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E-Commerce/88606539-5034-4f6c-aa8b-089f9230525d_content_list.json b/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/88606539-5034-4f6c-aa8b-089f9230525d_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..5b6c3607222a5f893913fb306576f949df62ac9b --- /dev/null +++ b/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/88606539-5034-4f6c-aa8b-089f9230525d_content_list.json @@ -0,0 +1,1622 @@ +[ + { + "type": "text", + "text": "Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-commerce", + "text_level": 1, + "bbox": [ + 133, + 130, + 834, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yang Jin $^{1,2}$ , Yongzhi Li $^{2}$ , Zehuan Yuan $^{2}$ , Yadong Mu $^{1*}$ $^{1}$ Peking University $\\mathbf{\\Pi}^{2}$ ByteDance Inc.", + "bbox": [ + 271, + 202, + 700, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "jiny@stu.pku.edu.cn, liyongzhi.ailab@bytedance.com,", + "bbox": [ + 258, + 241, + 707, + 257 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "yuanzehuan@bytedance.com, myd@pku.edu.cn", + "bbox": [ + 300, + 258, + 658, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 308, + 313, + 325 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "This paper aims to establish a generic multi-modal foundation model that has the scalable capability to massive downstream applications in E-commerce. Recently, large-scale vision-language pretraining approaches have achieved remarkable advances in the general domain. However, due to the significant differences between natural and product images, directly applying these frameworks for modeling image-level representations to E-commerce will be inevitably sub-optimal. To this end, we propose an instance-centric multi-modal pretraining paradigm called ECLIP in this work. In detail, we craft a decoder architecture that introduces a set of learnable instance queries to explicitly aggregate instance-level semantics. Moreover, to enable the model to focus on the desired product instance without reliance on expensive manual annotations, two specially configured pretext tasks are further proposed. Pretrained on the 100 million E-commerce-related data, ECLIP successfully extracts more generic, semantic-rich, and robust representations. Extensive experimental results show that, without further fine-tuning, ECLIP surpasses existing methods by a large margin on a broad range of downstream tasks, demonstrating the strong transferability to real-world E-commerce applications.", + "bbox": [ + 75, + 340, + 473, + 690 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 715, + 209, + 732 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Nowadays, the flourishing growth of E-commerce has brought great convenience to people's daily life. And a wide range of product-based application tasks has subsequently emerged, such as item classification [18, 29], product retrieval [7, 35], commodity recommendation [21, 28], and so on. Compared to developing individual task-specific models, building a general-purpose foundation model that works for massive E-commercial applications simultaneously can enhance applicability and reduce training costs.", + "bbox": [ + 75, + 742, + 468, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/cda2c2194e2842ce76a85d8a32befab13a10fcd293a95f3afbf76a67fe4e34f0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 511, + 306, + 633, + 421 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/1f81ad7128207b166829c846f6c18f23986ffc11f5d643b0d74b7d73247a9328.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 635, + 306, + 862, + 421 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/b63b68434f38cfc5d5b308d3f4a273ece4df8488e4c31651e94d6db2c719b807.jpg", + "image_caption": [ + "Figure 1. Domain difference between natural and product images. For natural images, it is the frequent case that most pixels are semantically correlated to the textual sentence. However, in E-commerce, such correlation is much more sparse (e.g., \"frying pan\" or \"coffee machine\" only occupy small portions of the entire images). Moreover, images for a product are often provided in a group from multiple sources such as (a) advertisement videos, (b) product pages, (c) customer comments (see the bottom examples)." + ], + "image_footnote": [], + "bbox": [ + 522, + 434, + 629, + 523 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/95341b20c11bff08a3df3a191172197048a7083ec9f9eddada9ef137f46a0e6d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 637, + 424, + 861, + 532 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recent developments in vision-language pretraining (VLP) [9, 12, 17, 20, 31, 34] have demonstrated remarkable advances in diverse VL downstream tasks. Profiting from large-scale image-text pairs, these methods are able to learn generic multimodal representations that are reused across various tasks. In E-commerce scenario, the related data naturally contains cross-modal information to describe a corresponding product. Motivated by the tremendous success achieved by VL modeling, several approaches [4, 33, 35, 37] have made attempts at designing a commerce-specific multimodal representation learning paradigm. They imitate the existing VLP methods (e.g., CLIP [20], VilBERT [17]) to learn the image-level representations of the product via pretraining on abundant commerce image-text pairs.", + "bbox": [ + 496, + 657, + 892, + 869 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Though promising results have been achieved, directly applying these VLP methods in the general domain to E-", + "bbox": [ + 500, + 869, + 890, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding Author.", + "bbox": [ + 94, + 887, + 225, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "11060", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "commerce still suffers from inherent deficiencies. The properties of natural and product images appear to be dramatically different. Given a natural image-text pair, almost every pixel in the natural image is mentioned by the corresponding textual description. In contrast, as shown in Figure 1, in a real E-commerce scenario, the images are mostly product-oriented. Only very few instances are related to the product description. Simply treating the whole image as a monolithic entity to perform cross-modal alignment with text will inevitably confound the foreground and noisy background. Hence, to establish a foundation model that generalizes well to diverse E-commerce applications, it is of great significance to learn the product-related instance-level representation. With this goal in mind, a crucial challenge needs to be addressed: How can we enable the model to focus on the product instance in the presence of background interference?", + "bbox": [ + 75, + 90, + 472, + 347 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "A straightforward way to tackle this problem would be to resort to object-level human annotations, but it is laborious and infeasible to scale on larger data from the Internet. In this work, we strive to derive the capability of grounding product instances from uncurated data. Our motivation is built on the natural characteristics of E-commerce data itself. As illustrated in Figure 1, a product usually has multiple image samples from different sources (e.g., merchant, customer comments, attached advertisement videos, etc.). Although the appearance of these samples may be diverse due to the changes of camera view or scenes, they all include the identical product entity. This fact strongly spurs us to pursue an instance-centric multi-modal learning paradigm by leveraging such explicit correlation.", + "bbox": [ + 75, + 349, + 472, + 561 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The proposed pretraining framework, dubbed as ECLIP (E for \"E-commerce\"), employs two separate encoders to embed the images and texts of products. Our key idea is to develop a decoder architecture built upon the abovementioned encoders, which aims to aggregate the instance-centric product representations without additional handcrafted annotation. Inspired by [1, 15, 30], the decoder introduces a set of learnable tokens that we refer to as instance query. At each decoder block, these instance queries are updated via interacting with the encoded visual features. Through the stack of multiple blocks, they will gradually probe the potential product instance from the entire image. Moreover, each instance query is conditioned on a concrete text or image called multi-modal prompt. Such a design renders it dedicated to a particular instance type indicated by the content of its associated prompt. Therefore, by specifying the content of multi-modal prompt, the decoder can adaptively discover the corresponding instance. During pretraining, there is only one positive prompt for a given sample. The rest are negative ones sampled from other products.", + "bbox": [ + 75, + 565, + 472, + 867 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To effectively optimize the generated instance representations, we newly craft two pretext tasks: inter-product", + "bbox": [ + 76, + 869, + 470, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "and intra-product multi-modal learning. The first one is in charge of pulling the representations of the identical product closer to each other and pushing away the unmatched ones. It is noteworthy that the appearance of the positive image samples varies a lot except for the presented product. Bringing their representations closer than negative pairs in the feature space will implicitly encourage the instance query to focus on the visual region that corresponds to the desired product. The second one aims to ensure that only positive queries can aggregate the semantics of the foreground instance, rather than negative ones. Coupling these two novel pretext tasks together, we find that the whole framework is capable of learning a generic product representation. Our core contributions can be summarized as follows:", + "bbox": [ + 496, + 90, + 893, + 301 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "(1) We propose ECLIP, an effective and simple multimodal representation learning paradigm in the E-commerce scenario. Going beyond regular global representations, it can successfully obtain instance-centric product representations via a decoder architecture.", + "(2) By fully exploiting the natural characteristics of E-commerce data and the proposed pretext tasks, ECLIP obtains the fine-grained alignment capability to ground the desired product instance (see Figure 4a) without reliance on any manual annotation.", + "(3) Pre-trained on large-scale product data, the resulting foundation model can seamlessly generalize to downstream E-commerce applications. Comprehensive experimental results further demonstrate the superiority of ECLIP: without any fine-tuning, it achieves substantial improvements over the existing state-of-the-art methods on diverse real-world E-commerce tasks." + ], + "bbox": [ + 496, + 301, + 893, + 556 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 571, + 640, + 589 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Vision-Language Representation Learning. In recent years, vision-language pretraining (VLP) has attracted the attention of numerous researchers and has been widely explored [6], which aims to learn from tremendous image-text paired data to obtain knowledge that can be generalized to downstream tasks. Some pioneer works (e.g. LXMERT [23], UNITER [2], VinVL [36]) rely on pretrained object detection modules such as Faster-RCNN [22] to extract visual representations. Later efforts such as ViLT [10] and VLMo [26] unify the vision and language transformers, and train a multimodal transformer from scratch. Then, CLIP [20] and ALIGN [9] demonstrate that dual-encoder models pretrained with contrastive objectives on noisy image-text pairs can learn strong image and text representations for crossmodal alignment tasks and zero-shot image classification. While ALBEF [12] additionally trains a fusion-encoder to jointly learn the multi-modal representations. GLIP [13] unifies object detection and phrase grounding for pretraining and surpasses many baselines in the detection field. Another line of researches [19,25,27,32]", + "bbox": [ + 496, + 598, + 893, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "11061", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "develop encoder-decoder models that are trained using generative losses and show strong generation performances in vision-language benchmarks, while the visual encoder still performs competitively on image classification. But most aforementioned VLP methods devote themselves to the coarse correlation between the text and the entire image and ignore the instance-level information, which is critical in an e-commerce scenario (as shown in Figure 1).", + "bbox": [ + 75, + 90, + 472, + 210 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "MultiModal Pre-training for E-commerce. Early works like FashionBERT [7], Kaleido-BERT [38] leverage a transformer-based model and tailored masking strategy to perform pretraining to generate more fine-grained features for cloth retrieval. Then CAPTURE [35] generates discriminative instance features via masked multi-modal learning as well as cross-modal contrastive pretraining achieve surprising performance in the instance-level product retrieval task. K3M [37] further introduces the knowledge modality in multi-modal pretraining to correct the noise and supplement the missing of image and text modalities. SCALE [4] proposes a self-harmonized contrastive learning framework that can integrate six different modalities into a unified model. Recent CommerceMM in [33] design a contrastive and MLM-based pretraining paradigm on 14 different tasks. However, all the existing methods only consider the global alignment between images and text, without exploring the special characteristic contained in the e-commercial data for learning instance-centric representation.", + "bbox": [ + 75, + 212, + 472, + 501 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Approach", + "text_level": 1, + "bbox": [ + 76, + 517, + 187, + 534 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we begin with the overview of our proposed ECLIP in Section 3.1. Then the core decoder architecture that aims at aggregating instance-level representation of the desired product is introduced in Section 3.2. To optimize the entire framework, we carefully designed several pretraining objectives in Section 3.3. Finally, we delineate how to transfer the resulting foundation model to various downstream tasks in Section 3.4.", + "bbox": [ + 75, + 542, + 472, + 664 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Model Overview", + "text_level": 1, + "bbox": [ + 76, + 676, + 241, + 691 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As illustrated in Figure 2, ECLIP is composed of an image encoder, a text encoder, and an instance decoder. Given an input sample $x = (x^{I}, x^{T})$ , where $x^{I}$ and $x^{T}$ are the image and text describing the corresponding product information, respectively. These two encoders first encode the image-text pair as a sequence of feature embeddings. Then, a modality-dependent projection layer is employed to map them linearly into a joint multi-modal feature space. These projected embeddings are further decoded to produce an instance-centric representation. Details of the two unimodal encoders are elaborated as follows.", + "bbox": [ + 75, + 700, + 468, + 864 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Image Encoder. Following the vision transformers [5], the product image $x^{I} \\in \\mathcal{R}^{H \\times W \\times C}$ is partitioned into $N$ non-", + "bbox": [ + 75, + 869, + 470, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "overlapping patches. These patches are flattened to 1D input tokens and then projected linearly, with position embeddings added. Through hierarchical feature encoding, we can obtain a sequence of visual embeddings $\\{v_{cls}, v_1, \\dots, v_N\\}$ , where $v_{cls}$ indicates the special token [CLS] for encoding the entire image information.", + "bbox": [ + 496, + 90, + 890, + 181 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Text Encoder. This encoder adopts analogous transformer-style architecture. For the input product description $x_{T}$ , it tokenizes the text to $M$ subwords as in BERT [3]. Similar to the image encoder, a special [CLS] token is appended to the beginning of the textual input to summarize the text semantics. After encoding, the resulting linguistic embedding sequence is denoted as $\\{w_{cls}, w_{1}, \\dots, w_{M}\\}$ .", + "bbox": [ + 496, + 184, + 893, + 292 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Extract Instance-Centric Representation", + "text_level": 1, + "bbox": [ + 498, + 300, + 848, + 316 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "After obtaining the contextualized embeddings, existing VLP approaches leverage $g_{I}(v_{cls}) \\in \\mathcal{R}^{D}$ and $g_{T}(w_{cls}) \\in \\mathcal{R}^{D}$ to align positive image-text pairs via contrastive learning. Here $g_{I}(\\cdot)$ and $g_{T}(\\cdot)$ are the aforementioned projections. While effective in the general domain, this design only considers the alignment between the global image-text semantics. However, in the E-commerce image, only several regions containing the desired product instance are informative foregrounds corresponding to the text description. Modeling such image-level alignment will fail to learn strong and robust product semantics. Hence, we are committed to learning instance-centric representation.", + "bbox": [ + 496, + 321, + 890, + 503 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Instance Query. Motivated by [1, 15], a set of learnable tokens called instance query are introduced to ground the potential instance in the product image. As Figure 2 shows, each query is correlated to a specific text or image that we refer to as multi-modal prompt. The insight behind this design is that we wish the instance that a query should probe to be specified by the prompt content. Formally, the proposed instance query is denoted as $\\mathbf{Q} = \\{q_t \\in \\mathcal{R}^D\\}_{t=1}^T$ , which can be obtained by:", + "bbox": [ + 496, + 508, + 890, + 645 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nq _ {t} = q _ {t} ^ {\\text {p r o m p t}} + q _ {t} ^ {\\text {p o s}} + q _ {t} ^ {\\text {t y p e}}. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 604, + 655, + 890, + 672 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Here, $q_{t}^{\\text{prompt}}$ denotes $g_{I}(v_{cls})$ or $g_{T}(w_{cls})$ , $q_{t}^{\\text{pos}}$ and $q_{t}^{\\text{type}}$ are learnable position and type embedding, indicating the probing area of a query and the modality type of the binding prompt. These queries are responsible for aggregating the instance-centric representations $H = \\{h_t\\}_{t=1}^T$ from the encoded visual features via a decoder architecture. During pretraining, there is only one positive prompt (w.r.t the same product) for a given sample, and the rest $T - 1$ are negative ones sampled from other products.", + "bbox": [ + 496, + 683, + 890, + 820 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Instance Decoder. We first project all the encoded $\\{v_{i}\\}_{i = 1}^{N}$ into the same feature space as the prompt, yielding an embedding sequence $Z = \\{z_{i}\\in \\mathcal{R}^{D}\\}_{i = 1}^{N}$ . Moreover, the instance representations $\\pmb{H}^0$ are zero-initialized, and then before feeding to the decoder. The proposed decoder then", + "bbox": [ + 496, + 824, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "11062", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/47b7105cfaabec726a10edcccbf9e00bb4a855dd523dcc74ad75a66113323505.jpg", + "image_caption": [ + "(a) The overall Pipeline" + ], + "image_footnote": [], + "bbox": [ + 117, + 90, + 504, + 362 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/ead454f46f5697a2722c45f32963d93448f6f115996fe3768af0198031d4b196.jpg", + "image_caption": [ + "(b) The $i$ -th Decoder Block", + "Figure 2. (a) The architecture of the proposed instance-level representation learning paradigm (ECLIP), which is composed of an image encoder, a text encoder, and an instance decoder. The whole framework is optimized by three pretext tasks when pretraining on a large-scale E-commerce dataset. (b) The core decoder architecture that aims at aggregating the instance representation of the desired product." + ], + "image_footnote": [], + "bbox": [ + 612, + 89, + 851, + 363 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "reads all the above-described embeddings: $Z$ , $Q$ and $H^0$ as its input. It has $L$ stacked blocks, and each one consists of a slot-attention and a self-attention layer.", + "bbox": [ + 75, + 433, + 468, + 479 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The goal of the slot-attention layer is to adaptively update query representations through the interaction with the encoded visual embeddings. In detail, for the $l$ -th slot-attention layer, it first calculates a similarity matrix $M \\in \\mathcal{R}^{N \\times T}$ , which is implemented by the dot-product attention mechanism [24]. Formally, it is formulated by:", + "bbox": [ + 76, + 479, + 468, + 570 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nM = \\frac {1}{\\sqrt {D}} \\left(Z W _ {z}\\right) \\cdot \\left(\\left(Q + H ^ {l - 1}\\right) W _ {q}\\right) ^ {\\top}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 138, + 582, + 468, + 614 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $W_{z}$ and $W_{q}$ are the learnable projections parameter matrices, $H^{l - 1}$ is the instance representation produced by the $(l - 1)^{\\mathrm{th}}$ decoder block. The similarity matrix $M$ is further normalized by a softmax function over $T$ queries:", + "bbox": [ + 76, + 625, + 468, + 686 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nM _ {i j} = \\frac {\\exp (M _ {i j})}{\\sum_ {t = 1} ^ {T} \\exp (M _ {i t})}. \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 187, + 696, + 468, + 734 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The generated matrix $M$ actually performs soft assignment via computing semantic similarity between $N$ visual tokens and $T$ instance queries. In doing so, it is capable of distributing each visual token to a specific query according to their similarity score. To aggregate the information of the visual tokens into their assigned input query, we compute a weighted mean update based on $M$ :", + "bbox": [ + 75, + 744, + 470, + 849 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\Delta h _ {t} ^ {l - 1} = \\frac {1}{\\sum_ {i = 1} ^ {N} M _ {i t}} \\sum_ {i = 1} ^ {N} M _ {i t} \\left(W _ {v} z _ {i}\\right). \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 145, + 862, + 468, + 904 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, the instance representation $\\pmb{H}^l$ at the $l$ -th layer can be updated by a residual connection:", + "bbox": [ + 498, + 433, + 890, + 464 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nh _ {t} ^ {l} = h _ {t} ^ {l - 1} + W _ {o} \\Delta h _ {t} ^ {l - 1}. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 612, + 476, + 890, + 494 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $W_{v}$ and $W_{o}$ are linear transformation parameters.", + "bbox": [ + 498, + 508, + 870, + 523 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "On the top of slot-attention layer, there is a self-attention module that performs information propagation between each query. In detail, given the previously updated $H^{l}$ , it employs standard multi-head self-attention (MSA) followed by a fully connected feed-forward network as in [24]. After $L$ successive decoder blocks, we can obtain the final instance representation $H^{L}$ . It is noteworthy that, since the multi-modal prompts just participate in the similarity calculation in Eq. 2, the resulting $H^{L}$ thus contains only visual-modality information.", + "bbox": [ + 496, + 525, + 890, + 676 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Discussion: The proposed decoder works like conducting a clustering on the image tokens, where each instance query serves as the centroid of a cluster. At each decoder block, it determines where each token belongs by measuring its distance from the centroids in the semantic space. The cluster centroid is then updated via a soft manner (Eq.4) based on the calculated distance. By stacking multiple decoder blocks, it can implicitly force each query to attend to a specific region and aggregate instance-level representations.", + "bbox": [ + 496, + 681, + 890, + 818 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Multi-Modal Pretraining Objectives", + "text_level": 1, + "bbox": [ + 500, + 830, + 812, + 847 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our ECLIP is optimized on large-scale uncurated product data with several pretraining proxy tasks. In the following, we describe each task in detail.", + "bbox": [ + 498, + 854, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "11063", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/b3d0370d28972c3795325d9e93624bdb7da601c924663ea41b1a77647b8a5d36.jpg", + "image_caption": [ + "Figure 3. The illustration of inter-product contrastive learning. It aims to bring the positive samples from the same product ( $x$ and $x^{+}$ ) closer than the negative ones ( $x$ and $x^{-}$ ), which contributes to grounding the desired product (outlined with the green box)." + ], + "image_footnote": [], + "bbox": [ + 107, + 88, + 436, + 220 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Image-Text Contrastive Learning. As in [9, 12, 20], this task contributes to learning better unimodal representations. Given a batch of product samples $\\{(x_i^I,x_i^T)\\}_{i = 1}^B$ , the similarity between image $x^{I}$ and text $x^{T}$ is estimated as:", + "bbox": [ + 75, + 294, + 470, + 354 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\ns (x ^ {I}, x ^ {T}) = g _ {I} \\left(v _ {c l s}\\right) ^ {\\top} g _ {T} \\left(w _ {c l s}\\right).\n$$\n", + "text_format": "latex", + "bbox": [ + 163, + 366, + 382, + 385 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This pretraining objective brings the image-text pairs of the same product in the embedding space closer than the unmatched ones, which consists of image-to-text term $\\mathcal{L}_{i2t}$ :", + "bbox": [ + 75, + 397, + 468, + 444 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {i 2 t} = - \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp (s \\left(x _ {i} ^ {I} , x _ {i} ^ {T}\\right) / \\tau)}{\\sum_ {j = 1} ^ {B} \\exp (s \\left(x _ {i} ^ {I} , x _ {j} ^ {T}\\right) / \\tau)}, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 127, + 455, + 468, + 500 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "and a text-to-image term $\\mathcal{L}_{t2i}$ :", + "bbox": [ + 76, + 505, + 279, + 520 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {t 2 i} = - \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp \\left(s \\left(x _ {i} ^ {T} , x _ {i} ^ {I}\\right) / \\tau\\right)}{\\sum_ {j = 1} ^ {B} \\exp \\left(s \\left(x _ {i} ^ {T} , x _ {j} ^ {I}\\right) / \\tau\\right)}, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 127, + 532, + 468, + 574 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\tau$ is a learnable temperature parameter. The whole objective is then defined as $\\mathcal{L}_{itc} = \\frac{1}{2} (\\mathcal{L}_{i2t} + \\mathcal{L}_{t2i})$ .", + "bbox": [ + 75, + 585, + 468, + 616 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Inter-Product Multi-modal Learning. As shown in Figure 3, we maintain a momentum model during pretraining that is an exponential-moving-average of the origin model like [8]. For a product sample $x_{i}$ , we denote the representation of the positive prompt produced by the base and momentum model as $h_{\\theta}^{i}$ and $h_{\\xi}^{i}$ . An inter-product contrastive loss $\\mathcal{L}_{inter}$ is computed by:", + "bbox": [ + 75, + 619, + 468, + 719 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {i n t e r} = - \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp \\left(h _ {\\theta} ^ {i} ^ {\\top} h _ {\\xi} ^ {j} / \\tau\\right)}{\\exp \\left(h _ {\\theta} ^ {i} ^ {\\top} h _ {\\xi} ^ {j} / \\tau\\right) + \\sum_ {k \\in \\mathcal {N} ^ {-}} \\exp \\left(h _ {\\theta} ^ {i} ^ {\\top} h _ {\\xi} ^ {k} / \\tau\\right)}, \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 76, + 729, + 468, + 777 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where sample $i$ and $j$ are a positive pair, $\\mathcal{N}^{-}$ is a negative sample set that belongs to other products. This objective maximizes the similarity between different samples of the identical product, while minimizing those of the unmatched ones. Since the images of a product are collected from different sources, their background appearances are usually diverse. Hence, $\\mathcal{L}_{inter}$ will encourage the yielded representation to be highly correlated with the desired product and", + "bbox": [ + 75, + 779, + 468, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "thus contributes to aligning the positive prompt with the corresponding image tokens in a fine-grained manner.", + "bbox": [ + 498, + 90, + 890, + 119 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This pretext task also incorporates an additional instance-text matching loss that predicts whether an instance and a text description are matched. Formally, given an instance-text pair, we obtain their match logit defined by: $f(h_{\\theta}^{i}\\odot g_{T}(w_{\\mathrm{cls}}^{i}))$ , where $\\odot$ is Hadamard product and $f(\\cdot)$ is a mapping layer: $\\mathcal{R}^D\\to \\mathcal{R}^2$ . This match logit is optimized by a typical binary cross entropy loss $\\mathcal{L}_{itm}$ .", + "bbox": [ + 498, + 121, + 890, + 227 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Intra-Product Multi-modal Learning. For a product sample, there is only one positive prompt describing the presented product during pretraining, and the rest $T - 1$ prompts are sampled from other products. The motivation behind this pretext task is to ensure that only positive queries can probe the foreground instance, rather than negative ones. To this end, we apply an intra-product contrastive loss using text supervision. Suppose index $r$ indicates the positive query, then $\\mathcal{L}_{\\text{intra}}$ can be formulated as:", + "bbox": [ + 496, + 231, + 890, + 367 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {i n t r a} = - \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp \\left(h _ {r} ^ {i} ^ {\\top} g _ {T} \\left(w _ {\\mathrm {c l s}} ^ {i}\\right) / \\tau\\right)}{\\sum_ {t = 1} ^ {T} \\exp \\left(h _ {t} ^ {i} ^ {\\top} g _ {T} \\left(w _ {\\mathrm {c l s}} ^ {i}\\right) / \\tau\\right)}, \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 524, + 376, + 890, + 419 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "which serves to bring the positive query and the product description closer than all $T - 1$ negative ones. Moreover, we also introduce an entropy regularization term for $M$ :", + "bbox": [ + 498, + 426, + 892, + 472 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\mathcal {R}} = \\sum_ {i = 1} ^ {N} M _ {i, r} \\log \\left(\\frac {1}{M _ {i , r}}\\right) + \\\\ \\sum_ {j = 1, j \\neq r} ^ {T} \\left(\\log N - \\sum_ {i = 1} ^ {N} M _ {i, j} \\log \\left(\\frac {1}{M _ {i , j}}\\right)\\right). \\tag {10} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 519, + 481, + 890, + 580 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This regularization term encourages the positive query to focus on several tokens that may contain product instances. While for $T - 1$ negative ones, it prevents too sharp similarity distributions over $N$ image tokens. Finally, the overall pretraining objective of ECLIP is the sum of all aforementioned loss terms.", + "bbox": [ + 498, + 582, + 890, + 671 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Transfer to Downstream Tasks", + "text_level": 1, + "bbox": [ + 500, + 681, + 771, + 696 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Once pretrained, the resulting foundation model can be leveraged to extract the product instance representation with minimal surgery. Specifically, given a product sample $(x_{i}^{I}, x_{i}^{T})$ , we first encode the image-text pair into an embedding sequence via the unimodal encoders. Then, the global representation of text description $g_{T}(w_{\\mathrm{cls}})$ is treated as the positive query and fed into the decoder concatenated with $T - 1$ negative ones. Here, the negative queries $\\{q_{t}\\}_{t = 2}^{T}$ are sampled from a standard Gaussian distribution for convenience. We also explore different negative query setting manners in Section 4.3. The yielded representation $h_0^L$ belonging to the positive query is then applied to a wide range of E-commerce downstream tasks.", + "bbox": [ + 498, + 704, + 890, + 898 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "11064", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 90, + 209, + 107 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Pretraining Details", + "text_level": 1, + "bbox": [ + 76, + 114, + 259, + 131 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Pretraining Dataset We collected a large-scale pretraining dataset from a popular E-commerce website. It consists of 15M different products and over 100M various images, covering about 9K diverse categories such as clothes, daily necessities, instruments, and so on. For each product item, it has a corresponding textual description and several images from the product details pages, customer comments, and attached advertisement videos. During pretraining, the positive data pairs are constructed by sampling images belonging to the same product from different sources.", + "bbox": [ + 75, + 138, + 468, + 289 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation Details. The image encoder adopts the same network configuration as the standard ViT [5] and is initialized from the weight pre-trained on ImageNet. Our text encoder is implemented with the same architecture as $\\mathrm{BERT}_{\\mathrm{base}}$ [3]. The decoder has 6 identical blocks and 20 instance queries. We here explore two variants of ViTs: ViTB/16 and ViT-L/16. There is a total of $220 / 450$ million parameters for the base and large version. During pretraining, the input images are resized to $224 \\times 224$ with random crop and horizontal flip augmentation, and the texts are tokenized by WordPiece with a maximum length of 55. We pretrain for 15 epochs with a batch size of 6400 (ViT-B) / 4096 (ViT-L) on 32 NVIDIA A100 GPUs. The whole framework is learned using AdamW [16] optimizer and the learning rate is warmed up to 1e-4 and then decayed linearly. More details are elaborated in the supplementary.", + "bbox": [ + 75, + 290, + 470, + 532 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Compared Baselines. We mainly compare the ECLIP with several state-of-the-art VLP methods: CLIP [20], FILIP [31], DeCLIP [14], ALBEF [12] and BLIP [11]. For a fair comparison, we also leverage ViT-B/16 as the image encoder, $\\mathrm{BERT}_{\\mathrm{base}}$ as the text encoder, and pretrain these baselines on the same 100M E-commerce data using their official public implementations.", + "bbox": [ + 75, + 532, + 468, + 640 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Evaluation on Downstream Tasks", + "text_level": 1, + "bbox": [ + 76, + 650, + 370, + 666 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Next, we delineate the evaluation performances for five specific E-commerce downstream tasks in turn.", + "bbox": [ + 76, + 674, + 468, + 705 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2.1 Zero-Shot Product Classification", + "text_level": 1, + "bbox": [ + 76, + 709, + 359, + 724 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We first transfer ECLIP to product item classification. It is a recognition task that aims to map a product sample to a specific category. We evaluate the performance on a large-scale publicly available E-commerce dataset called M5Product [4], which covers 1.1M images and 5,679 various product categories. Here, we consider the multimodal setting, which uses both the product image and related textual description for classification. To demonstrate the strong zero-shot ability of ECLIP, we apply it directly to the classification evaluation without further finetune. It is achieved by measuring the similarity between the category text like", + "bbox": [ + 75, + 734, + 468, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "CLIP [20]. The left part of Table 1 summarizes the Top-1 classification accuracy of all the compared methods. As presented, our ECLIP greatly exceeds all the existing baselines by a large margin (e.g., $+6.6\\%$ v.s. CLIP), demonstrating the superiority of instance-level representation.", + "bbox": [ + 496, + 90, + 890, + 167 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2.2 Zero-Shot Image-Text Retrieval", + "text_level": 1, + "bbox": [ + 498, + 179, + 774, + 194 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "ECLIP is also transferred to test zero-shot performance for image-to-text and text-to-image retrieval. To this end, we collect a large dataset that contains 205K image-text pairs of E-commerce products. Since only unimodal information is available in this task, we simply use our image and text encoders to embed image-text pairs and complete the retrieval based on their pairwise similarity. We utilize the widely-used Recall@K metric for evaluation. Detailed comparison results are shown in the right part of Table 1. We can see that, despite training on the same dataset, our method achieves superior performance owing to fine-grained alignment modeling between text and product instance.", + "bbox": [ + 496, + 203, + 890, + 385 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/8b4a7515f79b4e6096cf707edb5fb3ecac39258d299bbd255f605f94067d6f8f.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodClassificationImage-to-TextText-to-Image
Acc@1R@1R@5R@1R@5
CLIP [20]37.252.674.158.784.0
FILIP [31]37.152.373.858.083.5
DeCLIP [14]37.853.175.858.883.9
ALBEF [12]38.552.974.458.283.3
BLIP [11]39.353.375.659.184.4
OursViT-B/1643.853.876.059.984.6
OursViT-L/1644.858.279.663.887.4
", + "bbox": [ + 503, + 398, + 888, + 537 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Performance comparisons of zero-shot product classification and zero-shot image-text retrieval on M5Product dataset [4].", + "bbox": [ + 498, + 547, + 890, + 575 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2.3 Zero-Shot Product Retrieval", + "text_level": 1, + "bbox": [ + 498, + 589, + 751, + 603 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "This task aims to find the most relevant target product given a query (image-text pair of a product). It has a wide range of applications in real e-commerce scenarios such as recommending relevant products for users. We first evaluate the coarse-level retrieval. Following [4], a product pair is considered a match if both belong to the same category during evaluation. The results on M5Product benchmark are reported on the left of Table 2. It can be seen that exploiting instance-centric representation significantly boosts performance. To further demonstrate the effectiveness of instance-level representation, we then conduct a more complicated fine-grained level product retrieval task, where a pair is considered a match if and only if they are the same product. This task requires more adequate fine-grained understanding ability since it focuses on the specific product instance. Detailed comparison results are shown on the right part of Table 2. One can find that our ECLIP achieves a substantial improvement in retrieval performance (e.g., $44.3\\%$ v.s. $35.6\\%$ (BLIP) on $\\mathbf{R}@\\mathbf{1}$ ).", + "bbox": [ + 496, + 613, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "11065", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/423d8d87c9d010dfcaf4f1cff21573e5f2bd22c61b54bfc672802aa715448739.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodPretraining DatasetCoarse Product RetrievalFine-grained Product Retrieval
mAP@1mAP@5mAP@10R@1R@5R@10mAP@1mAP@5mAP@10
ViBERT [17]58.661.760.1------
UNITER [2]M5Product58.962.860.9------
SCALE [4]59.864.162.2------
CLIP [20]68.273.270.734.854.262.934.840.239.9
FILIP [31]67.873.070.334.653.962.234.640.139.7
DeCLIP [14]68.573.470.835.356.465.535.341.240.8
ALBEF [12]ECLIP 100M68.773.671.235.156.165.235.140.740.4
BLIP [11]69.174.171.635.656.866.035.641.641.3
OursViT-B/1669.674.972.544.363.471.143.848.648.2
OursViT-L/1670.275.372.945.064.272.145.050.049.5
", + "bbox": [ + 81, + 88, + 890, + 277 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We also consider another setting introduced in [35], called instance-level retrieval, where the query image encompasses multiple different kinds of product instances. And the model needs to find all the related products from a large gallery. As shown in Table 3, ECLIP still achieves superior performance than all the previous approaches. Although the CAPTURE leverages a specially-trained object detector to extract instances, ECLIP still surpasses it by a clear margin with no box annotation.", + "bbox": [ + 75, + 314, + 470, + 450 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.2.4 Zero-Shot Visual Grounding", + "text_level": 1, + "bbox": [ + 76, + 462, + 331, + 478 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To demonstrate whether our model possesses the capability of localizing the desired product instance after pretraining, we further evaluate ECLIP on the zero-shot product grounding, which requires localizing the product instance in an image according to a textual description. Specifically, the input image-text pair is first fed to our ECLIP to obtain a score map $S \\in \\mathcal{R}^{H \\times W}$ that measures the similarity between the text and each image location. Then, we use $S$ to rank the candidate regions produced by an off-the-shelf region proposal network. The performance is evaluated by the top-1 accuracy at IoU thresholds $\\{0.5, 0.7\\}$ on an annotated grounding dataset consisting of 450K product images. Detailed comparison results are listed in Table 4. As we can see, compared to methods aimed at global representation, our model has learned fine-grained cross-modal understanding ability and thus obtains substantial performance gain (e.g., +14.5% v.s. BLIP on Acc@0.7). Since ECLIP supports image prompts during pretraining, we also conduct zero-shot image-conditioned grounding. The results and analysis can be found in the supplementary.", + "bbox": [ + 75, + 486, + 470, + 789 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.2.5 Transfer to Object Detection", + "text_level": 1, + "bbox": [ + 76, + 800, + 331, + 816 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We also transfer ECLIP to object detection to further validate its fine-grained understanding ability. Following DETR [1], we utilize the image encoder to embed visual features, and utilize the decoder with a newly added prediction head to decode the potential objects. Moreover, we col", + "bbox": [ + 75, + 824, + 468, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/7e61a748237a44ac06643dbcd4e4294ae5bb6a3614f3138378fa7938b95d8bf2.jpg", + "table_caption": [ + "Table 2. Performance comparisons of zero-shot coarse level and fine-grained level product retrieval task." + ], + "table_footnote": [], + "table_body": "
MethodInstance-Level Product Retrieval
mAP@10mAP@50mAR@10mAR@50
CAPTURE [35]40.436.817.215.9
CLIP [20]86.682.854.459.5
FILIP [31]86.983.054.659.8
DeCLIP [14]87.183.354.960.0
BLIP [11]87.583.555.160.4
OursViT-B/1689.684.655.961.2
OursViT-L/1689.586.456.362.1
", + "bbox": [ + 504, + 311, + 890, + 441 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/48192d0113c01fcf43e4e8f33603a4d029bf9a81bf82dff84b3d6d071c821fef.jpg", + "table_caption": [ + "Table 3. Performance comparisons of zero-shot instance-level product retrieval task on Product1M dataset [35]." + ], + "table_footnote": [], + "table_body": "
MethodVisual GroundingObject Detection
Acc@0.5Acc@0.7mAP@0.3mAP@0.5
CLIP [20]80.975.217.214.3
FILIP [31]81.375.617.514.6
DeCLIP [14]81.075.317.014.2
ALBEF [12]80.974.717.113.9
BLIP [11]81.175.117.314.1
OursViT-B/1691.289.620.216.5
", + "bbox": [ + 504, + 479, + 890, + 603 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Performance comparisons of zero-shot visual grounding and finetuned object detection.", + "bbox": [ + 500, + 608, + 890, + 637 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "lect a manually annotated detection dataset covering 160K images. We split a 20K subset for evaluation and leave the rest for model finetuning. The supplementary provides experiment details of baselines and ECLIP. It can be observed from Table 4 and Figure 4b that it outperforms the existing VLP methods, demonstrating the superiority of ECLIP to learning fine-grained semantics in E-commerce.", + "bbox": [ + 496, + 641, + 893, + 748 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation Study", + "text_level": 1, + "bbox": [ + 498, + 756, + 653, + 772 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effect of Pretext Task. To demonstrate the effectiveness of the inter- and intra- product learning pretext task, we designed the experiments on different task combinations for the product retrieval task. All the ablations were conducted on a smaller pretraining dataset that includes only 5M images due to the costly training time. The complete results are listed in Table 5. One can observe that canceling either of these two tasks will result in worse performance. No", + "bbox": [ + 496, + 779, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "11066", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/380e598b2bb0d1a266a6da393b17ee838151742dd7f3b10b39c3f86a321b34b5.jpg", + "image_caption": [ + "Figure 4. Qualitative examples of visual grounding and object detection. See the main text for more explanation." + ], + "image_footnote": [], + "bbox": [ + 83, + 88, + 467, + 281 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/379459f62b025a761d5f074f44dcc5a73975f5e7a4ab69a0fb164fbb55a9a99c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 514, + 99, + 848, + 281 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "tably, the inter-product task brings a more significant performance boost compared to the intra-product. We speculate that because the former contrasts with more negative samples from different product images.", + "bbox": [ + 75, + 321, + 467, + 380 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effect of Negative Query. Since the negative instance queries are used when transferring to classification and retrieval tasks, we also ran ablations on different ways of setting these negative ones. We tried the following cases: 1) Leverage the description text from other products and encode with the text encoder. 2) Randomly sample the negative queries from the standard Gaussian distribution. 3) Adopt the exponential moving average of queries on the whole dataset during pretraining. Table 6 summarizes the results of this ablation study on the product retrieval task. As observed, there is little to no difference between different manners of setting up negative queries. We thus adopt random sampling for implementation simplicity.", + "bbox": [ + 75, + 381, + 467, + 577 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/cde276447d629a1cb846aa11afb2307d88d87625d655d0acb95589061a125fea.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
LinterLintramAP@1 (%)mAP@5 (%)
61.665.2
✓64.468.7
✓62.266.5
✓✓65.269.3
", + "bbox": [ + 127, + 587, + 419, + 666 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/99d15465f953f5de8c79bb556ce26ecd0407ddb9f463465d92f3d5238a48d629.jpg", + "table_caption": [ + "Table 5. Ablation of different pretraining pretext task combinations on coarse-level product retrieval task (ViT-B/16)." + ], + "table_footnote": [], + "table_body": "
MetricmAP@10mAR@10
Negative Text84.952.5
EMA Update85.153.3
Random Sampling86.854.1
", + "bbox": [ + 127, + 705, + 418, + 775 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 6. Ablation of different negative query setting manners on the instance-level product retrieval task (ViT-B/16).", + "bbox": [ + 75, + 780, + 467, + 808 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.4. Qualitative Analysis", + "text_level": 1, + "bbox": [ + 76, + 816, + 267, + 832 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this section, we first qualitatively showcase that ECLIP can learn fine-grained cross-modal alignment to ground the desired product. Figure 4a presents the visualization of the similarity score map between a product image", + "bbox": [ + 75, + 839, + 467, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/9164ea0d5096395ef38467abac77e2afe66bd087783bfdea8b2e5e0102783b38.jpg", + "image_caption": [ + "(a) CLIP", + "Figure 5. The T-SNE visualization for the learned representations of CLIP and our ECLIP. Both of them are pretrained on the same dataset. We first randomly select 50 kinds of products and then draw 100 samples for each. Points belonging to the same category are of the same color. See the main text for more explanation." + ], + "image_footnote": [], + "bbox": [ + 498, + 319, + 684, + 455 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/9052fcad9962fec36a5a79db8011bb8bcbfacc302af210a8455d37d76d8e8003.jpg", + "image_caption": [ + "(b) ECLIP" + ], + "image_footnote": [], + "bbox": [ + 705, + 319, + 890, + 455 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "and its corresponding text description, where darker color indicates image locations with higher similarity to text. We can clearly observe that our model can rightly attend to the desired instance depicted by the text. Furthermore, T-SNE is used for visualizing the visual embedding of different kinds of product samples. As shown in Figure 5, compared to CLIP, our ECLIP can extract semantic-rich yet compact representations that better distinguish products belonging to different categories. More visualization examples and analysis are provided in our supplementary.", + "bbox": [ + 496, + 558, + 890, + 710 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 723, + 617, + 739 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we develop an effective large-scale multimodal pretraining paradigm called ECLIP in E-commerce. Beyond regular global representation, it instead aims to learn the instance-level representation via a novel decoder and the carefully-designed pretraining proxy tasks. Extensive experimental results further demonstrate the superior generalization capability of the proposed framework.", + "bbox": [ + 496, + 750, + 890, + 854 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement: This work is supported by National Key R&D Program of China (2022ZD0160305) and Beijing Natural Science Foundation (Z190001).", + "bbox": [ + 496, + 856, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "11067", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 2, 3, 7", + "[2] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 2, 7", + "[3] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 3, 6", + "[4] Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Michael C Kampffmeyer, Xiaoyong Wei, Minlong Lu, Yaowei Wang, and Xiaodan Liang. M5product: Selfharmonized contrastive learning for e-commercial multimodal pretraining. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21252-21262, 2022. 1, 3, 6, 7", + "[5] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.3, 6", + "[6] Zhe Gan, Linjie Li, Chunyuan Li, Lijuan Wang, Zicheng Liu, and Jianfeng Gao. Vision-language pre-training: Basics, recent advances, and future trends. arXiv preprint arXiv:2210.09263, 2022. 2", + "[7] Dehong Gao, Linbo Jin, Ben Chen, Minghui Qiu, Peng Li, Yi Wei, Yi Hu, and Hao Wang. Fashionbert: Text and image matching with adaptive loss for cross-modal retrieval. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2251-2260, 2020. 1, 3", + "[8] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 5", + "[9] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 2, 5", + "[10] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning, pages 5583-5594. PMLR, 2021. 2", + "[11] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. arXiv preprint arXiv:2201.12086, 2022. 6, 7" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[12] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems, 34:9694-9705, 2021. 1, 2, 5, 6, 7", + "[13] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, and Jianfeng Gao. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10965–10975, June 2022. 2", + "[14] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208, 2021. 6, 7", + "[15] Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. Object-centric learning with slot attention. Advances in Neural Information Processing Systems, 33:11525-11538, 2020. 2, 3", + "[16] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6", + "[17] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems, 32, 2019. 1, 7", + "[18] Mieczysław Pawlowski. Machine learning based product classification for ecommerce. Journal of Computer Information Systems, 62(4):730-739, 2022. 1", + "[19] AJ Piergiovanni, Wei Li, Weicheng Kuo, Mohammad Saffar, Fred Bertsch, and Anelia Angelova. Answer-me: Multi-task open-vocabulary visual question answering. arXiv preprint arXiv:2205.00949, 2022. 2", + "[20] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 5, 6, 7", + "[21] Puji Rahayu, Dana Indra Sensuse, Betty Purwandari, Indra Budi, F Khalid, and N Zulkarnaim. A systematic review of recommender system for e-portfolio domain. In Proceedings of the 5th International Conference on Information and Education Technology, pages 21–26, 2017. 1", + "[22] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 2", + "[23] Hao Tan and Mohit Bansal. Lxmert: Learning cross-modality encoder representations from transformers. arXiv preprint arXiv:1908.07490, 2019. 2", + "[24] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia" + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "11068", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 4", + "[25] Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework. arXiv preprint arXiv:2202.03052, 2022. 2", + "[26] Wenhui Wang, Hangbo Bao, Li Dong, and Furu Wei. Vlmo: Unified vision-language pre-training with mixture-of-modality-experts. arXiv preprint arXiv:2111.02358, 2021. 2", + "[27] Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao. Simvlm: Simple visual language model pretraining with weak supervision. arXiv preprint arXiv:2108.10904, 2021. 2", + "[28] Chi-Man Wong, Fan Feng, Wen Zhang, Chi-Man Vong, and Chen. Improving conversational recommender system by pretraining billion-scale knowledge graph. In International Conference on Data Engineering (ICDE), pages 2607-2612, 2021. 1", + "[29] Hu Xu, Bing Liu, Lei Shu, and P Yu. Open-world learning and application to product classification. In The World Wide Web Conference, pages 3413-3419, 2019. 1", + "[30] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18134-18144, 2022. 2", + "[31] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. Filip: Fine-grained interactive language-image pre-training. arXiv preprint arXiv:2111.07783, 2021. 1, 6, 7", + "[32] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917, 2022. 2", + "[33] Licheng Yu, Jun Chen, Animesh Sinha, Mengjiao Wang, Yu Chen, Tamara L Berg, and Ning Zhang. Commercemm: Large-scale commerce multimodal representation learning with omni retrieval. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 4433-4442, 2022. 1, 3", + "[34] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. arXiv preprint arXiv:2111.08276, 2021. 1", + "[35] Xunlin Zhan, Yangxin Wu, Xiao Dong, Yunchao Wei, Minlong Lu, Yichi Zhang, Hang Xu, and Xiaodan Liang. Product1m: Towards weakly supervised instance-level product retrieval via cross-modal pretraining. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11782-11791, 2021. 1, 3, 7", + "[36] Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, and Jianfeng Gao. Vinvl: Revisiting visual representations in vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5579-5588, 2021. 2" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[37] Yushan Zhu, Huaixiao Zhao, Wen Zhang, Ganqiang Ye, Hui Chen, Ningyu Zhang, and Huajun Chen. Knowledge perceived multi-modal pretraining in e-commerce. In Proceedings of the 29th ACM International Conference on Multimedia, pages 2744-2752, 2021. 1, 3", + "[38] Mingchen Zhuge, Dehong Gao, Deng-Ping Fan, Linbo Jin, Ben Chen, Haoming Zhou, Minghui Qiu, and Ling Shao. Kaleido-bert: Vision-language pre-training on fashion domain. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12647-12657, 2021. 3" + ], + "bbox": [ + 501, + 90, + 892, + 244 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "11069", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/88606539-5034-4f6c-aa8b-089f9230525d_model.json b/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/88606539-5034-4f6c-aa8b-089f9230525d_model.json new file mode 100644 index 0000000000000000000000000000000000000000..3e29810362fe98d0afe007200a1a9fa425f6f6d1 --- /dev/null +++ b/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/88606539-5034-4f6c-aa8b-089f9230525d_model.json @@ -0,0 +1,2090 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.135, + 0.131, + 0.836, + 0.177 + ], + "angle": 0, + "content": "Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-commerce" + }, + { + "type": "text", + "bbox": [ + 0.272, + 0.203, + 0.701, + 0.24 + ], + "angle": 0, + "content": "Yang Jin\\(^{1,2}\\), Yongzhi Li\\(^{2}\\), Zehuan Yuan\\(^{2}\\), Yadong Mu\\(^{1*}\\) \n\\(^{1}\\)Peking University \\(\\mathbf{\\Pi}^{2}\\)ByteDance Inc." + }, + { + "type": "text", + "bbox": [ + 0.259, + 0.242, + 0.708, + 0.258 + ], + "angle": 0, + "content": "jiny@stu.pku.edu.cn, liyongzhi.ailab@bytedance.com," + }, + { + "type": "text", + "bbox": [ + 0.302, + 0.26, + 0.659, + 0.275 + ], + "angle": 0, + "content": "yuanzehuan@bytedance.com, myd@pku.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.309, + 0.314, + 0.326 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.342, + 0.474, + 0.691 + ], + "angle": 0, + "content": "This paper aims to establish a generic multi-modal foundation model that has the scalable capability to massive downstream applications in E-commerce. Recently, large-scale vision-language pretraining approaches have achieved remarkable advances in the general domain. However, due to the significant differences between natural and product images, directly applying these frameworks for modeling image-level representations to E-commerce will be inevitably sub-optimal. To this end, we propose an instance-centric multi-modal pretraining paradigm called ECLIP in this work. In detail, we craft a decoder architecture that introduces a set of learnable instance queries to explicitly aggregate instance-level semantics. Moreover, to enable the model to focus on the desired product instance without reliance on expensive manual annotations, two specially configured pretext tasks are further proposed. Pretrained on the 100 million E-commerce-related data, ECLIP successfully extracts more generic, semantic-rich, and robust representations. Extensive experimental results show that, without further fine-tuning, ECLIP surpasses existing methods by a large margin on a broad range of downstream tasks, demonstrating the strong transferability to real-world E-commerce applications." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.717, + 0.21, + 0.733 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.743, + 0.47, + 0.879 + ], + "angle": 0, + "content": "Nowadays, the flourishing growth of E-commerce has brought great convenience to people's daily life. And a wide range of product-based application tasks has subsequently emerged, such as item classification [18, 29], product retrieval [7, 35], commodity recommendation [21, 28], and so on. Compared to developing individual task-specific models, building a general-purpose foundation model that works for massive E-commercial applications simultaneously can enhance applicability and reduce training costs." + }, + { + "type": "image", + "bbox": [ + 0.513, + 0.308, + 0.634, + 0.422 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.636, + 0.308, + 0.864, + 0.422 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.435, + 0.63, + 0.524 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.638, + 0.425, + 0.862, + 0.534 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.539, + 0.892, + 0.651 + ], + "angle": 0, + "content": "Figure 1. Domain difference between natural and product images. For natural images, it is the frequent case that most pixels are semantically correlated to the textual sentence. However, in E-commerce, such correlation is much more sparse (e.g., \"frying pan\" or \"coffee machine\" only occupy small portions of the entire images). Moreover, images for a product are often provided in a group from multiple sources such as (a) advertisement videos, (b) product pages, (c) customer comments (see the bottom examples)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.893, + 0.87 + ], + "angle": 0, + "content": "Recent developments in vision-language pretraining (VLP) [9, 12, 17, 20, 31, 34] have demonstrated remarkable advances in diverse VL downstream tasks. Profiting from large-scale image-text pairs, these methods are able to learn generic multimodal representations that are reused across various tasks. In E-commerce scenario, the related data naturally contains cross-modal information to describe a corresponding product. Motivated by the tremendous success achieved by VL modeling, several approaches [4, 33, 35, 37] have made attempts at designing a commerce-specific multimodal representation learning paradigm. They imitate the existing VLP methods (e.g., CLIP [20], VilBERT [17]) to learn the image-level representations of the product via pretraining on abundant commerce image-text pairs." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Though promising results have been achieved, directly applying these VLP methods in the general domain to E-" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.888, + 0.227, + 0.9 + ], + "angle": 0, + "content": "*Corresponding Author." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11060" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.348 + ], + "angle": 0, + "content": "commerce still suffers from inherent deficiencies. The properties of natural and product images appear to be dramatically different. Given a natural image-text pair, almost every pixel in the natural image is mentioned by the corresponding textual description. In contrast, as shown in Figure 1, in a real E-commerce scenario, the images are mostly product-oriented. Only very few instances are related to the product description. Simply treating the whole image as a monolithic entity to perform cross-modal alignment with text will inevitably confound the foreground and noisy background. Hence, to establish a foundation model that generalizes well to diverse E-commerce applications, it is of great significance to learn the product-related instance-level representation. With this goal in mind, a crucial challenge needs to be addressed: How can we enable the model to focus on the product instance in the presence of background interference?" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.351, + 0.473, + 0.563 + ], + "angle": 0, + "content": "A straightforward way to tackle this problem would be to resort to object-level human annotations, but it is laborious and infeasible to scale on larger data from the Internet. In this work, we strive to derive the capability of grounding product instances from uncurated data. Our motivation is built on the natural characteristics of E-commerce data itself. As illustrated in Figure 1, a product usually has multiple image samples from different sources (e.g., merchant, customer comments, attached advertisement videos, etc.). Although the appearance of these samples may be diverse due to the changes of camera view or scenes, they all include the identical product entity. This fact strongly spurs us to pursue an instance-centric multi-modal learning paradigm by leveraging such explicit correlation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.566, + 0.473, + 0.868 + ], + "angle": 0, + "content": "The proposed pretraining framework, dubbed as ECLIP (E for \"E-commerce\"), employs two separate encoders to embed the images and texts of products. Our key idea is to develop a decoder architecture built upon the abovementioned encoders, which aims to aggregate the instance-centric product representations without additional handcrafted annotation. Inspired by [1, 15, 30], the decoder introduces a set of learnable tokens that we refer to as instance query. At each decoder block, these instance queries are updated via interacting with the encoded visual features. Through the stack of multiple blocks, they will gradually probe the potential product instance from the entire image. Moreover, each instance query is conditioned on a concrete text or image called multi-modal prompt. Such a design renders it dedicated to a particular instance type indicated by the content of its associated prompt. Therefore, by specifying the content of multi-modal prompt, the decoder can adaptively discover the corresponding instance. During pretraining, there is only one positive prompt for a given sample. The rest are negative ones sampled from other products." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.472, + 0.902 + ], + "angle": 0, + "content": "To effectively optimize the generated instance representations, we newly craft two pretext tasks: inter-product" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.302 + ], + "angle": 0, + "content": "and intra-product multi-modal learning. The first one is in charge of pulling the representations of the identical product closer to each other and pushing away the unmatched ones. It is noteworthy that the appearance of the positive image samples varies a lot except for the presented product. Bringing their representations closer than negative pairs in the feature space will implicitly encourage the instance query to focus on the visual region that corresponds to the desired product. The second one aims to ensure that only positive queries can aggregate the semantics of the foreground instance, rather than negative ones. Coupling these two novel pretext tasks together, we find that the whole framework is capable of learning a generic product representation. Our core contributions can be summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.303, + 0.894, + 0.377 + ], + "angle": 0, + "content": "(1) We propose ECLIP, an effective and simple multimodal representation learning paradigm in the E-commerce scenario. Going beyond regular global representations, it can successfully obtain instance-centric product representations via a decoder architecture." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.379, + 0.894, + 0.453 + ], + "angle": 0, + "content": "(2) By fully exploiting the natural characteristics of E-commerce data and the proposed pretext tasks, ECLIP obtains the fine-grained alignment capability to ground the desired product instance (see Figure 4a) without reliance on any manual annotation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.455, + 0.895, + 0.558 + ], + "angle": 0, + "content": "(3) Pre-trained on large-scale product data, the resulting foundation model can seamlessly generalize to downstream E-commerce applications. Comprehensive experimental results further demonstrate the superiority of ECLIP: without any fine-tuning, it achieves substantial improvements over the existing state-of-the-art methods on diverse real-world E-commerce tasks." + }, + { + "type": "list", + "bbox": [ + 0.498, + 0.303, + 0.895, + 0.558 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.573, + 0.642, + 0.59 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.599, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Vision-Language Representation Learning. In recent years, vision-language pretraining (VLP) has attracted the attention of numerous researchers and has been widely explored [6], which aims to learn from tremendous image-text paired data to obtain knowledge that can be generalized to downstream tasks. Some pioneer works (e.g. LXMERT [23], UNITER [2], VinVL [36]) rely on pretrained object detection modules such as Faster-RCNN [22] to extract visual representations. Later efforts such as ViLT [10] and VLMo [26] unify the vision and language transformers, and train a multimodal transformer from scratch. Then, CLIP [20] and ALIGN [9] demonstrate that dual-encoder models pretrained with contrastive objectives on noisy image-text pairs can learn strong image and text representations for crossmodal alignment tasks and zero-shot image classification. While ALBEF [12] additionally trains a fusion-encoder to jointly learn the multi-modal representations. GLIP [13] unifies object detection and phrase grounding for pretraining and surpasses many baselines in the detection field. Another line of researches [19,25,27,32]" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "11061" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.212 + ], + "angle": 0, + "content": "develop encoder-decoder models that are trained using generative losses and show strong generation performances in vision-language benchmarks, while the visual encoder still performs competitively on image classification. But most aforementioned VLP methods devote themselves to the coarse correlation between the text and the entire image and ignore the instance-level information, which is critical in an e-commerce scenario (as shown in Figure 1)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.213, + 0.473, + 0.502 + ], + "angle": 0, + "content": "MultiModal Pre-training for E-commerce. Early works like FashionBERT [7], Kaleido-BERT [38] leverage a transformer-based model and tailored masking strategy to perform pretraining to generate more fine-grained features for cloth retrieval. Then CAPTURE [35] generates discriminative instance features via masked multi-modal learning as well as cross-modal contrastive pretraining achieve surprising performance in the instance-level product retrieval task. K3M [37] further introduces the knowledge modality in multi-modal pretraining to correct the noise and supplement the missing of image and text modalities. SCALE [4] proposes a self-harmonized contrastive learning framework that can integrate six different modalities into a unified model. Recent CommerceMM in [33] design a contrastive and MLM-based pretraining paradigm on 14 different tasks. However, all the existing methods only consider the global alignment between images and text, without exploring the special characteristic contained in the e-commercial data for learning instance-centric representation." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.518, + 0.188, + 0.535 + ], + "angle": 0, + "content": "3. Approach" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.543, + 0.473, + 0.665 + ], + "angle": 0, + "content": "In this section, we begin with the overview of our proposed ECLIP in Section 3.1. Then the core decoder architecture that aims at aggregating instance-level representation of the desired product is introduced in Section 3.2. To optimize the entire framework, we carefully designed several pretraining objectives in Section 3.3. Finally, we delineate how to transfer the resulting foundation model to various downstream tasks in Section 3.4." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.677, + 0.242, + 0.692 + ], + "angle": 0, + "content": "3.1. Model Overview" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.701, + 0.47, + 0.866 + ], + "angle": 0, + "content": "As illustrated in Figure 2, ECLIP is composed of an image encoder, a text encoder, and an instance decoder. Given an input sample \\( x = (x^{I}, x^{T}) \\), where \\( x^{I} \\) and \\( x^{T} \\) are the image and text describing the corresponding product information, respectively. These two encoders first encode the image-text pair as a sequence of feature embeddings. Then, a modality-dependent projection layer is employed to map them linearly into a joint multi-modal feature space. These projected embeddings are further decoded to produce an instance-centric representation. Details of the two unimodal encoders are elaborated as follows." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.472, + 0.903 + ], + "angle": 0, + "content": "Image Encoder. Following the vision transformers [5], the product image \\( x^{I} \\in \\mathcal{R}^{H \\times W \\times C} \\) is partitioned into \\( N \\) non-" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.182 + ], + "angle": 0, + "content": "overlapping patches. These patches are flattened to 1D input tokens and then projected linearly, with position embeddings added. Through hierarchical feature encoding, we can obtain a sequence of visual embeddings \\(\\{v_{cls}, v_1, \\dots, v_N\\}\\), where \\(v_{cls}\\) indicates the special token [CLS] for encoding the entire image information." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.185, + 0.895, + 0.293 + ], + "angle": 0, + "content": "Text Encoder. This encoder adopts analogous transformer-style architecture. For the input product description \\( x_{T} \\), it tokenizes the text to \\( M \\) subwords as in BERT [3]. Similar to the image encoder, a special [CLS] token is appended to the beginning of the textual input to summarize the text semantics. After encoding, the resulting linguistic embedding sequence is denoted as \\( \\{w_{cls}, w_{1}, \\dots, w_{M}\\} \\)." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.301, + 0.849, + 0.317 + ], + "angle": 0, + "content": "3.2. Extract Instance-Centric Representation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.323, + 0.892, + 0.504 + ], + "angle": 0, + "content": "After obtaining the contextualized embeddings, existing VLP approaches leverage \\( g_{I}(v_{cls}) \\in \\mathcal{R}^{D} \\) and \\( g_{T}(w_{cls}) \\in \\mathcal{R}^{D} \\) to align positive image-text pairs via contrastive learning. Here \\( g_{I}(\\cdot) \\) and \\( g_{T}(\\cdot) \\) are the aforementioned projections. While effective in the general domain, this design only considers the alignment between the global image-text semantics. However, in the E-commerce image, only several regions containing the desired product instance are informative foregrounds corresponding to the text description. Modeling such image-level alignment will fail to learn strong and robust product semantics. Hence, we are committed to learning instance-centric representation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.509, + 0.892, + 0.646 + ], + "angle": 0, + "content": "Instance Query. Motivated by [1, 15], a set of learnable tokens called instance query are introduced to ground the potential instance in the product image. As Figure 2 shows, each query is correlated to a specific text or image that we refer to as multi-modal prompt. The insight behind this design is that we wish the instance that a query should probe to be specified by the prompt content. Formally, the proposed instance query is denoted as \\( \\mathbf{Q} = \\{q_t \\in \\mathcal{R}^D\\}_{t=1}^T \\), which can be obtained by:" + }, + { + "type": "equation", + "bbox": [ + 0.606, + 0.656, + 0.892, + 0.674 + ], + "angle": 0, + "content": "\\[\nq _ {t} = q _ {t} ^ {\\text {p r o m p t}} + q _ {t} ^ {\\text {p o s}} + q _ {t} ^ {\\text {t y p e}}. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.684, + 0.892, + 0.821 + ], + "angle": 0, + "content": "Here, \\( q_{t}^{\\text{prompt}} \\) denotes \\( g_{I}(v_{cls}) \\) or \\( g_{T}(w_{cls}) \\), \\( q_{t}^{\\text{pos}} \\) and \\( q_{t}^{\\text{type}} \\) are learnable position and type embedding, indicating the probing area of a query and the modality type of the binding prompt. These queries are responsible for aggregating the instance-centric representations \\( H = \\{h_t\\}_{t=1}^T \\) from the encoded visual features via a decoder architecture. During pretraining, there is only one positive prompt (w.r.t the same product) for a given sample, and the rest \\( T - 1 \\) are negative ones sampled from other products." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Instance Decoder. We first project all the encoded \\(\\{v_{i}\\}_{i = 1}^{N}\\) into the same feature space as the prompt, yielding an embedding sequence \\(Z = \\{z_{i}\\in \\mathcal{R}^{D}\\}_{i = 1}^{N}\\). Moreover, the instance representations \\(\\pmb{H}^0\\) are zero-initialized, and then before feeding to the decoder. The proposed decoder then" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11062" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.119, + 0.092, + 0.506, + 0.363 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.235, + 0.366, + 0.384, + 0.38 + ], + "angle": 0, + "content": "(a) The overall Pipeline" + }, + { + "type": "image", + "bbox": [ + 0.613, + 0.09, + 0.852, + 0.364 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.631, + 0.366, + 0.802, + 0.38 + ], + "angle": 0, + "content": "(b) The \\(i\\)-th Decoder Block" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.386, + 0.895, + 0.429 + ], + "angle": 0, + "content": "Figure 2. (a) The architecture of the proposed instance-level representation learning paradigm (ECLIP), which is composed of an image encoder, a text encoder, and an instance decoder. The whole framework is optimized by three pretext tasks when pretraining on a large-scale E-commerce dataset. (b) The core decoder architecture that aims at aggregating the instance representation of the desired product." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.434, + 0.47, + 0.48 + ], + "angle": 0, + "content": "reads all the above-described embeddings: \\( Z \\), \\( Q \\) and \\( H^0 \\) as its input. It has \\( L \\) stacked blocks, and each one consists of a slot-attention and a self-attention layer." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.481, + 0.47, + 0.571 + ], + "angle": 0, + "content": "The goal of the slot-attention layer is to adaptively update query representations through the interaction with the encoded visual embeddings. In detail, for the \\(l\\)-th slot-attention layer, it first calculates a similarity matrix \\(M \\in \\mathcal{R}^{N \\times T}\\), which is implemented by the dot-product attention mechanism [24]. Formally, it is formulated by:" + }, + { + "type": "equation", + "bbox": [ + 0.139, + 0.583, + 0.47, + 0.615 + ], + "angle": 0, + "content": "\\[\nM = \\frac {1}{\\sqrt {D}} \\left(Z W _ {z}\\right) \\cdot \\left(\\left(Q + H ^ {l - 1}\\right) W _ {q}\\right) ^ {\\top}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.625, + 0.47, + 0.687 + ], + "angle": 0, + "content": "where \\( W_{z} \\) and \\( W_{q} \\) are the learnable projections parameter matrices, \\( H^{l - 1} \\) is the instance representation produced by the \\( (l - 1)^{\\mathrm{th}} \\) decoder block. The similarity matrix \\( M \\) is further normalized by a softmax function over \\( T \\) queries:" + }, + { + "type": "equation", + "bbox": [ + 0.189, + 0.698, + 0.47, + 0.735 + ], + "angle": 0, + "content": "\\[\nM _ {i j} = \\frac {\\exp (M _ {i j})}{\\sum_ {t = 1} ^ {T} \\exp (M _ {i t})}. \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.745, + 0.471, + 0.851 + ], + "angle": 0, + "content": "The generated matrix \\( M \\) actually performs soft assignment via computing semantic similarity between \\( N \\) visual tokens and \\( T \\) instance queries. In doing so, it is capable of distributing each visual token to a specific query according to their similarity score. To aggregate the information of the visual tokens into their assigned input query, we compute a weighted mean update based on \\( M \\):" + }, + { + "type": "equation", + "bbox": [ + 0.147, + 0.863, + 0.47, + 0.905 + ], + "angle": 0, + "content": "\\[\n\\Delta h _ {t} ^ {l - 1} = \\frac {1}{\\sum_ {i = 1} ^ {N} M _ {i t}} \\sum_ {i = 1} ^ {N} M _ {i t} \\left(W _ {v} z _ {i}\\right). \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.434, + 0.892, + 0.465 + ], + "angle": 0, + "content": "Finally, the instance representation \\( \\pmb{H}^l \\) at the \\( l \\)-th layer can be updated by a residual connection:" + }, + { + "type": "equation", + "bbox": [ + 0.613, + 0.477, + 0.892, + 0.496 + ], + "angle": 0, + "content": "\\[\nh _ {t} ^ {l} = h _ {t} ^ {l - 1} + W _ {o} \\Delta h _ {t} ^ {l - 1}. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.51, + 0.871, + 0.525 + ], + "angle": 0, + "content": "where \\(W_{v}\\) and \\(W_{o}\\) are linear transformation parameters." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.526, + 0.892, + 0.678 + ], + "angle": 0, + "content": "On the top of slot-attention layer, there is a self-attention module that performs information propagation between each query. In detail, given the previously updated \\( H^{l} \\), it employs standard multi-head self-attention (MSA) followed by a fully connected feed-forward network as in [24]. After \\( L \\) successive decoder blocks, we can obtain the final instance representation \\( H^{L} \\). It is noteworthy that, since the multi-modal prompts just participate in the similarity calculation in Eq. 2, the resulting \\( H^{L} \\) thus contains only visual-modality information." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.683, + 0.892, + 0.819 + ], + "angle": 0, + "content": "Discussion: The proposed decoder works like conducting a clustering on the image tokens, where each instance query serves as the centroid of a cluster. At each decoder block, it determines where each token belongs by measuring its distance from the centroids in the semantic space. The cluster centroid is then updated via a soft manner (Eq.4) based on the calculated distance. By stacking multiple decoder blocks, it can implicitly force each query to attend to a specific region and aggregate instance-level representations." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.831, + 0.813, + 0.848 + ], + "angle": 0, + "content": "3.3. Multi-Modal Pretraining Objectives" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Our ECLIP is optimized on large-scale uncurated product data with several pretraining proxy tasks. In the following, we describe each task in detail." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11063" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.109, + 0.089, + 0.437, + 0.221 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.23, + 0.473, + 0.288 + ], + "angle": 0, + "content": "Figure 3. The illustration of inter-product contrastive learning. It aims to bring the positive samples from the same product (\\(x\\) and \\(x^{+}\\)) closer than the negative ones (\\(x\\) and \\(x^{-}\\)), which contributes to grounding the desired product (outlined with the green box)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.295, + 0.471, + 0.356 + ], + "angle": 0, + "content": "Image-Text Contrastive Learning. As in [9, 12, 20], this task contributes to learning better unimodal representations. Given a batch of product samples \\(\\{(x_i^I,x_i^T)\\}_{i = 1}^B\\), the similarity between image \\(x^{I}\\) and text \\(x^{T}\\) is estimated as:" + }, + { + "type": "equation", + "bbox": [ + 0.164, + 0.367, + 0.383, + 0.386 + ], + "angle": 0, + "content": "\\[\ns (x ^ {I}, x ^ {T}) = g _ {I} \\left(v _ {c l s}\\right) ^ {\\top} g _ {T} \\left(w _ {c l s}\\right).\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.398, + 0.47, + 0.445 + ], + "angle": 0, + "content": "This pretraining objective brings the image-text pairs of the same product in the embedding space closer than the unmatched ones, which consists of image-to-text term \\(\\mathcal{L}_{i2t}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.128, + 0.457, + 0.47, + 0.5 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {i 2 t} = - \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp (s \\left(x _ {i} ^ {I} , x _ {i} ^ {T}\\right) / \\tau)}{\\sum_ {j = 1} ^ {B} \\exp (s \\left(x _ {i} ^ {I} , x _ {j} ^ {T}\\right) / \\tau)}, \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.506, + 0.281, + 0.521 + ], + "angle": 0, + "content": "and a text-to-image term \\(\\mathcal{L}_{t2i}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.128, + 0.533, + 0.47, + 0.575 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {t 2 i} = - \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp \\left(s \\left(x _ {i} ^ {T} , x _ {i} ^ {I}\\right) / \\tau\\right)}{\\sum_ {j = 1} ^ {B} \\exp \\left(s \\left(x _ {i} ^ {T} , x _ {j} ^ {I}\\right) / \\tau\\right)}, \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.586, + 0.469, + 0.617 + ], + "angle": 0, + "content": "where \\(\\tau\\) is a learnable temperature parameter. The whole objective is then defined as \\(\\mathcal{L}_{itc} = \\frac{1}{2} (\\mathcal{L}_{i2t} + \\mathcal{L}_{t2i})\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.62, + 0.469, + 0.72 + ], + "angle": 0, + "content": "Inter-Product Multi-modal Learning. As shown in Figure 3, we maintain a momentum model during pretraining that is an exponential-moving-average of the origin model like [8]. For a product sample \\( x_{i} \\), we denote the representation of the positive prompt produced by the base and momentum model as \\( h_{\\theta}^{i} \\) and \\( h_{\\xi}^{i} \\). An inter-product contrastive loss \\( \\mathcal{L}_{inter} \\) is computed by:" + }, + { + "type": "equation", + "bbox": [ + 0.078, + 0.73, + 0.469, + 0.779 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {i n t e r} = - \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp \\left(h _ {\\theta} ^ {i} ^ {\\top} h _ {\\xi} ^ {j} / \\tau\\right)}{\\exp \\left(h _ {\\theta} ^ {i} ^ {\\top} h _ {\\xi} ^ {j} / \\tau\\right) + \\sum_ {k \\in \\mathcal {N} ^ {-}} \\exp \\left(h _ {\\theta} ^ {i} ^ {\\top} h _ {\\xi} ^ {k} / \\tau\\right)}, \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.47, + 0.902 + ], + "angle": 0, + "content": "where sample \\(i\\) and \\(j\\) are a positive pair, \\(\\mathcal{N}^{-}\\) is a negative sample set that belongs to other products. This objective maximizes the similarity between different samples of the identical product, while minimizing those of the unmatched ones. Since the images of a product are collected from different sources, their background appearances are usually diverse. Hence, \\(\\mathcal{L}_{inter}\\) will encourage the yielded representation to be highly correlated with the desired product and" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.891, + 0.121 + ], + "angle": 0, + "content": "thus contributes to aligning the positive prompt with the corresponding image tokens in a fine-grained manner." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.122, + 0.892, + 0.228 + ], + "angle": 0, + "content": "This pretext task also incorporates an additional instance-text matching loss that predicts whether an instance and a text description are matched. Formally, given an instance-text pair, we obtain their match logit defined by: \\( f(h_{\\theta}^{i}\\odot g_{T}(w_{\\mathrm{cls}}^{i})) \\), where \\( \\odot \\) is Hadamard product and \\( f(\\cdot) \\) is a mapping layer: \\( \\mathcal{R}^D\\to \\mathcal{R}^2 \\). This match logit is optimized by a typical binary cross entropy loss \\( \\mathcal{L}_{itm} \\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.232, + 0.892, + 0.368 + ], + "angle": 0, + "content": "Intra-Product Multi-modal Learning. For a product sample, there is only one positive prompt describing the presented product during pretraining, and the rest \\( T - 1 \\) prompts are sampled from other products. The motivation behind this pretext task is to ensure that only positive queries can probe the foreground instance, rather than negative ones. To this end, we apply an intra-product contrastive loss using text supervision. Suppose index \\( r \\) indicates the positive query, then \\( \\mathcal{L}_{\\text{intra}} \\) can be formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.525, + 0.377, + 0.892, + 0.42 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {i n t r a} = - \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp \\left(h _ {r} ^ {i} ^ {\\top} g _ {T} \\left(w _ {\\mathrm {c l s}} ^ {i}\\right) / \\tau\\right)}{\\sum_ {t = 1} ^ {T} \\exp \\left(h _ {t} ^ {i} ^ {\\top} g _ {T} \\left(w _ {\\mathrm {c l s}} ^ {i}\\right) / \\tau\\right)}, \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.427, + 0.893, + 0.473 + ], + "angle": 0, + "content": "which serves to bring the positive query and the product description closer than all \\( T - 1 \\) negative ones. Moreover, we also introduce an entropy regularization term for \\( M \\):" + }, + { + "type": "equation", + "bbox": [ + 0.521, + 0.482, + 0.892, + 0.582 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {\\mathcal {R}} = \\sum_ {i = 1} ^ {N} M _ {i, r} \\log \\left(\\frac {1}{M _ {i , r}}\\right) + \\\\ \\sum_ {j = 1, j \\neq r} ^ {T} \\left(\\log N - \\sum_ {i = 1} ^ {N} M _ {i, j} \\log \\left(\\frac {1}{M _ {i , j}}\\right)\\right). \\tag {10} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.583, + 0.892, + 0.672 + ], + "angle": 0, + "content": "This regularization term encourages the positive query to focus on several tokens that may contain product instances. While for \\( T - 1 \\) negative ones, it prevents too sharp similarity distributions over \\( N \\) image tokens. Finally, the overall pretraining objective of ECLIP is the sum of all aforementioned loss terms." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.682, + 0.772, + 0.697 + ], + "angle": 0, + "content": "3.4. Transfer to Downstream Tasks" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.705, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Once pretrained, the resulting foundation model can be leveraged to extract the product instance representation with minimal surgery. Specifically, given a product sample \\((x_{i}^{I}, x_{i}^{T})\\), we first encode the image-text pair into an embedding sequence via the unimodal encoders. Then, the global representation of text description \\(g_{T}(w_{\\mathrm{cls}})\\) is treated as the positive query and fed into the decoder concatenated with \\(T - 1\\) negative ones. Here, the negative queries \\(\\{q_{t}\\}_{t = 2}^{T}\\) are sampled from a standard Gaussian distribution for convenience. We also explore different negative query setting manners in Section 4.3. The yielded representation \\(h_0^L\\) belonging to the positive query is then applied to a wide range of E-commerce downstream tasks." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11064" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.077, + 0.091, + 0.21, + 0.108 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.115, + 0.26, + 0.132 + ], + "angle": 0, + "content": "4.1. Pretraining Details" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.139, + 0.47, + 0.29 + ], + "angle": 0, + "content": "Pretraining Dataset We collected a large-scale pretraining dataset from a popular E-commerce website. It consists of 15M different products and over 100M various images, covering about 9K diverse categories such as clothes, daily necessities, instruments, and so on. For each product item, it has a corresponding textual description and several images from the product details pages, customer comments, and attached advertisement videos. During pretraining, the positive data pairs are constructed by sampling images belonging to the same product from different sources." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.291, + 0.471, + 0.534 + ], + "angle": 0, + "content": "Implementation Details. The image encoder adopts the same network configuration as the standard ViT [5] and is initialized from the weight pre-trained on ImageNet. Our text encoder is implemented with the same architecture as \\(\\mathrm{BERT}_{\\mathrm{base}}\\) [3]. The decoder has 6 identical blocks and 20 instance queries. We here explore two variants of ViTs: ViTB/16 and ViT-L/16. There is a total of \\(220 / 450\\) million parameters for the base and large version. During pretraining, the input images are resized to \\(224 \\times 224\\) with random crop and horizontal flip augmentation, and the texts are tokenized by WordPiece with a maximum length of 55. We pretrain for 15 epochs with a batch size of 6400 (ViT-B) / 4096 (ViT-L) on 32 NVIDIA A100 GPUs. The whole framework is learned using AdamW [16] optimizer and the learning rate is warmed up to 1e-4 and then decayed linearly. More details are elaborated in the supplementary." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.534, + 0.47, + 0.641 + ], + "angle": 0, + "content": "Compared Baselines. We mainly compare the ECLIP with several state-of-the-art VLP methods: CLIP [20], FILIP [31], DeCLIP [14], ALBEF [12] and BLIP [11]. For a fair comparison, we also leverage ViT-B/16 as the image encoder, \\(\\mathrm{BERT}_{\\mathrm{base}}\\) as the text encoder, and pretrain these baselines on the same 100M E-commerce data using their official public implementations." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.651, + 0.371, + 0.667 + ], + "angle": 0, + "content": "4.2. Evaluation on Downstream Tasks" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.675, + 0.469, + 0.707 + ], + "angle": 0, + "content": "Next, we delineate the evaluation performances for five specific E-commerce downstream tasks in turn." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.71, + 0.36, + 0.725 + ], + "angle": 0, + "content": "4.2.1 Zero-Shot Product Classification" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.47, + 0.903 + ], + "angle": 0, + "content": "We first transfer ECLIP to product item classification. It is a recognition task that aims to map a product sample to a specific category. We evaluate the performance on a large-scale publicly available E-commerce dataset called M5Product [4], which covers 1.1M images and 5,679 various product categories. Here, we consider the multimodal setting, which uses both the product image and related textual description for classification. To demonstrate the strong zero-shot ability of ECLIP, we apply it directly to the classification evaluation without further finetune. It is achieved by measuring the similarity between the category text like" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.091, + 0.892, + 0.168 + ], + "angle": 0, + "content": "CLIP [20]. The left part of Table 1 summarizes the Top-1 classification accuracy of all the compared methods. As presented, our ECLIP greatly exceeds all the existing baselines by a large margin (e.g., \\(+6.6\\%\\) v.s. CLIP), demonstrating the superiority of instance-level representation." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.18, + 0.776, + 0.195 + ], + "angle": 0, + "content": "4.2.2 Zero-Shot Image-Text Retrieval" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.204, + 0.892, + 0.386 + ], + "angle": 0, + "content": "ECLIP is also transferred to test zero-shot performance for image-to-text and text-to-image retrieval. To this end, we collect a large dataset that contains 205K image-text pairs of E-commerce products. Since only unimodal information is available in this task, we simply use our image and text encoders to embed image-text pairs and complete the retrieval based on their pairwise similarity. We utilize the widely-used Recall@K metric for evaluation. Detailed comparison results are shown in the right part of Table 1. We can see that, despite training on the same dataset, our method achieves superior performance owing to fine-grained alignment modeling between text and product instance." + }, + { + "type": "table", + "bbox": [ + 0.504, + 0.399, + 0.89, + 0.538 + ], + "angle": 0, + "content": "
MethodClassificationImage-to-TextText-to-Image
Acc@1R@1R@5R@1R@5
CLIP [20]37.252.674.158.784.0
FILIP [31]37.152.373.858.083.5
DeCLIP [14]37.853.175.858.883.9
ALBEF [12]38.552.974.458.283.3
BLIP [11]39.353.375.659.184.4
OursViT-B/1643.853.876.059.984.6
OursViT-L/1644.858.279.663.887.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.548, + 0.892, + 0.577 + ], + "angle": 0, + "content": "Table 1. Performance comparisons of zero-shot product classification and zero-shot image-text retrieval on M5Product dataset [4]." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.59, + 0.753, + 0.604 + ], + "angle": 0, + "content": "4.2.3 Zero-Shot Product Retrieval" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.614, + 0.892, + 0.901 + ], + "angle": 0, + "content": "This task aims to find the most relevant target product given a query (image-text pair of a product). It has a wide range of applications in real e-commerce scenarios such as recommending relevant products for users. We first evaluate the coarse-level retrieval. Following [4], a product pair is considered a match if both belong to the same category during evaluation. The results on M5Product benchmark are reported on the left of Table 2. It can be seen that exploiting instance-centric representation significantly boosts performance. To further demonstrate the effectiveness of instance-level representation, we then conduct a more complicated fine-grained level product retrieval task, where a pair is considered a match if and only if they are the same product. This task requires more adequate fine-grained understanding ability since it focuses on the specific product instance. Detailed comparison results are shown on the right part of Table 2. One can find that our ECLIP achieves a substantial improvement in retrieval performance (e.g., \\(44.3\\%\\) v.s. \\(35.6\\%\\) (BLIP) on \\(\\mathbf{R}@\\mathbf{1}\\))." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11065" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.082, + 0.089, + 0.891, + 0.278 + ], + "angle": 0, + "content": "
MethodPretraining DatasetCoarse Product RetrievalFine-grained Product Retrieval
mAP@1mAP@5mAP@10R@1R@5R@10mAP@1mAP@5mAP@10
ViBERT [17]58.661.760.1------
UNITER [2]M5Product58.962.860.9------
SCALE [4]59.864.162.2------
CLIP [20]68.273.270.734.854.262.934.840.239.9
FILIP [31]67.873.070.334.653.962.234.640.139.7
DeCLIP [14]68.573.470.835.356.465.535.341.240.8
ALBEF [12]ECLIP 100M68.773.671.235.156.165.235.140.740.4
BLIP [11]69.174.171.635.656.866.035.641.641.3
OursViT-B/1669.674.972.544.363.471.143.848.648.2
OursViT-L/1670.275.372.945.064.272.145.050.049.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.175, + 0.286, + 0.794, + 0.302 + ], + "angle": 0, + "content": "Table 2. Performance comparisons of zero-shot coarse level and fine-grained level product retrieval task." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.315, + 0.471, + 0.451 + ], + "angle": 0, + "content": "We also consider another setting introduced in [35], called instance-level retrieval, where the query image encompasses multiple different kinds of product instances. And the model needs to find all the related products from a large gallery. As shown in Table 3, ECLIP still achieves superior performance than all the previous approaches. Although the CAPTURE leverages a specially-trained object detector to extract instances, ECLIP still surpasses it by a clear margin with no box annotation." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.463, + 0.332, + 0.479 + ], + "angle": 0, + "content": "4.2.4 Zero-Shot Visual Grounding" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.487, + 0.471, + 0.79 + ], + "angle": 0, + "content": "To demonstrate whether our model possesses the capability of localizing the desired product instance after pretraining, we further evaluate ECLIP on the zero-shot product grounding, which requires localizing the product instance in an image according to a textual description. Specifically, the input image-text pair is first fed to our ECLIP to obtain a score map \\( S \\in \\mathcal{R}^{H \\times W} \\) that measures the similarity between the text and each image location. Then, we use \\( S \\) to rank the candidate regions produced by an off-the-shelf region proposal network. The performance is evaluated by the top-1 accuracy at IoU thresholds \\( \\{0.5, 0.7\\} \\) on an annotated grounding dataset consisting of 450K product images. Detailed comparison results are listed in Table 4. As we can see, compared to methods aimed at global representation, our model has learned fine-grained cross-modal understanding ability and thus obtains substantial performance gain (e.g., +14.5% v.s. BLIP on Acc@0.7). Since ECLIP supports image prompts during pretraining, we also conduct zero-shot image-conditioned grounding. The results and analysis can be found in the supplementary." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.801, + 0.332, + 0.817 + ], + "angle": 0, + "content": "4.2.5 Transfer to Object Detection" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.47, + 0.903 + ], + "angle": 0, + "content": "We also transfer ECLIP to object detection to further validate its fine-grained understanding ability. Following DETR [1], we utilize the image encoder to embed visual features, and utilize the decoder with a newly added prediction head to decode the potential objects. Moreover, we col" + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.312, + 0.891, + 0.442 + ], + "angle": 0, + "content": "
MethodInstance-Level Product Retrieval
mAP@10mAP@50mAR@10mAR@50
CAPTURE [35]40.436.817.215.9
CLIP [20]86.682.854.459.5
FILIP [31]86.983.054.659.8
DeCLIP [14]87.183.354.960.0
BLIP [11]87.583.555.160.4
OursViT-B/1689.684.655.961.2
OursViT-L/1689.586.456.362.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.447, + 0.892, + 0.476 + ], + "angle": 0, + "content": "Table 3. Performance comparisons of zero-shot instance-level product retrieval task on Product1M dataset [35]." + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.48, + 0.891, + 0.604 + ], + "angle": 0, + "content": "
MethodVisual GroundingObject Detection
Acc@0.5Acc@0.7mAP@0.3mAP@0.5
CLIP [20]80.975.217.214.3
FILIP [31]81.375.617.514.6
DeCLIP [14]81.075.317.014.2
ALBEF [12]80.974.717.113.9
BLIP [11]81.175.117.314.1
OursViT-B/1691.289.620.216.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.609, + 0.892, + 0.638 + ], + "angle": 0, + "content": "Table 4. Performance comparisons of zero-shot visual grounding and finetuned object detection." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.642, + 0.894, + 0.749 + ], + "angle": 0, + "content": "lect a manually annotated detection dataset covering 160K images. We split a 20K subset for evaluation and leave the rest for model finetuning. The supplementary provides experiment details of baselines and ECLIP. It can be observed from Table 4 and Figure 4b that it outperforms the existing VLP methods, demonstrating the superiority of ECLIP to learning fine-grained semantics in E-commerce." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.757, + 0.655, + 0.773 + ], + "angle": 0, + "content": "4.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Effect of Pretext Task. To demonstrate the effectiveness of the inter- and intra- product learning pretext task, we designed the experiments on different task combinations for the product retrieval task. All the ablations were conducted on a smaller pretraining dataset that includes only 5M images due to the costly training time. The complete results are listed in Table 5. One can observe that canceling either of these two tasks will result in worse performance. No" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "11066" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.089, + 0.468, + 0.282 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.516, + 0.1, + 0.849, + 0.282 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.15, + 0.293, + 0.816, + 0.308 + ], + "angle": 0, + "content": "Figure 4. Qualitative examples of visual grounding and object detection. See the main text for more explanation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.322, + 0.468, + 0.381 + ], + "angle": 0, + "content": "tably, the inter-product task brings a more significant performance boost compared to the intra-product. We speculate that because the former contrasts with more negative samples from different product images." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.382, + 0.468, + 0.578 + ], + "angle": 0, + "content": "Effect of Negative Query. Since the negative instance queries are used when transferring to classification and retrieval tasks, we also ran ablations on different ways of setting these negative ones. We tried the following cases: 1) Leverage the description text from other products and encode with the text encoder. 2) Randomly sample the negative queries from the standard Gaussian distribution. 3) Adopt the exponential moving average of queries on the whole dataset during pretraining. Table 6 summarizes the results of this ablation study on the product retrieval task. As observed, there is little to no difference between different manners of setting up negative queries. We thus adopt random sampling for implementation simplicity." + }, + { + "type": "table", + "bbox": [ + 0.129, + 0.588, + 0.42, + 0.667 + ], + "angle": 0, + "content": "
LinterLintramAP@1 (%)mAP@5 (%)
61.665.2
✓64.468.7
✓62.266.5
✓✓65.269.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.672, + 0.468, + 0.7 + ], + "angle": 0, + "content": "Table 5. Ablation of different pretraining pretext task combinations on coarse-level product retrieval task (ViT-B/16)." + }, + { + "type": "table", + "bbox": [ + 0.129, + 0.706, + 0.419, + 0.776 + ], + "angle": 0, + "content": "
MetricmAP@10mAR@10
Negative Text84.952.5
EMA Update85.153.3
Random Sampling86.854.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.781, + 0.468, + 0.809 + ], + "angle": 0, + "content": "Table 6. Ablation of different negative query setting manners on the instance-level product retrieval task (ViT-B/16)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.818, + 0.268, + 0.833 + ], + "angle": 0, + "content": "4.4. Qualitative Analysis" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.468, + 0.901 + ], + "angle": 0, + "content": "In this section, we first qualitatively showcase that ECLIP can learn fine-grained cross-modal alignment to ground the desired product. Figure 4a presents the visualization of the similarity score map between a product image" + }, + { + "type": "image", + "bbox": [ + 0.499, + 0.32, + 0.685, + 0.456 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.556, + 0.461, + 0.621, + 0.476 + ], + "angle": 0, + "content": "(a) CLIP" + }, + { + "type": "image", + "bbox": [ + 0.706, + 0.32, + 0.891, + 0.456 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.765, + 0.461, + 0.842, + 0.476 + ], + "angle": 0, + "content": "(b) ECLIP" + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.482, + 0.892, + 0.552 + ], + "angle": 0, + "content": "Figure 5. The T-SNE visualization for the learned representations of CLIP and our ECLIP. Both of them are pretrained on the same dataset. We first randomly select 50 kinds of products and then draw 100 samples for each. Points belonging to the same category are of the same color. See the main text for more explanation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.559, + 0.892, + 0.711 + ], + "angle": 0, + "content": "and its corresponding text description, where darker color indicates image locations with higher similarity to text. We can clearly observe that our model can rightly attend to the desired instance depicted by the text. Furthermore, T-SNE is used for visualizing the visual embedding of different kinds of product samples. As shown in Figure 5, compared to CLIP, our ECLIP can extract semantic-rich yet compact representations that better distinguish products belonging to different categories. More visualization examples and analysis are provided in our supplementary." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.724, + 0.618, + 0.74 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.892, + 0.856 + ], + "angle": 0, + "content": "In this paper, we develop an effective large-scale multimodal pretraining paradigm called ECLIP in E-commerce. Beyond regular global representation, it instead aims to learn the instance-level representation via a novel decoder and the carefully-designed pretraining proxy tasks. Extensive experimental results further demonstrate the superior generalization capability of the proposed framework." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.857, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Acknowledgement: This work is supported by National Key R&D Program of China (2022ZD0160305) and Beijing Natural Science Foundation (Z190001)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "11067" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.184 + ], + "angle": 0, + "content": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 2, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.185, + 0.472, + 0.254 + ], + "angle": 0, + "content": "[2] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.256, + 0.471, + 0.311 + ], + "angle": 0, + "content": "[3] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.312, + 0.471, + 0.408 + ], + "angle": 0, + "content": "[4] Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Michael C Kampffmeyer, Xiaoyong Wei, Minlong Lu, Yaowei Wang, and Xiaodan Liang. M5product: Selfharmonized contrastive learning for e-commercial multimodal pretraining. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21252-21262, 2022. 1, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.41, + 0.471, + 0.492 + ], + "angle": 0, + "content": "[5] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.494, + 0.471, + 0.549 + ], + "angle": 0, + "content": "[6] Zhe Gan, Linjie Li, Chunyuan Li, Lijuan Wang, Zicheng Liu, and Jianfeng Gao. Vision-language pre-training: Basics, recent advances, and future trends. arXiv preprint arXiv:2210.09263, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.55, + 0.471, + 0.634 + ], + "angle": 0, + "content": "[7] Dehong Gao, Linbo Jin, Ben Chen, Minghui Qiu, Peng Li, Yi Wei, Yi Hu, and Hao Wang. Fashionbert: Text and image matching with adaptive loss for cross-modal retrieval. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2251-2260, 2020. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.635, + 0.471, + 0.703 + ], + "angle": 0, + "content": "[8] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.704, + 0.471, + 0.787 + ], + "angle": 0, + "content": "[9] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.789, + 0.471, + 0.844 + ], + "angle": 0, + "content": "[10] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning, pages 5583-5594. PMLR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.845, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[11] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. arXiv preprint arXiv:2201.12086, 2022. 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.174 + ], + "angle": 0, + "content": "[12] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems, 34:9694-9705, 2021. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.178, + 0.892, + 0.273 + ], + "angle": 0, + "content": "[13] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, and Jianfeng Gao. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10965–10975, June 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.276, + 0.892, + 0.344 + ], + "angle": 0, + "content": "[14] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208, 2021. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.346, + 0.892, + 0.428 + ], + "angle": 0, + "content": "[15] Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. Object-centric learning with slot attention. Advances in Neural Information Processing Systems, 33:11525-11538, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.431, + 0.892, + 0.459 + ], + "angle": 0, + "content": "[16] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.461, + 0.892, + 0.516 + ], + "angle": 0, + "content": "[17] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems, 32, 2019. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.518, + 0.892, + 0.559 + ], + "angle": 0, + "content": "[18] Mieczysław Pawlowski. Machine learning based product classification for ecommerce. Journal of Computer Information Systems, 62(4):730-739, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.561, + 0.892, + 0.615 + ], + "angle": 0, + "content": "[19] AJ Piergiovanni, Wei Li, Weicheng Kuo, Mohammad Saffar, Fred Bertsch, and Anelia Angelova. Answer-me: Multi-task open-vocabulary visual question answering. arXiv preprint arXiv:2205.00949, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.617, + 0.892, + 0.701 + ], + "angle": 0, + "content": "[20] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.702, + 0.892, + 0.771 + ], + "angle": 0, + "content": "[21] Puji Rahayu, Dana Indra Sensuse, Betty Purwandari, Indra Budi, F Khalid, and N Zulkarnaim. A systematic review of recommender system for e-portfolio domain. In Proceedings of the 5th International Conference on Information and Education Technology, pages 21–26, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.773, + 0.892, + 0.828 + ], + "angle": 0, + "content": "[22] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.83, + 0.892, + 0.872 + ], + "angle": 0, + "content": "[23] Hao Tan and Mohit Bansal. Lxmert: Learning cross-modality encoder representations from transformers. arXiv preprint arXiv:1908.07490, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.873, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[24] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11068" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.092, + 0.47, + 0.12 + ], + "angle": 0, + "content": "Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.123, + 0.47, + 0.192 + ], + "angle": 0, + "content": "[25] Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework. arXiv preprint arXiv:2202.03052, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.195, + 0.47, + 0.237 + ], + "angle": 0, + "content": "[26] Wenhui Wang, Hangbo Bao, Li Dong, and Furu Wei. Vlmo: Unified vision-language pre-training with mixture-of-modality-experts. arXiv preprint arXiv:2111.02358, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.239, + 0.47, + 0.294 + ], + "angle": 0, + "content": "[27] Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao. Simvlm: Simple visual language model pretraining with weak supervision. arXiv preprint arXiv:2108.10904, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.297, + 0.47, + 0.366 + ], + "angle": 0, + "content": "[28] Chi-Man Wong, Fan Feng, Wen Zhang, Chi-Man Vong, and Chen. Improving conversational recommender system by pretraining billion-scale knowledge graph. In International Conference on Data Engineering (ICDE), pages 2607-2612, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.369, + 0.47, + 0.411 + ], + "angle": 0, + "content": "[29] Hu Xu, Bing Liu, Lei Shu, and P Yu. Open-world learning and application to product classification. In The World Wide Web Conference, pages 3413-3419, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.413, + 0.47, + 0.483 + ], + "angle": 0, + "content": "[30] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18134-18144, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.486, + 0.47, + 0.542 + ], + "angle": 0, + "content": "[31] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. Filip: Fine-grained interactive language-image pre-training. arXiv preprint arXiv:2111.07783, 2021. 1, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.544, + 0.47, + 0.598 + ], + "angle": 0, + "content": "[32] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.602, + 0.47, + 0.685 + ], + "angle": 0, + "content": "[33] Licheng Yu, Jun Chen, Animesh Sinha, Mengjiao Wang, Yu Chen, Tamara L Berg, and Ning Zhang. Commercemm: Large-scale commerce multimodal representation learning with omni retrieval. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 4433-4442, 2022. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.688, + 0.47, + 0.73 + ], + "angle": 0, + "content": "[34] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. arXiv preprint arXiv:2111.08276, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.732, + 0.47, + 0.815 + ], + "angle": 0, + "content": "[35] Xunlin Zhan, Yangxin Wu, Xiao Dong, Yunchao Wei, Minlong Lu, Yichi Zhang, Hang Xu, and Xiaodan Liang. Product1m: Towards weakly supervised instance-level product retrieval via cross-modal pretraining. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11782-11791, 2021. 1, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.818, + 0.47, + 0.9 + ], + "angle": 0, + "content": "[36] Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, and Jianfeng Gao. Vinvl: Revisiting visual representations in vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5579-5588, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.092, + 0.893, + 0.162 + ], + "angle": 0, + "content": "[37] Yushan Zhu, Huaixiao Zhao, Wen Zhang, Ganqiang Ye, Hui Chen, Ningyu Zhang, and Huajun Chen. Knowledge perceived multi-modal pretraining in e-commerce. In Proceedings of the 29th ACM International Conference on Multimedia, pages 2744-2752, 2021. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.164, + 0.893, + 0.245 + ], + "angle": 0, + "content": "[38] Mingchen Zhuge, Dehong Gao, Deng-Ping Fan, Linbo Jin, Ben Chen, Haoming Zhou, Minghui Qiu, and Ling Shao. Kaleido-bert: Vision-language pre-training on fashion domain. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12647-12657, 2021. 3" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.092, + 0.893, + 0.245 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11069" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/88606539-5034-4f6c-aa8b-089f9230525d_origin.pdf b/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/88606539-5034-4f6c-aa8b-089f9230525d_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..6fd3824c34933553a2563ed21003f53636c3e569 --- /dev/null +++ b/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/88606539-5034-4f6c-aa8b-089f9230525d_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e451abde79d003092fd636df8128638fea16247ccc97545e9c11b851da2a1d01 +size 3653335 diff --git a/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/full.md b/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/full.md new file mode 100644 index 0000000000000000000000000000000000000000..0c5c051d56d69b0996173fd7a6aae70c21b8f953 --- /dev/null +++ b/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/full.md @@ -0,0 +1,315 @@ +# Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-commerce + +Yang Jin $^{1,2}$ , Yongzhi Li $^{2}$ , Zehuan Yuan $^{2}$ , Yadong Mu $^{1*}$ $^{1}$ Peking University $\mathbf{\Pi}^{2}$ ByteDance Inc. + +jiny@stu.pku.edu.cn, liyongzhi.ailab@bytedance.com, + +yuanzehuan@bytedance.com, myd@pku.edu.cn + +# Abstract + +This paper aims to establish a generic multi-modal foundation model that has the scalable capability to massive downstream applications in E-commerce. Recently, large-scale vision-language pretraining approaches have achieved remarkable advances in the general domain. However, due to the significant differences between natural and product images, directly applying these frameworks for modeling image-level representations to E-commerce will be inevitably sub-optimal. To this end, we propose an instance-centric multi-modal pretraining paradigm called ECLIP in this work. In detail, we craft a decoder architecture that introduces a set of learnable instance queries to explicitly aggregate instance-level semantics. Moreover, to enable the model to focus on the desired product instance without reliance on expensive manual annotations, two specially configured pretext tasks are further proposed. Pretrained on the 100 million E-commerce-related data, ECLIP successfully extracts more generic, semantic-rich, and robust representations. Extensive experimental results show that, without further fine-tuning, ECLIP surpasses existing methods by a large margin on a broad range of downstream tasks, demonstrating the strong transferability to real-world E-commerce applications. + +# 1. Introduction + +Nowadays, the flourishing growth of E-commerce has brought great convenience to people's daily life. And a wide range of product-based application tasks has subsequently emerged, such as item classification [18, 29], product retrieval [7, 35], commodity recommendation [21, 28], and so on. Compared to developing individual task-specific models, building a general-purpose foundation model that works for massive E-commercial applications simultaneously can enhance applicability and reduce training costs. + +![](images/cda2c2194e2842ce76a85d8a32befab13a10fcd293a95f3afbf76a67fe4e34f0.jpg) + +![](images/1f81ad7128207b166829c846f6c18f23986ffc11f5d643b0d74b7d73247a9328.jpg) + +![](images/b63b68434f38cfc5d5b308d3f4a273ece4df8488e4c31651e94d6db2c719b807.jpg) +Figure 1. Domain difference between natural and product images. For natural images, it is the frequent case that most pixels are semantically correlated to the textual sentence. However, in E-commerce, such correlation is much more sparse (e.g., "frying pan" or "coffee machine" only occupy small portions of the entire images). Moreover, images for a product are often provided in a group from multiple sources such as (a) advertisement videos, (b) product pages, (c) customer comments (see the bottom examples). + +![](images/95341b20c11bff08a3df3a191172197048a7083ec9f9eddada9ef137f46a0e6d.jpg) + +Recent developments in vision-language pretraining (VLP) [9, 12, 17, 20, 31, 34] have demonstrated remarkable advances in diverse VL downstream tasks. Profiting from large-scale image-text pairs, these methods are able to learn generic multimodal representations that are reused across various tasks. In E-commerce scenario, the related data naturally contains cross-modal information to describe a corresponding product. Motivated by the tremendous success achieved by VL modeling, several approaches [4, 33, 35, 37] have made attempts at designing a commerce-specific multimodal representation learning paradigm. They imitate the existing VLP methods (e.g., CLIP [20], VilBERT [17]) to learn the image-level representations of the product via pretraining on abundant commerce image-text pairs. + +Though promising results have been achieved, directly applying these VLP methods in the general domain to E- + +commerce still suffers from inherent deficiencies. The properties of natural and product images appear to be dramatically different. Given a natural image-text pair, almost every pixel in the natural image is mentioned by the corresponding textual description. In contrast, as shown in Figure 1, in a real E-commerce scenario, the images are mostly product-oriented. Only very few instances are related to the product description. Simply treating the whole image as a monolithic entity to perform cross-modal alignment with text will inevitably confound the foreground and noisy background. Hence, to establish a foundation model that generalizes well to diverse E-commerce applications, it is of great significance to learn the product-related instance-level representation. With this goal in mind, a crucial challenge needs to be addressed: How can we enable the model to focus on the product instance in the presence of background interference? + +A straightforward way to tackle this problem would be to resort to object-level human annotations, but it is laborious and infeasible to scale on larger data from the Internet. In this work, we strive to derive the capability of grounding product instances from uncurated data. Our motivation is built on the natural characteristics of E-commerce data itself. As illustrated in Figure 1, a product usually has multiple image samples from different sources (e.g., merchant, customer comments, attached advertisement videos, etc.). Although the appearance of these samples may be diverse due to the changes of camera view or scenes, they all include the identical product entity. This fact strongly spurs us to pursue an instance-centric multi-modal learning paradigm by leveraging such explicit correlation. + +The proposed pretraining framework, dubbed as ECLIP (E for "E-commerce"), employs two separate encoders to embed the images and texts of products. Our key idea is to develop a decoder architecture built upon the abovementioned encoders, which aims to aggregate the instance-centric product representations without additional handcrafted annotation. Inspired by [1, 15, 30], the decoder introduces a set of learnable tokens that we refer to as instance query. At each decoder block, these instance queries are updated via interacting with the encoded visual features. Through the stack of multiple blocks, they will gradually probe the potential product instance from the entire image. Moreover, each instance query is conditioned on a concrete text or image called multi-modal prompt. Such a design renders it dedicated to a particular instance type indicated by the content of its associated prompt. Therefore, by specifying the content of multi-modal prompt, the decoder can adaptively discover the corresponding instance. During pretraining, there is only one positive prompt for a given sample. The rest are negative ones sampled from other products. + +To effectively optimize the generated instance representations, we newly craft two pretext tasks: inter-product + +and intra-product multi-modal learning. The first one is in charge of pulling the representations of the identical product closer to each other and pushing away the unmatched ones. It is noteworthy that the appearance of the positive image samples varies a lot except for the presented product. Bringing their representations closer than negative pairs in the feature space will implicitly encourage the instance query to focus on the visual region that corresponds to the desired product. The second one aims to ensure that only positive queries can aggregate the semantics of the foreground instance, rather than negative ones. Coupling these two novel pretext tasks together, we find that the whole framework is capable of learning a generic product representation. Our core contributions can be summarized as follows: + +(1) We propose ECLIP, an effective and simple multimodal representation learning paradigm in the E-commerce scenario. Going beyond regular global representations, it can successfully obtain instance-centric product representations via a decoder architecture. +(2) By fully exploiting the natural characteristics of E-commerce data and the proposed pretext tasks, ECLIP obtains the fine-grained alignment capability to ground the desired product instance (see Figure 4a) without reliance on any manual annotation. +(3) Pre-trained on large-scale product data, the resulting foundation model can seamlessly generalize to downstream E-commerce applications. Comprehensive experimental results further demonstrate the superiority of ECLIP: without any fine-tuning, it achieves substantial improvements over the existing state-of-the-art methods on diverse real-world E-commerce tasks. + +# 2. Related Work + +Vision-Language Representation Learning. In recent years, vision-language pretraining (VLP) has attracted the attention of numerous researchers and has been widely explored [6], which aims to learn from tremendous image-text paired data to obtain knowledge that can be generalized to downstream tasks. Some pioneer works (e.g. LXMERT [23], UNITER [2], VinVL [36]) rely on pretrained object detection modules such as Faster-RCNN [22] to extract visual representations. Later efforts such as ViLT [10] and VLMo [26] unify the vision and language transformers, and train a multimodal transformer from scratch. Then, CLIP [20] and ALIGN [9] demonstrate that dual-encoder models pretrained with contrastive objectives on noisy image-text pairs can learn strong image and text representations for crossmodal alignment tasks and zero-shot image classification. While ALBEF [12] additionally trains a fusion-encoder to jointly learn the multi-modal representations. GLIP [13] unifies object detection and phrase grounding for pretraining and surpasses many baselines in the detection field. Another line of researches [19,25,27,32] + +develop encoder-decoder models that are trained using generative losses and show strong generation performances in vision-language benchmarks, while the visual encoder still performs competitively on image classification. But most aforementioned VLP methods devote themselves to the coarse correlation between the text and the entire image and ignore the instance-level information, which is critical in an e-commerce scenario (as shown in Figure 1). + +MultiModal Pre-training for E-commerce. Early works like FashionBERT [7], Kaleido-BERT [38] leverage a transformer-based model and tailored masking strategy to perform pretraining to generate more fine-grained features for cloth retrieval. Then CAPTURE [35] generates discriminative instance features via masked multi-modal learning as well as cross-modal contrastive pretraining achieve surprising performance in the instance-level product retrieval task. K3M [37] further introduces the knowledge modality in multi-modal pretraining to correct the noise and supplement the missing of image and text modalities. SCALE [4] proposes a self-harmonized contrastive learning framework that can integrate six different modalities into a unified model. Recent CommerceMM in [33] design a contrastive and MLM-based pretraining paradigm on 14 different tasks. However, all the existing methods only consider the global alignment between images and text, without exploring the special characteristic contained in the e-commercial data for learning instance-centric representation. + +# 3. Approach + +In this section, we begin with the overview of our proposed ECLIP in Section 3.1. Then the core decoder architecture that aims at aggregating instance-level representation of the desired product is introduced in Section 3.2. To optimize the entire framework, we carefully designed several pretraining objectives in Section 3.3. Finally, we delineate how to transfer the resulting foundation model to various downstream tasks in Section 3.4. + +# 3.1. Model Overview + +As illustrated in Figure 2, ECLIP is composed of an image encoder, a text encoder, and an instance decoder. Given an input sample $x = (x^{I}, x^{T})$ , where $x^{I}$ and $x^{T}$ are the image and text describing the corresponding product information, respectively. These two encoders first encode the image-text pair as a sequence of feature embeddings. Then, a modality-dependent projection layer is employed to map them linearly into a joint multi-modal feature space. These projected embeddings are further decoded to produce an instance-centric representation. Details of the two unimodal encoders are elaborated as follows. + +Image Encoder. Following the vision transformers [5], the product image $x^{I} \in \mathcal{R}^{H \times W \times C}$ is partitioned into $N$ non- + +overlapping patches. These patches are flattened to 1D input tokens and then projected linearly, with position embeddings added. Through hierarchical feature encoding, we can obtain a sequence of visual embeddings $\{v_{cls}, v_1, \dots, v_N\}$ , where $v_{cls}$ indicates the special token [CLS] for encoding the entire image information. + +Text Encoder. This encoder adopts analogous transformer-style architecture. For the input product description $x_{T}$ , it tokenizes the text to $M$ subwords as in BERT [3]. Similar to the image encoder, a special [CLS] token is appended to the beginning of the textual input to summarize the text semantics. After encoding, the resulting linguistic embedding sequence is denoted as $\{w_{cls}, w_{1}, \dots, w_{M}\}$ . + +# 3.2. Extract Instance-Centric Representation + +After obtaining the contextualized embeddings, existing VLP approaches leverage $g_{I}(v_{cls}) \in \mathcal{R}^{D}$ and $g_{T}(w_{cls}) \in \mathcal{R}^{D}$ to align positive image-text pairs via contrastive learning. Here $g_{I}(\cdot)$ and $g_{T}(\cdot)$ are the aforementioned projections. While effective in the general domain, this design only considers the alignment between the global image-text semantics. However, in the E-commerce image, only several regions containing the desired product instance are informative foregrounds corresponding to the text description. Modeling such image-level alignment will fail to learn strong and robust product semantics. Hence, we are committed to learning instance-centric representation. + +Instance Query. Motivated by [1, 15], a set of learnable tokens called instance query are introduced to ground the potential instance in the product image. As Figure 2 shows, each query is correlated to a specific text or image that we refer to as multi-modal prompt. The insight behind this design is that we wish the instance that a query should probe to be specified by the prompt content. Formally, the proposed instance query is denoted as $\mathbf{Q} = \{q_t \in \mathcal{R}^D\}_{t=1}^T$ , which can be obtained by: + +$$ +q _ {t} = q _ {t} ^ {\text {p r o m p t}} + q _ {t} ^ {\text {p o s}} + q _ {t} ^ {\text {t y p e}}. \tag {1} +$$ + +Here, $q_{t}^{\text{prompt}}$ denotes $g_{I}(v_{cls})$ or $g_{T}(w_{cls})$ , $q_{t}^{\text{pos}}$ and $q_{t}^{\text{type}}$ are learnable position and type embedding, indicating the probing area of a query and the modality type of the binding prompt. These queries are responsible for aggregating the instance-centric representations $H = \{h_t\}_{t=1}^T$ from the encoded visual features via a decoder architecture. During pretraining, there is only one positive prompt (w.r.t the same product) for a given sample, and the rest $T - 1$ are negative ones sampled from other products. + +Instance Decoder. We first project all the encoded $\{v_{i}\}_{i = 1}^{N}$ into the same feature space as the prompt, yielding an embedding sequence $Z = \{z_{i}\in \mathcal{R}^{D}\}_{i = 1}^{N}$ . Moreover, the instance representations $\pmb{H}^0$ are zero-initialized, and then before feeding to the decoder. The proposed decoder then + +![](images/47b7105cfaabec726a10edcccbf9e00bb4a855dd523dcc74ad75a66113323505.jpg) +(a) The overall Pipeline + +![](images/ead454f46f5697a2722c45f32963d93448f6f115996fe3768af0198031d4b196.jpg) +(b) The $i$ -th Decoder Block +Figure 2. (a) The architecture of the proposed instance-level representation learning paradigm (ECLIP), which is composed of an image encoder, a text encoder, and an instance decoder. The whole framework is optimized by three pretext tasks when pretraining on a large-scale E-commerce dataset. (b) The core decoder architecture that aims at aggregating the instance representation of the desired product. + +reads all the above-described embeddings: $Z$ , $Q$ and $H^0$ as its input. It has $L$ stacked blocks, and each one consists of a slot-attention and a self-attention layer. + +The goal of the slot-attention layer is to adaptively update query representations through the interaction with the encoded visual embeddings. In detail, for the $l$ -th slot-attention layer, it first calculates a similarity matrix $M \in \mathcal{R}^{N \times T}$ , which is implemented by the dot-product attention mechanism [24]. Formally, it is formulated by: + +$$ +M = \frac {1}{\sqrt {D}} \left(Z W _ {z}\right) \cdot \left(\left(Q + H ^ {l - 1}\right) W _ {q}\right) ^ {\top}, \tag {2} +$$ + +where $W_{z}$ and $W_{q}$ are the learnable projections parameter matrices, $H^{l - 1}$ is the instance representation produced by the $(l - 1)^{\mathrm{th}}$ decoder block. The similarity matrix $M$ is further normalized by a softmax function over $T$ queries: + +$$ +M _ {i j} = \frac {\exp (M _ {i j})}{\sum_ {t = 1} ^ {T} \exp (M _ {i t})}. \tag {3} +$$ + +The generated matrix $M$ actually performs soft assignment via computing semantic similarity between $N$ visual tokens and $T$ instance queries. In doing so, it is capable of distributing each visual token to a specific query according to their similarity score. To aggregate the information of the visual tokens into their assigned input query, we compute a weighted mean update based on $M$ : + +$$ +\Delta h _ {t} ^ {l - 1} = \frac {1}{\sum_ {i = 1} ^ {N} M _ {i t}} \sum_ {i = 1} ^ {N} M _ {i t} \left(W _ {v} z _ {i}\right). \tag {4} +$$ + +Finally, the instance representation $\pmb{H}^l$ at the $l$ -th layer can be updated by a residual connection: + +$$ +h _ {t} ^ {l} = h _ {t} ^ {l - 1} + W _ {o} \Delta h _ {t} ^ {l - 1}. \tag {5} +$$ + +where $W_{v}$ and $W_{o}$ are linear transformation parameters. + +On the top of slot-attention layer, there is a self-attention module that performs information propagation between each query. In detail, given the previously updated $H^{l}$ , it employs standard multi-head self-attention (MSA) followed by a fully connected feed-forward network as in [24]. After $L$ successive decoder blocks, we can obtain the final instance representation $H^{L}$ . It is noteworthy that, since the multi-modal prompts just participate in the similarity calculation in Eq. 2, the resulting $H^{L}$ thus contains only visual-modality information. + +Discussion: The proposed decoder works like conducting a clustering on the image tokens, where each instance query serves as the centroid of a cluster. At each decoder block, it determines where each token belongs by measuring its distance from the centroids in the semantic space. The cluster centroid is then updated via a soft manner (Eq.4) based on the calculated distance. By stacking multiple decoder blocks, it can implicitly force each query to attend to a specific region and aggregate instance-level representations. + +# 3.3. Multi-Modal Pretraining Objectives + +Our ECLIP is optimized on large-scale uncurated product data with several pretraining proxy tasks. In the following, we describe each task in detail. + +![](images/b3d0370d28972c3795325d9e93624bdb7da601c924663ea41b1a77647b8a5d36.jpg) +Figure 3. The illustration of inter-product contrastive learning. It aims to bring the positive samples from the same product ( $x$ and $x^{+}$ ) closer than the negative ones ( $x$ and $x^{-}$ ), which contributes to grounding the desired product (outlined with the green box). + +Image-Text Contrastive Learning. As in [9, 12, 20], this task contributes to learning better unimodal representations. Given a batch of product samples $\{(x_i^I,x_i^T)\}_{i = 1}^B$ , the similarity between image $x^{I}$ and text $x^{T}$ is estimated as: + +$$ +s (x ^ {I}, x ^ {T}) = g _ {I} \left(v _ {c l s}\right) ^ {\top} g _ {T} \left(w _ {c l s}\right). +$$ + +This pretraining objective brings the image-text pairs of the same product in the embedding space closer than the unmatched ones, which consists of image-to-text term $\mathcal{L}_{i2t}$ : + +$$ +\mathcal {L} _ {i 2 t} = - \sum_ {i = 1} ^ {B} \log \frac {\exp (s \left(x _ {i} ^ {I} , x _ {i} ^ {T}\right) / \tau)}{\sum_ {j = 1} ^ {B} \exp (s \left(x _ {i} ^ {I} , x _ {j} ^ {T}\right) / \tau)}, \tag {6} +$$ + +and a text-to-image term $\mathcal{L}_{t2i}$ : + +$$ +\mathcal {L} _ {t 2 i} = - \sum_ {i = 1} ^ {B} \log \frac {\exp \left(s \left(x _ {i} ^ {T} , x _ {i} ^ {I}\right) / \tau\right)}{\sum_ {j = 1} ^ {B} \exp \left(s \left(x _ {i} ^ {T} , x _ {j} ^ {I}\right) / \tau\right)}, \tag {7} +$$ + +where $\tau$ is a learnable temperature parameter. The whole objective is then defined as $\mathcal{L}_{itc} = \frac{1}{2} (\mathcal{L}_{i2t} + \mathcal{L}_{t2i})$ . + +Inter-Product Multi-modal Learning. As shown in Figure 3, we maintain a momentum model during pretraining that is an exponential-moving-average of the origin model like [8]. For a product sample $x_{i}$ , we denote the representation of the positive prompt produced by the base and momentum model as $h_{\theta}^{i}$ and $h_{\xi}^{i}$ . An inter-product contrastive loss $\mathcal{L}_{inter}$ is computed by: + +$$ +\mathcal {L} _ {i n t e r} = - \sum_ {i = 1} ^ {B} \log \frac {\exp \left(h _ {\theta} ^ {i} ^ {\top} h _ {\xi} ^ {j} / \tau\right)}{\exp \left(h _ {\theta} ^ {i} ^ {\top} h _ {\xi} ^ {j} / \tau\right) + \sum_ {k \in \mathcal {N} ^ {-}} \exp \left(h _ {\theta} ^ {i} ^ {\top} h _ {\xi} ^ {k} / \tau\right)}, \tag {8} +$$ + +where sample $i$ and $j$ are a positive pair, $\mathcal{N}^{-}$ is a negative sample set that belongs to other products. This objective maximizes the similarity between different samples of the identical product, while minimizing those of the unmatched ones. Since the images of a product are collected from different sources, their background appearances are usually diverse. Hence, $\mathcal{L}_{inter}$ will encourage the yielded representation to be highly correlated with the desired product and + +thus contributes to aligning the positive prompt with the corresponding image tokens in a fine-grained manner. + +This pretext task also incorporates an additional instance-text matching loss that predicts whether an instance and a text description are matched. Formally, given an instance-text pair, we obtain their match logit defined by: $f(h_{\theta}^{i}\odot g_{T}(w_{\mathrm{cls}}^{i}))$ , where $\odot$ is Hadamard product and $f(\cdot)$ is a mapping layer: $\mathcal{R}^D\to \mathcal{R}^2$ . This match logit is optimized by a typical binary cross entropy loss $\mathcal{L}_{itm}$ . + +Intra-Product Multi-modal Learning. For a product sample, there is only one positive prompt describing the presented product during pretraining, and the rest $T - 1$ prompts are sampled from other products. The motivation behind this pretext task is to ensure that only positive queries can probe the foreground instance, rather than negative ones. To this end, we apply an intra-product contrastive loss using text supervision. Suppose index $r$ indicates the positive query, then $\mathcal{L}_{\text{intra}}$ can be formulated as: + +$$ +\mathcal {L} _ {i n t r a} = - \sum_ {i = 1} ^ {B} \log \frac {\exp \left(h _ {r} ^ {i} ^ {\top} g _ {T} \left(w _ {\mathrm {c l s}} ^ {i}\right) / \tau\right)}{\sum_ {t = 1} ^ {T} \exp \left(h _ {t} ^ {i} ^ {\top} g _ {T} \left(w _ {\mathrm {c l s}} ^ {i}\right) / \tau\right)}, \tag {9} +$$ + +which serves to bring the positive query and the product description closer than all $T - 1$ negative ones. Moreover, we also introduce an entropy regularization term for $M$ : + +$$ +\begin{array}{l} \mathcal {L} _ {\mathcal {R}} = \sum_ {i = 1} ^ {N} M _ {i, r} \log \left(\frac {1}{M _ {i , r}}\right) + \\ \sum_ {j = 1, j \neq r} ^ {T} \left(\log N - \sum_ {i = 1} ^ {N} M _ {i, j} \log \left(\frac {1}{M _ {i , j}}\right)\right). \tag {10} \\ \end{array} +$$ + +This regularization term encourages the positive query to focus on several tokens that may contain product instances. While for $T - 1$ negative ones, it prevents too sharp similarity distributions over $N$ image tokens. Finally, the overall pretraining objective of ECLIP is the sum of all aforementioned loss terms. + +# 3.4. Transfer to Downstream Tasks + +Once pretrained, the resulting foundation model can be leveraged to extract the product instance representation with minimal surgery. Specifically, given a product sample $(x_{i}^{I}, x_{i}^{T})$ , we first encode the image-text pair into an embedding sequence via the unimodal encoders. Then, the global representation of text description $g_{T}(w_{\mathrm{cls}})$ is treated as the positive query and fed into the decoder concatenated with $T - 1$ negative ones. Here, the negative queries $\{q_{t}\}_{t = 2}^{T}$ are sampled from a standard Gaussian distribution for convenience. We also explore different negative query setting manners in Section 4.3. The yielded representation $h_0^L$ belonging to the positive query is then applied to a wide range of E-commerce downstream tasks. + +# 4. Experiments + +# 4.1. Pretraining Details + +Pretraining Dataset We collected a large-scale pretraining dataset from a popular E-commerce website. It consists of 15M different products and over 100M various images, covering about 9K diverse categories such as clothes, daily necessities, instruments, and so on. For each product item, it has a corresponding textual description and several images from the product details pages, customer comments, and attached advertisement videos. During pretraining, the positive data pairs are constructed by sampling images belonging to the same product from different sources. + +Implementation Details. The image encoder adopts the same network configuration as the standard ViT [5] and is initialized from the weight pre-trained on ImageNet. Our text encoder is implemented with the same architecture as $\mathrm{BERT}_{\mathrm{base}}$ [3]. The decoder has 6 identical blocks and 20 instance queries. We here explore two variants of ViTs: ViTB/16 and ViT-L/16. There is a total of $220 / 450$ million parameters for the base and large version. During pretraining, the input images are resized to $224 \times 224$ with random crop and horizontal flip augmentation, and the texts are tokenized by WordPiece with a maximum length of 55. We pretrain for 15 epochs with a batch size of 6400 (ViT-B) / 4096 (ViT-L) on 32 NVIDIA A100 GPUs. The whole framework is learned using AdamW [16] optimizer and the learning rate is warmed up to 1e-4 and then decayed linearly. More details are elaborated in the supplementary. + +Compared Baselines. We mainly compare the ECLIP with several state-of-the-art VLP methods: CLIP [20], FILIP [31], DeCLIP [14], ALBEF [12] and BLIP [11]. For a fair comparison, we also leverage ViT-B/16 as the image encoder, $\mathrm{BERT}_{\mathrm{base}}$ as the text encoder, and pretrain these baselines on the same 100M E-commerce data using their official public implementations. + +# 4.2. Evaluation on Downstream Tasks + +Next, we delineate the evaluation performances for five specific E-commerce downstream tasks in turn. + +# 4.2.1 Zero-Shot Product Classification + +We first transfer ECLIP to product item classification. It is a recognition task that aims to map a product sample to a specific category. We evaluate the performance on a large-scale publicly available E-commerce dataset called M5Product [4], which covers 1.1M images and 5,679 various product categories. Here, we consider the multimodal setting, which uses both the product image and related textual description for classification. To demonstrate the strong zero-shot ability of ECLIP, we apply it directly to the classification evaluation without further finetune. It is achieved by measuring the similarity between the category text like + +CLIP [20]. The left part of Table 1 summarizes the Top-1 classification accuracy of all the compared methods. As presented, our ECLIP greatly exceeds all the existing baselines by a large margin (e.g., $+6.6\%$ v.s. CLIP), demonstrating the superiority of instance-level representation. + +# 4.2.2 Zero-Shot Image-Text Retrieval + +ECLIP is also transferred to test zero-shot performance for image-to-text and text-to-image retrieval. To this end, we collect a large dataset that contains 205K image-text pairs of E-commerce products. Since only unimodal information is available in this task, we simply use our image and text encoders to embed image-text pairs and complete the retrieval based on their pairwise similarity. We utilize the widely-used Recall@K metric for evaluation. Detailed comparison results are shown in the right part of Table 1. We can see that, despite training on the same dataset, our method achieves superior performance owing to fine-grained alignment modeling between text and product instance. + +
MethodClassificationImage-to-TextText-to-Image
Acc@1R@1R@5R@1R@5
CLIP [20]37.252.674.158.784.0
FILIP [31]37.152.373.858.083.5
DeCLIP [14]37.853.175.858.883.9
ALBEF [12]38.552.974.458.283.3
BLIP [11]39.353.375.659.184.4
OursViT-B/1643.853.876.059.984.6
OursViT-L/1644.858.279.663.887.4
+ +Table 1. Performance comparisons of zero-shot product classification and zero-shot image-text retrieval on M5Product dataset [4]. + +# 4.2.3 Zero-Shot Product Retrieval + +This task aims to find the most relevant target product given a query (image-text pair of a product). It has a wide range of applications in real e-commerce scenarios such as recommending relevant products for users. We first evaluate the coarse-level retrieval. Following [4], a product pair is considered a match if both belong to the same category during evaluation. The results on M5Product benchmark are reported on the left of Table 2. It can be seen that exploiting instance-centric representation significantly boosts performance. To further demonstrate the effectiveness of instance-level representation, we then conduct a more complicated fine-grained level product retrieval task, where a pair is considered a match if and only if they are the same product. This task requires more adequate fine-grained understanding ability since it focuses on the specific product instance. Detailed comparison results are shown on the right part of Table 2. One can find that our ECLIP achieves a substantial improvement in retrieval performance (e.g., $44.3\%$ v.s. $35.6\%$ (BLIP) on $\mathbf{R}@\mathbf{1}$ ). + +
MethodPretraining DatasetCoarse Product RetrievalFine-grained Product Retrieval
mAP@1mAP@5mAP@10R@1R@5R@10mAP@1mAP@5mAP@10
ViBERT [17]58.661.760.1------
UNITER [2]M5Product58.962.860.9------
SCALE [4]59.864.162.2------
CLIP [20]68.273.270.734.854.262.934.840.239.9
FILIP [31]67.873.070.334.653.962.234.640.139.7
DeCLIP [14]68.573.470.835.356.465.535.341.240.8
ALBEF [12]ECLIP 100M68.773.671.235.156.165.235.140.740.4
BLIP [11]69.174.171.635.656.866.035.641.641.3
OursViT-B/1669.674.972.544.363.471.143.848.648.2
OursViT-L/1670.275.372.945.064.272.145.050.049.5
+ +We also consider another setting introduced in [35], called instance-level retrieval, where the query image encompasses multiple different kinds of product instances. And the model needs to find all the related products from a large gallery. As shown in Table 3, ECLIP still achieves superior performance than all the previous approaches. Although the CAPTURE leverages a specially-trained object detector to extract instances, ECLIP still surpasses it by a clear margin with no box annotation. + +# 4.2.4 Zero-Shot Visual Grounding + +To demonstrate whether our model possesses the capability of localizing the desired product instance after pretraining, we further evaluate ECLIP on the zero-shot product grounding, which requires localizing the product instance in an image according to a textual description. Specifically, the input image-text pair is first fed to our ECLIP to obtain a score map $S \in \mathcal{R}^{H \times W}$ that measures the similarity between the text and each image location. Then, we use $S$ to rank the candidate regions produced by an off-the-shelf region proposal network. The performance is evaluated by the top-1 accuracy at IoU thresholds $\{0.5, 0.7\}$ on an annotated grounding dataset consisting of 450K product images. Detailed comparison results are listed in Table 4. As we can see, compared to methods aimed at global representation, our model has learned fine-grained cross-modal understanding ability and thus obtains substantial performance gain (e.g., +14.5% v.s. BLIP on Acc@0.7). Since ECLIP supports image prompts during pretraining, we also conduct zero-shot image-conditioned grounding. The results and analysis can be found in the supplementary. + +# 4.2.5 Transfer to Object Detection + +We also transfer ECLIP to object detection to further validate its fine-grained understanding ability. Following DETR [1], we utilize the image encoder to embed visual features, and utilize the decoder with a newly added prediction head to decode the potential objects. Moreover, we col + +Table 2. Performance comparisons of zero-shot coarse level and fine-grained level product retrieval task. + +
MethodInstance-Level Product Retrieval
mAP@10mAP@50mAR@10mAR@50
CAPTURE [35]40.436.817.215.9
CLIP [20]86.682.854.459.5
FILIP [31]86.983.054.659.8
DeCLIP [14]87.183.354.960.0
BLIP [11]87.583.555.160.4
OursViT-B/1689.684.655.961.2
OursViT-L/1689.586.456.362.1
+ +Table 3. Performance comparisons of zero-shot instance-level product retrieval task on Product1M dataset [35]. + +
MethodVisual GroundingObject Detection
Acc@0.5Acc@0.7mAP@0.3mAP@0.5
CLIP [20]80.975.217.214.3
FILIP [31]81.375.617.514.6
DeCLIP [14]81.075.317.014.2
ALBEF [12]80.974.717.113.9
BLIP [11]81.175.117.314.1
OursViT-B/1691.289.620.216.5
+ +Table 4. Performance comparisons of zero-shot visual grounding and finetuned object detection. + +lect a manually annotated detection dataset covering 160K images. We split a 20K subset for evaluation and leave the rest for model finetuning. The supplementary provides experiment details of baselines and ECLIP. It can be observed from Table 4 and Figure 4b that it outperforms the existing VLP methods, demonstrating the superiority of ECLIP to learning fine-grained semantics in E-commerce. + +# 4.3. Ablation Study + +Effect of Pretext Task. To demonstrate the effectiveness of the inter- and intra- product learning pretext task, we designed the experiments on different task combinations for the product retrieval task. All the ablations were conducted on a smaller pretraining dataset that includes only 5M images due to the costly training time. The complete results are listed in Table 5. One can observe that canceling either of these two tasks will result in worse performance. No + +![](images/380e598b2bb0d1a266a6da393b17ee838151742dd7f3b10b39c3f86a321b34b5.jpg) +Figure 4. Qualitative examples of visual grounding and object detection. See the main text for more explanation. + +![](images/379459f62b025a761d5f074f44dcc5a73975f5e7a4ab69a0fb164fbb55a9a99c.jpg) + +tably, the inter-product task brings a more significant performance boost compared to the intra-product. We speculate that because the former contrasts with more negative samples from different product images. + +Effect of Negative Query. Since the negative instance queries are used when transferring to classification and retrieval tasks, we also ran ablations on different ways of setting these negative ones. We tried the following cases: 1) Leverage the description text from other products and encode with the text encoder. 2) Randomly sample the negative queries from the standard Gaussian distribution. 3) Adopt the exponential moving average of queries on the whole dataset during pretraining. Table 6 summarizes the results of this ablation study on the product retrieval task. As observed, there is little to no difference between different manners of setting up negative queries. We thus adopt random sampling for implementation simplicity. + +
LinterLintramAP@1 (%)mAP@5 (%)
61.665.2
✓64.468.7
✓62.266.5
✓✓65.269.3
+ +Table 5. Ablation of different pretraining pretext task combinations on coarse-level product retrieval task (ViT-B/16). + +
MetricmAP@10mAR@10
Negative Text84.952.5
EMA Update85.153.3
Random Sampling86.854.1
+ +Table 6. Ablation of different negative query setting manners on the instance-level product retrieval task (ViT-B/16). + +# 4.4. Qualitative Analysis + +In this section, we first qualitatively showcase that ECLIP can learn fine-grained cross-modal alignment to ground the desired product. Figure 4a presents the visualization of the similarity score map between a product image + +![](images/9164ea0d5096395ef38467abac77e2afe66bd087783bfdea8b2e5e0102783b38.jpg) +(a) CLIP +Figure 5. The T-SNE visualization for the learned representations of CLIP and our ECLIP. Both of them are pretrained on the same dataset. We first randomly select 50 kinds of products and then draw 100 samples for each. Points belonging to the same category are of the same color. See the main text for more explanation. + +![](images/9052fcad9962fec36a5a79db8011bb8bcbfacc302af210a8455d37d76d8e8003.jpg) +(b) ECLIP + +and its corresponding text description, where darker color indicates image locations with higher similarity to text. We can clearly observe that our model can rightly attend to the desired instance depicted by the text. Furthermore, T-SNE is used for visualizing the visual embedding of different kinds of product samples. As shown in Figure 5, compared to CLIP, our ECLIP can extract semantic-rich yet compact representations that better distinguish products belonging to different categories. More visualization examples and analysis are provided in our supplementary. + +# 5. Conclusion + +In this paper, we develop an effective large-scale multimodal pretraining paradigm called ECLIP in E-commerce. Beyond regular global representation, it instead aims to learn the instance-level representation via a novel decoder and the carefully-designed pretraining proxy tasks. Extensive experimental results further demonstrate the superior generalization capability of the proposed framework. + +Acknowledgement: This work is supported by National Key R&D Program of China (2022ZD0160305) and Beijing Natural Science Foundation (Z190001). + +# References + +[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 2, 3, 7 +[2] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 2, 7 +[3] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 3, 6 +[4] Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Michael C Kampffmeyer, Xiaoyong Wei, Minlong Lu, Yaowei Wang, and Xiaodan Liang. M5product: Selfharmonized contrastive learning for e-commercial multimodal pretraining. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21252-21262, 2022. 1, 3, 6, 7 +[5] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.3, 6 +[6] Zhe Gan, Linjie Li, Chunyuan Li, Lijuan Wang, Zicheng Liu, and Jianfeng Gao. Vision-language pre-training: Basics, recent advances, and future trends. arXiv preprint arXiv:2210.09263, 2022. 2 +[7] Dehong Gao, Linbo Jin, Ben Chen, Minghui Qiu, Peng Li, Yi Wei, Yi Hu, and Hao Wang. Fashionbert: Text and image matching with adaptive loss for cross-modal retrieval. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2251-2260, 2020. 1, 3 +[8] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 5 +[9] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 2, 5 +[10] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning, pages 5583-5594. PMLR, 2021. 2 +[11] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. arXiv preprint arXiv:2201.12086, 2022. 6, 7 + +[12] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems, 34:9694-9705, 2021. 1, 2, 5, 6, 7 +[13] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, and Jianfeng Gao. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10965–10975, June 2022. 2 +[14] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208, 2021. 6, 7 +[15] Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. Object-centric learning with slot attention. Advances in Neural Information Processing Systems, 33:11525-11538, 2020. 2, 3 +[16] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6 +[17] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems, 32, 2019. 1, 7 +[18] Mieczysław Pawlowski. Machine learning based product classification for ecommerce. Journal of Computer Information Systems, 62(4):730-739, 2022. 1 +[19] AJ Piergiovanni, Wei Li, Weicheng Kuo, Mohammad Saffar, Fred Bertsch, and Anelia Angelova. Answer-me: Multi-task open-vocabulary visual question answering. arXiv preprint arXiv:2205.00949, 2022. 2 +[20] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 5, 6, 7 +[21] Puji Rahayu, Dana Indra Sensuse, Betty Purwandari, Indra Budi, F Khalid, and N Zulkarnaim. A systematic review of recommender system for e-portfolio domain. In Proceedings of the 5th International Conference on Information and Education Technology, pages 21–26, 2017. 1 +[22] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 2 +[23] Hao Tan and Mohit Bansal. Lxmert: Learning cross-modality encoder representations from transformers. arXiv preprint arXiv:1908.07490, 2019. 2 +[24] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia + +Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 4 +[25] Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework. arXiv preprint arXiv:2202.03052, 2022. 2 +[26] Wenhui Wang, Hangbo Bao, Li Dong, and Furu Wei. Vlmo: Unified vision-language pre-training with mixture-of-modality-experts. arXiv preprint arXiv:2111.02358, 2021. 2 +[27] Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao. Simvlm: Simple visual language model pretraining with weak supervision. arXiv preprint arXiv:2108.10904, 2021. 2 +[28] Chi-Man Wong, Fan Feng, Wen Zhang, Chi-Man Vong, and Chen. Improving conversational recommender system by pretraining billion-scale knowledge graph. In International Conference on Data Engineering (ICDE), pages 2607-2612, 2021. 1 +[29] Hu Xu, Bing Liu, Lei Shu, and P Yu. Open-world learning and application to product classification. In The World Wide Web Conference, pages 3413-3419, 2019. 1 +[30] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18134-18144, 2022. 2 +[31] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. Filip: Fine-grained interactive language-image pre-training. arXiv preprint arXiv:2111.07783, 2021. 1, 6, 7 +[32] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917, 2022. 2 +[33] Licheng Yu, Jun Chen, Animesh Sinha, Mengjiao Wang, Yu Chen, Tamara L Berg, and Ning Zhang. Commercemm: Large-scale commerce multimodal representation learning with omni retrieval. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 4433-4442, 2022. 1, 3 +[34] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. arXiv preprint arXiv:2111.08276, 2021. 1 +[35] Xunlin Zhan, Yangxin Wu, Xiao Dong, Yunchao Wei, Minlong Lu, Yichi Zhang, Hang Xu, and Xiaodan Liang. Product1m: Towards weakly supervised instance-level product retrieval via cross-modal pretraining. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11782-11791, 2021. 1, 3, 7 +[36] Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, and Jianfeng Gao. Vinvl: Revisiting visual representations in vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5579-5588, 2021. 2 + +[37] Yushan Zhu, Huaixiao Zhao, Wen Zhang, Ganqiang Ye, Hui Chen, Ningyu Zhang, and Huajun Chen. Knowledge perceived multi-modal pretraining in e-commerce. In Proceedings of the 29th ACM International Conference on Multimedia, pages 2744-2752, 2021. 1, 3 +[38] Mingchen Zhuge, Dehong Gao, Deng-Ping Fan, Linbo Jin, Ben Chen, Haoming Zhou, Minghui Qiu, and Ling Shao. Kaleido-bert: Vision-language pre-training on fashion domain. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12647-12657, 2021. 3 \ No newline at end of file diff --git a/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/images.zip b/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..521ba88ec2669f404fd9f6a875312ceedceb4a04 --- /dev/null +++ b/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:45f2c3f80f356d1f3d0ea86db846936eb79270afa38b201e915e983b0dc8ddae +size 542529 diff --git a/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/layout.json b/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..ca249cbfbe7559b35f2fffa35897acd353411b67 --- /dev/null +++ b/2023/Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-Commerce/layout.json @@ -0,0 +1,8496 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 82, + 103, + 511, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 103, + 511, + 140 + ], + "spans": [ + { + "bbox": [ + 82, + 103, + 511, + 140 + ], + "type": "text", + "content": "Learning Instance-Level Representation for Large-Scale Multi-Modal Pretraining in E-commerce" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "spans": [ + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "type": "text", + "content": "Yang Jin" + }, + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "type": "inline_equation", + "content": "^{1,2}" + }, + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "type": "text", + "content": ", Yongzhi Li" + }, + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "type": "text", + "content": ", Zehuan Yuan" + }, + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "type": "text", + "content": ", Yadong Mu" + }, + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "type": "text", + "content": "Peking University " + }, + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "type": "inline_equation", + "content": "\\mathbf{\\Pi}^{2}" + }, + { + "bbox": [ + 166, + 160, + 429, + 190 + ], + "type": "text", + "content": "ByteDance Inc." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 158, + 191, + 433, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 158, + 191, + 433, + 204 + ], + "spans": [ + { + "bbox": [ + 158, + 191, + 433, + 204 + ], + "type": "text", + "content": "jiny@stu.pku.edu.cn, liyongzhi.ailab@bytedance.com," + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 184, + 205, + 403, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 184, + 205, + 403, + 217 + ], + "spans": [ + { + "bbox": [ + 184, + 205, + 403, + 217 + ], + "type": "text", + "content": "yuanzehuan@bytedance.com, myd@pku.edu.cn" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 143, + 244, + 192, + 258 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 244, + 192, + 258 + ], + "spans": [ + { + "bbox": [ + 143, + 244, + 192, + 258 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 270, + 290, + 547 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 270, + 290, + 547 + ], + "spans": [ + { + "bbox": [ + 46, + 270, + 290, + 547 + ], + "type": "text", + "content": "This paper aims to establish a generic multi-modal foundation model that has the scalable capability to massive downstream applications in E-commerce. Recently, large-scale vision-language pretraining approaches have achieved remarkable advances in the general domain. However, due to the significant differences between natural and product images, directly applying these frameworks for modeling image-level representations to E-commerce will be inevitably sub-optimal. To this end, we propose an instance-centric multi-modal pretraining paradigm called ECLIP in this work. In detail, we craft a decoder architecture that introduces a set of learnable instance queries to explicitly aggregate instance-level semantics. Moreover, to enable the model to focus on the desired product instance without reliance on expensive manual annotations, two specially configured pretext tasks are further proposed. Pretrained on the 100 million E-commerce-related data, ECLIP successfully extracts more generic, semantic-rich, and robust representations. Extensive experimental results show that, without further fine-tuning, ECLIP surpasses existing methods by a large margin on a broad range of downstream tasks, demonstrating the strong transferability to real-world E-commerce applications." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 567, + 128, + 580 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 567, + 128, + 580 + ], + "spans": [ + { + "bbox": [ + 47, + 567, + 128, + 580 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 588, + 287, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 588, + 287, + 696 + ], + "spans": [ + { + "bbox": [ + 46, + 588, + 287, + 696 + ], + "type": "text", + "content": "Nowadays, the flourishing growth of E-commerce has brought great convenience to people's daily life. And a wide range of product-based application tasks has subsequently emerged, such as item classification [18, 29], product retrieval [7, 35], commodity recommendation [21, 28], and so on. 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Domain difference between natural and product images. For natural images, it is the frequent case that most pixels are semantically correlated to the textual sentence. However, in E-commerce, such correlation is much more sparse (e.g., \"frying pan\" or \"coffee machine\" only occupy small portions of the entire images). Moreover, images for a product are often provided in a group from multiple sources such as (a) advertisement videos, (b) product pages, (c) customer comments (see the bottom examples)." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 390, + 336, + 527, + 422 + ], + "blocks": [ + { + "bbox": [ + 390, + 336, + 527, + 422 + ], + "lines": [ + { + "bbox": [ + 390, + 336, + 527, + 422 + ], + "spans": [ + { + "bbox": [ + 390, + 336, + 527, + 422 + ], + "type": "image", + "image_path": "95341b20c11bff08a3df3a191172197048a7083ec9f9eddada9ef137f46a0e6d.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 521, + 546, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 546, + 689 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 546, + 689 + ], + "type": "text", + "content": "Recent developments in vision-language pretraining (VLP) [9, 12, 17, 20, 31, 34] have demonstrated remarkable advances in diverse VL downstream tasks. Profiting from large-scale image-text pairs, these methods are able to learn generic multimodal representations that are reused across various tasks. In E-commerce scenario, the related data naturally contains cross-modal information to describe a corresponding product. Motivated by the tremendous success achieved by VL modeling, several approaches [4, 33, 35, 37] have made attempts at designing a commerce-specific multimodal representation learning paradigm. They imitate the existing VLP methods (e.g., CLIP [20], VilBERT [17]) to learn the image-level representations of the product via pretraining on abundant commerce image-text pairs." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 689, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 545, + 714 + ], + "type": "text", + "content": "Though promising results have been achieved, directly applying these VLP methods in the general domain to E-" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 703, + 138, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 703, + 138, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 703, + 138, + 712 + ], + "type": "text", + "content": "*Corresponding Author." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11060" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 275 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 275 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 275 + ], + "type": "text", + "content": "commerce still suffers from inherent deficiencies. The properties of natural and product images appear to be dramatically different. Given a natural image-text pair, almost every pixel in the natural image is mentioned by the corresponding textual description. In contrast, as shown in Figure 1, in a real E-commerce scenario, the images are mostly product-oriented. Only very few instances are related to the product description. Simply treating the whole image as a monolithic entity to perform cross-modal alignment with text will inevitably confound the foreground and noisy background. Hence, to establish a foundation model that generalizes well to diverse E-commerce applications, it is of great significance to learn the product-related instance-level representation. With this goal in mind, a crucial challenge needs to be addressed: How can we enable the model to focus on the product instance in the presence of background interference?" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 277, + 289, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 277, + 289, + 445 + ], + "spans": [ + { + "bbox": [ + 46, + 277, + 289, + 445 + ], + "type": "text", + "content": "A straightforward way to tackle this problem would be to resort to object-level human annotations, but it is laborious and infeasible to scale on larger data from the Internet. In this work, we strive to derive the capability of grounding product instances from uncurated data. Our motivation is built on the natural characteristics of E-commerce data itself. As illustrated in Figure 1, a product usually has multiple image samples from different sources (e.g., merchant, customer comments, attached advertisement videos, etc.). Although the appearance of these samples may be diverse due to the changes of camera view or scenes, they all include the identical product entity. This fact strongly spurs us to pursue an instance-centric multi-modal learning paradigm by leveraging such explicit correlation." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 448, + 289, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 448, + 289, + 687 + ], + "spans": [ + { + "bbox": [ + 46, + 448, + 289, + 687 + ], + "type": "text", + "content": "The proposed pretraining framework, dubbed as ECLIP (E for \"E-commerce\"), employs two separate encoders to embed the images and texts of products. Our key idea is to develop a decoder architecture built upon the abovementioned encoders, which aims to aggregate the instance-centric product representations without additional handcrafted annotation. Inspired by [1, 15, 30], the decoder introduces a set of learnable tokens that we refer to as instance query. At each decoder block, these instance queries are updated via interacting with the encoded visual features. Through the stack of multiple blocks, they will gradually probe the potential product instance from the entire image. Moreover, each instance query is conditioned on a concrete text or image called multi-modal prompt. Such a design renders it dedicated to a particular instance type indicated by the content of its associated prompt. Therefore, by specifying the content of multi-modal prompt, the decoder can adaptively discover the corresponding instance. During pretraining, there is only one positive prompt for a given sample. The rest are negative ones sampled from other products." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "type": "text", + "content": "To effectively optimize the generated instance representations, we newly craft two pretext tasks: inter-product" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 72, + 547, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 239 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 239 + ], + "type": "text", + "content": "and intra-product multi-modal learning. The first one is in charge of pulling the representations of the identical product closer to each other and pushing away the unmatched ones. It is noteworthy that the appearance of the positive image samples varies a lot except for the presented product. Bringing their representations closer than negative pairs in the feature space will implicitly encourage the instance query to focus on the visual region that corresponds to the desired product. The second one aims to ensure that only positive queries can aggregate the semantics of the foreground instance, rather than negative ones. Coupling these two novel pretext tasks together, we find that the whole framework is capable of learning a generic product representation. Our core contributions can be summarized as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 239, + 547, + 441 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 304, + 239, + 547, + 298 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 239, + 547, + 298 + ], + "spans": [ + { + "bbox": [ + 304, + 239, + 547, + 298 + ], + "type": "text", + "content": "(1) We propose ECLIP, an effective and simple multimodal representation learning paradigm in the E-commerce scenario. Going beyond regular global representations, it can successfully obtain instance-centric product representations via a decoder architecture." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 300, + 547, + 358 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 300, + 547, + 358 + ], + "spans": [ + { + "bbox": [ + 304, + 300, + 547, + 358 + ], + "type": "text", + "content": "(2) By fully exploiting the natural characteristics of E-commerce data and the proposed pretext tasks, ECLIP obtains the fine-grained alignment capability to ground the desired product instance (see Figure 4a) without reliance on any manual annotation." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 360, + 547, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 360, + 547, + 441 + ], + "spans": [ + { + "bbox": [ + 304, + 360, + 547, + 441 + ], + "type": "text", + "content": "(3) Pre-trained on large-scale product data, the resulting foundation model can seamlessly generalize to downstream E-commerce applications. Comprehensive experimental results further demonstrate the superiority of ECLIP: without any fine-tuning, it achieves substantial improvements over the existing state-of-the-art methods on diverse real-world E-commerce tasks." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 453, + 392, + 467 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 453, + 392, + 467 + ], + "spans": [ + { + "bbox": [ + 306, + 453, + 392, + 467 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 474, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 474, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 474, + 547, + 715 + ], + "type": "text", + "content": "Vision-Language Representation Learning. In recent years, vision-language pretraining (VLP) has attracted the attention of numerous researchers and has been widely explored [6], which aims to learn from tremendous image-text paired data to obtain knowledge that can be generalized to downstream tasks. Some pioneer works (e.g. LXMERT [23], UNITER [2], VinVL [36]) rely on pretrained object detection modules such as Faster-RCNN [22] to extract visual representations. Later efforts such as ViLT [10] and VLMo [26] unify the vision and language transformers, and train a multimodal transformer from scratch. Then, CLIP [20] and ALIGN [9] demonstrate that dual-encoder models pretrained with contrastive objectives on noisy image-text pairs can learn strong image and text representations for crossmodal alignment tasks and zero-shot image classification. While ALBEF [12] additionally trains a fusion-encoder to jointly learn the multi-modal representations. GLIP [13] unifies object detection and phrase grounding for pretraining and surpasses many baselines in the detection field. Another line of researches [19,25,27,32]" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11061" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 167 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 167 + ], + "type": "text", + "content": "develop encoder-decoder models that are trained using generative losses and show strong generation performances in vision-language benchmarks, while the visual encoder still performs competitively on image classification. But most aforementioned VLP methods devote themselves to the coarse correlation between the text and the entire image and ignore the instance-level information, which is critical in an e-commerce scenario (as shown in Figure 1)." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 168, + 289, + 397 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 168, + 289, + 397 + ], + "spans": [ + { + "bbox": [ + 46, + 168, + 289, + 397 + ], + "type": "text", + "content": "MultiModal Pre-training for E-commerce. Early works like FashionBERT [7], Kaleido-BERT [38] leverage a transformer-based model and tailored masking strategy to perform pretraining to generate more fine-grained features for cloth retrieval. Then CAPTURE [35] generates discriminative instance features via masked multi-modal learning as well as cross-modal contrastive pretraining achieve surprising performance in the instance-level product retrieval task. K3M [37] further introduces the knowledge modality in multi-modal pretraining to correct the noise and supplement the missing of image and text modalities. SCALE [4] proposes a self-harmonized contrastive learning framework that can integrate six different modalities into a unified model. Recent CommerceMM in [33] design a contrastive and MLM-based pretraining paradigm on 14 different tasks. However, all the existing methods only consider the global alignment between images and text, without exploring the special characteristic contained in the e-commercial data for learning instance-centric representation." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 410, + 115, + 423 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 410, + 115, + 423 + ], + "spans": [ + { + "bbox": [ + 47, + 410, + 115, + 423 + ], + "type": "text", + "content": "3. Approach" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 430, + 289, + 526 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 430, + 289, + 526 + ], + "spans": [ + { + "bbox": [ + 46, + 430, + 289, + 526 + ], + "type": "text", + "content": "In this section, we begin with the overview of our proposed ECLIP in Section 3.1. Then the core decoder architecture that aims at aggregating instance-level representation of the desired product is introduced in Section 3.2. To optimize the entire framework, we carefully designed several pretraining objectives in Section 3.3. Finally, we delineate how to transfer the resulting foundation model to various downstream tasks in Section 3.4." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 536, + 148, + 548 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 536, + 148, + 548 + ], + "spans": [ + { + "bbox": [ + 47, + 536, + 148, + 548 + ], + "type": "text", + "content": "3.1. Model Overview" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 555, + 287, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 555, + 287, + 685 + ], + "spans": [ + { + "bbox": [ + 46, + 555, + 287, + 685 + ], + "type": "text", + "content": "As illustrated in Figure 2, ECLIP is composed of an image encoder, a text encoder, and an instance decoder. Given an input sample " + }, + { + "bbox": [ + 46, + 555, + 287, + 685 + ], + "type": "inline_equation", + "content": "x = (x^{I}, x^{T})" + }, + { + "bbox": [ + 46, + 555, + 287, + 685 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 555, + 287, + 685 + ], + "type": "inline_equation", + "content": "x^{I}" + }, + { + "bbox": [ + 46, + 555, + 287, + 685 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 555, + 287, + 685 + ], + "type": "inline_equation", + "content": "x^{T}" + }, + { + "bbox": [ + 46, + 555, + 287, + 685 + ], + "type": "text", + "content": " are the image and text describing the corresponding product information, respectively. These two encoders first encode the image-text pair as a sequence of feature embeddings. Then, a modality-dependent projection layer is employed to map them linearly into a joint multi-modal feature space. These projected embeddings are further decoded to produce an instance-centric representation. Details of the two unimodal encoders are elaborated as follows." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "type": "text", + "content": "Image Encoder. Following the vision transformers [5], the product image " + }, + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "type": "inline_equation", + "content": "x^{I} \\in \\mathcal{R}^{H \\times W \\times C}" + }, + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "type": "text", + "content": " is partitioned into " + }, + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "type": "text", + "content": " non-" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "content": "overlapping patches. These patches are flattened to 1D input tokens and then projected linearly, with position embeddings added. Through hierarchical feature encoding, we can obtain a sequence of visual embeddings " + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "inline_equation", + "content": "\\{v_{cls}, v_1, \\dots, v_N\\}" + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "inline_equation", + "content": "v_{cls}" + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "content": " indicates the special token [CLS] for encoding the entire image information." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 146, + 547, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 146, + 547, + 232 + ], + "spans": [ + { + "bbox": [ + 304, + 146, + 547, + 232 + ], + "type": "text", + "content": "Text Encoder. This encoder adopts analogous transformer-style architecture. For the input product description " + }, + { + "bbox": [ + 304, + 146, + 547, + 232 + ], + "type": "inline_equation", + "content": "x_{T}" + }, + { + "bbox": [ + 304, + 146, + 547, + 232 + ], + "type": "text", + "content": ", it tokenizes the text to " + }, + { + "bbox": [ + 304, + 146, + 547, + 232 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 146, + 547, + 232 + ], + "type": "text", + "content": " subwords as in BERT [3]. Similar to the image encoder, a special [CLS] token is appended to the beginning of the textual input to summarize the text semantics. After encoding, the resulting linguistic embedding sequence is denoted as " + }, + { + "bbox": [ + 304, + 146, + 547, + 232 + ], + "type": "inline_equation", + "content": "\\{w_{cls}, w_{1}, \\dots, w_{M}\\}" + }, + { + "bbox": [ + 304, + 146, + 547, + 232 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 238, + 519, + 251 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 238, + 519, + 251 + ], + "spans": [ + { + "bbox": [ + 305, + 238, + 519, + 251 + ], + "type": "text", + "content": "3.2. Extract Instance-Centric Representation" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 255, + 545, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 255, + 545, + 399 + ], + "spans": [ + { + "bbox": [ + 304, + 255, + 545, + 399 + ], + "type": "text", + "content": "After obtaining the contextualized embeddings, existing VLP approaches leverage " + }, + { + "bbox": [ + 304, + 255, + 545, + 399 + ], + "type": "inline_equation", + "content": "g_{I}(v_{cls}) \\in \\mathcal{R}^{D}" + }, + { + "bbox": [ + 304, + 255, + 545, + 399 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 255, + 545, + 399 + ], + "type": "inline_equation", + "content": "g_{T}(w_{cls}) \\in \\mathcal{R}^{D}" + }, + { + "bbox": [ + 304, + 255, + 545, + 399 + ], + "type": "text", + "content": " to align positive image-text pairs via contrastive learning. Here " + }, + { + "bbox": [ + 304, + 255, + 545, + 399 + ], + "type": "inline_equation", + "content": "g_{I}(\\cdot)" + }, + { + "bbox": [ + 304, + 255, + 545, + 399 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 255, + 545, + 399 + ], + "type": "inline_equation", + "content": "g_{T}(\\cdot)" + }, + { + "bbox": [ + 304, + 255, + 545, + 399 + ], + "type": "text", + "content": " are the aforementioned projections. While effective in the general domain, this design only considers the alignment between the global image-text semantics. However, in the E-commerce image, only several regions containing the desired product instance are informative foregrounds corresponding to the text description. Modeling such image-level alignment will fail to learn strong and robust product semantics. Hence, we are committed to learning instance-centric representation." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 403, + 545, + 511 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 403, + 545, + 511 + ], + "spans": [ + { + "bbox": [ + 304, + 403, + 545, + 511 + ], + "type": "text", + "content": "Instance Query. Motivated by [1, 15], a set of learnable tokens called instance query are introduced to ground the potential instance in the product image. As Figure 2 shows, each query is correlated to a specific text or image that we refer to as multi-modal prompt. The insight behind this design is that we wish the instance that a query should probe to be specified by the prompt content. Formally, the proposed instance query is denoted as " + }, + { + "bbox": [ + 304, + 403, + 545, + 511 + ], + "type": "inline_equation", + "content": "\\mathbf{Q} = \\{q_t \\in \\mathcal{R}^D\\}_{t=1}^T" + }, + { + "bbox": [ + 304, + 403, + 545, + 511 + ], + "type": "text", + "content": ", which can be obtained by:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 370, + 519, + 545, + 533 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 370, + 519, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 370, + 519, + 545, + 533 + ], + "type": "interline_equation", + "content": "q _ {t} = q _ {t} ^ {\\text {p r o m p t}} + q _ {t} ^ {\\text {p o s}} + q _ {t} ^ {\\text {t y p e}}. \\tag {1}", + "image_path": "511afc365fa49d08131a4d60ca634ee801df90171da81d6746e2edc6c1da0b1f.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "spans": [ + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "inline_equation", + "content": "q_{t}^{\\text{prompt}}" + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "text", + "content": " denotes " + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "inline_equation", + "content": "g_{I}(v_{cls})" + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "text", + "content": " or " + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "inline_equation", + "content": "g_{T}(w_{cls})" + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "inline_equation", + "content": "q_{t}^{\\text{pos}}" + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "inline_equation", + "content": "q_{t}^{\\text{type}}" + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "text", + "content": " are learnable position and type embedding, indicating the probing area of a query and the modality type of the binding prompt. These queries are responsible for aggregating the instance-centric representations " + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "inline_equation", + "content": "H = \\{h_t\\}_{t=1}^T" + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "text", + "content": " from the encoded visual features via a decoder architecture. During pretraining, there is only one positive prompt (w.r.t the same product) for a given sample, and the rest " + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "inline_equation", + "content": "T - 1" + }, + { + "bbox": [ + 304, + 541, + 545, + 650 + ], + "type": "text", + "content": " are negative ones sampled from other products." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "content": "Instance Decoder. We first project all the encoded " + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\{v_{i}\\}_{i = 1}^{N}" + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "content": " into the same feature space as the prompt, yielding an embedding sequence " + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "inline_equation", + "content": "Z = \\{z_{i}\\in \\mathcal{R}^{D}\\}_{i = 1}^{N}" + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "content": ". Moreover, the instance representations " + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\pmb{H}^0" + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "content": " are zero-initialized, and then before feeding to the decoder. The proposed decoder then" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11062" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 72, + 72, + 309, + 287 + ], + "blocks": [ + { + "bbox": [ + 72, + 72, + 309, + 287 + ], + "lines": [ + { + "bbox": [ + 72, + 72, + 309, + 287 + ], + "spans": [ + { + "bbox": [ + 72, + 72, + 309, + 287 + ], + "type": "image", + "image_path": "47b7105cfaabec726a10edcccbf9e00bb4a855dd523dcc74ad75a66113323505.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 143, + 289, + 235, + 300 + ], + "lines": [ + { + "bbox": [ + 143, + 289, + 235, + 300 + ], + "spans": [ + { + "bbox": [ + 143, + 289, + 235, + 300 + ], + "type": "text", + "content": "(a) The overall Pipeline" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 375, + 71, + 521, + 288 + ], + "blocks": [ + { + "bbox": [ + 375, + 71, + 521, + 288 + ], + "lines": [ + { + "bbox": [ + 375, + 71, + 521, + 288 + ], + "spans": [ + { + "bbox": [ + 375, + 71, + 521, + 288 + ], + "type": "image", + "image_path": "ead454f46f5697a2722c45f32963d93448f6f115996fe3768af0198031d4b196.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 386, + 289, + 490, + 300 + ], + "lines": [ + { + "bbox": [ + 386, + 289, + 490, + 300 + ], + "spans": [ + { + "bbox": [ + 386, + 289, + 490, + 300 + ], + "type": "text", + "content": "(b) The " + }, + { + "bbox": [ + 386, + 289, + 490, + 300 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 386, + 289, + 490, + 300 + ], + "type": "text", + "content": "-th Decoder Block" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 46, + 305, + 547, + 339 + ], + "lines": [ + { + "bbox": [ + 46, + 305, + 547, + 339 + ], + "spans": [ + { + "bbox": [ + 46, + 305, + 547, + 339 + ], + "type": "text", + "content": "Figure 2. (a) The architecture of the proposed instance-level representation learning paradigm (ECLIP), which is composed of an image encoder, a text encoder, and an instance decoder. The whole framework is optimized by three pretext tasks when pretraining on a large-scale E-commerce dataset. (b) The core decoder architecture that aims at aggregating the instance representation of the desired product." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 343, + 287, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 343, + 287, + 380 + ], + "spans": [ + { + "bbox": [ + 46, + 343, + 287, + 380 + ], + "type": "text", + "content": "reads all the above-described embeddings: " + }, + { + "bbox": [ + 46, + 343, + 287, + 380 + ], + "type": "inline_equation", + "content": "Z" + }, + { + "bbox": [ + 46, + 343, + 287, + 380 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 343, + 287, + 380 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 46, + 343, + 287, + 380 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 343, + 287, + 380 + ], + "type": "inline_equation", + "content": "H^0" + }, + { + "bbox": [ + 46, + 343, + 287, + 380 + ], + "type": "text", + "content": " as its input. It has " + }, + { + "bbox": [ + 46, + 343, + 287, + 380 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 46, + 343, + 287, + 380 + ], + "type": "text", + "content": " stacked blocks, and each one consists of a slot-attention and a self-attention layer." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 380, + 287, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 380, + 287, + 452 + ], + "spans": [ + { + "bbox": [ + 47, + 380, + 287, + 452 + ], + "type": "text", + "content": "The goal of the slot-attention layer is to adaptively update query representations through the interaction with the encoded visual embeddings. In detail, for the " + }, + { + "bbox": [ + 47, + 380, + 287, + 452 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 47, + 380, + 287, + 452 + ], + "type": "text", + "content": "-th slot-attention layer, it first calculates a similarity matrix " + }, + { + "bbox": [ + 47, + 380, + 287, + 452 + ], + "type": "inline_equation", + "content": "M \\in \\mathcal{R}^{N \\times T}" + }, + { + "bbox": [ + 47, + 380, + 287, + 452 + ], + "type": "text", + "content": ", which is implemented by the dot-product attention mechanism [24]. Formally, it is formulated by:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 85, + 461, + 287, + 487 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 461, + 287, + 487 + ], + "spans": [ + { + "bbox": [ + 85, + 461, + 287, + 487 + ], + "type": "interline_equation", + "content": "M = \\frac {1}{\\sqrt {D}} \\left(Z W _ {z}\\right) \\cdot \\left(\\left(Q + H ^ {l - 1}\\right) W _ {q}\\right) ^ {\\top}, \\tag {2}", + "image_path": "1657cbc4165a2a368ad0d385ead9063a7a75410f5e0bc3ebdd685dafece22cc6.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "spans": [ + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "inline_equation", + "content": "W_{z}" + }, + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "inline_equation", + "content": "W_{q}" + }, + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "text", + "content": " are the learnable projections parameter matrices, " + }, + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "inline_equation", + "content": "H^{l - 1}" + }, + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "text", + "content": " is the instance representation produced by the " + }, + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "inline_equation", + "content": "(l - 1)^{\\mathrm{th}}" + }, + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "text", + "content": " decoder block. The similarity matrix " + }, + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "text", + "content": " is further normalized by a softmax function over " + }, + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 47, + 495, + 287, + 544 + ], + "type": "text", + "content": " queries:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 115, + 552, + 287, + 582 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 115, + 552, + 287, + 582 + ], + "spans": [ + { + "bbox": [ + 115, + 552, + 287, + 582 + ], + "type": "interline_equation", + "content": "M _ {i j} = \\frac {\\exp (M _ {i j})}{\\sum_ {t = 1} ^ {T} \\exp (M _ {i t})}. \\tag {3}", + "image_path": "6ca226564903173b077ad1407e0e07db0467ba6b640ee15071a606941af4f5ba.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 590, + 288, + 673 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 590, + 288, + 673 + ], + "spans": [ + { + "bbox": [ + 46, + 590, + 288, + 673 + ], + "type": "text", + "content": "The generated matrix " + }, + { + "bbox": [ + 46, + 590, + 288, + 673 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 590, + 288, + 673 + ], + "type": "text", + "content": " actually performs soft assignment via computing semantic similarity between " + }, + { + "bbox": [ + 46, + 590, + 288, + 673 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 590, + 288, + 673 + ], + "type": "text", + "content": " visual tokens and " + }, + { + "bbox": [ + 46, + 590, + 288, + 673 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 46, + 590, + 288, + 673 + ], + "type": "text", + "content": " instance queries. In doing so, it is capable of distributing each visual token to a specific query according to their similarity score. To aggregate the information of the visual tokens into their assigned input query, we compute a weighted mean update based on " + }, + { + "bbox": [ + 46, + 590, + 288, + 673 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 590, + 288, + 673 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 89, + 683, + 287, + 716 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 89, + 683, + 287, + 716 + ], + "spans": [ + { + "bbox": [ + 89, + 683, + 287, + 716 + ], + "type": "interline_equation", + "content": "\\Delta h _ {t} ^ {l - 1} = \\frac {1}{\\sum_ {i = 1} ^ {N} M _ {i t}} \\sum_ {i = 1} ^ {N} M _ {i t} \\left(W _ {v} z _ {i}\\right). \\tag {4}", + "image_path": "5017185bc1d74bb006c1223a044facdd13866917af58fdabb9415660b3eedaef.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 343, + 545, + 368 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 343, + 545, + 368 + ], + "spans": [ + { + "bbox": [ + 305, + 343, + 545, + 368 + ], + "type": "text", + "content": "Finally, the instance representation " + }, + { + "bbox": [ + 305, + 343, + 545, + 368 + ], + "type": "inline_equation", + "content": "\\pmb{H}^l" + }, + { + "bbox": [ + 305, + 343, + 545, + 368 + ], + "type": "text", + "content": " at the " + }, + { + "bbox": [ + 305, + 343, + 545, + 368 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 305, + 343, + 545, + 368 + ], + "type": "text", + "content": "-th layer can be updated by a residual connection:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 375, + 377, + 545, + 392 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 375, + 377, + 545, + 392 + ], + "spans": [ + { + "bbox": [ + 375, + 377, + 545, + 392 + ], + "type": "interline_equation", + "content": "h _ {t} ^ {l} = h _ {t} ^ {l - 1} + W _ {o} \\Delta h _ {t} ^ {l - 1}. \\tag {5}", + "image_path": "c7ce3342adbab2b14ebbc4b6607b01a75379e3b97807148fd70660f13150cd6b.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 403, + 533, + 415 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 403, + 533, + 415 + ], + "spans": [ + { + "bbox": [ + 305, + 403, + 533, + 415 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 403, + 533, + 415 + ], + "type": "inline_equation", + "content": "W_{v}" + }, + { + "bbox": [ + 305, + 403, + 533, + 415 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 403, + 533, + 415 + ], + "type": "inline_equation", + "content": "W_{o}" + }, + { + "bbox": [ + 305, + 403, + 533, + 415 + ], + "type": "text", + "content": " are linear transformation parameters." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 416, + 545, + 536 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 416, + 545, + 536 + ], + "spans": [ + { + "bbox": [ + 304, + 416, + 545, + 536 + ], + "type": "text", + "content": "On the top of slot-attention layer, there is a self-attention module that performs information propagation between each query. In detail, given the previously updated " + }, + { + "bbox": [ + 304, + 416, + 545, + 536 + ], + "type": "inline_equation", + "content": "H^{l}" + }, + { + "bbox": [ + 304, + 416, + 545, + 536 + ], + "type": "text", + "content": ", it employs standard multi-head self-attention (MSA) followed by a fully connected feed-forward network as in [24]. After " + }, + { + "bbox": [ + 304, + 416, + 545, + 536 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 304, + 416, + 545, + 536 + ], + "type": "text", + "content": " successive decoder blocks, we can obtain the final instance representation " + }, + { + "bbox": [ + 304, + 416, + 545, + 536 + ], + "type": "inline_equation", + "content": "H^{L}" + }, + { + "bbox": [ + 304, + 416, + 545, + 536 + ], + "type": "text", + "content": ". It is noteworthy that, since the multi-modal prompts just participate in the similarity calculation in Eq. 2, the resulting " + }, + { + "bbox": [ + 304, + 416, + 545, + 536 + ], + "type": "inline_equation", + "content": "H^{L}" + }, + { + "bbox": [ + 304, + 416, + 545, + 536 + ], + "type": "text", + "content": " thus contains only visual-modality information." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 540, + 545, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 540, + 545, + 648 + ], + "spans": [ + { + "bbox": [ + 304, + 540, + 545, + 648 + ], + "type": "text", + "content": "Discussion: The proposed decoder works like conducting a clustering on the image tokens, where each instance query serves as the centroid of a cluster. At each decoder block, it determines where each token belongs by measuring its distance from the centroids in the semantic space. The cluster centroid is then updated via a soft manner (Eq.4) based on the calculated distance. By stacking multiple decoder blocks, it can implicitly force each query to attend to a specific region and aggregate instance-level representations." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 658, + 497, + 671 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 658, + 497, + 671 + ], + "spans": [ + { + "bbox": [ + 306, + 658, + 497, + 671 + ], + "type": "text", + "content": "3.3. Multi-Modal Pretraining Objectives" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": "Our ECLIP is optimized on large-scale uncurated product data with several pretraining proxy tasks. In the following, we describe each task in detail." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11063" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 66, + 70, + 267, + 175 + ], + "blocks": [ + { + "bbox": [ + 66, + 70, + 267, + 175 + ], + "lines": [ + { + "bbox": [ + 66, + 70, + 267, + 175 + ], + "spans": [ + { + "bbox": [ + 66, + 70, + 267, + 175 + ], + "type": "image", + "image_path": "b3d0370d28972c3795325d9e93624bdb7da601c924663ea41b1a77647b8a5d36.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 182, + 289, + 228 + ], + "lines": [ + { + "bbox": [ + 46, + 182, + 289, + 228 + ], + "spans": [ + { + "bbox": [ + 46, + 182, + 289, + 228 + ], + "type": "text", + "content": "Figure 3. The illustration of inter-product contrastive learning. It aims to bring the positive samples from the same product (" + }, + { + "bbox": [ + 46, + 182, + 289, + 228 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 46, + 182, + 289, + 228 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 182, + 289, + 228 + ], + "type": "inline_equation", + "content": "x^{+}" + }, + { + "bbox": [ + 46, + 182, + 289, + 228 + ], + "type": "text", + "content": ") closer than the negative ones (" + }, + { + "bbox": [ + 46, + 182, + 289, + 228 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 46, + 182, + 289, + 228 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 182, + 289, + 228 + ], + "type": "inline_equation", + "content": "x^{-}" + }, + { + "bbox": [ + 46, + 182, + 289, + 228 + ], + "type": "text", + "content": "), which contributes to grounding the desired product (outlined with the green box)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 233, + 288, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 233, + 288, + 281 + ], + "spans": [ + { + "bbox": [ + 46, + 233, + 288, + 281 + ], + "type": "text", + "content": "Image-Text Contrastive Learning. As in [9, 12, 20], this task contributes to learning better unimodal representations. Given a batch of product samples " + }, + { + "bbox": [ + 46, + 233, + 288, + 281 + ], + "type": "inline_equation", + "content": "\\{(x_i^I,x_i^T)\\}_{i = 1}^B" + }, + { + "bbox": [ + 46, + 233, + 288, + 281 + ], + "type": "text", + "content": ", the similarity between image " + }, + { + "bbox": [ + 46, + 233, + 288, + 281 + ], + "type": "inline_equation", + "content": "x^{I}" + }, + { + "bbox": [ + 46, + 233, + 288, + 281 + ], + "type": "text", + "content": " and text " + }, + { + "bbox": [ + 46, + 233, + 288, + 281 + ], + "type": "inline_equation", + "content": "x^{T}" + }, + { + "bbox": [ + 46, + 233, + 288, + 281 + ], + "type": "text", + "content": " is estimated as:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 100, + 290, + 234, + 305 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 290, + 234, + 305 + ], + "spans": [ + { + "bbox": [ + 100, + 290, + 234, + 305 + ], + "type": "interline_equation", + "content": "s (x ^ {I}, x ^ {T}) = g _ {I} \\left(v _ {c l s}\\right) ^ {\\top} g _ {T} \\left(w _ {c l s}\\right).", + "image_path": "4126ecf07ee35d511304892839327588428323f42ad775d6fb89e3cca32cd632.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 315, + 287, + 352 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 315, + 287, + 352 + ], + "spans": [ + { + "bbox": [ + 46, + 315, + 287, + 352 + ], + "type": "text", + "content": "This pretraining objective brings the image-text pairs of the same product in the embedding space closer than the unmatched ones, which consists of image-to-text term " + }, + { + "bbox": [ + 46, + 315, + 287, + 352 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{i2t}" + }, + { + "bbox": [ + 46, + 315, + 287, + 352 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 78, + 361, + 287, + 396 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 361, + 287, + 396 + ], + "spans": [ + { + "bbox": [ + 78, + 361, + 287, + 396 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {i 2 t} = - \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp (s \\left(x _ {i} ^ {I} , x _ {i} ^ {T}\\right) / \\tau)}{\\sum_ {j = 1} ^ {B} \\exp (s \\left(x _ {i} ^ {I} , x _ {j} ^ {T}\\right) / \\tau)}, \\tag {6}", + "image_path": "0602ba8e1a9330d537f8548314445d746e70b70cf991054afed98dbb715f83aa.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 400, + 171, + 412 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 400, + 171, + 412 + ], + "spans": [ + { + "bbox": [ + 47, + 400, + 171, + 412 + ], + "type": "text", + "content": "and a text-to-image term " + }, + { + "bbox": [ + 47, + 400, + 171, + 412 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{t2i}" + }, + { + "bbox": [ + 47, + 400, + 171, + 412 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 78, + 422, + 287, + 455 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 422, + 287, + 455 + ], + "spans": [ + { + "bbox": [ + 78, + 422, + 287, + 455 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {t 2 i} = - \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp \\left(s \\left(x _ {i} ^ {T} , x _ {i} ^ {I}\\right) / \\tau\\right)}{\\sum_ {j = 1} ^ {B} \\exp \\left(s \\left(x _ {i} ^ {T} , x _ {j} ^ {I}\\right) / \\tau\\right)}, \\tag {7}", + "image_path": "d1eb4ed2485acda12b31953cc22821d58609e42741ca18278b07ce12e8a757b8.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 464, + 287, + 488 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 464, + 287, + 488 + ], + "spans": [ + { + "bbox": [ + 46, + 464, + 287, + 488 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 464, + 287, + 488 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 46, + 464, + 287, + 488 + ], + "type": "text", + "content": " is a learnable temperature parameter. The whole objective is then defined as " + }, + { + "bbox": [ + 46, + 464, + 287, + 488 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{itc} = \\frac{1}{2} (\\mathcal{L}_{i2t} + \\mathcal{L}_{t2i})" + }, + { + "bbox": [ + 46, + 464, + 287, + 488 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 491, + 287, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 491, + 287, + 570 + ], + "spans": [ + { + "bbox": [ + 46, + 491, + 287, + 570 + ], + "type": "text", + "content": "Inter-Product Multi-modal Learning. As shown in Figure 3, we maintain a momentum model during pretraining that is an exponential-moving-average of the origin model like [8]. For a product sample " + }, + { + "bbox": [ + 46, + 491, + 287, + 570 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 46, + 491, + 287, + 570 + ], + "type": "text", + "content": ", we denote the representation of the positive prompt produced by the base and momentum model as " + }, + { + "bbox": [ + 46, + 491, + 287, + 570 + ], + "type": "inline_equation", + "content": "h_{\\theta}^{i}" + }, + { + "bbox": [ + 46, + 491, + 287, + 570 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 491, + 287, + 570 + ], + "type": "inline_equation", + "content": "h_{\\xi}^{i}" + }, + { + "bbox": [ + 46, + 491, + 287, + 570 + ], + "type": "text", + "content": ". An inter-product contrastive loss " + }, + { + "bbox": [ + 46, + 491, + 287, + 570 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{inter}" + }, + { + "bbox": [ + 46, + 491, + 287, + 570 + ], + "type": "text", + "content": " is computed by:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 578, + 287, + 616 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 578, + 287, + 616 + ], + "spans": [ + { + "bbox": [ + 47, + 578, + 287, + 616 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {i n t e r} = - \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp \\left(h _ {\\theta} ^ {i} ^ {\\top} h _ {\\xi} ^ {j} / \\tau\\right)}{\\exp \\left(h _ {\\theta} ^ {i} ^ {\\top} h _ {\\xi} ^ {j} / \\tau\\right) + \\sum_ {k \\in \\mathcal {N} ^ {-}} \\exp \\left(h _ {\\theta} ^ {i} ^ {\\top} h _ {\\xi} ^ {k} / \\tau\\right)}, \\tag {8}", + "image_path": "a1fe6974dd5b22b836626ea9c264dbcb967541f1e05907767ae1e0e9974bd4bd.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "text", + "content": "where sample " + }, + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "text", + "content": " are a positive pair, " + }, + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{N}^{-}" + }, + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "text", + "content": " is a negative sample set that belongs to other products. This objective maximizes the similarity between different samples of the identical product, while minimizing those of the unmatched ones. Since the images of a product are collected from different sources, their background appearances are usually diverse. Hence, " + }, + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{inter}" + }, + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "text", + "content": " will encourage the yielded representation to be highly correlated with the desired product and" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 72, + 545, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 95 + ], + "type": "text", + "content": "thus contributes to aligning the positive prompt with the corresponding image tokens in a fine-grained manner." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 96, + 545, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 96, + 545, + 180 + ], + "spans": [ + { + "bbox": [ + 305, + 96, + 545, + 180 + ], + "type": "text", + "content": "This pretext task also incorporates an additional instance-text matching loss that predicts whether an instance and a text description are matched. Formally, given an instance-text pair, we obtain their match logit defined by: " + }, + { + "bbox": [ + 305, + 96, + 545, + 180 + ], + "type": "inline_equation", + "content": "f(h_{\\theta}^{i}\\odot g_{T}(w_{\\mathrm{cls}}^{i}))" + }, + { + "bbox": [ + 305, + 96, + 545, + 180 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 305, + 96, + 545, + 180 + ], + "type": "inline_equation", + "content": "\\odot" + }, + { + "bbox": [ + 305, + 96, + 545, + 180 + ], + "type": "text", + "content": " is Hadamard product and " + }, + { + "bbox": [ + 305, + 96, + 545, + 180 + ], + "type": "inline_equation", + "content": "f(\\cdot)" + }, + { + "bbox": [ + 305, + 96, + 545, + 180 + ], + "type": "text", + "content": " is a mapping layer: " + }, + { + "bbox": [ + 305, + 96, + 545, + 180 + ], + "type": "inline_equation", + "content": "\\mathcal{R}^D\\to \\mathcal{R}^2" + }, + { + "bbox": [ + 305, + 96, + 545, + 180 + ], + "type": "text", + "content": ". This match logit is optimized by a typical binary cross entropy loss " + }, + { + "bbox": [ + 305, + 96, + 545, + 180 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{itm}" + }, + { + "bbox": [ + 305, + 96, + 545, + 180 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 183, + 545, + 291 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 183, + 545, + 291 + ], + "spans": [ + { + "bbox": [ + 304, + 183, + 545, + 291 + ], + "type": "text", + "content": "Intra-Product Multi-modal Learning. For a product sample, there is only one positive prompt describing the presented product during pretraining, and the rest " + }, + { + "bbox": [ + 304, + 183, + 545, + 291 + ], + "type": "inline_equation", + "content": "T - 1" + }, + { + "bbox": [ + 304, + 183, + 545, + 291 + ], + "type": "text", + "content": " prompts are sampled from other products. The motivation behind this pretext task is to ensure that only positive queries can probe the foreground instance, rather than negative ones. To this end, we apply an intra-product contrastive loss using text supervision. Suppose index " + }, + { + "bbox": [ + 304, + 183, + 545, + 291 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 304, + 183, + 545, + 291 + ], + "type": "text", + "content": " indicates the positive query, then " + }, + { + "bbox": [ + 304, + 183, + 545, + 291 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text{intra}}" + }, + { + "bbox": [ + 304, + 183, + 545, + 291 + ], + "type": "text", + "content": " can be formulated as:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 321, + 298, + 545, + 332 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 298, + 545, + 332 + ], + "spans": [ + { + "bbox": [ + 321, + 298, + 545, + 332 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {i n t r a} = - \\sum_ {i = 1} ^ {B} \\log \\frac {\\exp \\left(h _ {r} ^ {i} ^ {\\top} g _ {T} \\left(w _ {\\mathrm {c l s}} ^ {i}\\right) / \\tau\\right)}{\\sum_ {t = 1} ^ {T} \\exp \\left(h _ {t} ^ {i} ^ {\\top} g _ {T} \\left(w _ {\\mathrm {c l s}} ^ {i}\\right) / \\tau\\right)}, \\tag {9}", + "image_path": "3c5f4af26e0642450102e2a291fe70dfa98f1071542b0037be7fbb4821c20aee.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 338, + 546, + 374 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 338, + 546, + 374 + ], + "spans": [ + { + "bbox": [ + 305, + 338, + 546, + 374 + ], + "type": "text", + "content": "which serves to bring the positive query and the product description closer than all " + }, + { + "bbox": [ + 305, + 338, + 546, + 374 + ], + "type": "inline_equation", + "content": "T - 1" + }, + { + "bbox": [ + 305, + 338, + 546, + 374 + ], + "type": "text", + "content": " negative ones. Moreover, we also introduce an entropy regularization term for " + }, + { + "bbox": [ + 305, + 338, + 546, + 374 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 305, + 338, + 546, + 374 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 318, + 381, + 545, + 460 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 381, + 545, + 460 + ], + "spans": [ + { + "bbox": [ + 318, + 381, + 545, + 460 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {\\mathcal {R}} = \\sum_ {i = 1} ^ {N} M _ {i, r} \\log \\left(\\frac {1}{M _ {i , r}}\\right) + \\\\ \\sum_ {j = 1, j \\neq r} ^ {T} \\left(\\log N - \\sum_ {i = 1} ^ {N} M _ {i, j} \\log \\left(\\frac {1}{M _ {i , j}}\\right)\\right). \\tag {10} \\\\ \\end{array}", + "image_path": "2cc72bd013e0d472669b8d3a0288ee9ade4f4ceaa55fbe6061a8f97e164b8b15.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 461, + 545, + 532 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 461, + 545, + 532 + ], + "spans": [ + { + "bbox": [ + 305, + 461, + 545, + 532 + ], + "type": "text", + "content": "This regularization term encourages the positive query to focus on several tokens that may contain product instances. While for " + }, + { + "bbox": [ + 305, + 461, + 545, + 532 + ], + "type": "inline_equation", + "content": "T - 1" + }, + { + "bbox": [ + 305, + 461, + 545, + 532 + ], + "type": "text", + "content": " negative ones, it prevents too sharp similarity distributions over " + }, + { + "bbox": [ + 305, + 461, + 545, + 532 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 305, + 461, + 545, + 532 + ], + "type": "text", + "content": " image tokens. Finally, the overall pretraining objective of ECLIP is the sum of all aforementioned loss terms." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 540, + 472, + 552 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 540, + 472, + 552 + ], + "spans": [ + { + "bbox": [ + 306, + 540, + 472, + 552 + ], + "type": "text", + "content": "3.4. Transfer to Downstream Tasks" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 305, + 558, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 558, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 305, + 558, + 545, + 712 + ], + "type": "text", + "content": "Once pretrained, the resulting foundation model can be leveraged to extract the product instance representation with minimal surgery. Specifically, given a product sample " + }, + { + "bbox": [ + 305, + 558, + 545, + 712 + ], + "type": "inline_equation", + "content": "(x_{i}^{I}, x_{i}^{T})" + }, + { + "bbox": [ + 305, + 558, + 545, + 712 + ], + "type": "text", + "content": ", we first encode the image-text pair into an embedding sequence via the unimodal encoders. Then, the global representation of text description " + }, + { + "bbox": [ + 305, + 558, + 545, + 712 + ], + "type": "inline_equation", + "content": "g_{T}(w_{\\mathrm{cls}})" + }, + { + "bbox": [ + 305, + 558, + 545, + 712 + ], + "type": "text", + "content": " is treated as the positive query and fed into the decoder concatenated with " + }, + { + "bbox": [ + 305, + 558, + 545, + 712 + ], + "type": "inline_equation", + "content": "T - 1" + }, + { + "bbox": [ + 305, + 558, + 545, + 712 + ], + "type": "text", + "content": " negative ones. Here, the negative queries " + }, + { + "bbox": [ + 305, + 558, + 545, + 712 + ], + "type": "inline_equation", + "content": "\\{q_{t}\\}_{t = 2}^{T}" + }, + { + "bbox": [ + 305, + 558, + 545, + 712 + ], + "type": "text", + "content": " are sampled from a standard Gaussian distribution for convenience. We also explore different negative query setting manners in Section 4.3. The yielded representation " + }, + { + "bbox": [ + 305, + 558, + 545, + 712 + ], + "type": "inline_equation", + "content": "h_0^L" + }, + { + "bbox": [ + 305, + 558, + 545, + 712 + ], + "type": "text", + "content": " belonging to the positive query is then applied to a wide range of E-commerce downstream tasks." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11064" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 128, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 128, + 85 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 128, + 85 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 91, + 159, + 104 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 91, + 159, + 104 + ], + "spans": [ + { + "bbox": [ + 47, + 91, + 159, + 104 + ], + "type": "text", + "content": "4.1. Pretraining Details" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 110, + 287, + 229 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 110, + 287, + 229 + ], + "spans": [ + { + "bbox": [ + 46, + 110, + 287, + 229 + ], + "type": "text", + "content": "Pretraining Dataset We collected a large-scale pretraining dataset from a popular E-commerce website. It consists of 15M different products and over 100M various images, covering about 9K diverse categories such as clothes, daily necessities, instruments, and so on. For each product item, it has a corresponding textual description and several images from the product details pages, customer comments, and attached advertisement videos. During pretraining, the positive data pairs are constructed by sampling images belonging to the same product from different sources." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 230, + 288, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 230, + 288, + 422 + ], + "spans": [ + { + "bbox": [ + 46, + 230, + 288, + 422 + ], + "type": "text", + "content": "Implementation Details. The image encoder adopts the same network configuration as the standard ViT [5] and is initialized from the weight pre-trained on ImageNet. Our text encoder is implemented with the same architecture as " + }, + { + "bbox": [ + 46, + 230, + 288, + 422 + ], + "type": "inline_equation", + "content": "\\mathrm{BERT}_{\\mathrm{base}}" + }, + { + "bbox": [ + 46, + 230, + 288, + 422 + ], + "type": "text", + "content": " [3]. The decoder has 6 identical blocks and 20 instance queries. We here explore two variants of ViTs: ViTB/16 and ViT-L/16. There is a total of " + }, + { + "bbox": [ + 46, + 230, + 288, + 422 + ], + "type": "inline_equation", + "content": "220 / 450" + }, + { + "bbox": [ + 46, + 230, + 288, + 422 + ], + "type": "text", + "content": " million parameters for the base and large version. During pretraining, the input images are resized to " + }, + { + "bbox": [ + 46, + 230, + 288, + 422 + ], + "type": "inline_equation", + "content": "224 \\times 224" + }, + { + "bbox": [ + 46, + 230, + 288, + 422 + ], + "type": "text", + "content": " with random crop and horizontal flip augmentation, and the texts are tokenized by WordPiece with a maximum length of 55. We pretrain for 15 epochs with a batch size of 6400 (ViT-B) / 4096 (ViT-L) on 32 NVIDIA A100 GPUs. The whole framework is learned using AdamW [16] optimizer and the learning rate is warmed up to 1e-4 and then decayed linearly. More details are elaborated in the supplementary." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 422, + 287, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 422, + 287, + 507 + ], + "spans": [ + { + "bbox": [ + 46, + 422, + 287, + 507 + ], + "type": "text", + "content": "Compared Baselines. We mainly compare the ECLIP with several state-of-the-art VLP methods: CLIP [20], FILIP [31], DeCLIP [14], ALBEF [12] and BLIP [11]. For a fair comparison, we also leverage ViT-B/16 as the image encoder, " + }, + { + "bbox": [ + 46, + 422, + 287, + 507 + ], + "type": "inline_equation", + "content": "\\mathrm{BERT}_{\\mathrm{base}}" + }, + { + "bbox": [ + 46, + 422, + 287, + 507 + ], + "type": "text", + "content": " as the text encoder, and pretrain these baselines on the same 100M E-commerce data using their official public implementations." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 515, + 227, + 528 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 515, + 227, + 528 + ], + "spans": [ + { + "bbox": [ + 47, + 515, + 227, + 528 + ], + "type": "text", + "content": "4.2. Evaluation on Downstream Tasks" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 534, + 287, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 534, + 287, + 559 + ], + "spans": [ + { + "bbox": [ + 47, + 534, + 287, + 559 + ], + "type": "text", + "content": "Next, we delineate the evaluation performances for five specific E-commerce downstream tasks in turn." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 562, + 220, + 574 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 562, + 220, + 574 + ], + "spans": [ + { + "bbox": [ + 47, + 562, + 220, + 574 + ], + "type": "text", + "content": "4.2.1 Zero-Shot Product Classification" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 582, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 287, + 715 + ], + "type": "text", + "content": "We first transfer ECLIP to product item classification. It is a recognition task that aims to map a product sample to a specific category. We evaluate the performance on a large-scale publicly available E-commerce dataset called M5Product [4], which covers 1.1M images and 5,679 various product categories. Here, we consider the multimodal setting, which uses both the product image and related textual description for classification. To demonstrate the strong zero-shot ability of ECLIP, we apply it directly to the classification evaluation without further finetune. It is achieved by measuring the similarity between the category text like" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "content": "CLIP [20]. The left part of Table 1 summarizes the Top-1 classification accuracy of all the compared methods. As presented, our ECLIP greatly exceeds all the existing baselines by a large margin (e.g., " + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "inline_equation", + "content": "+6.6\\%" + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "content": " v.s. CLIP), demonstrating the superiority of instance-level representation." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 142, + 474, + 154 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 142, + 474, + 154 + ], + "spans": [ + { + "bbox": [ + 305, + 142, + 474, + 154 + ], + "type": "text", + "content": "4.2.2 Zero-Shot Image-Text Retrieval" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 161, + 545, + 305 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 161, + 545, + 305 + ], + "spans": [ + { + "bbox": [ + 304, + 161, + 545, + 305 + ], + "type": "text", + "content": "ECLIP is also transferred to test zero-shot performance for image-to-text and text-to-image retrieval. To this end, we collect a large dataset that contains 205K image-text pairs of E-commerce products. Since only unimodal information is available in this task, we simply use our image and text encoders to embed image-text pairs and complete the retrieval based on their pairwise similarity. We utilize the widely-used Recall@K metric for evaluation. Detailed comparison results are shown in the right part of Table 1. We can see that, despite training on the same dataset, our method achieves superior performance owing to fine-grained alignment modeling between text and product instance." + } + ] + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 308, + 316, + 544, + 426 + ], + "blocks": [ + { + "bbox": [ + 308, + 316, + 544, + 426 + ], + "lines": [ + { + "bbox": [ + 308, + 316, + 544, + 426 + ], + "spans": [ + { + "bbox": [ + 308, + 316, + 544, + 426 + ], + "type": "table", + "html": "
MethodClassificationImage-to-TextText-to-Image
Acc@1R@1R@5R@1R@5
CLIP [20]37.252.674.158.784.0
FILIP [31]37.152.373.858.083.5
DeCLIP [14]37.853.175.858.883.9
ALBEF [12]38.552.974.458.283.3
BLIP [11]39.353.375.659.184.4
OursViT-B/1643.853.876.059.984.6
OursViT-L/1644.858.279.663.887.4
", + "image_path": "8b4a7515f79b4e6096cf707edb5fb3ecac39258d299bbd255f605f94067d6f8f.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 434, + 545, + 456 + ], + "lines": [ + { + "bbox": [ + 305, + 434, + 545, + 456 + ], + "spans": [ + { + "bbox": [ + 305, + 434, + 545, + 456 + ], + "type": "text", + "content": "Table 1. Performance comparisons of zero-shot product classification and zero-shot image-text retrieval on M5Product dataset [4]." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 305, + 467, + 460, + 478 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 467, + 460, + 478 + ], + "spans": [ + { + "bbox": [ + 305, + 467, + 460, + 478 + ], + "type": "text", + "content": "4.2.3 Zero-Shot Product Retrieval" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 486, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 486, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 486, + 545, + 713 + ], + "type": "text", + "content": "This task aims to find the most relevant target product given a query (image-text pair of a product). It has a wide range of applications in real e-commerce scenarios such as recommending relevant products for users. We first evaluate the coarse-level retrieval. Following [4], a product pair is considered a match if both belong to the same category during evaluation. The results on M5Product benchmark are reported on the left of Table 2. It can be seen that exploiting instance-centric representation significantly boosts performance. To further demonstrate the effectiveness of instance-level representation, we then conduct a more complicated fine-grained level product retrieval task, where a pair is considered a match if and only if they are the same product. This task requires more adequate fine-grained understanding ability since it focuses on the specific product instance. Detailed comparison results are shown on the right part of Table 2. One can find that our ECLIP achieves a substantial improvement in retrieval performance (e.g., " + }, + { + "bbox": [ + 304, + 486, + 545, + 713 + ], + "type": "inline_equation", + "content": "44.3\\%" + }, + { + "bbox": [ + 304, + 486, + 545, + 713 + ], + "type": "text", + "content": " v.s. " + }, + { + "bbox": [ + 304, + 486, + 545, + 713 + ], + "type": "inline_equation", + "content": "35.6\\%" + }, + { + "bbox": [ + 304, + 486, + 545, + 713 + ], + "type": "text", + "content": " (BLIP) on " + }, + { + "bbox": [ + 304, + 486, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{R}@\\mathbf{1}" + }, + { + "bbox": [ + 304, + 486, + 545, + 713 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11065" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 50, + 70, + 545, + 220 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 545, + 220 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 545, + 220 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 545, + 220 + ], + "type": "table", + "html": "
MethodPretraining DatasetCoarse Product RetrievalFine-grained Product Retrieval
mAP@1mAP@5mAP@10R@1R@5R@10mAP@1mAP@5mAP@10
ViBERT [17]58.661.760.1------
UNITER [2]M5Product58.962.860.9------
SCALE [4]59.864.162.2------
CLIP [20]68.273.270.734.854.262.934.840.239.9
FILIP [31]67.873.070.334.653.962.234.640.139.7
DeCLIP [14]68.573.470.835.356.465.535.341.240.8
ALBEF [12]ECLIP 100M68.773.671.235.156.165.235.140.740.4
BLIP [11]69.174.171.635.656.866.035.641.641.3
OursViT-B/1669.674.972.544.363.471.143.848.648.2
OursViT-L/1670.275.372.945.064.272.145.050.049.5
", + "image_path": "423d8d87c9d010dfcaf4f1cff21573e5f2bd22c61b54bfc672802aa715448739.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 249, + 288, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 249, + 288, + 357 + ], + "spans": [ + { + "bbox": [ + 46, + 249, + 288, + 357 + ], + "type": "text", + "content": "We also consider another setting introduced in [35], called instance-level retrieval, where the query image encompasses multiple different kinds of product instances. And the model needs to find all the related products from a large gallery. As shown in Table 3, ECLIP still achieves superior performance than all the previous approaches. Although the CAPTURE leverages a specially-trained object detector to extract instances, ECLIP still surpasses it by a clear margin with no box annotation." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 366, + 203, + 379 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 366, + 203, + 379 + ], + "spans": [ + { + "bbox": [ + 47, + 366, + 203, + 379 + ], + "type": "text", + "content": "4.2.4 Zero-Shot Visual Grounding" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 385, + 288, + 625 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 385, + 288, + 625 + ], + "spans": [ + { + "bbox": [ + 46, + 385, + 288, + 625 + ], + "type": "text", + "content": "To demonstrate whether our model possesses the capability of localizing the desired product instance after pretraining, we further evaluate ECLIP on the zero-shot product grounding, which requires localizing the product instance in an image according to a textual description. Specifically, the input image-text pair is first fed to our ECLIP to obtain a score map " + }, + { + "bbox": [ + 46, + 385, + 288, + 625 + ], + "type": "inline_equation", + "content": "S \\in \\mathcal{R}^{H \\times W}" + }, + { + "bbox": [ + 46, + 385, + 288, + 625 + ], + "type": "text", + "content": " that measures the similarity between the text and each image location. Then, we use " + }, + { + "bbox": [ + 46, + 385, + 288, + 625 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 46, + 385, + 288, + 625 + ], + "type": "text", + "content": " to rank the candidate regions produced by an off-the-shelf region proposal network. The performance is evaluated by the top-1 accuracy at IoU thresholds " + }, + { + "bbox": [ + 46, + 385, + 288, + 625 + ], + "type": "inline_equation", + "content": "\\{0.5, 0.7\\}" + }, + { + "bbox": [ + 46, + 385, + 288, + 625 + ], + "type": "text", + "content": " on an annotated grounding dataset consisting of 450K product images. Detailed comparison results are listed in Table 4. As we can see, compared to methods aimed at global representation, our model has learned fine-grained cross-modal understanding ability and thus obtains substantial performance gain (e.g., +14.5% v.s. BLIP on Acc@0.7). Since ECLIP supports image prompts during pretraining, we also conduct zero-shot image-conditioned grounding. The results and analysis can be found in the supplementary." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 634, + 203, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 634, + 203, + 647 + ], + "spans": [ + { + "bbox": [ + 47, + 634, + 203, + 647 + ], + "type": "text", + "content": "4.2.5 Transfer to Object Detection" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "text", + "content": "We also transfer ECLIP to object detection to further validate its fine-grained understanding ability. Following DETR [1], we utilize the image encoder to embed visual features, and utilize the decoder with a newly added prediction head to decode the potential objects. Moreover, we col" + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 309, + 247, + 545, + 350 + ], + "blocks": [ + { + "bbox": [ + 107, + 226, + 485, + 239 + ], + "lines": [ + { + "bbox": [ + 107, + 226, + 485, + 239 + ], + "spans": [ + { + "bbox": [ + 107, + 226, + 485, + 239 + ], + "type": "text", + "content": "Table 2. Performance comparisons of zero-shot coarse level and fine-grained level product retrieval task." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 247, + 545, + 350 + ], + "lines": [ + { + "bbox": [ + 309, + 247, + 545, + 350 + ], + "spans": [ + { + "bbox": [ + 309, + 247, + 545, + 350 + ], + "type": "table", + "html": "
MethodInstance-Level Product Retrieval
mAP@10mAP@50mAR@10mAR@50
CAPTURE [35]40.436.817.215.9
CLIP [20]86.682.854.459.5
FILIP [31]86.983.054.659.8
DeCLIP [14]87.183.354.960.0
BLIP [11]87.583.555.160.4
OursViT-B/1689.684.655.961.2
OursViT-L/1689.586.456.362.1
", + "image_path": "7e61a748237a44ac06643dbcd4e4294ae5bb6a3614f3138378fa7938b95d8bf2.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 309, + 380, + 545, + 478 + ], + "blocks": [ + { + "bbox": [ + 306, + 354, + 545, + 376 + ], + "lines": [ + { + "bbox": [ + 306, + 354, + 545, + 376 + ], + "spans": [ + { + "bbox": [ + 306, + 354, + 545, + 376 + ], + "type": "text", + "content": "Table 3. Performance comparisons of zero-shot instance-level product retrieval task on Product1M dataset [35]." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 380, + 545, + 478 + ], + "lines": [ + { + "bbox": [ + 309, + 380, + 545, + 478 + ], + "spans": [ + { + "bbox": [ + 309, + 380, + 545, + 478 + ], + "type": "table", + "html": "
MethodVisual GroundingObject Detection
Acc@0.5Acc@0.7mAP@0.3mAP@0.5
CLIP [20]80.975.217.214.3
FILIP [31]81.375.617.514.6
DeCLIP [14]81.075.317.014.2
ALBEF [12]80.974.717.113.9
BLIP [11]81.175.117.314.1
OursViT-B/1691.289.620.216.5
", + "image_path": "48192d0113c01fcf43e4e8f33603a4d029bf9a81bf82dff84b3d6d071c821fef.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 482, + 545, + 505 + ], + "lines": [ + { + "bbox": [ + 306, + 482, + 545, + 505 + ], + "spans": [ + { + "bbox": [ + 306, + 482, + 545, + 505 + ], + "type": "text", + "content": "Table 4. Performance comparisons of zero-shot visual grounding and finetuned object detection." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 508, + 547, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 508, + 547, + 593 + ], + "spans": [ + { + "bbox": [ + 304, + 508, + 547, + 593 + ], + "type": "text", + "content": "lect a manually annotated detection dataset covering 160K images. We split a 20K subset for evaluation and leave the rest for model finetuning. The supplementary provides experiment details of baselines and ECLIP. It can be observed from Table 4 and Figure 4b that it outperforms the existing VLP methods, demonstrating the superiority of ECLIP to learning fine-grained semantics in E-commerce." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 599, + 400, + 612 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 599, + 400, + 612 + ], + "spans": [ + { + "bbox": [ + 305, + 599, + 400, + 612 + ], + "type": "text", + "content": "4.3. Ablation Study" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": "Effect of Pretext Task. To demonstrate the effectiveness of the inter- and intra- product learning pretext task, we designed the experiments on different task combinations for the product retrieval task. All the ablations were conducted on a smaller pretraining dataset that includes only 5M images due to the costly training time. The complete results are listed in Table 5. One can observe that canceling either of these two tasks will result in worse performance. No" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "11066" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 51, + 70, + 286, + 223 + ], + "blocks": [ + { + "bbox": [ + 51, + 70, + 286, + 223 + ], + "lines": [ + { + "bbox": [ + 51, + 70, + 286, + 223 + ], + "spans": [ + { + "bbox": [ + 51, + 70, + 286, + 223 + ], + "type": "image", + "image_path": "380e598b2bb0d1a266a6da393b17ee838151742dd7f3b10b39c3f86a321b34b5.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 91, + 232, + 499, + 243 + ], + "lines": [ + { + "bbox": [ + 91, + 232, + 499, + 243 + ], + "spans": [ + { + "bbox": [ + 91, + 232, + 499, + 243 + ], + "type": "text", + "content": "Figure 4. Qualitative examples of visual grounding and object detection. See the main text for more explanation." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 315, + 79, + 519, + 223 + ], + "blocks": [ + { + "bbox": [ + 315, + 79, + 519, + 223 + ], + "lines": [ + { + "bbox": [ + 315, + 79, + 519, + 223 + ], + "spans": [ + { + "bbox": [ + 315, + 79, + 519, + 223 + ], + "type": "image", + "image_path": "379459f62b025a761d5f074f44dcc5a73975f5e7a4ab69a0fb164fbb55a9a99c.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 255, + 286, + 301 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 255, + 286, + 301 + ], + "spans": [ + { + "bbox": [ + 46, + 255, + 286, + 301 + ], + "type": "text", + "content": "tably, the inter-product task brings a more significant performance boost compared to the intra-product. We speculate that because the former contrasts with more negative samples from different product images." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 302, + 286, + 457 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 302, + 286, + 457 + ], + "spans": [ + { + "bbox": [ + 46, + 302, + 286, + 457 + ], + "type": "text", + "content": "Effect of Negative Query. Since the negative instance queries are used when transferring to classification and retrieval tasks, we also ran ablations on different ways of setting these negative ones. We tried the following cases: 1) Leverage the description text from other products and encode with the text encoder. 2) Randomly sample the negative queries from the standard Gaussian distribution. 3) Adopt the exponential moving average of queries on the whole dataset during pretraining. Table 6 summarizes the results of this ablation study on the product retrieval task. As observed, there is little to no difference between different manners of setting up negative queries. We thus adopt random sampling for implementation simplicity." + } + ] + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 78, + 465, + 257, + 528 + ], + "blocks": [ + { + "bbox": [ + 78, + 465, + 257, + 528 + ], + "lines": [ + { + "bbox": [ + 78, + 465, + 257, + 528 + ], + "spans": [ + { + "bbox": [ + 78, + 465, + 257, + 528 + ], + "type": "table", + "html": "
LinterLintramAP@1 (%)mAP@5 (%)
61.665.2
✓64.468.7
✓62.266.5
✓✓65.269.3
", + "image_path": "cde276447d629a1cb846aa11afb2307d88d87625d655d0acb95589061a125fea.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 78, + 559, + 256, + 614 + ], + "blocks": [ + { + "bbox": [ + 46, + 532, + 286, + 554 + ], + "lines": [ + { + "bbox": [ + 46, + 532, + 286, + 554 + ], + "spans": [ + { + "bbox": [ + 46, + 532, + 286, + 554 + ], + "type": "text", + "content": "Table 5. Ablation of different pretraining pretext task combinations on coarse-level product retrieval task (ViT-B/16)." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 78, + 559, + 256, + 614 + ], + "lines": [ + { + "bbox": [ + 78, + 559, + 256, + 614 + ], + "spans": [ + { + "bbox": [ + 78, + 559, + 256, + 614 + ], + "type": "table", + "html": "
MetricmAP@10mAR@10
Negative Text84.952.5
EMA Update85.153.3
Random Sampling86.854.1
", + "image_path": "99d15465f953f5de8c79bb556ce26ecd0407ddb9f463465d92f3d5238a48d629.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 618, + 286, + 640 + ], + "lines": [ + { + "bbox": [ + 46, + 618, + 286, + 640 + ], + "spans": [ + { + "bbox": [ + 46, + 618, + 286, + 640 + ], + "type": "text", + "content": "Table 6. Ablation of different negative query setting manners on the instance-level product retrieval task (ViT-B/16)." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 47, + 647, + 164, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 647, + 164, + 659 + ], + "spans": [ + { + "bbox": [ + 47, + 647, + 164, + 659 + ], + "type": "text", + "content": "4.4. Qualitative Analysis" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 665, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 286, + 713 + ], + "type": "text", + "content": "In this section, we first qualitatively showcase that ECLIP can learn fine-grained cross-modal alignment to ground the desired product. Figure 4a presents the visualization of the similarity score map between a product image" + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 305, + 253, + 419, + 361 + ], + "blocks": [ + { + "bbox": [ + 305, + 253, + 419, + 361 + ], + "lines": [ + { + "bbox": [ + 305, + 253, + 419, + 361 + ], + "spans": [ + { + "bbox": [ + 305, + 253, + 419, + 361 + ], + "type": "image", + "image_path": "9164ea0d5096395ef38467abac77e2afe66bd087783bfdea8b2e5e0102783b38.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 340, + 365, + 380, + 376 + ], + "lines": [ + { + "bbox": [ + 340, + 365, + 380, + 376 + ], + "spans": [ + { + "bbox": [ + 340, + 365, + 380, + 376 + ], + "type": "text", + "content": "(a) CLIP" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 304, + 381, + 545, + 437 + ], + "lines": [ + { + "bbox": [ + 304, + 381, + 545, + 437 + ], + "spans": [ + { + "bbox": [ + 304, + 381, + 545, + 437 + ], + "type": "text", + "content": "Figure 5. The T-SNE visualization for the learned representations of CLIP and our ECLIP. Both of them are pretrained on the same dataset. We first randomly select 50 kinds of products and then draw 100 samples for each. Points belonging to the same category are of the same color. See the main text for more explanation." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 432, + 253, + 545, + 361 + ], + "blocks": [ + { + "bbox": [ + 432, + 253, + 545, + 361 + ], + "lines": [ + { + "bbox": [ + 432, + 253, + 545, + 361 + ], + "spans": [ + { + "bbox": [ + 432, + 253, + 545, + 361 + ], + "type": "image", + "image_path": "9052fcad9962fec36a5a79db8011bb8bcbfacc302af210a8455d37d76d8e8003.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 468, + 365, + 515, + 376 + ], + "lines": [ + { + "bbox": [ + 468, + 365, + 515, + 376 + ], + "spans": [ + { + "bbox": [ + 468, + 365, + 515, + 376 + ], + "type": "text", + "content": "(b) ECLIP" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 442, + 545, + 563 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 442, + 545, + 563 + ], + "spans": [ + { + "bbox": [ + 304, + 442, + 545, + 563 + ], + "type": "text", + "content": "and its corresponding text description, where darker color indicates image locations with higher similarity to text. We can clearly observe that our model can rightly attend to the desired instance depicted by the text. Furthermore, T-SNE is used for visualizing the visual embedding of different kinds of product samples. As shown in Figure 5, compared to CLIP, our ECLIP can extract semantic-rich yet compact representations that better distinguish products belonging to different categories. More visualization examples and analysis are provided in our supplementary." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 573, + 378, + 586 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 573, + 378, + 586 + ], + "spans": [ + { + "bbox": [ + 306, + 573, + 378, + 586 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 594, + 545, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 545, + 677 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 545, + 677 + ], + "type": "text", + "content": "In this paper, we develop an effective large-scale multimodal pretraining paradigm called ECLIP in E-commerce. Beyond regular global representation, it instead aims to learn the instance-level representation via a novel decoder and the carefully-designed pretraining proxy tasks. Extensive experimental results further demonstrate the superior generalization capability of the proposed framework." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 678, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 678, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 678, + 545, + 712 + ], + "type": "text", + "content": "Acknowledgement: This work is supported by National Key R&D Program of China (2022ZD0160305) and Beijing Natural Science Foundation (Z190001)." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11067" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "text", + "content": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 2, 3, 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 146, + 288, + 201 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 146, + 288, + 201 + ], + "spans": [ + { + "bbox": [ + 53, + 146, + 288, + 201 + ], + "type": "text", + "content": "[2] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 2, 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 202, + 288, + 246 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 202, + 288, + 246 + ], + "spans": [ + { + "bbox": [ + 53, + 202, + 288, + 246 + ], + "type": "text", + "content": "[3] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 3, 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 247, + 288, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 247, + 288, + 323 + ], + "spans": [ + { + "bbox": [ + 53, + 247, + 288, + 323 + ], + "type": "text", + "content": "[4] Xiao Dong, Xunlin Zhan, Yangxin Wu, Yunchao Wei, Michael C Kampffmeyer, Xiaoyong Wei, Minlong Lu, Yaowei Wang, and Xiaodan Liang. M5product: Selfharmonized contrastive learning for e-commercial multimodal pretraining. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21252-21262, 2022. 1, 3, 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 324, + 288, + 389 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 324, + 288, + 389 + ], + "spans": [ + { + "bbox": [ + 53, + 324, + 288, + 389 + ], + "type": "text", + "content": "[5] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.3, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 391, + 288, + 434 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 391, + 288, + 434 + ], + "spans": [ + { + "bbox": [ + 53, + 391, + 288, + 434 + ], + "type": "text", + "content": "[6] Zhe Gan, Linjie Li, Chunyuan Li, Lijuan Wang, Zicheng Liu, and Jianfeng Gao. Vision-language pre-training: Basics, recent advances, and future trends. arXiv preprint arXiv:2210.09263, 2022. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 435, + 288, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 435, + 288, + 502 + ], + "spans": [ + { + "bbox": [ + 53, + 435, + 288, + 502 + ], + "type": "text", + "content": "[7] Dehong Gao, Linbo Jin, Ben Chen, Minghui Qiu, Peng Li, Yi Wei, Yi Hu, and Hao Wang. Fashionbert: Text and image matching with adaptive loss for cross-modal retrieval. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 2251-2260, 2020. 1, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 502, + 288, + 556 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 502, + 288, + 556 + ], + "spans": [ + { + "bbox": [ + 53, + 502, + 288, + 556 + ], + "type": "text", + "content": "[8] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 557, + 288, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 557, + 288, + 623 + ], + "spans": [ + { + "bbox": [ + 53, + 557, + 288, + 623 + ], + "type": "text", + "content": "[9] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 2, 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 624, + 288, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 288, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 288, + 668 + ], + "type": "text", + "content": "[10] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning, pages 5583-5594. PMLR, 2021. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 669, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 669, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 669, + 288, + 713 + ], + "type": "text", + "content": "[11] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. arXiv preprint arXiv:2201.12086, 2022. 6, 7" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 137 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 137 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 137 + ], + "type": "text", + "content": "[12] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems, 34:9694-9705, 2021. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 307, + 140, + 545, + 216 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 140, + 545, + 216 + ], + "spans": [ + { + "bbox": [ + 307, + 140, + 545, + 216 + ], + "type": "text", + "content": "[13] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, and Jianfeng Gao. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10965–10975, June 2022. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 218, + 545, + 272 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 218, + 545, + 272 + ], + "spans": [ + { + "bbox": [ + 307, + 218, + 545, + 272 + ], + "type": "text", + "content": "[14] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208, 2021. 6, 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 274, + 545, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 274, + 545, + 338 + ], + "spans": [ + { + "bbox": [ + 308, + 274, + 545, + 338 + ], + "type": "text", + "content": "[15] Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. Object-centric learning with slot attention. Advances in Neural Information Processing Systems, 33:11525-11538, 2020. 2, 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 341, + 545, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 341, + 545, + 363 + ], + "spans": [ + { + "bbox": [ + 308, + 341, + 545, + 363 + ], + "type": "text", + "content": "[16] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 365, + 545, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 365, + 545, + 408 + ], + "spans": [ + { + "bbox": [ + 308, + 365, + 545, + 408 + ], + "type": "text", + "content": "[17] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems, 32, 2019. 1, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 410, + 545, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 410, + 545, + 442 + ], + "spans": [ + { + "bbox": [ + 308, + 410, + 545, + 442 + ], + "type": "text", + "content": "[18] Mieczysław Pawlowski. Machine learning based product classification for ecommerce. Journal of Computer Information Systems, 62(4):730-739, 2022. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 444, + 545, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 444, + 545, + 487 + ], + "spans": [ + { + "bbox": [ + 308, + 444, + 545, + 487 + ], + "type": "text", + "content": "[19] AJ Piergiovanni, Wei Li, Weicheng Kuo, Mohammad Saffar, Fred Bertsch, and Anelia Angelova. Answer-me: Multi-task open-vocabulary visual question answering. arXiv preprint arXiv:2205.00949, 2022. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 488, + 545, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 488, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 308, + 488, + 545, + 555 + ], + "type": "text", + "content": "[20] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 555, + 545, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 555, + 545, + 610 + ], + "spans": [ + { + "bbox": [ + 308, + 555, + 545, + 610 + ], + "type": "text", + "content": "[21] Puji Rahayu, Dana Indra Sensuse, Betty Purwandari, Indra Budi, F Khalid, and N Zulkarnaim. A systematic review of recommender system for e-portfolio domain. In Proceedings of the 5th International Conference on Information and Education Technology, pages 21–26, 2017. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 612, + 545, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 612, + 545, + 655 + ], + "spans": [ + { + "bbox": [ + 308, + 612, + 545, + 655 + ], + "type": "text", + "content": "[22] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 657, + 545, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 657, + 545, + 690 + ], + "spans": [ + { + "bbox": [ + 308, + 657, + 545, + 690 + ], + "type": "text", + "content": "[23] Hao Tan and Mohit Bansal. Lxmert: Learning cross-modality encoder representations from transformers. arXiv preprint arXiv:1908.07490, 2019. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "type": "text", + "content": "[24] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11068" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 66, + 72, + 287, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 72, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 287, + 95 + ], + "type": "text", + "content": "Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 4" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 97, + 287, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 97, + 287, + 152 + ], + "spans": [ + { + "bbox": [ + 48, + 97, + 287, + 152 + ], + "type": "text", + "content": "[25] Peng Wang, An Yang, Rui Men, Junyang Lin, Shuai Bai, Zhikang Li, Jianxin Ma, Chang Zhou, Jingren Zhou, and Hongxia Yang. Unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework. arXiv preprint arXiv:2202.03052, 2022. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 154, + 287, + 187 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 154, + 287, + 187 + ], + "spans": [ + { + "bbox": [ + 48, + 154, + 287, + 187 + ], + "type": "text", + "content": "[26] Wenhui Wang, Hangbo Bao, Li Dong, and Furu Wei. Vlmo: Unified vision-language pre-training with mixture-of-modality-experts. arXiv preprint arXiv:2111.02358, 2021. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 189, + 287, + 232 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 189, + 287, + 232 + ], + "spans": [ + { + "bbox": [ + 48, + 189, + 287, + 232 + ], + "type": "text", + "content": "[27] Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao. Simvlm: Simple visual language model pretraining with weak supervision. arXiv preprint arXiv:2108.10904, 2021. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 235, + 287, + 289 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 235, + 287, + 289 + ], + "spans": [ + { + "bbox": [ + 48, + 235, + 287, + 289 + ], + "type": "text", + "content": "[28] Chi-Man Wong, Fan Feng, Wen Zhang, Chi-Man Vong, and Chen. Improving conversational recommender system by pretraining billion-scale knowledge graph. In International Conference on Data Engineering (ICDE), pages 2607-2612, 2021. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 292, + 287, + 325 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 292, + 287, + 325 + ], + "spans": [ + { + "bbox": [ + 48, + 292, + 287, + 325 + ], + "type": "text", + "content": "[29] Hu Xu, Bing Liu, Lei Shu, and P Yu. Open-world learning and application to product classification. In The World Wide Web Conference, pages 3413-3419, 2019. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 327, + 287, + 382 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 327, + 287, + 382 + ], + "spans": [ + { + "bbox": [ + 48, + 327, + 287, + 382 + ], + "type": "text", + "content": "[30] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18134-18144, 2022. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 384, + 287, + 429 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 384, + 287, + 429 + ], + "spans": [ + { + "bbox": [ + 48, + 384, + 287, + 429 + ], + "type": "text", + "content": "[31] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. Filip: Fine-grained interactive language-image pre-training. arXiv preprint arXiv:2111.07783, 2021. 1, 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 430, + 287, + 473 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 430, + 287, + 473 + ], + "spans": [ + { + "bbox": [ + 48, + 430, + 287, + 473 + ], + "type": "text", + "content": "[32] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917, 2022. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 476, + 287, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 476, + 287, + 542 + ], + "spans": [ + { + "bbox": [ + 48, + 476, + 287, + 542 + ], + "type": "text", + "content": "[33] Licheng Yu, Jun Chen, Animesh Sinha, Mengjiao Wang, Yu Chen, Tamara L Berg, and Ning Zhang. Commercemm: Large-scale commerce multimodal representation learning with omni retrieval. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 4433-4442, 2022. 1, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 544, + 287, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 544, + 287, + 578 + ], + "spans": [ + { + "bbox": [ + 48, + 544, + 287, + 578 + ], + "type": "text", + "content": "[34] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. arXiv preprint arXiv:2111.08276, 2021. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 579, + 287, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 579, + 287, + 645 + ], + "spans": [ + { + "bbox": [ + 48, + 579, + 287, + 645 + ], + "type": "text", + "content": "[35] Xunlin Zhan, Yangxin Wu, Xiao Dong, Yunchao Wei, Minlong Lu, Yichi Zhang, Hang Xu, and Xiaodan Liang. Product1m: Towards weakly supervised instance-level product retrieval via cross-modal pretraining. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11782-11791, 2021. 1, 3, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 647, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 287, + 712 + ], + "type": "text", + "content": "[36] Pengchuan Zhang, Xiujun Li, Xiaowei Hu, Jianwei Yang, Lei Zhang, Lijuan Wang, Yejin Choi, and Jianfeng Gao. Vinvl: Revisiting visual representations in vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5579-5588, 2021. 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 72, + 546, + 194 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 307, + 72, + 546, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 72, + 546, + 128 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 546, + 128 + ], + "type": "text", + "content": "[37] Yushan Zhu, Huaixiao Zhao, Wen Zhang, Ganqiang Ye, Hui Chen, Ningyu Zhang, and Huajun Chen. Knowledge perceived multi-modal pretraining in e-commerce. In Proceedings of the 29th ACM International Conference on Multimedia, pages 2744-2752, 2021. 1, 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 129, + 546, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 546, + 194 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 546, + 194 + ], + "type": "text", + "content": "[38] Mingchen Zhuge, Dehong Gao, Deng-Ping Fan, Linbo Jin, Ben Chen, Haoming Zhou, Minghui Qiu, and Ling Shao. Kaleido-bert: Vision-language pre-training on fashion domain. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12647-12657, 2021. 3" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "11069" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/cb972244-96fa-449d-98c3-61aa444fc92d_content_list.json b/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/cb972244-96fa-449d-98c3-61aa444fc92d_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..eeb942b32a544316b3cc3ef17c484c7e8957328b --- /dev/null +++ b/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/cb972244-96fa-449d-98c3-61aa444fc92d_content_list.json @@ -0,0 +1,1828 @@ +[ + { + "type": "text", + "text": "Learning Joint Latent Space EBM Prior Model for Multi-layer Generator", + "text_level": 1, + "bbox": [ + 112, + 130, + 854, + 151 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Jiali Cui $^{1}$ , Ying Nian Wu $^{2}$ , Tian Han $^{1}$ \\\n $^{1}$ Department of Computer Science, Stevens Institute of Technology\\\n $^{2}$ Department of Statistics, University of California, Los Angeles\\\n{jcui7,than6}@stevens.edu, ywu@stat.ucla.edu", + "bbox": [ + 218, + 179, + 750, + 251 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 286, + 313, + 301 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "This paper studies the fundamental problem of learning multi-layer generator models. The multi-layer generator model builds multiple layers of latent variables as a prior model on top of the generator, which benefits learning complex data distribution and hierarchical representations. However, such a prior model usually focuses on modeling inter-layer relations between latent variables by assuming non-informative (conditional) Gaussian distributions, which can be limited in model expressivity. To tackle this issue and learn more expressive prior models, we propose an energy-based model (EBM) on the joint latent space over all layers of latent variables with the multi-layer generator as its backbone. Such joint latent space EBM prior model captures the intra-layer contextual relations at each layer through layer-wise energy terms, and latent variables across different layers are jointly corrected. We develop a joint training scheme via maximum likelihood estimation (MLE), which involves Markov Chain Monte Carlo (MCMC) sampling for both prior and posterior distributions of the latent variables from different layers. To ensure efficient inference and learning, we further propose a variational training scheme where an inference model is used to amortize the costly posterior MCMC sampling. Our experiments demonstrate that the learned model can be expressive in generating high-quality images and capturing hierarchical features for better outlier detection.", + "bbox": [ + 76, + 316, + 472, + 710 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 739, + 209, + 753 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Deep generative models (a.k.a, generator models) have made promising progress in learning complex data distributions and achieved great successes in image and video synthesis [21, 34, 37, 39] as well as representation learning [5, 48]. Such models usually consist of low-dimensional latent variables together with a top-down generation model that maps such latent factors to the observed data. The latent factors can serve as an abstract data representation, but it is often modelled via a single latent vector with non-", + "bbox": [ + 75, + 763, + 468, + 900 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "informative prior distribution which leads to limited model expressivity and fails to capture different levels of abstractions. Learning an informative prior model for hierarchical representations is needed, yet research in this direction is still under-developed.", + "bbox": [ + 496, + 287, + 890, + 363 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "A principled way to learn such a prior model is by learning the generator models with multiple layers of latent variables. However, the learning of multi-layer generator model can be challenging as the inter-layer structural relation (i.e., latent variables across different layers) and the intra-layer contextual relation (i.e., latent units within the same layer) have to be effectively modelled and efficiently learned. Various methods have been proposed [5,28,32,35,40], but they only focused on inter-layer modeling by assuming the conditional Gaussian distribution across different layers while ignoring the intra-layer contextual modeling as the latent units are conditional independent within each layer.", + "bbox": [ + 496, + 366, + 892, + 547 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The energy-based models (EBMs), on the other hand, are shown to be expressive and proved to be powerful in capturing contextual and non-structural data regularities. Notably, [33] considers the EBM in the latent space for the non-hierarchical generator model, where the energy function is considered as a correction of the non-informative Gaussian prior. The low dimensionality of the latent space makes EBM effective in capturing regularities in the data. However, a single latent vector in [33] is infeasible for capturing the patterns at multiple layers of abstractions, which limits its model capacity.", + "bbox": [ + 496, + 550, + 890, + 717 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we propose to combine the strengths of the latent space EBM and the generator with multiple layers of latent variables for better hierarchical representations and a more expressive prior model. Specifically, we introduce layer-wise energy terms to exponentially tilt the non-informative Gaussian conditional at each layer, and latent variables across different layers are modelled jointly through EBM with the multi-layer generator model as its backbone. Such a joint EBM prior model seamlessly integrates the intra-layer contextual modeling via layer-wise energy terms and inter-layer structural modeling with multi-layer latent variables.", + "bbox": [ + 496, + 719, + 892, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "3603", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The joint EBM prior model can be learned by maximum likelihood estimation (MLE). Each learning iteration involves Markov chain Monte Carlo (MCMC) sampling of latent variables in each layer from both the prior and posterior distributions. The prior sampling can be efficiently done due to the low dimensionality of the latent variables and, more importantly, the lightweight networks for energy functions, while the posterior sampling can be less efficient. Therefore, we further develop the variational training scheme where an additional inference model is used for posterior approximation and is jointly trained with the joint EBM prior model.", + "bbox": [ + 75, + 90, + 472, + 271 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Contributions: 1) We propose a joint latent space EBM prior model for the generator model with multiple layers of latent variables; 2) We develop the maximum likelihood learning algorithm that learns the joint EBM prior model based on MCMC prior and posterior sampling across different layers. We further propose the variational joint training scheme for efficient learning and inference; 3) We provide strong empirical results through extensive experiments.", + "bbox": [ + 75, + 272, + 472, + 393 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Background", + "text_level": 1, + "bbox": [ + 76, + 407, + 205, + 424 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this section, we present the background of multi-layer latent variable model and latent space EBM prior model, which shall serve as the foundation of the proposed model.", + "bbox": [ + 75, + 431, + 468, + 478 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Multi-layer latent variable model", + "text_level": 1, + "bbox": [ + 76, + 487, + 369, + 503 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Let $\\mathbf{x}$ be the high-dimensional observed example, and $\\mathbf{z}$ be the low-dimensional latent variables. The latent variable generative model, or generator model, factorizes a joint distribution of $(\\mathbf{x},\\mathbf{z})$ as", + "bbox": [ + 75, + 511, + 468, + 571 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\beta} (\\mathbf {x}, \\mathbf {z}) = p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) p _ {\\beta_ {> 0}} (\\mathbf {z}) \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 158, + 583, + 468, + 599 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "where $p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})$ is the generation model with parameter $\\beta_0$ that maps from latent space to data space, and $p_{\\beta >0}(\\mathbf{z})$ is the prior distribution over latent variables with parameter $\\beta_{>0}$ . $\\beta = \\{\\beta_0,\\beta_{>0}\\}$", + "bbox": [ + 75, + 612, + 468, + 670 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Gaussian prior model: For non-hierarchical models [12, 23], $p_{\\beta > 0}(\\mathbf{z})$ is defined on single layer of latent variables and is typically assumed to be uniform or unit Gaussian. For hierarchical models with multiple layers of latent variables [32, 35], $p_{\\beta > 0}(\\mathbf{z})$ can be further decomposed into conditional distributions between consecutive layers of latent variables as", + "bbox": [ + 75, + 672, + 468, + 777 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\beta_ {> 0}} (\\mathbf {z}) = \\prod_ {i = 1} ^ {L - 1} p _ {\\beta_ {i}} \\left(\\mathbf {z} _ {i} \\mid \\mathbf {z} _ {i + 1}\\right) p \\left(\\mathbf {z} _ {L}\\right) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 138, + 786, + 468, + 828 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "where $p_{\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1})\\sim \\mathcal{N}(\\mu_{\\beta_i}(\\mathbf{z}_{i + 1}),\\sigma_{\\beta_i}^2 (\\mathbf{z}_{i + 1}))$ and is parameterized by a network with parameter $\\beta_{i}$ , and $p(\\mathbf{z}_L)$ is chosen to be a simple distribution, such as uniform or unit Gaussian.", + "bbox": [ + 75, + 839, + 468, + 898 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Maximum likelihood learning: Learning such latent variable generative models can be done using maximum likelihood estimation (MLE). The marginal distribution is $p_{\\beta}(\\mathbf{x}) = \\int p_{\\beta}(\\mathbf{x},\\mathbf{z})d\\mathbf{z}$ with the gradient:", + "bbox": [ + 496, + 90, + 890, + 152 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\n\\nabla_ {\\beta} \\log p _ {\\beta} (\\mathbf {x}) = \\mathbb {E} _ {p _ {\\beta} (\\mathbf {z} | \\mathbf {x})} [ \\nabla_ {\\beta} \\log p _ {\\beta} (\\mathbf {x}, \\mathbf {z}) ] \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 537, + 167, + 890, + 185 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "where the expectation can be approximated via Monte Carlo sampling from the posterior distribution $p_{\\beta}(\\mathbf{z}|\\mathbf{x})$ . The MLE can then be accomplished through gradient ascent using such gradients. The posterior sampling usually requires the Markov Chain Monte Carlo (MCMC) such as Langevin dynamics [13, 32]", + "bbox": [ + 496, + 199, + 890, + 289 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Variational learning: To alleviate the computational burden of MCMC, variational approach [4] introduces an additional inference model $q_{\\omega}(\\mathbf{z}|\\mathbf{x})$ with a separate set of parameters $\\omega = (\\omega_{1},\\dots,\\omega_{L})$ for posterior approximation,", + "bbox": [ + 496, + 291, + 890, + 353 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\nq _ {\\omega} (\\mathbf {z} | \\mathbf {x}) = q _ {\\omega_ {1}} (\\mathbf {z} _ {1} | \\mathbf {x}) \\prod_ {i = 1} ^ {L - 1} q _ {\\omega_ {i + 1}} (\\mathbf {z} _ {i + 1} | \\mathbf {z} _ {i}) \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 542, + 367, + 890, + 407 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "where $q_{\\omega_1}(\\mathbf{z}_1|\\mathbf{x})$ and $q_{\\omega_{i + 1}}(\\mathbf{z}_{i + 1}|\\mathbf{z}_i)$ are usually assumed as conditional Gaussian distributions, forming a \"bottom-up\" inference structure. The generator and inference model can be jointly learned via maximizing the evidence lower bound (ELBO), i.e., $\\max_{\\beta ,\\omega}\\mathrm{ELBO}(\\beta ,\\omega)$ where ELBO is defined as $\\mathrm{ELBO}(\\beta ,\\omega) = \\mathbb{E}_{q_{\\omega}(\\mathbf{z}|\\mathbf{x})}[\\log p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})] - D_{\\mathrm{KL}}(q_{\\omega}(\\mathbf{z}|\\mathbf{x})||p_{\\beta >0}(\\mathbf{z}))$", + "bbox": [ + 496, + 422, + 890, + 531 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Latent space energy-based model", + "text_level": 1, + "bbox": [ + 500, + 542, + 790, + 559 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The energy-based model (EBM) offers a flexible approach for learning the data distribution and is shown to be expressive in capturing data regularities [7,8,10,31,43,45]. Most existing works focus on learning the EBM on data space, which is high-dimensional and can be challenging. To tackle this challenge, [2,33] propose to learn latent space EBM as an informative prior model. With low-dimensional latent space, learning the EBM can be more efficient and effective, which in turn benefits the expressivity of the whole model. Specifically, [33] considers the latent space energy-based prior model on a single layer of latent variables,", + "bbox": [ + 496, + 568, + 890, + 734 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\alpha} (\\mathbf {z}) = \\frac {1}{\\mathrm {Z} (\\alpha)} \\exp [ f _ {\\alpha} (\\mathbf {z}) ] p _ {0} (\\mathbf {z}) \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 568, + 748, + 890, + 780 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "where $-f_{\\alpha}(\\mathbf{z})$ is the energy function, $Z(\\alpha)$ is the normalizing constant, i.e., $Z(\\alpha) = \\int \\exp [f_{\\alpha}(\\mathbf{z})]p_0(\\mathbf{z})d\\mathbf{z}$ , and $p_0(\\mathbf{z})$ is the reference distribution assumed to be unit Gaussian. Compared to data space EBMs in which the energy function needs to support the entire high-dimensional space, such exponential tilting latent space EBMs can be more efficient in capturing data regularities.", + "bbox": [ + 496, + 794, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "3604", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Model and Learning", + "text_level": 1, + "bbox": [ + 76, + 89, + 274, + 107 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Joint latent space EBM prior model", + "text_level": 1, + "bbox": [ + 76, + 114, + 388, + 132 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For generator models with multi-layer latent variables (or multi-layer generator model), consecutive layers are modelled by conditional Gaussian distributions (see Eqn.2), which essentially assumes the conditional independence for latent units within the $i$ -th layer given the $(i + 1)$ -th layer of latent variables. Such a conditional independence assumption limits the model capacity as the contextual relation between latent units within each layer is largely ignored (see Fig.1), and needs to be improved for informative conditional modeling and better model expressivity. In this paper, we propose the joint EBM prior for multi-layer generator models,", + "bbox": [ + 75, + 138, + 473, + 320 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} p _ {\\alpha , \\beta > 0} (\\mathbf {z}) = \\frac {1}{\\mathrm {Z} _ {\\alpha , \\beta > 0}} \\exp \\left[ f _ {\\alpha} (\\mathbf {z}) \\right] p _ {\\beta > 0} (\\mathbf {z}) \\tag {6} \\\\ = \\frac {1}{\\mathbf {Z} _ {\\alpha , \\beta > 0}} \\exp \\left[ \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right] \\prod_ {i = 1} ^ {L - 1} p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1}) p (\\mathbf {z} _ {L}) \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 101, + 332, + 468, + 409 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where we denote $\\alpha = (\\alpha_{1},\\dots,\\alpha_{L})$ for EBM parameters and $\\mathbf{z} = (\\mathbf{z}_1,\\dots,\\mathbf{z}_L)$ for latent variables in different layers with layer $L$ being the top layer. $\\mathrm{Z}_{\\alpha ,\\beta >0} = \\int \\exp [f_{\\alpha}(\\mathbf{z})]p_{\\beta >0}(\\mathbf{z})d\\mathbf{z}$ is the normalizing constant regarding latent variables for all layers. Thus, the latent variables across different layers are jointly corrected via EBM prior as in Eqn.6, where $f_{\\alpha}(\\mathbf{z})$ is the energy function for latent variables from all layers.", + "bbox": [ + 75, + 421, + 468, + 542 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this paper, we consider a simple factorized layer-wise parameterization, i.e., $f_{\\alpha}(\\mathbf{z}) = \\sum_{i=1}^{L} f_{\\alpha_i}(\\mathbf{z}_i)$ , but other parameterizations are also feasible, which we will explore in future work. With such energy parameterization, it's worth noting that the un-normalized prior model can be viewed as layer-wise exponential tilting,", + "bbox": [ + 75, + 542, + 468, + 635 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\underbrace {\\exp \\left[ \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right]} _ {\\text {E n e r g y c o r r e c t i o n}} \\underbrace {\\prod_ {i = 1} ^ {L - 1} p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1}) p (\\mathbf {z} _ {L})} _ {\\text {G a u s s i a n p r i o r}} \\tag {7} \\\\ = \\underbrace {\\exp \\left[ f _ {\\alpha_ {L}} (\\mathbf {z} _ {L}) \\right] p (\\mathbf {z} _ {L})} _ {\\text {C o r r e c t i o n o n t o p l a y e r}} \\prod_ {i = 1} ^ {L - 1} \\underbrace {\\exp \\left[ f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right] p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1})} _ {\\text {C o r r e c t i o n o n i n t e r m i d a t e l a y e r}} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 86, + 646, + 468, + 760 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "See Fig.1 for an illustration and comparison with multi-layer generator model with Gaussian prior.", + "bbox": [ + 75, + 773, + 468, + 803 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Joint vs. conditional EBM prior: Besides the proposed joint modeling, it is also tempting to consider EBM prior for layer-wise Gaussian conditional, i.e., $\\tilde{p}_{\\alpha_i,\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1}) = \\frac{1}{Z(\\mathbf{z}_{i + 1})}\\exp [f_{\\alpha_i}(\\mathbf{z}_i)]p_{\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1})$ and form the overall prior $p_{\\alpha ,\\beta >0}(\\mathbf{z}) = \\prod_{i = 1}^{L}\\tilde{p}_{\\alpha_i,\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1})p(\\mathbf{z}_L)$ . Such a scheme is closely related to autoregressive energy machine", + "bbox": [ + 75, + 804, + 470, + 901 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/d6d83883fce2ab3b8f68a3486cbced6b8c910245636dd726c56f9487f807f50a.jpg", + "image_caption": [ + "Figure 1. Left panel: Gaussian prior model. Right panel: Joint EBM prior model. Black solid lines with arrow: inter-layer relations modelling. Red solid lines: intra-layer contextual relations modelling. Blue dashed lines: joint modelling upon all layers." + ], + "image_footnote": [], + "bbox": [ + 511, + 87, + 880, + 271 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "[9] and is adopted in NCP-VAE [2]. However, the normalizing constant $\\mathrm{Z}(\\mathbf{z}_{i + 1})$ in $\\tilde{p}_{\\alpha_i,\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1})$ involves the latent variable $\\mathbf{z}_{i + 1}$ from the upper layer which can be intractable and needs an additional inner-loop for sampling or optimization. The proposed joint EBM prior couples the latent variables across different layers via energy function and can be learned effectively and efficiently.", + "bbox": [ + 496, + 366, + 890, + 473 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Maximum Likelihood Estimation", + "text_level": 1, + "bbox": [ + 500, + 486, + 792, + 501 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our joint EBM prior model can be trained using MLE. Let $\\theta = (\\alpha, \\beta)$ denotes the model parameters and $\\theta$ can be learned by maximizing the log-likelihood on $n$ training observations", + "bbox": [ + 496, + 510, + 890, + 570 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nL (\\theta) = \\sum_ {i = 1} ^ {n} \\log p _ {\\theta} (\\mathbf {x} _ {i}) = \\sum_ {i = 1} ^ {n} \\log \\int p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) p _ {\\alpha , \\beta_ {> 0}} (\\mathbf {z}) d \\mathbf {z}\n$$\n", + "text_format": "latex", + "bbox": [ + 500, + 582, + 883, + 622 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "When $n$ becomes sufficiently large, maximizing the above log-likelihood is equivalent to minimizing the Kullback-Leibler (KL) divergence between model distribution and empirical data distribution, i.e., $\\min_{\\theta} D_{\\mathrm{KL}}(p_{\\mathrm{data}}(\\mathbf{x})||p_{\\theta}(\\mathbf{x}))$ .", + "bbox": [ + 496, + 633, + 890, + 710 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To update the parameter $\\theta$ , we can compute the gradient of log-likelihood $\\nabla_{\\theta} \\log p_{\\theta}(\\mathbf{x})$ as", + "bbox": [ + 496, + 710, + 890, + 742 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\nabla_ {\\theta} \\log p _ {\\theta} (\\mathbf {x}) = \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} [ \\nabla_ {\\theta} \\log p _ {\\theta} (\\mathbf {x}, \\mathbf {z}) ] \\\\ = \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} \\left[ \\nabla_ {\\theta} \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) \\right] \\tag {8} \\\\ + \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} \\left[ \\nabla_ {\\theta} \\log p _ {\\alpha , \\beta > 0} (\\mathbf {z}) \\right] \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 550, + 755, + 890, + 811 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "With such a gradient, we can learn $\\theta$ using gradient ascent.", + "bbox": [ + 496, + 823, + 888, + 838 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Learning generation model $\\beta_0$ : $p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})$ is assumed to be Gaussian distribution, i.e., $p_{\\beta_0}(\\mathbf{x}|\\mathbf{z}) \\sim \\mathcal{N}(g_{\\beta_0}(\\mathbf{z}), \\sigma^2 I)$ , with generation network $g_{\\beta_0}$ with parameter $\\beta_0$ and pre-specified $\\sigma^2$ for simplicity. The learning gradient", + "bbox": [ + 496, + 839, + 890, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "3605", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "$\\nabla_{\\beta_0}\\log p_\\theta (\\mathbf{x})$ can then be expressed as", + "bbox": [ + 76, + 90, + 339, + 107 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\nabla_ {\\beta_ {0}} \\log p _ {\\theta} (\\mathbf {x}) = \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} [ \\nabla_ {\\beta_ {0}} \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) ] \\tag {9} \\\\ { = } { \\mathbb { E } _ { p _ { \\theta } ( \\mathbf { z } | \\mathbf { x } ) } \\left[ - \\nabla _ { \\beta _ { 0 } } \\frac { | | \\mathbf { x } - g _ { \\beta _ { 0 } } ( \\mathbf { z } ) | | ^ { 2 } } { 2 \\sigma ^ { 2 } } \\right] } \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 91, + 118, + 468, + 172 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Learning prior model $\\alpha, \\beta_{>0}$ : Learning $\\alpha_{i}$ can be done by computing the gradient $\\nabla_{\\alpha_i} \\log p_\\theta(\\mathbf{x})$ as", + "bbox": [ + 76, + 183, + 468, + 214 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\nabla_ {\\alpha_ {i}} \\log p _ {\\theta} (\\mathbf {x}) = \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} \\left[ \\nabla_ {\\alpha_ {i}} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right] \\tag {10} \\\\ - \\mathbb {E} _ {p _ {\\alpha , \\beta > 0} (\\mathbf {z})} \\left[ \\nabla_ {\\alpha_ {i}} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right] \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 122, + 226, + 468, + 263 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For updating $\\beta_{>0}$ , the gradient $\\nabla_{\\beta_i} \\log p_\\theta(\\mathbf{x})$ is", + "bbox": [ + 76, + 276, + 393, + 292 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\nabla_ {\\beta_ {i}} \\log p _ {\\theta} (\\mathbf {x}) = \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} \\left[ \\nabla_ {\\beta_ {i}} \\log p _ {\\beta_ {i}} \\left(\\mathbf {z} _ {i} \\mid \\mathbf {z} _ {i + 1}\\right) \\right] \\tag {11} \\\\ - \\mathbb {E} _ {p _ {\\alpha , \\beta > 0} (\\mathbf {z})} \\left[ \\nabla_ {\\beta_ {i}} \\log p _ {\\beta_ {i}} \\left(\\mathbf {z} _ {i} \\mid \\mathbf {z} _ {i + 1}\\right) \\right] \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 303, + 468, + 340 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Sampling: Both Eqn.10 and Eqn.11 require sampling from the posterior and prior distribution, which can be done via Langevin dynamic (LD) [26]. Given a target distribution $p(\\mathbf{z})$ , Langevin dynamic samples $\\mathbf{z} \\sim p(\\mathbf{z})$ by computing the gradient $\\nabla_{\\mathbf{z}} \\log p(\\mathbf{z})$ and iteratively update $\\mathbf{z}$ as", + "bbox": [ + 76, + 352, + 468, + 428 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {z} _ {t} = \\mathbf {z} _ {t - 1} + s \\nabla_ {\\mathbf {z}} \\log p \\left(\\mathbf {z} _ {t - 1}\\right) + \\sqrt {2 s} \\epsilon_ {t - 1} \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 117, + 440, + 468, + 458 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $t$ indexes the time step, $s$ is the step size, and $\\epsilon$ is the Gaussian noise for each time step.", + "bbox": [ + 76, + 470, + 468, + 500 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Prior sampling: By replacing target $p(\\mathbf{z})$ with $p_{\\alpha, \\beta > 0}(\\mathbf{z})$ , the prior sampling computes $\\nabla_{\\mathbf{z}} \\log p_{\\alpha, \\beta > 0}(\\mathbf{z})$ as", + "bbox": [ + 76, + 501, + 467, + 532 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\nabla_ {\\mathbf {z}} \\left[ \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) + \\sum_ {i = 1} ^ {L - 1} \\log p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1}) + \\log p (\\mathbf {z} _ {L}) \\right] \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 76, + 544, + 468, + 585 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Posterior sampling: By replacing $p(\\mathbf{z})$ with $p_{\\theta}(\\mathbf{z}|\\mathbf{x})$ where $p_{\\theta}(\\mathbf{z}|\\mathbf{x}) \\propto p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})p_{\\alpha, \\beta > 0}(\\mathbf{z})$ , the posterior sampling computes $\\nabla_{\\mathbf{z}} \\log p_{\\theta}(\\mathbf{z}|\\mathbf{x})$ as", + "bbox": [ + 76, + 598, + 468, + 643 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\nabla_ {\\mathbf {z}} \\log p _ {\\theta} (\\mathbf {z} | \\mathbf {x}) = \\nabla_ {\\mathbf {z}} \\left[ \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) + \\log p _ {\\alpha , \\beta_ {> 0}} (\\mathbf {z}) \\right] \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 84, + 656, + 468, + 672 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Notice that posterior sampling can be computationally inefficient as $\\nabla_{\\mathbf{z}}[\\log p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})]$ requires back-propagation through the deep generation model.", + "bbox": [ + 76, + 685, + 468, + 729 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Variational Learning", + "text_level": 1, + "bbox": [ + 76, + 741, + 274, + 757 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For efficient posterior sampling, an inference model $q_{\\omega}(\\mathbf{z}|\\mathbf{x})$ with a separate set of parameters $\\omega$ can be used for the posterior approximation. In this paper, we use the bottom-up inference model as Eqn.4 for amortizing the costly posterior MCMC sampling. Particularly, instead of KL minimization between marginal distributions as in MLE (see Sec.3.2), we consider the KL optimization between two joint densities, one for generator model density, i.e., $p_{\\theta}(\\mathbf{x},\\mathbf{z}) = p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})p_{\\alpha ,\\beta >0}(\\mathbf{z})$ , and one for data density,", + "bbox": [ + 76, + 763, + 468, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "i.e., $q_{\\omega}(\\mathbf{x}, \\mathbf{z}) = p_{\\mathrm{data}}(\\mathbf{x}) q_{\\omega}(\\mathbf{z}|\\mathbf{x})$ . We propose joint learning through KL minimization, denoting the objective to be $L(\\theta, \\omega)$ , i.e.,", + "bbox": [ + 498, + 90, + 890, + 136 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\theta} \\min _ {\\omega} L (\\theta , \\omega) = \\min _ {\\theta} \\min _ {\\omega} D _ {\\mathrm {K L}} \\left(q _ {\\omega} (\\mathbf {x}, \\mathbf {z}) \\| p _ {\\theta} (\\mathbf {x}, \\mathbf {z})\\right) \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 500, + 148, + 890, + 170 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Learning generation model $\\beta_0$ : For learning $\\beta_0$ , we can compute the gradient as", + "bbox": [ + 498, + 180, + 890, + 210 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n- \\nabla_ {\\beta_ {0}} L (\\theta , \\omega) = \\mathbb {E} _ {p _ {\\text {d a t a}} (\\mathbf {x})} \\mathbb {E} _ {q _ {\\omega} (\\mathbf {z} | \\mathbf {x})} \\left[ \\nabla_ {\\beta_ {0}} \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) \\right] \\tag {16}\n$$\n", + "text_format": "latex", + "bbox": [ + 503, + 222, + 890, + 239 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Learning prior model $\\alpha$ , $\\beta_{>0}$ : For learning $\\alpha_{i}$ , the gradient is computed as", + "bbox": [ + 498, + 250, + 890, + 280 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} - \\nabla_ {\\alpha_ {i}} L (\\theta , \\omega) = \\underset {\\mathbb {E}} {\\mathbb {E}} _ {p _ {\\text {d a t a}} (\\mathbf {x})} \\underset {\\mathbb {E} _ {q _ {\\omega} (\\mathbf {z} | \\mathbf {x})}} {\\mathbb {E}} \\left[ \\nabla_ {\\alpha_ {i}} f _ {\\alpha_ {i}} \\left(\\mathbf {z} _ {i}\\right) \\right] \\tag {17} \\\\ - \\mathbb {E} _ {p _ {\\alpha , \\beta > 0} (\\mathbf {z})} \\left[ \\nabla_ {\\alpha_ {i}} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right] \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 524, + 292, + 890, + 329 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For learning $\\beta_{>0}$ , we compute the gradient as", + "bbox": [ + 500, + 340, + 802, + 354 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} - \\nabla_ {\\beta_ {i}} L (\\theta , \\omega) = \\mathbb {E} _ {p _ {\\mathrm {d a t a}} (\\mathbf {x}) q _ {\\omega} (\\mathbf {z} | \\mathbf {x})} [ \\nabla_ {\\beta_ {i}} \\log p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1}) ] \\\\ - \\mathbb {E} _ {p _ {\\alpha , \\beta > 0} (\\mathbf {z})} \\left[ \\nabla_ {\\beta_ {i}} \\log p _ {\\beta_ {i}} \\left(\\mathbf {z} _ {i} \\mid \\mathbf {z} _ {i + 1}\\right) \\right] \\tag {18} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 514, + 366, + 890, + 416 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Learning inference model $\\omega$ : For learning $\\omega_{i}$ , the gradient is", + "bbox": [ + 500, + 417, + 890, + 445 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} - \\nabla_ {\\omega_ {i}} L (\\theta , \\omega) = \\nabla_ {\\omega_ {i}} \\mathbb {E} _ {p _ {\\mathrm {d a t a}} (\\mathbf {x})} [ \\mathbb {E} _ {q _ {\\omega} (\\mathbf {z} | \\mathbf {x})} \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) \\\\ - D _ {\\mathrm {K L}} \\left(q _ {\\omega} (\\mathbf {z} | \\mathbf {x}) \\| p _ {\\beta > 0} (\\mathbf {z})\\right) \\tag {19} \\\\ + \\mathbb {E} _ {q _ {\\omega} (\\mathbf {z} | \\mathbf {x})} [ \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) ] ] \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 509, + 455, + 890, + 534 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Divergence Perturbation. The KL joint minimization (Eqn.15) can be viewed as a surrogate of the MLE objective with the KL perturbation term,", + "bbox": [ + 498, + 546, + 890, + 590 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} D _ {\\mathrm {K L}} \\left(q _ {\\omega} (\\mathbf {x}, \\mathbf {z}) | | p _ {\\theta} (\\mathbf {x}, \\mathbf {z})\\right) \\\\ = D _ {\\mathrm {K L}} \\left(p _ {\\text {d a t a}} (\\mathbf {x}) | | p _ {\\theta} (\\mathbf {x})\\right) + D _ {\\mathrm {K L}} \\left(q _ {\\omega} (\\mathbf {z} | \\mathbf {x}) | | p _ {\\theta} (\\mathbf {z} | \\mathbf {x})\\right) \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 524, + 603, + 883, + 638 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where the perturbation term $D_{\\mathrm{KL}}(q_{\\omega}(\\mathbf{z}|\\mathbf{x})||p_{\\theta}(\\mathbf{z}|\\mathbf{x}))$ measures the KL-divergence between inference distribution and generator posterior. The inference model is learned to directly match the posterior distribution of the generator without expensive posterior sampling. In fact, such KL minimization in the joint space is closely related to evidence lower bound (ELBO) with the joint EBM as the prior model.", + "bbox": [ + 498, + 650, + 890, + 755 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Related Work", + "text_level": 1, + "bbox": [ + 500, + 768, + 640, + 784 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Hierarchical VAEs: Variational auto-encoder (VAE) [23] proposes variational learning by introducing an approximation of the true intractable posterior, which allows a tractable bound on log-likelihood to be formed. But the non-hierarchical structure can be limited in model expressivity and fails to capture different levels of abstraction. Hierarchical VAEs (HVAEs) [5, 28, 35, 40] consist of multiple", + "bbox": [ + 498, + 794, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "3606", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "layers of latent variables on top of the generator as a prior model, which can be used for learning complex data distribution and hierarchical representations. However, such models still focus on layer-wise relations while ignoring the intra-layer contextual relations at each layer.", + "bbox": [ + 75, + 90, + 470, + 167 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Energy-based models: The energy-based models receive attention for being expressive and powerful in capturing contextual data regularities. The majority of existing works focus on the pixel space [7, 8, 10, 14, 15, 43-45]. Learning such EBMs can be done using MLE, where MCMC sampling is typically required in each learning iteration which can be computationally expensive upon data space. Instead, [33] proposes to build EBM on latent space where the energy function is considered as a correction of the noninformative Gaussian prior. The low dimensionality of the latent space makes EBM effective in capturing data regularities and can alleviate the burden of MCMC sampling.", + "bbox": [ + 75, + 167, + 468, + 348 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Generator models with informative prior: For generator models, the assumed Gaussian or uniform prior distribution can be non-informative and less expressive. To address this problem, recent works [2, 6, 11, 33, 38, 42] propose to learn generator models with an informative prior, where RAE [11] constructs priors using rejection sampling, and Two-stage VAE [6] propose to train an extra model for simple prior at the second stage to match the aggregated posterior distribution, while LEBM [33] and NCP-VAE [2] instead learn EBMs on latent space to improve the expressivity of generator models.", + "bbox": [ + 75, + 349, + 468, + 515 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 76, + 531, + 209, + 547 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To demonstrate the proposed method, we present extensive experiments, including (i) image synthesis, (ii) hierarchical representations, and (iii) analysis of latent space, as well as various ablation studies based on the proposed EBM prior. The parameter complexity is discussed in Sec.5.5.", + "bbox": [ + 75, + 556, + 468, + 633 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1. Image Synthesis", + "text_level": 1, + "bbox": [ + 76, + 643, + 238, + 660 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Generator models with informative prior. We first evaluate the generation performance of the proposed joint model. If the model is well-trained, the multi-layer EBM prior model should render an expressive prior distribution leading to realistic synthesis. We benchmark our model against other generator models that assume standard Gaussian prior, such as VAE [23], Alternating Back-propagation (ABP) [13], Ladder VAE (LVAE) [35], and Short-run Inference (SRI) [32], as well as other generator models using informative prior, such as RAE [11], Two-stages VAE (2s-VAE) [6], NCP-VAE [2], and LEBM [33], where LEBM builds EBM for single layer latent variables, while ours contains a multi-layer structure.", + "bbox": [ + 75, + 667, + 468, + 863 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/e7d13f11872e72072107695b7813ce30b8591c83576715b36085266a98332b2d.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ModelSVHNCelebA-64CIFAR-10
VAE [23]46.7865.75106.37
LVAE (L=5) [35]39.2653.40-
ABP [13]49.7151.50-
SRI (L=5) [32]35.3247.95-
RAE [11]42.0240.9574.16
2s-VAE [6]42.8144.4072.90
NCP-VAE [2]33.2342.0778.06
LEBM [33]29.4437.8770.15
Ours (L=2)26.8133.6066.32
", + "bbox": [ + 506, + 88, + 887, + 266 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 1. FID(↓) for our model and baselines on SVHN, CelebA (64 x 64), and CIFAR-10.", + "bbox": [ + 498, + 275, + 890, + 306 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We train our model on SVHN [29], CIFAR-10 [24] and CelebA-64 [27] and use Fréchet Inception Distance (FID) [17] to quantitatively evaluate the generation quality. To make fair comparisons, we follow the standard protocol as in [33] and use the same generation model with convolutional structures. We use Langevin posterior sampling for the training, and the generation model is jointly learned (the result for variational learning is shown in Ablation Studies). The comparisons are shown in Tab.1, where the superior generation performance indicates the effectiveness of our model in learning a more expressive prior.", + "bbox": [ + 496, + 316, + 890, + 484 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Toward deep hierarchical models. We then consider the modern deep hierarchical structures as our multi-layer generator and explore the potential of the joint EBM prior for better generation. We adopt the two-stage training [2, 43] where the deep multi-layer generator $p_{\\beta >0}(\\mathbf{z})$ and inference model $q_{\\omega}(\\mathbf{z}|\\mathbf{x})$ are trained in the first stage by maximizing the ELBO as in VAEs, with the pre-trained models, our joint EBM prior model can then be learned in the second stage where the posterior samples are directly obtained from the pre-trained inference model $q_{\\omega}(\\mathbf{z}|\\mathbf{x})$ and prior samples can be obtained via Langevin sampling with change of variable on the generator $p_{\\beta >0}(\\mathbf{z})$ .", + "bbox": [ + 496, + 484, + 892, + 666 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We consider NVAE [40], a modern hierarchical VAE, for the first stage training, and we train our joint EBM prior in the second stage. For prior sampling in the second stage training, we employ similar reparametrized sampling scheme as in [43] via provided code1 in order to better traverse the deep hierarchical latent space with different scales. We examine our model on CIFAR-10, CelebA-HQ-256 [19], and LSUN-Church-64 [46]. The qualitative results for CelebA-HQ-256 and LSUN-Church-64 are shown in Fig.2 and Fig.3. For CelebA-HQ-256, we synthesize with adjusted batch-normalization as used in [2, 43]. We also visualize the Langevin transition on CIFAR-10 in Fig.4 where the quality of synthesis improves as the Langevin progresses.", + "bbox": [ + 496, + 666, + 893, + 878 + ], + "page_idx": 4 + }, + { + "type": "page_footnote", + "text": "Our project page is available at https://jcui1224.github.io/hierarchical-joint-ebm-proj.", + "bbox": [ + 76, + 875, + 467, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_footnote", + "text": "1 https://github.com/NVlabs/VAEBM", + "bbox": [ + 517, + 886, + 718, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "3607", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/b1de0f75521028c206b73376081cc8443cc62974d67960d742f3283085c59f30.jpg", + "image_caption": [ + "Figure 2. Generated samples on CelebA-HQ-256 . FID = 9.89." + ], + "image_footnote": [], + "bbox": [ + 99, + 88, + 870, + 167 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/79bf36e5f722dc59372f529509fa01584dd4956adde7279b02a3669967cf07e9.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 98, + 214, + 450, + 289 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/5cb33d6e0af0fbabcffb832746f56e2002126535f9b4f0c387f0e0370577333b.jpg", + "table_caption": [ + "Figure 3. Generated samples on LSUN-Church-64. FID = 8.38." + ], + "table_footnote": [], + "table_body": "
MethodISFID
NVAE* [40]5.3037.73
Ours8.9911.34
NCP-VAE [2]-24.08
VAEBM [43]8.4312.19
Other EBMs
IGEBM [8]6.7838.2
ImprovedCD [7]7.8525.1
Divergence Triangle [14]-30.10
Adv-EBM [45]9.1013.21
Other Likelihood Models
GLOW [22]3.9248.9
PixelCNN [41]4.6065.93
GANs+Score-based Models
BigGAN [3]9.2214.73
StyleGANv2 w/o ADA [20]8.999.9
NCSN [36]8.8725.32
DDPM [18]9.463.17
", + "bbox": [ + 112, + 333, + 436, + 645 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. IS(↑) and FID(↓) for our model and baselines on CIFAR-10. Model* indicates our backbone model.", + "bbox": [ + 76, + 657, + 468, + 686 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The quantitative results are shown in Tab.2 and Tab.3. We consider the baseline models, including NCP-VAE [2] and VAEBM [43], which also recruit NVAE as their backbone model, and other powerful deep generative models, such as GANs [3, 20], score-based models [18, 36] and EBMs [7,8,14,45] on data space. Compared to NVAE backbone model, our joint EBM prior model can significantly improve the fidelity of generated samples while only accounting for negligible overhead (see Parameter Efficiency in Sec.5.5). In comparison with other powerful deep generative models, we also achieve competitive generation performance.", + "bbox": [ + 75, + 719, + 468, + 898 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/55b8541ab8eef1a25afc1a7ac54346491ae06771ece358ab299826164fdfbb06.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 519, + 214, + 870, + 289 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/8c60405c78d1893622aee31eb7677a5d9cb7b27ea5654806e0d611723d2195dd.jpg", + "table_caption": [ + "Figure 4. Langevin transitions on CIFAR-10. FID = 11.34." + ], + "table_footnote": [], + "table_body": "
ModelCelebA-HQ-256LSUN-Church-64
NVAE* [40]30.2538.13
Ours9.898.38
NCP-VAE [2]24.79-
VAEBM [43]20.3813.51
Adv-EBM [45]17.3110.84
GLOW [22]68.9359.35
PGGAN [19]8.036.42
", + "bbox": [ + 506, + 327, + 885, + 470 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. FID(↓) for our model and baselines on CelebA-HQ-256 and LSUN-Church-64. Model* indicates backbone model.", + "bbox": [ + 500, + 479, + 890, + 508 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Hierarchical Representations", + "text_level": 1, + "bbox": [ + 500, + 536, + 759, + 551 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Hierarchical sampling. To examine our model in learning hierarchical representation, we employ hierarchical sampling to illustrate the learned representation at different layers. In particular, we first sample one group of latent vectors from EBM prior and hold them as fixed constants, then we randomly sample multiple groups of latent vectors to replace the fixed latent vectors at different layers. This allows us to visualize the variation in representation across layers.", + "bbox": [ + 496, + 558, + 890, + 680 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We apply our joint EBM prior to BIVA [28] on CelebA-64. For training, we reuse the two-stage training scheme, where we recruit $\\mathrm{BIVA}^2$ for the first stage training and train our EBM prior model in the second stage by using the reparametrized sampling [43] method (similar as deep hierarchical models in Sec.5.1). We show the results of hierarchical sampling in Fig.6 and observe that BIVA presents minor changes at the bottom and middle layers, while the proposed joint EBM prior model can show variations of different levels. Note that it is a challenging task for conditional hierarchical models [48], on which the improvement thus suggests that the proposed method is capable of learning hierarchical representations for multi-layer generator models.", + "bbox": [ + 496, + 680, + 890, + 876 + ], + "page_idx": 5 + }, + { + "type": "page_footnote", + "text": "$^{2}$ https://github.com/vlievin/biva-pytorch", + "bbox": [ + 514, + 886, + 732, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "3608", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/515393744524ac48cec77123635ab40fd11b2cba941d728b91019553703d5df9.jpg", + "image_caption": [ + "Figure 5. Histograms of density of $L_{\\mathrm{EBM}}^{>k}$ with AUROC(↑) and AUPRC(↑) for CIFAR-10 (in) / SVHN (out)." + ], + "image_footnote": [], + "bbox": [ + 78, + 87, + 893, + 156 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8bc26b65f5c2625625f1f9056a192f8de072a21db2684fcf91b47aface5b9e42.jpg", + "image_caption": [ + "Figure 6. Hierarchical sampling for Gaussian prior model (bottom) and EBM prior model (top). From left panel to right panel, latent vectors are sampled from the bottom layers to the top layers. Detailed sampling method can be referred in Figure 3 in [48]." + ], + "image_footnote": [], + "bbox": [ + 84, + 204, + 460, + 397 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Out-of-distribution detection. Next, we conduct out-of-distribution (OOD) detection to further evaluate the hierarchical representations. Typically, low-level representations (e.g., edges, corners) can be shared across data which in turn leads to high-confidence reconstructions for OOD examples, while high-level semantic ones have fewer correlations across different data and shall be more discriminative for OOD detection. Inspired by [16], we consider an unnormalized log-posterior as the decision function for EBM prior model, which is defined as", + "bbox": [ + 75, + 503, + 468, + 654 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} L _ {\\mathrm {E B M}} ^ {> k} = \\mathbb {E} _ {\\mathbf {z} > k \\sim q _ {\\omega} (\\mathbf {z} | \\mathbf {x}), \\mathbf {z} \\leq k \\sim p _ {\\beta > 0}, \\alpha (\\mathbf {z})} [ \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) \\\\ + \\log p _ {\\beta > 0} (\\mathbf {z}) + \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) ] \\tag {20} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 681, + 468, + 744 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "where latent codes above the $k$ -th layer are inferred from inference model and kept fixed, and those below the $k$ -th layer are sampled from EBM prior via the reparametrized sampling3 [43] with fixed inferred latent codes. With $k = 0$ , all layers of latent vectors are inferred from $q_{\\omega}(\\mathbf{z}|\\mathbf{x})$ . With a higher value of $k$ , less inferred low-level representations are used, which should render better performance in OOD detection. In addition, we can also compute a subtraction", + "bbox": [ + 75, + 756, + 468, + 876 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "between $L_{\\mathrm{EBM}}^{>0}$ and $L_{\\mathrm{EBM}}^{>k}$ as a surrogate of the likelihood-ratio which is shown to be effective for OOD detection [16]. We compute the subtraction as", + "bbox": [ + 500, + 205, + 890, + 252 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\nL L R _ {\\mathrm {E B M}} ^ {> k} = L _ {\\mathrm {E B M}} ^ {> 0} - L _ {\\mathrm {E B M}} ^ {> k} \\tag {21}\n$$\n", + "text_format": "latex", + "bbox": [ + 593, + 263, + 890, + 282 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We follow standard protocols and apply our EBM prior model with BIVA on CIFAR-10 and use SVHN as OOD data for testing. In Fig.5, we show the density of in-distribution and OOD data by computing the unnormalized log-posterior with increased $k$ , and we use AUROC, AUPRC to quantitatively evaluate the performance. It can be seen that as $k$ increases, relatively lower log-likelihoods are assigned to OOD data, which in turn renders better detection performance (higher AUROC and AUPRC). More importantly, we observe that the backbone model BIVA achieves the best detection performance of 0.885 for AUROC, while our models achieve 0.927 with the adapted decision function. This further verifies that the hierarchical representations can be learned within our multi-layer structure.", + "bbox": [ + 496, + 292, + 890, + 518 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Analysis of Latent Space", + "text_level": 1, + "bbox": [ + 500, + 529, + 725, + 546 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Long-run Langevin transition. In this section, we examine the energy landscape of our joint EBM prior model. If the EBM is well-learned, the energy prior should naturally render local modes of the energy function, and traversing these local modes should present realistic synthesized examples and steady-state energy scores. Existing EBMs typically have oversaturated images via long-run Langevin dynamics as observed in [30]. Training an EBM that learns steady-state energy scores over realistic images can be useful but challenging.", + "bbox": [ + 496, + 553, + 890, + 703 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We train our model on CelebA-64 using Langevin dynamic for 40 steps. We then run 100 and 2500 Langevin steps to examine the learned energy landscape. We show the synthesis and corresponding energy profile in Fig.7. It can be seen that generated examples become sharper for the first 40 steps as it starts from the referenced distribution $p_{\\beta > 0}(\\mathbf{z})$ toward the learned energy prior $p_{\\beta > 0, \\alpha}(\\mathbf{z})$ , and the energy fluctuates around some constant. For long-run 2500 steps, it is worth noting that our EBM prior model delivers diverse and realistic synthesis, and it does not exhibit the oversaturated phenomenon. This suggests that the learned EBM could mix well between different local modes of the learned energy prior.", + "bbox": [ + 496, + 704, + 892, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_footnote", + "text": "3https://github.com/NVlabs/VAEBM", + "bbox": [ + 94, + 886, + 292, + 898 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "3609", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/42ab7ce732358ffba47a2d6fe1d1a18de6f877c268effa3dcd14ef059a213e09.jpg", + "image_caption": [ + "Figure 7. Trajectory in data space and energy profile in Langevin transition. Top: Langevin transition with 100 steps. Bottom: Langevin transition with 2500 steps." + ], + "image_footnote": [], + "bbox": [ + 86, + 88, + 467, + 263 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Anomaly Detection. We further evaluate how our joint EBM prior model could benefit the anomaly detection (AD) task. Different from OOD detection, AD requires one class (e.g., one-digit class from MNIST) of data to be held out as anomaly for training, and both normal (e.g., other nine-digit classes from MNIST) and anomalous data are used for testing.", + "bbox": [ + 75, + 342, + 468, + 446 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The proposed prior model is built on the joint of all layers of latent variables. If it is well learned, the posterior $q_{\\omega}(\\mathbf{z}|\\mathbf{x})$ could form a discriminative joint latent space that has separated probability densities for normal and anomalous data. We use un-normalized log-posterior $L_{\\mathrm{EBM}}^{>0}$ as our decision function and train our model on MNIST with each class held out as an anomalous class. We consider the baseline models that also adopt an inferential mechanism, such as VAE [23], MEG [25], BiGAN- $\\sigma$ [47], OT-SRI [1], and LEBM [33] which assumes single-layer latent space and is closely related to our method. Tab.4 shows the results of AUPRC scores averaged over the last 10 epochs to account for the variance. To make fair comparisons, we follow the protocols in [1,25,33,47].", + "bbox": [ + 75, + 449, + 470, + 659 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/f043aa8088085b71fa2238bd0d0e60234424e3373878ebfc5f076ac14ee8028e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Heldout Digit14579
VAE [23]0.0630.3370.3250.1480.104
MEG [25]0.281 ± 0.0350.401 ± 0.0610.402 ± 0.0620.290 ± 0.0400.342 ± 0.034
BiGAN-σ [47]0.287 ± 0.0230.443 ± 0.0290.514 ± 0.0290.347 ± 0.0170.307 ± 0.028
OT-SRI [1]0.353 ± 0.0210.770 ± 0.0240.726 ± 0.0300.550 ± 0.0130.555 ± 0.023
LEBM [33]0.336 ± 0.0080.630 ± 0.0170.619 ± 0.0130.463 ± 0.0090.413 ± 0.010
Ours0.470 ± 0.0090.941 ± 0.0010.964 ± 0.0030.815 ± 0.0040.796 ± 0.004
", + "bbox": [ + 81, + 672, + 467, + 736 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.4. Ablation Studies.", + "text_level": 1, + "bbox": [ + 76, + 786, + 246, + 801 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "MCMC sampling vs. Inference model. Two posterior sampling schemes using MCMC and inference model are compared in Tab.5 in terms of FID and wall-clock training time (per-iteration). The MCMC posterior sampling renders better FID as it is more accurate in inference [13, 32], but it can be computationally heavy. While the inference model", + "bbox": [ + 75, + 809, + 468, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "is efficient in learning but can be less accurate. For deep hierarchical structures, the variational learning with inference model is preferred due to its efficiency.", + "bbox": [ + 498, + 90, + 890, + 137 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/10c8c9b95391dd2c4a14a9afb12d6cdec3a9b1390495a7513e1a60f1b7ff5df5.jpg", + "table_caption": [ + "Table 4. AUPRC scores for unsupervised anomaly detection." + ], + "table_footnote": [], + "table_body": "
MCMC / InfSVHNCelebA-64CIFAR-10
FID26.81 / 28.6033.60 / 36.1266.32 / 68.45
Time(s)0.478 / 0.2320.920 / 0.2460.568 / 0.256
", + "bbox": [ + 532, + 146, + 859, + 196 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Langevin steps. We explore the different number of Langevin steps in prior sampling for training on CIFAR-10. The results of FID and corresponding training time are shown in Tab.6. We observe that the Langevin step $k$ increasing from 10 to 40 can improve the generation quality, while for steps more than 40, it only has minor impacts on the improvement but with increased training overhead. We thus report the result of $k = 40$ in Tab.1.", + "bbox": [ + 498, + 250, + 893, + 369 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/c9b96bb92c47ed03f93c0f57c359377b9aa6c6574538745c9d983c7c4d218141.jpg", + "table_caption": [ + "Table 5. FID and training time for MCMC posterior sampling and variational learning." + ], + "table_footnote": [], + "table_body": "
steps kk = 10k = 20k = 40k = 80k = 100
FID69.4267.5866.3266.0365.86
Time(s)0.3120.4800.5680.7410.837
", + "bbox": [ + 532, + 380, + 859, + 433 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.5. Parameter Efficiency", + "text_level": 1, + "bbox": [ + 500, + 494, + 697, + 510 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "It is crucial to analyze the parameter complexity when comparing the generation performance. In Tab.1, we build our model with two layers of latent variables on top of the generator used in [33]. The additional layer accounts for only $1\\%$ overhead in total parameter complexity compared to LEBM [33]. For deep hierarchical models, we apply our joint EBM prior model on latent space which brings minimum overhead. The parameter complexity of the backbone NVAE and our EBM model is shown in Tab.7.", + "bbox": [ + 496, + 517, + 890, + 652 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/e0c05e2dbe90c0c512806f5245c668ec9f03d49e26c69d0699490f66e1821785.jpg", + "table_caption": [ + "Table 6. FID and training time for increasing MCMC steps in prior sampling." + ], + "table_footnote": [], + "table_body": "
NVAE / EBMCIFAR-10CelebA-HQ-256LSUN-Church-64
FID39.73 / 11.3430.25 / 9.8938.13 / 8.38
Parameters257M / 9M (3%)375M / 18M (4%)65M / 5M (7%)
", + "bbox": [ + 504, + 661, + 887, + 708 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 7. FID and parameter complexity for backbone model and EBM.", + "bbox": [ + 498, + 718, + 890, + 744 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 768, + 619, + 784 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "we propose a joint EBM prior for multi-layer generator models, which can effectively capture the intra-layer relations at each layer and jointly correct the latent variables from all layers. We present a joint training scheme via MLE and further develop a variational learning scheme for efficient inference. Our comprehensive experiments demonstrate the effectiveness of the proposed method.", + "bbox": [ + 496, + 794, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "3610", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Dongsheng An, Jianwen Xie, and Ping Li. Learning deep latent variable models by short-run mcmc inference with optimal transport correction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15415–15424, June 2021. 8", + "[2] Jyoti Aneja, Alex Schwing, Jan Kautz, and Arash Vahdat. A contrastive learning approach for training variational autoencoder priors. Advances in neural information processing systems, 34:480-493, 2021. 2, 3, 5, 6", + "[3] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096, 2018. 6", + "[4] Yuri Burda, Roger B. Grosse, and Ruslan Salakhutdinov. Importance weighted autoencoders. In Yoshua Bengio and Yann LeCun, editors, 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, 2016. 2", + "[5] Rewon Child. Very deep vaes generalize autoregressive models and can outperform them on images. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021. 1, 4", + "[6] Bin Dai and David Wipf. Diagnosing and enhancing vae models. arXiv preprint arXiv:1903.05789, 2019. 5", + "[7] Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch. Improved contrastive divergence training of energy based models. arXiv preprint arXiv:2012.01316, 2020. 2, 5, 6", + "[8] Yilun Du and Igor Mordatch. Implicit generation and generalization in energy-based models. arXiv preprint arXiv:1903.08689, 2019. 2, 5, 6", + "[9] Conor Durkan and Charlie Nash. Autoregressive energy machines. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 1735-1744. PMLR, 2019. 3", + "[10] Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, and Diederik P Kingma. Learning energy-based models by diffusion recovery likelihood. arXiv preprint arXiv:2012.08125, 2020. 2, 5", + "[11] Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black, and Bernhard Schölkopf. From variational to deterministic autoencoders. arXiv preprint arXiv:1903.12436, 2019. 5", + "[12] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014. 2", + "[13] Tian Han, Yang Lu, Song-Chun Zhu, and Ying Nian Wu. Alternating back-propagation for generator network. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31, 2017. 2, 5, 8" + ], + "bbox": [ + 78, + 114, + 470, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu. Divergence triangle for joint training of generator model, energy-based model, and inferential model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. 5, 6", + "[15] Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, and Ying Nian Wu. Joint training of variational autoencoder and latent energy-based model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7978-7987, 2020. 5", + "[16] Jakob D Drachmann Havtorn, Jes Frellsen, Soren Hauberg, and Lars Maaløe. Hierarchical vaes know what they don't know. In International Conference on Machine Learning, pages 4117-4128. PMLR, 2021. 7", + "[17] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017. 5", + "[18] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. 6", + "[19] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196, 2017. 5, 6", + "[20] Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Training generative adversarial networks with limited data. Advances in Neural Information Processing Systems, 33:12104-12114, 2020. 6", + "[21] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401-4410, 2019. 1", + "[22] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. Advances in neural information processing systems, 31, 2018. 6", + "[23] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 2, 4, 5, 8", + "[24] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 5", + "[25] Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, and Yoshua Bengio. Maximum entropy generators for energy-based models. arXiv preprint arXiv:1901.08508, 2019. 8", + "[26] Don S Lemons and Anthony Gythiel. Paul Langevin's 1908 paper \"on the theory of brownian motion\"[“sur la théorie du mouvement brownien,” cr acad. sci.(paris) 146, 530–533 (1908)]. American Journal of Physics, 65(11):1079–1081, 1997. 4", + "[27] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. CoRR, abs/1411.7766, 2014. 5", + "[28] Lars Maaløe, Marco Fraccaro, Valentin Lievin, and Ole Winther. Biva: A very deep hierarchy of latent variables for" + ], + "bbox": [ + 501, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "3611", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "generative modeling. Advances in neural information processing systems, 32, 2019. 1, 4, 6", + "[29] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng. Reading digits in natural images with unsupervised feature learning. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011, 2011. 5", + "[30] Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, and Ying Nian Wu. On the anatomy of mcmc-based maximum likelihood learning of energy-based models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 5272-5280, 2020. 7", + "[31] Erik Nijkamp, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu. Learning non-convergent non-persistent short-run mcmc toward energy-based model. Advances in Neural Information Processing Systems, 32, 2019. 2", + "[32] Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, and Ying Nian Wu. Learning multi-layer latent variable model via variational optimization of short run mcmc for approximate inference. In European Conference on Computer Vision, pages 361-378. Springer, 2020. 1, 2, 5, 8", + "[33] Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu. Learning latent space energy-based prior model. Advances in Neural Information Processing Systems, 33:21994-22008, 2020. 1, 2, 5, 8", + "[34] Masaki Saito, Shunta Saito, Masanori Koyama, and Sosuke Kobayashi. Train sparsely, generate densely: Memory-efficient unsupervised training of high-resolution temporal gan. International Journal of Computer Vision, 128(10):2586-2606, 2020. 1", + "[35] Casper Kaae Sønderby, Tapani Raiko, Lars Maalø e, Søren Kaae Sønderby, and Ole Winther. Ladder variational autoencoders. In D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc., 2016. 1, 2, 4, 5", + "[36] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in Neural Information Processing Systems, 32, 2019. 6", + "[37] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020. 1", + "[38] Jakub Tomczak and Max Welling. Vae with a vampprior. In International Conference on Artificial Intelligence and Statistics, pages 1214-1223. PMLR, 2018. 5", + "[39] Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz. Mocogan: Decomposing motion and content for video generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018. 1", + "[40] Arash Vahdat and Jan Kautz. Nvae: A deep hierarchical variational autoencoder. Advances in Neural Information Processing Systems, 33:19667-19679, 2020. 1, 4, 5, 6", + "[41] Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In International conference on machine learning, pages 1747-1756. PMLR, 2016. 6" + ], + "bbox": [ + 78, + 92, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[42] Zhisheng Xiao and Tian Han. Adaptive multi-stage density ratio estimation for learning latent space energy-based model. arXiv preprint arXiv:2209.08739, 2022. 5", + "[43] Zhisheng Xiao, Karsten Kreis, Jan Kautz, and Arash Vahdat. Vaebm: A symbiosis between variational autoencoders and energy-based models. arXiv preprint arXiv:2010.00654, 2020. 2, 5, 6, 7", + "[44] Jianwen Xie, Yaxuan Zhu, Jun Li, and Ping Li. A tale of two flows: Cooperative learning of Langevin flow and normalizing flow toward energy-based model. In International Conference on Learning Representations, 2022. 5", + "[45] Xuwang Yin, Shiying Li, and Gustavo K Rohde. Analyzing and improving generative adversarial training for generative modeling and out-of-distribution detection. arXiv preprint arXiv:2012.06568, 2020. 2, 5, 6", + "[46] Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015. 5", + "[47] Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar. Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222, 2018. 8", + "[48] Shengjia Zhao, Jiaming Song, and Stefano Ermon. Learning hierarchical features from generative models. arXiv preprint arXiv:1702.08396, 2017. 1, 6, 7" + ], + "bbox": [ + 501, + 92, + 890, + 458 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "3612", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/cb972244-96fa-449d-98c3-61aa444fc92d_model.json b/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/cb972244-96fa-449d-98c3-61aa444fc92d_model.json new file mode 100644 index 0000000000000000000000000000000000000000..fb916f10c5e0c049d9a016eac4eea0816cf78868 --- /dev/null +++ b/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/cb972244-96fa-449d-98c3-61aa444fc92d_model.json @@ -0,0 +1,2343 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.113, + 0.131, + 0.856, + 0.152 + ], + "angle": 0, + "content": "Learning Joint Latent Space EBM Prior Model for Multi-layer Generator" + }, + { + "type": "text", + "bbox": [ + 0.22, + 0.18, + 0.75, + 0.252 + ], + "angle": 0, + "content": "Jiali Cui\\(^{1}\\), Ying Nian Wu\\(^{2}\\), Tian Han\\(^{1}\\)\\\n\\(^{1}\\)Department of Computer Science, Stevens Institute of Technology\\\n\\(^{2}\\)Department of Statistics, University of California, Los Angeles\\\n{jcui7,than6}@stevens.edu, ywu@stat.ucla.edu" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.287, + 0.314, + 0.303 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.318, + 0.473, + 0.712 + ], + "angle": 0, + "content": "This paper studies the fundamental problem of learning multi-layer generator models. The multi-layer generator model builds multiple layers of latent variables as a prior model on top of the generator, which benefits learning complex data distribution and hierarchical representations. However, such a prior model usually focuses on modeling inter-layer relations between latent variables by assuming non-informative (conditional) Gaussian distributions, which can be limited in model expressivity. To tackle this issue and learn more expressive prior models, we propose an energy-based model (EBM) on the joint latent space over all layers of latent variables with the multi-layer generator as its backbone. Such joint latent space EBM prior model captures the intra-layer contextual relations at each layer through layer-wise energy terms, and latent variables across different layers are jointly corrected. We develop a joint training scheme via maximum likelihood estimation (MLE), which involves Markov Chain Monte Carlo (MCMC) sampling for both prior and posterior distributions of the latent variables from different layers. To ensure efficient inference and learning, we further propose a variational training scheme where an inference model is used to amortize the costly posterior MCMC sampling. Our experiments demonstrate that the learned model can be expressive in generating high-quality images and capturing hierarchical features for better outlier detection." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.74, + 0.21, + 0.755 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.765, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Deep generative models (a.k.a, generator models) have made promising progress in learning complex data distributions and achieved great successes in image and video synthesis [21, 34, 37, 39] as well as representation learning [5, 48]. Such models usually consist of low-dimensional latent variables together with a top-down generation model that maps such latent factors to the observed data. The latent factors can serve as an abstract data representation, but it is often modelled via a single latent vector with non-" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.288, + 0.892, + 0.364 + ], + "angle": 0, + "content": "informative prior distribution which leads to limited model expressivity and fails to capture different levels of abstractions. Learning an informative prior model for hierarchical representations is needed, yet research in this direction is still under-developed." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.367, + 0.893, + 0.548 + ], + "angle": 0, + "content": "A principled way to learn such a prior model is by learning the generator models with multiple layers of latent variables. However, the learning of multi-layer generator model can be challenging as the inter-layer structural relation (i.e., latent variables across different layers) and the intra-layer contextual relation (i.e., latent units within the same layer) have to be effectively modelled and efficiently learned. Various methods have been proposed [5,28,32,35,40], but they only focused on inter-layer modeling by assuming the conditional Gaussian distribution across different layers while ignoring the intra-layer contextual modeling as the latent units are conditional independent within each layer." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.551, + 0.892, + 0.718 + ], + "angle": 0, + "content": "The energy-based models (EBMs), on the other hand, are shown to be expressive and proved to be powerful in capturing contextual and non-structural data regularities. Notably, [33] considers the EBM in the latent space for the non-hierarchical generator model, where the energy function is considered as a correction of the non-informative Gaussian prior. The low dimensionality of the latent space makes EBM effective in capturing regularities in the data. However, a single latent vector in [33] is infeasible for capturing the patterns at multiple layers of abstractions, which limits its model capacity." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.893, + 0.901 + ], + "angle": 0, + "content": "In this paper, we propose to combine the strengths of the latent space EBM and the generator with multiple layers of latent variables for better hierarchical representations and a more expressive prior model. Specifically, we introduce layer-wise energy terms to exponentially tilt the non-informative Gaussian conditional at each layer, and latent variables across different layers are modelled jointly through EBM with the multi-layer generator model as its backbone. Such a joint EBM prior model seamlessly integrates the intra-layer contextual modeling via layer-wise energy terms and inter-layer structural modeling with multi-layer latent variables." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3603" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.272 + ], + "angle": 0, + "content": "The joint EBM prior model can be learned by maximum likelihood estimation (MLE). Each learning iteration involves Markov chain Monte Carlo (MCMC) sampling of latent variables in each layer from both the prior and posterior distributions. The prior sampling can be efficiently done due to the low dimensionality of the latent variables and, more importantly, the lightweight networks for energy functions, while the posterior sampling can be less efficient. Therefore, we further develop the variational training scheme where an additional inference model is used for posterior approximation and is jointly trained with the joint EBM prior model." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.273, + 0.473, + 0.395 + ], + "angle": 0, + "content": "Contributions: 1) We propose a joint latent space EBM prior model for the generator model with multiple layers of latent variables; 2) We develop the maximum likelihood learning algorithm that learns the joint EBM prior model based on MCMC prior and posterior sampling across different layers. We further propose the variational joint training scheme for efficient learning and inference; 3) We provide strong empirical results through extensive experiments." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.408, + 0.206, + 0.425 + ], + "angle": 0, + "content": "2. Background" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.433, + 0.47, + 0.479 + ], + "angle": 0, + "content": "In this section, we present the background of multi-layer latent variable model and latent space EBM prior model, which shall serve as the foundation of the proposed model." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.488, + 0.37, + 0.504 + ], + "angle": 0, + "content": "2.1. Multi-layer latent variable model" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.512, + 0.47, + 0.572 + ], + "angle": 0, + "content": "Let \\(\\mathbf{x}\\) be the high-dimensional observed example, and \\(\\mathbf{z}\\) be the low-dimensional latent variables. The latent variable generative model, or generator model, factorizes a joint distribution of \\((\\mathbf{x},\\mathbf{z})\\) as" + }, + { + "type": "equation", + "bbox": [ + 0.16, + 0.584, + 0.47, + 0.601 + ], + "angle": 0, + "content": "\\[\np _ {\\beta} (\\mathbf {x}, \\mathbf {z}) = p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) p _ {\\beta_ {> 0}} (\\mathbf {z}) \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.613, + 0.469, + 0.671 + ], + "angle": 0, + "content": "where \\(p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})\\) is the generation model with parameter \\(\\beta_0\\) that maps from latent space to data space, and \\(p_{\\beta >0}(\\mathbf{z})\\) is the prior distribution over latent variables with parameter \\(\\beta_{>0}\\). \\(\\beta = \\{\\beta_0,\\beta_{>0}\\}\\)" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.673, + 0.47, + 0.779 + ], + "angle": 0, + "content": "Gaussian prior model: For non-hierarchical models [12, 23], \\( p_{\\beta > 0}(\\mathbf{z}) \\) is defined on single layer of latent variables and is typically assumed to be uniform or unit Gaussian. For hierarchical models with multiple layers of latent variables [32, 35], \\( p_{\\beta > 0}(\\mathbf{z}) \\) can be further decomposed into conditional distributions between consecutive layers of latent variables as" + }, + { + "type": "equation", + "bbox": [ + 0.14, + 0.787, + 0.47, + 0.829 + ], + "angle": 0, + "content": "\\[\np _ {\\beta_ {> 0}} (\\mathbf {z}) = \\prod_ {i = 1} ^ {L - 1} p _ {\\beta_ {i}} \\left(\\mathbf {z} _ {i} \\mid \\mathbf {z} _ {i + 1}\\right) p \\left(\\mathbf {z} _ {L}\\right) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.47, + 0.9 + ], + "angle": 0, + "content": "where \\(p_{\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1})\\sim \\mathcal{N}(\\mu_{\\beta_i}(\\mathbf{z}_{i + 1}),\\sigma_{\\beta_i}^2 (\\mathbf{z}_{i + 1}))\\) and is parameterized by a network with parameter \\(\\beta_{i}\\), and \\(p(\\mathbf{z}_L)\\) is chosen to be a simple distribution, such as uniform or unit Gaussian." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.153 + ], + "angle": 0, + "content": "Maximum likelihood learning: Learning such latent variable generative models can be done using maximum likelihood estimation (MLE). The marginal distribution is \\( p_{\\beta}(\\mathbf{x}) = \\int p_{\\beta}(\\mathbf{x},\\mathbf{z})d\\mathbf{z} \\) with the gradient:" + }, + { + "type": "equation", + "bbox": [ + 0.538, + 0.168, + 0.892, + 0.186 + ], + "angle": 0, + "content": "\\[\n\\nabla_ {\\beta} \\log p _ {\\beta} (\\mathbf {x}) = \\mathbb {E} _ {p _ {\\beta} (\\mathbf {z} | \\mathbf {x})} [ \\nabla_ {\\beta} \\log p _ {\\beta} (\\mathbf {x}, \\mathbf {z}) ] \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.2, + 0.892, + 0.29 + ], + "angle": 0, + "content": "where the expectation can be approximated via Monte Carlo sampling from the posterior distribution \\( p_{\\beta}(\\mathbf{z}|\\mathbf{x}) \\). The MLE can then be accomplished through gradient ascent using such gradients. The posterior sampling usually requires the Markov Chain Monte Carlo (MCMC) such as Langevin dynamics [13, 32]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.292, + 0.892, + 0.354 + ], + "angle": 0, + "content": "Variational learning: To alleviate the computational burden of MCMC, variational approach [4] introduces an additional inference model \\( q_{\\omega}(\\mathbf{z}|\\mathbf{x}) \\) with a separate set of parameters \\( \\omega = (\\omega_{1},\\dots,\\omega_{L}) \\) for posterior approximation," + }, + { + "type": "equation", + "bbox": [ + 0.543, + 0.368, + 0.892, + 0.409 + ], + "angle": 0, + "content": "\\[\nq _ {\\omega} (\\mathbf {z} | \\mathbf {x}) = q _ {\\omega_ {1}} (\\mathbf {z} _ {1} | \\mathbf {x}) \\prod_ {i = 1} ^ {L - 1} q _ {\\omega_ {i + 1}} (\\mathbf {z} _ {i + 1} | \\mathbf {z} _ {i}) \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.424, + 0.892, + 0.532 + ], + "angle": 0, + "content": "where \\(q_{\\omega_1}(\\mathbf{z}_1|\\mathbf{x})\\) and \\(q_{\\omega_{i + 1}}(\\mathbf{z}_{i + 1}|\\mathbf{z}_i)\\) are usually assumed as conditional Gaussian distributions, forming a \"bottom-up\" inference structure. The generator and inference model can be jointly learned via maximizing the evidence lower bound (ELBO), i.e., \\(\\max_{\\beta ,\\omega}\\mathrm{ELBO}(\\beta ,\\omega)\\) where ELBO is defined as \\(\\mathrm{ELBO}(\\beta ,\\omega) = \\mathbb{E}_{q_{\\omega}(\\mathbf{z}|\\mathbf{x})}[\\log p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})] - D_{\\mathrm{KL}}(q_{\\omega}(\\mathbf{z}|\\mathbf{x})||p_{\\beta >0}(\\mathbf{z}))\\)" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.544, + 0.792, + 0.56 + ], + "angle": 0, + "content": "2.2. Latent space energy-based model" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.569, + 0.892, + 0.736 + ], + "angle": 0, + "content": "The energy-based model (EBM) offers a flexible approach for learning the data distribution and is shown to be expressive in capturing data regularities [7,8,10,31,43,45]. Most existing works focus on learning the EBM on data space, which is high-dimensional and can be challenging. To tackle this challenge, [2,33] propose to learn latent space EBM as an informative prior model. With low-dimensional latent space, learning the EBM can be more efficient and effective, which in turn benefits the expressivity of the whole model. Specifically, [33] considers the latent space energy-based prior model on a single layer of latent variables," + }, + { + "type": "equation", + "bbox": [ + 0.57, + 0.749, + 0.892, + 0.781 + ], + "angle": 0, + "content": "\\[\np _ {\\alpha} (\\mathbf {z}) = \\frac {1}{\\mathrm {Z} (\\alpha)} \\exp [ f _ {\\alpha} (\\mathbf {z}) ] p _ {0} (\\mathbf {z}) \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.795, + 0.892, + 0.901 + ], + "angle": 0, + "content": "where \\(-f_{\\alpha}(\\mathbf{z})\\) is the energy function, \\(Z(\\alpha)\\) is the normalizing constant, i.e., \\(Z(\\alpha) = \\int \\exp [f_{\\alpha}(\\mathbf{z})]p_0(\\mathbf{z})d\\mathbf{z}\\), and \\(p_0(\\mathbf{z})\\) is the reference distribution assumed to be unit Gaussian. Compared to data space EBMs in which the energy function needs to support the entire high-dimensional space, such exponential tilting latent space EBMs can be more efficient in capturing data regularities." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3604" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.077, + 0.09, + 0.276, + 0.108 + ], + "angle": 0, + "content": "3. Model and Learning" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.115, + 0.39, + 0.133 + ], + "angle": 0, + "content": "3.1. Joint latent space EBM prior model" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.14, + 0.474, + 0.321 + ], + "angle": 0, + "content": "For generator models with multi-layer latent variables (or multi-layer generator model), consecutive layers are modelled by conditional Gaussian distributions (see Eqn.2), which essentially assumes the conditional independence for latent units within the \\(i\\)-th layer given the \\((i + 1)\\)-th layer of latent variables. Such a conditional independence assumption limits the model capacity as the contextual relation between latent units within each layer is largely ignored (see Fig.1), and needs to be improved for informative conditional modeling and better model expressivity. In this paper, we propose the joint EBM prior for multi-layer generator models," + }, + { + "type": "equation", + "bbox": [ + 0.102, + 0.333, + 0.47, + 0.41 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} p _ {\\alpha , \\beta > 0} (\\mathbf {z}) = \\frac {1}{\\mathrm {Z} _ {\\alpha , \\beta > 0}} \\exp \\left[ f _ {\\alpha} (\\mathbf {z}) \\right] p _ {\\beta > 0} (\\mathbf {z}) \\tag {6} \\\\ = \\frac {1}{\\mathbf {Z} _ {\\alpha , \\beta > 0}} \\exp \\left[ \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right] \\prod_ {i = 1} ^ {L - 1} p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1}) p (\\mathbf {z} _ {L}) \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.422, + 0.47, + 0.543 + ], + "angle": 0, + "content": "where we denote \\(\\alpha = (\\alpha_{1},\\dots,\\alpha_{L})\\) for EBM parameters and \\(\\mathbf{z} = (\\mathbf{z}_1,\\dots,\\mathbf{z}_L)\\) for latent variables in different layers with layer \\(L\\) being the top layer. \\(\\mathrm{Z}_{\\alpha ,\\beta >0} = \\int \\exp [f_{\\alpha}(\\mathbf{z})]p_{\\beta >0}(\\mathbf{z})d\\mathbf{z}\\) is the normalizing constant regarding latent variables for all layers. Thus, the latent variables across different layers are jointly corrected via EBM prior as in Eqn.6, where \\(f_{\\alpha}(\\mathbf{z})\\) is the energy function for latent variables from all layers." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.544, + 0.47, + 0.636 + ], + "angle": 0, + "content": "In this paper, we consider a simple factorized layer-wise parameterization, i.e., \\( f_{\\alpha}(\\mathbf{z}) = \\sum_{i=1}^{L} f_{\\alpha_i}(\\mathbf{z}_i) \\), but other parameterizations are also feasible, which we will explore in future work. With such energy parameterization, it's worth noting that the un-normalized prior model can be viewed as layer-wise exponential tilting," + }, + { + "type": "equation", + "bbox": [ + 0.087, + 0.647, + 0.47, + 0.761 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\underbrace {\\exp \\left[ \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right]} _ {\\text {E n e r g y c o r r e c t i o n}} \\underbrace {\\prod_ {i = 1} ^ {L - 1} p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1}) p (\\mathbf {z} _ {L})} _ {\\text {G a u s s i a n p r i o r}} \\tag {7} \\\\ = \\underbrace {\\exp \\left[ f _ {\\alpha_ {L}} (\\mathbf {z} _ {L}) \\right] p (\\mathbf {z} _ {L})} _ {\\text {C o r r e c t i o n o n t o p l a y e r}} \\prod_ {i = 1} ^ {L - 1} \\underbrace {\\exp \\left[ f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right] p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1})} _ {\\text {C o r r e c t i o n o n i n t e r m i d a t e l a y e r}} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.774, + 0.47, + 0.804 + ], + "angle": 0, + "content": "See Fig.1 for an illustration and comparison with multi-layer generator model with Gaussian prior." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.805, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Joint vs. conditional EBM prior: Besides the proposed joint modeling, it is also tempting to consider EBM prior for layer-wise Gaussian conditional, i.e., \\(\\tilde{p}_{\\alpha_i,\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1}) = \\frac{1}{Z(\\mathbf{z}_{i + 1})}\\exp [f_{\\alpha_i}(\\mathbf{z}_i)]p_{\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1})\\) and form the overall prior \\(p_{\\alpha ,\\beta >0}(\\mathbf{z}) = \\prod_{i = 1}^{L}\\tilde{p}_{\\alpha_i,\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1})p(\\mathbf{z}_L)\\). Such a scheme is closely related to autoregressive energy machine" + }, + { + "type": "image", + "bbox": [ + 0.513, + 0.088, + 0.882, + 0.272 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.282, + 0.895, + 0.34 + ], + "angle": 0, + "content": "Figure 1. Left panel: Gaussian prior model. Right panel: Joint EBM prior model. Black solid lines with arrow: inter-layer relations modelling. Red solid lines: intra-layer contextual relations modelling. Blue dashed lines: joint modelling upon all layers." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.367, + 0.892, + 0.474 + ], + "angle": 0, + "content": "[9] and is adopted in NCP-VAE [2]. However, the normalizing constant \\( \\mathrm{Z}(\\mathbf{z}_{i + 1}) \\) in \\( \\tilde{p}_{\\alpha_i,\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1}) \\) involves the latent variable \\( \\mathbf{z}_{i + 1} \\) from the upper layer which can be intractable and needs an additional inner-loop for sampling or optimization. The proposed joint EBM prior couples the latent variables across different layers via energy function and can be learned effectively and efficiently." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.487, + 0.794, + 0.502 + ], + "angle": 0, + "content": "3.2. Maximum Likelihood Estimation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.511, + 0.892, + 0.571 + ], + "angle": 0, + "content": "Our joint EBM prior model can be trained using MLE. Let \\(\\theta = (\\alpha, \\beta)\\) denotes the model parameters and \\(\\theta\\) can be learned by maximizing the log-likelihood on \\(n\\) training observations" + }, + { + "type": "equation", + "bbox": [ + 0.5, + 0.583, + 0.885, + 0.623 + ], + "angle": 0, + "content": "\\[\nL (\\theta) = \\sum_ {i = 1} ^ {n} \\log p _ {\\theta} (\\mathbf {x} _ {i}) = \\sum_ {i = 1} ^ {n} \\log \\int p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) p _ {\\alpha , \\beta_ {> 0}} (\\mathbf {z}) d \\mathbf {z}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.635, + 0.892, + 0.711 + ], + "angle": 0, + "content": "When \\( n \\) becomes sufficiently large, maximizing the above log-likelihood is equivalent to minimizing the Kullback-Leibler (KL) divergence between model distribution and empirical data distribution, i.e., \\( \\min_{\\theta} D_{\\mathrm{KL}}(p_{\\mathrm{data}}(\\mathbf{x})||p_{\\theta}(\\mathbf{x})) \\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.712, + 0.892, + 0.743 + ], + "angle": 0, + "content": "To update the parameter \\(\\theta\\), we can compute the gradient of log-likelihood \\(\\nabla_{\\theta} \\log p_{\\theta}(\\mathbf{x})\\) as" + }, + { + "type": "equation", + "bbox": [ + 0.552, + 0.756, + 0.891, + 0.812 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\nabla_ {\\theta} \\log p _ {\\theta} (\\mathbf {x}) = \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} [ \\nabla_ {\\theta} \\log p _ {\\theta} (\\mathbf {x}, \\mathbf {z}) ] \\\\ = \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} \\left[ \\nabla_ {\\theta} \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) \\right] \\tag {8} \\\\ + \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} \\left[ \\nabla_ {\\theta} \\log p _ {\\alpha , \\beta > 0} (\\mathbf {z}) \\right] \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.824, + 0.89, + 0.839 + ], + "angle": 0, + "content": "With such a gradient, we can learn \\(\\theta\\) using gradient ascent." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Learning generation model \\(\\beta_0\\): \\(p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})\\) is assumed to be Gaussian distribution, i.e., \\(p_{\\beta_0}(\\mathbf{x}|\\mathbf{z}) \\sim \\mathcal{N}(g_{\\beta_0}(\\mathbf{z}), \\sigma^2 I)\\), with generation network \\(g_{\\beta_0}\\) with parameter \\(\\beta_0\\) and pre-specified \\(\\sigma^2\\) for simplicity. The learning gradient" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3605" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.078, + 0.092, + 0.34, + 0.108 + ], + "angle": 0, + "content": "\\(\\nabla_{\\beta_0}\\log p_\\theta (\\mathbf{x})\\) can then be expressed as" + }, + { + "type": "equation", + "bbox": [ + 0.093, + 0.119, + 0.469, + 0.173 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\nabla_ {\\beta_ {0}} \\log p _ {\\theta} (\\mathbf {x}) = \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} [ \\nabla_ {\\beta_ {0}} \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) ] \\tag {9} \\\\ { = } { \\mathbb { E } _ { p _ { \\theta } ( \\mathbf { z } | \\mathbf { x } ) } \\left[ - \\nabla _ { \\beta _ { 0 } } \\frac { | | \\mathbf { x } - g _ { \\beta _ { 0 } } ( \\mathbf { z } ) | | ^ { 2 } } { 2 \\sigma ^ { 2 } } \\right] } \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.184, + 0.469, + 0.215 + ], + "angle": 0, + "content": "Learning prior model \\(\\alpha, \\beta_{>0}\\): Learning \\(\\alpha_{i}\\) can be done by computing the gradient \\(\\nabla_{\\alpha_i} \\log p_\\theta(\\mathbf{x})\\) as" + }, + { + "type": "equation", + "bbox": [ + 0.124, + 0.227, + 0.469, + 0.265 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\nabla_ {\\alpha_ {i}} \\log p _ {\\theta} (\\mathbf {x}) = \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} \\left[ \\nabla_ {\\alpha_ {i}} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right] \\tag {10} \\\\ - \\mathbb {E} _ {p _ {\\alpha , \\beta > 0} (\\mathbf {z})} \\left[ \\nabla_ {\\alpha_ {i}} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right] \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.277, + 0.394, + 0.293 + ], + "angle": 0, + "content": "For updating \\(\\beta_{>0}\\), the gradient \\(\\nabla_{\\beta_i} \\log p_\\theta(\\mathbf{x})\\) is" + }, + { + "type": "equation", + "bbox": [ + 0.095, + 0.304, + 0.469, + 0.342 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\nabla_ {\\beta_ {i}} \\log p _ {\\theta} (\\mathbf {x}) = \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} \\left[ \\nabla_ {\\beta_ {i}} \\log p _ {\\beta_ {i}} \\left(\\mathbf {z} _ {i} \\mid \\mathbf {z} _ {i + 1}\\right) \\right] \\tag {11} \\\\ - \\mathbb {E} _ {p _ {\\alpha , \\beta > 0} (\\mathbf {z})} \\left[ \\nabla_ {\\beta_ {i}} \\log p _ {\\beta_ {i}} \\left(\\mathbf {z} _ {i} \\mid \\mathbf {z} _ {i + 1}\\right) \\right] \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.353, + 0.469, + 0.429 + ], + "angle": 0, + "content": "Sampling: Both Eqn.10 and Eqn.11 require sampling from the posterior and prior distribution, which can be done via Langevin dynamic (LD) [26]. Given a target distribution \\( p(\\mathbf{z}) \\), Langevin dynamic samples \\( \\mathbf{z} \\sim p(\\mathbf{z}) \\) by computing the gradient \\( \\nabla_{\\mathbf{z}} \\log p(\\mathbf{z}) \\) and iteratively update \\( \\mathbf{z} \\) as" + }, + { + "type": "equation", + "bbox": [ + 0.118, + 0.441, + 0.469, + 0.459 + ], + "angle": 0, + "content": "\\[\n\\mathbf {z} _ {t} = \\mathbf {z} _ {t - 1} + s \\nabla_ {\\mathbf {z}} \\log p \\left(\\mathbf {z} _ {t - 1}\\right) + \\sqrt {2 s} \\epsilon_ {t - 1} \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.472, + 0.469, + 0.501 + ], + "angle": 0, + "content": "where \\(t\\) indexes the time step, \\(s\\) is the step size, and \\(\\epsilon\\) is the Gaussian noise for each time step." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.502, + 0.468, + 0.533 + ], + "angle": 0, + "content": "Prior sampling: By replacing target \\(p(\\mathbf{z})\\) with \\(p_{\\alpha, \\beta > 0}(\\mathbf{z})\\), the prior sampling computes \\(\\nabla_{\\mathbf{z}} \\log p_{\\alpha, \\beta > 0}(\\mathbf{z})\\) as" + }, + { + "type": "equation", + "bbox": [ + 0.078, + 0.545, + 0.47, + 0.587 + ], + "angle": 0, + "content": "\\[\n\\nabla_ {\\mathbf {z}} \\left[ \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) + \\sum_ {i = 1} ^ {L - 1} \\log p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1}) + \\log p (\\mathbf {z} _ {L}) \\right] \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.599, + 0.469, + 0.644 + ], + "angle": 0, + "content": "Posterior sampling: By replacing \\(p(\\mathbf{z})\\) with \\(p_{\\theta}(\\mathbf{z}|\\mathbf{x})\\) where \\(p_{\\theta}(\\mathbf{z}|\\mathbf{x}) \\propto p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})p_{\\alpha, \\beta > 0}(\\mathbf{z})\\), the posterior sampling computes \\(\\nabla_{\\mathbf{z}} \\log p_{\\theta}(\\mathbf{z}|\\mathbf{x})\\) as" + }, + { + "type": "equation", + "bbox": [ + 0.085, + 0.657, + 0.469, + 0.674 + ], + "angle": 0, + "content": "\\[\n\\nabla_ {\\mathbf {z}} \\log p _ {\\theta} (\\mathbf {z} | \\mathbf {x}) = \\nabla_ {\\mathbf {z}} \\left[ \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) + \\log p _ {\\alpha , \\beta_ {> 0}} (\\mathbf {z}) \\right] \\tag {14}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.686, + 0.469, + 0.731 + ], + "angle": 0, + "content": "Notice that posterior sampling can be computationally inefficient as \\(\\nabla_{\\mathbf{z}}[\\log p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})]\\) requires back-propagation through the deep generation model." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.742, + 0.276, + 0.758 + ], + "angle": 0, + "content": "3.3. Variational Learning" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.765, + 0.469, + 0.903 + ], + "angle": 0, + "content": "For efficient posterior sampling, an inference model \\(q_{\\omega}(\\mathbf{z}|\\mathbf{x})\\) with a separate set of parameters \\(\\omega\\) can be used for the posterior approximation. In this paper, we use the bottom-up inference model as Eqn.4 for amortizing the costly posterior MCMC sampling. Particularly, instead of KL minimization between marginal distributions as in MLE (see Sec.3.2), we consider the KL optimization between two joint densities, one for generator model density, i.e., \\(p_{\\theta}(\\mathbf{x},\\mathbf{z}) = p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})p_{\\alpha ,\\beta >0}(\\mathbf{z})\\), and one for data density," + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.137 + ], + "angle": 0, + "content": "i.e., \\( q_{\\omega}(\\mathbf{x}, \\mathbf{z}) = p_{\\mathrm{data}}(\\mathbf{x}) q_{\\omega}(\\mathbf{z}|\\mathbf{x}) \\). We propose joint learning through KL minimization, denoting the objective to be \\( L(\\theta, \\omega) \\), i.e.," + }, + { + "type": "equation", + "bbox": [ + 0.5, + 0.149, + 0.892, + 0.171 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\theta} \\min _ {\\omega} L (\\theta , \\omega) = \\min _ {\\theta} \\min _ {\\omega} D _ {\\mathrm {K L}} \\left(q _ {\\omega} (\\mathbf {x}, \\mathbf {z}) \\| p _ {\\theta} (\\mathbf {x}, \\mathbf {z})\\right) \\tag {15}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.181, + 0.892, + 0.211 + ], + "angle": 0, + "content": "Learning generation model \\(\\beta_0\\): For learning \\(\\beta_0\\), we can compute the gradient as" + }, + { + "type": "equation", + "bbox": [ + 0.504, + 0.223, + 0.892, + 0.241 + ], + "angle": 0, + "content": "\\[\n- \\nabla_ {\\beta_ {0}} L (\\theta , \\omega) = \\mathbb {E} _ {p _ {\\text {d a t a}} (\\mathbf {x})} \\mathbb {E} _ {q _ {\\omega} (\\mathbf {z} | \\mathbf {x})} \\left[ \\nabla_ {\\beta_ {0}} \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) \\right] \\tag {16}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.251, + 0.892, + 0.281 + ], + "angle": 0, + "content": "Learning prior model \\(\\alpha\\), \\(\\beta_{>0}\\): For learning \\(\\alpha_{i}\\), the gradient is computed as" + }, + { + "type": "equation", + "bbox": [ + 0.525, + 0.293, + 0.892, + 0.33 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} - \\nabla_ {\\alpha_ {i}} L (\\theta , \\omega) = \\underset {\\mathbb {E}} {\\mathbb {E}} _ {p _ {\\text {d a t a}} (\\mathbf {x})} \\underset {\\mathbb {E} _ {q _ {\\omega} (\\mathbf {z} | \\mathbf {x})}} {\\mathbb {E}} \\left[ \\nabla_ {\\alpha_ {i}} f _ {\\alpha_ {i}} \\left(\\mathbf {z} _ {i}\\right) \\right] \\tag {17} \\\\ - \\mathbb {E} _ {p _ {\\alpha , \\beta > 0} (\\mathbf {z})} \\left[ \\nabla_ {\\alpha_ {i}} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right] \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.341, + 0.803, + 0.356 + ], + "angle": 0, + "content": "For learning \\(\\beta_{>0}\\), we compute the gradient as" + }, + { + "type": "equation", + "bbox": [ + 0.515, + 0.367, + 0.891, + 0.417 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} - \\nabla_ {\\beta_ {i}} L (\\theta , \\omega) = \\mathbb {E} _ {p _ {\\mathrm {d a t a}} (\\mathbf {x}) q _ {\\omega} (\\mathbf {z} | \\mathbf {x})} [ \\nabla_ {\\beta_ {i}} \\log p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1}) ] \\\\ - \\mathbb {E} _ {p _ {\\alpha , \\beta > 0} (\\mathbf {z})} \\left[ \\nabla_ {\\beta_ {i}} \\log p _ {\\beta_ {i}} \\left(\\mathbf {z} _ {i} \\mid \\mathbf {z} _ {i + 1}\\right) \\right] \\tag {18} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.418, + 0.892, + 0.446 + ], + "angle": 0, + "content": "Learning inference model \\(\\omega\\): For learning \\(\\omega_{i}\\), the gradient is" + }, + { + "type": "equation", + "bbox": [ + 0.51, + 0.456, + 0.891, + 0.535 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} - \\nabla_ {\\omega_ {i}} L (\\theta , \\omega) = \\nabla_ {\\omega_ {i}} \\mathbb {E} _ {p _ {\\mathrm {d a t a}} (\\mathbf {x})} [ \\mathbb {E} _ {q _ {\\omega} (\\mathbf {z} | \\mathbf {x})} \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) \\\\ - D _ {\\mathrm {K L}} \\left(q _ {\\omega} (\\mathbf {z} | \\mathbf {x}) \\| p _ {\\beta > 0} (\\mathbf {z})\\right) \\tag {19} \\\\ + \\mathbb {E} _ {q _ {\\omega} (\\mathbf {z} | \\mathbf {x})} [ \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) ] ] \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.547, + 0.892, + 0.592 + ], + "angle": 0, + "content": "Divergence Perturbation. The KL joint minimization (Eqn.15) can be viewed as a surrogate of the MLE objective with the KL perturbation term," + }, + { + "type": "equation", + "bbox": [ + 0.526, + 0.604, + 0.884, + 0.64 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} D _ {\\mathrm {K L}} \\left(q _ {\\omega} (\\mathbf {x}, \\mathbf {z}) | | p _ {\\theta} (\\mathbf {x}, \\mathbf {z})\\right) \\\\ = D _ {\\mathrm {K L}} \\left(p _ {\\text {d a t a}} (\\mathbf {x}) | | p _ {\\theta} (\\mathbf {x})\\right) + D _ {\\mathrm {K L}} \\left(q _ {\\omega} (\\mathbf {z} | \\mathbf {x}) | | p _ {\\theta} (\\mathbf {z} | \\mathbf {x})\\right) \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.651, + 0.892, + 0.756 + ], + "angle": 0, + "content": "where the perturbation term \\( D_{\\mathrm{KL}}(q_{\\omega}(\\mathbf{z}|\\mathbf{x})||p_{\\theta}(\\mathbf{z}|\\mathbf{x})) \\) measures the KL-divergence between inference distribution and generator posterior. The inference model is learned to directly match the posterior distribution of the generator without expensive posterior sampling. In fact, such KL minimization in the joint space is closely related to evidence lower bound (ELBO) with the joint EBM as the prior model." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.77, + 0.641, + 0.785 + ], + "angle": 0, + "content": "4. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.795, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Hierarchical VAEs: Variational auto-encoder (VAE) [23] proposes variational learning by introducing an approximation of the true intractable posterior, which allows a tractable bound on log-likelihood to be formed. But the non-hierarchical structure can be limited in model expressivity and fails to capture different levels of abstraction. Hierarchical VAEs (HVAEs) [5, 28, 35, 40] consist of multiple" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3606" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.471, + 0.168 + ], + "angle": 0, + "content": "layers of latent variables on top of the generator as a prior model, which can be used for learning complex data distribution and hierarchical representations. However, such models still focus on layer-wise relations while ignoring the intra-layer contextual relations at each layer." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.168, + 0.47, + 0.349 + ], + "angle": 0, + "content": "Energy-based models: The energy-based models receive attention for being expressive and powerful in capturing contextual data regularities. The majority of existing works focus on the pixel space [7, 8, 10, 14, 15, 43-45]. Learning such EBMs can be done using MLE, where MCMC sampling is typically required in each learning iteration which can be computationally expensive upon data space. Instead, [33] proposes to build EBM on latent space where the energy function is considered as a correction of the noninformative Gaussian prior. The low dimensionality of the latent space makes EBM effective in capturing data regularities and can alleviate the burden of MCMC sampling." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.35, + 0.47, + 0.516 + ], + "angle": 0, + "content": "Generator models with informative prior: For generator models, the assumed Gaussian or uniform prior distribution can be non-informative and less expressive. To address this problem, recent works [2, 6, 11, 33, 38, 42] propose to learn generator models with an informative prior, where RAE [11] constructs priors using rejection sampling, and Two-stage VAE [6] propose to train an extra model for simple prior at the second stage to match the aggregated posterior distribution, while LEBM [33] and NCP-VAE [2] instead learn EBMs on latent space to improve the expressivity of generator models." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.532, + 0.21, + 0.549 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.557, + 0.47, + 0.634 + ], + "angle": 0, + "content": "To demonstrate the proposed method, we present extensive experiments, including (i) image synthesis, (ii) hierarchical representations, and (iii) analysis of latent space, as well as various ablation studies based on the proposed EBM prior. The parameter complexity is discussed in Sec.5.5." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.644, + 0.24, + 0.661 + ], + "angle": 0, + "content": "5.1. Image Synthesis" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.668, + 0.47, + 0.864 + ], + "angle": 0, + "content": "Generator models with informative prior. We first evaluate the generation performance of the proposed joint model. If the model is well-trained, the multi-layer EBM prior model should render an expressive prior distribution leading to realistic synthesis. We benchmark our model against other generator models that assume standard Gaussian prior, such as VAE [23], Alternating Back-propagation (ABP) [13], Ladder VAE (LVAE) [35], and Short-run Inference (SRI) [32], as well as other generator models using informative prior, such as RAE [11], Two-stages VAE (2s-VAE) [6], NCP-VAE [2], and LEBM [33], where LEBM builds EBM for single layer latent variables, while ours contains a multi-layer structure." + }, + { + "type": "table", + "bbox": [ + 0.507, + 0.089, + 0.888, + 0.267 + ], + "angle": 0, + "content": "
ModelSVHNCelebA-64CIFAR-10
VAE [23]46.7865.75106.37
LVAE (L=5) [35]39.2653.40-
ABP [13]49.7151.50-
SRI (L=5) [32]35.3247.95-
RAE [11]42.0240.9574.16
2s-VAE [6]42.8144.4072.90
NCP-VAE [2]33.2342.0778.06
LEBM [33]29.4437.8770.15
Ours (L=2)26.8133.6066.32
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.276, + 0.892, + 0.307 + ], + "angle": 0, + "content": "Table 1. FID(↓) for our model and baselines on SVHN, CelebA (64 x 64), and CIFAR-10." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.318, + 0.892, + 0.485 + ], + "angle": 0, + "content": "We train our model on SVHN [29], CIFAR-10 [24] and CelebA-64 [27] and use Fréchet Inception Distance (FID) [17] to quantitatively evaluate the generation quality. To make fair comparisons, we follow the standard protocol as in [33] and use the same generation model with convolutional structures. We use Langevin posterior sampling for the training, and the generation model is jointly learned (the result for variational learning is shown in Ablation Studies). The comparisons are shown in Tab.1, where the superior generation performance indicates the effectiveness of our model in learning a more expressive prior." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.486, + 0.893, + 0.667 + ], + "angle": 0, + "content": "Toward deep hierarchical models. We then consider the modern deep hierarchical structures as our multi-layer generator and explore the potential of the joint EBM prior for better generation. We adopt the two-stage training [2, 43] where the deep multi-layer generator \\( p_{\\beta >0}(\\mathbf{z}) \\) and inference model \\( q_{\\omega}(\\mathbf{z}|\\mathbf{x}) \\) are trained in the first stage by maximizing the ELBO as in VAEs, with the pre-trained models, our joint EBM prior model can then be learned in the second stage where the posterior samples are directly obtained from the pre-trained inference model \\( q_{\\omega}(\\mathbf{z}|\\mathbf{x}) \\) and prior samples can be obtained via Langevin sampling with change of variable on the generator \\( p_{\\beta >0}(\\mathbf{z}) \\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.667, + 0.894, + 0.879 + ], + "angle": 0, + "content": "We consider NVAE [40], a modern hierarchical VAE, for the first stage training, and we train our joint EBM prior in the second stage. For prior sampling in the second stage training, we employ similar reparametrized sampling scheme as in [43] via provided code1 in order to better traverse the deep hierarchical latent space with different scales. We examine our model on CIFAR-10, CelebA-HQ-256 [19], and LSUN-Church-64 [46]. The qualitative results for CelebA-HQ-256 and LSUN-Church-64 are shown in Fig.2 and Fig.3. For CelebA-HQ-256, we synthesize with adjusted batch-normalization as used in [2, 43]. We also visualize the Langevin transition on CIFAR-10 in Fig.4 where the quality of synthesis improves as the Langevin progresses." + }, + { + "type": "page_footnote", + "bbox": [ + 0.078, + 0.876, + 0.468, + 0.903 + ], + "angle": 0, + "content": "Our project page is available at https://jcui1224.github.io/hierarchical-joint-ebm-proj." + }, + { + "type": "page_footnote", + "bbox": [ + 0.518, + 0.887, + 0.719, + 0.902 + ], + "angle": 0, + "content": "1 https://github.com/NVlabs/VAEBM" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3607" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.1, + 0.089, + 0.872, + 0.168 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.297, + 0.177, + 0.672, + 0.192 + ], + "angle": 0, + "content": "Figure 2. Generated samples on CelebA-HQ-256 . FID = 9.89." + }, + { + "type": "image", + "bbox": [ + 0.099, + 0.215, + 0.451, + 0.29 + ], + "angle": 0, + "content": null + }, + { + "type": "table_caption", + "bbox": [ + 0.084, + 0.3, + 0.462, + 0.314 + ], + "angle": 0, + "content": "Figure 3. Generated samples on LSUN-Church-64. FID = 8.38." + }, + { + "type": "table", + "bbox": [ + 0.113, + 0.334, + 0.437, + 0.646 + ], + "angle": 0, + "content": "
MethodISFID
NVAE* [40]5.3037.73
Ours8.9911.34
NCP-VAE [2]-24.08
VAEBM [43]8.4312.19
Other EBMs
IGEBM [8]6.7838.2
ImprovedCD [7]7.8525.1
Divergence Triangle [14]-30.10
Adv-EBM [45]9.1013.21
Other Likelihood Models
GLOW [22]3.9248.9
PixelCNN [41]4.6065.93
GANs+Score-based Models
BigGAN [3]9.2214.73
StyleGANv2 w/o ADA [20]8.999.9
NCSN [36]8.8725.32
DDPM [18]9.463.17
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.659, + 0.47, + 0.687 + ], + "angle": 0, + "content": "Table 2. IS(↑) and FID(↓) for our model and baselines on CIFAR-10. Model* indicates our backbone model." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.72, + 0.47, + 0.9 + ], + "angle": 0, + "content": "The quantitative results are shown in Tab.2 and Tab.3. We consider the baseline models, including NCP-VAE [2] and VAEBM [43], which also recruit NVAE as their backbone model, and other powerful deep generative models, such as GANs [3, 20], score-based models [18, 36] and EBMs [7,8,14,45] on data space. Compared to NVAE backbone model, our joint EBM prior model can significantly improve the fidelity of generated samples while only accounting for negligible overhead (see Parameter Efficiency in Sec.5.5). In comparison with other powerful deep generative models, we also achieve competitive generation performance." + }, + { + "type": "image", + "bbox": [ + 0.521, + 0.215, + 0.872, + 0.29 + ], + "angle": 0, + "content": null + }, + { + "type": "table_caption", + "bbox": [ + 0.521, + 0.3, + 0.871, + 0.314 + ], + "angle": 0, + "content": "Figure 4. Langevin transitions on CIFAR-10. FID = 11.34." + }, + { + "type": "table", + "bbox": [ + 0.508, + 0.328, + 0.887, + 0.472 + ], + "angle": 0, + "content": "
ModelCelebA-HQ-256LSUN-Church-64
NVAE* [40]30.2538.13
Ours9.898.38
NCP-VAE [2]24.79-
VAEBM [43]20.3813.51
Adv-EBM [45]17.3110.84
GLOW [22]68.9359.35
PGGAN [19]8.036.42
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.481, + 0.892, + 0.51 + ], + "angle": 0, + "content": "Table 3. FID(↓) for our model and baselines on CelebA-HQ-256 and LSUN-Church-64. Model* indicates backbone model." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.537, + 0.761, + 0.553 + ], + "angle": 0, + "content": "5.2. Hierarchical Representations" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.559, + 0.892, + 0.681 + ], + "angle": 0, + "content": "Hierarchical sampling. To examine our model in learning hierarchical representation, we employ hierarchical sampling to illustrate the learned representation at different layers. In particular, we first sample one group of latent vectors from EBM prior and hold them as fixed constants, then we randomly sample multiple groups of latent vectors to replace the fixed latent vectors at different layers. This allows us to visualize the variation in representation across layers." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.681, + 0.892, + 0.877 + ], + "angle": 0, + "content": "We apply our joint EBM prior to BIVA [28] on CelebA-64. For training, we reuse the two-stage training scheme, where we recruit \\(\\mathrm{BIVA}^2\\) for the first stage training and train our EBM prior model in the second stage by using the reparametrized sampling [43] method (similar as deep hierarchical models in Sec.5.1). We show the results of hierarchical sampling in Fig.6 and observe that BIVA presents minor changes at the bottom and middle layers, while the proposed joint EBM prior model can show variations of different levels. Note that it is a challenging task for conditional hierarchical models [48], on which the improvement thus suggests that the proposed method is capable of learning hierarchical representations for multi-layer generator models." + }, + { + "type": "page_footnote", + "bbox": [ + 0.516, + 0.887, + 0.733, + 0.901 + ], + "angle": 0, + "content": "\\(^{2}\\)https://github.com/vlievin/biva-pytorch" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3608" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.08, + 0.088, + 0.895, + 0.157 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.164, + 0.167, + 0.804, + 0.182 + ], + "angle": 0, + "content": "Figure 5. Histograms of density of \\( L_{\\mathrm{EBM}}^{>k} \\) with AUROC(↑) and AUPRC(↑) for CIFAR-10 (in) / SVHN (out)." + }, + { + "type": "image", + "bbox": [ + 0.086, + 0.205, + 0.462, + 0.398 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.407, + 0.471, + 0.478 + ], + "angle": 0, + "content": "Figure 6. Hierarchical sampling for Gaussian prior model (bottom) and EBM prior model (top). From left panel to right panel, latent vectors are sampled from the bottom layers to the top layers. Detailed sampling method can be referred in Figure 3 in [48]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.505, + 0.47, + 0.655 + ], + "angle": 0, + "content": "Out-of-distribution detection. Next, we conduct out-of-distribution (OOD) detection to further evaluate the hierarchical representations. Typically, low-level representations (e.g., edges, corners) can be shared across data which in turn leads to high-confidence reconstructions for OOD examples, while high-level semantic ones have fewer correlations across different data and shall be more discriminative for OOD detection. Inspired by [16], we consider an unnormalized log-posterior as the decision function for EBM prior model, which is defined as" + }, + { + "type": "equation", + "bbox": [ + 0.096, + 0.682, + 0.469, + 0.745 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} L _ {\\mathrm {E B M}} ^ {> k} = \\mathbb {E} _ {\\mathbf {z} > k \\sim q _ {\\omega} (\\mathbf {z} | \\mathbf {x}), \\mathbf {z} \\leq k \\sim p _ {\\beta > 0}, \\alpha (\\mathbf {z})} [ \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) \\\\ + \\log p _ {\\beta > 0} (\\mathbf {z}) + \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) ] \\tag {20} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.757, + 0.47, + 0.877 + ], + "angle": 0, + "content": "where latent codes above the \\(k\\)-th layer are inferred from inference model and kept fixed, and those below the \\(k\\)-th layer are sampled from EBM prior via the reparametrized sampling3 [43] with fixed inferred latent codes. With \\(k = 0\\), all layers of latent vectors are inferred from \\(q_{\\omega}(\\mathbf{z}|\\mathbf{x})\\). With a higher value of \\(k\\), less inferred low-level representations are used, which should render better performance in OOD detection. In addition, we can also compute a subtraction" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.207, + 0.891, + 0.253 + ], + "angle": 0, + "content": "between \\( L_{\\mathrm{EBM}}^{>0} \\) and \\( L_{\\mathrm{EBM}}^{>k} \\) as a surrogate of the likelihood-ratio which is shown to be effective for OOD detection [16]. We compute the subtraction as" + }, + { + "type": "equation", + "bbox": [ + 0.594, + 0.264, + 0.891, + 0.283 + ], + "angle": 0, + "content": "\\[\nL L R _ {\\mathrm {E B M}} ^ {> k} = L _ {\\mathrm {E B M}} ^ {> 0} - L _ {\\mathrm {E B M}} ^ {> k} \\tag {21}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.294, + 0.892, + 0.519 + ], + "angle": 0, + "content": "We follow standard protocols and apply our EBM prior model with BIVA on CIFAR-10 and use SVHN as OOD data for testing. In Fig.5, we show the density of in-distribution and OOD data by computing the unnormalized log-posterior with increased \\( k \\), and we use AUROC, AUPRC to quantitatively evaluate the performance. It can be seen that as \\( k \\) increases, relatively lower log-likelihoods are assigned to OOD data, which in turn renders better detection performance (higher AUROC and AUPRC). More importantly, we observe that the backbone model BIVA achieves the best detection performance of 0.885 for AUROC, while our models achieve 0.927 with the adapted decision function. This further verifies that the hierarchical representations can be learned within our multi-layer structure." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.53, + 0.726, + 0.547 + ], + "angle": 0, + "content": "5.3. Analysis of Latent Space" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.554, + 0.892, + 0.704 + ], + "angle": 0, + "content": "Long-run Langevin transition. In this section, we examine the energy landscape of our joint EBM prior model. If the EBM is well-learned, the energy prior should naturally render local modes of the energy function, and traversing these local modes should present realistic synthesized examples and steady-state energy scores. Existing EBMs typically have oversaturated images via long-run Langevin dynamics as observed in [30]. Training an EBM that learns steady-state energy scores over realistic images can be useful but challenging." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.893, + 0.901 + ], + "angle": 0, + "content": "We train our model on CelebA-64 using Langevin dynamic for 40 steps. We then run 100 and 2500 Langevin steps to examine the learned energy landscape. We show the synthesis and corresponding energy profile in Fig.7. It can be seen that generated examples become sharper for the first 40 steps as it starts from the referenced distribution \\( p_{\\beta > 0}(\\mathbf{z}) \\) toward the learned energy prior \\( p_{\\beta > 0, \\alpha}(\\mathbf{z}) \\), and the energy fluctuates around some constant. For long-run 2500 steps, it is worth noting that our EBM prior model delivers diverse and realistic synthesis, and it does not exhibit the oversaturated phenomenon. This suggests that the learned EBM could mix well between different local modes of the learned energy prior." + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.293, + 0.9 + ], + "angle": 0, + "content": "3https://github.com/NVlabs/VAEBM" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3609" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.088, + 0.089, + 0.468, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.274, + 0.472, + 0.318 + ], + "angle": 0, + "content": "Figure 7. Trajectory in data space and energy profile in Langevin transition. Top: Langevin transition with 100 steps. Bottom: Langevin transition with 2500 steps." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.343, + 0.47, + 0.448 + ], + "angle": 0, + "content": "Anomaly Detection. We further evaluate how our joint EBM prior model could benefit the anomaly detection (AD) task. Different from OOD detection, AD requires one class (e.g., one-digit class from MNIST) of data to be held out as anomaly for training, and both normal (e.g., other nine-digit classes from MNIST) and anomalous data are used for testing." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.45, + 0.472, + 0.66 + ], + "angle": 0, + "content": "The proposed prior model is built on the joint of all layers of latent variables. If it is well learned, the posterior \\( q_{\\omega}(\\mathbf{z}|\\mathbf{x}) \\) could form a discriminative joint latent space that has separated probability densities for normal and anomalous data. We use un-normalized log-posterior \\( L_{\\mathrm{EBM}}^{>0} \\) as our decision function and train our model on MNIST with each class held out as an anomalous class. We consider the baseline models that also adopt an inferential mechanism, such as VAE [23], MEG [25], BiGAN- \\( \\sigma \\) [47], OT-SRI [1], and LEBM [33] which assumes single-layer latent space and is closely related to our method. Tab.4 shows the results of AUPRC scores averaged over the last 10 epochs to account for the variance. To make fair comparisons, we follow the protocols in [1,25,33,47]." + }, + { + "type": "table", + "bbox": [ + 0.083, + 0.673, + 0.468, + 0.737 + ], + "angle": 0, + "content": "
Heldout Digit14579
VAE [23]0.0630.3370.3250.1480.104
MEG [25]0.281 ± 0.0350.401 ± 0.0610.402 ± 0.0620.290 ± 0.0400.342 ± 0.034
BiGAN-σ [47]0.287 ± 0.0230.443 ± 0.0290.514 ± 0.0290.347 ± 0.0170.307 ± 0.028
OT-SRI [1]0.353 ± 0.0210.770 ± 0.0240.726 ± 0.0300.550 ± 0.0130.555 ± 0.023
LEBM [33]0.336 ± 0.0080.630 ± 0.0170.619 ± 0.0130.463 ± 0.0090.413 ± 0.010
Ours0.470 ± 0.0090.941 ± 0.0010.964 ± 0.0030.815 ± 0.0040.796 ± 0.004
" + }, + { + "type": "table_caption", + "bbox": [ + 0.092, + 0.746, + 0.454, + 0.761 + ], + "angle": 0, + "content": "Table 4. AUPRC scores for unsupervised anomaly detection." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.787, + 0.248, + 0.803 + ], + "angle": 0, + "content": "5.4. Ablation Studies." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.47, + 0.903 + ], + "angle": 0, + "content": "MCMC sampling vs. Inference model. Two posterior sampling schemes using MCMC and inference model are compared in Tab.5 in terms of FID and wall-clock training time (per-iteration). The MCMC posterior sampling renders better FID as it is more accurate in inference [13, 32], but it can be computationally heavy. While the inference model" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.138 + ], + "angle": 0, + "content": "is efficient in learning but can be less accurate. For deep hierarchical structures, the variational learning with inference model is preferred due to its efficiency." + }, + { + "type": "table", + "bbox": [ + 0.533, + 0.147, + 0.861, + 0.198 + ], + "angle": 0, + "content": "
MCMC / InfSVHNCelebA-64CIFAR-10
FID26.81 / 28.6033.60 / 36.1266.32 / 68.45
Time(s)0.478 / 0.2320.920 / 0.2460.568 / 0.256
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.207, + 0.894, + 0.236 + ], + "angle": 0, + "content": "Table 5. FID and training time for MCMC posterior sampling and variational learning." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.25, + 0.895, + 0.371 + ], + "angle": 0, + "content": "Langevin steps. We explore the different number of Langevin steps in prior sampling for training on CIFAR-10. The results of FID and corresponding training time are shown in Tab.6. We observe that the Langevin step \\( k \\) increasing from 10 to 40 can improve the generation quality, while for steps more than 40, it only has minor impacts on the improvement but with increased training overhead. We thus report the result of \\( k = 40 \\) in Tab.1." + }, + { + "type": "table", + "bbox": [ + 0.533, + 0.381, + 0.861, + 0.434 + ], + "angle": 0, + "content": "
steps kk = 10k = 20k = 40k = 80k = 100
FID69.4267.5866.3266.0365.86
Time(s)0.3120.4800.5680.7410.837
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.444, + 0.894, + 0.473 + ], + "angle": 0, + "content": "Table 6. FID and training time for increasing MCMC steps in prior sampling." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.495, + 0.698, + 0.511 + ], + "angle": 0, + "content": "5.5. Parameter Efficiency" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.518, + 0.892, + 0.653 + ], + "angle": 0, + "content": "It is crucial to analyze the parameter complexity when comparing the generation performance. In Tab.1, we build our model with two layers of latent variables on top of the generator used in [33]. The additional layer accounts for only \\(1\\%\\) overhead in total parameter complexity compared to LEBM [33]. For deep hierarchical models, we apply our joint EBM prior model on latent space which brings minimum overhead. The parameter complexity of the backbone NVAE and our EBM model is shown in Tab.7." + }, + { + "type": "table", + "bbox": [ + 0.506, + 0.662, + 0.888, + 0.709 + ], + "angle": 0, + "content": "
NVAE / EBMCIFAR-10CelebA-HQ-256LSUN-Church-64
FID39.73 / 11.3430.25 / 9.8938.13 / 8.38
Parameters257M / 9M (3%)375M / 18M (4%)65M / 5M (7%)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.719, + 0.892, + 0.746 + ], + "angle": 0, + "content": "Table 7. FID and parameter complexity for backbone model and EBM." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.77, + 0.62, + 0.785 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.795, + 0.892, + 0.901 + ], + "angle": 0, + "content": "we propose a joint EBM prior for multi-layer generator models, which can effectively capture the intra-layer relations at each layer and jointly correct the latent variables from all layers. We present a joint training scheme via MLE and further develop a variational learning scheme for efficient inference. Our comprehensive experiments demonstrate the effectiveness of the proposed method." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3610" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.186 + ], + "angle": 0, + "content": "[1] Dongsheng An, Jianwen Xie, and Ping Li. Learning deep latent variable models by short-run mcmc inference with optimal transport correction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15415–15424, June 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.187, + 0.472, + 0.243 + ], + "angle": 0, + "content": "[2] Jyoti Aneja, Alex Schwing, Jan Kautz, and Arash Vahdat. A contrastive learning approach for training variational autoencoder priors. Advances in neural information processing systems, 34:480-493, 2021. 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.245, + 0.47, + 0.286 + ], + "angle": 0, + "content": "[3] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.288, + 0.47, + 0.37 + ], + "angle": 0, + "content": "[4] Yuri Burda, Roger B. Grosse, and Ruslan Salakhutdinov. Importance weighted autoencoders. In Yoshua Bengio and Yann LeCun, editors, 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.373, + 0.47, + 0.442 + ], + "angle": 0, + "content": "[5] Rewon Child. Very deep vaes generalize autoregressive models and can outperform them on images. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021. 1, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.444, + 0.47, + 0.472 + ], + "angle": 0, + "content": "[6] Bin Dai and David Wipf. Diagnosing and enhancing vae models. arXiv preprint arXiv:1903.05789, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.474, + 0.47, + 0.528 + ], + "angle": 0, + "content": "[7] Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch. Improved contrastive divergence training of energy based models. arXiv preprint arXiv:2012.01316, 2020. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.531, + 0.47, + 0.572 + ], + "angle": 0, + "content": "[8] Yilun Du and Igor Mordatch. Implicit generation and generalization in energy-based models. arXiv preprint arXiv:1903.08689, 2019. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.575, + 0.47, + 0.67 + ], + "angle": 0, + "content": "[9] Conor Durkan and Charlie Nash. Autoregressive energy machines. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 1735-1744. PMLR, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.673, + 0.47, + 0.728 + ], + "angle": 0, + "content": "[10] Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, and Diederik P Kingma. Learning energy-based models by diffusion recovery likelihood. arXiv preprint arXiv:2012.08125, 2020. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.731, + 0.47, + 0.785 + ], + "angle": 0, + "content": "[11] Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black, and Bernhard Schölkopf. From variational to deterministic autoencoders. arXiv preprint arXiv:1903.12436, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.788, + 0.47, + 0.844 + ], + "angle": 0, + "content": "[12] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[13] Tian Han, Yang Lu, Song-Chun Zhu, and Ying Nian Wu. Alternating back-propagation for generator network. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31, 2017. 2, 5, 8" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.472, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.175 + ], + "angle": 0, + "content": "[14] Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu. Divergence triangle for joint training of generator model, energy-based model, and inferential model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.177, + 0.895, + 0.245 + ], + "angle": 0, + "content": "[15] Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, and Ying Nian Wu. Joint training of variational autoencoder and latent energy-based model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7978-7987, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.247, + 0.894, + 0.3 + ], + "angle": 0, + "content": "[16] Jakob D Drachmann Havtorn, Jes Frellsen, Soren Hauberg, and Lars Maaløe. Hierarchical vaes know what they don't know. In International Conference on Machine Learning, pages 4117-4128. PMLR, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.302, + 0.894, + 0.398 + ], + "angle": 0, + "content": "[17] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.4, + 0.894, + 0.44 + ], + "angle": 0, + "content": "[18] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.442, + 0.894, + 0.482 + ], + "angle": 0, + "content": "[19] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196, 2017. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.484, + 0.894, + 0.538 + ], + "angle": 0, + "content": "[20] Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Training generative adversarial networks with limited data. Advances in Neural Information Processing Systems, 33:12104-12114, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.539, + 0.894, + 0.594 + ], + "angle": 0, + "content": "[21] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401-4410, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.596, + 0.894, + 0.636 + ], + "angle": 0, + "content": "[22] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. Advances in neural information processing systems, 31, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.637, + 0.894, + 0.676 + ], + "angle": 0, + "content": "[23] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 2, 4, 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.679, + 0.894, + 0.706 + ], + "angle": 0, + "content": "[24] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.707, + 0.894, + 0.761 + ], + "angle": 0, + "content": "[25] Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, and Yoshua Bengio. Maximum entropy generators for energy-based models. arXiv preprint arXiv:1901.08508, 2019. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.762, + 0.894, + 0.829 + ], + "angle": 0, + "content": "[26] Don S Lemons and Anthony Gythiel. Paul Langevin's 1908 paper \"on the theory of brownian motion\"[“sur la théorie du mouvement brownien,” cr acad. sci.(paris) 146, 530–533 (1908)]. American Journal of Physics, 65(11):1079–1081, 1997. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.832, + 0.894, + 0.871 + ], + "angle": 0, + "content": "[27] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. CoRR, abs/1411.7766, 2014. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.873, + 0.894, + 0.901 + ], + "angle": 0, + "content": "[28] Lars Maaløe, Marco Fraccaro, Valentin Lievin, and Ole Winther. Biva: A very deep hierarchy of latent variables for" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.895, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "3611" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.109, + 0.093, + 0.468, + 0.12 + ], + "angle": 0, + "content": "generative modeling. Advances in neural information processing systems, 32, 2019. 1, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.121, + 0.469, + 0.188 + ], + "angle": 0, + "content": "[29] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng. Reading digits in natural images with unsupervised feature learning. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011, 2011. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.19, + 0.469, + 0.259 + ], + "angle": 0, + "content": "[30] Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, and Ying Nian Wu. On the anatomy of mcmc-based maximum likelihood learning of energy-based models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 5272-5280, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.261, + 0.469, + 0.315 + ], + "angle": 0, + "content": "[31] Erik Nijkamp, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu. Learning non-convergent non-persistent short-run mcmc toward energy-based model. Advances in Neural Information Processing Systems, 32, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.316, + 0.469, + 0.384 + ], + "angle": 0, + "content": "[32] Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, and Ying Nian Wu. Learning multi-layer latent variable model via variational optimization of short run mcmc for approximate inference. In European Conference on Computer Vision, pages 361-378. Springer, 2020. 1, 2, 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.385, + 0.469, + 0.44 + ], + "angle": 0, + "content": "[33] Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu. Learning latent space energy-based prior model. Advances in Neural Information Processing Systems, 33:21994-22008, 2020. 1, 2, 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.442, + 0.469, + 0.509 + ], + "angle": 0, + "content": "[34] Masaki Saito, Shunta Saito, Masanori Koyama, and Sosuke Kobayashi. Train sparsely, generate densely: Memory-efficient unsupervised training of high-resolution temporal gan. International Journal of Computer Vision, 128(10):2586-2606, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.511, + 0.469, + 0.593 + ], + "angle": 0, + "content": "[35] Casper Kaae Sønderby, Tapani Raiko, Lars Maalø e, Søren Kaae Sønderby, and Ole Winther. Ladder variational autoencoders. In D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc., 2016. 1, 2, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.595, + 0.469, + 0.635 + ], + "angle": 0, + "content": "[36] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in Neural Information Processing Systems, 32, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.637, + 0.469, + 0.691 + ], + "angle": 0, + "content": "[37] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.692, + 0.469, + 0.733 + ], + "angle": 0, + "content": "[38] Jakub Tomczak and Max Welling. Vae with a vampprior. In International Conference on Artificial Intelligence and Statistics, pages 1214-1223. PMLR, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.735, + 0.469, + 0.801 + ], + "angle": 0, + "content": "[39] Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz. Mocogan: Decomposing motion and content for video generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.804, + 0.469, + 0.844 + ], + "angle": 0, + "content": "[40] Arash Vahdat and Jan Kautz. Nvae: A deep hierarchical variational autoencoder. Advances in Neural Information Processing Systems, 33:19667-19679, 2020. 1, 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[41] Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In International conference on machine learning, pages 1747-1756. PMLR, 2016. 6" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.093, + 0.469, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.134 + ], + "angle": 0, + "content": "[42] Zhisheng Xiao and Tian Han. Adaptive multi-stage density ratio estimation for learning latent space energy-based model. arXiv preprint arXiv:2209.08739, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[43] Zhisheng Xiao, Karsten Kreis, Jan Kautz, and Arash Vahdat. Vaebm: A symbiosis between variational autoencoders and energy-based models. arXiv preprint arXiv:2010.00654, 2020. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.192, + 0.892, + 0.247 + ], + "angle": 0, + "content": "[44] Jianwen Xie, Yaxuan Zhu, Jun Li, and Ping Li. A tale of two flows: Cooperative learning of Langevin flow and normalizing flow toward energy-based model. In International Conference on Learning Representations, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.249, + 0.892, + 0.303 + ], + "angle": 0, + "content": "[45] Xuwang Yin, Shiying Li, and Gustavo K Rohde. Analyzing and improving generative adversarial training for generative modeling and out-of-distribution detection. arXiv preprint arXiv:2012.06568, 2020. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.305, + 0.892, + 0.359 + ], + "angle": 0, + "content": "[46] Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.362, + 0.892, + 0.416 + ], + "angle": 0, + "content": "[47] Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar. Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222, 2018. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.418, + 0.892, + 0.459 + ], + "angle": 0, + "content": "[48] Shengjia Zhao, Jiaming Song, and Stefano Ermon. Learning hierarchical features from generative models. arXiv preprint arXiv:1702.08396, 2017. 1, 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.459 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "3612" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/cb972244-96fa-449d-98c3-61aa444fc92d_origin.pdf b/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/cb972244-96fa-449d-98c3-61aa444fc92d_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..906a943d1a146af208364cd9b6f51cf4fbb69b59 --- /dev/null +++ b/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/cb972244-96fa-449d-98c3-61aa444fc92d_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:968e36b85e5b3927601557a2b7228d8d56104dfa19c4e3e85d6bf0f86bf4804d +size 7499736 diff --git a/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/full.md b/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/full.md new file mode 100644 index 0000000000000000000000000000000000000000..91ee3999d7bb354d4aebde536559d9d223d0ccfb --- /dev/null +++ b/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/full.md @@ -0,0 +1,380 @@ +# Learning Joint Latent Space EBM Prior Model for Multi-layer Generator + +Jiali Cui $^{1}$ , Ying Nian Wu $^{2}$ , Tian Han $^{1}$ \ + $^{1}$ Department of Computer Science, Stevens Institute of Technology\ + $^{2}$ Department of Statistics, University of California, Los Angeles\ +{jcui7,than6}@stevens.edu, ywu@stat.ucla.edu + +# Abstract + +This paper studies the fundamental problem of learning multi-layer generator models. The multi-layer generator model builds multiple layers of latent variables as a prior model on top of the generator, which benefits learning complex data distribution and hierarchical representations. However, such a prior model usually focuses on modeling inter-layer relations between latent variables by assuming non-informative (conditional) Gaussian distributions, which can be limited in model expressivity. To tackle this issue and learn more expressive prior models, we propose an energy-based model (EBM) on the joint latent space over all layers of latent variables with the multi-layer generator as its backbone. Such joint latent space EBM prior model captures the intra-layer contextual relations at each layer through layer-wise energy terms, and latent variables across different layers are jointly corrected. We develop a joint training scheme via maximum likelihood estimation (MLE), which involves Markov Chain Monte Carlo (MCMC) sampling for both prior and posterior distributions of the latent variables from different layers. To ensure efficient inference and learning, we further propose a variational training scheme where an inference model is used to amortize the costly posterior MCMC sampling. Our experiments demonstrate that the learned model can be expressive in generating high-quality images and capturing hierarchical features for better outlier detection. + +# 1. Introduction + +Deep generative models (a.k.a, generator models) have made promising progress in learning complex data distributions and achieved great successes in image and video synthesis [21, 34, 37, 39] as well as representation learning [5, 48]. Such models usually consist of low-dimensional latent variables together with a top-down generation model that maps such latent factors to the observed data. The latent factors can serve as an abstract data representation, but it is often modelled via a single latent vector with non- + +informative prior distribution which leads to limited model expressivity and fails to capture different levels of abstractions. Learning an informative prior model for hierarchical representations is needed, yet research in this direction is still under-developed. + +A principled way to learn such a prior model is by learning the generator models with multiple layers of latent variables. However, the learning of multi-layer generator model can be challenging as the inter-layer structural relation (i.e., latent variables across different layers) and the intra-layer contextual relation (i.e., latent units within the same layer) have to be effectively modelled and efficiently learned. Various methods have been proposed [5,28,32,35,40], but they only focused on inter-layer modeling by assuming the conditional Gaussian distribution across different layers while ignoring the intra-layer contextual modeling as the latent units are conditional independent within each layer. + +The energy-based models (EBMs), on the other hand, are shown to be expressive and proved to be powerful in capturing contextual and non-structural data regularities. Notably, [33] considers the EBM in the latent space for the non-hierarchical generator model, where the energy function is considered as a correction of the non-informative Gaussian prior. The low dimensionality of the latent space makes EBM effective in capturing regularities in the data. However, a single latent vector in [33] is infeasible for capturing the patterns at multiple layers of abstractions, which limits its model capacity. + +In this paper, we propose to combine the strengths of the latent space EBM and the generator with multiple layers of latent variables for better hierarchical representations and a more expressive prior model. Specifically, we introduce layer-wise energy terms to exponentially tilt the non-informative Gaussian conditional at each layer, and latent variables across different layers are modelled jointly through EBM with the multi-layer generator model as its backbone. Such a joint EBM prior model seamlessly integrates the intra-layer contextual modeling via layer-wise energy terms and inter-layer structural modeling with multi-layer latent variables. + +The joint EBM prior model can be learned by maximum likelihood estimation (MLE). Each learning iteration involves Markov chain Monte Carlo (MCMC) sampling of latent variables in each layer from both the prior and posterior distributions. The prior sampling can be efficiently done due to the low dimensionality of the latent variables and, more importantly, the lightweight networks for energy functions, while the posterior sampling can be less efficient. Therefore, we further develop the variational training scheme where an additional inference model is used for posterior approximation and is jointly trained with the joint EBM prior model. + +Contributions: 1) We propose a joint latent space EBM prior model for the generator model with multiple layers of latent variables; 2) We develop the maximum likelihood learning algorithm that learns the joint EBM prior model based on MCMC prior and posterior sampling across different layers. We further propose the variational joint training scheme for efficient learning and inference; 3) We provide strong empirical results through extensive experiments. + +# 2. Background + +In this section, we present the background of multi-layer latent variable model and latent space EBM prior model, which shall serve as the foundation of the proposed model. + +# 2.1. Multi-layer latent variable model + +Let $\mathbf{x}$ be the high-dimensional observed example, and $\mathbf{z}$ be the low-dimensional latent variables. The latent variable generative model, or generator model, factorizes a joint distribution of $(\mathbf{x},\mathbf{z})$ as + +$$ +p _ {\beta} (\mathbf {x}, \mathbf {z}) = p _ {\beta_ {0}} (\mathbf {x} | \mathbf {z}) p _ {\beta_ {> 0}} (\mathbf {z}) \tag {1} +$$ + +where $p_{\beta_0}(\mathbf{x}|\mathbf{z})$ is the generation model with parameter $\beta_0$ that maps from latent space to data space, and $p_{\beta >0}(\mathbf{z})$ is the prior distribution over latent variables with parameter $\beta_{>0}$ . $\beta = \{\beta_0,\beta_{>0}\}$ + +Gaussian prior model: For non-hierarchical models [12, 23], $p_{\beta > 0}(\mathbf{z})$ is defined on single layer of latent variables and is typically assumed to be uniform or unit Gaussian. For hierarchical models with multiple layers of latent variables [32, 35], $p_{\beta > 0}(\mathbf{z})$ can be further decomposed into conditional distributions between consecutive layers of latent variables as + +$$ +p _ {\beta_ {> 0}} (\mathbf {z}) = \prod_ {i = 1} ^ {L - 1} p _ {\beta_ {i}} \left(\mathbf {z} _ {i} \mid \mathbf {z} _ {i + 1}\right) p \left(\mathbf {z} _ {L}\right) \tag {2} +$$ + +where $p_{\beta_i}(\mathbf{z}_i|\mathbf{z}_{i + 1})\sim \mathcal{N}(\mu_{\beta_i}(\mathbf{z}_{i + 1}),\sigma_{\beta_i}^2 (\mathbf{z}_{i + 1}))$ and is parameterized by a network with parameter $\beta_{i}$ , and $p(\mathbf{z}_L)$ is chosen to be a simple distribution, such as uniform or unit Gaussian. + +Maximum likelihood learning: Learning such latent variable generative models can be done using maximum likelihood estimation (MLE). The marginal distribution is $p_{\beta}(\mathbf{x}) = \int p_{\beta}(\mathbf{x},\mathbf{z})d\mathbf{z}$ with the gradient: + +$$ +\nabla_ {\beta} \log p _ {\beta} (\mathbf {x}) = \mathbb {E} _ {p _ {\beta} (\mathbf {z} | \mathbf {x})} [ \nabla_ {\beta} \log p _ {\beta} (\mathbf {x}, \mathbf {z}) ] \tag {3} +$$ + +where the expectation can be approximated via Monte Carlo sampling from the posterior distribution $p_{\beta}(\mathbf{z}|\mathbf{x})$ . The MLE can then be accomplished through gradient ascent using such gradients. The posterior sampling usually requires the Markov Chain Monte Carlo (MCMC) such as Langevin dynamics [13, 32] + +Variational learning: To alleviate the computational burden of MCMC, variational approach [4] introduces an additional inference model $q_{\omega}(\mathbf{z}|\mathbf{x})$ with a separate set of parameters $\omega = (\omega_{1},\dots,\omega_{L})$ for posterior approximation, + +$$ +q _ {\omega} (\mathbf {z} | \mathbf {x}) = q _ {\omega_ {1}} (\mathbf {z} _ {1} | \mathbf {x}) \prod_ {i = 1} ^ {L - 1} q _ {\omega_ {i + 1}} (\mathbf {z} _ {i + 1} | \mathbf {z} _ {i}) \tag {4} +$$ + +where $q_{\omega_1}(\mathbf{z}_1|\mathbf{x})$ and $q_{\omega_{i + 1}}(\mathbf{z}_{i + 1}|\mathbf{z}_i)$ are usually assumed as conditional Gaussian distributions, forming a "bottom-up" inference structure. The generator and inference model can be jointly learned via maximizing the evidence lower bound (ELBO), i.e., $\max_{\beta ,\omega}\mathrm{ELBO}(\beta ,\omega)$ where ELBO is defined as $\mathrm{ELBO}(\beta ,\omega) = \mathbb{E}_{q_{\omega}(\mathbf{z}|\mathbf{x})}[\log p_{\beta_0}(\mathbf{x}|\mathbf{z})] - D_{\mathrm{KL}}(q_{\omega}(\mathbf{z}|\mathbf{x})||p_{\beta >0}(\mathbf{z}))$ + +# 2.2. Latent space energy-based model + +The energy-based model (EBM) offers a flexible approach for learning the data distribution and is shown to be expressive in capturing data regularities [7,8,10,31,43,45]. Most existing works focus on learning the EBM on data space, which is high-dimensional and can be challenging. To tackle this challenge, [2,33] propose to learn latent space EBM as an informative prior model. With low-dimensional latent space, learning the EBM can be more efficient and effective, which in turn benefits the expressivity of the whole model. Specifically, [33] considers the latent space energy-based prior model on a single layer of latent variables, + +$$ +p _ {\alpha} (\mathbf {z}) = \frac {1}{\mathrm {Z} (\alpha)} \exp [ f _ {\alpha} (\mathbf {z}) ] p _ {0} (\mathbf {z}) \tag {5} +$$ + +where $-f_{\alpha}(\mathbf{z})$ is the energy function, $Z(\alpha)$ is the normalizing constant, i.e., $Z(\alpha) = \int \exp [f_{\alpha}(\mathbf{z})]p_0(\mathbf{z})d\mathbf{z}$ , and $p_0(\mathbf{z})$ is the reference distribution assumed to be unit Gaussian. Compared to data space EBMs in which the energy function needs to support the entire high-dimensional space, such exponential tilting latent space EBMs can be more efficient in capturing data regularities. + +# 3. Model and Learning + +# 3.1. Joint latent space EBM prior model + +For generator models with multi-layer latent variables (or multi-layer generator model), consecutive layers are modelled by conditional Gaussian distributions (see Eqn.2), which essentially assumes the conditional independence for latent units within the $i$ -th layer given the $(i + 1)$ -th layer of latent variables. Such a conditional independence assumption limits the model capacity as the contextual relation between latent units within each layer is largely ignored (see Fig.1), and needs to be improved for informative conditional modeling and better model expressivity. In this paper, we propose the joint EBM prior for multi-layer generator models, + +$$ +\begin{array}{l} p _ {\alpha , \beta > 0} (\mathbf {z}) = \frac {1}{\mathrm {Z} _ {\alpha , \beta > 0}} \exp \left[ f _ {\alpha} (\mathbf {z}) \right] p _ {\beta > 0} (\mathbf {z}) \tag {6} \\ = \frac {1}{\mathbf {Z} _ {\alpha , \beta > 0}} \exp \left[ \sum_ {i = 1} ^ {L} f _ {\alpha_ {i}} (\mathbf {z} _ {i}) \right] \prod_ {i = 1} ^ {L - 1} p _ {\beta_ {i}} (\mathbf {z} _ {i} | \mathbf {z} _ {i + 1}) p (\mathbf {z} _ {L}) \\ \end{array} +$$ + +where we denote $\alpha = (\alpha_{1},\dots,\alpha_{L})$ for EBM parameters and $\mathbf{z} = (\mathbf{z}_1,\dots,\mathbf{z}_L)$ for latent variables in different layers with layer $L$ being the top layer. $\mathrm{Z}_{\alpha ,\beta >0} = \int \exp [f_{\alpha}(\mathbf{z})]p_{\beta >0}(\mathbf{z})d\mathbf{z}$ is the normalizing constant regarding latent variables for all layers. Thus, the latent variables across different layers are jointly corrected via EBM prior as in Eqn.6, where $f_{\alpha}(\mathbf{z})$ is the energy function for latent variables from all layers. + +In this paper, we consider a simple factorized layer-wise parameterization, i.e., $f_{\alpha}(\mathbf{z}) = \sum_{i=1}^{L} f_{\alpha_i}(\mathbf{z}_i)$ , but other parameterizations are also feasible, which we will explore in future work. With such energy parameterization, it's worth noting that the un-normalized prior model can be viewed as layer-wise exponential tilting, + +$$ +\begin{array}{l} \underbrace {\exp \left[ \sum_ {i = 1} ^ {L} f _ {\alpha_ {i}} (\mathbf {z} _ {i}) \right]} _ {\text {E n e r g y c o r r e c t i o n}} \underbrace {\prod_ {i = 1} ^ {L - 1} p _ {\beta_ {i}} (\mathbf {z} _ {i} | \mathbf {z} _ {i + 1}) p (\mathbf {z} _ {L})} _ {\text {G a u s s i a n p r i o r}} \tag {7} \\ = \underbrace {\exp \left[ f _ {\alpha_ {L}} (\mathbf {z} _ {L}) \right] p (\mathbf {z} _ {L})} _ {\text {C o r r e c t i o n o n t o p l a y e r}} \prod_ {i = 1} ^ {L - 1} \underbrace {\exp \left[ f _ {\alpha_ {i}} (\mathbf {z} _ {i}) \right] p _ {\beta_ {i}} (\mathbf {z} _ {i} | \mathbf {z} _ {i + 1})} _ {\text {C o r r e c t i o n o n i n t e r m i d a t e l a y e r}} \\ \end{array} +$$ + +See Fig.1 for an illustration and comparison with multi-layer generator model with Gaussian prior. + +Joint vs. conditional EBM prior: Besides the proposed joint modeling, it is also tempting to consider EBM prior for layer-wise Gaussian conditional, i.e., $\tilde{p}_{\alpha_i,\beta_i}(\mathbf{z}_i|\mathbf{z}_{i + 1}) = \frac{1}{Z(\mathbf{z}_{i + 1})}\exp [f_{\alpha_i}(\mathbf{z}_i)]p_{\beta_i}(\mathbf{z}_i|\mathbf{z}_{i + 1})$ and form the overall prior $p_{\alpha ,\beta >0}(\mathbf{z}) = \prod_{i = 1}^{L}\tilde{p}_{\alpha_i,\beta_i}(\mathbf{z}_i|\mathbf{z}_{i + 1})p(\mathbf{z}_L)$ . Such a scheme is closely related to autoregressive energy machine + +![](images/d6d83883fce2ab3b8f68a3486cbced6b8c910245636dd726c56f9487f807f50a.jpg) +Figure 1. Left panel: Gaussian prior model. Right panel: Joint EBM prior model. Black solid lines with arrow: inter-layer relations modelling. Red solid lines: intra-layer contextual relations modelling. Blue dashed lines: joint modelling upon all layers. + +[9] and is adopted in NCP-VAE [2]. However, the normalizing constant $\mathrm{Z}(\mathbf{z}_{i + 1})$ in $\tilde{p}_{\alpha_i,\beta_i}(\mathbf{z}_i|\mathbf{z}_{i + 1})$ involves the latent variable $\mathbf{z}_{i + 1}$ from the upper layer which can be intractable and needs an additional inner-loop for sampling or optimization. The proposed joint EBM prior couples the latent variables across different layers via energy function and can be learned effectively and efficiently. + +# 3.2. Maximum Likelihood Estimation + +Our joint EBM prior model can be trained using MLE. Let $\theta = (\alpha, \beta)$ denotes the model parameters and $\theta$ can be learned by maximizing the log-likelihood on $n$ training observations + +$$ +L (\theta) = \sum_ {i = 1} ^ {n} \log p _ {\theta} (\mathbf {x} _ {i}) = \sum_ {i = 1} ^ {n} \log \int p _ {\beta_ {0}} (\mathbf {x} | \mathbf {z}) p _ {\alpha , \beta_ {> 0}} (\mathbf {z}) d \mathbf {z} +$$ + +When $n$ becomes sufficiently large, maximizing the above log-likelihood is equivalent to minimizing the Kullback-Leibler (KL) divergence between model distribution and empirical data distribution, i.e., $\min_{\theta} D_{\mathrm{KL}}(p_{\mathrm{data}}(\mathbf{x})||p_{\theta}(\mathbf{x}))$ . + +To update the parameter $\theta$ , we can compute the gradient of log-likelihood $\nabla_{\theta} \log p_{\theta}(\mathbf{x})$ as + +$$ +\begin{array}{l} \nabla_ {\theta} \log p _ {\theta} (\mathbf {x}) = \mathbb {E} _ {p _ {\theta} (\mathbf {z} | \mathbf {x})} [ \nabla_ {\theta} \log p _ {\theta} (\mathbf {x}, \mathbf {z}) ] \\ = \mathbb {E} _ {p _ {\theta} (\mathbf {z} | \mathbf {x})} \left[ \nabla_ {\theta} \log p _ {\beta_ {0}} (\mathbf {x} | \mathbf {z}) \right] \tag {8} \\ + \mathbb {E} _ {p _ {\theta} (\mathbf {z} | \mathbf {x})} \left[ \nabla_ {\theta} \log p _ {\alpha , \beta > 0} (\mathbf {z}) \right] \\ \end{array} +$$ + +With such a gradient, we can learn $\theta$ using gradient ascent. + +Learning generation model $\beta_0$ : $p_{\beta_0}(\mathbf{x}|\mathbf{z})$ is assumed to be Gaussian distribution, i.e., $p_{\beta_0}(\mathbf{x}|\mathbf{z}) \sim \mathcal{N}(g_{\beta_0}(\mathbf{z}), \sigma^2 I)$ , with generation network $g_{\beta_0}$ with parameter $\beta_0$ and pre-specified $\sigma^2$ for simplicity. The learning gradient + +$\nabla_{\beta_0}\log p_\theta (\mathbf{x})$ can then be expressed as + +$$ +\begin{array}{l} \nabla_ {\beta_ {0}} \log p _ {\theta} (\mathbf {x}) = \mathbb {E} _ {p _ {\theta} (\mathbf {z} | \mathbf {x})} [ \nabla_ {\beta_ {0}} \log p _ {\beta_ {0}} (\mathbf {x} | \mathbf {z}) ] \tag {9} \\ { = } { \mathbb { E } _ { p _ { \theta } ( \mathbf { z } | \mathbf { x } ) } \left[ - \nabla _ { \beta _ { 0 } } \frac { | | \mathbf { x } - g _ { \beta _ { 0 } } ( \mathbf { z } ) | | ^ { 2 } } { 2 \sigma ^ { 2 } } \right] } \\ \end{array} +$$ + +Learning prior model $\alpha, \beta_{>0}$ : Learning $\alpha_{i}$ can be done by computing the gradient $\nabla_{\alpha_i} \log p_\theta(\mathbf{x})$ as + +$$ +\begin{array}{l} \nabla_ {\alpha_ {i}} \log p _ {\theta} (\mathbf {x}) = \mathbb {E} _ {p _ {\theta} (\mathbf {z} | \mathbf {x})} \left[ \nabla_ {\alpha_ {i}} f _ {\alpha_ {i}} (\mathbf {z} _ {i}) \right] \tag {10} \\ - \mathbb {E} _ {p _ {\alpha , \beta > 0} (\mathbf {z})} \left[ \nabla_ {\alpha_ {i}} f _ {\alpha_ {i}} (\mathbf {z} _ {i}) \right] \\ \end{array} +$$ + +For updating $\beta_{>0}$ , the gradient $\nabla_{\beta_i} \log p_\theta(\mathbf{x})$ is + +$$ +\begin{array}{l} \nabla_ {\beta_ {i}} \log p _ {\theta} (\mathbf {x}) = \mathbb {E} _ {p _ {\theta} (\mathbf {z} | \mathbf {x})} \left[ \nabla_ {\beta_ {i}} \log p _ {\beta_ {i}} \left(\mathbf {z} _ {i} \mid \mathbf {z} _ {i + 1}\right) \right] \tag {11} \\ - \mathbb {E} _ {p _ {\alpha , \beta > 0} (\mathbf {z})} \left[ \nabla_ {\beta_ {i}} \log p _ {\beta_ {i}} \left(\mathbf {z} _ {i} \mid \mathbf {z} _ {i + 1}\right) \right] \\ \end{array} +$$ + +Sampling: Both Eqn.10 and Eqn.11 require sampling from the posterior and prior distribution, which can be done via Langevin dynamic (LD) [26]. Given a target distribution $p(\mathbf{z})$ , Langevin dynamic samples $\mathbf{z} \sim p(\mathbf{z})$ by computing the gradient $\nabla_{\mathbf{z}} \log p(\mathbf{z})$ and iteratively update $\mathbf{z}$ as + +$$ +\mathbf {z} _ {t} = \mathbf {z} _ {t - 1} + s \nabla_ {\mathbf {z}} \log p \left(\mathbf {z} _ {t - 1}\right) + \sqrt {2 s} \epsilon_ {t - 1} \tag {12} +$$ + +where $t$ indexes the time step, $s$ is the step size, and $\epsilon$ is the Gaussian noise for each time step. + +Prior sampling: By replacing target $p(\mathbf{z})$ with $p_{\alpha, \beta > 0}(\mathbf{z})$ , the prior sampling computes $\nabla_{\mathbf{z}} \log p_{\alpha, \beta > 0}(\mathbf{z})$ as + +$$ +\nabla_ {\mathbf {z}} \left[ \sum_ {i = 1} ^ {L} f _ {\alpha_ {i}} (\mathbf {z} _ {i}) + \sum_ {i = 1} ^ {L - 1} \log p _ {\beta_ {i}} (\mathbf {z} _ {i} | \mathbf {z} _ {i + 1}) + \log p (\mathbf {z} _ {L}) \right] \tag {13} +$$ + +Posterior sampling: By replacing $p(\mathbf{z})$ with $p_{\theta}(\mathbf{z}|\mathbf{x})$ where $p_{\theta}(\mathbf{z}|\mathbf{x}) \propto p_{\beta_0}(\mathbf{x}|\mathbf{z})p_{\alpha, \beta > 0}(\mathbf{z})$ , the posterior sampling computes $\nabla_{\mathbf{z}} \log p_{\theta}(\mathbf{z}|\mathbf{x})$ as + +$$ +\nabla_ {\mathbf {z}} \log p _ {\theta} (\mathbf {z} | \mathbf {x}) = \nabla_ {\mathbf {z}} \left[ \log p _ {\beta_ {0}} (\mathbf {x} | \mathbf {z}) + \log p _ {\alpha , \beta_ {> 0}} (\mathbf {z}) \right] \tag {14} +$$ + +Notice that posterior sampling can be computationally inefficient as $\nabla_{\mathbf{z}}[\log p_{\beta_0}(\mathbf{x}|\mathbf{z})]$ requires back-propagation through the deep generation model. + +# 3.3. Variational Learning + +For efficient posterior sampling, an inference model $q_{\omega}(\mathbf{z}|\mathbf{x})$ with a separate set of parameters $\omega$ can be used for the posterior approximation. In this paper, we use the bottom-up inference model as Eqn.4 for amortizing the costly posterior MCMC sampling. Particularly, instead of KL minimization between marginal distributions as in MLE (see Sec.3.2), we consider the KL optimization between two joint densities, one for generator model density, i.e., $p_{\theta}(\mathbf{x},\mathbf{z}) = p_{\beta_0}(\mathbf{x}|\mathbf{z})p_{\alpha ,\beta >0}(\mathbf{z})$ , and one for data density, + +i.e., $q_{\omega}(\mathbf{x}, \mathbf{z}) = p_{\mathrm{data}}(\mathbf{x}) q_{\omega}(\mathbf{z}|\mathbf{x})$ . We propose joint learning through KL minimization, denoting the objective to be $L(\theta, \omega)$ , i.e., + +$$ +\min _ {\theta} \min _ {\omega} L (\theta , \omega) = \min _ {\theta} \min _ {\omega} D _ {\mathrm {K L}} \left(q _ {\omega} (\mathbf {x}, \mathbf {z}) \| p _ {\theta} (\mathbf {x}, \mathbf {z})\right) \tag {15} +$$ + +Learning generation model $\beta_0$ : For learning $\beta_0$ , we can compute the gradient as + +$$ +- \nabla_ {\beta_ {0}} L (\theta , \omega) = \mathbb {E} _ {p _ {\text {d a t a}} (\mathbf {x})} \mathbb {E} _ {q _ {\omega} (\mathbf {z} | \mathbf {x})} \left[ \nabla_ {\beta_ {0}} \log p _ {\beta_ {0}} (\mathbf {x} | \mathbf {z}) \right] \tag {16} +$$ + +Learning prior model $\alpha$ , $\beta_{>0}$ : For learning $\alpha_{i}$ , the gradient is computed as + +$$ +\begin{array}{l} - \nabla_ {\alpha_ {i}} L (\theta , \omega) = \underset {\mathbb {E}} {\mathbb {E}} _ {p _ {\text {d a t a}} (\mathbf {x})} \underset {\mathbb {E} _ {q _ {\omega} (\mathbf {z} | \mathbf {x})}} {\mathbb {E}} \left[ \nabla_ {\alpha_ {i}} f _ {\alpha_ {i}} \left(\mathbf {z} _ {i}\right) \right] \tag {17} \\ - \mathbb {E} _ {p _ {\alpha , \beta > 0} (\mathbf {z})} \left[ \nabla_ {\alpha_ {i}} f _ {\alpha_ {i}} (\mathbf {z} _ {i}) \right] \\ \end{array} +$$ + +For learning $\beta_{>0}$ , we compute the gradient as + +$$ +\begin{array}{l} - \nabla_ {\beta_ {i}} L (\theta , \omega) = \mathbb {E} _ {p _ {\mathrm {d a t a}} (\mathbf {x}) q _ {\omega} (\mathbf {z} | \mathbf {x})} [ \nabla_ {\beta_ {i}} \log p _ {\beta_ {i}} (\mathbf {z} _ {i} | \mathbf {z} _ {i + 1}) ] \\ - \mathbb {E} _ {p _ {\alpha , \beta > 0} (\mathbf {z})} \left[ \nabla_ {\beta_ {i}} \log p _ {\beta_ {i}} \left(\mathbf {z} _ {i} \mid \mathbf {z} _ {i + 1}\right) \right] \tag {18} \\ \end{array} +$$ + +Learning inference model $\omega$ : For learning $\omega_{i}$ , the gradient is + +$$ +\begin{array}{l} - \nabla_ {\omega_ {i}} L (\theta , \omega) = \nabla_ {\omega_ {i}} \mathbb {E} _ {p _ {\mathrm {d a t a}} (\mathbf {x})} [ \mathbb {E} _ {q _ {\omega} (\mathbf {z} | \mathbf {x})} \log p _ {\beta_ {0}} (\mathbf {x} | \mathbf {z}) \\ - D _ {\mathrm {K L}} \left(q _ {\omega} (\mathbf {z} | \mathbf {x}) \| p _ {\beta > 0} (\mathbf {z})\right) \tag {19} \\ + \mathbb {E} _ {q _ {\omega} (\mathbf {z} | \mathbf {x})} [ \sum_ {i = 1} ^ {L} f _ {\alpha_ {i}} (\mathbf {z} _ {i}) ] ] \\ \end{array} +$$ + +Divergence Perturbation. The KL joint minimization (Eqn.15) can be viewed as a surrogate of the MLE objective with the KL perturbation term, + +$$ +\begin{array}{l} D _ {\mathrm {K L}} \left(q _ {\omega} (\mathbf {x}, \mathbf {z}) | | p _ {\theta} (\mathbf {x}, \mathbf {z})\right) \\ = D _ {\mathrm {K L}} \left(p _ {\text {d a t a}} (\mathbf {x}) | | p _ {\theta} (\mathbf {x})\right) + D _ {\mathrm {K L}} \left(q _ {\omega} (\mathbf {z} | \mathbf {x}) | | p _ {\theta} (\mathbf {z} | \mathbf {x})\right) \\ \end{array} +$$ + +where the perturbation term $D_{\mathrm{KL}}(q_{\omega}(\mathbf{z}|\mathbf{x})||p_{\theta}(\mathbf{z}|\mathbf{x}))$ measures the KL-divergence between inference distribution and generator posterior. The inference model is learned to directly match the posterior distribution of the generator without expensive posterior sampling. In fact, such KL minimization in the joint space is closely related to evidence lower bound (ELBO) with the joint EBM as the prior model. + +# 4. Related Work + +Hierarchical VAEs: Variational auto-encoder (VAE) [23] proposes variational learning by introducing an approximation of the true intractable posterior, which allows a tractable bound on log-likelihood to be formed. But the non-hierarchical structure can be limited in model expressivity and fails to capture different levels of abstraction. Hierarchical VAEs (HVAEs) [5, 28, 35, 40] consist of multiple + +layers of latent variables on top of the generator as a prior model, which can be used for learning complex data distribution and hierarchical representations. However, such models still focus on layer-wise relations while ignoring the intra-layer contextual relations at each layer. + +Energy-based models: The energy-based models receive attention for being expressive and powerful in capturing contextual data regularities. The majority of existing works focus on the pixel space [7, 8, 10, 14, 15, 43-45]. Learning such EBMs can be done using MLE, where MCMC sampling is typically required in each learning iteration which can be computationally expensive upon data space. Instead, [33] proposes to build EBM on latent space where the energy function is considered as a correction of the noninformative Gaussian prior. The low dimensionality of the latent space makes EBM effective in capturing data regularities and can alleviate the burden of MCMC sampling. + +Generator models with informative prior: For generator models, the assumed Gaussian or uniform prior distribution can be non-informative and less expressive. To address this problem, recent works [2, 6, 11, 33, 38, 42] propose to learn generator models with an informative prior, where RAE [11] constructs priors using rejection sampling, and Two-stage VAE [6] propose to train an extra model for simple prior at the second stage to match the aggregated posterior distribution, while LEBM [33] and NCP-VAE [2] instead learn EBMs on latent space to improve the expressivity of generator models. + +# 5. Experiments + +To demonstrate the proposed method, we present extensive experiments, including (i) image synthesis, (ii) hierarchical representations, and (iii) analysis of latent space, as well as various ablation studies based on the proposed EBM prior. The parameter complexity is discussed in Sec.5.5. + +# 5.1. Image Synthesis + +Generator models with informative prior. We first evaluate the generation performance of the proposed joint model. If the model is well-trained, the multi-layer EBM prior model should render an expressive prior distribution leading to realistic synthesis. We benchmark our model against other generator models that assume standard Gaussian prior, such as VAE [23], Alternating Back-propagation (ABP) [13], Ladder VAE (LVAE) [35], and Short-run Inference (SRI) [32], as well as other generator models using informative prior, such as RAE [11], Two-stages VAE (2s-VAE) [6], NCP-VAE [2], and LEBM [33], where LEBM builds EBM for single layer latent variables, while ours contains a multi-layer structure. + +
ModelSVHNCelebA-64CIFAR-10
VAE [23]46.7865.75106.37
LVAE (L=5) [35]39.2653.40-
ABP [13]49.7151.50-
SRI (L=5) [32]35.3247.95-
RAE [11]42.0240.9574.16
2s-VAE [6]42.8144.4072.90
NCP-VAE [2]33.2342.0778.06
LEBM [33]29.4437.8770.15
Ours (L=2)26.8133.6066.32
+ +Table 1. FID(↓) for our model and baselines on SVHN, CelebA (64 x 64), and CIFAR-10. + +We train our model on SVHN [29], CIFAR-10 [24] and CelebA-64 [27] and use Fréchet Inception Distance (FID) [17] to quantitatively evaluate the generation quality. To make fair comparisons, we follow the standard protocol as in [33] and use the same generation model with convolutional structures. We use Langevin posterior sampling for the training, and the generation model is jointly learned (the result for variational learning is shown in Ablation Studies). The comparisons are shown in Tab.1, where the superior generation performance indicates the effectiveness of our model in learning a more expressive prior. + +Toward deep hierarchical models. We then consider the modern deep hierarchical structures as our multi-layer generator and explore the potential of the joint EBM prior for better generation. We adopt the two-stage training [2, 43] where the deep multi-layer generator $p_{\beta >0}(\mathbf{z})$ and inference model $q_{\omega}(\mathbf{z}|\mathbf{x})$ are trained in the first stage by maximizing the ELBO as in VAEs, with the pre-trained models, our joint EBM prior model can then be learned in the second stage where the posterior samples are directly obtained from the pre-trained inference model $q_{\omega}(\mathbf{z}|\mathbf{x})$ and prior samples can be obtained via Langevin sampling with change of variable on the generator $p_{\beta >0}(\mathbf{z})$ . + +We consider NVAE [40], a modern hierarchical VAE, for the first stage training, and we train our joint EBM prior in the second stage. For prior sampling in the second stage training, we employ similar reparametrized sampling scheme as in [43] via provided code1 in order to better traverse the deep hierarchical latent space with different scales. We examine our model on CIFAR-10, CelebA-HQ-256 [19], and LSUN-Church-64 [46]. The qualitative results for CelebA-HQ-256 and LSUN-Church-64 are shown in Fig.2 and Fig.3. For CelebA-HQ-256, we synthesize with adjusted batch-normalization as used in [2, 43]. We also visualize the Langevin transition on CIFAR-10 in Fig.4 where the quality of synthesis improves as the Langevin progresses. + +![](images/b1de0f75521028c206b73376081cc8443cc62974d67960d742f3283085c59f30.jpg) +Figure 2. Generated samples on CelebA-HQ-256 . FID = 9.89. + +![](images/79bf36e5f722dc59372f529509fa01584dd4956adde7279b02a3669967cf07e9.jpg) + +Figure 3. Generated samples on LSUN-Church-64. FID = 8.38. + +
MethodISFID
NVAE* [40]5.3037.73
Ours8.9911.34
NCP-VAE [2]-24.08
VAEBM [43]8.4312.19
Other EBMs
IGEBM [8]6.7838.2
ImprovedCD [7]7.8525.1
Divergence Triangle [14]-30.10
Adv-EBM [45]9.1013.21
Other Likelihood Models
GLOW [22]3.9248.9
PixelCNN [41]4.6065.93
GANs+Score-based Models
BigGAN [3]9.2214.73
StyleGANv2 w/o ADA [20]8.999.9
NCSN [36]8.8725.32
DDPM [18]9.463.17
+ +Table 2. IS(↑) and FID(↓) for our model and baselines on CIFAR-10. Model* indicates our backbone model. + +The quantitative results are shown in Tab.2 and Tab.3. We consider the baseline models, including NCP-VAE [2] and VAEBM [43], which also recruit NVAE as their backbone model, and other powerful deep generative models, such as GANs [3, 20], score-based models [18, 36] and EBMs [7,8,14,45] on data space. Compared to NVAE backbone model, our joint EBM prior model can significantly improve the fidelity of generated samples while only accounting for negligible overhead (see Parameter Efficiency in Sec.5.5). In comparison with other powerful deep generative models, we also achieve competitive generation performance. + +![](images/55b8541ab8eef1a25afc1a7ac54346491ae06771ece358ab299826164fdfbb06.jpg) + +Figure 4. Langevin transitions on CIFAR-10. FID = 11.34. + +
ModelCelebA-HQ-256LSUN-Church-64
NVAE* [40]30.2538.13
Ours9.898.38
NCP-VAE [2]24.79-
VAEBM [43]20.3813.51
Adv-EBM [45]17.3110.84
GLOW [22]68.9359.35
PGGAN [19]8.036.42
+ +Table 3. FID(↓) for our model and baselines on CelebA-HQ-256 and LSUN-Church-64. Model* indicates backbone model. + +# 5.2. Hierarchical Representations + +Hierarchical sampling. To examine our model in learning hierarchical representation, we employ hierarchical sampling to illustrate the learned representation at different layers. In particular, we first sample one group of latent vectors from EBM prior and hold them as fixed constants, then we randomly sample multiple groups of latent vectors to replace the fixed latent vectors at different layers. This allows us to visualize the variation in representation across layers. + +We apply our joint EBM prior to BIVA [28] on CelebA-64. For training, we reuse the two-stage training scheme, where we recruit $\mathrm{BIVA}^2$ for the first stage training and train our EBM prior model in the second stage by using the reparametrized sampling [43] method (similar as deep hierarchical models in Sec.5.1). We show the results of hierarchical sampling in Fig.6 and observe that BIVA presents minor changes at the bottom and middle layers, while the proposed joint EBM prior model can show variations of different levels. Note that it is a challenging task for conditional hierarchical models [48], on which the improvement thus suggests that the proposed method is capable of learning hierarchical representations for multi-layer generator models. + +![](images/515393744524ac48cec77123635ab40fd11b2cba941d728b91019553703d5df9.jpg) +Figure 5. Histograms of density of $L_{\mathrm{EBM}}^{>k}$ with AUROC(↑) and AUPRC(↑) for CIFAR-10 (in) / SVHN (out). + +![](images/8bc26b65f5c2625625f1f9056a192f8de072a21db2684fcf91b47aface5b9e42.jpg) +Figure 6. Hierarchical sampling for Gaussian prior model (bottom) and EBM prior model (top). From left panel to right panel, latent vectors are sampled from the bottom layers to the top layers. Detailed sampling method can be referred in Figure 3 in [48]. + +Out-of-distribution detection. Next, we conduct out-of-distribution (OOD) detection to further evaluate the hierarchical representations. Typically, low-level representations (e.g., edges, corners) can be shared across data which in turn leads to high-confidence reconstructions for OOD examples, while high-level semantic ones have fewer correlations across different data and shall be more discriminative for OOD detection. Inspired by [16], we consider an unnormalized log-posterior as the decision function for EBM prior model, which is defined as + +$$ +\begin{array}{l} L _ {\mathrm {E B M}} ^ {> k} = \mathbb {E} _ {\mathbf {z} > k \sim q _ {\omega} (\mathbf {z} | \mathbf {x}), \mathbf {z} \leq k \sim p _ {\beta > 0}, \alpha (\mathbf {z})} [ \log p _ {\beta_ {0}} (\mathbf {x} | \mathbf {z}) \\ + \log p _ {\beta > 0} (\mathbf {z}) + \sum_ {i = 1} ^ {L} f _ {\alpha_ {i}} (\mathbf {z} _ {i}) ] \tag {20} \\ \end{array} +$$ + +where latent codes above the $k$ -th layer are inferred from inference model and kept fixed, and those below the $k$ -th layer are sampled from EBM prior via the reparametrized sampling3 [43] with fixed inferred latent codes. With $k = 0$ , all layers of latent vectors are inferred from $q_{\omega}(\mathbf{z}|\mathbf{x})$ . With a higher value of $k$ , less inferred low-level representations are used, which should render better performance in OOD detection. In addition, we can also compute a subtraction + +between $L_{\mathrm{EBM}}^{>0}$ and $L_{\mathrm{EBM}}^{>k}$ as a surrogate of the likelihood-ratio which is shown to be effective for OOD detection [16]. We compute the subtraction as + +$$ +L L R _ {\mathrm {E B M}} ^ {> k} = L _ {\mathrm {E B M}} ^ {> 0} - L _ {\mathrm {E B M}} ^ {> k} \tag {21} +$$ + +We follow standard protocols and apply our EBM prior model with BIVA on CIFAR-10 and use SVHN as OOD data for testing. In Fig.5, we show the density of in-distribution and OOD data by computing the unnormalized log-posterior with increased $k$ , and we use AUROC, AUPRC to quantitatively evaluate the performance. It can be seen that as $k$ increases, relatively lower log-likelihoods are assigned to OOD data, which in turn renders better detection performance (higher AUROC and AUPRC). More importantly, we observe that the backbone model BIVA achieves the best detection performance of 0.885 for AUROC, while our models achieve 0.927 with the adapted decision function. This further verifies that the hierarchical representations can be learned within our multi-layer structure. + +# 5.3. Analysis of Latent Space + +Long-run Langevin transition. In this section, we examine the energy landscape of our joint EBM prior model. If the EBM is well-learned, the energy prior should naturally render local modes of the energy function, and traversing these local modes should present realistic synthesized examples and steady-state energy scores. Existing EBMs typically have oversaturated images via long-run Langevin dynamics as observed in [30]. Training an EBM that learns steady-state energy scores over realistic images can be useful but challenging. + +We train our model on CelebA-64 using Langevin dynamic for 40 steps. We then run 100 and 2500 Langevin steps to examine the learned energy landscape. We show the synthesis and corresponding energy profile in Fig.7. It can be seen that generated examples become sharper for the first 40 steps as it starts from the referenced distribution $p_{\beta > 0}(\mathbf{z})$ toward the learned energy prior $p_{\beta > 0, \alpha}(\mathbf{z})$ , and the energy fluctuates around some constant. For long-run 2500 steps, it is worth noting that our EBM prior model delivers diverse and realistic synthesis, and it does not exhibit the oversaturated phenomenon. This suggests that the learned EBM could mix well between different local modes of the learned energy prior. + +![](images/42ab7ce732358ffba47a2d6fe1d1a18de6f877c268effa3dcd14ef059a213e09.jpg) +Figure 7. Trajectory in data space and energy profile in Langevin transition. Top: Langevin transition with 100 steps. Bottom: Langevin transition with 2500 steps. + +Anomaly Detection. We further evaluate how our joint EBM prior model could benefit the anomaly detection (AD) task. Different from OOD detection, AD requires one class (e.g., one-digit class from MNIST) of data to be held out as anomaly for training, and both normal (e.g., other nine-digit classes from MNIST) and anomalous data are used for testing. + +The proposed prior model is built on the joint of all layers of latent variables. If it is well learned, the posterior $q_{\omega}(\mathbf{z}|\mathbf{x})$ could form a discriminative joint latent space that has separated probability densities for normal and anomalous data. We use un-normalized log-posterior $L_{\mathrm{EBM}}^{>0}$ as our decision function and train our model on MNIST with each class held out as an anomalous class. We consider the baseline models that also adopt an inferential mechanism, such as VAE [23], MEG [25], BiGAN- $\sigma$ [47], OT-SRI [1], and LEBM [33] which assumes single-layer latent space and is closely related to our method. Tab.4 shows the results of AUPRC scores averaged over the last 10 epochs to account for the variance. To make fair comparisons, we follow the protocols in [1,25,33,47]. + +
Heldout Digit14579
VAE [23]0.0630.3370.3250.1480.104
MEG [25]0.281 ± 0.0350.401 ± 0.0610.402 ± 0.0620.290 ± 0.0400.342 ± 0.034
BiGAN-σ [47]0.287 ± 0.0230.443 ± 0.0290.514 ± 0.0290.347 ± 0.0170.307 ± 0.028
OT-SRI [1]0.353 ± 0.0210.770 ± 0.0240.726 ± 0.0300.550 ± 0.0130.555 ± 0.023
LEBM [33]0.336 ± 0.0080.630 ± 0.0170.619 ± 0.0130.463 ± 0.0090.413 ± 0.010
Ours0.470 ± 0.0090.941 ± 0.0010.964 ± 0.0030.815 ± 0.0040.796 ± 0.004
+ +# 5.4. Ablation Studies. + +MCMC sampling vs. Inference model. Two posterior sampling schemes using MCMC and inference model are compared in Tab.5 in terms of FID and wall-clock training time (per-iteration). The MCMC posterior sampling renders better FID as it is more accurate in inference [13, 32], but it can be computationally heavy. While the inference model + +is efficient in learning but can be less accurate. For deep hierarchical structures, the variational learning with inference model is preferred due to its efficiency. + +Table 4. AUPRC scores for unsupervised anomaly detection. + +
MCMC / InfSVHNCelebA-64CIFAR-10
FID26.81 / 28.6033.60 / 36.1266.32 / 68.45
Time(s)0.478 / 0.2320.920 / 0.2460.568 / 0.256
+ +Langevin steps. We explore the different number of Langevin steps in prior sampling for training on CIFAR-10. The results of FID and corresponding training time are shown in Tab.6. We observe that the Langevin step $k$ increasing from 10 to 40 can improve the generation quality, while for steps more than 40, it only has minor impacts on the improvement but with increased training overhead. We thus report the result of $k = 40$ in Tab.1. + +Table 5. FID and training time for MCMC posterior sampling and variational learning. + +
steps kk = 10k = 20k = 40k = 80k = 100
FID69.4267.5866.3266.0365.86
Time(s)0.3120.4800.5680.7410.837
+ +# 5.5. Parameter Efficiency + +It is crucial to analyze the parameter complexity when comparing the generation performance. In Tab.1, we build our model with two layers of latent variables on top of the generator used in [33]. The additional layer accounts for only $1\%$ overhead in total parameter complexity compared to LEBM [33]. For deep hierarchical models, we apply our joint EBM prior model on latent space which brings minimum overhead. The parameter complexity of the backbone NVAE and our EBM model is shown in Tab.7. + +Table 6. FID and training time for increasing MCMC steps in prior sampling. + +
NVAE / EBMCIFAR-10CelebA-HQ-256LSUN-Church-64
FID39.73 / 11.3430.25 / 9.8938.13 / 8.38
Parameters257M / 9M (3%)375M / 18M (4%)65M / 5M (7%)
+ +Table 7. FID and parameter complexity for backbone model and EBM. + +# 6. Conclusion + +we propose a joint EBM prior for multi-layer generator models, which can effectively capture the intra-layer relations at each layer and jointly correct the latent variables from all layers. We present a joint training scheme via MLE and further develop a variational learning scheme for efficient inference. Our comprehensive experiments demonstrate the effectiveness of the proposed method. + +# References + +[1] Dongsheng An, Jianwen Xie, and Ping Li. Learning deep latent variable models by short-run mcmc inference with optimal transport correction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15415–15424, June 2021. 8 +[2] Jyoti Aneja, Alex Schwing, Jan Kautz, and Arash Vahdat. A contrastive learning approach for training variational autoencoder priors. Advances in neural information processing systems, 34:480-493, 2021. 2, 3, 5, 6 +[3] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096, 2018. 6 +[4] Yuri Burda, Roger B. Grosse, and Ruslan Salakhutdinov. Importance weighted autoencoders. In Yoshua Bengio and Yann LeCun, editors, 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, 2016. 2 +[5] Rewon Child. Very deep vaes generalize autoregressive models and can outperform them on images. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021. 1, 4 +[6] Bin Dai and David Wipf. Diagnosing and enhancing vae models. arXiv preprint arXiv:1903.05789, 2019. 5 +[7] Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch. Improved contrastive divergence training of energy based models. arXiv preprint arXiv:2012.01316, 2020. 2, 5, 6 +[8] Yilun Du and Igor Mordatch. Implicit generation and generalization in energy-based models. arXiv preprint arXiv:1903.08689, 2019. 2, 5, 6 +[9] Conor Durkan and Charlie Nash. Autoregressive energy machines. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 1735-1744. PMLR, 2019. 3 +[10] Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, and Diederik P Kingma. Learning energy-based models by diffusion recovery likelihood. arXiv preprint arXiv:2012.08125, 2020. 2, 5 +[11] Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black, and Bernhard Schölkopf. From variational to deterministic autoencoders. arXiv preprint arXiv:1903.12436, 2019. 5 +[12] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014. 2 +[13] Tian Han, Yang Lu, Song-Chun Zhu, and Ying Nian Wu. Alternating back-propagation for generator network. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31, 2017. 2, 5, 8 + +[14] Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu. Divergence triangle for joint training of generator model, energy-based model, and inferential model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. 5, 6 +[15] Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, and Ying Nian Wu. Joint training of variational autoencoder and latent energy-based model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7978-7987, 2020. 5 +[16] Jakob D Drachmann Havtorn, Jes Frellsen, Soren Hauberg, and Lars Maaløe. Hierarchical vaes know what they don't know. In International Conference on Machine Learning, pages 4117-4128. PMLR, 2021. 7 +[17] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017. 5 +[18] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. 6 +[19] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196, 2017. 5, 6 +[20] Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Training generative adversarial networks with limited data. Advances in Neural Information Processing Systems, 33:12104-12114, 2020. 6 +[21] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401-4410, 2019. 1 +[22] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. Advances in neural information processing systems, 31, 2018. 6 +[23] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 2, 4, 5, 8 +[24] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 5 +[25] Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, and Yoshua Bengio. Maximum entropy generators for energy-based models. arXiv preprint arXiv:1901.08508, 2019. 8 +[26] Don S Lemons and Anthony Gythiel. Paul Langevin's 1908 paper "on the theory of brownian motion"[“sur la théorie du mouvement brownien,” cr acad. sci.(paris) 146, 530–533 (1908)]. American Journal of Physics, 65(11):1079–1081, 1997. 4 +[27] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. CoRR, abs/1411.7766, 2014. 5 +[28] Lars Maaløe, Marco Fraccaro, Valentin Lievin, and Ole Winther. Biva: A very deep hierarchy of latent variables for + +generative modeling. Advances in neural information processing systems, 32, 2019. 1, 4, 6 +[29] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng. Reading digits in natural images with unsupervised feature learning. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011, 2011. 5 +[30] Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, and Ying Nian Wu. On the anatomy of mcmc-based maximum likelihood learning of energy-based models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 5272-5280, 2020. 7 +[31] Erik Nijkamp, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu. Learning non-convergent non-persistent short-run mcmc toward energy-based model. Advances in Neural Information Processing Systems, 32, 2019. 2 +[32] Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, and Ying Nian Wu. Learning multi-layer latent variable model via variational optimization of short run mcmc for approximate inference. In European Conference on Computer Vision, pages 361-378. Springer, 2020. 1, 2, 5, 8 +[33] Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu. Learning latent space energy-based prior model. Advances in Neural Information Processing Systems, 33:21994-22008, 2020. 1, 2, 5, 8 +[34] Masaki Saito, Shunta Saito, Masanori Koyama, and Sosuke Kobayashi. Train sparsely, generate densely: Memory-efficient unsupervised training of high-resolution temporal gan. International Journal of Computer Vision, 128(10):2586-2606, 2020. 1 +[35] Casper Kaae Sønderby, Tapani Raiko, Lars Maalø e, Søren Kaae Sønderby, and Ole Winther. Ladder variational autoencoders. In D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc., 2016. 1, 2, 4, 5 +[36] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in Neural Information Processing Systems, 32, 2019. 6 +[37] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020. 1 +[38] Jakub Tomczak and Max Welling. Vae with a vampprior. In International Conference on Artificial Intelligence and Statistics, pages 1214-1223. PMLR, 2018. 5 +[39] Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz. Mocogan: Decomposing motion and content for video generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018. 1 +[40] Arash Vahdat and Jan Kautz. Nvae: A deep hierarchical variational autoencoder. Advances in Neural Information Processing Systems, 33:19667-19679, 2020. 1, 4, 5, 6 +[41] Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In International conference on machine learning, pages 1747-1756. PMLR, 2016. 6 + +[42] Zhisheng Xiao and Tian Han. Adaptive multi-stage density ratio estimation for learning latent space energy-based model. arXiv preprint arXiv:2209.08739, 2022. 5 +[43] Zhisheng Xiao, Karsten Kreis, Jan Kautz, and Arash Vahdat. Vaebm: A symbiosis between variational autoencoders and energy-based models. arXiv preprint arXiv:2010.00654, 2020. 2, 5, 6, 7 +[44] Jianwen Xie, Yaxuan Zhu, Jun Li, and Ping Li. A tale of two flows: Cooperative learning of Langevin flow and normalizing flow toward energy-based model. In International Conference on Learning Representations, 2022. 5 +[45] Xuwang Yin, Shiying Li, and Gustavo K Rohde. Analyzing and improving generative adversarial training for generative modeling and out-of-distribution detection. arXiv preprint arXiv:2012.06568, 2020. 2, 5, 6 +[46] Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015. 5 +[47] Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar. Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222, 2018. 8 +[48] Shengjia Zhao, Jiaming Song, and Stefano Ermon. Learning hierarchical features from generative models. arXiv preprint arXiv:1702.08396, 2017. 1, 6, 7 \ No newline at end of file diff --git a/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/images.zip b/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..d350e0df8a1edd162ccdcc578b5aa41e00b61e13 --- /dev/null +++ b/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a5a8e7b30e67eda5b76a070c2f9710250fc36bb308ec68579d882fae25603d3 +size 687366 diff --git a/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/layout.json b/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..77f0c7daeca6209e5c0be79cbde536e42bffae5c --- /dev/null +++ b/2023/Learning Joint Latent Space EBM Prior Model for Multi-Layer Generator/layout.json @@ -0,0 +1,9481 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 69, + 103, + 523, + 120 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 103, + 523, + 120 + ], + "spans": [ + { + "bbox": [ + 69, + 103, + 523, + 120 + ], + "type": "text", + "content": "Learning Joint Latent Space EBM Prior Model for Multi-layer Generator" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 134, + 142, + 459, + 199 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 134, + 142, + 459, + 199 + ], + "spans": [ + { + "bbox": [ + 134, + 142, + 459, + 199 + ], + "type": "text", + "content": "Jiali Cui" + }, + { + "bbox": [ + 134, + 142, + 459, + 199 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 134, + 142, + 459, + 199 + ], + "type": "text", + "content": ", Ying Nian Wu" + }, + { + "bbox": [ + 134, + 142, + 459, + 199 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 134, + 142, + 459, + 199 + ], + "type": "text", + "content": ", Tian Han" + }, + { + "bbox": [ + 134, + 142, + 459, + 199 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 134, + 142, + 459, + 199 + ], + "type": "text", + "content": "\\\n" + }, + { + "bbox": [ + 134, + 142, + 459, + 199 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 134, + 142, + 459, + 199 + ], + "type": "text", + "content": "Department of Computer Science, Stevens Institute of Technology\\\n" + }, + { + "bbox": [ + 134, + 142, + 459, + 199 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 134, + 142, + 459, + 199 + ], + "type": "text", + "content": "Department of Statistics, University of California, Los Angeles\\\n{jcui7,than6}@stevens.edu, ywu@stat.ucla.edu" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 143, + 227, + 192, + 239 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 227, + 192, + 239 + ], + "spans": [ + { + "bbox": [ + 143, + 227, + 192, + 239 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 251, + 289, + 563 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 251, + 289, + 563 + ], + "spans": [ + { + "bbox": [ + 47, + 251, + 289, + 563 + ], + "type": "text", + "content": "This paper studies the fundamental problem of learning multi-layer generator models. The multi-layer generator model builds multiple layers of latent variables as a prior model on top of the generator, which benefits learning complex data distribution and hierarchical representations. However, such a prior model usually focuses on modeling inter-layer relations between latent variables by assuming non-informative (conditional) Gaussian distributions, which can be limited in model expressivity. To tackle this issue and learn more expressive prior models, we propose an energy-based model (EBM) on the joint latent space over all layers of latent variables with the multi-layer generator as its backbone. Such joint latent space EBM prior model captures the intra-layer contextual relations at each layer through layer-wise energy terms, and latent variables across different layers are jointly corrected. We develop a joint training scheme via maximum likelihood estimation (MLE), which involves Markov Chain Monte Carlo (MCMC) sampling for both prior and posterior distributions of the latent variables from different layers. To ensure efficient inference and learning, we further propose a variational training scheme where an inference model is used to amortize the costly posterior MCMC sampling. Our experiments demonstrate that the learned model can be expressive in generating high-quality images and capturing hierarchical features for better outlier detection." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 586, + 128, + 597 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 586, + 128, + 597 + ], + "spans": [ + { + "bbox": [ + 47, + 586, + 128, + 597 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 605, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 287, + 713 + ], + "type": "text", + "content": "Deep generative models (a.k.a, generator models) have made promising progress in learning complex data distributions and achieved great successes in image and video synthesis [21, 34, 37, 39] as well as representation learning [5, 48]. Such models usually consist of low-dimensional latent variables together with a top-down generation model that maps such latent factors to the observed data. The latent factors can serve as an abstract data representation, but it is often modelled via a single latent vector with non-" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 228, + 545, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 228, + 545, + 288 + ], + "spans": [ + { + "bbox": [ + 304, + 228, + 545, + 288 + ], + "type": "text", + "content": "informative prior distribution which leads to limited model expressivity and fails to capture different levels of abstractions. Learning an informative prior model for hierarchical representations is needed, yet research in this direction is still under-developed." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 290, + 546, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 290, + 546, + 434 + ], + "spans": [ + { + "bbox": [ + 304, + 290, + 546, + 434 + ], + "type": "text", + "content": "A principled way to learn such a prior model is by learning the generator models with multiple layers of latent variables. However, the learning of multi-layer generator model can be challenging as the inter-layer structural relation (i.e., latent variables across different layers) and the intra-layer contextual relation (i.e., latent units within the same layer) have to be effectively modelled and efficiently learned. Various methods have been proposed [5,28,32,35,40], but they only focused on inter-layer modeling by assuming the conditional Gaussian distribution across different layers while ignoring the intra-layer contextual modeling as the latent units are conditional independent within each layer." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 436, + 545, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 436, + 545, + 568 + ], + "spans": [ + { + "bbox": [ + 304, + 436, + 545, + 568 + ], + "type": "text", + "content": "The energy-based models (EBMs), on the other hand, are shown to be expressive and proved to be powerful in capturing contextual and non-structural data regularities. Notably, [33] considers the EBM in the latent space for the non-hierarchical generator model, where the energy function is considered as a correction of the non-informative Gaussian prior. The low dimensionality of the latent space makes EBM effective in capturing regularities in the data. However, a single latent vector in [33] is infeasible for capturing the patterns at multiple layers of abstractions, which limits its model capacity." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 570, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 546, + 713 + ], + "type": "text", + "content": "In this paper, we propose to combine the strengths of the latent space EBM and the generator with multiple layers of latent variables for better hierarchical representations and a more expressive prior model. Specifically, we introduce layer-wise energy terms to exponentially tilt the non-informative Gaussian conditional at each layer, and latent variables across different layers are modelled jointly through EBM with the multi-layer generator model as its backbone. Such a joint EBM prior model seamlessly integrates the intra-layer contextual modeling via layer-wise energy terms and inter-layer structural modeling with multi-layer latent variables." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3603" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 215 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 215 + ], + "type": "text", + "content": "The joint EBM prior model can be learned by maximum likelihood estimation (MLE). Each learning iteration involves Markov chain Monte Carlo (MCMC) sampling of latent variables in each layer from both the prior and posterior distributions. The prior sampling can be efficiently done due to the low dimensionality of the latent variables and, more importantly, the lightweight networks for energy functions, while the posterior sampling can be less efficient. Therefore, we further develop the variational training scheme where an additional inference model is used for posterior approximation and is jointly trained with the joint EBM prior model." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 216, + 289, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 216, + 289, + 312 + ], + "spans": [ + { + "bbox": [ + 46, + 216, + 289, + 312 + ], + "type": "text", + "content": "Contributions: 1) We propose a joint latent space EBM prior model for the generator model with multiple layers of latent variables; 2) We develop the maximum likelihood learning algorithm that learns the joint EBM prior model based on MCMC prior and posterior sampling across different layers. We further propose the variational joint training scheme for efficient learning and inference; 3) We provide strong empirical results through extensive experiments." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 323, + 126, + 336 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 323, + 126, + 336 + ], + "spans": [ + { + "bbox": [ + 47, + 323, + 126, + 336 + ], + "type": "text", + "content": "2. Background" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 342, + 287, + 379 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 342, + 287, + 379 + ], + "spans": [ + { + "bbox": [ + 46, + 342, + 287, + 379 + ], + "type": "text", + "content": "In this section, we present the background of multi-layer latent variable model and latent space EBM prior model, which shall serve as the foundation of the proposed model." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 386, + 226, + 399 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 386, + 226, + 399 + ], + "spans": [ + { + "bbox": [ + 47, + 386, + 226, + 399 + ], + "type": "text", + "content": "2.1. Multi-layer latent variable model" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 405, + 287, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 405, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 46, + 405, + 287, + 453 + ], + "type": "text", + "content": "Let " + }, + { + "bbox": [ + 46, + 405, + 287, + 453 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 405, + 287, + 453 + ], + "type": "text", + "content": " be the high-dimensional observed example, and " + }, + { + "bbox": [ + 46, + 405, + 287, + 453 + ], + "type": "inline_equation", + "content": "\\mathbf{z}" + }, + { + "bbox": [ + 46, + 405, + 287, + 453 + ], + "type": "text", + "content": " be the low-dimensional latent variables. The latent variable generative model, or generator model, factorizes a joint distribution of " + }, + { + "bbox": [ + 46, + 405, + 287, + 453 + ], + "type": "inline_equation", + "content": "(\\mathbf{x},\\mathbf{z})" + }, + { + "bbox": [ + 46, + 405, + 287, + 453 + ], + "type": "text", + "content": " as" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 97, + 462, + 287, + 475 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 462, + 287, + 475 + ], + "spans": [ + { + "bbox": [ + 97, + 462, + 287, + 475 + ], + "type": "interline_equation", + "content": "p _ {\\beta} (\\mathbf {x}, \\mathbf {z}) = p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) p _ {\\beta_ {> 0}} (\\mathbf {z}) \\tag {1}", + "image_path": "4c88ea721cc80c2224cdae13ee41fd35808c4be6c004ddd89b663539e11642e6.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 485, + 287, + 531 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 485, + 287, + 531 + ], + "spans": [ + { + "bbox": [ + 46, + 485, + 287, + 531 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 485, + 287, + 531 + ], + "type": "inline_equation", + "content": "p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})" + }, + { + "bbox": [ + 46, + 485, + 287, + 531 + ], + "type": "text", + "content": " is the generation model with parameter " + }, + { + "bbox": [ + 46, + 485, + 287, + 531 + ], + "type": "inline_equation", + "content": "\\beta_0" + }, + { + "bbox": [ + 46, + 485, + 287, + 531 + ], + "type": "text", + "content": " that maps from latent space to data space, and " + }, + { + "bbox": [ + 46, + 485, + 287, + 531 + ], + "type": "inline_equation", + "content": "p_{\\beta >0}(\\mathbf{z})" + }, + { + "bbox": [ + 46, + 485, + 287, + 531 + ], + "type": "text", + "content": " is the prior distribution over latent variables with parameter " + }, + { + "bbox": [ + 46, + 485, + 287, + 531 + ], + "type": "inline_equation", + "content": "\\beta_{>0}" + }, + { + "bbox": [ + 46, + 485, + 287, + 531 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 46, + 485, + 287, + 531 + ], + "type": "inline_equation", + "content": "\\beta = \\{\\beta_0,\\beta_{>0}\\}" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 533, + 287, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 533, + 287, + 616 + ], + "spans": [ + { + "bbox": [ + 46, + 533, + 287, + 616 + ], + "type": "text", + "content": "Gaussian prior model: For non-hierarchical models [12, 23], " + }, + { + "bbox": [ + 46, + 533, + 287, + 616 + ], + "type": "inline_equation", + "content": "p_{\\beta > 0}(\\mathbf{z})" + }, + { + "bbox": [ + 46, + 533, + 287, + 616 + ], + "type": "text", + "content": " is defined on single layer of latent variables and is typically assumed to be uniform or unit Gaussian. For hierarchical models with multiple layers of latent variables [32, 35], " + }, + { + "bbox": [ + 46, + 533, + 287, + 616 + ], + "type": "inline_equation", + "content": "p_{\\beta > 0}(\\mathbf{z})" + }, + { + "bbox": [ + 46, + 533, + 287, + 616 + ], + "type": "text", + "content": " can be further decomposed into conditional distributions between consecutive layers of latent variables as" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 85, + 623, + 287, + 656 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 623, + 287, + 656 + ], + "spans": [ + { + "bbox": [ + 85, + 623, + 287, + 656 + ], + "type": "interline_equation", + "content": "p _ {\\beta_ {> 0}} (\\mathbf {z}) = \\prod_ {i = 1} ^ {L - 1} p _ {\\beta_ {i}} \\left(\\mathbf {z} _ {i} \\mid \\mathbf {z} _ {i + 1}\\right) p \\left(\\mathbf {z} _ {L}\\right) \\tag {2}", + "image_path": "9f5ad2eee8b1b62cef2ecee7324c1ade508c5583c660477bbcd70658da3400c3.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 665, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 712 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 665, + 287, + 712 + ], + "type": "inline_equation", + "content": "p_{\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1})\\sim \\mathcal{N}(\\mu_{\\beta_i}(\\mathbf{z}_{i + 1}),\\sigma_{\\beta_i}^2 (\\mathbf{z}_{i + 1}))" + }, + { + "bbox": [ + 46, + 665, + 287, + 712 + ], + "type": "text", + "content": " and is parameterized by a network with parameter " + }, + { + "bbox": [ + 46, + 665, + 287, + 712 + ], + "type": "inline_equation", + "content": "\\beta_{i}" + }, + { + "bbox": [ + 46, + 665, + 287, + 712 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 665, + 287, + 712 + ], + "type": "inline_equation", + "content": "p(\\mathbf{z}_L)" + }, + { + "bbox": [ + 46, + 665, + 287, + 712 + ], + "type": "text", + "content": " is chosen to be a simple distribution, such as uniform or unit Gaussian." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 72, + 545, + 121 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 121 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 121 + ], + "type": "text", + "content": "Maximum likelihood learning: Learning such latent variable generative models can be done using maximum likelihood estimation (MLE). The marginal distribution is " + }, + { + "bbox": [ + 304, + 72, + 545, + 121 + ], + "type": "inline_equation", + "content": "p_{\\beta}(\\mathbf{x}) = \\int p_{\\beta}(\\mathbf{x},\\mathbf{z})d\\mathbf{z}" + }, + { + "bbox": [ + 304, + 72, + 545, + 121 + ], + "type": "text", + "content": " with the gradient:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 329, + 133, + 545, + 147 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 329, + 133, + 545, + 147 + ], + "spans": [ + { + "bbox": [ + 329, + 133, + 545, + 147 + ], + "type": "interline_equation", + "content": "\\nabla_ {\\beta} \\log p _ {\\beta} (\\mathbf {x}) = \\mathbb {E} _ {p _ {\\beta} (\\mathbf {z} | \\mathbf {x})} [ \\nabla_ {\\beta} \\log p _ {\\beta} (\\mathbf {x}, \\mathbf {z}) ] \\tag {3}", + "image_path": "5649caf5f9b150bb9f25d510f8d43f3e7f456c2a41233b0e16bfecfdc3f8d0bf.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 158, + 545, + 229 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 158, + 545, + 229 + ], + "spans": [ + { + "bbox": [ + 304, + 158, + 545, + 229 + ], + "type": "text", + "content": "where the expectation can be approximated via Monte Carlo sampling from the posterior distribution " + }, + { + "bbox": [ + 304, + 158, + 545, + 229 + ], + "type": "inline_equation", + "content": "p_{\\beta}(\\mathbf{z}|\\mathbf{x})" + }, + { + "bbox": [ + 304, + 158, + 545, + 229 + ], + "type": "text", + "content": ". The MLE can then be accomplished through gradient ascent using such gradients. The posterior sampling usually requires the Markov Chain Monte Carlo (MCMC) such as Langevin dynamics [13, 32]" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 231, + 545, + 280 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 231, + 545, + 280 + ], + "spans": [ + { + "bbox": [ + 304, + 231, + 545, + 280 + ], + "type": "text", + "content": "Variational learning: To alleviate the computational burden of MCMC, variational approach [4] introduces an additional inference model " + }, + { + "bbox": [ + 304, + 231, + 545, + 280 + ], + "type": "inline_equation", + "content": "q_{\\omega}(\\mathbf{z}|\\mathbf{x})" + }, + { + "bbox": [ + 304, + 231, + 545, + 280 + ], + "type": "text", + "content": " with a separate set of parameters " + }, + { + "bbox": [ + 304, + 231, + 545, + 280 + ], + "type": "inline_equation", + "content": "\\omega = (\\omega_{1},\\dots,\\omega_{L})" + }, + { + "bbox": [ + 304, + 231, + 545, + 280 + ], + "type": "text", + "content": " for posterior approximation," + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 332, + 291, + 545, + 323 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 332, + 291, + 545, + 323 + ], + "spans": [ + { + "bbox": [ + 332, + 291, + 545, + 323 + ], + "type": "interline_equation", + "content": "q _ {\\omega} (\\mathbf {z} | \\mathbf {x}) = q _ {\\omega_ {1}} (\\mathbf {z} _ {1} | \\mathbf {x}) \\prod_ {i = 1} ^ {L - 1} q _ {\\omega_ {i + 1}} (\\mathbf {z} _ {i + 1} | \\mathbf {z} _ {i}) \\tag {4}", + "image_path": "288e62b6af4a3494f8740d7507eaf499b98741930e2d4c25801aa9a8dea92705.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 335, + 545, + 421 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 335, + 545, + 421 + ], + "spans": [ + { + "bbox": [ + 304, + 335, + 545, + 421 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 335, + 545, + 421 + ], + "type": "inline_equation", + "content": "q_{\\omega_1}(\\mathbf{z}_1|\\mathbf{x})" + }, + { + "bbox": [ + 304, + 335, + 545, + 421 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 335, + 545, + 421 + ], + "type": "inline_equation", + "content": "q_{\\omega_{i + 1}}(\\mathbf{z}_{i + 1}|\\mathbf{z}_i)" + }, + { + "bbox": [ + 304, + 335, + 545, + 421 + ], + "type": "text", + "content": " are usually assumed as conditional Gaussian distributions, forming a \"bottom-up\" inference structure. The generator and inference model can be jointly learned via maximizing the evidence lower bound (ELBO), i.e., " + }, + { + "bbox": [ + 304, + 335, + 545, + 421 + ], + "type": "inline_equation", + "content": "\\max_{\\beta ,\\omega}\\mathrm{ELBO}(\\beta ,\\omega)" + }, + { + "bbox": [ + 304, + 335, + 545, + 421 + ], + "type": "text", + "content": " where ELBO is defined as " + }, + { + "bbox": [ + 304, + 335, + 545, + 421 + ], + "type": "inline_equation", + "content": "\\mathrm{ELBO}(\\beta ,\\omega) = \\mathbb{E}_{q_{\\omega}(\\mathbf{z}|\\mathbf{x})}[\\log p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})] - D_{\\mathrm{KL}}(q_{\\omega}(\\mathbf{z}|\\mathbf{x})||p_{\\beta >0}(\\mathbf{z}))" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 430, + 484, + 443 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 430, + 484, + 443 + ], + "spans": [ + { + "bbox": [ + 306, + 430, + 484, + 443 + ], + "type": "text", + "content": "2.2. Latent space energy-based model" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 450, + 545, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 450, + 545, + 582 + ], + "spans": [ + { + "bbox": [ + 304, + 450, + 545, + 582 + ], + "type": "text", + "content": "The energy-based model (EBM) offers a flexible approach for learning the data distribution and is shown to be expressive in capturing data regularities [7,8,10,31,43,45]. Most existing works focus on learning the EBM on data space, which is high-dimensional and can be challenging. To tackle this challenge, [2,33] propose to learn latent space EBM as an informative prior model. With low-dimensional latent space, learning the EBM can be more efficient and effective, which in turn benefits the expressivity of the whole model. Specifically, [33] considers the latent space energy-based prior model on a single layer of latent variables," + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 348, + 593, + 545, + 618 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 348, + 593, + 545, + 618 + ], + "spans": [ + { + "bbox": [ + 348, + 593, + 545, + 618 + ], + "type": "interline_equation", + "content": "p _ {\\alpha} (\\mathbf {z}) = \\frac {1}{\\mathrm {Z} (\\alpha)} \\exp [ f _ {\\alpha} (\\mathbf {z}) ] p _ {0} (\\mathbf {z}) \\tag {5}", + "image_path": "1dd49ee046084f77fd510c801b89806aef47454ebb44f18f02362548127c8df1.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "inline_equation", + "content": "-f_{\\alpha}(\\mathbf{z})" + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "content": " is the energy function, " + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "inline_equation", + "content": "Z(\\alpha)" + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "content": " is the normalizing constant, i.e., " + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "inline_equation", + "content": "Z(\\alpha) = \\int \\exp [f_{\\alpha}(\\mathbf{z})]p_0(\\mathbf{z})d\\mathbf{z}" + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "inline_equation", + "content": "p_0(\\mathbf{z})" + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "content": " is the reference distribution assumed to be unit Gaussian. Compared to data space EBMs in which the energy function needs to support the entire high-dimensional space, such exponential tilting latent space EBMs can be more efficient in capturing data regularities." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3604" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 71, + 168, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 71, + 168, + 85 + ], + "spans": [ + { + "bbox": [ + 47, + 71, + 168, + 85 + ], + "type": "text", + "content": "3. Model and Learning" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 91, + 238, + 105 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 91, + 238, + 105 + ], + "spans": [ + { + "bbox": [ + 47, + 91, + 238, + 105 + ], + "type": "text", + "content": "3.1. Joint latent space EBM prior model" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 110, + 290, + 254 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 110, + 290, + 254 + ], + "spans": [ + { + "bbox": [ + 46, + 110, + 290, + 254 + ], + "type": "text", + "content": "For generator models with multi-layer latent variables (or multi-layer generator model), consecutive layers are modelled by conditional Gaussian distributions (see Eqn.2), which essentially assumes the conditional independence for latent units within the " + }, + { + "bbox": [ + 46, + 110, + 290, + 254 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 110, + 290, + 254 + ], + "type": "text", + "content": "-th layer given the " + }, + { + "bbox": [ + 46, + 110, + 290, + 254 + ], + "type": "inline_equation", + "content": "(i + 1)" + }, + { + "bbox": [ + 46, + 110, + 290, + 254 + ], + "type": "text", + "content": "-th layer of latent variables. Such a conditional independence assumption limits the model capacity as the contextual relation between latent units within each layer is largely ignored (see Fig.1), and needs to be improved for informative conditional modeling and better model expressivity. In this paper, we propose the joint EBM prior for multi-layer generator models," + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 62, + 263, + 287, + 324 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 263, + 287, + 324 + ], + "spans": [ + { + "bbox": [ + 62, + 263, + 287, + 324 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} p _ {\\alpha , \\beta > 0} (\\mathbf {z}) = \\frac {1}{\\mathrm {Z} _ {\\alpha , \\beta > 0}} \\exp \\left[ f _ {\\alpha} (\\mathbf {z}) \\right] p _ {\\beta > 0} (\\mathbf {z}) \\tag {6} \\\\ = \\frac {1}{\\mathbf {Z} _ {\\alpha , \\beta > 0}} \\exp \\left[ \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right] \\prod_ {i = 1} ^ {L - 1} p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1}) p (\\mathbf {z} _ {L}) \\\\ \\end{array}", + "image_path": "a9ec2e2dc02cdc3a1aed7edc418cdd49382d35b245bfeaffa06da7b89dfde9ed.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 334, + 287, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 334, + 287, + 430 + ], + "spans": [ + { + "bbox": [ + 46, + 334, + 287, + 430 + ], + "type": "text", + "content": "where we denote " + }, + { + "bbox": [ + 46, + 334, + 287, + 430 + ], + "type": "inline_equation", + "content": "\\alpha = (\\alpha_{1},\\dots,\\alpha_{L})" + }, + { + "bbox": [ + 46, + 334, + 287, + 430 + ], + "type": "text", + "content": " for EBM parameters and " + }, + { + "bbox": [ + 46, + 334, + 287, + 430 + ], + "type": "inline_equation", + "content": "\\mathbf{z} = (\\mathbf{z}_1,\\dots,\\mathbf{z}_L)" + }, + { + "bbox": [ + 46, + 334, + 287, + 430 + ], + "type": "text", + "content": " for latent variables in different layers with layer " + }, + { + "bbox": [ + 46, + 334, + 287, + 430 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 46, + 334, + 287, + 430 + ], + "type": "text", + "content": " being the top layer. " + }, + { + "bbox": [ + 46, + 334, + 287, + 430 + ], + "type": "inline_equation", + "content": "\\mathrm{Z}_{\\alpha ,\\beta >0} = \\int \\exp [f_{\\alpha}(\\mathbf{z})]p_{\\beta >0}(\\mathbf{z})d\\mathbf{z}" + }, + { + "bbox": [ + 46, + 334, + 287, + 430 + ], + "type": "text", + "content": " is the normalizing constant regarding latent variables for all layers. Thus, the latent variables across different layers are jointly corrected via EBM prior as in Eqn.6, where " + }, + { + "bbox": [ + 46, + 334, + 287, + 430 + ], + "type": "inline_equation", + "content": "f_{\\alpha}(\\mathbf{z})" + }, + { + "bbox": [ + 46, + 334, + 287, + 430 + ], + "type": "text", + "content": " is the energy function for latent variables from all layers." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 430, + 287, + 503 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 430, + 287, + 503 + ], + "spans": [ + { + "bbox": [ + 46, + 430, + 287, + 503 + ], + "type": "text", + "content": "In this paper, we consider a simple factorized layer-wise parameterization, i.e., " + }, + { + "bbox": [ + 46, + 430, + 287, + 503 + ], + "type": "inline_equation", + "content": "f_{\\alpha}(\\mathbf{z}) = \\sum_{i=1}^{L} f_{\\alpha_i}(\\mathbf{z}_i)" + }, + { + "bbox": [ + 46, + 430, + 287, + 503 + ], + "type": "text", + "content": ", but other parameterizations are also feasible, which we will explore in future work. With such energy parameterization, it's worth noting that the un-normalized prior model can be viewed as layer-wise exponential tilting," + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 512, + 287, + 602 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 512, + 287, + 602 + ], + "spans": [ + { + "bbox": [ + 53, + 512, + 287, + 602 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\underbrace {\\exp \\left[ \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right]} _ {\\text {E n e r g y c o r r e c t i o n}} \\underbrace {\\prod_ {i = 1} ^ {L - 1} p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1}) p (\\mathbf {z} _ {L})} _ {\\text {G a u s s i a n p r i o r}} \\tag {7} \\\\ = \\underbrace {\\exp \\left[ f _ {\\alpha_ {L}} (\\mathbf {z} _ {L}) \\right] p (\\mathbf {z} _ {L})} _ {\\text {C o r r e c t i o n o n t o p l a y e r}} \\prod_ {i = 1} ^ {L - 1} \\underbrace {\\exp \\left[ f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right] p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1})} _ {\\text {C o r r e c t i o n o n i n t e r m i d a t e l a y e r}} \\\\ \\end{array}", + "image_path": "3ed619f09cf60a2f49a96bfe611bd824caa9ee616bf8c56ba40afeb16a38c7b8.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 613, + 287, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 613, + 287, + 636 + ], + "spans": [ + { + "bbox": [ + 46, + 613, + 287, + 636 + ], + "type": "text", + "content": "See Fig.1 for an illustration and comparison with multi-layer generator model with Gaussian prior." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 637, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 637, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 637, + 288, + 714 + ], + "type": "text", + "content": "Joint vs. conditional EBM prior: Besides the proposed joint modeling, it is also tempting to consider EBM prior for layer-wise Gaussian conditional, i.e., " + }, + { + "bbox": [ + 46, + 637, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\tilde{p}_{\\alpha_i,\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1}) = \\frac{1}{Z(\\mathbf{z}_{i + 1})}\\exp [f_{\\alpha_i}(\\mathbf{z}_i)]p_{\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1})" + }, + { + "bbox": [ + 46, + 637, + 288, + 714 + ], + "type": "text", + "content": " and form the overall prior " + }, + { + "bbox": [ + 46, + 637, + 288, + 714 + ], + "type": "inline_equation", + "content": "p_{\\alpha ,\\beta >0}(\\mathbf{z}) = \\prod_{i = 1}^{L}\\tilde{p}_{\\alpha_i,\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1})p(\\mathbf{z}_L)" + }, + { + "bbox": [ + 46, + 637, + 288, + 714 + ], + "type": "text", + "content": ". Such a scheme is closely related to autoregressive energy machine" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 313, + 69, + 539, + 215 + ], + "blocks": [ + { + "bbox": [ + 313, + 69, + 539, + 215 + ], + "lines": [ + { + "bbox": [ + 313, + 69, + 539, + 215 + ], + "spans": [ + { + "bbox": [ + 313, + 69, + 539, + 215 + ], + "type": "image", + "image_path": "d6d83883fce2ab3b8f68a3486cbced6b8c910245636dd726c56f9487f807f50a.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 223, + 547, + 269 + ], + "lines": [ + { + "bbox": [ + 304, + 223, + 547, + 269 + ], + "spans": [ + { + "bbox": [ + 304, + 223, + 547, + 269 + ], + "type": "text", + "content": "Figure 1. Left panel: Gaussian prior model. Right panel: Joint EBM prior model. Black solid lines with arrow: inter-layer relations modelling. Red solid lines: intra-layer contextual relations modelling. Blue dashed lines: joint modelling upon all layers." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 290, + 545, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 290, + 545, + 375 + ], + "spans": [ + { + "bbox": [ + 304, + 290, + 545, + 375 + ], + "type": "text", + "content": "[9] and is adopted in NCP-VAE [2]. However, the normalizing constant " + }, + { + "bbox": [ + 304, + 290, + 545, + 375 + ], + "type": "inline_equation", + "content": "\\mathrm{Z}(\\mathbf{z}_{i + 1})" + }, + { + "bbox": [ + 304, + 290, + 545, + 375 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 304, + 290, + 545, + 375 + ], + "type": "inline_equation", + "content": "\\tilde{p}_{\\alpha_i,\\beta_i}(\\mathbf{z}_i|\\mathbf{z}_{i + 1})" + }, + { + "bbox": [ + 304, + 290, + 545, + 375 + ], + "type": "text", + "content": " involves the latent variable " + }, + { + "bbox": [ + 304, + 290, + 545, + 375 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_{i + 1}" + }, + { + "bbox": [ + 304, + 290, + 545, + 375 + ], + "type": "text", + "content": " from the upper layer which can be intractable and needs an additional inner-loop for sampling or optimization. The proposed joint EBM prior couples the latent variables across different layers via energy function and can be learned effectively and efficiently." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 385, + 485, + 397 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 385, + 485, + 397 + ], + "spans": [ + { + "bbox": [ + 306, + 385, + 485, + 397 + ], + "type": "text", + "content": "3.2. Maximum Likelihood Estimation" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 404, + 545, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 404, + 545, + 452 + ], + "spans": [ + { + "bbox": [ + 304, + 404, + 545, + 452 + ], + "type": "text", + "content": "Our joint EBM prior model can be trained using MLE. Let " + }, + { + "bbox": [ + 304, + 404, + 545, + 452 + ], + "type": "inline_equation", + "content": "\\theta = (\\alpha, \\beta)" + }, + { + "bbox": [ + 304, + 404, + 545, + 452 + ], + "type": "text", + "content": " denotes the model parameters and " + }, + { + "bbox": [ + 304, + 404, + 545, + 452 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 304, + 404, + 545, + 452 + ], + "type": "text", + "content": " can be learned by maximizing the log-likelihood on " + }, + { + "bbox": [ + 304, + 404, + 545, + 452 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 304, + 404, + 545, + 452 + ], + "type": "text", + "content": " training observations" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 461, + 541, + 493 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 461, + 541, + 493 + ], + "spans": [ + { + "bbox": [ + 306, + 461, + 541, + 493 + ], + "type": "interline_equation", + "content": "L (\\theta) = \\sum_ {i = 1} ^ {n} \\log p _ {\\theta} (\\mathbf {x} _ {i}) = \\sum_ {i = 1} ^ {n} \\log \\int p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) p _ {\\alpha , \\beta_ {> 0}} (\\mathbf {z}) d \\mathbf {z}", + "image_path": "9d3c2514a5743610cc06b77f84e47f1daabc976aa874b6ad4404daea9dcacefc.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 502, + 545, + 563 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 502, + 545, + 563 + ], + "spans": [ + { + "bbox": [ + 304, + 502, + 545, + 563 + ], + "type": "text", + "content": "When " + }, + { + "bbox": [ + 304, + 502, + 545, + 563 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 304, + 502, + 545, + 563 + ], + "type": "text", + "content": " becomes sufficiently large, maximizing the above log-likelihood is equivalent to minimizing the Kullback-Leibler (KL) divergence between model distribution and empirical data distribution, i.e., " + }, + { + "bbox": [ + 304, + 502, + 545, + 563 + ], + "type": "inline_equation", + "content": "\\min_{\\theta} D_{\\mathrm{KL}}(p_{\\mathrm{data}}(\\mathbf{x})||p_{\\theta}(\\mathbf{x}))" + }, + { + "bbox": [ + 304, + 502, + 545, + 563 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 563, + 545, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 563, + 545, + 588 + ], + "spans": [ + { + "bbox": [ + 304, + 563, + 545, + 588 + ], + "type": "text", + "content": "To update the parameter " + }, + { + "bbox": [ + 304, + 563, + 545, + 588 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 304, + 563, + 545, + 588 + ], + "type": "text", + "content": ", we can compute the gradient of log-likelihood " + }, + { + "bbox": [ + 304, + 563, + 545, + 588 + ], + "type": "inline_equation", + "content": "\\nabla_{\\theta} \\log p_{\\theta}(\\mathbf{x})" + }, + { + "bbox": [ + 304, + 563, + 545, + 588 + ], + "type": "text", + "content": " as" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 337, + 598, + 545, + 643 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 337, + 598, + 545, + 643 + ], + "spans": [ + { + "bbox": [ + 337, + 598, + 545, + 643 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\nabla_ {\\theta} \\log p _ {\\theta} (\\mathbf {x}) = \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} [ \\nabla_ {\\theta} \\log p _ {\\theta} (\\mathbf {x}, \\mathbf {z}) ] \\\\ = \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} \\left[ \\nabla_ {\\theta} \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) \\right] \\tag {8} \\\\ + \\mathbb {E} _ {p _ {\\theta} (\\mathbf {z} | \\mathbf {x})} \\left[ \\nabla_ {\\theta} \\log p _ {\\alpha , \\beta > 0} (\\mathbf {z}) \\right] \\\\ \\end{array}", + "image_path": "a055b7787dbb931d1103c08b3233960cb7be5d7fcfbfdc6adfc3505a937d1554.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 652, + 544, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 652, + 544, + 664 + ], + "spans": [ + { + "bbox": [ + 304, + 652, + 544, + 664 + ], + "type": "text", + "content": "With such a gradient, we can learn " + }, + { + "bbox": [ + 304, + 652, + 544, + 664 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 304, + 652, + 544, + 664 + ], + "type": "text", + "content": " using gradient ascent." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 665, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 545, + 714 + ], + "type": "text", + "content": "Learning generation model " + }, + { + "bbox": [ + 304, + 665, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\beta_0" + }, + { + "bbox": [ + 304, + 665, + 545, + 714 + ], + "type": "text", + "content": ": " + }, + { + "bbox": [ + 304, + 665, + 545, + 714 + ], + "type": "inline_equation", + "content": "p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})" + }, + { + "bbox": [ + 304, + 665, + 545, + 714 + ], + "type": "text", + "content": " is assumed to be Gaussian distribution, i.e., " + }, + { + "bbox": [ + 304, + 665, + 545, + 714 + ], + "type": "inline_equation", + "content": "p_{\\beta_0}(\\mathbf{x}|\\mathbf{z}) \\sim \\mathcal{N}(g_{\\beta_0}(\\mathbf{z}), \\sigma^2 I)" + }, + { + "bbox": [ + 304, + 665, + 545, + 714 + ], + "type": "text", + "content": ", with generation network " + }, + { + "bbox": [ + 304, + 665, + 545, + 714 + ], + "type": "inline_equation", + "content": "g_{\\beta_0}" + }, + { + "bbox": [ + 304, + 665, + 545, + 714 + ], + "type": "text", + "content": " with parameter " + }, + { + "bbox": [ + 304, + 665, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\beta_0" + }, + { + "bbox": [ + 304, + 665, + 545, + 714 + ], + "type": "text", + "content": " and pre-specified " + }, + { + "bbox": [ + 304, + 665, 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Variational Learning" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 47, + 605, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 605, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 605, + 287, + 715 + ], + "type": "text", + "content": "For efficient posterior sampling, an inference model " + }, + { + "bbox": [ + 47, + 605, + 287, + 715 + ], + "type": "inline_equation", + "content": "q_{\\omega}(\\mathbf{z}|\\mathbf{x})" + }, + { + "bbox": [ + 47, + 605, + 287, + 715 + ], + "type": "text", + "content": " with a separate set of parameters " + }, + { + "bbox": [ + 47, + 605, + 287, + 715 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 47, + 605, + 287, + 715 + ], + "type": "text", + "content": " can be used for the posterior approximation. In this paper, we use the bottom-up inference model as Eqn.4 for amortizing the costly posterior MCMC sampling. Particularly, instead of KL minimization between marginal distributions as in MLE (see Sec.3.2), we consider the KL optimization between two joint densities, one for generator model density, i.e., " + }, + { + "bbox": [ + 47, + 605, + 287, + 715 + ], + "type": "inline_equation", + "content": "p_{\\theta}(\\mathbf{x},\\mathbf{z}) = p_{\\beta_0}(\\mathbf{x}|\\mathbf{z})p_{\\alpha ,\\beta >0}(\\mathbf{z})" + }, + { + "bbox": [ + 47, + 605, + 287, + 715 + ], + "type": "text", + "content": ", and one for data density," + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "type": "text", + "content": "i.e., " + }, + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "type": "inline_equation", + "content": "q_{\\omega}(\\mathbf{x}, \\mathbf{z}) = p_{\\mathrm{data}}(\\mathbf{x}) q_{\\omega}(\\mathbf{z}|\\mathbf{x})" + }, + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "type": "text", + "content": ". We propose joint learning through KL minimization, denoting the objective to be " + }, + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "type": "inline_equation", + "content": "L(\\theta, \\omega)" + }, + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "type": "text", + "content": ", i.e.," + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 118, + 545, + 135 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 118, + 545, + 135 + ], + "spans": [ + { + "bbox": [ + 306, + 118, + 545, + 135 + ], + "type": "interline_equation", + "content": "\\min _ {\\theta} \\min _ {\\omega} L (\\theta , \\omega) = \\min _ {\\theta} \\min _ {\\omega} D _ {\\mathrm {K L}} \\left(q _ {\\omega} (\\mathbf {x}, \\mathbf {z}) \\| p _ {\\theta} (\\mathbf {x}, \\mathbf {z})\\right) \\tag {15}", + "image_path": "abf9bd5d5eca7e1aedf497de48fc501b5c2ac8c09942aa94543d9d84c05d45c1.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 143, + 545, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 143, + 545, + 167 + ], + "spans": [ + { + "bbox": [ + 305, + 143, + 545, + 167 + ], + "type": "text", + "content": "Learning generation model " + }, + { + "bbox": [ + 305, + 143, + 545, + 167 + ], + "type": "inline_equation", + "content": "\\beta_0" + }, + { + "bbox": [ + 305, + 143, + 545, + 167 + ], + "type": "text", + "content": ": For learning " + }, + { + "bbox": [ + 305, + 143, + 545, + 167 + ], + "type": "inline_equation", + "content": "\\beta_0" + }, + { + "bbox": [ + 305, + 143, + 545, + 167 + ], + "type": "text", + "content": ", we can compute the gradient as" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 176, + 545, + 190 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 176, + 545, + 190 + ], + "spans": [ + { + "bbox": [ + 308, + 176, + 545, + 190 + ], + "type": "interline_equation", + "content": "- \\nabla_ {\\beta_ {0}} L (\\theta , \\omega) = \\mathbb {E} _ {p _ {\\text {d a t a}} (\\mathbf {x})} \\mathbb {E} _ {q _ {\\omega} (\\mathbf {z} | \\mathbf {x})} \\left[ \\nabla_ {\\beta_ {0}} \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) \\right] \\tag {16}", + "image_path": "c44970c73b4a461625a805911bd5328bc21cb3a5c35e29fccb9dd549afaf3c6a.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 305, + 198, + 545, + 222 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 198, + 545, + 222 + ], + "spans": [ + { + "bbox": [ + 305, + 198, + 545, + 222 + ], + "type": "text", + "content": "Learning prior model " + }, + { + "bbox": [ + 305, + 198, + 545, + 222 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 305, + 198, + 545, + 222 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 305, + 198, + 545, + 222 + ], + "type": "inline_equation", + "content": "\\beta_{>0}" + }, + { + "bbox": [ + 305, + 198, + 545, + 222 + ], + "type": "text", + "content": ": For learning " + }, + { + "bbox": [ + 305, + 198, + 545, + 222 + ], + "type": "inline_equation", + "content": "\\alpha_{i}" + }, + { + "bbox": [ + 305, + 198, + 545, + 222 + ], + "type": "text", + "content": ", the gradient is computed as" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 321, + 232, + 545, + 261 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 232, + 545, + 261 + ], + "spans": [ + { + "bbox": [ + 321, + 232, + 545, + 261 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} - \\nabla_ {\\alpha_ {i}} L (\\theta , \\omega) = \\underset {\\mathbb {E}} {\\mathbb {E}} _ {p _ {\\text {d a t a}} (\\mathbf {x})} \\underset {\\mathbb {E} _ {q _ {\\omega} (\\mathbf {z} | \\mathbf {x})}} {\\mathbb {E}} \\left[ \\nabla_ {\\alpha_ {i}} f _ {\\alpha_ {i}} \\left(\\mathbf {z} _ {i}\\right) \\right] \\tag {17} \\\\ - \\mathbb {E} _ {p _ {\\alpha , \\beta > 0} (\\mathbf {z})} \\left[ \\nabla_ {\\alpha_ {i}} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) \\right] \\\\ \\end{array}", + "image_path": "068ad5f947f759f42a32a6ceb7ff73e95d07b8e71a386f265e1f5dd579c1f2e5.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 306, + 270, + 491, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 270, + 491, + 281 + ], + "spans": [ + { + "bbox": [ + 306, + 270, + 491, + 281 + ], + "type": "text", + "content": "For learning " + }, + { + "bbox": [ + 306, + 270, + 491, + 281 + ], + "type": "inline_equation", + "content": "\\beta_{>0}" + }, + { + "bbox": [ + 306, + 270, + 491, + 281 + ], + "type": "text", + "content": ", we compute the gradient as" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 315, + 290, + 545, + 330 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 290, + 545, + 330 + ], + "spans": [ + { + "bbox": [ + 315, + 290, + 545, + 330 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} - \\nabla_ {\\beta_ {i}} L (\\theta , \\omega) = \\mathbb {E} _ {p _ {\\mathrm {d a t a}} (\\mathbf {x}) q _ {\\omega} (\\mathbf {z} | \\mathbf {x})} [ \\nabla_ {\\beta_ {i}} \\log p _ {\\beta_ {i}} (\\mathbf {z} _ {i} | \\mathbf {z} _ {i + 1}) ] \\\\ - \\mathbb {E} _ {p _ {\\alpha , \\beta > 0} (\\mathbf {z})} \\left[ \\nabla_ {\\beta_ {i}} \\log p _ {\\beta_ {i}} \\left(\\mathbf {z} _ {i} \\mid \\mathbf {z} _ {i + 1}\\right) \\right] \\tag {18} \\\\ \\end{array}", + "image_path": "35fed906ff3dde9cc848b8165d577c641d434bc257fac3530edcb8abdb06e5e1.jpg" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 306, + 331, + 545, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 331, + 545, + 353 + ], + "spans": [ + { + "bbox": [ + 306, + 331, + 545, + 353 + ], + "type": "text", + "content": "Learning inference model " + }, + { + "bbox": [ + 306, + 331, + 545, + 353 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 306, + 331, + 545, + 353 + ], + "type": "text", + "content": ": For learning " + }, + { + "bbox": [ + 306, + 331, + 545, + 353 + ], + "type": "inline_equation", + "content": "\\omega_{i}" + }, + { + "bbox": [ + 306, + 331, + 545, + 353 + ], + "type": "text", + "content": ", the gradient is" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 312, + 361, + 545, + 423 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 361, + 545, + 423 + ], + "spans": [ + { + "bbox": [ + 312, + 361, + 545, + 423 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} - \\nabla_ {\\omega_ {i}} L (\\theta , \\omega) = \\nabla_ {\\omega_ {i}} \\mathbb {E} _ {p _ {\\mathrm {d a t a}} (\\mathbf {x})} [ \\mathbb {E} _ {q _ {\\omega} (\\mathbf {z} | \\mathbf {x})} \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) \\\\ - D _ {\\mathrm {K L}} \\left(q _ {\\omega} (\\mathbf {z} | \\mathbf {x}) \\| p _ {\\beta > 0} (\\mathbf {z})\\right) \\tag {19} \\\\ + \\mathbb {E} _ {q _ {\\omega} (\\mathbf {z} | \\mathbf {x})} [ \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) ] ] \\\\ \\end{array}", + "image_path": "0c903d0c525bf894c3a238a6e2d5f85d1ec0aebc3bfff74d7d1bb209e02a97a1.jpg" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 305, + 433, + 545, + 468 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 433, + 545, + 468 + ], + "spans": [ + { + "bbox": [ + 305, + 433, + 545, + 468 + ], + "type": "text", + "content": "Divergence Perturbation. The KL joint minimization (Eqn.15) can be viewed as a surrogate of the MLE objective with the KL perturbation term," + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 321, + 478, + 541, + 506 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 478, + 541, + 506 + ], + "spans": [ + { + "bbox": [ + 321, + 478, + 541, + 506 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} D _ {\\mathrm {K L}} \\left(q _ {\\omega} (\\mathbf {x}, \\mathbf {z}) | | p _ {\\theta} (\\mathbf {x}, \\mathbf {z})\\right) \\\\ = D _ {\\mathrm {K L}} \\left(p _ {\\text {d a t a}} (\\mathbf {x}) | | p _ {\\theta} (\\mathbf {x})\\right) + D _ {\\mathrm {K L}} \\left(q _ {\\omega} (\\mathbf {z} | \\mathbf {x}) | | p _ {\\theta} (\\mathbf {z} | \\mathbf {x})\\right) \\\\ \\end{array}", + "image_path": "f6ba74da29127d2200525316e6a06330acf9b399201293ae93aa80cde51fdb59.jpg" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 305, + 515, + 545, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 515, + 545, + 598 + ], + "spans": [ + { + "bbox": [ + 305, + 515, + 545, + 598 + ], + "type": "text", + "content": "where the perturbation term " + }, + { + "bbox": [ + 305, + 515, + 545, + 598 + ], + "type": "inline_equation", + "content": "D_{\\mathrm{KL}}(q_{\\omega}(\\mathbf{z}|\\mathbf{x})||p_{\\theta}(\\mathbf{z}|\\mathbf{x}))" + }, + { + "bbox": [ + 305, + 515, + 545, + 598 + ], + "type": "text", + "content": " measures the KL-divergence between inference distribution and generator posterior. The inference model is learned to directly match the posterior distribution of the generator without expensive posterior sampling. In fact, such KL minimization in the joint space is closely related to evidence lower bound (ELBO) with the joint EBM as the prior model." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 306, + 609, + 392, + 621 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 609, + 392, + 621 + ], + "spans": [ + { + "bbox": [ + 306, + 609, + 392, + 621 + ], + "type": "text", + "content": "4. Related Work" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 305, + 629, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 629, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 629, + 545, + 713 + ], + "type": "text", + "content": "Hierarchical VAEs: Variational auto-encoder (VAE) [23] proposes variational learning by introducing an approximation of the true intractable posterior, which allows a tractable bound on log-likelihood to be formed. But the non-hierarchical structure can be limited in model expressivity and fails to capture different levels of abstraction. Hierarchical VAEs (HVAEs) [5, 28, 35, 40] consist of multiple" + } + ] + } + ], + "index": 30 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3606" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 288, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 288, + 133 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 288, + 133 + ], + "type": "text", + "content": "layers of latent variables on top of the generator as a prior model, which can be used for learning complex data distribution and hierarchical representations. However, such models still focus on layer-wise relations while ignoring the intra-layer contextual relations at each layer." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 133, + 287, + 276 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 133, + 287, + 276 + ], + "spans": [ + { + "bbox": [ + 46, + 133, + 287, + 276 + ], + "type": "text", + "content": "Energy-based models: The energy-based models receive attention for being expressive and powerful in capturing contextual data regularities. The majority of existing works focus on the pixel space [7, 8, 10, 14, 15, 43-45]. Learning such EBMs can be done using MLE, where MCMC sampling is typically required in each learning iteration which can be computationally expensive upon data space. Instead, [33] proposes to build EBM on latent space where the energy function is considered as a correction of the noninformative Gaussian prior. The low dimensionality of the latent space makes EBM effective in capturing data regularities and can alleviate the burden of MCMC sampling." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 277, + 287, + 408 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 277, + 287, + 408 + ], + "spans": [ + { + "bbox": [ + 46, + 277, + 287, + 408 + ], + "type": "text", + "content": "Generator models with informative prior: For generator models, the assumed Gaussian or uniform prior distribution can be non-informative and less expressive. To address this problem, recent works [2, 6, 11, 33, 38, 42] propose to learn generator models with an informative prior, where RAE [11] constructs priors using rejection sampling, and Two-stage VAE [6] propose to train an extra model for simple prior at the second stage to match the aggregated posterior distribution, while LEBM [33] and NCP-VAE [2] instead learn EBMs on latent space to improve the expressivity of generator models." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 421, + 128, + 434 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 421, + 128, + 434 + ], + "spans": [ + { + "bbox": [ + 47, + 421, + 128, + 434 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 441, + 287, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 441, + 287, + 502 + ], + "spans": [ + { + "bbox": [ + 46, + 441, + 287, + 502 + ], + "type": "text", + "content": "To demonstrate the proposed method, we present extensive experiments, including (i) image synthesis, (ii) hierarchical representations, and (iii) analysis of latent space, as well as various ablation studies based on the proposed EBM prior. The parameter complexity is discussed in Sec.5.5." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 510, + 146, + 523 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 510, + 146, + 523 + ], + "spans": [ + { + "bbox": [ + 47, + 510, + 146, + 523 + ], + "type": "text", + "content": "5.1. Image Synthesis" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 529, + 287, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 529, + 287, + 684 + ], + "spans": [ + { + "bbox": [ + 46, + 529, + 287, + 684 + ], + "type": "text", + "content": "Generator models with informative prior. We first evaluate the generation performance of the proposed joint model. If the model is well-trained, the multi-layer EBM prior model should render an expressive prior distribution leading to realistic synthesis. We benchmark our model against other generator models that assume standard Gaussian prior, such as VAE [23], Alternating Back-propagation (ABP) [13], Ladder VAE (LVAE) [35], and Short-run Inference (SRI) [32], as well as other generator models using informative prior, such as RAE [11], Two-stages VAE (2s-VAE) [6], NCP-VAE [2], and LEBM [33], where LEBM builds EBM for single layer latent variables, while ours contains a multi-layer structure." + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 310, + 70, + 543, + 211 + ], + "blocks": [ + { + "bbox": [ + 310, + 70, + 543, + 211 + ], + "lines": [ + { + "bbox": [ + 310, + 70, + 543, + 211 + ], + "spans": [ + { + "bbox": [ + 310, + 70, + 543, + 211 + ], + "type": "table", + "html": "
ModelSVHNCelebA-64CIFAR-10
VAE [23]46.7865.75106.37
LVAE (L=5) [35]39.2653.40-
ABP [13]49.7151.50-
SRI (L=5) [32]35.3247.95-
RAE [11]42.0240.9574.16
2s-VAE [6]42.8144.4072.90
NCP-VAE [2]33.2342.0778.06
LEBM [33]29.4437.8770.15
Ours (L=2)26.8133.6066.32
", + "image_path": "e7d13f11872e72072107695b7813ce30b8591c83576715b36085266a98332b2d.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 218, + 545, + 243 + ], + "lines": [ + { + "bbox": [ + 305, + 218, + 545, + 243 + ], + "spans": [ + { + "bbox": [ + 305, + 218, + 545, + 243 + ], + "type": "text", + "content": "Table 1. FID(↓) for our model and baselines on SVHN, CelebA (64 x 64), and CIFAR-10." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 251, + 545, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 251, + 545, + 384 + ], + "spans": [ + { + "bbox": [ + 304, + 251, + 545, + 384 + ], + "type": "text", + "content": "We train our model on SVHN [29], CIFAR-10 [24] and CelebA-64 [27] and use Fréchet Inception Distance (FID) [17] to quantitatively evaluate the generation quality. To make fair comparisons, we follow the standard protocol as in [33] and use the same generation model with convolutional structures. We use Langevin posterior sampling for the training, and the generation model is jointly learned (the result for variational learning is shown in Ablation Studies). The comparisons are shown in Tab.1, where the superior generation performance indicates the effectiveness of our model in learning a more expressive prior." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 384, + 546, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 384, + 546, + 528 + ], + "spans": [ + { + "bbox": [ + 304, + 384, + 546, + 528 + ], + "type": "text", + "content": "Toward deep hierarchical models. We then consider the modern deep hierarchical structures as our multi-layer generator and explore the potential of the joint EBM prior for better generation. We adopt the two-stage training [2, 43] where the deep multi-layer generator " + }, + { + "bbox": [ + 304, + 384, + 546, + 528 + ], + "type": "inline_equation", + "content": "p_{\\beta >0}(\\mathbf{z})" + }, + { + "bbox": [ + 304, + 384, + 546, + 528 + ], + "type": "text", + "content": " and inference model " + }, + { + "bbox": [ + 304, + 384, + 546, + 528 + ], + "type": "inline_equation", + "content": "q_{\\omega}(\\mathbf{z}|\\mathbf{x})" + }, + { + "bbox": [ + 304, + 384, + 546, + 528 + ], + "type": "text", + "content": " are trained in the first stage by maximizing the ELBO as in VAEs, with the pre-trained models, our joint EBM prior model can then be learned in the second stage where the posterior samples are directly obtained from the pre-trained inference model " + }, + { + "bbox": [ + 304, + 384, + 546, + 528 + ], + "type": "inline_equation", + "content": "q_{\\omega}(\\mathbf{z}|\\mathbf{x})" + }, + { + "bbox": [ + 304, + 384, + 546, + 528 + ], + "type": "text", + "content": " and prior samples can be obtained via Langevin sampling with change of variable on the generator " + }, + { + "bbox": [ + 304, + 384, + 546, + 528 + ], + "type": "inline_equation", + "content": "p_{\\beta >0}(\\mathbf{z})" + }, + { + "bbox": [ + 304, + 384, + 546, + 528 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 528, + 547, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 528, + 547, + 696 + ], + "spans": [ + { + "bbox": [ + 304, + 528, + 547, + 696 + ], + "type": "text", + "content": "We consider NVAE [40], a modern hierarchical VAE, for the first stage training, and we train our joint EBM prior in the second stage. For prior sampling in the second stage training, we employ similar reparametrized sampling scheme as in [43] via provided code1 in order to better traverse the deep hierarchical latent space with different scales. We examine our model on CIFAR-10, CelebA-HQ-256 [19], and LSUN-Church-64 [46]. The qualitative results for CelebA-HQ-256 and LSUN-Church-64 are shown in Fig.2 and Fig.3. For CelebA-HQ-256, we synthesize with adjusted batch-normalization as used in [2, 43]. We also visualize the Langevin transition on CIFAR-10 in Fig.4 where the quality of synthesis improves as the Langevin progresses." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 47, + 693, + 286, + 715 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 693, + 286, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 693, + 286, + 715 + ], + "type": "text", + "content": "Our project page is available at https://jcui1224.github.io/hierarchical-joint-ebm-proj." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 317, + 702, + 440, + 714 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 702, + 440, + 714 + ], + "spans": [ + { + "bbox": [ + 317, + 702, + 440, + 714 + ], + "type": "text", + "content": "1 https://github.com/NVlabs/VAEBM" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3607" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 70, + 533, + 133 + ], + "blocks": [ + { + "bbox": [ + 61, + 70, + 533, + 133 + ], + "lines": [ + { + "bbox": [ + 61, + 70, + 533, + 133 + ], + "spans": [ + { + "bbox": [ + 61, + 70, + 533, + 133 + ], + "type": "image", + "image_path": "b1de0f75521028c206b73376081cc8443cc62974d67960d742f3283085c59f30.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 181, + 140, + 411, + 152 + ], + "lines": [ + { + "bbox": [ + 181, + 140, + 411, + 152 + ], + "spans": [ + { + "bbox": [ + 181, + 140, + 411, + 152 + ], + "type": "text", + "content": "Figure 2. Generated samples on CelebA-HQ-256 . FID = 9.89." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 60, + 170, + 276, + 229 + ], + "blocks": [ + { + "bbox": [ + 60, + 170, + 276, + 229 + ], + "lines": [ + { + "bbox": [ + 60, + 170, + 276, + 229 + ], + "spans": [ + { + "bbox": [ + 60, + 170, + 276, + 229 + ], + "type": "image", + "image_path": "79bf36e5f722dc59372f529509fa01584dd4956adde7279b02a3669967cf07e9.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 69, + 264, + 267, + 511 + ], + "blocks": [ + { + "bbox": [ + 51, + 237, + 282, + 248 + ], + "lines": [ + { + "bbox": [ + 51, + 237, + 282, + 248 + ], + "spans": [ + { + "bbox": [ + 51, + 237, + 282, + 248 + ], + "type": "text", + "content": "Figure 3. Generated samples on LSUN-Church-64. FID = 8.38." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 69, + 264, + 267, + 511 + ], + "lines": [ + { + "bbox": [ + 69, + 264, + 267, + 511 + ], + "spans": [ + { + "bbox": [ + 69, + 264, + 267, + 511 + ], + "type": "table", + "html": "
MethodISFID
NVAE* [40]5.3037.73
Ours8.9911.34
NCP-VAE [2]-24.08
VAEBM [43]8.4312.19
Other EBMs
IGEBM [8]6.7838.2
ImprovedCD [7]7.8525.1
Divergence Triangle [14]-30.10
Adv-EBM [45]9.1013.21
Other Likelihood Models
GLOW [22]3.9248.9
PixelCNN [41]4.6065.93
GANs+Score-based Models
BigGAN [3]9.2214.73
StyleGANv2 w/o ADA [20]8.999.9
NCSN [36]8.8725.32
DDPM [18]9.463.17
", + "image_path": "5cb33d6e0af0fbabcffb832746f56e2002126535f9b4f0c387f0e0370577333b.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 521, + 287, + 544 + ], + "lines": [ + { + "bbox": [ + 47, + 521, + 287, + 544 + ], + "spans": [ + { + "bbox": [ + 47, + 521, + 287, + 544 + ], + "type": "text", + "content": "Table 2. IS(↑) and FID(↓) for our model and baselines on CIFAR-10. Model* indicates our backbone model." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 570, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 570, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 570, + 287, + 712 + ], + "type": "text", + "content": "The quantitative results are shown in Tab.2 and Tab.3. We consider the baseline models, including NCP-VAE [2] and VAEBM [43], which also recruit NVAE as their backbone model, and other powerful deep generative models, such as GANs [3, 20], score-based models [18, 36] and EBMs [7,8,14,45] on data space. Compared to NVAE backbone model, our joint EBM prior model can significantly improve the fidelity of generated samples while only accounting for negligible overhead (see Parameter Efficiency in Sec.5.5). In comparison with other powerful deep generative models, we also achieve competitive generation performance." + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 318, + 170, + 533, + 229 + ], + "blocks": [ + { + "bbox": [ + 318, + 170, + 533, + 229 + ], + "lines": [ + { + "bbox": [ + 318, + 170, + 533, + 229 + ], + "spans": [ + { + "bbox": [ + 318, + 170, + 533, + 229 + ], + "type": "image", + "image_path": "55b8541ab8eef1a25afc1a7ac54346491ae06771ece358ab299826164fdfbb06.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 310, + 259, + 542, + 373 + ], + "blocks": [ + { + "bbox": [ + 318, + 237, + 533, + 248 + ], + "lines": [ + { + "bbox": [ + 318, + 237, + 533, + 248 + ], + "spans": [ + { + "bbox": [ + 318, + 237, + 533, + 248 + ], + "type": "text", + "content": "Figure 4. Langevin transitions on CIFAR-10. FID = 11.34." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 310, + 259, + 542, + 373 + ], + "lines": [ + { + "bbox": [ + 310, + 259, + 542, + 373 + ], + "spans": [ + { + "bbox": [ + 310, + 259, + 542, + 373 + ], + "type": "table", + "html": "
ModelCelebA-HQ-256LSUN-Church-64
NVAE* [40]30.2538.13
Ours9.898.38
NCP-VAE [2]24.79-
VAEBM [43]20.3813.51
Adv-EBM [45]17.3110.84
GLOW [22]68.9359.35
PGGAN [19]8.036.42
", + "image_path": "8c60405c78d1893622aee31eb7677a5d9cb7b27ea5654806e0d611723d2195dd.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 380, + 545, + 403 + ], + "lines": [ + { + "bbox": [ + 306, + 380, + 545, + 403 + ], + "spans": [ + { + "bbox": [ + 306, + 380, + 545, + 403 + ], + "type": "text", + "content": "Table 3. FID(↓) for our model and baselines on CelebA-HQ-256 and LSUN-Church-64. Model* indicates backbone model." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 306, + 425, + 465, + 437 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 425, + 465, + 437 + ], + "spans": [ + { + "bbox": [ + 306, + 425, + 465, + 437 + ], + "type": "text", + "content": "5.2. Hierarchical Representations" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 442, + 545, + 539 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 442, + 545, + 539 + ], + "spans": [ + { + "bbox": [ + 304, + 442, + 545, + 539 + ], + "type": "text", + "content": "Hierarchical sampling. To examine our model in learning hierarchical representation, we employ hierarchical sampling to illustrate the learned representation at different layers. In particular, we first sample one group of latent vectors from EBM prior and hold them as fixed constants, then we randomly sample multiple groups of latent vectors to replace the fixed latent vectors at different layers. This allows us to visualize the variation in representation across layers." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 539, + 545, + 694 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 539, + 545, + 694 + ], + "spans": [ + { + "bbox": [ + 304, + 539, + 545, + 694 + ], + "type": "text", + "content": "We apply our joint EBM prior to BIVA [28] on CelebA-64. For training, we reuse the two-stage training scheme, where we recruit " + }, + { + "bbox": [ + 304, + 539, + 545, + 694 + ], + "type": "inline_equation", + "content": "\\mathrm{BIVA}^2" + }, + { + "bbox": [ + 304, + 539, + 545, + 694 + ], + "type": "text", + "content": " for the first stage training and train our EBM prior model in the second stage by using the reparametrized sampling [43] method (similar as deep hierarchical models in Sec.5.1). We show the results of hierarchical sampling in Fig.6 and observe that BIVA presents minor changes at the bottom and middle layers, while the proposed joint EBM prior model can show variations of different levels. Note that it is a challenging task for conditional hierarchical models [48], on which the improvement thus suggests that the proposed method is capable of learning hierarchical representations for multi-layer generator models." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 315, + 702, + 448, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 702, + 448, + 713 + ], + "spans": [ + { + "bbox": [ + 315, + 702, + 448, + 713 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 315, + 702, + 448, + 713 + ], + "type": "text", + "content": "https://github.com/vlievin/biva-pytorch" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3608" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 69, + 547, + 124 + ], + "blocks": [ + { + "bbox": [ + 48, + 69, + 547, + 124 + ], + "lines": [ + { + "bbox": [ + 48, + 69, + 547, + 124 + ], + "spans": [ + { + "bbox": [ + 48, + 69, + 547, + 124 + ], + "type": "image", + "image_path": "515393744524ac48cec77123635ab40fd11b2cba941d728b91019553703d5df9.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 100, + 132, + 492, + 144 + ], + "lines": [ + { + "bbox": [ + 100, + 132, + 492, + 144 + ], + "spans": [ + { + "bbox": [ + 100, + 132, + 492, + 144 + ], + "type": "text", + "content": "Figure 5. Histograms of density of " + }, + { + "bbox": [ + 100, + 132, + 492, + 144 + ], + "type": "inline_equation", + "content": "L_{\\mathrm{EBM}}^{>k}" + }, + { + "bbox": [ + 100, + 132, + 492, + 144 + ], + "type": "text", + "content": " with AUROC(↑) and AUPRC(↑) for CIFAR-10 (in) / SVHN (out)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 52, + 162, + 282, + 315 + ], + "blocks": [ + { + "bbox": [ + 52, + 162, + 282, + 315 + ], + "lines": [ + { + "bbox": [ + 52, + 162, + 282, + 315 + ], + "spans": [ + { + "bbox": [ + 52, + 162, + 282, + 315 + ], + "type": "image", + "image_path": "8bc26b65f5c2625625f1f9056a192f8de072a21db2684fcf91b47aface5b9e42.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 322, + 288, + 378 + ], + "lines": [ + { + "bbox": [ + 46, + 322, + 288, + 378 + ], + "spans": [ + { + "bbox": [ + 46, + 322, + 288, + 378 + ], + "type": "text", + "content": "Figure 6. Hierarchical sampling for Gaussian prior model (bottom) and EBM prior model (top). From left panel to right panel, latent vectors are sampled from the bottom layers to the top layers. Detailed sampling method can be referred in Figure 3 in [48]." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 399, + 287, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 399, + 287, + 518 + ], + "spans": [ + { + "bbox": [ + 46, + 399, + 287, + 518 + ], + "type": "text", + "content": "Out-of-distribution detection. Next, we conduct out-of-distribution (OOD) detection to further evaluate the hierarchical representations. Typically, low-level representations (e.g., edges, corners) can be shared across data which in turn leads to high-confidence reconstructions for OOD examples, while high-level semantic ones have fewer correlations across different data and shall be more discriminative for OOD detection. Inspired by [16], we consider an unnormalized log-posterior as the decision function for EBM prior model, which is defined as" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 58, + 540, + 287, + 590 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 540, + 287, + 590 + ], + "spans": [ + { + "bbox": [ + 58, + 540, + 287, + 590 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} L _ {\\mathrm {E B M}} ^ {> k} = \\mathbb {E} _ {\\mathbf {z} > k \\sim q _ {\\omega} (\\mathbf {z} | \\mathbf {x}), \\mathbf {z} \\leq k \\sim p _ {\\beta > 0}, \\alpha (\\mathbf {z})} [ \\log p _ {\\beta_ {0}} (\\mathbf {x} | \\mathbf {z}) \\\\ + \\log p _ {\\beta > 0} (\\mathbf {z}) + \\sum_ {i = 1} ^ {L} f _ {\\alpha_ {i}} (\\mathbf {z} _ {i}) ] \\tag {20} \\\\ \\end{array}", + "image_path": "7ba75cb1e9776cbc56030cc294c077b96326df18b7f47ffa045a800605a9ce43.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 599, + 287, + 694 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 599, + 287, + 694 + ], + "spans": [ + { + "bbox": [ + 46, + 599, + 287, + 694 + ], + "type": "text", + "content": "where latent codes above the " + }, + { + "bbox": [ + 46, + 599, + 287, + 694 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 599, + 287, + 694 + ], + "type": "text", + "content": "-th layer are inferred from inference model and kept fixed, and those below the " + }, + { + "bbox": [ + 46, + 599, + 287, + 694 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 599, + 287, + 694 + ], + "type": "text", + "content": "-th layer are sampled from EBM prior via the reparametrized sampling3 [43] with fixed inferred latent codes. With " + }, + { + "bbox": [ + 46, + 599, + 287, + 694 + ], + "type": "inline_equation", + "content": "k = 0" + }, + { + "bbox": [ + 46, + 599, + 287, + 694 + ], + "type": "text", + "content": ", all layers of latent vectors are inferred from " + }, + { + "bbox": [ + 46, + 599, + 287, + 694 + ], + "type": "inline_equation", + "content": "q_{\\omega}(\\mathbf{z}|\\mathbf{x})" + }, + { + "bbox": [ + 46, + 599, + 287, + 694 + ], + "type": "text", + "content": ". With a higher value of " + }, + { + "bbox": [ + 46, + 599, + 287, + 694 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 599, + 287, + 694 + ], + "type": "text", + "content": ", less inferred low-level representations are used, which should render better performance in OOD detection. In addition, we can also compute a subtraction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 163, + 545, + 200 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 163, + 545, + 200 + ], + "spans": [ + { + "bbox": [ + 306, + 163, + 545, + 200 + ], + "type": "text", + "content": "between " + }, + { + "bbox": [ + 306, + 163, + 545, + 200 + ], + "type": "inline_equation", + "content": "L_{\\mathrm{EBM}}^{>0}" + }, + { + "bbox": [ + 306, + 163, + 545, + 200 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 306, + 163, + 545, + 200 + ], + "type": "inline_equation", + "content": "L_{\\mathrm{EBM}}^{>k}" + }, + { + "bbox": [ + 306, + 163, + 545, + 200 + ], + "type": "text", + "content": " as a surrogate of the likelihood-ratio which is shown to be effective for OOD detection [16]. We compute the subtraction as" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 363, + 209, + 545, + 224 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 363, + 209, + 545, + 224 + ], + "spans": [ + { + "bbox": [ + 363, + 209, + 545, + 224 + ], + "type": "interline_equation", + "content": "L L R _ {\\mathrm {E B M}} ^ {> k} = L _ {\\mathrm {E B M}} ^ {> 0} - L _ {\\mathrm {E B M}} ^ {> k} \\tag {21}", + "image_path": "61a562d22a2bb2c54ef38d81602b1d71792e9e2b05d1bc5f710978251f531d9d.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 232, + 545, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 232, + 545, + 411 + ], + "spans": [ + { + "bbox": [ + 304, + 232, + 545, + 411 + ], + "type": "text", + "content": "We follow standard protocols and apply our EBM prior model with BIVA on CIFAR-10 and use SVHN as OOD data for testing. In Fig.5, we show the density of in-distribution and OOD data by computing the unnormalized log-posterior with increased " + }, + { + "bbox": [ + 304, + 232, + 545, + 411 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 232, + 545, + 411 + ], + "type": "text", + "content": ", and we use AUROC, AUPRC to quantitatively evaluate the performance. It can be seen that as " + }, + { + "bbox": [ + 304, + 232, + 545, + 411 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 232, + 545, + 411 + ], + "type": "text", + "content": " increases, relatively lower log-likelihoods are assigned to OOD data, which in turn renders better detection performance (higher AUROC and AUPRC). More importantly, we observe that the backbone model BIVA achieves the best detection performance of 0.885 for AUROC, while our models achieve 0.927 with the adapted decision function. This further verifies that the hierarchical representations can be learned within our multi-layer structure." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 419, + 444, + 433 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 419, + 444, + 433 + ], + "spans": [ + { + "bbox": [ + 306, + 419, + 444, + 433 + ], + "type": "text", + "content": "5.3. Analysis of Latent Space" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 438, + 545, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 438, + 545, + 557 + ], + "spans": [ + { + "bbox": [ + 304, + 438, + 545, + 557 + ], + "type": "text", + "content": "Long-run Langevin transition. In this section, we examine the energy landscape of our joint EBM prior model. If the EBM is well-learned, the energy prior should naturally render local modes of the energy function, and traversing these local modes should present realistic synthesized examples and steady-state energy scores. Existing EBMs typically have oversaturated images via long-run Langevin dynamics as observed in [30]. Training an EBM that learns steady-state energy scores over realistic images can be useful but challenging." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 558, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 546, + 713 + ], + "type": "text", + "content": "We train our model on CelebA-64 using Langevin dynamic for 40 steps. We then run 100 and 2500 Langevin steps to examine the learned energy landscape. We show the synthesis and corresponding energy profile in Fig.7. It can be seen that generated examples become sharper for the first 40 steps as it starts from the referenced distribution " + }, + { + "bbox": [ + 304, + 558, + 546, + 713 + ], + "type": "inline_equation", + "content": "p_{\\beta > 0}(\\mathbf{z})" + }, + { + "bbox": [ + 304, + 558, + 546, + 713 + ], + "type": "text", + "content": " toward the learned energy prior " + }, + { + "bbox": [ + 304, + 558, + 546, + 713 + ], + "type": "inline_equation", + "content": "p_{\\beta > 0, \\alpha}(\\mathbf{z})" + }, + { + "bbox": [ + 304, + 558, + 546, + 713 + ], + "type": "text", + "content": ", and the energy fluctuates around some constant. For long-run 2500 steps, it is worth noting that our EBM prior model delivers diverse and realistic synthesis, and it does not exhibit the oversaturated phenomenon. This suggests that the learned EBM could mix well between different local modes of the learned energy prior." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 58, + 702, + 179, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 179, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 179, + 712 + ], + "type": "text", + "content": "3https://github.com/NVlabs/VAEBM" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3609" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 70, + 286, + 209 + ], + "blocks": [ + { + "bbox": [ + 53, + 70, + 286, + 209 + ], + "lines": [ + { + "bbox": [ + 53, + 70, + 286, + 209 + ], + "spans": [ + { + "bbox": [ + 53, + 70, + 286, + 209 + ], + "type": "image", + "image_path": "42ab7ce732358ffba47a2d6fe1d1a18de6f877c268effa3dcd14ef059a213e09.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 217, + 288, + 251 + ], + "lines": [ + { + "bbox": [ + 46, + 217, + 288, + 251 + ], + "spans": [ + { + "bbox": [ + 46, + 217, + 288, + 251 + ], + "type": "text", + "content": "Figure 7. Trajectory in data space and energy profile in Langevin transition. Top: Langevin transition with 100 steps. Bottom: Langevin transition with 2500 steps." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 271, + 287, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 271, + 287, + 354 + ], + "spans": [ + { + "bbox": [ + 46, + 271, + 287, + 354 + ], + "type": "text", + "content": "Anomaly Detection. We further evaluate how our joint EBM prior model could benefit the anomaly detection (AD) task. Different from OOD detection, AD requires one class (e.g., one-digit class from MNIST) of data to be held out as anomaly for training, and both normal (e.g., other nine-digit classes from MNIST) and anomalous data are used for testing." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 356, + 288, + 522 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 356, + 288, + 522 + ], + "spans": [ + { + "bbox": [ + 46, + 356, + 288, + 522 + ], + "type": "text", + "content": "The proposed prior model is built on the joint of all layers of latent variables. If it is well learned, the posterior " + }, + { + "bbox": [ + 46, + 356, + 288, + 522 + ], + "type": "inline_equation", + "content": "q_{\\omega}(\\mathbf{z}|\\mathbf{x})" + }, + { + "bbox": [ + 46, + 356, + 288, + 522 + ], + "type": "text", + "content": " could form a discriminative joint latent space that has separated probability densities for normal and anomalous data. We use un-normalized log-posterior " + }, + { + "bbox": [ + 46, + 356, + 288, + 522 + ], + "type": "inline_equation", + "content": "L_{\\mathrm{EBM}}^{>0}" + }, + { + "bbox": [ + 46, + 356, + 288, + 522 + ], + "type": "text", + "content": " as our decision function and train our model on MNIST with each class held out as an anomalous class. We consider the baseline models that also adopt an inferential mechanism, such as VAE [23], MEG [25], BiGAN- " + }, + { + "bbox": [ + 46, + 356, + 288, + 522 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 46, + 356, + 288, + 522 + ], + "type": "text", + "content": " [47], OT-SRI [1], and LEBM [33] which assumes single-layer latent space and is closely related to our method. Tab.4 shows the results of AUPRC scores averaged over the last 10 epochs to account for the variance. To make fair comparisons, we follow the protocols in [1,25,33,47]." + } + ] + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 50, + 533, + 286, + 583 + ], + "blocks": [ + { + "bbox": [ + 50, + 533, + 286, + 583 + ], + "lines": [ + { + "bbox": [ + 50, + 533, + 286, + 583 + ], + "spans": [ + { + "bbox": [ + 50, + 533, + 286, + 583 + ], + "type": "table", + "html": "
Heldout Digit14579
VAE [23]0.0630.3370.3250.1480.104
MEG [25]0.281 ± 0.0350.401 ± 0.0610.402 ± 0.0620.290 ± 0.0400.342 ± 0.034
BiGAN-σ [47]0.287 ± 0.0230.443 ± 0.0290.514 ± 0.0290.347 ± 0.0170.307 ± 0.028
OT-SRI [1]0.353 ± 0.0210.770 ± 0.0240.726 ± 0.0300.550 ± 0.0130.555 ± 0.023
LEBM [33]0.336 ± 0.0080.630 ± 0.0170.619 ± 0.0130.463 ± 0.0090.413 ± 0.010
Ours0.470 ± 0.0090.941 ± 0.0010.964 ± 0.0030.815 ± 0.0040.796 ± 0.004
", + "image_path": "f043aa8088085b71fa2238bd0d0e60234424e3373878ebfc5f076ac14ee8028e.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 623, + 151, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 623, + 151, + 635 + ], + "spans": [ + { + "bbox": [ + 47, + 623, + 151, + 635 + ], + "type": "text", + "content": "5.4. Ablation Studies." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 641, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 287, + 715 + ], + "type": "text", + "content": "MCMC sampling vs. Inference model. Two posterior sampling schemes using MCMC and inference model are compared in Tab.5 in terms of FID and wall-clock training time (per-iteration). The MCMC posterior sampling renders better FID as it is more accurate in inference [13, 32], but it can be computationally heavy. While the inference model" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 72, + 545, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 109 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 109 + ], + "type": "text", + "content": "is efficient in learning but can be less accurate. For deep hierarchical structures, the variational learning with inference model is preferred due to its efficiency." + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 326, + 116, + 526, + 156 + ], + "blocks": [ + { + "bbox": [ + 56, + 590, + 277, + 602 + ], + "lines": [ + { + "bbox": [ + 56, + 590, + 277, + 602 + ], + "spans": [ + { + "bbox": [ + 56, + 590, + 277, + 602 + ], + "type": "text", + "content": "Table 4. AUPRC scores for unsupervised anomaly detection." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 326, + 116, + 526, + 156 + ], + "lines": [ + { + "bbox": [ + 326, + 116, + 526, + 156 + ], + "spans": [ + { + "bbox": [ + 326, + 116, + 526, + 156 + ], + "type": "table", + "html": "
MCMC / InfSVHNCelebA-64CIFAR-10
FID26.81 / 28.6033.60 / 36.1266.32 / 68.45
Time(s)0.478 / 0.2320.920 / 0.2460.568 / 0.256
", + "image_path": "10c8c9b95391dd2c4a14a9afb12d6cdec3a9b1390495a7513e1a60f1b7ff5df5.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 198, + 547, + 293 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 198, + 547, + 293 + ], + "spans": [ + { + "bbox": [ + 305, + 198, + 547, + 293 + ], + "type": "text", + "content": "Langevin steps. We explore the different number of Langevin steps in prior sampling for training on CIFAR-10. The results of FID and corresponding training time are shown in Tab.6. We observe that the Langevin step " + }, + { + "bbox": [ + 305, + 198, + 547, + 293 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 305, + 198, + 547, + 293 + ], + "type": "text", + "content": " increasing from 10 to 40 can improve the generation quality, while for steps more than 40, it only has minor impacts on the improvement but with increased training overhead. We thus report the result of " + }, + { + "bbox": [ + 305, + 198, + 547, + 293 + ], + "type": "inline_equation", + "content": "k = 40" + }, + { + "bbox": [ + 305, + 198, + 547, + 293 + ], + "type": "text", + "content": " in Tab.1." + } + ] + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 326, + 301, + 526, + 343 + ], + "blocks": [ + { + "bbox": [ + 305, + 163, + 547, + 186 + ], + "lines": [ + { + "bbox": [ + 305, + 163, + 547, + 186 + ], + "spans": [ + { + "bbox": [ + 305, + 163, + 547, + 186 + ], + "type": "text", + "content": "Table 5. FID and training time for MCMC posterior sampling and variational learning." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 326, + 301, + 526, + 343 + ], + "lines": [ + { + "bbox": [ + 326, + 301, + 526, + 343 + ], + "spans": [ + { + "bbox": [ + 326, + 301, + 526, + 343 + ], + "type": "table", + "html": "
steps kk = 10k = 20k = 40k = 80k = 100
FID69.4267.5866.3266.0365.86
Time(s)0.3120.4800.5680.7410.837
", + "image_path": "c9b96bb92c47ed03f93c0f57c359377b9aa6c6574538745c9d983c7c4d218141.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 392, + 427, + 404 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 392, + 427, + 404 + ], + "spans": [ + { + "bbox": [ + 306, + 392, + 427, + 404 + ], + "type": "text", + "content": "5.5. Parameter Efficiency" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 410, + 545, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 410, + 545, + 517 + ], + "spans": [ + { + "bbox": [ + 304, + 410, + 545, + 517 + ], + "type": "text", + "content": "It is crucial to analyze the parameter complexity when comparing the generation performance. In Tab.1, we build our model with two layers of latent variables on top of the generator used in [33]. The additional layer accounts for only " + }, + { + "bbox": [ + 304, + 410, + 545, + 517 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 304, + 410, + 545, + 517 + ], + "type": "text", + "content": " overhead in total parameter complexity compared to LEBM [33]. For deep hierarchical models, we apply our joint EBM prior model on latent space which brings minimum overhead. The parameter complexity of the backbone NVAE and our EBM model is shown in Tab.7." + } + ] + } + ], + "index": 15 + }, + { + "type": "table", + "bbox": [ + 309, + 524, + 543, + 561 + ], + "blocks": [ + { + "bbox": [ + 305, + 351, + 547, + 374 + ], + "lines": [ + { + "bbox": [ + 305, + 351, + 547, + 374 + ], + "spans": [ + { + "bbox": [ + 305, + 351, + 547, + 374 + ], + "type": "text", + "content": "Table 6. FID and training time for increasing MCMC steps in prior sampling." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 524, + 543, + 561 + ], + "lines": [ + { + "bbox": [ + 309, + 524, + 543, + 561 + ], + "spans": [ + { + "bbox": [ + 309, + 524, + 543, + 561 + ], + "type": "table", + "html": "
NVAE / EBMCIFAR-10CelebA-HQ-256LSUN-Church-64
FID39.73 / 11.3430.25 / 9.8938.13 / 8.38
Parameters257M / 9M (3%)375M / 18M (4%)65M / 5M (7%)
", + "image_path": "e0c05e2dbe90c0c512806f5245c668ec9f03d49e26c69d0699490f66e1821785.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "table_body" + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 569, + 545, + 590 + ], + "lines": [ + { + "bbox": [ + 305, + 569, + 545, + 590 + ], + "spans": [ + { + "bbox": [ + 305, + 569, + 545, + 590 + ], + "type": "text", + "content": "Table 7. FID and parameter complexity for backbone model and EBM." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 306, + 609, + 379, + 621 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 609, + 379, + 621 + ], + "spans": [ + { + "bbox": [ + 306, + 609, + 379, + 621 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "content": "we propose a joint EBM prior for multi-layer generator models, which can effectively capture the intra-layer relations at each layer and jointly correct the latent variables from all layers. We present a joint training scheme via MLE and further develop a variational learning scheme for efficient inference. Our comprehensive experiments demonstrate the effectiveness of the proposed method." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3610" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 147 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 147 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 147 + ], + "type": "text", + "content": "[1] Dongsheng An, Jianwen Xie, and Ping Li. Learning deep latent variable models by short-run mcmc inference with optimal transport correction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15415–15424, June 2021. 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 148, + 288, + 192 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 148, + 288, + 192 + ], + "spans": [ + { + "bbox": [ + 53, + 148, + 288, + 192 + ], + "type": "text", + "content": "[2] Jyoti Aneja, Alex Schwing, Jan Kautz, and Arash Vahdat. A contrastive learning approach for training variational autoencoder priors. Advances in neural information processing systems, 34:480-493, 2021. 2, 3, 5, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 194, + 287, + 226 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 194, + 287, + 226 + ], + "spans": [ + { + "bbox": [ + 53, + 194, + 287, + 226 + ], + "type": "text", + "content": "[3] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096, 2018. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 228, + 287, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 228, + 287, + 293 + ], + "spans": [ + { + "bbox": [ + 53, + 228, + 287, + 293 + ], + "type": "text", + "content": "[4] Yuri Burda, Roger B. Grosse, and Ruslan Salakhutdinov. Importance weighted autoencoders. In Yoshua Bengio and Yann LeCun, editors, 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings, 2016. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 295, + 287, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 295, + 287, + 350 + ], + "spans": [ + { + "bbox": [ + 53, + 295, + 287, + 350 + ], + "type": "text", + "content": "[5] Rewon Child. Very deep vaes generalize autoregressive models and can outperform them on images. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net, 2021. 1, 4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 351, + 287, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 351, + 287, + 373 + ], + "spans": [ + { + "bbox": [ + 53, + 351, + 287, + 373 + ], + "type": "text", + "content": "[6] Bin Dai and David Wipf. Diagnosing and enhancing vae models. arXiv preprint arXiv:1903.05789, 2019. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 375, + 287, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 375, + 287, + 418 + ], + "spans": [ + { + "bbox": [ + 53, + 375, + 287, + 418 + ], + "type": "text", + "content": "[7] Yilun Du, Shuang Li, Joshua Tenenbaum, and Igor Mordatch. Improved contrastive divergence training of energy based models. arXiv preprint arXiv:2012.01316, 2020. 2, 5, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 420, + 287, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 420, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 53, + 420, + 287, + 453 + ], + "type": "text", + "content": "[8] Yilun Du and Igor Mordatch. Implicit generation and generalization in energy-based models. arXiv preprint arXiv:1903.08689, 2019. 2, 5, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 455, + 287, + 530 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 455, + 287, + 530 + ], + "spans": [ + { + "bbox": [ + 53, + 455, + 287, + 530 + ], + "type": "text", + "content": "[9] Conor Durkan and Charlie Nash. Autoregressive energy machines. In Kamalika Chaudhuri and Ruslan Salakhutdinov, editors, Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, volume 97 of Proceedings of Machine Learning Research, pages 1735-1744. PMLR, 2019. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 533, + 287, + 576 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 533, + 287, + 576 + ], + "spans": [ + { + "bbox": [ + 48, + 533, + 287, + 576 + ], + "type": "text", + "content": "[10] Ruiqi Gao, Yang Song, Ben Poole, Ying Nian Wu, and Diederik P Kingma. Learning energy-based models by diffusion recovery likelihood. arXiv preprint arXiv:2012.08125, 2020. 2, 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 578, + 287, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 578, + 287, + 621 + ], + "spans": [ + { + "bbox": [ + 48, + 578, + 287, + 621 + ], + "type": "text", + "content": "[11] Partha Ghosh, Mehdi SM Sajjadi, Antonio Vergari, Michael Black, and Bernhard Schölkopf. From variational to deterministic autoencoders. arXiv preprint arXiv:1903.12436, 2019. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 624, + 287, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 287, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 287, + 668 + ], + "type": "text", + "content": "[12] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "text", + "content": "[13] Tian Han, Yang Lu, Song-Chun Zhu, and Ying Nian Wu. Alternating back-propagation for generator network. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 31, 2017. 2, 5, 8" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 307, + 73, + 547, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 547, + 138 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 547, + 138 + ], + "type": "text", + "content": "[14] Tian Han, Erik Nijkamp, Xiaolin Fang, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu. Divergence triangle for joint training of generator model, energy-based model, and inferential model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019. 5, 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 140, + 547, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 140, + 547, + 194 + ], + "spans": [ + { + "bbox": [ + 307, + 140, + 547, + 194 + ], + "type": "text", + "content": "[15] Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, and Ying Nian Wu. Joint training of variational autoencoder and latent energy-based model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7978-7987, 2020. 5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 195, + 547, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 195, + 547, + 237 + ], + "spans": [ + { + "bbox": [ + 307, + 195, + 547, + 237 + ], + "type": "text", + "content": "[16] Jakob D Drachmann Havtorn, Jes Frellsen, Soren Hauberg, and Lars Maaløe. Hierarchical vaes know what they don't know. In International Conference on Machine Learning, pages 4117-4128. PMLR, 2021. 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 239, + 547, + 315 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 239, + 547, + 315 + ], + "spans": [ + { + "bbox": [ + 307, + 239, + 547, + 315 + ], + "type": "text", + "content": "[17] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017. 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 316, + 547, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 316, + 547, + 348 + ], + "spans": [ + { + "bbox": [ + 307, + 316, + 547, + 348 + ], + "type": "text", + "content": "[18] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 350, + 547, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 350, + 547, + 381 + ], + "spans": [ + { + "bbox": [ + 307, + 350, + 547, + 381 + ], + "type": "text", + "content": "[19] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196, 2017. 5, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 383, + 547, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 383, + 547, + 426 + ], + "spans": [ + { + "bbox": [ + 307, + 383, + 547, + 426 + ], + "type": "text", + "content": "[20] Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Training generative adversarial networks with limited data. Advances in Neural Information Processing Systems, 33:12104-12114, 2020. 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 426, + 547, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 426, + 547, + 470 + ], + "spans": [ + { + "bbox": [ + 307, + 426, + 547, + 470 + ], + "type": "text", + "content": "[21] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401-4410, 2019. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 472, + 547, + 503 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 472, + 547, + 503 + ], + "spans": [ + { + "bbox": [ + 307, + 472, + 547, + 503 + ], + "type": "text", + "content": "[22] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. Advances in neural information processing systems, 31, 2018. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 504, + 547, + 535 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 504, + 547, + 535 + ], + "spans": [ + { + "bbox": [ + 307, + 504, + 547, + 535 + ], + "type": "text", + "content": "[23] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 2, 4, 5, 8" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 537, + 547, + 559 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 537, + 547, + 559 + ], + "spans": [ + { + "bbox": [ + 307, + 537, + 547, + 559 + ], + "type": "text", + "content": "[24] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 559, + 547, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 559, + 547, + 602 + ], + "spans": [ + { + "bbox": [ + 307, + 559, + 547, + 602 + ], + "type": "text", + "content": "[25] Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, and Yoshua Bengio. Maximum entropy generators for energy-based models. arXiv preprint arXiv:1901.08508, 2019. 8" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 603, + 547, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 603, + 547, + 656 + ], + "spans": [ + { + "bbox": [ + 307, + 603, + 547, + 656 + ], + "type": "text", + "content": "[26] Don S Lemons and Anthony Gythiel. Paul Langevin's 1908 paper \"on the theory of brownian motion\"[“sur la théorie du mouvement brownien,” cr acad. sci.(paris) 146, 530–533 (1908)]. American Journal of Physics, 65(11):1079–1081, 1997. 4" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 658, + 547, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 658, + 547, + 689 + ], + "spans": [ + { + "bbox": [ + 307, + 658, + 547, + 689 + ], + "type": "text", + "content": "[27] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. CoRR, abs/1411.7766, 2014. 5" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 691, + 547, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 691, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 691, + 547, + 713 + ], + "type": "text", + "content": "[28] Lars Maaløe, Marco Fraccaro, Valentin Lievin, and Ole Winther. Biva: A very deep hierarchy of latent variables for" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "3611" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 73, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 66, + 73, + 286, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 73, + 286, + 95 + ], + "spans": [ + { + "bbox": [ + 66, + 73, + 286, + 95 + ], + "type": "text", + "content": "generative modeling. Advances in neural information processing systems, 32, 2019. 1, 4, 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 95, + 287, + 148 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 95, + 287, + 148 + ], + "spans": [ + { + "bbox": [ + 48, + 95, + 287, + 148 + ], + "type": "text", + "content": "[29] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng. Reading digits in natural images with unsupervised feature learning. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011, 2011. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 150, + 287, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 150, + 287, + 205 + ], + "spans": [ + { + "bbox": [ + 48, + 150, + 287, + 205 + ], + "type": "text", + "content": "[30] Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, and Ying Nian Wu. On the anatomy of mcmc-based maximum likelihood learning of energy-based models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 5272-5280, 2020. 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 206, + 287, + 249 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 206, + 287, + 249 + ], + "spans": [ + { + "bbox": [ + 48, + 206, + 287, + 249 + ], + "type": "text", + "content": "[31] Erik Nijkamp, Mitch Hill, Song-Chun Zhu, and Ying Nian Wu. Learning non-convergent non-persistent short-run mcmc toward energy-based model. Advances in Neural Information Processing Systems, 32, 2019. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 250, + 287, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 250, + 287, + 304 + ], + "spans": [ + { + "bbox": [ + 48, + 250, + 287, + 304 + ], + "type": "text", + "content": "[32] Erik Nijkamp, Bo Pang, Tian Han, Linqi Zhou, Song-Chun Zhu, and Ying Nian Wu. Learning multi-layer latent variable model via variational optimization of short run mcmc for approximate inference. In European Conference on Computer Vision, pages 361-378. Springer, 2020. 1, 2, 5, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 304, + 287, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 304, + 287, + 348 + ], + "spans": [ + { + "bbox": [ + 48, + 304, + 287, + 348 + ], + "type": "text", + "content": "[33] Bo Pang, Tian Han, Erik Nijkamp, Song-Chun Zhu, and Ying Nian Wu. Learning latent space energy-based prior model. Advances in Neural Information Processing Systems, 33:21994-22008, 2020. 1, 2, 5, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 350, + 287, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 350, + 287, + 403 + ], + "spans": [ + { + "bbox": [ + 48, + 350, + 287, + 403 + ], + "type": "text", + "content": "[34] Masaki Saito, Shunta Saito, Masanori Koyama, and Sosuke Kobayashi. Train sparsely, generate densely: Memory-efficient unsupervised training of high-resolution temporal gan. International Journal of Computer Vision, 128(10):2586-2606, 2020. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 404, + 287, + 469 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 404, + 287, + 469 + ], + "spans": [ + { + "bbox": [ + 48, + 404, + 287, + 469 + ], + "type": "text", + "content": "[35] Casper Kaae Sønderby, Tapani Raiko, Lars Maalø e, Søren Kaae Sønderby, and Ole Winther. Ladder variational autoencoders. In D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 29. Curran Associates, Inc., 2016. 1, 2, 4, 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 471, + 287, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 471, + 287, + 502 + ], + "spans": [ + { + "bbox": [ + 48, + 471, + 287, + 502 + ], + "type": "text", + "content": "[36] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. Advances in Neural Information Processing Systems, 32, 2019. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 504, + 287, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 504, + 287, + 547 + ], + "spans": [ + { + "bbox": [ + 48, + 504, + 287, + 547 + ], + "type": "text", + "content": "[37] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 548, + 287, + 580 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 548, + 287, + 580 + ], + "spans": [ + { + "bbox": [ + 48, + 548, + 287, + 580 + ], + "type": "text", + "content": "[38] Jakub Tomczak and Max Welling. Vae with a vampprior. In International Conference on Artificial Intelligence and Statistics, pages 1214-1223. PMLR, 2018. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 582, + 287, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 582, + 287, + 634 + ], + "spans": [ + { + "bbox": [ + 48, + 582, + 287, + 634 + ], + "type": "text", + "content": "[39] Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz. Mocogan: Decomposing motion and content for video generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018. 1" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 636, + 287, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 636, + 287, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 636, + 287, + 668 + ], + "type": "text", + "content": "[40] Arash Vahdat and Jan Kautz. Nvae: A deep hierarchical variational autoencoder. Advances in Neural Information Processing Systems, 33:19667-19679, 2020. 1, 4, 5, 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "text", + "content": "[41] Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In International conference on machine learning, pages 1747-1756. PMLR, 2016. 6" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 363 + ], + "type": "list", + "angle": 0, + "index": 22, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "text", + "content": "[42] Zhisheng Xiao and Tian Han. Adaptive multi-stage density ratio estimation for learning latent space energy-based model. arXiv preprint arXiv:2209.08739, 2022. 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "type": "text", + "content": "[43] Zhisheng Xiao, Karsten Kreis, Jan Kautz, and Arash Vahdat. Vaebm: A symbiosis between variational autoencoders and energy-based models. arXiv preprint arXiv:2010.00654, 2020. 2, 5, 6, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "text", + "content": "[44] Jianwen Xie, Yaxuan Zhu, Jun Li, and Ping Li. A tale of two flows: Cooperative learning of Langevin flow and normalizing flow toward energy-based model. In International Conference on Learning Representations, 2022. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 197, + 545, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 197, + 545, + 239 + ], + "spans": [ + { + "bbox": [ + 307, + 197, + 545, + 239 + ], + "type": "text", + "content": "[45] Xuwang Yin, Shiying Li, and Gustavo K Rohde. Analyzing and improving generative adversarial training for generative modeling and out-of-distribution detection. arXiv preprint arXiv:2012.06568, 2020. 2, 5, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 241, + 545, + 284 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 241, + 545, + 284 + ], + "spans": [ + { + "bbox": [ + 307, + 241, + 545, + 284 + ], + "type": "text", + "content": "[46] Fisher Yu, Yinda Zhang, Shuran Song, Ari Seff, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 286, + 545, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 286, + 545, + 329 + ], + "spans": [ + { + "bbox": [ + 307, + 286, + 545, + 329 + ], + "type": "text", + "content": "[47] Houssam Zenati, Chuan Sheng Foo, Bruno Lecouat, Gaurav Manek, and Vijay Ramaseshan Chandrasekhar. Efficient gan-based anomaly detection. arXiv preprint arXiv:1802.06222, 2018. 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 331, + 545, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 331, + 545, + 363 + ], + "spans": [ + { + "bbox": [ + 307, + 331, + 545, + 363 + ], + "type": "text", + "content": "[48] Shengjia Zhao, Jiaming Song, and Stefano Ermon. Learning hierarchical features from generative models. arXiv preprint arXiv:1702.08396, 2017. 1, 6, 7" + } + ] + } + ], + "index": 21 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3612" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Locally Editable Virtual Humans/81a768fa-0f17-4406-9706-ef4fe06ddff4_content_list.json b/2023/Learning Locally Editable Virtual Humans/81a768fa-0f17-4406-9706-ef4fe06ddff4_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..ea7ef85868b959081e212b73016cc41904726ca2 --- /dev/null +++ b/2023/Learning Locally Editable Virtual Humans/81a768fa-0f17-4406-9706-ef4fe06ddff4_content_list.json @@ -0,0 +1,1531 @@ +[ + { + "type": "text", + "text": "Learning Locally Editable Virtual Humans", + "text_level": 1, + "bbox": [ + 266, + 130, + 702, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Hsuan-I Ho Lixin Xue Jie Song Otmar Hilliges Department of Computer Science, ETH Zürich", + "bbox": [ + 212, + 180, + 756, + 219 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 251, + 313, + 266 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we propose a novel hybrid representation and end-to-end trainable network architecture to model fully editable and customizable neural avatars. At the core of our work lies a representation that combines the modeling power of neural fields with the ease of use and inherent 3D consistency of skinned meshes. To this end, we construct a trainable feature codebook to store local geometry and texture features on the vertices of a deformable body model, thus exploiting its consistent topology under articulation. This representation is then employed in a generative auto-decoder architecture that admits fitting to unseen scans and sampling of realistic avatars with varied appearances and geometries. Furthermore, our representation allows local editing by swapping local features between 3D assets. To verify our method for avatar creation and editing, we contribute a new high-quality dataset, dubbed CustomHumans, for training and evaluation. Our experiments quantitatively and qualitatively show that our method generates diverse detailed avatars and achieves better model fitting performance compared to state-of-the-art methods. Our code and dataset are available at https://aith.ethz.ch/custom-humans.", + "bbox": [ + 75, + 282, + 473, + 614 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 632, + 209, + 648 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "3D Avatars are an important aspect of many emerging applications such as 3D games or the Metaverse. Allowing for easy personalization of such avatars, holds the promise of increased user engagement. Traditionally, editing 3D assets requires knowledge of computer graphics tools and relies on standardized data formats to represent shapes and appearances. While methods for reconstruction or generative modeling of learned avatars achieve impressive results, it is unknown how such neural avatars can be edited and customized. Thus, the goal of our work is to contribute a simple, yet powerful data-driven method for avatar creation and customization (Fig. 1): our method enables (a) the ability to transfer partial geometric and appearance details between 3D assets, and (b) the ability to author details via 2D-3D transfer. The resulting avatars (c) retain consistent local details when posed.", + "bbox": [ + 75, + 659, + 470, + 900 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/772d487918a176891d66c97850bdb62f18b2108564301f4a69a7767616fcc67a.jpg", + "image_caption": [ + "Figure 1. Creating locally editable avatars: Given an input avatar, (a) the avatar can be edited by transferring clothing geometry and color details from existing, yet unseen 3D assets. (b) Users can customize clothing details such as logos and letters via drawing on 2D images. (c) The avatars retain local detail consistently under pose changes." + ], + "image_footnote": [], + "bbox": [ + 501, + 252, + 890, + 512 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Existing methods do not allow for such capabilities. While 3D generative models of articulated human bodies [5, 21, 25, 42, 75] leverage differentiable neural rendering to learn from images, they cannot control local details due to highly entangled color and geometry in the 2D supervision signal. Generative models trained on 3D data [9, 13, 36, 44, 45] can produce geometric details for surfaces and clothing. However, the diversity of generated samples is low due to the lack of high-quality 3D human scans and not all methods model appearance.", + "bbox": [ + 496, + 611, + 892, + 763 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "At the core of the issue lies the question of representation: graphics tools use meshes, UV, and texture maps which provide consistent topologies under deformation. However, human avatar methods that are built on mesh-based representations and linear blend skinning (LBS) are limited in their representational power with respect to challenging geometry (e.g., puffy garments) and flexible topologies (e.g., jackets), even with adaptations of additional displacement parameters [36] and mesh subdivision [66].", + "bbox": [ + 496, + 763, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "21024", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Inspired by the recent neural 3D representations [40, 62, 70, 72], we propose a novel hybrid representation for digital humans. Our representation combines the advantages of consistent topologies of LBS models with the representational power of neural fields. The key idea is to decompose the tasks of deformation consistency on one hand and local surface and appearance description on the other. For the former, we leverage existing parametric body models (e.g., SMPL [33] and SMPL-X [48]). For the latter, we leverage the fixed topology of the poseable mesh to store local feature codebooks. A decoder, shared across subjects, is then conditioned on the local features to predict the final signed distance and color values. Since only local information [15] is exposed to the decoder, overfitting and memorization can be mitigated. We experimentally show that this is crucial for 3D avatar fitting and reposing.", + "bbox": [ + 76, + 90, + 472, + 332 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Complementing this hybrid representation, we propose a training pipeline in the auto-decoding generative framework [9, 46, 52]. To this end, we jointly optimize multi-subject feature codebooks and the shared decoder weights via 3D reconstruction and 2D adversarial losses. The 3D losses help in disentangling appearance and geometric information from the input scans, while the latter improves the perceptual quality of randomly generated samples. To showcase the hybrid representation and the generative model we implement a prototypical avatar editing workflow shown in Fig. 1.", + "bbox": [ + 75, + 333, + 472, + 498 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Furthermore, to enable research on high-quality 3D avatars we contribute training data for generative 3D human models. We record a large-scale dataset (more than 600 scans of 80 subjects in 120 garments) using a volumetric capture stage [11]. Our dataset consists of high-quality 3D meshes alongside accurately registered SMPL-X [48] models and will be made available for research purposes. Finally, we assess our design decisions in detailed evaluations, both on existing and the proposed datasets.", + "bbox": [ + 75, + 500, + 472, + 635 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In summary, our contributions are threefold: (a) a novel hybrid representation for 3D virtual humans that allows for local editing across subjects, (b) a generative pipeline of 3D avatars creation that allows for fitting to unseen 3D scans and random sampling, and (c) a new large-scale high-quality dataset of 3D human scans containing diverse subjects, body poses and garments.", + "bbox": [ + 75, + 635, + 472, + 742 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 76, + 753, + 218, + 768 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Controllable human representations. Topics of virtual humans have received much attention in the graphics literature, such as skinning and rigging of articulated meshes [33, 43, 48, 55], physical simulation of clothing [19, 22, 41], and deferred rendering [24, 49, 65]. With the advances in neural rendering [63, 64] and the availability of large-scale human datasets [18, 28, 29, 32, 47, 67, 73, 74], numerous approaches have been proposed to reconstruct [4, 56, 57] and explicitly", + "bbox": [ + 75, + 779, + 470, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "control [10,30,50] human avatars in a data-driven manner.", + "bbox": [ + 498, + 90, + 887, + 106 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "One branch of work focuses on 2D image synthesis via generative adversarial networks (GANs) [20] and techniques of feature manipulation [28, 54, 60]. Typically, a 2D neural renderer creates human images corresponding to pose and appearance latent codes learned from the training data. Related applications such as virtual try-on [16, 17, 71] and video retargeting [7, 31, 68] have shown promising results in light of photo-realistic image synthesis by GANs [18,26]. However, these methods do not explicitly reason about complex 3D human geometry and can therefore not produce 3D-consistent results.", + "bbox": [ + 496, + 107, + 890, + 271 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "A newly emerging line of work aims to create controllable avatars with 3D consistency. Some methods extend existing body models with neural networks to predict displacement layers [3, 6, 36] or textures [51]. Other methods learn to model challenging pose-dependent deformations on avatars either by predicting LBS weights [8, 10, 14, 58, 59, 76] or improving the capabilities of body models [23, 30, 34, 35, 37, 50, 53] with the power of implicit neural fields. However, these approaches mainly focus on modeling a single subject in specific clothing and do not scale to create diverse avatars. Our method overcomes this issue by learning a multi-subject generative model which produces realistic virtual humans with disentangled controllability over body poses, clothing geometry, and texture.", + "bbox": [ + 496, + 273, + 892, + 484 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Generative 3D human models. Existing generative models of human avatars can be loosely split into two main streams: learning 3D-aware neural rendering from 2D images [5, 21, 25, 42, 75] and learning body shapes from 3D supervision [9, 13, 36, 44, 45]. Powered by recent advances in differentiable neural rendering [64] and neural fields [70], much progress has been made in 3D-aware generative models [61]. However, learning to generate detailed clothed avatars from pure 2D supervision [5, 21, 25, 42, 75] is still challenging due to the complex appearance and articulation of bodies, self-occlusions, and highly entangled colors and geometries in images.", + "bbox": [ + 496, + 492, + 892, + 672 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "More closely related to our setting are methods that learn to generate detailed body shapes from 3D scans or RGB-D data. For instance, CAPE [36] and SMPLict [13] are generative models for clothed humans. The former exploits VAE-GAN to predict additive displacements based on the SMPL vertices while the latter drape an implicitly modeled garment layer onto SMPL. NPMs [44], and SPAM [45] learn pose and shape latent spaces from 3D supervision, which enables latent code inversion using point clouds or depth sequences. gDNA [9] learns to synthesize body shapes in the canonical space and further improves clothing details with adversarial losses. However, none of the above-mentioned works is able to generate human bodies with appearance and neither allows fine-grained editing of the generated avatars. Our method addresses both issues by learning disentangled", + "bbox": [ + 496, + 674, + 893, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "21025", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/2fec53d2035886f0f9a4358c02ae195ae9cbe8ac6f2664d0ea91ab6c73a60f2c.jpg", + "image_caption": [ + "Figure 2. Proposed framework. Given a posed scan registered with body pose and shape LBS parameters $(\\theta, \\beta)$ , our proposed human representation stores its local geometry and texture features in a codebook $\\mathbf{C}$ which is indexed by the vertex indices of $M$ -vertex LBS body mesh $\\mathcal{M}$ (Sec. 3.1). Given a query point coordinates $\\mathbf{x}_g$ , two separated MLP decoders $\\Psi$ , $\\Phi$ predict signed distances and colors conditioned on the positional features $(\\mathbf{x}_l, \\vec{\\mathbf{n}})$ and the local geometry/texture features $(\\mathbf{f_s} / \\mathbf{f_c})$ respectively. We train a generative auto-decoder using $N$ posed scans, whose feature codebooks are stored in the dictionary $\\mathbf{D_s}$ , $\\mathbf{D_c}$ . We introduce two sampling strategies to sample codebooks (denoted as $\\mathbf{C}_i / \\mathbf{C}_r$ respectively) and jointly train our dictionaries and shared MLP decoders with a 3D reconstruction loss and a 2D adversarial loss (Sec. 3.2 & Sec. 3.3)." + ], + "image_footnote": [], + "bbox": [ + 81, + 88, + 890, + 327 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "local representations for multiple subjects. In addition, we experimentally show that our representation significantly improves the performance of model fitting against state-of-the-art human generative models.", + "bbox": [ + 75, + 441, + 468, + 502 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 76, + 518, + 168, + 534 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our proposed method is summarized in Fig. 2. We first contribute a novel hybrid human representation that stores local geometric and textural information into two aligned feature spaces (Sec. 3.1 and Fig. 3). To allow fitting to new 3D scans and drawing random samples from the underlying data distribution, we design a training strategy to learn a meaningful latent space under the generative adversarial framework to bring in additional 2D adversarial supervision (Sec. 3.2 & Sec. 3.3). Finally, we demonstrate the utility of our method for creating avatars by enabling local feature editing through existing 3D assets or images (Sec. 3.4).", + "bbox": [ + 75, + 545, + 468, + 712 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Hybrid Representation of Humans", + "text_level": 1, + "bbox": [ + 76, + 724, + 379, + 739 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To enable 3D avatars with high-fidelity representational power and local editing capabilities, a suitable representation is needed. To this end, we propose a novel hybrid representation that combines the advantages of neural fields (flexibility and modeling power) with LBS-articulated mesh models (ease of deformation and full explicit control).", + "bbox": [ + 75, + 748, + 468, + 839 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "An overview of how we leverage the proposed representation is provided in Fig. 2 in the dotted blue box. Given a human scan, we first create a posed, coarse body mesh $\\mathcal{M}$ (shown in red) using the registered body parameters", + "bbox": [ + 75, + 840, + 468, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "$(\\theta, \\beta)$ of an LBS body model. The mesh consists of $M$ vertices ( $\\mathcal{V} \\in \\mathbb{R}^{M \\times 3}$ ) in the posed space and $M_f$ faces where $\\mathcal{F} \\in \\{1, \\dots, M\\}^{M_f \\times 3}$ defines the vertex indices on each face. We then construct a trainable feature codebook $\\mathbf{C} \\in \\mathbb{R}^{M \\times 2F}$ , which stores $F$ -dimensional local geometry and texture features respectively for each vertex.", + "bbox": [ + 496, + 441, + 890, + 532 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Similar to coordinate-based implicit fields, a 3D coordinate $\\mathbf{x}_g\\in \\mathbb{R}^3$ is used to predict its corresponding signed distance $s(\\mathbf{x}_g)$ , and RGB color $c(\\mathbf{x}_g)$ . Instead of using global coordinates directly as inputs, we condition neural field decoders on the local triangle coordinates $\\mathbf{x}_l\\in \\mathbb{R}^3$ and the local geometry and texture features $\\mathbf{f_s},\\mathbf{f_c}\\in \\mathbb{R}^F$ . We illustrate this conversion from global coordinates to local triangle coordinates in Fig. 3. The global coordinates $\\mathbf{x}_g$ are first projected onto the mesh by finding the closest point $\\mathbf{x}_c^*$ (Fig. 3, blue dot):", + "bbox": [ + 496, + 532, + 890, + 684 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathbf {x} _ {c} ^ {*} = \\arg \\min _ {\\mathbf {x} _ {c}} \\| \\mathbf {x} _ {g} - \\mathbf {x} _ {c} \\| _ {2}, \\\\ \\mathbf {x} _ {c} = \\mathcal {B} _ {u, v} (\\mathcal {V} [ m _ {0}, m _ {1}, m _ {2} ]), \\tag {1} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 598, + 695, + 890, + 738 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $(m_0, m_1, m_2)$ are vertex indices of faces $\\mathcal{F}$ and $\\mathcal{B}_{u,v}(.)$ is the barycentric interpolation function with barycentric coordinates $(u, v, 1 - u - v)$ . The closest point $\\mathbf{x}_c^*$ within the face is used to transform $\\mathbf{x}_g$ into a local triangle coordinate system. Hence, $\\mathbf{x}_l$ consists of the barycentric coordinates $(u, v)$ , the signed distance $d$ between $\\mathbf{x}_g$ and $\\mathbf{x}_c^*$ , i.e., $\\mathbf{x}_l \\coloneqq (u, v, d)$ . We also compute a direction vector $\\vec{\\mathbf{n}}$ between $\\mathbf{x}_g$ and $\\mathbf{x}_c^*$ as an additional feature to distinguish points near triangle edges. To query local features $(\\mathbf{f_s}, \\mathbf{f_c})$ , we use the vertex indices on the triangle $(m_0^*, m_1^*, m_2^*)$ to", + "bbox": [ + 496, + 750, + 890, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "21026", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/aed628843baf272ee99fcfe945856dc0bc3c7b57cadd16d8ed6f5ff8032e100e.jpg", + "image_caption": [ + "Figure 3. Local feature querying. Given an LBS body mesh posed by the parameters $(\\theta, \\beta)$ , we represent a detailed human body as a codebook that stores local texture and geometry features indexed by the vertices on the mesh. An input query point $\\mathbf{x_g}$ finds the nearest triangle on the LBS body mesh and returns its vertex indices for local feature lookup. To prevent decoders from memorizing any global information, we transform the position of $\\mathbf{x_g}$ into local triangle coordinates $(u, v)$ , distance $d$ , and direction $\\vec{\\mathbf{n}}$ of the closest point (i.e., the blue dot). The final geometry and texture features $\\mathbf{f_s}, \\mathbf{f_c}$ are fused via barycentric interpolation." + ], + "image_footnote": [], + "bbox": [ + 86, + 92, + 460, + 306 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "look up three elements in the feature codebook C. We then fuse the three local features via barycentric interpolation.", + "bbox": [ + 75, + 465, + 468, + 496 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, we take all the local features $(\\mathbf{f_s},\\mathbf{f_c},\\mathbf{x}_l,\\vec{\\mathbf{n}})$ as input to two separate decoders $\\Phi$ and $\\Psi$ to predict SDF and RGB values respectively:", + "bbox": [ + 75, + 496, + 470, + 542 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\Phi : \\mathbb {R} ^ {F} \\times \\mathbb {R} ^ {3} \\times \\mathbb {R} ^ {3} \\rightarrow \\mathbb {R} \\tag {2} \\\\ \\left(\\mathbf {f} _ {\\mathbf {s}}, \\mathbf {x} _ {l}, \\vec {\\mathbf {n}}\\right) \\mapsto s \\left(\\mathbf {x} _ {g}\\right), \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 550, + 468, + 588 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\boldsymbol {\\Psi}: \\mathbb {R} ^ {F} \\times \\mathbb {R} ^ {3} \\times \\mathbb {R} ^ {3} \\rightarrow \\mathbb {R} ^ {3} \\tag {3} \\\\ \\left(\\mathbf {f} _ {\\mathbf {c}}, \\mathbf {x} _ {l}, \\vec {\\mathbf {n}}\\right) \\mapsto c \\left(\\mathbf {x} _ {g}\\right). \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 173, + 598, + 468, + 637 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Note that only local information is exposed to the decoders, which allows us to use the same MLPs across different vertices and subjects. We show that preventing networks from memorizing global information in this way is necessary for local editing and reposing in our experiments (Fig. 9).", + "bbox": [ + 75, + 641, + 468, + 718 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Generative Codebook Sampling", + "text_level": 1, + "bbox": [ + 76, + 726, + 359, + 744 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our goal is to provide means to create and personalize avatars with diverse body shapes, appearances, and local details. To this end, we leverage the above representation to train a single multi-subject model which enables the transfer of local features across subjects. We note that since the mesh topology of the LBS model is identical, this enables us to learn a shared feature space from multiple posed scans.", + "bbox": [ + 75, + 750, + 468, + 854 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To learn the feature representation over a dataset of $N$ scans, it is sufficient to store the codebooks $\\mathbf{C}_i$ in two dictionaries $\\mathbf{D_s},\\mathbf{D_c}\\in \\mathbb{R}^{N\\times (MF)}$ to represent shape and color", + "bbox": [ + 75, + 854, + 470, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "information of the $i$ -th subject respectively. The entries $\\mathbf{C}_i$ can then be learned jointly with the decoder weights via direct 3D supervision using the $i$ -th scan (Fig. 2). However, we experimentally show that this is insufficient to learn a well-behaved latent space from which we can draw novel samples (see Fig. 10).", + "bbox": [ + 496, + 90, + 890, + 181 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Therefore we introduce a codebook sampling strategy that allows us to draw random samples and update the entries of the dictionaries $\\mathbf{D}_{\\mathbf{s}}$ , $\\mathbf{D}_{\\mathbf{c}}$ via an additional 2D adversarial loss. More specifically, we follow the auto-decoder architecture [52] and perform PCA on the reshaped dictionary to compute eigenvectors $\\mathbf{V} \\in \\mathbb{R}^{D \\times (MF)}$ and fit a normal distribution to the $D$ -dimensional PCA coefficients of $N$ samples. A new random codebook $\\mathbf{C}_r$ can then be generated by sampling $D$ -dimensional PCA parameters and multiplying them with the eigenvectors $\\mathbf{V}$ (See SuppB.1 for details). Note that our representation disentangles shapes from appearances with separated geometry and texture branches, which enables independent sampling of geometry and texture features.", + "bbox": [ + 496, + 181, + 892, + 393 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Model Training", + "text_level": 1, + "bbox": [ + 500, + 402, + 656, + 419 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3D reconstruction loss. To train a codebook $\\mathbf{C}_i$ with a single scan, we sample points in a thin shell around the scan. For each coordinate we compute its signed distance $s$ to the input scan, closest texture color $\\mathbf{c}$ , and surface normal $\\mathbf{n}$ on the input scan to attain ground truth values. The codebooks and the decoder weights are then optimized via the following losses:", + "bbox": [ + 496, + 425, + 890, + 532 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {s d f} = \\| s - s (\\mathbf {x} _ {\\mathbf {g}}) \\| _ {1} + \\lambda_ {n} \\| 1 - \\mathbf {n} \\cdot \\nabla_ {\\mathbf {x} _ {\\mathbf {g}}} s (\\mathbf {x} _ {\\mathbf {g}}) \\| _ {1}, \\quad (4)\n$$\n", + "text_format": "latex", + "bbox": [ + 519, + 542, + 890, + 561 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {r g b} = \\left\\| \\mathbf {c} - c \\left(\\mathbf {x} _ {\\mathbf {g}}\\right) \\right\\| _ {1}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 619, + 585, + 890, + 604 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {3 D} = \\lambda_ {s d f} \\mathcal {L} _ {s d f} + \\lambda_ {r g b} \\mathcal {L} _ {r g b}. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 594, + 625, + 890, + 642 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "2D adversarial loss. Adversarial learning does not require exact ground-truth annotations but is trained via a collection of real and fake (rendered) images. Thus, real images are obtained by rasterizing the ground-truth scan to which the coarse body mesh $\\mathcal{M}$ was fitted. Color and normal images (denoted as \"Real Patch\" in Fig. 2) are used for learning texture and geometry respectively. Using the same virtual camera parameters and the coarse mesh $\\mathcal{M}$ , we attain rendered patches (denoted as \"Rendered Patch\" Fig. 2) via implicit surface rendering of a sampled codebook $\\mathbf{C}_r$ . Please refer to Supp-B.2 for more details.", + "bbox": [ + 496, + 657, + 890, + 824 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Using these 2D patches, we train dictionaries, decoders, and discriminators jointly with a non-saturating logistic loss $\\mathcal{L}_{adv}$ [20], R1 regularization $\\mathcal{L}_{R1}$ [38], and path length regularization $\\mathcal{L}_{path}$ [27]. Note that these losses do not require exact ground-truth replication. Furthermore, we regu", + "bbox": [ + 496, + 825, + 890, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "21027", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "larize the feature dictionaries to follow a Gaussian distribution [46] with $\\mathcal{L}_{reg} = \\| \\mathbf{D}\\| _F$ . In summary, we optimize the discriminator:", + "bbox": [ + 75, + 90, + 468, + 135 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {d i s}} = \\mathcal {L} _ {\\text {a d v}} + \\lambda_ {R 1} \\mathcal {L} _ {R 1}, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 189, + 150, + 468, + 167 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "while updating the remaining components $(\\mathbf{D}_{\\mathbf{c}},\\mathbf{D}_{\\mathbf{s}},\\Phi ,\\Psi)$", + "bbox": [ + 76, + 178, + 464, + 194 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = - \\mathcal {L} _ {a d v} + \\mathcal {L} _ {3 D} + \\lambda_ {\\text {p a t h}} \\mathcal {L} _ {\\text {p a t h}} + \\lambda_ {\\text {r e g}} \\mathcal {L} _ {\\text {r e g}}, \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 114, + 205, + 468, + 223 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\lambda_{(\\cdot)}$ denotes weights to balance the losses.", + "bbox": [ + 76, + 234, + 398, + 250 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Since we sample on the fly during training (see Sec. 3.2), the 2D adversarial loss does affect the shared decoders and the whole feature dictionaries (See Supp-Fig.14 for details).", + "bbox": [ + 75, + 250, + 468, + 296 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Feature Editing and Avatar Customization", + "text_level": 1, + "bbox": [ + 76, + 306, + 439, + 321 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We now describe how we integrate the above-mentioned human representation and the generative architecture into the avatar creation workflow shown in Fig. 1.", + "bbox": [ + 75, + 330, + 468, + 375 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Avatar initialization. To simplify the avatar creation process, our method allows users to start with a default example $\\mathbf{C}_d$ , which can be queried from the trained codebook dictionaries directly with index $i$ ( $\\mathbf{C}_i$ ) or randomly sampled from the learned D-dimensional PCA parameters distribution ( $\\mathbf{C}_r$ in Sec. 3.2).", + "bbox": [ + 75, + 383, + 468, + 473 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Model fitting. Being able to extract elements of interest or copying from existing 3D assets is necessary for avatar creation and editing. To this end, we leverage a similar technique to GAN inversion [69], where decoder parameters $\\Phi$ and $\\Psi$ are frozen and we only optimize a new feature codebook $\\mathbf{C}_{fit}$ to fit a 3D scan.", + "bbox": [ + 75, + 481, + 468, + 571 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Given a 3D target scan and its corresponding body model parameters, we calculate the 3D reconstruction loss in Eq. (6) between the prediction conditioned on $\\mathbf{C}_{fit}$ and the ground-truth scan, i.e.,", + "bbox": [ + 75, + 571, + 468, + 632 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {C} _ {f i t} = \\arg \\min _ {\\mathbf {C}} \\left(\\lambda_ {s d f} \\mathcal {L} _ {s d f} + \\lambda_ {r g b} \\mathcal {L} _ {r g b}\\right). \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 138, + 645, + 468, + 667 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Note that our generative neural fields (MLP decoders) are conditioned on descriptive local features. We show that it allows us to accurately fits complex clothing geometry and unseen textural patterns in Sec. 4.3.", + "bbox": [ + 75, + 678, + 468, + 739 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Cross-subjects feature editing. With multiple codebooks each representing different 3D assets, we can easily transfer local geometry and texture from one avatar to another, for instance, changing the top wear from the fitted scan $\\mathbf{C}_{fit}$ to the initial $\\mathbf{C}_d$ . Recalling that all codebooks are indexed by an identical mesh topology, users can easily retrieve the index numbers of $\\nu_{body} \\subset \\nu$ via standard mesh visualization tools such as Blender [12]. Finally, swapping of the corresponding rows in $\\mathbf{C}_{fit}$ and $\\mathbf{C}_d$ given the vertex indices also swaps the local appearance.", + "bbox": [ + 75, + 758, + 468, + 910 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/d092e9f4fee83fe4cf392f1f1675d2a182e1d84b6a5419cd58daf1e392dd09b1.jpg", + "image_caption": [ + "Figure 4. Randomly sampled texture and body geometry from the model trained on THuman2.0. Top: Given any poses, our randomly sampled geometries contain realistic details such as wrinkles in garments and facial expressions. Bottom: Given arbitrary poses and body geometries, our model produces reasonable colors on skin, hair, clothes, and pants in each sample (We turn off the Shader for visualizing pure texture colors)." + ], + "image_footnote": [], + "bbox": [ + 501, + 92, + 880, + 375 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Personalized texture drawing. One can further customize clothing by drawing directly onto 2D images. Due to the learned disentangled feature spaces, we are able to update texture features in $\\mathbf{C}_{fit}$ while keeping the geometry features unchanged. When fitting 2D images, users draw on the rasterized images from arbitrary target scans. We then finetune only texture features in $\\mathbf{C}_{fit}$ via the RGB loss in Eq. (5) given the corresponding 3D coordinates and colors.", + "bbox": [ + 498, + 497, + 890, + 618 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Avatar reposing. Our representation combines a base mesh $\\mathcal{M}$ and underlying feature codebooks $\\mathbf{C}_{fit}$ that learn local geometry and texture on the 3D scans. Hence, one can repose the mesh $\\mathcal{M}$ using $(\\theta, \\beta)$ parameters, which also reposes the avatar correspondingly. Since the geometry codebook does not contain global pose $(\\theta, \\beta)$ , local information can be consistently applied to $\\mathcal{M}$ under unseen poses.", + "bbox": [ + 498, + 640, + 890, + 746 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 767, + 632, + 784 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our goal is locally editable 3D avatar creation. Since we are the first to discuss this problem, we visualize our editing results in Sec. 4.2. Next, we highlight the capability of model fitting by comparing our method with SOTA human generative models in Sec. 4.3. Finally, controlled experiments are presented in Sec. 4.4 and Sec. 4.5 to verify the effectiveness of our design.", + "bbox": [ + 498, + 794, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "21028", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/08f796d97ccd518aa6921982dfa6ada2a55fd08f5a54bd8f34657813f0bba8c5.jpg", + "image_caption": [ + "Figure 5. Cross-subject feature editing results. We partially transfer local clothing details from the unseen scans (upper and lower body) to the input avatars. The results of the edited avatars are shown in the right column." + ], + "image_footnote": [], + "bbox": [ + 83, + 89, + 468, + 333 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Experiment Settings", + "text_level": 1, + "bbox": [ + 76, + 407, + 269, + 422 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Dataset. Most generative human works [9,44,45] exploit commercial data [1,2] for training, which is not easily accessible and limits reproducibility. Furthermore, the quality of publicly available 3D human datasets [66,73] is not satisfactory. Issues such as non-watertight topologies and noise are very common (See Supp-A.2 for examples and comparison). To bridge this gap, we collect a new dataset named CustomHumans for training and evaluation. Here we summarize the datasets used in our experiments.", + "bbox": [ + 75, + 430, + 468, + 566 + ], + "page_idx": 5 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- CustomHumans (Ours) contains more than 600 high-quality scans of 80 participants in 120 garments in varied poses from a volumetric capture stage [11], which is equipped with 106 synchronized cameras (53 RGB and 53 IR cameras). We use our dataset to train models of all quantitative experiments. (Sec. 4.3 ~ Sec. 4.5)", + "- THuman2.0 [73] is a dataset containing about 500 scans of humans wearing 150 garments in various poses. Since this dataset has more textural diversity, we train our method on it for qualitative random sampling experiments (Sec. 4.2 and Sec. 4.4).", + "- SIZER [66] is a widely used 3D scan dataset containing A-posed human meshes of 97 subjects in 22 garments. These meshes are used as unseen test scans in our fitting experiment (Sec. 4.3)." + ], + "bbox": [ + 94, + 575, + 468, + 821 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation protocol. Following the evaluation protocol in OccNet [39], we quantitatively evaluate the model fitting accuracy using three metrics: Chamfer distance (CD), normal consistency (NC), and f-Score.", + "bbox": [ + 75, + 839, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/3698524d046b85c70bb257d34dc20a565828d0a4c33a93db41f91ba65045105c.jpg", + "image_caption": [ + "Figure 6. Personalized texture editing. We draw personalized logos on 2D images and fit avatars' texture features to the images. These local textures remain consistent under pose changes." + ], + "image_footnote": [], + "bbox": [ + 500, + 90, + 890, + 202 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2.Customized Avatars", + "text_level": 1, + "bbox": [ + 500, + 253, + 689, + 268 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We visualize the results of our proposed avatar customization workflow described in Sec. 3.4.", + "bbox": [ + 500, + 277, + 888, + 306 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Avatar initialization. In Fig. 4, we show random textures and geometries sampled from the model trained on THuman2.0. Our method is able to generate reasonable colors and wrinkles in arbitrary poses. Note that the sampled geometries are shown as the real meshes but not as rendered normals as in [9] (See Supp-C.2 for comparisons).", + "bbox": [ + 498, + 311, + 890, + 402 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Cross-subjects feature editing. After fitting feature codebooks to 3D scans, we can change the clothes on our avatars by swapping the local features stored on the body vertices. We select the features within the upper body and lower body areas. We then copy these local features to the initial avatars' feature codebooks. As shown in Fig. 5, our method is able to handle multiple garments on different human subjects and preserves consistent details under different body poses or shapes.", + "bbox": [ + 498, + 407, + 890, + 545 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Personalized texture drawing. Our method allows users to draw complex letters and logos on images for personalized texture editing. We perform the model fitting and feature editing process but only optimize the texture features in the codebooks using user-edited images and the RGB loss (Eq. (5)). Fig. 6 shows that new texture can be seamlessly applied to the 3D avatars. It is worth noting that resulting avatars enable detailed pose control via the SMPL-X parameters without affecting the fitted texture and geometry.", + "bbox": [ + 498, + 549, + 890, + 686 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Model Fitting Comparison", + "text_level": 1, + "bbox": [ + 500, + 696, + 740, + 712 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Since model fitting is an important step in our avatar creation workflow, we compare the capability of feature inversion using unseen 3D scans. The goal of this task is to invert a 3D scan into latent codes while keeping the remaining model parameters fixed. We compare our method with the 3D human generative model gDNA [9], which has achieved state-of-the-art performance in fitting the geometry of 3D human bodies. We also directly compare with SMPL [33] and SMPL+D [3]. Note that SMPL+D is a stronger vertex-based extension that uses a subdivided version of SMPL to directly register surfaces to scans while our method and gDNA optimize latent codes.", + "bbox": [ + 498, + 719, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "21029", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/bdc903a5558034f6550624b0c802219c9e7182cb2514cc54d195e506ffd7b357.jpg", + "image_caption": [ + "Figure 7. Qualitative comparison of model fitting on SIZER. We visualize the fitting results from gDNA [9], SMPL+D [3], and our method. Our results are perceptually close to the ground truth even on the challenging test cases of jackets and loose t-shirts." + ], + "image_footnote": [], + "bbox": [ + 78, + 89, + 467, + 291 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/779ffaae00391ed9ec726853e6dc50f2875c43cb70e2b03afab5da49e4df7a54.jpg", + "image_caption": [ + "Figure 8. Qualitative comparison of texture fitting. We compare our method with SMPL+D [3] by fitting to unseen textured meshes. The performance of SMPL+D is limited by its geometry and texture resolution." + ], + "image_footnote": [], + "bbox": [ + 117, + 373, + 429, + 472 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "From Fig. 7 we can see that SMPL+D handles loose clothing, such as a business suite, better than gDNA. However, the surfaces of SMPL+D results are over-smoothed and do not contain high-frequency details while ours can preserve them. Quantitatively, our method consistently outperforms these methods on all metrics as shown in Tab. 1.", + "bbox": [ + 75, + 547, + 468, + 638 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Fig. 8 depicts the result of texture fitting against the SMPL+D baseline. While both methods inherit a fixed mesh topology the quality of SMPL+D is limited by its model resolution. Our method addresses this issue via local neural fields that enable cross-subject feature editing of texture and geometry with enhanced representational power.", + "bbox": [ + 75, + 638, + 468, + 729 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation Study", + "text_level": 1, + "bbox": [ + 76, + 739, + 230, + 757 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effectiveness of local features and shared decoders. To verify the design choice of using local features, we replace the local features $\\mathbf{x}_l$ by the global coordinates $\\mathbf{x}_g$ for conditioning the decoders. Fig. 9 shows that even though the shared decoders are able to achieve similar reconstruction results when training with global information, they do not maintain consistent performance for model fitting and avatar reposing. This is because the shared decoders tend to memorize global coordinates information in a \"per-subject\"", + "bbox": [ + 75, + 763, + 470, + 902 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/bf4e38a142b897967b1714d2123ef9299747f2c32a283263d044fe279c4456d7.jpg", + "image_caption": [ + "Figure 9. Comparison of using global/local features for conditioning decoders. The use of global coordinates causes overfitting and memorization to the shared decoders, which makes it struggle to handle unseen scans or novel body poses." + ], + "image_footnote": [], + "bbox": [ + 506, + 90, + 885, + 361 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/ca8908cc11016565690cfa22bc64f84999cfb057ae4d2098602f58b3ba3c664f.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodPred-to-Scan / \nScan-to-Pred (mm)↓NC↑f-Score↑
SMPL [33]13.60 / 18.030.8490.458
gDNA [9]8.374 / 8.0060.8420.718
SMPL+D [3]5.192 / 2.8540.9110.962
Ours1.364 / 1.4230.9490.997
", + "bbox": [ + 514, + 450, + 879, + 551 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 1. Model fitting comparison on SIZER. We report Chamfer distance, normal consistency (NC), and f-score between ground truth and the meshes fitted by different methods.", + "bbox": [ + 498, + 561, + 892, + 603 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "manner, rather than learning shareable information that can be used across vertices and subjects. On the other hand, our representation ensures only local features defined on the triangle coordinates are exposed to the shared decoders. In such cases, the decoders can better handle unseen body poses or out-of-distribution samples for model fitting and avatar editing.", + "bbox": [ + 496, + 619, + 893, + 726 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Importance of 2D adversarial loss and 3D disentanglement. As discussed in Sec. 3.3, we introduce feature disentanglement and generative adversarial learning in our training framework. As shown in Fig. 10, sampling within the feature spaces learned without adversarial loss does not yield reasonable body textures. Similarly, training only a single decoder for both geometry and texture does not allow us to maintain desired body geometries when sampling random textures. Our full model can produce disentangled textures, given arbitrary body geometries and poses.", + "bbox": [ + 496, + 750, + 893, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "21030", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/f687f2f486386137f00d0325add4112a70fd2ca58578812656488c1554574394.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Training Data Percentage10%25%50 %75%100%
Chamfer Distance (mm) \nS-to-P / P-to-S ↓1.933 / 1.7981.754 / 1.5901.543 / 1.4561.463 / 1.3851.423 / 1.364
Normal Consistency↑0.9180.9310.9350.9470.949
f-Score (%) ↑99.2599.3899.6599.7499.75
", + "bbox": [ + 125, + 88, + 844, + 180 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 2. Generalization analysis on CustomHumans. We analyze the model fitting performances with regard to different amounts of training data (100% = 100 training scans). We observe consistent performance gain on all evaluation metrics when using more training subjects to train the shared decoders.", + "bbox": [ + 75, + 191, + 893, + 232 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/a2c3420356ae7cfdc1108abec096c6dac78c4a8d6cdc05b77a10e202bc8e7125.jpg", + "image_caption": [ + "Figure 10. Ablative comparison of our framework designs. We visualize the results of transferring random texture to given body geometry. Our full model produces reasonable body texture and is able to maintain fixed geometry for texture editing." + ], + "image_footnote": [], + "bbox": [ + 76, + 234, + 468, + 529 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.5. Generalization Ability Analysis", + "text_level": 1, + "bbox": [ + 76, + 614, + 354, + 632 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We are interested in how the amount of training data affects the capacities of the MLP decoders. To analyze this, we design three evaluation protocols: 3D model fitting, avatar reposing, and 2D texture fitting. Tab. 2 summarizes the model fitting performance using different percentages of training data. We observe a $25\\%$ accuracy improvement when using the full training set. In Fig. 11 (Top) we show that the reposing artifacts caused by self-contact (e.g., fist and elbow) can be reduced when training the MLP decoders with more poses and subjects. In addition, Fig. 11 (Bottom) depicts a qualitative comparison of 2D texture editing under different training data percentages. We evaluate texture editing quality by fitting a 2D image with unseen geometric shapes and colors. It can be seen that the model trained on more samples is able to handle a wider range of color distribution. These results confirm the necessity for learning multi-subject shared decoders in our task.", + "bbox": [ + 75, + 643, + 470, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/9b02ee386727fe317ceb85097835bb232307ffac0eb0d536bb42dafdebe3bf56.jpg", + "image_caption": [ + "Figure 11. Qualitative comparison of generalization. Top: We visualize the results of avatar reposing using different percentages of training data (i.e., 10 meshes vs 100 meshes). Artifacts caused by self-contact (e.g., fist and elbow) can be reduced when more training subjects are introduced. Bottom: We visualize the results using different percentages of training data. Using more data results in more robust shared decoders (with a wider color range), which is necessary for the avatar creation task." + ], + "image_footnote": [], + "bbox": [ + 506, + 234, + 890, + 445 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 582, + 617, + 598 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We propose an end-to-end trainable framework for learning 3D human avatars with high fidelity and full editability. By combining neural fields with explicit skinned meshes, our representation addresses the controllability issue of many previous implicit representations. Moreover, we uniquely integrate the proposed human representation into a generative auto-decoding pipeline that enables local editing across multiple animation-ready avatars. Through our evaluation on the newly contributed CustomHumans dataset, we demonstrate that our approach achieves higher model fitting accuracy and generates diverse detailed avatars. We believe that this work opens up exciting possibilities for accelerating content creation in the Metaverse.", + "bbox": [ + 496, + 608, + 890, + 803 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements We express our gratitude to Stefan Walter and Dean Bakker for infrastructure support, Juan Zarate for managing the capture stage, and Deniz Yildiz and Laura Wulfroth for data capture assistance. We thank Andrew Searle for supporting the capturing system and all the dataset participants.", + "bbox": [ + 496, + 810, + 892, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "21031", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] https://3dpeople.com/.6", + "[2] https://renderpeople.com/.6", + "[3] Thiemo Alldieck, Marcus Magnor, Bharat Lal Bhatnagar, Christian Theobalt, and Gerard Pons-Moll. Learning to reconstruct people in clothing from a single rgb camera. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1175-1186, 2019. 2, 6, 7", + "[4] Thiemo Alldieck, Mihai Zanfir, and Cristian Sminchisescu. Photorealistic monocular 3d reconstruction of humans wearing clothing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2", + "[5] Alexander W Bergman, Petr Kellnhofer, Yifan Wang, Eric R Chan, David B Lindell, and Gordon Wetzstein. Generative neural articulated radiance fields. In Advances in Neural Information Processing Systems (NeurIPS), 2022. 1, 2", + "[6] Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Combining implicit function learning and parametric models for 3d human reconstruction. In Proceedings of the European Conference on Computer Vision (ECCV), pages 311-329. Springer, 2020. 2", + "[7] Caroline Chan, Shiry Ginosar, Tinghui Zhou, and Alexei A Efros. Everybody dance now. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 5933-5942, 2019. 2", + "[8] Xu Chen, Tianjian Jiang, Jie Song, Max Rietmann, Andreas Geiger, Michael J. Black, and Otmar Hilliges. FastSNARF: A fast deformer for articulated neural fields. arXiv, abs/2211.15601, 2022. 2", + "[9] Xu Chen, Tianjian Jiang, Jie Song, Jinlong Yang, Michael J Black, Andreas Geiger, and Otmar Hilliges. gdna: Towards generative detailed neural avatars. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 20427-20437, 2022. 1, 2, 6, 7", + "[10] Xu Chen, Yufeng Zheng, Michael J Black, Otmar Hilliges, and Andreas Geiger. Snarf: Differentiable forward skimming for animating non-rigid neural implicit shapes. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021. 2", + "[11] Alvaro Collet, Ming Chuang, Pat Sweeney, Don Gillett, Dennis Evseev, David Calabrese, Hugues Hoppe, Adam Kirk, and Steve Sullivan. High-quality streamable free-viewpoint video. ACM Transactions on Graphics (TOG), 34(4):1-13, 2015. 2, 6", + "[12] Blender Online Community. Blender - a 3D modelling and rendering package. Blender Foundation, Stichting Blender Foundation, Amsterdam, 2018. 5", + "[13] Enric Corona, Albert Pumarola, Guillem Alenyà, Gerard Pons-Moll, and Francesc Moreno-Noguer. Smplicit: Topology-aware generative model for clothed people. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 1, 2", + "[14] Boyang Deng, JP Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, and Andrea Tagliasacchi. Neural articulated shape approximation. In" + ], + "bbox": [ + 78, + 114, + 468, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Proceedings of the European Conference on Computer Vision (ECCV). Springer, August 2020. 2", + "[15] Terrance DeVries, Miguel Angel Bautista, Nitish Srivastava, Graham W Taylor, and Joshua M Susskind. Unconstrained scene generation with locally conditioned radiance fields. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 14304-14313, 2021. 2", + "[16] Haoye Dong, Xiaodan Liang, Xiaohui Shen, Bowen Wu, Bing-Cheng Chen, and Jian Yin. Fw-gan: Flow-navigated warping gan for video virtual try-on. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 1161-1170, 2019. 2", + "[17] Xin Dong, Fuwei Zhao, Zhenyu Xie, Xijin Zhang, Daniel K. Du, Min Zheng, Xiang Long, Xiaodan Liang, and Jianchao Yang. Dressing in the wild by watching dance videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3480-3489, June 2022. 2", + "[18] Jianglin Fu, Shikai Li, Yuming Jiang, Kwan-Yee Lin, Chen Qian, Chen Change Loy, Wayne Wu, and Ziwei Liu. Stylegan-human: A data-centric odyssey of human generation. In Proceedings of the European Conference on Computer Vision (ECCV), pages 1-19. Springer, 2022. 2", + "[19] Rony Goldenthal, David Harmon, Raanan Fattal, Michel Bercovier, and Eitan Grinspun. Efficient simulation of inextensible cloth. ACM Transactions on Graphics (TOG), pages 49-es, 2007. 2", + "[20] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems (NeurIPS), pages 2672–2680, 2014. 2, 4", + "[21] Artur Grigorev, Karim Iskakov, Anastasia Iinina, Renat Bashirov, Ilya Zakharkin, Alexander Vakhitov, and Victor Lempitsky. Stylepeople: A generative model of fullbody human avatars. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5151-5160, 2021. 1, 2", + "[22] Artur Grigorev, Bernhard Thomaszewski, Michael J Black, and Otmar Hilliges. HOOd: Hierarchical graphs for generalized modelling of clothing dynamics. 2023. 2", + "[23] Chen Guo, Tianjian Jiang, Xu Chen, Jie Song, and Otmar Hilliges. Vid2 avatar: 3d avatar reconstruction from videos in the wild via self-supervised scene decomposition. In Computer Vision and Pattern Recognition (CVPR), 2023. 2", + "[24] Pat Hanrahan and Paul Haeberli. Direct wysiwyg painting and texturing on 3d shapes: (an error occurred during the printing of this article that reversed the print order of pages 118 and 119. while we have corrected the sort order of the 2 pages in the dl, the pdf did not allow us to repaginae the 2 pages.). ACM Transactions on Graphics (TOG), 24(4):215-223, 1990. 2", + "[25] Fangzhou Hong, Zhaoxi Chen, Yushi Lan, Liang Pan, and Ziwei Liu. Eva3d: Compositional 3d human generation from 2d image collections. arXiv preprint arXiv:2210.04888, 2022. 1, 2", + "[26] Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Alias-free" + ], + "bbox": [ + 503, + 92, + 890, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "21032", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "generative adversarial networks. Advances in Neural Information Processing Systems (NeurIPS), 34:852-863, 2021. 2", + "[27] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110-8119, 2020. 4", + "[28] Christoph Lassner, Gerard Pons-Moll, and Peter V Gehler. A generative model of people in clothing. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 853-862, 2017. 2", + "[29] Ruilong Li, Shan Yang, David A. Ross, and Angjoo Kanazawa. Ai choreographer: Music conditioned 3d dance generation with aist++. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021. 2", + "[30] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural actor: Neural free-view synthesis of human actors with pose control. ACM Transactions on Graphics (TOG), 40(6):1-16, 2021. 2", + "[31] Wen Liu, Zhixin Piao, Jie Min, Wenhan Luo, Lin Ma, and Shenghua Gao. Liquid warping gan: A unified framework for human motion imitation, appearance transfer and novel view synthesis. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 5904-5913, 2019. 2", + "[32] Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaou Tang. Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1096-1104, 2016. 2", + "[33] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black. SMPL: A skinned multiperson linear model. ACM Transactions on Graphics (TOG), 34(6):248:1-248:16, October 2015. 2, 6, 7", + "[34] Qianli Ma, Shunsuke Saito, Jinlong Yang, Siyu Tang, and Michael J. Black. SCALE: Modeling clothed humans with a surface codec of articulated local elements. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 16082-16093, June 2021. 2", + "[35] Qianli Ma, Jinlong Yang, Michael J. Black, and Siyu Tang. Neural point-based shape modeling of humans in challenging clothing. In 2022 International Conference on 3D Vision (3DV), September 2022. 2", + "[36] Qianli Ma, Jinlong Yang, Anurag Ranjan, Sergi Pujades, Gerard Pons-Moll, Siyu Tang, and Michael J. Black. Learning to Dress 3D People in Generative Clothing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 1, 2", + "[37] Qianli Ma, Jinlong Yang, Siyu Tang, and Michael J. Black. The power of points for modeling humans in clothing. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 2021. 2", + "[38] Lars Mescheder, Andreas Geiger, and Sebastian Nowozin. Which training methods for gans do actually converge? In International Conference on Machine Learning (ICML), pages 3481-3490. PMLR, 2018. 4" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[39] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4460-4470, 2019. 6", + "[40] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-102:15, July 2022. 2", + "[41] Rahul Narain, Armin Samii, and James F O'brien. Adaptive anisotropic remeshing for cloth simulation. ACM Transactions on Graphics (TOG), 31(6):1-10, 2012. 2", + "[42] Atsuhiro Noguchi, Xiao Sun, Stephen Lin, and Tatsuya Harada. Unsupervised learning of efficient geometry-aware neural articulated representations. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 1, 2", + "[43] Ahmed AA Osman, Timo Bolkart, and Michael J Black. Star: Sparse trained articulated human body regressor. In Proceedings of the European Conference on Computer Vision (ECCV), pages 598-613. Springer, 2020. 2", + "[44] Pablo Palafox, Aljaž Božić, Justus Thies, Matthias Nießner, and Angela Dai. Npms: Neural parametric models for 3d deformable shapes. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021. 1, 2, 6", + "[45] Pablo Palafox, Nikolaos Sarafianos, Tony Tung, and Angela Dai. Spams: Structured implicit parametric models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 1, 2, 6", + "[46] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 165-174, 2019. 2, 5", + "[47] Priyanka Patel, Chun-Hao P Huang, Joachim Tesch, David T Hoffmann, Shashank Tripathi, and Michael J Black. Agora: Avatars in geography optimized for regression analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 13468-13478, 2021. 2", + "[48] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3d hands, face, and body from a single image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 10975-10985, 2019. 2", + "[49] Hans Köhling Pedersen. Decorating implicit surfaces. In ACM Transactions on Graphics (TOG), pages 291-300, 1995. 2", + "[50] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 9054–9063, 2021. 2", + "[51] Sergey Prokudin, Michael J Black, and Javier Romero. Smplpix: Neural avatars from 3d human models. In Proceed-" + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "21033", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1810-1819, 2021. 2", + "[52] Daniel Rebain, Mark Matthews, Kwang Moo Yi, Dmitry Lagun, and Andrea Tagliasacchi. Lolnerf: Learn from one look. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1558-1567, 2022. 2, 4", + "[53] Edoardo Remelli, Timur Bagautdinov, Shunsuke Saito, Chenglei Wu, Tomas Simon, Shih-En Wei, Kaiwen Guo, Zhe Cao, Fabian Prada, Jason Saragih, et al. Drivable volumetric avatars using texel-aligned features. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1–9, 2022. 2", + "[54] Daniel Roich, Ron Mokady, Amit H. Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. ACM Transactions on Graphics (TOG), 42(1), aug 2022. 2", + "[55] Javier Romero, Dimitrios Tzionas, and Michael J. Black. Embodied hands: Modeling and capturing hands and bodies together. ACM Transactions on Graphics (TOG), 36(6), Nov. 2017. 2", + "[56] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 2019. 2", + "[57] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo. Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 84-93, 2020. 2", + "[58] Shunsuke Saito, Jinlong Yang, Qianli Ma, and Michael J. Black. SCANimate: Weakly supervised learning of skinned clothed avatar networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2021. 2", + "[59] Kaiyue Shen, Chen Guo, Manuel Kaufmann, Juan Zarate, Julien Valentin, Jie Song, and Otmar Hilliges. X- avatar: Expressive human avatars. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 2", + "[60] Yichun Shi, Divyansh Aggarwal, and Anil K Jain. Lifting 2d stylegan for 3d-aware face generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6258-6266, 2021. 2", + "[61] Zifan Shi, Sida Peng, Yinghao Xu, Yiyi Liao, and Yujun Shen. Deep generative models on 3d representations: A survey. arXiv preprint arXiv:2210.15663, 2022. 2", + "[62] Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler. Neural geometric level of detail: Real-time rendering with implicit 3D shapes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 2", + "[63] Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo MartinBrualla, Tomas Simon, Jason Saragih, Matthias Nießner, et al. State of the art on neural rendering. In Annual Conference of the European Association for Computer Graphics" + ], + "bbox": [ + 78, + 92, + 470, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "(EUROGRAPHICS), volume 39, pages 701-727. Wiley Online Library, 2020. 2", + "[64] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, W Yifan, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, et al. Advances in neural rendering. In Annual Conference of the European Association for Computer Graphics (EUROGRAPHICS), volume 41, pages 703-735. Wiley Online Library, 2022. 2", + "[65] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics (TOG), 38(4):1-12, 2019. 2", + "[66] Garvita Tiwari, Bharat Lal Bhatnagar, Tony Tung, and Gerard Pons-Moll. Sizer: A dataset and model for parsing 3d clothing and learning size sensitive 3d clothing. In Proceedings of the European Conference on Computer Vision (ECCV). Springer, August 2020. 1, 6", + "[67] Gul Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J Black, Ivan Laptev, and Cordelia Schmid. Learning from synthetic humans. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 109-117, 2017. 2", + "[68] Ting-Chun Wang, Arun Mallya, and Ming-Yu Liu. One-shot free-view neural talking-head synthesis for video conferencing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 10039-10049, 2021. 2", + "[69] Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang. Gan inversion: A survey. TPAMI, 2022. 5", + "[70] Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. Neural fields in visual computing and beyond. In Annual Conference of the European Association for Computer Graphics (EUROGRAPHICS), volume 41, pages 641-676. Wiley Online Library, 2022. 2", + "[71] Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, and Xiaodan Liang. Towards scalable unpaired virtual try-on via patch-routed spatially-adaptive gan. Advances in Neural Information Processing Systems (NeurIPS), 34:2598-2610, 2021. 2", + "[72] Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In Proceedings of the European Conference on Computer Vision (ECCV), pages 597-614. Springer, 2022. 2", + "[73] Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, and Yebin Liu. Function4d: Real-time human volumetric capture from very sparse consumer rgbd sensors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2021. 2, 6", + "[74] Zhixuan Yu, Jae Shin Yoon, In Kyu Lee, Prashanth Venkatesh, Jaesik Park, Jihun Yu, and Hyun Soo Park. Humbi: A large multiview dataset of human body expressions. In Proceedings of the IEEE Conference on Computer" + ], + "bbox": [ + 503, + 92, + 890, + 900 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "21034", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Vision and Pattern Recognition (CVPR), pages 2990-3000, 2020. 2", + "[75] Jianfeng Zhang, Zihang Jiang, Dingdong Yang, Hongyi Xu, Yichun Shi, Guoxian Song, Zhongcong Xu, Xinchao Wang, and Jiashi Feng. Avatargen: A 3d generative model for animatable human avatars. In Arxiv, 2022. 1, 2", + "[76] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C. Bühler, Xu Chen, Michael J. Black, and Otmar Hilliges. I M Avatar: Implicit morphable head avatars from videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + ], + "bbox": [ + 78, + 90, + 468, + 247 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "21035", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/2023/Learning Locally Editable Virtual Humans/81a768fa-0f17-4406-9706-ef4fe06ddff4_model.json b/2023/Learning Locally Editable Virtual Humans/81a768fa-0f17-4406-9706-ef4fe06ddff4_model.json new file mode 100644 index 0000000000000000000000000000000000000000..9da2ae3cf77c5b8a233cd005f6c94d09609c7031 --- /dev/null +++ b/2023/Learning Locally Editable Virtual Humans/81a768fa-0f17-4406-9706-ef4fe06ddff4_model.json @@ -0,0 +1,2424 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.267, + 0.131, + 0.703, + 0.154 + ], + "angle": 0, + "content": "Learning Locally Editable Virtual Humans" + }, + { + "type": "text", + "bbox": [ + 0.214, + 0.181, + 0.757, + 0.22 + ], + "angle": 0, + "content": "Hsuan-I Ho Lixin Xue Jie Song Otmar Hilliges Department of Computer Science, ETH Zürich" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.252, + 0.314, + 0.267 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.284, + 0.474, + 0.616 + ], + "angle": 0, + "content": "In this paper, we propose a novel hybrid representation and end-to-end trainable network architecture to model fully editable and customizable neural avatars. At the core of our work lies a representation that combines the modeling power of neural fields with the ease of use and inherent 3D consistency of skinned meshes. To this end, we construct a trainable feature codebook to store local geometry and texture features on the vertices of a deformable body model, thus exploiting its consistent topology under articulation. This representation is then employed in a generative auto-decoder architecture that admits fitting to unseen scans and sampling of realistic avatars with varied appearances and geometries. Furthermore, our representation allows local editing by swapping local features between 3D assets. To verify our method for avatar creation and editing, we contribute a new high-quality dataset, dubbed CustomHumans, for training and evaluation. Our experiments quantitatively and qualitatively show that our method generates diverse detailed avatars and achieves better model fitting performance compared to state-of-the-art methods. Our code and dataset are available at https://aith.ethz.ch/custom-humans." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.633, + 0.21, + 0.649 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.66, + 0.471, + 0.901 + ], + "angle": 0, + "content": "3D Avatars are an important aspect of many emerging applications such as 3D games or the Metaverse. Allowing for easy personalization of such avatars, holds the promise of increased user engagement. Traditionally, editing 3D assets requires knowledge of computer graphics tools and relies on standardized data formats to represent shapes and appearances. While methods for reconstruction or generative modeling of learned avatars achieve impressive results, it is unknown how such neural avatars can be edited and customized. Thus, the goal of our work is to contribute a simple, yet powerful data-driven method for avatar creation and customization (Fig. 1): our method enables (a) the ability to transfer partial geometric and appearance details between 3D assets, and (b) the ability to author details via 2D-3D transfer. The resulting avatars (c) retain consistent local details when posed." + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.253, + 0.891, + 0.513 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.523, + 0.894, + 0.607 + ], + "angle": 0, + "content": "Figure 1. Creating locally editable avatars: Given an input avatar, (a) the avatar can be edited by transferring clothing geometry and color details from existing, yet unseen 3D assets. (b) Users can customize clothing details such as logos and letters via drawing on 2D images. (c) The avatars retain local detail consistently under pose changes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.612, + 0.893, + 0.764 + ], + "angle": 0, + "content": "Existing methods do not allow for such capabilities. While 3D generative models of articulated human bodies [5, 21, 25, 42, 75] leverage differentiable neural rendering to learn from images, they cannot control local details due to highly entangled color and geometry in the 2D supervision signal. Generative models trained on 3D data [9, 13, 36, 44, 45] can produce geometric details for surfaces and clothing. However, the diversity of generated samples is low due to the lack of high-quality 3D human scans and not all methods model appearance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.894, + 0.903 + ], + "angle": 0, + "content": "At the core of the issue lies the question of representation: graphics tools use meshes, UV, and texture maps which provide consistent topologies under deformation. However, human avatar methods that are built on mesh-based representations and linear blend skinning (LBS) are limited in their representational power with respect to challenging geometry (e.g., puffy garments) and flexible topologies (e.g., jackets), even with adaptations of additional displacement parameters [36] and mesh subdivision [66]." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21024" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.473, + 0.333 + ], + "angle": 0, + "content": "Inspired by the recent neural 3D representations [40, 62, 70, 72], we propose a novel hybrid representation for digital humans. Our representation combines the advantages of consistent topologies of LBS models with the representational power of neural fields. The key idea is to decompose the tasks of deformation consistency on one hand and local surface and appearance description on the other. For the former, we leverage existing parametric body models (e.g., SMPL [33] and SMPL-X [48]). For the latter, we leverage the fixed topology of the poseable mesh to store local feature codebooks. A decoder, shared across subjects, is then conditioned on the local features to predict the final signed distance and color values. Since only local information [15] is exposed to the decoder, overfitting and memorization can be mitigated. We experimentally show that this is crucial for 3D avatar fitting and reposing." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.334, + 0.473, + 0.499 + ], + "angle": 0, + "content": "Complementing this hybrid representation, we propose a training pipeline in the auto-decoding generative framework [9, 46, 52]. To this end, we jointly optimize multi-subject feature codebooks and the shared decoder weights via 3D reconstruction and 2D adversarial losses. The 3D losses help in disentangling appearance and geometric information from the input scans, while the latter improves the perceptual quality of randomly generated samples. To showcase the hybrid representation and the generative model we implement a prototypical avatar editing workflow shown in Fig. 1." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.5, + 0.473, + 0.636 + ], + "angle": 0, + "content": "Furthermore, to enable research on high-quality 3D avatars we contribute training data for generative 3D human models. We record a large-scale dataset (more than 600 scans of 80 subjects in 120 garments) using a volumetric capture stage [11]. Our dataset consists of high-quality 3D meshes alongside accurately registered SMPL-X [48] models and will be made available for research purposes. Finally, we assess our design decisions in detailed evaluations, both on existing and the proposed datasets." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.636, + 0.473, + 0.743 + ], + "angle": 0, + "content": "In summary, our contributions are threefold: (a) a novel hybrid representation for 3D virtual humans that allows for local editing across subjects, (b) a generative pipeline of 3D avatars creation that allows for fitting to unseen 3D scans and random sampling, and (c) a new large-scale high-quality dataset of 3D human scans containing diverse subjects, body poses and garments." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.755, + 0.22, + 0.77 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.472, + 0.903 + ], + "angle": 0, + "content": "Controllable human representations. Topics of virtual humans have received much attention in the graphics literature, such as skinning and rigging of articulated meshes [33, 43, 48, 55], physical simulation of clothing [19, 22, 41], and deferred rendering [24, 49, 65]. With the advances in neural rendering [63, 64] and the availability of large-scale human datasets [18, 28, 29, 32, 47, 67, 73, 74], numerous approaches have been proposed to reconstruct [4, 56, 57] and explicitly" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.888, + 0.107 + ], + "angle": 0, + "content": "control [10,30,50] human avatars in a data-driven manner." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.108, + 0.892, + 0.272 + ], + "angle": 0, + "content": "One branch of work focuses on 2D image synthesis via generative adversarial networks (GANs) [20] and techniques of feature manipulation [28, 54, 60]. Typically, a 2D neural renderer creates human images corresponding to pose and appearance latent codes learned from the training data. Related applications such as virtual try-on [16, 17, 71] and video retargeting [7, 31, 68] have shown promising results in light of photo-realistic image synthesis by GANs [18,26]. However, these methods do not explicitly reason about complex 3D human geometry and can therefore not produce 3D-consistent results." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.274, + 0.893, + 0.486 + ], + "angle": 0, + "content": "A newly emerging line of work aims to create controllable avatars with 3D consistency. Some methods extend existing body models with neural networks to predict displacement layers [3, 6, 36] or textures [51]. Other methods learn to model challenging pose-dependent deformations on avatars either by predicting LBS weights [8, 10, 14, 58, 59, 76] or improving the capabilities of body models [23, 30, 34, 35, 37, 50, 53] with the power of implicit neural fields. However, these approaches mainly focus on modeling a single subject in specific clothing and do not scale to create diverse avatars. Our method overcomes this issue by learning a multi-subject generative model which produces realistic virtual humans with disentangled controllability over body poses, clothing geometry, and texture." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.493, + 0.893, + 0.674 + ], + "angle": 0, + "content": "Generative 3D human models. Existing generative models of human avatars can be loosely split into two main streams: learning 3D-aware neural rendering from 2D images [5, 21, 25, 42, 75] and learning body shapes from 3D supervision [9, 13, 36, 44, 45]. Powered by recent advances in differentiable neural rendering [64] and neural fields [70], much progress has been made in 3D-aware generative models [61]. However, learning to generate detailed clothed avatars from pure 2D supervision [5, 21, 25, 42, 75] is still challenging due to the complex appearance and articulation of bodies, self-occlusions, and highly entangled colors and geometries in images." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.675, + 0.895, + 0.902 + ], + "angle": 0, + "content": "More closely related to our setting are methods that learn to generate detailed body shapes from 3D scans or RGB-D data. For instance, CAPE [36] and SMPLict [13] are generative models for clothed humans. The former exploits VAE-GAN to predict additive displacements based on the SMPL vertices while the latter drape an implicitly modeled garment layer onto SMPL. NPMs [44], and SPAM [45] learn pose and shape latent spaces from 3D supervision, which enables latent code inversion using point clouds or depth sequences. gDNA [9] learns to synthesize body shapes in the canonical space and further improves clothing details with adversarial losses. However, none of the above-mentioned works is able to generate human bodies with appearance and neither allows fine-grained editing of the generated avatars. Our method addresses both issues by learning disentangled" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21025" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.083, + 0.089, + 0.891, + 0.328 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.337, + 0.892, + 0.435 + ], + "angle": 0, + "content": "Figure 2. Proposed framework. Given a posed scan registered with body pose and shape LBS parameters \\((\\theta, \\beta)\\), our proposed human representation stores its local geometry and texture features in a codebook \\(\\mathbf{C}\\) which is indexed by the vertex indices of \\(M\\)-vertex LBS body mesh \\(\\mathcal{M}\\) (Sec. 3.1). Given a query point coordinates \\(\\mathbf{x}_g\\), two separated MLP decoders \\(\\Psi\\), \\(\\Phi\\) predict signed distances and colors conditioned on the positional features \\((\\mathbf{x}_l, \\vec{\\mathbf{n}})\\) and the local geometry/texture features \\((\\mathbf{f_s} / \\mathbf{f_c})\\) respectively. We train a generative auto-decoder using \\(N\\) posed scans, whose feature codebooks are stored in the dictionary \\(\\mathbf{D_s}\\), \\(\\mathbf{D_c}\\). We introduce two sampling strategies to sample codebooks (denoted as \\(\\mathbf{C}_i / \\mathbf{C}_r\\) respectively) and jointly train our dictionaries and shared MLP decoders with a 3D reconstruction loss and a 2D adversarial loss (Sec. 3.2 & Sec. 3.3)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.442, + 0.47, + 0.503 + ], + "angle": 0, + "content": "local representations for multiple subjects. In addition, we experimentally show that our representation significantly improves the performance of model fitting against state-of-the-art human generative models." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.519, + 0.169, + 0.535 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.546, + 0.47, + 0.713 + ], + "angle": 0, + "content": "Our proposed method is summarized in Fig. 2. We first contribute a novel hybrid human representation that stores local geometric and textural information into two aligned feature spaces (Sec. 3.1 and Fig. 3). To allow fitting to new 3D scans and drawing random samples from the underlying data distribution, we design a training strategy to learn a meaningful latent space under the generative adversarial framework to bring in additional 2D adversarial supervision (Sec. 3.2 & Sec. 3.3). Finally, we demonstrate the utility of our method for creating avatars by enabling local feature editing through existing 3D assets or images (Sec. 3.4)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.725, + 0.38, + 0.741 + ], + "angle": 0, + "content": "3.1. Hybrid Representation of Humans" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.749, + 0.47, + 0.84 + ], + "angle": 0, + "content": "To enable 3D avatars with high-fidelity representational power and local editing capabilities, a suitable representation is needed. To this end, we propose a novel hybrid representation that combines the advantages of neural fields (flexibility and modeling power) with LBS-articulated mesh models (ease of deformation and full explicit control)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.841, + 0.47, + 0.902 + ], + "angle": 0, + "content": "An overview of how we leverage the proposed representation is provided in Fig. 2 in the dotted blue box. Given a human scan, we first create a posed, coarse body mesh \\(\\mathcal{M}\\) (shown in red) using the registered body parameters" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.442, + 0.892, + 0.533 + ], + "angle": 0, + "content": "\\((\\theta, \\beta)\\) of an LBS body model. The mesh consists of \\(M\\) vertices (\\(\\mathcal{V} \\in \\mathbb{R}^{M \\times 3}\\)) in the posed space and \\(M_f\\) faces where \\(\\mathcal{F} \\in \\{1, \\dots, M\\}^{M_f \\times 3}\\) defines the vertex indices on each face. We then construct a trainable feature codebook \\(\\mathbf{C} \\in \\mathbb{R}^{M \\times 2F}\\), which stores \\(F\\)-dimensional local geometry and texture features respectively for each vertex." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.533, + 0.892, + 0.685 + ], + "angle": 0, + "content": "Similar to coordinate-based implicit fields, a 3D coordinate \\(\\mathbf{x}_g\\in \\mathbb{R}^3\\) is used to predict its corresponding signed distance \\(s(\\mathbf{x}_g)\\), and RGB color \\(c(\\mathbf{x}_g)\\). Instead of using global coordinates directly as inputs, we condition neural field decoders on the local triangle coordinates \\(\\mathbf{x}_l\\in \\mathbb{R}^3\\) and the local geometry and texture features \\(\\mathbf{f_s},\\mathbf{f_c}\\in \\mathbb{R}^F\\). We illustrate this conversion from global coordinates to local triangle coordinates in Fig. 3. The global coordinates \\(\\mathbf{x}_g\\) are first projected onto the mesh by finding the closest point \\(\\mathbf{x}_c^*\\) (Fig. 3, blue dot):" + }, + { + "type": "equation", + "bbox": [ + 0.599, + 0.696, + 0.891, + 0.739 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathbf {x} _ {c} ^ {*} = \\arg \\min _ {\\mathbf {x} _ {c}} \\| \\mathbf {x} _ {g} - \\mathbf {x} _ {c} \\| _ {2}, \\\\ \\mathbf {x} _ {c} = \\mathcal {B} _ {u, v} (\\mathcal {V} [ m _ {0}, m _ {1}, m _ {2} ]), \\tag {1} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.892, + 0.903 + ], + "angle": 0, + "content": "where \\((m_0, m_1, m_2)\\) are vertex indices of faces \\(\\mathcal{F}\\) and \\(\\mathcal{B}_{u,v}(.)\\) is the barycentric interpolation function with barycentric coordinates \\((u, v, 1 - u - v)\\). The closest point \\(\\mathbf{x}_c^*\\) within the face is used to transform \\(\\mathbf{x}_g\\) into a local triangle coordinate system. Hence, \\(\\mathbf{x}_l\\) consists of the barycentric coordinates \\((u, v)\\), the signed distance \\(d\\) between \\(\\mathbf{x}_g\\) and \\(\\mathbf{x}_c^*\\), i.e., \\(\\mathbf{x}_l \\coloneqq (u, v, d)\\). We also compute a direction vector \\(\\vec{\\mathbf{n}}\\) between \\(\\mathbf{x}_g\\) and \\(\\mathbf{x}_c^*\\) as an additional feature to distinguish points near triangle edges. To query local features \\((\\mathbf{f_s}, \\mathbf{f_c})\\), we use the vertex indices on the triangle \\((m_0^*, m_1^*, m_2^*)\\) to" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "21026" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.088, + 0.093, + 0.462, + 0.307 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.32, + 0.473, + 0.46 + ], + "angle": 0, + "content": "Figure 3. Local feature querying. Given an LBS body mesh posed by the parameters \\((\\theta, \\beta)\\), we represent a detailed human body as a codebook that stores local texture and geometry features indexed by the vertices on the mesh. An input query point \\(\\mathbf{x_g}\\) finds the nearest triangle on the LBS body mesh and returns its vertex indices for local feature lookup. To prevent decoders from memorizing any global information, we transform the position of \\(\\mathbf{x_g}\\) into local triangle coordinates \\((u, v)\\), distance \\(d\\), and direction \\(\\vec{\\mathbf{n}}\\) of the closest point (i.e., the blue dot). The final geometry and texture features \\(\\mathbf{f_s}, \\mathbf{f_c}\\) are fused via barycentric interpolation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.466, + 0.47, + 0.497 + ], + "angle": 0, + "content": "look up three elements in the feature codebook C. We then fuse the three local features via barycentric interpolation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.497, + 0.471, + 0.543 + ], + "angle": 0, + "content": "Finally, we take all the local features \\((\\mathbf{f_s},\\mathbf{f_c},\\mathbf{x}_l,\\vec{\\mathbf{n}})\\) as input to two separate decoders \\(\\Phi\\) and \\(\\Psi\\) to predict SDF and RGB values respectively:" + }, + { + "type": "equation", + "bbox": [ + 0.172, + 0.551, + 0.47, + 0.589 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\Phi : \\mathbb {R} ^ {F} \\times \\mathbb {R} ^ {3} \\times \\mathbb {R} ^ {3} \\rightarrow \\mathbb {R} \\tag {2} \\\\ \\left(\\mathbf {f} _ {\\mathbf {s}}, \\mathbf {x} _ {l}, \\vec {\\mathbf {n}}\\right) \\mapsto s \\left(\\mathbf {x} _ {g}\\right), \\\\ \\end{array}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.174, + 0.599, + 0.469, + 0.638 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\boldsymbol {\\Psi}: \\mathbb {R} ^ {F} \\times \\mathbb {R} ^ {3} \\times \\mathbb {R} ^ {3} \\rightarrow \\mathbb {R} ^ {3} \\tag {3} \\\\ \\left(\\mathbf {f} _ {\\mathbf {c}}, \\mathbf {x} _ {l}, \\vec {\\mathbf {n}}\\right) \\mapsto c \\left(\\mathbf {x} _ {g}\\right). \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.642, + 0.47, + 0.719 + ], + "angle": 0, + "content": "Note that only local information is exposed to the decoders, which allows us to use the same MLPs across different vertices and subjects. We show that preventing networks from memorizing global information in this way is necessary for local editing and reposing in our experiments (Fig. 9)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.727, + 0.36, + 0.745 + ], + "angle": 0, + "content": "3.2. Generative Codebook Sampling" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.47, + 0.856 + ], + "angle": 0, + "content": "Our goal is to provide means to create and personalize avatars with diverse body shapes, appearances, and local details. To this end, we leverage the above representation to train a single multi-subject model which enables the transfer of local features across subjects. We note that since the mesh topology of the LBS model is identical, this enables us to learn a shared feature space from multiple posed scans." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.471, + 0.902 + ], + "angle": 0, + "content": "To learn the feature representation over a dataset of \\(N\\) scans, it is sufficient to store the codebooks \\(\\mathbf{C}_i\\) in two dictionaries \\(\\mathbf{D_s},\\mathbf{D_c}\\in \\mathbb{R}^{N\\times (MF)}\\) to represent shape and color" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.182 + ], + "angle": 0, + "content": "information of the \\(i\\)-th subject respectively. The entries \\(\\mathbf{C}_i\\) can then be learned jointly with the decoder weights via direct 3D supervision using the \\(i\\)-th scan (Fig. 2). However, we experimentally show that this is insufficient to learn a well-behaved latent space from which we can draw novel samples (see Fig. 10)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.183, + 0.893, + 0.394 + ], + "angle": 0, + "content": "Therefore we introduce a codebook sampling strategy that allows us to draw random samples and update the entries of the dictionaries \\(\\mathbf{D}_{\\mathbf{s}}\\), \\(\\mathbf{D}_{\\mathbf{c}}\\) via an additional 2D adversarial loss. More specifically, we follow the auto-decoder architecture [52] and perform PCA on the reshaped dictionary to compute eigenvectors \\(\\mathbf{V} \\in \\mathbb{R}^{D \\times (MF)}\\) and fit a normal distribution to the \\(D\\)-dimensional PCA coefficients of \\(N\\) samples. A new random codebook \\(\\mathbf{C}_r\\) can then be generated by sampling \\(D\\)-dimensional PCA parameters and multiplying them with the eigenvectors \\(\\mathbf{V}\\) (See SuppB.1 for details). Note that our representation disentangles shapes from appearances with separated geometry and texture branches, which enables independent sampling of geometry and texture features." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.403, + 0.658, + 0.42 + ], + "angle": 0, + "content": "3.3. Model Training" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.426, + 0.892, + 0.533 + ], + "angle": 0, + "content": "3D reconstruction loss. To train a codebook \\(\\mathbf{C}_i\\) with a single scan, we sample points in a thin shell around the scan. For each coordinate we compute its signed distance \\(s\\) to the input scan, closest texture color \\(\\mathbf{c}\\), and surface normal \\(\\mathbf{n}\\) on the input scan to attain ground truth values. The codebooks and the decoder weights are then optimized via the following losses:" + }, + { + "type": "equation", + "bbox": [ + 0.52, + 0.544, + 0.892, + 0.563 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {s d f} = \\| s - s (\\mathbf {x} _ {\\mathbf {g}}) \\| _ {1} + \\lambda_ {n} \\| 1 - \\mathbf {n} \\cdot \\nabla_ {\\mathbf {x} _ {\\mathbf {g}}} s (\\mathbf {x} _ {\\mathbf {g}}) \\| _ {1}, \\quad (4)\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.62, + 0.587, + 0.892, + 0.605 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {r g b} = \\left\\| \\mathbf {c} - c \\left(\\mathbf {x} _ {\\mathbf {g}}\\right) \\right\\| _ {1}, \\tag {5}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.596, + 0.626, + 0.892, + 0.643 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {3 D} = \\lambda_ {s d f} \\mathcal {L} _ {s d f} + \\lambda_ {r g b} \\mathcal {L} _ {r g b}. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.892, + 0.825 + ], + "angle": 0, + "content": "2D adversarial loss. Adversarial learning does not require exact ground-truth annotations but is trained via a collection of real and fake (rendered) images. Thus, real images are obtained by rasterizing the ground-truth scan to which the coarse body mesh \\(\\mathcal{M}\\) was fitted. Color and normal images (denoted as \"Real Patch\" in Fig. 2) are used for learning texture and geometry respectively. Using the same virtual camera parameters and the coarse mesh \\(\\mathcal{M}\\), we attain rendered patches (denoted as \"Rendered Patch\" Fig. 2) via implicit surface rendering of a sampled codebook \\(\\mathbf{C}_r\\). Please refer to Supp-B.2 for more details." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.826, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Using these 2D patches, we train dictionaries, decoders, and discriminators jointly with a non-saturating logistic loss \\(\\mathcal{L}_{adv}\\) [20], R1 regularization \\(\\mathcal{L}_{R1}\\) [38], and path length regularization \\(\\mathcal{L}_{path}\\) [27]. Note that these losses do not require exact ground-truth replication. Furthermore, we regu" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21027" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.136 + ], + "angle": 0, + "content": "larize the feature dictionaries to follow a Gaussian distribution [46] with \\(\\mathcal{L}_{reg} = \\| \\mathbf{D}\\| _F\\). In summary, we optimize the discriminator:" + }, + { + "type": "equation", + "bbox": [ + 0.19, + 0.151, + 0.469, + 0.168 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {d i s}} = \\mathcal {L} _ {\\text {a d v}} + \\lambda_ {R 1} \\mathcal {L} _ {R 1}, \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.179, + 0.465, + 0.195 + ], + "angle": 0, + "content": "while updating the remaining components \\((\\mathbf{D}_{\\mathbf{c}},\\mathbf{D}_{\\mathbf{s}},\\Phi ,\\Psi)\\)" + }, + { + "type": "equation", + "bbox": [ + 0.116, + 0.207, + 0.47, + 0.224 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = - \\mathcal {L} _ {a d v} + \\mathcal {L} _ {3 D} + \\lambda_ {\\text {p a t h}} \\mathcal {L} _ {\\text {p a t h}} + \\lambda_ {\\text {r e g}} \\mathcal {L} _ {\\text {r e g}}, \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.236, + 0.4, + 0.251 + ], + "angle": 0, + "content": "where \\(\\lambda_{(\\cdot)}\\) denotes weights to balance the losses." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.251, + 0.469, + 0.297 + ], + "angle": 0, + "content": "Since we sample on the fly during training (see Sec. 3.2), the 2D adversarial loss does affect the shared decoders and the whole feature dictionaries (See Supp-Fig.14 for details)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.307, + 0.44, + 0.323 + ], + "angle": 0, + "content": "3.4. Feature Editing and Avatar Customization" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.331, + 0.469, + 0.376 + ], + "angle": 0, + "content": "We now describe how we integrate the above-mentioned human representation and the generative architecture into the avatar creation workflow shown in Fig. 1." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.384, + 0.469, + 0.474 + ], + "angle": 0, + "content": "Avatar initialization. To simplify the avatar creation process, our method allows users to start with a default example \\(\\mathbf{C}_d\\), which can be queried from the trained codebook dictionaries directly with index \\(i\\) (\\(\\mathbf{C}_i\\)) or randomly sampled from the learned D-dimensional PCA parameters distribution (\\(\\mathbf{C}_r\\) in Sec. 3.2)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.482, + 0.469, + 0.572 + ], + "angle": 0, + "content": "Model fitting. Being able to extract elements of interest or copying from existing 3D assets is necessary for avatar creation and editing. To this end, we leverage a similar technique to GAN inversion [69], where decoder parameters \\(\\Phi\\) and \\(\\Psi\\) are frozen and we only optimize a new feature codebook \\(\\mathbf{C}_{fit}\\) to fit a 3D scan." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.573, + 0.469, + 0.633 + ], + "angle": 0, + "content": "Given a 3D target scan and its corresponding body model parameters, we calculate the 3D reconstruction loss in Eq. (6) between the prediction conditioned on \\(\\mathbf{C}_{fit}\\) and the ground-truth scan, i.e.," + }, + { + "type": "equation", + "bbox": [ + 0.139, + 0.646, + 0.469, + 0.668 + ], + "angle": 0, + "content": "\\[\n\\mathbf {C} _ {f i t} = \\arg \\min _ {\\mathbf {C}} \\left(\\lambda_ {s d f} \\mathcal {L} _ {s d f} + \\lambda_ {r g b} \\mathcal {L} _ {r g b}\\right). \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.679, + 0.469, + 0.74 + ], + "angle": 0, + "content": "Note that our generative neural fields (MLP decoders) are conditioned on descriptive local features. We show that it allows us to accurately fits complex clothing geometry and unseen textural patterns in Sec. 4.3." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.76, + 0.469, + 0.911 + ], + "angle": 0, + "content": "Cross-subjects feature editing. With multiple codebooks each representing different 3D assets, we can easily transfer local geometry and texture from one avatar to another, for instance, changing the top wear from the fitted scan \\(\\mathbf{C}_{fit}\\) to the initial \\(\\mathbf{C}_d\\). Recalling that all codebooks are indexed by an identical mesh topology, users can easily retrieve the index numbers of \\(\\nu_{body} \\subset \\nu\\) via standard mesh visualization tools such as Blender [12]. Finally, swapping of the corresponding rows in \\(\\mathbf{C}_{fit}\\) and \\(\\mathbf{C}_d\\) given the vertex indices also swaps the local appearance." + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.093, + 0.882, + 0.375 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.387, + 0.892, + 0.485 + ], + "angle": 0, + "content": "Figure 4. Randomly sampled texture and body geometry from the model trained on THuman2.0. Top: Given any poses, our randomly sampled geometries contain realistic details such as wrinkles in garments and facial expressions. Bottom: Given arbitrary poses and body geometries, our model produces reasonable colors on skin, hair, clothes, and pants in each sample (We turn off the Shader for visualizing pure texture colors)." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.498, + 0.892, + 0.619 + ], + "angle": 0, + "content": "Personalized texture drawing. One can further customize clothing by drawing directly onto 2D images. Due to the learned disentangled feature spaces, we are able to update texture features in \\(\\mathbf{C}_{fit}\\) while keeping the geometry features unchanged. When fitting 2D images, users draw on the rasterized images from arbitrary target scans. We then finetune only texture features in \\(\\mathbf{C}_{fit}\\) via the RGB loss in Eq. (5) given the corresponding 3D coordinates and colors." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.641, + 0.892, + 0.747 + ], + "angle": 0, + "content": "Avatar reposing. Our representation combines a base mesh \\(\\mathcal{M}\\) and underlying feature codebooks \\(\\mathbf{C}_{fit}\\) that learn local geometry and texture on the 3D scans. Hence, one can repose the mesh \\(\\mathcal{M}\\) using \\((\\theta, \\beta)\\) parameters, which also reposes the avatar correspondingly. Since the geometry codebook does not contain global pose \\((\\theta, \\beta)\\), local information can be consistently applied to \\(\\mathcal{M}\\) under unseen poses." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.768, + 0.633, + 0.785 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.795, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Our goal is locally editable 3D avatar creation. Since we are the first to discuss this problem, we visualize our editing results in Sec. 4.2. Next, we highlight the capability of model fitting by comparing our method with SOTA human generative models in Sec. 4.3. Finally, controlled experiments are presented in Sec. 4.4 and Sec. 4.5 to verify the effectiveness of our design." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "21028" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.09, + 0.47, + 0.334 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.345, + 0.471, + 0.402 + ], + "angle": 0, + "content": "Figure 5. Cross-subject feature editing results. We partially transfer local clothing details from the unseen scans (upper and lower body) to the input avatars. The results of the edited avatars are shown in the right column." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.408, + 0.271, + 0.423 + ], + "angle": 0, + "content": "4.1. Experiment Settings" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.431, + 0.47, + 0.567 + ], + "angle": 0, + "content": "Dataset. Most generative human works [9,44,45] exploit commercial data [1,2] for training, which is not easily accessible and limits reproducibility. Furthermore, the quality of publicly available 3D human datasets [66,73] is not satisfactory. Issues such as non-watertight topologies and noise are very common (See Supp-A.2 for examples and comparison). To bridge this gap, we collect a new dataset named CustomHumans for training and evaluation. Here we summarize the datasets used in our experiments." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.577, + 0.469, + 0.669 + ], + "angle": 0, + "content": "- CustomHumans (Ours) contains more than 600 high-quality scans of 80 participants in 120 garments in varied poses from a volumetric capture stage [11], which is equipped with 106 synchronized cameras (53 RGB and 53 IR cameras). We use our dataset to train models of all quantitative experiments. (Sec. 4.3 ~ Sec. 4.5)" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.677, + 0.469, + 0.754 + ], + "angle": 0, + "content": "- THuman2.0 [73] is a dataset containing about 500 scans of humans wearing 150 garments in various poses. Since this dataset has more textural diversity, we train our method on it for qualitative random sampling experiments (Sec. 4.2 and Sec. 4.4)." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.762, + 0.469, + 0.822 + ], + "angle": 0, + "content": "- SIZER [66] is a widely used 3D scan dataset containing A-posed human meshes of 97 subjects in 22 garments. These meshes are used as unseen test scans in our fitting experiment (Sec. 4.3)." + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.577, + 0.469, + 0.822 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Evaluation protocol. Following the evaluation protocol in OccNet [39], we quantitatively evaluate the model fitting accuracy using three metrics: Chamfer distance (CD), normal consistency (NC), and f-Score." + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.091, + 0.891, + 0.203 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.207, + 0.892, + 0.249 + ], + "angle": 0, + "content": "Figure 6. Personalized texture editing. We draw personalized logos on 2D images and fit avatars' texture features to the images. These local textures remain consistent under pose changes." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.255, + 0.691, + 0.269 + ], + "angle": 0, + "content": "4.2.Customized Avatars" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.278, + 0.89, + 0.308 + ], + "angle": 0, + "content": "We visualize the results of our proposed avatar customization workflow described in Sec. 3.4." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.313, + 0.892, + 0.404 + ], + "angle": 0, + "content": "Avatar initialization. In Fig. 4, we show random textures and geometries sampled from the model trained on THuman2.0. Our method is able to generate reasonable colors and wrinkles in arbitrary poses. Note that the sampled geometries are shown as the real meshes but not as rendered normals as in [9] (See Supp-C.2 for comparisons)." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.408, + 0.892, + 0.546 + ], + "angle": 0, + "content": "Cross-subjects feature editing. After fitting feature codebooks to 3D scans, we can change the clothes on our avatars by swapping the local features stored on the body vertices. We select the features within the upper body and lower body areas. We then copy these local features to the initial avatars' feature codebooks. As shown in Fig. 5, our method is able to handle multiple garments on different human subjects and preserves consistent details under different body poses or shapes." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.55, + 0.892, + 0.687 + ], + "angle": 0, + "content": "Personalized texture drawing. Our method allows users to draw complex letters and logos on images for personalized texture editing. We perform the model fitting and feature editing process but only optimize the texture features in the codebooks using user-edited images and the RGB loss (Eq. (5)). Fig. 6 shows that new texture can be seamlessly applied to the 3D avatars. It is worth noting that resulting avatars enable detailed pose control via the SMPL-X parameters without affecting the fitted texture and geometry." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.697, + 0.741, + 0.713 + ], + "angle": 0, + "content": "4.3. Model Fitting Comparison" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.72, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Since model fitting is an important step in our avatar creation workflow, we compare the capability of feature inversion using unseen 3D scans. The goal of this task is to invert a 3D scan into latent codes while keeping the remaining model parameters fixed. We compare our method with the 3D human generative model gDNA [9], which has achieved state-of-the-art performance in fitting the geometry of 3D human bodies. We also directly compare with SMPL [33] and SMPL+D [3]. Note that SMPL+D is a stronger vertex-based extension that uses a subdivided version of SMPL to directly register surfaces to scans while our method and gDNA optimize latent codes." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "21029" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.08, + 0.09, + 0.468, + 0.292 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.301, + 0.47, + 0.358 + ], + "angle": 0, + "content": "Figure 7. Qualitative comparison of model fitting on SIZER. We visualize the fitting results from gDNA [9], SMPL+D [3], and our method. Our results are perceptually close to the ground truth even on the challenging test cases of jackets and loose t-shirts." + }, + { + "type": "image", + "bbox": [ + 0.118, + 0.374, + 0.431, + 0.473 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.483, + 0.47, + 0.54 + ], + "angle": 0, + "content": "Figure 8. Qualitative comparison of texture fitting. We compare our method with SMPL+D [3] by fitting to unseen textured meshes. The performance of SMPL+D is limited by its geometry and texture resolution." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.549, + 0.47, + 0.639 + ], + "angle": 0, + "content": "From Fig. 7 we can see that SMPL+D handles loose clothing, such as a business suite, better than gDNA. However, the surfaces of SMPL+D results are over-smoothed and do not contain high-frequency details while ours can preserve them. Quantitatively, our method consistently outperforms these methods on all metrics as shown in Tab. 1." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.64, + 0.47, + 0.731 + ], + "angle": 0, + "content": "Fig. 8 depicts the result of texture fitting against the SMPL+D baseline. While both methods inherit a fixed mesh topology the quality of SMPL+D is limited by its model resolution. Our method addresses this issue via local neural fields that enable cross-subject feature editing of texture and geometry with enhanced representational power." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.741, + 0.231, + 0.758 + ], + "angle": 0, + "content": "4.4. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.765, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Effectiveness of local features and shared decoders. To verify the design choice of using local features, we replace the local features \\(\\mathbf{x}_l\\) by the global coordinates \\(\\mathbf{x}_g\\) for conditioning the decoders. Fig. 9 shows that even though the shared decoders are able to achieve similar reconstruction results when training with global information, they do not maintain consistent performance for model fitting and avatar reposing. This is because the shared decoders tend to memorize global coordinates information in a \"per-subject\"" + }, + { + "type": "image", + "bbox": [ + 0.507, + 0.091, + 0.887, + 0.362 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.375, + 0.893, + 0.432 + ], + "angle": 0, + "content": "Figure 9. Comparison of using global/local features for conditioning decoders. The use of global coordinates causes overfitting and memorization to the shared decoders, which makes it struggle to handle unseen scans or novel body poses." + }, + { + "type": "table", + "bbox": [ + 0.515, + 0.452, + 0.88, + 0.553 + ], + "angle": 0, + "content": "
MethodPred-to-Scan / \nScan-to-Pred (mm)↓NC↑f-Score↑
SMPL [33]13.60 / 18.030.8490.458
gDNA [9]8.374 / 8.0060.8420.718
SMPL+D [3]5.192 / 2.8540.9110.962
Ours1.364 / 1.4230.9490.997
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.562, + 0.893, + 0.604 + ], + "angle": 0, + "content": "Table 1. Model fitting comparison on SIZER. We report Chamfer distance, normal consistency (NC), and f-score between ground truth and the meshes fitted by different methods." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.621, + 0.894, + 0.727 + ], + "angle": 0, + "content": "manner, rather than learning shareable information that can be used across vertices and subjects. On the other hand, our representation ensures only local features defined on the triangle coordinates are exposed to the shared decoders. In such cases, the decoders can better handle unseen body poses or out-of-distribution samples for model fitting and avatar editing." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Importance of 2D adversarial loss and 3D disentanglement. As discussed in Sec. 3.3, we introduce feature disentanglement and generative adversarial learning in our training framework. As shown in Fig. 10, sampling within the feature spaces learned without adversarial loss does not yield reasonable body textures. Similarly, training only a single decoder for both geometry and texture does not allow us to maintain desired body geometries when sampling random textures. Our full model can produce disentangled textures, given arbitrary body geometries and poses." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21030" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.127, + 0.089, + 0.845, + 0.181 + ], + "angle": 0, + "content": "
Training Data Percentage10%25%50 %75%100%
Chamfer Distance (mm) \nS-to-P / P-to-S ↓1.933 / 1.7981.754 / 1.5901.543 / 1.4561.463 / 1.3851.423 / 1.364
Normal Consistency↑0.9180.9310.9350.9470.949
f-Score (%) ↑99.2599.3899.6599.7499.75
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.192, + 0.894, + 0.233 + ], + "angle": 0, + "content": "Table 2. Generalization analysis on CustomHumans. We analyze the model fitting performances with regard to different amounts of training data (100% = 100 training scans). We observe consistent performance gain on all evaluation metrics when using more training subjects to train the shared decoders." + }, + { + "type": "image", + "bbox": [ + 0.078, + 0.236, + 0.47, + 0.53 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.541, + 0.471, + 0.598 + ], + "angle": 0, + "content": "Figure 10. Ablative comparison of our framework designs. We visualize the results of transferring random texture to given body geometry. Our full model produces reasonable body texture and is able to maintain fixed geometry for texture editing." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.616, + 0.355, + 0.633 + ], + "angle": 0, + "content": "4.5. Generalization Ability Analysis" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.644, + 0.471, + 0.901 + ], + "angle": 0, + "content": "We are interested in how the amount of training data affects the capacities of the MLP decoders. To analyze this, we design three evaluation protocols: 3D model fitting, avatar reposing, and 2D texture fitting. Tab. 2 summarizes the model fitting performance using different percentages of training data. We observe a \\(25\\%\\) accuracy improvement when using the full training set. In Fig. 11 (Top) we show that the reposing artifacts caused by self-contact (e.g., fist and elbow) can be reduced when training the MLP decoders with more poses and subjects. In addition, Fig. 11 (Bottom) depicts a qualitative comparison of 2D texture editing under different training data percentages. We evaluate texture editing quality by fitting a 2D image with unseen geometric shapes and colors. It can be seen that the model trained on more samples is able to handle a wider range of color distribution. These results confirm the necessity for learning multi-subject shared decoders in our task." + }, + { + "type": "image", + "bbox": [ + 0.507, + 0.235, + 0.891, + 0.446 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.46, + 0.893, + 0.571 + ], + "angle": 0, + "content": "Figure 11. Qualitative comparison of generalization. Top: We visualize the results of avatar reposing using different percentages of training data (i.e., 10 meshes vs 100 meshes). Artifacts caused by self-contact (e.g., fist and elbow) can be reduced when more training subjects are introduced. Bottom: We visualize the results using different percentages of training data. Using more data results in more robust shared decoders (with a wider color range), which is necessary for the avatar creation task." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.583, + 0.619, + 0.599 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.609, + 0.892, + 0.804 + ], + "angle": 0, + "content": "We propose an end-to-end trainable framework for learning 3D human avatars with high fidelity and full editability. By combining neural fields with explicit skinned meshes, our representation addresses the controllability issue of many previous implicit representations. Moreover, we uniquely integrate the proposed human representation into a generative auto-decoding pipeline that enables local editing across multiple animation-ready avatars. Through our evaluation on the newly contributed CustomHumans dataset, we demonstrate that our approach achieves higher model fitting accuracy and generates diverse detailed avatars. We believe that this work opens up exciting possibilities for accelerating content creation in the Metaverse." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Acknowledgements We express our gratitude to Stefan Walter and Dean Bakker for infrastructure support, Juan Zarate for managing the capture stage, and Deniz Yildiz and Laura Wulfroth for data capture assistance. We thank Andrew Searle for supporting the capturing system and all the dataset participants." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "21031" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.176, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.316, + 0.129 + ], + "angle": 0, + "content": "[1] https://3dpeople.com/.6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.131, + 0.353, + 0.144 + ], + "angle": 0, + "content": "[2] https://renderpeople.com/.6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.145, + 0.469, + 0.226 + ], + "angle": 0, + "content": "[3] Thiemo Alldieck, Marcus Magnor, Bharat Lal Bhatnagar, Christian Theobalt, and Gerard Pons-Moll. Learning to reconstruct people in clothing from a single rgb camera. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1175-1186, 2019. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.228, + 0.469, + 0.296 + ], + "angle": 0, + "content": "[4] Thiemo Alldieck, Mihai Zanfir, and Cristian Sminchisescu. Photorealistic monocular 3d reconstruction of humans wearing clothing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.298, + 0.469, + 0.354 + ], + "angle": 0, + "content": "[5] Alexander W Bergman, Petr Kellnhofer, Yifan Wang, Eric R Chan, David B Lindell, and Gordon Wetzstein. Generative neural articulated radiance fields. In Advances in Neural Information Processing Systems (NeurIPS), 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.355, + 0.469, + 0.424 + ], + "angle": 0, + "content": "[6] Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Combining implicit function learning and parametric models for 3d human reconstruction. In Proceedings of the European Conference on Computer Vision (ECCV), pages 311-329. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.425, + 0.469, + 0.479 + ], + "angle": 0, + "content": "[7] Caroline Chan, Shiry Ginosar, Tinghui Zhou, and Alexei A Efros. Everybody dance now. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 5933-5942, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.481, + 0.469, + 0.535 + ], + "angle": 0, + "content": "[8] Xu Chen, Tianjian Jiang, Jie Song, Max Rietmann, Andreas Geiger, Michael J. Black, and Otmar Hilliges. FastSNARF: A fast deformer for articulated neural fields. arXiv, abs/2211.15601, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.537, + 0.469, + 0.606 + ], + "angle": 0, + "content": "[9] Xu Chen, Tianjian Jiang, Jie Song, Jinlong Yang, Michael J Black, Andreas Geiger, and Otmar Hilliges. gdna: Towards generative detailed neural avatars. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 20427-20437, 2022. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.607, + 0.469, + 0.675 + ], + "angle": 0, + "content": "[10] Xu Chen, Yufeng Zheng, Michael J Black, Otmar Hilliges, and Andreas Geiger. Snarf: Differentiable forward skimming for animating non-rigid neural implicit shapes. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.677, + 0.469, + 0.745 + ], + "angle": 0, + "content": "[11] Alvaro Collet, Ming Chuang, Pat Sweeney, Don Gillett, Dennis Evseev, David Calabrese, Hugues Hoppe, Adam Kirk, and Steve Sullivan. High-quality streamable free-viewpoint video. ACM Transactions on Graphics (TOG), 34(4):1-13, 2015. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.747, + 0.469, + 0.788 + ], + "angle": 0, + "content": "[12] Blender Online Community. Blender - a 3D modelling and rendering package. Blender Foundation, Stichting Blender Foundation, Amsterdam, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.789, + 0.469, + 0.858 + ], + "angle": 0, + "content": "[13] Enric Corona, Albert Pumarola, Guillem Alenyà, Gerard Pons-Moll, and Francesc Moreno-Noguer. Smplicit: Topology-aware generative model for clothed people. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.859, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[14] Boyang Deng, JP Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, and Andrea Tagliasacchi. Neural articulated shape approximation. In" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.116, + 0.469, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "Proceedings of the European Conference on Computer Vision (ECCV). Springer, August 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.121, + 0.892, + 0.189 + ], + "angle": 0, + "content": "[15] Terrance DeVries, Miguel Angel Bautista, Nitish Srivastava, Graham W Taylor, and Joshua M Susskind. Unconstrained scene generation with locally conditioned radiance fields. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 14304-14313, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.19, + 0.892, + 0.259 + ], + "angle": 0, + "content": "[16] Haoye Dong, Xiaodan Liang, Xiaohui Shen, Bowen Wu, Bing-Cheng Chen, and Jian Yin. Fw-gan: Flow-navigated warping gan for video virtual try-on. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 1161-1170, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.26, + 0.892, + 0.342 + ], + "angle": 0, + "content": "[17] Xin Dong, Fuwei Zhao, Zhenyu Xie, Xijin Zhang, Daniel K. Du, Min Zheng, Xiang Long, Xiaodan Liang, and Jianchao Yang. Dressing in the wild by watching dance videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3480-3489, June 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.343, + 0.892, + 0.413 + ], + "angle": 0, + "content": "[18] Jianglin Fu, Shikai Li, Yuming Jiang, Kwan-Yee Lin, Chen Qian, Chen Change Loy, Wayne Wu, and Ziwei Liu. Stylegan-human: A data-centric odyssey of human generation. In Proceedings of the European Conference on Computer Vision (ECCV), pages 1-19. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.414, + 0.892, + 0.468 + ], + "angle": 0, + "content": "[19] Rony Goldenthal, David Harmon, Raanan Fattal, Michel Bercovier, and Eitan Grinspun. Efficient simulation of inextensible cloth. ACM Transactions on Graphics (TOG), pages 49-es, 2007. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.469, + 0.892, + 0.537 + ], + "angle": 0, + "content": "[20] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems (NeurIPS), pages 2672–2680, 2014. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.539, + 0.892, + 0.621 + ], + "angle": 0, + "content": "[21] Artur Grigorev, Karim Iskakov, Anastasia Iinina, Renat Bashirov, Ilya Zakharkin, Alexander Vakhitov, and Victor Lempitsky. Stylepeople: A generative model of fullbody human avatars. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5151-5160, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.622, + 0.892, + 0.663 + ], + "angle": 0, + "content": "[22] Artur Grigorev, Bernhard Thomaszewski, Michael J Black, and Otmar Hilliges. HOOd: Hierarchical graphs for generalized modelling of clothing dynamics. 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.664, + 0.892, + 0.719 + ], + "angle": 0, + "content": "[23] Chen Guo, Tianjian Jiang, Xu Chen, Jie Song, and Otmar Hilliges. Vid2 avatar: 3d avatar reconstruction from videos in the wild via self-supervised scene decomposition. In Computer Vision and Pattern Recognition (CVPR), 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.72, + 0.892, + 0.815 + ], + "angle": 0, + "content": "[24] Pat Hanrahan and Paul Haeberli. Direct wysiwyg painting and texturing on 3d shapes: (an error occurred during the printing of this article that reversed the print order of pages 118 and 119. while we have corrected the sort order of the 2 pages in the dl, the pdf did not allow us to repaginae the 2 pages.). ACM Transactions on Graphics (TOG), 24(4):215-223, 1990. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.817, + 0.892, + 0.871 + ], + "angle": 0, + "content": "[25] Fangzhou Hong, Zhaoxi Chen, Yushi Lan, Liang Pan, and Ziwei Liu. Eva3d: Compositional 3d human generation from 2d image collections. arXiv preprint arXiv:2210.04888, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.872, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[26] Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Alias-free" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "21032" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.092, + 0.47, + 0.12 + ], + "angle": 0, + "content": "generative adversarial networks. Advances in Neural Information Processing Systems (NeurIPS), 34:852-863, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.472, + 0.191 + ], + "angle": 0, + "content": "[27] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110-8119, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.193, + 0.471, + 0.248 + ], + "angle": 0, + "content": "[28] Christoph Lassner, Gerard Pons-Moll, and Peter V Gehler. A generative model of people in clothing. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 853-862, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.25, + 0.471, + 0.306 + ], + "angle": 0, + "content": "[29] Ruilong Li, Shan Yang, David A. Ross, and Angjoo Kanazawa. Ai choreographer: Music conditioned 3d dance generation with aist++. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.307, + 0.471, + 0.375 + ], + "angle": 0, + "content": "[30] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural actor: Neural free-view synthesis of human actors with pose control. ACM Transactions on Graphics (TOG), 40(6):1-16, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.378, + 0.471, + 0.459 + ], + "angle": 0, + "content": "[31] Wen Liu, Zhixin Piao, Jie Min, Wenhan Luo, Lin Ma, and Shenghua Gao. Liquid warping gan: A unified framework for human motion imitation, appearance transfer and novel view synthesis. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 5904-5913, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.462, + 0.471, + 0.532 + ], + "angle": 0, + "content": "[32] Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaou Tang. Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1096-1104, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.533, + 0.471, + 0.588 + ], + "angle": 0, + "content": "[33] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black. SMPL: A skinned multiperson linear model. ACM Transactions on Graphics (TOG), 34(6):248:1-248:16, October 2015. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.59, + 0.471, + 0.659 + ], + "angle": 0, + "content": "[34] Qianli Ma, Shunsuke Saito, Jinlong Yang, Siyu Tang, and Michael J. Black. SCALE: Modeling clothed humans with a surface codec of articulated local elements. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 16082-16093, June 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.661, + 0.471, + 0.716 + ], + "angle": 0, + "content": "[35] Qianli Ma, Jinlong Yang, Michael J. Black, and Siyu Tang. Neural point-based shape modeling of humans in challenging clothing. In 2022 International Conference on 3D Vision (3DV), September 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.718, + 0.471, + 0.786 + ], + "angle": 0, + "content": "[36] Qianli Ma, Jinlong Yang, Anurag Ranjan, Sergi Pujades, Gerard Pons-Moll, Siyu Tang, and Michael J. Black. Learning to Dress 3D People in Generative Clothing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.789, + 0.471, + 0.844 + ], + "angle": 0, + "content": "[37] Qianli Ma, Jinlong Yang, Siyu Tang, and Michael J. Black. The power of points for modeling humans in clothing. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[38] Lars Mescheder, Andreas Geiger, and Sebastian Nowozin. Which training methods for gans do actually converge? In International Conference on Machine Learning (ICML), pages 3481-3490. PMLR, 2018. 4" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.162 + ], + "angle": 0, + "content": "[39] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4460-4470, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.164, + 0.892, + 0.219 + ], + "angle": 0, + "content": "[40] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-102:15, July 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.221, + 0.892, + 0.262 + ], + "angle": 0, + "content": "[41] Rahul Narain, Armin Samii, and James F O'brien. Adaptive anisotropic remeshing for cloth simulation. ACM Transactions on Graphics (TOG), 31(6):1-10, 2012. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.264, + 0.892, + 0.331 + ], + "angle": 0, + "content": "[42] Atsuhiro Noguchi, Xiao Sun, Stephen Lin, and Tatsuya Harada. Unsupervised learning of efficient geometry-aware neural articulated representations. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.334, + 0.892, + 0.39 + ], + "angle": 0, + "content": "[43] Ahmed AA Osman, Timo Bolkart, and Michael J Black. Star: Sparse trained articulated human body regressor. In Proceedings of the European Conference on Computer Vision (ECCV), pages 598-613. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.392, + 0.892, + 0.447 + ], + "angle": 0, + "content": "[44] Pablo Palafox, Aljaž Božić, Justus Thies, Matthias Nießner, and Angela Dai. Npms: Neural parametric models for 3d deformable shapes. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.449, + 0.892, + 0.504 + ], + "angle": 0, + "content": "[45] Pablo Palafox, Nikolaos Sarafianos, Tony Tung, and Angela Dai. Spams: Structured implicit parametric models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.505, + 0.892, + 0.575 + ], + "angle": 0, + "content": "[46] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 165-174, 2019. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.577, + 0.892, + 0.657 + ], + "angle": 0, + "content": "[47] Priyanka Patel, Chun-Hao P Huang, Joachim Tesch, David T Hoffmann, Shashank Tripathi, and Michael J Black. Agora: Avatars in geography optimized for regression analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 13468-13478, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.661, + 0.892, + 0.744 + ], + "angle": 0, + "content": "[48] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3d hands, face, and body from a single image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 10975-10985, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.746, + 0.892, + 0.786 + ], + "angle": 0, + "content": "[49] Hans Köhling Pedersen. Decorating implicit surfaces. In ACM Transactions on Graphics (TOG), pages 291-300, 1995. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.789, + 0.892, + 0.871 + ], + "angle": 0, + "content": "[50] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 9054–9063, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.873, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[51] Sergey Prokudin, Michael J Black, and Javier Romero. Smplpix: Neural avatars from 3d human models. In Proceed-" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "21033" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.111, + 0.093, + 0.471, + 0.12 + ], + "angle": 0, + "content": "ings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1810-1819, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.471, + 0.188 + ], + "angle": 0, + "content": "[52] Daniel Rebain, Mark Matthews, Kwang Moo Yi, Dmitry Lagun, and Andrea Tagliasacchi. Lolnerf: Learn from one look. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1558-1567, 2022. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.19, + 0.471, + 0.26 + ], + "angle": 0, + "content": "[53] Edoardo Remelli, Timur Bagautdinov, Shunsuke Saito, Chenglei Wu, Tomas Simon, Shih-En Wei, Kaiwen Guo, Zhe Cao, Fabian Prada, Jason Saragih, et al. Drivable volumetric avatars using texel-aligned features. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1–9, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.261, + 0.471, + 0.315 + ], + "angle": 0, + "content": "[54] Daniel Roich, Ron Mokady, Amit H. Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. ACM Transactions on Graphics (TOG), 42(1), aug 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.316, + 0.471, + 0.37 + ], + "angle": 0, + "content": "[55] Javier Romero, Dimitrios Tzionas, and Michael J. Black. Embodied hands: Modeling and capturing hands and bodies together. ACM Transactions on Graphics (TOG), 36(6), Nov. 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.372, + 0.471, + 0.44 + ], + "angle": 0, + "content": "[56] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.442, + 0.471, + 0.511 + ], + "angle": 0, + "content": "[57] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo. Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 84-93, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.511, + 0.471, + 0.58 + ], + "angle": 0, + "content": "[58] Shunsuke Saito, Jinlong Yang, Qianli Ma, and Michael J. Black. SCANimate: Weakly supervised learning of skinned clothed avatar networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.581, + 0.471, + 0.648 + ], + "angle": 0, + "content": "[59] Kaiyue Shen, Chen Guo, Manuel Kaufmann, Juan Zarate, Julien Valentin, Jie Song, and Otmar Hilliges. X- avatar: Expressive human avatars. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.65, + 0.471, + 0.705 + ], + "angle": 0, + "content": "[60] Yichun Shi, Divyansh Aggarwal, and Anil K Jain. Lifting 2d stylegan for 3d-aware face generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6258-6266, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.707, + 0.471, + 0.748 + ], + "angle": 0, + "content": "[61] Zifan Shi, Sida Peng, Yinghao Xu, Yiyi Liao, and Yujun Shen. Deep generative models on 3d representations: A survey. arXiv preprint arXiv:2210.15663, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.749, + 0.471, + 0.831 + ], + "angle": 0, + "content": "[62] Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler. Neural geometric level of detail: Real-time rendering with implicit 3D shapes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.832, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[63] Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo MartinBrualla, Tomas Simon, Jason Saragih, Matthias Nießner, et al. State of the art on neural rendering. In Annual Conference of the European Association for Computer Graphics" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.093, + 0.471, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "(EUROGRAPHICS), volume 39, pages 701-727. Wiley Online Library, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.121, + 0.892, + 0.218 + ], + "angle": 0, + "content": "[64] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, W Yifan, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, et al. Advances in neural rendering. In Annual Conference of the European Association for Computer Graphics (EUROGRAPHICS), volume 41, pages 703-735. Wiley Online Library, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.219, + 0.892, + 0.272 + ], + "angle": 0, + "content": "[65] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics (TOG), 38(4):1-12, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.274, + 0.892, + 0.343 + ], + "angle": 0, + "content": "[66] Garvita Tiwari, Bharat Lal Bhatnagar, Tony Tung, and Gerard Pons-Moll. Sizer: A dataset and model for parsing 3d clothing and learning size sensitive 3d clothing. In Proceedings of the European Conference on Computer Vision (ECCV). Springer, August 2020. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.344, + 0.892, + 0.413 + ], + "angle": 0, + "content": "[67] Gul Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J Black, Ivan Laptev, and Cordelia Schmid. Learning from synthetic humans. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 109-117, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.414, + 0.892, + 0.481 + ], + "angle": 0, + "content": "[68] Ting-Chun Wang, Arun Mallya, and Ming-Yu Liu. One-shot free-view neural talking-head synthesis for video conferencing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 10039-10049, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.484, + 0.892, + 0.524 + ], + "angle": 0, + "content": "[69] Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang. Gan inversion: A survey. TPAMI, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.525, + 0.892, + 0.622 + ], + "angle": 0, + "content": "[70] Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. Neural fields in visual computing and beyond. In Annual Conference of the European Association for Computer Graphics (EUROGRAPHICS), volume 41, pages 641-676. Wiley Online Library, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.623, + 0.892, + 0.691 + ], + "angle": 0, + "content": "[71] Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, and Xiaodan Liang. Towards scalable unpaired virtual try-on via patch-routed spatially-adaptive gan. Advances in Neural Information Processing Systems (NeurIPS), 34:2598-2610, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.692, + 0.892, + 0.775 + ], + "angle": 0, + "content": "[72] Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In Proceedings of the European Conference on Computer Vision (ECCV), pages 597-614. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.776, + 0.892, + 0.845 + ], + "angle": 0, + "content": "[73] Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, and Yebin Liu. Function4d: Real-time human volumetric capture from very sparse consumer rgbd sensors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.846, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[74] Zhixuan Yu, Jae Shin Yoon, In Kyu Lee, Prashanth Venkatesh, Jaesik Park, Jihun Yu, and Hyun Soo Park. Humbi: A large multiview dataset of human body expressions. In Proceedings of the IEEE Conference on Computer" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "21034" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.092, + 0.469, + 0.119 + ], + "angle": 0, + "content": "Vision and Pattern Recognition (CVPR), pages 2990-3000, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.469, + 0.177 + ], + "angle": 0, + "content": "[75] Jianfeng Zhang, Zihang Jiang, Dingdong Yang, Hongyi Xu, Yichun Shi, Guoxian Song, Zhongcong Xu, Xinchao Wang, and Jiashi Feng. Avatargen: A 3d generative model for animatable human avatars. In Arxiv, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.179, + 0.469, + 0.248 + ], + "angle": 0, + "content": "[76] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C. Bühler, Xu Chen, Michael J. Black, and Otmar Hilliges. I M Avatar: Implicit morphable head avatars from videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.469, + 0.248 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.956 + ], + "angle": 0, + "content": "21035" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Locally Editable Virtual Humans/81a768fa-0f17-4406-9706-ef4fe06ddff4_origin.pdf b/2023/Learning Locally Editable Virtual Humans/81a768fa-0f17-4406-9706-ef4fe06ddff4_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..71b3aab29096b297824288db07678aebe6484b59 --- /dev/null +++ b/2023/Learning Locally Editable Virtual Humans/81a768fa-0f17-4406-9706-ef4fe06ddff4_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fe4b7cae475fe7d3e3504c0a0ba54a2550ea6ee40f2471eecad963a8372bb3b4 +size 5424828 diff --git a/2023/Learning Locally Editable Virtual Humans/full.md b/2023/Learning Locally Editable Virtual Humans/full.md new file mode 100644 index 0000000000000000000000000000000000000000..0b29732a6f4c97b5b11cbcdd55fdf363491cb1b6 --- /dev/null +++ b/2023/Learning Locally Editable Virtual Humans/full.md @@ -0,0 +1,324 @@ +# Learning Locally Editable Virtual Humans + +Hsuan-I Ho Lixin Xue Jie Song Otmar Hilliges Department of Computer Science, ETH Zürich + +# Abstract + +In this paper, we propose a novel hybrid representation and end-to-end trainable network architecture to model fully editable and customizable neural avatars. At the core of our work lies a representation that combines the modeling power of neural fields with the ease of use and inherent 3D consistency of skinned meshes. To this end, we construct a trainable feature codebook to store local geometry and texture features on the vertices of a deformable body model, thus exploiting its consistent topology under articulation. This representation is then employed in a generative auto-decoder architecture that admits fitting to unseen scans and sampling of realistic avatars with varied appearances and geometries. Furthermore, our representation allows local editing by swapping local features between 3D assets. To verify our method for avatar creation and editing, we contribute a new high-quality dataset, dubbed CustomHumans, for training and evaluation. Our experiments quantitatively and qualitatively show that our method generates diverse detailed avatars and achieves better model fitting performance compared to state-of-the-art methods. Our code and dataset are available at https://aith.ethz.ch/custom-humans. + +# 1. Introduction + +3D Avatars are an important aspect of many emerging applications such as 3D games or the Metaverse. Allowing for easy personalization of such avatars, holds the promise of increased user engagement. Traditionally, editing 3D assets requires knowledge of computer graphics tools and relies on standardized data formats to represent shapes and appearances. While methods for reconstruction or generative modeling of learned avatars achieve impressive results, it is unknown how such neural avatars can be edited and customized. Thus, the goal of our work is to contribute a simple, yet powerful data-driven method for avatar creation and customization (Fig. 1): our method enables (a) the ability to transfer partial geometric and appearance details between 3D assets, and (b) the ability to author details via 2D-3D transfer. The resulting avatars (c) retain consistent local details when posed. + +![](images/772d487918a176891d66c97850bdb62f18b2108564301f4a69a7767616fcc67a.jpg) +Figure 1. Creating locally editable avatars: Given an input avatar, (a) the avatar can be edited by transferring clothing geometry and color details from existing, yet unseen 3D assets. (b) Users can customize clothing details such as logos and letters via drawing on 2D images. (c) The avatars retain local detail consistently under pose changes. + +Existing methods do not allow for such capabilities. While 3D generative models of articulated human bodies [5, 21, 25, 42, 75] leverage differentiable neural rendering to learn from images, they cannot control local details due to highly entangled color and geometry in the 2D supervision signal. Generative models trained on 3D data [9, 13, 36, 44, 45] can produce geometric details for surfaces and clothing. However, the diversity of generated samples is low due to the lack of high-quality 3D human scans and not all methods model appearance. + +At the core of the issue lies the question of representation: graphics tools use meshes, UV, and texture maps which provide consistent topologies under deformation. However, human avatar methods that are built on mesh-based representations and linear blend skinning (LBS) are limited in their representational power with respect to challenging geometry (e.g., puffy garments) and flexible topologies (e.g., jackets), even with adaptations of additional displacement parameters [36] and mesh subdivision [66]. + +Inspired by the recent neural 3D representations [40, 62, 70, 72], we propose a novel hybrid representation for digital humans. Our representation combines the advantages of consistent topologies of LBS models with the representational power of neural fields. The key idea is to decompose the tasks of deformation consistency on one hand and local surface and appearance description on the other. For the former, we leverage existing parametric body models (e.g., SMPL [33] and SMPL-X [48]). For the latter, we leverage the fixed topology of the poseable mesh to store local feature codebooks. A decoder, shared across subjects, is then conditioned on the local features to predict the final signed distance and color values. Since only local information [15] is exposed to the decoder, overfitting and memorization can be mitigated. We experimentally show that this is crucial for 3D avatar fitting and reposing. + +Complementing this hybrid representation, we propose a training pipeline in the auto-decoding generative framework [9, 46, 52]. To this end, we jointly optimize multi-subject feature codebooks and the shared decoder weights via 3D reconstruction and 2D adversarial losses. The 3D losses help in disentangling appearance and geometric information from the input scans, while the latter improves the perceptual quality of randomly generated samples. To showcase the hybrid representation and the generative model we implement a prototypical avatar editing workflow shown in Fig. 1. + +Furthermore, to enable research on high-quality 3D avatars we contribute training data for generative 3D human models. We record a large-scale dataset (more than 600 scans of 80 subjects in 120 garments) using a volumetric capture stage [11]. Our dataset consists of high-quality 3D meshes alongside accurately registered SMPL-X [48] models and will be made available for research purposes. Finally, we assess our design decisions in detailed evaluations, both on existing and the proposed datasets. + +In summary, our contributions are threefold: (a) a novel hybrid representation for 3D virtual humans that allows for local editing across subjects, (b) a generative pipeline of 3D avatars creation that allows for fitting to unseen 3D scans and random sampling, and (c) a new large-scale high-quality dataset of 3D human scans containing diverse subjects, body poses and garments. + +# 2. Related Work + +Controllable human representations. Topics of virtual humans have received much attention in the graphics literature, such as skinning and rigging of articulated meshes [33, 43, 48, 55], physical simulation of clothing [19, 22, 41], and deferred rendering [24, 49, 65]. With the advances in neural rendering [63, 64] and the availability of large-scale human datasets [18, 28, 29, 32, 47, 67, 73, 74], numerous approaches have been proposed to reconstruct [4, 56, 57] and explicitly + +control [10,30,50] human avatars in a data-driven manner. + +One branch of work focuses on 2D image synthesis via generative adversarial networks (GANs) [20] and techniques of feature manipulation [28, 54, 60]. Typically, a 2D neural renderer creates human images corresponding to pose and appearance latent codes learned from the training data. Related applications such as virtual try-on [16, 17, 71] and video retargeting [7, 31, 68] have shown promising results in light of photo-realistic image synthesis by GANs [18,26]. However, these methods do not explicitly reason about complex 3D human geometry and can therefore not produce 3D-consistent results. + +A newly emerging line of work aims to create controllable avatars with 3D consistency. Some methods extend existing body models with neural networks to predict displacement layers [3, 6, 36] or textures [51]. Other methods learn to model challenging pose-dependent deformations on avatars either by predicting LBS weights [8, 10, 14, 58, 59, 76] or improving the capabilities of body models [23, 30, 34, 35, 37, 50, 53] with the power of implicit neural fields. However, these approaches mainly focus on modeling a single subject in specific clothing and do not scale to create diverse avatars. Our method overcomes this issue by learning a multi-subject generative model which produces realistic virtual humans with disentangled controllability over body poses, clothing geometry, and texture. + +Generative 3D human models. Existing generative models of human avatars can be loosely split into two main streams: learning 3D-aware neural rendering from 2D images [5, 21, 25, 42, 75] and learning body shapes from 3D supervision [9, 13, 36, 44, 45]. Powered by recent advances in differentiable neural rendering [64] and neural fields [70], much progress has been made in 3D-aware generative models [61]. However, learning to generate detailed clothed avatars from pure 2D supervision [5, 21, 25, 42, 75] is still challenging due to the complex appearance and articulation of bodies, self-occlusions, and highly entangled colors and geometries in images. + +More closely related to our setting are methods that learn to generate detailed body shapes from 3D scans or RGB-D data. For instance, CAPE [36] and SMPLict [13] are generative models for clothed humans. The former exploits VAE-GAN to predict additive displacements based on the SMPL vertices while the latter drape an implicitly modeled garment layer onto SMPL. NPMs [44], and SPAM [45] learn pose and shape latent spaces from 3D supervision, which enables latent code inversion using point clouds or depth sequences. gDNA [9] learns to synthesize body shapes in the canonical space and further improves clothing details with adversarial losses. However, none of the above-mentioned works is able to generate human bodies with appearance and neither allows fine-grained editing of the generated avatars. Our method addresses both issues by learning disentangled + +![](images/2fec53d2035886f0f9a4358c02ae195ae9cbe8ac6f2664d0ea91ab6c73a60f2c.jpg) +Figure 2. Proposed framework. Given a posed scan registered with body pose and shape LBS parameters $(\theta, \beta)$ , our proposed human representation stores its local geometry and texture features in a codebook $\mathbf{C}$ which is indexed by the vertex indices of $M$ -vertex LBS body mesh $\mathcal{M}$ (Sec. 3.1). Given a query point coordinates $\mathbf{x}_g$ , two separated MLP decoders $\Psi$ , $\Phi$ predict signed distances and colors conditioned on the positional features $(\mathbf{x}_l, \vec{\mathbf{n}})$ and the local geometry/texture features $(\mathbf{f_s} / \mathbf{f_c})$ respectively. We train a generative auto-decoder using $N$ posed scans, whose feature codebooks are stored in the dictionary $\mathbf{D_s}$ , $\mathbf{D_c}$ . We introduce two sampling strategies to sample codebooks (denoted as $\mathbf{C}_i / \mathbf{C}_r$ respectively) and jointly train our dictionaries and shared MLP decoders with a 3D reconstruction loss and a 2D adversarial loss (Sec. 3.2 & Sec. 3.3). + +local representations for multiple subjects. In addition, we experimentally show that our representation significantly improves the performance of model fitting against state-of-the-art human generative models. + +# 3. Method + +Our proposed method is summarized in Fig. 2. We first contribute a novel hybrid human representation that stores local geometric and textural information into two aligned feature spaces (Sec. 3.1 and Fig. 3). To allow fitting to new 3D scans and drawing random samples from the underlying data distribution, we design a training strategy to learn a meaningful latent space under the generative adversarial framework to bring in additional 2D adversarial supervision (Sec. 3.2 & Sec. 3.3). Finally, we demonstrate the utility of our method for creating avatars by enabling local feature editing through existing 3D assets or images (Sec. 3.4). + +# 3.1. Hybrid Representation of Humans + +To enable 3D avatars with high-fidelity representational power and local editing capabilities, a suitable representation is needed. To this end, we propose a novel hybrid representation that combines the advantages of neural fields (flexibility and modeling power) with LBS-articulated mesh models (ease of deformation and full explicit control). + +An overview of how we leverage the proposed representation is provided in Fig. 2 in the dotted blue box. Given a human scan, we first create a posed, coarse body mesh $\mathcal{M}$ (shown in red) using the registered body parameters + +$(\theta, \beta)$ of an LBS body model. The mesh consists of $M$ vertices ( $\mathcal{V} \in \mathbb{R}^{M \times 3}$ ) in the posed space and $M_f$ faces where $\mathcal{F} \in \{1, \dots, M\}^{M_f \times 3}$ defines the vertex indices on each face. We then construct a trainable feature codebook $\mathbf{C} \in \mathbb{R}^{M \times 2F}$ , which stores $F$ -dimensional local geometry and texture features respectively for each vertex. + +Similar to coordinate-based implicit fields, a 3D coordinate $\mathbf{x}_g\in \mathbb{R}^3$ is used to predict its corresponding signed distance $s(\mathbf{x}_g)$ , and RGB color $c(\mathbf{x}_g)$ . Instead of using global coordinates directly as inputs, we condition neural field decoders on the local triangle coordinates $\mathbf{x}_l\in \mathbb{R}^3$ and the local geometry and texture features $\mathbf{f_s},\mathbf{f_c}\in \mathbb{R}^F$ . We illustrate this conversion from global coordinates to local triangle coordinates in Fig. 3. The global coordinates $\mathbf{x}_g$ are first projected onto the mesh by finding the closest point $\mathbf{x}_c^*$ (Fig. 3, blue dot): + +$$ +\begin{array}{l} \mathbf {x} _ {c} ^ {*} = \arg \min _ {\mathbf {x} _ {c}} \| \mathbf {x} _ {g} - \mathbf {x} _ {c} \| _ {2}, \\ \mathbf {x} _ {c} = \mathcal {B} _ {u, v} (\mathcal {V} [ m _ {0}, m _ {1}, m _ {2} ]), \tag {1} \\ \end{array} +$$ + +where $(m_0, m_1, m_2)$ are vertex indices of faces $\mathcal{F}$ and $\mathcal{B}_{u,v}(.)$ is the barycentric interpolation function with barycentric coordinates $(u, v, 1 - u - v)$ . The closest point $\mathbf{x}_c^*$ within the face is used to transform $\mathbf{x}_g$ into a local triangle coordinate system. Hence, $\mathbf{x}_l$ consists of the barycentric coordinates $(u, v)$ , the signed distance $d$ between $\mathbf{x}_g$ and $\mathbf{x}_c^*$ , i.e., $\mathbf{x}_l \coloneqq (u, v, d)$ . We also compute a direction vector $\vec{\mathbf{n}}$ between $\mathbf{x}_g$ and $\mathbf{x}_c^*$ as an additional feature to distinguish points near triangle edges. To query local features $(\mathbf{f_s}, \mathbf{f_c})$ , we use the vertex indices on the triangle $(m_0^*, m_1^*, m_2^*)$ to + +![](images/aed628843baf272ee99fcfe945856dc0bc3c7b57cadd16d8ed6f5ff8032e100e.jpg) +Figure 3. Local feature querying. Given an LBS body mesh posed by the parameters $(\theta, \beta)$ , we represent a detailed human body as a codebook that stores local texture and geometry features indexed by the vertices on the mesh. An input query point $\mathbf{x_g}$ finds the nearest triangle on the LBS body mesh and returns its vertex indices for local feature lookup. To prevent decoders from memorizing any global information, we transform the position of $\mathbf{x_g}$ into local triangle coordinates $(u, v)$ , distance $d$ , and direction $\vec{\mathbf{n}}$ of the closest point (i.e., the blue dot). The final geometry and texture features $\mathbf{f_s}, \mathbf{f_c}$ are fused via barycentric interpolation. + +look up three elements in the feature codebook C. We then fuse the three local features via barycentric interpolation. + +Finally, we take all the local features $(\mathbf{f_s},\mathbf{f_c},\mathbf{x}_l,\vec{\mathbf{n}})$ as input to two separate decoders $\Phi$ and $\Psi$ to predict SDF and RGB values respectively: + +$$ +\begin{array}{l} \Phi : \mathbb {R} ^ {F} \times \mathbb {R} ^ {3} \times \mathbb {R} ^ {3} \rightarrow \mathbb {R} \tag {2} \\ \left(\mathbf {f} _ {\mathbf {s}}, \mathbf {x} _ {l}, \vec {\mathbf {n}}\right) \mapsto s \left(\mathbf {x} _ {g}\right), \\ \end{array} +$$ + +$$ +\begin{array}{l} \boldsymbol {\Psi}: \mathbb {R} ^ {F} \times \mathbb {R} ^ {3} \times \mathbb {R} ^ {3} \rightarrow \mathbb {R} ^ {3} \tag {3} \\ \left(\mathbf {f} _ {\mathbf {c}}, \mathbf {x} _ {l}, \vec {\mathbf {n}}\right) \mapsto c \left(\mathbf {x} _ {g}\right). \\ \end{array} +$$ + +Note that only local information is exposed to the decoders, which allows us to use the same MLPs across different vertices and subjects. We show that preventing networks from memorizing global information in this way is necessary for local editing and reposing in our experiments (Fig. 9). + +# 3.2. Generative Codebook Sampling + +Our goal is to provide means to create and personalize avatars with diverse body shapes, appearances, and local details. To this end, we leverage the above representation to train a single multi-subject model which enables the transfer of local features across subjects. We note that since the mesh topology of the LBS model is identical, this enables us to learn a shared feature space from multiple posed scans. + +To learn the feature representation over a dataset of $N$ scans, it is sufficient to store the codebooks $\mathbf{C}_i$ in two dictionaries $\mathbf{D_s},\mathbf{D_c}\in \mathbb{R}^{N\times (MF)}$ to represent shape and color + +information of the $i$ -th subject respectively. The entries $\mathbf{C}_i$ can then be learned jointly with the decoder weights via direct 3D supervision using the $i$ -th scan (Fig. 2). However, we experimentally show that this is insufficient to learn a well-behaved latent space from which we can draw novel samples (see Fig. 10). + +Therefore we introduce a codebook sampling strategy that allows us to draw random samples and update the entries of the dictionaries $\mathbf{D}_{\mathbf{s}}$ , $\mathbf{D}_{\mathbf{c}}$ via an additional 2D adversarial loss. More specifically, we follow the auto-decoder architecture [52] and perform PCA on the reshaped dictionary to compute eigenvectors $\mathbf{V} \in \mathbb{R}^{D \times (MF)}$ and fit a normal distribution to the $D$ -dimensional PCA coefficients of $N$ samples. A new random codebook $\mathbf{C}_r$ can then be generated by sampling $D$ -dimensional PCA parameters and multiplying them with the eigenvectors $\mathbf{V}$ (See SuppB.1 for details). Note that our representation disentangles shapes from appearances with separated geometry and texture branches, which enables independent sampling of geometry and texture features. + +# 3.3. Model Training + +3D reconstruction loss. To train a codebook $\mathbf{C}_i$ with a single scan, we sample points in a thin shell around the scan. For each coordinate we compute its signed distance $s$ to the input scan, closest texture color $\mathbf{c}$ , and surface normal $\mathbf{n}$ on the input scan to attain ground truth values. The codebooks and the decoder weights are then optimized via the following losses: + +$$ +\mathcal {L} _ {s d f} = \| s - s (\mathbf {x} _ {\mathbf {g}}) \| _ {1} + \lambda_ {n} \| 1 - \mathbf {n} \cdot \nabla_ {\mathbf {x} _ {\mathbf {g}}} s (\mathbf {x} _ {\mathbf {g}}) \| _ {1}, \quad (4) +$$ + +$$ +\mathcal {L} _ {r g b} = \left\| \mathbf {c} - c \left(\mathbf {x} _ {\mathbf {g}}\right) \right\| _ {1}, \tag {5} +$$ + +$$ +\mathcal {L} _ {3 D} = \lambda_ {s d f} \mathcal {L} _ {s d f} + \lambda_ {r g b} \mathcal {L} _ {r g b}. \tag {6} +$$ + +2D adversarial loss. Adversarial learning does not require exact ground-truth annotations but is trained via a collection of real and fake (rendered) images. Thus, real images are obtained by rasterizing the ground-truth scan to which the coarse body mesh $\mathcal{M}$ was fitted. Color and normal images (denoted as "Real Patch" in Fig. 2) are used for learning texture and geometry respectively. Using the same virtual camera parameters and the coarse mesh $\mathcal{M}$ , we attain rendered patches (denoted as "Rendered Patch" Fig. 2) via implicit surface rendering of a sampled codebook $\mathbf{C}_r$ . Please refer to Supp-B.2 for more details. + +Using these 2D patches, we train dictionaries, decoders, and discriminators jointly with a non-saturating logistic loss $\mathcal{L}_{adv}$ [20], R1 regularization $\mathcal{L}_{R1}$ [38], and path length regularization $\mathcal{L}_{path}$ [27]. Note that these losses do not require exact ground-truth replication. Furthermore, we regu + +larize the feature dictionaries to follow a Gaussian distribution [46] with $\mathcal{L}_{reg} = \| \mathbf{D}\| _F$ . In summary, we optimize the discriminator: + +$$ +\mathcal {L} _ {\text {d i s}} = \mathcal {L} _ {\text {a d v}} + \lambda_ {R 1} \mathcal {L} _ {R 1}, \tag {7} +$$ + +while updating the remaining components $(\mathbf{D}_{\mathbf{c}},\mathbf{D}_{\mathbf{s}},\Phi ,\Psi)$ + +$$ +\mathcal {L} = - \mathcal {L} _ {a d v} + \mathcal {L} _ {3 D} + \lambda_ {\text {p a t h}} \mathcal {L} _ {\text {p a t h}} + \lambda_ {\text {r e g}} \mathcal {L} _ {\text {r e g}}, \tag {8} +$$ + +where $\lambda_{(\cdot)}$ denotes weights to balance the losses. + +Since we sample on the fly during training (see Sec. 3.2), the 2D adversarial loss does affect the shared decoders and the whole feature dictionaries (See Supp-Fig.14 for details). + +# 3.4. Feature Editing and Avatar Customization + +We now describe how we integrate the above-mentioned human representation and the generative architecture into the avatar creation workflow shown in Fig. 1. + +Avatar initialization. To simplify the avatar creation process, our method allows users to start with a default example $\mathbf{C}_d$ , which can be queried from the trained codebook dictionaries directly with index $i$ ( $\mathbf{C}_i$ ) or randomly sampled from the learned D-dimensional PCA parameters distribution ( $\mathbf{C}_r$ in Sec. 3.2). + +Model fitting. Being able to extract elements of interest or copying from existing 3D assets is necessary for avatar creation and editing. To this end, we leverage a similar technique to GAN inversion [69], where decoder parameters $\Phi$ and $\Psi$ are frozen and we only optimize a new feature codebook $\mathbf{C}_{fit}$ to fit a 3D scan. + +Given a 3D target scan and its corresponding body model parameters, we calculate the 3D reconstruction loss in Eq. (6) between the prediction conditioned on $\mathbf{C}_{fit}$ and the ground-truth scan, i.e., + +$$ +\mathbf {C} _ {f i t} = \arg \min _ {\mathbf {C}} \left(\lambda_ {s d f} \mathcal {L} _ {s d f} + \lambda_ {r g b} \mathcal {L} _ {r g b}\right). \tag {9} +$$ + +Note that our generative neural fields (MLP decoders) are conditioned on descriptive local features. We show that it allows us to accurately fits complex clothing geometry and unseen textural patterns in Sec. 4.3. + +Cross-subjects feature editing. With multiple codebooks each representing different 3D assets, we can easily transfer local geometry and texture from one avatar to another, for instance, changing the top wear from the fitted scan $\mathbf{C}_{fit}$ to the initial $\mathbf{C}_d$ . Recalling that all codebooks are indexed by an identical mesh topology, users can easily retrieve the index numbers of $\nu_{body} \subset \nu$ via standard mesh visualization tools such as Blender [12]. Finally, swapping of the corresponding rows in $\mathbf{C}_{fit}$ and $\mathbf{C}_d$ given the vertex indices also swaps the local appearance. + +![](images/d092e9f4fee83fe4cf392f1f1675d2a182e1d84b6a5419cd58daf1e392dd09b1.jpg) +Figure 4. Randomly sampled texture and body geometry from the model trained on THuman2.0. Top: Given any poses, our randomly sampled geometries contain realistic details such as wrinkles in garments and facial expressions. Bottom: Given arbitrary poses and body geometries, our model produces reasonable colors on skin, hair, clothes, and pants in each sample (We turn off the Shader for visualizing pure texture colors). + +Personalized texture drawing. One can further customize clothing by drawing directly onto 2D images. Due to the learned disentangled feature spaces, we are able to update texture features in $\mathbf{C}_{fit}$ while keeping the geometry features unchanged. When fitting 2D images, users draw on the rasterized images from arbitrary target scans. We then finetune only texture features in $\mathbf{C}_{fit}$ via the RGB loss in Eq. (5) given the corresponding 3D coordinates and colors. + +Avatar reposing. Our representation combines a base mesh $\mathcal{M}$ and underlying feature codebooks $\mathbf{C}_{fit}$ that learn local geometry and texture on the 3D scans. Hence, one can repose the mesh $\mathcal{M}$ using $(\theta, \beta)$ parameters, which also reposes the avatar correspondingly. Since the geometry codebook does not contain global pose $(\theta, \beta)$ , local information can be consistently applied to $\mathcal{M}$ under unseen poses. + +# 4. Experiments + +Our goal is locally editable 3D avatar creation. Since we are the first to discuss this problem, we visualize our editing results in Sec. 4.2. Next, we highlight the capability of model fitting by comparing our method with SOTA human generative models in Sec. 4.3. Finally, controlled experiments are presented in Sec. 4.4 and Sec. 4.5 to verify the effectiveness of our design. + +![](images/08f796d97ccd518aa6921982dfa6ada2a55fd08f5a54bd8f34657813f0bba8c5.jpg) +Figure 5. Cross-subject feature editing results. We partially transfer local clothing details from the unseen scans (upper and lower body) to the input avatars. The results of the edited avatars are shown in the right column. + +# 4.1. Experiment Settings + +Dataset. Most generative human works [9,44,45] exploit commercial data [1,2] for training, which is not easily accessible and limits reproducibility. Furthermore, the quality of publicly available 3D human datasets [66,73] is not satisfactory. Issues such as non-watertight topologies and noise are very common (See Supp-A.2 for examples and comparison). To bridge this gap, we collect a new dataset named CustomHumans for training and evaluation. Here we summarize the datasets used in our experiments. + +- CustomHumans (Ours) contains more than 600 high-quality scans of 80 participants in 120 garments in varied poses from a volumetric capture stage [11], which is equipped with 106 synchronized cameras (53 RGB and 53 IR cameras). We use our dataset to train models of all quantitative experiments. (Sec. 4.3 ~ Sec. 4.5) +- THuman2.0 [73] is a dataset containing about 500 scans of humans wearing 150 garments in various poses. Since this dataset has more textural diversity, we train our method on it for qualitative random sampling experiments (Sec. 4.2 and Sec. 4.4). +- SIZER [66] is a widely used 3D scan dataset containing A-posed human meshes of 97 subjects in 22 garments. These meshes are used as unseen test scans in our fitting experiment (Sec. 4.3). + +Evaluation protocol. Following the evaluation protocol in OccNet [39], we quantitatively evaluate the model fitting accuracy using three metrics: Chamfer distance (CD), normal consistency (NC), and f-Score. + +![](images/3698524d046b85c70bb257d34dc20a565828d0a4c33a93db41f91ba65045105c.jpg) +Figure 6. Personalized texture editing. We draw personalized logos on 2D images and fit avatars' texture features to the images. These local textures remain consistent under pose changes. + +# 4.2.Customized Avatars + +We visualize the results of our proposed avatar customization workflow described in Sec. 3.4. + +Avatar initialization. In Fig. 4, we show random textures and geometries sampled from the model trained on THuman2.0. Our method is able to generate reasonable colors and wrinkles in arbitrary poses. Note that the sampled geometries are shown as the real meshes but not as rendered normals as in [9] (See Supp-C.2 for comparisons). + +Cross-subjects feature editing. After fitting feature codebooks to 3D scans, we can change the clothes on our avatars by swapping the local features stored on the body vertices. We select the features within the upper body and lower body areas. We then copy these local features to the initial avatars' feature codebooks. As shown in Fig. 5, our method is able to handle multiple garments on different human subjects and preserves consistent details under different body poses or shapes. + +Personalized texture drawing. Our method allows users to draw complex letters and logos on images for personalized texture editing. We perform the model fitting and feature editing process but only optimize the texture features in the codebooks using user-edited images and the RGB loss (Eq. (5)). Fig. 6 shows that new texture can be seamlessly applied to the 3D avatars. It is worth noting that resulting avatars enable detailed pose control via the SMPL-X parameters without affecting the fitted texture and geometry. + +# 4.3. Model Fitting Comparison + +Since model fitting is an important step in our avatar creation workflow, we compare the capability of feature inversion using unseen 3D scans. The goal of this task is to invert a 3D scan into latent codes while keeping the remaining model parameters fixed. We compare our method with the 3D human generative model gDNA [9], which has achieved state-of-the-art performance in fitting the geometry of 3D human bodies. We also directly compare with SMPL [33] and SMPL+D [3]. Note that SMPL+D is a stronger vertex-based extension that uses a subdivided version of SMPL to directly register surfaces to scans while our method and gDNA optimize latent codes. + +![](images/bdc903a5558034f6550624b0c802219c9e7182cb2514cc54d195e506ffd7b357.jpg) +Figure 7. Qualitative comparison of model fitting on SIZER. We visualize the fitting results from gDNA [9], SMPL+D [3], and our method. Our results are perceptually close to the ground truth even on the challenging test cases of jackets and loose t-shirts. + +![](images/779ffaae00391ed9ec726853e6dc50f2875c43cb70e2b03afab5da49e4df7a54.jpg) +Figure 8. Qualitative comparison of texture fitting. We compare our method with SMPL+D [3] by fitting to unseen textured meshes. The performance of SMPL+D is limited by its geometry and texture resolution. + +From Fig. 7 we can see that SMPL+D handles loose clothing, such as a business suite, better than gDNA. However, the surfaces of SMPL+D results are over-smoothed and do not contain high-frequency details while ours can preserve them. Quantitatively, our method consistently outperforms these methods on all metrics as shown in Tab. 1. + +Fig. 8 depicts the result of texture fitting against the SMPL+D baseline. While both methods inherit a fixed mesh topology the quality of SMPL+D is limited by its model resolution. Our method addresses this issue via local neural fields that enable cross-subject feature editing of texture and geometry with enhanced representational power. + +# 4.4. Ablation Study + +Effectiveness of local features and shared decoders. To verify the design choice of using local features, we replace the local features $\mathbf{x}_l$ by the global coordinates $\mathbf{x}_g$ for conditioning the decoders. Fig. 9 shows that even though the shared decoders are able to achieve similar reconstruction results when training with global information, they do not maintain consistent performance for model fitting and avatar reposing. This is because the shared decoders tend to memorize global coordinates information in a "per-subject" + +![](images/bf4e38a142b897967b1714d2123ef9299747f2c32a283263d044fe279c4456d7.jpg) +Figure 9. Comparison of using global/local features for conditioning decoders. The use of global coordinates causes overfitting and memorization to the shared decoders, which makes it struggle to handle unseen scans or novel body poses. + +
MethodPred-to-Scan / +Scan-to-Pred (mm)↓NC↑f-Score↑
SMPL [33]13.60 / 18.030.8490.458
gDNA [9]8.374 / 8.0060.8420.718
SMPL+D [3]5.192 / 2.8540.9110.962
Ours1.364 / 1.4230.9490.997
+ +Table 1. Model fitting comparison on SIZER. We report Chamfer distance, normal consistency (NC), and f-score between ground truth and the meshes fitted by different methods. + +manner, rather than learning shareable information that can be used across vertices and subjects. On the other hand, our representation ensures only local features defined on the triangle coordinates are exposed to the shared decoders. In such cases, the decoders can better handle unseen body poses or out-of-distribution samples for model fitting and avatar editing. + +Importance of 2D adversarial loss and 3D disentanglement. As discussed in Sec. 3.3, we introduce feature disentanglement and generative adversarial learning in our training framework. As shown in Fig. 10, sampling within the feature spaces learned without adversarial loss does not yield reasonable body textures. Similarly, training only a single decoder for both geometry and texture does not allow us to maintain desired body geometries when sampling random textures. Our full model can produce disentangled textures, given arbitrary body geometries and poses. + +
Training Data Percentage10%25%50 %75%100%
Chamfer Distance (mm) +S-to-P / P-to-S ↓1.933 / 1.7981.754 / 1.5901.543 / 1.4561.463 / 1.3851.423 / 1.364
Normal Consistency↑0.9180.9310.9350.9470.949
f-Score (%) ↑99.2599.3899.6599.7499.75
+ +Table 2. Generalization analysis on CustomHumans. We analyze the model fitting performances with regard to different amounts of training data (100% = 100 training scans). We observe consistent performance gain on all evaluation metrics when using more training subjects to train the shared decoders. + +![](images/a2c3420356ae7cfdc1108abec096c6dac78c4a8d6cdc05b77a10e202bc8e7125.jpg) +Figure 10. Ablative comparison of our framework designs. We visualize the results of transferring random texture to given body geometry. Our full model produces reasonable body texture and is able to maintain fixed geometry for texture editing. + +# 4.5. Generalization Ability Analysis + +We are interested in how the amount of training data affects the capacities of the MLP decoders. To analyze this, we design three evaluation protocols: 3D model fitting, avatar reposing, and 2D texture fitting. Tab. 2 summarizes the model fitting performance using different percentages of training data. We observe a $25\%$ accuracy improvement when using the full training set. In Fig. 11 (Top) we show that the reposing artifacts caused by self-contact (e.g., fist and elbow) can be reduced when training the MLP decoders with more poses and subjects. In addition, Fig. 11 (Bottom) depicts a qualitative comparison of 2D texture editing under different training data percentages. We evaluate texture editing quality by fitting a 2D image with unseen geometric shapes and colors. It can be seen that the model trained on more samples is able to handle a wider range of color distribution. These results confirm the necessity for learning multi-subject shared decoders in our task. + +![](images/9b02ee386727fe317ceb85097835bb232307ffac0eb0d536bb42dafdebe3bf56.jpg) +Figure 11. Qualitative comparison of generalization. Top: We visualize the results of avatar reposing using different percentages of training data (i.e., 10 meshes vs 100 meshes). Artifacts caused by self-contact (e.g., fist and elbow) can be reduced when more training subjects are introduced. Bottom: We visualize the results using different percentages of training data. Using more data results in more robust shared decoders (with a wider color range), which is necessary for the avatar creation task. + +# 5. Conclusion + +We propose an end-to-end trainable framework for learning 3D human avatars with high fidelity and full editability. By combining neural fields with explicit skinned meshes, our representation addresses the controllability issue of many previous implicit representations. Moreover, we uniquely integrate the proposed human representation into a generative auto-decoding pipeline that enables local editing across multiple animation-ready avatars. Through our evaluation on the newly contributed CustomHumans dataset, we demonstrate that our approach achieves higher model fitting accuracy and generates diverse detailed avatars. We believe that this work opens up exciting possibilities for accelerating content creation in the Metaverse. + +Acknowledgements We express our gratitude to Stefan Walter and Dean Bakker for infrastructure support, Juan Zarate for managing the capture stage, and Deniz Yildiz and Laura Wulfroth for data capture assistance. We thank Andrew Searle for supporting the capturing system and all the dataset participants. + +# References + +[1] https://3dpeople.com/.6 +[2] https://renderpeople.com/.6 +[3] Thiemo Alldieck, Marcus Magnor, Bharat Lal Bhatnagar, Christian Theobalt, and Gerard Pons-Moll. Learning to reconstruct people in clothing from a single rgb camera. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1175-1186, 2019. 2, 6, 7 +[4] Thiemo Alldieck, Mihai Zanfir, and Cristian Sminchisescu. Photorealistic monocular 3d reconstruction of humans wearing clothing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2 +[5] Alexander W Bergman, Petr Kellnhofer, Yifan Wang, Eric R Chan, David B Lindell, and Gordon Wetzstein. Generative neural articulated radiance fields. In Advances in Neural Information Processing Systems (NeurIPS), 2022. 1, 2 +[6] Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Combining implicit function learning and parametric models for 3d human reconstruction. In Proceedings of the European Conference on Computer Vision (ECCV), pages 311-329. Springer, 2020. 2 +[7] Caroline Chan, Shiry Ginosar, Tinghui Zhou, and Alexei A Efros. Everybody dance now. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 5933-5942, 2019. 2 +[8] Xu Chen, Tianjian Jiang, Jie Song, Max Rietmann, Andreas Geiger, Michael J. Black, and Otmar Hilliges. FastSNARF: A fast deformer for articulated neural fields. arXiv, abs/2211.15601, 2022. 2 +[9] Xu Chen, Tianjian Jiang, Jie Song, Jinlong Yang, Michael J Black, Andreas Geiger, and Otmar Hilliges. gdna: Towards generative detailed neural avatars. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 20427-20437, 2022. 1, 2, 6, 7 +[10] Xu Chen, Yufeng Zheng, Michael J Black, Otmar Hilliges, and Andreas Geiger. Snarf: Differentiable forward skimming for animating non-rigid neural implicit shapes. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021. 2 +[11] Alvaro Collet, Ming Chuang, Pat Sweeney, Don Gillett, Dennis Evseev, David Calabrese, Hugues Hoppe, Adam Kirk, and Steve Sullivan. High-quality streamable free-viewpoint video. ACM Transactions on Graphics (TOG), 34(4):1-13, 2015. 2, 6 +[12] Blender Online Community. Blender - a 3D modelling and rendering package. Blender Foundation, Stichting Blender Foundation, Amsterdam, 2018. 5 +[13] Enric Corona, Albert Pumarola, Guillem Alenyà, Gerard Pons-Moll, and Francesc Moreno-Noguer. Smplicit: Topology-aware generative model for clothed people. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 1, 2 +[14] Boyang Deng, JP Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, and Andrea Tagliasacchi. Neural articulated shape approximation. In + +Proceedings of the European Conference on Computer Vision (ECCV). Springer, August 2020. 2 +[15] Terrance DeVries, Miguel Angel Bautista, Nitish Srivastava, Graham W Taylor, and Joshua M Susskind. Unconstrained scene generation with locally conditioned radiance fields. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 14304-14313, 2021. 2 +[16] Haoye Dong, Xiaodan Liang, Xiaohui Shen, Bowen Wu, Bing-Cheng Chen, and Jian Yin. Fw-gan: Flow-navigated warping gan for video virtual try-on. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 1161-1170, 2019. 2 +[17] Xin Dong, Fuwei Zhao, Zhenyu Xie, Xijin Zhang, Daniel K. Du, Min Zheng, Xiang Long, Xiaodan Liang, and Jianchao Yang. Dressing in the wild by watching dance videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3480-3489, June 2022. 2 +[18] Jianglin Fu, Shikai Li, Yuming Jiang, Kwan-Yee Lin, Chen Qian, Chen Change Loy, Wayne Wu, and Ziwei Liu. Stylegan-human: A data-centric odyssey of human generation. In Proceedings of the European Conference on Computer Vision (ECCV), pages 1-19. Springer, 2022. 2 +[19] Rony Goldenthal, David Harmon, Raanan Fattal, Michel Bercovier, and Eitan Grinspun. Efficient simulation of inextensible cloth. ACM Transactions on Graphics (TOG), pages 49-es, 2007. 2 +[20] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems (NeurIPS), pages 2672–2680, 2014. 2, 4 +[21] Artur Grigorev, Karim Iskakov, Anastasia Iinina, Renat Bashirov, Ilya Zakharkin, Alexander Vakhitov, and Victor Lempitsky. Stylepeople: A generative model of fullbody human avatars. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5151-5160, 2021. 1, 2 +[22] Artur Grigorev, Bernhard Thomaszewski, Michael J Black, and Otmar Hilliges. HOOd: Hierarchical graphs for generalized modelling of clothing dynamics. 2023. 2 +[23] Chen Guo, Tianjian Jiang, Xu Chen, Jie Song, and Otmar Hilliges. Vid2 avatar: 3d avatar reconstruction from videos in the wild via self-supervised scene decomposition. In Computer Vision and Pattern Recognition (CVPR), 2023. 2 +[24] Pat Hanrahan and Paul Haeberli. Direct wysiwyg painting and texturing on 3d shapes: (an error occurred during the printing of this article that reversed the print order of pages 118 and 119. while we have corrected the sort order of the 2 pages in the dl, the pdf did not allow us to repaginae the 2 pages.). ACM Transactions on Graphics (TOG), 24(4):215-223, 1990. 2 +[25] Fangzhou Hong, Zhaoxi Chen, Yushi Lan, Liang Pan, and Ziwei Liu. Eva3d: Compositional 3d human generation from 2d image collections. arXiv preprint arXiv:2210.04888, 2022. 1, 2 +[26] Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Alias-free + +generative adversarial networks. Advances in Neural Information Processing Systems (NeurIPS), 34:852-863, 2021. 2 +[27] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110-8119, 2020. 4 +[28] Christoph Lassner, Gerard Pons-Moll, and Peter V Gehler. A generative model of people in clothing. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 853-862, 2017. 2 +[29] Ruilong Li, Shan Yang, David A. Ross, and Angjoo Kanazawa. Ai choreographer: Music conditioned 3d dance generation with aist++. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021. 2 +[30] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural actor: Neural free-view synthesis of human actors with pose control. ACM Transactions on Graphics (TOG), 40(6):1-16, 2021. 2 +[31] Wen Liu, Zhixin Piao, Jie Min, Wenhan Luo, Lin Ma, and Shenghua Gao. Liquid warping gan: A unified framework for human motion imitation, appearance transfer and novel view synthesis. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 5904-5913, 2019. 2 +[32] Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaou Tang. Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1096-1104, 2016. 2 +[33] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black. SMPL: A skinned multiperson linear model. ACM Transactions on Graphics (TOG), 34(6):248:1-248:16, October 2015. 2, 6, 7 +[34] Qianli Ma, Shunsuke Saito, Jinlong Yang, Siyu Tang, and Michael J. Black. SCALE: Modeling clothed humans with a surface codec of articulated local elements. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 16082-16093, June 2021. 2 +[35] Qianli Ma, Jinlong Yang, Michael J. Black, and Siyu Tang. Neural point-based shape modeling of humans in challenging clothing. In 2022 International Conference on 3D Vision (3DV), September 2022. 2 +[36] Qianli Ma, Jinlong Yang, Anurag Ranjan, Sergi Pujades, Gerard Pons-Moll, Siyu Tang, and Michael J. Black. Learning to Dress 3D People in Generative Clothing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 1, 2 +[37] Qianli Ma, Jinlong Yang, Siyu Tang, and Michael J. Black. The power of points for modeling humans in clothing. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 2021. 2 +[38] Lars Mescheder, Andreas Geiger, and Sebastian Nowozin. Which training methods for gans do actually converge? In International Conference on Machine Learning (ICML), pages 3481-3490. PMLR, 2018. 4 + +[39] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4460-4470, 2019. 6 +[40] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-102:15, July 2022. 2 +[41] Rahul Narain, Armin Samii, and James F O'brien. Adaptive anisotropic remeshing for cloth simulation. ACM Transactions on Graphics (TOG), 31(6):1-10, 2012. 2 +[42] Atsuhiro Noguchi, Xiao Sun, Stephen Lin, and Tatsuya Harada. Unsupervised learning of efficient geometry-aware neural articulated representations. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 1, 2 +[43] Ahmed AA Osman, Timo Bolkart, and Michael J Black. Star: Sparse trained articulated human body regressor. In Proceedings of the European Conference on Computer Vision (ECCV), pages 598-613. Springer, 2020. 2 +[44] Pablo Palafox, Aljaž Božić, Justus Thies, Matthias Nießner, and Angela Dai. Npms: Neural parametric models for 3d deformable shapes. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021. 1, 2, 6 +[45] Pablo Palafox, Nikolaos Sarafianos, Tony Tung, and Angela Dai. Spams: Structured implicit parametric models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 1, 2, 6 +[46] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 165-174, 2019. 2, 5 +[47] Priyanka Patel, Chun-Hao P Huang, Joachim Tesch, David T Hoffmann, Shashank Tripathi, and Michael J Black. Agora: Avatars in geography optimized for regression analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 13468-13478, 2021. 2 +[48] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3d hands, face, and body from a single image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 10975-10985, 2019. 2 +[49] Hans Köhling Pedersen. Decorating implicit surfaces. In ACM Transactions on Graphics (TOG), pages 291-300, 1995. 2 +[50] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 9054–9063, 2021. 2 +[51] Sergey Prokudin, Michael J Black, and Javier Romero. Smplpix: Neural avatars from 3d human models. In Proceed- + +ings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1810-1819, 2021. 2 +[52] Daniel Rebain, Mark Matthews, Kwang Moo Yi, Dmitry Lagun, and Andrea Tagliasacchi. Lolnerf: Learn from one look. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1558-1567, 2022. 2, 4 +[53] Edoardo Remelli, Timur Bagautdinov, Shunsuke Saito, Chenglei Wu, Tomas Simon, Shih-En Wei, Kaiwen Guo, Zhe Cao, Fabian Prada, Jason Saragih, et al. Drivable volumetric avatars using texel-aligned features. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1–9, 2022. 2 +[54] Daniel Roich, Ron Mokady, Amit H. Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. ACM Transactions on Graphics (TOG), 42(1), aug 2022. 2 +[55] Javier Romero, Dimitrios Tzionas, and Michael J. Black. Embodied hands: Modeling and capturing hands and bodies together. ACM Transactions on Graphics (TOG), 36(6), Nov. 2017. 2 +[56] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 2019. 2 +[57] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo. Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 84-93, 2020. 2 +[58] Shunsuke Saito, Jinlong Yang, Qianli Ma, and Michael J. Black. SCANimate: Weakly supervised learning of skinned clothed avatar networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2021. 2 +[59] Kaiyue Shen, Chen Guo, Manuel Kaufmann, Juan Zarate, Julien Valentin, Jie Song, and Otmar Hilliges. X- avatar: Expressive human avatars. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 2 +[60] Yichun Shi, Divyansh Aggarwal, and Anil K Jain. Lifting 2d stylegan for 3d-aware face generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6258-6266, 2021. 2 +[61] Zifan Shi, Sida Peng, Yinghao Xu, Yiyi Liao, and Yujun Shen. Deep generative models on 3d representations: A survey. arXiv preprint arXiv:2210.15663, 2022. 2 +[62] Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler. Neural geometric level of detail: Real-time rendering with implicit 3D shapes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 2 +[63] Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo MartinBrualla, Tomas Simon, Jason Saragih, Matthias Nießner, et al. State of the art on neural rendering. In Annual Conference of the European Association for Computer Graphics + +(EUROGRAPHICS), volume 39, pages 701-727. Wiley Online Library, 2020. 2 +[64] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, W Yifan, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, et al. Advances in neural rendering. In Annual Conference of the European Association for Computer Graphics (EUROGRAPHICS), volume 41, pages 703-735. Wiley Online Library, 2022. 2 +[65] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics (TOG), 38(4):1-12, 2019. 2 +[66] Garvita Tiwari, Bharat Lal Bhatnagar, Tony Tung, and Gerard Pons-Moll. Sizer: A dataset and model for parsing 3d clothing and learning size sensitive 3d clothing. In Proceedings of the European Conference on Computer Vision (ECCV). Springer, August 2020. 1, 6 +[67] Gul Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J Black, Ivan Laptev, and Cordelia Schmid. Learning from synthetic humans. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 109-117, 2017. 2 +[68] Ting-Chun Wang, Arun Mallya, and Ming-Yu Liu. One-shot free-view neural talking-head synthesis for video conferencing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 10039-10049, 2021. 2 +[69] Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang. Gan inversion: A survey. TPAMI, 2022. 5 +[70] Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. Neural fields in visual computing and beyond. In Annual Conference of the European Association for Computer Graphics (EUROGRAPHICS), volume 41, pages 641-676. Wiley Online Library, 2022. 2 +[71] Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, and Xiaodan Liang. Towards scalable unpaired virtual try-on via patch-routed spatially-adaptive gan. Advances in Neural Information Processing Systems (NeurIPS), 34:2598-2610, 2021. 2 +[72] Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In Proceedings of the European Conference on Computer Vision (ECCV), pages 597-614. Springer, 2022. 2 +[73] Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, and Yebin Liu. Function4d: Real-time human volumetric capture from very sparse consumer rgbd sensors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2021. 2, 6 +[74] Zhixuan Yu, Jae Shin Yoon, In Kyu Lee, Prashanth Venkatesh, Jaesik Park, Jihun Yu, and Hyun Soo Park. Humbi: A large multiview dataset of human body expressions. In Proceedings of the IEEE Conference on Computer + +Vision and Pattern Recognition (CVPR), pages 2990-3000, 2020. 2 +[75] Jianfeng Zhang, Zihang Jiang, Dingdong Yang, Hongyi Xu, Yichun Shi, Guoxian Song, Zhongcong Xu, Xinchao Wang, and Jiashi Feng. Avatargen: A 3d generative model for animatable human avatars. In Arxiv, 2022. 1, 2 +[76] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C. Bühler, Xu Chen, Michael J. Black, and Otmar Hilliges. I M Avatar: Implicit morphable head avatars from videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2 \ No newline at end of file diff --git a/2023/Learning Locally Editable Virtual Humans/images.zip b/2023/Learning Locally Editable Virtual Humans/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..a6490088d10bc5136043ed25347235ef9bea3149 --- /dev/null +++ b/2023/Learning Locally Editable Virtual Humans/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:023f05f2979b841d654bbf65ebbdc4dda3ee348ac099c3d16e3fcaa929347ab8 +size 578668 diff --git a/2023/Learning Locally Editable Virtual Humans/layout.json b/2023/Learning Locally Editable Virtual Humans/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..d71505248d96128abd7c7cf5dcbe50ff20856126 --- /dev/null +++ b/2023/Learning Locally Editable Virtual Humans/layout.json @@ -0,0 +1,9447 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 163, + 103, + 430, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 163, + 103, + 430, + 121 + ], + "spans": [ + { + "bbox": [ + 163, + 103, + 430, + 121 + ], + "type": "text", + "content": "Learning Locally Editable Virtual Humans" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 130, + 143, + 463, + 174 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 130, + 143, + 463, + 174 + ], + "spans": [ + { + "bbox": [ + 130, + 143, + 463, + 174 + ], + "type": "text", + "content": "Hsuan-I Ho Lixin Xue Jie Song Otmar Hilliges Department of Computer Science, ETH Zürich" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 143, + 199, + 192, + 211 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 199, + 192, + 211 + ], + "spans": [ + { + "bbox": [ + 143, + 199, + 192, + 211 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 224, + 290, + 487 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 224, + 290, + 487 + ], + "spans": [ + { + "bbox": [ + 46, + 224, + 290, + 487 + ], + "type": "text", + "content": "In this paper, we propose a novel hybrid representation and end-to-end trainable network architecture to model fully editable and customizable neural avatars. At the core of our work lies a representation that combines the modeling power of neural fields with the ease of use and inherent 3D consistency of skinned meshes. To this end, we construct a trainable feature codebook to store local geometry and texture features on the vertices of a deformable body model, thus exploiting its consistent topology under articulation. This representation is then employed in a generative auto-decoder architecture that admits fitting to unseen scans and sampling of realistic avatars with varied appearances and geometries. Furthermore, our representation allows local editing by swapping local features between 3D assets. To verify our method for avatar creation and editing, we contribute a new high-quality dataset, dubbed CustomHumans, for training and evaluation. Our experiments quantitatively and qualitatively show that our method generates diverse detailed avatars and achieves better model fitting performance compared to state-of-the-art methods. Our code and dataset are available at https://aith.ethz.ch/custom-humans." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 501, + 128, + 514 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 501, + 128, + 514 + ], + "spans": [ + { + "bbox": [ + 47, + 501, + 128, + 514 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "text", + "content": "3D Avatars are an important aspect of many emerging applications such as 3D games or the Metaverse. Allowing for easy personalization of such avatars, holds the promise of increased user engagement. Traditionally, editing 3D assets requires knowledge of computer graphics tools and relies on standardized data formats to represent shapes and appearances. While methods for reconstruction or generative modeling of learned avatars achieve impressive results, it is unknown how such neural avatars can be edited and customized. Thus, the goal of our work is to contribute a simple, yet powerful data-driven method for avatar creation and customization (Fig. 1): our method enables (a) the ability to transfer partial geometric and appearance details between 3D assets, and (b) the ability to author details via 2D-3D transfer. The resulting avatars (c) retain consistent local details when posed." + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 307, + 200, + 545, + 406 + ], + "blocks": [ + { + "bbox": [ + 307, + 200, + 545, + 406 + ], + "lines": [ + { + "bbox": [ + 307, + 200, + 545, + 406 + ], + "spans": [ + { + "bbox": [ + 307, + 200, + 545, + 406 + ], + "type": "image", + "image_path": "772d487918a176891d66c97850bdb62f18b2108564301f4a69a7767616fcc67a.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 414, + 547, + 480 + ], + "lines": [ + { + "bbox": [ + 304, + 414, + 547, + 480 + ], + "spans": [ + { + "bbox": [ + 304, + 414, + 547, + 480 + ], + "type": "text", + "content": "Figure 1. Creating locally editable avatars: Given an input avatar, (a) the avatar can be edited by transferring clothing geometry and color details from existing, yet unseen 3D assets. (b) Users can customize clothing details such as logos and letters via drawing on 2D images. (c) The avatars retain local detail consistently under pose changes." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 484, + 546, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 484, + 546, + 605 + ], + "spans": [ + { + "bbox": [ + 304, + 484, + 546, + 605 + ], + "type": "text", + "content": "Existing methods do not allow for such capabilities. While 3D generative models of articulated human bodies [5, 21, 25, 42, 75] leverage differentiable neural rendering to learn from images, they cannot control local details due to highly entangled color and geometry in the 2D supervision signal. Generative models trained on 3D data [9, 13, 36, 44, 45] can produce geometric details for surfaces and clothing. However, the diversity of generated samples is low due to the lack of high-quality 3D human scans and not all methods model appearance." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "type": "text", + "content": "At the core of the issue lies the question of representation: graphics tools use meshes, UV, and texture maps which provide consistent topologies under deformation. However, human avatar methods that are built on mesh-based representations and linear blend skinning (LBS) are limited in their representational power with respect to challenging geometry (e.g., puffy garments) and flexible topologies (e.g., jackets), even with adaptations of additional displacement parameters [36] and mesh subdivision [66]." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21024" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 289, + 263 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 289, + 263 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 289, + 263 + ], + "type": "text", + "content": "Inspired by the recent neural 3D representations [40, 62, 70, 72], we propose a novel hybrid representation for digital humans. Our representation combines the advantages of consistent topologies of LBS models with the representational power of neural fields. The key idea is to decompose the tasks of deformation consistency on one hand and local surface and appearance description on the other. For the former, we leverage existing parametric body models (e.g., SMPL [33] and SMPL-X [48]). For the latter, we leverage the fixed topology of the poseable mesh to store local feature codebooks. A decoder, shared across subjects, is then conditioned on the local features to predict the final signed distance and color values. Since only local information [15] is exposed to the decoder, overfitting and memorization can be mitigated. We experimentally show that this is crucial for 3D avatar fitting and reposing." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 264, + 289, + 395 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 264, + 289, + 395 + ], + "spans": [ + { + "bbox": [ + 46, + 264, + 289, + 395 + ], + "type": "text", + "content": "Complementing this hybrid representation, we propose a training pipeline in the auto-decoding generative framework [9, 46, 52]. To this end, we jointly optimize multi-subject feature codebooks and the shared decoder weights via 3D reconstruction and 2D adversarial losses. The 3D losses help in disentangling appearance and geometric information from the input scans, while the latter improves the perceptual quality of randomly generated samples. To showcase the hybrid representation and the generative model we implement a prototypical avatar editing workflow shown in Fig. 1." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 396, + 289, + 503 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 396, + 289, + 503 + ], + "spans": [ + { + "bbox": [ + 46, + 396, + 289, + 503 + ], + "type": "text", + "content": "Furthermore, to enable research on high-quality 3D avatars we contribute training data for generative 3D human models. We record a large-scale dataset (more than 600 scans of 80 subjects in 120 garments) using a volumetric capture stage [11]. Our dataset consists of high-quality 3D meshes alongside accurately registered SMPL-X [48] models and will be made available for research purposes. Finally, we assess our design decisions in detailed evaluations, both on existing and the proposed datasets." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 503, + 289, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 503, + 289, + 588 + ], + "spans": [ + { + "bbox": [ + 46, + 503, + 289, + 588 + ], + "type": "text", + "content": "In summary, our contributions are threefold: (a) a novel hybrid representation for 3D virtual humans that allows for local editing across subjects, (b) a generative pipeline of 3D avatars creation that allows for fitting to unseen 3D scans and random sampling, and (c) a new large-scale high-quality dataset of 3D human scans containing diverse subjects, body poses and garments." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 597, + 134, + 609 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 597, + 134, + 609 + ], + "spans": [ + { + "bbox": [ + 47, + 597, + 134, + 609 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "type": "text", + "content": "Controllable human representations. Topics of virtual humans have received much attention in the graphics literature, such as skinning and rigging of articulated meshes [33, 43, 48, 55], physical simulation of clothing [19, 22, 41], and deferred rendering [24, 49, 65]. With the advances in neural rendering [63, 64] and the availability of large-scale human datasets [18, 28, 29, 32, 47, 67, 73, 74], numerous approaches have been proposed to reconstruct [4, 56, 57] and explicitly" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 72, + 543, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 543, + 84 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 543, + 84 + ], + "type": "text", + "content": "control [10,30,50] human avatars in a data-driven manner." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 85, + 545, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 85, + 545, + 215 + ], + "spans": [ + { + "bbox": [ + 304, + 85, + 545, + 215 + ], + "type": "text", + "content": "One branch of work focuses on 2D image synthesis via generative adversarial networks (GANs) [20] and techniques of feature manipulation [28, 54, 60]. Typically, a 2D neural renderer creates human images corresponding to pose and appearance latent codes learned from the training data. Related applications such as virtual try-on [16, 17, 71] and video retargeting [7, 31, 68] have shown promising results in light of photo-realistic image synthesis by GANs [18,26]. However, these methods do not explicitly reason about complex 3D human geometry and can therefore not produce 3D-consistent results." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 217, + 546, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 217, + 546, + 384 + ], + "spans": [ + { + "bbox": [ + 304, + 217, + 546, + 384 + ], + "type": "text", + "content": "A newly emerging line of work aims to create controllable avatars with 3D consistency. Some methods extend existing body models with neural networks to predict displacement layers [3, 6, 36] or textures [51]. Other methods learn to model challenging pose-dependent deformations on avatars either by predicting LBS weights [8, 10, 14, 58, 59, 76] or improving the capabilities of body models [23, 30, 34, 35, 37, 50, 53] with the power of implicit neural fields. However, these approaches mainly focus on modeling a single subject in specific clothing and do not scale to create diverse avatars. Our method overcomes this issue by learning a multi-subject generative model which produces realistic virtual humans with disentangled controllability over body poses, clothing geometry, and texture." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 390, + 546, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 390, + 546, + 533 + ], + "spans": [ + { + "bbox": [ + 304, + 390, + 546, + 533 + ], + "type": "text", + "content": "Generative 3D human models. Existing generative models of human avatars can be loosely split into two main streams: learning 3D-aware neural rendering from 2D images [5, 21, 25, 42, 75] and learning body shapes from 3D supervision [9, 13, 36, 44, 45]. Powered by recent advances in differentiable neural rendering [64] and neural fields [70], much progress has been made in 3D-aware generative models [61]. However, learning to generate detailed clothed avatars from pure 2D supervision [5, 21, 25, 42, 75] is still challenging due to the complex appearance and articulation of bodies, self-occlusions, and highly entangled colors and geometries in images." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 534, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 534, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 534, + 547, + 714 + ], + "type": "text", + "content": "More closely related to our setting are methods that learn to generate detailed body shapes from 3D scans or RGB-D data. For instance, CAPE [36] and SMPLict [13] are generative models for clothed humans. The former exploits VAE-GAN to predict additive displacements based on the SMPL vertices while the latter drape an implicitly modeled garment layer onto SMPL. NPMs [44], and SPAM [45] learn pose and shape latent spaces from 3D supervision, which enables latent code inversion using point clouds or depth sequences. gDNA [9] learns to synthesize body shapes in the canonical space and further improves clothing details with adversarial losses. However, none of the above-mentioned works is able to generate human bodies with appearance and neither allows fine-grained editing of the generated avatars. Our method addresses both issues by learning disentangled" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21025" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 70, + 545, + 259 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 545, + 259 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 545, + 259 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 545, + 259 + ], + "type": "image", + "image_path": "2fec53d2035886f0f9a4358c02ae195ae9cbe8ac6f2664d0ea91ab6c73a60f2c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "lines": [ + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "spans": [ + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": "Figure 2. Proposed framework. Given a posed scan registered with body pose and shape LBS parameters " + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "inline_equation", + "content": "(\\theta, \\beta)" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": ", our proposed human representation stores its local geometry and texture features in a codebook " + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "inline_equation", + "content": "\\mathbf{C}" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": " which is indexed by the vertex indices of " + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": "-vertex LBS body mesh " + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": " (Sec. 3.1). Given a query point coordinates " + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_g" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": ", two separated MLP decoders " + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "inline_equation", + "content": "\\Psi" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "inline_equation", + "content": "\\Phi" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": " predict signed distances and colors conditioned on the positional features " + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "inline_equation", + "content": "(\\mathbf{x}_l, \\vec{\\mathbf{n}})" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": " and the local geometry/texture features " + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "inline_equation", + "content": "(\\mathbf{f_s} / \\mathbf{f_c})" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": " respectively. We train a generative auto-decoder using " + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": " posed scans, whose feature codebooks are stored in the dictionary " + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "inline_equation", + "content": "\\mathbf{D_s}" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "inline_equation", + "content": "\\mathbf{D_c}" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": ". We introduce two sampling strategies to sample codebooks (denoted as " + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_i / \\mathbf{C}_r" + }, + { + "bbox": [ + 46, + 266, + 545, + 344 + ], + "type": "text", + "content": " respectively) and jointly train our dictionaries and shared MLP decoders with a 3D reconstruction loss and a 2D adversarial loss (Sec. 3.2 & Sec. 3.3)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 350, + 287, + 398 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 350, + 287, + 398 + ], + "spans": [ + { + "bbox": [ + 46, + 350, + 287, + 398 + ], + "type": "text", + "content": "local representations for multiple subjects. In addition, we experimentally show that our representation significantly improves the performance of model fitting against state-of-the-art human generative models." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 411, + 103, + 423 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 411, + 103, + 423 + ], + "spans": [ + { + "bbox": [ + 47, + 411, + 103, + 423 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 432, + 287, + 564 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 432, + 287, + 564 + ], + "spans": [ + { + "bbox": [ + 46, + 432, + 287, + 564 + ], + "type": "text", + "content": "Our proposed method is summarized in Fig. 2. We first contribute a novel hybrid human representation that stores local geometric and textural information into two aligned feature spaces (Sec. 3.1 and Fig. 3). To allow fitting to new 3D scans and drawing random samples from the underlying data distribution, we design a training strategy to learn a meaningful latent space under the generative adversarial framework to bring in additional 2D adversarial supervision (Sec. 3.2 & Sec. 3.3). Finally, we demonstrate the utility of our method for creating avatars by enabling local feature editing through existing 3D assets or images (Sec. 3.4)." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 574, + 232, + 586 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 574, + 232, + 586 + ], + "spans": [ + { + "bbox": [ + 47, + 574, + 232, + 586 + ], + "type": "text", + "content": "3.1. Hybrid Representation of Humans" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 593, + 287, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 593, + 287, + 665 + ], + "spans": [ + { + "bbox": [ + 46, + 593, + 287, + 665 + ], + "type": "text", + "content": "To enable 3D avatars with high-fidelity representational power and local editing capabilities, a suitable representation is needed. To this end, we propose a novel hybrid representation that combines the advantages of neural fields (flexibility and modeling power) with LBS-articulated mesh models (ease of deformation and full explicit control)." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 666, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 666, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 666, + 287, + 714 + ], + "type": "text", + "content": "An overview of how we leverage the proposed representation is provided in Fig. 2 in the dotted blue box. Given a human scan, we first create a posed, coarse body mesh " + }, + { + "bbox": [ + 46, + 666, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 46, + 666, + 287, + 714 + ], + "type": "text", + "content": " (shown in red) using the registered body parameters" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "spans": [ + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "inline_equation", + "content": "(\\theta, \\beta)" + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "text", + "content": " of an LBS body model. The mesh consists of " + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "text", + "content": " vertices (" + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "inline_equation", + "content": "\\mathcal{V} \\in \\mathbb{R}^{M \\times 3}" + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "text", + "content": ") in the posed space and " + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "inline_equation", + "content": "M_f" + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "text", + "content": " faces where " + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "inline_equation", + "content": "\\mathcal{F} \\in \\{1, \\dots, M\\}^{M_f \\times 3}" + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "text", + "content": " defines the vertex indices on each face. We then construct a trainable feature codebook " + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "inline_equation", + "content": "\\mathbf{C} \\in \\mathbb{R}^{M \\times 2F}" + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "text", + "content": ", which stores " + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "inline_equation", + "content": "F" + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "text", + "content": "-dimensional local geometry and texture features respectively for each vertex." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "spans": [ + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "text", + "content": "Similar to coordinate-based implicit fields, a 3D coordinate " + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_g\\in \\mathbb{R}^3" + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "text", + "content": " is used to predict its corresponding signed distance " + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "inline_equation", + "content": "s(\\mathbf{x}_g)" + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "text", + "content": ", and RGB color " + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "inline_equation", + "content": "c(\\mathbf{x}_g)" + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "text", + "content": ". Instead of using global coordinates directly as inputs, we condition neural field decoders on the local triangle coordinates " + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_l\\in \\mathbb{R}^3" + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "text", + "content": " and the local geometry and texture features " + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "inline_equation", + "content": "\\mathbf{f_s},\\mathbf{f_c}\\in \\mathbb{R}^F" + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "text", + "content": ". We illustrate this conversion from global coordinates to local triangle coordinates in Fig. 3. The global coordinates " + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_g" + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "text", + "content": " are first projected onto the mesh by finding the closest point " + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_c^*" + }, + { + "bbox": [ + 304, + 422, + 545, + 542 + ], + "type": "text", + "content": " (Fig. 3, blue dot):" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 366, + 551, + 545, + 585 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 366, + 551, + 545, + 585 + ], + "spans": [ + { + "bbox": [ + 366, + 551, + 545, + 585 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathbf {x} _ {c} ^ {*} = \\arg \\min _ {\\mathbf {x} _ {c}} \\| \\mathbf {x} _ {g} - \\mathbf {x} _ {c} \\| _ {2}, \\\\ \\mathbf {x} _ {c} = \\mathcal {B} _ {u, v} (\\mathcal {V} [ m _ {0}, m _ {1}, m _ {2} ]), \\tag {1} \\\\ \\end{array}", + "image_path": "115e6284a4c0f5c95380f973f162ac2cae40567982f7780bca5ee946985d2db2.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "(m_0, m_1, m_2)" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": " are vertex indices of faces " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_{u,v}(.)" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": " is the barycentric interpolation function with barycentric coordinates " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "(u, v, 1 - u - v)" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": ". The closest point " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_c^*" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": " within the face is used to transform " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_g" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": " into a local triangle coordinate system. Hence, " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_l" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": " consists of the barycentric coordinates " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "(u, v)" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": ", the signed distance " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": " between " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_g" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_c^*" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": ", i.e., " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_l \\coloneqq (u, v, d)" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": ". We also compute a direction vector " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\vec{\\mathbf{n}}" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": " between " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_g" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_c^*" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": " as an additional feature to distinguish points near triangle edges. To query local features " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "(\\mathbf{f_s}, \\mathbf{f_c})" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": ", we use the vertex indices on the triangle " + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "inline_equation", + "content": "(m_0^*, m_1^*, m_2^*)" + }, + { + "bbox": [ + 304, + 594, + 545, + 715 + ], + "type": "text", + "content": " to" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21026" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 73, + 282, + 243 + ], + "blocks": [ + { + "bbox": [ + 53, + 73, + 282, + 243 + ], + "lines": [ + { + "bbox": [ + 53, + 73, + 282, + 243 + ], + "spans": [ + { + "bbox": [ + 53, + 73, + 282, + 243 + ], + "type": "image", + "image_path": "aed628843baf272ee99fcfe945856dc0bc3c7b57cadd16d8ed6f5ff8032e100e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "lines": [ + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "spans": [ + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "text", + "content": "Figure 3. Local feature querying. Given an LBS body mesh posed by the parameters " + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "inline_equation", + "content": "(\\theta, \\beta)" + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "text", + "content": ", we represent a detailed human body as a codebook that stores local texture and geometry features indexed by the vertices on the mesh. An input query point " + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "inline_equation", + "content": "\\mathbf{x_g}" + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "text", + "content": " finds the nearest triangle on the LBS body mesh and returns its vertex indices for local feature lookup. To prevent decoders from memorizing any global information, we transform the position of " + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "inline_equation", + "content": "\\mathbf{x_g}" + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "text", + "content": " into local triangle coordinates " + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "inline_equation", + "content": "(u, v)" + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "text", + "content": ", distance " + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "text", + "content": ", and direction " + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "inline_equation", + "content": "\\vec{\\mathbf{n}}" + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "text", + "content": " of the closest point (i.e., the blue dot). The final geometry and texture features " + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "inline_equation", + "content": "\\mathbf{f_s}, \\mathbf{f_c}" + }, + { + "bbox": [ + 46, + 253, + 289, + 364 + ], + "type": "text", + "content": " are fused via barycentric interpolation." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 369, + 287, + 393 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 369, + 287, + 393 + ], + "spans": [ + { + "bbox": [ + 46, + 369, + 287, + 393 + ], + "type": "text", + "content": "look up three elements in the feature codebook C. We then fuse the three local features via barycentric interpolation." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 393, + 288, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 393, + 288, + 430 + ], + "spans": [ + { + "bbox": [ + 46, + 393, + 288, + 430 + ], + "type": "text", + "content": "Finally, we take all the local features " + }, + { + "bbox": [ + 46, + 393, + 288, + 430 + ], + "type": "inline_equation", + "content": "(\\mathbf{f_s},\\mathbf{f_c},\\mathbf{x}_l,\\vec{\\mathbf{n}})" + }, + { + "bbox": [ + 46, + 393, + 288, + 430 + ], + "type": "text", + "content": " as input to two separate decoders " + }, + { + "bbox": [ + 46, + 393, + 288, + 430 + ], + "type": "inline_equation", + "content": "\\Phi" + }, + { + "bbox": [ + 46, + 393, + 288, + 430 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 393, + 288, + 430 + ], + "type": "inline_equation", + "content": "\\Psi" + }, + { + "bbox": [ + 46, + 393, + 288, + 430 + ], + "type": "text", + "content": " to predict SDF and RGB values respectively:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 436, + 287, + 466 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 436, + 287, + 466 + ], + "spans": [ + { + "bbox": [ + 105, + 436, + 287, + 466 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\Phi : \\mathbb {R} ^ {F} \\times \\mathbb {R} ^ {3} \\times \\mathbb {R} ^ {3} \\rightarrow \\mathbb {R} \\tag {2} \\\\ \\left(\\mathbf {f} _ {\\mathbf {s}}, \\mathbf {x} _ {l}, \\vec {\\mathbf {n}}\\right) \\mapsto s \\left(\\mathbf {x} _ {g}\\right), \\\\ \\end{array}", + "image_path": "fe9bfb40a7f557cd83143c8320988a393ed3e09b08df8e1693c95e75f3b4d43d.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 474, + 287, + 505 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 106, + 474, + 287, + 505 + ], + "spans": [ + { + "bbox": [ + 106, + 474, + 287, + 505 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\boldsymbol {\\Psi}: \\mathbb {R} ^ {F} \\times \\mathbb {R} ^ {3} \\times \\mathbb {R} ^ {3} \\rightarrow \\mathbb {R} ^ {3} \\tag {3} \\\\ \\left(\\mathbf {f} _ {\\mathbf {c}}, \\mathbf {x} _ {l}, \\vec {\\mathbf {n}}\\right) \\mapsto c \\left(\\mathbf {x} _ {g}\\right). \\\\ \\end{array}", + "image_path": "0759bc8d52a3f7686153fe0a08543af48403479602565600333cfaddaa6b6b13.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 508, + 287, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 508, + 287, + 569 + ], + "spans": [ + { + "bbox": [ + 46, + 508, + 287, + 569 + ], + "type": "text", + "content": "Note that only local information is exposed to the decoders, which allows us to use the same MLPs across different vertices and subjects. We show that preventing networks from memorizing global information in this way is necessary for local editing and reposing in our experiments (Fig. 9)." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 575, + 220, + 590 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 575, + 220, + 590 + ], + "spans": [ + { + "bbox": [ + 47, + 575, + 220, + 590 + ], + "type": "text", + "content": "3.2. Generative Codebook Sampling" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 594, + 287, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 287, + 677 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 287, + 677 + ], + "type": "text", + "content": "Our goal is to provide means to create and personalize avatars with diverse body shapes, appearances, and local details. To this end, we leverage the above representation to train a single multi-subject model which enables the transfer of local features across subjects. We note that since the mesh topology of the LBS model is identical, this enables us to learn a shared feature space from multiple posed scans." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "content": "To learn the feature representation over a dataset of " + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "content": " scans, it is sufficient to store the codebooks " + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_i" + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "content": " in two dictionaries " + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{D_s},\\mathbf{D_c}\\in \\mathbb{R}^{N\\times (MF)}" + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "content": " to represent shape and color" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "content": "information of the " + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "content": "-th subject respectively. The entries " + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_i" + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "content": " can then be learned jointly with the decoder weights via direct 3D supervision using the " + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "content": "-th scan (Fig. 2). However, we experimentally show that this is insufficient to learn a well-behaved latent space from which we can draw novel samples (see Fig. 10)." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "spans": [ + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "text", + "content": "Therefore we introduce a codebook sampling strategy that allows us to draw random samples and update the entries of the dictionaries " + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{D}_{\\mathbf{s}}" + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{D}_{\\mathbf{c}}" + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "text", + "content": " via an additional 2D adversarial loss. More specifically, we follow the auto-decoder architecture [52] and perform PCA on the reshaped dictionary to compute eigenvectors " + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{V} \\in \\mathbb{R}^{D \\times (MF)}" + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "text", + "content": " and fit a normal distribution to the " + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "text", + "content": "-dimensional PCA coefficients of " + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "text", + "content": " samples. A new random codebook " + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_r" + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "text", + "content": " can then be generated by sampling " + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "text", + "content": "-dimensional PCA parameters and multiplying them with the eigenvectors " + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{V}" + }, + { + "bbox": [ + 304, + 144, + 546, + 312 + ], + "type": "text", + "content": " (See SuppB.1 for details). Note that our representation disentangles shapes from appearances with separated geometry and texture branches, which enables independent sampling of geometry and texture features." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 319, + 402, + 332 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 319, + 402, + 332 + ], + "spans": [ + { + "bbox": [ + 306, + 319, + 402, + 332 + ], + "type": "text", + "content": "3.3. Model Training" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 337, + 545, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 337, + 545, + 422 + ], + "spans": [ + { + "bbox": [ + 304, + 337, + 545, + 422 + ], + "type": "text", + "content": "3D reconstruction loss. To train a codebook " + }, + { + "bbox": [ + 304, + 337, + 545, + 422 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_i" + }, + { + "bbox": [ + 304, + 337, + 545, + 422 + ], + "type": "text", + "content": " with a single scan, we sample points in a thin shell around the scan. For each coordinate we compute its signed distance " + }, + { + "bbox": [ + 304, + 337, + 545, + 422 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 337, + 545, + 422 + ], + "type": "text", + "content": " to the input scan, closest texture color " + }, + { + "bbox": [ + 304, + 337, + 545, + 422 + ], + "type": "inline_equation", + "content": "\\mathbf{c}" + }, + { + "bbox": [ + 304, + 337, + 545, + 422 + ], + "type": "text", + "content": ", and surface normal " + }, + { + "bbox": [ + 304, + 337, + 545, + 422 + ], + "type": "inline_equation", + "content": "\\mathbf{n}" + }, + { + "bbox": [ + 304, + 337, + 545, + 422 + ], + "type": "text", + "content": " on the input scan to attain ground truth values. The codebooks and the decoder weights are then optimized via the following losses:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 318, + 430, + 545, + 445 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 430, + 545, + 445 + ], + "spans": [ + { + "bbox": [ + 318, + 430, + 545, + 445 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {s d f} = \\| s - s (\\mathbf {x} _ {\\mathbf {g}}) \\| _ {1} + \\lambda_ {n} \\| 1 - \\mathbf {n} \\cdot \\nabla_ {\\mathbf {x} _ {\\mathbf {g}}} s (\\mathbf {x} _ {\\mathbf {g}}) \\| _ {1}, \\quad (4)", + "image_path": "15d7d945d6f2bf5371fa052efd3d15327e2733ea0e551cfa57d143b360c91ade.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 379, + 464, + 545, + 479 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 379, + 464, + 545, + 479 + ], + "spans": [ + { + "bbox": [ + 379, + 464, + 545, + 479 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {r g b} = \\left\\| \\mathbf {c} - c \\left(\\mathbf {x} _ {\\mathbf {g}}\\right) \\right\\| _ {1}, \\tag {5}", + "image_path": "fd80560f283e19eebaa60c135c68c462ad4bc02541b35a9e983b77c4845c8011.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 364, + 495, + 545, + 509 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 364, + 495, + 545, + 509 + ], + "spans": [ + { + "bbox": [ + 364, + 495, + 545, + 509 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {3 D} = \\lambda_ {s d f} \\mathcal {L} _ {s d f} + \\lambda_ {r g b} \\mathcal {L} _ {r g b}. \\tag {6}", + "image_path": "eea79cb730e554489402c82a31cd6eb79f55dca79c1b736cc9e1b5baa78fc5f5.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 521, + 545, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 545, + 653 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 545, + 653 + ], + "type": "text", + "content": "2D adversarial loss. Adversarial learning does not require exact ground-truth annotations but is trained via a collection of real and fake (rendered) images. Thus, real images are obtained by rasterizing the ground-truth scan to which the coarse body mesh " + }, + { + "bbox": [ + 304, + 521, + 545, + 653 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 304, + 521, + 545, + 653 + ], + "type": "text", + "content": " was fitted. Color and normal images (denoted as \"Real Patch\" in Fig. 2) are used for learning texture and geometry respectively. Using the same virtual camera parameters and the coarse mesh " + }, + { + "bbox": [ + 304, + 521, + 545, + 653 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 304, + 521, + 545, + 653 + ], + "type": "text", + "content": ", we attain rendered patches (denoted as \"Rendered Patch\" Fig. 2) via implicit surface rendering of a sampled codebook " + }, + { + "bbox": [ + 304, + 521, + 545, + 653 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_r" + }, + { + "bbox": [ + 304, + 521, + 545, + 653 + ], + "type": "text", + "content": ". Please refer to Supp-B.2 for more details." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 654, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 654, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 654, + 545, + 714 + ], + "type": "text", + "content": "Using these 2D patches, we train dictionaries, decoders, and discriminators jointly with a non-saturating logistic loss " + }, + { + "bbox": [ + 304, + 654, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{adv}" + }, + { + "bbox": [ + 304, + 654, + 545, + 714 + ], + "type": "text", + "content": " [20], R1 regularization " + }, + { + "bbox": [ + 304, + 654, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{R1}" + }, + { + "bbox": [ + 304, + 654, + 545, + 714 + ], + "type": "text", + "content": " [38], and path length regularization " + }, + { + "bbox": [ + 304, + 654, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{path}" + }, + { + "bbox": [ + 304, + 654, + 545, + 714 + ], + "type": "text", + "content": " [27]. Note that these losses do not require exact ground-truth replication. Furthermore, we regu" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21027" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 107 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 107 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 107 + ], + "type": "text", + "content": "larize the feature dictionaries to follow a Gaussian distribution [46] with " + }, + { + "bbox": [ + 46, + 72, + 287, + 107 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{reg} = \\| \\mathbf{D}\\| _F" + }, + { + "bbox": [ + 46, + 72, + 287, + 107 + ], + "type": "text", + "content": ". In summary, we optimize the discriminator:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 116, + 119, + 287, + 133 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 119, + 287, + 133 + ], + "spans": [ + { + "bbox": [ + 116, + 119, + 287, + 133 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {d i s}} = \\mathcal {L} _ {\\text {a d v}} + \\lambda_ {R 1} \\mathcal {L} _ {R 1}, \\tag {7}", + "image_path": "c85eee43cf2a9f581ac6fcce14cb52c3307e71b20dc854536040b9de1bf37502.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 141, + 284, + 154 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 141, + 284, + 154 + ], + "spans": [ + { + "bbox": [ + 47, + 141, + 284, + 154 + ], + "type": "text", + "content": "while updating the remaining components " + }, + { + "bbox": [ + 47, + 141, + 284, + 154 + ], + "type": "inline_equation", + "content": "(\\mathbf{D}_{\\mathbf{c}},\\mathbf{D}_{\\mathbf{s}},\\Phi ,\\Psi)" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 70, + 163, + 287, + 177 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 163, + 287, + 177 + ], + "spans": [ + { + "bbox": [ + 70, + 163, + 287, + 177 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = - \\mathcal {L} _ {a d v} + \\mathcal {L} _ {3 D} + \\lambda_ {\\text {p a t h}} \\mathcal {L} _ {\\text {p a t h}} + \\lambda_ {\\text {r e g}} \\mathcal {L} _ {\\text {r e g}}, \\tag {8}", + "image_path": "3baf528735f3375432065f718279f1f9905774223b87f01825352ce9eff3bb7f.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 186, + 244, + 198 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 186, + 244, + 198 + ], + "spans": [ + { + "bbox": [ + 47, + 186, + 244, + 198 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 186, + 244, + 198 + ], + "type": "inline_equation", + "content": "\\lambda_{(\\cdot)}" + }, + { + "bbox": [ + 47, + 186, + 244, + 198 + ], + "type": "text", + "content": " denotes weights to balance the losses." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 198, + 287, + 235 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 198, + 287, + 235 + ], + "spans": [ + { + "bbox": [ + 46, + 198, + 287, + 235 + ], + "type": "text", + "content": "Since we sample on the fly during training (see Sec. 3.2), the 2D adversarial loss does affect the shared decoders and the whole feature dictionaries (See Supp-Fig.14 for details)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 243, + 269, + 255 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 243, + 269, + 255 + ], + "spans": [ + { + "bbox": [ + 47, + 243, + 269, + 255 + ], + "type": "text", + "content": "3.4. Feature Editing and Avatar Customization" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 262, + 287, + 297 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 262, + 287, + 297 + ], + "spans": [ + { + "bbox": [ + 46, + 262, + 287, + 297 + ], + "type": "text", + "content": "We now describe how we integrate the above-mentioned human representation and the generative architecture into the avatar creation workflow shown in Fig. 1." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 304, + 287, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 304, + 287, + 375 + ], + "spans": [ + { + "bbox": [ + 46, + 304, + 287, + 375 + ], + "type": "text", + "content": "Avatar initialization. To simplify the avatar creation process, our method allows users to start with a default example " + }, + { + "bbox": [ + 46, + 304, + 287, + 375 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_d" + }, + { + "bbox": [ + 46, + 304, + 287, + 375 + ], + "type": "text", + "content": ", which can be queried from the trained codebook dictionaries directly with index " + }, + { + "bbox": [ + 46, + 304, + 287, + 375 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 304, + 287, + 375 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 46, + 304, + 287, + 375 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_i" + }, + { + "bbox": [ + 46, + 304, + 287, + 375 + ], + "type": "text", + "content": ") or randomly sampled from the learned D-dimensional PCA parameters distribution (" + }, + { + "bbox": [ + 46, + 304, + 287, + 375 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_r" + }, + { + "bbox": [ + 46, + 304, + 287, + 375 + ], + "type": "text", + "content": " in Sec. 3.2)." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 381, + 287, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 381, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 46, + 381, + 287, + 453 + ], + "type": "text", + "content": "Model fitting. Being able to extract elements of interest or copying from existing 3D assets is necessary for avatar creation and editing. To this end, we leverage a similar technique to GAN inversion [69], where decoder parameters " + }, + { + "bbox": [ + 46, + 381, + 287, + 453 + ], + "type": "inline_equation", + "content": "\\Phi" + }, + { + "bbox": [ + 46, + 381, + 287, + 453 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 381, + 287, + 453 + ], + "type": "inline_equation", + "content": "\\Psi" + }, + { + "bbox": [ + 46, + 381, + 287, + 453 + ], + "type": "text", + "content": " are frozen and we only optimize a new feature codebook " + }, + { + "bbox": [ + 46, + 381, + 287, + 453 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{fit}" + }, + { + "bbox": [ + 46, + 381, + 287, + 453 + ], + "type": "text", + "content": " to fit a 3D scan." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 453, + 287, + 501 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 453, + 287, + 501 + ], + "spans": [ + { + "bbox": [ + 46, + 453, + 287, + 501 + ], + "type": "text", + "content": "Given a 3D target scan and its corresponding body model parameters, we calculate the 3D reconstruction loss in Eq. (6) between the prediction conditioned on " + }, + { + "bbox": [ + 46, + 453, + 287, + 501 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{fit}" + }, + { + "bbox": [ + 46, + 453, + 287, + 501 + ], + "type": "text", + "content": " and the ground-truth scan, i.e.," + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 85, + 511, + 287, + 529 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 511, + 287, + 529 + ], + "spans": [ + { + "bbox": [ + 85, + 511, + 287, + 529 + ], + "type": "interline_equation", + "content": "\\mathbf {C} _ {f i t} = \\arg \\min _ {\\mathbf {C}} \\left(\\lambda_ {s d f} \\mathcal {L} _ {s d f} + \\lambda_ {r g b} \\mathcal {L} _ {r g b}\\right). \\tag {9}", + "image_path": "bda97895adfff91fec40b29110b5781fb952b2e26d9f4499c395322b5a4ddacd.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 537, + 287, + 586 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 537, + 287, + 586 + ], + "spans": [ + { + "bbox": [ + 46, + 537, + 287, + 586 + ], + "type": "text", + "content": "Note that our generative neural fields (MLP decoders) are conditioned on descriptive local features. We show that it allows us to accurately fits complex clothing geometry and unseen textural patterns in Sec. 4.3." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 601, + 287, + 721 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 601, + 287, + 721 + ], + "spans": [ + { + "bbox": [ + 46, + 601, + 287, + 721 + ], + "type": "text", + "content": "Cross-subjects feature editing. With multiple codebooks each representing different 3D assets, we can easily transfer local geometry and texture from one avatar to another, for instance, changing the top wear from the fitted scan " + }, + { + "bbox": [ + 46, + 601, + 287, + 721 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{fit}" + }, + { + "bbox": [ + 46, + 601, + 287, + 721 + ], + "type": "text", + "content": " to the initial " + }, + { + "bbox": [ + 46, + 601, + 287, + 721 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_d" + }, + { + "bbox": [ + 46, + 601, + 287, + 721 + ], + "type": "text", + "content": ". Recalling that all codebooks are indexed by an identical mesh topology, users can easily retrieve the index numbers of " + }, + { + "bbox": [ + 46, + 601, + 287, + 721 + ], + "type": "inline_equation", + "content": "\\nu_{body} \\subset \\nu" + }, + { + "bbox": [ + 46, + 601, + 287, + 721 + ], + "type": "text", + "content": " via standard mesh visualization tools such as Blender [12]. Finally, swapping of the corresponding rows in " + }, + { + "bbox": [ + 46, + 601, + 287, + 721 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{fit}" + }, + { + "bbox": [ + 46, + 601, + 287, + 721 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 601, + 287, + 721 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_d" + }, + { + "bbox": [ + 46, + 601, + 287, + 721 + ], + "type": "text", + "content": " given the vertex indices also swaps the local appearance." + } + ] + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 307, + 73, + 539, + 297 + ], + "blocks": [ + { + "bbox": [ + 307, + 73, + 539, + 297 + ], + "lines": [ + { + "bbox": [ + 307, + 73, + 539, + 297 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 539, + 297 + ], + "type": "image", + "image_path": "d092e9f4fee83fe4cf392f1f1675d2a182e1d84b6a5419cd58daf1e392dd09b1.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 306, + 545, + 384 + ], + "lines": [ + { + "bbox": [ + 305, + 306, + 545, + 384 + ], + "spans": [ + { + "bbox": [ + 305, + 306, + 545, + 384 + ], + "type": "text", + "content": "Figure 4. Randomly sampled texture and body geometry from the model trained on THuman2.0. Top: Given any poses, our randomly sampled geometries contain realistic details such as wrinkles in garments and facial expressions. Bottom: Given arbitrary poses and body geometries, our model produces reasonable colors on skin, hair, clothes, and pants in each sample (We turn off the Shader for visualizing pure texture colors)." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 394, + 545, + 490 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 394, + 545, + 490 + ], + "spans": [ + { + "bbox": [ + 305, + 394, + 545, + 490 + ], + "type": "text", + "content": "Personalized texture drawing. One can further customize clothing by drawing directly onto 2D images. Due to the learned disentangled feature spaces, we are able to update texture features in " + }, + { + "bbox": [ + 305, + 394, + 545, + 490 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{fit}" + }, + { + "bbox": [ + 305, + 394, + 545, + 490 + ], + "type": "text", + "content": " while keeping the geometry features unchanged. When fitting 2D images, users draw on the rasterized images from arbitrary target scans. We then finetune only texture features in " + }, + { + "bbox": [ + 305, + 394, + 545, + 490 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{fit}" + }, + { + "bbox": [ + 305, + 394, + 545, + 490 + ], + "type": "text", + "content": " via the RGB loss in Eq. (5) given the corresponding 3D coordinates and colors." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "spans": [ + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "text", + "content": "Avatar reposing. Our representation combines a base mesh " + }, + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "text", + "content": " and underlying feature codebooks " + }, + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{fit}" + }, + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "text", + "content": " that learn local geometry and texture on the 3D scans. Hence, one can repose the mesh " + }, + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "text", + "content": " using " + }, + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "inline_equation", + "content": "(\\theta, \\beta)" + }, + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "text", + "content": " parameters, which also reposes the avatar correspondingly. Since the geometry codebook does not contain global pose " + }, + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "inline_equation", + "content": "(\\theta, \\beta)" + }, + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "text", + "content": ", local information can be consistently applied to " + }, + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 305, + 507, + 545, + 591 + ], + "type": "text", + "content": " under unseen poses." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 306, + 608, + 387, + 621 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 608, + 387, + 621 + ], + "spans": [ + { + "bbox": [ + 306, + 608, + 387, + 621 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 305, + 629, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 629, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 629, + 545, + 713 + ], + "type": "text", + "content": "Our goal is locally editable 3D avatar creation. Since we are the first to discuss this problem, we visualize our editing results in Sec. 4.2. Next, we highlight the capability of model fitting by comparing our method with SOTA human generative models in Sec. 4.3. Finally, controlled experiments are presented in Sec. 4.4 and Sec. 4.5 to verify the effectiveness of our design." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21028" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 51, + 71, + 287, + 264 + ], + "blocks": [ + { + "bbox": [ + 51, + 71, + 287, + 264 + ], + "lines": [ + { + "bbox": [ + 51, + 71, + 287, + 264 + ], + "spans": [ + { + "bbox": [ + 51, + 71, + 287, + 264 + ], + "type": "image", + "image_path": "08f796d97ccd518aa6921982dfa6ada2a55fd08f5a54bd8f34657813f0bba8c5.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 273, + 288, + 318 + ], + "lines": [ + { + "bbox": [ + 46, + 273, + 288, + 318 + ], + "spans": [ + { + "bbox": [ + 46, + 273, + 288, + 318 + ], + "type": "text", + "content": "Figure 5. Cross-subject feature editing results. We partially transfer local clothing details from the unseen scans (upper and lower body) to the input avatars. The results of the edited avatars are shown in the right column." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 323, + 165, + 335 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 323, + 165, + 335 + ], + "spans": [ + { + "bbox": [ + 47, + 323, + 165, + 335 + ], + "type": "text", + "content": "4.1. Experiment Settings" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 341, + 287, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 341, + 287, + 449 + ], + "spans": [ + { + "bbox": [ + 46, + 341, + 287, + 449 + ], + "type": "text", + "content": "Dataset. Most generative human works [9,44,45] exploit commercial data [1,2] for training, which is not easily accessible and limits reproducibility. Furthermore, the quality of publicly available 3D human datasets [66,73] is not satisfactory. Issues such as non-watertight topologies and noise are very common (See Supp-A.2 for examples and comparison). To bridge this gap, we collect a new dataset named CustomHumans for training and evaluation. Here we summarize the datasets used in our experiments." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 58, + 456, + 287, + 651 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 58, + 456, + 287, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 456, + 287, + 529 + ], + "spans": [ + { + "bbox": [ + 58, + 456, + 287, + 529 + ], + "type": "text", + "content": "- CustomHumans (Ours) contains more than 600 high-quality scans of 80 participants in 120 garments in varied poses from a volumetric capture stage [11], which is equipped with 106 synchronized cameras (53 RGB and 53 IR cameras). We use our dataset to train models of all quantitative experiments. (Sec. 4.3 ~ Sec. 4.5)" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 58, + 536, + 287, + 597 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 536, + 287, + 597 + ], + "spans": [ + { + "bbox": [ + 58, + 536, + 287, + 597 + ], + "type": "text", + "content": "- THuman2.0 [73] is a dataset containing about 500 scans of humans wearing 150 garments in various poses. Since this dataset has more textural diversity, we train our method on it for qualitative random sampling experiments (Sec. 4.2 and Sec. 4.4)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 58, + 603, + 287, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 603, + 287, + 651 + ], + "spans": [ + { + "bbox": [ + 58, + 603, + 287, + 651 + ], + "type": "text", + "content": "- SIZER [66] is a widely used 3D scan dataset containing A-posed human meshes of 97 subjects in 22 garments. These meshes are used as unseen test scans in our fitting experiment (Sec. 4.3)." + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "type": "text", + "content": "Evaluation protocol. Following the evaluation protocol in OccNet [39], we quantitatively evaluate the model fitting accuracy using three metrics: Chamfer distance (CD), normal consistency (NC), and f-Score." + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 306, + 72, + 545, + 160 + ], + "blocks": [ + { + "bbox": [ + 306, + 72, + 545, + 160 + ], + "lines": [ + { + "bbox": [ + 306, + 72, + 545, + 160 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 545, + 160 + ], + "type": "image", + "image_path": "3698524d046b85c70bb257d34dc20a565828d0a4c33a93db41f91ba65045105c.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 163, + 545, + 197 + ], + "lines": [ + { + "bbox": [ + 305, + 163, + 545, + 197 + ], + "spans": [ + { + "bbox": [ + 305, + 163, + 545, + 197 + ], + "type": "text", + "content": "Figure 6. Personalized texture editing. We draw personalized logos on 2D images and fit avatars' texture features to the images. These local textures remain consistent under pose changes." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 201, + 422, + 213 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 201, + 422, + 213 + ], + "spans": [ + { + "bbox": [ + 306, + 201, + 422, + 213 + ], + "type": "text", + "content": "4.2.Customized Avatars" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 220, + 544, + 243 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 220, + 544, + 243 + ], + "spans": [ + { + "bbox": [ + 306, + 220, + 544, + 243 + ], + "type": "text", + "content": "We visualize the results of our proposed avatar customization workflow described in Sec. 3.4." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 247, + 545, + 319 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 247, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 305, + 247, + 545, + 319 + ], + "type": "text", + "content": "Avatar initialization. In Fig. 4, we show random textures and geometries sampled from the model trained on THuman2.0. Our method is able to generate reasonable colors and wrinkles in arbitrary poses. Note that the sampled geometries are shown as the real meshes but not as rendered normals as in [9] (See Supp-C.2 for comparisons)." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 323, + 545, + 432 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 323, + 545, + 432 + ], + "spans": [ + { + "bbox": [ + 305, + 323, + 545, + 432 + ], + "type": "text", + "content": "Cross-subjects feature editing. After fitting feature codebooks to 3D scans, we can change the clothes on our avatars by swapping the local features stored on the body vertices. We select the features within the upper body and lower body areas. We then copy these local features to the initial avatars' feature codebooks. As shown in Fig. 5, our method is able to handle multiple garments on different human subjects and preserves consistent details under different body poses or shapes." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 435, + 545, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 435, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 305, + 435, + 545, + 544 + ], + "type": "text", + "content": "Personalized texture drawing. Our method allows users to draw complex letters and logos on images for personalized texture editing. We perform the model fitting and feature editing process but only optimize the texture features in the codebooks using user-edited images and the RGB loss (Eq. (5)). Fig. 6 shows that new texture can be seamlessly applied to the 3D avatars. It is worth noting that resulting avatars enable detailed pose control via the SMPL-X parameters without affecting the fitted texture and geometry." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 552, + 453, + 564 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 552, + 453, + 564 + ], + "spans": [ + { + "bbox": [ + 306, + 552, + 453, + 564 + ], + "type": "text", + "content": "4.3. Model Fitting Comparison" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 570, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 570, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 570, + 545, + 713 + ], + "type": "text", + "content": "Since model fitting is an important step in our avatar creation workflow, we compare the capability of feature inversion using unseen 3D scans. The goal of this task is to invert a 3D scan into latent codes while keeping the remaining model parameters fixed. We compare our method with the 3D human generative model gDNA [9], which has achieved state-of-the-art performance in fitting the geometry of 3D human bodies. We also directly compare with SMPL [33] and SMPL+D [3]. Note that SMPL+D is a stronger vertex-based extension that uses a subdivided version of SMPL to directly register surfaces to scans while our method and gDNA optimize latent codes." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21029" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 71, + 286, + 231 + ], + "blocks": [ + { + "bbox": [ + 48, + 71, + 286, + 231 + ], + "lines": [ + { + "bbox": [ + 48, + 71, + 286, + 231 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 286, + 231 + ], + "type": "image", + "image_path": "bdc903a5558034f6550624b0c802219c9e7182cb2514cc54d195e506ffd7b357.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 238, + 287, + 283 + ], + "lines": [ + { + "bbox": [ + 46, + 238, + 287, + 283 + ], + "spans": [ + { + "bbox": [ + 46, + 238, + 287, + 283 + ], + "type": "text", + "content": "Figure 7. Qualitative comparison of model fitting on SIZER. We visualize the fitting results from gDNA [9], SMPL+D [3], and our method. Our results are perceptually close to the ground truth even on the challenging test cases of jackets and loose t-shirts." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 72, + 296, + 263, + 374 + ], + "blocks": [ + { + "bbox": [ + 72, + 296, + 263, + 374 + ], + "lines": [ + { + "bbox": [ + 72, + 296, + 263, + 374 + ], + "spans": [ + { + "bbox": [ + 72, + 296, + 263, + 374 + ], + "type": "image", + "image_path": "779ffaae00391ed9ec726853e6dc50f2875c43cb70e2b03afab5da49e4df7a54.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 382, + 287, + 427 + ], + "lines": [ + { + "bbox": [ + 46, + 382, + 287, + 427 + ], + "spans": [ + { + "bbox": [ + 46, + 382, + 287, + 427 + ], + "type": "text", + "content": "Figure 8. Qualitative comparison of texture fitting. We compare our method with SMPL+D [3] by fitting to unseen textured meshes. The performance of SMPL+D is limited by its geometry and texture resolution." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 434, + 287, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 434, + 287, + 506 + ], + "spans": [ + { + "bbox": [ + 46, + 434, + 287, + 506 + ], + "type": "text", + "content": "From Fig. 7 we can see that SMPL+D handles loose clothing, such as a business suite, better than gDNA. However, the surfaces of SMPL+D results are over-smoothed and do not contain high-frequency details while ours can preserve them. Quantitatively, our method consistently outperforms these methods on all metrics as shown in Tab. 1." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 506, + 287, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 506, + 287, + 578 + ], + "spans": [ + { + "bbox": [ + 46, + 506, + 287, + 578 + ], + "type": "text", + "content": "Fig. 8 depicts the result of texture fitting against the SMPL+D baseline. While both methods inherit a fixed mesh topology the quality of SMPL+D is limited by its model resolution. Our method addresses this issue via local neural fields that enable cross-subject feature editing of texture and geometry with enhanced representational power." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 586, + 141, + 600 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 586, + 141, + 600 + ], + "spans": [ + { + "bbox": [ + 47, + 586, + 141, + 600 + ], + "type": "text", + "content": "4.4. Ablation Study" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 605, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 288, + 715 + ], + "type": "text", + "content": "Effectiveness of local features and shared decoders. To verify the design choice of using local features, we replace the local features " + }, + { + "bbox": [ + 46, + 605, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_l" + }, + { + "bbox": [ + 46, + 605, + 288, + 715 + ], + "type": "text", + "content": " by the global coordinates " + }, + { + "bbox": [ + 46, + 605, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_g" + }, + { + "bbox": [ + 46, + 605, + 288, + 715 + ], + "type": "text", + "content": " for conditioning the decoders. Fig. 9 shows that even though the shared decoders are able to achieve similar reconstruction results when training with global information, they do not maintain consistent performance for model fitting and avatar reposing. This is because the shared decoders tend to memorize global coordinates information in a \"per-subject\"" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 310, + 72, + 542, + 286 + ], + "blocks": [ + { + "bbox": [ + 310, + 72, + 542, + 286 + ], + "lines": [ + { + "bbox": [ + 310, + 72, + 542, + 286 + ], + "spans": [ + { + "bbox": [ + 310, + 72, + 542, + 286 + ], + "type": "image", + "image_path": "bf4e38a142b897967b1714d2123ef9299747f2c32a283263d044fe279c4456d7.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 297, + 546, + 342 + ], + "lines": [ + { + "bbox": [ + 305, + 297, + 546, + 342 + ], + "spans": [ + { + "bbox": [ + 305, + 297, + 546, + 342 + ], + "type": "text", + "content": "Figure 9. Comparison of using global/local features for conditioning decoders. The use of global coordinates causes overfitting and memorization to the shared decoders, which makes it struggle to handle unseen scans or novel body poses." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 315, + 357, + 538, + 437 + ], + "blocks": [ + { + "bbox": [ + 315, + 357, + 538, + 437 + ], + "lines": [ + { + "bbox": [ + 315, + 357, + 538, + 437 + ], + "spans": [ + { + "bbox": [ + 315, + 357, + 538, + 437 + ], + "type": "table", + "html": "
MethodPred-to-Scan / \nScan-to-Pred (mm)↓NC↑f-Score↑
SMPL [33]13.60 / 18.030.8490.458
gDNA [9]8.374 / 8.0060.8420.718
SMPL+D [3]5.192 / 2.8540.9110.962
Ours1.364 / 1.4230.9490.997
", + "image_path": "ca8908cc11016565690cfa22bc64f84999cfb057ae4d2098602f58b3ba3c664f.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 445, + 546, + 478 + ], + "lines": [ + { + "bbox": [ + 305, + 445, + 546, + 478 + ], + "spans": [ + { + "bbox": [ + 305, + 445, + 546, + 478 + ], + "type": "text", + "content": "Table 1. Model fitting comparison on SIZER. We report Chamfer distance, normal consistency (NC), and f-score between ground truth and the meshes fitted by different methods." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 491, + 547, + 575 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 491, + 547, + 575 + ], + "spans": [ + { + "bbox": [ + 304, + 491, + 547, + 575 + ], + "type": "text", + "content": "manner, rather than learning shareable information that can be used across vertices and subjects. On the other hand, our representation ensures only local features defined on the triangle coordinates are exposed to the shared decoders. In such cases, the decoders can better handle unseen body poses or out-of-distribution samples for model fitting and avatar editing." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "type": "text", + "content": "Importance of 2D adversarial loss and 3D disentanglement. As discussed in Sec. 3.3, we introduce feature disentanglement and generative adversarial learning in our training framework. As shown in Fig. 10, sampling within the feature spaces learned without adversarial loss does not yield reasonable body textures. Similarly, training only a single decoder for both geometry and texture does not allow us to maintain desired body geometries when sampling random textures. Our full model can produce disentangled textures, given arbitrary body geometries and poses." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21030" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 77, + 70, + 517, + 143 + ], + "blocks": [ + { + "bbox": [ + 77, + 70, + 517, + 143 + ], + "lines": [ + { + "bbox": [ + 77, + 70, + 517, + 143 + ], + "spans": [ + { + "bbox": [ + 77, + 70, + 517, + 143 + ], + "type": "table", + "html": "
Training Data Percentage10%25%50 %75%100%
Chamfer Distance (mm) \nS-to-P / P-to-S ↓1.933 / 1.7981.754 / 1.5901.543 / 1.4561.463 / 1.3851.423 / 1.364
Normal Consistency↑0.9180.9310.9350.9470.949
f-Score (%) ↑99.2599.3899.6599.7499.75
", + "image_path": "f687f2f486386137f00d0325add4112a70fd2ca58578812656488c1554574394.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 152, + 547, + 184 + ], + "lines": [ + { + "bbox": [ + 46, + 152, + 547, + 184 + ], + "spans": [ + { + "bbox": [ + 46, + 152, + 547, + 184 + ], + "type": "text", + "content": "Table 2. Generalization analysis on CustomHumans. We analyze the model fitting performances with regard to different amounts of training data (100% = 100 training scans). We observe consistent performance gain on all evaluation metrics when using more training subjects to train the shared decoders." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 47, + 186, + 287, + 419 + ], + "blocks": [ + { + "bbox": [ + 47, + 186, + 287, + 419 + ], + "lines": [ + { + "bbox": [ + 47, + 186, + 287, + 419 + ], + "spans": [ + { + "bbox": [ + 47, + 186, + 287, + 419 + ], + "type": "image", + "image_path": "a2c3420356ae7cfdc1108abec096c6dac78c4a8d6cdc05b77a10e202bc8e7125.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 428, + 288, + 473 + ], + "lines": [ + { + "bbox": [ + 46, + 428, + 288, + 473 + ], + "spans": [ + { + "bbox": [ + 46, + 428, + 288, + 473 + ], + "type": "text", + "content": "Figure 10. Ablative comparison of our framework designs. We visualize the results of transferring random texture to given body geometry. Our full model produces reasonable body texture and is able to maintain fixed geometry for texture editing." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 487, + 217, + 501 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 487, + 217, + 501 + ], + "spans": [ + { + "bbox": [ + 47, + 487, + 217, + 501 + ], + "type": "text", + "content": "4.5. Generalization Ability Analysis" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 510, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 510, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 510, + 288, + 713 + ], + "type": "text", + "content": "We are interested in how the amount of training data affects the capacities of the MLP decoders. To analyze this, we design three evaluation protocols: 3D model fitting, avatar reposing, and 2D texture fitting. Tab. 2 summarizes the model fitting performance using different percentages of training data. We observe a " + }, + { + "bbox": [ + 46, + 510, + 288, + 713 + ], + "type": "inline_equation", + "content": "25\\%" + }, + { + "bbox": [ + 46, + 510, + 288, + 713 + ], + "type": "text", + "content": " accuracy improvement when using the full training set. In Fig. 11 (Top) we show that the reposing artifacts caused by self-contact (e.g., fist and elbow) can be reduced when training the MLP decoders with more poses and subjects. In addition, Fig. 11 (Bottom) depicts a qualitative comparison of 2D texture editing under different training data percentages. We evaluate texture editing quality by fitting a 2D image with unseen geometric shapes and colors. It can be seen that the model trained on more samples is able to handle a wider range of color distribution. These results confirm the necessity for learning multi-subject shared decoders in our task." + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 310, + 186, + 545, + 353 + ], + "blocks": [ + { + "bbox": [ + 310, + 186, + 545, + 353 + ], + "lines": [ + { + "bbox": [ + 310, + 186, + 545, + 353 + ], + "spans": [ + { + "bbox": [ + 310, + 186, + 545, + 353 + ], + "type": "image", + "image_path": "9b02ee386727fe317ceb85097835bb232307ffac0eb0d536bb42dafdebe3bf56.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 364, + 546, + 452 + ], + "lines": [ + { + "bbox": [ + 304, + 364, + 546, + 452 + ], + "spans": [ + { + "bbox": [ + 304, + 364, + 546, + 452 + ], + "type": "text", + "content": "Figure 11. Qualitative comparison of generalization. Top: We visualize the results of avatar reposing using different percentages of training data (i.e., 10 meshes vs 100 meshes). Artifacts caused by self-contact (e.g., fist and elbow) can be reduced when more training subjects are introduced. Bottom: We visualize the results using different percentages of training data. Using more data results in more robust shared decoders (with a wider color range), which is necessary for the avatar creation task." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 461, + 378, + 474 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 461, + 378, + 474 + ], + "spans": [ + { + "bbox": [ + 306, + 461, + 378, + 474 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 482, + 545, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 482, + 545, + 636 + ], + "spans": [ + { + "bbox": [ + 304, + 482, + 545, + 636 + ], + "type": "text", + "content": "We propose an end-to-end trainable framework for learning 3D human avatars with high fidelity and full editability. By combining neural fields with explicit skinned meshes, our representation addresses the controllability issue of many previous implicit representations. Moreover, we uniquely integrate the proposed human representation into a generative auto-decoding pipeline that enables local editing across multiple animation-ready avatars. Through our evaluation on the newly contributed CustomHumans dataset, we demonstrate that our approach achieves higher model fitting accuracy and generates diverse detailed avatars. We believe that this work opens up exciting possibilities for accelerating content creation in the Metaverse." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 642, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 546, + 713 + ], + "type": "text", + "content": "Acknowledgements We express our gratitude to Stefan Walter and Dean Bakker for infrastructure support, Juan Zarate for managing the capture stage, and Deniz Yildiz and Laura Wulfroth for data capture assistance. We thank Andrew Searle for supporting the capturing system and all the dataset participants." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21031" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 287, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 53, + 91, + 193, + 102 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 193, + 102 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 193, + 102 + ], + "type": "text", + "content": "[1] https://3dpeople.com/.6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 103, + 216, + 114 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 103, + 216, + 114 + ], + "spans": [ + { + "bbox": [ + 53, + 103, + 216, + 114 + ], + "type": "text", + "content": "[2] https://renderpeople.com/.6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 114, + 287, + 178 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 114, + 287, + 178 + ], + "spans": [ + { + "bbox": [ + 53, + 114, + 287, + 178 + ], + "type": "text", + "content": "[3] Thiemo Alldieck, Marcus Magnor, Bharat Lal Bhatnagar, Christian Theobalt, and Gerard Pons-Moll. Learning to reconstruct people in clothing from a single rgb camera. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1175-1186, 2019. 2, 6, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 180, + 287, + 234 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 180, + 287, + 234 + ], + "spans": [ + { + "bbox": [ + 53, + 180, + 287, + 234 + ], + "type": "text", + "content": "[4] Thiemo Alldieck, Mihai Zanfir, and Cristian Sminchisescu. Photorealistic monocular 3d reconstruction of humans wearing clothing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 236, + 287, + 280 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 236, + 287, + 280 + ], + "spans": [ + { + "bbox": [ + 53, + 236, + 287, + 280 + ], + "type": "text", + "content": "[5] Alexander W Bergman, Petr Kellnhofer, Yifan Wang, Eric R Chan, David B Lindell, and Gordon Wetzstein. Generative neural articulated radiance fields. In Advances in Neural Information Processing Systems (NeurIPS), 2022. 1, 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 281, + 287, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 281, + 287, + 335 + ], + "spans": [ + { + "bbox": [ + 53, + 281, + 287, + 335 + ], + "type": "text", + "content": "[6] Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Combining implicit function learning and parametric models for 3d human reconstruction. In Proceedings of the European Conference on Computer Vision (ECCV), pages 311-329. Springer, 2020. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 336, + 287, + 379 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 336, + 287, + 379 + ], + "spans": [ + { + "bbox": [ + 53, + 336, + 287, + 379 + ], + "type": "text", + "content": "[7] Caroline Chan, Shiry Ginosar, Tinghui Zhou, and Alexei A Efros. Everybody dance now. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 5933-5942, 2019. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 380, + 287, + 423 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 380, + 287, + 423 + ], + "spans": [ + { + "bbox": [ + 53, + 380, + 287, + 423 + ], + "type": "text", + "content": "[8] Xu Chen, Tianjian Jiang, Jie Song, Max Rietmann, Andreas Geiger, Michael J. Black, and Otmar Hilliges. FastSNARF: A fast deformer for articulated neural fields. arXiv, abs/2211.15601, 2022. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 425, + 287, + 479 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 425, + 287, + 479 + ], + "spans": [ + { + "bbox": [ + 53, + 425, + 287, + 479 + ], + "type": "text", + "content": "[9] Xu Chen, Tianjian Jiang, Jie Song, Jinlong Yang, Michael J Black, Andreas Geiger, and Otmar Hilliges. gdna: Towards generative detailed neural avatars. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 20427-20437, 2022. 1, 2, 6, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 480, + 287, + 534 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 480, + 287, + 534 + ], + "spans": [ + { + "bbox": [ + 48, + 480, + 287, + 534 + ], + "type": "text", + "content": "[10] Xu Chen, Yufeng Zheng, Michael J Black, Otmar Hilliges, and Andreas Geiger. Snarf: Differentiable forward skimming for animating non-rigid neural implicit shapes. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 536, + 287, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 536, + 287, + 590 + ], + "spans": [ + { + "bbox": [ + 48, + 536, + 287, + 590 + ], + "type": "text", + "content": "[11] Alvaro Collet, Ming Chuang, Pat Sweeney, Don Gillett, Dennis Evseev, David Calabrese, Hugues Hoppe, Adam Kirk, and Steve Sullivan. High-quality streamable free-viewpoint video. ACM Transactions on Graphics (TOG), 34(4):1-13, 2015. 2, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 591, + 287, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 591, + 287, + 624 + ], + "spans": [ + { + "bbox": [ + 48, + 591, + 287, + 624 + ], + "type": "text", + "content": "[12] Blender Online Community. Blender - a 3D modelling and rendering package. Blender Foundation, Stichting Blender Foundation, Amsterdam, 2018. 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 624, + 287, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 287, + 679 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 287, + 679 + ], + "type": "text", + "content": "[13] Enric Corona, Albert Pumarola, Guillem Alenyà, Gerard Pons-Moll, and Francesc Moreno-Noguer. Smplicit: Topology-aware generative model for clothed people. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 1, 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "text", + "content": "[14] Boyang Deng, JP Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, and Andrea Tagliasacchi. Neural articulated shape approximation. In" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "Proceedings of the European Conference on Computer Vision (ECCV). Springer, August 2020. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 95, + 545, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 95, + 545, + 149 + ], + "spans": [ + { + "bbox": [ + 308, + 95, + 545, + 149 + ], + "type": "text", + "content": "[15] Terrance DeVries, Miguel Angel Bautista, Nitish Srivastava, Graham W Taylor, and Joshua M Susskind. Unconstrained scene generation with locally conditioned radiance fields. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 14304-14313, 2021. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 150, + 545, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 150, + 545, + 205 + ], + "spans": [ + { + "bbox": [ + 308, + 150, + 545, + 205 + ], + "type": "text", + "content": "[16] Haoye Dong, Xiaodan Liang, Xiaohui Shen, Bowen Wu, Bing-Cheng Chen, and Jian Yin. Fw-gan: Flow-navigated warping gan for video virtual try-on. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 1161-1170, 2019. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 205, + 545, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 205, + 545, + 270 + ], + "spans": [ + { + "bbox": [ + 308, + 205, + 545, + 270 + ], + "type": "text", + "content": "[17] Xin Dong, Fuwei Zhao, Zhenyu Xie, Xijin Zhang, Daniel K. Du, Min Zheng, Xiang Long, Xiaodan Liang, and Jianchao Yang. Dressing in the wild by watching dance videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3480-3489, June 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 271, + 545, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 271, + 545, + 327 + ], + "spans": [ + { + "bbox": [ + 308, + 271, + 545, + 327 + ], + "type": "text", + "content": "[18] Jianglin Fu, Shikai Li, Yuming Jiang, Kwan-Yee Lin, Chen Qian, Chen Change Loy, Wayne Wu, and Ziwei Liu. Stylegan-human: A data-centric odyssey of human generation. In Proceedings of the European Conference on Computer Vision (ECCV), pages 1-19. Springer, 2022. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 327, + 545, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 327, + 545, + 370 + ], + "spans": [ + { + "bbox": [ + 308, + 327, + 545, + 370 + ], + "type": "text", + "content": "[19] Rony Goldenthal, David Harmon, Raanan Fattal, Michel Bercovier, and Eitan Grinspun. Efficient simulation of inextensible cloth. ACM Transactions on Graphics (TOG), pages 49-es, 2007. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 371, + 545, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 371, + 545, + 425 + ], + "spans": [ + { + "bbox": [ + 308, + 371, + 545, + 425 + ], + "type": "text", + "content": "[20] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems (NeurIPS), pages 2672–2680, 2014. 2, 4" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 426, + 545, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 426, + 545, + 491 + ], + "spans": [ + { + "bbox": [ + 308, + 426, + 545, + 491 + ], + "type": "text", + "content": "[21] Artur Grigorev, Karim Iskakov, Anastasia Iinina, Renat Bashirov, Ilya Zakharkin, Alexander Vakhitov, and Victor Lempitsky. Stylepeople: A generative model of fullbody human avatars. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5151-5160, 2021. 1, 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 492, + 545, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 492, + 545, + 525 + ], + "spans": [ + { + "bbox": [ + 308, + 492, + 545, + 525 + ], + "type": "text", + "content": "[22] Artur Grigorev, Bernhard Thomaszewski, Michael J Black, and Otmar Hilliges. HOOd: Hierarchical graphs for generalized modelling of clothing dynamics. 2023. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 525, + 545, + 569 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 525, + 545, + 569 + ], + "spans": [ + { + "bbox": [ + 308, + 525, + 545, + 569 + ], + "type": "text", + "content": "[23] Chen Guo, Tianjian Jiang, Xu Chen, Jie Song, and Otmar Hilliges. Vid2 avatar: 3d avatar reconstruction from videos in the wild via self-supervised scene decomposition. In Computer Vision and Pattern Recognition (CVPR), 2023. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 570, + 545, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 570, + 545, + 645 + ], + "spans": [ + { + "bbox": [ + 308, + 570, + 545, + 645 + ], + "type": "text", + "content": "[24] Pat Hanrahan and Paul Haeberli. Direct wysiwyg painting and texturing on 3d shapes: (an error occurred during the printing of this article that reversed the print order of pages 118 and 119. while we have corrected the sort order of the 2 pages in the dl, the pdf did not allow us to repaginae the 2 pages.). ACM Transactions on Graphics (TOG), 24(4):215-223, 1990. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 647, + 545, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 647, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 308, + 647, + 545, + 689 + ], + "type": "text", + "content": "[25] Fangzhou Hong, Zhaoxi Chen, Yushi Lan, Liang Pan, and Ziwei Liu. Eva3d: Compositional 3d human generation from 2d image collections. arXiv preprint arXiv:2210.04888, 2022. 1, 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 690, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 690, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 308, + 690, + 545, + 712 + ], + "type": "text", + "content": "[26] Tero Karras, Miika Aittala, Samuli Laine, Erik Härkönen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Alias-free" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "21032" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 66, + 72, + 287, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 72, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 287, + 95 + ], + "type": "text", + "content": "generative adversarial networks. Advances in Neural Information Processing Systems (NeurIPS), 34:852-863, 2021. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 288, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 288, + 151 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 288, + 151 + ], + "type": "text", + "content": "[27] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110-8119, 2020. 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 152, + 288, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 152, + 288, + 196 + ], + "spans": [ + { + "bbox": [ + 48, + 152, + 288, + 196 + ], + "type": "text", + "content": "[28] Christoph Lassner, Gerard Pons-Moll, and Peter V Gehler. A generative model of people in clothing. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 853-862, 2017. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 198, + 288, + 242 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 198, + 288, + 242 + ], + "spans": [ + { + "bbox": [ + 48, + 198, + 288, + 242 + ], + "type": "text", + "content": "[29] Ruilong Li, Shan Yang, David A. Ross, and Angjoo Kanazawa. Ai choreographer: Music conditioned 3d dance generation with aist++. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 243, + 288, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 243, + 288, + 297 + ], + "spans": [ + { + "bbox": [ + 48, + 243, + 288, + 297 + ], + "type": "text", + "content": "[30] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural actor: Neural free-view synthesis of human actors with pose control. ACM Transactions on Graphics (TOG), 40(6):1-16, 2021. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 299, + 288, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 299, + 288, + 363 + ], + "spans": [ + { + "bbox": [ + 48, + 299, + 288, + 363 + ], + "type": "text", + "content": "[31] Wen Liu, Zhixin Piao, Jie Min, Wenhan Luo, Lin Ma, and Shenghua Gao. Liquid warping gan: A unified framework for human motion imitation, appearance transfer and novel view synthesis. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 5904-5913, 2019. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 365, + 288, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 365, + 288, + 421 + ], + "spans": [ + { + "bbox": [ + 48, + 365, + 288, + 421 + ], + "type": "text", + "content": "[32] Ziwei Liu, Ping Luo, Shi Qiu, Xiaogang Wang, and Xiaou Tang. Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1096-1104, 2016. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 422, + 288, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 422, + 288, + 465 + ], + "spans": [ + { + "bbox": [ + 48, + 422, + 288, + 465 + ], + "type": "text", + "content": "[33] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black. SMPL: A skinned multiperson linear model. ACM Transactions on Graphics (TOG), 34(6):248:1-248:16, October 2015. 2, 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 467, + 288, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 467, + 288, + 521 + ], + "spans": [ + { + "bbox": [ + 48, + 467, + 288, + 521 + ], + "type": "text", + "content": "[34] Qianli Ma, Shunsuke Saito, Jinlong Yang, Siyu Tang, and Michael J. Black. SCALE: Modeling clothed humans with a surface codec of articulated local elements. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 16082-16093, June 2021. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 523, + 288, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 523, + 288, + 567 + ], + "spans": [ + { + "bbox": [ + 48, + 523, + 288, + 567 + ], + "type": "text", + "content": "[35] Qianli Ma, Jinlong Yang, Michael J. Black, and Siyu Tang. Neural point-based shape modeling of humans in challenging clothing. In 2022 International Conference on 3D Vision (3DV), September 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 568, + 288, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 568, + 288, + 622 + ], + "spans": [ + { + "bbox": [ + 48, + 568, + 288, + 622 + ], + "type": "text", + "content": "[36] Qianli Ma, Jinlong Yang, Anurag Ranjan, Sergi Pujades, Gerard Pons-Moll, Siyu Tang, and Michael J. Black. Learning to Dress 3D People in Generative Clothing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 1, 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 624, + 288, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 288, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 288, + 668 + ], + "type": "text", + "content": "[37] Qianli Ma, Jinlong Yang, Siyu Tang, and Michael J. Black. The power of points for modeling humans in clothing. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "type": "text", + "content": "[38] Lars Mescheder, Andreas Geiger, and Sebastian Nowozin. Which training methods for gans do actually converge? In International Conference on Machine Learning (ICML), pages 3481-3490. PMLR, 2018. 4" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "type": "text", + "content": "[39] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4460-4470, 2019. 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 129, + 545, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 545, + 173 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 545, + 173 + ], + "type": "text", + "content": "[40] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-102:15, July 2022. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 175, + 545, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 175, + 545, + 207 + ], + "spans": [ + { + "bbox": [ + 307, + 175, + 545, + 207 + ], + "type": "text", + "content": "[41] Rahul Narain, Armin Samii, and James F O'brien. Adaptive anisotropic remeshing for cloth simulation. ACM Transactions on Graphics (TOG), 31(6):1-10, 2012. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 209, + 545, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 209, + 545, + 262 + ], + "spans": [ + { + "bbox": [ + 308, + 209, + 545, + 262 + ], + "type": "text", + "content": "[42] Atsuhiro Noguchi, Xiao Sun, Stephen Lin, and Tatsuya Harada. Unsupervised learning of efficient geometry-aware neural articulated representations. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 1, 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 264, + 545, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 264, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 308, + 264, + 545, + 308 + ], + "type": "text", + "content": "[43] Ahmed AA Osman, Timo Bolkart, and Michael J Black. Star: Sparse trained articulated human body regressor. In Proceedings of the European Conference on Computer Vision (ECCV), pages 598-613. Springer, 2020. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 310, + 545, + 354 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 310, + 545, + 354 + ], + "spans": [ + { + "bbox": [ + 308, + 310, + 545, + 354 + ], + "type": "text", + "content": "[44] Pablo Palafox, Aljaž Božić, Justus Thies, Matthias Nießner, and Angela Dai. Npms: Neural parametric models for 3d deformable shapes. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2021. 1, 2, 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 355, + 545, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 355, + 545, + 399 + ], + "spans": [ + { + "bbox": [ + 308, + 355, + 545, + 399 + ], + "type": "text", + "content": "[45] Pablo Palafox, Nikolaos Sarafianos, Tony Tung, and Angela Dai. Spams: Structured implicit parametric models. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 1, 2, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 399, + 545, + 455 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 399, + 545, + 455 + ], + "spans": [ + { + "bbox": [ + 308, + 399, + 545, + 455 + ], + "type": "text", + "content": "[46] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 165-174, 2019. 2, 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 456, + 545, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 456, + 545, + 520 + ], + "spans": [ + { + "bbox": [ + 308, + 456, + 545, + 520 + ], + "type": "text", + "content": "[47] Priyanka Patel, Chun-Hao P Huang, Joachim Tesch, David T Hoffmann, Shashank Tripathi, and Michael J Black. Agora: Avatars in geography optimized for regression analysis. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 13468-13478, 2021. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 523, + 545, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 523, + 545, + 589 + ], + "spans": [ + { + "bbox": [ + 308, + 523, + 545, + 589 + ], + "type": "text", + "content": "[48] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3d hands, face, and body from a single image. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 10975-10985, 2019. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 590, + 545, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 590, + 545, + 622 + ], + "spans": [ + { + "bbox": [ + 308, + 590, + 545, + 622 + ], + "type": "text", + "content": "[49] Hans Köhling Pedersen. Decorating implicit surfaces. In ACM Transactions on Graphics (TOG), pages 291-300, 1995. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 624, + 545, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 624, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 308, + 624, + 545, + 689 + ], + "type": "text", + "content": "[50] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 9054–9063, 2021. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "type": "text", + "content": "[51] Sergey Prokudin, Michael J Black, and Javier Romero. Smplpix: Neural avatars from 3d human models. In Proceed-" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "21033" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 73, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 67, + 73, + 288, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 73, + 288, + 95 + ], + "spans": [ + { + "bbox": [ + 67, + 73, + 288, + 95 + ], + "type": "text", + "content": "ings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1810-1819, 2021. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 288, + 148 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 288, + 148 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 288, + 148 + ], + "type": "text", + "content": "[52] Daniel Rebain, Mark Matthews, Kwang Moo Yi, Dmitry Lagun, and Andrea Tagliasacchi. Lolnerf: Learn from one look. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1558-1567, 2022. 2, 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 150, + 288, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 150, + 288, + 205 + ], + "spans": [ + { + "bbox": [ + 48, + 150, + 288, + 205 + ], + "type": "text", + "content": "[53] Edoardo Remelli, Timur Bagautdinov, Shunsuke Saito, Chenglei Wu, Tomas Simon, Shih-En Wei, Kaiwen Guo, Zhe Cao, Fabian Prada, Jason Saragih, et al. Drivable volumetric avatars using texel-aligned features. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1–9, 2022. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 206, + 288, + 249 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 206, + 288, + 249 + ], + "spans": [ + { + "bbox": [ + 48, + 206, + 288, + 249 + ], + "type": "text", + "content": "[54] Daniel Roich, Ron Mokady, Amit H. Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. ACM Transactions on Graphics (TOG), 42(1), aug 2022. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 250, + 288, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 250, + 288, + 293 + ], + "spans": [ + { + "bbox": [ + 48, + 250, + 288, + 293 + ], + "type": "text", + "content": "[55] Javier Romero, Dimitrios Tzionas, and Michael J. Black. Embodied hands: Modeling and capturing hands and bodies together. ACM Transactions on Graphics (TOG), 36(6), Nov. 2017. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 294, + 288, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 294, + 288, + 348 + ], + "spans": [ + { + "bbox": [ + 48, + 294, + 288, + 348 + ], + "type": "text", + "content": "[56] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), October 2019. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 350, + 288, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 350, + 288, + 404 + ], + "spans": [ + { + "bbox": [ + 48, + 350, + 288, + 404 + ], + "type": "text", + "content": "[57] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo. Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 84-93, 2020. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 404, + 288, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 404, + 288, + 459 + ], + "spans": [ + { + "bbox": [ + 48, + 404, + 288, + 459 + ], + "type": "text", + "content": "[58] Shunsuke Saito, Jinlong Yang, Qianli Ma, and Michael J. Black. SCANimate: Weakly supervised learning of skinned clothed avatar networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2021. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 460, + 288, + 513 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 460, + 288, + 513 + ], + "spans": [ + { + "bbox": [ + 48, + 460, + 288, + 513 + ], + "type": "text", + "content": "[59] Kaiyue Shen, Chen Guo, Manuel Kaufmann, Juan Zarate, Julien Valentin, Jie Song, and Otmar Hilliges. X- avatar: Expressive human avatars. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 514, + 288, + 558 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 514, + 288, + 558 + ], + "spans": [ + { + "bbox": [ + 48, + 514, + 288, + 558 + ], + "type": "text", + "content": "[60] Yichun Shi, Divyansh Aggarwal, and Anil K Jain. Lifting 2d stylegan for 3d-aware face generation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6258-6266, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 559, + 288, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 559, + 288, + 592 + ], + "spans": [ + { + "bbox": [ + 48, + 559, + 288, + 592 + ], + "type": "text", + "content": "[61] Zifan Shi, Sida Peng, Yinghao Xu, Yiyi Liao, and Yujun Shen. Deep generative models on 3d representations: A survey. arXiv preprint arXiv:2210.15663, 2022. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 593, + 288, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 593, + 288, + 658 + ], + "spans": [ + { + "bbox": [ + 48, + 593, + 288, + 658 + ], + "type": "text", + "content": "[62] Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler. Neural geometric level of detail: Real-time rendering with implicit 3D shapes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 658, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 288, + 713 + ], + "type": "text", + "content": "[63] Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo MartinBrualla, Tomas Simon, Jason Saragih, Matthias Nießner, et al. State of the art on neural rendering. In Annual Conference of the European Association for Computer Graphics" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "text", + "content": "(EUROGRAPHICS), volume 39, pages 701-727. Wiley Online Library, 2020. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 308, + 95, + 545, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 95, + 545, + 172 + ], + "spans": [ + { + "bbox": [ + 308, + 95, + 545, + 172 + ], + "type": "text", + "content": "[64] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, W Yifan, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, et al. Advances in neural rendering. In Annual Conference of the European Association for Computer Graphics (EUROGRAPHICS), volume 41, pages 703-735. Wiley Online Library, 2022. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 173, + 545, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 173, + 545, + 215 + ], + "spans": [ + { + "bbox": [ + 308, + 173, + 545, + 215 + ], + "type": "text", + "content": "[65] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics (TOG), 38(4):1-12, 2019. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 217, + 545, + 271 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 217, + 545, + 271 + ], + "spans": [ + { + "bbox": [ + 308, + 217, + 545, + 271 + ], + "type": "text", + "content": "[66] Garvita Tiwari, Bharat Lal Bhatnagar, Tony Tung, and Gerard Pons-Moll. Sizer: A dataset and model for parsing 3d clothing and learning size sensitive 3d clothing. In Proceedings of the European Conference on Computer Vision (ECCV). Springer, August 2020. 1, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 272, + 545, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 272, + 545, + 327 + ], + "spans": [ + { + "bbox": [ + 308, + 272, + 545, + 327 + ], + "type": "text", + "content": "[67] Gul Varol, Javier Romero, Xavier Martin, Naureen Mahmood, Michael J Black, Ivan Laptev, and Cordelia Schmid. Learning from synthetic humans. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 109-117, 2017. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 327, + 545, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 327, + 545, + 380 + ], + "spans": [ + { + "bbox": [ + 308, + 327, + 545, + 380 + ], + "type": "text", + "content": "[68] Ting-Chun Wang, Arun Mallya, and Ming-Yu Liu. One-shot free-view neural talking-head synthesis for video conferencing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 10039-10049, 2021. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 383, + 545, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 383, + 545, + 415 + ], + "spans": [ + { + "bbox": [ + 308, + 383, + 545, + 415 + ], + "type": "text", + "content": "[69] Weihao Xia, Yulun Zhang, Yujiu Yang, Jing-Hao Xue, Bolei Zhou, and Ming-Hsuan Yang. Gan inversion: A survey. TPAMI, 2022. 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 415, + 545, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 415, + 545, + 492 + ], + "spans": [ + { + "bbox": [ + 308, + 415, + 545, + 492 + ], + "type": "text", + "content": "[70] Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. Neural fields in visual computing and beyond. In Annual Conference of the European Association for Computer Graphics (EUROGRAPHICS), volume 41, pages 641-676. Wiley Online Library, 2022. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 493, + 545, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 493, + 545, + 547 + ], + "spans": [ + { + "bbox": [ + 308, + 493, + 545, + 547 + ], + "type": "text", + "content": "[71] Zhenyu Xie, Zaiyu Huang, Fuwei Zhao, Haoye Dong, Michael Kampffmeyer, and Xiaodan Liang. Towards scalable unpaired virtual try-on via patch-routed spatially-adaptive gan. Advances in Neural Information Processing Systems (NeurIPS), 34:2598-2610, 2021. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 548, + 545, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 548, + 545, + 613 + ], + "spans": [ + { + "bbox": [ + 308, + 548, + 545, + 613 + ], + "type": "text", + "content": "[72] Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In Proceedings of the European Conference on Computer Vision (ECCV), pages 597-614. Springer, 2022. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 614, + 545, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 614, + 545, + 669 + ], + "spans": [ + { + "bbox": [ + 308, + 614, + 545, + 669 + ], + "type": "text", + "content": "[73] Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, and Yebin Liu. Function4d: Real-time human volumetric capture from very sparse consumer rgbd sensors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2021. 2, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "text", + "content": "[74] Zhixuan Yu, Jae Shin Yoon, In Kyu Lee, Prashanth Venkatesh, Jaesik Park, Jihun Yu, and Hyun Soo Park. Humbi: A large multiview dataset of human body expressions. In Proceedings of the IEEE Conference on Computer" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "21034" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 196 + ], + "type": "list", + "angle": 0, + "index": 3, + "blocks": [ + { + "bbox": [ + 67, + 72, + 287, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 72, + 287, + 94 + ], + "spans": [ + { + "bbox": [ + 67, + 72, + 287, + 94 + ], + "type": "text", + "content": "Vision and Pattern Recognition (CVPR), pages 2990-3000, 2020. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 287, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 287, + 140 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 287, + 140 + ], + "type": "text", + "content": "[75] Jianfeng Zhang, Zihang Jiang, Dingdong Yang, Hongyi Xu, Yichun Shi, Guoxian Song, Zhongcong Xu, Xinchao Wang, and Jiashi Feng. Avatargen: A 3d generative model for animatable human avatars. In Arxiv, 2022. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 141, + 287, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 141, + 287, + 196 + ], + "spans": [ + { + "bbox": [ + 48, + 141, + 287, + 196 + ], + "type": "text", + "content": "[76] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C. Bühler, Xu Chen, Michael J. Black, and Otmar Hilliges. I M Avatar: Implicit morphable head avatars from videos. 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Due to the limited ability to address intra-class variations, the image classifier cannot properly associate the pixel features, leading to inaccurate dense localization maps. In this paper, we propose to explicitly construct multi-modal class representations by leveraging the Contrastive Language-Image Pretraining (CLIP), to guide dense localization. More specifically, we propose a unified transformer framework to learn two-modalities of class-specific tokens, i.e., class-specific visual and textual tokens. The former captures semantics from the target visual data while the latter exploits the class-related language priors from CLIP, providing complementary information to better perceive the intra-class diversities. In addition, we propose to enrich the multi-modal class-specific tokens with sample-specific contexts comprising visual context and image-language context. This enables more adaptive class representation learning, which further facilitates dense localization. Extensive experiments show the superiority of the proposed method for WSDOL on two multi-label datasets, i.e., PASCAL VOC and MS COCO, and one single-label dataset, i.e., OpenImages. Our dense localization maps also lead to the state-of-the-art weakly supervised semantic segmentation (WSSS) results on PASCAL VOC and MS COCO.", + "bbox": [ + 75, + 359, + 472, + 765 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 786, + 209, + 801 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Fully supervised dense prediction tasks have achieved great success, which however comes at the cost of expensive pixel-level annotations. To address this issue, recent works have investigated the use of weak labels, such as image-", + "bbox": [ + 75, + 811, + 468, + 875 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/985161060fc3d76af78cb522c0970075b3ac7ac44f5526c63a456f5bfd0c5096.jpg", + "image_caption": [ + "Figure 1. (a) CAM exploits the correlation between the image classifier and the pixel features. (b) We propose to construct multimodal class-specific tokens to guide dense object localization." + ], + "image_footnote": [], + "bbox": [ + 521, + 324, + 870, + 491 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "level labels, to generate dense object localization maps as pseudo labels for those tasks. For the weakly supervised object localization (WSOL) task, most methods evaluate localization results on the bounding-box level and a few recent methods [5] evaluate on the pixel level. We use WSDOL to focus on the pixel-level evaluation, which is critical for downstream dense prediction tasks such as WSSS.", + "bbox": [ + 496, + 550, + 890, + 655 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Previous works have exploited Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) [7] for WSDOL with image-level labels [21, 40]. These methods have generally relied on Class Activation Mapping (CAM) [48], which generates class-specific localization maps by computing the correlation between the class-specific weight vectors of the image classifier and every pixel feature vector. However, image classifiers generally have a limited ability to address the intra-class variation, let alone at the pixel level. This thus leads to inaccurate dense localization results. In the conventional fully supervised learning paradigm, the image classification model aims to convert images to numeric labels, ignoring the context of the labels. Hence, it tends to learn the pattern that maximizes the inter-class differences but disregards the intra-class diversities. This largely restricts the model's ability of", + "bbox": [ + 496, + 657, + 892, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "1https://github.com/xulianuwa/MMCST", + "bbox": [ + 94, + 886, + 370, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "19596", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "understanding semantic objects.", + "bbox": [ + 76, + 90, + 292, + 104 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recently, Vision-Language (VL) models have attracted much attention. In particular, CLIP, a representative VL model, pre-trained on 400 million image-text pairs that are readily available publicly, has been successfully applied to a number of downstream tasks, due to its strong generalization ability. CLIP introduces a contrastive representation learning method that constrains an image to match its related text while dis-matching the remaining texts from the same batch, in a multi-modal embedding space. This enables the model to perceive the differences across images, thus facilitating it to better discriminate intra-class samples.", + "bbox": [ + 75, + 106, + 468, + 271 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Motivated by these observations, we propose to leverage the strong representations of visual concepts encoded by the pre-trained CLIP language model to guide the dense object localization. More specifically, we extract the class-related text embeddings by feeding the label prompts to the pre-trained CLIP language model. As shown in Figure 1, we propose a unified transformer framework which includes multi-modal class-specific tokens, i.e., class-specific visual tokens and class-specific textual tokens. The class-specific visual tokens aim to capture visual representations from the target image dataset, while the class-specific textual tokens take the rich language semantics from the CLIP label text embeddings. These two modalities of class-specific tokens, with complementary information, are jointly used to correlate pixel features, contributing to better dense localization.", + "bbox": [ + 75, + 272, + 470, + 498 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In order to construct more adaptive class representations, which can better associate the sample-specific local features for dense localization, we propose to enhance the global multi-modal class-specific tokens with sample-specific contextual information. To this end, we introduce two designs: (i) at the feature level, we use the sample-specific visual context to enhance both the class-specific visual and textual tokens. This is achieved by combining these global tokens with their output local counterparts which aggregate the patch tokens of the image through the self-attention layers; (ii) at the loss level, we introduce a regularization contrastive loss to encourage the output text tokens to match the CLIP image embeddings. This allows the CLIP model to be better adapted to our target datasets. Moreover, due to its image-language matching pre-training objective, the CLIP image encoder is learned to extract the image embeddings that match the CLIP text embeddings of their corresponding image captions. We thus argue that through this contrastive loss, the rich image-related language context from the CLIP could be implicitly transferred to the text tokens, which are more beneficial for guiding the dense object localization, compared to the simple label prompts.", + "bbox": [ + 75, + 498, + 470, + 830 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In summary, the contribution of this work is three-fold:", + "bbox": [ + 94, + 830, + 460, + 845 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- We propose a new WSDOL method by explicitly constructing multi-modal class representations in a unified transformer framework.", + "bbox": [ + 76, + 854, + 468, + 898 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- The proposed transformer includes class-specific visual tokens and class-specific textual tokens, which are learned from different data modalities with diverse supervisions, thus providing complementary information for more discriminative dense localization.", + "- We propose to enhance the multi-modal global class representations by using sample-specific visual context via the global-local token fusion and transferring the image-language context from the pre-trained CLIP via a regularization loss. This enables more adaptive class representations for more accurate dense localization." + ], + "bbox": [ + 500, + 90, + 890, + 265 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The proposed method achieved the state-of-the-art results on PASCAL VOC 2012 (72.2% on the test set) and MS COCO 2014 (45.9% on the validation set) for WSSS.", + "bbox": [ + 500, + 273, + 890, + 319 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 330, + 640, + 347 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Weakly supervised dense object localization. Most existing methods rely on CAM which generates class-specific localization maps based on the correlation between the class weights of the image classifier and the pixel features. However, CAM produces inaccurate localization maps where only the most discriminative object regions are activated. Most previous works are based on CNNs and they have generally focused on improving image feature learning in various ways, such as manipulating training images [20, 32], modifying the classification architecture [35, 41], and designing new losses [4, 8, 14]. Recently, ViTs have made breakthroughs in many computer vision tasks, outperforming CNNs, due to their self-attention based processing blocks that enable long-dependence modeling and cater for different data modalities. Recent ViT-based methods, TSCAM [10] and MCTformer [40] have exploited the transformer attention [12] to generate localization maps, but they still need to integrate CAM into ViTs to achieve a decent localization performance. For these CAM-based methods, one problem that has not been well investigated, is that the class weights used to associate pixel features are the global class representations for the entire dataset. Thus, they cannot properly address the complex pixel-level intra-class variations across samples. Chen et al. [3] proposed the Image-Specific CAM (IS-CAM) using the image-specific class prototypes constructed by applying the masked-average pooling on the image feature maps, and imposed a consistency loss between the IS-CAM and the raw CAM to enhance the feature learning for better localization.", + "bbox": [ + 496, + 357, + 890, + 792 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In contrast to all these CAM-based methods, we propose to explicitly construct multi-modal class representations for dense object localization.", + "bbox": [ + 496, + 795, + 890, + 839 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Vision-language models. Recent progress in vision-language pre-training with large-scale datasets has provided a rich source of transferable information. In particular, a representative VL model, i.e., CLIP, brought a significant", + "bbox": [ + 496, + 839, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "19597", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/48f61ab4599a763cd0115699f85f4761e9b9646dd54d9b043d011828fde0aeee.jpg", + "image_caption": [ + "Figure 2. An overview of the proposed transformer framework. An input RGB image is first split and then embedded into patch tokens. The proposed transformer additionally includes class-specific visual tokens (i.e., input class tokens) and class-specific textual tokens (i.e., input text tokens), in which the input text tokens are initialized by the pretrained CLIP text embeddings of the class-related prompts. The output class tokens are averaged to predict class scores. We fuse the input and output class tokens to correlate the output patch tokens. This generates the class-to-patch correlation maps, which are then globally pooled to produce class scores. Similarly for the text tokens. We also apply a contrastive regularization loss (Figure 4) on the averaged output text tokens to transfer the rich VL context from CLIP." + ], + "image_footnote": [], + "bbox": [ + 117, + 87, + 851, + 313 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "insight that one can use natural language to connect the visual concept, thus allowing a flexible prediction space for a wide knowledge transfer. The pre-trained CLIP model has been used in a variety of open-world visual tasks. Gu et al. [11] proposed to distill knowledge from the pre-trained CLIP image and text encoders to learn open-vocabulary object detectors. Li et al. [24] used the pre-trained text embeddings as semantic label representations to correlate visual features for semantic segmentation. Similar language-driven segmentation methods have been proposed for 2D images [29] and 3D point clouds [30]. In contrast to these methods which have dense supervision, we investigate a pixel-text matching problem in a weakly supervised setting.", + "bbox": [ + 75, + 419, + 472, + 616 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Xie et al. [37] proposed a CLIP-based WSDOL method, which constrains the CAM activated object regions and the background image regions to match and dis-match the corresponding label prompt in the CLIP embedding space, respectively. It also relies on the additionally pre-defined text descriptions to suppress the co-occurring backgrounds. In contrast, without introducing new visual concepts, we propose to directly use the CLIP label text embeddings to infer dense object localization maps, and further refine them with sample-specific contexts, achieving better results in both WSDOL and WSSS (see Table 1 and Table 3).", + "bbox": [ + 75, + 617, + 468, + 784 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 76, + 799, + 169, + 814 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Overview. We propose a unified transformer framework to construct multi-modal class-specific tokens for weakly supervised dense object localization. As illustrated in Figure 2, an input RGB image is split into patches and then embedded into a sequence of patch tokens. Additionally, the", + "bbox": [ + 75, + 824, + 468, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "proposed transformer has two sets of class-specific tokens, i.e., class-specific visual and textual tokens. The class-specific visual tokens are all initialized by the pre-trained weights of the class token of ViT, and the class-specific textual tokens are initialized by the pre-trained CLIP text embeddings of the class-related prompts and kept constant during training. With the added positional embeddings, these three types of tokens are concatenated and serve as input to the proposed transformer. The input tokens go through consecutive masked self-attention based transformer encoding layers. The output class tokens are averaged along the channel dimension to predict class scores. The input and output class/text tokens are fused and then correlated with the output patch tokens, generating the class-to-patch and text-to-patch correlation maps. These two correlation maps are then globally pooled to produce class scores, respectively.", + "bbox": [ + 496, + 419, + 890, + 661 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "During training, the class predictions are supervised by the image-level ground-truth labels via the classification loss. In addition, a batch-softmax contrastive loss is used to encourage the averaged output text token to match the CLIP image embedding. This allows the image-language context transfer, further enabling the class representations to be more sample-adaptive for better localization. During inference, a multi-modal correlation map is generated by fusing the two correlation maps, which are further refined by the transformer attention as suggested in [40], generating the final dense object localization maps.", + "bbox": [ + 496, + 662, + 892, + 830 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Multi-modal class-specific token learning", + "text_level": 1, + "bbox": [ + 500, + 844, + 852, + 861 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In order to construct multi-modal class representations to guide dense object localization, we propose a multi-modal", + "bbox": [ + 500, + 869, + 892, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "19598", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/e8f95b54269c1397f33a98677d000611e0a007b29b4a14002010295833885329.jpg", + "image_caption": [ + "Figure 3. The detailed structure of the proposed multi-modal token transformer encoder." + ], + "image_footnote": [], + "bbox": [ + 89, + 87, + 459, + 233 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "token transformer framework. As illustrated in Figure 2, an input RGB image is first split into $N \\times N$ patches, which are then embedded into a sequence of $N^2$ patch tokens. We additionally create $C$ class-specific visual tokens and $C$ class-specific textual tokens, where $C$ is the number of classes. With the added learnable positional embeddings, these three types of tokens are first concatenated and then fed into the transformer encoder (Figure 3). In order to allow class tokens and text tokens to fully interact with patch tokens but not competing with each other, we use the masked self-attention as shown in Figure 3 (right). Finally, the transformer encoder outputs three types of tokens accordingly.", + "bbox": [ + 75, + 281, + 468, + 462 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Class-specific visual token learning. Inspired by MCTformer [40], we use $C$ class tokens to learn class-specific representations. The output class tokens $\\mathbf{T}_{cls}^{out} \\in \\mathbb{R}^{C \\times D}$ from the proposed multi-modal token transformer encoder are processed by channel-wise averaging, producing $C$ class scores $\\mathbf{y}_{cls} \\in \\mathbb{R}^C$ . The class scores are supervised by the image-level ground-truth labels $\\mathbf{y} \\in \\mathbb{R}^C$ using the multi-label soft margin loss (MLSM):", + "bbox": [ + 75, + 462, + 468, + 583 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {c l s} = M L S M (\\mathbf {y} _ {c l s}, \\mathbf {y}) = - \\\\ \\frac {1}{C} \\sum_ {i = 1} ^ {C} \\mathbf {y} ^ {i} \\log \\sigma \\left(\\mathbf {y} _ {c l s} ^ {i}\\right) + (1 - \\mathbf {y} ^ {i}) \\log \\left(1 - \\sigma \\left(\\mathbf {y} _ {c l s} ^ {i}\\right)\\right). \\tag {1} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 590, + 468, + 652 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Class-specific textual token learning. CLIP presents a novel way of representing visual concepts by using textual prompts, which provide highly complementary information to the class representations learned from only visual data. It thus exhibits a great potential to better facilitate the dense object localization. This motivates us to construct $C$ class-specific textual tokens by leveraging the rich semantics learned by the pre-trained CLIP language model. More specifically, we create $C$ text prompts using the template of \"a photo of [CLS]\", where [CLS] refers to each class name in the label set of a given dataset. These text prompts are then fed into the pre-trained CLIP text encoder, generating $C$ text embeddings. We use the $C$ pre-trained label text embeddings to initialize the input text tokens, which are kept constant during training to retain the powerful representation ability of the pre-trained CLIP.", + "bbox": [ + 75, + 659, + 470, + 900 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/f844af8419163d14d08c492dc4c7a2120c528b043c53002fd810720012f4c482.jpg", + "image_caption": [ + "Figure 4. The regularization loss. The proposed multi-modal token transformer is encouraged to produce text tokens to match the image embeddings of the pre-trained CLIP image encoder, enabling image-language context transfer." + ], + "image_footnote": [], + "bbox": [ + 513, + 88, + 883, + 207 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Sample-specific local contexts", + "text_level": 1, + "bbox": [ + 500, + 287, + 764, + 304 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To construct class-specific tokens tailored for each sample, we propose to exploit sample-specific local contexts to complement the global input class-specific tokens.", + "bbox": [ + 496, + 311, + 890, + 356 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Visual context. Inspired by the context-aware prompting [29], we also use the visual context to refine the text embeddings. In contrast to [29] which employs an additional cross-attention module to model the interaction between textual and visual features, the inherent self-attention blocks of the proposed transformer encoder enables the text tokens to aggregate information from patch tokens, capturing the sample-specific visual context. Intuitively, the text embeddings of simple prompts (i.e., \"a photo of [CLS]\", after being refined by the sample-specific visual context, could represent more accurate image-related text. Given that the input text tokens $\\mathbf{T}_{txt}^{in}\\in \\mathbb{R}^{C\\times D}$ are global textual representations for each label while the output text tokens $\\mathbf{T}_{txt}^{out}\\in \\mathbb{R}^{C\\times D}$ capture sample-specific representations, we can obtain the enhanced class-specific textual tokens by fusing the input and output text tokens: $\\mathbf{T}_{txt} = \\mathbf{T}_{txt}^{in} + \\lambda_{1}\\cdot \\mathbf{T}_{txt}^{out}$ . Similarly, the enhanced class-specific visual tokens are obtained by $\\mathbf{T}_{cls} = \\mathbf{T}_{cls}^{in} + \\lambda_{2}\\cdot \\mathbf{T}_{cls}^{out}$ , where $\\lambda_{1}$ and $\\lambda_{2}$ are two learnable weights.", + "bbox": [ + 496, + 357, + 892, + 643 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Image-language context. To further ensure that the output text tokens can capture meaningful sample-specific context, we propose to impose a contrastive loss on the output text tokens by leveraging the pre-trained CLIP image model. More specifically, as illustrated in Figure 4, given a batch of $B$ input image-text pairs (the input text for each image is the same, i.e., $C$ label prompts), the proposed transformer encoder outputs $B$ text tokens. These $B$ text tokens are used to compute the similarity matrix $\\mathbf{S} \\in \\mathbb{R}^{B \\times B}$ with $B$ visual embeddings by feeding the input images to the pre-trained CLIP image encoder. When the output text token and the CLIP image embedding are from the same image, they form a positive pair. Otherwise, they form a negative pair. Such pair construction is reasonable in that: (i) The CLIP image embedding of an image matches its corresponding text description which commonly contains richer information than image-level labels. Thus, even two images with the same", + "bbox": [ + 496, + 643, + 893, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "19599", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "class label generally have distinct CLIP image embeddings. (ii) Although the input text tokens are the same for each image, they are progressively refined through interactions with patch tokens at every layer of our model. This enables the output text tokens to represent specific input image content, i.e., different images have different output text tokens. Therefore, the $B$ scores in the diagonal of the similarity matrix are encouraged to be maximized and the other $B^2 - B$ similarity scores are minimized. This is implemented by computing the cross-entropy classification loss between the similarity matrix $\\mathbf{S}$ and the ground-truth labels, i.e., an identity matrix $\\mathbf{I} \\in \\mathbb{R}^{B \\times B}$ :", + "bbox": [ + 75, + 90, + 472, + 272 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {c o n t r a s t}} = \\operatorname {C r o s s E n t r o p y} (\\mathbf {S}, \\mathbf {I}). \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 282, + 468, + 299 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The benefit is two-fold: (i) encouraging the output text tokens to match the pre-trained CLIP image embeddings, allows us to better adapt the pre-trained CLIP model to our target datasets. This also implicitly transfers richer image-related language contexts to the output text tokens, which are more beneficial for guiding the dense object localization, compared to the simple label prompts; (ii) through the batch-softmax cross-entropy loss on the similarity matrix, each text token is also constrained to be discriminative from other text tokens in the same batch, thereby enhancing the sample-specific characteristic of the text tokens.", + "bbox": [ + 75, + 310, + 472, + 474 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Image-label supervised multi-modal class-top-patch correlation learning", + "text_level": 1, + "bbox": [ + 76, + 484, + 468, + 516 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Given the multi-modal class-specific tokens, i.e., $\\mathbf{T}_{txt}$ and $\\mathbf{T}_{cls}$ , the class-specific dense localization can be inferred by computing the correlation between the class-specific tokens and the patch tokens. More specifically, the output patch tokens from the proposed multi-modal token transformer encoder are first linearly projected and then transposed to 2D feature maps $\\mathbf{F} \\in \\mathbb{R}^{N \\times N \\times D}$ , where $D$ is the feature dimension. The text-to-patch correlation maps $\\mathbf{C}_{t2p} \\in \\mathbb{R}^{N \\times N \\times C}$ and the class-to-patch correlation maps $\\mathbf{C}_{c2p} \\in \\mathbb{R}^{N \\times N \\times C}$ can be computed as: $\\mathbf{C}_{t2p} = \\mathbf{FT}_{txt}^{\\top}$ and $\\mathbf{C}_{c2p} = \\mathbf{FT}_{cls}^{\\top}$ , respectively.", + "bbox": [ + 75, + 523, + 468, + 688 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In contrast to recent language-driven dense prediction works [24, 29] where pixel-level ground-truth labels are used to supervise the learning of the class-to-patch correlations, only image-level labels are available in our case. Based on the assumption that the weighted sum of a class-to-patch correlation map can be regarded as the global correlation between the class and the whole image, we relax this class-to-patch correlation problem into a class-to-image correlation problem, which can thus be formulated into a classification task. Given that the commonly used global average pooling (GAP) and global max pooling inevitably either over-estimate or under-estimate the size of the object regions when generating the global correlation scores, respectively [16], we thus adopt the Global Weighted Rank", + "bbox": [ + 75, + 689, + 470, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "ing Pooling (GwRP) method [16]. Compared to GAP which assigns the same weight to each patch for aggregation, GwRP assigns different weights according to the ranking of the correlation scores of all patches for each class:", + "bbox": [ + 496, + 90, + 892, + 151 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nG _ {c} \\left(\\mathbf {X} _ {\\star}\\right) = \\frac {1}{Z (d)} \\sum_ {j = 1} ^ {N ^ {2}} d ^ {j - 1} X _ {\\star} ^ {r _ {j}, c}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 584, + 159, + 890, + 203 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathbf{X}_{\\star} \\in \\mathbb{R}^{N^2 \\times C}$ are the flattened correlation maps; $\\mathbf{y}_{\\star} = G(\\mathbf{X}_{\\star}) \\in \\mathbb{R}^{C}$ are the aggregated class correlation scores; $r_j$ is the index of the ranking, i.e., for a class $c$ , $X_{\\star}^{r_1, c} > X_{\\star}^{r_2, c} > \\dots > X_{\\star}^{r_{N^2}, c}$ ; $Z(d) = \\sum_{j=1}^{N^2} d^{j-1}$ , $d$ is a decay parameter. Therefore, the aggregated class-to-patch and text-to-patch correlation scores can be obtained as:", + "bbox": [ + 496, + 212, + 893, + 306 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {y} _ {c 2 p} = G \\left(\\mathbf {X} _ {c 2 p}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 633, + 316, + 890, + 332 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {y} _ {t 2 p} = G \\left(\\mathbf {X} _ {t 2 p}\\right). \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 635, + 335, + 890, + 351 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "These class scores can then be supervised by the groundtruth image-level labels $\\mathbf{y}$ via the classification loss:", + "bbox": [ + 496, + 358, + 890, + 388 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {c l a s s - p a t c h}} = M L S M \\left(\\mathbf {y} _ {c 2 p}, \\mathbf {y}\\right), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 576, + 397, + 890, + 412 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {t e x t - p a t c h}} = M L S M \\left(\\mathbf {y} _ {t 2 p}, \\mathbf {y}\\right). \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 583, + 417, + 890, + 431 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The total objective loss function for training the proposed multi-modal token transformer is as follows:", + "bbox": [ + 496, + 440, + 890, + 469 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {t o t a l}} = \\mathcal {L} _ {\\text {c l s}} + \\mathcal {L} _ {\\text {c o n t r a s t}} + \\mathcal {L} _ {\\text {t e x t - p a t c h}} + \\mathcal {L} _ {\\text {c l a s s - p a t c h}} \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 506, + 478, + 890, + 494 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Class-specific dense localization inference", + "text_level": 1, + "bbox": [ + 500, + 501, + 854, + 516 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Once the training of the proposed multi-modal token transformer is completed, the correlations between two modalities of class-specific tokens and the patch tokens are computed to produce two maps, i.e., text-to-patch correlation maps $\\mathbf{C}_{t2p}$ and class-to-patch correlation maps $\\mathbf{C}_{c2p}$ , respectively. We further combine these two maps via an element-wise sum to obtain the multi-modal class-specific dense localization maps $\\mathbf{C}_{mm} \\in \\mathbb{R}^{N \\times N \\times C}$ :", + "bbox": [ + 496, + 523, + 890, + 645 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {C} _ {m m} = \\mathbf {C} _ {t 2 p} + \\mathbf {C} _ {c 2 p}. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 617, + 652, + 890, + 670 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Moreover, similar to MCTformer [40], the multi-class token based transformer can produce class-specific attention maps $\\mathbf{A}_{c2p}$ and a patch-level pairwise affinity map $\\mathbf{A}_{p2p}$ from the transformer self-attention maps. Given their different localization mechanisms, the transformer self-attention maps can well complement the proposed multi-modal class-specific localization maps. We thus perform an element-wise multiplication (o) on these two maps to produce the fused maps $\\hat{\\mathbf{M}}\\in \\mathbb{R}^{N\\times N\\times C}$ , which are further refined by the patch-level pairwise affinity map:", + "bbox": [ + 496, + 676, + 890, + 829 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\mathbf {M}} = \\mathbf {C} _ {m m} \\circ \\mathbf {A} _ {c 2 p}, \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 835, + 890, + 852 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {M} (i, j, c) = \\sum_ {k = 1} ^ {N} \\sum_ {l = 1} ^ {N} \\mathbf {A} _ {p 2 p} (i, j, k, l) \\cdot \\hat {\\mathbf {M}} (k, l, c), \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 519, + 854, + 890, + 897 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "19600", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathbf{M} \\in \\mathbb{R}^{N \\times N \\times C}$ are the refined fused maps, which are then up-sampled to the original size of the input image before being normalized via the min-max normalization, to generate the final dense object localization maps.", + "bbox": [ + 75, + 89, + 470, + 151 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 167, + 209, + 183 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Experimental settings", + "text_level": 1, + "bbox": [ + 76, + 191, + 279, + 208 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets. We evaluated the proposed method on three datasets including two multi-label datasets, i.e., PASCAL VOC 2012 [9] and MS COCO 2014 [26], and one single-label dataset, i.e., OpenImages [5]. PASCAL VOC 2012 has 20 foreground object classes and one background class. It is split into three subsets, i.e., training (train), validation (val) and test sets including 1,464, 1,449, and 1,456 images, respectively. Following the common practice [18, 19], the training set was augmented to 10,582 images by adding the data from [13]. MS COCO 2014 has 80 foreground object classes and one background class. It has around 80K training images and 40K images for evaluation. OpenImages has 37,319 images in total and 100 object classes. It is split into a training set with 29,819 images, a validation set with 2,500 images and a test set with 5,000 images.", + "bbox": [ + 75, + 215, + 468, + 441 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation metrics. For the multi-label datasets, we followed the common practice [18, 20, 21] to use the mean Intersection-over-Union (mIoU) to evaluate the multi-label dense localization maps on the train set, and used mIoU to evaluate WSSS results on the val and test sets. Results on the PASCAL VOC test set were obtained from the online official evaluation server. For the single-label dataset, we focused on the pixel-level evaluation and followed the prior works [5, 52] which used the peak Intersection-over-Union (pIoU) and the pixel average precision (PxAP) to evaluate the single-label dense localization maps on the test set.", + "bbox": [ + 75, + 441, + 468, + 608 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation details. We built the proposed transformer using ViT-base as backbone. We used the Adam optimizer with the initial learning rate of 5e-4 and a batch size of 32. We trained our network for 60 epochs on PASCAL VOC and MS COCO and 10 epochs on OpenImages. For the VL model, we used the publicly available pre-trained CLIP model (ViT-B/16). To generate pseudo masks for WSSS, we followed prior works [20, 21, 32, 44] to use IR-Net [1] to post-process our dense localization maps. For semantic segmentation, we use ResNet38-based Deeplab-V1. More details can be found in the Supplementary Materials.", + "bbox": [ + 75, + 609, + 468, + 776 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Comparison with State-of-the-art of WSDOL", + "text_level": 1, + "bbox": [ + 76, + 786, + 460, + 801 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Multi-label dense localization. Table 1 reports the evaluation results of the generated multi-label dense localization maps, which are commonly used as seeds to generate pseudo masks in WSSS. The proposed method achieved mIoUs of $66.3\\%$ and $40.9\\%$ on the train sets of PASCAL VOC 2012 and MS COCO 2014, respectively, outperform-", + "bbox": [ + 75, + 809, + 468, + 902 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/4de67478fd63b1c93252b73a7d5139a3b321870c0d446d76472ff2e76347d6a3.jpg", + "table_caption": [ + "Table 1. Evaluation of the generated multi-class multi-label dense localization maps in terms of mIoU (\\%) on the PASCAL VOC 2012 and MS COCO 2014 train sets. Cls.: Classification. $\\dagger$ denotes the reproduced result by [18], $\\ddagger$ denotes the reproduced result by [44], and $\\star$ denotes our reproduced result." + ], + "table_footnote": [], + "table_body": "
MethodCls. BackboneVOCCOCO
CAM (CVPR16) [48]ResNet5048.833.5†
SEAM (CVPR20) [34]ResNet3855.425.1‡
RIB (NeurIPS21) [18]ResNet5056.536.5
AdvCAM (CVPR21) [19]ResNet3855.637.2
CLIMS (CVPR22) [37]ResNet5056.6-
SIPE (CVPR22) [4]ResNet5058.6-
W-OoD (CVPR22) [21]ResNet5059.1-
Du et al. (CVPR22) [8]ResNet3861.5-
TS-CAM (ICCV21) [10]ViT-small41.3-
MCTformer (CVPR22) [40]ViT-small61.7-
MCTformer (CVPR22) [40]ViT-base62.3*-
OursViT-base66.340.9
", + "bbox": [ + 511, + 146, + 880, + 325 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/e54532d1227b563fd788f96fe5f7cabeaf0e5e574043595dbad2ca67e0994184.jpg", + "table_caption": [ + "Table 2. Evaluation of multi-class single-label dense localization on the OpenImages test set." + ], + "table_footnote": [], + "table_body": "
MethodCls. backbonepIoUPxAP
CAM (CVPR16) [48]ResNet5043.058.2
HAS (ICCV17) [31]ResNet5041.955.1
ACoL (CVPR18) [47]ResNet5041.756.4
SPG (ECCV18) [46]ResNet5041.855.8
ADL (CVPR19) [6]ResNet5042.155.0
CutMix (ICCV19) [43]ResNet5042.757.6
PAS (ECCV20) [2]ResNet50-60.9
IVR (ICCV21) [15]ResNet50-58.9
Zhu et al. (CVPR22) [52]ResNet5049.765.4
CREAM (CVPR22) [38]ResNet50-64.7
Zhu et al. (ECCV22) [51]ResNet5052.267.7
OursViT-base57.673.3
", + "bbox": [ + 511, + 358, + 880, + 551 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "ing the state-of-the-art methods by significant margins.", + "bbox": [ + 498, + 555, + 861, + 570 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Single-label dense localization. We also evaluated the effectiveness of the proposed method for single-label dense object localization. We used the recently proposed OpenImages dataset [5], which has more challenging background context than the commonly used ones for weakly supervised object localization. As shown in Table 2, the proposed method attained a pIoU of $57.6\\%$ and a PxAP of $73.3\\%$ , achieving better results than the state-of-the-art methods.", + "bbox": [ + 496, + 571, + 890, + 691 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Comparison with State-of-the-art of WSSS", + "text_level": 1, + "bbox": [ + 498, + 707, + 864, + 723 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "PASCAL VOC 2012. As shown in Table 3, the proposed method achieved mIoUs of $72.2\\%$ and $72.2\\%$ on the val and test sets of PASCAL VOC 2012, respectively, outperforming other WSSS methods only using image-level labels.", + "bbox": [ + 496, + 732, + 890, + 792 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "MS COCO 2014. Table 4 also shows that, on a more challenging dataset, i.e., MS COCO 2014, the proposed method attained the best mIoU of $45.9\\%$ , which is significantly better than those relying on saliency maps, achieving the new state-of-the-art result. These results clearly demonstrate the effectiveness and the good generalization ability of the proposed method for WSSS.", + "bbox": [ + 496, + 794, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "19601", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/3b396d390bdd32cb9e9a6547bd8b7d45c03173f31139408970a3afba3f7e95e6.jpg", + "table_caption": [ + "Table 3. Performance comparison of the state-of-the-art WSSS methods on the PASCAL VOC 2012 val and test sets. Seg.: DeepLab version. Sup.: supervision; I: image-level ground-truth labels; S: off-the-shelf saliency maps; L: pre-trained VL model." + ], + "table_footnote": [], + "table_body": "
MethodBackboneSeg.Sup.ValTest
EDAM (CVPR21) [36]ResNet101V2I+S70.970.6
EPS (CVPR21) [23]ResNet101V1I+S71.071.8
Yao et al. (CVPR21) [41]ResNet101V2I+S68.368.5
AuxSegNet (ICCV21) [39]ResNet38V1I+S69.068.6
Li et al. (CVPR22) [25]ResNet101V2I+S72.072.9
Du et al. (CVPR22) [8]ResNet101V2I+S72.673.6
RCA (CVPR22) [50]ResNet38V2I+S72.272.8
L2G (CVPR22) [14]ResNet38V1I+S72.073.0
AdvCAM (CVPR21) [19]ResNet101V2I68.168.0
ECS-Net (ICCV21) [33]ResNet38V1I66.667.6
Kweon et al. (ICCV21) [17]ResNet38V1I68.468.2
CDA (ICCV21) [32]ResNet38V1I66.166.8
Zhang et al. (ICCV21) [45]ResNet38V1I67.868.5
MCTformer (CVPR22) [40]ResNet38V1I71.971.6
AMN (CVPR22) [22]ResNet101V2I70.770.6
W-OoD (CVPR22) [21]ResNet38V1I70.770.1
SIPE (CVPR22) [3]ResNet38V1I68.269.7
Yoon et al. (ECCV22) [42]ResNet38V1I70.971.7
CLIMS (CVPR22) [37]ResNet101V2I+L69.368.7
OursResNet38V1I+L72.272.2
", + "bbox": [ + 80, + 143, + 468, + 416 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/b26ec6088621aa6ac0a618301e50a2b0d003f56d38f61805e92e0a3f7171a0bd.jpg", + "table_caption": [ + "Table 4. Performance comparison of state-of-the-art WSSS methods on the MS COCO 2014 val set in terms of mIoU (\\%)." + ], + "table_footnote": [], + "table_body": "
MethodBackboneSeg.Sup.Val
EPS (CVPR21) [23]VGG16V2I+S35.7
RCA (CVPR22) [50]VGG16V2I+S36.8
AuxSegNet (ICCV21) [39]ResNet38V1I+S33.9
L2G (CVPR22) [14]ResNet101V2I+S44.2
Kweon et al. (ICCV21) [17]ResNet38V1I36.4
CDA (ICCV21) [32]ResNet38V1I33.2
AdvCAM (CVPR21) [19]ResNet101V2I44.4
MCTformer (CVPR22) [40]ResNet38V1I42.0
Li et al. (CVPR22) [25]ResNet101V2I44.7
AMN (CVPR22) [22]ResNet101V2I44.7
SIPE (CVPR22) [3]ResNet38V1I43.6
Yoon et al. (ECCV22) [42]ResNet38V1I44.8
OursResNet38V1I+L45.9
", + "bbox": [ + 99, + 449, + 450, + 637 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation studies", + "text_level": 1, + "bbox": [ + 76, + 648, + 236, + 662 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effect of learning multi-modal class-specific tokens. As shown in Table 5, without text tokens, only relying on the multi-class token transformer attention results in a dense localization mIoU of $57.5\\%$ ; By learning the multi-class tokens to correlate patch tokens using image-level supervision with GAP, a significant gain of $4.6\\%$ was achieved in mIoU. GwRP outperforms GAP, improving the dense localization result to $62.7\\%$ . By additionally learning class-specific textual tokens, the resulting multi-modal correlation maps driven by the global/input multi-modal class-specific tokens, attained an better mIoU of $64.1\\%$ , compared to that of the multi-modal correlation maps driven by the local/output multi-modal class-specific tokens. The fusion of the global and local multi-modal class-specific tokens leads to the best dense localization maps with an mIoU of $66.3\\%$ . These re", + "bbox": [ + 75, + 672, + 470, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/c2dcd91152529e87d26bf2ec03c53b9bc692598aecdf7f98913a1b1eb8d90d7c.jpg", + "table_caption": [ + "Table 5. Evaluation of the class-specific dense localization maps generated by learning correlations (cor.) between class-specific tokens and patch tokens on the PASCAL VOC 2012 train set." + ], + "table_footnote": [], + "table_body": "
ConfigurationPoolingmIoU
Multi-class token attention-57.5
+ class-to-patch correlationGAP62.1
+ class-to-patch correlation (C2P cor.)GWRP62.7
+ Multi-modal (C2P + text-to-patch) cor. (global)GWRP64.1
+ Multi-modal cor. (local)GWRP63.3
+ Multi-modal cor. (global + local)GWRP66.3
", + "bbox": [ + 504, + 130, + 890, + 229 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/96005767f65f4a5d58ce04b28739dece54a31ec6747b61de2da77f5fdaa2b361.jpg", + "table_caption": [ + "Table 6. Evaluation of the class-specific dense localization by different regularization methods for sample-specific local context learning on the PASCAL VOC 2012 train set. CE: cross-entropy." + ], + "table_footnote": [], + "table_body": "
Visual contextImage-language contextmIoU
Prior knowledgeRegularization loss
X--64.1
✓--64.8
✓CLIP caption embed.L163.7
✓CLIP caption embed.Batch-contrast CE65.1
✓CLIP image embed.Batch-contrast CE66.3
", + "bbox": [ + 504, + 282, + 890, + 393 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "sults demonstrate the effectiveness of the proposed method of learning multi-modal class-specific tokens in producing accurate class-specific dense localization maps.", + "bbox": [ + 496, + 398, + 890, + 445 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Figure 5 visualizes the generated dense object localization maps in different configurations. The multi-class token transformer attention only localizes partial object regions and includes many noises, such as the dog and the background in the third and second rows of Figure 5 (b), respectively. Learning the multi-class tokens to correlate patch tokens leads to more complete object localization maps, such as the sofa in the third row. This also results in several falsely activated regions near the object, such as the chair in the second rows of Figure 5 (c). Adding class-specific textual tokens leads to improved localization maps (Figure 5 (d)) with expanded true object regions and reduced falsely activated backgrounds. Further incorporating sample-specific context produces the best localization maps (Figure 5 (e)) which are close to the ground-truth. This demonstrates the advantage of the proposed method in generating dense localization maps for objects of different scales in various challenging scenarios.", + "bbox": [ + 496, + 446, + 890, + 718 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effect of sample-specific context learning. As shown in Table 6, without sample-specific context learning, the learned multi-modal class-specific tokens result in the class-specific localization maps with an mIoU of $64.1\\%$ . Incorporating the visual context information aggregated by the output text tokens leads to an improved mIoU of $64.8\\%$ . To further learn image-language context to enhance the text token learning, we investigated two regularization methods: (i) Leveraging the pre-trained CLIP text embeddings of image captions. More specifically, we used a pre-trained image captioning model, ClipCap [27], to generate captions for each image. By minimizing the distance between the text", + "bbox": [ + 496, + 719, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "19602", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7dc582e77cf79972370724017bc4daf788abe7f8e94fa2ec3bf4e58f289a3266.jpg", + "image_caption": [ + "(a)", + "Figure 5. Visualization of the multi-label dense object localization results using different configurations of the proposed method on the PASCAL VOC 2012 train set. (a) Input; (b) MCTAttn (Multi-class token attention); (c) MCTAttn + C2P-CMap (class-to-patch correlation maps); (d) MCTAttn + MM-CMap (multi-modal correlation maps, i.e., class-to-patch correlation maps + text-to-patch correlation maps); (e) MCTAttn + MM-CMap + sample-specific context; (f) Ground-truth." + ], + "image_footnote": [], + "bbox": [ + 99, + 89, + 220, + 289 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/5613f827da8a76697a5eab736a7420005af8f6422da8bc0f5c86637bb40e51bf.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 227, + 88, + 352, + 287 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4aff8e6dad1d49d4b29dc0d914515618aa4d15286a5d6c9eb0f13112b78d533d.jpg", + "image_caption": [ + "(c)" + ], + "image_footnote": [], + "bbox": [ + 357, + 88, + 480, + 287 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/319f09ad4562a4cfd5fa60701bfab4f0c114b6836e243b3f6605874c43fd4da4.jpg", + "image_caption": [ + "(d)" + ], + "image_footnote": [], + "bbox": [ + 486, + 88, + 612, + 287 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d216250e1668e2152124c23142130a9e3311e2b5a281fea5656dd848b1d4bddd.jpg", + "image_caption": [ + "(e)" + ], + "image_footnote": [], + "bbox": [ + 616, + 88, + 740, + 287 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/07fdeef3823b929ca431cf15abadb7f9e966a880c0e01712a7152a82798438a0.jpg", + "image_caption": [ + "(f)" + ], + "image_footnote": [], + "bbox": [ + 746, + 88, + 870, + 287 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/784ffbf92435f1065035efdda4e599e736358789fe75d5e37a63c6d2efbaf0c5.jpg", + "table_caption": [ + "Table 7. Evaluation of the class-specific dense localization maps by using different prompting methods for constructing class-specific textual tokens on the PASCAL VOC 2012 train set." + ], + "table_footnote": [], + "table_body": "
PromptmIoU
a photo of [class]66.3
Prompt ensembling65.5
CoOp (learning-based prompts) [49]66.0
", + "bbox": [ + 138, + 409, + 410, + 474 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "tokens and the pre-trained CLIP caption embeddings via L1 loss, the localization performance drops by $1.1\\%$ . Using the batch-softmax cross-entropy loss yields a slightly improved localization mIoU of $65.1\\%$ . L1 loss on the features allows to transfer all information of the generated image captions including useful and interfering contexts that could reduce the discriminative ability of the class-specific textual tokens, thus degrading the localization performance. In contrast, the batch-softmax cross-entropy loss on the feature similarities explicitly separates each text token from other text tokens in a same batch, which facilitates the sample-specific token learning for better localization. (ii) Leveraging the pre-trained CLIP image embeddings. By applying the batch-softmax cross-entropy loss on the similarity matrix of our text tokens and the pre-trained CLIP image embeddings, the class-specific localization attains a performance gain of $1.5\\%$ with the best mIoU of $66.3\\%$ . Compared to learning from the generated image captions, the batch-contrastive image-text matching loss enables the text tokens to better align with the visual features, thus leading to better dense localization results.", + "bbox": [ + 75, + 476, + 468, + 792 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effects of different prompts. We used different prompting methods to construct the class-specific textual tokens and evaluated their effects in the resulting class-specific dense localization maps. More specifically, we investigated three types of prompting methods including (i) the most commonly used prompt, i.e., \"a photo of [class]\"; (ii) Prompt ensembling, which ensembles 8 best prompts over the em", + "bbox": [ + 75, + 795, + 470, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "bedding space via averaging as suggested in [28]; (iii) A learning-based prompting method, CoOp [49]. As shown in Table 7, these three prompting methods lead to comparable dense localization results. Among them, the general prompt template \"a photo of [class]\" produces the best localization mIoU. We speculate this is due to the general prompt template better preserving the class-discriminative language priors from the pre-trained CLIP model, compared to ensembling several prompts or learning extra parameters.", + "bbox": [ + 496, + 368, + 893, + 506 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 531, + 619, + 547 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We proposed a new weakly supervised dense object localization method by explicitly constructing multi-modal class representations via a transformer-based framework. The proposed transformer includes class-specific visual and textual tokens by learning visual information from the target image data and exploiting language information from the pre-trained CLIP model, respectively. These diverse supervisions enable the multi-modal class-specific tokens to provide complementary information for more discriminative dense localization. By further incorporating the sample-specific contexts from visual context and image-language context, more adaptive multi-modal class-specific tokens can be learned to facilitate better dense localization. Our superior WSDOL results on two multi-label datasets, (i.e., PASCAL VOC and MS COCO) and one single-label dataset, (i.e., OpenImages), demonstrate the effectiveness of the proposed method. We also report state-of-the-art WSSS results on PASCAL VOC and MS COCO.", + "bbox": [ + 496, + 555, + 890, + 825 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgment. This research is supported in part by Australian Research Council Grant DP210101682, DP210102674, IH180100002, the Early Career Scheme of the Research Grants Council (RGC) of the Hong Kong SAR under grant No. 26202321 and HKUST Startup Fund No. R9253.", + "bbox": [ + 496, + 830, + 893, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "19603", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Jiwoon Ahn, Sunghyun Cho, and Suha Kwak. Weakly supervised learning of instance segmentation with inter-pixel relations. In CVPR, 2019. 6", + "[2] Wonho Bae, Junhyug Noh, and Gunhee Kim. Rethinking class activation mapping for weakly supervised object localization. In ECCV, 2020. 6", + "[3] Qi Chen, Lingxiao Yang, Jian-Huang Lai, and Xiaohua Xie. Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation. In CVPR, 2022. 2, 7", + "[4] Zhaozheng Chen, Tan Wang, Xiongwei Wu, Xian-Sheng Hua, Hanwang Zhang, and Qianru Sun. Class re-activation maps for weakly-supervised semantic segmentation. In CVPR, 2022. 2, 6", + "[5] Junsuk Choe, Seong Joon Oh, Seungho Lee, Sanghyuk Chun, Zeynep Akata, and Hyunjung Shim. Evaluating weakly supervised object localization methods right. In CVPR, 2020. 1, 6", + "[6] Junsuk Choe and Hyunjung Shim. Attention-based dropout layer for weakly supervised object localization. In CVPR, 2019. 6", + "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 1", + "[8] Ye Du, Zehua Fu, Qingjie Liu, and Yunhong Wang. Weakly supervised semantic segmentation by pixel-to-prototype contrast. In CVPR, 2022. 2, 6, 7", + "[9] Mark Everingham and John Winn. The pascal visual object classes challenge 2012 (voc2012) development kit. Pattern Anal. Stat. Model. Comput. Learn., Tech. Rep, 2007:1-45, 2012. 6", + "[10] Wei Gao, Fang Wan, Xingjia Pan, Zhiliang Peng, Qi Tian, Zhenjun Han, Bolei Zhou, and Qixiang Ye. Ts-cam: Token semantic coupled attention map for weakly supervised object localization. In ICCV, 2021. 2, 6", + "[11] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. In ICLR, 2022. 3", + "[12] Saurav Gupta, Sourav Lakhotia, Abhay Rawat, and Rahul Tallamraju. Vitol: Vision transformer for weakly supervised object localization. In CVPR, 2022. 2", + "[13] Bharath Hariharan, Pablo Arbeláez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik. Semantic contours from inverse detectors. In ICCV, 2011. 6", + "[14] Peng-Tao Jiang, Yuqi Yang, Qibin Hou, and Yunchao Wei. L2g: A simple local-to-global knowledge transfer framework for weakly supervised semantic segmentation. In CVPR, 2022. 2, 7", + "[15] Jeesoo Kim, Junsuk Choe, Sangdoo Yun, and Nojun Kwak. Normalization matters in weakly supervised object localization. In ICCV, 2021. 6", + "[16] Alexander Kolesnikov and Christoph H Lampert. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In ECCV, 2016. 5" + ], + "bbox": [ + 78, + 114, + 470, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[17] Hyeokjun Kweon, Sung-Hoon Yoon, Hyeonseong Kim, Daehee Park, and Kuk-Jin Yoon. Unlocking the potential of ordinary classifier: Class-specific adversarial erasing framework for weakly supervised semantic segmentation. In ICCV, 2021. 7", + "[18] Jungbeom Lee, Jooyoung Choi, Jisoo Mok, and Sungroh Yoon. Reducing information bottleneck for weakly supervised semantic segmentation. NeurIPS, 2021. 6", + "[19] Jungbeom Lee, Eunji Kim, Jisoo Mok, and Sungroh Yoon. Anti-adversarily manipulated attributions for weakly supervised semantic segmentation and object localization. PAMI, 2022. 6, 7", + "[20] Jungbeom Lee, Eunji Kim, and Sungroh Yoon. Antiadversarily manipulated attributions for weakly and semi-supervised semantic segmentation. In CVPR, 2021. 2, 6", + "[21] Jungbeom Lee, Seong Joon Oh, Sangdoo Yun, Junsuk Choe, Eunji Kim, and Sungroh Yoon. Weakly supervised semantic segmentation using out-of-distribution data. In CVPR, 2022. 1, 6, 7", + "[22] Minhyun Lee, Dongseob Kim, and Hyunjung Shim. Threshold matters in wsss: Manipulating the activation for the robust and accurate segmentation model against thresholds. In CVPR, 2022. 7", + "[23] Seungho Lee, Minhyun Lee, Jongwuk Lee, and Hyunjung Shim. Railroad is not a train: Saliency as pseudo-pixel supervision for weakly supervised semantic segmentation. In CVPR, 2021. 7", + "[24] Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and René Ranftl. Language-driven semantic segmentation. In ICLR, 2022. 3, 5", + "[25] Jing Li, Junsong Fan, and Zhaoxiang Zhang. Towards noiseless object contours for weakly supervised semantic segmentation. In CVPR, 2022. 7", + "[26] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 6", + "[27] Ron Mokady, Amir Hertz, and Amit H Bermano. Clipcap: Clip prefix for image captioning. arXiv preprint arXiv:2111.09734, 2021. 7", + "[28] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 8", + "[29] Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In CVPR, 2022. 3, 4, 5", + "[30] David Rozenberszki, Or Litany, and Angela Dai. Language-grounded indoor 3d semantic segmentation in the wild. In ECCV, 2022. 3", + "[31] Krishna Kumar Singh and Yong Jae Lee. Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization. In ICCV, 2017. 6", + "[32] Yukun Su, Ruizhou Sun, Guosheng Lin, and Qingyao Wu. Context decoupling augmentation for weakly supervised semantic segmentation. In ICCV, 2021. 2, 6, 7" + ], + "bbox": [ + 501, + 92, + 890, + 901 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "19604", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[33] Kunyang Sun, Haoqing Shi, Zhengming Zhang, and Yongming Huang. ECS-net: Improving weakly supervised semantic segmentation by using connections between class activation maps. In ICCV, 2021. 7", + "[34] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In CVPR, 2020. 6", + "[35] Yunchao Wei, Huaxin Xiao, Honghui Shi, Zequn Jie, Jiashi Feng, and Thomas S Huang. Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation. In CVPR, 2018. 2", + "[36] Tong Wu, Junshi Huang, Guangyu Gao, Xiaoming Wei, Xiaolin Wei, Xuan Luo, and Chi Harold Liu. Embedded discriminative attention mechanism for weakly supervised semantic segmentation. In CVPR, 2021. 7", + "[37] Jinheng Xie, Xianxu Hou, Kai Ye, and Linlin Shen. Clims: Cross language image matching for weakly supervised semantic segmentation. In CVPR, 2022. 3, 6, 7", + "[38] Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng, Rui-Wei Zhao, Tao Zhang, Xuequan Lu, and Shang Gao. Cream: Weakly supervised object localization via class re-activation mapping. In CVPR, 2022. 6", + "[39] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, Ferdous Sohel, and Dan Xu. Leveraging auxiliary tasks with affinity learning for weakly supervised semantic segmentation. In ICCV, 2021. 7", + "[40] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, and Dan Xu. Multi-class token transformer for weakly supervised semantic segmentation. In CVPR, 2022. 1, 2, 3, 4, 5, 6, 7", + "[41] Yazhou Yao, Tao Chen, Guo-Sen Xie, Chuanyi Zhang, Fumin Shen, Qi Wu, Zhenmin Tang, and Jian Zhang. Nonsalient region object mining for weakly supervised semantic segmentation. In CVPR, 2021. 2, 7", + "[42] Sung-Hoon Yoon, Hyeokjun Kweon, Jegyeong Cho, Shinjeong Kim, and Kuk-Jin Yoon. Adversarial erasing framework via triplet with gated pyramid pooling layer for weakly supervised semantic segmentation. In ECCV, 2022. 7", + "[43] Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. Cutmix: Regularization strategy to train strong classifiers with localizable features. In ICCV, 2019. 6", + "[44] Dong Zhang, Hanwang Zhang, Jinhui Tang, Xiansheng Hua, and Qianru Sun. Causal intervention for weakly-supervised semantic segmentation. In NeurIPS, 2020. 6", + "[45] Fei Zhang, Chaochen Gu, Chenyue Zhang, and Yuchao Dai. Complementary patch for weakly supervised semantic segmentation. In ICCV, 2021. 7", + "[46] Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. Self-attention generative adversarial networks. In ICML, 2019. 6", + "[47] Xiaolin Zhang, Yunchao Wei, Jiashi Feng, Yi Yang, and Thomas S Huang. Adversarial complementary learning for weakly supervised object localization. In CVPR, 2018. 6", + "[48] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In CVPR, 2016. 1, 6" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[49] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9):2337-2348, 2022. 8", + "[50] Tianfei Zhou, Meijie Zhang, Fang Zhao, and Jianwu Li. Regional semantic contrast and aggregation for weakly supervised semantic segmentation. In CVPR, 2022. 7", + "[51] Lei Zhu, Qian Chen, Lujia Jin, Yunfei You, and Yanye Lu. Bagging regional classification activation maps for weakly supervised object localization. In ECCV, 2022. 6", + "[52] Lei Zhu, Qi She, Qian Chen, Yunfei You, Boyu Wang, and Yanye Lu. Weakly supervised object localization as domain adaption. In CVPR, 2022. 6" + ], + "bbox": [ + 501, + 92, + 890, + 275 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "19605", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/56c83ae8-f628-4d2b-be75-d685154654de_model.json b/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/56c83ae8-f628-4d2b-be75-d685154654de_model.json new file mode 100644 index 0000000000000000000000000000000000000000..601a084044c8c9a8ff6ecc43da4bc6f6f9744c74 --- /dev/null +++ b/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/56c83ae8-f628-4d2b-be75-d685154654de_model.json @@ -0,0 +1,2123 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.0, + 0.812, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.11, + 0.13, + 0.861, + 0.177 + ], + "angle": 0, + "content": "Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization" + }, + { + "type": "text", + "bbox": [ + 0.151, + 0.203, + 0.818, + 0.258 + ], + "angle": 0, + "content": "Lian Xu\\(^{1}\\), Wanli Ouyang\\(^{2}\\), Mohammed Bennamoun\\(^{1}\\), Farid Boussaid\\(^{1}\\), and Dan Xu\\(^{3}\\) \n\\(^{1}\\)The University of Western Australia \\(^{2}\\)Shanghai AI Laboratory \n\\(^{3}\\)Hong Kong University of Science and Technology" + }, + { + "type": "text", + "bbox": [ + 0.246, + 0.26, + 0.728, + 0.275 + ], + "angle": 0, + "content": "{lian.xu,mohammed.bennamoun,farid.boussaid}@uwa.edu.au," + }, + { + "type": "text", + "bbox": [ + 0.286, + 0.278, + 0.678, + 0.292 + ], + "angle": 0, + "content": "wanli.ouyang@sydney.edu.au, danxu@cse.ust.hk" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.327, + 0.314, + 0.344 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.36, + 0.473, + 0.766 + ], + "angle": 0, + "content": "Weakly supervised dense object localization (WSDOL) relies generally on Class Activation Mapping (CAM), which exploits the correlation between the class weights of the image classifier and the pixel-level features. Due to the limited ability to address intra-class variations, the image classifier cannot properly associate the pixel features, leading to inaccurate dense localization maps. In this paper, we propose to explicitly construct multi-modal class representations by leveraging the Contrastive Language-Image Pretraining (CLIP), to guide dense localization. More specifically, we propose a unified transformer framework to learn two-modalities of class-specific tokens, i.e., class-specific visual and textual tokens. The former captures semantics from the target visual data while the latter exploits the class-related language priors from CLIP, providing complementary information to better perceive the intra-class diversities. In addition, we propose to enrich the multi-modal class-specific tokens with sample-specific contexts comprising visual context and image-language context. This enables more adaptive class representation learning, which further facilitates dense localization. Extensive experiments show the superiority of the proposed method for WSDOL on two multi-label datasets, i.e., PASCAL VOC and MS COCO, and one single-label dataset, i.e., OpenImages. Our dense localization maps also lead to the state-of-the-art weakly supervised semantic segmentation (WSSS) results on PASCAL VOC and MS COCO." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.787, + 0.21, + 0.803 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.813, + 0.47, + 0.875 + ], + "angle": 0, + "content": "Fully supervised dense prediction tasks have achieved great success, which however comes at the cost of expensive pixel-level annotations. To address this issue, recent works have investigated the use of weak labels, such as image-" + }, + { + "type": "image", + "bbox": [ + 0.522, + 0.325, + 0.872, + 0.492 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.501, + 0.892, + 0.544 + ], + "angle": 0, + "content": "Figure 1. (a) CAM exploits the correlation between the image classifier and the pixel features. (b) We propose to construct multimodal class-specific tokens to guide dense object localization." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.551, + 0.892, + 0.656 + ], + "angle": 0, + "content": "level labels, to generate dense object localization maps as pseudo labels for those tasks. For the weakly supervised object localization (WSOL) task, most methods evaluate localization results on the bounding-box level and a few recent methods [5] evaluate on the pixel level. We use WSDOL to focus on the pixel-level evaluation, which is critical for downstream dense prediction tasks such as WSSS." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Previous works have exploited Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) [7] for WSDOL with image-level labels [21, 40]. These methods have generally relied on Class Activation Mapping (CAM) [48], which generates class-specific localization maps by computing the correlation between the class-specific weight vectors of the image classifier and every pixel feature vector. However, image classifiers generally have a limited ability to address the intra-class variation, let alone at the pixel level. This thus leads to inaccurate dense localization results. In the conventional fully supervised learning paradigm, the image classification model aims to convert images to numeric labels, ignoring the context of the labels. Hence, it tends to learn the pattern that maximizes the inter-class differences but disregards the intra-class diversities. This largely restricts the model's ability of" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.372, + 0.9 + ], + "angle": 0, + "content": "1https://github.com/xulianuwa/MMCST" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "19596" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.078, + 0.092, + 0.293, + 0.106 + ], + "angle": 0, + "content": "understanding semantic objects." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.107, + 0.47, + 0.272 + ], + "angle": 0, + "content": "Recently, Vision-Language (VL) models have attracted much attention. In particular, CLIP, a representative VL model, pre-trained on 400 million image-text pairs that are readily available publicly, has been successfully applied to a number of downstream tasks, due to its strong generalization ability. CLIP introduces a contrastive representation learning method that constrains an image to match its related text while dis-matching the remaining texts from the same batch, in a multi-modal embedding space. This enables the model to perceive the differences across images, thus facilitating it to better discriminate intra-class samples." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.273, + 0.471, + 0.499 + ], + "angle": 0, + "content": "Motivated by these observations, we propose to leverage the strong representations of visual concepts encoded by the pre-trained CLIP language model to guide the dense object localization. More specifically, we extract the class-related text embeddings by feeding the label prompts to the pre-trained CLIP language model. As shown in Figure 1, we propose a unified transformer framework which includes multi-modal class-specific tokens, i.e., class-specific visual tokens and class-specific textual tokens. The class-specific visual tokens aim to capture visual representations from the target image dataset, while the class-specific textual tokens take the rich language semantics from the CLIP label text embeddings. These two modalities of class-specific tokens, with complementary information, are jointly used to correlate pixel features, contributing to better dense localization." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.499, + 0.471, + 0.831 + ], + "angle": 0, + "content": "In order to construct more adaptive class representations, which can better associate the sample-specific local features for dense localization, we propose to enhance the global multi-modal class-specific tokens with sample-specific contextual information. To this end, we introduce two designs: (i) at the feature level, we use the sample-specific visual context to enhance both the class-specific visual and textual tokens. This is achieved by combining these global tokens with their output local counterparts which aggregate the patch tokens of the image through the self-attention layers; (ii) at the loss level, we introduce a regularization contrastive loss to encourage the output text tokens to match the CLIP image embeddings. This allows the CLIP model to be better adapted to our target datasets. Moreover, due to its image-language matching pre-training objective, the CLIP image encoder is learned to extract the image embeddings that match the CLIP text embeddings of their corresponding image captions. We thus argue that through this contrastive loss, the rich image-related language context from the CLIP could be implicitly transferred to the text tokens, which are more beneficial for guiding the dense object localization, compared to the simple label prompts." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.832, + 0.462, + 0.846 + ], + "angle": 0, + "content": "In summary, the contribution of this work is three-fold:" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.856, + 0.47, + 0.9 + ], + "angle": 0, + "content": "- We propose a new WSDOL method by explicitly constructing multi-modal class representations in a unified transformer framework." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.092, + 0.892, + 0.166 + ], + "angle": 0, + "content": "- The proposed transformer includes class-specific visual tokens and class-specific textual tokens, which are learned from different data modalities with diverse supervisions, thus providing complementary information for more discriminative dense localization." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.176, + 0.892, + 0.266 + ], + "angle": 0, + "content": "- We propose to enhance the multi-modal global class representations by using sample-specific visual context via the global-local token fusion and transferring the image-language context from the pre-trained CLIP via a regularization loss. This enables more adaptive class representations for more accurate dense localization." + }, + { + "type": "list", + "bbox": [ + 0.5, + 0.092, + 0.892, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.275, + 0.892, + 0.32 + ], + "angle": 0, + "content": "The proposed method achieved the state-of-the-art results on PASCAL VOC 2012 (72.2% on the test set) and MS COCO 2014 (45.9% on the validation set) for WSSS." + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.332, + 0.642, + 0.348 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.358, + 0.892, + 0.794 + ], + "angle": 0, + "content": "Weakly supervised dense object localization. Most existing methods rely on CAM which generates class-specific localization maps based on the correlation between the class weights of the image classifier and the pixel features. However, CAM produces inaccurate localization maps where only the most discriminative object regions are activated. Most previous works are based on CNNs and they have generally focused on improving image feature learning in various ways, such as manipulating training images [20, 32], modifying the classification architecture [35, 41], and designing new losses [4, 8, 14]. Recently, ViTs have made breakthroughs in many computer vision tasks, outperforming CNNs, due to their self-attention based processing blocks that enable long-dependence modeling and cater for different data modalities. Recent ViT-based methods, TSCAM [10] and MCTformer [40] have exploited the transformer attention [12] to generate localization maps, but they still need to integrate CAM into ViTs to achieve a decent localization performance. For these CAM-based methods, one problem that has not been well investigated, is that the class weights used to associate pixel features are the global class representations for the entire dataset. Thus, they cannot properly address the complex pixel-level intra-class variations across samples. Chen et al. [3] proposed the Image-Specific CAM (IS-CAM) using the image-specific class prototypes constructed by applying the masked-average pooling on the image feature maps, and imposed a consistency loss between the IS-CAM and the raw CAM to enhance the feature learning for better localization." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.892, + 0.84 + ], + "angle": 0, + "content": "In contrast to all these CAM-based methods, we propose to explicitly construct multi-modal class representations for dense object localization." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Vision-language models. Recent progress in vision-language pre-training with large-scale datasets has provided a rich source of transferable information. In particular, a representative VL model, i.e., CLIP, brought a significant" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19597" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.118, + 0.088, + 0.852, + 0.314 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.322, + 0.896, + 0.409 + ], + "angle": 0, + "content": "Figure 2. An overview of the proposed transformer framework. An input RGB image is first split and then embedded into patch tokens. The proposed transformer additionally includes class-specific visual tokens (i.e., input class tokens) and class-specific textual tokens (i.e., input text tokens), in which the input text tokens are initialized by the pretrained CLIP text embeddings of the class-related prompts. The output class tokens are averaged to predict class scores. We fuse the input and output class tokens to correlate the output patch tokens. This generates the class-to-patch correlation maps, which are then globally pooled to produce class scores. Similarly for the text tokens. We also apply a contrastive regularization loss (Figure 4) on the averaged output text tokens to transfer the rich VL context from CLIP." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.42, + 0.473, + 0.617 + ], + "angle": 0, + "content": "insight that one can use natural language to connect the visual concept, thus allowing a flexible prediction space for a wide knowledge transfer. The pre-trained CLIP model has been used in a variety of open-world visual tasks. Gu et al. [11] proposed to distill knowledge from the pre-trained CLIP image and text encoders to learn open-vocabulary object detectors. Li et al. [24] used the pre-trained text embeddings as semantic label representations to correlate visual features for semantic segmentation. Similar language-driven segmentation methods have been proposed for 2D images [29] and 3D point clouds [30]. In contrast to these methods which have dense supervision, we investigate a pixel-text matching problem in a weakly supervised setting." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.618, + 0.47, + 0.785 + ], + "angle": 0, + "content": "Xie et al. [37] proposed a CLIP-based WSDOL method, which constrains the CAM activated object regions and the background image regions to match and dis-match the corresponding label prompt in the CLIP embedding space, respectively. It also relies on the additionally pre-defined text descriptions to suppress the co-occurring backgrounds. In contrast, without introducing new visual concepts, we propose to directly use the CLIP label text embeddings to infer dense object localization maps, and further refine them with sample-specific contexts, achieving better results in both WSDOL and WSSS (see Table 1 and Table 3)." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.8, + 0.17, + 0.815 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.47, + 0.903 + ], + "angle": 0, + "content": "Overview. We propose a unified transformer framework to construct multi-modal class-specific tokens for weakly supervised dense object localization. As illustrated in Figure 2, an input RGB image is split into patches and then embedded into a sequence of patch tokens. Additionally, the" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.42, + 0.892, + 0.662 + ], + "angle": 0, + "content": "proposed transformer has two sets of class-specific tokens, i.e., class-specific visual and textual tokens. The class-specific visual tokens are all initialized by the pre-trained weights of the class token of ViT, and the class-specific textual tokens are initialized by the pre-trained CLIP text embeddings of the class-related prompts and kept constant during training. With the added positional embeddings, these three types of tokens are concatenated and serve as input to the proposed transformer. The input tokens go through consecutive masked self-attention based transformer encoding layers. The output class tokens are averaged along the channel dimension to predict class scores. The input and output class/text tokens are fused and then correlated with the output patch tokens, generating the class-to-patch and text-to-patch correlation maps. These two correlation maps are then globally pooled to produce class scores, respectively." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.664, + 0.893, + 0.831 + ], + "angle": 0, + "content": "During training, the class predictions are supervised by the image-level ground-truth labels via the classification loss. In addition, a batch-softmax contrastive loss is used to encourage the averaged output text token to match the CLIP image embedding. This allows the image-language context transfer, further enabling the class representations to be more sample-adaptive for better localization. During inference, a multi-modal correlation map is generated by fusing the two correlation maps, which are further refined by the transformer attention as suggested in [40], generating the final dense object localization maps." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.845, + 0.853, + 0.862 + ], + "angle": 0, + "content": "3.1. Multi-modal class-specific token learning" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.893, + 0.902 + ], + "angle": 0, + "content": "In order to construct multi-modal class representations to guide dense object localization, we propose a multi-modal" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19598" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.091, + 0.088, + 0.46, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.243, + 0.47, + 0.271 + ], + "angle": 0, + "content": "Figure 3. The detailed structure of the proposed multi-modal token transformer encoder." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.282, + 0.47, + 0.463 + ], + "angle": 0, + "content": "token transformer framework. As illustrated in Figure 2, an input RGB image is first split into \\( N \\times N \\) patches, which are then embedded into a sequence of \\( N^2 \\) patch tokens. We additionally create \\( C \\) class-specific visual tokens and \\( C \\) class-specific textual tokens, where \\( C \\) is the number of classes. With the added learnable positional embeddings, these three types of tokens are first concatenated and then fed into the transformer encoder (Figure 3). In order to allow class tokens and text tokens to fully interact with patch tokens but not competing with each other, we use the masked self-attention as shown in Figure 3 (right). Finally, the transformer encoder outputs three types of tokens accordingly." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.463, + 0.47, + 0.584 + ], + "angle": 0, + "content": "Class-specific visual token learning. Inspired by MCTformer [40], we use \\(C\\) class tokens to learn class-specific representations. The output class tokens \\(\\mathbf{T}_{cls}^{out} \\in \\mathbb{R}^{C \\times D}\\) from the proposed multi-modal token transformer encoder are processed by channel-wise averaging, producing \\(C\\) class scores \\(\\mathbf{y}_{cls} \\in \\mathbb{R}^C\\). The class scores are supervised by the image-level ground-truth labels \\(\\mathbf{y} \\in \\mathbb{R}^C\\) using the multi-label soft margin loss (MLSM):" + }, + { + "type": "equation", + "bbox": [ + 0.095, + 0.591, + 0.47, + 0.653 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {c l s} = M L S M (\\mathbf {y} _ {c l s}, \\mathbf {y}) = - \\\\ \\frac {1}{C} \\sum_ {i = 1} ^ {C} \\mathbf {y} ^ {i} \\log \\sigma \\left(\\mathbf {y} _ {c l s} ^ {i}\\right) + (1 - \\mathbf {y} ^ {i}) \\log \\left(1 - \\sigma \\left(\\mathbf {y} _ {c l s} ^ {i}\\right)\\right). \\tag {1} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.66, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Class-specific textual token learning. CLIP presents a novel way of representing visual concepts by using textual prompts, which provide highly complementary information to the class representations learned from only visual data. It thus exhibits a great potential to better facilitate the dense object localization. This motivates us to construct \\( C \\) class-specific textual tokens by leveraging the rich semantics learned by the pre-trained CLIP language model. More specifically, we create \\( C \\) text prompts using the template of \"a photo of [CLS]\", where [CLS] refers to each class name in the label set of a given dataset. These text prompts are then fed into the pre-trained CLIP text encoder, generating \\( C \\) text embeddings. We use the \\( C \\) pre-trained label text embeddings to initialize the input text tokens, which are kept constant during training to retain the powerful representation ability of the pre-trained CLIP." + }, + { + "type": "image", + "bbox": [ + 0.514, + 0.089, + 0.885, + 0.208 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.218, + 0.892, + 0.275 + ], + "angle": 0, + "content": "Figure 4. The regularization loss. The proposed multi-modal token transformer is encouraged to produce text tokens to match the image embeddings of the pre-trained CLIP image encoder, enabling image-language context transfer." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.288, + 0.766, + 0.305 + ], + "angle": 0, + "content": "3.2. Sample-specific local contexts" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.312, + 0.892, + 0.357 + ], + "angle": 0, + "content": "To construct class-specific tokens tailored for each sample, we propose to exploit sample-specific local contexts to complement the global input class-specific tokens." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.358, + 0.893, + 0.644 + ], + "angle": 0, + "content": "Visual context. Inspired by the context-aware prompting [29], we also use the visual context to refine the text embeddings. In contrast to [29] which employs an additional cross-attention module to model the interaction between textual and visual features, the inherent self-attention blocks of the proposed transformer encoder enables the text tokens to aggregate information from patch tokens, capturing the sample-specific visual context. Intuitively, the text embeddings of simple prompts (i.e., \"a photo of [CLS]\", after being refined by the sample-specific visual context, could represent more accurate image-related text. Given that the input text tokens \\(\\mathbf{T}_{txt}^{in}\\in \\mathbb{R}^{C\\times D}\\) are global textual representations for each label while the output text tokens \\(\\mathbf{T}_{txt}^{out}\\in \\mathbb{R}^{C\\times D}\\) capture sample-specific representations, we can obtain the enhanced class-specific textual tokens by fusing the input and output text tokens: \\(\\mathbf{T}_{txt} = \\mathbf{T}_{txt}^{in} + \\lambda_{1}\\cdot \\mathbf{T}_{txt}^{out}\\). Similarly, the enhanced class-specific visual tokens are obtained by \\(\\mathbf{T}_{cls} = \\mathbf{T}_{cls}^{in} + \\lambda_{2}\\cdot \\mathbf{T}_{cls}^{out}\\), where \\(\\lambda_{1}\\) and \\(\\lambda_{2}\\) are two learnable weights." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.645, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Image-language context. To further ensure that the output text tokens can capture meaningful sample-specific context, we propose to impose a contrastive loss on the output text tokens by leveraging the pre-trained CLIP image model. More specifically, as illustrated in Figure 4, given a batch of \\( B \\) input image-text pairs (the input text for each image is the same, i.e., \\( C \\) label prompts), the proposed transformer encoder outputs \\( B \\) text tokens. These \\( B \\) text tokens are used to compute the similarity matrix \\( \\mathbf{S} \\in \\mathbb{R}^{B \\times B} \\) with \\( B \\) visual embeddings by feeding the input images to the pre-trained CLIP image encoder. When the output text token and the CLIP image embedding are from the same image, they form a positive pair. Otherwise, they form a negative pair. Such pair construction is reasonable in that: (i) The CLIP image embedding of an image matches its corresponding text description which commonly contains richer information than image-level labels. Thus, even two images with the same" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "19599" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.273 + ], + "angle": 0, + "content": "class label generally have distinct CLIP image embeddings. (ii) Although the input text tokens are the same for each image, they are progressively refined through interactions with patch tokens at every layer of our model. This enables the output text tokens to represent specific input image content, i.e., different images have different output text tokens. Therefore, the \\(B\\) scores in the diagonal of the similarity matrix are encouraged to be maximized and the other \\(B^2 - B\\) similarity scores are minimized. This is implemented by computing the cross-entropy classification loss between the similarity matrix \\(\\mathbf{S}\\) and the ground-truth labels, i.e., an identity matrix \\(\\mathbf{I} \\in \\mathbb{R}^{B \\times B}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.155, + 0.284, + 0.47, + 0.3 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {c o n t r a s t}} = \\operatorname {C r o s s E n t r o p y} (\\mathbf {S}, \\mathbf {I}). \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.311, + 0.473, + 0.476 + ], + "angle": 0, + "content": "The benefit is two-fold: (i) encouraging the output text tokens to match the pre-trained CLIP image embeddings, allows us to better adapt the pre-trained CLIP model to our target datasets. This also implicitly transfers richer image-related language contexts to the output text tokens, which are more beneficial for guiding the dense object localization, compared to the simple label prompts; (ii) through the batch-softmax cross-entropy loss on the similarity matrix, each text token is also constrained to be discriminative from other text tokens in the same batch, thereby enhancing the sample-specific characteristic of the text tokens." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.485, + 0.47, + 0.517 + ], + "angle": 0, + "content": "3.3. Image-label supervised multi-modal class-top-patch correlation learning" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.524, + 0.47, + 0.689 + ], + "angle": 0, + "content": "Given the multi-modal class-specific tokens, i.e., \\(\\mathbf{T}_{txt}\\) and \\(\\mathbf{T}_{cls}\\), the class-specific dense localization can be inferred by computing the correlation between the class-specific tokens and the patch tokens. More specifically, the output patch tokens from the proposed multi-modal token transformer encoder are first linearly projected and then transposed to 2D feature maps \\(\\mathbf{F} \\in \\mathbb{R}^{N \\times N \\times D}\\), where \\(D\\) is the feature dimension. The text-to-patch correlation maps \\(\\mathbf{C}_{t2p} \\in \\mathbb{R}^{N \\times N \\times C}\\) and the class-to-patch correlation maps \\(\\mathbf{C}_{c2p} \\in \\mathbb{R}^{N \\times N \\times C}\\) can be computed as: \\(\\mathbf{C}_{t2p} = \\mathbf{FT}_{txt}^{\\top}\\) and \\(\\mathbf{C}_{c2p} = \\mathbf{FT}_{cls}^{\\top}\\), respectively." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.69, + 0.471, + 0.902 + ], + "angle": 0, + "content": "In contrast to recent language-driven dense prediction works [24, 29] where pixel-level ground-truth labels are used to supervise the learning of the class-to-patch correlations, only image-level labels are available in our case. Based on the assumption that the weighted sum of a class-to-patch correlation map can be regarded as the global correlation between the class and the whole image, we relax this class-to-patch correlation problem into a class-to-image correlation problem, which can thus be formulated into a classification task. Given that the commonly used global average pooling (GAP) and global max pooling inevitably either over-estimate or under-estimate the size of the object regions when generating the global correlation scores, respectively [16], we thus adopt the Global Weighted Rank" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.893, + 0.152 + ], + "angle": 0, + "content": "ing Pooling (GwRP) method [16]. Compared to GAP which assigns the same weight to each patch for aggregation, GwRP assigns different weights according to the ranking of the correlation scores of all patches for each class:" + }, + { + "type": "equation", + "bbox": [ + 0.585, + 0.16, + 0.892, + 0.204 + ], + "angle": 0, + "content": "\\[\nG _ {c} \\left(\\mathbf {X} _ {\\star}\\right) = \\frac {1}{Z (d)} \\sum_ {j = 1} ^ {N ^ {2}} d ^ {j - 1} X _ {\\star} ^ {r _ {j}, c}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.213, + 0.894, + 0.308 + ], + "angle": 0, + "content": "where \\(\\mathbf{X}_{\\star} \\in \\mathbb{R}^{N^2 \\times C}\\) are the flattened correlation maps; \\(\\mathbf{y}_{\\star} = G(\\mathbf{X}_{\\star}) \\in \\mathbb{R}^{C}\\) are the aggregated class correlation scores; \\(r_j\\) is the index of the ranking, i.e., for a class \\(c\\), \\(X_{\\star}^{r_1, c} > X_{\\star}^{r_2, c} > \\dots > X_{\\star}^{r_{N^2}, c}\\); \\(Z(d) = \\sum_{j=1}^{N^2} d^{j-1}\\), \\(d\\) is a decay parameter. Therefore, the aggregated class-to-patch and text-to-patch correlation scores can be obtained as:" + }, + { + "type": "equation", + "bbox": [ + 0.634, + 0.317, + 0.891, + 0.333 + ], + "angle": 0, + "content": "\\[\n\\mathbf {y} _ {c 2 p} = G \\left(\\mathbf {X} _ {c 2 p}\\right), \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.636, + 0.336, + 0.892, + 0.352 + ], + "angle": 0, + "content": "\\[\n\\mathbf {y} _ {t 2 p} = G \\left(\\mathbf {X} _ {t 2 p}\\right). \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.359, + 0.892, + 0.39 + ], + "angle": 0, + "content": "These class scores can then be supervised by the groundtruth image-level labels \\(\\mathbf{y}\\) via the classification loss:" + }, + { + "type": "equation", + "bbox": [ + 0.577, + 0.398, + 0.891, + 0.414 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {c l a s s - p a t c h}} = M L S M \\left(\\mathbf {y} _ {c 2 p}, \\mathbf {y}\\right), \\tag {6}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.584, + 0.418, + 0.892, + 0.433 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {t e x t - p a t c h}} = M L S M \\left(\\mathbf {y} _ {t 2 p}, \\mathbf {y}\\right). \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.441, + 0.892, + 0.47 + ], + "angle": 0, + "content": "The total objective loss function for training the proposed multi-modal token transformer is as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.507, + 0.479, + 0.891, + 0.495 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {t o t a l}} = \\mathcal {L} _ {\\text {c l s}} + \\mathcal {L} _ {\\text {c o n t r a s t}} + \\mathcal {L} _ {\\text {t e x t - p a t c h}} + \\mathcal {L} _ {\\text {c l a s s - p a t c h}} \\tag {8}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.502, + 0.855, + 0.517 + ], + "angle": 0, + "content": "3.4. Class-specific dense localization inference" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.525, + 0.892, + 0.646 + ], + "angle": 0, + "content": "Once the training of the proposed multi-modal token transformer is completed, the correlations between two modalities of class-specific tokens and the patch tokens are computed to produce two maps, i.e., text-to-patch correlation maps \\(\\mathbf{C}_{t2p}\\) and class-to-patch correlation maps \\(\\mathbf{C}_{c2p}\\), respectively. We further combine these two maps via an element-wise sum to obtain the multi-modal class-specific dense localization maps \\(\\mathbf{C}_{mm} \\in \\mathbb{R}^{N \\times N \\times C}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.618, + 0.654, + 0.891, + 0.671 + ], + "angle": 0, + "content": "\\[\n\\mathbf {C} _ {m m} = \\mathbf {C} _ {t 2 p} + \\mathbf {C} _ {c 2 p}. \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.678, + 0.892, + 0.83 + ], + "angle": 0, + "content": "Moreover, similar to MCTformer [40], the multi-class token based transformer can produce class-specific attention maps \\(\\mathbf{A}_{c2p}\\) and a patch-level pairwise affinity map \\(\\mathbf{A}_{p2p}\\) from the transformer self-attention maps. Given their different localization mechanisms, the transformer self-attention maps can well complement the proposed multi-modal class-specific localization maps. We thus perform an element-wise multiplication (o) on these two maps to produce the fused maps \\(\\hat{\\mathbf{M}}\\in \\mathbb{R}^{N\\times N\\times C}\\), which are further refined by the patch-level pairwise affinity map:" + }, + { + "type": "equation", + "bbox": [ + 0.568, + 0.836, + 0.891, + 0.853 + ], + "angle": 0, + "content": "\\[\n\\hat {\\mathbf {M}} = \\mathbf {C} _ {m m} \\circ \\mathbf {A} _ {c 2 p}, \\tag {10}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.521, + 0.856, + 0.892, + 0.898 + ], + "angle": 0, + "content": "\\[\n\\mathbf {M} (i, j, c) = \\sum_ {k = 1} ^ {N} \\sum_ {l = 1} ^ {N} \\mathbf {A} _ {p 2 p} (i, j, k, l) \\cdot \\hat {\\mathbf {M}} (k, l, c), \\tag {11}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "19600" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.09, + 0.471, + 0.152 + ], + "angle": 0, + "content": "where \\(\\mathbf{M} \\in \\mathbb{R}^{N \\times N \\times C}\\) are the refined fused maps, which are then up-sampled to the original size of the input image before being normalized via the min-max normalization, to generate the final dense object localization maps." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.168, + 0.21, + 0.184 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.193, + 0.281, + 0.209 + ], + "angle": 0, + "content": "4.1. Experimental settings" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.216, + 0.47, + 0.442 + ], + "angle": 0, + "content": "Datasets. We evaluated the proposed method on three datasets including two multi-label datasets, i.e., PASCAL VOC 2012 [9] and MS COCO 2014 [26], and one single-label dataset, i.e., OpenImages [5]. PASCAL VOC 2012 has 20 foreground object classes and one background class. It is split into three subsets, i.e., training (train), validation (val) and test sets including 1,464, 1,449, and 1,456 images, respectively. Following the common practice [18, 19], the training set was augmented to 10,582 images by adding the data from [13]. MS COCO 2014 has 80 foreground object classes and one background class. It has around 80K training images and 40K images for evaluation. OpenImages has 37,319 images in total and 100 object classes. It is split into a training set with 29,819 images, a validation set with 2,500 images and a test set with 5,000 images." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.443, + 0.47, + 0.609 + ], + "angle": 0, + "content": "Evaluation metrics. For the multi-label datasets, we followed the common practice [18, 20, 21] to use the mean Intersection-over-Union (mIoU) to evaluate the multi-label dense localization maps on the train set, and used mIoU to evaluate WSSS results on the val and test sets. Results on the PASCAL VOC test set were obtained from the online official evaluation server. For the single-label dataset, we focused on the pixel-level evaluation and followed the prior works [5, 52] which used the peak Intersection-over-Union (pIoU) and the pixel average precision (PxAP) to evaluate the single-label dense localization maps on the test set." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.61, + 0.47, + 0.777 + ], + "angle": 0, + "content": "Implementation details. We built the proposed transformer using ViT-base as backbone. We used the Adam optimizer with the initial learning rate of 5e-4 and a batch size of 32. We trained our network for 60 epochs on PASCAL VOC and MS COCO and 10 epochs on OpenImages. For the VL model, we used the publicly available pre-trained CLIP model (ViT-B/16). To generate pseudo masks for WSSS, we followed prior works [20, 21, 32, 44] to use IR-Net [1] to post-process our dense localization maps. For semantic segmentation, we use ResNet38-based Deeplab-V1. More details can be found in the Supplementary Materials." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.787, + 0.462, + 0.803 + ], + "angle": 0, + "content": "4.2. Comparison with State-of-the-art of WSDOL" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.47, + 0.903 + ], + "angle": 0, + "content": "Multi-label dense localization. Table 1 reports the evaluation results of the generated multi-label dense localization maps, which are commonly used as seeds to generate pseudo masks in WSSS. The proposed method achieved mIoUs of \\(66.3\\%\\) and \\(40.9\\%\\) on the train sets of PASCAL VOC 2012 and MS COCO 2014, respectively, outperform-" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.077, + 0.892, + 0.146 + ], + "angle": 0, + "content": "Table 1. Evaluation of the generated multi-class multi-label dense localization maps in terms of mIoU (\\%) on the PASCAL VOC 2012 and MS COCO 2014 train sets. Cls.: Classification. \\(\\dagger\\) denotes the reproduced result by [18], \\(\\ddagger\\) denotes the reproduced result by [44], and \\(\\star\\) denotes our reproduced result." + }, + { + "type": "table", + "bbox": [ + 0.513, + 0.147, + 0.882, + 0.327 + ], + "angle": 0, + "content": "
MethodCls. BackboneVOCCOCO
CAM (CVPR16) [48]ResNet5048.833.5†
SEAM (CVPR20) [34]ResNet3855.425.1‡
RIB (NeurIPS21) [18]ResNet5056.536.5
AdvCAM (CVPR21) [19]ResNet3855.637.2
CLIMS (CVPR22) [37]ResNet5056.6-
SIPE (CVPR22) [4]ResNet5058.6-
W-OoD (CVPR22) [21]ResNet5059.1-
Du et al. (CVPR22) [8]ResNet3861.5-
TS-CAM (ICCV21) [10]ViT-small41.3-
MCTformer (CVPR22) [40]ViT-small61.7-
MCTformer (CVPR22) [40]ViT-base62.3*-
OursViT-base66.340.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.332, + 0.892, + 0.358 + ], + "angle": 0, + "content": "Table 2. Evaluation of multi-class single-label dense localization on the OpenImages test set." + }, + { + "type": "table", + "bbox": [ + 0.513, + 0.359, + 0.882, + 0.553 + ], + "angle": 0, + "content": "
MethodCls. backbonepIoUPxAP
CAM (CVPR16) [48]ResNet5043.058.2
HAS (ICCV17) [31]ResNet5041.955.1
ACoL (CVPR18) [47]ResNet5041.756.4
SPG (ECCV18) [46]ResNet5041.855.8
ADL (CVPR19) [6]ResNet5042.155.0
CutMix (ICCV19) [43]ResNet5042.757.6
PAS (ECCV20) [2]ResNet50-60.9
IVR (ICCV21) [15]ResNet50-58.9
Zhu et al. (CVPR22) [52]ResNet5049.765.4
CREAM (CVPR22) [38]ResNet50-64.7
Zhu et al. (ECCV22) [51]ResNet5052.267.7
OursViT-base57.673.3
" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.556, + 0.862, + 0.571 + ], + "angle": 0, + "content": "ing the state-of-the-art methods by significant margins." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.572, + 0.892, + 0.693 + ], + "angle": 0, + "content": "Single-label dense localization. We also evaluated the effectiveness of the proposed method for single-label dense object localization. We used the recently proposed OpenImages dataset [5], which has more challenging background context than the commonly used ones for weakly supervised object localization. As shown in Table 2, the proposed method attained a pIoU of \\(57.6\\%\\) and a PxAP of \\(73.3\\%\\), achieving better results than the state-of-the-art methods." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.708, + 0.866, + 0.724 + ], + "angle": 0, + "content": "4.3. Comparison with State-of-the-art of WSSS" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.733, + 0.892, + 0.794 + ], + "angle": 0, + "content": "PASCAL VOC 2012. As shown in Table 3, the proposed method achieved mIoUs of \\(72.2\\%\\) and \\(72.2\\%\\) on the val and test sets of PASCAL VOC 2012, respectively, outperforming other WSSS methods only using image-level labels." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.795, + 0.892, + 0.901 + ], + "angle": 0, + "content": "MS COCO 2014. Table 4 also shows that, on a more challenging dataset, i.e., MS COCO 2014, the proposed method attained the best mIoU of \\(45.9\\%\\), which is significantly better than those relying on saliency maps, achieving the new state-of-the-art result. These results clearly demonstrate the effectiveness and the good generalization ability of the proposed method for WSSS." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "19601" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.089, + 0.47, + 0.145 + ], + "angle": 0, + "content": "Table 3. Performance comparison of the state-of-the-art WSSS methods on the PASCAL VOC 2012 val and test sets. Seg.: DeepLab version. Sup.: supervision; I: image-level ground-truth labels; S: off-the-shelf saliency maps; L: pre-trained VL model." + }, + { + "type": "table", + "bbox": [ + 0.081, + 0.145, + 0.47, + 0.417 + ], + "angle": 0, + "content": "
MethodBackboneSeg.Sup.ValTest
EDAM (CVPR21) [36]ResNet101V2I+S70.970.6
EPS (CVPR21) [23]ResNet101V1I+S71.071.8
Yao et al. (CVPR21) [41]ResNet101V2I+S68.368.5
AuxSegNet (ICCV21) [39]ResNet38V1I+S69.068.6
Li et al. (CVPR22) [25]ResNet101V2I+S72.072.9
Du et al. (CVPR22) [8]ResNet101V2I+S72.673.6
RCA (CVPR22) [50]ResNet38V2I+S72.272.8
L2G (CVPR22) [14]ResNet38V1I+S72.073.0
AdvCAM (CVPR21) [19]ResNet101V2I68.168.0
ECS-Net (ICCV21) [33]ResNet38V1I66.667.6
Kweon et al. (ICCV21) [17]ResNet38V1I68.468.2
CDA (ICCV21) [32]ResNet38V1I66.166.8
Zhang et al. (ICCV21) [45]ResNet38V1I67.868.5
MCTformer (CVPR22) [40]ResNet38V1I71.971.6
AMN (CVPR22) [22]ResNet101V2I70.770.6
W-OoD (CVPR22) [21]ResNet38V1I70.770.1
SIPE (CVPR22) [3]ResNet38V1I68.269.7
Yoon et al. (ECCV22) [42]ResNet38V1I70.971.7
CLIMS (CVPR22) [37]ResNet101V2I+L69.368.7
OursResNet38V1I+L72.272.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.423, + 0.468, + 0.449 + ], + "angle": 0, + "content": "Table 4. Performance comparison of state-of-the-art WSSS methods on the MS COCO 2014 val set in terms of mIoU (\\%)." + }, + { + "type": "table", + "bbox": [ + 0.101, + 0.45, + 0.451, + 0.638 + ], + "angle": 0, + "content": "
MethodBackboneSeg.Sup.Val
EPS (CVPR21) [23]VGG16V2I+S35.7
RCA (CVPR22) [50]VGG16V2I+S36.8
AuxSegNet (ICCV21) [39]ResNet38V1I+S33.9
L2G (CVPR22) [14]ResNet101V2I+S44.2
Kweon et al. (ICCV21) [17]ResNet38V1I36.4
CDA (ICCV21) [32]ResNet38V1I33.2
AdvCAM (CVPR21) [19]ResNet101V2I44.4
MCTformer (CVPR22) [40]ResNet38V1I42.0
Li et al. (CVPR22) [25]ResNet101V2I44.7
AMN (CVPR22) [22]ResNet101V2I44.7
SIPE (CVPR22) [3]ResNet38V1I43.6
Yoon et al. (ECCV22) [42]ResNet38V1I44.8
OursResNet38V1I+L45.9
" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.649, + 0.238, + 0.663 + ], + "angle": 0, + "content": "4.4. Ablation studies" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.674, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Effect of learning multi-modal class-specific tokens. As shown in Table 5, without text tokens, only relying on the multi-class token transformer attention results in a dense localization mIoU of \\(57.5\\%\\); By learning the multi-class tokens to correlate patch tokens using image-level supervision with GAP, a significant gain of \\(4.6\\%\\) was achieved in mIoU. GwRP outperforms GAP, improving the dense localization result to \\(62.7\\%\\). By additionally learning class-specific textual tokens, the resulting multi-modal correlation maps driven by the global/input multi-modal class-specific tokens, attained an better mIoU of \\(64.1\\%\\), compared to that of the multi-modal correlation maps driven by the local/output multi-modal class-specific tokens. The fusion of the global and local multi-modal class-specific tokens leads to the best dense localization maps with an mIoU of \\(66.3\\%\\). These re" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.089, + 0.892, + 0.131 + ], + "angle": 0, + "content": "Table 5. Evaluation of the class-specific dense localization maps generated by learning correlations (cor.) between class-specific tokens and patch tokens on the PASCAL VOC 2012 train set." + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.131, + 0.892, + 0.23 + ], + "angle": 0, + "content": "
ConfigurationPoolingmIoU
Multi-class token attention-57.5
+ class-to-patch correlationGAP62.1
+ class-to-patch correlation (C2P cor.)GWRP62.7
+ Multi-modal (C2P + text-to-patch) cor. (global)GWRP64.1
+ Multi-modal cor. (local)GWRP63.3
+ Multi-modal cor. (global + local)GWRP66.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.241, + 0.892, + 0.283 + ], + "angle": 0, + "content": "Table 6. Evaluation of the class-specific dense localization by different regularization methods for sample-specific local context learning on the PASCAL VOC 2012 train set. CE: cross-entropy." + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.283, + 0.892, + 0.395 + ], + "angle": 0, + "content": "
Visual contextImage-language contextmIoU
Prior knowledgeRegularization loss
X--64.1
✓--64.8
✓CLIP caption embed.L163.7
✓CLIP caption embed.Batch-contrast CE65.1
✓CLIP image embed.Batch-contrast CE66.3
" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.4, + 0.892, + 0.446 + ], + "angle": 0, + "content": "sults demonstrate the effectiveness of the proposed method of learning multi-modal class-specific tokens in producing accurate class-specific dense localization maps." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.447, + 0.892, + 0.719 + ], + "angle": 0, + "content": "Figure 5 visualizes the generated dense object localization maps in different configurations. The multi-class token transformer attention only localizes partial object regions and includes many noises, such as the dog and the background in the third and second rows of Figure 5 (b), respectively. Learning the multi-class tokens to correlate patch tokens leads to more complete object localization maps, such as the sofa in the third row. This also results in several falsely activated regions near the object, such as the chair in the second rows of Figure 5 (c). Adding class-specific textual tokens leads to improved localization maps (Figure 5 (d)) with expanded true object regions and reduced falsely activated backgrounds. Further incorporating sample-specific context produces the best localization maps (Figure 5 (e)) which are close to the ground-truth. This demonstrates the advantage of the proposed method in generating dense localization maps for objects of different scales in various challenging scenarios." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Effect of sample-specific context learning. As shown in Table 6, without sample-specific context learning, the learned multi-modal class-specific tokens result in the class-specific localization maps with an mIoU of \\(64.1\\%\\). Incorporating the visual context information aggregated by the output text tokens leads to an improved mIoU of \\(64.8\\%\\). To further learn image-language context to enhance the text token learning, we investigated two regularization methods: (i) Leveraging the pre-trained CLIP text embeddings of image captions. More specifically, we used a pre-trained image captioning model, ClipCap [27], to generate captions for each image. By minimizing the distance between the text" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19602" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.101, + 0.09, + 0.222, + 0.29 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.155, + 0.29, + 0.171, + 0.3 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.228, + 0.089, + 0.353, + 0.289 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.283, + 0.29, + 0.3, + 0.3 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.358, + 0.089, + 0.482, + 0.289 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.413, + 0.29, + 0.43, + 0.3 + ], + "angle": 0, + "content": "(c)" + }, + { + "type": "image", + "bbox": [ + 0.488, + 0.089, + 0.613, + 0.289 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.542, + 0.29, + 0.56, + 0.3 + ], + "angle": 0, + "content": "(d)" + }, + { + "type": "image", + "bbox": [ + 0.617, + 0.089, + 0.741, + 0.289 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.673, + 0.29, + 0.69, + 0.3 + ], + "angle": 0, + "content": "(e)" + }, + { + "type": "image", + "bbox": [ + 0.748, + 0.089, + 0.871, + 0.289 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.804, + 0.29, + 0.818, + 0.3 + ], + "angle": 0, + "content": "(f)" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.305, + 0.894, + 0.362 + ], + "angle": 0, + "content": "Figure 5. Visualization of the multi-label dense object localization results using different configurations of the proposed method on the PASCAL VOC 2012 train set. (a) Input; (b) MCTAttn (Multi-class token attention); (c) MCTAttn + C2P-CMap (class-to-patch correlation maps); (d) MCTAttn + MM-CMap (multi-modal correlation maps, i.e., class-to-patch correlation maps + text-to-patch correlation maps); (e) MCTAttn + MM-CMap + sample-specific context; (f) Ground-truth." + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.368, + 0.471, + 0.409 + ], + "angle": 0, + "content": "Table 7. Evaluation of the class-specific dense localization maps by using different prompting methods for constructing class-specific textual tokens on the PASCAL VOC 2012 train set." + }, + { + "type": "table", + "bbox": [ + 0.139, + 0.41, + 0.411, + 0.475 + ], + "angle": 0, + "content": "
PromptmIoU
a photo of [class]66.3
Prompt ensembling65.5
CoOp (learning-based prompts) [49]66.0
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.477, + 0.47, + 0.793 + ], + "angle": 0, + "content": "tokens and the pre-trained CLIP caption embeddings via L1 loss, the localization performance drops by \\(1.1\\%\\). Using the batch-softmax cross-entropy loss yields a slightly improved localization mIoU of \\(65.1\\%\\). L1 loss on the features allows to transfer all information of the generated image captions including useful and interfering contexts that could reduce the discriminative ability of the class-specific textual tokens, thus degrading the localization performance. In contrast, the batch-softmax cross-entropy loss on the feature similarities explicitly separates each text token from other text tokens in a same batch, which facilitates the sample-specific token learning for better localization. (ii) Leveraging the pre-trained CLIP image embeddings. By applying the batch-softmax cross-entropy loss on the similarity matrix of our text tokens and the pre-trained CLIP image embeddings, the class-specific localization attains a performance gain of \\(1.5\\%\\) with the best mIoU of \\(66.3\\%\\). Compared to learning from the generated image captions, the batch-contrastive image-text matching loss enables the text tokens to better align with the visual features, thus leading to better dense localization results." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.796, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Effects of different prompts. We used different prompting methods to construct the class-specific textual tokens and evaluated their effects in the resulting class-specific dense localization maps. More specifically, we investigated three types of prompting methods including (i) the most commonly used prompt, i.e., \"a photo of [class]\"; (ii) Prompt ensembling, which ensembles 8 best prompts over the em" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.369, + 0.895, + 0.507 + ], + "angle": 0, + "content": "bedding space via averaging as suggested in [28]; (iii) A learning-based prompting method, CoOp [49]. As shown in Table 7, these three prompting methods lead to comparable dense localization results. Among them, the general prompt template \"a photo of [class]\" produces the best localization mIoU. We speculate this is due to the general prompt template better preserving the class-discriminative language priors from the pre-trained CLIP model, compared to ensembling several prompts or learning extra parameters." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.532, + 0.62, + 0.548 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.556, + 0.892, + 0.827 + ], + "angle": 0, + "content": "We proposed a new weakly supervised dense object localization method by explicitly constructing multi-modal class representations via a transformer-based framework. The proposed transformer includes class-specific visual and textual tokens by learning visual information from the target image data and exploiting language information from the pre-trained CLIP model, respectively. These diverse supervisions enable the multi-modal class-specific tokens to provide complementary information for more discriminative dense localization. By further incorporating the sample-specific contexts from visual context and image-language context, more adaptive multi-modal class-specific tokens can be learned to facilitate better dense localization. Our superior WSDOL results on two multi-label datasets, (i.e., PASCAL VOC and MS COCO) and one single-label dataset, (i.e., OpenImages), demonstrate the effectiveness of the proposed method. We also report state-of-the-art WSSS results on PASCAL VOC and MS COCO." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.831, + 0.894, + 0.901 + ], + "angle": 0, + "content": "Acknowledgment. This research is supported in part by Australian Research Council Grant DP210101682, DP210102674, IH180100002, the Early Career Scheme of the Research Grants Council (RGC) of the Hong Kong SAR under grant No. 26202321 and HKUST Startup Fund No. R9253." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19603" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.156 + ], + "angle": 0, + "content": "[1] Jiwoon Ahn, Sunghyun Cho, and Suha Kwak. Weakly supervised learning of instance segmentation with inter-pixel relations. In CVPR, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.157, + 0.47, + 0.198 + ], + "angle": 0, + "content": "[2] Wonho Bae, Junhyug Noh, and Gunhee Kim. Rethinking class activation mapping for weakly supervised object localization. In ECCV, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.2, + 0.471, + 0.255 + ], + "angle": 0, + "content": "[3] Qi Chen, Lingxiao Yang, Jian-Huang Lai, and Xiaohua Xie. Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation. In CVPR, 2022. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.256, + 0.471, + 0.311 + ], + "angle": 0, + "content": "[4] Zhaozheng Chen, Tan Wang, Xiongwei Wu, Xian-Sheng Hua, Hanwang Zhang, and Qianru Sun. Class re-activation maps for weakly-supervised semantic segmentation. In CVPR, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.312, + 0.471, + 0.367 + ], + "angle": 0, + "content": "[5] Junsuk Choe, Seong Joon Oh, Seungho Lee, Sanghyuk Chun, Zeynep Akata, and Hyunjung Shim. Evaluating weakly supervised object localization methods right. In CVPR, 2020. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.368, + 0.471, + 0.409 + ], + "angle": 0, + "content": "[6] Junsuk Choe and Hyunjung Shim. Attention-based dropout layer for weakly supervised object localization. In CVPR, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.41, + 0.471, + 0.48 + ], + "angle": 0, + "content": "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.48, + 0.471, + 0.522 + ], + "angle": 0, + "content": "[8] Ye Du, Zehua Fu, Qingjie Liu, and Yunhong Wang. Weakly supervised semantic segmentation by pixel-to-prototype contrast. In CVPR, 2022. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.523, + 0.471, + 0.577 + ], + "angle": 0, + "content": "[9] Mark Everingham and John Winn. The pascal visual object classes challenge 2012 (voc2012) development kit. Pattern Anal. Stat. Model. Comput. Learn., Tech. Rep, 2007:1-45, 2012. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.578, + 0.47, + 0.633 + ], + "angle": 0, + "content": "[10] Wei Gao, Fang Wan, Xingjia Pan, Zhiliang Peng, Qi Tian, Zhenjun Han, Bolei Zhou, and Qixiang Ye. Ts-cam: Token semantic coupled attention map for weakly supervised object localization. In ICCV, 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.634, + 0.47, + 0.676 + ], + "angle": 0, + "content": "[11] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. In ICLR, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.677, + 0.47, + 0.718 + ], + "angle": 0, + "content": "[12] Saurav Gupta, Sourav Lakhotia, Abhay Rawat, and Rahul Tallamraju. Vitol: Vision transformer for weakly supervised object localization. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.719, + 0.47, + 0.761 + ], + "angle": 0, + "content": "[13] Bharath Hariharan, Pablo Arbeláez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik. Semantic contours from inverse detectors. In ICCV, 2011. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.761, + 0.47, + 0.815 + ], + "angle": 0, + "content": "[14] Peng-Tao Jiang, Yuqi Yang, Qibin Hou, and Yunchao Wei. L2g: A simple local-to-global knowledge transfer framework for weakly supervised semantic segmentation. In CVPR, 2022. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.817, + 0.47, + 0.859 + ], + "angle": 0, + "content": "[15] Jeesoo Kim, Junsuk Choe, Sangdoo Yun, and Nojun Kwak. Normalization matters in weakly supervised object localization. In ICCV, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.86, + 0.47, + 0.902 + ], + "angle": 0, + "content": "[16] Alexander Kolesnikov and Christoph H Lampert. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In ECCV, 2016. 5" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.471, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.161 + ], + "angle": 0, + "content": "[17] Hyeokjun Kweon, Sung-Hoon Yoon, Hyeonseong Kim, Daehee Park, and Kuk-Jin Yoon. Unlocking the potential of ordinary classifier: Class-specific adversarial erasing framework for weakly supervised semantic segmentation. In ICCV, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.162, + 0.892, + 0.204 + ], + "angle": 0, + "content": "[18] Jungbeom Lee, Jooyoung Choi, Jisoo Mok, and Sungroh Yoon. Reducing information bottleneck for weakly supervised semantic segmentation. NeurIPS, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.205, + 0.892, + 0.259 + ], + "angle": 0, + "content": "[19] Jungbeom Lee, Eunji Kim, Jisoo Mok, and Sungroh Yoon. Anti-adversarily manipulated attributions for weakly supervised semantic segmentation and object localization. PAMI, 2022. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.26, + 0.892, + 0.302 + ], + "angle": 0, + "content": "[20] Jungbeom Lee, Eunji Kim, and Sungroh Yoon. Antiadversarily manipulated attributions for weakly and semi-supervised semantic segmentation. In CVPR, 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.302, + 0.892, + 0.357 + ], + "angle": 0, + "content": "[21] Jungbeom Lee, Seong Joon Oh, Sangdoo Yun, Junsuk Choe, Eunji Kim, and Sungroh Yoon. Weakly supervised semantic segmentation using out-of-distribution data. In CVPR, 2022. 1, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.358, + 0.892, + 0.412 + ], + "angle": 0, + "content": "[22] Minhyun Lee, Dongseob Kim, and Hyunjung Shim. Threshold matters in wsss: Manipulating the activation for the robust and accurate segmentation model against thresholds. In CVPR, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.413, + 0.892, + 0.468 + ], + "angle": 0, + "content": "[23] Seungho Lee, Minhyun Lee, Jongwuk Lee, and Hyunjung Shim. Railroad is not a train: Saliency as pseudo-pixel supervision for weakly supervised semantic segmentation. In CVPR, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.469, + 0.892, + 0.511 + ], + "angle": 0, + "content": "[24] Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and René Ranftl. Language-driven semantic segmentation. In ICLR, 2022. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.511, + 0.892, + 0.552 + ], + "angle": 0, + "content": "[25] Jing Li, Junsong Fan, and Zhaoxiang Zhang. Towards noiseless object contours for weakly supervised semantic segmentation. In CVPR, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.553, + 0.892, + 0.607 + ], + "angle": 0, + "content": "[26] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.608, + 0.892, + 0.65 + ], + "angle": 0, + "content": "[27] Ron Mokady, Amir Hertz, and Amit H Bermano. Clipcap: Clip prefix for image captioning. arXiv preprint arXiv:2111.09734, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.65, + 0.892, + 0.718 + ], + "angle": 0, + "content": "[28] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.719, + 0.892, + 0.776 + ], + "angle": 0, + "content": "[29] Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In CVPR, 2022. 3, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.776, + 0.892, + 0.817 + ], + "angle": 0, + "content": "[30] David Rozenberszki, Or Litany, and Angela Dai. Language-grounded indoor 3d semantic segmentation in the wild. In ECCV, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.818, + 0.892, + 0.859 + ], + "angle": 0, + "content": "[31] Krishna Kumar Singh and Yong Jae Lee. Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization. In ICCV, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.86, + 0.892, + 0.902 + ], + "angle": 0, + "content": "[32] Yukun Su, Ruizhou Sun, Guosheng Lin, and Qingyao Wu. Context decoupling augmentation for weakly supervised semantic segmentation. In ICCV, 2021. 2, 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "19604" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "[33] Kunyang Sun, Haoqing Shi, Zhengming Zhang, and Yongming Huang. ECS-net: Improving weakly supervised semantic segmentation by using connections between class activation maps. In ICCV, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.149, + 0.47, + 0.203 + ], + "angle": 0, + "content": "[34] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In CVPR, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.205, + 0.469, + 0.259 + ], + "angle": 0, + "content": "[35] Yunchao Wei, Huaxin Xiao, Honghui Shi, Zequn Jie, Jiashi Feng, and Thomas S Huang. Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation. In CVPR, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.261, + 0.469, + 0.315 + ], + "angle": 0, + "content": "[36] Tong Wu, Junshi Huang, Guangyu Gao, Xiaoming Wei, Xiaolin Wei, Xuan Luo, and Chi Harold Liu. Embedded discriminative attention mechanism for weakly supervised semantic segmentation. In CVPR, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.317, + 0.469, + 0.357 + ], + "angle": 0, + "content": "[37] Jinheng Xie, Xianxu Hou, Kai Ye, and Linlin Shen. Clims: Cross language image matching for weakly supervised semantic segmentation. In CVPR, 2022. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.359, + 0.469, + 0.412 + ], + "angle": 0, + "content": "[38] Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng, Rui-Wei Zhao, Tao Zhang, Xuequan Lu, and Shang Gao. Cream: Weakly supervised object localization via class re-activation mapping. In CVPR, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.414, + 0.469, + 0.468 + ], + "angle": 0, + "content": "[39] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, Ferdous Sohel, and Dan Xu. Leveraging auxiliary tasks with affinity learning for weakly supervised semantic segmentation. In ICCV, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.47, + 0.469, + 0.523 + ], + "angle": 0, + "content": "[40] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, and Dan Xu. Multi-class token transformer for weakly supervised semantic segmentation. In CVPR, 2022. 1, 2, 3, 4, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.526, + 0.469, + 0.579 + ], + "angle": 0, + "content": "[41] Yazhou Yao, Tao Chen, Guo-Sen Xie, Chuanyi Zhang, Fumin Shen, Qi Wu, Zhenmin Tang, and Jian Zhang. Nonsalient region object mining for weakly supervised semantic segmentation. In CVPR, 2021. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.581, + 0.469, + 0.635 + ], + "angle": 0, + "content": "[42] Sung-Hoon Yoon, Hyeokjun Kweon, Jegyeong Cho, Shinjeong Kim, and Kuk-Jin Yoon. Adversarial erasing framework via triplet with gated pyramid pooling layer for weakly supervised semantic segmentation. In ECCV, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.637, + 0.469, + 0.69 + ], + "angle": 0, + "content": "[43] Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. Cutmix: Regularization strategy to train strong classifiers with localizable features. In ICCV, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.693, + 0.469, + 0.733 + ], + "angle": 0, + "content": "[44] Dong Zhang, Hanwang Zhang, Jinhui Tang, Xiansheng Hua, and Qianru Sun. Causal intervention for weakly-supervised semantic segmentation. In NeurIPS, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.735, + 0.469, + 0.774 + ], + "angle": 0, + "content": "[45] Fei Zhang, Chaochen Gu, Chenyue Zhang, and Yuchao Dai. Complementary patch for weakly supervised semantic segmentation. In ICCV, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.777, + 0.469, + 0.816 + ], + "angle": 0, + "content": "[46] Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. Self-attention generative adversarial networks. In ICML, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.818, + 0.469, + 0.859 + ], + "angle": 0, + "content": "[47] Xiaolin Zhang, Yunchao Wei, Jiashi Feng, Yi Yang, and Thomas S Huang. Adversarial complementary learning for weakly supervised object localization. In CVPR, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.861, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[48] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In CVPR, 2016. 1, 6" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.146 + ], + "angle": 0, + "content": "[49] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9):2337-2348, 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[50] Tianfei Zhou, Meijie Zhang, Fang Zhao, and Jianwu Li. Regional semantic contrast and aggregation for weakly supervised semantic segmentation. In CVPR, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.193, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[51] Lei Zhu, Qian Chen, Lujia Jin, Yunfei You, and Yanye Lu. Bagging regional classification activation maps for weakly supervised object localization. In ECCV, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.235, + 0.892, + 0.276 + ], + "angle": 0, + "content": "[52] Lei Zhu, Qi She, Qian Chen, Yunfei You, Boyu Wang, and Yanye Lu. Weakly supervised object localization as domain adaption. In CVPR, 2022. 6" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.276 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19605" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/56c83ae8-f628-4d2b-be75-d685154654de_origin.pdf b/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/56c83ae8-f628-4d2b-be75-d685154654de_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..16aafb0499a312784bcb3a6550396661145f4488 --- /dev/null +++ b/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/56c83ae8-f628-4d2b-be75-d685154654de_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d1e7f598e79f4244adc2d81d672a02fe8189d793704a14be5e8965c20c782b0c +size 878844 diff --git a/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/full.md b/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/full.md new file mode 100644 index 0000000000000000000000000000000000000000..a3b2e2873b1308ccb74e5a0d478142494e7fefc7 --- /dev/null +++ b/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/full.md @@ -0,0 +1,308 @@ +# Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization + +Lian Xu $^{1}$ , Wanli Ouyang $^{2}$ , Mohammed Bennamoun $^{1}$ , Farid Boussaid $^{1}$ , and Dan Xu $^{3}$ $^{1}$ The University of Western Australia $^{2}$ Shanghai AI Laboratory + $^{3}$ Hong Kong University of Science and Technology + +{lian.xu,mohammed.bennamoun,farid.boussaid}@uwa.edu.au, + +wanli.ouyang@sydney.edu.au, danxu@cse.ust.hk + +# Abstract + +Weakly supervised dense object localization (WSDOL) relies generally on Class Activation Mapping (CAM), which exploits the correlation between the class weights of the image classifier and the pixel-level features. Due to the limited ability to address intra-class variations, the image classifier cannot properly associate the pixel features, leading to inaccurate dense localization maps. In this paper, we propose to explicitly construct multi-modal class representations by leveraging the Contrastive Language-Image Pretraining (CLIP), to guide dense localization. More specifically, we propose a unified transformer framework to learn two-modalities of class-specific tokens, i.e., class-specific visual and textual tokens. The former captures semantics from the target visual data while the latter exploits the class-related language priors from CLIP, providing complementary information to better perceive the intra-class diversities. In addition, we propose to enrich the multi-modal class-specific tokens with sample-specific contexts comprising visual context and image-language context. This enables more adaptive class representation learning, which further facilitates dense localization. Extensive experiments show the superiority of the proposed method for WSDOL on two multi-label datasets, i.e., PASCAL VOC and MS COCO, and one single-label dataset, i.e., OpenImages. Our dense localization maps also lead to the state-of-the-art weakly supervised semantic segmentation (WSSS) results on PASCAL VOC and MS COCO. + +# 1. Introduction + +Fully supervised dense prediction tasks have achieved great success, which however comes at the cost of expensive pixel-level annotations. To address this issue, recent works have investigated the use of weak labels, such as image- + +![](images/985161060fc3d76af78cb522c0970075b3ac7ac44f5526c63a456f5bfd0c5096.jpg) +Figure 1. (a) CAM exploits the correlation between the image classifier and the pixel features. (b) We propose to construct multimodal class-specific tokens to guide dense object localization. + +level labels, to generate dense object localization maps as pseudo labels for those tasks. For the weakly supervised object localization (WSOL) task, most methods evaluate localization results on the bounding-box level and a few recent methods [5] evaluate on the pixel level. We use WSDOL to focus on the pixel-level evaluation, which is critical for downstream dense prediction tasks such as WSSS. + +Previous works have exploited Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) [7] for WSDOL with image-level labels [21, 40]. These methods have generally relied on Class Activation Mapping (CAM) [48], which generates class-specific localization maps by computing the correlation between the class-specific weight vectors of the image classifier and every pixel feature vector. However, image classifiers generally have a limited ability to address the intra-class variation, let alone at the pixel level. This thus leads to inaccurate dense localization results. In the conventional fully supervised learning paradigm, the image classification model aims to convert images to numeric labels, ignoring the context of the labels. Hence, it tends to learn the pattern that maximizes the inter-class differences but disregards the intra-class diversities. This largely restricts the model's ability of + +understanding semantic objects. + +Recently, Vision-Language (VL) models have attracted much attention. In particular, CLIP, a representative VL model, pre-trained on 400 million image-text pairs that are readily available publicly, has been successfully applied to a number of downstream tasks, due to its strong generalization ability. CLIP introduces a contrastive representation learning method that constrains an image to match its related text while dis-matching the remaining texts from the same batch, in a multi-modal embedding space. This enables the model to perceive the differences across images, thus facilitating it to better discriminate intra-class samples. + +Motivated by these observations, we propose to leverage the strong representations of visual concepts encoded by the pre-trained CLIP language model to guide the dense object localization. More specifically, we extract the class-related text embeddings by feeding the label prompts to the pre-trained CLIP language model. As shown in Figure 1, we propose a unified transformer framework which includes multi-modal class-specific tokens, i.e., class-specific visual tokens and class-specific textual tokens. The class-specific visual tokens aim to capture visual representations from the target image dataset, while the class-specific textual tokens take the rich language semantics from the CLIP label text embeddings. These two modalities of class-specific tokens, with complementary information, are jointly used to correlate pixel features, contributing to better dense localization. + +In order to construct more adaptive class representations, which can better associate the sample-specific local features for dense localization, we propose to enhance the global multi-modal class-specific tokens with sample-specific contextual information. To this end, we introduce two designs: (i) at the feature level, we use the sample-specific visual context to enhance both the class-specific visual and textual tokens. This is achieved by combining these global tokens with their output local counterparts which aggregate the patch tokens of the image through the self-attention layers; (ii) at the loss level, we introduce a regularization contrastive loss to encourage the output text tokens to match the CLIP image embeddings. This allows the CLIP model to be better adapted to our target datasets. Moreover, due to its image-language matching pre-training objective, the CLIP image encoder is learned to extract the image embeddings that match the CLIP text embeddings of their corresponding image captions. We thus argue that through this contrastive loss, the rich image-related language context from the CLIP could be implicitly transferred to the text tokens, which are more beneficial for guiding the dense object localization, compared to the simple label prompts. + +In summary, the contribution of this work is three-fold: + +- We propose a new WSDOL method by explicitly constructing multi-modal class representations in a unified transformer framework. + +- The proposed transformer includes class-specific visual tokens and class-specific textual tokens, which are learned from different data modalities with diverse supervisions, thus providing complementary information for more discriminative dense localization. +- We propose to enhance the multi-modal global class representations by using sample-specific visual context via the global-local token fusion and transferring the image-language context from the pre-trained CLIP via a regularization loss. This enables more adaptive class representations for more accurate dense localization. + +The proposed method achieved the state-of-the-art results on PASCAL VOC 2012 (72.2% on the test set) and MS COCO 2014 (45.9% on the validation set) for WSSS. + +# 2. Related Work + +Weakly supervised dense object localization. Most existing methods rely on CAM which generates class-specific localization maps based on the correlation between the class weights of the image classifier and the pixel features. However, CAM produces inaccurate localization maps where only the most discriminative object regions are activated. Most previous works are based on CNNs and they have generally focused on improving image feature learning in various ways, such as manipulating training images [20, 32], modifying the classification architecture [35, 41], and designing new losses [4, 8, 14]. Recently, ViTs have made breakthroughs in many computer vision tasks, outperforming CNNs, due to their self-attention based processing blocks that enable long-dependence modeling and cater for different data modalities. Recent ViT-based methods, TSCAM [10] and MCTformer [40] have exploited the transformer attention [12] to generate localization maps, but they still need to integrate CAM into ViTs to achieve a decent localization performance. For these CAM-based methods, one problem that has not been well investigated, is that the class weights used to associate pixel features are the global class representations for the entire dataset. Thus, they cannot properly address the complex pixel-level intra-class variations across samples. Chen et al. [3] proposed the Image-Specific CAM (IS-CAM) using the image-specific class prototypes constructed by applying the masked-average pooling on the image feature maps, and imposed a consistency loss between the IS-CAM and the raw CAM to enhance the feature learning for better localization. + +In contrast to all these CAM-based methods, we propose to explicitly construct multi-modal class representations for dense object localization. + +Vision-language models. Recent progress in vision-language pre-training with large-scale datasets has provided a rich source of transferable information. In particular, a representative VL model, i.e., CLIP, brought a significant + +![](images/48f61ab4599a763cd0115699f85f4761e9b9646dd54d9b043d011828fde0aeee.jpg) +Figure 2. An overview of the proposed transformer framework. An input RGB image is first split and then embedded into patch tokens. The proposed transformer additionally includes class-specific visual tokens (i.e., input class tokens) and class-specific textual tokens (i.e., input text tokens), in which the input text tokens are initialized by the pretrained CLIP text embeddings of the class-related prompts. The output class tokens are averaged to predict class scores. We fuse the input and output class tokens to correlate the output patch tokens. This generates the class-to-patch correlation maps, which are then globally pooled to produce class scores. Similarly for the text tokens. We also apply a contrastive regularization loss (Figure 4) on the averaged output text tokens to transfer the rich VL context from CLIP. + +insight that one can use natural language to connect the visual concept, thus allowing a flexible prediction space for a wide knowledge transfer. The pre-trained CLIP model has been used in a variety of open-world visual tasks. Gu et al. [11] proposed to distill knowledge from the pre-trained CLIP image and text encoders to learn open-vocabulary object detectors. Li et al. [24] used the pre-trained text embeddings as semantic label representations to correlate visual features for semantic segmentation. Similar language-driven segmentation methods have been proposed for 2D images [29] and 3D point clouds [30]. In contrast to these methods which have dense supervision, we investigate a pixel-text matching problem in a weakly supervised setting. + +Xie et al. [37] proposed a CLIP-based WSDOL method, which constrains the CAM activated object regions and the background image regions to match and dis-match the corresponding label prompt in the CLIP embedding space, respectively. It also relies on the additionally pre-defined text descriptions to suppress the co-occurring backgrounds. In contrast, without introducing new visual concepts, we propose to directly use the CLIP label text embeddings to infer dense object localization maps, and further refine them with sample-specific contexts, achieving better results in both WSDOL and WSSS (see Table 1 and Table 3). + +# 3. Method + +Overview. We propose a unified transformer framework to construct multi-modal class-specific tokens for weakly supervised dense object localization. As illustrated in Figure 2, an input RGB image is split into patches and then embedded into a sequence of patch tokens. Additionally, the + +proposed transformer has two sets of class-specific tokens, i.e., class-specific visual and textual tokens. The class-specific visual tokens are all initialized by the pre-trained weights of the class token of ViT, and the class-specific textual tokens are initialized by the pre-trained CLIP text embeddings of the class-related prompts and kept constant during training. With the added positional embeddings, these three types of tokens are concatenated and serve as input to the proposed transformer. The input tokens go through consecutive masked self-attention based transformer encoding layers. The output class tokens are averaged along the channel dimension to predict class scores. The input and output class/text tokens are fused and then correlated with the output patch tokens, generating the class-to-patch and text-to-patch correlation maps. These two correlation maps are then globally pooled to produce class scores, respectively. + +During training, the class predictions are supervised by the image-level ground-truth labels via the classification loss. In addition, a batch-softmax contrastive loss is used to encourage the averaged output text token to match the CLIP image embedding. This allows the image-language context transfer, further enabling the class representations to be more sample-adaptive for better localization. During inference, a multi-modal correlation map is generated by fusing the two correlation maps, which are further refined by the transformer attention as suggested in [40], generating the final dense object localization maps. + +# 3.1. Multi-modal class-specific token learning + +In order to construct multi-modal class representations to guide dense object localization, we propose a multi-modal + +![](images/e8f95b54269c1397f33a98677d000611e0a007b29b4a14002010295833885329.jpg) +Figure 3. The detailed structure of the proposed multi-modal token transformer encoder. + +token transformer framework. As illustrated in Figure 2, an input RGB image is first split into $N \times N$ patches, which are then embedded into a sequence of $N^2$ patch tokens. We additionally create $C$ class-specific visual tokens and $C$ class-specific textual tokens, where $C$ is the number of classes. With the added learnable positional embeddings, these three types of tokens are first concatenated and then fed into the transformer encoder (Figure 3). In order to allow class tokens and text tokens to fully interact with patch tokens but not competing with each other, we use the masked self-attention as shown in Figure 3 (right). Finally, the transformer encoder outputs three types of tokens accordingly. + +Class-specific visual token learning. Inspired by MCTformer [40], we use $C$ class tokens to learn class-specific representations. The output class tokens $\mathbf{T}_{cls}^{out} \in \mathbb{R}^{C \times D}$ from the proposed multi-modal token transformer encoder are processed by channel-wise averaging, producing $C$ class scores $\mathbf{y}_{cls} \in \mathbb{R}^C$ . The class scores are supervised by the image-level ground-truth labels $\mathbf{y} \in \mathbb{R}^C$ using the multi-label soft margin loss (MLSM): + +$$ +\begin{array}{l} \mathcal {L} _ {c l s} = M L S M (\mathbf {y} _ {c l s}, \mathbf {y}) = - \\ \frac {1}{C} \sum_ {i = 1} ^ {C} \mathbf {y} ^ {i} \log \sigma \left(\mathbf {y} _ {c l s} ^ {i}\right) + (1 - \mathbf {y} ^ {i}) \log \left(1 - \sigma \left(\mathbf {y} _ {c l s} ^ {i}\right)\right). \tag {1} \\ \end{array} +$$ + +Class-specific textual token learning. CLIP presents a novel way of representing visual concepts by using textual prompts, which provide highly complementary information to the class representations learned from only visual data. It thus exhibits a great potential to better facilitate the dense object localization. This motivates us to construct $C$ class-specific textual tokens by leveraging the rich semantics learned by the pre-trained CLIP language model. More specifically, we create $C$ text prompts using the template of "a photo of [CLS]", where [CLS] refers to each class name in the label set of a given dataset. These text prompts are then fed into the pre-trained CLIP text encoder, generating $C$ text embeddings. We use the $C$ pre-trained label text embeddings to initialize the input text tokens, which are kept constant during training to retain the powerful representation ability of the pre-trained CLIP. + +![](images/f844af8419163d14d08c492dc4c7a2120c528b043c53002fd810720012f4c482.jpg) +Figure 4. The regularization loss. The proposed multi-modal token transformer is encouraged to produce text tokens to match the image embeddings of the pre-trained CLIP image encoder, enabling image-language context transfer. + +# 3.2. Sample-specific local contexts + +To construct class-specific tokens tailored for each sample, we propose to exploit sample-specific local contexts to complement the global input class-specific tokens. + +Visual context. Inspired by the context-aware prompting [29], we also use the visual context to refine the text embeddings. In contrast to [29] which employs an additional cross-attention module to model the interaction between textual and visual features, the inherent self-attention blocks of the proposed transformer encoder enables the text tokens to aggregate information from patch tokens, capturing the sample-specific visual context. Intuitively, the text embeddings of simple prompts (i.e., "a photo of [CLS]", after being refined by the sample-specific visual context, could represent more accurate image-related text. Given that the input text tokens $\mathbf{T}_{txt}^{in}\in \mathbb{R}^{C\times D}$ are global textual representations for each label while the output text tokens $\mathbf{T}_{txt}^{out}\in \mathbb{R}^{C\times D}$ capture sample-specific representations, we can obtain the enhanced class-specific textual tokens by fusing the input and output text tokens: $\mathbf{T}_{txt} = \mathbf{T}_{txt}^{in} + \lambda_{1}\cdot \mathbf{T}_{txt}^{out}$ . Similarly, the enhanced class-specific visual tokens are obtained by $\mathbf{T}_{cls} = \mathbf{T}_{cls}^{in} + \lambda_{2}\cdot \mathbf{T}_{cls}^{out}$ , where $\lambda_{1}$ and $\lambda_{2}$ are two learnable weights. + +Image-language context. To further ensure that the output text tokens can capture meaningful sample-specific context, we propose to impose a contrastive loss on the output text tokens by leveraging the pre-trained CLIP image model. More specifically, as illustrated in Figure 4, given a batch of $B$ input image-text pairs (the input text for each image is the same, i.e., $C$ label prompts), the proposed transformer encoder outputs $B$ text tokens. These $B$ text tokens are used to compute the similarity matrix $\mathbf{S} \in \mathbb{R}^{B \times B}$ with $B$ visual embeddings by feeding the input images to the pre-trained CLIP image encoder. When the output text token and the CLIP image embedding are from the same image, they form a positive pair. Otherwise, they form a negative pair. Such pair construction is reasonable in that: (i) The CLIP image embedding of an image matches its corresponding text description which commonly contains richer information than image-level labels. Thus, even two images with the same + +class label generally have distinct CLIP image embeddings. (ii) Although the input text tokens are the same for each image, they are progressively refined through interactions with patch tokens at every layer of our model. This enables the output text tokens to represent specific input image content, i.e., different images have different output text tokens. Therefore, the $B$ scores in the diagonal of the similarity matrix are encouraged to be maximized and the other $B^2 - B$ similarity scores are minimized. This is implemented by computing the cross-entropy classification loss between the similarity matrix $\mathbf{S}$ and the ground-truth labels, i.e., an identity matrix $\mathbf{I} \in \mathbb{R}^{B \times B}$ : + +$$ +\mathcal {L} _ {\text {c o n t r a s t}} = \operatorname {C r o s s E n t r o p y} (\mathbf {S}, \mathbf {I}). \tag {2} +$$ + +The benefit is two-fold: (i) encouraging the output text tokens to match the pre-trained CLIP image embeddings, allows us to better adapt the pre-trained CLIP model to our target datasets. This also implicitly transfers richer image-related language contexts to the output text tokens, which are more beneficial for guiding the dense object localization, compared to the simple label prompts; (ii) through the batch-softmax cross-entropy loss on the similarity matrix, each text token is also constrained to be discriminative from other text tokens in the same batch, thereby enhancing the sample-specific characteristic of the text tokens. + +# 3.3. Image-label supervised multi-modal class-top-patch correlation learning + +Given the multi-modal class-specific tokens, i.e., $\mathbf{T}_{txt}$ and $\mathbf{T}_{cls}$ , the class-specific dense localization can be inferred by computing the correlation between the class-specific tokens and the patch tokens. More specifically, the output patch tokens from the proposed multi-modal token transformer encoder are first linearly projected and then transposed to 2D feature maps $\mathbf{F} \in \mathbb{R}^{N \times N \times D}$ , where $D$ is the feature dimension. The text-to-patch correlation maps $\mathbf{C}_{t2p} \in \mathbb{R}^{N \times N \times C}$ and the class-to-patch correlation maps $\mathbf{C}_{c2p} \in \mathbb{R}^{N \times N \times C}$ can be computed as: $\mathbf{C}_{t2p} = \mathbf{FT}_{txt}^{\top}$ and $\mathbf{C}_{c2p} = \mathbf{FT}_{cls}^{\top}$ , respectively. + +In contrast to recent language-driven dense prediction works [24, 29] where pixel-level ground-truth labels are used to supervise the learning of the class-to-patch correlations, only image-level labels are available in our case. Based on the assumption that the weighted sum of a class-to-patch correlation map can be regarded as the global correlation between the class and the whole image, we relax this class-to-patch correlation problem into a class-to-image correlation problem, which can thus be formulated into a classification task. Given that the commonly used global average pooling (GAP) and global max pooling inevitably either over-estimate or under-estimate the size of the object regions when generating the global correlation scores, respectively [16], we thus adopt the Global Weighted Rank + +ing Pooling (GwRP) method [16]. Compared to GAP which assigns the same weight to each patch for aggregation, GwRP assigns different weights according to the ranking of the correlation scores of all patches for each class: + +$$ +G _ {c} \left(\mathbf {X} _ {\star}\right) = \frac {1}{Z (d)} \sum_ {j = 1} ^ {N ^ {2}} d ^ {j - 1} X _ {\star} ^ {r _ {j}, c}, \tag {3} +$$ + +where $\mathbf{X}_{\star} \in \mathbb{R}^{N^2 \times C}$ are the flattened correlation maps; $\mathbf{y}_{\star} = G(\mathbf{X}_{\star}) \in \mathbb{R}^{C}$ are the aggregated class correlation scores; $r_j$ is the index of the ranking, i.e., for a class $c$ , $X_{\star}^{r_1, c} > X_{\star}^{r_2, c} > \dots > X_{\star}^{r_{N^2}, c}$ ; $Z(d) = \sum_{j=1}^{N^2} d^{j-1}$ , $d$ is a decay parameter. Therefore, the aggregated class-to-patch and text-to-patch correlation scores can be obtained as: + +$$ +\mathbf {y} _ {c 2 p} = G \left(\mathbf {X} _ {c 2 p}\right), \tag {4} +$$ + +$$ +\mathbf {y} _ {t 2 p} = G \left(\mathbf {X} _ {t 2 p}\right). \tag {5} +$$ + +These class scores can then be supervised by the groundtruth image-level labels $\mathbf{y}$ via the classification loss: + +$$ +\mathcal {L} _ {\text {c l a s s - p a t c h}} = M L S M \left(\mathbf {y} _ {c 2 p}, \mathbf {y}\right), \tag {6} +$$ + +$$ +\mathcal {L} _ {\text {t e x t - p a t c h}} = M L S M \left(\mathbf {y} _ {t 2 p}, \mathbf {y}\right). \tag {7} +$$ + +The total objective loss function for training the proposed multi-modal token transformer is as follows: + +$$ +\mathcal {L} _ {\text {t o t a l}} = \mathcal {L} _ {\text {c l s}} + \mathcal {L} _ {\text {c o n t r a s t}} + \mathcal {L} _ {\text {t e x t - p a t c h}} + \mathcal {L} _ {\text {c l a s s - p a t c h}} \tag {8} +$$ + +# 3.4. Class-specific dense localization inference + +Once the training of the proposed multi-modal token transformer is completed, the correlations between two modalities of class-specific tokens and the patch tokens are computed to produce two maps, i.e., text-to-patch correlation maps $\mathbf{C}_{t2p}$ and class-to-patch correlation maps $\mathbf{C}_{c2p}$ , respectively. We further combine these two maps via an element-wise sum to obtain the multi-modal class-specific dense localization maps $\mathbf{C}_{mm} \in \mathbb{R}^{N \times N \times C}$ : + +$$ +\mathbf {C} _ {m m} = \mathbf {C} _ {t 2 p} + \mathbf {C} _ {c 2 p}. \tag {9} +$$ + +Moreover, similar to MCTformer [40], the multi-class token based transformer can produce class-specific attention maps $\mathbf{A}_{c2p}$ and a patch-level pairwise affinity map $\mathbf{A}_{p2p}$ from the transformer self-attention maps. Given their different localization mechanisms, the transformer self-attention maps can well complement the proposed multi-modal class-specific localization maps. We thus perform an element-wise multiplication (o) on these two maps to produce the fused maps $\hat{\mathbf{M}}\in \mathbb{R}^{N\times N\times C}$ , which are further refined by the patch-level pairwise affinity map: + +$$ +\hat {\mathbf {M}} = \mathbf {C} _ {m m} \circ \mathbf {A} _ {c 2 p}, \tag {10} +$$ + +$$ +\mathbf {M} (i, j, c) = \sum_ {k = 1} ^ {N} \sum_ {l = 1} ^ {N} \mathbf {A} _ {p 2 p} (i, j, k, l) \cdot \hat {\mathbf {M}} (k, l, c), \tag {11} +$$ + +where $\mathbf{M} \in \mathbb{R}^{N \times N \times C}$ are the refined fused maps, which are then up-sampled to the original size of the input image before being normalized via the min-max normalization, to generate the final dense object localization maps. + +# 4. Experiments + +# 4.1. Experimental settings + +Datasets. We evaluated the proposed method on three datasets including two multi-label datasets, i.e., PASCAL VOC 2012 [9] and MS COCO 2014 [26], and one single-label dataset, i.e., OpenImages [5]. PASCAL VOC 2012 has 20 foreground object classes and one background class. It is split into three subsets, i.e., training (train), validation (val) and test sets including 1,464, 1,449, and 1,456 images, respectively. Following the common practice [18, 19], the training set was augmented to 10,582 images by adding the data from [13]. MS COCO 2014 has 80 foreground object classes and one background class. It has around 80K training images and 40K images for evaluation. OpenImages has 37,319 images in total and 100 object classes. It is split into a training set with 29,819 images, a validation set with 2,500 images and a test set with 5,000 images. + +Evaluation metrics. For the multi-label datasets, we followed the common practice [18, 20, 21] to use the mean Intersection-over-Union (mIoU) to evaluate the multi-label dense localization maps on the train set, and used mIoU to evaluate WSSS results on the val and test sets. Results on the PASCAL VOC test set were obtained from the online official evaluation server. For the single-label dataset, we focused on the pixel-level evaluation and followed the prior works [5, 52] which used the peak Intersection-over-Union (pIoU) and the pixel average precision (PxAP) to evaluate the single-label dense localization maps on the test set. + +Implementation details. We built the proposed transformer using ViT-base as backbone. We used the Adam optimizer with the initial learning rate of 5e-4 and a batch size of 32. We trained our network for 60 epochs on PASCAL VOC and MS COCO and 10 epochs on OpenImages. For the VL model, we used the publicly available pre-trained CLIP model (ViT-B/16). To generate pseudo masks for WSSS, we followed prior works [20, 21, 32, 44] to use IR-Net [1] to post-process our dense localization maps. For semantic segmentation, we use ResNet38-based Deeplab-V1. More details can be found in the Supplementary Materials. + +# 4.2. Comparison with State-of-the-art of WSDOL + +Multi-label dense localization. Table 1 reports the evaluation results of the generated multi-label dense localization maps, which are commonly used as seeds to generate pseudo masks in WSSS. The proposed method achieved mIoUs of $66.3\%$ and $40.9\%$ on the train sets of PASCAL VOC 2012 and MS COCO 2014, respectively, outperform- + +Table 1. Evaluation of the generated multi-class multi-label dense localization maps in terms of mIoU (\%) on the PASCAL VOC 2012 and MS COCO 2014 train sets. Cls.: Classification. $\dagger$ denotes the reproduced result by [18], $\ddagger$ denotes the reproduced result by [44], and $\star$ denotes our reproduced result. + +
MethodCls. BackboneVOCCOCO
CAM (CVPR16) [48]ResNet5048.833.5†
SEAM (CVPR20) [34]ResNet3855.425.1‡
RIB (NeurIPS21) [18]ResNet5056.536.5
AdvCAM (CVPR21) [19]ResNet3855.637.2
CLIMS (CVPR22) [37]ResNet5056.6-
SIPE (CVPR22) [4]ResNet5058.6-
W-OoD (CVPR22) [21]ResNet5059.1-
Du et al. (CVPR22) [8]ResNet3861.5-
TS-CAM (ICCV21) [10]ViT-small41.3-
MCTformer (CVPR22) [40]ViT-small61.7-
MCTformer (CVPR22) [40]ViT-base62.3*-
OursViT-base66.340.9
+ +Table 2. Evaluation of multi-class single-label dense localization on the OpenImages test set. + +
MethodCls. backbonepIoUPxAP
CAM (CVPR16) [48]ResNet5043.058.2
HAS (ICCV17) [31]ResNet5041.955.1
ACoL (CVPR18) [47]ResNet5041.756.4
SPG (ECCV18) [46]ResNet5041.855.8
ADL (CVPR19) [6]ResNet5042.155.0
CutMix (ICCV19) [43]ResNet5042.757.6
PAS (ECCV20) [2]ResNet50-60.9
IVR (ICCV21) [15]ResNet50-58.9
Zhu et al. (CVPR22) [52]ResNet5049.765.4
CREAM (CVPR22) [38]ResNet50-64.7
Zhu et al. (ECCV22) [51]ResNet5052.267.7
OursViT-base57.673.3
+ +ing the state-of-the-art methods by significant margins. + +Single-label dense localization. We also evaluated the effectiveness of the proposed method for single-label dense object localization. We used the recently proposed OpenImages dataset [5], which has more challenging background context than the commonly used ones for weakly supervised object localization. As shown in Table 2, the proposed method attained a pIoU of $57.6\%$ and a PxAP of $73.3\%$ , achieving better results than the state-of-the-art methods. + +# 4.3. Comparison with State-of-the-art of WSSS + +PASCAL VOC 2012. As shown in Table 3, the proposed method achieved mIoUs of $72.2\%$ and $72.2\%$ on the val and test sets of PASCAL VOC 2012, respectively, outperforming other WSSS methods only using image-level labels. + +MS COCO 2014. Table 4 also shows that, on a more challenging dataset, i.e., MS COCO 2014, the proposed method attained the best mIoU of $45.9\%$ , which is significantly better than those relying on saliency maps, achieving the new state-of-the-art result. These results clearly demonstrate the effectiveness and the good generalization ability of the proposed method for WSSS. + +Table 3. Performance comparison of the state-of-the-art WSSS methods on the PASCAL VOC 2012 val and test sets. Seg.: DeepLab version. Sup.: supervision; I: image-level ground-truth labels; S: off-the-shelf saliency maps; L: pre-trained VL model. + +
MethodBackboneSeg.Sup.ValTest
EDAM (CVPR21) [36]ResNet101V2I+S70.970.6
EPS (CVPR21) [23]ResNet101V1I+S71.071.8
Yao et al. (CVPR21) [41]ResNet101V2I+S68.368.5
AuxSegNet (ICCV21) [39]ResNet38V1I+S69.068.6
Li et al. (CVPR22) [25]ResNet101V2I+S72.072.9
Du et al. (CVPR22) [8]ResNet101V2I+S72.673.6
RCA (CVPR22) [50]ResNet38V2I+S72.272.8
L2G (CVPR22) [14]ResNet38V1I+S72.073.0
AdvCAM (CVPR21) [19]ResNet101V2I68.168.0
ECS-Net (ICCV21) [33]ResNet38V1I66.667.6
Kweon et al. (ICCV21) [17]ResNet38V1I68.468.2
CDA (ICCV21) [32]ResNet38V1I66.166.8
Zhang et al. (ICCV21) [45]ResNet38V1I67.868.5
MCTformer (CVPR22) [40]ResNet38V1I71.971.6
AMN (CVPR22) [22]ResNet101V2I70.770.6
W-OoD (CVPR22) [21]ResNet38V1I70.770.1
SIPE (CVPR22) [3]ResNet38V1I68.269.7
Yoon et al. (ECCV22) [42]ResNet38V1I70.971.7
CLIMS (CVPR22) [37]ResNet101V2I+L69.368.7
OursResNet38V1I+L72.272.2
+ +Table 4. Performance comparison of state-of-the-art WSSS methods on the MS COCO 2014 val set in terms of mIoU (\%). + +
MethodBackboneSeg.Sup.Val
EPS (CVPR21) [23]VGG16V2I+S35.7
RCA (CVPR22) [50]VGG16V2I+S36.8
AuxSegNet (ICCV21) [39]ResNet38V1I+S33.9
L2G (CVPR22) [14]ResNet101V2I+S44.2
Kweon et al. (ICCV21) [17]ResNet38V1I36.4
CDA (ICCV21) [32]ResNet38V1I33.2
AdvCAM (CVPR21) [19]ResNet101V2I44.4
MCTformer (CVPR22) [40]ResNet38V1I42.0
Li et al. (CVPR22) [25]ResNet101V2I44.7
AMN (CVPR22) [22]ResNet101V2I44.7
SIPE (CVPR22) [3]ResNet38V1I43.6
Yoon et al. (ECCV22) [42]ResNet38V1I44.8
OursResNet38V1I+L45.9
+ +# 4.4. Ablation studies + +Effect of learning multi-modal class-specific tokens. As shown in Table 5, without text tokens, only relying on the multi-class token transformer attention results in a dense localization mIoU of $57.5\%$ ; By learning the multi-class tokens to correlate patch tokens using image-level supervision with GAP, a significant gain of $4.6\%$ was achieved in mIoU. GwRP outperforms GAP, improving the dense localization result to $62.7\%$ . By additionally learning class-specific textual tokens, the resulting multi-modal correlation maps driven by the global/input multi-modal class-specific tokens, attained an better mIoU of $64.1\%$ , compared to that of the multi-modal correlation maps driven by the local/output multi-modal class-specific tokens. The fusion of the global and local multi-modal class-specific tokens leads to the best dense localization maps with an mIoU of $66.3\%$ . These re + +Table 5. Evaluation of the class-specific dense localization maps generated by learning correlations (cor.) between class-specific tokens and patch tokens on the PASCAL VOC 2012 train set. + +
ConfigurationPoolingmIoU
Multi-class token attention-57.5
+ class-to-patch correlationGAP62.1
+ class-to-patch correlation (C2P cor.)GWRP62.7
+ Multi-modal (C2P + text-to-patch) cor. (global)GWRP64.1
+ Multi-modal cor. (local)GWRP63.3
+ Multi-modal cor. (global + local)GWRP66.3
+ +Table 6. Evaluation of the class-specific dense localization by different regularization methods for sample-specific local context learning on the PASCAL VOC 2012 train set. CE: cross-entropy. + +
Visual contextImage-language contextmIoU
Prior knowledgeRegularization loss
X--64.1
✓--64.8
✓CLIP caption embed.L163.7
✓CLIP caption embed.Batch-contrast CE65.1
✓CLIP image embed.Batch-contrast CE66.3
+ +sults demonstrate the effectiveness of the proposed method of learning multi-modal class-specific tokens in producing accurate class-specific dense localization maps. + +Figure 5 visualizes the generated dense object localization maps in different configurations. The multi-class token transformer attention only localizes partial object regions and includes many noises, such as the dog and the background in the third and second rows of Figure 5 (b), respectively. Learning the multi-class tokens to correlate patch tokens leads to more complete object localization maps, such as the sofa in the third row. This also results in several falsely activated regions near the object, such as the chair in the second rows of Figure 5 (c). Adding class-specific textual tokens leads to improved localization maps (Figure 5 (d)) with expanded true object regions and reduced falsely activated backgrounds. Further incorporating sample-specific context produces the best localization maps (Figure 5 (e)) which are close to the ground-truth. This demonstrates the advantage of the proposed method in generating dense localization maps for objects of different scales in various challenging scenarios. + +Effect of sample-specific context learning. As shown in Table 6, without sample-specific context learning, the learned multi-modal class-specific tokens result in the class-specific localization maps with an mIoU of $64.1\%$ . Incorporating the visual context information aggregated by the output text tokens leads to an improved mIoU of $64.8\%$ . To further learn image-language context to enhance the text token learning, we investigated two regularization methods: (i) Leveraging the pre-trained CLIP text embeddings of image captions. More specifically, we used a pre-trained image captioning model, ClipCap [27], to generate captions for each image. By minimizing the distance between the text + +![](images/7dc582e77cf79972370724017bc4daf788abe7f8e94fa2ec3bf4e58f289a3266.jpg) +(a) +Figure 5. Visualization of the multi-label dense object localization results using different configurations of the proposed method on the PASCAL VOC 2012 train set. (a) Input; (b) MCTAttn (Multi-class token attention); (c) MCTAttn + C2P-CMap (class-to-patch correlation maps); (d) MCTAttn + MM-CMap (multi-modal correlation maps, i.e., class-to-patch correlation maps + text-to-patch correlation maps); (e) MCTAttn + MM-CMap + sample-specific context; (f) Ground-truth. + +![](images/5613f827da8a76697a5eab736a7420005af8f6422da8bc0f5c86637bb40e51bf.jpg) +(b) + +![](images/4aff8e6dad1d49d4b29dc0d914515618aa4d15286a5d6c9eb0f13112b78d533d.jpg) +(c) + +![](images/319f09ad4562a4cfd5fa60701bfab4f0c114b6836e243b3f6605874c43fd4da4.jpg) +(d) + +![](images/d216250e1668e2152124c23142130a9e3311e2b5a281fea5656dd848b1d4bddd.jpg) +(e) + +![](images/07fdeef3823b929ca431cf15abadb7f9e966a880c0e01712a7152a82798438a0.jpg) +(f) + +Table 7. Evaluation of the class-specific dense localization maps by using different prompting methods for constructing class-specific textual tokens on the PASCAL VOC 2012 train set. + +
PromptmIoU
a photo of [class]66.3
Prompt ensembling65.5
CoOp (learning-based prompts) [49]66.0
+ +tokens and the pre-trained CLIP caption embeddings via L1 loss, the localization performance drops by $1.1\%$ . Using the batch-softmax cross-entropy loss yields a slightly improved localization mIoU of $65.1\%$ . L1 loss on the features allows to transfer all information of the generated image captions including useful and interfering contexts that could reduce the discriminative ability of the class-specific textual tokens, thus degrading the localization performance. In contrast, the batch-softmax cross-entropy loss on the feature similarities explicitly separates each text token from other text tokens in a same batch, which facilitates the sample-specific token learning for better localization. (ii) Leveraging the pre-trained CLIP image embeddings. By applying the batch-softmax cross-entropy loss on the similarity matrix of our text tokens and the pre-trained CLIP image embeddings, the class-specific localization attains a performance gain of $1.5\%$ with the best mIoU of $66.3\%$ . Compared to learning from the generated image captions, the batch-contrastive image-text matching loss enables the text tokens to better align with the visual features, thus leading to better dense localization results. + +Effects of different prompts. We used different prompting methods to construct the class-specific textual tokens and evaluated their effects in the resulting class-specific dense localization maps. More specifically, we investigated three types of prompting methods including (i) the most commonly used prompt, i.e., "a photo of [class]"; (ii) Prompt ensembling, which ensembles 8 best prompts over the em + +bedding space via averaging as suggested in [28]; (iii) A learning-based prompting method, CoOp [49]. As shown in Table 7, these three prompting methods lead to comparable dense localization results. Among them, the general prompt template "a photo of [class]" produces the best localization mIoU. We speculate this is due to the general prompt template better preserving the class-discriminative language priors from the pre-trained CLIP model, compared to ensembling several prompts or learning extra parameters. + +# 5. Conclusion + +We proposed a new weakly supervised dense object localization method by explicitly constructing multi-modal class representations via a transformer-based framework. The proposed transformer includes class-specific visual and textual tokens by learning visual information from the target image data and exploiting language information from the pre-trained CLIP model, respectively. These diverse supervisions enable the multi-modal class-specific tokens to provide complementary information for more discriminative dense localization. By further incorporating the sample-specific contexts from visual context and image-language context, more adaptive multi-modal class-specific tokens can be learned to facilitate better dense localization. Our superior WSDOL results on two multi-label datasets, (i.e., PASCAL VOC and MS COCO) and one single-label dataset, (i.e., OpenImages), demonstrate the effectiveness of the proposed method. We also report state-of-the-art WSSS results on PASCAL VOC and MS COCO. + +Acknowledgment. This research is supported in part by Australian Research Council Grant DP210101682, DP210102674, IH180100002, the Early Career Scheme of the Research Grants Council (RGC) of the Hong Kong SAR under grant No. 26202321 and HKUST Startup Fund No. R9253. + +# References + +[1] Jiwoon Ahn, Sunghyun Cho, and Suha Kwak. Weakly supervised learning of instance segmentation with inter-pixel relations. In CVPR, 2019. 6 +[2] Wonho Bae, Junhyug Noh, and Gunhee Kim. Rethinking class activation mapping for weakly supervised object localization. In ECCV, 2020. 6 +[3] Qi Chen, Lingxiao Yang, Jian-Huang Lai, and Xiaohua Xie. Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation. In CVPR, 2022. 2, 7 +[4] Zhaozheng Chen, Tan Wang, Xiongwei Wu, Xian-Sheng Hua, Hanwang Zhang, and Qianru Sun. Class re-activation maps for weakly-supervised semantic segmentation. In CVPR, 2022. 2, 6 +[5] Junsuk Choe, Seong Joon Oh, Seungho Lee, Sanghyuk Chun, Zeynep Akata, and Hyunjung Shim. Evaluating weakly supervised object localization methods right. In CVPR, 2020. 1, 6 +[6] Junsuk Choe and Hyunjung Shim. Attention-based dropout layer for weakly supervised object localization. In CVPR, 2019. 6 +[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 1 +[8] Ye Du, Zehua Fu, Qingjie Liu, and Yunhong Wang. Weakly supervised semantic segmentation by pixel-to-prototype contrast. In CVPR, 2022. 2, 6, 7 +[9] Mark Everingham and John Winn. The pascal visual object classes challenge 2012 (voc2012) development kit. Pattern Anal. Stat. Model. Comput. Learn., Tech. Rep, 2007:1-45, 2012. 6 +[10] Wei Gao, Fang Wan, Xingjia Pan, Zhiliang Peng, Qi Tian, Zhenjun Han, Bolei Zhou, and Qixiang Ye. Ts-cam: Token semantic coupled attention map for weakly supervised object localization. In ICCV, 2021. 2, 6 +[11] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. In ICLR, 2022. 3 +[12] Saurav Gupta, Sourav Lakhotia, Abhay Rawat, and Rahul Tallamraju. Vitol: Vision transformer for weakly supervised object localization. In CVPR, 2022. 2 +[13] Bharath Hariharan, Pablo Arbeláez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik. Semantic contours from inverse detectors. In ICCV, 2011. 6 +[14] Peng-Tao Jiang, Yuqi Yang, Qibin Hou, and Yunchao Wei. L2g: A simple local-to-global knowledge transfer framework for weakly supervised semantic segmentation. In CVPR, 2022. 2, 7 +[15] Jeesoo Kim, Junsuk Choe, Sangdoo Yun, and Nojun Kwak. Normalization matters in weakly supervised object localization. In ICCV, 2021. 6 +[16] Alexander Kolesnikov and Christoph H Lampert. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In ECCV, 2016. 5 + +[17] Hyeokjun Kweon, Sung-Hoon Yoon, Hyeonseong Kim, Daehee Park, and Kuk-Jin Yoon. Unlocking the potential of ordinary classifier: Class-specific adversarial erasing framework for weakly supervised semantic segmentation. In ICCV, 2021. 7 +[18] Jungbeom Lee, Jooyoung Choi, Jisoo Mok, and Sungroh Yoon. Reducing information bottleneck for weakly supervised semantic segmentation. NeurIPS, 2021. 6 +[19] Jungbeom Lee, Eunji Kim, Jisoo Mok, and Sungroh Yoon. Anti-adversarily manipulated attributions for weakly supervised semantic segmentation and object localization. PAMI, 2022. 6, 7 +[20] Jungbeom Lee, Eunji Kim, and Sungroh Yoon. Antiadversarily manipulated attributions for weakly and semi-supervised semantic segmentation. In CVPR, 2021. 2, 6 +[21] Jungbeom Lee, Seong Joon Oh, Sangdoo Yun, Junsuk Choe, Eunji Kim, and Sungroh Yoon. Weakly supervised semantic segmentation using out-of-distribution data. In CVPR, 2022. 1, 6, 7 +[22] Minhyun Lee, Dongseob Kim, and Hyunjung Shim. Threshold matters in wsss: Manipulating the activation for the robust and accurate segmentation model against thresholds. In CVPR, 2022. 7 +[23] Seungho Lee, Minhyun Lee, Jongwuk Lee, and Hyunjung Shim. Railroad is not a train: Saliency as pseudo-pixel supervision for weakly supervised semantic segmentation. In CVPR, 2021. 7 +[24] Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and René Ranftl. Language-driven semantic segmentation. In ICLR, 2022. 3, 5 +[25] Jing Li, Junsong Fan, and Zhaoxiang Zhang. Towards noiseless object contours for weakly supervised semantic segmentation. In CVPR, 2022. 7 +[26] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 6 +[27] Ron Mokady, Amir Hertz, and Amit H Bermano. Clipcap: Clip prefix for image captioning. arXiv preprint arXiv:2111.09734, 2021. 7 +[28] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 8 +[29] Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In CVPR, 2022. 3, 4, 5 +[30] David Rozenberszki, Or Litany, and Angela Dai. Language-grounded indoor 3d semantic segmentation in the wild. In ECCV, 2022. 3 +[31] Krishna Kumar Singh and Yong Jae Lee. Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization. In ICCV, 2017. 6 +[32] Yukun Su, Ruizhou Sun, Guosheng Lin, and Qingyao Wu. Context decoupling augmentation for weakly supervised semantic segmentation. In ICCV, 2021. 2, 6, 7 + +[33] Kunyang Sun, Haoqing Shi, Zhengming Zhang, and Yongming Huang. ECS-net: Improving weakly supervised semantic segmentation by using connections between class activation maps. In ICCV, 2021. 7 +[34] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In CVPR, 2020. 6 +[35] Yunchao Wei, Huaxin Xiao, Honghui Shi, Zequn Jie, Jiashi Feng, and Thomas S Huang. Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation. In CVPR, 2018. 2 +[36] Tong Wu, Junshi Huang, Guangyu Gao, Xiaoming Wei, Xiaolin Wei, Xuan Luo, and Chi Harold Liu. Embedded discriminative attention mechanism for weakly supervised semantic segmentation. In CVPR, 2021. 7 +[37] Jinheng Xie, Xianxu Hou, Kai Ye, and Linlin Shen. Clims: Cross language image matching for weakly supervised semantic segmentation. In CVPR, 2022. 3, 6, 7 +[38] Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng, Rui-Wei Zhao, Tao Zhang, Xuequan Lu, and Shang Gao. Cream: Weakly supervised object localization via class re-activation mapping. In CVPR, 2022. 6 +[39] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, Ferdous Sohel, and Dan Xu. Leveraging auxiliary tasks with affinity learning for weakly supervised semantic segmentation. In ICCV, 2021. 7 +[40] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, and Dan Xu. Multi-class token transformer for weakly supervised semantic segmentation. In CVPR, 2022. 1, 2, 3, 4, 5, 6, 7 +[41] Yazhou Yao, Tao Chen, Guo-Sen Xie, Chuanyi Zhang, Fumin Shen, Qi Wu, Zhenmin Tang, and Jian Zhang. Nonsalient region object mining for weakly supervised semantic segmentation. In CVPR, 2021. 2, 7 +[42] Sung-Hoon Yoon, Hyeokjun Kweon, Jegyeong Cho, Shinjeong Kim, and Kuk-Jin Yoon. Adversarial erasing framework via triplet with gated pyramid pooling layer for weakly supervised semantic segmentation. In ECCV, 2022. 7 +[43] Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. Cutmix: Regularization strategy to train strong classifiers with localizable features. In ICCV, 2019. 6 +[44] Dong Zhang, Hanwang Zhang, Jinhui Tang, Xiansheng Hua, and Qianru Sun. Causal intervention for weakly-supervised semantic segmentation. In NeurIPS, 2020. 6 +[45] Fei Zhang, Chaochen Gu, Chenyue Zhang, and Yuchao Dai. Complementary patch for weakly supervised semantic segmentation. In ICCV, 2021. 7 +[46] Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. Self-attention generative adversarial networks. In ICML, 2019. 6 +[47] Xiaolin Zhang, Yunchao Wei, Jiashi Feng, Yi Yang, and Thomas S Huang. Adversarial complementary learning for weakly supervised object localization. In CVPR, 2018. 6 +[48] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In CVPR, 2016. 1, 6 + +[49] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9):2337-2348, 2022. 8 +[50] Tianfei Zhou, Meijie Zhang, Fang Zhao, and Jianwu Li. Regional semantic contrast and aggregation for weakly supervised semantic segmentation. In CVPR, 2022. 7 +[51] Lei Zhu, Qian Chen, Lujia Jin, Yunfei You, and Yanye Lu. Bagging regional classification activation maps for weakly supervised object localization. In ECCV, 2022. 6 +[52] Lei Zhu, Qi She, Qian Chen, Yunfei You, Boyu Wang, and Yanye Lu. Weakly supervised object localization as domain adaption. In CVPR, 2022. 6 \ No newline at end of file diff --git a/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/images.zip b/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..5687be5766443e2fa29f9e5813da471e6b480e76 --- /dev/null +++ b/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:570751fc612ccd502f8a8da624c56777b95ccd831500b9c38529d31e5798070a +size 648943 diff --git a/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/layout.json b/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..dbcf71aca8e6a6d61822a8526ebd3b3701d9fa60 --- /dev/null +++ b/2023/Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization/layout.json @@ -0,0 +1,8176 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 67, + 102, + 526, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 102, + 526, + 140 + ], + "spans": [ + { + "bbox": [ + 67, + 102, + 526, + 140 + ], + "type": "text", + "content": "Learning Multi-Modal Class-Specific Tokens for Weakly Supervised Dense Object Localization" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "spans": [ + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "text", + "content": "Lian Xu" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "text", + "content": ", Wanli Ouyang" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "text", + "content": ", Mohammed Bennamoun" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "text", + "content": ", Farid Boussaid" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "text", + "content": ", and Dan Xu" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "text", + "content": "The University of Western Australia " + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "text", + "content": "Shanghai AI Laboratory \n" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 92, + 160, + 500, + 204 + ], + "type": "text", + "content": "Hong Kong University of Science and Technology" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 150, + 205, + 445, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 150, + 205, + 445, + 217 + ], + "spans": [ + { + "bbox": [ + 150, + 205, + 445, + 217 + ], + "type": "text", + "content": "{lian.xu,mohammed.bennamoun,farid.boussaid}@uwa.edu.au," + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 175, + 220, + 414, + 231 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 175, + 220, + 414, + 231 + ], + "spans": [ + { + "bbox": [ + 175, + 220, + 414, + 231 + ], + "type": "text", + "content": "wanli.ouyang@sydney.edu.au, danxu@cse.ust.hk" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 143, + 258, + 192, + 272 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 258, + 192, + 272 + ], + "spans": [ + { + "bbox": [ + 143, + 258, + 192, + 272 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 285, + 289, + 606 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 285, + 289, + 606 + ], + "spans": [ + { + "bbox": [ + 46, + 285, + 289, + 606 + ], + "type": "text", + "content": "Weakly supervised dense object localization (WSDOL) relies generally on Class Activation Mapping (CAM), which exploits the correlation between the class weights of the image classifier and the pixel-level features. Due to the limited ability to address intra-class variations, the image classifier cannot properly associate the pixel features, leading to inaccurate dense localization maps. In this paper, we propose to explicitly construct multi-modal class representations by leveraging the Contrastive Language-Image Pretraining (CLIP), to guide dense localization. More specifically, we propose a unified transformer framework to learn two-modalities of class-specific tokens, i.e., class-specific visual and textual tokens. The former captures semantics from the target visual data while the latter exploits the class-related language priors from CLIP, providing complementary information to better perceive the intra-class diversities. In addition, we propose to enrich the multi-modal class-specific tokens with sample-specific contexts comprising visual context and image-language context. This enables more adaptive class representation learning, which further facilitates dense localization. Extensive experiments show the superiority of the proposed method for WSDOL on two multi-label datasets, i.e., PASCAL VOC and MS COCO, and one single-label dataset, i.e., OpenImages. Our dense localization maps also lead to the state-of-the-art weakly supervised semantic segmentation (WSSS) results on PASCAL VOC and MS COCO." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 623, + 128, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 623, + 128, + 635 + ], + "spans": [ + { + "bbox": [ + 47, + 623, + 128, + 635 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 643, + 287, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 643, + 287, + 693 + ], + "spans": [ + { + "bbox": [ + 46, + 643, + 287, + 693 + ], + "type": "text", + "content": "Fully supervised dense prediction tasks have achieved great success, which however comes at the cost of expensive pixel-level annotations. To address this issue, recent works have investigated the use of weak labels, such as image-" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 319, + 257, + 533, + 389 + ], + "blocks": [ + { + "bbox": [ + 319, + 257, + 533, + 389 + ], + "lines": [ + { + "bbox": [ + 319, + 257, + 533, + 389 + ], + "spans": [ + { + "bbox": [ + 319, + 257, + 533, + 389 + ], + "type": "image", + "image_path": "985161060fc3d76af78cb522c0970075b3ac7ac44f5526c63a456f5bfd0c5096.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 396, + 545, + 430 + ], + "lines": [ + { + "bbox": [ + 305, + 396, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 305, + 396, + 545, + 430 + ], + "type": "text", + "content": "Figure 1. (a) CAM exploits the correlation between the image classifier and the pixel features. (b) We propose to construct multimodal class-specific tokens to guide dense object localization." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 436, + 545, + 519 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 436, + 545, + 519 + ], + "spans": [ + { + "bbox": [ + 304, + 436, + 545, + 519 + ], + "type": "text", + "content": "level labels, to generate dense object localization maps as pseudo labels for those tasks. For the weakly supervised object localization (WSOL) task, most methods evaluate localization results on the bounding-box level and a few recent methods [5] evaluate on the pixel level. We use WSDOL to focus on the pixel-level evaluation, which is critical for downstream dense prediction tasks such as WSSS." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 521, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 546, + 713 + ], + "type": "text", + "content": "Previous works have exploited Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) [7] for WSDOL with image-level labels [21, 40]. These methods have generally relied on Class Activation Mapping (CAM) [48], which generates class-specific localization maps by computing the correlation between the class-specific weight vectors of the image classifier and every pixel feature vector. However, image classifiers generally have a limited ability to address the intra-class variation, let alone at the pixel level. This thus leads to inaccurate dense localization results. In the conventional fully supervised learning paradigm, the image classification model aims to convert images to numeric labels, ignoring the context of the labels. Hence, it tends to learn the pattern that maximizes the inter-class differences but disregards the intra-class diversities. This largely restricts the model's ability of" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 702, + 227, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 227, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 227, + 712 + ], + "type": "text", + "content": "1https://github.com/xulianuwa/MMCST" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "19596" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 179, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 179, + 83 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 179, + 83 + ], + "type": "text", + "content": "understanding semantic objects." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 84, + 287, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 84, + 287, + 215 + ], + "spans": [ + { + "bbox": [ + 46, + 84, + 287, + 215 + ], + "type": "text", + "content": "Recently, Vision-Language (VL) models have attracted much attention. In particular, CLIP, a representative VL model, pre-trained on 400 million image-text pairs that are readily available publicly, has been successfully applied to a number of downstream tasks, due to its strong generalization ability. CLIP introduces a contrastive representation learning method that constrains an image to match its related text while dis-matching the remaining texts from the same batch, in a multi-modal embedding space. This enables the model to perceive the differences across images, thus facilitating it to better discriminate intra-class samples." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 216, + 288, + 395 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 216, + 288, + 395 + ], + "spans": [ + { + "bbox": [ + 46, + 216, + 288, + 395 + ], + "type": "text", + "content": "Motivated by these observations, we propose to leverage the strong representations of visual concepts encoded by the pre-trained CLIP language model to guide the dense object localization. More specifically, we extract the class-related text embeddings by feeding the label prompts to the pre-trained CLIP language model. As shown in Figure 1, we propose a unified transformer framework which includes multi-modal class-specific tokens, i.e., class-specific visual tokens and class-specific textual tokens. The class-specific visual tokens aim to capture visual representations from the target image dataset, while the class-specific textual tokens take the rich language semantics from the CLIP label text embeddings. These two modalities of class-specific tokens, with complementary information, are jointly used to correlate pixel features, contributing to better dense localization." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 395, + 288, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 395, + 288, + 658 + ], + "spans": [ + { + "bbox": [ + 46, + 395, + 288, + 658 + ], + "type": "text", + "content": "In order to construct more adaptive class representations, which can better associate the sample-specific local features for dense localization, we propose to enhance the global multi-modal class-specific tokens with sample-specific contextual information. To this end, we introduce two designs: (i) at the feature level, we use the sample-specific visual context to enhance both the class-specific visual and textual tokens. This is achieved by combining these global tokens with their output local counterparts which aggregate the patch tokens of the image through the self-attention layers; (ii) at the loss level, we introduce a regularization contrastive loss to encourage the output text tokens to match the CLIP image embeddings. This allows the CLIP model to be better adapted to our target datasets. Moreover, due to its image-language matching pre-training objective, the CLIP image encoder is learned to extract the image embeddings that match the CLIP text embeddings of their corresponding image captions. We thus argue that through this contrastive loss, the rich image-related language context from the CLIP could be implicitly transferred to the text tokens, which are more beneficial for guiding the dense object localization, compared to the simple label prompts." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 58, + 658, + 282, + 670 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 658, + 282, + 670 + ], + "spans": [ + { + "bbox": [ + 58, + 658, + 282, + 670 + ], + "type": "text", + "content": "In summary, the contribution of this work is three-fold:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 677, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 677, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 47, + 677, + 287, + 712 + ], + "type": "text", + "content": "- We propose a new WSDOL method by explicitly constructing multi-modal class representations in a unified transformer framework." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 306, + 72, + 545, + 210 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 306, + 72, + 545, + 131 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 72, + 545, + 131 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 545, + 131 + ], + "type": "text", + "content": "- The proposed transformer includes class-specific visual tokens and class-specific textual tokens, which are learned from different data modalities with diverse supervisions, thus providing complementary information for more discriminative dense localization." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 139, + 545, + 210 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 139, + 545, + 210 + ], + "spans": [ + { + "bbox": [ + 306, + 139, + 545, + 210 + ], + "type": "text", + "content": "- We propose to enhance the multi-modal global class representations by using sample-specific visual context via the global-local token fusion and transferring the image-language context from the pre-trained CLIP via a regularization loss. This enables more adaptive class representations for more accurate dense localization." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 217, + 545, + 253 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 217, + 545, + 253 + ], + "spans": [ + { + "bbox": [ + 306, + 217, + 545, + 253 + ], + "type": "text", + "content": "The proposed method achieved the state-of-the-art results on PASCAL VOC 2012 (72.2% on the test set) and MS COCO 2014 (45.9% on the validation set) for WSSS." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 262, + 392, + 275 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 262, + 392, + 275 + ], + "spans": [ + { + "bbox": [ + 306, + 262, + 392, + 275 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 283, + 545, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 283, + 545, + 628 + ], + "spans": [ + { + "bbox": [ + 304, + 283, + 545, + 628 + ], + "type": "text", + "content": "Weakly supervised dense object localization. Most existing methods rely on CAM which generates class-specific localization maps based on the correlation between the class weights of the image classifier and the pixel features. However, CAM produces inaccurate localization maps where only the most discriminative object regions are activated. Most previous works are based on CNNs and they have generally focused on improving image feature learning in various ways, such as manipulating training images [20, 32], modifying the classification architecture [35, 41], and designing new losses [4, 8, 14]. Recently, ViTs have made breakthroughs in many computer vision tasks, outperforming CNNs, due to their self-attention based processing blocks that enable long-dependence modeling and cater for different data modalities. Recent ViT-based methods, TSCAM [10] and MCTformer [40] have exploited the transformer attention [12] to generate localization maps, but they still need to integrate CAM into ViTs to achieve a decent localization performance. For these CAM-based methods, one problem that has not been well investigated, is that the class weights used to associate pixel features are the global class representations for the entire dataset. Thus, they cannot properly address the complex pixel-level intra-class variations across samples. Chen et al. [3] proposed the Image-Specific CAM (IS-CAM) using the image-specific class prototypes constructed by applying the masked-average pooling on the image feature maps, and imposed a consistency loss between the IS-CAM and the raw CAM to enhance the feature learning for better localization." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 630, + 545, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 545, + 665 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 545, + 665 + ], + "type": "text", + "content": "In contrast to all these CAM-based methods, we propose to explicitly construct multi-modal class representations for dense object localization." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "content": "Vision-language models. Recent progress in vision-language pre-training with large-scale datasets has provided a rich source of transferable information. In particular, a representative VL model, i.e., CLIP, brought a significant" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19597" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 72, + 69, + 521, + 248 + ], + "blocks": [ + { + "bbox": [ + 72, + 69, + 521, + 248 + ], + "lines": [ + { + "bbox": [ + 72, + 69, + 521, + 248 + ], + "spans": [ + { + "bbox": [ + 72, + 69, + 521, + 248 + ], + "type": "image", + "image_path": "48f61ab4599a763cd0115699f85f4761e9b9646dd54d9b043d011828fde0aeee.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 255, + 548, + 323 + ], + "lines": [ + { + "bbox": [ + 46, + 255, + 548, + 323 + ], + "spans": [ + { + "bbox": [ + 46, + 255, + 548, + 323 + ], + "type": "text", + "content": "Figure 2. An overview of the proposed transformer framework. An input RGB image is first split and then embedded into patch tokens. The proposed transformer additionally includes class-specific visual tokens (i.e., input class tokens) and class-specific textual tokens (i.e., input text tokens), in which the input text tokens are initialized by the pretrained CLIP text embeddings of the class-related prompts. The output class tokens are averaged to predict class scores. We fuse the input and output class tokens to correlate the output patch tokens. This generates the class-to-patch correlation maps, which are then globally pooled to produce class scores. Similarly for the text tokens. We also apply a contrastive regularization loss (Figure 4) on the averaged output text tokens to transfer the rich VL context from CLIP." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 332, + 289, + 488 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 332, + 289, + 488 + ], + "spans": [ + { + "bbox": [ + 46, + 332, + 289, + 488 + ], + "type": "text", + "content": "insight that one can use natural language to connect the visual concept, thus allowing a flexible prediction space for a wide knowledge transfer. The pre-trained CLIP model has been used in a variety of open-world visual tasks. Gu et al. [11] proposed to distill knowledge from the pre-trained CLIP image and text encoders to learn open-vocabulary object detectors. Li et al. [24] used the pre-trained text embeddings as semantic label representations to correlate visual features for semantic segmentation. Similar language-driven segmentation methods have been proposed for 2D images [29] and 3D point clouds [30]. In contrast to these methods which have dense supervision, we investigate a pixel-text matching problem in a weakly supervised setting." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 489, + 287, + 621 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 489, + 287, + 621 + ], + "spans": [ + { + "bbox": [ + 46, + 489, + 287, + 621 + ], + "type": "text", + "content": "Xie et al. [37] proposed a CLIP-based WSDOL method, which constrains the CAM activated object regions and the background image regions to match and dis-match the corresponding label prompt in the CLIP embedding space, respectively. It also relies on the additionally pre-defined text descriptions to suppress the co-occurring backgrounds. In contrast, without introducing new visual concepts, we propose to directly use the CLIP label text embeddings to infer dense object localization maps, and further refine them with sample-specific contexts, achieving better results in both WSDOL and WSSS (see Table 1 and Table 3)." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 633, + 104, + 645 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 633, + 104, + 645 + ], + "spans": [ + { + "bbox": [ + 47, + 633, + 104, + 645 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "text", + "content": "Overview. We propose a unified transformer framework to construct multi-modal class-specific tokens for weakly supervised dense object localization. As illustrated in Figure 2, an input RGB image is split into patches and then embedded into a sequence of patch tokens. Additionally, the" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 332, + 545, + 524 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 332, + 545, + 524 + ], + "spans": [ + { + "bbox": [ + 304, + 332, + 545, + 524 + ], + "type": "text", + "content": "proposed transformer has two sets of class-specific tokens, i.e., class-specific visual and textual tokens. The class-specific visual tokens are all initialized by the pre-trained weights of the class token of ViT, and the class-specific textual tokens are initialized by the pre-trained CLIP text embeddings of the class-related prompts and kept constant during training. With the added positional embeddings, these three types of tokens are concatenated and serve as input to the proposed transformer. The input tokens go through consecutive masked self-attention based transformer encoding layers. The output class tokens are averaged along the channel dimension to predict class scores. The input and output class/text tokens are fused and then correlated with the output patch tokens, generating the class-to-patch and text-to-patch correlation maps. These two correlation maps are then globally pooled to produce class scores, respectively." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 525, + 546, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 525, + 546, + 658 + ], + "spans": [ + { + "bbox": [ + 304, + 525, + 546, + 658 + ], + "type": "text", + "content": "During training, the class predictions are supervised by the image-level ground-truth labels via the classification loss. In addition, a batch-softmax contrastive loss is used to encourage the averaged output text token to match the CLIP image embedding. This allows the image-language context transfer, further enabling the class representations to be more sample-adaptive for better localization. During inference, a multi-modal correlation map is generated by fusing the two correlation maps, which are further refined by the transformer attention as suggested in [40], generating the final dense object localization maps." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 669, + 522, + 682 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 669, + 522, + 682 + ], + "spans": [ + { + "bbox": [ + 306, + 669, + 522, + 682 + ], + "type": "text", + "content": "3.1. Multi-modal class-specific token learning" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 689, + 546, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 546, + 714 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 546, + 714 + ], + "type": "text", + "content": "In order to construct multi-modal class representations to guide dense object localization, we propose a multi-modal" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19598" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 55, + 69, + 281, + 185 + ], + "blocks": [ + { + "bbox": [ + 55, + 69, + 281, + 185 + ], + "lines": [ + { + "bbox": [ + 55, + 69, + 281, + 185 + ], + "spans": [ + { + "bbox": [ + 55, + 69, + 281, + 185 + ], + "type": "image", + "image_path": "e8f95b54269c1397f33a98677d000611e0a007b29b4a14002010295833885329.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 192, + 287, + 214 + ], + "lines": [ + { + "bbox": [ + 46, + 192, + 287, + 214 + ], + "spans": [ + { + "bbox": [ + 46, + 192, + 287, + 214 + ], + "type": "text", + "content": "Figure 3. The detailed structure of the proposed multi-modal token transformer encoder." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 223, + 287, + 366 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 223, + 287, + 366 + ], + "spans": [ + { + "bbox": [ + 46, + 223, + 287, + 366 + ], + "type": "text", + "content": "token transformer framework. As illustrated in Figure 2, an input RGB image is first split into " + }, + { + "bbox": [ + 46, + 223, + 287, + 366 + ], + "type": "inline_equation", + "content": "N \\times N" + }, + { + "bbox": [ + 46, + 223, + 287, + 366 + ], + "type": "text", + "content": " patches, which are then embedded into a sequence of " + }, + { + "bbox": [ + 46, + 223, + 287, + 366 + ], + "type": "inline_equation", + "content": "N^2" + }, + { + "bbox": [ + 46, + 223, + 287, + 366 + ], + "type": "text", + "content": " patch tokens. We additionally create " + }, + { + "bbox": [ + 46, + 223, + 287, + 366 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 46, + 223, + 287, + 366 + ], + "type": "text", + "content": " class-specific visual tokens and " + }, + { + "bbox": [ + 46, + 223, + 287, + 366 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 46, + 223, + 287, + 366 + ], + "type": "text", + "content": " class-specific textual tokens, where " + }, + { + "bbox": [ + 46, + 223, + 287, + 366 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 46, + 223, + 287, + 366 + ], + "type": "text", + "content": " is the number of classes. With the added learnable positional embeddings, these three types of tokens are first concatenated and then fed into the transformer encoder (Figure 3). In order to allow class tokens and text tokens to fully interact with patch tokens but not competing with each other, we use the masked self-attention as shown in Figure 3 (right). Finally, the transformer encoder outputs three types of tokens accordingly." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 366, + 287, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 366, + 287, + 462 + ], + "spans": [ + { + "bbox": [ + 46, + 366, + 287, + 462 + ], + "type": "text", + "content": "Class-specific visual token learning. Inspired by MCTformer [40], we use " + }, + { + "bbox": [ + 46, + 366, + 287, + 462 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 46, + 366, + 287, + 462 + ], + "type": "text", + "content": " class tokens to learn class-specific representations. The output class tokens " + }, + { + "bbox": [ + 46, + 366, + 287, + 462 + ], + "type": "inline_equation", + "content": "\\mathbf{T}_{cls}^{out} \\in \\mathbb{R}^{C \\times D}" + }, + { + "bbox": [ + 46, + 366, + 287, + 462 + ], + "type": "text", + "content": " from the proposed multi-modal token transformer encoder are processed by channel-wise averaging, producing " + }, + { + "bbox": [ + 46, + 366, + 287, + 462 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 46, + 366, + 287, + 462 + ], + "type": "text", + "content": " class scores " + }, + { + "bbox": [ + 46, + 366, + 287, + 462 + ], + "type": "inline_equation", + "content": "\\mathbf{y}_{cls} \\in \\mathbb{R}^C" + }, + { + "bbox": [ + 46, + 366, + 287, + 462 + ], + "type": "text", + "content": ". The class scores are supervised by the image-level ground-truth labels " + }, + { + "bbox": [ + 46, + 366, + 287, + 462 + ], + "type": "inline_equation", + "content": "\\mathbf{y} \\in \\mathbb{R}^C" + }, + { + "bbox": [ + 46, + 366, + 287, + 462 + ], + "type": "text", + "content": " using the multi-label soft margin loss (MLSM):" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 58, + 468, + 287, + 517 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 468, + 287, + 517 + ], + "spans": [ + { + "bbox": [ + 58, + 468, + 287, + 517 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {c l s} = M L S M (\\mathbf {y} _ {c l s}, \\mathbf {y}) = - \\\\ \\frac {1}{C} \\sum_ {i = 1} ^ {C} \\mathbf {y} ^ {i} \\log \\sigma \\left(\\mathbf {y} _ {c l s} ^ {i}\\right) + (1 - \\mathbf {y} ^ {i}) \\log \\left(1 - \\sigma \\left(\\mathbf {y} _ {c l s} ^ {i}\\right)\\right). \\tag {1} \\\\ \\end{array}", + "image_path": "34fc8ff7039c1120ae17f3d57a71c404007ee65fc788b62fa6cdf4a91a9a6924.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "text", + "content": "Class-specific textual token learning. CLIP presents a novel way of representing visual concepts by using textual prompts, which provide highly complementary information to the class representations learned from only visual data. It thus exhibits a great potential to better facilitate the dense object localization. This motivates us to construct " + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "text", + "content": " class-specific textual tokens by leveraging the rich semantics learned by the pre-trained CLIP language model. More specifically, we create " + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "text", + "content": " text prompts using the template of \"a photo of [CLS]\", where [CLS] refers to each class name in the label set of a given dataset. These text prompts are then fed into the pre-trained CLIP text encoder, generating " + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "text", + "content": " text embeddings. We use the " + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "text", + "content": " pre-trained label text embeddings to initialize the input text tokens, which are kept constant during training to retain the powerful representation ability of the pre-trained CLIP." + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 314, + 70, + 541, + 164 + ], + "blocks": [ + { + "bbox": [ + 314, + 70, + 541, + 164 + ], + "lines": [ + { + "bbox": [ + 314, + 70, + 541, + 164 + ], + "spans": [ + { + "bbox": [ + 314, + 70, + 541, + 164 + ], + "type": "image", + "image_path": "f844af8419163d14d08c492dc4c7a2120c528b043c53002fd810720012f4c482.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 172, + 545, + 217 + ], + "lines": [ + { + "bbox": [ + 304, + 172, + 545, + 217 + ], + "spans": [ + { + "bbox": [ + 304, + 172, + 545, + 217 + ], + "type": "text", + "content": "Figure 4. The regularization loss. The proposed multi-modal token transformer is encouraged to produce text tokens to match the image embeddings of the pre-trained CLIP image encoder, enabling image-language context transfer." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 228, + 468, + 241 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 228, + 468, + 241 + ], + "spans": [ + { + "bbox": [ + 306, + 228, + 468, + 241 + ], + "type": "text", + "content": "3.2. Sample-specific local contexts" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 247, + 545, + 282 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 247, + 545, + 282 + ], + "spans": [ + { + "bbox": [ + 304, + 247, + 545, + 282 + ], + "type": "text", + "content": "To construct class-specific tokens tailored for each sample, we propose to exploit sample-specific local contexts to complement the global input class-specific tokens." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "spans": [ + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "text", + "content": "Visual context. Inspired by the context-aware prompting [29], we also use the visual context to refine the text embeddings. In contrast to [29] which employs an additional cross-attention module to model the interaction between textual and visual features, the inherent self-attention blocks of the proposed transformer encoder enables the text tokens to aggregate information from patch tokens, capturing the sample-specific visual context. Intuitively, the text embeddings of simple prompts (i.e., \"a photo of [CLS]\", after being refined by the sample-specific visual context, could represent more accurate image-related text. Given that the input text tokens " + }, + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "inline_equation", + "content": "\\mathbf{T}_{txt}^{in}\\in \\mathbb{R}^{C\\times D}" + }, + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "text", + "content": " are global textual representations for each label while the output text tokens " + }, + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "inline_equation", + "content": "\\mathbf{T}_{txt}^{out}\\in \\mathbb{R}^{C\\times D}" + }, + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "text", + "content": " capture sample-specific representations, we can obtain the enhanced class-specific textual tokens by fusing the input and output text tokens: " + }, + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "inline_equation", + "content": "\\mathbf{T}_{txt} = \\mathbf{T}_{txt}^{in} + \\lambda_{1}\\cdot \\mathbf{T}_{txt}^{out}" + }, + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "text", + "content": ". Similarly, the enhanced class-specific visual tokens are obtained by " + }, + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "inline_equation", + "content": "\\mathbf{T}_{cls} = \\mathbf{T}_{cls}^{in} + \\lambda_{2}\\cdot \\mathbf{T}_{cls}^{out}" + }, + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "inline_equation", + "content": "\\lambda_{1}" + }, + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "inline_equation", + "content": "\\lambda_{2}" + }, + { + "bbox": [ + 304, + 283, + 546, + 510 + ], + "type": "text", + "content": " are two learnable weights." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "text", + "content": "Image-language context. To further ensure that the output text tokens can capture meaningful sample-specific context, we propose to impose a contrastive loss on the output text tokens by leveraging the pre-trained CLIP image model. More specifically, as illustrated in Figure 4, given a batch of " + }, + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "text", + "content": " input image-text pairs (the input text for each image is the same, i.e., " + }, + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "text", + "content": " label prompts), the proposed transformer encoder outputs " + }, + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "text", + "content": " text tokens. These " + }, + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "text", + "content": " text tokens are used to compute the similarity matrix " + }, + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{S} \\in \\mathbb{R}^{B \\times B}" + }, + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 304, + 510, + 547, + 713 + ], + "type": "text", + "content": " visual embeddings by feeding the input images to the pre-trained CLIP image encoder. When the output text token and the CLIP image embedding are from the same image, they form a positive pair. Otherwise, they form a negative pair. Such pair construction is reasonable in that: (i) The CLIP image embedding of an image matches its corresponding text description which commonly contains richer information than image-level labels. Thus, even two images with the same" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "19599" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 216 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 216 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 216 + ], + "type": "text", + "content": "class label generally have distinct CLIP image embeddings. (ii) Although the input text tokens are the same for each image, they are progressively refined through interactions with patch tokens at every layer of our model. This enables the output text tokens to represent specific input image content, i.e., different images have different output text tokens. Therefore, the " + }, + { + "bbox": [ + 46, + 72, + 289, + 216 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 46, + 72, + 289, + 216 + ], + "type": "text", + "content": " scores in the diagonal of the similarity matrix are encouraged to be maximized and the other " + }, + { + "bbox": [ + 46, + 72, + 289, + 216 + ], + "type": "inline_equation", + "content": "B^2 - B" + }, + { + "bbox": [ + 46, + 72, + 289, + 216 + ], + "type": "text", + "content": " similarity scores are minimized. This is implemented by computing the cross-entropy classification loss between the similarity matrix " + }, + { + "bbox": [ + 46, + 72, + 289, + 216 + ], + "type": "inline_equation", + "content": "\\mathbf{S}" + }, + { + "bbox": [ + 46, + 72, + 289, + 216 + ], + "type": "text", + "content": " and the ground-truth labels, i.e., an identity matrix " + }, + { + "bbox": [ + 46, + 72, + 289, + 216 + ], + "type": "inline_equation", + "content": "\\mathbf{I} \\in \\mathbb{R}^{B \\times B}" + }, + { + "bbox": [ + 46, + 72, + 289, + 216 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 94, + 224, + 287, + 237 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 224, + 287, + 237 + ], + "spans": [ + { + "bbox": [ + 94, + 224, + 287, + 237 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {c o n t r a s t}} = \\operatorname {C r o s s E n t r o p y} (\\mathbf {S}, \\mathbf {I}). \\tag {2}", + "image_path": "a15ddb60ef8a4a04f0921e884dd60079d7fb237928bafa335bddf51dd7311be6.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 246, + 289, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 246, + 289, + 376 + ], + "spans": [ + { + "bbox": [ + 46, + 246, + 289, + 376 + ], + "type": "text", + "content": "The benefit is two-fold: (i) encouraging the output text tokens to match the pre-trained CLIP image embeddings, allows us to better adapt the pre-trained CLIP model to our target datasets. This also implicitly transfers richer image-related language contexts to the output text tokens, which are more beneficial for guiding the dense object localization, compared to the simple label prompts; (ii) through the batch-softmax cross-entropy loss on the similarity matrix, each text token is also constrained to be discriminative from other text tokens in the same batch, thereby enhancing the sample-specific characteristic of the text tokens." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 384, + 287, + 409 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 384, + 287, + 409 + ], + "spans": [ + { + "bbox": [ + 47, + 384, + 287, + 409 + ], + "type": "text", + "content": "3.3. Image-label supervised multi-modal class-top-patch correlation learning" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "spans": [ + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "text", + "content": "Given the multi-modal class-specific tokens, i.e., " + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "inline_equation", + "content": "\\mathbf{T}_{txt}" + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "inline_equation", + "content": "\\mathbf{T}_{cls}" + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "text", + "content": ", the class-specific dense localization can be inferred by computing the correlation between the class-specific tokens and the patch tokens. More specifically, the output patch tokens from the proposed multi-modal token transformer encoder are first linearly projected and then transposed to 2D feature maps " + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "inline_equation", + "content": "\\mathbf{F} \\in \\mathbb{R}^{N \\times N \\times D}" + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "text", + "content": " is the feature dimension. The text-to-patch correlation maps " + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{t2p} \\in \\mathbb{R}^{N \\times N \\times C}" + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "text", + "content": " and the class-to-patch correlation maps " + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{c2p} \\in \\mathbb{R}^{N \\times N \\times C}" + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "text", + "content": " can be computed as: " + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{t2p} = \\mathbf{FT}_{txt}^{\\top}" + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{c2p} = \\mathbf{FT}_{cls}^{\\top}" + }, + { + "bbox": [ + 46, + 415, + 287, + 545 + ], + "type": "text", + "content": ", respectively." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "text", + "content": "In contrast to recent language-driven dense prediction works [24, 29] where pixel-level ground-truth labels are used to supervise the learning of the class-to-patch correlations, only image-level labels are available in our case. Based on the assumption that the weighted sum of a class-to-patch correlation map can be regarded as the global correlation between the class and the whole image, we relax this class-to-patch correlation problem into a class-to-image correlation problem, which can thus be formulated into a classification task. Given that the commonly used global average pooling (GAP) and global max pooling inevitably either over-estimate or under-estimate the size of the object regions when generating the global correlation scores, respectively [16], we thus adopt the Global Weighted Rank" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 72, + 546, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 546, + 120 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 546, + 120 + ], + "type": "text", + "content": "ing Pooling (GwRP) method [16]. Compared to GAP which assigns the same weight to each patch for aggregation, GwRP assigns different weights according to the ranking of the correlation scores of all patches for each class:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 358, + 126, + 545, + 161 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 358, + 126, + 545, + 161 + ], + "spans": [ + { + "bbox": [ + 358, + 126, + 545, + 161 + ], + "type": "interline_equation", + "content": "G _ {c} \\left(\\mathbf {X} _ {\\star}\\right) = \\frac {1}{Z (d)} \\sum_ {j = 1} ^ {N ^ {2}} d ^ {j - 1} X _ {\\star} ^ {r _ {j}, c}, \\tag {3}", + "image_path": "b77f8eab7be51271adfe0e40533295d4a5f60025ad158db67721418825b4f6dd.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "spans": [ + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_{\\star} \\in \\mathbb{R}^{N^2 \\times C}" + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "text", + "content": " are the flattened correlation maps; " + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "inline_equation", + "content": "\\mathbf{y}_{\\star} = G(\\mathbf{X}_{\\star}) \\in \\mathbb{R}^{C}" + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "text", + "content": " are the aggregated class correlation scores; " + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "inline_equation", + "content": "r_j" + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "text", + "content": " is the index of the ranking, i.e., for a class " + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "inline_equation", + "content": "X_{\\star}^{r_1, c} > X_{\\star}^{r_2, c} > \\dots > X_{\\star}^{r_{N^2}, c}" + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "text", + "content": "; " + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "inline_equation", + "content": "Z(d) = \\sum_{j=1}^{N^2} d^{j-1}" + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 304, + 168, + 547, + 243 + ], + "type": "text", + "content": " is a decay parameter. Therefore, the aggregated class-to-patch and text-to-patch correlation scores can be obtained as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 388, + 251, + 545, + 263 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 388, + 251, + 545, + 263 + ], + "spans": [ + { + "bbox": [ + 388, + 251, + 545, + 263 + ], + "type": "interline_equation", + "content": "\\mathbf {y} _ {c 2 p} = G \\left(\\mathbf {X} _ {c 2 p}\\right), \\tag {4}", + "image_path": "62a0dbfba2fb14b91cea0b78642c4cc47f35e26fc873fcad0f87dacae554e3ec.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 389, + 266, + 545, + 278 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 389, + 266, + 545, + 278 + ], + "spans": [ + { + "bbox": [ + 389, + 266, + 545, + 278 + ], + "type": "interline_equation", + "content": "\\mathbf {y} _ {t 2 p} = G \\left(\\mathbf {X} _ {t 2 p}\\right). \\tag {5}", + "image_path": "8bc5069f99a1d66de9c29fe46d8d88b852ab0810b07a0028dda57e6404f377e8.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 284, + 545, + 308 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 284, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 304, + 284, + 545, + 308 + ], + "type": "text", + "content": "These class scores can then be supervised by the groundtruth image-level labels " + }, + { + "bbox": [ + 304, + 284, + 545, + 308 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 304, + 284, + 545, + 308 + ], + "type": "text", + "content": " via the classification loss:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 353, + 315, + 545, + 327 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 353, + 315, + 545, + 327 + ], + "spans": [ + { + "bbox": [ + 353, + 315, + 545, + 327 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {c l a s s - p a t c h}} = M L S M \\left(\\mathbf {y} _ {c 2 p}, \\mathbf {y}\\right), \\tag {6}", + "image_path": "2347c856bd62f2aee7d9f37f11c62a12fb0be2375a13d8dc1bcfb4e3dd950de8.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 357, + 331, + 545, + 342 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 357, + 331, + 545, + 342 + ], + "spans": [ + { + "bbox": [ + 357, + 331, + 545, + 342 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {t e x t - p a t c h}} = M L S M \\left(\\mathbf {y} _ {t 2 p}, \\mathbf {y}\\right). \\tag {7}", + "image_path": "3495e38a3cd81d1001be2d3130384cc5b88a005eec4717c574a1af385acf79f4.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 349, + 545, + 372 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 349, + 545, + 372 + ], + "spans": [ + { + "bbox": [ + 304, + 349, + 545, + 372 + ], + "type": "text", + "content": "The total objective loss function for training the proposed multi-modal token transformer is as follows:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 310, + 379, + 545, + 392 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 310, + 379, + 545, + 392 + ], + "spans": [ + { + "bbox": [ + 310, + 379, + 545, + 392 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {t o t a l}} = \\mathcal {L} _ {\\text {c l s}} + \\mathcal {L} _ {\\text {c o n t r a s t}} + \\mathcal {L} _ {\\text {t e x t - p a t c h}} + \\mathcal {L} _ {\\text {c l a s s - p a t c h}} \\tag {8}", + "image_path": "0a13dcdf2cd0c6e69b0351598aadbcab994e578ec3c4be0cbb487de0b8adf661.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 397, + 523, + 409 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 397, + 523, + 409 + ], + "spans": [ + { + "bbox": [ + 306, + 397, + 523, + 409 + ], + "type": "text", + "content": "3.4. Class-specific dense localization inference" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 415, + 545, + 511 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 415, + 545, + 511 + ], + "spans": [ + { + "bbox": [ + 304, + 415, + 545, + 511 + ], + "type": "text", + "content": "Once the training of the proposed multi-modal token transformer is completed, the correlations between two modalities of class-specific tokens and the patch tokens are computed to produce two maps, i.e., text-to-patch correlation maps " + }, + { + "bbox": [ + 304, + 415, + 545, + 511 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{t2p}" + }, + { + "bbox": [ + 304, + 415, + 545, + 511 + ], + "type": "text", + "content": " and class-to-patch correlation maps " + }, + { + "bbox": [ + 304, + 415, + 545, + 511 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{c2p}" + }, + { + "bbox": [ + 304, + 415, + 545, + 511 + ], + "type": "text", + "content": ", respectively. We further combine these two maps via an element-wise sum to obtain the multi-modal class-specific dense localization maps " + }, + { + "bbox": [ + 304, + 415, + 545, + 511 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{mm} \\in \\mathbb{R}^{N \\times N \\times C}" + }, + { + "bbox": [ + 304, + 415, + 545, + 511 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 378, + 517, + 545, + 531 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 378, + 517, + 545, + 531 + ], + "spans": [ + { + "bbox": [ + 378, + 517, + 545, + 531 + ], + "type": "interline_equation", + "content": "\\mathbf {C} _ {m m} = \\mathbf {C} _ {t 2 p} + \\mathbf {C} _ {c 2 p}. \\tag {9}", + "image_path": "bcb4bd112a0ba9948968321126b0f2522fedfca92fe9792fd92301643288c586.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 536, + 545, + 657 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 536, + 545, + 657 + ], + "spans": [ + { + "bbox": [ + 304, + 536, + 545, + 657 + ], + "type": "text", + "content": "Moreover, similar to MCTformer [40], the multi-class token based transformer can produce class-specific attention maps " + }, + { + "bbox": [ + 304, + 536, + 545, + 657 + ], + "type": "inline_equation", + "content": "\\mathbf{A}_{c2p}" + }, + { + "bbox": [ + 304, + 536, + 545, + 657 + ], + "type": "text", + "content": " and a patch-level pairwise affinity map " + }, + { + "bbox": [ + 304, + 536, + 545, + 657 + ], + "type": "inline_equation", + "content": "\\mathbf{A}_{p2p}" + }, + { + "bbox": [ + 304, + 536, + 545, + 657 + ], + "type": "text", + "content": " from the transformer self-attention maps. Given their different localization mechanisms, the transformer self-attention maps can well complement the proposed multi-modal class-specific localization maps. We thus perform an element-wise multiplication (o) on these two maps to produce the fused maps " + }, + { + "bbox": [ + 304, + 536, + 545, + 657 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{M}}\\in \\mathbb{R}^{N\\times N\\times C}" + }, + { + "bbox": [ + 304, + 536, + 545, + 657 + ], + "type": "text", + "content": ", which are further refined by the patch-level pairwise affinity map:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 347, + 662, + 545, + 675 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 662, + 545, + 675 + ], + "spans": [ + { + "bbox": [ + 347, + 662, + 545, + 675 + ], + "type": "interline_equation", + "content": "\\hat {\\mathbf {M}} = \\mathbf {C} _ {m m} \\circ \\mathbf {A} _ {c 2 p}, \\tag {10}", + "image_path": "d4ebe4cd5706d43f6196ef7d390af85c23301a6a90b02e9ee4af0b0920697ef0.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 318, + 677, + 545, + 711 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 677, + 545, + 711 + ], + "spans": [ + { + "bbox": [ + 318, + 677, + 545, + 711 + ], + "type": "interline_equation", + "content": "\\mathbf {M} (i, j, c) = \\sum_ {k = 1} ^ {N} \\sum_ {l = 1} ^ {N} \\mathbf {A} _ {p 2 p} (i, j, k, l) \\cdot \\hat {\\mathbf {M}} (k, l, c), \\tag {11}", + "image_path": "ee3655f94b96266c07de2039ed163f56ce8caafc9940aedfb7cb6c3841080f56.jpg" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "19600" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 71, + 288, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 71, + 288, + 120 + ], + "spans": [ + { + "bbox": [ + 46, + 71, + 288, + 120 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 71, + 288, + 120 + ], + "type": "inline_equation", + "content": "\\mathbf{M} \\in \\mathbb{R}^{N \\times N \\times C}" + }, + { + "bbox": [ + 46, + 71, + 288, + 120 + ], + "type": "text", + "content": " are the refined fused maps, which are then up-sampled to the original size of the input image before being normalized via the min-max normalization, to generate the final dense object localization maps." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 133, + 128, + 145 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 133, + 128, + 145 + ], + "spans": [ + { + "bbox": [ + 47, + 133, + 128, + 145 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 152, + 171, + 165 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 152, + 171, + 165 + ], + "spans": [ + { + "bbox": [ + 47, + 152, + 171, + 165 + ], + "type": "text", + "content": "4.1. Experimental settings" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 171, + 287, + 350 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 171, + 287, + 350 + ], + "spans": [ + { + "bbox": [ + 46, + 171, + 287, + 350 + ], + "type": "text", + "content": "Datasets. We evaluated the proposed method on three datasets including two multi-label datasets, i.e., PASCAL VOC 2012 [9] and MS COCO 2014 [26], and one single-label dataset, i.e., OpenImages [5]. PASCAL VOC 2012 has 20 foreground object classes and one background class. It is split into three subsets, i.e., training (train), validation (val) and test sets including 1,464, 1,449, and 1,456 images, respectively. Following the common practice [18, 19], the training set was augmented to 10,582 images by adding the data from [13]. MS COCO 2014 has 80 foreground object classes and one background class. It has around 80K training images and 40K images for evaluation. OpenImages has 37,319 images in total and 100 object classes. It is split into a training set with 29,819 images, a validation set with 2,500 images and a test set with 5,000 images." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 350, + 287, + 482 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 350, + 287, + 482 + ], + "spans": [ + { + "bbox": [ + 46, + 350, + 287, + 482 + ], + "type": "text", + "content": "Evaluation metrics. For the multi-label datasets, we followed the common practice [18, 20, 21] to use the mean Intersection-over-Union (mIoU) to evaluate the multi-label dense localization maps on the train set, and used mIoU to evaluate WSSS results on the val and test sets. Results on the PASCAL VOC test set were obtained from the online official evaluation server. For the single-label dataset, we focused on the pixel-level evaluation and followed the prior works [5, 52] which used the peak Intersection-over-Union (pIoU) and the pixel average precision (PxAP) to evaluate the single-label dense localization maps on the test set." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 483, + 287, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 483, + 287, + 615 + ], + "spans": [ + { + "bbox": [ + 46, + 483, + 287, + 615 + ], + "type": "text", + "content": "Implementation details. We built the proposed transformer using ViT-base as backbone. We used the Adam optimizer with the initial learning rate of 5e-4 and a batch size of 32. We trained our network for 60 epochs on PASCAL VOC and MS COCO and 10 epochs on OpenImages. For the VL model, we used the publicly available pre-trained CLIP model (ViT-B/16). To generate pseudo masks for WSSS, we followed prior works [20, 21, 32, 44] to use IR-Net [1] to post-process our dense localization maps. For semantic segmentation, we use ResNet38-based Deeplab-V1. More details can be found in the Supplementary Materials." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 623, + 282, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 623, + 282, + 635 + ], + "spans": [ + { + "bbox": [ + 47, + 623, + 282, + 635 + ], + "type": "text", + "content": "4.2. Comparison with State-of-the-art of WSDOL" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 641, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 287, + 715 + ], + "type": "text", + "content": "Multi-label dense localization. Table 1 reports the evaluation results of the generated multi-label dense localization maps, which are commonly used as seeds to generate pseudo masks in WSSS. The proposed method achieved mIoUs of " + }, + { + "bbox": [ + 46, + 641, + 287, + 715 + ], + "type": "inline_equation", + "content": "66.3\\%" + }, + { + "bbox": [ + 46, + 641, + 287, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 641, + 287, + 715 + ], + "type": "inline_equation", + "content": "40.9\\%" + }, + { + "bbox": [ + 46, + 641, + 287, + 715 + ], + "type": "text", + "content": " on the train sets of PASCAL VOC 2012 and MS COCO 2014, respectively, outperform-" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 313, + 116, + 539, + 258 + ], + "blocks": [ + { + "bbox": [ + 305, + 60, + 545, + 115 + ], + "lines": [ + { + "bbox": [ + 305, + 60, + 545, + 115 + ], + "spans": [ + { + "bbox": [ + 305, + 60, + 545, + 115 + ], + "type": "text", + "content": "Table 1. Evaluation of the generated multi-class multi-label dense localization maps in terms of mIoU (\\%) on the PASCAL VOC 2012 and MS COCO 2014 train sets. Cls.: Classification. " + }, + { + "bbox": [ + 305, + 60, + 545, + 115 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 305, + 60, + 545, + 115 + ], + "type": "text", + "content": " denotes the reproduced result by [18], " + }, + { + "bbox": [ + 305, + 60, + 545, + 115 + ], + "type": "inline_equation", + "content": "\\ddagger" + }, + { + "bbox": [ + 305, + 60, + 545, + 115 + ], + "type": "text", + "content": " denotes the reproduced result by [44], and " + }, + { + "bbox": [ + 305, + 60, + 545, + 115 + ], + "type": "inline_equation", + "content": "\\star" + }, + { + "bbox": [ + 305, + 60, + 545, + 115 + ], + "type": "text", + "content": " denotes our reproduced result." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 313, + 116, + 539, + 258 + ], + "lines": [ + { + "bbox": [ + 313, + 116, + 539, + 258 + ], + "spans": [ + { + "bbox": [ + 313, + 116, + 539, + 258 + ], + "type": "table", + "html": "
MethodCls. BackboneVOCCOCO
CAM (CVPR16) [48]ResNet5048.833.5†
SEAM (CVPR20) [34]ResNet3855.425.1‡
RIB (NeurIPS21) [18]ResNet5056.536.5
AdvCAM (CVPR21) [19]ResNet3855.637.2
CLIMS (CVPR22) [37]ResNet5056.6-
SIPE (CVPR22) [4]ResNet5058.6-
W-OoD (CVPR22) [21]ResNet5059.1-
Du et al. (CVPR22) [8]ResNet3861.5-
TS-CAM (ICCV21) [10]ViT-small41.3-
MCTformer (CVPR22) [40]ViT-small61.7-
MCTformer (CVPR22) [40]ViT-base62.3*-
OursViT-base66.340.9
", + "image_path": "4de67478fd63b1c93252b73a7d5139a3b321870c0d446d76472ff2e76347d6a3.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 313, + 284, + 539, + 437 + ], + "blocks": [ + { + "bbox": [ + 306, + 262, + 545, + 283 + ], + "lines": [ + { + "bbox": [ + 306, + 262, + 545, + 283 + ], + "spans": [ + { + "bbox": [ + 306, + 262, + 545, + 283 + ], + "type": "text", + "content": "Table 2. Evaluation of multi-class single-label dense localization on the OpenImages test set." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 313, + 284, + 539, + 437 + ], + "lines": [ + { + "bbox": [ + 313, + 284, + 539, + 437 + ], + "spans": [ + { + "bbox": [ + 313, + 284, + 539, + 437 + ], + "type": "table", + "html": "
MethodCls. backbonepIoUPxAP
CAM (CVPR16) [48]ResNet5043.058.2
HAS (ICCV17) [31]ResNet5041.955.1
ACoL (CVPR18) [47]ResNet5041.756.4
SPG (ECCV18) [46]ResNet5041.855.8
ADL (CVPR19) [6]ResNet5042.155.0
CutMix (ICCV19) [43]ResNet5042.757.6
PAS (ECCV20) [2]ResNet50-60.9
IVR (ICCV21) [15]ResNet50-58.9
Zhu et al. (CVPR22) [52]ResNet5049.765.4
CREAM (CVPR22) [38]ResNet50-64.7
Zhu et al. (ECCV22) [51]ResNet5052.267.7
OursViT-base57.673.3
", + "image_path": "e54532d1227b563fd788f96fe5f7cabeaf0e5e574043595dbad2ca67e0994184.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 440, + 527, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 440, + 527, + 452 + ], + "spans": [ + { + "bbox": [ + 305, + 440, + 527, + 452 + ], + "type": "text", + "content": "ing the state-of-the-art methods by significant margins." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 453, + 545, + 548 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 453, + 545, + 548 + ], + "spans": [ + { + "bbox": [ + 304, + 453, + 545, + 548 + ], + "type": "text", + "content": "Single-label dense localization. We also evaluated the effectiveness of the proposed method for single-label dense object localization. We used the recently proposed OpenImages dataset [5], which has more challenging background context than the commonly used ones for weakly supervised object localization. As shown in Table 2, the proposed method attained a pIoU of " + }, + { + "bbox": [ + 304, + 453, + 545, + 548 + ], + "type": "inline_equation", + "content": "57.6\\%" + }, + { + "bbox": [ + 304, + 453, + 545, + 548 + ], + "type": "text", + "content": " and a PxAP of " + }, + { + "bbox": [ + 304, + 453, + 545, + 548 + ], + "type": "inline_equation", + "content": "73.3\\%" + }, + { + "bbox": [ + 304, + 453, + 545, + 548 + ], + "type": "text", + "content": ", achieving better results than the state-of-the-art methods." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 560, + 529, + 573 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 560, + 529, + 573 + ], + "spans": [ + { + "bbox": [ + 305, + 560, + 529, + 573 + ], + "type": "text", + "content": "4.3. Comparison with State-of-the-art of WSSS" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 580, + 545, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 580, + 545, + 628 + ], + "spans": [ + { + "bbox": [ + 304, + 580, + 545, + 628 + ], + "type": "text", + "content": "PASCAL VOC 2012. As shown in Table 3, the proposed method achieved mIoUs of " + }, + { + "bbox": [ + 304, + 580, + 545, + 628 + ], + "type": "inline_equation", + "content": "72.2\\%" + }, + { + "bbox": [ + 304, + 580, + 545, + 628 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 580, + 545, + 628 + ], + "type": "inline_equation", + "content": "72.2\\%" + }, + { + "bbox": [ + 304, + 580, + 545, + 628 + ], + "type": "text", + "content": " on the val and test sets of PASCAL VOC 2012, respectively, outperforming other WSSS methods only using image-level labels." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "content": "MS COCO 2014. Table 4 also shows that, on a more challenging dataset, i.e., MS COCO 2014, the proposed method attained the best mIoU of " + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "inline_equation", + "content": "45.9\\%" + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "content": ", which is significantly better than those relying on saliency maps, achieving the new state-of-the-art result. These results clearly demonstrate the effectiveness and the good generalization ability of the proposed method for WSSS." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19601" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 49, + 114, + 287, + 330 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 287, + 114 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 287, + 114 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 287, + 114 + ], + "type": "text", + "content": "Table 3. Performance comparison of the state-of-the-art WSSS methods on the PASCAL VOC 2012 val and test sets. Seg.: DeepLab version. Sup.: supervision; I: image-level ground-truth labels; S: off-the-shelf saliency maps; L: pre-trained VL model." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 49, + 114, + 287, + 330 + ], + "lines": [ + { + "bbox": [ + 49, + 114, + 287, + 330 + ], + "spans": [ + { + "bbox": [ + 49, + 114, + 287, + 330 + ], + "type": "table", + "html": "
MethodBackboneSeg.Sup.ValTest
EDAM (CVPR21) [36]ResNet101V2I+S70.970.6
EPS (CVPR21) [23]ResNet101V1I+S71.071.8
Yao et al. (CVPR21) [41]ResNet101V2I+S68.368.5
AuxSegNet (ICCV21) [39]ResNet38V1I+S69.068.6
Li et al. (CVPR22) [25]ResNet101V2I+S72.072.9
Du et al. (CVPR22) [8]ResNet101V2I+S72.673.6
RCA (CVPR22) [50]ResNet38V2I+S72.272.8
L2G (CVPR22) [14]ResNet38V1I+S72.073.0
AdvCAM (CVPR21) [19]ResNet101V2I68.168.0
ECS-Net (ICCV21) [33]ResNet38V1I66.667.6
Kweon et al. (ICCV21) [17]ResNet38V1I68.468.2
CDA (ICCV21) [32]ResNet38V1I66.166.8
Zhang et al. (ICCV21) [45]ResNet38V1I67.868.5
MCTformer (CVPR22) [40]ResNet38V1I71.971.6
AMN (CVPR22) [22]ResNet101V2I70.770.6
W-OoD (CVPR22) [21]ResNet38V1I70.770.1
SIPE (CVPR22) [3]ResNet38V1I68.269.7
Yoon et al. (ECCV22) [42]ResNet38V1I70.971.7
CLIMS (CVPR22) [37]ResNet101V2I+L69.368.7
OursResNet38V1I+L72.272.2
", + "image_path": "3b396d390bdd32cb9e9a6547bd8b7d45c03173f31139408970a3afba3f7e95e6.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 61, + 356, + 276, + 505 + ], + "blocks": [ + { + "bbox": [ + 47, + 335, + 286, + 355 + ], + "lines": [ + { + "bbox": [ + 47, + 335, + 286, + 355 + ], + "spans": [ + { + "bbox": [ + 47, + 335, + 286, + 355 + ], + "type": "text", + "content": "Table 4. Performance comparison of state-of-the-art WSSS methods on the MS COCO 2014 val set in terms of mIoU (\\%)." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 61, + 356, + 276, + 505 + ], + "lines": [ + { + "bbox": [ + 61, + 356, + 276, + 505 + ], + "spans": [ + { + "bbox": [ + 61, + 356, + 276, + 505 + ], + "type": "table", + "html": "
MethodBackboneSeg.Sup.Val
EPS (CVPR21) [23]VGG16V2I+S35.7
RCA (CVPR22) [50]VGG16V2I+S36.8
AuxSegNet (ICCV21) [39]ResNet38V1I+S33.9
L2G (CVPR22) [14]ResNet101V2I+S44.2
Kweon et al. (ICCV21) [17]ResNet38V1I36.4
CDA (ICCV21) [32]ResNet38V1I33.2
AdvCAM (CVPR21) [19]ResNet101V2I44.4
MCTformer (CVPR22) [40]ResNet38V1I42.0
Li et al. (CVPR22) [25]ResNet101V2I44.7
AMN (CVPR22) [22]ResNet101V2I44.7
SIPE (CVPR22) [3]ResNet38V1I43.6
Yoon et al. (ECCV22) [42]ResNet38V1I44.8
OursResNet38V1I+L45.9
", + "image_path": "b26ec6088621aa6ac0a618301e50a2b0d003f56d38f61805e92e0a3f7171a0bd.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 514, + 145, + 525 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 514, + 145, + 525 + ], + "spans": [ + { + "bbox": [ + 47, + 514, + 145, + 525 + ], + "type": "text", + "content": "4.4. Ablation studies" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 533, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 533, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 533, + 288, + 713 + ], + "type": "text", + "content": "Effect of learning multi-modal class-specific tokens. As shown in Table 5, without text tokens, only relying on the multi-class token transformer attention results in a dense localization mIoU of " + }, + { + "bbox": [ + 46, + 533, + 288, + 713 + ], + "type": "inline_equation", + "content": "57.5\\%" + }, + { + "bbox": [ + 46, + 533, + 288, + 713 + ], + "type": "text", + "content": "; By learning the multi-class tokens to correlate patch tokens using image-level supervision with GAP, a significant gain of " + }, + { + "bbox": [ + 46, + 533, + 288, + 713 + ], + "type": "inline_equation", + "content": "4.6\\%" + }, + { + "bbox": [ + 46, + 533, + 288, + 713 + ], + "type": "text", + "content": " was achieved in mIoU. GwRP outperforms GAP, improving the dense localization result to " + }, + { + "bbox": [ + 46, + 533, + 288, + 713 + ], + "type": "inline_equation", + "content": "62.7\\%" + }, + { + "bbox": [ + 46, + 533, + 288, + 713 + ], + "type": "text", + "content": ". By additionally learning class-specific textual tokens, the resulting multi-modal correlation maps driven by the global/input multi-modal class-specific tokens, attained an better mIoU of " + }, + { + "bbox": [ + 46, + 533, + 288, + 713 + ], + "type": "inline_equation", + "content": "64.1\\%" + }, + { + "bbox": [ + 46, + 533, + 288, + 713 + ], + "type": "text", + "content": ", compared to that of the multi-modal correlation maps driven by the local/output multi-modal class-specific tokens. The fusion of the global and local multi-modal class-specific tokens leads to the best dense localization maps with an mIoU of " + }, + { + "bbox": [ + 46, + 533, + 288, + 713 + ], + "type": "inline_equation", + "content": "66.3\\%" + }, + { + "bbox": [ + 46, + 533, + 288, + 713 + ], + "type": "text", + "content": ". These re" + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 309, + 103, + 545, + 182 + ], + "blocks": [ + { + "bbox": [ + 305, + 70, + 545, + 103 + ], + "lines": [ + { + "bbox": [ + 305, + 70, + 545, + 103 + ], + "spans": [ + { + "bbox": [ + 305, + 70, + 545, + 103 + ], + "type": "text", + "content": "Table 5. Evaluation of the class-specific dense localization maps generated by learning correlations (cor.) between class-specific tokens and patch tokens on the PASCAL VOC 2012 train set." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 103, + 545, + 182 + ], + "lines": [ + { + "bbox": [ + 309, + 103, + 545, + 182 + ], + "spans": [ + { + "bbox": [ + 309, + 103, + 545, + 182 + ], + "type": "table", + "html": "
ConfigurationPoolingmIoU
Multi-class token attention-57.5
+ class-to-patch correlationGAP62.1
+ class-to-patch correlation (C2P cor.)GWRP62.7
+ Multi-modal (C2P + text-to-patch) cor. (global)GWRP64.1
+ Multi-modal cor. (local)GWRP63.3
+ Multi-modal cor. (global + local)GWRP66.3
", + "image_path": "c2dcd91152529e87d26bf2ec03c53b9bc692598aecdf7f98913a1b1eb8d90d7c.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 309, + 224, + 545, + 312 + ], + "blocks": [ + { + "bbox": [ + 305, + 190, + 545, + 224 + ], + "lines": [ + { + "bbox": [ + 305, + 190, + 545, + 224 + ], + "spans": [ + { + "bbox": [ + 305, + 190, + 545, + 224 + ], + "type": "text", + "content": "Table 6. Evaluation of the class-specific dense localization by different regularization methods for sample-specific local context learning on the PASCAL VOC 2012 train set. CE: cross-entropy." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 224, + 545, + 312 + ], + "lines": [ + { + "bbox": [ + 309, + 224, + 545, + 312 + ], + "spans": [ + { + "bbox": [ + 309, + 224, + 545, + 312 + ], + "type": "table", + "html": "
Visual contextImage-language contextmIoU
Prior knowledgeRegularization loss
X--64.1
✓--64.8
✓CLIP caption embed.L163.7
✓CLIP caption embed.Batch-contrast CE65.1
✓CLIP image embed.Batch-contrast CE66.3
", + "image_path": "96005767f65f4a5d58ce04b28739dece54a31ec6747b61de2da77f5fdaa2b361.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 316, + 545, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 316, + 545, + 353 + ], + "spans": [ + { + "bbox": [ + 304, + 316, + 545, + 353 + ], + "type": "text", + "content": "sults demonstrate the effectiveness of the proposed method of learning multi-modal class-specific tokens in producing accurate class-specific dense localization maps." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 354, + 545, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 354, + 545, + 569 + ], + "spans": [ + { + "bbox": [ + 304, + 354, + 545, + 569 + ], + "type": "text", + "content": "Figure 5 visualizes the generated dense object localization maps in different configurations. The multi-class token transformer attention only localizes partial object regions and includes many noises, such as the dog and the background in the third and second rows of Figure 5 (b), respectively. Learning the multi-class tokens to correlate patch tokens leads to more complete object localization maps, such as the sofa in the third row. This also results in several falsely activated regions near the object, such as the chair in the second rows of Figure 5 (c). Adding class-specific textual tokens leads to improved localization maps (Figure 5 (d)) with expanded true object regions and reduced falsely activated backgrounds. Further incorporating sample-specific context produces the best localization maps (Figure 5 (e)) which are close to the ground-truth. This demonstrates the advantage of the proposed method in generating dense localization maps for objects of different scales in various challenging scenarios." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": "Effect of sample-specific context learning. As shown in Table 6, without sample-specific context learning, the learned multi-modal class-specific tokens result in the class-specific localization maps with an mIoU of " + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "inline_equation", + "content": "64.1\\%" + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": ". Incorporating the visual context information aggregated by the output text tokens leads to an improved mIoU of " + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "inline_equation", + "content": "64.8\\%" + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": ". To further learn image-language context to enhance the text token learning, we investigated two regularization methods: (i) Leveraging the pre-trained CLIP text embeddings of image captions. More specifically, we used a pre-trained image captioning model, ClipCap [27], to generate captions for each image. By minimizing the distance between the text" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19602" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 71, + 135, + 229 + ], + "blocks": [ + { + "bbox": [ + 61, + 71, + 135, + 229 + ], + "lines": [ + { + "bbox": [ + 61, + 71, + 135, + 229 + ], + "spans": [ + { + "bbox": [ + 61, + 71, + 135, + 229 + ], + "type": "image", + "image_path": "7dc582e77cf79972370724017bc4daf788abe7f8e94fa2ec3bf4e58f289a3266.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 94, + 229, + 104, + 237 + ], + "lines": [ + { + "bbox": [ + 94, + 229, + 104, + 237 + ], + "spans": [ + { + "bbox": [ + 94, + 229, + 104, + 237 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 46, + 241, + 547, + 286 + ], + "lines": [ + { + "bbox": [ + 46, + 241, + 547, + 286 + ], + "spans": [ + { + "bbox": [ + 46, + 241, + 547, + 286 + ], + "type": "text", + "content": "Figure 5. Visualization of the multi-label dense object localization results using different configurations of the proposed method on the PASCAL VOC 2012 train set. (a) Input; (b) MCTAttn (Multi-class token attention); (c) MCTAttn + C2P-CMap (class-to-patch correlation maps); (d) MCTAttn + MM-CMap (multi-modal correlation maps, i.e., class-to-patch correlation maps + text-to-patch correlation maps); (e) MCTAttn + MM-CMap + sample-specific context; (f) Ground-truth." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 139, + 70, + 216, + 228 + ], + "blocks": [ + { + "bbox": [ + 139, + 70, + 216, + 228 + ], + "lines": [ + { + "bbox": [ + 139, + 70, + 216, + 228 + ], + "spans": [ + { + "bbox": [ + 139, + 70, + 216, + 228 + ], + "type": "image", + "image_path": "5613f827da8a76697a5eab736a7420005af8f6422da8bc0f5c86637bb40e51bf.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 173, + 229, + 183, + 237 + ], + "lines": [ + { + "bbox": [ + 173, + 229, + 183, + 237 + ], + "spans": [ + { + "bbox": [ + 173, + 229, + 183, + 237 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 219, + 70, + 294, + 228 + ], + "blocks": [ + { + "bbox": [ + 219, + 70, + 294, + 228 + ], + "lines": [ + { + "bbox": [ + 219, + 70, + 294, + 228 + ], + "spans": [ + { + "bbox": [ + 219, + 70, + 294, + 228 + ], + "type": "image", + "image_path": "4aff8e6dad1d49d4b29dc0d914515618aa4d15286a5d6c9eb0f13112b78d533d.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 252, + 229, + 263, + 237 + ], + "lines": [ + { + "bbox": [ + 252, + 229, + 263, + 237 + ], + "spans": [ + { + "bbox": [ + 252, + 229, + 263, + 237 + ], + "type": "text", + "content": "(c)" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 298, + 70, + 375, + 228 + ], + "blocks": [ + { + "bbox": [ + 298, + 70, + 375, + 228 + ], + "lines": [ + { + "bbox": [ + 298, + 70, + 375, + 228 + ], + "spans": [ + { + "bbox": [ + 298, + 70, + 375, + 228 + ], + "type": "image", + "image_path": "319f09ad4562a4cfd5fa60701bfab4f0c114b6836e243b3f6605874c43fd4da4.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 331, + 229, + 342, + 237 + ], + "lines": [ + { + "bbox": [ + 331, + 229, + 342, + 237 + ], + "spans": [ + { + "bbox": [ + 331, + 229, + 342, + 237 + ], + "type": "text", + "content": "(d)" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 377, + 70, + 453, + 228 + ], + "blocks": [ + { + "bbox": [ + 377, + 70, + 453, + 228 + ], + "lines": [ + { + "bbox": [ + 377, + 70, + 453, + 228 + ], + "spans": [ + { + "bbox": [ + 377, + 70, + 453, + 228 + ], + "type": "image", + "image_path": "d216250e1668e2152124c23142130a9e3311e2b5a281fea5656dd848b1d4bddd.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 411, + 229, + 422, + 237 + ], + "lines": [ + { + "bbox": [ + 411, + 229, + 422, + 237 + ], + "spans": [ + { + "bbox": [ + 411, + 229, + 422, + 237 + ], + "type": "text", + "content": "(e)" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 457, + 70, + 533, + 228 + ], + "blocks": [ + { + "bbox": [ + 457, + 70, + 533, + 228 + ], + "lines": [ + { + "bbox": [ + 457, + 70, + 533, + 228 + ], + "spans": [ + { + "bbox": [ + 457, + 70, + 533, + 228 + ], + "type": "image", + "image_path": "07fdeef3823b929ca431cf15abadb7f9e966a880c0e01712a7152a82798438a0.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 492, + 229, + 500, + 237 + ], + "lines": [ + { + "bbox": [ + 492, + 229, + 500, + 237 + ], + "spans": [ + { + "bbox": [ + 492, + 229, + 500, + 237 + ], + "type": "text", + "content": "(f)" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 85, + 324, + 251, + 376 + ], + "blocks": [ + { + "bbox": [ + 46, + 291, + 288, + 323 + ], + "lines": [ + { + "bbox": [ + 46, + 291, + 288, + 323 + ], + "spans": [ + { + "bbox": [ + 46, + 291, + 288, + 323 + ], + "type": "text", + "content": "Table 7. Evaluation of the class-specific dense localization maps by using different prompting methods for constructing class-specific textual tokens on the PASCAL VOC 2012 train set." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 85, + 324, + 251, + 376 + ], + "lines": [ + { + "bbox": [ + 85, + 324, + 251, + 376 + ], + "spans": [ + { + "bbox": [ + 85, + 324, + 251, + 376 + ], + "type": "table", + "html": "
PromptmIoU
a photo of [class]66.3
Prompt ensembling65.5
CoOp (learning-based prompts) [49]66.0
", + "image_path": "784ffbf92435f1065035efdda4e599e736358789fe75d5e37a63c6d2efbaf0c5.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_body" + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 377, + 287, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 377, + 287, + 628 + ], + "spans": [ + { + "bbox": [ + 46, + 377, + 287, + 628 + ], + "type": "text", + "content": "tokens and the pre-trained CLIP caption embeddings via L1 loss, the localization performance drops by " + }, + { + "bbox": [ + 46, + 377, + 287, + 628 + ], + "type": "inline_equation", + "content": "1.1\\%" + }, + { + "bbox": [ + 46, + 377, + 287, + 628 + ], + "type": "text", + "content": ". Using the batch-softmax cross-entropy loss yields a slightly improved localization mIoU of " + }, + { + "bbox": [ + 46, + 377, + 287, + 628 + ], + "type": "inline_equation", + "content": "65.1\\%" + }, + { + "bbox": [ + 46, + 377, + 287, + 628 + ], + "type": "text", + "content": ". L1 loss on the features allows to transfer all information of the generated image captions including useful and interfering contexts that could reduce the discriminative ability of the class-specific textual tokens, thus degrading the localization performance. In contrast, the batch-softmax cross-entropy loss on the feature similarities explicitly separates each text token from other text tokens in a same batch, which facilitates the sample-specific token learning for better localization. (ii) Leveraging the pre-trained CLIP image embeddings. By applying the batch-softmax cross-entropy loss on the similarity matrix of our text tokens and the pre-trained CLIP image embeddings, the class-specific localization attains a performance gain of " + }, + { + "bbox": [ + 46, + 377, + 287, + 628 + ], + "type": "inline_equation", + "content": "1.5\\%" + }, + { + "bbox": [ + 46, + 377, + 287, + 628 + ], + "type": "text", + "content": " with the best mIoU of " + }, + { + "bbox": [ + 46, + 377, + 287, + 628 + ], + "type": "inline_equation", + "content": "66.3\\%" + }, + { + "bbox": [ + 46, + 377, + 287, + 628 + ], + "type": "text", + "content": ". Compared to learning from the generated image captions, the batch-contrastive image-text matching loss enables the text tokens to better align with the visual features, thus leading to better dense localization results." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "type": "text", + "content": "Effects of different prompts. We used different prompting methods to construct the class-specific textual tokens and evaluated their effects in the resulting class-specific dense localization maps. More specifically, we investigated three types of prompting methods including (i) the most commonly used prompt, i.e., \"a photo of [class]\"; (ii) Prompt ensembling, which ensembles 8 best prompts over the em" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 292, + 547, + 401 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 292, + 547, + 401 + ], + "spans": [ + { + "bbox": [ + 304, + 292, + 547, + 401 + ], + "type": "text", + "content": "bedding space via averaging as suggested in [28]; (iii) A learning-based prompting method, CoOp [49]. As shown in Table 7, these three prompting methods lead to comparable dense localization results. Among them, the general prompt template \"a photo of [class]\" produces the best localization mIoU. We speculate this is due to the general prompt template better preserving the class-discriminative language priors from the pre-trained CLIP model, compared to ensembling several prompts or learning extra parameters." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 306, + 421, + 379, + 434 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 421, + 379, + 434 + ], + "spans": [ + { + "bbox": [ + 306, + 421, + 379, + 434 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 440, + 545, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 440, + 545, + 654 + ], + "spans": [ + { + "bbox": [ + 304, + 440, + 545, + 654 + ], + "type": "text", + "content": "We proposed a new weakly supervised dense object localization method by explicitly constructing multi-modal class representations via a transformer-based framework. The proposed transformer includes class-specific visual and textual tokens by learning visual information from the target image data and exploiting language information from the pre-trained CLIP model, respectively. These diverse supervisions enable the multi-modal class-specific tokens to provide complementary information for more discriminative dense localization. By further incorporating the sample-specific contexts from visual context and image-language context, more adaptive multi-modal class-specific tokens can be learned to facilitate better dense localization. Our superior WSDOL results on two multi-label datasets, (i.e., PASCAL VOC and MS COCO) and one single-label dataset, (i.e., OpenImages), demonstrate the effectiveness of the proposed method. We also report state-of-the-art WSSS results on PASCAL VOC and MS COCO." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 658, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 658, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 658, + 547, + 713 + ], + "type": "text", + "content": "Acknowledgment. This research is supported in part by Australian Research Council Grant DP210101682, DP210102674, IH180100002, the Early Career Scheme of the Research Grants Council (RGC) of the Hong Kong SAR under grant No. 26202321 and HKUST Startup Fund No. R9253." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19603" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "type": "text", + "content": "[1] Jiwoon Ahn, Sunghyun Cho, and Suha Kwak. Weakly supervised learning of instance segmentation with inter-pixel relations. In CVPR, 2019. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 124, + 287, + 156 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 124, + 287, + 156 + ], + "spans": [ + { + "bbox": [ + 53, + 124, + 287, + 156 + ], + "type": "text", + "content": "[2] Wonho Bae, Junhyug Noh, and Gunhee Kim. Rethinking class activation mapping for weakly supervised object localization. In ECCV, 2020. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 158, + 288, + 201 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 158, + 288, + 201 + ], + "spans": [ + { + "bbox": [ + 53, + 158, + 288, + 201 + ], + "type": "text", + "content": "[3] Qi Chen, Lingxiao Yang, Jian-Huang Lai, and Xiaohua Xie. Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation. In CVPR, 2022. 2, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 202, + 288, + 246 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 202, + 288, + 246 + ], + "spans": [ + { + "bbox": [ + 53, + 202, + 288, + 246 + ], + "type": "text", + "content": "[4] Zhaozheng Chen, Tan Wang, Xiongwei Wu, Xian-Sheng Hua, Hanwang Zhang, and Qianru Sun. Class re-activation maps for weakly-supervised semantic segmentation. In CVPR, 2022. 2, 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 247, + 288, + 290 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 247, + 288, + 290 + ], + "spans": [ + { + "bbox": [ + 53, + 247, + 288, + 290 + ], + "type": "text", + "content": "[5] Junsuk Choe, Seong Joon Oh, Seungho Lee, Sanghyuk Chun, Zeynep Akata, and Hyunjung Shim. Evaluating weakly supervised object localization methods right. In CVPR, 2020. 1, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 291, + 288, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 291, + 288, + 323 + ], + "spans": [ + { + "bbox": [ + 53, + 291, + 288, + 323 + ], + "type": "text", + "content": "[6] Junsuk Choe and Hyunjung Shim. Attention-based dropout layer for weakly supervised object localization. In CVPR, 2019. 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 324, + 288, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 324, + 288, + 380 + ], + "spans": [ + { + "bbox": [ + 53, + 324, + 288, + 380 + ], + "type": "text", + "content": "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 380, + 288, + 413 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 380, + 288, + 413 + ], + "spans": [ + { + "bbox": [ + 53, + 380, + 288, + 413 + ], + "type": "text", + "content": "[8] Ye Du, Zehua Fu, Qingjie Liu, and Yunhong Wang. Weakly supervised semantic segmentation by pixel-to-prototype contrast. In CVPR, 2022. 2, 6, 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 414, + 288, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 414, + 288, + 456 + ], + "spans": [ + { + "bbox": [ + 53, + 414, + 288, + 456 + ], + "type": "text", + "content": "[9] Mark Everingham and John Winn. The pascal visual object classes challenge 2012 (voc2012) development kit. Pattern Anal. Stat. Model. Comput. Learn., Tech. Rep, 2007:1-45, 2012. 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 457, + 287, + 501 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 457, + 287, + 501 + ], + "spans": [ + { + "bbox": [ + 48, + 457, + 287, + 501 + ], + "type": "text", + "content": "[10] Wei Gao, Fang Wan, Xingjia Pan, Zhiliang Peng, Qi Tian, Zhenjun Han, Bolei Zhou, and Qixiang Ye. Ts-cam: Token semantic coupled attention map for weakly supervised object localization. In ICCV, 2021. 2, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 502, + 287, + 535 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 502, + 287, + 535 + ], + "spans": [ + { + "bbox": [ + 48, + 502, + 287, + 535 + ], + "type": "text", + "content": "[11] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. In ICLR, 2022. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 536, + 287, + 568 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 536, + 287, + 568 + ], + "spans": [ + { + "bbox": [ + 48, + 536, + 287, + 568 + ], + "type": "text", + "content": "[12] Saurav Gupta, Sourav Lakhotia, Abhay Rawat, and Rahul Tallamraju. Vitol: Vision transformer for weakly supervised object localization. In CVPR, 2022. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 569, + 287, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 569, + 287, + 602 + ], + "spans": [ + { + "bbox": [ + 48, + 569, + 287, + 602 + ], + "type": "text", + "content": "[13] Bharath Hariharan, Pablo Arbeláez, Lubomir Bourdev, Subhransu Maji, and Jitendra Malik. Semantic contours from inverse detectors. In ICCV, 2011. 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 602, + 287, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 602, + 287, + 645 + ], + "spans": [ + { + "bbox": [ + 48, + 602, + 287, + 645 + ], + "type": "text", + "content": "[14] Peng-Tao Jiang, Yuqi Yang, Qibin Hou, and Yunchao Wei. L2g: A simple local-to-global knowledge transfer framework for weakly supervised semantic segmentation. In CVPR, 2022. 2, 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 647, + 287, + 680 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 287, + 680 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 287, + 680 + ], + "type": "text", + "content": "[15] Jeesoo Kim, Junsuk Choe, Sangdoo Yun, and Nojun Kwak. Normalization matters in weakly supervised object localization. In ICCV, 2021. 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 681, + 287, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 681, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 48, + 681, + 287, + 714 + ], + "type": "text", + "content": "[16] Alexander Kolesnikov and Christoph H Lampert. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In ECCV, 2016. 5" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 714 + ], + "type": "list", + "angle": 0, + "index": 34, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 127 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 127 + ], + "type": "text", + "content": "[17] Hyeokjun Kweon, Sung-Hoon Yoon, Hyeonseong Kim, Daehee Park, and Kuk-Jin Yoon. Unlocking the potential of ordinary classifier: Class-specific adversarial erasing framework for weakly supervised semantic segmentation. In ICCV, 2021. 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 128, + 545, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 128, + 545, + 161 + ], + "spans": [ + { + "bbox": [ + 307, + 128, + 545, + 161 + ], + "type": "text", + "content": "[18] Jungbeom Lee, Jooyoung Choi, Jisoo Mok, and Sungroh Yoon. Reducing information bottleneck for weakly supervised semantic segmentation. NeurIPS, 2021. 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 162, + 545, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 162, + 545, + 205 + ], + "spans": [ + { + "bbox": [ + 308, + 162, + 545, + 205 + ], + "type": "text", + "content": "[19] Jungbeom Lee, Eunji Kim, Jisoo Mok, and Sungroh Yoon. Anti-adversarily manipulated attributions for weakly supervised semantic segmentation and object localization. PAMI, 2022. 6, 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 205, + 545, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 205, + 545, + 239 + ], + "spans": [ + { + "bbox": [ + 308, + 205, + 545, + 239 + ], + "type": "text", + "content": "[20] Jungbeom Lee, Eunji Kim, and Sungroh Yoon. Antiadversarily manipulated attributions for weakly and semi-supervised semantic segmentation. In CVPR, 2021. 2, 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 239, + 545, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 239, + 545, + 282 + ], + "spans": [ + { + "bbox": [ + 308, + 239, + 545, + 282 + ], + "type": "text", + "content": "[21] Jungbeom Lee, Seong Joon Oh, Sangdoo Yun, Junsuk Choe, Eunji Kim, and Sungroh Yoon. Weakly supervised semantic segmentation using out-of-distribution data. In CVPR, 2022. 1, 6, 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 283, + 545, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 283, + 545, + 326 + ], + "spans": [ + { + "bbox": [ + 308, + 283, + 545, + 326 + ], + "type": "text", + "content": "[22] Minhyun Lee, Dongseob Kim, and Hyunjung Shim. Threshold matters in wsss: Manipulating the activation for the robust and accurate segmentation model against thresholds. In CVPR, 2022. 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 327, + 545, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 327, + 545, + 370 + ], + "spans": [ + { + "bbox": [ + 308, + 327, + 545, + 370 + ], + "type": "text", + "content": "[23] Seungho Lee, Minhyun Lee, Jongwuk Lee, and Hyunjung Shim. Railroad is not a train: Saliency as pseudo-pixel supervision for weakly supervised semantic segmentation. In CVPR, 2021. 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 371, + 545, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 371, + 545, + 404 + ], + "spans": [ + { + "bbox": [ + 308, + 371, + 545, + 404 + ], + "type": "text", + "content": "[24] Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and René Ranftl. Language-driven semantic segmentation. In ICLR, 2022. 3, 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 404, + 545, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 404, + 545, + 437 + ], + "spans": [ + { + "bbox": [ + 308, + 404, + 545, + 437 + ], + "type": "text", + "content": "[25] Jing Li, Junsong Fan, and Zhaoxiang Zhang. Towards noiseless object contours for weakly supervised semantic segmentation. In CVPR, 2022. 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 437, + 545, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 437, + 545, + 480 + ], + "spans": [ + { + "bbox": [ + 308, + 437, + 545, + 480 + ], + "type": "text", + "content": "[26] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 6" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 481, + 545, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 481, + 545, + 514 + ], + "spans": [ + { + "bbox": [ + 308, + 481, + 545, + 514 + ], + "type": "text", + "content": "[27] Ron Mokady, Amir Hertz, and Amit H Bermano. Clipcap: Clip prefix for image captioning. arXiv preprint arXiv:2111.09734, 2021. 7" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 514, + 545, + 568 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 514, + 545, + 568 + ], + "spans": [ + { + "bbox": [ + 308, + 514, + 545, + 568 + ], + "type": "text", + "content": "[28] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 8" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 569, + 545, + 614 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 569, + 545, + 614 + ], + "spans": [ + { + "bbox": [ + 308, + 569, + 545, + 614 + ], + "type": "text", + "content": "[29] Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In CVPR, 2022. 3, 4, 5" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 614, + 545, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 614, + 545, + 647 + ], + "spans": [ + { + "bbox": [ + 308, + 614, + 545, + 647 + ], + "type": "text", + "content": "[30] David Rozenberszki, Or Litany, and Angela Dai. Language-grounded indoor 3d semantic segmentation in the wild. In ECCV, 2022. 3" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 308, + 647, + 545, + 680 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 647, + 545, + 680 + ], + "spans": [ + { + "bbox": [ + 308, + 647, + 545, + 680 + ], + "type": "text", + "content": "[31] Krishna Kumar Singh and Yong Jae Lee. Hide-and-seek: Forcing a network to be meticulous for weakly-supervised object and action localization. In ICCV, 2017. 6" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 308, + 681, + 545, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 681, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 308, + 681, + 545, + 714 + ], + "type": "text", + "content": "[32] Yukun Su, Ruizhou Sun, Guosheng Lin, and Qingyao Wu. Context decoupling augmentation for weakly supervised semantic segmentation. In ICCV, 2021. 2, 6, 7" + } + ] + } + ], + "index": 33 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "19604" + } + ] + } + ], + "index": 35 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "text", + "content": "[33] Kunyang Sun, Haoqing Shi, Zhengming Zhang, and Yongming Huang. ECS-net: Improving weakly supervised semantic segmentation by using connections between class activation maps. In ICCV, 2021. 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 118, + 287, + 160 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 118, + 287, + 160 + ], + "spans": [ + { + "bbox": [ + 48, + 118, + 287, + 160 + ], + "type": "text", + "content": "[34] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In CVPR, 2020. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 162, + 287, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 162, + 287, + 205 + ], + "spans": [ + { + "bbox": [ + 48, + 162, + 287, + 205 + ], + "type": "text", + "content": "[35] Yunchao Wei, Huaxin Xiao, Honghui Shi, Zequn Jie, Jiashi Feng, and Thomas S Huang. Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation. In CVPR, 2018. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 206, + 287, + 249 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 206, + 287, + 249 + ], + "spans": [ + { + "bbox": [ + 48, + 206, + 287, + 249 + ], + "type": "text", + "content": "[36] Tong Wu, Junshi Huang, Guangyu Gao, Xiaoming Wei, Xiaolin Wei, Xuan Luo, and Chi Harold Liu. Embedded discriminative attention mechanism for weakly supervised semantic segmentation. In CVPR, 2021. 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 251, + 287, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 251, + 287, + 282 + ], + "spans": [ + { + "bbox": [ + 48, + 251, + 287, + 282 + ], + "type": "text", + "content": "[37] Jinheng Xie, Xianxu Hou, Kai Ye, and Linlin Shen. Clims: Cross language image matching for weakly supervised semantic segmentation. In CVPR, 2022. 3, 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 284, + 287, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 284, + 287, + 326 + ], + "spans": [ + { + "bbox": [ + 48, + 284, + 287, + 326 + ], + "type": "text", + "content": "[38] Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng, Rui-Wei Zhao, Tao Zhang, Xuequan Lu, and Shang Gao. Cream: Weakly supervised object localization via class re-activation mapping. In CVPR, 2022. 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 327, + 287, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 327, + 287, + 370 + ], + "spans": [ + { + "bbox": [ + 48, + 327, + 287, + 370 + ], + "type": "text", + "content": "[39] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, Ferdous Sohel, and Dan Xu. Leveraging auxiliary tasks with affinity learning for weakly supervised semantic segmentation. In ICCV, 2021. 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 372, + 287, + 414 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 372, + 287, + 414 + ], + "spans": [ + { + "bbox": [ + 48, + 372, + 287, + 414 + ], + "type": "text", + "content": "[40] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, and Dan Xu. Multi-class token transformer for weakly supervised semantic segmentation. In CVPR, 2022. 1, 2, 3, 4, 5, 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 416, + 287, + 458 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 416, + 287, + 458 + ], + "spans": [ + { + "bbox": [ + 48, + 416, + 287, + 458 + ], + "type": "text", + "content": "[41] Yazhou Yao, Tao Chen, Guo-Sen Xie, Chuanyi Zhang, Fumin Shen, Qi Wu, Zhenmin Tang, and Jian Zhang. Nonsalient region object mining for weakly supervised semantic segmentation. In CVPR, 2021. 2, 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 460, + 287, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 460, + 287, + 502 + ], + "spans": [ + { + "bbox": [ + 48, + 460, + 287, + 502 + ], + "type": "text", + "content": "[42] Sung-Hoon Yoon, Hyeokjun Kweon, Jegyeong Cho, Shinjeong Kim, and Kuk-Jin Yoon. Adversarial erasing framework via triplet with gated pyramid pooling layer for weakly supervised semantic segmentation. In ECCV, 2022. 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 504, + 287, + 546 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 504, + 287, + 546 + ], + "spans": [ + { + "bbox": [ + 48, + 504, + 287, + 546 + ], + "type": "text", + "content": "[43] Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. Cutmix: Regularization strategy to train strong classifiers with localizable features. In ICCV, 2019. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 548, + 287, + 580 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 548, + 287, + 580 + ], + "spans": [ + { + "bbox": [ + 48, + 548, + 287, + 580 + ], + "type": "text", + "content": "[44] Dong Zhang, Hanwang Zhang, Jinhui Tang, Xiansheng Hua, and Qianru Sun. Causal intervention for weakly-supervised semantic segmentation. In NeurIPS, 2020. 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 582, + 287, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 582, + 287, + 613 + ], + "spans": [ + { + "bbox": [ + 48, + 582, + 287, + 613 + ], + "type": "text", + "content": "[45] Fei Zhang, Chaochen Gu, Chenyue Zhang, and Yuchao Dai. Complementary patch for weakly supervised semantic segmentation. In ICCV, 2021. 7" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 615, + 287, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 615, + 287, + 646 + ], + "spans": [ + { + "bbox": [ + 48, + 615, + 287, + 646 + ], + "type": "text", + "content": "[46] Han Zhang, Ian Goodfellow, Dimitris Metaxas, and Augustus Odena. Self-attention generative adversarial networks. In ICML, 2019. 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 647, + 287, + 680 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 287, + 680 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 287, + 680 + ], + "type": "text", + "content": "[47] Xiaolin Zhang, Yunchao Wei, Jiashi Feng, Yi Yang, and Thomas S Huang. Adversarial complementary learning for weakly supervised object localization. In CVPR, 2018. 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 681, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 681, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 681, + 287, + 712 + ], + "type": "text", + "content": "[48] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In CVPR, 2016. 1, 6" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 218 + ], + "type": "list", + "angle": 0, + "index": 21, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 115 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 115 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 115 + ], + "type": "text", + "content": "[49] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9):2337-2348, 2022. 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 118, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 150 + ], + "type": "text", + "content": "[50] Tianfei Zhou, Meijie Zhang, Fang Zhao, and Jianwu Li. Regional semantic contrast and aggregation for weakly supervised semantic segmentation. In CVPR, 2022. 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 152, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 184 + ], + "type": "text", + "content": "[51] Lei Zhu, Qian Chen, Lujia Jin, Yunfei You, and Yanye Lu. Bagging regional classification activation maps for weakly supervised object localization. In ECCV, 2022. 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 186, + 545, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 545, + 218 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 545, + 218 + ], + "type": "text", + "content": "[52] Lei Zhu, Qi She, Qian Chen, Yunfei You, Boyu Wang, and Yanye Lu. Weakly supervised object localization as domain adaption. In CVPR, 2022. 6" + } + ] + } + ], + "index": 20 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "19605" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Neural Duplex Radiance Fields for Real-Time View Synthesis/0aecd6d6-eb8c-49cd-ab29-e7461ef9c969_content_list.json b/2023/Learning Neural Duplex Radiance Fields for Real-Time View Synthesis/0aecd6d6-eb8c-49cd-ab29-e7461ef9c969_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..0d7fcdcb4158decb8080727aaff90f0310d4cbe1 --- /dev/null +++ b/2023/Learning Neural Duplex Radiance Fields for Real-Time View Synthesis/0aecd6d6-eb8c-49cd-ab29-e7461ef9c969_content_list.json @@ -0,0 +1,1720 @@ +[ + { + "type": "text", + "text": "Learning Neural Duplex Radiance Fields for Real-Time View Synthesis", + "text_level": 1, + "bbox": [ + 125, + 130, + 844, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Ziyu Wan", + "bbox": [ + 153, + 180, + 243, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Christian Richardt2", + "bbox": [ + 267, + 180, + 421, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Aljaž Božić2", + "bbox": [ + 444, + 181, + 545, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Chao Li2", + "bbox": [ + 568, + 181, + 642, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Vijay Rengarajan2", + "bbox": [ + 663, + 181, + 810, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Seonghyeon Nam²", + "bbox": [ + 119, + 198, + 269, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Xiaoyu Xiang2", + "bbox": [ + 290, + 198, + 411, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Tuotuo Li²", + "bbox": [ + 433, + 198, + 522, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Bo Zhu²", + "bbox": [ + 544, + 198, + 612, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Rakesh Ranjan2", + "bbox": [ + 635, + 198, + 761, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Jing Liao $^{1*}$", + "bbox": [ + 784, + 198, + 874, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1City University of Hong Kong", + "bbox": [ + 253, + 215, + 501, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Meta Reality Labs", + "bbox": [ + 560, + 215, + 715, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "raywzy.com/NDRF/", + "bbox": [ + 387, + 234, + 576, + 252 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/42e73709aa06789c7fa3febe6fda7b016d903b2a7f3a90064faa3d697c0eb281.jpg", + "image_caption": [ + "Figure 1. Our framework efficiently learns the neural duplex radiance field from a NeRF model for high-quality real-time view synthesis." + ], + "image_footnote": [], + "bbox": [ + 78, + 253, + 874, + 406 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 434, + 313, + 450 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Neural radiance fields (NeRFs) enable novel-view synthesis with unprecedented visual quality. However, to render photorealistic images, NeRFs require hundreds of deep multilayer perceptron (MLP) evaluations – for each pixel. This is prohibitively expensive and makes real-time rendering infeasible, even on powerful modern GPUs. In this paper, we propose a novel approach to distill and bake NeRFs into highly efficient mesh-based neural representations that are fully compatible with the massively parallel graphics rendering pipeline. We represent scenes as neural radiance features encoded on a two-layer duplex mesh, which effectively overcomes the inherent inaccuracies in 3D surface reconstruction by learning the aggregated radiance information from a reliable interval of ray-surface intersections. To exploit local geometric relationships of nearby pixels, we leverage screen-space convolutions instead of the MLPs used in NeRFs to achieve high-quality appearance. Finally, the performance of the whole framework is further boosted by a novel multiview distillation optimization strategy. We demonstrate the effectiveness and superiority of our approach via extensive experiments on a range of standard datasets.", + "bbox": [ + 75, + 467, + 473, + 785 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 816, + 209, + 832 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Reconstructing 3D scenes by a representation that can be rendered from unobserved viewpoints using only a few posed", + "bbox": [ + 76, + 843, + 470, + 875 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "images has been a long-standing goal in the computer graphics and computer vision communities. Significant progress has recently been achieved by neural radiance fields (NeRFs) [26], which are capable of generating photorealistic novel views and modeling view-dependent effects such as specular reflections. In particular, a radiance field is a volumetric function parameterized by MLPs that estimates density and emitted radiance at sampled 3D locations in a given direction. Differentiable volume rendering then allows the optimization of this function by minimizing the photometric discrepancy between the real observed color and the rendered color.", + "bbox": [ + 500, + 436, + 893, + 601 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Despite the unprecedented success and enormous practical potential of NeRF and its various extensions [3, 6, 57], an inescapable problem is the high computational cost of rendering novel views. For instance, even using a powerful modern GPU, NeRF requires about 30 seconds to render a single image with $800 \\times 800$ pixels, which prevents its use for interactive applications in virtual and augmented reality. On the other hand, the rapid development of NeRF has spawned abundant follow-up works that focus on optimization acceleration [18, 28, 54], generalization [5, 47, 53], and enabled different downstream tasks, including 3D stylization [11, 16, 30], editing [24, 50, 55, 56], or even perception [10, 17]. Thus, a generalized method that can learn and extract a real-time renderable representation given an arbitrary pretrained NeRF-based model, while maintaining high rendering quality, is highly desirable.", + "bbox": [ + 496, + 607, + 895, + 849 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The huge computational cost of rendering a NeRF representation mainly comes from two aspects: (1) For each individual pixel, NeRF requires sampling hundreds of loca", + "bbox": [ + 498, + 854, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* Corresponding author.", + "bbox": [ + 89, + 886, + 220, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "8307", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "tions along the corresponding ray, querying density and then accumulating radiance using volume rendering; and (2) A large model size is required to represent the geometric details of complex scenes well, so the MLPs used in NeRF architecture are relatively deep and wide, which incurs significant computation for the evaluation of each point sample.", + "bbox": [ + 75, + 90, + 470, + 181 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we present an approach that achieves high-fidelity real-time view synthesis by addressing these issues. To avoid the dense sampling along each ray, one solution is to generate a geometry proxy from a pretrained NeRF model, e.g., via marching cubes [25]. By exploiting the highly-optimized graphics pipeline, we could almost instantly obtain the sample location for each ray. However, due to inaccuracies in the den", + "bbox": [ + 75, + 186, + 307, + 382 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "sity field around surfaces, the extracted mesh may not faithfully represent the true underlying geometry and can contain artifacts, as shown in Figure 2. Dense local sampling could alleviate these errors to some degree, but cannot handle missing geometries or occlusions. An alternative approach is to use rasterization for fast neural rendering [1, 37, 38, 43] by directly baking neural features onto the surface of explicit the geometry or point cloud. This is usually accompanied by a deep CNN to translate rasterized features to colors and learn to resolve the existing errors, which is expensive to evaluate and can prevent real-time rendering.", + "bbox": [ + 75, + 383, + 468, + 547 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To significantly reduce the sampled numbers while efficiently handling the geometric errors, our first key technical innovation is to infer the final RGB color according to the radiance of duplex points along the ray. As illustrated in Figure 3, NeRFs represent a continuous density distribution along each ray. While it will generally be difficult to determine the exact location of a surface along the ray, it will be easier to extract a reliable interval that contributes the most to the final prediction by using an under- and an overestimation of the geometry. Motivated by this idea, instead of selecting a specific location for appearance calculation, or performing expensive dense volume rendering in this interval, we represent the scene using learnable features at the two intersection points of a ray with the duplex geometry and use a neural network to learn the color from this aggregated duplex radiance information. Although only two sampled locations are considered, we found this proposed neural duplex radiance field to be robust in compensating for the errors of the geometry proxy even without a deep neural network, while effectively preserving the efficiency of rasterization-based approaches. The NeRF MLP has become the most standard architecture for most neural implicit representations [7, 8, 26, 27, 31, 39]. Yet, with only a few points", + "bbox": [ + 75, + 551, + 470, + 900 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/517b97bea6450572236bbad78aad87553c1811f07bcd1d77d612f52bfdef0287.jpg", + "image_caption": [ + "Figure 2. NeRF surface." + ], + "image_footnote": [], + "bbox": [ + 310, + 199, + 467, + 354 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/733cb19546d706b40979e0933a86463f4761e11a781a49e0983cc2d70105a821.jpg", + "image_caption": [ + "Figure 3. Motivation. The continuous density distribution of NeRF (curve above the ray) makes it difficult to identify the accurate location of a surface $(\\star)$ for appearance calculation. We thus seek to extract a reliable interval from the density field (between dashed lines), and learn the duplex radiance combinations to tolerate errors." + ], + "image_footnote": [], + "bbox": [ + 501, + 93, + 890, + 207 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "considered along the ray, the MLP struggles to constrain the proposed neural duplex radiance field. Instead, we use a shallow convolutional network, which can effectively capture the local geometric information of neighboring pixels, and leads to a considerably better rendering quality. Finally, we found that directly training the neural duplex radiance field from scratch will lead to noticeable artifacts. We therefore propose a multi-view distillation optimization strategy that enables us to effectively approximate the rendering quality of the original NeRF models. Remarkably, our method improves run-time performance by 10,000 times compared to the original NeRF while maintaining high-quality rendering.", + "bbox": [ + 496, + 313, + 893, + 494 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 508, + 640, + 523 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our work is in the category of neural rendering. Since the introduction of the seminal NeRF approach [26], the growth of neural rendering papers is exponential [42]. Here, we focus on works closely related to our method along two directions: neural representations and real-time neural rendering.", + "bbox": [ + 496, + 534, + 893, + 609 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Neural Representations. NeRF [26] represents the scene as a continuous field of density and color, and generates images by volume rendering. Built upon NeRF, Mip-NeRF [3] represents the prefiltered radiance field for a continuous space of scales, which enables accuracy improvements for different resolutions. $\\mathrm{NeRF + + }$ [57] and Mip-NeRF 360 [3] further extend NeRF-like models for unbounded scenes, allowing reconstruction of scenes with far-away backgrounds. NeuS [46] and VolSDF [51] propose to learn a signed distance function representation by volume rendering to better reconstruct 3D objects. This has potential for fast surface rendering, but the visual quality may not reach the photorealism of NeRF. PixelNeRF [53], IBRNet [47] and MVSNeRF [5] learn more generalized neural representations to overcome NeRF's inability to share knowledge between scenes and can achieve high-quality novel-view synthesis given sparse input images. More recently, efforts are made to extremely reduce the training time of NeRF such as Instant-NGP [28], using multiresolution hash encoding for training acceleration, or", + "bbox": [ + 496, + 613, + 893, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "8308", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/a613e250a7fa176dbaa64e1a603e0eaec6eef62384336fc011c058c7bac1f5d3.jpg", + "image_caption": [ + "Figure 4. Architecture of Neural Duplex Radiance Fields. Our neural representation builds on a two-layer duplex surface extracted from a NeRF model, which approximates the reliable interval covering the true underlying geometry. For each sampled target camera ray with view direction $d$ , we find the duplex ray-surface intersections and derive the radiance features using barycentric interpolation. We use a shallow CNN to aggregate the local geometry and appearance information, and produce view-dependent colors, which effectively ensures rendering efficiency. The output color is directly compared with the pixels of rendered or ground-truth views to optimize the whole representation." + ], + "image_footnote": [], + "bbox": [ + 127, + 93, + 851, + 214 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "TensoRF [6], using tensor factorization to enable memory-efficient feature storage and so on [18, 41, 54]. Neural light field networks [40] learn to directly map rays to observed radiance, and thus only require a single evaluation per pixel. Recently, multiple ways have been proposed to parameterize camera rays, such as Plücker coordinates [40], Gegenbauer polynomials [12], or local affine embeddings [2]. Albeit with great progress, neural light fields still struggle to achieve view-consistent and high-quality 360-degree rendering due to the lack of an explicit geometry prior.", + "bbox": [ + 75, + 320, + 472, + 473 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Real-Time Neural Rendering. There are various directions to accelerate NeRF rendering by reducing the considerable computation needed for inference of large networks and dense sampling along view rays. For example, by deriving sparse intermediate representations from NeRF, such as sparse octrees [45, 52] or sparse voxel grids [14], by dividing the space into many smaller MLPs [34, 35], or by adopting an efficient sampling strategy [15, 21, 29]. Inspired by the rendering equation in computer graphics, FastNeRF [13] proposes to factorize the NeRF and uses caching to achieve efficient rendering. These voxel-based methods basically leverage the caching concept to skip abundant computations in exchange for a larger memory footprint. NeX [49] proposes to combine the MPI representation and neural basis functions to enable real-time view synthesis for forward-facing data. EG3D [4] targets 3D-aware synthesis and supports real-time application by low-resolution rendering followed by upsampling. ENeRF [23] tries to tackle interactive free-viewpoint video by skipping the sampling of empty space. More recently, concurrent work MobileNeRF [9] utilizes optimized sheet meshes as proxy geometry to achieve highly efficient rendering on commodity graphics hardware. In contrast to MobileNeRF [9], our approach neither requires complex multi-stage optimization nor retraining the scene from scratch; instead, our approach can serve as a post-processing step for any existing NeRFs. Another direction explores alternative representations instead of functional NeRFs, such as point clouds [1, 33, 38] or spheres [22].", + "bbox": [ + 75, + 477, + 472, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 500, + 319, + 591, + 335 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our main goal is to learn an efficient 3D scene representation that enables high-quality, real-time novel-view synthesis based on a given NeRF model. To this end, we introduce the neural duplex radiance field (NDRF), a novel concept to significantly decrease the number of sampled points along a ray from hundreds to two, while preserving the realistic details of complex scenes well (Section 3.1). Although the query frequency is bounded by using a two-layered mesh, the per-sample deep MLP-based forward pass in NeRF is still burdensome and lacks expressivity, especially for high-resolution rendering scenarios. Thus, we propose a more efficient hybrid radiance representation and shading mechanism (Section 3.2). To further suppress potential artifacts and improve the rendering quality, we leverage multi-view distillation information to guide the optimization procedure (Section 3.3). Finally, we demonstrate the complete rendering procedure and our shader-based implementation that enables real-time cross-platform rendering (Section 3.4).", + "bbox": [ + 496, + 345, + 895, + 619 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Neural Duplex Radiance Fields", + "text_level": 1, + "bbox": [ + 498, + 632, + 774, + 648 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Let us start with a quick review of NeRF basics. In a nutshell, NeRF approximates the 5D plenoptic function with a learnable MLP, which maps a spatial location $\\mathbf{x} \\in \\mathbb{R}^3$ and view direction $\\mathbf{d} \\in \\mathbb{S}^2$ to radiance $\\mathbf{c} \\in \\mathbb{R}^3$ and density $\\sigma \\in \\mathbb{R}$ . After casting a ray from the camera center through a pixel into the scene, its color is calculated by evaluating a network $F_{\\Theta}$ at multiple locations $\\mathbf{x}$ along the ray, and aggregating the radiance using volume rendering [26].", + "bbox": [ + 496, + 657, + 893, + 777 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "A key factor that impacts the rendering efficiency of NeRF is the number of sampled 3D locations for each ray. Since the volumetric representation of NeRF can be converted into a triangle mesh via marching cubes, a natural idea is to directly bake the appearance on a geometric surface, i.e., to only consider the radiance of ray-surface intersections. However, due to the discretization error of marching cubes and the inherent floater artifacts of NeRF [3], the generated mesh", + "bbox": [ + 496, + 779, + 895, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "8309", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/b93679c47d47cdf3a8948ed9d72477d7e01b2071a35a4fcfda80dea0c3c361dd.jpg", + "image_caption": [ + "Figure 5. Duplex radiance field vs local sampling. $\\star$ : real ray-mesh intersection. $\\bullet$ : local sampled points around proxy surface. Dashed curve: estimated surface. Solid curve: ground-truth surface. Gray curve: not currently estimated surface. Left: The estimated surface occludes the appearance location on the actual surface. Middle: The red ray may not hit the estimated surface, resulting in hole artifacts or incorrect intersections. Right: Our duplex radiance field instead learns to combine locations on two estimated surfaces to render the correct color, and effectively decreases ray misses." + ], + "image_footnote": [], + "bbox": [ + 80, + 89, + 200, + 175 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/bfb169ba0380f5329bc7d7e8df37dcf3383a55231f043f8012617127986f5d8b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 212, + 88, + 334, + 175 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/ffd368eb84c3e4dd30a7494ac0e5ef1b92ab2906ed98634a82b7dc0edb154317.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 344, + 88, + 467, + 175 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "quality is often inadequate (see Figure 2). Rasterization-based neural rendering methods overcome imperfections in the 3D proxy by correcting errors in screen space using a deep CNN, which inevitably sacrifices rendering efficiency [36, 37]. Since the geometric proxy already conveys the approximate 3D structure, an alternative way would be locally sampling around the surface. Although this can help counter the low-quality mesh, it struggles with missing geometry or visible occlusions, as illustrated in Figure 5.", + "bbox": [ + 75, + 316, + 470, + 454 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Representation. It is worth noting that the density distribution of NeRF, which determines the contribution of emitted radiance to the final color of a ray, may contain multiple peaks. This makes approximating the color of a pixel using a single ray-surface intersection a challenging proposition. To significantly reduce the sampled points for each pixel and simultaneously ensure the rendering fidelity, we propose the neural duplex radiance field $F_{\\Theta}:\\mathbb{R}^{8}\\mapsto \\mathbb{R}^{3}$ to represent a light ray, where $F_{\\Theta}$ learns to map a ray segment defined by its two endpoints, $\\mathbf{x}_1$ and $\\mathbf{x}_2$ , to integrated view-dependent colors. More specifically, our goal is to directly learn the color of any pixel from the ray segment defined by the reliable interval of the density distribution. We acquire the endpoints of a ray segment by casting the ray into two proxy mesh surfaces, the inner $\\mathcal{M}_{\\mathrm{i}}(\\mathcal{V}_{\\mathrm{i}},\\mathcal{T}_{\\mathrm{i}})$ and outer $\\mathcal{M}_{\\mathrm{o}}(\\mathcal{V}_{\\mathrm{o}},\\mathcal{T}_{\\mathrm{o}})$ with vertex positions $\\nu_{\\bullet}$ and triangle faces $T_{\\bullet}$ . These meshes are extracted from different level sets of NeRF's density field. More formally, the ray color $\\mathbf{c}$ could be calculated by", + "bbox": [ + 75, + 455, + 472, + 731 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {c} = F _ {\\Theta} \\left(\\mathbf {x} _ {1}, \\mathbf {x} _ {2}, \\mathbf {d}\\right) \\quad \\text {f o r} \\quad \\mathbf {x} _ {1} \\in \\mathcal {T} _ {o} \\text {a n d} \\mathbf {x} _ {2} \\in \\mathcal {T} _ {i}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 117, + 739, + 468, + 755 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{x}_1$ and $\\mathbf{x}_2$ are the depth-sorted intersections of a given ray with meshes $\\mathcal{M}_{\\mathrm{i}}$ and $\\mathcal{M}_{\\mathrm{o}}$ , and $\\mathbf{d}$ denotes the view direction. We still append the view direction considering not all rays will hit both surface proxies. The explicit triangle mesh then will allow us to efficiently acquire the ray-surface intersection, benefiting from the full parallelism of the graphics rasterization pipeline.", + "bbox": [ + 75, + 763, + 468, + 869 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In most implicit neural representations, different properties like SDF, radiance, or pixel colors are encoded by a", + "bbox": [ + 76, + 869, + 470, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "multilayer perceptron due to its smoothness and compactness properties. However, densely evaluating a deep MLP is not a wise choice for real-time applications considering the high computational cost of inference. Since our radiance field is defined via two geometric surfaces, our encoding is defined by directly attaching learnable features $\\mathbf{f}_k \\in \\mathbb{R}^N$ to each mesh vertex $\\mathbf{v}_k$ . To obtain features $\\mathbf{f}(\\mathbf{x})$ for any point $\\mathbf{x}$ on a triangle, we interpolate features within triangles using barycentric coordinates via hardware rasterization. Thus, the rendering equation can be re-written as", + "bbox": [ + 496, + 90, + 893, + 243 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {c} = F _ {\\Theta} \\left(\\mathbf {f} \\left(\\mathbf {x} _ {1}\\right), \\mathbf {f} \\left(\\mathbf {x} _ {2}\\right), \\mathbf {d}\\right) \\quad \\text {w i t h} \\quad \\mathbf {x} _ {1} \\in \\mathcal {T} _ {o}, \\mathbf {x} _ {2} \\in \\mathcal {T} _ {i}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 516, + 255, + 893, + 272 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\Theta$ parameterizes a shallow network to learn the aggregation of radiance features given each ray.", + "bbox": [ + 496, + 285, + 893, + 316 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Convolutional Shading", + "text_level": 1, + "bbox": [ + 500, + 327, + 712, + 343 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Many NeRF-based methods use a view direction conditioned MLP to learn view-dependent appearance effects. The dense sampling along rays provides a feasible way to constrain the learning of the radiance fields through shared 3D locations, which ultimately enables high-quality and coherent rendering across different views. This implicit constraint, however, does not apply to our case. To highly optimize run-time efficiency, neural duplex radiance only considers two points for each cast ray, which significantly reduces the capability of capturing internal correlation in training, often resulting in spatial artifacts and the degradation of rendering quality.", + "bbox": [ + 496, + 349, + 893, + 517 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We resolve this issue by a convolutional shading network that converts duplex features and view directions to per-pixel colors in image space using a small receptive field. More specifically, an independent pixel will be rendered by considering the neighboring local geometry positions $\\mathbf{x}_i$ whose projection $\\mathbf{K}(\\mathbf{R}\\mathbf{x}_i + \\mathbf{t})$ with the nearest $z$ -coordinate lies in the local window around this pixel location, where camera pose $\\mathbf{R} \\in \\mathrm{SO}(3)$ , $\\mathbf{t} \\in \\mathbb{R}^3$ and $\\mathbf{K}$ is the intrinsic matrix. This approach effectively increases the correlation of samples of a 3D scene. To ensure high inference speed, we only use $2 \\sim 3$ convolutional layers and limit convolution kernels to a small $2 \\times 2$ window for optimal visual fidelity and view consistency. Please refer to the supplemental material for more detailed network architecture and parameters.", + "bbox": [ + 496, + 517, + 893, + 729 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Multi-View Distillation", + "text_level": 1, + "bbox": [ + 500, + 739, + 712, + 755 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We observed that neural duplex radiance fields trained from scratch fail to match the high fidelity of the original NeRFs and produce many spatial high-frequency artifacts. To overcome this problem, we take inspiration from KiloNeRF [35] and use a pretrained NeRF as a teacher model to guide the optimization of our neural duplex radiance fields. KiloNeRF directly minimizes the differences of density and radiance in 3D volume space between teacher and student models without any rendering. Considering different parameterizations", + "bbox": [ + 496, + 763, + 893, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "8310", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/9478675118aeefdc8517ecdd82188a5c1b0af262551a593f69480c4d55aaecee.jpg", + "image_caption": [ + "Figure 6. Visualizations of distillation view generation. Yellow cameras: training views. Blue cameras: sampled distillation views." + ], + "image_footnote": [], + "bbox": [ + 86, + 88, + 465, + 200 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "of NeRF and our models, we instead use rendered multi-view images to distill knowledge from the teacher model.", + "bbox": [ + 75, + 246, + 468, + 276 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "One important question is how to generate effective distillation views for rendering based on the training dataset. For object-level datasets, camera views tend to lie on a hemisphere that can be estimated and used for sampling new distillation viewpoints similar with R2L [44]. For real-world captures, the camera poses may only occupy a small subspace. To produce meaningful interpolated viewpoints under both settings, we first align the view directions of all cameras with the origin and convert the camera poses into Spherical coordinates $(r,\\theta ,\\varphi)$ relative to the origin. For each distillation view, we then randomly sample a radius $r_s\\sim U\\big(r^{\\mathrm{min}},r^{\\mathrm{max}}\\big)$ , angles $\\theta_{s}\\sim U\\big(\\theta^{\\mathrm{min}},\\theta^{\\mathrm{max}}\\big)$ and $\\varphi_{s}\\sim U\\big(\\varphi^{\\mathrm{min}},\\varphi^{\\mathrm{max}}\\big)$ according to the ranges of spherical coordinates. After converting from spherical to Cartesian coordinates, we obtain many suitable interpolated poses following the original viewpoint distribution. We define the number of distillation views to be 1,000 in all settings. Our model will be trained on the distilled views first and then fine-tuned using the training images to produce sharper appearance.", + "bbox": [ + 75, + 276, + 473, + 566 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Real-Time Rendering", + "text_level": 1, + "bbox": [ + 76, + 574, + 279, + 590 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "A fully trained neural duplex radiance field comprises two meshes with per-vertex features as well as the shallow shading CNN. While we train the model using PyTorch [32], we implemented a real-time renderer in WebGL using GLSL shaders for cross-platform compatibility. To synthesize a novel view, we use hardware rasterization to efficiently render two feature buffers at the output image resolution. These buffers are sent into the CNN together with a per-pixel view direction to aggregate the local radiance features and produce view-dependent RGB colors. For efficiency, we implement the CNN in two rendering passes, one pass for each layer. Thanks to the compact size of the convolution kernels, each convolution layer can be executed in a pixel-parallel way, which is highly efficient, even without CUDA.", + "bbox": [ + 75, + 598, + 470, + 809 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 821, + 209, + 838 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Setup", + "text_level": 1, + "bbox": [ + 76, + 847, + 158, + 863 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. We evaluate and compare performance on the NeRF-Synthetic [26] benchmark dataset, which contains", + "bbox": [ + 75, + 869, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "eight objects with $360^{\\circ}$ views at $800 \\times 800$ resolution. Further, to comprehensively compare the quality and to test the upper bound of speed, we conduct experiments on the megapixel-level dataset of Tanks&Temples [20], which contains $1920 \\times 1080$ frames. We also leverage additional real-world datasets to perform ablation studies. All the training and testing splits follow the published literature.", + "bbox": [ + 496, + 90, + 893, + 196 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Baselines. We compare with two representative efficient novel-view synthesis approaches: KiloNeRF [35] and SNeRG [14]. For quantitative comparisons, we directly use the published numerical results. For qualitative comparisons, we retrain both methods on different datasets following original configurations, since not all checkpoints are released.", + "bbox": [ + 496, + 202, + 893, + 294 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation Metrics. We measure rendering quality by comparing predicted novel views and ground-truth images using standard metrics: peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) [48], and learned perceptual image patch similarity (LPIPS) [58]. Since our goal is highly efficient rendering, we also report frameterates using frames per second (FPS). We also compare the GPU memory consumption in GB for WebGL applications, to quantify the trade-off between speed, quality, and memory. Finally, we compare the training speed by recording the total optimization time in hours. Our test system consists of an NVIDIA RTX 2080 Ti GPU, an i7-9700K CPU, and 32 GB RAM.", + "bbox": [ + 496, + 299, + 893, + 481 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Implementation. We implement the CUDA version with 20-dimensional learnable radiance features and a three-layer convolutional network. To ensure high efficiency and match the Shader implementation, in the WebGL version, we decrease the learnable feature dimension attached to the geometry surface from 20 to 8 and remove the final convolution layer. This results in some performance degradation on both datasets compared with our CUDA version, but brings remarkable FPS improvement and cross-platform properties, since the renderer implementation no longer requires CUDA. More details regarding the architecture design and parameters setting can be found in the supplementary material.", + "bbox": [ + 496, + 486, + 895, + 667 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "**Optimization Details.** We build our method on top of PyTorch [32] and use the Adam optimizer [19] with $\\beta_{1} = 0.9$ and $\\beta_{2} = 0.999$ . Our method converges in 200,000 iterations, where the first half is optimized on the distillation views and the second half on the original training images. In each iteration, we randomly sample one view, cast all rays into the geometry to fetch the duplex radiance features, and generate complete screen-space buffers (or local patches if training memory is insufficient) for the convolutional shading. The learning rate is set to $10^{-3}$ initially, with exponential decay to $10^{-5}$ thereafter. We chose TensoRF [6] as the teacher NeRF model considering its high efficiency of reconstruction and synthesis of distillation views. To convert the density field into the duplex mesh geometry, the two thresholds are set empirically to $10^{-4}$ and $10^{-2}$ , respectively.", + "bbox": [ + 496, + 672, + 893, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "8311", + "bbox": [ + 480, + 944, + 513, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/0055c00b52efa4c110b1ac5e5819f54bfe7b304c296bfa69c1fb5c0dd7691d83.jpg", + "table_caption": [ + "Table 1. Quantitative results on Synthetic-NeRF [26] and Tanks&Temples [20] datasets. The best result is highlighted in bold. Ours (laptop): Run on an M1 MacBook Pro with 16 GB RAM. Others run on a desktop. N+M: N is the training time and M is the baking time." + ], + "table_footnote": [], + "table_body": "
DatasetMethodBackendFPS ↑PSNR ↑SSIM ↑LPIPS ↓Training (hours)Training Specs
Synthetic-NeRF 800×800KiloNeRF [35]CUDA33.4731.000.9500.030~35+141×V100
SNeRG [14]WebGL89.4330.380.9500.050~30+58×A100
OursCUDA59.7132.140.9570.0306.611×3090
OursWebGL282.6730.430.9450.0491.011×3090
Ours (laptop)WebGL62.2730.430.9450.0491.011×3090
Tanks&Temples 1920×1080KiloNeRFCUDA16.3028.410.9100.090~24+241×A100
SNeRGWebGL55.4824.950.8820.184~30+58×A100
OursCUDA27.4928.120.9150.0899.021×A100
OursWebGL186.9427.080.8950.1301.841×A100
Ours (laptop)WebGL33.6427.080.8950.1301.841×A100
", + "bbox": [ + 78, + 123, + 890, + 338 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/86610c4a8b729e4d7af336349f8e890c31ecbc2216ed7e0e2212fdb67bbaabf3.jpg", + "table_caption": [ + "Table 2. Memory-Quality-Speed trade-off. Peak GPU memory consumption (GB), rendering quality, run-time performance comparisons on Tanks&Temples [20] dataset for WebGL applications." + ], + "table_footnote": [], + "table_body": "
MethodMemory (GB) ↓PSNR ↑FPS ↑
SNeRG4.1924.9555.48
Ours0.9927.08186.94
Ours (laptop)0.9427.0833.64
", + "bbox": [ + 78, + 397, + 468, + 470 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/014dc29faea2b67fe085c5b2d51e772bea0c72b1086707f5c06e06f6c02287eb.jpg", + "table_caption": [ + "Table 3. Quantitative ablation study of proposed components. Our full model performs significantly better than all variations." + ], + "table_footnote": [], + "table_body": "
DuplexConvDistillPSNR ↑SSIM ↑LPIPS ↓
✓✓26.950.8840.12
✓✓30.030.9420.05
✓✓29.660.9320.06
✓✓✓32.140.9570.03
", + "bbox": [ + 78, + 517, + 468, + 595 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Results", + "text_level": 1, + "bbox": [ + 76, + 619, + 171, + 635 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Quantitative Comparison. We provide extensive quantitative comparisons in Table 1. Compared with CUDA-based KiloNeRF [35], our CUDA version achieves significantly better rendering performance in terms of PSNR (more than 1 dB) and SSIM on the Synthetic-NeRF dataset [26] with much higher run-time FPS and lower training time. The PSNR of our CUDA-based model on Tanks&Temples [20] is slightly lower than KiloNeRF, even though the novel views of KiloNeRF contain severe artifacts, as shown in Figure 7. This may be caused by the characteristics of PSNR, which only measures pixel-level differences and ignores the structural similarity. In this situation, SSIM would be a better metric to indicate the performance gap between our method and KiloNeRF (0.915 vs. 0.910).", + "bbox": [ + 75, + 643, + 470, + 853 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We also compare with WebGL-based SNeRG [14]. Even at $1920 \\times 1080$ resolution, our WebGL-based model achieves $180 +$ FPS on a desktop-level GPU, and $30 +$ FPS on a laptop", + "bbox": [ + 76, + 854, + 470, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "using Chrome, with better rendering fidelity than SNeRG. In addition, our method requires about two orders of magnitude less computation for training compared to SNeRG. We also show the trade-off comparison regarding memory consumption, image quality and rendering speed in Table 2 under high-resolution setting, which comprehensively demonstrates the superiority of our method.", + "bbox": [ + 496, + 352, + 893, + 458 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Qualitative Comparison. To further verify our method's benefit in terms of rendering quality over existing fast rendering NeRF techniques, we show qualitative comparisons in Figure 7. As we can see, both baselines, KiloNeRF and SNeRG, cannot render the high-quality details of the scenes, which are possibly bounded by the voxel resolution; however, simply increasing the voxel resolution will directly lead to cubic growth of memory cost. Besides, some texture artifacts and wrong view-dependent specular highlights also exist in the baselines, which are visually inferior to our method. More importantly, in the Tanks&Temples dataset, we could observe vibrant blocking degradation from the results of KiloNeRF. The potential reason is KiloNeRF has limited generalization capability to out-of-distribution (OOD) views since there are no correlations between space-isolated MLPs. By contrast, our method can render consistent and high-fidelity views with good view generalization ability.", + "bbox": [ + 496, + 460, + 893, + 718 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Ablation Study", + "text_level": 1, + "bbox": [ + 500, + 726, + 653, + 742 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Duplex Radiance. To verify the effectiveness of our proposed neural duplex radiance, we compare with a variation that only considers a single ray-surface intersection. More specifically, we only adopt the mesh extracted from NeRF model with threshold $10^{-4}$ to ensure geometry proxy could cover all pixels and avoid hole artifacts. As shown in Table 3 and Figure 8, rendering without duplex radiance dramatically downgrades the quality of novel-view synthesis since the coarse geometry extracted from NeRF could not provide reasonable shading locations. More results about using single", + "bbox": [ + 496, + 750, + 893, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "8312", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/c47af8293aa2c8387f0fb6845c9b00f31b71dfee3707622df6d3c361760477e7.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 84, + 87, + 302, + 223 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2b0e6ce022d3abe2d7b530a09012e9bf8f88ff55bc12fd9b986c73836415b5be.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 303, + 88, + 495, + 223 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a5de2c2869b6e6cb5e4d1268648a69bf52c005afe4b288e34e2dac4bd2e83754.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 496, + 88, + 687, + 223 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/dadb4463b0318332efc5a27a66f4c12b7fed511efe2341a2bdd799322aebb47b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 689, + 89, + 883, + 223 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b28dce01cbbff048234fcf3404a89049cc9dc213b56ebeba29b729b96952fb44.jpg", + "image_caption": [ + "Ground Truth" + ], + "image_footnote": [], + "bbox": [ + 86, + 223, + 302, + 371 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/89e1465277d6e477a84623a807142479e427734e1d56159d3c231c717810adae.jpg", + "image_caption": [ + "KiloNeRF [35]" + ], + "image_footnote": [], + "bbox": [ + 303, + 223, + 495, + 372 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/55efcb45e37fa91f6e3cb2fb6e610e699f16c22889f74d4117c98b591384452d.jpg", + "image_caption": [ + "SNeRG [14]" + ], + "image_footnote": [], + "bbox": [ + 496, + 223, + 689, + 372 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/37175f2783d9432d157894194b336f83ccb5eb484de2ab7e2282b2775fd4683f.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 689, + 223, + 883, + 372 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/175b6f147cff715c550f243e9a4a55e59e2972c9fb2db48a14cbc6caa7eb334e.jpg", + "image_caption": [ + "Figure 7. Qualitative comparisons with various baselines on Synthetic-NeRF (top) and Tanks&Temples (bottom) datasets. We show the novel views synthesized by KiloNeRF, SNeRG and ours. Our approach effectively speeds up the inference efficiency while maintaining the high quality of the rendered frames.", + "No Duplex", + "Figure 8. Qualitative results for each ablation study. Our full model achieves the best rendering quality among all variations." + ], + "image_footnote": [], + "bbox": [ + 78, + 454, + 178, + 537 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/795313d1519b2133fa4e5d89cd9e07cc38f9a60931daab64face5782a8518cab.jpg", + "image_caption": [ + "No Conv" + ], + "image_footnote": [], + "bbox": [ + 181, + 454, + 277, + 537 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4583884132975fe4dc932173de98aa16a78b655b43fc82e986fdbe6012cd05ff.jpg", + "image_caption": [ + "No Distill" + ], + "image_footnote": [], + "bbox": [ + 277, + 454, + 375, + 537 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/0ad5aad0333ea262be0541119958d4c99800e3146b340d46e937689ceeffb39a.jpg", + "image_caption": [ + "Full Model" + ], + "image_footnote": [], + "bbox": [ + 375, + 454, + 473, + 537 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "radiance feature but with different thresholds can be found in the supplementary materials.", + "bbox": [ + 75, + 601, + 468, + 632 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Convolution vs MLP. In contrast to most NeRF methods, which integrate radiance only along a single ray, we aggregate all the radiance information of the local geometry surface via convolution kernels. Figure 8 and Table 3 demonstrate that the convolutional shading network can render high-fidelity novel views more robustly, and effectively overcomes the under-constrained problem while only considering two sample locations for each ray.", + "bbox": [ + 75, + 635, + 470, + 755 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "No Distillation. In this ablation study, we remove all the distillation views and only train the model based on known views. Our results in Figure 8 show that optimization without distillation views will lead to high-frequency artifacts in the geometry, which the spatial convolutional kernels are unable to effectively handle as well.", + "bbox": [ + 75, + 760, + 468, + 851 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Influence of Network Size. We have noticed that decreasing the radiance feature dimension and network size will result in some quality degeneration in Table 1. Hence, one", + "bbox": [ + 75, + 854, + 470, + 900 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/335f5ff8946ed8ab86fc2109e3c37d41465c2d8dfdbcff5f8ed3b4e12b8a70c9.jpg", + "image_caption": [ + "Deeper", + "Figure 9. Ablation study on the network size. Increasing the depth and receptive field of the convolutional shading network does not lead to an improvement on rendering quality in a consistent manner. Zoom in for more details." + ], + "image_footnote": [], + "bbox": [ + 503, + 454, + 601, + 529 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d7323e85591436df67f06ffebf588b32abf9c79e49dda5e7047aa6172421095d.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 602, + 454, + 700, + 529 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/29ce24a4f0fa9fcf8513bd4eeecf68faa17f5b9701a6280cbef32f57a5e3dfc6.jpg", + "image_caption": [ + "Deeper" + ], + "image_footnote": [], + "bbox": [ + 700, + 454, + 797, + 529 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8fe33b81ce678f79c3768d4f47541fb59c97ad9ec902896fd112a0b705214b09.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 799, + 454, + 895, + 529 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "interesting question is whether performance can be boosted consistently by increasing the network parameters and receptive fields. We conduct this experiment on the Fox dataset. More specifically, we double the network layers and boost the original used convolution layers with larger receptive fields. As shown in Figure 9, the deeper network does not bring more quality gains. By contrast, the synthesized novel views become more blurry than our method and lose many textural details, which may be caused by the learning difficulty of a deep network. Leveraging more advanced architectures or loss functions, such as adversarial objectives, could potentially resolve this issue.", + "bbox": [ + 496, + 617, + 893, + 797 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Generalization to out-of-distribution views. NeRF has strong capabilities to interpolate novel views, but its rendering quality may drop for some novel views which don't follow the original distribution. This issue will be more serious for distillation-based methods that try to mimic the teacher behavior. We show out-of-distribution (OOD) syn", + "bbox": [ + 496, + 809, + 893, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "8313", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4dd9216670937e32e0e9e5a29d10dae9457d3e95232130b7e356a4f312e6261d.jpg", + "image_caption": [ + "KiloNeRF [35]" + ], + "image_footnote": [], + "bbox": [ + 101, + 88, + 346, + 172 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/45db3542676bbccf79cdc66b0582282b460ced6a2b3c441f58a75305e4cd2315.jpg", + "image_caption": [ + "SNeRG [14]" + ], + "image_footnote": [], + "bbox": [ + 359, + 88, + 606, + 171 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/28038dc52f4d1840012fae869d848d6c31e0c15be9e222f81496fe747916ee3c.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 619, + 89, + 866, + 171 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/6507c2f4917091b0f84ef7d811029d5d55bddc4ad33bc4a88af7d5da2ff54131.jpg", + "table_caption": [ + "Table 4. Quantitative comparisons with the teacher model on the Tanks&Temples [20] dataset (1920×1080)." + ], + "table_footnote": [], + "table_body": "
PSNR ↑SSIM ↑LPIPS ↓FPS ↑
Original NeRF [26]28.320.9000.110.01
Teacher TensoRF [6]28.480.9180.120.10
Ours w/ WebGL27.080.8950.13186.94
Ours w/ CUDA28.120.9150.0927.49
", + "bbox": [ + 76, + 261, + 468, + 349 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "thesis results in Figure 10. Both KiloNeRF [35] and SNeRG [14] do not generalize well to OOD views and produce inconsistent and blurry artifacts, while our method synthesizes sharper and cleaner appearance with appropriate lighting.", + "bbox": [ + 75, + 371, + 468, + 433 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Comparison with Teacher Model. Since our method involves multi-view distillation, we also measure the quantitative differences between our model and the teacher model [6] in Table 4. Although the numerical results of PSNR and SSIM are slightly inferior to the teacher model, the perceptual metric LPIPS, which better correlates with human perception, surprisingly attains better performance benefiting from the better generalization ability of neural duplex radiance field. On the other hand, the teacher model TensoRF [6] has already optimized the run-time performance by skipping the importance sampling step of original NeRF model and tensor decomposition, but the speed is still limited for real-world interactive applications. In contrast, our method achieves $30+$ FPS while maintaining comparable rendering quality. Significantly, our WebGL version reaches $10,000 \\times$ speed up over the original NeRF model.", + "bbox": [ + 75, + 436, + 470, + 678 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Comparison with MobileNeRF [9]. Recently, a concurrent work MobileNeRF [9] also explores the rasterization pipeline to accelerate the rendering speed of neural radiance fields. Nonetheless, their framework involves optimizations of both geometry and appearance leading to a high computational cost (21+ hours using $8 \\times \\mathrm{V}100$ ). By contrast, our method can convert a pretrained NeRF in a few hours using a single GPU. We provide some qualitative comparisons in Figure 11 as well. Our approach produces more appealing results and better view-dependent appearance.", + "bbox": [ + 75, + 681, + 468, + 832 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 76, + 844, + 194, + 859 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we proposed a novel approach for learning a neural duplex radiance field from a pretrained NeRF for real-", + "bbox": [ + 75, + 869, + 470, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/3dd06652431c95d0b411e1d0c66c27f8a512dab493a5b9676ebc7f87da29c444.jpg", + "image_caption": [ + "Figure 10. Comparison of out-of-distribution (OOD) view synthesis (No ground-truth). Our method could generate high-quality and view-dependent frames with sharper details for OOD views.", + "MobileNeRF [9]", + "Figure 11. Qualitative comparisons with MobileNeRF. Our method produces smoother appearance." + ], + "image_footnote": [], + "bbox": [ + 504, + 229, + 598, + 285 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/54b9d296ead0080eeda2fd48a617889d285f92a8e7ec9af01057b2727f1f2b6e.jpg", + "image_caption": [ + "Ours w/ WebGL" + ], + "image_footnote": [], + "bbox": [ + 601, + 229, + 697, + 285 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e74d1c01903cb6599982f083bf6d6becc9051ad323dbe02d291d3f8e1e589586.jpg", + "image_caption": [ + "MobileNeRF [9]" + ], + "image_footnote": [], + "bbox": [ + 702, + 229, + 795, + 285 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/92626797dcc756da88f0119a9a91ae1c9117a3f57475d870d515d2c692fa3550.jpg", + "image_caption": [ + "Ours w/ WebGL" + ], + "image_footnote": [], + "bbox": [ + 799, + 229, + 895, + 285 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "time novel-view synthesis. We employed a convolutional shading network to improve the rendering quality and also proposed a multi-view distillation strategy for better optimization. Our method provides significant improvements on run-time, bettering or preserving the rendering quality, and at the same time, operating on a much lower computational cost compared to existing efficient NeRF methods.", + "bbox": [ + 496, + 357, + 893, + 463 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations. It is challenging to extract useful duplex meshes from NeRFs to represent transparent or semi-transparent scenes and our method is no different. Integrating order-independent transparency (OIT) with the NeRF framework may be a potential solution to speed up its rendering. In addition, the quality of the geometry proxy will directly influence the learning of neural representations, thus we would also like to explore scene-specific thresholds rather than empirical defined values while generating meshes, for instance, based on signed distance functions or on the statistics of the density values, for more robust proxy extraction. Currently, our method focuses on object-level and bounded-scene datasets. Efficiently rendering real-world unbounded scenes is a promising future direction. Finally, our experiments demonstrate that sufficient learnable parameters are crucial to achieve better rendering fidelity. Hence, to counter the performance degradation in the WebGL version, boosting the running efficiency of larger CNN in the fragment shader, or leveraging other acceleration frameworks, could also be considered in future work.", + "bbox": [ + 496, + 470, + 893, + 772 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements. We thank the anonymous reviewers for their constructive comments. We also appreciate helpful discussions with Feng Liu, Chakravarty R. Alla Chaitanya, Simon Green, Daniel Maskit, Aayush Bansal, Zhiqin Chen, Vasu Agrawal, Hao Tang, Michael Zollhoefer, Huan Wang, Ayush Saraf and Zhaoyang Lv. This work was partially supported by a GRF grant (Project No. CityU 11216122) from the Research Grants Council (RGC) of Hong Kong.", + "bbox": [ + 496, + 779, + 893, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "8314", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graphics. In ECCV, pages 696-712, 2020.", + "[2] Benjamin Attal, Jia-Bin Huang, Michael Zollhöfer, Johannes Kopf, and Changil Kim. Learning neural light fields with ray-space embedding. In CVPR, pages 19819-19829, 2022.", + "[3] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-NeRF 360: Unbounded anti-aliased neural radiance fields. In CVPR, pages 5470–5479, 2022.", + "[4] Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In CVPR, pages 16123-16133, 2022.", + "[5] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. MVSNeRF: Fast generalizable radiance field reconstruction from multi-view stereo. In CVPR, pages 14124-14133, 2021.", + "[6] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. TensoRF: Tensorial radiance fields. In ECCV, 2022.", + "[7] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In CVPR, pages 8628-8638, 2021.", + "[8] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In CVPR, pages 5939-5948, 2019.", + "[9] Zhiqin Chen, Thomas Funkhouser, Peter Hedman, and Andrea Tagliasacchi. MobileNeRF: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In CVPR, 2023.", + "[10] Nianchen Deng, Zhenyi He, Jiannan Ye, Budmonde Duinkhar-jav, Praneeth Chakravarthula, Xubo Yang, and Qi Sun. FoV-NeRF: Foveated neural radiance fields for virtual reality. TVCG, 28(11):3854-3864, 2022.", + "[11] Zhiwen Fan, Yifan Jiang, Peihao Wang, Xinyu Gong, Dejia Xu, and Zhangyang Wang. Unified implicit neural stylization. In ECCV, 2022.", + "[12] Brandon Yushan Feng and Amitabh Varshney. Signet: Efficient neural representation for light fields. In ICCV, pages 14224-14233, 2021.", + "[13] Stephan J Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin. FastNeRF: High-fidelity neural rendering at 200fps. In CVPR, pages 14346-14355, 2021.", + "[14] Peter Hedman, Pratul P Srinivasan, Ben Mildenhall, Jonathan T Barron, and Paul Debevec. Baking neural radiance fields for real-time view synthesis. In CVPR, pages 5875-5884, 2021.", + "[15] Tao Hu, Shu Liu, Yilun Chen, Tiancheng Shen, and Jiaya Jia. EfficientNeRF – efficient neural radiance fields. In CVPR, pages 12902–12911, 2022.", + "[16] Yi-Hua Huang, Yue He, Yu-Jie Yuan, Yu-Kun Lai, and Lin Gao. StylizedNeRF: Consistent 3D scene stylization as stylized NeRF via 2D-3D mutual learning. In CVPR, 2022.", + "[17] Yoonwoo Jeong, Seungjoo Shin, Junha Lee, Chris Choy, Anima Anandkumar, Minsu Cho, and Jaesik Park. PeRFception:" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Perception using radiance fields. In NeurIPS Datasets and Benchmarks Track, 2022.", + "[18] Animesh Karnewar, Tobias Ritschel, Oliver Wang, and Niloy Mitra. ReLU fields: The little non-linearity that could. In SIGGRAPH Conference Proceedings, pages 27:1-9, 2022.", + "[19] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015.", + "[20] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Trans. Graph., 36(4):1-13, 2017.", + "[21] Andreas Kurz, Thomas Neff, Zhaoyang Lv, Michael Zollhöfer, and Markus Steinberger. AdaNeRF: Adaptive sampling for real-time rendering of neural radiance fields. In ECCV, 2022.", + "[22] Christoph Lassner and Michael Zollhofer. Pulsar: Efficient sphere-based neural rendering. In CVPR, pages 1440-1449, 2021.", + "[23] Haotong Lin, Sida Peng, Zhen Xu, Yunzhi Yan, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Efficient neural radiance fields for interactive free-viewpoint video. In SIGGRAPH Asia 2022 Conference Papers, pages 1-9, 2022.", + "[24] Steven Liu, Xiuming Zhang, Zhoutong Zhang, Richard Zhang, Jun-Yan Zhu, and Bryan Russell. Editing conditional radiance fields. In ICCV, pages 5773-5783, 2021.", + "[25] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3D surface construction algorithm. SIGGRAPH Computer Graphics, 21(4):163-169, 1987.", + "[26] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020.", + "[27] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021.", + "[28] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-15, 2022.", + "[29] Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Joerg H Mueller, Chakravarty R Alla Chaitanya, Anton Kaplanyan, and Markus Steinberger. DONeRF: Towards real-time rendering of compact neural radiance fields using depth oracle networks. Computer Graphics Forum, 40(4):45-59, 2021.", + "[30] Thu Nguyen-Phuoc, Feng Liu, and Lei Xiao. SNeRF: Stylized neural implicit representations for 3D scenes. ACM Trans. Graph., 41(4):142:1-11, 2022.", + "[31] Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, and Andreas Geiger. Texture fields: Learning texture representations in function space. In ICCV, pages 4531-4540, 2019.", + "[32] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu" + ], + "bbox": [ + 503, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "8315", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Fang, Junjie Bai, and Soumith Chintala. PyTorch: An imperative style, high-performance deep learning library. In NeurIPS, pages 8024-8035, 2019.", + "[33] Ruslan Rakhimov, Andrei-Timotei Ardelean, Victor Lempitsky, and Evgeny Burnaev. NPBG++: Accelerating neural point-based graphics. In CVPR, pages 15948-15958, 2022.", + "[34] Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, and Andrea Tagliasacchi. DeRF: Decomposed radiance fields. In CVPR, pages 14153-14161, 2021.", + "[35] Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger. KiloNeRF: Speeding up neural radiance fields with thousands of tiny MLPs. In ICCV, pages 14335-14345, 2021.", + "[36] Gernot Riegler and Vladlen Koltun. Free view synthesis. In ECCV, 2020.", + "[37] Gernot Riegler and Vladlen Koltun. Stable view synthesis. In CVPR, pages 12216-12225, 2021.", + "[38] Darius Rückert, Linus Franke, and Marc Stamminger. ADOP: Approximate differentiable one-pixel point rendering. ACM Trans. Graph., 41(4):99:1-14, 2022.", + "[39] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3D-structure-aware neural scene representations. In NeurIPS, 2019.", + "[40] Vincent Sitzmann, Semon Rezchikov, William T. Freeman, Joshua B. Tenenbaum, and Fredo Durand. Light field networks: Neural scene representations with single-evaluation rendering. In NeurIPS, 2021.", + "[41] Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In CVPR, 2022.", + "[42] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, Yifan Wang, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, Tomas Simon, Christian Theobalt, Matthias Niessner, Jonathan T. Barron, Gordon Wetzstein, Michael Zollhöfer, and Vladislav Golyanik. Advances in neural rendering. Computer Graphics Forum, 41(2):703-735, 2022.", + "[43] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM Trans. Graph., 38(4):66:1-12, 2019.", + "[44] Huan Wang, Jian Ren, Zeng Huang, Kyle Olszewski, Mengei Chai, Yun Fu, and Sergey Tulyakov. R2L: Distilling neural radiance field to neural light field for efficient novel view synthesis. In ECCV, pages 612-629, 2022.", + "[45] Liao Wang, Jiakai Zhang, Xinhang Liu, Fuqiang Zhao, Yanshun Zhang, Yingliang Zhang, Minye Wu, Jingyi Yu, and Lan Xu. Fourier PlenOctrees for dynamic radiance field rendering in real-time. In CVPR, pages 13524-13534, 2022.", + "[46] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In NeurIPS, 2021.", + "[47] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P Srinivasan, Howard Zhou, Jonathan T Barron, Ricardo MartinBrualla, Noah Snavely, and Thomas Funkhouser. IBRNet: Learning multi-view image-based rendering. In CVPR, pages 4690-4699, 2021." + ], + "bbox": [ + 78, + 90, + 470, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[48] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4): 600-612, 2004.", + "[49] Suttisak Wizadwongsa, Pakkapon Phongthawee, Jiraphon Yenphraphai, and Supasorn Suwajanakorn. NeX: Real-time view synthesis with neural basis expansion. In CVPR, pages 8534-8543, 2021.", + "[50] Bangbang Yang, Yinda Zhang, Yinghao Xu, Yijin Li, Han Zhou, Hujun Bao, Guofeng Zhang, and Zhaopeng Cui. Learning object-compositional neural radiance field for editable scene rendering. In ICCV, pages 13779-13788, 2021.", + "[51] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In NeurIPS, pages 4805-4815, 2021.", + "[52] Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. PlenOctrees for real-time rendering of neural radiance fields. In ICCV, 2021.", + "[53] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. PixelNeRF: Neural radiance fields from one or few images. In CVPR, pages 4578-4587, 2021.", + "[54] Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In CVPR, 2022.", + "[55] Yu-Jie Yuan, Yang-Tian Sun, Yu-Kun Lai, Yuewen Ma, Rongfei Jia, and Lin Gao. NeRF-editing: geometry editing of neural radiance fields. In CVPR, pages 18353-18364, 2022.", + "[56] Jingbo Zhang, Xiaoyu Li, Ziyu Wan, Can Wang, and Jing Liao. FDNeRF: Few-shot dynamic neural radiance fields for face reconstruction and expression editing. In SIGGRAPH Asia Conference Papers, pages 12:1-9, 2022.", + "[57] Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun. NeRF++: Analyzing and improving neural radiance fields. arXiv:2010.07492, 2020.", + "[58] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018." + ], + "bbox": [ + 501, + 92, + 893, + 628 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "8316", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Neural Duplex Radiance Fields for Real-Time View Synthesis/0aecd6d6-eb8c-49cd-ab29-e7461ef9c969_model.json b/2023/Learning Neural Duplex Radiance Fields for Real-Time View Synthesis/0aecd6d6-eb8c-49cd-ab29-e7461ef9c969_model.json new file mode 100644 index 0000000000000000000000000000000000000000..34f05ab875c7bd060d29a3d2fd42b6a1d1396e64 --- /dev/null +++ b/2023/Learning Neural Duplex Radiance Fields for Real-Time View Synthesis/0aecd6d6-eb8c-49cd-ab29-e7461ef9c969_model.json @@ -0,0 +1,2552 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.126, + 0.131, + 0.846, + 0.154 + ], + "angle": 0, + "content": "Learning Neural Duplex Radiance Fields for Real-Time View Synthesis" + }, + { + "type": "text", + "bbox": [ + 0.155, + 0.181, + 0.245, + 0.199 + ], + "angle": 0, + "content": "Ziyu Wan" + }, + { + "type": "text", + "bbox": [ + 0.268, + 0.181, + 0.423, + 0.198 + ], + "angle": 0, + "content": "Christian Richardt2" + }, + { + "type": "text", + "bbox": [ + 0.445, + 0.182, + 0.547, + 0.199 + ], + "angle": 0, + "content": "Aljaž Božić2" + }, + { + "type": "text", + "bbox": [ + 0.569, + 0.182, + 0.643, + 0.199 + ], + "angle": 0, + "content": "Chao Li2" + }, + { + "type": "text", + "bbox": [ + 0.665, + 0.182, + 0.812, + 0.199 + ], + "angle": 0, + "content": "Vijay Rengarajan2" + }, + { + "type": "text", + "bbox": [ + 0.12, + 0.199, + 0.271, + 0.216 + ], + "angle": 0, + "content": "Seonghyeon Nam²" + }, + { + "type": "text", + "bbox": [ + 0.292, + 0.199, + 0.413, + 0.216 + ], + "angle": 0, + "content": "Xiaoyu Xiang2" + }, + { + "type": "text", + "bbox": [ + 0.434, + 0.199, + 0.523, + 0.216 + ], + "angle": 0, + "content": "Tuotuo Li²" + }, + { + "type": "text", + "bbox": [ + 0.545, + 0.199, + 0.613, + 0.216 + ], + "angle": 0, + "content": "Bo Zhu²" + }, + { + "type": "text", + "bbox": [ + 0.637, + 0.199, + 0.763, + 0.216 + ], + "angle": 0, + "content": "Rakesh Ranjan2" + }, + { + "type": "text", + "bbox": [ + 0.785, + 0.199, + 0.875, + 0.216 + ], + "angle": 0, + "content": "Jing Liao\\(^{1*}\\)" + }, + { + "type": "text", + "bbox": [ + 0.254, + 0.217, + 0.502, + 0.235 + ], + "angle": 0, + "content": "1City University of Hong Kong" + }, + { + "type": "text", + "bbox": [ + 0.562, + 0.217, + 0.717, + 0.235 + ], + "angle": 0, + "content": "Meta Reality Labs" + }, + { + "type": "text", + "bbox": [ + 0.388, + 0.236, + 0.577, + 0.253 + ], + "angle": 0, + "content": "raywzy.com/NDRF/" + }, + { + "type": "image", + "bbox": [ + 0.08, + 0.255, + 0.875, + 0.407 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.079, + 0.414, + 0.89, + 0.43 + ], + "angle": 0, + "content": "Figure 1. Our framework efficiently learns the neural duplex radiance field from a NeRF model for high-quality real-time view synthesis." + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.435, + 0.314, + 0.451 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.468, + 0.474, + 0.786 + ], + "angle": 0, + "content": "Neural radiance fields (NeRFs) enable novel-view synthesis with unprecedented visual quality. However, to render photorealistic images, NeRFs require hundreds of deep multilayer perceptron (MLP) evaluations – for each pixel. This is prohibitively expensive and makes real-time rendering infeasible, even on powerful modern GPUs. In this paper, we propose a novel approach to distill and bake NeRFs into highly efficient mesh-based neural representations that are fully compatible with the massively parallel graphics rendering pipeline. We represent scenes as neural radiance features encoded on a two-layer duplex mesh, which effectively overcomes the inherent inaccuracies in 3D surface reconstruction by learning the aggregated radiance information from a reliable interval of ray-surface intersections. To exploit local geometric relationships of nearby pixels, we leverage screen-space convolutions instead of the MLPs used in NeRFs to achieve high-quality appearance. Finally, the performance of the whole framework is further boosted by a novel multiview distillation optimization strategy. We demonstrate the effectiveness and superiority of our approach via extensive experiments on a range of standard datasets." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.818, + 0.21, + 0.833 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.844, + 0.472, + 0.875 + ], + "angle": 0, + "content": "Reconstructing 3D scenes by a representation that can be rendered from unobserved viewpoints using only a few posed" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.437, + 0.895, + 0.602 + ], + "angle": 0, + "content": "images has been a long-standing goal in the computer graphics and computer vision communities. Significant progress has recently been achieved by neural radiance fields (NeRFs) [26], which are capable of generating photorealistic novel views and modeling view-dependent effects such as specular reflections. In particular, a radiance field is a volumetric function parameterized by MLPs that estimates density and emitted radiance at sampled 3D locations in a given direction. Differentiable volume rendering then allows the optimization of this function by minimizing the photometric discrepancy between the real observed color and the rendered color." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.608, + 0.896, + 0.851 + ], + "angle": 0, + "content": "Despite the unprecedented success and enormous practical potential of NeRF and its various extensions [3, 6, 57], an inescapable problem is the high computational cost of rendering novel views. For instance, even using a powerful modern GPU, NeRF requires about 30 seconds to render a single image with \\(800 \\times 800\\) pixels, which prevents its use for interactive applications in virtual and augmented reality. On the other hand, the rapid development of NeRF has spawned abundant follow-up works that focus on optimization acceleration [18, 28, 54], generalization [5, 47, 53], and enabled different downstream tasks, including 3D stylization [11, 16, 30], editing [24, 50, 55, 56], or even perception [10, 17]. Thus, a generalized method that can learn and extract a real-time renderable representation given an arbitrary pretrained NeRF-based model, while maintaining high rendering quality, is highly desirable." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.895, + 0.903 + ], + "angle": 0, + "content": "The huge computational cost of rendering a NeRF representation mainly comes from two aspects: (1) For each individual pixel, NeRF requires sampling hundreds of loca" + }, + { + "type": "page_footnote", + "bbox": [ + 0.091, + 0.887, + 0.222, + 0.9 + ], + "angle": 0, + "content": "* Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8307" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.471, + 0.182 + ], + "angle": 0, + "content": "tions along the corresponding ray, querying density and then accumulating radiance using volume rendering; and (2) A large model size is required to represent the geometric details of complex scenes well, so the MLPs used in NeRF architecture are relatively deep and wide, which incurs significant computation for the evaluation of each point sample." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.187, + 0.308, + 0.383 + ], + "angle": 0, + "content": "In this paper, we present an approach that achieves high-fidelity real-time view synthesis by addressing these issues. To avoid the dense sampling along each ray, one solution is to generate a geometry proxy from a pretrained NeRF model, e.g., via marching cubes [25]. By exploiting the highly-optimized graphics pipeline, we could almost instantly obtain the sample location for each ray. However, due to inaccuracies in the den" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.384, + 0.47, + 0.549 + ], + "angle": 0, + "content": "sity field around surfaces, the extracted mesh may not faithfully represent the true underlying geometry and can contain artifacts, as shown in Figure 2. Dense local sampling could alleviate these errors to some degree, but cannot handle missing geometries or occlusions. An alternative approach is to use rasterization for fast neural rendering [1, 37, 38, 43] by directly baking neural features onto the surface of explicit the geometry or point cloud. This is usually accompanied by a deep CNN to translate rasterized features to colors and learn to resolve the existing errors, which is expensive to evaluate and can prevent real-time rendering." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.553, + 0.471, + 0.901 + ], + "angle": 0, + "content": "To significantly reduce the sampled numbers while efficiently handling the geometric errors, our first key technical innovation is to infer the final RGB color according to the radiance of duplex points along the ray. As illustrated in Figure 3, NeRFs represent a continuous density distribution along each ray. While it will generally be difficult to determine the exact location of a surface along the ray, it will be easier to extract a reliable interval that contributes the most to the final prediction by using an under- and an overestimation of the geometry. Motivated by this idea, instead of selecting a specific location for appearance calculation, or performing expensive dense volume rendering in this interval, we represent the scene using learnable features at the two intersection points of a ray with the duplex geometry and use a neural network to learn the color from this aggregated duplex radiance information. Although only two sampled locations are considered, we found this proposed neural duplex radiance field to be robust in compensating for the errors of the geometry proxy even without a deep neural network, while effectively preserving the efficiency of rasterization-based approaches. The NeRF MLP has become the most standard architecture for most neural implicit representations [7, 8, 26, 27, 31, 39]. Yet, with only a few points" + }, + { + "type": "image", + "bbox": [ + 0.312, + 0.2, + 0.468, + 0.356 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.315, + 0.361, + 0.465, + 0.374 + ], + "angle": 0, + "content": "Figure 2. NeRF surface." + }, + { + "type": "image", + "bbox": [ + 0.502, + 0.094, + 0.891, + 0.208 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.225, + 0.893, + 0.294 + ], + "angle": 0, + "content": "Figure 3. Motivation. The continuous density distribution of NeRF (curve above the ray) makes it difficult to identify the accurate location of a surface \\((\\star)\\) for appearance calculation. We thus seek to extract a reliable interval from the density field (between dashed lines), and learn the duplex radiance combinations to tolerate errors." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.314, + 0.895, + 0.496 + ], + "angle": 0, + "content": "considered along the ray, the MLP struggles to constrain the proposed neural duplex radiance field. Instead, we use a shallow convolutional network, which can effectively capture the local geometric information of neighboring pixels, and leads to a considerably better rendering quality. Finally, we found that directly training the neural duplex radiance field from scratch will lead to noticeable artifacts. We therefore propose a multi-view distillation optimization strategy that enables us to effectively approximate the rendering quality of the original NeRF models. Remarkably, our method improves run-time performance by 10,000 times compared to the original NeRF while maintaining high-quality rendering." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.51, + 0.642, + 0.525 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.535, + 0.894, + 0.61 + ], + "angle": 0, + "content": "Our work is in the category of neural rendering. Since the introduction of the seminal NeRF approach [26], the growth of neural rendering papers is exponential [42]. Here, we focus on works closely related to our method along two directions: neural representations and real-time neural rendering." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.614, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Neural Representations. NeRF [26] represents the scene as a continuous field of density and color, and generates images by volume rendering. Built upon NeRF, Mip-NeRF [3] represents the prefiltered radiance field for a continuous space of scales, which enables accuracy improvements for different resolutions. \\(\\mathrm{NeRF + + }\\) [57] and Mip-NeRF 360 [3] further extend NeRF-like models for unbounded scenes, allowing reconstruction of scenes with far-away backgrounds. NeuS [46] and VolSDF [51] propose to learn a signed distance function representation by volume rendering to better reconstruct 3D objects. This has potential for fast surface rendering, but the visual quality may not reach the photorealism of NeRF. PixelNeRF [53], IBRNet [47] and MVSNeRF [5] learn more generalized neural representations to overcome NeRF's inability to share knowledge between scenes and can achieve high-quality novel-view synthesis given sparse input images. More recently, efforts are made to extremely reduce the training time of NeRF such as Instant-NGP [28], using multiresolution hash encoding for training acceleration, or" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8308" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.128, + 0.094, + 0.852, + 0.215 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.231, + 0.895, + 0.304 + ], + "angle": 0, + "content": "Figure 4. Architecture of Neural Duplex Radiance Fields. Our neural representation builds on a two-layer duplex surface extracted from a NeRF model, which approximates the reliable interval covering the true underlying geometry. For each sampled target camera ray with view direction \\(d\\), we find the duplex ray-surface intersections and derive the radiance features using barycentric interpolation. We use a shallow CNN to aggregate the local geometry and appearance information, and produce view-dependent colors, which effectively ensures rendering efficiency. The output color is directly compared with the pixels of rendered or ground-truth views to optimize the whole representation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.321, + 0.473, + 0.474 + ], + "angle": 0, + "content": "TensoRF [6], using tensor factorization to enable memory-efficient feature storage and so on [18, 41, 54]. Neural light field networks [40] learn to directly map rays to observed radiance, and thus only require a single evaluation per pixel. Recently, multiple ways have been proposed to parameterize camera rays, such as Plücker coordinates [40], Gegenbauer polynomials [12], or local affine embeddings [2]. Albeit with great progress, neural light fields still struggle to achieve view-consistent and high-quality 360-degree rendering due to the lack of an explicit geometry prior." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.478, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Real-Time Neural Rendering. There are various directions to accelerate NeRF rendering by reducing the considerable computation needed for inference of large networks and dense sampling along view rays. For example, by deriving sparse intermediate representations from NeRF, such as sparse octrees [45, 52] or sparse voxel grids [14], by dividing the space into many smaller MLPs [34, 35], or by adopting an efficient sampling strategy [15, 21, 29]. Inspired by the rendering equation in computer graphics, FastNeRF [13] proposes to factorize the NeRF and uses caching to achieve efficient rendering. These voxel-based methods basically leverage the caching concept to skip abundant computations in exchange for a larger memory footprint. NeX [49] proposes to combine the MPI representation and neural basis functions to enable real-time view synthesis for forward-facing data. EG3D [4] targets 3D-aware synthesis and supports real-time application by low-resolution rendering followed by upsampling. ENeRF [23] tries to tackle interactive free-viewpoint video by skipping the sampling of empty space. More recently, concurrent work MobileNeRF [9] utilizes optimized sheet meshes as proxy geometry to achieve highly efficient rendering on commodity graphics hardware. In contrast to MobileNeRF [9], our approach neither requires complex multi-stage optimization nor retraining the scene from scratch; instead, our approach can serve as a post-processing step for any existing NeRFs. Another direction explores alternative representations instead of functional NeRFs, such as point clouds [1, 33, 38] or spheres [22]." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.32, + 0.593, + 0.336 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.347, + 0.896, + 0.62 + ], + "angle": 0, + "content": "Our main goal is to learn an efficient 3D scene representation that enables high-quality, real-time novel-view synthesis based on a given NeRF model. To this end, we introduce the neural duplex radiance field (NDRF), a novel concept to significantly decrease the number of sampled points along a ray from hundreds to two, while preserving the realistic details of complex scenes well (Section 3.1). Although the query frequency is bounded by using a two-layered mesh, the per-sample deep MLP-based forward pass in NeRF is still burdensome and lacks expressivity, especially for high-resolution rendering scenarios. Thus, we propose a more efficient hybrid radiance representation and shading mechanism (Section 3.2). To further suppress potential artifacts and improve the rendering quality, we leverage multi-view distillation information to guide the optimization procedure (Section 3.3). Finally, we demonstrate the complete rendering procedure and our shader-based implementation that enables real-time cross-platform rendering (Section 3.4)." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.633, + 0.776, + 0.649 + ], + "angle": 0, + "content": "3.1. Neural Duplex Radiance Fields" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.658, + 0.895, + 0.779 + ], + "angle": 0, + "content": "Let us start with a quick review of NeRF basics. In a nutshell, NeRF approximates the 5D plenoptic function with a learnable MLP, which maps a spatial location \\(\\mathbf{x} \\in \\mathbb{R}^3\\) and view direction \\(\\mathbf{d} \\in \\mathbb{S}^2\\) to radiance \\(\\mathbf{c} \\in \\mathbb{R}^3\\) and density \\(\\sigma \\in \\mathbb{R}\\). After casting a ray from the camera center through a pixel into the scene, its color is calculated by evaluating a network \\(F_{\\Theta}\\) at multiple locations \\(\\mathbf{x}\\) along the ray, and aggregating the radiance using volume rendering [26]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.896, + 0.902 + ], + "angle": 0, + "content": "A key factor that impacts the rendering efficiency of NeRF is the number of sampled 3D locations for each ray. Since the volumetric representation of NeRF can be converted into a triangle mesh via marching cubes, a natural idea is to directly bake the appearance on a geometric surface, i.e., to only consider the radiance of ray-surface intersections. However, due to the discretization error of marching cubes and the inherent floater artifacts of NeRF [3], the generated mesh" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8309" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.09, + 0.201, + 0.176 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.213, + 0.089, + 0.335, + 0.176 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.345, + 0.089, + 0.468, + 0.176 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.18, + 0.47, + 0.306 + ], + "angle": 0, + "content": "Figure 5. Duplex radiance field vs local sampling. \\(\\star\\): real ray-mesh intersection. \\(\\bullet\\): local sampled points around proxy surface. Dashed curve: estimated surface. Solid curve: ground-truth surface. Gray curve: not currently estimated surface. Left: The estimated surface occludes the appearance location on the actual surface. Middle: The red ray may not hit the estimated surface, resulting in hole artifacts or incorrect intersections. Right: Our duplex radiance field instead learns to combine locations on two estimated surfaces to render the correct color, and effectively decreases ray misses." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.318, + 0.471, + 0.455 + ], + "angle": 0, + "content": "quality is often inadequate (see Figure 2). Rasterization-based neural rendering methods overcome imperfections in the 3D proxy by correcting errors in screen space using a deep CNN, which inevitably sacrifices rendering efficiency [36, 37]. Since the geometric proxy already conveys the approximate 3D structure, an alternative way would be locally sampling around the surface. Although this can help counter the low-quality mesh, it struggles with missing geometry or visible occlusions, as illustrated in Figure 5." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.457, + 0.473, + 0.732 + ], + "angle": 0, + "content": "Representation. It is worth noting that the density distribution of NeRF, which determines the contribution of emitted radiance to the final color of a ray, may contain multiple peaks. This makes approximating the color of a pixel using a single ray-surface intersection a challenging proposition. To significantly reduce the sampled points for each pixel and simultaneously ensure the rendering fidelity, we propose the neural duplex radiance field \\( F_{\\Theta}:\\mathbb{R}^{8}\\mapsto \\mathbb{R}^{3} \\) to represent a light ray, where \\( F_{\\Theta} \\) learns to map a ray segment defined by its two endpoints, \\( \\mathbf{x}_1 \\) and \\( \\mathbf{x}_2 \\), to integrated view-dependent colors. More specifically, our goal is to directly learn the color of any pixel from the ray segment defined by the reliable interval of the density distribution. We acquire the endpoints of a ray segment by casting the ray into two proxy mesh surfaces, the inner \\( \\mathcal{M}_{\\mathrm{i}}(\\mathcal{V}_{\\mathrm{i}},\\mathcal{T}_{\\mathrm{i}}) \\) and outer \\( \\mathcal{M}_{\\mathrm{o}}(\\mathcal{V}_{\\mathrm{o}},\\mathcal{T}_{\\mathrm{o}}) \\) with vertex positions \\( \\nu_{\\bullet} \\) and triangle faces \\( T_{\\bullet} \\). These meshes are extracted from different level sets of NeRF's density field. More formally, the ray color \\( \\mathbf{c} \\) could be calculated by" + }, + { + "type": "equation", + "bbox": [ + 0.119, + 0.74, + 0.47, + 0.756 + ], + "angle": 0, + "content": "\\[\n\\mathbf {c} = F _ {\\Theta} \\left(\\mathbf {x} _ {1}, \\mathbf {x} _ {2}, \\mathbf {d}\\right) \\quad \\text {f o r} \\quad \\mathbf {x} _ {1} \\in \\mathcal {T} _ {o} \\text {a n d} \\mathbf {x} _ {2} \\in \\mathcal {T} _ {i}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.765, + 0.47, + 0.87 + ], + "angle": 0, + "content": "where \\(\\mathbf{x}_1\\) and \\(\\mathbf{x}_2\\) are the depth-sorted intersections of a given ray with meshes \\(\\mathcal{M}_{\\mathrm{i}}\\) and \\(\\mathcal{M}_{\\mathrm{o}}\\), and \\(\\mathbf{d}\\) denotes the view direction. We still append the view direction considering not all rays will hit both surface proxies. The explicit triangle mesh then will allow us to efficiently acquire the ray-surface intersection, benefiting from the full parallelism of the graphics rasterization pipeline." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.471, + 0.902 + ], + "angle": 0, + "content": "In most implicit neural representations, different properties like SDF, radiance, or pixel colors are encoded by a" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.244 + ], + "angle": 0, + "content": "multilayer perceptron due to its smoothness and compactness properties. However, densely evaluating a deep MLP is not a wise choice for real-time applications considering the high computational cost of inference. Since our radiance field is defined via two geometric surfaces, our encoding is defined by directly attaching learnable features \\(\\mathbf{f}_k \\in \\mathbb{R}^N\\) to each mesh vertex \\(\\mathbf{v}_k\\). To obtain features \\(\\mathbf{f}(\\mathbf{x})\\) for any point \\(\\mathbf{x}\\) on a triangle, we interpolate features within triangles using barycentric coordinates via hardware rasterization. Thus, the rendering equation can be re-written as" + }, + { + "type": "equation", + "bbox": [ + 0.517, + 0.256, + 0.894, + 0.273 + ], + "angle": 0, + "content": "\\[\n\\mathbf {c} = F _ {\\Theta} \\left(\\mathbf {f} \\left(\\mathbf {x} _ {1}\\right), \\mathbf {f} \\left(\\mathbf {x} _ {2}\\right), \\mathbf {d}\\right) \\quad \\text {w i t h} \\quad \\mathbf {x} _ {1} \\in \\mathcal {T} _ {o}, \\mathbf {x} _ {2} \\in \\mathcal {T} _ {i}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.286, + 0.894, + 0.317 + ], + "angle": 0, + "content": "where \\(\\Theta\\) parameterizes a shallow network to learn the aggregation of radiance features given each ray." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.328, + 0.713, + 0.344 + ], + "angle": 0, + "content": "3.2. Convolutional Shading" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.351, + 0.895, + 0.518 + ], + "angle": 0, + "content": "Many NeRF-based methods use a view direction conditioned MLP to learn view-dependent appearance effects. The dense sampling along rays provides a feasible way to constrain the learning of the radiance fields through shared 3D locations, which ultimately enables high-quality and coherent rendering across different views. This implicit constraint, however, does not apply to our case. To highly optimize run-time efficiency, neural duplex radiance only considers two points for each cast ray, which significantly reduces the capability of capturing internal correlation in training, often resulting in spatial artifacts and the degradation of rendering quality." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.518, + 0.895, + 0.73 + ], + "angle": 0, + "content": "We resolve this issue by a convolutional shading network that converts duplex features and view directions to per-pixel colors in image space using a small receptive field. More specifically, an independent pixel will be rendered by considering the neighboring local geometry positions \\(\\mathbf{x}_i\\) whose projection \\(\\mathbf{K}(\\mathbf{R}\\mathbf{x}_i + \\mathbf{t})\\) with the nearest \\(z\\)-coordinate lies in the local window around this pixel location, where camera pose \\(\\mathbf{R} \\in \\mathrm{SO}(3)\\), \\(\\mathbf{t} \\in \\mathbb{R}^3\\) and \\(\\mathbf{K}\\) is the intrinsic matrix. This approach effectively increases the correlation of samples of a 3D scene. To ensure high inference speed, we only use \\(2 \\sim 3\\) convolutional layers and limit convolution kernels to a small \\(2 \\times 2\\) window for optimal visual fidelity and view consistency. Please refer to the supplemental material for more detailed network architecture and parameters." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.741, + 0.714, + 0.756 + ], + "angle": 0, + "content": "3.3. Multi-View Distillation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.895, + 0.902 + ], + "angle": 0, + "content": "We observed that neural duplex radiance fields trained from scratch fail to match the high fidelity of the original NeRFs and produce many spatial high-frequency artifacts. To overcome this problem, we take inspiration from KiloNeRF [35] and use a pretrained NeRF as a teacher model to guide the optimization of our neural duplex radiance fields. KiloNeRF directly minimizes the differences of density and radiance in 3D volume space between teacher and student models without any rendering. Considering different parameterizations" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8310" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.088, + 0.089, + 0.466, + 0.201 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.206, + 0.47, + 0.234 + ], + "angle": 0, + "content": "Figure 6. Visualizations of distillation view generation. Yellow cameras: training views. Blue cameras: sampled distillation views." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.247, + 0.47, + 0.277 + ], + "angle": 0, + "content": "of NeRF and our models, we instead use rendered multi-view images to distill knowledge from the teacher model." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.277, + 0.474, + 0.568 + ], + "angle": 0, + "content": "One important question is how to generate effective distillation views for rendering based on the training dataset. For object-level datasets, camera views tend to lie on a hemisphere that can be estimated and used for sampling new distillation viewpoints similar with R2L [44]. For real-world captures, the camera poses may only occupy a small subspace. To produce meaningful interpolated viewpoints under both settings, we first align the view directions of all cameras with the origin and convert the camera poses into Spherical coordinates \\((r,\\theta ,\\varphi)\\) relative to the origin. For each distillation view, we then randomly sample a radius \\(r_s\\sim U\\big(r^{\\mathrm{min}},r^{\\mathrm{max}}\\big)\\), angles \\(\\theta_{s}\\sim U\\big(\\theta^{\\mathrm{min}},\\theta^{\\mathrm{max}}\\big)\\) and \\(\\varphi_{s}\\sim U\\big(\\varphi^{\\mathrm{min}},\\varphi^{\\mathrm{max}}\\big)\\) according to the ranges of spherical coordinates. After converting from spherical to Cartesian coordinates, we obtain many suitable interpolated poses following the original viewpoint distribution. We define the number of distillation views to be 1,000 in all settings. Our model will be trained on the distilled views first and then fine-tuned using the training images to produce sharper appearance." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.575, + 0.281, + 0.592 + ], + "angle": 0, + "content": "3.4. Real-Time Rendering" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.599, + 0.472, + 0.81 + ], + "angle": 0, + "content": "A fully trained neural duplex radiance field comprises two meshes with per-vertex features as well as the shallow shading CNN. While we train the model using PyTorch [32], we implemented a real-time renderer in WebGL using GLSL shaders for cross-platform compatibility. To synthesize a novel view, we use hardware rasterization to efficiently render two feature buffers at the output image resolution. These buffers are sent into the CNN together with a per-pixel view direction to aggregate the local radiance features and produce view-dependent RGB colors. For efficiency, we implement the CNN in two rendering passes, one pass for each layer. Thanks to the compact size of the convolution kernels, each convolution layer can be executed in a pixel-parallel way, which is highly efficient, even without CUDA." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.822, + 0.21, + 0.839 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.848, + 0.159, + 0.864 + ], + "angle": 0, + "content": "4.1. Setup" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Datasets. We evaluate and compare performance on the NeRF-Synthetic [26] benchmark dataset, which contains" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.198 + ], + "angle": 0, + "content": "eight objects with \\(360^{\\circ}\\) views at \\(800 \\times 800\\) resolution. Further, to comprehensively compare the quality and to test the upper bound of speed, we conduct experiments on the megapixel-level dataset of Tanks&Temples [20], which contains \\(1920 \\times 1080\\) frames. We also leverage additional real-world datasets to perform ablation studies. All the training and testing splits follow the published literature." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.203, + 0.895, + 0.295 + ], + "angle": 0, + "content": "Baselines. We compare with two representative efficient novel-view synthesis approaches: KiloNeRF [35] and SNeRG [14]. For quantitative comparisons, we directly use the published numerical results. For qualitative comparisons, we retrain both methods on different datasets following original configurations, since not all checkpoints are released." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.3, + 0.895, + 0.482 + ], + "angle": 0, + "content": "Evaluation Metrics. We measure rendering quality by comparing predicted novel views and ground-truth images using standard metrics: peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) [48], and learned perceptual image patch similarity (LPIPS) [58]. Since our goal is highly efficient rendering, we also report frameterates using frames per second (FPS). We also compare the GPU memory consumption in GB for WebGL applications, to quantify the trade-off between speed, quality, and memory. Finally, we compare the training speed by recording the total optimization time in hours. Our test system consists of an NVIDIA RTX 2080 Ti GPU, an i7-9700K CPU, and 32 GB RAM." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.487, + 0.897, + 0.669 + ], + "angle": 0, + "content": "Implementation. We implement the CUDA version with 20-dimensional learnable radiance features and a three-layer convolutional network. To ensure high efficiency and match the Shader implementation, in the WebGL version, we decrease the learnable feature dimension attached to the geometry surface from 20 to 8 and remove the final convolution layer. This results in some performance degradation on both datasets compared with our CUDA version, but brings remarkable FPS improvement and cross-platform properties, since the renderer implementation no longer requires CUDA. More details regarding the architecture design and parameters setting can be found in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.674, + 0.895, + 0.901 + ], + "angle": 0, + "content": "**Optimization Details.** We build our method on top of PyTorch [32] and use the Adam optimizer [19] with \\(\\beta_{1} = 0.9\\) and \\(\\beta_{2} = 0.999\\). Our method converges in 200,000 iterations, where the first half is optimized on the distillation views and the second half on the original training images. In each iteration, we randomly sample one view, cast all rays into the geometry to fetch the duplex radiance features, and generate complete screen-space buffers (or local patches if training memory is insufficient) for the convolutional shading. The learning rate is set to \\(10^{-3}\\) initially, with exponential decay to \\(10^{-5}\\) thereafter. We chose TensoRF [6] as the teacher NeRF model considering its high efficiency of reconstruction and synthesis of distillation views. To convert the density field into the duplex mesh geometry, the two thresholds are set empirically to \\(10^{-4}\\) and \\(10^{-2}\\), respectively." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "8311" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.089, + 0.892, + 0.119 + ], + "angle": 0, + "content": "Table 1. Quantitative results on Synthetic-NeRF [26] and Tanks&Temples [20] datasets. The best result is highlighted in bold. Ours (laptop): Run on an M1 MacBook Pro with 16 GB RAM. Others run on a desktop. N+M: N is the training time and M is the baking time." + }, + { + "type": "table", + "bbox": [ + 0.08, + 0.124, + 0.892, + 0.339 + ], + "angle": 0, + "content": "
DatasetMethodBackendFPS ↑PSNR ↑SSIM ↑LPIPS ↓Training (hours)Training Specs
Synthetic-NeRF 800×800KiloNeRF [35]CUDA33.4731.000.9500.030~35+141×V100
SNeRG [14]WebGL89.4330.380.9500.050~30+58×A100
OursCUDA59.7132.140.9570.0306.611×3090
OursWebGL282.6730.430.9450.0491.011×3090
Ours (laptop)WebGL62.2730.430.9450.0491.011×3090
Tanks&Temples 1920×1080KiloNeRFCUDA16.3028.410.9100.090~24+241×A100
SNeRGWebGL55.4824.950.8820.184~30+58×A100
OursCUDA27.4928.120.9150.0899.021×A100
OursWebGL186.9427.080.8950.1301.841×A100
Ours (laptop)WebGL33.6427.080.8950.1301.841×A100
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.35, + 0.47, + 0.394 + ], + "angle": 0, + "content": "Table 2. Memory-Quality-Speed trade-off. Peak GPU memory consumption (GB), rendering quality, run-time performance comparisons on Tanks&Temples [20] dataset for WebGL applications." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.398, + 0.47, + 0.472 + ], + "angle": 0, + "content": "
MethodMemory (GB) ↓PSNR ↑FPS ↑
SNeRG4.1924.9555.48
Ours0.9927.08186.94
Ours (laptop)0.9427.0833.64
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.485, + 0.472, + 0.514 + ], + "angle": 0, + "content": "Table 3. Quantitative ablation study of proposed components. Our full model performs significantly better than all variations." + }, + { + "type": "table", + "bbox": [ + 0.08, + 0.518, + 0.469, + 0.596 + ], + "angle": 0, + "content": "
DuplexConvDistillPSNR ↑SSIM ↑LPIPS ↓
✓✓26.950.8840.12
✓✓30.030.9420.05
✓✓29.660.9320.06
✓✓✓32.140.9570.03
" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.621, + 0.172, + 0.636 + ], + "angle": 0, + "content": "4.2. Results" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.644, + 0.471, + 0.854 + ], + "angle": 0, + "content": "Quantitative Comparison. We provide extensive quantitative comparisons in Table 1. Compared with CUDA-based KiloNeRF [35], our CUDA version achieves significantly better rendering performance in terms of PSNR (more than 1 dB) and SSIM on the Synthetic-NeRF dataset [26] with much higher run-time FPS and lower training time. The PSNR of our CUDA-based model on Tanks&Temples [20] is slightly lower than KiloNeRF, even though the novel views of KiloNeRF contain severe artifacts, as shown in Figure 7. This may be caused by the characteristics of PSNR, which only measures pixel-level differences and ignores the structural similarity. In this situation, SSIM would be a better metric to indicate the performance gap between our method and KiloNeRF (0.915 vs. 0.910)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.856, + 0.471, + 0.902 + ], + "angle": 0, + "content": "We also compare with WebGL-based SNeRG [14]. Even at \\( 1920 \\times 1080 \\) resolution, our WebGL-based model achieves \\( 180 + \\) FPS on a desktop-level GPU, and \\( 30 + \\) FPS on a laptop" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.353, + 0.895, + 0.459 + ], + "angle": 0, + "content": "using Chrome, with better rendering fidelity than SNeRG. In addition, our method requires about two orders of magnitude less computation for training compared to SNeRG. We also show the trade-off comparison regarding memory consumption, image quality and rendering speed in Table 2 under high-resolution setting, which comprehensively demonstrates the superiority of our method." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.462, + 0.895, + 0.719 + ], + "angle": 0, + "content": "Qualitative Comparison. To further verify our method's benefit in terms of rendering quality over existing fast rendering NeRF techniques, we show qualitative comparisons in Figure 7. As we can see, both baselines, KiloNeRF and SNeRG, cannot render the high-quality details of the scenes, which are possibly bounded by the voxel resolution; however, simply increasing the voxel resolution will directly lead to cubic growth of memory cost. Besides, some texture artifacts and wrong view-dependent specular highlights also exist in the baselines, which are visually inferior to our method. More importantly, in the Tanks&Temples dataset, we could observe vibrant blocking degradation from the results of KiloNeRF. The potential reason is KiloNeRF has limited generalization capability to out-of-distribution (OOD) views since there are no correlations between space-isolated MLPs. By contrast, our method can render consistent and high-fidelity views with good view generalization ability." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.727, + 0.655, + 0.743 + ], + "angle": 0, + "content": "4.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Duplex Radiance. To verify the effectiveness of our proposed neural duplex radiance, we compare with a variation that only considers a single ray-surface intersection. More specifically, we only adopt the mesh extracted from NeRF model with threshold \\(10^{-4}\\) to ensure geometry proxy could cover all pixels and avoid hole artifacts. As shown in Table 3 and Figure 8, rendering without duplex radiance dramatically downgrades the quality of novel-view synthesis since the coarse geometry extracted from NeRF could not provide reasonable shading locations. More results about using single" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8312" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.086, + 0.088, + 0.303, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.304, + 0.089, + 0.496, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.497, + 0.089, + 0.688, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.69, + 0.09, + 0.885, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.087, + 0.224, + 0.303, + 0.372 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.304, + 0.224, + 0.496, + 0.373 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.497, + 0.224, + 0.69, + 0.373 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.691, + 0.224, + 0.885, + 0.373 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.16, + 0.387, + 0.253, + 0.401 + ], + "angle": 0, + "content": "Ground Truth" + }, + { + "type": "image_caption", + "bbox": [ + 0.349, + 0.387, + 0.451, + 0.401 + ], + "angle": 0, + "content": "KiloNeRF [35]" + }, + { + "type": "image_caption", + "bbox": [ + 0.551, + 0.387, + 0.636, + 0.401 + ], + "angle": 0, + "content": "SNeRG [14]" + }, + { + "type": "image_caption", + "bbox": [ + 0.769, + 0.388, + 0.804, + 0.4 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.405, + 0.893, + 0.448 + ], + "angle": 0, + "content": "Figure 7. Qualitative comparisons with various baselines on Synthetic-NeRF (top) and Tanks&Temples (bottom) datasets. We show the novel views synthesized by KiloNeRF, SNeRG and ours. Our approach effectively speeds up the inference efficiency while maintaining the high quality of the rendered frames." + }, + { + "type": "image", + "bbox": [ + 0.08, + 0.455, + 0.179, + 0.539 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.104, + 0.54, + 0.156, + 0.55 + ], + "angle": 0, + "content": "No Duplex" + }, + { + "type": "image", + "bbox": [ + 0.182, + 0.455, + 0.278, + 0.538 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.207, + 0.54, + 0.249, + 0.55 + ], + "angle": 0, + "content": "No Conv" + }, + { + "type": "image", + "bbox": [ + 0.279, + 0.455, + 0.376, + 0.538 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.303, + 0.54, + 0.35, + 0.55 + ], + "angle": 0, + "content": "No Distill" + }, + { + "type": "image", + "bbox": [ + 0.377, + 0.455, + 0.474, + 0.538 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.398, + 0.54, + 0.451, + 0.55 + ], + "angle": 0, + "content": "Full Model" + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.553, + 0.468, + 0.582 + ], + "angle": 0, + "content": "Figure 8. Qualitative results for each ablation study. Our full model achieves the best rendering quality among all variations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.602, + 0.469, + 0.633 + ], + "angle": 0, + "content": "radiance feature but with different thresholds can be found in the supplementary materials." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.636, + 0.471, + 0.756 + ], + "angle": 0, + "content": "Convolution vs MLP. In contrast to most NeRF methods, which integrate radiance only along a single ray, we aggregate all the radiance information of the local geometry surface via convolution kernels. Figure 8 and Table 3 demonstrate that the convolutional shading network can render high-fidelity novel views more robustly, and effectively overcomes the under-constrained problem while only considering two sample locations for each ray." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.761, + 0.469, + 0.852 + ], + "angle": 0, + "content": "No Distillation. In this ablation study, we remove all the distillation views and only train the model based on known views. Our results in Figure 8 show that optimization without distillation views will lead to high-frequency artifacts in the geometry, which the spatial convolutional kernels are unable to effectively handle as well." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Influence of Network Size. We have noticed that decreasing the radiance feature dimension and network size will result in some quality degeneration in Table 1. Hence, one" + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.455, + 0.602, + 0.53 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.535, + 0.532, + 0.571, + 0.543 + ], + "angle": 0, + "content": "Deeper" + }, + { + "type": "image", + "bbox": [ + 0.604, + 0.455, + 0.701, + 0.53 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.638, + 0.532, + 0.664, + 0.542 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image", + "bbox": [ + 0.701, + 0.455, + 0.798, + 0.53 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.732, + 0.532, + 0.767, + 0.543 + ], + "angle": 0, + "content": "Deeper" + }, + { + "type": "image", + "bbox": [ + 0.8, + 0.455, + 0.897, + 0.53 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.835, + 0.532, + 0.859, + 0.542 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.546, + 0.895, + 0.6 + ], + "angle": 0, + "content": "Figure 9. Ablation study on the network size. Increasing the depth and receptive field of the convolutional shading network does not lead to an improvement on rendering quality in a consistent manner. Zoom in for more details." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.618, + 0.895, + 0.799 + ], + "angle": 0, + "content": "interesting question is whether performance can be boosted consistently by increasing the network parameters and receptive fields. We conduct this experiment on the Fox dataset. More specifically, we double the network layers and boost the original used convolution layers with larger receptive fields. As shown in Figure 9, the deeper network does not bring more quality gains. By contrast, the synthesized novel views become more blurry than our method and lose many textural details, which may be caused by the learning difficulty of a deep network. Leveraging more advanced architectures or loss functions, such as adversarial objectives, could potentially resolve this issue." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Generalization to out-of-distribution views. NeRF has strong capabilities to interpolate novel views, but its rendering quality may drop for some novel views which don't follow the original distribution. This issue will be more serious for distillation-based methods that try to mimic the teacher behavior. We show out-of-distribution (OOD) syn" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8313" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.102, + 0.089, + 0.348, + 0.173 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.179, + 0.175, + 0.271, + 0.188 + ], + "angle": 0, + "content": "KiloNeRF [35]" + }, + { + "type": "image", + "bbox": [ + 0.361, + 0.089, + 0.607, + 0.172 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.445, + 0.175, + 0.522, + 0.188 + ], + "angle": 0, + "content": "SNeRG [14]" + }, + { + "type": "image", + "bbox": [ + 0.62, + 0.09, + 0.867, + 0.172 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.727, + 0.176, + 0.758, + 0.186 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.19, + 0.894, + 0.218 + ], + "angle": 0, + "content": "Figure 10. Comparison of out-of-distribution (OOD) view synthesis (No ground-truth). Our method could generate high-quality and view-dependent frames with sharper details for OOD views." + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.229, + 0.468, + 0.256 + ], + "angle": 0, + "content": "Table 4. Quantitative comparisons with the teacher model on the Tanks&Temples [20] dataset (1920×1080)." + }, + { + "type": "table", + "bbox": [ + 0.078, + 0.262, + 0.47, + 0.35 + ], + "angle": 0, + "content": "
PSNR ↑SSIM ↑LPIPS ↓FPS ↑
Original NeRF [26]28.320.9000.110.01
Teacher TensoRF [6]28.480.9180.120.10
Ours w/ WebGL27.080.8950.13186.94
Ours w/ CUDA28.120.9150.0927.49
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.372, + 0.47, + 0.434 + ], + "angle": 0, + "content": "thesis results in Figure 10. Both KiloNeRF [35] and SNeRG [14] do not generalize well to OOD views and produce inconsistent and blurry artifacts, while our method synthesizes sharper and cleaner appearance with appropriate lighting." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.437, + 0.471, + 0.679 + ], + "angle": 0, + "content": "Comparison with Teacher Model. Since our method involves multi-view distillation, we also measure the quantitative differences between our model and the teacher model [6] in Table 4. Although the numerical results of PSNR and SSIM are slightly inferior to the teacher model, the perceptual metric LPIPS, which better correlates with human perception, surprisingly attains better performance benefiting from the better generalization ability of neural duplex radiance field. On the other hand, the teacher model TensoRF [6] has already optimized the run-time performance by skipping the importance sampling step of original NeRF model and tensor decomposition, but the speed is still limited for real-world interactive applications. In contrast, our method achieves \\(30+\\) FPS while maintaining comparable rendering quality. Significantly, our WebGL version reaches \\(10,000 \\times\\) speed up over the original NeRF model." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.682, + 0.47, + 0.833 + ], + "angle": 0, + "content": "Comparison with MobileNeRF [9]. Recently, a concurrent work MobileNeRF [9] also explores the rasterization pipeline to accelerate the rendering speed of neural radiance fields. Nonetheless, their framework involves optimizations of both geometry and appearance leading to a high computational cost (21+ hours using \\(8 \\times \\mathrm{V}100\\)). By contrast, our method can convert a pretrained NeRF in a few hours using a single GPU. We provide some qualitative comparisons in Figure 11 as well. Our approach produces more appealing results and better view-dependent appearance." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.845, + 0.195, + 0.86 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.471, + 0.901 + ], + "angle": 0, + "content": "In this paper, we proposed a novel approach for learning a neural duplex radiance field from a pretrained NeRF for real-" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.231, + 0.599, + 0.286 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.513, + 0.288, + 0.592, + 0.298 + ], + "angle": 0, + "content": "MobileNeRF [9]" + }, + { + "type": "image", + "bbox": [ + 0.602, + 0.231, + 0.699, + 0.286 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.612, + 0.288, + 0.69, + 0.298 + ], + "angle": 0, + "content": "Ours w/ WebGL" + }, + { + "type": "image", + "bbox": [ + 0.703, + 0.23, + 0.797, + 0.286 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.71, + 0.288, + 0.788, + 0.298 + ], + "angle": 0, + "content": "MobileNeRF [9]" + }, + { + "type": "image", + "bbox": [ + 0.8, + 0.231, + 0.896, + 0.286 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.808, + 0.288, + 0.886, + 0.298 + ], + "angle": 0, + "content": "Ours w/ WebGL" + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.301, + 0.892, + 0.33 + ], + "angle": 0, + "content": "Figure 11. Qualitative comparisons with MobileNeRF. Our method produces smoother appearance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.358, + 0.894, + 0.464 + ], + "angle": 0, + "content": "time novel-view synthesis. We employed a convolutional shading network to improve the rendering quality and also proposed a multi-view distillation strategy for better optimization. Our method provides significant improvements on run-time, bettering or preserving the rendering quality, and at the same time, operating on a much lower computational cost compared to existing efficient NeRF methods." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.471, + 0.895, + 0.773 + ], + "angle": 0, + "content": "Limitations. It is challenging to extract useful duplex meshes from NeRFs to represent transparent or semi-transparent scenes and our method is no different. Integrating order-independent transparency (OIT) with the NeRF framework may be a potential solution to speed up its rendering. In addition, the quality of the geometry proxy will directly influence the learning of neural representations, thus we would also like to explore scene-specific thresholds rather than empirical defined values while generating meshes, for instance, based on signed distance functions or on the statistics of the density values, for more robust proxy extraction. Currently, our method focuses on object-level and bounded-scene datasets. Efficiently rendering real-world unbounded scenes is a promising future direction. Finally, our experiments demonstrate that sufficient learnable parameters are crucial to achieve better rendering fidelity. Hence, to counter the performance degradation in the WebGL version, boosting the running efficiency of larger CNN in the fragment shader, or leveraging other acceleration frameworks, could also be considered in future work." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.894, + 0.901 + ], + "angle": 0, + "content": "Acknowledgements. We thank the anonymous reviewers for their constructive comments. We also appreciate helpful discussions with Feng Liu, Chakravarty R. Alla Chaitanya, Simon Green, Daniel Maskit, Aayush Bansal, Zhiqin Chen, Vasu Agrawal, Hao Tang, Michael Zollhoefer, Huan Wang, Ayush Saraf and Zhaoyang Lv. This work was partially supported by a GRF grant (Project No. CityU 11216122) from the Research Grants Council (RGC) of Hong Kong." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8314" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.156 + ], + "angle": 0, + "content": "[1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graphics. In ECCV, pages 696-712, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.157, + 0.47, + 0.199 + ], + "angle": 0, + "content": "[2] Benjamin Attal, Jia-Bin Huang, Michael Zollhöfer, Johannes Kopf, and Changil Kim. Learning neural light fields with ray-space embedding. In CVPR, pages 19819-19829, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.2, + 0.472, + 0.254 + ], + "angle": 0, + "content": "[3] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-NeRF 360: Unbounded anti-aliased neural radiance fields. In CVPR, pages 5470–5479, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.256, + 0.471, + 0.324 + ], + "angle": 0, + "content": "[4] Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In CVPR, pages 16123-16133, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.326, + 0.471, + 0.38 + ], + "angle": 0, + "content": "[5] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. MVSNeRF: Fast generalizable radiance field reconstruction from multi-view stereo. In CVPR, pages 14124-14133, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.382, + 0.471, + 0.408 + ], + "angle": 0, + "content": "[6] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. TensoRF: Tensorial radiance fields. In ECCV, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.41, + 0.471, + 0.45 + ], + "angle": 0, + "content": "[7] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In CVPR, pages 8628-8638, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.452, + 0.471, + 0.492 + ], + "angle": 0, + "content": "[8] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In CVPR, pages 5939-5948, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.494, + 0.471, + 0.548 + ], + "angle": 0, + "content": "[9] Zhiqin Chen, Thomas Funkhouser, Peter Hedman, and Andrea Tagliasacchi. MobileNeRF: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In CVPR, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.551, + 0.471, + 0.605 + ], + "angle": 0, + "content": "[10] Nianchen Deng, Zhenyi He, Jiannan Ye, Budmonde Duinkhar-jav, Praneeth Chakravarthula, Xubo Yang, and Qi Sun. FoV-NeRF: Foveated neural radiance fields for virtual reality. TVCG, 28(11):3854-3864, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.607, + 0.471, + 0.646 + ], + "angle": 0, + "content": "[11] Zhiwen Fan, Yifan Jiang, Peihao Wang, Xinyu Gong, Dejia Xu, and Zhangyang Wang. Unified implicit neural stylization. In ECCV, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.649, + 0.471, + 0.689 + ], + "angle": 0, + "content": "[12] Brandon Yushan Feng and Amitabh Varshney. Signet: Efficient neural representation for light fields. In ICCV, pages 14224-14233, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.691, + 0.471, + 0.732 + ], + "angle": 0, + "content": "[13] Stephan J Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin. FastNeRF: High-fidelity neural rendering at 200fps. In CVPR, pages 14346-14355, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.734, + 0.471, + 0.787 + ], + "angle": 0, + "content": "[14] Peter Hedman, Pratul P Srinivasan, Ben Mildenhall, Jonathan T Barron, and Paul Debevec. Baking neural radiance fields for real-time view synthesis. In CVPR, pages 5875-5884, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.789, + 0.471, + 0.83 + ], + "angle": 0, + "content": "[15] Tao Hu, Shu Liu, Yilun Chen, Tiancheng Shen, and Jiaya Jia. EfficientNeRF – efficient neural radiance fields. In CVPR, pages 12902–12911, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.832, + 0.471, + 0.872 + ], + "angle": 0, + "content": "[16] Yi-Hua Huang, Yue He, Yu-Jie Yuan, Yu-Kun Lai, and Lin Gao. StylizedNeRF: Consistent 3D scene stylization as stylized NeRF via 2D-3D mutual learning. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.874, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[17] Yoonwoo Jeong, Seungjoo Shin, Junha Lee, Chris Choy, Anima Anandkumar, Minsu Cho, and Jaesik Park. PeRFception:" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "Perception using radiance fields. In NeurIPS Datasets and Benchmarks Track, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.892, + 0.163 + ], + "angle": 0, + "content": "[18] Animesh Karnewar, Tobias Ritschel, Oliver Wang, and Niloy Mitra. ReLU fields: The little non-linearity that could. In SIGGRAPH Conference Proceedings, pages 27:1-9, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.165, + 0.892, + 0.192 + ], + "angle": 0, + "content": "[19] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.194, + 0.892, + 0.234 + ], + "angle": 0, + "content": "[20] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Trans. Graph., 36(4):1-13, 2017." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.236, + 0.892, + 0.29 + ], + "angle": 0, + "content": "[21] Andreas Kurz, Thomas Neff, Zhaoyang Lv, Michael Zollhöfer, and Markus Steinberger. AdaNeRF: Adaptive sampling for real-time rendering of neural radiance fields. In ECCV, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.293, + 0.892, + 0.333 + ], + "angle": 0, + "content": "[22] Christoph Lassner and Michael Zollhofer. Pulsar: Efficient sphere-based neural rendering. In CVPR, pages 1440-1449, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.336, + 0.892, + 0.391 + ], + "angle": 0, + "content": "[23] Haotong Lin, Sida Peng, Zhen Xu, Yunzhi Yan, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Efficient neural radiance fields for interactive free-viewpoint video. In SIGGRAPH Asia 2022 Conference Papers, pages 1-9, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.393, + 0.892, + 0.434 + ], + "angle": 0, + "content": "[24] Steven Liu, Xiuming Zhang, Zhoutong Zhang, Richard Zhang, Jun-Yan Zhu, and Bryan Russell. Editing conditional radiance fields. In ICCV, pages 5773-5783, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.436, + 0.892, + 0.477 + ], + "angle": 0, + "content": "[25] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3D surface construction algorithm. SIGGRAPH Computer Graphics, 21(4):163-169, 1987." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.479, + 0.892, + 0.532 + ], + "angle": 0, + "content": "[26] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.535, + 0.892, + 0.589 + ], + "angle": 0, + "content": "[27] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.591, + 0.892, + 0.645 + ], + "angle": 0, + "content": "[28] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-15, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.648, + 0.892, + 0.73 + ], + "angle": 0, + "content": "[29] Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Joerg H Mueller, Chakravarty R Alla Chaitanya, Anton Kaplanyan, and Markus Steinberger. DONeRF: Towards real-time rendering of compact neural radiance fields using depth oracle networks. Computer Graphics Forum, 40(4):45-59, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.733, + 0.892, + 0.773 + ], + "angle": 0, + "content": "[30] Thu Nguyen-Phuoc, Feng Liu, and Lei Xiao. SNeRF: Stylized neural implicit representations for 3D scenes. ACM Trans. Graph., 41(4):142:1-11, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.776, + 0.892, + 0.829 + ], + "angle": 0, + "content": "[31] Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, and Andreas Geiger. Texture fields: Learning texture representations in function space. In ICCV, pages 4531-4540, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.832, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[32] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8315" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.092, + 0.47, + 0.134 + ], + "angle": 0, + "content": "Fang, Junjie Bai, and Soumith Chintala. PyTorch: An imperative style, high-performance deep learning library. In NeurIPS, pages 8024-8035, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.137, + 0.471, + 0.178 + ], + "angle": 0, + "content": "[33] Ruslan Rakhimov, Andrei-Timotei Ardelean, Victor Lempitsky, and Evgeny Burnaev. NPBG++: Accelerating neural point-based graphics. In CVPR, pages 15948-15958, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.181, + 0.472, + 0.222 + ], + "angle": 0, + "content": "[34] Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, and Andrea Tagliasacchi. DeRF: Decomposed radiance fields. In CVPR, pages 14153-14161, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.224, + 0.471, + 0.266 + ], + "angle": 0, + "content": "[35] Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger. KiloNeRF: Speeding up neural radiance fields with thousands of tiny MLPs. In ICCV, pages 14335-14345, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.268, + 0.47, + 0.295 + ], + "angle": 0, + "content": "[36] Gernot Riegler and Vladlen Koltun. Free view synthesis. In ECCV, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.298, + 0.47, + 0.325 + ], + "angle": 0, + "content": "[37] Gernot Riegler and Vladlen Koltun. Stable view synthesis. In CVPR, pages 12216-12225, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.328, + 0.471, + 0.369 + ], + "angle": 0, + "content": "[38] Darius Rückert, Linus Franke, and Marc Stamminger. ADOP: Approximate differentiable one-pixel point rendering. ACM Trans. Graph., 41(4):99:1-14, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.372, + 0.471, + 0.412 + ], + "angle": 0, + "content": "[39] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3D-structure-aware neural scene representations. In NeurIPS, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.415, + 0.471, + 0.47 + ], + "angle": 0, + "content": "[40] Vincent Sitzmann, Semon Rezchikov, William T. Freeman, Joshua B. Tenenbaum, and Fredo Durand. Light field networks: Neural scene representations with single-evaluation rendering. In NeurIPS, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.473, + 0.47, + 0.514 + ], + "angle": 0, + "content": "[41] Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.517, + 0.471, + 0.613 + ], + "angle": 0, + "content": "[42] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, Yifan Wang, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, Tomas Simon, Christian Theobalt, Matthias Niessner, Jonathan T. Barron, Gordon Wetzstein, Michael Zollhöfer, and Vladislav Golyanik. Advances in neural rendering. Computer Graphics Forum, 41(2):703-735, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.616, + 0.471, + 0.656 + ], + "angle": 0, + "content": "[43] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM Trans. Graph., 38(4):66:1-12, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.659, + 0.47, + 0.714 + ], + "angle": 0, + "content": "[44] Huan Wang, Jian Ren, Zeng Huang, Kyle Olszewski, Mengei Chai, Yun Fu, and Sergey Tulyakov. R2L: Distilling neural radiance field to neural light field for efficient novel view synthesis. In ECCV, pages 612-629, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.717, + 0.471, + 0.771 + ], + "angle": 0, + "content": "[45] Liao Wang, Jiakai Zhang, Xinhang Liu, Fuqiang Zhao, Yanshun Zhang, Yingliang Zhang, Minye Wu, Jingyi Yu, and Lan Xu. Fourier PlenOctrees for dynamic radiance field rendering in real-time. In CVPR, pages 13524-13534, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.775, + 0.471, + 0.829 + ], + "angle": 0, + "content": "[46] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In NeurIPS, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.832, + 0.471, + 0.9 + ], + "angle": 0, + "content": "[47] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P Srinivasan, Howard Zhou, Jonathan T Barron, Ricardo MartinBrualla, Noah Snavely, and Thomas Funkhouser. IBRNet: Learning multi-view image-based rendering. In CVPR, pages 4690-4699, 2021." + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.893, + 0.147 + ], + "angle": 0, + "content": "[48] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4): 600-612, 2004." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.893, + 0.204 + ], + "angle": 0, + "content": "[49] Suttisak Wizadwongsa, Pakkapon Phongthawee, Jiraphon Yenphraphai, and Supasorn Suwajanakorn. NeX: Real-time view synthesis with neural basis expansion. In CVPR, pages 8534-8543, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.206, + 0.894, + 0.261 + ], + "angle": 0, + "content": "[50] Bangbang Yang, Yinda Zhang, Yinghao Xu, Yijin Li, Han Zhou, Hujun Bao, Guofeng Zhang, and Zhaopeng Cui. Learning object-compositional neural radiance field for editable scene rendering. In ICCV, pages 13779-13788, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.263, + 0.894, + 0.303 + ], + "angle": 0, + "content": "[51] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In NeurIPS, pages 4805-4815, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.305, + 0.893, + 0.346 + ], + "angle": 0, + "content": "[52] Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. PlenOctrees for real-time rendering of neural radiance fields. In ICCV, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.348, + 0.894, + 0.389 + ], + "angle": 0, + "content": "[53] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. PixelNeRF: Neural radiance fields from one or few images. In CVPR, pages 4578-4587, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.391, + 0.893, + 0.432 + ], + "angle": 0, + "content": "[54] Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.434, + 0.893, + 0.475 + ], + "angle": 0, + "content": "[55] Yu-Jie Yuan, Yang-Tian Sun, Yu-Kun Lai, Yuewen Ma, Rongfei Jia, and Lin Gao. NeRF-editing: geometry editing of neural radiance fields. In CVPR, pages 18353-18364, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.477, + 0.893, + 0.532 + ], + "angle": 0, + "content": "[56] Jingbo Zhang, Xiaoyu Li, Ziyu Wan, Can Wang, and Jing Liao. FDNeRF: Few-shot dynamic neural radiance fields for face reconstruction and expression editing. In SIGGRAPH Asia Conference Papers, pages 12:1-9, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.534, + 0.893, + 0.574 + ], + "angle": 0, + "content": "[57] Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun. NeRF++: Analyzing and improving neural radiance fields. arXiv:2010.07492, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.576, + 0.893, + 0.63 + ], + "angle": 0, + "content": "[58] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018." + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.63 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "8316" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Neural Duplex Radiance Fields for Real-Time View Synthesis/0aecd6d6-eb8c-49cd-ab29-e7461ef9c969_origin.pdf b/2023/Learning Neural Duplex Radiance Fields for Real-Time View Synthesis/0aecd6d6-eb8c-49cd-ab29-e7461ef9c969_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..f97f373c282b9cf5bd603a095674b0bd2507ea9f --- /dev/null +++ b/2023/Learning Neural Duplex Radiance Fields for Real-Time View Synthesis/0aecd6d6-eb8c-49cd-ab29-e7461ef9c969_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:432a9530cd0681ac684d52f1e368a76070523f1b3d7a0ae6b26737078f34e5e6 +size 9992585 diff --git a/2023/Learning Neural Duplex Radiance Fields for Real-Time View Synthesis/full.md b/2023/Learning Neural Duplex Radiance Fields for Real-Time View Synthesis/full.md new file mode 100644 index 0000000000000000000000000000000000000000..662f3ead19e0dfe714df8b97d41e50aec98396a6 --- /dev/null +++ b/2023/Learning Neural Duplex Radiance Fields for Real-Time View Synthesis/full.md @@ -0,0 +1,343 @@ +# Learning Neural Duplex Radiance Fields for Real-Time View Synthesis + +Ziyu Wan + +Christian Richardt2 + +Aljaž Božić2 + +Chao Li2 + +Vijay Rengarajan2 + +Seonghyeon Nam² + +Xiaoyu Xiang2 + +Tuotuo Li² + +Bo Zhu² + +Rakesh Ranjan2 + +Jing Liao $^{1*}$ + +1City University of Hong Kong + +Meta Reality Labs + +raywzy.com/NDRF/ + +![](images/42e73709aa06789c7fa3febe6fda7b016d903b2a7f3a90064faa3d697c0eb281.jpg) +Figure 1. Our framework efficiently learns the neural duplex radiance field from a NeRF model for high-quality real-time view synthesis. + +# Abstract + +Neural radiance fields (NeRFs) enable novel-view synthesis with unprecedented visual quality. However, to render photorealistic images, NeRFs require hundreds of deep multilayer perceptron (MLP) evaluations – for each pixel. This is prohibitively expensive and makes real-time rendering infeasible, even on powerful modern GPUs. In this paper, we propose a novel approach to distill and bake NeRFs into highly efficient mesh-based neural representations that are fully compatible with the massively parallel graphics rendering pipeline. We represent scenes as neural radiance features encoded on a two-layer duplex mesh, which effectively overcomes the inherent inaccuracies in 3D surface reconstruction by learning the aggregated radiance information from a reliable interval of ray-surface intersections. To exploit local geometric relationships of nearby pixels, we leverage screen-space convolutions instead of the MLPs used in NeRFs to achieve high-quality appearance. Finally, the performance of the whole framework is further boosted by a novel multiview distillation optimization strategy. We demonstrate the effectiveness and superiority of our approach via extensive experiments on a range of standard datasets. + +# 1. Introduction + +Reconstructing 3D scenes by a representation that can be rendered from unobserved viewpoints using only a few posed + +images has been a long-standing goal in the computer graphics and computer vision communities. Significant progress has recently been achieved by neural radiance fields (NeRFs) [26], which are capable of generating photorealistic novel views and modeling view-dependent effects such as specular reflections. In particular, a radiance field is a volumetric function parameterized by MLPs that estimates density and emitted radiance at sampled 3D locations in a given direction. Differentiable volume rendering then allows the optimization of this function by minimizing the photometric discrepancy between the real observed color and the rendered color. + +Despite the unprecedented success and enormous practical potential of NeRF and its various extensions [3, 6, 57], an inescapable problem is the high computational cost of rendering novel views. For instance, even using a powerful modern GPU, NeRF requires about 30 seconds to render a single image with $800 \times 800$ pixels, which prevents its use for interactive applications in virtual and augmented reality. On the other hand, the rapid development of NeRF has spawned abundant follow-up works that focus on optimization acceleration [18, 28, 54], generalization [5, 47, 53], and enabled different downstream tasks, including 3D stylization [11, 16, 30], editing [24, 50, 55, 56], or even perception [10, 17]. Thus, a generalized method that can learn and extract a real-time renderable representation given an arbitrary pretrained NeRF-based model, while maintaining high rendering quality, is highly desirable. + +The huge computational cost of rendering a NeRF representation mainly comes from two aspects: (1) For each individual pixel, NeRF requires sampling hundreds of loca + +tions along the corresponding ray, querying density and then accumulating radiance using volume rendering; and (2) A large model size is required to represent the geometric details of complex scenes well, so the MLPs used in NeRF architecture are relatively deep and wide, which incurs significant computation for the evaluation of each point sample. + +In this paper, we present an approach that achieves high-fidelity real-time view synthesis by addressing these issues. To avoid the dense sampling along each ray, one solution is to generate a geometry proxy from a pretrained NeRF model, e.g., via marching cubes [25]. By exploiting the highly-optimized graphics pipeline, we could almost instantly obtain the sample location for each ray. However, due to inaccuracies in the den + +sity field around surfaces, the extracted mesh may not faithfully represent the true underlying geometry and can contain artifacts, as shown in Figure 2. Dense local sampling could alleviate these errors to some degree, but cannot handle missing geometries or occlusions. An alternative approach is to use rasterization for fast neural rendering [1, 37, 38, 43] by directly baking neural features onto the surface of explicit the geometry or point cloud. This is usually accompanied by a deep CNN to translate rasterized features to colors and learn to resolve the existing errors, which is expensive to evaluate and can prevent real-time rendering. + +To significantly reduce the sampled numbers while efficiently handling the geometric errors, our first key technical innovation is to infer the final RGB color according to the radiance of duplex points along the ray. As illustrated in Figure 3, NeRFs represent a continuous density distribution along each ray. While it will generally be difficult to determine the exact location of a surface along the ray, it will be easier to extract a reliable interval that contributes the most to the final prediction by using an under- and an overestimation of the geometry. Motivated by this idea, instead of selecting a specific location for appearance calculation, or performing expensive dense volume rendering in this interval, we represent the scene using learnable features at the two intersection points of a ray with the duplex geometry and use a neural network to learn the color from this aggregated duplex radiance information. Although only two sampled locations are considered, we found this proposed neural duplex radiance field to be robust in compensating for the errors of the geometry proxy even without a deep neural network, while effectively preserving the efficiency of rasterization-based approaches. The NeRF MLP has become the most standard architecture for most neural implicit representations [7, 8, 26, 27, 31, 39]. Yet, with only a few points + +![](images/517b97bea6450572236bbad78aad87553c1811f07bcd1d77d612f52bfdef0287.jpg) +Figure 2. NeRF surface. + +![](images/733cb19546d706b40979e0933a86463f4761e11a781a49e0983cc2d70105a821.jpg) +Figure 3. Motivation. The continuous density distribution of NeRF (curve above the ray) makes it difficult to identify the accurate location of a surface $(\star)$ for appearance calculation. We thus seek to extract a reliable interval from the density field (between dashed lines), and learn the duplex radiance combinations to tolerate errors. + +considered along the ray, the MLP struggles to constrain the proposed neural duplex radiance field. Instead, we use a shallow convolutional network, which can effectively capture the local geometric information of neighboring pixels, and leads to a considerably better rendering quality. Finally, we found that directly training the neural duplex radiance field from scratch will lead to noticeable artifacts. We therefore propose a multi-view distillation optimization strategy that enables us to effectively approximate the rendering quality of the original NeRF models. Remarkably, our method improves run-time performance by 10,000 times compared to the original NeRF while maintaining high-quality rendering. + +# 2. Related Work + +Our work is in the category of neural rendering. Since the introduction of the seminal NeRF approach [26], the growth of neural rendering papers is exponential [42]. Here, we focus on works closely related to our method along two directions: neural representations and real-time neural rendering. + +Neural Representations. NeRF [26] represents the scene as a continuous field of density and color, and generates images by volume rendering. Built upon NeRF, Mip-NeRF [3] represents the prefiltered radiance field for a continuous space of scales, which enables accuracy improvements for different resolutions. $\mathrm{NeRF + + }$ [57] and Mip-NeRF 360 [3] further extend NeRF-like models for unbounded scenes, allowing reconstruction of scenes with far-away backgrounds. NeuS [46] and VolSDF [51] propose to learn a signed distance function representation by volume rendering to better reconstruct 3D objects. This has potential for fast surface rendering, but the visual quality may not reach the photorealism of NeRF. PixelNeRF [53], IBRNet [47] and MVSNeRF [5] learn more generalized neural representations to overcome NeRF's inability to share knowledge between scenes and can achieve high-quality novel-view synthesis given sparse input images. More recently, efforts are made to extremely reduce the training time of NeRF such as Instant-NGP [28], using multiresolution hash encoding for training acceleration, or + +![](images/a613e250a7fa176dbaa64e1a603e0eaec6eef62384336fc011c058c7bac1f5d3.jpg) +Figure 4. Architecture of Neural Duplex Radiance Fields. Our neural representation builds on a two-layer duplex surface extracted from a NeRF model, which approximates the reliable interval covering the true underlying geometry. For each sampled target camera ray with view direction $d$ , we find the duplex ray-surface intersections and derive the radiance features using barycentric interpolation. We use a shallow CNN to aggregate the local geometry and appearance information, and produce view-dependent colors, which effectively ensures rendering efficiency. The output color is directly compared with the pixels of rendered or ground-truth views to optimize the whole representation. + +TensoRF [6], using tensor factorization to enable memory-efficient feature storage and so on [18, 41, 54]. Neural light field networks [40] learn to directly map rays to observed radiance, and thus only require a single evaluation per pixel. Recently, multiple ways have been proposed to parameterize camera rays, such as Plücker coordinates [40], Gegenbauer polynomials [12], or local affine embeddings [2]. Albeit with great progress, neural light fields still struggle to achieve view-consistent and high-quality 360-degree rendering due to the lack of an explicit geometry prior. + +Real-Time Neural Rendering. There are various directions to accelerate NeRF rendering by reducing the considerable computation needed for inference of large networks and dense sampling along view rays. For example, by deriving sparse intermediate representations from NeRF, such as sparse octrees [45, 52] or sparse voxel grids [14], by dividing the space into many smaller MLPs [34, 35], or by adopting an efficient sampling strategy [15, 21, 29]. Inspired by the rendering equation in computer graphics, FastNeRF [13] proposes to factorize the NeRF and uses caching to achieve efficient rendering. These voxel-based methods basically leverage the caching concept to skip abundant computations in exchange for a larger memory footprint. NeX [49] proposes to combine the MPI representation and neural basis functions to enable real-time view synthesis for forward-facing data. EG3D [4] targets 3D-aware synthesis and supports real-time application by low-resolution rendering followed by upsampling. ENeRF [23] tries to tackle interactive free-viewpoint video by skipping the sampling of empty space. More recently, concurrent work MobileNeRF [9] utilizes optimized sheet meshes as proxy geometry to achieve highly efficient rendering on commodity graphics hardware. In contrast to MobileNeRF [9], our approach neither requires complex multi-stage optimization nor retraining the scene from scratch; instead, our approach can serve as a post-processing step for any existing NeRFs. Another direction explores alternative representations instead of functional NeRFs, such as point clouds [1, 33, 38] or spheres [22]. + +# 3. Method + +Our main goal is to learn an efficient 3D scene representation that enables high-quality, real-time novel-view synthesis based on a given NeRF model. To this end, we introduce the neural duplex radiance field (NDRF), a novel concept to significantly decrease the number of sampled points along a ray from hundreds to two, while preserving the realistic details of complex scenes well (Section 3.1). Although the query frequency is bounded by using a two-layered mesh, the per-sample deep MLP-based forward pass in NeRF is still burdensome and lacks expressivity, especially for high-resolution rendering scenarios. Thus, we propose a more efficient hybrid radiance representation and shading mechanism (Section 3.2). To further suppress potential artifacts and improve the rendering quality, we leverage multi-view distillation information to guide the optimization procedure (Section 3.3). Finally, we demonstrate the complete rendering procedure and our shader-based implementation that enables real-time cross-platform rendering (Section 3.4). + +# 3.1. Neural Duplex Radiance Fields + +Let us start with a quick review of NeRF basics. In a nutshell, NeRF approximates the 5D plenoptic function with a learnable MLP, which maps a spatial location $\mathbf{x} \in \mathbb{R}^3$ and view direction $\mathbf{d} \in \mathbb{S}^2$ to radiance $\mathbf{c} \in \mathbb{R}^3$ and density $\sigma \in \mathbb{R}$ . After casting a ray from the camera center through a pixel into the scene, its color is calculated by evaluating a network $F_{\Theta}$ at multiple locations $\mathbf{x}$ along the ray, and aggregating the radiance using volume rendering [26]. + +A key factor that impacts the rendering efficiency of NeRF is the number of sampled 3D locations for each ray. Since the volumetric representation of NeRF can be converted into a triangle mesh via marching cubes, a natural idea is to directly bake the appearance on a geometric surface, i.e., to only consider the radiance of ray-surface intersections. However, due to the discretization error of marching cubes and the inherent floater artifacts of NeRF [3], the generated mesh + +![](images/b93679c47d47cdf3a8948ed9d72477d7e01b2071a35a4fcfda80dea0c3c361dd.jpg) +Figure 5. Duplex radiance field vs local sampling. $\star$ : real ray-mesh intersection. $\bullet$ : local sampled points around proxy surface. Dashed curve: estimated surface. Solid curve: ground-truth surface. Gray curve: not currently estimated surface. Left: The estimated surface occludes the appearance location on the actual surface. Middle: The red ray may not hit the estimated surface, resulting in hole artifacts or incorrect intersections. Right: Our duplex radiance field instead learns to combine locations on two estimated surfaces to render the correct color, and effectively decreases ray misses. + +![](images/bfb169ba0380f5329bc7d7e8df37dcf3383a55231f043f8012617127986f5d8b.jpg) + +![](images/ffd368eb84c3e4dd30a7494ac0e5ef1b92ab2906ed98634a82b7dc0edb154317.jpg) + +quality is often inadequate (see Figure 2). Rasterization-based neural rendering methods overcome imperfections in the 3D proxy by correcting errors in screen space using a deep CNN, which inevitably sacrifices rendering efficiency [36, 37]. Since the geometric proxy already conveys the approximate 3D structure, an alternative way would be locally sampling around the surface. Although this can help counter the low-quality mesh, it struggles with missing geometry or visible occlusions, as illustrated in Figure 5. + +Representation. It is worth noting that the density distribution of NeRF, which determines the contribution of emitted radiance to the final color of a ray, may contain multiple peaks. This makes approximating the color of a pixel using a single ray-surface intersection a challenging proposition. To significantly reduce the sampled points for each pixel and simultaneously ensure the rendering fidelity, we propose the neural duplex radiance field $F_{\Theta}:\mathbb{R}^{8}\mapsto \mathbb{R}^{3}$ to represent a light ray, where $F_{\Theta}$ learns to map a ray segment defined by its two endpoints, $\mathbf{x}_1$ and $\mathbf{x}_2$ , to integrated view-dependent colors. More specifically, our goal is to directly learn the color of any pixel from the ray segment defined by the reliable interval of the density distribution. We acquire the endpoints of a ray segment by casting the ray into two proxy mesh surfaces, the inner $\mathcal{M}_{\mathrm{i}}(\mathcal{V}_{\mathrm{i}},\mathcal{T}_{\mathrm{i}})$ and outer $\mathcal{M}_{\mathrm{o}}(\mathcal{V}_{\mathrm{o}},\mathcal{T}_{\mathrm{o}})$ with vertex positions $\nu_{\bullet}$ and triangle faces $T_{\bullet}$ . These meshes are extracted from different level sets of NeRF's density field. More formally, the ray color $\mathbf{c}$ could be calculated by + +$$ +\mathbf {c} = F _ {\Theta} \left(\mathbf {x} _ {1}, \mathbf {x} _ {2}, \mathbf {d}\right) \quad \text {f o r} \quad \mathbf {x} _ {1} \in \mathcal {T} _ {o} \text {a n d} \mathbf {x} _ {2} \in \mathcal {T} _ {i}, \tag {1} +$$ + +where $\mathbf{x}_1$ and $\mathbf{x}_2$ are the depth-sorted intersections of a given ray with meshes $\mathcal{M}_{\mathrm{i}}$ and $\mathcal{M}_{\mathrm{o}}$ , and $\mathbf{d}$ denotes the view direction. We still append the view direction considering not all rays will hit both surface proxies. The explicit triangle mesh then will allow us to efficiently acquire the ray-surface intersection, benefiting from the full parallelism of the graphics rasterization pipeline. + +In most implicit neural representations, different properties like SDF, radiance, or pixel colors are encoded by a + +multilayer perceptron due to its smoothness and compactness properties. However, densely evaluating a deep MLP is not a wise choice for real-time applications considering the high computational cost of inference. Since our radiance field is defined via two geometric surfaces, our encoding is defined by directly attaching learnable features $\mathbf{f}_k \in \mathbb{R}^N$ to each mesh vertex $\mathbf{v}_k$ . To obtain features $\mathbf{f}(\mathbf{x})$ for any point $\mathbf{x}$ on a triangle, we interpolate features within triangles using barycentric coordinates via hardware rasterization. Thus, the rendering equation can be re-written as + +$$ +\mathbf {c} = F _ {\Theta} \left(\mathbf {f} \left(\mathbf {x} _ {1}\right), \mathbf {f} \left(\mathbf {x} _ {2}\right), \mathbf {d}\right) \quad \text {w i t h} \quad \mathbf {x} _ {1} \in \mathcal {T} _ {o}, \mathbf {x} _ {2} \in \mathcal {T} _ {i}, \tag {2} +$$ + +where $\Theta$ parameterizes a shallow network to learn the aggregation of radiance features given each ray. + +# 3.2. Convolutional Shading + +Many NeRF-based methods use a view direction conditioned MLP to learn view-dependent appearance effects. The dense sampling along rays provides a feasible way to constrain the learning of the radiance fields through shared 3D locations, which ultimately enables high-quality and coherent rendering across different views. This implicit constraint, however, does not apply to our case. To highly optimize run-time efficiency, neural duplex radiance only considers two points for each cast ray, which significantly reduces the capability of capturing internal correlation in training, often resulting in spatial artifacts and the degradation of rendering quality. + +We resolve this issue by a convolutional shading network that converts duplex features and view directions to per-pixel colors in image space using a small receptive field. More specifically, an independent pixel will be rendered by considering the neighboring local geometry positions $\mathbf{x}_i$ whose projection $\mathbf{K}(\mathbf{R}\mathbf{x}_i + \mathbf{t})$ with the nearest $z$ -coordinate lies in the local window around this pixel location, where camera pose $\mathbf{R} \in \mathrm{SO}(3)$ , $\mathbf{t} \in \mathbb{R}^3$ and $\mathbf{K}$ is the intrinsic matrix. This approach effectively increases the correlation of samples of a 3D scene. To ensure high inference speed, we only use $2 \sim 3$ convolutional layers and limit convolution kernels to a small $2 \times 2$ window for optimal visual fidelity and view consistency. Please refer to the supplemental material for more detailed network architecture and parameters. + +# 3.3. Multi-View Distillation + +We observed that neural duplex radiance fields trained from scratch fail to match the high fidelity of the original NeRFs and produce many spatial high-frequency artifacts. To overcome this problem, we take inspiration from KiloNeRF [35] and use a pretrained NeRF as a teacher model to guide the optimization of our neural duplex radiance fields. KiloNeRF directly minimizes the differences of density and radiance in 3D volume space between teacher and student models without any rendering. Considering different parameterizations + +![](images/9478675118aeefdc8517ecdd82188a5c1b0af262551a593f69480c4d55aaecee.jpg) +Figure 6. Visualizations of distillation view generation. Yellow cameras: training views. Blue cameras: sampled distillation views. + +of NeRF and our models, we instead use rendered multi-view images to distill knowledge from the teacher model. + +One important question is how to generate effective distillation views for rendering based on the training dataset. For object-level datasets, camera views tend to lie on a hemisphere that can be estimated and used for sampling new distillation viewpoints similar with R2L [44]. For real-world captures, the camera poses may only occupy a small subspace. To produce meaningful interpolated viewpoints under both settings, we first align the view directions of all cameras with the origin and convert the camera poses into Spherical coordinates $(r,\theta ,\varphi)$ relative to the origin. For each distillation view, we then randomly sample a radius $r_s\sim U\big(r^{\mathrm{min}},r^{\mathrm{max}}\big)$ , angles $\theta_{s}\sim U\big(\theta^{\mathrm{min}},\theta^{\mathrm{max}}\big)$ and $\varphi_{s}\sim U\big(\varphi^{\mathrm{min}},\varphi^{\mathrm{max}}\big)$ according to the ranges of spherical coordinates. After converting from spherical to Cartesian coordinates, we obtain many suitable interpolated poses following the original viewpoint distribution. We define the number of distillation views to be 1,000 in all settings. Our model will be trained on the distilled views first and then fine-tuned using the training images to produce sharper appearance. + +# 3.4. Real-Time Rendering + +A fully trained neural duplex radiance field comprises two meshes with per-vertex features as well as the shallow shading CNN. While we train the model using PyTorch [32], we implemented a real-time renderer in WebGL using GLSL shaders for cross-platform compatibility. To synthesize a novel view, we use hardware rasterization to efficiently render two feature buffers at the output image resolution. These buffers are sent into the CNN together with a per-pixel view direction to aggregate the local radiance features and produce view-dependent RGB colors. For efficiency, we implement the CNN in two rendering passes, one pass for each layer. Thanks to the compact size of the convolution kernels, each convolution layer can be executed in a pixel-parallel way, which is highly efficient, even without CUDA. + +# 4. Experiments + +# 4.1. Setup + +Datasets. We evaluate and compare performance on the NeRF-Synthetic [26] benchmark dataset, which contains + +eight objects with $360^{\circ}$ views at $800 \times 800$ resolution. Further, to comprehensively compare the quality and to test the upper bound of speed, we conduct experiments on the megapixel-level dataset of Tanks&Temples [20], which contains $1920 \times 1080$ frames. We also leverage additional real-world datasets to perform ablation studies. All the training and testing splits follow the published literature. + +Baselines. We compare with two representative efficient novel-view synthesis approaches: KiloNeRF [35] and SNeRG [14]. For quantitative comparisons, we directly use the published numerical results. For qualitative comparisons, we retrain both methods on different datasets following original configurations, since not all checkpoints are released. + +Evaluation Metrics. We measure rendering quality by comparing predicted novel views and ground-truth images using standard metrics: peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) [48], and learned perceptual image patch similarity (LPIPS) [58]. Since our goal is highly efficient rendering, we also report frameterates using frames per second (FPS). We also compare the GPU memory consumption in GB for WebGL applications, to quantify the trade-off between speed, quality, and memory. Finally, we compare the training speed by recording the total optimization time in hours. Our test system consists of an NVIDIA RTX 2080 Ti GPU, an i7-9700K CPU, and 32 GB RAM. + +Implementation. We implement the CUDA version with 20-dimensional learnable radiance features and a three-layer convolutional network. To ensure high efficiency and match the Shader implementation, in the WebGL version, we decrease the learnable feature dimension attached to the geometry surface from 20 to 8 and remove the final convolution layer. This results in some performance degradation on both datasets compared with our CUDA version, but brings remarkable FPS improvement and cross-platform properties, since the renderer implementation no longer requires CUDA. More details regarding the architecture design and parameters setting can be found in the supplementary material. + +**Optimization Details.** We build our method on top of PyTorch [32] and use the Adam optimizer [19] with $\beta_{1} = 0.9$ and $\beta_{2} = 0.999$ . Our method converges in 200,000 iterations, where the first half is optimized on the distillation views and the second half on the original training images. In each iteration, we randomly sample one view, cast all rays into the geometry to fetch the duplex radiance features, and generate complete screen-space buffers (or local patches if training memory is insufficient) for the convolutional shading. The learning rate is set to $10^{-3}$ initially, with exponential decay to $10^{-5}$ thereafter. We chose TensoRF [6] as the teacher NeRF model considering its high efficiency of reconstruction and synthesis of distillation views. To convert the density field into the duplex mesh geometry, the two thresholds are set empirically to $10^{-4}$ and $10^{-2}$ , respectively. + +Table 1. Quantitative results on Synthetic-NeRF [26] and Tanks&Temples [20] datasets. The best result is highlighted in bold. Ours (laptop): Run on an M1 MacBook Pro with 16 GB RAM. Others run on a desktop. N+M: N is the training time and M is the baking time. + +
DatasetMethodBackendFPS ↑PSNR ↑SSIM ↑LPIPS ↓Training (hours)Training Specs
Synthetic-NeRF 800×800KiloNeRF [35]CUDA33.4731.000.9500.030~35+141×V100
SNeRG [14]WebGL89.4330.380.9500.050~30+58×A100
OursCUDA59.7132.140.9570.0306.611×3090
OursWebGL282.6730.430.9450.0491.011×3090
Ours (laptop)WebGL62.2730.430.9450.0491.011×3090
Tanks&Temples 1920×1080KiloNeRFCUDA16.3028.410.9100.090~24+241×A100
SNeRGWebGL55.4824.950.8820.184~30+58×A100
OursCUDA27.4928.120.9150.0899.021×A100
OursWebGL186.9427.080.8950.1301.841×A100
Ours (laptop)WebGL33.6427.080.8950.1301.841×A100
+ +Table 2. Memory-Quality-Speed trade-off. Peak GPU memory consumption (GB), rendering quality, run-time performance comparisons on Tanks&Temples [20] dataset for WebGL applications. + +
MethodMemory (GB) ↓PSNR ↑FPS ↑
SNeRG4.1924.9555.48
Ours0.9927.08186.94
Ours (laptop)0.9427.0833.64
+ +Table 3. Quantitative ablation study of proposed components. Our full model performs significantly better than all variations. + +
DuplexConvDistillPSNR ↑SSIM ↑LPIPS ↓
✓✓26.950.8840.12
✓✓30.030.9420.05
✓✓29.660.9320.06
✓✓✓32.140.9570.03
+ +# 4.2. Results + +Quantitative Comparison. We provide extensive quantitative comparisons in Table 1. Compared with CUDA-based KiloNeRF [35], our CUDA version achieves significantly better rendering performance in terms of PSNR (more than 1 dB) and SSIM on the Synthetic-NeRF dataset [26] with much higher run-time FPS and lower training time. The PSNR of our CUDA-based model on Tanks&Temples [20] is slightly lower than KiloNeRF, even though the novel views of KiloNeRF contain severe artifacts, as shown in Figure 7. This may be caused by the characteristics of PSNR, which only measures pixel-level differences and ignores the structural similarity. In this situation, SSIM would be a better metric to indicate the performance gap between our method and KiloNeRF (0.915 vs. 0.910). + +We also compare with WebGL-based SNeRG [14]. Even at $1920 \times 1080$ resolution, our WebGL-based model achieves $180 +$ FPS on a desktop-level GPU, and $30 +$ FPS on a laptop + +using Chrome, with better rendering fidelity than SNeRG. In addition, our method requires about two orders of magnitude less computation for training compared to SNeRG. We also show the trade-off comparison regarding memory consumption, image quality and rendering speed in Table 2 under high-resolution setting, which comprehensively demonstrates the superiority of our method. + +Qualitative Comparison. To further verify our method's benefit in terms of rendering quality over existing fast rendering NeRF techniques, we show qualitative comparisons in Figure 7. As we can see, both baselines, KiloNeRF and SNeRG, cannot render the high-quality details of the scenes, which are possibly bounded by the voxel resolution; however, simply increasing the voxel resolution will directly lead to cubic growth of memory cost. Besides, some texture artifacts and wrong view-dependent specular highlights also exist in the baselines, which are visually inferior to our method. More importantly, in the Tanks&Temples dataset, we could observe vibrant blocking degradation from the results of KiloNeRF. The potential reason is KiloNeRF has limited generalization capability to out-of-distribution (OOD) views since there are no correlations between space-isolated MLPs. By contrast, our method can render consistent and high-fidelity views with good view generalization ability. + +# 4.3. Ablation Study + +Duplex Radiance. To verify the effectiveness of our proposed neural duplex radiance, we compare with a variation that only considers a single ray-surface intersection. More specifically, we only adopt the mesh extracted from NeRF model with threshold $10^{-4}$ to ensure geometry proxy could cover all pixels and avoid hole artifacts. As shown in Table 3 and Figure 8, rendering without duplex radiance dramatically downgrades the quality of novel-view synthesis since the coarse geometry extracted from NeRF could not provide reasonable shading locations. More results about using single + +![](images/c47af8293aa2c8387f0fb6845c9b00f31b71dfee3707622df6d3c361760477e7.jpg) + +![](images/2b0e6ce022d3abe2d7b530a09012e9bf8f88ff55bc12fd9b986c73836415b5be.jpg) + +![](images/a5de2c2869b6e6cb5e4d1268648a69bf52c005afe4b288e34e2dac4bd2e83754.jpg) + +![](images/dadb4463b0318332efc5a27a66f4c12b7fed511efe2341a2bdd799322aebb47b.jpg) + +![](images/b28dce01cbbff048234fcf3404a89049cc9dc213b56ebeba29b729b96952fb44.jpg) +Ground Truth + +![](images/89e1465277d6e477a84623a807142479e427734e1d56159d3c231c717810adae.jpg) +KiloNeRF [35] + +![](images/55efcb45e37fa91f6e3cb2fb6e610e699f16c22889f74d4117c98b591384452d.jpg) +SNeRG [14] + +![](images/37175f2783d9432d157894194b336f83ccb5eb484de2ab7e2282b2775fd4683f.jpg) +Ours + +![](images/175b6f147cff715c550f243e9a4a55e59e2972c9fb2db48a14cbc6caa7eb334e.jpg) +Figure 7. Qualitative comparisons with various baselines on Synthetic-NeRF (top) and Tanks&Temples (bottom) datasets. We show the novel views synthesized by KiloNeRF, SNeRG and ours. Our approach effectively speeds up the inference efficiency while maintaining the high quality of the rendered frames. +No Duplex +Figure 8. Qualitative results for each ablation study. Our full model achieves the best rendering quality among all variations. + +![](images/795313d1519b2133fa4e5d89cd9e07cc38f9a60931daab64face5782a8518cab.jpg) +No Conv + +![](images/4583884132975fe4dc932173de98aa16a78b655b43fc82e986fdbe6012cd05ff.jpg) +No Distill + +![](images/0ad5aad0333ea262be0541119958d4c99800e3146b340d46e937689ceeffb39a.jpg) +Full Model + +radiance feature but with different thresholds can be found in the supplementary materials. + +Convolution vs MLP. In contrast to most NeRF methods, which integrate radiance only along a single ray, we aggregate all the radiance information of the local geometry surface via convolution kernels. Figure 8 and Table 3 demonstrate that the convolutional shading network can render high-fidelity novel views more robustly, and effectively overcomes the under-constrained problem while only considering two sample locations for each ray. + +No Distillation. In this ablation study, we remove all the distillation views and only train the model based on known views. Our results in Figure 8 show that optimization without distillation views will lead to high-frequency artifacts in the geometry, which the spatial convolutional kernels are unable to effectively handle as well. + +Influence of Network Size. We have noticed that decreasing the radiance feature dimension and network size will result in some quality degeneration in Table 1. Hence, one + +![](images/335f5ff8946ed8ab86fc2109e3c37d41465c2d8dfdbcff5f8ed3b4e12b8a70c9.jpg) +Deeper +Figure 9. Ablation study on the network size. Increasing the depth and receptive field of the convolutional shading network does not lead to an improvement on rendering quality in a consistent manner. Zoom in for more details. + +![](images/d7323e85591436df67f06ffebf588b32abf9c79e49dda5e7047aa6172421095d.jpg) +Ours + +![](images/29ce24a4f0fa9fcf8513bd4eeecf68faa17f5b9701a6280cbef32f57a5e3dfc6.jpg) +Deeper + +![](images/8fe33b81ce678f79c3768d4f47541fb59c97ad9ec902896fd112a0b705214b09.jpg) +Ours + +interesting question is whether performance can be boosted consistently by increasing the network parameters and receptive fields. We conduct this experiment on the Fox dataset. More specifically, we double the network layers and boost the original used convolution layers with larger receptive fields. As shown in Figure 9, the deeper network does not bring more quality gains. By contrast, the synthesized novel views become more blurry than our method and lose many textural details, which may be caused by the learning difficulty of a deep network. Leveraging more advanced architectures or loss functions, such as adversarial objectives, could potentially resolve this issue. + +Generalization to out-of-distribution views. NeRF has strong capabilities to interpolate novel views, but its rendering quality may drop for some novel views which don't follow the original distribution. This issue will be more serious for distillation-based methods that try to mimic the teacher behavior. We show out-of-distribution (OOD) syn + +![](images/4dd9216670937e32e0e9e5a29d10dae9457d3e95232130b7e356a4f312e6261d.jpg) +KiloNeRF [35] + +![](images/45db3542676bbccf79cdc66b0582282b460ced6a2b3c441f58a75305e4cd2315.jpg) +SNeRG [14] + +![](images/28038dc52f4d1840012fae869d848d6c31e0c15be9e222f81496fe747916ee3c.jpg) +Ours + +Table 4. Quantitative comparisons with the teacher model on the Tanks&Temples [20] dataset (1920×1080). + +
PSNR ↑SSIM ↑LPIPS ↓FPS ↑
Original NeRF [26]28.320.9000.110.01
Teacher TensoRF [6]28.480.9180.120.10
Ours w/ WebGL27.080.8950.13186.94
Ours w/ CUDA28.120.9150.0927.49
+ +thesis results in Figure 10. Both KiloNeRF [35] and SNeRG [14] do not generalize well to OOD views and produce inconsistent and blurry artifacts, while our method synthesizes sharper and cleaner appearance with appropriate lighting. + +Comparison with Teacher Model. Since our method involves multi-view distillation, we also measure the quantitative differences between our model and the teacher model [6] in Table 4. Although the numerical results of PSNR and SSIM are slightly inferior to the teacher model, the perceptual metric LPIPS, which better correlates with human perception, surprisingly attains better performance benefiting from the better generalization ability of neural duplex radiance field. On the other hand, the teacher model TensoRF [6] has already optimized the run-time performance by skipping the importance sampling step of original NeRF model and tensor decomposition, but the speed is still limited for real-world interactive applications. In contrast, our method achieves $30+$ FPS while maintaining comparable rendering quality. Significantly, our WebGL version reaches $10,000 \times$ speed up over the original NeRF model. + +Comparison with MobileNeRF [9]. Recently, a concurrent work MobileNeRF [9] also explores the rasterization pipeline to accelerate the rendering speed of neural radiance fields. Nonetheless, their framework involves optimizations of both geometry and appearance leading to a high computational cost (21+ hours using $8 \times \mathrm{V}100$ ). By contrast, our method can convert a pretrained NeRF in a few hours using a single GPU. We provide some qualitative comparisons in Figure 11 as well. Our approach produces more appealing results and better view-dependent appearance. + +# 5. Conclusion + +In this paper, we proposed a novel approach for learning a neural duplex radiance field from a pretrained NeRF for real- + +![](images/3dd06652431c95d0b411e1d0c66c27f8a512dab493a5b9676ebc7f87da29c444.jpg) +Figure 10. Comparison of out-of-distribution (OOD) view synthesis (No ground-truth). Our method could generate high-quality and view-dependent frames with sharper details for OOD views. +MobileNeRF [9] +Figure 11. Qualitative comparisons with MobileNeRF. Our method produces smoother appearance. + +![](images/54b9d296ead0080eeda2fd48a617889d285f92a8e7ec9af01057b2727f1f2b6e.jpg) +Ours w/ WebGL + +![](images/e74d1c01903cb6599982f083bf6d6becc9051ad323dbe02d291d3f8e1e589586.jpg) +MobileNeRF [9] + +![](images/92626797dcc756da88f0119a9a91ae1c9117a3f57475d870d515d2c692fa3550.jpg) +Ours w/ WebGL + +time novel-view synthesis. We employed a convolutional shading network to improve the rendering quality and also proposed a multi-view distillation strategy for better optimization. Our method provides significant improvements on run-time, bettering or preserving the rendering quality, and at the same time, operating on a much lower computational cost compared to existing efficient NeRF methods. + +Limitations. It is challenging to extract useful duplex meshes from NeRFs to represent transparent or semi-transparent scenes and our method is no different. Integrating order-independent transparency (OIT) with the NeRF framework may be a potential solution to speed up its rendering. In addition, the quality of the geometry proxy will directly influence the learning of neural representations, thus we would also like to explore scene-specific thresholds rather than empirical defined values while generating meshes, for instance, based on signed distance functions or on the statistics of the density values, for more robust proxy extraction. Currently, our method focuses on object-level and bounded-scene datasets. Efficiently rendering real-world unbounded scenes is a promising future direction. Finally, our experiments demonstrate that sufficient learnable parameters are crucial to achieve better rendering fidelity. Hence, to counter the performance degradation in the WebGL version, boosting the running efficiency of larger CNN in the fragment shader, or leveraging other acceleration frameworks, could also be considered in future work. + +Acknowledgements. We thank the anonymous reviewers for their constructive comments. We also appreciate helpful discussions with Feng Liu, Chakravarty R. Alla Chaitanya, Simon Green, Daniel Maskit, Aayush Bansal, Zhiqin Chen, Vasu Agrawal, Hao Tang, Michael Zollhoefer, Huan Wang, Ayush Saraf and Zhaoyang Lv. This work was partially supported by a GRF grant (Project No. CityU 11216122) from the Research Grants Council (RGC) of Hong Kong. + +# References + +[1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graphics. In ECCV, pages 696-712, 2020. +[2] Benjamin Attal, Jia-Bin Huang, Michael Zollhöfer, Johannes Kopf, and Changil Kim. Learning neural light fields with ray-space embedding. In CVPR, pages 19819-19829, 2022. +[3] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-NeRF 360: Unbounded anti-aliased neural radiance fields. In CVPR, pages 5470–5479, 2022. +[4] Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In CVPR, pages 16123-16133, 2022. +[5] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. MVSNeRF: Fast generalizable radiance field reconstruction from multi-view stereo. In CVPR, pages 14124-14133, 2021. +[6] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. TensoRF: Tensorial radiance fields. In ECCV, 2022. +[7] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In CVPR, pages 8628-8638, 2021. +[8] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In CVPR, pages 5939-5948, 2019. +[9] Zhiqin Chen, Thomas Funkhouser, Peter Hedman, and Andrea Tagliasacchi. MobileNeRF: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In CVPR, 2023. +[10] Nianchen Deng, Zhenyi He, Jiannan Ye, Budmonde Duinkhar-jav, Praneeth Chakravarthula, Xubo Yang, and Qi Sun. FoV-NeRF: Foveated neural radiance fields for virtual reality. TVCG, 28(11):3854-3864, 2022. +[11] Zhiwen Fan, Yifan Jiang, Peihao Wang, Xinyu Gong, Dejia Xu, and Zhangyang Wang. Unified implicit neural stylization. In ECCV, 2022. +[12] Brandon Yushan Feng and Amitabh Varshney. Signet: Efficient neural representation for light fields. In ICCV, pages 14224-14233, 2021. +[13] Stephan J Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin. FastNeRF: High-fidelity neural rendering at 200fps. In CVPR, pages 14346-14355, 2021. +[14] Peter Hedman, Pratul P Srinivasan, Ben Mildenhall, Jonathan T Barron, and Paul Debevec. Baking neural radiance fields for real-time view synthesis. In CVPR, pages 5875-5884, 2021. +[15] Tao Hu, Shu Liu, Yilun Chen, Tiancheng Shen, and Jiaya Jia. EfficientNeRF – efficient neural radiance fields. In CVPR, pages 12902–12911, 2022. +[16] Yi-Hua Huang, Yue He, Yu-Jie Yuan, Yu-Kun Lai, and Lin Gao. StylizedNeRF: Consistent 3D scene stylization as stylized NeRF via 2D-3D mutual learning. In CVPR, 2022. +[17] Yoonwoo Jeong, Seungjoo Shin, Junha Lee, Chris Choy, Anima Anandkumar, Minsu Cho, and Jaesik Park. PeRFception: + +Perception using radiance fields. In NeurIPS Datasets and Benchmarks Track, 2022. +[18] Animesh Karnewar, Tobias Ritschel, Oliver Wang, and Niloy Mitra. ReLU fields: The little non-linearity that could. In SIGGRAPH Conference Proceedings, pages 27:1-9, 2022. +[19] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. +[20] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Trans. Graph., 36(4):1-13, 2017. +[21] Andreas Kurz, Thomas Neff, Zhaoyang Lv, Michael Zollhöfer, and Markus Steinberger. AdaNeRF: Adaptive sampling for real-time rendering of neural radiance fields. In ECCV, 2022. +[22] Christoph Lassner and Michael Zollhofer. Pulsar: Efficient sphere-based neural rendering. In CVPR, pages 1440-1449, 2021. +[23] Haotong Lin, Sida Peng, Zhen Xu, Yunzhi Yan, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Efficient neural radiance fields for interactive free-viewpoint video. In SIGGRAPH Asia 2022 Conference Papers, pages 1-9, 2022. +[24] Steven Liu, Xiuming Zhang, Zhoutong Zhang, Richard Zhang, Jun-Yan Zhu, and Bryan Russell. Editing conditional radiance fields. In ICCV, pages 5773-5783, 2021. +[25] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3D surface construction algorithm. SIGGRAPH Computer Graphics, 21(4):163-169, 1987. +[26] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020. +[27] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. +[28] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-15, 2022. +[29] Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Joerg H Mueller, Chakravarty R Alla Chaitanya, Anton Kaplanyan, and Markus Steinberger. DONeRF: Towards real-time rendering of compact neural radiance fields using depth oracle networks. Computer Graphics Forum, 40(4):45-59, 2021. +[30] Thu Nguyen-Phuoc, Feng Liu, and Lei Xiao. SNeRF: Stylized neural implicit representations for 3D scenes. ACM Trans. Graph., 41(4):142:1-11, 2022. +[31] Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, and Andreas Geiger. Texture fields: Learning texture representations in function space. In ICCV, pages 4531-4540, 2019. +[32] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu + +Fang, Junjie Bai, and Soumith Chintala. PyTorch: An imperative style, high-performance deep learning library. In NeurIPS, pages 8024-8035, 2019. +[33] Ruslan Rakhimov, Andrei-Timotei Ardelean, Victor Lempitsky, and Evgeny Burnaev. NPBG++: Accelerating neural point-based graphics. In CVPR, pages 15948-15958, 2022. +[34] Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, and Andrea Tagliasacchi. DeRF: Decomposed radiance fields. In CVPR, pages 14153-14161, 2021. +[35] Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger. KiloNeRF: Speeding up neural radiance fields with thousands of tiny MLPs. In ICCV, pages 14335-14345, 2021. +[36] Gernot Riegler and Vladlen Koltun. Free view synthesis. In ECCV, 2020. +[37] Gernot Riegler and Vladlen Koltun. Stable view synthesis. In CVPR, pages 12216-12225, 2021. +[38] Darius Rückert, Linus Franke, and Marc Stamminger. ADOP: Approximate differentiable one-pixel point rendering. ACM Trans. Graph., 41(4):99:1-14, 2022. +[39] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3D-structure-aware neural scene representations. In NeurIPS, 2019. +[40] Vincent Sitzmann, Semon Rezchikov, William T. Freeman, Joshua B. Tenenbaum, and Fredo Durand. Light field networks: Neural scene representations with single-evaluation rendering. In NeurIPS, 2021. +[41] Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In CVPR, 2022. +[42] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, Yifan Wang, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, Tomas Simon, Christian Theobalt, Matthias Niessner, Jonathan T. Barron, Gordon Wetzstein, Michael Zollhöfer, and Vladislav Golyanik. Advances in neural rendering. Computer Graphics Forum, 41(2):703-735, 2022. +[43] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM Trans. Graph., 38(4):66:1-12, 2019. +[44] Huan Wang, Jian Ren, Zeng Huang, Kyle Olszewski, Mengei Chai, Yun Fu, and Sergey Tulyakov. R2L: Distilling neural radiance field to neural light field for efficient novel view synthesis. In ECCV, pages 612-629, 2022. +[45] Liao Wang, Jiakai Zhang, Xinhang Liu, Fuqiang Zhao, Yanshun Zhang, Yingliang Zhang, Minye Wu, Jingyi Yu, and Lan Xu. Fourier PlenOctrees for dynamic radiance field rendering in real-time. In CVPR, pages 13524-13534, 2022. +[46] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In NeurIPS, 2021. +[47] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P Srinivasan, Howard Zhou, Jonathan T Barron, Ricardo MartinBrualla, Noah Snavely, and Thomas Funkhouser. IBRNet: Learning multi-view image-based rendering. In CVPR, pages 4690-4699, 2021. + +[48] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4): 600-612, 2004. +[49] Suttisak Wizadwongsa, Pakkapon Phongthawee, Jiraphon Yenphraphai, and Supasorn Suwajanakorn. NeX: Real-time view synthesis with neural basis expansion. In CVPR, pages 8534-8543, 2021. +[50] Bangbang Yang, Yinda Zhang, Yinghao Xu, Yijin Li, Han Zhou, Hujun Bao, Guofeng Zhang, and Zhaopeng Cui. Learning object-compositional neural radiance field for editable scene rendering. In ICCV, pages 13779-13788, 2021. +[51] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In NeurIPS, pages 4805-4815, 2021. +[52] Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. PlenOctrees for real-time rendering of neural radiance fields. In ICCV, 2021. +[53] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. PixelNeRF: Neural radiance fields from one or few images. In CVPR, pages 4578-4587, 2021. +[54] Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In CVPR, 2022. +[55] Yu-Jie Yuan, Yang-Tian Sun, Yu-Kun Lai, Yuewen Ma, Rongfei Jia, and Lin Gao. NeRF-editing: geometry editing of neural radiance fields. In CVPR, pages 18353-18364, 2022. +[56] Jingbo Zhang, Xiaoyu Li, Ziyu Wan, Can Wang, and Jing Liao. FDNeRF: Few-shot dynamic neural radiance fields for face reconstruction and expression editing. In SIGGRAPH Asia Conference Papers, pages 12:1-9, 2022. +[57] Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun. NeRF++: Analyzing and improving neural radiance fields. arXiv:2010.07492, 2020. +[58] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. 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Our framework efficiently learns the neural duplex radiance field from a NeRF model for high-quality real-time view synthesis." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 17 + }, + { + "bbox": [ + 143, + 344, + 192, + 357 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 344, + 192, + 357 + ], + "spans": [ + { + "bbox": [ + 143, + 344, + 192, + 357 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 46, + 370, + 290, + 622 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 370, + 290, + 622 + ], + "spans": [ + { + "bbox": [ + 46, + 370, + 290, + 622 + ], + "type": "text", + "content": "Neural radiance fields (NeRFs) enable novel-view synthesis with unprecedented visual quality. However, to render photorealistic images, NeRFs require hundreds of deep multilayer perceptron (MLP) evaluations – for each pixel. This is prohibitively expensive and makes real-time rendering infeasible, even on powerful modern GPUs. In this paper, we propose a novel approach to distill and bake NeRFs into highly efficient mesh-based neural representations that are fully compatible with the massively parallel graphics rendering pipeline. We represent scenes as neural radiance features encoded on a two-layer duplex mesh, which effectively overcomes the inherent inaccuracies in 3D surface reconstruction by learning the aggregated radiance information from a reliable interval of ray-surface intersections. To exploit local geometric relationships of nearby pixels, we leverage screen-space convolutions instead of the MLPs used in NeRFs to achieve high-quality appearance. Finally, the performance of the whole framework is further boosted by a novel multiview distillation optimization strategy. We demonstrate the effectiveness and superiority of our approach via extensive experiments on a range of standard datasets." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 47, + 647, + 128, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 647, + 128, + 659 + ], + "spans": [ + { + "bbox": [ + 47, + 647, + 128, + 659 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 47, + 668, + 288, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 668, + 288, + 693 + ], + "spans": [ + { + "bbox": [ + 47, + 668, + 288, + 693 + ], + "type": "text", + "content": "Reconstructing 3D scenes by a representation that can be rendered from unobserved viewpoints using only a few posed" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 306, + 346, + 547, + 476 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 346, + 547, + 476 + ], + "spans": [ + { + "bbox": [ + 306, + 346, + 547, + 476 + ], + "type": "text", + "content": "images has been a long-standing goal in the computer graphics and computer vision communities. Significant progress has recently been achieved by neural radiance fields (NeRFs) [26], which are capable of generating photorealistic novel views and modeling view-dependent effects such as specular reflections. In particular, a radiance field is a volumetric function parameterized by MLPs that estimates density and emitted radiance at sampled 3D locations in a given direction. Differentiable volume rendering then allows the optimization of this function by minimizing the photometric discrepancy between the real observed color and the rendered color." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 481, + 548, + 673 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 481, + 548, + 673 + ], + "spans": [ + { + "bbox": [ + 304, + 481, + 548, + 673 + ], + "type": "text", + "content": "Despite the unprecedented success and enormous practical potential of NeRF and its various extensions [3, 6, 57], an inescapable problem is the high computational cost of rendering novel views. For instance, even using a powerful modern GPU, NeRF requires about 30 seconds to render a single image with " + }, + { + "bbox": [ + 304, + 481, + 548, + 673 + ], + "type": "inline_equation", + "content": "800 \\times 800" + }, + { + "bbox": [ + 304, + 481, + 548, + 673 + ], + "type": "text", + "content": " pixels, which prevents its use for interactive applications in virtual and augmented reality. On the other hand, the rapid development of NeRF has spawned abundant follow-up works that focus on optimization acceleration [18, 28, 54], generalization [5, 47, 53], and enabled different downstream tasks, including 3D stylization [11, 16, 30], editing [24, 50, 55, 56], or even perception [10, 17]. Thus, a generalized method that can learn and extract a real-time renderable representation given an arbitrary pretrained NeRF-based model, while maintaining high rendering quality, is highly desirable." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 305, + 677, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 547, + 715 + ], + "type": "text", + "content": "The huge computational cost of rendering a NeRF representation mainly comes from two aspects: (1) For each individual pixel, NeRF requires sampling hundreds of loca" + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 702, + 135, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 702, + 135, + 712 + ], + "spans": [ + { + "bbox": [ + 55, + 702, + 135, + 712 + ], + "type": "text", + "content": "* Corresponding author." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8307" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 288, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 288, + 144 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 288, + 144 + ], + "type": "text", + "content": "tions along the corresponding ray, querying density and then accumulating radiance using volume rendering; and (2) A large model size is required to represent the geometric details of complex scenes well, so the MLPs used in NeRF architecture are relatively deep and wide, which incurs significant computation for the evaluation of each point sample." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 148, + 188, + 303 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 148, + 188, + 303 + ], + "spans": [ + { + "bbox": [ + 46, + 148, + 188, + 303 + ], + "type": "text", + "content": "In this paper, we present an approach that achieves high-fidelity real-time view synthesis by addressing these issues. To avoid the dense sampling along each ray, one solution is to generate a geometry proxy from a pretrained NeRF model, e.g., via marching cubes [25]. By exploiting the highly-optimized graphics pipeline, we could almost instantly obtain the sample location for each ray. However, due to inaccuracies in the den" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 304, + 287, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 304, + 287, + 434 + ], + "spans": [ + { + "bbox": [ + 46, + 304, + 287, + 434 + ], + "type": "text", + "content": "sity field around surfaces, the extracted mesh may not faithfully represent the true underlying geometry and can contain artifacts, as shown in Figure 2. Dense local sampling could alleviate these errors to some degree, but cannot handle missing geometries or occlusions. An alternative approach is to use rasterization for fast neural rendering [1, 37, 38, 43] by directly baking neural features onto the surface of explicit the geometry or point cloud. This is usually accompanied by a deep CNN to translate rasterized features to colors and learn to resolve the existing errors, which is expensive to evaluate and can prevent real-time rendering." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 437, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 437, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 437, + 288, + 713 + ], + "type": "text", + "content": "To significantly reduce the sampled numbers while efficiently handling the geometric errors, our first key technical innovation is to infer the final RGB color according to the radiance of duplex points along the ray. As illustrated in Figure 3, NeRFs represent a continuous density distribution along each ray. While it will generally be difficult to determine the exact location of a surface along the ray, it will be easier to extract a reliable interval that contributes the most to the final prediction by using an under- and an overestimation of the geometry. Motivated by this idea, instead of selecting a specific location for appearance calculation, or performing expensive dense volume rendering in this interval, we represent the scene using learnable features at the two intersection points of a ray with the duplex geometry and use a neural network to learn the color from this aggregated duplex radiance information. Although only two sampled locations are considered, we found this proposed neural duplex radiance field to be robust in compensating for the errors of the geometry proxy even without a deep neural network, while effectively preserving the efficiency of rasterization-based approaches. The NeRF MLP has become the most standard architecture for most neural implicit representations [7, 8, 26, 27, 31, 39]. Yet, with only a few points" + } + ] + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 190, + 158, + 286, + 281 + ], + "blocks": [ + { + "bbox": [ + 190, + 158, + 286, + 281 + ], + "lines": [ + { + "bbox": [ + 190, + 158, + 286, + 281 + ], + "spans": [ + { + "bbox": [ + 190, + 158, + 286, + 281 + ], + "type": "image", + "image_path": "517b97bea6450572236bbad78aad87553c1811f07bcd1d77d612f52bfdef0287.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 192, + 285, + 284, + 296 + ], + "lines": [ + { + "bbox": [ + 192, + 285, + 284, + 296 + ], + "spans": [ + { + "bbox": [ + 192, + 285, + 284, + 296 + ], + "type": "text", + "content": "Figure 2. NeRF surface." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 307, + 74, + 545, + 164 + ], + "blocks": [ + { + "bbox": [ + 307, + 74, + 545, + 164 + ], + "lines": [ + { + "bbox": [ + 307, + 74, + 545, + 164 + ], + "spans": [ + { + "bbox": [ + 307, + 74, + 545, + 164 + ], + "type": "image", + "image_path": "733cb19546d706b40979e0933a86463f4761e11a781a49e0983cc2d70105a821.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 178, + 546, + 232 + ], + "lines": [ + { + "bbox": [ + 305, + 178, + 546, + 232 + ], + "spans": [ + { + "bbox": [ + 305, + 178, + 546, + 232 + ], + "type": "text", + "content": "Figure 3. Motivation. The continuous density distribution of NeRF (curve above the ray) makes it difficult to identify the accurate location of a surface " + }, + { + "bbox": [ + 305, + 178, + 546, + 232 + ], + "type": "inline_equation", + "content": "(\\star)" + }, + { + "bbox": [ + 305, + 178, + 546, + 232 + ], + "type": "text", + "content": " for appearance calculation. We thus seek to extract a reliable interval from the density field (between dashed lines), and learn the duplex radiance combinations to tolerate errors." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 248, + 547, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 248, + 547, + 392 + ], + "spans": [ + { + "bbox": [ + 304, + 248, + 547, + 392 + ], + "type": "text", + "content": "considered along the ray, the MLP struggles to constrain the proposed neural duplex radiance field. Instead, we use a shallow convolutional network, which can effectively capture the local geometric information of neighboring pixels, and leads to a considerably better rendering quality. Finally, we found that directly training the neural duplex radiance field from scratch will lead to noticeable artifacts. We therefore propose a multi-view distillation optimization strategy that enables us to effectively approximate the rendering quality of the original NeRF models. Remarkably, our method improves run-time performance by 10,000 times compared to the original NeRF while maintaining high-quality rendering." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 403, + 392, + 415 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 403, + 392, + 415 + ], + "spans": [ + { + "bbox": [ + 306, + 403, + 392, + 415 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 423, + 547, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 423, + 547, + 483 + ], + "spans": [ + { + "bbox": [ + 304, + 423, + 547, + 483 + ], + "type": "text", + "content": "Our work is in the category of neural rendering. Since the introduction of the seminal NeRF approach [26], the growth of neural rendering papers is exponential [42]. Here, we focus on works closely related to our method along two directions: neural representations and real-time neural rendering." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 486, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 486, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 486, + 547, + 713 + ], + "type": "text", + "content": "Neural Representations. NeRF [26] represents the scene as a continuous field of density and color, and generates images by volume rendering. Built upon NeRF, Mip-NeRF [3] represents the prefiltered radiance field for a continuous space of scales, which enables accuracy improvements for different resolutions. " + }, + { + "bbox": [ + 304, + 486, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{NeRF + + }" + }, + { + "bbox": [ + 304, + 486, + 547, + 713 + ], + "type": "text", + "content": " [57] and Mip-NeRF 360 [3] further extend NeRF-like models for unbounded scenes, allowing reconstruction of scenes with far-away backgrounds. NeuS [46] and VolSDF [51] propose to learn a signed distance function representation by volume rendering to better reconstruct 3D objects. This has potential for fast surface rendering, but the visual quality may not reach the photorealism of NeRF. PixelNeRF [53], IBRNet [47] and MVSNeRF [5] learn more generalized neural representations to overcome NeRF's inability to share knowledge between scenes and can achieve high-quality novel-view synthesis given sparse input images. More recently, efforts are made to extremely reduce the training time of NeRF such as Instant-NGP [28], using multiresolution hash encoding for training acceleration, or" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8308" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 78, + 74, + 521, + 170 + ], + "blocks": [ + { + "bbox": [ + 78, + 74, + 521, + 170 + ], + "lines": [ + { + "bbox": [ + 78, + 74, + 521, + 170 + ], + "spans": [ + { + "bbox": [ + 78, + 74, + 521, + 170 + ], + "type": "image", + "image_path": "a613e250a7fa176dbaa64e1a603e0eaec6eef62384336fc011c058c7bac1f5d3.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 182, + 547, + 240 + ], + "lines": [ + { + "bbox": [ + 46, + 182, + 547, + 240 + ], + "spans": [ + { + "bbox": [ + 46, + 182, + 547, + 240 + ], + "type": "text", + "content": "Figure 4. Architecture of Neural Duplex Radiance Fields. Our neural representation builds on a two-layer duplex surface extracted from a NeRF model, which approximates the reliable interval covering the true underlying geometry. For each sampled target camera ray with view direction " + }, + { + "bbox": [ + 46, + 182, + 547, + 240 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 46, + 182, + 547, + 240 + ], + "type": "text", + "content": ", we find the duplex ray-surface intersections and derive the radiance features using barycentric interpolation. We use a shallow CNN to aggregate the local geometry and appearance information, and produce view-dependent colors, which effectively ensures rendering efficiency. The output color is directly compared with the pixels of rendered or ground-truth views to optimize the whole representation." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 254, + 289, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 254, + 289, + 375 + ], + "spans": [ + { + "bbox": [ + 46, + 254, + 289, + 375 + ], + "type": "text", + "content": "TensoRF [6], using tensor factorization to enable memory-efficient feature storage and so on [18, 41, 54]. Neural light field networks [40] learn to directly map rays to observed radiance, and thus only require a single evaluation per pixel. Recently, multiple ways have been proposed to parameterize camera rays, such as Plücker coordinates [40], Gegenbauer polynomials [12], or local affine embeddings [2]. Albeit with great progress, neural light fields still struggle to achieve view-consistent and high-quality 360-degree rendering due to the lack of an explicit geometry prior." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 378, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 378, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 378, + 289, + 715 + ], + "type": "text", + "content": "Real-Time Neural Rendering. There are various directions to accelerate NeRF rendering by reducing the considerable computation needed for inference of large networks and dense sampling along view rays. For example, by deriving sparse intermediate representations from NeRF, such as sparse octrees [45, 52] or sparse voxel grids [14], by dividing the space into many smaller MLPs [34, 35], or by adopting an efficient sampling strategy [15, 21, 29]. Inspired by the rendering equation in computer graphics, FastNeRF [13] proposes to factorize the NeRF and uses caching to achieve efficient rendering. These voxel-based methods basically leverage the caching concept to skip abundant computations in exchange for a larger memory footprint. NeX [49] proposes to combine the MPI representation and neural basis functions to enable real-time view synthesis for forward-facing data. EG3D [4] targets 3D-aware synthesis and supports real-time application by low-resolution rendering followed by upsampling. ENeRF [23] tries to tackle interactive free-viewpoint video by skipping the sampling of empty space. More recently, concurrent work MobileNeRF [9] utilizes optimized sheet meshes as proxy geometry to achieve highly efficient rendering on commodity graphics hardware. In contrast to MobileNeRF [9], our approach neither requires complex multi-stage optimization nor retraining the scene from scratch; instead, our approach can serve as a post-processing step for any existing NeRFs. Another direction explores alternative representations instead of functional NeRFs, such as point clouds [1, 33, 38] or spheres [22]." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 306, + 253, + 362, + 266 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 253, + 362, + 266 + ], + "spans": [ + { + "bbox": [ + 306, + 253, + 362, + 266 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 274, + 548, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 274, + 548, + 491 + ], + "spans": [ + { + "bbox": [ + 304, + 274, + 548, + 491 + ], + "type": "text", + "content": "Our main goal is to learn an efficient 3D scene representation that enables high-quality, real-time novel-view synthesis based on a given NeRF model. To this end, we introduce the neural duplex radiance field (NDRF), a novel concept to significantly decrease the number of sampled points along a ray from hundreds to two, while preserving the realistic details of complex scenes well (Section 3.1). Although the query frequency is bounded by using a two-layered mesh, the per-sample deep MLP-based forward pass in NeRF is still burdensome and lacks expressivity, especially for high-resolution rendering scenarios. Thus, we propose a more efficient hybrid radiance representation and shading mechanism (Section 3.2). To further suppress potential artifacts and improve the rendering quality, we leverage multi-view distillation information to guide the optimization procedure (Section 3.3). Finally, we demonstrate the complete rendering procedure and our shader-based implementation that enables real-time cross-platform rendering (Section 3.4)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 501, + 474, + 514 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 501, + 474, + 514 + ], + "spans": [ + { + "bbox": [ + 305, + 501, + 474, + 514 + ], + "type": "text", + "content": "3.1. Neural Duplex Radiance Fields" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "text", + "content": "Let us start with a quick review of NeRF basics. In a nutshell, NeRF approximates the 5D plenoptic function with a learnable MLP, which maps a spatial location " + }, + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "inline_equation", + "content": "\\mathbf{x} \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "text", + "content": " and view direction " + }, + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "inline_equation", + "content": "\\mathbf{d} \\in \\mathbb{S}^2" + }, + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "text", + "content": " to radiance " + }, + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "inline_equation", + "content": "\\mathbf{c} \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "text", + "content": " and density " + }, + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "inline_equation", + "content": "\\sigma \\in \\mathbb{R}" + }, + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "text", + "content": ". After casting a ray from the camera center through a pixel into the scene, its color is calculated by evaluating a network " + }, + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "inline_equation", + "content": "F_{\\Theta}" + }, + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "text", + "content": " at multiple locations " + }, + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 521, + 547, + 616 + ], + "type": "text", + "content": " along the ray, and aggregating the radiance using volume rendering [26]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 617, + 548, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 548, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 548, + 714 + ], + "type": "text", + "content": "A key factor that impacts the rendering efficiency of NeRF is the number of sampled 3D locations for each ray. Since the volumetric representation of NeRF can be converted into a triangle mesh via marching cubes, a natural idea is to directly bake the appearance on a geometric surface, i.e., to only consider the radiance of ray-surface intersections. However, due to the discretization error of marching cubes and the inherent floater artifacts of NeRF [3], the generated mesh" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8309" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 71, + 123, + 139 + ], + "blocks": [ + { + "bbox": [ + 49, + 71, + 123, + 139 + ], + "lines": [ + { + "bbox": [ + 49, + 71, + 123, + 139 + ], + "spans": [ + { + "bbox": [ + 49, + 71, + 123, + 139 + ], + "type": "image", + "image_path": "b93679c47d47cdf3a8948ed9d72477d7e01b2071a35a4fcfda80dea0c3c361dd.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 47, + 142, + 287, + 242 + ], + "lines": [ + { + "bbox": [ + 47, + 142, + 287, + 242 + ], + "spans": [ + { + "bbox": [ + 47, + 142, + 287, + 242 + ], + "type": "text", + "content": "Figure 5. Duplex radiance field vs local sampling. " + }, + { + "bbox": [ + 47, + 142, + 287, + 242 + ], + "type": "inline_equation", + "content": "\\star" + }, + { + "bbox": [ + 47, + 142, + 287, + 242 + ], + "type": "text", + "content": ": real ray-mesh intersection. " + }, + { + "bbox": [ + 47, + 142, + 287, + 242 + ], + "type": "inline_equation", + "content": "\\bullet" + }, + { + "bbox": [ + 47, + 142, + 287, + 242 + ], + "type": "text", + "content": ": local sampled points around proxy surface. Dashed curve: estimated surface. Solid curve: ground-truth surface. Gray curve: not currently estimated surface. Left: The estimated surface occludes the appearance location on the actual surface. Middle: The red ray may not hit the estimated surface, resulting in hole artifacts or incorrect intersections. Right: Our duplex radiance field instead learns to combine locations on two estimated surfaces to render the correct color, and effectively decreases ray misses." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 130, + 70, + 205, + 139 + ], + "blocks": [ + { + "bbox": [ + 130, + 70, + 205, + 139 + ], + "lines": [ + { + "bbox": [ + 130, + 70, + 205, + 139 + ], + "spans": [ + { + "bbox": [ + 130, + 70, + 205, + 139 + ], + "type": "image", + "image_path": "bfb169ba0380f5329bc7d7e8df37dcf3383a55231f043f8012617127986f5d8b.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 211, + 70, + 286, + 139 + ], + "blocks": [ + { + "bbox": [ + 211, + 70, + 286, + 139 + ], + "lines": [ + { + "bbox": [ + 211, + 70, + 286, + 139 + ], + "spans": [ + { + "bbox": [ + 211, + 70, + 286, + 139 + ], + "type": "image", + "image_path": "ffd368eb84c3e4dd30a7494ac0e5ef1b92ab2906ed98634a82b7dc0edb154317.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 251, + 288, + 360 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 251, + 288, + 360 + ], + "spans": [ + { + "bbox": [ + 46, + 251, + 288, + 360 + ], + "type": "text", + "content": "quality is often inadequate (see Figure 2). Rasterization-based neural rendering methods overcome imperfections in the 3D proxy by correcting errors in screen space using a deep CNN, which inevitably sacrifices rendering efficiency [36, 37]. Since the geometric proxy already conveys the approximate 3D structure, an alternative way would be locally sampling around the surface. Although this can help counter the low-quality mesh, it struggles with missing geometry or visible occlusions, as illustrated in Figure 5." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "spans": [ + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "text", + "content": "Representation. It is worth noting that the density distribution of NeRF, which determines the contribution of emitted radiance to the final color of a ray, may contain multiple peaks. This makes approximating the color of a pixel using a single ray-surface intersection a challenging proposition. To significantly reduce the sampled points for each pixel and simultaneously ensure the rendering fidelity, we propose the neural duplex radiance field " + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "inline_equation", + "content": "F_{\\Theta}:\\mathbb{R}^{8}\\mapsto \\mathbb{R}^{3}" + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "text", + "content": " to represent a light ray, where " + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "inline_equation", + "content": "F_{\\Theta}" + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "text", + "content": " learns to map a ray segment defined by its two endpoints, " + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_1" + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_2" + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "text", + "content": ", to integrated view-dependent colors. More specifically, our goal is to directly learn the color of any pixel from the ray segment defined by the reliable interval of the density distribution. We acquire the endpoints of a ray segment by casting the ray into two proxy mesh surfaces, the inner " + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_{\\mathrm{i}}(\\mathcal{V}_{\\mathrm{i}},\\mathcal{T}_{\\mathrm{i}})" + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "text", + "content": " and outer " + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_{\\mathrm{o}}(\\mathcal{V}_{\\mathrm{o}},\\mathcal{T}_{\\mathrm{o}})" + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "text", + "content": " with vertex positions " + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "inline_equation", + "content": "\\nu_{\\bullet}" + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "text", + "content": " and triangle faces " + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "inline_equation", + "content": "T_{\\bullet}" + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "text", + "content": ". These meshes are extracted from different level sets of NeRF's density field. More formally, the ray color " + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "inline_equation", + "content": "\\mathbf{c}" + }, + { + "bbox": [ + 46, + 361, + 289, + 579 + ], + "type": "text", + "content": " could be calculated by" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 72, + 586, + 287, + 598 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 586, + 287, + 598 + ], + "spans": [ + { + "bbox": [ + 72, + 586, + 287, + 598 + ], + "type": "interline_equation", + "content": "\\mathbf {c} = F _ {\\Theta} \\left(\\mathbf {x} _ {1}, \\mathbf {x} _ {2}, \\mathbf {d}\\right) \\quad \\text {f o r} \\quad \\mathbf {x} _ {1} \\in \\mathcal {T} _ {o} \\text {a n d} \\mathbf {x} _ {2} \\in \\mathcal {T} _ {i}, \\tag {1}", + "image_path": "b47cd78defb4fa32c0ac40bdb49ef709a4c4f877de1f00e860d71492b673e36a.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_1" + }, + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_2" + }, + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "text", + "content": " are the depth-sorted intersections of a given ray with meshes " + }, + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_{\\mathrm{i}}" + }, + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_{\\mathrm{o}}" + }, + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "inline_equation", + "content": "\\mathbf{d}" + }, + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "text", + "content": " denotes the view direction. We still append the view direction considering not all rays will hit both surface proxies. The explicit triangle mesh then will allow us to efficiently acquire the ray-surface intersection, benefiting from the full parallelism of the graphics rasterization pipeline." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "type": "text", + "content": "In most implicit neural representations, different properties like SDF, radiance, or pixel colors are encoded by a" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 72, + 547, + 193 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 193 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 193 + ], + "type": "text", + "content": "multilayer perceptron due to its smoothness and compactness properties. However, densely evaluating a deep MLP is not a wise choice for real-time applications considering the high computational cost of inference. Since our radiance field is defined via two geometric surfaces, our encoding is defined by directly attaching learnable features " + }, + { + "bbox": [ + 304, + 72, + 547, + 193 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_k \\in \\mathbb{R}^N" + }, + { + "bbox": [ + 304, + 72, + 547, + 193 + ], + "type": "text", + "content": " to each mesh vertex " + }, + { + "bbox": [ + 304, + 72, + 547, + 193 + ], + "type": "inline_equation", + "content": "\\mathbf{v}_k" + }, + { + "bbox": [ + 304, + 72, + 547, + 193 + ], + "type": "text", + "content": ". To obtain features " + }, + { + "bbox": [ + 304, + 72, + 547, + 193 + ], + "type": "inline_equation", + "content": "\\mathbf{f}(\\mathbf{x})" + }, + { + "bbox": [ + 304, + 72, + 547, + 193 + ], + "type": "text", + "content": " for any point " + }, + { + "bbox": [ + 304, + 72, + 547, + 193 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 72, + 547, + 193 + ], + "type": "text", + "content": " on a triangle, we interpolate features within triangles using barycentric coordinates via hardware rasterization. Thus, the rendering equation can be re-written as" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 316, + 202, + 547, + 216 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 202, + 547, + 216 + ], + "spans": [ + { + "bbox": [ + 316, + 202, + 547, + 216 + ], + "type": "interline_equation", + "content": "\\mathbf {c} = F _ {\\Theta} \\left(\\mathbf {f} \\left(\\mathbf {x} _ {1}\\right), \\mathbf {f} \\left(\\mathbf {x} _ {2}\\right), \\mathbf {d}\\right) \\quad \\text {w i t h} \\quad \\mathbf {x} _ {1} \\in \\mathcal {T} _ {o}, \\mathbf {x} _ {2} \\in \\mathcal {T} _ {i}, \\tag {2}", + "image_path": "e566b4e19f7e8955c8db262a80f7e2aa09ca9594b1dcb688ec19c91e51a01428.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 226, + 547, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 226, + 547, + 251 + ], + "spans": [ + { + "bbox": [ + 304, + 226, + 547, + 251 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 226, + 547, + 251 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 304, + 226, + 547, + 251 + ], + "type": "text", + "content": " parameterizes a shallow network to learn the aggregation of radiance features given each ray." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 259, + 436, + 272 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 259, + 436, + 272 + ], + "spans": [ + { + "bbox": [ + 306, + 259, + 436, + 272 + ], + "type": "text", + "content": "3.2. Convolutional Shading" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 277, + 547, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 277, + 547, + 410 + ], + "spans": [ + { + "bbox": [ + 304, + 277, + 547, + 410 + ], + "type": "text", + "content": "Many NeRF-based methods use a view direction conditioned MLP to learn view-dependent appearance effects. The dense sampling along rays provides a feasible way to constrain the learning of the radiance fields through shared 3D locations, which ultimately enables high-quality and coherent rendering across different views. This implicit constraint, however, does not apply to our case. To highly optimize run-time efficiency, neural duplex radiance only considers two points for each cast ray, which significantly reduces the capability of capturing internal correlation in training, often resulting in spatial artifacts and the degradation of rendering quality." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "spans": [ + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "text", + "content": "We resolve this issue by a convolutional shading network that converts duplex features and view directions to per-pixel colors in image space using a small receptive field. More specifically, an independent pixel will be rendered by considering the neighboring local geometry positions " + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_i" + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "text", + "content": " whose projection " + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "inline_equation", + "content": "\\mathbf{K}(\\mathbf{R}\\mathbf{x}_i + \\mathbf{t})" + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "text", + "content": " with the nearest " + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "text", + "content": "-coordinate lies in the local window around this pixel location, where camera pose " + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "inline_equation", + "content": "\\mathbf{R} \\in \\mathrm{SO}(3)" + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "inline_equation", + "content": "\\mathbf{t} \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "inline_equation", + "content": "\\mathbf{K}" + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "text", + "content": " is the intrinsic matrix. This approach effectively increases the correlation of samples of a 3D scene. To ensure high inference speed, we only use " + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "inline_equation", + "content": "2 \\sim 3" + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "text", + "content": " convolutional layers and limit convolution kernels to a small " + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "inline_equation", + "content": "2 \\times 2" + }, + { + "bbox": [ + 304, + 410, + 547, + 578 + ], + "type": "text", + "content": " window for optimal visual fidelity and view consistency. Please refer to the supplemental material for more detailed network architecture and parameters." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 586, + 436, + 598 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 586, + 436, + 598 + ], + "spans": [ + { + "bbox": [ + 306, + 586, + 436, + 598 + ], + "type": "text", + "content": "3.3. Multi-View Distillation" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "content": "We observed that neural duplex radiance fields trained from scratch fail to match the high fidelity of the original NeRFs and produce many spatial high-frequency artifacts. To overcome this problem, we take inspiration from KiloNeRF [35] and use a pretrained NeRF as a teacher model to guide the optimization of our neural duplex radiance fields. KiloNeRF directly minimizes the differences of density and radiance in 3D volume space between teacher and student models without any rendering. Considering different parameterizations" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8310" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 70, + 285, + 159 + ], + "blocks": [ + { + "bbox": [ + 53, + 70, + 285, + 159 + ], + "lines": [ + { + "bbox": [ + 53, + 70, + 285, + 159 + ], + "spans": [ + { + "bbox": [ + 53, + 70, + 285, + 159 + ], + "type": "image", + "image_path": "9478675118aeefdc8517ecdd82188a5c1b0af262551a593f69480c4d55aaecee.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 163, + 287, + 185 + ], + "lines": [ + { + "bbox": [ + 46, + 163, + 287, + 185 + ], + "spans": [ + { + "bbox": [ + 46, + 163, + 287, + 185 + ], + "type": "text", + "content": "Figure 6. Visualizations of distillation view generation. Yellow cameras: training views. Blue cameras: sampled distillation views." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 195, + 287, + 219 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 195, + 287, + 219 + ], + "spans": [ + { + "bbox": [ + 46, + 195, + 287, + 219 + ], + "type": "text", + "content": "of NeRF and our models, we instead use rendered multi-view images to distill knowledge from the teacher model." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 219, + 290, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 219, + 290, + 449 + ], + "spans": [ + { + "bbox": [ + 46, + 219, + 290, + 449 + ], + "type": "text", + "content": "One important question is how to generate effective distillation views for rendering based on the training dataset. For object-level datasets, camera views tend to lie on a hemisphere that can be estimated and used for sampling new distillation viewpoints similar with R2L [44]. For real-world captures, the camera poses may only occupy a small subspace. To produce meaningful interpolated viewpoints under both settings, we first align the view directions of all cameras with the origin and convert the camera poses into Spherical coordinates " + }, + { + "bbox": [ + 46, + 219, + 290, + 449 + ], + "type": "inline_equation", + "content": "(r,\\theta ,\\varphi)" + }, + { + "bbox": [ + 46, + 219, + 290, + 449 + ], + "type": "text", + "content": " relative to the origin. For each distillation view, we then randomly sample a radius " + }, + { + "bbox": [ + 46, + 219, + 290, + 449 + ], + "type": "inline_equation", + "content": "r_s\\sim U\\big(r^{\\mathrm{min}},r^{\\mathrm{max}}\\big)" + }, + { + "bbox": [ + 46, + 219, + 290, + 449 + ], + "type": "text", + "content": ", angles " + }, + { + "bbox": [ + 46, + 219, + 290, + 449 + ], + "type": "inline_equation", + "content": "\\theta_{s}\\sim U\\big(\\theta^{\\mathrm{min}},\\theta^{\\mathrm{max}}\\big)" + }, + { + "bbox": [ + 46, + 219, + 290, + 449 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 219, + 290, + 449 + ], + "type": "inline_equation", + "content": "\\varphi_{s}\\sim U\\big(\\varphi^{\\mathrm{min}},\\varphi^{\\mathrm{max}}\\big)" + }, + { + "bbox": [ + 46, + 219, + 290, + 449 + ], + "type": "text", + "content": " according to the ranges of spherical coordinates. After converting from spherical to Cartesian coordinates, we obtain many suitable interpolated poses following the original viewpoint distribution. We define the number of distillation views to be 1,000 in all settings. Our model will be trained on the distilled views first and then fine-tuned using the training images to produce sharper appearance." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 455, + 171, + 468 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 455, + 171, + 468 + ], + "spans": [ + { + "bbox": [ + 47, + 455, + 171, + 468 + ], + "type": "text", + "content": "3.4. Real-Time Rendering" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 474, + 288, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 474, + 288, + 641 + ], + "spans": [ + { + "bbox": [ + 46, + 474, + 288, + 641 + ], + "type": "text", + "content": "A fully trained neural duplex radiance field comprises two meshes with per-vertex features as well as the shallow shading CNN. While we train the model using PyTorch [32], we implemented a real-time renderer in WebGL using GLSL shaders for cross-platform compatibility. To synthesize a novel view, we use hardware rasterization to efficiently render two feature buffers at the output image resolution. These buffers are sent into the CNN together with a per-pixel view direction to aggregate the local radiance features and produce view-dependent RGB colors. For efficiency, we implement the CNN in two rendering passes, one pass for each layer. Thanks to the compact size of the convolution kernels, each convolution layer can be executed in a pixel-parallel way, which is highly efficient, even without CUDA." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 651, + 128, + 664 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 651, + 128, + 664 + ], + "spans": [ + { + "bbox": [ + 47, + 651, + 128, + 664 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 671, + 97, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 671, + 97, + 684 + ], + "spans": [ + { + "bbox": [ + 47, + 671, + 97, + 684 + ], + "type": "text", + "content": "4.1. Setup" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "text", + "content": "Datasets. We evaluate and compare performance on the NeRF-Synthetic [26] benchmark dataset, which contains" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "text", + "content": "eight objects with " + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "text", + "content": " views at " + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "inline_equation", + "content": "800 \\times 800" + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "text", + "content": " resolution. Further, to comprehensively compare the quality and to test the upper bound of speed, we conduct experiments on the megapixel-level dataset of Tanks&Temples [20], which contains " + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "inline_equation", + "content": "1920 \\times 1080" + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "text", + "content": " frames. We also leverage additional real-world datasets to perform ablation studies. All the training and testing splits follow the published literature." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 160, + 547, + 233 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 160, + 547, + 233 + ], + "spans": [ + { + "bbox": [ + 304, + 160, + 547, + 233 + ], + "type": "text", + "content": "Baselines. We compare with two representative efficient novel-view synthesis approaches: KiloNeRF [35] and SNeRG [14]. For quantitative comparisons, we directly use the published numerical results. For qualitative comparisons, we retrain both methods on different datasets following original configurations, since not all checkpoints are released." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 237, + 547, + 381 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 237, + 547, + 381 + ], + "spans": [ + { + "bbox": [ + 304, + 237, + 547, + 381 + ], + "type": "text", + "content": "Evaluation Metrics. We measure rendering quality by comparing predicted novel views and ground-truth images using standard metrics: peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) [48], and learned perceptual image patch similarity (LPIPS) [58]. Since our goal is highly efficient rendering, we also report frameterates using frames per second (FPS). We also compare the GPU memory consumption in GB for WebGL applications, to quantify the trade-off between speed, quality, and memory. Finally, we compare the training speed by recording the total optimization time in hours. Our test system consists of an NVIDIA RTX 2080 Ti GPU, an i7-9700K CPU, and 32 GB RAM." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 385, + 548, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 385, + 548, + 529 + ], + "spans": [ + { + "bbox": [ + 304, + 385, + 548, + 529 + ], + "type": "text", + "content": "Implementation. We implement the CUDA version with 20-dimensional learnable radiance features and a three-layer convolutional network. To ensure high efficiency and match the Shader implementation, in the WebGL version, we decrease the learnable feature dimension attached to the geometry surface from 20 to 8 and remove the final convolution layer. This results in some performance degradation on both datasets compared with our CUDA version, but brings remarkable FPS improvement and cross-platform properties, since the renderer implementation no longer requires CUDA. More details regarding the architecture design and parameters setting can be found in the supplementary material." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "text", + "content": "**Optimization Details.** We build our method on top of PyTorch [32] and use the Adam optimizer [19] with " + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\beta_{1} = 0.9" + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\beta_{2} = 0.999" + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "text", + "content": ". Our method converges in 200,000 iterations, where the first half is optimized on the distillation views and the second half on the original training images. In each iteration, we randomly sample one view, cast all rays into the geometry to fetch the duplex radiance features, and generate complete screen-space buffers (or local patches if training memory is insufficient) for the convolutional shading. The learning rate is set to " + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "inline_equation", + "content": "10^{-3}" + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "text", + "content": " initially, with exponential decay to " + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "inline_equation", + "content": "10^{-5}" + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "text", + "content": " thereafter. We chose TensoRF [6] as the teacher NeRF model considering its high efficiency of reconstruction and synthesis of distillation views. To convert the density field into the duplex mesh geometry, the two thresholds are set empirically to " + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "inline_equation", + "content": "10^{-4}" + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "inline_equation", + "content": "10^{-2}" + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "text", + "content": ", respectively." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "type": "text", + "content": "8311" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 48, + 98, + 545, + 268 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "type": "text", + "content": "Table 1. Quantitative results on Synthetic-NeRF [26] and Tanks&Temples [20] datasets. The best result is highlighted in bold. Ours (laptop): Run on an M1 MacBook Pro with 16 GB RAM. Others run on a desktop. N+M: N is the training time and M is the baking time." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 98, + 545, + 268 + ], + "lines": [ + { + "bbox": [ + 48, + 98, + 545, + 268 + ], + "spans": [ + { + "bbox": [ + 48, + 98, + 545, + 268 + ], + "type": "table", + "html": "
DatasetMethodBackendFPS ↑PSNR ↑SSIM ↑LPIPS ↓Training (hours)Training Specs
Synthetic-NeRF 800×800KiloNeRF [35]CUDA33.4731.000.9500.030~35+141×V100
SNeRG [14]WebGL89.4330.380.9500.050~30+58×A100
OursCUDA59.7132.140.9570.0306.611×3090
OursWebGL282.6730.430.9450.0491.011×3090
Ours (laptop)WebGL62.2730.430.9450.0491.011×3090
Tanks&Temples 1920×1080KiloNeRFCUDA16.3028.410.9100.090~24+241×A100
SNeRGWebGL55.4824.950.8820.184~30+58×A100
OursCUDA27.4928.120.9150.0899.021×A100
OursWebGL186.9427.080.8950.1301.841×A100
Ours (laptop)WebGL33.6427.080.8950.1301.841×A100
", + "image_path": "0055c00b52efa4c110b1ac5e5819f54bfe7b304c296bfa69c1fb5c0dd7691d83.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 48, + 315, + 287, + 373 + ], + "blocks": [ + { + "bbox": [ + 47, + 277, + 287, + 312 + ], + "lines": [ + { + "bbox": [ + 47, + 277, + 287, + 312 + ], + "spans": [ + { + "bbox": [ + 47, + 277, + 287, + 312 + ], + "type": "text", + "content": "Table 2. Memory-Quality-Speed trade-off. Peak GPU memory consumption (GB), rendering quality, run-time performance comparisons on Tanks&Temples [20] dataset for WebGL applications." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 315, + 287, + 373 + ], + "lines": [ + { + "bbox": [ + 48, + 315, + 287, + 373 + ], + "spans": [ + { + "bbox": [ + 48, + 315, + 287, + 373 + ], + "type": "table", + "html": "
MethodMemory (GB) ↓PSNR ↑FPS ↑
SNeRG4.1924.9555.48
Ours0.9927.08186.94
Ours (laptop)0.9427.0833.64
", + "image_path": "86610c4a8b729e4d7af336349f8e890c31ecbc2216ed7e0e2212fdb67bbaabf3.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 48, + 410, + 287, + 472 + ], + "blocks": [ + { + "bbox": [ + 47, + 384, + 288, + 407 + ], + "lines": [ + { + "bbox": [ + 47, + 384, + 288, + 407 + ], + "spans": [ + { + "bbox": [ + 47, + 384, + 288, + 407 + ], + "type": "text", + "content": "Table 3. Quantitative ablation study of proposed components. Our full model performs significantly better than all variations." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 410, + 287, + 472 + ], + "lines": [ + { + "bbox": [ + 48, + 410, + 287, + 472 + ], + "spans": [ + { + "bbox": [ + 48, + 410, + 287, + 472 + ], + "type": "table", + "html": "
DuplexConvDistillPSNR ↑SSIM ↑LPIPS ↓
✓✓26.950.8840.12
✓✓30.030.9420.05
✓✓29.660.9320.06
✓✓✓32.140.9570.03
", + "image_path": "014dc29faea2b67fe085c5b2d51e772bea0c72b1086707f5c06e06f6c02287eb.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 491, + 105, + 503 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 491, + 105, + 503 + ], + "spans": [ + { + "bbox": [ + 47, + 491, + 105, + 503 + ], + "type": "text", + "content": "4.2. Results" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 510, + 288, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 510, + 288, + 676 + ], + "spans": [ + { + "bbox": [ + 46, + 510, + 288, + 676 + ], + "type": "text", + "content": "Quantitative Comparison. We provide extensive quantitative comparisons in Table 1. Compared with CUDA-based KiloNeRF [35], our CUDA version achieves significantly better rendering performance in terms of PSNR (more than 1 dB) and SSIM on the Synthetic-NeRF dataset [26] with much higher run-time FPS and lower training time. The PSNR of our CUDA-based model on Tanks&Temples [20] is slightly lower than KiloNeRF, even though the novel views of KiloNeRF contain severe artifacts, as shown in Figure 7. This may be caused by the characteristics of PSNR, which only measures pixel-level differences and ignores the structural similarity. In this situation, SSIM would be a better metric to indicate the performance gap between our method and KiloNeRF (0.915 vs. 0.910)." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 677, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 677, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 677, + 288, + 714 + ], + "type": "text", + "content": "We also compare with WebGL-based SNeRG [14]. Even at " + }, + { + "bbox": [ + 47, + 677, + 288, + 714 + ], + "type": "inline_equation", + "content": "1920 \\times 1080" + }, + { + "bbox": [ + 47, + 677, + 288, + 714 + ], + "type": "text", + "content": " resolution, our WebGL-based model achieves " + }, + { + "bbox": [ + 47, + 677, + 288, + 714 + ], + "type": "inline_equation", + "content": "180 +" + }, + { + "bbox": [ + 47, + 677, + 288, + 714 + ], + "type": "text", + "content": " FPS on a desktop-level GPU, and " + }, + { + "bbox": [ + 47, + 677, + 288, + 714 + ], + "type": "inline_equation", + "content": "30 +" + }, + { + "bbox": [ + 47, + 677, + 288, + 714 + ], + "type": "text", + "content": " FPS on a laptop" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 279, + 547, + 363 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 279, + 547, + 363 + ], + "spans": [ + { + "bbox": [ + 304, + 279, + 547, + 363 + ], + "type": "text", + "content": "using Chrome, with better rendering fidelity than SNeRG. In addition, our method requires about two orders of magnitude less computation for training compared to SNeRG. We also show the trade-off comparison regarding memory consumption, image quality and rendering speed in Table 2 under high-resolution setting, which comprehensively demonstrates the superiority of our method." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 365, + 547, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 365, + 547, + 569 + ], + "spans": [ + { + "bbox": [ + 304, + 365, + 547, + 569 + ], + "type": "text", + "content": "Qualitative Comparison. To further verify our method's benefit in terms of rendering quality over existing fast rendering NeRF techniques, we show qualitative comparisons in Figure 7. As we can see, both baselines, KiloNeRF and SNeRG, cannot render the high-quality details of the scenes, which are possibly bounded by the voxel resolution; however, simply increasing the voxel resolution will directly lead to cubic growth of memory cost. Besides, some texture artifacts and wrong view-dependent specular highlights also exist in the baselines, which are visually inferior to our method. More importantly, in the Tanks&Temples dataset, we could observe vibrant blocking degradation from the results of KiloNeRF. The potential reason is KiloNeRF has limited generalization capability to out-of-distribution (OOD) views since there are no correlations between space-isolated MLPs. By contrast, our method can render consistent and high-fidelity views with good view generalization ability." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 575, + 400, + 588 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 575, + 400, + 588 + ], + "spans": [ + { + "bbox": [ + 306, + 575, + 400, + 588 + ], + "type": "text", + "content": "4.3. Ablation Study" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 594, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 547, + 713 + ], + "type": "text", + "content": "Duplex Radiance. To verify the effectiveness of our proposed neural duplex radiance, we compare with a variation that only considers a single ray-surface intersection. More specifically, we only adopt the mesh extracted from NeRF model with threshold " + }, + { + "bbox": [ + 304, + 594, + 547, + 713 + ], + "type": "inline_equation", + "content": "10^{-4}" + }, + { + "bbox": [ + 304, + 594, + 547, + 713 + ], + "type": "text", + "content": " to ensure geometry proxy could cover all pixels and avoid hole artifacts. As shown in Table 3 and Figure 8, rendering without duplex radiance dramatically downgrades the quality of novel-view synthesis since the coarse geometry extracted from NeRF could not provide reasonable shading locations. More results about using single" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "8312" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 52, + 69, + 185, + 177 + ], + "blocks": [ + { + "bbox": [ + 52, + 69, + 185, + 177 + ], + "lines": [ + { + "bbox": [ + 52, + 69, + 185, + 177 + ], + "spans": [ + { + "bbox": [ + 52, + 69, + 185, + 177 + ], + "type": "image", + "image_path": "c47af8293aa2c8387f0fb6845c9b00f31b71dfee3707622df6d3c361760477e7.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 186, + 70, + 303, + 177 + ], + "blocks": [ + { + "bbox": 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+ 316 + ], + "spans": [ + { + "bbox": [ + 470, + 307, + 492, + 316 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 48, + 360, + 109, + 426 + ], + "blocks": [ + { + "bbox": [ + 46, + 320, + 546, + 354 + ], + "lines": [ + { + "bbox": [ + 46, + 320, + 546, + 354 + ], + "spans": [ + { + "bbox": [ + 46, + 320, + 546, + 354 + ], + "type": "text", + "content": "Figure 7. Qualitative comparisons with various baselines on Synthetic-NeRF (top) and Tanks&Temples (bottom) datasets. We show the novel views synthesized by KiloNeRF, SNeRG and ours. Our approach effectively speeds up the inference efficiency while maintaining the high quality of the rendered frames." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 48, + 360, + 109, + 426 + ], + "lines": [ + { + "bbox": [ + 48, + 360, + 109, + 426 + ], + "spans": [ + { + "bbox": [ + 48, + 360, + 109, + 426 + ], + "type": "image", + "image_path": "175b6f147cff715c550f243e9a4a55e59e2972c9fb2db48a14cbc6caa7eb334e.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 63, + 427, + 95, + 435 + ], + "lines": [ + { + "bbox": [ + 63, + 427, + 95, + 435 + ], + "spans": [ + { + "bbox": [ + 63, + 427, + 95, + 435 + ], + "type": "text", + "content": "No Duplex" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 47, + 437, + 286, + 460 + ], + "lines": [ + { + "bbox": [ + 47, + 437, + 286, + 460 + ], + "spans": [ + { + "bbox": [ + 47, + 437, + 286, + 460 + ], + "type": "text", + "content": "Figure 8. Qualitative results for each ablation study. Our full model achieves the best rendering quality among all variations." + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 111, + 360, + 170, + 426 + ], + "blocks": [ + { + "bbox": [ + 111, + 360, + 170, + 426 + ], + "lines": [ + { + "bbox": [ + 111, + 360, + 170, + 426 + ], + "spans": [ + { + "bbox": [ + 111, + 360, + 170, + 426 + ], + "type": "image", + "image_path": "795313d1519b2133fa4e5d89cd9e07cc38f9a60931daab64face5782a8518cab.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 126, + 427, + 152, + 435 + ], + "lines": [ + { + "bbox": [ + 126, + 427, + 152, + 435 + ], + "spans": [ + { + "bbox": [ + 126, + 427, + 152, + 435 + ], + "type": "text", + "content": "No Conv" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 170, + 360, + 230, + 426 + ], + "blocks": [ + { + "bbox": [ + 170, + 360, + 230, + 426 + ], + "lines": [ + { + "bbox": [ + 170, + 360, + 230, + 426 + ], + "spans": [ + { + "bbox": [ + 170, + 360, + 230, + 426 + ], + "type": "image", + "image_path": "4583884132975fe4dc932173de98aa16a78b655b43fc82e986fdbe6012cd05ff.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 185, + 427, + 214, + 435 + ], + "lines": [ + { + "bbox": [ + 185, + 427, + 214, + 435 + ], + "spans": [ + { + "bbox": [ + 185, + 427, + 214, + 435 + ], + "type": "text", + "content": "No Distill" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 230, + 360, + 290, + 426 + ], + "blocks": [ + { + "bbox": [ + 230, + 360, + 290, + 426 + ], + "lines": [ + { + "bbox": [ + 230, + 360, + 290, + 426 + ], + "spans": [ + { + "bbox": [ + 230, + 360, + 290, + 426 + ], + "type": "image", + "image_path": "0ad5aad0333ea262be0541119958d4c99800e3146b340d46e937689ceeffb39a.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 243, + 427, + 276, + 435 + ], + "lines": [ + { + "bbox": [ + 243, + 427, + 276, + 435 + ], + "spans": [ + { + "bbox": [ + 243, + 427, + 276, + 435 + ], + "type": "text", + "content": "Full Model" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 19 + }, + { + "bbox": [ + 46, + 476, + 287, + 501 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 476, + 287, + 501 + ], + "spans": [ + { + "bbox": [ + 46, + 476, + 287, + 501 + ], + "type": "text", + "content": "radiance feature but with different thresholds can be found in the supplementary materials." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 46, + 503, + 288, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 503, + 288, + 598 + ], + "spans": [ + { + "bbox": [ + 46, + 503, + 288, + 598 + ], + "type": "text", + "content": "Convolution vs MLP. In contrast to most NeRF methods, which integrate radiance only along a single ray, we aggregate all the radiance information of the local geometry surface via convolution kernels. Figure 8 and Table 3 demonstrate that the convolutional shading network can render high-fidelity novel views more robustly, and effectively overcomes the under-constrained problem while only considering two sample locations for each ray." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 46, + 602, + 287, + 674 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 602, + 287, + 674 + ], + "spans": [ + { + "bbox": [ + 46, + 602, + 287, + 674 + ], + "type": "text", + "content": "No Distillation. In this ablation study, we remove all the distillation views and only train the model based on known views. Our results in Figure 8 show that optimization without distillation views will lead to high-frequency artifacts in the geometry, which the spatial convolutional kernels are unable to effectively handle as well." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 46, + 677, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 288, + 713 + ], + "type": "text", + "content": "Influence of Network Size. We have noticed that decreasing the radiance feature dimension and network size will result in some quality degeneration in Table 1. Hence, one" + } + ] + } + ], + "index": 25 + }, + { + "type": "image", + "bbox": [ + 308, + 360, + 368, + 419 + ], + "blocks": [ + { + "bbox": [ + 308, + 360, + 368, + 419 + ], + "lines": [ + { + "bbox": [ + 308, + 360, + 368, + 419 + ], + "spans": [ + { + "bbox": [ + 308, + 360, + 368, + 419 + ], + "type": "image", + "image_path": "335f5ff8946ed8ab86fc2109e3c37d41465c2d8dfdbcff5f8ed3b4e12b8a70c9.jpg" + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 327, + 421, + 349, + 430 + ], + "lines": [ + { + "bbox": [ + 327, + 421, + 349, + 430 + ], + "spans": [ + { + "bbox": [ + 327, + 421, + 349, + 430 + ], + "type": "text", + "content": "Deeper" + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 305, + 432, + 547, + 475 + ], + "lines": [ + { + "bbox": [ + 305, + 432, + 547, + 475 + ], + "spans": [ + { + "bbox": [ + 305, + 432, + 547, + 475 + ], + "type": "text", + "content": "Figure 9. Ablation study on the network size. Increasing the depth and receptive field of the convolutional shading network does not lead to an improvement on rendering quality in a consistent manner. Zoom in for more details." + } + ] + } + ], + "index": 34, + "angle": 0, + "type": "image_caption" + } + ], + "index": 26 + }, + { + "type": "image", + "bbox": [ + 369, + 360, + 429, + 419 + ], + "blocks": [ + { + "bbox": [ + 369, + 360, + 429, + 419 + ], + "lines": [ + { + "bbox": [ + 369, + 360, + 429, + 419 + ], + "spans": [ + { + "bbox": [ + 369, + 360, + 429, + 419 + ], + "type": "image", + "image_path": "d7323e85591436df67f06ffebf588b32abf9c79e49dda5e7047aa6172421095d.jpg" + } + ] + } + ], + "index": 28, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 390, + 421, + 406, + 429 + ], + "lines": [ + { + "bbox": [ + 390, + 421, + 406, + 429 + ], + "spans": [ + { + "bbox": [ + 390, + 421, + 406, + 429 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 29, + "angle": 0, + "type": "image_caption" + } + ], + "index": 28 + }, + { + "type": "image", + "bbox": [ + 429, + 360, + 488, + 419 + ], + "blocks": [ + { + "bbox": [ + 429, + 360, + 488, + 419 + ], + "lines": [ + { + "bbox": [ + 429, + 360, + 488, + 419 + ], + "spans": [ + { + "bbox": [ + 429, + 360, + 488, + 419 + ], + "type": "image", + "image_path": "29ce24a4f0fa9fcf8513bd4eeecf68faa17f5b9701a6280cbef32f57a5e3dfc6.jpg" + } + ] + } + ], + "index": 30, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 447, + 421, + 469, + 430 + ], + "lines": [ + { + "bbox": [ + 447, + 421, + 469, + 430 + ], + "spans": [ + { + "bbox": [ + 447, + 421, + 469, + 430 + ], + "type": "text", + "content": "Deeper" + } + ] + } + ], + "index": 31, + "angle": 0, + "type": "image_caption" + } + ], + "index": 30 + }, + { + "type": "image", + "bbox": [ + 489, + 360, + 548, + 419 + ], + "blocks": [ + { + "bbox": [ + 489, + 360, + 548, + 419 + ], + "lines": [ + { + "bbox": [ + 489, + 360, + 548, + 419 + ], + "spans": [ + { + "bbox": [ + 489, + 360, + 548, + 419 + ], + "type": "image", + "image_path": "8fe33b81ce678f79c3768d4f47541fb59c97ad9ec902896fd112a0b705214b09.jpg" + } + ] + } + ], + "index": 32, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 511, + 421, + 525, + 429 + ], + "lines": [ + { + "bbox": [ + 511, + 421, + 525, + 429 + ], + "spans": [ + { + "bbox": [ + 511, + 421, + 525, + 429 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 33, + "angle": 0, + "type": "image_caption" + } + ], + "index": 32 + }, + { + "bbox": [ + 304, + 489, + 547, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 489, + 547, + 632 + ], + "spans": [ + { + "bbox": [ + 304, + 489, + 547, + 632 + ], + "type": "text", + "content": "interesting question is whether performance can be boosted consistently by increasing the network parameters and receptive fields. We conduct this experiment on the Fox dataset. More specifically, we double the network layers and boost the original used convolution layers with larger receptive fields. As shown in Figure 9, the deeper network does not bring more quality gains. By contrast, the synthesized novel views become more blurry than our method and lose many textural details, which may be caused by the learning difficulty of a deep network. Leveraging more advanced architectures or loss functions, such as adversarial objectives, could potentially resolve this issue." + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 304, + 641, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 641, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 641, + 547, + 713 + ], + "type": "text", + "content": "Generalization to out-of-distribution views. NeRF has strong capabilities to interpolate novel views, but its rendering quality may drop for some novel views which don't follow the original distribution. This issue will be more serious for distillation-based methods that try to mimic the teacher behavior. We show out-of-distribution (OOD) syn" + } + ] + } + ], + "index": 36 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8313" + } + ] + } + ], + "index": 37 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 62, + 70, + 212, + 137 + ], + "blocks": [ + { + "bbox": [ + 62, + 70, + 212, + 137 + ], + "lines": [ + { + "bbox": [ + 62, + 70, + 212, + 137 + ], + "spans": [ + { + "bbox": [ + 62, + 70, + 212, + 137 + ], + "type": "image", + "image_path": "4dd9216670937e32e0e9e5a29d10dae9457d3e95232130b7e356a4f312e6261d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 109, + 138, + 165, + 148 + ], + "lines": [ + { + "bbox": [ + 109, + 138, + 165, + 148 + ], + "spans": [ + { + "bbox": [ + 109, + 138, + 165, + 148 + ], + "type": "text", + "content": "KiloNeRF [35]" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 220, + 70, + 371, + 136 + ], + "blocks": [ + { + "bbox": [ + 220, + 70, + 371, + 136 + ], + "lines": [ + { + "bbox": [ + 220, + 70, + 371, + 136 + ], + "spans": [ + { + "bbox": [ + 220, + 70, + 371, + 136 + ], + "type": "image", + "image_path": "45db3542676bbccf79cdc66b0582282b460ced6a2b3c441f58a75305e4cd2315.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 272, + 138, + 319, + 148 + ], + "lines": [ + { + "bbox": [ + 272, + 138, + 319, + 148 + ], + "spans": [ + { + "bbox": [ + 272, + 138, + 319, + 148 + ], + "type": "text", + "content": "SNeRG [14]" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 379, + 71, + 530, + 136 + ], + "blocks": [ + { + "bbox": [ + 379, + 71, + 530, + 136 + ], + "lines": [ + { + "bbox": [ + 379, + 71, + 530, + 136 + ], + "spans": [ + { + "bbox": [ + 379, + 71, + 530, + 136 + ], + "type": "image", + "image_path": "28038dc52f4d1840012fae869d848d6c31e0c15be9e222f81496fe747916ee3c.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 444, + 139, + 463, + 147 + ], + "lines": [ + { + "bbox": [ + 444, + 139, + 463, + 147 + ], + "spans": [ + { + "bbox": [ + 444, + 139, + 463, + 147 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 47, + 207, + 287, + 277 + ], + "blocks": [ + { + "bbox": [ + 47, + 181, + 286, + 202 + ], + "lines": [ + { + "bbox": [ + 47, + 181, + 286, + 202 + ], + "spans": [ + { + "bbox": [ + 47, + 181, + 286, + 202 + ], + "type": "text", + "content": "Table 4. Quantitative comparisons with the teacher model on the Tanks&Temples [20] dataset (1920×1080)." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 47, + 207, + 287, + 277 + ], + "lines": [ + { + "bbox": [ + 47, + 207, + 287, + 277 + ], + "spans": [ + { + "bbox": [ + 47, + 207, + 287, + 277 + ], + "type": "table", + "html": "
PSNR ↑SSIM ↑LPIPS ↓FPS ↑
Original NeRF [26]28.320.9000.110.01
Teacher TensoRF [6]28.480.9180.120.10
Ours w/ WebGL27.080.8950.13186.94
Ours w/ CUDA28.120.9150.0927.49
", + "image_path": "6507c2f4917091b0f84ef7d811029d5d55bddc4ad33bc4a88af7d5da2ff54131.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 294, + 287, + 343 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 294, + 287, + 343 + ], + "spans": [ + { + "bbox": [ + 46, + 294, + 287, + 343 + ], + "type": "text", + "content": "thesis results in Figure 10. Both KiloNeRF [35] and SNeRG [14] do not generalize well to OOD views and produce inconsistent and blurry artifacts, while our method synthesizes sharper and cleaner appearance with appropriate lighting." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 346, + 288, + 537 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 346, + 288, + 537 + ], + "spans": [ + { + "bbox": [ + 46, + 346, + 288, + 537 + ], + "type": "text", + "content": "Comparison with Teacher Model. Since our method involves multi-view distillation, we also measure the quantitative differences between our model and the teacher model [6] in Table 4. Although the numerical results of PSNR and SSIM are slightly inferior to the teacher model, the perceptual metric LPIPS, which better correlates with human perception, surprisingly attains better performance benefiting from the better generalization ability of neural duplex radiance field. On the other hand, the teacher model TensoRF [6] has already optimized the run-time performance by skipping the importance sampling step of original NeRF model and tensor decomposition, but the speed is still limited for real-world interactive applications. In contrast, our method achieves " + }, + { + "bbox": [ + 46, + 346, + 288, + 537 + ], + "type": "inline_equation", + "content": "30+" + }, + { + "bbox": [ + 46, + 346, + 288, + 537 + ], + "type": "text", + "content": " FPS while maintaining comparable rendering quality. Significantly, our WebGL version reaches " + }, + { + "bbox": [ + 46, + 346, + 288, + 537 + ], + "type": "inline_equation", + "content": "10,000 \\times" + }, + { + "bbox": [ + 46, + 346, + 288, + 537 + ], + "type": "text", + "content": " speed up over the original NeRF model." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 540, + 287, + 659 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 540, + 287, + 659 + ], + "spans": [ + { + "bbox": [ + 46, + 540, + 287, + 659 + ], + "type": "text", + "content": "Comparison with MobileNeRF [9]. Recently, a concurrent work MobileNeRF [9] also explores the rasterization pipeline to accelerate the rendering speed of neural radiance fields. Nonetheless, their framework involves optimizations of both geometry and appearance leading to a high computational cost (21+ hours using " + }, + { + "bbox": [ + 46, + 540, + 287, + 659 + ], + "type": "inline_equation", + "content": "8 \\times \\mathrm{V}100" + }, + { + "bbox": [ + 46, + 540, + 287, + 659 + ], + "type": "text", + "content": "). By contrast, our method can convert a pretrained NeRF in a few hours using a single GPU. We provide some qualitative comparisons in Figure 11 as well. Our approach produces more appealing results and better view-dependent appearance." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 669, + 119, + 681 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 669, + 119, + 681 + ], + "spans": [ + { + "bbox": [ + 47, + 669, + 119, + 681 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 689, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 288, + 713 + ], + "type": "text", + "content": "In this paper, we proposed a novel approach for learning a neural duplex radiance field from a pretrained NeRF for real-" + } + ] + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 309, + 182, + 366, + 226 + ], + "blocks": [ + { + "bbox": [ + 46, + 150, + 547, + 172 + ], + "lines": [ + { + "bbox": [ + 46, + 150, + 547, + 172 + ], + "spans": [ + { + "bbox": [ + 46, + 150, + 547, + 172 + ], + "type": "text", + "content": "Figure 10. Comparison of out-of-distribution (OOD) view synthesis (No ground-truth). Our method could generate high-quality and view-dependent frames with sharper details for OOD views." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 309, + 182, + 366, + 226 + ], + "lines": [ + { + "bbox": [ + 309, + 182, + 366, + 226 + ], + "spans": [ + { + "bbox": [ + 309, + 182, + 366, + 226 + ], + "type": "image", + "image_path": "3dd06652431c95d0b411e1d0c66c27f8a512dab493a5b9676ebc7f87da29c444.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 228, + 362, + 236 + ], + "lines": [ + { + "bbox": [ + 313, + 228, + 362, + 236 + ], + "spans": [ + { + "bbox": [ + 313, + 228, + 362, + 236 + ], + "type": "text", + "content": "MobileNeRF [9]" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 306, + 238, + 545, + 261 + ], + "lines": [ + { + "bbox": [ + 306, + 238, + 545, + 261 + ], + "spans": [ + { + "bbox": [ + 306, + 238, + 545, + 261 + ], + "type": "text", + "content": "Figure 11. 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Our method produces smoother appearance." + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 368, + 182, + 427, + 226 + ], + "blocks": [ + { + "bbox": [ + 368, + 182, + 427, + 226 + ], + "lines": [ + { + "bbox": [ + 368, + 182, + 427, + 226 + ], + "spans": [ + { + "bbox": [ + 368, + 182, + 427, + 226 + ], + "type": "image", + "image_path": "54b9d296ead0080eeda2fd48a617889d285f92a8e7ec9af01057b2727f1f2b6e.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 374, + 228, + 422, + 236 + ], + "lines": [ + { + "bbox": [ + 374, + 228, + 422, + 236 + ], + "spans": [ + { + "bbox": [ + 374, + 228, + 422, + 236 + ], + "type": "text", + "content": "Ours w/ WebGL" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 430, + 182, + 487, + 226 + ], + "blocks": [ + { + "bbox": [ + 430, + 182, + 487, + 226 + ], + "lines": [ + { + "bbox": [ + 430, + 182, + 487, + 226 + ], + "spans": [ + { + "bbox": [ + 430, + 182, + 487, + 226 + ], + "type": "image", + "image_path": "e74d1c01903cb6599982f083bf6d6becc9051ad323dbe02d291d3f8e1e589586.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 434, + 228, + 482, + 236 + ], + "lines": [ + { + "bbox": [ + 434, + 228, + 482, + 236 + ], + "spans": [ + { + "bbox": [ + 434, + 228, + 482, + 236 + ], + "type": "text", + "content": "MobileNeRF [9]" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 489, + 182, + 548, + 226 + ], + "blocks": [ + { + "bbox": [ + 489, + 182, + 548, + 226 + ], + "lines": [ + { + "bbox": [ + 489, + 182, + 548, + 226 + ], + "spans": [ + { + "bbox": [ + 489, + 182, + 548, + 226 + ], + "type": "image", + "image_path": "92626797dcc756da88f0119a9a91ae1c9117a3f57475d870d515d2c692fa3550.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 494, + 228, + 542, + 236 + ], + "lines": [ + { + "bbox": [ + 494, + 228, + 542, + 236 + ], + "spans": [ + { + "bbox": [ + 494, + 228, + 542, + 236 + ], + "type": "text", + "content": "Ours w/ WebGL" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 283, + 547, + 367 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 283, + 547, + 367 + ], + "spans": [ + { + "bbox": [ + 304, + 283, + 547, + 367 + ], + "type": "text", + "content": "time novel-view synthesis. We employed a convolutional shading network to improve the rendering quality and also proposed a multi-view distillation strategy for better optimization. Our method provides significant improvements on run-time, bettering or preserving the rendering quality, and at the same time, operating on a much lower computational cost compared to existing efficient NeRF methods." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 373, + 547, + 612 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 373, + 547, + 612 + ], + "spans": [ + { + "bbox": [ + 304, + 373, + 547, + 612 + ], + "type": "text", + "content": "Limitations. It is challenging to extract useful duplex meshes from NeRFs to represent transparent or semi-transparent scenes and our method is no different. Integrating order-independent transparency (OIT) with the NeRF framework may be a potential solution to speed up its rendering. In addition, the quality of the geometry proxy will directly influence the learning of neural representations, thus we would also like to explore scene-specific thresholds rather than empirical defined values while generating meshes, for instance, based on signed distance functions or on the statistics of the density values, for more robust proxy extraction. Currently, our method focuses on object-level and bounded-scene datasets. Efficiently rendering real-world unbounded scenes is a promising future direction. Finally, our experiments demonstrate that sufficient learnable parameters are crucial to achieve better rendering fidelity. Hence, to counter the performance degradation in the WebGL version, boosting the running efficiency of larger CNN in the fragment shader, or leveraging other acceleration frameworks, could also be considered in future work." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 304, + 617, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 547, + 713 + ], + "type": "text", + "content": "Acknowledgements. We thank the anonymous reviewers for their constructive comments. We also appreciate helpful discussions with Feng Liu, Chakravarty R. Alla Chaitanya, Simon Green, Daniel Maskit, Aayush Bansal, Zhiqin Chen, Vasu Agrawal, Hao Tang, Michael Zollhoefer, Huan Wang, Ayush Saraf and Zhaoyang Lv. This work was partially supported by a GRF grant (Project No. CityU 11216122) from the Research Grants Council (RGC) of Hong Kong." + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "8314" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "type": "text", + "content": "[1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graphics. In ECCV, pages 696-712, 2020." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 124, + 287, + 157 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 124, + 287, + 157 + ], + "spans": [ + { + "bbox": [ + 53, + 124, + 287, + 157 + ], + "type": "text", + "content": "[2] Benjamin Attal, Jia-Bin Huang, Michael Zollhöfer, Johannes Kopf, and Changil Kim. Learning neural light fields with ray-space embedding. In CVPR, pages 19819-19829, 2022." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 158, + 288, + 201 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 158, + 288, + 201 + ], + "spans": [ + { + "bbox": [ + 53, + 158, + 288, + 201 + ], + "type": "text", + "content": "[3] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-NeRF 360: Unbounded anti-aliased neural radiance fields. In CVPR, pages 5470–5479, 2022." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 202, + 288, + 256 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 202, + 288, + 256 + ], + "spans": [ + { + "bbox": [ + 53, + 202, + 288, + 256 + ], + "type": "text", + "content": "[4] Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In CVPR, pages 16123-16133, 2022." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 258, + 288, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 258, + 288, + 300 + ], + "spans": [ + { + "bbox": [ + 53, + 258, + 288, + 300 + ], + "type": "text", + "content": "[5] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. MVSNeRF: Fast generalizable radiance field reconstruction from multi-view stereo. In CVPR, pages 14124-14133, 2021." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 302, + 288, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 302, + 288, + 323 + ], + "spans": [ + { + "bbox": [ + 53, + 302, + 288, + 323 + ], + "type": "text", + "content": "[6] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. TensoRF: Tensorial radiance fields. In ECCV, 2022." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 324, + 288, + 356 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 324, + 288, + 356 + ], + "spans": [ + { + "bbox": [ + 53, + 324, + 288, + 356 + ], + "type": "text", + "content": "[7] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In CVPR, pages 8628-8638, 2021." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 357, + 288, + 389 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 357, + 288, + 389 + ], + "spans": [ + { + "bbox": [ + 53, + 357, + 288, + 389 + ], + "type": "text", + "content": "[8] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In CVPR, pages 5939-5948, 2019." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 391, + 288, + 434 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 391, + 288, + 434 + ], + "spans": [ + { + "bbox": [ + 53, + 391, + 288, + 434 + ], + "type": "text", + "content": "[9] Zhiqin Chen, Thomas Funkhouser, Peter Hedman, and Andrea Tagliasacchi. MobileNeRF: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In CVPR, 2023." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 436, + 288, + 479 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 436, + 288, + 479 + ], + "spans": [ + { + "bbox": [ + 48, + 436, + 288, + 479 + ], + "type": "text", + "content": "[10] Nianchen Deng, Zhenyi He, Jiannan Ye, Budmonde Duinkhar-jav, Praneeth Chakravarthula, Xubo Yang, and Qi Sun. FoV-NeRF: Foveated neural radiance fields for virtual reality. TVCG, 28(11):3854-3864, 2022." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 480, + 288, + 511 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 480, + 288, + 511 + ], + "spans": [ + { + "bbox": [ + 48, + 480, + 288, + 511 + ], + "type": "text", + "content": "[11] Zhiwen Fan, Yifan Jiang, Peihao Wang, Xinyu Gong, Dejia Xu, and Zhangyang Wang. Unified implicit neural stylization. In ECCV, 2022." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 514, + 288, + 545 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 514, + 288, + 545 + ], + "spans": [ + { + "bbox": [ + 48, + 514, + 288, + 545 + ], + "type": "text", + "content": "[12] Brandon Yushan Feng and Amitabh Varshney. Signet: Efficient neural representation for light fields. In ICCV, pages 14224-14233, 2021." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 547, + 288, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 547, + 288, + 579 + ], + "spans": [ + { + "bbox": [ + 48, + 547, + 288, + 579 + ], + "type": "text", + "content": "[13] Stephan J Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin. FastNeRF: High-fidelity neural rendering at 200fps. In CVPR, pages 14346-14355, 2021." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 581, + 288, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 581, + 288, + 623 + ], + "spans": [ + { + "bbox": [ + 48, + 581, + 288, + 623 + ], + "type": "text", + "content": "[14] Peter Hedman, Pratul P Srinivasan, Ben Mildenhall, Jonathan T Barron, and Paul Debevec. Baking neural radiance fields for real-time view synthesis. In CVPR, pages 5875-5884, 2021." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 624, + 288, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 288, + 657 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 288, + 657 + ], + "type": "text", + "content": "[15] Tao Hu, Shu Liu, Yilun Chen, Tiancheng Shen, and Jiaya Jia. EfficientNeRF – efficient neural radiance fields. In CVPR, pages 12902–12911, 2022." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 658, + 288, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 288, + 690 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 288, + 690 + ], + "type": "text", + "content": "[16] Yi-Hua Huang, Yue He, Yu-Jie Yuan, Yu-Kun Lai, and Lin Gao. StylizedNeRF: Consistent 3D scene stylization as stylized NeRF via 2D-3D mutual learning. In CVPR, 2022." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 48, + 692, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 692, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 692, + 288, + 713 + ], + "type": "text", + "content": "[17] Yoonwoo Jeong, Seungjoo Shin, Junha Lee, Chris Choy, Anima Anandkumar, Minsu Cho, and Jaesik Park. PeRFception:" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 35, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "Perception using radiance fields. In NeurIPS Datasets and Benchmarks Track, 2022." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 96, + 545, + 129 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 545, + 129 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 545, + 129 + ], + "type": "text", + "content": "[18] Animesh Karnewar, Tobias Ritschel, Oliver Wang, and Niloy Mitra. ReLU fields: The little non-linearity that could. In SIGGRAPH Conference Proceedings, pages 27:1-9, 2022." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 130, + 545, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 130, + 545, + 152 + ], + "spans": [ + { + "bbox": [ + 308, + 130, + 545, + 152 + ], + "type": "text", + "content": "[19] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 153, + 545, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 153, + 545, + 185 + ], + "spans": [ + { + "bbox": [ + 308, + 153, + 545, + 185 + ], + "type": "text", + "content": "[20] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Trans. Graph., 36(4):1-13, 2017." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 186, + 545, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 186, + 545, + 229 + ], + "spans": [ + { + "bbox": [ + 308, + 186, + 545, + 229 + ], + "type": "text", + "content": "[21] Andreas Kurz, Thomas Neff, Zhaoyang Lv, Michael Zollhöfer, and Markus Steinberger. AdaNeRF: Adaptive sampling for real-time rendering of neural radiance fields. In ECCV, 2022." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 232, + 545, + 263 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 232, + 545, + 263 + ], + "spans": [ + { + "bbox": [ + 308, + 232, + 545, + 263 + ], + "type": "text", + "content": "[22] Christoph Lassner and Michael Zollhofer. Pulsar: Efficient sphere-based neural rendering. In CVPR, pages 1440-1449, 2021." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 266, + 545, + 309 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 266, + 545, + 309 + ], + "spans": [ + { + "bbox": [ + 308, + 266, + 545, + 309 + ], + "type": "text", + "content": "[23] Haotong Lin, Sida Peng, Zhen Xu, Yunzhi Yan, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Efficient neural radiance fields for interactive free-viewpoint video. In SIGGRAPH Asia 2022 Conference Papers, pages 1-9, 2022." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 311, + 545, + 343 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 311, + 545, + 343 + ], + "spans": [ + { + "bbox": [ + 308, + 311, + 545, + 343 + ], + "type": "text", + "content": "[24] Steven Liu, Xiuming Zhang, Zhoutong Zhang, Richard Zhang, Jun-Yan Zhu, and Bryan Russell. Editing conditional radiance fields. In ICCV, pages 5773-5783, 2021." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 345, + 545, + 377 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 345, + 545, + 377 + ], + "spans": [ + { + "bbox": [ + 308, + 345, + 545, + 377 + ], + "type": "text", + "content": "[25] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3D surface construction algorithm. SIGGRAPH Computer Graphics, 21(4):163-169, 1987." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 379, + 545, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 379, + 545, + 421 + ], + "spans": [ + { + "bbox": [ + 308, + 379, + 545, + 421 + ], + "type": "text", + "content": "[26] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 423, + 545, + 466 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 423, + 545, + 466 + ], + "spans": [ + { + "bbox": [ + 308, + 423, + 545, + 466 + ], + "type": "text", + "content": "[27] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 468, + 545, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 468, + 545, + 510 + ], + "spans": [ + { + "bbox": [ + 308, + 468, + 545, + 510 + ], + "type": "text", + "content": "[28] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-15, 2022." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 513, + 545, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 513, + 545, + 578 + ], + "spans": [ + { + "bbox": [ + 308, + 513, + 545, + 578 + ], + "type": "text", + "content": "[29] Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Joerg H Mueller, Chakravarty R Alla Chaitanya, Anton Kaplanyan, and Markus Steinberger. DONeRF: Towards real-time rendering of compact neural radiance fields using depth oracle networks. Computer Graphics Forum, 40(4):45-59, 2021." + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 308, + 580, + 545, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 580, + 545, + 612 + ], + "spans": [ + { + "bbox": [ + 308, + 580, + 545, + 612 + ], + "type": "text", + "content": "[30] Thu Nguyen-Phuoc, Feng Liu, and Lei Xiao. SNeRF: Stylized neural implicit representations for 3D scenes. ACM Trans. Graph., 41(4):142:1-11, 2022." + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 308, + 614, + 545, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 614, + 545, + 656 + ], + "spans": [ + { + "bbox": [ + 308, + 614, + 545, + 656 + ], + "type": "text", + "content": "[31] Michael Oechsle, Lars Mescheder, Michael Niemeyer, Thilo Strauss, and Andreas Geiger. Texture fields: Learning texture representations in function space. In ICCV, pages 4531-4540, 2019." + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 308, + 658, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 658, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 658, + 545, + 713 + ], + "type": "text", + "content": "[32] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu" + } + ] + } + ], + "index": 34 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8315" + } + ] + } + ], + "index": 36 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 66, + 72, + 287, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 72, + 287, + 106 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 287, + 106 + ], + "type": "text", + "content": "Fang, Junjie Bai, and Soumith Chintala. PyTorch: An imperative style, high-performance deep learning library. In NeurIPS, pages 8024-8035, 2019." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 108, + 288, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 108, + 288, + 140 + ], + "spans": [ + { + "bbox": [ + 48, + 108, + 288, + 140 + ], + "type": "text", + "content": "[33] Ruslan Rakhimov, Andrei-Timotei Ardelean, Victor Lempitsky, and Evgeny Burnaev. NPBG++: Accelerating neural point-based graphics. In CVPR, pages 15948-15958, 2022." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 143, + 288, + 175 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 143, + 288, + 175 + ], + "spans": [ + { + "bbox": [ + 48, + 143, + 288, + 175 + ], + "type": "text", + "content": "[34] Daniel Rebain, Wei Jiang, Soroosh Yazdani, Ke Li, Kwang Moo Yi, and Andrea Tagliasacchi. DeRF: Decomposed radiance fields. In CVPR, pages 14153-14161, 2021." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 177, + 288, + 210 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 177, + 288, + 210 + ], + "spans": [ + { + "bbox": [ + 48, + 177, + 288, + 210 + ], + "type": "text", + "content": "[35] Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger. KiloNeRF: Speeding up neural radiance fields with thousands of tiny MLPs. In ICCV, pages 14335-14345, 2021." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 212, + 287, + 233 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 212, + 287, + 233 + ], + "spans": [ + { + "bbox": [ + 48, + 212, + 287, + 233 + ], + "type": "text", + "content": "[36] Gernot Riegler and Vladlen Koltun. Free view synthesis. In ECCV, 2020." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 236, + 287, + 257 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 236, + 287, + 257 + ], + "spans": [ + { + "bbox": [ + 48, + 236, + 287, + 257 + ], + "type": "text", + "content": "[37] Gernot Riegler and Vladlen Koltun. Stable view synthesis. In CVPR, pages 12216-12225, 2021." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 259, + 288, + 292 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 259, + 288, + 292 + ], + "spans": [ + { + "bbox": [ + 48, + 259, + 288, + 292 + ], + "type": "text", + "content": "[38] Darius Rückert, Linus Franke, and Marc Stamminger. ADOP: Approximate differentiable one-pixel point rendering. ACM Trans. Graph., 41(4):99:1-14, 2022." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 294, + 288, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 294, + 288, + 326 + ], + "spans": [ + { + "bbox": [ + 48, + 294, + 288, + 326 + ], + "type": "text", + "content": "[39] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3D-structure-aware neural scene representations. In NeurIPS, 2019." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 328, + 288, + 372 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 328, + 288, + 372 + ], + "spans": [ + { + "bbox": [ + 48, + 328, + 288, + 372 + ], + "type": "text", + "content": "[40] Vincent Sitzmann, Semon Rezchikov, William T. Freeman, Joshua B. Tenenbaum, and Fredo Durand. Light field networks: Neural scene representations with single-evaluation rendering. In NeurIPS, 2021." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 374, + 287, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 374, + 287, + 407 + ], + "spans": [ + { + "bbox": [ + 48, + 374, + 287, + 407 + ], + "type": "text", + "content": "[41] Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In CVPR, 2022." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 409, + 288, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 409, + 288, + 485 + ], + "spans": [ + { + "bbox": [ + 48, + 409, + 288, + 485 + ], + "type": "text", + "content": "[42] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, Yifan Wang, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, Tomas Simon, Christian Theobalt, Matthias Niessner, Jonathan T. Barron, Gordon Wetzstein, Michael Zollhöfer, and Vladislav Golyanik. Advances in neural rendering. Computer Graphics Forum, 41(2):703-735, 2022." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 487, + 288, + 519 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 487, + 288, + 519 + ], + "spans": [ + { + "bbox": [ + 48, + 487, + 288, + 519 + ], + "type": "text", + "content": "[43] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM Trans. Graph., 38(4):66:1-12, 2019." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 521, + 287, + 565 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 521, + 287, + 565 + ], + "spans": [ + { + "bbox": [ + 48, + 521, + 287, + 565 + ], + "type": "text", + "content": "[44] Huan Wang, Jian Ren, Zeng Huang, Kyle Olszewski, Mengei Chai, Yun Fu, and Sergey Tulyakov. R2L: Distilling neural radiance field to neural light field for efficient novel view synthesis. In ECCV, pages 612-629, 2022." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 567, + 288, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 567, + 288, + 610 + ], + "spans": [ + { + "bbox": [ + 48, + 567, + 288, + 610 + ], + "type": "text", + "content": "[45] Liao Wang, Jiakai Zhang, Xinhang Liu, Fuqiang Zhao, Yanshun Zhang, Yingliang Zhang, Minye Wu, Jingyi Yu, and Lan Xu. Fourier PlenOctrees for dynamic radiance field rendering in real-time. In CVPR, pages 13524-13534, 2022." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 613, + 288, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 613, + 288, + 656 + ], + "spans": [ + { + "bbox": [ + 48, + 613, + 288, + 656 + ], + "type": "text", + "content": "[46] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In NeurIPS, 2021." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 658, + 288, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 288, + 712 + ], + "type": "text", + "content": "[47] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P Srinivasan, Howard Zhou, Jonathan T Barron, Ricardo MartinBrualla, Noah Snavely, and Thomas Funkhouser. IBRNet: Learning multi-view image-based rendering. In CVPR, pages 4690-4699, 2021." + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 498 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 307, + 73, + 546, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 546, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 546, + 116 + ], + "type": "text", + "content": "[48] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4): 600-612, 2004." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 118, + 546, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 546, + 161 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 546, + 161 + ], + "type": "text", + "content": "[49] Suttisak Wizadwongsa, Pakkapon Phongthawee, Jiraphon Yenphraphai, and Supasorn Suwajanakorn. NeX: Real-time view synthesis with neural basis expansion. In CVPR, pages 8534-8543, 2021." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 163, + 547, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 163, + 547, + 206 + ], + "spans": [ + { + "bbox": [ + 307, + 163, + 547, + 206 + ], + "type": "text", + "content": "[50] Bangbang Yang, Yinda Zhang, Yinghao Xu, Yijin Li, Han Zhou, Hujun Bao, Guofeng Zhang, and Zhaopeng Cui. Learning object-compositional neural radiance field for editable scene rendering. In ICCV, pages 13779-13788, 2021." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 208, + 547, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 208, + 547, + 239 + ], + "spans": [ + { + "bbox": [ + 307, + 208, + 547, + 239 + ], + "type": "text", + "content": "[51] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In NeurIPS, pages 4805-4815, 2021." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 241, + 546, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 241, + 546, + 274 + ], + "spans": [ + { + "bbox": [ + 307, + 241, + 546, + 274 + ], + "type": "text", + "content": "[52] Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. PlenOctrees for real-time rendering of neural radiance fields. In ICCV, 2021." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 275, + 547, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 275, + 547, + 308 + ], + "spans": [ + { + "bbox": [ + 307, + 275, + 547, + 308 + ], + "type": "text", + "content": "[53] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. PixelNeRF: Neural radiance fields from one or few images. In CVPR, pages 4578-4587, 2021." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 309, + 546, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 309, + 546, + 342 + ], + "spans": [ + { + "bbox": [ + 307, + 309, + 546, + 342 + ], + "type": "text", + "content": "[54] Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In CVPR, 2022." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 343, + 546, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 343, + 546, + 376 + ], + "spans": [ + { + "bbox": [ + 307, + 343, + 546, + 376 + ], + "type": "text", + "content": "[55] Yu-Jie Yuan, Yang-Tian Sun, Yu-Kun Lai, Yuewen Ma, Rongfei Jia, and Lin Gao. NeRF-editing: geometry editing of neural radiance fields. In CVPR, pages 18353-18364, 2022." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 377, + 546, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 377, + 546, + 421 + ], + "spans": [ + { + "bbox": [ + 307, + 377, + 546, + 421 + ], + "type": "text", + "content": "[56] Jingbo Zhang, Xiaoyu Li, Ziyu Wan, Can Wang, and Jing Liao. FDNeRF: Few-shot dynamic neural radiance fields for face reconstruction and expression editing. In SIGGRAPH Asia Conference Papers, pages 12:1-9, 2022." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 422, + 546, + 454 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 422, + 546, + 454 + ], + "spans": [ + { + "bbox": [ + 307, + 422, + 546, + 454 + ], + "type": "text", + "content": "[57] Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun. NeRF++: Analyzing and improving neural radiance fields. arXiv:2010.07492, 2020." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 456, + 546, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 456, + 546, + 498 + ], + "spans": [ + { + "bbox": [ + 307, + 456, + 546, + 498 + ], + "type": "text", + "content": "[58] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018." + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "8316" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Neural Parametric Head Models/062fa4bb-0e36-4eeb-adb0-d6de77f80c20_content_list.json b/2023/Learning Neural Parametric Head Models/062fa4bb-0e36-4eeb-adb0-d6de77f80c20_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..beb25106a96f5b9b2cd00856ff731457542317a1 --- /dev/null +++ b/2023/Learning Neural Parametric Head Models/062fa4bb-0e36-4eeb-adb0-d6de77f80c20_content_list.json @@ -0,0 +1,1746 @@ +[ + { + "type": "text", + "text": "Learning Neural Parametric Head Models", + "text_level": 1, + "bbox": [ + 269, + 130, + 699, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Simon Giebenhain", + "bbox": [ + 147, + 179, + 303, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Tobias Kirschstein1", + "bbox": [ + 325, + 180, + 480, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Markos Georgopoulos2", + "bbox": [ + 504, + 180, + 689, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Martin Rünz²", + "bbox": [ + 710, + 180, + 821, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Lourdes Agapito3 Matthias Nießner1", + "bbox": [ + 325, + 198, + 635, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Technical University of Munich", + "bbox": [ + 156, + 222, + 416, + 241 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ Synthesia", + "bbox": [ + 457, + 222, + 544, + 241 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "3University College London", + "bbox": [ + 584, + 222, + 808, + 241 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6ca1b31732ac7f9fcdc41df89381446ff90634060669af3badbab1c199a76dac.jpg", + "image_caption": [ + "Training" + ], + "image_footnote": [], + "bbox": [ + 83, + 270, + 633, + 428 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/50c54bb43c73f8c52cc0a74bc25aae55896ac443c349ca961d91764bbfd9b072.jpg", + "image_caption": [ + "Inference", + "Figure 1. We propose to learn a neural parametric head model based on neural fields: first, we capture a large dataset of over 3700 high-fidelity head scans with varying shapes and expressions (left). We then non-rigidly register these scans to generate our training data. As a result of training, we obtain a disentangled latent that spans the space of shapes $\\mathbf{z}^{\\mathrm{id}}$ and expressions $\\mathbf{z}^{\\mathrm{ex}}$ (middle). At inference time, we can leverage the prior of our learned representation by fitting our model to a sparse input point cloud by solving for the latent codes (right)." + ], + "image_footnote": [], + "bbox": [ + 642, + 271, + 875, + 428 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 529, + 313, + 544 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We propose a novel 3D morphable model for complete human heads based on hybrid neural fields. At the core of our model lies a neural parametric representation that disentangles identity and expressions in disjoint latent spaces. To this end, we capture a person's identity in a canonical space as a signed distance field (SDF), and model facial expressions with a neural deformation field. In addition, our representation achieves high-fidelity local detail by introducing an ensemble of local fields centered around facial anchor points. To facilitate generalization, we train our model on a newly-captured dataset of over 3700 head scans from 203 different identities using a custom high-end 3D scanning setup. Our dataset significantly exceeds comparable existing datasets, both with respect to quality and completeness of geometry, averaging around 3.5M mesh faces per scan1. Finally, we demonstrate that our approach outperforms state-of-the-art methods in terms of fitting error and reconstruction quality.", + "bbox": [ + 73, + 561, + 473, + 834 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 501, + 529, + 630, + 544 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Human faces and heads lie at the core of human visual perception, and hence are key to creating digital replica of someones identity, likeliness, and appearance. In particular, 3D reconstruction of human heads from sparse inputs, such as point clouds, is central to a wide range of applications in the context of gaming, augmented and virtual reality, and digitization in our modern digital era. One of the most successful lines of research to address this challenging problem are parametric face models, which represent both shape identities and expressions featuring a low-dimensional parametric space. These Blendshape and 3D morphable models (3DMMs) have achieved incredible success, since they can be fitted to sparse inputs, regularize out noise, and provide a compact 3D representation. As a result, many practical settings could be realized, ranging from face tracking and 3D avatar creation to facial-reenactment applications [49].", + "bbox": [ + 496, + 555, + 892, + 796 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Traditionally, 3DMMs, are based on a low-rank approximation of the underlying 3D mesh geometry. To this end, a template mesh with fixed topology is non-rigidly registered to a series of 3D scans. From this template registration, a 3DMM can be computed using dimensionality", + "bbox": [ + 496, + 797, + 890, + 875 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "1We will publicly release our dataset along with a public benchmark for both neural head avatar construction as well as an evaluation on a hidden test-set for inference-time fitting.", + "bbox": [ + 75, + 862, + 470, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "Website: https://simongiebenhain.github.io/NPHM", + "bbox": [ + 522, + 887, + 877, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "21003", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "reduction methods such as principal component analysis (PCA). The quality of the resulting parametric space depends strongly on the quality of 3D scans, their registration, and the ability to disentangle identity and expression variations. While these PCA-based models exhibit excellent regularizing properties, their inherent limitation lies in their inability to represent local surface detail and the reliance on a template mesh of fixed topology, which inhibits the representation of diverse hair styles.", + "bbox": [ + 75, + 90, + 468, + 224 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this work, we propose neural parametric head models (NPHM), which represent complete human head geometry in a canonical space using an SDF, and morph the resulting geometry to posed space using a forward deformation field. By decoupling the human head representation into these two spaces, we are able to learn disentangled latent spaces - one of the core concepts of 3DMMs. Furthermore, we decompose the implicit geometry representation in canonical space into an ensemble of local MLPs. Each part is represented by a small MLP that operates in a local coordinate system centered around face keypoints. Additionally, we exploit face symmetry by sharing network weights of symmetric regions. This decomposition into separate parts imposes a strong geometry prior and helps to improve both generalization and provide higher levels of detail.", + "bbox": [ + 75, + 227, + 470, + 450 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In order to train our model, we capture a new high-fidelity head dataset with a high-end capture rig, which is composed of over 3700 3D head scans from 203 different people. After rigidly aligning all scans in a canonical coordinate system, we train our identity network on scans in canonical expression. In order to train the deformation network, we non-rigidly register each scan against a template mesh, which we in turn use as training data for our neural deformation model. At inference time, we can then fit our model to a given input point cloud by optimizing for the latent code parameters for both expression and identity. In a series of experiments, we demonstrate that our neural parametric model outperforms state-of-the-art models and can represent complete heads, including fine details.", + "bbox": [ + 75, + 454, + 468, + 664 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In sum, our contributions are as follows:", + "bbox": [ + 76, + 665, + 344, + 679 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We introduce a novel 3D dataset captured with a high-end capture rig, including over 3700 3D scans of human heads from 203 different identities.", + "- We propose a new neural-field-based parametric head representation, which facilitates high-fidelity local details through an ensemble of local implicit models.", + "- We demonstrate that our neural parametric head model can be robustly fit to range data, regularize out noise, and outperform existing models." + ], + "bbox": [ + 94, + 691, + 467, + 829 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 76, + 844, + 217, + 859 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3D morphable face and head models. The seminal work of Blanz and Vetter [1] was one of the first to introduce", + "bbox": [ + 75, + 869, + 467, + 898 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "a model-based approach to represent variations in human faces using PCA. Since the scans were captured in constrained environments, the expressiveness of the model was relatively limited. As such, improvements in the registration [29], as well as the use of data captured in the wild [3,4,31], led to significant advances. Thereafter, more advanced face models were introduced, including multilinear models of identity and expression [2,6], as well as models that combined linear shape spaces with articulated head parts [18], and localized approaches [23].", + "bbox": [ + 496, + 90, + 890, + 242 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "With the advent of deep learning, various works focused on extending face and head 3DMMs beyond linear spaces. To this end, convolutional neural network based architectures have been proposed to both regress the model parameters and reconstruct the face [16,37-39,42,43]. At the same time, graph convolutions [5, 14] and attention modules [11] have been proposed to model the head mesh geometry.", + "bbox": [ + 496, + 244, + 890, + 349 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Neural field representations. Neural field-based networks have emerged as an efficient way to implicitly represent 3D scenes. In contrast to explicit representations (e.g., meshes or voxel grids), neural fields are well-suited to represent geometries of arbitrary topology. Park et al. [26] proposed to represent a class-specific SDF with an MLP that is conditioned on a latent variable. Similarly, Mescheder et al. [21] implicitly define a surface as the decision boundary of a binary classifier and Mildenhall et al. [22] represent a radiance field using an MLP by supervising a photometric loss on the rendered images.", + "bbox": [ + 496, + 353, + 890, + 518 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Building upon these approaches, a series of works focus on modeling deformations. These methods use a separate network to model the deformations that occur in a sequence (e.g., [27, 28]), and have been successfully applied to animation of human bodies [17, 19] and heads [46]. Following this paradigm, a number of neural parametric models have been proposed for bodies [9, 24, 25], faces [45], and most closely related to our work—heads [32, 41, 44]. For instance, H3D-Net [32] and MoRF [41] proposed 3D generative models of heads, but do not account for expression-specific deformations. Recently, neural parametric models for human faces [44, 45] and bodies [9, 10, 24, 25] have explored combinations of SDFs and deformation fields, to produce complex non-linear deformations, while maintaining the flexibility of an implicit geometry representation. Our work is greatly inspired by these lines; however, the key difference is that we tailor our neural field representation specifically to human heads through an ensemble of local MLPs. Thereby, our work is also related to local conditioning methods for neural fields of arbitrary objects [8, 12, 13, 30], human bodies [25, 48] and faces [45]. Compared to ImFace [45], our model is more local, incorporates a symmetry prior, represents a complete head and models forward instead of backward deformations, which allows much faster animation.", + "bbox": [ + 496, + 523, + 890, + 898 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "21004", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/0941e01b06dd78b2f85ae548278129dd0274ac01021cc534de50588aa12de256.jpg", + "image_caption": [ + "Figure 2. 3D head scans from our newly-captured dataset: for each person (rows), we first capture a neutral pose, followed by several scans in different expressions (columns). Overall, our dataset has more than 3700 3D scans from 203 people." + ], + "image_footnote": [], + "bbox": [ + 94, + 80, + 880, + 372 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Dataset Acquisition", + "text_level": 1, + "bbox": [ + 76, + 426, + 266, + 444 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our dataset comprises 203 subjects, $29\\%$ female, and contains over 3700 3D scans; see Table. 1. Our 3D head scans show great levels of detail and completeness, as shown in Fig. 2. Additionally, we do not require participants to wear a bathing cap as in the FaceScape dataset [43], allowing for the capture of natural hair styles to a certain degree. See Fig. 3 for a visual comparison of our new dataset to other 3D face datasets.", + "bbox": [ + 75, + 452, + 470, + 571 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/465486c640c96d18ce9282f607d0d83e269e7d132a3dfec09b44e4cd7fccbcce.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Num. Subjects203 (144m/59f)
Total num. Scans3720
Num. Vertices/Scan≈ 1.5M
", + "bbox": [ + 135, + 584, + 411, + 641 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Table 1. Statistics of our 3D scanning dataset.", + "bbox": [ + 135, + 642, + 410, + 657 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Capture Setup", + "text_level": 1, + "bbox": [ + 76, + 680, + 227, + 698 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our setup is composed of two Artec Eva scanners [35], that are rotated $360^{\\circ}$ around a subject's head using a robotic actuator. Each scan takes only 6 seconds, which is crucial to keep involuntary, non-rigid facial movements to a minimum. The scanners operate at 16 FPS, and are aligned through the scanning sequence and fused into a single mesh; each fused scan contains approximately 1.5M vertices and 3.5M triangles. Each participant is asked to perform 23 different expressions, which are adopted from the FACS coded expression proposed in FaceWarehouse [7], see our supplemental for details. Importantly, we capture a neutral expression with the mouth open, which later serves as canonical expression, as described in Section 4.", + "bbox": [ + 75, + 704, + 468, + 900 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/e105f3c402734aca46a8136af04f1e4c264aa2d4bc9a3316226de8438c19a7bd.jpg", + "image_caption": [ + "Figure 3. Compared to recent multi-view stereo 3D face dataset, our data exhibits sharper details and less noise." + ], + "image_footnote": [], + "bbox": [ + 506, + 425, + 885, + 606 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Registration Pipeline", + "text_level": 1, + "bbox": [ + 500, + 667, + 697, + 684 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Registering all head scans against a common template is a key requirement to effectively train our parametric head model. First, we start with a rigid alignment into our canonical coordinate system; second, we non-rigidly register all scans to a common template.", + "bbox": [ + 496, + 691, + 893, + 767 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2.1 Rigid Alignment", + "text_level": 1, + "bbox": [ + 500, + 786, + 669, + 801 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We leverage 2D face landmark detectors to obtain a rigid transformation into the canonical coordinate system of the FLAME model [18]. To this end, we deploy the MediaPipe [20] face mesh detector and back-project a subset of 48 landmarks corresponding to iBUG68 annotations [33] to the 3D scan. Since not all viewing angles of the scanner's", + "bbox": [ + 496, + 809, + 893, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "21005", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/fd923e221b17398b1bf2a446fc428b86c6b029ee476d6e3194dc435aaedc8ff0.jpg", + "image_caption": [ + "Figure 4. Method overview: at the core of our neural parametric head model lies a neural field representation that parameterizes shape and expressions in disentangled latent spaces. Specifically, we propose a local MLP ensemble that is anchored at face keypoints (left). We train this model by leveraging a set of high-fidelity 3D scans from our newly-captured dataset comprising various expressions for identity (middle). In order to obtain the ground truth deformation samples, we non-rigidly register all scans to a common template (right)." + ], + "image_footnote": [], + "bbox": [ + 80, + 95, + 308, + 318 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/9424245bbe5b9479bb46ca4ca157a3c4c971496a83961511403338b64423a380.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 310, + 95, + 504, + 321 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/ba95cf74f9d90a9ae524b0c8d1e231fa40488e19ad8c3227fe0e5f94b8e7e9c2.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 506, + 95, + 890, + 321 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "trajectories are suited for 2D facial landmark detection, we instead use frontal renderings of the colored meshes, which yields robust detection quality. Note that the initial landmark detection is the only time we use the scanner's color images. We then calculate a similarity transform using [40] to transform the detected landmarks to the average face of FLAME.", + "bbox": [ + 75, + 414, + 472, + 518 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2.2 Non-Rigid Registration", + "text_level": 1, + "bbox": [ + 76, + 546, + 294, + 561 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As a non-rigid registration prior, we first constrain the non-rigid deformation to FLAME parameter space, before optimizing an offset for each vertex. Additionally, we backproject 2D hair segmentation masks obtained by FaRL [47] to mask out the respective areas of the scans.", + "bbox": [ + 75, + 573, + 468, + 650 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Initialization. Given the 23 expression scans $\\{S_j\\}_{j=1}^{23}$ of a subject, we jointly estimate identity parameters $\\mathbf{z}^{\\mathrm{id}} \\in \\mathbb{R}^{100}$ , expression parameters $\\{\\mathbf{z}_j^{\\mathrm{ex}}\\}_{j=1}^{23}$ , and jaw poses $\\{\\theta_j\\}_{j=1}^{23}$ of the FLAME model, as well as a shared scale $s \\in \\mathbb{R}$ and per-scan rotation and translation corrections $\\{R_j\\}_{j=1}^{23}$ and $\\{t_j\\}_{j=1}^{23}$ . Updating the initial similarity transform is crucial to obtaining a more consistent canonical alignment.", + "bbox": [ + 76, + 674, + 468, + 800 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Let $\\Phi_j$ denote all parameters affecting the $j$ -th FLAME model and $V_{\\Phi_j}$ its vertices. We jointly optimize for these parameters by minimizing", + "bbox": [ + 76, + 801, + 470, + 848 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\underset {\\Phi_ {1}, \\dots \\Phi_ {2 3}} {\\arg \\min } \\sum_ {j = 1} ^ {2 3} \\left[ \\lambda_ {l} \\| L _ {j} - \\hat {L} _ {j} \\| _ {1} + d \\left(V _ {\\Phi_ {j}}, \\mathcal {S} _ {j}\\right) + \\mathcal {R} \\left(\\Phi_ {j}\\right) \\right], \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 93, + 859, + 468, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $L_{j}\\in \\mathbb{R}^{68\\times 3}$ denotes the back-projected 3D landmarks, $\\hat{L}_j$ are the 3D landmarks from $V_{\\Phi_j}$ , $d(V_{\\Phi_j},S_j)$ is the mean point-to-plane distance from $V_{\\Phi_j}$ to its nearest neighbors in scan $S_{j}$ , and $\\mathcal{R}(\\Phi_j)$ regularizes FLAME parameters.", + "bbox": [ + 496, + 412, + 892, + 491 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Fine tuning. Once the initial alignment has been obtained, we upsample the mesh resolution by a factor of 16 for the face region, and perform non-rigid registration using ARAP [36] for each scan individually.", + "bbox": [ + 496, + 512, + 892, + 571 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Let $V$ be the upsampled vertices, which we aim to register to the scan $\\mathcal{S}$ . We seek vertex-specific offsets $\\{\\delta_v\\}_{v\\in V}$ and auxiliary, vertex-specific rotation $\\{R_v\\}_{v\\in V}$ from the ARAP term. Therefore, we solve", + "bbox": [ + 496, + 573, + 890, + 633 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\underset {\\left\\{ \\begin{array}{l} \\delta_ {v} \\\\ R _ {v} \\end{array} \\right\\} _ {v \\in V}} {\\arg \\min } \\sum_ {v \\in V} \\left[ d (\\hat {v}, \\mathcal {S}) + \\sum_ {u \\in \\mathcal {N} _ {v}} \\| R (v - u) - (\\hat {v} - \\hat {u}) \\| _ {2} ^ {2} \\right], \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 509, + 643, + 890, + 694 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "using the L-BFGS optimizer, where $\\hat{v} = v + \\delta_v, \\mathcal{N}_v$ denotes all neighboring vertices, and $d(\\hat{v}, S)$ is as before. See the supplemental for more details.", + "bbox": [ + 496, + 704, + 890, + 750 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Neural Parametric Head Models", + "text_level": 1, + "bbox": [ + 500, + 763, + 797, + 780 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our neural parametric head model separately represents geometry in a canonical space and facial expression as forward deformations; see Sections 4.1 and 4.2, respectively.", + "bbox": [ + 496, + 791, + 890, + 835 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Identity Representation", + "text_level": 1, + "bbox": [ + 500, + 845, + 718, + 863 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We represent a person's identity-specific geometry implicitly in its canonical space as a SDF. Compared to", + "bbox": [ + 496, + 869, + 890, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "21006", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "template-mesh-based approaches, this offers the necessary flexibility that is required to model a complete head with hair. In accordance with related work on human body modeling, e.g. [9,24,25], we choose a canonical expression with an open mouth to avoid topological issues. While a canonical coordinate system already reduces the dimensionality of the learning problem at hand, we further tailor our neural identity representation to the domain of human heads; as described below.", + "bbox": [ + 75, + 90, + 472, + 224 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1.1 Local Decomposition", + "text_level": 1, + "bbox": [ + 76, + 244, + 277, + 258 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Instead of globally conditioning the SDF network on a specific identity, we exploit the structure of the human face to impose two important geometric priors. First, we embrace the fixed composition of human faces by decomposing the SDF network into an ensemble of several smaller local MLP-based networks, which are defined around certain facial anchors, as shown in Fig. 4. Thereby, we reduce the learning problem into smaller, more tractable ones. We choose facial anchor points as a trade-off between the relevance of an area and spatial uniformity. Second, we exploit the symmetry of the face by only learning SDFs on the left side of the face, which are shared with the right half after flipping spatial coordinates accordingly. More specifically, we divide the face into $K = 2K_{\\mathrm{symm}} + K_{\\mathrm{middle}}$ regions, which are centered at facial anchor points $\\mathbf{a} \\in \\mathbb{R}^{K \\times 3}$ . We use $\\mathcal{M}$ to denote the index set anchors lying on the symmetry axis, and $\\mathcal{S}$ and $\\mathcal{S}^*$ for symmetric regions on the left and right side respectively, such that for $k \\in \\mathcal{S}$ there is a $k^* \\in \\mathcal{S}^*$ that corresponds to the symmetric anchor point.", + "bbox": [ + 75, + 268, + 472, + 554 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In addition to a global latent vector $\\mathbf{z}_{\\mathrm{glob}} \\in \\mathbb{R}^{d_{\\mathrm{glob}}}$ , the $k$ -th region is equipped with a local latent vector $\\mathbf{z}_k^{\\mathrm{id}} \\in \\mathbb{R}^{d_{\\mathrm{loc}}}$ . Together, the $k$ -th region is represented by a small MLP", + "bbox": [ + 76, + 555, + 468, + 599 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nf _ {k}: \\mathbb {R} ^ {d _ {\\text {g l o b}} + d _ {\\text {l o c}} + 3} \\rightarrow \\mathbb {R} \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 101, + 607, + 468, + 626 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\left(x, \\mathbf {z} _ {\\text {g l o b}} ^ {\\mathrm {i d}}, \\mathbf {z} _ {k} ^ {\\mathrm {i d}}\\right) \\mapsto \\operatorname {M L P} _ {\\theta_ {k}} \\left(\\left[ x - \\mathbf {a} _ {k}, \\mathbf {z} _ {\\text {g l o b}} ^ {\\mathrm {i d}}, \\mathbf {z} _ {k} ^ {\\mathrm {i d}} \\right]\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 127, + 628, + 468, + 648 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "that predicts SDF values for points $x \\in \\mathbb{R}^3$ , where $[\\cdot]$ denotes the concatenation operator.", + "bbox": [ + 76, + 657, + 468, + 686 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In order to exploit face symmetry, we share the network parameters and mirror the coordinates for each pair $(k,k^{*})$ of symmetric regions:", + "bbox": [ + 76, + 688, + 468, + 733 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nf _ {k ^ {*}} \\left(x, \\mathbf {z} _ {\\text {g l o b}} ^ {\\text {i d}}, \\mathbf {z} _ {k ^ {*}} ^ {\\text {i d}}\\right) := f _ {k} \\left(\\operatorname {f l i p} \\left(x - a _ {k ^ {*}}\\right), \\mathbf {z} _ {\\text {g l o b}} ^ {\\text {i d}}, \\mathbf {z} _ {k ^ {*}} ^ {\\text {i d}}\\right), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 96, + 741, + 468, + 761 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathrm{flip}(\\cdot)$ represents a flip of the coordinates along the face symmetry axis.", + "bbox": [ + 76, + 768, + 468, + 799 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1.2 Global Blending", + "text_level": 1, + "bbox": [ + 76, + 816, + 246, + 830 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In order to facilitate a decomposition that helps generalization, it is crucial that reliable anchor positions $\\mathbf{a}$ are available. To this end, we train a small $\\mathrm{MLP}_{\\mathrm{pos}}$ that predicts $\\mathbf{a}$ from the global latent $\\mathbf{z}_{\\mathrm{glob}}^{\\mathrm{id}}$ .", + "bbox": [ + 76, + 839, + 468, + 904 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Since each local SDF focuses on a specific semantic region of the face, as defined by the anchors $\\mathbf{a}$ , we additionally introduce $f_{0}(x,\\mathbf{z}_{\\mathrm{glob}}^{\\mathrm{id}},\\mathbf{z}_{0}^{\\mathrm{id}}) = \\mathrm{MLP}_{0}(x,\\mathbf{z}_{\\mathrm{glob}}^{\\mathrm{id}},\\mathbf{z}_{0}^{\\mathrm{id}})$ , which operates in the global coordinate system, hence covering all SDF values far away from any anchor in $\\mathbf{a}$ . To clarify the notation, we set $a_0\\coloneqq \\mathbf{0}\\in \\mathbb{R}^3$", + "bbox": [ + 496, + 90, + 890, + 180 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, we blend all local fields $f_{k}$ into a global field", + "bbox": [ + 517, + 181, + 870, + 196 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {F} _ {\\mathrm {i d}} (x) = \\sum_ {k = 0} ^ {K} w _ {k} \\left(x, a _ {k}\\right) f _ {k} \\left(x, \\mathbf {z} _ {\\text {g l o b}} ^ {\\mathrm {i d}}, \\mathbf {z} _ {k} ^ {\\mathrm {i d}}\\right), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 562, + 208, + 890, + 248 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "using Gaussian kernels, similar to [12,48], where", + "bbox": [ + 500, + 258, + 826, + 273 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nw _ {k} ^ {*} (x, a _ {k}) = \\left\\{ \\begin{array}{l l} e ^ {\\frac {- | | x - a | | _ {2}}{2 \\sigma}}, & \\text {i f} k > 0 \\\\ c, & \\text {i f} k = 0 \\end{array} \\right. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 282, + 890, + 324 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\text {a n d} w _ {k} (x, a _ {k}) = \\frac {w _ {k} ^ {*} (x , a _ {k})}{\\sum_ {k ^ {\\prime}} w _ {k ^ {\\prime}} ^ {*} (x , a _ {k ^ {\\prime}})} \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 581, + 335, + 890, + 369 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We use a fixed isotropic kernel with standard deviation $\\sigma$ and a constant response $c$ for $f_0$ .", + "bbox": [ + 500, + 373, + 890, + 402 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Expression Representation", + "text_level": 1, + "bbox": [ + 500, + 412, + 741, + 429 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In contrast to our local geometry representation, we model expressions only with a globally conditioned deformation field; e.g. a smile will effect the cheeks corners of the mouth and eye region. In this context, we define $\\mathbf{z}^{\\mathrm{ex}}\\in \\mathbb{R}^{d_{\\mathrm{ex}}}$ as a latent expression description. Since such a deformation field is defined in the ambient Euclidean space, it is crucial to additionally condition the deformation network with an identity feature. By imposing an information bottleneck on the latent expression description, the deformation network is then forced to learn a disentangled representation of expressions.", + "bbox": [ + 496, + 436, + 890, + 601 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "More formally, we model deformations using an MLP", + "bbox": [ + 517, + 602, + 877, + 617 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {F} _ {\\mathrm {e x}} \\left(x, \\mathbf {z} ^ {\\mathrm {e x}}, \\hat {\\mathbf {z}} ^ {\\mathrm {i d}}\\right): \\mathbb {R} ^ {d _ {\\mathrm {e x}} + d _ {\\mathrm {i d - e x}}} \\rightarrow \\mathbb {R} ^ {3}. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 581, + 627, + 890, + 645 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Rather than directly feeding all identity information into $\\mathcal{F}_{\\mathrm{ex}}$ directly, we first project the information to a lower dimensional representation", + "bbox": [ + 496, + 656, + 890, + 700 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\mathbf {Z}} ^ {\\mathrm {i d}} = W \\left[ \\mathbf {z} _ {\\text {g l o b}} ^ {\\mathrm {i d}}, \\mathbf {z} _ {0} ^ {\\mathrm {i d}}, \\dots \\mathbf {z} _ {K} ^ {\\mathrm {i d}}, \\mathbf {a} _ {1}, \\dots , \\mathbf {a} _ {K} \\right], \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 562, + 710, + 890, + 729 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "using a single linear layer $W$ , where $d_{\\mathrm{id - ex}}$ denotes the dimensionality of the interdependence of identity and expression.", + "bbox": [ + 496, + 739, + 890, + 784 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Training Strategy", + "text_level": 1, + "bbox": [ + 500, + 794, + 671, + 810 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our training strategy closely follows NPMs [24] and sequentially trains the identity and expression networks in an auto-decoder fashion.", + "bbox": [ + 496, + 816, + 890, + 861 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Identity Representation For the identity space, we jointly train latent codes $\\mathbf{Z}_j^{\\mathrm{id}}\\coloneqq \\{\\mathbf{z}_{\\mathrm{glob},j}^{\\mathrm{id}},\\mathbf{z}_{0,j}^{\\mathrm{id}},\\ldots ,\\mathbf{z}_{K,j}^{\\mathrm{id}}\\}$ for each $j$", + "bbox": [ + 500, + 869, + 890, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "21007", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "in the set of training indices $J$ and network parameters $\\theta_{\\mathrm{pos}}$ and $\\theta_0,\\dots ,\\theta_K$ , by minimizing", + "bbox": [ + 75, + 90, + 468, + 122 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {i d}} = \\sum_ {j \\in J} \\mathcal {L} _ {\\mathrm {I G R}} + \\lambda_ {a} \\| \\hat {\\mathbf {a}} _ {j} - \\mathbf {a} _ {j} \\| _ {2} ^ {2} + \\lambda_ {\\mathrm {s y}} \\mathcal {L} _ {\\mathrm {s y}} + \\lambda_ {\\mathrm {r e g}} ^ {\\mathrm {i d}} \\| \\mathbf {Z} _ {j} ^ {\\mathrm {i d}} \\| _ {2} ^ {2}, \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 84, + 132, + 468, + 165 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{\\mathrm{IGR}}$ is the loss introduced in [15] which enforces SDF values to be zero on the surface and contains an Eikonal term. This ensures consistency between surface normals and SDF gradients and is in similar spirit to [15, 34]. For training, we directly sample points and surface normals from our ground truth scans.", + "bbox": [ + 75, + 176, + 468, + 267 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Additionally, we supervise anchor predictions $\\mathbf{a}_j$ using the corresponding vertices from our registrations $\\hat{\\mathbf{a}}_j$ . The last two terms serve regularization purposes, where", + "bbox": [ + 75, + 267, + 468, + 313 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {s y}} = \\sum_ {k \\in \\mathcal {S}} \\| \\mathbf {z} _ {k} ^ {\\mathrm {i d}} - \\mathbf {z} _ {k *} ^ {\\mathrm {i d}} \\| _ {2} ^ {2} \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 191, + 323, + 468, + 354 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "enforces the local latent description of symmetric regions to be close, and the final term encourages a well-behaved distribution of both global and local latent descriptions centered around zero.", + "bbox": [ + 75, + 366, + 468, + 425 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Expression Representation Once the identity representation is learned, we optimize for network parameters $\\theta_{\\mathrm{ex}}$ , $W$ and latent expression codes, $\\{\\mathbf{z}_{j,l}^{\\mathrm{ex}}\\}_{j\\in J,l\\in L}$ , where $j$ indexes identity and $l$ indexes expressions. The deformation loss", + "bbox": [ + 75, + 434, + 468, + 494 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {e x}} = \\sum_ {\\substack {i, j \\in J, L \\\\ x \\in X _ {j, l}}} \\| \\mathcal {F} _ {\\mathrm {e x}} (x, \\mathbf {z} _ {j, l} ^ {\\mathrm {e x}}, \\hat {\\mathbf {z}} _ {j} ^ {\\mathrm {i d}}) - \\delta (x) _ {j, l} \\| _ {2} ^ {2} + \\lambda_ {\\mathrm {reg}} ^ {e x} \\| \\mathbf {z} _ {j, l} ^ {\\mathrm {e x}} \\| _ {2} ^ {2} \\tag{13}\n$$\n", + "text_format": "latex", + "bbox": [ + 84, + 505, + 468, + 549 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "directly supervises the deformation field using samples $x \\in X_{j,l}$ , which have been precomputed from the registration. See the supplemental for more details.", + "bbox": [ + 75, + 559, + 468, + 604 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Results", + "text_level": 1, + "bbox": [ + 76, + 618, + 161, + 633 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We aim to evaluate how well our method generalizes from our training dataset of 87 identities to unseen ones, and their unique expressions. Our test dataset consists of 6 female and 12 male identities in 23 expressions each. We fit our model and baselines to frontal single view depth maps, which are generated by rendering the unseen validation meshes and randomly sampling 5000 points. For ablations with respect to the number of points and noise level, as well as for a demonstration of real-world tracking with NPHM using a commodity depth sensor, we refer to the supplementary material. In our evaluation, we isolate the reconstruction of identity and expression in section 5.1 and 5.2, respectively.", + "bbox": [ + 75, + 643, + 468, + 839 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Mesh-Based Baselines. We evaluate against the Basel Face Model (BFM) and FLAME as representatives of existing template-based PCA-models. Furthermore, we compare against a PCA model with delta expressions [1] trained on", + "bbox": [ + 75, + 839, + 468, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "our registered meshes and a local variant thereof. For the local PCA model we utilize the same facial anchors as in NPHM to divide each neutral registered mesh into regions, which are separately represented by local PCA models. To obtain a final prediction we use the same blending scheme as described in Section 4.1.2. For all these models we additionally provide the 68 facial landmarks as input.", + "bbox": [ + 496, + 90, + 890, + 196 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implicit Baselines. We compare against ImFace [45] as a neural backward deformation baseline. We evaluate a version trained on the FaceScape dataset [43] and one that we train on our dataset using their preprocessing (denoted as ImFace*). Additionally, we compare against NPMs [24], isolating the effect of our proposed identity representation.", + "bbox": [ + 496, + 196, + 890, + 287 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metrics. To evaluate the quality of the reconstructions, we report $L_{1}$ -Chamfer distance, normal consistency (N. C.), and F-Score with a threshold of $1.5\\mathrm{mm}$ .", + "bbox": [ + 496, + 297, + 890, + 342 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Identity Reconstruction", + "text_level": 1, + "bbox": [ + 498, + 353, + 718, + 369 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To separately evaluate the quality of our identity space, we fit against a single neutral expression scan for each identity. These scans are aligned to each method's canonical coordinate system. We assist baselines that use a closed mouth in their canonical space, i.e. baselines not trained on our data, by optimizing these over all scans instead. More details on the optimization strategy for each model can be found in the supplemental.", + "bbox": [ + 496, + 377, + 890, + 497 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Figure 5 and Table 2 present qualitative and quantitative results, respectively. We observe that all neural field methods consistently achieve more faithful reconstructions and further note that the proposed local conditioning allows NPHM to reconstruct details and statistically unlikely elements more reliably.", + "bbox": [ + 496, + 498, + 890, + 589 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/b9d3f4e503c36972244a689e1d4fe7d7e11e8c67fb9de22b49a29c840ad6203d.jpg", + "table_caption": [], + "table_footnote": [ + "* trained on our data" + ], + "table_body": "
MethodL1-Chamfer ↓N. C. ↑F-Score@1.5 ↑
BFM [29]1.341e-20.9360.319
FLAME [18]0.640e-20.9310.530
Global PCA [1]0.563e-20.9540.571
Local PCA [1]0.416e-20.9600.756
ImFace [45]0,404e-20.9540.832
ImFace* [45]0.312e-20.9710.883
NPM [24]0.200e-20.9750.947
Ours0.182e-20.9780.954
", + "bbox": [ + 501, + 603, + 888, + 753 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Identity fitting to a single depth map in neutral expression.", + "bbox": [ + 500, + 773, + 888, + 787 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Expression Reconstruction", + "text_level": 1, + "bbox": [ + 500, + 816, + 741, + 832 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To evaluate each model's expression space, we fit it to multiple expressions of the same person with the task of recovering one identity code per subject and one expression code per expression. For the neural forward deformation", + "bbox": [ + 496, + 839, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "21008", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/79c50e93df5d9593a8f4c3a433816bdc3ad04f4a6d3269137c76766b55f47bcc.jpg", + "image_caption": [ + "Figure 5. Model fitting: at inference time, we fit our model to sparse, partial input point clouds from single depth map. We compare our method to widely-used state-of-the-art parametric face models, including FLAME [18], a local PCA [1], ImFace [45] and neural parametric models (NPM) [24]. Our parametric model has significantly more surface detail and covers the entire head, including the hair region." + ], + "image_footnote": [], + "bbox": [ + 80, + 88, + 893, + 481 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "models, NPM and NPHM, we utilize iterative root finding [10] to fit the expression codes. For simplicity, we keep the identity code fixed after fitting to the neutral scan. For all other models we jointly solve for expression and identity codes. Figure 6 and Table 3 show qualitative and quantitative comparisons with our baselines, respectively. Owing to the ability of backward deformations to directly connect the observed with the canonical space, ImFace reliably reconstructs expressions. Nevertheless, it still suffers from blurry reconstructions, compared to both NPM and NPHM.", + "bbox": [ + 75, + 556, + 468, + 708 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "See our supplemental for more details and an additional comparison of jointly fitting identity and expression when only a single depth observation is available.", + "bbox": [ + 75, + 710, + 468, + 755 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Ablations", + "text_level": 1, + "bbox": [ + 76, + 768, + 187, + 784 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We ablate two main contributions of the proposed identity representation, by fitting identity codes to a neutral scan without involving expressions. First, we analyze the effect of the number of regions $K$ of our ensemble, by comparing against NPM [24], which effectively would be an ensemble of size 1, and against versions with 12 and 26 regions and adjusted number of latent dimensions. Additionally, we", + "bbox": [ + 75, + 794, + 470, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/41d3b36bf15c4c56939979f23d699e526b6dd0e8f8ce79c945313b13a4c15f79.jpg", + "table_caption": [], + "table_footnote": [ + "* trained on our data", + "Table 3. Expression fitting on 23 single depth maps per person." + ], + "table_body": "
MethodL1-Chamfer ↓N. C. ↑F-Score@1.5 ↑
BFM [29]1.271e-20.9370.508
FLAME [18]0.679e-20.9240.351
Global PCA [1]0.515e-20.9560.606
Local PCA [1]0.535e-20.9500.641
ImFace [45]0.369e-20.9590.824
ImFace* [45]0.321e-20.9710.879
NPM [24]0.299e-20.9620.891
Ours0.272e-20.9690.913
", + "bbox": [ + 501, + 554, + 890, + 705 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "confirm the benefit of sharing weights for symmetric keypoints. Table 4 shows a quantitative evaluation of these two ablations supporting our design choices.", + "bbox": [ + 498, + 760, + 890, + 806 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.4. Limitations", + "text_level": 1, + "bbox": [ + 500, + 816, + 625, + 830 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In our experiments, we show that NPHM can reconstruct high-quality human heads; however, at the same time, we believe that there are still several limitations and opportunities for future work. For instance we focus solely on the", + "bbox": [ + 498, + 839, + 892, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "21009", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/89b42c2feda44afa20321f7ea434b03868b53f001c78323749b9042a74dac86d.jpg", + "image_caption": [ + "Figure 6. Comparison on fitting expressions to sparse input point clouds: from a sparse set of depth observations of different expressions from a frontal view (left), we compare FLAME [18], a local PCA [1], ImFace [45], neural parametric models (NPM) [24], and our method against the respective ground truth scans." + ], + "image_footnote": [], + "bbox": [ + 76, + 77, + 893, + 378 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/40c13d52cd50b0f3e99fc2d9648b23d0a89d17f2275ceb2d28a5b9fdf1c1489e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodL1-Chamfer ↓N. C. ↑F-Score@1.5 ↑
NPM [24]0.2540.9720.906
K=12, w/ sy.0.2890.9660.876
K=26, w/ sy.0.2370.9710.913
K=39, w/o sy.0.2300.9740.917
Ours0.2060.9760.938
", + "bbox": [ + 86, + 452, + 460, + 559 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 4. Effect of the number of anchor points $K$ and symmetry on identity reconstruction performance. NPM represents the extreme case of using exactly 1 anchor point. Note that to be consistent with the original version, NPM differs to the other models in both width and depth of the underlying MLP.", + "bbox": [ + 75, + 561, + 468, + 633 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "geometry of heads while omitting any information about appearance. This makes our model ill-suited for fitting to RGB images using dense photometric terms. Here, an interesting future avenue would be to explore learning appearance, anchored on top of the geometric base model. In fact, as part of our dataset we also provide the RGB frames captured during the 3D scanning process, which should facilitate learning such a texture model.", + "bbox": [ + 75, + 678, + 468, + 800 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Another limitation is that currently we do not capture open hair, which limits general diversity; however, compared to other existing face models such as 3D morphable models, we significantly expand the application domain by covering the entirety of the human head. In the future, we still would like to cover a broader range of hairstyles.", + "bbox": [ + 75, + 809, + 470, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 452, + 619, + 469 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have introduced neural parametric head models, a neural representation which disentangles identity and expressions of human heads, by representing geometry in canonical space and modelling expressions as forward deformations. For our identity representation we have proposed and validated a local representation that is tailored towards human head. To train our model, we introduce a new dataset of over 3700 high-fidelity 3D scans. Once trained, our model can be fitted to sparse input point clouds, for instance, captured by a commodity range sensor. Compared to existing methods, such as widely-used PCA-based techniques, our model represents significantly more detail while being able to regularize out noise of the underlying point cloud inputs. Overall, we believe that our method is an important step towards high-fidelity face capture and our newly-introduced dataset opens up opportunities to further explore learning priors for neural face models.", + "bbox": [ + 496, + 478, + 893, + 736 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 500, + 756, + 640, + 771 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work was supported by the ERC Starting Grant Scan2CAD (804724), the German Research Foundation (DFG) Grant \"Making Machine Learning on Static and Dynamic 3D Data Practical\", the German Research Foundation (DFG) Research Unit \"Learning and Simulation in Visual Computing\", and Synthesia. We would like to thank Maximilian Knörl and Tim Walter for the help with scanning, and Angela Dai for the video voiceover.", + "bbox": [ + 496, + 779, + 893, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "21010", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 187-194, 1999. 2, 6, 7, 8", + "[2] Timo Bolkart and Stefanie Wuhrer. A groupwise multilinear correspondence optimization for 3d faces. In Proceedings of the IEEE international conference on computer vision, pages 3604-3612, 2015. 2", + "[3] James Booth, Epameinondas Antonakos, Stylianos Ploumpis, George Trigeorgis, Yannis Panagakis, and Stefanos Zafeiriou. 3d face morphable models\" in-the-wild\". In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 48-57, 2017. 2", + "[4] James Booth, Anastasios Roussos, Stefanos Zafeiriou, Allan Ponniah, and David Dunaway. A 3d morphable model learnt from 10,000 faces. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5543-5552, 2016. 2", + "[5] Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Michael Bronstein, and Stefanos Zafeiriou. Neural 3d morphable models: Spiral convolutional networks for 3d shape representation learning and generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7213-7222, 2019. 2", + "[6] Alan Brunton, Timo Bolkart, and Stefanie Wuhrer. Multilinear wavelets: A statistical shape space for human faces. In European Conference on Computer Vision, pages 297-312. Springer, 2014. 2", + "[7] Chen Cao, Yanlin Weng, Shun Zhou, Yiying Tong, and Kun Zhou. Facewarehouse: A 3d facial expression database for visual computing. 20(3):413-425, mar 2014. 3", + "[8] Rohan Chabra, Jan E. Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe. Deep local shapes: Learning local sdf priors for detailed 3d reconstruction. In Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIX, page 608-625, Berlin, Heidelberg, 2020. Springer-Verlag. 2", + "[9] Xu Chen, Tianjian Jiang, Jie Song, Jinlong Yang, Michael J. Black, Andreas Geiger, and Otmar Hilliges. gdna: Towards generative detailed neural avatars. CoRR, abs/2201.04123, 2022. 2, 5", + "[10] Xu Chen, Yufeng Zheng, Michael J Black, Otmar Hilliges, and Andreas Geiger. Snarf: Differentiable forward skimming for animating non-rigid neural implicit shapes. In International Conference on Computer Vision (ICCV), 2021. 2, 7", + "[11] Zhixiang Chen and Tae-Kyun Kim. Learning feature aggregation for deep 3d morphable models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13164-13173, 2021. 2", + "[12] Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser. Local deep implicit functions for 3d shape. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4857-4866, 2020. 2, 5" + ], + "bbox": [ + 78, + 114, + 470, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Simon Giebenhain and Bastian Goldluecke. Air-nets: An attention-based framework for locally conditioned implicit representations. In 2021 International Conference on 3D Vision (3DV). IEEE, 2021. 2", + "[14] Shunwang Gong, Lei Chen, Michael Bronstein, and Stefanos Zafeiriou. Spiralnet++: A fast and highly efficient mesh convolution operator. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pages 0-0, 2019. 2", + "[15] Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman. Implicit geometric regularization for learning shapes. arXiv preprint arXiv:2002.10099, 2020. 6", + "[16] Ruilong Li, Karl Bladin, Yajie Zhao, Chinmay Chinara, Owen Ingraham, Pengda Xiang, Xinglei Ren, Pratusha Prasad, Bipin Kishore, Jun Xing, and Hao Li. Learning formation of physically-based face attributes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 2", + "[17] Ruilong Li, Julian Tanke, Minh Vo, Michael Zollhofer, Jurgen Gall, Angjoo Kanazawa, and Christoph Lassner. Tava: Template-free animatable volumetric actors. arXiv preprint arXiv:2206.08929, 2022. 2", + "[18] Tianye Li, Timo Bolkart, Michael. J. Black, Hao Li, and Javier Romero. Learning a model of facial shape and expression from 4D scans. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6):194:1-194:17, 2017. 2, 3, 6, 7, 8", + "[19] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural actor: Neural free-view synthesis of human actors with pose control. ACM Transactions on Graphics (TOG), 40(6):1-16, 2021. 2", + "[20] Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Yong, Juhyun Lee, Wan-Teh Chang, Wei Hua, Manfred Georg, and Matthias Grundmann. Mediapipe: A framework for perceiving and processing reality. In Third Workshop on Computer Vision for AR/VR at IEEE Computer Vision and Pattern Recognition (CVPR) 2019, 2019. 3", + "[21] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4460-4470, 2019. 2", + "[22] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 2", + "[23] Thomas Neumann, Kiran Varanasi, Stephan Wenger, Markus Wacker, Marcus Magnor, and Christian Theobalt. Sparse localized deformation components. ACM Trans. Graph., 32(6), nov 2013. 2", + "[24] Pablo Palafox, Aljaž Božić, Justus Thies, Matthias Nießner, and Angela Dai. Npms: Neural parametric models for 3d deformable shapes. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 12695–12705, October 2021. 2, 5, 6, 7, 8" + ], + "bbox": [ + 501, + 92, + 893, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "21011", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[25] Pablo Palafox, Nikolaos Sarafianos, Tony Tung, and Angela Dai. Spams: Structured implicit parametric models. CVPR, 2022. 2, 5", + "[26] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019. 2", + "[27] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5865-5874, 2021. 2", + "[28] Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Ricardo MartinBrualla, and Steven M. Seitz. Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields. ACM Trans. Graph., 40(6), dec 2021. 2", + "[29] Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. In 2009 sixth IEEE international conference on advanced video and signal based surveillance, pages 296-301. IEEE, 2009. 2, 6, 7", + "[30] Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In European Conference on Computer Vision, pages 523-540. Springer, 2020. 2", + "[31] Stylianos Ploumpis, Haoyang Wang, Nick Pears, William AP Smith, and Stefanos Zafeiriou. Combining 3d morphable models: A large scale face-and-head model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10934-10943, 2019. 2", + "[32] Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. H3d-net: Few-shot high-fidelity 3d head reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5620-5629, 2021. 2", + "[33] Christos Sagonas, Georgios Tzimiropoulos, Stefanos Zafeiriou, and Maja Pantic. 300 faces in-the-wild challenge: The first facial landmark localization challenge. In Proceedings of the IEEE international conference on computer vision workshops, pages 397-403, 2013. 3", + "[34] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462-7473, 2020. 6", + "[35] Janujah Sivanandan, Eugene Liscio, and P Eng. Assessing structured light 3d scanning using artec eva for injury documentation during autopsy. J Assoc Crime Scene Reconstr, 21:5-14, 2017. 3", + "[36] Olga Sorkine and Marc Alexa. As-Rigid-As-Possible Surface Modeling. In Alexander Belyaev and Michael Garland, editors, Geometry Processing. The Eurographics Association, 2007. 4", + "[37] Luan Tran, Feng Liu, and Xiaoming Liu. Towards high-fidelity nonlinear 3d face morphable model. In Proceedings" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1126-1135, 2019. 2", + "[38] Luan Tran and Xiaoming Liu. Nonlinear 3d face morphable model. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7346-7355, 2018. 2", + "[39] Luan Tran and Xiaoming Liu. On learning 3d face morphable model from in-the-wild images. IEEE transactions on pattern analysis and machine intelligence, 43(1):157-171, 2019. 2", + "[40] Shinji Umeyama. Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(04):376-380, 1991. 4", + "[41] Daoye Wang, Prashanth Chandran, Gaspard Zoss, Derek Bradley, and Paulo Gotardo. Morf: Morphable radiance fields for multiview neural head modeling. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1-9, 2022. 2", + "[42] Lizhen Wang, Zhiyua Chen, Tao Yu, Chenguang Ma, Liang Li, and Yebin Liu. Faceverse: a fine-grained and detail-controllable 3d face morphable model from a hybrid dataset. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR2022), June 2022. 2, 3", + "[43] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. Facescape: A large-scale high quality 3d face dataset and detailed riggable 3d face prediction. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 2, 3, 6", + "[44] Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and Christian Theobalt. i3dmm: Deep implicit 3d morphable model of human heads. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12803-12813, 2021. 2", + "[45] Mingwu Zheng, Hongyu Yang, Di Huang, and Liming Chen. Imface: A nonlinear 3d morphable face model with implicit neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 2, 6, 7, 8", + "[46] Yufeng Zheng, Victoria Fernández Abrevaya, Xu Chen, Marcel C. Bühler, Michael J. Black, and Otmar Hilliges. I M avatar: Implicit morphable head avatars from videos. CoRR, abs/2112.07471, 2021. 2", + "[47] Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, and Fang Wen. General facial representation learning in a visual-linguistic manner. arXiv preprint arXiv:2112.03109, 2021. 4", + "[48] Zerong Zheng, Han Huang, Tao Yu, Hongwen Zhang, Yandong Guo, and Yebin Liu. Structured local radiance fields for human avatar modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022. 2, 5", + "[49] Michael Zollhöfer, Justus Thies, Pablo Garrido, Derek Bradley, Thabo Beeler, Patrick Pérez, Marc Stamminger, Matthias Nießner, and Christian Theobalt. State of the art on monocular 3d face reconstruction, tracking, and applications. In Computer graphics forum, volume 37, pages 523-550. Wiley Online Library, 2018. 1" + ], + "bbox": [ + 503, + 92, + 890, + 896 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "21012", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Neural Parametric Head Models/062fa4bb-0e36-4eeb-adb0-d6de77f80c20_model.json b/2023/Learning Neural Parametric Head Models/062fa4bb-0e36-4eeb-adb0-d6de77f80c20_model.json new file mode 100644 index 0000000000000000000000000000000000000000..74e9ce03847947dc2a58393289233508d1c9f38e --- /dev/null +++ b/2023/Learning Neural Parametric Head Models/062fa4bb-0e36-4eeb-adb0-d6de77f80c20_model.json @@ -0,0 +1,2332 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.271, + 0.131, + 0.7, + 0.153 + ], + "angle": 0, + "content": "Learning Neural Parametric Head Models" + }, + { + "type": "text", + "bbox": [ + 0.148, + 0.18, + 0.305, + 0.198 + ], + "angle": 0, + "content": "Simon Giebenhain" + }, + { + "type": "text", + "bbox": [ + 0.326, + 0.181, + 0.482, + 0.198 + ], + "angle": 0, + "content": "Tobias Kirschstein1" + }, + { + "type": "text", + "bbox": [ + 0.505, + 0.181, + 0.691, + 0.199 + ], + "angle": 0, + "content": "Markos Georgopoulos2" + }, + { + "type": "text", + "bbox": [ + 0.712, + 0.181, + 0.822, + 0.198 + ], + "angle": 0, + "content": "Martin Rünz²" + }, + { + "type": "text", + "bbox": [ + 0.326, + 0.199, + 0.637, + 0.217 + ], + "angle": 0, + "content": "Lourdes Agapito3 Matthias Nießner1" + }, + { + "type": "text", + "bbox": [ + 0.157, + 0.223, + 0.418, + 0.242 + ], + "angle": 0, + "content": "\\(^{1}\\)Technical University of Munich" + }, + { + "type": "text", + "bbox": [ + 0.458, + 0.223, + 0.545, + 0.242 + ], + "angle": 0, + "content": "\\(^{2}\\)Synthesia" + }, + { + "type": "text", + "bbox": [ + 0.585, + 0.223, + 0.809, + 0.242 + ], + "angle": 0, + "content": "3University College London" + }, + { + "type": "image", + "bbox": [ + 0.084, + 0.271, + 0.634, + 0.429 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.328, + 0.433, + 0.398, + 0.449 + ], + "angle": 0, + "content": "Training" + }, + { + "type": "image", + "bbox": [ + 0.643, + 0.272, + 0.876, + 0.429 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.72, + 0.433, + 0.801, + 0.447 + ], + "angle": 0, + "content": "Inference" + }, + { + "type": "image_caption", + "bbox": [ + 0.075, + 0.456, + 0.894, + 0.514 + ], + "angle": 0, + "content": "Figure 1. We propose to learn a neural parametric head model based on neural fields: first, we capture a large dataset of over 3700 high-fidelity head scans with varying shapes and expressions (left). We then non-rigidly register these scans to generate our training data. As a result of training, we obtain a disentangled latent that spans the space of shapes \\(\\mathbf{z}^{\\mathrm{id}}\\) and expressions \\(\\mathbf{z}^{\\mathrm{ex}}\\) (middle). At inference time, we can leverage the prior of our learned representation by fitting our model to a sparse input point cloud by solving for the latent codes (right)." + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.53, + 0.314, + 0.545 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.562, + 0.474, + 0.835 + ], + "angle": 0, + "content": "We propose a novel 3D morphable model for complete human heads based on hybrid neural fields. At the core of our model lies a neural parametric representation that disentangles identity and expressions in disjoint latent spaces. To this end, we capture a person's identity in a canonical space as a signed distance field (SDF), and model facial expressions with a neural deformation field. In addition, our representation achieves high-fidelity local detail by introducing an ensemble of local fields centered around facial anchor points. To facilitate generalization, we train our model on a newly-captured dataset of over 3700 head scans from 203 different identities using a custom high-end 3D scanning setup. Our dataset significantly exceeds comparable existing datasets, both with respect to quality and completeness of geometry, averaging around 3.5M mesh faces per scan1. Finally, we demonstrate that our approach outperforms state-of-the-art methods in terms of fitting error and reconstruction quality." + }, + { + "type": "title", + "bbox": [ + 0.502, + 0.53, + 0.631, + 0.545 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.556, + 0.893, + 0.797 + ], + "angle": 0, + "content": "Human faces and heads lie at the core of human visual perception, and hence are key to creating digital replica of someones identity, likeliness, and appearance. In particular, 3D reconstruction of human heads from sparse inputs, such as point clouds, is central to a wide range of applications in the context of gaming, augmented and virtual reality, and digitization in our modern digital era. One of the most successful lines of research to address this challenging problem are parametric face models, which represent both shape identities and expressions featuring a low-dimensional parametric space. These Blendshape and 3D morphable models (3DMMs) have achieved incredible success, since they can be fitted to sparse inputs, regularize out noise, and provide a compact 3D representation. As a result, many practical settings could be realized, ranging from face tracking and 3D avatar creation to facial-reenactment applications [49]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.798, + 0.892, + 0.875 + ], + "angle": 0, + "content": "Traditionally, 3DMMs, are based on a low-rank approximation of the underlying 3D mesh geometry. To this end, a template mesh with fixed topology is non-rigidly registered to a series of 3D scans. From this template registration, a 3DMM can be computed using dimensionality" + }, + { + "type": "page_footnote", + "bbox": [ + 0.076, + 0.863, + 0.471, + 0.9 + ], + "angle": 0, + "content": "1We will publicly release our dataset along with a public benchmark for both neural head avatar construction as well as an evaluation on a hidden test-set for inference-time fitting." + }, + { + "type": "page_footnote", + "bbox": [ + 0.524, + 0.888, + 0.878, + 0.9 + ], + "angle": 0, + "content": "Website: https://simongiebenhain.github.io/NPHM" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "21003" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.226 + ], + "angle": 0, + "content": "reduction methods such as principal component analysis (PCA). The quality of the resulting parametric space depends strongly on the quality of 3D scans, their registration, and the ability to disentangle identity and expression variations. While these PCA-based models exhibit excellent regularizing properties, their inherent limitation lies in their inability to represent local surface detail and the reliance on a template mesh of fixed topology, which inhibits the representation of diverse hair styles." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.228, + 0.471, + 0.452 + ], + "angle": 0, + "content": "In this work, we propose neural parametric head models (NPHM), which represent complete human head geometry in a canonical space using an SDF, and morph the resulting geometry to posed space using a forward deformation field. By decoupling the human head representation into these two spaces, we are able to learn disentangled latent spaces - one of the core concepts of 3DMMs. Furthermore, we decompose the implicit geometry representation in canonical space into an ensemble of local MLPs. Each part is represented by a small MLP that operates in a local coordinate system centered around face keypoints. Additionally, we exploit face symmetry by sharing network weights of symmetric regions. This decomposition into separate parts imposes a strong geometry prior and helps to improve both generalization and provide higher levels of detail." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.455, + 0.47, + 0.665 + ], + "angle": 0, + "content": "In order to train our model, we capture a new high-fidelity head dataset with a high-end capture rig, which is composed of over 3700 3D head scans from 203 different people. After rigidly aligning all scans in a canonical coordinate system, we train our identity network on scans in canonical expression. In order to train the deformation network, we non-rigidly register each scan against a template mesh, which we in turn use as training data for our neural deformation model. At inference time, we can then fit our model to a given input point cloud by optimizing for the latent code parameters for both expression and identity. In a series of experiments, we demonstrate that our neural parametric model outperforms state-of-the-art models and can represent complete heads, including fine details." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.666, + 0.345, + 0.68 + ], + "angle": 0, + "content": "In sum, our contributions are as follows:" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.692, + 0.468, + 0.735 + ], + "angle": 0, + "content": "- We introduce a novel 3D dataset captured with a high-end capture rig, including over 3700 3D scans of human heads from 203 different identities." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.739, + 0.468, + 0.782 + ], + "angle": 0, + "content": "- We propose a new neural-field-based parametric head representation, which facilitates high-fidelity local details through an ensemble of local implicit models." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.786, + 0.468, + 0.83 + ], + "angle": 0, + "content": "- We demonstrate that our neural parametric head model can be robustly fit to range data, regularize out noise, and outperform existing models." + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.692, + 0.468, + 0.83 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.845, + 0.218, + 0.86 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.468, + 0.899 + ], + "angle": 0, + "content": "3D morphable face and head models. The seminal work of Blanz and Vetter [1] was one of the first to introduce" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.243 + ], + "angle": 0, + "content": "a model-based approach to represent variations in human faces using PCA. Since the scans were captured in constrained environments, the expressiveness of the model was relatively limited. As such, improvements in the registration [29], as well as the use of data captured in the wild [3,4,31], led to significant advances. Thereafter, more advanced face models were introduced, including multilinear models of identity and expression [2,6], as well as models that combined linear shape spaces with articulated head parts [18], and localized approaches [23]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.245, + 0.892, + 0.351 + ], + "angle": 0, + "content": "With the advent of deep learning, various works focused on extending face and head 3DMMs beyond linear spaces. To this end, convolutional neural network based architectures have been proposed to both regress the model parameters and reconstruct the face [16,37-39,42,43]. At the same time, graph convolutions [5, 14] and attention modules [11] have been proposed to model the head mesh geometry." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.354, + 0.892, + 0.52 + ], + "angle": 0, + "content": "Neural field representations. Neural field-based networks have emerged as an efficient way to implicitly represent 3D scenes. In contrast to explicit representations (e.g., meshes or voxel grids), neural fields are well-suited to represent geometries of arbitrary topology. Park et al. [26] proposed to represent a class-specific SDF with an MLP that is conditioned on a latent variable. Similarly, Mescheder et al. [21] implicitly define a surface as the decision boundary of a binary classifier and Mildenhall et al. [22] represent a radiance field using an MLP by supervising a photometric loss on the rendered images." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.524, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Building upon these approaches, a series of works focus on modeling deformations. These methods use a separate network to model the deformations that occur in a sequence (e.g., [27, 28]), and have been successfully applied to animation of human bodies [17, 19] and heads [46]. Following this paradigm, a number of neural parametric models have been proposed for bodies [9, 24, 25], faces [45], and most closely related to our work—heads [32, 41, 44]. For instance, H3D-Net [32] and MoRF [41] proposed 3D generative models of heads, but do not account for expression-specific deformations. Recently, neural parametric models for human faces [44, 45] and bodies [9, 10, 24, 25] have explored combinations of SDFs and deformation fields, to produce complex non-linear deformations, while maintaining the flexibility of an implicit geometry representation. Our work is greatly inspired by these lines; however, the key difference is that we tailor our neural field representation specifically to human heads through an ensemble of local MLPs. Thereby, our work is also related to local conditioning methods for neural fields of arbitrary objects [8, 12, 13, 30], human bodies [25, 48] and faces [45]. Compared to ImFace [45], our model is more local, incorporates a symmetry prior, represents a complete head and models forward instead of backward deformations, which allows much faster animation." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "21004" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.096, + 0.082, + 0.881, + 0.373 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.381, + 0.893, + 0.41 + ], + "angle": 0, + "content": "Figure 2. 3D head scans from our newly-captured dataset: for each person (rows), we first capture a neutral pose, followed by several scans in different expressions (columns). Overall, our dataset has more than 3700 3D scans from 203 people." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.427, + 0.267, + 0.445 + ], + "angle": 0, + "content": "3. Dataset Acquisition" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.453, + 0.471, + 0.573 + ], + "angle": 0, + "content": "Our dataset comprises 203 subjects, \\(29\\%\\) female, and contains over 3700 3D scans; see Table. 1. Our 3D head scans show great levels of detail and completeness, as shown in Fig. 2. Additionally, we do not require participants to wear a bathing cap as in the FaceScape dataset [43], allowing for the capture of natural hair styles to a certain degree. See Fig. 3 for a visual comparison of our new dataset to other 3D face datasets." + }, + { + "type": "table", + "bbox": [ + 0.136, + 0.585, + 0.413, + 0.642 + ], + "angle": 0, + "content": "
Num. Subjects203 (144m/59f)
Total num. Scans3720
Num. Vertices/Scan≈ 1.5M
" + }, + { + "type": "table_caption", + "bbox": [ + 0.137, + 0.643, + 0.411, + 0.658 + ], + "angle": 0, + "content": "Table 1. Statistics of our 3D scanning dataset." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.681, + 0.228, + 0.699 + ], + "angle": 0, + "content": "3.1. Capture Setup" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.705, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Our setup is composed of two Artec Eva scanners [35], that are rotated \\(360^{\\circ}\\) around a subject's head using a robotic actuator. Each scan takes only 6 seconds, which is crucial to keep involuntary, non-rigid facial movements to a minimum. The scanners operate at 16 FPS, and are aligned through the scanning sequence and fused into a single mesh; each fused scan contains approximately 1.5M vertices and 3.5M triangles. Each participant is asked to perform 23 different expressions, which are adopted from the FACS coded expression proposed in FaceWarehouse [7], see our supplemental for details. Importantly, we capture a neutral expression with the mouth open, which later serves as canonical expression, as described in Section 4." + }, + { + "type": "image", + "bbox": [ + 0.508, + 0.426, + 0.887, + 0.607 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.614, + 0.892, + 0.644 + ], + "angle": 0, + "content": "Figure 3. Compared to recent multi-view stereo 3D face dataset, our data exhibits sharper details and less noise." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.669, + 0.698, + 0.685 + ], + "angle": 0, + "content": "3.2. Registration Pipeline" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.692, + 0.894, + 0.768 + ], + "angle": 0, + "content": "Registering all head scans against a common template is a key requirement to effectively train our parametric head model. First, we start with a rigid alignment into our canonical coordinate system; second, we non-rigidly register all scans to a common template." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.787, + 0.671, + 0.803 + ], + "angle": 0, + "content": "3.2.1 Rigid Alignment" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.895, + 0.903 + ], + "angle": 0, + "content": "We leverage 2D face landmark detectors to obtain a rigid transformation into the canonical coordinate system of the FLAME model [18]. To this end, we deploy the MediaPipe [20] face mesh detector and back-project a subset of 48 landmarks corresponding to iBUG68 annotations [33] to the 3D scan. Since not all viewing angles of the scanner's" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21005" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.096, + 0.309, + 0.319 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.312, + 0.096, + 0.506, + 0.322 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.508, + 0.096, + 0.891, + 0.323 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.332, + 0.894, + 0.391 + ], + "angle": 0, + "content": "Figure 4. Method overview: at the core of our neural parametric head model lies a neural field representation that parameterizes shape and expressions in disentangled latent spaces. Specifically, we propose a local MLP ensemble that is anchored at face keypoints (left). We train this model by leveraging a set of high-fidelity 3D scans from our newly-captured dataset comprising various expressions for identity (middle). In order to obtain the ground truth deformation samples, we non-rigidly register all scans to a common template (right)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.415, + 0.473, + 0.52 + ], + "angle": 0, + "content": "trajectories are suited for 2D facial landmark detection, we instead use frontal renderings of the colored meshes, which yields robust detection quality. Note that the initial landmark detection is the only time we use the scanner's color images. We then calculate a similarity transform using [40] to transform the detected landmarks to the average face of FLAME." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.547, + 0.295, + 0.563 + ], + "angle": 0, + "content": "3.2.2 Non-Rigid Registration" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.574, + 0.47, + 0.651 + ], + "angle": 0, + "content": "As a non-rigid registration prior, we first constrain the non-rigid deformation to FLAME parameter space, before optimizing an offset for each vertex. Additionally, we backproject 2D hair segmentation masks obtained by FaRL [47] to mask out the respective areas of the scans." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.675, + 0.47, + 0.801 + ], + "angle": 0, + "content": "Initialization. Given the 23 expression scans \\(\\{S_j\\}_{j=1}^{23}\\) of a subject, we jointly estimate identity parameters \\(\\mathbf{z}^{\\mathrm{id}} \\in \\mathbb{R}^{100}\\), expression parameters \\(\\{\\mathbf{z}_j^{\\mathrm{ex}}\\}_{j=1}^{23}\\), and jaw poses \\(\\{\\theta_j\\}_{j=1}^{23}\\) of the FLAME model, as well as a shared scale \\(s \\in \\mathbb{R}\\) and per-scan rotation and translation corrections \\(\\{R_j\\}_{j=1}^{23}\\) and \\(\\{t_j\\}_{j=1}^{23}\\). Updating the initial similarity transform is crucial to obtaining a more consistent canonical alignment." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.802, + 0.471, + 0.849 + ], + "angle": 0, + "content": "Let \\(\\Phi_j\\) denote all parameters affecting the \\(j\\)-th FLAME model and \\(V_{\\Phi_j}\\) its vertices. We jointly optimize for these parameters by minimizing" + }, + { + "type": "equation", + "bbox": [ + 0.094, + 0.861, + 0.47, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\underset {\\Phi_ {1}, \\dots \\Phi_ {2 3}} {\\arg \\min } \\sum_ {j = 1} ^ {2 3} \\left[ \\lambda_ {l} \\| L _ {j} - \\hat {L} _ {j} \\| _ {1} + d \\left(V _ {\\Phi_ {j}}, \\mathcal {S} _ {j}\\right) + \\mathcal {R} \\left(\\Phi_ {j}\\right) \\right], \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.414, + 0.893, + 0.492 + ], + "angle": 0, + "content": "where \\(L_{j}\\in \\mathbb{R}^{68\\times 3}\\) denotes the back-projected 3D landmarks, \\(\\hat{L}_j\\) are the 3D landmarks from \\(V_{\\Phi_j}\\), \\(d(V_{\\Phi_j},S_j)\\) is the mean point-to-plane distance from \\(V_{\\Phi_j}\\) to its nearest neighbors in scan \\(S_{j}\\), and \\(\\mathcal{R}(\\Phi_j)\\) regularizes FLAME parameters." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.513, + 0.893, + 0.573 + ], + "angle": 0, + "content": "Fine tuning. Once the initial alignment has been obtained, we upsample the mesh resolution by a factor of 16 for the face region, and perform non-rigid registration using ARAP [36] for each scan individually." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.574, + 0.892, + 0.634 + ], + "angle": 0, + "content": "Let \\(V\\) be the upsampled vertices, which we aim to register to the scan \\(\\mathcal{S}\\). We seek vertex-specific offsets \\(\\{\\delta_v\\}_{v\\in V}\\) and auxiliary, vertex-specific rotation \\(\\{R_v\\}_{v\\in V}\\) from the ARAP term. Therefore, we solve" + }, + { + "type": "equation", + "bbox": [ + 0.51, + 0.644, + 0.892, + 0.695 + ], + "angle": 0, + "content": "\\[\n\\underset {\\left\\{ \\begin{array}{l} \\delta_ {v} \\\\ R _ {v} \\end{array} \\right\\} _ {v \\in V}} {\\arg \\min } \\sum_ {v \\in V} \\left[ d (\\hat {v}, \\mathcal {S}) + \\sum_ {u \\in \\mathcal {N} _ {v}} \\| R (v - u) - (\\hat {v} - \\hat {u}) \\| _ {2} ^ {2} \\right], \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.892, + 0.751 + ], + "angle": 0, + "content": "using the L-BFGS optimizer, where \\(\\hat{v} = v + \\delta_v, \\mathcal{N}_v\\) denotes all neighboring vertices, and \\(d(\\hat{v}, S)\\) is as before. See the supplemental for more details." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.765, + 0.798, + 0.781 + ], + "angle": 0, + "content": "4. Neural Parametric Head Models" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.792, + 0.892, + 0.837 + ], + "angle": 0, + "content": "Our neural parametric head model separately represents geometry in a canonical space and facial expression as forward deformations; see Sections 4.1 and 4.2, respectively." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.847, + 0.72, + 0.864 + ], + "angle": 0, + "content": "4.1. Identity Representation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.871, + 0.892, + 0.902 + ], + "angle": 0, + "content": "We represent a person's identity-specific geometry implicitly in its canonical space as a SDF. Compared to" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21006" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.226 + ], + "angle": 0, + "content": "template-mesh-based approaches, this offers the necessary flexibility that is required to model a complete head with hair. In accordance with related work on human body modeling, e.g. [9,24,25], we choose a canonical expression with an open mouth to avoid topological issues. While a canonical coordinate system already reduces the dimensionality of the learning problem at hand, we further tailor our neural identity representation to the domain of human heads; as described below." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.246, + 0.279, + 0.26 + ], + "angle": 0, + "content": "4.1.1 Local Decomposition" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.269, + 0.473, + 0.555 + ], + "angle": 0, + "content": "Instead of globally conditioning the SDF network on a specific identity, we exploit the structure of the human face to impose two important geometric priors. First, we embrace the fixed composition of human faces by decomposing the SDF network into an ensemble of several smaller local MLP-based networks, which are defined around certain facial anchors, as shown in Fig. 4. Thereby, we reduce the learning problem into smaller, more tractable ones. We choose facial anchor points as a trade-off between the relevance of an area and spatial uniformity. Second, we exploit the symmetry of the face by only learning SDFs on the left side of the face, which are shared with the right half after flipping spatial coordinates accordingly. More specifically, we divide the face into \\( K = 2K_{\\mathrm{symm}} + K_{\\mathrm{middle}} \\) regions, which are centered at facial anchor points \\( \\mathbf{a} \\in \\mathbb{R}^{K \\times 3} \\). We use \\( \\mathcal{M} \\) to denote the index set anchors lying on the symmetry axis, and \\( \\mathcal{S} \\) and \\( \\mathcal{S}^* \\) for symmetric regions on the left and right side respectively, such that for \\( k \\in \\mathcal{S} \\) there is a \\( k^* \\in \\mathcal{S}^* \\) that corresponds to the symmetric anchor point." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.556, + 0.47, + 0.601 + ], + "angle": 0, + "content": "In addition to a global latent vector \\(\\mathbf{z}_{\\mathrm{glob}} \\in \\mathbb{R}^{d_{\\mathrm{glob}}}\\), the \\(k\\)-th region is equipped with a local latent vector \\(\\mathbf{z}_k^{\\mathrm{id}} \\in \\mathbb{R}^{d_{\\mathrm{loc}}}\\). Together, the \\(k\\)-th region is represented by a small MLP" + }, + { + "type": "equation", + "bbox": [ + 0.102, + 0.608, + 0.469, + 0.627 + ], + "angle": 0, + "content": "\\[\nf _ {k}: \\mathbb {R} ^ {d _ {\\text {g l o b}} + d _ {\\text {l o c}} + 3} \\rightarrow \\mathbb {R} \\tag {3}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.128, + 0.63, + 0.469, + 0.649 + ], + "angle": 0, + "content": "\\[\n\\left(x, \\mathbf {z} _ {\\text {g l o b}} ^ {\\mathrm {i d}}, \\mathbf {z} _ {k} ^ {\\mathrm {i d}}\\right) \\mapsto \\operatorname {M L P} _ {\\theta_ {k}} \\left(\\left[ x - \\mathbf {a} _ {k}, \\mathbf {z} _ {\\text {g l o b}} ^ {\\mathrm {i d}}, \\mathbf {z} _ {k} ^ {\\mathrm {i d}} \\right]\\right), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.658, + 0.469, + 0.688 + ], + "angle": 0, + "content": "that predicts SDF values for points \\( x \\in \\mathbb{R}^3 \\), where \\( [\\cdot] \\) denotes the concatenation operator." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.689, + 0.469, + 0.734 + ], + "angle": 0, + "content": "In order to exploit face symmetry, we share the network parameters and mirror the coordinates for each pair \\((k,k^{*})\\) of symmetric regions:" + }, + { + "type": "equation", + "bbox": [ + 0.098, + 0.742, + 0.469, + 0.762 + ], + "angle": 0, + "content": "\\[\nf _ {k ^ {*}} \\left(x, \\mathbf {z} _ {\\text {g l o b}} ^ {\\text {i d}}, \\mathbf {z} _ {k ^ {*}} ^ {\\text {i d}}\\right) := f _ {k} \\left(\\operatorname {f l i p} \\left(x - a _ {k ^ {*}}\\right), \\mathbf {z} _ {\\text {g l o b}} ^ {\\text {i d}}, \\mathbf {z} _ {k ^ {*}} ^ {\\text {i d}}\\right), \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.769, + 0.469, + 0.8 + ], + "angle": 0, + "content": "where \\(\\mathrm{flip}(\\cdot)\\) represents a flip of the coordinates along the face symmetry axis." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.817, + 0.247, + 0.832 + ], + "angle": 0, + "content": "4.1.2 Global Blending" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.84, + 0.47, + 0.905 + ], + "angle": 0, + "content": "In order to facilitate a decomposition that helps generalization, it is crucial that reliable anchor positions \\( \\mathbf{a} \\) are available. To this end, we train a small \\( \\mathrm{MLP}_{\\mathrm{pos}} \\) that predicts \\( \\mathbf{a} \\) from the global latent \\( \\mathbf{z}_{\\mathrm{glob}}^{\\mathrm{id}} \\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.181 + ], + "angle": 0, + "content": "Since each local SDF focuses on a specific semantic region of the face, as defined by the anchors \\(\\mathbf{a}\\), we additionally introduce \\(f_{0}(x,\\mathbf{z}_{\\mathrm{glob}}^{\\mathrm{id}},\\mathbf{z}_{0}^{\\mathrm{id}}) = \\mathrm{MLP}_{0}(x,\\mathbf{z}_{\\mathrm{glob}}^{\\mathrm{id}},\\mathbf{z}_{0}^{\\mathrm{id}})\\), which operates in the global coordinate system, hence covering all SDF values far away from any anchor in \\(\\mathbf{a}\\). To clarify the notation, we set \\(a_0\\coloneqq \\mathbf{0}\\in \\mathbb{R}^3\\)" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.183, + 0.872, + 0.198 + ], + "angle": 0, + "content": "Finally, we blend all local fields \\( f_{k} \\) into a global field" + }, + { + "type": "equation", + "bbox": [ + 0.563, + 0.209, + 0.892, + 0.249 + ], + "angle": 0, + "content": "\\[\n\\mathcal {F} _ {\\mathrm {i d}} (x) = \\sum_ {k = 0} ^ {K} w _ {k} \\left(x, a _ {k}\\right) f _ {k} \\left(x, \\mathbf {z} _ {\\text {g l o b}} ^ {\\mathrm {i d}}, \\mathbf {z} _ {k} ^ {\\mathrm {i d}}\\right), \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.26, + 0.828, + 0.274 + ], + "angle": 0, + "content": "using Gaussian kernels, similar to [12,48], where" + }, + { + "type": "equation", + "bbox": [ + 0.568, + 0.284, + 0.892, + 0.325 + ], + "angle": 0, + "content": "\\[\nw _ {k} ^ {*} (x, a _ {k}) = \\left\\{ \\begin{array}{l l} e ^ {\\frac {- | | x - a | | _ {2}}{2 \\sigma}}, & \\text {i f} k > 0 \\\\ c, & \\text {i f} k = 0 \\end{array} \\right. \\tag {7}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.583, + 0.337, + 0.892, + 0.37 + ], + "angle": 0, + "content": "\\[\n\\text {a n d} w _ {k} (x, a _ {k}) = \\frac {w _ {k} ^ {*} (x , a _ {k})}{\\sum_ {k ^ {\\prime}} w _ {k ^ {\\prime}} ^ {*} (x , a _ {k ^ {\\prime}})} \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.374, + 0.891, + 0.404 + ], + "angle": 0, + "content": "We use a fixed isotropic kernel with standard deviation \\(\\sigma\\) and a constant response \\(c\\) for \\(f_0\\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.414, + 0.742, + 0.43 + ], + "angle": 0, + "content": "4.2. Expression Representation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.437, + 0.892, + 0.602 + ], + "angle": 0, + "content": "In contrast to our local geometry representation, we model expressions only with a globally conditioned deformation field; e.g. a smile will effect the cheeks corners of the mouth and eye region. In this context, we define \\(\\mathbf{z}^{\\mathrm{ex}}\\in \\mathbb{R}^{d_{\\mathrm{ex}}}\\) as a latent expression description. Since such a deformation field is defined in the ambient Euclidean space, it is crucial to additionally condition the deformation network with an identity feature. By imposing an information bottleneck on the latent expression description, the deformation network is then forced to learn a disentangled representation of expressions." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.603, + 0.878, + 0.618 + ], + "angle": 0, + "content": "More formally, we model deformations using an MLP" + }, + { + "type": "equation", + "bbox": [ + 0.583, + 0.628, + 0.892, + 0.646 + ], + "angle": 0, + "content": "\\[\n\\mathcal {F} _ {\\mathrm {e x}} \\left(x, \\mathbf {z} ^ {\\mathrm {e x}}, \\hat {\\mathbf {z}} ^ {\\mathrm {i d}}\\right): \\mathbb {R} ^ {d _ {\\mathrm {e x}} + d _ {\\mathrm {i d - e x}}} \\rightarrow \\mathbb {R} ^ {3}. \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.657, + 0.891, + 0.702 + ], + "angle": 0, + "content": "Rather than directly feeding all identity information into \\(\\mathcal{F}_{\\mathrm{ex}}\\) directly, we first project the information to a lower dimensional representation" + }, + { + "type": "equation", + "bbox": [ + 0.563, + 0.711, + 0.892, + 0.731 + ], + "angle": 0, + "content": "\\[\n\\hat {\\mathbf {Z}} ^ {\\mathrm {i d}} = W \\left[ \\mathbf {z} _ {\\text {g l o b}} ^ {\\mathrm {i d}}, \\mathbf {z} _ {0} ^ {\\mathrm {i d}}, \\dots \\mathbf {z} _ {K} ^ {\\mathrm {i d}}, \\mathbf {a} _ {1}, \\dots , \\mathbf {a} _ {K} \\right], \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.74, + 0.891, + 0.785 + ], + "angle": 0, + "content": "using a single linear layer \\( W \\), where \\( d_{\\mathrm{id - ex}} \\) denotes the dimensionality of the interdependence of identity and expression." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.795, + 0.673, + 0.811 + ], + "angle": 0, + "content": "4.3. Training Strategy" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.818, + 0.891, + 0.862 + ], + "angle": 0, + "content": "Our training strategy closely follows NPMs [24] and sequentially trains the identity and expression networks in an auto-decoder fashion." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.892, + 0.904 + ], + "angle": 0, + "content": "Identity Representation For the identity space, we jointly train latent codes \\(\\mathbf{Z}_j^{\\mathrm{id}}\\coloneqq \\{\\mathbf{z}_{\\mathrm{glob},j}^{\\mathrm{id}},\\mathbf{z}_{0,j}^{\\mathrm{id}},\\ldots ,\\mathbf{z}_{K,j}^{\\mathrm{id}}\\}\\) for each \\(j\\)" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "21007" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.123 + ], + "angle": 0, + "content": "in the set of training indices \\(J\\) and network parameters \\(\\theta_{\\mathrm{pos}}\\) and \\(\\theta_0,\\dots ,\\theta_K\\) , by minimizing" + }, + { + "type": "equation", + "bbox": [ + 0.085, + 0.133, + 0.47, + 0.166 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {i d}} = \\sum_ {j \\in J} \\mathcal {L} _ {\\mathrm {I G R}} + \\lambda_ {a} \\| \\hat {\\mathbf {a}} _ {j} - \\mathbf {a} _ {j} \\| _ {2} ^ {2} + \\lambda_ {\\mathrm {s y}} \\mathcal {L} _ {\\mathrm {s y}} + \\lambda_ {\\mathrm {r e g}} ^ {\\mathrm {i d}} \\| \\mathbf {Z} _ {j} ^ {\\mathrm {i d}} \\| _ {2} ^ {2}, \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.177, + 0.469, + 0.268 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{\\mathrm{IGR}}\\) is the loss introduced in [15] which enforces SDF values to be zero on the surface and contains an Eikonal term. This ensures consistency between surface normals and SDF gradients and is in similar spirit to [15, 34]. For training, we directly sample points and surface normals from our ground truth scans." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.268, + 0.469, + 0.314 + ], + "angle": 0, + "content": "Additionally, we supervise anchor predictions \\(\\mathbf{a}_j\\) using the corresponding vertices from our registrations \\(\\hat{\\mathbf{a}}_j\\). The last two terms serve regularization purposes, where" + }, + { + "type": "equation", + "bbox": [ + 0.192, + 0.324, + 0.469, + 0.356 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {s y}} = \\sum_ {k \\in \\mathcal {S}} \\| \\mathbf {z} _ {k} ^ {\\mathrm {i d}} - \\mathbf {z} _ {k *} ^ {\\mathrm {i d}} \\| _ {2} ^ {2} \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.367, + 0.469, + 0.426 + ], + "angle": 0, + "content": "enforces the local latent description of symmetric regions to be close, and the final term encourages a well-behaved distribution of both global and local latent descriptions centered around zero." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.435, + 0.469, + 0.495 + ], + "angle": 0, + "content": "Expression Representation Once the identity representation is learned, we optimize for network parameters \\(\\theta_{\\mathrm{ex}}\\), \\(W\\) and latent expression codes, \\(\\{\\mathbf{z}_{j,l}^{\\mathrm{ex}}\\}_{j\\in J,l\\in L}\\), where \\(j\\) indexes identity and \\(l\\) indexes expressions. The deformation loss" + }, + { + "type": "equation", + "bbox": [ + 0.085, + 0.506, + 0.469, + 0.55 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {e x}} = \\sum_ {\\substack {i, j \\in J, L \\\\ x \\in X _ {j, l}}} \\| \\mathcal {F} _ {\\mathrm {e x}} (x, \\mathbf {z} _ {j, l} ^ {\\mathrm {e x}}, \\hat {\\mathbf {z}} _ {j} ^ {\\mathrm {i d}}) - \\delta (x) _ {j, l} \\| _ {2} ^ {2} + \\lambda_ {\\mathrm {reg}} ^ {e x} \\| \\mathbf {z} _ {j, l} ^ {\\mathrm {e x}} \\| _ {2} ^ {2} \\tag{13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.56, + 0.469, + 0.606 + ], + "angle": 0, + "content": "directly supervises the deformation field using samples \\( x \\in X_{j,l} \\), which have been precomputed from the registration. See the supplemental for more details." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.619, + 0.163, + 0.635 + ], + "angle": 0, + "content": "5. Results" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.644, + 0.469, + 0.84 + ], + "angle": 0, + "content": "We aim to evaluate how well our method generalizes from our training dataset of 87 identities to unseen ones, and their unique expressions. Our test dataset consists of 6 female and 12 male identities in 23 expressions each. We fit our model and baselines to frontal single view depth maps, which are generated by rendering the unseen validation meshes and randomly sampling 5000 points. For ablations with respect to the number of points and noise level, as well as for a demonstration of real-world tracking with NPHM using a commodity depth sensor, we refer to the supplementary material. In our evaluation, we isolate the reconstruction of identity and expression in section 5.1 and 5.2, respectively." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.469, + 0.902 + ], + "angle": 0, + "content": "Mesh-Based Baselines. We evaluate against the Basel Face Model (BFM) and FLAME as representatives of existing template-based PCA-models. Furthermore, we compare against a PCA model with delta expressions [1] trained on" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.198 + ], + "angle": 0, + "content": "our registered meshes and a local variant thereof. For the local PCA model we utilize the same facial anchors as in NPHM to divide each neutral registered mesh into regions, which are separately represented by local PCA models. To obtain a final prediction we use the same blending scheme as described in Section 4.1.2. For all these models we additionally provide the 68 facial landmarks as input." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.198, + 0.892, + 0.289 + ], + "angle": 0, + "content": "Implicit Baselines. We compare against ImFace [45] as a neural backward deformation baseline. We evaluate a version trained on the FaceScape dataset [43] and one that we train on our dataset using their preprocessing (denoted as ImFace*). Additionally, we compare against NPMs [24], isolating the effect of our proposed identity representation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.298, + 0.892, + 0.343 + ], + "angle": 0, + "content": "Metrics. To evaluate the quality of the reconstructions, we report \\( L_{1} \\)-Chamfer distance, normal consistency (N. C.), and F-Score with a threshold of \\( 1.5\\mathrm{mm} \\)." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.354, + 0.719, + 0.37 + ], + "angle": 0, + "content": "5.1. Identity Reconstruction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.378, + 0.892, + 0.498 + ], + "angle": 0, + "content": "To separately evaluate the quality of our identity space, we fit against a single neutral expression scan for each identity. These scans are aligned to each method's canonical coordinate system. We assist baselines that use a closed mouth in their canonical space, i.e. baselines not trained on our data, by optimizing these over all scans instead. More details on the optimization strategy for each model can be found in the supplemental." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.499, + 0.892, + 0.59 + ], + "angle": 0, + "content": "Figure 5 and Table 2 present qualitative and quantitative results, respectively. We observe that all neural field methods consistently achieve more faithful reconstructions and further note that the proposed local conditioning allows NPHM to reconstruct details and statistically unlikely elements more reliably." + }, + { + "type": "table", + "bbox": [ + 0.503, + 0.604, + 0.89, + 0.755 + ], + "angle": 0, + "content": "
MethodL1-Chamfer ↓N. C. ↑F-Score@1.5 ↑
BFM [29]1.341e-20.9360.319
FLAME [18]0.640e-20.9310.530
Global PCA [1]0.563e-20.9540.571
Local PCA [1]0.416e-20.9600.756
ImFace [45]0,404e-20.9540.832
ImFace* [45]0.312e-20.9710.883
NPM [24]0.200e-20.9750.947
Ours0.182e-20.9780.954
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.507, + 0.756, + 0.634, + 0.768 + ], + "angle": 0, + "content": "* trained on our data" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.775, + 0.89, + 0.789 + ], + "angle": 0, + "content": "Table 2. Identity fitting to a single depth map in neutral expression." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.817, + 0.742, + 0.833 + ], + "angle": 0, + "content": "5.2. Expression Reconstruction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.892, + 0.901 + ], + "angle": 0, + "content": "To evaluate each model's expression space, we fit it to multiple expressions of the same person with the task of recovering one identity code per subject and one expression code per expression. For the neural forward deformation" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "21008" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.089, + 0.894, + 0.482 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.49, + 0.893, + 0.534 + ], + "angle": 0, + "content": "Figure 5. Model fitting: at inference time, we fit our model to sparse, partial input point clouds from single depth map. We compare our method to widely-used state-of-the-art parametric face models, including FLAME [18], a local PCA [1], ImFace [45] and neural parametric models (NPM) [24]. Our parametric model has significantly more surface detail and covers the entire head, including the hair region." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.558, + 0.47, + 0.709 + ], + "angle": 0, + "content": "models, NPM and NPHM, we utilize iterative root finding [10] to fit the expression codes. For simplicity, we keep the identity code fixed after fitting to the neutral scan. For all other models we jointly solve for expression and identity codes. Figure 6 and Table 3 show qualitative and quantitative comparisons with our baselines, respectively. Owing to the ability of backward deformations to directly connect the observed with the canonical space, ImFace reliably reconstructs expressions. Nevertheless, it still suffers from blurry reconstructions, compared to both NPM and NPHM." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.711, + 0.47, + 0.756 + ], + "angle": 0, + "content": "See our supplemental for more details and an additional comparison of jointly fitting identity and expression when only a single depth observation is available." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.77, + 0.188, + 0.785 + ], + "angle": 0, + "content": "5.3. Ablations" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.795, + 0.471, + 0.901 + ], + "angle": 0, + "content": "We ablate two main contributions of the proposed identity representation, by fitting identity codes to a neutral scan without involving expressions. First, we analyze the effect of the number of regions \\( K \\) of our ensemble, by comparing against NPM [24], which effectively would be an ensemble of size 1, and against versions with 12 and 26 regions and adjusted number of latent dimensions. Additionally, we" + }, + { + "type": "table", + "bbox": [ + 0.503, + 0.555, + 0.892, + 0.707 + ], + "angle": 0, + "content": "
MethodL1-Chamfer ↓N. C. ↑F-Score@1.5 ↑
BFM [29]1.271e-20.9370.508
FLAME [18]0.679e-20.9240.351
Global PCA [1]0.515e-20.9560.606
Local PCA [1]0.535e-20.9500.641
ImFace [45]0.369e-20.9590.824
ImFace* [45]0.321e-20.9710.879
NPM [24]0.299e-20.9620.891
Ours0.272e-20.9690.913
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.504, + 0.708, + 0.633, + 0.72 + ], + "angle": 0, + "content": "* trained on our data" + }, + { + "type": "table_footnote", + "bbox": [ + 0.507, + 0.727, + 0.882, + 0.741 + ], + "angle": 0, + "content": "Table 3. Expression fitting on 23 single depth maps per person." + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.708, + 0.882, + 0.741 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.761, + 0.892, + 0.807 + ], + "angle": 0, + "content": "confirm the benefit of sharing weights for symmetric keypoints. Table 4 shows a quantitative evaluation of these two ablations supporting our design choices." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.817, + 0.627, + 0.832 + ], + "angle": 0, + "content": "5.4. Limitations" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.84, + 0.893, + 0.901 + ], + "angle": 0, + "content": "In our experiments, we show that NPHM can reconstruct high-quality human heads; however, at the same time, we believe that there are still several limitations and opportunities for future work. For instance we focus solely on the" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21009" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.078, + 0.078, + 0.895, + 0.38 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.387, + 0.895, + 0.43 + ], + "angle": 0, + "content": "Figure 6. Comparison on fitting expressions to sparse input point clouds: from a sparse set of depth observations of different expressions from a frontal view (left), we compare FLAME [18], a local PCA [1], ImFace [45], neural parametric models (NPM) [24], and our method against the respective ground truth scans." + }, + { + "type": "table", + "bbox": [ + 0.088, + 0.453, + 0.462, + 0.56 + ], + "angle": 0, + "content": "
MethodL1-Chamfer ↓N. C. ↑F-Score@1.5 ↑
NPM [24]0.2540.9720.906
K=12, w/ sy.0.2890.9660.876
K=26, w/ sy.0.2370.9710.913
K=39, w/o sy.0.2300.9740.917
Ours0.2060.9760.938
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.563, + 0.47, + 0.634 + ], + "angle": 0, + "content": "Table 4. Effect of the number of anchor points \\( K \\) and symmetry on identity reconstruction performance. NPM represents the extreme case of using exactly 1 anchor point. Note that to be consistent with the original version, NPM differs to the other models in both width and depth of the underlying MLP." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.679, + 0.47, + 0.801 + ], + "angle": 0, + "content": "geometry of heads while omitting any information about appearance. This makes our model ill-suited for fitting to RGB images using dense photometric terms. Here, an interesting future avenue would be to explore learning appearance, anchored on top of the geometric base model. In fact, as part of our dataset we also provide the RGB frames captured during the 3D scanning process, which should facilitate learning such a texture model." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Another limitation is that currently we do not capture open hair, which limits general diversity; however, compared to other existing face models such as 3D morphable models, we significantly expand the application domain by covering the entirety of the human head. In the future, we still would like to cover a broader range of hairstyles." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.453, + 0.62, + 0.47 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.479, + 0.895, + 0.737 + ], + "angle": 0, + "content": "We have introduced neural parametric head models, a neural representation which disentangles identity and expressions of human heads, by representing geometry in canonical space and modelling expressions as forward deformations. For our identity representation we have proposed and validated a local representation that is tailored towards human head. To train our model, we introduce a new dataset of over 3700 high-fidelity 3D scans. Once trained, our model can be fitted to sparse input point clouds, for instance, captured by a commodity range sensor. Compared to existing methods, such as widely-used PCA-based techniques, our model represents significantly more detail while being able to regularize out noise of the underlying point cloud inputs. Overall, we believe that our method is an important step towards high-fidelity face capture and our newly-introduced dataset opens up opportunities to further explore learning priors for neural face models." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.757, + 0.642, + 0.772 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.895, + 0.903 + ], + "angle": 0, + "content": "This work was supported by the ERC Starting Grant Scan2CAD (804724), the German Research Foundation (DFG) Grant \"Making Machine Learning on Static and Dynamic 3D Data Practical\", the German Research Foundation (DFG) Research Unit \"Learning and Simulation in Visual Computing\", and Synthesia. We would like to thank Maximilian Knörl and Tim Walter for the help with scanning, and Angela Dai for the video voiceover." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21010" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.171 + ], + "angle": 0, + "content": "[1] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 187-194, 1999. 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.173, + 0.472, + 0.228 + ], + "angle": 0, + "content": "[2] Timo Bolkart and Stefanie Wuhrer. A groupwise multilinear correspondence optimization for 3d faces. In Proceedings of the IEEE international conference on computer vision, pages 3604-3612, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.231, + 0.471, + 0.301 + ], + "angle": 0, + "content": "[3] James Booth, Epameinondas Antonakos, Stylianos Ploumpis, George Trigeorgis, Yannis Panagakis, and Stefanos Zafeiriou. 3d face morphable models\" in-the-wild\". In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 48-57, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.302, + 0.471, + 0.37 + ], + "angle": 0, + "content": "[4] James Booth, Anastasios Roussos, Stefanos Zafeiriou, Allan Ponniah, and David Dunaway. A 3d morphable model learnt from 10,000 faces. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5543-5552, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.373, + 0.471, + 0.457 + ], + "angle": 0, + "content": "[5] Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Michael Bronstein, and Stefanos Zafeiriou. Neural 3d morphable models: Spiral convolutional networks for 3d shape representation learning and generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7213-7222, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.459, + 0.47, + 0.514 + ], + "angle": 0, + "content": "[6] Alan Brunton, Timo Bolkart, and Stefanie Wuhrer. Multilinear wavelets: A statistical shape space for human faces. In European Conference on Computer Vision, pages 297-312. Springer, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.517, + 0.47, + 0.558 + ], + "angle": 0, + "content": "[7] Chen Cao, Yanlin Weng, Shun Zhou, Yiying Tong, and Kun Zhou. Facewarehouse: A 3d facial expression database for visual computing. 20(3):413-425, mar 2014. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.56, + 0.47, + 0.657 + ], + "angle": 0, + "content": "[8] Rohan Chabra, Jan E. Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe. Deep local shapes: Learning local sdf priors for detailed 3d reconstruction. In Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIX, page 608-625, Berlin, Heidelberg, 2020. Springer-Verlag. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.659, + 0.469, + 0.713 + ], + "angle": 0, + "content": "[9] Xu Chen, Tianjian Jiang, Jie Song, Jinlong Yang, Michael J. Black, Andreas Geiger, and Otmar Hilliges. gdna: Towards generative detailed neural avatars. CoRR, abs/2201.04123, 2022. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.716, + 0.469, + 0.771 + ], + "angle": 0, + "content": "[10] Xu Chen, Yufeng Zheng, Michael J Black, Otmar Hilliges, and Andreas Geiger. Snarf: Differentiable forward skimming for animating non-rigid neural implicit shapes. In International Conference on Computer Vision (ICCV), 2021. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.774, + 0.471, + 0.829 + ], + "angle": 0, + "content": "[11] Zhixiang Chen and Tae-Kyun Kim. Learning feature aggregation for deep 3d morphable models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13164-13173, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.831, + 0.47, + 0.9 + ], + "angle": 0, + "content": "[12] Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser. Local deep implicit functions for 3d shape. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4857-4866, 2020. 2, 5" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.472, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[13] Simon Giebenhain and Bastian Goldluecke. Air-nets: An attention-based framework for locally conditioned implicit representations. In 2021 International Conference on 3D Vision (3DV). IEEE, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.892, + 0.216 + ], + "angle": 0, + "content": "[14] Shunwang Gong, Lei Chen, Michael Bronstein, and Stefanos Zafeiriou. Spiralnet++: A fast and highly efficient mesh convolution operator. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pages 0-0, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.219, + 0.892, + 0.259 + ], + "angle": 0, + "content": "[15] Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman. Implicit geometric regularization for learning shapes. arXiv preprint arXiv:2002.10099, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.261, + 0.894, + 0.342 + ], + "angle": 0, + "content": "[16] Ruilong Li, Karl Bladin, Yajie Zhao, Chinmay Chinara, Owen Ingraham, Pengda Xiang, Xinglei Ren, Pratusha Prasad, Bipin Kishore, Jun Xing, and Hao Li. Learning formation of physically-based face attributes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.344, + 0.892, + 0.398 + ], + "angle": 0, + "content": "[17] Ruilong Li, Julian Tanke, Minh Vo, Michael Zollhofer, Jurgen Gall, Angjoo Kanazawa, and Christoph Lassner. Tava: Template-free animatable volumetric actors. arXiv preprint arXiv:2206.08929, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.4, + 0.892, + 0.467 + ], + "angle": 0, + "content": "[18] Tianye Li, Timo Bolkart, Michael. J. Black, Hao Li, and Javier Romero. Learning a model of facial shape and expression from 4D scans. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6):194:1-194:17, 2017. 2, 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.469, + 0.892, + 0.537 + ], + "angle": 0, + "content": "[19] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural actor: Neural free-view synthesis of human actors with pose control. ACM Transactions on Graphics (TOG), 40(6):1-16, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.539, + 0.892, + 0.635 + ], + "angle": 0, + "content": "[20] Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Yong, Juhyun Lee, Wan-Teh Chang, Wei Hua, Manfred Georg, and Matthias Grundmann. Mediapipe: A framework for perceiving and processing reality. In Third Workshop on Computer Vision for AR/VR at IEEE Computer Vision and Pattern Recognition (CVPR) 2019, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.637, + 0.892, + 0.705 + ], + "angle": 0, + "content": "[21] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4460-4470, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.707, + 0.892, + 0.773 + ], + "angle": 0, + "content": "[22] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.776, + 0.892, + 0.83 + ], + "angle": 0, + "content": "[23] Thomas Neumann, Kiran Varanasi, Stephan Wenger, Markus Wacker, Marcus Magnor, and Christian Theobalt. Sparse localized deformation components. ACM Trans. Graph., 32(6), nov 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.832, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[24] Pablo Palafox, Aljaž Božić, Justus Thies, Matthias Nießner, and Angela Dai. Npms: Neural parametric models for 3d deformable shapes. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 12695–12705, October 2021. 2, 5, 6, 7, 8" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "21011" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.133 + ], + "angle": 0, + "content": "[25] Pablo Palafox, Nikolaos Sarafianos, Tony Tung, and Angela Dai. Spams: Structured implicit parametric models. CVPR, 2022. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.136, + 0.47, + 0.205 + ], + "angle": 0, + "content": "[26] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.207, + 0.471, + 0.276 + ], + "angle": 0, + "content": "[27] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5865-5874, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.278, + 0.469, + 0.347 + ], + "angle": 0, + "content": "[28] Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Ricardo MartinBrualla, and Steven M. Seitz. Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields. ACM Trans. Graph., 40(6), dec 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.349, + 0.469, + 0.417 + ], + "angle": 0, + "content": "[29] Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. In 2009 sixth IEEE international conference on advanced video and signal based surveillance, pages 296-301. IEEE, 2009. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.42, + 0.469, + 0.475 + ], + "angle": 0, + "content": "[30] Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In European Conference on Computer Vision, pages 523-540. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.477, + 0.469, + 0.557 + ], + "angle": 0, + "content": "[31] Stylianos Ploumpis, Haoyang Wang, Nick Pears, William AP Smith, and Stefanos Zafeiriou. Combining 3d morphable models: A large scale face-and-head model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10934-10943, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.561, + 0.469, + 0.63 + ], + "angle": 0, + "content": "[32] Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. H3d-net: Few-shot high-fidelity 3d head reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5620-5629, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.632, + 0.469, + 0.7 + ], + "angle": 0, + "content": "[33] Christos Sagonas, Georgios Tzimiropoulos, Stefanos Zafeiriou, and Maja Pantic. 300 faces in-the-wild challenge: The first facial landmark localization challenge. In Proceedings of the IEEE international conference on computer vision workshops, pages 397-403, 2013. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.703, + 0.469, + 0.758 + ], + "angle": 0, + "content": "[34] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462-7473, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.76, + 0.469, + 0.813 + ], + "angle": 0, + "content": "[35] Janujah Sivanandan, Eugene Liscio, and P Eng. Assessing structured light 3d scanning using artec eva for injury documentation during autopsy. J Assoc Crime Scene Reconstr, 21:5-14, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.817, + 0.469, + 0.87 + ], + "angle": 0, + "content": "[36] Olga Sorkine and Marc Alexa. As-Rigid-As-Possible Surface Modeling. In Alexander Belyaev and Michael Garland, editors, Geometry Processing. The Eurographics Association, 2007. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.874, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[37] Luan Tran, Feng Liu, and Xiaoming Liu. Towards high-fidelity nonlinear 3d face morphable model. In Proceedings" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.471, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1126-1135, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.892, + 0.162 + ], + "angle": 0, + "content": "[38] Luan Tran and Xiaoming Liu. Nonlinear 3d face morphable model. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7346-7355, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.163, + 0.892, + 0.216 + ], + "angle": 0, + "content": "[39] Luan Tran and Xiaoming Liu. On learning 3d face morphable model from in-the-wild images. IEEE transactions on pattern analysis and machine intelligence, 43(1):157-171, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.218, + 0.892, + 0.272 + ], + "angle": 0, + "content": "[40] Shinji Umeyama. Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(04):376-380, 1991. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.274, + 0.892, + 0.328 + ], + "angle": 0, + "content": "[41] Daoye Wang, Prashanth Chandran, Gaspard Zoss, Derek Bradley, and Paulo Gotardo. Morf: Morphable radiance fields for multiview neural head modeling. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1-9, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.329, + 0.892, + 0.397 + ], + "angle": 0, + "content": "[42] Lizhen Wang, Zhiyua Chen, Tao Yu, Chenguang Ma, Liang Li, and Yebin Liu. Faceverse: a fine-grained and detail-controllable 3d face morphable model from a hybrid dataset. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR2022), June 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.399, + 0.892, + 0.467 + ], + "angle": 0, + "content": "[43] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. Facescape: A large-scale high quality 3d face dataset and detailed riggable 3d face prediction. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.468, + 0.892, + 0.55 + ], + "angle": 0, + "content": "[44] Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and Christian Theobalt. i3dmm: Deep implicit 3d morphable model of human heads. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12803-12813, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.551, + 0.892, + 0.619 + ], + "angle": 0, + "content": "[45] Mingwu Zheng, Hongyu Yang, Di Huang, and Liming Chen. Imface: A nonlinear 3d morphable face model with implicit neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.621, + 0.892, + 0.674 + ], + "angle": 0, + "content": "[46] Yufeng Zheng, Victoria Fernández Abrevaya, Xu Chen, Marcel C. Bühler, Michael J. Black, and Otmar Hilliges. I M avatar: Implicit morphable head avatars from videos. CoRR, abs/2112.07471, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.676, + 0.892, + 0.743 + ], + "angle": 0, + "content": "[47] Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, and Fang Wen. General facial representation learning in a visual-linguistic manner. arXiv preprint arXiv:2112.03109, 2021. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.745, + 0.892, + 0.813 + ], + "angle": 0, + "content": "[48] Zerong Zheng, Han Huang, Tao Yu, Hongwen Zhang, Yandong Guo, and Yebin Liu. Structured local radiance fields for human avatar modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.814, + 0.892, + 0.897 + ], + "angle": 0, + "content": "[49] Michael Zollhöfer, Justus Thies, Pablo Garrido, Derek Bradley, Thabo Beeler, Patrick Pérez, Marc Stamminger, Matthias Nießner, and Christian Theobalt. State of the art on monocular 3d face reconstruction, tracking, and applications. In Computer graphics forum, volume 37, pages 523-550. 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We propose to learn a neural parametric head model based on neural fields: first, we capture a large dataset of over 3700 high-fidelity head scans with varying shapes and expressions (left). We then non-rigidly register these scans to generate our training data. As a result of training, we obtain a disentangled latent that spans the space of shapes $\mathbf{z}^{\mathrm{id}}$ and expressions $\mathbf{z}^{\mathrm{ex}}$ (middle). At inference time, we can leverage the prior of our learned representation by fitting our model to a sparse input point cloud by solving for the latent codes (right). + +# Abstract + +We propose a novel 3D morphable model for complete human heads based on hybrid neural fields. At the core of our model lies a neural parametric representation that disentangles identity and expressions in disjoint latent spaces. To this end, we capture a person's identity in a canonical space as a signed distance field (SDF), and model facial expressions with a neural deformation field. In addition, our representation achieves high-fidelity local detail by introducing an ensemble of local fields centered around facial anchor points. To facilitate generalization, we train our model on a newly-captured dataset of over 3700 head scans from 203 different identities using a custom high-end 3D scanning setup. Our dataset significantly exceeds comparable existing datasets, both with respect to quality and completeness of geometry, averaging around 3.5M mesh faces per scan1. Finally, we demonstrate that our approach outperforms state-of-the-art methods in terms of fitting error and reconstruction quality. + +# 1. Introduction + +Human faces and heads lie at the core of human visual perception, and hence are key to creating digital replica of someones identity, likeliness, and appearance. In particular, 3D reconstruction of human heads from sparse inputs, such as point clouds, is central to a wide range of applications in the context of gaming, augmented and virtual reality, and digitization in our modern digital era. One of the most successful lines of research to address this challenging problem are parametric face models, which represent both shape identities and expressions featuring a low-dimensional parametric space. These Blendshape and 3D morphable models (3DMMs) have achieved incredible success, since they can be fitted to sparse inputs, regularize out noise, and provide a compact 3D representation. As a result, many practical settings could be realized, ranging from face tracking and 3D avatar creation to facial-reenactment applications [49]. + +Traditionally, 3DMMs, are based on a low-rank approximation of the underlying 3D mesh geometry. To this end, a template mesh with fixed topology is non-rigidly registered to a series of 3D scans. From this template registration, a 3DMM can be computed using dimensionality + +reduction methods such as principal component analysis (PCA). The quality of the resulting parametric space depends strongly on the quality of 3D scans, their registration, and the ability to disentangle identity and expression variations. While these PCA-based models exhibit excellent regularizing properties, their inherent limitation lies in their inability to represent local surface detail and the reliance on a template mesh of fixed topology, which inhibits the representation of diverse hair styles. + +In this work, we propose neural parametric head models (NPHM), which represent complete human head geometry in a canonical space using an SDF, and morph the resulting geometry to posed space using a forward deformation field. By decoupling the human head representation into these two spaces, we are able to learn disentangled latent spaces - one of the core concepts of 3DMMs. Furthermore, we decompose the implicit geometry representation in canonical space into an ensemble of local MLPs. Each part is represented by a small MLP that operates in a local coordinate system centered around face keypoints. Additionally, we exploit face symmetry by sharing network weights of symmetric regions. This decomposition into separate parts imposes a strong geometry prior and helps to improve both generalization and provide higher levels of detail. + +In order to train our model, we capture a new high-fidelity head dataset with a high-end capture rig, which is composed of over 3700 3D head scans from 203 different people. After rigidly aligning all scans in a canonical coordinate system, we train our identity network on scans in canonical expression. In order to train the deformation network, we non-rigidly register each scan against a template mesh, which we in turn use as training data for our neural deformation model. At inference time, we can then fit our model to a given input point cloud by optimizing for the latent code parameters for both expression and identity. In a series of experiments, we demonstrate that our neural parametric model outperforms state-of-the-art models and can represent complete heads, including fine details. + +In sum, our contributions are as follows: + +- We introduce a novel 3D dataset captured with a high-end capture rig, including over 3700 3D scans of human heads from 203 different identities. +- We propose a new neural-field-based parametric head representation, which facilitates high-fidelity local details through an ensemble of local implicit models. +- We demonstrate that our neural parametric head model can be robustly fit to range data, regularize out noise, and outperform existing models. + +# 2. Related Work + +3D morphable face and head models. The seminal work of Blanz and Vetter [1] was one of the first to introduce + +a model-based approach to represent variations in human faces using PCA. Since the scans were captured in constrained environments, the expressiveness of the model was relatively limited. As such, improvements in the registration [29], as well as the use of data captured in the wild [3,4,31], led to significant advances. Thereafter, more advanced face models were introduced, including multilinear models of identity and expression [2,6], as well as models that combined linear shape spaces with articulated head parts [18], and localized approaches [23]. + +With the advent of deep learning, various works focused on extending face and head 3DMMs beyond linear spaces. To this end, convolutional neural network based architectures have been proposed to both regress the model parameters and reconstruct the face [16,37-39,42,43]. At the same time, graph convolutions [5, 14] and attention modules [11] have been proposed to model the head mesh geometry. + +Neural field representations. Neural field-based networks have emerged as an efficient way to implicitly represent 3D scenes. In contrast to explicit representations (e.g., meshes or voxel grids), neural fields are well-suited to represent geometries of arbitrary topology. Park et al. [26] proposed to represent a class-specific SDF with an MLP that is conditioned on a latent variable. Similarly, Mescheder et al. [21] implicitly define a surface as the decision boundary of a binary classifier and Mildenhall et al. [22] represent a radiance field using an MLP by supervising a photometric loss on the rendered images. + +Building upon these approaches, a series of works focus on modeling deformations. These methods use a separate network to model the deformations that occur in a sequence (e.g., [27, 28]), and have been successfully applied to animation of human bodies [17, 19] and heads [46]. Following this paradigm, a number of neural parametric models have been proposed for bodies [9, 24, 25], faces [45], and most closely related to our work—heads [32, 41, 44]. For instance, H3D-Net [32] and MoRF [41] proposed 3D generative models of heads, but do not account for expression-specific deformations. Recently, neural parametric models for human faces [44, 45] and bodies [9, 10, 24, 25] have explored combinations of SDFs and deformation fields, to produce complex non-linear deformations, while maintaining the flexibility of an implicit geometry representation. Our work is greatly inspired by these lines; however, the key difference is that we tailor our neural field representation specifically to human heads through an ensemble of local MLPs. Thereby, our work is also related to local conditioning methods for neural fields of arbitrary objects [8, 12, 13, 30], human bodies [25, 48] and faces [45]. Compared to ImFace [45], our model is more local, incorporates a symmetry prior, represents a complete head and models forward instead of backward deformations, which allows much faster animation. + +![](images/0941e01b06dd78b2f85ae548278129dd0274ac01021cc534de50588aa12de256.jpg) +Figure 2. 3D head scans from our newly-captured dataset: for each person (rows), we first capture a neutral pose, followed by several scans in different expressions (columns). Overall, our dataset has more than 3700 3D scans from 203 people. + +# 3. Dataset Acquisition + +Our dataset comprises 203 subjects, $29\%$ female, and contains over 3700 3D scans; see Table. 1. Our 3D head scans show great levels of detail and completeness, as shown in Fig. 2. Additionally, we do not require participants to wear a bathing cap as in the FaceScape dataset [43], allowing for the capture of natural hair styles to a certain degree. See Fig. 3 for a visual comparison of our new dataset to other 3D face datasets. + +
Num. Subjects203 (144m/59f)
Total num. Scans3720
Num. Vertices/Scan≈ 1.5M
+ +Table 1. Statistics of our 3D scanning dataset. + +# 3.1. Capture Setup + +Our setup is composed of two Artec Eva scanners [35], that are rotated $360^{\circ}$ around a subject's head using a robotic actuator. Each scan takes only 6 seconds, which is crucial to keep involuntary, non-rigid facial movements to a minimum. The scanners operate at 16 FPS, and are aligned through the scanning sequence and fused into a single mesh; each fused scan contains approximately 1.5M vertices and 3.5M triangles. Each participant is asked to perform 23 different expressions, which are adopted from the FACS coded expression proposed in FaceWarehouse [7], see our supplemental for details. Importantly, we capture a neutral expression with the mouth open, which later serves as canonical expression, as described in Section 4. + +![](images/e105f3c402734aca46a8136af04f1e4c264aa2d4bc9a3316226de8438c19a7bd.jpg) +Figure 3. Compared to recent multi-view stereo 3D face dataset, our data exhibits sharper details and less noise. + +# 3.2. Registration Pipeline + +Registering all head scans against a common template is a key requirement to effectively train our parametric head model. First, we start with a rigid alignment into our canonical coordinate system; second, we non-rigidly register all scans to a common template. + +# 3.2.1 Rigid Alignment + +We leverage 2D face landmark detectors to obtain a rigid transformation into the canonical coordinate system of the FLAME model [18]. To this end, we deploy the MediaPipe [20] face mesh detector and back-project a subset of 48 landmarks corresponding to iBUG68 annotations [33] to the 3D scan. Since not all viewing angles of the scanner's + +![](images/fd923e221b17398b1bf2a446fc428b86c6b029ee476d6e3194dc435aaedc8ff0.jpg) +Figure 4. Method overview: at the core of our neural parametric head model lies a neural field representation that parameterizes shape and expressions in disentangled latent spaces. Specifically, we propose a local MLP ensemble that is anchored at face keypoints (left). We train this model by leveraging a set of high-fidelity 3D scans from our newly-captured dataset comprising various expressions for identity (middle). In order to obtain the ground truth deformation samples, we non-rigidly register all scans to a common template (right). + +![](images/9424245bbe5b9479bb46ca4ca157a3c4c971496a83961511403338b64423a380.jpg) + +![](images/ba95cf74f9d90a9ae524b0c8d1e231fa40488e19ad8c3227fe0e5f94b8e7e9c2.jpg) + +trajectories are suited for 2D facial landmark detection, we instead use frontal renderings of the colored meshes, which yields robust detection quality. Note that the initial landmark detection is the only time we use the scanner's color images. We then calculate a similarity transform using [40] to transform the detected landmarks to the average face of FLAME. + +# 3.2.2 Non-Rigid Registration + +As a non-rigid registration prior, we first constrain the non-rigid deformation to FLAME parameter space, before optimizing an offset for each vertex. Additionally, we backproject 2D hair segmentation masks obtained by FaRL [47] to mask out the respective areas of the scans. + +Initialization. Given the 23 expression scans $\{S_j\}_{j=1}^{23}$ of a subject, we jointly estimate identity parameters $\mathbf{z}^{\mathrm{id}} \in \mathbb{R}^{100}$ , expression parameters $\{\mathbf{z}_j^{\mathrm{ex}}\}_{j=1}^{23}$ , and jaw poses $\{\theta_j\}_{j=1}^{23}$ of the FLAME model, as well as a shared scale $s \in \mathbb{R}$ and per-scan rotation and translation corrections $\{R_j\}_{j=1}^{23}$ and $\{t_j\}_{j=1}^{23}$ . Updating the initial similarity transform is crucial to obtaining a more consistent canonical alignment. + +Let $\Phi_j$ denote all parameters affecting the $j$ -th FLAME model and $V_{\Phi_j}$ its vertices. We jointly optimize for these parameters by minimizing + +$$ +\underset {\Phi_ {1}, \dots \Phi_ {2 3}} {\arg \min } \sum_ {j = 1} ^ {2 3} \left[ \lambda_ {l} \| L _ {j} - \hat {L} _ {j} \| _ {1} + d \left(V _ {\Phi_ {j}}, \mathcal {S} _ {j}\right) + \mathcal {R} \left(\Phi_ {j}\right) \right], \tag {1} +$$ + +where $L_{j}\in \mathbb{R}^{68\times 3}$ denotes the back-projected 3D landmarks, $\hat{L}_j$ are the 3D landmarks from $V_{\Phi_j}$ , $d(V_{\Phi_j},S_j)$ is the mean point-to-plane distance from $V_{\Phi_j}$ to its nearest neighbors in scan $S_{j}$ , and $\mathcal{R}(\Phi_j)$ regularizes FLAME parameters. + +Fine tuning. Once the initial alignment has been obtained, we upsample the mesh resolution by a factor of 16 for the face region, and perform non-rigid registration using ARAP [36] for each scan individually. + +Let $V$ be the upsampled vertices, which we aim to register to the scan $\mathcal{S}$ . We seek vertex-specific offsets $\{\delta_v\}_{v\in V}$ and auxiliary, vertex-specific rotation $\{R_v\}_{v\in V}$ from the ARAP term. Therefore, we solve + +$$ +\underset {\left\{ \begin{array}{l} \delta_ {v} \\ R _ {v} \end{array} \right\} _ {v \in V}} {\arg \min } \sum_ {v \in V} \left[ d (\hat {v}, \mathcal {S}) + \sum_ {u \in \mathcal {N} _ {v}} \| R (v - u) - (\hat {v} - \hat {u}) \| _ {2} ^ {2} \right], \tag {2} +$$ + +using the L-BFGS optimizer, where $\hat{v} = v + \delta_v, \mathcal{N}_v$ denotes all neighboring vertices, and $d(\hat{v}, S)$ is as before. See the supplemental for more details. + +# 4. Neural Parametric Head Models + +Our neural parametric head model separately represents geometry in a canonical space and facial expression as forward deformations; see Sections 4.1 and 4.2, respectively. + +# 4.1. Identity Representation + +We represent a person's identity-specific geometry implicitly in its canonical space as a SDF. Compared to + +template-mesh-based approaches, this offers the necessary flexibility that is required to model a complete head with hair. In accordance with related work on human body modeling, e.g. [9,24,25], we choose a canonical expression with an open mouth to avoid topological issues. While a canonical coordinate system already reduces the dimensionality of the learning problem at hand, we further tailor our neural identity representation to the domain of human heads; as described below. + +# 4.1.1 Local Decomposition + +Instead of globally conditioning the SDF network on a specific identity, we exploit the structure of the human face to impose two important geometric priors. First, we embrace the fixed composition of human faces by decomposing the SDF network into an ensemble of several smaller local MLP-based networks, which are defined around certain facial anchors, as shown in Fig. 4. Thereby, we reduce the learning problem into smaller, more tractable ones. We choose facial anchor points as a trade-off between the relevance of an area and spatial uniformity. Second, we exploit the symmetry of the face by only learning SDFs on the left side of the face, which are shared with the right half after flipping spatial coordinates accordingly. More specifically, we divide the face into $K = 2K_{\mathrm{symm}} + K_{\mathrm{middle}}$ regions, which are centered at facial anchor points $\mathbf{a} \in \mathbb{R}^{K \times 3}$ . We use $\mathcal{M}$ to denote the index set anchors lying on the symmetry axis, and $\mathcal{S}$ and $\mathcal{S}^*$ for symmetric regions on the left and right side respectively, such that for $k \in \mathcal{S}$ there is a $k^* \in \mathcal{S}^*$ that corresponds to the symmetric anchor point. + +In addition to a global latent vector $\mathbf{z}_{\mathrm{glob}} \in \mathbb{R}^{d_{\mathrm{glob}}}$ , the $k$ -th region is equipped with a local latent vector $\mathbf{z}_k^{\mathrm{id}} \in \mathbb{R}^{d_{\mathrm{loc}}}$ . Together, the $k$ -th region is represented by a small MLP + +$$ +f _ {k}: \mathbb {R} ^ {d _ {\text {g l o b}} + d _ {\text {l o c}} + 3} \rightarrow \mathbb {R} \tag {3} +$$ + +$$ +\left(x, \mathbf {z} _ {\text {g l o b}} ^ {\mathrm {i d}}, \mathbf {z} _ {k} ^ {\mathrm {i d}}\right) \mapsto \operatorname {M L P} _ {\theta_ {k}} \left(\left[ x - \mathbf {a} _ {k}, \mathbf {z} _ {\text {g l o b}} ^ {\mathrm {i d}}, \mathbf {z} _ {k} ^ {\mathrm {i d}} \right]\right), \tag {4} +$$ + +that predicts SDF values for points $x \in \mathbb{R}^3$ , where $[\cdot]$ denotes the concatenation operator. + +In order to exploit face symmetry, we share the network parameters and mirror the coordinates for each pair $(k,k^{*})$ of symmetric regions: + +$$ +f _ {k ^ {*}} \left(x, \mathbf {z} _ {\text {g l o b}} ^ {\text {i d}}, \mathbf {z} _ {k ^ {*}} ^ {\text {i d}}\right) := f _ {k} \left(\operatorname {f l i p} \left(x - a _ {k ^ {*}}\right), \mathbf {z} _ {\text {g l o b}} ^ {\text {i d}}, \mathbf {z} _ {k ^ {*}} ^ {\text {i d}}\right), \tag {5} +$$ + +where $\mathrm{flip}(\cdot)$ represents a flip of the coordinates along the face symmetry axis. + +# 4.1.2 Global Blending + +In order to facilitate a decomposition that helps generalization, it is crucial that reliable anchor positions $\mathbf{a}$ are available. To this end, we train a small $\mathrm{MLP}_{\mathrm{pos}}$ that predicts $\mathbf{a}$ from the global latent $\mathbf{z}_{\mathrm{glob}}^{\mathrm{id}}$ . + +Since each local SDF focuses on a specific semantic region of the face, as defined by the anchors $\mathbf{a}$ , we additionally introduce $f_{0}(x,\mathbf{z}_{\mathrm{glob}}^{\mathrm{id}},\mathbf{z}_{0}^{\mathrm{id}}) = \mathrm{MLP}_{0}(x,\mathbf{z}_{\mathrm{glob}}^{\mathrm{id}},\mathbf{z}_{0}^{\mathrm{id}})$ , which operates in the global coordinate system, hence covering all SDF values far away from any anchor in $\mathbf{a}$ . To clarify the notation, we set $a_0\coloneqq \mathbf{0}\in \mathbb{R}^3$ + +Finally, we blend all local fields $f_{k}$ into a global field + +$$ +\mathcal {F} _ {\mathrm {i d}} (x) = \sum_ {k = 0} ^ {K} w _ {k} \left(x, a _ {k}\right) f _ {k} \left(x, \mathbf {z} _ {\text {g l o b}} ^ {\mathrm {i d}}, \mathbf {z} _ {k} ^ {\mathrm {i d}}\right), \tag {6} +$$ + +using Gaussian kernels, similar to [12,48], where + +$$ +w _ {k} ^ {*} (x, a _ {k}) = \left\{ \begin{array}{l l} e ^ {\frac {- | | x - a | | _ {2}}{2 \sigma}}, & \text {i f} k > 0 \\ c, & \text {i f} k = 0 \end{array} \right. \tag {7} +$$ + +$$ +\text {a n d} w _ {k} (x, a _ {k}) = \frac {w _ {k} ^ {*} (x , a _ {k})}{\sum_ {k ^ {\prime}} w _ {k ^ {\prime}} ^ {*} (x , a _ {k ^ {\prime}})} \tag {8} +$$ + +We use a fixed isotropic kernel with standard deviation $\sigma$ and a constant response $c$ for $f_0$ . + +# 4.2. Expression Representation + +In contrast to our local geometry representation, we model expressions only with a globally conditioned deformation field; e.g. a smile will effect the cheeks corners of the mouth and eye region. In this context, we define $\mathbf{z}^{\mathrm{ex}}\in \mathbb{R}^{d_{\mathrm{ex}}}$ as a latent expression description. Since such a deformation field is defined in the ambient Euclidean space, it is crucial to additionally condition the deformation network with an identity feature. By imposing an information bottleneck on the latent expression description, the deformation network is then forced to learn a disentangled representation of expressions. + +More formally, we model deformations using an MLP + +$$ +\mathcal {F} _ {\mathrm {e x}} \left(x, \mathbf {z} ^ {\mathrm {e x}}, \hat {\mathbf {z}} ^ {\mathrm {i d}}\right): \mathbb {R} ^ {d _ {\mathrm {e x}} + d _ {\mathrm {i d - e x}}} \rightarrow \mathbb {R} ^ {3}. \tag {9} +$$ + +Rather than directly feeding all identity information into $\mathcal{F}_{\mathrm{ex}}$ directly, we first project the information to a lower dimensional representation + +$$ +\hat {\mathbf {Z}} ^ {\mathrm {i d}} = W \left[ \mathbf {z} _ {\text {g l o b}} ^ {\mathrm {i d}}, \mathbf {z} _ {0} ^ {\mathrm {i d}}, \dots \mathbf {z} _ {K} ^ {\mathrm {i d}}, \mathbf {a} _ {1}, \dots , \mathbf {a} _ {K} \right], \tag {10} +$$ + +using a single linear layer $W$ , where $d_{\mathrm{id - ex}}$ denotes the dimensionality of the interdependence of identity and expression. + +# 4.3. Training Strategy + +Our training strategy closely follows NPMs [24] and sequentially trains the identity and expression networks in an auto-decoder fashion. + +Identity Representation For the identity space, we jointly train latent codes $\mathbf{Z}_j^{\mathrm{id}}\coloneqq \{\mathbf{z}_{\mathrm{glob},j}^{\mathrm{id}},\mathbf{z}_{0,j}^{\mathrm{id}},\ldots ,\mathbf{z}_{K,j}^{\mathrm{id}}\}$ for each $j$ + +in the set of training indices $J$ and network parameters $\theta_{\mathrm{pos}}$ and $\theta_0,\dots ,\theta_K$ , by minimizing + +$$ +\mathcal {L} _ {\mathrm {i d}} = \sum_ {j \in J} \mathcal {L} _ {\mathrm {I G R}} + \lambda_ {a} \| \hat {\mathbf {a}} _ {j} - \mathbf {a} _ {j} \| _ {2} ^ {2} + \lambda_ {\mathrm {s y}} \mathcal {L} _ {\mathrm {s y}} + \lambda_ {\mathrm {r e g}} ^ {\mathrm {i d}} \| \mathbf {Z} _ {j} ^ {\mathrm {i d}} \| _ {2} ^ {2}, \tag {11} +$$ + +where $\mathcal{L}_{\mathrm{IGR}}$ is the loss introduced in [15] which enforces SDF values to be zero on the surface and contains an Eikonal term. This ensures consistency between surface normals and SDF gradients and is in similar spirit to [15, 34]. For training, we directly sample points and surface normals from our ground truth scans. + +Additionally, we supervise anchor predictions $\mathbf{a}_j$ using the corresponding vertices from our registrations $\hat{\mathbf{a}}_j$ . The last two terms serve regularization purposes, where + +$$ +\mathcal {L} _ {\mathrm {s y}} = \sum_ {k \in \mathcal {S}} \| \mathbf {z} _ {k} ^ {\mathrm {i d}} - \mathbf {z} _ {k *} ^ {\mathrm {i d}} \| _ {2} ^ {2} \tag {12} +$$ + +enforces the local latent description of symmetric regions to be close, and the final term encourages a well-behaved distribution of both global and local latent descriptions centered around zero. + +Expression Representation Once the identity representation is learned, we optimize for network parameters $\theta_{\mathrm{ex}}$ , $W$ and latent expression codes, $\{\mathbf{z}_{j,l}^{\mathrm{ex}}\}_{j\in J,l\in L}$ , where $j$ indexes identity and $l$ indexes expressions. The deformation loss + +$$ +\mathcal {L} _ {\mathrm {e x}} = \sum_ {\substack {i, j \in J, L \\ x \in X _ {j, l}}} \| \mathcal {F} _ {\mathrm {e x}} (x, \mathbf {z} _ {j, l} ^ {\mathrm {e x}}, \hat {\mathbf {z}} _ {j} ^ {\mathrm {i d}}) - \delta (x) _ {j, l} \| _ {2} ^ {2} + \lambda_ {\mathrm {reg}} ^ {e x} \| \mathbf {z} _ {j, l} ^ {\mathrm {e x}} \| _ {2} ^ {2} \tag{13} +$$ + +directly supervises the deformation field using samples $x \in X_{j,l}$ , which have been precomputed from the registration. See the supplemental for more details. + +# 5. Results + +We aim to evaluate how well our method generalizes from our training dataset of 87 identities to unseen ones, and their unique expressions. Our test dataset consists of 6 female and 12 male identities in 23 expressions each. We fit our model and baselines to frontal single view depth maps, which are generated by rendering the unseen validation meshes and randomly sampling 5000 points. For ablations with respect to the number of points and noise level, as well as for a demonstration of real-world tracking with NPHM using a commodity depth sensor, we refer to the supplementary material. In our evaluation, we isolate the reconstruction of identity and expression in section 5.1 and 5.2, respectively. + +Mesh-Based Baselines. We evaluate against the Basel Face Model (BFM) and FLAME as representatives of existing template-based PCA-models. Furthermore, we compare against a PCA model with delta expressions [1] trained on + +our registered meshes and a local variant thereof. For the local PCA model we utilize the same facial anchors as in NPHM to divide each neutral registered mesh into regions, which are separately represented by local PCA models. To obtain a final prediction we use the same blending scheme as described in Section 4.1.2. For all these models we additionally provide the 68 facial landmarks as input. + +Implicit Baselines. We compare against ImFace [45] as a neural backward deformation baseline. We evaluate a version trained on the FaceScape dataset [43] and one that we train on our dataset using their preprocessing (denoted as ImFace*). Additionally, we compare against NPMs [24], isolating the effect of our proposed identity representation. + +Metrics. To evaluate the quality of the reconstructions, we report $L_{1}$ -Chamfer distance, normal consistency (N. C.), and F-Score with a threshold of $1.5\mathrm{mm}$ . + +# 5.1. Identity Reconstruction + +To separately evaluate the quality of our identity space, we fit against a single neutral expression scan for each identity. These scans are aligned to each method's canonical coordinate system. We assist baselines that use a closed mouth in their canonical space, i.e. baselines not trained on our data, by optimizing these over all scans instead. More details on the optimization strategy for each model can be found in the supplemental. + +Figure 5 and Table 2 present qualitative and quantitative results, respectively. We observe that all neural field methods consistently achieve more faithful reconstructions and further note that the proposed local conditioning allows NPHM to reconstruct details and statistically unlikely elements more reliably. + +
MethodL1-Chamfer ↓N. C. ↑F-Score@1.5 ↑
BFM [29]1.341e-20.9360.319
FLAME [18]0.640e-20.9310.530
Global PCA [1]0.563e-20.9540.571
Local PCA [1]0.416e-20.9600.756
ImFace [45]0,404e-20.9540.832
ImFace* [45]0.312e-20.9710.883
NPM [24]0.200e-20.9750.947
Ours0.182e-20.9780.954
+ +* trained on our data + +Table 2. Identity fitting to a single depth map in neutral expression. + +# 5.2. Expression Reconstruction + +To evaluate each model's expression space, we fit it to multiple expressions of the same person with the task of recovering one identity code per subject and one expression code per expression. For the neural forward deformation + +![](images/79c50e93df5d9593a8f4c3a433816bdc3ad04f4a6d3269137c76766b55f47bcc.jpg) +Figure 5. Model fitting: at inference time, we fit our model to sparse, partial input point clouds from single depth map. We compare our method to widely-used state-of-the-art parametric face models, including FLAME [18], a local PCA [1], ImFace [45] and neural parametric models (NPM) [24]. Our parametric model has significantly more surface detail and covers the entire head, including the hair region. + +models, NPM and NPHM, we utilize iterative root finding [10] to fit the expression codes. For simplicity, we keep the identity code fixed after fitting to the neutral scan. For all other models we jointly solve for expression and identity codes. Figure 6 and Table 3 show qualitative and quantitative comparisons with our baselines, respectively. Owing to the ability of backward deformations to directly connect the observed with the canonical space, ImFace reliably reconstructs expressions. Nevertheless, it still suffers from blurry reconstructions, compared to both NPM and NPHM. + +See our supplemental for more details and an additional comparison of jointly fitting identity and expression when only a single depth observation is available. + +# 5.3. Ablations + +We ablate two main contributions of the proposed identity representation, by fitting identity codes to a neutral scan without involving expressions. First, we analyze the effect of the number of regions $K$ of our ensemble, by comparing against NPM [24], which effectively would be an ensemble of size 1, and against versions with 12 and 26 regions and adjusted number of latent dimensions. Additionally, we + +
MethodL1-Chamfer ↓N. C. ↑F-Score@1.5 ↑
BFM [29]1.271e-20.9370.508
FLAME [18]0.679e-20.9240.351
Global PCA [1]0.515e-20.9560.606
Local PCA [1]0.535e-20.9500.641
ImFace [45]0.369e-20.9590.824
ImFace* [45]0.321e-20.9710.879
NPM [24]0.299e-20.9620.891
Ours0.272e-20.9690.913
+ +* trained on our data +Table 3. Expression fitting on 23 single depth maps per person. + +confirm the benefit of sharing weights for symmetric keypoints. Table 4 shows a quantitative evaluation of these two ablations supporting our design choices. + +# 5.4. Limitations + +In our experiments, we show that NPHM can reconstruct high-quality human heads; however, at the same time, we believe that there are still several limitations and opportunities for future work. For instance we focus solely on the + +![](images/89b42c2feda44afa20321f7ea434b03868b53f001c78323749b9042a74dac86d.jpg) +Figure 6. Comparison on fitting expressions to sparse input point clouds: from a sparse set of depth observations of different expressions from a frontal view (left), we compare FLAME [18], a local PCA [1], ImFace [45], neural parametric models (NPM) [24], and our method against the respective ground truth scans. + +
MethodL1-Chamfer ↓N. C. ↑F-Score@1.5 ↑
NPM [24]0.2540.9720.906
K=12, w/ sy.0.2890.9660.876
K=26, w/ sy.0.2370.9710.913
K=39, w/o sy.0.2300.9740.917
Ours0.2060.9760.938
+ +Table 4. Effect of the number of anchor points $K$ and symmetry on identity reconstruction performance. NPM represents the extreme case of using exactly 1 anchor point. Note that to be consistent with the original version, NPM differs to the other models in both width and depth of the underlying MLP. + +geometry of heads while omitting any information about appearance. This makes our model ill-suited for fitting to RGB images using dense photometric terms. Here, an interesting future avenue would be to explore learning appearance, anchored on top of the geometric base model. In fact, as part of our dataset we also provide the RGB frames captured during the 3D scanning process, which should facilitate learning such a texture model. + +Another limitation is that currently we do not capture open hair, which limits general diversity; however, compared to other existing face models such as 3D morphable models, we significantly expand the application domain by covering the entirety of the human head. In the future, we still would like to cover a broader range of hairstyles. + +# 6. Conclusion + +We have introduced neural parametric head models, a neural representation which disentangles identity and expressions of human heads, by representing geometry in canonical space and modelling expressions as forward deformations. For our identity representation we have proposed and validated a local representation that is tailored towards human head. To train our model, we introduce a new dataset of over 3700 high-fidelity 3D scans. Once trained, our model can be fitted to sparse input point clouds, for instance, captured by a commodity range sensor. Compared to existing methods, such as widely-used PCA-based techniques, our model represents significantly more detail while being able to regularize out noise of the underlying point cloud inputs. Overall, we believe that our method is an important step towards high-fidelity face capture and our newly-introduced dataset opens up opportunities to further explore learning priors for neural face models. + +# Acknowledgements + +This work was supported by the ERC Starting Grant Scan2CAD (804724), the German Research Foundation (DFG) Grant "Making Machine Learning on Static and Dynamic 3D Data Practical", the German Research Foundation (DFG) Research Unit "Learning and Simulation in Visual Computing", and Synthesia. We would like to thank Maximilian Knörl and Tim Walter for the help with scanning, and Angela Dai for the video voiceover. + +# References + +[1] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 187-194, 1999. 2, 6, 7, 8 +[2] Timo Bolkart and Stefanie Wuhrer. A groupwise multilinear correspondence optimization for 3d faces. In Proceedings of the IEEE international conference on computer vision, pages 3604-3612, 2015. 2 +[3] James Booth, Epameinondas Antonakos, Stylianos Ploumpis, George Trigeorgis, Yannis Panagakis, and Stefanos Zafeiriou. 3d face morphable models" in-the-wild". In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 48-57, 2017. 2 +[4] James Booth, Anastasios Roussos, Stefanos Zafeiriou, Allan Ponniah, and David Dunaway. A 3d morphable model learnt from 10,000 faces. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5543-5552, 2016. 2 +[5] Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Michael Bronstein, and Stefanos Zafeiriou. Neural 3d morphable models: Spiral convolutional networks for 3d shape representation learning and generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7213-7222, 2019. 2 +[6] Alan Brunton, Timo Bolkart, and Stefanie Wuhrer. Multilinear wavelets: A statistical shape space for human faces. In European Conference on Computer Vision, pages 297-312. Springer, 2014. 2 +[7] Chen Cao, Yanlin Weng, Shun Zhou, Yiying Tong, and Kun Zhou. Facewarehouse: A 3d facial expression database for visual computing. 20(3):413-425, mar 2014. 3 +[8] Rohan Chabra, Jan E. Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe. Deep local shapes: Learning local sdf priors for detailed 3d reconstruction. In Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIX, page 608-625, Berlin, Heidelberg, 2020. Springer-Verlag. 2 +[9] Xu Chen, Tianjian Jiang, Jie Song, Jinlong Yang, Michael J. Black, Andreas Geiger, and Otmar Hilliges. gdna: Towards generative detailed neural avatars. CoRR, abs/2201.04123, 2022. 2, 5 +[10] Xu Chen, Yufeng Zheng, Michael J Black, Otmar Hilliges, and Andreas Geiger. Snarf: Differentiable forward skimming for animating non-rigid neural implicit shapes. In International Conference on Computer Vision (ICCV), 2021. 2, 7 +[11] Zhixiang Chen and Tae-Kyun Kim. Learning feature aggregation for deep 3d morphable models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13164-13173, 2021. 2 +[12] Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser. Local deep implicit functions for 3d shape. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4857-4866, 2020. 2, 5 + +[13] Simon Giebenhain and Bastian Goldluecke. Air-nets: An attention-based framework for locally conditioned implicit representations. In 2021 International Conference on 3D Vision (3DV). IEEE, 2021. 2 +[14] Shunwang Gong, Lei Chen, Michael Bronstein, and Stefanos Zafeiriou. Spiralnet++: A fast and highly efficient mesh convolution operator. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pages 0-0, 2019. 2 +[15] Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman. Implicit geometric regularization for learning shapes. arXiv preprint arXiv:2002.10099, 2020. 6 +[16] Ruilong Li, Karl Bladin, Yajie Zhao, Chinmay Chinara, Owen Ingraham, Pengda Xiang, Xinglei Ren, Pratusha Prasad, Bipin Kishore, Jun Xing, and Hao Li. Learning formation of physically-based face attributes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 2 +[17] Ruilong Li, Julian Tanke, Minh Vo, Michael Zollhofer, Jurgen Gall, Angjoo Kanazawa, and Christoph Lassner. Tava: Template-free animatable volumetric actors. arXiv preprint arXiv:2206.08929, 2022. 2 +[18] Tianye Li, Timo Bolkart, Michael. J. Black, Hao Li, and Javier Romero. Learning a model of facial shape and expression from 4D scans. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6):194:1-194:17, 2017. 2, 3, 6, 7, 8 +[19] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural actor: Neural free-view synthesis of human actors with pose control. ACM Transactions on Graphics (TOG), 40(6):1-16, 2021. 2 +[20] Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Yong, Juhyun Lee, Wan-Teh Chang, Wei Hua, Manfred Georg, and Matthias Grundmann. Mediapipe: A framework for perceiving and processing reality. In Third Workshop on Computer Vision for AR/VR at IEEE Computer Vision and Pattern Recognition (CVPR) 2019, 2019. 3 +[21] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4460-4470, 2019. 2 +[22] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 2 +[23] Thomas Neumann, Kiran Varanasi, Stephan Wenger, Markus Wacker, Marcus Magnor, and Christian Theobalt. Sparse localized deformation components. ACM Trans. Graph., 32(6), nov 2013. 2 +[24] Pablo Palafox, Aljaž Božić, Justus Thies, Matthias Nießner, and Angela Dai. Npms: Neural parametric models for 3d deformable shapes. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 12695–12705, October 2021. 2, 5, 6, 7, 8 + +[25] Pablo Palafox, Nikolaos Sarafianos, Tony Tung, and Angela Dai. Spams: Structured implicit parametric models. CVPR, 2022. 2, 5 +[26] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019. 2 +[27] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5865-5874, 2021. 2 +[28] Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Ricardo MartinBrualla, and Steven M. Seitz. Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields. ACM Trans. Graph., 40(6), dec 2021. 2 +[29] Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. In 2009 sixth IEEE international conference on advanced video and signal based surveillance, pages 296-301. IEEE, 2009. 2, 6, 7 +[30] Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In European Conference on Computer Vision, pages 523-540. Springer, 2020. 2 +[31] Stylianos Ploumpis, Haoyang Wang, Nick Pears, William AP Smith, and Stefanos Zafeiriou. Combining 3d morphable models: A large scale face-and-head model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10934-10943, 2019. 2 +[32] Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. H3d-net: Few-shot high-fidelity 3d head reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5620-5629, 2021. 2 +[33] Christos Sagonas, Georgios Tzimiropoulos, Stefanos Zafeiriou, and Maja Pantic. 300 faces in-the-wild challenge: The first facial landmark localization challenge. In Proceedings of the IEEE international conference on computer vision workshops, pages 397-403, 2013. 3 +[34] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462-7473, 2020. 6 +[35] Janujah Sivanandan, Eugene Liscio, and P Eng. Assessing structured light 3d scanning using artec eva for injury documentation during autopsy. J Assoc Crime Scene Reconstr, 21:5-14, 2017. 3 +[36] Olga Sorkine and Marc Alexa. As-Rigid-As-Possible Surface Modeling. In Alexander Belyaev and Michael Garland, editors, Geometry Processing. The Eurographics Association, 2007. 4 +[37] Luan Tran, Feng Liu, and Xiaoming Liu. Towards high-fidelity nonlinear 3d face morphable model. In Proceedings + +of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1126-1135, 2019. 2 +[38] Luan Tran and Xiaoming Liu. Nonlinear 3d face morphable model. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7346-7355, 2018. 2 +[39] Luan Tran and Xiaoming Liu. On learning 3d face morphable model from in-the-wild images. IEEE transactions on pattern analysis and machine intelligence, 43(1):157-171, 2019. 2 +[40] Shinji Umeyama. Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(04):376-380, 1991. 4 +[41] Daoye Wang, Prashanth Chandran, Gaspard Zoss, Derek Bradley, and Paulo Gotardo. Morf: Morphable radiance fields for multiview neural head modeling. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1-9, 2022. 2 +[42] Lizhen Wang, Zhiyua Chen, Tao Yu, Chenguang Ma, Liang Li, and Yebin Liu. Faceverse: a fine-grained and detail-controllable 3d face morphable model from a hybrid dataset. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR2022), June 2022. 2, 3 +[43] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. Facescape: A large-scale high quality 3d face dataset and detailed riggable 3d face prediction. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 2, 3, 6 +[44] Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and Christian Theobalt. i3dmm: Deep implicit 3d morphable model of human heads. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12803-12813, 2021. 2 +[45] Mingwu Zheng, Hongyu Yang, Di Huang, and Liming Chen. Imface: A nonlinear 3d morphable face model with implicit neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 2, 6, 7, 8 +[46] Yufeng Zheng, Victoria Fernández Abrevaya, Xu Chen, Marcel C. Bühler, Michael J. Black, and Otmar Hilliges. I M avatar: Implicit morphable head avatars from videos. CoRR, abs/2112.07471, 2021. 2 +[47] Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, and Fang Wen. General facial representation learning in a visual-linguistic manner. arXiv preprint arXiv:2112.03109, 2021. 4 +[48] Zerong Zheng, Han Huang, Tao Yu, Hongwen Zhang, Yandong Guo, and Yebin Liu. Structured local radiance fields for human avatar modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022. 2, 5 +[49] Michael Zollhöfer, Justus Thies, Pablo Garrido, Derek Bradley, Thabo Beeler, Patrick Pérez, Marc Stamminger, Matthias Nießner, and Christian Theobalt. State of the art on monocular 3d face reconstruction, tracking, and applications. In Computer graphics forum, volume 37, pages 523-550. 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We propose to learn a neural parametric head model based on neural fields: first, we capture a large dataset of over 3700 high-fidelity head scans with varying shapes and expressions (left). We then non-rigidly register these scans to generate our training data. As a result of training, we obtain a disentangled latent that spans the space of shapes " + }, + { + "bbox": [ + 45, + 361, + 547, + 407 + ], + "type": "inline_equation", + "content": "\\mathbf{z}^{\\mathrm{id}}" + }, + { + "bbox": [ + 45, + 361, + 547, + 407 + ], + "type": "text", + "content": " and expressions " + }, + { + "bbox": [ + 45, + 361, + 547, + 407 + ], + "type": "inline_equation", + "content": "\\mathbf{z}^{\\mathrm{ex}}" + }, + { + "bbox": [ + 45, + 361, + 547, + 407 + ], + "type": "text", + "content": " (middle). At inference time, we can leverage the prior of our learned representation by fitting our model to a sparse input point cloud by solving for the latent codes (right)." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 143, + 419, + 192, + 431 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 419, + 192, + 431 + ], + "spans": [ + { + "bbox": [ + 143, + 419, + 192, + 431 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 45, + 445, + 290, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 445, + 290, + 661 + ], + "spans": [ + { + "bbox": [ + 45, + 445, + 290, + 661 + ], + "type": "text", + "content": "We propose a novel 3D morphable model for complete human heads based on hybrid neural fields. At the core of our model lies a neural parametric representation that disentangles identity and expressions in disjoint latent spaces. To this end, we capture a person's identity in a canonical space as a signed distance field (SDF), and model facial expressions with a neural deformation field. In addition, our representation achieves high-fidelity local detail by introducing an ensemble of local fields centered around facial anchor points. To facilitate generalization, we train our model on a newly-captured dataset of over 3700 head scans from 203 different identities using a custom high-end 3D scanning setup. Our dataset significantly exceeds comparable existing datasets, both with respect to quality and completeness of geometry, averaging around 3.5M mesh faces per scan1. Finally, we demonstrate that our approach outperforms state-of-the-art methods in terms of fitting error and reconstruction quality." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 419, + 386, + 431 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 419, + 386, + 431 + ], + "spans": [ + { + "bbox": [ + 307, + 419, + 386, + 431 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 440, + 546, + 631 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 440, + 546, + 631 + ], + "spans": [ + { + "bbox": [ + 304, + 440, + 546, + 631 + ], + "type": "text", + "content": "Human faces and heads lie at the core of human visual perception, and hence are key to creating digital replica of someones identity, likeliness, and appearance. In particular, 3D reconstruction of human heads from sparse inputs, such as point clouds, is central to a wide range of applications in the context of gaming, augmented and virtual reality, and digitization in our modern digital era. One of the most successful lines of research to address this challenging problem are parametric face models, which represent both shape identities and expressions featuring a low-dimensional parametric space. These Blendshape and 3D morphable models (3DMMs) have achieved incredible success, since they can be fitted to sparse inputs, regularize out noise, and provide a compact 3D representation. As a result, many practical settings could be realized, ranging from face tracking and 3D avatar creation to facial-reenactment applications [49]." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 632, + 545, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 632, + 545, + 693 + ], + "spans": [ + { + "bbox": [ + 304, + 632, + 545, + 693 + ], + "type": "text", + "content": "Traditionally, 3DMMs, are based on a low-rank approximation of the underlying 3D mesh geometry. To this end, a template mesh with fixed topology is non-rigidly registered to a series of 3D scans. From this template registration, a 3DMM can be computed using dimensionality" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 683, + 288, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 683, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 683, + 288, + 712 + ], + "type": "text", + "content": "1We will publicly release our dataset along with a public benchmark for both neural head avatar construction as well as an evaluation on a hidden test-set for inference-time fitting." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 320, + 703, + 537, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 703, + 537, + 712 + ], + "spans": [ + { + "bbox": [ + 320, + 703, + 537, + 712 + ], + "type": "text", + "content": "Website: https://simongiebenhain.github.io/NPHM" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21003" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 178 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 178 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 178 + ], + "type": "text", + "content": "reduction methods such as principal component analysis (PCA). The quality of the resulting parametric space depends strongly on the quality of 3D scans, their registration, and the ability to disentangle identity and expression variations. While these PCA-based models exhibit excellent regularizing properties, their inherent limitation lies in their inability to represent local surface detail and the reliance on a template mesh of fixed topology, which inhibits the representation of diverse hair styles." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 180, + 288, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 180, + 288, + 357 + ], + "spans": [ + { + "bbox": [ + 46, + 180, + 288, + 357 + ], + "type": "text", + "content": "In this work, we propose neural parametric head models (NPHM), which represent complete human head geometry in a canonical space using an SDF, and morph the resulting geometry to posed space using a forward deformation field. By decoupling the human head representation into these two spaces, we are able to learn disentangled latent spaces - one of the core concepts of 3DMMs. Furthermore, we decompose the implicit geometry representation in canonical space into an ensemble of local MLPs. Each part is represented by a small MLP that operates in a local coordinate system centered around face keypoints. Additionally, we exploit face symmetry by sharing network weights of symmetric regions. This decomposition into separate parts imposes a strong geometry prior and helps to improve both generalization and provide higher levels of detail." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 360, + 287, + 526 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 360, + 287, + 526 + ], + "spans": [ + { + "bbox": [ + 46, + 360, + 287, + 526 + ], + "type": "text", + "content": "In order to train our model, we capture a new high-fidelity head dataset with a high-end capture rig, which is composed of over 3700 3D head scans from 203 different people. After rigidly aligning all scans in a canonical coordinate system, we train our identity network on scans in canonical expression. In order to train the deformation network, we non-rigidly register each scan against a template mesh, which we in turn use as training data for our neural deformation model. At inference time, we can then fit our model to a given input point cloud by optimizing for the latent code parameters for both expression and identity. In a series of experiments, we demonstrate that our neural parametric model outperforms state-of-the-art models and can represent complete heads, including fine details." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 527, + 211, + 538 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 527, + 211, + 538 + ], + "spans": [ + { + "bbox": [ + 47, + 527, + 211, + 538 + ], + "type": "text", + "content": "In sum, our contributions are as follows:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 58, + 548, + 286, + 657 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 58, + 548, + 286, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 548, + 286, + 582 + ], + "spans": [ + { + "bbox": [ + 58, + 548, + 286, + 582 + ], + "type": "text", + "content": "- We introduce a novel 3D dataset captured with a high-end capture rig, including over 3700 3D scans of human heads from 203 different identities." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 58, + 585, + 286, + 619 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 585, + 286, + 619 + ], + "spans": [ + { + "bbox": [ + 58, + 585, + 286, + 619 + ], + "type": "text", + "content": "- We propose a new neural-field-based parametric head representation, which facilitates high-fidelity local details through an ensemble of local implicit models." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 58, + 622, + 286, + 657 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 622, + 286, + 657 + ], + "spans": [ + { + "bbox": [ + 58, + 622, + 286, + 657 + ], + "type": "text", + "content": "- We demonstrate that our neural parametric head model can be robustly fit to range data, regularize out noise, and outperform existing models." + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 47, + 669, + 133, + 681 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 669, + 133, + 681 + ], + "spans": [ + { + "bbox": [ + 47, + 669, + 133, + 681 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 689, + 286, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 286, + 712 + ], + "type": "text", + "content": "3D morphable face and head models. The seminal work of Blanz and Vetter [1] was one of the first to introduce" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 72, + 545, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 192 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 192 + ], + "type": "text", + "content": "a model-based approach to represent variations in human faces using PCA. Since the scans were captured in constrained environments, the expressiveness of the model was relatively limited. As such, improvements in the registration [29], as well as the use of data captured in the wild [3,4,31], led to significant advances. Thereafter, more advanced face models were introduced, including multilinear models of identity and expression [2,6], as well as models that combined linear shape spaces with articulated head parts [18], and localized approaches [23]." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 194, + 545, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 194, + 545, + 277 + ], + "spans": [ + { + "bbox": [ + 304, + 194, + 545, + 277 + ], + "type": "text", + "content": "With the advent of deep learning, various works focused on extending face and head 3DMMs beyond linear spaces. To this end, convolutional neural network based architectures have been proposed to both regress the model parameters and reconstruct the face [16,37-39,42,43]. At the same time, graph convolutions [5, 14] and attention modules [11] have been proposed to model the head mesh geometry." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 280, + 545, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 280, + 545, + 411 + ], + "spans": [ + { + "bbox": [ + 304, + 280, + 545, + 411 + ], + "type": "text", + "content": "Neural field representations. Neural field-based networks have emerged as an efficient way to implicitly represent 3D scenes. In contrast to explicit representations (e.g., meshes or voxel grids), neural fields are well-suited to represent geometries of arbitrary topology. Park et al. [26] proposed to represent a class-specific SDF with an MLP that is conditioned on a latent variable. Similarly, Mescheder et al. [21] implicitly define a surface as the decision boundary of a binary classifier and Mildenhall et al. [22] represent a radiance field using an MLP by supervising a photometric loss on the rendered images." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 415, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 415, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 415, + 545, + 712 + ], + "type": "text", + "content": "Building upon these approaches, a series of works focus on modeling deformations. These methods use a separate network to model the deformations that occur in a sequence (e.g., [27, 28]), and have been successfully applied to animation of human bodies [17, 19] and heads [46]. Following this paradigm, a number of neural parametric models have been proposed for bodies [9, 24, 25], faces [45], and most closely related to our work—heads [32, 41, 44]. For instance, H3D-Net [32] and MoRF [41] proposed 3D generative models of heads, but do not account for expression-specific deformations. Recently, neural parametric models for human faces [44, 45] and bodies [9, 10, 24, 25] have explored combinations of SDFs and deformation fields, to produce complex non-linear deformations, while maintaining the flexibility of an implicit geometry representation. Our work is greatly inspired by these lines; however, the key difference is that we tailor our neural field representation specifically to human heads through an ensemble of local MLPs. Thereby, our work is also related to local conditioning methods for neural fields of arbitrary objects [8, 12, 13, 30], human bodies [25, 48] and faces [45]. Compared to ImFace [45], our model is more local, incorporates a symmetry prior, represents a complete head and models forward instead of backward deformations, which allows much faster animation." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "21004" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 64, + 539, + 295 + ], + "blocks": [ + { + "bbox": [ + 58, + 64, + 539, + 295 + ], + "lines": [ + { + "bbox": [ + 58, + 64, + 539, + 295 + ], + "spans": [ + { + "bbox": [ + 58, + 64, + 539, + 295 + ], + "type": "image", + "image_path": "0941e01b06dd78b2f85ae548278129dd0274ac01021cc534de50588aa12de256.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 301, + 546, + 324 + ], + "lines": [ + { + "bbox": [ + 46, + 301, + 546, + 324 + ], + "spans": [ + { + "bbox": [ + 46, + 301, + 546, + 324 + ], + "type": "text", + "content": "Figure 2. 3D head scans from our newly-captured dataset: for each person (rows), we first capture a neutral pose, followed by several scans in different expressions (columns). Overall, our dataset has more than 3700 3D scans from 203 people." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 338, + 163, + 352 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 338, + 163, + 352 + ], + "spans": [ + { + "bbox": [ + 47, + 338, + 163, + 352 + ], + "type": "text", + "content": "3. Dataset Acquisition" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 358, + 288, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 358, + 288, + 453 + ], + "spans": [ + { + "bbox": [ + 46, + 358, + 288, + 453 + ], + "type": "text", + "content": "Our dataset comprises 203 subjects, " + }, + { + "bbox": [ + 46, + 358, + 288, + 453 + ], + "type": "inline_equation", + "content": "29\\%" + }, + { + "bbox": [ + 46, + 358, + 288, + 453 + ], + "type": "text", + "content": " female, and contains over 3700 3D scans; see Table. 1. Our 3D head scans show great levels of detail and completeness, as shown in Fig. 2. Additionally, we do not require participants to wear a bathing cap as in the FaceScape dataset [43], allowing for the capture of natural hair styles to a certain degree. See Fig. 3 for a visual comparison of our new dataset to other 3D face datasets." + } + ] + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 83, + 463, + 252, + 508 + ], + "blocks": [ + { + "bbox": [ + 83, + 463, + 252, + 508 + ], + "lines": [ + { + "bbox": [ + 83, + 463, + 252, + 508 + ], + "spans": [ + { + "bbox": [ + 83, + 463, + 252, + 508 + ], + "type": "table", + "html": "
Num. Subjects203 (144m/59f)
Total num. Scans3720
Num. Vertices/Scan≈ 1.5M
", + "image_path": "465486c640c96d18ce9282f607d0d83e269e7d132a3dfec09b44e4cd7fccbcce.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 83, + 509, + 251, + 521 + ], + "lines": [ + { + "bbox": [ + 83, + 509, + 251, + 521 + ], + "spans": [ + { + "bbox": [ + 83, + 509, + 251, + 521 + ], + "type": "text", + "content": "Table 1. Statistics of our 3D scanning dataset." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 47, + 539, + 139, + 553 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 539, + 139, + 553 + ], + "spans": [ + { + "bbox": [ + 47, + 539, + 139, + 553 + ], + "type": "text", + "content": "3.1. Capture Setup" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 558, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 558, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 558, + 287, + 713 + ], + "type": "text", + "content": "Our setup is composed of two Artec Eva scanners [35], that are rotated " + }, + { + "bbox": [ + 46, + 558, + 287, + 713 + ], + "type": "inline_equation", + "content": "360^{\\circ}" + }, + { + "bbox": [ + 46, + 558, + 287, + 713 + ], + "type": "text", + "content": " around a subject's head using a robotic actuator. Each scan takes only 6 seconds, which is crucial to keep involuntary, non-rigid facial movements to a minimum. The scanners operate at 16 FPS, and are aligned through the scanning sequence and fused into a single mesh; each fused scan contains approximately 1.5M vertices and 3.5M triangles. Each participant is asked to perform 23 different expressions, which are adopted from the FACS coded expression proposed in FaceWarehouse [7], see our supplemental for details. Importantly, we capture a neutral expression with the mouth open, which later serves as canonical expression, as described in Section 4." + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 310, + 337, + 542, + 480 + ], + "blocks": [ + { + "bbox": [ + 310, + 337, + 542, + 480 + ], + "lines": [ + { + "bbox": [ + 310, + 337, + 542, + 480 + ], + "spans": [ + { + "bbox": [ + 310, + 337, + 542, + 480 + ], + "type": "image", + "image_path": "e105f3c402734aca46a8136af04f1e4c264aa2d4bc9a3316226de8438c19a7bd.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 306, + 486, + 545, + 510 + ], + "lines": [ + { + "bbox": [ + 306, + 486, + 545, + 510 + ], + "spans": [ + { + "bbox": [ + 306, + 486, + 545, + 510 + ], + "type": "text", + "content": "Figure 3. Compared to recent multi-view stereo 3D face dataset, our data exhibits sharper details and less noise." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 529, + 427, + 542 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 529, + 427, + 542 + ], + "spans": [ + { + "bbox": [ + 306, + 529, + 427, + 542 + ], + "type": "text", + "content": "3.2. Registration Pipeline" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 548, + 547, + 608 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 548, + 547, + 608 + ], + "spans": [ + { + "bbox": [ + 304, + 548, + 547, + 608 + ], + "type": "text", + "content": "Registering all head scans against a common template is a key requirement to effectively train our parametric head model. First, we start with a rigid alignment into our canonical coordinate system; second, we non-rigidly register all scans to a common template." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 623, + 410, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 623, + 410, + 635 + ], + "spans": [ + { + "bbox": [ + 306, + 623, + 410, + 635 + ], + "type": "text", + "content": "3.2.1 Rigid Alignment" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 641, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 641, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 641, + 547, + 715 + ], + "type": "text", + "content": "We leverage 2D face landmark detectors to obtain a rigid transformation into the canonical coordinate system of the FLAME model [18]. To this end, we deploy the MediaPipe [20] face mesh detector and back-project a subset of 48 landmarks corresponding to iBUG68 annotations [33] to the 3D scan. Since not all viewing angles of the scanner's" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21005" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 76, + 189, + 252 + ], + "blocks": [ + { + "bbox": [ + 49, + 76, + 189, + 252 + ], + "lines": [ + { + "bbox": [ + 49, + 76, + 189, + 252 + ], + "spans": [ + { + "bbox": [ + 49, + 76, + 189, + 252 + ], + "type": "image", + "image_path": "fd923e221b17398b1bf2a446fc428b86c6b029ee476d6e3194dc435aaedc8ff0.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 262, + 547, + 309 + ], + "lines": [ + { + "bbox": [ + 46, + 262, + 547, + 309 + ], + "spans": [ + { + "bbox": [ + 46, + 262, + 547, + 309 + ], + "type": "text", + "content": "Figure 4. Method overview: at the core of our neural parametric head model lies a neural field representation that parameterizes shape and expressions in disentangled latent spaces. Specifically, we propose a local MLP ensemble that is anchored at face keypoints (left). We train this model by leveraging a set of high-fidelity 3D scans from our newly-captured dataset comprising various expressions for identity (middle). In order to obtain the ground truth deformation samples, we non-rigidly register all scans to a common template (right)." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 190, + 76, + 309, + 255 + ], + "blocks": [ + { + "bbox": [ + 190, + 76, + 309, + 255 + ], + "lines": [ + { + "bbox": [ + 190, + 76, + 309, + 255 + ], + "spans": [ + { + "bbox": [ + 190, + 76, + 309, + 255 + ], + "type": "image", + "image_path": "9424245bbe5b9479bb46ca4ca157a3c4c971496a83961511403338b64423a380.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 310, + 76, + 545, + 255 + ], + "blocks": [ + { + "bbox": [ + 310, + 76, + 545, + 255 + ], + "lines": [ + { + "bbox": [ + 310, + 76, + 545, + 255 + ], + "spans": [ + { + "bbox": [ + 310, + 76, + 545, + 255 + ], + "type": "image", + "image_path": "ba95cf74f9d90a9ae524b0c8d1e231fa40488e19ad8c3227fe0e5f94b8e7e9c2.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 328, + 289, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 328, + 289, + 411 + ], + "spans": [ + { + "bbox": [ + 46, + 328, + 289, + 411 + ], + "type": "text", + "content": "trajectories are suited for 2D facial landmark detection, we instead use frontal renderings of the colored meshes, which yields robust detection quality. Note that the initial landmark detection is the only time we use the scanner's color images. We then calculate a similarity transform using [40] to transform the detected landmarks to the average face of FLAME." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 433, + 180, + 445 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 433, + 180, + 445 + ], + "spans": [ + { + "bbox": [ + 47, + 433, + 180, + 445 + ], + "type": "text", + "content": "3.2.2 Non-Rigid Registration" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 454, + 287, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 454, + 287, + 515 + ], + "spans": [ + { + "bbox": [ + 46, + 454, + 287, + 515 + ], + "type": "text", + "content": "As a non-rigid registration prior, we first constrain the non-rigid deformation to FLAME parameter space, before optimizing an offset for each vertex. Additionally, we backproject 2D hair segmentation masks obtained by FaRL [47] to mask out the respective areas of the scans." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "spans": [ + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "text", + "content": "Initialization. Given the 23 expression scans " + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "inline_equation", + "content": "\\{S_j\\}_{j=1}^{23}" + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "text", + "content": " of a subject, we jointly estimate identity parameters " + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "inline_equation", + "content": "\\mathbf{z}^{\\mathrm{id}} \\in \\mathbb{R}^{100}" + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "text", + "content": ", expression parameters " + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{z}_j^{\\mathrm{ex}}\\}_{j=1}^{23}" + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "text", + "content": ", and jaw poses " + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "inline_equation", + "content": "\\{\\theta_j\\}_{j=1}^{23}" + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "text", + "content": " of the FLAME model, as well as a shared scale " + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "inline_equation", + "content": "s \\in \\mathbb{R}" + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "text", + "content": " and per-scan rotation and translation corrections " + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "inline_equation", + "content": "\\{R_j\\}_{j=1}^{23}" + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "inline_equation", + "content": "\\{t_j\\}_{j=1}^{23}" + }, + { + "bbox": [ + 47, + 534, + 287, + 634 + ], + "type": "text", + "content": ". Updating the initial similarity transform is crucial to obtaining a more consistent canonical alignment." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 635, + 288, + 672 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 635, + 288, + 672 + ], + "spans": [ + { + "bbox": [ + 47, + 635, + 288, + 672 + ], + "type": "text", + "content": "Let " + }, + { + "bbox": [ + 47, + 635, + 288, + 672 + ], + "type": "inline_equation", + "content": "\\Phi_j" + }, + { + "bbox": [ + 47, + 635, + 288, + 672 + ], + "type": "text", + "content": " denote all parameters affecting the " + }, + { + "bbox": [ + 47, + 635, + 288, + 672 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 47, + 635, + 288, + 672 + ], + "type": "text", + "content": "-th FLAME model and " + }, + { + "bbox": [ + 47, + 635, + 288, + 672 + ], + "type": "inline_equation", + "content": "V_{\\Phi_j}" + }, + { + "bbox": [ + 47, + 635, + 288, + 672 + ], + "type": "text", + "content": " its vertices. We jointly optimize for these parameters by minimizing" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 57, + 681, + 287, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 681, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 57, + 681, + 287, + 715 + ], + "type": "interline_equation", + "content": "\\underset {\\Phi_ {1}, \\dots \\Phi_ {2 3}} {\\arg \\min } \\sum_ {j = 1} ^ {2 3} \\left[ \\lambda_ {l} \\| L _ {j} - \\hat {L} _ {j} \\| _ {1} + d \\left(V _ {\\Phi_ {j}}, \\mathcal {S} _ {j}\\right) + \\mathcal {R} \\left(\\Phi_ {j}\\right) \\right], \\tag {1}", + "image_path": "2598c4a8cff4676f5fef4a8c0389ba509c5786914d2e9b3f5fbc308d27566dea.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "spans": [ + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "inline_equation", + "content": "L_{j}\\in \\mathbb{R}^{68\\times 3}" + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "text", + "content": " denotes the back-projected 3D landmarks, " + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "inline_equation", + "content": "\\hat{L}_j" + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "text", + "content": " are the 3D landmarks from " + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "inline_equation", + "content": "V_{\\Phi_j}" + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "inline_equation", + "content": "d(V_{\\Phi_j},S_j)" + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "text", + "content": " is the mean point-to-plane distance from " + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "inline_equation", + "content": "V_{\\Phi_j}" + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "text", + "content": " to its nearest neighbors in scan " + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "inline_equation", + "content": "S_{j}" + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "inline_equation", + "content": "\\mathcal{R}(\\Phi_j)" + }, + { + "bbox": [ + 304, + 327, + 546, + 389 + ], + "type": "text", + "content": " regularizes FLAME parameters." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 406, + 546, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 406, + 546, + 453 + ], + "spans": [ + { + "bbox": [ + 304, + 406, + 546, + 453 + ], + "type": "text", + "content": "Fine tuning. Once the initial alignment has been obtained, we upsample the mesh resolution by a factor of 16 for the face region, and perform non-rigid registration using ARAP [36] for each scan individually." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 454, + 545, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 454, + 545, + 502 + ], + "spans": [ + { + "bbox": [ + 304, + 454, + 545, + 502 + ], + "type": "text", + "content": "Let " + }, + { + "bbox": [ + 304, + 454, + 545, + 502 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 304, + 454, + 545, + 502 + ], + "type": "text", + "content": " be the upsampled vertices, which we aim to register to the scan " + }, + { + "bbox": [ + 304, + 454, + 545, + 502 + ], + "type": "inline_equation", + "content": "\\mathcal{S}" + }, + { + "bbox": [ + 304, + 454, + 545, + 502 + ], + "type": "text", + "content": ". We seek vertex-specific offsets " + }, + { + "bbox": [ + 304, + 454, + 545, + 502 + ], + "type": "inline_equation", + "content": "\\{\\delta_v\\}_{v\\in V}" + }, + { + "bbox": [ + 304, + 454, + 545, + 502 + ], + "type": "text", + "content": " and auxiliary, vertex-specific rotation " + }, + { + "bbox": [ + 304, + 454, + 545, + 502 + ], + "type": "inline_equation", + "content": "\\{R_v\\}_{v\\in V}" + }, + { + "bbox": [ + 304, + 454, + 545, + 502 + ], + "type": "text", + "content": " from the ARAP term. Therefore, we solve" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 312, + 510, + 545, + 550 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 510, + 545, + 550 + ], + "spans": [ + { + "bbox": [ + 312, + 510, + 545, + 550 + ], + "type": "interline_equation", + "content": "\\underset {\\left\\{ \\begin{array}{l} \\delta_ {v} \\\\ R _ {v} \\end{array} \\right\\} _ {v \\in V}} {\\arg \\min } \\sum_ {v \\in V} \\left[ d (\\hat {v}, \\mathcal {S}) + \\sum_ {u \\in \\mathcal {N} _ {v}} \\| R (v - u) - (\\hat {v} - \\hat {u}) \\| _ {2} ^ {2} \\right], \\tag {2}", + "image_path": "15fcacc8202eb86fc22e93a4344a973e7cf83a147b8b62961489d4ed3e9de11c.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "text", + "content": "using the L-BFGS optimizer, where " + }, + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "inline_equation", + "content": "\\hat{v} = v + \\delta_v, \\mathcal{N}_v" + }, + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "text", + "content": " denotes all neighboring vertices, and " + }, + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "inline_equation", + "content": "d(\\hat{v}, S)" + }, + { + "bbox": [ + 304, + 558, + 545, + 594 + ], + "type": "text", + "content": " is as before. See the supplemental for more details." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 605, + 488, + 618 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 605, + 488, + 618 + ], + "spans": [ + { + "bbox": [ + 306, + 605, + 488, + 618 + ], + "type": "text", + "content": "4. Neural Parametric Head Models" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 627, + 545, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 627, + 545, + 662 + ], + "spans": [ + { + "bbox": [ + 304, + 627, + 545, + 662 + ], + "type": "text", + "content": "Our neural parametric head model separately represents geometry in a canonical space and facial expression as forward deformations; see Sections 4.1 and 4.2, respectively." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 670, + 440, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 670, + 440, + 684 + ], + "spans": [ + { + "bbox": [ + 306, + 670, + 440, + 684 + ], + "type": "text", + "content": "4.1. Identity Representation" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "text", + "content": "We represent a person's identity-specific geometry implicitly in its canonical space as a SDF. Compared to" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21006" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 178 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 178 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 178 + ], + "type": "text", + "content": "template-mesh-based approaches, this offers the necessary flexibility that is required to model a complete head with hair. In accordance with related work on human body modeling, e.g. [9,24,25], we choose a canonical expression with an open mouth to avoid topological issues. While a canonical coordinate system already reduces the dimensionality of the learning problem at hand, we further tailor our neural identity representation to the domain of human heads; as described below." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 194, + 170, + 205 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 194, + 170, + 205 + ], + "spans": [ + { + "bbox": [ + 47, + 194, + 170, + 205 + ], + "type": "text", + "content": "4.1.1 Local Decomposition" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "spans": [ + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "text", + "content": "Instead of globally conditioning the SDF network on a specific identity, we exploit the structure of the human face to impose two important geometric priors. First, we embrace the fixed composition of human faces by decomposing the SDF network into an ensemble of several smaller local MLP-based networks, which are defined around certain facial anchors, as shown in Fig. 4. Thereby, we reduce the learning problem into smaller, more tractable ones. We choose facial anchor points as a trade-off between the relevance of an area and spatial uniformity. Second, we exploit the symmetry of the face by only learning SDFs on the left side of the face, which are shared with the right half after flipping spatial coordinates accordingly. More specifically, we divide the face into " + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "inline_equation", + "content": "K = 2K_{\\mathrm{symm}} + K_{\\mathrm{middle}}" + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "text", + "content": " regions, which are centered at facial anchor points " + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "inline_equation", + "content": "\\mathbf{a} \\in \\mathbb{R}^{K \\times 3}" + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "text", + "content": ". We use " + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "text", + "content": " to denote the index set anchors lying on the symmetry axis, and " + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "inline_equation", + "content": "\\mathcal{S}" + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "inline_equation", + "content": "\\mathcal{S}^*" + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "text", + "content": " for symmetric regions on the left and right side respectively, such that for " + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "inline_equation", + "content": "k \\in \\mathcal{S}" + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "text", + "content": " there is a " + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "inline_equation", + "content": "k^* \\in \\mathcal{S}^*" + }, + { + "bbox": [ + 46, + 213, + 289, + 439 + ], + "type": "text", + "content": " that corresponds to the symmetric anchor point." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 440, + 287, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 440, + 287, + 475 + ], + "spans": [ + { + "bbox": [ + 47, + 440, + 287, + 475 + ], + "type": "text", + "content": "In addition to a global latent vector " + }, + { + "bbox": [ + 47, + 440, + 287, + 475 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_{\\mathrm{glob}} \\in \\mathbb{R}^{d_{\\mathrm{glob}}}" + }, + { + "bbox": [ + 47, + 440, + 287, + 475 + ], + "type": "text", + "content": ", the " + }, + { + "bbox": [ + 47, + 440, + 287, + 475 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 47, + 440, + 287, + 475 + ], + "type": "text", + "content": "-th region is equipped with a local latent vector " + }, + { + "bbox": [ + 47, + 440, + 287, + 475 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_k^{\\mathrm{id}} \\in \\mathbb{R}^{d_{\\mathrm{loc}}}" + }, + { + "bbox": [ + 47, + 440, + 287, + 475 + ], + "type": "text", + "content": ". Together, the " + }, + { + "bbox": [ + 47, + 440, + 287, + 475 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 47, + 440, + 287, + 475 + ], + "type": "text", + "content": "-th region is represented by a small MLP" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 481, + 287, + 496 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 481, + 287, + 496 + ], + "spans": [ + { + "bbox": [ + 62, + 481, + 287, + 496 + ], + "type": "interline_equation", + "content": "f _ {k}: \\mathbb {R} ^ {d _ {\\text {g l o b}} + d _ {\\text {l o c}} + 3} \\rightarrow \\mathbb {R} \\tag {3}", + "image_path": "ad17e8436d5ce5def434918ef1a3173b3ba2bbc157ecd4444e59ca2c9c28eea8.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 78, + 498, + 287, + 514 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 498, + 287, + 514 + ], + "spans": [ + { + "bbox": [ + 78, + 498, + 287, + 514 + ], + "type": "interline_equation", + "content": "\\left(x, \\mathbf {z} _ {\\text {g l o b}} ^ {\\mathrm {i d}}, \\mathbf {z} _ {k} ^ {\\mathrm {i d}}\\right) \\mapsto \\operatorname {M L P} _ {\\theta_ {k}} \\left(\\left[ x - \\mathbf {a} _ {k}, \\mathbf {z} _ {\\text {g l o b}} ^ {\\mathrm {i d}}, \\mathbf {z} _ {k} ^ {\\mathrm {i d}} \\right]\\right), \\tag {4}", + "image_path": "c31cefee23ca1b6ff1c0f61708122fc5ca3ae179b0ec9f6f13293618ce247890.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 521, + 287, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 521, + 287, + 544 + ], + "spans": [ + { + "bbox": [ + 47, + 521, + 287, + 544 + ], + "type": "text", + "content": "that predicts SDF values for points " + }, + { + "bbox": [ + 47, + 521, + 287, + 544 + ], + "type": "inline_equation", + "content": "x \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 47, + 521, + 287, + 544 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 47, + 521, + 287, + 544 + ], + "type": "inline_equation", + "content": "[\\cdot]" + }, + { + "bbox": [ + 47, + 521, + 287, + 544 + ], + "type": "text", + "content": " denotes the concatenation operator." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 545, + 287, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 545, + 287, + 581 + ], + "spans": [ + { + "bbox": [ + 47, + 545, + 287, + 581 + ], + "type": "text", + "content": "In order to exploit face symmetry, we share the network parameters and mirror the coordinates for each pair " + }, + { + "bbox": [ + 47, + 545, + 287, + 581 + ], + "type": "inline_equation", + "content": "(k,k^{*})" + }, + { + "bbox": [ + 47, + 545, + 287, + 581 + ], + "type": "text", + "content": " of symmetric regions:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 59, + 587, + 287, + 603 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 587, + 287, + 603 + ], + "spans": [ + { + "bbox": [ + 59, + 587, + 287, + 603 + ], + "type": "interline_equation", + "content": "f _ {k ^ {*}} \\left(x, \\mathbf {z} _ {\\text {g l o b}} ^ {\\text {i d}}, \\mathbf {z} _ {k ^ {*}} ^ {\\text {i d}}\\right) := f _ {k} \\left(\\operatorname {f l i p} \\left(x - a _ {k ^ {*}}\\right), \\mathbf {z} _ {\\text {g l o b}} ^ {\\text {i d}}, \\mathbf {z} _ {k ^ {*}} ^ {\\text {i d}}\\right), \\tag {5}", + "image_path": "8bb742e40c467985fa1cc2f401461a00bba18adf825c00f7141e63d7e69f2ef6.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 609, + 287, + 633 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 609, + 287, + 633 + ], + "spans": [ + { + "bbox": [ + 47, + 609, + 287, + 633 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 609, + 287, + 633 + ], + "type": "inline_equation", + "content": "\\mathrm{flip}(\\cdot)" + }, + { + "bbox": [ + 47, + 609, + 287, + 633 + ], + "type": "text", + "content": " represents a flip of the coordinates along the face symmetry axis." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 647, + 151, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 647, + 151, + 658 + ], + "spans": [ + { + "bbox": [ + 47, + 647, + 151, + 658 + ], + "type": "text", + "content": "4.1.2 Global Blending" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 665, + 287, + 716 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 665, + 287, + 716 + ], + "spans": [ + { + "bbox": [ + 47, + 665, + 287, + 716 + ], + "type": "text", + "content": "In order to facilitate a decomposition that helps generalization, it is crucial that reliable anchor positions " + }, + { + "bbox": [ + 47, + 665, + 287, + 716 + ], + "type": "inline_equation", + "content": "\\mathbf{a}" + }, + { + "bbox": [ + 47, + 665, + 287, + 716 + ], + "type": "text", + "content": " are available. To this end, we train a small " + }, + { + "bbox": [ + 47, + 665, + 287, + 716 + ], + "type": "inline_equation", + "content": "\\mathrm{MLP}_{\\mathrm{pos}}" + }, + { + "bbox": [ + 47, + 665, + 287, + 716 + ], + "type": "text", + "content": " that predicts " + }, + { + "bbox": [ + 47, + 665, + 287, + 716 + ], + "type": "inline_equation", + "content": "\\mathbf{a}" + }, + { + "bbox": [ + 47, + 665, + 287, + 716 + ], + "type": "text", + "content": " from the global latent " + }, + { + "bbox": [ + 47, + 665, + 287, + 716 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_{\\mathrm{glob}}^{\\mathrm{id}}" + }, + { + "bbox": [ + 47, + 665, + 287, + 716 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "text", + "content": "Since each local SDF focuses on a specific semantic region of the face, as defined by the anchors " + }, + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "inline_equation", + "content": "\\mathbf{a}" + }, + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "text", + "content": ", we additionally introduce " + }, + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "inline_equation", + "content": "f_{0}(x,\\mathbf{z}_{\\mathrm{glob}}^{\\mathrm{id}},\\mathbf{z}_{0}^{\\mathrm{id}}) = \\mathrm{MLP}_{0}(x,\\mathbf{z}_{\\mathrm{glob}}^{\\mathrm{id}},\\mathbf{z}_{0}^{\\mathrm{id}})" + }, + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "text", + "content": ", which operates in the global coordinate system, hence covering all SDF values far away from any anchor in " + }, + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "inline_equation", + "content": "\\mathbf{a}" + }, + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "text", + "content": ". To clarify the notation, we set " + }, + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "inline_equation", + "content": "a_0\\coloneqq \\mathbf{0}\\in \\mathbb{R}^3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 317, + 144, + 533, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 144, + 533, + 156 + ], + "spans": [ + { + "bbox": [ + 317, + 144, + 533, + 156 + ], + "type": "text", + "content": "Finally, we blend all local fields " + }, + { + "bbox": [ + 317, + 144, + 533, + 156 + ], + "type": "inline_equation", + "content": "f_{k}" + }, + { + "bbox": [ + 317, + 144, + 533, + 156 + ], + "type": "text", + "content": " into a global field" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 344, + 165, + 545, + 197 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 344, + 165, + 545, + 197 + ], + "spans": [ + { + "bbox": [ + 344, + 165, + 545, + 197 + ], + "type": "interline_equation", + "content": "\\mathcal {F} _ {\\mathrm {i d}} (x) = \\sum_ {k = 0} ^ {K} w _ {k} \\left(x, a _ {k}\\right) f _ {k} \\left(x, \\mathbf {z} _ {\\text {g l o b}} ^ {\\mathrm {i d}}, \\mathbf {z} _ {k} ^ {\\mathrm {i d}}\\right), \\tag {6}", + "image_path": "5d1bea4484fec95511c416b6439c3dc4c12bffe997191be5c9231a6a4f8c2d42.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 205, + 506, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 205, + 506, + 217 + ], + "spans": [ + { + "bbox": [ + 306, + 205, + 506, + 217 + ], + "type": "text", + "content": "using Gaussian kernels, similar to [12,48], where" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 347, + 224, + 545, + 257 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 224, + 545, + 257 + ], + "spans": [ + { + "bbox": [ + 347, + 224, + 545, + 257 + ], + "type": "interline_equation", + "content": "w _ {k} ^ {*} (x, a _ {k}) = \\left\\{ \\begin{array}{l l} e ^ {\\frac {- | | x - a | | _ {2}}{2 \\sigma}}, & \\text {i f} k > 0 \\\\ c, & \\text {i f} k = 0 \\end{array} \\right. \\tag {7}", + "image_path": "7c233f170ff7634a6dec45d00d478543812f54ea39769d6aef5abc70d2ce799f.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 356, + 266, + 545, + 293 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 356, + 266, + 545, + 293 + ], + "spans": [ + { + "bbox": [ + 356, + 266, + 545, + 293 + ], + "type": "interline_equation", + "content": "\\text {a n d} w _ {k} (x, a _ {k}) = \\frac {w _ {k} ^ {*} (x , a _ {k})}{\\sum_ {k ^ {\\prime}} w _ {k ^ {\\prime}} ^ {*} (x , a _ {k ^ {\\prime}})} \\tag {8}", + "image_path": "c1076c07cbb763df09f7e3b994b2883fe7cd9434e74d51e84a5878cfa11abec9.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 306, + 296, + 545, + 319 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 296, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 306, + 296, + 545, + 319 + ], + "type": "text", + "content": "We use a fixed isotropic kernel with standard deviation " + }, + { + "bbox": [ + 306, + 296, + 545, + 319 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 306, + 296, + 545, + 319 + ], + "type": "text", + "content": " and a constant response " + }, + { + "bbox": [ + 306, + 296, + 545, + 319 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 306, + 296, + 545, + 319 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 306, + 296, + 545, + 319 + ], + "type": "inline_equation", + "content": "f_0" + }, + { + "bbox": [ + 306, + 296, + 545, + 319 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 327, + 454, + 340 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 327, + 454, + 340 + ], + "spans": [ + { + "bbox": [ + 306, + 327, + 454, + 340 + ], + "type": "text", + "content": "4.2. Expression Representation" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 346, + 545, + 476 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 346, + 545, + 476 + ], + "spans": [ + { + "bbox": [ + 304, + 346, + 545, + 476 + ], + "type": "text", + "content": "In contrast to our local geometry representation, we model expressions only with a globally conditioned deformation field; e.g. a smile will effect the cheeks corners of the mouth and eye region. In this context, we define " + }, + { + "bbox": [ + 304, + 346, + 545, + 476 + ], + "type": "inline_equation", + "content": "\\mathbf{z}^{\\mathrm{ex}}\\in \\mathbb{R}^{d_{\\mathrm{ex}}}" + }, + { + "bbox": [ + 304, + 346, + 545, + 476 + ], + "type": "text", + "content": " as a latent expression description. Since such a deformation field is defined in the ambient Euclidean space, it is crucial to additionally condition the deformation network with an identity feature. By imposing an information bottleneck on the latent expression description, the deformation network is then forced to learn a disentangled representation of expressions." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 477, + 537, + 489 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 477, + 537, + 489 + ], + "spans": [ + { + "bbox": [ + 317, + 477, + 537, + 489 + ], + "type": "text", + "content": "More formally, we model deformations using an MLP" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 356, + 497, + 545, + 511 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 356, + 497, + 545, + 511 + ], + "spans": [ + { + "bbox": [ + 356, + 497, + 545, + 511 + ], + "type": "interline_equation", + "content": "\\mathcal {F} _ {\\mathrm {e x}} \\left(x, \\mathbf {z} ^ {\\mathrm {e x}}, \\hat {\\mathbf {z}} ^ {\\mathrm {i d}}\\right): \\mathbb {R} ^ {d _ {\\mathrm {e x}} + d _ {\\mathrm {i d - e x}}} \\rightarrow \\mathbb {R} ^ {3}. \\tag {9}", + "image_path": "0c291a62a645df60f0658c61d97f52527e06505f458b27c251146b2951b95201.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 520, + 545, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 520, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 304, + 520, + 545, + 555 + ], + "type": "text", + "content": "Rather than directly feeding all identity information into " + }, + { + "bbox": [ + 304, + 520, + 545, + 555 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_{\\mathrm{ex}}" + }, + { + "bbox": [ + 304, + 520, + 545, + 555 + ], + "type": "text", + "content": " directly, we first project the information to a lower dimensional representation" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 344, + 563, + 545, + 578 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 344, + 563, + 545, + 578 + ], + "spans": [ + { + "bbox": [ + 344, + 563, + 545, + 578 + ], + "type": "interline_equation", + "content": "\\hat {\\mathbf {Z}} ^ {\\mathrm {i d}} = W \\left[ \\mathbf {z} _ {\\text {g l o b}} ^ {\\mathrm {i d}}, \\mathbf {z} _ {0} ^ {\\mathrm {i d}}, \\dots \\mathbf {z} _ {K} ^ {\\mathrm {i d}}, \\mathbf {a} _ {1}, \\dots , \\mathbf {a} _ {K} \\right], \\tag {10}", + "image_path": "867f72dfca0a86943cb3ae3963a781361d1da39a94a642c4ee671d0ccd8761ba.jpg" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 304, + 586, + 545, + 621 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 586, + 545, + 621 + ], + "spans": [ + { + "bbox": [ + 304, + 586, + 545, + 621 + ], + "type": "text", + "content": "using a single linear layer " + }, + { + "bbox": [ + 304, + 586, + 545, + 621 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 304, + 586, + 545, + 621 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 586, + 545, + 621 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{id - ex}}" + }, + { + "bbox": [ + 304, + 586, + 545, + 621 + ], + "type": "text", + "content": " denotes the dimensionality of the interdependence of identity and expression." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 306, + 629, + 411, + 642 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 629, + 411, + 642 + ], + "spans": [ + { + "bbox": [ + 306, + 629, + 411, + 642 + ], + "type": "text", + "content": "4.3. Training Strategy" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 304, + 647, + 545, + 682 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 647, + 545, + 682 + ], + "spans": [ + { + "bbox": [ + 304, + 647, + 545, + 682 + ], + "type": "text", + "content": "Our training strategy closely follows NPMs [24] and sequentially trains the identity and expression networks in an auto-decoder fashion." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 306, + 689, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 545, + 715 + ], + "type": "text", + "content": "Identity Representation For the identity space, we jointly train latent codes " + }, + { + "bbox": [ + 306, + 689, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{Z}_j^{\\mathrm{id}}\\coloneqq \\{\\mathbf{z}_{\\mathrm{glob},j}^{\\mathrm{id}},\\mathbf{z}_{0,j}^{\\mathrm{id}},\\ldots ,\\mathbf{z}_{K,j}^{\\mathrm{id}}\\}" + }, + { + "bbox": [ + 306, + 689, + 545, + 715 + ], + "type": "text", + "content": " for each " + }, + { + "bbox": [ + 306, + 689, + 545, + 715 + ], + "type": "inline_equation", + "content": "j" + } + ] + } + ], + "index": 28 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21007" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 97 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 97 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 97 + ], + "type": "text", + "content": "in the set of training indices " + }, + { + "bbox": [ + 46, + 72, + 287, + 97 + ], + "type": "inline_equation", + "content": "J" + }, + { + "bbox": [ + 46, + 72, + 287, + 97 + ], + "type": "text", + "content": " and network parameters " + }, + { + "bbox": [ + 46, + 72, + 287, + 97 + ], + "type": "inline_equation", + "content": "\\theta_{\\mathrm{pos}}" + }, + { + "bbox": [ + 46, + 72, + 287, + 97 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 72, + 287, + 97 + ], + "type": "inline_equation", + "content": "\\theta_0,\\dots ,\\theta_K" + }, + { + "bbox": [ + 46, + 72, + 287, + 97 + ], + "type": "text", + "content": " , by minimizing" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 52, + 105, + 287, + 131 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 105, + 287, + 131 + ], + "spans": [ + { + "bbox": [ + 52, + 105, + 287, + 131 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {i d}} = \\sum_ {j \\in J} \\mathcal {L} _ {\\mathrm {I G R}} + \\lambda_ {a} \\| \\hat {\\mathbf {a}} _ {j} - \\mathbf {a} _ {j} \\| _ {2} ^ {2} + \\lambda_ {\\mathrm {s y}} \\mathcal {L} _ {\\mathrm {s y}} + \\lambda_ {\\mathrm {r e g}} ^ {\\mathrm {i d}} \\| \\mathbf {Z} _ {j} ^ {\\mathrm {i d}} \\| _ {2} ^ {2}, \\tag {11}", + "image_path": "9401f11a88ba480a8468102f0f68a5b2f299515c4bce4654f71d97a778ea5386.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 140, + 287, + 212 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 140, + 287, + 212 + ], + "spans": [ + { + "bbox": [ + 46, + 140, + 287, + 212 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 140, + 287, + 212 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{IGR}}" + }, + { + "bbox": [ + 46, + 140, + 287, + 212 + ], + "type": "text", + "content": " is the loss introduced in [15] which enforces SDF values to be zero on the surface and contains an Eikonal term. This ensures consistency between surface normals and SDF gradients and is in similar spirit to [15, 34]. For training, we directly sample points and surface normals from our ground truth scans." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 212, + 287, + 248 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 212, + 287, + 248 + ], + "spans": [ + { + "bbox": [ + 46, + 212, + 287, + 248 + ], + "type": "text", + "content": "Additionally, we supervise anchor predictions " + }, + { + "bbox": [ + 46, + 212, + 287, + 248 + ], + "type": "inline_equation", + "content": "\\mathbf{a}_j" + }, + { + "bbox": [ + 46, + 212, + 287, + 248 + ], + "type": "text", + "content": " using the corresponding vertices from our registrations " + }, + { + "bbox": [ + 46, + 212, + 287, + 248 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{a}}_j" + }, + { + "bbox": [ + 46, + 212, + 287, + 248 + ], + "type": "text", + "content": ". The last two terms serve regularization purposes, where" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 117, + 256, + 287, + 281 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 117, + 256, + 287, + 281 + ], + "spans": [ + { + "bbox": [ + 117, + 256, + 287, + 281 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {s y}} = \\sum_ {k \\in \\mathcal {S}} \\| \\mathbf {z} _ {k} ^ {\\mathrm {i d}} - \\mathbf {z} _ {k *} ^ {\\mathrm {i d}} \\| _ {2} ^ {2} \\tag {12}", + "image_path": "2b17b65282845ba2877b5516e635c637e548741a47831b0e7f7e0299aebd8991.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 290, + 287, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 290, + 287, + 337 + ], + "spans": [ + { + "bbox": [ + 46, + 290, + 287, + 337 + ], + "type": "text", + "content": "enforces the local latent description of symmetric regions to be close, and the final term encourages a well-behaved distribution of both global and local latent descriptions centered around zero." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 344, + 287, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 344, + 287, + 392 + ], + "spans": [ + { + "bbox": [ + 46, + 344, + 287, + 392 + ], + "type": "text", + "content": "Expression Representation Once the identity representation is learned, we optimize for network parameters " + }, + { + "bbox": [ + 46, + 344, + 287, + 392 + ], + "type": "inline_equation", + "content": "\\theta_{\\mathrm{ex}}" + }, + { + "bbox": [ + 46, + 344, + 287, + 392 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 344, + 287, + 392 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 46, + 344, + 287, + 392 + ], + "type": "text", + "content": " and latent expression codes, " + }, + { + "bbox": [ + 46, + 344, + 287, + 392 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{z}_{j,l}^{\\mathrm{ex}}\\}_{j\\in J,l\\in L}" + }, + { + "bbox": [ + 46, + 344, + 287, + 392 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 344, + 287, + 392 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 46, + 344, + 287, + 392 + ], + "type": "text", + "content": " indexes identity and " + }, + { + "bbox": [ + 46, + 344, + 287, + 392 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 46, + 344, + 287, + 392 + ], + "type": "text", + "content": " indexes expressions. The deformation loss" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 52, + 400, + 287, + 435 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 400, + 287, + 435 + ], + "spans": [ + { + "bbox": [ + 52, + 400, + 287, + 435 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {e x}} = \\sum_ {\\substack {i, j \\in J, L \\\\ x \\in X _ {j, l}}} \\| \\mathcal {F} _ {\\mathrm {e x}} (x, \\mathbf {z} _ {j, l} ^ {\\mathrm {e x}}, \\hat {\\mathbf {z}} _ {j} ^ {\\mathrm {i d}}) - \\delta (x) _ {j, l} \\| _ {2} ^ {2} + \\lambda_ {\\mathrm {reg}} ^ {e x} \\| \\mathbf {z} _ {j, l} ^ {\\mathrm {e x}} \\| _ {2} ^ {2} \\tag{13}", + "image_path": "de962c9fc124168e246ba84473af833f2434d82dc9c12ee2a640eb708592fd95.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 443, + 287, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 443, + 287, + 479 + ], + "spans": [ + { + "bbox": [ + 46, + 443, + 287, + 479 + ], + "type": "text", + "content": "directly supervises the deformation field using samples " + }, + { + "bbox": [ + 46, + 443, + 287, + 479 + ], + "type": "inline_equation", + "content": "x \\in X_{j,l}" + }, + { + "bbox": [ + 46, + 443, + 287, + 479 + ], + "type": "text", + "content": ", which have been precomputed from the registration. See the supplemental for more details." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 490, + 99, + 502 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 490, + 99, + 502 + ], + "spans": [ + { + "bbox": [ + 47, + 490, + 99, + 502 + ], + "type": "text", + "content": "5. Results" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 510, + 287, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 510, + 287, + 665 + ], + "spans": [ + { + "bbox": [ + 46, + 510, + 287, + 665 + ], + "type": "text", + "content": "We aim to evaluate how well our method generalizes from our training dataset of 87 identities to unseen ones, and their unique expressions. Our test dataset consists of 6 female and 12 male identities in 23 expressions each. We fit our model and baselines to frontal single view depth maps, which are generated by rendering the unseen validation meshes and randomly sampling 5000 points. For ablations with respect to the number of points and noise level, as well as for a demonstration of real-world tracking with NPHM using a commodity depth sensor, we refer to the supplementary material. In our evaluation, we isolate the reconstruction of identity and expression in section 5.1 and 5.2, respectively." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": "Mesh-Based Baselines. We evaluate against the Basel Face Model (BFM) and FLAME as representatives of existing template-based PCA-models. Furthermore, we compare against a PCA model with delta expressions [1] trained on" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "content": "our registered meshes and a local variant thereof. For the local PCA model we utilize the same facial anchors as in NPHM to divide each neutral registered mesh into regions, which are separately represented by local PCA models. To obtain a final prediction we use the same blending scheme as described in Section 4.1.2. For all these models we additionally provide the 68 facial landmarks as input." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 156, + 545, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 156, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 304, + 156, + 545, + 228 + ], + "type": "text", + "content": "Implicit Baselines. We compare against ImFace [45] as a neural backward deformation baseline. We evaluate a version trained on the FaceScape dataset [43] and one that we train on our dataset using their preprocessing (denoted as ImFace*). Additionally, we compare against NPMs [24], isolating the effect of our proposed identity representation." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 236, + 545, + 271 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 236, + 545, + 271 + ], + "spans": [ + { + "bbox": [ + 304, + 236, + 545, + 271 + ], + "type": "text", + "content": "Metrics. To evaluate the quality of the reconstructions, we report " + }, + { + "bbox": [ + 304, + 236, + 545, + 271 + ], + "type": "inline_equation", + "content": "L_{1}" + }, + { + "bbox": [ + 304, + 236, + 545, + 271 + ], + "type": "text", + "content": "-Chamfer distance, normal consistency (N. C.), and F-Score with a threshold of " + }, + { + "bbox": [ + 304, + 236, + 545, + 271 + ], + "type": "inline_equation", + "content": "1.5\\mathrm{mm}" + }, + { + "bbox": [ + 304, + 236, + 545, + 271 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 280, + 440, + 293 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 280, + 440, + 293 + ], + "spans": [ + { + "bbox": [ + 305, + 280, + 440, + 293 + ], + "type": "text", + "content": "5.1. Identity Reconstruction" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 299, + 545, + 394 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 299, + 545, + 394 + ], + "spans": [ + { + "bbox": [ + 304, + 299, + 545, + 394 + ], + "type": "text", + "content": "To separately evaluate the quality of our identity space, we fit against a single neutral expression scan for each identity. These scans are aligned to each method's canonical coordinate system. We assist baselines that use a closed mouth in their canonical space, i.e. baselines not trained on our data, by optimizing these over all scans instead. More details on the optimization strategy for each model can be found in the supplemental." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 395, + 545, + 467 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 395, + 545, + 467 + ], + "spans": [ + { + "bbox": [ + 304, + 395, + 545, + 467 + ], + "type": "text", + "content": "Figure 5 and Table 2 present qualitative and quantitative results, respectively. We observe that all neural field methods consistently achieve more faithful reconstructions and further note that the proposed local conditioning allows NPHM to reconstruct details and statistically unlikely elements more reliably." + } + ] + } + ], + "index": 17 + }, + { + "type": "table", + "bbox": [ + 307, + 478, + 544, + 597 + ], + "blocks": [ + { + "bbox": [ + 307, + 478, + 544, + 597 + ], + "lines": [ + { + "bbox": [ + 307, + 478, + 544, + 597 + ], + "spans": [ + { + "bbox": [ + 307, + 478, + 544, + 597 + ], + "type": "table", + "html": "
MethodL1-Chamfer ↓N. C. ↑F-Score@1.5 ↑
BFM [29]1.341e-20.9360.319
FLAME [18]0.640e-20.9310.530
Global PCA [1]0.563e-20.9540.571
Local PCA [1]0.416e-20.9600.756
ImFace [45]0,404e-20.9540.832
ImFace* [45]0.312e-20.9710.883
NPM [24]0.200e-20.9750.947
Ours0.182e-20.9780.954
", + "image_path": "b9d3f4e503c36972244a689e1d4fe7d7e11e8c67fb9de22b49a29c840ad6203d.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 310, + 598, + 388, + 608 + ], + "lines": [ + { + "bbox": [ + 310, + 598, + 388, + 608 + ], + "spans": [ + { + "bbox": [ + 310, + 598, + 388, + 608 + ], + "type": "text", + "content": "* trained on our data" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 613, + 544, + 624 + ], + "lines": [ + { + "bbox": [ + 306, + 613, + 544, + 624 + ], + "spans": [ + { + "bbox": [ + 306, + 613, + 544, + 624 + ], + "type": "text", + "content": "Table 2. Identity fitting to a single depth map in neutral expression." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 306, + 647, + 454, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 647, + 454, + 659 + ], + "spans": [ + { + "bbox": [ + 306, + 647, + 454, + 659 + ], + "type": "text", + "content": "5.2. Expression Reconstruction" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "content": "To evaluate each model's expression space, we fit it to multiple expressions of the same person with the task of recovering one identity code per subject and one expression code per expression. For the neural forward deformation" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21008" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 70, + 547, + 381 + ], + "blocks": [ + { + "bbox": [ + 49, + 70, + 547, + 381 + ], + "lines": [ + { + "bbox": [ + 49, + 70, + 547, + 381 + ], + "spans": [ + { + "bbox": [ + 49, + 70, + 547, + 381 + ], + "type": "image", + "image_path": "79c50e93df5d9593a8f4c3a433816bdc3ad04f4a6d3269137c76766b55f47bcc.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 388, + 546, + 422 + ], + "lines": [ + { + "bbox": [ + 46, + 388, + 546, + 422 + ], + "spans": [ + { + "bbox": [ + 46, + 388, + 546, + 422 + ], + "type": "text", + "content": "Figure 5. Model fitting: at inference time, we fit our model to sparse, partial input point clouds from single depth map. We compare our method to widely-used state-of-the-art parametric face models, including FLAME [18], a local PCA [1], ImFace [45] and neural parametric models (NPM) [24]. Our parametric model has significantly more surface detail and covers the entire head, including the hair region." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 441, + 287, + 561 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 441, + 287, + 561 + ], + "spans": [ + { + "bbox": [ + 46, + 441, + 287, + 561 + ], + "type": "text", + "content": "models, NPM and NPHM, we utilize iterative root finding [10] to fit the expression codes. For simplicity, we keep the identity code fixed after fitting to the neutral scan. For all other models we jointly solve for expression and identity codes. Figure 6 and Table 3 show qualitative and quantitative comparisons with our baselines, respectively. Owing to the ability of backward deformations to directly connect the observed with the canonical space, ImFace reliably reconstructs expressions. Nevertheless, it still suffers from blurry reconstructions, compared to both NPM and NPHM." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 563, + 287, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 563, + 287, + 598 + ], + "spans": [ + { + "bbox": [ + 46, + 563, + 287, + 598 + ], + "type": "text", + "content": "See our supplemental for more details and an additional comparison of jointly fitting identity and expression when only a single depth observation is available." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 609, + 115, + 621 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 609, + 115, + 621 + ], + "spans": [ + { + "bbox": [ + 47, + 609, + 115, + 621 + ], + "type": "text", + "content": "5.3. Ablations" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 629, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 629, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 629, + 288, + 713 + ], + "type": "text", + "content": "We ablate two main contributions of the proposed identity representation, by fitting identity codes to a neutral scan without involving expressions. First, we analyze the effect of the number of regions " + }, + { + "bbox": [ + 46, + 629, + 288, + 713 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 629, + 288, + 713 + ], + "type": "text", + "content": " of our ensemble, by comparing against NPM [24], which effectively would be an ensemble of size 1, and against versions with 12 and 26 regions and adjusted number of latent dimensions. Additionally, we" + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 307, + 439, + 545, + 559 + ], + "blocks": [ + { + "bbox": [ + 307, + 439, + 545, + 559 + ], + "lines": [ + { + "bbox": [ + 307, + 439, + 545, + 559 + ], + "spans": [ + { + "bbox": [ + 307, + 439, + 545, + 559 + ], + "type": "table", + "html": "
MethodL1-Chamfer ↓N. C. ↑F-Score@1.5 ↑
BFM [29]1.271e-20.9370.508
FLAME [18]0.679e-20.9240.351
Global PCA [1]0.515e-20.9560.606
Local PCA [1]0.535e-20.9500.641
ImFace [45]0.369e-20.9590.824
ImFace* [45]0.321e-20.9710.879
NPM [24]0.299e-20.9620.891
Ours0.272e-20.9690.913
", + "image_path": "41d3b36bf15c4c56939979f23d699e526b6dd0e8f8ce79c945313b13a4c15f79.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 308, + 560, + 387, + 570 + ], + "lines": [ + { + "bbox": [ + 308, + 560, + 387, + 570 + ], + "spans": [ + { + "bbox": [ + 308, + 560, + 387, + 570 + ], + "type": "text", + "content": "* trained on our data" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_footnote" + }, + { + "bbox": [ + 310, + 575, + 539, + 586 + ], + "lines": [ + { + "bbox": [ + 310, + 575, + 539, + 586 + ], + "spans": [ + { + "bbox": [ + 310, + 575, + 539, + 586 + ], + "type": "text", + "content": "Table 3. Expression fitting on 23 single depth maps per person." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 602, + 545, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 602, + 545, + 639 + ], + "spans": [ + { + "bbox": [ + 305, + 602, + 545, + 639 + ], + "type": "text", + "content": "confirm the benefit of sharing weights for symmetric keypoints. Table 4 shows a quantitative evaluation of these two ablations supporting our design choices." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 647, + 383, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 647, + 383, + 658 + ], + "spans": [ + { + "bbox": [ + 306, + 647, + 383, + 658 + ], + "type": "text", + "content": "5.4. Limitations" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 665, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 665, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 665, + 546, + 713 + ], + "type": "text", + "content": "In our experiments, we show that NPHM can reconstruct high-quality human heads; however, at the same time, we believe that there are still several limitations and opportunities for future work. For instance we focus solely on the" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21009" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 61, + 547, + 300 + ], + "blocks": [ + { + "bbox": [ + 47, + 61, + 547, + 300 + ], + "lines": [ + { + "bbox": [ + 47, + 61, + 547, + 300 + ], + "spans": [ + { + "bbox": [ + 47, + 61, + 547, + 300 + ], + "type": "image", + "image_path": "89b42c2feda44afa20321f7ea434b03868b53f001c78323749b9042a74dac86d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 306, + 547, + 340 + ], + "lines": [ + { + "bbox": [ + 46, + 306, + 547, + 340 + ], + "spans": [ + { + "bbox": [ + 46, + 306, + 547, + 340 + ], + "type": "text", + "content": "Figure 6. Comparison on fitting expressions to sparse input point clouds: from a sparse set of depth observations of different expressions from a frontal view (left), we compare FLAME [18], a local PCA [1], ImFace [45], neural parametric models (NPM) [24], and our method against the respective ground truth scans." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 53, + 358, + 282, + 443 + ], + "blocks": [ + { + "bbox": [ + 53, + 358, + 282, + 443 + ], + "lines": [ + { + "bbox": [ + 53, + 358, + 282, + 443 + ], + "spans": [ + { + "bbox": [ + 53, + 358, + 282, + 443 + ], + "type": "table", + "html": "
MethodL1-Chamfer ↓N. C. ↑F-Score@1.5 ↑
NPM [24]0.2540.9720.906
K=12, w/ sy.0.2890.9660.876
K=26, w/ sy.0.2370.9710.913
K=39, w/o sy.0.2300.9740.917
Ours0.2060.9760.938
", + "image_path": "40c13d52cd50b0f3e99fc2d9648b23d0a89d17f2275ceb2d28a5b9fdf1c1489e.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 445, + 287, + 502 + ], + "lines": [ + { + "bbox": [ + 46, + 445, + 287, + 502 + ], + "spans": [ + { + "bbox": [ + 46, + 445, + 287, + 502 + ], + "type": "text", + "content": "Table 4. Effect of the number of anchor points " + }, + { + "bbox": [ + 46, + 445, + 287, + 502 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 445, + 287, + 502 + ], + "type": "text", + "content": " and symmetry on identity reconstruction performance. NPM represents the extreme case of using exactly 1 anchor point. Note that to be consistent with the original version, NPM differs to the other models in both width and depth of the underlying MLP." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 537, + 287, + 634 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 537, + 287, + 634 + ], + "spans": [ + { + "bbox": [ + 46, + 537, + 287, + 634 + ], + "type": "text", + "content": "geometry of heads while omitting any information about appearance. This makes our model ill-suited for fitting to RGB images using dense photometric terms. Here, an interesting future avenue would be to explore learning appearance, anchored on top of the geometric base model. In fact, as part of our dataset we also provide the RGB frames captured during the 3D scanning process, which should facilitate learning such a texture model." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 641, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 288, + 715 + ], + "type": "text", + "content": "Another limitation is that currently we do not capture open hair, which limits general diversity; however, compared to other existing face models such as 3D morphable models, we significantly expand the application domain by covering the entirety of the human head. In the future, we still would like to cover a broader range of hairstyles." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 306, + 358, + 379, + 372 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 358, + 379, + 372 + ], + "spans": [ + { + "bbox": [ + 306, + 358, + 379, + 372 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 379, + 547, + 583 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 379, + 547, + 583 + ], + "spans": [ + { + "bbox": [ + 304, + 379, + 547, + 583 + ], + "type": "text", + "content": "We have introduced neural parametric head models, a neural representation which disentangles identity and expressions of human heads, by representing geometry in canonical space and modelling expressions as forward deformations. For our identity representation we have proposed and validated a local representation that is tailored towards human head. To train our model, we introduce a new dataset of over 3700 high-fidelity 3D scans. Once trained, our model can be fitted to sparse input point clouds, for instance, captured by a commodity range sensor. Compared to existing methods, such as widely-used PCA-based techniques, our model represents significantly more detail while being able to regularize out noise of the underlying point cloud inputs. Overall, we believe that our method is an important step towards high-fidelity face capture and our newly-introduced dataset opens up opportunities to further explore learning priors for neural face models." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 599, + 392, + 611 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 599, + 392, + 611 + ], + "spans": [ + { + "bbox": [ + 306, + 599, + 392, + 611 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 617, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 547, + 715 + ], + "type": "text", + "content": "This work was supported by the ERC Starting Grant Scan2CAD (804724), the German Research Foundation (DFG) Grant \"Making Machine Learning on Static and Dynamic 3D Data Practical\", the German Research Foundation (DFG) Research Unit \"Learning and Simulation in Visual Computing\", and Synthesia. We would like to thank Maximilian Knörl and Tim Walter for the help with scanning, and Angela Dai for the video voiceover." + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21010" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "text", + "content": "[1] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 187-194, 1999. 2, 6, 7, 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 137, + 288, + 180 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 137, + 288, + 180 + ], + "spans": [ + { + "bbox": [ + 53, + 137, + 288, + 180 + ], + "type": "text", + "content": "[2] Timo Bolkart and Stefanie Wuhrer. A groupwise multilinear correspondence optimization for 3d faces. In Proceedings of the IEEE international conference on computer vision, pages 3604-3612, 2015. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 182, + 288, + 238 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 182, + 288, + 238 + ], + "spans": [ + { + "bbox": [ + 53, + 182, + 288, + 238 + ], + "type": "text", + "content": "[3] James Booth, Epameinondas Antonakos, Stylianos Ploumpis, George Trigeorgis, Yannis Panagakis, and Stefanos Zafeiriou. 3d face morphable models\" in-the-wild\". In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 48-57, 2017. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 239, + 288, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 239, + 288, + 293 + ], + "spans": [ + { + "bbox": [ + 53, + 239, + 288, + 293 + ], + "type": "text", + "content": "[4] James Booth, Anastasios Roussos, Stefanos Zafeiriou, Allan Ponniah, and David Dunaway. A 3d morphable model learnt from 10,000 faces. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5543-5552, 2016. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 295, + 288, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 295, + 288, + 361 + ], + "spans": [ + { + "bbox": [ + 53, + 295, + 288, + 361 + ], + "type": "text", + "content": "[5] Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Michael Bronstein, and Stefanos Zafeiriou. Neural 3d morphable models: Spiral convolutional networks for 3d shape representation learning and generation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7213-7222, 2019. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 363, + 287, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 363, + 287, + 407 + ], + "spans": [ + { + "bbox": [ + 53, + 363, + 287, + 407 + ], + "type": "text", + "content": "[6] Alan Brunton, Timo Bolkart, and Stefanie Wuhrer. Multilinear wavelets: A statistical shape space for human faces. In European Conference on Computer Vision, pages 297-312. Springer, 2014. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 409, + 287, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 409, + 287, + 441 + ], + "spans": [ + { + "bbox": [ + 53, + 409, + 287, + 441 + ], + "type": "text", + "content": "[7] Chen Cao, Yanlin Weng, Shun Zhou, Yiying Tong, and Kun Zhou. Facewarehouse: A 3d facial expression database for visual computing. 20(3):413-425, mar 2014. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 443, + 287, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 443, + 287, + 520 + ], + "spans": [ + { + "bbox": [ + 53, + 443, + 287, + 520 + ], + "type": "text", + "content": "[8] Rohan Chabra, Jan E. Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe. Deep local shapes: Learning local sdf priors for detailed 3d reconstruction. In Computer Vision - ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIX, page 608-625, Berlin, Heidelberg, 2020. Springer-Verlag. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 521, + 287, + 564 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 521, + 287, + 564 + ], + "spans": [ + { + "bbox": [ + 53, + 521, + 287, + 564 + ], + "type": "text", + "content": "[9] Xu Chen, Tianjian Jiang, Jie Song, Jinlong Yang, Michael J. Black, Andreas Geiger, and Otmar Hilliges. gdna: Towards generative detailed neural avatars. CoRR, abs/2201.04123, 2022. 2, 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 567, + 287, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 567, + 287, + 610 + ], + "spans": [ + { + "bbox": [ + 48, + 567, + 287, + 610 + ], + "type": "text", + "content": "[10] Xu Chen, Yufeng Zheng, Michael J Black, Otmar Hilliges, and Andreas Geiger. Snarf: Differentiable forward skimming for animating non-rigid neural implicit shapes. In International Conference on Computer Vision (ICCV), 2021. 2, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 613, + 288, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 613, + 288, + 656 + ], + "spans": [ + { + "bbox": [ + 48, + 613, + 288, + 656 + ], + "type": "text", + "content": "[11] Zhixiang Chen and Tae-Kyun Kim. Learning feature aggregation for deep 3d morphable models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13164-13173, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "type": "text", + "content": "[12] Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser. Local deep implicit functions for 3d shape. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4857-4866, 2020. 2, 5" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 712 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "text", + "content": "[13] Simon Giebenhain and Bastian Goldluecke. Air-nets: An attention-based framework for locally conditioned implicit representations. In 2021 International Conference on 3D Vision (3DV). IEEE, 2021. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 118, + 545, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 171 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 171 + ], + "type": "text", + "content": "[14] Shunwang Gong, Lei Chen, Michael Bronstein, and Stefanos Zafeiriou. Spiralnet++: A fast and highly efficient mesh convolution operator. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pages 0-0, 2019. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 173, + 545, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 173, + 545, + 205 + ], + "spans": [ + { + "bbox": [ + 307, + 173, + 545, + 205 + ], + "type": "text", + "content": "[15] Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman. Implicit geometric regularization for learning shapes. arXiv preprint arXiv:2002.10099, 2020. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 206, + 547, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 206, + 547, + 270 + ], + "spans": [ + { + "bbox": [ + 307, + 206, + 547, + 270 + ], + "type": "text", + "content": "[16] Ruilong Li, Karl Bladin, Yajie Zhao, Chinmay Chinara, Owen Ingraham, Pengda Xiang, Xinglei Ren, Pratusha Prasad, Bipin Kishore, Jun Xing, and Hao Li. Learning formation of physically-based face attributes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 272, + 545, + 315 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 272, + 545, + 315 + ], + "spans": [ + { + "bbox": [ + 307, + 272, + 545, + 315 + ], + "type": "text", + "content": "[17] Ruilong Li, Julian Tanke, Minh Vo, Michael Zollhofer, Jurgen Gall, Angjoo Kanazawa, and Christoph Lassner. Tava: Template-free animatable volumetric actors. arXiv preprint arXiv:2206.08929, 2022. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 316, + 545, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 316, + 545, + 369 + ], + "spans": [ + { + "bbox": [ + 307, + 316, + 545, + 369 + ], + "type": "text", + "content": "[18] Tianye Li, Timo Bolkart, Michael. J. Black, Hao Li, and Javier Romero. Learning a model of facial shape and expression from 4D scans. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6):194:1-194:17, 2017. 2, 3, 6, 7, 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 371, + 545, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 371, + 545, + 425 + ], + "spans": [ + { + "bbox": [ + 307, + 371, + 545, + 425 + ], + "type": "text", + "content": "[19] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural actor: Neural free-view synthesis of human actors with pose control. ACM Transactions on Graphics (TOG), 40(6):1-16, 2021. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 426, + 545, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 426, + 545, + 502 + ], + "spans": [ + { + "bbox": [ + 307, + 426, + 545, + 502 + ], + "type": "text", + "content": "[20] Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Yong, Juhyun Lee, Wan-Teh Chang, Wei Hua, Manfred Georg, and Matthias Grundmann. Mediapipe: A framework for perceiving and processing reality. In Third Workshop on Computer Vision for AR/VR at IEEE Computer Vision and Pattern Recognition (CVPR) 2019, 2019. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 504, + 545, + 558 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 504, + 545, + 558 + ], + "spans": [ + { + "bbox": [ + 307, + 504, + 545, + 558 + ], + "type": "text", + "content": "[21] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4460-4470, 2019. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 559, + 545, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 559, + 545, + 612 + ], + "spans": [ + { + "bbox": [ + 307, + 559, + 545, + 612 + ], + "type": "text", + "content": "[22] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 614, + 545, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 614, + 545, + 657 + ], + "spans": [ + { + "bbox": [ + 307, + 614, + 545, + 657 + ], + "type": "text", + "content": "[23] Thomas Neumann, Kiran Varanasi, Stephan Wenger, Markus Wacker, Marcus Magnor, and Christian Theobalt. Sparse localized deformation components. ACM Trans. Graph., 32(6), nov 2013. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 658, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 658, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 307, + 658, + 545, + 712 + ], + "type": "text", + "content": "[24] Pablo Palafox, Aljaž Božić, Justus Thies, Matthias Nießner, and Angela Dai. Npms: Neural parametric models for 3d deformable shapes. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 12695–12705, October 2021. 2, 5, 6, 7, 8" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "21011" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 105 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 105 + ], + "type": "text", + "content": "[25] Pablo Palafox, Nikolaos Sarafianos, Tony Tung, and Angela Dai. Spams: Structured implicit parametric models. CVPR, 2022. 2, 5" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 107, + 287, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 107, + 287, + 162 + ], + "spans": [ + { + "bbox": [ + 49, + 107, + 287, + 162 + ], + "type": "text", + "content": "[26] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 163, + 288, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 163, + 288, + 218 + ], + "spans": [ + { + "bbox": [ + 49, + 163, + 288, + 218 + ], + "type": "text", + "content": "[27] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5865-5874, 2021. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 220, + 287, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 220, + 287, + 274 + ], + "spans": [ + { + "bbox": [ + 49, + 220, + 287, + 274 + ], + "type": "text", + "content": "[28] Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Ricardo MartinBrualla, and Steven M. Seitz. Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields. ACM Trans. Graph., 40(6), dec 2021. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 276, + 287, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 276, + 287, + 330 + ], + "spans": [ + { + "bbox": [ + 49, + 276, + 287, + 330 + ], + "type": "text", + "content": "[29] Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. In 2009 sixth IEEE international conference on advanced video and signal based surveillance, pages 296-301. IEEE, 2009. 2, 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 332, + 287, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 332, + 287, + 376 + ], + "spans": [ + { + "bbox": [ + 49, + 332, + 287, + 376 + ], + "type": "text", + "content": "[30] Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In European Conference on Computer Vision, pages 523-540. Springer, 2020. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 377, + 287, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 377, + 287, + 441 + ], + "spans": [ + { + "bbox": [ + 49, + 377, + 287, + 441 + ], + "type": "text", + "content": "[31] Stylianos Ploumpis, Haoyang Wang, Nick Pears, William AP Smith, and Stefanos Zafeiriou. Combining 3d morphable models: A large scale face-and-head model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10934-10943, 2019. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 49, + 444, + 287, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 444, + 287, + 498 + ], + "spans": [ + { + "bbox": [ + 49, + 444, + 287, + 498 + ], + "type": "text", + "content": "[32] Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. H3d-net: Few-shot high-fidelity 3d head reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5620-5629, 2021. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 49, + 500, + 287, + 554 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 500, + 287, + 554 + ], + "spans": [ + { + "bbox": [ + 49, + 500, + 287, + 554 + ], + "type": "text", + "content": "[33] Christos Sagonas, Georgios Tzimiropoulos, Stefanos Zafeiriou, and Maja Pantic. 300 faces in-the-wild challenge: The first facial landmark localization challenge. In Proceedings of the IEEE international conference on computer vision workshops, pages 397-403, 2013. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 49, + 556, + 287, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 556, + 287, + 600 + ], + "spans": [ + { + "bbox": [ + 49, + 556, + 287, + 600 + ], + "type": "text", + "content": "[34] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462-7473, 2020. 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 49, + 601, + 287, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 601, + 287, + 643 + ], + "spans": [ + { + "bbox": [ + 49, + 601, + 287, + 643 + ], + "type": "text", + "content": "[35] Janujah Sivanandan, Eugene Liscio, and P Eng. Assessing structured light 3d scanning using artec eva for injury documentation during autopsy. J Assoc Crime Scene Reconstr, 21:5-14, 2017. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 49, + 647, + 287, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 647, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 49, + 647, + 287, + 689 + ], + "type": "text", + "content": "[36] Olga Sorkine and Marc Alexa. As-Rigid-As-Possible Surface Modeling. In Alexander Belyaev and Michael Garland, editors, Geometry Processing. The Eurographics Association, 2007. 4" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 49, + 692, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 692, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 49, + 692, + 287, + 713 + ], + "type": "text", + "content": "[37] Luan Tran, Feng Liu, and Xiaoming Liu. Towards high-fidelity nonlinear 3d face morphable model. In Proceedings" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 710 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "text", + "content": "of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1126-1135, 2019. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 308, + 96, + 545, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 545, + 128 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 545, + 128 + ], + "type": "text", + "content": "[38] Luan Tran and Xiaoming Liu. Nonlinear 3d face morphable model. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7346-7355, 2018. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 129, + 545, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 129, + 545, + 171 + ], + "spans": [ + { + "bbox": [ + 308, + 129, + 545, + 171 + ], + "type": "text", + "content": "[39] Luan Tran and Xiaoming Liu. On learning 3d face morphable model from in-the-wild images. IEEE transactions on pattern analysis and machine intelligence, 43(1):157-171, 2019. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 172, + 545, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 172, + 545, + 215 + ], + "spans": [ + { + "bbox": [ + 308, + 172, + 545, + 215 + ], + "type": "text", + "content": "[40] Shinji Umeyama. Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(04):376-380, 1991. 4" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 217, + 545, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 217, + 545, + 259 + ], + "spans": [ + { + "bbox": [ + 308, + 217, + 545, + 259 + ], + "type": "text", + "content": "[41] Daoye Wang, Prashanth Chandran, Gaspard Zoss, Derek Bradley, and Paulo Gotardo. Morf: Morphable radiance fields for multiview neural head modeling. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1-9, 2022. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 260, + 545, + 314 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 260, + 545, + 314 + ], + "spans": [ + { + "bbox": [ + 308, + 260, + 545, + 314 + ], + "type": "text", + "content": "[42] Lizhen Wang, Zhiyua Chen, Tao Yu, Chenguang Ma, Liang Li, and Yebin Liu. Faceverse: a fine-grained and detail-controllable 3d face morphable model from a hybrid dataset. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR2022), June 2022. 2, 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 316, + 545, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 316, + 545, + 369 + ], + "spans": [ + { + "bbox": [ + 308, + 316, + 545, + 369 + ], + "type": "text", + "content": "[43] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. Facescape: A large-scale high quality 3d face dataset and detailed riggable 3d face prediction. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 2, 3, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 370, + 545, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 370, + 545, + 435 + ], + "spans": [ + { + "bbox": [ + 308, + 370, + 545, + 435 + ], + "type": "text", + "content": "[44] Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and Christian Theobalt. i3dmm: Deep implicit 3d morphable model of human heads. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12803-12813, 2021. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 436, + 545, + 490 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 436, + 545, + 490 + ], + "spans": [ + { + "bbox": [ + 308, + 436, + 545, + 490 + ], + "type": "text", + "content": "[45] Mingwu Zheng, Hongyu Yang, Di Huang, and Liming Chen. Imface: A nonlinear 3d morphable face model with implicit neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 2, 6, 7, 8" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 491, + 545, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 491, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 308, + 491, + 545, + 533 + ], + "type": "text", + "content": "[46] Yufeng Zheng, Victoria Fernández Abrevaya, Xu Chen, Marcel C. Bühler, Michael J. Black, and Otmar Hilliges. I M avatar: Implicit morphable head avatars from videos. CoRR, abs/2112.07471, 2021. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 535, + 545, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 535, + 545, + 588 + ], + "spans": [ + { + "bbox": [ + 308, + 535, + 545, + 588 + ], + "type": "text", + "content": "[47] Yinglin Zheng, Hao Yang, Ting Zhang, Jianmin Bao, Dongdong Chen, Yangyu Huang, Lu Yuan, Dong Chen, Ming Zeng, and Fang Wen. General facial representation learning in a visual-linguistic manner. arXiv preprint arXiv:2112.03109, 2021. 4" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 590, + 545, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 590, + 545, + 643 + ], + "spans": [ + { + "bbox": [ + 308, + 590, + 545, + 643 + ], + "type": "text", + "content": "[48] Zerong Zheng, Han Huang, Tao Yu, Hongwen Zhang, Yandong Guo, and Yebin Liu. Structured local radiance fields for human avatar modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022. 2, 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 644, + 545, + 710 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 644, + 545, + 710 + ], + "spans": [ + { + "bbox": [ + 308, + 644, + 545, + 710 + ], + "type": "text", + "content": "[49] Michael Zollhöfer, Justus Thies, Pablo Garrido, Derek Bradley, Thabo Beeler, Patrick Pérez, Marc Stamminger, Matthias Nießner, and Christian Theobalt. State of the art on monocular 3d face reconstruction, tracking, and applications. In Computer graphics forum, volume 37, pages 523-550. Wiley Online Library, 2018. 1" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "21012" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild/5ebd965c-2fcd-48b5-82dd-8be4b32c7eaf_content_list.json b/2023/Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild/5ebd965c-2fcd-48b5-82dd-8be4b32c7eaf_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..04eade6a4becef2821c926cec4d6751d08fcc2fd --- /dev/null +++ b/2023/Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild/5ebd965c-2fcd-48b5-82dd-8be4b32c7eaf_content_list.json @@ -0,0 +1,1530 @@ +[ + { + "type": "text", + "text": "Learning Neural Proto-face Field for Disentangled 3D Face Modeling In the Wild", + "text_level": 1, + "bbox": [ + 230, + 161, + 739, + 201 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Zhenyu Zhang $^{1}$ , Renwang Chen $^{1}$ , Weijian Cao $^{1}$ , Ying Tai $^{1*}$ , Chengjie Wang $^{1,2*}$ \nTencent Youtu Lab, Shanghai, China $^{1}$ \nShanghai Jiao Tong University, Shanghai, China $^{2}$", + "bbox": [ + 174, + 226, + 800, + 277 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "zhangjesse@foxmail.com", + "bbox": [ + 386, + 280, + 583, + 292 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "renwangchen, weijiancao, yingtai, jasoncjwang@tencent.com", + "bbox": [ + 228, + 296, + 734, + 309 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 340, + 312, + 355 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Generative models show good potential for recovering 3D faces beyond limited shape assumptions. While plausible details and resolutions are achieved, these models easily fail under extreme conditions of pose, shadow or appearance, due to the entangled fitting or lack of multiview priors. To address this problem, this paper presents a novel Neural Proto-face Field (NPF) for unsupervised robust 3D face modeling. Instead of using constrained images as Neural Radiance Field (NeRF), NPF disentangles the common/specific facial cues, i.e., ID, expression and scene-specific details from in-the-wild photo collections. Specifically, NPF learns a face prototype to aggregate 3D-consistent identity via uncertainty modeling, extracting multi-image priors from a photo collection. NPF then learns to deform the prototype with the appropriate facial expressions, constrained by a loss of expression consistency and personal idiosyncrasies. Finally, NPF is optimized to fit a target image in the collection, recovering specific details of appearance and geometry. In this way, the generative model benefits from multi-image priors and meaningful facial structures. Extensive experiments on benchmarks show that NPF recovers superior or competitive facial shapes and textures, compared to state-of-the-art methods.", + "bbox": [ + 75, + 371, + 473, + 690 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 78, + 719, + 208, + 733 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "3D face reconstruction is a long-standing problem with applications including games, digital human and mobile photography. It is ill-posed in many cases requiring strong assumptions e.g., shape from shading [99]. With the 3D Morphable Model (3DMM) [10] proposed, such a problem can be solved by fitting parameters to the target faces [67, 68, 107]. Recently, deep-learning methods [22, 25, 43, 64,", + "bbox": [ + 75, + 743, + 468, + 840 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/1d5b92629b0a0a31ff39bdc5ad65f02ea7692cfc2c9cc1429f5034d90658f06f.jpg", + "image_caption": [ + "Figure 1. (a) Comparison with graphics-renderer-based methods LAP [100] and D3DFR [20]. Our method models geometry details and photo-realistic texture. (b) Results of neural rendering methods EG3D [13] + PTI [66], HeadNeRF [34] and our method. Our method produces high-quality geometry, robust texture modeling under rotation and deformation." + ], + "image_footnote": [], + "bbox": [ + 521, + 339, + 870, + 589 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "105] are proposed to regress 3DMM parameters from input images. These approaches are then improved by nonlinear modeling [24, 29, 79, 81, 84, 94] and multi-view consistency [7, 15, 76, 90]. Besides 3DMM methods, recent efforts [91, 100] attempt to model 3D face without shape assumptions. These non-parametric methods have potential ability to improve the modeling quality beyond 3DMM.", + "bbox": [ + 497, + 685, + 890, + 782 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Although the aforementioned methods achieve impressive performance, they also have obvious drawbacks. On the one hand, as the parametric models are usually built from a small amount of subjects (e.g., BFM [58] with 200 subjects) and rigidly controlled conditions, they may be fragile to large variations of identity [106], and have limi", + "bbox": [ + 497, + 783, + 892, + 866 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 129, + 49, + 200, + 85 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 252, + 48, + 791, + 86 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Chengjie Wang and Ying Tai are corresponding authors", + "bbox": [ + 94, + 853, + 394, + 866 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "382", + "bbox": [ + 472, + 887, + 497, + 897 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "tations on building teeth, skin details or anatomic grounded muscles [23]. On the other hand, all of these methods depend on graphics renderers [42, 44, 46] in the analysis-by-synthesis fitting procedure, and thus yields hand-crafted approximation or ill-posed decomposition on intrinsic clues. Hence, as illustrated in Fig. 1-(a), these methods struggle to produce photo-realistic texture or geometric details.", + "bbox": [ + 75, + 125, + 468, + 223 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Against these limitations, efforts are made to use a neural renderer such as StyleGAN [38, 39] to model faces by inverting the corresponding images [1,2] into $\\mathcal{W}$ space. Existing methods [11, 18, 59, 62, 92] mainly learn to embed 3DMM coefficients to implicitly leverage 3D clues, but they have difficulty achieving precise 3D controls due to their entangled image formation. To disentangle neural rendering, recent works [13, 14, 34, 54] employ explicit 3D pipelines, e.g., Neural Radiance Field (NeRF) [52] into the StyleGANs' framework, so that face shapes and camera views can be extracted. In this way, precise 3D controls and detailed geometry can be obtained. However, these methods still show fragile performance under challenging conditions as shown in Fig. 1-(b). When confronting large poses, extreme appearance or lighting, the lack of facial priors disturbs the reconstruction and results in severe distortions. This is due to the essentially overfitting objective of inverting single target image, where the geometry ambiguity is unavoidable.", + "bbox": [ + 75, + 224, + 470, + 486 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "On top of this, one solution is to leverage reliable priors, e.g., multi-image consistency as a complement. While NeRF provides a natural paradigm to dig multi-view cues, it requires fully constrained images that are difficult to obtain. Even conditioned by style codes [13, 14, 54], there is no direct way to build 3D faces from unconstrained portrait collections in such a neural rendering mechanism. In this work, we present a novel Neural Proto-face Field (NPF) for unsupervised robust 3D face modeling, where ID, expression and scene-specific details can be disentangled from in-the-wild photo collections. To aggregate ID-aware cues, NPF leverages uncertainty modeling to extract multi-image priors and recovers a face prototype with ID-consistent face shape. To disentangle the expression, NPF then learns appropriate representations to deform the prototype, constrained by a expression consistency loss. In this way, the learned face shape is properly blended to avoid geometric ambiguity. Finally, to recover the scene-specific details, NPF is optimized to fit a target image in the collection. The robustness of fitting is guaranteed by a geometry and appearance regularization. As shown in Fig. 1-(b), NPF makes the generative method benefit from multi-image priors in unconstrained environments, and produces high-quality 3D faces under challenging conditions.", + "bbox": [ + 75, + 489, + 468, + 820 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In summary, our contributions are as follows:", + "bbox": [ + 95, + 823, + 396, + 836 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "1) A novel Neural Proto-face Field (NPF) is proposed to disentangle ID, expression and specific details from 3D face", + "bbox": [ + 75, + 838, + 468, + 866 + ], + "page_idx": 1 + }, + { + "type": "table", + "img_path": "images/aee8e5cf8135add311a21fe67b72e10d6471f7cbfbd184fd0da0d7ef3a9a1570.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodsRenderingPipelineMulti-view
EMOCA [81], DECA [24], Unsup3D [91]GraphicsDisentangled×
LAP [100], FML [76], MVF [90]GraphicsDisentangled✓
DFG [18], StyleRig [77], StyleFlow [3]NeuralEntangled×
Pi-GAN [14], StyleSDF [54], EG3D [13]NeuralDisentangled×
OursNeuralDisentangled✓
", + "bbox": [ + 504, + 124, + 890, + 187 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Table 1. Discussion with selected existing methods.", + "bbox": [ + 541, + 195, + 847, + 207 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "modeling, which uses in-the-wild photo collections to benefit the 3D generative model under challenging conditions.", + "bbox": [ + 497, + 222, + 890, + 250 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "2) With a novel face prototype aggregation method, NPF integrates multi-image face priors against the large variations in unconstrained environments.", + "3) With a series of novel consistency losses, NPF is well fit to specific scenes with personalized details, based on the guidance of face prototypes." + ], + "bbox": [ + 497, + 250, + 890, + 333 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 499, + 345, + 648, + 359 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In Table 1, we make a discussion on existing methods. NPF benefits from neural rendering and avoids hand-crafted approximations of graphics renderers. In contrast to neural rendering methods, our approach has explicit 3D pipelines, and leverages multi-image consistency in the wild.", + "bbox": [ + 497, + 369, + 890, + 437 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3D Face Reconstruction: As a long-standing problem, the studies mainly start from the pioneer work 3DMM [10]. With the parametric model, early works try to find suitable parameters via optimization [67, 68, 107], while recent approaches [25, 64, 105, 106] leverage deep neural networks to regress the results from input images. With the differentiable renderers proposed, efforts are made on aspects of unsupervised learning [30, 65, 81], improving the non-linear feasibility [16, 21, 24, 29, 81, 84, 104] and multi-view consistency [7, 15, 76, 90]. More recent works attempt to learn complete 3DMM basis [79] or implicit functions [94, 102] which brings new possibilities to this topic.", + "bbox": [ + 497, + 437, + 890, + 603 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Beyond 3DMM, non-parametric models are also developed by data-driven supervised training [4, 35, 97]. With shape-from-shading algorithm [99], unsupervised methods are proposed including SFS-Net [70], Unsup3D [91] and LAP [100]. Recently, Gan2Shape [55] and LiftedGAN [72] try to distill knowledge from 2D GANs for 3D reconstruction. De3D [89] de-renders more intrinsic factors based on reliable priors. Different from the these discussed methods, NPF benefits 3D face modeling via neural rendering mechanisms, thus provides better performance on details, resolution and non-facial objects.", + "bbox": [ + 497, + 604, + 890, + 755 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Neural Scene Representation: Neural scene representation is a novel way to parameterize signals, and attracts more attention on learning geometry [12, 49, 51, 74, 93]. Recent effort Neural Radiance Field (NeRF) [8, 9, 52] shows impressive performance on recovering appearance and shapes from multi-view images. Based on NeRF, 3D face can also be modeled [28,61,86,87] from a few or single views. Recent efforts are developed to perform reconstruc", + "bbox": [ + 497, + 756, + 890, + 866 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "383", + "bbox": [ + 472, + 887, + 497, + 897 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "tion against non-rigid deformation [56,57,60]. Conditioned on specific expressions, NeRF can also be used to realize editing [5, 36] or 4D facial avatars [27, 31, 103]. In summary, these methods depend on fully constrained environments, i.e., the used images have to be captured at a same time. In contrast, our method leverages face consistency from in-the-wild photo sets that are easy to collect, and addresses large variations of lighting, appearance or artifacts.", + "bbox": [ + 75, + 125, + 468, + 236 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Neural Rendering on Face Modeling: Neural rendering aims to render images using neural networks [50, 78, 80] with much relaxed inputs. The style-based approaches, i.e., StyleGANs [37-39] obtain the state-of-the-art performance on face generation. With the pretrained StyleGANs, face modeling can be achieved by inverting the target images [1,2,63,66]. 3D embedding (e.g., 3DMM parameter) is then employed to implicitly control the StyleGAN's prediction on pose, identity and lighting [3,18,77]. However, they cannot guarantee the robustness on physical perspective, nor obtain explicit geometry. More recent works attempt to employ 3D pipelines, such as NeRF or deferred neural rendering [82], into StyleGANs [11, 13, 14, 19, 32, 53, 54, 75, 101]. While these methods achieve more precise controls and facial geometry prediction, they are fragile to large pose and ambiguous appearance due to the single-image fitting. In contrast, NPF well leverages multi-image consistency as complementary priors and shows more robust performance.", + "bbox": [ + 75, + 237, + 470, + 485 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Preliminary", + "text_level": 1, + "bbox": [ + 75, + 500, + 201, + 515 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Exploiting the consistency of multiple images, especially from unconstrained environments, is non-trivial for neural rendering methods because there is no explicit topology that can be shared within the image set. While NeRF provides a natural paradigm to dig consistency from different views, it requires photos taken at the same time or constrained scene. Recent 3D-aware generative methods [13, 14, 32, 54] show that NeRF can represent different faces conditioned on style codes. Such an evidence makes it possible to learn common hybrid 3D representations from in-the-wild photo collections. Without loss of generality, we build our model on EG3D [13] as it achieves the state-of-the-art performance.", + "bbox": [ + 75, + 523, + 468, + 688 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "EG3D represents the 3D scene as a tri-plane denoted as:", + "bbox": [ + 95, + 688, + 467, + 703 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {F} _ {x y}, \\mathbf {F} _ {x z}, \\mathbf {F} _ {y z} = \\Phi_ {g} (\\mathbf {s}), \\mathbf {s} = \\left\\{s ^ {j} \\right\\} _ {j = 1} ^ {M}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 139, + 717, + 468, + 735 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathbf{F} \\in [H, W, C]$ is the orthogonal feature plane along different axis, and $\\Phi_g$ is the StyleGAN generator. $\\mathbf{s} \\in \\mathbb{R}^{14 \\times 512}$ is the style code with stages $M = 14$ , generated from a random noise $\\mathbf{z}$ by a mapping network. Then any 3D position $x \\in \\mathbb{R}^3$ can be projected onto each of the three feature planes, retrieving its feature $f = f_{xy} + f_{xz} + f_{yz}$ via bilinear interpolation. The corresponding density $\\sigma$ and color $\\mathbf{c}$ is predicted by a tri-plane decoder $\\Phi_d$ from $f$ . In this way, given a camera pose $p$ , origin $\\mathbf{o}$ and near/far bound $t_n, t_f$ ,", + "bbox": [ + 75, + 741, + 468, + 866 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "the pixel color $\\mathbf{C}$ for a ray $\\mathbf{r}(t) = \\mathbf{o} + t\\mathbf{d}$ can be obtained using volume rendering equation [48]:", + "bbox": [ + 497, + 125, + 890, + 153 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {C} (\\mathbf {r}) = \\int_ {t _ {n}} ^ {t _ {f}} T (t) \\sigma (\\mathbf {r} (t)) \\mathbf {c} (\\mathbf {r} (t), \\mathbf {d}) d t, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 564, + 162, + 890, + 195 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $T(t) = \\exp(\\int_{t_n}^t \\sigma(\\mathbf{r}(s)) ds$ , and $t$ is the sampled point along each ray. Using Eqn. 2, EG3D renders a low-resolution image $\\mathbf{I}_l$ as well as a feature image $\\mathbf{I}_f$ . $\\mathbf{I}_f$ is then fed into a super-resolution module $\\Phi_u$ to generate final face image $\\mathbf{I}$ . Adversarial loss is calculated between $\\mathbf{I}, \\mathbf{I}_l$ and the real image to update $\\Phi_g, \\Phi_d$ and $\\Phi_u$ . Once trained, given a target image $\\mathbf{I}_t$ , face modeling can be achieved by image inversion [1,63,66]. As discussed in Sec. 1, this kind of approaches suffer from ambiguous appearance and large poses. In contrast, NPF tackles the problem by further disentangling a face using multi-image priors, which is introduced in the following.", + "bbox": [ + 497, + 205, + 890, + 372 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Methodology", + "text_level": 1, + "bbox": [ + 497, + 385, + 633, + 401 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we introduce the proposed Neural Protoface Field (NPF). Our aim is digging multi-image priors to improve the robustness of 3D face modeling. This is achieved by disentangling ID-consistent shape, expression and specific details from in-the-wild photo collection. The overview is shown in Fig. 2, where NPF contains identity-aware aggregation and deformation modeling to recover face prototypes (Sec. 4.1). After NPF is learned, the target face is reconstructed by a fitting procedure (Sec. 4.2).", + "bbox": [ + 497, + 408, + 890, + 532 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.1. Neural Proto-face Field Learning", + "text_level": 1, + "bbox": [ + 497, + 541, + 791, + 555 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Face appearance of an identity shares consistent cues due to the invariant face geometry, even under different in-the-wild environments. Such consistency has been widely used in previous methods by sharing the coefficients [20,69,79,90] or combining the UV space [100, 101]. Inspired by these efforts, we propose NPF to aggregate a common hybrid 3D representation from each photo collection, which models a face prototype with ID-consistent shape from reliable priors.", + "bbox": [ + 497, + 562, + 890, + 686 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Identity-aware Aggregation: Our method starts from the style code $\\mathbf{s} = \\{s^j\\}_{j=1}^M$ , which well conditions the radiance field of tri-planes to render a face image $\\mathbf{I}$ . Theoretically, $\\mathbf{s}$ can be obtained from various methods [1,2,63,83]. Without specially statement, we use the most direct scheme proposed in [38] to optimize $\\mathbf{s}$ for real-image inversion. Given a photo collection $\\{\\mathbf{I}_i\\}_{i=1}^N$ of a same ID, we obtain $\\{\\mathbf{s}_i\\}_{i=1}^N$ in the $\\mathcal{W}$ space. Although finding optimal solution is difficult, $\\mathbf{s}_i$ still represents the face shape of $\\mathbf{I}_i$ , and such consistent cues should have much lower uncertainty than any other information within the image set. As a result, we propose an uncertainty-aware aggregation method inspired by [73] to extract the consistency. As illustrated in Fig. 2,", + "bbox": [ + 497, + 687, + 890, + 866 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "384", + "bbox": [ + 472, + 887, + 497, + 897 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/fcbff586a5927862544ee285a176adf69e4e9d88b45e1f6e0701c0a0ee6e4630.jpg", + "image_caption": [ + "Figure 2. Overview of NPF learning method. For an in-the-wild photo collection $\\{\\mathbf{I}_i\\}_{i=1}^N$ , we first obtain its style codes $\\{\\mathbf{s}_i\\}_{i=1}^N$ and uncertainties $\\{\\omega_i\\}_{i=1}^N$ , then perform identity-aware aggregation to get the pivotal proto code $\\bar{s}$ . To model deformation, we use 3DMM expression parameters $\\{\\beta_i\\}_{i=1}^N$ as conditions, and train deformation networks $\\{\\Phi_{\\Delta}^j\\}_{j=1}^M$ to predict deformation code $\\Delta \\mathbf{s}_i$ . We also learn global face representation $\\bar{h}^j$ at $j$ -th stage to complement for personalized idiosyncrasies. In this way, the ID-consistent face is suitably blended on the manifold by Eqn. 5. Training by Eqn. 7, NPF well leverages the consistency within in-the-wild portrait sets." + ], + "image_footnote": [], + "bbox": [ + 164, + 124, + 811, + 357 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "we use an encoder $\\Phi_{a}$ to get uncertainty $\\omega_{i} \\in \\mathbb{R}^{512}$ from $\\mathbf{I}_i$ , and assume $\\mathbf{I}_i$ to have a multivariate Gaussian representation $\\mathcal{N}(\\mathbf{s}_i, \\omega_i)$ in the latent space. Then the aggregation is performed as:", + "bbox": [ + 75, + 445, + 470, + 502 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\bar {\\mathbf {s}} = \\sum_ {i = 1} ^ {N} \\frac {\\omega^ {2}}{\\omega_ {i} ^ {2}} \\mathbf {s} _ {i}, \\quad \\frac {1}{\\omega^ {2}} = \\sum_ {i = 1} ^ {N} \\frac {1}{\\omega_ {i} ^ {2}}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 169, + 511, + 468, + 551 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\bar{\\mathbf{s}}$ is the pivotal proto code similar to PTI [66] but from the photo collection instead of a single image. When forcing $\\bar{\\mathbf{s}}$ to approximate each image, the consistency which has lower uncertainty within $\\{\\mathbf{I}_i\\}_{i=1}^N$ is remained, while other information is suppressed. In this way, the common facial shape and appearance is well aggregated into $\\bar{\\mathbf{s}}$ as a face prototype. Further, compared with average pooling, the adaptively fusion algorithm of Eqn. 3 can be jointly learned with deformation to improve the modeling quality, which is introduced in the following.", + "bbox": [ + 75, + 561, + 468, + 699 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Deformation Modeling: Although $\\bar{\\mathbf{s}}$ has the common prior of an identity, it considers no facial deformation which is suppressed after the aggregation, resulting in a mean expression. Actually, modeling deformation is crucial for face reconstruction [10]. On the one hand, if the face prototype has a different expression from the target image, then recovering the gap will be difficult in the fitting procedure. On the other hand, face images have different appearance and silhouettes due to expression variation, and thus discarding the expression brings ambiguity or conflicts to the consistent geometry. Hence, we propose deformation modeling method to disentangle expressions of face prototype.", + "bbox": [ + 75, + 700, + 470, + 866 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Previous works [56, 57] learn deformation during NeRF sampling, which depends on plentiful images captured in a constrained environment. As the data we use is in-the-wild with limited numbers, using such a deformation strategy is difficult. In contrast, we model expression by modifying style codes. As illustrated in Fig. 2, we first extracts the 3DMM expression parameters $\\{\\beta_i\\}_{i=1}^N$ , $\\beta_i \\in \\mathbb{R}^{64}$ and camera poses $\\{p_i\\}_{i=1}^N$ from a pretrained 3D face network $\\Phi_{3d}$ [20], which provides guidance on tuning $\\bar{s}$ . Note that, $\\{\\beta_i\\}_{i=1}^N$ cannot well represent the personalized expression with unique idiosyncrasies, as it is unrelated to the identity. Hence, we extract global image representations from $\\{\\mathbf{I}_i\\}_{i=1}^N$ to complement for the characteristics. We use a similar encoder $\\Phi_e$ as PSP [63] to get the representation $h_i^j \\in \\mathbb{R}^{512}$ , $j = 1, 2, \\dots, M$ and the corresponding uncertainty $\\mu_i^j$ from $\\mathbf{I}_i$ . Then following Eqn. 3, we fuse the $\\{h_i^j\\}_{i=1}^N$ using $\\{\\mu_i^j\\}_{i=1}^N$ to get the global representation $\\bar{h}^j$ at $j$ -th stage. With the global $\\bar{h}^j$ of the collection, we propose deformation networks $\\{\\Phi_\\Delta^j\\}_{j=1}^M$ to predict the deformation code on each stage, which can be defined as:", + "bbox": [ + 497, + 445, + 890, + 725 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\Delta s _ {i} ^ {j} = \\Phi_ {\\Delta} ^ {j} \\left(\\bar {h} ^ {j}, \\beta_ {i}\\right). \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 628, + 730, + 890, + 748 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Denoting $\\{\\Delta s_i^j\\}_{j = 1}^M$ as $\\Delta \\mathbf{s}_i$ for simplification, the personalized deformation of $\\mathbf{I}_i$ can be achieved via modifying the pivotal proto code:", + "bbox": [ + 497, + 754, + 890, + 796 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\mathbf {s}} _ {i} = \\bar {\\mathbf {s}} + \\Delta \\mathbf {s} _ {i}. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 643, + 802, + 890, + 817 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, the face prototype $\\hat{\\mathbf{I}}_i$ can be obtained from $\\hat{\\mathbf{s}}_i$ and $p_i$ using pretrained EG3D networks. In this way, $\\hat{\\mathbf{I}}_i$ suffers from less appearance conflicts to $\\mathbf{I}_i$ . Further, by jointly", + "bbox": [ + 497, + 824, + 890, + 866 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "385", + "bbox": [ + 472, + 885, + 497, + 897 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "learning of $\\Delta \\mathbf{s}_i$ and $\\bar{\\mathbf{s}}$ , the consistent face shape within a collection is adaptively blended against non-rigid ambiguity, and have more precise geometry and texture.", + "bbox": [ + 75, + 125, + 468, + 167 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training Loss: Note that we do not require $\\hat{\\mathbf{I}}_i$ to be exactly the same as $\\mathbf{I}_i$ , but have a proper expression and a consistent blended shape among $\\{\\mathbf{I}_i\\}_{i=1}^N$ . To achieve such an aim, we propose a series of consistency losses to learn $\\Phi_a, \\Phi_e$ and $\\Phi_\\Delta$ . The reconstruction loss is defined as $\\mathcal{L}_{re} = \\mathcal{L}_{lpips}(\\hat{\\mathbf{I}}_i - \\mathbf{I}_i) + \\mathcal{L}_{L2}(\\hat{\\mathbf{I}}_i - \\mathbf{I}_i)$ , where $\\mathcal{L}_{lpips}$ is the perceptual loss [98] and $\\mathcal{L}_{L2}$ is the L2 distance. To constrain the expression of $\\hat{\\mathbf{I}}_i$ , we propose an expression consistency loss using pretrained $\\Phi_{3d}$ :", + "bbox": [ + 75, + 167, + 470, + 294 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {c} \\left(\\hat {\\mathbf {I}} _ {i}, \\beta_ {i}\\right) = \\frac {1}{\\Omega} \\left| \\Phi_ {3 d} \\left(\\hat {\\mathbf {I}} _ {i}\\right) - \\beta_ {i} \\right|, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 169, + 304, + 468, + 331 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\Omega$ is the normalization factor. To prevent distortion, we limit the deformation code via a regularization as $\\mathcal{L}_d = \\|\\Delta \\mathbf{s}_i\\|_2^2$ . The final loss function is denoted as:", + "bbox": [ + 77, + 340, + 468, + 384 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {p r o t o} = \\sum_ {i = 1} ^ {N} \\mathcal {L} _ {r e} \\left(\\hat {\\mathbf {I}} _ {i}, \\mathbf {I} _ {i}\\right) + \\lambda_ {c} \\mathcal {L} _ {c} \\left(\\hat {\\mathbf {I}} _ {i}, \\beta_ {i}\\right) + \\lambda_ {d} \\mathcal {L} _ {d} \\left(\\mathbf {s} _ {i}\\right), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 85, + 395, + 468, + 433 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\lambda_c, \\lambda_d$ are the weights. When using $\\mathcal{L}_{proto}$ to update $\\Phi_a, \\Phi_e$ and $\\Phi_\\Delta$ , the networks of EG3D are all frozen. In this way, we learn neural proto-face field from in-the-wild photo collections, and disentangle identities and expressions of the face prototypes.", + "bbox": [ + 75, + 444, + 468, + 514 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Neural Proto-face Field Fitting", + "text_level": 1, + "bbox": [ + 75, + 524, + 349, + 540 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Once NPF is learned, we get a 3D hybrid initialization containing consistent cues of the photo collection, constrained by reliable multi-image priors. To model the precise 3D faces, scene-specific details need to be recovered. Such an aim can be achieved by tuning the generator using pivotal tuning (PTI) algorithm [66]. However, PTI has obvious drawbacks of two aspects: 1) its pivotal code has no multi-view prior; 2) its tuning procedure easily overfits to a single image. The former one has been addressed by NPF. For the latter, we propose a consistent fitting method to overcome the monocular ambiguity.", + "bbox": [ + 75, + 547, + 468, + 699 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Multi-image Warm-Up: Fitting is achieved by tuning the generator $\\Phi_g$ of EG3D while freezing other networks. In the early stage of fitting, we need to prevent the generator converging to a local optimum that provides distortions due to challenging conditions. In contrast to fitting a single image, we propose a multi-image warm-up method to fit $\\Phi_g$ to the photo collection $\\{\\mathbf{I}_i\\}_{i=1}^N$ . We use $\\mathcal{L}_{re}$ to approach the target image set. To guarantee the geometry consistency at unseen views, we randomly sample a camera pose $p'$ at every step, and use the volume renderer of EG3D to predict the corresponding normal maps $\\hat{\\mathbf{n}}_i$ and $\\dot{\\mathbf{n}}_i$ from the original/new weights of $\\Phi_g$ , respectively. Then we calculate the", + "bbox": [ + 75, + 700, + 468, + 866 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/31aff62a3ce8861dc6f1e132ca7c1d76de02ec397e472cb4504e274d70c78d06.jpg", + "image_caption": [ + "Figure 3. The proposed consistency losses of NPF fitting. (a) Multi-image Warm-up; (b) Robust Target-image Fitting." + ], + "image_footnote": [], + "bbox": [ + 502, + 124, + 890, + 280 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "geometry consistency loss $\\mathcal{L}_{gc}(\\dot{\\mathbf{n}}_i,\\hat{\\mathbf{n}}_i) = \\| \\dot{\\mathbf{n}}_i - \\hat{\\mathbf{n}}_i\\| _2^2$ to constrain the robustness of facial geometry. The final objective of multi-image warm-up is denoted as:", + "bbox": [ + 497, + 328, + 890, + 372 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {w a r m}} = \\sum_ {i = 1} ^ {N} \\mathcal {L} _ {r e} \\left(\\dot {\\mathbf {I}} _ {i}, \\mathbf {I} _ {i}\\right) + \\lambda_ {g} \\mathcal {L} _ {g c} \\left(\\dot {\\mathbf {n}} _ {i}, \\hat {\\mathbf {n}} _ {i}\\right), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 551, + 382, + 890, + 421 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\dot{\\mathbf{I}}_i$ is the reconstructed image. Using $\\mathcal{L}_{warm}$ , we make $\\Phi_g$ initialize to a solution near the optimum of each image, and suppress distortions of unseen poses.", + "bbox": [ + 497, + 432, + 890, + 473 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Robust Target-image Fitting: After the multi-image warm-up, we fit $\\Phi_g$ to the target image $\\mathbf{I}_i$ and model specific details. Besides the reconstruction loss $\\mathcal{L}_{re}$ , we constrain the recovered appearance of unseen poses. Similar to $\\mathcal{L}_{gc}$ we randomly sample a camera pose $p'$ at each step and render an image $\\dot{\\mathbf{I}}_i'$ , then encourage $\\dot{\\mathbf{I}}_i'$ and the target $\\mathbf{I}_i$ to have similar representations via $\\mathcal{L}_{ac} = \\|\\Phi_e(\\dot{\\mathbf{I}}_i') - \\Phi_e(\\mathbf{I}_i)\\|_2^2$ . The final loss of robust target-image fitting is:", + "bbox": [ + 497, + 475, + 890, + 586 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {t a r g e t}} = \\mathcal {L} _ {\\text {r e}} \\left(\\mathbf {I} _ {i}, \\mathbf {I} _ {i}\\right) + \\lambda_ {a} \\mathcal {L} _ {\\text {a c}}. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 589, + 596, + 890, + 612 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this way, we improve the robustness of the scene-specific details recovering.", + "bbox": [ + 497, + 622, + 890, + 650 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experiment", + "text_level": 1, + "bbox": [ + 499, + 663, + 623, + 679 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Dataset: We first train our $\\Phi_{a},\\Phi_{e}$ and $\\Phi_{\\Delta}$ on CelebA [45] and CASIA-WebFace [95], then fine-tune them on a high-resolution dataset CelebAMask-HQ [41]. Following [100], we organize CelebA and CASIAWebFace using ID-labels and keep each identity with at least 6 photos. This provides $600\\mathrm{K}$ images with $16\\mathrm{K}$ identities. We select images of $12\\mathrm{K} / 2\\mathrm{K} / 2\\mathrm{K}$ identities as train/val/test set. For CelebAMask-HQ, we organize it into $24\\mathrm{K}$ different identities using ground truth ID-labels, and randomly select $20\\mathrm{K} / 1\\mathrm{K} / 3\\mathrm{K}$ identities as train/val/test set. Following [4, 91, 100], we perform testing on MICC [6], Photoface [96] and FG3D [26] dataset. Photoface dataset contains $12\\mathrm{K}$ images of 453 people with face/normal image", + "bbox": [ + 497, + 686, + 890, + 866 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "386", + "bbox": [ + 472, + 887, + 497, + 897 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/6d281a27d26d2c8fd0537a3a50bfc7bfd5db942c004d060facd357f4a195e45e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
No.methodp2p (mm) ↓MAD (deg.) ↓IDE ↓
(1)Ours1.7711.870.257
(2)Ours face prototype1.9612.390.307
(3)w/o uncertainty = avg pooling1.8712.130.298
(4)w/o deformation modeling2.2512.900.401
(5)w/o Φe2.0112.460.350
(6)w/o multi-image warm-up1.8412.300.301
(7)w/o Lgc1.8012.150.274
(8)w/o Lac1.7911.870.262
(9)single-image PTI [66]2.3512.100.482
", + "bbox": [ + 90, + 122, + 455, + 225 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Comparison with Different Baselines and Settings.", + "bbox": [ + 95, + 235, + 448, + 249 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "pairs, and we follow the protocol of [4,70] for testing. More details are introduced in Appendix.", + "bbox": [ + 75, + 255, + 468, + 282 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation Details: We build $\\Phi_{a}$ using a similar architecture to the encoder of [91, 100]. The deformation network $\\Phi_{\\Delta}^{j}$ of $j$ -th stage contains 3 MLP layers to encode $\\beta_{i}$ , and another 3 MLP layers to predict $\\Delta s_{i}^{j}$ from the combination of $\\beta_{i}, \\bar{\\mathbf{h}}^{j}$ . We set $\\lambda_{c} = 1, \\lambda_{d} = 1e - 3, \\lambda_{g} = \\lambda_{a} = 0.2$ for the losses. Following [13], the output images of the model are of $512 \\times 512$ , and the meshes are extracted using Marching Cubes [47] with a same size. During training, we randomly set $N$ from 1 to 6 to adapt different sizes of a photo collection. We train the model with batch size of 4 on CelebA and CASIA-WebFace for 15 epochs, and fine-tune it on CelebAMaskHQ for 30 epochs. During fitting, the multi-image warm-up lasts 100 steps and robust target-image fitting lasts another 100 steps. We use Adam [40] as the optimizer, and set the learning rate as 0.0001 on a V-100 GPU. More details are in the Appendix.", + "bbox": [ + 75, + 283, + 468, + 505 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation Protocol: Without special statements, we use 4-image results to compare with other methods. To evaluate the modeled texture, we calculate Structural Similarity Index (SSIM) [88], Cosine-similarity of Arcface [17] representation and RMSE of identity parameters (IDE) of 3D face network [20] between the original high-quality images and rendered ones. We render images under different poses to measure the robustness. To evaluate the geometry, we rigid-align predictions to the ground truth via ICP using hand-crafted key points. Mean Angle Deviation (MAD) of normal maps and point-to-plane $(\\mathrm{p2p})$ distance between the aligned prediction and ground truth are utilized as metrics. Please see Appendix for more details.", + "bbox": [ + 75, + 505, + 470, + 685 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Ablation Study", + "text_level": 1, + "bbox": [ + 77, + 693, + 231, + 707 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparison with Baselines: We first analyse different settings of NPF in Table 2. We calculate NME, MAD and IDE metrics on MICC, Photoface and CelebaMaskHQ dataset, respectively. In row (1), our full method obtains the best performance in all metrics, approving the effectiveness of each proposed component. In row (2), we observe that the face prototype gets satisfactory, or even better results than several baselines, which reveals that NPF well integrates consistency from photo collection. In row (3), we replace the adaptive uncertainty with average pooling to get the proto code $\\overline{\\mathbf{s}}$ , and the performance is reduced to", + "bbox": [ + 75, + 713, + 468, + 866 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/b1c69de88b51beec4d17f0a3153ea50cd43427602159b3b02c7946ed1eb46573.jpg", + "image_caption": [ + "Figure 4. Analysis on the deformation modeling. (a) How the deformation modeling improves the details and shape accuracy. (b) How the settings of deformation modules influence the results." + ], + "image_footnote": [], + "bbox": [ + 502, + 122, + 894, + 280 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "some extent. This reveals our identity-aware aggregation well contributes on digging consistent cues. In row (4), the significant accuracy drop demonstrates the effectiveness of deformation modeling on avoiding expression-aware shape ambiguity. Row (5) also proves that the global face representation from $\\Phi_e$ improves the quality of deformation modeling. Rows (6)-(8) reveal that the multi-image warm-up and consistency losses also improve the robustness. Finally, row (9) is the state-of-the-art single-image baseline, where it achieves even worse results than face prototype in IDE. This is due to the distortion caused by overfitting or ambiguity under challenging conditions of pose, lighting and occlusion.", + "bbox": [ + 497, + 349, + 892, + 528 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Analysis on Deformation Modeling: We analyse how the proposed deformation modeling method improves the performance. In Fig. 4-(a), we highlight the differences between this two settings. Without deformation modeling, fusing the style codes yields $\\bar{\\mathbf{s}}$ to represent a mean expression on the face prototype, which is different from the target face. Hence, it brings difficulty to recover the target's expression during NPF fitting. Further, as the face shapes with various expressions show different appearance and silhouettes on the images, ignoring the deformation brings ambiguity when digging the multi-image consistency. Hence, such a model produces improper shapes and details. In contrast, modeling the deformation recovers the target's expression, the corresponding shapes and appearance effects. In Fig. 4-(b), we show the influence of different settings. Without deformation modeling, the target expression cannot be recovered. Without the global representation $\\bar{\\mathbf{h}}$ , the expression effect is produced but not accurate enough. Finally, our full method recovers precise target deformation.", + "bbox": [ + 497, + 532, + 890, + 794 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Analysis on Multi-image Consistency: We analyse how our method leverages the multi-image priors from inthe-wild photo collections. In Fig. 5-(a), we show the influence of the size $N$ of photo collections. Compared with the single-image setting (same as PTI [66]), $N = 2$ sig", + "bbox": [ + 497, + 796, + 890, + 866 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "387", + "bbox": [ + 472, + 887, + 497, + 897 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/08e04bf735f0b0c504255f29a0d64e9b2838bfa959ee6da5a77b76b447827258.jpg", + "image_caption": [ + "Figure 5. Analysis on the multi-image consistency. (a) (b) Performance under different photo collection size $N$ ; (c) The effectiveness of multi-image warm-up. Red box signs the target image." + ], + "image_footnote": [], + "bbox": [ + 80, + 125, + 465, + 289 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/e4c2960563a2047f5b023805eaae78e34b17487d1999fc997a1c1b0375f4fecb.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MICCFG3D
Method3DMMindooroutdoorLQHQ
Extreme3D [85]✓3.663.703.493.58
PRN [25]✓2.322.472.382.06
RingNet [69]✓1.932.022.082.02
3DDFA v2 [33]✓1.881.962.101.91
D3DFR [20] (mv)✓1.661.691.901.95
MGCnet [71]✓1.721.781.951.90
DECA [24]✓1.691.701.911.89
Cross-modal [4]×2.392.332.302.04
Unsup3D [91]×2.482.522.412.29
LAP [100] (mv)×2.322.252.182.16
PhyDIR [101] (mv)×2.292.202.252.09
Ours×1.751.812.151.98
", + "bbox": [ + 112, + 352, + 433, + 488 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "nificantly improves the accuracy, revealing that even two images still provide reliable priors. With $N$ increasing, the accuracy gets better. Fig. 5-(b) illustrates the qualitative performance. The single-image result has obvious distortion of facial shape due to the target's large pose and challenging appearance. Increasing $N = 3$ obviously suppresses the degradation, and the setting $N = 6$ obtains best performance with proper geometry. In Fig. 5-(c), we observe that during fitting, the proposed multi-image warm-up suppresses the overfitting on occlusion or shadows, boosted by the consistency from other images.", + "bbox": [ + 75, + 526, + 468, + 679 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "6. Comparison with State-of-the-art", + "text_level": 1, + "bbox": [ + 75, + 691, + 381, + 706 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Evaluation on Geometry: We first analyse the modeled geometry on MICC and FG3D dataset. We use the provided indoor/outdoor videos of MICC, low-quality (LQ) and high-quality (HQ) image sets to optimize the trained NPF. The geometry for each identity is obtained from 4 images, and calculated the average results. The comparisons are shown in Table 3, where we divide the methods into 3DMM and non-3DMM group. The tag 'mv' means the method uses multi-view input. We observe that our method obtains the best performance among non-3DMM approaches, but higher errors than D3DFR, MGCnet and", + "bbox": [ + 75, + 713, + 470, + 866 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/2314b41d0c74441fdbc141b89ff0f65f00c2d42b9964f44a3c67ca193f305867.jpg", + "table_caption": [ + "Table 3. Point-to-plane distance (mm) on benchmarks." + ], + "table_footnote": [], + "table_body": "
MAD↓<20°↑<25°↑<30°↑
SfSNet [70]25.5±9.343.6%57.5%68.7%
PRN [25]24.8±6.843.1%62.9%74.1%
DF2Net [97] (GT)24.3±5.742.2%62.7%74.5%
D3DFR [20] (mv)23.5±6.146.1%61.8%73.3%
Cross-Modal [4] (GT)22.8±6.549.0%62.9%74.1%
DECA [24]22.5±5.348.7%62.3%73.7%
LAP [100] (mv)23.0±5.148.2%63.1%74.9%
PhyDIR [101] (mv)22.7±4.349.2%63.4%75.3%
Ours prototype23.7±4.745.6%61.4%73.0%
SfSNet-ft [70]12.8±5.483.7%90.8%94.5%
Cross-Modal-ft [4] (GT)12.0±5.385.2%92.0%95.6%
LAP-ft [100] (mv)12.3±4.584.9%92.4%96.3%
PhyDir-ft [101] (mv)12.0±4.985.3%92.7%96.9%
Ours11.87±5.285.9%93.0%96.9%
", + "bbox": [ + 534, + 123, + 857, + 256 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Facial normal evaluation on Photoface dataset.", + "bbox": [ + 529, + 268, + 858, + 279 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/71eb93256fa96a9952eda7706a7f42d2492b68a4e91c7cc936f866cc4134197b.jpg", + "image_caption": [ + "Figure 6. Qualitative comparison on predicted facial geometry." + ], + "image_footnote": [], + "bbox": [ + 504, + 287, + 889, + 581 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "DECA. Note that, 3DMM is a highly reliable face shape assumption made from real 3D ground truth, and thus it fundamentally boosts the models' accuracy. Even though, our method still outperforms Extreme3D, PRN, RingNet and competes with 3DDFA v2. We further analyse the modeled facial normal on Photoface dataset in Table 4, where '-ft' means finetuning. Our prototype has already outperforms several methods. After finetuning, our method obtains best performance. Finally, we illustrate qualitative results in Fig. 6, where our method provides finer details. Compared with the EG3D [13] + PTI [66] baseline, our method is much more robust against distortion from large pose, extreme lighting and appearance.", + "bbox": [ + 497, + 630, + 890, + 809 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Evaluation on Texture: We then evaluate the modeled texture on CelebAMaskHQ dataset. Besides the reconstructed image, we render images along the yaw angle in 3 ranges: $[0^{\\circ}, \\pm 20^{\\circ}]$ , $[\\pm 20^{\\circ}, \\pm 40^{\\circ}]$ and $[\\pm 40^{\\circ}, \\pm 60^{\\circ}]$ ,", + "bbox": [ + 497, + 811, + 892, + 866 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "388", + "bbox": [ + 472, + 887, + 497, + 897 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/fd288c6b4d6489d320b9c78058194b1d8fec0c61755ac02943b9c2242470df8b.jpg", + "image_caption": [ + "Figure 7. Qualitative comparison with EG3D [13] + PTI [66] baseline, PhyDIR [101], HeadNeRF [34] and DFG [18] on the robustness of rotation and expression editing. Our method produces robust and photo-realistic rendering results. Zooming in to see the details." + ], + "image_footnote": [], + "bbox": [ + 119, + 122, + 850, + 368 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/7febf5a12d5f07116cfe586231d5f87916ea767f2c804aa50b1d12b42283dcf1.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Method\\( \\left\\lbrack {{0}^{ \\circ }, \\pm {20}^{ \\circ }}\\right\\rbrack \\)\\( \\left\\lbrack {\\pm {20}^{ \\circ }, \\pm {40}^{ \\circ }}\\right\\rbrack \\)\\( \\left\\lbrack {\\pm {40}^{ \\circ }, \\pm {60}^{ \\circ }}\\right\\rbrack \\)
Cos-simIDECos-simIDECos-simIDESSIM
DFG [18]0.7320.3420.6440.3850.5810.4300.751
Unsup3D [91]0.7010.3180.6770.3340.6320.3870.514
LAP [100] (mv)0.7400.2930.6850.3010.6580.3590.623
PhyDIR [101] (mv)0.8260.2670.7920.2940.7450.3280.880
MoFANeRF [108]0.6930.3370.6660.3600.6400.3950.523
HeadNeRF [34]0.8300.2400.8010.2680.6710.3640.823
EG3D + PTI0.8350.2480.7830.2790.7090.3470.919
Ours0.8420.2330.8060.2510.7500.2870.921
", + "bbox": [ + 92, + 414, + 453, + 498 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Quality of rendered images on CelebAMask-HQ.", + "bbox": [ + 100, + 507, + 443, + 520 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "and calculate the cosine similarity and ID parameter error between them and target images. The performance is illustrated in Table 5, where DFG cannot provide satisfactory results due to its entangled image formation. For Unsup3D and LAP, the quality of texture is limited by the graphics renderer. MoFANeRF suffers from limited data diversity of constrained environments. PhyDIR and HeadNeRF suffer from large-pose degradation. The single-image baseline $\\mathrm{EG3D} + \\mathrm{PTI}$ obtains high reconstruction performance, but fragile results under rotation due to the degraded shapes. In contrast, our method obtains the best performance in all the metrics, and shows robust and high-quality modeling results. Finally, we show qualitative results in Fig. 7 under challenging conditions of extreme lighting and pose. EG3D $^+$ PTI suffers from overfitting and produces flatten shapes, while our method obtains best performance on the rendering quality and robustness. Our method also show satisfactory ability on editing the expressions via deformation codes. More results and comparisons can be seen in the Appendix.", + "bbox": [ + 75, + 543, + 468, + 819 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitation: Our method requires multiple images of a same person as input, which may limit the application under limiting conditions. This problem can be suppressed", + "bbox": [ + 75, + 824, + 468, + 866 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "by searching images with similar identity and building a 'relaxed' photo collection. Further, the performance also depends on the pretrained EG3D models. As a result, extreme expressions and poses may bring degradation on accuracy. Such issues can be addressed by re-training EG3D via targeted dataset. We make the discussions in the Appendix.", + "bbox": [ + 497, + 416, + 890, + 501 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "7. Conclusion & Future Work", + "text_level": 1, + "bbox": [ + 499, + 521, + 754, + 536 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we propose a Neural Proto-face Field (NPF) method for unsupervised robust 3D face modeling. NPF digs multi-image priors from in-the-wild photo collections to boost the 3D generative models, and well complements the single-image overfitting inversion procedure. To aggregate consistency against large variations in unconstrained environments, a novel identity-aware aggregation method is proposed to adaptively combine the style codes, and build ID-consistent face prototypes. To suppress the non-rigid ambiguity, NPF blends the consistent hybrid representation via a novel deformation modeling method. In this way, NPF obtains face prototypes containing common facial cues within the collection, and disentangles specific expressions. The final recovered 3D face is obtained via fitting NPF with consistency losses, and thus scene-specific details can be recovered. Extensive experiments demonstrate that our method models robust and detailed face shapes under challenging conditions, and recovers photorealistic texture with pose/expression controlling. In the future, the works could be proposed to tackle the limitation such as supporting single-image input. Further, decomposing the NeRF pipeline, the texture could be disentangled into different intrinsic factors.", + "bbox": [ + 497, + 548, + 890, + 866 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "389", + "bbox": [ + 472, + 887, + 497, + 897 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 124, + 171, + 138 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Rameen Abdal, Yipeng Qin, and Peter Wonka. Image2stylegan: How to embed images into the stylegan latent space? In CVPR, pages 4432-4441, 2019. 2, 3", + "[2] Rameen Abdal, Yipeng Qin, and Peter Wonka. Image2stylegan++: How to edit the embedded images? In CVPR, pages 8296-8305, 2020. 2, 3", + "[3] Rameen Abdal, Peihao Zhu, Niloy J Mitra, and Peter Wonka. Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. ACM Transactions on Graphics (TOG), 40(3):1-21, 2021. 2, 3", + "[4] Victoria Fernández Abrevaya, Adnane Boukhayma, Philip HS Torr, and Edmond Boyer. Cross-modal deep face normals with deactivable skip connections. In CVPR, pages 4979-4989, 2020. 2, 5, 6, 7", + "[5] ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, and Zhixin Shu. Rignerf: Fully controllable neural 3d portraits. In CVPR, pages 20364-20373, 2022. 3", + "[6] Andrew D Bagdanov, Alberto Del Bimbo, and Iacopo Masi. The florence 2d/3d hybrid face dataset. In Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding, pages 79-80, 2011. 5", + "[7] Ziqian Bai, Zhaopeng Cui, Jamal Ahmed Rahim, Xiaoming Liu, and Ping Tan. Deep facial non-rigid multi-view stereo. In CVPR, pages 5850-5860, 2020. 1, 2", + "[8] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In CVPR, pages 5855–5864, 2021. 2", + "[9] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In CVPR, pages 5470–5479, 2022. 2", + "[10] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 187-194, 1999. 1, 2, 4", + "[11] Marcel C Bühler, Abhimitra Meka, Gengyan Li, Thabo Beeler, and Otmar Hilliges. Varitex: Variational neural face textures. In ICCV, pages 13890-13899, 2021. 2, 3", + "[12] Rohan Chabra, Jan E Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe. Deep local shapes: Learning local sdf priors for detailed 3d reconstruction. In ECCV, pages 608-625. Springer, 2020. 2", + "[13] Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In CVPR, pages 16123-16133, 2022. 1, 2, 3, 6, 7, 8", + "[14] Eric R Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein. pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In CVPR, pages 5799-5809, 2021. 2, 3" + ], + "bbox": [ + 87, + 148, + 468, + 865 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[15] Bindita Chaudhuri, Noranart Vesdapunt, Linda Shapiro, and Baoyuan Wang. Personalized face modeling for improved face reconstruction and motion retargeting. In ECCV, 2020. 1, 2", + "[16] Radek Daneček, Michael J Black, and Timo Bolkart. Emoca: Emotion driven monocular face capture and animation. In CVPR, pages 20311-20322, 2022. 2", + "[17] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In CVPR, pages 4690-4699, 2019. 6", + "[18] Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, and Xin Tong. Disentangled and controllable face image generation via 3d imitative-contrastive learning. In CVPR, pages 5154–5163, 2020. 2, 3, 8", + "[19] Yu Deng, Jiaolong Yang, Jianfeng Xiang, and Xin Tong. Gram: Generative radiance manifolds for 3d-aware image generation. In CVPR, pages 10673-10683, 2022. 3", + "[20] Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set. In CVPRW, 2019. 1, 3, 4, 6, 7", + "[21] Abdallah Dib, Cedric Thebault, Junghyun Ahn, Philippe-Henri Gosselin, Christian Theobalt, and Louis Chevallier. Towards high fidelity monocular face reconstruction with rich reflectance using self-supervised learning and ray tracing. arXiv preprint arXiv:2103.15432, 2021. 2", + "[22] Pengfei Dou, Shishir K Shah, and Ioannis A Kakadiaris. End-to-end 3d face reconstruction with deep neural networks. In CVPR, pages 5908-5917, 2017. 1", + "[23] Bernhard Egger, William AP Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Rondhani, et al. 3d morphable face models—past, present, and future. ACM Transactions on Graphics (TOG), 39(5):1-38, 2020. 2", + "[24] Yao Feng, Haiwen Feng, Michael J Black, and Timo Bolkart. Learning an animatable detailed 3d face model from in-the-wild images. ACM Transactions on Graphics (TOG), 40(4):1-13, 2021. 1, 2, 7", + "[25] Yao Feng, Fan Wu, Xiaohu Shao, Yanfeng Wang, and Xi Zhou. Joint 3d face reconstruction and dense alignment with position map regression network. In ECCV, pages 534-551, 2018. 1, 2, 7", + "[26] Zhen-Hua Feng, Patrik Huber, Josef Kittler, Peter Hancock, Xiao-Jun Wu, Qijun Zhao, Paul Koppen, and Matthias Ratsch. Evaluation of dense 3d reconstruction from 2d face images in the wild. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pages 780-786, 2018. 5", + "[27] Guy Gafni, Justus Thies, Michael Zollhofer, and Matthias Nießner. Dynamic neural radiance fields for monocular 4d facial avatar reconstruction. In CVPR, pages 8649-8658, 2021. 3", + "[28] Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang. Portrait neural radiance fields from a single image. arXiv preprint arXiv:2012.05903, 2020. 2", + "[29] Baris Gecer, Stylianos Ploumpis, Irene Kotsia, and Stefanos Zafeiriou. Ganfit: Generative adversarial network fitting for" + ], + "bbox": [ + 510, + 127, + 890, + 865 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "390", + "bbox": [ + 472, + 887, + 497, + 897 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "high fidelity 3d face reconstruction. In CVPR, pages 1155-1164, 2019. 1, 2", + "[30] Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, and William T Freeman. Unsupervised training for 3d morphable model regression. In CVPR, pages 8377-8386, 2018. 2", + "[31] Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. Neural head avatars from monocular rgb videos. In CVPR, pages 18653–18664, 2022. 3", + "[32] Jiatao Gu, Lingjie Liu, Peng Wang, and Christian Theobalt. Stylenerf: A style-based 3d-aware generator for high-resolution image synthesis. arXiv preprint arXiv:2110.08985, 2021. 3", + "[33] Jianzhu Guo, Xiangyu Zhu, Yang Yang, Fan Yang, Zhen Lei, and Stan Z Li. Towards fast, accurate and stable 3d dense face alignment. In ECCV, 2020. 7", + "[34] Yang Hong, Bo Peng, Haiyao Xiao, Ligang Liu, and Juyong Zhang. Headnerf: A real-time nerf-based parametric head model. In CVPR, pages 20374-20384, 2022. 1, 2, 8", + "[35] Aaron S Jackson, Adrian Bulat, Vasileios Argyriou, and Georgios Tzimiropoulos. Large pose 3d face reconstruction from a single image via direct volumetric cnn regression. In ICCV, pages 1031-1039, 2017. 2", + "[36] Kacper Kania, Kwang Moo Yi, Marek Kowalski, Tomasz Trzeciński, and Andrea Tagliasacchi. Conerf: Controllable neural radiance fields. In CVPR, pages 18623-18632, 2022. 3", + "[37] Tero Karras, Miika Aittala, Samuli Laine, Erik Härkonen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Alias-free generative adversarial networks. NeurIPS, 34:852-863, 2021. 3", + "[38] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In CVPR, pages 4401-4410, 2019. 2, 3", + "[39] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In CVPR, pages 8110-8119, 2020. 2, 3", + "[40] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6", + "[41] Cheng-Han Lee, Ziwei Liu, Lingyun Wu, and Ping Luo. Maskgan: Towards diverse and interactive facial image manipulation. In CVPR, 2020. 5", + "[42] Tzu-Mao Li, Miika Aittala, Frédo Durand, and Jaakko Lehtinen. Differentiable monte carlo ray tracing through edge sampling. ACM Transactions on Graphics (TOG), 37(6):1-11, 2018. 2", + "[43] Feng Liu, Dan Zeng, Qijun Zhao, and Xiaoming Liu. Joint face alignment and 3d face reconstruction. In ECCV, pages 545-560. Springer, 2016. 1", + "[44] Shichen Liu, Tianye Li, Weikai Chen, and Hao Li. Soft rasterizer: A differentiable renderer for image-based 3d reasoning. In ICCV, pages 7708-7717, 2019. 2", + "[45] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In ICCV, pages 3730-3738, 2015. 5" + ], + "bbox": [ + 87, + 125, + 468, + 864 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[46] Matthew M Loper and Michael J Black. Opendr: An approximate differentiable renderer. In ECCV, pages 154-169, 2014. 2", + "[47] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. ACM siggraph computer graphics, 21(4):163-169, 1987. 6", + "[48] Nelson Max. Optical models for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics, 1(2):99-108, 1995. 3", + "[49] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In CVPR, pages 4460-4470, 2019. 2", + "[50] Moustafa Meshry, Dan B Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snively, and Ricardo Martin-Brualla. Neural rerendering in the wild. In CVPR, pages 6878–6887, 2019. 3", + "[51] Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson. Implicit surface representations as layers in neural networks. In CVPR, pages 4743-4752, 2019. 2", + "[52] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, pages 405-421, 2020. 2", + "[53] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional generative neural feature fields. In CVPR, pages 11453-11464, 2021. 3", + "[54] Roy Or-El, Xuan Luo, Mengyi Shan, Eli Shechtman, Jeong Joon Park, and Ira Kemelmacher-Shlizerman. Stylesdf: High-resolution 3d-consistent image and geometry generation. In CVPR, pages 13503–13513, 2022. 2, 3", + "[55] Xingang Pan, Bo Dai, Ziwei Liu, Chen Change Loy, and Ping Luo. Do 2d gans know 3d shape? unsupervised 3d shape reconstruction from 2d image gans. arXiv preprint arXiv:2011.00844, 2020. 2", + "[56] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In ICCV, pages 5865-5874, 2021. 3, 4", + "[57] Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Ricardo MartinBrualla, and Steven M Seitz. Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields. arXiv preprint arXiv:2106.13228, 2021. 3, 4", + "[58] Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. In IEEE International Conference on Advanced Video and Signal Based Surveillance, pages 296-301, 2009. 1", + "[59] Jingtan Piao, Keqiang Sun, Quan Wang, Kwan-Yee Lin, and Hongsheng Li. Inverting generative adversarial renderer for face reconstruction. In CVPR, pages 15619-15628, 2021. 2", + "[60] Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-nerf: Neural radiance fields" + ], + "bbox": [ + 510, + 127, + 890, + 865 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "391", + "bbox": [ + 472, + 887, + 495, + 897 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "for dynamic scenes. In CVPR, pages 10318-10327, 2021. 3", + "[61] Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. H3d-net: Few-shot high-fidelity 3d head reconstruction. In ICCV, pages 5620-5629, 2021. 2", + "[62] Yurui Ren, Ge Li, Yuanqi Chen, Thomas H Li, and Shan Liu. Pirenderer: Controllable portrait image generation via semantic neural rendering. In ICCV, pages 13759-13768, 2021. 2", + "[63] Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or. Encoding in style: a stylegan encoder for image-to-image translation. In CVPR, pages 2287–2296, 2021. 3, 4", + "[64] Elad Richardson, Matan Sela, and Ron Kimmel. 3d face reconstruction by learning from synthetic data. In 3DV, pages 460-469, 2016. 1, 2", + "[65] Elad Richardson, Matan Sela, Roy Or-El, and Ron Kimmel. Learning detailed face reconstruction from a single image. In CVPR, pages 1259–1268, 2017. 2", + "[66] Daniel Roich, Ron Mokady, Amit H Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. ACM Transactions on Graphics (TOG), 42(1):1-13, 2022. 1, 3, 4, 5, 6, 7, 8", + "[67] Sami Romdhani and Thomas Vetter. Efficient, robust and accurate fitting of a 3d morphable model. In Computer Vision, page 59. IEEE, 2003. 1, 2", + "[68] Sami Romdhani and Thomas Vetter. Estimating 3d shape and texture using pixel intensity, edges, specular highlights, texture constraints and a prior. In CVPR, volume 2, pages 986-993, 2005. 1, 2", + "[69] Soubhik Sanyal, Timo Bolkart, Haiwen Feng, and Michael J Black. Learning to regress 3d face shape and expression from an image without 3d supervision. In CVPR, pages 7763-7772, 2019. 3, 7", + "[70] Soumyadip Sengupta, Angjoo Kanazawa, Carlos D Castillo, and David W Jacobs. Sfsnet: Learning shape, reflectance and illuminance of faces in the wild. In CVPR, pages 6296-6305, 2018. 2, 6, 7", + "[71] Jiaxiang Shang, Tianwei Shen, Shiwei Li, Lei Zhou, Mingmin Zhen, Tian Fang, and Long Quan. Self-supervised monocular 3d face reconstruction by occlusion-aware multi-view geometry consistency. arXiv preprint arXiv:2007.12494, 2020. 7", + "[72] Yichun Shi, Divyansh Aggarwal, and Anil K Jain. Lifting 2d stylegan for 3d-aware face generation. In CVPR, pages 6258-6266, 2021. 2", + "[73] Yichun Shi and Anil K Jain. Probabilistic face embeddings. In ICCV, pages 6902-6911, 2019. 3", + "[74] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3d-structure-aware neural scene representations. NeurIPS, 32, 2019. 2", + "[75] Keqiang Sun, Shangzhe Wu, Zhaoyang Huang, Ning Zhang, Quan Wang, and Hongsheng Li. Controllable 3d face synthesis with conditional generative occupancy fields. In Advances in Neural Information Processing Systems, 2021. 3" + ], + "bbox": [ + 87, + 125, + 468, + 864 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[76] Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Fml: Face model learning from videos. In CVPR, pages 10812-10822, 2019. 1, 2", + "[77] Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Stylerig: Rigging stylegan for 3d control over portrait images. In CVPR, pages 6142-6151, 2020. 2, 3", + "[78] Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo MartinBrualla, Tomas Simon, Jason Saragih, Matthias Nießner, et al. State of the art on neural rendering. In Computer Graphics Forum, volume 39, pages 701-727. Wiley Online Library, 2020. 3", + "[79] Ayush Tewari, Hans-Peter Seidel, Mohamed Elgharib, Christian Theobalt, et al. Learning complete 3d morphable face models from images and videos. In CVPR, pages 3361-3371, 2021. 1, 2, 3", + "[80] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, W Yifan, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, et al. Advances in neural rendering. In Computer Graphics Forum, volume 41, pages 703-735. Wiley Online Library, 2022. 3", + "[81] Ayush Tewari, Michael Zollhofer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, Patrick Perez, and Christian Theobalt. Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction. In ICCVW, pages 1274-1283, 2017. 1, 2", + "[82] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics (TOG), 38(4):1-12, 2019. 3", + "[83] Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, and Daniel Cohen-Or. Designing an encoder for stylegan image manipulation. ACM Transactions on Graphics (TOG), 40(4):1-14, 2021. 3", + "[84] Luan Tran and Xiaoming Liu. Nonlinear 3d face morphable model. In CVPR, pages 7346-7355, 2018. 1, 2", + "[85] Anh Tuan Tran, Tal Hassner, Iacopo Masi, Eran Paz, Yuval Nirkin, and Gerard Medioni. Extreme 3d face reconstruction: Seeing through occlusions. In CVPR, pages 3935-3944, 2018. 7", + "[86] Daoye Wang, Prashanth Chandran, Gaspard Zoss, Derek Bradley, and Paulo Gotardo. Morf: Morphable radiance fields for multiview neural head modeling. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1-9, 2022. 2", + "[87] Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, and Michael Zollhofer. Learning compositional radiance fields of dynamic human heads. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5704-5713, 2021. 2" + ], + "bbox": [ + 510, + 127, + 890, + 864 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "392", + "bbox": [ + 472, + 887, + 497, + 897 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[88] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. TIP, 13(4):600-612, 2004. 6", + "[89] Felix Wimbauer, Shangzhe Wu, and Christian Rupprecht. De-rendering 3d objects in the wild. In CVPR, pages 18490-18499, 2022. 2", + "[90] Fanzi Wu, Linchao Bao, Yajing Chen, Yonggen Ling, Yibing Song, Songnan Li, King Ngi Ngan, and Wei Liu. Mvfnet: Multi-view 3d face morphable model regression. In CVPR, pages 959-968, 2019. 1, 2, 3", + "[91] Shangzhe Wu, Christian Rupprecht, and Andrea Vedaldi. Unsupervised learning of probably symmetric deformable 3d objects from images in the wild. In CVPR, pages 1-10, 2020. 1, 2, 5, 6, 7, 8", + "[92] Sicheng Xu, Jiaolong Yang, Dong Chen, Fang Wen, Yu Deng, Yunde Jia, and Xin Tong. Deep 3d portrait from a single image. In CVPR, pages 7710-7720, 2020. 2", + "[93] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. NeurIPS, 34:4805-4815, 2021. 2", + "[94] Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and Christian Theobalt. i3dmm: Deep implicit 3d morphable model of human heads. In CVPR, pages 12803-12813, 2021. 1, 2", + "[95] Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li. Learning face representation from scratch. arXiv preprint arXiv:1411.7923, 2014. 5", + "[96] Stefanos Zafeiriou, Mark Hansen, Gary Atkinson, Vasileios Argyriou, Maria Petrou, Melvyn Smith, and Lyndon Smith. The photoface database. In CVPRW, pages 132-139, 2011. 5", + "[97] Xiaoxing Zeng, Xiaojiang Peng, and Yu Qiao. Df2net: A dense-fine-finer network for detailed 3d face reconstruction. In ICCV, pages 2315-2324, 2019. 2, 7", + "[98] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 5", + "[99] Ruo Zhang, Ping-Sing Tsai, James Edwin Cryer, and Mubarak Shah. Shape-from-shading: a survey. IEEE TPAMI, 21(8):690-706, 1999. 1, 2", + "[100] Zhenyu Zhang, Yanhao Ge, Renwang Chen, Ying Tai, Yan Yan, Jian Yang, Chengjie Wang, Jilin Li, and Feiyue Huang. Learning to aggregate and personalize 3d face from in-the-wild photo collection. In CVPR, pages 14214-14224, 2021. 1, 2, 3, 5, 6, 7, 8", + "[101] Zhenyu Zhang, Yanhao Ge, Ying Tai, Weijian Cao, Renwang Chen, Kunlin Liu, Hao Tang, Xiaoming Huang, Chengjie Wang, Zhifeng Xie, et al. Physically-guided disentangled implicit rendering for 3d face modeling. In CVPR, pages 20353-20363, 2022. 3, 7, 8", + "[102] Mingwu Zheng, Hongyu Yang, Di Huang, and Liming Chen. Imface: A nonlinear 3d morphable face model with implicit neural representations. In CVPR, pages 20343-20352, 2022. 2" + ], + "bbox": [ + 78, + 125, + 468, + 864 + ], + "page_idx": 11 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[103] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. Im avatar: Implicit morphable head avatars from videos. In CVPR, pages 13545–13555, 2022. 3", + "[104] Yuxiang Zhou, Jiankang Deng, Irene Kotsia, and Stefanos Zafeiriou. Dense 3d face decoding over 2500fps: Joint texture & shape convolutional mesh decoders. In CVPR, pages 1097-1106, 2019. 2", + "[105] Xiangyu Zhu, Zhen Lei, Xiaoming Liu, Hailin Shi, and Stan Z Li. Face alignment across large poses: A 3d solution. In CVPR, pages 146-155, 2016. 1, 2", + "[106] Xiangyu Zhu, Fan Yang, Chang Yu Di Huang, Hao Wang, Jianzhu Guo, Zhen Lei, and Stan Z Li. Beyond 3dmm space: Towards fine-grained 3d face reconstruction. In ECCV, 2020. 1, 2", + "[107] Xiangyu Zhu, Dong Yi, Zhen Lei, and Stan Z Li. Robust 3d morphable model fitting by sparse sift flow. In ICCV, pages 4044-4049. IEEE, 2014. 1, 2", + "[108] Yiyu Zhuang, Hao Zhu, Xusen Sun, and Xun Cao. Mofan-erf: Morphable facial neural radiance field. arXiv preprint arXiv:2112.02308, 2021. 8" + ], + "bbox": [ + 502, + 125, + 890, + 397 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "393", + "bbox": [ + 472, + 887, + 497, + 897 + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/2023/Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild/5ebd965c-2fcd-48b5-82dd-8be4b32c7eaf_model.json b/2023/Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild/5ebd965c-2fcd-48b5-82dd-8be4b32c7eaf_model.json new file mode 100644 index 0000000000000000000000000000000000000000..d4fe005dd692061f78ad819aa2feb2b19c18f395 --- /dev/null +++ b/2023/Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild/5ebd965c-2fcd-48b5-82dd-8be4b32c7eaf_model.json @@ -0,0 +1,2677 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.13, + 0.051, + 0.201, + 0.086 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.253, + 0.049, + 0.792, + 0.087 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.231, + 0.162, + 0.74, + 0.203 + ], + "angle": 0, + "content": "Learning Neural Proto-face Field for Disentangled 3D Face Modeling In the Wild" + }, + { + "type": "text", + "bbox": [ + 0.175, + 0.228, + 0.8, + 0.279 + ], + "angle": 0, + "content": "Zhenyu Zhang\\(^{1}\\), Renwang Chen\\(^{1}\\), Weijian Cao\\(^{1}\\), Ying Tai\\(^{1*}\\), Chengjie Wang\\(^{1,2*}\\) \nTencent Youtu Lab, Shanghai, China\\(^{1}\\) \nShanghai Jiao Tong University, Shanghai, China\\(^{2}\\)" + }, + { + "type": "text", + "bbox": [ + 0.388, + 0.281, + 0.584, + 0.293 + ], + "angle": 0, + "content": "zhangjesse@foxmail.com" + }, + { + "type": "text", + "bbox": [ + 0.23, + 0.297, + 0.735, + 0.31 + ], + "angle": 0, + "content": "renwangchen, weijiancao, yingtai, jasoncjwang@tencent.com" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.342, + 0.313, + 0.356 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.372, + 0.474, + 0.691 + ], + "angle": 0, + "content": "Generative models show good potential for recovering 3D faces beyond limited shape assumptions. While plausible details and resolutions are achieved, these models easily fail under extreme conditions of pose, shadow or appearance, due to the entangled fitting or lack of multiview priors. To address this problem, this paper presents a novel Neural Proto-face Field (NPF) for unsupervised robust 3D face modeling. Instead of using constrained images as Neural Radiance Field (NeRF), NPF disentangles the common/specific facial cues, i.e., ID, expression and scene-specific details from in-the-wild photo collections. Specifically, NPF learns a face prototype to aggregate 3D-consistent identity via uncertainty modeling, extracting multi-image priors from a photo collection. NPF then learns to deform the prototype with the appropriate facial expressions, constrained by a loss of expression consistency and personal idiosyncrasies. Finally, NPF is optimized to fit a target image in the collection, recovering specific details of appearance and geometry. In this way, the generative model benefits from multi-image priors and meaningful facial structures. Extensive experiments on benchmarks show that NPF recovers superior or competitive facial shapes and textures, compared to state-of-the-art methods." + }, + { + "type": "title", + "bbox": [ + 0.079, + 0.72, + 0.21, + 0.734 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.744, + 0.47, + 0.842 + ], + "angle": 0, + "content": "3D face reconstruction is a long-standing problem with applications including games, digital human and mobile photography. It is ill-posed in many cases requiring strong assumptions e.g., shape from shading [99]. With the 3D Morphable Model (3DMM) [10] proposed, such a problem can be solved by fitting parameters to the target faces [67, 68, 107]. Recently, deep-learning methods [22, 25, 43, 64," + }, + { + "type": "image", + "bbox": [ + 0.522, + 0.34, + 0.871, + 0.59 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.601, + 0.892, + 0.678 + ], + "angle": 0, + "content": "Figure 1. (a) Comparison with graphics-renderer-based methods LAP [100] and D3DFR [20]. Our method models geometry details and photo-realistic texture. (b) Results of neural rendering methods EG3D [13] + PTI [66], HeadNeRF [34] and our method. Our method produces high-quality geometry, robust texture modeling under rotation and deformation." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.686, + 0.892, + 0.783 + ], + "angle": 0, + "content": "105] are proposed to regress 3DMM parameters from input images. These approaches are then improved by nonlinear modeling [24, 29, 79, 81, 84, 94] and multi-view consistency [7, 15, 76, 90]. Besides 3DMM methods, recent efforts [91, 100] attempt to model 3D face without shape assumptions. These non-parametric methods have potential ability to improve the modeling quality beyond 3DMM." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.784, + 0.893, + 0.868 + ], + "angle": 0, + "content": "Although the aforementioned methods achieve impressive performance, they also have obvious drawbacks. On the one hand, as the parametric models are usually built from a small amount of subjects (e.g., BFM [58] with 200 subjects) and rigidly controlled conditions, they may be fragile to large variations of identity [106], and have limi" + }, + { + "type": "page_footnote", + "bbox": [ + 0.095, + 0.854, + 0.396, + 0.867 + ], + "angle": 0, + "content": "*Chengjie Wang and Ying Tai are corresponding authors" + }, + { + "type": "page_number", + "bbox": [ + 0.473, + 0.888, + 0.498, + 0.898 + ], + "angle": 0, + "content": "382" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.126, + 0.47, + 0.224 + ], + "angle": 0, + "content": "tations on building teeth, skin details or anatomic grounded muscles [23]. On the other hand, all of these methods depend on graphics renderers [42, 44, 46] in the analysis-by-synthesis fitting procedure, and thus yields hand-crafted approximation or ill-posed decomposition on intrinsic clues. Hence, as illustrated in Fig. 1-(a), these methods struggle to produce photo-realistic texture or geometric details." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.225, + 0.471, + 0.487 + ], + "angle": 0, + "content": "Against these limitations, efforts are made to use a neural renderer such as StyleGAN [38, 39] to model faces by inverting the corresponding images [1,2] into \\(\\mathcal{W}\\) space. Existing methods [11, 18, 59, 62, 92] mainly learn to embed 3DMM coefficients to implicitly leverage 3D clues, but they have difficulty achieving precise 3D controls due to their entangled image formation. To disentangle neural rendering, recent works [13, 14, 34, 54] employ explicit 3D pipelines, e.g., Neural Radiance Field (NeRF) [52] into the StyleGANs' framework, so that face shapes and camera views can be extracted. In this way, precise 3D controls and detailed geometry can be obtained. However, these methods still show fragile performance under challenging conditions as shown in Fig. 1-(b). When confronting large poses, extreme appearance or lighting, the lack of facial priors disturbs the reconstruction and results in severe distortions. This is due to the essentially overfitting objective of inverting single target image, where the geometry ambiguity is unavoidable." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.49, + 0.47, + 0.821 + ], + "angle": 0, + "content": "On top of this, one solution is to leverage reliable priors, e.g., multi-image consistency as a complement. While NeRF provides a natural paradigm to dig multi-view cues, it requires fully constrained images that are difficult to obtain. Even conditioned by style codes [13, 14, 54], there is no direct way to build 3D faces from unconstrained portrait collections in such a neural rendering mechanism. In this work, we present a novel Neural Proto-face Field (NPF) for unsupervised robust 3D face modeling, where ID, expression and scene-specific details can be disentangled from in-the-wild photo collections. To aggregate ID-aware cues, NPF leverages uncertainty modeling to extract multi-image priors and recovers a face prototype with ID-consistent face shape. To disentangle the expression, NPF then learns appropriate representations to deform the prototype, constrained by a expression consistency loss. In this way, the learned face shape is properly blended to avoid geometric ambiguity. Finally, to recover the scene-specific details, NPF is optimized to fit a target image in the collection. The robustness of fitting is guaranteed by a geometry and appearance regularization. As shown in Fig. 1-(b), NPF makes the generative method benefit from multi-image priors in unconstrained environments, and produces high-quality 3D faces under challenging conditions." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.824, + 0.397, + 0.837 + ], + "angle": 0, + "content": "In summary, our contributions are as follows:" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.839, + 0.47, + 0.868 + ], + "angle": 0, + "content": "1) A novel Neural Proto-face Field (NPF) is proposed to disentangle ID, expression and specific details from 3D face" + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.125, + 0.892, + 0.188 + ], + "angle": 0, + "content": "
MethodsRenderingPipelineMulti-view
EMOCA [81], DECA [24], Unsup3D [91]GraphicsDisentangled×
LAP [100], FML [76], MVF [90]GraphicsDisentangled✓
DFG [18], StyleRig [77], StyleFlow [3]NeuralEntangled×
Pi-GAN [14], StyleSDF [54], EG3D [13]NeuralDisentangled×
OursNeuralDisentangled✓
" + }, + { + "type": "table_caption", + "bbox": [ + 0.542, + 0.196, + 0.848, + 0.208 + ], + "angle": 0, + "content": "Table 1. Discussion with selected existing methods." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.223, + 0.891, + 0.251 + ], + "angle": 0, + "content": "modeling, which uses in-the-wild photo collections to benefit the 3D generative model under challenging conditions." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.251, + 0.891, + 0.291 + ], + "angle": 0, + "content": "2) With a novel face prototype aggregation method, NPF integrates multi-image face priors against the large variations in unconstrained environments." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.292, + 0.891, + 0.334 + ], + "angle": 0, + "content": "3) With a series of novel consistency losses, NPF is well fit to specific scenes with personalized details, based on the guidance of face prototypes." + }, + { + "type": "list", + "bbox": [ + 0.499, + 0.251, + 0.891, + 0.334 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.346, + 0.649, + 0.36 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.37, + 0.891, + 0.438 + ], + "angle": 0, + "content": "In Table 1, we make a discussion on existing methods. NPF benefits from neural rendering and avoids hand-crafted approximations of graphics renderers. In contrast to neural rendering methods, our approach has explicit 3D pipelines, and leverages multi-image consistency in the wild." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.438, + 0.892, + 0.604 + ], + "angle": 0, + "content": "3D Face Reconstruction: As a long-standing problem, the studies mainly start from the pioneer work 3DMM [10]. With the parametric model, early works try to find suitable parameters via optimization [67, 68, 107], while recent approaches [25, 64, 105, 106] leverage deep neural networks to regress the results from input images. With the differentiable renderers proposed, efforts are made on aspects of unsupervised learning [30, 65, 81], improving the non-linear feasibility [16, 21, 24, 29, 81, 84, 104] and multi-view consistency [7, 15, 76, 90]. More recent works attempt to learn complete 3DMM basis [79] or implicit functions [94, 102] which brings new possibilities to this topic." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.605, + 0.892, + 0.756 + ], + "angle": 0, + "content": "Beyond 3DMM, non-parametric models are also developed by data-driven supervised training [4, 35, 97]. With shape-from-shading algorithm [99], unsupervised methods are proposed including SFS-Net [70], Unsup3D [91] and LAP [100]. Recently, Gan2Shape [55] and LiftedGAN [72] try to distill knowledge from 2D GANs for 3D reconstruction. De3D [89] de-renders more intrinsic factors based on reliable priors. Different from the these discussed methods, NPF benefits 3D face modeling via neural rendering mechanisms, thus provides better performance on details, resolution and non-facial objects." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.757, + 0.892, + 0.867 + ], + "angle": 0, + "content": "Neural Scene Representation: Neural scene representation is a novel way to parameterize signals, and attracts more attention on learning geometry [12, 49, 51, 74, 93]. Recent effort Neural Radiance Field (NeRF) [8, 9, 52] shows impressive performance on recovering appearance and shapes from multi-view images. Based on NeRF, 3D face can also be modeled [28,61,86,87] from a few or single views. Recent efforts are developed to perform reconstruc" + }, + { + "type": "page_number", + "bbox": [ + 0.473, + 0.888, + 0.498, + 0.898 + ], + "angle": 0, + "content": "383" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.126, + 0.469, + 0.237 + ], + "angle": 0, + "content": "tion against non-rigid deformation [56,57,60]. Conditioned on specific expressions, NeRF can also be used to realize editing [5, 36] or 4D facial avatars [27, 31, 103]. In summary, these methods depend on fully constrained environments, i.e., the used images have to be captured at a same time. In contrast, our method leverages face consistency from in-the-wild photo sets that are easy to collect, and addresses large variations of lighting, appearance or artifacts." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.238, + 0.471, + 0.486 + ], + "angle": 0, + "content": "Neural Rendering on Face Modeling: Neural rendering aims to render images using neural networks [50, 78, 80] with much relaxed inputs. The style-based approaches, i.e., StyleGANs [37-39] obtain the state-of-the-art performance on face generation. With the pretrained StyleGANs, face modeling can be achieved by inverting the target images [1,2,63,66]. 3D embedding (e.g., 3DMM parameter) is then employed to implicitly control the StyleGAN's prediction on pose, identity and lighting [3,18,77]. However, they cannot guarantee the robustness on physical perspective, nor obtain explicit geometry. More recent works attempt to employ 3D pipelines, such as NeRF or deferred neural rendering [82], into StyleGANs [11, 13, 14, 19, 32, 53, 54, 75, 101]. While these methods achieve more precise controls and facial geometry prediction, they are fragile to large pose and ambiguous appearance due to the single-image fitting. In contrast, NPF well leverages multi-image consistency as complementary priors and shows more robust performance." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.5, + 0.203, + 0.516 + ], + "angle": 0, + "content": "3. Preliminary" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.524, + 0.469, + 0.689 + ], + "angle": 0, + "content": "Exploiting the consistency of multiple images, especially from unconstrained environments, is non-trivial for neural rendering methods because there is no explicit topology that can be shared within the image set. While NeRF provides a natural paradigm to dig consistency from different views, it requires photos taken at the same time or constrained scene. Recent 3D-aware generative methods [13, 14, 32, 54] show that NeRF can represent different faces conditioned on style codes. Such an evidence makes it possible to learn common hybrid 3D representations from in-the-wild photo collections. Without loss of generality, we build our model on EG3D [13] as it achieves the state-of-the-art performance." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.69, + 0.468, + 0.704 + ], + "angle": 0, + "content": "EG3D represents the 3D scene as a tri-plane denoted as:" + }, + { + "type": "equation", + "bbox": [ + 0.14, + 0.718, + 0.469, + 0.736 + ], + "angle": 0, + "content": "\\[\n\\mathbf {F} _ {x y}, \\mathbf {F} _ {x z}, \\mathbf {F} _ {y z} = \\Phi_ {g} (\\mathbf {s}), \\mathbf {s} = \\left\\{s ^ {j} \\right\\} _ {j = 1} ^ {M}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.742, + 0.469, + 0.867 + ], + "angle": 0, + "content": "where \\(\\mathbf{F} \\in [H, W, C]\\) is the orthogonal feature plane along different axis, and \\(\\Phi_g\\) is the StyleGAN generator. \\(\\mathbf{s} \\in \\mathbb{R}^{14 \\times 512}\\) is the style code with stages \\(M = 14\\), generated from a random noise \\(\\mathbf{z}\\) by a mapping network. Then any 3D position \\(x \\in \\mathbb{R}^3\\) can be projected onto each of the three feature planes, retrieving its feature \\(f = f_{xy} + f_{xz} + f_{yz}\\) via bilinear interpolation. The corresponding density \\(\\sigma\\) and color \\(\\mathbf{c}\\) is predicted by a tri-plane decoder \\(\\Phi_d\\) from \\(f\\). In this way, given a camera pose \\(p\\), origin \\(\\mathbf{o}\\) and near/far bound \\(t_n, t_f\\)," + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.126, + 0.892, + 0.154 + ], + "angle": 0, + "content": "the pixel color \\(\\mathbf{C}\\) for a ray \\(\\mathbf{r}(t) = \\mathbf{o} + t\\mathbf{d}\\) can be obtained using volume rendering equation [48]:" + }, + { + "type": "equation", + "bbox": [ + 0.565, + 0.163, + 0.892, + 0.196 + ], + "angle": 0, + "content": "\\[\n\\mathbf {C} (\\mathbf {r}) = \\int_ {t _ {n}} ^ {t _ {f}} T (t) \\sigma (\\mathbf {r} (t)) \\mathbf {c} (\\mathbf {r} (t), \\mathbf {d}) d t, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.206, + 0.892, + 0.374 + ], + "angle": 0, + "content": "where \\( T(t) = \\exp(\\int_{t_n}^t \\sigma(\\mathbf{r}(s)) ds \\), and \\( t \\) is the sampled point along each ray. Using Eqn. 2, EG3D renders a low-resolution image \\( \\mathbf{I}_l \\) as well as a feature image \\( \\mathbf{I}_f \\). \\( \\mathbf{I}_f \\) is then fed into a super-resolution module \\( \\Phi_u \\) to generate final face image \\( \\mathbf{I} \\). Adversarial loss is calculated between \\( \\mathbf{I}, \\mathbf{I}_l \\) and the real image to update \\( \\Phi_g, \\Phi_d \\) and \\( \\Phi_u \\). Once trained, given a target image \\( \\mathbf{I}_t \\), face modeling can be achieved by image inversion [1,63,66]. As discussed in Sec. 1, this kind of approaches suffer from ambiguous appearance and large poses. In contrast, NPF tackles the problem by further disentangling a face using multi-image priors, which is introduced in the following." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.386, + 0.635, + 0.402 + ], + "angle": 0, + "content": "4. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.409, + 0.892, + 0.533 + ], + "angle": 0, + "content": "In this section, we introduce the proposed Neural Protoface Field (NPF). Our aim is digging multi-image priors to improve the robustness of 3D face modeling. This is achieved by disentangling ID-consistent shape, expression and specific details from in-the-wild photo collection. The overview is shown in Fig. 2, where NPF contains identity-aware aggregation and deformation modeling to recover face prototypes (Sec. 4.1). After NPF is learned, the target face is reconstructed by a fitting procedure (Sec. 4.2)." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.542, + 0.792, + 0.557 + ], + "angle": 0, + "content": "4.1. Neural Proto-face Field Learning" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.563, + 0.891, + 0.687 + ], + "angle": 0, + "content": "Face appearance of an identity shares consistent cues due to the invariant face geometry, even under different in-the-wild environments. Such consistency has been widely used in previous methods by sharing the coefficients [20,69,79,90] or combining the UV space [100, 101]. Inspired by these efforts, we propose NPF to aggregate a common hybrid 3D representation from each photo collection, which models a face prototype with ID-consistent shape from reliable priors." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.688, + 0.892, + 0.867 + ], + "angle": 0, + "content": "Identity-aware Aggregation: Our method starts from the style code \\(\\mathbf{s} = \\{s^j\\}_{j=1}^M\\), which well conditions the radiance field of tri-planes to render a face image \\(\\mathbf{I}\\). Theoretically, \\(\\mathbf{s}\\) can be obtained from various methods [1,2,63,83]. Without specially statement, we use the most direct scheme proposed in [38] to optimize \\(\\mathbf{s}\\) for real-image inversion. Given a photo collection \\(\\{\\mathbf{I}_i\\}_{i=1}^N\\) of a same ID, we obtain \\(\\{\\mathbf{s}_i\\}_{i=1}^N\\) in the \\(\\mathcal{W}\\) space. Although finding optimal solution is difficult, \\(\\mathbf{s}_i\\) still represents the face shape of \\(\\mathbf{I}_i\\), and such consistent cues should have much lower uncertainty than any other information within the image set. As a result, we propose an uncertainty-aware aggregation method inspired by [73] to extract the consistency. As illustrated in Fig. 2," + }, + { + "type": "page_number", + "bbox": [ + 0.473, + 0.888, + 0.498, + 0.898 + ], + "angle": 0, + "content": "384" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.166, + 0.125, + 0.812, + 0.358 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.367, + 0.895, + 0.435 + ], + "angle": 0, + "content": "Figure 2. Overview of NPF learning method. For an in-the-wild photo collection \\(\\{\\mathbf{I}_i\\}_{i=1}^N\\), we first obtain its style codes \\(\\{\\mathbf{s}_i\\}_{i=1}^N\\) and uncertainties \\(\\{\\omega_i\\}_{i=1}^N\\), then perform identity-aware aggregation to get the pivotal proto code \\(\\bar{s}\\). To model deformation, we use 3DMM expression parameters \\(\\{\\beta_i\\}_{i=1}^N\\) as conditions, and train deformation networks \\(\\{\\Phi_{\\Delta}^j\\}_{j=1}^M\\) to predict deformation code \\(\\Delta \\mathbf{s}_i\\). We also learn global face representation \\(\\bar{h}^j\\) at \\(j\\)-th stage to complement for personalized idiosyncrasies. In this way, the ID-consistent face is suitably blended on the manifold by Eqn. 5. Training by Eqn. 7, NPF well leverages the consistency within in-the-wild portrait sets." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.446, + 0.471, + 0.503 + ], + "angle": 0, + "content": "we use an encoder \\(\\Phi_{a}\\) to get uncertainty \\(\\omega_{i} \\in \\mathbb{R}^{512}\\) from \\(\\mathbf{I}_i\\), and assume \\(\\mathbf{I}_i\\) to have a multivariate Gaussian representation \\(\\mathcal{N}(\\mathbf{s}_i, \\omega_i)\\) in the latent space. Then the aggregation is performed as:" + }, + { + "type": "equation", + "bbox": [ + 0.17, + 0.513, + 0.47, + 0.552 + ], + "angle": 0, + "content": "\\[\n\\bar {\\mathbf {s}} = \\sum_ {i = 1} ^ {N} \\frac {\\omega^ {2}}{\\omega_ {i} ^ {2}} \\mathbf {s} _ {i}, \\quad \\frac {1}{\\omega^ {2}} = \\sum_ {i = 1} ^ {N} \\frac {1}{\\omega_ {i} ^ {2}}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.562, + 0.47, + 0.7 + ], + "angle": 0, + "content": "where \\(\\bar{\\mathbf{s}}\\) is the pivotal proto code similar to PTI [66] but from the photo collection instead of a single image. When forcing \\(\\bar{\\mathbf{s}}\\) to approximate each image, the consistency which has lower uncertainty within \\(\\{\\mathbf{I}_i\\}_{i=1}^N\\) is remained, while other information is suppressed. In this way, the common facial shape and appearance is well aggregated into \\(\\bar{\\mathbf{s}}\\) as a face prototype. Further, compared with average pooling, the adaptively fusion algorithm of Eqn. 3 can be jointly learned with deformation to improve the modeling quality, which is introduced in the following." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.701, + 0.471, + 0.868 + ], + "angle": 0, + "content": "Deformation Modeling: Although \\(\\bar{\\mathbf{s}}\\) has the common prior of an identity, it considers no facial deformation which is suppressed after the aggregation, resulting in a mean expression. Actually, modeling deformation is crucial for face reconstruction [10]. On the one hand, if the face prototype has a different expression from the target image, then recovering the gap will be difficult in the fitting procedure. On the other hand, face images have different appearance and silhouettes due to expression variation, and thus discarding the expression brings ambiguity or conflicts to the consistent geometry. Hence, we propose deformation modeling method to disentangle expressions of face prototype." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.446, + 0.892, + 0.726 + ], + "angle": 0, + "content": "Previous works [56, 57] learn deformation during NeRF sampling, which depends on plentiful images captured in a constrained environment. As the data we use is in-the-wild with limited numbers, using such a deformation strategy is difficult. In contrast, we model expression by modifying style codes. As illustrated in Fig. 2, we first extracts the 3DMM expression parameters \\(\\{\\beta_i\\}_{i=1}^N\\), \\(\\beta_i \\in \\mathbb{R}^{64}\\) and camera poses \\(\\{p_i\\}_{i=1}^N\\) from a pretrained 3D face network \\(\\Phi_{3d}\\) [20], which provides guidance on tuning \\(\\bar{s}\\). Note that, \\(\\{\\beta_i\\}_{i=1}^N\\) cannot well represent the personalized expression with unique idiosyncrasies, as it is unrelated to the identity. Hence, we extract global image representations from \\(\\{\\mathbf{I}_i\\}_{i=1}^N\\) to complement for the characteristics. We use a similar encoder \\(\\Phi_e\\) as PSP [63] to get the representation \\(h_i^j \\in \\mathbb{R}^{512}\\), \\(j = 1, 2, \\dots, M\\) and the corresponding uncertainty \\(\\mu_i^j\\) from \\(\\mathbf{I}_i\\). Then following Eqn. 3, we fuse the \\(\\{h_i^j\\}_{i=1}^N\\) using \\(\\{\\mu_i^j\\}_{i=1}^N\\) to get the global representation \\(\\bar{h}^j\\) at \\(j\\)-th stage. With the global \\(\\bar{h}^j\\) of the collection, we propose deformation networks \\(\\{\\Phi_\\Delta^j\\}_{j=1}^M\\) to predict the deformation code on each stage, which can be defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.629, + 0.731, + 0.891, + 0.749 + ], + "angle": 0, + "content": "\\[\n\\Delta s _ {i} ^ {j} = \\Phi_ {\\Delta} ^ {j} \\left(\\bar {h} ^ {j}, \\beta_ {i}\\right). \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.755, + 0.892, + 0.797 + ], + "angle": 0, + "content": "Denoting \\(\\{\\Delta s_i^j\\}_{j = 1}^M\\) as \\(\\Delta \\mathbf{s}_i\\) for simplification, the personalized deformation of \\(\\mathbf{I}_i\\) can be achieved via modifying the pivotal proto code:" + }, + { + "type": "equation", + "bbox": [ + 0.645, + 0.804, + 0.891, + 0.818 + ], + "angle": 0, + "content": "\\[\n\\hat {\\mathbf {s}} _ {i} = \\bar {\\mathbf {s}} + \\Delta \\mathbf {s} _ {i}. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.825, + 0.892, + 0.868 + ], + "angle": 0, + "content": "Finally, the face prototype \\(\\hat{\\mathbf{I}}_i\\) can be obtained from \\(\\hat{\\mathbf{s}}_i\\) and \\(p_i\\) using pretrained EG3D networks. In this way, \\(\\hat{\\mathbf{I}}_i\\) suffers from less appearance conflicts to \\(\\mathbf{I}_i\\). Further, by jointly" + }, + { + "type": "page_number", + "bbox": [ + 0.473, + 0.887, + 0.498, + 0.898 + ], + "angle": 0, + "content": "385" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.127, + 0.47, + 0.168 + ], + "angle": 0, + "content": "learning of \\(\\Delta \\mathbf{s}_i\\) and \\(\\bar{\\mathbf{s}}\\), the consistent face shape within a collection is adaptively blended against non-rigid ambiguity, and have more precise geometry and texture." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.168, + 0.471, + 0.295 + ], + "angle": 0, + "content": "Training Loss: Note that we do not require \\(\\hat{\\mathbf{I}}_i\\) to be exactly the same as \\(\\mathbf{I}_i\\), but have a proper expression and a consistent blended shape among \\(\\{\\mathbf{I}_i\\}_{i=1}^N\\). To achieve such an aim, we propose a series of consistency losses to learn \\(\\Phi_a, \\Phi_e\\) and \\(\\Phi_\\Delta\\). The reconstruction loss is defined as \\(\\mathcal{L}_{re} = \\mathcal{L}_{lpips}(\\hat{\\mathbf{I}}_i - \\mathbf{I}_i) + \\mathcal{L}_{L2}(\\hat{\\mathbf{I}}_i - \\mathbf{I}_i)\\), where \\(\\mathcal{L}_{lpips}\\) is the perceptual loss [98] and \\(\\mathcal{L}_{L2}\\) is the L2 distance. To constrain the expression of \\(\\hat{\\mathbf{I}}_i\\), we propose an expression consistency loss using pretrained \\(\\Phi_{3d}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.17, + 0.305, + 0.47, + 0.332 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {c} \\left(\\hat {\\mathbf {I}} _ {i}, \\beta_ {i}\\right) = \\frac {1}{\\Omega} \\left| \\Phi_ {3 d} \\left(\\hat {\\mathbf {I}} _ {i}\\right) - \\beta_ {i} \\right|, \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.342, + 0.469, + 0.385 + ], + "angle": 0, + "content": "where \\(\\Omega\\) is the normalization factor. To prevent distortion, we limit the deformation code via a regularization as \\(\\mathcal{L}_d = \\|\\Delta \\mathbf{s}_i\\|_2^2\\). The final loss function is denoted as:" + }, + { + "type": "equation", + "bbox": [ + 0.087, + 0.396, + 0.469, + 0.434 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {p r o t o} = \\sum_ {i = 1} ^ {N} \\mathcal {L} _ {r e} \\left(\\hat {\\mathbf {I}} _ {i}, \\mathbf {I} _ {i}\\right) + \\lambda_ {c} \\mathcal {L} _ {c} \\left(\\hat {\\mathbf {I}} _ {i}, \\beta_ {i}\\right) + \\lambda_ {d} \\mathcal {L} _ {d} \\left(\\mathbf {s} _ {i}\\right), \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.445, + 0.469, + 0.515 + ], + "angle": 0, + "content": "where \\(\\lambda_c, \\lambda_d\\) are the weights. When using \\(\\mathcal{L}_{proto}\\) to update \\(\\Phi_a, \\Phi_e\\) and \\(\\Phi_\\Delta\\), the networks of EG3D are all frozen. In this way, we learn neural proto-face field from in-the-wild photo collections, and disentangle identities and expressions of the face prototypes." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.526, + 0.351, + 0.541 + ], + "angle": 0, + "content": "4.2. Neural Proto-face Field Fitting" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.548, + 0.469, + 0.7 + ], + "angle": 0, + "content": "Once NPF is learned, we get a 3D hybrid initialization containing consistent cues of the photo collection, constrained by reliable multi-image priors. To model the precise 3D faces, scene-specific details need to be recovered. Such an aim can be achieved by tuning the generator using pivotal tuning (PTI) algorithm [66]. However, PTI has obvious drawbacks of two aspects: 1) its pivotal code has no multi-view prior; 2) its tuning procedure easily overfits to a single image. The former one has been addressed by NPF. For the latter, we propose a consistent fitting method to overcome the monocular ambiguity." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.701, + 0.469, + 0.867 + ], + "angle": 0, + "content": "Multi-image Warm-Up: Fitting is achieved by tuning the generator \\(\\Phi_g\\) of EG3D while freezing other networks. In the early stage of fitting, we need to prevent the generator converging to a local optimum that provides distortions due to challenging conditions. In contrast to fitting a single image, we propose a multi-image warm-up method to fit \\(\\Phi_g\\) to the photo collection \\(\\{\\mathbf{I}_i\\}_{i=1}^N\\). We use \\(\\mathcal{L}_{re}\\) to approach the target image set. To guarantee the geometry consistency at unseen views, we randomly sample a camera pose \\(p'\\) at every step, and use the volume renderer of EG3D to predict the corresponding normal maps \\(\\hat{\\mathbf{n}}_i\\) and \\(\\dot{\\mathbf{n}}_i\\) from the original/new weights of \\(\\Phi_g\\), respectively. Then we calculate the" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.125, + 0.891, + 0.281 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.29, + 0.892, + 0.317 + ], + "angle": 0, + "content": "Figure 3. The proposed consistency losses of NPF fitting. (a) Multi-image Warm-up; (b) Robust Target-image Fitting." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.33, + 0.892, + 0.373 + ], + "angle": 0, + "content": "geometry consistency loss \\(\\mathcal{L}_{gc}(\\dot{\\mathbf{n}}_i,\\hat{\\mathbf{n}}_i) = \\| \\dot{\\mathbf{n}}_i - \\hat{\\mathbf{n}}_i\\| _2^2\\) to constrain the robustness of facial geometry. The final objective of multi-image warm-up is denoted as:" + }, + { + "type": "equation", + "bbox": [ + 0.552, + 0.383, + 0.891, + 0.422 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {w a r m}} = \\sum_ {i = 1} ^ {N} \\mathcal {L} _ {r e} \\left(\\dot {\\mathbf {I}} _ {i}, \\mathbf {I} _ {i}\\right) + \\lambda_ {g} \\mathcal {L} _ {g c} \\left(\\dot {\\mathbf {n}} _ {i}, \\hat {\\mathbf {n}} _ {i}\\right), \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.433, + 0.891, + 0.475 + ], + "angle": 0, + "content": "where \\(\\dot{\\mathbf{I}}_i\\) is the reconstructed image. Using \\(\\mathcal{L}_{warm}\\), we make \\(\\Phi_g\\) initialize to a solution near the optimum of each image, and suppress distortions of unseen poses." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.476, + 0.892, + 0.587 + ], + "angle": 0, + "content": "Robust Target-image Fitting: After the multi-image warm-up, we fit \\(\\Phi_g\\) to the target image \\(\\mathbf{I}_i\\) and model specific details. Besides the reconstruction loss \\(\\mathcal{L}_{re}\\), we constrain the recovered appearance of unseen poses. Similar to \\(\\mathcal{L}_{gc}\\) we randomly sample a camera pose \\(p'\\) at each step and render an image \\(\\dot{\\mathbf{I}}_i'\\), then encourage \\(\\dot{\\mathbf{I}}_i'\\) and the target \\(\\mathbf{I}_i\\) to have similar representations via \\(\\mathcal{L}_{ac} = \\|\\Phi_e(\\dot{\\mathbf{I}}_i') - \\Phi_e(\\mathbf{I}_i)\\|_2^2\\). The final loss of robust target-image fitting is:" + }, + { + "type": "equation", + "bbox": [ + 0.59, + 0.597, + 0.891, + 0.613 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {t a r g e t}} = \\mathcal {L} _ {\\text {r e}} \\left(\\mathbf {I} _ {i}, \\mathbf {I} _ {i}\\right) + \\lambda_ {a} \\mathcal {L} _ {\\text {a c}}. \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.623, + 0.891, + 0.651 + ], + "angle": 0, + "content": "In this way, we improve the robustness of the scene-specific details recovering." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.664, + 0.625, + 0.68 + ], + "angle": 0, + "content": "5. Experiment" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.687, + 0.892, + 0.867 + ], + "angle": 0, + "content": "Dataset: We first train our \\(\\Phi_{a},\\Phi_{e}\\) and \\(\\Phi_{\\Delta}\\) on CelebA [45] and CASIA-WebFace [95], then fine-tune them on a high-resolution dataset CelebAMask-HQ [41]. Following [100], we organize CelebA and CASIAWebFace using ID-labels and keep each identity with at least 6 photos. This provides \\(600\\mathrm{K}\\) images with \\(16\\mathrm{K}\\) identities. We select images of \\(12\\mathrm{K} / 2\\mathrm{K} / 2\\mathrm{K}\\) identities as train/val/test set. For CelebAMask-HQ, we organize it into \\(24\\mathrm{K}\\) different identities using ground truth ID-labels, and randomly select \\(20\\mathrm{K} / 1\\mathrm{K} / 3\\mathrm{K}\\) identities as train/val/test set. Following [4, 91, 100], we perform testing on MICC [6], Photoface [96] and FG3D [26] dataset. Photoface dataset contains \\(12\\mathrm{K}\\) images of 453 people with face/normal image" + }, + { + "type": "page_number", + "bbox": [ + 0.473, + 0.888, + 0.498, + 0.898 + ], + "angle": 0, + "content": "386" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.092, + 0.123, + 0.457, + 0.226 + ], + "angle": 0, + "content": "
No.methodp2p (mm) ↓MAD (deg.) ↓IDE ↓
(1)Ours1.7711.870.257
(2)Ours face prototype1.9612.390.307
(3)w/o uncertainty = avg pooling1.8712.130.298
(4)w/o deformation modeling2.2512.900.401
(5)w/o Φe2.0112.460.350
(6)w/o multi-image warm-up1.8412.300.301
(7)w/o Lgc1.8012.150.274
(8)w/o Lac1.7911.870.262
(9)single-image PTI [66]2.3512.100.482
" + }, + { + "type": "table_caption", + "bbox": [ + 0.096, + 0.236, + 0.45, + 0.25 + ], + "angle": 0, + "content": "Table 2. Comparison with Different Baselines and Settings." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.256, + 0.47, + 0.283 + ], + "angle": 0, + "content": "pairs, and we follow the protocol of [4,70] for testing. More details are introduced in Appendix." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.284, + 0.47, + 0.506 + ], + "angle": 0, + "content": "Implementation Details: We build \\(\\Phi_{a}\\) using a similar architecture to the encoder of [91, 100]. The deformation network \\(\\Phi_{\\Delta}^{j}\\) of \\(j\\)-th stage contains 3 MLP layers to encode \\(\\beta_{i}\\), and another 3 MLP layers to predict \\(\\Delta s_{i}^{j}\\) from the combination of \\(\\beta_{i}, \\bar{\\mathbf{h}}^{j}\\). We set \\(\\lambda_{c} = 1, \\lambda_{d} = 1e - 3, \\lambda_{g} = \\lambda_{a} = 0.2\\) for the losses. Following [13], the output images of the model are of \\(512 \\times 512\\), and the meshes are extracted using Marching Cubes [47] with a same size. During training, we randomly set \\(N\\) from 1 to 6 to adapt different sizes of a photo collection. We train the model with batch size of 4 on CelebA and CASIA-WebFace for 15 epochs, and fine-tune it on CelebAMaskHQ for 30 epochs. During fitting, the multi-image warm-up lasts 100 steps and robust target-image fitting lasts another 100 steps. We use Adam [40] as the optimizer, and set the learning rate as 0.0001 on a V-100 GPU. More details are in the Appendix." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.507, + 0.471, + 0.686 + ], + "angle": 0, + "content": "Evaluation Protocol: Without special statements, we use 4-image results to compare with other methods. To evaluate the modeled texture, we calculate Structural Similarity Index (SSIM) [88], Cosine-similarity of Arcface [17] representation and RMSE of identity parameters (IDE) of 3D face network [20] between the original high-quality images and rendered ones. We render images under different poses to measure the robustness. To evaluate the geometry, we rigid-align predictions to the ground truth via ICP using hand-crafted key points. Mean Angle Deviation (MAD) of normal maps and point-to-plane \\((\\mathrm{p2p})\\) distance between the aligned prediction and ground truth are utilized as metrics. Please see Appendix for more details." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.694, + 0.232, + 0.708 + ], + "angle": 0, + "content": "5.1. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.714, + 0.47, + 0.867 + ], + "angle": 0, + "content": "Comparison with Baselines: We first analyse different settings of NPF in Table 2. We calculate NME, MAD and IDE metrics on MICC, Photoface and CelebaMaskHQ dataset, respectively. In row (1), our full method obtains the best performance in all metrics, approving the effectiveness of each proposed component. In row (2), we observe that the face prototype gets satisfactory, or even better results than several baselines, which reveals that NPF well integrates consistency from photo collection. In row (3), we replace the adaptive uncertainty with average pooling to get the proto code \\(\\overline{\\mathbf{s}}\\), and the performance is reduced to" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.123, + 0.895, + 0.281 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.292, + 0.893, + 0.332 + ], + "angle": 0, + "content": "Figure 4. Analysis on the deformation modeling. (a) How the deformation modeling improves the details and shape accuracy. (b) How the settings of deformation modules influence the results." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.35, + 0.893, + 0.529 + ], + "angle": 0, + "content": "some extent. This reveals our identity-aware aggregation well contributes on digging consistent cues. In row (4), the significant accuracy drop demonstrates the effectiveness of deformation modeling on avoiding expression-aware shape ambiguity. Row (5) also proves that the global face representation from \\(\\Phi_e\\) improves the quality of deformation modeling. Rows (6)-(8) reveal that the multi-image warm-up and consistency losses also improve the robustness. Finally, row (9) is the state-of-the-art single-image baseline, where it achieves even worse results than face prototype in IDE. This is due to the distortion caused by overfitting or ambiguity under challenging conditions of pose, lighting and occlusion." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.533, + 0.892, + 0.795 + ], + "angle": 0, + "content": "Analysis on Deformation Modeling: We analyse how the proposed deformation modeling method improves the performance. In Fig. 4-(a), we highlight the differences between this two settings. Without deformation modeling, fusing the style codes yields \\(\\bar{\\mathbf{s}}\\) to represent a mean expression on the face prototype, which is different from the target face. Hence, it brings difficulty to recover the target's expression during NPF fitting. Further, as the face shapes with various expressions show different appearance and silhouettes on the images, ignoring the deformation brings ambiguity when digging the multi-image consistency. Hence, such a model produces improper shapes and details. In contrast, modeling the deformation recovers the target's expression, the corresponding shapes and appearance effects. In Fig. 4-(b), we show the influence of different settings. Without deformation modeling, the target expression cannot be recovered. Without the global representation \\(\\bar{\\mathbf{h}}\\), the expression effect is produced but not accurate enough. Finally, our full method recovers precise target deformation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.798, + 0.892, + 0.868 + ], + "angle": 0, + "content": "Analysis on Multi-image Consistency: We analyse how our method leverages the multi-image priors from inthe-wild photo collections. In Fig. 5-(a), we show the influence of the size \\(N\\) of photo collections. Compared with the single-image setting (same as PTI [66]), \\(N = 2\\) sig" + }, + { + "type": "page_number", + "bbox": [ + 0.473, + 0.888, + 0.498, + 0.898 + ], + "angle": 0, + "content": "387" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.126, + 0.466, + 0.29 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.308, + 0.47, + 0.348 + ], + "angle": 0, + "content": "Figure 5. Analysis on the multi-image consistency. (a) (b) Performance under different photo collection size \\( N \\); (c) The effectiveness of multi-image warm-up. Red box signs the target image." + }, + { + "type": "table", + "bbox": [ + 0.113, + 0.353, + 0.435, + 0.489 + ], + "angle": 0, + "content": "
MICCFG3D
Method3DMMindooroutdoorLQHQ
Extreme3D [85]✓3.663.703.493.58
PRN [25]✓2.322.472.382.06
RingNet [69]✓1.932.022.082.02
3DDFA v2 [33]✓1.881.962.101.91
D3DFR [20] (mv)✓1.661.691.901.95
MGCnet [71]✓1.721.781.951.90
DECA [24]✓1.691.701.911.89
Cross-modal [4]×2.392.332.302.04
Unsup3D [91]×2.482.522.412.29
LAP [100] (mv)×2.322.252.182.16
PhyDIR [101] (mv)×2.292.202.252.09
Ours×1.751.812.151.98
" + }, + { + "type": "table_caption", + "bbox": [ + 0.111, + 0.501, + 0.435, + 0.514 + ], + "angle": 0, + "content": "Table 3. Point-to-plane distance (mm) on benchmarks." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.527, + 0.47, + 0.68 + ], + "angle": 0, + "content": "nificantly improves the accuracy, revealing that even two images still provide reliable priors. With \\(N\\) increasing, the accuracy gets better. Fig. 5-(b) illustrates the qualitative performance. The single-image result has obvious distortion of facial shape due to the target's large pose and challenging appearance. Increasing \\(N = 3\\) obviously suppresses the degradation, and the setting \\(N = 6\\) obtains best performance with proper geometry. In Fig. 5-(c), we observe that during fitting, the proposed multi-image warm-up suppresses the overfitting on occlusion or shadows, boosted by the consistency from other images." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.692, + 0.383, + 0.707 + ], + "angle": 0, + "content": "6. Comparison with State-of-the-art" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.714, + 0.471, + 0.867 + ], + "angle": 0, + "content": "Evaluation on Geometry: We first analyse the modeled geometry on MICC and FG3D dataset. We use the provided indoor/outdoor videos of MICC, low-quality (LQ) and high-quality (HQ) image sets to optimize the trained NPF. The geometry for each identity is obtained from 4 images, and calculated the average results. The comparisons are shown in Table 3, where we divide the methods into 3DMM and non-3DMM group. The tag 'mv' means the method uses multi-view input. We observe that our method obtains the best performance among non-3DMM approaches, but higher errors than D3DFR, MGCnet and" + }, + { + "type": "table", + "bbox": [ + 0.536, + 0.124, + 0.858, + 0.257 + ], + "angle": 0, + "content": "
MAD↓<20°↑<25°↑<30°↑
SfSNet [70]25.5±9.343.6%57.5%68.7%
PRN [25]24.8±6.843.1%62.9%74.1%
DF2Net [97] (GT)24.3±5.742.2%62.7%74.5%
D3DFR [20] (mv)23.5±6.146.1%61.8%73.3%
Cross-Modal [4] (GT)22.8±6.549.0%62.9%74.1%
DECA [24]22.5±5.348.7%62.3%73.7%
LAP [100] (mv)23.0±5.148.2%63.1%74.9%
PhyDIR [101] (mv)22.7±4.349.2%63.4%75.3%
Ours prototype23.7±4.745.6%61.4%73.0%
SfSNet-ft [70]12.8±5.483.7%90.8%94.5%
Cross-Modal-ft [4] (GT)12.0±5.385.2%92.0%95.6%
LAP-ft [100] (mv)12.3±4.584.9%92.4%96.3%
PhyDir-ft [101] (mv)12.0±4.985.3%92.7%96.9%
Ours11.87±5.285.9%93.0%96.9%
" + }, + { + "type": "table_caption", + "bbox": [ + 0.531, + 0.269, + 0.86, + 0.28 + ], + "angle": 0, + "content": "Table 4. Facial normal evaluation on Photoface dataset." + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.288, + 0.89, + 0.582 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.509, + 0.6, + 0.882, + 0.613 + ], + "angle": 0, + "content": "Figure 6. Qualitative comparison on predicted facial geometry." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.631, + 0.892, + 0.811 + ], + "angle": 0, + "content": "DECA. Note that, 3DMM is a highly reliable face shape assumption made from real 3D ground truth, and thus it fundamentally boosts the models' accuracy. Even though, our method still outperforms Extreme3D, PRN, RingNet and competes with 3DDFA v2. We further analyse the modeled facial normal on Photoface dataset in Table 4, where '-ft' means finetuning. Our prototype has already outperforms several methods. After finetuning, our method obtains best performance. Finally, we illustrate qualitative results in Fig. 6, where our method provides finer details. Compared with the EG3D [13] + PTI [66] baseline, our method is much more robust against distortion from large pose, extreme lighting and appearance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.812, + 0.893, + 0.868 + ], + "angle": 0, + "content": "Evaluation on Texture: We then evaluate the modeled texture on CelebAMaskHQ dataset. Besides the reconstructed image, we render images along the yaw angle in 3 ranges: \\([0^{\\circ}, \\pm 20^{\\circ}]\\), \\([\\pm 20^{\\circ}, \\pm 40^{\\circ}]\\) and \\([\\pm 40^{\\circ}, \\pm 60^{\\circ}]\\)," + }, + { + "type": "page_number", + "bbox": [ + 0.473, + 0.888, + 0.498, + 0.898 + ], + "angle": 0, + "content": "388" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.121, + 0.123, + 0.852, + 0.369 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.377, + 0.893, + 0.404 + ], + "angle": 0, + "content": "Figure 7. Qualitative comparison with EG3D [13] + PTI [66] baseline, PhyDIR [101], HeadNeRF [34] and DFG [18] on the robustness of rotation and expression editing. Our method produces robust and photo-realistic rendering results. Zooming in to see the details." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.415, + 0.455, + 0.499 + ], + "angle": 0, + "content": "
Method\\( \\left\\lbrack {{0}^{ \\circ }, \\pm {20}^{ \\circ }}\\right\\rbrack \\)\\( \\left\\lbrack {\\pm {20}^{ \\circ }, \\pm {40}^{ \\circ }}\\right\\rbrack \\)\\( \\left\\lbrack {\\pm {40}^{ \\circ }, \\pm {60}^{ \\circ }}\\right\\rbrack \\)
Cos-simIDECos-simIDECos-simIDESSIM
DFG [18]0.7320.3420.6440.3850.5810.4300.751
Unsup3D [91]0.7010.3180.6770.3340.6320.3870.514
LAP [100] (mv)0.7400.2930.6850.3010.6580.3590.623
PhyDIR [101] (mv)0.8260.2670.7920.2940.7450.3280.880
MoFANeRF [108]0.6930.3370.6660.3600.6400.3950.523
HeadNeRF [34]0.8300.2400.8010.2680.6710.3640.823
EG3D + PTI0.8350.2480.7830.2790.7090.3470.919
Ours0.8420.2330.8060.2510.7500.2870.921
" + }, + { + "type": "table_caption", + "bbox": [ + 0.101, + 0.508, + 0.444, + 0.521 + ], + "angle": 0, + "content": "Table 5. Quality of rendered images on CelebAMask-HQ." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.544, + 0.47, + 0.82 + ], + "angle": 0, + "content": "and calculate the cosine similarity and ID parameter error between them and target images. The performance is illustrated in Table 5, where DFG cannot provide satisfactory results due to its entangled image formation. For Unsup3D and LAP, the quality of texture is limited by the graphics renderer. MoFANeRF suffers from limited data diversity of constrained environments. PhyDIR and HeadNeRF suffer from large-pose degradation. The single-image baseline \\(\\mathrm{EG3D} + \\mathrm{PTI}\\) obtains high reconstruction performance, but fragile results under rotation due to the degraded shapes. In contrast, our method obtains the best performance in all the metrics, and shows robust and high-quality modeling results. Finally, we show qualitative results in Fig. 7 under challenging conditions of extreme lighting and pose. EG3D \\(^+\\) PTI suffers from overfitting and produces flatten shapes, while our method obtains best performance on the rendering quality and robustness. Our method also show satisfactory ability on editing the expressions via deformation codes. More results and comparisons can be seen in the Appendix." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.825, + 0.47, + 0.867 + ], + "angle": 0, + "content": "Limitation: Our method requires multiple images of a same person as input, which may limit the application under limiting conditions. This problem can be suppressed" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.418, + 0.892, + 0.502 + ], + "angle": 0, + "content": "by searching images with similar identity and building a 'relaxed' photo collection. Further, the performance also depends on the pretrained EG3D models. As a result, extreme expressions and poses may bring degradation on accuracy. Such issues can be addressed by re-training EG3D via targeted dataset. We make the discussions in the Appendix." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.522, + 0.755, + 0.537 + ], + "angle": 0, + "content": "7. Conclusion & Future Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.549, + 0.892, + 0.867 + ], + "angle": 0, + "content": "In this paper, we propose a Neural Proto-face Field (NPF) method for unsupervised robust 3D face modeling. NPF digs multi-image priors from in-the-wild photo collections to boost the 3D generative models, and well complements the single-image overfitting inversion procedure. To aggregate consistency against large variations in unconstrained environments, a novel identity-aware aggregation method is proposed to adaptively combine the style codes, and build ID-consistent face prototypes. To suppress the non-rigid ambiguity, NPF blends the consistent hybrid representation via a novel deformation modeling method. In this way, NPF obtains face prototypes containing common facial cues within the collection, and disentangles specific expressions. The final recovered 3D face is obtained via fitting NPF with consistency losses, and thus scene-specific details can be recovered. Extensive experiments demonstrate that our method models robust and detailed face shapes under challenging conditions, and recovers photorealistic texture with pose/expression controlling. In the future, the works could be proposed to tackle the limitation such as supporting single-image input. Further, decomposing the NeRF pipeline, the texture could be disentangled into different intrinsic factors." + }, + { + "type": "page_number", + "bbox": [ + 0.473, + 0.888, + 0.498, + 0.898 + ], + "angle": 0, + "content": "389" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.125, + 0.173, + 0.139 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.149, + 0.469, + 0.186 + ], + "angle": 0, + "content": "[1] Rameen Abdal, Yipeng Qin, and Peter Wonka. Image2stylegan: How to embed images into the stylegan latent space? In CVPR, pages 4432-4441, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.189, + 0.469, + 0.226 + ], + "angle": 0, + "content": "[2] Rameen Abdal, Yipeng Qin, and Peter Wonka. Image2stylegan++: How to edit the embedded images? In CVPR, pages 8296-8305, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.229, + 0.469, + 0.291 + ], + "angle": 0, + "content": "[3] Rameen Abdal, Peihao Zhu, Niloy J Mitra, and Peter Wonka. Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. ACM Transactions on Graphics (TOG), 40(3):1-21, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.294, + 0.469, + 0.343 + ], + "angle": 0, + "content": "[4] Victoria Fernández Abrevaya, Adnane Boukhayma, Philip HS Torr, and Edmond Boyer. Cross-modal deep face normals with deactivable skip connections. In CVPR, pages 4979-4989, 2020. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.346, + 0.468, + 0.383 + ], + "angle": 0, + "content": "[5] ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, and Zhixin Shu. Rignerf: Fully controllable neural 3d portraits. In CVPR, pages 20364-20373, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.385, + 0.469, + 0.435 + ], + "angle": 0, + "content": "[6] Andrew D Bagdanov, Alberto Del Bimbo, and Iacopo Masi. The florence 2d/3d hybrid face dataset. In Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding, pages 79-80, 2011. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.438, + 0.468, + 0.474 + ], + "angle": 0, + "content": "[7] Ziqian Bai, Zhaopeng Cui, Jamal Ahmed Rahim, Xiaoming Liu, and Ping Tan. Deep facial non-rigid multi-view stereo. In CVPR, pages 5850-5860, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.477, + 0.469, + 0.527 + ], + "angle": 0, + "content": "[8] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In CVPR, pages 5855–5864, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.53, + 0.469, + 0.578 + ], + "angle": 0, + "content": "[9] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In CVPR, pages 5470–5479, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.582, + 0.469, + 0.632 + ], + "angle": 0, + "content": "[10] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 187-194, 1999. 1, 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.634, + 0.468, + 0.671 + ], + "angle": 0, + "content": "[11] Marcel C Bühler, Abhimitra Meka, Gengyan Li, Thabo Beeler, and Otmar Hilliges. Varitex: Variational neural face textures. In ICCV, pages 13890-13899, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.674, + 0.469, + 0.734 + ], + "angle": 0, + "content": "[12] Rohan Chabra, Jan E Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe. Deep local shapes: Learning local sdf priors for detailed 3d reconstruction. In ECCV, pages 608-625. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.738, + 0.468, + 0.813 + ], + "angle": 0, + "content": "[13] Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In CVPR, pages 16123-16133, 2022. 1, 2, 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.816, + 0.468, + 0.866 + ], + "angle": 0, + "content": "[14] Eric R Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein. pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In CVPR, pages 5799-5809, 2021. 2, 3" + }, + { + "type": "list", + "bbox": [ + 0.088, + 0.149, + 0.469, + 0.866 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.128, + 0.891, + 0.177 + ], + "angle": 0, + "content": "[15] Bindita Chaudhuri, Noranart Vesdapunt, Linda Shapiro, and Baoyuan Wang. Personalized face modeling for improved face reconstruction and motion retargeting. In ECCV, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.179, + 0.891, + 0.216 + ], + "angle": 0, + "content": "[16] Radek Daneček, Michael J Black, and Timo Bolkart. Emoca: Emotion driven monocular face capture and animation. In CVPR, pages 20311-20322, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.218, + 0.891, + 0.254 + ], + "angle": 0, + "content": "[17] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In CVPR, pages 4690-4699, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.256, + 0.891, + 0.304 + ], + "angle": 0, + "content": "[18] Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, and Xin Tong. Disentangled and controllable face image generation via 3d imitative-contrastive learning. In CVPR, pages 5154–5163, 2020. 2, 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.307, + 0.891, + 0.343 + ], + "angle": 0, + "content": "[19] Yu Deng, Jiaolong Yang, Jianfeng Xiang, and Xin Tong. Gram: Generative radiance manifolds for 3d-aware image generation. In CVPR, pages 10673-10683, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.346, + 0.891, + 0.393 + ], + "angle": 0, + "content": "[20] Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set. In CVPRW, 2019. 1, 3, 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.396, + 0.891, + 0.458 + ], + "angle": 0, + "content": "[21] Abdallah Dib, Cedric Thebault, Junghyun Ahn, Philippe-Henri Gosselin, Christian Theobalt, and Louis Chevallier. Towards high fidelity monocular face reconstruction with rich reflectance using self-supervised learning and ray tracing. arXiv preprint arXiv:2103.15432, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.46, + 0.891, + 0.496 + ], + "angle": 0, + "content": "[22] Pengfei Dou, Shishir K Shah, and Ioannis A Kakadiaris. End-to-end 3d face reconstruction with deep neural networks. In CVPR, pages 5908-5917, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.498, + 0.891, + 0.571 + ], + "angle": 0, + "content": "[23] Bernhard Egger, William AP Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Rondhani, et al. 3d morphable face models—past, present, and future. ACM Transactions on Graphics (TOG), 39(5):1-38, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.574, + 0.891, + 0.623 + ], + "angle": 0, + "content": "[24] Yao Feng, Haiwen Feng, Michael J Black, and Timo Bolkart. Learning an animatable detailed 3d face model from in-the-wild images. ACM Transactions on Graphics (TOG), 40(4):1-13, 2021. 1, 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.626, + 0.891, + 0.674 + ], + "angle": 0, + "content": "[25] Yao Feng, Fan Wu, Xiaohu Shao, Yanfeng Wang, and Xi Zhou. Joint 3d face reconstruction and dense alignment with position map regression network. In ECCV, pages 534-551, 2018. 1, 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.676, + 0.891, + 0.751 + ], + "angle": 0, + "content": "[26] Zhen-Hua Feng, Patrik Huber, Josef Kittler, Peter Hancock, Xiao-Jun Wu, Qijun Zhao, Paul Koppen, and Matthias Ratsch. Evaluation of dense 3d reconstruction from 2d face images in the wild. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pages 780-786, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.753, + 0.891, + 0.8 + ], + "angle": 0, + "content": "[27] Guy Gafni, Justus Thies, Michael Zollhofer, and Matthias Nießner. Dynamic neural radiance fields for monocular 4d facial avatar reconstruction. In CVPR, pages 8649-8658, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.803, + 0.891, + 0.84 + ], + "angle": 0, + "content": "[28] Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang. Portrait neural radiance fields from a single image. arXiv preprint arXiv:2012.05903, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.842, + 0.891, + 0.866 + ], + "angle": 0, + "content": "[29] Baris Gecer, Stylianos Ploumpis, Irene Kotsia, and Stefanos Zafeiriou. Ganfit: Generative adversarial network fitting for" + }, + { + "type": "list", + "bbox": [ + 0.511, + 0.128, + 0.891, + 0.866 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.473, + 0.888, + 0.498, + 0.898 + ], + "angle": 0, + "content": "390" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.118, + 0.127, + 0.47, + 0.152 + ], + "angle": 0, + "content": "high fidelity 3d face reconstruction. In CVPR, pages 1155-1164, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.153, + 0.47, + 0.204 + ], + "angle": 0, + "content": "[30] Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, and William T Freeman. Unsupervised training for 3d morphable model regression. In CVPR, pages 8377-8386, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.089, + 0.205, + 0.469, + 0.253 + ], + "angle": 0, + "content": "[31] Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. Neural head avatars from monocular rgb videos. In CVPR, pages 18653–18664, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.089, + 0.255, + 0.469, + 0.304 + ], + "angle": 0, + "content": "[32] Jiatao Gu, Lingjie Liu, Peng Wang, and Christian Theobalt. Stylenerf: A style-based 3d-aware generator for high-resolution image synthesis. arXiv preprint arXiv:2110.08985, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.089, + 0.305, + 0.469, + 0.344 + ], + "angle": 0, + "content": "[33] Jianzhu Guo, Xiangyu Zhu, Yang Yang, Fan Yang, Zhen Lei, and Stan Z Li. Towards fast, accurate and stable 3d dense face alignment. In ECCV, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.344, + 0.469, + 0.382 + ], + "angle": 0, + "content": "[34] Yang Hong, Bo Peng, Haiyao Xiao, Ligang Liu, and Juyong Zhang. Headnerf: A real-time nerf-based parametric head model. In CVPR, pages 20374-20384, 2022. 1, 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.383, + 0.469, + 0.433 + ], + "angle": 0, + "content": "[35] Aaron S Jackson, Adrian Bulat, Vasileios Argyriou, and Georgios Tzimiropoulos. Large pose 3d face reconstruction from a single image via direct volumetric cnn regression. In ICCV, pages 1031-1039, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.434, + 0.469, + 0.482 + ], + "angle": 0, + "content": "[36] Kacper Kania, Kwang Moo Yi, Marek Kowalski, Tomasz Trzeciński, and Andrea Tagliasacchi. Conerf: Controllable neural radiance fields. In CVPR, pages 18623-18632, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.484, + 0.469, + 0.534 + ], + "angle": 0, + "content": "[37] Tero Karras, Miika Aittala, Samuli Laine, Erik Härkonen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Alias-free generative adversarial networks. NeurIPS, 34:852-863, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.535, + 0.469, + 0.573 + ], + "angle": 0, + "content": "[38] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In CVPR, pages 4401-4410, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.574, + 0.469, + 0.623 + ], + "angle": 0, + "content": "[39] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In CVPR, pages 8110-8119, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.625, + 0.469, + 0.661 + ], + "angle": 0, + "content": "[40] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.662, + 0.469, + 0.7 + ], + "angle": 0, + "content": "[41] Cheng-Han Lee, Ziwei Liu, Lingyun Wu, and Ping Luo. Maskgan: Towards diverse and interactive facial image manipulation. In CVPR, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.701, + 0.469, + 0.75 + ], + "angle": 0, + "content": "[42] Tzu-Mao Li, Miika Aittala, Frédo Durand, and Jaakko Lehtinen. Differentiable monte carlo ray tracing through edge sampling. ACM Transactions on Graphics (TOG), 37(6):1-11, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.752, + 0.469, + 0.789 + ], + "angle": 0, + "content": "[43] Feng Liu, Dan Zeng, Qijun Zhao, and Xiaoming Liu. Joint face alignment and 3d face reconstruction. In ECCV, pages 545-560. Springer, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.79, + 0.469, + 0.828 + ], + "angle": 0, + "content": "[44] Shichen Liu, Tianye Li, Weikai Chen, and Hao Li. Soft rasterizer: A differentiable renderer for image-based 3d reasoning. In ICCV, pages 7708-7717, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.829, + 0.469, + 0.865 + ], + "angle": 0, + "content": "[45] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In ICCV, pages 3730-3738, 2015. 5" + }, + { + "type": "list", + "bbox": [ + 0.088, + 0.127, + 0.47, + 0.865 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.128, + 0.892, + 0.164 + ], + "angle": 0, + "content": "[46] Matthew M Loper and Michael J Black. Opendr: An approximate differentiable renderer. In ECCV, pages 154-169, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.166, + 0.892, + 0.204 + ], + "angle": 0, + "content": "[47] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. ACM siggraph computer graphics, 21(4):163-169, 1987. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.205, + 0.892, + 0.242 + ], + "angle": 0, + "content": "[48] Nelson Max. Optical models for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics, 1(2):99-108, 1995. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.243, + 0.892, + 0.292 + ], + "angle": 0, + "content": "[49] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In CVPR, pages 4460-4470, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.293, + 0.892, + 0.343 + ], + "angle": 0, + "content": "[50] Moustafa Meshry, Dan B Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snively, and Ricardo Martin-Brualla. Neural rerendering in the wild. In CVPR, pages 6878–6887, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.344, + 0.892, + 0.394 + ], + "angle": 0, + "content": "[51] Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson. Implicit surface representations as layers in neural networks. In CVPR, pages 4743-4752, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.395, + 0.892, + 0.445 + ], + "angle": 0, + "content": "[52] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, pages 405-421, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.446, + 0.892, + 0.484 + ], + "angle": 0, + "content": "[53] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional generative neural feature fields. In CVPR, pages 11453-11464, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.485, + 0.892, + 0.546 + ], + "angle": 0, + "content": "[54] Roy Or-El, Xuan Luo, Mengyi Shan, Eli Shechtman, Jeong Joon Park, and Ira Kemelmacher-Shlizerman. Stylesdf: High-resolution 3d-consistent image and geometry generation. In CVPR, pages 13503–13513, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.548, + 0.892, + 0.598 + ], + "angle": 0, + "content": "[55] Xingang Pan, Bo Dai, Ziwei Liu, Chen Change Loy, and Ping Luo. Do 2d gans know 3d shape? unsupervised 3d shape reconstruction from 2d image gans. arXiv preprint arXiv:2011.00844, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.6, + 0.892, + 0.65 + ], + "angle": 0, + "content": "[56] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In ICCV, pages 5865-5874, 2021. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.65, + 0.892, + 0.725 + ], + "angle": 0, + "content": "[57] Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Ricardo MartinBrualla, and Steven M Seitz. Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields. arXiv preprint arXiv:2106.13228, 2021. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.726, + 0.892, + 0.789 + ], + "angle": 0, + "content": "[58] Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. In IEEE International Conference on Advanced Video and Signal Based Surveillance, pages 296-301, 2009. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.79, + 0.892, + 0.839 + ], + "angle": 0, + "content": "[59] Jingtan Piao, Keqiang Sun, Quan Wang, Kwan-Yee Lin, and Hongsheng Li. Inverting generative adversarial renderer for face reconstruction. In CVPR, pages 15619-15628, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.841, + 0.892, + 0.866 + ], + "angle": 0, + "content": "[60] Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-nerf: Neural radiance fields" + }, + { + "type": "list", + "bbox": [ + 0.511, + 0.128, + 0.892, + 0.866 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.473, + 0.888, + 0.496, + 0.898 + ], + "angle": 0, + "content": "391" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.118, + 0.127, + 0.468, + 0.152 + ], + "angle": 0, + "content": "for dynamic scenes. In CVPR, pages 10318-10327, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.153, + 0.47, + 0.203 + ], + "angle": 0, + "content": "[61] Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. H3d-net: Few-shot high-fidelity 3d head reconstruction. In ICCV, pages 5620-5629, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.089, + 0.204, + 0.469, + 0.253 + ], + "angle": 0, + "content": "[62] Yurui Ren, Ge Li, Yuanqi Chen, Thomas H Li, and Shan Liu. Pirenderer: Controllable portrait image generation via semantic neural rendering. In ICCV, pages 13759-13768, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.089, + 0.255, + 0.469, + 0.305 + ], + "angle": 0, + "content": "[63] Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or. Encoding in style: a stylegan encoder for image-to-image translation. In CVPR, pages 2287–2296, 2021. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.089, + 0.307, + 0.469, + 0.343 + ], + "angle": 0, + "content": "[64] Elad Richardson, Matan Sela, and Ron Kimmel. 3d face reconstruction by learning from synthetic data. In 3DV, pages 460-469, 2016. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.344, + 0.468, + 0.382 + ], + "angle": 0, + "content": "[65] Elad Richardson, Matan Sela, Roy Or-El, and Ron Kimmel. Learning detailed face reconstruction from a single image. In CVPR, pages 1259–1268, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.383, + 0.469, + 0.433 + ], + "angle": 0, + "content": "[66] Daniel Roich, Ron Mokady, Amit H Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. ACM Transactions on Graphics (TOG), 42(1):1-13, 2022. 1, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.434, + 0.469, + 0.47 + ], + "angle": 0, + "content": "[67] Sami Romdhani and Thomas Vetter. Efficient, robust and accurate fitting of a 3d morphable model. In Computer Vision, page 59. IEEE, 2003. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.472, + 0.469, + 0.521 + ], + "angle": 0, + "content": "[68] Sami Romdhani and Thomas Vetter. Estimating 3d shape and texture using pixel intensity, edges, specular highlights, texture constraints and a prior. In CVPR, volume 2, pages 986-993, 2005. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.523, + 0.469, + 0.573 + ], + "angle": 0, + "content": "[69] Soubhik Sanyal, Timo Bolkart, Haiwen Feng, and Michael J Black. Learning to regress 3d face shape and expression from an image without 3d supervision. In CVPR, pages 7763-7772, 2019. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.574, + 0.469, + 0.624 + ], + "angle": 0, + "content": "[70] Soumyadip Sengupta, Angjoo Kanazawa, Carlos D Castillo, and David W Jacobs. Sfsnet: Learning shape, reflectance and illuminance of faces in the wild. In CVPR, pages 6296-6305, 2018. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.625, + 0.469, + 0.687 + ], + "angle": 0, + "content": "[71] Jiaxiang Shang, Tianwei Shen, Shiwei Li, Lei Zhou, Mingmin Zhen, Tian Fang, and Long Quan. Self-supervised monocular 3d face reconstruction by occlusion-aware multi-view geometry consistency. arXiv preprint arXiv:2007.12494, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.689, + 0.469, + 0.725 + ], + "angle": 0, + "content": "[72] Yichun Shi, Divyansh Aggarwal, and Anil K Jain. Lifting 2d stylegan for 3d-aware face generation. In CVPR, pages 6258-6266, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.727, + 0.468, + 0.751 + ], + "angle": 0, + "content": "[73] Yichun Shi and Anil K Jain. Probabilistic face embeddings. In ICCV, pages 6902-6911, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.753, + 0.469, + 0.801 + ], + "angle": 0, + "content": "[74] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3d-structure-aware neural scene representations. NeurIPS, 32, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.803, + 0.469, + 0.865 + ], + "angle": 0, + "content": "[75] Keqiang Sun, Shangzhe Wu, Zhaoyang Huang, Ning Zhang, Quan Wang, and Hongsheng Li. Controllable 3d face synthesis with conditional generative occupancy fields. In Advances in Neural Information Processing Systems, 2021. 3" + }, + { + "type": "list", + "bbox": [ + 0.088, + 0.127, + 0.47, + 0.865 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.128, + 0.892, + 0.189 + ], + "angle": 0, + "content": "[76] Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Fml: Face model learning from videos. In CVPR, pages 10812-10822, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.194, + 0.892, + 0.255 + ], + "angle": 0, + "content": "[77] Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Stylerig: Rigging stylegan for 3d control over portrait images. In CVPR, pages 6142-6151, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.259, + 0.892, + 0.334 + ], + "angle": 0, + "content": "[78] Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo MartinBrualla, Tomas Simon, Jason Saragih, Matthias Nießner, et al. State of the art on neural rendering. In Computer Graphics Forum, volume 39, pages 701-727. Wiley Online Library, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.338, + 0.892, + 0.387 + ], + "angle": 0, + "content": "[79] Ayush Tewari, Hans-Peter Seidel, Mohamed Elgharib, Christian Theobalt, et al. Learning complete 3d morphable face models from images and videos. In CVPR, pages 3361-3371, 2021. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.391, + 0.892, + 0.466 + ], + "angle": 0, + "content": "[80] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, W Yifan, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, et al. Advances in neural rendering. In Computer Graphics Forum, volume 41, pages 703-735. Wiley Online Library, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.47, + 0.892, + 0.533 + ], + "angle": 0, + "content": "[81] Ayush Tewari, Michael Zollhofer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, Patrick Perez, and Christian Theobalt. Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction. In ICCVW, pages 1274-1283, 2017. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.536, + 0.892, + 0.585 + ], + "angle": 0, + "content": "[82] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics (TOG), 38(4):1-12, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.589, + 0.892, + 0.639 + ], + "angle": 0, + "content": "[83] Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, and Daniel Cohen-Or. Designing an encoder for stylegan image manipulation. ACM Transactions on Graphics (TOG), 40(4):1-14, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.643, + 0.892, + 0.668 + ], + "angle": 0, + "content": "[84] Luan Tran and Xiaoming Liu. Nonlinear 3d face morphable model. In CVPR, pages 7346-7355, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.671, + 0.892, + 0.72 + ], + "angle": 0, + "content": "[85] Anh Tuan Tran, Tal Hassner, Iacopo Masi, Eran Paz, Yuval Nirkin, and Gerard Medioni. Extreme 3d face reconstruction: Seeing through occlusions. In CVPR, pages 3935-3944, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.724, + 0.892, + 0.786 + ], + "angle": 0, + "content": "[86] Daoye Wang, Prashanth Chandran, Gaspard Zoss, Derek Bradley, and Paulo Gotardo. Morf: Morphable radiance fields for multiview neural head modeling. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1-9, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.79, + 0.892, + 0.865 + ], + "angle": 0, + "content": "[87] Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, and Michael Zollhofer. Learning compositional radiance fields of dynamic human heads. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5704-5713, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.511, + 0.128, + 0.892, + 0.865 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.473, + 0.888, + 0.498, + 0.898 + ], + "angle": 0, + "content": "392" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.127, + 0.47, + 0.165 + ], + "angle": 0, + "content": "[88] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. TIP, 13(4):600-612, 2004. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.168, + 0.47, + 0.204 + ], + "angle": 0, + "content": "[89] Felix Wimbauer, Shangzhe Wu, and Christian Rupprecht. De-rendering 3d objects in the wild. In CVPR, pages 18490-18499, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.208, + 0.47, + 0.258 + ], + "angle": 0, + "content": "[90] Fanzi Wu, Linchao Bao, Yajing Chen, Yonggen Ling, Yibing Song, Songnan Li, King Ngi Ngan, and Wei Liu. Mvfnet: Multi-view 3d face morphable model regression. In CVPR, pages 959-968, 2019. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.261, + 0.469, + 0.31 + ], + "angle": 0, + "content": "[91] Shangzhe Wu, Christian Rupprecht, and Andrea Vedaldi. Unsupervised learning of probably symmetric deformable 3d objects from images in the wild. In CVPR, pages 1-10, 2020. 1, 2, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.314, + 0.469, + 0.351 + ], + "angle": 0, + "content": "[92] Sicheng Xu, Jiaolong Yang, Dong Chen, Fang Wen, Yu Deng, Yunde Jia, and Xin Tong. Deep 3d portrait from a single image. In CVPR, pages 7710-7720, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.354, + 0.469, + 0.391 + ], + "angle": 0, + "content": "[93] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. NeurIPS, 34:4805-4815, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.394, + 0.469, + 0.456 + ], + "angle": 0, + "content": "[94] Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and Christian Theobalt. i3dmm: Deep implicit 3d morphable model of human heads. In CVPR, pages 12803-12813, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.46, + 0.469, + 0.496 + ], + "angle": 0, + "content": "[95] Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li. Learning face representation from scratch. arXiv preprint arXiv:1411.7923, 2014. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.499, + 0.469, + 0.548 + ], + "angle": 0, + "content": "[96] Stefanos Zafeiriou, Mark Hansen, Gary Atkinson, Vasileios Argyriou, Maria Petrou, Melvyn Smith, and Lyndon Smith. The photoface database. In CVPRW, pages 132-139, 2011. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.552, + 0.469, + 0.589 + ], + "angle": 0, + "content": "[97] Xiaoxing Zeng, Xiaojiang Peng, and Yu Qiao. Df2net: A dense-fine-finer network for detailed 3d face reconstruction. In ICCV, pages 2315-2324, 2019. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.593, + 0.469, + 0.641 + ], + "angle": 0, + "content": "[98] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.645, + 0.469, + 0.682 + ], + "angle": 0, + "content": "[99] Ruo Zhang, Ping-Sing Tsai, James Edwin Cryer, and Mubarak Shah. Shape-from-shading: a survey. IEEE TPAMI, 21(8):690-706, 1999. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.686, + 0.469, + 0.747 + ], + "angle": 0, + "content": "[100] Zhenyu Zhang, Yanhao Ge, Renwang Chen, Ying Tai, Yan Yan, Jian Yang, Chengjie Wang, Jilin Li, and Feiyue Huang. Learning to aggregate and personalize 3d face from in-the-wild photo collection. In CVPR, pages 14214-14224, 2021. 1, 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.751, + 0.469, + 0.813 + ], + "angle": 0, + "content": "[101] Zhenyu Zhang, Yanhao Ge, Ying Tai, Weijian Cao, Renwang Chen, Kunlin Liu, Hao Tang, Xiaoming Huang, Chengjie Wang, Zhifeng Xie, et al. Physically-guided disentangled implicit rendering for 3d face modeling. In CVPR, pages 20353-20363, 2022. 3, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.816, + 0.469, + 0.865 + ], + "angle": 0, + "content": "[102] Mingwu Zheng, Hongyu Yang, Di Huang, and Liming Chen. Imface: A nonlinear 3d morphable face model with implicit neural representations. In CVPR, pages 20343-20352, 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.127, + 0.47, + 0.865 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.127, + 0.892, + 0.178 + ], + "angle": 0, + "content": "[103] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. Im avatar: Implicit morphable head avatars from videos. In CVPR, pages 13545–13555, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.18, + 0.892, + 0.228 + ], + "angle": 0, + "content": "[104] Yuxiang Zhou, Jiankang Deng, Irene Kotsia, and Stefanos Zafeiriou. Dense 3d face decoding over 2500fps: Joint texture & shape convolutional mesh decoders. In CVPR, pages 1097-1106, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.231, + 0.892, + 0.269 + ], + "angle": 0, + "content": "[105] Xiangyu Zhu, Zhen Lei, Xiaoming Liu, Hailin Shi, and Stan Z Li. Face alignment across large poses: A 3d solution. In CVPR, pages 146-155, 2016. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.271, + 0.892, + 0.319 + ], + "angle": 0, + "content": "[106] Xiangyu Zhu, Fan Yang, Chang Yu Di Huang, Hao Wang, Jianzhu Guo, Zhen Lei, and Stan Z Li. Beyond 3dmm space: Towards fine-grained 3d face reconstruction. In ECCV, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.322, + 0.892, + 0.358 + ], + "angle": 0, + "content": "[107] Xiangyu Zhu, Dong Yi, Zhen Lei, and Stan Z Li. Robust 3d morphable model fitting by sparse sift flow. In ICCV, pages 4044-4049. IEEE, 2014. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.361, + 0.892, + 0.398 + ], + "angle": 0, + "content": "[108] Yiyu Zhuang, Hao Zhu, Xusen Sun, and Xun Cao. Mofan-erf: Morphable facial neural radiance field. arXiv preprint arXiv:2112.02308, 2021. 8" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.127, + 0.892, + 0.398 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.473, + 0.888, + 0.498, + 0.898 + ], + "angle": 0, + "content": "393" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild/5ebd965c-2fcd-48b5-82dd-8be4b32c7eaf_origin.pdf b/2023/Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild/5ebd965c-2fcd-48b5-82dd-8be4b32c7eaf_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ecdb400161ae3ddffd64bd2deb0bc53d8b4c2c7a --- /dev/null +++ b/2023/Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild/5ebd965c-2fcd-48b5-82dd-8be4b32c7eaf_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc80a3d1bc612bf9cff55bbaccd98dc8d7a25bf84b58f071504176c039b96410 +size 1230646 diff --git a/2023/Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild/full.md b/2023/Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild/full.md new file mode 100644 index 0000000000000000000000000000000000000000..f7b6d4b9a4830ba1304a0bf457f3eca5975b094e --- /dev/null +++ b/2023/Learning Neural Proto-Face Field for Disentangled 3D Face Modeling in the Wild/full.md @@ -0,0 +1,343 @@ +# Learning Neural Proto-face Field for Disentangled 3D Face Modeling In the Wild + +Zhenyu Zhang $^{1}$ , Renwang Chen $^{1}$ , Weijian Cao $^{1}$ , Ying Tai $^{1*}$ , Chengjie Wang $^{1,2*}$ +Tencent Youtu Lab, Shanghai, China $^{1}$ +Shanghai Jiao Tong University, Shanghai, China $^{2}$ + +zhangjesse@foxmail.com + +renwangchen, weijiancao, yingtai, jasoncjwang@tencent.com + +# Abstract + +Generative models show good potential for recovering 3D faces beyond limited shape assumptions. While plausible details and resolutions are achieved, these models easily fail under extreme conditions of pose, shadow or appearance, due to the entangled fitting or lack of multiview priors. To address this problem, this paper presents a novel Neural Proto-face Field (NPF) for unsupervised robust 3D face modeling. Instead of using constrained images as Neural Radiance Field (NeRF), NPF disentangles the common/specific facial cues, i.e., ID, expression and scene-specific details from in-the-wild photo collections. Specifically, NPF learns a face prototype to aggregate 3D-consistent identity via uncertainty modeling, extracting multi-image priors from a photo collection. NPF then learns to deform the prototype with the appropriate facial expressions, constrained by a loss of expression consistency and personal idiosyncrasies. Finally, NPF is optimized to fit a target image in the collection, recovering specific details of appearance and geometry. In this way, the generative model benefits from multi-image priors and meaningful facial structures. Extensive experiments on benchmarks show that NPF recovers superior or competitive facial shapes and textures, compared to state-of-the-art methods. + +# 1. Introduction + +3D face reconstruction is a long-standing problem with applications including games, digital human and mobile photography. It is ill-posed in many cases requiring strong assumptions e.g., shape from shading [99]. With the 3D Morphable Model (3DMM) [10] proposed, such a problem can be solved by fitting parameters to the target faces [67, 68, 107]. Recently, deep-learning methods [22, 25, 43, 64, + +![](images/1d5b92629b0a0a31ff39bdc5ad65f02ea7692cfc2c9cc1429f5034d90658f06f.jpg) +Figure 1. (a) Comparison with graphics-renderer-based methods LAP [100] and D3DFR [20]. Our method models geometry details and photo-realistic texture. (b) Results of neural rendering methods EG3D [13] + PTI [66], HeadNeRF [34] and our method. Our method produces high-quality geometry, robust texture modeling under rotation and deformation. + +105] are proposed to regress 3DMM parameters from input images. These approaches are then improved by nonlinear modeling [24, 29, 79, 81, 84, 94] and multi-view consistency [7, 15, 76, 90]. Besides 3DMM methods, recent efforts [91, 100] attempt to model 3D face without shape assumptions. These non-parametric methods have potential ability to improve the modeling quality beyond 3DMM. + +Although the aforementioned methods achieve impressive performance, they also have obvious drawbacks. On the one hand, as the parametric models are usually built from a small amount of subjects (e.g., BFM [58] with 200 subjects) and rigidly controlled conditions, they may be fragile to large variations of identity [106], and have limi + +tations on building teeth, skin details or anatomic grounded muscles [23]. On the other hand, all of these methods depend on graphics renderers [42, 44, 46] in the analysis-by-synthesis fitting procedure, and thus yields hand-crafted approximation or ill-posed decomposition on intrinsic clues. Hence, as illustrated in Fig. 1-(a), these methods struggle to produce photo-realistic texture or geometric details. + +Against these limitations, efforts are made to use a neural renderer such as StyleGAN [38, 39] to model faces by inverting the corresponding images [1,2] into $\mathcal{W}$ space. Existing methods [11, 18, 59, 62, 92] mainly learn to embed 3DMM coefficients to implicitly leverage 3D clues, but they have difficulty achieving precise 3D controls due to their entangled image formation. To disentangle neural rendering, recent works [13, 14, 34, 54] employ explicit 3D pipelines, e.g., Neural Radiance Field (NeRF) [52] into the StyleGANs' framework, so that face shapes and camera views can be extracted. In this way, precise 3D controls and detailed geometry can be obtained. However, these methods still show fragile performance under challenging conditions as shown in Fig. 1-(b). When confronting large poses, extreme appearance or lighting, the lack of facial priors disturbs the reconstruction and results in severe distortions. This is due to the essentially overfitting objective of inverting single target image, where the geometry ambiguity is unavoidable. + +On top of this, one solution is to leverage reliable priors, e.g., multi-image consistency as a complement. While NeRF provides a natural paradigm to dig multi-view cues, it requires fully constrained images that are difficult to obtain. Even conditioned by style codes [13, 14, 54], there is no direct way to build 3D faces from unconstrained portrait collections in such a neural rendering mechanism. In this work, we present a novel Neural Proto-face Field (NPF) for unsupervised robust 3D face modeling, where ID, expression and scene-specific details can be disentangled from in-the-wild photo collections. To aggregate ID-aware cues, NPF leverages uncertainty modeling to extract multi-image priors and recovers a face prototype with ID-consistent face shape. To disentangle the expression, NPF then learns appropriate representations to deform the prototype, constrained by a expression consistency loss. In this way, the learned face shape is properly blended to avoid geometric ambiguity. Finally, to recover the scene-specific details, NPF is optimized to fit a target image in the collection. The robustness of fitting is guaranteed by a geometry and appearance regularization. As shown in Fig. 1-(b), NPF makes the generative method benefit from multi-image priors in unconstrained environments, and produces high-quality 3D faces under challenging conditions. + +In summary, our contributions are as follows: + +1) A novel Neural Proto-face Field (NPF) is proposed to disentangle ID, expression and specific details from 3D face + +
MethodsRenderingPipelineMulti-view
EMOCA [81], DECA [24], Unsup3D [91]GraphicsDisentangled×
LAP [100], FML [76], MVF [90]GraphicsDisentangled✓
DFG [18], StyleRig [77], StyleFlow [3]NeuralEntangled×
Pi-GAN [14], StyleSDF [54], EG3D [13]NeuralDisentangled×
OursNeuralDisentangled✓
+ +Table 1. Discussion with selected existing methods. + +modeling, which uses in-the-wild photo collections to benefit the 3D generative model under challenging conditions. + +2) With a novel face prototype aggregation method, NPF integrates multi-image face priors against the large variations in unconstrained environments. +3) With a series of novel consistency losses, NPF is well fit to specific scenes with personalized details, based on the guidance of face prototypes. + +# 2. Related Works + +In Table 1, we make a discussion on existing methods. NPF benefits from neural rendering and avoids hand-crafted approximations of graphics renderers. In contrast to neural rendering methods, our approach has explicit 3D pipelines, and leverages multi-image consistency in the wild. + +3D Face Reconstruction: As a long-standing problem, the studies mainly start from the pioneer work 3DMM [10]. With the parametric model, early works try to find suitable parameters via optimization [67, 68, 107], while recent approaches [25, 64, 105, 106] leverage deep neural networks to regress the results from input images. With the differentiable renderers proposed, efforts are made on aspects of unsupervised learning [30, 65, 81], improving the non-linear feasibility [16, 21, 24, 29, 81, 84, 104] and multi-view consistency [7, 15, 76, 90]. More recent works attempt to learn complete 3DMM basis [79] or implicit functions [94, 102] which brings new possibilities to this topic. + +Beyond 3DMM, non-parametric models are also developed by data-driven supervised training [4, 35, 97]. With shape-from-shading algorithm [99], unsupervised methods are proposed including SFS-Net [70], Unsup3D [91] and LAP [100]. Recently, Gan2Shape [55] and LiftedGAN [72] try to distill knowledge from 2D GANs for 3D reconstruction. De3D [89] de-renders more intrinsic factors based on reliable priors. Different from the these discussed methods, NPF benefits 3D face modeling via neural rendering mechanisms, thus provides better performance on details, resolution and non-facial objects. + +Neural Scene Representation: Neural scene representation is a novel way to parameterize signals, and attracts more attention on learning geometry [12, 49, 51, 74, 93]. Recent effort Neural Radiance Field (NeRF) [8, 9, 52] shows impressive performance on recovering appearance and shapes from multi-view images. Based on NeRF, 3D face can also be modeled [28,61,86,87] from a few or single views. Recent efforts are developed to perform reconstruc + +tion against non-rigid deformation [56,57,60]. Conditioned on specific expressions, NeRF can also be used to realize editing [5, 36] or 4D facial avatars [27, 31, 103]. In summary, these methods depend on fully constrained environments, i.e., the used images have to be captured at a same time. In contrast, our method leverages face consistency from in-the-wild photo sets that are easy to collect, and addresses large variations of lighting, appearance or artifacts. + +Neural Rendering on Face Modeling: Neural rendering aims to render images using neural networks [50, 78, 80] with much relaxed inputs. The style-based approaches, i.e., StyleGANs [37-39] obtain the state-of-the-art performance on face generation. With the pretrained StyleGANs, face modeling can be achieved by inverting the target images [1,2,63,66]. 3D embedding (e.g., 3DMM parameter) is then employed to implicitly control the StyleGAN's prediction on pose, identity and lighting [3,18,77]. However, they cannot guarantee the robustness on physical perspective, nor obtain explicit geometry. More recent works attempt to employ 3D pipelines, such as NeRF or deferred neural rendering [82], into StyleGANs [11, 13, 14, 19, 32, 53, 54, 75, 101]. While these methods achieve more precise controls and facial geometry prediction, they are fragile to large pose and ambiguous appearance due to the single-image fitting. In contrast, NPF well leverages multi-image consistency as complementary priors and shows more robust performance. + +# 3. Preliminary + +Exploiting the consistency of multiple images, especially from unconstrained environments, is non-trivial for neural rendering methods because there is no explicit topology that can be shared within the image set. While NeRF provides a natural paradigm to dig consistency from different views, it requires photos taken at the same time or constrained scene. Recent 3D-aware generative methods [13, 14, 32, 54] show that NeRF can represent different faces conditioned on style codes. Such an evidence makes it possible to learn common hybrid 3D representations from in-the-wild photo collections. Without loss of generality, we build our model on EG3D [13] as it achieves the state-of-the-art performance. + +EG3D represents the 3D scene as a tri-plane denoted as: + +$$ +\mathbf {F} _ {x y}, \mathbf {F} _ {x z}, \mathbf {F} _ {y z} = \Phi_ {g} (\mathbf {s}), \mathbf {s} = \left\{s ^ {j} \right\} _ {j = 1} ^ {M}, \tag {1} +$$ + +where $\mathbf{F} \in [H, W, C]$ is the orthogonal feature plane along different axis, and $\Phi_g$ is the StyleGAN generator. $\mathbf{s} \in \mathbb{R}^{14 \times 512}$ is the style code with stages $M = 14$ , generated from a random noise $\mathbf{z}$ by a mapping network. Then any 3D position $x \in \mathbb{R}^3$ can be projected onto each of the three feature planes, retrieving its feature $f = f_{xy} + f_{xz} + f_{yz}$ via bilinear interpolation. The corresponding density $\sigma$ and color $\mathbf{c}$ is predicted by a tri-plane decoder $\Phi_d$ from $f$ . In this way, given a camera pose $p$ , origin $\mathbf{o}$ and near/far bound $t_n, t_f$ , + +the pixel color $\mathbf{C}$ for a ray $\mathbf{r}(t) = \mathbf{o} + t\mathbf{d}$ can be obtained using volume rendering equation [48]: + +$$ +\mathbf {C} (\mathbf {r}) = \int_ {t _ {n}} ^ {t _ {f}} T (t) \sigma (\mathbf {r} (t)) \mathbf {c} (\mathbf {r} (t), \mathbf {d}) d t, \tag {2} +$$ + +where $T(t) = \exp(\int_{t_n}^t \sigma(\mathbf{r}(s)) ds$ , and $t$ is the sampled point along each ray. Using Eqn. 2, EG3D renders a low-resolution image $\mathbf{I}_l$ as well as a feature image $\mathbf{I}_f$ . $\mathbf{I}_f$ is then fed into a super-resolution module $\Phi_u$ to generate final face image $\mathbf{I}$ . Adversarial loss is calculated between $\mathbf{I}, \mathbf{I}_l$ and the real image to update $\Phi_g, \Phi_d$ and $\Phi_u$ . Once trained, given a target image $\mathbf{I}_t$ , face modeling can be achieved by image inversion [1,63,66]. As discussed in Sec. 1, this kind of approaches suffer from ambiguous appearance and large poses. In contrast, NPF tackles the problem by further disentangling a face using multi-image priors, which is introduced in the following. + +# 4. Methodology + +In this section, we introduce the proposed Neural Protoface Field (NPF). Our aim is digging multi-image priors to improve the robustness of 3D face modeling. This is achieved by disentangling ID-consistent shape, expression and specific details from in-the-wild photo collection. The overview is shown in Fig. 2, where NPF contains identity-aware aggregation and deformation modeling to recover face prototypes (Sec. 4.1). After NPF is learned, the target face is reconstructed by a fitting procedure (Sec. 4.2). + +# 4.1. Neural Proto-face Field Learning + +Face appearance of an identity shares consistent cues due to the invariant face geometry, even under different in-the-wild environments. Such consistency has been widely used in previous methods by sharing the coefficients [20,69,79,90] or combining the UV space [100, 101]. Inspired by these efforts, we propose NPF to aggregate a common hybrid 3D representation from each photo collection, which models a face prototype with ID-consistent shape from reliable priors. + +Identity-aware Aggregation: Our method starts from the style code $\mathbf{s} = \{s^j\}_{j=1}^M$ , which well conditions the radiance field of tri-planes to render a face image $\mathbf{I}$ . Theoretically, $\mathbf{s}$ can be obtained from various methods [1,2,63,83]. Without specially statement, we use the most direct scheme proposed in [38] to optimize $\mathbf{s}$ for real-image inversion. Given a photo collection $\{\mathbf{I}_i\}_{i=1}^N$ of a same ID, we obtain $\{\mathbf{s}_i\}_{i=1}^N$ in the $\mathcal{W}$ space. Although finding optimal solution is difficult, $\mathbf{s}_i$ still represents the face shape of $\mathbf{I}_i$ , and such consistent cues should have much lower uncertainty than any other information within the image set. As a result, we propose an uncertainty-aware aggregation method inspired by [73] to extract the consistency. As illustrated in Fig. 2, + +![](images/fcbff586a5927862544ee285a176adf69e4e9d88b45e1f6e0701c0a0ee6e4630.jpg) +Figure 2. Overview of NPF learning method. For an in-the-wild photo collection $\{\mathbf{I}_i\}_{i=1}^N$ , we first obtain its style codes $\{\mathbf{s}_i\}_{i=1}^N$ and uncertainties $\{\omega_i\}_{i=1}^N$ , then perform identity-aware aggregation to get the pivotal proto code $\bar{s}$ . To model deformation, we use 3DMM expression parameters $\{\beta_i\}_{i=1}^N$ as conditions, and train deformation networks $\{\Phi_{\Delta}^j\}_{j=1}^M$ to predict deformation code $\Delta \mathbf{s}_i$ . We also learn global face representation $\bar{h}^j$ at $j$ -th stage to complement for personalized idiosyncrasies. In this way, the ID-consistent face is suitably blended on the manifold by Eqn. 5. Training by Eqn. 7, NPF well leverages the consistency within in-the-wild portrait sets. + +we use an encoder $\Phi_{a}$ to get uncertainty $\omega_{i} \in \mathbb{R}^{512}$ from $\mathbf{I}_i$ , and assume $\mathbf{I}_i$ to have a multivariate Gaussian representation $\mathcal{N}(\mathbf{s}_i, \omega_i)$ in the latent space. Then the aggregation is performed as: + +$$ +\bar {\mathbf {s}} = \sum_ {i = 1} ^ {N} \frac {\omega^ {2}}{\omega_ {i} ^ {2}} \mathbf {s} _ {i}, \quad \frac {1}{\omega^ {2}} = \sum_ {i = 1} ^ {N} \frac {1}{\omega_ {i} ^ {2}}, \tag {3} +$$ + +where $\bar{\mathbf{s}}$ is the pivotal proto code similar to PTI [66] but from the photo collection instead of a single image. When forcing $\bar{\mathbf{s}}$ to approximate each image, the consistency which has lower uncertainty within $\{\mathbf{I}_i\}_{i=1}^N$ is remained, while other information is suppressed. In this way, the common facial shape and appearance is well aggregated into $\bar{\mathbf{s}}$ as a face prototype. Further, compared with average pooling, the adaptively fusion algorithm of Eqn. 3 can be jointly learned with deformation to improve the modeling quality, which is introduced in the following. + +Deformation Modeling: Although $\bar{\mathbf{s}}$ has the common prior of an identity, it considers no facial deformation which is suppressed after the aggregation, resulting in a mean expression. Actually, modeling deformation is crucial for face reconstruction [10]. On the one hand, if the face prototype has a different expression from the target image, then recovering the gap will be difficult in the fitting procedure. On the other hand, face images have different appearance and silhouettes due to expression variation, and thus discarding the expression brings ambiguity or conflicts to the consistent geometry. Hence, we propose deformation modeling method to disentangle expressions of face prototype. + +Previous works [56, 57] learn deformation during NeRF sampling, which depends on plentiful images captured in a constrained environment. As the data we use is in-the-wild with limited numbers, using such a deformation strategy is difficult. In contrast, we model expression by modifying style codes. As illustrated in Fig. 2, we first extracts the 3DMM expression parameters $\{\beta_i\}_{i=1}^N$ , $\beta_i \in \mathbb{R}^{64}$ and camera poses $\{p_i\}_{i=1}^N$ from a pretrained 3D face network $\Phi_{3d}$ [20], which provides guidance on tuning $\bar{s}$ . Note that, $\{\beta_i\}_{i=1}^N$ cannot well represent the personalized expression with unique idiosyncrasies, as it is unrelated to the identity. Hence, we extract global image representations from $\{\mathbf{I}_i\}_{i=1}^N$ to complement for the characteristics. We use a similar encoder $\Phi_e$ as PSP [63] to get the representation $h_i^j \in \mathbb{R}^{512}$ , $j = 1, 2, \dots, M$ and the corresponding uncertainty $\mu_i^j$ from $\mathbf{I}_i$ . Then following Eqn. 3, we fuse the $\{h_i^j\}_{i=1}^N$ using $\{\mu_i^j\}_{i=1}^N$ to get the global representation $\bar{h}^j$ at $j$ -th stage. With the global $\bar{h}^j$ of the collection, we propose deformation networks $\{\Phi_\Delta^j\}_{j=1}^M$ to predict the deformation code on each stage, which can be defined as: + +$$ +\Delta s _ {i} ^ {j} = \Phi_ {\Delta} ^ {j} \left(\bar {h} ^ {j}, \beta_ {i}\right). \tag {4} +$$ + +Denoting $\{\Delta s_i^j\}_{j = 1}^M$ as $\Delta \mathbf{s}_i$ for simplification, the personalized deformation of $\mathbf{I}_i$ can be achieved via modifying the pivotal proto code: + +$$ +\hat {\mathbf {s}} _ {i} = \bar {\mathbf {s}} + \Delta \mathbf {s} _ {i}. \tag {5} +$$ + +Finally, the face prototype $\hat{\mathbf{I}}_i$ can be obtained from $\hat{\mathbf{s}}_i$ and $p_i$ using pretrained EG3D networks. In this way, $\hat{\mathbf{I}}_i$ suffers from less appearance conflicts to $\mathbf{I}_i$ . Further, by jointly + +learning of $\Delta \mathbf{s}_i$ and $\bar{\mathbf{s}}$ , the consistent face shape within a collection is adaptively blended against non-rigid ambiguity, and have more precise geometry and texture. + +Training Loss: Note that we do not require $\hat{\mathbf{I}}_i$ to be exactly the same as $\mathbf{I}_i$ , but have a proper expression and a consistent blended shape among $\{\mathbf{I}_i\}_{i=1}^N$ . To achieve such an aim, we propose a series of consistency losses to learn $\Phi_a, \Phi_e$ and $\Phi_\Delta$ . The reconstruction loss is defined as $\mathcal{L}_{re} = \mathcal{L}_{lpips}(\hat{\mathbf{I}}_i - \mathbf{I}_i) + \mathcal{L}_{L2}(\hat{\mathbf{I}}_i - \mathbf{I}_i)$ , where $\mathcal{L}_{lpips}$ is the perceptual loss [98] and $\mathcal{L}_{L2}$ is the L2 distance. To constrain the expression of $\hat{\mathbf{I}}_i$ , we propose an expression consistency loss using pretrained $\Phi_{3d}$ : + +$$ +\mathcal {L} _ {c} \left(\hat {\mathbf {I}} _ {i}, \beta_ {i}\right) = \frac {1}{\Omega} \left| \Phi_ {3 d} \left(\hat {\mathbf {I}} _ {i}\right) - \beta_ {i} \right|, \tag {6} +$$ + +where $\Omega$ is the normalization factor. To prevent distortion, we limit the deformation code via a regularization as $\mathcal{L}_d = \|\Delta \mathbf{s}_i\|_2^2$ . The final loss function is denoted as: + +$$ +\mathcal {L} _ {p r o t o} = \sum_ {i = 1} ^ {N} \mathcal {L} _ {r e} \left(\hat {\mathbf {I}} _ {i}, \mathbf {I} _ {i}\right) + \lambda_ {c} \mathcal {L} _ {c} \left(\hat {\mathbf {I}} _ {i}, \beta_ {i}\right) + \lambda_ {d} \mathcal {L} _ {d} \left(\mathbf {s} _ {i}\right), \tag {7} +$$ + +where $\lambda_c, \lambda_d$ are the weights. When using $\mathcal{L}_{proto}$ to update $\Phi_a, \Phi_e$ and $\Phi_\Delta$ , the networks of EG3D are all frozen. In this way, we learn neural proto-face field from in-the-wild photo collections, and disentangle identities and expressions of the face prototypes. + +# 4.2. Neural Proto-face Field Fitting + +Once NPF is learned, we get a 3D hybrid initialization containing consistent cues of the photo collection, constrained by reliable multi-image priors. To model the precise 3D faces, scene-specific details need to be recovered. Such an aim can be achieved by tuning the generator using pivotal tuning (PTI) algorithm [66]. However, PTI has obvious drawbacks of two aspects: 1) its pivotal code has no multi-view prior; 2) its tuning procedure easily overfits to a single image. The former one has been addressed by NPF. For the latter, we propose a consistent fitting method to overcome the monocular ambiguity. + +Multi-image Warm-Up: Fitting is achieved by tuning the generator $\Phi_g$ of EG3D while freezing other networks. In the early stage of fitting, we need to prevent the generator converging to a local optimum that provides distortions due to challenging conditions. In contrast to fitting a single image, we propose a multi-image warm-up method to fit $\Phi_g$ to the photo collection $\{\mathbf{I}_i\}_{i=1}^N$ . We use $\mathcal{L}_{re}$ to approach the target image set. To guarantee the geometry consistency at unseen views, we randomly sample a camera pose $p'$ at every step, and use the volume renderer of EG3D to predict the corresponding normal maps $\hat{\mathbf{n}}_i$ and $\dot{\mathbf{n}}_i$ from the original/new weights of $\Phi_g$ , respectively. Then we calculate the + +![](images/31aff62a3ce8861dc6f1e132ca7c1d76de02ec397e472cb4504e274d70c78d06.jpg) +Figure 3. The proposed consistency losses of NPF fitting. (a) Multi-image Warm-up; (b) Robust Target-image Fitting. + +geometry consistency loss $\mathcal{L}_{gc}(\dot{\mathbf{n}}_i,\hat{\mathbf{n}}_i) = \| \dot{\mathbf{n}}_i - \hat{\mathbf{n}}_i\| _2^2$ to constrain the robustness of facial geometry. The final objective of multi-image warm-up is denoted as: + +$$ +\mathcal {L} _ {\text {w a r m}} = \sum_ {i = 1} ^ {N} \mathcal {L} _ {r e} \left(\dot {\mathbf {I}} _ {i}, \mathbf {I} _ {i}\right) + \lambda_ {g} \mathcal {L} _ {g c} \left(\dot {\mathbf {n}} _ {i}, \hat {\mathbf {n}} _ {i}\right), \tag {8} +$$ + +where $\dot{\mathbf{I}}_i$ is the reconstructed image. Using $\mathcal{L}_{warm}$ , we make $\Phi_g$ initialize to a solution near the optimum of each image, and suppress distortions of unseen poses. + +Robust Target-image Fitting: After the multi-image warm-up, we fit $\Phi_g$ to the target image $\mathbf{I}_i$ and model specific details. Besides the reconstruction loss $\mathcal{L}_{re}$ , we constrain the recovered appearance of unseen poses. Similar to $\mathcal{L}_{gc}$ we randomly sample a camera pose $p'$ at each step and render an image $\dot{\mathbf{I}}_i'$ , then encourage $\dot{\mathbf{I}}_i'$ and the target $\mathbf{I}_i$ to have similar representations via $\mathcal{L}_{ac} = \|\Phi_e(\dot{\mathbf{I}}_i') - \Phi_e(\mathbf{I}_i)\|_2^2$ . The final loss of robust target-image fitting is: + +$$ +\mathcal {L} _ {\text {t a r g e t}} = \mathcal {L} _ {\text {r e}} \left(\mathbf {I} _ {i}, \mathbf {I} _ {i}\right) + \lambda_ {a} \mathcal {L} _ {\text {a c}}. \tag {9} +$$ + +In this way, we improve the robustness of the scene-specific details recovering. + +# 5. Experiment + +Dataset: We first train our $\Phi_{a},\Phi_{e}$ and $\Phi_{\Delta}$ on CelebA [45] and CASIA-WebFace [95], then fine-tune them on a high-resolution dataset CelebAMask-HQ [41]. Following [100], we organize CelebA and CASIAWebFace using ID-labels and keep each identity with at least 6 photos. This provides $600\mathrm{K}$ images with $16\mathrm{K}$ identities. We select images of $12\mathrm{K} / 2\mathrm{K} / 2\mathrm{K}$ identities as train/val/test set. For CelebAMask-HQ, we organize it into $24\mathrm{K}$ different identities using ground truth ID-labels, and randomly select $20\mathrm{K} / 1\mathrm{K} / 3\mathrm{K}$ identities as train/val/test set. Following [4, 91, 100], we perform testing on MICC [6], Photoface [96] and FG3D [26] dataset. Photoface dataset contains $12\mathrm{K}$ images of 453 people with face/normal image + +
No.methodp2p (mm) ↓MAD (deg.) ↓IDE ↓
(1)Ours1.7711.870.257
(2)Ours face prototype1.9612.390.307
(3)w/o uncertainty = avg pooling1.8712.130.298
(4)w/o deformation modeling2.2512.900.401
(5)w/o Φe2.0112.460.350
(6)w/o multi-image warm-up1.8412.300.301
(7)w/o Lgc1.8012.150.274
(8)w/o Lac1.7911.870.262
(9)single-image PTI [66]2.3512.100.482
+ +Table 2. Comparison with Different Baselines and Settings. + +pairs, and we follow the protocol of [4,70] for testing. More details are introduced in Appendix. + +Implementation Details: We build $\Phi_{a}$ using a similar architecture to the encoder of [91, 100]. The deformation network $\Phi_{\Delta}^{j}$ of $j$ -th stage contains 3 MLP layers to encode $\beta_{i}$ , and another 3 MLP layers to predict $\Delta s_{i}^{j}$ from the combination of $\beta_{i}, \bar{\mathbf{h}}^{j}$ . We set $\lambda_{c} = 1, \lambda_{d} = 1e - 3, \lambda_{g} = \lambda_{a} = 0.2$ for the losses. Following [13], the output images of the model are of $512 \times 512$ , and the meshes are extracted using Marching Cubes [47] with a same size. During training, we randomly set $N$ from 1 to 6 to adapt different sizes of a photo collection. We train the model with batch size of 4 on CelebA and CASIA-WebFace for 15 epochs, and fine-tune it on CelebAMaskHQ for 30 epochs. During fitting, the multi-image warm-up lasts 100 steps and robust target-image fitting lasts another 100 steps. We use Adam [40] as the optimizer, and set the learning rate as 0.0001 on a V-100 GPU. More details are in the Appendix. + +Evaluation Protocol: Without special statements, we use 4-image results to compare with other methods. To evaluate the modeled texture, we calculate Structural Similarity Index (SSIM) [88], Cosine-similarity of Arcface [17] representation and RMSE of identity parameters (IDE) of 3D face network [20] between the original high-quality images and rendered ones. We render images under different poses to measure the robustness. To evaluate the geometry, we rigid-align predictions to the ground truth via ICP using hand-crafted key points. Mean Angle Deviation (MAD) of normal maps and point-to-plane $(\mathrm{p2p})$ distance between the aligned prediction and ground truth are utilized as metrics. Please see Appendix for more details. + +# 5.1. Ablation Study + +Comparison with Baselines: We first analyse different settings of NPF in Table 2. We calculate NME, MAD and IDE metrics on MICC, Photoface and CelebaMaskHQ dataset, respectively. In row (1), our full method obtains the best performance in all metrics, approving the effectiveness of each proposed component. In row (2), we observe that the face prototype gets satisfactory, or even better results than several baselines, which reveals that NPF well integrates consistency from photo collection. In row (3), we replace the adaptive uncertainty with average pooling to get the proto code $\overline{\mathbf{s}}$ , and the performance is reduced to + +![](images/b1c69de88b51beec4d17f0a3153ea50cd43427602159b3b02c7946ed1eb46573.jpg) +Figure 4. Analysis on the deformation modeling. (a) How the deformation modeling improves the details and shape accuracy. (b) How the settings of deformation modules influence the results. + +some extent. This reveals our identity-aware aggregation well contributes on digging consistent cues. In row (4), the significant accuracy drop demonstrates the effectiveness of deformation modeling on avoiding expression-aware shape ambiguity. Row (5) also proves that the global face representation from $\Phi_e$ improves the quality of deformation modeling. Rows (6)-(8) reveal that the multi-image warm-up and consistency losses also improve the robustness. Finally, row (9) is the state-of-the-art single-image baseline, where it achieves even worse results than face prototype in IDE. This is due to the distortion caused by overfitting or ambiguity under challenging conditions of pose, lighting and occlusion. + +Analysis on Deformation Modeling: We analyse how the proposed deformation modeling method improves the performance. In Fig. 4-(a), we highlight the differences between this two settings. Without deformation modeling, fusing the style codes yields $\bar{\mathbf{s}}$ to represent a mean expression on the face prototype, which is different from the target face. Hence, it brings difficulty to recover the target's expression during NPF fitting. Further, as the face shapes with various expressions show different appearance and silhouettes on the images, ignoring the deformation brings ambiguity when digging the multi-image consistency. Hence, such a model produces improper shapes and details. In contrast, modeling the deformation recovers the target's expression, the corresponding shapes and appearance effects. In Fig. 4-(b), we show the influence of different settings. Without deformation modeling, the target expression cannot be recovered. Without the global representation $\bar{\mathbf{h}}$ , the expression effect is produced but not accurate enough. Finally, our full method recovers precise target deformation. + +Analysis on Multi-image Consistency: We analyse how our method leverages the multi-image priors from inthe-wild photo collections. In Fig. 5-(a), we show the influence of the size $N$ of photo collections. Compared with the single-image setting (same as PTI [66]), $N = 2$ sig + +![](images/08e04bf735f0b0c504255f29a0d64e9b2838bfa959ee6da5a77b76b447827258.jpg) +Figure 5. Analysis on the multi-image consistency. (a) (b) Performance under different photo collection size $N$ ; (c) The effectiveness of multi-image warm-up. Red box signs the target image. + +
MICCFG3D
Method3DMMindooroutdoorLQHQ
Extreme3D [85]✓3.663.703.493.58
PRN [25]✓2.322.472.382.06
RingNet [69]✓1.932.022.082.02
3DDFA v2 [33]✓1.881.962.101.91
D3DFR [20] (mv)✓1.661.691.901.95
MGCnet [71]✓1.721.781.951.90
DECA [24]✓1.691.701.911.89
Cross-modal [4]×2.392.332.302.04
Unsup3D [91]×2.482.522.412.29
LAP [100] (mv)×2.322.252.182.16
PhyDIR [101] (mv)×2.292.202.252.09
Ours×1.751.812.151.98
+ +nificantly improves the accuracy, revealing that even two images still provide reliable priors. With $N$ increasing, the accuracy gets better. Fig. 5-(b) illustrates the qualitative performance. The single-image result has obvious distortion of facial shape due to the target's large pose and challenging appearance. Increasing $N = 3$ obviously suppresses the degradation, and the setting $N = 6$ obtains best performance with proper geometry. In Fig. 5-(c), we observe that during fitting, the proposed multi-image warm-up suppresses the overfitting on occlusion or shadows, boosted by the consistency from other images. + +# 6. Comparison with State-of-the-art + +Evaluation on Geometry: We first analyse the modeled geometry on MICC and FG3D dataset. We use the provided indoor/outdoor videos of MICC, low-quality (LQ) and high-quality (HQ) image sets to optimize the trained NPF. The geometry for each identity is obtained from 4 images, and calculated the average results. The comparisons are shown in Table 3, where we divide the methods into 3DMM and non-3DMM group. The tag 'mv' means the method uses multi-view input. We observe that our method obtains the best performance among non-3DMM approaches, but higher errors than D3DFR, MGCnet and + +Table 3. Point-to-plane distance (mm) on benchmarks. + +
MAD↓<20°↑<25°↑<30°↑
SfSNet [70]25.5±9.343.6%57.5%68.7%
PRN [25]24.8±6.843.1%62.9%74.1%
DF2Net [97] (GT)24.3±5.742.2%62.7%74.5%
D3DFR [20] (mv)23.5±6.146.1%61.8%73.3%
Cross-Modal [4] (GT)22.8±6.549.0%62.9%74.1%
DECA [24]22.5±5.348.7%62.3%73.7%
LAP [100] (mv)23.0±5.148.2%63.1%74.9%
PhyDIR [101] (mv)22.7±4.349.2%63.4%75.3%
Ours prototype23.7±4.745.6%61.4%73.0%
SfSNet-ft [70]12.8±5.483.7%90.8%94.5%
Cross-Modal-ft [4] (GT)12.0±5.385.2%92.0%95.6%
LAP-ft [100] (mv)12.3±4.584.9%92.4%96.3%
PhyDir-ft [101] (mv)12.0±4.985.3%92.7%96.9%
Ours11.87±5.285.9%93.0%96.9%
+ +Table 4. Facial normal evaluation on Photoface dataset. + +![](images/71eb93256fa96a9952eda7706a7f42d2492b68a4e91c7cc936f866cc4134197b.jpg) +Figure 6. Qualitative comparison on predicted facial geometry. + +DECA. Note that, 3DMM is a highly reliable face shape assumption made from real 3D ground truth, and thus it fundamentally boosts the models' accuracy. Even though, our method still outperforms Extreme3D, PRN, RingNet and competes with 3DDFA v2. We further analyse the modeled facial normal on Photoface dataset in Table 4, where '-ft' means finetuning. Our prototype has already outperforms several methods. After finetuning, our method obtains best performance. Finally, we illustrate qualitative results in Fig. 6, where our method provides finer details. Compared with the EG3D [13] + PTI [66] baseline, our method is much more robust against distortion from large pose, extreme lighting and appearance. + +Evaluation on Texture: We then evaluate the modeled texture on CelebAMaskHQ dataset. Besides the reconstructed image, we render images along the yaw angle in 3 ranges: $[0^{\circ}, \pm 20^{\circ}]$ , $[\pm 20^{\circ}, \pm 40^{\circ}]$ and $[\pm 40^{\circ}, \pm 60^{\circ}]$ , + +![](images/fd288c6b4d6489d320b9c78058194b1d8fec0c61755ac02943b9c2242470df8b.jpg) +Figure 7. Qualitative comparison with EG3D [13] + PTI [66] baseline, PhyDIR [101], HeadNeRF [34] and DFG [18] on the robustness of rotation and expression editing. Our method produces robust and photo-realistic rendering results. Zooming in to see the details. + +
Method\( \left\lbrack {{0}^{ \circ }, \pm {20}^{ \circ }}\right\rbrack \)\( \left\lbrack {\pm {20}^{ \circ }, \pm {40}^{ \circ }}\right\rbrack \)\( \left\lbrack {\pm {40}^{ \circ }, \pm {60}^{ \circ }}\right\rbrack \)
Cos-simIDECos-simIDECos-simIDESSIM
DFG [18]0.7320.3420.6440.3850.5810.4300.751
Unsup3D [91]0.7010.3180.6770.3340.6320.3870.514
LAP [100] (mv)0.7400.2930.6850.3010.6580.3590.623
PhyDIR [101] (mv)0.8260.2670.7920.2940.7450.3280.880
MoFANeRF [108]0.6930.3370.6660.3600.6400.3950.523
HeadNeRF [34]0.8300.2400.8010.2680.6710.3640.823
EG3D + PTI0.8350.2480.7830.2790.7090.3470.919
Ours0.8420.2330.8060.2510.7500.2870.921
+ +Table 5. Quality of rendered images on CelebAMask-HQ. + +and calculate the cosine similarity and ID parameter error between them and target images. The performance is illustrated in Table 5, where DFG cannot provide satisfactory results due to its entangled image formation. For Unsup3D and LAP, the quality of texture is limited by the graphics renderer. MoFANeRF suffers from limited data diversity of constrained environments. PhyDIR and HeadNeRF suffer from large-pose degradation. The single-image baseline $\mathrm{EG3D} + \mathrm{PTI}$ obtains high reconstruction performance, but fragile results under rotation due to the degraded shapes. In contrast, our method obtains the best performance in all the metrics, and shows robust and high-quality modeling results. Finally, we show qualitative results in Fig. 7 under challenging conditions of extreme lighting and pose. EG3D $^+$ PTI suffers from overfitting and produces flatten shapes, while our method obtains best performance on the rendering quality and robustness. Our method also show satisfactory ability on editing the expressions via deformation codes. More results and comparisons can be seen in the Appendix. + +Limitation: Our method requires multiple images of a same person as input, which may limit the application under limiting conditions. This problem can be suppressed + +by searching images with similar identity and building a 'relaxed' photo collection. Further, the performance also depends on the pretrained EG3D models. As a result, extreme expressions and poses may bring degradation on accuracy. Such issues can be addressed by re-training EG3D via targeted dataset. We make the discussions in the Appendix. + +# 7. Conclusion & Future Work + +In this paper, we propose a Neural Proto-face Field (NPF) method for unsupervised robust 3D face modeling. NPF digs multi-image priors from in-the-wild photo collections to boost the 3D generative models, and well complements the single-image overfitting inversion procedure. To aggregate consistency against large variations in unconstrained environments, a novel identity-aware aggregation method is proposed to adaptively combine the style codes, and build ID-consistent face prototypes. To suppress the non-rigid ambiguity, NPF blends the consistent hybrid representation via a novel deformation modeling method. In this way, NPF obtains face prototypes containing common facial cues within the collection, and disentangles specific expressions. The final recovered 3D face is obtained via fitting NPF with consistency losses, and thus scene-specific details can be recovered. Extensive experiments demonstrate that our method models robust and detailed face shapes under challenging conditions, and recovers photorealistic texture with pose/expression controlling. In the future, the works could be proposed to tackle the limitation such as supporting single-image input. Further, decomposing the NeRF pipeline, the texture could be disentangled into different intrinsic factors. + +# References + +[1] Rameen Abdal, Yipeng Qin, and Peter Wonka. Image2stylegan: How to embed images into the stylegan latent space? In CVPR, pages 4432-4441, 2019. 2, 3 +[2] Rameen Abdal, Yipeng Qin, and Peter Wonka. Image2stylegan++: How to edit the embedded images? In CVPR, pages 8296-8305, 2020. 2, 3 +[3] Rameen Abdal, Peihao Zhu, Niloy J Mitra, and Peter Wonka. Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. ACM Transactions on Graphics (TOG), 40(3):1-21, 2021. 2, 3 +[4] Victoria Fernández Abrevaya, Adnane Boukhayma, Philip HS Torr, and Edmond Boyer. Cross-modal deep face normals with deactivable skip connections. In CVPR, pages 4979-4989, 2020. 2, 5, 6, 7 +[5] ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, and Zhixin Shu. Rignerf: Fully controllable neural 3d portraits. In CVPR, pages 20364-20373, 2022. 3 +[6] Andrew D Bagdanov, Alberto Del Bimbo, and Iacopo Masi. The florence 2d/3d hybrid face dataset. In Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding, pages 79-80, 2011. 5 +[7] Ziqian Bai, Zhaopeng Cui, Jamal Ahmed Rahim, Xiaoming Liu, and Ping Tan. Deep facial non-rigid multi-view stereo. In CVPR, pages 5850-5860, 2020. 1, 2 +[8] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In CVPR, pages 5855–5864, 2021. 2 +[9] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In CVPR, pages 5470–5479, 2022. 2 +[10] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 187-194, 1999. 1, 2, 4 +[11] Marcel C Bühler, Abhimitra Meka, Gengyan Li, Thabo Beeler, and Otmar Hilliges. Varitex: Variational neural face textures. In ICCV, pages 13890-13899, 2021. 2, 3 +[12] Rohan Chabra, Jan E Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe. Deep local shapes: Learning local sdf priors for detailed 3d reconstruction. In ECCV, pages 608-625. Springer, 2020. 2 +[13] Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In CVPR, pages 16123-16133, 2022. 1, 2, 3, 6, 7, 8 +[14] Eric R Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein. pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In CVPR, pages 5799-5809, 2021. 2, 3 + +[15] Bindita Chaudhuri, Noranart Vesdapunt, Linda Shapiro, and Baoyuan Wang. Personalized face modeling for improved face reconstruction and motion retargeting. In ECCV, 2020. 1, 2 +[16] Radek Daneček, Michael J Black, and Timo Bolkart. Emoca: Emotion driven monocular face capture and animation. In CVPR, pages 20311-20322, 2022. 2 +[17] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In CVPR, pages 4690-4699, 2019. 6 +[18] Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, and Xin Tong. Disentangled and controllable face image generation via 3d imitative-contrastive learning. In CVPR, pages 5154–5163, 2020. 2, 3, 8 +[19] Yu Deng, Jiaolong Yang, Jianfeng Xiang, and Xin Tong. Gram: Generative radiance manifolds for 3d-aware image generation. In CVPR, pages 10673-10683, 2022. 3 +[20] Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set. In CVPRW, 2019. 1, 3, 4, 6, 7 +[21] Abdallah Dib, Cedric Thebault, Junghyun Ahn, Philippe-Henri Gosselin, Christian Theobalt, and Louis Chevallier. Towards high fidelity monocular face reconstruction with rich reflectance using self-supervised learning and ray tracing. arXiv preprint arXiv:2103.15432, 2021. 2 +[22] Pengfei Dou, Shishir K Shah, and Ioannis A Kakadiaris. End-to-end 3d face reconstruction with deep neural networks. In CVPR, pages 5908-5917, 2017. 1 +[23] Bernhard Egger, William AP Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Rondhani, et al. 3d morphable face models—past, present, and future. ACM Transactions on Graphics (TOG), 39(5):1-38, 2020. 2 +[24] Yao Feng, Haiwen Feng, Michael J Black, and Timo Bolkart. Learning an animatable detailed 3d face model from in-the-wild images. ACM Transactions on Graphics (TOG), 40(4):1-13, 2021. 1, 2, 7 +[25] Yao Feng, Fan Wu, Xiaohu Shao, Yanfeng Wang, and Xi Zhou. Joint 3d face reconstruction and dense alignment with position map regression network. In ECCV, pages 534-551, 2018. 1, 2, 7 +[26] Zhen-Hua Feng, Patrik Huber, Josef Kittler, Peter Hancock, Xiao-Jun Wu, Qijun Zhao, Paul Koppen, and Matthias Ratsch. Evaluation of dense 3d reconstruction from 2d face images in the wild. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pages 780-786, 2018. 5 +[27] Guy Gafni, Justus Thies, Michael Zollhofer, and Matthias Nießner. Dynamic neural radiance fields for monocular 4d facial avatar reconstruction. In CVPR, pages 8649-8658, 2021. 3 +[28] Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang. Portrait neural radiance fields from a single image. arXiv preprint arXiv:2012.05903, 2020. 2 +[29] Baris Gecer, Stylianos Ploumpis, Irene Kotsia, and Stefanos Zafeiriou. Ganfit: Generative adversarial network fitting for + +high fidelity 3d face reconstruction. In CVPR, pages 1155-1164, 2019. 1, 2 +[30] Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, and William T Freeman. Unsupervised training for 3d morphable model regression. In CVPR, pages 8377-8386, 2018. 2 +[31] Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. Neural head avatars from monocular rgb videos. In CVPR, pages 18653–18664, 2022. 3 +[32] Jiatao Gu, Lingjie Liu, Peng Wang, and Christian Theobalt. Stylenerf: A style-based 3d-aware generator for high-resolution image synthesis. arXiv preprint arXiv:2110.08985, 2021. 3 +[33] Jianzhu Guo, Xiangyu Zhu, Yang Yang, Fan Yang, Zhen Lei, and Stan Z Li. Towards fast, accurate and stable 3d dense face alignment. In ECCV, 2020. 7 +[34] Yang Hong, Bo Peng, Haiyao Xiao, Ligang Liu, and Juyong Zhang. Headnerf: A real-time nerf-based parametric head model. In CVPR, pages 20374-20384, 2022. 1, 2, 8 +[35] Aaron S Jackson, Adrian Bulat, Vasileios Argyriou, and Georgios Tzimiropoulos. Large pose 3d face reconstruction from a single image via direct volumetric cnn regression. In ICCV, pages 1031-1039, 2017. 2 +[36] Kacper Kania, Kwang Moo Yi, Marek Kowalski, Tomasz Trzeciński, and Andrea Tagliasacchi. Conerf: Controllable neural radiance fields. In CVPR, pages 18623-18632, 2022. 3 +[37] Tero Karras, Miika Aittala, Samuli Laine, Erik Härkonen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Alias-free generative adversarial networks. NeurIPS, 34:852-863, 2021. 3 +[38] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In CVPR, pages 4401-4410, 2019. 2, 3 +[39] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In CVPR, pages 8110-8119, 2020. 2, 3 +[40] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6 +[41] Cheng-Han Lee, Ziwei Liu, Lingyun Wu, and Ping Luo. Maskgan: Towards diverse and interactive facial image manipulation. In CVPR, 2020. 5 +[42] Tzu-Mao Li, Miika Aittala, Frédo Durand, and Jaakko Lehtinen. Differentiable monte carlo ray tracing through edge sampling. ACM Transactions on Graphics (TOG), 37(6):1-11, 2018. 2 +[43] Feng Liu, Dan Zeng, Qijun Zhao, and Xiaoming Liu. Joint face alignment and 3d face reconstruction. In ECCV, pages 545-560. Springer, 2016. 1 +[44] Shichen Liu, Tianye Li, Weikai Chen, and Hao Li. Soft rasterizer: A differentiable renderer for image-based 3d reasoning. In ICCV, pages 7708-7717, 2019. 2 +[45] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In ICCV, pages 3730-3738, 2015. 5 + +[46] Matthew M Loper and Michael J Black. Opendr: An approximate differentiable renderer. In ECCV, pages 154-169, 2014. 2 +[47] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. ACM siggraph computer graphics, 21(4):163-169, 1987. 6 +[48] Nelson Max. Optical models for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics, 1(2):99-108, 1995. 3 +[49] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In CVPR, pages 4460-4470, 2019. 2 +[50] Moustafa Meshry, Dan B Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snively, and Ricardo Martin-Brualla. Neural rerendering in the wild. In CVPR, pages 6878–6887, 2019. 3 +[51] Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson. Implicit surface representations as layers in neural networks. In CVPR, pages 4743-4752, 2019. 2 +[52] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, pages 405-421, 2020. 2 +[53] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional generative neural feature fields. In CVPR, pages 11453-11464, 2021. 3 +[54] Roy Or-El, Xuan Luo, Mengyi Shan, Eli Shechtman, Jeong Joon Park, and Ira Kemelmacher-Shlizerman. Stylesdf: High-resolution 3d-consistent image and geometry generation. In CVPR, pages 13503–13513, 2022. 2, 3 +[55] Xingang Pan, Bo Dai, Ziwei Liu, Chen Change Loy, and Ping Luo. Do 2d gans know 3d shape? unsupervised 3d shape reconstruction from 2d image gans. arXiv preprint arXiv:2011.00844, 2020. 2 +[56] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In ICCV, pages 5865-5874, 2021. 3, 4 +[57] Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Ricardo MartinBrualla, and Steven M Seitz. Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields. arXiv preprint arXiv:2106.13228, 2021. 3, 4 +[58] Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. In IEEE International Conference on Advanced Video and Signal Based Surveillance, pages 296-301, 2009. 1 +[59] Jingtan Piao, Keqiang Sun, Quan Wang, Kwan-Yee Lin, and Hongsheng Li. Inverting generative adversarial renderer for face reconstruction. In CVPR, pages 15619-15628, 2021. 2 +[60] Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-nerf: Neural radiance fields + +for dynamic scenes. In CVPR, pages 10318-10327, 2021. 3 +[61] Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. H3d-net: Few-shot high-fidelity 3d head reconstruction. In ICCV, pages 5620-5629, 2021. 2 +[62] Yurui Ren, Ge Li, Yuanqi Chen, Thomas H Li, and Shan Liu. Pirenderer: Controllable portrait image generation via semantic neural rendering. In ICCV, pages 13759-13768, 2021. 2 +[63] Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or. Encoding in style: a stylegan encoder for image-to-image translation. In CVPR, pages 2287–2296, 2021. 3, 4 +[64] Elad Richardson, Matan Sela, and Ron Kimmel. 3d face reconstruction by learning from synthetic data. In 3DV, pages 460-469, 2016. 1, 2 +[65] Elad Richardson, Matan Sela, Roy Or-El, and Ron Kimmel. Learning detailed face reconstruction from a single image. In CVPR, pages 1259–1268, 2017. 2 +[66] Daniel Roich, Ron Mokady, Amit H Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. ACM Transactions on Graphics (TOG), 42(1):1-13, 2022. 1, 3, 4, 5, 6, 7, 8 +[67] Sami Romdhani and Thomas Vetter. Efficient, robust and accurate fitting of a 3d morphable model. In Computer Vision, page 59. IEEE, 2003. 1, 2 +[68] Sami Romdhani and Thomas Vetter. Estimating 3d shape and texture using pixel intensity, edges, specular highlights, texture constraints and a prior. In CVPR, volume 2, pages 986-993, 2005. 1, 2 +[69] Soubhik Sanyal, Timo Bolkart, Haiwen Feng, and Michael J Black. Learning to regress 3d face shape and expression from an image without 3d supervision. In CVPR, pages 7763-7772, 2019. 3, 7 +[70] Soumyadip Sengupta, Angjoo Kanazawa, Carlos D Castillo, and David W Jacobs. Sfsnet: Learning shape, reflectance and illuminance of faces in the wild. In CVPR, pages 6296-6305, 2018. 2, 6, 7 +[71] Jiaxiang Shang, Tianwei Shen, Shiwei Li, Lei Zhou, Mingmin Zhen, Tian Fang, and Long Quan. Self-supervised monocular 3d face reconstruction by occlusion-aware multi-view geometry consistency. arXiv preprint arXiv:2007.12494, 2020. 7 +[72] Yichun Shi, Divyansh Aggarwal, and Anil K Jain. Lifting 2d stylegan for 3d-aware face generation. In CVPR, pages 6258-6266, 2021. 2 +[73] Yichun Shi and Anil K Jain. Probabilistic face embeddings. In ICCV, pages 6902-6911, 2019. 3 +[74] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3d-structure-aware neural scene representations. NeurIPS, 32, 2019. 2 +[75] Keqiang Sun, Shangzhe Wu, Zhaoyang Huang, Ning Zhang, Quan Wang, and Hongsheng Li. Controllable 3d face synthesis with conditional generative occupancy fields. In Advances in Neural Information Processing Systems, 2021. 3 + +[76] Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Fml: Face model learning from videos. In CVPR, pages 10812-10822, 2019. 1, 2 +[77] Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Stylerig: Rigging stylegan for 3d control over portrait images. In CVPR, pages 6142-6151, 2020. 2, 3 +[78] Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo MartinBrualla, Tomas Simon, Jason Saragih, Matthias Nießner, et al. State of the art on neural rendering. In Computer Graphics Forum, volume 39, pages 701-727. Wiley Online Library, 2020. 3 +[79] Ayush Tewari, Hans-Peter Seidel, Mohamed Elgharib, Christian Theobalt, et al. Learning complete 3d morphable face models from images and videos. In CVPR, pages 3361-3371, 2021. 1, 2, 3 +[80] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, W Yifan, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, et al. Advances in neural rendering. In Computer Graphics Forum, volume 41, pages 703-735. Wiley Online Library, 2022. 3 +[81] Ayush Tewari, Michael Zollhofer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, Patrick Perez, and Christian Theobalt. Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction. In ICCVW, pages 1274-1283, 2017. 1, 2 +[82] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics (TOG), 38(4):1-12, 2019. 3 +[83] Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, and Daniel Cohen-Or. Designing an encoder for stylegan image manipulation. ACM Transactions on Graphics (TOG), 40(4):1-14, 2021. 3 +[84] Luan Tran and Xiaoming Liu. Nonlinear 3d face morphable model. In CVPR, pages 7346-7355, 2018. 1, 2 +[85] Anh Tuan Tran, Tal Hassner, Iacopo Masi, Eran Paz, Yuval Nirkin, and Gerard Medioni. Extreme 3d face reconstruction: Seeing through occlusions. In CVPR, pages 3935-3944, 2018. 7 +[86] Daoye Wang, Prashanth Chandran, Gaspard Zoss, Derek Bradley, and Paulo Gotardo. Morf: Morphable radiance fields for multiview neural head modeling. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1-9, 2022. 2 +[87] Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, and Michael Zollhofer. Learning compositional radiance fields of dynamic human heads. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5704-5713, 2021. 2 + +[88] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. TIP, 13(4):600-612, 2004. 6 +[89] Felix Wimbauer, Shangzhe Wu, and Christian Rupprecht. De-rendering 3d objects in the wild. In CVPR, pages 18490-18499, 2022. 2 +[90] Fanzi Wu, Linchao Bao, Yajing Chen, Yonggen Ling, Yibing Song, Songnan Li, King Ngi Ngan, and Wei Liu. Mvfnet: Multi-view 3d face morphable model regression. In CVPR, pages 959-968, 2019. 1, 2, 3 +[91] Shangzhe Wu, Christian Rupprecht, and Andrea Vedaldi. Unsupervised learning of probably symmetric deformable 3d objects from images in the wild. In CVPR, pages 1-10, 2020. 1, 2, 5, 6, 7, 8 +[92] Sicheng Xu, Jiaolong Yang, Dong Chen, Fang Wen, Yu Deng, Yunde Jia, and Xin Tong. Deep 3d portrait from a single image. In CVPR, pages 7710-7720, 2020. 2 +[93] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. NeurIPS, 34:4805-4815, 2021. 2 +[94] Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and Christian Theobalt. i3dmm: Deep implicit 3d morphable model of human heads. In CVPR, pages 12803-12813, 2021. 1, 2 +[95] Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li. Learning face representation from scratch. arXiv preprint arXiv:1411.7923, 2014. 5 +[96] Stefanos Zafeiriou, Mark Hansen, Gary Atkinson, Vasileios Argyriou, Maria Petrou, Melvyn Smith, and Lyndon Smith. The photoface database. In CVPRW, pages 132-139, 2011. 5 +[97] Xiaoxing Zeng, Xiaojiang Peng, and Yu Qiao. Df2net: A dense-fine-finer network for detailed 3d face reconstruction. In ICCV, pages 2315-2324, 2019. 2, 7 +[98] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 5 +[99] Ruo Zhang, Ping-Sing Tsai, James Edwin Cryer, and Mubarak Shah. Shape-from-shading: a survey. IEEE TPAMI, 21(8):690-706, 1999. 1, 2 +[100] Zhenyu Zhang, Yanhao Ge, Renwang Chen, Ying Tai, Yan Yan, Jian Yang, Chengjie Wang, Jilin Li, and Feiyue Huang. Learning to aggregate and personalize 3d face from in-the-wild photo collection. In CVPR, pages 14214-14224, 2021. 1, 2, 3, 5, 6, 7, 8 +[101] Zhenyu Zhang, Yanhao Ge, Ying Tai, Weijian Cao, Renwang Chen, Kunlin Liu, Hao Tang, Xiaoming Huang, Chengjie Wang, Zhifeng Xie, et al. Physically-guided disentangled implicit rendering for 3d face modeling. In CVPR, pages 20353-20363, 2022. 3, 7, 8 +[102] Mingwu Zheng, Hongyu Yang, Di Huang, and Liming Chen. Imface: A nonlinear 3d morphable face model with implicit neural representations. In CVPR, pages 20343-20352, 2022. 2 + +[103] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. Im avatar: Implicit morphable head avatars from videos. In CVPR, pages 13545–13555, 2022. 3 +[104] Yuxiang Zhou, Jiankang Deng, Irene Kotsia, and Stefanos Zafeiriou. Dense 3d face decoding over 2500fps: Joint texture & shape convolutional mesh decoders. In CVPR, pages 1097-1106, 2019. 2 +[105] Xiangyu Zhu, Zhen Lei, Xiaoming Liu, Hailin Shi, and Stan Z Li. Face alignment across large poses: A 3d solution. In CVPR, pages 146-155, 2016. 1, 2 +[106] Xiangyu Zhu, Fan Yang, Chang Yu Di Huang, Hao Wang, Jianzhu Guo, Zhen Lei, and Stan Z Li. Beyond 3dmm space: Towards fine-grained 3d face reconstruction. In ECCV, 2020. 1, 2 +[107] Xiangyu Zhu, Dong Yi, Zhen Lei, and Stan Z Li. Robust 3d morphable model fitting by sparse sift flow. In ICCV, pages 4044-4049. IEEE, 2014. 1, 2 +[108] Yiyu Zhuang, Hao Zhu, Xusen Sun, and Xun Cao. 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While plausible details and resolutions are achieved, these models easily fail under extreme conditions of pose, shadow or appearance, due to the entangled fitting or lack of multiview priors. To address this problem, this paper presents a novel Neural Proto-face Field (NPF) for unsupervised robust 3D face modeling. Instead of using constrained images as Neural Radiance Field (NeRF), NPF disentangles the common/specific facial cues, i.e., ID, expression and scene-specific details from in-the-wild photo collections. Specifically, NPF learns a face prototype to aggregate 3D-consistent identity via uncertainty modeling, extracting multi-image priors from a photo collection. NPF then learns to deform the prototype with the appropriate facial expressions, constrained by a loss of expression consistency and personal idiosyncrasies. Finally, NPF is optimized to fit a target image in the collection, recovering specific details of appearance and geometry. In this way, the generative model benefits from multi-image priors and meaningful facial structures. Extensive experiments on benchmarks show that NPF recovers superior or competitive facial shapes and textures, compared to state-of-the-art methods." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 606, + 124, + 618 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 606, + 124, + 618 + ], + "spans": [ + { + "bbox": [ + 47, + 606, + 124, + 618 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 45, + 626, + 279, + 708 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 626, + 279, + 708 + ], + "spans": [ + { + "bbox": [ + 45, + 626, + 279, + 708 + ], + "type": "text", + "content": "3D face reconstruction is a long-standing problem with applications including games, digital human and mobile photography. It is ill-posed in many cases requiring strong assumptions e.g., shape from shading [99]. With the 3D Morphable Model (3DMM) [10] proposed, such a problem can be solved by fitting parameters to the target faces [67, 68, 107]. Recently, deep-learning methods [22, 25, 43, 64," + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 310, + 286, + 518, + 496 + ], + "blocks": [ + { + "bbox": [ + 310, + 286, + 518, + 496 + ], + "lines": [ + { + "bbox": [ + 310, + 286, + 518, + 496 + ], + "spans": [ + { + "bbox": [ + 310, + 286, + 518, + 496 + ], + "type": "image", + "image_path": "1d5b92629b0a0a31ff39bdc5ad65f02ea7692cfc2c9cc1429f5034d90658f06f.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 296, + 506, + 530, + 570 + ], + "lines": [ + { + "bbox": [ + 296, + 506, + 530, + 570 + ], + "spans": [ + { + "bbox": [ + 296, + 506, + 530, + 570 + ], + "type": "text", + "content": "Figure 1. (a) Comparison with graphics-renderer-based methods LAP [100] and D3DFR [20]. Our method models geometry details and photo-realistic texture. (b) Results of neural rendering methods EG3D [13] + PTI [66], HeadNeRF [34] and our method. Our method produces high-quality geometry, robust texture modeling under rotation and deformation." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 577, + 530, + 659 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 577, + 530, + 659 + ], + "spans": [ + { + "bbox": [ + 296, + 577, + 530, + 659 + ], + "type": "text", + "content": "105] are proposed to regress 3DMM parameters from input images. These approaches are then improved by nonlinear modeling [24, 29, 79, 81, 84, 94] and multi-view consistency [7, 15, 76, 90]. Besides 3DMM methods, recent efforts [91, 100] attempt to model 3D face without shape assumptions. These non-parametric methods have potential ability to improve the modeling quality beyond 3DMM." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 660, + 531, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 660, + 531, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 660, + 531, + 730 + ], + "type": "text", + "content": "Although the aforementioned methods achieve impressive performance, they also have obvious drawbacks. On the one hand, as the parametric models are usually built from a small amount of subjects (e.g., BFM [58] with 200 subjects) and rigidly controlled conditions, they may be fragile to large variations of identity [106], and have limi" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 77, + 42, + 119, + 72 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 42, + 119, + 72 + ], + "spans": [ + { + "bbox": [ + 77, + 42, + 119, + 72 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 150, + 41, + 471, + 73 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 150, + 41, + 471, + 73 + ], + "spans": [ + { + "bbox": [ + 150, + 41, + 471, + 73 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 719, + 235, + 730 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 719, + 235, + 730 + ], + "spans": [ + { + "bbox": [ + 56, + 719, + 235, + 730 + ], + "type": "text", + "content": "*Chengjie Wang and Ying Tai are corresponding authors" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "spans": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "text", + "content": "382" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 45, + 106, + 279, + 188 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 106, + 279, + 188 + ], + "spans": [ + { + "bbox": [ + 45, + 106, + 279, + 188 + ], + "type": "text", + "content": "tations on building teeth, skin details or anatomic grounded muscles [23]. On the other hand, all of these methods depend on graphics renderers [42, 44, 46] in the analysis-by-synthesis fitting procedure, and thus yields hand-crafted approximation or ill-posed decomposition on intrinsic clues. Hence, as illustrated in Fig. 1-(a), these methods struggle to produce photo-realistic texture or geometric details." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 45, + 189, + 280, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 189, + 280, + 410 + ], + "spans": [ + { + "bbox": [ + 45, + 189, + 280, + 410 + ], + "type": "text", + "content": "Against these limitations, efforts are made to use a neural renderer such as StyleGAN [38, 39] to model faces by inverting the corresponding images [1,2] into " + }, + { + "bbox": [ + 45, + 189, + 280, + 410 + ], + "type": "inline_equation", + "content": "\\mathcal{W}" + }, + { + "bbox": [ + 45, + 189, + 280, + 410 + ], + "type": "text", + "content": " space. Existing methods [11, 18, 59, 62, 92] mainly learn to embed 3DMM coefficients to implicitly leverage 3D clues, but they have difficulty achieving precise 3D controls due to their entangled image formation. To disentangle neural rendering, recent works [13, 14, 34, 54] employ explicit 3D pipelines, e.g., Neural Radiance Field (NeRF) [52] into the StyleGANs' framework, so that face shapes and camera views can be extracted. In this way, precise 3D controls and detailed geometry can be obtained. However, these methods still show fragile performance under challenging conditions as shown in Fig. 1-(b). When confronting large poses, extreme appearance or lighting, the lack of facial priors disturbs the reconstruction and results in severe distortions. This is due to the essentially overfitting objective of inverting single target image, where the geometry ambiguity is unavoidable." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 45, + 412, + 279, + 691 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 412, + 279, + 691 + ], + "spans": [ + { + "bbox": [ + 45, + 412, + 279, + 691 + ], + "type": "text", + "content": "On top of this, one solution is to leverage reliable priors, e.g., multi-image consistency as a complement. While NeRF provides a natural paradigm to dig multi-view cues, it requires fully constrained images that are difficult to obtain. Even conditioned by style codes [13, 14, 54], there is no direct way to build 3D faces from unconstrained portrait collections in such a neural rendering mechanism. In this work, we present a novel Neural Proto-face Field (NPF) for unsupervised robust 3D face modeling, where ID, expression and scene-specific details can be disentangled from in-the-wild photo collections. To aggregate ID-aware cues, NPF leverages uncertainty modeling to extract multi-image priors and recovers a face prototype with ID-consistent face shape. To disentangle the expression, NPF then learns appropriate representations to deform the prototype, constrained by a expression consistency loss. In this way, the learned face shape is properly blended to avoid geometric ambiguity. Finally, to recover the scene-specific details, NPF is optimized to fit a target image in the collection. The robustness of fitting is guaranteed by a geometry and appearance regularization. As shown in Fig. 1-(b), NPF makes the generative method benefit from multi-image priors in unconstrained environments, and produces high-quality 3D faces under challenging conditions." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 693, + 236, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 693, + 236, + 704 + ], + "spans": [ + { + "bbox": [ + 57, + 693, + 236, + 704 + ], + "type": "text", + "content": "In summary, our contributions are as follows:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 45, + 706, + 279, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 706, + 279, + 730 + ], + "spans": [ + { + "bbox": [ + 45, + 706, + 279, + 730 + ], + "type": "text", + "content": "1) A novel Neural Proto-face Field (NPF) is proposed to disentangle ID, expression and specific details from 3D face" + } + ] + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 300, + 105, + 530, + 158 + ], + "blocks": [ + { + "bbox": [ + 300, + 105, + 530, + 158 + ], + "lines": [ + { + "bbox": [ + 300, + 105, + 530, + 158 + ], + "spans": [ + { + "bbox": [ + 300, + 105, + 530, + 158 + ], + "type": "table", + "html": "
MethodsRenderingPipelineMulti-view
EMOCA [81], DECA [24], Unsup3D [91]GraphicsDisentangled×
LAP [100], FML [76], MVF [90]GraphicsDisentangled✓
DFG [18], StyleRig [77], StyleFlow [3]NeuralEntangled×
Pi-GAN [14], StyleSDF [54], EG3D [13]NeuralDisentangled×
OursNeuralDisentangled✓
", + "image_path": "aee8e5cf8135add311a21fe67b72e10d6471f7cbfbd184fd0da0d7ef3a9a1570.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 322, + 165, + 504, + 175 + ], + "lines": [ + { + "bbox": [ + 322, + 165, + 504, + 175 + ], + "spans": [ + { + "bbox": [ + 322, + 165, + 504, + 175 + ], + "type": "text", + "content": "Table 1. Discussion with selected existing methods." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 296, + 187, + 530, + 211 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 187, + 530, + 211 + ], + "spans": [ + { + "bbox": [ + 296, + 187, + 530, + 211 + ], + "type": "text", + "content": "modeling, which uses in-the-wild photo collections to benefit the 3D generative model under challenging conditions." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 296, + 211, + 530, + 281 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 296, + 211, + 530, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 211, + 530, + 245 + ], + "spans": [ + { + "bbox": [ + 296, + 211, + 530, + 245 + ], + "type": "text", + "content": "2) With a novel face prototype aggregation method, NPF integrates multi-image face priors against the large variations in unconstrained environments." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 296, + 245, + 530, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 245, + 530, + 281 + ], + "spans": [ + { + "bbox": [ + 296, + 245, + 530, + 281 + ], + "type": "text", + "content": "3) With a series of novel consistency losses, NPF is well fit to specific scenes with personalized details, based on the guidance of face prototypes." + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 297, + 291, + 386, + 303 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 291, + 386, + 303 + ], + "spans": [ + { + "bbox": [ + 297, + 291, + 386, + 303 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 311, + 530, + 368 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 311, + 530, + 368 + ], + "spans": [ + { + "bbox": [ + 296, + 311, + 530, + 368 + ], + "type": "text", + "content": "In Table 1, we make a discussion on existing methods. NPF benefits from neural rendering and avoids hand-crafted approximations of graphics renderers. In contrast to neural rendering methods, our approach has explicit 3D pipelines, and leverages multi-image consistency in the wild." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 368, + 530, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 368, + 530, + 508 + ], + "spans": [ + { + "bbox": [ + 296, + 368, + 530, + 508 + ], + "type": "text", + "content": "3D Face Reconstruction: As a long-standing problem, the studies mainly start from the pioneer work 3DMM [10]. With the parametric model, early works try to find suitable parameters via optimization [67, 68, 107], while recent approaches [25, 64, 105, 106] leverage deep neural networks to regress the results from input images. With the differentiable renderers proposed, efforts are made on aspects of unsupervised learning [30, 65, 81], improving the non-linear feasibility [16, 21, 24, 29, 81, 84, 104] and multi-view consistency [7, 15, 76, 90]. More recent works attempt to learn complete 3DMM basis [79] or implicit functions [94, 102] which brings new possibilities to this topic." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 296, + 509, + 530, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 509, + 530, + 636 + ], + "spans": [ + { + "bbox": [ + 296, + 509, + 530, + 636 + ], + "type": "text", + "content": "Beyond 3DMM, non-parametric models are also developed by data-driven supervised training [4, 35, 97]. With shape-from-shading algorithm [99], unsupervised methods are proposed including SFS-Net [70], Unsup3D [91] and LAP [100]. Recently, Gan2Shape [55] and LiftedGAN [72] try to distill knowledge from 2D GANs for 3D reconstruction. De3D [89] de-renders more intrinsic factors based on reliable priors. Different from the these discussed methods, NPF benefits 3D face modeling via neural rendering mechanisms, thus provides better performance on details, resolution and non-facial objects." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 296, + 637, + 530, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 637, + 530, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 637, + 530, + 730 + ], + "type": "text", + "content": "Neural Scene Representation: Neural scene representation is a novel way to parameterize signals, and attracts more attention on learning geometry [12, 49, 51, 74, 93]. Recent effort Neural Radiance Field (NeRF) [8, 9, 52] shows impressive performance on recovering appearance and shapes from multi-view images. Based on NeRF, 3D face can also be modeled [28,61,86,87] from a few or single views. Recent efforts are developed to perform reconstruc" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "spans": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "text", + "content": "383" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 45, + 106, + 279, + 199 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 106, + 279, + 199 + ], + "spans": [ + { + "bbox": [ + 45, + 106, + 279, + 199 + ], + "type": "text", + "content": "tion against non-rigid deformation [56,57,60]. Conditioned on specific expressions, NeRF can also be used to realize editing [5, 36] or 4D facial avatars [27, 31, 103]. In summary, these methods depend on fully constrained environments, i.e., the used images have to be captured at a same time. In contrast, our method leverages face consistency from in-the-wild photo sets that are easy to collect, and addresses large variations of lighting, appearance or artifacts." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 45, + 200, + 280, + 409 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 200, + 280, + 409 + ], + "spans": [ + { + "bbox": [ + 45, + 200, + 280, + 409 + ], + "type": "text", + "content": "Neural Rendering on Face Modeling: Neural rendering aims to render images using neural networks [50, 78, 80] with much relaxed inputs. The style-based approaches, i.e., StyleGANs [37-39] obtain the state-of-the-art performance on face generation. With the pretrained StyleGANs, face modeling can be achieved by inverting the target images [1,2,63,66]. 3D embedding (e.g., 3DMM parameter) is then employed to implicitly control the StyleGAN's prediction on pose, identity and lighting [3,18,77]. However, they cannot guarantee the robustness on physical perspective, nor obtain explicit geometry. More recent works attempt to employ 3D pipelines, such as NeRF or deferred neural rendering [82], into StyleGANs [11, 13, 14, 19, 32, 53, 54, 75, 101]. While these methods achieve more precise controls and facial geometry prediction, they are fragile to large pose and ambiguous appearance due to the single-image fitting. In contrast, NPF well leverages multi-image consistency as complementary priors and shows more robust performance." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 45, + 421, + 120, + 434 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 421, + 120, + 434 + ], + "spans": [ + { + "bbox": [ + 45, + 421, + 120, + 434 + ], + "type": "text", + "content": "3. Preliminary" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 45, + 441, + 279, + 580 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 441, + 279, + 580 + ], + "spans": [ + { + "bbox": [ + 45, + 441, + 279, + 580 + ], + "type": "text", + "content": "Exploiting the consistency of multiple images, especially from unconstrained environments, is non-trivial for neural rendering methods because there is no explicit topology that can be shared within the image set. While NeRF provides a natural paradigm to dig consistency from different views, it requires photos taken at the same time or constrained scene. Recent 3D-aware generative methods [13, 14, 32, 54] show that NeRF can represent different faces conditioned on style codes. Such an evidence makes it possible to learn common hybrid 3D representations from in-the-wild photo collections. Without loss of generality, we build our model on EG3D [13] as it achieves the state-of-the-art performance." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 57, + 580, + 278, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 580, + 278, + 592 + ], + "spans": [ + { + "bbox": [ + 57, + 580, + 278, + 592 + ], + "type": "text", + "content": "EG3D represents the 3D scene as a tri-plane denoted as:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 83, + 604, + 279, + 619 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 604, + 279, + 619 + ], + "spans": [ + { + "bbox": [ + 83, + 604, + 279, + 619 + ], + "type": "interline_equation", + "content": "\\mathbf {F} _ {x y}, \\mathbf {F} _ {x z}, \\mathbf {F} _ {y z} = \\Phi_ {g} (\\mathbf {s}), \\mathbf {s} = \\left\\{s ^ {j} \\right\\} _ {j = 1} ^ {M}, \\tag {1}", + "image_path": "a1778f197f2ba9ce936cfc2e0ab71dcacafc53c17865be3d64b1d3f932bb89d3.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "spans": [ + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\mathbf{F} \\in [H, W, C]" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": " is the orthogonal feature plane along different axis, and " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\Phi_g" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": " is the StyleGAN generator. " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\mathbf{s} \\in \\mathbb{R}^{14 \\times 512}" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": " is the style code with stages " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "M = 14" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": ", generated from a random noise " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\mathbf{z}" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": " by a mapping network. Then any 3D position " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "x \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": " can be projected onto each of the three feature planes, retrieving its feature " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "f = f_{xy} + f_{xz} + f_{yz}" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": " via bilinear interpolation. The corresponding density " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": " and color " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\mathbf{c}" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": " is predicted by a tri-plane decoder " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\Phi_d" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": ". In this way, given a camera pose " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": ", origin " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\mathbf{o}" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": " and near/far bound " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "t_n, t_f" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": "," + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 296, + 106, + 530, + 129 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 106, + 530, + 129 + ], + "spans": [ + { + "bbox": [ + 296, + 106, + 530, + 129 + ], + "type": "text", + "content": "the pixel color " + }, + { + "bbox": [ + 296, + 106, + 530, + 129 + ], + "type": "inline_equation", + "content": "\\mathbf{C}" + }, + { + "bbox": [ + 296, + 106, + 530, + 129 + ], + "type": "text", + "content": " for a ray " + }, + { + "bbox": [ + 296, + 106, + 530, + 129 + ], + "type": "inline_equation", + "content": "\\mathbf{r}(t) = \\mathbf{o} + t\\mathbf{d}" + }, + { + "bbox": [ + 296, + 106, + 530, + 129 + ], + "type": "text", + "content": " can be obtained using volume rendering equation [48]:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 336, + 137, + 530, + 165 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 336, + 137, + 530, + 165 + ], + "spans": [ + { + "bbox": [ + 336, + 137, + 530, + 165 + ], + "type": "interline_equation", + "content": "\\mathbf {C} (\\mathbf {r}) = \\int_ {t _ {n}} ^ {t _ {f}} T (t) \\sigma (\\mathbf {r} (t)) \\mathbf {c} (\\mathbf {r} (t), \\mathbf {d}) d t, \\tag {2}", + "image_path": "909fcf86a7aa5a9c1e6dfd325f119051782d15e712df607021e4621021dea6dd.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "spans": [ + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "inline_equation", + "content": "T(t) = \\exp(\\int_{t_n}^t \\sigma(\\mathbf{r}(s)) ds" + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "text", + "content": " is the sampled point along each ray. Using Eqn. 2, EG3D renders a low-resolution image " + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_l" + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "text", + "content": " as well as a feature image " + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_f" + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_f" + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "text", + "content": " is then fed into a super-resolution module " + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "inline_equation", + "content": "\\Phi_u" + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "text", + "content": " to generate final face image " + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "inline_equation", + "content": "\\mathbf{I}" + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "text", + "content": ". Adversarial loss is calculated between " + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "inline_equation", + "content": "\\mathbf{I}, \\mathbf{I}_l" + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "text", + "content": " and the real image to update " + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "inline_equation", + "content": "\\Phi_g, \\Phi_d" + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "inline_equation", + "content": "\\Phi_u" + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "text", + "content": ". Once trained, given a target image " + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_t" + }, + { + "bbox": [ + 296, + 173, + 530, + 314 + ], + "type": "text", + "content": ", face modeling can be achieved by image inversion [1,63,66]. As discussed in Sec. 1, this kind of approaches suffer from ambiguous appearance and large poses. In contrast, NPF tackles the problem by further disentangling a face using multi-image priors, which is introduced in the following." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 296, + 325, + 377, + 338 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 325, + 377, + 338 + ], + "spans": [ + { + "bbox": [ + 296, + 325, + 377, + 338 + ], + "type": "text", + "content": "4. Methodology" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 344, + 530, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 344, + 530, + 448 + ], + "spans": [ + { + "bbox": [ + 296, + 344, + 530, + 448 + ], + "type": "text", + "content": "In this section, we introduce the proposed Neural Protoface Field (NPF). Our aim is digging multi-image priors to improve the robustness of 3D face modeling. This is achieved by disentangling ID-consistent shape, expression and specific details from in-the-wild photo collection. The overview is shown in Fig. 2, where NPF contains identity-aware aggregation and deformation modeling to recover face prototypes (Sec. 4.1). After NPF is learned, the target face is reconstructed by a fitting procedure (Sec. 4.2)." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 456, + 471, + 468 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 456, + 471, + 468 + ], + "spans": [ + { + "bbox": [ + 296, + 456, + 471, + 468 + ], + "type": "text", + "content": "4.1. Neural Proto-face Field Learning" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 474, + 530, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 474, + 530, + 578 + ], + "spans": [ + { + "bbox": [ + 296, + 474, + 530, + 578 + ], + "type": "text", + "content": "Face appearance of an identity shares consistent cues due to the invariant face geometry, even under different in-the-wild environments. Such consistency has been widely used in previous methods by sharing the coefficients [20,69,79,90] or combining the UV space [100, 101]. Inspired by these efforts, we propose NPF to aggregate a common hybrid 3D representation from each photo collection, which models a face prototype with ID-consistent shape from reliable priors." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "text", + "content": "Identity-aware Aggregation: Our method starts from the style code " + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\mathbf{s} = \\{s^j\\}_{j=1}^M" + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "text", + "content": ", which well conditions the radiance field of tri-planes to render a face image " + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\mathbf{I}" + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "text", + "content": ". Theoretically, " + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\mathbf{s}" + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "text", + "content": " can be obtained from various methods [1,2,63,83]. Without specially statement, we use the most direct scheme proposed in [38] to optimize " + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\mathbf{s}" + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "text", + "content": " for real-image inversion. Given a photo collection " + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{I}_i\\}_{i=1}^N" + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "text", + "content": " of a same ID, we obtain " + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{s}_i\\}_{i=1}^N" + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "text", + "content": " in the " + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\mathcal{W}" + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "text", + "content": " space. Although finding optimal solution is difficult, " + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\mathbf{s}_i" + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "text", + "content": " still represents the face shape of " + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_i" + }, + { + "bbox": [ + 296, + 579, + 530, + 730 + ], + "type": "text", + "content": ", and such consistent cues should have much lower uncertainty than any other information within the image set. As a result, we propose an uncertainty-aware aggregation method inspired by [73] to extract the consistency. As illustrated in Fig. 2," + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "spans": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "text", + "content": "384" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 98, + 105, + 483, + 301 + ], + "blocks": [ + { + "bbox": [ + 98, + 105, + 483, + 301 + ], + "lines": [ + { + "bbox": [ + 98, + 105, + 483, + 301 + ], + "spans": [ + { + "bbox": [ + 98, + 105, + 483, + 301 + ], + "type": "image", + "image_path": "fcbff586a5927862544ee285a176adf69e4e9d88b45e1f6e0701c0a0ee6e4630.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "lines": [ + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "spans": [ + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "text", + "content": "Figure 2. Overview of NPF learning method. For an in-the-wild photo collection " + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{I}_i\\}_{i=1}^N" + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "text", + "content": ", we first obtain its style codes " + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{s}_i\\}_{i=1}^N" + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "text", + "content": " and uncertainties " + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "inline_equation", + "content": "\\{\\omega_i\\}_{i=1}^N" + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "text", + "content": ", then perform identity-aware aggregation to get the pivotal proto code " + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "inline_equation", + "content": "\\bar{s}" + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "text", + "content": ". To model deformation, we use 3DMM expression parameters " + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "inline_equation", + "content": "\\{\\beta_i\\}_{i=1}^N" + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "text", + "content": " as conditions, and train deformation networks " + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "inline_equation", + "content": "\\{\\Phi_{\\Delta}^j\\}_{j=1}^M" + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "text", + "content": " to predict deformation code " + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "inline_equation", + "content": "\\Delta \\mathbf{s}_i" + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "text", + "content": ". We also learn global face representation " + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "inline_equation", + "content": "\\bar{h}^j" + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "text", + "content": " at " + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 45, + 309, + 532, + 366 + ], + "type": "text", + "content": "-th stage to complement for personalized idiosyncrasies. In this way, the ID-consistent face is suitably blended on the manifold by Eqn. 5. Training by Eqn. 7, NPF well leverages the consistency within in-the-wild portrait sets." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 45, + 375, + 280, + 423 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 375, + 280, + 423 + ], + "spans": [ + { + "bbox": [ + 45, + 375, + 280, + 423 + ], + "type": "text", + "content": "we use an encoder " + }, + { + "bbox": [ + 45, + 375, + 280, + 423 + ], + "type": "inline_equation", + "content": "\\Phi_{a}" + }, + { + "bbox": [ + 45, + 375, + 280, + 423 + ], + "type": "text", + "content": " to get uncertainty " + }, + { + "bbox": [ + 45, + 375, + 280, + 423 + ], + "type": "inline_equation", + "content": "\\omega_{i} \\in \\mathbb{R}^{512}" + }, + { + "bbox": [ + 45, + 375, + 280, + 423 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 45, + 375, + 280, + 423 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_i" + }, + { + "bbox": [ + 45, + 375, + 280, + 423 + ], + "type": "text", + "content": ", and assume " + }, + { + "bbox": [ + 45, + 375, + 280, + 423 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_i" + }, + { + "bbox": [ + 45, + 375, + 280, + 423 + ], + "type": "text", + "content": " to have a multivariate Gaussian representation " + }, + { + "bbox": [ + 45, + 375, + 280, + 423 + ], + "type": "inline_equation", + "content": "\\mathcal{N}(\\mathbf{s}_i, \\omega_i)" + }, + { + "bbox": [ + 45, + 375, + 280, + 423 + ], + "type": "text", + "content": " in the latent space. Then the aggregation is performed as:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 101, + 431, + 279, + 464 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 101, + 431, + 279, + 464 + ], + "spans": [ + { + "bbox": [ + 101, + 431, + 279, + 464 + ], + "type": "interline_equation", + "content": "\\bar {\\mathbf {s}} = \\sum_ {i = 1} ^ {N} \\frac {\\omega^ {2}}{\\omega_ {i} ^ {2}} \\mathbf {s} _ {i}, \\quad \\frac {1}{\\omega^ {2}} = \\sum_ {i = 1} ^ {N} \\frac {1}{\\omega_ {i} ^ {2}}, \\tag {3}", + "image_path": "915581dc4e71b3fe76b065064bdbb955f0ae6eb97531c60d0e7ca2313312b629.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 45, + 473, + 279, + 589 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 473, + 279, + 589 + ], + "spans": [ + { + "bbox": [ + 45, + 473, + 279, + 589 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 45, + 473, + 279, + 589 + ], + "type": "inline_equation", + "content": "\\bar{\\mathbf{s}}" + }, + { + "bbox": [ + 45, + 473, + 279, + 589 + ], + "type": "text", + "content": " is the pivotal proto code similar to PTI [66] but from the photo collection instead of a single image. When forcing " + }, + { + "bbox": [ + 45, + 473, + 279, + 589 + ], + "type": "inline_equation", + "content": "\\bar{\\mathbf{s}}" + }, + { + "bbox": [ + 45, + 473, + 279, + 589 + ], + "type": "text", + "content": " to approximate each image, the consistency which has lower uncertainty within " + }, + { + "bbox": [ + 45, + 473, + 279, + 589 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{I}_i\\}_{i=1}^N" + }, + { + "bbox": [ + 45, + 473, + 279, + 589 + ], + "type": "text", + "content": " is remained, while other information is suppressed. In this way, the common facial shape and appearance is well aggregated into " + }, + { + "bbox": [ + 45, + 473, + 279, + 589 + ], + "type": "inline_equation", + "content": "\\bar{\\mathbf{s}}" + }, + { + "bbox": [ + 45, + 473, + 279, + 589 + ], + "type": "text", + "content": " as a face prototype. Further, compared with average pooling, the adaptively fusion algorithm of Eqn. 3 can be jointly learned with deformation to improve the modeling quality, which is introduced in the following." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 45, + 590, + 280, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 590, + 280, + 730 + ], + "spans": [ + { + "bbox": [ + 45, + 590, + 280, + 730 + ], + "type": "text", + "content": "Deformation Modeling: Although " + }, + { + "bbox": [ + 45, + 590, + 280, + 730 + ], + "type": "inline_equation", + "content": "\\bar{\\mathbf{s}}" + }, + { + "bbox": [ + 45, + 590, + 280, + 730 + ], + "type": "text", + "content": " has the common prior of an identity, it considers no facial deformation which is suppressed after the aggregation, resulting in a mean expression. Actually, modeling deformation is crucial for face reconstruction [10]. On the one hand, if the face prototype has a different expression from the target image, then recovering the gap will be difficult in the fitting procedure. On the other hand, face images have different appearance and silhouettes due to expression variation, and thus discarding the expression brings ambiguity or conflicts to the consistent geometry. Hence, we propose deformation modeling method to disentangle expressions of face prototype." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "spans": [ + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": "Previous works [56, 57] learn deformation during NeRF sampling, which depends on plentiful images captured in a constrained environment. As the data we use is in-the-wild with limited numbers, using such a deformation strategy is difficult. In contrast, we model expression by modifying style codes. As illustrated in Fig. 2, we first extracts the 3DMM expression parameters " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\{\\beta_i\\}_{i=1}^N" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\beta_i \\in \\mathbb{R}^{64}" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": " and camera poses " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\{p_i\\}_{i=1}^N" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": " from a pretrained 3D face network " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\Phi_{3d}" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": " [20], which provides guidance on tuning " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\bar{s}" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": ". Note that, " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\{\\beta_i\\}_{i=1}^N" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": " cannot well represent the personalized expression with unique idiosyncrasies, as it is unrelated to the identity. Hence, we extract global image representations from " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{I}_i\\}_{i=1}^N" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": " to complement for the characteristics. We use a similar encoder " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\Phi_e" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": " as PSP [63] to get the representation " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "h_i^j \\in \\mathbb{R}^{512}" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "j = 1, 2, \\dots, M" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": " and the corresponding uncertainty " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\mu_i^j" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_i" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": ". Then following Eqn. 3, we fuse the " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\{h_i^j\\}_{i=1}^N" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": " using " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\{\\mu_i^j\\}_{i=1}^N" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": " to get the global representation " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\bar{h}^j" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": " at " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": "-th stage. With the global " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\bar{h}^j" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": " of the collection, we propose deformation networks " + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "inline_equation", + "content": "\\{\\Phi_\\Delta^j\\}_{j=1}^M" + }, + { + "bbox": [ + 296, + 375, + 530, + 611 + ], + "type": "text", + "content": " to predict the deformation code on each stage, which can be defined as:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 374, + 615, + 530, + 630 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 374, + 615, + 530, + 630 + ], + "spans": [ + { + "bbox": [ + 374, + 615, + 530, + 630 + ], + "type": "interline_equation", + "content": "\\Delta s _ {i} ^ {j} = \\Phi_ {\\Delta} ^ {j} \\left(\\bar {h} ^ {j}, \\beta_ {i}\\right). \\tag {4}", + "image_path": "216a572d3ab2c54d73fddaa82c33f7ae2144c2c88634b1a0133d45d1f1e03c8d.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 296, + 635, + 530, + 671 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 635, + 530, + 671 + ], + "spans": [ + { + "bbox": [ + 296, + 635, + 530, + 671 + ], + "type": "text", + "content": "Denoting " + }, + { + "bbox": [ + 296, + 635, + 530, + 671 + ], + "type": "inline_equation", + "content": "\\{\\Delta s_i^j\\}_{j = 1}^M" + }, + { + "bbox": [ + 296, + 635, + 530, + 671 + ], + "type": "text", + "content": " as " + }, + { + "bbox": [ + 296, + 635, + 530, + 671 + ], + "type": "inline_equation", + "content": "\\Delta \\mathbf{s}_i" + }, + { + "bbox": [ + 296, + 635, + 530, + 671 + ], + "type": "text", + "content": " for simplification, the personalized deformation of " + }, + { + "bbox": [ + 296, + 635, + 530, + 671 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_i" + }, + { + "bbox": [ + 296, + 635, + 530, + 671 + ], + "type": "text", + "content": " can be achieved via modifying the pivotal proto code:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 383, + 676, + 530, + 688 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 383, + 676, + 530, + 688 + ], + "spans": [ + { + "bbox": [ + 383, + 676, + 530, + 688 + ], + "type": "interline_equation", + "content": "\\hat {\\mathbf {s}} _ {i} = \\bar {\\mathbf {s}} + \\Delta \\mathbf {s} _ {i}. \\tag {5}", + "image_path": "9c746134b80b2969f243d29cebbe88f8a32a81d3424fd1241a21cb14620d2402.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "text", + "content": "Finally, the face prototype " + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{I}}_i" + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "text", + "content": " can be obtained from " + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{s}}_i" + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "inline_equation", + "content": "p_i" + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "text", + "content": " using pretrained EG3D networks. In this way, " + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{I}}_i" + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "text", + "content": " suffers from less appearance conflicts to " + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_i" + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "text", + "content": ". Further, by jointly" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 281, + 746, + 296, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 281, + 746, + 296, + 756 + ], + "spans": [ + { + "bbox": [ + 281, + 746, + 296, + 756 + ], + "type": "text", + "content": "385" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 45, + 106, + 279, + 141 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 106, + 279, + 141 + ], + "spans": [ + { + "bbox": [ + 45, + 106, + 279, + 141 + ], + "type": "text", + "content": "learning of " + }, + { + "bbox": [ + 45, + 106, + 279, + 141 + ], + "type": "inline_equation", + "content": "\\Delta \\mathbf{s}_i" + }, + { + "bbox": [ + 45, + 106, + 279, + 141 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 45, + 106, + 279, + 141 + ], + "type": "inline_equation", + "content": "\\bar{\\mathbf{s}}" + }, + { + "bbox": [ + 45, + 106, + 279, + 141 + ], + "type": "text", + "content": ", the consistent face shape within a collection is adaptively blended against non-rigid ambiguity, and have more precise geometry and texture." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "spans": [ + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "text", + "content": "Training Loss: Note that we do not require " + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{I}}_i" + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "text", + "content": " to be exactly the same as " + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_i" + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "text", + "content": ", but have a proper expression and a consistent blended shape among " + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{I}_i\\}_{i=1}^N" + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "text", + "content": ". To achieve such an aim, we propose a series of consistency losses to learn " + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "inline_equation", + "content": "\\Phi_a, \\Phi_e" + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "inline_equation", + "content": "\\Phi_\\Delta" + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "text", + "content": ". The reconstruction loss is defined as " + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{re} = \\mathcal{L}_{lpips}(\\hat{\\mathbf{I}}_i - \\mathbf{I}_i) + \\mathcal{L}_{L2}(\\hat{\\mathbf{I}}_i - \\mathbf{I}_i)" + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{lpips}" + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "text", + "content": " is the perceptual loss [98] and " + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{L2}" + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "text", + "content": " is the L2 distance. To constrain the expression of " + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{I}}_i" + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "text", + "content": ", we propose an expression consistency loss using pretrained " + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "inline_equation", + "content": "\\Phi_{3d}" + }, + { + "bbox": [ + 45, + 141, + 280, + 248 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 101, + 256, + 279, + 279 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 101, + 256, + 279, + 279 + ], + "spans": [ + { + "bbox": [ + 101, + 256, + 279, + 279 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {c} \\left(\\hat {\\mathbf {I}} _ {i}, \\beta_ {i}\\right) = \\frac {1}{\\Omega} \\left| \\Phi_ {3 d} \\left(\\hat {\\mathbf {I}} _ {i}\\right) - \\beta_ {i} \\right|, \\tag {6}", + "image_path": "e094e4cdaa8738c9c2b39b61ff6d8f5f073817f812009a9279c9a24b4d1f6206.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 287, + 279, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 287, + 279, + 324 + ], + "spans": [ + { + "bbox": [ + 46, + 287, + 279, + 324 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 287, + 279, + 324 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 46, + 287, + 279, + 324 + ], + "type": "text", + "content": " is the normalization factor. To prevent distortion, we limit the deformation code via a regularization as " + }, + { + "bbox": [ + 46, + 287, + 279, + 324 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_d = \\|\\Delta \\mathbf{s}_i\\|_2^2" + }, + { + "bbox": [ + 46, + 287, + 279, + 324 + ], + "type": "text", + "content": ". The final loss function is denoted as:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 51, + 333, + 279, + 365 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 333, + 279, + 365 + ], + "spans": [ + { + "bbox": [ + 51, + 333, + 279, + 365 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {p r o t o} = \\sum_ {i = 1} ^ {N} \\mathcal {L} _ {r e} \\left(\\hat {\\mathbf {I}} _ {i}, \\mathbf {I} _ {i}\\right) + \\lambda_ {c} \\mathcal {L} _ {c} \\left(\\hat {\\mathbf {I}} _ {i}, \\beta_ {i}\\right) + \\lambda_ {d} \\mathcal {L} _ {d} \\left(\\mathbf {s} _ {i}\\right), \\tag {7}", + "image_path": "dd6e5af7fb06d52d38bad5a7a541d8aacede860f47973511530ebe6b147c3a0d.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 45, + 374, + 279, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 374, + 279, + 433 + ], + "spans": [ + { + "bbox": [ + 45, + 374, + 279, + 433 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 45, + 374, + 279, + 433 + ], + "type": "inline_equation", + "content": "\\lambda_c, \\lambda_d" + }, + { + "bbox": [ + 45, + 374, + 279, + 433 + ], + "type": "text", + "content": " are the weights. When using " + }, + { + "bbox": [ + 45, + 374, + 279, + 433 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{proto}" + }, + { + "bbox": [ + 45, + 374, + 279, + 433 + ], + "type": "text", + "content": " to update " + }, + { + "bbox": [ + 45, + 374, + 279, + 433 + ], + "type": "inline_equation", + "content": "\\Phi_a, \\Phi_e" + }, + { + "bbox": [ + 45, + 374, + 279, + 433 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 45, + 374, + 279, + 433 + ], + "type": "inline_equation", + "content": "\\Phi_\\Delta" + }, + { + "bbox": [ + 45, + 374, + 279, + 433 + ], + "type": "text", + "content": ", the networks of EG3D are all frozen. In this way, we learn neural proto-face field from in-the-wild photo collections, and disentangle identities and expressions of the face prototypes." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 45, + 442, + 208, + 455 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 442, + 208, + 455 + ], + "spans": [ + { + "bbox": [ + 45, + 442, + 208, + 455 + ], + "type": "text", + "content": "4.2. Neural Proto-face Field Fitting" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 45, + 461, + 279, + 589 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 461, + 279, + 589 + ], + "spans": [ + { + "bbox": [ + 45, + 461, + 279, + 589 + ], + "type": "text", + "content": "Once NPF is learned, we get a 3D hybrid initialization containing consistent cues of the photo collection, constrained by reliable multi-image priors. To model the precise 3D faces, scene-specific details need to be recovered. Such an aim can be achieved by tuning the generator using pivotal tuning (PTI) algorithm [66]. However, PTI has obvious drawbacks of two aspects: 1) its pivotal code has no multi-view prior; 2) its tuning procedure easily overfits to a single image. The former one has been addressed by NPF. For the latter, we propose a consistent fitting method to overcome the monocular ambiguity." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "spans": [ + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "text", + "content": "Multi-image Warm-Up: Fitting is achieved by tuning the generator " + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\Phi_g" + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "text", + "content": " of EG3D while freezing other networks. In the early stage of fitting, we need to prevent the generator converging to a local optimum that provides distortions due to challenging conditions. In contrast to fitting a single image, we propose a multi-image warm-up method to fit " + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\Phi_g" + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "text", + "content": " to the photo collection " + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{I}_i\\}_{i=1}^N" + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "text", + "content": ". We use " + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{re}" + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "text", + "content": " to approach the target image set. To guarantee the geometry consistency at unseen views, we randomly sample a camera pose " + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "inline_equation", + "content": "p'" + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "text", + "content": " at every step, and use the volume renderer of EG3D to predict the corresponding normal maps " + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{n}}_i" + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\dot{\\mathbf{n}}_i" + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "text", + "content": " from the original/new weights of " + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\Phi_g" + }, + { + "bbox": [ + 45, + 590, + 279, + 730 + ], + "type": "text", + "content": ", respectively. Then we calculate the" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 299, + 105, + 530, + 236 + ], + "blocks": [ + { + "bbox": [ + 299, + 105, + 530, + 236 + ], + "lines": [ + { + "bbox": [ + 299, + 105, + 530, + 236 + ], + "spans": [ + { + "bbox": [ + 299, + 105, + 530, + 236 + ], + "type": "image", + "image_path": "31aff62a3ce8861dc6f1e132ca7c1d76de02ec397e472cb4504e274d70c78d06.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 296, + 244, + 530, + 266 + ], + "lines": [ + { + "bbox": [ + 296, + 244, + 530, + 266 + ], + "spans": [ + { + "bbox": [ + 296, + 244, + 530, + 266 + ], + "type": "text", + "content": "Figure 3. The proposed consistency losses of NPF fitting. (a) Multi-image Warm-up; (b) Robust Target-image Fitting." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 296, + 277, + 530, + 314 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 277, + 530, + 314 + ], + "spans": [ + { + "bbox": [ + 296, + 277, + 530, + 314 + ], + "type": "text", + "content": "geometry consistency loss " + }, + { + "bbox": [ + 296, + 277, + 530, + 314 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{gc}(\\dot{\\mathbf{n}}_i,\\hat{\\mathbf{n}}_i) = \\| \\dot{\\mathbf{n}}_i - \\hat{\\mathbf{n}}_i\\| _2^2" + }, + { + "bbox": [ + 296, + 277, + 530, + 314 + ], + "type": "text", + "content": " to constrain the robustness of facial geometry. The final objective of multi-image warm-up is denoted as:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 328, + 322, + 530, + 355 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 328, + 322, + 530, + 355 + ], + "spans": [ + { + "bbox": [ + 328, + 322, + 530, + 355 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {w a r m}} = \\sum_ {i = 1} ^ {N} \\mathcal {L} _ {r e} \\left(\\dot {\\mathbf {I}} _ {i}, \\mathbf {I} _ {i}\\right) + \\lambda_ {g} \\mathcal {L} _ {g c} \\left(\\dot {\\mathbf {n}} _ {i}, \\hat {\\mathbf {n}} _ {i}\\right), \\tag {8}", + "image_path": "6122cf7d4e7c66c6bdec584bdf2e2027c02192ed17c8836ca56383b3a872b99b.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 364, + 530, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 364, + 530, + 399 + ], + "spans": [ + { + "bbox": [ + 296, + 364, + 530, + 399 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 296, + 364, + 530, + 399 + ], + "type": "inline_equation", + "content": "\\dot{\\mathbf{I}}_i" + }, + { + "bbox": [ + 296, + 364, + 530, + 399 + ], + "type": "text", + "content": " is the reconstructed image. Using " + }, + { + "bbox": [ + 296, + 364, + 530, + 399 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{warm}" + }, + { + "bbox": [ + 296, + 364, + 530, + 399 + ], + "type": "text", + "content": ", we make " + }, + { + "bbox": [ + 296, + 364, + 530, + 399 + ], + "type": "inline_equation", + "content": "\\Phi_g" + }, + { + "bbox": [ + 296, + 364, + 530, + 399 + ], + "type": "text", + "content": " initialize to a solution near the optimum of each image, and suppress distortions of unseen poses." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "spans": [ + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "text", + "content": "Robust Target-image Fitting: After the multi-image warm-up, we fit " + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "inline_equation", + "content": "\\Phi_g" + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "text", + "content": " to the target image " + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_i" + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "text", + "content": " and model specific details. Besides the reconstruction loss " + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{re}" + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "text", + "content": ", we constrain the recovered appearance of unseen poses. Similar to " + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{gc}" + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "text", + "content": " we randomly sample a camera pose " + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "inline_equation", + "content": "p'" + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "text", + "content": " at each step and render an image " + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "inline_equation", + "content": "\\dot{\\mathbf{I}}_i'" + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "text", + "content": ", then encourage " + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "inline_equation", + "content": "\\dot{\\mathbf{I}}_i'" + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "text", + "content": " and the target " + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_i" + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "text", + "content": " to have similar representations via " + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{ac} = \\|\\Phi_e(\\dot{\\mathbf{I}}_i') - \\Phi_e(\\mathbf{I}_i)\\|_2^2" + }, + { + "bbox": [ + 296, + 400, + 530, + 494 + ], + "type": "text", + "content": ". The final loss of robust target-image fitting is:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 351, + 502, + 530, + 516 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 351, + 502, + 530, + 516 + ], + "spans": [ + { + "bbox": [ + 351, + 502, + 530, + 516 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {t a r g e t}} = \\mathcal {L} _ {\\text {r e}} \\left(\\mathbf {I} _ {i}, \\mathbf {I} _ {i}\\right) + \\lambda_ {a} \\mathcal {L} _ {\\text {a c}}. \\tag {9}", + "image_path": "757f206ee6deb8c30b424585fe9682e77798ad080c5841d6604e8c02b5319282.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 296, + 524, + 530, + 548 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 524, + 530, + 548 + ], + "spans": [ + { + "bbox": [ + 296, + 524, + 530, + 548 + ], + "type": "text", + "content": "In this way, we improve the robustness of the scene-specific details recovering." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 297, + 559, + 371, + 572 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 559, + 371, + 572 + ], + "spans": [ + { + "bbox": [ + 297, + 559, + 371, + 572 + ], + "type": "text", + "content": "5. Experiment" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "text", + "content": "Dataset: We first train our " + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\Phi_{a},\\Phi_{e}" + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\Phi_{\\Delta}" + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "text", + "content": " on CelebA [45] and CASIA-WebFace [95], then fine-tune them on a high-resolution dataset CelebAMask-HQ [41]. Following [100], we organize CelebA and CASIAWebFace using ID-labels and keep each identity with at least 6 photos. This provides " + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "inline_equation", + "content": "600\\mathrm{K}" + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "text", + "content": " images with " + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "inline_equation", + "content": "16\\mathrm{K}" + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "text", + "content": " identities. We select images of " + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "inline_equation", + "content": "12\\mathrm{K} / 2\\mathrm{K} / 2\\mathrm{K}" + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "text", + "content": " identities as train/val/test set. For CelebAMask-HQ, we organize it into " + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "inline_equation", + "content": "24\\mathrm{K}" + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "text", + "content": " different identities using ground truth ID-labels, and randomly select " + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "inline_equation", + "content": "20\\mathrm{K} / 1\\mathrm{K} / 3\\mathrm{K}" + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "text", + "content": " identities as train/val/test set. Following [4, 91, 100], we perform testing on MICC [6], Photoface [96] and FG3D [26] dataset. Photoface dataset contains " + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "inline_equation", + "content": "12\\mathrm{K}" + }, + { + "bbox": [ + 296, + 578, + 530, + 730 + ], + "type": "text", + "content": " images of 453 people with face/normal image" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "spans": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "text", + "content": "386" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 54, + 103, + 271, + 190 + ], + "blocks": [ + { + "bbox": [ + 54, + 103, + 271, + 190 + ], + "lines": [ + { + "bbox": [ + 54, + 103, + 271, + 190 + ], + "spans": [ + { + "bbox": [ + 54, + 103, + 271, + 190 + ], + "type": "table", + "html": "
No.methodp2p (mm) ↓MAD (deg.) ↓IDE ↓
(1)Ours1.7711.870.257
(2)Ours face prototype1.9612.390.307
(3)w/o uncertainty = avg pooling1.8712.130.298
(4)w/o deformation modeling2.2512.900.401
(5)w/o Φe2.0112.460.350
(6)w/o multi-image warm-up1.8412.300.301
(7)w/o Lgc1.8012.150.274
(8)w/o Lac1.7911.870.262
(9)single-image PTI [66]2.3512.100.482
", + "image_path": "6d281a27d26d2c8fd0537a3a50bfc7bfd5db942c004d060facd357f4a195e45e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 57, + 198, + 267, + 210 + ], + "lines": [ + { + "bbox": [ + 57, + 198, + 267, + 210 + ], + "spans": [ + { + "bbox": [ + 57, + 198, + 267, + 210 + ], + "type": "text", + "content": "Table 2. Comparison with Different Baselines and Settings." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 45, + 215, + 279, + 238 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 215, + 279, + 238 + ], + "spans": [ + { + "bbox": [ + 45, + 215, + 279, + 238 + ], + "type": "text", + "content": "pairs, and we follow the protocol of [4,70] for testing. More details are introduced in Appendix." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "spans": [ + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "text", + "content": "Implementation Details: We build " + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "inline_equation", + "content": "\\Phi_{a}" + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "text", + "content": " using a similar architecture to the encoder of [91, 100]. The deformation network " + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "inline_equation", + "content": "\\Phi_{\\Delta}^{j}" + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "text", + "content": "-th stage contains 3 MLP layers to encode " + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "inline_equation", + "content": "\\beta_{i}" + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "text", + "content": ", and another 3 MLP layers to predict " + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "inline_equation", + "content": "\\Delta s_{i}^{j}" + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "text", + "content": " from the combination of " + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "inline_equation", + "content": "\\beta_{i}, \\bar{\\mathbf{h}}^{j}" + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "text", + "content": ". We set " + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "inline_equation", + "content": "\\lambda_{c} = 1, \\lambda_{d} = 1e - 3, \\lambda_{g} = \\lambda_{a} = 0.2" + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "text", + "content": " for the losses. Following [13], the output images of the model are of " + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "text", + "content": ", and the meshes are extracted using Marching Cubes [47] with a same size. During training, we randomly set " + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 45, + 239, + 279, + 426 + ], + "type": "text", + "content": " from 1 to 6 to adapt different sizes of a photo collection. We train the model with batch size of 4 on CelebA and CASIA-WebFace for 15 epochs, and fine-tune it on CelebAMaskHQ for 30 epochs. During fitting, the multi-image warm-up lasts 100 steps and robust target-image fitting lasts another 100 steps. We use Adam [40] as the optimizer, and set the learning rate as 0.0001 on a V-100 GPU. More details are in the Appendix." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 45, + 426, + 280, + 577 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 426, + 280, + 577 + ], + "spans": [ + { + "bbox": [ + 45, + 426, + 280, + 577 + ], + "type": "text", + "content": "Evaluation Protocol: Without special statements, we use 4-image results to compare with other methods. To evaluate the modeled texture, we calculate Structural Similarity Index (SSIM) [88], Cosine-similarity of Arcface [17] representation and RMSE of identity parameters (IDE) of 3D face network [20] between the original high-quality images and rendered ones. We render images under different poses to measure the robustness. To evaluate the geometry, we rigid-align predictions to the ground truth via ICP using hand-crafted key points. Mean Angle Deviation (MAD) of normal maps and point-to-plane " + }, + { + "bbox": [ + 45, + 426, + 280, + 577 + ], + "type": "inline_equation", + "content": "(\\mathrm{p2p})" + }, + { + "bbox": [ + 45, + 426, + 280, + 577 + ], + "type": "text", + "content": " distance between the aligned prediction and ground truth are utilized as metrics. Please see Appendix for more details." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 584, + 138, + 596 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 584, + 138, + 596 + ], + "spans": [ + { + "bbox": [ + 46, + 584, + 138, + 596 + ], + "type": "text", + "content": "5.1. Ablation Study" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 45, + 601, + 279, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 601, + 279, + 730 + ], + "spans": [ + { + "bbox": [ + 45, + 601, + 279, + 730 + ], + "type": "text", + "content": "Comparison with Baselines: We first analyse different settings of NPF in Table 2. We calculate NME, MAD and IDE metrics on MICC, Photoface and CelebaMaskHQ dataset, respectively. In row (1), our full method obtains the best performance in all metrics, approving the effectiveness of each proposed component. In row (2), we observe that the face prototype gets satisfactory, or even better results than several baselines, which reveals that NPF well integrates consistency from photo collection. In row (3), we replace the adaptive uncertainty with average pooling to get the proto code " + }, + { + "bbox": [ + 45, + 601, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\overline{\\mathbf{s}}" + }, + { + "bbox": [ + 45, + 601, + 279, + 730 + ], + "type": "text", + "content": ", and the performance is reduced to" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 299, + 103, + 532, + 236 + ], + "blocks": [ + { + "bbox": [ + 299, + 103, + 532, + 236 + ], + "lines": [ + { + "bbox": [ + 299, + 103, + 532, + 236 + ], + "spans": [ + { + "bbox": [ + 299, + 103, + 532, + 236 + ], + "type": "image", + "image_path": "b1c69de88b51beec4d17f0a3153ea50cd43427602159b3b02c7946ed1eb46573.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 296, + 245, + 531, + 279 + ], + "lines": [ + { + "bbox": [ + 296, + 245, + 531, + 279 + ], + "spans": [ + { + "bbox": [ + 296, + 245, + 531, + 279 + ], + "type": "text", + "content": "Figure 4. Analysis on the deformation modeling. (a) How the deformation modeling improves the details and shape accuracy. (b) How the settings of deformation modules influence the results." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 296, + 294, + 531, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 294, + 531, + 445 + ], + "spans": [ + { + "bbox": [ + 296, + 294, + 531, + 445 + ], + "type": "text", + "content": "some extent. This reveals our identity-aware aggregation well contributes on digging consistent cues. In row (4), the significant accuracy drop demonstrates the effectiveness of deformation modeling on avoiding expression-aware shape ambiguity. Row (5) also proves that the global face representation from " + }, + { + "bbox": [ + 296, + 294, + 531, + 445 + ], + "type": "inline_equation", + "content": "\\Phi_e" + }, + { + "bbox": [ + 296, + 294, + 531, + 445 + ], + "type": "text", + "content": " improves the quality of deformation modeling. Rows (6)-(8) reveal that the multi-image warm-up and consistency losses also improve the robustness. Finally, row (9) is the state-of-the-art single-image baseline, where it achieves even worse results than face prototype in IDE. This is due to the distortion caused by overfitting or ambiguity under challenging conditions of pose, lighting and occlusion." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 296, + 448, + 530, + 669 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 448, + 530, + 669 + ], + "spans": [ + { + "bbox": [ + 296, + 448, + 530, + 669 + ], + "type": "text", + "content": "Analysis on Deformation Modeling: We analyse how the proposed deformation modeling method improves the performance. In Fig. 4-(a), we highlight the differences between this two settings. Without deformation modeling, fusing the style codes yields " + }, + { + "bbox": [ + 296, + 448, + 530, + 669 + ], + "type": "inline_equation", + "content": "\\bar{\\mathbf{s}}" + }, + { + "bbox": [ + 296, + 448, + 530, + 669 + ], + "type": "text", + "content": " to represent a mean expression on the face prototype, which is different from the target face. Hence, it brings difficulty to recover the target's expression during NPF fitting. Further, as the face shapes with various expressions show different appearance and silhouettes on the images, ignoring the deformation brings ambiguity when digging the multi-image consistency. Hence, such a model produces improper shapes and details. In contrast, modeling the deformation recovers the target's expression, the corresponding shapes and appearance effects. In Fig. 4-(b), we show the influence of different settings. Without deformation modeling, the target expression cannot be recovered. Without the global representation " + }, + { + "bbox": [ + 296, + 448, + 530, + 669 + ], + "type": "inline_equation", + "content": "\\bar{\\mathbf{h}}" + }, + { + "bbox": [ + 296, + 448, + 530, + 669 + ], + "type": "text", + "content": ", the expression effect is produced but not accurate enough. Finally, our full method recovers precise target deformation." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 671, + 530, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 671, + 530, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 671, + 530, + 730 + ], + "type": "text", + "content": "Analysis on Multi-image Consistency: We analyse how our method leverages the multi-image priors from inthe-wild photo collections. In Fig. 5-(a), we show the influence of the size " + }, + { + "bbox": [ + 296, + 671, + 530, + 730 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 296, + 671, + 530, + 730 + ], + "type": "text", + "content": " of photo collections. Compared with the single-image setting (same as PTI [66]), " + }, + { + "bbox": [ + 296, + 671, + 530, + 730 + ], + "type": "inline_equation", + "content": "N = 2" + }, + { + "bbox": [ + 296, + 671, + 530, + 730 + ], + "type": "text", + "content": " sig" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "spans": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "text", + "content": "387" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 106, + 277, + 244 + ], + "blocks": [ + { + "bbox": [ + 48, + 106, + 277, + 244 + ], + "lines": [ + { + "bbox": [ + 48, + 106, + 277, + 244 + ], + "spans": [ + { + "bbox": [ + 48, + 106, + 277, + 244 + ], + "type": "image", + "image_path": "08e04bf735f0b0c504255f29a0d64e9b2838bfa959ee6da5a77b76b447827258.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 259, + 279, + 293 + ], + "lines": [ + { + "bbox": [ + 45, + 259, + 279, + 293 + ], + "spans": [ + { + "bbox": [ + 45, + 259, + 279, + 293 + ], + "type": "text", + "content": "Figure 5. Analysis on the multi-image consistency. (a) (b) Performance under different photo collection size " + }, + { + "bbox": [ + 45, + 259, + 279, + 293 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 45, + 259, + 279, + 293 + ], + "type": "text", + "content": "; (c) The effectiveness of multi-image warm-up. Red box signs the target image." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 67, + 297, + 258, + 411 + ], + "blocks": [ + { + "bbox": [ + 67, + 297, + 258, + 411 + ], + "lines": [ + { + "bbox": [ + 67, + 297, + 258, + 411 + ], + "spans": [ + { + "bbox": [ + 67, + 297, + 258, + 411 + ], + "type": "table", + "html": "
MICCFG3D
Method3DMMindooroutdoorLQHQ
Extreme3D [85]✓3.663.703.493.58
PRN [25]✓2.322.472.382.06
RingNet [69]✓1.932.022.082.02
3DDFA v2 [33]✓1.881.962.101.91
D3DFR [20] (mv)✓1.661.691.901.95
MGCnet [71]✓1.721.781.951.90
DECA [24]✓1.691.701.911.89
Cross-modal [4]×2.392.332.302.04
Unsup3D [91]×2.482.522.412.29
LAP [100] (mv)×2.322.252.182.16
PhyDIR [101] (mv)×2.292.202.252.09
Ours×1.751.812.151.98
", + "image_path": "e4c2960563a2047f5b023805eaae78e34b17487d1999fc997a1c1b0375f4fecb.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 45, + 443, + 279, + 572 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 443, + 279, + 572 + ], + "spans": [ + { + "bbox": [ + 45, + 443, + 279, + 572 + ], + "type": "text", + "content": "nificantly improves the accuracy, revealing that even two images still provide reliable priors. With " + }, + { + "bbox": [ + 45, + 443, + 279, + 572 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 45, + 443, + 279, + 572 + ], + "type": "text", + "content": " increasing, the accuracy gets better. Fig. 5-(b) illustrates the qualitative performance. The single-image result has obvious distortion of facial shape due to the target's large pose and challenging appearance. Increasing " + }, + { + "bbox": [ + 45, + 443, + 279, + 572 + ], + "type": "inline_equation", + "content": "N = 3" + }, + { + "bbox": [ + 45, + 443, + 279, + 572 + ], + "type": "text", + "content": " obviously suppresses the degradation, and the setting " + }, + { + "bbox": [ + 45, + 443, + 279, + 572 + ], + "type": "inline_equation", + "content": "N = 6" + }, + { + "bbox": [ + 45, + 443, + 279, + 572 + ], + "type": "text", + "content": " obtains best performance with proper geometry. In Fig. 5-(c), we observe that during fitting, the proposed multi-image warm-up suppresses the overfitting on occlusion or shadows, boosted by the consistency from other images." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 45, + 582, + 227, + 595 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 582, + 227, + 595 + ], + "spans": [ + { + "bbox": [ + 45, + 582, + 227, + 595 + ], + "type": "text", + "content": "6. Comparison with State-of-the-art" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 45, + 601, + 280, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 601, + 280, + 730 + ], + "spans": [ + { + "bbox": [ + 45, + 601, + 280, + 730 + ], + "type": "text", + "content": "Evaluation on Geometry: We first analyse the modeled geometry on MICC and FG3D dataset. We use the provided indoor/outdoor videos of MICC, low-quality (LQ) and high-quality (HQ) image sets to optimize the trained NPF. The geometry for each identity is obtained from 4 images, and calculated the average results. The comparisons are shown in Table 3, where we divide the methods into 3DMM and non-3DMM group. The tag 'mv' means the method uses multi-view input. We observe that our method obtains the best performance among non-3DMM approaches, but higher errors than D3DFR, MGCnet and" + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 318, + 104, + 510, + 216 + ], + "blocks": [ + { + "bbox": [ + 66, + 421, + 258, + 432 + ], + "lines": [ + { + "bbox": [ + 66, + 421, + 258, + 432 + ], + "spans": [ + { + "bbox": [ + 66, + 421, + 258, + 432 + ], + "type": "text", + "content": "Table 3. Point-to-plane distance (mm) on benchmarks." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 318, + 104, + 510, + 216 + ], + "lines": [ + { + "bbox": [ + 318, + 104, + 510, + 216 + ], + "spans": [ + { + "bbox": [ + 318, + 104, + 510, + 216 + ], + "type": "table", + "html": "
MAD↓<20°↑<25°↑<30°↑
SfSNet [70]25.5±9.343.6%57.5%68.7%
PRN [25]24.8±6.843.1%62.9%74.1%
DF2Net [97] (GT)24.3±5.742.2%62.7%74.5%
D3DFR [20] (mv)23.5±6.146.1%61.8%73.3%
Cross-Modal [4] (GT)22.8±6.549.0%62.9%74.1%
DECA [24]22.5±5.348.7%62.3%73.7%
LAP [100] (mv)23.0±5.148.2%63.1%74.9%
PhyDIR [101] (mv)22.7±4.349.2%63.4%75.3%
Ours prototype23.7±4.745.6%61.4%73.0%
SfSNet-ft [70]12.8±5.483.7%90.8%94.5%
Cross-Modal-ft [4] (GT)12.0±5.385.2%92.0%95.6%
LAP-ft [100] (mv)12.3±4.584.9%92.4%96.3%
PhyDir-ft [101] (mv)12.0±4.985.3%92.7%96.9%
Ours11.87±5.285.9%93.0%96.9%
", + "image_path": "2314b41d0c74441fdbc141b89ff0f65f00c2d42b9964f44a3c67ca193f305867.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 315, + 226, + 511, + 235 + ], + "lines": [ + { + "bbox": [ + 315, + 226, + 511, + 235 + ], + "spans": [ + { + "bbox": [ + 315, + 226, + 511, + 235 + ], + "type": "text", + "content": "Table 4. Facial normal evaluation on Photoface dataset." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 300, + 242, + 529, + 490 + ], + "blocks": [ + { + "bbox": [ + 300, + 242, + 529, + 490 + ], + "lines": [ + { + "bbox": [ + 300, + 242, + 529, + 490 + ], + "spans": [ + { + "bbox": [ + 300, + 242, + 529, + 490 + ], + "type": "image", + "image_path": "71eb93256fa96a9952eda7706a7f42d2492b68a4e91c7cc936f866cc4134197b.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 302, + 505, + 524, + 516 + ], + "lines": [ + { + "bbox": [ + 302, + 505, + 524, + 516 + ], + "spans": [ + { + "bbox": [ + 302, + 505, + 524, + 516 + ], + "type": "text", + "content": "Figure 6. Qualitative comparison on predicted facial geometry." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 296, + 531, + 530, + 682 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 531, + 530, + 682 + ], + "spans": [ + { + "bbox": [ + 296, + 531, + 530, + 682 + ], + "type": "text", + "content": "DECA. Note that, 3DMM is a highly reliable face shape assumption made from real 3D ground truth, and thus it fundamentally boosts the models' accuracy. Even though, our method still outperforms Extreme3D, PRN, RingNet and competes with 3DDFA v2. We further analyse the modeled facial normal on Photoface dataset in Table 4, where '-ft' means finetuning. Our prototype has already outperforms several methods. After finetuning, our method obtains best performance. Finally, we illustrate qualitative results in Fig. 6, where our method provides finer details. Compared with the EG3D [13] + PTI [66] baseline, our method is much more robust against distortion from large pose, extreme lighting and appearance." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 683, + 531, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 683, + 531, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 683, + 531, + 730 + ], + "type": "text", + "content": "Evaluation on Texture: We then evaluate the modeled texture on CelebAMaskHQ dataset. Besides the reconstructed image, we render images along the yaw angle in 3 ranges: " + }, + { + "bbox": [ + 296, + 683, + 531, + 730 + ], + "type": "inline_equation", + "content": "[0^{\\circ}, \\pm 20^{\\circ}]" + }, + { + "bbox": [ + 296, + 683, + 531, + 730 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 296, + 683, + 531, + 730 + ], + "type": "inline_equation", + "content": "[\\pm 20^{\\circ}, \\pm 40^{\\circ}]" + }, + { + "bbox": [ + 296, + 683, + 531, + 730 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 683, + 531, + 730 + ], + "type": "inline_equation", + "content": "[\\pm 40^{\\circ}, \\pm 60^{\\circ}]" + }, + { + "bbox": [ + 296, + 683, + 531, + 730 + ], + "type": "text", + "content": "," + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "spans": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "text", + "content": "388" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 71, + 103, + 506, + 310 + ], + "blocks": [ + { + "bbox": [ + 71, + 103, + 506, + 310 + ], + "lines": [ + { + "bbox": [ + 71, + 103, + 506, + 310 + ], + "spans": [ + { + "bbox": [ + 71, + 103, + 506, + 310 + ], + "type": "image", + "image_path": "fd288c6b4d6489d320b9c78058194b1d8fec0c61755ac02943b9c2242470df8b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 317, + 531, + 340 + ], + "lines": [ + { + "bbox": [ + 45, + 317, + 531, + 340 + ], + "spans": [ + { + "bbox": [ + 45, + 317, + 531, + 340 + ], + "type": "text", + "content": "Figure 7. Qualitative comparison with EG3D [13] + PTI [66] baseline, PhyDIR [101], HeadNeRF [34] and DFG [18] on the robustness of rotation and expression editing. Our method produces robust and photo-realistic rendering results. Zooming in to see the details." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 55, + 349, + 270, + 420 + ], + "blocks": [ + { + "bbox": [ + 55, + 349, + 270, + 420 + ], + "lines": [ + { + "bbox": [ + 55, + 349, + 270, + 420 + ], + "spans": [ + { + "bbox": [ + 55, + 349, + 270, + 420 + ], + "type": "table", + "html": "
Method\\( \\left\\lbrack {{0}^{ \\circ }, \\pm {20}^{ \\circ }}\\right\\rbrack \\)\\( \\left\\lbrack {\\pm {20}^{ \\circ }, \\pm {40}^{ \\circ }}\\right\\rbrack \\)\\( \\left\\lbrack {\\pm {40}^{ \\circ }, \\pm {60}^{ \\circ }}\\right\\rbrack \\)
Cos-simIDECos-simIDECos-simIDESSIM
DFG [18]0.7320.3420.6440.3850.5810.4300.751
Unsup3D [91]0.7010.3180.6770.3340.6320.3870.514
LAP [100] (mv)0.7400.2930.6850.3010.6580.3590.623
PhyDIR [101] (mv)0.8260.2670.7920.2940.7450.3280.880
MoFANeRF [108]0.6930.3370.6660.3600.6400.3950.523
HeadNeRF [34]0.8300.2400.8010.2680.6710.3640.823
EG3D + PTI0.8350.2480.7830.2790.7090.3470.919
Ours0.8420.2330.8060.2510.7500.2870.921
", + "image_path": "7febf5a12d5f07116cfe586231d5f87916ea767f2c804aa50b1d12b42283dcf1.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 60, + 427, + 264, + 438 + ], + "lines": [ + { + "bbox": [ + 60, + 427, + 264, + 438 + ], + "spans": [ + { + "bbox": [ + 60, + 427, + 264, + 438 + ], + "type": "text", + "content": "Table 5. Quality of rendered images on CelebAMask-HQ." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 45, + 458, + 279, + 690 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 458, + 279, + 690 + ], + "spans": [ + { + "bbox": [ + 45, + 458, + 279, + 690 + ], + "type": "text", + "content": "and calculate the cosine similarity and ID parameter error between them and target images. The performance is illustrated in Table 5, where DFG cannot provide satisfactory results due to its entangled image formation. For Unsup3D and LAP, the quality of texture is limited by the graphics renderer. MoFANeRF suffers from limited data diversity of constrained environments. PhyDIR and HeadNeRF suffer from large-pose degradation. The single-image baseline " + }, + { + "bbox": [ + 45, + 458, + 279, + 690 + ], + "type": "inline_equation", + "content": "\\mathrm{EG3D} + \\mathrm{PTI}" + }, + { + "bbox": [ + 45, + 458, + 279, + 690 + ], + "type": "text", + "content": " obtains high reconstruction performance, but fragile results under rotation due to the degraded shapes. In contrast, our method obtains the best performance in all the metrics, and shows robust and high-quality modeling results. Finally, we show qualitative results in Fig. 7 under challenging conditions of extreme lighting and pose. EG3D " + }, + { + "bbox": [ + 45, + 458, + 279, + 690 + ], + "type": "inline_equation", + "content": "^+" + }, + { + "bbox": [ + 45, + 458, + 279, + 690 + ], + "type": "text", + "content": " PTI suffers from overfitting and produces flatten shapes, while our method obtains best performance on the rendering quality and robustness. Our method also show satisfactory ability on editing the expressions via deformation codes. More results and comparisons can be seen in the Appendix." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 45, + 694, + 279, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 694, + 279, + 730 + ], + "spans": [ + { + "bbox": [ + 45, + 694, + 279, + 730 + ], + "type": "text", + "content": "Limitation: Our method requires multiple images of a same person as input, which may limit the application under limiting conditions. This problem can be suppressed" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 296, + 351, + 530, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 351, + 530, + 422 + ], + "spans": [ + { + "bbox": [ + 296, + 351, + 530, + 422 + ], + "type": "text", + "content": "by searching images with similar identity and building a 'relaxed' photo collection. Further, the performance also depends on the pretrained EG3D models. As a result, extreme expressions and poses may bring degradation on accuracy. Such issues can be addressed by re-training EG3D via targeted dataset. We make the discussions in the Appendix." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 297, + 439, + 449, + 452 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 439, + 449, + 452 + ], + "spans": [ + { + "bbox": [ + 297, + 439, + 449, + 452 + ], + "type": "text", + "content": "7. Conclusion & Future Work" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 296, + 462, + 530, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 462, + 530, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 462, + 530, + 730 + ], + "type": "text", + "content": "In this paper, we propose a Neural Proto-face Field (NPF) method for unsupervised robust 3D face modeling. NPF digs multi-image priors from in-the-wild photo collections to boost the 3D generative models, and well complements the single-image overfitting inversion procedure. To aggregate consistency against large variations in unconstrained environments, a novel identity-aware aggregation method is proposed to adaptively combine the style codes, and build ID-consistent face prototypes. To suppress the non-rigid ambiguity, NPF blends the consistent hybrid representation via a novel deformation modeling method. In this way, NPF obtains face prototypes containing common facial cues within the collection, and disentangles specific expressions. The final recovered 3D face is obtained via fitting NPF with consistency losses, and thus scene-specific details can be recovered. Extensive experiments demonstrate that our method models robust and detailed face shapes under challenging conditions, and recovers photorealistic texture with pose/expression controlling. In the future, the works could be proposed to tackle the limitation such as supporting single-image input. Further, decomposing the NeRF pipeline, the texture could be disentangled into different intrinsic factors." + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "spans": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "text", + "content": "389" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 105, + 102, + 117 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 105, + 102, + 117 + ], + "spans": [ + { + "bbox": [ + 47, + 105, + 102, + 117 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 52, + 125, + 279, + 729 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 57, + 125, + 279, + 156 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 125, + 279, + 156 + ], + "spans": [ + { + "bbox": [ + 57, + 125, + 279, + 156 + ], + "type": "text", + "content": "[1] Rameen Abdal, Yipeng Qin, and Peter Wonka. Image2stylegan: How to embed images into the stylegan latent space? In CVPR, pages 4432-4441, 2019. 2, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 159, + 279, + 190 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 159, + 279, + 190 + ], + "spans": [ + { + "bbox": [ + 57, + 159, + 279, + 190 + ], + "type": "text", + "content": "[2] Rameen Abdal, Yipeng Qin, and Peter Wonka. Image2stylegan++: How to edit the embedded images? In CVPR, pages 8296-8305, 2020. 2, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 192, + 279, + 245 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 192, + 279, + 245 + ], + "spans": [ + { + "bbox": [ + 57, + 192, + 279, + 245 + ], + "type": "text", + "content": "[3] Rameen Abdal, Peihao Zhu, Niloy J Mitra, and Peter Wonka. Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. ACM Transactions on Graphics (TOG), 40(3):1-21, 2021. 2, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 57, + 247, + 279, + 288 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 247, + 279, + 288 + ], + "spans": [ + { + "bbox": [ + 57, + 247, + 279, + 288 + ], + "type": "text", + "content": "[4] Victoria Fernández Abrevaya, Adnane Boukhayma, Philip HS Torr, and Edmond Boyer. Cross-modal deep face normals with deactivable skip connections. In CVPR, pages 4979-4989, 2020. 2, 5, 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 57, + 291, + 278, + 322 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 291, + 278, + 322 + ], + "spans": [ + { + "bbox": [ + 57, + 291, + 278, + 322 + ], + "type": "text", + "content": "[5] ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, and Zhixin Shu. Rignerf: Fully controllable neural 3d portraits. In CVPR, pages 20364-20373, 2022. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 57, + 324, + 279, + 366 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 324, + 279, + 366 + ], + "spans": [ + { + "bbox": [ + 57, + 324, + 279, + 366 + ], + "type": "text", + "content": "[6] Andrew D Bagdanov, Alberto Del Bimbo, and Iacopo Masi. The florence 2d/3d hybrid face dataset. In Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding, pages 79-80, 2011. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 57, + 368, + 278, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 368, + 278, + 399 + ], + "spans": [ + { + "bbox": [ + 57, + 368, + 278, + 399 + ], + "type": "text", + "content": "[7] Ziqian Bai, Zhaopeng Cui, Jamal Ahmed Rahim, Xiaoming Liu, and Ping Tan. Deep facial non-rigid multi-view stereo. In CVPR, pages 5850-5860, 2020. 1, 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 57, + 401, + 279, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 401, + 279, + 443 + ], + "spans": [ + { + "bbox": [ + 57, + 401, + 279, + 443 + ], + "type": "text", + "content": "[8] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In CVPR, pages 5855–5864, 2021. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 57, + 446, + 279, + 486 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 446, + 279, + 486 + ], + "spans": [ + { + "bbox": [ + 57, + 446, + 279, + 486 + ], + "type": "text", + "content": "[9] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In CVPR, pages 5470–5479, 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 52, + 490, + 279, + 532 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 490, + 279, + 532 + ], + "spans": [ + { + "bbox": [ + 52, + 490, + 279, + 532 + ], + "type": "text", + "content": "[10] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 187-194, 1999. 1, 2, 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 52, + 533, + 278, + 564 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 533, + 278, + 564 + ], + "spans": [ + { + "bbox": [ + 52, + 533, + 278, + 564 + ], + "type": "text", + "content": "[11] Marcel C Bühler, Abhimitra Meka, Gengyan Li, Thabo Beeler, and Otmar Hilliges. Varitex: Variational neural face textures. In ICCV, pages 13890-13899, 2021. 2, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 52, + 567, + 279, + 618 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 567, + 279, + 618 + ], + "spans": [ + { + "bbox": [ + 52, + 567, + 279, + 618 + ], + "type": "text", + "content": "[12] Rohan Chabra, Jan E Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe. Deep local shapes: Learning local sdf priors for detailed 3d reconstruction. In ECCV, pages 608-625. Springer, 2020. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 52, + 621, + 278, + 684 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 621, + 278, + 684 + ], + "spans": [ + { + "bbox": [ + 52, + 621, + 278, + 684 + ], + "type": "text", + "content": "[13] Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In CVPR, pages 16123-16133, 2022. 1, 2, 3, 6, 7, 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 52, + 687, + 278, + 729 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 687, + 278, + 729 + ], + "spans": [ + { + "bbox": [ + 52, + 687, + 278, + 729 + ], + "type": "text", + "content": "[14] Eric R Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein. pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In CVPR, pages 5799-5809, 2021. 2, 3" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 304, + 107, + 530, + 729 + ], + "type": "list", + "angle": 0, + "index": 31, + "blocks": [ + { + "bbox": [ + 304, + 107, + 530, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 107, + 530, + 149 + ], + "spans": [ + { + "bbox": [ + 304, + 107, + 530, + 149 + ], + "type": "text", + "content": "[15] Bindita Chaudhuri, Noranart Vesdapunt, Linda Shapiro, and Baoyuan Wang. Personalized face modeling for improved face reconstruction and motion retargeting. In ECCV, 2020. 1, 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 150, + 530, + 181 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 150, + 530, + 181 + ], + "spans": [ + { + "bbox": [ + 304, + 150, + 530, + 181 + ], + "type": "text", + "content": "[16] Radek Daneček, Michael J Black, and Timo Bolkart. Emoca: Emotion driven monocular face capture and animation. In CVPR, pages 20311-20322, 2022. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 183, + 530, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 183, + 530, + 213 + ], + "spans": [ + { + "bbox": [ + 304, + 183, + 530, + 213 + ], + "type": "text", + "content": "[17] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In CVPR, pages 4690-4699, 2019. 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 215, + 530, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 215, + 530, + 255 + ], + "spans": [ + { + "bbox": [ + 304, + 215, + 530, + 255 + ], + "type": "text", + "content": "[18] Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, and Xin Tong. Disentangled and controllable face image generation via 3d imitative-contrastive learning. In CVPR, pages 5154–5163, 2020. 2, 3, 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 258, + 530, + 288 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 258, + 530, + 288 + ], + "spans": [ + { + "bbox": [ + 304, + 258, + 530, + 288 + ], + "type": "text", + "content": "[19] Yu Deng, Jiaolong Yang, Jianfeng Xiang, and Xin Tong. Gram: Generative radiance manifolds for 3d-aware image generation. In CVPR, pages 10673-10683, 2022. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 291, + 530, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 291, + 530, + 330 + ], + "spans": [ + { + "bbox": [ + 304, + 291, + 530, + 330 + ], + "type": "text", + "content": "[20] Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set. In CVPRW, 2019. 1, 3, 4, 6, 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 333, + 530, + 385 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 333, + 530, + 385 + ], + "spans": [ + { + "bbox": [ + 304, + 333, + 530, + 385 + ], + "type": "text", + "content": "[21] Abdallah Dib, Cedric Thebault, Junghyun Ahn, Philippe-Henri Gosselin, Christian Theobalt, and Louis Chevallier. Towards high fidelity monocular face reconstruction with rich reflectance using self-supervised learning and ray tracing. arXiv preprint arXiv:2103.15432, 2021. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 387, + 530, + 417 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 387, + 530, + 417 + ], + "spans": [ + { + "bbox": [ + 304, + 387, + 530, + 417 + ], + "type": "text", + "content": "[22] Pengfei Dou, Shishir K Shah, and Ioannis A Kakadiaris. End-to-end 3d face reconstruction with deep neural networks. In CVPR, pages 5908-5917, 2017. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 419, + 530, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 419, + 530, + 480 + ], + "spans": [ + { + "bbox": [ + 304, + 419, + 530, + 480 + ], + "type": "text", + "content": "[23] Bernhard Egger, William AP Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Rondhani, et al. 3d morphable face models—past, present, and future. ACM Transactions on Graphics (TOG), 39(5):1-38, 2020. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 304, + 483, + 530, + 524 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 483, + 530, + 524 + ], + "spans": [ + { + "bbox": [ + 304, + 483, + 530, + 524 + ], + "type": "text", + "content": "[24] Yao Feng, Haiwen Feng, Michael J Black, and Timo Bolkart. Learning an animatable detailed 3d face model from in-the-wild images. ACM Transactions on Graphics (TOG), 40(4):1-13, 2021. 1, 2, 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 304, + 527, + 530, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 527, + 530, + 567 + ], + "spans": [ + { + "bbox": [ + 304, + 527, + 530, + 567 + ], + "type": "text", + "content": "[25] Yao Feng, Fan Wu, Xiaohu Shao, Yanfeng Wang, and Xi Zhou. Joint 3d face reconstruction and dense alignment with position map regression network. In ECCV, pages 534-551, 2018. 1, 2, 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 304, + 569, + 530, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 569, + 530, + 632 + ], + "spans": [ + { + "bbox": [ + 304, + 569, + 530, + 632 + ], + "type": "text", + "content": "[26] Zhen-Hua Feng, Patrik Huber, Josef Kittler, Peter Hancock, Xiao-Jun Wu, Qijun Zhao, Paul Koppen, and Matthias Ratsch. Evaluation of dense 3d reconstruction from 2d face images in the wild. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pages 780-786, 2018. 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 304, + 634, + 530, + 673 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 634, + 530, + 673 + ], + "spans": [ + { + "bbox": [ + 304, + 634, + 530, + 673 + ], + "type": "text", + "content": "[27] Guy Gafni, Justus Thies, Michael Zollhofer, and Matthias Nießner. Dynamic neural radiance fields for monocular 4d facial avatar reconstruction. In CVPR, pages 8649-8658, 2021. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 304, + 676, + 530, + 707 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 676, + 530, + 707 + ], + "spans": [ + { + "bbox": [ + 304, + 676, + 530, + 707 + ], + "type": "text", + "content": "[28] Chen Gao, Yichang Shih, Wei-Sheng Lai, Chia-Kai Liang, and Jia-Bin Huang. Portrait neural radiance fields from a single image. arXiv preprint arXiv:2012.05903, 2020. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 304, + 708, + 530, + 729 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 708, + 530, + 729 + ], + "spans": [ + { + "bbox": [ + 304, + 708, + 530, + 729 + ], + "type": "text", + "content": "[29] Baris Gecer, Stylianos Ploumpis, Irene Kotsia, and Stefanos Zafeiriou. Ganfit: Generative adversarial network fitting for" + } + ] + } + ], + "index": 30 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "spans": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "text", + "content": "390" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 52, + 106, + 279, + 728 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 70, + 106, + 279, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 106, + 279, + 127 + ], + "spans": [ + { + "bbox": [ + 70, + 106, + 279, + 127 + ], + "type": "text", + "content": "high fidelity 3d face reconstruction. In CVPR, pages 1155-1164, 2019. 1, 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 52, + 128, + 279, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 128, + 279, + 171 + ], + "spans": [ + { + "bbox": [ + 52, + 128, + 279, + 171 + ], + "type": "text", + "content": "[30] Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, and William T Freeman. Unsupervised training for 3d morphable model regression. In CVPR, pages 8377-8386, 2018. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 52, + 172, + 279, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 172, + 279, + 213 + ], + "spans": [ + { + "bbox": [ + 52, + 172, + 279, + 213 + ], + "type": "text", + "content": "[31] Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. Neural head avatars from monocular rgb videos. In CVPR, pages 18653–18664, 2022. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 52, + 214, + 279, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 214, + 279, + 255 + ], + "spans": [ + { + "bbox": [ + 52, + 214, + 279, + 255 + ], + "type": "text", + "content": "[32] Jiatao Gu, Lingjie Liu, Peng Wang, and Christian Theobalt. Stylenerf: A style-based 3d-aware generator for high-resolution image synthesis. arXiv preprint arXiv:2110.08985, 2021. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 52, + 256, + 279, + 289 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 256, + 279, + 289 + ], + "spans": [ + { + "bbox": [ + 52, + 256, + 279, + 289 + ], + "type": "text", + "content": "[33] Jianzhu Guo, Xiangyu Zhu, Yang Yang, Fan Yang, Zhen Lei, and Stan Z Li. Towards fast, accurate and stable 3d dense face alignment. In ECCV, 2020. 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 52, + 289, + 279, + 321 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 289, + 279, + 321 + ], + "spans": [ + { + "bbox": [ + 52, + 289, + 279, + 321 + ], + "type": "text", + "content": "[34] Yang Hong, Bo Peng, Haiyao Xiao, Ligang Liu, and Juyong Zhang. Headnerf: A real-time nerf-based parametric head model. In CVPR, pages 20374-20384, 2022. 1, 2, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 52, + 322, + 279, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 322, + 279, + 364 + ], + "spans": [ + { + "bbox": [ + 52, + 322, + 279, + 364 + ], + "type": "text", + "content": "[35] Aaron S Jackson, Adrian Bulat, Vasileios Argyriou, and Georgios Tzimiropoulos. Large pose 3d face reconstruction from a single image via direct volumetric cnn regression. In ICCV, pages 1031-1039, 2017. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 52, + 365, + 279, + 405 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 365, + 279, + 405 + ], + "spans": [ + { + "bbox": [ + 52, + 365, + 279, + 405 + ], + "type": "text", + "content": "[36] Kacper Kania, Kwang Moo Yi, Marek Kowalski, Tomasz Trzeciński, and Andrea Tagliasacchi. Conerf: Controllable neural radiance fields. In CVPR, pages 18623-18632, 2022. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 52, + 407, + 279, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 407, + 279, + 449 + ], + "spans": [ + { + "bbox": [ + 52, + 407, + 279, + 449 + ], + "type": "text", + "content": "[37] Tero Karras, Miika Aittala, Samuli Laine, Erik Härkonen, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Alias-free generative adversarial networks. NeurIPS, 34:852-863, 2021. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 52, + 450, + 279, + 482 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 450, + 279, + 482 + ], + "spans": [ + { + "bbox": [ + 52, + 450, + 279, + 482 + ], + "type": "text", + "content": "[38] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In CVPR, pages 4401-4410, 2019. 2, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 52, + 483, + 279, + 524 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 483, + 279, + 524 + ], + "spans": [ + { + "bbox": [ + 52, + 483, + 279, + 524 + ], + "type": "text", + "content": "[39] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In CVPR, pages 8110-8119, 2020. 2, 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 52, + 526, + 279, + 556 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 526, + 279, + 556 + ], + "spans": [ + { + "bbox": [ + 52, + 526, + 279, + 556 + ], + "type": "text", + "content": "[40] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 52, + 557, + 279, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 557, + 279, + 589 + ], + "spans": [ + { + "bbox": [ + 52, + 557, + 279, + 589 + ], + "type": "text", + "content": "[41] Cheng-Han Lee, Ziwei Liu, Lingyun Wu, and Ping Luo. Maskgan: Towards diverse and interactive facial image manipulation. In CVPR, 2020. 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 52, + 590, + 279, + 631 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 590, + 279, + 631 + ], + "spans": [ + { + "bbox": [ + 52, + 590, + 279, + 631 + ], + "type": "text", + "content": "[42] Tzu-Mao Li, Miika Aittala, Frédo Durand, and Jaakko Lehtinen. Differentiable monte carlo ray tracing through edge sampling. ACM Transactions on Graphics (TOG), 37(6):1-11, 2018. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 52, + 633, + 279, + 664 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 633, + 279, + 664 + ], + "spans": [ + { + "bbox": [ + 52, + 633, + 279, + 664 + ], + "type": "text", + "content": "[43] Feng Liu, Dan Zeng, Qijun Zhao, and Xiaoming Liu. Joint face alignment and 3d face reconstruction. In ECCV, pages 545-560. Springer, 2016. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 52, + 665, + 279, + 697 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 665, + 279, + 697 + ], + "spans": [ + { + "bbox": [ + 52, + 665, + 279, + 697 + ], + "type": "text", + "content": "[44] Shichen Liu, Tianye Li, Weikai Chen, and Hao Li. Soft rasterizer: A differentiable renderer for image-based 3d reasoning. In ICCV, pages 7708-7717, 2019. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 52, + 698, + 279, + 728 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 698, + 279, + 728 + ], + "spans": [ + { + "bbox": [ + 52, + 698, + 279, + 728 + ], + "type": "text", + "content": "[45] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In ICCV, pages 3730-3738, 2015. 5" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 304, + 107, + 530, + 729 + ], + "type": "list", + "angle": 0, + "index": 33, + "blocks": [ + { + "bbox": [ + 304, + 107, + 530, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 107, + 530, + 138 + ], + "spans": [ + { + "bbox": [ + 304, + 107, + 530, + 138 + ], + "type": "text", + "content": "[46] Matthew M Loper and Michael J Black. Opendr: An approximate differentiable renderer. In ECCV, pages 154-169, 2014. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 139, + 530, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 139, + 530, + 171 + ], + "spans": [ + { + "bbox": [ + 304, + 139, + 530, + 171 + ], + "type": "text", + "content": "[47] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. ACM siggraph computer graphics, 21(4):163-169, 1987. 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 172, + 530, + 203 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 172, + 530, + 203 + ], + "spans": [ + { + "bbox": [ + 304, + 172, + 530, + 203 + ], + "type": "text", + "content": "[48] Nelson Max. Optical models for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics, 1(2):99-108, 1995. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 204, + 530, + 245 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 204, + 530, + 245 + ], + "spans": [ + { + "bbox": [ + 304, + 204, + 530, + 245 + ], + "type": "text", + "content": "[49] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In CVPR, pages 4460-4470, 2019. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 246, + 530, + 288 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 246, + 530, + 288 + ], + "spans": [ + { + "bbox": [ + 304, + 246, + 530, + 288 + ], + "type": "text", + "content": "[50] Moustafa Meshry, Dan B Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snively, and Ricardo Martin-Brualla. Neural rerendering in the wild. In CVPR, pages 6878–6887, 2019. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 289, + 530, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 289, + 530, + 331 + ], + "spans": [ + { + "bbox": [ + 304, + 289, + 530, + 331 + ], + "type": "text", + "content": "[51] Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson. Implicit surface representations as layers in neural networks. In CVPR, pages 4743-4752, 2019. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 332, + 530, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 332, + 530, + 374 + ], + "spans": [ + { + "bbox": [ + 304, + 332, + 530, + 374 + ], + "type": "text", + "content": "[52] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, pages 405-421, 2020. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 304, + 375, + 530, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 375, + 530, + 407 + ], + "spans": [ + { + "bbox": [ + 304, + 375, + 530, + 407 + ], + "type": "text", + "content": "[53] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional generative neural feature fields. In CVPR, pages 11453-11464, 2021. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 304, + 408, + 530, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 408, + 530, + 459 + ], + "spans": [ + { + "bbox": [ + 304, + 408, + 530, + 459 + ], + "type": "text", + "content": "[54] Roy Or-El, Xuan Luo, Mengyi Shan, Eli Shechtman, Jeong Joon Park, and Ira Kemelmacher-Shlizerman. Stylesdf: High-resolution 3d-consistent image and geometry generation. In CVPR, pages 13503–13513, 2022. 2, 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 304, + 461, + 530, + 503 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 461, + 530, + 503 + ], + "spans": [ + { + "bbox": [ + 304, + 461, + 530, + 503 + ], + "type": "text", + "content": "[55] Xingang Pan, Bo Dai, Ziwei Liu, Chen Change Loy, and Ping Luo. Do 2d gans know 3d shape? unsupervised 3d shape reconstruction from 2d image gans. arXiv preprint arXiv:2011.00844, 2020. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 304, + 505, + 530, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 505, + 530, + 547 + ], + "spans": [ + { + "bbox": [ + 304, + 505, + 530, + 547 + ], + "type": "text", + "content": "[56] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In ICCV, pages 5865-5874, 2021. 3, 4" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 304, + 547, + 530, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 547, + 530, + 610 + ], + "spans": [ + { + "bbox": [ + 304, + 547, + 530, + 610 + ], + "type": "text", + "content": "[57] Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Ricardo MartinBrualla, and Steven M Seitz. Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields. arXiv preprint arXiv:2106.13228, 2021. 3, 4" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 304, + 611, + 530, + 664 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 611, + 530, + 664 + ], + "spans": [ + { + "bbox": [ + 304, + 611, + 530, + 664 + ], + "type": "text", + "content": "[58] Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. In IEEE International Conference on Advanced Video and Signal Based Surveillance, pages 296-301, 2009. 1" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 304, + 665, + 530, + 706 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 530, + 706 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 530, + 706 + ], + "type": "text", + "content": "[59] Jingtan Piao, Keqiang Sun, Quan Wang, Kwan-Yee Lin, and Hongsheng Li. Inverting generative adversarial renderer for face reconstruction. In CVPR, pages 15619-15628, 2021. 2" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 304, + 708, + 530, + 729 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 708, + 530, + 729 + ], + "spans": [ + { + "bbox": [ + 304, + 708, + 530, + 729 + ], + "type": "text", + "content": "[60] Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-nerf: Neural radiance fields" + } + ] + } + ], + "index": 32 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 281, + 747, + 295, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 281, + 747, + 295, + 756 + ], + "spans": [ + { + "bbox": [ + 281, + 747, + 295, + 756 + ], + "type": "text", + "content": "391" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 52, + 106, + 279, + 728 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 70, + 106, + 278, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 106, + 278, + 127 + ], + "spans": [ + { + "bbox": [ + 70, + 106, + 278, + 127 + ], + "type": "text", + "content": "for dynamic scenes. In CVPR, pages 10318-10327, 2021. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 52, + 128, + 279, + 170 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 128, + 279, + 170 + ], + "spans": [ + { + "bbox": [ + 52, + 128, + 279, + 170 + ], + "type": "text", + "content": "[61] Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. H3d-net: Few-shot high-fidelity 3d head reconstruction. In ICCV, pages 5620-5629, 2021. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 52, + 171, + 279, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 171, + 279, + 213 + ], + "spans": [ + { + "bbox": [ + 52, + 171, + 279, + 213 + ], + "type": "text", + "content": "[62] Yurui Ren, Ge Li, Yuanqi Chen, Thomas H Li, and Shan Liu. Pirenderer: Controllable portrait image generation via semantic neural rendering. In ICCV, pages 13759-13768, 2021. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 52, + 214, + 279, + 256 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 214, + 279, + 256 + ], + "spans": [ + { + "bbox": [ + 52, + 214, + 279, + 256 + ], + "type": "text", + "content": "[63] Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or. Encoding in style: a stylegan encoder for image-to-image translation. In CVPR, pages 2287–2296, 2021. 3, 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 52, + 258, + 279, + 288 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 258, + 279, + 288 + ], + "spans": [ + { + "bbox": [ + 52, + 258, + 279, + 288 + ], + "type": "text", + "content": "[64] Elad Richardson, Matan Sela, and Ron Kimmel. 3d face reconstruction by learning from synthetic data. In 3DV, pages 460-469, 2016. 1, 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 52, + 289, + 278, + 321 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 289, + 278, + 321 + ], + "spans": [ + { + "bbox": [ + 52, + 289, + 278, + 321 + ], + "type": "text", + "content": "[65] Elad Richardson, Matan Sela, Roy Or-El, and Ron Kimmel. Learning detailed face reconstruction from a single image. In CVPR, pages 1259–1268, 2017. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 52, + 322, + 279, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 322, + 279, + 364 + ], + "spans": [ + { + "bbox": [ + 52, + 322, + 279, + 364 + ], + "type": "text", + "content": "[66] Daniel Roich, Ron Mokady, Amit H Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. ACM Transactions on Graphics (TOG), 42(1):1-13, 2022. 1, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 52, + 365, + 279, + 395 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 365, + 279, + 395 + ], + "spans": [ + { + "bbox": [ + 52, + 365, + 279, + 395 + ], + "type": "text", + "content": "[67] Sami Romdhani and Thomas Vetter. Efficient, robust and accurate fitting of a 3d morphable model. In Computer Vision, page 59. IEEE, 2003. 1, 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 52, + 397, + 279, + 438 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 397, + 279, + 438 + ], + "spans": [ + { + "bbox": [ + 52, + 397, + 279, + 438 + ], + "type": "text", + "content": "[68] Sami Romdhani and Thomas Vetter. Estimating 3d shape and texture using pixel intensity, edges, specular highlights, texture constraints and a prior. In CVPR, volume 2, pages 986-993, 2005. 1, 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 52, + 440, + 279, + 482 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 440, + 279, + 482 + ], + "spans": [ + { + "bbox": [ + 52, + 440, + 279, + 482 + ], + "type": "text", + "content": "[69] Soubhik Sanyal, Timo Bolkart, Haiwen Feng, and Michael J Black. Learning to regress 3d face shape and expression from an image without 3d supervision. In CVPR, pages 7763-7772, 2019. 3, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 52, + 483, + 279, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 483, + 279, + 525 + ], + "spans": [ + { + "bbox": [ + 52, + 483, + 279, + 525 + ], + "type": "text", + "content": "[70] Soumyadip Sengupta, Angjoo Kanazawa, Carlos D Castillo, and David W Jacobs. Sfsnet: Learning shape, reflectance and illuminance of faces in the wild. In CVPR, pages 6296-6305, 2018. 2, 6, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 52, + 526, + 279, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 526, + 279, + 578 + ], + "spans": [ + { + "bbox": [ + 52, + 526, + 279, + 578 + ], + "type": "text", + "content": "[71] Jiaxiang Shang, Tianwei Shen, Shiwei Li, Lei Zhou, Mingmin Zhen, Tian Fang, and Long Quan. Self-supervised monocular 3d face reconstruction by occlusion-aware multi-view geometry consistency. arXiv preprint arXiv:2007.12494, 2020. 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 52, + 580, + 279, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 580, + 279, + 610 + ], + "spans": [ + { + "bbox": [ + 52, + 580, + 279, + 610 + ], + "type": "text", + "content": "[72] Yichun Shi, Divyansh Aggarwal, and Anil K Jain. Lifting 2d stylegan for 3d-aware face generation. In CVPR, pages 6258-6266, 2021. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 52, + 612, + 278, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 612, + 278, + 632 + ], + "spans": [ + { + "bbox": [ + 52, + 612, + 278, + 632 + ], + "type": "text", + "content": "[73] Yichun Shi and Anil K Jain. Probabilistic face embeddings. In ICCV, pages 6902-6911, 2019. 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 52, + 634, + 279, + 674 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 634, + 279, + 674 + ], + "spans": [ + { + "bbox": [ + 52, + 634, + 279, + 674 + ], + "type": "text", + "content": "[74] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3d-structure-aware neural scene representations. NeurIPS, 32, 2019. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 52, + 676, + 279, + 728 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 676, + 279, + 728 + ], + "spans": [ + { + "bbox": [ + 52, + 676, + 279, + 728 + ], + "type": "text", + "content": "[75] Keqiang Sun, Shangzhe Wu, Zhaoyang Huang, Ning Zhang, Quan Wang, and Hongsheng Li. Controllable 3d face synthesis with conditional generative occupancy fields. In Advances in Neural Information Processing Systems, 2021. 3" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 304, + 107, + 530, + 728 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 304, + 107, + 530, + 159 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 107, + 530, + 159 + ], + "spans": [ + { + "bbox": [ + 304, + 107, + 530, + 159 + ], + "type": "text", + "content": "[76] Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Fml: Face model learning from videos. In CVPR, pages 10812-10822, 2019. 1, 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 163, + 530, + 214 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 163, + 530, + 214 + ], + "spans": [ + { + "bbox": [ + 304, + 163, + 530, + 214 + ], + "type": "text", + "content": "[77] Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Stylerig: Rigging stylegan for 3d control over portrait images. In CVPR, pages 6142-6151, 2020. 2, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 218, + 530, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 218, + 530, + 281 + ], + "spans": [ + { + "bbox": [ + 304, + 218, + 530, + 281 + ], + "type": "text", + "content": "[78] Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo MartinBrualla, Tomas Simon, Jason Saragih, Matthias Nießner, et al. State of the art on neural rendering. In Computer Graphics Forum, volume 39, pages 701-727. Wiley Online Library, 2020. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 284, + 530, + 325 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 284, + 530, + 325 + ], + "spans": [ + { + "bbox": [ + 304, + 284, + 530, + 325 + ], + "type": "text", + "content": "[79] Ayush Tewari, Hans-Peter Seidel, Mohamed Elgharib, Christian Theobalt, et al. Learning complete 3d morphable face models from images and videos. In CVPR, pages 3361-3371, 2021. 1, 2, 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 329, + 530, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 329, + 530, + 392 + ], + "spans": [ + { + "bbox": [ + 304, + 329, + 530, + 392 + ], + "type": "text", + "content": "[80] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, W Yifan, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, et al. Advances in neural rendering. In Computer Graphics Forum, volume 41, pages 703-735. Wiley Online Library, 2022. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 395, + 530, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 395, + 530, + 448 + ], + "spans": [ + { + "bbox": [ + 304, + 395, + 530, + 448 + ], + "type": "text", + "content": "[81] Ayush Tewari, Michael Zollhofer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, Patrick Perez, and Christian Theobalt. Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction. In ICCVW, pages 1274-1283, 2017. 1, 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 451, + 530, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 451, + 530, + 492 + ], + "spans": [ + { + "bbox": [ + 304, + 451, + 530, + 492 + ], + "type": "text", + "content": "[82] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics (TOG), 38(4):1-12, 2019. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 495, + 530, + 538 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 495, + 530, + 538 + ], + "spans": [ + { + "bbox": [ + 304, + 495, + 530, + 538 + ], + "type": "text", + "content": "[83] Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, and Daniel Cohen-Or. Designing an encoder for stylegan image manipulation. ACM Transactions on Graphics (TOG), 40(4):1-14, 2021. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 304, + 541, + 530, + 562 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 541, + 530, + 562 + ], + "spans": [ + { + "bbox": [ + 304, + 541, + 530, + 562 + ], + "type": "text", + "content": "[84] Luan Tran and Xiaoming Liu. Nonlinear 3d face morphable model. In CVPR, pages 7346-7355, 2018. 1, 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 304, + 564, + 530, + 606 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 564, + 530, + 606 + ], + "spans": [ + { + "bbox": [ + 304, + 564, + 530, + 606 + ], + "type": "text", + "content": "[85] Anh Tuan Tran, Tal Hassner, Iacopo Masi, Eran Paz, Yuval Nirkin, and Gerard Medioni. Extreme 3d face reconstruction: Seeing through occlusions. In CVPR, pages 3935-3944, 2018. 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 304, + 609, + 530, + 661 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 609, + 530, + 661 + ], + "spans": [ + { + "bbox": [ + 304, + 609, + 530, + 661 + ], + "type": "text", + "content": "[86] Daoye Wang, Prashanth Chandran, Gaspard Zoss, Derek Bradley, and Paulo Gotardo. Morf: Morphable radiance fields for multiview neural head modeling. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1-9, 2022. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 304, + 665, + 530, + 728 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 530, + 728 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 530, + 728 + ], + "type": "text", + "content": "[87] Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon, Jason Saragih, Jessica Hodgins, and Michael Zollhofer. Learning compositional radiance fields of dynamic human heads. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5704-5713, 2021. 2" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "spans": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "text", + "content": "392" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 106, + 279, + 728 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 51, + 106, + 279, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 106, + 279, + 138 + ], + "spans": [ + { + "bbox": [ + 51, + 106, + 279, + 138 + ], + "type": "text", + "content": "[88] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. TIP, 13(4):600-612, 2004. 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 51, + 141, + 279, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 141, + 279, + 171 + ], + "spans": [ + { + "bbox": [ + 51, + 141, + 279, + 171 + ], + "type": "text", + "content": "[89] Felix Wimbauer, Shangzhe Wu, and Christian Rupprecht. De-rendering 3d objects in the wild. In CVPR, pages 18490-18499, 2022. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 51, + 175, + 279, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 175, + 279, + 217 + ], + "spans": [ + { + "bbox": [ + 51, + 175, + 279, + 217 + ], + "type": "text", + "content": "[90] Fanzi Wu, Linchao Bao, Yajing Chen, Yonggen Ling, Yibing Song, Songnan Li, King Ngi Ngan, and Wei Liu. Mvfnet: Multi-view 3d face morphable model regression. In CVPR, pages 959-968, 2019. 1, 2, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 51, + 219, + 279, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 219, + 279, + 261 + ], + "spans": [ + { + "bbox": [ + 51, + 219, + 279, + 261 + ], + "type": "text", + "content": "[91] Shangzhe Wu, Christian Rupprecht, and Andrea Vedaldi. Unsupervised learning of probably symmetric deformable 3d objects from images in the wild. In CVPR, pages 1-10, 2020. 1, 2, 5, 6, 7, 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 51, + 264, + 279, + 295 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 264, + 279, + 295 + ], + "spans": [ + { + "bbox": [ + 51, + 264, + 279, + 295 + ], + "type": "text", + "content": "[92] Sicheng Xu, Jiaolong Yang, Dong Chen, Fang Wen, Yu Deng, Yunde Jia, and Xin Tong. Deep 3d portrait from a single image. In CVPR, pages 7710-7720, 2020. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 51, + 298, + 279, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 298, + 279, + 329 + ], + "spans": [ + { + "bbox": [ + 51, + 298, + 279, + 329 + ], + "type": "text", + "content": "[93] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. NeurIPS, 34:4805-4815, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 51, + 331, + 279, + 383 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 331, + 279, + 383 + ], + "spans": [ + { + "bbox": [ + 51, + 331, + 279, + 383 + ], + "type": "text", + "content": "[94] Tarun Yenamandra, Ayush Tewari, Florian Bernard, Hans-Peter Seidel, Mohamed Elgharib, Daniel Cremers, and Christian Theobalt. i3dmm: Deep implicit 3d morphable model of human heads. In CVPR, pages 12803-12813, 2021. 1, 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 51, + 387, + 279, + 417 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 387, + 279, + 417 + ], + "spans": [ + { + "bbox": [ + 51, + 387, + 279, + 417 + ], + "type": "text", + "content": "[95] Dong Yi, Zhen Lei, Shengcai Liao, and Stan Z Li. Learning face representation from scratch. arXiv preprint arXiv:1411.7923, 2014. 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 51, + 420, + 279, + 461 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 420, + 279, + 461 + ], + "spans": [ + { + "bbox": [ + 51, + 420, + 279, + 461 + ], + "type": "text", + "content": "[96] Stefanos Zafeiriou, Mark Hansen, Gary Atkinson, Vasileios Argyriou, Maria Petrou, Melvyn Smith, and Lyndon Smith. The photoface database. In CVPRW, pages 132-139, 2011. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 51, + 464, + 279, + 495 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 464, + 279, + 495 + ], + "spans": [ + { + "bbox": [ + 51, + 464, + 279, + 495 + ], + "type": "text", + "content": "[97] Xiaoxing Zeng, Xiaojiang Peng, and Yu Qiao. Df2net: A dense-fine-finer network for detailed 3d face reconstruction. In ICCV, pages 2315-2324, 2019. 2, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 51, + 499, + 279, + 539 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 499, + 279, + 539 + ], + "spans": [ + { + "bbox": [ + 51, + 499, + 279, + 539 + ], + "type": "text", + "content": "[98] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 51, + 543, + 279, + 574 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 543, + 279, + 574 + ], + "spans": [ + { + "bbox": [ + 51, + 543, + 279, + 574 + ], + "type": "text", + "content": "[99] Ruo Zhang, Ping-Sing Tsai, James Edwin Cryer, and Mubarak Shah. Shape-from-shading: a survey. IEEE TPAMI, 21(8):690-706, 1999. 1, 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 577, + 279, + 628 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 577, + 279, + 628 + ], + "spans": [ + { + "bbox": [ + 47, + 577, + 279, + 628 + ], + "type": "text", + "content": "[100] Zhenyu Zhang, Yanhao Ge, Renwang Chen, Ying Tai, Yan Yan, Jian Yang, Chengjie Wang, Jilin Li, and Feiyue Huang. Learning to aggregate and personalize 3d face from in-the-wild photo collection. In CVPR, pages 14214-14224, 2021. 1, 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 47, + 632, + 279, + 684 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 632, + 279, + 684 + ], + "spans": [ + { + "bbox": [ + 47, + 632, + 279, + 684 + ], + "type": "text", + "content": "[101] Zhenyu Zhang, Yanhao Ge, Ying Tai, Weijian Cao, Renwang Chen, Kunlin Liu, Hao Tang, Xiaoming Huang, Chengjie Wang, Zhifeng Xie, et al. Physically-guided disentangled implicit rendering for 3d face modeling. In CVPR, pages 20353-20363, 2022. 3, 7, 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 47, + 687, + 279, + 728 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 687, + 279, + 728 + ], + "spans": [ + { + "bbox": [ + 47, + 687, + 279, + 728 + ], + "type": "text", + "content": "[102] Mingwu Zheng, Hongyu Yang, Di Huang, and Liming Chen. Imface: A nonlinear 3d morphable face model with implicit neural representations. In CVPR, pages 20343-20352, 2022. 2" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 299, + 106, + 530, + 335 + ], + "type": "list", + "angle": 0, + "index": 22, + "blocks": [ + { + "bbox": [ + 299, + 106, + 530, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 106, + 530, + 149 + ], + "spans": [ + { + "bbox": [ + 299, + 106, + 530, + 149 + ], + "type": "text", + "content": "[103] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. Im avatar: Implicit morphable head avatars from videos. In CVPR, pages 13545–13555, 2022. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 299, + 151, + 530, + 191 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 151, + 530, + 191 + ], + "spans": [ + { + "bbox": [ + 299, + 151, + 530, + 191 + ], + "type": "text", + "content": "[104] Yuxiang Zhou, Jiankang Deng, Irene Kotsia, and Stefanos Zafeiriou. Dense 3d face decoding over 2500fps: Joint texture & shape convolutional mesh decoders. In CVPR, pages 1097-1106, 2019. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 299, + 194, + 530, + 226 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 194, + 530, + 226 + ], + "spans": [ + { + "bbox": [ + 299, + 194, + 530, + 226 + ], + "type": "text", + "content": "[105] Xiangyu Zhu, Zhen Lei, Xiaoming Liu, Hailin Shi, and Stan Z Li. Face alignment across large poses: A 3d solution. In CVPR, pages 146-155, 2016. 1, 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 299, + 228, + 530, + 268 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 228, + 530, + 268 + ], + "spans": [ + { + "bbox": [ + 299, + 228, + 530, + 268 + ], + "type": "text", + "content": "[106] Xiangyu Zhu, Fan Yang, Chang Yu Di Huang, Hao Wang, Jianzhu Guo, Zhen Lei, and Stan Z Li. Beyond 3dmm space: Towards fine-grained 3d face reconstruction. In ECCV, 2020. 1, 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 299, + 271, + 530, + 301 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 271, + 530, + 301 + ], + "spans": [ + { + "bbox": [ + 299, + 271, + 530, + 301 + ], + "type": "text", + "content": "[107] Xiangyu Zhu, Dong Yi, Zhen Lei, and Stan Z Li. Robust 3d morphable model fitting by sparse sift flow. In ICCV, pages 4044-4049. IEEE, 2014. 1, 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 299, + 303, + 530, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 303, + 530, + 335 + ], + "spans": [ + { + "bbox": [ + 299, + 303, + 530, + 335 + ], + "type": "text", + "content": "[108] Yiyu Zhuang, Hao Zhu, Xusen Sun, and Xun Cao. Mofan-erf: Morphable facial neural radiance field. arXiv preprint arXiv:2112.02308, 2021. 8" + } + ] + } + ], + "index": 21 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "spans": [ + { + "bbox": [ + 281, + 747, + 296, + 756 + ], + "type": "text", + "content": "393" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 11 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Neural Volumetric Representations of Dynamic Humans in Minutes/4b95dcfc-29a4-4f2f-b770-89d83ba2b62f_content_list.json b/2023/Learning Neural Volumetric Representations of Dynamic Humans in Minutes/4b95dcfc-29a4-4f2f-b770-89d83ba2b62f_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..e2c785f42f65a8956393019c78039742afec5851 --- /dev/null +++ b/2023/Learning Neural Volumetric Representations of Dynamic Humans in Minutes/4b95dcfc-29a4-4f2f-b770-89d83ba2b62f_content_list.json @@ -0,0 +1,1694 @@ +[ + { + "type": "text", + "text": "Learning Neural Volumetric Representations of Dynamic Humans in Minutes", + "text_level": 1, + "bbox": [ + 94, + 130, + 875, + 154 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Chen Geng*", + "bbox": [ + 176, + 179, + 277, + 199 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Sida Peng*", + "bbox": [ + 308, + 180, + 398, + 199 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Zhen $\\mathrm{Xu^{*}}$", + "bbox": [ + 433, + 181, + 513, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Hujun Bao", + "bbox": [ + 545, + 181, + 635, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Xiaowei Zhou", + "bbox": [ + 674, + 180, + 797, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "State Key Laboratory of CAD&CG, Zhejiang University", + "bbox": [ + 259, + 210, + 709, + 229 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/72f2b519b2a6c3a631d3ad75b565dd48c99f39cd1ae4caca057aa942d22aa9e5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 91, + 287, + 241, + 405 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6b94307a825b4c73176cd7a5febf53952d0c2a27b1415c91e5d9b975b73f5064.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 243, + 292, + 369, + 406 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6ca6c9748afc1ef6917b2c1343d4757c3c1ad7ec00a9329221d7c8b57db37319.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 372, + 292, + 496, + 406 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/19297576584d1f7b530c07a93b0b4c2421da4108ce8e82213d8020fe7ca3d61f.jpg", + "image_caption": [ + "1 min" + ], + "image_footnote": [], + "bbox": [ + 91, + 407, + 241, + 521 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/17c1f1a7aaa48d633c4b59545b76bd64f999fcf68219e201a54b70e85813a217.jpg", + "image_caption": [ + "5 min" + ], + "image_footnote": [], + "bbox": [ + 243, + 407, + 367, + 521 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f0045714a267a604eee70d02e0f430e016b2242c2190832fc4a08a6125e3ff02.jpg", + "image_caption": [ + "10 min" + ], + "image_footnote": [], + "bbox": [ + 372, + 407, + 496, + 521 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/689735e0f2fb89e8ab2518dadf175db1e77e035308867473476ffc553a201432.jpg", + "image_caption": [ + "Figure 1. Convergence rate of training. Given a monocular video of a human performer, our model can be learned in $\\sim 5$ minutes to produce photorealistic novel view rendering, which is 100 times faster than Neural Body [58]." + ], + "image_footnote": [], + "bbox": [ + 511, + 286, + 859, + 539 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 607, + 313, + 622 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "This paper addresses the challenge of efficiently reconstructing volumetric videos of dynamic humans from sparse multi-view videos. Some recent works represent a dynamic human as a canonical neural radiance field (NeRF) and a motion field, which are learned from input videos through differentiable rendering. But the per-scene optimization generally requires hours. Other generalizable NeRF models leverage learned prior from datasets to reduce the optimization time by only finetuning on new scenes at the cost of visual fidelity. In this paper, we propose a novel method for learning neural volumetric representations of dynamic humans in minutes with competitive visual quality. Specifically, we define a novel part-based voxelized human representation to better distribute the representational power of the network to different human parts. Furthermore, we propose", + "bbox": [ + 75, + 641, + 470, + 868 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "a novel 2D motion parameterization scheme to increase the convergence rate of deformation field learning. Experiments demonstrate that our model can be learned 100 times faster than previous per-scene optimization methods while being competitive in the rendering quality. Training our model on a $512 \\times 512$ video with 100 frames typically takes about 5 minutes on a single RTX 3090 GPU. The code is available on our project page: https://zju3dv.github.io/instant_nvr.", + "bbox": [ + 500, + 608, + 893, + 729 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 500, + 766, + 632, + 782 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Creating volumetric videos of human performers has many applications, such as immersive telepresence, video games, and movie production. Recently, some methods [58, 93] have shown that high-quality volumetric videos can be recovered from sparse multi-view videos by representing dynamic humans with neural scene representations. However, they typically require more than 10 hours of training on", + "bbox": [ + 498, + 794, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Equal contribution. † Corresponding author.", + "bbox": [ + 76, + 886, + 316, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "8759", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "a single GPU. The expensive time and computational costs limit the large-scale application of volumetric videos. Generalizable methods [34, 100] utilize learned prior from datasets of dynamic humans to reduce the training time by only finetuning on novel human performers. These techniques could increase the optimization speed by a factor of 2-5 at the cost of some visual fidelity.", + "bbox": [ + 75, + 90, + 470, + 196 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To speed up the process of optimizing a neural representation for view synthesis of dynamic humans, we analyze the structural prior of the human body and motion, and propose a novel dynamic human representation that achieves 100x speedup during optimization while maintaining competitive visual fidelity. Specifically, to model a dynamic human, we first transform world-space points to a canonical space using a novel motion parameterization scheme and inverse linear blend skinning (LBS) [35]. Then, the color and density of these points are estimated using the canonical human model.", + "bbox": [ + 75, + 199, + 470, + 349 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The innovation of our proposed representation is two-fold. First, we observe that human body parts have different levels of complexity in terms of both shape and texture. For example, the face of a human performer typically exhibits higher complexity than a flatly textured torso region, thus requiring more representational power to depict. Motivated by this, our method decomposes the canonical human body into multiple parts and represents the human body with a structured set of voxelized NeRF [47] networks to bring the convergence rate of these different parts to the same level. In contrast to a single-resolution representation, the part-based body model utilizes the human body prior to represent the human shape and texture efficiently, heuristically distributing variational representational power to human parts with spatially varying complexity.", + "bbox": [ + 75, + 352, + 470, + 579 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Second, we notice that non-rigid deformation of human geometry typically occurs around a surface instead of in a volume, that is, nearby surface points on a parametric human model tend to have similar motion behavior. Thus we propose a novel motion parameterization technique that models the 3D human deformation in a 2D domain, enabling modeling of the motion field using 3D voxelized representation to accelerate the learning. This idea is similar to the displacement map and bump map [14, 15] in traditional computer graphics to represent detailed deformation on a 2D texture domain. We extend the technique of displacement map [14, 15] to represent human motions by restricting the originally 3D deformation field [40, 56, 59] on the 2D surface of a parametric human model, such as SMPL [43]. This technique allows us to use hybrid representations [48, 98] to model non-rigid deformation by reducing the memory footprint, largely boosting the convergence of the field.", + "bbox": [ + 75, + 580, + 470, + 838 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Experiments demonstrate that our method significantly accelerates the optimization of neural human representations while being competitive with state-of-the-art human modeling methods on rendering quality. As shown in Figure 1, our", + "bbox": [ + 75, + 839, + 470, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "model can be trained within 5 minutes to produce a volumetric video of a dynamic human from a 100-frame monocular video of a $512 \\times 512$ resolution on an RTX 3090 GPU.", + "bbox": [ + 496, + 90, + 893, + 135 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To summarize, our key contributions are:", + "bbox": [ + 517, + 137, + 792, + 151 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- A novel part-based voxelized human representation for more efficient human body modeling.", + "- A 2D motion parameterization scheme for more efficient deformation field modeling.", + "- 100x speedup in optimization compared to previous neural human representations while maintaining competitive rendering quality." + ], + "bbox": [ + 517, + 162, + 893, + 271 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related work", + "text_level": 1, + "bbox": [ + 500, + 296, + 637, + 311 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Implicit neural representation and rendering. There have been many 3D scene representations, such as multiview images [60, 78], textured meshes [38, 86], point clouds [1, 61], and voxels [42, 76]. Recently, some methods [11, 41, 44, 51, 77, 97, 104] propose implicit neural representations to represent scenes, which uses MLP networks to predict scene properties for any point in 3D space, such as occupancy [44, 67], signed distance [41, 51], and semantics [23, 104]. This enables them to describe continuous and high-resolution 3D scenes. To perform novel view synthesis, neural radiance field (NeRF) [47] models scenes as implicit fields of density and color. NeRF is optimized from images with volume rendering techniques, which produces impressive image synthesis results. Many works improve NeRF in various aspects, including rendering quality [3, 4], rendering speed [22, 27, 39, 63, 99], scene scale [64, 82, 89, 94], and reconstruction quality [50, 90, 96]. Some methods [18, 37, 52, 59] extend NeRF to dynamic scenes.", + "bbox": [ + 496, + 321, + 893, + 594 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Human modeling. Reconstructing high-quality 3D human models is essential for synthesizing free-viewpoint videos of human performers. Traditional methods leverage multi-view stereo techniques [24, 71, 72] or depth fusion [13, 17, 80] to reconstruct human geometries, which require complicated hardware, such as dense camera arrays or depth sensors. To reduce the requirement of the capture equipment, some methods [2, 67, 68] train networks to learn human priors from datasets containing a large amount of 3D ground-truth human models, enabling them to infer human geometry and texture from even a single image. However, due to the limited diversity of training data, these methods do not generalize well to humans under complex poses. Recently, some methods [9, 46, 69, 87] model the shapes of dynamic humans as implicit neural representations and attempt to optimize them from human scans. Another line of works [28, 30, 34, 36, 40, 56, 58, 65, 92, 93, 95, 101-103, 105] exploits dynamic implicit neural representations and differentiable renderers to reconstruct 3D human models from", + "bbox": [ + 496, + 613, + 895, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "8760", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/da5e57f8f072b40730e509918d78e8ff12f1bd63ed38b43c3663309d49ceae75.jpg", + "image_caption": [ + "Figure 2. Overview of the proposed representation. Given a query point $\\mathbf{x}$ at frame $t$ , we find its nearest surface point on each human part of the SMPL mesh, which gives the blend weight $\\mathbf{w}_k$ and the UV coordinates $(u_k, v_k)$ . Consider the $k$ -th part. The motion field consists of an inverse LBS module and a residual deformation module. (a) The inverse LBS module takes body pose $\\rho$ , blend weight $\\mathbf{w}_k$ , and query point $\\mathbf{x}$ as input and outputs the transformed point $\\mathbf{x}'$ . The residual deformation module applies the multiresolution hash encoding (MHE) to $(u_k, v_k, t)$ and uses an MLP network to regress the residual translation $\\Delta \\mathbf{x}$ , which is added to $\\mathbf{x}'$ to obtain the canonical point $\\mathbf{x}^{\\mathrm{can}}$ . (b) We then feed $\\mathbf{x}^{\\mathrm{can}}$ to networks of $k$ -th human part to predict the density $\\sigma_k$ and color $\\mathbf{c}_k$ . With $\\left\\{\\left(\\sigma_k, \\mathbf{c}_k\\right)\\right\\}_{k=1}^{K}$ , we select the one with the largest density as the density and color of the query point." + ], + "image_footnote": [], + "bbox": [ + 116, + 87, + 537, + 287 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/8f8f74e7019caf9d8314a78d714eda47af3dda6e55612880b8ad68381e5fe05d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 540, + 85, + 849, + 287 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "videos. To represent dynamic humans, Neural Actor [40] augments the neural radiance field with the linear blend skinning model [35]. It additionally adopts a residual deformation field to better predict human motions. To overcome the inaccuracy of input human poses, [79, 93] optimize the parameters of human poses jointly with the human representations during training. These methods typically require a lengthy training process to produce high-quality human models. In contrast, we introduce a part-based voxelized human representation to model the canonical human body, which significantly accelerates the optimization process. Although [16, 46] have proposed part-based implicit functions, they focus on human shape modeling and do not show that the part-based representation can be used to reduce the training time.", + "bbox": [ + 75, + 417, + 473, + 643 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Accelerating the optimization of neural representations. Many differentiable rendering-based methods [3, 39, 47] optimize a separate neural representation for each scene. The optimization process generally takes several hours on a modern GPU, which is time-consuming and costly to scale. Inspired by multi-view stereo matching [72], some methods [8, 12, 45, 73, 88, 91, 100] train a network on multi-view datasets to learn to infer radiance fields from input images. This enables them to quickly fine-tune neural representations to unseen scenes. [19, 62] leverage the auto-decoder [51] to capture the scene priors for efficient fine-tuning. [5, 83] utilize meta-learning techniques [20, 49] to initialize network parameters, thereby improving the training speed. Some methods [7, 10, 55, 70, 81, 98] attempt to design scene representations that support efficient training. [21, 48, 75, 84]", + "bbox": [ + 75, + 674, + 472, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "augments the approximation ability of networks by designing encoding techniques. Multiresolution hash encoding [48] defines multiresolution feature vector arrays for a scene and uses the hash technique [85] to assign each input coordinate a feature vector as the encoded input, which significantly improves the training speed.", + "bbox": [ + 496, + 417, + 895, + 508 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methods", + "text_level": 1, + "bbox": [ + 500, + 522, + 599, + 537 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "This paper aims to quickly create a volumetric video from a sparse multi-view video that captures a dynamic human. We assume that the cameras are calibrated and the human pose and foreground human mask of each image are provided. Section 3.1 introduces our dynamic human model that is comprised of a part-based voxelized human representation and a dimensionality reduction motion parameterization scheme. Section 3.2 discusses how to efficiently optimize the proposed representation. Finally, Section 3.3 provides implementation details.", + "bbox": [ + 496, + 547, + 895, + 700 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Proposed human representation", + "text_level": 1, + "bbox": [ + 500, + 710, + 782, + 726 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As shown in Figure 2, our dynamic human representation consists of a motion parameterization field and a part-based voxelized human model. (a) For a query point $\\mathbf{x}$ , the motion parameterization field first transforms it to the canonical space correspondence $\\mathbf{x}^{\\mathrm{can}}$ using the inverse LBS [35] algorithm and by parameterizing the 3D points onto 2D UV coordinates to predict the residual deformation $\\Delta \\mathbf{x}$ . (b) Then, $\\mathbf{x}^{\\mathrm{can}}$ is fed into the part-based voxelized human model in canonical space to predict and aggregate the density and color $(\\sigma, \\mathbf{c})$ , where the canonical human body is decomposed into $K$ parts, each of which is represented using an", + "bbox": [ + 496, + 734, + 895, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "8761", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "MHE-augmented [48] NeRF network.", + "bbox": [ + 76, + 90, + 331, + 104 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Motion parameterization on 2D surface domain. To regress the canonical correspondence $\\mathbf{x}^{\\mathrm{can}}$ of a query point $\\mathbf{x}$ , we first find its nearest surface point $\\mathbf{p}$ on the posed SMPL mesh. Using the strategy in [40], the blend weight $\\mathbf{w}$ and UV coordinate $(u,v)$ of surface point $\\mathbf{p}$ are obtained from the SMPL model.", + "bbox": [ + 75, + 125, + 468, + 213 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Given the blend weight $\\mathbf{w}$ and UV coordinate $(u, v)$ , the motion field maps the query point $\\mathbf{x}$ to the canonical space correspondence $\\mathbf{x}^{\\mathrm{can}}$ . The motion field is comprised of an inverse LBS module [35] and a residual deformation module. Given a query point $\\mathbf{x}$ and blend weight $\\mathbf{w}$ , we use the inverse LBS module to transform it to the unposed space, which is defined as:", + "bbox": [ + 75, + 214, + 470, + 319 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\Phi_ {\\mathrm {L B S}} (\\mathbf {x}, \\mathbf {w}, \\boldsymbol {\\rho}) = \\left(\\sum_ {j = 1} ^ {J} w _ {k, j} G _ {j}\\right) ^ {- 1} \\mathbf {x}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 142, + 328, + 468, + 380 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\rho$ denotes the human pose and $\\{G_j\\}_{j=1}^J$ are transformation matrices derived from $\\rho$ [43]. The detailed derivation of the inverse LBS algorithm can be found in the supplementary material.", + "bbox": [ + 75, + 388, + 470, + 449 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The transformed point $\\Phi_{\\mathrm{LBS}}(\\mathbf{x},\\mathbf{w},\\pmb {\\rho})$ is then deformed to the human surface using the residual deformation module. Specifically, the current time $t$ is first concatenated with the UV coordinate $(u,v)$ to serve as the parameterization of the query point $\\mathbf{x}$ at frame $t$ . This motion parameterization is inspired by the displacement map and bump map techniques [14, 15] in the traditional Computer Graphics pipelines. It essentially reduces the dimensionality of a 4D space-time sequence down to the 3D surface-time domain utilizing the human deformation prior. Then, we apply the multiresolution hash encoding [48] $\\psi_{\\mathrm{res}}$ to $(u,v,t)$ and forward the encoded input through a network $\\mathrm{MLP}_{\\mathrm{res}}$ to regress the residual $\\Delta \\Phi (u,v,t)$ . The full human motion at frame $t$ is defined as:", + "bbox": [ + 75, + 450, + 472, + 660 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\Delta \\Phi (u, v, t) = \\mathrm {M L P} _ {\\text {r e s}} \\left(\\psi_ {\\text {r e s}} (u, v, t)\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 143, + 672, + 468, + 689 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {x} ^ {\\text {c a n}} (\\mathbf {x}, \\mathbf {w}, u, v, \\boldsymbol {\\rho}, t) = \\Phi_ {\\mathrm {L B S}} (\\mathbf {x}, \\mathbf {w}, \\boldsymbol {\\rho}) + \\Delta \\Phi (u, v, t). \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 86, + 691, + 468, + 709 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "There are two main observations that inspired us to use the $(u,v,t)$ motion parameterization. First, we observe that a typical human motion happens at a surface level instead of a volumetric level. Near-surface points sharing similar UV coordinate of the parametric model shows similar motions. Utilizing this prior with a surface parameterization [14, 15], we can reduce the required 4D volumetric motion to the 3D surface-time domain, greatly decreasing the amount of information the deformation network has to learn. Based on a similar idea, [6] diffuses the surface motion to the full 3D space. Second, a naive $(x,y,z,t)$ encoding would introduce quartic memory overhead on an explicitly defined voxel", + "bbox": [ + 75, + 719, + 472, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "structure, which is intractable to use in practice. Instead, by parameterizing the motion to $(u,v,t)$ , we can reduce the memory footprint to a more practical cubic level. Experiments demonstrate that the motion parameterization scheme effectively reduces the dimensionality of the deformation field, thus greatly increasing the convergence rate of the human model.", + "bbox": [ + 496, + 90, + 893, + 196 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Part-based voxelized human representation. Muller et al. [48] propose the multi-resolution hash encoding (MHE) to improve the approximation ability and training speed of implicit neural representations. MHE is defined on an explicit set of voxel grids of different resolutions. Given an input coordinate, it applies the hash encoding on each level and queries the corresponding voxel grid to trilinearly interpolate the feature of the input point for this level. Then, the concatenated multi-resolution feature is fed into a small MLP network to predict the target value. Note that [48] concatenates the features of multi-resolution hash encoding for the same point to mitigate the effect of hash collision, while our part-based voxelized human representation introduces spatially varying resolution to efficiently encode human parts with different complexity.", + "bbox": [ + 496, + 218, + 893, + 445 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In contrast to [40, 56] which use a single neural radiance field (NeRF) to represent the canonical human model, we decompose the human body into multiple parts with different complexity and adopt a structured set of MHE-augmented NeRF with varying resolutions as the body representation. Specifically, we manually divide the human body into multiple parts based on a parametric human model (such as SMPL [43]), as shown in Figure 2. Note that other parametric human models [54,66] can also be used in our method. We use the blend weights defined in SMPL model [43] to decompose SMPL template mesh $\\mathcal{M} = (\\mathcal{V},\\mathcal{E})$ , where $\\mathcal{V}$ represents the vertices and $\\mathcal{E}$ represents the edges. Let the $i$ -th vertex $v_{i}$ have the blend weight $w_{i}$ and for each part $k$ we define $\\Omega_{k}$ as the set of bones that belong to this part. The detailed setting of $\\Omega_{k}$ can be found in the supplementary material. The mesh of the $k$ -th part is defined as $\\mathcal{M}_k = (\\mathcal{V}_k,\\mathcal{E}_k)$ , where:", + "bbox": [ + 496, + 446, + 895, + 688 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {V} _ {k} = \\left\\{v _ {i} \\mid \\operatorname {a r g m a x} w _ {i} \\in \\Omega_ {k} \\right\\}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 611, + 715, + 890, + 732 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {E} _ {k} = \\left\\{\\left(v _ {i}, v _ {j}\\right) \\mid v _ {i} \\in \\mathcal {V} _ {k}, v _ {j} \\in \\mathcal {V} _ {k} \\right\\}. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 581, + 734, + 890, + 752 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To regress the density and color of a query point $\\mathbf{x}$ , we first find the nearest surface point $\\mathbf{p}_k$ on each human part $\\mathcal{M}_k$ of the posed SMPL mesh. Using the strategy in [40], the blend weight $\\mathbf{w}_k$ and UV coordinate $(u_k, v_k)$ of surface point $\\mathbf{p}_k$ are obtained from the SMPL model. With $(u_k, v_k, t)$ , we use the motion parameterization scheme defined in Section 3.1 to transform the query point to the space of the $k$ -th human part. We predefine the parameters of the multiresolution hash encoding function $\\psi_k$ for the $k$ -th part.", + "bbox": [ + 496, + 763, + 893, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "8762", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Given the transformed point, we first apply the multiresolution hash encoding to the transformed point and then feed the encoded point $\\psi_{k}(\\mathbf{x})$ to a small NeRF network to predict the density and color. The density network $\\mathrm{MLP}_{\\sigma_k}$ is defined as:", + "bbox": [ + 75, + 90, + 468, + 165 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\left(\\sigma_ {k}, \\mathbf {z}\\right) = \\operatorname {M L P} _ {\\sigma_ {k}} \\left(\\psi_ {k} (\\mathbf {x})\\right), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 183, + 167, + 468, + 183 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\sigma_{k}$ means the density and $\\mathbf{z}$ is a feature vector. Then, we take the feature vector $\\mathbf{z}$ and the viewing direction $\\mathbf{d}$ as the input for the color regression. Similar to [52], a latent embedding $\\ell_t$ for each video frame $t$ is introduced to model the temporally-varying appearance. The color network is defined as:", + "bbox": [ + 75, + 188, + 468, + 277 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {c} _ {k} = \\operatorname {M L P} _ {\\mathbf {c} _ {k}} (\\mathbf {z}, \\mathbf {d}, \\ell_ {t}). \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 194, + 279, + 468, + 295 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, we have $K$ predictions $\\{(\\sigma_k,\\mathbf{c}_k)\\}_{k = 1}^K$ . The density and color $(\\sigma ,\\mathbf{c})$ of the query point $\\mathbf{x}$ is calculated based on:", + "bbox": [ + 75, + 300, + 468, + 333 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n(\\sigma , \\mathbf {c}) = (\\sigma_ {k ^ {*}}, \\mathbf {c} _ {k ^ {*}}), \\text {w h e r e} k ^ {*} = \\underset {k} {\\operatorname {a r g m a x}} \\sigma_ {k}. \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 120, + 342, + 468, + 366 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In contrast to [40, 56] that represent the body with a single NeRF network, our part-based voxelized human representation can assign different densities of model parameters to different human parts with different complexity, thereby enabling us to efficiently distribute the representational power of the network. Experiments show that our proposed body representation significantly improves the rate of convergence.", + "bbox": [ + 75, + 376, + 470, + 497 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.2. Training", + "text_level": 1, + "bbox": [ + 76, + 505, + 181, + 522 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The proposed representation can be learned from sparse multi-view videos by minimizing the difference between rendered and observed images. The volume rendering technique [32, 47] is used to synthesize the pixel color. Give a pixel at frame $t$ , we emit a camera ray and sample points along the ray. Then, the sampled points are fed into the dynamic human representation to predict their colors and densities, which are finally accumulated into the pixel color. During each training iteration, we randomly sample an image patch from the input image and compute the Mean Squared Error (MSE) loss and perceptual loss [31] to train the model parameters, which are defined as:", + "bbox": [ + 75, + 527, + 468, + 710 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL _ {\\mathrm {r g b}} = \\| \\tilde {I} _ {P} - I _ {P} \\| _ {2} + \\| F _ {\\mathrm {v g g}} (\\tilde {I} _ {P}) - F _ {\\mathrm {v g g}} (I _ {P}) \\| _ {2}, \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 112, + 719, + 468, + 739 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\tilde{I}_P$ is the rendered image patch, $I_P$ is the ground truth image patch, and $F_{\\mathrm{vgg}}$ extracts image features using the pretrained VGG network [31]. Ablation study demonstrates that perceptual loss is essential for rendering quality and fast training.", + "bbox": [ + 75, + 750, + 468, + 824 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In addition to the image rendering loss, two regularization techniques are used to facilitate the learning of the neural representations. First, we apply the regularizer in [4] to concentrate densities on the human surface. Second, the residual deformation field is regularized to be small and smooth.", + "bbox": [ + 75, + 825, + 468, + 898 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "More details of the loss terms are described in the supplementary material.", + "bbox": [ + 498, + 90, + 893, + 121 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Implementation details", + "text_level": 1, + "bbox": [ + 500, + 131, + 714, + 147 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We adopt the Adam optimizer [33] with a learning rate of $5e^{-4}$ . We train our model on an RTX 3090 GPU, which takes around 5 minutes to produce photorealistic results. Our method is implemented purely with the PyTorch framework [53] to demonstrate the effectiveness of our representation. It also enables us to fairly compare with baseline methods [34,56,58] implemented in PyTorch. The details of the network architectures and hyper-parameters are presented in the supplementary material.", + "bbox": [ + 496, + 154, + 893, + 290 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 305, + 630, + 321 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets", + "text_level": 1, + "bbox": [ + 500, + 330, + 601, + 344 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "ZJU-MoCap [58] dataset is a widely-used benchmark for human modeling from videos. It provides foreground human masks and SMPL parameters. Following [93], we select 6 human subjects (377, 386, 387, 392, 393, 394) from this dataset to conduct our experiments. One camera is used for training, and the remaining cameras are used for evaluation. For each human subject, we select 1 frame every 5 frames and collect 100 frames for training. Please refer to the supplementary material for more detailed experiment settings of all characters.", + "bbox": [ + 496, + 353, + 893, + 503 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "MonoCap dataset contains four multi-view videos collected by [57] from the DeepCap dataset [26] and the DynaCap dataset [25]. It provides camera parameters and human masks. [57] additionally estimate the SMPL parameters for each image. We adopt the setting of training and test camera views in [57]. For each subject, 100 frames are selected for training, and we sample 1 frame every 5 frames. Detailed configurations of all sequences are described in the supplementary material.", + "bbox": [ + 496, + 503, + 893, + 640 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Comparison with the state-of-the-art methods", + "text_level": 1, + "bbox": [ + 498, + 650, + 885, + 666 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Baselines. We compare our method with subject-specific optimization methods [56-58, 93], generalizable methods [34, 100]. All the baselines are implemented in pure PyTorch [53] for a fair comparison. Here we list only the average metric values of all selected characters on a dataset due to the size limit. We provide more detailed qualitative and quantitative comparisons in the supplementary material.", + "bbox": [ + 496, + 674, + 890, + 779 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "(1) Subject-specific optimization methods. Neural Body (NB) [58] anchors a set of latent codes to the SMPL mesh and regresses the radiance field from the posed latent codes. Animatable NeRF (AN) [56] deforms the canonical NeRF with the skeleton-driven framework and models non-rigid deformations by learning blend weight fields. [57] extend [56] with a signed distance field and pose-dependent deformation field to better model the residual deformation and geometric", + "bbox": [ + 496, + 780, + 893, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "8763", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/84dc96aeae2125d189b0a7619b167eeaa2e37e2e1d6a0f679daf2edb419193dc.jpg", + "image_caption": [ + "Figure 3. Qualitative results of novel view synthesis on the ZJU-MoCap and MonoCap datasets. Our method produces better rendering results while only requiring 1/100 of the training time. The bottom row lists the training time of each method." + ], + "image_footnote": [], + "bbox": [ + 138, + 88, + 834, + 502 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "details of dynamic humans. [93] optimizes for a volumetric representation of the person in a canonical space along with the estimated human pose.", + "bbox": [ + 75, + 568, + 470, + 613 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "(2) Generalizable methods. PixelNeRF [100] trains a network to infer the radiance field from a single image. Neural Human Performer (NHP) [34] anchors image features to vertices of the SMPL mesh and aggregates temporal features using a transformer, which are decoded into a human model. For each evaluated subject (e.g. one subject of MonoCap), we first pre-train the network on the other dataset (e.g. ZJUMoCap) and then finetune it on the evaluated subject until it converges.", + "bbox": [ + 75, + 614, + 470, + 751 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Results on the ZJU-MoCap dataset. Table 1 compares our method with NB [58], AN [56], PixelNeRF [100], NHP [34], HN [93] and AS [57] on novel view synthesis. Our proposed representation can be optimized within around 5 minutes to produce photorealistic rendering results, while [56-58, 93] require around 10 hours to finish training and [34, 100] require 10 hours of pretraining and 1 hour of fine-tuning. [57, 93] exhibit better results than [56, 58].", + "bbox": [ + 75, + 779, + 472, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "However, they all require a lengthy optimization process and fail to produce reasonable renderings in only 5 minutes because their models have not converged yet. Generalizable methods [34, 100] failed to render humans with reasonable shapes under the monocular setting. Our method achieves comparable results on all of the three evaluated metrics even when only trained in minutes, which shows the effectiveness of our novel human representation. We present qualitative results of our method and baselines in Figure 3.", + "bbox": [ + 496, + 568, + 893, + 705 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Results on the MonoCap dataset. Table 1 summarizes the quantitative comparison between our method and other baselines on the MonoCap dataset. Our model again achieves competitive visual quality while only requiring 1/100 of the training time due to our efficient part-based voxelized human representation and effective motion parameterization scheme. Figure 3 indicates that our method can produce better appearance details than [56-58]. Although [56,58] have shown impressive rendering results given 4-view videos, they do not perform well on monocular inputs. [58] implicitly aggregates the temporal information using structured latent", + "bbox": [ + 496, + 734, + 895, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "8764", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/20b08069d357f2bc282db71ad188b5c540b362ee15a32ef6b4131faf707a4eeb.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Training TimeZJU-MoCapMonoCap
PSNR ↑SSIM ↑LPIPS* ↓PSNR ↑SSIM ↑LPIPS* ↓
Ours~5 min31.010.97138.4532.610.98816.68
HumanNeRF [93]~10 h30.660.96933.3832.680.98715.52
AS [57]~10 h30.380.97537.2332.480.98813.18
AN [56]~10 h29.770.96546.8931.070.98519.47
NB [58]~10 h29.030.96442.4732.360.98616.70
NHP [34]~1 h fine-tuning28.250.95564.7730.510.98027.14
PixelNeRF [100]~1 h fine-tuning24.710.892121.8626.430.96043.98
", + "bbox": [ + 156, + 87, + 812, + 227 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/70d286cf6acfc23fa00f01876cd0fb5f7d4aab5c6609fb7704f73aab26730060.jpg", + "table_caption": [ + "Table 1. Quantitative comparison of our method and baseline methods on the ZJU-MoCap and MonoCap datasets. We use bold text for the best and underlined text for the second best metric value across methods. Our method achieves the fastest training speed and shows competitive rendering results. Note that the NHP [34] and PixelNeRF [100] are additionally pretrained for 10 hours. LPIPS* = LPIPS × 10^3.", + "(a) Ablation studies on proposed components." + ], + "table_footnote": [], + "table_body": "
PSNRSSIMLPIPS*
Ours32.090.98223.47
Ours w/o Part30.110.97445.84
Ours w/o UV31.400.97930.99
Ours w/o Perc30.550.97644.33
", + "bbox": [ + 107, + 319, + 357, + 395 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/a8cbe96e65e0921e82aaac8ca4f9619b354b564472463eec72d4c0acb189cf7a.jpg", + "table_caption": [ + "(b) Ablation studies on variants of $\\mathrm{MLP}_{\\mathrm{res}}$ input." + ], + "table_footnote": [], + "table_body": "
PSNRSSIMLPIPS*
Ours32.090.98223.47
PE31.940.98126.76
XYZ-Code31.320.97931.19
XYZ-Pose31.510.97934.42
", + "bbox": [ + 370, + 319, + 604, + 395 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/5d172a63db7b8047df9fffdd80ecc17b399953100e1574faae9ed9ef2eb039c1.jpg", + "table_caption": [ + "(c) Ablation studies on the part parameters." + ], + "table_footnote": [], + "table_body": "
PSNRSSIMLPIPS*
Ours32.090.98223.47
Table size 21530.690.97635.67
Table size 22031.180.97833.58
", + "bbox": [ + 617, + 319, + 864, + 395 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2. Quantitative results of ablation studies on the 377 sequence of ZJU-MoCap dataset. The description of each model can be found in Section 4.3. All models are trained for 5 minutes.", + "bbox": [ + 75, + 407, + 892, + 434 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "codes, which may not work well on monocular videos with complex human motions. [56] uses a learnable blend weight field to model human motion, which has a higher dimension and could be hard to converge well given single-view supervision. [57, 93] demonstrate similar visual quality and show that representing the human motion with the LBS model and residual deformation works particularly well, but their models require $100\\mathrm{x}$ more time to optimize.", + "bbox": [ + 75, + 474, + 472, + 597 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/69bfc411a9fb74630bf36f1f1b8de9fd49df812faf8e3ade165c7bc2ecef9410.jpg", + "image_caption": [ + "Figure 4. Comparison of convergence LPIPS* and time needed for convergence among different variants of the proposed pipeline. The description of each model can be found in Section 4.3. The proposed components accelerate the training and enhance the rendering quality significantly. LPIPS* = LPIPS × 10^3." + ], + "image_footnote": [], + "bbox": [ + 161, + 611, + 385, + 736 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation Studies", + "text_level": 1, + "bbox": [ + 76, + 832, + 241, + 847 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We perform ablation studies on the \"377\" sequence of the ZJU-MoCap dataset to analyze how the proposed components affect the performance and training speed of our", + "bbox": [ + 75, + 854, + 472, + 902 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "method.", + "bbox": [ + 500, + 474, + 558, + 488 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3.1 Ablation Studies on Proposed Components.", + "text_level": 1, + "bbox": [ + 500, + 508, + 856, + 525 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2(a) lists the quantitative result of ablation studies on our proposed components. All models are trained for 5 minutes. \"Ours w/o Part\" represents the canonical human body with a single MHE-augmented NeRF [48] network, which drastically degrades the rate of convergence. To keep the comparison fair, this variant of our method has a similar number of parameters (302M) to ours (286M). However, this change leads to a significant decrease in PSNR of 1.98dB because of its unwise design of considering all parts equally complex and wasting representational power. In \"Ours w/o UV\", the residual deformation network $\\mathrm{MLP}_{\\mathrm{res}}$ takes hash encoded $(\\mathbf{x},t)$ as input, which observed a PSNR degradation from 32.09dB to 31.40dB with the same training time because of severe hash collision and limited resolution. \"Ours w/o Perc\" does not adopt the perceptual loss during training, which in turn increases the LPIPS distance. This comparison illustrates the importance of perceptual loss for visual fidelity. Figure 4 and Figure 5 provide a more intuitive qualitative comparison among variants of the proposed method.", + "bbox": [ + 496, + 532, + 895, + 821 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Analysis of the part-based voxelized human representation. MHE [48] defines a multiresolution hash table of trainable features to embed input coordinates to a high-dimension space. We find that simply increasing the size", + "bbox": [ + 496, + 840, + 893, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "8765", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/1b3ffeb53c674a544c7235a3f841b95df1fb76cc7e7fea4ccb80a79620375373.jpg", + "image_caption": [ + "Ground Truth" + ], + "image_footnote": [], + "bbox": [ + 209, + 104, + 318, + 224 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/459b96fe011c420d85a8c30f47ca8cf82c3f034a1d30d48c66f120126377f33e.jpg", + "image_caption": [ + "Ours", + "Figure 5. Ablation studies on the 377 sequence of ZJU-MoCap dataset. \"Ours w/o part\" means that we use an MHE-augmented NeRF network to represent the whole body. \"Ours w/o UV\" indicates that the residual deformation network $\\mathrm{MLP}_{\\mathrm{res}}$ takes the hash encoded $(\\mathbf{x},t)$ as input. \"Ours w/o Perc\" represents that we do not use perceptual loss during training." + ], + "image_footnote": [], + "bbox": [ + 320, + 104, + 429, + 224 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/f910efd07b3e303fc32feacd88b0bb6f28437ac5feb08d797c39903fd4b04904.jpg", + "image_caption": [ + "Ours w/o Part" + ], + "image_footnote": [], + "bbox": [ + 431, + 104, + 540, + 224 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0b3fda2a2692b48484194523be78fd38907de2a2434cfdcf3befbc6c754a44bb.jpg", + "image_caption": [ + "Ours w/o UV" + ], + "image_footnote": [], + "bbox": [ + 540, + 104, + 650, + 224 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0653bd7198263efbf1afdb147c64cb7f37f2ce4e96ebbd6819fc231a29ff7424.jpg", + "image_caption": [ + "Ours w/o Perc" + ], + "image_footnote": [], + "bbox": [ + 651, + 104, + 761, + 224 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "of the hash table does not always result in better performance with the same training time, because a bigger hash table leads to higher memory consumption and increases the time of each training iteration. The proposed part-based voxelized human representation allows us to adapt the hash table size according to the complexity of the human part, allowing us to efficiently represent the human body. Table 2(a) demonstrates the effectiveness of the part-based voxelized human representation. To further validate this representation, we design two variants that use the hash tables of size $2^{15}$ and $2^{20}$ in all human parts respectively. Table 2(c) summarizes the ablation studies, indicating that varying the model parameters in human parts improves the performance.", + "bbox": [ + 75, + 295, + 472, + 492 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Analysis of the motion parameterization scheme. Table 2(a) shows that our model works better when the residual deformation network $\\mathrm{MLP}_{\\mathrm{res}}$ takes parameterized 3D surface-time $(u,v,t)$ coordinates as input, compared with taking 4D space-time $(\\mathbf{x},t)$ as input. Note that $(u,v,t)$ makes MHE much more memory efficient than $(\\mathbf{x},t)$ . To further validate the effectiveness of our motion parameterization, we additionally design three variants of the $\\mathrm{MLP}_{\\mathrm{res}}$ input. (1) PE: positional encoded $(\\mathbf{x},t)$ . (2) XYZ-Code: hash encoded $\\mathbf{x}$ and a per-frame learnable latent code [57]. (3) XYZ-Pose: hash encoded $\\mathbf{x}$ and the pose parameter [40,93]. When taking the positional encoded $(\\mathbf{x},t)$ as input, we use a larger network for $\\mathrm{MLP}_{\\mathrm{res}}$ . The results in Table 2(b) indicate that hash encoded $(u,v,t)$ achieves the best performance.", + "bbox": [ + 75, + 510, + 472, + 722 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Analysis of Robustness. To evaluate the robustness of the proposed system, we measure the time needed to achieve an evaluation PSNR of 30 for five times on the \"377\" sequence. This results in a training time with a mean value of $76.00s$ and a standard derivation of $13.56s$ , showing the stability of the proposed method.", + "bbox": [ + 75, + 739, + 470, + 830 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Limitations", + "text_level": 1, + "bbox": [ + 76, + 844, + 199, + 859 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Although our method can quickly reconstruct high-quality human models from videos, it has some limitations to be fur-", + "bbox": [ + 75, + 869, + 470, + 898 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "ther addressed. First, our method currently relies on accurate SMPL parameters, which could be difficult to obtain under in-the-wild settings. It is interesting to utilize the techniques in [79, 93] to optimize the human pose parameters along with the training of human avatars. Second, we can only reconstruct foreground dynamic humans, while dynamic scenes typically include foreground and background entities. It might be plausible to combine our method with ST-NeRF [29] or MultiNB [74] to quickly reconstruct dynamic scenes containing foreground and background objects.", + "bbox": [ + 496, + 295, + 893, + 446 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 465, + 617, + 482 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We introduce a novel dynamic human representation that can be quickly optimized from videos and used for generating free-viewpoint videos of the human performer. This representation consists of a part-based voxelized human model in the canonical space and a motion parameterization scheme that transforms points from the world space to the canonical space. The part-based voxelized human model decomposes the human body into multiple parts and represents each part with an MHE-augmented NeRF network, which efficiently distributes network representational power and significantly improves the training speed. When predicting the motion of a query point, the motion field reparameterizes the point coordinate as 2D surface-level UV coordinate, which effectively reduces the dimensionality of motion the network is required to model, resulting in a boost in convergence rate. Experiments demonstrate that our proposed representation can be optimized at 1/100 of the time of previous methods while still maintaining competitive rendering quality. We show that given a 100-frame monocular video of a $512 \\times 512$ resolution, our method can produce photorealistic free-viewpoint videos in minutes on an RTX 3090 GPU.", + "bbox": [ + 496, + 493, + 893, + 811 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements. This work was supported by the Key Research Project of Zhejiang Lab (No. K2022PG1BB01), NSFC (No. 62172364), and Information Technology Center and State Key Lab of CAD&CG, Zhejiang University.", + "bbox": [ + 496, + 840, + 893, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "8766", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graphics. In ECCV, 2020. 2", + "[2] Thiemo Alldieck, Mihai Zanfir, and Cristian Sminchisescu. Photorealistic monocular 3d reconstruction of humans wearing clothing. arXiv preprint arXiv:2204.08906, 2022. 2", + "[3] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5855–5864, 2021. 2, 3", + "[4] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. arXiv preprint arXiv:2111.12077, 2021. 2, 5", + "[5] Alexander Bergman, Petr Kellnhofer, and Gordon Wetzstein. Fast training of neural lumigraph representations using meta learning. Advances in Neural Information Processing Systems, 34, 2021. 3", + "[6] Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Loopreg: Self-supervised learning of implicit surface correspondences, pose and shape for 3d human mesh registration. In NeurlPS, 2020. 4", + "[7] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. arXiv preprint arXiv:2203.09517, 2022. 3", + "[8] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14124-14133, 2021. 3", + "[9] Xu Chen, Yufeng Zheng, Michael J Black, Otmar Hilliges, and Andreas Geiger. Snarf: Differentiable forward skinning for animating non-rigid neural implicit shapes. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11594-11604, 2021. 2", + "[10] Yue Chen, Xuan Wang, Qi Zhang, Xiaoyu Li, Xingyu Chen, Yu Guo, Jue Wang, and Fei Wang. Uv volumes for real-time rendering of editable free-view human performance. arXiv preprint arXiv:2203.14402, 2022. 3", + "[11] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In CVPR, 2019. 2", + "[12] Julian Chibane, Aayush Bansal, Verica Lazova, and Gerard Pons-Moll. Stereo radiance fields (srf): Learning view synthesis for sparse views of novel scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7911-7920, 2021. 3", + "[13] Alvaro Collet, Ming Chuang, Pat Sweeney, Don Gillett, Dennis Evseev, David Calabrese, Hugues Hoppe, Adam Kirk, and Steve Sullivan. High-quality streamable free-viewpoint video. ACM TOG, 2015. 2", + "[14] Robert L Cook. Shade trees. In Proceedings of the 11th annual conference on Computer graphics and interactive techniques, pages 223-231, 1984. 2, 4" + ], + "bbox": [ + 86, + 116, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[15] Robert L Cook, Loren Carpenter, and Edwin Catmull. The reyes image rendering architecture. ACM SIGGRAPH Computer Graphics, 21(4):95-102, 1987. 2, 4", + "[16] Boyang Deng, John P Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, and Andrea Tagliasacchi. Nasa neural articulated shape approximation. In European Conference on Computer Vision, pages 612-628. Springer, 2020. 3", + "[17] Mingsong Dou, Sameh Khamis, Yury Degtyarev, Philip Davidson, Sean Ryan Fanello, Adarsh Kowdle, Sergio Orts Escolano, Christoph Rhemann, David Kim, Jonathan Taylor, et al. Fusion4d: Real-time performance capture of challenging scenes. ACM TOG, 2016. 2", + "[18] Yilun Du, Yinan Zhang, Hong-Xing Yu, Joshua B Tenenbaum, and Jiajun Wu. Neural radiance flow for 4d view synthesis and video processing. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 14304-14314. IEEE Computer Society, 2021. 2", + "[19] Emilien Dupont, Hyunjik Kim, SM Eslami, Danilo Rezende, and Dan Rosenbaum. From data to functa: Your data point is a function and you should treat it like one. arXiv preprint arXiv:2201.12204, 2022. 3", + "[20] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning, pages 1126–1135. PMLR, 2017. 3", + "[21] Xuan Gao, Chenglai Zhong, Jun Xiang, Yang Hong, Yudong Guo, and Juyong Zhang. Reconstructing personalized semantic facial nerf models from monocular video. arXiv preprint arXiv:2210.06108, 2022.3", + "[22] Stephan J Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin. Fastnerf: High-fidelity neural rendering at 200fps. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14346-14355, 2021. 2", + "[23] Haoyu Guo, Sida Peng, Haotong Lin, Qianqian Wang, Guofeng Zhang, Hujun Bao, and Xiaowei Zhou. Neural 3d scene reconstruction with the Manhattan-world assumption. In CVPR, 2022. 2", + "[24] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, et al. The relightables: Volumetric performance capture of humans with realistic relighting. ACM TOG, 2019. 2", + "[25] Marc Habermann, Lingjie Liu, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, and Christian Theobalt. Real-time deep dynamic characters. In SIGGRAPH, 2021. 5", + "[26] Marc Habermann, Weipeng Xu, Michael Zollhofer, Gerard Pons-Moll, and Christian Theobalt. Deepcap: Monocular human performance capture using weak supervision. In CVPR, 2020. 5", + "[27] Peter Hedman, Pratul P Srinivasan, Ben Mildenhall, Jonathan T Barron, and Paul Debevec. Baking neural radiance fields for real-time view synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5875-5884, 2021. 2" + ], + "bbox": [ + 509, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "8767", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[28] Tao Hu, Tao Yu, Zerong Zheng, He Zhang, Yebin Liu, and Matthias Zwicker. Hvtr: Hybrid volumetric-textural rendering for human avatars. arXiv preprint arXiv:2112.10203, 2021. 2", + "[29] Zhang Jiakai, Liu Xinhang, Ye Xinyi, Zhao Fuqiang, Zhang Yanshun, Wu Minye, Zhang Yingliang, Xu Lan, and Yu Jingyi. Editable free-viewpoint video using a layered neural representation. In SIGGRAPH, 2021. 8", + "[30] Boyi Jiang, Yang Hong, Hujun Bao, and Juyong Zhang. Self-recon: Self reconstruction your digital avatar from monocular video. arXiv preprint arXiv:2201.12792, 2022. 2", + "[31] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016. 5", + "[32] James T Kajiya and Brian P Von Herzen. Ray tracing volume densities. ACM SIGGRAPH computer graphics, 18(3):165-174, 1984. 5", + "[33] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 5", + "[34] Youngjoong Kwon, Dahun Kim, Duygu Ceylan, and Henry Fuchs. Neural human performer: Learning generalizable radiance fields for human performance rendering. Advances in Neural Information Processing Systems, 34, 2021. 2, 5, 6, 7", + "[35] John P Lewis, Matt Cordner, and Nickson Fong. Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation. In SIGGRAPH, 2000. 2, 3, 4", + "[36] Ruilong Li, Julian Tanke, Minh Vo, Michael Zollhoffer, Jurgen Gall, Angjoo Kanazawa, and Christoph Lassner. Tava: Template-free animatable volumetric actors. arXiv preprint arXiv:2206.08929, 2022. 2", + "[37] Zhengqi Li, Simon Niklaus, Noah Snavely, and Oliver Wang. Neural scene flow fields for space-time view synthesis of dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6498-6508, 2021. 2", + "[38] Yiyi Liao, Katja Schwarz, Lars Mescheder, and Andreas Geiger. Towards unsupervised learning of generative models for 3d controllable image synthesis. In CVPR, 2020. 2", + "[39] Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. Advances in Neural Information Processing Systems, 33:15651-15663, 2020. 2, 3", + "[40] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural actor: Neural free-view synthesis of human actors with pose control. In SIGGRAPH Asia, 2021. 2, 3, 4, 5, 8", + "[41] Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui. Dist: Rendering deep implicit signed distance function with differentiable sphere tracing. In CVPR, 2020. 2", + "[42] Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural volumes: Learning dynamic renderable volumes from images. In SIGGRAPH, 2019. 2", + "[43] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. Smpl: A skinned multiperson linear model. ACM TOG, 2015. 2, 4" + ], + "bbox": [ + 86, + 90, + 470, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[44] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In CVPR, 2019. 2", + "[45] Marko Mihajlovic, Aayush Bansal, Michael Zollhoefer, Siyu Tang, and Shunsuke Saito. Keypointnerf: Generalizing image-based volumetric avatars using relative spatial encoding of keypoints. In European Conference on Computer Vision, pages 179-197. Springer, 2022. 3", + "[46] Marko Mihajlovic, Shunsuke Saito, Aayush Bansal, Michael Zollhoefer, and Siyu Tang. COAP: Compositional articulated occupancy of people. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 3", + "[47] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020. 2, 3, 5", + "[48] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. In SIGGRAPH, 2022. 2, 3, 4, 7", + "[49] Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018. 3", + "[50] Michael Oechsle, Songyou Peng, and Andreas Geiger. Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5589-5599, 2021. 2", + "[51] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, 2019. 2, 3", + "[52] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5865-5874, 2021. 2, 5", + "[53] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, 2019. 5", + "[54] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3d hands, face, and body from a single image. In CVPR, 2019. 4", + "[55] Bo Peng, Jun Hu, Jingtao Zhou, and Juyong Zhang. Selfnerf: Fast training nerf for human from monocular self-rotating video. arXiv preprint arXiv:2210.01651, 2022. 3", + "[56] Sida Peng, Junting Dong, Qianqian Wang, Shangzhan Zhang, Qing Shuai, Xiaowei Zhou, and Hujun Bao. Animatable neural radiance fields for modeling dynamic human bodies." + ], + "bbox": [ + 509, + 90, + 893, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "8768", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14314-14323, 2021. 2, 4, 5, 6, 7", + "[57] Sida Peng, Shangzhan Zhang, Zhen Xu, Chen Geng, Boyi Jiang, Hujun Bao, and Xiaowei Zhou. Animatable neural implicit surfaces for creating avatars from videos. arXiv preprint arXiv:2203.08133, 2022. 5, 6, 7, 8", + "[58] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9054-9063, 2021. 1, 2, 5, 6, 7", + "[59] Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-nerf: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10318-10327, 2021. 2", + "[60] Charles R Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas. Volumetric and multi-view cnns for object classification on 3d data. In CVPR, 2016. 2", + "[61] Amit Raj, Julian Tanke, James Hays, Minh Vo, Carsten Stoll, and Christoph Lassner. Anr: Articulated neural rendering for virtual avatars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3722-3731, 2021. 2", + "[62] Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. H3d-net: Few-shot high-fidelity 3d head reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5620-5629, 2021. 3", + "[63] Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger. Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14335-14345, 2021. 2", + "[64] Konstantinos Rematas, Andrew Liu, Pratul P Srinivasan, Jonathan T Barron, Andrea Tagliasacchi, Thomas Funkhouser, and Vittorio Ferrari. Urban radiance fields. arXiv preprint arXiv:2111.14643, 2021. 2", + "[65] Edoardo Remelli, Timur Bagautdinov, Shunsuke Saito, Chenglei Wu, Tomas Simon, Shih-En Wei, Kaiwen Guo, Zhe Cao, Fabian Prada, Jason Saragih, et al. Drivable volumetric avatars using texel-aligned features. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1–9, 2022. 2", + "[66] Javier Romero, Dimitrios Tzionas, and Michael J Black. Embodied hands: Modeling and capturing hands and bodies together. ACM ToG, 2017. 4", + "[67] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In ICCV, 2019. 2", + "[68] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo. Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization. In CVPR, 2020. 2", + "[69] Shunsuke Saito, Jinlong Yang, Qianli Ma, and Michael J Black. Scanimate: Weakly supervised learning of skinned" + ], + "bbox": [ + 86, + 92, + 470, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "clothed avatar networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2886-2897, 2021. 2", + "[70] Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G Baraniuk, and Ashok Veeraraghavan. Miner: Multiscale implicit neural representations. arXiv preprint arXiv:2202.03532, 2022. 3", + "[71] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In CVPR, 2016. 2", + "[72] Johannes L Schonberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys. Pixelwise view selection for unstructured multi-view stereo. In ECCV, 2016. 2, 3", + "[73] Ruizhi Shao, Hongwen Zhang, He Zhang, Yanpei Cao, Tao Yu, and Yebin Liu. Doublefield: Bridging the neural surface and radiance fields for high-fidelity human rendering. arXiv preprint arXiv:2106.03798, 2021. 3", + "[74] Qing Shuai, Chen Geng, Qi Fang, Sida Peng, Wenhao Shen, Xiaowei Zhou, and Hujun Bao. Novel view synthesis of human interactions from sparse multi-view videos. In SIGGRAPH Conference Proceedings, 2022. 8", + "[75] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462-7473, 2020. 3", + "[76] Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhofer. Deepvoxels: Learning persistent 3d feature embeddings. In CVPR, 2019. 2", + "[77] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3d-structure-aware neural scene representations. In NeurIPS, 2019. 2", + "[78] Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller. Multi-view convolutional neural networks for 3d shape recognition. In Proceedings of the IEEE international conference on computer vision, pages 945-953, 2015. 2", + "[79] Shih-Yang Su, Frank Yu, Michael Zollhöfer, and Helge Rhodin. A-nerf: Articulated neural radiance fields for learning human shape, appearance, and pose. Advances in Neural Information Processing Systems, 34, 2021. 3, 8", + "[80] Zhuo Su, Lan Xu, Zerong Zheng, Tao Yu, Yebin Liu, et al. Robustfusion: Human volumetric capture with data-driven visual cues using a rgbd camera. In ECCV, 2020. 2", + "[81] Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. arXiv preprint arXiv:2111.11215, 2021. 3", + "[82] Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Mildenhall, Pratul P Srinivasan, Jonathan T Barron, and Henrik Kretzschmar. Block-nerf: Scalable large scene neural view synthesis. arXiv preprint arXiv:2202.05263, 2022. 2", + "[83] Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P Srinivasan, Jonathan T Barron, and Ren Ng. Learned initializations for optimizing coordinate-based neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2846-2855, 2021. 3" + ], + "bbox": [ + 509, + 92, + 893, + 900 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "8769", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[84] Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems, 33:7537-7547, 2020. 3", + "[85] Matthias Teschner, Bruno Heidelberger, Matthias Müller, Danat Pomerantes, and Markus H Gross. Optimized spatial hashing for collision detection of deformable objects. In Vmv, volume 3, pages 47-54, 2003. 3", + "[86] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM TOG, 2019. 2", + "[87] Garvita Tiwari, Nikolaos Sarafianos, Tony Tung, and Gerard Pons-Moll. Neural-gif: Neural generalized implicit functions for animating people in clothing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11708-11718, 2021. 2", + "[88] Alex Trevithick and Bo Yang. Grf: Learning a general radiance field for 3d representation and rendering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15182-15192, 2021. 3", + "[89] Haithem Turki, Deva Ramanan, and Mahadev Satyanarayanan. Mega-nerf: Scalable construction of large-scale nerfs for virtual fly-throughs. arXiv preprint arXiv:2112.10703, 2021. 2", + "[90] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In NeurIPS, 2021. 2", + "[91] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P Srinivasan, Howard Zhou, Jonathan T Barron, Ricardo Martin-Brualla, Noah Snavely, and Thomas Funkhouser. Ibrnet: Learning multi-view image-based rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4690-4699, 2021. 3", + "[92] Shaofei Wang, Katja Schwarz, Andreas Geiger, and Siyu Tang. Arah: animatable volume rendering of articulated human sdfs. arXiv preprint arXiv:2210.10036, 2022. 2", + "[93] Chung-Yi Weng, Brian Curless, Pratul P Srinivasan, Jonathan T Barron, and Ira Kemelmacher-Shlizerman. Humanerf: Free-viewpoint rendering of moving people from monocular video. arXiv preprint arXiv:2201.04127, 2022. 1, 2, 3, 5, 6, 7, 8", + "[94] Yuanbo Xiangli, Linning Xu, Xingang Pan, Nanxuan Zhao, Anyi Rao, Christian Theobalt, Bo Dai, and Dahua Lin. Citynerf: Building nerf at city scale. arXiv preprint arXiv:2112.05504, 2021. 2", + "[95] Hongyi Xu, Thiemo Alldieck, and Cristian Sminchisescu. H-nerf: Neural radiance fields for rendering and temporal reconstruction of humans in motion. Advances in Neural Information Processing Systems, 34, 2021. 2", + "[96] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In NeurIPS, 2021. 2", + "[97] Jianglong Ye, Yuntao Chen, Naiyan Wang, and Xiaolong Wang. Gifs: Neural implicit function for general shape representation. arXiv preprint arXiv:2204.07126, 2022. 2" + ], + "bbox": [ + 86, + 92, + 470, + 900 + ], + "page_idx": 11 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[98] Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. arXiv preprint arXiv:2112.05131, 2021. 2, 3", + "[99] Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. Plenoptrees for real-time rendering of neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5752-5761, 2021. 2", + "[100] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelnerf: Neural radiance fields from one or few images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4578-4587, 2021. 2, 3, 5, 6, 7", + "[101] Ruiqi Zhang and Jie Chen. Ndf: Neural deformable fields for dynamic human modelling. arXiv preprint arXiv:2207.09193, 2022. 2", + "[102] Fuqiang Zhao, Wei Yang, Jiakai Zhang, Pei Lin, Yingliang Zhang, Jingyi Yu, and Lan Xu. Humannerf: Generalizable neural human radiance field from sparse inputs. arXiv preprint arXiv:2112.02789, 2021. 2", + "[103] Zerong Zheng, Han Huang, Tao Yu, Hongwen Zhang, Yandong Guo, and Yebin Liu. Structured local radiance fields for human avatar modeling. arXiv preprint arXiv:2203.14478, 2022. 2", + "[104] Shuaifeng Zhi, Tristan Laidlow, Stefan Leutenegger, and Andrew J Davison. In-place scene labelling and understanding with implicit scene representation. In ICCV, 2021. 2", + "[105] Yihao Zhi, Shenhan Qian, Xinhao Yan, and Shenghua Gao. Dual-space nerf: Learning animatable avatars and scene lighting in separate spaces. arXiv preprint arXiv:2208.14851, 2022. 2" + ], + "bbox": [ + 503, + 92, + 893, + 542 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "8770", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/2023/Learning Neural Volumetric Representations of Dynamic Humans in Minutes/4b95dcfc-29a4-4f2f-b770-89d83ba2b62f_model.json b/2023/Learning Neural Volumetric Representations of Dynamic Humans in Minutes/4b95dcfc-29a4-4f2f-b770-89d83ba2b62f_model.json new file mode 100644 index 0000000000000000000000000000000000000000..e3e1a2596715be035956905003e296268d168111 --- /dev/null +++ b/2023/Learning Neural Volumetric Representations of Dynamic Humans in Minutes/4b95dcfc-29a4-4f2f-b770-89d83ba2b62f_model.json @@ -0,0 +1,2886 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.095, + 0.131, + 0.877, + 0.155 + ], + "angle": 0, + "content": "Learning Neural Volumetric Representations of Dynamic Humans in Minutes" + }, + { + "type": "text", + "bbox": [ + 0.177, + 0.18, + 0.278, + 0.2 + ], + "angle": 0, + "content": "Chen Geng*" + }, + { + "type": "text", + "bbox": [ + 0.31, + 0.181, + 0.4, + 0.2 + ], + "angle": 0, + "content": "Sida Peng*" + }, + { + "type": "text", + "bbox": [ + 0.434, + 0.182, + 0.514, + 0.199 + ], + "angle": 0, + "content": "Zhen \\(\\mathrm{Xu^{*}}\\)" + }, + { + "type": "text", + "bbox": [ + 0.547, + 0.182, + 0.637, + 0.199 + ], + "angle": 0, + "content": "Hujun Bao" + }, + { + "type": "text", + "bbox": [ + 0.675, + 0.181, + 0.798, + 0.198 + ], + "angle": 0, + "content": "Xiaowei Zhou" + }, + { + "type": "text", + "bbox": [ + 0.26, + 0.211, + 0.71, + 0.23 + ], + "angle": 0, + "content": "State Key Laboratory of CAD&CG, Zhejiang University" + }, + { + "type": "image", + "bbox": [ + 0.092, + 0.288, + 0.242, + 0.406 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.245, + 0.293, + 0.37, + 0.407 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.373, + 0.293, + 0.498, + 0.407 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.093, + 0.408, + 0.242, + 0.522 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.159, + 0.526, + 0.199, + 0.538 + ], + "angle": 0, + "content": "1 min" + }, + { + "type": "image", + "bbox": [ + 0.245, + 0.408, + 0.369, + 0.522 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.287, + 0.526, + 0.327, + 0.538 + ], + "angle": 0, + "content": "5 min" + }, + { + "type": "image", + "bbox": [ + 0.373, + 0.408, + 0.498, + 0.522 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.409, + 0.526, + 0.457, + 0.538 + ], + "angle": 0, + "content": "10 min" + }, + { + "type": "image", + "bbox": [ + 0.512, + 0.287, + 0.861, + 0.54 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.548, + 0.895, + 0.578 + ], + "angle": 0, + "content": "Figure 1. Convergence rate of training. Given a monocular video of a human performer, our model can be learned in \\(\\sim 5\\) minutes to produce photorealistic novel view rendering, which is 100 times faster than Neural Body [58]." + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.608, + 0.314, + 0.623 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.642, + 0.472, + 0.869 + ], + "angle": 0, + "content": "This paper addresses the challenge of efficiently reconstructing volumetric videos of dynamic humans from sparse multi-view videos. Some recent works represent a dynamic human as a canonical neural radiance field (NeRF) and a motion field, which are learned from input videos through differentiable rendering. But the per-scene optimization generally requires hours. Other generalizable NeRF models leverage learned prior from datasets to reduce the optimization time by only finetuning on new scenes at the cost of visual fidelity. In this paper, we propose a novel method for learning neural volumetric representations of dynamic humans in minutes with competitive visual quality. Specifically, we define a novel part-based voxelized human representation to better distribute the representational power of the network to different human parts. Furthermore, we propose" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.609, + 0.895, + 0.731 + ], + "angle": 0, + "content": "a novel 2D motion parameterization scheme to increase the convergence rate of deformation field learning. Experiments demonstrate that our model can be learned 100 times faster than previous per-scene optimization methods while being competitive in the rendering quality. Training our model on a \\(512 \\times 512\\) video with 100 frames typically takes about 5 minutes on a single RTX 3090 GPU. The code is available on our project page: https://zju3dv.github.io/instant_nvr." + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.767, + 0.633, + 0.783 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.795, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Creating volumetric videos of human performers has many applications, such as immersive telepresence, video games, and movie production. Recently, some methods [58, 93] have shown that high-quality volumetric videos can be recovered from sparse multi-view videos by representing dynamic humans with neural scene representations. However, they typically require more than 10 hours of training on" + }, + { + "type": "page_footnote", + "bbox": [ + 0.078, + 0.887, + 0.318, + 0.901 + ], + "angle": 0, + "content": "*Equal contribution. † Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8759" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.471, + 0.198 + ], + "angle": 0, + "content": "a single GPU. The expensive time and computational costs limit the large-scale application of volumetric videos. Generalizable methods [34, 100] utilize learned prior from datasets of dynamic humans to reduce the training time by only finetuning on novel human performers. These techniques could increase the optimization speed by a factor of 2-5 at the cost of some visual fidelity." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.2, + 0.472, + 0.351 + ], + "angle": 0, + "content": "To speed up the process of optimizing a neural representation for view synthesis of dynamic humans, we analyze the structural prior of the human body and motion, and propose a novel dynamic human representation that achieves 100x speedup during optimization while maintaining competitive visual fidelity. Specifically, to model a dynamic human, we first transform world-space points to a canonical space using a novel motion parameterization scheme and inverse linear blend skinning (LBS) [35]. Then, the color and density of these points are estimated using the canonical human model." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.353, + 0.472, + 0.58 + ], + "angle": 0, + "content": "The innovation of our proposed representation is two-fold. First, we observe that human body parts have different levels of complexity in terms of both shape and texture. For example, the face of a human performer typically exhibits higher complexity than a flatly textured torso region, thus requiring more representational power to depict. Motivated by this, our method decomposes the canonical human body into multiple parts and represents the human body with a structured set of voxelized NeRF [47] networks to bring the convergence rate of these different parts to the same level. In contrast to a single-resolution representation, the part-based body model utilizes the human body prior to represent the human shape and texture efficiently, heuristically distributing variational representational power to human parts with spatially varying complexity." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.582, + 0.472, + 0.839 + ], + "angle": 0, + "content": "Second, we notice that non-rigid deformation of human geometry typically occurs around a surface instead of in a volume, that is, nearby surface points on a parametric human model tend to have similar motion behavior. Thus we propose a novel motion parameterization technique that models the 3D human deformation in a 2D domain, enabling modeling of the motion field using 3D voxelized representation to accelerate the learning. This idea is similar to the displacement map and bump map [14, 15] in traditional computer graphics to represent detailed deformation on a 2D texture domain. We extend the technique of displacement map [14, 15] to represent human motions by restricting the originally 3D deformation field [40, 56, 59] on the 2D surface of a parametric human model, such as SMPL [43]. This technique allows us to use hybrid representations [48, 98] to model non-rigid deformation by reducing the memory footprint, largely boosting the convergence of the field." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.472, + 0.902 + ], + "angle": 0, + "content": "Experiments demonstrate that our method significantly accelerates the optimization of neural human representations while being competitive with state-of-the-art human modeling methods on rendering quality. As shown in Figure 1, our" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.894, + 0.136 + ], + "angle": 0, + "content": "model can be trained within 5 minutes to produce a volumetric video of a dynamic human from a 100-frame monocular video of a \\(512 \\times 512\\) resolution on an RTX 3090 GPU." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.138, + 0.794, + 0.152 + ], + "angle": 0, + "content": "To summarize, our key contributions are:" + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.163, + 0.892, + 0.193 + ], + "angle": 0, + "content": "- A novel part-based voxelized human representation for more efficient human body modeling." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.195, + 0.894, + 0.224 + ], + "angle": 0, + "content": "- A 2D motion parameterization scheme for more efficient deformation field modeling." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.227, + 0.894, + 0.272 + ], + "angle": 0, + "content": "- 100x speedup in optimization compared to previous neural human representations while maintaining competitive rendering quality." + }, + { + "type": "list", + "bbox": [ + 0.519, + 0.163, + 0.894, + 0.272 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.297, + 0.638, + 0.312 + ], + "angle": 0, + "content": "2. Related work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.322, + 0.895, + 0.595 + ], + "angle": 0, + "content": "Implicit neural representation and rendering. There have been many 3D scene representations, such as multiview images [60, 78], textured meshes [38, 86], point clouds [1, 61], and voxels [42, 76]. Recently, some methods [11, 41, 44, 51, 77, 97, 104] propose implicit neural representations to represent scenes, which uses MLP networks to predict scene properties for any point in 3D space, such as occupancy [44, 67], signed distance [41, 51], and semantics [23, 104]. This enables them to describe continuous and high-resolution 3D scenes. To perform novel view synthesis, neural radiance field (NeRF) [47] models scenes as implicit fields of density and color. NeRF is optimized from images with volume rendering techniques, which produces impressive image synthesis results. Many works improve NeRF in various aspects, including rendering quality [3, 4], rendering speed [22, 27, 39, 63, 99], scene scale [64, 82, 89, 94], and reconstruction quality [50, 90, 96]. Some methods [18, 37, 52, 59] extend NeRF to dynamic scenes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.614, + 0.897, + 0.901 + ], + "angle": 0, + "content": "Human modeling. Reconstructing high-quality 3D human models is essential for synthesizing free-viewpoint videos of human performers. Traditional methods leverage multi-view stereo techniques [24, 71, 72] or depth fusion [13, 17, 80] to reconstruct human geometries, which require complicated hardware, such as dense camera arrays or depth sensors. To reduce the requirement of the capture equipment, some methods [2, 67, 68] train networks to learn human priors from datasets containing a large amount of 3D ground-truth human models, enabling them to infer human geometry and texture from even a single image. However, due to the limited diversity of training data, these methods do not generalize well to humans under complex poses. Recently, some methods [9, 46, 69, 87] model the shapes of dynamic humans as implicit neural representations and attempt to optimize them from human scans. Another line of works [28, 30, 34, 36, 40, 56, 58, 65, 92, 93, 95, 101-103, 105] exploits dynamic implicit neural representations and differentiable renderers to reconstruct 3D human models from" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8760" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.117, + 0.088, + 0.538, + 0.289 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.542, + 0.087, + 0.851, + 0.289 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.294, + 0.896, + 0.394 + ], + "angle": 0, + "content": "Figure 2. Overview of the proposed representation. Given a query point \\(\\mathbf{x}\\) at frame \\(t\\), we find its nearest surface point on each human part of the SMPL mesh, which gives the blend weight \\(\\mathbf{w}_k\\) and the UV coordinates \\((u_k, v_k)\\). Consider the \\(k\\)-th part. The motion field consists of an inverse LBS module and a residual deformation module. (a) The inverse LBS module takes body pose \\(\\rho\\), blend weight \\(\\mathbf{w}_k\\), and query point \\(\\mathbf{x}\\) as input and outputs the transformed point \\(\\mathbf{x}'\\). The residual deformation module applies the multiresolution hash encoding (MHE) to \\((u_k, v_k, t)\\) and uses an MLP network to regress the residual translation \\(\\Delta \\mathbf{x}\\), which is added to \\(\\mathbf{x}'\\) to obtain the canonical point \\(\\mathbf{x}^{\\mathrm{can}}\\). (b) We then feed \\(\\mathbf{x}^{\\mathrm{can}}\\) to networks of \\(k\\)-th human part to predict the density \\(\\sigma_k\\) and color \\(\\mathbf{c}_k\\). With \\(\\left\\{\\left(\\sigma_k, \\mathbf{c}_k\\right)\\right\\}_{k=1}^{K}\\), we select the one with the largest density as the density and color of the query point." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.418, + 0.475, + 0.645 + ], + "angle": 0, + "content": "videos. To represent dynamic humans, Neural Actor [40] augments the neural radiance field with the linear blend skinning model [35]. It additionally adopts a residual deformation field to better predict human motions. To overcome the inaccuracy of input human poses, [79, 93] optimize the parameters of human poses jointly with the human representations during training. These methods typically require a lengthy training process to produce high-quality human models. In contrast, we introduce a part-based voxelized human representation to model the canonical human body, which significantly accelerates the optimization process. Although [16, 46] have proposed part-based implicit functions, they focus on human shape modeling and do not show that the part-based representation can be used to reduce the training time." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.675, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Accelerating the optimization of neural representations. Many differentiable rendering-based methods [3, 39, 47] optimize a separate neural representation for each scene. The optimization process generally takes several hours on a modern GPU, which is time-consuming and costly to scale. Inspired by multi-view stereo matching [72], some methods [8, 12, 45, 73, 88, 91, 100] train a network on multi-view datasets to learn to infer radiance fields from input images. This enables them to quickly fine-tune neural representations to unseen scenes. [19, 62] leverage the auto-decoder [51] to capture the scene priors for efficient fine-tuning. [5, 83] utilize meta-learning techniques [20, 49] to initialize network parameters, thereby improving the training speed. Some methods [7, 10, 55, 70, 81, 98] attempt to design scene representations that support efficient training. [21, 48, 75, 84]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.418, + 0.896, + 0.51 + ], + "angle": 0, + "content": "augments the approximation ability of networks by designing encoding techniques. Multiresolution hash encoding [48] defines multiresolution feature vector arrays for a scene and uses the hash technique [85] to assign each input coordinate a feature vector as the encoded input, which significantly improves the training speed." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.523, + 0.6, + 0.538 + ], + "angle": 0, + "content": "3. Methods" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.549, + 0.896, + 0.701 + ], + "angle": 0, + "content": "This paper aims to quickly create a volumetric video from a sparse multi-view video that captures a dynamic human. We assume that the cameras are calibrated and the human pose and foreground human mask of each image are provided. Section 3.1 introduces our dynamic human model that is comprised of a part-based voxelized human representation and a dimensionality reduction motion parameterization scheme. Section 3.2 discusses how to efficiently optimize the proposed representation. Finally, Section 3.3 provides implementation details." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.711, + 0.783, + 0.727 + ], + "angle": 0, + "content": "3.1. Proposed human representation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.897, + 0.903 + ], + "angle": 0, + "content": "As shown in Figure 2, our dynamic human representation consists of a motion parameterization field and a part-based voxelized human model. (a) For a query point \\(\\mathbf{x}\\), the motion parameterization field first transforms it to the canonical space correspondence \\(\\mathbf{x}^{\\mathrm{can}}\\) using the inverse LBS [35] algorithm and by parameterizing the 3D points onto 2D UV coordinates to predict the residual deformation \\(\\Delta \\mathbf{x}\\). (b) Then, \\(\\mathbf{x}^{\\mathrm{can}}\\) is fed into the part-based voxelized human model in canonical space to predict and aggregate the density and color \\((\\sigma, \\mathbf{c})\\), where the canonical human body is decomposed into \\(K\\) parts, each of which is represented using an" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "8761" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.078, + 0.092, + 0.332, + 0.106 + ], + "angle": 0, + "content": "MHE-augmented [48] NeRF network." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.125, + 0.47, + 0.214 + ], + "angle": 0, + "content": "Motion parameterization on 2D surface domain. To regress the canonical correspondence \\(\\mathbf{x}^{\\mathrm{can}}\\) of a query point \\(\\mathbf{x}\\), we first find its nearest surface point \\(\\mathbf{p}\\) on the posed SMPL mesh. Using the strategy in [40], the blend weight \\(\\mathbf{w}\\) and UV coordinate \\((u,v)\\) of surface point \\(\\mathbf{p}\\) are obtained from the SMPL model." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.215, + 0.471, + 0.32 + ], + "angle": 0, + "content": "Given the blend weight \\(\\mathbf{w}\\) and UV coordinate \\((u, v)\\), the motion field maps the query point \\(\\mathbf{x}\\) to the canonical space correspondence \\(\\mathbf{x}^{\\mathrm{can}}\\). The motion field is comprised of an inverse LBS module [35] and a residual deformation module. Given a query point \\(\\mathbf{x}\\) and blend weight \\(\\mathbf{w}\\), we use the inverse LBS module to transform it to the unposed space, which is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.143, + 0.329, + 0.47, + 0.381 + ], + "angle": 0, + "content": "\\[\n\\Phi_ {\\mathrm {L B S}} (\\mathbf {x}, \\mathbf {w}, \\boldsymbol {\\rho}) = \\left(\\sum_ {j = 1} ^ {J} w _ {k, j} G _ {j}\\right) ^ {- 1} \\mathbf {x}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.39, + 0.471, + 0.45 + ], + "angle": 0, + "content": "where \\(\\rho\\) denotes the human pose and \\(\\{G_j\\}_{j=1}^J\\) are transformation matrices derived from \\(\\rho\\) [43]. The detailed derivation of the inverse LBS algorithm can be found in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.451, + 0.473, + 0.661 + ], + "angle": 0, + "content": "The transformed point \\(\\Phi_{\\mathrm{LBS}}(\\mathbf{x},\\mathbf{w},\\pmb {\\rho})\\) is then deformed to the human surface using the residual deformation module. Specifically, the current time \\(t\\) is first concatenated with the UV coordinate \\((u,v)\\) to serve as the parameterization of the query point \\(\\mathbf{x}\\) at frame \\(t\\). This motion parameterization is inspired by the displacement map and bump map techniques [14, 15] in the traditional Computer Graphics pipelines. It essentially reduces the dimensionality of a 4D space-time sequence down to the 3D surface-time domain utilizing the human deformation prior. Then, we apply the multiresolution hash encoding [48] \\(\\psi_{\\mathrm{res}}\\) to \\((u,v,t)\\) and forward the encoded input through a network \\(\\mathrm{MLP}_{\\mathrm{res}}\\) to regress the residual \\(\\Delta \\Phi (u,v,t)\\). The full human motion at frame \\(t\\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.144, + 0.674, + 0.47, + 0.69 + ], + "angle": 0, + "content": "\\[\n\\Delta \\Phi (u, v, t) = \\mathrm {M L P} _ {\\text {r e s}} \\left(\\psi_ {\\text {r e s}} (u, v, t)\\right), \\tag {2}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.088, + 0.693, + 0.47, + 0.71 + ], + "angle": 0, + "content": "\\[\n\\mathbf {x} ^ {\\text {c a n}} (\\mathbf {x}, \\mathbf {w}, u, v, \\boldsymbol {\\rho}, t) = \\Phi_ {\\mathrm {L B S}} (\\mathbf {x}, \\mathbf {w}, \\boldsymbol {\\rho}) + \\Delta \\Phi (u, v, t). \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.72, + 0.473, + 0.902 + ], + "angle": 0, + "content": "There are two main observations that inspired us to use the \\((u,v,t)\\) motion parameterization. First, we observe that a typical human motion happens at a surface level instead of a volumetric level. Near-surface points sharing similar UV coordinate of the parametric model shows similar motions. Utilizing this prior with a surface parameterization [14, 15], we can reduce the required 4D volumetric motion to the 3D surface-time domain, greatly decreasing the amount of information the deformation network has to learn. Based on a similar idea, [6] diffuses the surface motion to the full 3D space. Second, a naive \\((x,y,z,t)\\) encoding would introduce quartic memory overhead on an explicitly defined voxel" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.198 + ], + "angle": 0, + "content": "structure, which is intractable to use in practice. Instead, by parameterizing the motion to \\((u,v,t)\\), we can reduce the memory footprint to a more practical cubic level. Experiments demonstrate that the motion parameterization scheme effectively reduces the dimensionality of the deformation field, thus greatly increasing the convergence rate of the human model." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.219, + 0.895, + 0.446 + ], + "angle": 0, + "content": "Part-based voxelized human representation. Muller et al. [48] propose the multi-resolution hash encoding (MHE) to improve the approximation ability and training speed of implicit neural representations. MHE is defined on an explicit set of voxel grids of different resolutions. Given an input coordinate, it applies the hash encoding on each level and queries the corresponding voxel grid to trilinearly interpolate the feature of the input point for this level. Then, the concatenated multi-resolution feature is fed into a small MLP network to predict the target value. Note that [48] concatenates the features of multi-resolution hash encoding for the same point to mitigate the effect of hash collision, while our part-based voxelized human representation introduces spatially varying resolution to efficiently encode human parts with different complexity." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.447, + 0.897, + 0.689 + ], + "angle": 0, + "content": "In contrast to [40, 56] which use a single neural radiance field (NeRF) to represent the canonical human model, we decompose the human body into multiple parts with different complexity and adopt a structured set of MHE-augmented NeRF with varying resolutions as the body representation. Specifically, we manually divide the human body into multiple parts based on a parametric human model (such as SMPL [43]), as shown in Figure 2. Note that other parametric human models [54,66] can also be used in our method. We use the blend weights defined in SMPL model [43] to decompose SMPL template mesh \\(\\mathcal{M} = (\\mathcal{V},\\mathcal{E})\\), where \\(\\mathcal{V}\\) represents the vertices and \\(\\mathcal{E}\\) represents the edges. Let the \\(i\\)-th vertex \\(v_{i}\\) have the blend weight \\(w_{i}\\) and for each part \\(k\\) we define \\(\\Omega_{k}\\) as the set of bones that belong to this part. The detailed setting of \\(\\Omega_{k}\\) can be found in the supplementary material. The mesh of the \\(k\\)-th part is defined as \\(\\mathcal{M}_k = (\\mathcal{V}_k,\\mathcal{E}_k)\\), where:" + }, + { + "type": "equation", + "bbox": [ + 0.612, + 0.716, + 0.892, + 0.733 + ], + "angle": 0, + "content": "\\[\n\\mathcal {V} _ {k} = \\left\\{v _ {i} \\mid \\operatorname {a r g m a x} w _ {i} \\in \\Omega_ {k} \\right\\}, \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.582, + 0.736, + 0.892, + 0.753 + ], + "angle": 0, + "content": "\\[\n\\mathcal {E} _ {k} = \\left\\{\\left(v _ {i}, v _ {j}\\right) \\mid v _ {i} \\in \\mathcal {V} _ {k}, v _ {j} \\in \\mathcal {V} _ {k} \\right\\}. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.895, + 0.902 + ], + "angle": 0, + "content": "To regress the density and color of a query point \\(\\mathbf{x}\\), we first find the nearest surface point \\(\\mathbf{p}_k\\) on each human part \\(\\mathcal{M}_k\\) of the posed SMPL mesh. Using the strategy in [40], the blend weight \\(\\mathbf{w}_k\\) and UV coordinate \\((u_k, v_k)\\) of surface point \\(\\mathbf{p}_k\\) are obtained from the SMPL model. With \\((u_k, v_k, t)\\), we use the motion parameterization scheme defined in Section 3.1 to transform the query point to the space of the \\(k\\)-th human part. We predefine the parameters of the multiresolution hash encoding function \\(\\psi_k\\) for the \\(k\\)-th part." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8762" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.166 + ], + "angle": 0, + "content": "Given the transformed point, we first apply the multiresolution hash encoding to the transformed point and then feed the encoded point \\(\\psi_{k}(\\mathbf{x})\\) to a small NeRF network to predict the density and color. The density network \\(\\mathrm{MLP}_{\\sigma_k}\\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.184, + 0.168, + 0.47, + 0.184 + ], + "angle": 0, + "content": "\\[\n\\left(\\sigma_ {k}, \\mathbf {z}\\right) = \\operatorname {M L P} _ {\\sigma_ {k}} \\left(\\psi_ {k} (\\mathbf {x})\\right), \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.189, + 0.47, + 0.278 + ], + "angle": 0, + "content": "where \\(\\sigma_{k}\\) means the density and \\(\\mathbf{z}\\) is a feature vector. Then, we take the feature vector \\(\\mathbf{z}\\) and the viewing direction \\(\\mathbf{d}\\) as the input for the color regression. Similar to [52], a latent embedding \\(\\ell_t\\) for each video frame \\(t\\) is introduced to model the temporally-varying appearance. The color network is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.195, + 0.28, + 0.47, + 0.296 + ], + "angle": 0, + "content": "\\[\n\\mathbf {c} _ {k} = \\operatorname {M L P} _ {\\mathbf {c} _ {k}} (\\mathbf {z}, \\mathbf {d}, \\ell_ {t}). \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.301, + 0.469, + 0.334 + ], + "angle": 0, + "content": "Finally, we have \\(K\\) predictions \\(\\{(\\sigma_k,\\mathbf{c}_k)\\}_{k = 1}^K\\). The density and color \\((\\sigma ,\\mathbf{c})\\) of the query point \\(\\mathbf{x}\\) is calculated based on:" + }, + { + "type": "equation", + "bbox": [ + 0.122, + 0.343, + 0.47, + 0.367 + ], + "angle": 0, + "content": "\\[\n(\\sigma , \\mathbf {c}) = (\\sigma_ {k ^ {*}}, \\mathbf {c} _ {k ^ {*}}), \\text {w h e r e} k ^ {*} = \\underset {k} {\\operatorname {a r g m a x}} \\sigma_ {k}. \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.377, + 0.471, + 0.498 + ], + "angle": 0, + "content": "In contrast to [40, 56] that represent the body with a single NeRF network, our part-based voxelized human representation can assign different densities of model parameters to different human parts with different complexity, thereby enabling us to efficiently distribute the representational power of the network. Experiments show that our proposed body representation significantly improves the rate of convergence." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.506, + 0.182, + 0.523 + ], + "angle": 0, + "content": "3.2. Training" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.529, + 0.47, + 0.711 + ], + "angle": 0, + "content": "The proposed representation can be learned from sparse multi-view videos by minimizing the difference between rendered and observed images. The volume rendering technique [32, 47] is used to synthesize the pixel color. Give a pixel at frame \\( t \\), we emit a camera ray and sample points along the ray. Then, the sampled points are fed into the dynamic human representation to predict their colors and densities, which are finally accumulated into the pixel color. During each training iteration, we randomly sample an image patch from the input image and compute the Mean Squared Error (MSE) loss and perceptual loss [31] to train the model parameters, which are defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.114, + 0.72, + 0.47, + 0.74 + ], + "angle": 0, + "content": "\\[\nL _ {\\mathrm {r g b}} = \\| \\tilde {I} _ {P} - I _ {P} \\| _ {2} + \\| F _ {\\mathrm {v g g}} (\\tilde {I} _ {P}) - F _ {\\mathrm {v g g}} (I _ {P}) \\| _ {2}, \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.469, + 0.825 + ], + "angle": 0, + "content": "where \\(\\tilde{I}_P\\) is the rendered image patch, \\(I_P\\) is the ground truth image patch, and \\(F_{\\mathrm{vgg}}\\) extracts image features using the pretrained VGG network [31]. Ablation study demonstrates that perceptual loss is essential for rendering quality and fast training." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.47, + 0.9 + ], + "angle": 0, + "content": "In addition to the image rendering loss, two regularization techniques are used to facilitate the learning of the neural representations. First, we apply the regularizer in [4] to concentrate densities on the human surface. Second, the residual deformation field is regularized to be small and smooth." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.894, + 0.122 + ], + "angle": 0, + "content": "More details of the loss terms are described in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.132, + 0.715, + 0.148 + ], + "angle": 0, + "content": "3.3. Implementation details" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.155, + 0.894, + 0.291 + ], + "angle": 0, + "content": "We adopt the Adam optimizer [33] with a learning rate of \\(5e^{-4}\\). We train our model on an RTX 3090 GPU, which takes around 5 minutes to produce photorealistic results. Our method is implemented purely with the PyTorch framework [53] to demonstrate the effectiveness of our representation. It also enables us to fairly compare with baseline methods [34,56,58] implemented in PyTorch. The details of the network architectures and hyper-parameters are presented in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.306, + 0.632, + 0.323 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.331, + 0.602, + 0.345 + ], + "angle": 0, + "content": "4.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.354, + 0.894, + 0.504 + ], + "angle": 0, + "content": "ZJU-MoCap [58] dataset is a widely-used benchmark for human modeling from videos. It provides foreground human masks and SMPL parameters. Following [93], we select 6 human subjects (377, 386, 387, 392, 393, 394) from this dataset to conduct our experiments. One camera is used for training, and the remaining cameras are used for evaluation. For each human subject, we select 1 frame every 5 frames and collect 100 frames for training. Please refer to the supplementary material for more detailed experiment settings of all characters." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.505, + 0.894, + 0.641 + ], + "angle": 0, + "content": "MonoCap dataset contains four multi-view videos collected by [57] from the DeepCap dataset [26] and the DynaCap dataset [25]. It provides camera parameters and human masks. [57] additionally estimate the SMPL parameters for each image. We adopt the setting of training and test camera views in [57]. For each subject, 100 frames are selected for training, and we sample 1 frame every 5 frames. Detailed configurations of all sequences are described in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.651, + 0.887, + 0.667 + ], + "angle": 0, + "content": "4.2. Comparison with the state-of-the-art methods" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.675, + 0.892, + 0.78 + ], + "angle": 0, + "content": "Baselines. We compare our method with subject-specific optimization methods [56-58, 93], generalizable methods [34, 100]. All the baselines are implemented in pure PyTorch [53] for a fair comparison. Here we list only the average metric values of all selected characters on a dataset due to the size limit. We provide more detailed qualitative and quantitative comparisons in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.781, + 0.894, + 0.901 + ], + "angle": 0, + "content": "(1) Subject-specific optimization methods. Neural Body (NB) [58] anchors a set of latent codes to the SMPL mesh and regresses the radiance field from the posed latent codes. Animatable NeRF (AN) [56] deforms the canonical NeRF with the skeleton-driven framework and models non-rigid deformations by learning blend weight fields. [57] extend [56] with a signed distance field and pose-dependent deformation field to better model the residual deformation and geometric" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8763" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.139, + 0.089, + 0.835, + 0.503 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.514, + 0.893, + 0.545 + ], + "angle": 0, + "content": "Figure 3. Qualitative results of novel view synthesis on the ZJU-MoCap and MonoCap datasets. Our method produces better rendering results while only requiring 1/100 of the training time. The bottom row lists the training time of each method." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.569, + 0.471, + 0.614 + ], + "angle": 0, + "content": "details of dynamic humans. [93] optimizes for a volumetric representation of the person in a canonical space along with the estimated human pose." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.616, + 0.472, + 0.752 + ], + "angle": 0, + "content": "(2) Generalizable methods. PixelNeRF [100] trains a network to infer the radiance field from a single image. Neural Human Performer (NHP) [34] anchors image features to vertices of the SMPL mesh and aggregates temporal features using a transformer, which are decoded into a human model. For each evaluated subject (e.g. one subject of MonoCap), we first pre-train the network on the other dataset (e.g. ZJUMoCap) and then finetune it on the evaluated subject until it converges." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Results on the ZJU-MoCap dataset. Table 1 compares our method with NB [58], AN [56], PixelNeRF [100], NHP [34], HN [93] and AS [57] on novel view synthesis. Our proposed representation can be optimized within around 5 minutes to produce photorealistic rendering results, while [56-58, 93] require around 10 hours to finish training and [34, 100] require 10 hours of pretraining and 1 hour of fine-tuning. [57, 93] exhibit better results than [56, 58]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.569, + 0.895, + 0.706 + ], + "angle": 0, + "content": "However, they all require a lengthy optimization process and fail to produce reasonable renderings in only 5 minutes because their models have not converged yet. Generalizable methods [34, 100] failed to render humans with reasonable shapes under the monocular setting. Our method achieves comparable results on all of the three evaluated metrics even when only trained in minutes, which shows the effectiveness of our novel human representation. We present qualitative results of our method and baselines in Figure 3." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.897, + 0.903 + ], + "angle": 0, + "content": "Results on the MonoCap dataset. Table 1 summarizes the quantitative comparison between our method and other baselines on the MonoCap dataset. Our model again achieves competitive visual quality while only requiring 1/100 of the training time due to our efficient part-based voxelized human representation and effective motion parameterization scheme. Figure 3 indicates that our method can produce better appearance details than [56-58]. Although [56,58] have shown impressive rendering results given 4-view videos, they do not perform well on monocular inputs. [58] implicitly aggregates the temporal information using structured latent" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8764" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.158, + 0.088, + 0.813, + 0.228 + ], + "angle": 0, + "content": "
Training TimeZJU-MoCapMonoCap
PSNR ↑SSIM ↑LPIPS* ↓PSNR ↑SSIM ↑LPIPS* ↓
Ours~5 min31.010.97138.4532.610.98816.68
HumanNeRF [93]~10 h30.660.96933.3832.680.98715.52
AS [57]~10 h30.380.97537.2332.480.98813.18
AN [56]~10 h29.770.96546.8931.070.98519.47
NB [58]~10 h29.030.96442.4732.360.98616.70
NHP [34]~1 h fine-tuning28.250.95564.7730.510.98027.14
PixelNeRF [100]~1 h fine-tuning24.710.892121.8626.430.96043.98
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.239, + 0.895, + 0.285 + ], + "angle": 0, + "content": "Table 1. Quantitative comparison of our method and baseline methods on the ZJU-MoCap and MonoCap datasets. We use bold text for the best and underlined text for the second best metric value across methods. Our method achieves the fastest training speed and shows competitive rendering results. Note that the NHP [34] and PixelNeRF [100] are additionally pretrained for 10 hours. LPIPS* = LPIPS × 10^3." + }, + { + "type": "table_caption", + "bbox": [ + 0.133, + 0.308, + 0.333, + 0.319 + ], + "angle": 0, + "content": "(a) Ablation studies on proposed components." + }, + { + "type": "table", + "bbox": [ + 0.109, + 0.32, + 0.359, + 0.396 + ], + "angle": 0, + "content": "
PSNRSSIMLPIPS*
Ours32.090.98223.47
Ours w/o Part30.110.97445.84
Ours w/o UV31.400.97930.99
Ours w/o Perc30.550.97644.33
" + }, + { + "type": "table_caption", + "bbox": [ + 0.382, + 0.308, + 0.593, + 0.319 + ], + "angle": 0, + "content": "(b) Ablation studies on variants of \\(\\mathrm{MLP}_{\\mathrm{res}}\\) input." + }, + { + "type": "table", + "bbox": [ + 0.371, + 0.32, + 0.605, + 0.396 + ], + "angle": 0, + "content": "
PSNRSSIMLPIPS*
Ours32.090.98223.47
PE31.940.98126.76
XYZ-Code31.320.97931.19
XYZ-Pose31.510.97934.42
" + }, + { + "type": "table_caption", + "bbox": [ + 0.648, + 0.309, + 0.835, + 0.319 + ], + "angle": 0, + "content": "(c) Ablation studies on the part parameters." + }, + { + "type": "table", + "bbox": [ + 0.618, + 0.32, + 0.866, + 0.396 + ], + "angle": 0, + "content": "
PSNRSSIMLPIPS*
Ours32.090.98223.47
Table size 21530.690.97635.67
Table size 22031.180.97833.58
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.408, + 0.893, + 0.435 + ], + "angle": 0, + "content": "Table 2. Quantitative results of ablation studies on the 377 sequence of ZJU-MoCap dataset. The description of each model can be found in Section 4.3. All models are trained for 5 minutes." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.475, + 0.473, + 0.598 + ], + "angle": 0, + "content": "codes, which may not work well on monocular videos with complex human motions. [56] uses a learnable blend weight field to model human motion, which has a higher dimension and could be hard to converge well given single-view supervision. [57, 93] demonstrate similar visual quality and show that representing the human motion with the LBS model and residual deformation works particularly well, but their models require \\(100\\mathrm{x}\\) more time to optimize." + }, + { + "type": "image", + "bbox": [ + 0.163, + 0.612, + 0.387, + 0.737 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.752, + 0.472, + 0.822 + ], + "angle": 0, + "content": "Figure 4. Comparison of convergence LPIPS* and time needed for convergence among different variants of the proposed pipeline. The description of each model can be found in Section 4.3. The proposed components accelerate the training and enhance the rendering quality significantly. LPIPS* = LPIPS × 10^3." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.833, + 0.242, + 0.848 + ], + "angle": 0, + "content": "4.3. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.473, + 0.903 + ], + "angle": 0, + "content": "We perform ablation studies on the \"377\" sequence of the ZJU-MoCap dataset to analyze how the proposed components affect the performance and training speed of our" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.476, + 0.559, + 0.489 + ], + "angle": 0, + "content": "method." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.51, + 0.857, + 0.526 + ], + "angle": 0, + "content": "4.3.1 Ablation Studies on Proposed Components." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.534, + 0.896, + 0.822 + ], + "angle": 0, + "content": "Table 2(a) lists the quantitative result of ablation studies on our proposed components. All models are trained for 5 minutes. \"Ours w/o Part\" represents the canonical human body with a single MHE-augmented NeRF [48] network, which drastically degrades the rate of convergence. To keep the comparison fair, this variant of our method has a similar number of parameters (302M) to ours (286M). However, this change leads to a significant decrease in PSNR of 1.98dB because of its unwise design of considering all parts equally complex and wasting representational power. In \"Ours w/o UV\", the residual deformation network \\(\\mathrm{MLP}_{\\mathrm{res}}\\) takes hash encoded \\((\\mathbf{x},t)\\) as input, which observed a PSNR degradation from 32.09dB to 31.40dB with the same training time because of severe hash collision and limited resolution. \"Ours w/o Perc\" does not adopt the perceptual loss during training, which in turn increases the LPIPS distance. This comparison illustrates the importance of perceptual loss for visual fidelity. Figure 4 and Figure 5 provide a more intuitive qualitative comparison among variants of the proposed method." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.841, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Analysis of the part-based voxelized human representation. MHE [48] defines a multiresolution hash table of trainable features to embed input coordinates to a high-dimension space. We find that simply increasing the size" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8765" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.218, + 0.092, + 0.311, + 0.103 + ], + "angle": 0, + "content": "Ground Truth" + }, + { + "type": "image_caption", + "bbox": [ + 0.361, + 0.092, + 0.392, + 0.102 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.441, + 0.092, + 0.532, + 0.103 + ], + "angle": 0, + "content": "Ours w/o Part" + }, + { + "type": "image_caption", + "bbox": [ + 0.555, + 0.092, + 0.64, + 0.104 + ], + "angle": 0, + "content": "Ours w/o UV" + }, + { + "type": "image_caption", + "bbox": [ + 0.663, + 0.092, + 0.754, + 0.104 + ], + "angle": 0, + "content": "Ours w/o Perc" + }, + { + "type": "image", + "bbox": [ + 0.21, + 0.106, + 0.32, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.321, + 0.106, + 0.43, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.432, + 0.106, + 0.541, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.542, + 0.106, + 0.651, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.653, + 0.106, + 0.762, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.239, + 0.895, + 0.282 + ], + "angle": 0, + "content": "Figure 5. Ablation studies on the 377 sequence of ZJU-MoCap dataset. \"Ours w/o part\" means that we use an MHE-augmented NeRF network to represent the whole body. \"Ours w/o UV\" indicates that the residual deformation network \\(\\mathrm{MLP}_{\\mathrm{res}}\\) takes the hash encoded \\((\\mathbf{x},t)\\) as input. \"Ours w/o Perc\" represents that we do not use perceptual loss during training." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.296, + 0.473, + 0.493 + ], + "angle": 0, + "content": "of the hash table does not always result in better performance with the same training time, because a bigger hash table leads to higher memory consumption and increases the time of each training iteration. The proposed part-based voxelized human representation allows us to adapt the hash table size according to the complexity of the human part, allowing us to efficiently represent the human body. Table 2(a) demonstrates the effectiveness of the part-based voxelized human representation. To further validate this representation, we design two variants that use the hash tables of size \\(2^{15}\\) and \\(2^{20}\\) in all human parts respectively. Table 2(c) summarizes the ablation studies, indicating that varying the model parameters in human parts improves the performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.511, + 0.473, + 0.723 + ], + "angle": 0, + "content": "Analysis of the motion parameterization scheme. Table 2(a) shows that our model works better when the residual deformation network \\(\\mathrm{MLP}_{\\mathrm{res}}\\) takes parameterized 3D surface-time \\((u,v,t)\\) coordinates as input, compared with taking 4D space-time \\((\\mathbf{x},t)\\) as input. Note that \\((u,v,t)\\) makes MHE much more memory efficient than \\((\\mathbf{x},t)\\). To further validate the effectiveness of our motion parameterization, we additionally design three variants of the \\(\\mathrm{MLP}_{\\mathrm{res}}\\) input. (1) PE: positional encoded \\((\\mathbf{x},t)\\). (2) XYZ-Code: hash encoded \\(\\mathbf{x}\\) and a per-frame learnable latent code [57]. (3) XYZ-Pose: hash encoded \\(\\mathbf{x}\\) and the pose parameter [40,93]. When taking the positional encoded \\((\\mathbf{x},t)\\) as input, we use a larger network for \\(\\mathrm{MLP}_{\\mathrm{res}}\\). The results in Table 2(b) indicate that hash encoded \\((u,v,t)\\) achieves the best performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.741, + 0.471, + 0.832 + ], + "angle": 0, + "content": "Analysis of Robustness. To evaluate the robustness of the proposed system, we measure the time needed to achieve an evaluation PSNR of 30 for five times on the \"377\" sequence. This results in a training time with a mean value of \\(76.00s\\) and a standard derivation of \\(13.56s\\), showing the stability of the proposed method." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.845, + 0.2, + 0.861 + ], + "angle": 0, + "content": "5. Limitations" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.472, + 0.9 + ], + "angle": 0, + "content": "Although our method can quickly reconstruct high-quality human models from videos, it has some limitations to be fur-" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.296, + 0.895, + 0.448 + ], + "angle": 0, + "content": "ther addressed. First, our method currently relies on accurate SMPL parameters, which could be difficult to obtain under in-the-wild settings. It is interesting to utilize the techniques in [79, 93] to optimize the human pose parameters along with the training of human avatars. Second, we can only reconstruct foreground dynamic humans, while dynamic scenes typically include foreground and background entities. It might be plausible to combine our method with ST-NeRF [29] or MultiNB [74] to quickly reconstruct dynamic scenes containing foreground and background objects." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.467, + 0.619, + 0.483 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.494, + 0.895, + 0.812 + ], + "angle": 0, + "content": "We introduce a novel dynamic human representation that can be quickly optimized from videos and used for generating free-viewpoint videos of the human performer. This representation consists of a part-based voxelized human model in the canonical space and a motion parameterization scheme that transforms points from the world space to the canonical space. The part-based voxelized human model decomposes the human body into multiple parts and represents each part with an MHE-augmented NeRF network, which efficiently distributes network representational power and significantly improves the training speed. When predicting the motion of a query point, the motion field reparameterizes the point coordinate as 2D surface-level UV coordinate, which effectively reduces the dimensionality of motion the network is required to model, resulting in a boost in convergence rate. Experiments demonstrate that our proposed representation can be optimized at 1/100 of the time of previous methods while still maintaining competitive rendering quality. We show that given a 100-frame monocular video of a \\(512 \\times 512\\) resolution, our method can produce photorealistic free-viewpoint videos in minutes on an RTX 3090 GPU." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.841, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Acknowledgements. This work was supported by the Key Research Project of Zhejiang Lab (No. K2022PG1BB01), NSFC (No. 62172364), and Information Technology Center and State Key Lab of CAD&CG, Zhejiang University." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8766" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.175, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.117, + 0.47, + 0.156 + ], + "angle": 0, + "content": "[1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graphics. In ECCV, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.16, + 0.472, + 0.201 + ], + "angle": 0, + "content": "[2] Thiemo Alldieck, Mihai Zanfir, and Cristian Sminchisescu. Photorealistic monocular 3d reconstruction of humans wearing clothing. arXiv preprint arXiv:2204.08906, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.095, + 0.203, + 0.472, + 0.285 + ], + "angle": 0, + "content": "[3] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5855–5864, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.288, + 0.471, + 0.342 + ], + "angle": 0, + "content": "[4] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. arXiv preprint arXiv:2111.12077, 2021. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.345, + 0.471, + 0.399 + ], + "angle": 0, + "content": "[5] Alexander Bergman, Petr Kellnhofer, and Gordon Wetzstein. Fast training of neural lumigraph representations using meta learning. Advances in Neural Information Processing Systems, 34, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.403, + 0.469, + 0.457 + ], + "angle": 0, + "content": "[6] Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Loopreg: Self-supervised learning of implicit surface correspondences, pose and shape for 3d human mesh registration. In NeurlPS, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.46, + 0.469, + 0.5 + ], + "angle": 0, + "content": "[7] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. arXiv preprint arXiv:2203.09517, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.503, + 0.47, + 0.572 + ], + "angle": 0, + "content": "[8] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14124-14133, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.096, + 0.574, + 0.47, + 0.643 + ], + "angle": 0, + "content": "[9] Xu Chen, Yufeng Zheng, Michael J Black, Otmar Hilliges, and Andreas Geiger. Snarf: Differentiable forward skinning for animating non-rigid neural implicit shapes. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11594-11604, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.645, + 0.47, + 0.7 + ], + "angle": 0, + "content": "[10] Yue Chen, Xuan Wang, Qi Zhang, Xiaoyu Li, Xingyu Chen, Yu Guo, Jue Wang, and Fei Wang. Uv volumes for real-time rendering of editable free-view human performance. arXiv preprint arXiv:2203.14402, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.702, + 0.47, + 0.73 + ], + "angle": 0, + "content": "[11] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In CVPR, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.732, + 0.471, + 0.8 + ], + "angle": 0, + "content": "[12] Julian Chibane, Aayush Bansal, Verica Lazova, and Gerard Pons-Moll. Stereo radiance fields (srf): Learning view synthesis for sparse views of novel scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7911-7920, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.803, + 0.471, + 0.857 + ], + "angle": 0, + "content": "[13] Alvaro Collet, Ming Chuang, Pat Sweeney, Don Gillett, Dennis Evseev, David Calabrese, Hugues Hoppe, Adam Kirk, and Steve Sullivan. High-quality streamable free-viewpoint video. ACM TOG, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.86, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[14] Robert L Cook. Shade trees. In Proceedings of the 11th annual conference on Computer graphics and interactive techniques, pages 223-231, 1984. 2, 4" + }, + { + "type": "list", + "bbox": [ + 0.088, + 0.117, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.093, + 0.894, + 0.135 + ], + "angle": 0, + "content": "[15] Robert L Cook, Loren Carpenter, and Edwin Catmull. The reyes image rendering architecture. ACM SIGGRAPH Computer Graphics, 21(4):95-102, 1987. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.137, + 0.894, + 0.207 + ], + "angle": 0, + "content": "[16] Boyang Deng, John P Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, and Andrea Tagliasacchi. Nasa neural articulated shape approximation. In European Conference on Computer Vision, pages 612-628. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.209, + 0.894, + 0.278 + ], + "angle": 0, + "content": "[17] Mingsong Dou, Sameh Khamis, Yury Degtyarev, Philip Davidson, Sean Ryan Fanello, Adarsh Kowdle, Sergio Orts Escolano, Christoph Rhemann, David Kim, Jonathan Taylor, et al. Fusion4d: Real-time performance capture of challenging scenes. ACM TOG, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.281, + 0.894, + 0.35 + ], + "angle": 0, + "content": "[18] Yilun Du, Yinan Zhang, Hong-Xing Yu, Joshua B Tenenbaum, and Jiajun Wu. Neural radiance flow for 4d view synthesis and video processing. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 14304-14314. IEEE Computer Society, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.353, + 0.893, + 0.407 + ], + "angle": 0, + "content": "[19] Emilien Dupont, Hyunjik Kim, SM Eslami, Danilo Rezende, and Dan Rosenbaum. From data to functa: Your data point is a function and you should treat it like one. arXiv preprint arXiv:2201.12204, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.411, + 0.894, + 0.466 + ], + "angle": 0, + "content": "[20] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning, pages 1126–1135. PMLR, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.47, + 0.893, + 0.525 + ], + "angle": 0, + "content": "[21] Xuan Gao, Chenglai Zhong, Jun Xiang, Yang Hong, Yudong Guo, and Juyong Zhang. Reconstructing personalized semantic facial nerf models from monocular video. arXiv preprint arXiv:2210.06108, 2022.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.528, + 0.894, + 0.595 + ], + "angle": 0, + "content": "[22] Stephan J Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin. Fastnerf: High-fidelity neural rendering at 200fps. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14346-14355, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.6, + 0.893, + 0.654 + ], + "angle": 0, + "content": "[23] Haoyu Guo, Sida Peng, Haotong Lin, Qianqian Wang, Guofeng Zhang, Hujun Bao, and Xiaowei Zhou. Neural 3d scene reconstruction with the Manhattan-world assumption. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.657, + 0.894, + 0.727 + ], + "angle": 0, + "content": "[24] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, et al. The relightables: Volumetric performance capture of humans with realistic relighting. ACM TOG, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.73, + 0.893, + 0.771 + ], + "angle": 0, + "content": "[25] Marc Habermann, Lingjie Liu, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, and Christian Theobalt. Real-time deep dynamic characters. In SIGGRAPH, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.774, + 0.893, + 0.828 + ], + "angle": 0, + "content": "[26] Marc Habermann, Weipeng Xu, Michael Zollhofer, Gerard Pons-Moll, and Christian Theobalt. Deepcap: Monocular human performance capture using weak supervision. In CVPR, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.832, + 0.894, + 0.901 + ], + "angle": 0, + "content": "[27] Peter Hedman, Pratul P Srinivasan, Ben Mildenhall, Jonathan T Barron, and Paul Debevec. Baking neural radiance fields for real-time view synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5875-5884, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.511, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8767" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.092, + 0.472, + 0.146 + ], + "angle": 0, + "content": "[28] Tao Hu, Tao Yu, Zerong Zheng, He Zhang, Yebin Liu, and Matthias Zwicker. Hvtr: Hybrid volumetric-textural rendering for human avatars. arXiv preprint arXiv:2112.10203, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.148, + 0.472, + 0.204 + ], + "angle": 0, + "content": "[29] Zhang Jiakai, Liu Xinhang, Ye Xinyi, Zhao Fuqiang, Zhang Yanshun, Wu Minye, Zhang Yingliang, Xu Lan, and Yu Jingyi. Editable free-viewpoint video using a layered neural representation. In SIGGRAPH, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.205, + 0.472, + 0.247 + ], + "angle": 0, + "content": "[30] Boyi Jiang, Yang Hong, Hujun Bao, and Juyong Zhang. Self-recon: Self reconstruction your digital avatar from monocular video. arXiv preprint arXiv:2201.12792, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.247, + 0.472, + 0.287 + ], + "angle": 0, + "content": "[31] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.288, + 0.472, + 0.328 + ], + "angle": 0, + "content": "[32] James T Kajiya and Brian P Von Herzen. Ray tracing volume densities. ACM SIGGRAPH computer graphics, 18(3):165-174, 1984. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.33, + 0.472, + 0.357 + ], + "angle": 0, + "content": "[33] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.359, + 0.472, + 0.425 + ], + "angle": 0, + "content": "[34] Youngjoong Kwon, Dahun Kim, Duygu Ceylan, and Henry Fuchs. Neural human performer: Learning generalizable radiance fields for human performance rendering. Advances in Neural Information Processing Systems, 34, 2021. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.427, + 0.472, + 0.469 + ], + "angle": 0, + "content": "[35] John P Lewis, Matt Cordner, and Nickson Fong. Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation. In SIGGRAPH, 2000. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.47, + 0.472, + 0.523 + ], + "angle": 0, + "content": "[36] Ruilong Li, Julian Tanke, Minh Vo, Michael Zollhoffer, Jurgen Gall, Angjoo Kanazawa, and Christoph Lassner. Tava: Template-free animatable volumetric actors. arXiv preprint arXiv:2206.08929, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.525, + 0.472, + 0.593 + ], + "angle": 0, + "content": "[37] Zhengqi Li, Simon Niklaus, Noah Snavely, and Oliver Wang. Neural scene flow fields for space-time view synthesis of dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6498-6508, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.595, + 0.472, + 0.637 + ], + "angle": 0, + "content": "[38] Yiyi Liao, Katja Schwarz, Lars Mescheder, and Andreas Geiger. Towards unsupervised learning of generative models for 3d controllable image synthesis. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.638, + 0.472, + 0.69 + ], + "angle": 0, + "content": "[39] Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. Advances in Neural Information Processing Systems, 33:15651-15663, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.692, + 0.472, + 0.747 + ], + "angle": 0, + "content": "[40] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural actor: Neural free-view synthesis of human actors with pose control. In SIGGRAPH Asia, 2021. 2, 3, 4, 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.748, + 0.472, + 0.802 + ], + "angle": 0, + "content": "[41] Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui. Dist: Rendering deep implicit signed distance function with differentiable sphere tracing. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.804, + 0.472, + 0.858 + ], + "angle": 0, + "content": "[42] Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural volumes: Learning dynamic renderable volumes from images. In SIGGRAPH, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.86, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[43] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. Smpl: A skinned multiperson linear model. ACM TOG, 2015. 2, 4" + }, + { + "type": "list", + "bbox": [ + 0.087, + 0.092, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.092, + 0.894, + 0.146 + ], + "angle": 0, + "content": "[44] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In CVPR, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.149, + 0.894, + 0.219 + ], + "angle": 0, + "content": "[45] Marko Mihajlovic, Aayush Bansal, Michael Zollhoefer, Siyu Tang, and Shunsuke Saito. Keypointnerf: Generalizing image-based volumetric avatars using relative spatial encoding of keypoints. In European Conference on Computer Vision, pages 179-197. Springer, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.221, + 0.894, + 0.288 + ], + "angle": 0, + "content": "[46] Marko Mihajlovic, Shunsuke Saito, Aayush Bansal, Michael Zollhoefer, and Siyu Tang. COAP: Compositional articulated occupancy of people. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.291, + 0.894, + 0.346 + ], + "angle": 0, + "content": "[47] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.349, + 0.894, + 0.401 + ], + "angle": 0, + "content": "[48] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. In SIGGRAPH, 2022. 2, 3, 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.405, + 0.894, + 0.446 + ], + "angle": 0, + "content": "[49] Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.449, + 0.894, + 0.517 + ], + "angle": 0, + "content": "[50] Michael Oechsle, Songyou Peng, and Andreas Geiger. Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5589-5599, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.519, + 0.894, + 0.573 + ], + "angle": 0, + "content": "[51] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.576, + 0.894, + 0.645 + ], + "angle": 0, + "content": "[52] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5865-5874, 2021. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.647, + 0.894, + 0.757 + ], + "angle": 0, + "content": "[53] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.759, + 0.894, + 0.814 + ], + "angle": 0, + "content": "[54] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3d hands, face, and body from a single image. In CVPR, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.816, + 0.894, + 0.858 + ], + "angle": 0, + "content": "[55] Bo Peng, Jun Hu, Jingtao Zhou, and Juyong Zhang. Selfnerf: Fast training nerf for human from monocular self-rotating video. arXiv preprint arXiv:2210.01651, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.86, + 0.894, + 0.901 + ], + "angle": 0, + "content": "[56] Sida Peng, Junting Dong, Qianqian Wang, Shangzhan Zhang, Qing Shuai, Xiaowei Zhou, and Hujun Bao. Animatable neural radiance fields for modeling dynamic human bodies." + }, + { + "type": "list", + "bbox": [ + 0.51, + 0.092, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8768" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.118, + 0.093, + 0.47, + 0.12 + ], + "angle": 0, + "content": "In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14314-14323, 2021. 2, 4, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.122, + 0.47, + 0.177 + ], + "angle": 0, + "content": "[57] Sida Peng, Shangzhan Zhang, Zhen Xu, Chen Geng, Boyi Jiang, Hujun Bao, and Xiaowei Zhou. Animatable neural implicit surfaces for creating avatars from videos. arXiv preprint arXiv:2203.08133, 2022. 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.179, + 0.471, + 0.262 + ], + "angle": 0, + "content": "[58] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9054-9063, 2021. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.089, + 0.264, + 0.471, + 0.331 + ], + "angle": 0, + "content": "[59] Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-nerf: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10318-10327, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.334, + 0.471, + 0.389 + ], + "angle": 0, + "content": "[60] Charles R Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas. Volumetric and multi-view cnns for object classification on 3d data. In CVPR, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.391, + 0.471, + 0.459 + ], + "angle": 0, + "content": "[61] Amit Raj, Julian Tanke, James Hays, Minh Vo, Carsten Stoll, and Christoph Lassner. Anr: Articulated neural rendering for virtual avatars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3722-3731, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.462, + 0.471, + 0.532 + ], + "angle": 0, + "content": "[62] Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. H3d-net: Few-shot high-fidelity 3d head reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5620-5629, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.533, + 0.471, + 0.6 + ], + "angle": 0, + "content": "[63] Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger. Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14335-14345, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.603, + 0.471, + 0.658 + ], + "angle": 0, + "content": "[64] Konstantinos Rematas, Andrew Liu, Pratul P Srinivasan, Jonathan T Barron, Andrea Tagliasacchi, Thomas Funkhouser, and Vittorio Ferrari. Urban radiance fields. arXiv preprint arXiv:2111.14643, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.66, + 0.471, + 0.729 + ], + "angle": 0, + "content": "[65] Edoardo Remelli, Timur Bagautdinov, Shunsuke Saito, Chenglei Wu, Tomas Simon, Shih-En Wei, Kaiwen Guo, Zhe Cao, Fabian Prada, Jason Saragih, et al. Drivable volumetric avatars using texel-aligned features. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1–9, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.731, + 0.471, + 0.771 + ], + "angle": 0, + "content": "[66] Javier Romero, Dimitrios Tzionas, and Michael J Black. Embodied hands: Modeling and capturing hands and bodies together. ACM ToG, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.774, + 0.471, + 0.828 + ], + "angle": 0, + "content": "[67] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In ICCV, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.831, + 0.471, + 0.871 + ], + "angle": 0, + "content": "[68] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo. Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.874, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[69] Shunsuke Saito, Jinlong Yang, Qianli Ma, and Michael J Black. Scanimate: Weakly supervised learning of skinned" + }, + { + "type": "list", + "bbox": [ + 0.088, + 0.093, + 0.471, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.541, + 0.093, + 0.894, + 0.135 + ], + "angle": 0, + "content": "clothed avatar networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2886-2897, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.137, + 0.894, + 0.19 + ], + "angle": 0, + "content": "[70] Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G Baraniuk, and Ashok Veeraraghavan. Miner: Multiscale implicit neural representations. arXiv preprint arXiv:2202.03532, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.193, + 0.894, + 0.22 + ], + "angle": 0, + "content": "[71] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In CVPR, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.222, + 0.894, + 0.262 + ], + "angle": 0, + "content": "[72] Johannes L Schonberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys. Pixelwise view selection for unstructured multi-view stereo. In ECCV, 2016. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.265, + 0.894, + 0.319 + ], + "angle": 0, + "content": "[73] Ruizhi Shao, Hongwen Zhang, He Zhang, Yanpei Cao, Tao Yu, and Yebin Liu. Doublefield: Bridging the neural surface and radiance fields for high-fidelity human rendering. arXiv preprint arXiv:2106.03798, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.322, + 0.894, + 0.376 + ], + "angle": 0, + "content": "[74] Qing Shuai, Chen Geng, Qi Fang, Sida Peng, Wenhao Shen, Xiaowei Zhou, and Hujun Bao. Novel view synthesis of human interactions from sparse multi-view videos. In SIGGRAPH Conference Proceedings, 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.379, + 0.894, + 0.433 + ], + "angle": 0, + "content": "[75] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462-7473, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.435, + 0.894, + 0.489 + ], + "angle": 0, + "content": "[76] Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhofer. Deepvoxels: Learning persistent 3d feature embeddings. In CVPR, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.492, + 0.894, + 0.532 + ], + "angle": 0, + "content": "[77] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3d-structure-aware neural scene representations. In NeurIPS, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.535, + 0.894, + 0.603 + ], + "angle": 0, + "content": "[78] Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller. Multi-view convolutional neural networks for 3d shape recognition. In Proceedings of the IEEE international conference on computer vision, pages 945-953, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.606, + 0.894, + 0.66 + ], + "angle": 0, + "content": "[79] Shih-Yang Su, Frank Yu, Michael Zollhöfer, and Helge Rhodin. A-nerf: Articulated neural radiance fields for learning human shape, appearance, and pose. Advances in Neural Information Processing Systems, 34, 2021. 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.662, + 0.894, + 0.703 + ], + "angle": 0, + "content": "[80] Zhuo Su, Lan Xu, Zerong Zheng, Tao Yu, Yebin Liu, et al. Robustfusion: Human volumetric capture with data-driven visual cues using a rgbd camera. In ECCV, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.705, + 0.894, + 0.746 + ], + "angle": 0, + "content": "[81] Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. arXiv preprint arXiv:2111.11215, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.748, + 0.894, + 0.815 + ], + "angle": 0, + "content": "[82] Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Mildenhall, Pratul P Srinivasan, Jonathan T Barron, and Henrik Kretzschmar. Block-nerf: Scalable large scene neural view synthesis. arXiv preprint arXiv:2202.05263, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.818, + 0.894, + 0.901 + ], + "angle": 0, + "content": "[83] Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P Srinivasan, Jonathan T Barron, and Ren Ng. Learned initializations for optimizing coordinate-based neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2846-2855, 2021. 3" + }, + { + "type": "list", + "bbox": [ + 0.511, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8769" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.093, + 0.472, + 0.175 + ], + "angle": 0, + "content": "[84] Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems, 33:7537-7547, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.176, + 0.472, + 0.232 + ], + "angle": 0, + "content": "[85] Matthias Teschner, Bruno Heidelberger, Matthias Müller, Danat Pomerantes, and Markus H Gross. Optimized spatial hashing for collision detection of deformable objects. In Vmv, volume 3, pages 47-54, 2003. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.233, + 0.472, + 0.273 + ], + "angle": 0, + "content": "[86] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM TOG, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.274, + 0.472, + 0.343 + ], + "angle": 0, + "content": "[87] Garvita Tiwari, Nikolaos Sarafianos, Tony Tung, and Gerard Pons-Moll. Neural-gif: Neural generalized implicit functions for animating people in clothing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11708-11718, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.344, + 0.472, + 0.399 + ], + "angle": 0, + "content": "[88] Alex Trevithick and Bo Yang. Grf: Learning a general radiance field for 3d representation and rendering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15182-15192, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.4, + 0.472, + 0.453 + ], + "angle": 0, + "content": "[89] Haithem Turki, Deva Ramanan, and Mahadev Satyanarayanan. Mega-nerf: Scalable construction of large-scale nerfs for virtual fly-throughs. arXiv preprint arXiv:2112.10703, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.455, + 0.472, + 0.509 + ], + "angle": 0, + "content": "[90] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In NeurIPS, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.511, + 0.472, + 0.594 + ], + "angle": 0, + "content": "[91] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P Srinivasan, Howard Zhou, Jonathan T Barron, Ricardo Martin-Brualla, Noah Snavely, and Thomas Funkhouser. Ibrnet: Learning multi-view image-based rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4690-4699, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.595, + 0.472, + 0.636 + ], + "angle": 0, + "content": "[92] Shaofei Wang, Katja Schwarz, Andreas Geiger, and Siyu Tang. Arah: animatable volume rendering of articulated human sdfs. arXiv preprint arXiv:2210.10036, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.637, + 0.472, + 0.704 + ], + "angle": 0, + "content": "[93] Chung-Yi Weng, Brian Curless, Pratul P Srinivasan, Jonathan T Barron, and Ira Kemelmacher-Shlizerman. Humanerf: Free-viewpoint rendering of moving people from monocular video. arXiv preprint arXiv:2201.04127, 2022. 1, 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.705, + 0.472, + 0.761 + ], + "angle": 0, + "content": "[94] Yuanbo Xiangli, Linning Xu, Xingang Pan, Nanxuan Zhao, Anyi Rao, Christian Theobalt, Bo Dai, and Dahua Lin. Citynerf: Building nerf at city scale. arXiv preprint arXiv:2112.05504, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.762, + 0.472, + 0.817 + ], + "angle": 0, + "content": "[95] Hongyi Xu, Thiemo Alldieck, and Cristian Sminchisescu. H-nerf: Neural radiance fields for rendering and temporal reconstruction of humans in motion. Advances in Neural Information Processing Systems, 34, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.818, + 0.472, + 0.857 + ], + "angle": 0, + "content": "[96] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In NeurIPS, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.859, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[97] Jianglong Ye, Yuntao Chen, Naiyan Wang, and Xiaolong Wang. Gifs: Neural implicit function for general shape representation. arXiv preprint arXiv:2204.07126, 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.087, + 0.093, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.093, + 0.894, + 0.147 + ], + "angle": 0, + "content": "[98] Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. arXiv preprint arXiv:2112.05131, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.149, + 0.894, + 0.218 + ], + "angle": 0, + "content": "[99] Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. Plenoptrees for real-time rendering of neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5752-5761, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.22, + 0.894, + 0.288 + ], + "angle": 0, + "content": "[100] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelnerf: Neural radiance fields from one or few images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4578-4587, 2021. 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.29, + 0.892, + 0.33 + ], + "angle": 0, + "content": "[101] Ruiqi Zhang and Jie Chen. Ndf: Neural deformable fields for dynamic human modelling. arXiv preprint arXiv:2207.09193, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.332, + 0.894, + 0.388 + ], + "angle": 0, + "content": "[102] Fuqiang Zhao, Wei Yang, Jiakai Zhang, Pei Lin, Yingliang Zhang, Jingyi Yu, and Lan Xu. Humannerf: Generalizable neural human radiance field from sparse inputs. arXiv preprint arXiv:2112.02789, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.39, + 0.894, + 0.444 + ], + "angle": 0, + "content": "[103] Zerong Zheng, Han Huang, Tao Yu, Hongwen Zhang, Yandong Guo, and Yebin Liu. Structured local radiance fields for human avatar modeling. arXiv preprint arXiv:2203.14478, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.446, + 0.894, + 0.488 + ], + "angle": 0, + "content": "[104] Shuaifeng Zhi, Tristan Laidlow, Stefan Leutenegger, and Andrew J Davison. In-place scene labelling and understanding with implicit scene representation. In ICCV, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.489, + 0.892, + 0.543 + ], + "angle": 0, + "content": "[105] Yihao Zhi, Shenhan Qian, Xinhao Yan, and Shenghua Gao. Dual-space nerf: Learning animatable avatars and scene lighting in separate spaces. arXiv preprint arXiv:2208.14851, 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.894, + 0.543 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8770" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Neural Volumetric Representations of Dynamic Humans in Minutes/4b95dcfc-29a4-4f2f-b770-89d83ba2b62f_origin.pdf b/2023/Learning Neural Volumetric Representations of Dynamic Humans in Minutes/4b95dcfc-29a4-4f2f-b770-89d83ba2b62f_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..90888f9677d5fcfa8c593d251331db206052dbee --- /dev/null +++ b/2023/Learning Neural Volumetric Representations of Dynamic Humans in Minutes/4b95dcfc-29a4-4f2f-b770-89d83ba2b62f_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:dfe9b6e75a5f5b4d24830a3195c8cfbd7c78d68b2c0bb43ee77ffd404604649d +size 9607185 diff --git a/2023/Learning Neural Volumetric Representations of Dynamic Humans in Minutes/full.md b/2023/Learning Neural Volumetric Representations of Dynamic Humans in Minutes/full.md new file mode 100644 index 0000000000000000000000000000000000000000..020cf2dad3c5044a30cd45cc6ed2147526155679 --- /dev/null +++ b/2023/Learning Neural Volumetric Representations of Dynamic Humans in Minutes/full.md @@ -0,0 +1,374 @@ +# Learning Neural Volumetric Representations of Dynamic Humans in Minutes + +Chen Geng* + +Sida Peng* + +Zhen $\mathrm{Xu^{*}}$ + +Hujun Bao + +Xiaowei Zhou + +State Key Laboratory of CAD&CG, Zhejiang University + +![](images/72f2b519b2a6c3a631d3ad75b565dd48c99f39cd1ae4caca057aa942d22aa9e5.jpg) + +![](images/6b94307a825b4c73176cd7a5febf53952d0c2a27b1415c91e5d9b975b73f5064.jpg) + +![](images/6ca6c9748afc1ef6917b2c1343d4757c3c1ad7ec00a9329221d7c8b57db37319.jpg) + +![](images/19297576584d1f7b530c07a93b0b4c2421da4108ce8e82213d8020fe7ca3d61f.jpg) +1 min + +![](images/17c1f1a7aaa48d633c4b59545b76bd64f999fcf68219e201a54b70e85813a217.jpg) +5 min + +![](images/f0045714a267a604eee70d02e0f430e016b2242c2190832fc4a08a6125e3ff02.jpg) +10 min + +![](images/689735e0f2fb89e8ab2518dadf175db1e77e035308867473476ffc553a201432.jpg) +Figure 1. Convergence rate of training. Given a monocular video of a human performer, our model can be learned in $\sim 5$ minutes to produce photorealistic novel view rendering, which is 100 times faster than Neural Body [58]. + +# Abstract + +This paper addresses the challenge of efficiently reconstructing volumetric videos of dynamic humans from sparse multi-view videos. Some recent works represent a dynamic human as a canonical neural radiance field (NeRF) and a motion field, which are learned from input videos through differentiable rendering. But the per-scene optimization generally requires hours. Other generalizable NeRF models leverage learned prior from datasets to reduce the optimization time by only finetuning on new scenes at the cost of visual fidelity. In this paper, we propose a novel method for learning neural volumetric representations of dynamic humans in minutes with competitive visual quality. Specifically, we define a novel part-based voxelized human representation to better distribute the representational power of the network to different human parts. Furthermore, we propose + +a novel 2D motion parameterization scheme to increase the convergence rate of deformation field learning. Experiments demonstrate that our model can be learned 100 times faster than previous per-scene optimization methods while being competitive in the rendering quality. Training our model on a $512 \times 512$ video with 100 frames typically takes about 5 minutes on a single RTX 3090 GPU. The code is available on our project page: https://zju3dv.github.io/instant_nvr. + +# 1. Introduction + +Creating volumetric videos of human performers has many applications, such as immersive telepresence, video games, and movie production. Recently, some methods [58, 93] have shown that high-quality volumetric videos can be recovered from sparse multi-view videos by representing dynamic humans with neural scene representations. However, they typically require more than 10 hours of training on + +a single GPU. The expensive time and computational costs limit the large-scale application of volumetric videos. Generalizable methods [34, 100] utilize learned prior from datasets of dynamic humans to reduce the training time by only finetuning on novel human performers. These techniques could increase the optimization speed by a factor of 2-5 at the cost of some visual fidelity. + +To speed up the process of optimizing a neural representation for view synthesis of dynamic humans, we analyze the structural prior of the human body and motion, and propose a novel dynamic human representation that achieves 100x speedup during optimization while maintaining competitive visual fidelity. Specifically, to model a dynamic human, we first transform world-space points to a canonical space using a novel motion parameterization scheme and inverse linear blend skinning (LBS) [35]. Then, the color and density of these points are estimated using the canonical human model. + +The innovation of our proposed representation is two-fold. First, we observe that human body parts have different levels of complexity in terms of both shape and texture. For example, the face of a human performer typically exhibits higher complexity than a flatly textured torso region, thus requiring more representational power to depict. Motivated by this, our method decomposes the canonical human body into multiple parts and represents the human body with a structured set of voxelized NeRF [47] networks to bring the convergence rate of these different parts to the same level. In contrast to a single-resolution representation, the part-based body model utilizes the human body prior to represent the human shape and texture efficiently, heuristically distributing variational representational power to human parts with spatially varying complexity. + +Second, we notice that non-rigid deformation of human geometry typically occurs around a surface instead of in a volume, that is, nearby surface points on a parametric human model tend to have similar motion behavior. Thus we propose a novel motion parameterization technique that models the 3D human deformation in a 2D domain, enabling modeling of the motion field using 3D voxelized representation to accelerate the learning. This idea is similar to the displacement map and bump map [14, 15] in traditional computer graphics to represent detailed deformation on a 2D texture domain. We extend the technique of displacement map [14, 15] to represent human motions by restricting the originally 3D deformation field [40, 56, 59] on the 2D surface of a parametric human model, such as SMPL [43]. This technique allows us to use hybrid representations [48, 98] to model non-rigid deformation by reducing the memory footprint, largely boosting the convergence of the field. + +Experiments demonstrate that our method significantly accelerates the optimization of neural human representations while being competitive with state-of-the-art human modeling methods on rendering quality. As shown in Figure 1, our + +model can be trained within 5 minutes to produce a volumetric video of a dynamic human from a 100-frame monocular video of a $512 \times 512$ resolution on an RTX 3090 GPU. + +To summarize, our key contributions are: + +- A novel part-based voxelized human representation for more efficient human body modeling. +- A 2D motion parameterization scheme for more efficient deformation field modeling. +- 100x speedup in optimization compared to previous neural human representations while maintaining competitive rendering quality. + +# 2. Related work + +Implicit neural representation and rendering. There have been many 3D scene representations, such as multiview images [60, 78], textured meshes [38, 86], point clouds [1, 61], and voxels [42, 76]. Recently, some methods [11, 41, 44, 51, 77, 97, 104] propose implicit neural representations to represent scenes, which uses MLP networks to predict scene properties for any point in 3D space, such as occupancy [44, 67], signed distance [41, 51], and semantics [23, 104]. This enables them to describe continuous and high-resolution 3D scenes. To perform novel view synthesis, neural radiance field (NeRF) [47] models scenes as implicit fields of density and color. NeRF is optimized from images with volume rendering techniques, which produces impressive image synthesis results. Many works improve NeRF in various aspects, including rendering quality [3, 4], rendering speed [22, 27, 39, 63, 99], scene scale [64, 82, 89, 94], and reconstruction quality [50, 90, 96]. Some methods [18, 37, 52, 59] extend NeRF to dynamic scenes. + +Human modeling. Reconstructing high-quality 3D human models is essential for synthesizing free-viewpoint videos of human performers. Traditional methods leverage multi-view stereo techniques [24, 71, 72] or depth fusion [13, 17, 80] to reconstruct human geometries, which require complicated hardware, such as dense camera arrays or depth sensors. To reduce the requirement of the capture equipment, some methods [2, 67, 68] train networks to learn human priors from datasets containing a large amount of 3D ground-truth human models, enabling them to infer human geometry and texture from even a single image. However, due to the limited diversity of training data, these methods do not generalize well to humans under complex poses. Recently, some methods [9, 46, 69, 87] model the shapes of dynamic humans as implicit neural representations and attempt to optimize them from human scans. Another line of works [28, 30, 34, 36, 40, 56, 58, 65, 92, 93, 95, 101-103, 105] exploits dynamic implicit neural representations and differentiable renderers to reconstruct 3D human models from + +![](images/da5e57f8f072b40730e509918d78e8ff12f1bd63ed38b43c3663309d49ceae75.jpg) +Figure 2. Overview of the proposed representation. Given a query point $\mathbf{x}$ at frame $t$ , we find its nearest surface point on each human part of the SMPL mesh, which gives the blend weight $\mathbf{w}_k$ and the UV coordinates $(u_k, v_k)$ . Consider the $k$ -th part. The motion field consists of an inverse LBS module and a residual deformation module. (a) The inverse LBS module takes body pose $\rho$ , blend weight $\mathbf{w}_k$ , and query point $\mathbf{x}$ as input and outputs the transformed point $\mathbf{x}'$ . The residual deformation module applies the multiresolution hash encoding (MHE) to $(u_k, v_k, t)$ and uses an MLP network to regress the residual translation $\Delta \mathbf{x}$ , which is added to $\mathbf{x}'$ to obtain the canonical point $\mathbf{x}^{\mathrm{can}}$ . (b) We then feed $\mathbf{x}^{\mathrm{can}}$ to networks of $k$ -th human part to predict the density $\sigma_k$ and color $\mathbf{c}_k$ . With $\left\{\left(\sigma_k, \mathbf{c}_k\right)\right\}_{k=1}^{K}$ , we select the one with the largest density as the density and color of the query point. + +![](images/8f8f74e7019caf9d8314a78d714eda47af3dda6e55612880b8ad68381e5fe05d.jpg) + +videos. To represent dynamic humans, Neural Actor [40] augments the neural radiance field with the linear blend skinning model [35]. It additionally adopts a residual deformation field to better predict human motions. To overcome the inaccuracy of input human poses, [79, 93] optimize the parameters of human poses jointly with the human representations during training. These methods typically require a lengthy training process to produce high-quality human models. In contrast, we introduce a part-based voxelized human representation to model the canonical human body, which significantly accelerates the optimization process. Although [16, 46] have proposed part-based implicit functions, they focus on human shape modeling and do not show that the part-based representation can be used to reduce the training time. + +Accelerating the optimization of neural representations. Many differentiable rendering-based methods [3, 39, 47] optimize a separate neural representation for each scene. The optimization process generally takes several hours on a modern GPU, which is time-consuming and costly to scale. Inspired by multi-view stereo matching [72], some methods [8, 12, 45, 73, 88, 91, 100] train a network on multi-view datasets to learn to infer radiance fields from input images. This enables them to quickly fine-tune neural representations to unseen scenes. [19, 62] leverage the auto-decoder [51] to capture the scene priors for efficient fine-tuning. [5, 83] utilize meta-learning techniques [20, 49] to initialize network parameters, thereby improving the training speed. Some methods [7, 10, 55, 70, 81, 98] attempt to design scene representations that support efficient training. [21, 48, 75, 84] + +augments the approximation ability of networks by designing encoding techniques. Multiresolution hash encoding [48] defines multiresolution feature vector arrays for a scene and uses the hash technique [85] to assign each input coordinate a feature vector as the encoded input, which significantly improves the training speed. + +# 3. Methods + +This paper aims to quickly create a volumetric video from a sparse multi-view video that captures a dynamic human. We assume that the cameras are calibrated and the human pose and foreground human mask of each image are provided. Section 3.1 introduces our dynamic human model that is comprised of a part-based voxelized human representation and a dimensionality reduction motion parameterization scheme. Section 3.2 discusses how to efficiently optimize the proposed representation. Finally, Section 3.3 provides implementation details. + +# 3.1. Proposed human representation + +As shown in Figure 2, our dynamic human representation consists of a motion parameterization field and a part-based voxelized human model. (a) For a query point $\mathbf{x}$ , the motion parameterization field first transforms it to the canonical space correspondence $\mathbf{x}^{\mathrm{can}}$ using the inverse LBS [35] algorithm and by parameterizing the 3D points onto 2D UV coordinates to predict the residual deformation $\Delta \mathbf{x}$ . (b) Then, $\mathbf{x}^{\mathrm{can}}$ is fed into the part-based voxelized human model in canonical space to predict and aggregate the density and color $(\sigma, \mathbf{c})$ , where the canonical human body is decomposed into $K$ parts, each of which is represented using an + +MHE-augmented [48] NeRF network. + +Motion parameterization on 2D surface domain. To regress the canonical correspondence $\mathbf{x}^{\mathrm{can}}$ of a query point $\mathbf{x}$ , we first find its nearest surface point $\mathbf{p}$ on the posed SMPL mesh. Using the strategy in [40], the blend weight $\mathbf{w}$ and UV coordinate $(u,v)$ of surface point $\mathbf{p}$ are obtained from the SMPL model. + +Given the blend weight $\mathbf{w}$ and UV coordinate $(u, v)$ , the motion field maps the query point $\mathbf{x}$ to the canonical space correspondence $\mathbf{x}^{\mathrm{can}}$ . The motion field is comprised of an inverse LBS module [35] and a residual deformation module. Given a query point $\mathbf{x}$ and blend weight $\mathbf{w}$ , we use the inverse LBS module to transform it to the unposed space, which is defined as: + +$$ +\Phi_ {\mathrm {L B S}} (\mathbf {x}, \mathbf {w}, \boldsymbol {\rho}) = \left(\sum_ {j = 1} ^ {J} w _ {k, j} G _ {j}\right) ^ {- 1} \mathbf {x}, \tag {1} +$$ + +where $\rho$ denotes the human pose and $\{G_j\}_{j=1}^J$ are transformation matrices derived from $\rho$ [43]. The detailed derivation of the inverse LBS algorithm can be found in the supplementary material. + +The transformed point $\Phi_{\mathrm{LBS}}(\mathbf{x},\mathbf{w},\pmb {\rho})$ is then deformed to the human surface using the residual deformation module. Specifically, the current time $t$ is first concatenated with the UV coordinate $(u,v)$ to serve as the parameterization of the query point $\mathbf{x}$ at frame $t$ . This motion parameterization is inspired by the displacement map and bump map techniques [14, 15] in the traditional Computer Graphics pipelines. It essentially reduces the dimensionality of a 4D space-time sequence down to the 3D surface-time domain utilizing the human deformation prior. Then, we apply the multiresolution hash encoding [48] $\psi_{\mathrm{res}}$ to $(u,v,t)$ and forward the encoded input through a network $\mathrm{MLP}_{\mathrm{res}}$ to regress the residual $\Delta \Phi (u,v,t)$ . The full human motion at frame $t$ is defined as: + +$$ +\Delta \Phi (u, v, t) = \mathrm {M L P} _ {\text {r e s}} \left(\psi_ {\text {r e s}} (u, v, t)\right), \tag {2} +$$ + +$$ +\mathbf {x} ^ {\text {c a n}} (\mathbf {x}, \mathbf {w}, u, v, \boldsymbol {\rho}, t) = \Phi_ {\mathrm {L B S}} (\mathbf {x}, \mathbf {w}, \boldsymbol {\rho}) + \Delta \Phi (u, v, t). \tag {3} +$$ + +There are two main observations that inspired us to use the $(u,v,t)$ motion parameterization. First, we observe that a typical human motion happens at a surface level instead of a volumetric level. Near-surface points sharing similar UV coordinate of the parametric model shows similar motions. Utilizing this prior with a surface parameterization [14, 15], we can reduce the required 4D volumetric motion to the 3D surface-time domain, greatly decreasing the amount of information the deformation network has to learn. Based on a similar idea, [6] diffuses the surface motion to the full 3D space. Second, a naive $(x,y,z,t)$ encoding would introduce quartic memory overhead on an explicitly defined voxel + +structure, which is intractable to use in practice. Instead, by parameterizing the motion to $(u,v,t)$ , we can reduce the memory footprint to a more practical cubic level. Experiments demonstrate that the motion parameterization scheme effectively reduces the dimensionality of the deformation field, thus greatly increasing the convergence rate of the human model. + +Part-based voxelized human representation. Muller et al. [48] propose the multi-resolution hash encoding (MHE) to improve the approximation ability and training speed of implicit neural representations. MHE is defined on an explicit set of voxel grids of different resolutions. Given an input coordinate, it applies the hash encoding on each level and queries the corresponding voxel grid to trilinearly interpolate the feature of the input point for this level. Then, the concatenated multi-resolution feature is fed into a small MLP network to predict the target value. Note that [48] concatenates the features of multi-resolution hash encoding for the same point to mitigate the effect of hash collision, while our part-based voxelized human representation introduces spatially varying resolution to efficiently encode human parts with different complexity. + +In contrast to [40, 56] which use a single neural radiance field (NeRF) to represent the canonical human model, we decompose the human body into multiple parts with different complexity and adopt a structured set of MHE-augmented NeRF with varying resolutions as the body representation. Specifically, we manually divide the human body into multiple parts based on a parametric human model (such as SMPL [43]), as shown in Figure 2. Note that other parametric human models [54,66] can also be used in our method. We use the blend weights defined in SMPL model [43] to decompose SMPL template mesh $\mathcal{M} = (\mathcal{V},\mathcal{E})$ , where $\mathcal{V}$ represents the vertices and $\mathcal{E}$ represents the edges. Let the $i$ -th vertex $v_{i}$ have the blend weight $w_{i}$ and for each part $k$ we define $\Omega_{k}$ as the set of bones that belong to this part. The detailed setting of $\Omega_{k}$ can be found in the supplementary material. The mesh of the $k$ -th part is defined as $\mathcal{M}_k = (\mathcal{V}_k,\mathcal{E}_k)$ , where: + +$$ +\mathcal {V} _ {k} = \left\{v _ {i} \mid \operatorname {a r g m a x} w _ {i} \in \Omega_ {k} \right\}, \tag {4} +$$ + +$$ +\mathcal {E} _ {k} = \left\{\left(v _ {i}, v _ {j}\right) \mid v _ {i} \in \mathcal {V} _ {k}, v _ {j} \in \mathcal {V} _ {k} \right\}. \tag {5} +$$ + +To regress the density and color of a query point $\mathbf{x}$ , we first find the nearest surface point $\mathbf{p}_k$ on each human part $\mathcal{M}_k$ of the posed SMPL mesh. Using the strategy in [40], the blend weight $\mathbf{w}_k$ and UV coordinate $(u_k, v_k)$ of surface point $\mathbf{p}_k$ are obtained from the SMPL model. With $(u_k, v_k, t)$ , we use the motion parameterization scheme defined in Section 3.1 to transform the query point to the space of the $k$ -th human part. We predefine the parameters of the multiresolution hash encoding function $\psi_k$ for the $k$ -th part. + +Given the transformed point, we first apply the multiresolution hash encoding to the transformed point and then feed the encoded point $\psi_{k}(\mathbf{x})$ to a small NeRF network to predict the density and color. The density network $\mathrm{MLP}_{\sigma_k}$ is defined as: + +$$ +\left(\sigma_ {k}, \mathbf {z}\right) = \operatorname {M L P} _ {\sigma_ {k}} \left(\psi_ {k} (\mathbf {x})\right), \tag {6} +$$ + +where $\sigma_{k}$ means the density and $\mathbf{z}$ is a feature vector. Then, we take the feature vector $\mathbf{z}$ and the viewing direction $\mathbf{d}$ as the input for the color regression. Similar to [52], a latent embedding $\ell_t$ for each video frame $t$ is introduced to model the temporally-varying appearance. The color network is defined as: + +$$ +\mathbf {c} _ {k} = \operatorname {M L P} _ {\mathbf {c} _ {k}} (\mathbf {z}, \mathbf {d}, \ell_ {t}). \tag {7} +$$ + +Finally, we have $K$ predictions $\{(\sigma_k,\mathbf{c}_k)\}_{k = 1}^K$ . The density and color $(\sigma ,\mathbf{c})$ of the query point $\mathbf{x}$ is calculated based on: + +$$ +(\sigma , \mathbf {c}) = (\sigma_ {k ^ {*}}, \mathbf {c} _ {k ^ {*}}), \text {w h e r e} k ^ {*} = \underset {k} {\operatorname {a r g m a x}} \sigma_ {k}. \tag {8} +$$ + +In contrast to [40, 56] that represent the body with a single NeRF network, our part-based voxelized human representation can assign different densities of model parameters to different human parts with different complexity, thereby enabling us to efficiently distribute the representational power of the network. Experiments show that our proposed body representation significantly improves the rate of convergence. + +# 3.2. Training + +The proposed representation can be learned from sparse multi-view videos by minimizing the difference between rendered and observed images. The volume rendering technique [32, 47] is used to synthesize the pixel color. Give a pixel at frame $t$ , we emit a camera ray and sample points along the ray. Then, the sampled points are fed into the dynamic human representation to predict their colors and densities, which are finally accumulated into the pixel color. During each training iteration, we randomly sample an image patch from the input image and compute the Mean Squared Error (MSE) loss and perceptual loss [31] to train the model parameters, which are defined as: + +$$ +L _ {\mathrm {r g b}} = \| \tilde {I} _ {P} - I _ {P} \| _ {2} + \| F _ {\mathrm {v g g}} (\tilde {I} _ {P}) - F _ {\mathrm {v g g}} (I _ {P}) \| _ {2}, \tag {9} +$$ + +where $\tilde{I}_P$ is the rendered image patch, $I_P$ is the ground truth image patch, and $F_{\mathrm{vgg}}$ extracts image features using the pretrained VGG network [31]. Ablation study demonstrates that perceptual loss is essential for rendering quality and fast training. + +In addition to the image rendering loss, two regularization techniques are used to facilitate the learning of the neural representations. First, we apply the regularizer in [4] to concentrate densities on the human surface. Second, the residual deformation field is regularized to be small and smooth. + +More details of the loss terms are described in the supplementary material. + +# 3.3. Implementation details + +We adopt the Adam optimizer [33] with a learning rate of $5e^{-4}$ . We train our model on an RTX 3090 GPU, which takes around 5 minutes to produce photorealistic results. Our method is implemented purely with the PyTorch framework [53] to demonstrate the effectiveness of our representation. It also enables us to fairly compare with baseline methods [34,56,58] implemented in PyTorch. The details of the network architectures and hyper-parameters are presented in the supplementary material. + +# 4. Experiments + +# 4.1. Datasets + +ZJU-MoCap [58] dataset is a widely-used benchmark for human modeling from videos. It provides foreground human masks and SMPL parameters. Following [93], we select 6 human subjects (377, 386, 387, 392, 393, 394) from this dataset to conduct our experiments. One camera is used for training, and the remaining cameras are used for evaluation. For each human subject, we select 1 frame every 5 frames and collect 100 frames for training. Please refer to the supplementary material for more detailed experiment settings of all characters. + +MonoCap dataset contains four multi-view videos collected by [57] from the DeepCap dataset [26] and the DynaCap dataset [25]. It provides camera parameters and human masks. [57] additionally estimate the SMPL parameters for each image. We adopt the setting of training and test camera views in [57]. For each subject, 100 frames are selected for training, and we sample 1 frame every 5 frames. Detailed configurations of all sequences are described in the supplementary material. + +# 4.2. Comparison with the state-of-the-art methods + +Baselines. We compare our method with subject-specific optimization methods [56-58, 93], generalizable methods [34, 100]. All the baselines are implemented in pure PyTorch [53] for a fair comparison. Here we list only the average metric values of all selected characters on a dataset due to the size limit. We provide more detailed qualitative and quantitative comparisons in the supplementary material. + +(1) Subject-specific optimization methods. Neural Body (NB) [58] anchors a set of latent codes to the SMPL mesh and regresses the radiance field from the posed latent codes. Animatable NeRF (AN) [56] deforms the canonical NeRF with the skeleton-driven framework and models non-rigid deformations by learning blend weight fields. [57] extend [56] with a signed distance field and pose-dependent deformation field to better model the residual deformation and geometric + +![](images/84dc96aeae2125d189b0a7619b167eeaa2e37e2e1d6a0f679daf2edb419193dc.jpg) +Figure 3. Qualitative results of novel view synthesis on the ZJU-MoCap and MonoCap datasets. Our method produces better rendering results while only requiring 1/100 of the training time. The bottom row lists the training time of each method. + +details of dynamic humans. [93] optimizes for a volumetric representation of the person in a canonical space along with the estimated human pose. + +(2) Generalizable methods. PixelNeRF [100] trains a network to infer the radiance field from a single image. Neural Human Performer (NHP) [34] anchors image features to vertices of the SMPL mesh and aggregates temporal features using a transformer, which are decoded into a human model. For each evaluated subject (e.g. one subject of MonoCap), we first pre-train the network on the other dataset (e.g. ZJUMoCap) and then finetune it on the evaluated subject until it converges. + +Results on the ZJU-MoCap dataset. Table 1 compares our method with NB [58], AN [56], PixelNeRF [100], NHP [34], HN [93] and AS [57] on novel view synthesis. Our proposed representation can be optimized within around 5 minutes to produce photorealistic rendering results, while [56-58, 93] require around 10 hours to finish training and [34, 100] require 10 hours of pretraining and 1 hour of fine-tuning. [57, 93] exhibit better results than [56, 58]. + +However, they all require a lengthy optimization process and fail to produce reasonable renderings in only 5 minutes because their models have not converged yet. Generalizable methods [34, 100] failed to render humans with reasonable shapes under the monocular setting. Our method achieves comparable results on all of the three evaluated metrics even when only trained in minutes, which shows the effectiveness of our novel human representation. We present qualitative results of our method and baselines in Figure 3. + +Results on the MonoCap dataset. Table 1 summarizes the quantitative comparison between our method and other baselines on the MonoCap dataset. Our model again achieves competitive visual quality while only requiring 1/100 of the training time due to our efficient part-based voxelized human representation and effective motion parameterization scheme. Figure 3 indicates that our method can produce better appearance details than [56-58]. Although [56,58] have shown impressive rendering results given 4-view videos, they do not perform well on monocular inputs. [58] implicitly aggregates the temporal information using structured latent + +
Training TimeZJU-MoCapMonoCap
PSNR ↑SSIM ↑LPIPS* ↓PSNR ↑SSIM ↑LPIPS* ↓
Ours~5 min31.010.97138.4532.610.98816.68
HumanNeRF [93]~10 h30.660.96933.3832.680.98715.52
AS [57]~10 h30.380.97537.2332.480.98813.18
AN [56]~10 h29.770.96546.8931.070.98519.47
NB [58]~10 h29.030.96442.4732.360.98616.70
NHP [34]~1 h fine-tuning28.250.95564.7730.510.98027.14
PixelNeRF [100]~1 h fine-tuning24.710.892121.8626.430.96043.98
+ +Table 1. Quantitative comparison of our method and baseline methods on the ZJU-MoCap and MonoCap datasets. We use bold text for the best and underlined text for the second best metric value across methods. Our method achieves the fastest training speed and shows competitive rendering results. Note that the NHP [34] and PixelNeRF [100] are additionally pretrained for 10 hours. LPIPS* = LPIPS × 10^3. +(a) Ablation studies on proposed components. + +
PSNRSSIMLPIPS*
Ours32.090.98223.47
Ours w/o Part30.110.97445.84
Ours w/o UV31.400.97930.99
Ours w/o Perc30.550.97644.33
+ +(b) Ablation studies on variants of $\mathrm{MLP}_{\mathrm{res}}$ input. + +
PSNRSSIMLPIPS*
Ours32.090.98223.47
PE31.940.98126.76
XYZ-Code31.320.97931.19
XYZ-Pose31.510.97934.42
+ +(c) Ablation studies on the part parameters. + +
PSNRSSIMLPIPS*
Ours32.090.98223.47
Table size 21530.690.97635.67
Table size 22031.180.97833.58
+ +Table 2. Quantitative results of ablation studies on the 377 sequence of ZJU-MoCap dataset. The description of each model can be found in Section 4.3. All models are trained for 5 minutes. + +codes, which may not work well on monocular videos with complex human motions. [56] uses a learnable blend weight field to model human motion, which has a higher dimension and could be hard to converge well given single-view supervision. [57, 93] demonstrate similar visual quality and show that representing the human motion with the LBS model and residual deformation works particularly well, but their models require $100\mathrm{x}$ more time to optimize. + +![](images/69bfc411a9fb74630bf36f1f1b8de9fd49df812faf8e3ade165c7bc2ecef9410.jpg) +Figure 4. Comparison of convergence LPIPS* and time needed for convergence among different variants of the proposed pipeline. The description of each model can be found in Section 4.3. The proposed components accelerate the training and enhance the rendering quality significantly. LPIPS* = LPIPS × 10^3. + +# 4.3. Ablation Studies + +We perform ablation studies on the "377" sequence of the ZJU-MoCap dataset to analyze how the proposed components affect the performance and training speed of our + +method. + +# 4.3.1 Ablation Studies on Proposed Components. + +Table 2(a) lists the quantitative result of ablation studies on our proposed components. All models are trained for 5 minutes. "Ours w/o Part" represents the canonical human body with a single MHE-augmented NeRF [48] network, which drastically degrades the rate of convergence. To keep the comparison fair, this variant of our method has a similar number of parameters (302M) to ours (286M). However, this change leads to a significant decrease in PSNR of 1.98dB because of its unwise design of considering all parts equally complex and wasting representational power. In "Ours w/o UV", the residual deformation network $\mathrm{MLP}_{\mathrm{res}}$ takes hash encoded $(\mathbf{x},t)$ as input, which observed a PSNR degradation from 32.09dB to 31.40dB with the same training time because of severe hash collision and limited resolution. "Ours w/o Perc" does not adopt the perceptual loss during training, which in turn increases the LPIPS distance. This comparison illustrates the importance of perceptual loss for visual fidelity. Figure 4 and Figure 5 provide a more intuitive qualitative comparison among variants of the proposed method. + +Analysis of the part-based voxelized human representation. MHE [48] defines a multiresolution hash table of trainable features to embed input coordinates to a high-dimension space. We find that simply increasing the size + +![](images/1b3ffeb53c674a544c7235a3f841b95df1fb76cc7e7fea4ccb80a79620375373.jpg) +Ground Truth + +![](images/459b96fe011c420d85a8c30f47ca8cf82c3f034a1d30d48c66f120126377f33e.jpg) +Ours +Figure 5. Ablation studies on the 377 sequence of ZJU-MoCap dataset. "Ours w/o part" means that we use an MHE-augmented NeRF network to represent the whole body. "Ours w/o UV" indicates that the residual deformation network $\mathrm{MLP}_{\mathrm{res}}$ takes the hash encoded $(\mathbf{x},t)$ as input. "Ours w/o Perc" represents that we do not use perceptual loss during training. + +![](images/f910efd07b3e303fc32feacd88b0bb6f28437ac5feb08d797c39903fd4b04904.jpg) +Ours w/o Part + +![](images/0b3fda2a2692b48484194523be78fd38907de2a2434cfdcf3befbc6c754a44bb.jpg) +Ours w/o UV + +![](images/0653bd7198263efbf1afdb147c64cb7f37f2ce4e96ebbd6819fc231a29ff7424.jpg) +Ours w/o Perc + +of the hash table does not always result in better performance with the same training time, because a bigger hash table leads to higher memory consumption and increases the time of each training iteration. The proposed part-based voxelized human representation allows us to adapt the hash table size according to the complexity of the human part, allowing us to efficiently represent the human body. Table 2(a) demonstrates the effectiveness of the part-based voxelized human representation. To further validate this representation, we design two variants that use the hash tables of size $2^{15}$ and $2^{20}$ in all human parts respectively. Table 2(c) summarizes the ablation studies, indicating that varying the model parameters in human parts improves the performance. + +Analysis of the motion parameterization scheme. Table 2(a) shows that our model works better when the residual deformation network $\mathrm{MLP}_{\mathrm{res}}$ takes parameterized 3D surface-time $(u,v,t)$ coordinates as input, compared with taking 4D space-time $(\mathbf{x},t)$ as input. Note that $(u,v,t)$ makes MHE much more memory efficient than $(\mathbf{x},t)$ . To further validate the effectiveness of our motion parameterization, we additionally design three variants of the $\mathrm{MLP}_{\mathrm{res}}$ input. (1) PE: positional encoded $(\mathbf{x},t)$ . (2) XYZ-Code: hash encoded $\mathbf{x}$ and a per-frame learnable latent code [57]. (3) XYZ-Pose: hash encoded $\mathbf{x}$ and the pose parameter [40,93]. When taking the positional encoded $(\mathbf{x},t)$ as input, we use a larger network for $\mathrm{MLP}_{\mathrm{res}}$ . The results in Table 2(b) indicate that hash encoded $(u,v,t)$ achieves the best performance. + +Analysis of Robustness. To evaluate the robustness of the proposed system, we measure the time needed to achieve an evaluation PSNR of 30 for five times on the "377" sequence. This results in a training time with a mean value of $76.00s$ and a standard derivation of $13.56s$ , showing the stability of the proposed method. + +# 5. Limitations + +Although our method can quickly reconstruct high-quality human models from videos, it has some limitations to be fur- + +ther addressed. First, our method currently relies on accurate SMPL parameters, which could be difficult to obtain under in-the-wild settings. It is interesting to utilize the techniques in [79, 93] to optimize the human pose parameters along with the training of human avatars. Second, we can only reconstruct foreground dynamic humans, while dynamic scenes typically include foreground and background entities. It might be plausible to combine our method with ST-NeRF [29] or MultiNB [74] to quickly reconstruct dynamic scenes containing foreground and background objects. + +# 6. Conclusion + +We introduce a novel dynamic human representation that can be quickly optimized from videos and used for generating free-viewpoint videos of the human performer. This representation consists of a part-based voxelized human model in the canonical space and a motion parameterization scheme that transforms points from the world space to the canonical space. The part-based voxelized human model decomposes the human body into multiple parts and represents each part with an MHE-augmented NeRF network, which efficiently distributes network representational power and significantly improves the training speed. When predicting the motion of a query point, the motion field reparameterizes the point coordinate as 2D surface-level UV coordinate, which effectively reduces the dimensionality of motion the network is required to model, resulting in a boost in convergence rate. Experiments demonstrate that our proposed representation can be optimized at 1/100 of the time of previous methods while still maintaining competitive rendering quality. We show that given a 100-frame monocular video of a $512 \times 512$ resolution, our method can produce photorealistic free-viewpoint videos in minutes on an RTX 3090 GPU. + +Acknowledgements. This work was supported by the Key Research Project of Zhejiang Lab (No. K2022PG1BB01), NSFC (No. 62172364), and Information Technology Center and State Key Lab of CAD&CG, Zhejiang University. + +# References + +[1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graphics. In ECCV, 2020. 2 +[2] Thiemo Alldieck, Mihai Zanfir, and Cristian Sminchisescu. Photorealistic monocular 3d reconstruction of humans wearing clothing. arXiv preprint arXiv:2204.08906, 2022. 2 +[3] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5855–5864, 2021. 2, 3 +[4] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. arXiv preprint arXiv:2111.12077, 2021. 2, 5 +[5] Alexander Bergman, Petr Kellnhofer, and Gordon Wetzstein. Fast training of neural lumigraph representations using meta learning. Advances in Neural Information Processing Systems, 34, 2021. 3 +[6] Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Loopreg: Self-supervised learning of implicit surface correspondences, pose and shape for 3d human mesh registration. In NeurlPS, 2020. 4 +[7] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. arXiv preprint arXiv:2203.09517, 2022. 3 +[8] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14124-14133, 2021. 3 +[9] Xu Chen, Yufeng Zheng, Michael J Black, Otmar Hilliges, and Andreas Geiger. Snarf: Differentiable forward skinning for animating non-rigid neural implicit shapes. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11594-11604, 2021. 2 +[10] Yue Chen, Xuan Wang, Qi Zhang, Xiaoyu Li, Xingyu Chen, Yu Guo, Jue Wang, and Fei Wang. Uv volumes for real-time rendering of editable free-view human performance. arXiv preprint arXiv:2203.14402, 2022. 3 +[11] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In CVPR, 2019. 2 +[12] Julian Chibane, Aayush Bansal, Verica Lazova, and Gerard Pons-Moll. Stereo radiance fields (srf): Learning view synthesis for sparse views of novel scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7911-7920, 2021. 3 +[13] Alvaro Collet, Ming Chuang, Pat Sweeney, Don Gillett, Dennis Evseev, David Calabrese, Hugues Hoppe, Adam Kirk, and Steve Sullivan. High-quality streamable free-viewpoint video. ACM TOG, 2015. 2 +[14] Robert L Cook. Shade trees. In Proceedings of the 11th annual conference on Computer graphics and interactive techniques, pages 223-231, 1984. 2, 4 + +[15] Robert L Cook, Loren Carpenter, and Edwin Catmull. The reyes image rendering architecture. ACM SIGGRAPH Computer Graphics, 21(4):95-102, 1987. 2, 4 +[16] Boyang Deng, John P Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, and Andrea Tagliasacchi. Nasa neural articulated shape approximation. In European Conference on Computer Vision, pages 612-628. Springer, 2020. 3 +[17] Mingsong Dou, Sameh Khamis, Yury Degtyarev, Philip Davidson, Sean Ryan Fanello, Adarsh Kowdle, Sergio Orts Escolano, Christoph Rhemann, David Kim, Jonathan Taylor, et al. Fusion4d: Real-time performance capture of challenging scenes. ACM TOG, 2016. 2 +[18] Yilun Du, Yinan Zhang, Hong-Xing Yu, Joshua B Tenenbaum, and Jiajun Wu. Neural radiance flow for 4d view synthesis and video processing. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 14304-14314. IEEE Computer Society, 2021. 2 +[19] Emilien Dupont, Hyunjik Kim, SM Eslami, Danilo Rezende, and Dan Rosenbaum. From data to functa: Your data point is a function and you should treat it like one. arXiv preprint arXiv:2201.12204, 2022. 3 +[20] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning, pages 1126–1135. PMLR, 2017. 3 +[21] Xuan Gao, Chenglai Zhong, Jun Xiang, Yang Hong, Yudong Guo, and Juyong Zhang. Reconstructing personalized semantic facial nerf models from monocular video. arXiv preprint arXiv:2210.06108, 2022.3 +[22] Stephan J Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin. Fastnerf: High-fidelity neural rendering at 200fps. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14346-14355, 2021. 2 +[23] Haoyu Guo, Sida Peng, Haotong Lin, Qianqian Wang, Guofeng Zhang, Hujun Bao, and Xiaowei Zhou. Neural 3d scene reconstruction with the Manhattan-world assumption. In CVPR, 2022. 2 +[24] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, et al. The relightables: Volumetric performance capture of humans with realistic relighting. ACM TOG, 2019. 2 +[25] Marc Habermann, Lingjie Liu, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, and Christian Theobalt. Real-time deep dynamic characters. In SIGGRAPH, 2021. 5 +[26] Marc Habermann, Weipeng Xu, Michael Zollhofer, Gerard Pons-Moll, and Christian Theobalt. Deepcap: Monocular human performance capture using weak supervision. In CVPR, 2020. 5 +[27] Peter Hedman, Pratul P Srinivasan, Ben Mildenhall, Jonathan T Barron, and Paul Debevec. Baking neural radiance fields for real-time view synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5875-5884, 2021. 2 + +[28] Tao Hu, Tao Yu, Zerong Zheng, He Zhang, Yebin Liu, and Matthias Zwicker. Hvtr: Hybrid volumetric-textural rendering for human avatars. arXiv preprint arXiv:2112.10203, 2021. 2 +[29] Zhang Jiakai, Liu Xinhang, Ye Xinyi, Zhao Fuqiang, Zhang Yanshun, Wu Minye, Zhang Yingliang, Xu Lan, and Yu Jingyi. Editable free-viewpoint video using a layered neural representation. In SIGGRAPH, 2021. 8 +[30] Boyi Jiang, Yang Hong, Hujun Bao, and Juyong Zhang. Self-recon: Self reconstruction your digital avatar from monocular video. arXiv preprint arXiv:2201.12792, 2022. 2 +[31] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016. 5 +[32] James T Kajiya and Brian P Von Herzen. Ray tracing volume densities. ACM SIGGRAPH computer graphics, 18(3):165-174, 1984. 5 +[33] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 5 +[34] Youngjoong Kwon, Dahun Kim, Duygu Ceylan, and Henry Fuchs. Neural human performer: Learning generalizable radiance fields for human performance rendering. Advances in Neural Information Processing Systems, 34, 2021. 2, 5, 6, 7 +[35] John P Lewis, Matt Cordner, and Nickson Fong. Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation. In SIGGRAPH, 2000. 2, 3, 4 +[36] Ruilong Li, Julian Tanke, Minh Vo, Michael Zollhoffer, Jurgen Gall, Angjoo Kanazawa, and Christoph Lassner. Tava: Template-free animatable volumetric actors. arXiv preprint arXiv:2206.08929, 2022. 2 +[37] Zhengqi Li, Simon Niklaus, Noah Snavely, and Oliver Wang. Neural scene flow fields for space-time view synthesis of dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6498-6508, 2021. 2 +[38] Yiyi Liao, Katja Schwarz, Lars Mescheder, and Andreas Geiger. Towards unsupervised learning of generative models for 3d controllable image synthesis. In CVPR, 2020. 2 +[39] Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. Advances in Neural Information Processing Systems, 33:15651-15663, 2020. 2, 3 +[40] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural actor: Neural free-view synthesis of human actors with pose control. In SIGGRAPH Asia, 2021. 2, 3, 4, 5, 8 +[41] Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui. Dist: Rendering deep implicit signed distance function with differentiable sphere tracing. In CVPR, 2020. 2 +[42] Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural volumes: Learning dynamic renderable volumes from images. In SIGGRAPH, 2019. 2 +[43] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. Smpl: A skinned multiperson linear model. ACM TOG, 2015. 2, 4 + +[44] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In CVPR, 2019. 2 +[45] Marko Mihajlovic, Aayush Bansal, Michael Zollhoefer, Siyu Tang, and Shunsuke Saito. Keypointnerf: Generalizing image-based volumetric avatars using relative spatial encoding of keypoints. In European Conference on Computer Vision, pages 179-197. Springer, 2022. 3 +[46] Marko Mihajlovic, Shunsuke Saito, Aayush Bansal, Michael Zollhoefer, and Siyu Tang. COAP: Compositional articulated occupancy of people. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 3 +[47] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020. 2, 3, 5 +[48] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. In SIGGRAPH, 2022. 2, 3, 4, 7 +[49] Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018. 3 +[50] Michael Oechsle, Songyou Peng, and Andreas Geiger. Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5589-5599, 2021. 2 +[51] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, 2019. 2, 3 +[52] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5865-5874, 2021. 2, 5 +[53] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, 2019. 5 +[54] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3d hands, face, and body from a single image. In CVPR, 2019. 4 +[55] Bo Peng, Jun Hu, Jingtao Zhou, and Juyong Zhang. Selfnerf: Fast training nerf for human from monocular self-rotating video. arXiv preprint arXiv:2210.01651, 2022. 3 +[56] Sida Peng, Junting Dong, Qianqian Wang, Shangzhan Zhang, Qing Shuai, Xiaowei Zhou, and Hujun Bao. Animatable neural radiance fields for modeling dynamic human bodies. + +In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14314-14323, 2021. 2, 4, 5, 6, 7 +[57] Sida Peng, Shangzhan Zhang, Zhen Xu, Chen Geng, Boyi Jiang, Hujun Bao, and Xiaowei Zhou. Animatable neural implicit surfaces for creating avatars from videos. arXiv preprint arXiv:2203.08133, 2022. 5, 6, 7, 8 +[58] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9054-9063, 2021. 1, 2, 5, 6, 7 +[59] Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-nerf: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10318-10327, 2021. 2 +[60] Charles R Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas. Volumetric and multi-view cnns for object classification on 3d data. In CVPR, 2016. 2 +[61] Amit Raj, Julian Tanke, James Hays, Minh Vo, Carsten Stoll, and Christoph Lassner. Anr: Articulated neural rendering for virtual avatars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3722-3731, 2021. 2 +[62] Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. H3d-net: Few-shot high-fidelity 3d head reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5620-5629, 2021. 3 +[63] Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger. Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14335-14345, 2021. 2 +[64] Konstantinos Rematas, Andrew Liu, Pratul P Srinivasan, Jonathan T Barron, Andrea Tagliasacchi, Thomas Funkhouser, and Vittorio Ferrari. Urban radiance fields. arXiv preprint arXiv:2111.14643, 2021. 2 +[65] Edoardo Remelli, Timur Bagautdinov, Shunsuke Saito, Chenglei Wu, Tomas Simon, Shih-En Wei, Kaiwen Guo, Zhe Cao, Fabian Prada, Jason Saragih, et al. Drivable volumetric avatars using texel-aligned features. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1–9, 2022. 2 +[66] Javier Romero, Dimitrios Tzionas, and Michael J Black. Embodied hands: Modeling and capturing hands and bodies together. ACM ToG, 2017. 4 +[67] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In ICCV, 2019. 2 +[68] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo. Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization. In CVPR, 2020. 2 +[69] Shunsuke Saito, Jinlong Yang, Qianli Ma, and Michael J Black. Scanimate: Weakly supervised learning of skinned + +clothed avatar networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2886-2897, 2021. 2 +[70] Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G Baraniuk, and Ashok Veeraraghavan. Miner: Multiscale implicit neural representations. arXiv preprint arXiv:2202.03532, 2022. 3 +[71] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In CVPR, 2016. 2 +[72] Johannes L Schonberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys. Pixelwise view selection for unstructured multi-view stereo. In ECCV, 2016. 2, 3 +[73] Ruizhi Shao, Hongwen Zhang, He Zhang, Yanpei Cao, Tao Yu, and Yebin Liu. Doublefield: Bridging the neural surface and radiance fields for high-fidelity human rendering. arXiv preprint arXiv:2106.03798, 2021. 3 +[74] Qing Shuai, Chen Geng, Qi Fang, Sida Peng, Wenhao Shen, Xiaowei Zhou, and Hujun Bao. Novel view synthesis of human interactions from sparse multi-view videos. In SIGGRAPH Conference Proceedings, 2022. 8 +[75] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462-7473, 2020. 3 +[76] Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhofer. Deepvoxels: Learning persistent 3d feature embeddings. In CVPR, 2019. 2 +[77] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3d-structure-aware neural scene representations. In NeurIPS, 2019. 2 +[78] Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller. Multi-view convolutional neural networks for 3d shape recognition. In Proceedings of the IEEE international conference on computer vision, pages 945-953, 2015. 2 +[79] Shih-Yang Su, Frank Yu, Michael Zollhöfer, and Helge Rhodin. A-nerf: Articulated neural radiance fields for learning human shape, appearance, and pose. Advances in Neural Information Processing Systems, 34, 2021. 3, 8 +[80] Zhuo Su, Lan Xu, Zerong Zheng, Tao Yu, Yebin Liu, et al. Robustfusion: Human volumetric capture with data-driven visual cues using a rgbd camera. In ECCV, 2020. 2 +[81] Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. arXiv preprint arXiv:2111.11215, 2021. 3 +[82] Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Mildenhall, Pratul P Srinivasan, Jonathan T Barron, and Henrik Kretzschmar. Block-nerf: Scalable large scene neural view synthesis. arXiv preprint arXiv:2202.05263, 2022. 2 +[83] Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P Srinivasan, Jonathan T Barron, and Ren Ng. Learned initializations for optimizing coordinate-based neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2846-2855, 2021. 3 + +[84] Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems, 33:7537-7547, 2020. 3 +[85] Matthias Teschner, Bruno Heidelberger, Matthias Müller, Danat Pomerantes, and Markus H Gross. Optimized spatial hashing for collision detection of deformable objects. In Vmv, volume 3, pages 47-54, 2003. 3 +[86] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM TOG, 2019. 2 +[87] Garvita Tiwari, Nikolaos Sarafianos, Tony Tung, and Gerard Pons-Moll. Neural-gif: Neural generalized implicit functions for animating people in clothing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11708-11718, 2021. 2 +[88] Alex Trevithick and Bo Yang. Grf: Learning a general radiance field for 3d representation and rendering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15182-15192, 2021. 3 +[89] Haithem Turki, Deva Ramanan, and Mahadev Satyanarayanan. Mega-nerf: Scalable construction of large-scale nerfs for virtual fly-throughs. arXiv preprint arXiv:2112.10703, 2021. 2 +[90] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In NeurIPS, 2021. 2 +[91] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P Srinivasan, Howard Zhou, Jonathan T Barron, Ricardo Martin-Brualla, Noah Snavely, and Thomas Funkhouser. Ibrnet: Learning multi-view image-based rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4690-4699, 2021. 3 +[92] Shaofei Wang, Katja Schwarz, Andreas Geiger, and Siyu Tang. Arah: animatable volume rendering of articulated human sdfs. arXiv preprint arXiv:2210.10036, 2022. 2 +[93] Chung-Yi Weng, Brian Curless, Pratul P Srinivasan, Jonathan T Barron, and Ira Kemelmacher-Shlizerman. Humanerf: Free-viewpoint rendering of moving people from monocular video. arXiv preprint arXiv:2201.04127, 2022. 1, 2, 3, 5, 6, 7, 8 +[94] Yuanbo Xiangli, Linning Xu, Xingang Pan, Nanxuan Zhao, Anyi Rao, Christian Theobalt, Bo Dai, and Dahua Lin. Citynerf: Building nerf at city scale. arXiv preprint arXiv:2112.05504, 2021. 2 +[95] Hongyi Xu, Thiemo Alldieck, and Cristian Sminchisescu. H-nerf: Neural radiance fields for rendering and temporal reconstruction of humans in motion. Advances in Neural Information Processing Systems, 34, 2021. 2 +[96] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In NeurIPS, 2021. 2 +[97] Jianglong Ye, Yuntao Chen, Naiyan Wang, and Xiaolong Wang. Gifs: Neural implicit function for general shape representation. arXiv preprint arXiv:2204.07126, 2022. 2 + +[98] Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. arXiv preprint arXiv:2112.05131, 2021. 2, 3 +[99] Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. Plenoptrees for real-time rendering of neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5752-5761, 2021. 2 +[100] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelnerf: Neural radiance fields from one or few images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4578-4587, 2021. 2, 3, 5, 6, 7 +[101] Ruiqi Zhang and Jie Chen. Ndf: Neural deformable fields for dynamic human modelling. arXiv preprint arXiv:2207.09193, 2022. 2 +[102] Fuqiang Zhao, Wei Yang, Jiakai Zhang, Pei Lin, Yingliang Zhang, Jingyi Yu, and Lan Xu. Humannerf: Generalizable neural human radiance field from sparse inputs. arXiv preprint arXiv:2112.02789, 2021. 2 +[103] Zerong Zheng, Han Huang, Tao Yu, Hongwen Zhang, Yandong Guo, and Yebin Liu. Structured local radiance fields for human avatar modeling. arXiv preprint arXiv:2203.14478, 2022. 2 +[104] Shuaifeng Zhi, Tristan Laidlow, Stefan Leutenegger, and Andrew J Davison. In-place scene labelling and understanding with implicit scene representation. In ICCV, 2021. 2 +[105] Yihao Zhi, Shenhan Qian, Xinhao Yan, and Shenghua Gao. 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Convergence rate of training. Given a monocular video of a human performer, our model can be learned in " + }, + { + "bbox": [ + 46, + 434, + 547, + 457 + ], + "type": "inline_equation", + "content": "\\sim 5" + }, + { + "bbox": [ + 46, + 434, + 547, + 457 + ], + "type": "text", + "content": " minutes to produce photorealistic novel view rendering, which is 100 times faster than Neural Body [58]." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "bbox": [ + 143, + 481, + 192, + 493 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 481, + 192, + 493 + ], + "spans": [ + { + "bbox": [ + 143, + 481, + 192, + 493 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 46, + 508, + 288, + 688 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 508, + 288, + 688 + ], + "spans": [ + { + "bbox": [ + 46, + 508, + 288, + 688 + ], + "type": "text", + "content": "This paper addresses the challenge of efficiently reconstructing volumetric videos of dynamic humans from sparse multi-view videos. Some recent works represent a dynamic human as a canonical neural radiance field (NeRF) and a motion field, which are learned from input videos through differentiable rendering. But the per-scene optimization generally requires hours. Other generalizable NeRF models leverage learned prior from datasets to reduce the optimization time by only finetuning on new scenes at the cost of visual fidelity. In this paper, we propose a novel method for learning neural volumetric representations of dynamic humans in minutes with competitive visual quality. Specifically, we define a novel part-based voxelized human representation to better distribute the representational power of the network to different human parts. Furthermore, we propose" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 306, + 482, + 547, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 482, + 547, + 578 + ], + "spans": [ + { + "bbox": [ + 306, + 482, + 547, + 578 + ], + "type": "text", + "content": "a novel 2D motion parameterization scheme to increase the convergence rate of deformation field learning. Experiments demonstrate that our model can be learned 100 times faster than previous per-scene optimization methods while being competitive in the rendering quality. Training our model on a " + }, + { + "bbox": [ + 306, + 482, + 547, + 578 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 306, + 482, + 547, + 578 + ], + "type": "text", + "content": " video with 100 frames typically takes about 5 minutes on a single RTX 3090 GPU. The code is available on our project page: https://zju3dv.github.io/instant_nvr." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 306, + 607, + 387, + 620 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 607, + 387, + 620 + ], + "spans": [ + { + "bbox": [ + 306, + 607, + 387, + 620 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 305, + 629, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 629, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 305, + 629, + 547, + 715 + ], + "type": "text", + "content": "Creating volumetric videos of human performers has many applications, such as immersive telepresence, video games, and movie production. Recently, some methods [58, 93] have shown that high-quality volumetric videos can be recovered from sparse multi-view videos by representing dynamic humans with neural scene representations. However, they typically require more than 10 hours of training on" + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 702, + 194, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 702, + 194, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 702, + 194, + 713 + ], + "type": "text", + "content": "*Equal contribution. † Corresponding author." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8759" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 288, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 288, + 156 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 288, + 156 + ], + "type": "text", + "content": "a single GPU. The expensive time and computational costs limit the large-scale application of volumetric videos. Generalizable methods [34, 100] utilize learned prior from datasets of dynamic humans to reduce the training time by only finetuning on novel human performers. These techniques could increase the optimization speed by a factor of 2-5 at the cost of some visual fidelity." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 158, + 288, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 158, + 288, + 277 + ], + "spans": [ + { + "bbox": [ + 46, + 158, + 288, + 277 + ], + "type": "text", + "content": "To speed up the process of optimizing a neural representation for view synthesis of dynamic humans, we analyze the structural prior of the human body and motion, and propose a novel dynamic human representation that achieves 100x speedup during optimization while maintaining competitive visual fidelity. Specifically, to model a dynamic human, we first transform world-space points to a canonical space using a novel motion parameterization scheme and inverse linear blend skinning (LBS) [35]. Then, the color and density of these points are estimated using the canonical human model." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 279, + 288, + 459 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 279, + 288, + 459 + ], + "spans": [ + { + "bbox": [ + 46, + 279, + 288, + 459 + ], + "type": "text", + "content": "The innovation of our proposed representation is two-fold. First, we observe that human body parts have different levels of complexity in terms of both shape and texture. For example, the face of a human performer typically exhibits higher complexity than a flatly textured torso region, thus requiring more representational power to depict. Motivated by this, our method decomposes the canonical human body into multiple parts and represents the human body with a structured set of voxelized NeRF [47] networks to bring the convergence rate of these different parts to the same level. In contrast to a single-resolution representation, the part-based body model utilizes the human body prior to represent the human shape and texture efficiently, heuristically distributing variational representational power to human parts with spatially varying complexity." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 460, + 288, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 460, + 288, + 664 + ], + "spans": [ + { + "bbox": [ + 46, + 460, + 288, + 664 + ], + "type": "text", + "content": "Second, we notice that non-rigid deformation of human geometry typically occurs around a surface instead of in a volume, that is, nearby surface points on a parametric human model tend to have similar motion behavior. Thus we propose a novel motion parameterization technique that models the 3D human deformation in a 2D domain, enabling modeling of the motion field using 3D voxelized representation to accelerate the learning. This idea is similar to the displacement map and bump map [14, 15] in traditional computer graphics to represent detailed deformation on a 2D texture domain. We extend the technique of displacement map [14, 15] to represent human motions by restricting the originally 3D deformation field [40, 56, 59] on the 2D surface of a parametric human model, such as SMPL [43]. This technique allows us to use hybrid representations [48, 98] to model non-rigid deformation by reducing the memory footprint, largely boosting the convergence of the field." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "text", + "content": "Experiments demonstrate that our method significantly accelerates the optimization of neural human representations while being competitive with state-of-the-art human modeling methods on rendering quality. As shown in Figure 1, our" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 72, + 547, + 107 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 107 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 107 + ], + "type": "text", + "content": "model can be trained within 5 minutes to produce a volumetric video of a dynamic human from a 100-frame monocular video of a " + }, + { + "bbox": [ + 304, + 72, + 547, + 107 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 304, + 72, + 547, + 107 + ], + "type": "text", + "content": " resolution on an RTX 3090 GPU." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 317, + 109, + 485, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 109, + 485, + 120 + ], + "spans": [ + { + "bbox": [ + 317, + 109, + 485, + 120 + ], + "type": "text", + "content": "To summarize, our key contributions are:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 317, + 129, + 547, + 215 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 317, + 129, + 545, + 152 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 129, + 545, + 152 + ], + "spans": [ + { + "bbox": [ + 317, + 129, + 545, + 152 + ], + "type": "text", + "content": "- A novel part-based voxelized human representation for more efficient human body modeling." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 317, + 154, + 547, + 177 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 154, + 547, + 177 + ], + "spans": [ + { + "bbox": [ + 317, + 154, + 547, + 177 + ], + "type": "text", + "content": "- A 2D motion parameterization scheme for more efficient deformation field modeling." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 317, + 179, + 547, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 179, + 547, + 215 + ], + "spans": [ + { + "bbox": [ + 317, + 179, + 547, + 215 + ], + "type": "text", + "content": "- 100x speedup in optimization compared to previous neural human representations while maintaining competitive rendering quality." + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 235, + 390, + 247 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 235, + 390, + 247 + ], + "spans": [ + { + "bbox": [ + 306, + 235, + 390, + 247 + ], + "type": "text", + "content": "2. Related work" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 255, + 547, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 255, + 547, + 471 + ], + "spans": [ + { + "bbox": [ + 304, + 255, + 547, + 471 + ], + "type": "text", + "content": "Implicit neural representation and rendering. There have been many 3D scene representations, such as multiview images [60, 78], textured meshes [38, 86], point clouds [1, 61], and voxels [42, 76]. Recently, some methods [11, 41, 44, 51, 77, 97, 104] propose implicit neural representations to represent scenes, which uses MLP networks to predict scene properties for any point in 3D space, such as occupancy [44, 67], signed distance [41, 51], and semantics [23, 104]. This enables them to describe continuous and high-resolution 3D scenes. To perform novel view synthesis, neural radiance field (NeRF) [47] models scenes as implicit fields of density and color. NeRF is optimized from images with volume rendering techniques, which produces impressive image synthesis results. Many works improve NeRF in various aspects, including rendering quality [3, 4], rendering speed [22, 27, 39, 63, 99], scene scale [64, 82, 89, 94], and reconstruction quality [50, 90, 96]. Some methods [18, 37, 52, 59] extend NeRF to dynamic scenes." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 486, + 548, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 486, + 548, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 486, + 548, + 713 + ], + "type": "text", + "content": "Human modeling. Reconstructing high-quality 3D human models is essential for synthesizing free-viewpoint videos of human performers. Traditional methods leverage multi-view stereo techniques [24, 71, 72] or depth fusion [13, 17, 80] to reconstruct human geometries, which require complicated hardware, such as dense camera arrays or depth sensors. To reduce the requirement of the capture equipment, some methods [2, 67, 68] train networks to learn human priors from datasets containing a large amount of 3D ground-truth human models, enabling them to infer human geometry and texture from even a single image. However, due to the limited diversity of training data, these methods do not generalize well to humans under complex poses. Recently, some methods [9, 46, 69, 87] model the shapes of dynamic humans as implicit neural representations and attempt to optimize them from human scans. Another line of works [28, 30, 34, 36, 40, 56, 58, 65, 92, 93, 95, 101-103, 105] exploits dynamic implicit neural representations and differentiable renderers to reconstruct 3D human models from" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8760" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 71, + 69, + 329, + 228 + ], + "blocks": [ + { + "bbox": [ + 71, + 69, + 329, + 228 + ], + "lines": [ + { + "bbox": [ + 71, + 69, + 329, + 228 + ], + "spans": [ + { + "bbox": [ + 71, + 69, + 329, + 228 + ], + "type": "image", + "image_path": "da5e57f8f072b40730e509918d78e8ff12f1bd63ed38b43c3663309d49ceae75.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "lines": [ + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "spans": [ + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": "Figure 2. Overview of the proposed representation. Given a query point " + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": " at frame " + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": ", we find its nearest surface point on each human part of the SMPL mesh, which gives the blend weight " + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{w}_k" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": " and the UV coordinates " + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "inline_equation", + "content": "(u_k, v_k)" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": ". 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The residual deformation module applies the multiresolution hash encoding (MHE) to " + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "inline_equation", + "content": "(u_k, v_k, t)" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": " and uses an MLP network to regress the residual translation " + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "inline_equation", + "content": "\\Delta \\mathbf{x}" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": ", which is added to " + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{x}'" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": " to obtain the canonical point " + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{x}^{\\mathrm{can}}" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": ". (b) We then feed " + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{x}^{\\mathrm{can}}" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": " to networks of " + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": "-th human part to predict the density " + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "inline_equation", + "content": "\\sigma_k" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": " and color " + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{c}_k" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": ". With " + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "inline_equation", + "content": "\\left\\{\\left(\\sigma_k, \\mathbf{c}_k\\right)\\right\\}_{k=1}^{K}" + }, + { + "bbox": [ + 46, + 232, + 548, + 312 + ], + "type": "text", + "content": ", we select the one with the largest density as the density and color of the query point." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 331, + 68, + 520, + 228 + ], + "blocks": [ + { + "bbox": [ + 331, + 68, + 520, + 228 + ], + "lines": [ + { + "bbox": [ + 331, + 68, + 520, + 228 + ], + "spans": [ + { + "bbox": [ + 331, + 68, + 520, + 228 + ], + "type": "image", + "image_path": "8f8f74e7019caf9d8314a78d714eda47af3dda6e55612880b8ad68381e5fe05d.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 331, + 290, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 331, + 290, + 510 + ], + "spans": [ + { + "bbox": [ + 46, + 331, + 290, + 510 + ], + "type": "text", + "content": "videos. To represent dynamic humans, Neural Actor [40] augments the neural radiance field with the linear blend skinning model [35]. It additionally adopts a residual deformation field to better predict human motions. To overcome the inaccuracy of input human poses, [79, 93] optimize the parameters of human poses jointly with the human representations during training. These methods typically require a lengthy training process to produce high-quality human models. In contrast, we introduce a part-based voxelized human representation to model the canonical human body, which significantly accelerates the optimization process. Although [16, 46] have proposed part-based implicit functions, they focus on human shape modeling and do not show that the part-based representation can be used to reduce the training time." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 534, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 534, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 534, + 289, + 715 + ], + "type": "text", + "content": "Accelerating the optimization of neural representations. Many differentiable rendering-based methods [3, 39, 47] optimize a separate neural representation for each scene. The optimization process generally takes several hours on a modern GPU, which is time-consuming and costly to scale. Inspired by multi-view stereo matching [72], some methods [8, 12, 45, 73, 88, 91, 100] train a network on multi-view datasets to learn to infer radiance fields from input images. This enables them to quickly fine-tune neural representations to unseen scenes. [19, 62] leverage the auto-decoder [51] to capture the scene priors for efficient fine-tuning. [5, 83] utilize meta-learning techniques [20, 49] to initialize network parameters, thereby improving the training speed. Some methods [7, 10, 55, 70, 81, 98] attempt to design scene representations that support efficient training. [21, 48, 75, 84]" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 331, + 548, + 403 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 331, + 548, + 403 + ], + "spans": [ + { + "bbox": [ + 304, + 331, + 548, + 403 + ], + "type": "text", + "content": "augments the approximation ability of networks by designing encoding techniques. Multiresolution hash encoding [48] defines multiresolution feature vector arrays for a scene and uses the hash technique [85] to assign each input coordinate a feature vector as the encoded input, which significantly improves the training speed." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 306, + 414, + 367, + 426 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 414, + 367, + 426 + ], + "spans": [ + { + "bbox": [ + 306, + 414, + 367, + 426 + ], + "type": "text", + "content": "3. Methods" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 434, + 548, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 434, + 548, + 555 + ], + "spans": [ + { + "bbox": [ + 304, + 434, + 548, + 555 + ], + "type": "text", + "content": "This paper aims to quickly create a volumetric video from a sparse multi-view video that captures a dynamic human. We assume that the cameras are calibrated and the human pose and foreground human mask of each image are provided. Section 3.1 introduces our dynamic human model that is comprised of a part-based voxelized human representation and a dimensionality reduction motion parameterization scheme. Section 3.2 discusses how to efficiently optimize the proposed representation. Finally, Section 3.3 provides implementation details." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 563, + 479, + 575 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 563, + 479, + 575 + ], + "spans": [ + { + "bbox": [ + 306, + 563, + 479, + 575 + ], + "type": "text", + "content": "3.1. Proposed human representation" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "content": "As shown in Figure 2, our dynamic human representation consists of a motion parameterization field and a part-based voxelized human model. (a) For a query point " + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "content": ", the motion parameterization field first transforms it to the canonical space correspondence " + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}^{\\mathrm{can}}" + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "content": " using the inverse LBS [35] algorithm and by parameterizing the 3D points onto 2D UV coordinates to predict the residual deformation " + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "inline_equation", + "content": "\\Delta \\mathbf{x}" + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "content": ". (b) Then, " + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}^{\\mathrm{can}}" + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "content": " is fed into the part-based voxelized human model in canonical space to predict and aggregate the density and color " + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "inline_equation", + "content": "(\\sigma, \\mathbf{c})" + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "content": ", where the canonical human body is decomposed into " + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "content": " parts, each of which is represented using an" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "8761" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 203, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 203, + 83 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 203, + 83 + ], + "type": "text", + "content": "MHE-augmented [48] NeRF network." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "spans": [ + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "text", + "content": "Motion parameterization on 2D surface domain. To regress the canonical correspondence " + }, + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "inline_equation", + "content": "\\mathbf{x}^{\\mathrm{can}}" + }, + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "text", + "content": " of a query point " + }, + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "text", + "content": ", we first find its nearest surface point " + }, + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "inline_equation", + "content": "\\mathbf{p}" + }, + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "text", + "content": " on the posed SMPL mesh. Using the strategy in [40], the blend weight " + }, + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "inline_equation", + "content": "\\mathbf{w}" + }, + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "text", + "content": " and UV coordinate " + }, + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "inline_equation", + "content": "(u,v)" + }, + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "text", + "content": " of surface point " + }, + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "inline_equation", + "content": "\\mathbf{p}" + }, + { + "bbox": [ + 46, + 99, + 287, + 169 + ], + "type": "text", + "content": " are obtained from the SMPL model." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "spans": [ + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "text", + "content": "Given the blend weight " + }, + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "inline_equation", + "content": "\\mathbf{w}" + }, + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "text", + "content": " and UV coordinate " + }, + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "inline_equation", + "content": "(u, v)" + }, + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "text", + "content": ", the motion field maps the query point " + }, + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "text", + "content": " to the canonical space correspondence " + }, + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "inline_equation", + "content": "\\mathbf{x}^{\\mathrm{can}}" + }, + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "text", + "content": ". The motion field is comprised of an inverse LBS module [35] and a residual deformation module. Given a query point " + }, + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "text", + "content": " and blend weight " + }, + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "inline_equation", + "content": "\\mathbf{w}" + }, + { + "bbox": [ + 46, + 170, + 288, + 253 + ], + "type": "text", + "content": ", we use the inverse LBS module to transform it to the unposed space, which is defined as:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 87, + 260, + 287, + 301 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 260, + 287, + 301 + ], + "spans": [ + { + "bbox": [ + 87, + 260, + 287, + 301 + ], + "type": "interline_equation", + "content": "\\Phi_ {\\mathrm {L B S}} (\\mathbf {x}, \\mathbf {w}, \\boldsymbol {\\rho}) = \\left(\\sum_ {j = 1} ^ {J} w _ {k, j} G _ {j}\\right) ^ {- 1} \\mathbf {x}, \\tag {1}", + "image_path": "7c249d4e51a0ade810852cc4cc520bb5f9e24295d7ef6be22f0161334e0f59d3.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 308, + 288, + 356 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 308, + 288, + 356 + ], + "spans": [ + { + "bbox": [ + 46, + 308, + 288, + 356 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 308, + 288, + 356 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 46, + 308, + 288, + 356 + ], + "type": "text", + "content": " denotes the human pose and " + }, + { + "bbox": [ + 46, + 308, + 288, + 356 + ], + "type": "inline_equation", + "content": "\\{G_j\\}_{j=1}^J" + }, + { + "bbox": [ + 46, + 308, + 288, + 356 + ], + "type": "text", + "content": " are transformation matrices derived from " + }, + { + "bbox": [ + 46, + 308, + 288, + 356 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 46, + 308, + 288, + 356 + ], + "type": "text", + "content": " [43]. The detailed derivation of the inverse LBS algorithm can be found in the supplementary material." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "spans": [ + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "text", + "content": "The transformed point " + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "inline_equation", + "content": "\\Phi_{\\mathrm{LBS}}(\\mathbf{x},\\mathbf{w},\\pmb {\\rho})" + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "text", + "content": " is then deformed to the human surface using the residual deformation module. Specifically, the current time " + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "text", + "content": " is first concatenated with the UV coordinate " + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "inline_equation", + "content": "(u,v)" + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "text", + "content": " to serve as the parameterization of the query point " + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "text", + "content": " at frame " + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "text", + "content": ". This motion parameterization is inspired by the displacement map and bump map techniques [14, 15] in the traditional Computer Graphics pipelines. It essentially reduces the dimensionality of a 4D space-time sequence down to the 3D surface-time domain utilizing the human deformation prior. Then, we apply the multiresolution hash encoding [48] " + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "inline_equation", + "content": "\\psi_{\\mathrm{res}}" + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "inline_equation", + "content": "(u,v,t)" + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "text", + "content": " and forward the encoded input through a network " + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "inline_equation", + "content": "\\mathrm{MLP}_{\\mathrm{res}}" + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "text", + "content": " to regress the residual " + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "inline_equation", + "content": "\\Delta \\Phi (u,v,t)" + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "text", + "content": ". The full human motion at frame " + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 357, + 289, + 523 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 88, + 533, + 287, + 546 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 88, + 533, + 287, + 546 + ], + "spans": [ + { + "bbox": [ + 88, + 533, + 287, + 546 + ], + "type": "interline_equation", + "content": "\\Delta \\Phi (u, v, t) = \\mathrm {M L P} _ {\\text {r e s}} \\left(\\psi_ {\\text {r e s}} (u, v, t)\\right), \\tag {2}", + "image_path": "e40c15a971dce9ad770d024640b67265eec05bbd8d9ee70b3eeb945fdccf1acf.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 548, + 287, + 562 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 548, + 287, + 562 + ], + "spans": [ + { + "bbox": [ + 53, + 548, + 287, + 562 + ], + "type": "interline_equation", + "content": "\\mathbf {x} ^ {\\text {c a n}} (\\mathbf {x}, \\mathbf {w}, u, v, \\boldsymbol {\\rho}, t) = \\Phi_ {\\mathrm {L B S}} (\\mathbf {x}, \\mathbf {w}, \\boldsymbol {\\rho}) + \\Delta \\Phi (u, v, t). \\tag {3}", + "image_path": "7958145754864b89dd5fbf23b8d33f5291090747e9b51f3ffd6aaa8a9e005abb.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "text", + "content": "There are two main observations that inspired us to use the " + }, + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "inline_equation", + "content": "(u,v,t)" + }, + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "text", + "content": " motion parameterization. First, we observe that a typical human motion happens at a surface level instead of a volumetric level. Near-surface points sharing similar UV coordinate of the parametric model shows similar motions. Utilizing this prior with a surface parameterization [14, 15], we can reduce the required 4D volumetric motion to the 3D surface-time domain, greatly decreasing the amount of information the deformation network has to learn. Based on a similar idea, [6] diffuses the surface motion to the full 3D space. Second, a naive " + }, + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "inline_equation", + "content": "(x,y,z,t)" + }, + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "text", + "content": " encoding would introduce quartic memory overhead on an explicitly defined voxel" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "text", + "content": "structure, which is intractable to use in practice. Instead, by parameterizing the motion to " + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "inline_equation", + "content": "(u,v,t)" + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "text", + "content": ", we can reduce the memory footprint to a more practical cubic level. Experiments demonstrate that the motion parameterization scheme effectively reduces the dimensionality of the deformation field, thus greatly increasing the convergence rate of the human model." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 173, + 547, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 173, + 547, + 353 + ], + "spans": [ + { + "bbox": [ + 304, + 173, + 547, + 353 + ], + "type": "text", + "content": "Part-based voxelized human representation. Muller et al. [48] propose the multi-resolution hash encoding (MHE) to improve the approximation ability and training speed of implicit neural representations. MHE is defined on an explicit set of voxel grids of different resolutions. Given an input coordinate, it applies the hash encoding on each level and queries the corresponding voxel grid to trilinearly interpolate the feature of the input point for this level. Then, the concatenated multi-resolution feature is fed into a small MLP network to predict the target value. Note that [48] concatenates the features of multi-resolution hash encoding for the same point to mitigate the effect of hash collision, while our part-based voxelized human representation introduces spatially varying resolution to efficiently encode human parts with different complexity." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "spans": [ + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "text", + "content": "In contrast to [40, 56] which use a single neural radiance field (NeRF) to represent the canonical human model, we decompose the human body into multiple parts with different complexity and adopt a structured set of MHE-augmented NeRF with varying resolutions as the body representation. Specifically, we manually divide the human body into multiple parts based on a parametric human model (such as SMPL [43]), as shown in Figure 2. Note that other parametric human models [54,66] can also be used in our method. We use the blend weights defined in SMPL model [43] to decompose SMPL template mesh " + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "inline_equation", + "content": "\\mathcal{M} = (\\mathcal{V},\\mathcal{E})" + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "inline_equation", + "content": "\\mathcal{V}" + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "text", + "content": " represents the vertices and " + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "inline_equation", + "content": "\\mathcal{E}" + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "text", + "content": " represents the edges. Let the " + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "text", + "content": "-th vertex " + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "inline_equation", + "content": "v_{i}" + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "text", + "content": " have the blend weight " + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "inline_equation", + "content": "w_{i}" + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "text", + "content": " and for each part " + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "text", + "content": " we define " + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "inline_equation", + "content": "\\Omega_{k}" + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "text", + "content": " as the set of bones that belong to this part. The detailed setting of " + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "inline_equation", + "content": "\\Omega_{k}" + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "text", + "content": " can be found in the supplementary material. The mesh of the " + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "text", + "content": "-th part is defined as " + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_k = (\\mathcal{V}_k,\\mathcal{E}_k)" + }, + { + "bbox": [ + 304, + 354, + 548, + 545 + ], + "type": "text", + "content": ", where:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 374, + 567, + 545, + 580 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 374, + 567, + 545, + 580 + ], + "spans": [ + { + "bbox": [ + 374, + 567, + 545, + 580 + ], + "type": "interline_equation", + "content": "\\mathcal {V} _ {k} = \\left\\{v _ {i} \\mid \\operatorname {a r g m a x} w _ {i} \\in \\Omega_ {k} \\right\\}, \\tag {4}", + "image_path": "7abdb174b7cbc66dc9fa8d57a45b7c7f14153f29d809ef2613518e34a2a7be82.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 356, + 582, + 545, + 596 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 356, + 582, + 545, + 596 + ], + "spans": [ + { + "bbox": [ + 356, + 582, + 545, + 596 + ], + "type": "interline_equation", + "content": "\\mathcal {E} _ {k} = \\left\\{\\left(v _ {i}, v _ {j}\\right) \\mid v _ {i} \\in \\mathcal {V} _ {k}, v _ {j} \\in \\mathcal {V} _ {k} \\right\\}. \\tag {5}", + "image_path": "08a2b14b8c6bdc02bedc4ff9a417bbc5dfdb072055597405b9fda7e7d37c1a9b.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "content": "To regress the density and color of a query point " + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "content": ", we first find the nearest surface point " + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_k" + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "content": " on each human part " + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_k" + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "content": " of the posed SMPL mesh. Using the strategy in [40], the blend weight " + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{w}_k" + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "content": " and UV coordinate " + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "inline_equation", + "content": "(u_k, v_k)" + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "content": " of surface point " + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{p}_k" + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "content": " are obtained from the SMPL model. With " + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "inline_equation", + "content": "(u_k, v_k, t)" + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "content": ", we use the motion parameterization scheme defined in Section 3.1 to transform the query point to the space of the " + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "content": "-th human part. We predefine the parameters of the multiresolution hash encoding function " + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\psi_k" + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "content": " for the " + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 605, + 547, + 714 + ], + "type": "text", + "content": "-th part." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "8762" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 131 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 131 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 131 + ], + "type": "text", + "content": "Given the transformed point, we first apply the multiresolution hash encoding to the transformed point and then feed the encoded point " + }, + { + "bbox": [ + 46, + 72, + 287, + 131 + ], + "type": "inline_equation", + "content": "\\psi_{k}(\\mathbf{x})" + }, + { + "bbox": [ + 46, + 72, + 287, + 131 + ], + "type": "text", + "content": " to a small NeRF network to predict the density and color. The density network " + }, + { + "bbox": [ + 46, + 72, + 287, + 131 + ], + "type": "inline_equation", + "content": "\\mathrm{MLP}_{\\sigma_k}" + }, + { + "bbox": [ + 46, + 72, + 287, + 131 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 112, + 133, + 287, + 145 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 112, + 133, + 287, + 145 + ], + "spans": [ + { + "bbox": [ + 112, + 133, + 287, + 145 + ], + "type": "interline_equation", + "content": "\\left(\\sigma_ {k}, \\mathbf {z}\\right) = \\operatorname {M L P} _ {\\sigma_ {k}} \\left(\\psi_ {k} (\\mathbf {x})\\right), \\tag {6}", + "image_path": "ae2b24a72f22e7623f8c2fc91c31e726ce016b2115334f5605bcd1c3c7ed21ab.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "spans": [ + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "inline_equation", + "content": "\\sigma_{k}" + }, + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "text", + "content": " means the density and " + }, + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "inline_equation", + "content": "\\mathbf{z}" + }, + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "text", + "content": " is a feature vector. Then, we take the feature vector " + }, + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "inline_equation", + "content": "\\mathbf{z}" + }, + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "text", + "content": " and the viewing direction " + }, + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "inline_equation", + "content": "\\mathbf{d}" + }, + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "text", + "content": " as the input for the color regression. Similar to [52], a latent embedding " + }, + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "inline_equation", + "content": "\\ell_t" + }, + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "text", + "content": " for each video frame " + }, + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 149, + 287, + 220 + ], + "type": "text", + "content": " is introduced to model the temporally-varying appearance. The color network is defined as:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 119, + 221, + 287, + 234 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 119, + 221, + 287, + 234 + ], + "spans": [ + { + "bbox": [ + 119, + 221, + 287, + 234 + ], + "type": "interline_equation", + "content": "\\mathbf {c} _ {k} = \\operatorname {M L P} _ {\\mathbf {c} _ {k}} (\\mathbf {z}, \\mathbf {d}, \\ell_ {t}). \\tag {7}", + "image_path": "2950383df3a4bcea5189a4a82501499efa8f5a2afeb75297e04b476a0cbc91b3.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 238, + 287, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 238, + 287, + 264 + ], + "spans": [ + { + "bbox": [ + 46, + 238, + 287, + 264 + ], + "type": "text", + "content": "Finally, we have " + }, + { + "bbox": [ + 46, + 238, + 287, + 264 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 238, + 287, + 264 + ], + "type": "text", + "content": " predictions " + }, + { + "bbox": [ + 46, + 238, + 287, + 264 + ], + "type": "inline_equation", + "content": "\\{(\\sigma_k,\\mathbf{c}_k)\\}_{k = 1}^K" + }, + { + "bbox": [ + 46, + 238, + 287, + 264 + ], + "type": "text", + "content": ". The density and color " + }, + { + "bbox": [ + 46, + 238, + 287, + 264 + ], + "type": "inline_equation", + "content": "(\\sigma ,\\mathbf{c})" + }, + { + "bbox": [ + 46, + 238, + 287, + 264 + ], + "type": "text", + "content": " of the query point " + }, + { + "bbox": [ + 46, + 238, + 287, + 264 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 238, + 287, + 264 + ], + "type": "text", + "content": " is calculated based on:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 74, + 271, + 287, + 290 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 271, + 287, + 290 + ], + "spans": [ + { + "bbox": [ + 74, + 271, + 287, + 290 + ], + "type": "interline_equation", + "content": "(\\sigma , \\mathbf {c}) = (\\sigma_ {k ^ {*}}, \\mathbf {c} _ {k ^ {*}}), \\text {w h e r e} k ^ {*} = \\underset {k} {\\operatorname {a r g m a x}} \\sigma_ {k}. \\tag {8}", + "image_path": "e970d68a637e82f4421ebeefbba41884a5968aa7f0bffb8a624dfb266c27fdd0.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 298, + 288, + 394 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 298, + 288, + 394 + ], + "spans": [ + { + "bbox": [ + 46, + 298, + 288, + 394 + ], + "type": "text", + "content": "In contrast to [40, 56] that represent the body with a single NeRF network, our part-based voxelized human representation can assign different densities of model parameters to different human parts with different complexity, thereby enabling us to efficiently distribute the representational power of the network. Experiments show that our proposed body representation significantly improves the rate of convergence." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 400, + 111, + 414 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 400, + 111, + 414 + ], + "spans": [ + { + "bbox": [ + 47, + 400, + 111, + 414 + ], + "type": "text", + "content": "3.2. Training" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 418, + 287, + 563 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 418, + 287, + 563 + ], + "spans": [ + { + "bbox": [ + 46, + 418, + 287, + 563 + ], + "type": "text", + "content": "The proposed representation can be learned from sparse multi-view videos by minimizing the difference between rendered and observed images. The volume rendering technique [32, 47] is used to synthesize the pixel color. Give a pixel at frame " + }, + { + "bbox": [ + 46, + 418, + 287, + 563 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 418, + 287, + 563 + ], + "type": "text", + "content": ", we emit a camera ray and sample points along the ray. Then, the sampled points are fed into the dynamic human representation to predict their colors and densities, which are finally accumulated into the pixel color. During each training iteration, we randomly sample an image patch from the input image and compute the Mean Squared Error (MSE) loss and perceptual loss [31] to train the model parameters, which are defined as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 69, + 570, + 287, + 586 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 570, + 287, + 586 + ], + "spans": [ + { + "bbox": [ + 69, + 570, + 287, + 586 + ], + "type": "interline_equation", + "content": "L _ {\\mathrm {r g b}} = \\| \\tilde {I} _ {P} - I _ {P} \\| _ {2} + \\| F _ {\\mathrm {v g g}} (\\tilde {I} _ {P}) - F _ {\\mathrm {v g g}} (I _ {P}) \\| _ {2}, \\tag {9}", + "image_path": "d630935695e6d40ad666528829a9401bd35030d122d1bd912a13a43699e15371.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 594, + 287, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 287, + 653 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 287, + 653 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 594, + 287, + 653 + ], + "type": "inline_equation", + "content": "\\tilde{I}_P" + }, + { + "bbox": [ + 46, + 594, + 287, + 653 + ], + "type": "text", + "content": " is the rendered image patch, " + }, + { + "bbox": [ + 46, + 594, + 287, + 653 + ], + "type": "inline_equation", + "content": "I_P" + }, + { + "bbox": [ + 46, + 594, + 287, + 653 + ], + "type": "text", + "content": " is the ground truth image patch, and " + }, + { + "bbox": [ + 46, + 594, + 287, + 653 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{vgg}}" + }, + { + "bbox": [ + 46, + 594, + 287, + 653 + ], + "type": "text", + "content": " extracts image features using the pretrained VGG network [31]. Ablation study demonstrates that perceptual loss is essential for rendering quality and fast training." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 654, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 287, + 712 + ], + "type": "text", + "content": "In addition to the image rendering loss, two regularization techniques are used to facilitate the learning of the neural representations. First, we apply the regularizer in [4] to concentrate densities on the human surface. Second, the residual deformation field is regularized to be small and smooth." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 72, + 547, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 547, + 96 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 547, + 96 + ], + "type": "text", + "content": "More details of the loss terms are described in the supplementary material." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 104, + 437, + 117 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 104, + 437, + 117 + ], + "spans": [ + { + "bbox": [ + 306, + 104, + 437, + 117 + ], + "type": "text", + "content": "3.3. Implementation details" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 122, + 547, + 230 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 122, + 547, + 230 + ], + "spans": [ + { + "bbox": [ + 304, + 122, + 547, + 230 + ], + "type": "text", + "content": "We adopt the Adam optimizer [33] with a learning rate of " + }, + { + "bbox": [ + 304, + 122, + 547, + 230 + ], + "type": "inline_equation", + "content": "5e^{-4}" + }, + { + "bbox": [ + 304, + 122, + 547, + 230 + ], + "type": "text", + "content": ". We train our model on an RTX 3090 GPU, which takes around 5 minutes to produce photorealistic results. Our method is implemented purely with the PyTorch framework [53] to demonstrate the effectiveness of our representation. It also enables us to fairly compare with baseline methods [34,56,58] implemented in PyTorch. The details of the network architectures and hyper-parameters are presented in the supplementary material." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 242, + 386, + 255 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 242, + 386, + 255 + ], + "spans": [ + { + "bbox": [ + 306, + 242, + 386, + 255 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 262, + 368, + 273 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 262, + 368, + 273 + ], + "spans": [ + { + "bbox": [ + 306, + 262, + 368, + 273 + ], + "type": "text", + "content": "4.1. Datasets" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 280, + 547, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 280, + 547, + 399 + ], + "spans": [ + { + "bbox": [ + 304, + 280, + 547, + 399 + ], + "type": "text", + "content": "ZJU-MoCap [58] dataset is a widely-used benchmark for human modeling from videos. It provides foreground human masks and SMPL parameters. Following [93], we select 6 human subjects (377, 386, 387, 392, 393, 394) from this dataset to conduct our experiments. One camera is used for training, and the remaining cameras are used for evaluation. For each human subject, we select 1 frame every 5 frames and collect 100 frames for training. Please refer to the supplementary material for more detailed experiment settings of all characters." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 399, + 547, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 399, + 547, + 507 + ], + "spans": [ + { + "bbox": [ + 304, + 399, + 547, + 507 + ], + "type": "text", + "content": "MonoCap dataset contains four multi-view videos collected by [57] from the DeepCap dataset [26] and the DynaCap dataset [25]. It provides camera parameters and human masks. [57] additionally estimate the SMPL parameters for each image. We adopt the setting of training and test camera views in [57]. For each subject, 100 frames are selected for training, and we sample 1 frame every 5 frames. Detailed configurations of all sequences are described in the supplementary material." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 305, + 515, + 542, + 528 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 515, + 542, + 528 + ], + "spans": [ + { + "bbox": [ + 305, + 515, + 542, + 528 + ], + "type": "text", + "content": "4.2. Comparison with the state-of-the-art methods" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 534, + 545, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 534, + 545, + 617 + ], + "spans": [ + { + "bbox": [ + 304, + 534, + 545, + 617 + ], + "type": "text", + "content": "Baselines. We compare our method with subject-specific optimization methods [56-58, 93], generalizable methods [34, 100]. All the baselines are implemented in pure PyTorch [53] for a fair comparison. Here we list only the average metric values of all selected characters on a dataset due to the size limit. We provide more detailed qualitative and quantitative comparisons in the supplementary material." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 618, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 618, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 618, + 547, + 713 + ], + "type": "text", + "content": "(1) Subject-specific optimization methods. Neural Body (NB) [58] anchors a set of latent codes to the SMPL mesh and regresses the radiance field from the posed latent codes. Animatable NeRF (AN) [56] deforms the canonical NeRF with the skeleton-driven framework and models non-rigid deformations by learning blend weight fields. [57] extend [56] with a signed distance field and pose-dependent deformation field to better model the residual deformation and geometric" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "8763" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 85, + 70, + 511, + 398 + ], + "blocks": [ + { + "bbox": [ + 85, + 70, + 511, + 398 + ], + "lines": [ + { + "bbox": [ + 85, + 70, + 511, + 398 + ], + "spans": [ + { + "bbox": [ + 85, + 70, + 511, + 398 + ], + "type": "image", + "image_path": "84dc96aeae2125d189b0a7619b167eeaa2e37e2e1d6a0f679daf2edb419193dc.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 407, + 546, + 431 + ], + "lines": [ + { + "bbox": [ + 46, + 407, + 546, + 431 + ], + "spans": [ + { + "bbox": [ + 46, + 407, + 546, + 431 + ], + "type": "text", + "content": "Figure 3. Qualitative results of novel view synthesis on the ZJU-MoCap and MonoCap datasets. Our method produces better rendering results while only requiring 1/100 of the training time. The bottom row lists the training time of each method." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 450, + 288, + 486 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 450, + 288, + 486 + ], + "spans": [ + { + "bbox": [ + 46, + 450, + 288, + 486 + ], + "type": "text", + "content": "details of dynamic humans. [93] optimizes for a volumetric representation of the person in a canonical space along with the estimated human pose." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 487, + 288, + 595 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 487, + 288, + 595 + ], + "spans": [ + { + "bbox": [ + 46, + 487, + 288, + 595 + ], + "type": "text", + "content": "(2) Generalizable methods. PixelNeRF [100] trains a network to infer the radiance field from a single image. Neural Human Performer (NHP) [34] anchors image features to vertices of the SMPL mesh and aggregates temporal features using a transformer, which are decoded into a human model. For each evaluated subject (e.g. one subject of MonoCap), we first pre-train the network on the other dataset (e.g. ZJUMoCap) and then finetune it on the evaluated subject until it converges." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 617, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 289, + 715 + ], + "type": "text", + "content": "Results on the ZJU-MoCap dataset. Table 1 compares our method with NB [58], AN [56], PixelNeRF [100], NHP [34], HN [93] and AS [57] on novel view synthesis. Our proposed representation can be optimized within around 5 minutes to produce photorealistic rendering results, while [56-58, 93] require around 10 hours to finish training and [34, 100] require 10 hours of pretraining and 1 hour of fine-tuning. [57, 93] exhibit better results than [56, 58]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 450, + 547, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 450, + 547, + 559 + ], + "spans": [ + { + "bbox": [ + 304, + 450, + 547, + 559 + ], + "type": "text", + "content": "However, they all require a lengthy optimization process and fail to produce reasonable renderings in only 5 minutes because their models have not converged yet. Generalizable methods [34, 100] failed to render humans with reasonable shapes under the monocular setting. Our method achieves comparable results on all of the three evaluated metrics even when only trained in minutes, which shows the effectiveness of our novel human representation. We present qualitative results of our method and baselines in Figure 3." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "content": "Results on the MonoCap dataset. Table 1 summarizes the quantitative comparison between our method and other baselines on the MonoCap dataset. Our model again achieves competitive visual quality while only requiring 1/100 of the training time due to our efficient part-based voxelized human representation and effective motion parameterization scheme. Figure 3 indicates that our method can produce better appearance details than [56-58]. Although [56,58] have shown impressive rendering results given 4-view videos, they do not perform well on monocular inputs. [58] implicitly aggregates the temporal information using structured latent" + } + ] + } + ], + "index": 6 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8764" + } + ] + } + ], + "index": 7 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 96, + 69, + 497, + 180 + ], + "blocks": [ + { + "bbox": [ + 96, + 69, + 497, + 180 + ], + "lines": [ + { + "bbox": [ + 96, + 69, + 497, + 180 + ], + "spans": [ + { + "bbox": [ + 96, + 69, + 497, + 180 + ], + "type": "table", + "html": "
Training TimeZJU-MoCapMonoCap
PSNR ↑SSIM ↑LPIPS* ↓PSNR ↑SSIM ↑LPIPS* ↓
Ours~5 min31.010.97138.4532.610.98816.68
HumanNeRF [93]~10 h30.660.96933.3832.680.98715.52
AS [57]~10 h30.380.97537.2332.480.98813.18
AN [56]~10 h29.770.96546.8931.070.98519.47
NB [58]~10 h29.030.96442.4732.360.98616.70
NHP [34]~1 h fine-tuning28.250.95564.7730.510.98027.14
PixelNeRF [100]~1 h fine-tuning24.710.892121.8626.430.96043.98
", + "image_path": "20b08069d357f2bc282db71ad188b5c540b362ee15a32ef6b4131faf707a4eeb.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 66, + 253, + 219, + 313 + ], + "blocks": [ + { + "bbox": [ + 46, + 189, + 547, + 225 + ], + "lines": [ + { + "bbox": [ + 46, + 189, + 547, + 225 + ], + "spans": [ + { + "bbox": [ + 46, + 189, + 547, + 225 + ], + "type": "text", + "content": "Table 1. Quantitative comparison of our method and baseline methods on the ZJU-MoCap and MonoCap datasets. We use bold text for the best and underlined text for the second best metric value across methods. Our method achieves the fastest training speed and shows competitive rendering results. Note that the NHP [34] and PixelNeRF [100] are additionally pretrained for 10 hours. LPIPS* = LPIPS × 10^3." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 81, + 243, + 203, + 252 + ], + "lines": [ + { + "bbox": [ + 81, + 243, + 203, + 252 + ], + "spans": [ + { + "bbox": [ + 81, + 243, + 203, + 252 + ], + "type": "text", + "content": "(a) Ablation studies on proposed components." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 66, + 253, + 219, + 313 + ], + "lines": [ + { + "bbox": [ + 66, + 253, + 219, + 313 + ], + "spans": [ + { + "bbox": [ + 66, + 253, + 219, + 313 + ], + "type": "table", + "html": "
PSNRSSIMLPIPS*
Ours32.090.98223.47
Ours w/o Part30.110.97445.84
Ours w/o UV31.400.97930.99
Ours w/o Perc30.550.97644.33
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PSNRSSIMLPIPS*
Ours32.090.98223.47
PE31.940.98126.76
XYZ-Code31.320.97931.19
XYZ-Pose31.510.97934.42
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PSNRSSIMLPIPS*
Ours32.090.98223.47
Table size 21530.690.97635.67
Table size 22031.180.97833.58
", + "image_path": "5d172a63db7b8047df9fffdd80ecc17b399953100e1574faae9ed9ef2eb039c1.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 323, + 546, + 344 + ], + "lines": [ + { + "bbox": [ + 46, + 323, + 546, + 344 + ], + "spans": [ + { + "bbox": [ + 46, + 323, + 546, + 344 + ], + "type": "text", + "content": "Table 2. Quantitative results of ablation studies on the 377 sequence of ZJU-MoCap dataset. The description of each model can be found in Section 4.3. All models are trained for 5 minutes." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 376, + 289, + 473 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 376, + 289, + 473 + ], + "spans": [ + { + "bbox": [ + 46, + 376, + 289, + 473 + ], + "type": "text", + "content": "codes, which may not work well on monocular videos with complex human motions. [56] uses a learnable blend weight field to model human motion, which has a higher dimension and could be hard to converge well given single-view supervision. [57, 93] demonstrate similar visual quality and show that representing the human motion with the LBS model and residual deformation works particularly well, but their models require " + }, + { + "bbox": [ + 46, + 376, + 289, + 473 + ], + "type": "inline_equation", + "content": "100\\mathrm{x}" + }, + { + "bbox": [ + 46, + 376, + 289, + 473 + ], + "type": "text", + "content": " more time to optimize." + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 99, + 484, + 236, + 583 + ], + "blocks": [ + { + "bbox": [ + 99, + 484, + 236, + 583 + ], + "lines": [ + { + "bbox": [ + 99, + 484, + 236, + 583 + ], + "spans": [ + { + "bbox": [ + 99, + 484, + 236, + 583 + ], + "type": "image", + "image_path": "69bfc411a9fb74630bf36f1f1b8de9fd49df812faf8e3ade165c7bc2ecef9410.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 595, + 288, + 651 + ], + "lines": [ + { + "bbox": [ + 46, + 595, + 288, + 651 + ], + "spans": [ + { + "bbox": [ + 46, + 595, + 288, + 651 + ], + "type": "text", + "content": "Figure 4. Comparison of convergence LPIPS* and time needed for convergence among different variants of the proposed pipeline. The description of each model can be found in Section 4.3. The proposed components accelerate the training and enhance the rendering quality significantly. LPIPS* = LPIPS × 10^3." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 659, + 148, + 671 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 659, + 148, + 671 + ], + "spans": [ + { + "bbox": [ + 47, + 659, + 148, + 671 + ], + "type": "text", + "content": "4.3. Ablation Studies" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 677, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 289, + 715 + ], + "type": "text", + "content": "We perform ablation studies on the \"377\" sequence of the ZJU-MoCap dataset to analyze how the proposed components affect the performance and training speed of our" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 376, + 342, + 387 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 376, + 342, + 387 + ], + "spans": [ + { + "bbox": [ + 306, + 376, + 342, + 387 + ], + "type": "text", + "content": "method." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 403, + 524, + 416 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 403, + 524, + 416 + ], + "spans": [ + { + "bbox": [ + 306, + 403, + 524, + 416 + ], + "type": "text", + "content": "4.3.1 Ablation Studies on Proposed Components." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 422, + 548, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 422, + 548, + 651 + ], + "spans": [ + { + "bbox": [ + 304, + 422, + 548, + 651 + ], + "type": "text", + "content": "Table 2(a) lists the quantitative result of ablation studies on our proposed components. All models are trained for 5 minutes. \"Ours w/o Part\" represents the canonical human body with a single MHE-augmented NeRF [48] network, which drastically degrades the rate of convergence. To keep the comparison fair, this variant of our method has a similar number of parameters (302M) to ours (286M). However, this change leads to a significant decrease in PSNR of 1.98dB because of its unwise design of considering all parts equally complex and wasting representational power. In \"Ours w/o UV\", the residual deformation network " + }, + { + "bbox": [ + 304, + 422, + 548, + 651 + ], + "type": "inline_equation", + "content": "\\mathrm{MLP}_{\\mathrm{res}}" + }, + { + "bbox": [ + 304, + 422, + 548, + 651 + ], + "type": "text", + "content": " takes hash encoded " + }, + { + "bbox": [ + 304, + 422, + 548, + 651 + ], + "type": "inline_equation", + "content": "(\\mathbf{x},t)" + }, + { + "bbox": [ + 304, + 422, + 548, + 651 + ], + "type": "text", + "content": " as input, which observed a PSNR degradation from 32.09dB to 31.40dB with the same training time because of severe hash collision and limited resolution. \"Ours w/o Perc\" does not adopt the perceptual loss during training, which in turn increases the LPIPS distance. This comparison illustrates the importance of perceptual loss for visual fidelity. Figure 4 and Figure 5 provide a more intuitive qualitative comparison among variants of the proposed method." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 666, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 666, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 666, + 547, + 715 + ], + "type": "text", + "content": "Analysis of the part-based voxelized human representation. MHE [48] defines a multiresolution hash table of trainable features to embed input coordinates to a high-dimension space. We find that simply increasing the size" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8765" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 128, + 83, + 195, + 178 + ], + "blocks": [ + { + "bbox": [ + 133, + 72, + 190, + 81 + ], + "lines": [ + { + "bbox": [ + 133, + 72, + 190, + 81 + ], + "spans": [ + { + "bbox": [ + 133, + 72, + 190, + 81 + ], + "type": "text", + "content": "Ground Truth" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 128, + 83, + 195, + 178 + ], + "lines": [ + { + "bbox": [ + 128, + 83, + 195, + 178 + ], + "spans": [ + { + "bbox": [ + 128, + 83, + 195, + 178 + ], + "type": "image", + "image_path": "1b3ffeb53c674a544c7235a3f841b95df1fb76cc7e7fea4ccb80a79620375373.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 196, + 83, + 263, + 178 + ], + "blocks": [ + { + "bbox": [ + 220, + 72, + 239, + 80 + ], + "lines": [ + { + "bbox": [ + 220, + 72, + 239, + 80 + ], + "spans": [ + { + "bbox": [ + 220, + 72, + 239, + 80 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 196, + 83, + 263, + 178 + ], + "lines": [ + { + "bbox": [ + 196, + 83, + 263, + 178 + ], + "spans": [ + { + "bbox": [ + 196, + 83, + 263, + 178 + ], + "type": "image", + "image_path": "459b96fe011c420d85a8c30f47ca8cf82c3f034a1d30d48c66f120126377f33e.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 189, + 547, + 223 + ], + "lines": [ + { + "bbox": [ + 46, + 189, + 547, + 223 + ], + "spans": [ + { + "bbox": [ + 46, + 189, + 547, + 223 + ], + "type": "text", + "content": "Figure 5. Ablation studies on the 377 sequence of ZJU-MoCap dataset. \"Ours w/o part\" means that we use an MHE-augmented NeRF network to represent the whole body. \"Ours w/o UV\" indicates that the residual deformation network " + }, + { + "bbox": [ + 46, + 189, + 547, + 223 + ], + "type": "inline_equation", + "content": "\\mathrm{MLP}_{\\mathrm{res}}" + }, + { + "bbox": [ + 46, + 189, + 547, + 223 + ], + "type": "text", + "content": " takes the hash encoded " + }, + { + "bbox": [ + 46, + 189, + 547, + 223 + ], + "type": "inline_equation", + "content": "(\\mathbf{x},t)" + }, + { + "bbox": [ + 46, + 189, + 547, + 223 + ], + "type": "text", + "content": " as input. \"Ours w/o Perc\" represents that we do not use perceptual loss during training." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 264, + 83, + 331, + 178 + ], + "blocks": [ + { + "bbox": [ + 269, + 72, + 325, + 81 + ], + "lines": [ + { + "bbox": [ + 269, + 72, + 325, + 81 + ], + "spans": [ + { + "bbox": [ + 269, + 72, + 325, + 81 + ], + "type": "text", + "content": "Ours w/o Part" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 264, + 83, + 331, + 178 + ], + "lines": [ + { + "bbox": [ + 264, + 83, + 331, + 178 + ], + "spans": [ + { + "bbox": [ + 264, + 83, + 331, + 178 + ], + "type": "image", + "image_path": "f910efd07b3e303fc32feacd88b0bb6f28437ac5feb08d797c39903fd4b04904.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 331, + 83, + 398, + 178 + ], + "blocks": [ + { + "bbox": [ + 339, + 72, + 391, + 82 + ], + "lines": [ + { + "bbox": [ + 339, + 72, + 391, + 82 + ], + "spans": [ + { + "bbox": [ + 339, + 72, + 391, + 82 + ], + "type": "text", + "content": "Ours w/o UV" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 331, + 83, + 398, + 178 + ], + "lines": [ + { + "bbox": [ + 331, + 83, + 398, + 178 + ], + "spans": [ + { + "bbox": [ + 331, + 83, + 398, + 178 + ], + "type": "image", + "image_path": "0b3fda2a2692b48484194523be78fd38907de2a2434cfdcf3befbc6c754a44bb.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 399, + 83, + 466, + 178 + ], + "blocks": [ + { + "bbox": [ + 405, + 72, + 461, + 82 + ], + "lines": [ + { + "bbox": [ + 405, + 72, + 461, + 82 + ], + "spans": [ + { + "bbox": [ + 405, + 72, + 461, + 82 + ], + "type": "text", + "content": "Ours w/o Perc" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 399, + 83, + 466, + 178 + ], + "lines": [ + { + "bbox": [ + 399, + 83, + 466, + 178 + ], + "spans": [ + { + "bbox": [ + 399, + 83, + 466, + 178 + ], + "type": "image", + "image_path": "0653bd7198263efbf1afdb147c64cb7f37f2ce4e96ebbd6819fc231a29ff7424.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 234, + 289, + 390 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 234, + 289, + 390 + ], + "spans": [ + { + "bbox": [ + 46, + 234, + 289, + 390 + ], + "type": "text", + "content": "of the hash table does not always result in better performance with the same training time, because a bigger hash table leads to higher memory consumption and increases the time of each training iteration. The proposed part-based voxelized human representation allows us to adapt the hash table size according to the complexity of the human part, allowing us to efficiently represent the human body. Table 2(a) demonstrates the effectiveness of the part-based voxelized human representation. To further validate this representation, we design two variants that use the hash tables of size " + }, + { + "bbox": [ + 46, + 234, + 289, + 390 + ], + "type": "inline_equation", + "content": "2^{15}" + }, + { + "bbox": [ + 46, + 234, + 289, + 390 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 234, + 289, + 390 + ], + "type": "inline_equation", + "content": "2^{20}" + }, + { + "bbox": [ + 46, + 234, + 289, + 390 + ], + "type": "text", + "content": " in all human parts respectively. Table 2(c) summarizes the ablation studies, indicating that varying the model parameters in human parts improves the performance." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "spans": [ + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "content": "Analysis of the motion parameterization scheme. Table 2(a) shows that our model works better when the residual deformation network " + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "inline_equation", + "content": "\\mathrm{MLP}_{\\mathrm{res}}" + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "content": " takes parameterized 3D surface-time " + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "inline_equation", + "content": "(u,v,t)" + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "content": " coordinates as input, compared with taking 4D space-time " + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "inline_equation", + "content": "(\\mathbf{x},t)" + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "content": " as input. Note that " + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "inline_equation", + "content": "(u,v,t)" + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "content": " makes MHE much more memory efficient than " + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "inline_equation", + "content": "(\\mathbf{x},t)" + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "content": ". To further validate the effectiveness of our motion parameterization, we additionally design three variants of the " + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "inline_equation", + "content": "\\mathrm{MLP}_{\\mathrm{res}}" + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "content": " input. (1) PE: positional encoded " + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "inline_equation", + "content": "(\\mathbf{x},t)" + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "content": ". (2) XYZ-Code: hash encoded " + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "content": " and a per-frame learnable latent code [57]. (3) XYZ-Pose: hash encoded " + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "content": " and the pose parameter [40,93]. When taking the positional encoded " + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "inline_equation", + "content": "(\\mathbf{x},t)" + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "content": " as input, we use a larger network for " + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "inline_equation", + "content": "\\mathrm{MLP}_{\\mathrm{res}}" + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "content": ". The results in Table 2(b) indicate that hash encoded " + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "inline_equation", + "content": "(u,v,t)" + }, + { + "bbox": [ + 46, + 404, + 289, + 572 + ], + "type": "text", + "content": " achieves the best performance." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 586, + 288, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 586, + 288, + 658 + ], + "spans": [ + { + "bbox": [ + 46, + 586, + 288, + 658 + ], + "type": "text", + "content": "Analysis of Robustness. To evaluate the robustness of the proposed system, we measure the time needed to achieve an evaluation PSNR of 30 for five times on the \"377\" sequence. This results in a training time with a mean value of " + }, + { + "bbox": [ + 46, + 586, + 288, + 658 + ], + "type": "inline_equation", + "content": "76.00s" + }, + { + "bbox": [ + 46, + 586, + 288, + 658 + ], + "type": "text", + "content": " and a standard derivation of " + }, + { + "bbox": [ + 46, + 586, + 288, + 658 + ], + "type": "inline_equation", + "content": "13.56s" + }, + { + "bbox": [ + 46, + 586, + 288, + 658 + ], + "type": "text", + "content": ", showing the stability of the proposed method." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 47, + 669, + 122, + 681 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 669, + 122, + 681 + ], + "spans": [ + { + "bbox": [ + 47, + 669, + 122, + 681 + ], + "type": "text", + "content": "5. Limitations" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 689, + 288, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 288, + 712 + ], + "type": "text", + "content": "Although our method can quickly reconstruct high-quality human models from videos, it has some limitations to be fur-" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 234, + 547, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 234, + 547, + 354 + ], + "spans": [ + { + "bbox": [ + 304, + 234, + 547, + 354 + ], + "type": "text", + "content": "ther addressed. First, our method currently relies on accurate SMPL parameters, which could be difficult to obtain under in-the-wild settings. It is interesting to utilize the techniques in [79, 93] to optimize the human pose parameters along with the training of human avatars. Second, we can only reconstruct foreground dynamic humans, while dynamic scenes typically include foreground and background entities. It might be plausible to combine our method with ST-NeRF [29] or MultiNB [74] to quickly reconstruct dynamic scenes containing foreground and background objects." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 369, + 378, + 382 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 369, + 378, + 382 + ], + "spans": [ + { + "bbox": [ + 306, + 369, + 378, + 382 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 391, + 547, + 643 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 391, + 547, + 643 + ], + "spans": [ + { + "bbox": [ + 304, + 391, + 547, + 643 + ], + "type": "text", + "content": "We introduce a novel dynamic human representation that can be quickly optimized from videos and used for generating free-viewpoint videos of the human performer. This representation consists of a part-based voxelized human model in the canonical space and a motion parameterization scheme that transforms points from the world space to the canonical space. The part-based voxelized human model decomposes the human body into multiple parts and represents each part with an MHE-augmented NeRF network, which efficiently distributes network representational power and significantly improves the training speed. When predicting the motion of a query point, the motion field reparameterizes the point coordinate as 2D surface-level UV coordinate, which effectively reduces the dimensionality of motion the network is required to model, resulting in a boost in convergence rate. Experiments demonstrate that our proposed representation can be optimized at 1/100 of the time of previous methods while still maintaining competitive rendering quality. We show that given a 100-frame monocular video of a " + }, + { + "bbox": [ + 304, + 391, + 547, + 643 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 304, + 391, + 547, + 643 + ], + "type": "text", + "content": " resolution, our method can produce photorealistic free-viewpoint videos in minutes on an RTX 3090 GPU." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 666, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 666, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 666, + 547, + 715 + ], + "type": "text", + "content": "Acknowledgements. This work was supported by the Key Research Project of Zhejiang Lab (No. K2022PG1BB01), NSFC (No. 62172364), and Information Technology Center and State Key Lab of CAD&CG, Zhejiang University." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8766" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 92, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 58, + 92, + 287, + 123 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 92, + 287, + 123 + ], + "spans": [ + { + "bbox": [ + 58, + 92, + 287, + 123 + ], + "type": "text", + "content": "[1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graphics. In ECCV, 2020. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 126, + 288, + 159 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 126, + 288, + 159 + ], + "spans": [ + { + "bbox": [ + 58, + 126, + 288, + 159 + ], + "type": "text", + "content": "[2] Thiemo Alldieck, Mihai Zanfir, and Cristian Sminchisescu. Photorealistic monocular 3d reconstruction of humans wearing clothing. arXiv preprint arXiv:2204.08906, 2022. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 58, + 160, + 288, + 225 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 160, + 288, + 225 + ], + "spans": [ + { + "bbox": [ + 58, + 160, + 288, + 225 + ], + "type": "text", + "content": "[3] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5855–5864, 2021. 2, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 58, + 228, + 288, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 228, + 288, + 270 + ], + "spans": [ + { + "bbox": [ + 58, + 228, + 288, + 270 + ], + "type": "text", + "content": "[4] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. arXiv preprint arXiv:2111.12077, 2021. 2, 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 58, + 273, + 288, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 273, + 288, + 316 + ], + "spans": [ + { + "bbox": [ + 58, + 273, + 288, + 316 + ], + "type": "text", + "content": "[5] Alexander Bergman, Petr Kellnhofer, and Gordon Wetzstein. Fast training of neural lumigraph representations using meta learning. Advances in Neural Information Processing Systems, 34, 2021. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 58, + 319, + 287, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 319, + 287, + 361 + ], + "spans": [ + { + "bbox": [ + 58, + 319, + 287, + 361 + ], + "type": "text", + "content": "[6] Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Loopreg: Self-supervised learning of implicit surface correspondences, pose and shape for 3d human mesh registration. In NeurlPS, 2020. 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 58, + 364, + 287, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 364, + 287, + 396 + ], + "spans": [ + { + "bbox": [ + 58, + 364, + 287, + 396 + ], + "type": "text", + "content": "[7] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. arXiv preprint arXiv:2203.09517, 2022. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 58, + 398, + 287, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 398, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 58, + 398, + 287, + 453 + ], + "type": "text", + "content": "[8] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14124-14133, 2021. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 58, + 454, + 287, + 509 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 454, + 287, + 509 + ], + "spans": [ + { + "bbox": [ + 58, + 454, + 287, + 509 + ], + "type": "text", + "content": "[9] Xu Chen, Yufeng Zheng, Michael J Black, Otmar Hilliges, and Andreas Geiger. Snarf: Differentiable forward skinning for animating non-rigid neural implicit shapes. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11594-11604, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 510, + 287, + 554 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 510, + 287, + 554 + ], + "spans": [ + { + "bbox": [ + 53, + 510, + 287, + 554 + ], + "type": "text", + "content": "[10] Yue Chen, Xuan Wang, Qi Zhang, Xiaoyu Li, Xingyu Chen, Yu Guo, Jue Wang, and Fei Wang. Uv volumes for real-time rendering of editable free-view human performance. arXiv preprint arXiv:2203.14402, 2022. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 53, + 555, + 287, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 555, + 287, + 578 + ], + "spans": [ + { + "bbox": [ + 53, + 555, + 287, + 578 + ], + "type": "text", + "content": "[11] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In CVPR, 2019. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 53, + 579, + 288, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 579, + 288, + 633 + ], + "spans": [ + { + "bbox": [ + 53, + 579, + 288, + 633 + ], + "type": "text", + "content": "[12] Julian Chibane, Aayush Bansal, Verica Lazova, and Gerard Pons-Moll. Stereo radiance fields (srf): Learning view synthesis for sparse views of novel scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7911-7920, 2021. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 53, + 635, + 288, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 635, + 288, + 678 + ], + "spans": [ + { + "bbox": [ + 53, + 635, + 288, + 678 + ], + "type": "text", + "content": "[13] Alvaro Collet, Ming Chuang, Pat Sweeney, Don Gillett, Dennis Evseev, David Calabrese, Hugues Hoppe, Adam Kirk, and Steve Sullivan. High-quality streamable free-viewpoint video. ACM TOG, 2015. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 53, + 681, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 681, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 53, + 681, + 287, + 713 + ], + "type": "text", + "content": "[14] Robert L Cook. Shade trees. In Proceedings of the 11th annual conference on Computer graphics and interactive techniques, pages 223-231, 1984. 2, 4" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 312, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 312, + 73, + 547, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 73, + 547, + 106 + ], + "spans": [ + { + "bbox": [ + 312, + 73, + 547, + 106 + ], + "type": "text", + "content": "[15] Robert L Cook, Loren Carpenter, and Edwin Catmull. The reyes image rendering architecture. ACM SIGGRAPH Computer Graphics, 21(4):95-102, 1987. 2, 4" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 312, + 108, + 547, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 108, + 547, + 163 + ], + "spans": [ + { + "bbox": [ + 312, + 108, + 547, + 163 + ], + "type": "text", + "content": "[16] Boyang Deng, John P Lewis, Timothy Jeruzalski, Gerard Pons-Moll, Geoffrey Hinton, Mohammad Norouzi, and Andrea Tagliasacchi. Nasa neural articulated shape approximation. In European Conference on Computer Vision, pages 612-628. Springer, 2020. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 312, + 165, + 547, + 220 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 165, + 547, + 220 + ], + "spans": [ + { + "bbox": [ + 312, + 165, + 547, + 220 + ], + "type": "text", + "content": "[17] Mingsong Dou, Sameh Khamis, Yury Degtyarev, Philip Davidson, Sean Ryan Fanello, Adarsh Kowdle, Sergio Orts Escolano, Christoph Rhemann, David Kim, Jonathan Taylor, et al. Fusion4d: Real-time performance capture of challenging scenes. ACM TOG, 2016. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 312, + 222, + 547, + 277 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 222, + 547, + 277 + ], + "spans": [ + { + "bbox": [ + 312, + 222, + 547, + 277 + ], + "type": "text", + "content": "[18] Yilun Du, Yinan Zhang, Hong-Xing Yu, Joshua B Tenenbaum, and Jiajun Wu. Neural radiance flow for 4d view synthesis and video processing. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 14304-14314. IEEE Computer Society, 2021. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 312, + 279, + 546, + 322 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 279, + 546, + 322 + ], + "spans": [ + { + "bbox": [ + 312, + 279, + 546, + 322 + ], + "type": "text", + "content": "[19] Emilien Dupont, Hyunjik Kim, SM Eslami, Danilo Rezende, and Dan Rosenbaum. From data to functa: Your data point is a function and you should treat it like one. arXiv preprint arXiv:2201.12204, 2022. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 312, + 325, + 547, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 325, + 547, + 369 + ], + "spans": [ + { + "bbox": [ + 312, + 325, + 547, + 369 + ], + "type": "text", + "content": "[20] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In International conference on machine learning, pages 1126–1135. PMLR, 2017. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 312, + 372, + 546, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 372, + 546, + 415 + ], + "spans": [ + { + "bbox": [ + 312, + 372, + 546, + 415 + ], + "type": "text", + "content": "[21] Xuan Gao, Chenglai Zhong, Jun Xiang, Yang Hong, Yudong Guo, and Juyong Zhang. Reconstructing personalized semantic facial nerf models from monocular video. arXiv preprint arXiv:2210.06108, 2022.3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 312, + 418, + 547, + 471 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 418, + 547, + 471 + ], + "spans": [ + { + "bbox": [ + 312, + 418, + 547, + 471 + ], + "type": "text", + "content": "[22] Stephan J Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, and Julien Valentin. Fastnerf: High-fidelity neural rendering at 200fps. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14346-14355, 2021. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 312, + 475, + 546, + 517 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 475, + 546, + 517 + ], + "spans": [ + { + "bbox": [ + 312, + 475, + 546, + 517 + ], + "type": "text", + "content": "[23] Haoyu Guo, Sida Peng, Haotong Lin, Qianqian Wang, Guofeng Zhang, Hujun Bao, and Xiaowei Zhou. Neural 3d scene reconstruction with the Manhattan-world assumption. In CVPR, 2022. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 312, + 520, + 547, + 575 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 520, + 547, + 575 + ], + "spans": [ + { + "bbox": [ + 312, + 520, + 547, + 575 + ], + "type": "text", + "content": "[24] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, et al. The relightables: Volumetric performance capture of humans with realistic relighting. ACM TOG, 2019. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 312, + 578, + 546, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 578, + 546, + 610 + ], + "spans": [ + { + "bbox": [ + 312, + 578, + 546, + 610 + ], + "type": "text", + "content": "[25] Marc Habermann, Lingjie Liu, Weipeng Xu, Michael Zollhoefer, Gerard Pons-Moll, and Christian Theobalt. Real-time deep dynamic characters. In SIGGRAPH, 2021. 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 312, + 613, + 546, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 613, + 546, + 655 + ], + "spans": [ + { + "bbox": [ + 312, + 613, + 546, + 655 + ], + "type": "text", + "content": "[26] Marc Habermann, Weipeng Xu, Michael Zollhofer, Gerard Pons-Moll, and Christian Theobalt. Deepcap: Monocular human performance capture using weak supervision. In CVPR, 2020. 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 312, + 658, + 547, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 658, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 312, + 658, + 547, + 713 + ], + "type": "text", + "content": "[27] Peter Hedman, Pratul P Srinivasan, Ben Mildenhall, Jonathan T Barron, and Paul Debevec. Baking neural radiance fields for real-time view synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5875-5884, 2021. 2" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8767" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 53, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 53, + 72, + 288, + 115 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 72, + 288, + 115 + ], + "spans": [ + { + "bbox": [ + 53, + 72, + 288, + 115 + ], + "type": "text", + "content": "[28] Tao Hu, Tao Yu, Zerong Zheng, He Zhang, Yebin Liu, and Matthias Zwicker. Hvtr: Hybrid volumetric-textural rendering for human avatars. arXiv preprint arXiv:2112.10203, 2021. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 117, + 288, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 117, + 288, + 161 + ], + "spans": [ + { + "bbox": [ + 53, + 117, + 288, + 161 + ], + "type": "text", + "content": "[29] Zhang Jiakai, Liu Xinhang, Ye Xinyi, Zhao Fuqiang, Zhang Yanshun, Wu Minye, Zhang Yingliang, Xu Lan, and Yu Jingyi. Editable free-viewpoint video using a layered neural representation. In SIGGRAPH, 2021. 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 162, + 288, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 162, + 288, + 195 + ], + "spans": [ + { + "bbox": [ + 53, + 162, + 288, + 195 + ], + "type": "text", + "content": "[30] Boyi Jiang, Yang Hong, Hujun Bao, and Juyong Zhang. Self-recon: Self reconstruction your digital avatar from monocular video. arXiv preprint arXiv:2201.12792, 2022. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 195, + 288, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 195, + 288, + 227 + ], + "spans": [ + { + "bbox": [ + 53, + 195, + 288, + 227 + ], + "type": "text", + "content": "[31] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 228, + 288, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 228, + 288, + 259 + ], + "spans": [ + { + "bbox": [ + 53, + 228, + 288, + 259 + ], + "type": "text", + "content": "[32] James T Kajiya and Brian P Von Herzen. Ray tracing volume densities. ACM SIGGRAPH computer graphics, 18(3):165-174, 1984. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 261, + 288, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 261, + 288, + 282 + ], + "spans": [ + { + "bbox": [ + 53, + 261, + 288, + 282 + ], + "type": "text", + "content": "[33] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 284, + 288, + 336 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 284, + 288, + 336 + ], + "spans": [ + { + "bbox": [ + 53, + 284, + 288, + 336 + ], + "type": "text", + "content": "[34] Youngjoong Kwon, Dahun Kim, Duygu Ceylan, and Henry Fuchs. Neural human performer: Learning generalizable radiance fields for human performance rendering. Advances in Neural Information Processing Systems, 34, 2021. 2, 5, 6, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 338, + 288, + 371 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 338, + 288, + 371 + ], + "spans": [ + { + "bbox": [ + 53, + 338, + 288, + 371 + ], + "type": "text", + "content": "[35] John P Lewis, Matt Cordner, and Nickson Fong. Pose space deformation: a unified approach to shape interpolation and skeleton-driven deformation. In SIGGRAPH, 2000. 2, 3, 4" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 372, + 288, + 414 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 372, + 288, + 414 + ], + "spans": [ + { + "bbox": [ + 53, + 372, + 288, + 414 + ], + "type": "text", + "content": "[36] Ruilong Li, Julian Tanke, Minh Vo, Michael Zollhoffer, Jurgen Gall, Angjoo Kanazawa, and Christoph Lassner. Tava: Template-free animatable volumetric actors. arXiv preprint arXiv:2206.08929, 2022. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 415, + 288, + 469 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 415, + 288, + 469 + ], + "spans": [ + { + "bbox": [ + 53, + 415, + 288, + 469 + ], + "type": "text", + "content": "[37] Zhengqi Li, Simon Niklaus, Noah Snavely, and Oliver Wang. Neural scene flow fields for space-time view synthesis of dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6498-6508, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 471, + 288, + 504 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 471, + 288, + 504 + ], + "spans": [ + { + "bbox": [ + 53, + 471, + 288, + 504 + ], + "type": "text", + "content": "[38] Yiyi Liao, Katja Schwarz, Lars Mescheder, and Andreas Geiger. Towards unsupervised learning of generative models for 3d controllable image synthesis. In CVPR, 2020. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 53, + 505, + 288, + 546 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 505, + 288, + 546 + ], + "spans": [ + { + "bbox": [ + 53, + 505, + 288, + 546 + ], + "type": "text", + "content": "[39] Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. Advances in Neural Information Processing Systems, 33:15651-15663, 2020. 2, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 53, + 548, + 288, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 548, + 288, + 591 + ], + "spans": [ + { + "bbox": [ + 53, + 548, + 288, + 591 + ], + "type": "text", + "content": "[40] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural actor: Neural free-view synthesis of human actors with pose control. In SIGGRAPH Asia, 2021. 2, 3, 4, 5, 8" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 53, + 592, + 288, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 592, + 288, + 635 + ], + "spans": [ + { + "bbox": [ + 53, + 592, + 288, + 635 + ], + "type": "text", + "content": "[41] Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui. Dist: Rendering deep implicit signed distance function with differentiable sphere tracing. In CVPR, 2020. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 53, + 636, + 288, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 636, + 288, + 679 + ], + "spans": [ + { + "bbox": [ + 53, + 636, + 288, + 679 + ], + "type": "text", + "content": "[42] Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural volumes: Learning dynamic renderable volumes from images. In SIGGRAPH, 2019. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 53, + 681, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 681, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 53, + 681, + 288, + 713 + ], + "type": "text", + "content": "[43] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. Smpl: A skinned multiperson linear model. ACM TOG, 2015. 2, 4" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 312, + 72, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 312, + 72, + 547, + 115 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 72, + 547, + 115 + ], + "spans": [ + { + "bbox": [ + 312, + 72, + 547, + 115 + ], + "type": "text", + "content": "[44] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In CVPR, 2019. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 312, + 118, + 547, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 118, + 547, + 173 + ], + "spans": [ + { + "bbox": [ + 312, + 118, + 547, + 173 + ], + "type": "text", + "content": "[45] Marko Mihajlovic, Aayush Bansal, Michael Zollhoefer, Siyu Tang, and Shunsuke Saito. Keypointnerf: Generalizing image-based volumetric avatars using relative spatial encoding of keypoints. In European Conference on Computer Vision, pages 179-197. Springer, 2022. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 312, + 175, + 547, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 175, + 547, + 228 + ], + "spans": [ + { + "bbox": [ + 312, + 175, + 547, + 228 + ], + "type": "text", + "content": "[46] Marko Mihajlovic, Shunsuke Saito, Aayush Bansal, Michael Zollhoefer, and Siyu Tang. COAP: Compositional articulated occupancy of people. In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 312, + 230, + 547, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 230, + 547, + 274 + ], + "spans": [ + { + "bbox": [ + 312, + 230, + 547, + 274 + ], + "type": "text", + "content": "[47] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020. 2, 3, 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 312, + 276, + 547, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 276, + 547, + 317 + ], + "spans": [ + { + "bbox": [ + 312, + 276, + 547, + 317 + ], + "type": "text", + "content": "[48] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. In SIGGRAPH, 2022. 2, 3, 4, 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 312, + 320, + 547, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 320, + 547, + 353 + ], + "spans": [ + { + "bbox": [ + 312, + 320, + 547, + 353 + ], + "type": "text", + "content": "[49] Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 312, + 355, + 547, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 355, + 547, + 409 + ], + "spans": [ + { + "bbox": [ + 312, + 355, + 547, + 409 + ], + "type": "text", + "content": "[50] Michael Oechsle, Songyou Peng, and Andreas Geiger. Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5589-5599, 2021. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 312, + 411, + 547, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 411, + 547, + 453 + ], + "spans": [ + { + "bbox": [ + 312, + 411, + 547, + 453 + ], + "type": "text", + "content": "[51] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, 2019. 2, 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 312, + 456, + 547, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 456, + 547, + 510 + ], + "spans": [ + { + "bbox": [ + 312, + 456, + 547, + 510 + ], + "type": "text", + "content": "[52] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5865-5874, 2021. 2, 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 312, + 512, + 547, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 512, + 547, + 599 + ], + "spans": [ + { + "bbox": [ + 312, + 512, + 547, + 599 + ], + "type": "text", + "content": "[53] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, 2019. 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 312, + 601, + 547, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 601, + 547, + 644 + ], + "spans": [ + { + "bbox": [ + 312, + 601, + 547, + 644 + ], + "type": "text", + "content": "[54] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3d hands, face, and body from a single image. In CVPR, 2019. 4" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 312, + 646, + 547, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 646, + 547, + 679 + ], + "spans": [ + { + "bbox": [ + 312, + 646, + 547, + 679 + ], + "type": "text", + "content": "[55] Bo Peng, Jun Hu, Jingtao Zhou, and Juyong Zhang. Selfnerf: Fast training nerf for human from monocular self-rotating video. arXiv preprint arXiv:2210.01651, 2022. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 312, + 681, + 547, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 681, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 312, + 681, + 547, + 713 + ], + "type": "text", + "content": "[56] Sida Peng, Junting Dong, Qianqian Wang, Shangzhan Zhang, Qing Shuai, Xiaowei Zhou, and Hujun Bao. Animatable neural radiance fields for modeling dynamic human bodies." + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "8768" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 53, + 73, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 72, + 73, + 287, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 73, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 72, + 73, + 287, + 95 + ], + "type": "text", + "content": "In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14314-14323, 2021. 2, 4, 5, 6, 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 96, + 287, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 96, + 287, + 140 + ], + "spans": [ + { + "bbox": [ + 53, + 96, + 287, + 140 + ], + "type": "text", + "content": "[57] Sida Peng, Shangzhan Zhang, Zhen Xu, Chen Geng, Boyi Jiang, Hujun Bao, and Xiaowei Zhou. Animatable neural implicit surfaces for creating avatars from videos. arXiv preprint arXiv:2203.08133, 2022. 5, 6, 7, 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 141, + 288, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 141, + 288, + 207 + ], + "spans": [ + { + "bbox": [ + 53, + 141, + 288, + 207 + ], + "type": "text", + "content": "[58] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9054-9063, 2021. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 54, + 209, + 288, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 209, + 288, + 262 + ], + "spans": [ + { + "bbox": [ + 54, + 209, + 288, + 262 + ], + "type": "text", + "content": "[59] Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. D-nerf: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10318-10327, 2021. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 264, + 288, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 264, + 288, + 308 + ], + "spans": [ + { + "bbox": [ + 53, + 264, + 288, + 308 + ], + "type": "text", + "content": "[60] Charles R Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J Guibas. Volumetric and multi-view cnns for object classification on 3d data. In CVPR, 2016. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 309, + 288, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 309, + 288, + 363 + ], + "spans": [ + { + "bbox": [ + 53, + 309, + 288, + 363 + ], + "type": "text", + "content": "[61] Amit Raj, Julian Tanke, James Hays, Minh Vo, Carsten Stoll, and Christoph Lassner. Anr: Articulated neural rendering for virtual avatars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3722-3731, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 365, + 288, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 365, + 288, + 421 + ], + "spans": [ + { + "bbox": [ + 53, + 365, + 288, + 421 + ], + "type": "text", + "content": "[62] Eduard Ramon, Gil Triginer, Janna Escur, Albert Pumarola, Jaime Garcia, Xavier Giro-i Nieto, and Francesc Moreno-Noguer. H3d-net: Few-shot high-fidelity 3d head reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5620-5629, 2021. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 422, + 288, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 422, + 288, + 475 + ], + "spans": [ + { + "bbox": [ + 53, + 422, + 288, + 475 + ], + "type": "text", + "content": "[63] Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger. Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14335-14345, 2021. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 477, + 288, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 477, + 288, + 521 + ], + "spans": [ + { + "bbox": [ + 53, + 477, + 288, + 521 + ], + "type": "text", + "content": "[64] Konstantinos Rematas, Andrew Liu, Pratul P Srinivasan, Jonathan T Barron, Andrea Tagliasacchi, Thomas Funkhouser, and Vittorio Ferrari. Urban radiance fields. arXiv preprint arXiv:2111.14643, 2021. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 522, + 288, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 522, + 288, + 577 + ], + "spans": [ + { + "bbox": [ + 53, + 522, + 288, + 577 + ], + "type": "text", + "content": "[65] Edoardo Remelli, Timur Bagautdinov, Shunsuke Saito, Chenglei Wu, Tomas Simon, Shih-En Wei, Kaiwen Guo, Zhe Cao, Fabian Prada, Jason Saragih, et al. Drivable volumetric avatars using texel-aligned features. In ACM SIGGRAPH 2022 Conference Proceedings, pages 1–9, 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 578, + 288, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 578, + 288, + 610 + ], + "spans": [ + { + "bbox": [ + 53, + 578, + 288, + 610 + ], + "type": "text", + "content": "[66] Javier Romero, Dimitrios Tzionas, and Michael J Black. Embodied hands: Modeling and capturing hands and bodies together. ACM ToG, 2017. 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 53, + 613, + 288, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 613, + 288, + 655 + ], + "spans": [ + { + "bbox": [ + 53, + 613, + 288, + 655 + ], + "type": "text", + "content": "[67] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In ICCV, 2019. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 53, + 658, + 288, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 658, + 288, + 689 + ], + "spans": [ + { + "bbox": [ + 53, + 658, + 288, + 689 + ], + "type": "text", + "content": "[68] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo. Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization. In CVPR, 2020. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 53, + 692, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 692, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 53, + 692, + 288, + 713 + ], + "type": "text", + "content": "[69] Shunsuke Saito, Jinlong Yang, Qianli Ma, and Michael J Black. Scanimate: Weakly supervised learning of skinned" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 312, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 331, + 73, + 547, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 331, + 73, + 547, + 106 + ], + "spans": [ + { + "bbox": [ + 331, + 73, + 547, + 106 + ], + "type": "text", + "content": "clothed avatar networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2886-2897, 2021. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 312, + 108, + 547, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 108, + 547, + 150 + ], + "spans": [ + { + "bbox": [ + 312, + 108, + 547, + 150 + ], + "type": "text", + "content": "[70] Vishwanath Saragadam, Jasper Tan, Guha Balakrishnan, Richard G Baraniuk, and Ashok Veeraraghavan. Miner: Multiscale implicit neural representations. arXiv preprint arXiv:2202.03532, 2022. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 312, + 152, + 547, + 174 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 152, + 547, + 174 + ], + "spans": [ + { + "bbox": [ + 312, + 152, + 547, + 174 + ], + "type": "text", + "content": "[71] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In CVPR, 2016. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 312, + 175, + 547, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 175, + 547, + 207 + ], + "spans": [ + { + "bbox": [ + 312, + 175, + 547, + 207 + ], + "type": "text", + "content": "[72] Johannes L Schonberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys. Pixelwise view selection for unstructured multi-view stereo. In ECCV, 2016. 2, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 312, + 209, + 547, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 209, + 547, + 252 + ], + "spans": [ + { + "bbox": [ + 312, + 209, + 547, + 252 + ], + "type": "text", + "content": "[73] Ruizhi Shao, Hongwen Zhang, He Zhang, Yanpei Cao, Tao Yu, and Yebin Liu. Doublefield: Bridging the neural surface and radiance fields for high-fidelity human rendering. arXiv preprint arXiv:2106.03798, 2021. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 312, + 255, + 547, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 255, + 547, + 297 + ], + "spans": [ + { + "bbox": [ + 312, + 255, + 547, + 297 + ], + "type": "text", + "content": "[74] Qing Shuai, Chen Geng, Qi Fang, Sida Peng, Wenhao Shen, Xiaowei Zhou, and Hujun Bao. Novel view synthesis of human interactions from sparse multi-view videos. In SIGGRAPH Conference Proceedings, 2022. 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 312, + 300, + 547, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 300, + 547, + 342 + ], + "spans": [ + { + "bbox": [ + 312, + 300, + 547, + 342 + ], + "type": "text", + "content": "[75] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462-7473, 2020. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 312, + 344, + 547, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 344, + 547, + 387 + ], + "spans": [ + { + "bbox": [ + 312, + 344, + 547, + 387 + ], + "type": "text", + "content": "[76] Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhofer. Deepvoxels: Learning persistent 3d feature embeddings. In CVPR, 2019. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 312, + 389, + 547, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 389, + 547, + 421 + ], + "spans": [ + { + "bbox": [ + 312, + 389, + 547, + 421 + ], + "type": "text", + "content": "[77] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3d-structure-aware neural scene representations. In NeurIPS, 2019. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 312, + 423, + 547, + 477 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 423, + 547, + 477 + ], + "spans": [ + { + "bbox": [ + 312, + 423, + 547, + 477 + ], + "type": "text", + "content": "[78] Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik Learned-Miller. Multi-view convolutional neural networks for 3d shape recognition. In Proceedings of the IEEE international conference on computer vision, pages 945-953, 2015. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 312, + 479, + 547, + 522 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 479, + 547, + 522 + ], + "spans": [ + { + "bbox": [ + 312, + 479, + 547, + 522 + ], + "type": "text", + "content": "[79] Shih-Yang Su, Frank Yu, Michael Zollhöfer, and Helge Rhodin. A-nerf: Articulated neural radiance fields for learning human shape, appearance, and pose. Advances in Neural Information Processing Systems, 34, 2021. 3, 8" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 312, + 524, + 547, + 556 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 524, + 547, + 556 + ], + "spans": [ + { + "bbox": [ + 312, + 524, + 547, + 556 + ], + "type": "text", + "content": "[80] Zhuo Su, Lan Xu, Zerong Zheng, Tao Yu, Yebin Liu, et al. Robustfusion: Human volumetric capture with data-driven visual cues using a rgbd camera. In ECCV, 2020. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 312, + 558, + 547, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 558, + 547, + 590 + ], + "spans": [ + { + "bbox": [ + 312, + 558, + 547, + 590 + ], + "type": "text", + "content": "[81] Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. arXiv preprint arXiv:2111.11215, 2021. 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 312, + 592, + 547, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 592, + 547, + 645 + ], + "spans": [ + { + "bbox": [ + 312, + 592, + 547, + 645 + ], + "type": "text", + "content": "[82] Matthew Tancik, Vincent Casser, Xinchen Yan, Sabeek Pradhan, Ben Mildenhall, Pratul P Srinivasan, Jonathan T Barron, and Henrik Kretzschmar. Block-nerf: Scalable large scene neural view synthesis. arXiv preprint arXiv:2202.05263, 2022. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 312, + 647, + 547, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 647, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 312, + 647, + 547, + 713 + ], + "type": "text", + "content": "[83] Matthew Tancik, Ben Mildenhall, Terrance Wang, Divi Schmidt, Pratul P Srinivasan, Jonathan T Barron, and Ren Ng. Learned initializations for optimizing coordinate-based neural representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2846-2855, 2021. 3" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "8769" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 53, + 73, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 73, + 288, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 73, + 288, + 138 + ], + "spans": [ + { + "bbox": [ + 53, + 73, + 288, + 138 + ], + "type": "text", + "content": "[84] Matthew Tancik, Pratul Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan Barron, and Ren Ng. Fourier features let networks learn high frequency functions in low dimensional domains. Advances in Neural Information Processing Systems, 33:7537-7547, 2020. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 139, + 288, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 139, + 288, + 183 + ], + "spans": [ + { + "bbox": [ + 53, + 139, + 288, + 183 + ], + "type": "text", + "content": "[85] Matthias Teschner, Bruno Heidelberger, Matthias Müller, Danat Pomerantes, and Markus H Gross. Optimized spatial hashing for collision detection of deformable objects. In Vmv, volume 3, pages 47-54, 2003. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 184, + 288, + 216 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 184, + 288, + 216 + ], + "spans": [ + { + "bbox": [ + 53, + 184, + 288, + 216 + ], + "type": "text", + "content": "[86] Justus Thies, Michael Zollhöfer, and Matthias Nießner. Deferred neural rendering: Image synthesis using neural textures. ACM TOG, 2019. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 217, + 288, + 271 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 217, + 288, + 271 + ], + "spans": [ + { + "bbox": [ + 53, + 217, + 288, + 271 + ], + "type": "text", + "content": "[87] Garvita Tiwari, Nikolaos Sarafianos, Tony Tung, and Gerard Pons-Moll. Neural-gif: Neural generalized implicit functions for animating people in clothing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11708-11718, 2021. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 272, + 288, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 272, + 288, + 316 + ], + "spans": [ + { + "bbox": [ + 53, + 272, + 288, + 316 + ], + "type": "text", + "content": "[88] Alex Trevithick and Bo Yang. Grf: Learning a general radiance field for 3d representation and rendering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15182-15192, 2021. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 316, + 288, + 358 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 316, + 288, + 358 + ], + "spans": [ + { + "bbox": [ + 53, + 316, + 288, + 358 + ], + "type": "text", + "content": "[89] Haithem Turki, Deva Ramanan, and Mahadev Satyanarayanan. Mega-nerf: Scalable construction of large-scale nerfs for virtual fly-throughs. arXiv preprint arXiv:2112.10703, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 360, + 288, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 360, + 288, + 403 + ], + "spans": [ + { + "bbox": [ + 53, + 360, + 288, + 403 + ], + "type": "text", + "content": "[90] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In NeurIPS, 2021. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 404, + 288, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 404, + 288, + 470 + ], + "spans": [ + { + "bbox": [ + 53, + 404, + 288, + 470 + ], + "type": "text", + "content": "[91] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul P Srinivasan, Howard Zhou, Jonathan T Barron, Ricardo Martin-Brualla, Noah Snavely, and Thomas Funkhouser. Ibrnet: Learning multi-view image-based rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4690-4699, 2021. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 471, + 288, + 503 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 471, + 288, + 503 + ], + "spans": [ + { + "bbox": [ + 53, + 471, + 288, + 503 + ], + "type": "text", + "content": "[92] Shaofei Wang, Katja Schwarz, Andreas Geiger, and Siyu Tang. Arah: animatable volume rendering of articulated human sdfs. arXiv preprint arXiv:2210.10036, 2022. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 504, + 288, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 504, + 288, + 557 + ], + "spans": [ + { + "bbox": [ + 53, + 504, + 288, + 557 + ], + "type": "text", + "content": "[93] Chung-Yi Weng, Brian Curless, Pratul P Srinivasan, Jonathan T Barron, and Ira Kemelmacher-Shlizerman. Humanerf: Free-viewpoint rendering of moving people from monocular video. arXiv preprint arXiv:2201.04127, 2022. 1, 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 558, + 288, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 558, + 288, + 602 + ], + "spans": [ + { + "bbox": [ + 53, + 558, + 288, + 602 + ], + "type": "text", + "content": "[94] Yuanbo Xiangli, Linning Xu, Xingang Pan, Nanxuan Zhao, Anyi Rao, Christian Theobalt, Bo Dai, and Dahua Lin. Citynerf: Building nerf at city scale. arXiv preprint arXiv:2112.05504, 2021. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 53, + 603, + 288, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 603, + 288, + 647 + ], + "spans": [ + { + "bbox": [ + 53, + 603, + 288, + 647 + ], + "type": "text", + "content": "[95] Hongyi Xu, Thiemo Alldieck, and Cristian Sminchisescu. H-nerf: Neural radiance fields for rendering and temporal reconstruction of humans in motion. Advances in Neural Information Processing Systems, 34, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 53, + 647, + 288, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 647, + 288, + 678 + ], + "spans": [ + { + "bbox": [ + 53, + 647, + 288, + 678 + ], + "type": "text", + "content": "[96] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In NeurIPS, 2021. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 53, + 680, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 680, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 53, + 680, + 288, + 713 + ], + "type": "text", + "content": "[97] Jianglong Ye, Yuntao Chen, Naiyan Wang, and Xiaolong Wang. Gifs: Neural implicit function for general shape representation. arXiv preprint arXiv:2204.07126, 2022. 2" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 547, + 430 + ], + "type": "list", + "angle": 0, + "index": 23, + "blocks": [ + { + "bbox": [ + 312, + 73, + 547, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 73, + 547, + 116 + ], + "spans": [ + { + "bbox": [ + 312, + 73, + 547, + 116 + ], + "type": "text", + "content": "[98] Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. arXiv preprint arXiv:2112.05131, 2021. 2, 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 312, + 118, + 547, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 118, + 547, + 172 + ], + "spans": [ + { + "bbox": [ + 312, + 118, + 547, + 172 + ], + "type": "text", + "content": "[99] Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. Plenoptrees for real-time rendering of neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5752-5761, 2021. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 174, + 547, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 174, + 547, + 228 + ], + "spans": [ + { + "bbox": [ + 308, + 174, + 547, + 228 + ], + "type": "text", + "content": "[100] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelnerf: Neural radiance fields from one or few images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4578-4587, 2021. 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 229, + 545, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 229, + 545, + 261 + ], + "spans": [ + { + "bbox": [ + 308, + 229, + 545, + 261 + ], + "type": "text", + "content": "[101] Ruiqi Zhang and Jie Chen. Ndf: Neural deformable fields for dynamic human modelling. arXiv preprint arXiv:2207.09193, 2022. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 262, + 547, + 307 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 262, + 547, + 307 + ], + "spans": [ + { + "bbox": [ + 308, + 262, + 547, + 307 + ], + "type": "text", + "content": "[102] Fuqiang Zhao, Wei Yang, Jiakai Zhang, Pei Lin, Yingliang Zhang, Jingyi Yu, and Lan Xu. Humannerf: Generalizable neural human radiance field from sparse inputs. arXiv preprint arXiv:2112.02789, 2021. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 308, + 547, + 351 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 308, + 547, + 351 + ], + "spans": [ + { + "bbox": [ + 308, + 308, + 547, + 351 + ], + "type": "text", + "content": "[103] Zerong Zheng, Han Huang, Tao Yu, Hongwen Zhang, Yandong Guo, and Yebin Liu. Structured local radiance fields for human avatar modeling. arXiv preprint arXiv:2203.14478, 2022. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 353, + 547, + 386 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 353, + 547, + 386 + ], + "spans": [ + { + "bbox": [ + 308, + 353, + 547, + 386 + ], + "type": "text", + "content": "[104] Shuaifeng Zhi, Tristan Laidlow, Stefan Leutenegger, and Andrew J Davison. In-place scene labelling and understanding with implicit scene representation. In ICCV, 2021. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 387, + 545, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 387, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 308, + 387, + 545, + 430 + ], + "type": "text", + "content": "[105] Yihao Zhi, Shenhan Qian, Xinhao Yan, and Shenghua Gao. Dual-space nerf: Learning animatable avatars and scene lighting in separate spaces. arXiv preprint arXiv:2208.14851, 2022. 2" + } + ] + } + ], + "index": 22 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "8770" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 11 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision/c64e9aaf-56b9-414a-803b-d9922f77f018_content_list.json b/2023/Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision/c64e9aaf-56b9-414a-803b-d9922f77f018_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..33ee1eaeb1f2f0576329a97163b303a1b86929da --- /dev/null +++ b/2023/Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision/c64e9aaf-56b9-414a-803b-d9922f77f018_content_list.json @@ -0,0 +1,1560 @@ +[ + { + "type": "text", + "text": "Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision", + "text_level": 1, + "bbox": [ + 187, + 130, + 782, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Jilan Xu $^{1,2}$ Junlin Hou $^{1}$ Yuejie Zhang $^{1*}$ Rui Feng $^{1*}$ Yi Wang $^{2}$ Yu Qiao $^{2}$ Weidi Xie $^{2,3}$", + "bbox": [ + 140, + 202, + 828, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ School of Computer Science, Shanghai Key Lab of Intelligent Information Processing, Shanghai Collaborative Innovation Center of Intelligent Visual Computing, Fudan University $^{2}$ Shanghai AI Laboratory $^{3}$ CMIC, Shanghai Jiao Tong University", + "bbox": [ + 119, + 220, + 849, + 276 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 308, + 313, + 325 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "This paper considers the problem of open-vocabulary semantic segmentation (OVS), that aims to segment objects of arbitrary classes beyond a pre-defined, closed-set categories. The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor, which only exploits web-crawled image-text pairs for pre-training without using any mask annotations. OVSegmentor assembles the image pixels into a set of learnable group tokens via a slot-attention based binding module, then aligns the group tokens to corresponding caption embeddings. Second, we propose two proxy tasks for training, namely masked entity completion and cross-image mask consistency. The former aims to infer all masked entities in the caption given group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities. The latter enforces consistent mask predictions between images that contain shared entities, encouraging the model to learn visual invariance. Third, we construct CC4M dataset for pre-training by filtering CC12M with frequently appeared entities, which significantly improves training efficiency. Fourth, we perform zero-shot transfer on four benchmark datasets, PASCAL VOC, PASCAL Context, COCO Object, and ADE20K. OVSegmentor achieves superior results over state-of-the-art approaches on PASCAL VOC using only $3\\%$ data (4M vs 134M) for pre-training.", + "bbox": [ + 76, + 340, + 472, + 734 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 744, + 209, + 760 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Semantic segmentation considers the problem of assigning class labels to each pixel in the image. It plays critical roles in a wide range of real-world scenarios, including autonomous driving, computer-aided diagnosis and satellite image analysis, to name a few. Generally speaking, two lines of research dominate semantic segmentation, one way is to cluster the pixels into different groups and assign a", + "bbox": [ + 75, + 770, + 468, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/3e5628a889cc5c8d3645c2cb18ca92a68d39aa22c62b1cdf5bb761bfc113c381.jpg", + "image_caption": [ + "Figure 1. An illustration of open-vocabulary semantic segmentation. Models trained on closed-set classes (cat and dog) fail to segment novel class (fire hydrant). We train a visual encoder and a text encoder on web-collected image-text pairs without using any mask labels, and our model can segment arbitrary object classes." + ], + "image_footnote": [], + "bbox": [ + 501, + 306, + 893, + 477 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "semantic label to each group; the other idea treats segmentation as pixel-wise classification, casting each pixel into one category. Despite tremendous progress, the scalability of existing approaches that rely on supervised training has been fundamentally limited: (1) costly annotation procedure. Extensive manual pixel-wise annotations are required for training segmentation models; (2) closed-set segmentation. The model is restricted to segmenting objects from a closed-set of categories. Whenever a new dataset comes, the model requires re-training.", + "bbox": [ + 496, + 565, + 890, + 715 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, our goal is to train an open-vocabulary semantic segmentation (OVS) model, by exploiting freely available image-caption pairs on Internet, as illustrated in Fig. 1. The recent work, for example, CLIP [41] and ALIGN [23] have demonstrated that a combination of large-scale image-caption pairs and simple noise contrastive estimation can learn powerful image-text embeddings from scratch, and show strong \"zero-shot\" generalization abilities for open-vocabulary classification. Additionally, GroupViT [53] extends the idea towards semantic segmentation by training a segmentation model with text supervision only. They perform hierarchical grouping of", + "bbox": [ + 496, + 719, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 94, + 887, + 220, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "2935", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "visual tokens, which are then aligned to the corresponding text embeddings via a contrastive loss. However, the following issues remain challenging and unsolved: First, the captions only provide coarse, image-level descriptions, which are insufficient for training semantic segmentation models where fine-grained, pixel-wise supervision is usually needed. Second, the diversity of web-collected data is large, that requires the model to learn visual invariance on objects of interest, with only weak supervision provided. For instance, the visual appearance of two images with similar captions can be drastically different.", + "bbox": [ + 75, + 90, + 472, + 257 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To tackle the above challenges, (i) we propose a transformer-based model for open-vocabulary semantic segmentation, dubbed as OVSegmentor, that can segment objects of arbitrary categories via zero-shot transfer, with only image-caption pairs for pre-training. Specifically, we introduce learnable group tokens to cluster image patches via a slot-attention [33] based binding module, and align the group tokens with corresponding caption embeddings. Note that our model neither requires ground-truth masks for training nor additional re-training on target segmentation datasets, substantially alleviating the annotation efforts and improving transfer efficiency; (ii) As for training on the image-caption dataset, we propose two proxy tasks, namely masked entity completion and cross-image mask consistency, the former trains the model to infer all the masked entities in the sentence given the group tokens, and the latter enforces consistent mask prediction for images with the common entity. Both tasks have shown to be beneficial in learning entity-specific, fine-grained and visually invariant group semantics; (iii) We construct an image-caption dataset, termed as CC4M, by designing an automatic approach to filter CC12M [7] with frequently appeared visual entities, significantly improving the training efficiency.", + "bbox": [ + 75, + 257, + 472, + 604 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We pre-train the proposed OVSegmentor on our filtered image-caption dataset (CC4M), without using any manual segmentation masks whatsoever. The model is evaluated on four segmentation benchmarks, PASCAL VOC 2012 [18], PASCAL Context [38], COCO Object [31], and ADE20K [59] in a zero-shot manner, i.e., the model is directly evaluated on target datasets without any finetuning. Extensive experiments demonstrate that our model surpasses existing models that are trained with full supervision and outperforms state-of-the-art self-supervised approaches on PASCAL VOC by using only $3\\%$ data (4M vs 134M) for pre-training, significantly improving the training efficiency.", + "bbox": [ + 75, + 604, + 472, + 787 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 76, + 797, + 218, + 811 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Vision-Language Pre-training. Vision-language pretraining (VLP) aims to learn joint visual-textual representations for a variety of multimodal downstream tasks. Existing works either learn unimodal encoders by distinguishing the positive pair(s) from the unpaired samples [3,28,41]", + "bbox": [ + 75, + 825, + 470, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "or focus on one multimodal encoder for joint feature learning with masked image/language modeling and image-text matching losses [12,27,29]. Additionally, some approaches seek fine-grained supervision for cross-modal interaction [20, 30, 55-57]. For example, GLIP [30] proposed to align the bounding boxes with corresponding phrases in the text. However, they still rely on ground-truth grounding annotations. In contrast, our work explores fine-grained information with only weak supervision provided. Despite remarkable performance on multimodal downstream tasks, few of these vision-language models have been designed for fundamental vision tasks (e.g., semantic segmentation).", + "bbox": [ + 496, + 90, + 893, + 272 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Zero-shot/Open-vocabulary Semantic Segmentation. The goal of zero-shot semantic segmentation is to segment objects of interest that are not seen in the training set. Prior works [22, 40, 51] mainly transfer the knowledge from the training set (seen) to the testing set (unseen) via visual-semantic mapping. Inspired by the open-vocabulary nature of language, current approaches [17, 21, 26, 37, 44, 45, 60] exploit vision-language models (e.g., CLIP [41]) pretrained on large-scale image-caption pairs. They need either finetuning [17, 21, 26] or self-training [60] on the target segmentation dataset, which is less efficient and flexible than zero-shot transfer. VLP for open-vocabulary segmentation combines the merits of both zero-shot transfer and open-vocabulary recognition. GroupViT [53] designed a grouping vision transformer and learned the alignment between groups and text via the contrastive loss. ViL-Seg [32] combined image-text contrastive loss with online pixel clustering for segmentation. A concurrent work CLIPpy [42] explored different aggregation operations for training spatial-aware vision-language models for segmentation. Beyond the global image-text matching, we further design masked entity completion and cross-image mask consistency to enrich the group semantics.", + "bbox": [ + 496, + 276, + 893, + 625 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Fully-/Weakly-/Semi-Supervised Semantic Segmentation. Fully-supervised semantic segmentation emerges from per-pixel classification [8, 34] to mask classification [13, 48]. To relieve the laborious annotations, extensive efforts have been made to address semantic segmentation with less supervision. In the family of weakly-supervised object localization [24,50,54] and semantic segmentation [1,9,52], only class labels are available for supervision. Generally, the class activation maps [58] derived from the classification network serve as the initial segmentation results. Another line of research focuses on semi-supervised semantic segmentation [11, 19, 36, 39, 49] where a few samples have dense per-pixel labels and the remaining samples are unlabeled. These works mainly perform supervised training on labeled samples with additional consistency regularizations posed on unlabeled samples. Despite promising results, these approaches are still limited to closed-set object categories.", + "bbox": [ + 496, + 628, + 895, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "2936", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/49f51875dff4317c8e35ff871f9ee8da5f21cdc5deaeb5d9c31c6fd97d293a6d.jpg", + "image_caption": [ + "Figure 2. Overall framework. The model takes an image-text pair as input, and outputs the image features and a set of learnable group tokens, which is aligned to the caption embedding via $\\mathcal{L}_{\\mathrm{contrast}}$ . The visual encoder consists of Transformer encoder layers, with a slot-attention based binding module in between to cluster the image tokens into groups. To enrich group semantics, (1) $\\mathcal{L}_{\\mathrm{entity}}$ trains the model to infer all masked entities in the caption given the group tokens. (2) $\\mathcal{L}_{\\mathrm{mask}}$ enforces consistent mask predictions of images with the same entity (right figure). During inference, only visual and text encoders and learned group tokens are used to generate the segmentation mask." + ], + "image_footnote": [], + "bbox": [ + 86, + 85, + 584, + 411 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/8a146145b190280f3ef0ec79eb8efc2eb19e517294cef3e8bf3d94353b43c34e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 602, + 85, + 883, + 410 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 76, + 505, + 212, + 523 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Assuming we are given a dataset of image-caption pairs, i.e., $\\mathcal{D}_{\\mathrm{train}} = \\{(I_1,T_1),\\dots ,(I_N,T_N)\\}$ , where $I_{i}\\in$ $\\mathbb{R}^{H\\times W\\times 3}$ denotes the image, and $T_{i}$ refers to its corresponding alt-text downloaded from the Internet, the goal is to train an open-vocabulary semantic segmentation (OVS) model only with these (very) weak labels, that can segment objects of arbitrary classes. For each pair $(I,T)$ ,we first split the image into $L = HW / P^2$ non-overlapping patches with patch size $P$ . These patches are then transformed into a sequence of image tokens with MLPs, we reuse $I\\in \\mathbb{R}^{L\\times D}$ to represent them for ease of understanding. Additionally, we introduce $K$ learnable tokens $G\\in \\mathbb{R}^{K\\times D}$ , that aims to group the image tokens into clusters.", + "bbox": [ + 75, + 530, + 468, + 726 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We present a VLP framework, termed OVSegmentor, it assembles the image patches into groups and aligns the group to categories, by only exploiting weak supervisions from image-caption pairs. Conceptually, the architecture consists of two stages, namely, a patch-to-group binding that assigns all patches with same semantics into one group, and group-to-category alignment that computes matching scores between each of these group tokens with semantic categories, the segmentation mask $m$ can be computed as:", + "bbox": [ + 75, + 726, + 470, + 863 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nm = \\underset {C} {\\arg \\max } \\mathbb {A} \\cdot \\mathcal {G} \\cdot \\mathcal {T} ^ {\\top}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 184, + 872, + 468, + 897 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathbb{A} \\in \\mathbb{R}^{L \\times K}$ characterises a soft binding procedure, denoting the likelihood of each image patch being assigned into the output group embeddings $(\\mathcal{G} \\in \\mathbb{R}^{K \\times D})$ ; and $\\mathcal{T} \\in \\mathbb{R}^{C \\times D}$ denotes the embeddings with dimension $D$ for a total of $C$ categories, computed from a text encoder. Note that, at training time, manual segmentation masks are not present, and the intermediate binding and assignment can only be learnt from the image-caption pairs implicitly.", + "bbox": [ + 496, + 506, + 892, + 627 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "At inference stage, the patch-group affinity $\\mathbb{A}$ can be computed by the product between the image features $\\mathcal{I} \\in \\mathbb{R}^{L \\times D}$ and the learned group tokens $\\mathcal{G}$ , and $\\mathcal{T}$ can be derived by encoding the prompted class labels (e.g., A photo of {class}). Thus the patch-level mask prediction can be calculated via Eq. 1. The pixel-level segmentation mask can then be obtained by up-sampling the patch-level mask $(m)$ . In the following sections, we detail the overall architecture and training procedure of each component. The overall framework is illustrated in Fig. 2.", + "bbox": [ + 496, + 628, + 893, + 779 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Architecture", + "text_level": 1, + "bbox": [ + 500, + 790, + 635, + 806 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we will detail the proposed architecture at training stage, including a visual encoder for learning group tokens and the binding procedure, a text encoder that computes variants of caption embeddings. We describe the encoding procedure for image and caption pair, the subscripts are thus ignored for simplicity.", + "bbox": [ + 496, + 814, + 892, + 906 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "2937", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1.1 Visual Encoder", + "text_level": 1, + "bbox": [ + 76, + 90, + 238, + 104 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The visual encoder consists of two components, namely, Transformer encoders and binding modules. Specifically, the image tokens and learnable group tokens are concatenated, and iteratively processed by the Transformer encoders, and a binding module with slot-attention [33] is adopted for grouping. The visual encoder is defined as:", + "bbox": [ + 75, + 114, + 468, + 205 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n[ \\mathcal {G}; \\mathcal {I} ] = \\Phi_ {\\mathrm {e n c}} ^ {2} (\\cdot) \\circ \\Phi_ {\\mathrm {b i n d}} (\\cdot) \\circ \\Phi_ {\\mathrm {e n c}} ^ {1} ([ G; I ]), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 135, + 215, + 468, + 234 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\Phi_{\\mathrm{enc}}^{1}(\\cdot)$ and $\\Phi_{\\mathrm{enc}}^{2}(\\cdot)$ refer to modules with Transformer encoder layers, and $\\Phi_{\\mathrm{bind}}(\\cdot)$ is the binding module. The output $\\mathcal{G}\\in \\mathbb{R}^{K\\times D}$ is the encoded group tokens, and $\\mathcal{I}\\in \\mathbb{R}^{L\\times D}$ refers to the output image tokens.", + "bbox": [ + 76, + 244, + 468, + 306 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Transformer Encoder. Both $\\Phi_{\\mathrm{enc}}^{1}$ and $\\Phi_{\\mathrm{enc}}^{2}$ consist of 6 Transformer encoder layers [47], where each layer is composed of a multi-head self-attention (MHSA) layer followed by layer normalisation (LN) and a feed-forward network (FFN). $\\Phi_{\\mathrm{enc}}^{1}$ takes the concatenation of image patches and randomly initialised group tokens as input, and outputs intermediately encoded group and image tokens ( $G'$ and $I'$ ); $\\Phi_{\\mathrm{enc}}^{2}$ processes the output from the binding module.", + "bbox": [ + 76, + 310, + 468, + 433 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Binding Module. The binding module uses slot-attention to cluster image tokens into groups in a data-dependent manner, i.e., image patches with similar appearance and semantics are encouraged to be grouped together. Formally, the binding module accepts the output from the Transformer encoder $\\Phi_{\\mathrm{enc}}^{1}$ , and transforms them into query, key and values with linear transformations:", + "bbox": [ + 75, + 435, + 468, + 540 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {Q} = W _ {q} ^ {\\text {b i n d}} G ^ {\\prime}, \\quad \\mathbf {K}, \\mathbf {V} = W _ {k} ^ {\\text {b i n d}} I ^ {\\prime}, W _ {v} ^ {\\text {b i n d}} I ^ {\\prime}. \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 125, + 551, + 468, + 571 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In contrast to the standard cross attention in Transformer Decoders [47], slot-attention performs normalisation over queries, encouraging each image token to be claimed by one of the group tokens. The binding process can be defined as:", + "bbox": [ + 75, + 582, + 468, + 643 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbb {A} _ {j, k} = \\frac {\\exp \\left(\\mathbf {K} _ {j} \\cdot \\mathbf {Q} _ {k}\\right)}{\\sum_ {l} \\exp \\left(\\mathbf {K} _ {j} \\cdot \\mathbf {Q} _ {l}\\right)}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 184, + 654, + 468, + 686 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbb{A}_{j,k}$ refers to the probability of the $j$ -th image token being assigned to the $k$ -th group. Then, each updated group token $\\tilde{G}_k'$ is computed by taking the weighted average of the image tokens that are assigned to it. The output of the binding module $G^{\\mathrm{bind}}$ is defined as:", + "bbox": [ + 75, + 696, + 468, + 773 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {G} _ {k} ^ {\\prime} = \\frac {\\sum_ {j = 1} ^ {L} \\mathbb {A} _ {j , k} \\mathbf {V} _ {j}}{\\sum_ {j = 1} ^ {L} \\mathbb {A} _ {j , k}}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 205, + 784, + 468, + 825 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nG ^ {\\text {b i n d}} = G ^ {\\prime} + W _ {o} ^ {\\text {b i n d}} \\tilde {G} ^ {\\prime}, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 194, + 828, + 468, + 847 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $W_{o}^{\\mathrm{bind}}$ is a linear transformation. Now we have obtained the correspondence between each patch and groups, next we describe the procedure for encoding captions.", + "bbox": [ + 75, + 859, + 468, + 906 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.1.2 Text Encoder", + "text_level": 1, + "bbox": [ + 500, + 90, + 648, + 104 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Till this end, we filter all the captions and only keep the ones with informative entities, followed by exploiting three variants of the caption encoding, namely, the entire caption embedding, the masked caption embedding and the prompted entity embedding. In all cases we use a pretrained BERT [16] as the text encoder $\\Phi_{\\mathrm{text}}$ .", + "bbox": [ + 496, + 114, + 890, + 205 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Constructing Entity Set. We adopt the nltk toolkit [4] to extract entities from all the captions, and construct an entity set $\\Omega = \\Phi_{\\text{entity}}(\\{T_1, \\ldots, T_N\\})$ that only maintains the frequently appeared entities (e.g., people, cat, shirt, etc.), and exclude the abstract nouns (e.g., art, view, etc) as they usually do not correspond to any specific region in the image. For each image-caption pair, we can thus obtain an image-caption-entity triplet $(I, T, E)$ , where $E = \\{e | e \\in T \\cap e \\in \\Omega\\}$ includes all frequent entities in the caption.", + "bbox": [ + 496, + 208, + 890, + 345 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Caption Embedding. Here, for each caption $T$ , it is tokenised using the BERT tokeniser [16], with the start-of-token [SOT] and end-of-token [EOT] added to the beginning/ending. The caption embedding is computed as $\\mathcal{T}^{\\mathrm{cap}} = \\Phi_{\\mathrm{text}}(T) \\in \\mathbb{R}^{M \\times D}$ , where $M$ is length after tokenisation.", + "bbox": [ + 496, + 348, + 890, + 425 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Masked Caption Embedding. In the second variant, we mask all the frequent entities in the caption, and compute the masked caption embedding as $\\mathcal{T}^{\\mathrm{m - cap}} = \\Phi_{\\mathrm{text}}(g(T))\\in$ $\\mathbb{R}^{M\\times D}$ , where $g(\\cdot)$ denotes an masking operation, that replaces the entity with a special [MASK] token.", + "bbox": [ + 496, + 428, + 890, + 503 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Prompted Entity Embedding. We construct manual prompts for each of the entities in the triplets, and compute its textual embedding as $\\mathcal{T}^{\\mathrm{entity}} = \\Phi_{\\mathrm{text}}(h(E)) \\in \\mathbb{R}^{M \\times D}$ , where $h(\\cdot)$ refers to the procedure for constructing manual prompts, for example, we randomly sample a prompt template provided in [41], and fill in the entities: A painting of $a$ $\\{entity_1\\}$ and $\\{entity_2\\}$ and $\\{entity_3\\}$ . This sentence is padded to the same length as the caption after tokenisation.", + "bbox": [ + 496, + 507, + 893, + 628 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Training", + "text_level": 1, + "bbox": [ + 500, + 637, + 604, + 654 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As for training, we aim to learn the alignment between group tokens and caption embeddings via three proxy tasks, namely, image-caption alignment, masked entity completion, and cross-image mask consistency.", + "bbox": [ + 496, + 660, + 890, + 720 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Image-caption Alignment. For each image-text pair, the objective is to align their visual and textual embeddings. The visual embedding $z^{\\mathrm{I}}$ is the average of group tokens, and the textual embedding $z^{\\mathrm{T}}$ is obtained by taking the [EOT] token feature of the caption embedding $\\mathcal{T}^{\\mathrm{cap}}$ , both projected to a 256-d joint feature space followed by normalisation. The image-caption contrastive loss $\\mathcal{L}_{\\mathrm{contrast}}$ is formulated as:", + "bbox": [ + 496, + 724, + 890, + 830 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {c o n t r a s t}} = \\frac {1}{2} \\left(\\frac {\\exp \\left(z _ {i} ^ {\\mathrm {I}} \\cdot z _ {i} ^ {\\mathrm {T}}\\right)}{\\sum_ {l} \\exp \\left(z _ {i} ^ {\\mathrm {I}} \\cdot z _ {l} ^ {\\mathrm {T}}\\right)} + \\frac {\\exp \\left(z _ {i} ^ {\\mathrm {I}} \\cdot z _ {i} ^ {\\mathrm {T}}\\right)}{\\sum_ {l} \\exp \\left(z _ {l} ^ {\\mathrm {I}} \\cdot z _ {i} ^ {\\mathrm {T}}\\right)}\\right). \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 508, + 840, + 890, + 877 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here, we omit the temperature parameter for simplicity.", + "bbox": [ + 500, + 885, + 867, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "2938", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Masked Entity Completion. The goal of masked entity completion is to infer all the masked entities in the sentence given the group tokens. In specific, we adopt a Transformer decoder layer, where a projection of the masked caption embedding is treated as query, and two linear transformations of group tokens are treated as key and values, respectively.", + "bbox": [ + 75, + 90, + 468, + 183 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\tilde {\\mathcal {T}} ^ {\\mathrm {m} - \\mathrm {c a p}} = \\Phi_ {\\mathrm {d e c}} \\left(W _ {q} ^ {\\mathrm {d e c}} \\mathcal {T} ^ {\\mathrm {m} - \\mathrm {c a p}}, W _ {k} ^ {\\mathrm {d e c}} \\mathcal {G}, W _ {v} ^ {\\mathrm {d e c}} \\mathcal {G}\\right) \\in \\mathbb {R} ^ {M \\times D}, \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 89, + 188, + 468, + 222 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $W_{q}^{\\mathrm{dec}}$ , $W_{k}^{\\mathrm{dec}}$ and $W_{v}^{\\mathrm{dec}}$ are linear transformations, and $\\tilde{T}^{\\mathrm{m - cap}}$ denotes the updated vector sequence, with the masked entities being completed by querying group tokens. For training, we extract the [EOT] token features $z^{\\mathrm{M}}$ and $z^{\\mathrm{E}}$ from $\\tilde{T}^{\\mathrm{m - cap}}$ and $T^{\\mathrm{entity}}$ , and construct a contrastive loss:", + "bbox": [ + 75, + 232, + 468, + 311 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {e n t i t y}} = \\frac {1}{2} \\left(\\frac {\\exp \\left(z _ {i} ^ {\\mathrm {M}} \\cdot z _ {i} ^ {\\mathrm {E}}\\right)}{\\sum_ {l} \\exp \\left(z _ {i} ^ {\\mathrm {M}} \\cdot z _ {l} ^ {\\mathrm {E}}\\right)} + \\frac {\\exp \\left(z _ {i} ^ {\\mathrm {M}} \\cdot z _ {i} ^ {\\mathrm {E}}\\right)}{\\sum_ {l} \\exp \\left(z _ {l} ^ {\\mathrm {M}} \\cdot z _ {i} ^ {\\mathrm {E}}\\right)}\\right). \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 93, + 316, + 468, + 367 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Intuitively, the entity completion task enables better alignment between the groups and entities.", + "bbox": [ + 75, + 376, + 468, + 406 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Cross-image Mask Consistency. To encourage visual invariance, we enforce consistent mask predictions between images that contain shared entities. Specifically, for each entity of interest, we can easily source multiple image-caption pairs from the dataset by text searching. Given one sampled image-caption-entity triplet, $(I_1, T_1, E_1)$ , we can easily search for another sample $(I_2, T_2, E_2)$ , with both triplet sharing one entity, i.e., $e \\in E_1 \\cap E_2$ . Given encoded groups $\\mathcal{G}_1, \\mathcal{G}_2$ , two sets of subgroups $\\mathcal{G}_1^{\\mathrm{sub}}, \\mathcal{G}_2^{\\mathrm{sub}} \\in \\mathbb{R}^{K' \\times D}$ that represent the common entity $e$ (e.g., cat in Fig. 2) are obtained by choosing groups with higher similarity to the entity embedding, where $K' = rK$ and $r$ is the selection ratio. The masks of co-attentive entity in image $I_1$ can be grounded by both entity-specific subgroups $\\mathcal{G}_1^{\\mathrm{sub}}, \\mathcal{G}_2^{\\mathrm{sub}}$ as:", + "bbox": [ + 75, + 409, + 468, + 622 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {M} _ {1} = \\sigma \\left(\\mathcal {I} _ {1} ^ {\\top} \\mathcal {G} _ {1} ^ {\\text {s u b}}\\right), \\hat {\\mathcal {M}} _ {1} = \\sigma \\left(\\mathcal {I} _ {1} ^ {\\top} \\mathcal {G} _ {2} ^ {\\text {s u b}}\\right) \\in [ 0, 1 ] ^ {L \\times K ^ {\\prime}}, \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 84, + 628, + 468, + 648 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\sigma$ is the sigmoid activation; both $\\mathcal{M}_1 = \\{m_k^1\\}_{k=1}^{K'}$ and $\\hat{\\mathcal{M}}_1 = \\{\\hat{m}_k^1\\}_{k=1}^{K'}$ consist of $K'$ unordered masks.", + "bbox": [ + 75, + 655, + 467, + 689 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To align $\\hat{\\mathcal{M}}_1$ with $\\mathcal{M}_1$ , we first adopt the bipartite matching to find the optimal permutation $p_1^*$ over $K'$ subgroups with the lowest matching cost as:", + "bbox": [ + 75, + 690, + 468, + 734 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\np _ {1} ^ {*} = \\underset {p \\in \\mathcal {P}} {\\arg \\min } \\sum_ {k} - \\cos \\left(m _ {k} ^ {1}, \\hat {m} _ {p (k)} ^ {1}\\right), \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 140, + 743, + 468, + 775 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{P}$ is the full permutation and $\\cos (\\cdot)$ denotes the cosine similarity. Eq. 11 is solved via the efficient Hungarian algorithm [25]. In this way, the symmetric cross-image mask consistency loss $\\mathcal{L}_{mask}$ is defined as:", + "bbox": [ + 75, + 784, + 468, + 845 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {m a s k}} = \\frac {1}{2} \\left(\\sum_ {k} \\mathrm {D} \\left(\\operatorname {s g} \\left(\\mathbf {m} _ {k} ^ {1}\\right), \\hat {m} _ {p _ {1} ^ {*} (k)} ^ {1}\\right) + \\mathrm {D} \\left(\\operatorname {s g} \\left(\\mathbf {m} _ {k} ^ {2}\\right), \\hat {m} _ {p _ {2} ^ {*} (k)} ^ {2}\\right)\\right), \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 76, + 851, + 480, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathrm{sg}(\\cdot)$ denotes the stop gradient operation; the target mask $\\mathbf{m}_k$ is achieved by binarizing $m_{k}$ with a threshold $\\delta$ ; $\\mathrm{D}(\\mathbf{m},\\hat{m}) = 1 - 2|\\mathbf{m}\\bigcap \\hat{m} | / (|\\mathbf{m}| + |\\hat{m} |)$ stands for the standard Dice loss. To guarantee the quality of pseudo mask targets, the masks are generated by an extra momentum model, which is updated by the exponential-moving-average (EMA) of the online model.", + "bbox": [ + 496, + 90, + 890, + 196 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training Objective. We adopt a combination of three different loss functions:", + "bbox": [ + 496, + 200, + 890, + 229 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {t o t a l}} = \\mathcal {L} _ {\\text {c o n t r a s t}} + \\mathcal {L} _ {\\text {e n t i t y}} + \\lambda \\mathcal {L} _ {\\text {m a s k}}, \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 576, + 244, + 890, + 260 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\lambda$ is the weight for balancing the mask consistency.", + "bbox": [ + 496, + 272, + 880, + 287 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Discussion", + "text_level": 1, + "bbox": [ + 500, + 297, + 617, + 311 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "One work that is closely related to ours is GroupViT [53]. They extracted multiple nouns and prompted each noun to a sentence to serve as extra matched captions for the image. The model is thus supervised by a multi-label contrastive loss. In contrast, our paper differs from GroupViT in three critical aspects: (1) Entities vs nouns. Rather than using all nouns, we leverage the entities that match to visual objects, enabling high-quality image-caption correspondence. (2) Network architecture. We only adopt single-stage grouping (i.e., one binding module) as we observe no further performance gain of applying multi-stage grouping in GroupViT. The hard-assignment trick [53] is also adopted. Besides, we devise a (very) lightweight decoder to model the fine-grained, token-wise group-word correlation. (3) Proxy tasks for training. We propose two different proxy tasks, i.e., masked entity completion and cross-image mask consistency to improve the entity-specific group semantics and further encourage visual invariance.", + "bbox": [ + 496, + 321, + 890, + 593 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 498, + 607, + 633, + 625 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Setups", + "text_level": 1, + "bbox": [ + 498, + 632, + 696, + 648 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Pre-training Dataset. Following [32, 53], we use Conceptual Captions 12M [7] for training, which is originally constructed with over 12M image-text pairs collected from the Internet. However, due to some links have been expired, we have downloaded about 10M image-text pairs. The constructed entity set in Sec. 3.1.2 includes a total number of 100 frequently appeared entities while abstract nouns (e.g., art, view) are discarded. After filtering CC12M, we obtain 4.3 million image-text pairs for pre-training, which is termed as CC4M. Examples of entities include people, car, cup, chair, T-shirt, house, bed, cat, ball, pizza, etc. Please refer to the supplementary material for the full entity set.", + "bbox": [ + 496, + 656, + 890, + 837 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Downstream Evaluation Datasets. We evaluate our model on four benchmarks, namely, PASCAL VOC 2012 [18], PASCAL Context [38], COCO Object [31], and ADE20K [59] with 20, 59, 80 and 150 foreground classes,", + "bbox": [ + 496, + 840, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "2939", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/358f97ff57551b5411e630c893bca4c67569405a20898bef4b5647a21bde3e02.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodBackbonePretrain datasetSupervisionZero-shot transferDownstream datasets
PASCAL VOCPASCAL ContextCOCO ObjectADE20K
DeiT [46]ViT-SIN-1Kclass labelX53.035.9--
MoCo [10]ViT-SIN-1KselfX34.321.3--
DINO [6]ViT-SIN-1KselfX39.120.4--
MoCo [10]ViT-SCC12M+YFCC15MselfX36.123.0--
DINO [6]ViT-SCC12M+YFCC15MselfX37.622.8--
ViL-Seg [32]ViT-BCC12Mself+text✓33.615.9--
GroupViT [53]ViT-SCC12Mtext✓41.1---
GroupViT [53]ViT-SCC12M+YFCC15Mtext✓51.222.320.9-
ViewCo [43]ViT-SCC12M+YFCC15Mself+text✓52.423.023.5-
CLIPpy [42]ViT-BHQITP-134Mtext✓52.2-25.5†13.5
GroupViT* [53]ViT-SCC4Mtext✓19.88.89.13.4
GroupViT* [53]ViT-BCC4Mtext✓25.811.310.73.6
GroupViT* [53]ViT-SCC12Mtext✓40.218.717.76.2
OVSegmentor (ours)ViT-SCC4Mtext✓44.518.319.04.3
OVSegmentor (ours)ViT-BCC4Mtext✓53.820.425.15.6
", + "bbox": [ + 80, + 88, + 893, + 339 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Comparison with existing methods. Models in the first five rows are finetuned on target datasets while the rest perform zero-shot transfer. GroupViT (with *) refers to the results by training officially released code. Results of DeiT, MoCo, and DINO are copied from [53]. Bold fonts refer to the best results among the models that enable zero-shot transfer. CLIPpy uses 134M in-house data while we use an order of magnitude less data. † indicates evaluation on 133 COCO classes claimed by CLIPpy, which differs from ours (80 classes).", + "bbox": [ + 75, + 345, + 893, + 402 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "respectively. An extra background class is considered in VOC, Context, and COCO. We ignore their training sets and directly evaluate our method on the validation sets without any finetuning, including 1449, 5105, 5000, and 2000 images, respectively. In general, we report the mean Intersection-over-Union (mIoU) on all the classes.", + "bbox": [ + 75, + 412, + 470, + 503 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation Details. In our model, the self-attention layers in the visual encoder are initialised with DINO [6] pre-trained on ImageNet. The text encoder is initialized with BERT [16] model pre-trained on BookCorpus and English Wikipedia. Our decoder with one randomly initialized Transformer Decoder layer performs reasonably well. The input image is randomly cropped to $224\\times 224$ at training time, and the batch size is set to 2048 with an initial learning rate $3.2\\times 10^{-4}$ . We train our model for 40 epochs using the Adam optimizer with weight decay set to 0.5. The coefficient for updating the momentum model is 0.99. As the generated masks are unreliable in early epochs, we set the mask consistency coefficient $\\lambda = 0$ for the first 30 epochs and $\\lambda = 0.1$ for the remaining epochs. The group selection ratio $r$ is 0.5. As for the threshold in mask consistency loss, we use $\\delta = 0.65$ . At inference time, the image is resized with a shorter length of 448. We follow [53] to set a threshold for the background class, which is 0.9, 0.35, 0.9 and 0.95 on VOC, Context, COCO, and ADE20K, respectively.", + "bbox": [ + 75, + 508, + 472, + 797 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Comparison with Existing Methods", + "text_level": 1, + "bbox": [ + 76, + 813, + 387, + 830 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In Table 1, we compare our model with the existing models that have been trained with (1) fully-supervised finetuning transfer and (2) zero-shot transfer. In Table 2, zero-shot segmentation (ZSS) approaches are listed for comparison.", + "bbox": [ + 75, + 839, + 470, + 902 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/843c99e8c48689860c4522a44c8a88f978c35e1c91410e25ec4da541a5961a28.jpg", + "image_caption": [ + "Figure 3. Qualitative results on PASCAL VOC. We show the learned groups in the last column with each color representing a group. In the last row, the model fails to segment the person's leg." + ], + "image_footnote": [], + "bbox": [ + 498, + 410, + 890, + 542 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparison with Finetuning Transfer. We compare our method with DeiT [46], MoCo [10] and DINO [6], which are pre-trained on ImageNet [15] or CC12M+YFCC15M datasets with class labels [46] or self-supervision [6, 10], and finetuned on the training set from downstream benchmarks, with a randomly initialised convolution head appended on the backbone network. As shown in Table 1, our model achieves competitive performance on PASCAL Context, and outperforms the self-supervised methods by over $10\\%$ on PASCAL VOC, with zero-shot transfer.", + "bbox": [ + 496, + 607, + 893, + 758 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparison with Zero-shot Transfer. Here, we compare with existing works under the zero-shot transfer scenario, including GroupViT [53], ViL-Seg [32] and CLIPpy [42], with the pre-training data ranging from CC12M [7] to 134M in-house dataset HQITP-134M [42]. For fair comparison, we re-train GroupViT [53] with their official codebase on our CC4M and CC12M datasets, with the same pre-trained weights as ours being adopted on CC4M. However, we observe no further performance gain of either applying pre", + "bbox": [ + 496, + 763, + 893, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "2940", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/9176d1ca1a1d7af72b52c19210f667f0d8e75a45886358b6649259b13bed2b18.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodPretrain datasetSeenUnseenVOCContext
SPNet [51]-✓✗15.64.0
ZS3 [5]-✓✓17.77.7
GaGNet [22]-✓✓29.915.0
SIGN [14]-✓✓28.914.9
Joint [2]-✓✗32.5-
ZegFormer [17]CLIP400M✓✗63.6
MaskCLIP+ [60]CLIP400M✓✗86.166.7†
ViL-Seg [32]CC12M✗✗37.318.9
GroupViT [53]CC12M+YFCC15M✗✗43.751.3
GroupViT* [53]CC4M✗✗22.424.5
GroupViT* [53]CC12M✗✗33.145.3
OVSegmentorCC4M✗✗46.654.5
", + "bbox": [ + 81, + 88, + 467, + 272 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "trained weights to GroupViT or using ViT-B as the backbone on CC12M. The results on CC12M match the reported ones (40.2 vs 41.1). Under the same pre-trained ViT-B backbone and CC4M dataset, our method surpasses the original GroupViT by $35.7\\%$ . Additionally, by only using 4M pre-training data, our model yields the best segmentation performance on PASCAL VOC, even outperforming CLIPpy, which is a concurrent work to ours, and pre-trained on 134M data, indicating the effectiveness and training efficiency of our proposed model.", + "bbox": [ + 75, + 361, + 468, + 513 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Comparison with Zero-shot Segmentation Methods. In this line of research [17, 22, 51], the idea is to train the model with full mask labels (obtained either from manual groundtruth or pseudo-labelling [60]) on the seen classes and transfer the model to unseen classes, and the task is thus dubbed as zero-shot semantic segmentation (ZSS). To be specific, 5 classes (potted plant, sheep, sofa, train and tv-monitor) in PASCAL VOC and 4 classes (cow, motorbike, sofa and cat) in PASCAL Context are considered unseen while the remaining classes belong to seen. For comparison, we also report the zero-shot transfer performance of our model on these unseen classes, however, note that, we do not use any manual mask annotations for training. As observed in Table 2, our model surpasses majority of the models trained under the ZSS scenario, except for [17, 60] that adopted CLIP model pre-trained on 400M data. In terms of transfer efficiency, our model excels at zero-shot transfer ability without the need of training on seen classes.", + "bbox": [ + 75, + 515, + 470, + 787 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation Study", + "text_level": 1, + "bbox": [ + 76, + 797, + 230, + 814 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this section, we conduct thorough ablation studies to validate the necessity of each proposed component.", + "bbox": [ + 76, + 821, + 467, + 852 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablation Study on Proxy Tasks. Here, we aim to understand the effects of our proposed proxy tasks, i.e., masked entity completion and cross-image mask consistency. As", + "bbox": [ + 75, + 854, + 467, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/5ec6e112727b101d02a658d31f1047e28490196620287080f2b9d302d9961eff.jpg", + "table_caption": [ + "Table 2. Comparison with zero-shot segmentation methods on unseen classes. Seen/Unseen denotes whether the model is trained on seen/unseen classes. Our method outperforms most zero-shot segmentation models even without training on seen classes. $\\dagger$ indicates a different set of unseen classes." + ], + "table_footnote": [], + "table_body": "
Baseline\\( {\\mathcal{L}}_{\\text{entity }} \\)\\( {\\mathcal{L}}_{\\text{mask }} \\)PASCAL VOCPASCAL Context
✓40.515.1
✓✓48.919.9
✓✓44.817.0
✓✓✓53.820.4
", + "bbox": [ + 504, + 88, + 887, + 172 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/f424e2c09d07b52c4cf48681ba2aad432493731fe787543b45bd219ecfc16088.jpg", + "table_caption": [ + "Table 3. Ablation study on the masked entity completion loss $(\\mathcal{L}_{\\mathrm{entity}})$ and cross-image mask consistency loss $(\\mathcal{L}_{\\mathrm{mask}})$ . The baseline only uses the image-caption contrastive loss $(\\mathcal{L}_{\\mathrm{contrast}})$ ." + ], + "table_footnote": [], + "table_body": "
Masking objectivePASCAL VOCPASCAL Context
All entities (ours)48.919.9
Single entity47.018.5
All nouns45.417.4
Multi-label contrastive44.517.0
MLM (w/ groups)42.816.3
MLM (w/o groups)36.314.6
", + "bbox": [ + 511, + 232, + 875, + 335 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Comparison on masking objectives. MLM (w/ groups) refers to masked language modeling [16] given the group tokens as keys and values in the decoder while MLM (w/o groups) directly predicts masked words from the output of text encoder.", + "bbox": [ + 496, + 340, + 890, + 397 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "shown in Table 3, the baseline model uses the image-text contrastive loss $\\mathcal{L}_{\\mathrm{contrast}}$ only, while adding the entity completion task, $\\mathcal{L}_{\\mathrm{entity}}$ , we observe a significant improvement by $8.4\\%$ and $4.8\\%$ on PASCAL VOC and PASCAL Context, respectively. The performance gain is due to the ability of better aligning the pixel groups with the visual entities. Additionally, the mask consistency also brings improvements, and combing both leads to the best performance. Qualitative results can be seen in Fig. 3. We refer the readers for more visualisations in the supplementary material.", + "bbox": [ + 496, + 412, + 890, + 564 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "On the Choice of Masking Objectives. We compare our masked entity completion with a series of variants as shown in Table 4. Our proposed objective of masking all entities is listed in the first row. (1) All entities vs one entity: the masked language modeling (MLM) in prior works [16, 35] normally choose $15\\%$ of the token positions in the sentence for prediction, which results in one entity in most of our cases. We observe that masking all entities is $2.9\\%$ mIoU better than single entity masking, as it forces the network to infer all possible object categories in the image, that is potentially beneficial for the group to category alignment. (2) Entities vs nouns: masking and predicting noun phrases in the sentence [20] is one feasible option to learn fine-grained vision-text matching. However, noun masking leads to $3.5\\%$ lower mIoU than our entity masking strategy. This is because not all of the nouns in the sentence are visually corresponding to the objects in the image (e.g., illustration, night, etc), thus the group tokens are not expected to align with these nouns. Our method avoids this issue by only masking the visual entities. (3) Masked entity completion vs multi-label contrastive loss: comparing with the multi-label contrastive loss used in GroupViT [53], our proposed", + "bbox": [ + 496, + 568, + 892, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "2941", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/31486f25a583772e6931f67c7fdb0681334e02104e3ca6f7aec5ff51b431d8cb.jpg", + "image_caption": [ + "Figure 4. Qualitative results of the masked entity completion loss. We show different groups that match the given class. With the boost of group semantics, our model improves visual grouping (left) and solves false matching between group and text (right) of the baseline." + ], + "image_footnote": [], + "bbox": [ + 78, + 87, + 274, + 186 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/5614c2757de50d322408add4ef27eccd1156cf05261ac98e47a5e77ecc08974d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 279, + 88, + 475, + 186 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/02b8222b0918f2120e84f2cce607cd9fb2489b0f1223898ae8e7de60c9cae974.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
K/τ0.250.50.751.0
849.053.651.850.4
1642.343.243.142.9
", + "bbox": [ + 81, + 231, + 269, + 277 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/2672267657dfdc32aee1fe625a0e31b872aeb30b28ea337ab4514fa5e024d97b.jpg", + "table_caption": [ + "Table 5. Comparison on group numbers $K$ and selection ratios $\\tau$ on VOC." + ], + "table_footnote": [], + "table_body": "
ObjectiveLossmIoU
Mask ConsistencyDice53.8
Group ConsistencyNCE47.9
", + "bbox": [ + 276, + 232, + 464, + 277 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "strategy shows superior performance. (4) Masked entity completion vs masked language modeling: MLM [16] originally predicts the masked token over the entire vocabulary via a cross-entropy loss. Here, we restrict the vocabulary to our constructed entity set for fair comparison. Our masking strategy surpasses both MLM variants by a clear margin, which we conjecture is because: (1) MLM classifies each masked token individually, and the model can easily refer to the context words without relating to groups. (2) MLM focuses on word-level representations, which is not in accordance with the sentence-level representation of the class embeddings we used during inference. Fig. 4 shows the effect of the masked entity completion in improving visual grouping (left) and group-text alignment (right).", + "bbox": [ + 75, + 335, + 467, + 546 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "On Cross-image Mask Consistency. Here, we analyze two key factors in cross-image consistency: (1) group numbers and selection ratios. As shown in Table 5, 8 groups perform comparably well for all selection ratios, and we pick 0.5 as the default. Smaller ratios miss the entities encoded in remaining groups while larger ratios introduce entity-irrelevant information (e.g., background). However, while increasing the group number to 16, it brings over-segmentation for large objects, deteriorating the performance. (2) the objective for cross-image consistency. We study another variant of cross-image consistency by directly aligning two sets of group tokens encoded with shared entity. We adopt the contrastive loss (NCE in Table 6) to pull two sets of group tokens closer, while other group tokens in each mini-batch are pushed farther. Table 6 reveals the superiority of our proposed mask consistency over group consistency, which we believe is because mask consistency involves the image content to realize visual invariance.", + "bbox": [ + 75, + 550, + 467, + 821 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Performance on Unseen Entities. We measure the mean IoU for objects within entity set (e.g., person, bus) and those out of entity set on each dataset. As shown in Table 7, our model still achieves a decent improvement over the baseline on unseen entities, indicating it retains strong", + "bbox": [ + 75, + 825, + 467, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/241e8d11af046f243799c2d39e6411b4a5250bd20cbf42d3626d59c157f1c889.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 491, + 88, + 691, + 186 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d818cf0fec8993a2a20701e9fe47d78833da0cda482e8c1310eb32fbfcbd0276.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 694, + 88, + 890, + 185 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/a2cd5875c5c7f60159d77292ee1b62a3da5742989fae10b83ebe655b38bf2493.jpg", + "table_caption": [ + "Table 6. Comparison on different objectives for cross-image consistency on VOC." + ], + "table_footnote": [], + "table_body": "
MethodVOCCOCOContextADE20K
S20U0S65U16S30U29S31U119
Baseline38.5-17.215.420.58.87.13.7
Ours(CC4M)52.4-24.825.927.910.98.64.8
Ours(CC12M)44.8-18.621.525.412.010.16.3
", + "bbox": [ + 501, + 231, + 888, + 301 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 7. Performance on seen(S)/unseen(U) entities, where indices refer to entity numbers. Background class is not considered.", + "bbox": [ + 500, + 304, + 890, + 330 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "open-vocabulary segmentation ability. The text encoder pre-trained on large text corpus remains its ability to encode the semantic concept of objects out of entity set.", + "bbox": [ + 496, + 345, + 890, + 392 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Scalability. We scale up the datasets by increasing the number of entities, i.e., training the model on the entire CC12M. Ours (CC12M) outperforms ours (CC4M) on the challenging ADE20K and PASCAL Context, reflecting the effect of the scalability of our model. The performance drop on VOC is because the extremely large entity set in CC12M poses extra challenge for our model to learn the entities in VOC.", + "bbox": [ + 496, + 393, + 890, + 500 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 505, + 617, + 520 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we present OVSegmentor, a transformer-based model for open-vocabulary semantic segmentation. The model exploits web-collected image-caption pairs for pre-training without any mask annotations, and transfers to target benchmark segmentation datasets in a zero-shot manner. The model clusters the image pixels into learnable group tokens, which are then aligned with the corresponding caption embeddings. We further devise two proxy tasks, namely masked entity completion and cross-image mask consistency, to learn entity-specific, fine-grained and visually invariant group semantics. OVSegmentor outperforms the state-of-the-art method on PASCAL VOC by using only $3\\%$ (4M vs 134M) for pre-training, indicating the effectiveness and training efficiency of our model.", + "bbox": [ + 496, + 530, + 890, + 742 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement. This work is supported by Natural Science Foundation of China (No. 61976057; No. 62172101), the Science and Technology Commission of Shanghai Municipality (No. 21511101000; No. 22511106003), the Science and Technology Major Project of Commission of Science and Technology of Shanghai (No. 2021SHZDZX0103). This work is also partially supported by the National Key R&D Program of China (No. 2022ZD0160100), and in part by Shanghai Committee of Science and Technology (No. 21DZ1100100). This work was done during Jilan's parttime internship at Shanghai Artificial Intelligence Laboratory.", + "bbox": [ + 496, + 747, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "2942", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Jiwoon Ahn and Suha Kwak. Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation. In CVPR, 2018. 2", + "[2] Donghyeon Baek, Youngmin Oh, and Bumsub Ham. Exploiting a joint embedding space for generalized zero-shot semantic segmentation. In ICCV, 2021. 7", + "[3] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, 2021. 2", + "[4] Steven Bird, Ewan Klein, and Edward Loper. Natural language processing with Python: analyzing text with the natural language toolkit. \"O'Reilly Media, Inc.\", 2009. 4", + "[5] Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, and Patrick Pérez. Zero-shot semantic segmentation. NeurIPS, 2019. 7", + "[6] Mathilde Caron, Hugo Touvron, Ishan Misra, Herve Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In ICCV, 2021. 6", + "[7] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In CVPR, 2021. 2, 5, 6", + "[8] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. PAMI, 2017. 2", + "[9] Qi Chen, Lingxiao Yang, Jian-Huang Lai, and Xiaohua Xie. Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation. In CVPR, 2022. 2", + "[10] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In ICCV, 2021. 6", + "[11] Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang. Semi-supervised semantic segmentation with cross pseudo supervision. In CVPR, 2021. 2", + "[12] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, 2020. 2", + "[13] Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. NeurIPS, 2021. 2", + "[14] Jiaxin Cheng, Soumyaroop Nandi, Prem Natarajan, and Wael Abd-Almageed. Sign: Spatial-information incorporated generative network for generalized zero-shot semantic segmentation. In ICCV, 2021. 7", + "[15] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 6", + "[16] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In NAACL, 2019. 4, 6, 7, 8" + ], + "bbox": [ + 78, + 116, + 470, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[17] Jian Ding, Nan Xue, Gui-Song Xia, and Dengxin Dai. Decoupling zero-shot semantic segmentation. In CVPR, 2022, 2, 7", + "[18] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. IJCV, 2010. 2, 5", + "[19] Geoffrey French, Samuli Laine, Timo Aila, Michal Mackiewicz, and Graham D. Finlayson. Semi-supervised semantic segmentation needs strong, varied perturbations. In BMVC, 2020. 2", + "[20] Yuying Ge, Yixiao Ge, Xihui Liu, Dian Li, Ying Shan, Xiaohu Qie, and Ping Luo. Bridging video-text retrieval with multiple choice questions. In CVPR, 2022. 2, 7", + "[21] Golnaz Ghiasi, Xiuye Gu, Yin Cui, and Tsung-Yi Lin. Scaling open-vocabulary image segmentation with image-level labels. In ECCV, 2022. 2", + "[22] Zhangxuan Gu, Siyuan Zhou, Li Niu, Zihan Zhao, and Liqing Zhang. Context-aware feature generation for zero-shot semantic segmentation. In ACM MM, 2020. 2, 7", + "[23] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICML, 2021. 1", + "[24] Eunji Kim, Siwon Kim, Jungbeom Lee, Hyunwoo Kim, and Sungroh Yoon. Bridging the gap between classification and localization for weakly supervised object localization. In CVPR, 2022. 2", + "[25] Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83-97, 1955. 5", + "[26] Boyi Li, Kilian Q. Weinberger, Serge J. Belongie, Vladlen Koltun, and René Ranftl. Language-driven semantic segmentation. In ICLR, 2022. 2", + "[27] Dongxu Li, Junnan Li, Hongdong Li, Juan Carlos Niebles, and Steven CH Hoi. Align and prompt: Video-and-language pre-training with entity prompts. In CVPR, 2022. 2", + "[28] Juncheng Li, Xin He, Longhui Wei, Long Qian, Linchao Zhu, Lingxi Xie, Yueting Zhuang, Qi Tian, and Siliang Tang. Fine-grained semantically aligned vision-language pre-training. NeurIPS, 2022. 2", + "[29] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. NeurIPS, 2021. 2", + "[30] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In CVPR, 2022. 2", + "[31] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV. Springer, 2014. 2, 5", + "[32] Quande Liu, Youpeng Wen, Jianhua Han, Chunjing Xu, Hang Xu, and Xiaodan Liang. Open-world semantic segmentation via contrasting and clustering vision-language embedding. In ECCV, 2022. 2, 5, 6, 7" + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "2943", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[33] Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. Object-centric learning with slot attention. NeurIPS, 2020. 2, 4", + "[34] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015. 2", + "[35] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS, 2019. 7", + "[36] Wenfeng Luo and Meng Yang. Semi-supervised semantic segmentation via strong-weak dual-branch network. In ECCV, 2020. 2", + "[37] Chaofan Ma, Yuhuan Yang, Yanfeng Wang, Ya Zhang, and Weidi Xie. Open-vocabulary semantic segmentation with frozen vision-language models. BMVC, 2022. 2", + "[38] Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic segmentation in the wild. In CVPR, 2014. 2, 5", + "[39] Yassine Ouali, Céline Hudelot, and Myriam Tami. Semi-supervised semantic segmentation with cross-consistency training. In CVPR, 2020. 2", + "[40] Giuseppe Pastore, Fabio Cermelli, Yongqin Xian, Massimiliano Mancini, Zeynep Akata, and Barbara Caputo. A closer look at self-training for zero-label semantic segmentation. In CVPR, 2021. 2", + "[41] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 1, 2, 4", + "[42] Kanchana Ranasinghe, Brandon McKinzie, Sachin Ravi, Yinfei Yang, Alexander Toshev, and Jonathon Shlens. Perceptual grouping in vision-language models. arXiv preprint arXiv:2210.09996, 2022. 2, 6", + "[43] Pengzhen Ren, Changlin Li, Hang Xu, Yi Zhu, Guangrun Wang, Jianzhuang Liu, Xiaojun Chang, and Xiaodan Liang. Viewco: Discovering text-supervised segmentation masks via multi-view semantic consistency. arXiv preprint arXiv:2302.10307, 2023. 6", + "[44] Gyungin Shin, Weidi Xie, and Samuel Albanie. Named-mask: Distilling segmenters from complementary foundation models. arXiv preprint arXiv:2209.11228, 2022. 2", + "[45] Gyungin Shin, Weidi Xie, and Samuel Albanie. Reco: Retrieve and co-segment for zero-shot transfer. NeurIPS, 2022. 2", + "[46] Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through attention. In ICML, 2021. 6", + "[47] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. NeurIPS, 2017. 4", + "[48] Huiyu Wang, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen. Max-deeplab: End-to-end panoptic segmentation with mask transformers. In CVPR, 2021. 2" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[49] Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, and Xinyi Le. Semi-supervised semantic segmentation using unreliable pseudo-labels. In CVPR, 2022. 2", + "[50] Pingyu Wu, Wei Zhai, and Yang Cao. Background activation suppression for weakly supervised object localization. In CVPR, 2022. 2", + "[51] Yongqin Xian, Subhabrata Choudhury, Yang He, Bernt Schiele, and Zeynep Akata. Semantic projection network for zero-and few-label semantic segmentation. In CVPR, 2019. 2, 7", + "[52] Jinheng Xie, Jianfeng Xiang, Junliang Chen, Xianxu Hou, Xiaodong Zhao, and Linlin Shen. C2am: Contrastive learning of class-agnostic activation map for weakly supervised object localization and semantic segmentation. In CVPR, 2022. 2", + "[53] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In CVPR, 2022. 1, 2, 5, 6, 7", + "[54] Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng, Rui-Wei Zhao, Tao Zhang, Xuequan Lu, and Shang Gao. Cream: Weakly supervised object localization via class re-activation mapping. In CVPR, 2022. 2", + "[55] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. FILIP: fine-grained interactive language-image pre-training. In ICLR, 2022. 2", + "[56] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. In ICML, 2022. 2", + "[57] Haotian Zhang, Pengchuan Zhang, Xiaowei Hu, Yen-Chun Chen, Liunian Harold Li, Xiyang Dai, Lijuan Wang, Lu Yuan, Jenq-Neng Hwang, and Jianfeng Gao. Glipv2: Unifying localization and vision-language understanding. NeurIPS, 2022. 2", + "[58] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In CVPR, 2016. 2", + "[59] Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127:302-321, 2019. 2, 5", + "[60] Chong Zhou, Chen Change Loy, and Bo Dai. Denseclip: Extract free dense labels from clip. ECCV, 2022. 2, 7" + ], + "bbox": [ + 501, + 92, + 890, + 729 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "2944", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision/c64e9aaf-56b9-414a-803b-d9922f77f018_model.json b/2023/Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision/c64e9aaf-56b9-414a-803b-d9922f77f018_model.json new file mode 100644 index 0000000000000000000000000000000000000000..d2ecb2e6c17567690195ed8387eb350f55519bd0 --- /dev/null +++ b/2023/Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision/c64e9aaf-56b9-414a-803b-d9922f77f018_model.json @@ -0,0 +1,2178 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.188, + 0.131, + 0.784, + 0.177 + ], + "angle": 0, + "content": "Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision" + }, + { + "type": "text", + "bbox": [ + 0.142, + 0.203, + 0.829, + 0.222 + ], + "angle": 0, + "content": "Jilan Xu\\(^{1,2}\\) Junlin Hou\\(^{1}\\) Yuejie Zhang\\(^{1*}\\) Rui Feng\\(^{1*}\\) Yi Wang\\(^{2}\\) Yu Qiao\\(^{2}\\) Weidi Xie\\(^{2,3}\\)" + }, + { + "type": "text", + "bbox": [ + 0.12, + 0.222, + 0.851, + 0.277 + ], + "angle": 0, + "content": "\\(^{1}\\)School of Computer Science, Shanghai Key Lab of Intelligent Information Processing, Shanghai Collaborative Innovation Center of Intelligent Visual Computing, Fudan University \\(^{2}\\)Shanghai AI Laboratory \\(^{3}\\)CMIC, Shanghai Jiao Tong University" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.309, + 0.314, + 0.326 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.342, + 0.473, + 0.735 + ], + "angle": 0, + "content": "This paper considers the problem of open-vocabulary semantic segmentation (OVS), that aims to segment objects of arbitrary classes beyond a pre-defined, closed-set categories. The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor, which only exploits web-crawled image-text pairs for pre-training without using any mask annotations. OVSegmentor assembles the image pixels into a set of learnable group tokens via a slot-attention based binding module, then aligns the group tokens to corresponding caption embeddings. Second, we propose two proxy tasks for training, namely masked entity completion and cross-image mask consistency. The former aims to infer all masked entities in the caption given group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities. The latter enforces consistent mask predictions between images that contain shared entities, encouraging the model to learn visual invariance. Third, we construct CC4M dataset for pre-training by filtering CC12M with frequently appeared entities, which significantly improves training efficiency. Fourth, we perform zero-shot transfer on four benchmark datasets, PASCAL VOC, PASCAL Context, COCO Object, and ADE20K. OVSegmentor achieves superior results over state-of-the-art approaches on PASCAL VOC using only \\(3\\%\\) data (4M vs 134M) for pre-training." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.746, + 0.21, + 0.761 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.771, + 0.47, + 0.879 + ], + "angle": 0, + "content": "Semantic segmentation considers the problem of assigning class labels to each pixel in the image. It plays critical roles in a wide range of real-world scenarios, including autonomous driving, computer-aided diagnosis and satellite image analysis, to name a few. Generally speaking, two lines of research dominate semantic segmentation, one way is to cluster the pixels into different groups and assign a" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.307, + 0.895, + 0.478 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.481, + 0.894, + 0.552 + ], + "angle": 0, + "content": "Figure 1. An illustration of open-vocabulary semantic segmentation. Models trained on closed-set classes (cat and dog) fail to segment novel class (fire hydrant). We train a visual encoder and a text encoder on web-collected image-text pairs without using any mask labels, and our model can segment arbitrary object classes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.566, + 0.892, + 0.717 + ], + "angle": 0, + "content": "semantic label to each group; the other idea treats segmentation as pixel-wise classification, casting each pixel into one category. Despite tremendous progress, the scalability of existing approaches that rely on supervised training has been fundamentally limited: (1) costly annotation procedure. Extensive manual pixel-wise annotations are required for training segmentation models; (2) closed-set segmentation. The model is restricted to segmenting objects from a closed-set of categories. Whenever a new dataset comes, the model requires re-training." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.895, + 0.903 + ], + "angle": 0, + "content": "In this paper, our goal is to train an open-vocabulary semantic segmentation (OVS) model, by exploiting freely available image-caption pairs on Internet, as illustrated in Fig. 1. The recent work, for example, CLIP [41] and ALIGN [23] have demonstrated that a combination of large-scale image-caption pairs and simple noise contrastive estimation can learn powerful image-text embeddings from scratch, and show strong \"zero-shot\" generalization abilities for open-vocabulary classification. Additionally, GroupViT [53] extends the idea towards semantic segmentation by training a segmentation model with text supervision only. They perform hierarchical grouping of" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.888, + 0.221, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2935" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.258 + ], + "angle": 0, + "content": "visual tokens, which are then aligned to the corresponding text embeddings via a contrastive loss. However, the following issues remain challenging and unsolved: First, the captions only provide coarse, image-level descriptions, which are insufficient for training semantic segmentation models where fine-grained, pixel-wise supervision is usually needed. Second, the diversity of web-collected data is large, that requires the model to learn visual invariance on objects of interest, with only weak supervision provided. For instance, the visual appearance of two images with similar captions can be drastically different." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.258, + 0.473, + 0.605 + ], + "angle": 0, + "content": "To tackle the above challenges, (i) we propose a transformer-based model for open-vocabulary semantic segmentation, dubbed as OVSegmentor, that can segment objects of arbitrary categories via zero-shot transfer, with only image-caption pairs for pre-training. Specifically, we introduce learnable group tokens to cluster image patches via a slot-attention [33] based binding module, and align the group tokens with corresponding caption embeddings. Note that our model neither requires ground-truth masks for training nor additional re-training on target segmentation datasets, substantially alleviating the annotation efforts and improving transfer efficiency; (ii) As for training on the image-caption dataset, we propose two proxy tasks, namely masked entity completion and cross-image mask consistency, the former trains the model to infer all the masked entities in the sentence given the group tokens, and the latter enforces consistent mask prediction for images with the common entity. Both tasks have shown to be beneficial in learning entity-specific, fine-grained and visually invariant group semantics; (iii) We construct an image-caption dataset, termed as CC4M, by designing an automatic approach to filter CC12M [7] with frequently appeared visual entities, significantly improving the training efficiency." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.606, + 0.473, + 0.788 + ], + "angle": 0, + "content": "We pre-train the proposed OVSegmentor on our filtered image-caption dataset (CC4M), without using any manual segmentation masks whatsoever. The model is evaluated on four segmentation benchmarks, PASCAL VOC 2012 [18], PASCAL Context [38], COCO Object [31], and ADE20K [59] in a zero-shot manner, i.e., the model is directly evaluated on target datasets without any finetuning. Extensive experiments demonstrate that our model surpasses existing models that are trained with full supervision and outperforms state-of-the-art self-supervised approaches on PASCAL VOC by using only \\(3\\%\\) data (4M vs 134M) for pre-training, significantly improving the training efficiency." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.798, + 0.22, + 0.813 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.472, + 0.903 + ], + "angle": 0, + "content": "Vision-Language Pre-training. Vision-language pretraining (VLP) aims to learn joint visual-textual representations for a variety of multimodal downstream tasks. Existing works either learn unimodal encoders by distinguishing the positive pair(s) from the unpaired samples [3,28,41]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.273 + ], + "angle": 0, + "content": "or focus on one multimodal encoder for joint feature learning with masked image/language modeling and image-text matching losses [12,27,29]. Additionally, some approaches seek fine-grained supervision for cross-modal interaction [20, 30, 55-57]. For example, GLIP [30] proposed to align the bounding boxes with corresponding phrases in the text. However, they still rely on ground-truth grounding annotations. In contrast, our work explores fine-grained information with only weak supervision provided. Despite remarkable performance on multimodal downstream tasks, few of these vision-language models have been designed for fundamental vision tasks (e.g., semantic segmentation)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.277, + 0.895, + 0.626 + ], + "angle": 0, + "content": "Zero-shot/Open-vocabulary Semantic Segmentation. The goal of zero-shot semantic segmentation is to segment objects of interest that are not seen in the training set. Prior works [22, 40, 51] mainly transfer the knowledge from the training set (seen) to the testing set (unseen) via visual-semantic mapping. Inspired by the open-vocabulary nature of language, current approaches [17, 21, 26, 37, 44, 45, 60] exploit vision-language models (e.g., CLIP [41]) pretrained on large-scale image-caption pairs. They need either finetuning [17, 21, 26] or self-training [60] on the target segmentation dataset, which is less efficient and flexible than zero-shot transfer. VLP for open-vocabulary segmentation combines the merits of both zero-shot transfer and open-vocabulary recognition. GroupViT [53] designed a grouping vision transformer and learned the alignment between groups and text via the contrastive loss. ViL-Seg [32] combined image-text contrastive loss with online pixel clustering for segmentation. A concurrent work CLIPpy [42] explored different aggregation operations for training spatial-aware vision-language models for segmentation. Beyond the global image-text matching, we further design masked entity completion and cross-image mask consistency to enrich the group semantics." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.629, + 0.897, + 0.902 + ], + "angle": 0, + "content": "Fully-/Weakly-/Semi-Supervised Semantic Segmentation. Fully-supervised semantic segmentation emerges from per-pixel classification [8, 34] to mask classification [13, 48]. To relieve the laborious annotations, extensive efforts have been made to address semantic segmentation with less supervision. In the family of weakly-supervised object localization [24,50,54] and semantic segmentation [1,9,52], only class labels are available for supervision. Generally, the class activation maps [58] derived from the classification network serve as the initial segmentation results. Another line of research focuses on semi-supervised semantic segmentation [11, 19, 36, 39, 49] where a few samples have dense per-pixel labels and the remaining samples are unlabeled. These works mainly perform supervised training on labeled samples with additional consistency regularizations posed on unlabeled samples. Despite promising results, these approaches are still limited to closed-set object categories." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "2936" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.087, + 0.087, + 0.585, + 0.412 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.604, + 0.087, + 0.885, + 0.411 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.417, + 0.893, + 0.488 + ], + "angle": 0, + "content": "Figure 2. Overall framework. The model takes an image-text pair as input, and outputs the image features and a set of learnable group tokens, which is aligned to the caption embedding via \\(\\mathcal{L}_{\\mathrm{contrast}}\\). The visual encoder consists of Transformer encoder layers, with a slot-attention based binding module in between to cluster the image tokens into groups. To enrich group semantics, (1) \\(\\mathcal{L}_{\\mathrm{entity}}\\) trains the model to infer all masked entities in the caption given the group tokens. (2) \\(\\mathcal{L}_{\\mathrm{mask}}\\) enforces consistent mask predictions of images with the same entity (right figure). During inference, only visual and text encoders and learned group tokens are used to generate the segmentation mask." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.506, + 0.214, + 0.525 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.531, + 0.47, + 0.727 + ], + "angle": 0, + "content": "Assuming we are given a dataset of image-caption pairs, i.e., \\(\\mathcal{D}_{\\mathrm{train}} = \\{(I_1,T_1),\\dots ,(I_N,T_N)\\}\\) , where \\(I_{i}\\in\\) \\(\\mathbb{R}^{H\\times W\\times 3}\\) denotes the image, and \\(T_{i}\\) refers to its corresponding alt-text downloaded from the Internet, the goal is to train an open-vocabulary semantic segmentation (OVS) model only with these (very) weak labels, that can segment objects of arbitrary classes. For each pair \\((I,T)\\) ,we first split the image into \\(L = HW / P^2\\) non-overlapping patches with patch size \\(P\\) . These patches are then transformed into a sequence of image tokens with MLPs, we reuse \\(I\\in \\mathbb{R}^{L\\times D}\\) to represent them for ease of understanding. Additionally, we introduce \\(K\\) learnable tokens \\(G\\in \\mathbb{R}^{K\\times D}\\) , that aims to group the image tokens into clusters." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.727, + 0.471, + 0.864 + ], + "angle": 0, + "content": "We present a VLP framework, termed OVSegmentor, it assembles the image patches into groups and aligns the group to categories, by only exploiting weak supervisions from image-caption pairs. Conceptually, the architecture consists of two stages, namely, a patch-to-group binding that assigns all patches with same semantics into one group, and group-to-category alignment that computes matching scores between each of these group tokens with semantic categories, the segmentation mask \\( m \\) can be computed as:" + }, + { + "type": "equation", + "bbox": [ + 0.185, + 0.873, + 0.47, + 0.898 + ], + "angle": 0, + "content": "\\[\nm = \\underset {C} {\\arg \\max } \\mathbb {A} \\cdot \\mathcal {G} \\cdot \\mathcal {T} ^ {\\top}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.507, + 0.893, + 0.628 + ], + "angle": 0, + "content": "where \\(\\mathbb{A} \\in \\mathbb{R}^{L \\times K}\\) characterises a soft binding procedure, denoting the likelihood of each image patch being assigned into the output group embeddings \\((\\mathcal{G} \\in \\mathbb{R}^{K \\times D})\\); and \\(\\mathcal{T} \\in \\mathbb{R}^{C \\times D}\\) denotes the embeddings with dimension \\(D\\) for a total of \\(C\\) categories, computed from a text encoder. Note that, at training time, manual segmentation masks are not present, and the intermediate binding and assignment can only be learnt from the image-caption pairs implicitly." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.629, + 0.895, + 0.78 + ], + "angle": 0, + "content": "At inference stage, the patch-group affinity \\(\\mathbb{A}\\) can be computed by the product between the image features \\(\\mathcal{I} \\in \\mathbb{R}^{L \\times D}\\) and the learned group tokens \\(\\mathcal{G}\\), and \\(\\mathcal{T}\\) can be derived by encoding the prompted class labels (e.g., A photo of {class}). Thus the patch-level mask prediction can be calculated via Eq. 1. The pixel-level segmentation mask can then be obtained by up-sampling the patch-level mask \\((m)\\). In the following sections, we detail the overall architecture and training procedure of each component. The overall framework is illustrated in Fig. 2." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.791, + 0.636, + 0.807 + ], + "angle": 0, + "content": "3.1. Architecture" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.815, + 0.893, + 0.907 + ], + "angle": 0, + "content": "In this section, we will detail the proposed architecture at training stage, including a visual encoder for learning group tokens and the binding procedure, a text encoder that computes variants of caption embeddings. We describe the encoding procedure for image and caption pair, the subscripts are thus ignored for simplicity." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2937" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.078, + 0.092, + 0.24, + 0.106 + ], + "angle": 0, + "content": "3.1.1 Visual Encoder" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.115, + 0.47, + 0.206 + ], + "angle": 0, + "content": "The visual encoder consists of two components, namely, Transformer encoders and binding modules. Specifically, the image tokens and learnable group tokens are concatenated, and iteratively processed by the Transformer encoders, and a binding module with slot-attention [33] is adopted for grouping. The visual encoder is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.137, + 0.217, + 0.469, + 0.236 + ], + "angle": 0, + "content": "\\[\n[ \\mathcal {G}; \\mathcal {I} ] = \\Phi_ {\\mathrm {e n c}} ^ {2} (\\cdot) \\circ \\Phi_ {\\mathrm {b i n d}} (\\cdot) \\circ \\Phi_ {\\mathrm {e n c}} ^ {1} ([ G; I ]), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.246, + 0.469, + 0.307 + ], + "angle": 0, + "content": "where \\(\\Phi_{\\mathrm{enc}}^{1}(\\cdot)\\) and \\(\\Phi_{\\mathrm{enc}}^{2}(\\cdot)\\) refer to modules with Transformer encoder layers, and \\(\\Phi_{\\mathrm{bind}}(\\cdot)\\) is the binding module. The output \\(\\mathcal{G}\\in \\mathbb{R}^{K\\times D}\\) is the encoded group tokens, and \\(\\mathcal{I}\\in \\mathbb{R}^{L\\times D}\\) refers to the output image tokens." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.311, + 0.469, + 0.434 + ], + "angle": 0, + "content": "Transformer Encoder. Both \\(\\Phi_{\\mathrm{enc}}^{1}\\) and \\(\\Phi_{\\mathrm{enc}}^{2}\\) consist of 6 Transformer encoder layers [47], where each layer is composed of a multi-head self-attention (MHSA) layer followed by layer normalisation (LN) and a feed-forward network (FFN). \\(\\Phi_{\\mathrm{enc}}^{1}\\) takes the concatenation of image patches and randomly initialised group tokens as input, and outputs intermediately encoded group and image tokens (\\(G'\\) and \\(I'\\)); \\(\\Phi_{\\mathrm{enc}}^{2}\\) processes the output from the binding module." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.436, + 0.469, + 0.541 + ], + "angle": 0, + "content": "Binding Module. The binding module uses slot-attention to cluster image tokens into groups in a data-dependent manner, i.e., image patches with similar appearance and semantics are encouraged to be grouped together. Formally, the binding module accepts the output from the Transformer encoder \\(\\Phi_{\\mathrm{enc}}^{1}\\), and transforms them into query, key and values with linear transformations:" + }, + { + "type": "equation", + "bbox": [ + 0.127, + 0.553, + 0.469, + 0.573 + ], + "angle": 0, + "content": "\\[\n\\mathbf {Q} = W _ {q} ^ {\\text {b i n d}} G ^ {\\prime}, \\quad \\mathbf {K}, \\mathbf {V} = W _ {k} ^ {\\text {b i n d}} I ^ {\\prime}, W _ {v} ^ {\\text {b i n d}} I ^ {\\prime}. \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.583, + 0.47, + 0.644 + ], + "angle": 0, + "content": "In contrast to the standard cross attention in Transformer Decoders [47], slot-attention performs normalisation over queries, encouraging each image token to be claimed by one of the group tokens. The binding process can be defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.185, + 0.655, + 0.469, + 0.688 + ], + "angle": 0, + "content": "\\[\n\\mathbb {A} _ {j, k} = \\frac {\\exp \\left(\\mathbf {K} _ {j} \\cdot \\mathbf {Q} _ {k}\\right)}{\\sum_ {l} \\exp \\left(\\mathbf {K} _ {j} \\cdot \\mathbf {Q} _ {l}\\right)}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.698, + 0.47, + 0.774 + ], + "angle": 0, + "content": "where \\(\\mathbb{A}_{j,k}\\) refers to the probability of the \\(j\\)-th image token being assigned to the \\(k\\)-th group. Then, each updated group token \\(\\tilde{G}_k'\\) is computed by taking the weighted average of the image tokens that are assigned to it. The output of the binding module \\(G^{\\mathrm{bind}}\\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.206, + 0.785, + 0.469, + 0.827 + ], + "angle": 0, + "content": "\\[\n\\tilde {G} _ {k} ^ {\\prime} = \\frac {\\sum_ {j = 1} ^ {L} \\mathbb {A} _ {j , k} \\mathbf {V} _ {j}}{\\sum_ {j = 1} ^ {L} \\mathbb {A} _ {j , k}}, \\tag {5}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.195, + 0.829, + 0.469, + 0.848 + ], + "angle": 0, + "content": "\\[\nG ^ {\\text {b i n d}} = G ^ {\\prime} + W _ {o} ^ {\\text {b i n d}} \\tilde {G} ^ {\\prime}, \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.86, + 0.469, + 0.907 + ], + "angle": 0, + "content": "where \\( W_{o}^{\\mathrm{bind}} \\) is a linear transformation. Now we have obtained the correspondence between each patch and groups, next we describe the procedure for encoding captions." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.092, + 0.649, + 0.106 + ], + "angle": 0, + "content": "3.1.2 Text Encoder" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.115, + 0.892, + 0.206 + ], + "angle": 0, + "content": "Till this end, we filter all the captions and only keep the ones with informative entities, followed by exploiting three variants of the caption encoding, namely, the entire caption embedding, the masked caption embedding and the prompted entity embedding. In all cases we use a pretrained BERT [16] as the text encoder \\(\\Phi_{\\mathrm{text}}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.209, + 0.892, + 0.346 + ], + "angle": 0, + "content": "Constructing Entity Set. We adopt the nltk toolkit [4] to extract entities from all the captions, and construct an entity set \\(\\Omega = \\Phi_{\\text{entity}}(\\{T_1, \\ldots, T_N\\})\\) that only maintains the frequently appeared entities (e.g., people, cat, shirt, etc.), and exclude the abstract nouns (e.g., art, view, etc) as they usually do not correspond to any specific region in the image. For each image-caption pair, we can thus obtain an image-caption-entity triplet \\((I, T, E)\\), where \\(E = \\{e | e \\in T \\cap e \\in \\Omega\\}\\) includes all frequent entities in the caption." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.349, + 0.892, + 0.426 + ], + "angle": 0, + "content": "Caption Embedding. Here, for each caption \\( T \\), it is tokenised using the BERT tokeniser [16], with the start-of-token [SOT] and end-of-token [EOT] added to the beginning/ending. The caption embedding is computed as \\( \\mathcal{T}^{\\mathrm{cap}} = \\Phi_{\\mathrm{text}}(T) \\in \\mathbb{R}^{M \\times D} \\), where \\( M \\) is length after tokenisation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.429, + 0.892, + 0.504 + ], + "angle": 0, + "content": "Masked Caption Embedding. In the second variant, we mask all the frequent entities in the caption, and compute the masked caption embedding as \\(\\mathcal{T}^{\\mathrm{m - cap}} = \\Phi_{\\mathrm{text}}(g(T))\\in\\) \\(\\mathbb{R}^{M\\times D}\\), where \\(g(\\cdot)\\) denotes an masking operation, that replaces the entity with a special [MASK] token." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.508, + 0.894, + 0.629 + ], + "angle": 0, + "content": "Prompted Entity Embedding. We construct manual prompts for each of the entities in the triplets, and compute its textual embedding as \\(\\mathcal{T}^{\\mathrm{entity}} = \\Phi_{\\mathrm{text}}(h(E)) \\in \\mathbb{R}^{M \\times D}\\), where \\(h(\\cdot)\\) refers to the procedure for constructing manual prompts, for example, we randomly sample a prompt template provided in [41], and fill in the entities: A painting of \\(a\\) \\(\\{entity_1\\}\\) and \\(\\{entity_2\\}\\) and \\(\\{entity_3\\}\\). This sentence is padded to the same length as the caption after tokenisation." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.638, + 0.605, + 0.655 + ], + "angle": 0, + "content": "3.2. Training" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.661, + 0.892, + 0.722 + ], + "angle": 0, + "content": "As for training, we aim to learn the alignment between group tokens and caption embeddings via three proxy tasks, namely, image-caption alignment, masked entity completion, and cross-image mask consistency." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.726, + 0.892, + 0.832 + ], + "angle": 0, + "content": "Image-caption Alignment. For each image-text pair, the objective is to align their visual and textual embeddings. The visual embedding \\( z^{\\mathrm{I}} \\) is the average of group tokens, and the textual embedding \\( z^{\\mathrm{T}} \\) is obtained by taking the [EOT] token feature of the caption embedding \\( \\mathcal{T}^{\\mathrm{cap}} \\), both projected to a 256-d joint feature space followed by normalisation. The image-caption contrastive loss \\( \\mathcal{L}_{\\mathrm{contrast}} \\) is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.509, + 0.841, + 0.892, + 0.878 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {c o n t r a s t}} = \\frac {1}{2} \\left(\\frac {\\exp \\left(z _ {i} ^ {\\mathrm {I}} \\cdot z _ {i} ^ {\\mathrm {T}}\\right)}{\\sum_ {l} \\exp \\left(z _ {i} ^ {\\mathrm {I}} \\cdot z _ {l} ^ {\\mathrm {T}}\\right)} + \\frac {\\exp \\left(z _ {i} ^ {\\mathrm {I}} \\cdot z _ {i} ^ {\\mathrm {T}}\\right)}{\\sum_ {l} \\exp \\left(z _ {l} ^ {\\mathrm {I}} \\cdot z _ {i} ^ {\\mathrm {T}}\\right)}\\right). \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.886, + 0.868, + 0.902 + ], + "angle": 0, + "content": "Here, we omit the temperature parameter for simplicity." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2938" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.184 + ], + "angle": 0, + "content": "Masked Entity Completion. The goal of masked entity completion is to infer all the masked entities in the sentence given the group tokens. In specific, we adopt a Transformer decoder layer, where a projection of the masked caption embedding is treated as query, and two linear transformations of group tokens are treated as key and values, respectively." + }, + { + "type": "equation", + "bbox": [ + 0.091, + 0.189, + 0.469, + 0.223 + ], + "angle": 0, + "content": "\\[\n\\tilde {\\mathcal {T}} ^ {\\mathrm {m} - \\mathrm {c a p}} = \\Phi_ {\\mathrm {d e c}} \\left(W _ {q} ^ {\\mathrm {d e c}} \\mathcal {T} ^ {\\mathrm {m} - \\mathrm {c a p}}, W _ {k} ^ {\\mathrm {d e c}} \\mathcal {G}, W _ {v} ^ {\\mathrm {d e c}} \\mathcal {G}\\right) \\in \\mathbb {R} ^ {M \\times D}, \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.233, + 0.469, + 0.312 + ], + "angle": 0, + "content": "where \\( W_{q}^{\\mathrm{dec}} \\), \\( W_{k}^{\\mathrm{dec}} \\) and \\( W_{v}^{\\mathrm{dec}} \\) are linear transformations, and \\( \\tilde{T}^{\\mathrm{m - cap}} \\) denotes the updated vector sequence, with the masked entities being completed by querying group tokens. For training, we extract the [EOT] token features \\( z^{\\mathrm{M}} \\) and \\( z^{\\mathrm{E}} \\) from \\( \\tilde{T}^{\\mathrm{m - cap}} \\) and \\( T^{\\mathrm{entity}} \\), and construct a contrastive loss:" + }, + { + "type": "equation", + "bbox": [ + 0.094, + 0.318, + 0.469, + 0.368 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {e n t i t y}} = \\frac {1}{2} \\left(\\frac {\\exp \\left(z _ {i} ^ {\\mathrm {M}} \\cdot z _ {i} ^ {\\mathrm {E}}\\right)}{\\sum_ {l} \\exp \\left(z _ {i} ^ {\\mathrm {M}} \\cdot z _ {l} ^ {\\mathrm {E}}\\right)} + \\frac {\\exp \\left(z _ {i} ^ {\\mathrm {M}} \\cdot z _ {i} ^ {\\mathrm {E}}\\right)}{\\sum_ {l} \\exp \\left(z _ {l} ^ {\\mathrm {M}} \\cdot z _ {i} ^ {\\mathrm {E}}\\right)}\\right). \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.377, + 0.469, + 0.407 + ], + "angle": 0, + "content": "Intuitively, the entity completion task enables better alignment between the groups and entities." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.41, + 0.469, + 0.623 + ], + "angle": 0, + "content": "Cross-image Mask Consistency. To encourage visual invariance, we enforce consistent mask predictions between images that contain shared entities. Specifically, for each entity of interest, we can easily source multiple image-caption pairs from the dataset by text searching. Given one sampled image-caption-entity triplet, \\((I_1, T_1, E_1)\\), we can easily search for another sample \\((I_2, T_2, E_2)\\), with both triplet sharing one entity, i.e., \\(e \\in E_1 \\cap E_2\\). Given encoded groups \\(\\mathcal{G}_1, \\mathcal{G}_2\\), two sets of subgroups \\(\\mathcal{G}_1^{\\mathrm{sub}}, \\mathcal{G}_2^{\\mathrm{sub}} \\in \\mathbb{R}^{K' \\times D}\\) that represent the common entity \\(e\\) (e.g., cat in Fig. 2) are obtained by choosing groups with higher similarity to the entity embedding, where \\(K' = rK\\) and \\(r\\) is the selection ratio. The masks of co-attentive entity in image \\(I_1\\) can be grounded by both entity-specific subgroups \\(\\mathcal{G}_1^{\\mathrm{sub}}, \\mathcal{G}_2^{\\mathrm{sub}}\\) as:" + }, + { + "type": "equation", + "bbox": [ + 0.085, + 0.63, + 0.469, + 0.65 + ], + "angle": 0, + "content": "\\[\n\\mathcal {M} _ {1} = \\sigma \\left(\\mathcal {I} _ {1} ^ {\\top} \\mathcal {G} _ {1} ^ {\\text {s u b}}\\right), \\hat {\\mathcal {M}} _ {1} = \\sigma \\left(\\mathcal {I} _ {1} ^ {\\top} \\mathcal {G} _ {2} ^ {\\text {s u b}}\\right) \\in [ 0, 1 ] ^ {L \\times K ^ {\\prime}}, \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.656, + 0.468, + 0.69 + ], + "angle": 0, + "content": "where \\(\\sigma\\) is the sigmoid activation; both \\(\\mathcal{M}_1 = \\{m_k^1\\}_{k=1}^{K'}\\) and \\(\\hat{\\mathcal{M}}_1 = \\{\\hat{m}_k^1\\}_{k=1}^{K'}\\) consist of \\(K'\\) unordered masks." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.691, + 0.469, + 0.736 + ], + "angle": 0, + "content": "To align \\(\\hat{\\mathcal{M}}_1\\) with \\(\\mathcal{M}_1\\), we first adopt the bipartite matching to find the optimal permutation \\(p_1^*\\) over \\(K'\\) subgroups with the lowest matching cost as:" + }, + { + "type": "equation", + "bbox": [ + 0.142, + 0.744, + 0.469, + 0.776 + ], + "angle": 0, + "content": "\\[\np _ {1} ^ {*} = \\underset {p \\in \\mathcal {P}} {\\arg \\min } \\sum_ {k} - \\cos \\left(m _ {k} ^ {1}, \\hat {m} _ {p (k)} ^ {1}\\right), \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.785, + 0.469, + 0.846 + ], + "angle": 0, + "content": "where \\(\\mathcal{P}\\) is the full permutation and \\(\\cos (\\cdot)\\) denotes the cosine similarity. Eq. 11 is solved via the efficient Hungarian algorithm [25]. In this way, the symmetric cross-image mask consistency loss \\(\\mathcal{L}_{mask}\\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.077, + 0.852, + 0.482, + 0.901 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {m a s k}} = \\frac {1}{2} \\left(\\sum_ {k} \\mathrm {D} \\left(\\operatorname {s g} \\left(\\mathbf {m} _ {k} ^ {1}\\right), \\hat {m} _ {p _ {1} ^ {*} (k)} ^ {1}\\right) + \\mathrm {D} \\left(\\operatorname {s g} \\left(\\mathbf {m} _ {k} ^ {2}\\right), \\hat {m} _ {p _ {2} ^ {*} (k)} ^ {2}\\right)\\right), \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.198 + ], + "angle": 0, + "content": "where \\(\\mathrm{sg}(\\cdot)\\) denotes the stop gradient operation; the target mask \\(\\mathbf{m}_k\\) is achieved by binarizing \\(m_{k}\\) with a threshold \\(\\delta\\); \\(\\mathrm{D}(\\mathbf{m},\\hat{m}) = 1 - 2|\\mathbf{m}\\bigcap \\hat{m} | / (|\\mathbf{m}| + |\\hat{m} |)\\) stands for the standard Dice loss. To guarantee the quality of pseudo mask targets, the masks are generated by an extra momentum model, which is updated by the exponential-moving-average (EMA) of the online model." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.201, + 0.892, + 0.23 + ], + "angle": 0, + "content": "Training Objective. We adopt a combination of three different loss functions:" + }, + { + "type": "equation", + "bbox": [ + 0.578, + 0.245, + 0.892, + 0.261 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {t o t a l}} = \\mathcal {L} _ {\\text {c o n t r a s t}} + \\mathcal {L} _ {\\text {e n t i t y}} + \\lambda \\mathcal {L} _ {\\text {m a s k}}, \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.273, + 0.882, + 0.289 + ], + "angle": 0, + "content": "where \\(\\lambda\\) is the weight for balancing the mask consistency." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.298, + 0.618, + 0.313 + ], + "angle": 0, + "content": "3.3. Discussion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.322, + 0.892, + 0.594 + ], + "angle": 0, + "content": "One work that is closely related to ours is GroupViT [53]. They extracted multiple nouns and prompted each noun to a sentence to serve as extra matched captions for the image. The model is thus supervised by a multi-label contrastive loss. In contrast, our paper differs from GroupViT in three critical aspects: (1) Entities vs nouns. Rather than using all nouns, we leverage the entities that match to visual objects, enabling high-quality image-caption correspondence. (2) Network architecture. We only adopt single-stage grouping (i.e., one binding module) as we observe no further performance gain of applying multi-stage grouping in GroupViT. The hard-assignment trick [53] is also adopted. Besides, we devise a (very) lightweight decoder to model the fine-grained, token-wise group-word correlation. (3) Proxy tasks for training. We propose two different proxy tasks, i.e., masked entity completion and cross-image mask consistency to improve the entity-specific group semantics and further encourage visual invariance." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.608, + 0.634, + 0.625 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.633, + 0.697, + 0.649 + ], + "angle": 0, + "content": "4.1. Experimental Setups" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.657, + 0.892, + 0.838 + ], + "angle": 0, + "content": "Pre-training Dataset. Following [32, 53], we use Conceptual Captions 12M [7] for training, which is originally constructed with over 12M image-text pairs collected from the Internet. However, due to some links have been expired, we have downloaded about 10M image-text pairs. The constructed entity set in Sec. 3.1.2 includes a total number of 100 frequently appeared entities while abstract nouns (e.g., art, view) are discarded. After filtering CC12M, we obtain 4.3 million image-text pairs for pre-training, which is termed as CC4M. Examples of entities include people, car, cup, chair, T-shirt, house, bed, cat, ball, pizza, etc. Please refer to the supplementary material for the full entity set." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.841, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Downstream Evaluation Datasets. We evaluate our model on four benchmarks, namely, PASCAL VOC 2012 [18], PASCAL Context [38], COCO Object [31], and ADE20K [59] with 20, 59, 80 and 150 foreground classes," + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2939" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.081, + 0.089, + 0.894, + 0.34 + ], + "angle": 0, + "content": "
MethodBackbonePretrain datasetSupervisionZero-shot transferDownstream datasets
PASCAL VOCPASCAL ContextCOCO ObjectADE20K
DeiT [46]ViT-SIN-1Kclass labelX53.035.9--
MoCo [10]ViT-SIN-1KselfX34.321.3--
DINO [6]ViT-SIN-1KselfX39.120.4--
MoCo [10]ViT-SCC12M+YFCC15MselfX36.123.0--
DINO [6]ViT-SCC12M+YFCC15MselfX37.622.8--
ViL-Seg [32]ViT-BCC12Mself+text✓33.615.9--
GroupViT [53]ViT-SCC12Mtext✓41.1---
GroupViT [53]ViT-SCC12M+YFCC15Mtext✓51.222.320.9-
ViewCo [43]ViT-SCC12M+YFCC15Mself+text✓52.423.023.5-
CLIPpy [42]ViT-BHQITP-134Mtext✓52.2-25.5†13.5
GroupViT* [53]ViT-SCC4Mtext✓19.88.89.13.4
GroupViT* [53]ViT-BCC4Mtext✓25.811.310.73.6
GroupViT* [53]ViT-SCC12Mtext✓40.218.717.76.2
OVSegmentor (ours)ViT-SCC4Mtext✓44.518.319.04.3
OVSegmentor (ours)ViT-BCC4Mtext✓53.820.425.15.6
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.346, + 0.895, + 0.404 + ], + "angle": 0, + "content": "Table 1. Comparison with existing methods. Models in the first five rows are finetuned on target datasets while the rest perform zero-shot transfer. GroupViT (with *) refers to the results by training officially released code. Results of DeiT, MoCo, and DINO are copied from [53]. Bold fonts refer to the best results among the models that enable zero-shot transfer. CLIPpy uses 134M in-house data while we use an order of magnitude less data. † indicates evaluation on 133 COCO classes claimed by CLIPpy, which differs from ours (80 classes)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.414, + 0.471, + 0.504 + ], + "angle": 0, + "content": "respectively. An extra background class is considered in VOC, Context, and COCO. We ignore their training sets and directly evaluate our method on the validation sets without any finetuning, including 1449, 5105, 5000, and 2000 images, respectively. In general, we report the mean Intersection-over-Union (mIoU) on all the classes." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.51, + 0.473, + 0.798 + ], + "angle": 0, + "content": "Implementation Details. In our model, the self-attention layers in the visual encoder are initialised with DINO [6] pre-trained on ImageNet. The text encoder is initialized with BERT [16] model pre-trained on BookCorpus and English Wikipedia. Our decoder with one randomly initialized Transformer Decoder layer performs reasonably well. The input image is randomly cropped to \\(224\\times 224\\) at training time, and the batch size is set to 2048 with an initial learning rate \\(3.2\\times 10^{-4}\\). We train our model for 40 epochs using the Adam optimizer with weight decay set to 0.5. The coefficient for updating the momentum model is 0.99. As the generated masks are unreliable in early epochs, we set the mask consistency coefficient \\(\\lambda = 0\\) for the first 30 epochs and \\(\\lambda = 0.1\\) for the remaining epochs. The group selection ratio \\(r\\) is 0.5. As for the threshold in mask consistency loss, we use \\(\\delta = 0.65\\). At inference time, the image is resized with a shorter length of 448. We follow [53] to set a threshold for the background class, which is 0.9, 0.35, 0.9 and 0.95 on VOC, Context, COCO, and ADE20K, respectively." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.814, + 0.388, + 0.831 + ], + "angle": 0, + "content": "4.2. Comparison with Existing Methods" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.903 + ], + "angle": 0, + "content": "In Table 1, we compare our model with the existing models that have been trained with (1) fully-supervised finetuning transfer and (2) zero-shot transfer. In Table 2, zero-shot segmentation (ZSS) approaches are listed for comparison." + }, + { + "type": "image", + "bbox": [ + 0.499, + 0.411, + 0.892, + 0.544 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.545, + 0.894, + 0.588 + ], + "angle": 0, + "content": "Figure 3. Qualitative results on PASCAL VOC. We show the learned groups in the last column with each color representing a group. In the last row, the model fails to segment the person's leg." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.608, + 0.895, + 0.76 + ], + "angle": 0, + "content": "Comparison with Finetuning Transfer. We compare our method with DeiT [46], MoCo [10] and DINO [6], which are pre-trained on ImageNet [15] or CC12M+YFCC15M datasets with class labels [46] or self-supervision [6, 10], and finetuned on the training set from downstream benchmarks, with a randomly initialised convolution head appended on the backbone network. As shown in Table 1, our model achieves competitive performance on PASCAL Context, and outperforms the self-supervised methods by over \\(10\\%\\) on PASCAL VOC, with zero-shot transfer." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Comparison with Zero-shot Transfer. Here, we compare with existing works under the zero-shot transfer scenario, including GroupViT [53], ViL-Seg [32] and CLIPpy [42], with the pre-training data ranging from CC12M [7] to 134M in-house dataset HQITP-134M [42]. For fair comparison, we re-train GroupViT [53] with their official codebase on our CC4M and CC12M datasets, with the same pre-trained weights as ours being adopted on CC4M. However, we observe no further performance gain of either applying pre" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "2940" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.082, + 0.089, + 0.468, + 0.273 + ], + "angle": 0, + "content": "
MethodPretrain datasetSeenUnseenVOCContext
SPNet [51]-✓✗15.64.0
ZS3 [5]-✓✓17.77.7
GaGNet [22]-✓✓29.915.0
SIGN [14]-✓✓28.914.9
Joint [2]-✓✗32.5-
ZegFormer [17]CLIP400M✓✗63.6
MaskCLIP+ [60]CLIP400M✓✗86.166.7†
ViL-Seg [32]CC12M✗✗37.318.9
GroupViT [53]CC12M+YFCC15M✗✗43.751.3
GroupViT* [53]CC4M✗✗22.424.5
GroupViT* [53]CC12M✗✗33.145.3
OVSegmentorCC4M✗✗46.654.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.279, + 0.47, + 0.349 + ], + "angle": 0, + "content": "Table 2. Comparison with zero-shot segmentation methods on unseen classes. Seen/Unseen denotes whether the model is trained on seen/unseen classes. Our method outperforms most zero-shot segmentation models even without training on seen classes. \\(\\dagger\\) indicates a different set of unseen classes." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.362, + 0.47, + 0.514 + ], + "angle": 0, + "content": "trained weights to GroupViT or using ViT-B as the backbone on CC12M. The results on CC12M match the reported ones (40.2 vs 41.1). Under the same pre-trained ViT-B backbone and CC4M dataset, our method surpasses the original GroupViT by \\(35.7\\%\\). Additionally, by only using 4M pre-training data, our model yields the best segmentation performance on PASCAL VOC, even outperforming CLIPpy, which is a concurrent work to ours, and pre-trained on 134M data, indicating the effectiveness and training efficiency of our proposed model." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.516, + 0.471, + 0.789 + ], + "angle": 0, + "content": "Comparison with Zero-shot Segmentation Methods. In this line of research [17, 22, 51], the idea is to train the model with full mask labels (obtained either from manual groundtruth or pseudo-labelling [60]) on the seen classes and transfer the model to unseen classes, and the task is thus dubbed as zero-shot semantic segmentation (ZSS). To be specific, 5 classes (potted plant, sheep, sofa, train and tv-monitor) in PASCAL VOC and 4 classes (cow, motorbike, sofa and cat) in PASCAL Context are considered unseen while the remaining classes belong to seen. For comparison, we also report the zero-shot transfer performance of our model on these unseen classes, however, note that, we do not use any manual mask annotations for training. As observed in Table 2, our model surpasses majority of the models trained under the ZSS scenario, except for [17, 60] that adopted CLIP model pre-trained on 400M data. In terms of transfer efficiency, our model excels at zero-shot transfer ability without the need of training on seen classes." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.799, + 0.231, + 0.815 + ], + "angle": 0, + "content": "4.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.823, + 0.468, + 0.853 + ], + "angle": 0, + "content": "In this section, we conduct thorough ablation studies to validate the necessity of each proposed component." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.468, + 0.901 + ], + "angle": 0, + "content": "Ablation Study on Proxy Tasks. Here, we aim to understand the effects of our proposed proxy tasks, i.e., masked entity completion and cross-image mask consistency. As" + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.089, + 0.888, + 0.174 + ], + "angle": 0, + "content": "
Baseline\\( {\\mathcal{L}}_{\\text{entity }} \\)\\( {\\mathcal{L}}_{\\text{mask }} \\)PASCAL VOCPASCAL Context
✓40.515.1
✓✓48.919.9
✓✓44.817.0
✓✓✓53.820.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.181, + 0.892, + 0.223 + ], + "angle": 0, + "content": "Table 3. Ablation study on the masked entity completion loss \\((\\mathcal{L}_{\\mathrm{entity}})\\) and cross-image mask consistency loss \\((\\mathcal{L}_{\\mathrm{mask}})\\). The baseline only uses the image-caption contrastive loss \\((\\mathcal{L}_{\\mathrm{contrast}})\\)." + }, + { + "type": "table", + "bbox": [ + 0.513, + 0.233, + 0.876, + 0.337 + ], + "angle": 0, + "content": "
Masking objectivePASCAL VOCPASCAL Context
All entities (ours)48.919.9
Single entity47.018.5
All nouns45.417.4
Multi-label contrastive44.517.0
MLM (w/ groups)42.816.3
MLM (w/o groups)36.314.6
" + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.342, + 0.892, + 0.398 + ], + "angle": 0, + "content": "Table 4. Comparison on masking objectives. MLM (w/ groups) refers to masked language modeling [16] given the group tokens as keys and values in the decoder while MLM (w/o groups) directly predicts masked words from the output of text encoder." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.413, + 0.892, + 0.565 + ], + "angle": 0, + "content": "shown in Table 3, the baseline model uses the image-text contrastive loss \\(\\mathcal{L}_{\\mathrm{contrast}}\\) only, while adding the entity completion task, \\(\\mathcal{L}_{\\mathrm{entity}}\\), we observe a significant improvement by \\(8.4\\%\\) and \\(4.8\\%\\) on PASCAL VOC and PASCAL Context, respectively. The performance gain is due to the ability of better aligning the pixel groups with the visual entities. Additionally, the mask consistency also brings improvements, and combing both leads to the best performance. Qualitative results can be seen in Fig. 3. We refer the readers for more visualisations in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.569, + 0.893, + 0.902 + ], + "angle": 0, + "content": "On the Choice of Masking Objectives. We compare our masked entity completion with a series of variants as shown in Table 4. Our proposed objective of masking all entities is listed in the first row. (1) All entities vs one entity: the masked language modeling (MLM) in prior works [16, 35] normally choose \\(15\\%\\) of the token positions in the sentence for prediction, which results in one entity in most of our cases. We observe that masking all entities is \\(2.9\\%\\) mIoU better than single entity masking, as it forces the network to infer all possible object categories in the image, that is potentially beneficial for the group to category alignment. (2) Entities vs nouns: masking and predicting noun phrases in the sentence [20] is one feasible option to learn fine-grained vision-text matching. However, noun masking leads to \\(3.5\\%\\) lower mIoU than our entity masking strategy. This is because not all of the nouns in the sentence are visually corresponding to the objects in the image (e.g., illustration, night, etc), thus the group tokens are not expected to align with these nouns. Our method avoids this issue by only masking the visual entities. (3) Masked entity completion vs multi-label contrastive loss: comparing with the multi-label contrastive loss used in GroupViT [53], our proposed" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "2941" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.08, + 0.088, + 0.275, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.28, + 0.089, + 0.477, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.189, + 0.89, + 0.218 + ], + "angle": 0, + "content": "Figure 4. Qualitative results of the masked entity completion loss. We show different groups that match the given class. With the boost of group semantics, our model improves visual grouping (left) and solves false matching between group and text (right) of the baseline." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.232, + 0.27, + 0.279 + ], + "angle": 0, + "content": "
K/τ0.250.50.751.0
849.053.651.850.4
1642.343.243.142.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.284, + 0.268, + 0.325 + ], + "angle": 0, + "content": "Table 5. Comparison on group numbers \\(K\\) and selection ratios \\(\\tau\\) on VOC." + }, + { + "type": "table", + "bbox": [ + 0.277, + 0.233, + 0.465, + 0.278 + ], + "angle": 0, + "content": "
ObjectiveLossmIoU
Mask ConsistencyDice53.8
Group ConsistencyNCE47.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.274, + 0.283, + 0.465, + 0.325 + ], + "angle": 0, + "content": "Table 6. Comparison on different objectives for cross-image consistency on VOC." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.336, + 0.468, + 0.547 + ], + "angle": 0, + "content": "strategy shows superior performance. (4) Masked entity completion vs masked language modeling: MLM [16] originally predicts the masked token over the entire vocabulary via a cross-entropy loss. Here, we restrict the vocabulary to our constructed entity set for fair comparison. Our masking strategy surpasses both MLM variants by a clear margin, which we conjecture is because: (1) MLM classifies each masked token individually, and the model can easily refer to the context words without relating to groups. (2) MLM focuses on word-level representations, which is not in accordance with the sentence-level representation of the class embeddings we used during inference. Fig. 4 shows the effect of the masked entity completion in improving visual grouping (left) and group-text alignment (right)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.551, + 0.468, + 0.822 + ], + "angle": 0, + "content": "On Cross-image Mask Consistency. Here, we analyze two key factors in cross-image consistency: (1) group numbers and selection ratios. As shown in Table 5, 8 groups perform comparably well for all selection ratios, and we pick 0.5 as the default. Smaller ratios miss the entities encoded in remaining groups while larger ratios introduce entity-irrelevant information (e.g., background). However, while increasing the group number to 16, it brings over-segmentation for large objects, deteriorating the performance. (2) the objective for cross-image consistency. We study another variant of cross-image consistency by directly aligning two sets of group tokens encoded with shared entity. We adopt the contrastive loss (NCE in Table 6) to pull two sets of group tokens closer, while other group tokens in each mini-batch are pushed farther. Table 6 reveals the superiority of our proposed mask consistency over group consistency, which we believe is because mask consistency involves the image content to realize visual invariance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.468, + 0.901 + ], + "angle": 0, + "content": "Performance on Unseen Entities. We measure the mean IoU for objects within entity set (e.g., person, bus) and those out of entity set on each dataset. As shown in Table 7, our model still achieves a decent improvement over the baseline on unseen entities, indicating it retains strong" + }, + { + "type": "image", + "bbox": [ + 0.493, + 0.089, + 0.692, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.695, + 0.089, + 0.892, + 0.186 + ], + "angle": 0, + "content": null + }, + { + "type": "table", + "bbox": [ + 0.503, + 0.232, + 0.89, + 0.302 + ], + "angle": 0, + "content": "
MethodVOCCOCOContextADE20K
S20U0S65U16S30U29S31U119
Baseline38.5-17.215.420.58.87.13.7
Ours(CC4M)52.4-24.825.927.910.98.64.8
Ours(CC12M)44.8-18.621.525.412.010.16.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.305, + 0.891, + 0.332 + ], + "angle": 0, + "content": "Table 7. Performance on seen(S)/unseen(U) entities, where indices refer to entity numbers. Background class is not considered." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.347, + 0.891, + 0.393 + ], + "angle": 0, + "content": "open-vocabulary segmentation ability. The text encoder pre-trained on large text corpus remains its ability to encode the semantic concept of objects out of entity set." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.395, + 0.892, + 0.5 + ], + "angle": 0, + "content": "Scalability. We scale up the datasets by increasing the number of entities, i.e., training the model on the entire CC12M. Ours (CC12M) outperforms ours (CC4M) on the challenging ADE20K and PASCAL Context, reflecting the effect of the scalability of our model. The performance drop on VOC is because the extremely large entity set in CC12M poses extra challenge for our model to learn the entities in VOC." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.506, + 0.619, + 0.521 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.531, + 0.892, + 0.743 + ], + "angle": 0, + "content": "In this paper, we present OVSegmentor, a transformer-based model for open-vocabulary semantic segmentation. The model exploits web-collected image-caption pairs for pre-training without any mask annotations, and transfers to target benchmark segmentation datasets in a zero-shot manner. The model clusters the image pixels into learnable group tokens, which are then aligned with the corresponding caption embeddings. We further devise two proxy tasks, namely masked entity completion and cross-image mask consistency, to learn entity-specific, fine-grained and visually invariant group semantics. OVSegmentor outperforms the state-of-the-art method on PASCAL VOC by using only \\(3\\%\\) (4M vs 134M) for pre-training, indicating the effectiveness and training efficiency of our model." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.748, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Acknowledgement. This work is supported by Natural Science Foundation of China (No. 61976057; No. 62172101), the Science and Technology Commission of Shanghai Municipality (No. 21511101000; No. 22511106003), the Science and Technology Major Project of Commission of Science and Technology of Shanghai (No. 2021SHZDZX0103). This work is also partially supported by the National Key R&D Program of China (No. 2022ZD0160100), and in part by Shanghai Committee of Science and Technology (No. 21DZ1100100). This work was done during Jilan's parttime internship at Shanghai Artificial Intelligence Laboratory." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2942" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.175, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.117, + 0.47, + 0.158 + ], + "angle": 0, + "content": "[1] Jiwoon Ahn and Suha Kwak. Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation. In CVPR, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.161, + 0.47, + 0.202 + ], + "angle": 0, + "content": "[2] Donghyeon Baek, Youngmin Oh, and Bumsub Ham. Exploiting a joint embedding space for generalized zero-shot semantic segmentation. In ICCV, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.205, + 0.471, + 0.247 + ], + "angle": 0, + "content": "[3] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.249, + 0.47, + 0.29 + ], + "angle": 0, + "content": "[4] Steven Bird, Ewan Klein, and Edward Loper. Natural language processing with Python: analyzing text with the natural language toolkit. \"O'Reilly Media, Inc.\", 2009. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.293, + 0.469, + 0.321 + ], + "angle": 0, + "content": "[5] Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, and Patrick Pérez. Zero-shot semantic segmentation. NeurIPS, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.323, + 0.469, + 0.377 + ], + "angle": 0, + "content": "[6] Mathilde Caron, Hugo Touvron, Ishan Misra, Herve Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In ICCV, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.381, + 0.469, + 0.435 + ], + "angle": 0, + "content": "[7] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In CVPR, 2021. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.439, + 0.469, + 0.493 + ], + "angle": 0, + "content": "[8] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. PAMI, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.497, + 0.469, + 0.55 + ], + "angle": 0, + "content": "[9] Qi Chen, Lingxiao Yang, Jian-Huang Lai, and Xiaohua Xie. Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.554, + 0.469, + 0.595 + ], + "angle": 0, + "content": "[10] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In ICCV, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.598, + 0.469, + 0.639 + ], + "angle": 0, + "content": "[11] Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang. Semi-supervised semantic segmentation with cross pseudo supervision. In CVPR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.642, + 0.469, + 0.696 + ], + "angle": 0, + "content": "[12] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.7, + 0.469, + 0.74 + ], + "angle": 0, + "content": "[13] Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. NeurIPS, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.744, + 0.469, + 0.799 + ], + "angle": 0, + "content": "[14] Jiaxin Cheng, Soumyaroop Nandi, Prem Natarajan, and Wael Abd-Almageed. Sign: Spatial-information incorporated generative network for generalized zero-shot semantic segmentation. In ICCV, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.802, + 0.469, + 0.842 + ], + "angle": 0, + "content": "[15] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.469, + 0.899 + ], + "angle": 0, + "content": "[16] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In NAACL, 2019. 4, 6, 7, 8" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.117, + 0.471, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.134 + ], + "angle": 0, + "content": "[17] Jian Ding, Nan Xue, Gui-Song Xia, and Dengxin Dai. Decoupling zero-shot semantic segmentation. In CVPR, 2022, 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.137, + 0.892, + 0.178 + ], + "angle": 0, + "content": "[18] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. IJCV, 2010. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.181, + 0.892, + 0.234 + ], + "angle": 0, + "content": "[19] Geoffrey French, Samuli Laine, Timo Aila, Michal Mackiewicz, and Graham D. Finlayson. Semi-supervised semantic segmentation needs strong, varied perturbations. In BMVC, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.238, + 0.892, + 0.279 + ], + "angle": 0, + "content": "[20] Yuying Ge, Yixiao Ge, Xihui Liu, Dian Li, Ying Shan, Xiaohu Qie, and Ping Luo. Bridging video-text retrieval with multiple choice questions. In CVPR, 2022. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.282, + 0.892, + 0.322 + ], + "angle": 0, + "content": "[21] Golnaz Ghiasi, Xiuye Gu, Yin Cui, and Tsung-Yi Lin. Scaling open-vocabulary image segmentation with image-level labels. In ECCV, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.326, + 0.892, + 0.367 + ], + "angle": 0, + "content": "[22] Zhangxuan Gu, Siyuan Zhou, Li Niu, Zihan Zhao, and Liqing Zhang. Context-aware feature generation for zero-shot semantic segmentation. In ACM MM, 2020. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.369, + 0.892, + 0.424 + ], + "angle": 0, + "content": "[23] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICML, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.427, + 0.892, + 0.48 + ], + "angle": 0, + "content": "[24] Eunji Kim, Siwon Kim, Jungbeom Lee, Hyunwoo Kim, and Sungroh Yoon. Bridging the gap between classification and localization for weakly supervised object localization. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.484, + 0.892, + 0.524 + ], + "angle": 0, + "content": "[25] Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83-97, 1955. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.528, + 0.892, + 0.569 + ], + "angle": 0, + "content": "[26] Boyi Li, Kilian Q. Weinberger, Serge J. Belongie, Vladlen Koltun, and René Ranftl. Language-driven semantic segmentation. In ICLR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.572, + 0.892, + 0.613 + ], + "angle": 0, + "content": "[27] Dongxu Li, Junnan Li, Hongdong Li, Juan Carlos Niebles, and Steven CH Hoi. Align and prompt: Video-and-language pre-training with entity prompts. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.616, + 0.892, + 0.67 + ], + "angle": 0, + "content": "[28] Juncheng Li, Xin He, Longhui Wei, Long Qian, Linchao Zhu, Lingxi Xie, Yueting Zhuang, Qi Tian, and Siliang Tang. Fine-grained semantically aligned vision-language pre-training. NeurIPS, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.673, + 0.892, + 0.728 + ], + "angle": 0, + "content": "[29] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. NeurIPS, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.731, + 0.892, + 0.786 + ], + "angle": 0, + "content": "[30] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.789, + 0.892, + 0.843 + ], + "angle": 0, + "content": "[31] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV. Springer, 2014. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.846, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[32] Quande Liu, Youpeng Wen, Jianhua Han, Chunjing Xu, Hang Xu, and Xiaodan Liang. Open-world semantic segmentation via contrasting and clustering vision-language embedding. In ECCV, 2022. 2, 5, 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2943" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "[33] Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. Object-centric learning with slot attention. NeurIPS, 2020. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.149, + 0.469, + 0.19 + ], + "angle": 0, + "content": "[34] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.192, + 0.469, + 0.233 + ], + "angle": 0, + "content": "[35] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.235, + 0.469, + 0.275 + ], + "angle": 0, + "content": "[36] Wenfeng Luo and Meng Yang. Semi-supervised semantic segmentation via strong-weak dual-branch network. In ECCV, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.278, + 0.469, + 0.319 + ], + "angle": 0, + "content": "[37] Chaofan Ma, Yuhuan Yang, Yanfeng Wang, Ya Zhang, and Weidi Xie. Open-vocabulary semantic segmentation with frozen vision-language models. BMVC, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.321, + 0.469, + 0.375 + ], + "angle": 0, + "content": "[38] Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic segmentation in the wild. In CVPR, 2014. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.378, + 0.469, + 0.418 + ], + "angle": 0, + "content": "[39] Yassine Ouali, Céline Hudelot, and Myriam Tami. Semi-supervised semantic segmentation with cross-consistency training. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.421, + 0.469, + 0.473 + ], + "angle": 0, + "content": "[40] Giuseppe Pastore, Fabio Cermelli, Yongqin Xian, Massimiliano Mancini, Zeynep Akata, and Barbara Caputo. A closer look at self-training for zero-label semantic segmentation. In CVPR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.477, + 0.469, + 0.545 + ], + "angle": 0, + "content": "[41] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 1, 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.548, + 0.469, + 0.601 + ], + "angle": 0, + "content": "[42] Kanchana Ranasinghe, Brandon McKinzie, Sachin Ravi, Yinfei Yang, Alexander Toshev, and Jonathon Shlens. Perceptual grouping in vision-language models. arXiv preprint arXiv:2210.09996, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.604, + 0.469, + 0.672 + ], + "angle": 0, + "content": "[43] Pengzhen Ren, Changlin Li, Hang Xu, Yi Zhu, Guangrun Wang, Jianzhuang Liu, Xiaojun Chang, and Xiaodan Liang. Viewco: Discovering text-supervised segmentation masks via multi-view semantic consistency. arXiv preprint arXiv:2302.10307, 2023. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.675, + 0.469, + 0.715 + ], + "angle": 0, + "content": "[44] Gyungin Shin, Weidi Xie, and Samuel Albanie. Named-mask: Distilling segmenters from complementary foundation models. arXiv preprint arXiv:2209.11228, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.717, + 0.469, + 0.757 + ], + "angle": 0, + "content": "[45] Gyungin Shin, Weidi Xie, and Samuel Albanie. Reco: Retrieve and co-segment for zero-shot transfer. NeurIPS, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.76, + 0.469, + 0.814 + ], + "angle": 0, + "content": "[46] Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through attention. In ICML, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.817, + 0.469, + 0.857 + ], + "angle": 0, + "content": "[47] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. NeurIPS, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[48] Huiyu Wang, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen. Max-deeplab: End-to-end panoptic segmentation with mask transformers. In CVPR, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[49] Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, and Xinyi Le. Semi-supervised semantic segmentation using unreliable pseudo-labels. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[50] Pingyu Wu, Wei Zhai, and Yang Cao. Background activation suppression for weakly supervised object localization. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.192, + 0.892, + 0.246 + ], + "angle": 0, + "content": "[51] Yongqin Xian, Subhabrata Choudhury, Yang He, Bernt Schiele, and Zeynep Akata. Semantic projection network for zero-and few-label semantic segmentation. In CVPR, 2019. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.249, + 0.892, + 0.316 + ], + "angle": 0, + "content": "[52] Jinheng Xie, Jianfeng Xiang, Junliang Chen, Xianxu Hou, Xiaodong Zhao, and Linlin Shen. C2am: Contrastive learning of class-agnostic activation map for weakly supervised object localization and semantic segmentation. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.319, + 0.892, + 0.373 + ], + "angle": 0, + "content": "[53] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In CVPR, 2022. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.376, + 0.892, + 0.431 + ], + "angle": 0, + "content": "[54] Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng, Rui-Wei Zhao, Tao Zhang, Xuequan Lu, and Shang Gao. Cream: Weakly supervised object localization via class re-activation mapping. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.433, + 0.892, + 0.488 + ], + "angle": 0, + "content": "[55] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. FILIP: fine-grained interactive language-image pre-training. In ICLR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.49, + 0.892, + 0.53 + ], + "angle": 0, + "content": "[56] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. In ICML, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.532, + 0.892, + 0.599 + ], + "angle": 0, + "content": "[57] Haotian Zhang, Pengchuan Zhang, Xiaowei Hu, Yen-Chun Chen, Liunian Harold Li, Xiyang Dai, Lijuan Wang, Lu Yuan, Jenq-Neng Hwang, and Jianfeng Gao. Glipv2: Unifying localization and vision-language understanding. NeurIPS, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.602, + 0.892, + 0.643 + ], + "angle": 0, + "content": "[58] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In CVPR, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.645, + 0.892, + 0.7 + ], + "angle": 0, + "content": "[59] Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127:302-321, 2019. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.702, + 0.892, + 0.73 + ], + "angle": 0, + "content": "[60] Chong Zhou, Chen Change Loy, and Bo Dai. Denseclip: Extract free dense labels from clip. ECCV, 2022. 2, 7" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.73 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "2944" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision/c64e9aaf-56b9-414a-803b-d9922f77f018_origin.pdf b/2023/Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision/c64e9aaf-56b9-414a-803b-d9922f77f018_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..775d96913ccf2f1c39c91a135bbcd4827283e851 --- /dev/null +++ b/2023/Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision/c64e9aaf-56b9-414a-803b-d9922f77f018_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a3b536ac8e9bb4e3aabdc08746b2512f89a665f8394f0dff9334b45df67d2e22 +size 859312 diff --git a/2023/Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision/full.md b/2023/Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision/full.md new file mode 100644 index 0000000000000000000000000000000000000000..7f908b9439faa9e833511826fbe6063969c22089 --- /dev/null +++ b/2023/Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision/full.md @@ -0,0 +1,323 @@ +# Learning Open-vocabulary Semantic Segmentation Models From Natural Language Supervision + +Jilan Xu $^{1,2}$ Junlin Hou $^{1}$ Yuejie Zhang $^{1*}$ Rui Feng $^{1*}$ Yi Wang $^{2}$ Yu Qiao $^{2}$ Weidi Xie $^{2,3}$ + +$^{1}$ School of Computer Science, Shanghai Key Lab of Intelligent Information Processing, Shanghai Collaborative Innovation Center of Intelligent Visual Computing, Fudan University $^{2}$ Shanghai AI Laboratory $^{3}$ CMIC, Shanghai Jiao Tong University + +# Abstract + +This paper considers the problem of open-vocabulary semantic segmentation (OVS), that aims to segment objects of arbitrary classes beyond a pre-defined, closed-set categories. The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor, which only exploits web-crawled image-text pairs for pre-training without using any mask annotations. OVSegmentor assembles the image pixels into a set of learnable group tokens via a slot-attention based binding module, then aligns the group tokens to corresponding caption embeddings. Second, we propose two proxy tasks for training, namely masked entity completion and cross-image mask consistency. The former aims to infer all masked entities in the caption given group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities. The latter enforces consistent mask predictions between images that contain shared entities, encouraging the model to learn visual invariance. Third, we construct CC4M dataset for pre-training by filtering CC12M with frequently appeared entities, which significantly improves training efficiency. Fourth, we perform zero-shot transfer on four benchmark datasets, PASCAL VOC, PASCAL Context, COCO Object, and ADE20K. OVSegmentor achieves superior results over state-of-the-art approaches on PASCAL VOC using only $3\%$ data (4M vs 134M) for pre-training. + +# 1. Introduction + +Semantic segmentation considers the problem of assigning class labels to each pixel in the image. It plays critical roles in a wide range of real-world scenarios, including autonomous driving, computer-aided diagnosis and satellite image analysis, to name a few. Generally speaking, two lines of research dominate semantic segmentation, one way is to cluster the pixels into different groups and assign a + +![](images/3e5628a889cc5c8d3645c2cb18ca92a68d39aa22c62b1cdf5bb761bfc113c381.jpg) +Figure 1. An illustration of open-vocabulary semantic segmentation. Models trained on closed-set classes (cat and dog) fail to segment novel class (fire hydrant). We train a visual encoder and a text encoder on web-collected image-text pairs without using any mask labels, and our model can segment arbitrary object classes. + +semantic label to each group; the other idea treats segmentation as pixel-wise classification, casting each pixel into one category. Despite tremendous progress, the scalability of existing approaches that rely on supervised training has been fundamentally limited: (1) costly annotation procedure. Extensive manual pixel-wise annotations are required for training segmentation models; (2) closed-set segmentation. The model is restricted to segmenting objects from a closed-set of categories. Whenever a new dataset comes, the model requires re-training. + +In this paper, our goal is to train an open-vocabulary semantic segmentation (OVS) model, by exploiting freely available image-caption pairs on Internet, as illustrated in Fig. 1. The recent work, for example, CLIP [41] and ALIGN [23] have demonstrated that a combination of large-scale image-caption pairs and simple noise contrastive estimation can learn powerful image-text embeddings from scratch, and show strong "zero-shot" generalization abilities for open-vocabulary classification. Additionally, GroupViT [53] extends the idea towards semantic segmentation by training a segmentation model with text supervision only. They perform hierarchical grouping of + +visual tokens, which are then aligned to the corresponding text embeddings via a contrastive loss. However, the following issues remain challenging and unsolved: First, the captions only provide coarse, image-level descriptions, which are insufficient for training semantic segmentation models where fine-grained, pixel-wise supervision is usually needed. Second, the diversity of web-collected data is large, that requires the model to learn visual invariance on objects of interest, with only weak supervision provided. For instance, the visual appearance of two images with similar captions can be drastically different. + +To tackle the above challenges, (i) we propose a transformer-based model for open-vocabulary semantic segmentation, dubbed as OVSegmentor, that can segment objects of arbitrary categories via zero-shot transfer, with only image-caption pairs for pre-training. Specifically, we introduce learnable group tokens to cluster image patches via a slot-attention [33] based binding module, and align the group tokens with corresponding caption embeddings. Note that our model neither requires ground-truth masks for training nor additional re-training on target segmentation datasets, substantially alleviating the annotation efforts and improving transfer efficiency; (ii) As for training on the image-caption dataset, we propose two proxy tasks, namely masked entity completion and cross-image mask consistency, the former trains the model to infer all the masked entities in the sentence given the group tokens, and the latter enforces consistent mask prediction for images with the common entity. Both tasks have shown to be beneficial in learning entity-specific, fine-grained and visually invariant group semantics; (iii) We construct an image-caption dataset, termed as CC4M, by designing an automatic approach to filter CC12M [7] with frequently appeared visual entities, significantly improving the training efficiency. + +We pre-train the proposed OVSegmentor on our filtered image-caption dataset (CC4M), without using any manual segmentation masks whatsoever. The model is evaluated on four segmentation benchmarks, PASCAL VOC 2012 [18], PASCAL Context [38], COCO Object [31], and ADE20K [59] in a zero-shot manner, i.e., the model is directly evaluated on target datasets without any finetuning. Extensive experiments demonstrate that our model surpasses existing models that are trained with full supervision and outperforms state-of-the-art self-supervised approaches on PASCAL VOC by using only $3\%$ data (4M vs 134M) for pre-training, significantly improving the training efficiency. + +# 2. Related Work + +Vision-Language Pre-training. Vision-language pretraining (VLP) aims to learn joint visual-textual representations for a variety of multimodal downstream tasks. Existing works either learn unimodal encoders by distinguishing the positive pair(s) from the unpaired samples [3,28,41] + +or focus on one multimodal encoder for joint feature learning with masked image/language modeling and image-text matching losses [12,27,29]. Additionally, some approaches seek fine-grained supervision for cross-modal interaction [20, 30, 55-57]. For example, GLIP [30] proposed to align the bounding boxes with corresponding phrases in the text. However, they still rely on ground-truth grounding annotations. In contrast, our work explores fine-grained information with only weak supervision provided. Despite remarkable performance on multimodal downstream tasks, few of these vision-language models have been designed for fundamental vision tasks (e.g., semantic segmentation). + +Zero-shot/Open-vocabulary Semantic Segmentation. The goal of zero-shot semantic segmentation is to segment objects of interest that are not seen in the training set. Prior works [22, 40, 51] mainly transfer the knowledge from the training set (seen) to the testing set (unseen) via visual-semantic mapping. Inspired by the open-vocabulary nature of language, current approaches [17, 21, 26, 37, 44, 45, 60] exploit vision-language models (e.g., CLIP [41]) pretrained on large-scale image-caption pairs. They need either finetuning [17, 21, 26] or self-training [60] on the target segmentation dataset, which is less efficient and flexible than zero-shot transfer. VLP for open-vocabulary segmentation combines the merits of both zero-shot transfer and open-vocabulary recognition. GroupViT [53] designed a grouping vision transformer and learned the alignment between groups and text via the contrastive loss. ViL-Seg [32] combined image-text contrastive loss with online pixel clustering for segmentation. A concurrent work CLIPpy [42] explored different aggregation operations for training spatial-aware vision-language models for segmentation. Beyond the global image-text matching, we further design masked entity completion and cross-image mask consistency to enrich the group semantics. + +Fully-/Weakly-/Semi-Supervised Semantic Segmentation. Fully-supervised semantic segmentation emerges from per-pixel classification [8, 34] to mask classification [13, 48]. To relieve the laborious annotations, extensive efforts have been made to address semantic segmentation with less supervision. In the family of weakly-supervised object localization [24,50,54] and semantic segmentation [1,9,52], only class labels are available for supervision. Generally, the class activation maps [58] derived from the classification network serve as the initial segmentation results. Another line of research focuses on semi-supervised semantic segmentation [11, 19, 36, 39, 49] where a few samples have dense per-pixel labels and the remaining samples are unlabeled. These works mainly perform supervised training on labeled samples with additional consistency regularizations posed on unlabeled samples. Despite promising results, these approaches are still limited to closed-set object categories. + +![](images/49f51875dff4317c8e35ff871f9ee8da5f21cdc5deaeb5d9c31c6fd97d293a6d.jpg) +Figure 2. Overall framework. The model takes an image-text pair as input, and outputs the image features and a set of learnable group tokens, which is aligned to the caption embedding via $\mathcal{L}_{\mathrm{contrast}}$ . The visual encoder consists of Transformer encoder layers, with a slot-attention based binding module in between to cluster the image tokens into groups. To enrich group semantics, (1) $\mathcal{L}_{\mathrm{entity}}$ trains the model to infer all masked entities in the caption given the group tokens. (2) $\mathcal{L}_{\mathrm{mask}}$ enforces consistent mask predictions of images with the same entity (right figure). During inference, only visual and text encoders and learned group tokens are used to generate the segmentation mask. + +![](images/8a146145b190280f3ef0ec79eb8efc2eb19e517294cef3e8bf3d94353b43c34e.jpg) + +# 3. Methodology + +Assuming we are given a dataset of image-caption pairs, i.e., $\mathcal{D}_{\mathrm{train}} = \{(I_1,T_1),\dots ,(I_N,T_N)\}$ , where $I_{i}\in$ $\mathbb{R}^{H\times W\times 3}$ denotes the image, and $T_{i}$ refers to its corresponding alt-text downloaded from the Internet, the goal is to train an open-vocabulary semantic segmentation (OVS) model only with these (very) weak labels, that can segment objects of arbitrary classes. For each pair $(I,T)$ ,we first split the image into $L = HW / P^2$ non-overlapping patches with patch size $P$ . These patches are then transformed into a sequence of image tokens with MLPs, we reuse $I\in \mathbb{R}^{L\times D}$ to represent them for ease of understanding. Additionally, we introduce $K$ learnable tokens $G\in \mathbb{R}^{K\times D}$ , that aims to group the image tokens into clusters. + +We present a VLP framework, termed OVSegmentor, it assembles the image patches into groups and aligns the group to categories, by only exploiting weak supervisions from image-caption pairs. Conceptually, the architecture consists of two stages, namely, a patch-to-group binding that assigns all patches with same semantics into one group, and group-to-category alignment that computes matching scores between each of these group tokens with semantic categories, the segmentation mask $m$ can be computed as: + +$$ +m = \underset {C} {\arg \max } \mathbb {A} \cdot \mathcal {G} \cdot \mathcal {T} ^ {\top}, \tag {1} +$$ + +where $\mathbb{A} \in \mathbb{R}^{L \times K}$ characterises a soft binding procedure, denoting the likelihood of each image patch being assigned into the output group embeddings $(\mathcal{G} \in \mathbb{R}^{K \times D})$ ; and $\mathcal{T} \in \mathbb{R}^{C \times D}$ denotes the embeddings with dimension $D$ for a total of $C$ categories, computed from a text encoder. Note that, at training time, manual segmentation masks are not present, and the intermediate binding and assignment can only be learnt from the image-caption pairs implicitly. + +At inference stage, the patch-group affinity $\mathbb{A}$ can be computed by the product between the image features $\mathcal{I} \in \mathbb{R}^{L \times D}$ and the learned group tokens $\mathcal{G}$ , and $\mathcal{T}$ can be derived by encoding the prompted class labels (e.g., A photo of {class}). Thus the patch-level mask prediction can be calculated via Eq. 1. The pixel-level segmentation mask can then be obtained by up-sampling the patch-level mask $(m)$ . In the following sections, we detail the overall architecture and training procedure of each component. The overall framework is illustrated in Fig. 2. + +# 3.1. Architecture + +In this section, we will detail the proposed architecture at training stage, including a visual encoder for learning group tokens and the binding procedure, a text encoder that computes variants of caption embeddings. We describe the encoding procedure for image and caption pair, the subscripts are thus ignored for simplicity. + +# 3.1.1 Visual Encoder + +The visual encoder consists of two components, namely, Transformer encoders and binding modules. Specifically, the image tokens and learnable group tokens are concatenated, and iteratively processed by the Transformer encoders, and a binding module with slot-attention [33] is adopted for grouping. The visual encoder is defined as: + +$$ +[ \mathcal {G}; \mathcal {I} ] = \Phi_ {\mathrm {e n c}} ^ {2} (\cdot) \circ \Phi_ {\mathrm {b i n d}} (\cdot) \circ \Phi_ {\mathrm {e n c}} ^ {1} ([ G; I ]), \tag {2} +$$ + +where $\Phi_{\mathrm{enc}}^{1}(\cdot)$ and $\Phi_{\mathrm{enc}}^{2}(\cdot)$ refer to modules with Transformer encoder layers, and $\Phi_{\mathrm{bind}}(\cdot)$ is the binding module. The output $\mathcal{G}\in \mathbb{R}^{K\times D}$ is the encoded group tokens, and $\mathcal{I}\in \mathbb{R}^{L\times D}$ refers to the output image tokens. + +Transformer Encoder. Both $\Phi_{\mathrm{enc}}^{1}$ and $\Phi_{\mathrm{enc}}^{2}$ consist of 6 Transformer encoder layers [47], where each layer is composed of a multi-head self-attention (MHSA) layer followed by layer normalisation (LN) and a feed-forward network (FFN). $\Phi_{\mathrm{enc}}^{1}$ takes the concatenation of image patches and randomly initialised group tokens as input, and outputs intermediately encoded group and image tokens ( $G'$ and $I'$ ); $\Phi_{\mathrm{enc}}^{2}$ processes the output from the binding module. + +Binding Module. The binding module uses slot-attention to cluster image tokens into groups in a data-dependent manner, i.e., image patches with similar appearance and semantics are encouraged to be grouped together. Formally, the binding module accepts the output from the Transformer encoder $\Phi_{\mathrm{enc}}^{1}$ , and transforms them into query, key and values with linear transformations: + +$$ +\mathbf {Q} = W _ {q} ^ {\text {b i n d}} G ^ {\prime}, \quad \mathbf {K}, \mathbf {V} = W _ {k} ^ {\text {b i n d}} I ^ {\prime}, W _ {v} ^ {\text {b i n d}} I ^ {\prime}. \tag {3} +$$ + +In contrast to the standard cross attention in Transformer Decoders [47], slot-attention performs normalisation over queries, encouraging each image token to be claimed by one of the group tokens. The binding process can be defined as: + +$$ +\mathbb {A} _ {j, k} = \frac {\exp \left(\mathbf {K} _ {j} \cdot \mathbf {Q} _ {k}\right)}{\sum_ {l} \exp \left(\mathbf {K} _ {j} \cdot \mathbf {Q} _ {l}\right)}, \tag {4} +$$ + +where $\mathbb{A}_{j,k}$ refers to the probability of the $j$ -th image token being assigned to the $k$ -th group. Then, each updated group token $\tilde{G}_k'$ is computed by taking the weighted average of the image tokens that are assigned to it. The output of the binding module $G^{\mathrm{bind}}$ is defined as: + +$$ +\tilde {G} _ {k} ^ {\prime} = \frac {\sum_ {j = 1} ^ {L} \mathbb {A} _ {j , k} \mathbf {V} _ {j}}{\sum_ {j = 1} ^ {L} \mathbb {A} _ {j , k}}, \tag {5} +$$ + +$$ +G ^ {\text {b i n d}} = G ^ {\prime} + W _ {o} ^ {\text {b i n d}} \tilde {G} ^ {\prime}, \tag {6} +$$ + +where $W_{o}^{\mathrm{bind}}$ is a linear transformation. Now we have obtained the correspondence between each patch and groups, next we describe the procedure for encoding captions. + +# 3.1.2 Text Encoder + +Till this end, we filter all the captions and only keep the ones with informative entities, followed by exploiting three variants of the caption encoding, namely, the entire caption embedding, the masked caption embedding and the prompted entity embedding. In all cases we use a pretrained BERT [16] as the text encoder $\Phi_{\mathrm{text}}$ . + +Constructing Entity Set. We adopt the nltk toolkit [4] to extract entities from all the captions, and construct an entity set $\Omega = \Phi_{\text{entity}}(\{T_1, \ldots, T_N\})$ that only maintains the frequently appeared entities (e.g., people, cat, shirt, etc.), and exclude the abstract nouns (e.g., art, view, etc) as they usually do not correspond to any specific region in the image. For each image-caption pair, we can thus obtain an image-caption-entity triplet $(I, T, E)$ , where $E = \{e | e \in T \cap e \in \Omega\}$ includes all frequent entities in the caption. + +Caption Embedding. Here, for each caption $T$ , it is tokenised using the BERT tokeniser [16], with the start-of-token [SOT] and end-of-token [EOT] added to the beginning/ending. The caption embedding is computed as $\mathcal{T}^{\mathrm{cap}} = \Phi_{\mathrm{text}}(T) \in \mathbb{R}^{M \times D}$ , where $M$ is length after tokenisation. + +Masked Caption Embedding. In the second variant, we mask all the frequent entities in the caption, and compute the masked caption embedding as $\mathcal{T}^{\mathrm{m - cap}} = \Phi_{\mathrm{text}}(g(T))\in$ $\mathbb{R}^{M\times D}$ , where $g(\cdot)$ denotes an masking operation, that replaces the entity with a special [MASK] token. + +Prompted Entity Embedding. We construct manual prompts for each of the entities in the triplets, and compute its textual embedding as $\mathcal{T}^{\mathrm{entity}} = \Phi_{\mathrm{text}}(h(E)) \in \mathbb{R}^{M \times D}$ , where $h(\cdot)$ refers to the procedure for constructing manual prompts, for example, we randomly sample a prompt template provided in [41], and fill in the entities: A painting of $a$ $\{entity_1\}$ and $\{entity_2\}$ and $\{entity_3\}$ . This sentence is padded to the same length as the caption after tokenisation. + +# 3.2. Training + +As for training, we aim to learn the alignment between group tokens and caption embeddings via three proxy tasks, namely, image-caption alignment, masked entity completion, and cross-image mask consistency. + +Image-caption Alignment. For each image-text pair, the objective is to align their visual and textual embeddings. The visual embedding $z^{\mathrm{I}}$ is the average of group tokens, and the textual embedding $z^{\mathrm{T}}$ is obtained by taking the [EOT] token feature of the caption embedding $\mathcal{T}^{\mathrm{cap}}$ , both projected to a 256-d joint feature space followed by normalisation. The image-caption contrastive loss $\mathcal{L}_{\mathrm{contrast}}$ is formulated as: + +$$ +\mathcal {L} _ {\text {c o n t r a s t}} = \frac {1}{2} \left(\frac {\exp \left(z _ {i} ^ {\mathrm {I}} \cdot z _ {i} ^ {\mathrm {T}}\right)}{\sum_ {l} \exp \left(z _ {i} ^ {\mathrm {I}} \cdot z _ {l} ^ {\mathrm {T}}\right)} + \frac {\exp \left(z _ {i} ^ {\mathrm {I}} \cdot z _ {i} ^ {\mathrm {T}}\right)}{\sum_ {l} \exp \left(z _ {l} ^ {\mathrm {I}} \cdot z _ {i} ^ {\mathrm {T}}\right)}\right). \tag {7} +$$ + +Here, we omit the temperature parameter for simplicity. + +Masked Entity Completion. The goal of masked entity completion is to infer all the masked entities in the sentence given the group tokens. In specific, we adopt a Transformer decoder layer, where a projection of the masked caption embedding is treated as query, and two linear transformations of group tokens are treated as key and values, respectively. + +$$ +\tilde {\mathcal {T}} ^ {\mathrm {m} - \mathrm {c a p}} = \Phi_ {\mathrm {d e c}} \left(W _ {q} ^ {\mathrm {d e c}} \mathcal {T} ^ {\mathrm {m} - \mathrm {c a p}}, W _ {k} ^ {\mathrm {d e c}} \mathcal {G}, W _ {v} ^ {\mathrm {d e c}} \mathcal {G}\right) \in \mathbb {R} ^ {M \times D}, \tag {8} +$$ + +where $W_{q}^{\mathrm{dec}}$ , $W_{k}^{\mathrm{dec}}$ and $W_{v}^{\mathrm{dec}}$ are linear transformations, and $\tilde{T}^{\mathrm{m - cap}}$ denotes the updated vector sequence, with the masked entities being completed by querying group tokens. For training, we extract the [EOT] token features $z^{\mathrm{M}}$ and $z^{\mathrm{E}}$ from $\tilde{T}^{\mathrm{m - cap}}$ and $T^{\mathrm{entity}}$ , and construct a contrastive loss: + +$$ +\mathcal {L} _ {\text {e n t i t y}} = \frac {1}{2} \left(\frac {\exp \left(z _ {i} ^ {\mathrm {M}} \cdot z _ {i} ^ {\mathrm {E}}\right)}{\sum_ {l} \exp \left(z _ {i} ^ {\mathrm {M}} \cdot z _ {l} ^ {\mathrm {E}}\right)} + \frac {\exp \left(z _ {i} ^ {\mathrm {M}} \cdot z _ {i} ^ {\mathrm {E}}\right)}{\sum_ {l} \exp \left(z _ {l} ^ {\mathrm {M}} \cdot z _ {i} ^ {\mathrm {E}}\right)}\right). \tag {9} +$$ + +Intuitively, the entity completion task enables better alignment between the groups and entities. + +Cross-image Mask Consistency. To encourage visual invariance, we enforce consistent mask predictions between images that contain shared entities. Specifically, for each entity of interest, we can easily source multiple image-caption pairs from the dataset by text searching. Given one sampled image-caption-entity triplet, $(I_1, T_1, E_1)$ , we can easily search for another sample $(I_2, T_2, E_2)$ , with both triplet sharing one entity, i.e., $e \in E_1 \cap E_2$ . Given encoded groups $\mathcal{G}_1, \mathcal{G}_2$ , two sets of subgroups $\mathcal{G}_1^{\mathrm{sub}}, \mathcal{G}_2^{\mathrm{sub}} \in \mathbb{R}^{K' \times D}$ that represent the common entity $e$ (e.g., cat in Fig. 2) are obtained by choosing groups with higher similarity to the entity embedding, where $K' = rK$ and $r$ is the selection ratio. The masks of co-attentive entity in image $I_1$ can be grounded by both entity-specific subgroups $\mathcal{G}_1^{\mathrm{sub}}, \mathcal{G}_2^{\mathrm{sub}}$ as: + +$$ +\mathcal {M} _ {1} = \sigma \left(\mathcal {I} _ {1} ^ {\top} \mathcal {G} _ {1} ^ {\text {s u b}}\right), \hat {\mathcal {M}} _ {1} = \sigma \left(\mathcal {I} _ {1} ^ {\top} \mathcal {G} _ {2} ^ {\text {s u b}}\right) \in [ 0, 1 ] ^ {L \times K ^ {\prime}}, \tag {10} +$$ + +where $\sigma$ is the sigmoid activation; both $\mathcal{M}_1 = \{m_k^1\}_{k=1}^{K'}$ and $\hat{\mathcal{M}}_1 = \{\hat{m}_k^1\}_{k=1}^{K'}$ consist of $K'$ unordered masks. + +To align $\hat{\mathcal{M}}_1$ with $\mathcal{M}_1$ , we first adopt the bipartite matching to find the optimal permutation $p_1^*$ over $K'$ subgroups with the lowest matching cost as: + +$$ +p _ {1} ^ {*} = \underset {p \in \mathcal {P}} {\arg \min } \sum_ {k} - \cos \left(m _ {k} ^ {1}, \hat {m} _ {p (k)} ^ {1}\right), \tag {11} +$$ + +where $\mathcal{P}$ is the full permutation and $\cos (\cdot)$ denotes the cosine similarity. Eq. 11 is solved via the efficient Hungarian algorithm [25]. In this way, the symmetric cross-image mask consistency loss $\mathcal{L}_{mask}$ is defined as: + +$$ +\mathcal {L} _ {\text {m a s k}} = \frac {1}{2} \left(\sum_ {k} \mathrm {D} \left(\operatorname {s g} \left(\mathbf {m} _ {k} ^ {1}\right), \hat {m} _ {p _ {1} ^ {*} (k)} ^ {1}\right) + \mathrm {D} \left(\operatorname {s g} \left(\mathbf {m} _ {k} ^ {2}\right), \hat {m} _ {p _ {2} ^ {*} (k)} ^ {2}\right)\right), \tag {12} +$$ + +where $\mathrm{sg}(\cdot)$ denotes the stop gradient operation; the target mask $\mathbf{m}_k$ is achieved by binarizing $m_{k}$ with a threshold $\delta$ ; $\mathrm{D}(\mathbf{m},\hat{m}) = 1 - 2|\mathbf{m}\bigcap \hat{m} | / (|\mathbf{m}| + |\hat{m} |)$ stands for the standard Dice loss. To guarantee the quality of pseudo mask targets, the masks are generated by an extra momentum model, which is updated by the exponential-moving-average (EMA) of the online model. + +Training Objective. We adopt a combination of three different loss functions: + +$$ +\mathcal {L} _ {\text {t o t a l}} = \mathcal {L} _ {\text {c o n t r a s t}} + \mathcal {L} _ {\text {e n t i t y}} + \lambda \mathcal {L} _ {\text {m a s k}}, \tag {13} +$$ + +where $\lambda$ is the weight for balancing the mask consistency. + +# 3.3. Discussion + +One work that is closely related to ours is GroupViT [53]. They extracted multiple nouns and prompted each noun to a sentence to serve as extra matched captions for the image. The model is thus supervised by a multi-label contrastive loss. In contrast, our paper differs from GroupViT in three critical aspects: (1) Entities vs nouns. Rather than using all nouns, we leverage the entities that match to visual objects, enabling high-quality image-caption correspondence. (2) Network architecture. We only adopt single-stage grouping (i.e., one binding module) as we observe no further performance gain of applying multi-stage grouping in GroupViT. The hard-assignment trick [53] is also adopted. Besides, we devise a (very) lightweight decoder to model the fine-grained, token-wise group-word correlation. (3) Proxy tasks for training. We propose two different proxy tasks, i.e., masked entity completion and cross-image mask consistency to improve the entity-specific group semantics and further encourage visual invariance. + +# 4. Experiments + +# 4.1. Experimental Setups + +Pre-training Dataset. Following [32, 53], we use Conceptual Captions 12M [7] for training, which is originally constructed with over 12M image-text pairs collected from the Internet. However, due to some links have been expired, we have downloaded about 10M image-text pairs. The constructed entity set in Sec. 3.1.2 includes a total number of 100 frequently appeared entities while abstract nouns (e.g., art, view) are discarded. After filtering CC12M, we obtain 4.3 million image-text pairs for pre-training, which is termed as CC4M. Examples of entities include people, car, cup, chair, T-shirt, house, bed, cat, ball, pizza, etc. Please refer to the supplementary material for the full entity set. + +Downstream Evaluation Datasets. We evaluate our model on four benchmarks, namely, PASCAL VOC 2012 [18], PASCAL Context [38], COCO Object [31], and ADE20K [59] with 20, 59, 80 and 150 foreground classes, + +
MethodBackbonePretrain datasetSupervisionZero-shot transferDownstream datasets
PASCAL VOCPASCAL ContextCOCO ObjectADE20K
DeiT [46]ViT-SIN-1Kclass labelX53.035.9--
MoCo [10]ViT-SIN-1KselfX34.321.3--
DINO [6]ViT-SIN-1KselfX39.120.4--
MoCo [10]ViT-SCC12M+YFCC15MselfX36.123.0--
DINO [6]ViT-SCC12M+YFCC15MselfX37.622.8--
ViL-Seg [32]ViT-BCC12Mself+text✓33.615.9--
GroupViT [53]ViT-SCC12Mtext✓41.1---
GroupViT [53]ViT-SCC12M+YFCC15Mtext✓51.222.320.9-
ViewCo [43]ViT-SCC12M+YFCC15Mself+text✓52.423.023.5-
CLIPpy [42]ViT-BHQITP-134Mtext✓52.2-25.5†13.5
GroupViT* [53]ViT-SCC4Mtext✓19.88.89.13.4
GroupViT* [53]ViT-BCC4Mtext✓25.811.310.73.6
GroupViT* [53]ViT-SCC12Mtext✓40.218.717.76.2
OVSegmentor (ours)ViT-SCC4Mtext✓44.518.319.04.3
OVSegmentor (ours)ViT-BCC4Mtext✓53.820.425.15.6
+ +Table 1. Comparison with existing methods. Models in the first five rows are finetuned on target datasets while the rest perform zero-shot transfer. GroupViT (with *) refers to the results by training officially released code. Results of DeiT, MoCo, and DINO are copied from [53]. Bold fonts refer to the best results among the models that enable zero-shot transfer. CLIPpy uses 134M in-house data while we use an order of magnitude less data. † indicates evaluation on 133 COCO classes claimed by CLIPpy, which differs from ours (80 classes). + +respectively. An extra background class is considered in VOC, Context, and COCO. We ignore their training sets and directly evaluate our method on the validation sets without any finetuning, including 1449, 5105, 5000, and 2000 images, respectively. In general, we report the mean Intersection-over-Union (mIoU) on all the classes. + +Implementation Details. In our model, the self-attention layers in the visual encoder are initialised with DINO [6] pre-trained on ImageNet. The text encoder is initialized with BERT [16] model pre-trained on BookCorpus and English Wikipedia. Our decoder with one randomly initialized Transformer Decoder layer performs reasonably well. The input image is randomly cropped to $224\times 224$ at training time, and the batch size is set to 2048 with an initial learning rate $3.2\times 10^{-4}$ . We train our model for 40 epochs using the Adam optimizer with weight decay set to 0.5. The coefficient for updating the momentum model is 0.99. As the generated masks are unreliable in early epochs, we set the mask consistency coefficient $\lambda = 0$ for the first 30 epochs and $\lambda = 0.1$ for the remaining epochs. The group selection ratio $r$ is 0.5. As for the threshold in mask consistency loss, we use $\delta = 0.65$ . At inference time, the image is resized with a shorter length of 448. We follow [53] to set a threshold for the background class, which is 0.9, 0.35, 0.9 and 0.95 on VOC, Context, COCO, and ADE20K, respectively. + +# 4.2. Comparison with Existing Methods + +In Table 1, we compare our model with the existing models that have been trained with (1) fully-supervised finetuning transfer and (2) zero-shot transfer. In Table 2, zero-shot segmentation (ZSS) approaches are listed for comparison. + +![](images/843c99e8c48689860c4522a44c8a88f978c35e1c91410e25ec4da541a5961a28.jpg) +Figure 3. Qualitative results on PASCAL VOC. We show the learned groups in the last column with each color representing a group. In the last row, the model fails to segment the person's leg. + +Comparison with Finetuning Transfer. We compare our method with DeiT [46], MoCo [10] and DINO [6], which are pre-trained on ImageNet [15] or CC12M+YFCC15M datasets with class labels [46] or self-supervision [6, 10], and finetuned on the training set from downstream benchmarks, with a randomly initialised convolution head appended on the backbone network. As shown in Table 1, our model achieves competitive performance on PASCAL Context, and outperforms the self-supervised methods by over $10\%$ on PASCAL VOC, with zero-shot transfer. + +Comparison with Zero-shot Transfer. Here, we compare with existing works under the zero-shot transfer scenario, including GroupViT [53], ViL-Seg [32] and CLIPpy [42], with the pre-training data ranging from CC12M [7] to 134M in-house dataset HQITP-134M [42]. For fair comparison, we re-train GroupViT [53] with their official codebase on our CC4M and CC12M datasets, with the same pre-trained weights as ours being adopted on CC4M. However, we observe no further performance gain of either applying pre + +
MethodPretrain datasetSeenUnseenVOCContext
SPNet [51]-✓✗15.64.0
ZS3 [5]-✓✓17.77.7
GaGNet [22]-✓✓29.915.0
SIGN [14]-✓✓28.914.9
Joint [2]-✓✗32.5-
ZegFormer [17]CLIP400M✓✗63.6
MaskCLIP+ [60]CLIP400M✓✗86.166.7†
ViL-Seg [32]CC12M✗✗37.318.9
GroupViT [53]CC12M+YFCC15M✗✗43.751.3
GroupViT* [53]CC4M✗✗22.424.5
GroupViT* [53]CC12M✗✗33.145.3
OVSegmentorCC4M✗✗46.654.5
+ +trained weights to GroupViT or using ViT-B as the backbone on CC12M. The results on CC12M match the reported ones (40.2 vs 41.1). Under the same pre-trained ViT-B backbone and CC4M dataset, our method surpasses the original GroupViT by $35.7\%$ . Additionally, by only using 4M pre-training data, our model yields the best segmentation performance on PASCAL VOC, even outperforming CLIPpy, which is a concurrent work to ours, and pre-trained on 134M data, indicating the effectiveness and training efficiency of our proposed model. + +Comparison with Zero-shot Segmentation Methods. In this line of research [17, 22, 51], the idea is to train the model with full mask labels (obtained either from manual groundtruth or pseudo-labelling [60]) on the seen classes and transfer the model to unseen classes, and the task is thus dubbed as zero-shot semantic segmentation (ZSS). To be specific, 5 classes (potted plant, sheep, sofa, train and tv-monitor) in PASCAL VOC and 4 classes (cow, motorbike, sofa and cat) in PASCAL Context are considered unseen while the remaining classes belong to seen. For comparison, we also report the zero-shot transfer performance of our model on these unseen classes, however, note that, we do not use any manual mask annotations for training. As observed in Table 2, our model surpasses majority of the models trained under the ZSS scenario, except for [17, 60] that adopted CLIP model pre-trained on 400M data. In terms of transfer efficiency, our model excels at zero-shot transfer ability without the need of training on seen classes. + +# 4.3. Ablation Study + +In this section, we conduct thorough ablation studies to validate the necessity of each proposed component. + +Ablation Study on Proxy Tasks. Here, we aim to understand the effects of our proposed proxy tasks, i.e., masked entity completion and cross-image mask consistency. As + +Table 2. Comparison with zero-shot segmentation methods on unseen classes. Seen/Unseen denotes whether the model is trained on seen/unseen classes. Our method outperforms most zero-shot segmentation models even without training on seen classes. $\dagger$ indicates a different set of unseen classes. + +
Baseline\( {\mathcal{L}}_{\text{entity }} \)\( {\mathcal{L}}_{\text{mask }} \)PASCAL VOCPASCAL Context
✓40.515.1
✓✓48.919.9
✓✓44.817.0
✓✓✓53.820.4
+ +Table 3. Ablation study on the masked entity completion loss $(\mathcal{L}_{\mathrm{entity}})$ and cross-image mask consistency loss $(\mathcal{L}_{\mathrm{mask}})$ . The baseline only uses the image-caption contrastive loss $(\mathcal{L}_{\mathrm{contrast}})$ . + +
Masking objectivePASCAL VOCPASCAL Context
All entities (ours)48.919.9
Single entity47.018.5
All nouns45.417.4
Multi-label contrastive44.517.0
MLM (w/ groups)42.816.3
MLM (w/o groups)36.314.6
+ +Table 4. Comparison on masking objectives. MLM (w/ groups) refers to masked language modeling [16] given the group tokens as keys and values in the decoder while MLM (w/o groups) directly predicts masked words from the output of text encoder. + +shown in Table 3, the baseline model uses the image-text contrastive loss $\mathcal{L}_{\mathrm{contrast}}$ only, while adding the entity completion task, $\mathcal{L}_{\mathrm{entity}}$ , we observe a significant improvement by $8.4\%$ and $4.8\%$ on PASCAL VOC and PASCAL Context, respectively. The performance gain is due to the ability of better aligning the pixel groups with the visual entities. Additionally, the mask consistency also brings improvements, and combing both leads to the best performance. Qualitative results can be seen in Fig. 3. We refer the readers for more visualisations in the supplementary material. + +On the Choice of Masking Objectives. We compare our masked entity completion with a series of variants as shown in Table 4. Our proposed objective of masking all entities is listed in the first row. (1) All entities vs one entity: the masked language modeling (MLM) in prior works [16, 35] normally choose $15\%$ of the token positions in the sentence for prediction, which results in one entity in most of our cases. We observe that masking all entities is $2.9\%$ mIoU better than single entity masking, as it forces the network to infer all possible object categories in the image, that is potentially beneficial for the group to category alignment. (2) Entities vs nouns: masking and predicting noun phrases in the sentence [20] is one feasible option to learn fine-grained vision-text matching. However, noun masking leads to $3.5\%$ lower mIoU than our entity masking strategy. This is because not all of the nouns in the sentence are visually corresponding to the objects in the image (e.g., illustration, night, etc), thus the group tokens are not expected to align with these nouns. Our method avoids this issue by only masking the visual entities. (3) Masked entity completion vs multi-label contrastive loss: comparing with the multi-label contrastive loss used in GroupViT [53], our proposed + +![](images/31486f25a583772e6931f67c7fdb0681334e02104e3ca6f7aec5ff51b431d8cb.jpg) +Figure 4. Qualitative results of the masked entity completion loss. We show different groups that match the given class. With the boost of group semantics, our model improves visual grouping (left) and solves false matching between group and text (right) of the baseline. + +![](images/5614c2757de50d322408add4ef27eccd1156cf05261ac98e47a5e77ecc08974d.jpg) + +
K/τ0.250.50.751.0
849.053.651.850.4
1642.343.243.142.9
+ +Table 5. Comparison on group numbers $K$ and selection ratios $\tau$ on VOC. + +
ObjectiveLossmIoU
Mask ConsistencyDice53.8
Group ConsistencyNCE47.9
+ +strategy shows superior performance. (4) Masked entity completion vs masked language modeling: MLM [16] originally predicts the masked token over the entire vocabulary via a cross-entropy loss. Here, we restrict the vocabulary to our constructed entity set for fair comparison. Our masking strategy surpasses both MLM variants by a clear margin, which we conjecture is because: (1) MLM classifies each masked token individually, and the model can easily refer to the context words without relating to groups. (2) MLM focuses on word-level representations, which is not in accordance with the sentence-level representation of the class embeddings we used during inference. Fig. 4 shows the effect of the masked entity completion in improving visual grouping (left) and group-text alignment (right). + +On Cross-image Mask Consistency. Here, we analyze two key factors in cross-image consistency: (1) group numbers and selection ratios. As shown in Table 5, 8 groups perform comparably well for all selection ratios, and we pick 0.5 as the default. Smaller ratios miss the entities encoded in remaining groups while larger ratios introduce entity-irrelevant information (e.g., background). However, while increasing the group number to 16, it brings over-segmentation for large objects, deteriorating the performance. (2) the objective for cross-image consistency. We study another variant of cross-image consistency by directly aligning two sets of group tokens encoded with shared entity. We adopt the contrastive loss (NCE in Table 6) to pull two sets of group tokens closer, while other group tokens in each mini-batch are pushed farther. Table 6 reveals the superiority of our proposed mask consistency over group consistency, which we believe is because mask consistency involves the image content to realize visual invariance. + +Performance on Unseen Entities. We measure the mean IoU for objects within entity set (e.g., person, bus) and those out of entity set on each dataset. As shown in Table 7, our model still achieves a decent improvement over the baseline on unseen entities, indicating it retains strong + +![](images/241e8d11af046f243799c2d39e6411b4a5250bd20cbf42d3626d59c157f1c889.jpg) + +![](images/d818cf0fec8993a2a20701e9fe47d78833da0cda482e8c1310eb32fbfcbd0276.jpg) + +Table 6. Comparison on different objectives for cross-image consistency on VOC. + +
MethodVOCCOCOContextADE20K
S20U0S65U16S30U29S31U119
Baseline38.5-17.215.420.58.87.13.7
Ours(CC4M)52.4-24.825.927.910.98.64.8
Ours(CC12M)44.8-18.621.525.412.010.16.3
+ +Table 7. Performance on seen(S)/unseen(U) entities, where indices refer to entity numbers. Background class is not considered. + +open-vocabulary segmentation ability. The text encoder pre-trained on large text corpus remains its ability to encode the semantic concept of objects out of entity set. + +Scalability. We scale up the datasets by increasing the number of entities, i.e., training the model on the entire CC12M. Ours (CC12M) outperforms ours (CC4M) on the challenging ADE20K and PASCAL Context, reflecting the effect of the scalability of our model. The performance drop on VOC is because the extremely large entity set in CC12M poses extra challenge for our model to learn the entities in VOC. + +# 5. Conclusion + +In this paper, we present OVSegmentor, a transformer-based model for open-vocabulary semantic segmentation. The model exploits web-collected image-caption pairs for pre-training without any mask annotations, and transfers to target benchmark segmentation datasets in a zero-shot manner. The model clusters the image pixels into learnable group tokens, which are then aligned with the corresponding caption embeddings. We further devise two proxy tasks, namely masked entity completion and cross-image mask consistency, to learn entity-specific, fine-grained and visually invariant group semantics. OVSegmentor outperforms the state-of-the-art method on PASCAL VOC by using only $3\%$ (4M vs 134M) for pre-training, indicating the effectiveness and training efficiency of our model. + +Acknowledgement. This work is supported by Natural Science Foundation of China (No. 61976057; No. 62172101), the Science and Technology Commission of Shanghai Municipality (No. 21511101000; No. 22511106003), the Science and Technology Major Project of Commission of Science and Technology of Shanghai (No. 2021SHZDZX0103). This work is also partially supported by the National Key R&D Program of China (No. 2022ZD0160100), and in part by Shanghai Committee of Science and Technology (No. 21DZ1100100). This work was done during Jilan's parttime internship at Shanghai Artificial Intelligence Laboratory. + +# References + +[1] Jiwoon Ahn and Suha Kwak. Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation. In CVPR, 2018. 2 +[2] Donghyeon Baek, Youngmin Oh, and Bumsub Ham. Exploiting a joint embedding space for generalized zero-shot semantic segmentation. In ICCV, 2021. 7 +[3] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, 2021. 2 +[4] Steven Bird, Ewan Klein, and Edward Loper. Natural language processing with Python: analyzing text with the natural language toolkit. "O'Reilly Media, Inc.", 2009. 4 +[5] Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, and Patrick Pérez. Zero-shot semantic segmentation. NeurIPS, 2019. 7 +[6] Mathilde Caron, Hugo Touvron, Ishan Misra, Herve Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In ICCV, 2021. 6 +[7] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In CVPR, 2021. 2, 5, 6 +[8] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. PAMI, 2017. 2 +[9] Qi Chen, Lingxiao Yang, Jian-Huang Lai, and Xiaohua Xie. Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation. In CVPR, 2022. 2 +[10] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In ICCV, 2021. 6 +[11] Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang. Semi-supervised semantic segmentation with cross pseudo supervision. In CVPR, 2021. 2 +[12] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, 2020. 2 +[13] Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. NeurIPS, 2021. 2 +[14] Jiaxin Cheng, Soumyaroop Nandi, Prem Natarajan, and Wael Abd-Almageed. Sign: Spatial-information incorporated generative network for generalized zero-shot semantic segmentation. In ICCV, 2021. 7 +[15] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 6 +[16] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In NAACL, 2019. 4, 6, 7, 8 + +[17] Jian Ding, Nan Xue, Gui-Song Xia, and Dengxin Dai. Decoupling zero-shot semantic segmentation. In CVPR, 2022, 2, 7 +[18] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. IJCV, 2010. 2, 5 +[19] Geoffrey French, Samuli Laine, Timo Aila, Michal Mackiewicz, and Graham D. Finlayson. Semi-supervised semantic segmentation needs strong, varied perturbations. In BMVC, 2020. 2 +[20] Yuying Ge, Yixiao Ge, Xihui Liu, Dian Li, Ying Shan, Xiaohu Qie, and Ping Luo. Bridging video-text retrieval with multiple choice questions. In CVPR, 2022. 2, 7 +[21] Golnaz Ghiasi, Xiuye Gu, Yin Cui, and Tsung-Yi Lin. Scaling open-vocabulary image segmentation with image-level labels. In ECCV, 2022. 2 +[22] Zhangxuan Gu, Siyuan Zhou, Li Niu, Zihan Zhao, and Liqing Zhang. Context-aware feature generation for zero-shot semantic segmentation. In ACM MM, 2020. 2, 7 +[23] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICML, 2021. 1 +[24] Eunji Kim, Siwon Kim, Jungbeom Lee, Hyunwoo Kim, and Sungroh Yoon. Bridging the gap between classification and localization for weakly supervised object localization. In CVPR, 2022. 2 +[25] Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83-97, 1955. 5 +[26] Boyi Li, Kilian Q. Weinberger, Serge J. Belongie, Vladlen Koltun, and René Ranftl. Language-driven semantic segmentation. In ICLR, 2022. 2 +[27] Dongxu Li, Junnan Li, Hongdong Li, Juan Carlos Niebles, and Steven CH Hoi. Align and prompt: Video-and-language pre-training with entity prompts. In CVPR, 2022. 2 +[28] Juncheng Li, Xin He, Longhui Wei, Long Qian, Linchao Zhu, Lingxi Xie, Yueting Zhuang, Qi Tian, and Siliang Tang. Fine-grained semantically aligned vision-language pre-training. NeurIPS, 2022. 2 +[29] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. NeurIPS, 2021. 2 +[30] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In CVPR, 2022. 2 +[31] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV. Springer, 2014. 2, 5 +[32] Quande Liu, Youpeng Wen, Jianhua Han, Chunjing Xu, Hang Xu, and Xiaodan Liang. Open-world semantic segmentation via contrasting and clustering vision-language embedding. In ECCV, 2022. 2, 5, 6, 7 + +[33] Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. Object-centric learning with slot attention. NeurIPS, 2020. 2, 4 +[34] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015. 2 +[35] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS, 2019. 7 +[36] Wenfeng Luo and Meng Yang. Semi-supervised semantic segmentation via strong-weak dual-branch network. In ECCV, 2020. 2 +[37] Chaofan Ma, Yuhuan Yang, Yanfeng Wang, Ya Zhang, and Weidi Xie. Open-vocabulary semantic segmentation with frozen vision-language models. BMVC, 2022. 2 +[38] Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic segmentation in the wild. In CVPR, 2014. 2, 5 +[39] Yassine Ouali, Céline Hudelot, and Myriam Tami. Semi-supervised semantic segmentation with cross-consistency training. In CVPR, 2020. 2 +[40] Giuseppe Pastore, Fabio Cermelli, Yongqin Xian, Massimiliano Mancini, Zeynep Akata, and Barbara Caputo. A closer look at self-training for zero-label semantic segmentation. In CVPR, 2021. 2 +[41] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 1, 2, 4 +[42] Kanchana Ranasinghe, Brandon McKinzie, Sachin Ravi, Yinfei Yang, Alexander Toshev, and Jonathon Shlens. Perceptual grouping in vision-language models. arXiv preprint arXiv:2210.09996, 2022. 2, 6 +[43] Pengzhen Ren, Changlin Li, Hang Xu, Yi Zhu, Guangrun Wang, Jianzhuang Liu, Xiaojun Chang, and Xiaodan Liang. Viewco: Discovering text-supervised segmentation masks via multi-view semantic consistency. arXiv preprint arXiv:2302.10307, 2023. 6 +[44] Gyungin Shin, Weidi Xie, and Samuel Albanie. Named-mask: Distilling segmenters from complementary foundation models. arXiv preprint arXiv:2209.11228, 2022. 2 +[45] Gyungin Shin, Weidi Xie, and Samuel Albanie. Reco: Retrieve and co-segment for zero-shot transfer. NeurIPS, 2022. 2 +[46] Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through attention. In ICML, 2021. 6 +[47] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. NeurIPS, 2017. 4 +[48] Huiyu Wang, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen. Max-deeplab: End-to-end panoptic segmentation with mask transformers. In CVPR, 2021. 2 + +[49] Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, and Xinyi Le. Semi-supervised semantic segmentation using unreliable pseudo-labels. In CVPR, 2022. 2 +[50] Pingyu Wu, Wei Zhai, and Yang Cao. Background activation suppression for weakly supervised object localization. In CVPR, 2022. 2 +[51] Yongqin Xian, Subhabrata Choudhury, Yang He, Bernt Schiele, and Zeynep Akata. Semantic projection network for zero-and few-label semantic segmentation. In CVPR, 2019. 2, 7 +[52] Jinheng Xie, Jianfeng Xiang, Junliang Chen, Xianxu Hou, Xiaodong Zhao, and Linlin Shen. C2am: Contrastive learning of class-agnostic activation map for weakly supervised object localization and semantic segmentation. In CVPR, 2022. 2 +[53] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In CVPR, 2022. 1, 2, 5, 6, 7 +[54] Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng, Rui-Wei Zhao, Tao Zhang, Xuequan Lu, and Shang Gao. Cream: Weakly supervised object localization via class re-activation mapping. In CVPR, 2022. 2 +[55] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. FILIP: fine-grained interactive language-image pre-training. In ICLR, 2022. 2 +[56] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. In ICML, 2022. 2 +[57] Haotian Zhang, Pengchuan Zhang, Xiaowei Hu, Yen-Chun Chen, Liunian Harold Li, Xiyang Dai, Lijuan Wang, Lu Yuan, Jenq-Neng Hwang, and Jianfeng Gao. Glipv2: Unifying localization and vision-language understanding. NeurIPS, 2022. 2 +[58] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In CVPR, 2016. 2 +[59] Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127:302-321, 2019. 2, 5 +[60] Chong Zhou, Chen Change Loy, and Bo Dai. Denseclip: Extract free dense labels from clip. 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The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor, which only exploits web-crawled image-text pairs for pre-training without using any mask annotations. OVSegmentor assembles the image pixels into a set of learnable group tokens via a slot-attention based binding module, then aligns the group tokens to corresponding caption embeddings. Second, we propose two proxy tasks for training, namely masked entity completion and cross-image mask consistency. The former aims to infer all masked entities in the caption given group tokens, that enables the model to learn fine-grained alignment between visual groups and text entities. The latter enforces consistent mask predictions between images that contain shared entities, encouraging the model to learn visual invariance. Third, we construct CC4M dataset for pre-training by filtering CC12M with frequently appeared entities, which significantly improves training efficiency. Fourth, we perform zero-shot transfer on four benchmark datasets, PASCAL VOC, PASCAL Context, COCO Object, and ADE20K. OVSegmentor achieves superior results over state-of-the-art approaches on PASCAL VOC using only " + }, + { + "bbox": [ + 47, + 270, + 289, + 582 + ], + "type": "inline_equation", + "content": "3\\%" + }, + { + "bbox": [ + 47, + 270, + 289, + 582 + ], + "type": "text", + "content": " data (4M vs 134M) for pre-training." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 590, + 128, + 602 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 590, + 128, + 602 + ], + "spans": [ + { + "bbox": [ + 47, + 590, + 128, + 602 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 610, + 287, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 610, + 287, + 696 + ], + "spans": [ + { + "bbox": [ + 46, + 610, + 287, + 696 + ], + "type": "text", + "content": "Semantic segmentation considers the problem of assigning class labels to each pixel in the image. It plays critical roles in a wide range of real-world scenarios, including autonomous driving, computer-aided diagnosis and satellite image analysis, to name a few. Generally speaking, two lines of research dominate semantic segmentation, one way is to cluster the pixels into different groups and assign a" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 307, + 243, + 547, + 378 + ], + "blocks": [ + { + "bbox": [ + 307, + 243, + 547, + 378 + ], + "lines": [ + { + "bbox": [ + 307, + 243, + 547, + 378 + ], + "spans": [ + { + "bbox": [ + 307, + 243, + 547, + 378 + ], + "type": "image", + "image_path": "3e5628a889cc5c8d3645c2cb18ca92a68d39aa22c62b1cdf5bb761bfc113c381.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 380, + 547, + 437 + ], + "lines": [ + { + "bbox": [ + 305, + 380, + 547, + 437 + ], + "spans": [ + { + "bbox": [ + 305, + 380, + 547, + 437 + ], + "type": "text", + "content": "Figure 1. An illustration of open-vocabulary semantic segmentation. Models trained on closed-set classes (cat and dog) fail to segment novel class (fire hydrant). We train a visual encoder and a text encoder on web-collected image-text pairs without using any mask labels, and our model can segment arbitrary object classes." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 448, + 545, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 448, + 545, + 567 + ], + "spans": [ + { + "bbox": [ + 304, + 448, + 545, + 567 + ], + "type": "text", + "content": "semantic label to each group; the other idea treats segmentation as pixel-wise classification, casting each pixel into one category. Despite tremendous progress, the scalability of existing approaches that rely on supervised training has been fundamentally limited: (1) costly annotation procedure. Extensive manual pixel-wise annotations are required for training segmentation models; (2) closed-set segmentation. The model is restricted to segmenting objects from a closed-set of categories. Whenever a new dataset comes, the model requires re-training." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 570, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 547, + 715 + ], + "type": "text", + "content": "In this paper, our goal is to train an open-vocabulary semantic segmentation (OVS) model, by exploiting freely available image-caption pairs on Internet, as illustrated in Fig. 1. The recent work, for example, CLIP [41] and ALIGN [23] have demonstrated that a combination of large-scale image-caption pairs and simple noise contrastive estimation can learn powerful image-text embeddings from scratch, and show strong \"zero-shot\" generalization abilities for open-vocabulary classification. Additionally, GroupViT [53] extends the idea towards semantic segmentation by training a segmentation model with text supervision only. They perform hierarchical grouping of" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 703, + 135, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 703, + 135, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 703, + 135, + 712 + ], + "type": "text", + "content": "*Corresponding author" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2935" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "content": "visual tokens, which are then aligned to the corresponding text embeddings via a contrastive loss. However, the following issues remain challenging and unsolved: First, the captions only provide coarse, image-level descriptions, which are insufficient for training semantic segmentation models where fine-grained, pixel-wise supervision is usually needed. Second, the diversity of web-collected data is large, that requires the model to learn visual invariance on objects of interest, with only weak supervision provided. For instance, the visual appearance of two images with similar captions can be drastically different." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 204, + 289, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 204, + 289, + 479 + ], + "spans": [ + { + "bbox": [ + 46, + 204, + 289, + 479 + ], + "type": "text", + "content": "To tackle the above challenges, (i) we propose a transformer-based model for open-vocabulary semantic segmentation, dubbed as OVSegmentor, that can segment objects of arbitrary categories via zero-shot transfer, with only image-caption pairs for pre-training. Specifically, we introduce learnable group tokens to cluster image patches via a slot-attention [33] based binding module, and align the group tokens with corresponding caption embeddings. Note that our model neither requires ground-truth masks for training nor additional re-training on target segmentation datasets, substantially alleviating the annotation efforts and improving transfer efficiency; (ii) As for training on the image-caption dataset, we propose two proxy tasks, namely masked entity completion and cross-image mask consistency, the former trains the model to infer all the masked entities in the sentence given the group tokens, and the latter enforces consistent mask prediction for images with the common entity. Both tasks have shown to be beneficial in learning entity-specific, fine-grained and visually invariant group semantics; (iii) We construct an image-caption dataset, termed as CC4M, by designing an automatic approach to filter CC12M [7] with frequently appeared visual entities, significantly improving the training efficiency." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 479, + 289, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 479, + 289, + 624 + ], + "spans": [ + { + "bbox": [ + 46, + 479, + 289, + 624 + ], + "type": "text", + "content": "We pre-train the proposed OVSegmentor on our filtered image-caption dataset (CC4M), without using any manual segmentation masks whatsoever. The model is evaluated on four segmentation benchmarks, PASCAL VOC 2012 [18], PASCAL Context [38], COCO Object [31], and ADE20K [59] in a zero-shot manner, i.e., the model is directly evaluated on target datasets without any finetuning. Extensive experiments demonstrate that our model surpasses existing models that are trained with full supervision and outperforms state-of-the-art self-supervised approaches on PASCAL VOC by using only " + }, + { + "bbox": [ + 46, + 479, + 289, + 624 + ], + "type": "inline_equation", + "content": "3\\%" + }, + { + "bbox": [ + 46, + 479, + 289, + 624 + ], + "type": "text", + "content": " data (4M vs 134M) for pre-training, significantly improving the training efficiency." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 632, + 134, + 643 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 632, + 134, + 643 + ], + "spans": [ + { + "bbox": [ + 47, + 632, + 134, + 643 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 654, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 288, + 715 + ], + "type": "text", + "content": "Vision-Language Pre-training. Vision-language pretraining (VLP) aims to learn joint visual-textual representations for a variety of multimodal downstream tasks. Existing works either learn unimodal encoders by distinguishing the positive pair(s) from the unpaired samples [3,28,41]" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 72, + 547, + 216 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 216 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 216 + ], + "type": "text", + "content": "or focus on one multimodal encoder for joint feature learning with masked image/language modeling and image-text matching losses [12,27,29]. Additionally, some approaches seek fine-grained supervision for cross-modal interaction [20, 30, 55-57]. For example, GLIP [30] proposed to align the bounding boxes with corresponding phrases in the text. However, they still rely on ground-truth grounding annotations. In contrast, our work explores fine-grained information with only weak supervision provided. Despite remarkable performance on multimodal downstream tasks, few of these vision-language models have been designed for fundamental vision tasks (e.g., semantic segmentation)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 219, + 547, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 219, + 547, + 495 + ], + "spans": [ + { + "bbox": [ + 304, + 219, + 547, + 495 + ], + "type": "text", + "content": "Zero-shot/Open-vocabulary Semantic Segmentation. The goal of zero-shot semantic segmentation is to segment objects of interest that are not seen in the training set. Prior works [22, 40, 51] mainly transfer the knowledge from the training set (seen) to the testing set (unseen) via visual-semantic mapping. Inspired by the open-vocabulary nature of language, current approaches [17, 21, 26, 37, 44, 45, 60] exploit vision-language models (e.g., CLIP [41]) pretrained on large-scale image-caption pairs. They need either finetuning [17, 21, 26] or self-training [60] on the target segmentation dataset, which is less efficient and flexible than zero-shot transfer. VLP for open-vocabulary segmentation combines the merits of both zero-shot transfer and open-vocabulary recognition. GroupViT [53] designed a grouping vision transformer and learned the alignment between groups and text via the contrastive loss. ViL-Seg [32] combined image-text contrastive loss with online pixel clustering for segmentation. A concurrent work CLIPpy [42] explored different aggregation operations for training spatial-aware vision-language models for segmentation. Beyond the global image-text matching, we further design masked entity completion and cross-image mask consistency to enrich the group semantics." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 498, + 548, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 548, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 548, + 714 + ], + "type": "text", + "content": "Fully-/Weakly-/Semi-Supervised Semantic Segmentation. Fully-supervised semantic segmentation emerges from per-pixel classification [8, 34] to mask classification [13, 48]. To relieve the laborious annotations, extensive efforts have been made to address semantic segmentation with less supervision. In the family of weakly-supervised object localization [24,50,54] and semantic segmentation [1,9,52], only class labels are available for supervision. Generally, the class activation maps [58] derived from the classification network serve as the initial segmentation results. Another line of research focuses on semi-supervised semantic segmentation [11, 19, 36, 39, 49] where a few samples have dense per-pixel labels and the remaining samples are unlabeled. These works mainly perform supervised training on labeled samples with additional consistency regularizations posed on unlabeled samples. Despite promising results, these approaches are still limited to closed-set object categories." + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "2936" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 68, + 358, + 326 + ], + "blocks": [ + { + "bbox": [ + 53, + 68, + 358, + 326 + ], + "lines": [ + { + "bbox": [ + 53, + 68, + 358, + 326 + ], + "spans": [ + { + "bbox": [ + 53, + 68, + 358, + 326 + ], + "type": "image", + "image_path": "49f51875dff4317c8e35ff871f9ee8da5f21cdc5deaeb5d9c31c6fd97d293a6d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 330, + 546, + 386 + ], + "lines": [ + { + "bbox": [ + 46, + 330, + 546, + 386 + ], + "spans": [ + { + "bbox": [ + 46, + 330, + 546, + 386 + ], + "type": "text", + "content": "Figure 2. Overall framework. The model takes an image-text pair as input, and outputs the image features and a set of learnable group tokens, which is aligned to the caption embedding via " + }, + { + "bbox": [ + 46, + 330, + 546, + 386 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{contrast}}" + }, + { + "bbox": [ + 46, + 330, + 546, + 386 + ], + "type": "text", + "content": ". The visual encoder consists of Transformer encoder layers, with a slot-attention based binding module in between to cluster the image tokens into groups. To enrich group semantics, (1) " + }, + { + "bbox": [ + 46, + 330, + 546, + 386 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{entity}}" + }, + { + "bbox": [ + 46, + 330, + 546, + 386 + ], + "type": "text", + "content": " trains the model to infer all masked entities in the caption given the group tokens. (2) " + }, + { + "bbox": [ + 46, + 330, + 546, + 386 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{mask}}" + }, + { + "bbox": [ + 46, + 330, + 546, + 386 + ], + "type": "text", + "content": " enforces consistent mask predictions of images with the same entity (right figure). During inference, only visual and text encoders and learned group tokens are used to generate the segmentation mask." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 369, + 68, + 541, + 325 + ], + "blocks": [ + { + "bbox": [ + 369, + 68, + 541, + 325 + ], + "lines": [ + { + "bbox": [ + 369, + 68, + 541, + 325 + ], + "spans": [ + { + "bbox": [ + 369, + 68, + 541, + 325 + ], + "type": "image", + "image_path": "8a146145b190280f3ef0ec79eb8efc2eb19e517294cef3e8bf3d94353b43c34e.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 400, + 130, + 415 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 400, + 130, + 415 + ], + "spans": [ + { + "bbox": [ + 47, + 400, + 130, + 415 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "spans": [ + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "text", + "content": "Assuming we are given a dataset of image-caption pairs, i.e., " + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\mathrm{train}} = \\{(I_1,T_1),\\dots ,(I_N,T_N)\\}" + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "text", + "content": " , where " + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "inline_equation", + "content": "I_{i}\\in" + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{H\\times W\\times 3}" + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "text", + "content": " denotes the image, and " + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "inline_equation", + "content": "T_{i}" + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "text", + "content": " refers to its corresponding alt-text downloaded from the Internet, the goal is to train an open-vocabulary semantic segmentation (OVS) model only with these (very) weak labels, that can segment objects of arbitrary classes. For each pair " + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "inline_equation", + "content": "(I,T)" + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "text", + "content": " ,we first split the image into " + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "inline_equation", + "content": "L = HW / P^2" + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "text", + "content": " non-overlapping patches with patch size " + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "text", + "content": " . These patches are then transformed into a sequence of image tokens with MLPs, we reuse " + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "inline_equation", + "content": "I\\in \\mathbb{R}^{L\\times D}" + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "text", + "content": " to represent them for ease of understanding. Additionally, we introduce " + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "text", + "content": " learnable tokens " + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "inline_equation", + "content": "G\\in \\mathbb{R}^{K\\times D}" + }, + { + "bbox": [ + 46, + 420, + 287, + 575 + ], + "type": "text", + "content": " , that aims to group the image tokens into clusters." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 575, + 288, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 575, + 288, + 684 + ], + "spans": [ + { + "bbox": [ + 46, + 575, + 288, + 684 + ], + "type": "text", + "content": "We present a VLP framework, termed OVSegmentor, it assembles the image patches into groups and aligns the group to categories, by only exploiting weak supervisions from image-caption pairs. Conceptually, the architecture consists of two stages, namely, a patch-to-group binding that assigns all patches with same semantics into one group, and group-to-category alignment that computes matching scores between each of these group tokens with semantic categories, the segmentation mask " + }, + { + "bbox": [ + 46, + 575, + 288, + 684 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 46, + 575, + 288, + 684 + ], + "type": "text", + "content": " can be computed as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 113, + 691, + 287, + 711 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 113, + 691, + 287, + 711 + ], + "spans": [ + { + "bbox": [ + 113, + 691, + 287, + 711 + ], + "type": "interline_equation", + "content": "m = \\underset {C} {\\arg \\max } \\mathbb {A} \\cdot \\mathcal {G} \\cdot \\mathcal {T} ^ {\\top}, \\tag {1}", + "image_path": "37b06eac2fa39440c053f6f93dd19ecabd3ea3d99018154adab9249b018fe4c4.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 401, + 546, + 497 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 401, + 546, + 497 + ], + "spans": [ + { + "bbox": [ + 304, + 401, + 546, + 497 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 401, + 546, + 497 + ], + "type": "inline_equation", + "content": "\\mathbb{A} \\in \\mathbb{R}^{L \\times K}" + }, + { + "bbox": [ + 304, + 401, + 546, + 497 + ], + "type": "text", + "content": " characterises a soft binding procedure, denoting the likelihood of each image patch being assigned into the output group embeddings " + }, + { + "bbox": [ + 304, + 401, + 546, + 497 + ], + "type": "inline_equation", + "content": "(\\mathcal{G} \\in \\mathbb{R}^{K \\times D})" + }, + { + "bbox": [ + 304, + 401, + 546, + 497 + ], + "type": "text", + "content": "; and " + }, + { + "bbox": [ + 304, + 401, + 546, + 497 + ], + "type": "inline_equation", + "content": "\\mathcal{T} \\in \\mathbb{R}^{C \\times D}" + }, + { + "bbox": [ + 304, + 401, + 546, + 497 + ], + "type": "text", + "content": " denotes the embeddings with dimension " + }, + { + "bbox": [ + 304, + 401, + 546, + 497 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 304, + 401, + 546, + 497 + ], + "type": "text", + "content": " for a total of " + }, + { + "bbox": [ + 304, + 401, + 546, + 497 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 304, + 401, + 546, + 497 + ], + "type": "text", + "content": " categories, computed from a text encoder. Note that, at training time, manual segmentation masks are not present, and the intermediate binding and assignment can only be learnt from the image-caption pairs implicitly." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 498, + 547, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 547, + 617 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 547, + 617 + ], + "type": "text", + "content": "At inference stage, the patch-group affinity " + }, + { + "bbox": [ + 304, + 498, + 547, + 617 + ], + "type": "inline_equation", + "content": "\\mathbb{A}" + }, + { + "bbox": [ + 304, + 498, + 547, + 617 + ], + "type": "text", + "content": " can be computed by the product between the image features " + }, + { + "bbox": [ + 304, + 498, + 547, + 617 + ], + "type": "inline_equation", + "content": "\\mathcal{I} \\in \\mathbb{R}^{L \\times D}" + }, + { + "bbox": [ + 304, + 498, + 547, + 617 + ], + "type": "text", + "content": " and the learned group tokens " + }, + { + "bbox": [ + 304, + 498, + 547, + 617 + ], + "type": "inline_equation", + "content": "\\mathcal{G}" + }, + { + "bbox": [ + 304, + 498, + 547, + 617 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 498, + 547, + 617 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 304, + 498, + 547, + 617 + ], + "type": "text", + "content": " can be derived by encoding the prompted class labels (e.g., A photo of {class}). Thus the patch-level mask prediction can be calculated via Eq. 1. The pixel-level segmentation mask can then be obtained by up-sampling the patch-level mask " + }, + { + "bbox": [ + 304, + 498, + 547, + 617 + ], + "type": "inline_equation", + "content": "(m)" + }, + { + "bbox": [ + 304, + 498, + 547, + 617 + ], + "type": "text", + "content": ". In the following sections, we detail the overall architecture and training procedure of each component. The overall framework is illustrated in Fig. 2." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 626, + 389, + 639 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 626, + 389, + 639 + ], + "spans": [ + { + "bbox": [ + 306, + 626, + 389, + 639 + ], + "type": "text", + "content": "3.1. Architecture" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 645, + 546, + 718 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 645, + 546, + 718 + ], + "spans": [ + { + "bbox": [ + 304, + 645, + 546, + 718 + ], + "type": "text", + "content": "In this section, we will detail the proposed architecture at training stage, including a visual encoder for learning group tokens and the binding procedure, a text encoder that computes variants of caption embeddings. We describe the encoding procedure for image and caption pair, the subscripts are thus ignored for simplicity." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2937" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 146, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 146, + 83 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 146, + 83 + ], + "type": "text", + "content": "3.1.1 Visual Encoder" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 91, + 287, + 163 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 91, + 287, + 163 + ], + "spans": [ + { + "bbox": [ + 46, + 91, + 287, + 163 + ], + "type": "text", + "content": "The visual encoder consists of two components, namely, Transformer encoders and binding modules. Specifically, the image tokens and learnable group tokens are concatenated, and iteratively processed by the Transformer encoders, and a binding module with slot-attention [33] is adopted for grouping. The visual encoder is defined as:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 83, + 171, + 287, + 186 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 171, + 287, + 186 + ], + "spans": [ + { + "bbox": [ + 83, + 171, + 287, + 186 + ], + "type": "interline_equation", + "content": "[ \\mathcal {G}; \\mathcal {I} ] = \\Phi_ {\\mathrm {e n c}} ^ {2} (\\cdot) \\circ \\Phi_ {\\mathrm {b i n d}} (\\cdot) \\circ \\Phi_ {\\mathrm {e n c}} ^ {1} ([ G; I ]), \\tag {2}", + "image_path": "aca3095d425bca4dc596757c726de3a93ef5364e85ca9dc5bf17f9d0111cf1cd.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 194, + 287, + 243 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 194, + 287, + 243 + ], + "spans": [ + { + "bbox": [ + 47, + 194, + 287, + 243 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 194, + 287, + 243 + ], + "type": "inline_equation", + "content": "\\Phi_{\\mathrm{enc}}^{1}(\\cdot)" + }, + { + "bbox": [ + 47, + 194, + 287, + 243 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 194, + 287, + 243 + ], + "type": "inline_equation", + "content": "\\Phi_{\\mathrm{enc}}^{2}(\\cdot)" + }, + { + "bbox": [ + 47, + 194, + 287, + 243 + ], + "type": "text", + "content": " refer to modules with Transformer encoder layers, and " + }, + { + "bbox": [ + 47, + 194, + 287, + 243 + ], + "type": "inline_equation", + "content": "\\Phi_{\\mathrm{bind}}(\\cdot)" + }, + { + "bbox": [ + 47, + 194, + 287, + 243 + ], + "type": "text", + "content": " is the binding module. The output " + }, + { + "bbox": [ + 47, + 194, + 287, + 243 + ], + "type": "inline_equation", + "content": "\\mathcal{G}\\in \\mathbb{R}^{K\\times D}" + }, + { + "bbox": [ + 47, + 194, + 287, + 243 + ], + "type": "text", + "content": " is the encoded group tokens, and " + }, + { + "bbox": [ + 47, + 194, + 287, + 243 + ], + "type": "inline_equation", + "content": "\\mathcal{I}\\in \\mathbb{R}^{L\\times D}" + }, + { + "bbox": [ + 47, + 194, + 287, + 243 + ], + "type": "text", + "content": " refers to the output image tokens." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "spans": [ + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "text", + "content": "Transformer Encoder. Both " + }, + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "inline_equation", + "content": "\\Phi_{\\mathrm{enc}}^{1}" + }, + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "inline_equation", + "content": "\\Phi_{\\mathrm{enc}}^{2}" + }, + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "text", + "content": " consist of 6 Transformer encoder layers [47], where each layer is composed of a multi-head self-attention (MHSA) layer followed by layer normalisation (LN) and a feed-forward network (FFN). " + }, + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "inline_equation", + "content": "\\Phi_{\\mathrm{enc}}^{1}" + }, + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "text", + "content": " takes the concatenation of image patches and randomly initialised group tokens as input, and outputs intermediately encoded group and image tokens (" + }, + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "inline_equation", + "content": "G'" + }, + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "inline_equation", + "content": "I'" + }, + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "text", + "content": "); " + }, + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "inline_equation", + "content": "\\Phi_{\\mathrm{enc}}^{2}" + }, + { + "bbox": [ + 47, + 246, + 287, + 343 + ], + "type": "text", + "content": " processes the output from the binding module." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 345, + 287, + 428 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 345, + 287, + 428 + ], + "spans": [ + { + "bbox": [ + 46, + 345, + 287, + 428 + ], + "type": "text", + "content": "Binding Module. The binding module uses slot-attention to cluster image tokens into groups in a data-dependent manner, i.e., image patches with similar appearance and semantics are encouraged to be grouped together. Formally, the binding module accepts the output from the Transformer encoder " + }, + { + "bbox": [ + 46, + 345, + 287, + 428 + ], + "type": "inline_equation", + "content": "\\Phi_{\\mathrm{enc}}^{1}" + }, + { + "bbox": [ + 46, + 345, + 287, + 428 + ], + "type": "text", + "content": ", and transforms them into query, key and values with linear transformations:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 77, + 437, + 287, + 453 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 437, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 77, + 437, + 287, + 453 + ], + "type": "interline_equation", + "content": "\\mathbf {Q} = W _ {q} ^ {\\text {b i n d}} G ^ {\\prime}, \\quad \\mathbf {K}, \\mathbf {V} = W _ {k} ^ {\\text {b i n d}} I ^ {\\prime}, W _ {v} ^ {\\text {b i n d}} I ^ {\\prime}. \\tag {3}", + "image_path": "ea6b310b91275f87239467db00ead04acbe46e5a6978f9250256849b724ae565.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 461, + 287, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 461, + 287, + 510 + ], + "spans": [ + { + "bbox": [ + 46, + 461, + 287, + 510 + ], + "type": "text", + "content": "In contrast to the standard cross attention in Transformer Decoders [47], slot-attention performs normalisation over queries, encouraging each image token to be claimed by one of the group tokens. The binding process can be defined as:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 113, + 518, + 287, + 544 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 113, + 518, + 287, + 544 + ], + "spans": [ + { + "bbox": [ + 113, + 518, + 287, + 544 + ], + "type": "interline_equation", + "content": "\\mathbb {A} _ {j, k} = \\frac {\\exp \\left(\\mathbf {K} _ {j} \\cdot \\mathbf {Q} _ {k}\\right)}{\\sum_ {l} \\exp \\left(\\mathbf {K} _ {j} \\cdot \\mathbf {Q} _ {l}\\right)}, \\tag {4}", + "image_path": "47ea112d92899d1ebb2d06b7b3992571b252181e2d57157ee39c6f249acd3a79.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 552, + 287, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 552, + 287, + 613 + ], + "spans": [ + { + "bbox": [ + 46, + 552, + 287, + 613 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 552, + 287, + 613 + ], + "type": "inline_equation", + "content": "\\mathbb{A}_{j,k}" + }, + { + "bbox": [ + 46, + 552, + 287, + 613 + ], + "type": "text", + "content": " refers to the probability of the " + }, + { + "bbox": [ + 46, + 552, + 287, + 613 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 46, + 552, + 287, + 613 + ], + "type": "text", + "content": "-th image token being assigned to the " + }, + { + "bbox": [ + 46, + 552, + 287, + 613 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 552, + 287, + 613 + ], + "type": "text", + "content": "-th group. Then, each updated group token " + }, + { + "bbox": [ + 46, + 552, + 287, + 613 + ], + "type": "inline_equation", + "content": "\\tilde{G}_k'" + }, + { + "bbox": [ + 46, + 552, + 287, + 613 + ], + "type": "text", + "content": " is computed by taking the weighted average of the image tokens that are assigned to it. The output of the binding module " + }, + { + "bbox": [ + 46, + 552, + 287, + 613 + ], + "type": "inline_equation", + "content": "G^{\\mathrm{bind}}" + }, + { + "bbox": [ + 46, + 552, + 287, + 613 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 126, + 621, + 287, + 654 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 126, + 621, + 287, + 654 + ], + "spans": [ + { + "bbox": [ + 126, + 621, + 287, + 654 + ], + "type": "interline_equation", + "content": "\\tilde {G} _ {k} ^ {\\prime} = \\frac {\\sum_ {j = 1} ^ {L} \\mathbb {A} _ {j , k} \\mathbf {V} _ {j}}{\\sum_ {j = 1} ^ {L} \\mathbb {A} _ {j , k}}, \\tag {5}", + "image_path": "f800a20e72f8627e8225d62a768d4a2c6983f28c6aa5c9c88c296e2ea37975e3.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 119, + 656, + 287, + 671 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 119, + 656, + 287, + 671 + ], + "spans": [ + { + "bbox": [ + 119, + 656, + 287, + 671 + ], + "type": "interline_equation", + "content": "G ^ {\\text {b i n d}} = G ^ {\\prime} + W _ {o} ^ {\\text {b i n d}} \\tilde {G} ^ {\\prime}, \\tag {6}", + "image_path": "f1ee17229fabb9c4a0a12cd2213aafe2c64e8a638e4b1bf44d85c9cd42608644.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 681, + 287, + 718 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 681, + 287, + 718 + ], + "spans": [ + { + "bbox": [ + 46, + 681, + 287, + 718 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 681, + 287, + 718 + ], + "type": "inline_equation", + "content": "W_{o}^{\\mathrm{bind}}" + }, + { + "bbox": [ + 46, + 681, + 287, + 718 + ], + "type": "text", + "content": " is a linear transformation. Now we have obtained the correspondence between each patch and groups, next we describe the procedure for encoding captions." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 72, + 397, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 72, + 397, + 83 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 397, + 83 + ], + "type": "text", + "content": "3.1.2 Text Encoder" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 91, + 545, + 163 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 91, + 545, + 163 + ], + "spans": [ + { + "bbox": [ + 304, + 91, + 545, + 163 + ], + "type": "text", + "content": "Till this end, we filter all the captions and only keep the ones with informative entities, followed by exploiting three variants of the caption encoding, namely, the entire caption embedding, the masked caption embedding and the prompted entity embedding. In all cases we use a pretrained BERT [16] as the text encoder " + }, + { + "bbox": [ + 304, + 91, + 545, + 163 + ], + "type": "inline_equation", + "content": "\\Phi_{\\mathrm{text}}" + }, + { + "bbox": [ + 304, + 91, + 545, + 163 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 165, + 545, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 165, + 545, + 274 + ], + "spans": [ + { + "bbox": [ + 304, + 165, + 545, + 274 + ], + "type": "text", + "content": "Constructing Entity Set. We adopt the nltk toolkit [4] to extract entities from all the captions, and construct an entity set " + }, + { + "bbox": [ + 304, + 165, + 545, + 274 + ], + "type": "inline_equation", + "content": "\\Omega = \\Phi_{\\text{entity}}(\\{T_1, \\ldots, T_N\\})" + }, + { + "bbox": [ + 304, + 165, + 545, + 274 + ], + "type": "text", + "content": " that only maintains the frequently appeared entities (e.g., people, cat, shirt, etc.), and exclude the abstract nouns (e.g., art, view, etc) as they usually do not correspond to any specific region in the image. For each image-caption pair, we can thus obtain an image-caption-entity triplet " + }, + { + "bbox": [ + 304, + 165, + 545, + 274 + ], + "type": "inline_equation", + "content": "(I, T, E)" + }, + { + "bbox": [ + 304, + 165, + 545, + 274 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 165, + 545, + 274 + ], + "type": "inline_equation", + "content": "E = \\{e | e \\in T \\cap e \\in \\Omega\\}" + }, + { + "bbox": [ + 304, + 165, + 545, + 274 + ], + "type": "text", + "content": " includes all frequent entities in the caption." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 276, + 545, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 276, + 545, + 337 + ], + "spans": [ + { + "bbox": [ + 304, + 276, + 545, + 337 + ], + "type": "text", + "content": "Caption Embedding. Here, for each caption " + }, + { + "bbox": [ + 304, + 276, + 545, + 337 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 276, + 545, + 337 + ], + "type": "text", + "content": ", it is tokenised using the BERT tokeniser [16], with the start-of-token [SOT] and end-of-token [EOT] added to the beginning/ending. The caption embedding is computed as " + }, + { + "bbox": [ + 304, + 276, + 545, + 337 + ], + "type": "inline_equation", + "content": "\\mathcal{T}^{\\mathrm{cap}} = \\Phi_{\\mathrm{text}}(T) \\in \\mathbb{R}^{M \\times D}" + }, + { + "bbox": [ + 304, + 276, + 545, + 337 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 276, + 545, + 337 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 276, + 545, + 337 + ], + "type": "text", + "content": " is length after tokenisation." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 339, + 545, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 339, + 545, + 399 + ], + "spans": [ + { + "bbox": [ + 304, + 339, + 545, + 399 + ], + "type": "text", + "content": "Masked Caption Embedding. In the second variant, we mask all the frequent entities in the caption, and compute the masked caption embedding as " + }, + { + "bbox": [ + 304, + 339, + 545, + 399 + ], + "type": "inline_equation", + "content": "\\mathcal{T}^{\\mathrm{m - cap}} = \\Phi_{\\mathrm{text}}(g(T))\\in" + }, + { + "bbox": [ + 304, + 339, + 545, + 399 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{M\\times D}" + }, + { + "bbox": [ + 304, + 339, + 545, + 399 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 339, + 545, + 399 + ], + "type": "inline_equation", + "content": "g(\\cdot)" + }, + { + "bbox": [ + 304, + 339, + 545, + 399 + ], + "type": "text", + "content": " denotes an masking operation, that replaces the entity with a special [MASK] token." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "spans": [ + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "type": "text", + "content": "Prompted Entity Embedding. We construct manual prompts for each of the entities in the triplets, and compute its textual embedding as " + }, + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "type": "inline_equation", + "content": "\\mathcal{T}^{\\mathrm{entity}} = \\Phi_{\\mathrm{text}}(h(E)) \\in \\mathbb{R}^{M \\times D}" + }, + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "type": "inline_equation", + "content": "h(\\cdot)" + }, + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "type": "text", + "content": " refers to the procedure for constructing manual prompts, for example, we randomly sample a prompt template provided in [41], and fill in the entities: A painting of " + }, + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "type": "inline_equation", + "content": "\\{entity_1\\}" + }, + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "type": "inline_equation", + "content": "\\{entity_2\\}" + }, + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "type": "inline_equation", + "content": "\\{entity_3\\}" + }, + { + "bbox": [ + 304, + 402, + 547, + 498 + ], + "type": "text", + "content": ". This sentence is padded to the same length as the caption after tokenisation." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 505, + 370, + 518 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 505, + 370, + 518 + ], + "spans": [ + { + "bbox": [ + 306, + 505, + 370, + 518 + ], + "type": "text", + "content": "3.2. Training" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 523, + 545, + 571 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 523, + 545, + 571 + ], + "spans": [ + { + "bbox": [ + 304, + 523, + 545, + 571 + ], + "type": "text", + "content": "As for training, we aim to learn the alignment between group tokens and caption embeddings via three proxy tasks, namely, image-caption alignment, masked entity completion, and cross-image mask consistency." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 574, + 545, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 574, + 545, + 658 + ], + "spans": [ + { + "bbox": [ + 304, + 574, + 545, + 658 + ], + "type": "text", + "content": "Image-caption Alignment. For each image-text pair, the objective is to align their visual and textual embeddings. The visual embedding " + }, + { + "bbox": [ + 304, + 574, + 545, + 658 + ], + "type": "inline_equation", + "content": "z^{\\mathrm{I}}" + }, + { + "bbox": [ + 304, + 574, + 545, + 658 + ], + "type": "text", + "content": " is the average of group tokens, and the textual embedding " + }, + { + "bbox": [ + 304, + 574, + 545, + 658 + ], + "type": "inline_equation", + "content": "z^{\\mathrm{T}}" + }, + { + "bbox": [ + 304, + 574, + 545, + 658 + ], + "type": "text", + "content": " is obtained by taking the [EOT] token feature of the caption embedding " + }, + { + "bbox": [ + 304, + 574, + 545, + 658 + ], + "type": "inline_equation", + "content": "\\mathcal{T}^{\\mathrm{cap}}" + }, + { + "bbox": [ + 304, + 574, + 545, + 658 + ], + "type": "text", + "content": ", both projected to a 256-d joint feature space followed by normalisation. The image-caption contrastive loss " + }, + { + "bbox": [ + 304, + 574, + 545, + 658 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{contrast}}" + }, + { + "bbox": [ + 304, + 574, + 545, + 658 + ], + "type": "text", + "content": " is formulated as:" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 311, + 666, + 545, + 695 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 311, + 666, + 545, + 695 + ], + "spans": [ + { + "bbox": [ + 311, + 666, + 545, + 695 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {c o n t r a s t}} = \\frac {1}{2} \\left(\\frac {\\exp \\left(z _ {i} ^ {\\mathrm {I}} \\cdot z _ {i} ^ {\\mathrm {T}}\\right)}{\\sum_ {l} \\exp \\left(z _ {i} ^ {\\mathrm {I}} \\cdot z _ {l} ^ {\\mathrm {T}}\\right)} + \\frac {\\exp \\left(z _ {i} ^ {\\mathrm {I}} \\cdot z _ {i} ^ {\\mathrm {T}}\\right)}{\\sum_ {l} \\exp \\left(z _ {l} ^ {\\mathrm {I}} \\cdot z _ {i} ^ {\\mathrm {T}}\\right)}\\right). \\tag {7}", + "image_path": "a1e842d234514297469c3a05bff05c1394039e8da7257aa2c5f09f490b859b1f.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 306, + 701, + 531, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 701, + 531, + 714 + ], + "spans": [ + { + "bbox": [ + 306, + 701, + 531, + 714 + ], + "type": "text", + "content": "Here, we omit the temperature parameter for simplicity." + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2938" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 145 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 145 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 145 + ], + "type": "text", + "content": "Masked Entity Completion. The goal of masked entity completion is to infer all the masked entities in the sentence given the group tokens. In specific, we adopt a Transformer decoder layer, where a projection of the masked caption embedding is treated as query, and two linear transformations of group tokens are treated as key and values, respectively." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 149, + 287, + 176 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 149, + 287, + 176 + ], + "spans": [ + { + "bbox": [ + 55, + 149, + 287, + 176 + ], + "type": "interline_equation", + "content": "\\tilde {\\mathcal {T}} ^ {\\mathrm {m} - \\mathrm {c a p}} = \\Phi_ {\\mathrm {d e c}} \\left(W _ {q} ^ {\\mathrm {d e c}} \\mathcal {T} ^ {\\mathrm {m} - \\mathrm {c a p}}, W _ {k} ^ {\\mathrm {d e c}} \\mathcal {G}, W _ {v} ^ {\\mathrm {d e c}} \\mathcal {G}\\right) \\in \\mathbb {R} ^ {M \\times D}, \\tag {8}", + "image_path": "5bb3bd6abe1539a0ba88b41ef179d954515553e3462004dfae728bf9e1db9317.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "spans": [ + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "inline_equation", + "content": "W_{q}^{\\mathrm{dec}}" + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "inline_equation", + "content": "W_{k}^{\\mathrm{dec}}" + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "inline_equation", + "content": "W_{v}^{\\mathrm{dec}}" + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "text", + "content": " are linear transformations, and " + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "inline_equation", + "content": "\\tilde{T}^{\\mathrm{m - cap}}" + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "text", + "content": " denotes the updated vector sequence, with the masked entities being completed by querying group tokens. For training, we extract the [EOT] token features " + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "inline_equation", + "content": "z^{\\mathrm{M}}" + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "inline_equation", + "content": "z^{\\mathrm{E}}" + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "inline_equation", + "content": "\\tilde{T}^{\\mathrm{m - cap}}" + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "inline_equation", + "content": "T^{\\mathrm{entity}}" + }, + { + "bbox": [ + 46, + 184, + 287, + 247 + ], + "type": "text", + "content": ", and construct a contrastive loss:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 251, + 287, + 291 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 251, + 287, + 291 + ], + "spans": [ + { + "bbox": [ + 57, + 251, + 287, + 291 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {e n t i t y}} = \\frac {1}{2} \\left(\\frac {\\exp \\left(z _ {i} ^ {\\mathrm {M}} \\cdot z _ {i} ^ {\\mathrm {E}}\\right)}{\\sum_ {l} \\exp \\left(z _ {i} ^ {\\mathrm {M}} \\cdot z _ {l} ^ {\\mathrm {E}}\\right)} + \\frac {\\exp \\left(z _ {i} ^ {\\mathrm {M}} \\cdot z _ {i} ^ {\\mathrm {E}}\\right)}{\\sum_ {l} \\exp \\left(z _ {l} ^ {\\mathrm {M}} \\cdot z _ {i} ^ {\\mathrm {E}}\\right)}\\right). \\tag {9}", + "image_path": "84434155673c749e6b57e1d18e015898aa668f15a1e814d5b072eb9f3ea42039.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 298, + 287, + 322 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 298, + 287, + 322 + ], + "spans": [ + { + "bbox": [ + 46, + 298, + 287, + 322 + ], + "type": "text", + "content": "Intuitively, the entity completion task enables better alignment between the groups and entities." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "spans": [ + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "text", + "content": "Cross-image Mask Consistency. To encourage visual invariance, we enforce consistent mask predictions between images that contain shared entities. Specifically, for each entity of interest, we can easily source multiple image-caption pairs from the dataset by text searching. Given one sampled image-caption-entity triplet, " + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "inline_equation", + "content": "(I_1, T_1, E_1)" + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "text", + "content": ", we can easily search for another sample " + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "inline_equation", + "content": "(I_2, T_2, E_2)" + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "text", + "content": ", with both triplet sharing one entity, i.e., " + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "inline_equation", + "content": "e \\in E_1 \\cap E_2" + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "text", + "content": ". Given encoded groups " + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_1, \\mathcal{G}_2" + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "text", + "content": ", two sets of subgroups " + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_1^{\\mathrm{sub}}, \\mathcal{G}_2^{\\mathrm{sub}} \\in \\mathbb{R}^{K' \\times D}" + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "text", + "content": " that represent the common entity " + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "inline_equation", + "content": "e" + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "text", + "content": " (e.g., cat in Fig. 2) are obtained by choosing groups with higher similarity to the entity embedding, where " + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "inline_equation", + "content": "K' = rK" + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "text", + "content": " is the selection ratio. The masks of co-attentive entity in image " + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "inline_equation", + "content": "I_1" + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "text", + "content": " can be grounded by both entity-specific subgroups " + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "inline_equation", + "content": "\\mathcal{G}_1^{\\mathrm{sub}}, \\mathcal{G}_2^{\\mathrm{sub}}" + }, + { + "bbox": [ + 46, + 324, + 287, + 493 + ], + "type": "text", + "content": " as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 52, + 498, + 287, + 514 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 498, + 287, + 514 + ], + "spans": [ + { + "bbox": [ + 52, + 498, + 287, + 514 + ], + "type": "interline_equation", + "content": "\\mathcal {M} _ {1} = \\sigma \\left(\\mathcal {I} _ {1} ^ {\\top} \\mathcal {G} _ {1} ^ {\\text {s u b}}\\right), \\hat {\\mathcal {M}} _ {1} = \\sigma \\left(\\mathcal {I} _ {1} ^ {\\top} \\mathcal {G} _ {2} ^ {\\text {s u b}}\\right) \\in [ 0, 1 ] ^ {L \\times K ^ {\\prime}}, \\tag {10}", + "image_path": "87f4b69e3868d7dc11e5893d49c200a512aad680b4d91c78bdc2b00a077c5b19.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 519, + 286, + 546 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 519, + 286, + 546 + ], + "spans": [ + { + "bbox": [ + 46, + 519, + 286, + 546 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 519, + 286, + 546 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 46, + 519, + 286, + 546 + ], + "type": "text", + "content": " is the sigmoid activation; both " + }, + { + "bbox": [ + 46, + 519, + 286, + 546 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_1 = \\{m_k^1\\}_{k=1}^{K'}" + }, + { + "bbox": [ + 46, + 519, + 286, + 546 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 519, + 286, + 546 + ], + "type": "inline_equation", + "content": "\\hat{\\mathcal{M}}_1 = \\{\\hat{m}_k^1\\}_{k=1}^{K'}" + }, + { + "bbox": [ + 46, + 519, + 286, + 546 + ], + "type": "text", + "content": " consist of " + }, + { + "bbox": [ + 46, + 519, + 286, + 546 + ], + "type": "inline_equation", + "content": "K'" + }, + { + "bbox": [ + 46, + 519, + 286, + 546 + ], + "type": "text", + "content": " unordered masks." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 547, + 287, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 547, + 287, + 582 + ], + "spans": [ + { + "bbox": [ + 46, + 547, + 287, + 582 + ], + "type": "text", + "content": "To align " + }, + { + "bbox": [ + 46, + 547, + 287, + 582 + ], + "type": "inline_equation", + "content": "\\hat{\\mathcal{M}}_1" + }, + { + "bbox": [ + 46, + 547, + 287, + 582 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 46, + 547, + 287, + 582 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_1" + }, + { + "bbox": [ + 46, + 547, + 287, + 582 + ], + "type": "text", + "content": ", we first adopt the bipartite matching to find the optimal permutation " + }, + { + "bbox": [ + 46, + 547, + 287, + 582 + ], + "type": "inline_equation", + "content": "p_1^*" + }, + { + "bbox": [ + 46, + 547, + 287, + 582 + ], + "type": "text", + "content": " over " + }, + { + "bbox": [ + 46, + 547, + 287, + 582 + ], + "type": "inline_equation", + "content": "K'" + }, + { + "bbox": [ + 46, + 547, + 287, + 582 + ], + "type": "text", + "content": " subgroups with the lowest matching cost as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 86, + 589, + 287, + 614 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 86, + 589, + 287, + 614 + ], + "spans": [ + { + "bbox": [ + 86, + 589, + 287, + 614 + ], + "type": "interline_equation", + "content": "p _ {1} ^ {*} = \\underset {p \\in \\mathcal {P}} {\\arg \\min } \\sum_ {k} - \\cos \\left(m _ {k} ^ {1}, \\hat {m} _ {p (k)} ^ {1}\\right), \\tag {11}", + "image_path": "372b2101968b2300600dec976ff29a0ff968e659ed2ca2be3e1ae02cd9bb16e5.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 621, + 287, + 670 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 621, + 287, + 670 + ], + "spans": [ + { + "bbox": [ + 46, + 621, + 287, + 670 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 621, + 287, + 670 + ], + "type": "inline_equation", + "content": "\\mathcal{P}" + }, + { + "bbox": [ + 46, + 621, + 287, + 670 + ], + "type": "text", + "content": " is the full permutation and " + }, + { + "bbox": [ + 46, + 621, + 287, + 670 + ], + "type": "inline_equation", + "content": "\\cos (\\cdot)" + }, + { + "bbox": [ + 46, + 621, + 287, + 670 + ], + "type": "text", + "content": " denotes the cosine similarity. Eq. 11 is solved via the efficient Hungarian algorithm [25]. In this way, the symmetric cross-image mask consistency loss " + }, + { + "bbox": [ + 46, + 621, + 287, + 670 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{mask}" + }, + { + "bbox": [ + 46, + 621, + 287, + 670 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 674, + 294, + 713 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 674, + 294, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 674, + 294, + 713 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {m a s k}} = \\frac {1}{2} \\left(\\sum_ {k} \\mathrm {D} \\left(\\operatorname {s g} \\left(\\mathbf {m} _ {k} ^ {1}\\right), \\hat {m} _ {p _ {1} ^ {*} (k)} ^ {1}\\right) + \\mathrm {D} \\left(\\operatorname {s g} \\left(\\mathbf {m} _ {k} ^ {2}\\right), \\hat {m} _ {p _ {2} ^ {*} (k)} ^ {2}\\right)\\right), \\tag {12}", + "image_path": "5e1f92dd729c126f43540cd1822c218abd7def3831babdfddd58efda46650be2.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "inline_equation", + "content": "\\mathrm{sg}(\\cdot)" + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "content": " denotes the stop gradient operation; the target mask " + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "inline_equation", + "content": "\\mathbf{m}_k" + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "content": " is achieved by binarizing " + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "inline_equation", + "content": "m_{k}" + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "content": " with a threshold " + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "content": "; " + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "inline_equation", + "content": "\\mathrm{D}(\\mathbf{m},\\hat{m}) = 1 - 2|\\mathbf{m}\\bigcap \\hat{m} | / (|\\mathbf{m}| + |\\hat{m} |)" + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "content": " stands for the standard Dice loss. To guarantee the quality of pseudo mask targets, the masks are generated by an extra momentum model, which is updated by the exponential-moving-average (EMA) of the online model." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 159, + 545, + 182 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 159, + 545, + 182 + ], + "spans": [ + { + "bbox": [ + 304, + 159, + 545, + 182 + ], + "type": "text", + "content": "Training Objective. We adopt a combination of three different loss functions:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 353, + 194, + 545, + 206 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 353, + 194, + 545, + 206 + ], + "spans": [ + { + "bbox": [ + 353, + 194, + 545, + 206 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {t o t a l}} = \\mathcal {L} _ {\\text {c o n t r a s t}} + \\mathcal {L} _ {\\text {e n t i t y}} + \\lambda \\mathcal {L} _ {\\text {m a s k}}, \\tag {13}", + "image_path": "0a36b9a7ea33bbb5ea6304f6b19c84d29d23ef15e192390d12d83e7e7863b404.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 216, + 539, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 216, + 539, + 228 + ], + "spans": [ + { + "bbox": [ + 304, + 216, + 539, + 228 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 216, + 539, + 228 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 304, + 216, + 539, + 228 + ], + "type": "text", + "content": " is the weight for balancing the mask consistency." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 236, + 378, + 247 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 236, + 378, + 247 + ], + "spans": [ + { + "bbox": [ + 306, + 236, + 378, + 247 + ], + "type": "text", + "content": "3.3. Discussion" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 255, + 545, + 470 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 255, + 545, + 470 + ], + "spans": [ + { + "bbox": [ + 304, + 255, + 545, + 470 + ], + "type": "text", + "content": "One work that is closely related to ours is GroupViT [53]. They extracted multiple nouns and prompted each noun to a sentence to serve as extra matched captions for the image. The model is thus supervised by a multi-label contrastive loss. In contrast, our paper differs from GroupViT in three critical aspects: (1) Entities vs nouns. Rather than using all nouns, we leverage the entities that match to visual objects, enabling high-quality image-caption correspondence. (2) Network architecture. We only adopt single-stage grouping (i.e., one binding module) as we observe no further performance gain of applying multi-stage grouping in GroupViT. The hard-assignment trick [53] is also adopted. Besides, we devise a (very) lightweight decoder to model the fine-grained, token-wise group-word correlation. (3) Proxy tasks for training. We propose two different proxy tasks, i.e., masked entity completion and cross-image mask consistency to improve the entity-specific group semantics and further encourage visual invariance." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 481, + 388, + 495 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 481, + 388, + 495 + ], + "spans": [ + { + "bbox": [ + 305, + 481, + 388, + 495 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 305, + 501, + 426, + 514 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 501, + 426, + 514 + ], + "spans": [ + { + "bbox": [ + 305, + 501, + 426, + 514 + ], + "type": "text", + "content": "4.1. Experimental Setups" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 520, + 545, + 663 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 520, + 545, + 663 + ], + "spans": [ + { + "bbox": [ + 304, + 520, + 545, + 663 + ], + "type": "text", + "content": "Pre-training Dataset. Following [32, 53], we use Conceptual Captions 12M [7] for training, which is originally constructed with over 12M image-text pairs collected from the Internet. However, due to some links have been expired, we have downloaded about 10M image-text pairs. The constructed entity set in Sec. 3.1.2 includes a total number of 100 frequently appeared entities while abstract nouns (e.g., art, view) are discarded. After filtering CC12M, we obtain 4.3 million image-text pairs for pre-training, which is termed as CC4M. Examples of entities include people, car, cup, chair, T-shirt, house, bed, cat, ball, pizza, etc. Please refer to the supplementary material for the full entity set." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "type": "text", + "content": "Downstream Evaluation Datasets. We evaluate our model on four benchmarks, namely, PASCAL VOC 2012 [18], PASCAL Context [38], COCO Object [31], and ADE20K [59] with 20, 59, 80 and 150 foreground classes," + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2939" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 49, + 70, + 547, + 269 + ], + "blocks": [ + { + "bbox": [ + 49, + 70, + 547, + 269 + ], + "lines": [ + { + "bbox": [ + 49, + 70, + 547, + 269 + ], + "spans": [ + { + "bbox": [ + 49, + 70, + 547, + 269 + ], + "type": "table", + "html": "
MethodBackbonePretrain datasetSupervisionZero-shot transferDownstream datasets
PASCAL VOCPASCAL ContextCOCO ObjectADE20K
DeiT [46]ViT-SIN-1Kclass labelX53.035.9--
MoCo [10]ViT-SIN-1KselfX34.321.3--
DINO [6]ViT-SIN-1KselfX39.120.4--
MoCo [10]ViT-SCC12M+YFCC15MselfX36.123.0--
DINO [6]ViT-SCC12M+YFCC15MselfX37.622.8--
ViL-Seg [32]ViT-BCC12Mself+text✓33.615.9--
GroupViT [53]ViT-SCC12Mtext✓41.1---
GroupViT [53]ViT-SCC12M+YFCC15Mtext✓51.222.320.9-
ViewCo [43]ViT-SCC12M+YFCC15Mself+text✓52.423.023.5-
CLIPpy [42]ViT-BHQITP-134Mtext✓52.2-25.5†13.5
GroupViT* [53]ViT-SCC4Mtext✓19.88.89.13.4
GroupViT* [53]ViT-BCC4Mtext✓25.811.310.73.6
GroupViT* [53]ViT-SCC12Mtext✓40.218.717.76.2
OVSegmentor (ours)ViT-SCC4Mtext✓44.518.319.04.3
OVSegmentor (ours)ViT-BCC4Mtext✓53.820.425.15.6
", + "image_path": "358f97ff57551b5411e630c893bca4c67569405a20898bef4b5647a21bde3e02.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 274, + 547, + 319 + ], + "lines": [ + { + "bbox": [ + 46, + 274, + 547, + 319 + ], + "spans": [ + { + "bbox": [ + 46, + 274, + 547, + 319 + ], + "type": "text", + "content": "Table 1. Comparison with existing methods. Models in the first five rows are finetuned on target datasets while the rest perform zero-shot transfer. GroupViT (with *) refers to the results by training officially released code. Results of DeiT, MoCo, and DINO are copied from [53]. Bold fonts refer to the best results among the models that enable zero-shot transfer. CLIPpy uses 134M in-house data while we use an order of magnitude less data. † indicates evaluation on 133 COCO classes claimed by CLIPpy, which differs from ours (80 classes)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 327, + 288, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 327, + 288, + 399 + ], + "spans": [ + { + "bbox": [ + 46, + 327, + 288, + 399 + ], + "type": "text", + "content": "respectively. An extra background class is considered in VOC, Context, and COCO. We ignore their training sets and directly evaluate our method on the validation sets without any finetuning, including 1449, 5105, 5000, and 2000 images, respectively. In general, we report the mean Intersection-over-Union (mIoU) on all the classes." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "spans": [ + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "text", + "content": "Implementation Details. In our model, the self-attention layers in the visual encoder are initialised with DINO [6] pre-trained on ImageNet. The text encoder is initialized with BERT [16] model pre-trained on BookCorpus and English Wikipedia. Our decoder with one randomly initialized Transformer Decoder layer performs reasonably well. The input image is randomly cropped to " + }, + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "inline_equation", + "content": "224\\times 224" + }, + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "text", + "content": " at training time, and the batch size is set to 2048 with an initial learning rate " + }, + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "inline_equation", + "content": "3.2\\times 10^{-4}" + }, + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "text", + "content": ". We train our model for 40 epochs using the Adam optimizer with weight decay set to 0.5. The coefficient for updating the momentum model is 0.99. As the generated masks are unreliable in early epochs, we set the mask consistency coefficient " + }, + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "inline_equation", + "content": "\\lambda = 0" + }, + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "text", + "content": " for the first 30 epochs and " + }, + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "inline_equation", + "content": "\\lambda = 0.1" + }, + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "text", + "content": " for the remaining epochs. The group selection ratio " + }, + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "text", + "content": " is 0.5. As for the threshold in mask consistency loss, we use " + }, + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "inline_equation", + "content": "\\delta = 0.65" + }, + { + "bbox": [ + 46, + 403, + 289, + 632 + ], + "type": "text", + "content": ". At inference time, the image is resized with a shorter length of 448. We follow [53] to set a threshold for the background class, which is 0.9, 0.35, 0.9 and 0.95 on VOC, Context, COCO, and ADE20K, respectively." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 644, + 237, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 644, + 237, + 658 + ], + "spans": [ + { + "bbox": [ + 47, + 644, + 237, + 658 + ], + "type": "text", + "content": "4.2. Comparison with Existing Methods" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "content": "In Table 1, we compare our model with the existing models that have been trained with (1) fully-supervised finetuning transfer and (2) zero-shot transfer. In Table 2, zero-shot segmentation (ZSS) approaches are listed for comparison." + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 305, + 325, + 545, + 430 + ], + "blocks": [ + { + "bbox": [ + 305, + 325, + 545, + 430 + ], + "lines": [ + { + "bbox": [ + 305, + 325, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 305, + 325, + 545, + 430 + ], + "type": "image", + "image_path": "843c99e8c48689860c4522a44c8a88f978c35e1c91410e25ec4da541a5961a28.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 431, + 547, + 465 + ], + "lines": [ + { + "bbox": [ + 305, + 431, + 547, + 465 + ], + "spans": [ + { + "bbox": [ + 305, + 431, + 547, + 465 + ], + "type": "text", + "content": "Figure 3. Qualitative results on PASCAL VOC. We show the learned groups in the last column with each color representing a group. In the last row, the model fails to segment the person's leg." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 481, + 547, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 481, + 547, + 601 + ], + "spans": [ + { + "bbox": [ + 304, + 481, + 547, + 601 + ], + "type": "text", + "content": "Comparison with Finetuning Transfer. We compare our method with DeiT [46], MoCo [10] and DINO [6], which are pre-trained on ImageNet [15] or CC12M+YFCC15M datasets with class labels [46] or self-supervision [6, 10], and finetuned on the training set from downstream benchmarks, with a randomly initialised convolution head appended on the backbone network. As shown in Table 1, our model achieves competitive performance on PASCAL Context, and outperforms the self-supervised methods by over " + }, + { + "bbox": [ + 304, + 481, + 547, + 601 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 304, + 481, + 547, + 601 + ], + "type": "text", + "content": " on PASCAL VOC, with zero-shot transfer." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "type": "text", + "content": "Comparison with Zero-shot Transfer. Here, we compare with existing works under the zero-shot transfer scenario, including GroupViT [53], ViL-Seg [32] and CLIPpy [42], with the pre-training data ranging from CC12M [7] to 134M in-house dataset HQITP-134M [42]. For fair comparison, we re-train GroupViT [53] with their official codebase on our CC4M and CC12M datasets, with the same pre-trained weights as ours being adopted on CC4M. However, we observe no further performance gain of either applying pre" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "2940" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 50, + 70, + 286, + 216 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 286, + 216 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 286, + 216 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 286, + 216 + ], + "type": "table", + "html": "
MethodPretrain datasetSeenUnseenVOCContext
SPNet [51]-✓✗15.64.0
ZS3 [5]-✓✓17.77.7
GaGNet [22]-✓✓29.915.0
SIGN [14]-✓✓28.914.9
Joint [2]-✓✗32.5-
ZegFormer [17]CLIP400M✓✗63.6
MaskCLIP+ [60]CLIP400M✓✗86.166.7†
ViL-Seg [32]CC12M✗✗37.318.9
GroupViT [53]CC12M+YFCC15M✗✗43.751.3
GroupViT* [53]CC4M✗✗22.424.5
GroupViT* [53]CC12M✗✗33.145.3
OVSegmentorCC4M✗✗46.654.5
", + "image_path": "9176d1ca1a1d7af72b52c19210f667f0d8e75a45886358b6649259b13bed2b18.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 286, + 287, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 286, + 287, + 407 + ], + "spans": [ + { + "bbox": [ + 46, + 286, + 287, + 407 + ], + "type": "text", + "content": "trained weights to GroupViT or using ViT-B as the backbone on CC12M. The results on CC12M match the reported ones (40.2 vs 41.1). Under the same pre-trained ViT-B backbone and CC4M dataset, our method surpasses the original GroupViT by " + }, + { + "bbox": [ + 46, + 286, + 287, + 407 + ], + "type": "inline_equation", + "content": "35.7\\%" + }, + { + "bbox": [ + 46, + 286, + 287, + 407 + ], + "type": "text", + "content": ". Additionally, by only using 4M pre-training data, our model yields the best segmentation performance on PASCAL VOC, even outperforming CLIPpy, which is a concurrent work to ours, and pre-trained on 134M data, indicating the effectiveness and training efficiency of our proposed model." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 408, + 288, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 408, + 288, + 624 + ], + "spans": [ + { + "bbox": [ + 46, + 408, + 288, + 624 + ], + "type": "text", + "content": "Comparison with Zero-shot Segmentation Methods. In this line of research [17, 22, 51], the idea is to train the model with full mask labels (obtained either from manual groundtruth or pseudo-labelling [60]) on the seen classes and transfer the model to unseen classes, and the task is thus dubbed as zero-shot semantic segmentation (ZSS). To be specific, 5 classes (potted plant, sheep, sofa, train and tv-monitor) in PASCAL VOC and 4 classes (cow, motorbike, sofa and cat) in PASCAL Context are considered unseen while the remaining classes belong to seen. For comparison, we also report the zero-shot transfer performance of our model on these unseen classes, however, note that, we do not use any manual mask annotations for training. As observed in Table 2, our model surpasses majority of the models trained under the ZSS scenario, except for [17, 60] that adopted CLIP model pre-trained on 400M data. In terms of transfer efficiency, our model excels at zero-shot transfer ability without the need of training on seen classes." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 632, + 141, + 645 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 632, + 141, + 645 + ], + "spans": [ + { + "bbox": [ + 47, + 632, + 141, + 645 + ], + "type": "text", + "content": "4.3. Ablation Study" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 651, + 286, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 651, + 286, + 675 + ], + "spans": [ + { + "bbox": [ + 47, + 651, + 286, + 675 + ], + "type": "text", + "content": "In this section, we conduct thorough ablation studies to validate the necessity of each proposed component." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 677, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 286, + 713 + ], + "type": "text", + "content": "Ablation Study on Proxy Tasks. Here, we aim to understand the effects of our proposed proxy tasks, i.e., masked entity completion and cross-image mask consistency. As" + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 309, + 70, + 543, + 137 + ], + "blocks": [ + { + "bbox": [ + 46, + 220, + 287, + 276 + ], + "lines": [ + { + "bbox": [ + 46, + 220, + 287, + 276 + ], + "spans": [ + { + "bbox": [ + 46, + 220, + 287, + 276 + ], + "type": "text", + "content": "Table 2. Comparison with zero-shot segmentation methods on unseen classes. Seen/Unseen denotes whether the model is trained on seen/unseen classes. Our method outperforms most zero-shot segmentation models even without training on seen classes. " + }, + { + "bbox": [ + 46, + 220, + 287, + 276 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 46, + 220, + 287, + 276 + ], + "type": "text", + "content": " indicates a different set of unseen classes." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 70, + 543, + 137 + ], + "lines": [ + { + "bbox": [ + 309, + 70, + 543, + 137 + ], + "spans": [ + { + "bbox": [ + 309, + 70, + 543, + 137 + ], + "type": "table", + "html": "
Baseline\\( {\\mathcal{L}}_{\\text{entity }} \\)\\( {\\mathcal{L}}_{\\text{mask }} \\)PASCAL VOCPASCAL Context
✓40.515.1
✓✓48.919.9
✓✓44.817.0
✓✓✓53.820.4
", + "image_path": "5ec6e112727b101d02a658d31f1047e28490196620287080f2b9d302d9961eff.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 313, + 184, + 536, + 266 + ], + "blocks": [ + { + "bbox": [ + 305, + 143, + 545, + 176 + ], + "lines": [ + { + "bbox": [ + 305, + 143, + 545, + 176 + ], + "spans": [ + { + "bbox": [ + 305, + 143, + 545, + 176 + ], + "type": "text", + "content": "Table 3. Ablation study on the masked entity completion loss " + }, + { + "bbox": [ + 305, + 143, + 545, + 176 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{\\mathrm{entity}})" + }, + { + "bbox": [ + 305, + 143, + 545, + 176 + ], + "type": "text", + "content": " and cross-image mask consistency loss " + }, + { + "bbox": [ + 305, + 143, + 545, + 176 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{\\mathrm{mask}})" + }, + { + "bbox": [ + 305, + 143, + 545, + 176 + ], + "type": "text", + "content": ". The baseline only uses the image-caption contrastive loss " + }, + { + "bbox": [ + 305, + 143, + 545, + 176 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{\\mathrm{contrast}})" + }, + { + "bbox": [ + 305, + 143, + 545, + 176 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 313, + 184, + 536, + 266 + ], + "lines": [ + { + "bbox": [ + 313, + 184, + 536, + 266 + ], + "spans": [ + { + "bbox": [ + 313, + 184, + 536, + 266 + ], + "type": "table", + "html": "
Masking objectivePASCAL VOCPASCAL Context
All entities (ours)48.919.9
Single entity47.018.5
All nouns45.417.4
Multi-label contrastive44.517.0
MLM (w/ groups)42.816.3
MLM (w/o groups)36.314.6
", + "image_path": "f424e2c09d07b52c4cf48681ba2aad432493731fe787543b45bd219ecfc16088.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 270, + 545, + 315 + ], + "lines": [ + { + "bbox": [ + 304, + 270, + 545, + 315 + ], + "spans": [ + { + "bbox": [ + 304, + 270, + 545, + 315 + ], + "type": "text", + "content": "Table 4. Comparison on masking objectives. MLM (w/ groups) refers to masked language modeling [16] given the group tokens as keys and values in the decoder while MLM (w/o groups) directly predicts masked words from the output of text encoder." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 327, + 545, + 447 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 327, + 545, + 447 + ], + "spans": [ + { + "bbox": [ + 304, + 327, + 545, + 447 + ], + "type": "text", + "content": "shown in Table 3, the baseline model uses the image-text contrastive loss " + }, + { + "bbox": [ + 304, + 327, + 545, + 447 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{contrast}}" + }, + { + "bbox": [ + 304, + 327, + 545, + 447 + ], + "type": "text", + "content": " only, while adding the entity completion task, " + }, + { + "bbox": [ + 304, + 327, + 545, + 447 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{entity}}" + }, + { + "bbox": [ + 304, + 327, + 545, + 447 + ], + "type": "text", + "content": ", we observe a significant improvement by " + }, + { + "bbox": [ + 304, + 327, + 545, + 447 + ], + "type": "inline_equation", + "content": "8.4\\%" + }, + { + "bbox": [ + 304, + 327, + 545, + 447 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 327, + 545, + 447 + ], + "type": "inline_equation", + "content": "4.8\\%" + }, + { + "bbox": [ + 304, + 327, + 545, + 447 + ], + "type": "text", + "content": " on PASCAL VOC and PASCAL Context, respectively. The performance gain is due to the ability of better aligning the pixel groups with the visual entities. Additionally, the mask consistency also brings improvements, and combing both leads to the best performance. Qualitative results can be seen in Fig. 3. We refer the readers for more visualisations in the supplementary material." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 450, + 546, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 450, + 546, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 450, + 546, + 714 + ], + "type": "text", + "content": "On the Choice of Masking Objectives. We compare our masked entity completion with a series of variants as shown in Table 4. Our proposed objective of masking all entities is listed in the first row. (1) All entities vs one entity: the masked language modeling (MLM) in prior works [16, 35] normally choose " + }, + { + "bbox": [ + 304, + 450, + 546, + 714 + ], + "type": "inline_equation", + "content": "15\\%" + }, + { + "bbox": [ + 304, + 450, + 546, + 714 + ], + "type": "text", + "content": " of the token positions in the sentence for prediction, which results in one entity in most of our cases. We observe that masking all entities is " + }, + { + "bbox": [ + 304, + 450, + 546, + 714 + ], + "type": "inline_equation", + "content": "2.9\\%" + }, + { + "bbox": [ + 304, + 450, + 546, + 714 + ], + "type": "text", + "content": " mIoU better than single entity masking, as it forces the network to infer all possible object categories in the image, that is potentially beneficial for the group to category alignment. (2) Entities vs nouns: masking and predicting noun phrases in the sentence [20] is one feasible option to learn fine-grained vision-text matching. However, noun masking leads to " + }, + { + "bbox": [ + 304, + 450, + 546, + 714 + ], + "type": "inline_equation", + "content": "3.5\\%" + }, + { + "bbox": [ + 304, + 450, + 546, + 714 + ], + "type": "text", + "content": " lower mIoU than our entity masking strategy. This is because not all of the nouns in the sentence are visually corresponding to the objects in the image (e.g., illustration, night, etc), thus the group tokens are not expected to align with these nouns. Our method avoids this issue by only masking the visual entities. (3) Masked entity completion vs multi-label contrastive loss: comparing with the multi-label contrastive loss used in GroupViT [53], our proposed" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "2941" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 69, + 168, + 148 + ], + "blocks": [ + { + "bbox": [ + 48, + 69, + 168, + 148 + ], + "lines": [ + { + "bbox": [ + 48, + 69, + 168, + 148 + ], + "spans": [ + { + "bbox": [ + 48, + 69, + 168, + 148 + ], + "type": "image", + "image_path": "31486f25a583772e6931f67c7fdb0681334e02104e3ca6f7aec5ff51b431d8cb.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 149, + 544, + 172 + ], + "lines": [ + { + "bbox": [ + 46, + 149, + 544, + 172 + ], + "spans": [ + { + "bbox": [ + 46, + 149, + 544, + 172 + ], + "type": "text", + "content": "Figure 4. Qualitative results of the masked entity completion loss. We show different groups that match the given class. With the boost of group semantics, our model improves visual grouping (left) and solves false matching between group and text (right) of the baseline." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 171, + 70, + 291, + 148 + ], + "blocks": [ + { + "bbox": [ + 171, + 70, + 291, + 148 + ], + "lines": [ + { + "bbox": [ + 171, + 70, + 291, + 148 + ], + "spans": [ + { + "bbox": [ + 171, + 70, + 291, + 148 + ], + "type": "image", + "image_path": "5614c2757de50d322408add4ef27eccd1156cf05261ac98e47a5e77ecc08974d.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 50, + 183, + 165, + 220 + ], + "blocks": [ + { + "bbox": [ + 50, + 183, + 165, + 220 + ], + "lines": [ + { + "bbox": [ + 50, + 183, + 165, + 220 + ], + "spans": [ + { + "bbox": [ + 50, + 183, + 165, + 220 + ], + "type": "table", + "html": "
K/τ0.250.50.751.0
849.053.651.850.4
1642.343.243.142.9
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ObjectiveLossmIoU
Mask ConsistencyDice53.8
Group ConsistencyNCE47.9
", + "image_path": "2672267657dfdc32aee1fe625a0e31b872aeb30b28ea337ab4514fa5e024d97b.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 266, + 286, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 266, + 286, + 433 + ], + "spans": [ + { + "bbox": [ + 46, + 266, + 286, + 433 + ], + "type": "text", + "content": "strategy shows superior performance. (4) Masked entity completion vs masked language modeling: MLM [16] originally predicts the masked token over the entire vocabulary via a cross-entropy loss. Here, we restrict the vocabulary to our constructed entity set for fair comparison. Our masking strategy surpasses both MLM variants by a clear margin, which we conjecture is because: (1) MLM classifies each masked token individually, and the model can easily refer to the context words without relating to groups. (2) MLM focuses on word-level representations, which is not in accordance with the sentence-level representation of the class embeddings we used during inference. Fig. 4 shows the effect of the masked entity completion in improving visual grouping (left) and group-text alignment (right)." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 436, + 286, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 436, + 286, + 651 + ], + "spans": [ + { + "bbox": [ + 46, + 436, + 286, + 651 + ], + "type": "text", + "content": "On Cross-image Mask Consistency. Here, we analyze two key factors in cross-image consistency: (1) group numbers and selection ratios. As shown in Table 5, 8 groups perform comparably well for all selection ratios, and we pick 0.5 as the default. Smaller ratios miss the entities encoded in remaining groups while larger ratios introduce entity-irrelevant information (e.g., background). However, while increasing the group number to 16, it brings over-segmentation for large objects, deteriorating the performance. (2) the objective for cross-image consistency. We study another variant of cross-image consistency by directly aligning two sets of group tokens encoded with shared entity. We adopt the contrastive loss (NCE in Table 6) to pull two sets of group tokens closer, while other group tokens in each mini-batch are pushed farther. Table 6 reveals the superiority of our proposed mask consistency over group consistency, which we believe is because mask consistency involves the image content to realize visual invariance." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 654, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 286, + 713 + ], + "type": "text", + "content": "Performance on Unseen Entities. We measure the mean IoU for objects within entity set (e.g., person, bus) and those out of entity set on each dataset. As shown in Table 7, our model still achieves a decent improvement over the baseline on unseen entities, indicating it retains strong" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 301, + 70, + 423, + 148 + ], + "blocks": [ + { + "bbox": [ + 301, + 70, + 423, + 148 + ], + "lines": [ + { + "bbox": [ + 301, + 70, + 423, + 148 + ], + "spans": [ + { + "bbox": [ + 301, + 70, + 423, + 148 + ], + "type": "image", + "image_path": "241e8d11af046f243799c2d39e6411b4a5250bd20cbf42d3626d59c157f1c889.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 425, + 70, + 545, + 147 + ], + "blocks": [ + { + "bbox": [ + 425, + 70, + 545, + 147 + ], + "lines": [ + { + "bbox": [ + 425, + 70, + 545, + 147 + ], + "spans": [ + { + "bbox": [ + 425, + 70, + 545, + 147 + ], + "type": "image", + "image_path": "d818cf0fec8993a2a20701e9fe47d78833da0cda482e8c1310eb32fbfcbd0276.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 307, + 183, + 544, + 239 + ], + "blocks": [ + { + "bbox": [ + 167, + 224, + 284, + 257 + ], + "lines": [ + { + "bbox": [ + 167, + 224, + 284, + 257 + ], + "spans": [ + { + "bbox": [ + 167, + 224, + 284, + 257 + ], + "type": "text", + "content": "Table 6. Comparison on different objectives for cross-image consistency on VOC." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 307, + 183, + 544, + 239 + ], + "lines": [ + { + "bbox": [ + 307, + 183, + 544, + 239 + ], + "spans": [ + { + "bbox": [ + 307, + 183, + 544, + 239 + ], + "type": "table", + "html": "
MethodVOCCOCOContextADE20K
S20U0S65U16S30U29S31U119
Baseline38.5-17.215.420.58.87.13.7
Ours(CC4M)52.4-24.825.927.910.98.64.8
Ours(CC12M)44.8-18.621.525.412.010.16.3
", + "image_path": "a2cd5875c5c7f60159d77292ee1b62a3da5742989fae10b83ebe655b38bf2493.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 241, + 545, + 262 + ], + "lines": [ + { + "bbox": [ + 306, + 241, + 545, + 262 + ], + "spans": [ + { + "bbox": [ + 306, + 241, + 545, + 262 + ], + "type": "text", + "content": "Table 7. Performance on seen(S)/unseen(U) entities, where indices refer to entity numbers. Background class is not considered." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 274, + 545, + 311 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 274, + 545, + 311 + ], + "spans": [ + { + "bbox": [ + 304, + 274, + 545, + 311 + ], + "type": "text", + "content": "open-vocabulary segmentation ability. The text encoder pre-trained on large text corpus remains its ability to encode the semantic concept of objects out of entity set." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 312, + 545, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 312, + 545, + 396 + ], + "spans": [ + { + "bbox": [ + 304, + 312, + 545, + 396 + ], + "type": "text", + "content": "Scalability. We scale up the datasets by increasing the number of entities, i.e., training the model on the entire CC12M. Ours (CC12M) outperforms ours (CC4M) on the challenging ADE20K and PASCAL Context, reflecting the effect of the scalability of our model. The performance drop on VOC is because the extremely large entity set in CC12M poses extra challenge for our model to learn the entities in VOC." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 400, + 378, + 412 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 400, + 378, + 412 + ], + "spans": [ + { + "bbox": [ + 306, + 400, + 378, + 412 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 420, + 545, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 420, + 545, + 588 + ], + "spans": [ + { + "bbox": [ + 304, + 420, + 545, + 588 + ], + "type": "text", + "content": "In this paper, we present OVSegmentor, a transformer-based model for open-vocabulary semantic segmentation. The model exploits web-collected image-caption pairs for pre-training without any mask annotations, and transfers to target benchmark segmentation datasets in a zero-shot manner. The model clusters the image pixels into learnable group tokens, which are then aligned with the corresponding caption embeddings. We further devise two proxy tasks, namely masked entity completion and cross-image mask consistency, to learn entity-specific, fine-grained and visually invariant group semantics. OVSegmentor outperforms the state-of-the-art method on PASCAL VOC by using only " + }, + { + "bbox": [ + 304, + 420, + 545, + 588 + ], + "type": "inline_equation", + "content": "3\\%" + }, + { + "bbox": [ + 304, + 420, + 545, + 588 + ], + "type": "text", + "content": " (4M vs 134M) for pre-training, indicating the effectiveness and training efficiency of our model." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 592, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 592, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 592, + 545, + 713 + ], + "type": "text", + "content": "Acknowledgement. This work is supported by Natural Science Foundation of China (No. 61976057; No. 62172101), the Science and Technology Commission of Shanghai Municipality (No. 21511101000; No. 22511106003), the Science and Technology Major Project of Commission of Science and Technology of Shanghai (No. 2021SHZDZX0103). This work is also partially supported by the National Key R&D Program of China (No. 2022ZD0160100), and in part by Shanghai Committee of Science and Technology (No. 21DZ1100100). This work was done during Jilan's parttime internship at Shanghai Artificial Intelligence Laboratory." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2942" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 92, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 53, + 92, + 287, + 125 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 92, + 287, + 125 + ], + "spans": [ + { + "bbox": [ + 53, + 92, + 287, + 125 + ], + "type": "text", + "content": "[1] Jiwoon Ahn and Suha Kwak. Learning pixel-level semantic affinity with image-level supervision for weakly supervised semantic segmentation. In CVPR, 2018. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 127, + 287, + 159 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 127, + 287, + 159 + ], + "spans": [ + { + "bbox": [ + 53, + 127, + 287, + 159 + ], + "type": "text", + "content": "[2] Donghyeon Baek, Youngmin Oh, and Bumsub Ham. Exploiting a joint embedding space for generalized zero-shot semantic segmentation. In ICCV, 2021. 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 162, + 288, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 162, + 288, + 195 + ], + "spans": [ + { + "bbox": [ + 53, + 162, + 288, + 195 + ], + "type": "text", + "content": "[3] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, 2021. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 197, + 287, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 197, + 287, + 229 + ], + "spans": [ + { + "bbox": [ + 53, + 197, + 287, + 229 + ], + "type": "text", + "content": "[4] Steven Bird, Ewan Klein, and Edward Loper. Natural language processing with Python: analyzing text with the natural language toolkit. \"O'Reilly Media, Inc.\", 2009. 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 232, + 287, + 254 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 232, + 287, + 254 + ], + "spans": [ + { + "bbox": [ + 53, + 232, + 287, + 254 + ], + "type": "text", + "content": "[5] Maxime Bucher, Tuan-Hung Vu, Matthieu Cord, and Patrick Pérez. Zero-shot semantic segmentation. NeurIPS, 2019. 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 255, + 287, + 298 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 255, + 287, + 298 + ], + "spans": [ + { + "bbox": [ + 53, + 255, + 287, + 298 + ], + "type": "text", + "content": "[6] Mathilde Caron, Hugo Touvron, Ishan Misra, Herve Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In ICCV, 2021. 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 301, + 287, + 344 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 301, + 287, + 344 + ], + "spans": [ + { + "bbox": [ + 53, + 301, + 287, + 344 + ], + "type": "text", + "content": "[7] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In CVPR, 2021. 2, 5, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 347, + 287, + 390 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 347, + 287, + 390 + ], + "spans": [ + { + "bbox": [ + 53, + 347, + 287, + 390 + ], + "type": "text", + "content": "[8] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. PAMI, 2017. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 393, + 287, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 393, + 287, + 435 + ], + "spans": [ + { + "bbox": [ + 53, + 393, + 287, + 435 + ], + "type": "text", + "content": "[9] Qi Chen, Lingxiao Yang, Jian-Huang Lai, and Xiaohua Xie. Self-supervised image-specific prototype exploration for weakly supervised semantic segmentation. In CVPR, 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 438, + 287, + 471 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 438, + 287, + 471 + ], + "spans": [ + { + "bbox": [ + 48, + 438, + 287, + 471 + ], + "type": "text", + "content": "[10] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In ICCV, 2021. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 473, + 287, + 506 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 473, + 287, + 506 + ], + "spans": [ + { + "bbox": [ + 48, + 473, + 287, + 506 + ], + "type": "text", + "content": "[11] Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang. Semi-supervised semantic segmentation with cross pseudo supervision. In CVPR, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 508, + 287, + 551 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 508, + 287, + 551 + ], + "spans": [ + { + "bbox": [ + 48, + 508, + 287, + 551 + ], + "type": "text", + "content": "[12] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, 2020. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 554, + 287, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 554, + 287, + 586 + ], + "spans": [ + { + "bbox": [ + 48, + 554, + 287, + 586 + ], + "type": "text", + "content": "[13] Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. NeurIPS, 2021. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 589, + 287, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 589, + 287, + 632 + ], + "spans": [ + { + "bbox": [ + 48, + 589, + 287, + 632 + ], + "type": "text", + "content": "[14] Jiaxin Cheng, Soumyaroop Nandi, Prem Natarajan, and Wael Abd-Almageed. Sign: Spatial-information incorporated generative network for generalized zero-shot semantic segmentation. In ICCV, 2021. 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 635, + 287, + 666 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 635, + 287, + 666 + ], + "spans": [ + { + "bbox": [ + 48, + 635, + 287, + 666 + ], + "type": "text", + "content": "[15] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, 2009. 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "text", + "content": "[16] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: pre-training of deep bidirectional transformers for language understanding. In NAACL, 2019. 4, 6, 7, 8" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 34, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "text", + "content": "[17] Jian Ding, Nan Xue, Gui-Song Xia, and Dengxin Dai. Decoupling zero-shot semantic segmentation. In CVPR, 2022, 2, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 108, + 545, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 108, + 545, + 140 + ], + "spans": [ + { + "bbox": [ + 307, + 108, + 545, + 140 + ], + "type": "text", + "content": "[18] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. IJCV, 2010. 2, 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 143, + 545, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 143, + 545, + 185 + ], + "spans": [ + { + "bbox": [ + 307, + 143, + 545, + 185 + ], + "type": "text", + "content": "[19] Geoffrey French, Samuli Laine, Timo Aila, Michal Mackiewicz, and Graham D. Finlayson. Semi-supervised semantic segmentation needs strong, varied perturbations. In BMVC, 2020. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 188, + 545, + 220 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 188, + 545, + 220 + ], + "spans": [ + { + "bbox": [ + 307, + 188, + 545, + 220 + ], + "type": "text", + "content": "[20] Yuying Ge, Yixiao Ge, Xihui Liu, Dian Li, Ying Shan, Xiaohu Qie, and Ping Luo. Bridging video-text retrieval with multiple choice questions. In CVPR, 2022. 2, 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 223, + 545, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 223, + 545, + 255 + ], + "spans": [ + { + "bbox": [ + 307, + 223, + 545, + 255 + ], + "type": "text", + "content": "[21] Golnaz Ghiasi, Xiuye Gu, Yin Cui, and Tsung-Yi Lin. Scaling open-vocabulary image segmentation with image-level labels. In ECCV, 2022. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 258, + 545, + 290 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 258, + 545, + 290 + ], + "spans": [ + { + "bbox": [ + 307, + 258, + 545, + 290 + ], + "type": "text", + "content": "[22] Zhangxuan Gu, Siyuan Zhou, Li Niu, Zihan Zhao, and Liqing Zhang. Context-aware feature generation for zero-shot semantic segmentation. In ACM MM, 2020. 2, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 292, + 545, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 292, + 545, + 335 + ], + "spans": [ + { + "bbox": [ + 307, + 292, + 545, + 335 + ], + "type": "text", + "content": "[23] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICML, 2021. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 338, + 545, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 338, + 545, + 380 + ], + "spans": [ + { + "bbox": [ + 307, + 338, + 545, + 380 + ], + "type": "text", + "content": "[24] Eunji Kim, Siwon Kim, Jungbeom Lee, Hyunwoo Kim, and Sungroh Yoon. Bridging the gap between classification and localization for weakly supervised object localization. In CVPR, 2022. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 383, + 545, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 383, + 545, + 415 + ], + "spans": [ + { + "bbox": [ + 307, + 383, + 545, + 415 + ], + "type": "text", + "content": "[25] Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83-97, 1955. 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 418, + 545, + 450 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 418, + 545, + 450 + ], + "spans": [ + { + "bbox": [ + 307, + 418, + 545, + 450 + ], + "type": "text", + "content": "[26] Boyi Li, Kilian Q. Weinberger, Serge J. Belongie, Vladlen Koltun, and René Ranftl. Language-driven semantic segmentation. In ICLR, 2022. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 453, + 545, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 453, + 545, + 485 + ], + "spans": [ + { + "bbox": [ + 307, + 453, + 545, + 485 + ], + "type": "text", + "content": "[27] Dongxu Li, Junnan Li, Hongdong Li, Juan Carlos Niebles, and Steven CH Hoi. Align and prompt: Video-and-language pre-training with entity prompts. In CVPR, 2022. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 487, + 545, + 530 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 487, + 545, + 530 + ], + "spans": [ + { + "bbox": [ + 307, + 487, + 545, + 530 + ], + "type": "text", + "content": "[28] Juncheng Li, Xin He, Longhui Wei, Long Qian, Linchao Zhu, Lingxi Xie, Yueting Zhuang, Qi Tian, and Siliang Tang. Fine-grained semantically aligned vision-language pre-training. NeurIPS, 2022. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 307, + 533, + 545, + 576 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 533, + 545, + 576 + ], + "spans": [ + { + "bbox": [ + 307, + 533, + 545, + 576 + ], + "type": "text", + "content": "[29] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. NeurIPS, 2021. 2" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 307, + 578, + 545, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 578, + 545, + 622 + ], + "spans": [ + { + "bbox": [ + 307, + 578, + 545, + 622 + ], + "type": "text", + "content": "[30] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In CVPR, 2022. 2" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 307, + 624, + 545, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 624, + 545, + 667 + ], + "spans": [ + { + "bbox": [ + 307, + 624, + 545, + 667 + ], + "type": "text", + "content": "[31] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV. Springer, 2014. 2, 5" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 307, + 670, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 670, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 307, + 670, + 545, + 712 + ], + "type": "text", + "content": "[32] Quande Liu, Youpeng Wen, Jianhua Han, Chunjing Xu, Hang Xu, and Xiaodan Liang. Open-world semantic segmentation via contrasting and clustering vision-language embedding. In ECCV, 2022. 2, 5, 6, 7" + } + ] + } + ], + "index": 33 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2943" + } + ] + } + ], + "index": 35 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "text", + "content": "[33] Francesco Locatello, Dirk Weissenborn, Thomas Unterthiner, Aravindh Mahendran, Georg Heigold, Jakob Uszkoreit, Alexey Dosovitskiy, and Thomas Kipf. Object-centric learning with slot attention. NeurIPS, 2020. 2, 4" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 118, + 287, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 118, + 287, + 150 + ], + "spans": [ + { + "bbox": [ + 49, + 118, + 287, + 150 + ], + "type": "text", + "content": "[34] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 152, + 287, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 152, + 287, + 184 + ], + "spans": [ + { + "bbox": [ + 48, + 152, + 287, + 184 + ], + "type": "text", + "content": "[35] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS, 2019. 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 186, + 287, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 186, + 287, + 217 + ], + "spans": [ + { + "bbox": [ + 48, + 186, + 287, + 217 + ], + "type": "text", + "content": "[36] Wenfeng Luo and Meng Yang. Semi-supervised semantic segmentation via strong-weak dual-branch network. In ECCV, 2020. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 220, + 287, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 220, + 287, + 252 + ], + "spans": [ + { + "bbox": [ + 48, + 220, + 287, + 252 + ], + "type": "text", + "content": "[37] Chaofan Ma, Yuhuan Yang, Yanfeng Wang, Ya Zhang, and Weidi Xie. Open-vocabulary semantic segmentation with frozen vision-language models. BMVC, 2022. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 254, + 287, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 254, + 287, + 297 + ], + "spans": [ + { + "bbox": [ + 48, + 254, + 287, + 297 + ], + "type": "text", + "content": "[38] Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic segmentation in the wild. In CVPR, 2014. 2, 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 299, + 287, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 299, + 287, + 331 + ], + "spans": [ + { + "bbox": [ + 48, + 299, + 287, + 331 + ], + "type": "text", + "content": "[39] Yassine Ouali, Céline Hudelot, and Myriam Tami. Semi-supervised semantic segmentation with cross-consistency training. In CVPR, 2020. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 333, + 287, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 333, + 287, + 374 + ], + "spans": [ + { + "bbox": [ + 48, + 333, + 287, + 374 + ], + "type": "text", + "content": "[40] Giuseppe Pastore, Fabio Cermelli, Yongqin Xian, Massimiliano Mancini, Zeynep Akata, and Barbara Caputo. A closer look at self-training for zero-label semantic segmentation. In CVPR, 2021. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 377, + 287, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 377, + 287, + 431 + ], + "spans": [ + { + "bbox": [ + 48, + 377, + 287, + 431 + ], + "type": "text", + "content": "[41] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICML, 2021. 1, 2, 4" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 434, + 287, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 434, + 287, + 475 + ], + "spans": [ + { + "bbox": [ + 48, + 434, + 287, + 475 + ], + "type": "text", + "content": "[42] Kanchana Ranasinghe, Brandon McKinzie, Sachin Ravi, Yinfei Yang, Alexander Toshev, and Jonathon Shlens. Perceptual grouping in vision-language models. arXiv preprint arXiv:2210.09996, 2022. 2, 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 478, + 287, + 532 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 478, + 287, + 532 + ], + "spans": [ + { + "bbox": [ + 48, + 478, + 287, + 532 + ], + "type": "text", + "content": "[43] Pengzhen Ren, Changlin Li, Hang Xu, Yi Zhu, Guangrun Wang, Jianzhuang Liu, Xiaojun Chang, and Xiaodan Liang. Viewco: Discovering text-supervised segmentation masks via multi-view semantic consistency. arXiv preprint arXiv:2302.10307, 2023. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 534, + 287, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 534, + 287, + 566 + ], + "spans": [ + { + "bbox": [ + 48, + 534, + 287, + 566 + ], + "type": "text", + "content": "[44] Gyungin Shin, Weidi Xie, and Samuel Albanie. Named-mask: Distilling segmenters from complementary foundation models. arXiv preprint arXiv:2209.11228, 2022. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 567, + 287, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 567, + 287, + 599 + ], + "spans": [ + { + "bbox": [ + 48, + 567, + 287, + 599 + ], + "type": "text", + "content": "[45] Gyungin Shin, Weidi Xie, and Samuel Albanie. Reco: Retrieve and co-segment for zero-shot transfer. NeurIPS, 2022. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 601, + 287, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 601, + 287, + 644 + ], + "spans": [ + { + "bbox": [ + 48, + 601, + 287, + 644 + ], + "type": "text", + "content": "[46] Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Hervé Jégou. Training data-efficient image transformers & distillation through attention. In ICML, 2021. 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 647, + 287, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 287, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 287, + 678 + ], + "type": "text", + "content": "[47] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. NeurIPS, 2017. 4" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 680, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 287, + 712 + ], + "type": "text", + "content": "[48] Huiyu Wang, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen. Max-deeplab: End-to-end panoptic segmentation with mask transformers. In CVPR, 2021. 2" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 578 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "text", + "content": "[49] Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, and Xinyi Le. Semi-supervised semantic segmentation using unreliable pseudo-labels. In CVPR, 2022. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 118, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 150 + ], + "type": "text", + "content": "[50] Pingyu Wu, Wei Zhai, and Yang Cao. Background activation suppression for weakly supervised object localization. In CVPR, 2022. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 152, + 545, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 194 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 194 + ], + "type": "text", + "content": "[51] Yongqin Xian, Subhabrata Choudhury, Yang He, Bernt Schiele, and Zeynep Akata. Semantic projection network for zero-and few-label semantic segmentation. In CVPR, 2019. 2, 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 197, + 545, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 197, + 545, + 250 + ], + "spans": [ + { + "bbox": [ + 307, + 197, + 545, + 250 + ], + "type": "text", + "content": "[52] Jinheng Xie, Jianfeng Xiang, Junliang Chen, Xianxu Hou, Xiaodong Zhao, and Linlin Shen. C2am: Contrastive learning of class-agnostic activation map for weakly supervised object localization and semantic segmentation. In CVPR, 2022. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 252, + 545, + 295 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 252, + 545, + 295 + ], + "spans": [ + { + "bbox": [ + 307, + 252, + 545, + 295 + ], + "type": "text", + "content": "[53] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In CVPR, 2022. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 297, + 545, + 341 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 297, + 545, + 341 + ], + "spans": [ + { + "bbox": [ + 307, + 297, + 545, + 341 + ], + "type": "text", + "content": "[54] Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng, Rui-Wei Zhao, Tao Zhang, Xuequan Lu, and Shang Gao. Cream: Weakly supervised object localization via class re-activation mapping. In CVPR, 2022. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 342, + 545, + 386 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 342, + 545, + 386 + ], + "spans": [ + { + "bbox": [ + 307, + 342, + 545, + 386 + ], + "type": "text", + "content": "[55] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. FILIP: fine-grained interactive language-image pre-training. In ICLR, 2022. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 388, + 545, + 419 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 388, + 545, + 419 + ], + "spans": [ + { + "bbox": [ + 307, + 388, + 545, + 419 + ], + "type": "text", + "content": "[56] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. In ICML, 2022. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 421, + 545, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 421, + 545, + 474 + ], + "spans": [ + { + "bbox": [ + 307, + 421, + 545, + 474 + ], + "type": "text", + "content": "[57] Haotian Zhang, Pengchuan Zhang, Xiaowei Hu, Yen-Chun Chen, Liunian Harold Li, Xiyang Dai, Lijuan Wang, Lu Yuan, Jenq-Neng Hwang, and Jianfeng Gao. Glipv2: Unifying localization and vision-language understanding. NeurIPS, 2022. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 476, + 545, + 509 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 476, + 545, + 509 + ], + "spans": [ + { + "bbox": [ + 307, + 476, + 545, + 509 + ], + "type": "text", + "content": "[58] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In CVPR, 2016. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 510, + 545, + 554 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 510, + 545, + 554 + ], + "spans": [ + { + "bbox": [ + 307, + 510, + 545, + 554 + ], + "type": "text", + "content": "[59] Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127:302-321, 2019. 2, 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 555, + 545, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 555, + 545, + 578 + ], + "spans": [ + { + "bbox": [ + 307, + 555, + 545, + 578 + ], + "type": "text", + "content": "[60] Chong Zhou, Chen Change Loy, and Bo Dai. Denseclip: Extract free dense labels from clip. ECCV, 2022. 2, 7" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2944" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Optical Expansion From Scale Matching/fadba611-fc8a-473b-9570-70ac81007770_content_list.json b/2023/Learning Optical Expansion From Scale Matching/fadba611-fc8a-473b-9570-70ac81007770_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b1efc3ad342cf927b1074209f46caf5af3a583c3 --- /dev/null +++ b/2023/Learning Optical Expansion From Scale Matching/fadba611-fc8a-473b-9570-70ac81007770_content_list.json @@ -0,0 +1,1919 @@ +[ + { + "type": "text", + "text": "Learning Optical Expansion from Scale Matching", + "text_level": 1, + "bbox": [ + 233, + 130, + 736, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Han Ling $^{1}$ Yinghui Sun $^{1}$ Quansen Sun $^{1*}$ Zhenwen Ren $^{1,2,3\\dagger}$", + "bbox": [ + 223, + 178, + 750, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Nanjing University of Science and Technology", + "bbox": [ + 295, + 198, + 673, + 217 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ Southwest University of Science and Technology", + "bbox": [ + 287, + 215, + 683, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "3SongShan Laboratory", + "bbox": [ + 393, + 233, + 575, + 252 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{321106010190,yinghuisun,sunquansen,rzw}@njust.edu.cn", + "bbox": [ + 250, + 253, + 714, + 268 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 304, + 312, + 319 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "This paper addresses the problem of optical expansion (OE). OE describes the object scale change between two frames, widely used in monocular 3D vision tasks. Previous methods estimate optical expansion mainly from optical flow results, but this two-stage architecture makes their results limited by the accuracy of optical flow and less robust. To solve these problems, we propose the concept of 3D optical flow by integrating optical expansion into the 2D optical flow, which is implemented by a plug-and-play module, namely TPCV. TPCV implements matching features at the correct location and scale, thus allowing the simultaneous optimization of optical flow and optical expansion tasks. Experimentally, we apply TPCV to the RAFT optical flow baseline. Experimental results show that the baseline optical flow performance is substantially improved. Moreover, we apply the optical flow and optical expansion results to various dynamic 3D vision tasks, including motion-in-depth, time-to-collision, and scene flow, often achieving significant improvement over the prior SOTA. Code is available at https://github.com/HanLingsgjk/TPCV.", + "bbox": [ + 75, + 335, + 473, + 652 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 684, + 207, + 699 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Optical expansion (OE) is a fundamental and important concept in monocular dynamic 3D vision tasks [1,3,22,23, 32]. OE describes the scale change of an object between two frames, which can be translated into motion in the depth direction. It has essential applications in time-to-collision, scene flow, and motion-in-depth estimation. OE schemes have unique advantages in 3D motion estimation tasks, requiring only a single camera and enabling dense and fixed baseline independent results. In this paper, we discuss a robust and novel approach for OE estimation.", + "bbox": [ + 75, + 710, + 468, + 862 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9a59211a90260a8615aa6c462b04af7edfb097b9bcca60b508164908a3cfcc3b.jpg", + "image_caption": [ + "Frame1" + ], + "image_footnote": [], + "bbox": [ + 514, + 304, + 686, + 382 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/5add814f79720e19aa514825e3c0b5c3768cc079f0ecc3ecbb556b843a71f474.jpg", + "image_caption": [ + "Multi Scale Frame2", + "Figure 1. Scale matching idea. Left: multi-scale matching between two frames. Right: texture around the matching point, where $s$ is the size of the image scaling. The core idea of scale matching is to match texture features at the correct location and scale. As seen above, the texture at the license plate can be better matched when the second frame is scaled 0.7 times in size, where the scaling magnification also reflects the optical expansion of that local pixel between the two frames. Furthermore, scale matching can better handle the motion in the depth direction and contains potential 3D motion information." + ], + "image_footnote": [], + "bbox": [ + 514, + 393, + 697, + 503 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/00e35e7bdeffd87e4d7571d9bcf8fa04c50bee1cc535b4fd4012193d73cca929.jpg", + "image_caption": [ + "frame1 $s = 1$" + ], + "image_footnote": [], + "bbox": [ + 710, + 306, + 826, + 352 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d9675181e54b252d31502189d9c8f94d3bd049a6b59ea61a0453dc688d2d88b2.jpg", + "image_caption": [ + "frame2 $s = 1$" + ], + "image_footnote": [], + "bbox": [ + 707, + 366, + 823, + 410 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/ea1a2426daad6fcaaf5bae764d45f2bbd742f5baf9f8929eb685f4c2f0f837ad.jpg", + "image_caption": [ + "$s = 0.7$" + ], + "image_footnote": [], + "bbox": [ + 709, + 412, + 823, + 455 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/209a8c1484f5a2552f110076a23cbce3d7deb6db01aa52e34e3058004afeca99.jpg", + "image_caption": [ + "$s = 0.5$" + ], + "image_footnote": [], + "bbox": [ + 709, + 458, + 821, + 502 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Prior works In early time-to-collision (TTC) studies [4, 22, 23], OE was obtained from motion modeling, where the motion estimation was provided by optical flow or SIFT [10]. Such algorithms relied on optical flow results and specific model assumptions, often yielding only sparse and low-accuracy results. Some recent methods [32] regress OE based on existing optical flow results and achieve better outcomes. However, these two-stage estimation methods rely on accurate optical flow results and decrease computational efficiency. Instead, we consider optical flow and expansion estimation as two complementary tasks. Introducing OE in optical flow can realize matching features at the correct location and scale. As shown in Fig. 1, this fusion", + "bbox": [ + 496, + 703, + 892, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 94, + 875, + 220, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding author", + "bbox": [ + 96, + 887, + 220, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "5445", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/7e541fff791ad69d0fa33382252bbcbab7bd50513122476d9e525358d4567df4.jpg", + "image_caption": [ + "(a) image to image" + ], + "image_footnote": [], + "bbox": [ + 127, + 88, + 415, + 145 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/20021b67457fea3ac6485b5505555a9ddb879f8989083aceed8943b9c80dc0b6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 127, + 161, + 419, + 241 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/acfe67daf232390dacaaa505cb90a91b09f755f57c18a0150d0265d7dbc6b2a7.jpg", + "image_caption": [ + "Figure 2. Three different matching modes. We match objects between two consecutive frames, where the cat is away from the camera and the car is close to the camera. (a) The 2D optical flow matches the cat and car in the original size image. (b) Match the second frame with the first frame after multiscale scaling, we found that cat can be better matched when magnified by 1.25 times, and car can be better matched when shrunk by 0.75 times. However, obtaining an accurate enlarged picture of a cat is impossible. (c) Transpose matching, where the cat zoomed in in frame 1 to 2 is equivalent to the cat zoomed out in frame 2 to 1. The core of TPCV is to convert the problem of zooming in the forward optical flow to zooming out in the reverse flow." + ], + "image_footnote": [], + "bbox": [ + 127, + 241, + 410, + 332 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "eliminates the effect of scale variation of moving objects on optical flow matching, allowing the algorithm to predict moving foreground objects more accurately. Specifically, we refer to the fused task as 3D optical flow, and the 3D optical flow consists of a 2D optical flow field $\\pmb{f}$ and a 1D scale flow field $s$ , where $s$ describes the scale change of objects between two frames.", + "bbox": [ + 75, + 537, + 468, + 642 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Solving for 3D Optical Flow We view solving 3D optical flow as a scale matching problem—matching the feature at the right location and scale. The matching idea has been applied in many successful optical flow methods [5, 27, 31, 36], where they iteratively update the optical flow by constructing a correlation volume, querying the correlation of pixel pairs in the correlation volume based on the current estimation. In this paper, we propose a plug-and-play upgrade module: TPCV, a multi-scale correlation volume from which the algorithm can query the correlation between pixel pairs at different locations and scales. We show that 3D optical flow can be robustly extracted from the correlation features provided by TPCV. Importantly, TPCV can be easily applied to matching-based optical flow methods or 3D visual tasks, increasing the performance.", + "bbox": [ + 75, + 643, + 468, + 869 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Why TPCV? The computation of 3D optical flow requires computing pixel pairs correlation between feature", + "bbox": [ + 76, + 869, + 468, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "maps at different scales. As shown in Fig. 2, firstly, we tried to directly scale the image for matching, but this did not present good results; the main reason is the unrealistic large-scale features. So we rethink the problem in reverse, where the size reduction in the forward optical flow is equivalent to the enlargement in the inverse optical flow. We convert the part that needs to be enlarged to the reduction part in the inverse flow, and combine the forward and inverse parts to form a transposed correlation volume (TPCV) to query the correlation of feature pairs.", + "bbox": [ + 496, + 90, + 890, + 241 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "TPCV Application In order to evaluate the performance of TPCV, we applied it to the classic and basic matched optical flow framework RAFT [27]. For upgrading 2D optical flow to 3D optical flow, only need to replace the original 4D correlation volume in RAFT with TPCV and add an output head of OE in the GRU iterative optimization layer. The relative OE and optical flow ground truth for training are obtained from the existing 3D scene flow training datasets [15, 19].", + "bbox": [ + 496, + 242, + 890, + 378 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Contributions We summarize our contributions as follows:", + "bbox": [ + 498, + 378, + 890, + 406 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a plug-and-play module: TPCV, which can be applied to matching-based optical flow framework. Based on TPCV, the original 2D optical flow can be easily upgraded to 3D optical flow, improving accuracy.", + "- We applied TPCV to the optical flow framework RAFT, which significantly improved the performance without changing the optimizer structure, especially on the moving foreground target.", + "- We display the effectiveness of TPCV across a variety of benchmark tasks, establishing new SOTA results for scene flow, time-to-collision (TTC) and motion-in-depth estimation - while maintaining a competitive speed." + ], + "bbox": [ + 517, + 419, + 890, + 654 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Relate works", + "text_level": 1, + "bbox": [ + 500, + 678, + 633, + 694 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Optical Flow The main task of optical flow is to estimate the dense pixel displacement between two frames. Recent matching-based methods have achieved great success [5, 25, 31, 36]. They query the correlation between pixel pairs through a 4D correlation volume for subsequent matching and regression. In these matching methods, the calculation of correlation is the algorithmic core. However, all of these methods ignore the correlation mutation caused by the scale change when the object moves in depth, resulting in the incapacity to handle the foreground moving objects. In this paper, we upgrade traditional image-to-image feature matching to image-to-scale-space matching based on scale matching. which allows our method to deal with", + "bbox": [ + 496, + 703, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "5446", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/56ee8aee0141899b96b5bfda911a6b8a5b1f68fe6c0f4747dcfb1db296983877.jpg", + "image_caption": [ + "Figure 3. A typical 3D optical flow calculation process. The estimation of 3D optical flow using TPCV is mainly divided into two parts. First, query the correlation features of optical flow and OE from TPCV according to the current estimation, and then regress the optical flow and OE results based on the correlation features. The TPCV module does not contain any parameters to be optimized, even the calculation of inverse optical flow in the green box, so it can be easily applied to other methods." + ], + "image_footnote": [], + "bbox": [ + 81, + 88, + 893, + 250 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "scale changes in motion. Although some previous methods [6, 11, 34] also introduced the idea of the multi-scale image pyramid, but their pyramid is often used to save computing costs, expand network receptive field, speed up reasoning, and optimize loss function, which is still essentially image-to-image matching.", + "bbox": [ + 75, + 342, + 468, + 433 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Scene flow Scene flow calculates the motion of dense pixels in 3D space. Recently, some successful scene flow methods [2,8,13,18,29,33] are based on the rigid assumption, which assumes that the motion state of pixels on the same rigid object should be the same. These hypothesis-based optimization methods transform the scene flow task into a segmentation problem. Judging the foreground rigid object mask has become a key factor affecting the method's performance. However, it will inevitably introduce too much semantic information to assist judgment. Although these methods perform well on existing datasets, but require additional semantic labels and depth information to aid training.", + "bbox": [ + 75, + 433, + 470, + 630 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "There is also a class of methods [9] that highly fuse depth and optical flow information by combining the inference process of depth and optical flow networks, so that the results can complement each other. However, the segmentation of rigid masks is still implicitly involved in this process. Another kind of Monocular method [1, 32] calculates scene flow by optical expansion. Owing to optical expansion is directly regressed from optical flow or RGB images, it often obtains poor accuracy. Unlike them, TPCV obtains scale changes based on scale matching, eliminating the dependence on depth and semantic information.", + "bbox": [ + 75, + 630, + 468, + 797 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 76, + 813, + 168, + 828 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We propose a general optical flow upgrade module: Transpose pyramid correlation volume (TPCV), which can be widely applied to various 2D optical flow frameworks and upgraded to 3D optical flow. In this section, we will", + "bbox": [ + 75, + 839, + 470, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "explain the construction details of TPCV, examples applied to RAFT, and supervision of 3D optical flow.", + "bbox": [ + 498, + 342, + 890, + 372 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Construction Details of TPCV", + "text_level": 1, + "bbox": [ + 500, + 381, + 767, + 396 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The main function of the TPCV module is to index the correlation features of pixel pairs from different scales at different locations. These features will be used for subsequent optical flow estimation and scale estimation.", + "bbox": [ + 498, + 404, + 890, + 465 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As shown in the Fig. 3, TPCV consists of three steps: Constructing the correlation volume of forward flow and reverse flow based on the input multi-scale features; Querying the correlation features from the correlation volume; Calculation of inverse optical flow.", + "bbox": [ + 498, + 465, + 890, + 541 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1.1 Correlation Volume", + "text_level": 1, + "bbox": [ + 500, + 559, + 692, + 573 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The correlation volume of TPCV is divided into forward and inverse, where the forward correlation volume is essentially the dot product of the original-scale feature map of the frame 1 and the multi-scale feature map of the frame 2. The structure of the inverse correlation volume is the same as the forward; only the order of dot products is swapped.", + "bbox": [ + 496, + 582, + 890, + 672 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Specifically, We extract the multi-scale features $F_{1}^{x}$ and $F_{2}^{s}$ respectively from the input image pyramid, where $x$ and $s$ refers to the scale change. The forward correlation volume consists of 4D correlation volumes $C_{s}$ with different scales:", + "bbox": [ + 498, + 672, + 890, + 734 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nC _ {s} (i, j, u, v) = F _ {1} ^ {1} (i, j) \\cdot F _ {2} ^ {s} (u, v) \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 578, + 747, + 890, + 766 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $(i,j)$ and $(u,v)$ refer to locations in $F_{1}^{1}$ and $F_{2}^{s}$ , $F_{1}^{x} \\in \\mathbb{R}^{xH \\times xW \\times C}$ , $F_{2}^{s} \\in \\mathbb{R}^{sH \\times sW \\times C}$ , $s,x = 0.5,0.5 + 1 / S, \\ldots, 1$ , $C_{s} \\in \\mathbb{R}^{H \\times W \\times sH \\times sW}$ , in our experiment $S = 4$ .", + "bbox": [ + 500, + 771, + 890, + 816 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Similarly, the inverse correlation volume $C_x'$ is computed as:", + "bbox": [ + 500, + 816, + 890, + 845 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nC _ {x} ^ {\\prime} (u, v, i, j) = F _ {2} ^ {1} (u, v) \\cdot F _ {1} ^ {x} (i, j) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 576, + 845, + 890, + 864 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $(u,v)$ and $(i,j)$ refer to locations in $F_{2}^{1}$ and $F_{1}^{x}$ , $C_x' \\in \\mathbb{R}^{H \\times W \\times xH \\times xW}$ .", + "bbox": [ + 500, + 869, + 890, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "5447", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/c29b11016aa76cc6a99073e1f93fdced8ad52d626eaf1f5d00fb45d4a835f6b6.jpg", + "image_caption": [ + "Figure 4. TPCV details. This figure shows the details of the inverse flow query of TPCV: Build the inverse correlation volume $C_x'$ ; Query the inverse correlation feature $cf_x'$ according to the optical flow estimation; Query the forward correlation feature $cf_s$ from $cf_x'$ ; Query the OE features from $cf_s$ according to the current OE estimation." + ], + "image_footnote": [], + "bbox": [ + 76, + 88, + 893, + 244 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.1.2 Query Correlation Feature", + "text_level": 1, + "bbox": [ + 75, + 324, + 318, + 339 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This section introduces how to query1 the correlation features from the correlation volume, which is mainly divided into two steps. First, the multi-scale optical flow features are queried from the correlation volume based on the optical flow results. Then, the multi-scale optical flow features are sampled in the scale dimension based on the current OE results. As shown in Fig. 4.", + "bbox": [ + 75, + 349, + 468, + 455 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "After obtaining the forward optical flow $\\pmb{f}$ and the correlation volume $C_s$ , the forward correlation feature $c f_s (s < 1)$ can be queried as follows:", + "bbox": [ + 76, + 458, + 468, + 503 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} c f _ {s} (i, j) = C _ {s} (i, j, s u, s v) \\\\ = C _ {s} (i, j, s (i + \\boldsymbol {f} (i, j, 0)), s (j + \\boldsymbol {f} (i, j, 1))) \\\\ = \\operatorname {C o r r} (p, q ^ {s}) \\tag {3} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 91, + 518, + 468, + 588 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $c f_{s} \\in \\mathbb{R}^{H \\times W}$ , $(i,j)$ and $(u,v)$ refer to the locations of $p$ and $q$ . $Corr(p,q^s)$ describes the Correlation between point $p$ and point $q$ scaled by s times, $s = 0.5, 0.5 + 1 / S, \\ldots, 1$ .", + "bbox": [ + 76, + 603, + 468, + 665 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Similarly, after acquiring the inverse optical flow $\\pmb{f}^{\\prime}$ and the correlation volume $C_x'$ , the inverse correlation feature $c f_x'(x < 1)$ can be queried as follows:", + "bbox": [ + 76, + 667, + 468, + 713 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} c f _ {x} ^ {\\prime} (u, v) = C _ {x} ^ {\\prime} (u, v, x i, x j) \\\\ = C _ {x} ^ {\\prime} (u, v, x \\left(u + \\boldsymbol {f} ^ {\\prime} (u, v, 0)\\right), x \\left(v + \\boldsymbol {f} ^ {\\prime} (u, v, 1)\\right)) \\\\ = \\operatorname {C o r r} \\left(q ^ {x}, p\\right) \\tag {4} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 76, + 726, + 475, + 796 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\pmb{f}^{\\prime}(u,v) = -\\pmb {f}(i,j),(u,v) = (i,j) + \\pmb {f}(i,j),cf_{x}^{\\prime}\\in$ $\\mathbb{R}^{H\\times W},x = 0.5,0.5 + 1 / S,\\dots,1.$", + "bbox": [ + 76, + 811, + 468, + 843 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Then based on the forward optical flow $f$ to query the", + "bbox": [ + 96, + 844, + 468, + 859 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "forward correlation features $c f_{s}$ when $s > 1$ :", + "bbox": [ + 500, + 324, + 797, + 339 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} c f _ {1 / x} (i, j) = c f _ {x} ^ {\\prime} (i + \\boldsymbol {f} (i, j, 0), j + \\boldsymbol {f} (i, j, 1)) \\tag {5} \\\\ = C o r r (p, q ^ {1 / x}) \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 539, + 348, + 890, + 386 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Combining $s < 1$ and $s > 1$ , the complete forward optical flow correlation feature $C_f$ is obtained:", + "bbox": [ + 498, + 395, + 890, + 426 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nC _ {f} = \\left\\{c f _ {0. 5}, \\dots , c f _ {1}, \\dots , c f _ {2} \\right\\} \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 438, + 890, + 455 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $C_f \\in \\mathbb{R}^{H \\times W \\times (S + 1)}$ .", + "bbox": [ + 500, + 464, + 686, + 481 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "After getting $C_f$ , the OE feature is obtained by querying the third scale dimension of $C_f$ based on the current OE estimation $s$ . So far, we have described the query method of the OE features $F_{oe}$ , and $cf_1$ is the optical flow correlation feature $F_{of}$ .", + "bbox": [ + 498, + 481, + 890, + 556 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In the process of applying TPCV to RAFT, we also query the neighborhood of optical flow and OE to obtain the optimization direction. We show more details about query in supplementary material.", + "bbox": [ + 498, + 556, + 890, + 617 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.1.3 Calculation of Inverse Optical Flow", + "text_level": 1, + "bbox": [ + 500, + 635, + 800, + 650 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this section, we consider designing a inverse optical flow algorithm. In the previous work [12, 17], inverse optical flow is calculated by an extra network. However, this method is not applicable to TPCV. First, the extra network will increase the time and memory cost; second, to ensure the stability of querying, we hope that the forward and inverse flow can be perfectly closed-loop (about closed-loop, see Eq. (4), where $\\pmb{f}^{\\prime}(u,v) = -\\pmb{f}(i,j)$ , $(u,v) = (i,j) + \\pmb{f}(i,j)$ , $(i,j) = (u,v) + \\pmb{f}^{\\prime}(u,v)$ ).", + "bbox": [ + 496, + 657, + 890, + 795 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In order to ensure the perfect closed-loop of the forward and inverse flow, we refer to the idea of Javier [24], that is, the forward flow directly solves the inverse flow. The specific process is shown in Algorithm 1, where $\\text{corr}$ is $cf_1$ , $x_w$ is the current point to be processed, $x_i$ is the four coordinate points around $x_w$ , $w_i$ and $wght$ are intermediate variables that store correlation weights.", + "bbox": [ + 496, + 795, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_footnote", + "text": "1In this paper, the query is implemented by the gridsample function in Pytorch.", + "bbox": [ + 75, + 875, + 468, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "5448", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "code", + "sub_type": "algorithm", + "code_caption": [], + "code_body": "Algorithm 1 Inverse optical flow \nInput: $f$ , $corr$ \nParameter: $wght$ \nOutput: $f'$ \n1: Initialize $f' = 0, wght = -\\infty$ . \n2: for all $x \\in \\Omega$ do \n3: $x_w \\gets x + f(x)$ \n4: Find the four neighbors of $x_w : \\{x_1, x_2, x_3, x_4\\}$ \n5: for $i = 1$ to 4 do \n6: $w_i \\gets corr(x_i, x_w)$ \n7: if $w_i > wght(x_i)$ then \n8: $f'(x_i) \\gets -f(x)$ \n9: $wght(x_i) \\gets w_i$ \n10: end if \n11: end for \n12: end for \n13: return $f'$", + "bbox": [ + 76, + 89, + 468, + 354 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.2. Apply TPCV in RAFT", + "text_level": 1, + "bbox": [ + 76, + 382, + 285, + 398 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "RAFT is a typical matching optical flow method, which iteratively maintains an optical flow field through the GRU module. In this section, we will explain how to apply TPCV to RAFT.", + "bbox": [ + 75, + 405, + 468, + 465 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We do not change the structure of the RAFT feature extractor and GRU optimizer. The only change is that we use the TPCV module to replace the original 4D correlation volume to obtain optical flow correlation features and OE correlation features. We provide more details of network construction in the supplementary material.", + "bbox": [ + 75, + 465, + 468, + 556 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Optimization After applying TPCV, we maintain an optical flow field $\\pmb{f}$ and a scale flow field $s$ at the same time. The calculation of the optical flow update amount is the same as RAFT, and we add a new output header to update the scale flow from hidden features $h_t$ generated by GRU:", + "bbox": [ + 75, + 556, + 468, + 632 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nh _ {t} = G R U \\left(h _ {t - 1}, F _ {o f}, F _ {o e}\\right) \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 137, + 645, + 468, + 667 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\Delta s = \\tanh (C o n v (R e L U (C o n v (h _ {t}))))\n$$\n", + "text_format": "latex", + "bbox": [ + 140, + 664, + 406, + 680 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\Delta s$ is the update amount of scale flow, initial $h_0$ is given by feature encoder.", + "bbox": [ + 75, + 686, + 468, + 717 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Supervision of 3D Optical Flow", + "text_level": 1, + "bbox": [ + 76, + 727, + 352, + 743 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3.1 2D Optical Flow Loss", + "text_level": 1, + "bbox": [ + 76, + 751, + 281, + 766 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We follow RAFT and supervise the 2D optical flow with $L_{1}$ distance between the predicted flow and ground truth, the 2D optical flow loss is defined as:", + "bbox": [ + 75, + 775, + 468, + 821 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {f} = \\sum_ {k = 1} ^ {N} \\gamma^ {N - k} \\left\\| \\boldsymbol {f} ^ {k} - \\boldsymbol {f} _ {g t} \\right\\| _ {1} \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 833, + 468, + 873 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where we set $\\gamma = 0.8$ , $N = 12$ in our experiments.", + "bbox": [ + 76, + 885, + 413, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3.2 Scale Flow Loss", + "text_level": 1, + "bbox": [ + 500, + 90, + 663, + 104 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Existing datasets do not directly provide supervised scale data. We refer to the scale change and motion in depth conversion formula established by Gengshan Yang [32] to train the 3D optical flow indirectly.", + "bbox": [ + 496, + 114, + 890, + 175 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Yang pointed out that under the assumption of small rotation, the scale change and motion-in-depth are approximately equal, which allows us to use the scene flow dataset to obtain the scale loss, which is defined as follows:", + "bbox": [ + 496, + 176, + 890, + 234 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {s} = \\sum_ {k = 1} ^ {N} \\gamma^ {N - k} \\left\\| \\boldsymbol {s} ^ {k} - z _ {g t} ^ {\\prime} / z _ {g t} \\right\\| _ {1} \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 583, + 247, + 890, + 287 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $z_{gt}^{\\prime}$ and $z_{gt}$ are the ground truth depths of the pixels in the second frame and the first frame, $s$ is the scale flow field.", + "bbox": [ + 496, + 300, + 890, + 344 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The overall loss function of 3D optical flow is:", + "bbox": [ + 517, + 345, + 828, + 361 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {f} + \\mathcal {L} _ {s} \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 647, + 375, + 890, + 390 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 417, + 630, + 434 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Experimental results are discussed in this section to demonstrate the effectiveness of TPCV. Experiments show that TPCV can significantly improve the estimation accuracy of the optical flow for foreground objects under the same number of optimizer parameters. Furthermore, the ability of motion-in-depth estimation far exceeds the prior SOTA method. Specifically, we first compared the optical flow performance between the TPCV and baseline models. Then we applied the 3D optical flow results to the 3D scene perception tasks, including motion-in-depth, time-to-collision and scene flow estimation.", + "bbox": [ + 496, + 443, + 890, + 608 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Setup We first pre-train our model on the Driving (D) for 80k iterations (batch = 6, lr = 0.002). For the optical flow estimation task, we finetune it on KITTI (K-200) with 60k iterations (batch = 6, lr = 0.00125). For motion-in-depth and time-to-collision task, we follow the split of optical expansion [32]. Specifically, we select one for every 5 images in K-200 for validation (K-40) and add the rest 160 images for training (K-160) with 60k iterations (batch = 6, lr = 0.00125). For the scene flow task, we use K-200 for training with 60k iterations (batch = 6, lr = 0.00125) and submit the results to the KITTI scene flow benchmark.", + "bbox": [ + 496, + 609, + 890, + 775 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Optical Flow", + "text_level": 1, + "bbox": [ + 500, + 786, + 635, + 801 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we compare the effect of applying TPCV on the baseline optical flow model. We chose RAFT [27] as the baseline model, not because it offers better performance, but because it is the basic framework for most advanced optical flow models [5, 16, 25, 26] and has strong representativeness.", + "bbox": [ + 496, + 809, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "5449", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/d21650245039141109a685f51f69d59d396bc5bea3c94e66bf3617e4f4bd7a2f.jpg", + "image_caption": [ + "Figure 5. Results for KITTI test image. The upper line is the optical flow error. The redder the colour represents the greater the error, where Noc Fl-fg is the outlier rate of the non-occluded part, and the lower line is the colour-coded optical flow result. Observing the car window in the red box, there is a large-scale change between the two frames, and only our TPCV can get the correct result." + ], + "image_footnote": [], + "bbox": [ + 80, + 88, + 893, + 212 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The main indicator we compare is the outlier rate of foreground and background optical flow, where outliers are the points whose endpoint error is greater than 3px or $5\\%$ .", + "bbox": [ + 75, + 289, + 470, + 335 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/090426c216356720ed7867bf346aa6e8661cd3f5f597d619e711d36c48d0ac21.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodFl-bgFl-fgFl-allImprovement
RAFT [27]4.746.875.1-
GMA [5]4.787.035.15+attention
CRAFT [25]4.585.854.79+attention
RAFT+AOIR [26]4.686.995.07regularisation
RAFT+TPCV(ours)4.535.524.69scale correlation
", + "bbox": [ + 78, + 345, + 472, + 459 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Validation performance We first compare the performance of TPCV and baseline RAFT. As shown in Tab. 1, TPCV improves the accuracy of the baseline model comprehensively, especially the foreground error is reduced from 6.87 to 5.52, which proves that TPCV can help the baseline model adapt to the foreground object scale changes effectively.", + "bbox": [ + 75, + 551, + 468, + 657 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We also compare some state-of-the-art RAFT-based improvement methods' performance, where CRAFT and GMA both introduce attention mechanisms into the RAFT framework to solve the occlusion problem. While RAFT+AOIR studies new regularization schemes, these methods all change the structure of RAFT's optimizer and feature extractor, increasing the computational cost and training cost. However, our TPCV still achieves the best results, although the optimizer structure is not changed. Fig. 5 shows optical flow results under large-scale changes. All methods except TPCV cannot handle large-scale changes, proving the superiority of TPCV's scale matching strategy.", + "bbox": [ + 75, + 657, + 470, + 840 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Motion-in-depth (MID)", + "text_level": 1, + "bbox": [ + 76, + 847, + 294, + 864 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In this section, we discuss the performance of 3D optical flow in the MID estimation task. Based on the relationship", + "bbox": [ + 76, + 869, + 470, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "established in the previous work [32], we can equate the scale change $s$ to MID $\\tau$ . For example, an object is doubled in size between two frames ( $s = 0.5$ , the second frame is scaled by 0.5 times and matches the first frame), which means that its distance from the camera is shortened by 0.5 times ( $\\tau = z' / z = s = 0.5$ , where $z'$ and $z$ are the depth of frame2 and frame1).", + "bbox": [ + 496, + 289, + 893, + 393 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "MID is a key indicator in tasks such as time-to-collision, scene flow, and depth estimation. To demonstrate the superiority of TPCV, we compare it with the current state-of-the-art MID methods. The error of MID is defined as:", + "bbox": [ + 496, + 396, + 893, + 455 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nM I D _ {e r r o r} = \\left\\| l o g (\\tau) - l o g \\left(\\tau_ {g t}\\right) \\right\\| _ {1} \\cdot 1 0 ^ {4} \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 557, + 469, + 890, + 488 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\tau_{gt}$ is obtained by dividing the matching depth of the second frame and the first frame.", + "bbox": [ + 496, + 500, + 890, + 530 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/753f475f9729bef78d9c03e28c21bbeeda021f4bba7eb6c9b806d62325415348.jpg", + "table_caption": [ + "Table 1. Quantitative optical flow performance comparison on KITTI test. Fl-bg and Fl-fg are the outlier rates of the foreground and background optical flow. The best among the all are bolded. Our method greatly improves the performance of foreground optical flow." + ], + "table_footnote": [], + "table_body": "
MethodInputMIDerrorTime/s
OSF [20]Stereo1153000
PRSM [30]Stereo124300
Binary TTC [1]Mono73.552.2
Optical expansion [32]Mono750.2
RAFT+TPCV(ours)Mono42.840.2
", + "bbox": [ + 501, + 542, + 893, + 650 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. MID estimation on K-40 validation set. Our method outperforms other methods by a large margin.", + "bbox": [ + 496, + 659, + 893, + 689 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We first compare with traditional optimization methods that use stereo information: PSMR and OSF decompose pixels into rigid blocks, and iteratively update the optimized optical flow and depth fields based on rigid assumptions and hand-designed priors. To calculate the MID we divide the depth of the second frame by the depth of the first frame. Experimental results show that our errors are much smaller than them (115 vs. 42.84) and in less time $^{2}$ .", + "bbox": [ + 496, + 710, + 890, + 830 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We also compare with the state-of-the-art MID methods Optical expansion and Binary TTC. TPCV outperforming", + "bbox": [ + 496, + 830, + 890, + 863 + ], + "page_idx": 5 + }, + { + "type": "page_footnote", + "text": "2Timing results are computed on 375x1242 images with an RTX 3090 GPU using 200 updates.", + "bbox": [ + 500, + 875, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "5450", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/b39b61bf4d14b5f3c90ab74f416061cfd269b29a1fe72d4ae1317604048fa73f.jpg", + "image_caption": [ + "Figure 6. Motion-in-depth results for image \"195\" in the K-40. Up: Ground truth and motion-in-depth results, where the darker indicates the objects moving towards the camera. Down: The input image and error map, where the whiter the pixels, the greater the error. In both foreground and background regions, our method is much more accurate than other methods." + ], + "image_footnote": [], + "bbox": [ + 80, + 89, + 284, + 188 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c3afa2185073475837446f273c7bfe58d3a671bebda13aa3679553ec854fd1b1.jpg", + "image_caption": [ + "OSF" + ], + "image_footnote": [], + "bbox": [ + 282, + 90, + 486, + 186 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a7430de52aeb57fda01d4c8daec9344a1f2b20a05c95a26e56a4e4dc3c9a7c86.jpg", + "image_caption": [ + "Optical expansion" + ], + "image_footnote": [], + "bbox": [ + 486, + 90, + 686, + 186 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b0e304beadeadcca702e8c7323218df347ffe6034d8545fee66b2bcce0d47235.jpg", + "image_caption": [ + "TPCV (ours)" + ], + "image_footnote": [], + "bbox": [ + 687, + 90, + 888, + 186 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "them by a large margin (73.55 vs 42.84), and the average error is reduced by $42\\%$ . Different from their directly regress MID, TPCV calculates MID based on matching, making the result robust and accurate. The above experiments prove that our TPCV module is progressiveness in depth motion estimation.", + "bbox": [ + 75, + 279, + 468, + 369 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Time-to-collision (TTC)", + "text_level": 1, + "bbox": [ + 76, + 378, + 295, + 393 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Time-to-collision estimation is essential for obstacle avoidance and path planning tasks [3, 7, 14, 21]. In fact, the time for a pixel to reach the camera plane (TTC) can be directly obtained from MID:", + "bbox": [ + 75, + 402, + 468, + 462 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\nT T C = \\frac {Z}{Z - Z ^ {\\prime}} T = \\frac {T}{1 - \\frac {Z ^ {\\prime}}{Z}} \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 470, + 468, + 507 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "where $T$ is the sampling interval of the camera (in KITTI $T = 0.1s$ ) and $Z' / Z$ is the motion-in-depth $\\tau$ .", + "bbox": [ + 76, + 513, + 468, + 546 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/f71060a90b04aebad8c00abea5b9df4d72fa37a55c0de5ee97aadfb2ed94e3cf.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodErr-1sErr-2sErr-5sTime/s
Binary TTC [1]2.43.163.250.0192
OSF [20]1.792.934.033000
Optical expansion [32]1.861.872.450.2
RAFT+TPCV(ours)1.141.651.570.2
", + "bbox": [ + 84, + 556, + 460, + 648 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Percentage errors of time-to-collision estimation on K-40 validation set", + "bbox": [ + 76, + 660, + 468, + 686 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We treat TTC as a binary classification problem, and for each pixel, we judge whether the TTC time is less than $\\{1\\mathrm{s}, 2\\mathrm{s}, 5\\mathrm{s}\\}$ . Only the points moving towards the camera are evaluated (the TTC of points farther away is meaningless).", + "bbox": [ + 75, + 703, + 468, + 763 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this experiment, we use the binary mode of Binary TTC. In this mode, a binary mask will be directly output. Although the accuracy is decreased, the reasoning speed is greatly improved. Optical expansion and OSF perform reasonably well, consistent with their high precision performance in the motion-in-depth task. Our monocular method achieves the lowest error in all time intervals, which proves that the 3D optical flow estimated by TPCV can well predict future collisions.", + "bbox": [ + 75, + 765, + 468, + 898 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Scene Flow", + "text_level": 1, + "bbox": [ + 500, + 277, + 622, + 292 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Correctly estimating the 3D motion of objects in space is crucial for autonomous driving and dynamic environment path planning for robots. The scene flow consists of the optical flow and the depth of matched pixels between two frames. In order to unify the standard with the most advanced methods, we use GA-Net [35] to obtain the depth of the first frame $d_{1}$ , and obtain the depth of the second frame $d_{2}$ based on the motion-in-depth $\\tau$ estimated by TPCV $(d_{2} = d_{1} * \\tau)$ . The model was trained on the complete K-200 dataset and tested on the KITTI public scene flow evaluation website3.", + "bbox": [ + 496, + 304, + 890, + 469 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We first compare TPCV with monocular methods, as shown in Tab. 4 TPCV leads in all monocular methods with significant advantages, consistent with the previous superior performance of TPCV in motion-in-depth and optical flow tasks. It proves the effectiveness of our matching strategy in the field of 3D motion estimation.", + "bbox": [ + 496, + 472, + 890, + 563 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We also compare with the state-of-the-art depth-based methods CamLiFlow, RigidMask+ISF and RAFT3D. Noticing that they tend to get smaller Fl-bg errors, which is thanks to the aid of depth information to optimize the background optical flow. Nonetheless, our monocular approach achieves the best results on all foreground metrics and maintains a competitive inference speed. We note that TPCV outperforms even most depth-based methods on the D2-all metric, which demonstrates the high potential of our 3D optical flow for estimating 3D motion.", + "bbox": [ + 496, + 566, + 890, + 717 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "RAFT3D + TPCV We apply TPCV to RAFT3D. Specifically, we directly use TPCV to replace the correlation volume in RAFT3D, and change the original direct regression depth change to the estimated depth change ratio. The experimental results show that the addition of TPCV significantly improves the performance of SF-all $(5.77\\rightarrow 5.00)$ . However, we found that Fl-fg did not improve. We think it is because RAFT3D introduced potential semantic information when estimating the foreground.", + "bbox": [ + 496, + 719, + 890, + 854 + ], + "page_idx": 6 + }, + { + "type": "page_footnote", + "text": "3http://www.cvlibs.net/datasets/kitti/evalscene_flow.php", + "bbox": [ + 500, + 875, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "5451", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/d0e316916920a2c240d9ee9747916e1e94b94ef93ea2afa48e60072508d37d8d.jpg", + "table_caption": [], + "table_footnote": [ + "Table 4. State-of-art published methods on KITTI scene flow benchmark. D+M means depth and monocular information, and M means monocular information. D1, D2, Fl, and SF is the percentage of disparity, optical flow and scene flow outliers. -bg,-fg and -all represent the percentage of outliers averaged only over background regions, foreground regions and overall ground truth pixels. The best among all are bolded, and the second best are underlined. Our monocular method performs quite well, not only far ahead of similar monocular methods, but also outperforming stereo-based methods in foreground scene flow estimation, which proves that the TPCV module can accurately estimate the 3D motion." + ], + "table_body": "
MethodInputD1-bgD1-fgD1-allD2-bgD2-fgD2-allFl-bgFl-fgFl-allSF-bgSF-fgSF-all
CamLiFlow [9]D+M1.483.461.811.928.142.952.317.043.12.8712.234.43
RigidMask+ISF [33]D+M1.533.651.892.098.923.232.637.853.53.2513.084.89
RAFT3D [28]D+M1.483.461.812.519.463.673.398.794.294.2713.275.77
RAFT3D+TPCV(ours)D+M1.483.461.811.938.793.072.4810.193.763.0314.825.0
Optical expansion [32]M1.483.461.813.398.544.255.838.666.37.0613.448.12
Binary TTC [1]M1.483.461.813.849.394.765.848.676.317.4513.748.5
RAFT+TPCV(ours)M1.483.461.812.297.633.184.535.524.695.3410.66.21
", + "bbox": [ + 78, + 88, + 895, + 232 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Ablation", + "text_level": 1, + "bbox": [ + 76, + 351, + 174, + 367 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Setup We conduct more extensive experiments to verify the advanced nature of the TPCV module. For all experiments, we pre-train with 80K on the Driving dataset (batch=6, lr = 0.00025), refinement training on K-160 (batch=6, lr = 0.000125), and testing on the K-40 dataset.", + "bbox": [ + 75, + 377, + 468, + 452 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/c988a74bc3bc2d368b175deebd079b26641437581be08b064c5ea8df77c848e5.jpg", + "table_caption": [], + "table_footnote": [ + "Table 5. Ablation study on optical flow and motion-in-depth estimation. $Flow_{epe}$ is the average endpoint pixel error of optical flow estimation, the lower the better." + ], + "table_body": "
MethodFlow_epeMID_errorMemory
RAFT*1.6156.22.7GB
RAFT+pyramid1.4248.95.2GB
RAFT+TPCV1.3142.83.0GB
", + "bbox": [ + 86, + 465, + 460, + 542 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Comparison to regression (RAFT*) We first replace the TPCV in \"RAFT+TPCV\" with 4D correlation volume, at this time, the estimation of scale flow degenerates into direct regression, similar to optical expansion [32]. As shown in Tab. 5, after replacement, the optical flow error increased from 1.31 to 1.61 (increases $22.9\\%$ ), and the motion-in-depth error increased from 42.8 to 56.2 (increases $31.3\\%$ ). It proves that the TPCV module plays a crucial role in advanced optical flow and motion-in-depth estimation.", + "bbox": [ + 75, + 613, + 467, + 748 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Comparison to pyramid matching (RAFT+pyramid) We also tried a pyramid matching scheme, shown in Fig. 2 (b), which matched the first frame feature with the same scale feature in the image pyramid of the second frame. we directly obtain large-scale images through interpolation. As shown in Tab. 5, the optical flow and motion-in-depth errors are greatly reduced after introducing the matching strategy, which once again proves the superiority of the matching method. However, it is still not as good as the performance of TPCV (1.42 vs. 1.31; 48.9 vs. 42.8); we believe that it is", + "bbox": [ + 75, + 750, + 468, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "mainly due to the construction of false large-scale features, which makes it difficult to perform correct feature matching for objects far away from the camera.", + "bbox": [ + 496, + 353, + 890, + 398 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. More experimental results", + "text_level": 1, + "bbox": [ + 498, + 414, + 745, + 431 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We followed the experimental procedure of RAFT3D [28] to conduct more experiments on the sizeable synthetic dataset Flyingthings3D. The results show that after applying TPCV, the end-to-end 3D error of RAFT3D is reduced from $0.062\\mathrm{m}$ to $0.048\\mathrm{m}$ , and the end-to-end 2D error dropped from $2.2\\mathrm{px}$ to $1.95\\mathrm{px}$ . See supplementary materials for details of results.", + "bbox": [ + 496, + 439, + 890, + 545 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitation We also conducted experiments on Sintel, but the baseline results did not improve significantly after applying TPCV; we believe this is because Sintel contains many non-rigid scenes, while the conversion between MID and OE depends on the rigid assumption.", + "bbox": [ + 496, + 546, + 890, + 622 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "7. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 638, + 619, + 654 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We combine optical flow and optical expansion, proposing the concept of 3D optical flow, which can eliminate the influence of scale change on optical flow matching and provide motion information in the depth direction. Then we design a plug-and-play 2D to 3D optical flow upgrade module: TPCV, associated with a set of supervised learning strategies. Experimentally, we apply TPCV to the RAFT, achieving significant performance improvements in 2D optical flow tasks and multiple 3D perception tasks, including motion-in-depth, scene flow, and time-to-collision.", + "bbox": [ + 496, + 664, + 890, + 815 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements This research was supported by the National Natural Science Foundation of China (Grant nos. 61673220, 62106209), and the Project of SongShan Laboratory (Grant no. YYJC012022015).", + "bbox": [ + 496, + 830, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "5452", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Abhishek Badki, Orazio Gallo, Jan Kautz, and Pradeep Sen. Binary ttc: A temporal geofence for autonomous navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12946-12955, 2021. 1, 3, 6, 7, 8", + "[2] Aseem Behl, Omid Hosseini Jafari, Siva Karthik Mustikovela, Hassan Abu Alhaija, Carsten Rother, and Andreas Geiger. Bounding boxes, segmentations and object coordinates: How important is recognition for 3d scene flow estimation in autonomous driving scenarios? In Proceedings of the IEEE International Conference on Computer Vision, pages 2574-2583, 2017. 3", + "[3] Jeffrey Byrne and Camillo J Taylor. Expansion segmentation for visual collision detection and estimation. In 2009 IEEE International Conference on Robotics and Automation, pages 875-882. IEEE, 2009. 1, 7", + "[4] TA Camus. Calculating time-to-contact using real-time quantized optical flow. 1995. 1", + "[5] Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, and Richard Hartley. Learning to estimate hidden motions with global motion aggregation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9772-9781, 2021. 2, 5, 6", + "[6] Rico Jonschkowski, Austin Stone, Jonathan T Barron, Ariel Gordon, Kurt Konolige, and Anelia Angelova. What matters in unsupervised optical flow. In European Conference on Computer Vision, pages 557-572. Springer, 2020. 3", + "[7] David N Lee. A theory of visual control of braking based on information about time-to-collision. Perception, 5(4):437-459, 1976. 7", + "[8] Congcong Li, Haoyu Ma, and Qingmin Liao. Two-stage adaptive object scene flow using hybrid cnn-crf model. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 3876-3883. IEEE, 2021. 3", + "[9] Haisong Liu, Tao Lu, Yihui Xu, Jia Liu, Wenjie Li, and Lijun Chen. Camliflow: Bidirectional camera-lidar fusion for joint optical flow and scene flow estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5791-5801, 2022. 3, 8", + "[10] David G Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91-110, 2004. 1", + "[11] Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, and Jian Sun. Upflow: Upsampling pyramid for unsupervised optical flow learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1045-1054, 2021. 3", + "[12] Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, and Jian Sun. Upflow: Upsampling pyramid for unsupervised optical flow learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1045-1054, June 2021. 4", + "[13] Wei-Chiu Ma, Shenlong Wang, Rui Hu, Yuwen Xiong, and Raquel Urtasun. Deep rigid instance scene flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3614-3622, 2019. 3" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] Thiago Marinho, Massinissa Amrouche, Venanzio Cichella, Dušan Stipanović, and Naira Hovakimyan. Guaranteed collision avoidance based on line-of-sight angle and time-to-collision. In 2018 Annual American Control Conference (ACC), pages 4305-4310. IEEE, 2018. 7", + "[15] Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4040-4048, 2016. 2", + "[16] Lukas Mehl, Cedric Beschle, Andrea Barth, and Andrés Bruhn. An anisotropic selection scheme for variational optical flow methods with order-adaptive regularisation. In International Conference on Scale Space and Variational Methods in Computer Vision, pages 140-152. Springer, 2021. 5", + "[17] Simon Meister, Junhwa Hur, and Stefan Roth. Unflow: Unsupervised learning of optical flow with a bidirectional census loss. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018. 4", + "[18] Moritz Menze and Andreas Geiger. Object scene flow for autonomous vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3061-3070, 2015. 3", + "[19] Moritz Menze, Christian Heipke, and Andreas Geiger. Joint 3d estimation of vehicles and scene flow. In ISPRS Workshop on Image Sequence Analysis (ISA), 2015. 2", + "[20] Moritz Menze, Christian Heipke, and Andreas Geiger. Object scene flow. ISPRS Journal of Photogrammetry and Remote Sensing, 140:60-76, 2018. 6, 7", + "[21] Tomoyuki Mori and Sebastian Scherer. First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles. In 2013 IEEE International Conference on Robotics and Automation, pages 1750-1757. IEEE, 2013. 7", + "[22] Dennis Muller, Josef Pauli, Christian Nunn, Steffen Gormer, and Stefan Muller-Schneiders. Time to contact estimation using interest points. In 2009 12th International IEEE Conference on Intelligent Transportation Systems, pages 1-6. IEEE, 2009. 1", + "[23] Maria Sagrebin, Anastasia Noglik, and Josef Pauli. Robust time-to-contact calculation for real time applications. In Proceedings of 18th International Conference on Computer Graphics and Vision, pages 128-133, 2008. 1", + "[24] Javier Sánchez, Agustín Salgado, and Nelson Monzon. Computing inverse optical flow. Pattern Recognition Letters, 52:32-39, 2015. 4", + "[25] Xiuchao Sui, Shaohua Li, Xue Geng, Yan Wu, Xinxing Xu, Yong Liu, Rick Goh, and Hongyuan Zhu. Craft: Cross-attentional flow transformer for robust optical flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17602-17611, 2022. 2, 5, 6", + "[26] Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T Freeman, and Ce Liu. Autoflow: Learning a better training set for optical flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10093-10102, 2021. 5, 6" + ], + "bbox": [ + 501, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "5453", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[27] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In European conference on computer vision, pages 402-419. Springer, 2020. 2, 5, 6", + "[28] Zachary Teed and Jia Deng. Raft-3d: Scene flow using rigid-motion embeddings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8375–8384, 2021. 8", + "[29] Christoph Vogel, Konrad Schindler, and Stefan Roth. 3d scene flow estimation with a rigid motion prior. In 2011 International Conference on Computer Vision, pages 1291-1298. IEEE, 2011. 3", + "[30] Christoph Vogel, Konrad Schindler, and Stefan Roth. 3d scene flow estimation with a piecewise rigid scene model. International Journal of Computer Vision, 115(1):1-28, 2015. 6", + "[31] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, and Dacheng Tao. Gmflow: Learning optical flow via global matching. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8121-8130, 2022. 2", + "[32] Gengshan Yang and Deva Ramanan. Upgrading optical flow to 3d scene flow through optical expansion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1334-1343, 2020. 1, 3, 5, 6, 7, 8", + "[33] Gengshan Yang and Deva Ramanan. Learning to segment rigid motions from two frames. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1266-1275, 2021. 3, 8", + "[34] Hanchao Yu, Xiao Chen, Humphrey Shi, Terrence Chen, Thomas S Huang, and Shanhui Sun. Motion pyramid networks for accurate and efficient cardiac motion estimation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 436-446. Springer, 2020. 3", + "[35] Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr. Ga-net: Guided aggregation net for end-to-end stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 185–194, 2019. 7", + "[36] Shiyu Zhao, Long Zhao, Zhixing Zhang, Enyu Zhou, and Dimitris Metaxas. Global matching with overlapping attention for optical flow estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17592-17601, June 2022. 2" + ], + "bbox": [ + 78, + 90, + 467, + 712 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "5454", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Optical Expansion From Scale Matching/fadba611-fc8a-473b-9570-70ac81007770_model.json b/2023/Learning Optical Expansion From Scale Matching/fadba611-fc8a-473b-9570-70ac81007770_model.json new file mode 100644 index 0000000000000000000000000000000000000000..6f21e1033fcaca7302c18cda9c4efef186484174 --- /dev/null +++ b/2023/Learning Optical Expansion From Scale Matching/fadba611-fc8a-473b-9570-70ac81007770_model.json @@ -0,0 +1,2420 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.234, + 0.131, + 0.738, + 0.154 + ], + "angle": 0, + "content": "Learning Optical Expansion from Scale Matching" + }, + { + "type": "text", + "bbox": [ + 0.224, + 0.179, + 0.751, + 0.199 + ], + "angle": 0, + "content": "Han Ling\\(^{1}\\) Yinghui Sun\\(^{1}\\) Quansen Sun\\(^{1*}\\) Zhenwen Ren\\(^{1,2,3\\dagger}\\)" + }, + { + "type": "text", + "bbox": [ + 0.297, + 0.199, + 0.674, + 0.218 + ], + "angle": 0, + "content": "\\(^{1}\\)Nanjing University of Science and Technology" + }, + { + "type": "text", + "bbox": [ + 0.288, + 0.217, + 0.684, + 0.235 + ], + "angle": 0, + "content": "\\(^{2}\\)Southwest University of Science and Technology" + }, + { + "type": "text", + "bbox": [ + 0.395, + 0.234, + 0.576, + 0.253 + ], + "angle": 0, + "content": "3SongShan Laboratory" + }, + { + "type": "text", + "bbox": [ + 0.25, + 0.255, + 0.715, + 0.269 + ], + "angle": 0, + "content": "{321106010190,yinghuisun,sunquansen,rzw}@njust.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.305, + 0.313, + 0.32 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.337, + 0.474, + 0.653 + ], + "angle": 0, + "content": "This paper addresses the problem of optical expansion (OE). OE describes the object scale change between two frames, widely used in monocular 3D vision tasks. Previous methods estimate optical expansion mainly from optical flow results, but this two-stage architecture makes their results limited by the accuracy of optical flow and less robust. To solve these problems, we propose the concept of 3D optical flow by integrating optical expansion into the 2D optical flow, which is implemented by a plug-and-play module, namely TPCV. TPCV implements matching features at the correct location and scale, thus allowing the simultaneous optimization of optical flow and optical expansion tasks. Experimentally, we apply TPCV to the RAFT optical flow baseline. Experimental results show that the baseline optical flow performance is substantially improved. Moreover, we apply the optical flow and optical expansion results to various dynamic 3D vision tasks, including motion-in-depth, time-to-collision, and scene flow, often achieving significant improvement over the prior SOTA. Code is available at https://github.com/HanLingsgjk/TPCV." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.685, + 0.208, + 0.7 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.712, + 0.47, + 0.863 + ], + "angle": 0, + "content": "Optical expansion (OE) is a fundamental and important concept in monocular dynamic 3D vision tasks [1,3,22,23, 32]. OE describes the scale change of an object between two frames, which can be translated into motion in the depth direction. It has essential applications in time-to-collision, scene flow, and motion-in-depth estimation. OE schemes have unique advantages in 3D motion estimation tasks, requiring only a single camera and enabling dense and fixed baseline independent results. In this paper, we discuss a robust and novel approach for OE estimation." + }, + { + "type": "image", + "bbox": [ + 0.516, + 0.305, + 0.687, + 0.383 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.576, + 0.383, + 0.629, + 0.395 + ], + "angle": 0, + "content": "Frame1" + }, + { + "type": "image", + "bbox": [ + 0.515, + 0.395, + 0.698, + 0.505 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.533, + 0.505, + 0.671, + 0.517 + ], + "angle": 0, + "content": "Multi Scale Frame2" + }, + { + "type": "image", + "bbox": [ + 0.712, + 0.308, + 0.827, + 0.353 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.709, + 0.367, + 0.825, + 0.411 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.71, + 0.413, + 0.825, + 0.457 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.71, + 0.459, + 0.823, + 0.503 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.829, + 0.319, + 0.879, + 0.343 + ], + "angle": 0, + "content": "frame1 \\(s = 1\\)" + }, + { + "type": "image_caption", + "bbox": [ + 0.831, + 0.38, + 0.879, + 0.404 + ], + "angle": 0, + "content": "frame2 \\(s = 1\\)" + }, + { + "type": "image_caption", + "bbox": [ + 0.834, + 0.431, + 0.879, + 0.442 + ], + "angle": 0, + "content": "\\(s = 0.7\\)" + }, + { + "type": "image_caption", + "bbox": [ + 0.834, + 0.474, + 0.879, + 0.484 + ], + "angle": 0, + "content": "\\(s = 0.5\\)" + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.529, + 0.892, + 0.668 + ], + "angle": 0, + "content": "Figure 1. Scale matching idea. Left: multi-scale matching between two frames. Right: texture around the matching point, where \\( s \\) is the size of the image scaling. The core idea of scale matching is to match texture features at the correct location and scale. As seen above, the texture at the license plate can be better matched when the second frame is scaled 0.7 times in size, where the scaling magnification also reflects the optical expansion of that local pixel between the two frames. Furthermore, scale matching can better handle the motion in the depth direction and contains potential 3D motion information." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.704, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Prior works In early time-to-collision (TTC) studies [4, 22, 23], OE was obtained from motion modeling, where the motion estimation was provided by optical flow or SIFT [10]. Such algorithms relied on optical flow results and specific model assumptions, often yielding only sparse and low-accuracy results. Some recent methods [32] regress OE based on existing optical flow results and achieve better outcomes. However, these two-stage estimation methods rely on accurate optical flow results and decrease computational efficiency. Instead, we consider optical flow and expansion estimation as two complementary tasks. Introducing OE in optical flow can realize matching features at the correct location and scale. As shown in Fig. 1, this fusion" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.875, + 0.221, + 0.888 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_footnote", + "bbox": [ + 0.098, + 0.888, + 0.221, + 0.9 + ], + "angle": 0, + "content": "† Corresponding author" + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.875, + 0.221, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.515, + 0.957 + ], + "angle": 0, + "content": "5445" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.129, + 0.089, + 0.416, + 0.146 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.204, + 0.147, + 0.317, + 0.158 + ], + "angle": 0, + "content": "(a) image to image" + }, + { + "type": "image", + "bbox": [ + 0.128, + 0.162, + 0.421, + 0.242 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.128, + 0.242, + 0.411, + 0.333 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.344, + 0.473, + 0.511 + ], + "angle": 0, + "content": "Figure 2. Three different matching modes. We match objects between two consecutive frames, where the cat is away from the camera and the car is close to the camera. (a) The 2D optical flow matches the cat and car in the original size image. (b) Match the second frame with the first frame after multiscale scaling, we found that cat can be better matched when magnified by 1.25 times, and car can be better matched when shrunk by 0.75 times. However, obtaining an accurate enlarged picture of a cat is impossible. (c) Transpose matching, where the cat zoomed in in frame 1 to 2 is equivalent to the cat zoomed out in frame 2 to 1. The core of TPCV is to convert the problem of zooming in the forward optical flow to zooming out in the reverse flow." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.538, + 0.47, + 0.643 + ], + "angle": 0, + "content": "eliminates the effect of scale variation of moving objects on optical flow matching, allowing the algorithm to predict moving foreground objects more accurately. Specifically, we refer to the fused task as 3D optical flow, and the 3D optical flow consists of a 2D optical flow field \\( \\pmb{f} \\) and a 1D scale flow field \\( s \\), where \\( s \\) describes the scale change of objects between two frames." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.644, + 0.47, + 0.87 + ], + "angle": 0, + "content": "Solving for 3D Optical Flow We view solving 3D optical flow as a scale matching problem—matching the feature at the right location and scale. The matching idea has been applied in many successful optical flow methods [5, 27, 31, 36], where they iteratively update the optical flow by constructing a correlation volume, querying the correlation of pixel pairs in the correlation volume based on the current estimation. In this paper, we propose a plug-and-play upgrade module: TPCV, a multi-scale correlation volume from which the algorithm can query the correlation between pixel pairs at different locations and scales. We show that 3D optical flow can be robustly extracted from the correlation features provided by TPCV. Importantly, TPCV can be easily applied to matching-based optical flow methods or 3D visual tasks, increasing the performance." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.469, + 0.902 + ], + "angle": 0, + "content": "Why TPCV? The computation of 3D optical flow requires computing pixel pairs correlation between feature" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.242 + ], + "angle": 0, + "content": "maps at different scales. As shown in Fig. 2, firstly, we tried to directly scale the image for matching, but this did not present good results; the main reason is the unrealistic large-scale features. So we rethink the problem in reverse, where the size reduction in the forward optical flow is equivalent to the enlargement in the inverse optical flow. We convert the part that needs to be enlarged to the reduction part in the inverse flow, and combine the forward and inverse parts to form a transposed correlation volume (TPCV) to query the correlation of feature pairs." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.243, + 0.892, + 0.379 + ], + "angle": 0, + "content": "TPCV Application In order to evaluate the performance of TPCV, we applied it to the classic and basic matched optical flow framework RAFT [27]. For upgrading 2D optical flow to 3D optical flow, only need to replace the original 4D correlation volume in RAFT with TPCV and add an output head of OE in the GRU iterative optimization layer. The relative OE and optical flow ground truth for training are obtained from the existing 3D scene flow training datasets [15, 19]." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.379, + 0.892, + 0.407 + ], + "angle": 0, + "content": "Contributions We summarize our contributions as follows:" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.42, + 0.892, + 0.496 + ], + "angle": 0, + "content": "- We propose a plug-and-play module: TPCV, which can be applied to matching-based optical flow framework. Based on TPCV, the original 2D optical flow can be easily upgraded to 3D optical flow, improving accuracy." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.507, + 0.892, + 0.568 + ], + "angle": 0, + "content": "- We applied TPCV to the optical flow framework RAFT, which significantly improved the performance without changing the optimizer structure, especially on the moving foreground target." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.579, + 0.892, + 0.655 + ], + "angle": 0, + "content": "- We display the effectiveness of TPCV across a variety of benchmark tasks, establishing new SOTA results for scene flow, time-to-collision (TTC) and motion-in-depth estimation - while maintaining a competitive speed." + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.42, + 0.892, + 0.655 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.679, + 0.634, + 0.695 + ], + "angle": 0, + "content": "2. Relate works" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.704, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Optical Flow The main task of optical flow is to estimate the dense pixel displacement between two frames. Recent matching-based methods have achieved great success [5, 25, 31, 36]. They query the correlation between pixel pairs through a 4D correlation volume for subsequent matching and regression. In these matching methods, the calculation of correlation is the algorithmic core. However, all of these methods ignore the correlation mutation caused by the scale change when the object moves in depth, resulting in the incapacity to handle the foreground moving objects. In this paper, we upgrade traditional image-to-image feature matching to image-to-scale-space matching based on scale matching. which allows our method to deal with" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "5446" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.089, + 0.895, + 0.251 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.26, + 0.895, + 0.318 + ], + "angle": 0, + "content": "Figure 3. A typical 3D optical flow calculation process. The estimation of 3D optical flow using TPCV is mainly divided into two parts. First, query the correlation features of optical flow and OE from TPCV according to the current estimation, and then regress the optical flow and OE results based on the correlation features. The TPCV module does not contain any parameters to be optimized, even the calculation of inverse optical flow in the green box, so it can be easily applied to other methods." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.343, + 0.47, + 0.434 + ], + "angle": 0, + "content": "scale changes in motion. Although some previous methods [6, 11, 34] also introduced the idea of the multi-scale image pyramid, but their pyramid is often used to save computing costs, expand network receptive field, speed up reasoning, and optimize loss function, which is still essentially image-to-image matching." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.434, + 0.471, + 0.631 + ], + "angle": 0, + "content": "Scene flow Scene flow calculates the motion of dense pixels in 3D space. Recently, some successful scene flow methods [2,8,13,18,29,33] are based on the rigid assumption, which assumes that the motion state of pixels on the same rigid object should be the same. These hypothesis-based optimization methods transform the scene flow task into a segmentation problem. Judging the foreground rigid object mask has become a key factor affecting the method's performance. However, it will inevitably introduce too much semantic information to assist judgment. Although these methods perform well on existing datasets, but require additional semantic labels and depth information to aid training." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.631, + 0.47, + 0.798 + ], + "angle": 0, + "content": "There is also a class of methods [9] that highly fuse depth and optical flow information by combining the inference process of depth and optical flow networks, so that the results can complement each other. However, the segmentation of rigid masks is still implicitly involved in this process. Another kind of Monocular method [1, 32] calculates scene flow by optical expansion. Owing to optical expansion is directly regressed from optical flow or RGB images, it often obtains poor accuracy. Unlike them, TPCV obtains scale changes based on scale matching, eliminating the dependence on depth and semantic information." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.814, + 0.169, + 0.829 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.903 + ], + "angle": 0, + "content": "We propose a general optical flow upgrade module: Transpose pyramid correlation volume (TPCV), which can be widely applied to various 2D optical flow frameworks and upgraded to 3D optical flow. In this section, we will" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.343, + 0.892, + 0.373 + ], + "angle": 0, + "content": "explain the construction details of TPCV, examples applied to RAFT, and supervision of 3D optical flow." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.382, + 0.768, + 0.397 + ], + "angle": 0, + "content": "3.1. Construction Details of TPCV" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.405, + 0.892, + 0.466 + ], + "angle": 0, + "content": "The main function of the TPCV module is to index the correlation features of pixel pairs from different scales at different locations. These features will be used for subsequent optical flow estimation and scale estimation." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.466, + 0.892, + 0.542 + ], + "angle": 0, + "content": "As shown in the Fig. 3, TPCV consists of three steps: Constructing the correlation volume of forward flow and reverse flow based on the input multi-scale features; Querying the correlation features from the correlation volume; Calculation of inverse optical flow." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.56, + 0.693, + 0.574 + ], + "angle": 0, + "content": "3.1.1 Correlation Volume" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.583, + 0.892, + 0.674 + ], + "angle": 0, + "content": "The correlation volume of TPCV is divided into forward and inverse, where the forward correlation volume is essentially the dot product of the original-scale feature map of the frame 1 and the multi-scale feature map of the frame 2. The structure of the inverse correlation volume is the same as the forward; only the order of dot products is swapped." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.674, + 0.892, + 0.735 + ], + "angle": 0, + "content": "Specifically, We extract the multi-scale features \\( F_{1}^{x} \\) and \\( F_{2}^{s} \\) respectively from the input image pyramid, where \\( x \\) and \\( s \\) refers to the scale change. The forward correlation volume consists of 4D correlation volumes \\( C_{s} \\) with different scales:" + }, + { + "type": "equation", + "bbox": [ + 0.579, + 0.748, + 0.892, + 0.767 + ], + "angle": 0, + "content": "\\[\nC _ {s} (i, j, u, v) = F _ {1} ^ {1} (i, j) \\cdot F _ {2} ^ {s} (u, v) \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.772, + 0.892, + 0.818 + ], + "angle": 0, + "content": "where \\((i,j)\\) and \\((u,v)\\) refer to locations in \\(F_{1}^{1}\\) and \\(F_{2}^{s}\\), \\(F_{1}^{x} \\in \\mathbb{R}^{xH \\times xW \\times C}\\), \\(F_{2}^{s} \\in \\mathbb{R}^{sH \\times sW \\times C}\\), \\(s,x = 0.5,0.5 + 1 / S, \\ldots, 1\\), \\(C_{s} \\in \\mathbb{R}^{H \\times W \\times sH \\times sW}\\), in our experiment \\(S = 4\\)." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.818, + 0.892, + 0.846 + ], + "angle": 0, + "content": "Similarly, the inverse correlation volume \\( C_x' \\) is computed as:" + }, + { + "type": "equation", + "bbox": [ + 0.578, + 0.847, + 0.892, + 0.865 + ], + "angle": 0, + "content": "\\[\nC _ {x} ^ {\\prime} (u, v, i, j) = F _ {2} ^ {1} (u, v) \\cdot F _ {1} ^ {x} (i, j) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.87, + 0.891, + 0.903 + ], + "angle": 0, + "content": "where \\((u,v)\\) and \\((i,j)\\) refer to locations in \\(F_{2}^{1}\\) and \\(F_{1}^{x}\\), \\(C_x' \\in \\mathbb{R}^{H \\times W \\times xH \\times xW}\\)." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5447" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.078, + 0.089, + 0.895, + 0.246 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.075, + 0.256, + 0.895, + 0.3 + ], + "angle": 0, + "content": "Figure 4. TPCV details. This figure shows the details of the inverse flow query of TPCV: Build the inverse correlation volume \\( C_x' \\); Query the inverse correlation feature \\( cf_x' \\) according to the optical flow estimation; Query the forward correlation feature \\( cf_s \\) from \\( cf_x' \\); Query the OE features from \\( cf_s \\) according to the current OE estimation." + }, + { + "type": "title", + "bbox": [ + 0.076, + 0.325, + 0.32, + 0.34 + ], + "angle": 0, + "content": "3.1.2 Query Correlation Feature" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.351, + 0.47, + 0.457 + ], + "angle": 0, + "content": "This section introduces how to query1 the correlation features from the correlation volume, which is mainly divided into two steps. First, the multi-scale optical flow features are queried from the correlation volume based on the optical flow results. Then, the multi-scale optical flow features are sampled in the scale dimension based on the current OE results. As shown in Fig. 4." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.459, + 0.47, + 0.505 + ], + "angle": 0, + "content": "After obtaining the forward optical flow \\( \\pmb{f} \\) and the correlation volume \\( C_s \\), the forward correlation feature \\( c f_s (s < 1) \\) can be queried as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.093, + 0.519, + 0.469, + 0.589 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} c f _ {s} (i, j) = C _ {s} (i, j, s u, s v) \\\\ = C _ {s} (i, j, s (i + \\boldsymbol {f} (i, j, 0)), s (j + \\boldsymbol {f} (i, j, 1))) \\\\ = \\operatorname {C o r r} (p, q ^ {s}) \\tag {3} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.604, + 0.469, + 0.666 + ], + "angle": 0, + "content": "where \\( c f_{s} \\in \\mathbb{R}^{H \\times W} \\), \\( (i,j) \\) and \\( (u,v) \\) refer to the locations of \\( p \\) and \\( q \\). \\( Corr(p,q^s) \\) describes the Correlation between point \\( p \\) and point \\( q \\) scaled by s times, \\( s = 0.5, 0.5 + 1 / S, \\ldots, 1 \\)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.668, + 0.47, + 0.714 + ], + "angle": 0, + "content": "Similarly, after acquiring the inverse optical flow \\( \\pmb{f}^{\\prime} \\) and the correlation volume \\( C_x' \\), the inverse correlation feature \\( c f_x'(x < 1) \\) can be queried as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.078, + 0.727, + 0.476, + 0.797 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} c f _ {x} ^ {\\prime} (u, v) = C _ {x} ^ {\\prime} (u, v, x i, x j) \\\\ = C _ {x} ^ {\\prime} (u, v, x \\left(u + \\boldsymbol {f} ^ {\\prime} (u, v, 0)\\right), x \\left(v + \\boldsymbol {f} ^ {\\prime} (u, v, 1)\\right)) \\\\ = \\operatorname {C o r r} \\left(q ^ {x}, p\\right) \\tag {4} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.812, + 0.469, + 0.844 + ], + "angle": 0, + "content": "where \\(\\pmb{f}^{\\prime}(u,v) = -\\pmb {f}(i,j),(u,v) = (i,j) + \\pmb {f}(i,j),cf_{x}^{\\prime}\\in\\) \\(\\mathbb{R}^{H\\times W},x = 0.5,0.5 + 1 / S,\\dots,1.\\)" + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.845, + 0.469, + 0.861 + ], + "angle": 0, + "content": "Then based on the forward optical flow \\( f \\) to query the" + }, + { + "type": "page_footnote", + "bbox": [ + 0.076, + 0.875, + 0.47, + 0.902 + ], + "angle": 0, + "content": "1In this paper, the query is implemented by the gridsample function in Pytorch." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.325, + 0.799, + 0.34 + ], + "angle": 0, + "content": "forward correlation features \\( c f_{s} \\) when \\( s > 1 \\):" + }, + { + "type": "equation", + "bbox": [ + 0.54, + 0.349, + 0.892, + 0.387 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} c f _ {1 / x} (i, j) = c f _ {x} ^ {\\prime} (i + \\boldsymbol {f} (i, j, 0), j + \\boldsymbol {f} (i, j, 1)) \\tag {5} \\\\ = C o r r (p, q ^ {1 / x}) \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.396, + 0.892, + 0.427 + ], + "angle": 0, + "content": "Combining \\( s < 1 \\) and \\( s > 1 \\), the complete forward optical flow correlation feature \\( C_f \\) is obtained:" + }, + { + "type": "equation", + "bbox": [ + 0.598, + 0.439, + 0.892, + 0.456 + ], + "angle": 0, + "content": "\\[\nC _ {f} = \\left\\{c f _ {0. 5}, \\dots , c f _ {1}, \\dots , c f _ {2} \\right\\} \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.465, + 0.687, + 0.482 + ], + "angle": 0, + "content": "where \\(C_f \\in \\mathbb{R}^{H \\times W \\times (S + 1)}\\)." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.482, + 0.892, + 0.558 + ], + "angle": 0, + "content": "After getting \\( C_f \\), the OE feature is obtained by querying the third scale dimension of \\( C_f \\) based on the current OE estimation \\( s \\). So far, we have described the query method of the OE features \\( F_{oe} \\), and \\( cf_1 \\) is the optical flow correlation feature \\( F_{of} \\)." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.558, + 0.892, + 0.618 + ], + "angle": 0, + "content": "In the process of applying TPCV to RAFT, we also query the neighborhood of optical flow and OE to obtain the optimization direction. We show more details about query in supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.636, + 0.802, + 0.651 + ], + "angle": 0, + "content": "3.1.3 Calculation of Inverse Optical Flow" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.892, + 0.796 + ], + "angle": 0, + "content": "In this section, we consider designing a inverse optical flow algorithm. In the previous work [12, 17], inverse optical flow is calculated by an extra network. However, this method is not applicable to TPCV. First, the extra network will increase the time and memory cost; second, to ensure the stability of querying, we hope that the forward and inverse flow can be perfectly closed-loop (about closed-loop, see Eq. (4), where \\( \\pmb{f}^{\\prime}(u,v) = -\\pmb{f}(i,j) \\), \\( (u,v) = (i,j) + \\pmb{f}(i,j) \\), \\( (i,j) = (u,v) + \\pmb{f}^{\\prime}(u,v) \\))." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.892, + 0.901 + ], + "angle": 0, + "content": "In order to ensure the perfect closed-loop of the forward and inverse flow, we refer to the idea of Javier [24], that is, the forward flow directly solves the inverse flow. The specific process is shown in Algorithm 1, where \\( \\text{corr} \\) is \\( cf_1 \\), \\( x_w \\) is the current point to be processed, \\( x_i \\) is the four coordinate points around \\( x_w \\), \\( w_i \\) and \\( wght \\) are intermediate variables that store correlation weights." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5448" + } + ], + [ + { + "type": "algorithm", + "bbox": [ + 0.078, + 0.09, + 0.47, + 0.356 + ], + "angle": 0, + "content": "Algorithm 1 Inverse optical flow \nInput: \\( f \\), \\( corr \\) \nParameter: \\( wght \\) \nOutput: \\( f' \\) \n1: Initialize \\( f' = 0, wght = -\\infty \\). \n2: for all \\( x \\in \\Omega \\) do \n3: \\( x_w \\gets x + f(x) \\) \n4: Find the four neighbors of \\( x_w : \\{x_1, x_2, x_3, x_4\\} \\) \n5: for \\( i = 1 \\) to 4 do \n6: \\( w_i \\gets corr(x_i, x_w) \\) \n7: if \\( w_i > wght(x_i) \\) then \n8: \\( f'(x_i) \\gets -f(x) \\) \n9: \\( wght(x_i) \\gets w_i \\) \n10: end if \n11: end for \n12: end for \n13: return \\( f' \\)" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.383, + 0.286, + 0.399 + ], + "angle": 0, + "content": "3.2. Apply TPCV in RAFT" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.406, + 0.469, + 0.466 + ], + "angle": 0, + "content": "RAFT is a typical matching optical flow method, which iteratively maintains an optical flow field through the GRU module. In this section, we will explain how to apply TPCV to RAFT." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.467, + 0.469, + 0.558 + ], + "angle": 0, + "content": "We do not change the structure of the RAFT feature extractor and GRU optimizer. The only change is that we use the TPCV module to replace the original 4D correlation volume to obtain optical flow correlation features and OE correlation features. We provide more details of network construction in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.558, + 0.469, + 0.633 + ], + "angle": 0, + "content": "Optimization After applying TPCV, we maintain an optical flow field \\( \\pmb{f} \\) and a scale flow field \\( s \\) at the same time. The calculation of the optical flow update amount is the same as RAFT, and we add a new output header to update the scale flow from hidden features \\( h_t \\) generated by GRU:" + }, + { + "type": "equation", + "bbox": [ + 0.138, + 0.646, + 0.469, + 0.669 + ], + "angle": 0, + "content": "\\[\nh _ {t} = G R U \\left(h _ {t - 1}, F _ {o f}, F _ {o e}\\right) \\tag {7}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.141, + 0.665, + 0.407, + 0.681 + ], + "angle": 0, + "content": "\\[\n\\Delta s = \\tanh (C o n v (R e L U (C o n v (h _ {t}))))\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.688, + 0.469, + 0.718 + ], + "angle": 0, + "content": "where \\(\\Delta s\\) is the update amount of scale flow, initial \\(h_0\\) is given by feature encoder." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.728, + 0.353, + 0.744 + ], + "angle": 0, + "content": "3.3. Supervision of 3D Optical Flow" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.752, + 0.282, + 0.767 + ], + "angle": 0, + "content": "3.3.1 2D Optical Flow Loss" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.776, + 0.469, + 0.822 + ], + "angle": 0, + "content": "We follow RAFT and supervise the 2D optical flow with \\( L_{1} \\) distance between the predicted flow and ground truth, the 2D optical flow loss is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.172, + 0.834, + 0.469, + 0.874 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {f} = \\sum_ {k = 1} ^ {N} \\gamma^ {N - k} \\left\\| \\boldsymbol {f} ^ {k} - \\boldsymbol {f} _ {g t} \\right\\| _ {1} \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.886, + 0.414, + 0.901 + ], + "angle": 0, + "content": "where we set \\(\\gamma = 0.8\\), \\(N = 12\\) in our experiments." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.092, + 0.665, + 0.105 + ], + "angle": 0, + "content": "3.3.2 Scale Flow Loss" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.116, + 0.892, + 0.176 + ], + "angle": 0, + "content": "Existing datasets do not directly provide supervised scale data. We refer to the scale change and motion in depth conversion formula established by Gengshan Yang [32] to train the 3D optical flow indirectly." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.177, + 0.892, + 0.236 + ], + "angle": 0, + "content": "Yang pointed out that under the assumption of small rotation, the scale change and motion-in-depth are approximately equal, which allows us to use the scene flow dataset to obtain the scale loss, which is defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.584, + 0.248, + 0.892, + 0.289 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {s} = \\sum_ {k = 1} ^ {N} \\gamma^ {N - k} \\left\\| \\boldsymbol {s} ^ {k} - z _ {g t} ^ {\\prime} / z _ {g t} \\right\\| _ {1} \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.301, + 0.891, + 0.345 + ], + "angle": 0, + "content": "where \\( z_{gt}^{\\prime} \\) and \\( z_{gt} \\) are the ground truth depths of the pixels in the second frame and the first frame, \\( s \\) is the scale flow field." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.347, + 0.829, + 0.362 + ], + "angle": 0, + "content": "The overall loss function of 3D optical flow is:" + }, + { + "type": "equation", + "bbox": [ + 0.648, + 0.375, + 0.891, + 0.391 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathcal {L} _ {f} + \\mathcal {L} _ {s} \\tag {10}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.419, + 0.632, + 0.435 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.444, + 0.892, + 0.609 + ], + "angle": 0, + "content": "Experimental results are discussed in this section to demonstrate the effectiveness of TPCV. Experiments show that TPCV can significantly improve the estimation accuracy of the optical flow for foreground objects under the same number of optimizer parameters. Furthermore, the ability of motion-in-depth estimation far exceeds the prior SOTA method. Specifically, we first compared the optical flow performance between the TPCV and baseline models. Then we applied the 3D optical flow results to the 3D scene perception tasks, including motion-in-depth, time-to-collision and scene flow estimation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.61, + 0.892, + 0.776 + ], + "angle": 0, + "content": "Setup We first pre-train our model on the Driving (D) for 80k iterations (batch = 6, lr = 0.002). For the optical flow estimation task, we finetune it on KITTI (K-200) with 60k iterations (batch = 6, lr = 0.00125). For motion-in-depth and time-to-collision task, we follow the split of optical expansion [32]. Specifically, we select one for every 5 images in K-200 for validation (K-40) and add the rest 160 images for training (K-160) with 60k iterations (batch = 6, lr = 0.00125). For the scene flow task, we use K-200 for training with 60k iterations (batch = 6, lr = 0.00125) and submit the results to the KITTI scene flow benchmark." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.787, + 0.636, + 0.803 + ], + "angle": 0, + "content": "4.1. Optical Flow" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.892, + 0.901 + ], + "angle": 0, + "content": "In this section, we compare the effect of applying TPCV on the baseline optical flow model. We chose RAFT [27] as the baseline model, not because it offers better performance, but because it is the basic framework for most advanced optical flow models [5, 16, 25, 26] and has strong representativeness." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5449" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.089, + 0.895, + 0.213 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.221, + 0.895, + 0.266 + ], + "angle": 0, + "content": "Figure 5. Results for KITTI test image. The upper line is the optical flow error. The redder the colour represents the greater the error, where Noc Fl-fg is the outlier rate of the non-occluded part, and the lower line is the colour-coded optical flow result. Observing the car window in the red box, there is a large-scale change between the two frames, and only our TPCV can get the correct result." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.29, + 0.471, + 0.337 + ], + "angle": 0, + "content": "The main indicator we compare is the outlier rate of foreground and background optical flow, where outliers are the points whose endpoint error is greater than 3px or \\(5\\%\\)." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.346, + 0.473, + 0.46 + ], + "angle": 0, + "content": "
MethodFl-bgFl-fgFl-allImprovement
RAFT [27]4.746.875.1-
GMA [5]4.787.035.15+attention
CRAFT [25]4.585.854.79+attention
RAFT+AOIR [26]4.686.995.07regularisation
RAFT+TPCV(ours)4.535.524.69scale correlation
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.47, + 0.47, + 0.539 + ], + "angle": 0, + "content": "Table 1. Quantitative optical flow performance comparison on KITTI test. Fl-bg and Fl-fg are the outlier rates of the foreground and background optical flow. The best among the all are bolded. Our method greatly improves the performance of foreground optical flow." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.553, + 0.47, + 0.658 + ], + "angle": 0, + "content": "Validation performance We first compare the performance of TPCV and baseline RAFT. As shown in Tab. 1, TPCV improves the accuracy of the baseline model comprehensively, especially the foreground error is reduced from 6.87 to 5.52, which proves that TPCV can help the baseline model adapt to the foreground object scale changes effectively." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.659, + 0.471, + 0.842 + ], + "angle": 0, + "content": "We also compare some state-of-the-art RAFT-based improvement methods' performance, where CRAFT and GMA both introduce attention mechanisms into the RAFT framework to solve the occlusion problem. While RAFT+AOIR studies new regularization schemes, these methods all change the structure of RAFT's optimizer and feature extractor, increasing the computational cost and training cost. However, our TPCV still achieves the best results, although the optimizer structure is not changed. Fig. 5 shows optical flow results under large-scale changes. All methods except TPCV cannot handle large-scale changes, proving the superiority of TPCV's scale matching strategy." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.848, + 0.295, + 0.865 + ], + "angle": 0, + "content": "4.2. Motion-in-depth (MID)" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.471, + 0.903 + ], + "angle": 0, + "content": "In this section, we discuss the performance of 3D optical flow in the MID estimation task. Based on the relationship" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.29, + 0.895, + 0.395 + ], + "angle": 0, + "content": "established in the previous work [32], we can equate the scale change \\( s \\) to MID \\( \\tau \\). For example, an object is doubled in size between two frames (\\( s = 0.5 \\), the second frame is scaled by 0.5 times and matches the first frame), which means that its distance from the camera is shortened by 0.5 times (\\( \\tau = z' / z = s = 0.5 \\), where \\( z' \\) and \\( z \\) are the depth of frame2 and frame1)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.397, + 0.895, + 0.457 + ], + "angle": 0, + "content": "MID is a key indicator in tasks such as time-to-collision, scene flow, and depth estimation. To demonstrate the superiority of TPCV, we compare it with the current state-of-the-art MID methods. The error of MID is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.558, + 0.47, + 0.892, + 0.489 + ], + "angle": 0, + "content": "\\[\nM I D _ {e r r o r} = \\left\\| l o g (\\tau) - l o g \\left(\\tau_ {g t}\\right) \\right\\| _ {1} \\cdot 1 0 ^ {4} \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.501, + 0.892, + 0.531 + ], + "angle": 0, + "content": "where \\(\\tau_{gt}\\) is obtained by dividing the matching depth of the second frame and the first frame." + }, + { + "type": "table", + "bbox": [ + 0.502, + 0.543, + 0.895, + 0.651 + ], + "angle": 0, + "content": "
MethodInputMIDerrorTime/s
OSF [20]Stereo1153000
PRSM [30]Stereo124300
Binary TTC [1]Mono73.552.2
Optical expansion [32]Mono750.2
RAFT+TPCV(ours)Mono42.840.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.66, + 0.895, + 0.69 + ], + "angle": 0, + "content": "Table 2. MID estimation on K-40 validation set. Our method outperforms other methods by a large margin." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.711, + 0.892, + 0.831 + ], + "angle": 0, + "content": "We first compare with traditional optimization methods that use stereo information: PSMR and OSF decompose pixels into rigid blocks, and iteratively update the optimized optical flow and depth fields based on rigid assumptions and hand-designed priors. To calculate the MID we divide the depth of the second frame by the depth of the first frame. Experimental results show that our errors are much smaller than them (115 vs. 42.84) and in less time\\(^{2}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.832, + 0.892, + 0.864 + ], + "angle": 0, + "content": "We also compare with the state-of-the-art MID methods Optical expansion and Binary TTC. TPCV outperforming" + }, + { + "type": "page_footnote", + "bbox": [ + 0.5, + 0.875, + 0.892, + 0.901 + ], + "angle": 0, + "content": "2Timing results are computed on 375x1242 images with an RTX 3090 GPU using 200 updates." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5450" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.09, + 0.285, + 0.189 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.284, + 0.091, + 0.487, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.372, + 0.189, + 0.399, + 0.2 + ], + "angle": 0, + "content": "OSF" + }, + { + "type": "image", + "bbox": [ + 0.487, + 0.091, + 0.687, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.541, + 0.189, + 0.637, + 0.201 + ], + "angle": 0, + "content": "Optical expansion" + }, + { + "type": "image", + "bbox": [ + 0.688, + 0.092, + 0.89, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.757, + 0.189, + 0.825, + 0.2 + ], + "angle": 0, + "content": "TPCV (ours)" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.211, + 0.894, + 0.253 + ], + "angle": 0, + "content": "Figure 6. Motion-in-depth results for image \"195\" in the K-40. Up: Ground truth and motion-in-depth results, where the darker indicates the objects moving towards the camera. Down: The input image and error map, where the whiter the pixels, the greater the error. In both foreground and background regions, our method is much more accurate than other methods." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.28, + 0.47, + 0.37 + ], + "angle": 0, + "content": "them by a large margin (73.55 vs 42.84), and the average error is reduced by \\(42\\%\\). Different from their directly regress MID, TPCV calculates MID based on matching, making the result robust and accurate. The above experiments prove that our TPCV module is progressiveness in depth motion estimation." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.379, + 0.296, + 0.395 + ], + "angle": 0, + "content": "4.3. Time-to-collision (TTC)" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.403, + 0.469, + 0.463 + ], + "angle": 0, + "content": "Time-to-collision estimation is essential for obstacle avoidance and path planning tasks [3, 7, 14, 21]. In fact, the time for a pixel to reach the camera plane (TTC) can be directly obtained from MID:" + }, + { + "type": "equation", + "bbox": [ + 0.172, + 0.472, + 0.469, + 0.508 + ], + "angle": 0, + "content": "\\[\nT T C = \\frac {Z}{Z - Z ^ {\\prime}} T = \\frac {T}{1 - \\frac {Z ^ {\\prime}}{Z}} \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.515, + 0.469, + 0.547 + ], + "angle": 0, + "content": "where \\(T\\) is the sampling interval of the camera (in KITTI \\(T = 0.1s\\)) and \\(Z' / Z\\) is the motion-in-depth \\(\\tau\\)." + }, + { + "type": "table", + "bbox": [ + 0.085, + 0.558, + 0.462, + 0.65 + ], + "angle": 0, + "content": "
MethodErr-1sErr-2sErr-5sTime/s
Binary TTC [1]2.43.163.250.0192
OSF [20]1.792.934.033000
Optical expansion [32]1.861.872.450.2
RAFT+TPCV(ours)1.141.651.570.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.661, + 0.469, + 0.688 + ], + "angle": 0, + "content": "Table 3. Percentage errors of time-to-collision estimation on K-40 validation set" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.704, + 0.469, + 0.765 + ], + "angle": 0, + "content": "We treat TTC as a binary classification problem, and for each pixel, we judge whether the TTC time is less than \\(\\{1\\mathrm{s}, 2\\mathrm{s}, 5\\mathrm{s}\\}\\). Only the points moving towards the camera are evaluated (the TTC of points farther away is meaningless)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.766, + 0.469, + 0.9 + ], + "angle": 0, + "content": "In this experiment, we use the binary mode of Binary TTC. In this mode, a binary mask will be directly output. Although the accuracy is decreased, the reasoning speed is greatly improved. Optical expansion and OSF perform reasonably well, consistent with their high precision performance in the motion-in-depth task. Our monocular method achieves the lowest error in all time intervals, which proves that the 3D optical flow estimated by TPCV can well predict future collisions." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.279, + 0.623, + 0.294 + ], + "angle": 0, + "content": "4.4. Scene Flow" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.305, + 0.892, + 0.47 + ], + "angle": 0, + "content": "Correctly estimating the 3D motion of objects in space is crucial for autonomous driving and dynamic environment path planning for robots. The scene flow consists of the optical flow and the depth of matched pixels between two frames. In order to unify the standard with the most advanced methods, we use GA-Net [35] to obtain the depth of the first frame \\( d_{1} \\), and obtain the depth of the second frame \\( d_{2} \\) based on the motion-in-depth \\( \\tau \\) estimated by TPCV \\( (d_{2} = d_{1} * \\tau) \\). The model was trained on the complete K-200 dataset and tested on the KITTI public scene flow evaluation website3." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.473, + 0.892, + 0.564 + ], + "angle": 0, + "content": "We first compare TPCV with monocular methods, as shown in Tab. 4 TPCV leads in all monocular methods with significant advantages, consistent with the previous superior performance of TPCV in motion-in-depth and optical flow tasks. It proves the effectiveness of our matching strategy in the field of 3D motion estimation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.567, + 0.892, + 0.718 + ], + "angle": 0, + "content": "We also compare with the state-of-the-art depth-based methods CamLiFlow, RigidMask+ISF and RAFT3D. Noticing that they tend to get smaller Fl-bg errors, which is thanks to the aid of depth information to optimize the background optical flow. Nonetheless, our monocular approach achieves the best results on all foreground metrics and maintains a competitive inference speed. We note that TPCV outperforms even most depth-based methods on the D2-all metric, which demonstrates the high potential of our 3D optical flow for estimating 3D motion." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.892, + 0.856 + ], + "angle": 0, + "content": "RAFT3D + TPCV We apply TPCV to RAFT3D. Specifically, we directly use TPCV to replace the correlation volume in RAFT3D, and change the original direct regression depth change to the estimated depth change ratio. The experimental results show that the addition of TPCV significantly improves the performance of SF-all \\((5.77\\rightarrow 5.00)\\). However, we found that Fl-fg did not improve. We think it is because RAFT3D introduced potential semantic information when estimating the foreground." + }, + { + "type": "page_footnote", + "bbox": [ + 0.501, + 0.875, + 0.892, + 0.901 + ], + "angle": 0, + "content": "3http://www.cvlibs.net/datasets/kitti/evalscene_flow.php" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "5451" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.08, + 0.089, + 0.897, + 0.233 + ], + "angle": 0, + "content": "
MethodInputD1-bgD1-fgD1-allD2-bgD2-fgD2-allFl-bgFl-fgFl-allSF-bgSF-fgSF-all
CamLiFlow [9]D+M1.483.461.811.928.142.952.317.043.12.8712.234.43
RigidMask+ISF [33]D+M1.533.651.892.098.923.232.637.853.53.2513.084.89
RAFT3D [28]D+M1.483.461.812.519.463.673.398.794.294.2713.275.77
RAFT3D+TPCV(ours)D+M1.483.461.811.938.793.072.4810.193.763.0314.825.0
Optical expansion [32]M1.483.461.813.398.544.255.838.666.37.0613.448.12
Binary TTC [1]M1.483.461.813.849.394.765.848.676.317.4513.748.5
RAFT+TPCV(ours)M1.483.461.812.297.633.184.535.524.695.3410.66.21
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.076, + 0.243, + 0.894, + 0.327 + ], + "angle": 0, + "content": "Table 4. State-of-art published methods on KITTI scene flow benchmark. D+M means depth and monocular information, and M means monocular information. D1, D2, Fl, and SF is the percentage of disparity, optical flow and scene flow outliers. -bg,-fg and -all represent the percentage of outliers averaged only over background regions, foreground regions and overall ground truth pixels. The best among all are bolded, and the second best are underlined. Our monocular method performs quite well, not only far ahead of similar monocular methods, but also outperforming stereo-based methods in foreground scene flow estimation, which proves that the TPCV module can accurately estimate the 3D motion." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.352, + 0.176, + 0.368 + ], + "angle": 0, + "content": "5. Ablation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.378, + 0.47, + 0.453 + ], + "angle": 0, + "content": "Setup We conduct more extensive experiments to verify the advanced nature of the TPCV module. For all experiments, we pre-train with 80K on the Driving dataset (batch=6, lr = 0.00025), refinement training on K-160 (batch=6, lr = 0.000125), and testing on the K-40 dataset." + }, + { + "type": "table", + "bbox": [ + 0.088, + 0.467, + 0.462, + 0.544 + ], + "angle": 0, + "content": "
MethodFlow_epeMID_errorMemory
RAFT*1.6156.22.7GB
RAFT+pyramid1.4248.95.2GB
RAFT+TPCV1.3142.83.0GB
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.076, + 0.554, + 0.47, + 0.595 + ], + "angle": 0, + "content": "Table 5. Ablation study on optical flow and motion-in-depth estimation. \\( Flow_{epe} \\) is the average endpoint pixel error of optical flow estimation, the lower the better." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.614, + 0.468, + 0.749 + ], + "angle": 0, + "content": "Comparison to regression (RAFT*) We first replace the TPCV in \"RAFT+TPCV\" with 4D correlation volume, at this time, the estimation of scale flow degenerates into direct regression, similar to optical expansion [32]. As shown in Tab. 5, after replacement, the optical flow error increased from 1.31 to 1.61 (increases \\(22.9\\%\\)), and the motion-in-depth error increased from 42.8 to 56.2 (increases \\(31.3\\%\\)). It proves that the TPCV module plays a crucial role in advanced optical flow and motion-in-depth estimation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Comparison to pyramid matching (RAFT+pyramid) We also tried a pyramid matching scheme, shown in Fig. 2 (b), which matched the first frame feature with the same scale feature in the image pyramid of the second frame. we directly obtain large-scale images through interpolation. As shown in Tab. 5, the optical flow and motion-in-depth errors are greatly reduced after introducing the matching strategy, which once again proves the superiority of the matching method. However, it is still not as good as the performance of TPCV (1.42 vs. 1.31; 48.9 vs. 42.8); we believe that it is" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.354, + 0.892, + 0.4 + ], + "angle": 0, + "content": "mainly due to the construction of false large-scale features, which makes it difficult to perform correct feature matching for objects far away from the camera." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.415, + 0.746, + 0.433 + ], + "angle": 0, + "content": "6. More experimental results" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.44, + 0.892, + 0.546 + ], + "angle": 0, + "content": "We followed the experimental procedure of RAFT3D [28] to conduct more experiments on the sizeable synthetic dataset Flyingthings3D. The results show that after applying TPCV, the end-to-end 3D error of RAFT3D is reduced from \\(0.062\\mathrm{m}\\) to \\(0.048\\mathrm{m}\\), and the end-to-end 2D error dropped from \\(2.2\\mathrm{px}\\) to \\(1.95\\mathrm{px}\\). See supplementary materials for details of results." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.547, + 0.892, + 0.623 + ], + "angle": 0, + "content": "Limitation We also conducted experiments on Sintel, but the baseline results did not improve significantly after applying TPCV; we believe this is because Sintel contains many non-rigid scenes, while the conversion between MID and OE depends on the rigid assumption." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.639, + 0.62, + 0.655 + ], + "angle": 0, + "content": "7. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.665, + 0.892, + 0.816 + ], + "angle": 0, + "content": "We combine optical flow and optical expansion, proposing the concept of 3D optical flow, which can eliminate the influence of scale change on optical flow matching and provide motion information in the depth direction. Then we design a plug-and-play 2D to 3D optical flow upgrade module: TPCV, associated with a set of supervised learning strategies. Experimentally, we apply TPCV to the RAFT, achieving significant performance improvements in 2D optical flow tasks and multiple 3D perception tasks, including motion-in-depth, scene flow, and time-to-collision." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.831, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Acknowledgements This research was supported by the National Natural Science Foundation of China (Grant nos. 61673220, 62106209), and the Project of SongShan Laboratory (Grant no. YYJC012022015)." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5452" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.184 + ], + "angle": 0, + "content": "[1] Abhishek Badki, Orazio Gallo, Jan Kautz, and Pradeep Sen. Binary ttc: A temporal geofence for autonomous navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12946-12955, 2021. 1, 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.186, + 0.472, + 0.282 + ], + "angle": 0, + "content": "[2] Aseem Behl, Omid Hosseini Jafari, Siva Karthik Mustikovela, Hassan Abu Alhaija, Carsten Rother, and Andreas Geiger. Bounding boxes, segmentations and object coordinates: How important is recognition for 3d scene flow estimation in autonomous driving scenarios? In Proceedings of the IEEE International Conference on Computer Vision, pages 2574-2583, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.284, + 0.471, + 0.338 + ], + "angle": 0, + "content": "[3] Jeffrey Byrne and Camillo J Taylor. Expansion segmentation for visual collision detection and estimation. In 2009 IEEE International Conference on Robotics and Automation, pages 875-882. IEEE, 2009. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.34, + 0.47, + 0.368 + ], + "angle": 0, + "content": "[4] TA Camus. Calculating time-to-contact using real-time quantized optical flow. 1995. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.369, + 0.47, + 0.436 + ], + "angle": 0, + "content": "[5] Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, and Richard Hartley. Learning to estimate hidden motions with global motion aggregation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9772-9781, 2021. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.438, + 0.47, + 0.494 + ], + "angle": 0, + "content": "[6] Rico Jonschkowski, Austin Stone, Jonathan T Barron, Ariel Gordon, Kurt Konolige, and Anelia Angelova. What matters in unsupervised optical flow. In European Conference on Computer Vision, pages 557-572. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.495, + 0.47, + 0.535 + ], + "angle": 0, + "content": "[7] David N Lee. A theory of visual control of braking based on information about time-to-collision. Perception, 5(4):437-459, 1976. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.537, + 0.47, + 0.592 + ], + "angle": 0, + "content": "[8] Congcong Li, Haoyu Ma, and Qingmin Liao. Two-stage adaptive object scene flow using hybrid cnn-crf model. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 3876-3883. IEEE, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.594, + 0.471, + 0.662 + ], + "angle": 0, + "content": "[9] Haisong Liu, Tao Lu, Yihui Xu, Jia Liu, Wenjie Li, and Lijun Chen. Camliflow: Bidirectional camera-lidar fusion for joint optical flow and scene flow estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5791-5801, 2022. 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.663, + 0.47, + 0.704 + ], + "angle": 0, + "content": "[10] David G Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91-110, 2004. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.705, + 0.471, + 0.775 + ], + "angle": 0, + "content": "[11] Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, and Jian Sun. Upflow: Upsampling pyramid for unsupervised optical flow learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1045-1054, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.776, + 0.471, + 0.845 + ], + "angle": 0, + "content": "[12] Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, and Jian Sun. Upflow: Upsampling pyramid for unsupervised optical flow learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1045-1054, June 2021. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[13] Wei-Chiu Ma, Shenlong Wang, Rui Hu, Yuwen Xiong, and Raquel Urtasun. Deep rigid instance scene flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3614-3622, 2019. 3" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.162 + ], + "angle": 0, + "content": "[14] Thiago Marinho, Massinissa Amrouche, Venanzio Cichella, Dušan Stipanović, and Naira Hovakimyan. Guaranteed collision avoidance based on line-of-sight angle and time-to-collision. In 2018 Annual American Control Conference (ACC), pages 4305-4310. IEEE, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.163, + 0.895, + 0.245 + ], + "angle": 0, + "content": "[15] Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4040-4048, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.247, + 0.894, + 0.315 + ], + "angle": 0, + "content": "[16] Lukas Mehl, Cedric Beschle, Andrea Barth, and Andrés Bruhn. An anisotropic selection scheme for variational optical flow methods with order-adaptive regularisation. In International Conference on Scale Space and Variational Methods in Computer Vision, pages 140-152. Springer, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.316, + 0.894, + 0.371 + ], + "angle": 0, + "content": "[17] Simon Meister, Junhwa Hur, and Stefan Roth. Unflow: Unsupervised learning of optical flow with a bidirectional census loss. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.372, + 0.894, + 0.426 + ], + "angle": 0, + "content": "[18] Moritz Menze and Andreas Geiger. Object scene flow for autonomous vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3061-3070, 2015. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.428, + 0.894, + 0.469 + ], + "angle": 0, + "content": "[19] Moritz Menze, Christian Heipke, and Andreas Geiger. Joint 3d estimation of vehicles and scene flow. In ISPRS Workshop on Image Sequence Analysis (ISA), 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.47, + 0.894, + 0.511 + ], + "angle": 0, + "content": "[20] Moritz Menze, Christian Heipke, and Andreas Geiger. Object scene flow. ISPRS Journal of Photogrammetry and Remote Sensing, 140:60-76, 2018. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.512, + 0.894, + 0.58 + ], + "angle": 0, + "content": "[21] Tomoyuki Mori and Sebastian Scherer. First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles. In 2013 IEEE International Conference on Robotics and Automation, pages 1750-1757. IEEE, 2013. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.581, + 0.894, + 0.649 + ], + "angle": 0, + "content": "[22] Dennis Muller, Josef Pauli, Christian Nunn, Steffen Gormer, and Stefan Muller-Schneiders. Time to contact estimation using interest points. In 2009 12th International IEEE Conference on Intelligent Transportation Systems, pages 1-6. IEEE, 2009. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.65, + 0.894, + 0.706 + ], + "angle": 0, + "content": "[23] Maria Sagrebin, Anastasia Noglik, and Josef Pauli. Robust time-to-contact calculation for real time applications. In Proceedings of 18th International Conference on Computer Graphics and Vision, pages 128-133, 2008. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.707, + 0.894, + 0.747 + ], + "angle": 0, + "content": "[24] Javier Sánchez, Agustín Salgado, and Nelson Monzon. Computing inverse optical flow. Pattern Recognition Letters, 52:32-39, 2015. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.748, + 0.894, + 0.817 + ], + "angle": 0, + "content": "[25] Xiuchao Sui, Shaohua Li, Xue Geng, Yan Wu, Xinxing Xu, Yong Liu, Rick Goh, and Hongyuan Zhu. Craft: Cross-attentional flow transformer for robust optical flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17602-17611, 2022. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.818, + 0.894, + 0.901 + ], + "angle": 0, + "content": "[26] Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T Freeman, and Ce Liu. Autoflow: Learning a better training set for optical flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10093-10102, 2021. 5, 6" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.895, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5453" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.468, + 0.135 + ], + "angle": 0, + "content": "[27] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In European conference on computer vision, pages 402-419. Springer, 2020. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.136, + 0.468, + 0.19 + ], + "angle": 0, + "content": "[28] Zachary Teed and Jia Deng. Raft-3d: Scene flow using rigid-motion embeddings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8375–8384, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.192, + 0.468, + 0.247 + ], + "angle": 0, + "content": "[29] Christoph Vogel, Konrad Schindler, and Stefan Roth. 3d scene flow estimation with a rigid motion prior. In 2011 International Conference on Computer Vision, pages 1291-1298. IEEE, 2011. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.249, + 0.468, + 0.303 + ], + "angle": 0, + "content": "[30] Christoph Vogel, Konrad Schindler, and Stefan Roth. 3d scene flow estimation with a piecewise rigid scene model. International Journal of Computer Vision, 115(1):1-28, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.305, + 0.468, + 0.373 + ], + "angle": 0, + "content": "[31] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, and Dacheng Tao. Gmflow: Learning optical flow via global matching. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8121-8130, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.375, + 0.468, + 0.432 + ], + "angle": 0, + "content": "[32] Gengshan Yang and Deva Ramanan. Upgrading optical flow to 3d scene flow through optical expansion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1334-1343, 2020. 1, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.434, + 0.468, + 0.488 + ], + "angle": 0, + "content": "[33] Gengshan Yang and Deva Ramanan. Learning to segment rigid motions from two frames. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1266-1275, 2021. 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.489, + 0.468, + 0.572 + ], + "angle": 0, + "content": "[34] Hanchao Yu, Xiao Chen, Humphrey Shi, Terrence Chen, Thomas S Huang, and Shanhui Sun. Motion pyramid networks for accurate and efficient cardiac motion estimation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 436-446. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.574, + 0.468, + 0.642 + ], + "angle": 0, + "content": "[35] Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr. Ga-net: Guided aggregation net for end-to-end stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 185–194, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.644, + 0.468, + 0.713 + ], + "angle": 0, + "content": "[36] Shiyu Zhao, Long Zhao, Zhixing Zhang, Enyu Zhou, and Dimitris Metaxas. Global matching with overlapping attention for optical flow estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17592-17601, June 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.468, + 0.713 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "5454" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Optical Expansion From Scale Matching/fadba611-fc8a-473b-9570-70ac81007770_origin.pdf b/2023/Learning Optical Expansion From Scale Matching/fadba611-fc8a-473b-9570-70ac81007770_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..dce40acff7945a2771ca577d67de1255b72dc100 --- /dev/null +++ b/2023/Learning Optical Expansion From Scale Matching/fadba611-fc8a-473b-9570-70ac81007770_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:00eff4af12583e97a88660a4b96dfbace3ee61cf85d1237294a69f716d131487 +size 2541491 diff --git a/2023/Learning Optical Expansion From Scale Matching/full.md b/2023/Learning Optical Expansion From Scale Matching/full.md new file mode 100644 index 0000000000000000000000000000000000000000..c32f8df764433bf143c78b8c4f3ca356cbb3df32 --- /dev/null +++ b/2023/Learning Optical Expansion From Scale Matching/full.md @@ -0,0 +1,382 @@ +# Learning Optical Expansion from Scale Matching + +Han Ling $^{1}$ Yinghui Sun $^{1}$ Quansen Sun $^{1*}$ Zhenwen Ren $^{1,2,3\dagger}$ + +$^{1}$ Nanjing University of Science and Technology + +$^{2}$ Southwest University of Science and Technology + +3SongShan Laboratory + +{321106010190,yinghuisun,sunquansen,rzw}@njust.edu.cn + +# Abstract + +This paper addresses the problem of optical expansion (OE). OE describes the object scale change between two frames, widely used in monocular 3D vision tasks. Previous methods estimate optical expansion mainly from optical flow results, but this two-stage architecture makes their results limited by the accuracy of optical flow and less robust. To solve these problems, we propose the concept of 3D optical flow by integrating optical expansion into the 2D optical flow, which is implemented by a plug-and-play module, namely TPCV. TPCV implements matching features at the correct location and scale, thus allowing the simultaneous optimization of optical flow and optical expansion tasks. Experimentally, we apply TPCV to the RAFT optical flow baseline. Experimental results show that the baseline optical flow performance is substantially improved. Moreover, we apply the optical flow and optical expansion results to various dynamic 3D vision tasks, including motion-in-depth, time-to-collision, and scene flow, often achieving significant improvement over the prior SOTA. Code is available at https://github.com/HanLingsgjk/TPCV. + +# 1. Introduction + +Optical expansion (OE) is a fundamental and important concept in monocular dynamic 3D vision tasks [1,3,22,23, 32]. OE describes the scale change of an object between two frames, which can be translated into motion in the depth direction. It has essential applications in time-to-collision, scene flow, and motion-in-depth estimation. OE schemes have unique advantages in 3D motion estimation tasks, requiring only a single camera and enabling dense and fixed baseline independent results. In this paper, we discuss a robust and novel approach for OE estimation. + +![](images/9a59211a90260a8615aa6c462b04af7edfb097b9bcca60b508164908a3cfcc3b.jpg) +Frame1 + +![](images/5add814f79720e19aa514825e3c0b5c3768cc079f0ecc3ecbb556b843a71f474.jpg) +Multi Scale Frame2 +Figure 1. Scale matching idea. Left: multi-scale matching between two frames. Right: texture around the matching point, where $s$ is the size of the image scaling. The core idea of scale matching is to match texture features at the correct location and scale. As seen above, the texture at the license plate can be better matched when the second frame is scaled 0.7 times in size, where the scaling magnification also reflects the optical expansion of that local pixel between the two frames. Furthermore, scale matching can better handle the motion in the depth direction and contains potential 3D motion information. + +![](images/00e35e7bdeffd87e4d7571d9bcf8fa04c50bee1cc535b4fd4012193d73cca929.jpg) +frame1 $s = 1$ + +![](images/d9675181e54b252d31502189d9c8f94d3bd049a6b59ea61a0453dc688d2d88b2.jpg) +frame2 $s = 1$ + +![](images/ea1a2426daad6fcaaf5bae764d45f2bbd742f5baf9f8929eb685f4c2f0f837ad.jpg) +$s = 0.7$ + +![](images/209a8c1484f5a2552f110076a23cbce3d7deb6db01aa52e34e3058004afeca99.jpg) +$s = 0.5$ + +Prior works In early time-to-collision (TTC) studies [4, 22, 23], OE was obtained from motion modeling, where the motion estimation was provided by optical flow or SIFT [10]. Such algorithms relied on optical flow results and specific model assumptions, often yielding only sparse and low-accuracy results. Some recent methods [32] regress OE based on existing optical flow results and achieve better outcomes. However, these two-stage estimation methods rely on accurate optical flow results and decrease computational efficiency. Instead, we consider optical flow and expansion estimation as two complementary tasks. Introducing OE in optical flow can realize matching features at the correct location and scale. As shown in Fig. 1, this fusion + +![](images/7e541fff791ad69d0fa33382252bbcbab7bd50513122476d9e525358d4567df4.jpg) +(a) image to image + +![](images/20021b67457fea3ac6485b5505555a9ddb879f8989083aceed8943b9c80dc0b6.jpg) + +![](images/acfe67daf232390dacaaa505cb90a91b09f755f57c18a0150d0265d7dbc6b2a7.jpg) +Figure 2. Three different matching modes. We match objects between two consecutive frames, where the cat is away from the camera and the car is close to the camera. (a) The 2D optical flow matches the cat and car in the original size image. (b) Match the second frame with the first frame after multiscale scaling, we found that cat can be better matched when magnified by 1.25 times, and car can be better matched when shrunk by 0.75 times. However, obtaining an accurate enlarged picture of a cat is impossible. (c) Transpose matching, where the cat zoomed in in frame 1 to 2 is equivalent to the cat zoomed out in frame 2 to 1. The core of TPCV is to convert the problem of zooming in the forward optical flow to zooming out in the reverse flow. + +eliminates the effect of scale variation of moving objects on optical flow matching, allowing the algorithm to predict moving foreground objects more accurately. Specifically, we refer to the fused task as 3D optical flow, and the 3D optical flow consists of a 2D optical flow field $\pmb{f}$ and a 1D scale flow field $s$ , where $s$ describes the scale change of objects between two frames. + +Solving for 3D Optical Flow We view solving 3D optical flow as a scale matching problem—matching the feature at the right location and scale. The matching idea has been applied in many successful optical flow methods [5, 27, 31, 36], where they iteratively update the optical flow by constructing a correlation volume, querying the correlation of pixel pairs in the correlation volume based on the current estimation. In this paper, we propose a plug-and-play upgrade module: TPCV, a multi-scale correlation volume from which the algorithm can query the correlation between pixel pairs at different locations and scales. We show that 3D optical flow can be robustly extracted from the correlation features provided by TPCV. Importantly, TPCV can be easily applied to matching-based optical flow methods or 3D visual tasks, increasing the performance. + +Why TPCV? The computation of 3D optical flow requires computing pixel pairs correlation between feature + +maps at different scales. As shown in Fig. 2, firstly, we tried to directly scale the image for matching, but this did not present good results; the main reason is the unrealistic large-scale features. So we rethink the problem in reverse, where the size reduction in the forward optical flow is equivalent to the enlargement in the inverse optical flow. We convert the part that needs to be enlarged to the reduction part in the inverse flow, and combine the forward and inverse parts to form a transposed correlation volume (TPCV) to query the correlation of feature pairs. + +TPCV Application In order to evaluate the performance of TPCV, we applied it to the classic and basic matched optical flow framework RAFT [27]. For upgrading 2D optical flow to 3D optical flow, only need to replace the original 4D correlation volume in RAFT with TPCV and add an output head of OE in the GRU iterative optimization layer. The relative OE and optical flow ground truth for training are obtained from the existing 3D scene flow training datasets [15, 19]. + +Contributions We summarize our contributions as follows: + +- We propose a plug-and-play module: TPCV, which can be applied to matching-based optical flow framework. Based on TPCV, the original 2D optical flow can be easily upgraded to 3D optical flow, improving accuracy. +- We applied TPCV to the optical flow framework RAFT, which significantly improved the performance without changing the optimizer structure, especially on the moving foreground target. +- We display the effectiveness of TPCV across a variety of benchmark tasks, establishing new SOTA results for scene flow, time-to-collision (TTC) and motion-in-depth estimation - while maintaining a competitive speed. + +# 2. Relate works + +Optical Flow The main task of optical flow is to estimate the dense pixel displacement between two frames. Recent matching-based methods have achieved great success [5, 25, 31, 36]. They query the correlation between pixel pairs through a 4D correlation volume for subsequent matching and regression. In these matching methods, the calculation of correlation is the algorithmic core. However, all of these methods ignore the correlation mutation caused by the scale change when the object moves in depth, resulting in the incapacity to handle the foreground moving objects. In this paper, we upgrade traditional image-to-image feature matching to image-to-scale-space matching based on scale matching. which allows our method to deal with + +![](images/56ee8aee0141899b96b5bfda911a6b8a5b1f68fe6c0f4747dcfb1db296983877.jpg) +Figure 3. A typical 3D optical flow calculation process. The estimation of 3D optical flow using TPCV is mainly divided into two parts. First, query the correlation features of optical flow and OE from TPCV according to the current estimation, and then regress the optical flow and OE results based on the correlation features. The TPCV module does not contain any parameters to be optimized, even the calculation of inverse optical flow in the green box, so it can be easily applied to other methods. + +scale changes in motion. Although some previous methods [6, 11, 34] also introduced the idea of the multi-scale image pyramid, but their pyramid is often used to save computing costs, expand network receptive field, speed up reasoning, and optimize loss function, which is still essentially image-to-image matching. + +Scene flow Scene flow calculates the motion of dense pixels in 3D space. Recently, some successful scene flow methods [2,8,13,18,29,33] are based on the rigid assumption, which assumes that the motion state of pixels on the same rigid object should be the same. These hypothesis-based optimization methods transform the scene flow task into a segmentation problem. Judging the foreground rigid object mask has become a key factor affecting the method's performance. However, it will inevitably introduce too much semantic information to assist judgment. Although these methods perform well on existing datasets, but require additional semantic labels and depth information to aid training. + +There is also a class of methods [9] that highly fuse depth and optical flow information by combining the inference process of depth and optical flow networks, so that the results can complement each other. However, the segmentation of rigid masks is still implicitly involved in this process. Another kind of Monocular method [1, 32] calculates scene flow by optical expansion. Owing to optical expansion is directly regressed from optical flow or RGB images, it often obtains poor accuracy. Unlike them, TPCV obtains scale changes based on scale matching, eliminating the dependence on depth and semantic information. + +# 3. Method + +We propose a general optical flow upgrade module: Transpose pyramid correlation volume (TPCV), which can be widely applied to various 2D optical flow frameworks and upgraded to 3D optical flow. In this section, we will + +explain the construction details of TPCV, examples applied to RAFT, and supervision of 3D optical flow. + +# 3.1. Construction Details of TPCV + +The main function of the TPCV module is to index the correlation features of pixel pairs from different scales at different locations. These features will be used for subsequent optical flow estimation and scale estimation. + +As shown in the Fig. 3, TPCV consists of three steps: Constructing the correlation volume of forward flow and reverse flow based on the input multi-scale features; Querying the correlation features from the correlation volume; Calculation of inverse optical flow. + +# 3.1.1 Correlation Volume + +The correlation volume of TPCV is divided into forward and inverse, where the forward correlation volume is essentially the dot product of the original-scale feature map of the frame 1 and the multi-scale feature map of the frame 2. The structure of the inverse correlation volume is the same as the forward; only the order of dot products is swapped. + +Specifically, We extract the multi-scale features $F_{1}^{x}$ and $F_{2}^{s}$ respectively from the input image pyramid, where $x$ and $s$ refers to the scale change. The forward correlation volume consists of 4D correlation volumes $C_{s}$ with different scales: + +$$ +C _ {s} (i, j, u, v) = F _ {1} ^ {1} (i, j) \cdot F _ {2} ^ {s} (u, v) \tag {1} +$$ + +where $(i,j)$ and $(u,v)$ refer to locations in $F_{1}^{1}$ and $F_{2}^{s}$ , $F_{1}^{x} \in \mathbb{R}^{xH \times xW \times C}$ , $F_{2}^{s} \in \mathbb{R}^{sH \times sW \times C}$ , $s,x = 0.5,0.5 + 1 / S, \ldots, 1$ , $C_{s} \in \mathbb{R}^{H \times W \times sH \times sW}$ , in our experiment $S = 4$ . + +Similarly, the inverse correlation volume $C_x'$ is computed as: + +$$ +C _ {x} ^ {\prime} (u, v, i, j) = F _ {2} ^ {1} (u, v) \cdot F _ {1} ^ {x} (i, j) \tag {2} +$$ + +where $(u,v)$ and $(i,j)$ refer to locations in $F_{2}^{1}$ and $F_{1}^{x}$ , $C_x' \in \mathbb{R}^{H \times W \times xH \times xW}$ . + +![](images/c29b11016aa76cc6a99073e1f93fdced8ad52d626eaf1f5d00fb45d4a835f6b6.jpg) +Figure 4. TPCV details. This figure shows the details of the inverse flow query of TPCV: Build the inverse correlation volume $C_x'$ ; Query the inverse correlation feature $cf_x'$ according to the optical flow estimation; Query the forward correlation feature $cf_s$ from $cf_x'$ ; Query the OE features from $cf_s$ according to the current OE estimation. + +# 3.1.2 Query Correlation Feature + +This section introduces how to query1 the correlation features from the correlation volume, which is mainly divided into two steps. First, the multi-scale optical flow features are queried from the correlation volume based on the optical flow results. Then, the multi-scale optical flow features are sampled in the scale dimension based on the current OE results. As shown in Fig. 4. + +After obtaining the forward optical flow $\pmb{f}$ and the correlation volume $C_s$ , the forward correlation feature $c f_s (s < 1)$ can be queried as follows: + +$$ +\begin{array}{l} c f _ {s} (i, j) = C _ {s} (i, j, s u, s v) \\ = C _ {s} (i, j, s (i + \boldsymbol {f} (i, j, 0)), s (j + \boldsymbol {f} (i, j, 1))) \\ = \operatorname {C o r r} (p, q ^ {s}) \tag {3} \\ \end{array} +$$ + +where $c f_{s} \in \mathbb{R}^{H \times W}$ , $(i,j)$ and $(u,v)$ refer to the locations of $p$ and $q$ . $Corr(p,q^s)$ describes the Correlation between point $p$ and point $q$ scaled by s times, $s = 0.5, 0.5 + 1 / S, \ldots, 1$ . + +Similarly, after acquiring the inverse optical flow $\pmb{f}^{\prime}$ and the correlation volume $C_x'$ , the inverse correlation feature $c f_x'(x < 1)$ can be queried as follows: + +$$ +\begin{array}{l} c f _ {x} ^ {\prime} (u, v) = C _ {x} ^ {\prime} (u, v, x i, x j) \\ = C _ {x} ^ {\prime} (u, v, x \left(u + \boldsymbol {f} ^ {\prime} (u, v, 0)\right), x \left(v + \boldsymbol {f} ^ {\prime} (u, v, 1)\right)) \\ = \operatorname {C o r r} \left(q ^ {x}, p\right) \tag {4} \\ \end{array} +$$ + +where $\pmb{f}^{\prime}(u,v) = -\pmb {f}(i,j),(u,v) = (i,j) + \pmb {f}(i,j),cf_{x}^{\prime}\in$ $\mathbb{R}^{H\times W},x = 0.5,0.5 + 1 / S,\dots,1.$ + +Then based on the forward optical flow $f$ to query the + +forward correlation features $c f_{s}$ when $s > 1$ : + +$$ +\begin{array}{l} c f _ {1 / x} (i, j) = c f _ {x} ^ {\prime} (i + \boldsymbol {f} (i, j, 0), j + \boldsymbol {f} (i, j, 1)) \tag {5} \\ = C o r r (p, q ^ {1 / x}) \\ \end{array} +$$ + +Combining $s < 1$ and $s > 1$ , the complete forward optical flow correlation feature $C_f$ is obtained: + +$$ +C _ {f} = \left\{c f _ {0. 5}, \dots , c f _ {1}, \dots , c f _ {2} \right\} \tag {6} +$$ + +where $C_f \in \mathbb{R}^{H \times W \times (S + 1)}$ . + +After getting $C_f$ , the OE feature is obtained by querying the third scale dimension of $C_f$ based on the current OE estimation $s$ . So far, we have described the query method of the OE features $F_{oe}$ , and $cf_1$ is the optical flow correlation feature $F_{of}$ . + +In the process of applying TPCV to RAFT, we also query the neighborhood of optical flow and OE to obtain the optimization direction. We show more details about query in supplementary material. + +# 3.1.3 Calculation of Inverse Optical Flow + +In this section, we consider designing a inverse optical flow algorithm. In the previous work [12, 17], inverse optical flow is calculated by an extra network. However, this method is not applicable to TPCV. First, the extra network will increase the time and memory cost; second, to ensure the stability of querying, we hope that the forward and inverse flow can be perfectly closed-loop (about closed-loop, see Eq. (4), where $\pmb{f}^{\prime}(u,v) = -\pmb{f}(i,j)$ , $(u,v) = (i,j) + \pmb{f}(i,j)$ , $(i,j) = (u,v) + \pmb{f}^{\prime}(u,v)$ ). + +In order to ensure the perfect closed-loop of the forward and inverse flow, we refer to the idea of Javier [24], that is, the forward flow directly solves the inverse flow. The specific process is shown in Algorithm 1, where $\text{corr}$ is $cf_1$ , $x_w$ is the current point to be processed, $x_i$ is the four coordinate points around $x_w$ , $w_i$ and $wght$ are intermediate variables that store correlation weights. + +Algorithm 1 Inverse optical flow +Input: $f$ , $corr$ +Parameter: $wght$ +Output: $f'$ +1: Initialize $f' = 0, wght = -\infty$ . +2: for all $x \in \Omega$ do +3: $x_w \gets x + f(x)$ +4: Find the four neighbors of $x_w : \{x_1, x_2, x_3, x_4\}$ +5: for $i = 1$ to 4 do +6: $w_i \gets corr(x_i, x_w)$ +7: if $w_i > wght(x_i)$ then +8: $f'(x_i) \gets -f(x)$ +9: $wght(x_i) \gets w_i$ +10: end if +11: end for +12: end for +13: return $f'$ + +# 3.2. Apply TPCV in RAFT + +RAFT is a typical matching optical flow method, which iteratively maintains an optical flow field through the GRU module. In this section, we will explain how to apply TPCV to RAFT. + +We do not change the structure of the RAFT feature extractor and GRU optimizer. The only change is that we use the TPCV module to replace the original 4D correlation volume to obtain optical flow correlation features and OE correlation features. We provide more details of network construction in the supplementary material. + +Optimization After applying TPCV, we maintain an optical flow field $\pmb{f}$ and a scale flow field $s$ at the same time. The calculation of the optical flow update amount is the same as RAFT, and we add a new output header to update the scale flow from hidden features $h_t$ generated by GRU: + +$$ +h _ {t} = G R U \left(h _ {t - 1}, F _ {o f}, F _ {o e}\right) \tag {7} +$$ + +$$ +\Delta s = \tanh (C o n v (R e L U (C o n v (h _ {t})))) +$$ + +where $\Delta s$ is the update amount of scale flow, initial $h_0$ is given by feature encoder. + +# 3.3. Supervision of 3D Optical Flow + +# 3.3.1 2D Optical Flow Loss + +We follow RAFT and supervise the 2D optical flow with $L_{1}$ distance between the predicted flow and ground truth, the 2D optical flow loss is defined as: + +$$ +\mathcal {L} _ {f} = \sum_ {k = 1} ^ {N} \gamma^ {N - k} \left\| \boldsymbol {f} ^ {k} - \boldsymbol {f} _ {g t} \right\| _ {1} \tag {8} +$$ + +where we set $\gamma = 0.8$ , $N = 12$ in our experiments. + +# 3.3.2 Scale Flow Loss + +Existing datasets do not directly provide supervised scale data. We refer to the scale change and motion in depth conversion formula established by Gengshan Yang [32] to train the 3D optical flow indirectly. + +Yang pointed out that under the assumption of small rotation, the scale change and motion-in-depth are approximately equal, which allows us to use the scene flow dataset to obtain the scale loss, which is defined as follows: + +$$ +\mathcal {L} _ {s} = \sum_ {k = 1} ^ {N} \gamma^ {N - k} \left\| \boldsymbol {s} ^ {k} - z _ {g t} ^ {\prime} / z _ {g t} \right\| _ {1} \tag {9} +$$ + +where $z_{gt}^{\prime}$ and $z_{gt}$ are the ground truth depths of the pixels in the second frame and the first frame, $s$ is the scale flow field. + +The overall loss function of 3D optical flow is: + +$$ +\mathcal {L} = \mathcal {L} _ {f} + \mathcal {L} _ {s} \tag {10} +$$ + +# 4. Experiments + +Experimental results are discussed in this section to demonstrate the effectiveness of TPCV. Experiments show that TPCV can significantly improve the estimation accuracy of the optical flow for foreground objects under the same number of optimizer parameters. Furthermore, the ability of motion-in-depth estimation far exceeds the prior SOTA method. Specifically, we first compared the optical flow performance between the TPCV and baseline models. Then we applied the 3D optical flow results to the 3D scene perception tasks, including motion-in-depth, time-to-collision and scene flow estimation. + +Setup We first pre-train our model on the Driving (D) for 80k iterations (batch = 6, lr = 0.002). For the optical flow estimation task, we finetune it on KITTI (K-200) with 60k iterations (batch = 6, lr = 0.00125). For motion-in-depth and time-to-collision task, we follow the split of optical expansion [32]. Specifically, we select one for every 5 images in K-200 for validation (K-40) and add the rest 160 images for training (K-160) with 60k iterations (batch = 6, lr = 0.00125). For the scene flow task, we use K-200 for training with 60k iterations (batch = 6, lr = 0.00125) and submit the results to the KITTI scene flow benchmark. + +# 4.1. Optical Flow + +In this section, we compare the effect of applying TPCV on the baseline optical flow model. We chose RAFT [27] as the baseline model, not because it offers better performance, but because it is the basic framework for most advanced optical flow models [5, 16, 25, 26] and has strong representativeness. + +![](images/d21650245039141109a685f51f69d59d396bc5bea3c94e66bf3617e4f4bd7a2f.jpg) +Figure 5. Results for KITTI test image. The upper line is the optical flow error. The redder the colour represents the greater the error, where Noc Fl-fg is the outlier rate of the non-occluded part, and the lower line is the colour-coded optical flow result. Observing the car window in the red box, there is a large-scale change between the two frames, and only our TPCV can get the correct result. + +The main indicator we compare is the outlier rate of foreground and background optical flow, where outliers are the points whose endpoint error is greater than 3px or $5\%$ . + +
MethodFl-bgFl-fgFl-allImprovement
RAFT [27]4.746.875.1-
GMA [5]4.787.035.15+attention
CRAFT [25]4.585.854.79+attention
RAFT+AOIR [26]4.686.995.07regularisation
RAFT+TPCV(ours)4.535.524.69scale correlation
+ +Validation performance We first compare the performance of TPCV and baseline RAFT. As shown in Tab. 1, TPCV improves the accuracy of the baseline model comprehensively, especially the foreground error is reduced from 6.87 to 5.52, which proves that TPCV can help the baseline model adapt to the foreground object scale changes effectively. + +We also compare some state-of-the-art RAFT-based improvement methods' performance, where CRAFT and GMA both introduce attention mechanisms into the RAFT framework to solve the occlusion problem. While RAFT+AOIR studies new regularization schemes, these methods all change the structure of RAFT's optimizer and feature extractor, increasing the computational cost and training cost. However, our TPCV still achieves the best results, although the optimizer structure is not changed. Fig. 5 shows optical flow results under large-scale changes. All methods except TPCV cannot handle large-scale changes, proving the superiority of TPCV's scale matching strategy. + +# 4.2. Motion-in-depth (MID) + +In this section, we discuss the performance of 3D optical flow in the MID estimation task. Based on the relationship + +established in the previous work [32], we can equate the scale change $s$ to MID $\tau$ . For example, an object is doubled in size between two frames ( $s = 0.5$ , the second frame is scaled by 0.5 times and matches the first frame), which means that its distance from the camera is shortened by 0.5 times ( $\tau = z' / z = s = 0.5$ , where $z'$ and $z$ are the depth of frame2 and frame1). + +MID is a key indicator in tasks such as time-to-collision, scene flow, and depth estimation. To demonstrate the superiority of TPCV, we compare it with the current state-of-the-art MID methods. The error of MID is defined as: + +$$ +M I D _ {e r r o r} = \left\| l o g (\tau) - l o g \left(\tau_ {g t}\right) \right\| _ {1} \cdot 1 0 ^ {4} \tag {11} +$$ + +where $\tau_{gt}$ is obtained by dividing the matching depth of the second frame and the first frame. + +Table 1. Quantitative optical flow performance comparison on KITTI test. Fl-bg and Fl-fg are the outlier rates of the foreground and background optical flow. The best among the all are bolded. Our method greatly improves the performance of foreground optical flow. + +
MethodInputMIDerrorTime/s
OSF [20]Stereo1153000
PRSM [30]Stereo124300
Binary TTC [1]Mono73.552.2
Optical expansion [32]Mono750.2
RAFT+TPCV(ours)Mono42.840.2
+ +Table 2. MID estimation on K-40 validation set. Our method outperforms other methods by a large margin. + +We first compare with traditional optimization methods that use stereo information: PSMR and OSF decompose pixels into rigid blocks, and iteratively update the optimized optical flow and depth fields based on rigid assumptions and hand-designed priors. To calculate the MID we divide the depth of the second frame by the depth of the first frame. Experimental results show that our errors are much smaller than them (115 vs. 42.84) and in less time $^{2}$ . + +We also compare with the state-of-the-art MID methods Optical expansion and Binary TTC. TPCV outperforming + +![](images/b39b61bf4d14b5f3c90ab74f416061cfd269b29a1fe72d4ae1317604048fa73f.jpg) +Figure 6. Motion-in-depth results for image "195" in the K-40. Up: Ground truth and motion-in-depth results, where the darker indicates the objects moving towards the camera. Down: The input image and error map, where the whiter the pixels, the greater the error. In both foreground and background regions, our method is much more accurate than other methods. + +![](images/c3afa2185073475837446f273c7bfe58d3a671bebda13aa3679553ec854fd1b1.jpg) +OSF + +![](images/a7430de52aeb57fda01d4c8daec9344a1f2b20a05c95a26e56a4e4dc3c9a7c86.jpg) +Optical expansion + +![](images/b0e304beadeadcca702e8c7323218df347ffe6034d8545fee66b2bcce0d47235.jpg) +TPCV (ours) + +them by a large margin (73.55 vs 42.84), and the average error is reduced by $42\%$ . Different from their directly regress MID, TPCV calculates MID based on matching, making the result robust and accurate. The above experiments prove that our TPCV module is progressiveness in depth motion estimation. + +# 4.3. Time-to-collision (TTC) + +Time-to-collision estimation is essential for obstacle avoidance and path planning tasks [3, 7, 14, 21]. In fact, the time for a pixel to reach the camera plane (TTC) can be directly obtained from MID: + +$$ +T T C = \frac {Z}{Z - Z ^ {\prime}} T = \frac {T}{1 - \frac {Z ^ {\prime}}{Z}} \tag {12} +$$ + +where $T$ is the sampling interval of the camera (in KITTI $T = 0.1s$ ) and $Z' / Z$ is the motion-in-depth $\tau$ . + +
MethodErr-1sErr-2sErr-5sTime/s
Binary TTC [1]2.43.163.250.0192
OSF [20]1.792.934.033000
Optical expansion [32]1.861.872.450.2
RAFT+TPCV(ours)1.141.651.570.2
+ +Table 3. Percentage errors of time-to-collision estimation on K-40 validation set + +We treat TTC as a binary classification problem, and for each pixel, we judge whether the TTC time is less than $\{1\mathrm{s}, 2\mathrm{s}, 5\mathrm{s}\}$ . Only the points moving towards the camera are evaluated (the TTC of points farther away is meaningless). + +In this experiment, we use the binary mode of Binary TTC. In this mode, a binary mask will be directly output. Although the accuracy is decreased, the reasoning speed is greatly improved. Optical expansion and OSF perform reasonably well, consistent with their high precision performance in the motion-in-depth task. Our monocular method achieves the lowest error in all time intervals, which proves that the 3D optical flow estimated by TPCV can well predict future collisions. + +# 4.4. Scene Flow + +Correctly estimating the 3D motion of objects in space is crucial for autonomous driving and dynamic environment path planning for robots. The scene flow consists of the optical flow and the depth of matched pixels between two frames. In order to unify the standard with the most advanced methods, we use GA-Net [35] to obtain the depth of the first frame $d_{1}$ , and obtain the depth of the second frame $d_{2}$ based on the motion-in-depth $\tau$ estimated by TPCV $(d_{2} = d_{1} * \tau)$ . The model was trained on the complete K-200 dataset and tested on the KITTI public scene flow evaluation website3. + +We first compare TPCV with monocular methods, as shown in Tab. 4 TPCV leads in all monocular methods with significant advantages, consistent with the previous superior performance of TPCV in motion-in-depth and optical flow tasks. It proves the effectiveness of our matching strategy in the field of 3D motion estimation. + +We also compare with the state-of-the-art depth-based methods CamLiFlow, RigidMask+ISF and RAFT3D. Noticing that they tend to get smaller Fl-bg errors, which is thanks to the aid of depth information to optimize the background optical flow. Nonetheless, our monocular approach achieves the best results on all foreground metrics and maintains a competitive inference speed. We note that TPCV outperforms even most depth-based methods on the D2-all metric, which demonstrates the high potential of our 3D optical flow for estimating 3D motion. + +RAFT3D + TPCV We apply TPCV to RAFT3D. Specifically, we directly use TPCV to replace the correlation volume in RAFT3D, and change the original direct regression depth change to the estimated depth change ratio. The experimental results show that the addition of TPCV significantly improves the performance of SF-all $(5.77\rightarrow 5.00)$ . However, we found that Fl-fg did not improve. We think it is because RAFT3D introduced potential semantic information when estimating the foreground. + +
MethodInputD1-bgD1-fgD1-allD2-bgD2-fgD2-allFl-bgFl-fgFl-allSF-bgSF-fgSF-all
CamLiFlow [9]D+M1.483.461.811.928.142.952.317.043.12.8712.234.43
RigidMask+ISF [33]D+M1.533.651.892.098.923.232.637.853.53.2513.084.89
RAFT3D [28]D+M1.483.461.812.519.463.673.398.794.294.2713.275.77
RAFT3D+TPCV(ours)D+M1.483.461.811.938.793.072.4810.193.763.0314.825.0
Optical expansion [32]M1.483.461.813.398.544.255.838.666.37.0613.448.12
Binary TTC [1]M1.483.461.813.849.394.765.848.676.317.4513.748.5
RAFT+TPCV(ours)M1.483.461.812.297.633.184.535.524.695.3410.66.21
+ +Table 4. State-of-art published methods on KITTI scene flow benchmark. D+M means depth and monocular information, and M means monocular information. D1, D2, Fl, and SF is the percentage of disparity, optical flow and scene flow outliers. -bg,-fg and -all represent the percentage of outliers averaged only over background regions, foreground regions and overall ground truth pixels. The best among all are bolded, and the second best are underlined. Our monocular method performs quite well, not only far ahead of similar monocular methods, but also outperforming stereo-based methods in foreground scene flow estimation, which proves that the TPCV module can accurately estimate the 3D motion. + +# 5. Ablation + +Setup We conduct more extensive experiments to verify the advanced nature of the TPCV module. For all experiments, we pre-train with 80K on the Driving dataset (batch=6, lr = 0.00025), refinement training on K-160 (batch=6, lr = 0.000125), and testing on the K-40 dataset. + +
MethodFlow_epeMID_errorMemory
RAFT*1.6156.22.7GB
RAFT+pyramid1.4248.95.2GB
RAFT+TPCV1.3142.83.0GB
+ +Table 5. Ablation study on optical flow and motion-in-depth estimation. $Flow_{epe}$ is the average endpoint pixel error of optical flow estimation, the lower the better. + +Comparison to regression (RAFT*) We first replace the TPCV in "RAFT+TPCV" with 4D correlation volume, at this time, the estimation of scale flow degenerates into direct regression, similar to optical expansion [32]. As shown in Tab. 5, after replacement, the optical flow error increased from 1.31 to 1.61 (increases $22.9\%$ ), and the motion-in-depth error increased from 42.8 to 56.2 (increases $31.3\%$ ). It proves that the TPCV module plays a crucial role in advanced optical flow and motion-in-depth estimation. + +Comparison to pyramid matching (RAFT+pyramid) We also tried a pyramid matching scheme, shown in Fig. 2 (b), which matched the first frame feature with the same scale feature in the image pyramid of the second frame. we directly obtain large-scale images through interpolation. As shown in Tab. 5, the optical flow and motion-in-depth errors are greatly reduced after introducing the matching strategy, which once again proves the superiority of the matching method. However, it is still not as good as the performance of TPCV (1.42 vs. 1.31; 48.9 vs. 42.8); we believe that it is + +mainly due to the construction of false large-scale features, which makes it difficult to perform correct feature matching for objects far away from the camera. + +# 6. More experimental results + +We followed the experimental procedure of RAFT3D [28] to conduct more experiments on the sizeable synthetic dataset Flyingthings3D. The results show that after applying TPCV, the end-to-end 3D error of RAFT3D is reduced from $0.062\mathrm{m}$ to $0.048\mathrm{m}$ , and the end-to-end 2D error dropped from $2.2\mathrm{px}$ to $1.95\mathrm{px}$ . See supplementary materials for details of results. + +Limitation We also conducted experiments on Sintel, but the baseline results did not improve significantly after applying TPCV; we believe this is because Sintel contains many non-rigid scenes, while the conversion between MID and OE depends on the rigid assumption. + +# 7. Conclusion + +We combine optical flow and optical expansion, proposing the concept of 3D optical flow, which can eliminate the influence of scale change on optical flow matching and provide motion information in the depth direction. Then we design a plug-and-play 2D to 3D optical flow upgrade module: TPCV, associated with a set of supervised learning strategies. Experimentally, we apply TPCV to the RAFT, achieving significant performance improvements in 2D optical flow tasks and multiple 3D perception tasks, including motion-in-depth, scene flow, and time-to-collision. + +Acknowledgements This research was supported by the National Natural Science Foundation of China (Grant nos. 61673220, 62106209), and the Project of SongShan Laboratory (Grant no. YYJC012022015). + +# References + +[1] Abhishek Badki, Orazio Gallo, Jan Kautz, and Pradeep Sen. Binary ttc: A temporal geofence for autonomous navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12946-12955, 2021. 1, 3, 6, 7, 8 +[2] Aseem Behl, Omid Hosseini Jafari, Siva Karthik Mustikovela, Hassan Abu Alhaija, Carsten Rother, and Andreas Geiger. Bounding boxes, segmentations and object coordinates: How important is recognition for 3d scene flow estimation in autonomous driving scenarios? In Proceedings of the IEEE International Conference on Computer Vision, pages 2574-2583, 2017. 3 +[3] Jeffrey Byrne and Camillo J Taylor. Expansion segmentation for visual collision detection and estimation. In 2009 IEEE International Conference on Robotics and Automation, pages 875-882. IEEE, 2009. 1, 7 +[4] TA Camus. Calculating time-to-contact using real-time quantized optical flow. 1995. 1 +[5] Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, and Richard Hartley. Learning to estimate hidden motions with global motion aggregation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9772-9781, 2021. 2, 5, 6 +[6] Rico Jonschkowski, Austin Stone, Jonathan T Barron, Ariel Gordon, Kurt Konolige, and Anelia Angelova. What matters in unsupervised optical flow. In European Conference on Computer Vision, pages 557-572. Springer, 2020. 3 +[7] David N Lee. A theory of visual control of braking based on information about time-to-collision. Perception, 5(4):437-459, 1976. 7 +[8] Congcong Li, Haoyu Ma, and Qingmin Liao. Two-stage adaptive object scene flow using hybrid cnn-crf model. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 3876-3883. IEEE, 2021. 3 +[9] Haisong Liu, Tao Lu, Yihui Xu, Jia Liu, Wenjie Li, and Lijun Chen. Camliflow: Bidirectional camera-lidar fusion for joint optical flow and scene flow estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5791-5801, 2022. 3, 8 +[10] David G Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91-110, 2004. 1 +[11] Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, and Jian Sun. Upflow: Upsampling pyramid for unsupervised optical flow learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1045-1054, 2021. 3 +[12] Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, and Jian Sun. Upflow: Upsampling pyramid for unsupervised optical flow learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1045-1054, June 2021. 4 +[13] Wei-Chiu Ma, Shenlong Wang, Rui Hu, Yuwen Xiong, and Raquel Urtasun. Deep rigid instance scene flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3614-3622, 2019. 3 + +[14] Thiago Marinho, Massinissa Amrouche, Venanzio Cichella, Dušan Stipanović, and Naira Hovakimyan. Guaranteed collision avoidance based on line-of-sight angle and time-to-collision. In 2018 Annual American Control Conference (ACC), pages 4305-4310. IEEE, 2018. 7 +[15] Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4040-4048, 2016. 2 +[16] Lukas Mehl, Cedric Beschle, Andrea Barth, and Andrés Bruhn. An anisotropic selection scheme for variational optical flow methods with order-adaptive regularisation. In International Conference on Scale Space and Variational Methods in Computer Vision, pages 140-152. Springer, 2021. 5 +[17] Simon Meister, Junhwa Hur, and Stefan Roth. Unflow: Unsupervised learning of optical flow with a bidirectional census loss. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018. 4 +[18] Moritz Menze and Andreas Geiger. Object scene flow for autonomous vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3061-3070, 2015. 3 +[19] Moritz Menze, Christian Heipke, and Andreas Geiger. Joint 3d estimation of vehicles and scene flow. In ISPRS Workshop on Image Sequence Analysis (ISA), 2015. 2 +[20] Moritz Menze, Christian Heipke, and Andreas Geiger. Object scene flow. ISPRS Journal of Photogrammetry and Remote Sensing, 140:60-76, 2018. 6, 7 +[21] Tomoyuki Mori and Sebastian Scherer. First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles. In 2013 IEEE International Conference on Robotics and Automation, pages 1750-1757. IEEE, 2013. 7 +[22] Dennis Muller, Josef Pauli, Christian Nunn, Steffen Gormer, and Stefan Muller-Schneiders. Time to contact estimation using interest points. In 2009 12th International IEEE Conference on Intelligent Transportation Systems, pages 1-6. IEEE, 2009. 1 +[23] Maria Sagrebin, Anastasia Noglik, and Josef Pauli. Robust time-to-contact calculation for real time applications. In Proceedings of 18th International Conference on Computer Graphics and Vision, pages 128-133, 2008. 1 +[24] Javier Sánchez, Agustín Salgado, and Nelson Monzon. Computing inverse optical flow. Pattern Recognition Letters, 52:32-39, 2015. 4 +[25] Xiuchao Sui, Shaohua Li, Xue Geng, Yan Wu, Xinxing Xu, Yong Liu, Rick Goh, and Hongyuan Zhu. Craft: Cross-attentional flow transformer for robust optical flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17602-17611, 2022. 2, 5, 6 +[26] Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T Freeman, and Ce Liu. Autoflow: Learning a better training set for optical flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10093-10102, 2021. 5, 6 + +[27] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In European conference on computer vision, pages 402-419. Springer, 2020. 2, 5, 6 +[28] Zachary Teed and Jia Deng. Raft-3d: Scene flow using rigid-motion embeddings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8375–8384, 2021. 8 +[29] Christoph Vogel, Konrad Schindler, and Stefan Roth. 3d scene flow estimation with a rigid motion prior. In 2011 International Conference on Computer Vision, pages 1291-1298. IEEE, 2011. 3 +[30] Christoph Vogel, Konrad Schindler, and Stefan Roth. 3d scene flow estimation with a piecewise rigid scene model. International Journal of Computer Vision, 115(1):1-28, 2015. 6 +[31] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, and Dacheng Tao. Gmflow: Learning optical flow via global matching. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8121-8130, 2022. 2 +[32] Gengshan Yang and Deva Ramanan. Upgrading optical flow to 3d scene flow through optical expansion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1334-1343, 2020. 1, 3, 5, 6, 7, 8 +[33] Gengshan Yang and Deva Ramanan. Learning to segment rigid motions from two frames. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1266-1275, 2021. 3, 8 +[34] Hanchao Yu, Xiao Chen, Humphrey Shi, Terrence Chen, Thomas S Huang, and Shanhui Sun. Motion pyramid networks for accurate and efficient cardiac motion estimation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 436-446. Springer, 2020. 3 +[35] Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr. Ga-net: Guided aggregation net for end-to-end stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 185–194, 2019. 7 +[36] Shiyu Zhao, Long Zhao, Zhixing Zhang, Enyu Zhou, and Dimitris Metaxas. Global matching with overlapping attention for optical flow estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17592-17601, June 2022. 2 \ No newline at end of file diff --git a/2023/Learning Optical Expansion From Scale Matching/images.zip b/2023/Learning Optical Expansion From Scale Matching/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..372389d70ed07be47425d96a51110b7856bc3520 --- /dev/null +++ b/2023/Learning Optical Expansion From Scale Matching/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1f396312e362f0009d84e1361117ced53594094f23a140e3eced1be1dadfe3f8 +size 593129 diff --git a/2023/Learning Optical Expansion From Scale Matching/layout.json b/2023/Learning Optical Expansion From Scale Matching/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..e98e9733738902d164283c10da168655b13f6c82 --- /dev/null +++ b/2023/Learning Optical Expansion From Scale Matching/layout.json @@ -0,0 +1,9984 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 143, + 103, + 451, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 103, + 451, + 121 + ], + "spans": [ + { + "bbox": [ + 143, + 103, + 451, + 121 + ], + "type": "text", + "content": "Learning Optical Expansion from Scale Matching" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 137, + 141, + 459, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 137, + 141, + 459, + 157 + ], + "spans": [ + { + "bbox": [ + 137, + 141, + 459, + 157 + ], + "type": "text", + "content": "Han Ling" + }, + { + "bbox": [ + 137, + 141, + 459, + 157 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 137, + 141, + 459, + 157 + ], + "type": "text", + "content": " Yinghui Sun" + }, + { + "bbox": [ + 137, + 141, + 459, + 157 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 137, + 141, + 459, + 157 + ], + "type": "text", + "content": " Quansen Sun" + }, + { + "bbox": [ + 137, + 141, + 459, + 157 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 137, + 141, + 459, + 157 + ], + "type": "text", + "content": " Zhenwen Ren" + }, + { + "bbox": [ + 137, + 141, + 459, + 157 + ], + "type": "inline_equation", + "content": "^{1,2,3\\dagger}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 181, + 157, + 412, + 172 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 181, + 157, + 412, + 172 + ], + "spans": [ + { + "bbox": [ + 181, + 157, + 412, + 172 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 181, + 157, + 412, + 172 + ], + "type": "text", + "content": "Nanjing University of Science and Technology" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 176, + 171, + 418, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 176, + 171, + 418, + 186 + ], + "spans": [ + { + "bbox": [ + 176, + 171, + 418, + 186 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 176, + 171, + 418, + 186 + ], + "type": "text", + "content": "Southwest University of Science and Technology" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 241, + 185, + 352, + 200 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 241, + 185, + 352, + 200 + ], + "spans": [ + { + "bbox": [ + 241, + 185, + 352, + 200 + ], + "type": "text", + "content": "3SongShan Laboratory" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 153, + 201, + 437, + 213 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 153, + 201, + 437, + 213 + ], + "spans": [ + { + "bbox": [ + 153, + 201, + 437, + 213 + ], + "type": "text", + "content": "{321106010190,yinghuisun,sunquansen,rzw}@njust.edu.cn" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 143, + 241, + 191, + 253 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 241, + 191, + 253 + ], + "spans": [ + { + "bbox": [ + 143, + 241, + 191, + 253 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 266, + 290, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 266, + 290, + 517 + ], + "spans": [ + { + "bbox": [ + 46, + 266, + 290, + 517 + ], + "type": "text", + "content": "This paper addresses the problem of optical expansion (OE). OE describes the object scale change between two frames, widely used in monocular 3D vision tasks. Previous methods estimate optical expansion mainly from optical flow results, but this two-stage architecture makes their results limited by the accuracy of optical flow and less robust. To solve these problems, we propose the concept of 3D optical flow by integrating optical expansion into the 2D optical flow, which is implemented by a plug-and-play module, namely TPCV. TPCV implements matching features at the correct location and scale, thus allowing the simultaneous optimization of optical flow and optical expansion tasks. Experimentally, we apply TPCV to the RAFT optical flow baseline. Experimental results show that the baseline optical flow performance is substantially improved. Moreover, we apply the optical flow and optical expansion results to various dynamic 3D vision tasks, including motion-in-depth, time-to-collision, and scene flow, often achieving significant improvement over the prior SOTA. Code is available at https://github.com/HanLingsgjk/TPCV." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 542, + 127, + 554 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 542, + 127, + 554 + ], + "spans": [ + { + "bbox": [ + 47, + 542, + 127, + 554 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 563, + 287, + 683 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 563, + 287, + 683 + ], + "spans": [ + { + "bbox": [ + 46, + 563, + 287, + 683 + ], + "type": "text", + "content": "Optical expansion (OE) is a fundamental and important concept in monocular dynamic 3D vision tasks [1,3,22,23, 32]. OE describes the scale change of an object between two frames, which can be translated into motion in the depth direction. It has essential applications in time-to-collision, scene flow, and motion-in-depth estimation. OE schemes have unique advantages in 3D motion estimation tasks, requiring only a single camera and enabling dense and fixed baseline independent results. 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Scale matching idea. Left: multi-scale matching between two frames. Right: texture around the matching point, where " + }, + { + "bbox": [ + 304, + 418, + 545, + 529 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 418, + 545, + 529 + ], + "type": "text", + "content": " is the size of the image scaling. The core idea of scale matching is to match texture features at the correct location and scale. As seen above, the texture at the license plate can be better matched when the second frame is scaled 0.7 times in size, where the scaling magnification also reflects the optical expansion of that local pixel between the two frames. 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Such algorithms relied on optical flow results and specific model assumptions, often yielding only sparse and low-accuracy results. Some recent methods [32] regress OE based on existing optical flow results and achieve better outcomes. However, these two-stage estimation methods rely on accurate optical flow results and decrease computational efficiency. Instead, we consider optical flow and expansion estimation as two complementary tasks. Introducing OE in optical flow can realize matching features at the correct location and scale. As shown in Fig. 1, this fusion" + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 693, + 135, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 693, + 135, + 703 + ], + "spans": [ + { + "bbox": [ + 58, + 693, + 135, + 703 + ], + "type": "text", + "content": "*Corresponding author" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 59, + 703, + 135, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 703, + 135, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 703, + 135, + 712 + ], + "type": "text", + "content": "† Corresponding author" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5445" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 78, + 70, + 254, + 115 + ], + "blocks": [ + { + "bbox": [ + 78, + 70, + 254, + 115 + ], + "lines": [ + { + "bbox": [ + 78, + 70, + 254, + 115 + ], + "spans": [ + { + "bbox": [ + 78, + 70, + 254, + 115 + ], + "type": "image", + "image_path": "7e541fff791ad69d0fa33382252bbcbab7bd50513122476d9e525358d4567df4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 124, + 116, + 194, + 125 + ], + "lines": [ + { + "bbox": [ + 124, + 116, + 194, + 125 + ], + "spans": [ + { + "bbox": [ + 124, + 116, + 194, + 125 + ], + "type": "text", + "content": "(a) image to image" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 78, + 128, + 257, + 191 + ], + "blocks": [ + { + "bbox": [ + 78, + 128, + 257, + 191 + ], + "lines": [ + { + "bbox": [ + 78, + 128, + 257, + 191 + ], + "spans": [ + { + "bbox": [ + 78, + 128, + 257, + 191 + ], + "type": "image", + "image_path": "20021b67457fea3ac6485b5505555a9ddb879f8989083aceed8943b9c80dc0b6.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 78, + 191, + 251, + 263 + ], + "blocks": [ + { + "bbox": [ + 78, + 191, + 251, + 263 + ], + "lines": [ + { + "bbox": [ + 78, + 191, + 251, + 263 + ], + "spans": [ + { + "bbox": [ + 78, + 191, + 251, + 263 + ], + "type": "image", + "image_path": "acfe67daf232390dacaaa505cb90a91b09f755f57c18a0150d0265d7dbc6b2a7.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 272, + 289, + 404 + ], + "lines": [ + { + "bbox": [ + 46, + 272, + 289, + 404 + ], + "spans": [ + { + "bbox": [ + 46, + 272, + 289, + 404 + ], + "type": "text", + "content": "Figure 2. Three different matching modes. We match objects between two consecutive frames, where the cat is away from the camera and the car is close to the camera. (a) The 2D optical flow matches the cat and car in the original size image. (b) Match the second frame with the first frame after multiscale scaling, we found that cat can be better matched when magnified by 1.25 times, and car can be better matched when shrunk by 0.75 times. However, obtaining an accurate enlarged picture of a cat is impossible. (c) Transpose matching, where the cat zoomed in in frame 1 to 2 is equivalent to the cat zoomed out in frame 2 to 1. The core of TPCV is to convert the problem of zooming in the forward optical flow to zooming out in the reverse flow." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 426, + 287, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 426, + 287, + 509 + ], + "spans": [ + { + "bbox": [ + 46, + 426, + 287, + 509 + ], + "type": "text", + "content": "eliminates the effect of scale variation of moving objects on optical flow matching, allowing the algorithm to predict moving foreground objects more accurately. Specifically, we refer to the fused task as 3D optical flow, and the 3D optical flow consists of a 2D optical flow field " + }, + { + "bbox": [ + 46, + 426, + 287, + 509 + ], + "type": "inline_equation", + "content": "\\pmb{f}" + }, + { + "bbox": [ + 46, + 426, + 287, + 509 + ], + "type": "text", + "content": " and a 1D scale flow field " + }, + { + "bbox": [ + 46, + 426, + 287, + 509 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 46, + 426, + 287, + 509 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 426, + 287, + 509 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 46, + 426, + 287, + 509 + ], + "type": "text", + "content": " describes the scale change of objects between two frames." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 510, + 287, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 510, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 46, + 510, + 287, + 689 + ], + "type": "text", + "content": "Solving for 3D Optical Flow We view solving 3D optical flow as a scale matching problem—matching the feature at the right location and scale. The matching idea has been applied in many successful optical flow methods [5, 27, 31, 36], where they iteratively update the optical flow by constructing a correlation volume, querying the correlation of pixel pairs in the correlation volume based on the current estimation. In this paper, we propose a plug-and-play upgrade module: TPCV, a multi-scale correlation volume from which the algorithm can query the correlation between pixel pairs at different locations and scales. We show that 3D optical flow can be robustly extracted from the correlation features provided by TPCV. Importantly, TPCV can be easily applied to matching-based optical flow methods or 3D visual tasks, increasing the performance." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "text", + "content": "Why TPCV? The computation of 3D optical flow requires computing pixel pairs correlation between feature" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 72, + 545, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 191 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 191 + ], + "type": "text", + "content": "maps at different scales. As shown in Fig. 2, firstly, we tried to directly scale the image for matching, but this did not present good results; the main reason is the unrealistic large-scale features. So we rethink the problem in reverse, where the size reduction in the forward optical flow is equivalent to the enlargement in the inverse optical flow. We convert the part that needs to be enlarged to the reduction part in the inverse flow, and combine the forward and inverse parts to form a transposed correlation volume (TPCV) to query the correlation of feature pairs." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 192, + 545, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 192, + 545, + 300 + ], + "spans": [ + { + "bbox": [ + 304, + 192, + 545, + 300 + ], + "type": "text", + "content": "TPCV Application In order to evaluate the performance of TPCV, we applied it to the classic and basic matched optical flow framework RAFT [27]. For upgrading 2D optical flow to 3D optical flow, only need to replace the original 4D correlation volume in RAFT with TPCV and add an output head of OE in the GRU iterative optimization layer. The relative OE and optical flow ground truth for training are obtained from the existing 3D scene flow training datasets [15, 19]." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 300, + 545, + 322 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 300, + 545, + 322 + ], + "spans": [ + { + "bbox": [ + 305, + 300, + 545, + 322 + ], + "type": "text", + "content": "Contributions We summarize our contributions as follows:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 317, + 332, + 545, + 518 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 317, + 332, + 545, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 332, + 545, + 392 + ], + "spans": [ + { + "bbox": [ + 317, + 332, + 545, + 392 + ], + "type": "text", + "content": "- We propose a plug-and-play module: TPCV, which can be applied to matching-based optical flow framework. Based on TPCV, the original 2D optical flow can be easily upgraded to 3D optical flow, improving accuracy." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 317, + 401, + 545, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 401, + 545, + 449 + ], + "spans": [ + { + "bbox": [ + 317, + 401, + 545, + 449 + ], + "type": "text", + "content": "- We applied TPCV to the optical flow framework RAFT, which significantly improved the performance without changing the optimizer structure, especially on the moving foreground target." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 317, + 458, + 545, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 458, + 545, + 518 + ], + "spans": [ + { + "bbox": [ + 317, + 458, + 545, + 518 + ], + "type": "text", + "content": "- We display the effectiveness of TPCV across a variety of benchmark tasks, establishing new SOTA results for scene flow, time-to-collision (TTC) and motion-in-depth estimation - while maintaining a competitive speed." + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 537, + 388, + 550 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 537, + 388, + 550 + ], + "spans": [ + { + "bbox": [ + 306, + 537, + 388, + 550 + ], + "type": "text", + "content": "2. Relate works" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 557, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 557, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 557, + 545, + 713 + ], + "type": "text", + "content": "Optical Flow The main task of optical flow is to estimate the dense pixel displacement between two frames. Recent matching-based methods have achieved great success [5, 25, 31, 36]. They query the correlation between pixel pairs through a 4D correlation volume for subsequent matching and regression. In these matching methods, the calculation of correlation is the algorithmic core. However, all of these methods ignore the correlation mutation caused by the scale change when the object moves in depth, resulting in the incapacity to handle the foreground moving objects. In this paper, we upgrade traditional image-to-image feature matching to image-to-scale-space matching based on scale matching. which allows our method to deal with" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5446" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 70, + 547, + 198 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 547, + 198 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 547, + 198 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 547, + 198 + ], + "type": "image", + "image_path": "56ee8aee0141899b96b5bfda911a6b8a5b1f68fe6c0f4747dcfb1db296983877.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 205, + 547, + 251 + ], + "lines": [ + { + "bbox": [ + 46, + 205, + 547, + 251 + ], + "spans": [ + { + "bbox": [ + 46, + 205, + 547, + 251 + ], + "type": "text", + "content": "Figure 3. A typical 3D optical flow calculation process. The estimation of 3D optical flow using TPCV is mainly divided into two parts. First, query the correlation features of optical flow and OE from TPCV according to the current estimation, and then regress the optical flow and OE results based on the correlation features. The TPCV module does not contain any parameters to be optimized, even the calculation of inverse optical flow in the green box, so it can be easily applied to other methods." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 271, + 287, + 343 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 271, + 287, + 343 + ], + "spans": [ + { + "bbox": [ + 46, + 271, + 287, + 343 + ], + "type": "text", + "content": "scale changes in motion. Although some previous methods [6, 11, 34] also introduced the idea of the multi-scale image pyramid, but their pyramid is often used to save computing costs, expand network receptive field, speed up reasoning, and optimize loss function, which is still essentially image-to-image matching." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 343, + 288, + 499 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 343, + 288, + 499 + ], + "spans": [ + { + "bbox": [ + 46, + 343, + 288, + 499 + ], + "type": "text", + "content": "Scene flow Scene flow calculates the motion of dense pixels in 3D space. Recently, some successful scene flow methods [2,8,13,18,29,33] are based on the rigid assumption, which assumes that the motion state of pixels on the same rigid object should be the same. These hypothesis-based optimization methods transform the scene flow task into a segmentation problem. Judging the foreground rigid object mask has become a key factor affecting the method's performance. However, it will inevitably introduce too much semantic information to assist judgment. Although these methods perform well on existing datasets, but require additional semantic labels and depth information to aid training." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 499, + 287, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 499, + 287, + 632 + ], + "spans": [ + { + "bbox": [ + 46, + 499, + 287, + 632 + ], + "type": "text", + "content": "There is also a class of methods [9] that highly fuse depth and optical flow information by combining the inference process of depth and optical flow networks, so that the results can complement each other. However, the segmentation of rigid masks is still implicitly involved in this process. Another kind of Monocular method [1, 32] calculates scene flow by optical expansion. Owing to optical expansion is directly regressed from optical flow or RGB images, it often obtains poor accuracy. Unlike them, TPCV obtains scale changes based on scale matching, eliminating the dependence on depth and semantic information." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 644, + 103, + 656 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 644, + 103, + 656 + ], + "spans": [ + { + "bbox": [ + 47, + 644, + 103, + 656 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "content": "We propose a general optical flow upgrade module: Transpose pyramid correlation volume (TPCV), which can be widely applied to various 2D optical flow frameworks and upgraded to 3D optical flow. In this section, we will" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 271, + 545, + 295 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 271, + 545, + 295 + ], + "spans": [ + { + "bbox": [ + 305, + 271, + 545, + 295 + ], + "type": "text", + "content": "explain the construction details of TPCV, examples applied to RAFT, and supervision of 3D optical flow." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 302, + 470, + 314 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 302, + 470, + 314 + ], + "spans": [ + { + "bbox": [ + 306, + 302, + 470, + 314 + ], + "type": "text", + "content": "3.1. Construction Details of TPCV" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 320, + 545, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 320, + 545, + 369 + ], + "spans": [ + { + "bbox": [ + 305, + 320, + 545, + 369 + ], + "type": "text", + "content": "The main function of the TPCV module is to index the correlation features of pixel pairs from different scales at different locations. These features will be used for subsequent optical flow estimation and scale estimation." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 369, + 545, + 429 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 369, + 545, + 429 + ], + "spans": [ + { + "bbox": [ + 305, + 369, + 545, + 429 + ], + "type": "text", + "content": "As shown in the Fig. 3, TPCV consists of three steps: Constructing the correlation volume of forward flow and reverse flow based on the input multi-scale features; Querying the correlation features from the correlation volume; Calculation of inverse optical flow." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 443, + 424, + 454 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 443, + 424, + 454 + ], + "spans": [ + { + "bbox": [ + 306, + 443, + 424, + 454 + ], + "type": "text", + "content": "3.1.1 Correlation Volume" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 461, + 545, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 461, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 304, + 461, + 545, + 533 + ], + "type": "text", + "content": "The correlation volume of TPCV is divided into forward and inverse, where the forward correlation volume is essentially the dot product of the original-scale feature map of the frame 1 and the multi-scale feature map of the frame 2. The structure of the inverse correlation volume is the same as the forward; only the order of dot products is swapped." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 533, + 545, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 533, + 545, + 582 + ], + "spans": [ + { + "bbox": [ + 305, + 533, + 545, + 582 + ], + "type": "text", + "content": "Specifically, We extract the multi-scale features " + }, + { + "bbox": [ + 305, + 533, + 545, + 582 + ], + "type": "inline_equation", + "content": "F_{1}^{x}" + }, + { + "bbox": [ + 305, + 533, + 545, + 582 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 533, + 545, + 582 + ], + "type": "inline_equation", + "content": "F_{2}^{s}" + }, + { + "bbox": [ + 305, + 533, + 545, + 582 + ], + "type": "text", + "content": " respectively from the input image pyramid, where " + }, + { + "bbox": [ + 305, + 533, + 545, + 582 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 305, + 533, + 545, + 582 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 533, + 545, + 582 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 305, + 533, + 545, + 582 + ], + "type": "text", + "content": " refers to the scale change. The forward correlation volume consists of 4D correlation volumes " + }, + { + "bbox": [ + 305, + 533, + 545, + 582 + ], + "type": "inline_equation", + "content": "C_{s}" + }, + { + "bbox": [ + 305, + 533, + 545, + 582 + ], + "type": "text", + "content": " with different scales:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 354, + 592, + 545, + 607 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 354, + 592, + 545, + 607 + ], + "spans": [ + { + "bbox": [ + 354, + 592, + 545, + 607 + ], + "type": "interline_equation", + "content": "C _ {s} (i, j, u, v) = F _ {1} ^ {1} (i, j) \\cdot F _ {2} ^ {s} (u, v) \\tag {1}", + "image_path": "6b884b1c535ba95bfcbe7c8e281b4121267c38d86d2dfc334e5a81d12d69018b.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 611, + 545, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 611, + 545, + 647 + ], + "spans": [ + { + "bbox": [ + 306, + 611, + 545, + 647 + ], + "type": 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"bbox": [ + 306, + 611, + 545, + 647 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 306, + 611, + 545, + 647 + ], + "type": "inline_equation", + "content": "F_{2}^{s} \\in \\mathbb{R}^{sH \\times sW \\times C}" + }, + { + "bbox": [ + 306, + 611, + 545, + 647 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 306, + 611, + 545, + 647 + ], + "type": "inline_equation", + "content": "s,x = 0.5,0.5 + 1 / S, \\ldots, 1" + }, + { + "bbox": [ + 306, + 611, + 545, + 647 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 306, + 611, + 545, + 647 + ], + "type": "inline_equation", + "content": "C_{s} \\in \\mathbb{R}^{H \\times W \\times sH \\times sW}" + }, + { + "bbox": [ + 306, + 611, + 545, + 647 + ], + "type": "text", + "content": ", in our experiment " + }, + { + "bbox": [ + 306, + 611, + 545, + 647 + ], + "type": "inline_equation", + "content": "S = 4" + }, + { + "bbox": [ + 306, + 611, + 545, + 647 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 647, + 545, + 670 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 647, + 545, + 670 + ], + "spans": [ + { + "bbox": [ + 306, + 647, + 545, + 670 + ], + "type": "text", + "content": "Similarly, the inverse correlation volume " + }, + { + "bbox": [ + 306, + 647, + 545, + 670 + ], + "type": "inline_equation", + "content": "C_x'" + }, + { + "bbox": [ + 306, + 647, + 545, + 670 + ], + "type": "text", + "content": " is computed as:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 353, + 670, + 545, + 685 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 353, + 670, + 545, + 685 + ], + "spans": [ + { + "bbox": [ + 353, + 670, + 545, + 685 + ], + "type": "interline_equation", + "content": "C _ {x} ^ {\\prime} (u, v, i, j) = F _ {2} ^ {1} (u, v) \\cdot F _ {1} ^ {x} (i, j) \\tag {2}", + "image_path": "ad49e0e3cf5b38e2cfdbe5bad026774fbb6097756c1fb7f98a2376df385030c7.jpg" + } + ] + 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[ + 306, + 689, + 545, + 715 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 306, + 689, + 545, + 715 + ], + "type": "inline_equation", + "content": "C_x' \\in \\mathbb{R}^{H \\times W \\times xH \\times xW}" + }, + { + "bbox": [ + 306, + 689, + 545, + 715 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5447" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 70, + 547, + 194 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 547, + 194 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 547, + 194 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 547, + 194 + ], + "type": "image", + "image_path": "c29b11016aa76cc6a99073e1f93fdced8ad52d626eaf1f5d00fb45d4a835f6b6.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 202, + 547, + 237 + ], + "lines": [ + { + "bbox": [ + 45, + 202, + 547, + 237 + ], + "spans": [ + { + "bbox": [ + 45, + 202, + 547, + 237 + ], + "type": "text", + "content": "Figure 4. TPCV details. This figure shows the details of the inverse flow query of TPCV: Build the inverse correlation volume " + }, + { + "bbox": [ + 45, + 202, + 547, + 237 + ], + "type": "inline_equation", + "content": "C_x'" + }, + { + "bbox": [ + 45, + 202, + 547, + 237 + ], + "type": "text", + "content": "; Query the inverse correlation feature " + }, + { + "bbox": [ + 45, + 202, + 547, + 237 + ], + "type": "inline_equation", + "content": "cf_x'" + }, + { + "bbox": [ + 45, + 202, + 547, + 237 + ], + "type": "text", + "content": " according to the optical flow estimation; Query the forward correlation feature " + }, + { + "bbox": [ + 45, + 202, + 547, + 237 + ], + "type": "inline_equation", + "content": "cf_s" + }, + { + "bbox": [ + 45, + 202, + 547, + 237 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 45, + 202, + 547, + 237 + ], + "type": "inline_equation", + "content": "cf_x'" + }, + { + "bbox": [ + 45, + 202, + 547, + 237 + ], + "type": "text", + "content": "; Query the OE features from " + }, + { + "bbox": [ + 45, + 202, + 547, + 237 + ], + "type": "inline_equation", + "content": "cf_s" + }, + { + "bbox": [ + 45, + 202, + 547, + 237 + ], + "type": "text", + "content": " according to the current OE estimation." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 257, + 195, + 269 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 257, + 195, + 269 + ], + "spans": [ + { + "bbox": [ + 46, + 257, + 195, + 269 + ], + "type": "text", + "content": "3.1.2 Query Correlation Feature" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 277, + 287, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 277, + 287, + 361 + ], + "spans": [ + { + "bbox": [ + 46, + 277, + 287, + 361 + ], + "type": "text", + "content": "This section introduces how to query1 the correlation features from the correlation volume, which is mainly divided into two steps. First, the multi-scale optical flow features are queried from the correlation volume based on the optical flow results. Then, the multi-scale optical flow features are sampled in the scale dimension based on the current OE results. As shown in Fig. 4." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 363, + 287, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 363, + 287, + 399 + ], + "spans": [ + { + "bbox": [ + 47, + 363, + 287, + 399 + ], + "type": "text", + "content": "After obtaining the forward optical flow " + }, + { + "bbox": [ + 47, + 363, + 287, + 399 + ], + "type": "inline_equation", + "content": "\\pmb{f}" + }, + { + "bbox": [ + 47, + 363, + 287, + 399 + ], + "type": "text", + "content": " and the correlation volume " + }, + { + "bbox": [ + 47, + 363, + 287, + 399 + ], + "type": "inline_equation", + "content": "C_s" + }, + { + "bbox": [ + 47, + 363, + 287, + 399 + ], + "type": "text", + "content": ", the forward correlation feature " + }, + { + "bbox": [ + 47, + 363, + 287, + 399 + ], + "type": "inline_equation", + "content": "c f_s (s < 1)" + }, + { + "bbox": [ + 47, + 363, + 287, + 399 + ], + "type": "text", + "content": " can be queried as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 411, + 287, + 466 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 411, + 287, + 466 + ], + "spans": [ + { + "bbox": [ + 56, + 411, + 287, + 466 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} c f _ {s} (i, j) = C _ {s} (i, j, s u, s v) \\\\ = C _ {s} (i, j, s (i + \\boldsymbol {f} (i, j, 0)), s (j + \\boldsymbol {f} (i, j, 1))) \\\\ = \\operatorname {C o r r} (p, q ^ {s}) \\tag {3} \\\\ \\end{array}", + "image_path": "77a00a9df5930e7577a8c7cc8036015966f21cee0a742aad68427179842af0fc.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "spans": [ + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "inline_equation", + "content": "c f_{s} \\in \\mathbb{R}^{H \\times W}" + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "inline_equation", + "content": "(i,j)" + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "inline_equation", + "content": "(u,v)" + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "text", + "content": " refer to the locations of " + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "inline_equation", + "content": "Corr(p,q^s)" + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "text", + "content": " describes the Correlation between point " + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "text", + "content": " and point " + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "text", + "content": " scaled by s times, " + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "inline_equation", + "content": "s = 0.5, 0.5 + 1 / S, \\ldots, 1" + }, + { + "bbox": [ + 47, + 478, + 287, + 527 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 529, + 287, + 565 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, 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{f}(i,j),cf_{x}^{\\prime}\\in" + }, + { + "bbox": [ + 47, + 643, + 287, + 668 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{H\\times W},x = 0.5,0.5 + 1 / S,\\dots,1." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 59, + 669, + 287, + 681 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 669, + 287, + 681 + ], + "spans": [ + { + "bbox": [ + 59, + 669, + 287, + 681 + ], + "type": "text", + "content": "Then based on the forward optical flow " + }, + { + "bbox": [ + 59, + 669, + 287, + 681 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 59, + 669, + 287, + 681 + ], + "type": "text", + "content": " to query the" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 257, + 488, + 269 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 257, + 488, + 269 + ], + "spans": [ + { + "bbox": [ + 306, + 257, + 488, + 269 + ], + "type": "text", + "content": "forward correlation features " + }, + { + 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"bbox": [ + 305, + 313, + 545, + 338 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 313, + 545, + 338 + ], + "spans": [ + { + "bbox": [ + 305, + 313, + 545, + 338 + ], + "type": "text", + "content": "Combining " + }, + { + "bbox": [ + 305, + 313, + 545, + 338 + ], + "type": "inline_equation", + "content": "s < 1" + }, + { + "bbox": [ + 305, + 313, + 545, + 338 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 313, + 545, + 338 + ], + "type": "inline_equation", + "content": "s > 1" + }, + { + "bbox": [ + 305, + 313, + 545, + 338 + ], + "type": "text", + "content": ", the complete forward optical flow correlation feature " + }, + { + "bbox": [ + 305, + 313, + 545, + 338 + ], + "type": "inline_equation", + "content": "C_f" + }, + { + "bbox": [ + 305, + 313, + 545, + 338 + ], + "type": "text", + "content": " is obtained:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 365, + 347, + 545, + 361 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 347, + 545, + 361 + ], + "spans": [ + { + "bbox": [ + 365, + 347, + 545, + 361 + ], + "type": "interline_equation", + "content": "C _ {f} = \\left\\{c f _ {0. 5}, \\dots , c f _ {1}, \\dots , c f _ {2} \\right\\} \\tag {6}", + "image_path": "c9a8916cf6b7c19fc5501e4f1780302aa8ad0ea2979ea1596d7a87a0ff71808e.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 368, + 420, + 381 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 368, + 420, + 381 + ], + "spans": [ + { + "bbox": [ + 306, + 368, + 420, + 381 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 306, + 368, + 420, + 381 + ], + "type": "inline_equation", + "content": "C_f \\in \\mathbb{R}^{H \\times W \\times (S + 1)}" + }, + { + "bbox": [ + 306, + 368, + 420, + 381 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "spans": [ + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "text", + "content": "After getting " + }, + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "inline_equation", + "content": "C_f" + }, + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "text", + "content": ", the OE feature is obtained by querying the third scale dimension of " + }, + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "inline_equation", + "content": "C_f" + }, + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "text", + "content": " based on the current OE estimation " + }, + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "text", + "content": ". So far, we have described the query method of the OE features " + }, + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "inline_equation", + "content": "F_{oe}" + }, + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "inline_equation", + "content": "cf_1" + }, + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "text", + "content": " is the optical flow correlation feature " + }, + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "inline_equation", + "content": "F_{of}" + }, + { + "bbox": [ + 305, + 381, + 545, + 441 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 441, + 545, + 489 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 441, + 545, + 489 + ], + "spans": [ + { + "bbox": [ + 305, + 441, + 545, + 489 + ], + "type": "text", + "content": "In the process of applying TPCV to RAFT, we also query the neighborhood of optical flow and OE to obtain the optimization direction. We show more details about query in supplementary material." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 503, + 490, + 515 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 503, + 490, + 515 + ], + "spans": [ + { + "bbox": [ + 306, + 503, + 490, + 515 + ], + "type": "text", + "content": "3.1.3 Calculation of Inverse Optical Flow" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 521, + 545, + 630 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 545, + 630 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 545, + 630 + ], + "type": "text", + "content": "In this section, we consider designing a inverse optical flow algorithm. In the previous work [12, 17], inverse optical flow is calculated by an extra network. However, this method is not applicable to TPCV. First, the extra network will increase the time and memory cost; second, to ensure the stability of querying, we hope that the forward and inverse flow can be perfectly closed-loop (about closed-loop, see Eq. (4), where " + }, + { + "bbox": [ + 304, + 521, + 545, + 630 + ], + "type": "inline_equation", + "content": "\\pmb{f}^{\\prime}(u,v) = -\\pmb{f}(i,j)" + }, + { + "bbox": [ + 304, + 521, + 545, + 630 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 521, + 545, + 630 + ], + "type": "inline_equation", + "content": "(u,v) = (i,j) + \\pmb{f}(i,j)" + }, + { + "bbox": [ + 304, + 521, + 545, + 630 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 521, + 545, + 630 + ], + "type": "inline_equation", + "content": "(i,j) = (u,v) + \\pmb{f}^{\\prime}(u,v)" + }, + { + "bbox": [ + 304, + 521, + 545, + 630 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": "In order to ensure the perfect closed-loop of the forward and inverse flow, we refer to the idea of Javier [24], that is, the forward flow directly solves the inverse flow. The specific process is shown in Algorithm 1, where " + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\text{corr}" + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": " is " + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "inline_equation", + "content": "cf_1" + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "inline_equation", + "content": "x_w" + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": " is the current point to be processed, " + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "inline_equation", + "content": "x_i" + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": " is the four coordinate points around " + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "inline_equation", + "content": "x_w" + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "inline_equation", + "content": "w_i" + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "inline_equation", + "content": "wght" + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": " are intermediate variables that store correlation weights." + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 46, + 693, + 287, + 714 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 693, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 693, + 287, + 714 + ], + "type": "text", + "content": "1In this paper, the query is implemented by the gridsample function in Pytorch." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5448" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "code", + "bbox": [ + 47, + 71, + 287, + 281 + ], + "blocks": [ + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "lines": [ + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "spans": [ + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": "Algorithm 1 Inverse optical flow \nInput: " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "corr" + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": " \nParameter: " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "wght" + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": " \nOutput: " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "f'" + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": " \n1: Initialize " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "f' = 0, wght = -\\infty" + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": ". \n2: for all " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "x \\in \\Omega" + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": " do \n3: " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "x_w \\gets x + f(x)" + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": " \n4: Find the four neighbors of " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "x_w : \\{x_1, x_2, x_3, x_4\\}" + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": " \n5: for " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "i = 1" + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": " to 4 do \n6: " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "w_i \\gets corr(x_i, x_w)" + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": " \n7: if " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "w_i > wght(x_i)" + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": " then \n8: " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "f'(x_i) \\gets -f(x)" + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": " \n9: " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "wght(x_i) \\gets w_i" + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "text", + "content": " \n10: end if \n11: end for \n12: end for \n13: return " + }, + { + "bbox": [ + 47, + 71, + 287, + 281 + ], + "type": "inline_equation", + "content": "f'" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "code_body" + } + ], + "index": 0, + "sub_type": "algorithm" + }, + { + "bbox": [ + 47, + 303, + 175, + 316 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 303, + 175, + 316 + ], + "spans": [ + { + "bbox": [ + 47, + 303, + 175, + 316 + ], + "type": "text", + "content": "3.2. Apply TPCV in RAFT" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 321, + 287, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 321, + 287, + 369 + ], + "spans": [ + { + "bbox": [ + 46, + 321, + 287, + 369 + ], + "type": "text", + "content": "RAFT is a typical matching optical flow method, which iteratively maintains an optical flow field through the GRU module. In this section, we will explain how to apply TPCV to RAFT." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 369, + 287, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 369, + 287, + 441 + ], + "spans": [ + { + "bbox": [ + 46, + 369, + 287, + 441 + ], + "type": "text", + "content": "We do not change the structure of the RAFT feature extractor and GRU optimizer. The only change is that we use the TPCV module to replace the original 4D correlation volume to obtain optical flow correlation features and OE correlation features. We provide more details of network construction in the supplementary material." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 441, + 287, + 501 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 441, + 287, + 501 + ], + "spans": [ + { + "bbox": [ + 46, + 441, + 287, + 501 + ], + "type": "text", + "content": "Optimization After applying TPCV, we maintain an optical flow field " + }, + { + "bbox": [ + 46, + 441, + 287, + 501 + ], + "type": "inline_equation", + "content": "\\pmb{f}" + }, + { + "bbox": [ + 46, + 441, + 287, + 501 + ], + "type": "text", + "content": " and a scale flow field " + }, + { + "bbox": [ + 46, + 441, + 287, + 501 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 46, + 441, + 287, + 501 + ], + "type": "text", + "content": " at the same time. The calculation of the optical flow update amount is the same as RAFT, and we add a new output header to update the scale flow from hidden features " + }, + { + "bbox": [ + 46, + 441, + 287, + 501 + ], + "type": "inline_equation", + "content": "h_t" + }, + { + "bbox": [ + 46, + 441, + 287, + 501 + ], + "type": "text", + "content": " generated by GRU:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 84, + 511, + 287, + 529 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 84, + 511, + 287, + 529 + ], + "spans": [ + { + "bbox": [ + 84, + 511, + 287, + 529 + ], + "type": "interline_equation", + "content": "h _ {t} = G R U \\left(h _ {t - 1}, F _ {o f}, F _ {o e}\\right) \\tag {7}", + "image_path": "b50c54bb0b8910b6eb805e8859f377b3427b7c2eeff77dd973e63703d7d76085.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 86, + 526, + 249, + 539 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 86, + 526, + 249, + 539 + ], + "spans": [ + { + "bbox": [ + 86, + 526, + 249, + 539 + ], + "type": "interline_equation", + "content": "\\Delta s = \\tanh (C o n v (R e L U (C o n v (h _ {t}))))", + "image_path": "57b705cbb29978d2b5c65c36ffea8726e494856e2b738f607653bbab94f879e2.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 544, + 287, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 544, + 287, + 568 + ], + "spans": [ + { + "bbox": [ + 46, + 544, + 287, + 568 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 544, + 287, + 568 + ], + "type": "inline_equation", + "content": "\\Delta s" + }, + { + "bbox": [ + 46, + 544, + 287, + 568 + ], + "type": "text", + "content": " is the update amount of scale flow, initial " + }, + { + "bbox": [ + 46, + 544, + 287, + 568 + ], + "type": "inline_equation", + "content": "h_0" + }, + { + "bbox": [ + 46, + 544, + 287, + 568 + ], + "type": "text", + "content": " is given by feature encoder." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 576, + 216, + 589 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 576, + 216, + 589 + ], + "spans": [ + { + "bbox": [ + 47, + 576, + 216, + 589 + ], + "type": "text", + "content": "3.3. Supervision of 3D Optical Flow" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 595, + 172, + 607 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 595, + 172, + 607 + ], + "spans": [ + { + "bbox": [ + 47, + 595, + 172, + 607 + ], + "type": "text", + "content": "3.3.1 2D Optical Flow Loss" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 614, + 287, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 614, + 287, + 651 + ], + "spans": [ + { + "bbox": [ + 46, + 614, + 287, + 651 + ], + "type": "text", + "content": "We follow RAFT and supervise the 2D optical flow with " + }, + { + "bbox": [ + 46, + 614, + 287, + 651 + ], + "type": "inline_equation", + "content": "L_{1}" + }, + { + "bbox": [ + 46, + 614, + 287, + 651 + ], + "type": "text", + "content": " distance between the predicted flow and ground truth, the 2D optical flow loss is defined as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 660, + 287, + 692 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 660, + 287, + 692 + ], + "spans": [ + { + "bbox": [ + 105, + 660, + 287, + 692 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {f} = \\sum_ {k = 1} ^ {N} \\gamma^ {N - k} \\left\\| \\boldsymbol {f} ^ {k} - \\boldsymbol {f} _ {g t} \\right\\| _ {1} \\tag {8}", + "image_path": "28d49f7eaa7037fb12985c961fa148daacf5b7bf2e39bf5c057745001907c0f9.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 701, + 253, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 701, + 253, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 701, + 253, + 713 + ], + "type": "text", + "content": "where we set " + }, + { + "bbox": [ + 47, + 701, + 253, + 713 + ], + "type": "inline_equation", + "content": "\\gamma = 0.8" + }, + { + "bbox": [ + 47, + 701, + 253, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 701, + 253, + 713 + ], + "type": "inline_equation", + "content": "N = 12" + }, + { + "bbox": [ + 47, + 701, + 253, + 713 + ], + "type": "text", + "content": " in our experiments." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 72, + 406, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 72, + 406, + 83 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 406, + 83 + ], + "type": "text", + "content": "3.3.2 Scale Flow Loss" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 91, + 545, + 139 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 91, + 545, + 139 + ], + "spans": [ + { + "bbox": [ + 304, + 91, + 545, + 139 + ], + "type": "text", + "content": "Existing datasets do not directly provide supervised scale data. We refer to the scale change and motion in depth conversion formula established by Gengshan Yang [32] to train the 3D optical flow indirectly." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 140, + 545, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 140, + 545, + 186 + ], + "spans": [ + { + "bbox": [ + 304, + 140, + 545, + 186 + ], + "type": "text", + "content": "Yang pointed out that under the assumption of small rotation, the scale change and motion-in-depth are approximately equal, which allows us to use the scene flow dataset to obtain the scale loss, which is defined as follows:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 357, + 196, + 545, + 228 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 357, + 196, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 357, + 196, + 545, + 228 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {s} = \\sum_ {k = 1} ^ {N} \\gamma^ {N - k} \\left\\| \\boldsymbol {s} ^ {k} - z _ {g t} ^ {\\prime} / z _ {g t} \\right\\| _ {1} \\tag {9}", + "image_path": "67fd91ecffccaf9ece5cf6a5f79c6c46c4006f9f62a465318e7526d4d917a757.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 238, + 545, + 273 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 238, + 545, + 273 + ], + "spans": [ + { + "bbox": [ + 304, + 238, + 545, + 273 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 238, + 545, + 273 + ], + "type": "inline_equation", + "content": "z_{gt}^{\\prime}" + }, + { + "bbox": [ + 304, + 238, + 545, + 273 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 238, + 545, + 273 + ], + "type": "inline_equation", + "content": "z_{gt}" + }, + { + "bbox": [ + 304, + 238, + 545, + 273 + ], + "type": "text", + "content": " are the ground truth depths of the pixels in the second frame and the first frame, " + }, + { + "bbox": [ + 304, + 238, + 545, + 273 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 238, + 545, + 273 + ], + "type": "text", + "content": " is the scale flow field." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 274, + 507, + 286 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 274, + 507, + 286 + ], + "spans": [ + { + "bbox": [ + 317, + 274, + 507, + 286 + ], + "type": "text", + "content": "The overall loss function of 3D optical flow is:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 396, + 297, + 545, + 309 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 396, + 297, + 545, + 309 + ], + "spans": [ + { + "bbox": [ + 396, + 297, + 545, + 309 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathcal {L} _ {f} + \\mathcal {L} _ {s} \\tag {10}", + "image_path": "be176e0d529541afc8bc39d3c603c21d05c26a31fa8806c1740733cc3ff1e949.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 331, + 386, + 344 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 331, + 386, + 344 + ], + "spans": [ + { + "bbox": [ + 306, + 331, + 386, + 344 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 351, + 545, + 482 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 351, + 545, + 482 + ], + "spans": [ + { + "bbox": [ + 304, + 351, + 545, + 482 + ], + "type": "text", + "content": "Experimental results are discussed in this section to demonstrate the effectiveness of TPCV. Experiments show that TPCV can significantly improve the estimation accuracy of the optical flow for foreground objects under the same number of optimizer parameters. Furthermore, the ability of motion-in-depth estimation far exceeds the prior SOTA method. Specifically, we first compared the optical flow performance between the TPCV and baseline models. Then we applied the 3D optical flow results to the 3D scene perception tasks, including motion-in-depth, time-to-collision and scene flow estimation." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 483, + 545, + 614 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 483, + 545, + 614 + ], + "spans": [ + { + "bbox": [ + 304, + 483, + 545, + 614 + ], + "type": "text", + "content": "Setup We first pre-train our model on the Driving (D) for 80k iterations (batch = 6, lr = 0.002). For the optical flow estimation task, we finetune it on KITTI (K-200) with 60k iterations (batch = 6, lr = 0.00125). For motion-in-depth and time-to-collision task, we follow the split of optical expansion [32]. Specifically, we select one for every 5 images in K-200 for validation (K-40) and add the rest 160 images for training (K-160) with 60k iterations (batch = 6, lr = 0.00125). For the scene flow task, we use K-200 for training with 60k iterations (batch = 6, lr = 0.00125) and submit the results to the KITTI scene flow benchmark." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 306, + 623, + 389, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 623, + 389, + 635 + ], + "spans": [ + { + "bbox": [ + 306, + 623, + 389, + 635 + ], + "type": "text", + "content": "4.1. Optical Flow" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "type": "text", + "content": "In this section, we compare the effect of applying TPCV on the baseline optical flow model. We chose RAFT [27] as the baseline model, not because it offers better performance, but because it is the basic framework for most advanced optical flow models [5, 16, 25, 26] and has strong representativeness." + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5449" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 70, + 547, + 168 + ], + "blocks": [ + { + "bbox": [ + 49, + 70, + 547, + 168 + ], + "lines": [ + { + "bbox": [ + 49, + 70, + 547, + 168 + ], + "spans": [ + { + "bbox": [ + 49, + 70, + 547, + 168 + ], + "type": "image", + "image_path": "d21650245039141109a685f51f69d59d396bc5bea3c94e66bf3617e4f4bd7a2f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 175, + 547, + 210 + ], + "lines": [ + { + "bbox": [ + 46, + 175, + 547, + 210 + ], + "spans": [ + { + "bbox": [ + 46, + 175, + 547, + 210 + ], + "type": "text", + "content": "Figure 5. Results for KITTI test image. The upper line is the optical flow error. The redder the colour represents the greater the error, where Noc Fl-fg is the outlier rate of the non-occluded part, and the lower line is the colour-coded optical flow result. Observing the car window in the red box, there is a large-scale change between the two frames, and only our TPCV can get the correct result." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 229, + 288, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 229, + 288, + 266 + ], + "spans": [ + { + "bbox": [ + 46, + 229, + 288, + 266 + ], + "type": "text", + "content": "The main indicator we compare is the outlier rate of foreground and background optical flow, where outliers are the points whose endpoint error is greater than 3px or " + }, + { + "bbox": [ + 46, + 229, + 288, + 266 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 46, + 229, + 288, + 266 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 48, + 274, + 289, + 364 + ], + "blocks": [ + { + "bbox": [ + 48, + 274, + 289, + 364 + ], + "lines": [ + { + "bbox": [ + 48, + 274, + 289, + 364 + ], + "spans": [ + { + "bbox": [ + 48, + 274, + 289, + 364 + ], + "type": "table", + "html": "
MethodFl-bgFl-fgFl-allImprovement
RAFT [27]4.746.875.1-
GMA [5]4.787.035.15+attention
CRAFT [25]4.585.854.79+attention
RAFT+AOIR [26]4.686.995.07regularisation
RAFT+TPCV(ours)4.535.524.69scale correlation
", + "image_path": "090426c216356720ed7867bf346aa6e8661cd3f5f597d619e711d36c48d0ac21.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 437, + 287, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 437, + 287, + 521 + ], + "spans": [ + { + "bbox": [ + 46, + 437, + 287, + 521 + ], + "type": "text", + "content": "Validation performance We first compare the performance of TPCV and baseline RAFT. As shown in Tab. 1, TPCV improves the accuracy of the baseline model comprehensively, especially the foreground error is reduced from 6.87 to 5.52, which proves that TPCV can help the baseline model adapt to the foreground object scale changes effectively." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 521, + 288, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 521, + 288, + 666 + ], + "spans": [ + { + "bbox": [ + 46, + 521, + 288, + 666 + ], + "type": "text", + "content": "We also compare some state-of-the-art RAFT-based improvement methods' performance, where CRAFT and GMA both introduce attention mechanisms into the RAFT framework to solve the occlusion problem. While RAFT+AOIR studies new regularization schemes, these methods all change the structure of RAFT's optimizer and feature extractor, increasing the computational cost and training cost. However, our TPCV still achieves the best results, although the optimizer structure is not changed. Fig. 5 shows optical flow results under large-scale changes. All methods except TPCV cannot handle large-scale changes, proving the superiority of TPCV's scale matching strategy." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 671, + 180, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 671, + 180, + 685 + ], + "spans": [ + { + "bbox": [ + 47, + 671, + 180, + 685 + ], + "type": "text", + "content": "4.2. Motion-in-depth (MID)" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 689, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 288, + 715 + ], + "type": "text", + "content": "In this section, we discuss the performance of 3D optical flow in the MID estimation task. Based on the relationship" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "spans": [ + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "text", + "content": "established in the previous work [32], we can equate the scale change " + }, + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "text", + "content": " to MID " + }, + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "text", + "content": ". For example, an object is doubled in size between two frames (" + }, + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "inline_equation", + "content": "s = 0.5" + }, + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "text", + "content": ", the second frame is scaled by 0.5 times and matches the first frame), which means that its distance from the camera is shortened by 0.5 times (" + }, + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "inline_equation", + "content": "\\tau = z' / z = s = 0.5" + }, + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "inline_equation", + "content": "z'" + }, + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 304, + 229, + 547, + 312 + ], + "type": "text", + "content": " are the depth of frame2 and frame1)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 314, + 547, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 314, + 547, + 361 + ], + "spans": [ + { + "bbox": [ + 304, + 314, + 547, + 361 + ], + "type": "text", + "content": "MID is a key indicator in tasks such as time-to-collision, scene flow, and depth estimation. To demonstrate the superiority of TPCV, we compare it with the current state-of-the-art MID methods. The error of MID is defined as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 341, + 372, + 545, + 387 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 341, + 372, + 545, + 387 + ], + "spans": [ + { + "bbox": [ + 341, + 372, + 545, + 387 + ], + "type": "interline_equation", + "content": "M I D _ {e r r o r} = \\left\\| l o g (\\tau) - l o g \\left(\\tau_ {g t}\\right) \\right\\| _ {1} \\cdot 1 0 ^ {4} \\tag {11}", + "image_path": "52e3728f91f03b2c3b1e5fd7e31aa25d01245613df94150765050c6bcfaf15fe.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 396, + 545, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 396, + 545, + 420 + ], + "spans": [ + { + "bbox": [ + 304, + 396, + 545, + 420 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 396, + 545, + 420 + ], + "type": "inline_equation", + "content": "\\tau_{gt}" + }, + { + "bbox": [ + 304, + 396, + 545, + 420 + ], + "type": "text", + "content": " is obtained by dividing the matching depth of the second frame and the first frame." + } + ] + } + ], + "index": 12 + }, + { + "type": "table", + "bbox": [ + 307, + 430, + 547, + 515 + ], + "blocks": [ + { + "bbox": [ + 46, + 372, + 287, + 426 + ], + "lines": [ + { + "bbox": [ + 46, + 372, + 287, + 426 + ], + "spans": [ + { + "bbox": [ + 46, + 372, + 287, + 426 + ], + "type": "text", + "content": "Table 1. Quantitative optical flow performance comparison on KITTI test. Fl-bg and Fl-fg are the outlier rates of the foreground and background optical flow. The best among the all are bolded. Our method greatly improves the performance of foreground optical flow." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 307, + 430, + 547, + 515 + ], + "lines": [ + { + "bbox": [ + 307, + 430, + 547, + 515 + ], + "spans": [ + { + "bbox": [ + 307, + 430, + 547, + 515 + ], + "type": "table", + "html": "
MethodInputMIDerrorTime/s
OSF [20]Stereo1153000
PRSM [30]Stereo124300
Binary TTC [1]Mono73.552.2
Optical expansion [32]Mono750.2
RAFT+TPCV(ours)Mono42.840.2
", + "image_path": "753f475f9729bef78d9c03e28c21bbeeda021f4bba7eb6c9b806d62325415348.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 522, + 547, + 546 + ], + "lines": [ + { + "bbox": [ + 304, + 522, + 547, + 546 + ], + "spans": [ + { + "bbox": [ + 304, + 522, + 547, + 546 + ], + "type": "text", + "content": "Table 2. MID estimation on K-40 validation set. Our method outperforms other methods by a large margin." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 563, + 545, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 563, + 545, + 658 + ], + "spans": [ + { + "bbox": [ + 304, + 563, + 545, + 658 + ], + "type": "text", + "content": "We first compare with traditional optimization methods that use stereo information: PSMR and OSF decompose pixels into rigid blocks, and iteratively update the optimized optical flow and depth fields based on rigid assumptions and hand-designed priors. To calculate the MID we divide the depth of the second frame by the depth of the first frame. Experimental results show that our errors are much smaller than them (115 vs. 42.84) and in less time" + }, + { + "bbox": [ + 304, + 563, + 545, + 658 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 304, + 563, + 545, + 658 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 658, + 545, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 658, + 545, + 684 + ], + "spans": [ + { + "bbox": [ + 304, + 658, + 545, + 684 + ], + "type": "text", + "content": "We also compare with the state-of-the-art MID methods Optical expansion and Binary TTC. TPCV outperforming" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 306, + 693, + 545, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 693, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 693, + 545, + 713 + ], + "type": "text", + "content": "2Timing results are computed on 375x1242 images with an RTX 3090 GPU using 200 updates." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5450" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 71, + 174, + 149 + ], + "blocks": [ + { + "bbox": [ + 49, + 71, + 174, + 149 + ], + "lines": [ + { + "bbox": [ + 49, + 71, + 174, + 149 + ], + "spans": [ + { + "bbox": [ + 49, + 71, + 174, + 149 + ], + "type": "image", + "image_path": "b39b61bf4d14b5f3c90ab74f416061cfd269b29a1fe72d4ae1317604048fa73f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 167, + 547, + 200 + ], + "lines": [ + { + "bbox": [ + 46, + 167, + 547, + 200 + ], + "spans": [ + { + "bbox": [ + 46, + 167, + 547, + 200 + ], + "type": "text", + "content": "Figure 6. Motion-in-depth results for image \"195\" in the K-40. Up: Ground truth and motion-in-depth results, where the darker indicates the objects moving towards the camera. Down: The input image and error map, where the whiter the pixels, the greater the error. In both foreground and background regions, our method is much more accurate than other methods." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 173, + 72, + 298, + 148 + ], + "blocks": [ + { + "bbox": [ + 173, + 72, + 298, + 148 + ], + "lines": [ + { + "bbox": [ + 173, + 72, + 298, + 148 + ], + "spans": [ + { + "bbox": [ + 173, + 72, + 298, + 148 + ], + "type": "image", + "image_path": "c3afa2185073475837446f273c7bfe58d3a671bebda13aa3679553ec854fd1b1.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 227, + 149, + 244, + 158 + ], + "lines": [ + { + "bbox": [ + 227, + 149, + 244, + 158 + ], + "spans": [ + { + "bbox": [ + 227, + 149, + 244, + 158 + ], + "type": "text", + "content": "OSF" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 298, + 72, + 420, + 148 + ], + "blocks": [ + { + "bbox": [ + 298, + 72, + 420, + 148 + ], + "lines": [ + { + "bbox": [ + 298, + 72, + 420, + 148 + ], + "spans": [ + { + "bbox": [ + 298, + 72, + 420, + 148 + ], + "type": "image", + "image_path": "a7430de52aeb57fda01d4c8daec9344a1f2b20a05c95a26e56a4e4dc3c9a7c86.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 331, + 149, + 389, + 159 + ], + "lines": [ + { + "bbox": [ + 331, + 149, + 389, + 159 + ], + "spans": [ + { + "bbox": [ + 331, + 149, + 389, + 159 + ], + "type": "text", + "content": "Optical expansion" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 421, + 72, + 544, + 148 + ], + "blocks": [ + { + "bbox": [ + 421, + 72, + 544, + 148 + ], + "lines": [ + { + "bbox": [ + 421, + 72, + 544, + 148 + ], + "spans": [ + { + "bbox": [ + 421, + 72, + 544, + 148 + ], + "type": "image", + "image_path": "b0e304beadeadcca702e8c7323218df347ffe6034d8545fee66b2bcce0d47235.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 463, + 149, + 504, + 158 + ], + "lines": [ + { + "bbox": [ + 463, + 149, + 504, + 158 + ], + "spans": [ + { + "bbox": [ + 463, + 149, + 504, + 158 + ], + "type": "text", + "content": "TPCV (ours)" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 221, + 287, + 293 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 221, + 287, + 293 + ], + "spans": [ + { + "bbox": [ + 46, + 221, + 287, + 293 + ], + "type": "text", + "content": "them by a large margin (73.55 vs 42.84), and the average error is reduced by " + }, + { + "bbox": [ + 46, + 221, + 287, + 293 + ], + "type": "inline_equation", + "content": "42\\%" + }, + { + "bbox": [ + 46, + 221, + 287, + 293 + ], + "type": "text", + "content": ". Different from their directly regress MID, TPCV calculates MID based on matching, making the result robust and accurate. The above experiments prove that our TPCV module is progressiveness in depth motion estimation." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 300, + 181, + 312 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 300, + 181, + 312 + ], + "spans": [ + { + "bbox": [ + 47, + 300, + 181, + 312 + ], + "type": "text", + "content": "4.3. Time-to-collision (TTC)" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 319, + 287, + 366 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 319, + 287, + 366 + ], + "spans": [ + { + "bbox": [ + 46, + 319, + 287, + 366 + ], + "type": "text", + "content": "Time-to-collision estimation is essential for obstacle avoidance and path planning tasks [3, 7, 14, 21]. In fact, the time for a pixel to reach the camera plane (TTC) can be directly obtained from MID:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 373, + 287, + 402 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 373, + 287, + 402 + ], + "spans": [ + { + "bbox": [ + 105, + 373, + 287, + 402 + ], + "type": "interline_equation", + "content": "T T C = \\frac {Z}{Z - Z ^ {\\prime}} T = \\frac {T}{1 - \\frac {Z ^ {\\prime}}{Z}} \\tag {12}", + "image_path": "e97033fca199872e811c0e7460b048ec7c7c1a30b3e799c0a6708b1c0aa4e8fe.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 407, + 287, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 407, + 287, + 433 + ], + "spans": [ + { + "bbox": [ + 47, + 407, + 287, + 433 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 407, + 287, + 433 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 47, + 407, + 287, + 433 + ], + "type": "text", + "content": " is the sampling interval of the camera (in KITTI " + }, + { + "bbox": [ + 47, + 407, + 287, + 433 + ], + "type": "inline_equation", + "content": "T = 0.1s" + }, + { + "bbox": [ + 47, + 407, + 287, + 433 + ], + "type": "text", + "content": ") and " + }, + { + "bbox": [ + 47, + 407, + 287, + 433 + ], + "type": "inline_equation", + "content": "Z' / Z" + }, + { + "bbox": [ + 47, + 407, + 287, + 433 + ], + "type": "text", + "content": " is the motion-in-depth " + }, + { + "bbox": [ + 47, + 407, + 287, + 433 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 47, + 407, + 287, + 433 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "type": "table", + "bbox": [ + 52, + 441, + 282, + 514 + ], + "blocks": [ + { + "bbox": [ + 52, + 441, + 282, + 514 + ], + "lines": [ + { + "bbox": [ + 52, + 441, + 282, + 514 + ], + "spans": [ + { + "bbox": [ + 52, + 441, + 282, + 514 + ], + "type": "table", + "html": "
MethodErr-1sErr-2sErr-5sTime/s
Binary TTC [1]2.43.163.250.0192
OSF [20]1.792.934.033000
Optical expansion [32]1.861.872.450.2
RAFT+TPCV(ours)1.141.651.570.2
", + "image_path": "f71060a90b04aebad8c00abea5b9df4d72fa37a55c0de5ee97aadfb2ed94e3cf.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 47, + 523, + 287, + 544 + ], + "lines": [ + { + "bbox": [ + 47, + 523, + 287, + 544 + ], + "spans": [ + { + "bbox": [ + 47, + 523, + 287, + 544 + ], + "type": "text", + "content": "Table 3. Percentage errors of time-to-collision estimation on K-40 validation set" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 557, + 287, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 557, + 287, + 605 + ], + "spans": [ + { + "bbox": [ + 46, + 557, + 287, + 605 + ], + "type": "text", + "content": "We treat TTC as a binary classification problem, and for each pixel, we judge whether the TTC time is less than " + }, + { + "bbox": [ + 46, + 557, + 287, + 605 + ], + "type": "inline_equation", + "content": "\\{1\\mathrm{s}, 2\\mathrm{s}, 5\\mathrm{s}\\}" + }, + { + "bbox": [ + 46, + 557, + 287, + 605 + ], + "type": "text", + "content": ". Only the points moving towards the camera are evaluated (the TTC of points farther away is meaningless)." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 46, + 606, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 606, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 606, + 287, + 712 + ], + "type": "text", + "content": "In this experiment, we use the binary mode of Binary TTC. In this mode, a binary mask will be directly output. Although the accuracy is decreased, the reasoning speed is greatly improved. Optical expansion and OSF perform reasonably well, consistent with their high precision performance in the motion-in-depth task. Our monocular method achieves the lowest error in all time intervals, which proves that the 3D optical flow estimated by TPCV can well predict future collisions." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 220, + 381, + 232 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 220, + 381, + 232 + ], + "spans": [ + { + "bbox": [ + 306, + 220, + 381, + 232 + ], + "type": "text", + "content": "4.4. Scene Flow" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 241, + 545, + 372 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 241, + 545, + 372 + ], + "spans": [ + { + "bbox": [ + 304, + 241, + 545, + 372 + ], + "type": "text", + "content": "Correctly estimating the 3D motion of objects in space is crucial for autonomous driving and dynamic environment path planning for robots. The scene flow consists of the optical flow and the depth of matched pixels between two frames. In order to unify the standard with the most advanced methods, we use GA-Net [35] to obtain the depth of the first frame " + }, + { + "bbox": [ + 304, + 241, + 545, + 372 + ], + "type": "inline_equation", + "content": "d_{1}" + }, + { + "bbox": [ + 304, + 241, + 545, + 372 + ], + "type": "text", + "content": ", and obtain the depth of the second frame " + }, + { + "bbox": [ + 304, + 241, + 545, + 372 + ], + "type": "inline_equation", + "content": "d_{2}" + }, + { + "bbox": [ + 304, + 241, + 545, + 372 + ], + "type": "text", + "content": " based on the motion-in-depth " + }, + { + "bbox": [ + 304, + 241, + 545, + 372 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 304, + 241, + 545, + 372 + ], + "type": "text", + "content": " estimated by TPCV " + }, + { + "bbox": [ + 304, + 241, + 545, + 372 + ], + "type": "inline_equation", + "content": "(d_{2} = d_{1} * \\tau)" + }, + { + "bbox": [ + 304, + 241, + 545, + 372 + ], + "type": "text", + "content": ". The model was trained on the complete K-200 dataset and tested on the KITTI public scene flow evaluation website3." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 374, + 545, + 446 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 374, + 545, + 446 + ], + "spans": [ + { + "bbox": [ + 304, + 374, + 545, + 446 + ], + "type": "text", + "content": "We first compare TPCV with monocular methods, as shown in Tab. 4 TPCV leads in all monocular methods with significant advantages, consistent with the previous superior performance of TPCV in motion-in-depth and optical flow tasks. It proves the effectiveness of our matching strategy in the field of 3D motion estimation." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 449, + 545, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 449, + 545, + 568 + ], + "spans": [ + { + "bbox": [ + 304, + 449, + 545, + 568 + ], + "type": "text", + "content": "We also compare with the state-of-the-art depth-based methods CamLiFlow, RigidMask+ISF and RAFT3D. Noticing that they tend to get smaller Fl-bg errors, which is thanks to the aid of depth information to optimize the background optical flow. Nonetheless, our monocular approach achieves the best results on all foreground metrics and maintains a competitive inference speed. We note that TPCV outperforms even most depth-based methods on the D2-all metric, which demonstrates the high potential of our 3D optical flow for estimating 3D motion." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 570, + 545, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 545, + 677 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 545, + 677 + ], + "type": "text", + "content": "RAFT3D + TPCV We apply TPCV to RAFT3D. Specifically, we directly use TPCV to replace the correlation volume in RAFT3D, and change the original direct regression depth change to the estimated depth change ratio. The experimental results show that the addition of TPCV significantly improves the performance of SF-all " + }, + { + "bbox": [ + 304, + 570, + 545, + 677 + ], + "type": "inline_equation", + "content": "(5.77\\rightarrow 5.00)" + }, + { + "bbox": [ + 304, + 570, + 545, + 677 + ], + "type": "text", + "content": ". However, we found that Fl-fg did not improve. We think it is because RAFT3D introduced potential semantic information when estimating the foreground." + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 306, + 693, + 545, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 693, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 693, + 545, + 713 + ], + "type": "text", + "content": "3http://www.cvlibs.net/datasets/kitti/evalscene_flow.php" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "5451" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 48, + 70, + 548, + 184 + ], + "blocks": [ + { + "bbox": [ + 48, + 70, + 548, + 184 + ], + "lines": [ + { + "bbox": [ + 48, + 70, + 548, + 184 + ], + "spans": [ + { + "bbox": [ + 48, + 70, + 548, + 184 + ], + "type": "table", + "html": "
MethodInputD1-bgD1-fgD1-allD2-bgD2-fgD2-allFl-bgFl-fgFl-allSF-bgSF-fgSF-all
CamLiFlow [9]D+M1.483.461.811.928.142.952.317.043.12.8712.234.43
RigidMask+ISF [33]D+M1.533.651.892.098.923.232.637.853.53.2513.084.89
RAFT3D [28]D+M1.483.461.812.519.463.673.398.794.294.2713.275.77
RAFT3D+TPCV(ours)D+M1.483.461.811.938.793.072.4810.193.763.0314.825.0
Optical expansion [32]M1.483.461.813.398.544.255.838.666.37.0613.448.12
Binary TTC [1]M1.483.461.813.849.394.765.848.676.317.4513.748.5
RAFT+TPCV(ours)M1.483.461.812.297.633.184.535.524.695.3410.66.21
", + "image_path": "d0e316916920a2c240d9ee9747916e1e94b94ef93ea2afa48e60072508d37d8d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 46, + 192, + 547, + 258 + ], + "lines": [ + { + "bbox": [ + 46, + 192, + 547, + 258 + ], + "spans": [ + { + "bbox": [ + 46, + 192, + 547, + 258 + ], + "type": "text", + "content": "Table 4. State-of-art published methods on KITTI scene flow benchmark. D+M means depth and monocular information, and M means monocular information. D1, D2, Fl, and SF is the percentage of disparity, optical flow and scene flow outliers. -bg,-fg and -all represent the percentage of outliers averaged only over background regions, foreground regions and overall ground truth pixels. The best among all are bolded, and the second best are underlined. Our monocular method performs quite well, not only far ahead of similar monocular methods, but also outperforming stereo-based methods in foreground scene flow estimation, which proves that the TPCV module can accurately estimate the 3D motion." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 278, + 107, + 291 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 278, + 107, + 291 + ], + "spans": [ + { + "bbox": [ + 47, + 278, + 107, + 291 + ], + "type": "text", + "content": "5. Ablation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 299, + 287, + 358 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 299, + 287, + 358 + ], + "spans": [ + { + "bbox": [ + 46, + 299, + 287, + 358 + ], + "type": "text", + "content": "Setup We conduct more extensive experiments to verify the advanced nature of the TPCV module. For all experiments, we pre-train with 80K on the Driving dataset (batch=6, lr = 0.00025), refinement training on K-160 (batch=6, lr = 0.000125), and testing on the K-40 dataset." + } + ] + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 53, + 369, + 282, + 430 + ], + "blocks": [ + { + "bbox": [ + 53, + 369, + 282, + 430 + ], + "lines": [ + { + "bbox": [ + 53, + 369, + 282, + 430 + ], + "spans": [ + { + "bbox": [ + 53, + 369, + 282, + 430 + ], + "type": "table", + "html": "
MethodFlow_epeMID_errorMemory
RAFT*1.6156.22.7GB
RAFT+pyramid1.4248.95.2GB
RAFT+TPCV1.3142.83.0GB
", + "image_path": "c988a74bc3bc2d368b175deebd079b26641437581be08b064c5ea8df77c848e5.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 46, + 438, + 287, + 471 + ], + "lines": [ + { + "bbox": [ + 46, + 438, + 287, + 471 + ], + "spans": [ + { + "bbox": [ + 46, + 438, + 287, + 471 + ], + "type": "text", + "content": "Table 5. Ablation study on optical flow and motion-in-depth estimation. " + }, + { + "bbox": [ + 46, + 438, + 287, + 471 + ], + "type": "inline_equation", + "content": "Flow_{epe}" + }, + { + "bbox": [ + 46, + 438, + 287, + 471 + ], + "type": "text", + "content": " is the average endpoint pixel error of optical flow estimation, the lower the better." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 486, + 286, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 486, + 286, + 593 + ], + "spans": [ + { + "bbox": [ + 46, + 486, + 286, + 593 + ], + "type": "text", + "content": "Comparison to regression (RAFT*) We first replace the TPCV in \"RAFT+TPCV\" with 4D correlation volume, at this time, the estimation of scale flow degenerates into direct regression, similar to optical expansion [32]. As shown in Tab. 5, after replacement, the optical flow error increased from 1.31 to 1.61 (increases " + }, + { + "bbox": [ + 46, + 486, + 286, + 593 + ], + "type": "inline_equation", + "content": "22.9\\%" + }, + { + "bbox": [ + 46, + 486, + 286, + 593 + ], + "type": "text", + "content": "), and the motion-in-depth error increased from 42.8 to 56.2 (increases " + }, + { + "bbox": [ + 46, + 486, + 286, + 593 + ], + "type": "inline_equation", + "content": "31.3\\%" + }, + { + "bbox": [ + 46, + 486, + 286, + 593 + ], + "type": "text", + "content": "). It proves that the TPCV module plays a crucial role in advanced optical flow and motion-in-depth estimation." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "text", + "content": "Comparison to pyramid matching (RAFT+pyramid) We also tried a pyramid matching scheme, shown in Fig. 2 (b), which matched the first frame feature with the same scale feature in the image pyramid of the second frame. we directly obtain large-scale images through interpolation. As shown in Tab. 5, the optical flow and motion-in-depth errors are greatly reduced after introducing the matching strategy, which once again proves the superiority of the matching method. However, it is still not as good as the performance of TPCV (1.42 vs. 1.31; 48.9 vs. 42.8); we believe that it is" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 280, + 545, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 280, + 545, + 316 + ], + "spans": [ + { + "bbox": [ + 304, + 280, + 545, + 316 + ], + "type": "text", + "content": "mainly due to the construction of false large-scale features, which makes it difficult to perform correct feature matching for objects far away from the camera." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 328, + 456, + 342 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 328, + 456, + 342 + ], + "spans": [ + { + "bbox": [ + 305, + 328, + 456, + 342 + ], + "type": "text", + "content": "6. More experimental results" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 348, + 545, + 432 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 348, + 545, + 432 + ], + "spans": [ + { + "bbox": [ + 304, + 348, + 545, + 432 + ], + "type": "text", + "content": "We followed the experimental procedure of RAFT3D [28] to conduct more experiments on the sizeable synthetic dataset Flyingthings3D. The results show that after applying TPCV, the end-to-end 3D error of RAFT3D is reduced from " + }, + { + "bbox": [ + 304, + 348, + 545, + 432 + ], + "type": "inline_equation", + "content": "0.062\\mathrm{m}" + }, + { + "bbox": [ + 304, + 348, + 545, + 432 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 304, + 348, + 545, + 432 + ], + "type": "inline_equation", + "content": "0.048\\mathrm{m}" + }, + { + "bbox": [ + 304, + 348, + 545, + 432 + ], + "type": "text", + "content": ", and the end-to-end 2D error dropped from " + }, + { + "bbox": [ + 304, + 348, + 545, + 432 + ], + "type": "inline_equation", + "content": "2.2\\mathrm{px}" + }, + { + "bbox": [ + 304, + 348, + 545, + 432 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 304, + 348, + 545, + 432 + ], + "type": "inline_equation", + "content": "1.95\\mathrm{px}" + }, + { + "bbox": [ + 304, + 348, + 545, + 432 + ], + "type": "text", + "content": ". See supplementary materials for details of results." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 433, + 545, + 493 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 433, + 545, + 493 + ], + "spans": [ + { + "bbox": [ + 304, + 433, + 545, + 493 + ], + "type": "text", + "content": "Limitation We also conducted experiments on Sintel, but the baseline results did not improve significantly after applying TPCV; we believe this is because Sintel contains many non-rigid scenes, while the conversion between MID and OE depends on the rigid assumption." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 506, + 379, + 518 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 506, + 379, + 518 + ], + "spans": [ + { + "bbox": [ + 306, + 506, + 379, + 518 + ], + "type": "text", + "content": "7. Conclusion" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 526, + 545, + 646 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 526, + 545, + 646 + ], + "spans": [ + { + "bbox": [ + 304, + 526, + 545, + 646 + ], + "type": "text", + "content": "We combine optical flow and optical expansion, proposing the concept of 3D optical flow, which can eliminate the influence of scale change on optical flow matching and provide motion information in the depth direction. Then we design a plug-and-play 2D to 3D optical flow upgrade module: TPCV, associated with a set of supervised learning strategies. Experimentally, we apply TPCV to the RAFT, achieving significant performance improvements in 2D optical flow tasks and multiple 3D perception tasks, including motion-in-depth, scene flow, and time-to-collision." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 658, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 658, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 658, + 545, + 713 + ], + "type": "text", + "content": "Acknowledgements This research was supported by the National Natural Science Foundation of China (Grant nos. 61673220, 62106209), and the Project of SongShan Laboratory (Grant no. YYJC012022015)." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5452" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "text", + "content": "[1] Abhishek Badki, Orazio Gallo, Jan Kautz, and Pradeep Sen. Binary ttc: A temporal geofence for autonomous navigation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12946-12955, 2021. 1, 3, 6, 7, 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 147, + 288, + 223 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 147, + 288, + 223 + ], + "spans": [ + { + "bbox": [ + 53, + 147, + 288, + 223 + ], + "type": "text", + "content": "[2] Aseem Behl, Omid Hosseini Jafari, Siva Karthik Mustikovela, Hassan Abu Alhaija, Carsten Rother, and Andreas Geiger. Bounding boxes, segmentations and object coordinates: How important is recognition for 3d scene flow estimation in autonomous driving scenarios? In Proceedings of the IEEE International Conference on Computer Vision, pages 2574-2583, 2017. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 224, + 288, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 224, + 288, + 267 + ], + "spans": [ + { + "bbox": [ + 53, + 224, + 288, + 267 + ], + "type": "text", + "content": "[3] Jeffrey Byrne and Camillo J Taylor. Expansion segmentation for visual collision detection and estimation. In 2009 IEEE International Conference on Robotics and Automation, pages 875-882. IEEE, 2009. 1, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 269, + 287, + 291 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 269, + 287, + 291 + ], + "spans": [ + { + "bbox": [ + 53, + 269, + 287, + 291 + ], + "type": "text", + "content": "[4] TA Camus. Calculating time-to-contact using real-time quantized optical flow. 1995. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 292, + 287, + 345 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 292, + 287, + 345 + ], + "spans": [ + { + "bbox": [ + 53, + 292, + 287, + 345 + ], + "type": "text", + "content": "[5] Shihao Jiang, Dylan Campbell, Yao Lu, Hongdong Li, and Richard Hartley. Learning to estimate hidden motions with global motion aggregation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9772-9781, 2021. 2, 5, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 346, + 287, + 391 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 346, + 287, + 391 + ], + "spans": [ + { + "bbox": [ + 53, + 346, + 287, + 391 + ], + "type": "text", + "content": "[6] Rico Jonschkowski, Austin Stone, Jonathan T Barron, Ariel Gordon, Kurt Konolige, and Anelia Angelova. What matters in unsupervised optical flow. In European Conference on Computer Vision, pages 557-572. Springer, 2020. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 392, + 287, + 423 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 392, + 287, + 423 + ], + "spans": [ + { + "bbox": [ + 53, + 392, + 287, + 423 + ], + "type": "text", + "content": "[7] David N Lee. A theory of visual control of braking based on information about time-to-collision. Perception, 5(4):437-459, 1976. 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 425, + 287, + 468 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 425, + 287, + 468 + ], + "spans": [ + { + "bbox": [ + 53, + 425, + 287, + 468 + ], + "type": "text", + "content": "[8] Congcong Li, Haoyu Ma, and Qingmin Liao. Two-stage adaptive object scene flow using hybrid cnn-crf model. In 2020 25th International Conference on Pattern Recognition (ICPR), pages 3876-3883. IEEE, 2021. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 470, + 288, + 524 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 470, + 288, + 524 + ], + "spans": [ + { + "bbox": [ + 53, + 470, + 288, + 524 + ], + "type": "text", + "content": "[9] Haisong Liu, Tao Lu, Yihui Xu, Jia Liu, Wenjie Li, and Lijun Chen. Camliflow: Bidirectional camera-lidar fusion for joint optical flow and scene flow estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5791-5801, 2022. 3, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 525, + 287, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 525, + 287, + 557 + ], + "spans": [ + { + "bbox": [ + 48, + 525, + 287, + 557 + ], + "type": "text", + "content": "[10] David G Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91-110, 2004. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 558, + 288, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 558, + 288, + 613 + ], + "spans": [ + { + "bbox": [ + 48, + 558, + 288, + 613 + ], + "type": "text", + "content": "[11] Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, and Jian Sun. Upflow: Upsampling pyramid for unsupervised optical flow learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1045-1054, 2021. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 614, + 288, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 614, + 288, + 669 + ], + "spans": [ + { + "bbox": [ + 48, + 614, + 288, + 669 + ], + "type": "text", + "content": "[12] Kunming Luo, Chuan Wang, Shuaicheng Liu, Haoqiang Fan, Jue Wang, and Jian Sun. Upflow: Upsampling pyramid for unsupervised optical flow learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 1045-1054, June 2021. 4" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "type": "text", + "content": "[13] Wei-Chiu Ma, Shenlong Wang, Rui Hu, Yuwen Xiong, and Raquel Urtasun. Deep rigid instance scene flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3614-3622, 2019. 3" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 307, + 73, + 547, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 547, + 128 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 547, + 128 + ], + "type": "text", + "content": "[14] Thiago Marinho, Massinissa Amrouche, Venanzio Cichella, Dušan Stipanović, and Naira Hovakimyan. Guaranteed collision avoidance based on line-of-sight angle and time-to-collision. In 2018 Annual American Control Conference (ACC), pages 4305-4310. IEEE, 2018. 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 129, + 547, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 547, + 194 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 547, + 194 + ], + "type": "text", + "content": "[15] Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4040-4048, 2016. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 195, + 547, + 249 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 195, + 547, + 249 + ], + "spans": [ + { + "bbox": [ + 307, + 195, + 547, + 249 + ], + "type": "text", + "content": "[16] Lukas Mehl, Cedric Beschle, Andrea Barth, and Andrés Bruhn. An anisotropic selection scheme for variational optical flow methods with order-adaptive regularisation. In International Conference on Scale Space and Variational Methods in Computer Vision, pages 140-152. Springer, 2021. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 250, + 547, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 250, + 547, + 293 + ], + "spans": [ + { + "bbox": [ + 307, + 250, + 547, + 293 + ], + "type": "text", + "content": "[17] Simon Meister, Junhwa Hur, and Stefan Roth. Unflow: Unsupervised learning of optical flow with a bidirectional census loss. In Proceedings of the AAAI conference on artificial intelligence, volume 32, 2018. 4" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 294, + 547, + 337 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 294, + 547, + 337 + ], + "spans": [ + { + "bbox": [ + 307, + 294, + 547, + 337 + ], + "type": "text", + "content": "[18] Moritz Menze and Andreas Geiger. Object scene flow for autonomous vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3061-3070, 2015. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 338, + 547, + 371 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 338, + 547, + 371 + ], + "spans": [ + { + "bbox": [ + 307, + 338, + 547, + 371 + ], + "type": "text", + "content": "[19] Moritz Menze, Christian Heipke, and Andreas Geiger. Joint 3d estimation of vehicles and scene flow. In ISPRS Workshop on Image Sequence Analysis (ISA), 2015. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 372, + 547, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 372, + 547, + 404 + ], + "spans": [ + { + "bbox": [ + 307, + 372, + 547, + 404 + ], + "type": "text", + "content": "[20] Moritz Menze, Christian Heipke, and Andreas Geiger. Object scene flow. ISPRS Journal of Photogrammetry and Remote Sensing, 140:60-76, 2018. 6, 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 405, + 547, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 405, + 547, + 459 + ], + "spans": [ + { + "bbox": [ + 307, + 405, + 547, + 459 + ], + "type": "text", + "content": "[21] Tomoyuki Mori and Sebastian Scherer. First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles. In 2013 IEEE International Conference on Robotics and Automation, pages 1750-1757. IEEE, 2013. 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 460, + 547, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 460, + 547, + 514 + ], + "spans": [ + { + "bbox": [ + 307, + 460, + 547, + 514 + ], + "type": "text", + "content": "[22] Dennis Muller, Josef Pauli, Christian Nunn, Steffen Gormer, and Stefan Muller-Schneiders. Time to contact estimation using interest points. In 2009 12th International IEEE Conference on Intelligent Transportation Systems, pages 1-6. IEEE, 2009. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 514, + 547, + 559 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 514, + 547, + 559 + ], + "spans": [ + { + "bbox": [ + 307, + 514, + 547, + 559 + ], + "type": "text", + "content": "[23] Maria Sagrebin, Anastasia Noglik, and Josef Pauli. Robust time-to-contact calculation for real time applications. In Proceedings of 18th International Conference on Computer Graphics and Vision, pages 128-133, 2008. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 559, + 547, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 559, + 547, + 591 + ], + "spans": [ + { + "bbox": [ + 307, + 559, + 547, + 591 + ], + "type": "text", + "content": "[24] Javier Sánchez, Agustín Salgado, and Nelson Monzon. Computing inverse optical flow. Pattern Recognition Letters, 52:32-39, 2015. 4" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 592, + 547, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 592, + 547, + 647 + ], + "spans": [ + { + "bbox": [ + 307, + 592, + 547, + 647 + ], + "type": "text", + "content": "[25] Xiuchao Sui, Shaohua Li, Xue Geng, Yan Wu, Xinxing Xu, Yong Liu, Rick Goh, and Hongyuan Zhu. Craft: Cross-attentional flow transformer for robust optical flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17602-17611, 2022. 2, 5, 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 647, + 547, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 647, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 647, + 547, + 713 + ], + "type": "text", + "content": "[26] Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang, Ramin Zabih, William T Freeman, and Ce Liu. Autoflow: Learning a better training set for optical flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10093-10102, 2021. 5, 6" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5453" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 286, + 564 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 48, + 72, + 286, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 286, + 106 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 286, + 106 + ], + "type": "text", + "content": "[27] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In European conference on computer vision, pages 402-419. Springer, 2020. 2, 5, 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 107, + 286, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 107, + 286, + 150 + ], + "spans": [ + { + "bbox": [ + 49, + 107, + 286, + 150 + ], + "type": "text", + "content": "[28] Zachary Teed and Jia Deng. Raft-3d: Scene flow using rigid-motion embeddings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8375–8384, 2021. 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 152, + 286, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 152, + 286, + 195 + ], + "spans": [ + { + "bbox": [ + 49, + 152, + 286, + 195 + ], + "type": "text", + "content": "[29] Christoph Vogel, Konrad Schindler, and Stefan Roth. 3d scene flow estimation with a rigid motion prior. In 2011 International Conference on Computer Vision, pages 1291-1298. IEEE, 2011. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 197, + 286, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 197, + 286, + 239 + ], + "spans": [ + { + "bbox": [ + 49, + 197, + 286, + 239 + ], + "type": "text", + "content": "[30] Christoph Vogel, Konrad Schindler, and Stefan Roth. 3d scene flow estimation with a piecewise rigid scene model. International Journal of Computer Vision, 115(1):1-28, 2015. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 241, + 286, + 295 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 241, + 286, + 295 + ], + "spans": [ + { + "bbox": [ + 49, + 241, + 286, + 295 + ], + "type": "text", + "content": "[31] Haofei Xu, Jing Zhang, Jianfei Cai, Hamid Rezatofighi, and Dacheng Tao. Gmflow: Learning optical flow via global matching. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8121-8130, 2022. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 297, + 286, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 297, + 286, + 342 + ], + "spans": [ + { + "bbox": [ + 49, + 297, + 286, + 342 + ], + "type": "text", + "content": "[32] Gengshan Yang and Deva Ramanan. Upgrading optical flow to 3d scene flow through optical expansion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1334-1343, 2020. 1, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 343, + 286, + 386 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 343, + 286, + 386 + ], + "spans": [ + { + "bbox": [ + 49, + 343, + 286, + 386 + ], + "type": "text", + "content": "[33] Gengshan Yang and Deva Ramanan. Learning to segment rigid motions from two frames. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1266-1275, 2021. 3, 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 49, + 387, + 286, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 387, + 286, + 453 + ], + "spans": [ + { + "bbox": [ + 49, + 387, + 286, + 453 + ], + "type": "text", + "content": "[34] Hanchao Yu, Xiao Chen, Humphrey Shi, Terrence Chen, Thomas S Huang, and Shanhui Sun. Motion pyramid networks for accurate and efficient cardiac motion estimation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 436-446. Springer, 2020. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 49, + 454, + 286, + 508 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 454, + 286, + 508 + ], + "spans": [ + { + "bbox": [ + 49, + 454, + 286, + 508 + ], + "type": "text", + "content": "[35] Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr. Ga-net: Guided aggregation net for end-to-end stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 185–194, 2019. 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 49, + 510, + 286, + 564 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 510, + 286, + 564 + ], + "spans": [ + { + "bbox": [ + 49, + 510, + 286, + 564 + ], + "type": "text", + "content": "[36] Shiyu Zhao, Long Zhao, Zhixing Zhang, Enyu Zhou, and Dimitris Metaxas. Global matching with overlapping attention for optical flow estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17592-17601, June 2022. 2" + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5454" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation/f55ad462-d47a-4afc-95bc-6a55486b79b1_content_list.json b/2023/Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation/f55ad462-d47a-4afc-95bc-6a55486b79b1_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..ea9b7255cc1b9fb7eb08d6ecbadfce138a97c872 --- /dev/null +++ b/2023/Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation/f55ad462-d47a-4afc-95bc-6a55486b79b1_content_list.json @@ -0,0 +1,1339 @@ +[ + { + "type": "text", + "text": "Learning Orthogonal Prototypes for Generalized Few-shot Semantic Segmentation", + "text_level": 1, + "bbox": [ + 233, + 130, + 736, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Sun-Ao Liu, Yiheng Zhang, Zhaofan Qiu, Hongtao Xie*, Yongdong Zhang, Ting Yao University of Science and Technology of China HiDream.ai Inc.", + "bbox": [ + 176, + 203, + 846, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{lsa1997.mail, htxie, zhyd73}@ustc.edu.cn, {yihengzhang.chn, zhaofanqiu, tingyao.ustc}@gmail.com", + "bbox": [ + 86, + 241, + 931, + 258 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 291, + 313, + 306 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Generalized few-shot semantic segmentation (GFSS) distinguishes pixels of base and novel classes from the background simultaneously, conditioning on sufficient data of base classes and a few examples from novel class. A typical GFSS approach has two training phases: base class learning and novel class updating. Nevertheless, such a standalone updating process often compromises the well-learnt features and results in performance drop on base classes. In this paper, we propose a new idea of leveraging Projection onto Orthogonal Prototypes (POP), which updates features to identify novel classes without compromising base classes. POP builds a set of orthogonal prototypes, each of which represents a semantic class, and makes the prediction for each class separately based on the features projected onto its prototype. Technically, POP first learns prototypes on base data, and then extends the prototype set to novel classes. The orthogonal constraint of POP encourages the orthogonality between the learnt prototypes and thus mitigates the influence on base class features when generalizing to novel prototypes. Moreover, we capitalize on the residual of feature projection as the background representation to dynamically fit semantic shifting (i.e., background no longer includes the pixels of novel classes in updating phase). Extensive experiments on two benchmarks demonstrate that our POP achieves superior performances on novel classes without sacrificing much accuracy on base classes. Notably, POP outperforms the state-of-the-art fine-tuning by $3.93\\%$ overall mIoU on PASCAL- $5^i$ in 5-shot scenario.", + "bbox": [ + 73, + 323, + 473, + 746 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 761, + 209, + 777 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Semantic segmentation is to assign semantic labels to every pixel of an image. With the recent development of CNNs [10, 13] and vision transformers [6, 18, 23, 24, 35, 41, 42], the state-of-the-art networks have successfully pushed the limits of semantic segmentation [1, 3, 25, 49] with remarkable performance improvements. Such achievements", + "bbox": [ + 75, + 786, + 470, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6a2a4028f5521dfaa08792e7c7decfea170297f1a71d89f3cc42798f1a430b7c.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 501, + 290, + 885, + 378 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/38a3f4455904532d9ecdd54cba2cf23551e3af8d0714e525dd8c25d5ef54736b.jpg", + "image_caption": [ + "(b)", + "Figure 1. Comparisons between fine-tuning [26] and Projection onto Orthogonal Prototypes (POP). In novel class updating phase, fine-tuning (a) updates the network to predict base and novel classes, and inevitably compromises the well-learnt representations for base classes. Instead, POP (b) only updates prototypes for novel classes and executes predictions for each class separately on the features projected onto different orthogonal prototypes." + ], + "image_footnote": [], + "bbox": [ + 503, + 391, + 883, + 478 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "heavily rely on the requirements of large quantities of pixel-level annotations and it is also difficult to directly apply the models to the classes unseen in the training set. A straightforward way to alleviate this issue is to leverage Few-shot Semantic Segmentation (FSS) [30, 36, 44], which utilizes the limited support annotations from unseen/novel classes to adapt the models. Nevertheless, FSS performs on the assumption that the support images and the query image contain the same novel classes, and solely emphasizes the segmentation of one novel class in the query image at a time. A more practical scenario namely Generalized Few-shot Semantic Segmentation (GFSS) [33] is recently presented to simultaneously identify the pixels of base and novel classes in a query image.", + "bbox": [ + 496, + 597, + 893, + 809 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "A typical GFSS solution has proceeded along two training phases: base class learning and novel class updating. In the first phase, models are trained on abundant base classes' annotations to classify the pixels of base categories, and then updated with the limited labeled novel examples in the second phase to additionally recognize pixels of novel", + "bbox": [ + 496, + 810, + 895, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author.", + "bbox": [ + 94, + 886, + 223, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "11319", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "classes. For instance, Myers Dean et al. [26] sample some base data plus novel examples as the supervision to finetune the network, as depicted in Figure 1(a). Despite having good performances on novel classes, there still exists a clear performance degradation on base classes. We speculate that this may be the results of compromising the well-learnt features of base classes in fine-tuning. As such, a valid question then emerges as is it possible for GFSS to nicely generalize the model to novel classes without sacrificing much segmentation capability on base classes. In an effort to answer this question, we seek to represent an image via a group of uncorrelated feature components each of which characterizes a specific class. By doing so, it is readily applicable to learn and integrate new components for novel classes without affecting the ones learnt for base classes.", + "bbox": [ + 75, + 90, + 472, + 316 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To materialize our idea, we propose a new Projection onto Orthogonal Prototypes (POP) framework for GFSS. POP learns a series of orthogonal prototypes and each prototype corresponds to one specific semantic class. As shown in Figure 1(b), POP employs an encoder to extract feature maps of a given query image and then projects them onto prototypes. The projection on each prototype is regarded as the discriminative representation with respect to the corresponding class and exploited to predict the probability map of pixels belonging to the class. More specifically, POP deliberately devises the learning of prototypes from three standpoints. The first one is to freeze the base prototypes when learning novel ones from support images in the updating phase. In this way, feature projections on base prototypes maintain their discriminability of base classes. Second, POP encourages the prototypes of base and novel classes to be orthogonal through a prototype orthogonality loss. Such a constraint decorrelates features projected onto different prototypes and mitigates the inter-class confusion caused by extending to novel classes. Finally, in view that background no longer contains the pixels of novel classes in updating phase, known as \"semantic shifting\", POP measures the residual of feature projection as the background representation instead of learning a prototype for \"background\". This way further improves the differentiation between novel classes and background.", + "bbox": [ + 75, + 330, + 472, + 723 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The main contribution of this work is the proposal of a Projection on Orthogonal Prototypes (POP) framework for generalized few-shot semantic segmentation. The solution also leads to the elegant views of how to adapt the trained model to novel classes without sacrificing well-learnt features, and how to represent background pixels dynamically in the context of semantic shifting, which are problems not yet fully explored in the literature. We demonstrate that POP outperforms the state-of-the-art fine-tuning [26] on two benchmarks (PASCAL-5 $^i$ and COCO-20 $^i$ ) with evident improvements on both base and novel classes.", + "bbox": [ + 75, + 734, + 472, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 89, + 640, + 106 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Semantic Segmentation. Extensive FCN-based methods have been proposed and achieved remarkable performances. For example, pyramid pooling [12, 48, 50] and parallel dilated convolutions [1, 2, 46, 47] are utilized to aggregate multi-scale context. The attention mechanism is widely exploited to model long-range dependencies [8, 14, 17, 21, 43, 45, 51]. Recently, vision transformer has also been proven effective for semantic segmentation [3, 32, 39]. Though these efforts lead to high-quality segmentation, it is still difficult to directly apply the learnt models to unseen classes.", + "bbox": [ + 496, + 114, + 890, + 263 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Few-shot Semantic Segmentation. Few-shot semantic segmentation (FSS) [30] executes pixel-wise labeling on new classes given a limited number of support examples. It mainly focuses on the 1-way scenario that requires binary maps for query images to identify the pixels belonging to the class labeled in support images. PL [5] and PANet [36] remould prototype learning for FSS by calculating the cosine similarities between pixels and prototypes obtained from support images. ASR [20] learns several orthogonal prototypes on the base data to represent novel categories. In addition, assigning multiple prototypes to each class is also a promising solution to improve FSS models [16,22,38,40].", + "bbox": [ + 496, + 266, + 890, + 446 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Generalized Few-shot Semantic Segmentation. Despite showing great potential, it is not trivial for FSS approaches to simultaneously identify the pixels of both base and novel classes in a query image. Generalized Few-shot Semantic Segmentation (GFSS) is further presented by Tian et al. [33] to address this problem. GFSS aims to segment both base and novel classes from query images through one inference process without paired support images. To achieve this goal, CAPL [33] mines contextual cues from both support and query samples to enrich the classifier for novel class segmentation. Fine-tuning [26] is another practical and effective way to adapt models for novel classes.", + "bbox": [ + 496, + 448, + 890, + 627 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Orthogonality. Feature orthogonality is known to be helpful to improve discriminative ability. DSN [31] constrains the orthogonality on base data to facilitate few-shot classification on novel classes. C-FSCIL [11] and OPL [29] rely on re-training the network to keep the features of each class orthogonal. Nevertheless, these approaches are not applicable for GFSS since they either ignore the influence between base and novel classes or compromise the segmentation capability on base classes after re-training.", + "bbox": [ + 496, + 628, + 890, + 763 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our work focuses on adapting the semantic segmentation model to novel classes without compromising well-learnt features for better generalization. Different from existing GFSS approaches may sacrifice base class segmentation quality for novel class updating, our POP contributes by studying not only learning orthogonal prototypes to independently indicate pixels of base and novel classes, but also how the background could be nicely represented in the context of semantic shifting.", + "bbox": [ + 496, + 765, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "11320", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/7f1053cbb34f3f85f9d3c8c8896a86b6950b0c0df28f40ec439956e957df6ada.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 81, + 89, + 883, + 204 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/e664af1377760018d80a7a39a39b40fc49a6a6af4655204735fb745d62bf8b2c.jpg", + "image_caption": [ + "Figure 2. An overview of our Projection onto Orthogonal Prototypes (POP) framework for generalized few-shot semantic segmentation. The input image is first encoded into dense feature map via a CNN encoder, which is then projected onto a series of orthogonal prototypes to make prediction of each class independently through a shared classifier. In the base class learning phase, the entire network and prototypes for base classes are learnt on sufficient base data. In the second phase of novel class updating, we freeze all the modules learnt in the first phase and only optimize the newly minted prototypes for novel classes along with an additional classifier for novel classes and background class. As such, the proposed POP generalizes well to novel classes while maintaining the segmentation capability on base classes." + ], + "image_footnote": [], + "bbox": [ + 80, + 205, + 883, + 388 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Our Method", + "text_level": 1, + "bbox": [ + 76, + 484, + 207, + 500 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this paper, we devise Projection onto Orthogonal Prototypes (POP) framework for generalized few-shot semantic segmentation (GFSS), by decoupling the feature learning of base classes and novel classes. Figure 2 illustrates an overview of the whole architecture of POP. We begin this section by elaborating the problem formulation of GFSS with the two-phase training scheme. Then, a projection-based framework is introduced to learn class-wise orthogonal prototypes, and extract the residual representation as background descriptor. Finally, we present the overall objective combining orthogonal constraint along with the semantic segmentation loss.", + "bbox": [ + 75, + 513, + 470, + 694 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Problem Formulation", + "text_level": 1, + "bbox": [ + 76, + 709, + 279, + 724 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In generalized few-shot semantic segmentation, we are given abundant annotated images for $M$ base classes $C^b = \\{c_1,\\dots ,c_M\\}$ and only $K$ labeled images per class for $N$ novel classes $C^n = \\{c_{M + 1},\\ldots ,c_{M + N}\\}$ . The background category $c_{0}$ presents the pixels that do not belong to any target classes. The goal of this task is to simultaneously distinguish both base and novel classes from backgrounds, i.e., $M + N + 1$ classes in total. Inspired by recent success of two-phase training scheme in generalized few-shot semantic segmentation [26,33], we formulate our POP framework in two-phase manner: base class learning and novel class", + "bbox": [ + 76, + 734, + 468, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "updating. Accordingly, POP is first trained with sufficient data for $(M + 1)$ -class segmentation $(C^b \\cup \\{c_0\\})$ in the first phase, and then updates the segmentation head with the few-shot support data targeting for pixel-wise prediction of all $M + N + 1$ classes $(C^b \\cup C^n \\cup \\{c_0\\})$ in the second phase.", + "bbox": [ + 498, + 487, + 890, + 564 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Projection onto Orthogonal Prototypes", + "text_level": 1, + "bbox": [ + 500, + 574, + 834, + 590 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "GFSS necessitates the capability to segment both base and novel classes. In this case, the novel updating process should learn and integrate new components for novel classes without affecting the ones learnt for base classes. However, the existing approaches [26, 33] in this area still show obvious performance degradation on base classes in the second phase. They often suffer from a challenge of whether to drastically overhaul the network for the performance on novel classes or preserve the well-learnt representation for base classes. We attribute this issue to the highly-relevant representations to identify different classes, which interfere with each other when integrating novel classes.", + "bbox": [ + 496, + 598, + 890, + 779 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To fulfill the objective of decoupling the feature learning of base classes and novel classes, we propose to orthogonally decompose the feature map into several uncorrelated components, each of which characterizes a specific class. Formally, we denote the dense feature map from the encoder network as $\\pmb{f} \\in \\mathbb{R}^{(H \\times W) \\times C}$ , where $H$ , $W$ and $C$ are the height, the width and the number of channels of feature map, respectively. Assuming the matrix rank of extracted", + "bbox": [ + 496, + 779, + 892, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "11321", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "features is $r$ ( $r \\leq C$ ), the feature map $f$ can be represented as the weighted summation of $r$ orthogonal bases as:", + "bbox": [ + 76, + 90, + 468, + 121 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {f} = \\sum_ {i = 1} ^ {r} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 184, + 133, + 468, + 170 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\text {s . t .} \\boldsymbol {u} _ {i} \\cdot \\boldsymbol {u} _ {j} ^ {T} = 0 (i \\neq j),\n$$\n", + "text_format": "latex", + "bbox": [ + 187, + 172, + 357, + 193 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\{\\pmb{u}_i\\in \\mathbb{R}^{1\\times C}|_{i = 1\\dots r}\\}$ is a group of unit bases that are orthogonal to each other, and $f^{(i)}\\in \\mathbb{R}^{(H\\times W)\\times 1}$ is the weight map of the $i$ -th component. This process is well known as orthogonal decomposition, and widely explored for Independent Component Analysis (ICA) [4]. In our work, we treat the basis $\\pmb{u}_i$ as the prototype of the $i$ -th class and the corresponding component $f^{(i)}\\cdot \\pmb{u}_i$ as the representation to identify the pixels of the $i$ -th class. As such, calculating the representation of the $i$ -th class is equivalent to projecting $\\pmb{f}$ to the $i$ -th basis $\\pmb{u}_i$ :", + "bbox": [ + 75, + 204, + 470, + 357 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {f} _ {c _ {i}} = f ^ {(i)} \\cdot \\boldsymbol {u} _ {i} = (\\boldsymbol {f} \\cdot \\boldsymbol {u} _ {i} ^ {T}) \\cdot \\boldsymbol {u} _ {i}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 161, + 368, + 468, + 388 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $f_{c_i}$ is the representation of the $i$ -th class and is then taken as the input of a shared classifier. In view that the learnt prototypes are orthogonal to each other, the representations of different classes are decorrelated and easy to be distinguished. More importantly, when integrating a new target class, we only need to learn a new prototype that is orthogonal to the existing ones, thereby preserving the well-learnt representations of other classes.", + "bbox": [ + 75, + 398, + 468, + 518 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Nevertheless, the limit of prototype orthogonality (i.e., $\\pmb{u}_i\\cdot \\pmb{u}_j^T = 0$ ) is not differentiable and hard to be implemented by gradient propagation. Hence, we present a practical alternative to relax the hard limit to a soft orthogonal constraint. Specifically, a prototype orthogonality loss is devised by minimizing absolute value of the inner product between prototypes:", + "bbox": [ + 76, + 518, + 468, + 626 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nL _ {\\text {o r t h}} = \\frac {1}{r ^ {\\prime} \\left(r ^ {\\prime} - 1\\right)} \\sum_ {i \\neq j} \\left| \\boldsymbol {u} _ {i} \\cdot \\boldsymbol {u} _ {j} ^ {T} \\right|, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 156, + 635, + 468, + 672 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $r' < r$ is the number of target classes. The orthogonality loss $L_{orth}$ restricts the averaged correlation of different prototype pairs, and is minimized when the prototypes are orthogonal to each other.", + "bbox": [ + 75, + 685, + 468, + 746 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Residual as Background Representation", + "text_level": 1, + "bbox": [ + 76, + 756, + 419, + 772 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The background class $c_{0}$ is a special category in segmentation tasks, and indicates the pixels that do not belong to any target classes. One common practice is to treat target classes and background class equally, which reaches reasonable performances on various segmentation tasks. However, such a simple strategy is not acclimatized to GFSS task due to the effect of the \"semantic shifting\" between two phases. More precisely, in the first phase, the pixels of both", + "bbox": [ + 75, + 780, + 468, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "novel classes $C^n$ and background class $c_0$ are labeled as background. When integrating novel classes in the second phase, the semantic meaning of background changes since it no longer includes the pixels of novel classes. We regard this gap as semantic shifting of background, which results in the confusion between novel classes and background class.", + "bbox": [ + 496, + 90, + 890, + 181 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In order to bridge the gap between two phases, we propose to exploit the residual of feature projection as the background representation in POP, instead of constructing an independent prototype. The residual here is defined as the summation of remaining components after projecting $\\mathbf{f}$ to the target classes. For instance, in the base class learning phase, the first $M$ components in Eq.(1) are the projections for $M$ base classes and the remaining $r - M$ components are background components:", + "bbox": [ + 496, + 181, + 892, + 316 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {f} = \\underbrace {\\sum_ {i = 1} ^ {M} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {b a s e c l a s s e s}} + \\underbrace {\\sum_ {i = M + 1} ^ {r} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {b a c k g r o u n d}}. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 571, + 325, + 890, + 388 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Note that the background here actually includes the pixels of novel and background classes. In view of the difficulty on learning the remaining $r - M$ components, we reformulate the background representation as the residual of feature projection by subtracting the components of bases classes:", + "bbox": [ + 496, + 397, + 890, + 473 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {f} _ {c _ {0}} = \\sum_ {i = M + 1} ^ {r} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i} = \\boldsymbol {f} - \\sum_ {i = 1} ^ {M} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 549, + 481, + 890, + 523 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\pmb{f}_{c_0}$ is orthogonal to the feature $\\pmb{f}_{c_i}$ of each class $c_i$ due to the orthogonality constraint between prototypes. Moreover, this reformulation avoids the estimation of matrix rank $r$ , which is more practical.", + "bbox": [ + 496, + 532, + 890, + 592 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For the novel class updating phase, the background components in Eq. (4) are separated into two parts, i.e., $N$ components for $N$ novel classes and $r - M - N$ components as the updated background representation. The decomposition of feature $\\pmb{f}$ in this phase is represented as", + "bbox": [ + 496, + 593, + 890, + 667 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {f} = \\underbrace {\\sum_ {i = 1} ^ {M} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {b a s e c l a s s e s}} + \\underbrace {\\sum_ {i = M + 1} ^ {M + N} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {n o v e l c l a s s e s}} + \\underbrace {\\sum_ {i = M + N + 1} ^ {r} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {b a c k g r o u n d}}. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 501, + 676, + 890, + 752 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Similar to Eq.(5), the updated background representation is measured by the residual of feature projection to base and novel classes:", + "bbox": [ + 496, + 753, + 890, + 797 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {f} _ {c _ {0}} = \\boldsymbol {f} - \\sum_ {i = 1} ^ {M} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i} - \\sum_ {i = M + 1} ^ {M + N} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 550, + 804, + 890, + 845 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this way, the background representation is dynamically adjusted with respect to the change of target classes. It remains orthogonal to the representations of both base classes", + "bbox": [ + 496, + 854, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "11322", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "and novel classes, such that the confusion between novel classes and background class is alleviated.", + "bbox": [ + 76, + 90, + 468, + 121 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Optimization", + "text_level": 1, + "bbox": [ + 76, + 137, + 217, + 155 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Following the common practice [26, 33], the optimization of our POP framework contains two phases: base class learning and novel class updating.", + "bbox": [ + 76, + 162, + 468, + 209 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Base class learning. In the first phase, the entire network is trained on sufficient base data to optimize the encoded representations and segment base classes. The overall training objective function of POP combines the semantic segmentation loss of base classes and the orthogonality loss between prototypes: $L = L_{seg} + \\lambda L_{orth}$ , where $L_{seg}$ denotes the cross entropy loss for segmentation and $\\lambda$ is a scale factor of the orthogonality loss.", + "bbox": [ + 75, + 210, + 468, + 332 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Novel class updating. The second phase involves the $K$ -shot support data of novel classes and expands the set of target classes. We freeze all the modules learnt in the first phase and only optimize the newly constructed prototypes for novel classes along with an additional classifier for novel classes and background class. The loss function is the same as that in the first phase (i.e., $L = L_{seg} + \\lambda L_{orth}$ ), and the orthogonality loss $L_{orth}$ is utilized over the prototypes of both base classes and novel classes.", + "bbox": [ + 75, + 334, + 468, + 470 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Data enrichment. The second phase demands the training images of both base and novel classes. As a result of highly unbalanced data between base classes and novel classes, simply merging the two training sets leads to poor performance on novel classes. Inspired by the fine-tuning approaches [26, 37], we establish a balanced training subset by randomly sampling $K$ examples per base class. Such a subset in the second phase has been proven to be effective for partially retraining the knowledge of base classes while integrating novel classes [26, 37]. Besides, we further promote the subset by two data enrichment strategies:", + "bbox": [ + 75, + 473, + 468, + 638 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1) Relabeling. Recall that there is semantic shifting problem between two training phases. In the training images of base classes, the pixels of novel classes are labeled as background. Such annotation misleads the network optimization in the second phase. Therefore, after each epoch, we utilize the latest POP model to relabel the background pixels in the image of base classes, and replace the background annotation with the class with the highest predicted probability across the novel classes and background class.", + "2) Resampling. The subset only selects a few samples from the images of base classes. It underuses the large amount of training data, and makes the network sensitive to the choice of few-shot samples. Hence, we propose to resample the training images of base classes after each epoch. This strategy, on one hand, well balances the number of training images for different classes, and on the other, makes full use of the entire base training set." + ], + "bbox": [ + 75, + 641, + 470, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 90, + 633, + 108 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We empirically verify the merit of our POP by conducting a thorough evaluation of generalized few-shot semantic segmentation on PASCAL- $5^{i}$ [30] and COCO- $20^{i}$ [27].", + "bbox": [ + 498, + 114, + 890, + 161 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets and Experimental Settings", + "text_level": 1, + "bbox": [ + 498, + 167, + 808, + 184 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. The PASCAL-5 $^i$ [30] is built on the PASCAL VOC 2012 [7] dataset with the extended annotations of SDS [9], and contains 12,031 images with high-quality pixel-level annotations of 20 classes. All the images are split into two sets of 10,582 and 1,449 for training and validation, respectively. Following the standard protocol in [30,33], the 20 classes are evenly partitioned into 4 folds (5 classes per fold) for cross-validation. Furthermore, we evaluate the capability of our POP on a more challenging COCO-20 $^i$ [27] dataset. COCO-20 $^i$ is created upon COCO [19], and includes 122,218 images being annotated with 80 object classes. In between, 82,081 and 40,137 images are exploited for training and validation. Similarly, experiments on COCO-20 $^i$ are also conducted with cross-validation on 4 folds (20 classes per fold) following [27,33].", + "bbox": [ + 496, + 190, + 890, + 417 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation Settings. For both datasets, once we validate the model on one fold, the classes in this fold serve as \"novel classes\" and the classes in the other 3 folds plus background play the role of \"base classes\". In the base class learning phase, we select all images that have at least one pixel belonging to base classes from the original training set for model training. Note that the pixels of novel classes in the selected images are considered as background at this stage. In the novel class updating phase, we mimic the few-shot setting by randomly sampling $K$ images containing pixels of novel classes from the original training set. All the images in the validation set are utilized for the evaluation on both base and novel classes. In the experiments, the Intersection over Union (IoU) per class and mean IoU (mIoU) over the base, novel, and all classes are adopted as the performance metrics. We average the mIoU over all the folds for cross-validation and regard the mean values as the final performance. For stability, we repeat each experiment five times with different random seeds and report the mean values. The same setting is also exploited in [26, 33].", + "bbox": [ + 496, + 417, + 890, + 719 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Network Structure. In order to make the capacity of our model comparable to the baselines of generalized few-shot semantic segmentation, we follow [33] and exploit the PSPNet [50] originated from the ImageNet pre-trained ResNet-50 as the encoder. The spatial resolution of the feature map extracted through the encoder is $\\frac{1}{8}$ of the input image and the probability maps produced by POP are upsampled via bilinear interpolation for pixel-level predictions. The number of prototypes depends on the datasets. For PASCAL $^5$ [30], POP has 15 prototypes in the base class learning phase, and then 5 prototypes are additionally included for novel class updating. Similarly, POP maintains 60 and 80", + "bbox": [ + 496, + 719, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "11323", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/c179cba15e9c496cfbc0b3347aca992a06f57788c1854ef6580e09b8c278bc26.jpg", + "table_caption": [ + "Table 1. Performance comparisons on PASCAL-5 $^i$ under the generalized few-shot segmentation settings. We report mIoU (\\%) over base classes (Base), novel classes (Novel), and all classes (Base + Novel = Total). All models are based on ResNet-50. $\\dagger$ indicates that the images containing novel classes are excluded by base class learning phase." + ], + "table_footnote": [], + "table_body": "
Methods1-shot5-shot10-shot
BaseNovelTotalBaseNovelTotalBaseNovelTotal
CANet [44]8.732.427.239.051.527.26---
PFENet [34]8.322.676.978.831.897.18---
PANet [36]31.8811.2526.9732.9515.2528.74---
BAM [15]73.6232.9663.9473.5734.3564.23---
\\( CAPL^† \\)[33]65.4818.8554.3866.1422.4155.7269.0927.1759.11
FT [26]66.8418.8255.4172.0346.4065.9373.0252.5568.14
FT-Triplet [26]66.4119.7155.3171.3150.4666.3572.8757.0069.10
\\( POP^† \\)67.7319.8556.4571.3443.4864.7172.2350.4967.05
POP73.9235.5164.7774.7855.8770.2874.9958.7771.13
", + "bbox": [ + 187, + 133, + 779, + 316 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/5aebcdd81fab74b2a627504db13b35df2aa24f36f3b5c4c8d4a24254e732fda0.jpg", + "table_caption": [ + "Table 2. Performance comparisons on COCO- ${20}^{i}$ under the generalized few-shot segmentation settings. Models are based on ResNet-50." + ], + "table_footnote": [], + "table_body": "
Methods1-shot5-shot10-shot
BaseNovelTotalBaseNovelTotalBaseNovelTotal
\\( \\text{CAPL}^\\dagger [33] \\)44.617.0535.4645.2411.0536.8045.5110.8236.95
FT [26]43.428.9434.9047.1824.7241.6348.1830.0343.70
FT-Triplet [26]43.649.2335.1446.6128.8441.3646.6134.4943.27
\\( \\text{POP}^\\dagger \\)47.097.2737.2648.6120.0741.5649.1326.4843.54
POP54.7115.3144.9854.9029.9748.7555.0135.0550.08
", + "bbox": [ + 187, + 337, + 779, + 457 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "prototypes in two training phases on COCO- $20^{i}$ , respectively. The dimension of the prototypes is set as 512.", + "bbox": [ + 75, + 468, + 468, + 500 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Training Strategy. Our POP is implemented on PyTorch [28]. The mini-batch stochastic gradient descent with momentum 0.9 and weight decay 0.0001 is exploited to optimize the model. During the base class learning, the initial learning rate is set to 0.01 which is annealed down to zero following a \"poly\" policy whose power is fixed to 0.9. The batch size is set as 8 for both datasets and the training of each model takes 50 epochs. For novel class updating, we update the model with a fixed learning rate 0.01 for 500 epochs, and the batch size is set as 2 and 8 for PASCAL $^{5^i}$ and COCO-20 $^{i}$ , respectively. For data augmentation, we employ the $473 \\times 473$ patches randomly cropped from the random scaled images. Each patch is randomly flipped along horizontal direction. The trade-off parameter $\\lambda$ is set as 10 empirically.", + "bbox": [ + 75, + 503, + 470, + 731 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Baseline Approaches. We compared the following approaches: (1) CAPL [33] capitalizes on the contextual co-occurrence cues to enrich the prototypes in the classifier for novel class segmentation. (2) FT [26] directly fine-tunes the model on some sampled data from base classes plus novel examples for novel class updating. Moreover, we also report the setting of FT which additionally leverages a triplet loss regularization to augment the learnt models. (3) Following [33], we form a prototype for each class by averaging features of pixels belonging to the corresponding class in all training images and utilize prototypes to", + "bbox": [ + 75, + 734, + 468, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "segment the query image. By doing so, we can remould the three prototype-based FSS approaches of CANet [44], PFENet [34] and PANet [36] for GFSS. The reported results are directly drawn from [33]. (4) BAM [15] is an FSS model that segments all base classes and one novel class through one inference process. To adapt for GFSS, we execute multiple inferences each of which takes the query image and support images of one novel class as the inputs. Following [15], we fuse the model outputs as the final prediction.", + "bbox": [ + 496, + 468, + 893, + 606 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Performance Comparison", + "text_level": 1, + "bbox": [ + 500, + 618, + 735, + 635 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Quantitative Analysis. We compare with several state-of-the-art techniques on PASCAL- $5^{i}$ and COCO- $20^{i}$ datasets. Table 1 summarizes the mIoU performances of capitalizing on different number of shots ( $K$ -shot) on PASCAL- $5^{i}$ . Overall, the results across three $K$ -shot settings consistently indicate that our POP successfully adapts the models for novel classes and achieves the better performances than other baselines including the remoulded FSS methods and GFSS approaches. In particular, the mIoU of POP on all classes, i.e., Total, reaches $64.77\\%$ , $70.28\\%$ and $71.13\\%$ on 1-shot, 5-shot and 10-shot scenarios, respectively, making the absolute gain over the best competitors by $0.83\\%$ , $3.93\\%$ , and $2.03\\%$ . CAPL, FT and POP generally exhibit higher mIoU against the reshaped FSS approaches of CANet, PFENet and PANet. This somewhat reveals the weakness of the episodic training/testing scheme of FSS, which once only distinguishes pixels be", + "bbox": [ + 496, + 643, + 893, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "11324", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/1f588cf9f709622c944d087c7fe2383f3af4a2294f08ac0c980d62afd75eb376.jpg", + "image_caption": [ + "Figure 3. Twelve examples of semantic segmentation from PASCAL-5 by POP and the state-of-the-art FT method under 5-shot setting." + ], + "image_footnote": [], + "bbox": [ + 80, + 87, + 890, + 329 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "longing to one specific class by abstracting the knowledge of such class from support images. As such, FSS models result in inferior performances for GFSS, where the models necessitate to identify novel classes and all possible base classes simultaneously. BAM in part alleviates the limitation of FSS scheme by additionally employing a base class segmentation branch, that is pre-trained on base data and frozen when FSS training to refine the predictions of novel classes. As such, adapting BAM to GFSS can segment the base classes well and yield good performances.", + "bbox": [ + 75, + 356, + 468, + 507 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Compared to GFSS models of CAPL and FT in which the segmentation of base classes may be affected by novel class updating, POP improves the segmentation quality of base classes. The results basically indicate the advantage of freezing the well-learnt representations of base classes in model updating. On novel classes, the mIoU of POP is $35.51\\%$ in 1-shot scenario and further increased to $55.87\\%$ when knowing 5 labeled images (5-shot) for each category. As a good performer in 1-shot setting, BAM benefits from the frozen base representations and a well-tuned base-novel fusion strategy. Nevertheless, BAM still suffers from interclass confusion and is inferior to POP, especially when having more available novel examples. The results in general verify the impact of learning orthogonal prototypes in POP. Please note that for a fair comparison, we follow CAPL [33] and also implement POP with the dataset filtration that excludes the images containing pixels of novel classes in base class learning phase. We denote such run as $\\mathsf{POP}^{\\dagger}$ . $\\mathsf{POP}^{\\dagger}$ constantly outperforms CAPL across different few-shot settings, demonstrating the effectiveness of representing an image via a group of uncorrelated features by POP.", + "bbox": [ + 75, + 507, + 470, + 824 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2 details the performance comparisons of 1-shot, 5-shot and 10-shot experiments on COCO- $20^{i}$ dataset. Similar to the observations on PASCAL- $5^{i}$ , POP surpasses the best-performing baseline FT-Triplet on 5-shot by $8.29\\%$ , $1.13\\%$ and $7.39\\%$ in terms of mIoU on base, novel and", + "bbox": [ + 76, + 825, + 468, + 898 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "all classes, respectively. Furthermore, POP obtains better segmentation quality on base classes against other methods across different k-shot settings. The results again empirically validate our proposal.", + "bbox": [ + 496, + 354, + 890, + 415 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Qualitative Analysis. Figure 3 showcases twelve semantic segmentation examples in PASCAL- $5^{i}$ , by our POP and the state-of-the-art FT [26] method under 5-shot setting of GFSS. Clearly, POP attains much more promising segmentation results. Taking the query images in the first three columns on the left as the examples, POP nicely identifies the novel class \"Person\", but FT fails to recognize this category. Moreover, the novel class \"Dog\" in the fourth example on the left is well segmented by both models and POP distinguishes the base class of \"Sofa\" more precisely.", + "bbox": [ + 496, + 416, + 892, + 566 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Experimental Analysis", + "text_level": 1, + "bbox": [ + 500, + 575, + 710, + 590 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Evaluation on POP Designs. We first examine the performance contribution of different factors in POP. In between, PRO projects the features on the learnt prototypes to predict the class of each pixel and base prototypes are frozen when learning novel ones in the updating phase. ORT further encourages the orthogonality between prototypes of different classes to alleviate inter-class confusion. RES is our residual background representation that measures the residual of feature projection to categorize background pixels rather than learn a prototype for \"background\".", + "bbox": [ + 496, + 598, + 890, + 750 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3 shows the mIoU improvement on the PASCAL $^5$ dataset by considering one more factors in POP for GFSS. Note that the data enrichment strategies are not employed in the experiments here. We start from the basic run of FT [26] that capitalizes on the same features to classify the pixels of base and novel classes in the images. PRO successfully boosts up the total mIoU performance from $55.41\\%$ to $59.59\\%$ under 1-shot setting and introduces $5.53\\%$ mIoU improvement on base classes. This basically proves that PRO is a very effective and practical way for", + "bbox": [ + 496, + 750, + 892, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "11325", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/c3f4f12a0b30838b55e86f247bfb4f9d835ff0b80df1529d3d59fc6459b9321c.jpg", + "table_caption": [ + "Table 3. Ablation studies of POP on PASCAL- $5^{i}$ . PRO represents segmentation via feature projection on learnt prototypes. ORT denotes the orthogonality constraint on prototypes. RES means that the background is represented by residual of feature projection." + ], + "table_footnote": [], + "table_body": "
PROORTRES1-shot5-shot
BaseNovelTotalBaseNovelTotal
---66.8418.8255.4172.0346.4065.93
✓--72.3718.7159.5973.3946.9967.10
✓✓-72.8223.6561.1173.7650.6968.27
✓✓✓73.1132.2963.3974.3253.4669.36
", + "bbox": [ + 80, + 148, + 467, + 247 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/f15312111971ea67909aff83963e737b06b32f4e33d3ac761e204c3b3bd25ac0.jpg", + "table_caption": [ + "Table 4. Ablation studies of data enrichment strategies for novel class updating on PASCAL- $5^{i}$ . RL denotes relabeling which provides pseudo labels for background pixels in images of base data. RS represents the resampling strategy that randomly selects different base data for each epoch of novel class updating." + ], + "table_footnote": [], + "table_body": "
RLRS1-shot5-shot
BaseNovelTotalBaseNovelTotal
--73.1132.2963.3974.3253.4669.36
✓-73.1035.0864.0474.3954.8169.73
-✓72.8627.7462.1274.3152.1269.03
✓✓73.9235.5164.7774.7855.8770.28
", + "bbox": [ + 78, + 337, + 465, + 436 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "GFSS to generalize the model to novel classes without sacrificing much accuracy on base classes. The performance gain of ORT is $0.45\\%$ and $4.94\\%$ in terms of mIoU on base and novel classes, respectively. The results verify the idea of learning orthogonal prototypes for feature projection. Measuring the residual of feature projection as background representation by RES further contributes a mIoU increase of $8.64\\%$ and $2.77\\%$ on novel classes in 1-shot and 5-shot settings. That empirically manifests the superiority of RES to dynamically fit semantic shifting in GFSS.", + "bbox": [ + 75, + 457, + 467, + 608 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Evaluation on Data Enrichment. To better study the contribution of each design in data enrichment, we then conduct the comparisons on POP configured with different enrichment strategies. Table 4 lists the results of both 1-shot and 5-shot scenarios. In particular, relabeling the background pixels in base samples leads to over $1\\%$ mIoU improvements on novel classes. The results indicate that assigning pseudo labels to novel class pixels in base data could refine noisy background labels and ease the prototype learning for novel classes. The performance gain tends to be larger when updating the model on more base data via resampling. The performance however drops if solely using resampling. We speculate that this may be the result of involving more novel class pixels which may be incorrectly regarded as background due to semantic shifting.", + "bbox": [ + 75, + 611, + 467, + 837 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Confusion Matrix Visualization. Figure 4 depicts the confusion matrices of FT and POP under 5-shot scenario on the second fold of PASCAL- $5^{i}$ . We visualize the matrix after each training phase and highlight the difference in", + "bbox": [ + 75, + 839, + 467, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/3214005e4cc134b52b48a482c75ae675137080a53ce0ce2cf5227771813d3770.jpg", + "image_caption": [ + "Figure 4. The difference of confusion matrices between two training phases. Compared to FT, POP obtains higher novel class performance after novel class updating without sacrificing much accuracy on base classes learnt in base class learning." + ], + "image_footnote": [], + "bbox": [ + 500, + 85, + 883, + 353 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "between. After the first phase of base class learning, both models achieve over $72.9\\%$ mIoU on base classes. Through the second phase of novel class updating, the mIoU on novel classes is $45.50\\%$ by FT but the performance decreases to $68.37\\%$ on base classes. In contrast, POP reaches $63.36\\%$ mIoU on novel class with only $0.13\\%$ mIoU drop on base classes. That again validates the good property of POP to identify novel classes without compromising base ones.", + "bbox": [ + 496, + 429, + 890, + 550 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 563, + 617, + 579 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have presented Projection onto Orthogonal Prototypes (POP) framework, which explores a principled way to generalize the model to novel classes without sacrificing much segmentation capability on base classes for GFSS. In particular, we study the problem by learning a group of prototypes and each represents a semantic class, thereby predicting each class separately with respect to the projections on its prototype. Technically, POP learns prototypes first on base classes and then updates the prototypes to support novel classes, with orthogonal constraint. Moreover, POP measures the residual of feature projection as the background representation to better mitigate semantic shifting. Experiments conducted on two datasets of PASCAL- $5^{i}$ and COCO- $20^{i}$ validate our proposal and analysis. Performance improvements are observed when comparing to both GFSS and remoulded FSS techniques.", + "bbox": [ + 496, + 589, + 890, + 830 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements This work is partly supported by the National Key Research and Development Program of China (2022YFB3104700), the National Nature Science Foundation of China (62121002, 62022076, U1936210).", + "bbox": [ + 496, + 830, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "11326", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE TPAMI, 2017. 1, 2", + "[2] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In ECCV, 2018. 2", + "[3] Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. In NeurIPS, 2021. 1, 2", + "[4] Pierre Comon. Independent component analysis, a new concept? Signal processing, 1994. 4", + "[5] Nanqing Dong and Eric P. Xing. Few-shot semantic segmentation with prototype learning. In BMVC, 2018. 2", + "[6] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 1", + "[7] Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. *IJCV*, 2009. 5", + "[8] Jun Fu, Jing Liu, Hajjie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In CVPR, 2019. 2", + "[9] Bharath Hariharan, Pablo Arbeláez, Lubomir D. Bourdev, Subhransu Maji, and Jitendra Malik. Semantic contours from inverse detectors. In ICCV, 2011. 5", + "[10] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016. 1", + "[11] Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, and Abbas Rahimi. Constrained few-shot class-incremental learning. In CVPR, 2022. 2", + "[12] Qibin Hou, Li Zhang, Ming-Ming Cheng, and Jiashi Feng. Strip pooling: Rethinking spatial pooling for scene parsing. In CVPR, 2020. 2", + "[13] Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In CVPR, 2018. 1", + "[14] Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu. Ccnet: Criss-cross attention for semantic segmentation. In ICCV, 2019. 2", + "[15] Chunbo Lang, Gong Cheng, Binfei Tu, and Junwei Han. Learning what not to segment: A new perspective on few-shot segmentation. In CVPR, 2022. 6", + "[16] Gen Li, Varun Jampani, Laura Sevilla-Lara, Deqing Sun, Jonghyun Kim, and Joongkyu Kim. Adaptive prototype learning and allocation for few-shot segmentation. In CVPR, 2021. 2", + "[17] Xia Li, Zhisheng Zhong, Jianlong Wu, Yibo Yang, Zhouchen Lin, and Hong Liu. Expectation-maximization attention networks for semantic segmentation. In ICCV, 2019. 2" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[18] Yehao Li, Ting Yao, Yingwei Pan, and Tao Mei. Contextual transformer networks for visual recognition. IEEE TPAMI, 2022. 1", + "[19] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 5", + "[20] Binghao Liu, Yao Ding, Jianbin Jiao, Xiangyang Ji, and Qixiang Ye. Anti-aliasing semantic reconstruction for few-shot semantic segmentation. In CVPR, 2021. 2", + "[21] Sun-Ao Liu, Hongtao Xie, Hai Xu, Yongdong Zhang, and Qi Tian. Partial class activation attention for semantic segmentation. In CVPR, 2022. 2", + "[22] Yongfei Liu, Xiangyi Zhang, Songyang Zhang, and Xuming He. Part-aware prototype network for few-shot semantic segmentation. In ECCV, 2020. 2", + "[23] Fuchen Long, Zhaofan Qiu, Yingwei Pan, Ting Yao, Jiebo Luo, and Tao Mei. Stand-alone inter-frame attention in video models. In CVPR, 2022. 1", + "[24] Fuchen Long, Zhaofan Qiu, Yingwei Pan, Ting Yao, Chong-Wah Ngo, and Tao Mei. Dynamic temporal filtering in video models. In ECCV, 2022. 1", + "[25] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015. 1", + "[26] Josh Myers-Dean, Yinan Zhao, Brian Price, Scott Cohen, and Danna Gurari. Generalized few-shot semantic segmentation: All you need is fine-tuning. arXiv preprint arXiv:2112.10982, 2021. 1, 2, 3, 5, 6, 7", + "[27] Khoi Duc Minh Nguyen and Sinisa Todorovic. Feature weighting and boosting for few-shot segmentation. In ICCV, 2019. 5", + "[28] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, 2019. 6", + "[29] Kanchana Ranasinghe, Muzammal Naseer, Munawar Hayat, Salman Khan, and Fahad Shahbaz Khan. Orthogonal projection loss. In ICCV, 2021. 2", + "[30] Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, and Byron Boots. One-shot learning for semantic segmentation. arXiv preprint arXiv:1709.03410, 2017. 1, 2, 5", + "[31] Christian Simon, Piotr Koniusz, Richard Nock, and Mehrtash Harandi. Adaptive subspaces for few-shot learning. In CVPR, 2020. 2", + "[32] Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmentation. In ICCV, 2021. 2", + "[33] Zhuotao Tian, Xin Lai, Li Jiang, Shu Liu, Michelle Shu, Hengshuang Zhao, and Jiaya Jia. Generalized few-shot semantic segmentation. In CVPR, 2022. 1, 2, 3, 5, 6, 7", + "[34] Zhuotao Tian, Hengshuang Zhao, Michelle Shu, Zhicheng Yang, Ruiyu Li, and Jiaya Jia. Prior guided feature en" + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "11327", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "richment network for few-shot segmentation. IEEE TPAMI, 2020. 6", + "[35] Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. Mlp-mixer: An all-mlp architecture for vision. In NeurIPS, 2021. 1", + "[36] Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng. Panet: Few-shot image semantic segmentation with prototype alignment. In ICCV, 2019. 1, 2, 6", + "[37] Xin Wang, Thomas Huang, Joseph Gonzalez, Trevor Darrell, and Fisher Yu. Frustratingly simple few-shot object detection. In ICML, 2020. 5", + "[38] Zhonghua Wu, Xiangxi Shi, Guosheng Lin, and Jianfei Cai. Learning meta-class memory for few-shot semantic segmentation. In ICCV, 2021. 2", + "[39] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. In NeurIPS, 2021. 2", + "[40] Boyu Yang, Chang Liu, Bohao Li, Jianbin Jiao, and Qixiang Ye. Prototype mixture models for few-shot semantic segmentation. In ECCV, 2020. 2", + "[41] Ting Yao, Yehao Li, Yingwei Pan, Yu Wang, Xiao-Ping Zhang, and Tao Mei. Dual vision transformer. arXiv preprint arXiv:2207.04976, 2022. 1", + "[42] Ting Yao, Yingwei Pan, Yehao Li, Chong-Wah Ngo, and Tao Mei. Wave-vit: Unifying wavelet and transformers for visual representation learning. In ECCV, 2022. 1", + "[43] Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object-contextual representations for semantic segmentation. In ECCV, 2020. 2", + "[44] Chi Zhang, Guosheng Lin, Fayao Liu, Rui Yao, and Chunhua Shen. Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. In CVPR, 2019. 1, 6", + "[45] Fan Zhang, Yanqin Chen, Zhihang Li, Zhibin Hong, Jingtuo Liu, Feifei Ma, Junyu Han, and Errui Ding. Acfnet: Attentional class feature network for semantic segmentation. In ICCV, 2019. 2", + "[46] Yiheng Zhang, Zhaofan Qiu, Jingen Liu, Ting Yao, Dong Liu, and Tao Mei. Customizable architecture search for semantic segmentation. In CVPR, 2019. 2", + "[47] Yiheng Zhang, Zhaofan Qiu, Ting Yao, Dong Liu, and Tao Mei. Fully convolutional adaptation networks for semantic segmentation. In CVPR, 2018. 2", + "[48] Yiheng Zhang, Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Dong Liu, and Tao Mei. Transferring and regularizing prediction for semantic segmentation. In CVPR, 2020. 2", + "[49] Yiheng Zhang, Ting Yao, Zhaofan Qiu, and Tao Mei. Lightweight and progressively-scalable networks for semantic segmentation. arXiv preprint arXiv:2207.13600, 2022. 1", + "[50] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In CVPR, 2017. 2, 5" + ], + "bbox": [ + 78, + 90, + 467, + 898 + ], + "page_idx": 9 + }, + { + "type": "ref_text", + "text": "[51] Zhen Zhu, Mengde Xu, Song Bai, Tengteng Huang, and Xiang Bai. Asymmetric non-local neural networks for semantic segmentation. In ICCV, 2019. 2", + "bbox": [ + 501, + 90, + 890, + 133 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "11328", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation/f55ad462-d47a-4afc-95bc-6a55486b79b1_model.json b/2023/Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation/f55ad462-d47a-4afc-95bc-6a55486b79b1_model.json new file mode 100644 index 0000000000000000000000000000000000000000..a48a0a4bf9ef73cf565b5a10e4c8845becafefdc --- /dev/null +++ b/2023/Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation/f55ad462-d47a-4afc-95bc-6a55486b79b1_model.json @@ -0,0 +1,1925 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.131, + 0.737, + 0.177 + ], + "angle": 0, + "content": "Learning Orthogonal Prototypes for Generalized Few-shot Semantic Segmentation" + }, + { + "type": "text", + "bbox": [ + 0.177, + 0.204, + 0.848, + 0.24 + ], + "angle": 0, + "content": "Sun-Ao Liu, Yiheng Zhang, Zhaofan Qiu, Hongtao Xie*, Yongdong Zhang, Ting Yao University of Science and Technology of China HiDream.ai Inc." + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.242, + 0.933, + 0.259 + ], + "angle": 0, + "content": "{lsa1997.mail, htxie, zhyd73}@ustc.edu.cn, {yihengzhang.chn, zhaofanqiu, tingyao.ustc}@gmail.com" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.292, + 0.314, + 0.308 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.324, + 0.474, + 0.747 + ], + "angle": 0, + "content": "Generalized few-shot semantic segmentation (GFSS) distinguishes pixels of base and novel classes from the background simultaneously, conditioning on sufficient data of base classes and a few examples from novel class. A typical GFSS approach has two training phases: base class learning and novel class updating. Nevertheless, such a standalone updating process often compromises the well-learnt features and results in performance drop on base classes. In this paper, we propose a new idea of leveraging Projection onto Orthogonal Prototypes (POP), which updates features to identify novel classes without compromising base classes. POP builds a set of orthogonal prototypes, each of which represents a semantic class, and makes the prediction for each class separately based on the features projected onto its prototype. Technically, POP first learns prototypes on base data, and then extends the prototype set to novel classes. The orthogonal constraint of POP encourages the orthogonality between the learnt prototypes and thus mitigates the influence on base class features when generalizing to novel prototypes. Moreover, we capitalize on the residual of feature projection as the background representation to dynamically fit semantic shifting (i.e., background no longer includes the pixels of novel classes in updating phase). Extensive experiments on two benchmarks demonstrate that our POP achieves superior performances on novel classes without sacrificing much accuracy on base classes. Notably, POP outperforms the state-of-the-art fine-tuning by \\(3.93\\%\\) overall mIoU on PASCAL-\\(5^i\\) in 5-shot scenario." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.762, + 0.21, + 0.778 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.787, + 0.471, + 0.879 + ], + "angle": 0, + "content": "Semantic segmentation is to assign semantic labels to every pixel of an image. With the recent development of CNNs [10, 13] and vision transformers [6, 18, 23, 24, 35, 41, 42], the state-of-the-art networks have successfully pushed the limits of semantic segmentation [1, 3, 25, 49] with remarkable performance improvements. Such achievements" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.291, + 0.886, + 0.38 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.679, + 0.38, + 0.694, + 0.39 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.392, + 0.884, + 0.479 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.679, + 0.479, + 0.695, + 0.488 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.487, + 0.895, + 0.585 + ], + "angle": 0, + "content": "Figure 1. Comparisons between fine-tuning [26] and Projection onto Orthogonal Prototypes (POP). In novel class updating phase, fine-tuning (a) updates the network to predict base and novel classes, and inevitably compromises the well-learnt representations for base classes. Instead, POP (b) only updates prototypes for novel classes and executes predictions for each class separately on the features projected onto different orthogonal prototypes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.598, + 0.895, + 0.81 + ], + "angle": 0, + "content": "heavily rely on the requirements of large quantities of pixel-level annotations and it is also difficult to directly apply the models to the classes unseen in the training set. A straightforward way to alleviate this issue is to leverage Few-shot Semantic Segmentation (FSS) [30, 36, 44], which utilizes the limited support annotations from unseen/novel classes to adapt the models. Nevertheless, FSS performs on the assumption that the support images and the query image contain the same novel classes, and solely emphasizes the segmentation of one novel class in the query image at a time. A more practical scenario namely Generalized Few-shot Semantic Segmentation (GFSS) [33] is recently presented to simultaneously identify the pixels of base and novel classes in a query image." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.896, + 0.903 + ], + "angle": 0, + "content": "A typical GFSS solution has proceeded along two training phases: base class learning and novel class updating. In the first phase, models are trained on abundant base classes' annotations to classify the pixels of base categories, and then updated with the limited labeled novel examples in the second phase to additionally recognize pixels of novel" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.224, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "11319" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.318 + ], + "angle": 0, + "content": "classes. For instance, Myers Dean et al. [26] sample some base data plus novel examples as the supervision to finetune the network, as depicted in Figure 1(a). Despite having good performances on novel classes, there still exists a clear performance degradation on base classes. We speculate that this may be the results of compromising the well-learnt features of base classes in fine-tuning. As such, a valid question then emerges as is it possible for GFSS to nicely generalize the model to novel classes without sacrificing much segmentation capability on base classes. In an effort to answer this question, we seek to represent an image via a group of uncorrelated feature components each of which characterizes a specific class. By doing so, it is readily applicable to learn and integrate new components for novel classes without affecting the ones learnt for base classes." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.331, + 0.473, + 0.724 + ], + "angle": 0, + "content": "To materialize our idea, we propose a new Projection onto Orthogonal Prototypes (POP) framework for GFSS. POP learns a series of orthogonal prototypes and each prototype corresponds to one specific semantic class. As shown in Figure 1(b), POP employs an encoder to extract feature maps of a given query image and then projects them onto prototypes. The projection on each prototype is regarded as the discriminative representation with respect to the corresponding class and exploited to predict the probability map of pixels belonging to the class. More specifically, POP deliberately devises the learning of prototypes from three standpoints. The first one is to freeze the base prototypes when learning novel ones from support images in the updating phase. In this way, feature projections on base prototypes maintain their discriminability of base classes. Second, POP encourages the prototypes of base and novel classes to be orthogonal through a prototype orthogonality loss. Such a constraint decorrelates features projected onto different prototypes and mitigates the inter-class confusion caused by extending to novel classes. Finally, in view that background no longer contains the pixels of novel classes in updating phase, known as \"semantic shifting\", POP measures the residual of feature projection as the background representation instead of learning a prototype for \"background\". This way further improves the differentiation between novel classes and background." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.473, + 0.903 + ], + "angle": 0, + "content": "The main contribution of this work is the proposal of a Projection on Orthogonal Prototypes (POP) framework for generalized few-shot semantic segmentation. The solution also leads to the elegant views of how to adapt the trained model to novel classes without sacrificing well-learnt features, and how to represent background pixels dynamically in the context of semantic shifting, which are problems not yet fully explored in the literature. We demonstrate that POP outperforms the state-of-the-art fine-tuning [26] on two benchmarks (PASCAL-5\\(^i\\) and COCO-20\\(^i\\)) with evident improvements on both base and novel classes." + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.09, + 0.642, + 0.107 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.115, + 0.892, + 0.265 + ], + "angle": 0, + "content": "Semantic Segmentation. Extensive FCN-based methods have been proposed and achieved remarkable performances. For example, pyramid pooling [12, 48, 50] and parallel dilated convolutions [1, 2, 46, 47] are utilized to aggregate multi-scale context. The attention mechanism is widely exploited to model long-range dependencies [8, 14, 17, 21, 43, 45, 51]. Recently, vision transformer has also been proven effective for semantic segmentation [3, 32, 39]. Though these efforts lead to high-quality segmentation, it is still difficult to directly apply the learnt models to unseen classes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.267, + 0.892, + 0.447 + ], + "angle": 0, + "content": "Few-shot Semantic Segmentation. Few-shot semantic segmentation (FSS) [30] executes pixel-wise labeling on new classes given a limited number of support examples. It mainly focuses on the 1-way scenario that requires binary maps for query images to identify the pixels belonging to the class labeled in support images. PL [5] and PANet [36] remould prototype learning for FSS by calculating the cosine similarities between pixels and prototypes obtained from support images. ASR [20] learns several orthogonal prototypes on the base data to represent novel categories. In addition, assigning multiple prototypes to each class is also a promising solution to improve FSS models [16,22,38,40]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.449, + 0.892, + 0.628 + ], + "angle": 0, + "content": "Generalized Few-shot Semantic Segmentation. Despite showing great potential, it is not trivial for FSS approaches to simultaneously identify the pixels of both base and novel classes in a query image. Generalized Few-shot Semantic Segmentation (GFSS) is further presented by Tian et al. [33] to address this problem. GFSS aims to segment both base and novel classes from query images through one inference process without paired support images. To achieve this goal, CAPL [33] mines contextual cues from both support and query samples to enrich the classifier for novel class segmentation. Fine-tuning [26] is another practical and effective way to adapt models for novel classes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.63, + 0.892, + 0.765 + ], + "angle": 0, + "content": "Orthogonality. Feature orthogonality is known to be helpful to improve discriminative ability. DSN [31] constrains the orthogonality on base data to facilitate few-shot classification on novel classes. C-FSCIL [11] and OPL [29] rely on re-training the network to keep the features of each class orthogonal. Nevertheless, these approaches are not applicable for GFSS since they either ignore the influence between base and novel classes or compromise the segmentation capability on base classes after re-training." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.766, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Our work focuses on adapting the semantic segmentation model to novel classes without compromising well-learnt features for better generalization. Different from existing GFSS approaches may sacrifice base class segmentation quality for novel class updating, our POP contributes by studying not only learning orthogonal prototypes to independently indicate pixels of base and novel classes, but also how the background could be nicely represented in the context of semantic shifting." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "11320" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.09, + 0.885, + 0.205 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.081, + 0.207, + 0.885, + 0.389 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.392, + 0.893, + 0.476 + ], + "angle": 0, + "content": "Figure 2. An overview of our Projection onto Orthogonal Prototypes (POP) framework for generalized few-shot semantic segmentation. The input image is first encoded into dense feature map via a CNN encoder, which is then projected onto a series of orthogonal prototypes to make prediction of each class independently through a shared classifier. In the base class learning phase, the entire network and prototypes for base classes are learnt on sufficient base data. In the second phase of novel class updating, we freeze all the modules learnt in the first phase and only optimize the newly minted prototypes for novel classes along with an additional classifier for novel classes and background class. As such, the proposed POP generalizes well to novel classes while maintaining the segmentation capability on base classes." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.486, + 0.208, + 0.501 + ], + "angle": 0, + "content": "3. Our Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.514, + 0.471, + 0.695 + ], + "angle": 0, + "content": "In this paper, we devise Projection onto Orthogonal Prototypes (POP) framework for generalized few-shot semantic segmentation (GFSS), by decoupling the feature learning of base classes and novel classes. Figure 2 illustrates an overview of the whole architecture of POP. We begin this section by elaborating the problem formulation of GFSS with the two-phase training scheme. Then, a projection-based framework is introduced to learn class-wise orthogonal prototypes, and extract the residual representation as background descriptor. Finally, we present the overall objective combining orthogonal constraint along with the semantic segmentation loss." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.71, + 0.281, + 0.725 + ], + "angle": 0, + "content": "3.1. Problem Formulation" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.735, + 0.47, + 0.902 + ], + "angle": 0, + "content": "In generalized few-shot semantic segmentation, we are given abundant annotated images for \\(M\\) base classes \\(C^b = \\{c_1,\\dots ,c_M\\}\\) and only \\(K\\) labeled images per class for \\(N\\) novel classes \\(C^n = \\{c_{M + 1},\\ldots ,c_{M + N}\\}\\). The background category \\(c_{0}\\) presents the pixels that do not belong to any target classes. The goal of this task is to simultaneously distinguish both base and novel classes from backgrounds, i.e., \\(M + N + 1\\) classes in total. Inspired by recent success of two-phase training scheme in generalized few-shot semantic segmentation [26,33], we formulate our POP framework in two-phase manner: base class learning and novel class" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.488, + 0.892, + 0.565 + ], + "angle": 0, + "content": "updating. Accordingly, POP is first trained with sufficient data for \\((M + 1)\\)-class segmentation \\((C^b \\cup \\{c_0\\})\\) in the first phase, and then updates the segmentation head with the few-shot support data targeting for pixel-wise prediction of all \\(M + N + 1\\) classes \\((C^b \\cup C^n \\cup \\{c_0\\})\\) in the second phase." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.575, + 0.836, + 0.591 + ], + "angle": 0, + "content": "3.2. Projection onto Orthogonal Prototypes" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.599, + 0.892, + 0.78 + ], + "angle": 0, + "content": "GFSS necessitates the capability to segment both base and novel classes. In this case, the novel updating process should learn and integrate new components for novel classes without affecting the ones learnt for base classes. However, the existing approaches [26, 33] in this area still show obvious performance degradation on base classes in the second phase. They often suffer from a challenge of whether to drastically overhaul the network for the performance on novel classes or preserve the well-learnt representation for base classes. We attribute this issue to the highly-relevant representations to identify different classes, which interfere with each other when integrating novel classes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.893, + 0.902 + ], + "angle": 0, + "content": "To fulfill the objective of decoupling the feature learning of base classes and novel classes, we propose to orthogonally decompose the feature map into several uncorrelated components, each of which characterizes a specific class. Formally, we denote the dense feature map from the encoder network as \\(\\pmb{f} \\in \\mathbb{R}^{(H \\times W) \\times C}\\), where \\(H\\), \\(W\\) and \\(C\\) are the height, the width and the number of channels of feature map, respectively. Assuming the matrix rank of extracted" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "11321" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.091, + 0.47, + 0.122 + ], + "angle": 0, + "content": "features is \\( r \\) (\\( r \\leq C \\)), the feature map \\( f \\) can be represented as the weighted summation of \\( r \\) orthogonal bases as:" + }, + { + "type": "equation", + "bbox": [ + 0.186, + 0.134, + 0.469, + 0.171 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {f} = \\sum_ {i = 1} ^ {r} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}, \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.188, + 0.174, + 0.359, + 0.194 + ], + "angle": 0, + "content": "\\[\n\\text {s . t .} \\boldsymbol {u} _ {i} \\cdot \\boldsymbol {u} _ {j} ^ {T} = 0 (i \\neq j),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.205, + 0.471, + 0.358 + ], + "angle": 0, + "content": "where \\(\\{\\pmb{u}_i\\in \\mathbb{R}^{1\\times C}|_{i = 1\\dots r}\\}\\) is a group of unit bases that are orthogonal to each other, and \\(f^{(i)}\\in \\mathbb{R}^{(H\\times W)\\times 1}\\) is the weight map of the \\(i\\)-th component. This process is well known as orthogonal decomposition, and widely explored for Independent Component Analysis (ICA) [4]. In our work, we treat the basis \\(\\pmb{u}_i\\) as the prototype of the \\(i\\)-th class and the corresponding component \\(f^{(i)}\\cdot \\pmb{u}_i\\) as the representation to identify the pixels of the \\(i\\)-th class. As such, calculating the representation of the \\(i\\)-th class is equivalent to projecting \\(\\pmb{f}\\) to the \\(i\\)-th basis \\(\\pmb{u}_i\\):" + }, + { + "type": "equation", + "bbox": [ + 0.163, + 0.369, + 0.47, + 0.389 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {f} _ {c _ {i}} = f ^ {(i)} \\cdot \\boldsymbol {u} _ {i} = (\\boldsymbol {f} \\cdot \\boldsymbol {u} _ {i} ^ {T}) \\cdot \\boldsymbol {u} _ {i}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.399, + 0.47, + 0.519 + ], + "angle": 0, + "content": "where \\( f_{c_i} \\) is the representation of the \\( i \\)-th class and is then taken as the input of a shared classifier. In view that the learnt prototypes are orthogonal to each other, the representations of different classes are decorrelated and easy to be distinguished. More importantly, when integrating a new target class, we only need to learn a new prototype that is orthogonal to the existing ones, thereby preserving the well-learnt representations of other classes." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.52, + 0.47, + 0.627 + ], + "angle": 0, + "content": "Nevertheless, the limit of prototype orthogonality (i.e., \\(\\pmb{u}_i\\cdot \\pmb{u}_j^T = 0\\)) is not differentiable and hard to be implemented by gradient propagation. Hence, we present a practical alternative to relax the hard limit to a soft orthogonal constraint. Specifically, a prototype orthogonality loss is devised by minimizing absolute value of the inner product between prototypes:" + }, + { + "type": "equation", + "bbox": [ + 0.157, + 0.636, + 0.469, + 0.673 + ], + "angle": 0, + "content": "\\[\nL _ {\\text {o r t h}} = \\frac {1}{r ^ {\\prime} \\left(r ^ {\\prime} - 1\\right)} \\sum_ {i \\neq j} \\left| \\boldsymbol {u} _ {i} \\cdot \\boldsymbol {u} _ {j} ^ {T} \\right|, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.686, + 0.469, + 0.747 + ], + "angle": 0, + "content": "where \\( r' < r \\) is the number of target classes. The orthogonality loss \\( L_{orth} \\) restricts the averaged correlation of different prototype pairs, and is minimized when the prototypes are orthogonal to each other." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.757, + 0.421, + 0.773 + ], + "angle": 0, + "content": "3.3. Residual as Background Representation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.781, + 0.469, + 0.901 + ], + "angle": 0, + "content": "The background class \\( c_{0} \\) is a special category in segmentation tasks, and indicates the pixels that do not belong to any target classes. One common practice is to treat target classes and background class equally, which reaches reasonable performances on various segmentation tasks. However, such a simple strategy is not acclimatized to GFSS task due to the effect of the \"semantic shifting\" between two phases. More precisely, in the first phase, the pixels of both" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.182 + ], + "angle": 0, + "content": "novel classes \\( C^n \\) and background class \\( c_0 \\) are labeled as background. When integrating novel classes in the second phase, the semantic meaning of background changes since it no longer includes the pixels of novel classes. We regard this gap as semantic shifting of background, which results in the confusion between novel classes and background class." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.183, + 0.893, + 0.318 + ], + "angle": 0, + "content": "In order to bridge the gap between two phases, we propose to exploit the residual of feature projection as the background representation in POP, instead of constructing an independent prototype. The residual here is defined as the summation of remaining components after projecting \\( \\mathbf{f} \\) to the target classes. For instance, in the base class learning phase, the first \\( M \\) components in Eq.(1) are the projections for \\( M \\) base classes and the remaining \\( r - M \\) components are background components:" + }, + { + "type": "equation", + "bbox": [ + 0.573, + 0.327, + 0.892, + 0.39 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {f} = \\underbrace {\\sum_ {i = 1} ^ {M} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {b a s e c l a s s e s}} + \\underbrace {\\sum_ {i = M + 1} ^ {r} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {b a c k g r o u n d}}. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.398, + 0.892, + 0.474 + ], + "angle": 0, + "content": "Note that the background here actually includes the pixels of novel and background classes. In view of the difficulty on learning the remaining \\( r - M \\) components, we reformulate the background representation as the residual of feature projection by subtracting the components of bases classes:" + }, + { + "type": "equation", + "bbox": [ + 0.55, + 0.482, + 0.892, + 0.524 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {f} _ {c _ {0}} = \\sum_ {i = M + 1} ^ {r} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i} = \\boldsymbol {f} - \\sum_ {i = 1} ^ {M} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.533, + 0.892, + 0.593 + ], + "angle": 0, + "content": "where \\( \\pmb{f}_{c_0} \\) is orthogonal to the feature \\( \\pmb{f}_{c_i} \\) of each class \\( c_i \\) due to the orthogonality constraint between prototypes. Moreover, this reformulation avoids the estimation of matrix rank \\( r \\), which is more practical." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.594, + 0.892, + 0.669 + ], + "angle": 0, + "content": "For the novel class updating phase, the background components in Eq. (4) are separated into two parts, i.e., \\(N\\) components for \\(N\\) novel classes and \\(r - M - N\\) components as the updated background representation. The decomposition of feature \\(\\pmb{f}\\) in this phase is represented as" + }, + { + "type": "equation", + "bbox": [ + 0.502, + 0.678, + 0.892, + 0.753 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {f} = \\underbrace {\\sum_ {i = 1} ^ {M} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {b a s e c l a s s e s}} + \\underbrace {\\sum_ {i = M + 1} ^ {M + N} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {n o v e l c l a s s e s}} + \\underbrace {\\sum_ {i = M + N + 1} ^ {r} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {b a c k g r o u n d}}. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.754, + 0.892, + 0.798 + ], + "angle": 0, + "content": "Similar to Eq.(5), the updated background representation is measured by the residual of feature projection to base and novel classes:" + }, + { + "type": "equation", + "bbox": [ + 0.552, + 0.805, + 0.892, + 0.847 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {f} _ {c _ {0}} = \\boldsymbol {f} - \\sum_ {i = 1} ^ {M} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i} - \\sum_ {i = M + 1} ^ {M + N} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "In this way, the background representation is dynamically adjusted with respect to the change of target classes. It remains orthogonal to the representations of both base classes" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11322" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.47, + 0.122 + ], + "angle": 0, + "content": "and novel classes, such that the confusion between novel classes and background class is alleviated." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.138, + 0.218, + 0.156 + ], + "angle": 0, + "content": "3.4. Optimization" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.164, + 0.47, + 0.21 + ], + "angle": 0, + "content": "Following the common practice [26, 33], the optimization of our POP framework contains two phases: base class learning and novel class updating." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.212, + 0.469, + 0.333 + ], + "angle": 0, + "content": "Base class learning. In the first phase, the entire network is trained on sufficient base data to optimize the encoded representations and segment base classes. The overall training objective function of POP combines the semantic segmentation loss of base classes and the orthogonality loss between prototypes: \\( L = L_{seg} + \\lambda L_{orth} \\), where \\( L_{seg} \\) denotes the cross entropy loss for segmentation and \\( \\lambda \\) is a scale factor of the orthogonality loss." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.335, + 0.47, + 0.472 + ], + "angle": 0, + "content": "Novel class updating. The second phase involves the \\(K\\)-shot support data of novel classes and expands the set of target classes. We freeze all the modules learnt in the first phase and only optimize the newly constructed prototypes for novel classes along with an additional classifier for novel classes and background class. The loss function is the same as that in the first phase (i.e., \\(L = L_{seg} + \\lambda L_{orth}\\)), and the orthogonality loss \\(L_{orth}\\) is utilized over the prototypes of both base classes and novel classes." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.474, + 0.47, + 0.64 + ], + "angle": 0, + "content": "Data enrichment. The second phase demands the training images of both base and novel classes. As a result of highly unbalanced data between base classes and novel classes, simply merging the two training sets leads to poor performance on novel classes. Inspired by the fine-tuning approaches [26, 37], we establish a balanced training subset by randomly sampling \\( K \\) examples per base class. Such a subset in the second phase has been proven to be effective for partially retraining the knowledge of base classes while integrating novel classes [26, 37]. Besides, we further promote the subset by two data enrichment strategies:" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.642, + 0.47, + 0.779 + ], + "angle": 0, + "content": "1) Relabeling. Recall that there is semantic shifting problem between two training phases. In the training images of base classes, the pixels of novel classes are labeled as background. Such annotation misleads the network optimization in the second phase. Therefore, after each epoch, we utilize the latest POP model to relabel the background pixels in the image of base classes, and replace the background annotation with the class with the highest predicted probability across the novel classes and background class." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.471, + 0.902 + ], + "angle": 0, + "content": "2) Resampling. The subset only selects a few samples from the images of base classes. It underuses the large amount of training data, and makes the network sensitive to the choice of few-shot samples. Hence, we propose to resample the training images of base classes after each epoch. This strategy, on one hand, well balances the number of training images for different classes, and on the other, makes full use of the entire base training set." + }, + { + "type": "list", + "bbox": [ + 0.076, + 0.642, + 0.471, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.091, + 0.634, + 0.109 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.115, + 0.892, + 0.162 + ], + "angle": 0, + "content": "We empirically verify the merit of our POP by conducting a thorough evaluation of generalized few-shot semantic segmentation on PASCAL- \\(5^{i}\\) [30] and COCO- \\(20^{i}\\) [27]." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.169, + 0.81, + 0.185 + ], + "angle": 0, + "content": "4.1. Datasets and Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.191, + 0.892, + 0.418 + ], + "angle": 0, + "content": "Datasets. The PASCAL-5\\(^i\\) [30] is built on the PASCAL VOC 2012 [7] dataset with the extended annotations of SDS [9], and contains 12,031 images with high-quality pixel-level annotations of 20 classes. All the images are split into two sets of 10,582 and 1,449 for training and validation, respectively. Following the standard protocol in [30,33], the 20 classes are evenly partitioned into 4 folds (5 classes per fold) for cross-validation. Furthermore, we evaluate the capability of our POP on a more challenging COCO-20\\(^i\\) [27] dataset. COCO-20\\(^i\\) is created upon COCO [19], and includes 122,218 images being annotated with 80 object classes. In between, 82,081 and 40,137 images are exploited for training and validation. Similarly, experiments on COCO-20\\(^i\\) are also conducted with cross-validation on 4 folds (20 classes per fold) following [27,33]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.419, + 0.892, + 0.72 + ], + "angle": 0, + "content": "Evaluation Settings. For both datasets, once we validate the model on one fold, the classes in this fold serve as \"novel classes\" and the classes in the other 3 folds plus background play the role of \"base classes\". In the base class learning phase, we select all images that have at least one pixel belonging to base classes from the original training set for model training. Note that the pixels of novel classes in the selected images are considered as background at this stage. In the novel class updating phase, we mimic the few-shot setting by randomly sampling \\( K \\) images containing pixels of novel classes from the original training set. All the images in the validation set are utilized for the evaluation on both base and novel classes. In the experiments, the Intersection over Union (IoU) per class and mean IoU (mIoU) over the base, novel, and all classes are adopted as the performance metrics. We average the mIoU over all the folds for cross-validation and regard the mean values as the final performance. For stability, we repeat each experiment five times with different random seeds and report the mean values. The same setting is also exploited in [26, 33]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Network Structure. In order to make the capacity of our model comparable to the baselines of generalized few-shot semantic segmentation, we follow [33] and exploit the PSPNet [50] originated from the ImageNet pre-trained ResNet-50 as the encoder. The spatial resolution of the feature map extracted through the encoder is \\(\\frac{1}{8}\\) of the input image and the probability maps produced by POP are upsampled via bilinear interpolation for pixel-level predictions. The number of prototypes depends on the datasets. For PASCAL\\(^5\\) [30], POP has 15 prototypes in the base class learning phase, and then 5 prototypes are additionally included for novel class updating. Similarly, POP maintains 60 and 80" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11323" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.09, + 0.895, + 0.133 + ], + "angle": 0, + "content": "Table 1. Performance comparisons on PASCAL-5\\(^i\\) under the generalized few-shot segmentation settings. We report mIoU (\\%) over base classes (Base), novel classes (Novel), and all classes (Base + Novel = Total). All models are based on ResNet-50. \\(\\dagger\\) indicates that the images containing novel classes are excluded by base class learning phase." + }, + { + "type": "table", + "bbox": [ + 0.188, + 0.135, + 0.78, + 0.317 + ], + "angle": 0, + "content": "
Methods1-shot5-shot10-shot
BaseNovelTotalBaseNovelTotalBaseNovelTotal
CANet [44]8.732.427.239.051.527.26---
PFENet [34]8.322.676.978.831.897.18---
PANet [36]31.8811.2526.9732.9515.2528.74---
BAM [15]73.6232.9663.9473.5734.3564.23---
\\( CAPL^† \\)[33]65.4818.8554.3866.1422.4155.7269.0927.1759.11
FT [26]66.8418.8255.4172.0346.4065.9373.0252.5568.14
FT-Triplet [26]66.4119.7155.3171.3150.4666.3572.8757.0069.10
\\( POP^† \\)67.7319.8556.4571.3443.4864.7172.2350.4967.05
POP73.9235.5164.7774.7855.8770.2874.9958.7771.13
" + }, + { + "type": "table_caption", + "bbox": [ + 0.08, + 0.32, + 0.89, + 0.335 + ], + "angle": 0, + "content": "Table 2. Performance comparisons on COCO- \\( {20}^{i} \\) under the generalized few-shot segmentation settings. Models are based on ResNet-50." + }, + { + "type": "table", + "bbox": [ + 0.188, + 0.338, + 0.78, + 0.458 + ], + "angle": 0, + "content": "
Methods1-shot5-shot10-shot
BaseNovelTotalBaseNovelTotalBaseNovelTotal
\\( \\text{CAPL}^\\dagger [33] \\)44.617.0535.4645.2411.0536.8045.5110.8236.95
FT [26]43.428.9434.9047.1824.7241.6348.1830.0343.70
FT-Triplet [26]43.649.2335.1446.6128.8441.3646.6134.4943.27
\\( \\text{POP}^\\dagger \\)47.097.2737.2648.6120.0741.5649.1326.4843.54
POP54.7115.3144.9854.9029.9748.7555.0135.0550.08
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.469, + 0.47, + 0.501 + ], + "angle": 0, + "content": "prototypes in two training phases on COCO- \\(20^{i}\\), respectively. The dimension of the prototypes is set as 512." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.504, + 0.471, + 0.732 + ], + "angle": 0, + "content": "Training Strategy. Our POP is implemented on PyTorch [28]. The mini-batch stochastic gradient descent with momentum 0.9 and weight decay 0.0001 is exploited to optimize the model. During the base class learning, the initial learning rate is set to 0.01 which is annealed down to zero following a \"poly\" policy whose power is fixed to 0.9. The batch size is set as 8 for both datasets and the training of each model takes 50 epochs. For novel class updating, we update the model with a fixed learning rate 0.01 for 500 epochs, and the batch size is set as 2 and 8 for PASCAL\\(^{5^i}\\) and COCO-20\\(^{i}\\), respectively. For data augmentation, we employ the \\(473 \\times 473\\) patches randomly cropped from the random scaled images. Each patch is randomly flipped along horizontal direction. The trade-off parameter \\(\\lambda\\) is set as 10 empirically." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.47, + 0.903 + ], + "angle": 0, + "content": "Baseline Approaches. We compared the following approaches: (1) CAPL [33] capitalizes on the contextual co-occurrence cues to enrich the prototypes in the classifier for novel class segmentation. (2) FT [26] directly fine-tunes the model on some sampled data from base classes plus novel examples for novel class updating. Moreover, we also report the setting of FT which additionally leverages a triplet loss regularization to augment the learnt models. (3) Following [33], we form a prototype for each class by averaging features of pixels belonging to the corresponding class in all training images and utilize prototypes to" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.469, + 0.895, + 0.607 + ], + "angle": 0, + "content": "segment the query image. By doing so, we can remould the three prototype-based FSS approaches of CANet [44], PFENet [34] and PANet [36] for GFSS. The reported results are directly drawn from [33]. (4) BAM [15] is an FSS model that segments all base classes and one novel class through one inference process. To adapt for GFSS, we execute multiple inferences each of which takes the query image and support images of one novel class as the inputs. Following [15], we fuse the model outputs as the final prediction." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.619, + 0.736, + 0.636 + ], + "angle": 0, + "content": "4.2. Performance Comparison" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.644, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Quantitative Analysis. We compare with several state-of-the-art techniques on PASCAL- \\(5^{i}\\) and COCO- \\(20^{i}\\) datasets. Table 1 summarizes the mIoU performances of capitalizing on different number of shots (\\(K\\)-shot) on PASCAL- \\(5^{i}\\). Overall, the results across three \\(K\\)-shot settings consistently indicate that our POP successfully adapts the models for novel classes and achieves the better performances than other baselines including the remoulded FSS methods and GFSS approaches. In particular, the mIoU of POP on all classes, i.e., Total, reaches \\(64.77\\%\\), \\(70.28\\%\\) and \\(71.13\\%\\) on 1-shot, 5-shot and 10-shot scenarios, respectively, making the absolute gain over the best competitors by \\(0.83\\%\\), \\(3.93\\%\\), and \\(2.03\\%\\). CAPL, FT and POP generally exhibit higher mIoU against the reshaped FSS approaches of CANet, PFENet and PANet. This somewhat reveals the weakness of the episodic training/testing scheme of FSS, which once only distinguishes pixels be" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "11324" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.088, + 0.891, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.082, + 0.333, + 0.886, + 0.348 + ], + "angle": 0, + "content": "Figure 3. Twelve examples of semantic segmentation from PASCAL-5 by POP and the state-of-the-art FT method under 5-shot setting." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.357, + 0.47, + 0.508 + ], + "angle": 0, + "content": "longing to one specific class by abstracting the knowledge of such class from support images. As such, FSS models result in inferior performances for GFSS, where the models necessitate to identify novel classes and all possible base classes simultaneously. BAM in part alleviates the limitation of FSS scheme by additionally employing a base class segmentation branch, that is pre-trained on base data and frozen when FSS training to refine the predictions of novel classes. As such, adapting BAM to GFSS can segment the base classes well and yield good performances." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.508, + 0.471, + 0.825 + ], + "angle": 0, + "content": "Compared to GFSS models of CAPL and FT in which the segmentation of base classes may be affected by novel class updating, POP improves the segmentation quality of base classes. The results basically indicate the advantage of freezing the well-learnt representations of base classes in model updating. On novel classes, the mIoU of POP is \\(35.51\\%\\) in 1-shot scenario and further increased to \\(55.87\\%\\) when knowing 5 labeled images (5-shot) for each category. As a good performer in 1-shot setting, BAM benefits from the frozen base representations and a well-tuned base-novel fusion strategy. Nevertheless, BAM still suffers from interclass confusion and is inferior to POP, especially when having more available novel examples. The results in general verify the impact of learning orthogonal prototypes in POP. Please note that for a fair comparison, we follow CAPL [33] and also implement POP with the dataset filtration that excludes the images containing pixels of novel classes in base class learning phase. We denote such run as \\(\\mathsf{POP}^{\\dagger}\\). \\(\\mathsf{POP}^{\\dagger}\\) constantly outperforms CAPL across different few-shot settings, demonstrating the effectiveness of representing an image via a group of uncorrelated features by POP." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.826, + 0.47, + 0.9 + ], + "angle": 0, + "content": "Table 2 details the performance comparisons of 1-shot, 5-shot and 10-shot experiments on COCO- \\(20^{i}\\) dataset. Similar to the observations on PASCAL- \\(5^{i}\\), POP surpasses the best-performing baseline FT-Triplet on 5-shot by \\(8.29\\%\\), \\(1.13\\%\\) and \\(7.39\\%\\) in terms of mIoU on base, novel and" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.356, + 0.892, + 0.416 + ], + "angle": 0, + "content": "all classes, respectively. Furthermore, POP obtains better segmentation quality on base classes against other methods across different k-shot settings. The results again empirically validate our proposal." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.417, + 0.893, + 0.568 + ], + "angle": 0, + "content": "Qualitative Analysis. Figure 3 showcases twelve semantic segmentation examples in PASCAL- \\(5^{i}\\), by our POP and the state-of-the-art FT [26] method under 5-shot setting of GFSS. Clearly, POP attains much more promising segmentation results. Taking the query images in the first three columns on the left as the examples, POP nicely identifies the novel class \"Person\", but FT fails to recognize this category. Moreover, the novel class \"Dog\" in the fourth example on the left is well segmented by both models and POP distinguishes the base class of \"Sofa\" more precisely." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.576, + 0.712, + 0.592 + ], + "angle": 0, + "content": "4.3. Experimental Analysis" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.599, + 0.892, + 0.75 + ], + "angle": 0, + "content": "Evaluation on POP Designs. We first examine the performance contribution of different factors in POP. In between, PRO projects the features on the learnt prototypes to predict the class of each pixel and base prototypes are frozen when learning novel ones in the updating phase. ORT further encourages the orthogonality between prototypes of different classes to alleviate inter-class confusion. RES is our residual background representation that measures the residual of feature projection to categorize background pixels rather than learn a prototype for \"background\"." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.893, + 0.902 + ], + "angle": 0, + "content": "Table 3 shows the mIoU improvement on the PASCAL\\(^5\\) dataset by considering one more factors in POP for GFSS. Note that the data enrichment strategies are not employed in the experiments here. We start from the basic run of FT [26] that capitalizes on the same features to classify the pixels of base and novel classes in the images. PRO successfully boosts up the total mIoU performance from \\(55.41\\%\\) to \\(59.59\\%\\) under 1-shot setting and introduces \\(5.53\\%\\) mIoU improvement on base classes. This basically proves that PRO is a very effective and practical way for" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11325" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.09, + 0.47, + 0.146 + ], + "angle": 0, + "content": "Table 3. Ablation studies of POP on PASCAL- \\(5^{i}\\). PRO represents segmentation via feature projection on learnt prototypes. ORT denotes the orthogonality constraint on prototypes. RES means that the background is represented by residual of feature projection." + }, + { + "type": "table", + "bbox": [ + 0.081, + 0.149, + 0.468, + 0.248 + ], + "angle": 0, + "content": "
PROORTRES1-shot5-shot
BaseNovelTotalBaseNovelTotal
---66.8418.8255.4172.0346.4065.93
✓--72.3718.7159.5973.3946.9967.10
✓✓-72.8223.6561.1173.7650.6968.27
✓✓✓73.1132.2963.3974.3253.4669.36
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.266, + 0.468, + 0.336 + ], + "angle": 0, + "content": "Table 4. Ablation studies of data enrichment strategies for novel class updating on PASCAL- \\(5^{i}\\). RL denotes relabeling which provides pseudo labels for background pixels in images of base data. RS represents the resampling strategy that randomly selects different base data for each epoch of novel class updating." + }, + { + "type": "table", + "bbox": [ + 0.08, + 0.338, + 0.466, + 0.438 + ], + "angle": 0, + "content": "
RLRS1-shot5-shot
BaseNovelTotalBaseNovelTotal
--73.1132.2963.3974.3253.4669.36
✓-73.1035.0864.0474.3954.8169.73
-✓72.8627.7462.1274.3152.1269.03
✓✓73.9235.5164.7774.7855.8770.28
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.458, + 0.468, + 0.609 + ], + "angle": 0, + "content": "GFSS to generalize the model to novel classes without sacrificing much accuracy on base classes. The performance gain of ORT is \\(0.45\\%\\) and \\(4.94\\%\\) in terms of mIoU on base and novel classes, respectively. The results verify the idea of learning orthogonal prototypes for feature projection. Measuring the residual of feature projection as background representation by RES further contributes a mIoU increase of \\(8.64\\%\\) and \\(2.77\\%\\) on novel classes in 1-shot and 5-shot settings. That empirically manifests the superiority of RES to dynamically fit semantic shifting in GFSS." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.612, + 0.468, + 0.838 + ], + "angle": 0, + "content": "Evaluation on Data Enrichment. To better study the contribution of each design in data enrichment, we then conduct the comparisons on POP configured with different enrichment strategies. Table 4 lists the results of both 1-shot and 5-shot scenarios. In particular, relabeling the background pixels in base samples leads to over \\(1\\%\\) mIoU improvements on novel classes. The results indicate that assigning pseudo labels to novel class pixels in base data could refine noisy background labels and ease the prototype learning for novel classes. The performance gain tends to be larger when updating the model on more base data via resampling. The performance however drops if solely using resampling. We speculate that this may be the result of involving more novel class pixels which may be incorrectly regarded as background due to semantic shifting." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.468, + 0.901 + ], + "angle": 0, + "content": "Confusion Matrix Visualization. Figure 4 depicts the confusion matrices of FT and POP under 5-shot scenario on the second fold of PASCAL- \\(5^{i}\\). We visualize the matrix after each training phase and highlight the difference in" + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.087, + 0.885, + 0.354 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.356, + 0.892, + 0.412 + ], + "angle": 0, + "content": "Figure 4. The difference of confusion matrices between two training phases. Compared to FT, POP obtains higher novel class performance after novel class updating without sacrificing much accuracy on base classes learnt in base class learning." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.43, + 0.892, + 0.551 + ], + "angle": 0, + "content": "between. After the first phase of base class learning, both models achieve over \\(72.9\\%\\) mIoU on base classes. Through the second phase of novel class updating, the mIoU on novel classes is \\(45.50\\%\\) by FT but the performance decreases to \\(68.37\\%\\) on base classes. In contrast, POP reaches \\(63.36\\%\\) mIoU on novel class with only \\(0.13\\%\\) mIoU drop on base classes. That again validates the good property of POP to identify novel classes without compromising base ones." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.564, + 0.618, + 0.58 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.59, + 0.892, + 0.831 + ], + "angle": 0, + "content": "We have presented Projection onto Orthogonal Prototypes (POP) framework, which explores a principled way to generalize the model to novel classes without sacrificing much segmentation capability on base classes for GFSS. In particular, we study the problem by learning a group of prototypes and each represents a semantic class, thereby predicting each class separately with respect to the projections on its prototype. Technically, POP learns prototypes first on base classes and then updates the prototypes to support novel classes, with orthogonal constraint. Moreover, POP measures the residual of feature projection as the background representation to better mitigate semantic shifting. Experiments conducted on two datasets of PASCAL- \\(5^{i}\\) and COCO- \\(20^{i}\\) validate our proposal and analysis. Performance improvements are observed when comparing to both GFSS and remoulded FSS techniques." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.831, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Acknowledgements This work is partly supported by the National Key Research and Development Program of China (2022YFB3104700), the National Nature Science Foundation of China (62121002, 62022076, U1936210)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11326" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.175, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.171 + ], + "angle": 0, + "content": "[1] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE TPAMI, 2017. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.173, + 0.472, + 0.227 + ], + "angle": 0, + "content": "[2] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In ECCV, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.229, + 0.471, + 0.271 + ], + "angle": 0, + "content": "[3] Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. In NeurIPS, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.273, + 0.471, + 0.3 + ], + "angle": 0, + "content": "[4] Pierre Comon. Independent component analysis, a new concept? Signal processing, 1994. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.302, + 0.471, + 0.329 + ], + "angle": 0, + "content": "[5] Nanqing Dong and Eric P. Xing. Few-shot semantic segmentation with prototype learning. In BMVC, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.331, + 0.471, + 0.413 + ], + "angle": 0, + "content": "[6] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.415, + 0.471, + 0.457 + ], + "angle": 0, + "content": "[7] Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. *IJCV*, 2009. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.459, + 0.471, + 0.5 + ], + "angle": 0, + "content": "[8] Jun Fu, Jing Liu, Hajjie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In CVPR, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.502, + 0.471, + 0.542 + ], + "angle": 0, + "content": "[9] Bharath Hariharan, Pablo Arbeláez, Lubomir D. Bourdev, Subhransu Maji, and Jitendra Malik. Semantic contours from inverse detectors. In ICCV, 2011. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.544, + 0.47, + 0.585 + ], + "angle": 0, + "content": "[10] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.588, + 0.471, + 0.642 + ], + "angle": 0, + "content": "[11] Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, and Abbas Rahimi. Constrained few-shot class-incremental learning. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.644, + 0.47, + 0.685 + ], + "angle": 0, + "content": "[12] Qibin Hou, Li Zhang, Ming-Ming Cheng, and Jiashi Feng. Strip pooling: Rethinking spatial pooling for scene parsing. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.688, + 0.47, + 0.714 + ], + "angle": 0, + "content": "[13] Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In CVPR, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.717, + 0.47, + 0.758 + ], + "angle": 0, + "content": "[14] Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu. Ccnet: Criss-cross attention for semantic segmentation. In ICCV, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.76, + 0.47, + 0.801 + ], + "angle": 0, + "content": "[15] Chunbo Lang, Gong Cheng, Binfei Tu, and Junwei Han. Learning what not to segment: A new perspective on few-shot segmentation. In CVPR, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.803, + 0.47, + 0.857 + ], + "angle": 0, + "content": "[16] Gen Li, Varun Jampani, Laura Sevilla-Lara, Deqing Sun, Jonghyun Kim, and Joongkyu Kim. Adaptive prototype learning and allocation for few-shot segmentation. In CVPR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[17] Xia Li, Zhisheng Zhong, Jianlong Wu, Yibo Yang, Zhouchen Lin, and Hong Liu. Expectation-maximization attention networks for semantic segmentation. In ICCV, 2019. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.133 + ], + "angle": 0, + "content": "[18] Yehao Li, Ting Yao, Yingwei Pan, and Tao Mei. Contextual transformer networks for visual recognition. IEEE TPAMI, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[19] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.192, + 0.892, + 0.234 + ], + "angle": 0, + "content": "[20] Binghao Liu, Yao Ding, Jianbin Jiao, Xiangyang Ji, and Qixiang Ye. Anti-aliasing semantic reconstruction for few-shot semantic segmentation. In CVPR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.235, + 0.892, + 0.275 + ], + "angle": 0, + "content": "[21] Sun-Ao Liu, Hongtao Xie, Hai Xu, Yongdong Zhang, and Qi Tian. Partial class activation attention for semantic segmentation. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.278, + 0.892, + 0.318 + ], + "angle": 0, + "content": "[22] Yongfei Liu, Xiangyi Zhang, Songyang Zhang, and Xuming He. Part-aware prototype network for few-shot semantic segmentation. In ECCV, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.32, + 0.892, + 0.36 + ], + "angle": 0, + "content": "[23] Fuchen Long, Zhaofan Qiu, Yingwei Pan, Ting Yao, Jiebo Luo, and Tao Mei. Stand-alone inter-frame attention in video models. In CVPR, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.363, + 0.892, + 0.403 + ], + "angle": 0, + "content": "[24] Fuchen Long, Zhaofan Qiu, Yingwei Pan, Ting Yao, Chong-Wah Ngo, and Tao Mei. Dynamic temporal filtering in video models. In ECCV, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.406, + 0.892, + 0.446 + ], + "angle": 0, + "content": "[25] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.449, + 0.892, + 0.503 + ], + "angle": 0, + "content": "[26] Josh Myers-Dean, Yinan Zhao, Brian Price, Scott Cohen, and Danna Gurari. Generalized few-shot semantic segmentation: All you need is fine-tuning. arXiv preprint arXiv:2112.10982, 2021. 1, 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.505, + 0.892, + 0.544 + ], + "angle": 0, + "content": "[27] Khoi Duc Minh Nguyen and Sinisa Todorovic. Feature weighting and boosting for few-shot segmentation. In ICCV, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.547, + 0.892, + 0.657 + ], + "angle": 0, + "content": "[28] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.66, + 0.892, + 0.7 + ], + "angle": 0, + "content": "[29] Kanchana Ranasinghe, Muzammal Naseer, Munawar Hayat, Salman Khan, and Fahad Shahbaz Khan. Orthogonal projection loss. In ICCV, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.702, + 0.892, + 0.743 + ], + "angle": 0, + "content": "[30] Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, and Byron Boots. One-shot learning for semantic segmentation. arXiv preprint arXiv:1709.03410, 2017. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.746, + 0.892, + 0.786 + ], + "angle": 0, + "content": "[31] Christian Simon, Piotr Koniusz, Richard Nock, and Mehrtash Harandi. Adaptive subspaces for few-shot learning. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.788, + 0.892, + 0.828 + ], + "angle": 0, + "content": "[32] Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmentation. In ICCV, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.831, + 0.892, + 0.871 + ], + "angle": 0, + "content": "[33] Zhuotao Tian, Xin Lai, Li Jiang, Shu Liu, Michelle Shu, Hengshuang Zhao, and Jiaya Jia. Generalized few-shot semantic segmentation. In CVPR, 2022. 1, 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.873, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[34] Zhuotao Tian, Hengshuang Zhao, Michelle Shu, Zhicheng Yang, Ruiyu Li, and Jiaya Jia. Prior guided feature en" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11327" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.092, + 0.468, + 0.12 + ], + "angle": 0, + "content": "richment network for few-shot segmentation. IEEE TPAMI, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.468, + 0.191 + ], + "angle": 0, + "content": "[35] Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. Mlp-mixer: An all-mlp architecture for vision. In NeurIPS, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.194, + 0.468, + 0.235 + ], + "angle": 0, + "content": "[36] Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng. Panet: Few-shot image semantic segmentation with prototype alignment. In ICCV, 2019. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.238, + 0.468, + 0.278 + ], + "angle": 0, + "content": "[37] Xin Wang, Thomas Huang, Joseph Gonzalez, Trevor Darrell, and Fisher Yu. Frustratingly simple few-shot object detection. In ICML, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.281, + 0.468, + 0.322 + ], + "angle": 0, + "content": "[38] Zhonghua Wu, Xiangxi Shi, Guosheng Lin, and Jianfei Cai. Learning meta-class memory for few-shot semantic segmentation. In ICCV, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.325, + 0.468, + 0.379 + ], + "angle": 0, + "content": "[39] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. In NeurIPS, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.382, + 0.468, + 0.422 + ], + "angle": 0, + "content": "[40] Boyu Yang, Chang Liu, Bohao Li, Jianbin Jiao, and Qixiang Ye. Prototype mixture models for few-shot semantic segmentation. In ECCV, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.426, + 0.468, + 0.466 + ], + "angle": 0, + "content": "[41] Ting Yao, Yehao Li, Yingwei Pan, Yu Wang, Xiao-Ping Zhang, and Tao Mei. Dual vision transformer. arXiv preprint arXiv:2207.04976, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.469, + 0.468, + 0.511 + ], + "angle": 0, + "content": "[42] Ting Yao, Yingwei Pan, Yehao Li, Chong-Wah Ngo, and Tao Mei. Wave-vit: Unifying wavelet and transformers for visual representation learning. In ECCV, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.513, + 0.468, + 0.553 + ], + "angle": 0, + "content": "[43] Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object-contextual representations for semantic segmentation. In ECCV, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.556, + 0.468, + 0.61 + ], + "angle": 0, + "content": "[44] Chi Zhang, Guosheng Lin, Fayao Liu, Rui Yao, and Chunhua Shen. Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. In CVPR, 2019. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.614, + 0.468, + 0.668 + ], + "angle": 0, + "content": "[45] Fan Zhang, Yanqin Chen, Zhihang Li, Zhibin Hong, Jingtuo Liu, Feifei Ma, Junyu Han, and Errui Ding. Acfnet: Attentional class feature network for semantic segmentation. In ICCV, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.671, + 0.468, + 0.712 + ], + "angle": 0, + "content": "[46] Yiheng Zhang, Zhaofan Qiu, Jingen Liu, Ting Yao, Dong Liu, and Tao Mei. Customizable architecture search for semantic segmentation. In CVPR, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.715, + 0.468, + 0.756 + ], + "angle": 0, + "content": "[47] Yiheng Zhang, Zhaofan Qiu, Ting Yao, Dong Liu, and Tao Mei. Fully convolutional adaptation networks for semantic segmentation. In CVPR, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.759, + 0.468, + 0.8 + ], + "angle": 0, + "content": "[48] Yiheng Zhang, Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Dong Liu, and Tao Mei. Transferring and regularizing prediction for semantic segmentation. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.802, + 0.468, + 0.855 + ], + "angle": 0, + "content": "[49] Yiheng Zhang, Ting Yao, Zhaofan Qiu, and Tao Mei. Lightweight and progressively-scalable networks for semantic segmentation. arXiv preprint arXiv:2207.13600, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.468, + 0.9 + ], + "angle": 0, + "content": "[50] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In CVPR, 2017. 2, 5" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.468, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.092, + 0.892, + 0.134 + ], + "angle": 0, + "content": "[51] Zhen Zhu, Mengde Xu, Song Bai, Tengteng Huang, and Xiang Bai. Asymmetric non-local neural networks for semantic segmentation. In ICCV, 2019. 2" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11328" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation/f55ad462-d47a-4afc-95bc-6a55486b79b1_origin.pdf b/2023/Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation/f55ad462-d47a-4afc-95bc-6a55486b79b1_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..9f0901abcc07aaf7aca7b3e96d0f72cc9c9b965f --- /dev/null +++ b/2023/Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation/f55ad462-d47a-4afc-95bc-6a55486b79b1_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c0a5566d5204915850321975da739efcc2e76fc14e9a0ab37c0ea8d3e143cbf7 +size 969948 diff --git a/2023/Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation/full.md b/2023/Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..c4b260a9fe12a08352ab57a0bb46b5898cb17408 --- /dev/null +++ b/2023/Learning Orthogonal Prototypes for Generalized Few-Shot Semantic Segmentation/full.md @@ -0,0 +1,272 @@ +# Learning Orthogonal Prototypes for Generalized Few-shot Semantic Segmentation + +Sun-Ao Liu, Yiheng Zhang, Zhaofan Qiu, Hongtao Xie*, Yongdong Zhang, Ting Yao University of Science and Technology of China HiDream.ai Inc. + +{lsa1997.mail, htxie, zhyd73}@ustc.edu.cn, {yihengzhang.chn, zhaofanqiu, tingyao.ustc}@gmail.com + +# Abstract + +Generalized few-shot semantic segmentation (GFSS) distinguishes pixels of base and novel classes from the background simultaneously, conditioning on sufficient data of base classes and a few examples from novel class. A typical GFSS approach has two training phases: base class learning and novel class updating. Nevertheless, such a standalone updating process often compromises the well-learnt features and results in performance drop on base classes. In this paper, we propose a new idea of leveraging Projection onto Orthogonal Prototypes (POP), which updates features to identify novel classes without compromising base classes. POP builds a set of orthogonal prototypes, each of which represents a semantic class, and makes the prediction for each class separately based on the features projected onto its prototype. Technically, POP first learns prototypes on base data, and then extends the prototype set to novel classes. The orthogonal constraint of POP encourages the orthogonality between the learnt prototypes and thus mitigates the influence on base class features when generalizing to novel prototypes. Moreover, we capitalize on the residual of feature projection as the background representation to dynamically fit semantic shifting (i.e., background no longer includes the pixels of novel classes in updating phase). Extensive experiments on two benchmarks demonstrate that our POP achieves superior performances on novel classes without sacrificing much accuracy on base classes. Notably, POP outperforms the state-of-the-art fine-tuning by $3.93\%$ overall mIoU on PASCAL- $5^i$ in 5-shot scenario. + +# 1. Introduction + +Semantic segmentation is to assign semantic labels to every pixel of an image. With the recent development of CNNs [10, 13] and vision transformers [6, 18, 23, 24, 35, 41, 42], the state-of-the-art networks have successfully pushed the limits of semantic segmentation [1, 3, 25, 49] with remarkable performance improvements. Such achievements + +![](images/6a2a4028f5521dfaa08792e7c7decfea170297f1a71d89f3cc42798f1a430b7c.jpg) +(a) + +![](images/38a3f4455904532d9ecdd54cba2cf23551e3af8d0714e525dd8c25d5ef54736b.jpg) +(b) +Figure 1. Comparisons between fine-tuning [26] and Projection onto Orthogonal Prototypes (POP). In novel class updating phase, fine-tuning (a) updates the network to predict base and novel classes, and inevitably compromises the well-learnt representations for base classes. Instead, POP (b) only updates prototypes for novel classes and executes predictions for each class separately on the features projected onto different orthogonal prototypes. + +heavily rely on the requirements of large quantities of pixel-level annotations and it is also difficult to directly apply the models to the classes unseen in the training set. A straightforward way to alleviate this issue is to leverage Few-shot Semantic Segmentation (FSS) [30, 36, 44], which utilizes the limited support annotations from unseen/novel classes to adapt the models. Nevertheless, FSS performs on the assumption that the support images and the query image contain the same novel classes, and solely emphasizes the segmentation of one novel class in the query image at a time. A more practical scenario namely Generalized Few-shot Semantic Segmentation (GFSS) [33] is recently presented to simultaneously identify the pixels of base and novel classes in a query image. + +A typical GFSS solution has proceeded along two training phases: base class learning and novel class updating. In the first phase, models are trained on abundant base classes' annotations to classify the pixels of base categories, and then updated with the limited labeled novel examples in the second phase to additionally recognize pixels of novel + +classes. For instance, Myers Dean et al. [26] sample some base data plus novel examples as the supervision to finetune the network, as depicted in Figure 1(a). Despite having good performances on novel classes, there still exists a clear performance degradation on base classes. We speculate that this may be the results of compromising the well-learnt features of base classes in fine-tuning. As such, a valid question then emerges as is it possible for GFSS to nicely generalize the model to novel classes without sacrificing much segmentation capability on base classes. In an effort to answer this question, we seek to represent an image via a group of uncorrelated feature components each of which characterizes a specific class. By doing so, it is readily applicable to learn and integrate new components for novel classes without affecting the ones learnt for base classes. + +To materialize our idea, we propose a new Projection onto Orthogonal Prototypes (POP) framework for GFSS. POP learns a series of orthogonal prototypes and each prototype corresponds to one specific semantic class. As shown in Figure 1(b), POP employs an encoder to extract feature maps of a given query image and then projects them onto prototypes. The projection on each prototype is regarded as the discriminative representation with respect to the corresponding class and exploited to predict the probability map of pixels belonging to the class. More specifically, POP deliberately devises the learning of prototypes from three standpoints. The first one is to freeze the base prototypes when learning novel ones from support images in the updating phase. In this way, feature projections on base prototypes maintain their discriminability of base classes. Second, POP encourages the prototypes of base and novel classes to be orthogonal through a prototype orthogonality loss. Such a constraint decorrelates features projected onto different prototypes and mitigates the inter-class confusion caused by extending to novel classes. Finally, in view that background no longer contains the pixels of novel classes in updating phase, known as "semantic shifting", POP measures the residual of feature projection as the background representation instead of learning a prototype for "background". This way further improves the differentiation between novel classes and background. + +The main contribution of this work is the proposal of a Projection on Orthogonal Prototypes (POP) framework for generalized few-shot semantic segmentation. The solution also leads to the elegant views of how to adapt the trained model to novel classes without sacrificing well-learnt features, and how to represent background pixels dynamically in the context of semantic shifting, which are problems not yet fully explored in the literature. We demonstrate that POP outperforms the state-of-the-art fine-tuning [26] on two benchmarks (PASCAL-5 $^i$ and COCO-20 $^i$ ) with evident improvements on both base and novel classes. + +# 2. Related Work + +Semantic Segmentation. Extensive FCN-based methods have been proposed and achieved remarkable performances. For example, pyramid pooling [12, 48, 50] and parallel dilated convolutions [1, 2, 46, 47] are utilized to aggregate multi-scale context. The attention mechanism is widely exploited to model long-range dependencies [8, 14, 17, 21, 43, 45, 51]. Recently, vision transformer has also been proven effective for semantic segmentation [3, 32, 39]. Though these efforts lead to high-quality segmentation, it is still difficult to directly apply the learnt models to unseen classes. + +Few-shot Semantic Segmentation. Few-shot semantic segmentation (FSS) [30] executes pixel-wise labeling on new classes given a limited number of support examples. It mainly focuses on the 1-way scenario that requires binary maps for query images to identify the pixels belonging to the class labeled in support images. PL [5] and PANet [36] remould prototype learning for FSS by calculating the cosine similarities between pixels and prototypes obtained from support images. ASR [20] learns several orthogonal prototypes on the base data to represent novel categories. In addition, assigning multiple prototypes to each class is also a promising solution to improve FSS models [16,22,38,40]. + +Generalized Few-shot Semantic Segmentation. Despite showing great potential, it is not trivial for FSS approaches to simultaneously identify the pixels of both base and novel classes in a query image. Generalized Few-shot Semantic Segmentation (GFSS) is further presented by Tian et al. [33] to address this problem. GFSS aims to segment both base and novel classes from query images through one inference process without paired support images. To achieve this goal, CAPL [33] mines contextual cues from both support and query samples to enrich the classifier for novel class segmentation. Fine-tuning [26] is another practical and effective way to adapt models for novel classes. + +Orthogonality. Feature orthogonality is known to be helpful to improve discriminative ability. DSN [31] constrains the orthogonality on base data to facilitate few-shot classification on novel classes. C-FSCIL [11] and OPL [29] rely on re-training the network to keep the features of each class orthogonal. Nevertheless, these approaches are not applicable for GFSS since they either ignore the influence between base and novel classes or compromise the segmentation capability on base classes after re-training. + +Our work focuses on adapting the semantic segmentation model to novel classes without compromising well-learnt features for better generalization. Different from existing GFSS approaches may sacrifice base class segmentation quality for novel class updating, our POP contributes by studying not only learning orthogonal prototypes to independently indicate pixels of base and novel classes, but also how the background could be nicely represented in the context of semantic shifting. + +![](images/7f1053cbb34f3f85f9d3c8c8896a86b6950b0c0df28f40ec439956e957df6ada.jpg) + +![](images/e664af1377760018d80a7a39a39b40fc49a6a6af4655204735fb745d62bf8b2c.jpg) +Figure 2. An overview of our Projection onto Orthogonal Prototypes (POP) framework for generalized few-shot semantic segmentation. The input image is first encoded into dense feature map via a CNN encoder, which is then projected onto a series of orthogonal prototypes to make prediction of each class independently through a shared classifier. In the base class learning phase, the entire network and prototypes for base classes are learnt on sufficient base data. In the second phase of novel class updating, we freeze all the modules learnt in the first phase and only optimize the newly minted prototypes for novel classes along with an additional classifier for novel classes and background class. As such, the proposed POP generalizes well to novel classes while maintaining the segmentation capability on base classes. + +# 3. Our Method + +In this paper, we devise Projection onto Orthogonal Prototypes (POP) framework for generalized few-shot semantic segmentation (GFSS), by decoupling the feature learning of base classes and novel classes. Figure 2 illustrates an overview of the whole architecture of POP. We begin this section by elaborating the problem formulation of GFSS with the two-phase training scheme. Then, a projection-based framework is introduced to learn class-wise orthogonal prototypes, and extract the residual representation as background descriptor. Finally, we present the overall objective combining orthogonal constraint along with the semantic segmentation loss. + +# 3.1. Problem Formulation + +In generalized few-shot semantic segmentation, we are given abundant annotated images for $M$ base classes $C^b = \{c_1,\dots ,c_M\}$ and only $K$ labeled images per class for $N$ novel classes $C^n = \{c_{M + 1},\ldots ,c_{M + N}\}$ . The background category $c_{0}$ presents the pixels that do not belong to any target classes. The goal of this task is to simultaneously distinguish both base and novel classes from backgrounds, i.e., $M + N + 1$ classes in total. Inspired by recent success of two-phase training scheme in generalized few-shot semantic segmentation [26,33], we formulate our POP framework in two-phase manner: base class learning and novel class + +updating. Accordingly, POP is first trained with sufficient data for $(M + 1)$ -class segmentation $(C^b \cup \{c_0\})$ in the first phase, and then updates the segmentation head with the few-shot support data targeting for pixel-wise prediction of all $M + N + 1$ classes $(C^b \cup C^n \cup \{c_0\})$ in the second phase. + +# 3.2. Projection onto Orthogonal Prototypes + +GFSS necessitates the capability to segment both base and novel classes. In this case, the novel updating process should learn and integrate new components for novel classes without affecting the ones learnt for base classes. However, the existing approaches [26, 33] in this area still show obvious performance degradation on base classes in the second phase. They often suffer from a challenge of whether to drastically overhaul the network for the performance on novel classes or preserve the well-learnt representation for base classes. We attribute this issue to the highly-relevant representations to identify different classes, which interfere with each other when integrating novel classes. + +To fulfill the objective of decoupling the feature learning of base classes and novel classes, we propose to orthogonally decompose the feature map into several uncorrelated components, each of which characterizes a specific class. Formally, we denote the dense feature map from the encoder network as $\pmb{f} \in \mathbb{R}^{(H \times W) \times C}$ , where $H$ , $W$ and $C$ are the height, the width and the number of channels of feature map, respectively. Assuming the matrix rank of extracted + +features is $r$ ( $r \leq C$ ), the feature map $f$ can be represented as the weighted summation of $r$ orthogonal bases as: + +$$ +\boldsymbol {f} = \sum_ {i = 1} ^ {r} f ^ {(i)} \cdot \boldsymbol {u} _ {i}, \tag {1} +$$ + +$$ +\text {s . t .} \boldsymbol {u} _ {i} \cdot \boldsymbol {u} _ {j} ^ {T} = 0 (i \neq j), +$$ + +where $\{\pmb{u}_i\in \mathbb{R}^{1\times C}|_{i = 1\dots r}\}$ is a group of unit bases that are orthogonal to each other, and $f^{(i)}\in \mathbb{R}^{(H\times W)\times 1}$ is the weight map of the $i$ -th component. This process is well known as orthogonal decomposition, and widely explored for Independent Component Analysis (ICA) [4]. In our work, we treat the basis $\pmb{u}_i$ as the prototype of the $i$ -th class and the corresponding component $f^{(i)}\cdot \pmb{u}_i$ as the representation to identify the pixels of the $i$ -th class. As such, calculating the representation of the $i$ -th class is equivalent to projecting $\pmb{f}$ to the $i$ -th basis $\pmb{u}_i$ : + +$$ +\boldsymbol {f} _ {c _ {i}} = f ^ {(i)} \cdot \boldsymbol {u} _ {i} = (\boldsymbol {f} \cdot \boldsymbol {u} _ {i} ^ {T}) \cdot \boldsymbol {u} _ {i}, \tag {2} +$$ + +where $f_{c_i}$ is the representation of the $i$ -th class and is then taken as the input of a shared classifier. In view that the learnt prototypes are orthogonal to each other, the representations of different classes are decorrelated and easy to be distinguished. More importantly, when integrating a new target class, we only need to learn a new prototype that is orthogonal to the existing ones, thereby preserving the well-learnt representations of other classes. + +Nevertheless, the limit of prototype orthogonality (i.e., $\pmb{u}_i\cdot \pmb{u}_j^T = 0$ ) is not differentiable and hard to be implemented by gradient propagation. Hence, we present a practical alternative to relax the hard limit to a soft orthogonal constraint. Specifically, a prototype orthogonality loss is devised by minimizing absolute value of the inner product between prototypes: + +$$ +L _ {\text {o r t h}} = \frac {1}{r ^ {\prime} \left(r ^ {\prime} - 1\right)} \sum_ {i \neq j} \left| \boldsymbol {u} _ {i} \cdot \boldsymbol {u} _ {j} ^ {T} \right|, \tag {3} +$$ + +where $r' < r$ is the number of target classes. The orthogonality loss $L_{orth}$ restricts the averaged correlation of different prototype pairs, and is minimized when the prototypes are orthogonal to each other. + +# 3.3. Residual as Background Representation + +The background class $c_{0}$ is a special category in segmentation tasks, and indicates the pixels that do not belong to any target classes. One common practice is to treat target classes and background class equally, which reaches reasonable performances on various segmentation tasks. However, such a simple strategy is not acclimatized to GFSS task due to the effect of the "semantic shifting" between two phases. More precisely, in the first phase, the pixels of both + +novel classes $C^n$ and background class $c_0$ are labeled as background. When integrating novel classes in the second phase, the semantic meaning of background changes since it no longer includes the pixels of novel classes. We regard this gap as semantic shifting of background, which results in the confusion between novel classes and background class. + +In order to bridge the gap between two phases, we propose to exploit the residual of feature projection as the background representation in POP, instead of constructing an independent prototype. The residual here is defined as the summation of remaining components after projecting $\mathbf{f}$ to the target classes. For instance, in the base class learning phase, the first $M$ components in Eq.(1) are the projections for $M$ base classes and the remaining $r - M$ components are background components: + +$$ +\boldsymbol {f} = \underbrace {\sum_ {i = 1} ^ {M} f ^ {(i)} \cdot \boldsymbol {u} _ {i}} _ {\text {b a s e c l a s s e s}} + \underbrace {\sum_ {i = M + 1} ^ {r} f ^ {(i)} \cdot \boldsymbol {u} _ {i}} _ {\text {b a c k g r o u n d}}. \tag {4} +$$ + +Note that the background here actually includes the pixels of novel and background classes. In view of the difficulty on learning the remaining $r - M$ components, we reformulate the background representation as the residual of feature projection by subtracting the components of bases classes: + +$$ +\boldsymbol {f} _ {c _ {0}} = \sum_ {i = M + 1} ^ {r} f ^ {(i)} \cdot \boldsymbol {u} _ {i} = \boldsymbol {f} - \sum_ {i = 1} ^ {M} f ^ {(i)} \cdot \boldsymbol {u} _ {i}, \tag {5} +$$ + +where $\pmb{f}_{c_0}$ is orthogonal to the feature $\pmb{f}_{c_i}$ of each class $c_i$ due to the orthogonality constraint between prototypes. Moreover, this reformulation avoids the estimation of matrix rank $r$ , which is more practical. + +For the novel class updating phase, the background components in Eq. (4) are separated into two parts, i.e., $N$ components for $N$ novel classes and $r - M - N$ components as the updated background representation. The decomposition of feature $\pmb{f}$ in this phase is represented as + +$$ +\boldsymbol {f} = \underbrace {\sum_ {i = 1} ^ {M} f ^ {(i)} \cdot \boldsymbol {u} _ {i}} _ {\text {b a s e c l a s s e s}} + \underbrace {\sum_ {i = M + 1} ^ {M + N} f ^ {(i)} \cdot \boldsymbol {u} _ {i}} _ {\text {n o v e l c l a s s e s}} + \underbrace {\sum_ {i = M + N + 1} ^ {r} f ^ {(i)} \cdot \boldsymbol {u} _ {i}} _ {\text {b a c k g r o u n d}}. \tag {6} +$$ + +Similar to Eq.(5), the updated background representation is measured by the residual of feature projection to base and novel classes: + +$$ +\boldsymbol {f} _ {c _ {0}} = \boldsymbol {f} - \sum_ {i = 1} ^ {M} f ^ {(i)} \cdot \boldsymbol {u} _ {i} - \sum_ {i = M + 1} ^ {M + N} f ^ {(i)} \cdot \boldsymbol {u} _ {i}. \tag {7} +$$ + +In this way, the background representation is dynamically adjusted with respect to the change of target classes. It remains orthogonal to the representations of both base classes + +and novel classes, such that the confusion between novel classes and background class is alleviated. + +# 3.4. Optimization + +Following the common practice [26, 33], the optimization of our POP framework contains two phases: base class learning and novel class updating. + +Base class learning. In the first phase, the entire network is trained on sufficient base data to optimize the encoded representations and segment base classes. The overall training objective function of POP combines the semantic segmentation loss of base classes and the orthogonality loss between prototypes: $L = L_{seg} + \lambda L_{orth}$ , where $L_{seg}$ denotes the cross entropy loss for segmentation and $\lambda$ is a scale factor of the orthogonality loss. + +Novel class updating. The second phase involves the $K$ -shot support data of novel classes and expands the set of target classes. We freeze all the modules learnt in the first phase and only optimize the newly constructed prototypes for novel classes along with an additional classifier for novel classes and background class. The loss function is the same as that in the first phase (i.e., $L = L_{seg} + \lambda L_{orth}$ ), and the orthogonality loss $L_{orth}$ is utilized over the prototypes of both base classes and novel classes. + +Data enrichment. The second phase demands the training images of both base and novel classes. As a result of highly unbalanced data between base classes and novel classes, simply merging the two training sets leads to poor performance on novel classes. Inspired by the fine-tuning approaches [26, 37], we establish a balanced training subset by randomly sampling $K$ examples per base class. Such a subset in the second phase has been proven to be effective for partially retraining the knowledge of base classes while integrating novel classes [26, 37]. Besides, we further promote the subset by two data enrichment strategies: + +1) Relabeling. Recall that there is semantic shifting problem between two training phases. In the training images of base classes, the pixels of novel classes are labeled as background. Such annotation misleads the network optimization in the second phase. Therefore, after each epoch, we utilize the latest POP model to relabel the background pixels in the image of base classes, and replace the background annotation with the class with the highest predicted probability across the novel classes and background class. +2) Resampling. The subset only selects a few samples from the images of base classes. It underuses the large amount of training data, and makes the network sensitive to the choice of few-shot samples. Hence, we propose to resample the training images of base classes after each epoch. This strategy, on one hand, well balances the number of training images for different classes, and on the other, makes full use of the entire base training set. + +# 4. Experiments + +We empirically verify the merit of our POP by conducting a thorough evaluation of generalized few-shot semantic segmentation on PASCAL- $5^{i}$ [30] and COCO- $20^{i}$ [27]. + +# 4.1. Datasets and Experimental Settings + +Datasets. The PASCAL-5 $^i$ [30] is built on the PASCAL VOC 2012 [7] dataset with the extended annotations of SDS [9], and contains 12,031 images with high-quality pixel-level annotations of 20 classes. All the images are split into two sets of 10,582 and 1,449 for training and validation, respectively. Following the standard protocol in [30,33], the 20 classes are evenly partitioned into 4 folds (5 classes per fold) for cross-validation. Furthermore, we evaluate the capability of our POP on a more challenging COCO-20 $^i$ [27] dataset. COCO-20 $^i$ is created upon COCO [19], and includes 122,218 images being annotated with 80 object classes. In between, 82,081 and 40,137 images are exploited for training and validation. Similarly, experiments on COCO-20 $^i$ are also conducted with cross-validation on 4 folds (20 classes per fold) following [27,33]. + +Evaluation Settings. For both datasets, once we validate the model on one fold, the classes in this fold serve as "novel classes" and the classes in the other 3 folds plus background play the role of "base classes". In the base class learning phase, we select all images that have at least one pixel belonging to base classes from the original training set for model training. Note that the pixels of novel classes in the selected images are considered as background at this stage. In the novel class updating phase, we mimic the few-shot setting by randomly sampling $K$ images containing pixels of novel classes from the original training set. All the images in the validation set are utilized for the evaluation on both base and novel classes. In the experiments, the Intersection over Union (IoU) per class and mean IoU (mIoU) over the base, novel, and all classes are adopted as the performance metrics. We average the mIoU over all the folds for cross-validation and regard the mean values as the final performance. For stability, we repeat each experiment five times with different random seeds and report the mean values. The same setting is also exploited in [26, 33]. + +Network Structure. In order to make the capacity of our model comparable to the baselines of generalized few-shot semantic segmentation, we follow [33] and exploit the PSPNet [50] originated from the ImageNet pre-trained ResNet-50 as the encoder. The spatial resolution of the feature map extracted through the encoder is $\frac{1}{8}$ of the input image and the probability maps produced by POP are upsampled via bilinear interpolation for pixel-level predictions. The number of prototypes depends on the datasets. For PASCAL $^5$ [30], POP has 15 prototypes in the base class learning phase, and then 5 prototypes are additionally included for novel class updating. Similarly, POP maintains 60 and 80 + +Table 1. Performance comparisons on PASCAL-5 $^i$ under the generalized few-shot segmentation settings. We report mIoU (\%) over base classes (Base), novel classes (Novel), and all classes (Base + Novel = Total). All models are based on ResNet-50. $\dagger$ indicates that the images containing novel classes are excluded by base class learning phase. + +
Methods1-shot5-shot10-shot
BaseNovelTotalBaseNovelTotalBaseNovelTotal
CANet [44]8.732.427.239.051.527.26---
PFENet [34]8.322.676.978.831.897.18---
PANet [36]31.8811.2526.9732.9515.2528.74---
BAM [15]73.6232.9663.9473.5734.3564.23---
\( CAPL^† \)[33]65.4818.8554.3866.1422.4155.7269.0927.1759.11
FT [26]66.8418.8255.4172.0346.4065.9373.0252.5568.14
FT-Triplet [26]66.4119.7155.3171.3150.4666.3572.8757.0069.10
\( POP^† \)67.7319.8556.4571.3443.4864.7172.2350.4967.05
POP73.9235.5164.7774.7855.8770.2874.9958.7771.13
+ +Table 2. Performance comparisons on COCO- ${20}^{i}$ under the generalized few-shot segmentation settings. Models are based on ResNet-50. + +
Methods1-shot5-shot10-shot
BaseNovelTotalBaseNovelTotalBaseNovelTotal
\( \text{CAPL}^\dagger [33] \)44.617.0535.4645.2411.0536.8045.5110.8236.95
FT [26]43.428.9434.9047.1824.7241.6348.1830.0343.70
FT-Triplet [26]43.649.2335.1446.6128.8441.3646.6134.4943.27
\( \text{POP}^\dagger \)47.097.2737.2648.6120.0741.5649.1326.4843.54
POP54.7115.3144.9854.9029.9748.7555.0135.0550.08
+ +prototypes in two training phases on COCO- $20^{i}$ , respectively. The dimension of the prototypes is set as 512. + +Training Strategy. Our POP is implemented on PyTorch [28]. The mini-batch stochastic gradient descent with momentum 0.9 and weight decay 0.0001 is exploited to optimize the model. During the base class learning, the initial learning rate is set to 0.01 which is annealed down to zero following a "poly" policy whose power is fixed to 0.9. The batch size is set as 8 for both datasets and the training of each model takes 50 epochs. For novel class updating, we update the model with a fixed learning rate 0.01 for 500 epochs, and the batch size is set as 2 and 8 for PASCAL $^{5^i}$ and COCO-20 $^{i}$ , respectively. For data augmentation, we employ the $473 \times 473$ patches randomly cropped from the random scaled images. Each patch is randomly flipped along horizontal direction. The trade-off parameter $\lambda$ is set as 10 empirically. + +Baseline Approaches. We compared the following approaches: (1) CAPL [33] capitalizes on the contextual co-occurrence cues to enrich the prototypes in the classifier for novel class segmentation. (2) FT [26] directly fine-tunes the model on some sampled data from base classes plus novel examples for novel class updating. Moreover, we also report the setting of FT which additionally leverages a triplet loss regularization to augment the learnt models. (3) Following [33], we form a prototype for each class by averaging features of pixels belonging to the corresponding class in all training images and utilize prototypes to + +segment the query image. By doing so, we can remould the three prototype-based FSS approaches of CANet [44], PFENet [34] and PANet [36] for GFSS. The reported results are directly drawn from [33]. (4) BAM [15] is an FSS model that segments all base classes and one novel class through one inference process. To adapt for GFSS, we execute multiple inferences each of which takes the query image and support images of one novel class as the inputs. Following [15], we fuse the model outputs as the final prediction. + +# 4.2. Performance Comparison + +Quantitative Analysis. We compare with several state-of-the-art techniques on PASCAL- $5^{i}$ and COCO- $20^{i}$ datasets. Table 1 summarizes the mIoU performances of capitalizing on different number of shots ( $K$ -shot) on PASCAL- $5^{i}$ . Overall, the results across three $K$ -shot settings consistently indicate that our POP successfully adapts the models for novel classes and achieves the better performances than other baselines including the remoulded FSS methods and GFSS approaches. In particular, the mIoU of POP on all classes, i.e., Total, reaches $64.77\%$ , $70.28\%$ and $71.13\%$ on 1-shot, 5-shot and 10-shot scenarios, respectively, making the absolute gain over the best competitors by $0.83\%$ , $3.93\%$ , and $2.03\%$ . CAPL, FT and POP generally exhibit higher mIoU against the reshaped FSS approaches of CANet, PFENet and PANet. This somewhat reveals the weakness of the episodic training/testing scheme of FSS, which once only distinguishes pixels be + +![](images/1f588cf9f709622c944d087c7fe2383f3af4a2294f08ac0c980d62afd75eb376.jpg) +Figure 3. Twelve examples of semantic segmentation from PASCAL-5 by POP and the state-of-the-art FT method under 5-shot setting. + +longing to one specific class by abstracting the knowledge of such class from support images. As such, FSS models result in inferior performances for GFSS, where the models necessitate to identify novel classes and all possible base classes simultaneously. BAM in part alleviates the limitation of FSS scheme by additionally employing a base class segmentation branch, that is pre-trained on base data and frozen when FSS training to refine the predictions of novel classes. As such, adapting BAM to GFSS can segment the base classes well and yield good performances. + +Compared to GFSS models of CAPL and FT in which the segmentation of base classes may be affected by novel class updating, POP improves the segmentation quality of base classes. The results basically indicate the advantage of freezing the well-learnt representations of base classes in model updating. On novel classes, the mIoU of POP is $35.51\%$ in 1-shot scenario and further increased to $55.87\%$ when knowing 5 labeled images (5-shot) for each category. As a good performer in 1-shot setting, BAM benefits from the frozen base representations and a well-tuned base-novel fusion strategy. Nevertheless, BAM still suffers from interclass confusion and is inferior to POP, especially when having more available novel examples. The results in general verify the impact of learning orthogonal prototypes in POP. Please note that for a fair comparison, we follow CAPL [33] and also implement POP with the dataset filtration that excludes the images containing pixels of novel classes in base class learning phase. We denote such run as $\mathsf{POP}^{\dagger}$ . $\mathsf{POP}^{\dagger}$ constantly outperforms CAPL across different few-shot settings, demonstrating the effectiveness of representing an image via a group of uncorrelated features by POP. + +Table 2 details the performance comparisons of 1-shot, 5-shot and 10-shot experiments on COCO- $20^{i}$ dataset. Similar to the observations on PASCAL- $5^{i}$ , POP surpasses the best-performing baseline FT-Triplet on 5-shot by $8.29\%$ , $1.13\%$ and $7.39\%$ in terms of mIoU on base, novel and + +all classes, respectively. Furthermore, POP obtains better segmentation quality on base classes against other methods across different k-shot settings. The results again empirically validate our proposal. + +Qualitative Analysis. Figure 3 showcases twelve semantic segmentation examples in PASCAL- $5^{i}$ , by our POP and the state-of-the-art FT [26] method under 5-shot setting of GFSS. Clearly, POP attains much more promising segmentation results. Taking the query images in the first three columns on the left as the examples, POP nicely identifies the novel class "Person", but FT fails to recognize this category. Moreover, the novel class "Dog" in the fourth example on the left is well segmented by both models and POP distinguishes the base class of "Sofa" more precisely. + +# 4.3. Experimental Analysis + +Evaluation on POP Designs. We first examine the performance contribution of different factors in POP. In between, PRO projects the features on the learnt prototypes to predict the class of each pixel and base prototypes are frozen when learning novel ones in the updating phase. ORT further encourages the orthogonality between prototypes of different classes to alleviate inter-class confusion. RES is our residual background representation that measures the residual of feature projection to categorize background pixels rather than learn a prototype for "background". + +Table 3 shows the mIoU improvement on the PASCAL $^5$ dataset by considering one more factors in POP for GFSS. Note that the data enrichment strategies are not employed in the experiments here. We start from the basic run of FT [26] that capitalizes on the same features to classify the pixels of base and novel classes in the images. PRO successfully boosts up the total mIoU performance from $55.41\%$ to $59.59\%$ under 1-shot setting and introduces $5.53\%$ mIoU improvement on base classes. This basically proves that PRO is a very effective and practical way for + +Table 3. Ablation studies of POP on PASCAL- $5^{i}$ . PRO represents segmentation via feature projection on learnt prototypes. ORT denotes the orthogonality constraint on prototypes. RES means that the background is represented by residual of feature projection. + +
PROORTRES1-shot5-shot
BaseNovelTotalBaseNovelTotal
---66.8418.8255.4172.0346.4065.93
✓--72.3718.7159.5973.3946.9967.10
✓✓-72.8223.6561.1173.7650.6968.27
✓✓✓73.1132.2963.3974.3253.4669.36
+ +Table 4. Ablation studies of data enrichment strategies for novel class updating on PASCAL- $5^{i}$ . RL denotes relabeling which provides pseudo labels for background pixels in images of base data. RS represents the resampling strategy that randomly selects different base data for each epoch of novel class updating. + +
RLRS1-shot5-shot
BaseNovelTotalBaseNovelTotal
--73.1132.2963.3974.3253.4669.36
✓-73.1035.0864.0474.3954.8169.73
-✓72.8627.7462.1274.3152.1269.03
✓✓73.9235.5164.7774.7855.8770.28
+ +GFSS to generalize the model to novel classes without sacrificing much accuracy on base classes. The performance gain of ORT is $0.45\%$ and $4.94\%$ in terms of mIoU on base and novel classes, respectively. The results verify the idea of learning orthogonal prototypes for feature projection. Measuring the residual of feature projection as background representation by RES further contributes a mIoU increase of $8.64\%$ and $2.77\%$ on novel classes in 1-shot and 5-shot settings. That empirically manifests the superiority of RES to dynamically fit semantic shifting in GFSS. + +Evaluation on Data Enrichment. To better study the contribution of each design in data enrichment, we then conduct the comparisons on POP configured with different enrichment strategies. Table 4 lists the results of both 1-shot and 5-shot scenarios. In particular, relabeling the background pixels in base samples leads to over $1\%$ mIoU improvements on novel classes. The results indicate that assigning pseudo labels to novel class pixels in base data could refine noisy background labels and ease the prototype learning for novel classes. The performance gain tends to be larger when updating the model on more base data via resampling. The performance however drops if solely using resampling. We speculate that this may be the result of involving more novel class pixels which may be incorrectly regarded as background due to semantic shifting. + +Confusion Matrix Visualization. Figure 4 depicts the confusion matrices of FT and POP under 5-shot scenario on the second fold of PASCAL- $5^{i}$ . We visualize the matrix after each training phase and highlight the difference in + +![](images/3214005e4cc134b52b48a482c75ae675137080a53ce0ce2cf5227771813d3770.jpg) +Figure 4. The difference of confusion matrices between two training phases. Compared to FT, POP obtains higher novel class performance after novel class updating without sacrificing much accuracy on base classes learnt in base class learning. + +between. After the first phase of base class learning, both models achieve over $72.9\%$ mIoU on base classes. Through the second phase of novel class updating, the mIoU on novel classes is $45.50\%$ by FT but the performance decreases to $68.37\%$ on base classes. In contrast, POP reaches $63.36\%$ mIoU on novel class with only $0.13\%$ mIoU drop on base classes. That again validates the good property of POP to identify novel classes without compromising base ones. + +# 5. Conclusion + +We have presented Projection onto Orthogonal Prototypes (POP) framework, which explores a principled way to generalize the model to novel classes without sacrificing much segmentation capability on base classes for GFSS. In particular, we study the problem by learning a group of prototypes and each represents a semantic class, thereby predicting each class separately with respect to the projections on its prototype. Technically, POP learns prototypes first on base classes and then updates the prototypes to support novel classes, with orthogonal constraint. Moreover, POP measures the residual of feature projection as the background representation to better mitigate semantic shifting. Experiments conducted on two datasets of PASCAL- $5^{i}$ and COCO- $20^{i}$ validate our proposal and analysis. Performance improvements are observed when comparing to both GFSS and remoulded FSS techniques. + +Acknowledgements This work is partly supported by the National Key Research and Development Program of China (2022YFB3104700), the National Nature Science Foundation of China (62121002, 62022076, U1936210). + +# References + +[1] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE TPAMI, 2017. 1, 2 +[2] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In ECCV, 2018. 2 +[3] Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. In NeurIPS, 2021. 1, 2 +[4] Pierre Comon. Independent component analysis, a new concept? Signal processing, 1994. 4 +[5] Nanqing Dong and Eric P. Xing. Few-shot semantic segmentation with prototype learning. In BMVC, 2018. 2 +[6] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 1 +[7] Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. *IJCV*, 2009. 5 +[8] Jun Fu, Jing Liu, Hajjie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In CVPR, 2019. 2 +[9] Bharath Hariharan, Pablo Arbeláez, Lubomir D. Bourdev, Subhransu Maji, and Jitendra Malik. Semantic contours from inverse detectors. In ICCV, 2011. 5 +[10] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016. 1 +[11] Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, and Abbas Rahimi. Constrained few-shot class-incremental learning. In CVPR, 2022. 2 +[12] Qibin Hou, Li Zhang, Ming-Ming Cheng, and Jiashi Feng. Strip pooling: Rethinking spatial pooling for scene parsing. In CVPR, 2020. 2 +[13] Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In CVPR, 2018. 1 +[14] Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu. Ccnet: Criss-cross attention for semantic segmentation. In ICCV, 2019. 2 +[15] Chunbo Lang, Gong Cheng, Binfei Tu, and Junwei Han. Learning what not to segment: A new perspective on few-shot segmentation. In CVPR, 2022. 6 +[16] Gen Li, Varun Jampani, Laura Sevilla-Lara, Deqing Sun, Jonghyun Kim, and Joongkyu Kim. Adaptive prototype learning and allocation for few-shot segmentation. In CVPR, 2021. 2 +[17] Xia Li, Zhisheng Zhong, Jianlong Wu, Yibo Yang, Zhouchen Lin, and Hong Liu. Expectation-maximization attention networks for semantic segmentation. In ICCV, 2019. 2 + +[18] Yehao Li, Ting Yao, Yingwei Pan, and Tao Mei. Contextual transformer networks for visual recognition. IEEE TPAMI, 2022. 1 +[19] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 5 +[20] Binghao Liu, Yao Ding, Jianbin Jiao, Xiangyang Ji, and Qixiang Ye. Anti-aliasing semantic reconstruction for few-shot semantic segmentation. In CVPR, 2021. 2 +[21] Sun-Ao Liu, Hongtao Xie, Hai Xu, Yongdong Zhang, and Qi Tian. Partial class activation attention for semantic segmentation. In CVPR, 2022. 2 +[22] Yongfei Liu, Xiangyi Zhang, Songyang Zhang, and Xuming He. Part-aware prototype network for few-shot semantic segmentation. In ECCV, 2020. 2 +[23] Fuchen Long, Zhaofan Qiu, Yingwei Pan, Ting Yao, Jiebo Luo, and Tao Mei. Stand-alone inter-frame attention in video models. In CVPR, 2022. 1 +[24] Fuchen Long, Zhaofan Qiu, Yingwei Pan, Ting Yao, Chong-Wah Ngo, and Tao Mei. Dynamic temporal filtering in video models. In ECCV, 2022. 1 +[25] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015. 1 +[26] Josh Myers-Dean, Yinan Zhao, Brian Price, Scott Cohen, and Danna Gurari. Generalized few-shot semantic segmentation: All you need is fine-tuning. arXiv preprint arXiv:2112.10982, 2021. 1, 2, 3, 5, 6, 7 +[27] Khoi Duc Minh Nguyen and Sinisa Todorovic. Feature weighting and boosting for few-shot segmentation. In ICCV, 2019. 5 +[28] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, 2019. 6 +[29] Kanchana Ranasinghe, Muzammal Naseer, Munawar Hayat, Salman Khan, and Fahad Shahbaz Khan. Orthogonal projection loss. In ICCV, 2021. 2 +[30] Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, and Byron Boots. One-shot learning for semantic segmentation. arXiv preprint arXiv:1709.03410, 2017. 1, 2, 5 +[31] Christian Simon, Piotr Koniusz, Richard Nock, and Mehrtash Harandi. Adaptive subspaces for few-shot learning. In CVPR, 2020. 2 +[32] Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmentation. In ICCV, 2021. 2 +[33] Zhuotao Tian, Xin Lai, Li Jiang, Shu Liu, Michelle Shu, Hengshuang Zhao, and Jiaya Jia. Generalized few-shot semantic segmentation. In CVPR, 2022. 1, 2, 3, 5, 6, 7 +[34] Zhuotao Tian, Hengshuang Zhao, Michelle Shu, Zhicheng Yang, Ruiyu Li, and Jiaya Jia. Prior guided feature en + +richment network for few-shot segmentation. IEEE TPAMI, 2020. 6 +[35] Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. Mlp-mixer: An all-mlp architecture for vision. In NeurIPS, 2021. 1 +[36] Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng. Panet: Few-shot image semantic segmentation with prototype alignment. In ICCV, 2019. 1, 2, 6 +[37] Xin Wang, Thomas Huang, Joseph Gonzalez, Trevor Darrell, and Fisher Yu. Frustratingly simple few-shot object detection. In ICML, 2020. 5 +[38] Zhonghua Wu, Xiangxi Shi, Guosheng Lin, and Jianfei Cai. Learning meta-class memory for few-shot semantic segmentation. In ICCV, 2021. 2 +[39] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. In NeurIPS, 2021. 2 +[40] Boyu Yang, Chang Liu, Bohao Li, Jianbin Jiao, and Qixiang Ye. Prototype mixture models for few-shot semantic segmentation. In ECCV, 2020. 2 +[41] Ting Yao, Yehao Li, Yingwei Pan, Yu Wang, Xiao-Ping Zhang, and Tao Mei. Dual vision transformer. arXiv preprint arXiv:2207.04976, 2022. 1 +[42] Ting Yao, Yingwei Pan, Yehao Li, Chong-Wah Ngo, and Tao Mei. Wave-vit: Unifying wavelet and transformers for visual representation learning. In ECCV, 2022. 1 +[43] Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object-contextual representations for semantic segmentation. In ECCV, 2020. 2 +[44] Chi Zhang, Guosheng Lin, Fayao Liu, Rui Yao, and Chunhua Shen. Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. In CVPR, 2019. 1, 6 +[45] Fan Zhang, Yanqin Chen, Zhihang Li, Zhibin Hong, Jingtuo Liu, Feifei Ma, Junyu Han, and Errui Ding. Acfnet: Attentional class feature network for semantic segmentation. In ICCV, 2019. 2 +[46] Yiheng Zhang, Zhaofan Qiu, Jingen Liu, Ting Yao, Dong Liu, and Tao Mei. Customizable architecture search for semantic segmentation. In CVPR, 2019. 2 +[47] Yiheng Zhang, Zhaofan Qiu, Ting Yao, Dong Liu, and Tao Mei. Fully convolutional adaptation networks for semantic segmentation. In CVPR, 2018. 2 +[48] Yiheng Zhang, Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Dong Liu, and Tao Mei. Transferring and regularizing prediction for semantic segmentation. In CVPR, 2020. 2 +[49] Yiheng Zhang, Ting Yao, Zhaofan Qiu, and Tao Mei. Lightweight and progressively-scalable networks for semantic segmentation. arXiv preprint arXiv:2207.13600, 2022. 1 +[50] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In CVPR, 2017. 2, 5 + +[51] Zhen Zhu, Mengde Xu, Song Bai, Tengteng Huang, and Xiang Bai. Asymmetric non-local neural networks for semantic segmentation. 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Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 623, + 288, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 623, + 288, + 696 + ], + "spans": [ + { + "bbox": [ + 46, + 623, + 288, + 696 + ], + "type": "text", + "content": "Semantic segmentation is to assign semantic labels to every pixel of an image. With the recent development of CNNs [10, 13] and vision transformers [6, 18, 23, 24, 35, 41, 42], the state-of-the-art networks have successfully pushed the limits of semantic segmentation [1, 3, 25, 49] with remarkable performance improvements. 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Comparisons between fine-tuning [26] and Projection onto Orthogonal Prototypes (POP). In novel class updating phase, fine-tuning (a) updates the network to predict base and novel classes, and inevitably compromises the well-learnt representations for base classes. Instead, POP (b) only updates prototypes for novel classes and executes predictions for each class separately on the features projected onto different orthogonal prototypes." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 473, + 547, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 473, + 547, + 641 + ], + "spans": [ + { + "bbox": [ + 304, + 473, + 547, + 641 + ], + "type": "text", + "content": "heavily rely on the requirements of large quantities of pixel-level annotations and it is also difficult to directly apply the models to the classes unseen in the training set. A straightforward way to alleviate this issue is to leverage Few-shot Semantic Segmentation (FSS) [30, 36, 44], which utilizes the limited support annotations from unseen/novel classes to adapt the models. Nevertheless, FSS performs on the assumption that the support images and the query image contain the same novel classes, and solely emphasizes the segmentation of one novel class in the query image at a time. A more practical scenario namely Generalized Few-shot Semantic Segmentation (GFSS) [33] is recently presented to simultaneously identify the pixels of base and novel classes in a query image." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 642, + 548, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 548, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 548, + 715 + ], + "type": "text", + "content": "A typical GFSS solution has proceeded along two training phases: base class learning and novel class updating. 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Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 702, + 137, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 137, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 137, + 712 + ], + "type": "text", + "content": "*Corresponding author." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "11319" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 251 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 251 + ], + "type": "text", + "content": "classes. For instance, Myers Dean et al. [26] sample some base data plus novel examples as the supervision to finetune the network, as depicted in Figure 1(a). Despite having good performances on novel classes, there still exists a clear performance degradation on base classes. We speculate that this may be the results of compromising the well-learnt features of base classes in fine-tuning. As such, a valid question then emerges as is it possible for GFSS to nicely generalize the model to novel classes without sacrificing much segmentation capability on base classes. In an effort to answer this question, we seek to represent an image via a group of uncorrelated feature components each of which characterizes a specific class. By doing so, it is readily applicable to learn and integrate new components for novel classes without affecting the ones learnt for base classes." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 262, + 289, + 573 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 262, + 289, + 573 + ], + "spans": [ + { + "bbox": [ + 46, + 262, + 289, + 573 + ], + "type": "text", + "content": "To materialize our idea, we propose a new Projection onto Orthogonal Prototypes (POP) framework for GFSS. POP learns a series of orthogonal prototypes and each prototype corresponds to one specific semantic class. As shown in Figure 1(b), POP employs an encoder to extract feature maps of a given query image and then projects them onto prototypes. The projection on each prototype is regarded as the discriminative representation with respect to the corresponding class and exploited to predict the probability map of pixels belonging to the class. More specifically, POP deliberately devises the learning of prototypes from three standpoints. The first one is to freeze the base prototypes when learning novel ones from support images in the updating phase. In this way, feature projections on base prototypes maintain their discriminability of base classes. Second, POP encourages the prototypes of base and novel classes to be orthogonal through a prototype orthogonality loss. Such a constraint decorrelates features projected onto different prototypes and mitigates the inter-class confusion caused by extending to novel classes. Finally, in view that background no longer contains the pixels of novel classes in updating phase, known as \"semantic shifting\", POP measures the residual of feature projection as the background representation instead of learning a prototype for \"background\". This way further improves the differentiation between novel classes and background." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 582, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 289, + 715 + ], + "type": "text", + "content": "The main contribution of this work is the proposal of a Projection on Orthogonal Prototypes (POP) framework for generalized few-shot semantic segmentation. The solution also leads to the elegant views of how to adapt the trained model to novel classes without sacrificing well-learnt features, and how to represent background pixels dynamically in the context of semantic shifting, which are problems not yet fully explored in the literature. We demonstrate that POP outperforms the state-of-the-art fine-tuning [26] on two benchmarks (PASCAL-5" + }, + { + "bbox": [ + 46, + 582, + 289, + 715 + ], + "type": "inline_equation", + "content": "^i" + }, + { + "bbox": [ + 46, + 582, + 289, + 715 + ], + "type": "text", + "content": " and COCO-20" + }, + { + "bbox": [ + 46, + 582, + 289, + 715 + ], + "type": "inline_equation", + "content": "^i" + }, + { + "bbox": [ + 46, + 582, + 289, + 715 + ], + "type": "text", + "content": ") with evident improvements on both base and novel classes." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 306, + 71, + 392, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 71, + 392, + 84 + ], + "spans": [ + { + "bbox": [ + 306, + 71, + 392, + 84 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 91, + 545, + 209 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 91, + 545, + 209 + ], + "spans": [ + { + "bbox": [ + 304, + 91, + 545, + 209 + ], + "type": "text", + "content": "Semantic Segmentation. Extensive FCN-based methods have been proposed and achieved remarkable performances. For example, pyramid pooling [12, 48, 50] and parallel dilated convolutions [1, 2, 46, 47] are utilized to aggregate multi-scale context. The attention mechanism is widely exploited to model long-range dependencies [8, 14, 17, 21, 43, 45, 51]. Recently, vision transformer has also been proven effective for semantic segmentation [3, 32, 39]. Though these efforts lead to high-quality segmentation, it is still difficult to directly apply the learnt models to unseen classes." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 211, + 545, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 211, + 545, + 354 + ], + "spans": [ + { + "bbox": [ + 304, + 211, + 545, + 354 + ], + "type": "text", + "content": "Few-shot Semantic Segmentation. Few-shot semantic segmentation (FSS) [30] executes pixel-wise labeling on new classes given a limited number of support examples. It mainly focuses on the 1-way scenario that requires binary maps for query images to identify the pixels belonging to the class labeled in support images. PL [5] and PANet [36] remould prototype learning for FSS by calculating the cosine similarities between pixels and prototypes obtained from support images. ASR [20] learns several orthogonal prototypes on the base data to represent novel categories. In addition, assigning multiple prototypes to each class is also a promising solution to improve FSS models [16,22,38,40]." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 355, + 545, + 497 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 355, + 545, + 497 + ], + "spans": [ + { + "bbox": [ + 304, + 355, + 545, + 497 + ], + "type": "text", + "content": "Generalized Few-shot Semantic Segmentation. Despite showing great potential, it is not trivial for FSS approaches to simultaneously identify the pixels of both base and novel classes in a query image. Generalized Few-shot Semantic Segmentation (GFSS) is further presented by Tian et al. [33] to address this problem. GFSS aims to segment both base and novel classes from query images through one inference process without paired support images. To achieve this goal, CAPL [33] mines contextual cues from both support and query samples to enrich the classifier for novel class segmentation. Fine-tuning [26] is another practical and effective way to adapt models for novel classes." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 498, + 545, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 545, + 605 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 545, + 605 + ], + "type": "text", + "content": "Orthogonality. Feature orthogonality is known to be helpful to improve discriminative ability. DSN [31] constrains the orthogonality on base data to facilitate few-shot classification on novel classes. C-FSCIL [11] and OPL [29] rely on re-training the network to keep the features of each class orthogonal. Nevertheless, these approaches are not applicable for GFSS since they either ignore the influence between base and novel classes or compromise the segmentation capability on base classes after re-training." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 606, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 606, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 606, + 545, + 713 + ], + "type": "text", + "content": "Our work focuses on adapting the semantic segmentation model to novel classes without compromising well-learnt features for better generalization. Different from existing GFSS approaches may sacrifice base class segmentation quality for novel class updating, our POP contributes by studying not only learning orthogonal prototypes to independently indicate pixels of base and novel classes, but also how the background could be nicely represented in the context of semantic shifting." + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "11320" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 71, + 541, + 162 + ], + "blocks": [ + { + "bbox": [ + 50, + 71, + 541, + 162 + ], + "lines": [ + { + "bbox": [ + 50, + 71, + 541, + 162 + ], + "spans": [ + { + "bbox": [ + 50, + 71, + 541, + 162 + ], + "type": "image", + "image_path": "7f1053cbb34f3f85f9d3c8c8896a86b6950b0c0df28f40ec439956e957df6ada.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 49, + 163, + 541, + 308 + ], + "blocks": [ + { + "bbox": [ + 49, + 163, + 541, + 308 + ], + "lines": [ + { + "bbox": [ + 49, + 163, + 541, + 308 + ], + "spans": [ + { + "bbox": [ + 49, + 163, + 541, + 308 + ], + "type": "image", + "image_path": "e664af1377760018d80a7a39a39b40fc49a6a6af4655204735fb745d62bf8b2c.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 310, + 546, + 376 + ], + "lines": [ + { + "bbox": [ + 46, + 310, + 546, + 376 + ], + "spans": [ + { + "bbox": [ + 46, + 310, + 546, + 376 + ], + "type": "text", + "content": "Figure 2. An overview of our Projection onto Orthogonal Prototypes (POP) framework for generalized few-shot semantic segmentation. The input image is first encoded into dense feature map via a CNN encoder, which is then projected onto a series of orthogonal prototypes to make prediction of each class independently through a shared classifier. In the base class learning phase, the entire network and prototypes for base classes are learnt on sufficient base data. In the second phase of novel class updating, we freeze all the modules learnt in the first phase and only optimize the newly minted prototypes for novel classes along with an additional classifier for novel classes and background class. As such, the proposed POP generalizes well to novel classes while maintaining the segmentation capability on base classes." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 384, + 127, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 384, + 127, + 396 + ], + "spans": [ + { + "bbox": [ + 47, + 384, + 127, + 396 + ], + "type": "text", + "content": "3. Our Method" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 407, + 288, + 550 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 407, + 288, + 550 + ], + "spans": [ + { + "bbox": [ + 46, + 407, + 288, + 550 + ], + "type": "text", + "content": "In this paper, we devise Projection onto Orthogonal Prototypes (POP) framework for generalized few-shot semantic segmentation (GFSS), by decoupling the feature learning of base classes and novel classes. Figure 2 illustrates an overview of the whole architecture of POP. We begin this section by elaborating the problem formulation of GFSS with the two-phase training scheme. Then, a projection-based framework is introduced to learn class-wise orthogonal prototypes, and extract the residual representation as background descriptor. Finally, we present the overall objective combining orthogonal constraint along with the semantic segmentation loss." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 562, + 171, + 574 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 562, + 171, + 574 + ], + "spans": [ + { + "bbox": [ + 47, + 562, + 171, + 574 + ], + "type": "text", + "content": "3.1. Problem Formulation" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "text", + "content": "In generalized few-shot semantic segmentation, we are given abundant annotated images for " + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "text", + "content": " base classes " + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "inline_equation", + "content": "C^b = \\{c_1,\\dots ,c_M\\}" + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "text", + "content": " and only " + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "text", + "content": " labeled images per class for " + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "text", + "content": " novel classes " + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "inline_equation", + "content": "C^n = \\{c_{M + 1},\\ldots ,c_{M + N}\\}" + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "text", + "content": ". The background category " + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "inline_equation", + "content": "c_{0}" + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "text", + "content": " presents the pixels that do not belong to any target classes. The goal of this task is to simultaneously distinguish both base and novel classes from backgrounds, i.e., " + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "inline_equation", + "content": "M + N + 1" + }, + { + "bbox": [ + 47, + 582, + 287, + 714 + ], + "type": "text", + "content": " classes in total. Inspired by recent success of two-phase training scheme in generalized few-shot semantic segmentation [26,33], we formulate our POP framework in two-phase manner: base class learning and novel class" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 386, + 545, + 447 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 386, + 545, + 447 + ], + "spans": [ + { + "bbox": [ + 305, + 386, + 545, + 447 + ], + "type": "text", + "content": "updating. Accordingly, POP is first trained with sufficient data for " + }, + { + "bbox": [ + 305, + 386, + 545, + 447 + ], + "type": "inline_equation", + "content": "(M + 1)" + }, + { + "bbox": [ + 305, + 386, + 545, + 447 + ], + "type": "text", + "content": "-class segmentation " + }, + { + "bbox": [ + 305, + 386, + 545, + 447 + ], + "type": "inline_equation", + "content": "(C^b \\cup \\{c_0\\})" + }, + { + "bbox": [ + 305, + 386, + 545, + 447 + ], + "type": "text", + "content": " in the first phase, and then updates the segmentation head with the few-shot support data targeting for pixel-wise prediction of all " + }, + { + "bbox": [ + 305, + 386, + 545, + 447 + ], + "type": "inline_equation", + "content": "M + N + 1" + }, + { + "bbox": [ + 305, + 386, + 545, + 447 + ], + "type": "text", + "content": " classes " + }, + { + "bbox": [ + 305, + 386, + 545, + 447 + ], + "type": "inline_equation", + "content": "(C^b \\cup C^n \\cup \\{c_0\\})" + }, + { + "bbox": [ + 305, + 386, + 545, + 447 + ], + "type": "text", + "content": " in the second phase." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 455, + 511, + 468 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 455, + 511, + 468 + ], + "spans": [ + { + "bbox": [ + 306, + 455, + 511, + 468 + ], + "type": "text", + "content": "3.2. Projection onto Orthogonal Prototypes" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 474, + 545, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 474, + 545, + 617 + ], + "spans": [ + { + "bbox": [ + 304, + 474, + 545, + 617 + ], + "type": "text", + "content": "GFSS necessitates the capability to segment both base and novel classes. In this case, the novel updating process should learn and integrate new components for novel classes without affecting the ones learnt for base classes. However, the existing approaches [26, 33] in this area still show obvious performance degradation on base classes in the second phase. They often suffer from a challenge of whether to drastically overhaul the network for the performance on novel classes or preserve the well-learnt representation for base classes. We attribute this issue to the highly-relevant representations to identify different classes, which interfere with each other when integrating novel classes." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 617, + 546, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 546, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 546, + 714 + ], + "type": "text", + "content": "To fulfill the objective of decoupling the feature learning of base classes and novel classes, we propose to orthogonally decompose the feature map into several uncorrelated components, each of which characterizes a specific class. Formally, we denote the dense feature map from the encoder network as " + }, + { + "bbox": [ + 304, + 617, + 546, + 714 + ], + "type": "inline_equation", + "content": "\\pmb{f} \\in \\mathbb{R}^{(H \\times W) \\times C}" + }, + { + "bbox": [ + 304, + 617, + 546, + 714 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 617, + 546, + 714 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 304, + 617, + 546, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 617, + 546, + 714 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 304, + 617, + 546, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 617, + 546, + 714 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 304, + 617, + 546, + 714 + ], + "type": "text", + "content": " are the height, the width and the number of channels of feature map, respectively. Assuming the matrix rank of extracted" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11321" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "content": "features is " + }, + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "inline_equation", + "content": "r \\leq C" + }, + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "content": "), the feature map " + }, + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "content": " can be represented as the weighted summation of " + }, + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "content": " orthogonal bases as:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 113, + 106, + 287, + 135 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 113, + 106, + 287, + 135 + ], + "spans": [ + { + "bbox": [ + 113, + 106, + 287, + 135 + ], + "type": "interline_equation", + "content": "\\boldsymbol {f} = \\sum_ {i = 1} ^ {r} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}, \\tag {1}", + "image_path": "3661e1e1ac4b76e0793530a4c1734214238296aba5dffe3a22bd27e1df98206b.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 115, + 137, + 219, + 153 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 115, + 137, + 219, + 153 + ], + "spans": [ + { + "bbox": [ + 115, + 137, + 219, + 153 + ], + "type": "interline_equation", + "content": "\\text {s . t .} \\boldsymbol {u} _ {i} \\cdot \\boldsymbol {u} _ {j} ^ {T} = 0 (i \\neq j),", + "image_path": "baaa15617d4c6ce6ce7c51da35c85a3f44149caf6dad00c04ce2b43db36e6b83.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "spans": [ + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "inline_equation", + "content": "\\{\\pmb{u}_i\\in \\mathbb{R}^{1\\times C}|_{i = 1\\dots r}\\}" + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "text", + "content": " is a group of unit bases that are orthogonal to each other, and " + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "inline_equation", + "content": "f^{(i)}\\in \\mathbb{R}^{(H\\times W)\\times 1}" + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "text", + "content": " is the weight map of the " + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "text", + "content": "-th component. This process is well known as orthogonal decomposition, and widely explored for Independent Component Analysis (ICA) [4]. In our work, we treat the basis " + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "inline_equation", + "content": "\\pmb{u}_i" + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "text", + "content": " as the prototype of the " + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "text", + "content": "-th class and the corresponding component " + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "inline_equation", + "content": "f^{(i)}\\cdot \\pmb{u}_i" + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "text", + "content": " as the representation to identify the pixels of the " + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "text", + "content": "-th class. As such, calculating the representation of the " + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "text", + "content": "-th class is equivalent to projecting " + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "inline_equation", + "content": "\\pmb{f}" + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "text", + "content": " to the " + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "text", + "content": "-th basis " + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "inline_equation", + "content": "\\pmb{u}_i" + }, + { + "bbox": [ + 46, + 162, + 288, + 283 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 99, + 292, + 287, + 308 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 99, + 292, + 287, + 308 + ], + "spans": [ + { + "bbox": [ + 99, + 292, + 287, + 308 + ], + "type": "interline_equation", + "content": "\\boldsymbol {f} _ {c _ {i}} = f ^ {(i)} \\cdot \\boldsymbol {u} _ {i} = (\\boldsymbol {f} \\cdot \\boldsymbol {u} _ {i} ^ {T}) \\cdot \\boldsymbol {u} _ {i}, \\tag {2}", + "image_path": "d7010dd03b70d1c397af6af1621ab010b8d9d8ff13322ec44a45fdc936f7e602.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 316, + 287, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 316, + 287, + 411 + ], + "spans": [ + { + "bbox": [ + 46, + 316, + 287, + 411 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 316, + 287, + 411 + ], + "type": "inline_equation", + "content": "f_{c_i}" + }, + { + "bbox": [ + 46, + 316, + 287, + 411 + ], + "type": "text", + "content": " is the representation of the " + }, + { + "bbox": [ + 46, + 316, + 287, + 411 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 316, + 287, + 411 + ], + "type": "text", + "content": "-th class and is then taken as the input of a shared classifier. In view that the learnt prototypes are orthogonal to each other, the representations of different classes are decorrelated and easy to be distinguished. More importantly, when integrating a new target class, we only need to learn a new prototype that is orthogonal to the existing ones, thereby preserving the well-learnt representations of other classes." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 411, + 287, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 411, + 287, + 496 + ], + "spans": [ + { + "bbox": [ + 47, + 411, + 287, + 496 + ], + "type": "text", + "content": "Nevertheless, the limit of prototype orthogonality (i.e., " + }, + { + "bbox": [ + 47, + 411, + 287, + 496 + ], + "type": "inline_equation", + "content": "\\pmb{u}_i\\cdot \\pmb{u}_j^T = 0" + }, + { + "bbox": [ + 47, + 411, + 287, + 496 + ], + "type": "text", + "content": ") is not differentiable and hard to be implemented by gradient propagation. Hence, we present a practical alternative to relax the hard limit to a soft orthogonal constraint. Specifically, a prototype orthogonality loss is devised by minimizing absolute value of the inner product between prototypes:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 96, + 503, + 287, + 533 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 503, + 287, + 533 + ], + "spans": [ + { + "bbox": [ + 96, + 503, + 287, + 533 + ], + "type": "interline_equation", + "content": "L _ {\\text {o r t h}} = \\frac {1}{r ^ {\\prime} \\left(r ^ {\\prime} - 1\\right)} \\sum_ {i \\neq j} \\left| \\boldsymbol {u} _ {i} \\cdot \\boldsymbol {u} _ {j} ^ {T} \\right|, \\tag {3}", + "image_path": "368838af8a08e7486142e8a1f64b37f5f732b568442e4d1babd5cf00cbd5d4d8.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 543, + 287, + 591 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 543, + 287, + 591 + ], + "spans": [ + { + "bbox": [ + 46, + 543, + 287, + 591 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 543, + 287, + 591 + ], + "type": "inline_equation", + "content": "r' < r" + }, + { + "bbox": [ + 46, + 543, + 287, + 591 + ], + "type": "text", + "content": " is the number of target classes. The orthogonality loss " + }, + { + "bbox": [ + 46, + 543, + 287, + 591 + ], + "type": "inline_equation", + "content": "L_{orth}" + }, + { + "bbox": [ + 46, + 543, + 287, + 591 + ], + "type": "text", + "content": " restricts the averaged correlation of different prototype pairs, and is minimized when the prototypes are orthogonal to each other." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 599, + 257, + 612 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 599, + 257, + 612 + ], + "spans": [ + { + "bbox": [ + 47, + 599, + 257, + 612 + ], + "type": "text", + "content": "3.3. Residual as Background Representation" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "type": "text", + "content": "The background class " + }, + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "type": "inline_equation", + "content": "c_{0}" + }, + { + "bbox": [ + 46, + 618, + 287, + 713 + ], + "type": "text", + "content": " is a special category in segmentation tasks, and indicates the pixels that do not belong to any target classes. One common practice is to treat target classes and background class equally, which reaches reasonable performances on various segmentation tasks. However, such a simple strategy is not acclimatized to GFSS task due to the effect of the \"semantic shifting\" between two phases. More precisely, in the first phase, the pixels of both" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "content": "novel classes " + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "inline_equation", + "content": "C^n" + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "content": " and background class " + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "inline_equation", + "content": "c_0" + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "content": " are labeled as background. When integrating novel classes in the second phase, the semantic meaning of background changes since it no longer includes the pixels of novel classes. We regard this gap as semantic shifting of background, which results in the confusion between novel classes and background class." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 144, + 546, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 144, + 546, + 251 + ], + "spans": [ + { + "bbox": [ + 304, + 144, + 546, + 251 + ], + "type": "text", + "content": "In order to bridge the gap between two phases, we propose to exploit the residual of feature projection as the background representation in POP, instead of constructing an independent prototype. The residual here is defined as the summation of remaining components after projecting " + }, + { + "bbox": [ + 304, + 144, + 546, + 251 + ], + "type": "inline_equation", + "content": "\\mathbf{f}" + }, + { + "bbox": [ + 304, + 144, + 546, + 251 + ], + "type": "text", + "content": " to the target classes. For instance, in the base class learning phase, the first " + }, + { + "bbox": [ + 304, + 144, + 546, + 251 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 144, + 546, + 251 + ], + "type": "text", + "content": " components in Eq.(1) are the projections for " + }, + { + "bbox": [ + 304, + 144, + 546, + 251 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 144, + 546, + 251 + ], + "type": "text", + "content": " base classes and the remaining " + }, + { + "bbox": [ + 304, + 144, + 546, + 251 + ], + "type": "inline_equation", + "content": "r - M" + }, + { + "bbox": [ + 304, + 144, + 546, + 251 + ], + "type": "text", + "content": " components are background components:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 350, + 258, + 545, + 308 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 350, + 258, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 350, + 258, + 545, + 308 + ], + "type": "interline_equation", + "content": "\\boldsymbol {f} = \\underbrace {\\sum_ {i = 1} ^ {M} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {b a s e c l a s s e s}} + \\underbrace {\\sum_ {i = M + 1} ^ {r} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {b a c k g r o u n d}}. \\tag {4}", + "image_path": "ce3cb658f2bc800e9c62e066307c603d19e2658d82c390f74ea0f29375492162.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 315, + 545, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 315, + 545, + 375 + ], + "spans": [ + { + "bbox": [ + 304, + 315, + 545, + 375 + ], + "type": "text", + "content": "Note that the background here actually includes the pixels of novel and background classes. In view of the difficulty on learning the remaining " + }, + { + "bbox": [ + 304, + 315, + 545, + 375 + ], + "type": "inline_equation", + "content": "r - M" + }, + { + "bbox": [ + 304, + 315, + 545, + 375 + ], + "type": "text", + "content": " components, we reformulate the background representation as the residual of feature projection by subtracting the components of bases classes:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 336, + 381, + 545, + 415 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 336, + 381, + 545, + 415 + ], + "spans": [ + { + "bbox": [ + 336, + 381, + 545, + 415 + ], + "type": "interline_equation", + "content": "\\boldsymbol {f} _ {c _ {0}} = \\sum_ {i = M + 1} ^ {r} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i} = \\boldsymbol {f} - \\sum_ {i = 1} ^ {M} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}, \\tag {5}", + "image_path": "7b5dbe9f34a04ed136b174ea34bb28ce157b67f0378bef306ea36b09d23e11a5.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 422, + 545, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 422, + 545, + 469 + ], + "spans": [ + { + "bbox": [ + 304, + 422, + 545, + 469 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 422, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\pmb{f}_{c_0}" + }, + { + "bbox": [ + 304, + 422, + 545, + 469 + ], + "type": "text", + "content": " is orthogonal to the feature " + }, + { + "bbox": [ + 304, + 422, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\pmb{f}_{c_i}" + }, + { + "bbox": [ + 304, + 422, + 545, + 469 + ], + "type": "text", + "content": " of each class " + }, + { + "bbox": [ + 304, + 422, + 545, + 469 + ], + "type": "inline_equation", + "content": "c_i" + }, + { + "bbox": [ + 304, + 422, + 545, + 469 + ], + "type": "text", + "content": " due to the orthogonality constraint between prototypes. Moreover, this reformulation avoids the estimation of matrix rank " + }, + { + "bbox": [ + 304, + 422, + 545, + 469 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 304, + 422, + 545, + 469 + ], + "type": "text", + "content": ", which is more practical." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 470, + 545, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 470, + 545, + 529 + ], + "spans": [ + { + "bbox": [ + 304, + 470, + 545, + 529 + ], + "type": "text", + "content": "For the novel class updating phase, the background components in Eq. (4) are separated into two parts, i.e., " + }, + { + "bbox": [ + 304, + 470, + 545, + 529 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 304, + 470, + 545, + 529 + ], + "type": "text", + "content": " components for " + }, + { + "bbox": [ + 304, + 470, + 545, + 529 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 304, + 470, + 545, + 529 + ], + "type": "text", + "content": " novel classes and " + }, + { + "bbox": [ + 304, + 470, + 545, + 529 + ], + "type": "inline_equation", + "content": "r - M - N" + }, + { + "bbox": [ + 304, + 470, + 545, + 529 + ], + "type": "text", + "content": " components as the updated background representation. The decomposition of feature " + }, + { + "bbox": [ + 304, + 470, + 545, + 529 + ], + "type": "inline_equation", + "content": "\\pmb{f}" + }, + { + "bbox": [ + 304, + 470, + 545, + 529 + ], + "type": "text", + "content": " in this phase is represented as" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 536, + 545, + 596 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 536, + 545, + 596 + ], + "spans": [ + { + "bbox": [ + 307, + 536, + 545, + 596 + ], + "type": "interline_equation", + "content": "\\boldsymbol {f} = \\underbrace {\\sum_ {i = 1} ^ {M} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {b a s e c l a s s e s}} + \\underbrace {\\sum_ {i = M + 1} ^ {M + N} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {n o v e l c l a s s e s}} + \\underbrace {\\sum_ {i = M + N + 1} ^ {r} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}} _ {\\text {b a c k g r o u n d}}. \\tag {6}", + "image_path": "07c7cf7fbbf3cf21eb52282b5e85685651a7363191da8b73dac039fb57ce3271.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 597, + 545, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 597, + 545, + 632 + ], + "spans": [ + { + "bbox": [ + 304, + 597, + 545, + 632 + ], + "type": "text", + "content": "Similar to Eq.(5), the updated background representation is measured by the residual of feature projection to base and novel classes:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 337, + 637, + 545, + 670 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 337, + 637, + 545, + 670 + ], + "spans": [ + { + "bbox": [ + 337, + 637, + 545, + 670 + ], + "type": "interline_equation", + "content": "\\boldsymbol {f} _ {c _ {0}} = \\boldsymbol {f} - \\sum_ {i = 1} ^ {M} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i} - \\sum_ {i = M + 1} ^ {M + N} f ^ {(i)} \\cdot \\boldsymbol {u} _ {i}. \\tag {7}", + "image_path": "03f266f039c5074cb5f35187cd972fac6e86312ec7de42b9e39e4b46c589dabc.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "type": "text", + "content": "In this way, the background representation is dynamically adjusted with respect to the change of target classes. It remains orthogonal to the representations of both base classes" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "11322" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "content": "and novel classes, such that the confusion between novel classes and background class is alleviated." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 109, + 133, + 123 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 109, + 133, + 123 + ], + "spans": [ + { + "bbox": [ + 47, + 109, + 133, + 123 + ], + "type": "text", + "content": "3.4. Optimization" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 129, + 287, + 166 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 129, + 287, + 166 + ], + "spans": [ + { + "bbox": [ + 47, + 129, + 287, + 166 + ], + "type": "text", + "content": "Following the common practice [26, 33], the optimization of our POP framework contains two phases: base class learning and novel class updating." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 167, + 287, + 263 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 167, + 287, + 263 + ], + "spans": [ + { + "bbox": [ + 46, + 167, + 287, + 263 + ], + "type": "text", + "content": "Base class learning. In the first phase, the entire network is trained on sufficient base data to optimize the encoded representations and segment base classes. The overall training objective function of POP combines the semantic segmentation loss of base classes and the orthogonality loss between prototypes: " + }, + { + "bbox": [ + 46, + 167, + 287, + 263 + ], + "type": "inline_equation", + "content": "L = L_{seg} + \\lambda L_{orth}" + }, + { + "bbox": [ + 46, + 167, + 287, + 263 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 167, + 287, + 263 + ], + "type": "inline_equation", + "content": "L_{seg}" + }, + { + "bbox": [ + 46, + 167, + 287, + 263 + ], + "type": "text", + "content": " denotes the cross entropy loss for segmentation and " + }, + { + "bbox": [ + 46, + 167, + 287, + 263 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 167, + 287, + 263 + ], + "type": "text", + "content": " is a scale factor of the orthogonality loss." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 265, + 287, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 265, + 287, + 373 + ], + "spans": [ + { + "bbox": [ + 46, + 265, + 287, + 373 + ], + "type": "text", + "content": "Novel class updating. The second phase involves the " + }, + { + "bbox": [ + 46, + 265, + 287, + 373 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 265, + 287, + 373 + ], + "type": "text", + "content": "-shot support data of novel classes and expands the set of target classes. We freeze all the modules learnt in the first phase and only optimize the newly constructed prototypes for novel classes along with an additional classifier for novel classes and background class. The loss function is the same as that in the first phase (i.e., " + }, + { + "bbox": [ + 46, + 265, + 287, + 373 + ], + "type": "inline_equation", + "content": "L = L_{seg} + \\lambda L_{orth}" + }, + { + "bbox": [ + 46, + 265, + 287, + 373 + ], + "type": "text", + "content": "), and the orthogonality loss " + }, + { + "bbox": [ + 46, + 265, + 287, + 373 + ], + "type": "inline_equation", + "content": "L_{orth}" + }, + { + "bbox": [ + 46, + 265, + 287, + 373 + ], + "type": "text", + "content": " is utilized over the prototypes of both base classes and novel classes." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 375, + 287, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 375, + 287, + 506 + ], + "spans": [ + { + "bbox": [ + 46, + 375, + 287, + 506 + ], + "type": "text", + "content": "Data enrichment. The second phase demands the training images of both base and novel classes. As a result of highly unbalanced data between base classes and novel classes, simply merging the two training sets leads to poor performance on novel classes. Inspired by the fine-tuning approaches [26, 37], we establish a balanced training subset by randomly sampling " + }, + { + "bbox": [ + 46, + 375, + 287, + 506 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 375, + 287, + 506 + ], + "type": "text", + "content": " examples per base class. Such a subset in the second phase has been proven to be effective for partially retraining the knowledge of base classes while integrating novel classes [26, 37]. Besides, we further promote the subset by two data enrichment strategies:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 508, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 46, + 508, + 287, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 508, + 287, + 616 + ], + "spans": [ + { + "bbox": [ + 46, + 508, + 287, + 616 + ], + "type": "text", + "content": "1) Relabeling. Recall that there is semantic shifting problem between two training phases. In the training images of base classes, the pixels of novel classes are labeled as background. Such annotation misleads the network optimization in the second phase. Therefore, after each epoch, we utilize the latest POP model to relabel the background pixels in the image of base classes, and replace the background annotation with the class with the highest predicted probability across the novel classes and background class." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 617, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 288, + 714 + ], + "type": "text", + "content": "2) Resampling. The subset only selects a few samples from the images of base classes. It underuses the large amount of training data, and makes the network sensitive to the choice of few-shot samples. Hence, we propose to resample the training images of base classes after each epoch. This strategy, on one hand, well balances the number of training images for different classes, and on the other, makes full use of the entire base training set." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 72, + 388, + 86 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 72, + 388, + 86 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 388, + 86 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 91, + 545, + 128 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 91, + 545, + 128 + ], + "spans": [ + { + "bbox": [ + 305, + 91, + 545, + 128 + ], + "type": "text", + "content": "We empirically verify the merit of our POP by conducting a thorough evaluation of generalized few-shot semantic segmentation on PASCAL- " + }, + { + "bbox": [ + 305, + 91, + 545, + 128 + ], + "type": "inline_equation", + "content": "5^{i}" + }, + { + "bbox": [ + 305, + 91, + 545, + 128 + ], + "type": "text", + "content": " [30] and COCO- " + }, + { + "bbox": [ + 305, + 91, + 545, + 128 + ], + "type": "inline_equation", + "content": "20^{i}" + }, + { + "bbox": [ + 305, + 91, + 545, + 128 + ], + "type": "text", + "content": " [27]." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 133, + 495, + 146 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 133, + 495, + 146 + ], + "spans": [ + { + "bbox": [ + 305, + 133, + 495, + 146 + ], + "type": "text", + "content": "4.1. Datasets and Experimental Settings" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 151, + 545, + 331 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 151, + 545, + 331 + ], + "spans": [ + { + "bbox": [ + 304, + 151, + 545, + 331 + ], + "type": "text", + "content": "Datasets. The PASCAL-5" + }, + { + "bbox": [ + 304, + 151, + 545, + 331 + ], + "type": "inline_equation", + "content": "^i" + }, + { + "bbox": [ + 304, + 151, + 545, + 331 + ], + "type": "text", + "content": " [30] is built on the PASCAL VOC 2012 [7] dataset with the extended annotations of SDS [9], and contains 12,031 images with high-quality pixel-level annotations of 20 classes. All the images are split into two sets of 10,582 and 1,449 for training and validation, respectively. Following the standard protocol in [30,33], the 20 classes are evenly partitioned into 4 folds (5 classes per fold) for cross-validation. Furthermore, we evaluate the capability of our POP on a more challenging COCO-20" + }, + { + "bbox": [ + 304, + 151, + 545, + 331 + ], + "type": "inline_equation", + "content": "^i" + }, + { + "bbox": [ + 304, + 151, + 545, + 331 + ], + "type": "text", + "content": " [27] dataset. COCO-20" + }, + { + "bbox": [ + 304, + 151, + 545, + 331 + ], + "type": "inline_equation", + "content": "^i" + }, + { + "bbox": [ + 304, + 151, + 545, + 331 + ], + "type": "text", + "content": " is created upon COCO [19], and includes 122,218 images being annotated with 80 object classes. In between, 82,081 and 40,137 images are exploited for training and validation. Similarly, experiments on COCO-20" + }, + { + "bbox": [ + 304, + 151, + 545, + 331 + ], + "type": "inline_equation", + "content": "^i" + }, + { + "bbox": [ + 304, + 151, + 545, + 331 + ], + "type": "text", + "content": " are also conducted with cross-validation on 4 folds (20 classes per fold) following [27,33]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 331, + 545, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 331, + 545, + 570 + ], + "spans": [ + { + "bbox": [ + 304, + 331, + 545, + 570 + ], + "type": "text", + "content": "Evaluation Settings. For both datasets, once we validate the model on one fold, the classes in this fold serve as \"novel classes\" and the classes in the other 3 folds plus background play the role of \"base classes\". In the base class learning phase, we select all images that have at least one pixel belonging to base classes from the original training set for model training. Note that the pixels of novel classes in the selected images are considered as background at this stage. In the novel class updating phase, we mimic the few-shot setting by randomly sampling " + }, + { + "bbox": [ + 304, + 331, + 545, + 570 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 331, + 545, + 570 + ], + "type": "text", + "content": " images containing pixels of novel classes from the original training set. All the images in the validation set are utilized for the evaluation on both base and novel classes. In the experiments, the Intersection over Union (IoU) per class and mean IoU (mIoU) over the base, novel, and all classes are adopted as the performance metrics. We average the mIoU over all the folds for cross-validation and regard the mean values as the final performance. For stability, we repeat each experiment five times with different random seeds and report the mean values. The same setting is also exploited in [26, 33]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": "Network Structure. In order to make the capacity of our model comparable to the baselines of generalized few-shot semantic segmentation, we follow [33] and exploit the PSPNet [50] originated from the ImageNet pre-trained ResNet-50 as the encoder. The spatial resolution of the feature map extracted through the encoder is " + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\frac{1}{8}" + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": " of the input image and the probability maps produced by POP are upsampled via bilinear interpolation for pixel-level predictions. The number of prototypes depends on the datasets. For PASCAL" + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "inline_equation", + "content": "^5" + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": " [30], POP has 15 prototypes in the base class learning phase, and then 5 prototypes are additionally included for novel class updating. Similarly, POP maintains 60 and 80" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11323" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 115, + 106, + 477, + 251 + ], + "blocks": [ + { + "bbox": [ + 46, + 71, + 547, + 105 + ], + "lines": [ + { + "bbox": [ + 46, + 71, + 547, + 105 + ], + "spans": [ + { + "bbox": [ + 46, + 71, + 547, + 105 + ], + "type": "text", + "content": "Table 1. Performance comparisons on PASCAL-5" + }, + { + "bbox": [ + 46, + 71, + 547, + 105 + ], + "type": "inline_equation", + "content": "^i" + }, + { + "bbox": [ + 46, + 71, + 547, + 105 + ], + "type": "text", + "content": " under the generalized few-shot segmentation settings. We report mIoU (\\%) over base classes (Base), novel classes (Novel), and all classes (Base + Novel = Total). All models are based on ResNet-50. " + }, + { + "bbox": [ + 46, + 71, + 547, + 105 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 46, + 71, + 547, + 105 + ], + "type": "text", + "content": " indicates that the images containing novel classes are excluded by base class learning phase." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 115, + 106, + 477, + 251 + ], + "lines": [ + { + "bbox": [ + 115, + 106, + 477, + 251 + ], + "spans": [ + { + "bbox": [ + 115, + 106, + 477, + 251 + ], + "type": "table", + "html": "
Methods1-shot5-shot10-shot
BaseNovelTotalBaseNovelTotalBaseNovelTotal
CANet [44]8.732.427.239.051.527.26---
PFENet [34]8.322.676.978.831.897.18---
PANet [36]31.8811.2526.9732.9515.2528.74---
BAM [15]73.6232.9663.9473.5734.3564.23---
\\( CAPL^† \\)[33]65.4818.8554.3866.1422.4155.7269.0927.1759.11
FT [26]66.8418.8255.4172.0346.4065.9373.0252.5568.14
FT-Triplet [26]66.4119.7155.3171.3150.4666.3572.8757.0069.10
\\( POP^† \\)67.7319.8556.4571.3443.4864.7172.2350.4967.05
POP73.9235.5164.7774.7855.8770.2874.9958.7771.13
", + "image_path": "c179cba15e9c496cfbc0b3347aca992a06f57788c1854ef6580e09b8c278bc26.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 115, + 267, + 477, + 362 + ], + "blocks": [ + { + "bbox": [ + 48, + 253, + 544, + 265 + ], + "lines": [ + { + "bbox": [ + 48, + 253, + 544, + 265 + ], + "spans": [ + { + "bbox": [ + 48, + 253, + 544, + 265 + ], + "type": "text", + "content": "Table 2. Performance comparisons on COCO- " + }, + { + "bbox": [ + 48, + 253, + 544, + 265 + ], + "type": "inline_equation", + "content": "{20}^{i}" + }, + { + "bbox": [ + 48, + 253, + 544, + 265 + ], + "type": "text", + "content": " under the generalized few-shot segmentation settings. Models are based on ResNet-50." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 115, + 267, + 477, + 362 + ], + "lines": [ + { + "bbox": [ + 115, + 267, + 477, + 362 + ], + "spans": [ + { + "bbox": [ + 115, + 267, + 477, + 362 + ], + "type": "table", + "html": "
Methods1-shot5-shot10-shot
BaseNovelTotalBaseNovelTotalBaseNovelTotal
\\( \\text{CAPL}^\\dagger [33] \\)44.617.0535.4645.2411.0536.8045.5110.8236.95
FT [26]43.428.9434.9047.1824.7241.6348.1830.0343.70
FT-Triplet [26]43.649.2335.1446.6128.8441.3646.6134.4943.27
\\( \\text{POP}^\\dagger \\)47.097.2737.2648.6120.0741.5649.1326.4843.54
POP54.7115.3144.9854.9029.9748.7555.0135.0550.08
", + "image_path": "5aebcdd81fab74b2a627504db13b35df2aa24f36f3b5c4c8d4a24254e732fda0.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 371, + 287, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 371, + 287, + 396 + ], + "spans": [ + { + "bbox": [ + 46, + 371, + 287, + 396 + ], + "type": "text", + "content": "prototypes in two training phases on COCO- " + }, + { + "bbox": [ + 46, + 371, + 287, + 396 + ], + "type": "inline_equation", + "content": "20^{i}" + }, + { + "bbox": [ + 46, + 371, + 287, + 396 + ], + "type": "text", + "content": ", respectively. The dimension of the prototypes is set as 512." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 399, + 288, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 399, + 288, + 579 + ], + "spans": [ + { + "bbox": [ + 46, + 399, + 288, + 579 + ], + "type": "text", + "content": "Training Strategy. Our POP is implemented on PyTorch [28]. The mini-batch stochastic gradient descent with momentum 0.9 and weight decay 0.0001 is exploited to optimize the model. During the base class learning, the initial learning rate is set to 0.01 which is annealed down to zero following a \"poly\" policy whose power is fixed to 0.9. The batch size is set as 8 for both datasets and the training of each model takes 50 epochs. For novel class updating, we update the model with a fixed learning rate 0.01 for 500 epochs, and the batch size is set as 2 and 8 for PASCAL" + }, + { + "bbox": [ + 46, + 399, + 288, + 579 + ], + "type": "inline_equation", + "content": "^{5^i}" + }, + { + "bbox": [ + 46, + 399, + 288, + 579 + ], + "type": "text", + "content": " and COCO-20" + }, + { + "bbox": [ + 46, + 399, + 288, + 579 + ], + "type": "inline_equation", + "content": "^{i}" + }, + { + "bbox": [ + 46, + 399, + 288, + 579 + ], + "type": "text", + "content": ", respectively. For data augmentation, we employ the " + }, + { + "bbox": [ + 46, + 399, + 288, + 579 + ], + "type": "inline_equation", + "content": "473 \\times 473" + }, + { + "bbox": [ + 46, + 399, + 288, + 579 + ], + "type": "text", + "content": " patches randomly cropped from the random scaled images. Each patch is randomly flipped along horizontal direction. The trade-off parameter " + }, + { + "bbox": [ + 46, + 399, + 288, + 579 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 399, + 288, + 579 + ], + "type": "text", + "content": " is set as 10 empirically." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 582, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 287, + 715 + ], + "type": "text", + "content": "Baseline Approaches. We compared the following approaches: (1) CAPL [33] capitalizes on the contextual co-occurrence cues to enrich the prototypes in the classifier for novel class segmentation. (2) FT [26] directly fine-tunes the model on some sampled data from base classes plus novel examples for novel class updating. Moreover, we also report the setting of FT which additionally leverages a triplet loss regularization to augment the learnt models. (3) Following [33], we form a prototype for each class by averaging features of pixels belonging to the corresponding class in all training images and utilize prototypes to" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 371, + 547, + 480 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 371, + 547, + 480 + ], + "spans": [ + { + "bbox": [ + 304, + 371, + 547, + 480 + ], + "type": "text", + "content": "segment the query image. By doing so, we can remould the three prototype-based FSS approaches of CANet [44], PFENet [34] and PANet [36] for GFSS. The reported results are directly drawn from [33]. (4) BAM [15] is an FSS model that segments all base classes and one novel class through one inference process. To adapt for GFSS, we execute multiple inferences each of which takes the query image and support images of one novel class as the inputs. Following [15], we fuse the model outputs as the final prediction." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 490, + 450, + 503 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 490, + 450, + 503 + ], + "spans": [ + { + "bbox": [ + 306, + 490, + 450, + 503 + ], + "type": "text", + "content": "4.2. Performance Comparison" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "content": "Quantitative Analysis. We compare with several state-of-the-art techniques on PASCAL- " + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "inline_equation", + "content": "5^{i}" + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "content": " and COCO- " + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "inline_equation", + "content": "20^{i}" + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "content": " datasets. Table 1 summarizes the mIoU performances of capitalizing on different number of shots (" + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "content": "-shot) on PASCAL- " + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "inline_equation", + "content": "5^{i}" + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "content": ". Overall, the results across three " + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "content": "-shot settings consistently indicate that our POP successfully adapts the models for novel classes and achieves the better performances than other baselines including the remoulded FSS methods and GFSS approaches. In particular, the mIoU of POP on all classes, i.e., Total, reaches " + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "inline_equation", + "content": "64.77\\%" + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "inline_equation", + "content": "70.28\\%" + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "inline_equation", + "content": "71.13\\%" + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "content": " on 1-shot, 5-shot and 10-shot scenarios, respectively, making the absolute gain over the best competitors by " + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "inline_equation", + "content": "0.83\\%" + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "inline_equation", + "content": "3.93\\%" + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "inline_equation", + "content": "2.03\\%" + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "content": ". CAPL, FT and POP generally exhibit higher mIoU against the reshaped FSS approaches of CANet, PFENet and PANet. This somewhat reveals the weakness of the episodic training/testing scheme of FSS, which once only distinguishes pixels be" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "11324" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 69, + 545, + 261 + ], + "blocks": [ + { + "bbox": [ + 49, + 69, + 545, + 261 + ], + "lines": [ + { + "bbox": [ + 49, + 69, + 545, + 261 + ], + "spans": [ + { + "bbox": [ + 49, + 69, + 545, + 261 + ], + "type": "image", + "image_path": "1f588cf9f709622c944d087c7fe2383f3af4a2294f08ac0c980d62afd75eb376.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 50, + 263, + 542, + 275 + ], + "lines": [ + { + "bbox": [ + 50, + 263, + 542, + 275 + ], + "spans": [ + { + "bbox": [ + 50, + 263, + 542, + 275 + ], + "type": "text", + "content": "Figure 3. Twelve examples of semantic segmentation from PASCAL-5 by POP and the state-of-the-art FT method under 5-shot setting." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 282, + 287, + 402 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 282, + 287, + 402 + ], + "spans": [ + { + "bbox": [ + 46, + 282, + 287, + 402 + ], + "type": "text", + "content": "longing to one specific class by abstracting the knowledge of such class from support images. As such, FSS models result in inferior performances for GFSS, where the models necessitate to identify novel classes and all possible base classes simultaneously. BAM in part alleviates the limitation of FSS scheme by additionally employing a base class segmentation branch, that is pre-trained on base data and frozen when FSS training to refine the predictions of novel classes. As such, adapting BAM to GFSS can segment the base classes well and yield good performances." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 402, + 288, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 402, + 288, + 653 + ], + "spans": [ + { + "bbox": [ + 46, + 402, + 288, + 653 + ], + "type": "text", + "content": "Compared to GFSS models of CAPL and FT in which the segmentation of base classes may be affected by novel class updating, POP improves the segmentation quality of base classes. The results basically indicate the advantage of freezing the well-learnt representations of base classes in model updating. On novel classes, the mIoU of POP is " + }, + { + "bbox": [ + 46, + 402, + 288, + 653 + ], + "type": "inline_equation", + "content": "35.51\\%" + }, + { + "bbox": [ + 46, + 402, + 288, + 653 + ], + "type": "text", + "content": " in 1-shot scenario and further increased to " + }, + { + "bbox": [ + 46, + 402, + 288, + 653 + ], + "type": "inline_equation", + "content": "55.87\\%" + }, + { + "bbox": [ + 46, + 402, + 288, + 653 + ], + "type": "text", + "content": " when knowing 5 labeled images (5-shot) for each category. As a good performer in 1-shot setting, BAM benefits from the frozen base representations and a well-tuned base-novel fusion strategy. Nevertheless, BAM still suffers from interclass confusion and is inferior to POP, especially when having more available novel examples. The results in general verify the impact of learning orthogonal prototypes in POP. Please note that for a fair comparison, we follow CAPL [33] and also implement POP with the dataset filtration that excludes the images containing pixels of novel classes in base class learning phase. We denote such run as " + }, + { + "bbox": [ + 46, + 402, + 288, + 653 + ], + "type": "inline_equation", + "content": "\\mathsf{POP}^{\\dagger}" + }, + { + "bbox": [ + 46, + 402, + 288, + 653 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 46, + 402, + 288, + 653 + ], + "type": "inline_equation", + "content": "\\mathsf{POP}^{\\dagger}" + }, + { + "bbox": [ + 46, + 402, + 288, + 653 + ], + "type": "text", + "content": " constantly outperforms CAPL across different few-shot settings, demonstrating the effectiveness of representing an image via a group of uncorrelated features by POP." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 654, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 654, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 47, + 654, + 287, + 712 + ], + "type": "text", + "content": "Table 2 details the performance comparisons of 1-shot, 5-shot and 10-shot experiments on COCO- " + }, + { + "bbox": [ + 47, + 654, + 287, + 712 + ], + "type": "inline_equation", + "content": "20^{i}" + }, + { + "bbox": [ + 47, + 654, + 287, + 712 + ], + "type": "text", + "content": " dataset. Similar to the observations on PASCAL- " + }, + { + "bbox": [ + 47, + 654, + 287, + 712 + ], + "type": "inline_equation", + "content": "5^{i}" + }, + { + "bbox": [ + 47, + 654, + 287, + 712 + ], + "type": "text", + "content": ", POP surpasses the best-performing baseline FT-Triplet on 5-shot by " + }, + { + "bbox": [ + 47, + 654, + 287, + 712 + ], + "type": "inline_equation", + "content": "8.29\\%" + }, + { + "bbox": [ + 47, + 654, + 287, + 712 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 654, + 287, + 712 + ], + "type": "inline_equation", + "content": "1.13\\%" + }, + { + "bbox": [ + 47, + 654, + 287, + 712 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 654, + 287, + 712 + ], + "type": "inline_equation", + "content": "7.39\\%" + }, + { + "bbox": [ + 47, + 654, + 287, + 712 + ], + "type": "text", + "content": " in terms of mIoU on base, novel and" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 281, + 545, + 329 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 281, + 545, + 329 + ], + "spans": [ + { + "bbox": [ + 304, + 281, + 545, + 329 + ], + "type": "text", + "content": "all classes, respectively. Furthermore, POP obtains better segmentation quality on base classes against other methods across different k-shot settings. The results again empirically validate our proposal." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 330, + 546, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 330, + 546, + 449 + ], + "spans": [ + { + "bbox": [ + 304, + 330, + 546, + 449 + ], + "type": "text", + "content": "Qualitative Analysis. Figure 3 showcases twelve semantic segmentation examples in PASCAL- " + }, + { + "bbox": [ + 304, + 330, + 546, + 449 + ], + "type": "inline_equation", + "content": "5^{i}" + }, + { + "bbox": [ + 304, + 330, + 546, + 449 + ], + "type": "text", + "content": ", by our POP and the state-of-the-art FT [26] method under 5-shot setting of GFSS. Clearly, POP attains much more promising segmentation results. Taking the query images in the first three columns on the left as the examples, POP nicely identifies the novel class \"Person\", but FT fails to recognize this category. Moreover, the novel class \"Dog\" in the fourth example on the left is well segmented by both models and POP distinguishes the base class of \"Sofa\" more precisely." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 456, + 435, + 468 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 456, + 435, + 468 + ], + "spans": [ + { + "bbox": [ + 306, + 456, + 435, + 468 + ], + "type": "text", + "content": "4.3. Experimental Analysis" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 474, + 545, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 474, + 545, + 594 + ], + "spans": [ + { + "bbox": [ + 304, + 474, + 545, + 594 + ], + "type": "text", + "content": "Evaluation on POP Designs. We first examine the performance contribution of different factors in POP. In between, PRO projects the features on the learnt prototypes to predict the class of each pixel and base prototypes are frozen when learning novel ones in the updating phase. ORT further encourages the orthogonality between prototypes of different classes to alleviate inter-class confusion. RES is our residual background representation that measures the residual of feature projection to categorize background pixels rather than learn a prototype for \"background\"." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "type": "text", + "content": "Table 3 shows the mIoU improvement on the PASCAL" + }, + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "type": "inline_equation", + "content": "^5" + }, + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "type": "text", + "content": " dataset by considering one more factors in POP for GFSS. Note that the data enrichment strategies are not employed in the experiments here. We start from the basic run of FT [26] that capitalizes on the same features to classify the pixels of base and novel classes in the images. PRO successfully boosts up the total mIoU performance from " + }, + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "type": "inline_equation", + "content": "55.41\\%" + }, + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "type": "inline_equation", + "content": "59.59\\%" + }, + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "type": "text", + "content": " under 1-shot setting and introduces " + }, + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "type": "inline_equation", + "content": "5.53\\%" + }, + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "type": "text", + "content": " mIoU improvement on base classes. This basically proves that PRO is a very effective and practical way for" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11325" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 49, + 118, + 286, + 196 + ], + "blocks": [ + { + "bbox": [ + 47, + 71, + 287, + 115 + ], + "lines": [ + { + "bbox": [ + 47, + 71, + 287, + 115 + ], + "spans": [ + { + "bbox": [ + 47, + 71, + 287, + 115 + ], + "type": "text", + "content": "Table 3. Ablation studies of POP on PASCAL- " + }, + { + "bbox": [ + 47, + 71, + 287, + 115 + ], + "type": "inline_equation", + "content": "5^{i}" + }, + { + "bbox": [ + 47, + 71, + 287, + 115 + ], + "type": "text", + "content": ". PRO represents segmentation via feature projection on learnt prototypes. ORT denotes the orthogonality constraint on prototypes. RES means that the background is represented by residual of feature projection." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 49, + 118, + 286, + 196 + ], + "lines": [ + { + "bbox": [ + 49, + 118, + 286, + 196 + ], + "spans": [ + { + "bbox": [ + 49, + 118, + 286, + 196 + ], + "type": "table", + "html": "
PROORTRES1-shot5-shot
BaseNovelTotalBaseNovelTotal
---66.8418.8255.4172.0346.4065.93
✓--72.3718.7159.5973.3946.9967.10
✓✓-72.8223.6561.1173.7650.6968.27
✓✓✓73.1132.2963.3974.3253.4669.36
", + "image_path": "c3f4f12a0b30838b55e86f247bfb4f9d835ff0b80df1529d3d59fc6459b9321c.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 48, + 267, + 285, + 346 + ], + "blocks": [ + { + "bbox": [ + 47, + 210, + 286, + 266 + ], + "lines": [ + { + "bbox": [ + 47, + 210, + 286, + 266 + ], + "spans": [ + { + "bbox": [ + 47, + 210, + 286, + 266 + ], + "type": "text", + "content": "Table 4. Ablation studies of data enrichment strategies for novel class updating on PASCAL- " + }, + { + "bbox": [ + 47, + 210, + 286, + 266 + ], + "type": "inline_equation", + "content": "5^{i}" + }, + { + "bbox": [ + 47, + 210, + 286, + 266 + ], + "type": "text", + "content": ". RL denotes relabeling which provides pseudo labels for background pixels in images of base data. RS represents the resampling strategy that randomly selects different base data for each epoch of novel class updating." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 267, + 285, + 346 + ], + "lines": [ + { + "bbox": [ + 48, + 267, + 285, + 346 + ], + "spans": [ + { + "bbox": [ + 48, + 267, + 285, + 346 + ], + "type": "table", + "html": "
RLRS1-shot5-shot
BaseNovelTotalBaseNovelTotal
--73.1132.2963.3974.3253.4669.36
✓-73.1035.0864.0474.3954.8169.73
-✓72.8627.7462.1274.3152.1269.03
✓✓73.9235.5164.7774.7855.8770.28
", + "image_path": "f15312111971ea67909aff83963e737b06b32f4e33d3ac761e204c3b3bd25ac0.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 362, + 286, + 482 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 362, + 286, + 482 + ], + "spans": [ + { + "bbox": [ + 46, + 362, + 286, + 482 + ], + "type": "text", + "content": "GFSS to generalize the model to novel classes without sacrificing much accuracy on base classes. The performance gain of ORT is " + }, + { + "bbox": [ + 46, + 362, + 286, + 482 + ], + "type": "inline_equation", + "content": "0.45\\%" + }, + { + "bbox": [ + 46, + 362, + 286, + 482 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 362, + 286, + 482 + ], + "type": "inline_equation", + "content": "4.94\\%" + }, + { + "bbox": [ + 46, + 362, + 286, + 482 + ], + "type": "text", + "content": " in terms of mIoU on base and novel classes, respectively. The results verify the idea of learning orthogonal prototypes for feature projection. Measuring the residual of feature projection as background representation by RES further contributes a mIoU increase of " + }, + { + "bbox": [ + 46, + 362, + 286, + 482 + ], + "type": "inline_equation", + "content": "8.64\\%" + }, + { + "bbox": [ + 46, + 362, + 286, + 482 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 362, + 286, + 482 + ], + "type": "inline_equation", + "content": "2.77\\%" + }, + { + "bbox": [ + 46, + 362, + 286, + 482 + ], + "type": "text", + "content": " on novel classes in 1-shot and 5-shot settings. That empirically manifests the superiority of RES to dynamically fit semantic shifting in GFSS." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 484, + 286, + 663 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 484, + 286, + 663 + ], + "spans": [ + { + "bbox": [ + 46, + 484, + 286, + 663 + ], + "type": "text", + "content": "Evaluation on Data Enrichment. To better study the contribution of each design in data enrichment, we then conduct the comparisons on POP configured with different enrichment strategies. Table 4 lists the results of both 1-shot and 5-shot scenarios. In particular, relabeling the background pixels in base samples leads to over " + }, + { + "bbox": [ + 46, + 484, + 286, + 663 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 46, + 484, + 286, + 663 + ], + "type": "text", + "content": " mIoU improvements on novel classes. The results indicate that assigning pseudo labels to novel class pixels in base data could refine noisy background labels and ease the prototype learning for novel classes. The performance gain tends to be larger when updating the model on more base data via resampling. The performance however drops if solely using resampling. We speculate that this may be the result of involving more novel class pixels which may be incorrectly regarded as background due to semantic shifting." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 665, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 286, + 713 + ], + "type": "text", + "content": "Confusion Matrix Visualization. Figure 4 depicts the confusion matrices of FT and POP under 5-shot scenario on the second fold of PASCAL- " + }, + { + "bbox": [ + 46, + 665, + 286, + 713 + ], + "type": "inline_equation", + "content": "5^{i}" + }, + { + "bbox": [ + 46, + 665, + 286, + 713 + ], + "type": "text", + "content": ". We visualize the matrix after each training phase and highlight the difference in" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 306, + 68, + 541, + 280 + ], + "blocks": [ + { + "bbox": [ + 306, + 68, + 541, + 280 + ], + "lines": [ + { + "bbox": [ + 306, + 68, + 541, + 280 + ], + "spans": [ + { + "bbox": [ + 306, + 68, + 541, + 280 + ], + "type": "image", + "image_path": "3214005e4cc134b52b48a482c75ae675137080a53ce0ce2cf5227771813d3770.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 281, + 545, + 326 + ], + "lines": [ + { + "bbox": [ + 305, + 281, + 545, + 326 + ], + "spans": [ + { + "bbox": [ + 305, + 281, + 545, + 326 + ], + "type": "text", + "content": "Figure 4. The difference of confusion matrices between two training phases. Compared to FT, POP obtains higher novel class performance after novel class updating without sacrificing much accuracy on base classes learnt in base class learning." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 340, + 545, + 436 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 340, + 545, + 436 + ], + "spans": [ + { + "bbox": [ + 304, + 340, + 545, + 436 + ], + "type": "text", + "content": "between. After the first phase of base class learning, both models achieve over " + }, + { + "bbox": [ + 304, + 340, + 545, + 436 + ], + "type": "inline_equation", + "content": "72.9\\%" + }, + { + "bbox": [ + 304, + 340, + 545, + 436 + ], + "type": "text", + "content": " mIoU on base classes. Through the second phase of novel class updating, the mIoU on novel classes is " + }, + { + "bbox": [ + 304, + 340, + 545, + 436 + ], + "type": "inline_equation", + "content": "45.50\\%" + }, + { + "bbox": [ + 304, + 340, + 545, + 436 + ], + "type": "text", + "content": " by FT but the performance decreases to " + }, + { + "bbox": [ + 304, + 340, + 545, + 436 + ], + "type": "inline_equation", + "content": "68.37\\%" + }, + { + "bbox": [ + 304, + 340, + 545, + 436 + ], + "type": "text", + "content": " on base classes. In contrast, POP reaches " + }, + { + "bbox": [ + 304, + 340, + 545, + 436 + ], + "type": "inline_equation", + "content": "63.36\\%" + }, + { + "bbox": [ + 304, + 340, + 545, + 436 + ], + "type": "text", + "content": " mIoU on novel class with only " + }, + { + "bbox": [ + 304, + 340, + 545, + 436 + ], + "type": "inline_equation", + "content": "0.13\\%" + }, + { + "bbox": [ + 304, + 340, + 545, + 436 + ], + "type": "text", + "content": " mIoU drop on base classes. That again validates the good property of POP to identify novel classes without compromising base ones." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 446, + 378, + 459 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 446, + 378, + 459 + ], + "spans": [ + { + "bbox": [ + 306, + 446, + 378, + 459 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 467, + 545, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 467, + 545, + 658 + ], + "spans": [ + { + "bbox": [ + 304, + 467, + 545, + 658 + ], + "type": "text", + "content": "We have presented Projection onto Orthogonal Prototypes (POP) framework, which explores a principled way to generalize the model to novel classes without sacrificing much segmentation capability on base classes for GFSS. In particular, we study the problem by learning a group of prototypes and each represents a semantic class, thereby predicting each class separately with respect to the projections on its prototype. Technically, POP learns prototypes first on base classes and then updates the prototypes to support novel classes, with orthogonal constraint. Moreover, POP measures the residual of feature projection as the background representation to better mitigate semantic shifting. Experiments conducted on two datasets of PASCAL- " + }, + { + "bbox": [ + 304, + 467, + 545, + 658 + ], + "type": "inline_equation", + "content": "5^{i}" + }, + { + "bbox": [ + 304, + 467, + 545, + 658 + ], + "type": "text", + "content": " and COCO- " + }, + { + "bbox": [ + 304, + 467, + 545, + 658 + ], + "type": "inline_equation", + "content": "20^{i}" + }, + { + "bbox": [ + 304, + 467, + 545, + 658 + ], + "type": "text", + "content": " validate our proposal and analysis. Performance improvements are observed when comparing to both GFSS and remoulded FSS techniques." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 658, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 658, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 658, + 545, + 712 + ], + "type": "text", + "content": "Acknowledgements This work is partly supported by the National Key Research and Development Program of China (2022YFB3104700), the National Nature Science Foundation of China (62121002, 62022076, U1936210)." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11326" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "text", + "content": "[1] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE TPAMI, 2017. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 137, + 288, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 137, + 288, + 179 + ], + "spans": [ + { + "bbox": [ + 53, + 137, + 288, + 179 + ], + "type": "text", + "content": "[2] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In ECCV, 2018. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 181, + 288, + 214 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 181, + 288, + 214 + ], + "spans": [ + { + "bbox": [ + 53, + 181, + 288, + 214 + ], + "type": "text", + "content": "[3] Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. In NeurIPS, 2021. 1, 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 216, + 288, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 216, + 288, + 237 + ], + "spans": [ + { + "bbox": [ + 53, + 216, + 288, + 237 + ], + "type": "text", + "content": "[4] Pierre Comon. Independent component analysis, a new concept? Signal processing, 1994. 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 239, + 288, + 260 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 239, + 288, + 260 + ], + "spans": [ + { + "bbox": [ + 53, + 239, + 288, + 260 + ], + "type": "text", + "content": "[5] Nanqing Dong and Eric P. Xing. Few-shot semantic segmentation with prototype learning. In BMVC, 2018. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 262, + 288, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 262, + 288, + 327 + ], + "spans": [ + { + "bbox": [ + 53, + 262, + 288, + 327 + ], + "type": "text", + "content": "[6] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 328, + 288, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 328, + 288, + 361 + ], + "spans": [ + { + "bbox": [ + 53, + 328, + 288, + 361 + ], + "type": "text", + "content": "[7] Mark Everingham, Luc Van Gool, Christopher K. I. Williams, John M. Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. *IJCV*, 2009. 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 363, + 288, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 363, + 288, + 396 + ], + "spans": [ + { + "bbox": [ + 53, + 363, + 288, + 396 + ], + "type": "text", + "content": "[8] Jun Fu, Jing Liu, Hajjie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In CVPR, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 397, + 288, + 429 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 397, + 288, + 429 + ], + "spans": [ + { + "bbox": [ + 53, + 397, + 288, + 429 + ], + "type": "text", + "content": "[9] Bharath Hariharan, Pablo Arbeláez, Lubomir D. Bourdev, Subhransu Maji, and Jitendra Malik. Semantic contours from inverse detectors. In ICCV, 2011. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 430, + 287, + 463 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 430, + 287, + 463 + ], + "spans": [ + { + "bbox": [ + 48, + 430, + 287, + 463 + ], + "type": "text", + "content": "[10] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 465, + 288, + 508 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 465, + 288, + 508 + ], + "spans": [ + { + "bbox": [ + 48, + 465, + 288, + 508 + ], + "type": "text", + "content": "[11] Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, and Abbas Rahimi. Constrained few-shot class-incremental learning. In CVPR, 2022. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 510, + 287, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 510, + 287, + 542 + ], + "spans": [ + { + "bbox": [ + 48, + 510, + 287, + 542 + ], + "type": "text", + "content": "[12] Qibin Hou, Li Zhang, Ming-Ming Cheng, and Jiashi Feng. Strip pooling: Rethinking spatial pooling for scene parsing. In CVPR, 2020. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 544, + 287, + 565 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 544, + 287, + 565 + ], + "spans": [ + { + "bbox": [ + 48, + 544, + 287, + 565 + ], + "type": "text", + "content": "[13] Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In CVPR, 2018. 1" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 567, + 287, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 567, + 287, + 600 + ], + "spans": [ + { + "bbox": [ + 48, + 567, + 287, + 600 + ], + "type": "text", + "content": "[14] Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu. Ccnet: Criss-cross attention for semantic segmentation. In ICCV, 2019. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 601, + 287, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 601, + 287, + 634 + ], + "spans": [ + { + "bbox": [ + 48, + 601, + 287, + 634 + ], + "type": "text", + "content": "[15] Chunbo Lang, Gong Cheng, Binfei Tu, and Junwei Han. Learning what not to segment: A new perspective on few-shot segmentation. In CVPR, 2022. 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 635, + 287, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 635, + 287, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 635, + 287, + 678 + ], + "type": "text", + "content": "[16] Gen Li, Varun Jampani, Laura Sevilla-Lara, Deqing Sun, Jonghyun Kim, and Joongkyu Kim. Adaptive prototype learning and allocation for few-shot segmentation. In CVPR, 2021. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "text", + "content": "[17] Xia Li, Zhisheng Zhong, Jianlong Wu, Yibo Yang, Zhouchen Lin, and Hong Liu. Expectation-maximization attention networks for semantic segmentation. In ICCV, 2019. 2" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 36, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 105 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 105 + ], + "type": "text", + "content": "[18] Yehao Li, Ting Yao, Yingwei Pan, and Tao Mei. Contextual transformer networks for visual recognition. IEEE TPAMI, 2022. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "type": "text", + "content": "[19] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 152, + 545, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 185 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 185 + ], + "type": "text", + "content": "[20] Binghao Liu, Yao Ding, Jianbin Jiao, Xiangyang Ji, and Qixiang Ye. Anti-aliasing semantic reconstruction for few-shot semantic segmentation. In CVPR, 2021. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 186, + 545, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 545, + 217 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 545, + 217 + ], + "type": "text", + "content": "[21] Sun-Ao Liu, Hongtao Xie, Hai Xu, Yongdong Zhang, and Qi Tian. Partial class activation attention for semantic segmentation. In CVPR, 2022. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 220, + 545, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 220, + 545, + 251 + ], + "spans": [ + { + "bbox": [ + 307, + 220, + 545, + 251 + ], + "type": "text", + "content": "[22] Yongfei Liu, Xiangyi Zhang, Songyang Zhang, and Xuming He. Part-aware prototype network for few-shot semantic segmentation. In ECCV, 2020. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 253, + 545, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 253, + 545, + 285 + ], + "spans": [ + { + "bbox": [ + 307, + 253, + 545, + 285 + ], + "type": "text", + "content": "[23] Fuchen Long, Zhaofan Qiu, Yingwei Pan, Ting Yao, Jiebo Luo, and Tao Mei. Stand-alone inter-frame attention in video models. In CVPR, 2022. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 287, + 545, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 287, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 307, + 287, + 545, + 319 + ], + "type": "text", + "content": "[24] Fuchen Long, Zhaofan Qiu, Yingwei Pan, Ting Yao, Chong-Wah Ngo, and Tao Mei. Dynamic temporal filtering in video models. In ECCV, 2022. 1" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 321, + 545, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 321, + 545, + 353 + ], + "spans": [ + { + "bbox": [ + 307, + 321, + 545, + 353 + ], + "type": "text", + "content": "[25] Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networks for semantic segmentation. In CVPR, 2015. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 355, + 545, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 355, + 545, + 398 + ], + "spans": [ + { + "bbox": [ + 307, + 355, + 545, + 398 + ], + "type": "text", + "content": "[26] Josh Myers-Dean, Yinan Zhao, Brian Price, Scott Cohen, and Danna Gurari. Generalized few-shot semantic segmentation: All you need is fine-tuning. arXiv preprint arXiv:2112.10982, 2021. 1, 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 399, + 545, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 399, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 307, + 399, + 545, + 430 + ], + "type": "text", + "content": "[27] Khoi Duc Minh Nguyen and Sinisa Todorovic. Feature weighting and boosting for few-shot segmentation. In ICCV, 2019. 5" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 433, + 545, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 433, + 545, + 520 + ], + "spans": [ + { + "bbox": [ + 307, + 433, + 545, + 520 + ], + "type": "text", + "content": "[28] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, 2019. 6" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 307, + 522, + 545, + 554 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 522, + 545, + 554 + ], + "spans": [ + { + "bbox": [ + 307, + 522, + 545, + 554 + ], + "type": "text", + "content": "[29] Kanchana Ranasinghe, Muzammal Naseer, Munawar Hayat, Salman Khan, and Fahad Shahbaz Khan. Orthogonal projection loss. In ICCV, 2021. 2" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 307, + 555, + 545, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 555, + 545, + 588 + ], + "spans": [ + { + "bbox": [ + 307, + 555, + 545, + 588 + ], + "type": "text", + "content": "[30] Amirreza Shaban, Shray Bansal, Zhen Liu, Irfan Essa, and Byron Boots. One-shot learning for semantic segmentation. arXiv preprint arXiv:1709.03410, 2017. 1, 2, 5" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 307, + 590, + 545, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 590, + 545, + 622 + ], + "spans": [ + { + "bbox": [ + 307, + 590, + 545, + 622 + ], + "type": "text", + "content": "[31] Christian Simon, Piotr Koniusz, Richard Nock, and Mehrtash Harandi. Adaptive subspaces for few-shot learning. In CVPR, 2020. 2" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 307, + 624, + 545, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 624, + 545, + 655 + ], + "spans": [ + { + "bbox": [ + 307, + 624, + 545, + 655 + ], + "type": "text", + "content": "[32] Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmentation. In ICCV, 2021. 2" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 307, + 658, + 545, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 658, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 307, + 658, + 545, + 689 + ], + "type": "text", + "content": "[33] Zhuotao Tian, Xin Lai, Li Jiang, Shu Liu, Michelle Shu, Hengshuang Zhao, and Jiaya Jia. Generalized few-shot semantic segmentation. In CVPR, 2022. 1, 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 307, + 691, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 691, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 691, + 545, + 713 + ], + "type": "text", + "content": "[34] Zhuotao Tian, Hengshuang Zhao, Michelle Shu, Zhicheng Yang, Ruiyu Li, and Jiaya Jia. Prior guided feature en" + } + ] + } + ], + "index": 35 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11327" + } + ] + } + ], + "index": 37 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 286, + 712 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 66, + 72, + 286, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 72, + 286, + 95 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 286, + 95 + ], + "type": "text", + "content": "richment network for few-shot segmentation. IEEE TPAMI, 2020. 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 286, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 286, + 151 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 286, + 151 + ], + "type": "text", + "content": "[35] Ilya O. Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy. Mlp-mixer: An all-mlp architecture for vision. In NeurIPS, 2021. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 153, + 286, + 186 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 153, + 286, + 186 + ], + "spans": [ + { + "bbox": [ + 48, + 153, + 286, + 186 + ], + "type": "text", + "content": "[36] Kaixin Wang, Jun Hao Liew, Yingtian Zou, Daquan Zhou, and Jiashi Feng. Panet: Few-shot image semantic segmentation with prototype alignment. In ICCV, 2019. 1, 2, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 188, + 286, + 220 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 188, + 286, + 220 + ], + "spans": [ + { + "bbox": [ + 48, + 188, + 286, + 220 + ], + "type": "text", + "content": "[37] Xin Wang, Thomas Huang, Joseph Gonzalez, Trevor Darrell, and Fisher Yu. Frustratingly simple few-shot object detection. In ICML, 2020. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 222, + 286, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 222, + 286, + 255 + ], + "spans": [ + { + "bbox": [ + 48, + 222, + 286, + 255 + ], + "type": "text", + "content": "[38] Zhonghua Wu, Xiangxi Shi, Guosheng Lin, and Jianfei Cai. Learning meta-class memory for few-shot semantic segmentation. In ICCV, 2021. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 257, + 286, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 257, + 286, + 300 + ], + "spans": [ + { + "bbox": [ + 48, + 257, + 286, + 300 + ], + "type": "text", + "content": "[39] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. In NeurIPS, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 302, + 286, + 334 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 302, + 286, + 334 + ], + "spans": [ + { + "bbox": [ + 48, + 302, + 286, + 334 + ], + "type": "text", + "content": "[40] Boyu Yang, Chang Liu, Bohao Li, Jianbin Jiao, and Qixiang Ye. Prototype mixture models for few-shot semantic segmentation. In ECCV, 2020. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 337, + 286, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 337, + 286, + 369 + ], + "spans": [ + { + "bbox": [ + 48, + 337, + 286, + 369 + ], + "type": "text", + "content": "[41] Ting Yao, Yehao Li, Yingwei Pan, Yu Wang, Xiao-Ping Zhang, and Tao Mei. Dual vision transformer. arXiv preprint arXiv:2207.04976, 2022. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 371, + 286, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 371, + 286, + 404 + ], + "spans": [ + { + "bbox": [ + 48, + 371, + 286, + 404 + ], + "type": "text", + "content": "[42] Ting Yao, Yingwei Pan, Yehao Li, Chong-Wah Ngo, and Tao Mei. Wave-vit: Unifying wavelet and transformers for visual representation learning. In ECCV, 2022. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 406, + 286, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 406, + 286, + 437 + ], + "spans": [ + { + "bbox": [ + 48, + 406, + 286, + 437 + ], + "type": "text", + "content": "[43] Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object-contextual representations for semantic segmentation. In ECCV, 2020. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 440, + 286, + 483 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 440, + 286, + 483 + ], + "spans": [ + { + "bbox": [ + 48, + 440, + 286, + 483 + ], + "type": "text", + "content": "[44] Chi Zhang, Guosheng Lin, Fayao Liu, Rui Yao, and Chunhua Shen. Canet: Class-agnostic segmentation networks with iterative refinement and attentive few-shot learning. In CVPR, 2019. 1, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 486, + 286, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 486, + 286, + 529 + ], + "spans": [ + { + "bbox": [ + 48, + 486, + 286, + 529 + ], + "type": "text", + "content": "[45] Fan Zhang, Yanqin Chen, Zhihang Li, Zhibin Hong, Jingtuo Liu, Feifei Ma, Junyu Han, and Errui Ding. Acfnet: Attentional class feature network for semantic segmentation. In ICCV, 2019. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 531, + 286, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 531, + 286, + 563 + ], + "spans": [ + { + "bbox": [ + 48, + 531, + 286, + 563 + ], + "type": "text", + "content": "[46] Yiheng Zhang, Zhaofan Qiu, Jingen Liu, Ting Yao, Dong Liu, and Tao Mei. Customizable architecture search for semantic segmentation. In CVPR, 2019. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 566, + 286, + 598 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 566, + 286, + 598 + ], + "spans": [ + { + "bbox": [ + 48, + 566, + 286, + 598 + ], + "type": "text", + "content": "[47] Yiheng Zhang, Zhaofan Qiu, Ting Yao, Dong Liu, and Tao Mei. Fully convolutional adaptation networks for semantic segmentation. In CVPR, 2018. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 601, + 286, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 601, + 286, + 633 + ], + "spans": [ + { + "bbox": [ + 48, + 601, + 286, + 633 + ], + "type": "text", + "content": "[48] Yiheng Zhang, Zhaofan Qiu, Ting Yao, Chong-Wah Ngo, Dong Liu, and Tao Mei. Transferring and regularizing prediction for semantic segmentation. In CVPR, 2020. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 635, + 286, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 635, + 286, + 677 + ], + "spans": [ + { + "bbox": [ + 48, + 635, + 286, + 677 + ], + "type": "text", + "content": "[49] Yiheng Zhang, Ting Yao, Zhaofan Qiu, and Tao Mei. Lightweight and progressively-scalable networks for semantic segmentation. arXiv preprint arXiv:2207.13600, 2022. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 680, + 286, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 286, + 712 + ], + "type": "text", + "content": "[50] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In CVPR, 2017. 2, 5" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 72, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 72, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 545, + 106 + ], + "type": "text", + "content": "[51] Zhen Zhu, Mengde Xu, Song Bai, Tengteng Huang, and Xiang Bai. Asymmetric non-local neural networks for semantic segmentation. In ICCV, 2019. 2" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "11328" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Partial Correlation Based Deep Visual Representation for Image Classification/87245566-23af-421b-80ad-1a8c0309df53_content_list.json b/2023/Learning Partial Correlation Based Deep Visual Representation for Image Classification/87245566-23af-421b-80ad-1a8c0309df53_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..010c3b2df3f8f3e8ad0158c6d18bcd7336cff8fb --- /dev/null +++ b/2023/Learning Partial Correlation Based Deep Visual Representation for Image Classification/87245566-23af-421b-80ad-1a8c0309df53_content_list.json @@ -0,0 +1,1641 @@ +[ + { + "type": "text", + "text": "Learning Partial Correlation based Deep Visual Representation for Image Classification", + "text_level": 1, + "bbox": [ + 112, + 128, + 857, + 174 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Saimunur Rahman $^{1,2}$ , Piotr Koniusz $^{*,1,3}$ , Lei Wang $^{2}$ , Luping Zhou $^{4}$ , Peyman Moghadam $^{1,5}$ , Changming Sun $^{1}$", + "bbox": [ + 84, + 202, + 937, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Data61♥CSIRO, $^{2}$ University of Wollongong, $^{3}$ Australian National University,", + "bbox": [ + 205, + 220, + 821, + 238 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "4University of Sydney, 5Queensland University of Technology", + "bbox": [ + 267, + 239, + 758, + 256 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "name.surname@data61.csiro.au, leiw@uow.edu.au, luping.zhou@sydney.edu.au", + "bbox": [ + 228, + 258, + 790, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 308, + 313, + 324 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolutional feature maps. However, pairwise correlation will become misleading once there is another channel correlating with both channels of interest, resulting in the \"confounding\" effect. For this case, \"partial correlation\" which removes the confounding effect shall be estimated instead. Nevertheless, reliably estimating partial correlation requires to solve a symmetric positive definite matrix optimisation, known as sparse inverse covariance estimation (SICE). How to incorporate this process into CNN remains an open issue. In this work, we formulate SICE as a novel structured layer of CNN. To ensure end-to-end trainability, we develop an iterative method to solve the above matrix optimisation during forward and backward propagation steps. Our work obtains a partial correlation based deep visual representation and mitigates the small sample problem often encountered by covariance matrix estimation in CNN. Computationally, our model can be effectively trained with GPU and works well with a large number of channels of advanced CNNs. Experiments show the efficacy and superior classification performance of our deep visual representation compared to covariance matrix based counterparts.", + "bbox": [ + 76, + 340, + 473, + 704 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 746, + 209, + 762 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Learning effective visual representation is a central issue in computer vision. In the past two decades, describing images with local features and pooling them to a global representation has shown promising performance. As one of the pooling methods, covariance matrix based pooling has attracted much attention due to its exploitation of second-order correlation information of features. A variety of tasks", + "bbox": [ + 75, + 771, + 468, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/01fcafd03a5d182fffdcf1d63296039b70dacc05cf245a0749c2886792e36018.jpg", + "image_caption": [ + "Figure 1. Understanding the partial correlation (a 3D toy case). Unlike the ordinary covariance (pairwise correlation of say $\\mathbf{x}$ and $\\mathbf{y}$ corresponding to channels), partial correlation between variables $\\mathbf{x}$ and $\\mathbf{y}$ removes the influence of the confounding variable $\\mathbf{z}$ . Let the number of samples $n = 3$ and channels $d = 3$ . For the 3D case, $\\mathbf{x}$ and $\\mathbf{y}$ are projected onto a plane perpendicular to $\\mathbf{z}$ . Then $\\rho_{xy} = \\cos \\varphi_{xy}$ (and $\\rho_{xz}$ and $\\rho_{yz}$ can be computed by analogy). Projected \"residuals\" $\\mathbf{r_x}$ and $\\mathbf{r_y}$ are computed as indicated in the plot, $\\mathbf{w}'_x = \\arg \\min_{\\mathbf{w}} \\sum_{i=1}^{3} (x_i - \\mathbf{w}_x^\\top \\mathbf{z}_i)$ where $\\mathbf{z}_i = [z_i, 1]^\\top$ (and $\\mathbf{w}'_y$ is computed by analogy). The green box: for $d > 3$ , the computation of partial correlation requires covariance inversion [7]." + ], + "image_footnote": [], + "bbox": [ + 501, + 297, + 901, + 449 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "such as fine-grained image classification [27], image segmentation [16], generic image classification [24, 26, 34], image set classification [44], action recognition [18], few-shot classification [50] and few-shot detection [52-54] have benefited from the covariance matrix based representation. A few pioneering works have integrated covariance matrix as a pooling method within convolutional neural networks (CNN) and investigated associated issues such as matrix function backpropagation [16], matrix normalisation [23, 28, 38], compact matrix estimation [11, 49] and kernel based extension [9]. The above works further improved visual representations based on covariance matrix.", + "bbox": [ + 496, + 642, + 893, + 823 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Despite the above progress, covariance matrix merely measures the pairwise correlation (more accurately, covariance) of two variables without taking any other variables into account. This can be easily verified because its $(i,j)$ -th entry solely depends on the $i$ -th and $j$ -th variables on a", + "bbox": [ + 496, + 824, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author. Code: https://github.com/csiro-robotics/iSICE", + "bbox": [ + 94, + 886, + 467, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "6231", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6f3a27138e8545a6b344d7e42a40eae3568220c7ab4746d78a5a2c631126050d.jpg", + "image_caption": [ + "Figure 2. Proposed iterative sparse inverse covariance estimation (iSICE) method in a CNN pipeline." + ], + "image_footnote": [], + "bbox": [ + 80, + 88, + 893, + 189 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "sample set. In Statistics, it is known that such a pairwise correlation will give misleading results once a third variable is correlated with both variables of interest due to the \"confounding\" effect. For this situation, the partial correlation is the right measure to use. It regresses out the effects of other variables from the two variables and then calculates the correlation of their residuals instead. Partial correlation can be conveniently obtained by computing inverse covariance matrix, also known as the precision matrix [7] in the statistical community. Figure 1 illustrates the geometrical interpretation of partial correlation.", + "bbox": [ + 75, + 239, + 472, + 406 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The above observation motivates us to investigate a visual representation for image classification based on the inverse covariance matrix. After all, it has better theoretical support on characterising the essential relationship of variables (e.g., the channels in a convolutional feature map) when other variables are present. Note that inverse covariance matrix can be used for many vision tasks but in this paper, we investigate it from the perspective of image classification. Nevertheless, reliably estimating inverse covariance matrix from the local descriptors of a CNN feature map is a challenging task. This is primarily due to the small spatial size of the feature map, i.e., sample size, and a higher number of channels, i.e., feature dimensions, and this issue becomes more pronounced for advanced CNN models. An unreliable estimate of inverse covariance matrix will critically affect its effectiveness as a visual representation. One might argue that by increasing the size of input images or using a dimension reduction layer to reduce the number of feature channels, such an issue could be resolved. In this paper, we investigate this issue from the perspective of robust precision matrix estimation.", + "bbox": [ + 75, + 411, + 470, + 729 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To achieve our goal, we explore the use of sparsity prior for inverse covariance matrix estimation in the literature. Specifically, the general principle of \"bet on sparsity\" [12] is adopted in estimating the structure of high-dimensional data, and this leads to an established technique called sparse inverse covariance estimation (SICE) [10]. It solves an optimisation in the space of symmetric and positive definite (SPD) matrix to estimate the inverse covariance matrix by imposing the sparsity prior on its entries. SICE is designed to handle small sample problems and it is known for its excellent effectiveness to that end [10]. An initial attempt", + "bbox": [ + 75, + 734, + 472, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "to apply SICE for visual representation is based on handcrafted or pre-extracted features of small size and an off-the-shelf SICE solver, and it does not have the ability to backpropagate through SICE due to optimisation of the SPD matrix with the imposed non-smooth sparsity term [51].", + "bbox": [ + 496, + 239, + 890, + 316 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our work is the first one that truly integrates SICE into CNN for end-to-end training. Clearly, such an integration will fully take advantage of the feature learning capability of CNN and the partial correlation offered by inverse covariance matrix. On the other hand, realising such an integration is not trivial. Unlike covariance matrix, which is obtained by simple arithmetic operations, SICE is obtained by solving an SPD matrix based optimisation. How to incorporate this optimisation process into CNN as a layer is an issue. Furthermore, this SICE optimisation needs to be solved for each training image during both forward and backward phases to generate a visual representation. Directly solving this optimisation within CNN will not be practical even for a medium-sized SICE problem.", + "bbox": [ + 496, + 316, + 892, + 527 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To efficiently integrate SICE into CNN, we propose a fast end-to-end training method for SICE by taking inspiration from Newton-Schulz iteration [14]. Our method solves the SICE optimisation with a smooth convex cost function by re-parameterising the non-smooth term in the original SICE cost function (see Eq. (1)), and it can therefore be optimised with standard optimisation techniques such as gradient descent. Furthermore, we effectively enforce the SPD constraint during optimisation so that the obtained SICE solution remains SPD as desired. Figure 2 shows our \"Iterative Sparse Inverse Covariance Estimation (iSICE)\". In contrast to SICE, iSICE works with end-to-end trainable deep learning models. Our iSICE involves simple matrix arithmetic operations fully compatible with GPU. It can approximately solve large SICE problems within CNN efficiently.", + "bbox": [ + 496, + 529, + 893, + 757 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our main contributions are summarised as follows.", + "bbox": [ + 517, + 765, + 857, + 779 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "1. To more precisely characterise the relationship of features for visual representation, this paper proposes to integrate sparse inverse covariance estimation (SICE) process into CNNs as a novel layer. To achieve this, we develop a method based on Newton-Schulz iteration and box constraints for $\\ell_1$ penalty to solve the SICE optimisation with CNN and maintain the end-to-end", + "bbox": [ + 513, + 794, + 893, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "6232", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "training efficiency. To the best of our knowledge, our iSICE is the first end-to-end SICE solution for CNN.", + "bbox": [ + 109, + 90, + 467, + 119 + ], + "page_idx": 2 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "2. Our iSICE method requires a minimal change of network architecture. Therefore, it can readily be integrated with existing works to replace those using deep network models to learn covariance matrix based visual representation. The iSICE is fully compatible with GPU and can be easily implemented with modern deep learning libraries.", + "3. As the objective of SICE is a combination of log det term (may change rapidly) and sparsity (changes slowly), achieving the balance between both terms during optimisation by the gradient descent is hard. To this end, we propose a minor contribution: a simple modulating network whose goal is to adapt on-the-fly learning rate and sparsity penalty." + ], + "bbox": [ + 89, + 130, + 467, + 351 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Experiments on multiple image classification datasets show the effectiveness of our proposed iSICE method.", + "bbox": [ + 76, + 359, + 467, + 390 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2. Related work", + "text_level": 1, + "bbox": [ + 76, + 402, + 212, + 419 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Since the advent of covariance representation methods in deep learning [11, 27], reliable estimation of the covariance matrix from a CNN feature map remains an issue. The issue is due to the small spatial size of feature map (corresponding to the number of samples) and the higher number of feature channels (corresponding to the feature dimensions), which could cause unreliable estimation and even matrix singularity due to the so-called \"curse of dimensionality\". Existing works either append a small positive constant to the diagonals of covariance matrix [27] or use matrix normalisation operation [16, 24, 28] to handle this issue. Matrix normalisation approaches mitigate unbalanced spectrum represented by eigenvalues of covariance matrix [19, 20, 24, 25, 55]. Different from the existing methods, in this paper, we approach the reliable covariance estimation problem in CNN from a perspective of partial correlations that can be efficiently captured by the inverse of covariance matrix, also known as the precision matrix [7], when a large number of samples is available for estimation.", + "bbox": [ + 76, + 428, + 467, + 714 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Covariance representation strives to capture the underlying structure of CNN feature channels. In the literature of knowledge representation [22], it is recommended to leverage prior knowledge to improve a learning task when sufficient data is not available1. Thus, prior knowledge can be used to improve the estimation of underlying structure of high-dimensional data captured by covariance representation. One common prior knowledge is \"structure sparsity\" which leads to the sparse inverse covariance matrix in the literature of statistical machine learning [10, 15].", + "bbox": [ + 76, + 715, + 467, + 864 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Structure sparsity cannot be readily applied to covariance representation as it requires the access to partial correlation between feature components. A covariance matrix captures pairwise correlation of feature components without taking into account the confounding effect of remaining components. Therefore, it is unlikely that the covariance matrix will be sparse by nature. To obtain partial correlation, SICE moves from covariance matrix to its inverse. An inverse covariance matrix captures partial correlation between feature components by regressing out the effects of other variables [15]. Once other variables are factored out, structure sparsity can be effectively enforced in SICE. In a recent work [51], SICE-based visual representation has been applied to image classification with handcrafted and pre-extracted features of small size. However, SICE has never been integrated into CNN for end-to-end training with the goal of adapting to such a representation. The existing solvers for computing SICE also have limited GPU support [8, 10]. Thus, we propose an end-to-end trainable iterative method for solving SICE optimisation with CNNs.", + "bbox": [ + 500, + 90, + 890, + 393 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Proposed method", + "text_level": 1, + "bbox": [ + 500, + 405, + 669, + 422 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we begin by discussing the background of SICE. Then we discuss how it can be estimated from CNN feature descriptors. Finally, we describe our proposed method which is trainable end-to-end with a CNN.", + "bbox": [ + 500, + 430, + 890, + 491 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. The basic idea of SICE", + "text_level": 1, + "bbox": [ + 500, + 500, + 710, + 513 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As a representation, the covariance matrix captures the underlying structure of a feature set. It uses a covariance matrix estimated from samples to capture this structure. Sparse inverse covariance estimation (SICE) focuses on the following two issues: (1) instability or singularity of sample-based covariance matrix estimated from a small number of high-dimensional feature vectors. This situation makes it less effective in capturing the underlying structure of data. As an example, in this case the smaller and larger eigenvalues of the estimated covariance matrix become poorly estimated. Thus, a suitable regularisation (a prior) is needed during the estimation to mitigate the bias of our estimator; (2) rigid estimation of covariance matrix for high-dimensional feature vectors is not always appropriate as the high-dimensional data usually presents a complex structure. If there is prior knowledge available, it should be used to improve the covariance estimation from a small number of samples.", + "bbox": [ + 500, + 523, + 890, + 792 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In terms of prior knowledge of high-dimensional data, structure sparsity [15] and the \"bet on sparsity\" principle [12] are the two common priors used in the literature. Suppose that we have a probabilistic graphical model, where each node corresponds to a feature and the statistical dependence between two nodes is expressed with an edge linking two nodes. Structure sparsity would specify how", + "bbox": [ + 500, + 795, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_footnote", + "text": "This is indeed the case with CNNs as the spatial size of a CNN feature map is usually small when compared to the number of its channels.", + "bbox": [ + 76, + 875, + 467, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "6233", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "sparse such a graph is, e.g., how many edges are presented in this graph. More generally, even if there is no clear prior knowledge on structure sparsity available, the \"bet on sparsity\" principle can still be applied to estimate the structure of the graph by imposing a sparsity prior. Its rationality is as follows. If the graph is indeed sparse, SICE will estimate its underlying structure with a correct prior, and if the graph is dense, it will not estimate the underlying structure accurately with such a prior. However, in the latter case, we will not lose much because we have known that we do not have enough sample to estimate the dense structure. The \"bet on sparsity\" principle has been widely adopted in high-dimensional data analysis, and also demonstrated its efficacy on covariance matrix based visual representation estimated from handcrafted or pre-extracted CNN features [51].", + "bbox": [ + 75, + 90, + 472, + 332 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "SICE strives to improve the covariance estimation with the use of prior knowledge. To incorporate prior knowledge, SICE switches to the inverse of covariance matrix from covariance matrix. In principle, the covariance matrix captures the apparent pairwise correlation between feature components, i.e., indirect correlation. In comparison, the inverse of covariance matrix is able to characterise the direct (i.e. partial) correlation between two feature components by regressing out the remaining features. Using the inverse covariance matrix not only helps to interpret the essential relationship between two features, but also allows the convenient incorporation of the sparsity prior.", + "bbox": [ + 75, + 333, + 472, + 517 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Our SICE estimation with CNN", + "text_level": 1, + "bbox": [ + 76, + 526, + 359, + 542 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Suppose, we have the sample-based covariance matrix $\\pmb{\\Sigma}$ computed from a set of CNN local descriptors presented in a convolutional feature map. Let $\\mathbf{S}$ denote the corresponding sparse inverse covariance matrix. The off-diagonal entries of $\\mathbf{S}$ capture the direct correlation between different descriptor components. They are zero if two components are independent under the removed influence of confounding variables. In the literature [10], the estimation of $\\mathbf{S}$ has been effectively resolved by the maximization of a penalised log-likelihood of data with an SPD constraint on $\\mathbf{S}$ and the sparsity prior to induce sparse graph connectivity. The optimal solution of the above problem is known as SICE.", + "bbox": [ + 75, + 551, + 472, + 733 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "SICE is defined as follows:", + "bbox": [ + 84, + 747, + 272, + 763 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {S} ^ {*} = \\underset {\\mathbf {S} > 0} {\\arg \\max } \\log \\det (\\mathbf {S}) - \\operatorname {t r a c e} (\\boldsymbol {\\Sigma} \\mathbf {S}) - \\lambda \\| \\mathbf {S} \\| _ {1}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 107, + 775, + 452, + 797 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\Sigma$ is a sample-based covariance matrix, and $\\operatorname{det}(\\cdot)$ , trace $(\\cdot)$ and $\\| \\cdot \\|_1$ denote the determinant, trace and $\\ell_1$ -norm of a vectorization of matrix, respectively.", + "bbox": [ + 83, + 808, + 454, + 854 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To obtain reliable and faithful SICE, the term $\\| \\mathbf{S}\\| _1$ imposes the structure sparsity on $\\mathbf{S}$ . $\\lambda$ controls the trade-off", + "bbox": [ + 76, + 869, + 470, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "between the amount of sparsity and the log-likelihood estimation. The problem in Eq. (1) is convex and can be solved by the off-the-shelf packages such as GLASSO [10] and CVXPY [8]. However, the objective is non-smooth due to the $\\ell_1$ penalty. The above optimisation packages cannot be used with CNN layers to conduct training with backpropagation. A recent extension of CVXPY called CVXPYLAYERS [1] provides differentiable optimisation layers. However, based on our investigation, it has the following issues: (1) it cannot efficiently solve large SICE problems, i.e., of size $128 \\times 128$ or higher; (2) it relies on multiple CPU based libraries including CVXPY to solve the optimisation problem and obtain gradients for backpropagation. This greatly limits its efficiency due to the lack of GPU support. The above limitations motivate us to develop an SICE method suitable for end-to-end training with GPU.", + "bbox": [ + 496, + 90, + 893, + 333 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Proposed end-to-end trainable SICE method", + "text_level": 1, + "bbox": [ + 498, + 343, + 879, + 359 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Let $J$ be the objective function of Eq. (1). $J$ can be optimised by taking the gradient with respect to $\\mathbf{S}$ as follows:", + "bbox": [ + 498, + 366, + 890, + 397 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\frac {\\partial J}{\\partial \\mathbf {S}} = \\frac {\\partial}{\\partial \\mathbf {S}} \\log \\det (\\mathbf {S}) - \\frac {\\partial}{\\partial \\mathbf {S}} \\operatorname {t r a c e} (\\boldsymbol {\\Sigma} \\mathbf {S}) - \\lambda \\frac {\\partial}{\\partial \\mathbf {S}} \\| \\mathbf {S} \\| _ {1} \\\\ = \\mathbf {S} ^ {- 1} - \\boldsymbol {\\Sigma} - \\lambda \\left(\\frac {\\partial}{\\partial \\mathbf {S}} \\mathbf {S} ^ {+} - \\frac {\\partial}{\\partial \\mathbf {S}} \\mathbf {S} ^ {-}\\right) \\\\ = \\mathbf {S} ^ {- 1} - \\boldsymbol {\\Sigma} - \\lambda (\\operatorname {s i g n} (\\mathbf {S} ^ {+}) - \\operatorname {s i g n} (\\mathbf {S} ^ {-})), \\tag {2} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 539, + 407, + 890, + 489 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{S}^{+}\\equiv \\max (0,\\mathbf{S})$ and $\\mathbf{S}^{-}\\equiv \\max (0, - \\mathbf{S})$ contain the positive and negative parts of $\\mathbf{S}$ , respectively. Eq. (2) can be optimised with the projected gradient descend which has the native backpropagation support on GPU and can take advantage of GPU parallel computing to improve their speed. Now we discuss how Eq. (2) can be effectively optimised using a few consecutive structured CNN layers.", + "bbox": [ + 496, + 500, + 890, + 606 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The overview of our method is given in Fig. 2. From the left, we pass an input image to the backbone and process it till the last convolution layer. We obtain the feature map, i.e., $h \\times w \\times d$ tensor, where $h$ is the height, $w$ is the width, and $d$ is the number of channels. By reshaping the feature map to a set of $n$ vectors of length $d$ , where $n = wh$ and stacking them as column vectors, we can create a $d \\times n$ data matrix $\\mathbf{X}$ . A sample-based covariance matrix $\\boldsymbol{\\Sigma}$ estimated from $\\mathbf{X}$ is defined as $\\boldsymbol{\\Sigma} \\equiv \\mathbf{X} \\bar{\\mathbf{X}}^{\\top}$ , where $\\bar{\\mathbf{I}} = \\frac{1}{n} (\\mathbf{I} - \\frac{1}{n} \\mathbf{1} \\mathbf{1}^{\\top})$ performs centering of matrix $\\mathbf{X}$ , where $\\mathbf{I}$ and $\\mathbf{1} \\mathbf{1}^{\\top}$ are $n \\times n$ dimensional identity matrix and matrix of all-ones, respectively. Below, we describe key steps of our method.", + "bbox": [ + 496, + 607, + 893, + 787 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Estimation of precision matrix $\\mathbf{S}_0 = \\boldsymbol{\\Sigma}^{\\prime -1}$ . Newton-Schulz iteration is popular as it can approximate the matrix square root $^2$ fast on GPU [23]. In contrast, during the estimation of $\\mathbf{S}$ , we use Newton-Schulz iteration [14] for a fast approxi-", + "bbox": [ + 496, + 791, + 893, + 853 + ], + "page_idx": 3 + }, + { + "type": "page_footnote", + "text": "2Notice we use Newton-Schulz iteration to obtain the precision matrix $\\Omega$ from covariance $\\pmb{\\Sigma}$ . We do not propose or use square rooting of $\\pmb{\\Sigma}$ , but we compare our iSICE to this kind of covariance normalisation.", + "bbox": [ + 500, + 862, + 893, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "6234", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Algorithm 1 Matrix inverse by Newton-Schulz iterations, named $\\mathrm{nsInv}(\\cdot)$ . It is used to compute \"Precision $\\Omega$ and is also used by Alg. 2, whereas $\\mathbf{Y}_{N_s}$ (line 4) gives iSQRT.", + "bbox": [ + 76, + 90, + 468, + 136 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Input: Covariance matrix $\\pmb{\\Sigma}$ , number of iterations $N_{s}$ .", + "bbox": [ + 76, + 138, + 429, + 152 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Output: Inverse covariance matrix $\\Sigma^{-1}$ .", + "bbox": [ + 76, + 152, + 341, + 167 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1: $\\mathbf{Y}_0 = \\pmb{\\Sigma}' = \\pmb{\\Sigma} / \\mathrm{trace}(\\pmb{\\Sigma})$ , $\\mathbf{Z}_0 = \\mathbf{I}$", + "2: for $i = 1$ to $N_{s}$ do", + "3: $\\mathbf{P} = \\frac{1}{2} (3\\mathbf{I} - \\mathbf{Z}_{i - 1}\\mathbf{Y}_{i - 1})$", + "4: $\\mathbf{Y}_i = \\mathbf{Y}_{i - 1}\\mathbf{P}$ and $\\mathbf{Z}_i = \\mathbf{P}\\mathbf{Z}_{i - 1}$" + ], + "bbox": [ + 86, + 170, + 320, + 231 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "5: end for", + "6: $\\mathbf{Q} = \\mathbf{Z}_{N_1}$", + "7: $\\pmb{\\Sigma}^{-1} = \\mathbf{Q}\\mathbf{Q}^{\\top} / \\mathrm{trace}(\\pmb{\\Sigma})$ {Inverse covariance matrix}" + ], + "bbox": [ + 86, + 232, + 429, + 277 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "mate inverse of matrix, which imposes a convergence condition $\\| \\mathbf{\\Sigma} - \\mathbf{I}\\| _2 < 1$ on Algorithm 1. Thus, we normalise $\\mathbf{\\Sigma}$ by its trace and use the trace-normalised $\\pmb{\\Sigma}' = \\pmb{\\Sigma} / \\mathrm{trace}(\\pmb{\\Sigma})$ . Then, the square of the inverse square root $\\mathbf{Q}$ is post-normalized by the trace to reverse it, i.e., $\\pmb{\\Sigma}^{-1} = \\pmb{Q}\\pmb{Q}^{\\top} / \\mathrm{trace}(\\pmb{\\Sigma})$ .", + "bbox": [ + 75, + 305, + 468, + 380 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As Eq. (2) has to start with an initial $\\mathbf{S}_0$ , if $\\pmb{\\Sigma}^{\\prime}$ is invertible, Alg. 1 approximates its inverse3. Although it is an approximation, we denote it in Alg. 1 as $\\pmb{\\Sigma}^{-1}$ for brevity.", + "bbox": [ + 75, + 381, + 468, + 426 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Estimation of sparse inverse covariance $\\mathbf{S}$ . Given the result obtained in the last step, $\\mathbf{S}_0$ , we start iterations of iSICE by applying the projected gradient descend (PGD) to the gradient of SICE (Eq. (1)), given in Eq. (2).", + "bbox": [ + 75, + 429, + 468, + 489 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Following methodology of optimisation by imposing box constraints (e.g., see an intuitive example by Schiele et al. [35]), we separate $\\mathbf{S}$ into its positive and negative parts:", + "bbox": [ + 75, + 489, + 468, + 535 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {S} _ {i} ^ {+} = \\max (0, \\mathbf {S} _ {i - 1}) \\quad \\text {a n d} \\quad \\mathbf {S} _ {i} ^ {-} = \\max (0, - \\mathbf {S} _ {i - 1}), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 116, + 545, + 468, + 561 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "and apply the PGD step to each of them separately.", + "bbox": [ + 75, + 571, + 413, + 585 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In such a case, the sparsity constraint imposed by the $\\ell_1$ norm simplifies, i.e., the gradient of $\\lambda \\mathbf{S}^{+}$ can be assumed $\\lambda$ and the gradient of $-\\lambda \\mathbf{S}^{-}$ can be also assumed $\\lambda$ . Thus, we firstly rewrite Eq. (2) into two parts:", + "bbox": [ + 75, + 585, + 468, + 646 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\nabla \\mathbf {S} _ {i} ^ {+} = \\mathbf {S} _ {i - 1} - \\boldsymbol {\\Sigma} - \\lambda \\quad \\text {a n d} \\quad - \\nabla \\mathbf {S} _ {i} ^ {-} = \\mathbf {S} _ {i - 1} - \\boldsymbol {\\Sigma} + \\lambda . \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 656, + 468, + 672 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Then we take one PGD step to update $\\mathbf{S}_i^+$ and $\\mathbf{S}_i^-$ :", + "bbox": [ + 94, + 681, + 423, + 699 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {S} _ {i} ^ {+} := \\Pi \\left(\\mathbf {S} _ {i} ^ {+} - \\eta \\beta \\nabla \\mathbf {S} _ {i} ^ {+}\\right) \\quad \\text {a n d} \\quad \\mathbf {S} _ {i} ^ {-} := \\Pi \\left(\\mathbf {S} _ {i} ^ {-} - \\eta \\beta \\nabla \\mathbf {S} _ {i} ^ {-}\\right), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 86, + 705, + 468, + 724 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\Pi (\\cdot)\\equiv \\max (0,\\cdot)\\equiv \\mathrm{ReLU}(\\cdot)$ is the gradient reprojection function of PGD into the feasible region of each box constraint (one for the non-negative $\\mathbf{S}_i^+$ , and one for nonpositive $\\mathbf{S}_i^-$ ). Constant $\\eta >0$ is a desired learning rate, whereas $\\beta >0$ controls the decay of learning rate.", + "bbox": [ + 75, + 732, + 468, + 808 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, we assemble the current estimate of $\\mathbf{S}_i$ from $\\mathbf{S}_i^+$ and $\\mathbf{S}_i^-$ :", + "bbox": [ + 75, + 808, + 467, + 838 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {S} _ {i} = \\operatorname {S y m} \\left(\\mathbf {S} _ {i} ^ {+} - \\mathbf {S} _ {i} ^ {-}\\right), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 204, + 838, + 468, + 854 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Algorithm 2 Iterative sparse inverse covariance estimation (iSICE).", + "bbox": [ + 500, + 90, + 890, + 119 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Input: Sample-based covariance matrix $\\pmb{\\Sigma}$ , sparsity constant $\\lambda$ , learning rate $\\eta$ , number of iterations N, small constant $\\alpha$ , i.e., $\\alpha = 1\\mathrm{e} - 9$ , regularisation parameter $\\beta$ .", + "bbox": [ + 500, + 122, + 890, + 167 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Output: Sparse inverse covariance matrix $\\mathbf{S}'$ .", + "bbox": [ + 500, + 167, + 800, + 181 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1: $\\nabla_{2} = \\pmb{\\Sigma}' = \\pmb{\\Sigma}/\\mathrm{trace}(\\pmb{\\Sigma})$ {Pre-normalisation using trace}", + "2: $\\mathbf{S}_0 = \\mathrm{nsInv}(\\mathbf{S}_{i - 1} + \\alpha \\mathbf{I})$ {Fast approx. inverse (Alg. 1)}", + "3: $\\nabla_{1} = \\mathbf{S}_{0}$", + "4: for $i = 1$ to $N$ do" + ], + "bbox": [ + 509, + 185, + 867, + 244 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "5: $\\mathbf{S}_i^+ = \\mathrm{ReLU}(\\mathbf{S}_{i - 1})$ and $\\mathbf{S}_i^- = \\mathrm{ReLU}(-\\mathbf{S}_{i - 1})$", + "6: if $i\\neq 1$", + "7: $\\nabla_{1} = \\mathrm{nsInv}(\\mathbf{S}_{i - 1} + \\alpha \\mathbf{I})$ {Fast approx. inv. (Alg. 1)}", + "8: end if", + "9: $\\nabla_{12} = \\nabla_{1} - \\nabla_{2}$", + "10: $\\beta = 1 - \\frac{i - 1}{\\max(1,N - 1)}$ {Decay the learning rate}", + "11: $\\mathbf{S}_i^+ := \\Pi (\\mathbf{S}_i^+ - \\eta \\beta (-\\nabla_{12} + \\lambda))$", + "12: $\\mathbf{S}_i^- \\coloneqq \\Pi (\\mathbf{S}_i^- - \\eta \\beta (+\\nabla_{12} + \\lambda))$", + "13: $\\mathbf{S}_i = \\mathrm{Sym}(\\mathbf{S}_i^+ -\\mathbf{S}_i^-)$", + "14: end for", + "15: $\\mathbf{S}^{*} = \\mathbf{S}_{N} / \\mathrm{trace}(\\mathbf{S}_{N})$" + ], + "bbox": [ + 504, + 244, + 877, + 412 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathrm{Sym}(\\mathbf{M}) = \\frac{1}{2} (\\mathbf{M} + \\mathbf{M}^{\\top})$ ensures the matrix $\\mathbf{M}$ is symmetric (and the intermediate estimate of SICE).", + "bbox": [ + 498, + 443, + 890, + 473 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Algorithm 2 starts with a dense precision matrix $\\mathbf{S}_0$ . If $N > 0$ , it loops over iterations $i = 1, \\dots, N$ , applying the above steps. For ease of tuning the learning rate $\\eta$ , the algorithm starts by the trace normalisation of $\\boldsymbol{\\Sigma}$ and it reverses the trace normalisation when it finishes. Otherwise, $\\eta$ has to be scaled depending on the value of the largest eigenvalue of $\\boldsymbol{\\Sigma}$ which is somewhat impractical when running CNN end-to-end over multiple mini-batches.", + "bbox": [ + 496, + 474, + 890, + 594 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As $\\mathbf{S}^*$ is a symmetric matrix, we only take its upper-triangular entries (plus the diagonal entries) and process them by fully connected layers for classification purposes.", + "bbox": [ + 496, + 595, + 890, + 641 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Algorithm 2 is implemented with modern deep learning library, PyTorch, to leverage the full GPU support and autograd package for optimisation. Due to the iterative nature of solving S, we call our method iterative SICE (iSICE).", + "bbox": [ + 496, + 641, + 890, + 702 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 715, + 632, + 733 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Below, we first describe experimental dataset benchmarks and then discuss the implementation of our proposed method. Subsequently, we present our experimental results and ablation study on key hyper-parameters. Finally, we compare our proposed method with the existing methods.", + "bbox": [ + 496, + 742, + 890, + 818 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets, Metric, and Implementation", + "text_level": 1, + "bbox": [ + 498, + 828, + 826, + 844 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. We conduct experiments using one scene and five fine-grained image datasets: the MIT Indoor dataset [33], Airplane [32], Birds [42], Cars [21], DTD [5] and iNatural-", + "bbox": [ + 496, + 854, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_footnote", + "text": "Note that the conventional matrix inverse requires matrix eigendecomposition which is not well supported on GPU [16]. In contrast, Newton-Schulz iteration [14] is known for fast convergence to $\\pmb{\\Sigma}^{-1}$ .", + "bbox": [ + 75, + 862, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "6235", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "ist [40]. We also use ImageNet100 (a subset of ImageNet1K dataset) proposed by Tian et al. [39] and mini-ImageNet [41]. We follow the widely used training and testing protocols of Bilinear CNN [27]. The details of datasets and protocols are provided in Appendix A.", + "bbox": [ + 75, + 90, + 468, + 167 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metric for evaluations. For evaluation of different methods, average classification accuracy is used. This metric is widely used in literature, e.g., [23, 27].", + "bbox": [ + 76, + 170, + 468, + 215 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation details. Our method is implemented using PyTorch 1.9. We use ImageNet-1K pre-trained backbones provided in Torchvision 0.13.0 library. Following the recent works [23, 24], the number of feature channels is reduced to 256 with $1 \\times 1$ convolution for efficiency and fair comparisons. All images are resized to $448 \\times 448$ and the training is conducted by randomly flipping them horizontally. We fine-tune all backbones for 50-100 epochs with AdamW optimiser [31] for an initial learning rate of 0.00012, and ConvNext-T CNN and Swin-T with an initial learning rate 0.00005. For all backbones, we decrease the learning rate by a factor of 10 at the 15th and 30th epochs. Depending on the dataset and backbones, our fine-tuning process lasts for about 3-8 hours on four P100 GPUs. For ImageNet100, ResNet-50 was trained for 100 epochs with the initial learning rate 0.01, reduced by 10 at the 15th, 30th and 45th epochs. Settings are detailed in Appendix B.", + "bbox": [ + 75, + 219, + 470, + 476 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Evaluations", + "text_level": 1, + "bbox": [ + 76, + 486, + 205, + 500 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We evaluate the performance of the proposed iSICE and compare it with its covariance-based competitors. We also include comparisons with our baseline, the inverse covariance matrix, called precision matrix (for simplicity denoted as Precision $\\Omega$ in experiments). In contrast to iSICE which represents a sparse graph, precision matrix is used as a baseline as it represents a graph without imposed sparsity.", + "bbox": [ + 75, + 508, + 468, + 616 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The covariance representations (denoted with COV) are widely used in literature [24, 27, 28]. We use the Newton-Schulz iteration for computing the matrix square root normalised covariance (iSQRT-COV) due to efficiency of the Newton-Schulz iteration with GPUs, as well as good empirical results reported by multiple authors [23, 25].", + "bbox": [ + 75, + 616, + 468, + 705 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Backbones. We choose VGG-16 [37] and ResNet-50 [13] CNNs as our backbones for the majority of experiments (we also include the VGG-19, ResNet-101, ResNeXt-101 [45], and latest ConvNext-T [30], Swin-T and Swin-B [29] in the main table). VGG-16 and ResNet-50 are popular in image classification (including fine-grained benchmarks). We choose these two backbones in order to better understand", + "bbox": [ + 75, + 709, + 468, + 815 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "the performance of our methods compared to baselines in the common testbed (the same backbones and experimental settings). Given an input image, we obtain a set of feature channels shaped as a tensor after the $1 \\times 1$ convolution operation. Using these feature channels, we compute the iSICE, iSQRT-COV and Precision $\\Omega$ representations. Since these representations are symmetric, we only use the upper-triangular entries (and the diagonal entries) passed to a fully-connected layer to obtain classification scores.", + "bbox": [ + 500, + 90, + 890, + 226 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Hyper-parameters. There are three hyper-parameters associated with iSICE: sparsity constant $\\lambda$ , learning rate $\\eta$ and number of iterations $N$ . We experiment with a large range of values for a better understanding, i.e., $\\lambda \\in \\{1.0, 0.5, 0.1, 0.01, 0.001, 0.0001, 0.00001\\}$ , $\\eta \\in \\{0.001, 0.01, 0.1, 1.0, 5.0, 10.0, 20.0\\}$ , and $N \\in \\{1, 5, 10\\}$ . Since the total combination of hyper-parameters in the table is 147, we choose the median values of each hyper-parameter range (highlighted in bold) and keep them throughout experiments on all datasets. Appendix C studies the impact of hyper-parameters on results.", + "bbox": [ + 498, + 233, + 890, + 383 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Overview of results. Table 1 shows the performance of several COV models (e.g., popular iSQRT-COV), and our Precision $\\Omega$ and iSICE models. The rightmost column summarizes the average performance over one scene and three fine-grained image classification benchmarks. It is clear that on average, iSICE outperforms MPN-COV, iSQRT-COV, DeepCOV, and DeepKSPD, etc. iSICE also outperforms our baseline Precision $\\Omega$ . This achievement is consistent in all four backbones. It is interesting to see that except a few cases, the inverse covariance method, i.e., Precision $\\Omega$ , performs slightly better than the covariance method, i.e., iSQRT-COV. This improved performance highlights the effectiveness of characterising partial correlations of features with inverse covariance instead of pairwise correlations of features based on the sample covariance. Our iSICE method makes the inverse covariance estimation more robust and reliable by enforcing sparsity, as demonstrated by improved performance over Precision $\\Omega$ baseline. However, there may be some situations when Precision $\\Omega$ outperforms iSICE (e.g., MIT with VGG-16 backbone). Notice that iSICE can be considered as sparse precision matrix. When $N = 0$ in Alg. 2, iSICE reduces to Precision $\\Omega$ . In further experiments we show that once the size of matrix is increased, iSICE does outperform Precision $\\Omega$ .", + "bbox": [ + 498, + 390, + 890, + 752 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2 corroborates that iSICE significantly outperforms Precision $\\Omega$ and iSQRT-COV.", + "bbox": [ + 500, + 755, + 888, + 785 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Robustness of iSICE to Hyper-parameters. We have conducted experiments with the hyper-parameter range given in Section 4.1. Appendix C shows that the performance of iSICE remains stable across a range of values.", + "bbox": [ + 500, + 787, + 890, + 848 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Detailed comparisons with SPD-based SOTA models. Table 1 compares the performance of iSICE to several prior works. We first compare our VGG-16 backbone", + "bbox": [ + 498, + 854, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_footnote", + "text": "4The use of precision matrix (and partial correlations) as a visual representation in place of the sample-based covariance matrix $\\pmb{\\Sigma}$ is also our minor but novel proposition. We obtain it via Alg. 1 from which we recover the inverse square root $\\mathbf{Q}$ and then $\\boldsymbol {\\Omega} = \\boldsymbol{\\Sigma}^{-1} = \\mathbf{QQ}^{\\top} / \\mathrm{trace}(\\boldsymbol {\\Sigma})$", + "bbox": [ + 75, + 823, + 468, + 872 + ], + "page_idx": 5 + }, + { + "type": "page_footnote", + "text": "In contrast to our precision matrix $\\pmb{\\Omega} = \\pmb{\\Sigma}^{-1} = \\pmb{Q}\\pmb{Q}^{\\top} / \\mathrm{trace}(\\pmb{\\Sigma})$ from Alg. 1, iSQRT-COV uses $\\pmb{\\Sigma}^{\\frac{1}{2}} = \\mathbf{Y}_N / \\sqrt{\\mathrm{trace}(\\pmb{\\Sigma})}$ from Alg. 1.", + "bbox": [ + 75, + 872, + 467, + 898 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "6236", + "bbox": [ + 480, + 945, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/a9d683d0b098b1df0b81ede401ee98ea92aa31e0349bb802946093811f8545ed.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodBackboneMITAirplaneBirdsCarsDTDiNatuarlistmini-ImageNet
GAP [37]-76.670.479.8---
NetVLAD [2]-81.881.688.6---
NetFV [28]-79.079.986.2---
BCNN [27]77.683.984.090.684.0--
CBP [11]76.284.184.391.284.0--
LRBP [17]-87.384.290.9---
KP [6]-86.986.292.4---
HIHCA [4]-88.385.391.7---
Improved BCNN [25]-88.585.892.0---
SMSO [46]VGG-1679.5-85.0----
MPN-COV [43] (reproduced)-86.182.989.8---
iSQRT-COV [23] (reproduced)76.190.084.591.271.356.276.2
DeepCOV [9]79.288.785.491.786.3--
DeepKSPD [9]81.090.084.891.686.3--
RUN [47]80.591.085.7----
FCBN [48]80.390.585.5----
TKPF [49]80.591.486.0----
Precision Ω80.289.483.492.074.057.974.0
iSICE (ours)78.792.286.594.074.759.878.7
CBP [11]-81.681.688.6---
KP [6]-85.784.791.1---
SMSO [46]79.7-85.8----
iSQRT-COV [23] (reproduced)78.890.984.392.173.057.770.7
DeepCOV-ResNet [34]ResNet-5083.483.986.085.084.6--
TKPF [49]84.192.185.7----
Precision Ω80.891.284.792.073.759.665.6
iSICE (ours)80.592.785.993.560.760.772.0
iSQRT-COV [23]76.390.384.191.471.856.975.4
Precision ΩVGG-1979.691.183.292.274.257.373.8
iSICE (ours)80.692.586.693.974.959.677.1
iSQRT-COV [23]79.391.084.492.373.070.673.9
Precision ΩResNet-10177.990.183.391.471.269.873.0
iSICE (ours)81.092.986.693.675.472.078.0
iSQRT-COV [23]81.691.386.292.475.772.276.1
Precision ΩResNeXt-10185.790.284.689.976.972.377.6
iSICE (ours)86.394.687.294.578.773.881.0
iSQRT-COV [23]77.888.183.589.484.761.582.0
Precision ΩConvNext-T78.581.283.792.283.959.383.6
iSICE (ours)85.490.486.793.188.965.085.1
iSQRT-COV [23]82.187.685.189.786.158.167.7
Precision ΩSwin-T82.588.284.990.586.559.165.6
iSICE (ours)85.989.686.591.388.361.969.1
iSQRT-COV [23]86.691.388.092.079.469.764.9
Precision ΩSwin-B87.090.787.793.180.167.366.4
iSICE (ours)87.692.988.393.379.872.468.4
", + "bbox": [ + 81, + 77, + 890, + 571 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/52414462a2abf5ecf6126f7e90ceb09a54e07911c70359cd67c2a2b9a9d40387.jpg", + "table_caption": [ + "Table 1. Comparison between iSICE, Precision $\\mathbf{\\Omega }$ and other SPD representations in terms of classification accuracy (%) . The performance of existing SPD representation methods is quoted from the original papers. Precision $\\mathbf{\\Omega }$ is given by Alg. 1. iSICE is given by Alg. 2." + ], + "table_footnote": [], + "table_body": "
MethodBackboneTop-1Top-5
GAP [13]71.0/69.590.9/88.9
iSQRT-COV [23]ResNet-50/71.5/70.290.5/89.7
Precision ΩVGG-1671.1/71.090.1/90.1
iSICE74.8/73.492.0/91.8
", + "bbox": [ + 81, + 635, + 468, + 700 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2. Results on the ImageNet100 dataset.", + "bbox": [ + 137, + 712, + 408, + 726 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "based iSICE with BCNN, CBP, LRBP, KP, HIHCA, Improved BCNN, SMSO, MPN-COV, iSQRT-COV, DeepCOV, DeepKSPD, RUN, FCBN and TKPF methods. The MPN-COV and iSQRT-COV methods use backbones pretrained with second-order pooling. For fair comparison with iSICE, we re-run those methods on our machine with the same backbone and evaluation protocols as ours. iSICE outperforms all existing methods on fine-grained datasets. On MIT dataset, our performance is better than CBP, BCNN and iSQRT-COV. iSICE could outperform DeepKSPD and", + "bbox": [ + 75, + 748, + 470, + 902 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "other methods on MIT if a large-dimensional matrix similar to those is used (see Table 3).", + "bbox": [ + 496, + 638, + 890, + 667 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Secondly, we compare our ResNet-50 backbone based iSICE with CBP, KP, SMO, iSQRT-COV, DeepCOV-ResNet and TKPF methods. iSICE achieves better performance than existing methods on both Airplane and Cars datasets. DeepCOV-ResNet uses $1024 \\times 1024$ -dimensional matrix which is four times larger than ours. TKPF uses an advanced feature projection to reduce the CNN feature channels and we use a simple linear projection with $1 \\times 1$ convolution. However, on average, we are still better than DeepCOV-ResNet and TKPF.", + "bbox": [ + 496, + 670, + 892, + 821 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Thirdly, we integrate iSICE with the popular VGG-19, ResNet-101, ResNeXt-101, ConvNext-T [30], Swin-T and Swin-B [29] backbones pre-trained on ImageNet-1K, and compare their performance with iSQRT-COV and Precision $\\Omega$ (Alg. 1). iSICE outperforms iSQRT-COV and Precision", + "bbox": [ + 496, + 824, + 893, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "6237", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/7aa0eadbb57fe377cd99d944c8359ec6bc16df562a70ba77a5c37b1d8777fe24.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodMatrix Dim.MITAirplaneBirdsCarsAverage
VGGResNetVGGResNetVGGResNetVGGResNetVGGResNet
iSQRT-COV256 × 25676.178.890.090.984.584.391.292.185.586.5
512 × 51276.982.891.591.185.084.592.292.186.487.6
Precision Ω256 × 25680.280.889.491.283.484.792.092.086.387.1
512 × 51280.782.790.191.584.984.092.592.687.087.7
SICE128 × 12871.073.185.586.977.378.087.087.980.281.5
256 × 25673.775.487.989.279.780.389.589.382.783.6
iSICE256 × 25678.780.592.292.786.585.99493.587.988.2
512 × 51281.181.792.992.686.88694.693.888.988.5
", + "bbox": [ + 81, + 78, + 888, + 203 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "$\\Omega$ methods across all datasets. This highlights that SPD-based visual representations (1) are still relevant for modern powerful classification backbones and (2) they improve results on large-scale datasets.", + "bbox": [ + 75, + 243, + 468, + 305 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablations on the size of Sparse Inverse Matrix. Table 3 shows results for $512 \\times 512$ vs. $256 \\times 256$ matrix size (we keep the hyper-parameters fixed). To produce $512 \\times 512$ dim. matrix, (1) from VGG-16, we simply remove the $1 \\times 1$ convolution layer to obtain 512 feature channels and (2) from ResNet-50, we set the output channels of $1 \\times 1$ convolution layer to 512. Generally, switching to a larger matrix improves the performance, e.g., on MIT our iSICE gains between 1.2 and $2.4\\%$ (sparsity helps with a larger matrix). On average, all methods improved performance by switching to a larger matrix at the computational expense. See runtimes in Table 5 and memory consumption in Appendix E. Finally, Table 3 also shows that iSICE performs much better than SICE (based on ADMM solver [3]) computed over pre-extracted features. Table 5 shows that iSICE is $3 \\times$ faster. SICE is almost intractable on larger datasets. This validates our claim that learning sparse inverse covariance matrix end-to-end produces robust visual representation.", + "bbox": [ + 75, + 311, + 472, + 583 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "iSICE with learning rate and sparsity modulators. As iSICE trades between log $\\operatorname{det}(\\cdot)$ (changes rapidly) and the $\\ell_1$ norm (changes linearly), optimizing Eq. (1) with PGD may struggle with non-optimal learning rates and sparsity. Thus, we design a simple modulator that updates $\\beta$ in Alg. 2 by setting $\\beta := \\beta \\cdot \\kappa$ in lines 10, where $\\kappa = \\tau + 2\\mathrm{Sigm}(\\mathrm{FC}(\\mathbf{X1} / n))$ , $\\mathrm{Sigm}(\\cdot)$ is a sigmoid, and FC layer is of $d \\times 1$ size. We also add a penalty $-\\gamma (\\kappa - 1)^2$ to the classification loss to encourage $\\kappa$ to be close to 1 unless classification loss gets smaller for $\\kappa \\neq 1$ while incurring the above penalty. We set $\\gamma = 0.0001$ . We use the above modulator (we do not claim this is the most optimal design) as a tool akin to Mod-Grad [36]. $\\tau = 0.01$ is a small offset to prevent zero learning rate. Another modulator with the same architecture is used to adapt sparsity parameter $\\lambda$ . Table 4 shows that modulating the learning rate and sparsity on-the-fly helps iSICE.", + "bbox": [ + 75, + 590, + 468, + 834 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Experiments on dense vs. sparse structure estimation w.r.t. sample size. Below we randomly generate a dense or sparse inverse covariance matrix $\\mathbf{P}$ (size $100\\times 100$ ) and sample various amounts of data from the resulted normal dis", + "bbox": [ + 75, + 840, + 468, + 902 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/59141ecf363bb00e19b1d9bcb000f49335e08451f43b125459e9b48aae1508e8.jpg", + "table_caption": [ + "Table 3. Performance of iSQRT-COV, Precision $\\mathbf{\\Omega }$ ,SICE and iSICE on various datasets when different matrix dimensions are used." + ], + "table_footnote": [], + "table_body": "
MethodMITAirplaneBirdsCarsImageNet100
iSICE80.592.785.993.574.8
iSCIE+MLP81.393.486.193.976.3
", + "bbox": [ + 503, + 241, + 888, + 284 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/568beb2923f8aa8a1ed15ba726ee06f2aa2f100ae1718b73e02286e2614adc15.jpg", + "table_caption": [ + "Table 4. Comparison between the classification performance of iSICE and iSICE+MLP on the ResNet-50 backbone." + ], + "table_footnote": [], + "table_body": "
GAPiSQRT-COVPrecision ΩSICEiSICEiSICE+MLP
Time/batch (sec.)29.032.032.8150.844.645.8
Time/epoch (min.)12.613.413.865.319.319.8
", + "bbox": [ + 503, + 337, + 888, + 381 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/43c445bdbe55f9ff879e65e5e6364d2b8135495f1d76e86b8042fc4041b636a0.jpg", + "image_caption": [ + "Table 5. Runtimes (256×256 matrix, ImageNet100, ResNet-50)." + ], + "image_footnote": [], + "bbox": [ + 504, + 412, + 697, + 516 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/27277345ba5218e2642c0c4087dc5bc376a24c89a8edeb1fae727f185c57b4eb.jpg", + "image_caption": [ + "Figure 3. Estimation errors on (left) dense & (right) sparse struct." + ], + "image_footnote": [], + "bbox": [ + 700, + 412, + 890, + 516 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "tribution (via mvnrnd( $\\cdot$ ) in Matlab) to get its estimate $\\hat{\\mathbf{P}}$ . Fig. 3 (left) shows estimation error $\\|\\hat{\\mathbf{P}} - \\mathbf{P}\\|_F$ for dense structure. Sparse and non-sparse estimation (by SICE and MLE, resp.) show high errors in low sample regime. For the sparse structure in Fig. 3 (right), sparse estimation works better.", + "bbox": [ + 498, + 569, + 890, + 646 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusions", + "text_level": 1, + "bbox": [ + 500, + 659, + 627, + 674 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we proposed a method for learning sparse inverse covariance representation with CNN. Our method estimates SICE within the CNN layers and facilitates backpropagation for end-to-end training. Our iSICE significantly outperforms other covariance representations on several datasets. iSICE exploits the sparsity prior to capture partial correlations under limited number of samples. Our method is of general purpose and can be readily applied in existing SPD-based models to improve their performance.", + "bbox": [ + 496, + 685, + 890, + 821 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements. This work was supported by CSIRO's Machine Learning & Artificial Intelligence Future Science Platform (MLAI FSP), University of Wollongong's Australia IPTA scholarship, and Australian Research Council (DP200101289). We thank Jianjia Zhang for discussions.", + "bbox": [ + 496, + 824, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "6238", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, and J Zico Kolter. Differentiable convex optimization layers. Advances in neural information processing systems, 32, 2019. 4", + "[2] Relja Arandjelovic, Petr Gronat, Akihiko Torii, Tomas Pajdla, and Josef Sivic. Netvlad: Cnn architecture for weakly supervised place recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5297-5307, 2016. 7", + "[3] Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine learning, 3(1):1-122, 2011. 8", + "[4] Sijia Cai, Wangmeng Zuo, and Lei Zhang. Higher-order integration of hierarchical convolutional activations for fine-grained visual categorization. In Proceedings of the IEEE international conference on computer vision, pages 511-520, 2017. 7", + "[5] Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describing textures in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3606-3613, 2014. 5, 11", + "[6] Yin Cui, Feng Zhou, Jiang Wang, Xiao Liu, Yuanqing Lin, and Serge Belongie. Kernel pooling for convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2921-2930, 2017. 7", + "[7] Morris H. DeGroot. Optimal statistical decisions. McGraw-Hill, New York, NY [u.a], 1970. 1, 2, 3", + "[8] Steven Diamond and Stephen Boyd. Cvxpy: A python-embedded modeling language for convex optimization. The Journal of Machine Learning Research, 17(1):2909-2913, 2016. 3, 4", + "[9] Melih Engin, Lei Wang, Luping Zhou, and Xinwang Liu. Deepkspd: Learning kernel-matrix-based spd representation for fine-grained image recognition. In Proceedings of the European Conference on Computer Vision, pages 612-627, 2018. 1, 7", + "[10] Jerome Friedman, Trevor Hastie, and Robert Tibshirani. Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3):432-441, 2008. 2, 3, 4", + "[11] Yang Gao, Oscar Beijbom, Ning Zhang, and Trevor Darrell. Compact bilinear pooling. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 317-326, 2016. 1, 3, 7", + "[12] Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Friedman. The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer, 2009. 2, 3", + "[13] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6, 7" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] Nicholas J Higham. Functions of matrices: theory and computation. SIAM, 2008. 2, 4, 5", + "[15] Junzhou Huang, Tong Zhang, and Dimitris Metaxas. Learning with structured sparsity. Journal of Machine Learning Research, 12(11), 2011. 3", + "[16] Catalin Ionescu, Orestis Vantzos, and Cristian Sminchisescu. Matrix backpropagation for deep networks with structured layers. In Proceedings of the IEEE international conference on computer vision, pages 2965-2973, 2015. 1, 3, 5", + "[17] Shu Kong and Charless Fowlkes. Low-rank bilinear pooling for fine-grained classification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 365-374, 2017. 7", + "[18] Piotr Koniusz, Lei Wang, and Anoop Cherian. Tensor representations for action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(2):648-665, 2021. 1", + "[19] Piotr Koniusz, Fei Yan, Philippe-Henri Gosselin, and Krystian Mikolajczyk. Higher-order occurrence pooling on mid- and low-level features: Visual concept detection. Tech. Report (hal-00922524), 2013. 3", + "[20] Piotr Koniusz and Hongguang Zhang. Power normalizations in fine-grained image, few-shot image and graph classification. In IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2021. 3", + "[21] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In Proceedings of the IEEE international conference on computer vision workshops, pages 554–561, 2013. 5", + "[22] Miroslav Kubat. Neural networks: a comprehensive foundation by simon haykin, macmillan, 1994, ISBN 0-02-352781-7. The Knowledge Engineering Review, 13(4):409-412, 1999. 3", + "[23] Peihua Li, Jiangtao Xie, Qilong Wang, and Zilin Gao. Towards faster training of global covariance pooling networks by iterative matrix square root normalization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 947-955, 2018. 1, 4, 6, 7, 13, 14", + "[24] Peihua Li, Jiangtao Xie, Qilong Wang, and Wangmeng Zuo. Is second-order information helpful for large-scale visual recognition? In Proceedings of the IEEE international conference on computer vision, pages 2070-2078, 2017. 1, 3, 6", + "[25] Tsung-Yu Lin and Subhransu Maji. Improved bilinear pooling with cnns. British Machine Vision Conference, 2017. 3, 6, 7", + "[26] Tsung-Yu Lin, Subhransu Maji, and Piotr Koniusz. Second-order democratic aggregation. In Proceedings of the European Conference on Computer Vision, pages 620-636, 2018. 1", + "[27] Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. Bilinear cnn models for fine-grained visual recognition. In Proceedings of the IEEE international conference on computer vision, pages 1449-1457, 2015. 1, 3, 6, 7", + "[28] Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. Bilinear convolutional neural networks for fine-grained visual recognition. IEEE transactions on pattern analysis and machine intelligence, 40(6):1309-1322, 2017. 1, 3, 6, 7" + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "6239", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[29] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10012-10022, 2021. 6, 7", + "[30] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11976-11986, 2022. 6, 7", + "[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6", + "[32] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, 2013. 5", + "[33] Ariadna Quattoni and Antonio Torralba. Recognizing indoor scenes. In 2009 IEEE conference on computer vision and pattern recognition, pages 413-420. IEEE, 2009. 5", + "[34] Saimunur Rahman, Lei Wang, Changming Sun, and Luping Zhou. Redro: Efficiently learning large-sized spd visual representation. In European Conference on Computer Vision, pages 1-17. Springer, 2020. 1, 7", + "[35] Bernt Schiele, Paul Swoboda, and Gerard Pons-Moll. Projected gradient descent for the lasso. Lecture Notes, 2018. 5", + "[36] Christian Simon, Piotr Koniusz, Richard Nock, and Mehrtash Harandi. On modulating the gradient for metalearning. In Proceedings of the European conference on computer vision, 2020. 8", + "[37] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 6, 7", + "[38] Yue Song, Nicu Sebe, and Wei Wang. Why approximate matrix square root outperforms accurate svd in global covariance pooling? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1115-1123, 2021. 1", + "[39] Yonglong Tian, Dilip Krishnan, and Phillip Isola. Contrastive multiview coding. In European conference on computer vision, pages 776-794. Springer, 2020. 6", + "[40] Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie. The inaturalist species classification and detection dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8769-8778, 2018. 6, 11", + "[41] Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. Matching networks for one shot learning. Advances in neural information processing systems, 29, 2016. 6, 11", + "[42] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200-2011 dataset. 2011. 5", + "[43] Qilong Wang, Jiangtao Xie, Wangmeng Zuo, Lei Zhang, and Peihua Li. Deep cnns meet global covariance pooling: Better representation and generalization. IEEE transactions on" + ], + "bbox": [ + 78, + 90, + 468, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "pattern analysis and machine intelligence, 43(8):2582-2597, 2020. 7", + "[44] Ruiping Wang, Huimin Guo, Larry S Davis, and Qionghai Dai. Covariance discriminative learning: A natural and efficient approach to image set classification. In 2012 IEEE conference on computer vision and pattern recognition, pages 2496-2503. IEEE, 2012. 1", + "[45] Saining Xie, Ross Girshick, Piotr Dolkar, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1492-1500, 2017. 6", + "[46] Kaicheng Yu and Mathieu Salzmann. Statistically-motivated second-order pooling. In Proceedings of the European Conference on Computer Vision, pages 600–616, 2018. 7", + "[47] Tan Yu, Yunfeng Cai, and Ping Li. Toward faster and simpler matrix normalization via rank-1 update. In European Conference on Computer Vision, pages 203–219. Springer, 2020. 7", + "[48] Tan Yu, Xiaoyun Li, and Ping Li. Fast and compact bilinear pooling by shifted random maclaurin. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 3243-3251, 2021. 7", + "[49] Tan Yu, Xiaoyun Li, and Ping Li. Efficient compact bilinear pooling via kronecker product. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 3170-3178, 2022. 1, 7", + "[50] Hongguang Zhang, Jing Zhang, and Piotr Koniusz. Few-shot learning via saliency-guided hallucination of samples. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2770-2779, 2019. 1", + "[51] Jianjia Zhang, Lei Wang, Luping Zhou, and Wanqing Li. Beyond covariance: Sice and kernel based visual feature representation. International Journal of Computer Vision, 129(2):300-320, 2021. 2, 3, 4", + "[52] Shan Zhang, Dawei Luo, Lei Wang, and Piotr Koniusz. Few-shot object detection by second-order pooling. In Proceedings of the Asian conference on computer vision, 2020. 1", + "[53] Shan Zhang, Naila Murray, Lei Wang, and Piotr Koniusz. Time-reversed diffusion tensor transformer: A new tenet of few-shot object detection. In Proceedings of the European conference on computer vision, 2022. 1", + "[54] Shan Zhang, Lei Wang, Naila Murray, and Piotr Koniusz. Kernelized few-shot object detection with efficient integral aggregation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1", + "[55] Yifei Zhang, Hao Zhu, Zixing Song, Piotr Koniusz, and Irwin King. Spectral feature augmentation for graph contrastive learning and beyond. In Proceedings of the AAAI Conference on Artificial Intelligence, 2023. 3" + ], + "bbox": [ + 501, + 92, + 890, + 799 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "6240", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Partial Correlation Based Deep Visual Representation for Image Classification/87245566-23af-421b-80ad-1a8c0309df53_model.json b/2023/Learning Partial Correlation Based Deep Visual Representation for Image Classification/87245566-23af-421b-80ad-1a8c0309df53_model.json new file mode 100644 index 0000000000000000000000000000000000000000..380476819cfd0cb90802ac23d5cfed1d1b01fcd5 --- /dev/null +++ b/2023/Learning Partial Correlation Based Deep Visual Representation for Image Classification/87245566-23af-421b-80ad-1a8c0309df53_model.json @@ -0,0 +1,2475 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.0, + 0.812, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.113, + 0.13, + 0.859, + 0.175 + ], + "angle": 0, + "content": "Learning Partial Correlation based Deep Visual Representation for Image Classification" + }, + { + "type": "text", + "bbox": [ + 0.086, + 0.203, + 0.938, + 0.221 + ], + "angle": 0, + "content": "Saimunur Rahman\\(^{1,2}\\), Piotr Koniusz\\(^{*,1,3}\\), Lei Wang\\(^{2}\\), Luping Zhou\\(^{4}\\), Peyman Moghadam\\(^{1,5}\\), Changming Sun\\(^{1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.207, + 0.222, + 0.822, + 0.239 + ], + "angle": 0, + "content": "\\(^{1}\\)Data61♥CSIRO, \\(^{2}\\)University of Wollongong, \\(^{3}\\)Australian National University," + }, + { + "type": "text", + "bbox": [ + 0.269, + 0.24, + 0.759, + 0.257 + ], + "angle": 0, + "content": "4University of Sydney, 5Queensland University of Technology" + }, + { + "type": "text", + "bbox": [ + 0.23, + 0.26, + 0.792, + 0.274 + ], + "angle": 0, + "content": "name.surname@data61.csiro.au, leiw@uow.edu.au, luping.zhou@sydney.edu.au" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.309, + 0.314, + 0.325 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.342, + 0.474, + 0.705 + ], + "angle": 0, + "content": "Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolutional feature maps. However, pairwise correlation will become misleading once there is another channel correlating with both channels of interest, resulting in the \"confounding\" effect. For this case, \"partial correlation\" which removes the confounding effect shall be estimated instead. Nevertheless, reliably estimating partial correlation requires to solve a symmetric positive definite matrix optimisation, known as sparse inverse covariance estimation (SICE). How to incorporate this process into CNN remains an open issue. In this work, we formulate SICE as a novel structured layer of CNN. To ensure end-to-end trainability, we develop an iterative method to solve the above matrix optimisation during forward and backward propagation steps. Our work obtains a partial correlation based deep visual representation and mitigates the small sample problem often encountered by covariance matrix estimation in CNN. Computationally, our model can be effectively trained with GPU and works well with a large number of channels of advanced CNNs. Experiments show the efficacy and superior classification performance of our deep visual representation compared to covariance matrix based counterparts." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.747, + 0.21, + 0.763 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.772, + 0.47, + 0.879 + ], + "angle": 0, + "content": "Learning effective visual representation is a central issue in computer vision. In the past two decades, describing images with local features and pooling them to a global representation has shown promising performance. As one of the pooling methods, covariance matrix based pooling has attracted much attention due to its exploitation of second-order correlation information of features. A variety of tasks" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.298, + 0.903, + 0.45 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.46, + 0.895, + 0.613 + ], + "angle": 0, + "content": "Figure 1. Understanding the partial correlation (a 3D toy case). Unlike the ordinary covariance (pairwise correlation of say \\(\\mathbf{x}\\) and \\(\\mathbf{y}\\) corresponding to channels), partial correlation between variables \\(\\mathbf{x}\\) and \\(\\mathbf{y}\\) removes the influence of the confounding variable \\(\\mathbf{z}\\). Let the number of samples \\(n = 3\\) and channels \\(d = 3\\). For the 3D case, \\(\\mathbf{x}\\) and \\(\\mathbf{y}\\) are projected onto a plane perpendicular to \\(\\mathbf{z}\\). Then \\(\\rho_{xy} = \\cos \\varphi_{xy}\\) (and \\(\\rho_{xz}\\) and \\(\\rho_{yz}\\) can be computed by analogy). Projected \"residuals\" \\(\\mathbf{r_x}\\) and \\(\\mathbf{r_y}\\) are computed as indicated in the plot, \\(\\mathbf{w}'_x = \\arg \\min_{\\mathbf{w}} \\sum_{i=1}^{3} (x_i - \\mathbf{w}_x^\\top \\mathbf{z}_i)\\) where \\(\\mathbf{z}_i = [z_i, 1]^\\top\\) (and \\(\\mathbf{w}'_y\\) is computed by analogy). The green box: for \\(d > 3\\), the computation of partial correlation requires covariance inversion [7]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.643, + 0.895, + 0.824 + ], + "angle": 0, + "content": "such as fine-grained image classification [27], image segmentation [16], generic image classification [24, 26, 34], image set classification [44], action recognition [18], few-shot classification [50] and few-shot detection [52-54] have benefited from the covariance matrix based representation. A few pioneering works have integrated covariance matrix as a pooling method within convolutional neural networks (CNN) and investigated associated issues such as matrix function backpropagation [16], matrix normalisation [23, 28, 38], compact matrix estimation [11, 49] and kernel based extension [9]. The above works further improved visual representations based on covariance matrix." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Despite the above progress, covariance matrix merely measures the pairwise correlation (more accurately, covariance) of two variables without taking any other variables into account. This can be easily verified because its \\((i,j)\\)-th entry solely depends on the \\(i\\)-th and \\(j\\)-th variables on a" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.468, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author. Code: https://github.com/csiro-robotics/iSICE" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "6231" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.089, + 0.895, + 0.19 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.185, + 0.199, + 0.784, + 0.215 + ], + "angle": 0, + "content": "Figure 2. Proposed iterative sparse inverse covariance estimation (iSICE) method in a CNN pipeline." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.241, + 0.473, + 0.407 + ], + "angle": 0, + "content": "sample set. In Statistics, it is known that such a pairwise correlation will give misleading results once a third variable is correlated with both variables of interest due to the \"confounding\" effect. For this situation, the partial correlation is the right measure to use. It regresses out the effects of other variables from the two variables and then calculates the correlation of their residuals instead. Partial correlation can be conveniently obtained by computing inverse covariance matrix, also known as the precision matrix [7] in the statistical community. Figure 1 illustrates the geometrical interpretation of partial correlation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.412, + 0.472, + 0.73 + ], + "angle": 0, + "content": "The above observation motivates us to investigate a visual representation for image classification based on the inverse covariance matrix. After all, it has better theoretical support on characterising the essential relationship of variables (e.g., the channels in a convolutional feature map) when other variables are present. Note that inverse covariance matrix can be used for many vision tasks but in this paper, we investigate it from the perspective of image classification. Nevertheless, reliably estimating inverse covariance matrix from the local descriptors of a CNN feature map is a challenging task. This is primarily due to the small spatial size of the feature map, i.e., sample size, and a higher number of channels, i.e., feature dimensions, and this issue becomes more pronounced for advanced CNN models. An unreliable estimate of inverse covariance matrix will critically affect its effectiveness as a visual representation. One might argue that by increasing the size of input images or using a dimension reduction layer to reduce the number of feature channels, such an issue could be resolved. In this paper, we investigate this issue from the perspective of robust precision matrix estimation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.473, + 0.903 + ], + "angle": 0, + "content": "To achieve our goal, we explore the use of sparsity prior for inverse covariance matrix estimation in the literature. Specifically, the general principle of \"bet on sparsity\" [12] is adopted in estimating the structure of high-dimensional data, and this leads to an established technique called sparse inverse covariance estimation (SICE) [10]. It solves an optimisation in the space of symmetric and positive definite (SPD) matrix to estimate the inverse covariance matrix by imposing the sparsity prior on its entries. SICE is designed to handle small sample problems and it is known for its excellent effectiveness to that end [10]. An initial attempt" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.241, + 0.892, + 0.317 + ], + "angle": 0, + "content": "to apply SICE for visual representation is based on handcrafted or pre-extracted features of small size and an off-the-shelf SICE solver, and it does not have the ability to backpropagate through SICE due to optimisation of the SPD matrix with the imposed non-smooth sparsity term [51]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.318, + 0.893, + 0.529 + ], + "angle": 0, + "content": "Our work is the first one that truly integrates SICE into CNN for end-to-end training. Clearly, such an integration will fully take advantage of the feature learning capability of CNN and the partial correlation offered by inverse covariance matrix. On the other hand, realising such an integration is not trivial. Unlike covariance matrix, which is obtained by simple arithmetic operations, SICE is obtained by solving an SPD matrix based optimisation. How to incorporate this optimisation process into CNN as a layer is an issue. Furthermore, this SICE optimisation needs to be solved for each training image during both forward and backward phases to generate a visual representation. Directly solving this optimisation within CNN will not be practical even for a medium-sized SICE problem." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.53, + 0.895, + 0.758 + ], + "angle": 0, + "content": "To efficiently integrate SICE into CNN, we propose a fast end-to-end training method for SICE by taking inspiration from Newton-Schulz iteration [14]. Our method solves the SICE optimisation with a smooth convex cost function by re-parameterising the non-smooth term in the original SICE cost function (see Eq. (1)), and it can therefore be optimised with standard optimisation techniques such as gradient descent. Furthermore, we effectively enforce the SPD constraint during optimisation so that the obtained SICE solution remains SPD as desired. Figure 2 shows our \"Iterative Sparse Inverse Covariance Estimation (iSICE)\". In contrast to SICE, iSICE works with end-to-end trainable deep learning models. Our iSICE involves simple matrix arithmetic operations fully compatible with GPU. It can approximately solve large SICE problems within CNN efficiently." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.766, + 0.858, + 0.78 + ], + "angle": 0, + "content": "Our main contributions are summarised as follows." + }, + { + "type": "text", + "bbox": [ + 0.514, + 0.795, + 0.895, + 0.903 + ], + "angle": 0, + "content": "1. To more precisely characterise the relationship of features for visual representation, this paper proposes to integrate sparse inverse covariance estimation (SICE) process into CNNs as a novel layer. To achieve this, we develop a method based on Newton-Schulz iteration and box constraints for \\(\\ell_1\\) penalty to solve the SICE optimisation with CNN and maintain the end-to-end" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6232" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.11, + 0.092, + 0.468, + 0.121 + ], + "angle": 0, + "content": "training efficiency. To the best of our knowledge, our iSICE is the first end-to-end SICE solution for CNN." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.131, + 0.468, + 0.236 + ], + "angle": 0, + "content": "2. Our iSICE method requires a minimal change of network architecture. Therefore, it can readily be integrated with existing works to replace those using deep network models to learn covariance matrix based visual representation. The iSICE is fully compatible with GPU and can be easily implemented with modern deep learning libraries." + }, + { + "type": "text", + "bbox": [ + 0.09, + 0.246, + 0.468, + 0.352 + ], + "angle": 0, + "content": "3. As the objective of SICE is a combination of log det term (may change rapidly) and sparsity (changes slowly), achieving the balance between both terms during optimisation by the gradient descent is hard. To this end, we propose a minor contribution: a simple modulating network whose goal is to adapt on-the-fly learning rate and sparsity penalty." + }, + { + "type": "list", + "bbox": [ + 0.09, + 0.131, + 0.468, + 0.352 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.361, + 0.468, + 0.391 + ], + "angle": 0, + "content": "Experiments on multiple image classification datasets show the effectiveness of our proposed iSICE method." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.404, + 0.214, + 0.42 + ], + "angle": 0, + "content": "2. Related work" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.429, + 0.468, + 0.715 + ], + "angle": 0, + "content": "Since the advent of covariance representation methods in deep learning [11, 27], reliable estimation of the covariance matrix from a CNN feature map remains an issue. The issue is due to the small spatial size of feature map (corresponding to the number of samples) and the higher number of feature channels (corresponding to the feature dimensions), which could cause unreliable estimation and even matrix singularity due to the so-called \"curse of dimensionality\". Existing works either append a small positive constant to the diagonals of covariance matrix [27] or use matrix normalisation operation [16, 24, 28] to handle this issue. Matrix normalisation approaches mitigate unbalanced spectrum represented by eigenvalues of covariance matrix [19, 20, 24, 25, 55]. Different from the existing methods, in this paper, we approach the reliable covariance estimation problem in CNN from a perspective of partial correlations that can be efficiently captured by the inverse of covariance matrix, also known as the precision matrix [7], when a large number of samples is available for estimation." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.716, + 0.468, + 0.866 + ], + "angle": 0, + "content": "Covariance representation strives to capture the underlying structure of CNN feature channels. In the literature of knowledge representation [22], it is recommended to leverage prior knowledge to improve a learning task when sufficient data is not available1. Thus, prior knowledge can be used to improve the estimation of underlying structure of high-dimensional data captured by covariance representation. One common prior knowledge is \"structure sparsity\" which leads to the sparse inverse covariance matrix in the literature of statistical machine learning [10, 15]." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.092, + 0.892, + 0.394 + ], + "angle": 0, + "content": "Structure sparsity cannot be readily applied to covariance representation as it requires the access to partial correlation between feature components. A covariance matrix captures pairwise correlation of feature components without taking into account the confounding effect of remaining components. Therefore, it is unlikely that the covariance matrix will be sparse by nature. To obtain partial correlation, SICE moves from covariance matrix to its inverse. An inverse covariance matrix captures partial correlation between feature components by regressing out the effects of other variables [15]. Once other variables are factored out, structure sparsity can be effectively enforced in SICE. In a recent work [51], SICE-based visual representation has been applied to image classification with handcrafted and pre-extracted features of small size. However, SICE has never been integrated into CNN for end-to-end training with the goal of adapting to such a representation. The existing solvers for computing SICE also have limited GPU support [8, 10]. Thus, we propose an end-to-end trainable iterative method for solving SICE optimisation with CNNs." + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.406, + 0.671, + 0.423 + ], + "angle": 0, + "content": "3. Proposed method" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.431, + 0.892, + 0.492 + ], + "angle": 0, + "content": "In this section, we begin by discussing the background of SICE. Then we discuss how it can be estimated from CNN feature descriptors. Finally, we describe our proposed method which is trainable end-to-end with a CNN." + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.5, + 0.712, + 0.515 + ], + "angle": 0, + "content": "3.1. The basic idea of SICE" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.524, + 0.892, + 0.794 + ], + "angle": 0, + "content": "As a representation, the covariance matrix captures the underlying structure of a feature set. It uses a covariance matrix estimated from samples to capture this structure. Sparse inverse covariance estimation (SICE) focuses on the following two issues: (1) instability or singularity of sample-based covariance matrix estimated from a small number of high-dimensional feature vectors. This situation makes it less effective in capturing the underlying structure of data. As an example, in this case the smaller and larger eigenvalues of the estimated covariance matrix become poorly estimated. Thus, a suitable regularisation (a prior) is needed during the estimation to mitigate the bias of our estimator; (2) rigid estimation of covariance matrix for high-dimensional feature vectors is not always appropriate as the high-dimensional data usually presents a complex structure. If there is prior knowledge available, it should be used to improve the covariance estimation from a small number of samples." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.796, + 0.892, + 0.901 + ], + "angle": 0, + "content": "In terms of prior knowledge of high-dimensional data, structure sparsity [15] and the \"bet on sparsity\" principle [12] are the two common priors used in the literature. Suppose that we have a probabilistic graphical model, where each node corresponds to a feature and the statistical dependence between two nodes is expressed with an edge linking two nodes. Structure sparsity would specify how" + }, + { + "type": "page_footnote", + "bbox": [ + 0.077, + 0.875, + 0.468, + 0.901 + ], + "angle": 0, + "content": "This is indeed the case with CNNs as the spatial size of a CNN feature map is usually small when compared to the number of its channels." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6233" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.333 + ], + "angle": 0, + "content": "sparse such a graph is, e.g., how many edges are presented in this graph. More generally, even if there is no clear prior knowledge on structure sparsity available, the \"bet on sparsity\" principle can still be applied to estimate the structure of the graph by imposing a sparsity prior. Its rationality is as follows. If the graph is indeed sparse, SICE will estimate its underlying structure with a correct prior, and if the graph is dense, it will not estimate the underlying structure accurately with such a prior. However, in the latter case, we will not lose much because we have known that we do not have enough sample to estimate the dense structure. The \"bet on sparsity\" principle has been widely adopted in high-dimensional data analysis, and also demonstrated its efficacy on covariance matrix based visual representation estimated from handcrafted or pre-extracted CNN features [51]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.334, + 0.473, + 0.518 + ], + "angle": 0, + "content": "SICE strives to improve the covariance estimation with the use of prior knowledge. To incorporate prior knowledge, SICE switches to the inverse of covariance matrix from covariance matrix. In principle, the covariance matrix captures the apparent pairwise correlation between feature components, i.e., indirect correlation. In comparison, the inverse of covariance matrix is able to characterise the direct (i.e. partial) correlation between two feature components by regressing out the remaining features. Using the inverse covariance matrix not only helps to interpret the essential relationship between two features, but also allows the convenient incorporation of the sparsity prior." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.527, + 0.36, + 0.543 + ], + "angle": 0, + "content": "3.2. Our SICE estimation with CNN" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.552, + 0.473, + 0.734 + ], + "angle": 0, + "content": "Suppose, we have the sample-based covariance matrix \\(\\pmb{\\Sigma}\\) computed from a set of CNN local descriptors presented in a convolutional feature map. Let \\(\\mathbf{S}\\) denote the corresponding sparse inverse covariance matrix. The off-diagonal entries of \\(\\mathbf{S}\\) capture the direct correlation between different descriptor components. They are zero if two components are independent under the removed influence of confounding variables. In the literature [10], the estimation of \\(\\mathbf{S}\\) has been effectively resolved by the maximization of a penalised log-likelihood of data with an SPD constraint on \\(\\mathbf{S}\\) and the sparsity prior to induce sparse graph connectivity. The optimal solution of the above problem is known as SICE." + }, + { + "type": "text", + "bbox": [ + 0.085, + 0.748, + 0.273, + 0.765 + ], + "angle": 0, + "content": "SICE is defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.108, + 0.776, + 0.454, + 0.799 + ], + "angle": 0, + "content": "\\[\n\\mathbf {S} ^ {*} = \\underset {\\mathbf {S} > 0} {\\arg \\max } \\log \\det (\\mathbf {S}) - \\operatorname {t r a c e} (\\boldsymbol {\\Sigma} \\mathbf {S}) - \\lambda \\| \\mathbf {S} \\| _ {1}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.084, + 0.809, + 0.455, + 0.856 + ], + "angle": 0, + "content": "where \\(\\Sigma\\) is a sample-based covariance matrix, and \\(\\operatorname{det}(\\cdot)\\), trace \\((\\cdot)\\) and \\(\\| \\cdot \\|_1\\) denote the determinant, trace and \\(\\ell_1\\)-norm of a vectorization of matrix, respectively." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.87, + 0.472, + 0.902 + ], + "angle": 0, + "content": "To obtain reliable and faithful SICE, the term \\(\\| \\mathbf{S}\\| _1\\) imposes the structure sparsity on \\(\\mathbf{S}\\). \\(\\lambda\\) controls the trade-off" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.334 + ], + "angle": 0, + "content": "between the amount of sparsity and the log-likelihood estimation. The problem in Eq. (1) is convex and can be solved by the off-the-shelf packages such as GLASSO [10] and CVXPY [8]. However, the objective is non-smooth due to the \\(\\ell_1\\) penalty. The above optimisation packages cannot be used with CNN layers to conduct training with backpropagation. A recent extension of CVXPY called CVXPYLAYERS [1] provides differentiable optimisation layers. However, based on our investigation, it has the following issues: (1) it cannot efficiently solve large SICE problems, i.e., of size \\(128 \\times 128\\) or higher; (2) it relies on multiple CPU based libraries including CVXPY to solve the optimisation problem and obtain gradients for backpropagation. This greatly limits its efficiency due to the lack of GPU support. The above limitations motivate us to develop an SICE method suitable for end-to-end training with GPU." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.344, + 0.88, + 0.36 + ], + "angle": 0, + "content": "3.3. Proposed end-to-end trainable SICE method" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.367, + 0.892, + 0.398 + ], + "angle": 0, + "content": "Let \\( J \\) be the objective function of Eq. (1). \\( J \\) can be optimised by taking the gradient with respect to \\( \\mathbf{S} \\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.54, + 0.409, + 0.892, + 0.49 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\frac {\\partial J}{\\partial \\mathbf {S}} = \\frac {\\partial}{\\partial \\mathbf {S}} \\log \\det (\\mathbf {S}) - \\frac {\\partial}{\\partial \\mathbf {S}} \\operatorname {t r a c e} (\\boldsymbol {\\Sigma} \\mathbf {S}) - \\lambda \\frac {\\partial}{\\partial \\mathbf {S}} \\| \\mathbf {S} \\| _ {1} \\\\ = \\mathbf {S} ^ {- 1} - \\boldsymbol {\\Sigma} - \\lambda \\left(\\frac {\\partial}{\\partial \\mathbf {S}} \\mathbf {S} ^ {+} - \\frac {\\partial}{\\partial \\mathbf {S}} \\mathbf {S} ^ {-}\\right) \\\\ = \\mathbf {S} ^ {- 1} - \\boldsymbol {\\Sigma} - \\lambda (\\operatorname {s i g n} (\\mathbf {S} ^ {+}) - \\operatorname {s i g n} (\\mathbf {S} ^ {-})), \\tag {2} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.501, + 0.892, + 0.607 + ], + "angle": 0, + "content": "where \\(\\mathbf{S}^{+}\\equiv \\max (0,\\mathbf{S})\\) and \\(\\mathbf{S}^{-}\\equiv \\max (0, - \\mathbf{S})\\) contain the positive and negative parts of \\(\\mathbf{S}\\), respectively. Eq. (2) can be optimised with the projected gradient descend which has the native backpropagation support on GPU and can take advantage of GPU parallel computing to improve their speed. Now we discuss how Eq. (2) can be effectively optimised using a few consecutive structured CNN layers." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.608, + 0.895, + 0.789 + ], + "angle": 0, + "content": "The overview of our method is given in Fig. 2. From the left, we pass an input image to the backbone and process it till the last convolution layer. We obtain the feature map, i.e., \\( h \\times w \\times d \\) tensor, where \\( h \\) is the height, \\( w \\) is the width, and \\( d \\) is the number of channels. By reshaping the feature map to a set of \\( n \\) vectors of length \\( d \\), where \\( n = wh \\) and stacking them as column vectors, we can create a \\( d \\times n \\) data matrix \\( \\mathbf{X} \\). A sample-based covariance matrix \\( \\boldsymbol{\\Sigma} \\) estimated from \\( \\mathbf{X} \\) is defined as \\( \\boldsymbol{\\Sigma} \\equiv \\mathbf{X} \\bar{\\mathbf{X}}^{\\top} \\), where \\( \\bar{\\mathbf{I}} = \\frac{1}{n} (\\mathbf{I} - \\frac{1}{n} \\mathbf{1} \\mathbf{1}^{\\top}) \\) performs centering of matrix \\( \\mathbf{X} \\), where \\( \\mathbf{I} \\) and \\( \\mathbf{1} \\mathbf{1}^{\\top} \\) are \\( n \\times n \\) dimensional identity matrix and matrix of all-ones, respectively. Below, we describe key steps of our method." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.792, + 0.895, + 0.854 + ], + "angle": 0, + "content": "Estimation of precision matrix \\(\\mathbf{S}_0 = \\boldsymbol{\\Sigma}^{\\prime -1}\\). Newton-Schulz iteration is popular as it can approximate the matrix square root\\(^2\\) fast on GPU [23]. In contrast, during the estimation of \\(\\mathbf{S}\\), we use Newton-Schulz iteration [14] for a fast approxi-" + }, + { + "type": "page_footnote", + "bbox": [ + 0.5, + 0.863, + 0.894, + 0.901 + ], + "angle": 0, + "content": "2Notice we use Newton-Schulz iteration to obtain the precision matrix \\(\\Omega\\) from covariance \\(\\pmb{\\Sigma}\\). We do not propose or use square rooting of \\(\\pmb{\\Sigma}\\), but we compare our iSICE to this kind of covariance normalisation." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6234" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.078, + 0.091, + 0.47, + 0.137 + ], + "angle": 0, + "content": "Algorithm 1 Matrix inverse by Newton-Schulz iterations, named \\(\\mathrm{nsInv}(\\cdot)\\). It is used to compute \"Precision \\(\\Omega\\) and is also used by Alg. 2, whereas \\(\\mathbf{Y}_{N_s}\\) (line 4) gives iSQRT." + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.139, + 0.431, + 0.154 + ], + "angle": 0, + "content": "Input: Covariance matrix \\(\\pmb{\\Sigma}\\), number of iterations \\(N_{s}\\)." + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.154, + 0.343, + 0.169 + ], + "angle": 0, + "content": "Output: Inverse covariance matrix \\(\\Sigma^{-1}\\)." + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.171, + 0.286, + 0.186 + ], + "angle": 0, + "content": "1: \\(\\mathbf{Y}_0 = \\pmb{\\Sigma}' = \\pmb{\\Sigma} / \\mathrm{trace}(\\pmb{\\Sigma})\\), \\(\\mathbf{Z}_0 = \\mathbf{I}\\)" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.188, + 0.227, + 0.201 + ], + "angle": 0, + "content": "2: for \\( i = 1 \\) to \\( N_{s} \\) do" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.202, + 0.26, + 0.217 + ], + "angle": 0, + "content": "3: \\(\\mathbf{P} = \\frac{1}{2} (3\\mathbf{I} - \\mathbf{Z}_{i - 1}\\mathbf{Y}_{i - 1})\\)" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.218, + 0.321, + 0.232 + ], + "angle": 0, + "content": "4: \\(\\mathbf{Y}_i = \\mathbf{Y}_{i - 1}\\mathbf{P}\\) and \\(\\mathbf{Z}_i = \\mathbf{P}\\mathbf{Z}_{i - 1}\\)" + }, + { + "type": "list", + "bbox": [ + 0.088, + 0.171, + 0.321, + 0.232 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.233, + 0.161, + 0.246 + ], + "angle": 0, + "content": "5: end for" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.248, + 0.163, + 0.262 + ], + "angle": 0, + "content": "6: \\(\\mathbf{Q} = \\mathbf{Z}_{N_1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.263, + 0.431, + 0.278 + ], + "angle": 0, + "content": "7: \\(\\pmb{\\Sigma}^{-1} = \\mathbf{Q}\\mathbf{Q}^{\\top} / \\mathrm{trace}(\\pmb{\\Sigma})\\) {Inverse covariance matrix}" + }, + { + "type": "list", + "bbox": [ + 0.088, + 0.233, + 0.431, + 0.278 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.306, + 0.469, + 0.381 + ], + "angle": 0, + "content": "mate inverse of matrix, which imposes a convergence condition \\( \\| \\mathbf{\\Sigma} - \\mathbf{I}\\| _2 < 1 \\) on Algorithm 1. Thus, we normalise \\( \\mathbf{\\Sigma} \\) by its trace and use the trace-normalised \\( \\pmb{\\Sigma}' = \\pmb{\\Sigma} / \\mathrm{trace}(\\pmb{\\Sigma}) \\). Then, the square of the inverse square root \\( \\mathbf{Q} \\) is post-normalized by the trace to reverse it, i.e., \\( \\pmb{\\Sigma}^{-1} = \\pmb{Q}\\pmb{Q}^{\\top} / \\mathrm{trace}(\\pmb{\\Sigma}) \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.382, + 0.469, + 0.427 + ], + "angle": 0, + "content": "As Eq. (2) has to start with an initial \\(\\mathbf{S}_0\\), if \\(\\pmb{\\Sigma}^{\\prime}\\) is invertible, Alg. 1 approximates its inverse3. Although it is an approximation, we denote it in Alg. 1 as \\(\\pmb{\\Sigma}^{-1}\\) for brevity." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.43, + 0.469, + 0.491 + ], + "angle": 0, + "content": "Estimation of sparse inverse covariance \\(\\mathbf{S}\\). Given the result obtained in the last step, \\(\\mathbf{S}_0\\), we start iterations of iSICE by applying the projected gradient descend (PGD) to the gradient of SICE (Eq. (1)), given in Eq. (2)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.491, + 0.469, + 0.536 + ], + "angle": 0, + "content": "Following methodology of optimisation by imposing box constraints (e.g., see an intuitive example by Schiele et al. [35]), we separate \\(\\mathbf{S}\\) into its positive and negative parts:" + }, + { + "type": "equation", + "bbox": [ + 0.117, + 0.546, + 0.469, + 0.563 + ], + "angle": 0, + "content": "\\[\n\\mathbf {S} _ {i} ^ {+} = \\max (0, \\mathbf {S} _ {i - 1}) \\quad \\text {a n d} \\quad \\mathbf {S} _ {i} ^ {-} = \\max (0, - \\mathbf {S} _ {i - 1}), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.572, + 0.415, + 0.587 + ], + "angle": 0, + "content": "and apply the PGD step to each of them separately." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.587, + 0.469, + 0.647 + ], + "angle": 0, + "content": "In such a case, the sparsity constraint imposed by the \\(\\ell_1\\) norm simplifies, i.e., the gradient of \\(\\lambda \\mathbf{S}^{+}\\) can be assumed \\(\\lambda\\) and the gradient of \\(-\\lambda \\mathbf{S}^{-}\\) can be also assumed \\(\\lambda\\). Thus, we firstly rewrite Eq. (2) into two parts:" + }, + { + "type": "equation", + "bbox": [ + 0.095, + 0.657, + 0.469, + 0.673 + ], + "angle": 0, + "content": "\\[\n\\nabla \\mathbf {S} _ {i} ^ {+} = \\mathbf {S} _ {i - 1} - \\boldsymbol {\\Sigma} - \\lambda \\quad \\text {a n d} \\quad - \\nabla \\mathbf {S} _ {i} ^ {-} = \\mathbf {S} _ {i - 1} - \\boldsymbol {\\Sigma} + \\lambda . \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.682, + 0.424, + 0.7 + ], + "angle": 0, + "content": "Then we take one PGD step to update \\(\\mathbf{S}_i^+\\) and \\(\\mathbf{S}_i^-\\):" + }, + { + "type": "equation", + "bbox": [ + 0.087, + 0.707, + 0.469, + 0.725 + ], + "angle": 0, + "content": "\\[\n\\mathbf {S} _ {i} ^ {+} := \\Pi \\left(\\mathbf {S} _ {i} ^ {+} - \\eta \\beta \\nabla \\mathbf {S} _ {i} ^ {+}\\right) \\quad \\text {a n d} \\quad \\mathbf {S} _ {i} ^ {-} := \\Pi \\left(\\mathbf {S} _ {i} ^ {-} - \\eta \\beta \\nabla \\mathbf {S} _ {i} ^ {-}\\right), \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.733, + 0.469, + 0.809 + ], + "angle": 0, + "content": "where \\(\\Pi (\\cdot)\\equiv \\max (0,\\cdot)\\equiv \\mathrm{ReLU}(\\cdot)\\) is the gradient reprojection function of PGD into the feasible region of each box constraint (one for the non-negative \\(\\mathbf{S}_i^+\\), and one for nonpositive \\(\\mathbf{S}_i^-\\)). Constant \\(\\eta >0\\) is a desired learning rate, whereas \\(\\beta >0\\) controls the decay of learning rate." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.809, + 0.468, + 0.839 + ], + "angle": 0, + "content": "Finally, we assemble the current estimate of \\(\\mathbf{S}_i\\) from \\(\\mathbf{S}_i^+\\) and \\(\\mathbf{S}_i^-\\):" + }, + { + "type": "equation", + "bbox": [ + 0.205, + 0.839, + 0.469, + 0.856 + ], + "angle": 0, + "content": "\\[\n\\mathbf {S} _ {i} = \\operatorname {S y m} \\left(\\mathbf {S} _ {i} ^ {+} - \\mathbf {S} _ {i} ^ {-}\\right), \\tag {6}\n\\]" + }, + { + "type": "page_footnote", + "bbox": [ + 0.076, + 0.863, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Note that the conventional matrix inverse requires matrix eigendecomposition which is not well supported on GPU [16]. In contrast, Newton-Schulz iteration [14] is known for fast convergence to \\(\\pmb{\\Sigma}^{-1}\\)." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.091, + 0.892, + 0.121 + ], + "angle": 0, + "content": "Algorithm 2 Iterative sparse inverse covariance estimation (iSICE)." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.123, + 0.892, + 0.168 + ], + "angle": 0, + "content": "Input: Sample-based covariance matrix \\(\\pmb{\\Sigma}\\), sparsity constant \\(\\lambda\\), learning rate \\(\\eta\\), number of iterations N, small constant \\(\\alpha\\), i.e., \\(\\alpha = 1\\mathrm{e} - 9\\), regularisation parameter \\(\\beta\\)." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.168, + 0.801, + 0.183 + ], + "angle": 0, + "content": "Output: Sparse inverse covariance matrix \\(\\mathbf{S}'\\)." + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.186, + 0.868, + 0.2 + ], + "angle": 0, + "content": "1: \\(\\nabla_{2} = \\pmb{\\Sigma}' = \\pmb{\\Sigma}/\\mathrm{trace}(\\pmb{\\Sigma})\\) {Pre-normalisation using trace}" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.201, + 0.866, + 0.216 + ], + "angle": 0, + "content": "2: \\(\\mathbf{S}_0 = \\mathrm{nsInv}(\\mathbf{S}_{i - 1} + \\alpha \\mathbf{I})\\) {Fast approx. inverse (Alg. 1)}" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.217, + 0.58, + 0.231 + ], + "angle": 0, + "content": "3: \\(\\nabla_{1} = \\mathbf{S}_{0}\\)" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.232, + 0.644, + 0.245 + ], + "angle": 0, + "content": "4: for \\( i = 1 \\) to \\( N \\) do" + }, + { + "type": "list", + "bbox": [ + 0.511, + 0.186, + 0.868, + 0.245 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.246, + 0.84, + 0.262 + ], + "angle": 0, + "content": "5: \\(\\mathbf{S}_i^+ = \\mathrm{ReLU}(\\mathbf{S}_{i - 1})\\) and \\(\\mathbf{S}_i^- = \\mathrm{ReLU}(-\\mathbf{S}_{i - 1})\\)" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.263, + 0.594, + 0.274 + ], + "angle": 0, + "content": "6: if \\(i\\neq 1\\)" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.276, + 0.878, + 0.292 + ], + "angle": 0, + "content": "7: \\(\\nabla_{1} = \\mathrm{nsInv}(\\mathbf{S}_{i - 1} + \\alpha \\mathbf{I})\\) {Fast approx. inv. (Alg. 1)}" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.293, + 0.59, + 0.305 + ], + "angle": 0, + "content": "8: end if" + }, + { + "type": "text", + "bbox": [ + 0.511, + 0.306, + 0.644, + 0.321 + ], + "angle": 0, + "content": "9: \\(\\nabla_{12} = \\nabla_{1} - \\nabla_{2}\\)" + }, + { + "type": "text", + "bbox": [ + 0.506, + 0.322, + 0.829, + 0.339 + ], + "angle": 0, + "content": "10: \\(\\beta = 1 - \\frac{i - 1}{\\max(1,N - 1)}\\) {Decay the learning rate}" + }, + { + "type": "text", + "bbox": [ + 0.506, + 0.339, + 0.729, + 0.354 + ], + "angle": 0, + "content": "11: \\(\\mathbf{S}_i^+ := \\Pi (\\mathbf{S}_i^+ - \\eta \\beta (-\\nabla_{12} + \\lambda))\\)" + }, + { + "type": "text", + "bbox": [ + 0.506, + 0.354, + 0.729, + 0.369 + ], + "angle": 0, + "content": "12: \\(\\mathbf{S}_i^- \\coloneqq \\Pi (\\mathbf{S}_i^- - \\eta \\beta (+\\nabla_{12} + \\lambda))\\)" + }, + { + "type": "text", + "bbox": [ + 0.506, + 0.37, + 0.671, + 0.385 + ], + "angle": 0, + "content": "13: \\(\\mathbf{S}_i = \\mathrm{Sym}(\\mathbf{S}_i^+ -\\mathbf{S}_i^-)\\)" + }, + { + "type": "text", + "bbox": [ + 0.506, + 0.386, + 0.583, + 0.397 + ], + "angle": 0, + "content": "14: end for" + }, + { + "type": "text", + "bbox": [ + 0.506, + 0.399, + 0.654, + 0.414 + ], + "angle": 0, + "content": "15: \\(\\mathbf{S}^{*} = \\mathbf{S}_{N} / \\mathrm{trace}(\\mathbf{S}_{N})\\)" + }, + { + "type": "list", + "bbox": [ + 0.506, + 0.246, + 0.878, + 0.414 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.444, + 0.892, + 0.474 + ], + "angle": 0, + "content": "where \\(\\mathrm{Sym}(\\mathbf{M}) = \\frac{1}{2} (\\mathbf{M} + \\mathbf{M}^{\\top})\\) ensures the matrix \\(\\mathbf{M}\\) is symmetric (and the intermediate estimate of SICE)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.475, + 0.892, + 0.595 + ], + "angle": 0, + "content": "Algorithm 2 starts with a dense precision matrix \\(\\mathbf{S}_0\\). If \\(N > 0\\), it loops over iterations \\(i = 1, \\dots, N\\), applying the above steps. For ease of tuning the learning rate \\(\\eta\\), the algorithm starts by the trace normalisation of \\(\\boldsymbol{\\Sigma}\\) and it reverses the trace normalisation when it finishes. Otherwise, \\(\\eta\\) has to be scaled depending on the value of the largest eigenvalue of \\(\\boldsymbol{\\Sigma}\\) which is somewhat impractical when running CNN end-to-end over multiple mini-batches." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.596, + 0.892, + 0.642 + ], + "angle": 0, + "content": "As \\(\\mathbf{S}^*\\) is a symmetric matrix, we only take its upper-triangular entries (plus the diagonal entries) and process them by fully connected layers for classification purposes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.642, + 0.892, + 0.703 + ], + "angle": 0, + "content": "Algorithm 2 is implemented with modern deep learning library, PyTorch, to leverage the full GPU support and autograd package for optimisation. Due to the iterative nature of solving S, we call our method iterative SICE (iSICE)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.717, + 0.633, + 0.734 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.743, + 0.892, + 0.819 + ], + "angle": 0, + "content": "Below, we first describe experimental dataset benchmarks and then discuss the implementation of our proposed method. Subsequently, we present our experimental results and ablation study on key hyper-parameters. Finally, we compare our proposed method with the existing methods." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.829, + 0.828, + 0.845 + ], + "angle": 0, + "content": "4.1. Datasets, Metric, and Implementation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Datasets. We conduct experiments using one scene and five fine-grained image datasets: the MIT Indoor dataset [33], Airplane [32], Birds [42], Cars [21], DTD [5] and iNatural-" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6235" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.168 + ], + "angle": 0, + "content": "ist [40]. We also use ImageNet100 (a subset of ImageNet1K dataset) proposed by Tian et al. [39] and mini-ImageNet [41]. We follow the widely used training and testing protocols of Bilinear CNN [27]. The details of datasets and protocols are provided in Appendix A." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.171, + 0.47, + 0.217 + ], + "angle": 0, + "content": "Metric for evaluations. For evaluation of different methods, average classification accuracy is used. This metric is widely used in literature, e.g., [23, 27]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.22, + 0.471, + 0.477 + ], + "angle": 0, + "content": "Implementation details. Our method is implemented using PyTorch 1.9. We use ImageNet-1K pre-trained backbones provided in Torchvision 0.13.0 library. Following the recent works [23, 24], the number of feature channels is reduced to 256 with \\(1 \\times 1\\) convolution for efficiency and fair comparisons. All images are resized to \\(448 \\times 448\\) and the training is conducted by randomly flipping them horizontally. We fine-tune all backbones for 50-100 epochs with AdamW optimiser [31] for an initial learning rate of 0.00012, and ConvNext-T CNN and Swin-T with an initial learning rate 0.00005. For all backbones, we decrease the learning rate by a factor of 10 at the 15th and 30th epochs. Depending on the dataset and backbones, our fine-tuning process lasts for about 3-8 hours on four P100 GPUs. For ImageNet100, ResNet-50 was trained for 100 epochs with the initial learning rate 0.01, reduced by 10 at the 15th, 30th and 45th epochs. Settings are detailed in Appendix B." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.487, + 0.206, + 0.501 + ], + "angle": 0, + "content": "4.2. Evaluations" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.51, + 0.469, + 0.617 + ], + "angle": 0, + "content": "We evaluate the performance of the proposed iSICE and compare it with its covariance-based competitors. We also include comparisons with our baseline, the inverse covariance matrix, called precision matrix (for simplicity denoted as Precision \\(\\Omega\\) in experiments). In contrast to iSICE which represents a sparse graph, precision matrix is used as a baseline as it represents a graph without imposed sparsity." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.617, + 0.469, + 0.707 + ], + "angle": 0, + "content": "The covariance representations (denoted with COV) are widely used in literature [24, 27, 28]. We use the Newton-Schulz iteration for computing the matrix square root normalised covariance (iSQRT-COV) due to efficiency of the Newton-Schulz iteration with GPUs, as well as good empirical results reported by multiple authors [23, 25]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.71, + 0.469, + 0.816 + ], + "angle": 0, + "content": "Backbones. We choose VGG-16 [37] and ResNet-50 [13] CNNs as our backbones for the majority of experiments (we also include the VGG-19, ResNet-101, ResNeXt-101 [45], and latest ConvNext-T [30], Swin-T and Swin-B [29] in the main table). VGG-16 and ResNet-50 are popular in image classification (including fine-grained benchmarks). We choose these two backbones in order to better understand" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.092, + 0.892, + 0.227 + ], + "angle": 0, + "content": "the performance of our methods compared to baselines in the common testbed (the same backbones and experimental settings). Given an input image, we obtain a set of feature channels shaped as a tensor after the \\(1 \\times 1\\) convolution operation. Using these feature channels, we compute the iSICE, iSQRT-COV and Precision \\(\\Omega\\) representations. Since these representations are symmetric, we only use the upper-triangular entries (and the diagonal entries) passed to a fully-connected layer to obtain classification scores." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.234, + 0.892, + 0.385 + ], + "angle": 0, + "content": "Hyper-parameters. There are three hyper-parameters associated with iSICE: sparsity constant \\(\\lambda\\), learning rate \\(\\eta\\) and number of iterations \\(N\\). We experiment with a large range of values for a better understanding, i.e., \\(\\lambda \\in \\{1.0, 0.5, 0.1, 0.01, 0.001, 0.0001, 0.00001\\}\\), \\(\\eta \\in \\{0.001, 0.01, 0.1, 1.0, 5.0, 10.0, 20.0\\}\\), and \\(N \\in \\{1, 5, 10\\}\\). Since the total combination of hyper-parameters in the table is 147, we choose the median values of each hyper-parameter range (highlighted in bold) and keep them throughout experiments on all datasets. Appendix C studies the impact of hyper-parameters on results." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.391, + 0.892, + 0.753 + ], + "angle": 0, + "content": "Overview of results. Table 1 shows the performance of several COV models (e.g., popular iSQRT-COV), and our Precision \\(\\Omega\\) and iSICE models. The rightmost column summarizes the average performance over one scene and three fine-grained image classification benchmarks. It is clear that on average, iSICE outperforms MPN-COV, iSQRT-COV, DeepCOV, and DeepKSPD, etc. iSICE also outperforms our baseline Precision \\(\\Omega\\). This achievement is consistent in all four backbones. It is interesting to see that except a few cases, the inverse covariance method, i.e., Precision \\(\\Omega\\), performs slightly better than the covariance method, i.e., iSQRT-COV. This improved performance highlights the effectiveness of characterising partial correlations of features with inverse covariance instead of pairwise correlations of features based on the sample covariance. Our iSICE method makes the inverse covariance estimation more robust and reliable by enforcing sparsity, as demonstrated by improved performance over Precision \\(\\Omega\\) baseline. However, there may be some situations when Precision \\(\\Omega\\) outperforms iSICE (e.g., MIT with VGG-16 backbone). Notice that iSICE can be considered as sparse precision matrix. When \\(N = 0\\) in Alg. 2, iSICE reduces to Precision \\(\\Omega\\). In further experiments we show that once the size of matrix is increased, iSICE does outperform Precision \\(\\Omega\\)." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.756, + 0.89, + 0.786 + ], + "angle": 0, + "content": "Table 2 corroborates that iSICE significantly outperforms Precision \\(\\Omega\\) and iSQRT-COV." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.789, + 0.892, + 0.849 + ], + "angle": 0, + "content": "Robustness of iSICE to Hyper-parameters. We have conducted experiments with the hyper-parameter range given in Section 4.1. Appendix C shows that the performance of iSICE remains stable across a range of values." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Detailed comparisons with SPD-based SOTA models. Table 1 compares the performance of iSICE to several prior works. We first compare our VGG-16 backbone" + }, + { + "type": "page_footnote", + "bbox": [ + 0.076, + 0.824, + 0.469, + 0.873 + ], + "angle": 0, + "content": "4The use of precision matrix (and partial correlations) as a visual representation in place of the sample-based covariance matrix \\(\\pmb{\\Sigma}\\) is also our minor but novel proposition. We obtain it via Alg. 1 from which we recover the inverse square root \\(\\mathbf{Q}\\) and then \\(\\boldsymbol {\\Omega} = \\boldsymbol{\\Sigma}^{-1} = \\mathbf{QQ}^{\\top} / \\mathrm{trace}(\\boldsymbol {\\Sigma})\\)" + }, + { + "type": "page_footnote", + "bbox": [ + 0.076, + 0.873, + 0.468, + 0.9 + ], + "angle": 0, + "content": "In contrast to our precision matrix \\(\\pmb{\\Omega} = \\pmb{\\Sigma}^{-1} = \\pmb{Q}\\pmb{Q}^{\\top} / \\mathrm{trace}(\\pmb{\\Sigma})\\) from Alg. 1, iSQRT-COV uses \\(\\pmb{\\Sigma}^{\\frac{1}{2}} = \\mathbf{Y}_N / \\sqrt{\\mathrm{trace}(\\pmb{\\Sigma})}\\) from Alg. 1." + }, + { + "type": "list", + "bbox": [ + 0.076, + 0.824, + 0.469, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "6236" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.082, + 0.078, + 0.892, + 0.573 + ], + "angle": 0, + "content": "
MethodBackboneMITAirplaneBirdsCarsDTDiNatuarlistmini-ImageNet
GAP [37]-76.670.479.8---
NetVLAD [2]-81.881.688.6---
NetFV [28]-79.079.986.2---
BCNN [27]77.683.984.090.684.0--
CBP [11]76.284.184.391.284.0--
LRBP [17]-87.384.290.9---
KP [6]-86.986.292.4---
HIHCA [4]-88.385.391.7---
Improved BCNN [25]-88.585.892.0---
SMSO [46]VGG-1679.5-85.0----
MPN-COV [43] (reproduced)-86.182.989.8---
iSQRT-COV [23] (reproduced)76.190.084.591.271.356.276.2
DeepCOV [9]79.288.785.491.786.3--
DeepKSPD [9]81.090.084.891.686.3--
RUN [47]80.591.085.7----
FCBN [48]80.390.585.5----
TKPF [49]80.591.486.0----
Precision Ω80.289.483.492.074.057.974.0
iSICE (ours)78.792.286.594.074.759.878.7
CBP [11]-81.681.688.6---
KP [6]-85.784.791.1---
SMSO [46]79.7-85.8----
iSQRT-COV [23] (reproduced)78.890.984.392.173.057.770.7
DeepCOV-ResNet [34]ResNet-5083.483.986.085.084.6--
TKPF [49]84.192.185.7----
Precision Ω80.891.284.792.073.759.665.6
iSICE (ours)80.592.785.993.560.760.772.0
iSQRT-COV [23]76.390.384.191.471.856.975.4
Precision ΩVGG-1979.691.183.292.274.257.373.8
iSICE (ours)80.692.586.693.974.959.677.1
iSQRT-COV [23]79.391.084.492.373.070.673.9
Precision ΩResNet-10177.990.183.391.471.269.873.0
iSICE (ours)81.092.986.693.675.472.078.0
iSQRT-COV [23]81.691.386.292.475.772.276.1
Precision ΩResNeXt-10185.790.284.689.976.972.377.6
iSICE (ours)86.394.687.294.578.773.881.0
iSQRT-COV [23]77.888.183.589.484.761.582.0
Precision ΩConvNext-T78.581.283.792.283.959.383.6
iSICE (ours)85.490.486.793.188.965.085.1
iSQRT-COV [23]82.187.685.189.786.158.167.7
Precision ΩSwin-T82.588.284.990.586.559.165.6
iSICE (ours)85.989.686.591.388.361.969.1
iSQRT-COV [23]86.691.388.092.079.469.764.9
Precision ΩSwin-B87.090.787.793.180.167.366.4
iSICE (ours)87.692.988.393.379.872.468.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.584, + 0.893, + 0.613 + ], + "angle": 0, + "content": "Table 1. Comparison between iSICE, Precision \\( \\mathbf{\\Omega } \\) and other SPD representations in terms of classification accuracy (%) . The performance of existing SPD representation methods is quoted from the original papers. Precision \\( \\mathbf{\\Omega } \\) is given by Alg. 1. iSICE is given by Alg. 2." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.636, + 0.47, + 0.702 + ], + "angle": 0, + "content": "
MethodBackboneTop-1Top-5
GAP [13]71.0/69.590.9/88.9
iSQRT-COV [23]ResNet-50/71.5/70.290.5/89.7
Precision ΩVGG-1671.1/71.090.1/90.1
iSICE74.8/73.492.0/91.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.138, + 0.713, + 0.409, + 0.727 + ], + "angle": 0, + "content": "Table 2. Results on the ImageNet100 dataset." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.749, + 0.471, + 0.903 + ], + "angle": 0, + "content": "based iSICE with BCNN, CBP, LRBP, KP, HIHCA, Improved BCNN, SMSO, MPN-COV, iSQRT-COV, DeepCOV, DeepKSPD, RUN, FCBN and TKPF methods. The MPN-COV and iSQRT-COV methods use backbones pretrained with second-order pooling. For fair comparison with iSICE, we re-run those methods on our machine with the same backbone and evaluation protocols as ours. iSICE outperforms all existing methods on fine-grained datasets. On MIT dataset, our performance is better than CBP, BCNN and iSQRT-COV. iSICE could outperform DeepKSPD and" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.639, + 0.892, + 0.669 + ], + "angle": 0, + "content": "other methods on MIT if a large-dimensional matrix similar to those is used (see Table 3)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.671, + 0.893, + 0.822 + ], + "angle": 0, + "content": "Secondly, we compare our ResNet-50 backbone based iSICE with CBP, KP, SMO, iSQRT-COV, DeepCOV-ResNet and TKPF methods. iSICE achieves better performance than existing methods on both Airplane and Cars datasets. DeepCOV-ResNet uses \\(1024 \\times 1024\\)-dimensional matrix which is four times larger than ours. TKPF uses an advanced feature projection to reduce the CNN feature channels and we use a simple linear projection with \\(1 \\times 1\\) convolution. However, on average, we are still better than DeepCOV-ResNet and TKPF." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.894, + 0.903 + ], + "angle": 0, + "content": "Thirdly, we integrate iSICE with the popular VGG-19, ResNet-101, ResNeXt-101, ConvNext-T [30], Swin-T and Swin-B [29] backbones pre-trained on ImageNet-1K, and compare their performance with iSQRT-COV and Precision \\(\\Omega\\) (Alg. 1). iSICE outperforms iSQRT-COV and Precision" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6237" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.082, + 0.079, + 0.89, + 0.204 + ], + "angle": 0, + "content": "
MethodMatrix Dim.MITAirplaneBirdsCarsAverage
VGGResNetVGGResNetVGGResNetVGGResNetVGGResNet
iSQRT-COV256 × 25676.178.890.090.984.584.391.292.185.586.5
512 × 51276.982.891.591.185.084.592.292.186.487.6
Precision Ω256 × 25680.280.889.491.283.484.792.092.086.387.1
512 × 51280.782.790.191.584.984.092.592.687.087.7
SICE128 × 12871.073.185.586.977.378.087.087.980.281.5
256 × 25673.775.487.989.279.780.389.589.382.783.6
iSICE256 × 25678.780.592.292.786.585.99493.587.988.2
512 × 51281.181.792.992.686.88694.693.888.988.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.096, + 0.214, + 0.872, + 0.227 + ], + "angle": 0, + "content": "Table 3. Performance of iSQRT-COV, Precision \\( \\mathbf{\\Omega } \\) ,SICE and iSICE on various datasets when different matrix dimensions are used." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.244, + 0.47, + 0.306 + ], + "angle": 0, + "content": "\\(\\Omega\\) methods across all datasets. This highlights that SPD-based visual representations (1) are still relevant for modern powerful classification backbones and (2) they improve results on large-scale datasets." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.312, + 0.473, + 0.584 + ], + "angle": 0, + "content": "Ablations on the size of Sparse Inverse Matrix. Table 3 shows results for \\(512 \\times 512\\) vs. \\(256 \\times 256\\) matrix size (we keep the hyper-parameters fixed). To produce \\(512 \\times 512\\) dim. matrix, (1) from VGG-16, we simply remove the \\(1 \\times 1\\) convolution layer to obtain 512 feature channels and (2) from ResNet-50, we set the output channels of \\(1 \\times 1\\) convolution layer to 512. Generally, switching to a larger matrix improves the performance, e.g., on MIT our iSICE gains between 1.2 and \\(2.4\\%\\) (sparsity helps with a larger matrix). On average, all methods improved performance by switching to a larger matrix at the computational expense. See runtimes in Table 5 and memory consumption in Appendix E. Finally, Table 3 also shows that iSICE performs much better than SICE (based on ADMM solver [3]) computed over pre-extracted features. Table 5 shows that iSICE is \\(3 \\times\\) faster. SICE is almost intractable on larger datasets. This validates our claim that learning sparse inverse covariance matrix end-to-end produces robust visual representation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.592, + 0.47, + 0.835 + ], + "angle": 0, + "content": "iSICE with learning rate and sparsity modulators. As iSICE trades between log \\(\\operatorname{det}(\\cdot)\\) (changes rapidly) and the \\(\\ell_1\\) norm (changes linearly), optimizing Eq. (1) with PGD may struggle with non-optimal learning rates and sparsity. Thus, we design a simple modulator that updates \\(\\beta\\) in Alg. 2 by setting \\(\\beta := \\beta \\cdot \\kappa\\) in lines 10, where \\(\\kappa = \\tau + 2\\mathrm{Sigm}(\\mathrm{FC}(\\mathbf{X1} / n))\\), \\(\\mathrm{Sigm}(\\cdot)\\) is a sigmoid, and FC layer is of \\(d \\times 1\\) size. We also add a penalty \\(-\\gamma (\\kappa - 1)^2\\) to the classification loss to encourage \\(\\kappa\\) to be close to 1 unless classification loss gets smaller for \\(\\kappa \\neq 1\\) while incurring the above penalty. We set \\(\\gamma = 0.0001\\). We use the above modulator (we do not claim this is the most optimal design) as a tool akin to Mod-Grad [36]. \\(\\tau = 0.01\\) is a small offset to prevent zero learning rate. Another modulator with the same architecture is used to adapt sparsity parameter \\(\\lambda\\). Table 4 shows that modulating the learning rate and sparsity on-the-fly helps iSICE." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.841, + 0.47, + 0.903 + ], + "angle": 0, + "content": "Experiments on dense vs. sparse structure estimation w.r.t. sample size. Below we randomly generate a dense or sparse inverse covariance matrix \\(\\mathbf{P}\\) (size \\(100\\times 100\\)) and sample various amounts of data from the resulted normal dis" + }, + { + "type": "table", + "bbox": [ + 0.504, + 0.242, + 0.89, + 0.285 + ], + "angle": 0, + "content": "
MethodMITAirplaneBirdsCarsImageNet100
iSICE80.592.785.993.574.8
iSCIE+MLP81.393.486.193.976.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.295, + 0.893, + 0.324 + ], + "angle": 0, + "content": "Table 4. Comparison between the classification performance of iSICE and iSICE+MLP on the ResNet-50 backbone." + }, + { + "type": "table", + "bbox": [ + 0.504, + 0.338, + 0.89, + 0.382 + ], + "angle": 0, + "content": "
GAPiSQRT-COVPrecision ΩSICEiSICEiSICE+MLP
Time/batch (sec.)29.032.032.8150.844.645.8
Time/epoch (min.)12.613.413.865.319.319.8
" + }, + { + "type": "image_caption", + "bbox": [ + 0.503, + 0.392, + 0.888, + 0.407 + ], + "angle": 0, + "content": "Table 5. Runtimes (256×256 matrix, ImageNet100, ResNet-50)." + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.414, + 0.698, + 0.517 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.701, + 0.414, + 0.892, + 0.517 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.501, + 0.536, + 0.89, + 0.551 + ], + "angle": 0, + "content": "Figure 3. Estimation errors on (left) dense & (right) sparse struct." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.57, + 0.892, + 0.647 + ], + "angle": 0, + "content": "tribution (via mvnrnd(\\(\\cdot\\)) in Matlab) to get its estimate \\(\\hat{\\mathbf{P}}\\). Fig. 3 (left) shows estimation error \\(\\|\\hat{\\mathbf{P}} - \\mathbf{P}\\|_F\\) for dense structure. Sparse and non-sparse estimation (by SICE and MLE, resp.) show high errors in low sample regime. For the sparse structure in Fig. 3 (right), sparse estimation works better." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.66, + 0.628, + 0.675 + ], + "angle": 0, + "content": "5. Conclusions" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.686, + 0.892, + 0.822 + ], + "angle": 0, + "content": "In this paper, we proposed a method for learning sparse inverse covariance representation with CNN. Our method estimates SICE within the CNN layers and facilitates backpropagation for end-to-end training. Our iSICE significantly outperforms other covariance representations on several datasets. iSICE exploits the sparsity prior to capture partial correlations under limited number of samples. Our method is of general purpose and can be readily applied in existing SPD-based models to improve their performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Acknowledgements. This work was supported by CSIRO's Machine Learning & Artificial Intelligence Future Science Platform (MLAI FSP), University of Wollongong's Australia IPTA scholarship, and Australian Research Council (DP200101289). We thank Jianjia Zhang for discussions." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6238" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.175, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.171 + ], + "angle": 0, + "content": "[1] Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, and J Zico Kolter. Differentiable convex optimization layers. Advances in neural information processing systems, 32, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.173, + 0.472, + 0.243 + ], + "angle": 0, + "content": "[2] Relja Arandjelovic, Petr Gronat, Akihiko Torii, Tomas Pajdla, and Josef Sivic. Netvlad: Cnn architecture for weakly supervised place recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5297-5307, 2016. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.245, + 0.471, + 0.313 + ], + "angle": 0, + "content": "[3] Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine learning, 3(1):1-122, 2011. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.316, + 0.471, + 0.383 + ], + "angle": 0, + "content": "[4] Sijia Cai, Wangmeng Zuo, and Lei Zhang. Higher-order integration of hierarchical convolutional activations for fine-grained visual categorization. In Proceedings of the IEEE international conference on computer vision, pages 511-520, 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.387, + 0.472, + 0.455 + ], + "angle": 0, + "content": "[5] Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describing textures in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3606-3613, 2014. 5, 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.458, + 0.47, + 0.526 + ], + "angle": 0, + "content": "[6] Yin Cui, Feng Zhou, Jiang Wang, Xiao Liu, Yuanqing Lin, and Serge Belongie. Kernel pooling for convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2921-2930, 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.529, + 0.47, + 0.557 + ], + "angle": 0, + "content": "[7] Morris H. DeGroot. Optimal statistical decisions. McGraw-Hill, New York, NY [u.a], 1970. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.559, + 0.47, + 0.613 + ], + "angle": 0, + "content": "[8] Steven Diamond and Stephen Boyd. Cvxpy: A python-embedded modeling language for convex optimization. The Journal of Machine Learning Research, 17(1):2909-2913, 2016. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.616, + 0.47, + 0.685 + ], + "angle": 0, + "content": "[9] Melih Engin, Lei Wang, Luping Zhou, and Xinwang Liu. Deepkspd: Learning kernel-matrix-based spd representation for fine-grained image recognition. In Proceedings of the European Conference on Computer Vision, pages 612-627, 2018. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.688, + 0.469, + 0.729 + ], + "angle": 0, + "content": "[10] Jerome Friedman, Trevor Hastie, and Robert Tibshirani. Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3):432-441, 2008. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.731, + 0.469, + 0.785 + ], + "angle": 0, + "content": "[11] Yang Gao, Oscar Beijbom, Ning Zhang, and Trevor Darrell. Compact bilinear pooling. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 317-326, 2016. 1, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.788, + 0.469, + 0.843 + ], + "angle": 0, + "content": "[12] Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Friedman. The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer, 2009. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[13] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.116, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "[14] Nicholas J Higham. Functions of matrices: theory and computation. SIAM, 2008. 2, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.892, + 0.162 + ], + "angle": 0, + "content": "[15] Junzhou Huang, Tong Zhang, and Dimitris Metaxas. Learning with structured sparsity. Journal of Machine Learning Research, 12(11), 2011. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.164, + 0.892, + 0.218 + ], + "angle": 0, + "content": "[16] Catalin Ionescu, Orestis Vantzos, and Cristian Sminchisescu. Matrix backpropagation for deep networks with structured layers. In Proceedings of the IEEE international conference on computer vision, pages 2965-2973, 2015. 1, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.22, + 0.892, + 0.274 + ], + "angle": 0, + "content": "[17] Shu Kong and Charless Fowlkes. Low-rank bilinear pooling for fine-grained classification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 365-374, 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.275, + 0.892, + 0.327 + ], + "angle": 0, + "content": "[18] Piotr Koniusz, Lei Wang, and Anoop Cherian. Tensor representations for action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(2):648-665, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.33, + 0.892, + 0.385 + ], + "angle": 0, + "content": "[19] Piotr Koniusz, Fei Yan, Philippe-Henri Gosselin, and Krystian Mikolajczyk. Higher-order occurrence pooling on mid- and low-level features: Visual concept detection. Tech. Report (hal-00922524), 2013. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.387, + 0.892, + 0.44 + ], + "angle": 0, + "content": "[20] Piotr Koniusz and Hongguang Zhang. Power normalizations in fine-grained image, few-shot image and graph classification. In IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.442, + 0.892, + 0.496 + ], + "angle": 0, + "content": "[21] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In Proceedings of the IEEE international conference on computer vision workshops, pages 554–561, 2013. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.498, + 0.892, + 0.55 + ], + "angle": 0, + "content": "[22] Miroslav Kubat. Neural networks: a comprehensive foundation by simon haykin, macmillan, 1994, ISBN 0-02-352781-7. The Knowledge Engineering Review, 13(4):409-412, 1999. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.553, + 0.892, + 0.622 + ], + "angle": 0, + "content": "[23] Peihua Li, Jiangtao Xie, Qilong Wang, and Zilin Gao. Towards faster training of global covariance pooling networks by iterative matrix square root normalization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 947-955, 2018. 1, 4, 6, 7, 13, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.624, + 0.892, + 0.691 + ], + "angle": 0, + "content": "[24] Peihua Li, Jiangtao Xie, Qilong Wang, and Wangmeng Zuo. Is second-order information helpful for large-scale visual recognition? In Proceedings of the IEEE international conference on computer vision, pages 2070-2078, 2017. 1, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.693, + 0.892, + 0.732 + ], + "angle": 0, + "content": "[25] Tsung-Yu Lin and Subhransu Maji. Improved bilinear pooling with cnns. British Machine Vision Conference, 2017. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.734, + 0.892, + 0.787 + ], + "angle": 0, + "content": "[26] Tsung-Yu Lin, Subhransu Maji, and Piotr Koniusz. Second-order democratic aggregation. In Proceedings of the European Conference on Computer Vision, pages 620-636, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.79, + 0.892, + 0.845 + ], + "angle": 0, + "content": "[27] Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. Bilinear cnn models for fine-grained visual recognition. In Proceedings of the IEEE international conference on computer vision, pages 1449-1457, 2015. 1, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.847, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[28] Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. Bilinear convolutional neural networks for fine-grained visual recognition. IEEE transactions on pattern analysis and machine intelligence, 40(6):1309-1322, 2017. 1, 3, 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6239" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.161 + ], + "angle": 0, + "content": "[29] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10012-10022, 2021. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.163, + 0.47, + 0.233 + ], + "angle": 0, + "content": "[30] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11976-11986, 2022. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.234, + 0.469, + 0.262 + ], + "angle": 0, + "content": "[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.263, + 0.469, + 0.317 + ], + "angle": 0, + "content": "[32] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, 2013. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.319, + 0.469, + 0.361 + ], + "angle": 0, + "content": "[33] Ariadna Quattoni and Antonio Torralba. Recognizing indoor scenes. In 2009 IEEE conference on computer vision and pattern recognition, pages 413-420. IEEE, 2009. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.362, + 0.469, + 0.418 + ], + "angle": 0, + "content": "[34] Saimunur Rahman, Lei Wang, Changming Sun, and Luping Zhou. Redro: Efficiently learning large-sized spd visual representation. In European Conference on Computer Vision, pages 1-17. Springer, 2020. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.42, + 0.469, + 0.46 + ], + "angle": 0, + "content": "[35] Bernt Schiele, Paul Swoboda, and Gerard Pons-Moll. Projected gradient descent for the lasso. Lecture Notes, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.462, + 0.469, + 0.517 + ], + "angle": 0, + "content": "[36] Christian Simon, Piotr Koniusz, Richard Nock, and Mehrtash Harandi. On modulating the gradient for metalearning. In Proceedings of the European conference on computer vision, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.519, + 0.469, + 0.56 + ], + "angle": 0, + "content": "[37] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.562, + 0.469, + 0.63 + ], + "angle": 0, + "content": "[38] Yue Song, Nicu Sebe, and Wei Wang. Why approximate matrix square root outperforms accurate svd in global covariance pooling? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1115-1123, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.632, + 0.469, + 0.674 + ], + "angle": 0, + "content": "[39] Yonglong Tian, Dilip Krishnan, and Phillip Isola. Contrastive multiview coding. In European conference on computer vision, pages 776-794. Springer, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.675, + 0.469, + 0.757 + ], + "angle": 0, + "content": "[40] Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie. The inaturalist species classification and detection dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8769-8778, 2018. 6, 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.759, + 0.469, + 0.814 + ], + "angle": 0, + "content": "[41] Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. Matching networks for one shot learning. Advances in neural information processing systems, 29, 2016. 6, 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.817, + 0.469, + 0.857 + ], + "angle": 0, + "content": "[42] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200-2011 dataset. 2011. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[43] Qilong Wang, Jiangtao Xie, Wangmeng Zuo, Lei Zhang, and Peihua Li. Deep cnns meet global covariance pooling: Better representation and generalization. IEEE transactions on" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.119 + ], + "angle": 0, + "content": "pattern analysis and machine intelligence, 43(8):2582-2597, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.122, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[44] Ruiping Wang, Huimin Guo, Larry S Davis, and Qionghai Dai. Covariance discriminative learning: A natural and efficient approach to image set classification. In 2012 IEEE conference on computer vision and pattern recognition, pages 2496-2503. IEEE, 2012. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.192, + 0.892, + 0.26 + ], + "angle": 0, + "content": "[45] Saining Xie, Ross Girshick, Piotr Dolkar, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1492-1500, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.262, + 0.892, + 0.304 + ], + "angle": 0, + "content": "[46] Kaicheng Yu and Mathieu Salzmann. Statistically-motivated second-order pooling. In Proceedings of the European Conference on Computer Vision, pages 600–616, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.305, + 0.892, + 0.358 + ], + "angle": 0, + "content": "[47] Tan Yu, Yunfeng Cai, and Ping Li. Toward faster and simpler matrix normalization via rank-1 update. In European Conference on Computer Vision, pages 203–219. Springer, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.362, + 0.892, + 0.416 + ], + "angle": 0, + "content": "[48] Tan Yu, Xiaoyun Li, and Ping Li. Fast and compact bilinear pooling by shifted random maclaurin. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 3243-3251, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.418, + 0.892, + 0.473 + ], + "angle": 0, + "content": "[49] Tan Yu, Xiaoyun Li, and Ping Li. Efficient compact bilinear pooling via kronecker product. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 3170-3178, 2022. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.475, + 0.892, + 0.531 + ], + "angle": 0, + "content": "[50] Hongguang Zhang, Jing Zhang, and Piotr Koniusz. Few-shot learning via saliency-guided hallucination of samples. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2770-2779, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.532, + 0.892, + 0.587 + ], + "angle": 0, + "content": "[51] Jianjia Zhang, Lei Wang, Luping Zhou, and Wanqing Li. Beyond covariance: Sice and kernel based visual feature representation. International Journal of Computer Vision, 129(2):300-320, 2021. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.589, + 0.892, + 0.63 + ], + "angle": 0, + "content": "[52] Shan Zhang, Dawei Luo, Lei Wang, and Piotr Koniusz. Few-shot object detection by second-order pooling. In Proceedings of the Asian conference on computer vision, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.631, + 0.892, + 0.686 + ], + "angle": 0, + "content": "[53] Shan Zhang, Naila Murray, Lei Wang, and Piotr Koniusz. Time-reversed diffusion tensor transformer: A new tenet of few-shot object detection. In Proceedings of the European conference on computer vision, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.688, + 0.892, + 0.743 + ], + "angle": 0, + "content": "[54] Shan Zhang, Lei Wang, Naila Murray, and Piotr Koniusz. Kernelized few-shot object detection with efficient integral aggregation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.744, + 0.892, + 0.8 + ], + "angle": 0, + "content": "[55] Yifei Zhang, Hao Zhu, Zixing Song, Piotr Koniusz, and Irwin King. Spectral feature augmentation for graph contrastive learning and beyond. In Proceedings of the AAAI Conference on Artificial Intelligence, 2023. 3" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.8 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "6240" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Partial Correlation Based Deep Visual Representation for Image Classification/87245566-23af-421b-80ad-1a8c0309df53_origin.pdf b/2023/Learning Partial Correlation Based Deep Visual Representation for Image Classification/87245566-23af-421b-80ad-1a8c0309df53_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ba39792d448df70326ea0f996962651d87131c17 --- /dev/null +++ b/2023/Learning Partial Correlation Based Deep Visual Representation for Image Classification/87245566-23af-421b-80ad-1a8c0309df53_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75ca1d208804230dfa762f0eb8a306fd0c9a4c6d68768fbfd9d06a2f4457df48 +size 695480 diff --git a/2023/Learning Partial Correlation Based Deep Visual Representation for Image Classification/full.md b/2023/Learning Partial Correlation Based Deep Visual Representation for Image Classification/full.md new file mode 100644 index 0000000000000000000000000000000000000000..881a04594798aef952e0743f2bd0eb5850406c77 --- /dev/null +++ b/2023/Learning Partial Correlation Based Deep Visual Representation for Image Classification/full.md @@ -0,0 +1,326 @@ +# Learning Partial Correlation based Deep Visual Representation for Image Classification + +Saimunur Rahman $^{1,2}$ , Piotr Koniusz $^{*,1,3}$ , Lei Wang $^{2}$ , Luping Zhou $^{4}$ , Peyman Moghadam $^{1,5}$ , Changming Sun $^{1}$ + +$^{1}$ Data61♥CSIRO, $^{2}$ University of Wollongong, $^{3}$ Australian National University, + +4University of Sydney, 5Queensland University of Technology + +name.surname@data61.csiro.au, leiw@uow.edu.au, luping.zhou@sydney.edu.au + +# Abstract + +Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolutional feature maps. However, pairwise correlation will become misleading once there is another channel correlating with both channels of interest, resulting in the "confounding" effect. For this case, "partial correlation" which removes the confounding effect shall be estimated instead. Nevertheless, reliably estimating partial correlation requires to solve a symmetric positive definite matrix optimisation, known as sparse inverse covariance estimation (SICE). How to incorporate this process into CNN remains an open issue. In this work, we formulate SICE as a novel structured layer of CNN. To ensure end-to-end trainability, we develop an iterative method to solve the above matrix optimisation during forward and backward propagation steps. Our work obtains a partial correlation based deep visual representation and mitigates the small sample problem often encountered by covariance matrix estimation in CNN. Computationally, our model can be effectively trained with GPU and works well with a large number of channels of advanced CNNs. Experiments show the efficacy and superior classification performance of our deep visual representation compared to covariance matrix based counterparts. + +# 1. Introduction + +Learning effective visual representation is a central issue in computer vision. In the past two decades, describing images with local features and pooling them to a global representation has shown promising performance. As one of the pooling methods, covariance matrix based pooling has attracted much attention due to its exploitation of second-order correlation information of features. A variety of tasks + +![](images/01fcafd03a5d182fffdcf1d63296039b70dacc05cf245a0749c2886792e36018.jpg) +Figure 1. Understanding the partial correlation (a 3D toy case). Unlike the ordinary covariance (pairwise correlation of say $\mathbf{x}$ and $\mathbf{y}$ corresponding to channels), partial correlation between variables $\mathbf{x}$ and $\mathbf{y}$ removes the influence of the confounding variable $\mathbf{z}$ . Let the number of samples $n = 3$ and channels $d = 3$ . For the 3D case, $\mathbf{x}$ and $\mathbf{y}$ are projected onto a plane perpendicular to $\mathbf{z}$ . Then $\rho_{xy} = \cos \varphi_{xy}$ (and $\rho_{xz}$ and $\rho_{yz}$ can be computed by analogy). Projected "residuals" $\mathbf{r_x}$ and $\mathbf{r_y}$ are computed as indicated in the plot, $\mathbf{w}'_x = \arg \min_{\mathbf{w}} \sum_{i=1}^{3} (x_i - \mathbf{w}_x^\top \mathbf{z}_i)$ where $\mathbf{z}_i = [z_i, 1]^\top$ (and $\mathbf{w}'_y$ is computed by analogy). The green box: for $d > 3$ , the computation of partial correlation requires covariance inversion [7]. + +such as fine-grained image classification [27], image segmentation [16], generic image classification [24, 26, 34], image set classification [44], action recognition [18], few-shot classification [50] and few-shot detection [52-54] have benefited from the covariance matrix based representation. A few pioneering works have integrated covariance matrix as a pooling method within convolutional neural networks (CNN) and investigated associated issues such as matrix function backpropagation [16], matrix normalisation [23, 28, 38], compact matrix estimation [11, 49] and kernel based extension [9]. The above works further improved visual representations based on covariance matrix. + +Despite the above progress, covariance matrix merely measures the pairwise correlation (more accurately, covariance) of two variables without taking any other variables into account. This can be easily verified because its $(i,j)$ -th entry solely depends on the $i$ -th and $j$ -th variables on a + +![](images/6f3a27138e8545a6b344d7e42a40eae3568220c7ab4746d78a5a2c631126050d.jpg) +Figure 2. Proposed iterative sparse inverse covariance estimation (iSICE) method in a CNN pipeline. + +sample set. In Statistics, it is known that such a pairwise correlation will give misleading results once a third variable is correlated with both variables of interest due to the "confounding" effect. For this situation, the partial correlation is the right measure to use. It regresses out the effects of other variables from the two variables and then calculates the correlation of their residuals instead. Partial correlation can be conveniently obtained by computing inverse covariance matrix, also known as the precision matrix [7] in the statistical community. Figure 1 illustrates the geometrical interpretation of partial correlation. + +The above observation motivates us to investigate a visual representation for image classification based on the inverse covariance matrix. After all, it has better theoretical support on characterising the essential relationship of variables (e.g., the channels in a convolutional feature map) when other variables are present. Note that inverse covariance matrix can be used for many vision tasks but in this paper, we investigate it from the perspective of image classification. Nevertheless, reliably estimating inverse covariance matrix from the local descriptors of a CNN feature map is a challenging task. This is primarily due to the small spatial size of the feature map, i.e., sample size, and a higher number of channels, i.e., feature dimensions, and this issue becomes more pronounced for advanced CNN models. An unreliable estimate of inverse covariance matrix will critically affect its effectiveness as a visual representation. One might argue that by increasing the size of input images or using a dimension reduction layer to reduce the number of feature channels, such an issue could be resolved. In this paper, we investigate this issue from the perspective of robust precision matrix estimation. + +To achieve our goal, we explore the use of sparsity prior for inverse covariance matrix estimation in the literature. Specifically, the general principle of "bet on sparsity" [12] is adopted in estimating the structure of high-dimensional data, and this leads to an established technique called sparse inverse covariance estimation (SICE) [10]. It solves an optimisation in the space of symmetric and positive definite (SPD) matrix to estimate the inverse covariance matrix by imposing the sparsity prior on its entries. SICE is designed to handle small sample problems and it is known for its excellent effectiveness to that end [10]. An initial attempt + +to apply SICE for visual representation is based on handcrafted or pre-extracted features of small size and an off-the-shelf SICE solver, and it does not have the ability to backpropagate through SICE due to optimisation of the SPD matrix with the imposed non-smooth sparsity term [51]. + +Our work is the first one that truly integrates SICE into CNN for end-to-end training. Clearly, such an integration will fully take advantage of the feature learning capability of CNN and the partial correlation offered by inverse covariance matrix. On the other hand, realising such an integration is not trivial. Unlike covariance matrix, which is obtained by simple arithmetic operations, SICE is obtained by solving an SPD matrix based optimisation. How to incorporate this optimisation process into CNN as a layer is an issue. Furthermore, this SICE optimisation needs to be solved for each training image during both forward and backward phases to generate a visual representation. Directly solving this optimisation within CNN will not be practical even for a medium-sized SICE problem. + +To efficiently integrate SICE into CNN, we propose a fast end-to-end training method for SICE by taking inspiration from Newton-Schulz iteration [14]. Our method solves the SICE optimisation with a smooth convex cost function by re-parameterising the non-smooth term in the original SICE cost function (see Eq. (1)), and it can therefore be optimised with standard optimisation techniques such as gradient descent. Furthermore, we effectively enforce the SPD constraint during optimisation so that the obtained SICE solution remains SPD as desired. Figure 2 shows our "Iterative Sparse Inverse Covariance Estimation (iSICE)". In contrast to SICE, iSICE works with end-to-end trainable deep learning models. Our iSICE involves simple matrix arithmetic operations fully compatible with GPU. It can approximately solve large SICE problems within CNN efficiently. + +Our main contributions are summarised as follows. + +1. To more precisely characterise the relationship of features for visual representation, this paper proposes to integrate sparse inverse covariance estimation (SICE) process into CNNs as a novel layer. To achieve this, we develop a method based on Newton-Schulz iteration and box constraints for $\ell_1$ penalty to solve the SICE optimisation with CNN and maintain the end-to-end + +training efficiency. To the best of our knowledge, our iSICE is the first end-to-end SICE solution for CNN. + +2. Our iSICE method requires a minimal change of network architecture. Therefore, it can readily be integrated with existing works to replace those using deep network models to learn covariance matrix based visual representation. The iSICE is fully compatible with GPU and can be easily implemented with modern deep learning libraries. +3. As the objective of SICE is a combination of log det term (may change rapidly) and sparsity (changes slowly), achieving the balance between both terms during optimisation by the gradient descent is hard. To this end, we propose a minor contribution: a simple modulating network whose goal is to adapt on-the-fly learning rate and sparsity penalty. + +Experiments on multiple image classification datasets show the effectiveness of our proposed iSICE method. + +# 2. Related work + +Since the advent of covariance representation methods in deep learning [11, 27], reliable estimation of the covariance matrix from a CNN feature map remains an issue. The issue is due to the small spatial size of feature map (corresponding to the number of samples) and the higher number of feature channels (corresponding to the feature dimensions), which could cause unreliable estimation and even matrix singularity due to the so-called "curse of dimensionality". Existing works either append a small positive constant to the diagonals of covariance matrix [27] or use matrix normalisation operation [16, 24, 28] to handle this issue. Matrix normalisation approaches mitigate unbalanced spectrum represented by eigenvalues of covariance matrix [19, 20, 24, 25, 55]. Different from the existing methods, in this paper, we approach the reliable covariance estimation problem in CNN from a perspective of partial correlations that can be efficiently captured by the inverse of covariance matrix, also known as the precision matrix [7], when a large number of samples is available for estimation. + +Covariance representation strives to capture the underlying structure of CNN feature channels. In the literature of knowledge representation [22], it is recommended to leverage prior knowledge to improve a learning task when sufficient data is not available1. Thus, prior knowledge can be used to improve the estimation of underlying structure of high-dimensional data captured by covariance representation. One common prior knowledge is "structure sparsity" which leads to the sparse inverse covariance matrix in the literature of statistical machine learning [10, 15]. + +Structure sparsity cannot be readily applied to covariance representation as it requires the access to partial correlation between feature components. A covariance matrix captures pairwise correlation of feature components without taking into account the confounding effect of remaining components. Therefore, it is unlikely that the covariance matrix will be sparse by nature. To obtain partial correlation, SICE moves from covariance matrix to its inverse. An inverse covariance matrix captures partial correlation between feature components by regressing out the effects of other variables [15]. Once other variables are factored out, structure sparsity can be effectively enforced in SICE. In a recent work [51], SICE-based visual representation has been applied to image classification with handcrafted and pre-extracted features of small size. However, SICE has never been integrated into CNN for end-to-end training with the goal of adapting to such a representation. The existing solvers for computing SICE also have limited GPU support [8, 10]. Thus, we propose an end-to-end trainable iterative method for solving SICE optimisation with CNNs. + +# 3. Proposed method + +In this section, we begin by discussing the background of SICE. Then we discuss how it can be estimated from CNN feature descriptors. Finally, we describe our proposed method which is trainable end-to-end with a CNN. + +# 3.1. The basic idea of SICE + +As a representation, the covariance matrix captures the underlying structure of a feature set. It uses a covariance matrix estimated from samples to capture this structure. Sparse inverse covariance estimation (SICE) focuses on the following two issues: (1) instability or singularity of sample-based covariance matrix estimated from a small number of high-dimensional feature vectors. This situation makes it less effective in capturing the underlying structure of data. As an example, in this case the smaller and larger eigenvalues of the estimated covariance matrix become poorly estimated. Thus, a suitable regularisation (a prior) is needed during the estimation to mitigate the bias of our estimator; (2) rigid estimation of covariance matrix for high-dimensional feature vectors is not always appropriate as the high-dimensional data usually presents a complex structure. If there is prior knowledge available, it should be used to improve the covariance estimation from a small number of samples. + +In terms of prior knowledge of high-dimensional data, structure sparsity [15] and the "bet on sparsity" principle [12] are the two common priors used in the literature. Suppose that we have a probabilistic graphical model, where each node corresponds to a feature and the statistical dependence between two nodes is expressed with an edge linking two nodes. Structure sparsity would specify how + +sparse such a graph is, e.g., how many edges are presented in this graph. More generally, even if there is no clear prior knowledge on structure sparsity available, the "bet on sparsity" principle can still be applied to estimate the structure of the graph by imposing a sparsity prior. Its rationality is as follows. If the graph is indeed sparse, SICE will estimate its underlying structure with a correct prior, and if the graph is dense, it will not estimate the underlying structure accurately with such a prior. However, in the latter case, we will not lose much because we have known that we do not have enough sample to estimate the dense structure. The "bet on sparsity" principle has been widely adopted in high-dimensional data analysis, and also demonstrated its efficacy on covariance matrix based visual representation estimated from handcrafted or pre-extracted CNN features [51]. + +SICE strives to improve the covariance estimation with the use of prior knowledge. To incorporate prior knowledge, SICE switches to the inverse of covariance matrix from covariance matrix. In principle, the covariance matrix captures the apparent pairwise correlation between feature components, i.e., indirect correlation. In comparison, the inverse of covariance matrix is able to characterise the direct (i.e. partial) correlation between two feature components by regressing out the remaining features. Using the inverse covariance matrix not only helps to interpret the essential relationship between two features, but also allows the convenient incorporation of the sparsity prior. + +# 3.2. Our SICE estimation with CNN + +Suppose, we have the sample-based covariance matrix $\pmb{\Sigma}$ computed from a set of CNN local descriptors presented in a convolutional feature map. Let $\mathbf{S}$ denote the corresponding sparse inverse covariance matrix. The off-diagonal entries of $\mathbf{S}$ capture the direct correlation between different descriptor components. They are zero if two components are independent under the removed influence of confounding variables. In the literature [10], the estimation of $\mathbf{S}$ has been effectively resolved by the maximization of a penalised log-likelihood of data with an SPD constraint on $\mathbf{S}$ and the sparsity prior to induce sparse graph connectivity. The optimal solution of the above problem is known as SICE. + +SICE is defined as follows: + +$$ +\mathbf {S} ^ {*} = \underset {\mathbf {S} > 0} {\arg \max } \log \det (\mathbf {S}) - \operatorname {t r a c e} (\boldsymbol {\Sigma} \mathbf {S}) - \lambda \| \mathbf {S} \| _ {1}, \tag {1} +$$ + +where $\Sigma$ is a sample-based covariance matrix, and $\operatorname{det}(\cdot)$ , trace $(\cdot)$ and $\| \cdot \|_1$ denote the determinant, trace and $\ell_1$ -norm of a vectorization of matrix, respectively. + +To obtain reliable and faithful SICE, the term $\| \mathbf{S}\| _1$ imposes the structure sparsity on $\mathbf{S}$ . $\lambda$ controls the trade-off + +between the amount of sparsity and the log-likelihood estimation. The problem in Eq. (1) is convex and can be solved by the off-the-shelf packages such as GLASSO [10] and CVXPY [8]. However, the objective is non-smooth due to the $\ell_1$ penalty. The above optimisation packages cannot be used with CNN layers to conduct training with backpropagation. A recent extension of CVXPY called CVXPYLAYERS [1] provides differentiable optimisation layers. However, based on our investigation, it has the following issues: (1) it cannot efficiently solve large SICE problems, i.e., of size $128 \times 128$ or higher; (2) it relies on multiple CPU based libraries including CVXPY to solve the optimisation problem and obtain gradients for backpropagation. This greatly limits its efficiency due to the lack of GPU support. The above limitations motivate us to develop an SICE method suitable for end-to-end training with GPU. + +# 3.3. Proposed end-to-end trainable SICE method + +Let $J$ be the objective function of Eq. (1). $J$ can be optimised by taking the gradient with respect to $\mathbf{S}$ as follows: + +$$ +\begin{array}{l} \frac {\partial J}{\partial \mathbf {S}} = \frac {\partial}{\partial \mathbf {S}} \log \det (\mathbf {S}) - \frac {\partial}{\partial \mathbf {S}} \operatorname {t r a c e} (\boldsymbol {\Sigma} \mathbf {S}) - \lambda \frac {\partial}{\partial \mathbf {S}} \| \mathbf {S} \| _ {1} \\ = \mathbf {S} ^ {- 1} - \boldsymbol {\Sigma} - \lambda \left(\frac {\partial}{\partial \mathbf {S}} \mathbf {S} ^ {+} - \frac {\partial}{\partial \mathbf {S}} \mathbf {S} ^ {-}\right) \\ = \mathbf {S} ^ {- 1} - \boldsymbol {\Sigma} - \lambda (\operatorname {s i g n} (\mathbf {S} ^ {+}) - \operatorname {s i g n} (\mathbf {S} ^ {-})), \tag {2} \\ \end{array} +$$ + +where $\mathbf{S}^{+}\equiv \max (0,\mathbf{S})$ and $\mathbf{S}^{-}\equiv \max (0, - \mathbf{S})$ contain the positive and negative parts of $\mathbf{S}$ , respectively. Eq. (2) can be optimised with the projected gradient descend which has the native backpropagation support on GPU and can take advantage of GPU parallel computing to improve their speed. Now we discuss how Eq. (2) can be effectively optimised using a few consecutive structured CNN layers. + +The overview of our method is given in Fig. 2. From the left, we pass an input image to the backbone and process it till the last convolution layer. We obtain the feature map, i.e., $h \times w \times d$ tensor, where $h$ is the height, $w$ is the width, and $d$ is the number of channels. By reshaping the feature map to a set of $n$ vectors of length $d$ , where $n = wh$ and stacking them as column vectors, we can create a $d \times n$ data matrix $\mathbf{X}$ . A sample-based covariance matrix $\boldsymbol{\Sigma}$ estimated from $\mathbf{X}$ is defined as $\boldsymbol{\Sigma} \equiv \mathbf{X} \bar{\mathbf{X}}^{\top}$ , where $\bar{\mathbf{I}} = \frac{1}{n} (\mathbf{I} - \frac{1}{n} \mathbf{1} \mathbf{1}^{\top})$ performs centering of matrix $\mathbf{X}$ , where $\mathbf{I}$ and $\mathbf{1} \mathbf{1}^{\top}$ are $n \times n$ dimensional identity matrix and matrix of all-ones, respectively. Below, we describe key steps of our method. + +Estimation of precision matrix $\mathbf{S}_0 = \boldsymbol{\Sigma}^{\prime -1}$ . Newton-Schulz iteration is popular as it can approximate the matrix square root $^2$ fast on GPU [23]. In contrast, during the estimation of $\mathbf{S}$ , we use Newton-Schulz iteration [14] for a fast approxi- + +Algorithm 1 Matrix inverse by Newton-Schulz iterations, named $\mathrm{nsInv}(\cdot)$ . It is used to compute "Precision $\Omega$ and is also used by Alg. 2, whereas $\mathbf{Y}_{N_s}$ (line 4) gives iSQRT. + +Input: Covariance matrix $\pmb{\Sigma}$ , number of iterations $N_{s}$ . + +Output: Inverse covariance matrix $\Sigma^{-1}$ . + +1: $\mathbf{Y}_0 = \pmb{\Sigma}' = \pmb{\Sigma} / \mathrm{trace}(\pmb{\Sigma})$ , $\mathbf{Z}_0 = \mathbf{I}$ +2: for $i = 1$ to $N_{s}$ do +3: $\mathbf{P} = \frac{1}{2} (3\mathbf{I} - \mathbf{Z}_{i - 1}\mathbf{Y}_{i - 1})$ +4: $\mathbf{Y}_i = \mathbf{Y}_{i - 1}\mathbf{P}$ and $\mathbf{Z}_i = \mathbf{P}\mathbf{Z}_{i - 1}$ + +5: end for +6: $\mathbf{Q} = \mathbf{Z}_{N_1}$ +7: $\pmb{\Sigma}^{-1} = \mathbf{Q}\mathbf{Q}^{\top} / \mathrm{trace}(\pmb{\Sigma})$ {Inverse covariance matrix} + +mate inverse of matrix, which imposes a convergence condition $\| \mathbf{\Sigma} - \mathbf{I}\| _2 < 1$ on Algorithm 1. Thus, we normalise $\mathbf{\Sigma}$ by its trace and use the trace-normalised $\pmb{\Sigma}' = \pmb{\Sigma} / \mathrm{trace}(\pmb{\Sigma})$ . Then, the square of the inverse square root $\mathbf{Q}$ is post-normalized by the trace to reverse it, i.e., $\pmb{\Sigma}^{-1} = \pmb{Q}\pmb{Q}^{\top} / \mathrm{trace}(\pmb{\Sigma})$ . + +As Eq. (2) has to start with an initial $\mathbf{S}_0$ , if $\pmb{\Sigma}^{\prime}$ is invertible, Alg. 1 approximates its inverse3. Although it is an approximation, we denote it in Alg. 1 as $\pmb{\Sigma}^{-1}$ for brevity. + +Estimation of sparse inverse covariance $\mathbf{S}$ . Given the result obtained in the last step, $\mathbf{S}_0$ , we start iterations of iSICE by applying the projected gradient descend (PGD) to the gradient of SICE (Eq. (1)), given in Eq. (2). + +Following methodology of optimisation by imposing box constraints (e.g., see an intuitive example by Schiele et al. [35]), we separate $\mathbf{S}$ into its positive and negative parts: + +$$ +\mathbf {S} _ {i} ^ {+} = \max (0, \mathbf {S} _ {i - 1}) \quad \text {a n d} \quad \mathbf {S} _ {i} ^ {-} = \max (0, - \mathbf {S} _ {i - 1}), \tag {3} +$$ + +and apply the PGD step to each of them separately. + +In such a case, the sparsity constraint imposed by the $\ell_1$ norm simplifies, i.e., the gradient of $\lambda \mathbf{S}^{+}$ can be assumed $\lambda$ and the gradient of $-\lambda \mathbf{S}^{-}$ can be also assumed $\lambda$ . Thus, we firstly rewrite Eq. (2) into two parts: + +$$ +\nabla \mathbf {S} _ {i} ^ {+} = \mathbf {S} _ {i - 1} - \boldsymbol {\Sigma} - \lambda \quad \text {a n d} \quad - \nabla \mathbf {S} _ {i} ^ {-} = \mathbf {S} _ {i - 1} - \boldsymbol {\Sigma} + \lambda . \tag {4} +$$ + +Then we take one PGD step to update $\mathbf{S}_i^+$ and $\mathbf{S}_i^-$ : + +$$ +\mathbf {S} _ {i} ^ {+} := \Pi \left(\mathbf {S} _ {i} ^ {+} - \eta \beta \nabla \mathbf {S} _ {i} ^ {+}\right) \quad \text {a n d} \quad \mathbf {S} _ {i} ^ {-} := \Pi \left(\mathbf {S} _ {i} ^ {-} - \eta \beta \nabla \mathbf {S} _ {i} ^ {-}\right), \tag {5} +$$ + +where $\Pi (\cdot)\equiv \max (0,\cdot)\equiv \mathrm{ReLU}(\cdot)$ is the gradient reprojection function of PGD into the feasible region of each box constraint (one for the non-negative $\mathbf{S}_i^+$ , and one for nonpositive $\mathbf{S}_i^-$ ). Constant $\eta >0$ is a desired learning rate, whereas $\beta >0$ controls the decay of learning rate. + +Finally, we assemble the current estimate of $\mathbf{S}_i$ from $\mathbf{S}_i^+$ and $\mathbf{S}_i^-$ : + +$$ +\mathbf {S} _ {i} = \operatorname {S y m} \left(\mathbf {S} _ {i} ^ {+} - \mathbf {S} _ {i} ^ {-}\right), \tag {6} +$$ + +Algorithm 2 Iterative sparse inverse covariance estimation (iSICE). + +Input: Sample-based covariance matrix $\pmb{\Sigma}$ , sparsity constant $\lambda$ , learning rate $\eta$ , number of iterations N, small constant $\alpha$ , i.e., $\alpha = 1\mathrm{e} - 9$ , regularisation parameter $\beta$ . + +Output: Sparse inverse covariance matrix $\mathbf{S}'$ . + +1: $\nabla_{2} = \pmb{\Sigma}' = \pmb{\Sigma}/\mathrm{trace}(\pmb{\Sigma})$ {Pre-normalisation using trace} +2: $\mathbf{S}_0 = \mathrm{nsInv}(\mathbf{S}_{i - 1} + \alpha \mathbf{I})$ {Fast approx. inverse (Alg. 1)} +3: $\nabla_{1} = \mathbf{S}_{0}$ +4: for $i = 1$ to $N$ do + +5: $\mathbf{S}_i^+ = \mathrm{ReLU}(\mathbf{S}_{i - 1})$ and $\mathbf{S}_i^- = \mathrm{ReLU}(-\mathbf{S}_{i - 1})$ +6: if $i\neq 1$ +7: $\nabla_{1} = \mathrm{nsInv}(\mathbf{S}_{i - 1} + \alpha \mathbf{I})$ {Fast approx. inv. (Alg. 1)} +8: end if +9: $\nabla_{12} = \nabla_{1} - \nabla_{2}$ +10: $\beta = 1 - \frac{i - 1}{\max(1,N - 1)}$ {Decay the learning rate} +11: $\mathbf{S}_i^+ := \Pi (\mathbf{S}_i^+ - \eta \beta (-\nabla_{12} + \lambda))$ +12: $\mathbf{S}_i^- \coloneqq \Pi (\mathbf{S}_i^- - \eta \beta (+\nabla_{12} + \lambda))$ +13: $\mathbf{S}_i = \mathrm{Sym}(\mathbf{S}_i^+ -\mathbf{S}_i^-)$ +14: end for +15: $\mathbf{S}^{*} = \mathbf{S}_{N} / \mathrm{trace}(\mathbf{S}_{N})$ + +where $\mathrm{Sym}(\mathbf{M}) = \frac{1}{2} (\mathbf{M} + \mathbf{M}^{\top})$ ensures the matrix $\mathbf{M}$ is symmetric (and the intermediate estimate of SICE). + +Algorithm 2 starts with a dense precision matrix $\mathbf{S}_0$ . If $N > 0$ , it loops over iterations $i = 1, \dots, N$ , applying the above steps. For ease of tuning the learning rate $\eta$ , the algorithm starts by the trace normalisation of $\boldsymbol{\Sigma}$ and it reverses the trace normalisation when it finishes. Otherwise, $\eta$ has to be scaled depending on the value of the largest eigenvalue of $\boldsymbol{\Sigma}$ which is somewhat impractical when running CNN end-to-end over multiple mini-batches. + +As $\mathbf{S}^*$ is a symmetric matrix, we only take its upper-triangular entries (plus the diagonal entries) and process them by fully connected layers for classification purposes. + +Algorithm 2 is implemented with modern deep learning library, PyTorch, to leverage the full GPU support and autograd package for optimisation. Due to the iterative nature of solving S, we call our method iterative SICE (iSICE). + +# 4. Experiments + +Below, we first describe experimental dataset benchmarks and then discuss the implementation of our proposed method. Subsequently, we present our experimental results and ablation study on key hyper-parameters. Finally, we compare our proposed method with the existing methods. + +# 4.1. Datasets, Metric, and Implementation + +Datasets. We conduct experiments using one scene and five fine-grained image datasets: the MIT Indoor dataset [33], Airplane [32], Birds [42], Cars [21], DTD [5] and iNatural- + +ist [40]. We also use ImageNet100 (a subset of ImageNet1K dataset) proposed by Tian et al. [39] and mini-ImageNet [41]. We follow the widely used training and testing protocols of Bilinear CNN [27]. The details of datasets and protocols are provided in Appendix A. + +Metric for evaluations. For evaluation of different methods, average classification accuracy is used. This metric is widely used in literature, e.g., [23, 27]. + +Implementation details. Our method is implemented using PyTorch 1.9. We use ImageNet-1K pre-trained backbones provided in Torchvision 0.13.0 library. Following the recent works [23, 24], the number of feature channels is reduced to 256 with $1 \times 1$ convolution for efficiency and fair comparisons. All images are resized to $448 \times 448$ and the training is conducted by randomly flipping them horizontally. We fine-tune all backbones for 50-100 epochs with AdamW optimiser [31] for an initial learning rate of 0.00012, and ConvNext-T CNN and Swin-T with an initial learning rate 0.00005. For all backbones, we decrease the learning rate by a factor of 10 at the 15th and 30th epochs. Depending on the dataset and backbones, our fine-tuning process lasts for about 3-8 hours on four P100 GPUs. For ImageNet100, ResNet-50 was trained for 100 epochs with the initial learning rate 0.01, reduced by 10 at the 15th, 30th and 45th epochs. Settings are detailed in Appendix B. + +# 4.2. Evaluations + +We evaluate the performance of the proposed iSICE and compare it with its covariance-based competitors. We also include comparisons with our baseline, the inverse covariance matrix, called precision matrix (for simplicity denoted as Precision $\Omega$ in experiments). In contrast to iSICE which represents a sparse graph, precision matrix is used as a baseline as it represents a graph without imposed sparsity. + +The covariance representations (denoted with COV) are widely used in literature [24, 27, 28]. We use the Newton-Schulz iteration for computing the matrix square root normalised covariance (iSQRT-COV) due to efficiency of the Newton-Schulz iteration with GPUs, as well as good empirical results reported by multiple authors [23, 25]. + +Backbones. We choose VGG-16 [37] and ResNet-50 [13] CNNs as our backbones for the majority of experiments (we also include the VGG-19, ResNet-101, ResNeXt-101 [45], and latest ConvNext-T [30], Swin-T and Swin-B [29] in the main table). VGG-16 and ResNet-50 are popular in image classification (including fine-grained benchmarks). We choose these two backbones in order to better understand + +the performance of our methods compared to baselines in the common testbed (the same backbones and experimental settings). Given an input image, we obtain a set of feature channels shaped as a tensor after the $1 \times 1$ convolution operation. Using these feature channels, we compute the iSICE, iSQRT-COV and Precision $\Omega$ representations. Since these representations are symmetric, we only use the upper-triangular entries (and the diagonal entries) passed to a fully-connected layer to obtain classification scores. + +Hyper-parameters. There are three hyper-parameters associated with iSICE: sparsity constant $\lambda$ , learning rate $\eta$ and number of iterations $N$ . We experiment with a large range of values for a better understanding, i.e., $\lambda \in \{1.0, 0.5, 0.1, 0.01, 0.001, 0.0001, 0.00001\}$ , $\eta \in \{0.001, 0.01, 0.1, 1.0, 5.0, 10.0, 20.0\}$ , and $N \in \{1, 5, 10\}$ . Since the total combination of hyper-parameters in the table is 147, we choose the median values of each hyper-parameter range (highlighted in bold) and keep them throughout experiments on all datasets. Appendix C studies the impact of hyper-parameters on results. + +Overview of results. Table 1 shows the performance of several COV models (e.g., popular iSQRT-COV), and our Precision $\Omega$ and iSICE models. The rightmost column summarizes the average performance over one scene and three fine-grained image classification benchmarks. It is clear that on average, iSICE outperforms MPN-COV, iSQRT-COV, DeepCOV, and DeepKSPD, etc. iSICE also outperforms our baseline Precision $\Omega$ . This achievement is consistent in all four backbones. It is interesting to see that except a few cases, the inverse covariance method, i.e., Precision $\Omega$ , performs slightly better than the covariance method, i.e., iSQRT-COV. This improved performance highlights the effectiveness of characterising partial correlations of features with inverse covariance instead of pairwise correlations of features based on the sample covariance. Our iSICE method makes the inverse covariance estimation more robust and reliable by enforcing sparsity, as demonstrated by improved performance over Precision $\Omega$ baseline. However, there may be some situations when Precision $\Omega$ outperforms iSICE (e.g., MIT with VGG-16 backbone). Notice that iSICE can be considered as sparse precision matrix. When $N = 0$ in Alg. 2, iSICE reduces to Precision $\Omega$ . In further experiments we show that once the size of matrix is increased, iSICE does outperform Precision $\Omega$ . + +Table 2 corroborates that iSICE significantly outperforms Precision $\Omega$ and iSQRT-COV. + +Robustness of iSICE to Hyper-parameters. We have conducted experiments with the hyper-parameter range given in Section 4.1. Appendix C shows that the performance of iSICE remains stable across a range of values. + +Detailed comparisons with SPD-based SOTA models. Table 1 compares the performance of iSICE to several prior works. We first compare our VGG-16 backbone + +
MethodBackboneMITAirplaneBirdsCarsDTDiNatuarlistmini-ImageNet
GAP [37]-76.670.479.8---
NetVLAD [2]-81.881.688.6---
NetFV [28]-79.079.986.2---
BCNN [27]77.683.984.090.684.0--
CBP [11]76.284.184.391.284.0--
LRBP [17]-87.384.290.9---
KP [6]-86.986.292.4---
HIHCA [4]-88.385.391.7---
Improved BCNN [25]-88.585.892.0---
SMSO [46]VGG-1679.5-85.0----
MPN-COV [43] (reproduced)-86.182.989.8---
iSQRT-COV [23] (reproduced)76.190.084.591.271.356.276.2
DeepCOV [9]79.288.785.491.786.3--
DeepKSPD [9]81.090.084.891.686.3--
RUN [47]80.591.085.7----
FCBN [48]80.390.585.5----
TKPF [49]80.591.486.0----
Precision Ω80.289.483.492.074.057.974.0
iSICE (ours)78.792.286.594.074.759.878.7
CBP [11]-81.681.688.6---
KP [6]-85.784.791.1---
SMSO [46]79.7-85.8----
iSQRT-COV [23] (reproduced)78.890.984.392.173.057.770.7
DeepCOV-ResNet [34]ResNet-5083.483.986.085.084.6--
TKPF [49]84.192.185.7----
Precision Ω80.891.284.792.073.759.665.6
iSICE (ours)80.592.785.993.560.760.772.0
iSQRT-COV [23]76.390.384.191.471.856.975.4
Precision ΩVGG-1979.691.183.292.274.257.373.8
iSICE (ours)80.692.586.693.974.959.677.1
iSQRT-COV [23]79.391.084.492.373.070.673.9
Precision ΩResNet-10177.990.183.391.471.269.873.0
iSICE (ours)81.092.986.693.675.472.078.0
iSQRT-COV [23]81.691.386.292.475.772.276.1
Precision ΩResNeXt-10185.790.284.689.976.972.377.6
iSICE (ours)86.394.687.294.578.773.881.0
iSQRT-COV [23]77.888.183.589.484.761.582.0
Precision ΩConvNext-T78.581.283.792.283.959.383.6
iSICE (ours)85.490.486.793.188.965.085.1
iSQRT-COV [23]82.187.685.189.786.158.167.7
Precision ΩSwin-T82.588.284.990.586.559.165.6
iSICE (ours)85.989.686.591.388.361.969.1
iSQRT-COV [23]86.691.388.092.079.469.764.9
Precision ΩSwin-B87.090.787.793.180.167.366.4
iSICE (ours)87.692.988.393.379.872.468.4
+ +Table 1. Comparison between iSICE, Precision $\mathbf{\Omega }$ and other SPD representations in terms of classification accuracy (%) . The performance of existing SPD representation methods is quoted from the original papers. Precision $\mathbf{\Omega }$ is given by Alg. 1. iSICE is given by Alg. 2. + +
MethodBackboneTop-1Top-5
GAP [13]71.0/69.590.9/88.9
iSQRT-COV [23]ResNet-50/71.5/70.290.5/89.7
Precision ΩVGG-1671.1/71.090.1/90.1
iSICE74.8/73.492.0/91.8
+ +Table 2. Results on the ImageNet100 dataset. + +based iSICE with BCNN, CBP, LRBP, KP, HIHCA, Improved BCNN, SMSO, MPN-COV, iSQRT-COV, DeepCOV, DeepKSPD, RUN, FCBN and TKPF methods. The MPN-COV and iSQRT-COV methods use backbones pretrained with second-order pooling. For fair comparison with iSICE, we re-run those methods on our machine with the same backbone and evaluation protocols as ours. iSICE outperforms all existing methods on fine-grained datasets. On MIT dataset, our performance is better than CBP, BCNN and iSQRT-COV. iSICE could outperform DeepKSPD and + +other methods on MIT if a large-dimensional matrix similar to those is used (see Table 3). + +Secondly, we compare our ResNet-50 backbone based iSICE with CBP, KP, SMO, iSQRT-COV, DeepCOV-ResNet and TKPF methods. iSICE achieves better performance than existing methods on both Airplane and Cars datasets. DeepCOV-ResNet uses $1024 \times 1024$ -dimensional matrix which is four times larger than ours. TKPF uses an advanced feature projection to reduce the CNN feature channels and we use a simple linear projection with $1 \times 1$ convolution. However, on average, we are still better than DeepCOV-ResNet and TKPF. + +Thirdly, we integrate iSICE with the popular VGG-19, ResNet-101, ResNeXt-101, ConvNext-T [30], Swin-T and Swin-B [29] backbones pre-trained on ImageNet-1K, and compare their performance with iSQRT-COV and Precision $\Omega$ (Alg. 1). iSICE outperforms iSQRT-COV and Precision + +
MethodMatrix Dim.MITAirplaneBirdsCarsAverage
VGGResNetVGGResNetVGGResNetVGGResNetVGGResNet
iSQRT-COV256 × 25676.178.890.090.984.584.391.292.185.586.5
512 × 51276.982.891.591.185.084.592.292.186.487.6
Precision Ω256 × 25680.280.889.491.283.484.792.092.086.387.1
512 × 51280.782.790.191.584.984.092.592.687.087.7
SICE128 × 12871.073.185.586.977.378.087.087.980.281.5
256 × 25673.775.487.989.279.780.389.589.382.783.6
iSICE256 × 25678.780.592.292.786.585.99493.587.988.2
512 × 51281.181.792.992.686.88694.693.888.988.5
+ +$\Omega$ methods across all datasets. This highlights that SPD-based visual representations (1) are still relevant for modern powerful classification backbones and (2) they improve results on large-scale datasets. + +Ablations on the size of Sparse Inverse Matrix. Table 3 shows results for $512 \times 512$ vs. $256 \times 256$ matrix size (we keep the hyper-parameters fixed). To produce $512 \times 512$ dim. matrix, (1) from VGG-16, we simply remove the $1 \times 1$ convolution layer to obtain 512 feature channels and (2) from ResNet-50, we set the output channels of $1 \times 1$ convolution layer to 512. Generally, switching to a larger matrix improves the performance, e.g., on MIT our iSICE gains between 1.2 and $2.4\%$ (sparsity helps with a larger matrix). On average, all methods improved performance by switching to a larger matrix at the computational expense. See runtimes in Table 5 and memory consumption in Appendix E. Finally, Table 3 also shows that iSICE performs much better than SICE (based on ADMM solver [3]) computed over pre-extracted features. Table 5 shows that iSICE is $3 \times$ faster. SICE is almost intractable on larger datasets. This validates our claim that learning sparse inverse covariance matrix end-to-end produces robust visual representation. + +iSICE with learning rate and sparsity modulators. As iSICE trades between log $\operatorname{det}(\cdot)$ (changes rapidly) and the $\ell_1$ norm (changes linearly), optimizing Eq. (1) with PGD may struggle with non-optimal learning rates and sparsity. Thus, we design a simple modulator that updates $\beta$ in Alg. 2 by setting $\beta := \beta \cdot \kappa$ in lines 10, where $\kappa = \tau + 2\mathrm{Sigm}(\mathrm{FC}(\mathbf{X1} / n))$ , $\mathrm{Sigm}(\cdot)$ is a sigmoid, and FC layer is of $d \times 1$ size. We also add a penalty $-\gamma (\kappa - 1)^2$ to the classification loss to encourage $\kappa$ to be close to 1 unless classification loss gets smaller for $\kappa \neq 1$ while incurring the above penalty. We set $\gamma = 0.0001$ . We use the above modulator (we do not claim this is the most optimal design) as a tool akin to Mod-Grad [36]. $\tau = 0.01$ is a small offset to prevent zero learning rate. Another modulator with the same architecture is used to adapt sparsity parameter $\lambda$ . Table 4 shows that modulating the learning rate and sparsity on-the-fly helps iSICE. + +Experiments on dense vs. sparse structure estimation w.r.t. sample size. Below we randomly generate a dense or sparse inverse covariance matrix $\mathbf{P}$ (size $100\times 100$ ) and sample various amounts of data from the resulted normal dis + +Table 3. Performance of iSQRT-COV, Precision $\mathbf{\Omega }$ ,SICE and iSICE on various datasets when different matrix dimensions are used. + +
MethodMITAirplaneBirdsCarsImageNet100
iSICE80.592.785.993.574.8
iSCIE+MLP81.393.486.193.976.3
+ +Table 4. Comparison between the classification performance of iSICE and iSICE+MLP on the ResNet-50 backbone. + +
GAPiSQRT-COVPrecision ΩSICEiSICEiSICE+MLP
Time/batch (sec.)29.032.032.8150.844.645.8
Time/epoch (min.)12.613.413.865.319.319.8
+ +![](images/43c445bdbe55f9ff879e65e5e6364d2b8135495f1d76e86b8042fc4041b636a0.jpg) +Table 5. Runtimes (256×256 matrix, ImageNet100, ResNet-50). + +![](images/27277345ba5218e2642c0c4087dc5bc376a24c89a8edeb1fae727f185c57b4eb.jpg) +Figure 3. Estimation errors on (left) dense & (right) sparse struct. + +tribution (via mvnrnd( $\cdot$ ) in Matlab) to get its estimate $\hat{\mathbf{P}}$ . Fig. 3 (left) shows estimation error $\|\hat{\mathbf{P}} - \mathbf{P}\|_F$ for dense structure. Sparse and non-sparse estimation (by SICE and MLE, resp.) show high errors in low sample regime. For the sparse structure in Fig. 3 (right), sparse estimation works better. + +# 5. Conclusions + +In this paper, we proposed a method for learning sparse inverse covariance representation with CNN. Our method estimates SICE within the CNN layers and facilitates backpropagation for end-to-end training. Our iSICE significantly outperforms other covariance representations on several datasets. iSICE exploits the sparsity prior to capture partial correlations under limited number of samples. Our method is of general purpose and can be readily applied in existing SPD-based models to improve their performance. + +Acknowledgements. This work was supported by CSIRO's Machine Learning & Artificial Intelligence Future Science Platform (MLAI FSP), University of Wollongong's Australia IPTA scholarship, and Australian Research Council (DP200101289). We thank Jianjia Zhang for discussions. + +# References + +[1] Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, and J Zico Kolter. Differentiable convex optimization layers. Advances in neural information processing systems, 32, 2019. 4 +[2] Relja Arandjelovic, Petr Gronat, Akihiko Torii, Tomas Pajdla, and Josef Sivic. Netvlad: Cnn architecture for weakly supervised place recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5297-5307, 2016. 7 +[3] Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine learning, 3(1):1-122, 2011. 8 +[4] Sijia Cai, Wangmeng Zuo, and Lei Zhang. Higher-order integration of hierarchical convolutional activations for fine-grained visual categorization. In Proceedings of the IEEE international conference on computer vision, pages 511-520, 2017. 7 +[5] Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describing textures in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3606-3613, 2014. 5, 11 +[6] Yin Cui, Feng Zhou, Jiang Wang, Xiao Liu, Yuanqing Lin, and Serge Belongie. Kernel pooling for convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2921-2930, 2017. 7 +[7] Morris H. DeGroot. Optimal statistical decisions. McGraw-Hill, New York, NY [u.a], 1970. 1, 2, 3 +[8] Steven Diamond and Stephen Boyd. Cvxpy: A python-embedded modeling language for convex optimization. The Journal of Machine Learning Research, 17(1):2909-2913, 2016. 3, 4 +[9] Melih Engin, Lei Wang, Luping Zhou, and Xinwang Liu. Deepkspd: Learning kernel-matrix-based spd representation for fine-grained image recognition. In Proceedings of the European Conference on Computer Vision, pages 612-627, 2018. 1, 7 +[10] Jerome Friedman, Trevor Hastie, and Robert Tibshirani. Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3):432-441, 2008. 2, 3, 4 +[11] Yang Gao, Oscar Beijbom, Ning Zhang, and Trevor Darrell. Compact bilinear pooling. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 317-326, 2016. 1, 3, 7 +[12] Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Friedman. The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer, 2009. 2, 3 +[13] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6, 7 + +[14] Nicholas J Higham. Functions of matrices: theory and computation. SIAM, 2008. 2, 4, 5 +[15] Junzhou Huang, Tong Zhang, and Dimitris Metaxas. Learning with structured sparsity. Journal of Machine Learning Research, 12(11), 2011. 3 +[16] Catalin Ionescu, Orestis Vantzos, and Cristian Sminchisescu. Matrix backpropagation for deep networks with structured layers. In Proceedings of the IEEE international conference on computer vision, pages 2965-2973, 2015. 1, 3, 5 +[17] Shu Kong and Charless Fowlkes. Low-rank bilinear pooling for fine-grained classification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 365-374, 2017. 7 +[18] Piotr Koniusz, Lei Wang, and Anoop Cherian. Tensor representations for action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(2):648-665, 2021. 1 +[19] Piotr Koniusz, Fei Yan, Philippe-Henri Gosselin, and Krystian Mikolajczyk. Higher-order occurrence pooling on mid- and low-level features: Visual concept detection. Tech. Report (hal-00922524), 2013. 3 +[20] Piotr Koniusz and Hongguang Zhang. Power normalizations in fine-grained image, few-shot image and graph classification. In IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2021. 3 +[21] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In Proceedings of the IEEE international conference on computer vision workshops, pages 554–561, 2013. 5 +[22] Miroslav Kubat. Neural networks: a comprehensive foundation by simon haykin, macmillan, 1994, ISBN 0-02-352781-7. The Knowledge Engineering Review, 13(4):409-412, 1999. 3 +[23] Peihua Li, Jiangtao Xie, Qilong Wang, and Zilin Gao. Towards faster training of global covariance pooling networks by iterative matrix square root normalization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 947-955, 2018. 1, 4, 6, 7, 13, 14 +[24] Peihua Li, Jiangtao Xie, Qilong Wang, and Wangmeng Zuo. Is second-order information helpful for large-scale visual recognition? In Proceedings of the IEEE international conference on computer vision, pages 2070-2078, 2017. 1, 3, 6 +[25] Tsung-Yu Lin and Subhransu Maji. Improved bilinear pooling with cnns. British Machine Vision Conference, 2017. 3, 6, 7 +[26] Tsung-Yu Lin, Subhransu Maji, and Piotr Koniusz. Second-order democratic aggregation. In Proceedings of the European Conference on Computer Vision, pages 620-636, 2018. 1 +[27] Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. Bilinear cnn models for fine-grained visual recognition. In Proceedings of the IEEE international conference on computer vision, pages 1449-1457, 2015. 1, 3, 6, 7 +[28] Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. Bilinear convolutional neural networks for fine-grained visual recognition. IEEE transactions on pattern analysis and machine intelligence, 40(6):1309-1322, 2017. 1, 3, 6, 7 + +[29] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10012-10022, 2021. 6, 7 +[30] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11976-11986, 2022. 6, 7 +[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6 +[32] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, 2013. 5 +[33] Ariadna Quattoni and Antonio Torralba. Recognizing indoor scenes. In 2009 IEEE conference on computer vision and pattern recognition, pages 413-420. IEEE, 2009. 5 +[34] Saimunur Rahman, Lei Wang, Changming Sun, and Luping Zhou. Redro: Efficiently learning large-sized spd visual representation. In European Conference on Computer Vision, pages 1-17. Springer, 2020. 1, 7 +[35] Bernt Schiele, Paul Swoboda, and Gerard Pons-Moll. Projected gradient descent for the lasso. Lecture Notes, 2018. 5 +[36] Christian Simon, Piotr Koniusz, Richard Nock, and Mehrtash Harandi. On modulating the gradient for metalearning. In Proceedings of the European conference on computer vision, 2020. 8 +[37] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 6, 7 +[38] Yue Song, Nicu Sebe, and Wei Wang. Why approximate matrix square root outperforms accurate svd in global covariance pooling? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1115-1123, 2021. 1 +[39] Yonglong Tian, Dilip Krishnan, and Phillip Isola. Contrastive multiview coding. In European conference on computer vision, pages 776-794. Springer, 2020. 6 +[40] Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie. The inaturalist species classification and detection dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8769-8778, 2018. 6, 11 +[41] Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. Matching networks for one shot learning. Advances in neural information processing systems, 29, 2016. 6, 11 +[42] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200-2011 dataset. 2011. 5 +[43] Qilong Wang, Jiangtao Xie, Wangmeng Zuo, Lei Zhang, and Peihua Li. Deep cnns meet global covariance pooling: Better representation and generalization. IEEE transactions on + +pattern analysis and machine intelligence, 43(8):2582-2597, 2020. 7 +[44] Ruiping Wang, Huimin Guo, Larry S Davis, and Qionghai Dai. Covariance discriminative learning: A natural and efficient approach to image set classification. In 2012 IEEE conference on computer vision and pattern recognition, pages 2496-2503. IEEE, 2012. 1 +[45] Saining Xie, Ross Girshick, Piotr Dolkar, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1492-1500, 2017. 6 +[46] Kaicheng Yu and Mathieu Salzmann. Statistically-motivated second-order pooling. In Proceedings of the European Conference on Computer Vision, pages 600–616, 2018. 7 +[47] Tan Yu, Yunfeng Cai, and Ping Li. Toward faster and simpler matrix normalization via rank-1 update. In European Conference on Computer Vision, pages 203–219. Springer, 2020. 7 +[48] Tan Yu, Xiaoyun Li, and Ping Li. Fast and compact bilinear pooling by shifted random maclaurin. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 3243-3251, 2021. 7 +[49] Tan Yu, Xiaoyun Li, and Ping Li. Efficient compact bilinear pooling via kronecker product. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 3170-3178, 2022. 1, 7 +[50] Hongguang Zhang, Jing Zhang, and Piotr Koniusz. Few-shot learning via saliency-guided hallucination of samples. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2770-2779, 2019. 1 +[51] Jianjia Zhang, Lei Wang, Luping Zhou, and Wanqing Li. Beyond covariance: Sice and kernel based visual feature representation. International Journal of Computer Vision, 129(2):300-320, 2021. 2, 3, 4 +[52] Shan Zhang, Dawei Luo, Lei Wang, and Piotr Koniusz. Few-shot object detection by second-order pooling. In Proceedings of the Asian conference on computer vision, 2020. 1 +[53] Shan Zhang, Naila Murray, Lei Wang, and Piotr Koniusz. Time-reversed diffusion tensor transformer: A new tenet of few-shot object detection. In Proceedings of the European conference on computer vision, 2022. 1 +[54] Shan Zhang, Lei Wang, Naila Murray, and Piotr Koniusz. Kernelized few-shot object detection with efficient integral aggregation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1 +[55] Yifei Zhang, Hao Zhu, Zixing Song, Piotr Koniusz, and Irwin King. Spectral feature augmentation for graph contrastive learning and beyond. 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"type": "text", + "content": "Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolutional feature maps. However, pairwise correlation will become misleading once there is another channel correlating with both channels of interest, resulting in the \"confounding\" effect. For this case, \"partial correlation\" which removes the confounding effect shall be estimated instead. Nevertheless, reliably estimating partial correlation requires to solve a symmetric positive definite matrix optimisation, known as sparse inverse covariance estimation (SICE). How to incorporate this process into CNN remains an open issue. In this work, we formulate SICE as a novel structured layer of CNN. To ensure end-to-end trainability, we develop an iterative method to solve the above matrix optimisation during forward and backward propagation steps. Our work obtains a partial correlation based deep visual representation and mitigates the small sample problem often encountered by covariance matrix estimation in CNN. Computationally, our model can be effectively trained with GPU and works well with a large number of channels of advanced CNNs. Experiments show the efficacy and superior classification performance of our deep visual representation compared to covariance matrix based counterparts." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 591, + 128, + 604 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 591, + 128, + 604 + ], + "spans": [ + { + "bbox": [ + 47, + 591, + 128, + 604 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 611, + 287, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 611, + 287, + 696 + ], + "spans": [ + { + "bbox": [ + 46, + 611, + 287, + 696 + ], + "type": "text", + "content": "Learning effective visual representation is a central issue in computer vision. In the past two decades, describing images with local features and pooling them to a global representation has shown promising performance. As one of the pooling methods, covariance matrix based pooling has attracted much attention due to its exploitation of second-order correlation information of features. A variety of tasks" + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 307, + 236, + 552, + 356 + ], + "blocks": [ + { + "bbox": [ + 307, + 236, + 552, + 356 + ], + "lines": [ + { + "bbox": [ + 307, + 236, + 552, + 356 + ], + "spans": [ + { + "bbox": [ + 307, + 236, + 552, + 356 + ], + "type": "image", + "image_path": "01fcafd03a5d182fffdcf1d63296039b70dacc05cf245a0749c2886792e36018.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "lines": [ + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "spans": [ + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": "Figure 1. Understanding the partial correlation (a 3D toy case). Unlike the ordinary covariance (pairwise correlation of say " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " corresponding to channels), partial correlation between variables " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " removes the influence of the confounding variable " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{z}" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": ". Let the number of samples " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "n = 3" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " and channels " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "d = 3" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": ". For the 3D case, " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " are projected onto a plane perpendicular to " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{z}" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": ". Then " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\rho_{xy} = \\cos \\varphi_{xy}" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " (and " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\rho_{xz}" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\rho_{yz}" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " can be computed by analogy). Projected \"residuals\" " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{r_x}" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{r_y}" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " are computed as indicated in the plot, " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{w}'_x = \\arg \\min_{\\mathbf{w}} \\sum_{i=1}^{3} (x_i - \\mathbf{w}_x^\\top \\mathbf{z}_i)" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_i = [z_i, 1]^\\top" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " (and " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{w}'_y" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": " is computed by analogy). The green box: for " + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "inline_equation", + "content": "d > 3" + }, + { + "bbox": [ + 304, + 364, + 547, + 485 + ], + "type": "text", + "content": ", the computation of partial correlation requires covariance inversion [7]." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 509, + 547, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 509, + 547, + 652 + ], + "spans": [ + { + "bbox": [ + 304, + 509, + 547, + 652 + ], + "type": "text", + "content": "such as fine-grained image classification [27], image segmentation [16], generic image classification [24, 26, 34], image set classification [44], action recognition [18], few-shot classification [50] and few-shot detection [52-54] have benefited from the covariance matrix based representation. A few pioneering works have integrated covariance matrix as a pooling method within convolutional neural networks (CNN) and investigated associated issues such as matrix function backpropagation [16], matrix normalisation [23, 28, 38], compact matrix estimation [11, 49] and kernel based extension [9]. The above works further improved visual representations based on covariance matrix." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "content": "Despite the above progress, covariance matrix merely measures the pairwise correlation (more accurately, covariance) of two variables without taking any other variables into account. This can be easily verified because its " + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "inline_equation", + "content": "(i,j)" + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "content": "-th entry solely depends on the " + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "content": "-th and " + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "content": "-th variables on a" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 702, + 286, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 286, + 712 + ], + "type": "text", + "content": "*Corresponding author. Code: https://github.com/csiro-robotics/iSICE" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "6231" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 70, + 547, + 150 + ], + "blocks": [ + { + "bbox": [ + 49, + 70, + 547, + 150 + ], + "lines": [ + { + "bbox": [ + 49, + 70, + 547, + 150 + ], + "spans": [ + { + "bbox": [ + 49, + 70, + 547, + 150 + ], + "type": "image", + "image_path": "6f3a27138e8545a6b344d7e42a40eae3568220c7ab4746d78a5a2c631126050d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 113, + 157, + 479, + 170 + ], + "lines": [ + { + "bbox": [ + 113, + 157, + 479, + 170 + ], + "spans": [ + { + "bbox": [ + 113, + 157, + 479, + 170 + ], + "type": "text", + "content": "Figure 2. Proposed iterative sparse inverse covariance estimation (iSICE) method in a CNN pipeline." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 190, + 289, + 322 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 190, + 289, + 322 + ], + "spans": [ + { + "bbox": [ + 46, + 190, + 289, + 322 + ], + "type": "text", + "content": "sample set. In Statistics, it is known that such a pairwise correlation will give misleading results once a third variable is correlated with both variables of interest due to the \"confounding\" effect. For this situation, the partial correlation is the right measure to use. It regresses out the effects of other variables from the two variables and then calculates the correlation of their residuals instead. Partial correlation can be conveniently obtained by computing inverse covariance matrix, also known as the precision matrix [7] in the statistical community. Figure 1 illustrates the geometrical interpretation of partial correlation." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 326, + 288, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 326, + 288, + 578 + ], + "spans": [ + { + "bbox": [ + 46, + 326, + 288, + 578 + ], + "type": "text", + "content": "The above observation motivates us to investigate a visual representation for image classification based on the inverse covariance matrix. After all, it has better theoretical support on characterising the essential relationship of variables (e.g., the channels in a convolutional feature map) when other variables are present. Note that inverse covariance matrix can be used for many vision tasks but in this paper, we investigate it from the perspective of image classification. Nevertheless, reliably estimating inverse covariance matrix from the local descriptors of a CNN feature map is a challenging task. This is primarily due to the small spatial size of the feature map, i.e., sample size, and a higher number of channels, i.e., feature dimensions, and this issue becomes more pronounced for advanced CNN models. An unreliable estimate of inverse covariance matrix will critically affect its effectiveness as a visual representation. One might argue that by increasing the size of input images or using a dimension reduction layer to reduce the number of feature channels, such an issue could be resolved. In this paper, we investigate this issue from the perspective of robust precision matrix estimation." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 582, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 289, + 715 + ], + "type": "text", + "content": "To achieve our goal, we explore the use of sparsity prior for inverse covariance matrix estimation in the literature. Specifically, the general principle of \"bet on sparsity\" [12] is adopted in estimating the structure of high-dimensional data, and this leads to an established technique called sparse inverse covariance estimation (SICE) [10]. It solves an optimisation in the space of symmetric and positive definite (SPD) matrix to estimate the inverse covariance matrix by imposing the sparsity prior on its entries. SICE is designed to handle small sample problems and it is known for its excellent effectiveness to that end [10]. An initial attempt" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 190, + 545, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 190, + 545, + 251 + ], + "spans": [ + { + "bbox": [ + 304, + 190, + 545, + 251 + ], + "type": "text", + "content": "to apply SICE for visual representation is based on handcrafted or pre-extracted features of small size and an off-the-shelf SICE solver, and it does not have the ability to backpropagate through SICE due to optimisation of the SPD matrix with the imposed non-smooth sparsity term [51]." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 251, + 546, + 418 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 251, + 546, + 418 + ], + "spans": [ + { + "bbox": [ + 304, + 251, + 546, + 418 + ], + "type": "text", + "content": "Our work is the first one that truly integrates SICE into CNN for end-to-end training. Clearly, such an integration will fully take advantage of the feature learning capability of CNN and the partial correlation offered by inverse covariance matrix. On the other hand, realising such an integration is not trivial. Unlike covariance matrix, which is obtained by simple arithmetic operations, SICE is obtained by solving an SPD matrix based optimisation. How to incorporate this optimisation process into CNN as a layer is an issue. Furthermore, this SICE optimisation needs to be solved for each training image during both forward and backward phases to generate a visual representation. Directly solving this optimisation within CNN will not be practical even for a medium-sized SICE problem." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 419, + 547, + 600 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 419, + 547, + 600 + ], + "spans": [ + { + "bbox": [ + 304, + 419, + 547, + 600 + ], + "type": "text", + "content": "To efficiently integrate SICE into CNN, we propose a fast end-to-end training method for SICE by taking inspiration from Newton-Schulz iteration [14]. Our method solves the SICE optimisation with a smooth convex cost function by re-parameterising the non-smooth term in the original SICE cost function (see Eq. (1)), and it can therefore be optimised with standard optimisation techniques such as gradient descent. Furthermore, we effectively enforce the SPD constraint during optimisation so that the obtained SICE solution remains SPD as desired. Figure 2 shows our \"Iterative Sparse Inverse Covariance Estimation (iSICE)\". In contrast to SICE, iSICE works with end-to-end trainable deep learning models. Our iSICE involves simple matrix arithmetic operations fully compatible with GPU. It can approximately solve large SICE problems within CNN efficiently." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 317, + 606, + 525, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 606, + 525, + 617 + ], + "spans": [ + { + "bbox": [ + 317, + 606, + 525, + 617 + ], + "type": "text", + "content": "Our main contributions are summarised as follows." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 314, + 629, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 629, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 314, + 629, + 547, + 715 + ], + "type": "text", + "content": "1. To more precisely characterise the relationship of features for visual representation, this paper proposes to integrate sparse inverse covariance estimation (SICE) process into CNNs as a novel layer. To achieve this, we develop a method based on Newton-Schulz iteration and box constraints for " + }, + { + "bbox": [ + 314, + 629, + 547, + 715 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 314, + 629, + 547, + 715 + ], + "type": "text", + "content": " penalty to solve the SICE optimisation with CNN and maintain the end-to-end" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6232" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 67, + 72, + 286, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 72, + 286, + 95 + ], + "spans": [ + { + "bbox": [ + 67, + 72, + 286, + 95 + ], + "type": "text", + "content": "training efficiency. To the best of our knowledge, our iSICE is the first end-to-end SICE solution for CNN." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 55, + 103, + 286, + 278 + ], + "type": "list", + "angle": 0, + "index": 3, + "blocks": [ + { + "bbox": [ + 55, + 103, + 286, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 103, + 286, + 186 + ], + "spans": [ + { + "bbox": [ + 55, + 103, + 286, + 186 + ], + "type": "text", + "content": "2. Our iSICE method requires a minimal change of network architecture. Therefore, it can readily be integrated with existing works to replace those using deep network models to learn covariance matrix based visual representation. The iSICE is fully compatible with GPU and can be easily implemented with modern deep learning libraries." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 55, + 194, + 286, + 278 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 194, + 286, + 278 + ], + "spans": [ + { + "bbox": [ + 55, + 194, + 286, + 278 + ], + "type": "text", + "content": "3. As the objective of SICE is a combination of log det term (may change rapidly) and sparsity (changes slowly), achieving the balance between both terms during optimisation by the gradient descent is hard. To this end, we propose a minor contribution: a simple modulating network whose goal is to adapt on-the-fly learning rate and sparsity penalty." + } + ] + } + ], + "index": 2 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 47, + 285, + 286, + 309 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 285, + 286, + 309 + ], + "spans": [ + { + "bbox": [ + 47, + 285, + 286, + 309 + ], + "type": "text", + "content": "Experiments on multiple image classification datasets show the effectiveness of our proposed iSICE method." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 319, + 130, + 332 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 319, + 130, + 332 + ], + "spans": [ + { + "bbox": [ + 47, + 319, + 130, + 332 + ], + "type": "text", + "content": "2. Related work" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 339, + 286, + 566 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 339, + 286, + 566 + ], + "spans": [ + { + "bbox": [ + 47, + 339, + 286, + 566 + ], + "type": "text", + "content": "Since the advent of covariance representation methods in deep learning [11, 27], reliable estimation of the covariance matrix from a CNN feature map remains an issue. The issue is due to the small spatial size of feature map (corresponding to the number of samples) and the higher number of feature channels (corresponding to the feature dimensions), which could cause unreliable estimation and even matrix singularity due to the so-called \"curse of dimensionality\". Existing works either append a small positive constant to the diagonals of covariance matrix [27] or use matrix normalisation operation [16, 24, 28] to handle this issue. Matrix normalisation approaches mitigate unbalanced spectrum represented by eigenvalues of covariance matrix [19, 20, 24, 25, 55]. Different from the existing methods, in this paper, we approach the reliable covariance estimation problem in CNN from a perspective of partial correlations that can be efficiently captured by the inverse of covariance matrix, also known as the precision matrix [7], when a large number of samples is available for estimation." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 567, + 286, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 567, + 286, + 685 + ], + "spans": [ + { + "bbox": [ + 47, + 567, + 286, + 685 + ], + "type": "text", + "content": "Covariance representation strives to capture the underlying structure of CNN feature channels. In the literature of knowledge representation [22], it is recommended to leverage prior knowledge to improve a learning task when sufficient data is not available1. Thus, prior knowledge can be used to improve the estimation of underlying structure of high-dimensional data captured by covariance representation. One common prior knowledge is \"structure sparsity\" which leads to the sparse inverse covariance matrix in the literature of statistical machine learning [10, 15]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 72, + 545, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 72, + 545, + 312 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 545, + 312 + ], + "type": "text", + "content": "Structure sparsity cannot be readily applied to covariance representation as it requires the access to partial correlation between feature components. A covariance matrix captures pairwise correlation of feature components without taking into account the confounding effect of remaining components. Therefore, it is unlikely that the covariance matrix will be sparse by nature. To obtain partial correlation, SICE moves from covariance matrix to its inverse. An inverse covariance matrix captures partial correlation between feature components by regressing out the effects of other variables [15]. Once other variables are factored out, structure sparsity can be effectively enforced in SICE. In a recent work [51], SICE-based visual representation has been applied to image classification with handcrafted and pre-extracted features of small size. However, SICE has never been integrated into CNN for end-to-end training with the goal of adapting to such a representation. The existing solvers for computing SICE also have limited GPU support [8, 10]. Thus, we propose an end-to-end trainable iterative method for solving SICE optimisation with CNNs." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 321, + 410, + 335 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 321, + 410, + 335 + ], + "spans": [ + { + "bbox": [ + 306, + 321, + 410, + 335 + ], + "type": "text", + "content": "3. Proposed method" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 341, + 545, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 341, + 545, + 389 + ], + "spans": [ + { + "bbox": [ + 306, + 341, + 545, + 389 + ], + "type": "text", + "content": "In this section, we begin by discussing the background of SICE. Then we discuss how it can be estimated from CNN feature descriptors. Finally, we describe our proposed method which is trainable end-to-end with a CNN." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 396, + 435, + 407 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 396, + 435, + 407 + ], + "spans": [ + { + "bbox": [ + 306, + 396, + 435, + 407 + ], + "type": "text", + "content": "3.1. The basic idea of SICE" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 415, + 545, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 415, + 545, + 628 + ], + "spans": [ + { + "bbox": [ + 306, + 415, + 545, + 628 + ], + "type": "text", + "content": "As a representation, the covariance matrix captures the underlying structure of a feature set. It uses a covariance matrix estimated from samples to capture this structure. Sparse inverse covariance estimation (SICE) focuses on the following two issues: (1) instability or singularity of sample-based covariance matrix estimated from a small number of high-dimensional feature vectors. This situation makes it less effective in capturing the underlying structure of data. As an example, in this case the smaller and larger eigenvalues of the estimated covariance matrix become poorly estimated. Thus, a suitable regularisation (a prior) is needed during the estimation to mitigate the bias of our estimator; (2) rigid estimation of covariance matrix for high-dimensional feature vectors is not always appropriate as the high-dimensional data usually presents a complex structure. If there is prior knowledge available, it should be used to improve the covariance estimation from a small number of samples." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 630, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 630, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 630, + 545, + 713 + ], + "type": "text", + "content": "In terms of prior knowledge of high-dimensional data, structure sparsity [15] and the \"bet on sparsity\" principle [12] are the two common priors used in the literature. Suppose that we have a probabilistic graphical model, where each node corresponds to a feature and the statistical dependence between two nodes is expressed with an edge linking two nodes. Structure sparsity would specify how" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 47, + 693, + 286, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 693, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 693, + 286, + 713 + ], + "type": "text", + "content": "This is indeed the case with CNNs as the spatial size of a CNN feature map is usually small when compared to the number of its channels." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6233" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 263 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 263 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 263 + ], + "type": "text", + "content": "sparse such a graph is, e.g., how many edges are presented in this graph. More generally, even if there is no clear prior knowledge on structure sparsity available, the \"bet on sparsity\" principle can still be applied to estimate the structure of the graph by imposing a sparsity prior. Its rationality is as follows. If the graph is indeed sparse, SICE will estimate its underlying structure with a correct prior, and if the graph is dense, it will not estimate the underlying structure accurately with such a prior. However, in the latter case, we will not lose much because we have known that we do not have enough sample to estimate the dense structure. The \"bet on sparsity\" principle has been widely adopted in high-dimensional data analysis, and also demonstrated its efficacy on covariance matrix based visual representation estimated from handcrafted or pre-extracted CNN features [51]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 264, + 289, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 264, + 289, + 410 + ], + "spans": [ + { + "bbox": [ + 46, + 264, + 289, + 410 + ], + "type": "text", + "content": "SICE strives to improve the covariance estimation with the use of prior knowledge. To incorporate prior knowledge, SICE switches to the inverse of covariance matrix from covariance matrix. In principle, the covariance matrix captures the apparent pairwise correlation between feature components, i.e., indirect correlation. In comparison, the inverse of covariance matrix is able to characterise the direct (i.e. partial) correlation between two feature components by regressing out the remaining features. Using the inverse covariance matrix not only helps to interpret the essential relationship between two features, but also allows the convenient incorporation of the sparsity prior." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 417, + 220, + 430 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 417, + 220, + 430 + ], + "spans": [ + { + "bbox": [ + 47, + 417, + 220, + 430 + ], + "type": "text", + "content": "3.2. Our SICE estimation with CNN" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 437, + 289, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 437, + 289, + 581 + ], + "spans": [ + { + "bbox": [ + 46, + 437, + 289, + 581 + ], + "type": "text", + "content": "Suppose, we have the sample-based covariance matrix " + }, + { + "bbox": [ + 46, + 437, + 289, + 581 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}" + }, + { + "bbox": [ + 46, + 437, + 289, + 581 + ], + "type": "text", + "content": " computed from a set of CNN local descriptors presented in a convolutional feature map. Let " + }, + { + "bbox": [ + 46, + 437, + 289, + 581 + ], + "type": "inline_equation", + "content": "\\mathbf{S}" + }, + { + "bbox": [ + 46, + 437, + 289, + 581 + ], + "type": "text", + "content": " denote the corresponding sparse inverse covariance matrix. The off-diagonal entries of " + }, + { + "bbox": [ + 46, + 437, + 289, + 581 + ], + "type": "inline_equation", + "content": "\\mathbf{S}" + }, + { + "bbox": [ + 46, + 437, + 289, + 581 + ], + "type": "text", + "content": " capture the direct correlation between different descriptor components. They are zero if two components are independent under the removed influence of confounding variables. In the literature [10], the estimation of " + }, + { + "bbox": [ + 46, + 437, + 289, + 581 + ], + "type": "inline_equation", + "content": "\\mathbf{S}" + }, + { + "bbox": [ + 46, + 437, + 289, + 581 + ], + "type": "text", + "content": " has been effectively resolved by the maximization of a penalised log-likelihood of data with an SPD constraint on " + }, + { + "bbox": [ + 46, + 437, + 289, + 581 + ], + "type": "inline_equation", + "content": "\\mathbf{S}" + }, + { + "bbox": [ + 46, + 437, + 289, + 581 + ], + "type": "text", + "content": " and the sparsity prior to induce sparse graph connectivity. The optimal solution of the above problem is known as SICE." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 52, + 592, + 167, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 592, + 167, + 605 + ], + "spans": [ + { + "bbox": [ + 52, + 592, + 167, + 605 + ], + "type": "text", + "content": "SICE is defined as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 66, + 614, + 277, + 632 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 614, + 277, + 632 + ], + "spans": [ + { + "bbox": [ + 66, + 614, + 277, + 632 + ], + "type": "interline_equation", + "content": "\\mathbf {S} ^ {*} = \\underset {\\mathbf {S} > 0} {\\arg \\max } \\log \\det (\\mathbf {S}) - \\operatorname {t r a c e} (\\boldsymbol {\\Sigma} \\mathbf {S}) - \\lambda \\| \\mathbf {S} \\| _ {1}, \\tag {1}", + "image_path": "8055b798ef7eacfa922a7810e5a211126f6c83126a68b1e67ffc7e811dee1e57.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 51, + 640, + 278, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 640, + 278, + 677 + ], + "spans": [ + { + "bbox": [ + 51, + 640, + 278, + 677 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 51, + 640, + 278, + 677 + ], + "type": "inline_equation", + "content": "\\Sigma" + }, + { + "bbox": [ + 51, + 640, + 278, + 677 + ], + "type": "text", + "content": " is a sample-based covariance matrix, and " + }, + { + "bbox": [ + 51, + 640, + 278, + 677 + ], + "type": "inline_equation", + "content": "\\operatorname{det}(\\cdot)" + }, + { + "bbox": [ + 51, + 640, + 278, + 677 + ], + "type": "text", + "content": ", trace " + }, + { + "bbox": [ + 51, + 640, + 278, + 677 + ], + "type": "inline_equation", + "content": "(\\cdot)" + }, + { + "bbox": [ + 51, + 640, + 278, + 677 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 51, + 640, + 278, + 677 + ], + "type": "inline_equation", + "content": "\\| \\cdot \\|_1" + }, + { + "bbox": [ + 51, + 640, + 278, + 677 + ], + "type": "text", + "content": " denote the determinant, trace and " + }, + { + "bbox": [ + 51, + 640, + 278, + 677 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 51, + 640, + 278, + 677 + ], + "type": "text", + "content": "-norm of a vectorization of matrix, respectively." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "type": "text", + "content": "To obtain reliable and faithful SICE, the term " + }, + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\| \\mathbf{S}\\| _1" + }, + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "type": "text", + "content": " imposes the structure sparsity on " + }, + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{S}" + }, + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 47, + 689, + 288, + 714 + ], + "type": "text", + "content": " controls the trade-off" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 72, + 547, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 264 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 264 + ], + "type": "text", + "content": "between the amount of sparsity and the log-likelihood estimation. The problem in Eq. (1) is convex and can be solved by the off-the-shelf packages such as GLASSO [10] and CVXPY [8]. However, the objective is non-smooth due to the " + }, + { + "bbox": [ + 304, + 72, + 547, + 264 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 304, + 72, + 547, + 264 + ], + "type": "text", + "content": " penalty. The above optimisation packages cannot be used with CNN layers to conduct training with backpropagation. A recent extension of CVXPY called CVXPYLAYERS [1] provides differentiable optimisation layers. However, based on our investigation, it has the following issues: (1) it cannot efficiently solve large SICE problems, i.e., of size " + }, + { + "bbox": [ + 304, + 72, + 547, + 264 + ], + "type": "inline_equation", + "content": "128 \\times 128" + }, + { + "bbox": [ + 304, + 72, + 547, + 264 + ], + "type": "text", + "content": " or higher; (2) it relies on multiple CPU based libraries including CVXPY to solve the optimisation problem and obtain gradients for backpropagation. This greatly limits its efficiency due to the lack of GPU support. The above limitations motivate us to develop an SICE method suitable for end-to-end training with GPU." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 272, + 538, + 285 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 272, + 538, + 285 + ], + "spans": [ + { + "bbox": [ + 305, + 272, + 538, + 285 + ], + "type": "text", + "content": "3.3. Proposed end-to-end trainable SICE method" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 290, + 545, + 315 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 290, + 545, + 315 + ], + "spans": [ + { + "bbox": [ + 305, + 290, + 545, + 315 + ], + "type": "text", + "content": "Let " + }, + { + "bbox": [ + 305, + 290, + 545, + 315 + ], + "type": "inline_equation", + "content": "J" + }, + { + "bbox": [ + 305, + 290, + 545, + 315 + ], + "type": "text", + "content": " be the objective function of Eq. (1). " + }, + { + "bbox": [ + 305, + 290, + 545, + 315 + ], + "type": "inline_equation", + "content": "J" + }, + { + "bbox": [ + 305, + 290, + 545, + 315 + ], + "type": "text", + "content": " can be optimised by taking the gradient with respect to " + }, + { + "bbox": [ + 305, + 290, + 545, + 315 + ], + "type": "inline_equation", + "content": "\\mathbf{S}" + }, + { + "bbox": [ + 305, + 290, + 545, + 315 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 330, + 323, + 545, + 388 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 330, + 323, + 545, + 388 + ], + "spans": [ + { + "bbox": [ + 330, + 323, + 545, + 388 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\frac {\\partial J}{\\partial \\mathbf {S}} = \\frac {\\partial}{\\partial \\mathbf {S}} \\log \\det (\\mathbf {S}) - \\frac {\\partial}{\\partial \\mathbf {S}} \\operatorname {t r a c e} (\\boldsymbol {\\Sigma} \\mathbf {S}) - \\lambda \\frac {\\partial}{\\partial \\mathbf {S}} \\| \\mathbf {S} \\| _ {1} \\\\ = \\mathbf {S} ^ {- 1} - \\boldsymbol {\\Sigma} - \\lambda \\left(\\frac {\\partial}{\\partial \\mathbf {S}} \\mathbf {S} ^ {+} - \\frac {\\partial}{\\partial \\mathbf {S}} \\mathbf {S} ^ {-}\\right) \\\\ = \\mathbf {S} ^ {- 1} - \\boldsymbol {\\Sigma} - \\lambda (\\operatorname {s i g n} (\\mathbf {S} ^ {+}) - \\operatorname {s i g n} (\\mathbf {S} ^ {-})), \\tag {2} \\\\ \\end{array}", + "image_path": "23fc20b733a8c54eb8ff59f68059e66a77ddfa7e994bd16d0d02befb317dbdee.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 396, + 545, + 480 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 396, + 545, + 480 + ], + "spans": [ + { + "bbox": [ + 304, + 396, + 545, + 480 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 396, + 545, + 480 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^{+}\\equiv \\max (0,\\mathbf{S})" + }, + { + "bbox": [ + 304, + 396, + 545, + 480 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 396, + 545, + 480 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^{-}\\equiv \\max (0, - \\mathbf{S})" + }, + { + "bbox": [ + 304, + 396, + 545, + 480 + ], + "type": "text", + "content": " contain the positive and negative parts of " + }, + { + "bbox": [ + 304, + 396, + 545, + 480 + ], + "type": "inline_equation", + "content": "\\mathbf{S}" + }, + { + "bbox": [ + 304, + 396, + 545, + 480 + ], + "type": "text", + "content": ", respectively. Eq. (2) can be optimised with the projected gradient descend which has the native backpropagation support on GPU and can take advantage of GPU parallel computing to improve their speed. Now we discuss how Eq. (2) can be effectively optimised using a few consecutive structured CNN layers." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "spans": [ + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": "The overview of our method is given in Fig. 2. From the left, we pass an input image to the backbone and process it till the last convolution layer. We obtain the feature map, i.e., " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "h \\times w \\times d" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": " tensor, where " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": " is the height, " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": " is the width, and " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": " is the number of channels. By reshaping the feature map to a set of " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": " vectors of length " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "n = wh" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": " and stacking them as column vectors, we can create a " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "d \\times n" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": " data matrix " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "\\mathbf{X}" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": ". A sample-based covariance matrix " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "\\boldsymbol{\\Sigma}" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": " estimated from " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "\\mathbf{X}" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": " is defined as " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "\\boldsymbol{\\Sigma} \\equiv \\mathbf{X} \\bar{\\mathbf{X}}^{\\top}" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "\\bar{\\mathbf{I}} = \\frac{1}{n} (\\mathbf{I} - \\frac{1}{n} \\mathbf{1} \\mathbf{1}^{\\top})" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": " performs centering of matrix " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "\\mathbf{X}" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "\\mathbf{I}" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "\\mathbf{1} \\mathbf{1}^{\\top}" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": " are " + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "inline_equation", + "content": "n \\times n" + }, + { + "bbox": [ + 304, + 481, + 547, + 624 + ], + "type": "text", + "content": " dimensional identity matrix and matrix of all-ones, respectively. Below, we describe key steps of our method." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 627, + 547, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 627, + 547, + 676 + ], + "spans": [ + { + "bbox": [ + 304, + 627, + 547, + 676 + ], + "type": "text", + "content": "Estimation of precision matrix " + }, + { + "bbox": [ + 304, + 627, + 547, + 676 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_0 = \\boldsymbol{\\Sigma}^{\\prime -1}" + }, + { + "bbox": [ + 304, + 627, + 547, + 676 + ], + "type": "text", + "content": ". Newton-Schulz iteration is popular as it can approximate the matrix square root" + }, + { + "bbox": [ + 304, + 627, + 547, + 676 + ], + "type": "inline_equation", + "content": "^2" + }, + { + "bbox": [ + 304, + 627, + 547, + 676 + ], + "type": "text", + "content": " fast on GPU [23]. In contrast, during the estimation of " + }, + { + "bbox": [ + 304, + 627, + 547, + 676 + ], + "type": "inline_equation", + "content": "\\mathbf{S}" + }, + { + "bbox": [ + 304, + 627, + 547, + 676 + ], + "type": "text", + "content": ", we use Newton-Schulz iteration [14] for a fast approxi-" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 306, + 683, + 547, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 683, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 683, + 547, + 713 + ], + "type": "text", + "content": "2Notice we use Newton-Schulz iteration to obtain the precision matrix " + }, + { + "bbox": [ + 306, + 683, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 306, + 683, + 547, + 713 + ], + "type": "text", + "content": " from covariance " + }, + { + "bbox": [ + 306, + 683, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}" + }, + { + "bbox": [ + 306, + 683, + 547, + 713 + ], + "type": "text", + "content": ". We do not propose or use square rooting of " + }, + { + "bbox": [ + 306, + 683, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}" + }, + { + "bbox": [ + 306, + 683, + 547, + 713 + ], + "type": "text", + "content": ", but we compare our iSICE to this kind of covariance normalisation." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6234" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 108 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 108 + ], + "type": "text", + "content": "Algorithm 1 Matrix inverse by Newton-Schulz iterations, named " + }, + { + "bbox": [ + 47, + 72, + 287, + 108 + ], + "type": "inline_equation", + "content": "\\mathrm{nsInv}(\\cdot)" + }, + { + "bbox": [ + 47, + 72, + 287, + 108 + ], + "type": "text", + "content": ". It is used to compute \"Precision " + }, + { + "bbox": [ + 47, + 72, + 287, + 108 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 47, + 72, + 287, + 108 + ], + "type": "text", + "content": " and is also used by Alg. 2, whereas " + }, + { + "bbox": [ + 47, + 72, + 287, + 108 + ], + "type": "inline_equation", + "content": "\\mathbf{Y}_{N_s}" + }, + { + "bbox": [ + 47, + 72, + 287, + 108 + ], + "type": "text", + "content": " (line 4) gives iSQRT." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 110, + 263, + 121 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 110, + 263, + 121 + ], + "spans": [ + { + "bbox": [ + 47, + 110, + 263, + 121 + ], + "type": "text", + "content": "Input: Covariance matrix " + }, + { + "bbox": [ + 47, + 110, + 263, + 121 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}" + }, + { + "bbox": [ + 47, + 110, + 263, + 121 + ], + "type": "text", + "content": ", number of iterations " + }, + { + "bbox": [ + 47, + 110, + 263, + 121 + ], + "type": "inline_equation", + "content": "N_{s}" + }, + { + "bbox": [ + 47, + 110, + 263, + 121 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 121, + 209, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 121, + 209, + 133 + ], + "spans": [ + { + "bbox": [ + 47, + 121, + 209, + 133 + ], + "type": "text", + "content": "Output: Inverse covariance matrix " + }, + { + "bbox": [ + 47, + 121, + 209, + 133 + ], + "type": "inline_equation", + "content": "\\Sigma^{-1}" + }, + { + "bbox": [ + 47, + 121, + 209, + 133 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 135, + 196, + 183 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 53, + 135, + 175, + 147 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 135, + 175, + 147 + ], + "spans": [ + { + "bbox": [ + 53, + 135, + 175, + 147 + ], + "type": "text", + "content": "1: " + }, + { + "bbox": [ + 53, + 135, + 175, + 147 + ], + "type": "inline_equation", + "content": "\\mathbf{Y}_0 = \\pmb{\\Sigma}' = \\pmb{\\Sigma} / \\mathrm{trace}(\\pmb{\\Sigma})" + }, + { + "bbox": [ + 53, + 135, + 175, + 147 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 53, + 135, + 175, + 147 + ], + "type": "inline_equation", + "content": "\\mathbf{Z}_0 = \\mathbf{I}" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 148, + 138, + 159 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 148, + 138, + 159 + ], + "spans": [ + { + "bbox": [ + 53, + 148, + 138, + 159 + ], + "type": "text", + "content": "2: for " + }, + { + "bbox": [ + 53, + 148, + 138, + 159 + ], + "type": "inline_equation", + "content": "i = 1" + }, + { + "bbox": [ + 53, + 148, + 138, + 159 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 53, + 148, + 138, + 159 + ], + "type": "inline_equation", + "content": "N_{s}" + }, + { + "bbox": [ + 53, + 148, + 138, + 159 + ], + "type": "text", + "content": " do" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 159, + 159, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 159, + 159, + 171 + ], + "spans": [ + { + "bbox": [ + 53, + 159, + 159, + 171 + ], + "type": "text", + "content": "3: " + }, + { + "bbox": [ + 53, + 159, + 159, + 171 + ], + "type": "inline_equation", + "content": "\\mathbf{P} = \\frac{1}{2} (3\\mathbf{I} - \\mathbf{Z}_{i - 1}\\mathbf{Y}_{i - 1})" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 172, + 196, + 183 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 172, + 196, + 183 + ], + "spans": [ + { + "bbox": [ + 53, + 172, + 196, + 183 + ], + "type": "text", + "content": "4: " + }, + { + "bbox": [ + 53, + 172, + 196, + 183 + ], + "type": "inline_equation", + "content": "\\mathbf{Y}_i = \\mathbf{Y}_{i - 1}\\mathbf{P}" + }, + { + "bbox": [ + 53, + 172, + 196, + 183 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 53, + 172, + 196, + 183 + ], + "type": "inline_equation", + "content": "\\mathbf{Z}_i = \\mathbf{P}\\mathbf{Z}_{i - 1}" + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 53, + 184, + 263, + 220 + ], + "type": "list", + "angle": 0, + "index": 11, + "blocks": [ + { + "bbox": [ + 53, + 184, + 98, + 194 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 184, + 98, + 194 + ], + "spans": [ + { + "bbox": [ + 53, + 184, + 98, + 194 + ], + "type": "text", + "content": "5: end for" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 196, + 99, + 207 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 196, + 99, + 207 + ], + "spans": [ + { + "bbox": [ + 53, + 196, + 99, + 207 + ], + "type": "text", + "content": "6: " + }, + { + "bbox": [ + 53, + 196, + 99, + 207 + ], + "type": "inline_equation", + "content": "\\mathbf{Q} = \\mathbf{Z}_{N_1}" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 208, + 263, + 220 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 208, + 263, + 220 + ], + "spans": [ + { + "bbox": [ + 53, + 208, + 263, + 220 + ], + "type": "text", + "content": "7: " + }, + { + "bbox": [ + 53, + 208, + 263, + 220 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}^{-1} = \\mathbf{Q}\\mathbf{Q}^{\\top} / \\mathrm{trace}(\\pmb{\\Sigma})" + }, + { + "bbox": [ + 53, + 208, + 263, + 220 + ], + "type": "text", + "content": " {Inverse covariance matrix}" + } + ] + } + ], + "index": 10 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 46, + 242, + 287, + 301 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 242, + 287, + 301 + ], + "spans": [ + { + "bbox": [ + 46, + 242, + 287, + 301 + ], + "type": "text", + "content": "mate inverse of matrix, which imposes a convergence condition " + }, + { + "bbox": [ + 46, + 242, + 287, + 301 + ], + "type": "inline_equation", + "content": "\\| \\mathbf{\\Sigma} - \\mathbf{I}\\| _2 < 1" + }, + { + "bbox": [ + 46, + 242, + 287, + 301 + ], + "type": "text", + "content": " on Algorithm 1. Thus, we normalise " + }, + { + "bbox": [ + 46, + 242, + 287, + 301 + ], + "type": "inline_equation", + "content": "\\mathbf{\\Sigma}" + }, + { + "bbox": [ + 46, + 242, + 287, + 301 + ], + "type": "text", + "content": " by its trace and use the trace-normalised " + }, + { + "bbox": [ + 46, + 242, + 287, + 301 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}' = \\pmb{\\Sigma} / \\mathrm{trace}(\\pmb{\\Sigma})" + }, + { + "bbox": [ + 46, + 242, + 287, + 301 + ], + "type": "text", + "content": ". Then, the square of the inverse square root " + }, + { + "bbox": [ + 46, + 242, + 287, + 301 + ], + "type": "inline_equation", + "content": "\\mathbf{Q}" + }, + { + "bbox": [ + 46, + 242, + 287, + 301 + ], + "type": "text", + "content": " is post-normalized by the trace to reverse it, i.e., " + }, + { + "bbox": [ + 46, + 242, + 287, + 301 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}^{-1} = \\pmb{Q}\\pmb{Q}^{\\top} / \\mathrm{trace}(\\pmb{\\Sigma})" + }, + { + "bbox": [ + 46, + 242, + 287, + 301 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 302, + 287, + 338 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 302, + 287, + 338 + ], + "spans": [ + { + "bbox": [ + 46, + 302, + 287, + 338 + ], + "type": "text", + "content": "As Eq. (2) has to start with an initial " + }, + { + "bbox": [ + 46, + 302, + 287, + 338 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_0" + }, + { + "bbox": [ + 46, + 302, + 287, + 338 + ], + "type": "text", + "content": ", if " + }, + { + "bbox": [ + 46, + 302, + 287, + 338 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}^{\\prime}" + }, + { + "bbox": [ + 46, + 302, + 287, + 338 + ], + "type": "text", + "content": " is invertible, Alg. 1 approximates its inverse3. Although it is an approximation, we denote it in Alg. 1 as " + }, + { + "bbox": [ + 46, + 302, + 287, + 338 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}^{-1}" + }, + { + "bbox": [ + 46, + 302, + 287, + 338 + ], + "type": "text", + "content": " for brevity." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 46, + 340, + 287, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 340, + 287, + 388 + ], + "spans": [ + { + "bbox": [ + 46, + 340, + 287, + 388 + ], + "type": "text", + "content": "Estimation of sparse inverse covariance " + }, + { + "bbox": [ + 46, + 340, + 287, + 388 + ], + "type": "inline_equation", + "content": "\\mathbf{S}" + }, + { + "bbox": [ + 46, + 340, + 287, + 388 + ], + "type": "text", + "content": ". Given the result obtained in the last step, " + }, + { + "bbox": [ + 46, + 340, + 287, + 388 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_0" + }, + { + "bbox": [ + 46, + 340, + 287, + 388 + ], + "type": "text", + "content": ", we start iterations of iSICE by applying the projected gradient descend (PGD) to the gradient of SICE (Eq. (1)), given in Eq. (2)." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 388, + 287, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 388, + 287, + 424 + ], + "spans": [ + { + "bbox": [ + 46, + 388, + 287, + 424 + ], + "type": "text", + "content": "Following methodology of optimisation by imposing box constraints (e.g., see an intuitive example by Schiele et al. [35]), we separate " + }, + { + "bbox": [ + 46, + 388, + 287, + 424 + ], + "type": "inline_equation", + "content": "\\mathbf{S}" + }, + { + "bbox": [ + 46, + 388, + 287, + 424 + ], + "type": "text", + "content": " into its positive and negative parts:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 71, + 432, + 287, + 445 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 432, + 287, + 445 + ], + "spans": [ + { + "bbox": [ + 71, + 432, + 287, + 445 + ], + "type": "interline_equation", + "content": "\\mathbf {S} _ {i} ^ {+} = \\max (0, \\mathbf {S} _ {i - 1}) \\quad \\text {a n d} \\quad \\mathbf {S} _ {i} ^ {-} = \\max (0, - \\mathbf {S} _ {i - 1}), \\tag {3}", + "image_path": "ae564f7fe53cbe6c318b8aaad2f2483515ce40d693f0357cd05d3b4e3e0924f3.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 46, + 453, + 253, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 453, + 253, + 464 + ], + "spans": [ + { + "bbox": [ + 46, + 453, + 253, + 464 + ], + "type": "text", + "content": "and apply the PGD step to each of them separately." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 46, + 464, + 287, + 512 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 464, + 287, + 512 + ], + "spans": [ + { + "bbox": [ + 46, + 464, + 287, + 512 + ], + "type": "text", + "content": "In such a case, the sparsity constraint imposed by the " + }, + { + "bbox": [ + 46, + 464, + 287, + 512 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 46, + 464, + 287, + 512 + ], + "type": "text", + "content": " norm simplifies, i.e., the gradient of " + }, + { + "bbox": [ + 46, + 464, + 287, + 512 + ], + "type": "inline_equation", + "content": "\\lambda \\mathbf{S}^{+}" + }, + { + "bbox": [ + 46, + 464, + 287, + 512 + ], + "type": "text", + "content": " can be assumed " + }, + { + "bbox": [ + 46, + 464, + 287, + 512 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 464, + 287, + 512 + ], + "type": "text", + "content": " and the gradient of " + }, + { + "bbox": [ + 46, + 464, + 287, + 512 + ], + "type": "inline_equation", + "content": "-\\lambda \\mathbf{S}^{-}" + }, + { + "bbox": [ + 46, + 464, + 287, + 512 + ], + "type": "text", + "content": " can be also assumed " + }, + { + "bbox": [ + 46, + 464, + 287, + 512 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 464, + 287, + 512 + ], + "type": "text", + "content": ". Thus, we firstly rewrite Eq. (2) into two parts:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 58, + 520, + 287, + 533 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 520, + 287, + 533 + ], + "spans": [ + { + "bbox": [ + 58, + 520, + 287, + 533 + ], + "type": "interline_equation", + "content": "\\nabla \\mathbf {S} _ {i} ^ {+} = \\mathbf {S} _ {i - 1} - \\boldsymbol {\\Sigma} - \\lambda \\quad \\text {a n d} \\quad - \\nabla \\mathbf {S} _ {i} ^ {-} = \\mathbf {S} _ {i - 1} - \\boldsymbol {\\Sigma} + \\lambda . \\tag {4}", + "image_path": "ccf1fc0a2d2856b9104f791774d051c428bcb92ad0db2727b3e173d5bac581be.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 58, + 540, + 259, + 554 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 540, + 259, + 554 + ], + "spans": [ + { + "bbox": [ + 58, + 540, + 259, + 554 + ], + "type": "text", + "content": "Then we take one PGD step to update " + }, + { + "bbox": [ + 58, + 540, + 259, + 554 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_i^+" + }, + { + "bbox": [ + 58, + 540, + 259, + 554 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 58, + 540, + 259, + 554 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_i^-" + }, + { + "bbox": [ + 58, + 540, + 259, + 554 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 53, + 559, + 287, + 574 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 559, + 287, + 574 + ], + "spans": [ + { + "bbox": [ + 53, + 559, + 287, + 574 + ], + "type": "interline_equation", + "content": "\\mathbf {S} _ {i} ^ {+} := \\Pi \\left(\\mathbf {S} _ {i} ^ {+} - \\eta \\beta \\nabla \\mathbf {S} _ {i} ^ {+}\\right) \\quad \\text {a n d} \\quad \\mathbf {S} _ {i} ^ {-} := \\Pi \\left(\\mathbf {S} _ {i} ^ {-} - \\eta \\beta \\nabla \\mathbf {S} _ {i} ^ {-}\\right), \\tag {5}", + "image_path": "2500fe23700f597f41df59a70f755d6363b4c4ec0ed9e130f766b645176c73da.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 46, + 580, + 287, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 580, + 287, + 640 + ], + "spans": [ + { + "bbox": [ + 46, + 580, + 287, + 640 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 580, + 287, + 640 + ], + "type": "inline_equation", + "content": "\\Pi (\\cdot)\\equiv \\max (0,\\cdot)\\equiv \\mathrm{ReLU}(\\cdot)" + }, + { + "bbox": [ + 46, + 580, + 287, + 640 + ], + "type": "text", + "content": " is the gradient reprojection function of PGD into the feasible region of each box constraint (one for the non-negative " + }, + { + "bbox": [ + 46, + 580, + 287, + 640 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_i^+" + }, + { + "bbox": [ + 46, + 580, + 287, + 640 + ], + "type": "text", + "content": ", and one for nonpositive " + }, + { + "bbox": [ + 46, + 580, + 287, + 640 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_i^-" + }, + { + "bbox": [ + 46, + 580, + 287, + 640 + ], + "type": "text", + "content": "). Constant " + }, + { + "bbox": [ + 46, + 580, + 287, + 640 + ], + "type": "inline_equation", + "content": "\\eta >0" + }, + { + "bbox": [ + 46, + 580, + 287, + 640 + ], + "type": "text", + "content": " is a desired learning rate, whereas " + }, + { + "bbox": [ + 46, + 580, + 287, + 640 + ], + "type": "inline_equation", + "content": "\\beta >0" + }, + { + "bbox": [ + 46, + 580, + 287, + 640 + ], + "type": "text", + "content": " controls the decay of learning rate." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 46, + 640, + 286, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 640, + 286, + 664 + ], + "spans": [ + { + "bbox": [ + 46, + 640, + 286, + 664 + ], + "type": "text", + "content": "Finally, we assemble the current estimate of " + }, + { + "bbox": [ + 46, + 640, + 286, + 664 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_i" + }, + { + "bbox": [ + 46, + 640, + 286, + 664 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 46, + 640, + 286, + 664 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_i^+" + }, + { + "bbox": [ + 46, + 640, + 286, + 664 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 640, + 286, + 664 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_i^-" + }, + { + "bbox": [ + 46, + 640, + 286, + 664 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 125, + 664, + 287, + 677 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 125, + 664, + 287, + 677 + ], + "spans": [ + { + "bbox": [ + 125, + 664, + 287, + 677 + ], + "type": "interline_equation", + "content": "\\mathbf {S} _ {i} = \\operatorname {S y m} \\left(\\mathbf {S} _ {i} ^ {+} - \\mathbf {S} _ {i} ^ {-}\\right), \\tag {6}", + "image_path": "a2a7eacbdea6450956dbaaec63484eddb0e7855e81f0dd99c3697afb0b4805e3.jpg" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 306, + 72, + 545, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 72, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 545, + 95 + ], + "type": "text", + "content": "Algorithm 2 Iterative sparse inverse covariance estimation (iSICE)." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "spans": [ + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "text", + "content": "Input: Sample-based covariance matrix " + }, + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}" + }, + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "text", + "content": ", sparsity constant " + }, + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "text", + "content": ", learning rate " + }, + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "inline_equation", + "content": "\\eta" + }, + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "text", + "content": ", number of iterations N, small constant " + }, + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "text", + "content": ", i.e., " + }, + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "inline_equation", + "content": "\\alpha = 1\\mathrm{e} - 9" + }, + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "text", + "content": ", regularisation parameter " + }, + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 306, + 97, + 545, + 133 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 306, + 133, + 490, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 133, + 490, + 144 + ], + "spans": [ + { + "bbox": [ + 306, + 133, + 490, + 144 + ], + "type": "text", + "content": "Output: Sparse inverse covariance matrix " + }, + { + "bbox": [ + 306, + 133, + 490, + 144 + ], + "type": "inline_equation", + "content": "\\mathbf{S}'" + }, + { + "bbox": [ + 306, + 133, + 490, + 144 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 312, + 147, + 531, + 194 + ], + "type": "list", + "angle": 0, + "index": 33, + "blocks": [ + { + "bbox": [ + 312, + 147, + 531, + 158 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 147, + 531, + 158 + ], + "spans": [ + { + "bbox": [ + 312, + 147, + 531, + 158 + ], + "type": "text", + "content": "1: " + }, + { + "bbox": [ + 312, + 147, + 531, + 158 + ], + "type": "inline_equation", + "content": "\\nabla_{2} = \\pmb{\\Sigma}' = \\pmb{\\Sigma}/\\mathrm{trace}(\\pmb{\\Sigma})" + }, + { + "bbox": [ + 312, + 147, + 531, + 158 + ], + "type": "text", + "content": " {Pre-normalisation using trace}" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 312, + 159, + 529, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 159, + 529, + 171 + ], + "spans": [ + { + "bbox": [ + 312, + 159, + 529, + 171 + ], + "type": "text", + "content": "2: " + }, + { + "bbox": [ + 312, + 159, + 529, + 171 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_0 = \\mathrm{nsInv}(\\mathbf{S}_{i - 1} + \\alpha \\mathbf{I})" + }, + { + "bbox": [ + 312, + 159, + 529, + 171 + ], + "type": "text", + "content": " {Fast approx. inverse (Alg. 1)}" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 312, + 171, + 354, + 182 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 171, + 354, + 182 + ], + "spans": [ + { + "bbox": [ + 312, + 171, + 354, + 182 + ], + "type": "text", + "content": "3: " + }, + { + "bbox": [ + 312, + 171, + 354, + 182 + ], + "type": "inline_equation", + "content": "\\nabla_{1} = \\mathbf{S}_{0}" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 312, + 183, + 394, + 194 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 183, + 394, + 194 + ], + "spans": [ + { + "bbox": [ + 312, + 183, + 394, + 194 + ], + "type": "text", + "content": "4: for " + }, + { + "bbox": [ + 312, + 183, + 394, + 194 + ], + "type": "inline_equation", + "content": "i = 1" + }, + { + "bbox": [ + 312, + 183, + 394, + 194 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 312, + 183, + 394, + 194 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 312, + 183, + 394, + 194 + ], + "type": "text", + "content": " do" + } + ] + } + ], + "index": 32 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 309, + 194, + 537, + 327 + ], + "type": "list", + "angle": 0, + "index": 45, + "blocks": [ + { + "bbox": [ + 312, + 194, + 514, + 207 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 194, + 514, + 207 + ], + "spans": [ + { + "bbox": [ + 312, + 194, + 514, + 207 + ], + "type": "text", + "content": "5: " + }, + { + "bbox": [ + 312, + 194, + 514, + 207 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_i^+ = \\mathrm{ReLU}(\\mathbf{S}_{i - 1})" + }, + { + "bbox": [ + 312, + 194, + 514, + 207 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 312, + 194, + 514, + 207 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_i^- = \\mathrm{ReLU}(-\\mathbf{S}_{i - 1})" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 312, + 208, + 363, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 208, + 363, + 217 + ], + "spans": [ + { + "bbox": [ + 312, + 208, + 363, + 217 + ], + "type": "text", + "content": "6: if " + }, + { + "bbox": [ + 312, + 208, + 363, + 217 + ], + "type": "inline_equation", + "content": "i\\neq 1" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 312, + 218, + 537, + 231 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 218, + 537, + 231 + ], + "spans": [ + { + "bbox": [ + 312, + 218, + 537, + 231 + ], + "type": "text", + "content": "7: " + }, + { + "bbox": [ + 312, + 218, + 537, + 231 + ], + "type": "inline_equation", + "content": "\\nabla_{1} = \\mathrm{nsInv}(\\mathbf{S}_{i - 1} + \\alpha \\mathbf{I})" + }, + { + "bbox": [ + 312, + 218, + 537, + 231 + ], + "type": "text", + "content": " {Fast approx. inv. (Alg. 1)}" + } + ] + } + ], + "index": 36 + }, + { + "bbox": [ + 312, + 232, + 361, + 241 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 232, + 361, + 241 + ], + "spans": [ + { + "bbox": [ + 312, + 232, + 361, + 241 + ], + "type": "text", + "content": "8: end if" + } + ] + } + ], + "index": 37 + }, + { + "bbox": [ + 312, + 242, + 394, + 254 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 242, + 394, + 254 + ], + "spans": [ + { + "bbox": [ + 312, + 242, + 394, + 254 + ], + "type": "text", + "content": "9: " + }, + { + "bbox": [ + 312, + 242, + 394, + 254 + ], + "type": "inline_equation", + "content": "\\nabla_{12} = \\nabla_{1} - \\nabla_{2}" + } + ] + } + ], + "index": 38 + }, + { + "bbox": [ + 309, + 255, + 507, + 268 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 309, + 255, + 507, + 268 + ], + "spans": [ + { + "bbox": [ + 309, + 255, + 507, + 268 + ], + "type": "text", + "content": "10: " + }, + { + "bbox": [ + 309, + 255, + 507, + 268 + ], + "type": "inline_equation", + "content": "\\beta = 1 - \\frac{i - 1}{\\max(1,N - 1)}" + }, + { + "bbox": [ + 309, + 255, + 507, + 268 + ], + "type": "text", + "content": " {Decay the learning rate}" + } + ] + } + ], + "index": 39 + }, + { + "bbox": [ + 309, + 268, + 446, + 280 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 309, + 268, + 446, + 280 + ], + "spans": [ + { + "bbox": [ + 309, + 268, + 446, + 280 + ], + "type": "text", + "content": "11: " + }, + { + "bbox": [ + 309, + 268, + 446, + 280 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_i^+ := \\Pi (\\mathbf{S}_i^+ - \\eta \\beta (-\\nabla_{12} + \\lambda))" + } + ] + } + ], + "index": 40 + }, + { + "bbox": [ + 309, + 280, + 446, + 292 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 309, + 280, + 446, + 292 + ], + "spans": [ + { + "bbox": [ + 309, + 280, + 446, + 292 + ], + "type": "text", + "content": "12: " + }, + { + "bbox": [ + 309, + 280, + 446, + 292 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_i^- \\coloneqq \\Pi (\\mathbf{S}_i^- - \\eta \\beta (+\\nabla_{12} + \\lambda))" + } + ] + } + ], + "index": 41 + }, + { + "bbox": [ + 309, + 293, + 410, + 304 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 309, + 293, + 410, + 304 + ], + "spans": [ + { + "bbox": [ + 309, + 293, + 410, + 304 + ], + "type": "text", + "content": "13: " + }, + { + "bbox": [ + 309, + 293, + 410, + 304 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_i = \\mathrm{Sym}(\\mathbf{S}_i^+ -\\mathbf{S}_i^-)" + } + ] + } + ], + "index": 42 + }, + { + "bbox": [ + 309, + 305, + 356, + 314 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 309, + 305, + 356, + 314 + ], + "spans": [ + { + "bbox": [ + 309, + 305, + 356, + 314 + ], + "type": "text", + "content": "14: end for" + } + ] + } + ], + "index": 43 + }, + { + "bbox": [ + 309, + 316, + 400, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 309, + 316, + 400, + 327 + ], + "spans": [ + { + "bbox": [ + 309, + 316, + 400, + 327 + ], + "type": "text", + "content": "15: " + }, + { + "bbox": [ + 309, + 316, + 400, + 327 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^{*} = \\mathbf{S}_{N} / \\mathrm{trace}(\\mathbf{S}_{N})" + } + ] + } + ], + "index": 44 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 305, + 351, + 545, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 351, + 545, + 375 + ], + "spans": [ + { + "bbox": [ + 305, + 351, + 545, + 375 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 351, + 545, + 375 + ], + "type": "inline_equation", + "content": "\\mathrm{Sym}(\\mathbf{M}) = \\frac{1}{2} (\\mathbf{M} + \\mathbf{M}^{\\top})" + }, + { + "bbox": [ + 305, + 351, + 545, + 375 + ], + "type": "text", + "content": " ensures the matrix " + }, + { + "bbox": [ + 305, + 351, + 545, + 375 + ], + "type": "inline_equation", + "content": "\\mathbf{M}" + }, + { + "bbox": [ + 305, + 351, + 545, + 375 + ], + "type": "text", + "content": " is symmetric (and the intermediate estimate of SICE)." + } + ] + } + ], + "index": 46 + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "spans": [ + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "text", + "content": "Algorithm 2 starts with a dense precision matrix " + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_0" + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "text", + "content": ". If " + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "inline_equation", + "content": "N > 0" + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "text", + "content": ", it loops over iterations " + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "inline_equation", + "content": "i = 1, \\dots, N" + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "text", + "content": ", applying the above steps. For ease of tuning the learning rate " + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "inline_equation", + "content": "\\eta" + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "text", + "content": ", the algorithm starts by the trace normalisation of " + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "inline_equation", + "content": "\\boldsymbol{\\Sigma}" + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "text", + "content": " and it reverses the trace normalisation when it finishes. Otherwise, " + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "inline_equation", + "content": "\\eta" + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "text", + "content": " has to be scaled depending on the value of the largest eigenvalue of " + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "inline_equation", + "content": "\\boldsymbol{\\Sigma}" + }, + { + "bbox": [ + 304, + 376, + 545, + 471 + ], + "type": "text", + "content": " which is somewhat impractical when running CNN end-to-end over multiple mini-batches." + } + ] + } + ], + "index": 47 + }, + { + "bbox": [ + 304, + 472, + 545, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 472, + 545, + 508 + ], + "spans": [ + { + "bbox": [ + 304, + 472, + 545, + 508 + ], + "type": "text", + "content": "As " + }, + { + "bbox": [ + 304, + 472, + 545, + 508 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^*" + }, + { + "bbox": [ + 304, + 472, + 545, + 508 + ], + "type": "text", + "content": " is a symmetric matrix, we only take its upper-triangular entries (plus the diagonal entries) and process them by fully connected layers for classification purposes." + } + ] + } + ], + "index": 48 + }, + { + "bbox": [ + 304, + 508, + 545, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 508, + 545, + 556 + ], + "spans": [ + { + "bbox": [ + 304, + 508, + 545, + 556 + ], + "type": "text", + "content": "Algorithm 2 is implemented with modern deep learning library, PyTorch, to leverage the full GPU support and autograd package for optimisation. Due to the iterative nature of solving S, we call our method iterative SICE (iSICE)." + } + ] + } + ], + "index": 49 + }, + { + "bbox": [ + 306, + 567, + 387, + 581 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 567, + 387, + 581 + ], + "spans": [ + { + "bbox": [ + 306, + 567, + 387, + 581 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 50 + }, + { + "bbox": [ + 304, + 588, + 545, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 588, + 545, + 648 + ], + "spans": [ + { + "bbox": [ + 304, + 588, + 545, + 648 + ], + "type": "text", + "content": "Below, we first describe experimental dataset benchmarks and then discuss the implementation of our proposed method. Subsequently, we present our experimental results and ablation study on key hyper-parameters. Finally, we compare our proposed method with the existing methods." + } + ] + } + ], + "index": 51 + }, + { + "bbox": [ + 305, + 656, + 506, + 669 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 656, + 506, + 669 + ], + "spans": [ + { + "bbox": [ + 305, + 656, + 506, + 669 + ], + "type": "text", + "content": "4.1. Datasets, Metric, and Implementation" + } + ] + } + ], + "index": 52 + }, + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "type": "text", + "content": "Datasets. We conduct experiments using one scene and five fine-grained image datasets: the MIT Indoor dataset [33], Airplane [32], Birds [42], Cars [21], DTD [5] and iNatural-" + } + ] + } + ], + "index": 53 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 46, + 683, + 287, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 683, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 683, + 287, + 713 + ], + "type": "text", + "content": "Note that the conventional matrix inverse requires matrix eigendecomposition which is not well supported on GPU [16]. In contrast, Newton-Schulz iteration [14] is known for fast convergence to " + }, + { + "bbox": [ + 46, + 683, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}^{-1}" + }, + { + "bbox": [ + 46, + 683, + 287, + 713 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6235" + } + ] + } + ], + "index": 54 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 133 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 133 + ], + "type": "text", + "content": "ist [40]. We also use ImageNet100 (a subset of ImageNet1K dataset) proposed by Tian et al. [39] and mini-ImageNet [41]. We follow the widely used training and testing protocols of Bilinear CNN [27]. The details of datasets and protocols are provided in Appendix A." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 135, + 287, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 135, + 287, + 171 + ], + "spans": [ + { + "bbox": [ + 47, + 135, + 287, + 171 + ], + "type": "text", + "content": "Metric for evaluations. For evaluation of different methods, average classification accuracy is used. This metric is widely used in literature, e.g., [23, 27]." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 174, + 288, + 377 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 174, + 288, + 377 + ], + "spans": [ + { + "bbox": [ + 46, + 174, + 288, + 377 + ], + "type": "text", + "content": "Implementation details. Our method is implemented using PyTorch 1.9. We use ImageNet-1K pre-trained backbones provided in Torchvision 0.13.0 library. Following the recent works [23, 24], the number of feature channels is reduced to 256 with " + }, + { + "bbox": [ + 46, + 174, + 288, + 377 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 46, + 174, + 288, + 377 + ], + "type": "text", + "content": " convolution for efficiency and fair comparisons. All images are resized to " + }, + { + "bbox": [ + 46, + 174, + 288, + 377 + ], + "type": "inline_equation", + "content": "448 \\times 448" + }, + { + "bbox": [ + 46, + 174, + 288, + 377 + ], + "type": "text", + "content": " and the training is conducted by randomly flipping them horizontally. We fine-tune all backbones for 50-100 epochs with AdamW optimiser [31] for an initial learning rate of 0.00012, and ConvNext-T CNN and Swin-T with an initial learning rate 0.00005. For all backbones, we decrease the learning rate by a factor of 10 at the 15th and 30th epochs. Depending on the dataset and backbones, our fine-tuning process lasts for about 3-8 hours on four P100 GPUs. For ImageNet100, ResNet-50 was trained for 100 epochs with the initial learning rate 0.01, reduced by 10 at the 15th, 30th and 45th epochs. Settings are detailed in Appendix B." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 385, + 126, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 385, + 126, + 396 + ], + "spans": [ + { + "bbox": [ + 47, + 385, + 126, + 396 + ], + "type": "text", + "content": "4.2. Evaluations" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 403, + 287, + 488 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 403, + 287, + 488 + ], + "spans": [ + { + "bbox": [ + 46, + 403, + 287, + 488 + ], + "type": "text", + "content": "We evaluate the performance of the proposed iSICE and compare it with its covariance-based competitors. We also include comparisons with our baseline, the inverse covariance matrix, called precision matrix (for simplicity denoted as Precision " + }, + { + "bbox": [ + 46, + 403, + 287, + 488 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 46, + 403, + 287, + 488 + ], + "type": "text", + "content": " in experiments). In contrast to iSICE which represents a sparse graph, precision matrix is used as a baseline as it represents a graph without imposed sparsity." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 488, + 287, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 488, + 287, + 559 + ], + "spans": [ + { + "bbox": [ + 46, + 488, + 287, + 559 + ], + "type": "text", + "content": "The covariance representations (denoted with COV) are widely used in literature [24, 27, 28]. We use the Newton-Schulz iteration for computing the matrix square root normalised covariance (iSQRT-COV) due to efficiency of the Newton-Schulz iteration with GPUs, as well as good empirical results reported by multiple authors [23, 25]." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 562, + 287, + 646 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 562, + 287, + 646 + ], + "spans": [ + { + "bbox": [ + 46, + 562, + 287, + 646 + ], + "type": "text", + "content": "Backbones. We choose VGG-16 [37] and ResNet-50 [13] CNNs as our backbones for the majority of experiments (we also include the VGG-19, ResNet-101, ResNeXt-101 [45], and latest ConvNext-T [30], Swin-T and Swin-B [29] in the main table). VGG-16 and ResNet-50 are popular in image classification (including fine-grained benchmarks). We choose these two backbones in order to better understand" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 72, + 545, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 72, + 545, + 179 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 545, + 179 + ], + "type": "text", + "content": "the performance of our methods compared to baselines in the common testbed (the same backbones and experimental settings). Given an input image, we obtain a set of feature channels shaped as a tensor after the " + }, + { + "bbox": [ + 306, + 72, + 545, + 179 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 306, + 72, + 545, + 179 + ], + "type": "text", + "content": " convolution operation. Using these feature channels, we compute the iSICE, iSQRT-COV and Precision " + }, + { + "bbox": [ + 306, + 72, + 545, + 179 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 306, + 72, + 545, + 179 + ], + "type": "text", + "content": " representations. Since these representations are symmetric, we only use the upper-triangular entries (and the diagonal entries) passed to a fully-connected layer to obtain classification scores." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "spans": [ + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "text", + "content": "Hyper-parameters. There are three hyper-parameters associated with iSICE: sparsity constant " + }, + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "text", + "content": ", learning rate " + }, + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "inline_equation", + "content": "\\eta" + }, + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "text", + "content": " and number of iterations " + }, + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "text", + "content": ". We experiment with a large range of values for a better understanding, i.e., " + }, + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "inline_equation", + "content": "\\lambda \\in \\{1.0, 0.5, 0.1, 0.01, 0.001, 0.0001, 0.00001\\}" + }, + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "inline_equation", + "content": "\\eta \\in \\{0.001, 0.01, 0.1, 1.0, 5.0, 10.0, 20.0\\}" + }, + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "inline_equation", + "content": "N \\in \\{1, 5, 10\\}" + }, + { + "bbox": [ + 305, + 185, + 545, + 304 + ], + "type": "text", + "content": ". Since the total combination of hyper-parameters in the table is 147, we choose the median values of each hyper-parameter range (highlighted in bold) and keep them throughout experiments on all datasets. Appendix C studies the impact of hyper-parameters on results." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "spans": [ + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "text", + "content": "Overview of results. Table 1 shows the performance of several COV models (e.g., popular iSQRT-COV), and our Precision " + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "text", + "content": " and iSICE models. The rightmost column summarizes the average performance over one scene and three fine-grained image classification benchmarks. It is clear that on average, iSICE outperforms MPN-COV, iSQRT-COV, DeepCOV, and DeepKSPD, etc. iSICE also outperforms our baseline Precision " + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "text", + "content": ". This achievement is consistent in all four backbones. It is interesting to see that except a few cases, the inverse covariance method, i.e., Precision " + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "text", + "content": ", performs slightly better than the covariance method, i.e., iSQRT-COV. This improved performance highlights the effectiveness of characterising partial correlations of features with inverse covariance instead of pairwise correlations of features based on the sample covariance. Our iSICE method makes the inverse covariance estimation more robust and reliable by enforcing sparsity, as demonstrated by improved performance over Precision " + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "text", + "content": " baseline. However, there may be some situations when Precision " + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "text", + "content": " outperforms iSICE (e.g., MIT with VGG-16 backbone). Notice that iSICE can be considered as sparse precision matrix. When " + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "inline_equation", + "content": "N = 0" + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "text", + "content": " in Alg. 2, iSICE reduces to Precision " + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "text", + "content": ". In further experiments we show that once the size of matrix is increased, iSICE does outperform Precision " + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 305, + 309, + 545, + 596 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 598, + 544, + 622 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 598, + 544, + 622 + ], + "spans": [ + { + "bbox": [ + 306, + 598, + 544, + 622 + ], + "type": "text", + "content": "Table 2 corroborates that iSICE significantly outperforms Precision " + }, + { + "bbox": [ + 306, + 598, + 544, + 622 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 306, + 598, + 544, + 622 + ], + "type": "text", + "content": " and iSQRT-COV." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 624, + 545, + 672 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 624, + 545, + 672 + ], + "spans": [ + { + "bbox": [ + 306, + 624, + 545, + 672 + ], + "type": "text", + "content": "Robustness of iSICE to Hyper-parameters. We have conducted experiments with the hyper-parameter range given in Section 4.1. Appendix C shows that the performance of iSICE remains stable across a range of values." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": "Detailed comparisons with SPD-based SOTA models. Table 1 compares the performance of iSICE to several prior works. We first compare our VGG-16 backbone" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 46, + 652, + 287, + 691 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 652, + 287, + 691 + ], + "spans": [ + { + "bbox": [ + 46, + 652, + 287, + 691 + ], + "type": "text", + "content": "4The use of precision matrix (and partial correlations) as a visual representation in place of the sample-based covariance matrix " + }, + { + "bbox": [ + 46, + 652, + 287, + 691 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}" + }, + { + "bbox": [ + 46, + 652, + 287, + 691 + ], + "type": "text", + "content": " is also our minor but novel proposition. We obtain it via Alg. 1 from which we recover the inverse square root " + }, + { + "bbox": [ + 46, + 652, + 287, + 691 + ], + "type": "inline_equation", + "content": "\\mathbf{Q}" + }, + { + "bbox": [ + 46, + 652, + 287, + 691 + ], + "type": "text", + "content": " and then " + }, + { + "bbox": [ + 46, + 652, + 287, + 691 + ], + "type": "inline_equation", + "content": "\\boldsymbol {\\Omega} = \\boldsymbol{\\Sigma}^{-1} = \\mathbf{QQ}^{\\top} / \\mathrm{trace}(\\boldsymbol {\\Sigma})" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 46, + 691, + 286, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 691, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 691, + 286, + 712 + ], + "type": "text", + "content": "In contrast to our precision matrix " + }, + { + "bbox": [ + 46, + 691, + 286, + 712 + ], + "type": "inline_equation", + "content": "\\pmb{\\Omega} = \\pmb{\\Sigma}^{-1} = \\pmb{Q}\\pmb{Q}^{\\top} / \\mathrm{trace}(\\pmb{\\Sigma})" + }, + { + "bbox": [ + 46, + 691, + 286, + 712 + ], + "type": "text", + "content": " from Alg. 1, iSQRT-COV uses " + }, + { + "bbox": [ + 46, + 691, + 286, + 712 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}^{\\frac{1}{2}} = \\mathbf{Y}_N / \\sqrt{\\mathrm{trace}(\\pmb{\\Sigma})}" + }, + { + "bbox": [ + 46, + 691, + 286, + 712 + ], + "type": "text", + "content": " from Alg. 1." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 294, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 315, + 757 + ], + "type": "text", + "content": "6236" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 50, + 61, + 545, + 453 + ], + "blocks": [ + { + "bbox": [ + 50, + 61, + 545, + 453 + ], + "lines": [ + { + "bbox": [ + 50, + 61, + 545, + 453 + ], + "spans": [ + { + "bbox": [ + 50, + 61, + 545, + 453 + ], + "type": "table", + "html": "
MethodBackboneMITAirplaneBirdsCarsDTDiNatuarlistmini-ImageNet
GAP [37]-76.670.479.8---
NetVLAD [2]-81.881.688.6---
NetFV [28]-79.079.986.2---
BCNN [27]77.683.984.090.684.0--
CBP [11]76.284.184.391.284.0--
LRBP [17]-87.384.290.9---
KP [6]-86.986.292.4---
HIHCA [4]-88.385.391.7---
Improved BCNN [25]-88.585.892.0---
SMSO [46]VGG-1679.5-85.0----
MPN-COV [43] (reproduced)-86.182.989.8---
iSQRT-COV [23] (reproduced)76.190.084.591.271.356.276.2
DeepCOV [9]79.288.785.491.786.3--
DeepKSPD [9]81.090.084.891.686.3--
RUN [47]80.591.085.7----
FCBN [48]80.390.585.5----
TKPF [49]80.591.486.0----
Precision Ω80.289.483.492.074.057.974.0
iSICE (ours)78.792.286.594.074.759.878.7
CBP [11]-81.681.688.6---
KP [6]-85.784.791.1---
SMSO [46]79.7-85.8----
iSQRT-COV [23] (reproduced)78.890.984.392.173.057.770.7
DeepCOV-ResNet [34]ResNet-5083.483.986.085.084.6--
TKPF [49]84.192.185.7----
Precision Ω80.891.284.792.073.759.665.6
iSICE (ours)80.592.785.993.560.760.772.0
iSQRT-COV [23]76.390.384.191.471.856.975.4
Precision ΩVGG-1979.691.183.292.274.257.373.8
iSICE (ours)80.692.586.693.974.959.677.1
iSQRT-COV [23]79.391.084.492.373.070.673.9
Precision ΩResNet-10177.990.183.391.471.269.873.0
iSICE (ours)81.092.986.693.675.472.078.0
iSQRT-COV [23]81.691.386.292.475.772.276.1
Precision ΩResNeXt-10185.790.284.689.976.972.377.6
iSICE (ours)86.394.687.294.578.773.881.0
iSQRT-COV [23]77.888.183.589.484.761.582.0
Precision ΩConvNext-T78.581.283.792.283.959.383.6
iSICE (ours)85.490.486.793.188.965.085.1
iSQRT-COV [23]82.187.685.189.786.158.167.7
Precision ΩSwin-T82.588.284.990.586.559.165.6
iSICE (ours)85.989.686.591.388.361.969.1
iSQRT-COV [23]86.691.388.092.079.469.764.9
Precision ΩSwin-B87.090.787.793.180.167.366.4
iSICE (ours)87.692.988.393.379.872.468.4
", + "image_path": "a9d683d0b098b1df0b81ede401ee98ea92aa31e0349bb802946093811f8545ed.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 50, + 503, + 287, + 555 + ], + "blocks": [ + { + "bbox": [ + 47, + 462, + 546, + 485 + ], + "lines": [ + { + "bbox": [ + 47, + 462, + 546, + 485 + ], + "spans": [ + { + "bbox": [ + 47, + 462, + 546, + 485 + ], + "type": "text", + "content": "Table 1. Comparison between iSICE, Precision " + }, + { + "bbox": [ + 47, + 462, + 546, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{\\Omega }" + }, + { + "bbox": [ + 47, + 462, + 546, + 485 + ], + "type": "text", + "content": " and other SPD representations in terms of classification accuracy (%) . The performance of existing SPD representation methods is quoted from the original papers. Precision " + }, + { + "bbox": [ + 47, + 462, + 546, + 485 + ], + "type": "inline_equation", + "content": "\\mathbf{\\Omega }" + }, + { + "bbox": [ + 47, + 462, + 546, + 485 + ], + "type": "text", + "content": " is given by Alg. 1. iSICE is given by Alg. 2." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 503, + 287, + 555 + ], + "lines": [ + { + "bbox": [ + 50, + 503, + 287, + 555 + ], + "spans": [ + { + "bbox": [ + 50, + 503, + 287, + 555 + ], + "type": "table", + "html": "
MethodBackboneTop-1Top-5
GAP [13]71.0/69.590.9/88.9
iSQRT-COV [23]ResNet-50/71.5/70.290.5/89.7
Precision ΩVGG-1671.1/71.090.1/90.1
iSICE74.8/73.492.0/91.8
", + "image_path": "52414462a2abf5ecf6126f7e90ceb09a54e07911c70359cd67c2a2b9a9d40387.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 84, + 564, + 250, + 575 + ], + "lines": [ + { + "bbox": [ + 84, + 564, + 250, + 575 + ], + "spans": [ + { + "bbox": [ + 84, + 564, + 250, + 575 + ], + "type": "text", + "content": "Table 2. Results on the ImageNet100 dataset." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 593, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 593, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 593, + 288, + 715 + ], + "type": "text", + "content": "based iSICE with BCNN, CBP, LRBP, KP, HIHCA, Improved BCNN, SMSO, MPN-COV, iSQRT-COV, DeepCOV, DeepKSPD, RUN, FCBN and TKPF methods. The MPN-COV and iSQRT-COV methods use backbones pretrained with second-order pooling. For fair comparison with iSICE, we re-run those methods on our machine with the same backbone and evaluation protocols as ours. iSICE outperforms all existing methods on fine-grained datasets. On MIT dataset, our performance is better than CBP, BCNN and iSQRT-COV. iSICE could outperform DeepKSPD and" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 506, + 545, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 506, + 545, + 529 + ], + "spans": [ + { + "bbox": [ + 304, + 506, + 545, + 529 + ], + "type": "text", + "content": "other methods on MIT if a large-dimensional matrix similar to those is used (see Table 3)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 531, + 546, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 531, + 546, + 651 + ], + "spans": [ + { + "bbox": [ + 304, + 531, + 546, + 651 + ], + "type": "text", + "content": "Secondly, we compare our ResNet-50 backbone based iSICE with CBP, KP, SMO, iSQRT-COV, DeepCOV-ResNet and TKPF methods. iSICE achieves better performance than existing methods on both Airplane and Cars datasets. DeepCOV-ResNet uses " + }, + { + "bbox": [ + 304, + 531, + 546, + 651 + ], + "type": "inline_equation", + "content": "1024 \\times 1024" + }, + { + "bbox": [ + 304, + 531, + 546, + 651 + ], + "type": "text", + "content": "-dimensional matrix which is four times larger than ours. TKPF uses an advanced feature projection to reduce the CNN feature channels and we use a simple linear projection with " + }, + { + "bbox": [ + 304, + 531, + 546, + 651 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 304, + 531, + 546, + 651 + ], + "type": "text", + "content": " convolution. However, on average, we are still better than DeepCOV-ResNet and TKPF." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "content": "Thirdly, we integrate iSICE with the popular VGG-19, ResNet-101, ResNeXt-101, ConvNext-T [30], Swin-T and Swin-B [29] backbones pre-trained on ImageNet-1K, and compare their performance with iSQRT-COV and Precision " + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "content": " (Alg. 1). iSICE outperforms iSQRT-COV and Precision" + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6237" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 50, + 62, + 544, + 161 + ], + "blocks": [ + { + "bbox": [ + 50, + 62, + 544, + 161 + ], + "lines": [ + { + "bbox": [ + 50, + 62, + 544, + 161 + ], + "spans": [ + { + "bbox": [ + 50, + 62, + 544, + 161 + ], + "type": "table", + "html": "
MethodMatrix Dim.MITAirplaneBirdsCarsAverage
VGGResNetVGGResNetVGGResNetVGGResNetVGGResNet
iSQRT-COV256 × 25676.178.890.090.984.584.391.292.185.586.5
512 × 51276.982.891.591.185.084.592.292.186.487.6
Precision Ω256 × 25680.280.889.491.283.484.792.092.086.387.1
512 × 51280.782.790.191.584.984.092.592.687.087.7
SICE128 × 12871.073.185.586.977.378.087.087.980.281.5
256 × 25673.775.487.989.279.780.389.589.382.783.6
iSICE256 × 25678.780.592.292.786.585.99493.587.988.2
512 × 51281.181.792.992.686.88694.693.888.988.5
", + "image_path": "7aa0eadbb57fe377cd99d944c8359ec6bc16df562a70ba77a5c37b1d8777fe24.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 193, + 287, + 242 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 193, + 287, + 242 + ], + "spans": [ + { + "bbox": [ + 46, + 193, + 287, + 242 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 46, + 193, + 287, + 242 + ], + "type": "text", + "content": " methods across all datasets. This highlights that SPD-based visual representations (1) are still relevant for modern powerful classification backbones and (2) they improve results on large-scale datasets." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "spans": [ + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "text", + "content": "Ablations on the size of Sparse Inverse Matrix. Table 3 shows results for " + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "text", + "content": " vs. " + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "text", + "content": " matrix size (we keep the hyper-parameters fixed). To produce " + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "text", + "content": " dim. matrix, (1) from VGG-16, we simply remove the " + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "text", + "content": " convolution layer to obtain 512 feature channels and (2) from ResNet-50, we set the output channels of " + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "text", + "content": " convolution layer to 512. Generally, switching to a larger matrix improves the performance, e.g., on MIT our iSICE gains between 1.2 and " + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "inline_equation", + "content": "2.4\\%" + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "text", + "content": " (sparsity helps with a larger matrix). On average, all methods improved performance by switching to a larger matrix at the computational expense. See runtimes in Table 5 and memory consumption in Appendix E. Finally, Table 3 also shows that iSICE performs much better than SICE (based on ADMM solver [3]) computed over pre-extracted features. Table 5 shows that iSICE is " + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "inline_equation", + "content": "3 \\times" + }, + { + "bbox": [ + 46, + 247, + 289, + 462 + ], + "type": "text", + "content": " faster. SICE is almost intractable on larger datasets. This validates our claim that learning sparse inverse covariance matrix end-to-end produces robust visual representation." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "spans": [ + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": "iSICE with learning rate and sparsity modulators. As iSICE trades between log " + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "inline_equation", + "content": "\\operatorname{det}(\\cdot)" + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": " (changes rapidly) and the " + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": " norm (changes linearly), optimizing Eq. (1) with PGD may struggle with non-optimal learning rates and sparsity. Thus, we design a simple modulator that updates " + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": " in Alg. 2 by setting " + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "inline_equation", + "content": "\\beta := \\beta \\cdot \\kappa" + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": " in lines 10, where " + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "inline_equation", + "content": "\\kappa = \\tau + 2\\mathrm{Sigm}(\\mathrm{FC}(\\mathbf{X1} / n))" + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "inline_equation", + "content": "\\mathrm{Sigm}(\\cdot)" + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": " is a sigmoid, and FC layer is of " + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "inline_equation", + "content": "d \\times 1" + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": " size. We also add a penalty " + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "inline_equation", + "content": "-\\gamma (\\kappa - 1)^2" + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": " to the classification loss to encourage " + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "inline_equation", + "content": "\\kappa" + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": " to be close to 1 unless classification loss gets smaller for " + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "inline_equation", + "content": "\\kappa \\neq 1" + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": " while incurring the above penalty. We set " + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "inline_equation", + "content": "\\gamma = 0.0001" + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": ". We use the above modulator (we do not claim this is the most optimal design) as a tool akin to Mod-Grad [36]. " + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "inline_equation", + "content": "\\tau = 0.01" + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": " is a small offset to prevent zero learning rate. Another modulator with the same architecture is used to adapt sparsity parameter " + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 468, + 287, + 661 + ], + "type": "text", + "content": ". Table 4 shows that modulating the learning rate and sparsity on-the-fly helps iSICE." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 666, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 666, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 666, + 287, + 715 + ], + "type": "text", + "content": "Experiments on dense vs. sparse structure estimation w.r.t. sample size. Below we randomly generate a dense or sparse inverse covariance matrix " + }, + { + "bbox": [ + 46, + 666, + 287, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{P}" + }, + { + "bbox": [ + 46, + 666, + 287, + 715 + ], + "type": "text", + "content": " (size " + }, + { + "bbox": [ + 46, + 666, + 287, + 715 + ], + "type": "inline_equation", + "content": "100\\times 100" + }, + { + "bbox": [ + 46, + 666, + 287, + 715 + ], + "type": "text", + "content": ") and sample various amounts of data from the resulted normal dis" + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 308, + 191, + 544, + 225 + ], + "blocks": [ + { + "bbox": [ + 58, + 169, + 533, + 179 + ], + "lines": [ + { + "bbox": [ + 58, + 169, + 533, + 179 + ], + "spans": [ + { + "bbox": [ + 58, + 169, + 533, + 179 + ], + "type": "text", + "content": "Table 3. Performance of iSQRT-COV, Precision " + }, + { + "bbox": [ + 58, + 169, + 533, + 179 + ], + "type": "inline_equation", + "content": "\\mathbf{\\Omega }" + }, + { + "bbox": [ + 58, + 169, + 533, + 179 + ], + "type": "text", + "content": " ,SICE and iSICE on various datasets when different matrix dimensions are used." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 308, + 191, + 544, + 225 + ], + "lines": [ + { + "bbox": [ + 308, + 191, + 544, + 225 + ], + "spans": [ + { + "bbox": [ + 308, + 191, + 544, + 225 + ], + "type": "table", + "html": "
MethodMITAirplaneBirdsCarsImageNet100
iSICE80.592.785.993.574.8
iSCIE+MLP81.393.486.193.976.3
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GAPiSQRT-COVPrecision ΩSICEiSICEiSICE+MLP
Time/batch (sec.)29.032.032.8150.844.645.8
Time/epoch (min.)12.613.413.865.319.319.8
", + "image_path": "568beb2923f8aa8a1ed15ba726ee06f2aa2f100ae1718b73e02286e2614adc15.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 309, + 327, + 427, + 409 + ], + "blocks": [ + { + "bbox": [ + 307, + 310, + 543, + 322 + ], + "lines": [ + { + "bbox": [ + 307, + 310, + 543, + 322 + ], + "spans": [ + { + "bbox": [ + 307, + 310, + 543, + 322 + ], + "type": "text", + "content": "Table 5. Runtimes (256×256 matrix, ImageNet100, ResNet-50)." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 309, + 327, + 427, + 409 + ], + "lines": [ + { + "bbox": [ + 309, + 327, + 427, + 409 + ], + "spans": [ + { + "bbox": [ + 309, + 327, + 427, + 409 + ], + "type": "image", + "image_path": "43c445bdbe55f9ff879e65e5e6364d2b8135495f1d76e86b8042fc4041b636a0.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 429, + 327, + 545, + 409 + ], + "blocks": [ + { + "bbox": [ + 429, + 327, + 545, + 409 + ], + "lines": [ + { + "bbox": [ + 429, + 327, + 545, + 409 + ], + "spans": [ + { + "bbox": [ + 429, + 327, + 545, + 409 + ], + "type": "image", + "image_path": "27277345ba5218e2642c0c4087dc5bc376a24c89a8edeb1fae727f185c57b4eb.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 306, + 424, + 544, + 436 + ], + "lines": [ + { + "bbox": [ + 306, + 424, + 544, + 436 + ], + "spans": [ + { + "bbox": [ + 306, + 424, + 544, + 436 + ], + "type": "text", + "content": "Figure 3. Estimation errors on (left) dense & (right) sparse struct." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 451, + 545, + 512 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 451, + 545, + 512 + ], + "spans": [ + { + "bbox": [ + 305, + 451, + 545, + 512 + ], + "type": "text", + "content": "tribution (via mvnrnd(" + }, + { + "bbox": [ + 305, + 451, + 545, + 512 + ], + "type": "inline_equation", + "content": "\\cdot" + }, + { + "bbox": [ + 305, + 451, + 545, + 512 + ], + "type": "text", + "content": ") in Matlab) to get its estimate " + }, + { + "bbox": [ + 305, + 451, + 545, + 512 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{P}}" + }, + { + "bbox": [ + 305, + 451, + 545, + 512 + ], + "type": "text", + "content": ". Fig. 3 (left) shows estimation error " + }, + { + "bbox": [ + 305, + 451, + 545, + 512 + ], + "type": "inline_equation", + "content": "\\|\\hat{\\mathbf{P}} - \\mathbf{P}\\|_F" + }, + { + "bbox": [ + 305, + 451, + 545, + 512 + ], + "type": "text", + "content": " for dense structure. Sparse and non-sparse estimation (by SICE and MLE, resp.) show high errors in low sample regime. For the sparse structure in Fig. 3 (right), sparse estimation works better." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 522, + 384, + 534 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 522, + 384, + 534 + ], + "spans": [ + { + "bbox": [ + 306, + 522, + 384, + 534 + ], + "type": "text", + "content": "5. Conclusions" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 543, + 545, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 543, + 545, + 651 + ], + "spans": [ + { + "bbox": [ + 304, + 543, + 545, + 651 + ], + "type": "text", + "content": "In this paper, we proposed a method for learning sparse inverse covariance representation with CNN. Our method estimates SICE within the CNN layers and facilitates backpropagation for end-to-end training. Our iSICE significantly outperforms other covariance representations on several datasets. iSICE exploits the sparsity prior to capture partial correlations under limited number of samples. Our method is of general purpose and can be readily applied in existing SPD-based models to improve their performance." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "content": "Acknowledgements. This work was supported by CSIRO's Machine Learning & Artificial Intelligence Future Science Platform (MLAI FSP), University of Wollongong's Australia IPTA scholarship, and Australian Research Council (DP200101289). We thank Jianjia Zhang for discussions." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "6238" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "text", + "content": "[1] Akshay Agrawal, Brandon Amos, Shane Barratt, Stephen Boyd, Steven Diamond, and J Zico Kolter. Differentiable convex optimization layers. Advances in neural information processing systems, 32, 2019. 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 137, + 288, + 192 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 137, + 288, + 192 + ], + "spans": [ + { + "bbox": [ + 53, + 137, + 288, + 192 + ], + "type": "text", + "content": "[2] Relja Arandjelovic, Petr Gronat, Akihiko Torii, Tomas Pajdla, and Josef Sivic. Netvlad: Cnn architecture for weakly supervised place recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5297-5307, 2016. 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 194, + 288, + 247 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 194, + 288, + 247 + ], + "spans": [ + { + "bbox": [ + 53, + 194, + 288, + 247 + ], + "type": "text", + "content": "[3] Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, Jonathan Eckstein, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine learning, 3(1):1-122, 2011. 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 250, + 288, + 303 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 250, + 288, + 303 + ], + "spans": [ + { + "bbox": [ + 53, + 250, + 288, + 303 + ], + "type": "text", + "content": "[4] Sijia Cai, Wangmeng Zuo, and Lei Zhang. Higher-order integration of hierarchical convolutional activations for fine-grained visual categorization. In Proceedings of the IEEE international conference on computer vision, pages 511-520, 2017. 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 306, + 288, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 306, + 288, + 360 + ], + "spans": [ + { + "bbox": [ + 53, + 306, + 288, + 360 + ], + "type": "text", + "content": "[5] Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describing textures in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3606-3613, 2014. 5, 11" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 362, + 287, + 416 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 362, + 287, + 416 + ], + "spans": [ + { + "bbox": [ + 53, + 362, + 287, + 416 + ], + "type": "text", + "content": "[6] Yin Cui, Feng Zhou, Jiang Wang, Xiao Liu, Yuanqing Lin, and Serge Belongie. Kernel pooling for convolutional neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2921-2930, 2017. 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 418, + 287, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 418, + 287, + 441 + ], + "spans": [ + { + "bbox": [ + 53, + 418, + 287, + 441 + ], + "type": "text", + "content": "[7] Morris H. DeGroot. Optimal statistical decisions. McGraw-Hill, New York, NY [u.a], 1970. 1, 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 442, + 287, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 442, + 287, + 485 + ], + "spans": [ + { + "bbox": [ + 53, + 442, + 287, + 485 + ], + "type": "text", + "content": "[8] Steven Diamond and Stephen Boyd. Cvxpy: A python-embedded modeling language for convex optimization. The Journal of Machine Learning Research, 17(1):2909-2913, 2016. 3, 4" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 487, + 287, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 487, + 287, + 542 + ], + "spans": [ + { + "bbox": [ + 53, + 487, + 287, + 542 + ], + "type": "text", + "content": "[9] Melih Engin, Lei Wang, Luping Zhou, and Xinwang Liu. Deepkspd: Learning kernel-matrix-based spd representation for fine-grained image recognition. In Proceedings of the European Conference on Computer Vision, pages 612-627, 2018. 1, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 544, + 287, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 544, + 287, + 577 + ], + "spans": [ + { + "bbox": [ + 48, + 544, + 287, + 577 + ], + "type": "text", + "content": "[10] Jerome Friedman, Trevor Hastie, and Robert Tibshirani. Sparse inverse covariance estimation with the graphical lasso. Biostatistics, 9(3):432-441, 2008. 2, 3, 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 578, + 287, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 578, + 287, + 621 + ], + "spans": [ + { + "bbox": [ + 48, + 578, + 287, + 621 + ], + "type": "text", + "content": "[11] Yang Gao, Oscar Beijbom, Ning Zhang, and Trevor Darrell. Compact bilinear pooling. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 317-326, 2016. 1, 3, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 624, + 287, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 287, + 667 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 287, + 667 + ], + "type": "text", + "content": "[12] Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Friedman. The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer, 2009. 2, 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "type": "text", + "content": "[13] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6, 7" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "type": "text", + "content": "[14] Nicholas J Higham. Functions of matrices: theory and computation. SIAM, 2008. 2, 4, 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 96, + 545, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 545, + 128 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 545, + 128 + ], + "type": "text", + "content": "[15] Junzhou Huang, Tong Zhang, and Dimitris Metaxas. Learning with structured sparsity. Journal of Machine Learning Research, 12(11), 2011. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 129, + 545, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 129, + 545, + 172 + ], + "spans": [ + { + "bbox": [ + 308, + 129, + 545, + 172 + ], + "type": "text", + "content": "[16] Catalin Ionescu, Orestis Vantzos, and Cristian Sminchisescu. Matrix backpropagation for deep networks with structured layers. In Proceedings of the IEEE international conference on computer vision, pages 2965-2973, 2015. 1, 3, 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 174, + 545, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 174, + 545, + 217 + ], + "spans": [ + { + "bbox": [ + 308, + 174, + 545, + 217 + ], + "type": "text", + "content": "[17] Shu Kong and Charless Fowlkes. Low-rank bilinear pooling for fine-grained classification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 365-374, 2017. 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 217, + 545, + 258 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 217, + 545, + 258 + ], + "spans": [ + { + "bbox": [ + 308, + 217, + 545, + 258 + ], + "type": "text", + "content": "[18] Piotr Koniusz, Lei Wang, and Anoop Cherian. Tensor representations for action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(2):648-665, 2021. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 261, + 545, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 261, + 545, + 304 + ], + "spans": [ + { + "bbox": [ + 308, + 261, + 545, + 304 + ], + "type": "text", + "content": "[19] Piotr Koniusz, Fei Yan, Philippe-Henri Gosselin, and Krystian Mikolajczyk. Higher-order occurrence pooling on mid- and low-level features: Visual concept detection. Tech. Report (hal-00922524), 2013. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 306, + 545, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 306, + 545, + 348 + ], + "spans": [ + { + "bbox": [ + 308, + 306, + 545, + 348 + ], + "type": "text", + "content": "[20] Piotr Koniusz and Hongguang Zhang. Power normalizations in fine-grained image, few-shot image and graph classification. In IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE, 2021. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 350, + 545, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 350, + 545, + 392 + ], + "spans": [ + { + "bbox": [ + 308, + 350, + 545, + 392 + ], + "type": "text", + "content": "[21] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In Proceedings of the IEEE international conference on computer vision workshops, pages 554–561, 2013. 5" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 394, + 545, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 394, + 545, + 435 + ], + "spans": [ + { + "bbox": [ + 308, + 394, + 545, + 435 + ], + "type": "text", + "content": "[22] Miroslav Kubat. Neural networks: a comprehensive foundation by simon haykin, macmillan, 1994, ISBN 0-02-352781-7. The Knowledge Engineering Review, 13(4):409-412, 1999. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 437, + 545, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 437, + 545, + 492 + ], + "spans": [ + { + "bbox": [ + 308, + 437, + 545, + 492 + ], + "type": "text", + "content": "[23] Peihua Li, Jiangtao Xie, Qilong Wang, and Zilin Gao. Towards faster training of global covariance pooling networks by iterative matrix square root normalization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 947-955, 2018. 1, 4, 6, 7, 13, 14" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 494, + 545, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 494, + 545, + 547 + ], + "spans": [ + { + "bbox": [ + 308, + 494, + 545, + 547 + ], + "type": "text", + "content": "[24] Peihua Li, Jiangtao Xie, Qilong Wang, and Wangmeng Zuo. Is second-order information helpful for large-scale visual recognition? In Proceedings of the IEEE international conference on computer vision, pages 2070-2078, 2017. 1, 3, 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 548, + 545, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 548, + 545, + 579 + ], + "spans": [ + { + "bbox": [ + 308, + 548, + 545, + 579 + ], + "type": "text", + "content": "[25] Tsung-Yu Lin and Subhransu Maji. Improved bilinear pooling with cnns. British Machine Vision Conference, 2017. 3, 6, 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 581, + 545, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 581, + 545, + 623 + ], + "spans": [ + { + "bbox": [ + 308, + 581, + 545, + 623 + ], + "type": "text", + "content": "[26] Tsung-Yu Lin, Subhransu Maji, and Piotr Koniusz. Second-order democratic aggregation. In Proceedings of the European Conference on Computer Vision, pages 620-636, 2018. 1" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 625, + 545, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 625, + 545, + 669 + ], + "spans": [ + { + "bbox": [ + 308, + 625, + 545, + 669 + ], + "type": "text", + "content": "[27] Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. Bilinear cnn models for fine-grained visual recognition. In Proceedings of the IEEE international conference on computer vision, pages 1449-1457, 2015. 1, 3, 6, 7" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "text", + "content": "[28] Tsung-Yu Lin, Aruni RoyChowdhury, and Subhransu Maji. Bilinear convolutional neural networks for fine-grained visual recognition. IEEE transactions on pattern analysis and machine intelligence, 40(6):1309-1322, 2017. 1, 3, 6, 7" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "6239" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "text", + "content": "[29] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10012-10022, 2021. 6, 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 129, + 287, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 129, + 287, + 184 + ], + "spans": [ + { + "bbox": [ + 48, + 129, + 287, + 184 + ], + "type": "text", + "content": "[30] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11976-11986, 2022. 6, 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 185, + 287, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 185, + 287, + 207 + ], + "spans": [ + { + "bbox": [ + 48, + 185, + 287, + 207 + ], + "type": "text", + "content": "[31] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 208, + 287, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 208, + 287, + 251 + ], + "spans": [ + { + "bbox": [ + 48, + 208, + 287, + 251 + ], + "type": "text", + "content": "[32] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, 2013. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 252, + 287, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 252, + 287, + 285 + ], + "spans": [ + { + "bbox": [ + 48, + 252, + 287, + 285 + ], + "type": "text", + "content": "[33] Ariadna Quattoni and Antonio Torralba. Recognizing indoor scenes. In 2009 IEEE conference on computer vision and pattern recognition, pages 413-420. IEEE, 2009. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 286, + 287, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 286, + 287, + 331 + ], + "spans": [ + { + "bbox": [ + 48, + 286, + 287, + 331 + ], + "type": "text", + "content": "[34] Saimunur Rahman, Lei Wang, Changming Sun, and Luping Zhou. Redro: Efficiently learning large-sized spd visual representation. In European Conference on Computer Vision, pages 1-17. Springer, 2020. 1, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 332, + 287, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 332, + 287, + 364 + ], + "spans": [ + { + "bbox": [ + 48, + 332, + 287, + 364 + ], + "type": "text", + "content": "[35] Bernt Schiele, Paul Swoboda, and Gerard Pons-Moll. Projected gradient descent for the lasso. Lecture Notes, 2018. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 365, + 287, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 365, + 287, + 409 + ], + "spans": [ + { + "bbox": [ + 48, + 365, + 287, + 409 + ], + "type": "text", + "content": "[36] Christian Simon, Piotr Koniusz, Richard Nock, and Mehrtash Harandi. On modulating the gradient for metalearning. In Proceedings of the European conference on computer vision, 2020. 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 411, + 287, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 411, + 287, + 443 + ], + "spans": [ + { + "bbox": [ + 48, + 411, + 287, + 443 + ], + "type": "text", + "content": "[37] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 6, 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 445, + 287, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 445, + 287, + 498 + ], + "spans": [ + { + "bbox": [ + 48, + 445, + 287, + 498 + ], + "type": "text", + "content": "[38] Yue Song, Nicu Sebe, and Wei Wang. Why approximate matrix square root outperforms accurate svd in global covariance pooling? In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1115-1123, 2021. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 500, + 287, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 500, + 287, + 533 + ], + "spans": [ + { + "bbox": [ + 48, + 500, + 287, + 533 + ], + "type": "text", + "content": "[39] Yonglong Tian, Dilip Krishnan, and Phillip Isola. Contrastive multiview coding. In European conference on computer vision, pages 776-794. Springer, 2020. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 534, + 287, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 534, + 287, + 599 + ], + "spans": [ + { + "bbox": [ + 48, + 534, + 287, + 599 + ], + "type": "text", + "content": "[40] Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie. The inaturalist species classification and detection dataset. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8769-8778, 2018. 6, 11" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 601, + 287, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 601, + 287, + 644 + ], + "spans": [ + { + "bbox": [ + 48, + 601, + 287, + 644 + ], + "type": "text", + "content": "[41] Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al. Matching networks for one shot learning. Advances in neural information processing systems, 29, 2016. 6, 11" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 647, + 287, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 287, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 287, + 678 + ], + "type": "text", + "content": "[42] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200-2011 dataset. 2011. 5" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "text", + "content": "[43] Qilong Wang, Jiangtao Xie, Wangmeng Zuo, Lei Zhang, and Peihua Li. Deep cnns meet global covariance pooling: Better representation and generalization. IEEE transactions on" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 633 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 94 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 94 + ], + "type": "text", + "content": "pattern analysis and machine intelligence, 43(8):2582-2597, 2020. 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 96, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 96, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 307, + 96, + 545, + 150 + ], + "type": "text", + "content": "[44] Ruiping Wang, Huimin Guo, Larry S Davis, and Qionghai Dai. Covariance discriminative learning: A natural and efficient approach to image set classification. In 2012 IEEE conference on computer vision and pattern recognition, pages 2496-2503. IEEE, 2012. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 152, + 545, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 205 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 205 + ], + "type": "text", + "content": "[45] Saining Xie, Ross Girshick, Piotr Dolkar, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1492-1500, 2017. 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 207, + 545, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 207, + 545, + 240 + ], + "spans": [ + { + "bbox": [ + 307, + 207, + 545, + 240 + ], + "type": "text", + "content": "[46] Kaicheng Yu and Mathieu Salzmann. Statistically-motivated second-order pooling. In Proceedings of the European Conference on Computer Vision, pages 600–616, 2018. 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 241, + 545, + 283 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 241, + 545, + 283 + ], + "spans": [ + { + "bbox": [ + 307, + 241, + 545, + 283 + ], + "type": "text", + "content": "[47] Tan Yu, Yunfeng Cai, and Ping Li. Toward faster and simpler matrix normalization via rank-1 update. In European Conference on Computer Vision, pages 203–219. Springer, 2020. 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 286, + 545, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 286, + 545, + 329 + ], + "spans": [ + { + "bbox": [ + 307, + 286, + 545, + 329 + ], + "type": "text", + "content": "[48] Tan Yu, Xiaoyun Li, and Ping Li. Fast and compact bilinear pooling by shifted random maclaurin. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 3243-3251, 2021. 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 331, + 545, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 331, + 545, + 374 + ], + "spans": [ + { + "bbox": [ + 307, + 331, + 545, + 374 + ], + "type": "text", + "content": "[49] Tan Yu, Xiaoyun Li, and Ping Li. Efficient compact bilinear pooling via kronecker product. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 3170-3178, 2022. 1, 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 376, + 545, + 420 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 376, + 545, + 420 + ], + "spans": [ + { + "bbox": [ + 307, + 376, + 545, + 420 + ], + "type": "text", + "content": "[50] Hongguang Zhang, Jing Zhang, and Piotr Koniusz. Few-shot learning via saliency-guided hallucination of samples. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2770-2779, 2019. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 421, + 545, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 421, + 545, + 464 + ], + "spans": [ + { + "bbox": [ + 307, + 421, + 545, + 464 + ], + "type": "text", + "content": "[51] Jianjia Zhang, Lei Wang, Luping Zhou, and Wanqing Li. Beyond covariance: Sice and kernel based visual feature representation. International Journal of Computer Vision, 129(2):300-320, 2021. 2, 3, 4" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 466, + 545, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 466, + 545, + 498 + ], + "spans": [ + { + "bbox": [ + 307, + 466, + 545, + 498 + ], + "type": "text", + "content": "[52] Shan Zhang, Dawei Luo, Lei Wang, and Piotr Koniusz. Few-shot object detection by second-order pooling. In Proceedings of the Asian conference on computer vision, 2020. 1" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 499, + 545, + 543 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 499, + 545, + 543 + ], + "spans": [ + { + "bbox": [ + 307, + 499, + 545, + 543 + ], + "type": "text", + "content": "[53] Shan Zhang, Naila Murray, Lei Wang, and Piotr Koniusz. Time-reversed diffusion tensor transformer: A new tenet of few-shot object detection. In Proceedings of the European conference on computer vision, 2022. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 544, + 545, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 544, + 545, + 588 + ], + "spans": [ + { + "bbox": [ + 307, + 544, + 545, + 588 + ], + "type": "text", + "content": "[54] Shan Zhang, Lei Wang, Naila Murray, and Piotr Koniusz. Kernelized few-shot object detection with efficient integral aggregation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 589, + 545, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 589, + 545, + 633 + ], + "spans": [ + { + "bbox": [ + 307, + 589, + 545, + 633 + ], + "type": "text", + "content": "[55] Yifei Zhang, Hao Zhu, Zixing Song, Piotr Koniusz, and Irwin King. Spectral feature augmentation for graph contrastive learning and beyond. 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Our technique builds a 3D avatar representation of a person using just a single short monocular RGB video (e.g., 1-2 minutes). We leverage a 3DMM to track the user's expressions. By anchoring a neural radiance field to the 3DMM geometry, we generate a volumetric photorealistic 3D avatar that can be rendered with user-defined expression and viewpoint. Note that our method works well on challenging materials, e.g., hair and dramatic expressions. Please see our webpage augmentedperception.github.io/monoavatar for more results." + ], + "image_footnote": [], + "bbox": [ + 287, + 398, + 388, + 487 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/777c3def0428ef6bb9d3054be7d8b15015940aa8c155a44d17e99873c352b86b.jpg", + "image_caption": [ + "Controllable Photorealistic Head Avatar" + ], + "image_footnote": [], + "bbox": [ + 406, + 398, + 514, + 487 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/eae6ba8175c15b1b683845a042771e8c4c0de99d98c9bac5af8a532560d67109.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 526, + 398, + 635, + 489 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d7ee5d7bc9bb220e44b97fb7f675fb57a2d197cd0c093a3cd68efc503d6f193b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 653, + 397, + 753, + 489 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/8d7186ce16c09e00ad88f6e3e722cb082d6a9201abf60d35789ad4364a7f7153.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 767, + 396, + 872, + 489 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 578, + 313, + 595 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We propose a method to learn a high-quality implicit 3D head avatar from a monocular RGB video captured in the wild. The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses. Our hybrid pipeline combines the geometry prior and dynamic tracking of a 3DMM with a neural radiance field to achieve fine-grained control and photorealism. To reduce over-smoothing and improve out-of-model expressions synthesis, we propose to predict local features anchored on the 3DMM geometry. These learnt features are driven by 3DMM deformation and interpolated in 3D space to yield the volumetric radiance at a designated query point. We further show that using a Convolutional Neural Network in the UV space is critical in incorporating spatial context and producing representative local features. Extensive experiments show that we are able to reconstruct high-quality avatars, with more accurate expression-dependent details, good generalization to out-of-training expressions, and quantitatively superior renderings compared to other", + "bbox": [ + 75, + 613, + 472, + 901 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "state-of-the-art approaches.", + "bbox": [ + 500, + 580, + 684, + 595 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 500, + 640, + 632, + 656 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Creating a controllable human avatar is a fundamental piece of technology for many downstream applications, such as AR/VR communication [20, 31], virtual try-on [37], virtual tourism [13], games [42], and visual effects for movies [12, 18]. Prior art in high-quality avatar generation typically requires extensive hardware configurations (i.e., camera arrays [6, 12, 31], light stages [18, 29], dedicated depth sensors [8]), or laborious manual intervention [1]. Alternatively, reconstructing avatars from monocular RGB videos significantly relaxes the dependency on equipment setup and broadens the application scenarios. However, monocular head avatar creation is highly ill-posed due to the dual problems of reconstructing and tracking highly articulated and deformable facial geometry, while model", + "bbox": [ + 496, + 665, + 890, + 878 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Work was conducted while Ziqian Bai was an intern at Google.", + "bbox": [ + 522, + 886, + 864, + 901 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "16890", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "ing sophisticated facial appearance. Traditionally, 3D Morphable Models (3DMM) [7, 24] have been used to model facial geometry and appearance for various applications including avatar generation [9, 16, 22]. However, 3DMMs do not fully capture subject-specific static details and dynamic variations, such as hair, glasses, and expression-dependent high frequency details such as wrinkles, due to the limited capacity of the underlying linear model.", + "bbox": [ + 80, + 90, + 467, + 210 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recent works [3, 15] have incorporated neural radiance fields in combination with 3DMMs for head avatar generation to achieve photorealistic renderings, especially improving challenging areas, such as hair, and adding view-dependent effects, such as reflections on glasses. The pioneering work of NerFACE [15] uses a neural radiance field parameterized by an MLP that is conditioned on 3DMM expression parameters and learnt per-frame latent codes. While they achieve photorealistic renderings, the reliance on an MLP to directly decode from 3DMM parameter space leads to the loss of fine-grain control over geometry and articulation. Alternatively, RigNeRF [3] learns the radiance field in a canonical space by warping the target head geometry using a 3DMM fit, which is further corrected by a learnt dense deformation field parameterized by another MLP. While they demonstrate in-the-wild head pose and expression control, the use of two global MLPs to model canonical appearance and deformations for the full spatial-temporal space leads to a loss of high frequency details, and an overall uncanny appearance of the avatar. Both of these works introduce new capabilities but suffer from lack of detail in both appearance and motion because they attempt to model the avatar's global appearance and deformation with an MLP network.", + "bbox": [ + 80, + 215, + 467, + 575 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we propose a method to learn a neural head avatar from a monocular RGB video. The avatar can be controlled by an underlying 3DMM model and deliver high-quality rendering of arbitrary facial expressions, head poses, and viewpoints, which retain fine-grained details and accurate articulations. We achieve this by learning to predict expression-dependent spatially local features on the surface of the 3DMM mesh. A radiance field for any given 3D point in the volume is then obtained by interpolating the features from K-nearest neighbor vertices on the deformed 3DMM mesh in target expression, and passing them through a local MLP to infer density and color. The local features and local MLP are trained jointly by supervising the radiance field through standard volumetric rendering on the training sequence [30]. Note that our networks rely on the local features to model appearance and deformation details, and leverages the 3DMM to model only the global geometry.", + "bbox": [ + 80, + 580, + 467, + 835 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Learning local features is critical in achieving a high-quality head avatar. To this end, we train an image-to-image translation U-Net that transforms the 3DMM deformations in the UV space to such local features. These UV", + "bbox": [ + 80, + 839, + 467, + 898 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "space features are then attached to the corresponding vertices of the 3DMM mesh geometry. We show that learning features from such explicit per-vertex local displacement of the 3DMM geometry makes the model retain high-frequency expression-dependent details and also generalizes better to out-of-training expressions, presumably because of the spatial context between nearby vertices incorporated by the convolutional neural network (CNN). An alternative approach is to feed the 3DMM parameters directly into a CNN decoder running on the UV space. However, we found this produces severe artifacts on out-of-training expressions, particularly given a limited amount of training data, e.g. for a lightweight, 1-minute data collection procedure during the avatar generation process.", + "bbox": [ + 503, + 90, + 888, + 301 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In summary, our contributions are as follows: we propose a neural head avatar representation based on a 3DMM-anchored neural radiance field, which can model complex expression-dependent variations, but requires only monocular RGB videos for training. We show that a convolutional neural network running on per-vertex displacement in UV space is effective in learning local expression-dependent features, and delivers favorable training stability and generalization to out-of-training expressions. Experiments on real-world datasets show that our model provides competitive controllability and generates sharper and detail enriched rendering compared to state-of-the-art approaches.", + "bbox": [ + 503, + 303, + 888, + 482 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 503, + 497, + 645, + 512 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Building photorealistic representations of humans has been widely researched in the past few decades. Here, we mainly discuss prior art in head avatar and refer readers to the state-of-the-art surveys [14, 55] for a comprehensive literature review.", + "bbox": [ + 503, + 523, + 888, + 597 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Monocular Explicit Surface Head (Face) Avatars. Traditionally, a typical approach to create head (or face) avatars from monocular RGB videos is using a 3D Morphable Models (3DMM) as the foundation and adding personalized representations, such as corrected blendshapes [16, 22], detail texture maps [16, 22], image-based representations [9], and secondary components [21]. Early works use various optimizations to obtain the personalized representations from monocular data, including analysis-by-synthesis [14, 16, 21, 55] as well as shape-from-shading [16, 22]. Recent approaches replace optimizations by regressions with Deep Neural Networks (DNNs) [10, 40, 48], or integrate optimizations with deep learning components [4, 5]. More recent methods leverage neural textures [17] to generate photorealistic appearances.", + "bbox": [ + 503, + 599, + 888, + 823 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The main drawback of these methods is that they rely on explicit meshes with a fixed topology, making it hard to handle out-of-model details such as hair and accessories like glasses and apparels. In contrast, our hybrid method combines geometric priors with implicit representations, lead-", + "bbox": [ + 503, + 825, + 888, + 898 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "16891", + "bbox": [ + 482, + 946, + 516, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/cfa3512d5b56a09d517ae695a0310721974858f1f3c370cf76579f717b01bb60.jpg", + "image_caption": [ + "Figure 2. Overview of our pipeline. The core of our method is the Avatar Representation (Sec. 3.1. Shown as the yellow area) based on a 3DMM-anchored neural radiance field (NeRF), which are decoded from local features attached on the 3DMM vertices. Then, we use volumetric rendering to compute the output image. To predict the vertex-attached features (Sec. 3.2. Shown as the green area), we first compute the vertex displacements from the 3DMM expression and pose, then process the displacements in UV space with Convolutional Neural Networks (CNNs), and sample the obtained features back to mesh vertices." + ], + "image_footnote": [], + "bbox": [ + 78, + 89, + 893, + 301 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "ing to a significantly larger representation capacity.", + "bbox": [ + 75, + 402, + 413, + 416 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Monocular Implicit Head Avatars. Recent work proposes to extend 3DMM with implicit 3D representations. NerFACE [15] introduces a dynamic neural radiance field (NeRF) conditioned on 3DMM expression codes which can render a view-consistent avatar with volumetric rendering. Since NerFACE directly inputs the 3DMM expression codes into MLPs without using any shape or spatial information from 3DMM, their model is quite under-constrained for monocular reconstruction, and suffers from severe artifacts for data with challenging expressions. RigNeRF [3] uses 3DMM derived warping field to deform the camera space into a canonical space, and defines a canonical NeRF conditioned on 3DMM codes. However, their model uses a dense MLP-based architecture to memorize the appearance and deformation for the full head, leading to oversmooth results due to limited network capacity. IMAAvatar [53] learns personalized implicit fields of blendshapes, pose correctives, and skinning weights, then formulates the avatar with linear summation of blendshapes followed by linear blend skinning. However, their linear formulation limits the amount of expression deformations.", + "bbox": [ + 73, + 416, + 468, + 733 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Geometry Anchored Implicit 3D Representation. Sparse local feature embedding attached on geometry has been demonstrated to be effective in improving the rendering quality of neural radiance field [26-28, 34, 54]. It also naturally supports neural radiance field editing since the modification on the geometry can be directly propagated to the rendering [11], which makes them a favorable representation to support the controllability for human avatar. We adapt this representation to head avatar and incorporate head specific priors. Differently from prior art, we leverage a CNN in UV space to learn local, per-vertex features", + "bbox": [ + 75, + 734, + 470, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "that are expression-dependent, improving generalization of out-of-train expressions.", + "bbox": [ + 496, + 402, + 890, + 430 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2D-based Head Avatars. There are numerous approaches that synthesize the head (or face) relying on 2D (explicit/implicit) representations, including 2D facial landmarks [43, 49, 50] and 2D warping fields [38, 39, 46]. Landmark-based avatar models [43, 49, 50] synthesize the face conditioned to the facial landmarks extracted with a, usually pre-trained, landmark detector. Specifically, an encoder is applied to extract an identity embedding from a reference image, a decoder is adapted by the identity code to animate the reference face with landmarks from the driving videos. X2Face [46] is the first approach to animate human heads by learning a dense warping field and producing the output video via image warping. MonkeyNet [38] and First Order Motion Model (FOMM) further propose to infer motion fields with self-learned keypoints, which significantly improves motion prediction and synthesizes higher quality renderings of heads. While most aforementioned methods can produce photorealistic results, they are not able to maintain geometry and multiview consistency due to their inherent 2D representation.", + "bbox": [ + 496, + 433, + 892, + 734 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 500, + 752, + 589, + 768 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given a monocular RGB video containing $M$ frames $\\{I_1, I_2, \\dots, I_M\\}$ , our method reconstructs a head avatar representation that can be rendered under arbitrary facial expressions, head poses, and camera viewpoints. We first preprocess the video to remove the background [19,32] and obtain camera and 3DMM parameters for each frame. More specifically, we use FLAME [24] as the 3DMM and denote the fitted face with shape $\\beta$ , expressions $\\psi_i$ , poses $\\theta_i$ (i.e.,", + "bbox": [ + 496, + 779, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "16892", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/8b7b225e882fe956ccb35038f9c3c8ffb0c2fd0c64e5ff26acc67ab4f464e6f6.jpg", + "image_caption": [ + "Figure 3. Illustration of Avatar Representation (Sec. 3.1). Given a query point, we find its k-Nearest-Neighbor (k-NN) vertices from the 3DMM. Then, we decode these vertices and features into a density and color with respect to the input camera view direction, via Multi-Layer-Perceptrons (MLPs) interleaved with inverse-distance based weighted sum." + ], + "image_footnote": [], + "bbox": [ + 81, + 89, + 467, + 224 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "neck, jaw, and eyes), where $i$ is the frame index, with which a head mesh can be obtained via $\\pmb{V}_i(\\pmb{\\beta},\\pmb{\\psi}_i,\\pmb{\\theta}_i)$ . Since $\\beta$ is fixed and does not depend on the pose or expressions for a given user, we omit it in the following sections for brevity.", + "bbox": [ + 75, + 339, + 468, + 400 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "An overview of our framework is shown in Fig. 2. We adopt the 3DMM-anchored neural radiance field (NeRF) as the core representation for our head avatar (Sec. 3.1), where local features are attached to the vertices of the deformable 3DMM mesh. During the inference, we first deform the 3DMM mesh based on the target configuration $V_{t} = (\\psi_{t},\\theta_{t})$ . Then, for an arbitrary 3D query point, we aggregate the features from neighboring vertices on $V_{t}$ to estimate the local density and color by Multi-Layer-Perceptrons (MLPs), which are then integrated in the volumetric rendering formulation to generate the color image. To learn local features, we train CNN-based networks in the UV space to incorporate spatial context (Sec. 3.2). Our model is trained end-to-end with RGB supervisions (Sec. 3.3).", + "bbox": [ + 75, + 401, + 468, + 626 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.1. Avatar Representation", + "text_level": 1, + "bbox": [ + 76, + 635, + 285, + 651 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "An ideal representation for a head avatar should have the following properties: 1) Provides intuitive control to achieve the desired expression and head pose; 2) Requires a moderate amount of training data, e.g., a short monocular video; 3) Produces expression-dependent rendering details; 4) Generalizes reasonably well to unseen expressions.", + "bbox": [ + 75, + 659, + 468, + 750 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To this end, we propose the 3DMM-anchored neural radiance field (NeRF) as shown in Fig. 3. Inspired by local feature based neural radiance field [34, 47], we attach feature vectors $z^{j}$ on each 3DMM vertex $v_{i}^{j}$ to encode the local radiance fields that can be decoded with MLPs, where $i$ denotes frame index and $j$ denotes vertex index. In this way, the radiance field can be deformed according to vertex locations, hence can be intuitively controlled by the 3DMM expression and pose $(\\psi_{i},\\theta_{i})$ . In addition, the 3DMM fitting on each frame provides a rough tracking across deformable", + "bbox": [ + 75, + 750, + 468, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "face geometries, such that all the frames can contribute into the learning of a unified set of local per-vertex features. We will discuss model capacity and generalization in Sec. 3.2.", + "bbox": [ + 498, + 90, + 890, + 136 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To decode the vertex features $\\{z^j\\}$ into the radiance field for the frame $i$ , given a 3D query point $\\pmb{q}$ , we first find its $k$ -Nearest-Neighbor ( $k$ -NN) vertices from the 3DMM mesh $\\{\\pmb{v}_i^j\\}_{j\\in \\mathcal{N}_k^q}$ with attached features $\\{z^j\\}_{j\\in \\mathcal{N}_k^q}$ . Then, we use two MLPs $\\mathcal{F}_0$ and $\\mathcal{F}_1$ with inverse-distance based weighted sum to decode local color and density. Formally,", + "bbox": [ + 498, + 136, + 890, + 227 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\boldsymbol {z}} _ {i} ^ {j} = \\mathcal {F} _ {0} (\\boldsymbol {v} _ {i} ^ {j} - \\boldsymbol {q}, \\boldsymbol {z} ^ {j})\n$$\n", + "text_format": "latex", + "bbox": [ + 666, + 237, + 808, + 257 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\boldsymbol {z}} _ {i} = \\sum_ {j} w ^ {j} \\hat {\\boldsymbol {z}} _ {i} ^ {j} \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 669, + 260, + 890, + 280 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {c} _ {i} (\\boldsymbol {q}, \\boldsymbol {d} _ {i}), \\sigma_ {i} (\\boldsymbol {q}) = \\mathcal {F} _ {1} (\\hat {\\boldsymbol {z}} _ {i}, \\boldsymbol {d} _ {i}),\n$$\n", + "text_format": "latex", + "bbox": [ + 580, + 282, + 781, + 299 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $w^{j} = \\frac{d^{j}}{\\sum_{k} d^{k}}$ , $d^{j} = \\frac{1}{\\|\\pmb{v}_{i}^{j} - \\pmb{q}\\|_{2}}$ with $j \\in \\mathcal{N}_k^q$ , and $\\pmb{d}_i$ denotes the camera view direction. Finally, we render the output image with volumetric rendering formulation as in vanilla NeRF [30] given the camera ray $r(t) = o + td$ :", + "bbox": [ + 498, + 311, + 890, + 378 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {C} _ {i} (\\boldsymbol {r}) = \\int_ {t _ {n}} ^ {t _ {f}} T (t) \\sigma_ {i} (\\boldsymbol {r} (t)) \\boldsymbol {c} _ {i} (\\boldsymbol {r} (t), \\boldsymbol {d}) \\mathrm {d} t, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 558, + 390, + 890, + 426 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\text {w h e r e} T (t) = \\exp \\left(- \\int_ {t _ {n}} ^ {t} \\sigma_ {i} (\\boldsymbol {r} (s)) \\mathrm {d} s\\right)\n$$\n", + "text_format": "latex", + "bbox": [ + 557, + 428, + 821, + 464 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To reduce misalignments caused by per-frame contents that cannot be captured by 3DMM (e.g., 3DMM fitting errors), we additionally learn error-correction warping fields during training inspired from prior works on deformable NeRF [23, 45]. More specifically, we input the original query point and a per-frame latent code $e_i$ , which is randomly initialized and optimized during the training, into the error-correction MLPs $\\mathcal{F}_{\\mathcal{E}}$ to predict a rigid transformation, and apply it to the query point. The transformation is denoted as $q' = \\mathcal{T}_i(q) = \\mathcal{F}_{\\mathcal{E}}(q, e_i)$ . Then we use the warped query point $q'$ to decode the color and density. Note that this warping field is disabled during testing. Please refer to the supplementary for detailed formulations of the warping field.", + "bbox": [ + 496, + 474, + 890, + 683 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Predicting Expression-Dependent Features", + "text_level": 1, + "bbox": [ + 500, + 695, + 862, + 710 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "While the proposed avatar representation (Sec. 3.1) enables intuitive controllability and convenience in learning, it still has limited capability for modeling complex expression-dependent variations due to the use of frame-shared vertex features $\\{z^j\\}$ .", + "bbox": [ + 496, + 719, + 890, + 794 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To overcome this, we propose to predict the dynamic vertex features $\\{z_i^j\\}$ conditioned on the 3DMM expression and pose $(\\psi_{i},\\theta_{i})$ . A common practice for NeRF-based methods is to use MLP-based architectures for dynamic feature prediction [3, 15, 53, 54]. However, we find that this leads to blurry rendering results, presumably because of the limited model capacity due to the lack of spatial context (i.e.,", + "bbox": [ + 496, + 795, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "16893", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/268536dfd2ffd16456bbaa26e2073a59c890479a77f576c76d2eb754c92687b2.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Subject0\nLPIPS / SSIM / PSNRSubject1\nLPIPS / SSIM / PSNRSubject2\nLPIPS / SSIM / PSNRSubject3\nLPIPS / SSIM / PSNRSubject4\nLPIPS / SSIM / PSNR
TPSMM [52]0.192 / 0.852 / 22.600.205 / 0.830 / 16.380.216 / 0.782 / 18.400.222 / 0.799 / 20.280.156 / 0.913 / 21.29
FOMM [39]0.171 / 0.841 / 22.930.179 / 0.827 / 16.020.202 / 0.777 / 18.980.186 / 0.798 / 22.280.122 / 0.915 / 23.94
NHA [17]0.165 / 0.836 / 20.200.166 / 0.840 / 15.480.178 / 0.809 / 17.990.153 / 0.798 / 21.310.091 / 0.926 / 23.78
IMAvatar [53]0.207 / 0.852 / 21.260.187 / 0.848 / 15.980.265 / 0.729 / 15.800.214 / 0.782 / 20.370.142 / 0.897 / 20.63
NerFACE [15]0.205 / 0.817 / 20.060.182 / 0.833 / 15.780.188 / 0.793 / 19.410.229 / 0.747 / 18.160.093 / 0.938 / 25.57
Ours-D0.144 / 0.864 / 21.920.152 / 0.855 / 16.230.141 / 0.841 / 20.420.156 / 0.833 / 23.050.075 / 0.944 / 25.71
", + "bbox": [ + 78, + 88, + 885, + 215 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 1. Quantitative Comparison with state-of-the-art (SOTA) approaches. Subject4 is the data from NerFACE [15], while other subjects are from our dataset. Our method achieves superior results than prior SOTAs.", + "bbox": [ + 75, + 220, + 892, + 250 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "each vertex does not know the feature of its neighboring vertices). Based on this intuition, we propose to process the 3DMM expression and pose $(\\psi_i,\\theta_i)$ with CNNs in the texture atlas space (UV space) to provide local spatial context.", + "bbox": [ + 75, + 275, + 468, + 335 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Specifically, we design two variations of CNN-based architecture to learn expression-dependent vertex features $\\{z_i^j\\}$ . The first variant, denoted as Ours-C, trains a decoder consisting of transposed convolutional blocks to predict the feature map in UV space directly from 1-D codes of 3DMM expression and pose. We empirically find that such a model is effective in improving the overall rendering quality, however tends to fail and produce severe artifacts on out-of-training expressions (See discussions in Sec. 4.4). We then propose the second variant, denoted as Ours-D, that uses the 3D deformation of 3DMM in UV space as the input for feature prediction, and observe that resulting avatar models are more resilient to stretchy and unseen expressions. Specifically, we first compute the vertex displacements using 3DMM expression and pose as $D_{i} = V_{i}(\\psi_{i},\\theta_{i}) - V_{neutral}(0,0)$ . We then rasterize the vertex displacements $D_{i}$ into UV space, and process it with a U-Net $\\mathcal{F}_{\\mathcal{D}}$ . Finally, the output UV feature map is sampled back to mesh vertices $V_{i}$ , serving as the dynamic vertex features $\\{z_i^j\\}$ (i.e., the expression-dependent version of frame-shared vertex features $\\{z^j\\}$ described in Sec. 3.1).", + "bbox": [ + 75, + 335, + 472, + 654 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Training Schema", + "text_level": 1, + "bbox": [ + 76, + 660, + 243, + 676 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our model is trained on monocular RGB videos mainly with the photometric loss, where we penalize the $l_{2}$ distance between the rendering and the ground truth images. Formally $\\mathcal{L}_{rgb} = \\sum_{i}\\sum_{\\boldsymbol{r}}\\| C_i(\\boldsymbol{r}) - I_i(\\boldsymbol{r})\\|_2$ , where $\\boldsymbol{r}$ denotes the camera ray of each pixel and $i$ denotes frame index. To regularize the learning of error-correction warping field $\\mathcal{T}(\\boldsymbol{q})$ , we adopt a elastic loss $\\mathcal{L}_{elastic}$ similar to Nerfies [33], and a magnitude loss defined as $\\mathcal{L}_{mag} = \\sum_q\\| q - \\mathcal{T}(q)\\|_2^2$ . The total loss is defined as:", + "bbox": [ + 75, + 683, + 468, + 821 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {r g b} + \\lambda_ {\\text {e l a s t i c}} \\mathcal {L} _ {\\text {e l a s t i c}} + \\lambda_ {\\text {m a g}} \\mathcal {L} _ {\\text {m a g}}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 129, + 830, + 468, + 848 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where we set $\\lambda_{elastic} = 10^{-4}$ at the beginning and decay to $10^{-5}$ after 155k iterations, and $\\lambda_{mag} = 10^{-2}$ . Please see supplementary for more details.", + "bbox": [ + 76, + 854, + 468, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 273, + 633, + 290 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We train and evaluate our method on casually captured monocular RGB videos (Sec. 4.1) and show that our method achieves superior rendering quality than prior state-of-the-art monocular RGB head avatars (Sec. 4.2). Then we verify our key observations on architectural choices to design a good avatar model, in terms of rendering quality and expression robustness (Sec. 4.4).", + "bbox": [ + 496, + 308, + 892, + 415 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets and Metrics", + "text_level": 1, + "bbox": [ + 500, + 450, + 697, + 465 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. Following the prior art [15], we captured monocular RGB videos of various subjects with smartphones or webcams for training and evaluation. Each video is 1-2 minutes long (around $1.5\\mathrm{k - }2\\mathrm{k}$ frames at 30 FPS) with the first 1000-1500 frames as training data and the rest frames for evaluation if not otherwise specified. For the training clip, the subjects are asked to first keep a neutral expression and rotate their heads, then perform different expressions during the head rotation, with extreme expressions included. For the testing clip, the subjects are asked to perform freely without any constraints. To demonstrate that our method also works for common talking head videos, we also include a video from NerFACE [15], which has significantly less variability in expressions when compared to our capture protocol. We mask out background [32] for each video, and obtain 3DMM and camera parameters with 3DMM fitting similar to that in NHA [17]. Please refer to the supplementary for examples of data. Please note that we collect relatively shorter training videos compared to related work. This favors a better user experience while still synthesizing high-quality avatars with more personalized and detailed expressions.", + "bbox": [ + 496, + 483, + 892, + 816 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Metrics. Following the prior art [15], we use the following standard image quality metrics for quantitative evaluations: the Learned Perceptual Image Patch Similarity (LPIPS) [51], Structure Similarity Index (SSIM) [44], and Peak Signal-to-Noise Ratio (PSNR).", + "bbox": [ + 496, + 824, + 893, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "16894", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/8e6d1ac6535f28aa5cc557d58bcbee2ce2e089c7573e0cdffb083778d670a523.jpg", + "image_caption": [ + "Figure 4. Qualitative Comparison to prior state-of-the-art monocular head avatars. Note how our approach more faithfully reconstructs the ground truth expressions while preserving most of the high frequency details." + ], + "image_footnote": [], + "bbox": [ + 76, + 89, + 890, + 531 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Comparisons with State-of-Art", + "text_level": 1, + "bbox": [ + 76, + 585, + 352, + 603 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We compare our method with five state-of-the-art methods of different types with publicly available implementations from the authors: NerFACE [15], IMAvatar [53], NHA [17], FOMM [39], and TPSMM [52]. For subject-specific methods (i.e. Ours, NerFACE [15], IMAvatar [53], and NHA [17]), we train the avatar for each subject separately with training frames of each video, then drive and render the trained avatar with 3DMM and camera parameters of testing frames. For few-shot methods (i.e., FOMM [39], and TPSMM [52]), we use the first frame of each video, which shows a frontal head, as the source image, then use testing frames as driving images in self-reenactment manner. Finally, the generated images of each method are compared with ground truth testing frames quantitatively (See Tab. 1) and qualitatively (See Fig. 4).", + "bbox": [ + 75, + 611, + 468, + 838 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As shown in Fig. 4, FOMM [39] and TPSMM [52] struggle with large head rotations and fail to produce 3D consistent results. NHA [17] uses explicit mesh surface with neural textures. The fix mesh topology makes it hard to", + "bbox": [ + 75, + 839, + 470, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "handle challenging hair in Subject 3. Also, NHA uses linear 3DMM blendshapes, making it hard to capture complex expressions (e.g., Subject 1 & 3) and wrinkles (e.g., Subject 4). IMAvatar [53] uses an occupancy field to model geometry, making it hard to handle challenging hair. Despite learning a personalized blendshape field, the linear formulation still limits their model capacity of handling complex expressions. In addition, we observed training instability of IMAvatar on the captured data, converging to oversmooth results. NerFACE [15] works relatively well on data with easy expressions (i.e., common talking heads in Subject 4), but struggles on our challenging data and estimates incorrect geometry (e.g., distorted head for Subject 3) and blurry rendering. In contrast, our method gives superior results on all the aspects discussed above. Tab. 1 further quantitatively confirms the good rendering quality of our method.", + "bbox": [ + 496, + 585, + 890, + 829 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Driving the Avatar", + "text_level": 1, + "bbox": [ + 500, + 844, + 679, + 861 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "After training, the learned avatar model can be driven by the same subject under different capture conditions, e.g. hair", + "bbox": [ + 500, + 869, + 892, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "16895", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/f917e938b70e11648ad95eb9cccd3f6ba46114e75633202ec2a5fd8c94c0d7e6.jpg", + "image_caption": [ + "Drive Frame" + ], + "image_footnote": [], + "bbox": [ + 80, + 103, + 176, + 210 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/88862a58742ee8ed3f9080bfd0bba84733c0d5256cff9694c7c3892e7058073c.jpg", + "image_caption": [ + "Drive View" + ], + "image_footnote": [], + "bbox": [ + 187, + 107, + 272, + 202 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/63e17120c5ab8b9d0cee090e884c5b6daeb1448372b64da9aaa0d766dbf2e827.jpg", + "image_caption": [ + "Side View" + ], + "image_footnote": [], + "bbox": [ + 287, + 107, + 370, + 202 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b7986626baaf30e74f5afebccef37b5277d2880ef814f276736d38a556021b82.jpg", + "image_caption": [ + "Depth" + ], + "image_footnote": [], + "bbox": [ + 380, + 109, + 467, + 202 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/3c34d5e10bf27da1bdb9aea82c4c5b26629fa7708943bbc0a512e5dbf77ce6d7.jpg", + "image_caption": [ + "Figure 5. Results on driving the learned avatar by the same subject under different capture conditions. Our method produces faithful expressions, multi-view consistent rendering, and good geometry." + ], + "image_footnote": [], + "bbox": [ + 80, + 213, + 176, + 319 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/27d29cbf3c21bf1ea341890c4a569998d019030581e60e605b38b23539665615.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 187, + 215, + 267, + 313 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8ada6b924f1dcda74bf7226e62c796f63d346b17aded717eb55f68e03935b669.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 281, + 215, + 367, + 313 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/36ffe8ff63775d054b498fb8300d73a3c10d6943136d7e432b12f54cafb1ff1d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 380, + 215, + 460, + 313 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c61ac4266f0fd60e21f5a948b9f2216adae7d0872670320746222fa55abea7d8.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 81, + 376, + 145, + 477 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ee01942802ce4a2fa8b7d1d8606707d5645e151a99745a499fb3655d58ed92bd.jpg", + "image_caption": [ + "Figure 6. Results on expressions out of the training distribution with different amount of training data. Ours- $D$ more robustly handles unseen expressions and degrades less with fewer training data." + ], + "image_footnote": [], + "bbox": [ + 158, + 377, + 225, + 478 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9861e135702f2237c11428c8cfd0dae1e71406bfcb88f2a17956474e7f789578.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 233, + 377, + 303, + 478 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b5dd1b42fd6dad49ab29bba7d89539943f0859934475ec26ff93453898674288.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 313, + 377, + 382, + 478 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/71089543f7f6e7e03142148a1bfe1b01db603292f9287a5c62d6c67a15868cd8.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 390, + 377, + 467, + 478 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "style, illumination, glasses. We show the driving results in Fig. 5. Our avatar faithfully reproduces the expressions of the driving frame, while also achieving multi-view consistent renderings and generating high quality geometry.", + "bbox": [ + 75, + 549, + 468, + 612 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation Study: Expression Features", + "text_level": 1, + "bbox": [ + 76, + 619, + 397, + 636 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Learning good local features is crucial for improving model capacity and capturing high frequency details, without losing the regularization from 3DMM (Sec. 3.2). We investigate and compare three alternative approaches and our two model variations for feature learning:", + "bbox": [ + 75, + 643, + 468, + 718 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Static Features. We use frame-shared (hence expression-shared) vertex features $\\{z^j\\}$ . As shown in Fig. 7, the expression-shared features struggle to capture strong expression-dependent variations, leading to incorrect geometry (e.g., incorrect cheek silhouette), blurry details (e.g., teeth), and inferior LPIPS scores in Tab. 2.", + "bbox": [ + 75, + 719, + 468, + 808 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "3DMM Codes. We extend Static Features by directly concatenating 3DMM expression and pose codes $(\\psi_{i},\\theta_{i})$ to the vertex features $\\{z^j\\}$ . This allows the model to have more capacity to capture expression-dependent variations. Although median-level expression characteristics are recognizable (e.g., cheek silhouette in Fig. 7), the results are even", + "bbox": [ + 75, + 810, + 468, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/7a306dab8f7c5cb56aa4026f957119dac47ee0de3a95cb23d2ae78f32db94478.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Subject0Subject1Subject2Subject3
Full Training Data
Static Features0.15590.15860.15520.1688
3DMM Codes0.15990.16200.17460.1738
3DMM Codes MLP0.15680.15510.15050.1686
Ours-C0.14170.14570.13830.1550
Ours-D0.14390.15230.14150.1559
First 50% Training Data
Ours-C0.20380.14830.15800.1566
+0.0621+0.0026+0.0197+0.0016
Ours-D0.17110.15110.15160.1558
+0.0272-0.0012+0.0101-0.0001
", + "bbox": [ + 504, + 88, + 888, + 272 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2. Quantitative ablations with baselines in LPIPS scores (lower is better). Bold denotes the best while underline denotes the second best.", + "bbox": [ + 498, + 282, + 890, + 324 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "blurrier for components less depend on expressions such as hairs, possibly due to the limited model capacity of shallow MLPs without spatial context.", + "bbox": [ + 496, + 352, + 890, + 396 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "3DMM Codes MLP. To further increase the model capacity, we use a more sophisticated MLP architecture inspired from Zheng et al. [54], which is a MLP-based conditional Variational AutoEncoder (cVAE), to predict the expression-dependent vertex features $\\{z_i^j\\}$ from the 3DMM expression and pose codes $(\\psi_i,\\theta_i)$ . More specifically, the cVAE is conditioned on 3DMM codes and vertex coordinates on neutral face mesh $V_{neutral}$ . Then, the cVAE encodes the frame index into a latent code and decodes it into the vertex features $\\{z_i^j\\}$ . Although the overall sharpness of results is improved (e.g., glasses and hair in Fig. 7), this model still produces blurry details such as teeth, eyes, and wrinkles. Tab. 2 further confirms that this more sophisticated MLP architecture still produces inferior results compared to CNN-based methods Ours-C and Ours-D.", + "bbox": [ + 496, + 397, + 892, + 623 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ours-C vs. Ours-D. By replacing MLPs with CNNs in UV space (Please refer to Sec. 3.2 for more details), both variations of Ours achieve superior rendering quality than MLP-based baselines, as shown in Fig. 7. Moreover, we find that Ours-D is more robust to expressions that are outside the training distribution compared to Ours-C (See Fig. 6). To further investigate the model robustness to out-of-training expressions, we train Ours-C and Ours-D with the first $50\\%$ of training frames, to simulate the scenario with less expression coverage during training. As shown in Tab. 2 and Fig. 6, Ours-D degrades less than Ours-C, indicating enhanced robustness.", + "bbox": [ + 496, + 625, + 890, + 804 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5. Robustness to Expression Extrapolation", + "text_level": 1, + "bbox": [ + 500, + 816, + 841, + 832 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We qualitatively test our method on expression extrapolation setting by artificially manipulating the expression code for a given test frame. In particular, we double the value of the expression code and compare the results with", + "bbox": [ + 496, + 839, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "16896", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d7e6cecf13863a8d3c50c15853fd1d1e52f7c2b635f1648510a204579b82ec6f.jpg", + "image_caption": [ + "Figure 7. Comparison between different designs for local vertex feature learning. See Sec. 4.4 for more details. \"Static feature\" struggles on capturing personalized expressions. \"3DMM Codes\" improves the personalization but suffers from overall blurriness. \"3DMM Codes MLP\" further improves the sharpness, but still cannot present the details. Overall, our convolution-based methods lead to superior renderings on areas such as cheek silhouette, glasses frames and reflections, and teeth." + ], + "image_footnote": [], + "bbox": [ + 83, + 95, + 890, + 335 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/ce37a664adb733227ebf020c22a4135fb06e8620e5c28c4308eb78f4a54aa81b.jpg", + "image_caption": [ + "Figure 8. Expression extrapolation results and comparisons. Compared to other work, our method performs well on extreme and out-of-training expression." + ], + "image_footnote": [], + "bbox": [ + 78, + 415, + 468, + 580 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "prior works. As shown in Fig. 8, NHA [17] gives reasonable expression extrapolation results for both geometry and appearance. However, it cannot faithfully capture the out-of-3DMM details when compared to the ground truth. IMAvatar [53] gives reasonable geometry for extrapolation, but struggles to produce sharp extrapolated appearances. NerveFACE [15] fails in producing reasonable renderings. In contrast, our method not only faithfully captures out-of-3DMM details, but also generalizes to extrapolated expressions.", + "bbox": [ + 75, + 661, + 468, + 797 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Discussion", + "text_level": 1, + "bbox": [ + 76, + 814, + 191, + 829 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we presented a framework to learn high-quality controllable 3D head avatars from monocular RGB videos. The core of our method is a 3DMM-anchored neural radiance field decoded from features attached on 3DMM", + "bbox": [ + 75, + 839, + 470, + 898 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "vertices. The vertex features can be either predicted from 3DMM expression and pose codes, or vertex displacements, via CNNs in UV space, where the former favors quality and the latter favors robustness. We experimentally demonstrate that it is possible to learn high-quality avatars with faithful, highly non-rigid expression deformations purely from monocular RGB videos, leading to a superior rendering quality of our method compared to other approaches.", + "bbox": [ + 496, + 420, + 893, + 542 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations. Compared to the state-of-the-art approaches, our proposed framework learns portrait avatars with superior rendering quality. Nevertheless, our method still inherits the disadvantages of NeRF [30] on time-consuming subject-specific training and slow rendering. Our method relies on the expression space of a 3DMM, thus cannot capture components that are completely missing in the 3DMM, such as the tongue. Moreover, extending our method to include the upper body or integrating into full body avatars are also interesting future directions.", + "bbox": [ + 496, + 561, + 893, + 714 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ethical Considerations. The rapid progress on avatar synthesis facilitates numerous downstream applications, but also raises concerns on ethical misuses. On the synthesis side, it would be ideal to actively use watermarking and avoid driving avatars with different identities. On the viewing side, forgery detection [2,25,35,36,41] is an active research field with promising progress. However, it is still hard to guarantee reliable detection of fake visual materials at the current stage. Encouraging the use of cryptographical signatures may also be a potential solution to ensure the authenticity of visual material.", + "bbox": [ + 496, + 734, + 893, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "16897", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] MetaHuman - Unreal Engine. https://www.unrealengine.com/en-US/metahuman, 2. Accessed: 2022-10-17. 1", + "[2] Shruti Agarwal, Hany Farid, Tarek El-Gaaly, and Ser-Nam Lim. Detecting deep-fake videos from appearance and behavior. In 2020 IEEE International Workshop on Information Forensics and Security (WIFS), pages 1-6. IEEE, 2020. 8", + "[3] ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, and Zhixin Shu. RigNeRF: Fully Controllable Neural 3D Portraits. pages 20364-20373, 2022. 2, 3, 4", + "[4] Ziqian Bai, Zhaopeng Cui, Xiaoming Liu, and Ping Tan. Riggable 3D Face Reconstruction via In-Network Optimization. pages 6216-6225, June 2021. 2", + "[5] Ziqian Bai, Zhaopeng Cui, Jamal Ahmed Rahim, Xiaoming Liu, and Ping Tan. Deep Facial Non-Rigid Multi-View Stereo. pages 5850–5860, 2020. 2", + "[6] Thabo Beeler, Fabian Hahn, Derek Bradley, Bernd Bickel, Paul Beardsley, Craig Gotsman, Robert W Sumner, and Markus Gross. High-Quality Passive Facial Performance Capture Using Anchor Frames. In ACM SIGGRAPH 2011 Papers, pages 1-10. 2011. 1", + "[7] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pages 187-194, 1999. 2", + "[8] Chen Cao, Tomas Simon, Jin Kyu Kim, Gabe Schwartz, Michael Zollhoefer, Shun-Suke Saito, Stephen Lombardi, Shih-En Wei, Danielle Belko, Shoou-I Yu, Yaser Sheikh, and Jason Saragih. Authentic Volumetric Avatars from a Phone Scan. ACM Transactions on Graphics (TOG), 41(4), 2022. 1", + "[9] Chen Cao, Hongzhi Wu, Yanlin Weng, Tianjia Shao, and Kun Zhou. Real-Time Facial Animation With Image-Based Dynamic Avatars. 35(4), 2016. 2", + "[10] Bindita Chaudhuri, Noranart Vesdapunt, Linda Shapiro, and Baoyuan Wang. Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting. In ECCV 2020: 16th European Conference on Computer Vision, pages 142-160, 2020. 2", + "[11] Chong Bao and Bangbang Yang, Zeng Junyi, Bao Hujun, Zhang Yinda, Cui Zhaopeng, and Zhang Guofeng. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In European Conference on Computer Vision (ECCV), 2022. 3", + "[12] Ruofei Du, Ming Chuang, Wayne Chang, Hugues Hoppe, and Amitabh Varshney. Montage4D: Real-time Seamless Fusion and Stylization of Multiview Video Textures. Journal of Computer Graphics Techniques, 8(1):1-34, Jan. 2019. 1", + "[13] Ruofei Du, David Li, and Amitabh Varshney. Geollery: A Mixed Reality Social Media Platform. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, number 685 in CHI. ACM, May 2019. 1", + "[14] Bernhard Egger, William AP Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani," + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "et al. 3D Morphable Face Models—Past, Present, and Future. 39(5):1-38, 2020. 2", + "[15] Guy Gafni, Justus Thies, Michael Zollhofer, and Matthias Nießner. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. pages 8649-8658, 2021. 2, 3, 4, 5, 6, 8", + "[16] Pablo Garrido, Michael Zollhöfer, Dan Casas, Levi Valgaerts, Kiran Varanasi, Patrick Pérez, and Christian Theobalt. Reconstruction of Personalized 3D Face Rigs From Monocular Video. ACM Transactions on Graphics (TOG), 35(3):28, 2016. 2", + "[17] Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. Neural Head Avatars From Monocular RGB Videos. pages 18653–18664, 2022. 2, 5, 6, 8", + "[18] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, Danhang Tang, Anastasia Tkach, Adarsh Kowdle, Emily Cooper, Ming-song Dou, Sean Fanello, Graham Fyffe, Christoph Rhemann, Jonathan Taylor, Paul Debevec, and Shahram Izadi. The Relightables: Volumetric Performance Capture of Humans With Realistic Relighting. ACM Transactions on Graphics, Nov. 2019. 1", + "[19] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, Danhang Tang, Anastasia Tkach, Adarsh Kowdle, Emily Cooper, Ming-song Dou, Sean Fanello, Graham Fyffe, Christoph Rhemann, Jonathan Taylor, Paul Debevec, and Shahram Izadi. The relightables: Volumetric performance capture of humans with realistic relighting. ACM Trans. Graph., 38(6), nov 2019. 3", + "[20] Zhenyi He, Ruofei Du, and Ken Perlin. CollaboVR: A Reconfigurable Framework for Multi-user to Communicate in Virtual Reality. In 2020 IEEE International Symposium on Mixed and Augmented Reality, ISMAR, pages 542-554. IEEE, Nov. 2020. 1", + "[21] Liwen Hu, Shunsuke Saito, Lingyu Wei, Koki Nagano, Jaewoo Seo, Jens Fursund, Iman Sadeghi, Carrie Sun, YenChun Chen, and Hao Li. Avatar Digitization From a Single Image for Real-Time Rendering. 36(6):1-14, 2017. 2", + "[22] Alexandru Eugen Ichim, Sofien Bouaziz, and Mark Pauly. Dynamic 3D Avatar Creation From Hand-Held Video Input. 34(4):1-14, 2015. 2", + "[23] Wei Jiang, Kwang Moo Yi, Golnoosh Samei, Oncel Tuzel, and Anurag Ranjan. NeuMan: Neural Human Radiance Field From a Single Video. 2022. 4", + "[24] Tianye Li, Timo Bolkart, Michael. J. Black, Hao Li, and Javier Romero. Learning a Model of Facial Shape and Expression From 4D Scans. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6):194:1-194:17, 2017. 2, 3", + "[25] Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. Celeb-df: A large-scale challenging dataset for deepfake forensics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3207-3216, 2020. 8" + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "16898", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[26] Siyou Lin, Hongwen Zhang, Zerong Zheng, Ruizhi Shao, and Yebin Liu. Learning implicit templates for point-based clothed human modeling. In ECCV, 2022. 3", + "[27] Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. Advances in Neural Information Processing Systems, 33:15651-15663, 2020. 3", + "[28] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Control. ACM Transactions on Graphics (TOG), 40(6):1-16, 2021. 3", + "[29] Abhimitra Meka, Rohit Pandey, Christian Haene, Sergio Orts-Escalano, Peter Barnum, Philip David-Son, Daniel Erickson, Yinda Zhang, Jonathan Taylor, Sofien Bouaziz, et al. Deep Relightable Textures: Volumetric Performance Capture With Neural Rendering. ACM Transactions on Graphics (TOG), 39(6):1-21, 2020. 1", + "[30] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing Scenes As Neural Radiance Fields for View Synthesis. Communications of the ACM, 65(1):99-106, 2021. 2, 4, 8", + "[31] Sergio Orts-Escolano, Christoph Rhemann, Sean Fanello, Wayne Chang, Adarsh Kowdle, Yury Degtyarev, David Kim, Philip Davidson, Sameh Khamis, Mingsong Dou, Vladimir Tankovich, Charles Loop, Qin Cai, Philip Chou, Sarah Mennicken, Julien Valentin, Vivek Pradeep, Shenlong Wang, Sing Bing Kang, Pushmeet Kohli, Yuliya Lutchyn, Cem Keskin, and Shahram Izadi. Holoportation: Virtual 3D Teleportation in Real-Time. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST). ACM, Oct. 2016. 1", + "[32] Rohit Pandey, Sergio Orts Escolano, Chloe Legendre, Christian Haene, Sofien Bouaziz, Christoph Rhemann, Paul Debevec, and Sean Fanello. Total relighting: learning to relight portraits for background replacement. ACM Transactions on Graphics (TOG), 40(4):1-21, 2021. 3, 5", + "[33] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable Neural Radiance Fields. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 5865-5874, 2021. 5", + "[34] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural Body: Implicit Neural Representations With Structured Latent Codes for Novel View Synthesis of Dynamic Humans. pages 9054-9063, 2021. 3, 4", + "[35] Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: A large-scale video dataset for forgery detection in human faces. arXiv preprint arXiv:1803.09179, 2018. 8", + "[36] Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: Learning to detect manipulated facial images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1-11, 2019. 8" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[37] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Hao Li, and Angjoo Kanazawa. PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, Oct. 2019. 1", + "[38] Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. Animating arbitrary objects via deep motion transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2377-2386, 2019. 3", + "[39] Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. First Order Motion Model for Image Animation. In Advances in Neural Information Processing Systems (NeurIPS), volume 32, 2019. 3, 5, 6", + "[40] Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. FML: Face Model Learning From Videos. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10812-10822, 2019. 2", + "[41] Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales, and Javier Ortega-Garcia. Deepfakes and beyond: A survey of face manipulation and fake detection. Information Fusion, 64:131-148, 2020. 8", + "[42] Zach Waggoner. My Avatar, My Self: Identity in Video Role-Playing Games. McFarland, 2009. 1", + "[43] Ting-Chun Wang, Ming-Yu Liu, Andrew Tao, Guilin Liu, Jan Kautz, and Bryan Catanzaro. Few-Shot Video-to-Video Synthesis. In Advances in Neural Information Processing Systems (NeurIPS), 2019. 3", + "[44] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image Quality Assessment: From Error Visibility to Structural Similarity. 13(4):600-612, 2004. 5", + "[45] Chung-Yi Weng, Brian Curless, Pratul P Srinivasan, Jonathan T Barron, and Ira Kemelmacher-Shlizerman. Humanerf: Free-viewpoint rendering of moving people from monocular video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16210-16220, 2022. 4", + "[46] Olivia Wiles, A Koepke, and Andrew Zisserman. X2face: A network for controlling face generation using images, audio, and pose codes. In Proceedings of the European conference on Computer Vision (ECCV), pages 670-686, 2018. 3", + "[47] Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In European Conference on Computer Vision, pages 597-614. Springer, 2022. 4", + "[48] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction. pages 601-610, 2020. 2", + "[49] Egor Zakharov, Aleksei Ivakhnenko, Aliaksandra Shysheya, and Victor Lempitsky. Fast bi-layer neural synthesis of one-shot realistic head avatars. In European Conference on Computer Vision, pages 524-540. Springer, 2020. 3" + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "16899", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[50] Egor Zakharov, Aliaksandra Shysheya, Egor Burkov, and Victor Lempitsky. Few-Shot Adversarial Learning of Realistic Neural Talking Head Models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9459-9468, 2019. 3", + "[51] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The Unreasonable Effectiveness of Deep Features As a Perceptual Metric. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 586-595, 2018. 5", + "[52] Jian Zhao and Hui Zhang. Thin-plate spline motion model for image animation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3657–3666, 2022. 5, 6", + "[53] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. Im avatar: Implicit morphable head avatars from videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13545–13555, 2022. 3, 4, 5, 6, 8", + "[54] Zerong Zheng, Han Huang, Tao Yu, Hongwen Zhang, Yandong Guo, and Yebin Liu. Structured Local Radiance Fields for Human Avatar Modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15893-15903, 2022. 3, 4, 7", + "[55] Michael Zollhöfer, Justus Thies, Pablo Garrido, Derek Bradley, Thabo Beeler, Patrick Pérez, Marc Stamminger, Matthias Nießner, and Christian Theobalt. State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications. volume 37, pages 523-550, 2018. 2" + ], + "bbox": [ + 78, + 90, + 468, + 513 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "16900", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Learning Personalized High Quality Volumetric Head Avatars From Monocular RGB Videos/f99f5e12-9192-46a6-b4e3-a025ac8e0105_model.json b/2023/Learning Personalized High Quality Volumetric Head Avatars From Monocular RGB Videos/f99f5e12-9192-46a6-b4e3-a025ac8e0105_model.json new file mode 100644 index 0000000000000000000000000000000000000000..091813a7150fa066aa08d20394c3dcacdb73326c --- /dev/null +++ b/2023/Learning Personalized High Quality Volumetric Head Avatars From Monocular RGB Videos/f99f5e12-9192-46a6-b4e3-a025ac8e0105_model.json @@ -0,0 +1,2246 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.0, + 0.812, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.172, + 0.131, + 0.8, + 0.175 + ], + "angle": 0, + "content": "Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos" + }, + { + "type": "text", + "bbox": [ + 0.135, + 0.202, + 0.834, + 0.277 + ], + "angle": 0, + "content": "Ziqian Bai\\(^{1,2*}\\) Feitong Tan\\(^{1}\\) Zeng Huang\\(^{1}\\) Kripasindhu Sarkar\\(^{1}\\) Danhang Tang\\(^{1}\\) Di Qiu\\(^{1}\\) Abhimitra Meka\\(^{1}\\) Ruofei Du\\(^{1}\\) Mingsong Dou\\(^{1}\\) Sergio Orts-Escalano\\(^{1}\\) Rohit Pandey\\(^{1}\\) Ping Tan\\(^{2}\\) Thabo Beeler\\(^{1}\\) Sean Fanello\\(^{1}\\) Yinda Zhang\\(^{1}\\) Google\\(^{2}\\) Simon Fraser University" + }, + { + "type": "image", + "bbox": [ + 0.082, + 0.279, + 0.236, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.082, + 0.393, + 0.236, + 0.493 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.092, + 0.494, + 0.228, + 0.505 + ], + "angle": 0, + "content": "Monocular RGB Video" + }, + { + "type": "image", + "bbox": [ + 0.238, + 0.328, + 0.291, + 0.345 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.296, + 0.288, + 0.372, + 0.39 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.379, + 0.287, + 0.455, + 0.39 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.468, + 0.286, + 0.535, + 0.391 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.547, + 0.287, + 0.614, + 0.39 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.629, + 0.288, + 0.7, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.721, + 0.288, + 0.786, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.802, + 0.288, + 0.872, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.24, + 0.431, + 0.288, + 0.447 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.289, + 0.4, + 0.39, + 0.488 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.408, + 0.4, + 0.516, + 0.488 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.527, + 0.4, + 0.637, + 0.491 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.655, + 0.398, + 0.754, + 0.491 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.768, + 0.397, + 0.874, + 0.49 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.482, + 0.494, + 0.721, + 0.506 + ], + "angle": 0, + "content": "Controllable Photorealistic Head Avatar" + }, + { + "type": "image_caption", + "bbox": [ + 0.075, + 0.511, + 0.895, + 0.568 + ], + "angle": 0, + "content": "Figure 1. Our technique builds a 3D avatar representation of a person using just a single short monocular RGB video (e.g., 1-2 minutes). We leverage a 3DMM to track the user's expressions. By anchoring a neural radiance field to the 3DMM geometry, we generate a volumetric photorealistic 3D avatar that can be rendered with user-defined expression and viewpoint. Note that our method works well on challenging materials, e.g., hair and dramatic expressions. Please see our webpage augmentedperception.github.io/monoavatar for more results." + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.579, + 0.314, + 0.596 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.614, + 0.473, + 0.902 + ], + "angle": 0, + "content": "We propose a method to learn a high-quality implicit 3D head avatar from a monocular RGB video captured in the wild. The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses. Our hybrid pipeline combines the geometry prior and dynamic tracking of a 3DMM with a neural radiance field to achieve fine-grained control and photorealism. To reduce over-smoothing and improve out-of-model expressions synthesis, we propose to predict local features anchored on the 3DMM geometry. These learnt features are driven by 3DMM deformation and interpolated in 3D space to yield the volumetric radiance at a designated query point. We further show that using a Convolutional Neural Network in the UV space is critical in incorporating spatial context and producing representative local features. Extensive experiments show that we are able to reconstruct high-quality avatars, with more accurate expression-dependent details, good generalization to out-of-training expressions, and quantitatively superior renderings compared to other" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.581, + 0.686, + 0.597 + ], + "angle": 0, + "content": "state-of-the-art approaches." + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.641, + 0.633, + 0.657 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.666, + 0.892, + 0.879 + ], + "angle": 0, + "content": "Creating a controllable human avatar is a fundamental piece of technology for many downstream applications, such as AR/VR communication [20, 31], virtual try-on [37], virtual tourism [13], games [42], and visual effects for movies [12, 18]. Prior art in high-quality avatar generation typically requires extensive hardware configurations (i.e., camera arrays [6, 12, 31], light stages [18, 29], dedicated depth sensors [8]), or laborious manual intervention [1]. Alternatively, reconstructing avatars from monocular RGB videos significantly relaxes the dependency on equipment setup and broadens the application scenarios. However, monocular head avatar creation is highly ill-posed due to the dual problems of reconstructing and tracking highly articulated and deformable facial geometry, while model" + }, + { + "type": "page_footnote", + "bbox": [ + 0.524, + 0.887, + 0.866, + 0.902 + ], + "angle": 0, + "content": "*Work was conducted while Ziqian Bai was an intern at Google." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "16890" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.081, + 0.092, + 0.468, + 0.212 + ], + "angle": 0, + "content": "ing sophisticated facial appearance. Traditionally, 3D Morphable Models (3DMM) [7, 24] have been used to model facial geometry and appearance for various applications including avatar generation [9, 16, 22]. However, 3DMMs do not fully capture subject-specific static details and dynamic variations, such as hair, glasses, and expression-dependent high frequency details such as wrinkles, due to the limited capacity of the underlying linear model." + }, + { + "type": "text", + "bbox": [ + 0.081, + 0.216, + 0.468, + 0.576 + ], + "angle": 0, + "content": "Recent works [3, 15] have incorporated neural radiance fields in combination with 3DMMs for head avatar generation to achieve photorealistic renderings, especially improving challenging areas, such as hair, and adding view-dependent effects, such as reflections on glasses. The pioneering work of NerFACE [15] uses a neural radiance field parameterized by an MLP that is conditioned on 3DMM expression parameters and learnt per-frame latent codes. While they achieve photorealistic renderings, the reliance on an MLP to directly decode from 3DMM parameter space leads to the loss of fine-grain control over geometry and articulation. Alternatively, RigNeRF [3] learns the radiance field in a canonical space by warping the target head geometry using a 3DMM fit, which is further corrected by a learnt dense deformation field parameterized by another MLP. While they demonstrate in-the-wild head pose and expression control, the use of two global MLPs to model canonical appearance and deformations for the full spatial-temporal space leads to a loss of high frequency details, and an overall uncanny appearance of the avatar. Both of these works introduce new capabilities but suffer from lack of detail in both appearance and motion because they attempt to model the avatar's global appearance and deformation with an MLP network." + }, + { + "type": "text", + "bbox": [ + 0.081, + 0.581, + 0.468, + 0.837 + ], + "angle": 0, + "content": "In this paper, we propose a method to learn a neural head avatar from a monocular RGB video. The avatar can be controlled by an underlying 3DMM model and deliver high-quality rendering of arbitrary facial expressions, head poses, and viewpoints, which retain fine-grained details and accurate articulations. We achieve this by learning to predict expression-dependent spatially local features on the surface of the 3DMM mesh. A radiance field for any given 3D point in the volume is then obtained by interpolating the features from K-nearest neighbor vertices on the deformed 3DMM mesh in target expression, and passing them through a local MLP to infer density and color. The local features and local MLP are trained jointly by supervising the radiance field through standard volumetric rendering on the training sequence [30]. Note that our networks rely on the local features to model appearance and deformation details, and leverages the 3DMM to model only the global geometry." + }, + { + "type": "text", + "bbox": [ + 0.081, + 0.84, + 0.468, + 0.899 + ], + "angle": 0, + "content": "Learning local features is critical in achieving a high-quality head avatar. To this end, we train an image-to-image translation U-Net that transforms the 3DMM deformations in the UV space to such local features. These UV" + }, + { + "type": "text", + "bbox": [ + 0.504, + 0.092, + 0.89, + 0.302 + ], + "angle": 0, + "content": "space features are then attached to the corresponding vertices of the 3DMM mesh geometry. We show that learning features from such explicit per-vertex local displacement of the 3DMM geometry makes the model retain high-frequency expression-dependent details and also generalizes better to out-of-training expressions, presumably because of the spatial context between nearby vertices incorporated by the convolutional neural network (CNN). An alternative approach is to feed the 3DMM parameters directly into a CNN decoder running on the UV space. However, we found this produces severe artifacts on out-of-training expressions, particularly given a limited amount of training data, e.g. for a lightweight, 1-minute data collection procedure during the avatar generation process." + }, + { + "type": "text", + "bbox": [ + 0.504, + 0.304, + 0.89, + 0.483 + ], + "angle": 0, + "content": "In summary, our contributions are as follows: we propose a neural head avatar representation based on a 3DMM-anchored neural radiance field, which can model complex expression-dependent variations, but requires only monocular RGB videos for training. We show that a convolutional neural network running on per-vertex displacement in UV space is effective in learning local expression-dependent features, and delivers favorable training stability and generalization to out-of-training expressions. Experiments on real-world datasets show that our model provides competitive controllability and generates sharper and detail enriched rendering compared to state-of-the-art approaches." + }, + { + "type": "title", + "bbox": [ + 0.504, + 0.498, + 0.647, + 0.513 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "text", + "bbox": [ + 0.504, + 0.524, + 0.89, + 0.598 + ], + "angle": 0, + "content": "Building photorealistic representations of humans has been widely researched in the past few decades. Here, we mainly discuss prior art in head avatar and refer readers to the state-of-the-art surveys [14, 55] for a comprehensive literature review." + }, + { + "type": "text", + "bbox": [ + 0.504, + 0.6, + 0.89, + 0.824 + ], + "angle": 0, + "content": "Monocular Explicit Surface Head (Face) Avatars. Traditionally, a typical approach to create head (or face) avatars from monocular RGB videos is using a 3D Morphable Models (3DMM) as the foundation and adding personalized representations, such as corrected blendshapes [16, 22], detail texture maps [16, 22], image-based representations [9], and secondary components [21]. Early works use various optimizations to obtain the personalized representations from monocular data, including analysis-by-synthesis [14, 16, 21, 55] as well as shape-from-shading [16, 22]. Recent approaches replace optimizations by regressions with Deep Neural Networks (DNNs) [10, 40, 48], or integrate optimizations with deep learning components [4, 5]. More recent methods leverage neural textures [17] to generate photorealistic appearances." + }, + { + "type": "text", + "bbox": [ + 0.504, + 0.826, + 0.89, + 0.899 + ], + "angle": 0, + "content": "The main drawback of these methods is that they rely on explicit meshes with a fixed topology, making it hard to handle out-of-model details such as hair and accessories like glasses and apparels. In contrast, our hybrid method combines geometric priors with implicit representations, lead-" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.947, + 0.517, + 0.956 + ], + "angle": 0, + "content": "16891" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.08, + 0.09, + 0.895, + 0.302 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.306, + 0.895, + 0.377 + ], + "angle": 0, + "content": "Figure 2. Overview of our pipeline. The core of our method is the Avatar Representation (Sec. 3.1. Shown as the yellow area) based on a 3DMM-anchored neural radiance field (NeRF), which are decoded from local features attached on the 3DMM vertices. Then, we use volumetric rendering to compute the output image. To predict the vertex-attached features (Sec. 3.2. Shown as the green area), we first compute the vertex displacements from the 3DMM expression and pose, then process the displacements in UV space with Convolutional Neural Networks (CNNs), and sample the obtained features back to mesh vertices." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.403, + 0.415, + 0.417 + ], + "angle": 0, + "content": "ing to a significantly larger representation capacity." + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.417, + 0.47, + 0.734 + ], + "angle": 0, + "content": "Monocular Implicit Head Avatars. Recent work proposes to extend 3DMM with implicit 3D representations. NerFACE [15] introduces a dynamic neural radiance field (NeRF) conditioned on 3DMM expression codes which can render a view-consistent avatar with volumetric rendering. Since NerFACE directly inputs the 3DMM expression codes into MLPs without using any shape or spatial information from 3DMM, their model is quite under-constrained for monocular reconstruction, and suffers from severe artifacts for data with challenging expressions. RigNeRF [3] uses 3DMM derived warping field to deform the camera space into a canonical space, and defines a canonical NeRF conditioned on 3DMM codes. However, their model uses a dense MLP-based architecture to memorize the appearance and deformation for the full head, leading to oversmooth results due to limited network capacity. IMAAvatar [53] learns personalized implicit fields of blendshapes, pose correctives, and skinning weights, then formulates the avatar with linear summation of blendshapes followed by linear blend skinning. However, their linear formulation limits the amount of expression deformations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Geometry Anchored Implicit 3D Representation. Sparse local feature embedding attached on geometry has been demonstrated to be effective in improving the rendering quality of neural radiance field [26-28, 34, 54]. It also naturally supports neural radiance field editing since the modification on the geometry can be directly propagated to the rendering [11], which makes them a favorable representation to support the controllability for human avatar. We adapt this representation to head avatar and incorporate head specific priors. Differently from prior art, we leverage a CNN in UV space to learn local, per-vertex features" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.403, + 0.892, + 0.431 + ], + "angle": 0, + "content": "that are expression-dependent, improving generalization of out-of-train expressions." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.434, + 0.893, + 0.736 + ], + "angle": 0, + "content": "2D-based Head Avatars. There are numerous approaches that synthesize the head (or face) relying on 2D (explicit/implicit) representations, including 2D facial landmarks [43, 49, 50] and 2D warping fields [38, 39, 46]. Landmark-based avatar models [43, 49, 50] synthesize the face conditioned to the facial landmarks extracted with a, usually pre-trained, landmark detector. Specifically, an encoder is applied to extract an identity embedding from a reference image, a decoder is adapted by the identity code to animate the reference face with landmarks from the driving videos. X2Face [46] is the first approach to animate human heads by learning a dense warping field and producing the output video via image warping. MonkeyNet [38] and First Order Motion Model (FOMM) further propose to infer motion fields with self-learned keypoints, which significantly improves motion prediction and synthesizes higher quality renderings of heads. While most aforementioned methods can produce photorealistic results, they are not able to maintain geometry and multiview consistency due to their inherent 2D representation." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.753, + 0.591, + 0.769 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Given a monocular RGB video containing \\(M\\) frames \\(\\{I_1, I_2, \\dots, I_M\\}\\), our method reconstructs a head avatar representation that can be rendered under arbitrary facial expressions, head poses, and camera viewpoints. We first preprocess the video to remove the background [19,32] and obtain camera and 3DMM parameters for each frame. More specifically, we use FLAME [24] as the 3DMM and denote the fitted face with shape \\(\\beta\\), expressions \\(\\psi_i\\), poses \\(\\theta_i\\) (i.e.," + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16892" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.09, + 0.468, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.231, + 0.47, + 0.314 + ], + "angle": 0, + "content": "Figure 3. Illustration of Avatar Representation (Sec. 3.1). Given a query point, we find its k-Nearest-Neighbor (k-NN) vertices from the 3DMM. Then, we decode these vertices and features into a density and color with respect to the input camera view direction, via Multi-Layer-Perceptrons (MLPs) interleaved with inverse-distance based weighted sum." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.34, + 0.469, + 0.401 + ], + "angle": 0, + "content": "neck, jaw, and eyes), where \\(i\\) is the frame index, with which a head mesh can be obtained via \\(\\pmb{V}_i(\\pmb{\\beta},\\pmb{\\psi}_i,\\pmb{\\theta}_i)\\). Since \\(\\beta\\) is fixed and does not depend on the pose or expressions for a given user, we omit it in the following sections for brevity." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.402, + 0.469, + 0.627 + ], + "angle": 0, + "content": "An overview of our framework is shown in Fig. 2. We adopt the 3DMM-anchored neural radiance field (NeRF) as the core representation for our head avatar (Sec. 3.1), where local features are attached to the vertices of the deformable 3DMM mesh. During the inference, we first deform the 3DMM mesh based on the target configuration \\( V_{t} = (\\psi_{t},\\theta_{t}) \\). Then, for an arbitrary 3D query point, we aggregate the features from neighboring vertices on \\( V_{t} \\) to estimate the local density and color by Multi-Layer-Perceptrons (MLPs), which are then integrated in the volumetric rendering formulation to generate the color image. To learn local features, we train CNN-based networks in the UV space to incorporate spatial context (Sec. 3.2). Our model is trained end-to-end with RGB supervisions (Sec. 3.3)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.636, + 0.286, + 0.652 + ], + "angle": 0, + "content": "3.1. Avatar Representation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.66, + 0.469, + 0.75 + ], + "angle": 0, + "content": "An ideal representation for a head avatar should have the following properties: 1) Provides intuitive control to achieve the desired expression and head pose; 2) Requires a moderate amount of training data, e.g., a short monocular video; 3) Produces expression-dependent rendering details; 4) Generalizes reasonably well to unseen expressions." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.469, + 0.901 + ], + "angle": 0, + "content": "To this end, we propose the 3DMM-anchored neural radiance field (NeRF) as shown in Fig. 3. Inspired by local feature based neural radiance field [34, 47], we attach feature vectors \\( z^{j} \\) on each 3DMM vertex \\( v_{i}^{j} \\) to encode the local radiance fields that can be decoded with MLPs, where \\( i \\) denotes frame index and \\( j \\) denotes vertex index. In this way, the radiance field can be deformed according to vertex locations, hence can be intuitively controlled by the 3DMM expression and pose \\( (\\psi_{i},\\theta_{i}) \\). In addition, the 3DMM fitting on each frame provides a rough tracking across deformable" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.891, + 0.137 + ], + "angle": 0, + "content": "face geometries, such that all the frames can contribute into the learning of a unified set of local per-vertex features. We will discuss model capacity and generalization in Sec. 3.2." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.137, + 0.892, + 0.228 + ], + "angle": 0, + "content": "To decode the vertex features \\(\\{z^j\\}\\) into the radiance field for the frame \\(i\\), given a 3D query point \\(\\pmb{q}\\), we first find its \\(k\\)-Nearest-Neighbor (\\(k\\)-NN) vertices from the 3DMM mesh \\(\\{\\pmb{v}_i^j\\}_{j\\in \\mathcal{N}_k^q}\\) with attached features \\(\\{z^j\\}_{j\\in \\mathcal{N}_k^q}\\). Then, we use two MLPs \\(\\mathcal{F}_0\\) and \\(\\mathcal{F}_1\\) with inverse-distance based weighted sum to decode local color and density. Formally," + }, + { + "type": "equation", + "bbox": [ + 0.667, + 0.238, + 0.81, + 0.258 + ], + "angle": 0, + "content": "\\[\n\\hat {\\boldsymbol {z}} _ {i} ^ {j} = \\mathcal {F} _ {0} (\\boldsymbol {v} _ {i} ^ {j} - \\boldsymbol {q}, \\boldsymbol {z} ^ {j})\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.67, + 0.261, + 0.891, + 0.281 + ], + "angle": 0, + "content": "\\[\n\\hat {\\boldsymbol {z}} _ {i} = \\sum_ {j} w ^ {j} \\hat {\\boldsymbol {z}} _ {i} ^ {j} \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.581, + 0.283, + 0.782, + 0.3 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {c} _ {i} (\\boldsymbol {q}, \\boldsymbol {d} _ {i}), \\sigma_ {i} (\\boldsymbol {q}) = \\mathcal {F} _ {1} (\\hat {\\boldsymbol {z}} _ {i}, \\boldsymbol {d} _ {i}),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.313, + 0.892, + 0.38 + ], + "angle": 0, + "content": "where \\( w^{j} = \\frac{d^{j}}{\\sum_{k} d^{k}} \\), \\( d^{j} = \\frac{1}{\\|\\pmb{v}_{i}^{j} - \\pmb{q}\\|_{2}} \\) with \\( j \\in \\mathcal{N}_k^q \\), and \\( \\pmb{d}_i \\) denotes the camera view direction. Finally, we render the output image with volumetric rendering formulation as in vanilla NeRF [30] given the camera ray \\( r(t) = o + td \\):" + }, + { + "type": "equation", + "bbox": [ + 0.56, + 0.391, + 0.891, + 0.427 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {C} _ {i} (\\boldsymbol {r}) = \\int_ {t _ {n}} ^ {t _ {f}} T (t) \\sigma_ {i} (\\boldsymbol {r} (t)) \\boldsymbol {c} _ {i} (\\boldsymbol {r} (t), \\boldsymbol {d}) \\mathrm {d} t, \\tag {2}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.558, + 0.429, + 0.822, + 0.465 + ], + "angle": 0, + "content": "\\[\n\\text {w h e r e} T (t) = \\exp \\left(- \\int_ {t _ {n}} ^ {t} \\sigma_ {i} (\\boldsymbol {r} (s)) \\mathrm {d} s\\right)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.475, + 0.892, + 0.684 + ], + "angle": 0, + "content": "To reduce misalignments caused by per-frame contents that cannot be captured by 3DMM (e.g., 3DMM fitting errors), we additionally learn error-correction warping fields during training inspired from prior works on deformable NeRF [23, 45]. More specifically, we input the original query point and a per-frame latent code \\( e_i \\), which is randomly initialized and optimized during the training, into the error-correction MLPs \\( \\mathcal{F}_{\\mathcal{E}} \\) to predict a rigid transformation, and apply it to the query point. The transformation is denoted as \\( q' = \\mathcal{T}_i(q) = \\mathcal{F}_{\\mathcal{E}}(q, e_i) \\). Then we use the warped query point \\( q' \\) to decode the color and density. Note that this warping field is disabled during testing. Please refer to the supplementary for detailed formulations of the warping field." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.696, + 0.864, + 0.712 + ], + "angle": 0, + "content": "3.2. Predicting Expression-Dependent Features" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.891, + 0.795 + ], + "angle": 0, + "content": "While the proposed avatar representation (Sec. 3.1) enables intuitive controllability and convenience in learning, it still has limited capability for modeling complex expression-dependent variations due to the use of frame-shared vertex features \\(\\{z^j\\}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.892, + 0.901 + ], + "angle": 0, + "content": "To overcome this, we propose to predict the dynamic vertex features \\(\\{z_i^j\\}\\) conditioned on the 3DMM expression and pose \\((\\psi_{i},\\theta_{i})\\). A common practice for NeRF-based methods is to use MLP-based architectures for dynamic feature prediction [3, 15, 53, 54]. However, we find that this leads to blurry rendering results, presumably because of the limited model capacity due to the lack of spatial context (i.e.," + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.956 + ], + "angle": 0, + "content": "16893" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.079, + 0.089, + 0.887, + 0.216 + ], + "angle": 0, + "content": "
Subject0\nLPIPS / SSIM / PSNRSubject1\nLPIPS / SSIM / PSNRSubject2\nLPIPS / SSIM / PSNRSubject3\nLPIPS / SSIM / PSNRSubject4\nLPIPS / SSIM / PSNR
TPSMM [52]0.192 / 0.852 / 22.600.205 / 0.830 / 16.380.216 / 0.782 / 18.400.222 / 0.799 / 20.280.156 / 0.913 / 21.29
FOMM [39]0.171 / 0.841 / 22.930.179 / 0.827 / 16.020.202 / 0.777 / 18.980.186 / 0.798 / 22.280.122 / 0.915 / 23.94
NHA [17]0.165 / 0.836 / 20.200.166 / 0.840 / 15.480.178 / 0.809 / 17.990.153 / 0.798 / 21.310.091 / 0.926 / 23.78
IMAvatar [53]0.207 / 0.852 / 21.260.187 / 0.848 / 15.980.265 / 0.729 / 15.800.214 / 0.782 / 20.370.142 / 0.897 / 20.63
NerFACE [15]0.205 / 0.817 / 20.060.182 / 0.833 / 15.780.188 / 0.793 / 19.410.229 / 0.747 / 18.160.093 / 0.938 / 25.57
Ours-D0.144 / 0.864 / 21.920.152 / 0.855 / 16.230.141 / 0.841 / 20.420.156 / 0.833 / 23.050.075 / 0.944 / 25.71
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.221, + 0.893, + 0.25 + ], + "angle": 0, + "content": "Table 1. Quantitative Comparison with state-of-the-art (SOTA) approaches. Subject4 is the data from NerFACE [15], while other subjects are from our dataset. Our method achieves superior results than prior SOTAs." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.276, + 0.469, + 0.336 + ], + "angle": 0, + "content": "each vertex does not know the feature of its neighboring vertices). Based on this intuition, we propose to process the 3DMM expression and pose \\((\\psi_i,\\theta_i)\\) with CNNs in the texture atlas space (UV space) to provide local spatial context." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.336, + 0.473, + 0.655 + ], + "angle": 0, + "content": "Specifically, we design two variations of CNN-based architecture to learn expression-dependent vertex features \\(\\{z_i^j\\}\\). The first variant, denoted as Ours-C, trains a decoder consisting of transposed convolutional blocks to predict the feature map in UV space directly from 1-D codes of 3DMM expression and pose. We empirically find that such a model is effective in improving the overall rendering quality, however tends to fail and produce severe artifacts on out-of-training expressions (See discussions in Sec. 4.4). We then propose the second variant, denoted as Ours-D, that uses the 3D deformation of 3DMM in UV space as the input for feature prediction, and observe that resulting avatar models are more resilient to stretchy and unseen expressions. Specifically, we first compute the vertex displacements using 3DMM expression and pose as \\( D_{i} = V_{i}(\\psi_{i},\\theta_{i}) - V_{neutral}(0,0) \\). We then rasterize the vertex displacements \\( D_{i} \\) into UV space, and process it with a U-Net \\( \\mathcal{F}_{\\mathcal{D}} \\). Finally, the output UV feature map is sampled back to mesh vertices \\( V_{i} \\), serving as the dynamic vertex features \\(\\{z_i^j\\}\\) (i.e., the expression-dependent version of frame-shared vertex features \\(\\{z^j\\}\\) described in Sec. 3.1)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.661, + 0.245, + 0.677 + ], + "angle": 0, + "content": "3.3. Training Schema" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.684, + 0.47, + 0.822 + ], + "angle": 0, + "content": "Our model is trained on monocular RGB videos mainly with the photometric loss, where we penalize the \\(l_{2}\\) distance between the rendering and the ground truth images. Formally \\(\\mathcal{L}_{rgb} = \\sum_{i}\\sum_{\\boldsymbol{r}}\\| C_i(\\boldsymbol{r}) - I_i(\\boldsymbol{r})\\|_2\\), where \\(\\boldsymbol{r}\\) denotes the camera ray of each pixel and \\(i\\) denotes frame index. To regularize the learning of error-correction warping field \\(\\mathcal{T}(\\boldsymbol{q})\\), we adopt a elastic loss \\(\\mathcal{L}_{elastic}\\) similar to Nerfies [33], and a magnitude loss defined as \\(\\mathcal{L}_{mag} = \\sum_q\\| q - \\mathcal{T}(q)\\|_2^2\\). The total loss is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.13, + 0.832, + 0.469, + 0.849 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathcal {L} _ {r g b} + \\lambda_ {\\text {e l a s t i c}} \\mathcal {L} _ {\\text {e l a s t i c}} + \\lambda_ {\\text {m a g}} \\mathcal {L} _ {\\text {m a g}}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.856, + 0.47, + 0.903 + ], + "angle": 0, + "content": "where we set \\(\\lambda_{elastic} = 10^{-4}\\) at the beginning and decay to \\(10^{-5}\\) after 155k iterations, and \\(\\lambda_{mag} = 10^{-2}\\). Please see supplementary for more details." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.274, + 0.634, + 0.291 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.309, + 0.893, + 0.416 + ], + "angle": 0, + "content": "We train and evaluate our method on casually captured monocular RGB videos (Sec. 4.1) and show that our method achieves superior rendering quality than prior state-of-the-art monocular RGB head avatars (Sec. 4.2). Then we verify our key observations on architectural choices to design a good avatar model, in terms of rendering quality and expression robustness (Sec. 4.4)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.452, + 0.698, + 0.467 + ], + "angle": 0, + "content": "4.1. Datasets and Metrics" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.484, + 0.893, + 0.817 + ], + "angle": 0, + "content": "Datasets. Following the prior art [15], we captured monocular RGB videos of various subjects with smartphones or webcams for training and evaluation. Each video is 1-2 minutes long (around \\(1.5\\mathrm{k - }2\\mathrm{k}\\) frames at 30 FPS) with the first 1000-1500 frames as training data and the rest frames for evaluation if not otherwise specified. For the training clip, the subjects are asked to first keep a neutral expression and rotate their heads, then perform different expressions during the head rotation, with extreme expressions included. For the testing clip, the subjects are asked to perform freely without any constraints. To demonstrate that our method also works for common talking head videos, we also include a video from NerFACE [15], which has significantly less variability in expressions when compared to our capture protocol. We mask out background [32] for each video, and obtain 3DMM and camera parameters with 3DMM fitting similar to that in NHA [17]. Please refer to the supplementary for examples of data. Please note that we collect relatively shorter training videos compared to related work. This favors a better user experience while still synthesizing high-quality avatars with more personalized and detailed expressions." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.894, + 0.901 + ], + "angle": 0, + "content": "Metrics. Following the prior art [15], we use the following standard image quality metrics for quantitative evaluations: the Learned Perceptual Image Patch Similarity (LPIPS) [51], Structure Similarity Index (SSIM) [44], and Peak Signal-to-Noise Ratio (PSNR)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16894" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.078, + 0.09, + 0.891, + 0.532 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.533, + 0.895, + 0.565 + ], + "angle": 0, + "content": "Figure 4. Qualitative Comparison to prior state-of-the-art monocular head avatars. Note how our approach more faithfully reconstructs the ground truth expressions while preserving most of the high frequency details." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.587, + 0.353, + 0.604 + ], + "angle": 0, + "content": "4.2. Comparisons with State-of-Art" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.612, + 0.47, + 0.839 + ], + "angle": 0, + "content": "We compare our method with five state-of-the-art methods of different types with publicly available implementations from the authors: NerFACE [15], IMAvatar [53], NHA [17], FOMM [39], and TPSMM [52]. For subject-specific methods (i.e. Ours, NerFACE [15], IMAvatar [53], and NHA [17]), we train the avatar for each subject separately with training frames of each video, then drive and render the trained avatar with 3DMM and camera parameters of testing frames. For few-shot methods (i.e., FOMM [39], and TPSMM [52]), we use the first frame of each video, which shows a frontal head, as the source image, then use testing frames as driving images in self-reenactment manner. Finally, the generated images of each method are compared with ground truth testing frames quantitatively (See Tab. 1) and qualitatively (See Fig. 4)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.902 + ], + "angle": 0, + "content": "As shown in Fig. 4, FOMM [39] and TPSMM [52] struggle with large head rotations and fail to produce 3D consistent results. NHA [17] uses explicit mesh surface with neural textures. The fix mesh topology makes it hard to" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.587, + 0.892, + 0.83 + ], + "angle": 0, + "content": "handle challenging hair in Subject 3. Also, NHA uses linear 3DMM blendshapes, making it hard to capture complex expressions (e.g., Subject 1 & 3) and wrinkles (e.g., Subject 4). IMAvatar [53] uses an occupancy field to model geometry, making it hard to handle challenging hair. Despite learning a personalized blendshape field, the linear formulation still limits their model capacity of handling complex expressions. In addition, we observed training instability of IMAvatar on the captured data, converging to oversmooth results. NerFACE [15] works relatively well on data with easy expressions (i.e., common talking heads in Subject 4), but struggles on our challenging data and estimates incorrect geometry (e.g., distorted head for Subject 3) and blurry rendering. In contrast, our method gives superior results on all the aspects discussed above. Tab. 1 further quantitatively confirms the good rendering quality of our method." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.845, + 0.681, + 0.862 + ], + "angle": 0, + "content": "4.3. Driving the Avatar" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.893, + 0.902 + ], + "angle": 0, + "content": "After training, the learned avatar model can be driven by the same subject under different capture conditions, e.g. hair" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16895" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.094, + 0.092, + 0.165, + 0.102 + ], + "angle": 0, + "content": "Drive Frame" + }, + { + "type": "image", + "bbox": [ + 0.081, + 0.104, + 0.178, + 0.211 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.198, + 0.092, + 0.261, + 0.102 + ], + "angle": 0, + "content": "Drive View" + }, + { + "type": "image", + "bbox": [ + 0.189, + 0.108, + 0.273, + 0.203 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.297, + 0.092, + 0.351, + 0.102 + ], + "angle": 0, + "content": "Side View" + }, + { + "type": "image", + "bbox": [ + 0.288, + 0.108, + 0.372, + 0.203 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.401, + 0.092, + 0.437, + 0.102 + ], + "angle": 0, + "content": "Depth" + }, + { + "type": "image", + "bbox": [ + 0.382, + 0.11, + 0.468, + 0.203 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.081, + 0.214, + 0.178, + 0.32 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.188, + 0.217, + 0.269, + 0.314 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.282, + 0.217, + 0.369, + 0.314 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.382, + 0.217, + 0.462, + 0.314 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.32, + 0.47, + 0.362 + ], + "angle": 0, + "content": "Figure 5. Results on driving the learned avatar by the same subject under different capture conditions. Our method produces faithful expressions, multi-view consistent rendering, and good geometry." + }, + { + "type": "image", + "bbox": [ + 0.082, + 0.377, + 0.147, + 0.478 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.16, + 0.378, + 0.226, + 0.479 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.235, + 0.378, + 0.304, + 0.479 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.315, + 0.378, + 0.383, + 0.479 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.392, + 0.378, + 0.468, + 0.479 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.482, + 0.469, + 0.524 + ], + "angle": 0, + "content": "Figure 6. Results on expressions out of the training distribution with different amount of training data. Ours- \\(D\\) more robustly handles unseen expressions and degrades less with fewer training data." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.55, + 0.47, + 0.613 + ], + "angle": 0, + "content": "style, illumination, glasses. We show the driving results in Fig. 5. Our avatar faithfully reproduces the expressions of the driving frame, while also achieving multi-view consistent renderings and generating high quality geometry." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.621, + 0.398, + 0.637 + ], + "angle": 0, + "content": "4.4. Ablation Study: Expression Features" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.644, + 0.469, + 0.719 + ], + "angle": 0, + "content": "Learning good local features is crucial for improving model capacity and capturing high frequency details, without losing the regularization from 3DMM (Sec. 3.2). We investigate and compare three alternative approaches and our two model variations for feature learning:" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.72, + 0.469, + 0.809 + ], + "angle": 0, + "content": "Static Features. We use frame-shared (hence expression-shared) vertex features \\(\\{z^j\\}\\). As shown in Fig. 7, the expression-shared features struggle to capture strong expression-dependent variations, leading to incorrect geometry (e.g., incorrect cheek silhouette), blurry details (e.g., teeth), and inferior LPIPS scores in Tab. 2." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.469, + 0.901 + ], + "angle": 0, + "content": "3DMM Codes. We extend Static Features by directly concatenating 3DMM expression and pose codes \\((\\psi_{i},\\theta_{i})\\) to the vertex features \\(\\{z^j\\}\\). This allows the model to have more capacity to capture expression-dependent variations. Although median-level expression characteristics are recognizable (e.g., cheek silhouette in Fig. 7), the results are even" + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.089, + 0.889, + 0.273 + ], + "angle": 0, + "content": "
Subject0Subject1Subject2Subject3
Full Training Data
Static Features0.15590.15860.15520.1688
3DMM Codes0.15990.16200.17460.1738
3DMM Codes MLP0.15680.15510.15050.1686
Ours-C0.14170.14570.13830.1550
Ours-D0.14390.15230.14150.1559
First 50% Training Data
Ours-C0.20380.14830.15800.1566
+0.0621+0.0026+0.0197+0.0016
Ours-D0.17110.15110.15160.1558
+0.0272-0.0012+0.0101-0.0001
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.283, + 0.892, + 0.325 + ], + "angle": 0, + "content": "Table 2. Quantitative ablations with baselines in LPIPS scores (lower is better). Bold denotes the best while underline denotes the second best." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.353, + 0.892, + 0.397 + ], + "angle": 0, + "content": "blurrier for components less depend on expressions such as hairs, possibly due to the limited model capacity of shallow MLPs without spatial context." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.398, + 0.893, + 0.624 + ], + "angle": 0, + "content": "3DMM Codes MLP. To further increase the model capacity, we use a more sophisticated MLP architecture inspired from Zheng et al. [54], which is a MLP-based conditional Variational AutoEncoder (cVAE), to predict the expression-dependent vertex features \\(\\{z_i^j\\}\\) from the 3DMM expression and pose codes \\((\\psi_i,\\theta_i)\\). More specifically, the cVAE is conditioned on 3DMM codes and vertex coordinates on neutral face mesh \\(V_{neutral}\\). Then, the cVAE encodes the frame index into a latent code and decodes it into the vertex features \\(\\{z_i^j\\}\\). Although the overall sharpness of results is improved (e.g., glasses and hair in Fig. 7), this model still produces blurry details such as teeth, eyes, and wrinkles. Tab. 2 further confirms that this more sophisticated MLP architecture still produces inferior results compared to CNN-based methods Ours-C and Ours-D." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.625, + 0.892, + 0.805 + ], + "angle": 0, + "content": "Ours-C vs. Ours-D. By replacing MLPs with CNNs in UV space (Please refer to Sec. 3.2 for more details), both variations of Ours achieve superior rendering quality than MLP-based baselines, as shown in Fig. 7. Moreover, we find that Ours-D is more robust to expressions that are outside the training distribution compared to Ours-C (See Fig. 6). To further investigate the model robustness to out-of-training expressions, we train Ours-C and Ours-D with the first \\(50\\%\\) of training frames, to simulate the scenario with less expression coverage during training. As shown in Tab. 2 and Fig. 6, Ours-D degrades less than Ours-C, indicating enhanced robustness." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.817, + 0.842, + 0.833 + ], + "angle": 0, + "content": "4.5. Robustness to Expression Extrapolation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.892, + 0.901 + ], + "angle": 0, + "content": "We qualitatively test our method on expression extrapolation setting by artificially manipulating the expression code for a given test frame. In particular, we double the value of the expression code and compare the results with" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16896" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.096, + 0.891, + 0.337 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.339, + 0.897, + 0.396 + ], + "angle": 0, + "content": "Figure 7. Comparison between different designs for local vertex feature learning. See Sec. 4.4 for more details. \"Static feature\" struggles on capturing personalized expressions. \"3DMM Codes\" improves the personalization but suffers from overall blurriness. \"3DMM Codes MLP\" further improves the sharpness, but still cannot present the details. Overall, our convolution-based methods lead to superior renderings on areas such as cheek silhouette, glasses frames and reflections, and teeth." + }, + { + "type": "image", + "bbox": [ + 0.08, + 0.416, + 0.47, + 0.582 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.591, + 0.471, + 0.634 + ], + "angle": 0, + "content": "Figure 8. Expression extrapolation results and comparisons. Compared to other work, our method performs well on extreme and out-of-training expression." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.662, + 0.47, + 0.799 + ], + "angle": 0, + "content": "prior works. As shown in Fig. 8, NHA [17] gives reasonable expression extrapolation results for both geometry and appearance. However, it cannot faithfully capture the out-of-3DMM details when compared to the ground truth. IMAvatar [53] gives reasonable geometry for extrapolation, but struggles to produce sharp extrapolated appearances. NerveFACE [15] fails in producing reasonable renderings. In contrast, our method not only faithfully captures out-of-3DMM details, but also generalizes to extrapolated expressions." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.815, + 0.192, + 0.83 + ], + "angle": 0, + "content": "5. Discussion" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.9 + ], + "angle": 0, + "content": "In this work, we presented a framework to learn high-quality controllable 3D head avatars from monocular RGB videos. The core of our method is a 3DMM-anchored neural radiance field decoded from features attached on 3DMM" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.421, + 0.895, + 0.543 + ], + "angle": 0, + "content": "vertices. The vertex features can be either predicted from 3DMM expression and pose codes, or vertex displacements, via CNNs in UV space, where the former favors quality and the latter favors robustness. We experimentally demonstrate that it is possible to learn high-quality avatars with faithful, highly non-rigid expression deformations purely from monocular RGB videos, leading to a superior rendering quality of our method compared to other approaches." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.563, + 0.895, + 0.715 + ], + "angle": 0, + "content": "Limitations. Compared to the state-of-the-art approaches, our proposed framework learns portrait avatars with superior rendering quality. Nevertheless, our method still inherits the disadvantages of NeRF [30] on time-consuming subject-specific training and slow rendering. Our method relies on the expression space of a 3DMM, thus cannot capture components that are completely missing in the 3DMM, such as the tongue. Moreover, extending our method to include the upper body or integrating into full body avatars are also interesting future directions." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Ethical Considerations. The rapid progress on avatar synthesis facilitates numerous downstream applications, but also raises concerns on ethical misuses. On the synthesis side, it would be ideal to actively use watermarking and avoid driving avatars with different identities. On the viewing side, forgery detection [2,25,35,36,41] is an active research field with promising progress. However, it is still hard to guarantee reliable detection of fake visual materials at the current stage. Encouraging the use of cryptographical signatures may also be a potential solution to ensure the authenticity of visual material." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16897" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.105 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.156 + ], + "angle": 0, + "content": "[1] MetaHuman - Unreal Engine. https://www.unrealengine.com/en-US/metahuman, 2. Accessed: 2022-10-17. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.158, + 0.471, + 0.225 + ], + "angle": 0, + "content": "[2] Shruti Agarwal, Hany Farid, Tarek El-Gaaly, and Ser-Nam Lim. Detecting deep-fake videos from appearance and behavior. In 2020 IEEE International Workshop on Information Forensics and Security (WIFS), pages 1-6. IEEE, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.228, + 0.471, + 0.269 + ], + "angle": 0, + "content": "[3] ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, and Zhixin Shu. RigNeRF: Fully Controllable Neural 3D Portraits. pages 20364-20373, 2022. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.271, + 0.47, + 0.311 + ], + "angle": 0, + "content": "[4] Ziqian Bai, Zhaopeng Cui, Xiaoming Liu, and Ping Tan. Riggable 3D Face Reconstruction via In-Network Optimization. pages 6216-6225, June 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.313, + 0.471, + 0.354 + ], + "angle": 0, + "content": "[5] Ziqian Bai, Zhaopeng Cui, Jamal Ahmed Rahim, Xiaoming Liu, and Ping Tan. Deep Facial Non-Rigid Multi-View Stereo. pages 5850–5860, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.355, + 0.47, + 0.424 + ], + "angle": 0, + "content": "[6] Thabo Beeler, Fabian Hahn, Derek Bradley, Bernd Bickel, Paul Beardsley, Craig Gotsman, Robert W Sumner, and Markus Gross. High-Quality Passive Facial Performance Capture Using Anchor Frames. In ACM SIGGRAPH 2011 Papers, pages 1-10. 2011. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.425, + 0.471, + 0.48 + ], + "angle": 0, + "content": "[7] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pages 187-194, 1999. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.481, + 0.471, + 0.563 + ], + "angle": 0, + "content": "[8] Chen Cao, Tomas Simon, Jin Kyu Kim, Gabe Schwartz, Michael Zollhoefer, Shun-Suke Saito, Stephen Lombardi, Shih-En Wei, Danielle Belko, Shoou-I Yu, Yaser Sheikh, and Jason Saragih. Authentic Volumetric Avatars from a Phone Scan. ACM Transactions on Graphics (TOG), 41(4), 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.565, + 0.471, + 0.606 + ], + "angle": 0, + "content": "[9] Chen Cao, Hongzhi Wu, Yanlin Weng, Tianjia Shao, and Kun Zhou. Real-Time Facial Animation With Image-Based Dynamic Avatars. 35(4), 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.607, + 0.471, + 0.675 + ], + "angle": 0, + "content": "[10] Bindita Chaudhuri, Noranart Vesdapunt, Linda Shapiro, and Baoyuan Wang. Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting. In ECCV 2020: 16th European Conference on Computer Vision, pages 142-160, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.677, + 0.471, + 0.746 + ], + "angle": 0, + "content": "[11] Chong Bao and Bangbang Yang, Zeng Junyi, Bao Hujun, Zhang Yinda, Cui Zhaopeng, and Zhang Guofeng. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In European Conference on Computer Vision (ECCV), 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.747, + 0.471, + 0.803 + ], + "angle": 0, + "content": "[12] Ruofei Du, Ming Chuang, Wayne Chang, Hugues Hoppe, and Amitabh Varshney. Montage4D: Real-time Seamless Fusion and Stylization of Multiview Video Textures. Journal of Computer Graphics Techniques, 8(1):1-34, Jan. 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.804, + 0.471, + 0.858 + ], + "angle": 0, + "content": "[13] Ruofei Du, David Li, and Amitabh Varshney. Geollery: A Mixed Reality Social Media Platform. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, number 685 in CHI. ACM, May 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.86, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[14] Bernhard Egger, William AP Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani," + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.115, + 0.471, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "et al. 3D Morphable Face Models—Past, Present, and Future. 39(5):1-38, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.123, + 0.892, + 0.178 + ], + "angle": 0, + "content": "[15] Guy Gafni, Justus Thies, Michael Zollhofer, and Matthias Nießner. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. pages 8649-8658, 2021. 2, 3, 4, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.182, + 0.892, + 0.25 + ], + "angle": 0, + "content": "[16] Pablo Garrido, Michael Zollhöfer, Dan Casas, Levi Valgaerts, Kiran Varanasi, Patrick Pérez, and Christian Theobalt. Reconstruction of Personalized 3D Face Rigs From Monocular Video. ACM Transactions on Graphics (TOG), 35(3):28, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.254, + 0.892, + 0.308 + ], + "angle": 0, + "content": "[17] Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. Neural Head Avatars From Monocular RGB Videos. pages 18653–18664, 2022. 2, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.312, + 0.892, + 0.435 + ], + "angle": 0, + "content": "[18] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, Danhang Tang, Anastasia Tkach, Adarsh Kowdle, Emily Cooper, Ming-song Dou, Sean Fanello, Graham Fyffe, Christoph Rhemann, Jonathan Taylor, Paul Debevec, and Shahram Izadi. The Relightables: Volumetric Performance Capture of Humans With Realistic Relighting. ACM Transactions on Graphics, Nov. 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.44, + 0.892, + 0.551 + ], + "angle": 0, + "content": "[19] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, Danhang Tang, Anastasia Tkach, Adarsh Kowdle, Emily Cooper, Ming-song Dou, Sean Fanello, Graham Fyffe, Christoph Rhemann, Jonathan Taylor, Paul Debevec, and Shahram Izadi. The relightables: Volumetric performance capture of humans with realistic relighting. ACM Trans. Graph., 38(6), nov 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.554, + 0.892, + 0.621 + ], + "angle": 0, + "content": "[20] Zhenyi He, Ruofei Du, and Ken Perlin. CollaboVR: A Reconfigurable Framework for Multi-user to Communicate in Virtual Reality. In 2020 IEEE International Symposium on Mixed and Augmented Reality, ISMAR, pages 542-554. IEEE, Nov. 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.626, + 0.892, + 0.681 + ], + "angle": 0, + "content": "[21] Liwen Hu, Shunsuke Saito, Lingyu Wei, Koki Nagano, Jaewoo Seo, Jens Fursund, Iman Sadeghi, Carrie Sun, YenChun Chen, and Hao Li. Avatar Digitization From a Single Image for Real-Time Rendering. 36(6):1-14, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.684, + 0.892, + 0.726 + ], + "angle": 0, + "content": "[22] Alexandru Eugen Ichim, Sofien Bouaziz, and Mark Pauly. Dynamic 3D Avatar Creation From Hand-Held Video Input. 34(4):1-14, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.729, + 0.892, + 0.77 + ], + "angle": 0, + "content": "[23] Wei Jiang, Kwang Moo Yi, Golnoosh Samei, Oncel Tuzel, and Anurag Ranjan. NeuMan: Neural Human Radiance Field From a Single Video. 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.773, + 0.892, + 0.828 + ], + "angle": 0, + "content": "[24] Tianye Li, Timo Bolkart, Michael. J. Black, Hao Li, and Javier Romero. Learning a Model of Facial Shape and Expression From 4D Scans. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6):194:1-194:17, 2017. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.831, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[25] Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. Celeb-df: A large-scale challenging dataset for deepfake forensics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3207-3216, 2020. 8" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.956 + ], + "angle": 0, + "content": "16898" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.469, + 0.134 + ], + "angle": 0, + "content": "[26] Siyou Lin, Hongwen Zhang, Zerong Zheng, Ruizhi Shao, and Yebin Liu. Learning implicit templates for point-based clothed human modeling. In ECCV, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.136, + 0.469, + 0.19 + ], + "angle": 0, + "content": "[27] Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. Advances in Neural Information Processing Systems, 33:15651-15663, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.194, + 0.469, + 0.261 + ], + "angle": 0, + "content": "[28] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Control. ACM Transactions on Graphics (TOG), 40(6):1-16, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.265, + 0.469, + 0.347 + ], + "angle": 0, + "content": "[29] Abhimitra Meka, Rohit Pandey, Christian Haene, Sergio Orts-Escalano, Peter Barnum, Philip David-Son, Daniel Erickson, Yinda Zhang, Jonathan Taylor, Sofien Bouaziz, et al. Deep Relightable Textures: Volumetric Performance Capture With Neural Rendering. ACM Transactions on Graphics (TOG), 39(6):1-21, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.35, + 0.469, + 0.417 + ], + "angle": 0, + "content": "[30] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing Scenes As Neural Radiance Fields for View Synthesis. Communications of the ACM, 65(1):99-106, 2021. 2, 4, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.421, + 0.469, + 0.558 + ], + "angle": 0, + "content": "[31] Sergio Orts-Escolano, Christoph Rhemann, Sean Fanello, Wayne Chang, Adarsh Kowdle, Yury Degtyarev, David Kim, Philip Davidson, Sameh Khamis, Mingsong Dou, Vladimir Tankovich, Charles Loop, Qin Cai, Philip Chou, Sarah Mennicken, Julien Valentin, Vivek Pradeep, Shenlong Wang, Sing Bing Kang, Pushmeet Kohli, Yuliya Lutchyn, Cem Keskin, and Shahram Izadi. Holoportation: Virtual 3D Teleportation in Real-Time. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST). ACM, Oct. 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.562, + 0.469, + 0.63 + ], + "angle": 0, + "content": "[32] Rohit Pandey, Sergio Orts Escolano, Chloe Legendre, Christian Haene, Sofien Bouaziz, Christoph Rhemann, Paul Debevec, and Sean Fanello. Total relighting: learning to relight portraits for background replacement. ACM Transactions on Graphics (TOG), 40(4):1-21, 2021. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.633, + 0.469, + 0.701 + ], + "angle": 0, + "content": "[33] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable Neural Radiance Fields. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 5865-5874, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.704, + 0.469, + 0.771 + ], + "angle": 0, + "content": "[34] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural Body: Implicit Neural Representations With Structured Latent Codes for Novel View Synthesis of Dynamic Humans. pages 9054-9063, 2021. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.775, + 0.469, + 0.829 + ], + "angle": 0, + "content": "[35] Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: A large-scale video dataset for forgery detection in human faces. arXiv preprint arXiv:1803.09179, 2018. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.832, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[36] Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: Learning to detect manipulated facial images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1-11, 2019. 8" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.469, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.162 + ], + "angle": 0, + "content": "[37] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Hao Li, and Angjoo Kanazawa. PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, Oct. 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.165, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[38] Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. Animating arbitrary objects via deep motion transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2377-2386, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.237, + 0.892, + 0.292 + ], + "angle": 0, + "content": "[39] Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. First Order Motion Model for Image Animation. In Advances in Neural Information Processing Systems (NeurIPS), volume 32, 2019. 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.295, + 0.892, + 0.378 + ], + "angle": 0, + "content": "[40] Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. FML: Face Model Learning From Videos. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10812-10822, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.381, + 0.892, + 0.435 + ], + "angle": 0, + "content": "[41] Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales, and Javier Ortega-Garcia. Deepfakes and beyond: A survey of face manipulation and fake detection. Information Fusion, 64:131-148, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.439, + 0.892, + 0.465 + ], + "angle": 0, + "content": "[42] Zach Waggoner. My Avatar, My Self: Identity in Video Role-Playing Games. McFarland, 2009. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.47, + 0.892, + 0.524 + ], + "angle": 0, + "content": "[43] Ting-Chun Wang, Ming-Yu Liu, Andrew Tao, Guilin Liu, Jan Kautz, and Bryan Catanzaro. Few-Shot Video-to-Video Synthesis. In Advances in Neural Information Processing Systems (NeurIPS), 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.528, + 0.892, + 0.568 + ], + "angle": 0, + "content": "[44] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image Quality Assessment: From Error Visibility to Structural Similarity. 13(4):600-612, 2004. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.572, + 0.892, + 0.653 + ], + "angle": 0, + "content": "[45] Chung-Yi Weng, Brian Curless, Pratul P Srinivasan, Jonathan T Barron, and Ira Kemelmacher-Shlizerman. Humanerf: Free-viewpoint rendering of moving people from monocular video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16210-16220, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.657, + 0.892, + 0.713 + ], + "angle": 0, + "content": "[46] Olivia Wiles, A Koepke, and Andrew Zisserman. X2face: A network for controlling face generation using images, audio, and pose codes. In Proceedings of the European conference on Computer Vision (ECCV), pages 670-686, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.716, + 0.892, + 0.784 + ], + "angle": 0, + "content": "[47] Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In European Conference on Computer Vision, pages 597-614. Springer, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.788, + 0.892, + 0.843 + ], + "angle": 0, + "content": "[48] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction. pages 601-610, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.846, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[49] Egor Zakharov, Aleksei Ivakhnenko, Aliaksandra Shysheya, and Victor Lempitsky. Fast bi-layer neural synthesis of one-shot realistic head avatars. In European Conference on Computer Vision, pages 524-540. Springer, 2020. 3" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "16899" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.161 + ], + "angle": 0, + "content": "[50] Egor Zakharov, Aliaksandra Shysheya, Egor Burkov, and Victor Lempitsky. Few-Shot Adversarial Learning of Realistic Neural Talking Head Models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9459-9468, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.163, + 0.47, + 0.232 + ], + "angle": 0, + "content": "[51] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The Unreasonable Effectiveness of Deep Features As a Perceptual Metric. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 586-595, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.234, + 0.47, + 0.288 + ], + "angle": 0, + "content": "[52] Jian Zhao and Hui Zhang. Thin-plate spline motion model for image animation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3657–3666, 2022. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.29, + 0.47, + 0.372 + ], + "angle": 0, + "content": "[53] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. Im avatar: Implicit morphable head avatars from videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13545–13555, 2022. 3, 4, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.374, + 0.47, + 0.444 + ], + "angle": 0, + "content": "[54] Zerong Zheng, Han Huang, Tao Yu, Hongwen Zhang, Yandong Guo, and Yebin Liu. Structured Local Radiance Fields for Human Avatar Modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15893-15903, 2022. 3, 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.445, + 0.469, + 0.514 + ], + "angle": 0, + "content": "[55] Michael Zollhöfer, Justus Thies, Pablo Garrido, Derek Bradley, Thabo Beeler, Patrick Pérez, Marc Stamminger, Matthias Nießner, and Christian Theobalt. State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications. volume 37, pages 523-550, 2018. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.514 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "16900" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Personalized High Quality Volumetric Head Avatars From Monocular RGB Videos/f99f5e12-9192-46a6-b4e3-a025ac8e0105_origin.pdf b/2023/Learning Personalized High Quality Volumetric Head Avatars From Monocular RGB Videos/f99f5e12-9192-46a6-b4e3-a025ac8e0105_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..6af96f0945d3cd6f2ade1704ce487ab20e8a432a --- /dev/null +++ b/2023/Learning Personalized High Quality Volumetric Head Avatars From Monocular RGB Videos/f99f5e12-9192-46a6-b4e3-a025ac8e0105_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f2dbcf35610a9b56523848c9be24ba9ad662945aea87f8fbf4e7ed9f78f72b0c +size 2244762 diff --git a/2023/Learning Personalized High Quality Volumetric Head Avatars From Monocular RGB Videos/full.md b/2023/Learning Personalized High Quality Volumetric Head Avatars From Monocular RGB Videos/full.md new file mode 100644 index 0000000000000000000000000000000000000000..ff036fc44c7120bbec2cabc56147e48a4b8ab0f8 --- /dev/null +++ b/2023/Learning Personalized High Quality Volumetric Head Avatars From Monocular RGB Videos/full.md @@ -0,0 +1,317 @@ +# Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos + +Ziqian Bai $^{1,2*}$ Feitong Tan $^{1}$ Zeng Huang $^{1}$ Kripasindhu Sarkar $^{1}$ Danhang Tang $^{1}$ Di Qiu $^{1}$ Abhimitra Meka $^{1}$ Ruofei Du $^{1}$ Mingsong Dou $^{1}$ Sergio Orts-Escalano $^{1}$ Rohit Pandey $^{1}$ Ping Tan $^{2}$ Thabo Beeler $^{1}$ Sean Fanello $^{1}$ Yinda Zhang $^{1}$ Google $^{2}$ Simon Fraser University + +![](images/9e2403b9e52c52a8fc09751961ab75d4a81ffb23fc8b7da7ada1cd0c3097fca0.jpg) + +![](images/77ff15b5287ee04d9ce1ec7ffbd06b8a2f64010126aa21b5e4ecb349132d9521.jpg) +Monocular RGB Video + +![](images/792fc6f37e89567c5b28d8716bac6be7226f109e4b469cc2afad61d90df6ea62.jpg) + +![](images/1d119608c5b304063bcc20f064e23cd5e372e8abb91d32df7a3b8b9bfd27bc6c.jpg) + +![](images/e3818684732b57a2226acca7d871e8c584aa96afd41f9b85fc114fbcbe18c097.jpg) + +![](images/d16319892da4c8ece3eb631d3e876277b7ea31babd6d79f68a2f0c33e5d2ec66.jpg) + +![](images/16367df513a55fbdebe4ac06ad3f06ca70c106a337b5c01e4cf1fcd4e410a3d6.jpg) + +![](images/26a09153f11681792ddea914176e74374d52e42ead0e0ccce2463a515e4e0d41.jpg) + +![](images/5c9911123951fd0b2562885222c764b63b64f6ee55f6ea23af35246a1226186c.jpg) + +![](images/82126a611c82af75443feee1d24cbccb137f6fc0b9d241218f9a4bd451fa6c34.jpg) + +![](images/22b4bedbe1ff591b8e1d9b364a6403ede560da5562874ca9cfe5c85549c7c34e.jpg) + +![](images/1e9672a8b97fab722f2de872c482ba075b4fda22962fbfcf21e3aa3f20f43c52.jpg) +Figure 1. Our technique builds a 3D avatar representation of a person using just a single short monocular RGB video (e.g., 1-2 minutes). We leverage a 3DMM to track the user's expressions. By anchoring a neural radiance field to the 3DMM geometry, we generate a volumetric photorealistic 3D avatar that can be rendered with user-defined expression and viewpoint. Note that our method works well on challenging materials, e.g., hair and dramatic expressions. Please see our webpage augmentedperception.github.io/monoavatar for more results. + +![](images/777c3def0428ef6bb9d3054be7d8b15015940aa8c155a44d17e99873c352b86b.jpg) +Controllable Photorealistic Head Avatar + +![](images/eae6ba8175c15b1b683845a042771e8c4c0de99d98c9bac5af8a532560d67109.jpg) + +![](images/d7ee5d7bc9bb220e44b97fb7f675fb57a2d197cd0c093a3cd68efc503d6f193b.jpg) + +![](images/8d7186ce16c09e00ad88f6e3e722cb082d6a9201abf60d35789ad4364a7f7153.jpg) + +# Abstract + +We propose a method to learn a high-quality implicit 3D head avatar from a monocular RGB video captured in the wild. The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses. Our hybrid pipeline combines the geometry prior and dynamic tracking of a 3DMM with a neural radiance field to achieve fine-grained control and photorealism. To reduce over-smoothing and improve out-of-model expressions synthesis, we propose to predict local features anchored on the 3DMM geometry. These learnt features are driven by 3DMM deformation and interpolated in 3D space to yield the volumetric radiance at a designated query point. We further show that using a Convolutional Neural Network in the UV space is critical in incorporating spatial context and producing representative local features. Extensive experiments show that we are able to reconstruct high-quality avatars, with more accurate expression-dependent details, good generalization to out-of-training expressions, and quantitatively superior renderings compared to other + +state-of-the-art approaches. + +# 1. Introduction + +Creating a controllable human avatar is a fundamental piece of technology for many downstream applications, such as AR/VR communication [20, 31], virtual try-on [37], virtual tourism [13], games [42], and visual effects for movies [12, 18]. Prior art in high-quality avatar generation typically requires extensive hardware configurations (i.e., camera arrays [6, 12, 31], light stages [18, 29], dedicated depth sensors [8]), or laborious manual intervention [1]. Alternatively, reconstructing avatars from monocular RGB videos significantly relaxes the dependency on equipment setup and broadens the application scenarios. However, monocular head avatar creation is highly ill-posed due to the dual problems of reconstructing and tracking highly articulated and deformable facial geometry, while model + +ing sophisticated facial appearance. Traditionally, 3D Morphable Models (3DMM) [7, 24] have been used to model facial geometry and appearance for various applications including avatar generation [9, 16, 22]. However, 3DMMs do not fully capture subject-specific static details and dynamic variations, such as hair, glasses, and expression-dependent high frequency details such as wrinkles, due to the limited capacity of the underlying linear model. + +Recent works [3, 15] have incorporated neural radiance fields in combination with 3DMMs for head avatar generation to achieve photorealistic renderings, especially improving challenging areas, such as hair, and adding view-dependent effects, such as reflections on glasses. The pioneering work of NerFACE [15] uses a neural radiance field parameterized by an MLP that is conditioned on 3DMM expression parameters and learnt per-frame latent codes. While they achieve photorealistic renderings, the reliance on an MLP to directly decode from 3DMM parameter space leads to the loss of fine-grain control over geometry and articulation. Alternatively, RigNeRF [3] learns the radiance field in a canonical space by warping the target head geometry using a 3DMM fit, which is further corrected by a learnt dense deformation field parameterized by another MLP. While they demonstrate in-the-wild head pose and expression control, the use of two global MLPs to model canonical appearance and deformations for the full spatial-temporal space leads to a loss of high frequency details, and an overall uncanny appearance of the avatar. Both of these works introduce new capabilities but suffer from lack of detail in both appearance and motion because they attempt to model the avatar's global appearance and deformation with an MLP network. + +In this paper, we propose a method to learn a neural head avatar from a monocular RGB video. The avatar can be controlled by an underlying 3DMM model and deliver high-quality rendering of arbitrary facial expressions, head poses, and viewpoints, which retain fine-grained details and accurate articulations. We achieve this by learning to predict expression-dependent spatially local features on the surface of the 3DMM mesh. A radiance field for any given 3D point in the volume is then obtained by interpolating the features from K-nearest neighbor vertices on the deformed 3DMM mesh in target expression, and passing them through a local MLP to infer density and color. The local features and local MLP are trained jointly by supervising the radiance field through standard volumetric rendering on the training sequence [30]. Note that our networks rely on the local features to model appearance and deformation details, and leverages the 3DMM to model only the global geometry. + +Learning local features is critical in achieving a high-quality head avatar. To this end, we train an image-to-image translation U-Net that transforms the 3DMM deformations in the UV space to such local features. These UV + +space features are then attached to the corresponding vertices of the 3DMM mesh geometry. We show that learning features from such explicit per-vertex local displacement of the 3DMM geometry makes the model retain high-frequency expression-dependent details and also generalizes better to out-of-training expressions, presumably because of the spatial context between nearby vertices incorporated by the convolutional neural network (CNN). An alternative approach is to feed the 3DMM parameters directly into a CNN decoder running on the UV space. However, we found this produces severe artifacts on out-of-training expressions, particularly given a limited amount of training data, e.g. for a lightweight, 1-minute data collection procedure during the avatar generation process. + +In summary, our contributions are as follows: we propose a neural head avatar representation based on a 3DMM-anchored neural radiance field, which can model complex expression-dependent variations, but requires only monocular RGB videos for training. We show that a convolutional neural network running on per-vertex displacement in UV space is effective in learning local expression-dependent features, and delivers favorable training stability and generalization to out-of-training expressions. Experiments on real-world datasets show that our model provides competitive controllability and generates sharper and detail enriched rendering compared to state-of-the-art approaches. + +# 2. Related Works + +Building photorealistic representations of humans has been widely researched in the past few decades. Here, we mainly discuss prior art in head avatar and refer readers to the state-of-the-art surveys [14, 55] for a comprehensive literature review. + +Monocular Explicit Surface Head (Face) Avatars. Traditionally, a typical approach to create head (or face) avatars from monocular RGB videos is using a 3D Morphable Models (3DMM) as the foundation and adding personalized representations, such as corrected blendshapes [16, 22], detail texture maps [16, 22], image-based representations [9], and secondary components [21]. Early works use various optimizations to obtain the personalized representations from monocular data, including analysis-by-synthesis [14, 16, 21, 55] as well as shape-from-shading [16, 22]. Recent approaches replace optimizations by regressions with Deep Neural Networks (DNNs) [10, 40, 48], or integrate optimizations with deep learning components [4, 5]. More recent methods leverage neural textures [17] to generate photorealistic appearances. + +The main drawback of these methods is that they rely on explicit meshes with a fixed topology, making it hard to handle out-of-model details such as hair and accessories like glasses and apparels. In contrast, our hybrid method combines geometric priors with implicit representations, lead- + +![](images/cfa3512d5b56a09d517ae695a0310721974858f1f3c370cf76579f717b01bb60.jpg) +Figure 2. Overview of our pipeline. The core of our method is the Avatar Representation (Sec. 3.1. Shown as the yellow area) based on a 3DMM-anchored neural radiance field (NeRF), which are decoded from local features attached on the 3DMM vertices. Then, we use volumetric rendering to compute the output image. To predict the vertex-attached features (Sec. 3.2. Shown as the green area), we first compute the vertex displacements from the 3DMM expression and pose, then process the displacements in UV space with Convolutional Neural Networks (CNNs), and sample the obtained features back to mesh vertices. + +ing to a significantly larger representation capacity. + +Monocular Implicit Head Avatars. Recent work proposes to extend 3DMM with implicit 3D representations. NerFACE [15] introduces a dynamic neural radiance field (NeRF) conditioned on 3DMM expression codes which can render a view-consistent avatar with volumetric rendering. Since NerFACE directly inputs the 3DMM expression codes into MLPs without using any shape or spatial information from 3DMM, their model is quite under-constrained for monocular reconstruction, and suffers from severe artifacts for data with challenging expressions. RigNeRF [3] uses 3DMM derived warping field to deform the camera space into a canonical space, and defines a canonical NeRF conditioned on 3DMM codes. However, their model uses a dense MLP-based architecture to memorize the appearance and deformation for the full head, leading to oversmooth results due to limited network capacity. IMAAvatar [53] learns personalized implicit fields of blendshapes, pose correctives, and skinning weights, then formulates the avatar with linear summation of blendshapes followed by linear blend skinning. However, their linear formulation limits the amount of expression deformations. + +Geometry Anchored Implicit 3D Representation. Sparse local feature embedding attached on geometry has been demonstrated to be effective in improving the rendering quality of neural radiance field [26-28, 34, 54]. It also naturally supports neural radiance field editing since the modification on the geometry can be directly propagated to the rendering [11], which makes them a favorable representation to support the controllability for human avatar. We adapt this representation to head avatar and incorporate head specific priors. Differently from prior art, we leverage a CNN in UV space to learn local, per-vertex features + +that are expression-dependent, improving generalization of out-of-train expressions. + +2D-based Head Avatars. There are numerous approaches that synthesize the head (or face) relying on 2D (explicit/implicit) representations, including 2D facial landmarks [43, 49, 50] and 2D warping fields [38, 39, 46]. Landmark-based avatar models [43, 49, 50] synthesize the face conditioned to the facial landmarks extracted with a, usually pre-trained, landmark detector. Specifically, an encoder is applied to extract an identity embedding from a reference image, a decoder is adapted by the identity code to animate the reference face with landmarks from the driving videos. X2Face [46] is the first approach to animate human heads by learning a dense warping field and producing the output video via image warping. MonkeyNet [38] and First Order Motion Model (FOMM) further propose to infer motion fields with self-learned keypoints, which significantly improves motion prediction and synthesizes higher quality renderings of heads. While most aforementioned methods can produce photorealistic results, they are not able to maintain geometry and multiview consistency due to their inherent 2D representation. + +# 3. Method + +Given a monocular RGB video containing $M$ frames $\{I_1, I_2, \dots, I_M\}$ , our method reconstructs a head avatar representation that can be rendered under arbitrary facial expressions, head poses, and camera viewpoints. We first preprocess the video to remove the background [19,32] and obtain camera and 3DMM parameters for each frame. More specifically, we use FLAME [24] as the 3DMM and denote the fitted face with shape $\beta$ , expressions $\psi_i$ , poses $\theta_i$ (i.e., + +![](images/8b7b225e882fe956ccb35038f9c3c8ffb0c2fd0c64e5ff26acc67ab4f464e6f6.jpg) +Figure 3. Illustration of Avatar Representation (Sec. 3.1). Given a query point, we find its k-Nearest-Neighbor (k-NN) vertices from the 3DMM. Then, we decode these vertices and features into a density and color with respect to the input camera view direction, via Multi-Layer-Perceptrons (MLPs) interleaved with inverse-distance based weighted sum. + +neck, jaw, and eyes), where $i$ is the frame index, with which a head mesh can be obtained via $\pmb{V}_i(\pmb{\beta},\pmb{\psi}_i,\pmb{\theta}_i)$ . Since $\beta$ is fixed and does not depend on the pose or expressions for a given user, we omit it in the following sections for brevity. + +An overview of our framework is shown in Fig. 2. We adopt the 3DMM-anchored neural radiance field (NeRF) as the core representation for our head avatar (Sec. 3.1), where local features are attached to the vertices of the deformable 3DMM mesh. During the inference, we first deform the 3DMM mesh based on the target configuration $V_{t} = (\psi_{t},\theta_{t})$ . Then, for an arbitrary 3D query point, we aggregate the features from neighboring vertices on $V_{t}$ to estimate the local density and color by Multi-Layer-Perceptrons (MLPs), which are then integrated in the volumetric rendering formulation to generate the color image. To learn local features, we train CNN-based networks in the UV space to incorporate spatial context (Sec. 3.2). Our model is trained end-to-end with RGB supervisions (Sec. 3.3). + +# 3.1. Avatar Representation + +An ideal representation for a head avatar should have the following properties: 1) Provides intuitive control to achieve the desired expression and head pose; 2) Requires a moderate amount of training data, e.g., a short monocular video; 3) Produces expression-dependent rendering details; 4) Generalizes reasonably well to unseen expressions. + +To this end, we propose the 3DMM-anchored neural radiance field (NeRF) as shown in Fig. 3. Inspired by local feature based neural radiance field [34, 47], we attach feature vectors $z^{j}$ on each 3DMM vertex $v_{i}^{j}$ to encode the local radiance fields that can be decoded with MLPs, where $i$ denotes frame index and $j$ denotes vertex index. In this way, the radiance field can be deformed according to vertex locations, hence can be intuitively controlled by the 3DMM expression and pose $(\psi_{i},\theta_{i})$ . In addition, the 3DMM fitting on each frame provides a rough tracking across deformable + +face geometries, such that all the frames can contribute into the learning of a unified set of local per-vertex features. We will discuss model capacity and generalization in Sec. 3.2. + +To decode the vertex features $\{z^j\}$ into the radiance field for the frame $i$ , given a 3D query point $\pmb{q}$ , we first find its $k$ -Nearest-Neighbor ( $k$ -NN) vertices from the 3DMM mesh $\{\pmb{v}_i^j\}_{j\in \mathcal{N}_k^q}$ with attached features $\{z^j\}_{j\in \mathcal{N}_k^q}$ . Then, we use two MLPs $\mathcal{F}_0$ and $\mathcal{F}_1$ with inverse-distance based weighted sum to decode local color and density. Formally, + +$$ +\hat {\boldsymbol {z}} _ {i} ^ {j} = \mathcal {F} _ {0} (\boldsymbol {v} _ {i} ^ {j} - \boldsymbol {q}, \boldsymbol {z} ^ {j}) +$$ + +$$ +\hat {\boldsymbol {z}} _ {i} = \sum_ {j} w ^ {j} \hat {\boldsymbol {z}} _ {i} ^ {j} \tag {1} +$$ + +$$ +\boldsymbol {c} _ {i} (\boldsymbol {q}, \boldsymbol {d} _ {i}), \sigma_ {i} (\boldsymbol {q}) = \mathcal {F} _ {1} (\hat {\boldsymbol {z}} _ {i}, \boldsymbol {d} _ {i}), +$$ + +where $w^{j} = \frac{d^{j}}{\sum_{k} d^{k}}$ , $d^{j} = \frac{1}{\|\pmb{v}_{i}^{j} - \pmb{q}\|_{2}}$ with $j \in \mathcal{N}_k^q$ , and $\pmb{d}_i$ denotes the camera view direction. Finally, we render the output image with volumetric rendering formulation as in vanilla NeRF [30] given the camera ray $r(t) = o + td$ : + +$$ +\boldsymbol {C} _ {i} (\boldsymbol {r}) = \int_ {t _ {n}} ^ {t _ {f}} T (t) \sigma_ {i} (\boldsymbol {r} (t)) \boldsymbol {c} _ {i} (\boldsymbol {r} (t), \boldsymbol {d}) \mathrm {d} t, \tag {2} +$$ + +$$ +\text {w h e r e} T (t) = \exp \left(- \int_ {t _ {n}} ^ {t} \sigma_ {i} (\boldsymbol {r} (s)) \mathrm {d} s\right) +$$ + +To reduce misalignments caused by per-frame contents that cannot be captured by 3DMM (e.g., 3DMM fitting errors), we additionally learn error-correction warping fields during training inspired from prior works on deformable NeRF [23, 45]. More specifically, we input the original query point and a per-frame latent code $e_i$ , which is randomly initialized and optimized during the training, into the error-correction MLPs $\mathcal{F}_{\mathcal{E}}$ to predict a rigid transformation, and apply it to the query point. The transformation is denoted as $q' = \mathcal{T}_i(q) = \mathcal{F}_{\mathcal{E}}(q, e_i)$ . Then we use the warped query point $q'$ to decode the color and density. Note that this warping field is disabled during testing. Please refer to the supplementary for detailed formulations of the warping field. + +# 3.2. Predicting Expression-Dependent Features + +While the proposed avatar representation (Sec. 3.1) enables intuitive controllability and convenience in learning, it still has limited capability for modeling complex expression-dependent variations due to the use of frame-shared vertex features $\{z^j\}$ . + +To overcome this, we propose to predict the dynamic vertex features $\{z_i^j\}$ conditioned on the 3DMM expression and pose $(\psi_{i},\theta_{i})$ . A common practice for NeRF-based methods is to use MLP-based architectures for dynamic feature prediction [3, 15, 53, 54]. However, we find that this leads to blurry rendering results, presumably because of the limited model capacity due to the lack of spatial context (i.e., + +
Subject0 +LPIPS / SSIM / PSNRSubject1 +LPIPS / SSIM / PSNRSubject2 +LPIPS / SSIM / PSNRSubject3 +LPIPS / SSIM / PSNRSubject4 +LPIPS / SSIM / PSNR
TPSMM [52]0.192 / 0.852 / 22.600.205 / 0.830 / 16.380.216 / 0.782 / 18.400.222 / 0.799 / 20.280.156 / 0.913 / 21.29
FOMM [39]0.171 / 0.841 / 22.930.179 / 0.827 / 16.020.202 / 0.777 / 18.980.186 / 0.798 / 22.280.122 / 0.915 / 23.94
NHA [17]0.165 / 0.836 / 20.200.166 / 0.840 / 15.480.178 / 0.809 / 17.990.153 / 0.798 / 21.310.091 / 0.926 / 23.78
IMAvatar [53]0.207 / 0.852 / 21.260.187 / 0.848 / 15.980.265 / 0.729 / 15.800.214 / 0.782 / 20.370.142 / 0.897 / 20.63
NerFACE [15]0.205 / 0.817 / 20.060.182 / 0.833 / 15.780.188 / 0.793 / 19.410.229 / 0.747 / 18.160.093 / 0.938 / 25.57
Ours-D0.144 / 0.864 / 21.920.152 / 0.855 / 16.230.141 / 0.841 / 20.420.156 / 0.833 / 23.050.075 / 0.944 / 25.71
+ +Table 1. Quantitative Comparison with state-of-the-art (SOTA) approaches. Subject4 is the data from NerFACE [15], while other subjects are from our dataset. Our method achieves superior results than prior SOTAs. + +each vertex does not know the feature of its neighboring vertices). Based on this intuition, we propose to process the 3DMM expression and pose $(\psi_i,\theta_i)$ with CNNs in the texture atlas space (UV space) to provide local spatial context. + +Specifically, we design two variations of CNN-based architecture to learn expression-dependent vertex features $\{z_i^j\}$ . The first variant, denoted as Ours-C, trains a decoder consisting of transposed convolutional blocks to predict the feature map in UV space directly from 1-D codes of 3DMM expression and pose. We empirically find that such a model is effective in improving the overall rendering quality, however tends to fail and produce severe artifacts on out-of-training expressions (See discussions in Sec. 4.4). We then propose the second variant, denoted as Ours-D, that uses the 3D deformation of 3DMM in UV space as the input for feature prediction, and observe that resulting avatar models are more resilient to stretchy and unseen expressions. Specifically, we first compute the vertex displacements using 3DMM expression and pose as $D_{i} = V_{i}(\psi_{i},\theta_{i}) - V_{neutral}(0,0)$ . We then rasterize the vertex displacements $D_{i}$ into UV space, and process it with a U-Net $\mathcal{F}_{\mathcal{D}}$ . Finally, the output UV feature map is sampled back to mesh vertices $V_{i}$ , serving as the dynamic vertex features $\{z_i^j\}$ (i.e., the expression-dependent version of frame-shared vertex features $\{z^j\}$ described in Sec. 3.1). + +# 3.3. Training Schema + +Our model is trained on monocular RGB videos mainly with the photometric loss, where we penalize the $l_{2}$ distance between the rendering and the ground truth images. Formally $\mathcal{L}_{rgb} = \sum_{i}\sum_{\boldsymbol{r}}\| C_i(\boldsymbol{r}) - I_i(\boldsymbol{r})\|_2$ , where $\boldsymbol{r}$ denotes the camera ray of each pixel and $i$ denotes frame index. To regularize the learning of error-correction warping field $\mathcal{T}(\boldsymbol{q})$ , we adopt a elastic loss $\mathcal{L}_{elastic}$ similar to Nerfies [33], and a magnitude loss defined as $\mathcal{L}_{mag} = \sum_q\| q - \mathcal{T}(q)\|_2^2$ . The total loss is defined as: + +$$ +\mathcal {L} = \mathcal {L} _ {r g b} + \lambda_ {\text {e l a s t i c}} \mathcal {L} _ {\text {e l a s t i c}} + \lambda_ {\text {m a g}} \mathcal {L} _ {\text {m a g}}, \tag {3} +$$ + +where we set $\lambda_{elastic} = 10^{-4}$ at the beginning and decay to $10^{-5}$ after 155k iterations, and $\lambda_{mag} = 10^{-2}$ . Please see supplementary for more details. + +# 4. Experiments + +We train and evaluate our method on casually captured monocular RGB videos (Sec. 4.1) and show that our method achieves superior rendering quality than prior state-of-the-art monocular RGB head avatars (Sec. 4.2). Then we verify our key observations on architectural choices to design a good avatar model, in terms of rendering quality and expression robustness (Sec. 4.4). + +# 4.1. Datasets and Metrics + +Datasets. Following the prior art [15], we captured monocular RGB videos of various subjects with smartphones or webcams for training and evaluation. Each video is 1-2 minutes long (around $1.5\mathrm{k - }2\mathrm{k}$ frames at 30 FPS) with the first 1000-1500 frames as training data and the rest frames for evaluation if not otherwise specified. For the training clip, the subjects are asked to first keep a neutral expression and rotate their heads, then perform different expressions during the head rotation, with extreme expressions included. For the testing clip, the subjects are asked to perform freely without any constraints. To demonstrate that our method also works for common talking head videos, we also include a video from NerFACE [15], which has significantly less variability in expressions when compared to our capture protocol. We mask out background [32] for each video, and obtain 3DMM and camera parameters with 3DMM fitting similar to that in NHA [17]. Please refer to the supplementary for examples of data. Please note that we collect relatively shorter training videos compared to related work. This favors a better user experience while still synthesizing high-quality avatars with more personalized and detailed expressions. + +Metrics. Following the prior art [15], we use the following standard image quality metrics for quantitative evaluations: the Learned Perceptual Image Patch Similarity (LPIPS) [51], Structure Similarity Index (SSIM) [44], and Peak Signal-to-Noise Ratio (PSNR). + +![](images/8e6d1ac6535f28aa5cc557d58bcbee2ce2e089c7573e0cdffb083778d670a523.jpg) +Figure 4. Qualitative Comparison to prior state-of-the-art monocular head avatars. Note how our approach more faithfully reconstructs the ground truth expressions while preserving most of the high frequency details. + +# 4.2. Comparisons with State-of-Art + +We compare our method with five state-of-the-art methods of different types with publicly available implementations from the authors: NerFACE [15], IMAvatar [53], NHA [17], FOMM [39], and TPSMM [52]. For subject-specific methods (i.e. Ours, NerFACE [15], IMAvatar [53], and NHA [17]), we train the avatar for each subject separately with training frames of each video, then drive and render the trained avatar with 3DMM and camera parameters of testing frames. For few-shot methods (i.e., FOMM [39], and TPSMM [52]), we use the first frame of each video, which shows a frontal head, as the source image, then use testing frames as driving images in self-reenactment manner. Finally, the generated images of each method are compared with ground truth testing frames quantitatively (See Tab. 1) and qualitatively (See Fig. 4). + +As shown in Fig. 4, FOMM [39] and TPSMM [52] struggle with large head rotations and fail to produce 3D consistent results. NHA [17] uses explicit mesh surface with neural textures. The fix mesh topology makes it hard to + +handle challenging hair in Subject 3. Also, NHA uses linear 3DMM blendshapes, making it hard to capture complex expressions (e.g., Subject 1 & 3) and wrinkles (e.g., Subject 4). IMAvatar [53] uses an occupancy field to model geometry, making it hard to handle challenging hair. Despite learning a personalized blendshape field, the linear formulation still limits their model capacity of handling complex expressions. In addition, we observed training instability of IMAvatar on the captured data, converging to oversmooth results. NerFACE [15] works relatively well on data with easy expressions (i.e., common talking heads in Subject 4), but struggles on our challenging data and estimates incorrect geometry (e.g., distorted head for Subject 3) and blurry rendering. In contrast, our method gives superior results on all the aspects discussed above. Tab. 1 further quantitatively confirms the good rendering quality of our method. + +# 4.3. Driving the Avatar + +After training, the learned avatar model can be driven by the same subject under different capture conditions, e.g. hair + +![](images/f917e938b70e11648ad95eb9cccd3f6ba46114e75633202ec2a5fd8c94c0d7e6.jpg) +Drive Frame + +![](images/88862a58742ee8ed3f9080bfd0bba84733c0d5256cff9694c7c3892e7058073c.jpg) +Drive View + +![](images/63e17120c5ab8b9d0cee090e884c5b6daeb1448372b64da9aaa0d766dbf2e827.jpg) +Side View + +![](images/b7986626baaf30e74f5afebccef37b5277d2880ef814f276736d38a556021b82.jpg) +Depth + +![](images/3c34d5e10bf27da1bdb9aea82c4c5b26629fa7708943bbc0a512e5dbf77ce6d7.jpg) +Figure 5. Results on driving the learned avatar by the same subject under different capture conditions. Our method produces faithful expressions, multi-view consistent rendering, and good geometry. + +![](images/27d29cbf3c21bf1ea341890c4a569998d019030581e60e605b38b23539665615.jpg) + +![](images/8ada6b924f1dcda74bf7226e62c796f63d346b17aded717eb55f68e03935b669.jpg) + +![](images/36ffe8ff63775d054b498fb8300d73a3c10d6943136d7e432b12f54cafb1ff1d.jpg) + +![](images/c61ac4266f0fd60e21f5a948b9f2216adae7d0872670320746222fa55abea7d8.jpg) + +![](images/ee01942802ce4a2fa8b7d1d8606707d5645e151a99745a499fb3655d58ed92bd.jpg) +Figure 6. Results on expressions out of the training distribution with different amount of training data. Ours- $D$ more robustly handles unseen expressions and degrades less with fewer training data. + +![](images/9861e135702f2237c11428c8cfd0dae1e71406bfcb88f2a17956474e7f789578.jpg) + +![](images/b5dd1b42fd6dad49ab29bba7d89539943f0859934475ec26ff93453898674288.jpg) + +![](images/71089543f7f6e7e03142148a1bfe1b01db603292f9287a5c62d6c67a15868cd8.jpg) + +style, illumination, glasses. We show the driving results in Fig. 5. Our avatar faithfully reproduces the expressions of the driving frame, while also achieving multi-view consistent renderings and generating high quality geometry. + +# 4.4. Ablation Study: Expression Features + +Learning good local features is crucial for improving model capacity and capturing high frequency details, without losing the regularization from 3DMM (Sec. 3.2). We investigate and compare three alternative approaches and our two model variations for feature learning: + +Static Features. We use frame-shared (hence expression-shared) vertex features $\{z^j\}$ . As shown in Fig. 7, the expression-shared features struggle to capture strong expression-dependent variations, leading to incorrect geometry (e.g., incorrect cheek silhouette), blurry details (e.g., teeth), and inferior LPIPS scores in Tab. 2. + +3DMM Codes. We extend Static Features by directly concatenating 3DMM expression and pose codes $(\psi_{i},\theta_{i})$ to the vertex features $\{z^j\}$ . This allows the model to have more capacity to capture expression-dependent variations. Although median-level expression characteristics are recognizable (e.g., cheek silhouette in Fig. 7), the results are even + +
Subject0Subject1Subject2Subject3
Full Training Data
Static Features0.15590.15860.15520.1688
3DMM Codes0.15990.16200.17460.1738
3DMM Codes MLP0.15680.15510.15050.1686
Ours-C0.14170.14570.13830.1550
Ours-D0.14390.15230.14150.1559
First 50% Training Data
Ours-C0.20380.14830.15800.1566
+0.0621+0.0026+0.0197+0.0016
Ours-D0.17110.15110.15160.1558
+0.0272-0.0012+0.0101-0.0001
+ +Table 2. Quantitative ablations with baselines in LPIPS scores (lower is better). Bold denotes the best while underline denotes the second best. + +blurrier for components less depend on expressions such as hairs, possibly due to the limited model capacity of shallow MLPs without spatial context. + +3DMM Codes MLP. To further increase the model capacity, we use a more sophisticated MLP architecture inspired from Zheng et al. [54], which is a MLP-based conditional Variational AutoEncoder (cVAE), to predict the expression-dependent vertex features $\{z_i^j\}$ from the 3DMM expression and pose codes $(\psi_i,\theta_i)$ . More specifically, the cVAE is conditioned on 3DMM codes and vertex coordinates on neutral face mesh $V_{neutral}$ . Then, the cVAE encodes the frame index into a latent code and decodes it into the vertex features $\{z_i^j\}$ . Although the overall sharpness of results is improved (e.g., glasses and hair in Fig. 7), this model still produces blurry details such as teeth, eyes, and wrinkles. Tab. 2 further confirms that this more sophisticated MLP architecture still produces inferior results compared to CNN-based methods Ours-C and Ours-D. + +Ours-C vs. Ours-D. By replacing MLPs with CNNs in UV space (Please refer to Sec. 3.2 for more details), both variations of Ours achieve superior rendering quality than MLP-based baselines, as shown in Fig. 7. Moreover, we find that Ours-D is more robust to expressions that are outside the training distribution compared to Ours-C (See Fig. 6). To further investigate the model robustness to out-of-training expressions, we train Ours-C and Ours-D with the first $50\%$ of training frames, to simulate the scenario with less expression coverage during training. As shown in Tab. 2 and Fig. 6, Ours-D degrades less than Ours-C, indicating enhanced robustness. + +# 4.5. Robustness to Expression Extrapolation + +We qualitatively test our method on expression extrapolation setting by artificially manipulating the expression code for a given test frame. In particular, we double the value of the expression code and compare the results with + +![](images/d7e6cecf13863a8d3c50c15853fd1d1e52f7c2b635f1648510a204579b82ec6f.jpg) +Figure 7. Comparison between different designs for local vertex feature learning. See Sec. 4.4 for more details. "Static feature" struggles on capturing personalized expressions. "3DMM Codes" improves the personalization but suffers from overall blurriness. "3DMM Codes MLP" further improves the sharpness, but still cannot present the details. Overall, our convolution-based methods lead to superior renderings on areas such as cheek silhouette, glasses frames and reflections, and teeth. + +![](images/ce37a664adb733227ebf020c22a4135fb06e8620e5c28c4308eb78f4a54aa81b.jpg) +Figure 8. Expression extrapolation results and comparisons. Compared to other work, our method performs well on extreme and out-of-training expression. + +prior works. As shown in Fig. 8, NHA [17] gives reasonable expression extrapolation results for both geometry and appearance. However, it cannot faithfully capture the out-of-3DMM details when compared to the ground truth. IMAvatar [53] gives reasonable geometry for extrapolation, but struggles to produce sharp extrapolated appearances. NerveFACE [15] fails in producing reasonable renderings. In contrast, our method not only faithfully captures out-of-3DMM details, but also generalizes to extrapolated expressions. + +# 5. Discussion + +In this work, we presented a framework to learn high-quality controllable 3D head avatars from monocular RGB videos. The core of our method is a 3DMM-anchored neural radiance field decoded from features attached on 3DMM + +vertices. The vertex features can be either predicted from 3DMM expression and pose codes, or vertex displacements, via CNNs in UV space, where the former favors quality and the latter favors robustness. We experimentally demonstrate that it is possible to learn high-quality avatars with faithful, highly non-rigid expression deformations purely from monocular RGB videos, leading to a superior rendering quality of our method compared to other approaches. + +Limitations. Compared to the state-of-the-art approaches, our proposed framework learns portrait avatars with superior rendering quality. Nevertheless, our method still inherits the disadvantages of NeRF [30] on time-consuming subject-specific training and slow rendering. Our method relies on the expression space of a 3DMM, thus cannot capture components that are completely missing in the 3DMM, such as the tongue. Moreover, extending our method to include the upper body or integrating into full body avatars are also interesting future directions. + +Ethical Considerations. The rapid progress on avatar synthesis facilitates numerous downstream applications, but also raises concerns on ethical misuses. On the synthesis side, it would be ideal to actively use watermarking and avoid driving avatars with different identities. On the viewing side, forgery detection [2,25,35,36,41] is an active research field with promising progress. However, it is still hard to guarantee reliable detection of fake visual materials at the current stage. Encouraging the use of cryptographical signatures may also be a potential solution to ensure the authenticity of visual material. + +# References + +[1] MetaHuman - Unreal Engine. https://www.unrealengine.com/en-US/metahuman, 2. Accessed: 2022-10-17. 1 +[2] Shruti Agarwal, Hany Farid, Tarek El-Gaaly, and Ser-Nam Lim. Detecting deep-fake videos from appearance and behavior. In 2020 IEEE International Workshop on Information Forensics and Security (WIFS), pages 1-6. IEEE, 2020. 8 +[3] ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, and Zhixin Shu. RigNeRF: Fully Controllable Neural 3D Portraits. pages 20364-20373, 2022. 2, 3, 4 +[4] Ziqian Bai, Zhaopeng Cui, Xiaoming Liu, and Ping Tan. Riggable 3D Face Reconstruction via In-Network Optimization. pages 6216-6225, June 2021. 2 +[5] Ziqian Bai, Zhaopeng Cui, Jamal Ahmed Rahim, Xiaoming Liu, and Ping Tan. Deep Facial Non-Rigid Multi-View Stereo. pages 5850–5860, 2020. 2 +[6] Thabo Beeler, Fabian Hahn, Derek Bradley, Bernd Bickel, Paul Beardsley, Craig Gotsman, Robert W Sumner, and Markus Gross. High-Quality Passive Facial Performance Capture Using Anchor Frames. In ACM SIGGRAPH 2011 Papers, pages 1-10. 2011. 1 +[7] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pages 187-194, 1999. 2 +[8] Chen Cao, Tomas Simon, Jin Kyu Kim, Gabe Schwartz, Michael Zollhoefer, Shun-Suke Saito, Stephen Lombardi, Shih-En Wei, Danielle Belko, Shoou-I Yu, Yaser Sheikh, and Jason Saragih. Authentic Volumetric Avatars from a Phone Scan. ACM Transactions on Graphics (TOG), 41(4), 2022. 1 +[9] Chen Cao, Hongzhi Wu, Yanlin Weng, Tianjia Shao, and Kun Zhou. Real-Time Facial Animation With Image-Based Dynamic Avatars. 35(4), 2016. 2 +[10] Bindita Chaudhuri, Noranart Vesdapunt, Linda Shapiro, and Baoyuan Wang. Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting. In ECCV 2020: 16th European Conference on Computer Vision, pages 142-160, 2020. 2 +[11] Chong Bao and Bangbang Yang, Zeng Junyi, Bao Hujun, Zhang Yinda, Cui Zhaopeng, and Zhang Guofeng. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In European Conference on Computer Vision (ECCV), 2022. 3 +[12] Ruofei Du, Ming Chuang, Wayne Chang, Hugues Hoppe, and Amitabh Varshney. Montage4D: Real-time Seamless Fusion and Stylization of Multiview Video Textures. Journal of Computer Graphics Techniques, 8(1):1-34, Jan. 2019. 1 +[13] Ruofei Du, David Li, and Amitabh Varshney. Geollery: A Mixed Reality Social Media Platform. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, number 685 in CHI. ACM, May 2019. 1 +[14] Bernhard Egger, William AP Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani, + +et al. 3D Morphable Face Models—Past, Present, and Future. 39(5):1-38, 2020. 2 +[15] Guy Gafni, Justus Thies, Michael Zollhofer, and Matthias Nießner. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. pages 8649-8658, 2021. 2, 3, 4, 5, 6, 8 +[16] Pablo Garrido, Michael Zollhöfer, Dan Casas, Levi Valgaerts, Kiran Varanasi, Patrick Pérez, and Christian Theobalt. Reconstruction of Personalized 3D Face Rigs From Monocular Video. ACM Transactions on Graphics (TOG), 35(3):28, 2016. 2 +[17] Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. Neural Head Avatars From Monocular RGB Videos. pages 18653–18664, 2022. 2, 5, 6, 8 +[18] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, Danhang Tang, Anastasia Tkach, Adarsh Kowdle, Emily Cooper, Ming-song Dou, Sean Fanello, Graham Fyffe, Christoph Rhemann, Jonathan Taylor, Paul Debevec, and Shahram Izadi. The Relightables: Volumetric Performance Capture of Humans With Realistic Relighting. ACM Transactions on Graphics, Nov. 2019. 1 +[19] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, Danhang Tang, Anastasia Tkach, Adarsh Kowdle, Emily Cooper, Ming-song Dou, Sean Fanello, Graham Fyffe, Christoph Rhemann, Jonathan Taylor, Paul Debevec, and Shahram Izadi. The relightables: Volumetric performance capture of humans with realistic relighting. ACM Trans. Graph., 38(6), nov 2019. 3 +[20] Zhenyi He, Ruofei Du, and Ken Perlin. CollaboVR: A Reconfigurable Framework for Multi-user to Communicate in Virtual Reality. In 2020 IEEE International Symposium on Mixed and Augmented Reality, ISMAR, pages 542-554. IEEE, Nov. 2020. 1 +[21] Liwen Hu, Shunsuke Saito, Lingyu Wei, Koki Nagano, Jaewoo Seo, Jens Fursund, Iman Sadeghi, Carrie Sun, YenChun Chen, and Hao Li. Avatar Digitization From a Single Image for Real-Time Rendering. 36(6):1-14, 2017. 2 +[22] Alexandru Eugen Ichim, Sofien Bouaziz, and Mark Pauly. Dynamic 3D Avatar Creation From Hand-Held Video Input. 34(4):1-14, 2015. 2 +[23] Wei Jiang, Kwang Moo Yi, Golnoosh Samei, Oncel Tuzel, and Anurag Ranjan. NeuMan: Neural Human Radiance Field From a Single Video. 2022. 4 +[24] Tianye Li, Timo Bolkart, Michael. J. Black, Hao Li, and Javier Romero. Learning a Model of Facial Shape and Expression From 4D Scans. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6):194:1-194:17, 2017. 2, 3 +[25] Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. Celeb-df: A large-scale challenging dataset for deepfake forensics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3207-3216, 2020. 8 + +[26] Siyou Lin, Hongwen Zhang, Zerong Zheng, Ruizhi Shao, and Yebin Liu. Learning implicit templates for point-based clothed human modeling. In ECCV, 2022. 3 +[27] Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. Advances in Neural Information Processing Systems, 33:15651-15663, 2020. 3 +[28] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Control. ACM Transactions on Graphics (TOG), 40(6):1-16, 2021. 3 +[29] Abhimitra Meka, Rohit Pandey, Christian Haene, Sergio Orts-Escalano, Peter Barnum, Philip David-Son, Daniel Erickson, Yinda Zhang, Jonathan Taylor, Sofien Bouaziz, et al. Deep Relightable Textures: Volumetric Performance Capture With Neural Rendering. ACM Transactions on Graphics (TOG), 39(6):1-21, 2020. 1 +[30] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing Scenes As Neural Radiance Fields for View Synthesis. Communications of the ACM, 65(1):99-106, 2021. 2, 4, 8 +[31] Sergio Orts-Escolano, Christoph Rhemann, Sean Fanello, Wayne Chang, Adarsh Kowdle, Yury Degtyarev, David Kim, Philip Davidson, Sameh Khamis, Mingsong Dou, Vladimir Tankovich, Charles Loop, Qin Cai, Philip Chou, Sarah Mennicken, Julien Valentin, Vivek Pradeep, Shenlong Wang, Sing Bing Kang, Pushmeet Kohli, Yuliya Lutchyn, Cem Keskin, and Shahram Izadi. Holoportation: Virtual 3D Teleportation in Real-Time. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST). ACM, Oct. 2016. 1 +[32] Rohit Pandey, Sergio Orts Escolano, Chloe Legendre, Christian Haene, Sofien Bouaziz, Christoph Rhemann, Paul Debevec, and Sean Fanello. Total relighting: learning to relight portraits for background replacement. ACM Transactions on Graphics (TOG), 40(4):1-21, 2021. 3, 5 +[33] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable Neural Radiance Fields. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 5865-5874, 2021. 5 +[34] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural Body: Implicit Neural Representations With Structured Latent Codes for Novel View Synthesis of Dynamic Humans. pages 9054-9063, 2021. 3, 4 +[35] Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: A large-scale video dataset for forgery detection in human faces. arXiv preprint arXiv:1803.09179, 2018. 8 +[36] Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: Learning to detect manipulated facial images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1-11, 2019. 8 + +[37] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Hao Li, and Angjoo Kanazawa. PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, Oct. 2019. 1 +[38] Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. Animating arbitrary objects via deep motion transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2377-2386, 2019. 3 +[39] Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. First Order Motion Model for Image Animation. In Advances in Neural Information Processing Systems (NeurIPS), volume 32, 2019. 3, 5, 6 +[40] Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. FML: Face Model Learning From Videos. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10812-10822, 2019. 2 +[41] Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales, and Javier Ortega-Garcia. Deepfakes and beyond: A survey of face manipulation and fake detection. Information Fusion, 64:131-148, 2020. 8 +[42] Zach Waggoner. My Avatar, My Self: Identity in Video Role-Playing Games. McFarland, 2009. 1 +[43] Ting-Chun Wang, Ming-Yu Liu, Andrew Tao, Guilin Liu, Jan Kautz, and Bryan Catanzaro. Few-Shot Video-to-Video Synthesis. In Advances in Neural Information Processing Systems (NeurIPS), 2019. 3 +[44] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image Quality Assessment: From Error Visibility to Structural Similarity. 13(4):600-612, 2004. 5 +[45] Chung-Yi Weng, Brian Curless, Pratul P Srinivasan, Jonathan T Barron, and Ira Kemelmacher-Shlizerman. Humanerf: Free-viewpoint rendering of moving people from monocular video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16210-16220, 2022. 4 +[46] Olivia Wiles, A Koepke, and Andrew Zisserman. X2face: A network for controlling face generation using images, audio, and pose codes. In Proceedings of the European conference on Computer Vision (ECCV), pages 670-686, 2018. 3 +[47] Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In European Conference on Computer Vision, pages 597-614. Springer, 2022. 4 +[48] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction. pages 601-610, 2020. 2 +[49] Egor Zakharov, Aleksei Ivakhnenko, Aliaksandra Shysheya, and Victor Lempitsky. Fast bi-layer neural synthesis of one-shot realistic head avatars. In European Conference on Computer Vision, pages 524-540. Springer, 2020. 3 + +[50] Egor Zakharov, Aliaksandra Shysheya, Egor Burkov, and Victor Lempitsky. Few-Shot Adversarial Learning of Realistic Neural Talking Head Models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9459-9468, 2019. 3 +[51] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The Unreasonable Effectiveness of Deep Features As a Perceptual Metric. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 586-595, 2018. 5 +[52] Jian Zhao and Hui Zhang. Thin-plate spline motion model for image animation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3657–3666, 2022. 5, 6 +[53] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. Im avatar: Implicit morphable head avatars from videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13545–13555, 2022. 3, 4, 5, 6, 8 +[54] Zerong Zheng, Han Huang, Tao Yu, Hongwen Zhang, Yandong Guo, and Yebin Liu. Structured Local Radiance Fields for Human Avatar Modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15893-15903, 2022. 3, 4, 7 +[55] Michael Zollhöfer, Justus Thies, Pablo Garrido, Derek Bradley, Thabo Beeler, Patrick Pérez, Marc Stamminger, Matthias Nießner, and Christian Theobalt. 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The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses. Our hybrid pipeline combines the geometry prior and dynamic tracking of a 3DMM with a neural radiance field to achieve fine-grained control and photorealism. To reduce over-smoothing and improve out-of-model expressions synthesis, we propose to predict local features anchored on the 3DMM geometry. These learnt features are driven by 3DMM deformation and interpolated in 3D space to yield the volumetric radiance at a designated query point. We further show that using a Convolutional Neural Network in the UV space is critical in incorporating spatial context and producing representative local features. Extensive experiments show that we are able to reconstruct high-quality avatars, with more accurate expression-dependent details, good generalization to out-of-training expressions, and quantitatively superior renderings compared to other" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 306, + 460, + 419, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 460, + 419, + 472 + ], + "spans": [ + { + "bbox": [ + 306, + 460, + 419, + 472 + ], + "type": "text", + "content": "state-of-the-art approaches." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 306, + 507, + 387, + 520 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 507, + 387, + 520 + ], + "spans": [ + { + "bbox": [ + 306, + 507, + 387, + 520 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 304, + 527, + 545, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 527, + 545, + 696 + ], + "spans": [ + { + "bbox": [ + 304, + 527, + 545, + 696 + ], + "type": "text", + "content": "Creating a controllable human avatar is a fundamental piece of technology for many downstream applications, such as AR/VR communication [20, 31], virtual try-on [37], virtual tourism [13], games [42], and visual effects for movies [12, 18]. Prior art in high-quality avatar generation typically requires extensive hardware configurations (i.e., camera arrays [6, 12, 31], light stages [18, 29], dedicated depth sensors [8]), or laborious manual intervention [1]. Alternatively, reconstructing avatars from monocular RGB videos significantly relaxes the dependency on equipment setup and broadens the application scenarios. However, monocular head avatar creation is highly ill-posed due to the dual problems of reconstructing and tracking highly articulated and deformable facial geometry, while model" + } + ] + } + ], + "index": 27 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 320, + 702, + 529, + 714 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 702, + 529, + 714 + ], + "spans": [ + { + "bbox": [ + 320, + 702, + 529, + 714 + ], + "type": "text", + "content": "*Work was conducted while Ziqian Bai was an intern at Google." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "16890" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 49, + 72, + 286, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 72, + 286, + 167 + ], + "spans": [ + { + "bbox": [ + 49, + 72, + 286, + 167 + ], + "type": "text", + "content": "ing sophisticated facial appearance. Traditionally, 3D Morphable Models (3DMM) [7, 24] have been used to model facial geometry and appearance for various applications including avatar generation [9, 16, 22]. However, 3DMMs do not fully capture subject-specific static details and dynamic variations, such as hair, glasses, and expression-dependent high frequency details such as wrinkles, due to the limited capacity of the underlying linear model." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 171, + 286, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 171, + 286, + 456 + ], + "spans": [ + { + "bbox": [ + 49, + 171, + 286, + 456 + ], + "type": "text", + "content": "Recent works [3, 15] have incorporated neural radiance fields in combination with 3DMMs for head avatar generation to achieve photorealistic renderings, especially improving challenging areas, such as hair, and adding view-dependent effects, such as reflections on glasses. The pioneering work of NerFACE [15] uses a neural radiance field parameterized by an MLP that is conditioned on 3DMM expression parameters and learnt per-frame latent codes. While they achieve photorealistic renderings, the reliance on an MLP to directly decode from 3DMM parameter space leads to the loss of fine-grain control over geometry and articulation. Alternatively, RigNeRF [3] learns the radiance field in a canonical space by warping the target head geometry using a 3DMM fit, which is further corrected by a learnt dense deformation field parameterized by another MLP. While they demonstrate in-the-wild head pose and expression control, the use of two global MLPs to model canonical appearance and deformations for the full spatial-temporal space leads to a loss of high frequency details, and an overall uncanny appearance of the avatar. Both of these works introduce new capabilities but suffer from lack of detail in both appearance and motion because they attempt to model the avatar's global appearance and deformation with an MLP network." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 460, + 286, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 460, + 286, + 662 + ], + "spans": [ + { + "bbox": [ + 49, + 460, + 286, + 662 + ], + "type": "text", + "content": "In this paper, we propose a method to learn a neural head avatar from a monocular RGB video. The avatar can be controlled by an underlying 3DMM model and deliver high-quality rendering of arbitrary facial expressions, head poses, and viewpoints, which retain fine-grained details and accurate articulations. We achieve this by learning to predict expression-dependent spatially local features on the surface of the 3DMM mesh. A radiance field for any given 3D point in the volume is then obtained by interpolating the features from K-nearest neighbor vertices on the deformed 3DMM mesh in target expression, and passing them through a local MLP to infer density and color. The local features and local MLP are trained jointly by supervising the radiance field through standard volumetric rendering on the training sequence [30]. Note that our networks rely on the local features to model appearance and deformation details, and leverages the 3DMM to model only the global geometry." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 665, + 286, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 665, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 49, + 665, + 286, + 712 + ], + "type": "text", + "content": "Learning local features is critical in achieving a high-quality head avatar. To this end, we train an image-to-image translation U-Net that transforms the 3DMM deformations in the UV space to such local features. These UV" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 308, + 72, + 544, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 72, + 544, + 239 + ], + "spans": [ + { + "bbox": [ + 308, + 72, + 544, + 239 + ], + "type": "text", + "content": "space features are then attached to the corresponding vertices of the 3DMM mesh geometry. We show that learning features from such explicit per-vertex local displacement of the 3DMM geometry makes the model retain high-frequency expression-dependent details and also generalizes better to out-of-training expressions, presumably because of the spatial context between nearby vertices incorporated by the convolutional neural network (CNN). An alternative approach is to feed the 3DMM parameters directly into a CNN decoder running on the UV space. However, we found this produces severe artifacts on out-of-training expressions, particularly given a limited amount of training data, e.g. for a lightweight, 1-minute data collection procedure during the avatar generation process." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 308, + 240, + 544, + 382 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 240, + 544, + 382 + ], + "spans": [ + { + "bbox": [ + 308, + 240, + 544, + 382 + ], + "type": "text", + "content": "In summary, our contributions are as follows: we propose a neural head avatar representation based on a 3DMM-anchored neural radiance field, which can model complex expression-dependent variations, but requires only monocular RGB videos for training. We show that a convolutional neural network running on per-vertex displacement in UV space is effective in learning local expression-dependent features, and delivers favorable training stability and generalization to out-of-training expressions. Experiments on real-world datasets show that our model provides competitive controllability and generates sharper and detail enriched rendering compared to state-of-the-art approaches." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 308, + 394, + 395, + 406 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 394, + 395, + 406 + ], + "spans": [ + { + "bbox": [ + 308, + 394, + 395, + 406 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 308, + 415, + 544, + 473 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 415, + 544, + 473 + ], + "spans": [ + { + "bbox": [ + 308, + 415, + 544, + 473 + ], + "type": "text", + "content": "Building photorealistic representations of humans has been widely researched in the past few decades. Here, we mainly discuss prior art in head avatar and refer readers to the state-of-the-art surveys [14, 55] for a comprehensive literature review." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 308, + 475, + 544, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 475, + 544, + 652 + ], + "spans": [ + { + "bbox": [ + 308, + 475, + 544, + 652 + ], + "type": "text", + "content": "Monocular Explicit Surface Head (Face) Avatars. Traditionally, a typical approach to create head (or face) avatars from monocular RGB videos is using a 3D Morphable Models (3DMM) as the foundation and adding personalized representations, such as corrected blendshapes [16, 22], detail texture maps [16, 22], image-based representations [9], and secondary components [21]. Early works use various optimizations to obtain the personalized representations from monocular data, including analysis-by-synthesis [14, 16, 21, 55] as well as shape-from-shading [16, 22]. Recent approaches replace optimizations by regressions with Deep Neural Networks (DNNs) [10, 40, 48], or integrate optimizations with deep learning components [4, 5]. More recent methods leverage neural textures [17] to generate photorealistic appearances." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 308, + 654, + 544, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 654, + 544, + 712 + ], + "spans": [ + { + "bbox": [ + 308, + 654, + 544, + 712 + ], + "type": "text", + "content": "The main drawback of these methods is that they rely on explicit meshes with a fixed topology, making it hard to handle out-of-model details such as hair and accessories like glasses and apparels. In contrast, our hybrid method combines geometric priors with implicit representations, lead-" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 750, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 750, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 750, + 316, + 757 + ], + "type": "text", + "content": "16891" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 71, + 547, + 239 + ], + "blocks": [ + { + "bbox": [ + 48, + 71, + 547, + 239 + ], + "lines": [ + { + "bbox": [ + 48, + 71, + 547, + 239 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 547, + 239 + ], + "type": "image", + "image_path": "cfa3512d5b56a09d517ae695a0310721974858f1f3c370cf76579f717b01bb60.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 242, + 547, + 298 + ], + "lines": [ + { + "bbox": [ + 46, + 242, + 547, + 298 + ], + "spans": [ + { + "bbox": [ + 46, + 242, + 547, + 298 + ], + "type": "text", + "content": "Figure 2. Overview of our pipeline. The core of our method is the Avatar Representation (Sec. 3.1. Shown as the yellow area) based on a 3DMM-anchored neural radiance field (NeRF), which are decoded from local features attached on the 3DMM vertices. Then, we use volumetric rendering to compute the output image. To predict the vertex-attached features (Sec. 3.2. Shown as the green area), we first compute the vertex displacements from the 3DMM expression and pose, then process the displacements in UV space with Convolutional Neural Networks (CNNs), and sample the obtained features back to mesh vertices." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 319, + 253, + 330 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 319, + 253, + 330 + ], + "spans": [ + { + "bbox": [ + 46, + 319, + 253, + 330 + ], + "type": "text", + "content": "ing to a significantly larger representation capacity." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 45, + 330, + 287, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 330, + 287, + 581 + ], + "spans": [ + { + "bbox": [ + 45, + 330, + 287, + 581 + ], + "type": "text", + "content": "Monocular Implicit Head Avatars. Recent work proposes to extend 3DMM with implicit 3D representations. NerFACE [15] introduces a dynamic neural radiance field (NeRF) conditioned on 3DMM expression codes which can render a view-consistent avatar with volumetric rendering. Since NerFACE directly inputs the 3DMM expression codes into MLPs without using any shape or spatial information from 3DMM, their model is quite under-constrained for monocular reconstruction, and suffers from severe artifacts for data with challenging expressions. RigNeRF [3] uses 3DMM derived warping field to deform the camera space into a canonical space, and defines a canonical NeRF conditioned on 3DMM codes. However, their model uses a dense MLP-based architecture to memorize the appearance and deformation for the full head, leading to oversmooth results due to limited network capacity. IMAAvatar [53] learns personalized implicit fields of blendshapes, pose correctives, and skinning weights, then formulates the avatar with linear summation of blendshapes followed by linear blend skinning. However, their linear formulation limits the amount of expression deformations." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "content": "Geometry Anchored Implicit 3D Representation. Sparse local feature embedding attached on geometry has been demonstrated to be effective in improving the rendering quality of neural radiance field [26-28, 34, 54]. It also naturally supports neural radiance field editing since the modification on the geometry can be directly propagated to the rendering [11], which makes them a favorable representation to support the controllability for human avatar. We adapt this representation to head avatar and incorporate head specific priors. Differently from prior art, we leverage a CNN in UV space to learn local, per-vertex features" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 319, + 545, + 341 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 319, + 545, + 341 + ], + "spans": [ + { + "bbox": [ + 304, + 319, + 545, + 341 + ], + "type": "text", + "content": "that are expression-dependent, improving generalization of out-of-train expressions." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 343, + 546, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 343, + 546, + 582 + ], + "spans": [ + { + "bbox": [ + 304, + 343, + 546, + 582 + ], + "type": "text", + "content": "2D-based Head Avatars. There are numerous approaches that synthesize the head (or face) relying on 2D (explicit/implicit) representations, including 2D facial landmarks [43, 49, 50] and 2D warping fields [38, 39, 46]. Landmark-based avatar models [43, 49, 50] synthesize the face conditioned to the facial landmarks extracted with a, usually pre-trained, landmark detector. Specifically, an encoder is applied to extract an identity embedding from a reference image, a decoder is adapted by the identity code to animate the reference face with landmarks from the driving videos. X2Face [46] is the first approach to animate human heads by learning a dense warping field and producing the output video via image warping. MonkeyNet [38] and First Order Motion Model (FOMM) further propose to infer motion fields with self-learned keypoints, which significantly improves motion prediction and synthesizes higher quality renderings of heads. While most aforementioned methods can produce photorealistic results, they are not able to maintain geometry and multiview consistency due to their inherent 2D representation." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 596, + 361, + 609 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 596, + 361, + 609 + ], + "spans": [ + { + "bbox": [ + 306, + 596, + 361, + 609 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": "Given a monocular RGB video containing " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": " frames " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\{I_1, I_2, \\dots, I_M\\}" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": ", our method reconstructs a head avatar representation that can be rendered under arbitrary facial expressions, head poses, and camera viewpoints. We first preprocess the video to remove the background [19,32] and obtain camera and 3DMM parameters for each frame. More specifically, we use FLAME [24] as the 3DMM and denote the fitted face with shape " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": ", expressions " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\psi_i" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": ", poses " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\theta_i" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": " (i.e.," + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16892" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 71, + 286, + 178 + ], + "blocks": [ + { + "bbox": [ + 50, + 71, + 286, + 178 + ], + "lines": [ + { + "bbox": [ + 50, + 71, + 286, + 178 + ], + "spans": [ + { + "bbox": [ + 50, + 71, + 286, + 178 + ], + "type": "image", + "image_path": "8b7b225e882fe956ccb35038f9c3c8ffb0c2fd0c64e5ff26acc67ab4f464e6f6.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 182, + 287, + 248 + ], + "lines": [ + { + "bbox": [ + 46, + 182, + 287, + 248 + ], + "spans": [ + { + "bbox": [ + 46, + 182, + 287, + 248 + ], + "type": "text", + "content": "Figure 3. Illustration of Avatar Representation (Sec. 3.1). Given a query point, we find its k-Nearest-Neighbor (k-NN) vertices from the 3DMM. Then, we decode these vertices and features into a density and color with respect to the input camera view direction, via Multi-Layer-Perceptrons (MLPs) interleaved with inverse-distance based weighted sum." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 269, + 287, + 317 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 269, + 287, + 317 + ], + "spans": [ + { + "bbox": [ + 46, + 269, + 287, + 317 + ], + "type": "text", + "content": "neck, jaw, and eyes), where " + }, + { + "bbox": [ + 46, + 269, + 287, + 317 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 269, + 287, + 317 + ], + "type": "text", + "content": " is the frame index, with which a head mesh can be obtained via " + }, + { + "bbox": [ + 46, + 269, + 287, + 317 + ], + "type": "inline_equation", + "content": "\\pmb{V}_i(\\pmb{\\beta},\\pmb{\\psi}_i,\\pmb{\\theta}_i)" + }, + { + "bbox": [ + 46, + 269, + 287, + 317 + ], + "type": "text", + "content": ". Since " + }, + { + "bbox": [ + 46, + 269, + 287, + 317 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 46, + 269, + 287, + 317 + ], + "type": "text", + "content": " is fixed and does not depend on the pose or expressions for a given user, we omit it in the following sections for brevity." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 318, + 287, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 318, + 287, + 496 + ], + "spans": [ + { + "bbox": [ + 46, + 318, + 287, + 496 + ], + "type": "text", + "content": "An overview of our framework is shown in Fig. 2. We adopt the 3DMM-anchored neural radiance field (NeRF) as the core representation for our head avatar (Sec. 3.1), where local features are attached to the vertices of the deformable 3DMM mesh. During the inference, we first deform the 3DMM mesh based on the target configuration " + }, + { + "bbox": [ + 46, + 318, + 287, + 496 + ], + "type": "inline_equation", + "content": "V_{t} = (\\psi_{t},\\theta_{t})" + }, + { + "bbox": [ + 46, + 318, + 287, + 496 + ], + "type": "text", + "content": ". Then, for an arbitrary 3D query point, we aggregate the features from neighboring vertices on " + }, + { + "bbox": [ + 46, + 318, + 287, + 496 + ], + "type": "inline_equation", + "content": "V_{t}" + }, + { + "bbox": [ + 46, + 318, + 287, + 496 + ], + "type": "text", + "content": " to estimate the local density and color by Multi-Layer-Perceptrons (MLPs), which are then integrated in the volumetric rendering formulation to generate the color image. To learn local features, we train CNN-based networks in the UV space to incorporate spatial context (Sec. 3.2). Our model is trained end-to-end with RGB supervisions (Sec. 3.3)." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 503, + 175, + 516 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 503, + 175, + 516 + ], + "spans": [ + { + "bbox": [ + 47, + 503, + 175, + 516 + ], + "type": "text", + "content": "3.1. Avatar Representation" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 522, + 287, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 522, + 287, + 594 + ], + "spans": [ + { + "bbox": [ + 46, + 522, + 287, + 594 + ], + "type": "text", + "content": "An ideal representation for a head avatar should have the following properties: 1) Provides intuitive control to achieve the desired expression and head pose; 2) Requires a moderate amount of training data, e.g., a short monocular video; 3) Produces expression-dependent rendering details; 4) Generalizes reasonably well to unseen expressions." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "text", + "content": "To this end, we propose the 3DMM-anchored neural radiance field (NeRF) as shown in Fig. 3. Inspired by local feature based neural radiance field [34, 47], we attach feature vectors " + }, + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "inline_equation", + "content": "z^{j}" + }, + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "text", + "content": " on each 3DMM vertex " + }, + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "inline_equation", + "content": "v_{i}^{j}" + }, + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "text", + "content": " to encode the local radiance fields that can be decoded with MLPs, where " + }, + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "text", + "content": " denotes frame index and " + }, + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "text", + "content": " denotes vertex index. In this way, the radiance field can be deformed according to vertex locations, hence can be intuitively controlled by the 3DMM expression and pose " + }, + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "inline_equation", + "content": "(\\psi_{i},\\theta_{i})" + }, + { + "bbox": [ + 46, + 594, + 287, + 713 + ], + "type": "text", + "content": ". In addition, the 3DMM fitting on each frame provides a rough tracking across deformable" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "type": "text", + "content": "face geometries, such that all the frames can contribute into the learning of a unified set of local per-vertex features. We will discuss model capacity and generalization in Sec. 3.2." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "spans": [ + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "text", + "content": "To decode the vertex features " + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "inline_equation", + "content": "\\{z^j\\}" + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "text", + "content": " into the radiance field for the frame " + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "text", + "content": ", given a 3D query point " + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "inline_equation", + "content": "\\pmb{q}" + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "text", + "content": ", we first find its " + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "text", + "content": "-Nearest-Neighbor (" + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "text", + "content": "-NN) vertices from the 3DMM mesh " + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "inline_equation", + "content": "\\{\\pmb{v}_i^j\\}_{j\\in \\mathcal{N}_k^q}" + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "text", + "content": " with attached features " + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "inline_equation", + "content": "\\{z^j\\}_{j\\in \\mathcal{N}_k^q}" + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "text", + "content": ". Then, we use two MLPs " + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_0" + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_1" + }, + { + "bbox": [ + 305, + 108, + 545, + 180 + ], + "type": "text", + "content": " with inverse-distance based weighted sum to decode local color and density. Formally," + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 408, + 188, + 495, + 204 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 408, + 188, + 495, + 204 + ], + "spans": [ + { + "bbox": [ + 408, + 188, + 495, + 204 + ], + "type": "interline_equation", + "content": "\\hat {\\boldsymbol {z}} _ {i} ^ {j} = \\mathcal {F} _ {0} (\\boldsymbol {v} _ {i} ^ {j} - \\boldsymbol {q}, \\boldsymbol {z} ^ {j})", + "image_path": "b93ce5ec3cdb1150e2e9e98762ef8095ccdc5ba44af02d61f50647cec9d9ad6a.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 410, + 206, + 545, + 222 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 410, + 206, + 545, + 222 + ], + "spans": [ + { + "bbox": [ + 410, + 206, + 545, + 222 + ], + "type": "interline_equation", + "content": "\\hat {\\boldsymbol {z}} _ {i} = \\sum_ {j} w ^ {j} \\hat {\\boldsymbol {z}} _ {i} ^ {j} \\tag {1}", + "image_path": "dec4288b9e4005262bb8f553b456c2404ed39bede4dc5e784ab7063748dca243.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 355, + 224, + 478, + 237 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 355, + 224, + 478, + 237 + ], + "spans": [ + { + "bbox": [ + 355, + 224, + 478, + 237 + ], + "type": "interline_equation", + "content": "\\boldsymbol {c} _ {i} (\\boldsymbol {q}, \\boldsymbol {d} _ {i}), \\sigma_ {i} (\\boldsymbol {q}) = \\mathcal {F} _ {1} (\\hat {\\boldsymbol {z}} _ {i}, \\boldsymbol {d} _ {i}),", + "image_path": "61e31050219df92e7d23a893e7a8f096c8388df57af435318b4c70d91a962928.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 247, + 545, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 247, + 545, + 300 + ], + "spans": [ + { + "bbox": [ + 305, + 247, + 545, + 300 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 247, + 545, + 300 + ], + "type": "inline_equation", + "content": "w^{j} = \\frac{d^{j}}{\\sum_{k} d^{k}}" + }, + { + "bbox": [ + 305, + 247, + 545, + 300 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 305, + 247, + 545, + 300 + ], + "type": "inline_equation", + "content": "d^{j} = \\frac{1}{\\|\\pmb{v}_{i}^{j} - \\pmb{q}\\|_{2}}" + }, + { + "bbox": [ + 305, + 247, + 545, + 300 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 305, + 247, + 545, + 300 + ], + "type": "inline_equation", + "content": "j \\in \\mathcal{N}_k^q" + }, + { + "bbox": [ + 305, + 247, + 545, + 300 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 305, + 247, + 545, + 300 + ], + "type": "inline_equation", + "content": "\\pmb{d}_i" + }, + { + "bbox": [ + 305, + 247, + 545, + 300 + ], + "type": "text", + "content": " denotes the camera view direction. Finally, we render the output image with volumetric rendering formulation as in vanilla NeRF [30] given the camera ray " + }, + { + "bbox": [ + 305, + 247, + 545, + 300 + ], + "type": "inline_equation", + "content": "r(t) = o + td" + }, + { + "bbox": [ + 305, + 247, + 545, + 300 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 342, + 309, + 545, + 338 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 342, + 309, + 545, + 338 + ], + "spans": [ + { + "bbox": [ + 342, + 309, + 545, + 338 + ], + "type": "interline_equation", + "content": "\\boldsymbol {C} _ {i} (\\boldsymbol {r}) = \\int_ {t _ {n}} ^ {t _ {f}} T (t) \\sigma_ {i} (\\boldsymbol {r} (t)) \\boldsymbol {c} _ {i} (\\boldsymbol {r} (t), \\boldsymbol {d}) \\mathrm {d} t, \\tag {2}", + "image_path": "51294d5002bdfe1a9e409771c9a193545ef2cc0cb35bdba3ea0f229872130468.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 341, + 339, + 503, + 368 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 341, + 339, + 503, + 368 + ], + "spans": [ + { + "bbox": [ + 341, + 339, + 503, + 368 + ], + "type": "interline_equation", + "content": "\\text {w h e r e} T (t) = \\exp \\left(- \\int_ {t _ {n}} ^ {t} \\sigma_ {i} (\\boldsymbol {r} (s)) \\mathrm {d} s\\right)", + "image_path": "9ce33d3e6fd788d92c977c291dbd3eb2568ce44428475c3c981692c8ee8e4423.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 376, + 545, + 541 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 376, + 545, + 541 + ], + "spans": [ + { + "bbox": [ + 304, + 376, + 545, + 541 + ], + "type": "text", + "content": "To reduce misalignments caused by per-frame contents that cannot be captured by 3DMM (e.g., 3DMM fitting errors), we additionally learn error-correction warping fields during training inspired from prior works on deformable NeRF [23, 45]. More specifically, we input the original query point and a per-frame latent code " + }, + { + "bbox": [ + 304, + 376, + 545, + 541 + ], + "type": "inline_equation", + "content": "e_i" + }, + { + "bbox": [ + 304, + 376, + 545, + 541 + ], + "type": "text", + "content": ", which is randomly initialized and optimized during the training, into the error-correction MLPs " + }, + { + "bbox": [ + 304, + 376, + 545, + 541 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_{\\mathcal{E}}" + }, + { + "bbox": [ + 304, + 376, + 545, + 541 + ], + "type": "text", + "content": " to predict a rigid transformation, and apply it to the query point. The transformation is denoted as " + }, + { + "bbox": [ + 304, + 376, + 545, + 541 + ], + "type": "inline_equation", + "content": "q' = \\mathcal{T}_i(q) = \\mathcal{F}_{\\mathcal{E}}(q, e_i)" + }, + { + "bbox": [ + 304, + 376, + 545, + 541 + ], + "type": "text", + "content": ". Then we use the warped query point " + }, + { + "bbox": [ + 304, + 376, + 545, + 541 + ], + "type": "inline_equation", + "content": "q'" + }, + { + "bbox": [ + 304, + 376, + 545, + 541 + ], + "type": "text", + "content": " to decode the color and density. Note that this warping field is disabled during testing. Please refer to the supplementary for detailed formulations of the warping field." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 551, + 528, + 563 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 551, + 528, + 563 + ], + "spans": [ + { + "bbox": [ + 306, + 551, + 528, + 563 + ], + "type": "text", + "content": "3.2. Predicting Expression-Dependent Features" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 570, + 545, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 545, + 629 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 545, + 629 + ], + "type": "text", + "content": "While the proposed avatar representation (Sec. 3.1) enables intuitive controllability and convenience in learning, it still has limited capability for modeling complex expression-dependent variations due to the use of frame-shared vertex features " + }, + { + "bbox": [ + 304, + 570, + 545, + 629 + ], + "type": "inline_equation", + "content": "\\{z^j\\}" + }, + { + "bbox": [ + 304, + 570, + 545, + 629 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": "To overcome this, we propose to predict the dynamic vertex features " + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\{z_i^j\\}" + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": " conditioned on the 3DMM expression and pose " + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "inline_equation", + "content": "(\\psi_{i},\\theta_{i})" + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": ". A common practice for NeRF-based methods is to use MLP-based architectures for dynamic feature prediction [3, 15, 53, 54]. However, we find that this leads to blurry rendering results, presumably because of the limited model capacity due to the lack of spatial context (i.e.," + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16893" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 48, + 70, + 542, + 171 + ], + "blocks": [ + { + "bbox": [ + 48, + 70, + 542, + 171 + ], + "lines": [ + { + "bbox": [ + 48, + 70, + 542, + 171 + ], + "spans": [ + { + "bbox": [ + 48, + 70, + 542, + 171 + ], + "type": "table", + "html": "
Subject0\nLPIPS / SSIM / PSNRSubject1\nLPIPS / SSIM / PSNRSubject2\nLPIPS / SSIM / PSNRSubject3\nLPIPS / SSIM / PSNRSubject4\nLPIPS / SSIM / PSNR
TPSMM [52]0.192 / 0.852 / 22.600.205 / 0.830 / 16.380.216 / 0.782 / 18.400.222 / 0.799 / 20.280.156 / 0.913 / 21.29
FOMM [39]0.171 / 0.841 / 22.930.179 / 0.827 / 16.020.202 / 0.777 / 18.980.186 / 0.798 / 22.280.122 / 0.915 / 23.94
NHA [17]0.165 / 0.836 / 20.200.166 / 0.840 / 15.480.178 / 0.809 / 17.990.153 / 0.798 / 21.310.091 / 0.926 / 23.78
IMAvatar [53]0.207 / 0.852 / 21.260.187 / 0.848 / 15.980.265 / 0.729 / 15.800.214 / 0.782 / 20.370.142 / 0.897 / 20.63
NerFACE [15]0.205 / 0.817 / 20.060.182 / 0.833 / 15.780.188 / 0.793 / 19.410.229 / 0.747 / 18.160.093 / 0.938 / 25.57
Ours-D0.144 / 0.864 / 21.920.152 / 0.855 / 16.230.141 / 0.841 / 20.420.156 / 0.833 / 23.050.075 / 0.944 / 25.71
", + "image_path": "268536dfd2ffd16456bbaa26e2073a59c890479a77f576c76d2eb754c92687b2.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 175, + 546, + 198 + ], + "lines": [ + { + "bbox": [ + 46, + 175, + 546, + 198 + ], + "spans": [ + { + "bbox": [ + 46, + 175, + 546, + 198 + ], + "type": "text", + "content": "Table 1. Quantitative Comparison with state-of-the-art (SOTA) approaches. Subject4 is the data from NerFACE [15], while other subjects are from our dataset. Our method achieves superior results than prior SOTAs." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 218, + 287, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 218, + 287, + 266 + ], + "spans": [ + { + "bbox": [ + 46, + 218, + 287, + 266 + ], + "type": "text", + "content": "each vertex does not know the feature of its neighboring vertices). Based on this intuition, we propose to process the 3DMM expression and pose " + }, + { + "bbox": [ + 46, + 218, + 287, + 266 + ], + "type": "inline_equation", + "content": "(\\psi_i,\\theta_i)" + }, + { + "bbox": [ + 46, + 218, + 287, + 266 + ], + "type": "text", + "content": " with CNNs in the texture atlas space (UV space) to provide local spatial context." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "spans": [ + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "text", + "content": "Specifically, we design two variations of CNN-based architecture to learn expression-dependent vertex features " + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "inline_equation", + "content": "\\{z_i^j\\}" + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "text", + "content": ". The first variant, denoted as Ours-C, trains a decoder consisting of transposed convolutional blocks to predict the feature map in UV space directly from 1-D codes of 3DMM expression and pose. We empirically find that such a model is effective in improving the overall rendering quality, however tends to fail and produce severe artifacts on out-of-training expressions (See discussions in Sec. 4.4). We then propose the second variant, denoted as Ours-D, that uses the 3D deformation of 3DMM in UV space as the input for feature prediction, and observe that resulting avatar models are more resilient to stretchy and unseen expressions. Specifically, we first compute the vertex displacements using 3DMM expression and pose as " + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "inline_equation", + "content": "D_{i} = V_{i}(\\psi_{i},\\theta_{i}) - V_{neutral}(0,0)" + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "text", + "content": ". We then rasterize the vertex displacements " + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "inline_equation", + "content": "D_{i}" + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "text", + "content": " into UV space, and process it with a U-Net " + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_{\\mathcal{D}}" + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "text", + "content": ". Finally, the output UV feature map is sampled back to mesh vertices " + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "inline_equation", + "content": "V_{i}" + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "text", + "content": ", serving as the dynamic vertex features " + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "inline_equation", + "content": "\\{z_i^j\\}" + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "text", + "content": " (i.e., the expression-dependent version of frame-shared vertex features " + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "inline_equation", + "content": "\\{z^j\\}" + }, + { + "bbox": [ + 46, + 266, + 289, + 518 + ], + "type": "text", + "content": " described in Sec. 3.1)." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 523, + 149, + 536 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 523, + 149, + 536 + ], + "spans": [ + { + "bbox": [ + 47, + 523, + 149, + 536 + ], + "type": "text", + "content": "3.3. Training Schema" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "spans": [ + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "text", + "content": "Our model is trained on monocular RGB videos mainly with the photometric loss, where we penalize the " + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "inline_equation", + "content": "l_{2}" + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "text", + "content": " distance between the rendering and the ground truth images. Formally " + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{rgb} = \\sum_{i}\\sum_{\\boldsymbol{r}}\\| C_i(\\boldsymbol{r}) - I_i(\\boldsymbol{r})\\|_2" + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "inline_equation", + "content": "\\boldsymbol{r}" + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "text", + "content": " denotes the camera ray of each pixel and " + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "text", + "content": " denotes frame index. To regularize the learning of error-correction warping field " + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "inline_equation", + "content": "\\mathcal{T}(\\boldsymbol{q})" + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "text", + "content": ", we adopt a elastic loss " + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{elastic}" + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "text", + "content": " similar to Nerfies [33], and a magnitude loss defined as " + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{mag} = \\sum_q\\| q - \\mathcal{T}(q)\\|_2^2" + }, + { + "bbox": [ + 46, + 541, + 287, + 651 + ], + "type": "text", + "content": ". The total loss is defined as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 79, + 658, + 287, + 672 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 658, + 287, + 672 + ], + "spans": [ + { + "bbox": [ + 79, + 658, + 287, + 672 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathcal {L} _ {r g b} + \\lambda_ {\\text {e l a s t i c}} \\mathcal {L} _ {\\text {e l a s t i c}} + \\lambda_ {\\text {m a g}} \\mathcal {L} _ {\\text {m a g}}, \\tag {3}", + "image_path": "9bfa764da94de8452197c41d23c5b4a38810e923bf9d7f4f4525f9951de0fe18.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "text", + "content": "where we set " + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "inline_equation", + "content": "\\lambda_{elastic} = 10^{-4}" + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "text", + "content": " at the beginning and decay to " + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "inline_equation", + "content": "10^{-5}" + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "text", + "content": " after 155k iterations, and " + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "inline_equation", + "content": "\\lambda_{mag} = 10^{-2}" + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "text", + "content": ". Please see supplementary for more details." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 217, + 388, + 230 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 217, + 388, + 230 + ], + "spans": [ + { + "bbox": [ + 306, + 217, + 388, + 230 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 244, + 546, + 329 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 244, + 546, + 329 + ], + "spans": [ + { + "bbox": [ + 304, + 244, + 546, + 329 + ], + "type": "text", + "content": "We train and evaluate our method on casually captured monocular RGB videos (Sec. 4.1) and show that our method achieves superior rendering quality than prior state-of-the-art monocular RGB head avatars (Sec. 4.2). Then we verify our key observations on architectural choices to design a good avatar model, in terms of rendering quality and expression robustness (Sec. 4.4)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 357, + 427, + 369 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 357, + 427, + 369 + ], + "spans": [ + { + "bbox": [ + 306, + 357, + 427, + 369 + ], + "type": "text", + "content": "4.1. Datasets and Metrics" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 383, + 546, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 383, + 546, + 647 + ], + "spans": [ + { + "bbox": [ + 304, + 383, + 546, + 647 + ], + "type": "text", + "content": "Datasets. Following the prior art [15], we captured monocular RGB videos of various subjects with smartphones or webcams for training and evaluation. Each video is 1-2 minutes long (around " + }, + { + "bbox": [ + 304, + 383, + 546, + 647 + ], + "type": "inline_equation", + "content": "1.5\\mathrm{k - }2\\mathrm{k}" + }, + { + "bbox": [ + 304, + 383, + 546, + 647 + ], + "type": "text", + "content": " frames at 30 FPS) with the first 1000-1500 frames as training data and the rest frames for evaluation if not otherwise specified. For the training clip, the subjects are asked to first keep a neutral expression and rotate their heads, then perform different expressions during the head rotation, with extreme expressions included. For the testing clip, the subjects are asked to perform freely without any constraints. To demonstrate that our method also works for common talking head videos, we also include a video from NerFACE [15], which has significantly less variability in expressions when compared to our capture protocol. We mask out background [32] for each video, and obtain 3DMM and camera parameters with 3DMM fitting similar to that in NHA [17]. Please refer to the supplementary for examples of data. Please note that we collect relatively shorter training videos compared to related work. This favors a better user experience while still synthesizing high-quality avatars with more personalized and detailed expressions." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 653, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 547, + 713 + ], + "type": "text", + "content": "Metrics. Following the prior art [15], we use the following standard image quality metrics for quantitative evaluations: the Learned Perceptual Image Patch Similarity (LPIPS) [51], Structure Similarity Index (SSIM) [44], and Peak Signal-to-Noise Ratio (PSNR)." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16894" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 71, + 545, + 421 + ], + "blocks": [ + { + "bbox": [ + 47, + 71, + 545, + 421 + ], + "lines": [ + { + "bbox": [ + 47, + 71, + 545, + 421 + ], + "spans": [ + { + "bbox": [ + 47, + 71, + 545, + 421 + ], + "type": "image", + "image_path": "8e6d1ac6535f28aa5cc557d58bcbee2ce2e089c7573e0cdffb083778d670a523.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 422, + 547, + 447 + ], + "lines": [ + { + "bbox": [ + 46, + 422, + 547, + 447 + ], + "spans": [ + { + "bbox": [ + 46, + 422, + 547, + 447 + ], + "type": "text", + "content": "Figure 4. Qualitative Comparison to prior state-of-the-art monocular head avatars. Note how our approach more faithfully reconstructs the ground truth expressions while preserving most of the high frequency details." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 464, + 216, + 478 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 464, + 216, + 478 + ], + "spans": [ + { + "bbox": [ + 47, + 464, + 216, + 478 + ], + "type": "text", + "content": "4.2. Comparisons with State-of-Art" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 484, + 287, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 484, + 287, + 664 + ], + "spans": [ + { + "bbox": [ + 46, + 484, + 287, + 664 + ], + "type": "text", + "content": "We compare our method with five state-of-the-art methods of different types with publicly available implementations from the authors: NerFACE [15], IMAvatar [53], NHA [17], FOMM [39], and TPSMM [52]. For subject-specific methods (i.e. Ours, NerFACE [15], IMAvatar [53], and NHA [17]), we train the avatar for each subject separately with training frames of each video, then drive and render the trained avatar with 3DMM and camera parameters of testing frames. For few-shot methods (i.e., FOMM [39], and TPSMM [52]), we use the first frame of each video, which shows a frontal head, as the source image, then use testing frames as driving images in self-reenactment manner. Finally, the generated images of each method are compared with ground truth testing frames quantitatively (See Tab. 1) and qualitatively (See Fig. 4)." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "text", + "content": "As shown in Fig. 4, FOMM [39] and TPSMM [52] struggle with large head rotations and fail to produce 3D consistent results. NHA [17] uses explicit mesh surface with neural textures. The fix mesh topology makes it hard to" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 464, + 545, + 657 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 464, + 545, + 657 + ], + "spans": [ + { + "bbox": [ + 304, + 464, + 545, + 657 + ], + "type": "text", + "content": "handle challenging hair in Subject 3. Also, NHA uses linear 3DMM blendshapes, making it hard to capture complex expressions (e.g., Subject 1 & 3) and wrinkles (e.g., Subject 4). IMAvatar [53] uses an occupancy field to model geometry, making it hard to handle challenging hair. Despite learning a personalized blendshape field, the linear formulation still limits their model capacity of handling complex expressions. In addition, we observed training instability of IMAvatar on the captured data, converging to oversmooth results. NerFACE [15] works relatively well on data with easy expressions (i.e., common talking heads in Subject 4), but struggles on our challenging data and estimates incorrect geometry (e.g., distorted head for Subject 3) and blurry rendering. In contrast, our method gives superior results on all the aspects discussed above. Tab. 1 further quantitatively confirms the good rendering quality of our method." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 306, + 669, + 416, + 682 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 669, + 416, + 682 + ], + "spans": [ + { + "bbox": [ + 306, + 669, + 416, + 682 + ], + "type": "text", + "content": "4.3. Driving the Avatar" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 689, + 546, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 546, + 714 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 546, + 714 + ], + "type": "text", + "content": "After training, the learned avatar model can be driven by the same subject under different capture conditions, e.g. hair" + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16895" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 82, + 108, + 167 + ], + "blocks": [ + { + "bbox": [ + 57, + 72, + 100, + 80 + ], + "lines": [ + { + "bbox": [ + 57, + 72, + 100, + 80 + ], + "spans": [ + { + "bbox": [ + 57, + 72, + 100, + 80 + ], + "type": "text", + "content": "Drive Frame" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 49, + 82, + 108, + 167 + ], + "lines": [ + { + "bbox": [ + 49, + 82, + 108, + 167 + ], + "spans": [ + { + "bbox": [ + 49, + 82, + 108, + 167 + ], + "type": "image", + "image_path": "f917e938b70e11648ad95eb9cccd3f6ba46114e75633202ec2a5fd8c94c0d7e6.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 115, + 85, + 167, + 160 + ], + "blocks": [ + { + "bbox": [ + 121, + 72, + 159, + 80 + ], + "lines": [ + { + "bbox": [ + 121, + 72, + 159, + 80 + ], + "spans": [ + { + "bbox": [ + 121, + 72, + 159, + 80 + ], + "type": "text", + "content": "Drive View" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 115, + 85, + 167, + 160 + ], + "lines": [ + { + "bbox": [ + 115, + 85, + 167, + 160 + ], + "spans": [ + { + "bbox": [ + 115, + 85, + 167, + 160 + ], + "type": "image", + "image_path": "88862a58742ee8ed3f9080bfd0bba84733c0d5256cff9694c7c3892e7058073c.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 176, + 85, + 227, + 160 + ], + "blocks": [ + { + "bbox": [ + 181, + 72, + 214, + 80 + ], + "lines": [ + { + "bbox": [ + 181, + 72, + 214, + 80 + ], + "spans": [ + { + "bbox": [ + 181, + 72, + 214, + 80 + ], + "type": "text", + "content": "Side View" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 176, + 85, + 227, + 160 + ], + "lines": [ + { + "bbox": [ + 176, + 85, + 227, + 160 + ], + "spans": [ + { + "bbox": [ + 176, + 85, + 227, + 160 + ], + "type": "image", + "image_path": "63e17120c5ab8b9d0cee090e884c5b6daeb1448372b64da9aaa0d766dbf2e827.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 233, + 87, + 286, + 160 + ], + "blocks": [ + { + "bbox": [ + 245, + 72, + 267, + 80 + ], + "lines": [ + { + "bbox": [ + 245, + 72, + 267, + 80 + ], + "spans": [ + { + "bbox": [ + 245, + 72, + 267, + 80 + ], + "type": "text", + "content": "Depth" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 233, + 87, + 286, + 160 + ], + "lines": [ + { + "bbox": [ + 233, + 87, + 286, + 160 + ], + "spans": [ + { + "bbox": [ + 233, + 87, + 286, + 160 + ], + "type": "image", + "image_path": "b7986626baaf30e74f5afebccef37b5277d2880ef814f276736d38a556021b82.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 49, + 169, + 108, + 253 + ], + "blocks": [ + { + "bbox": [ + 49, + 169, + 108, + 253 + ], + "lines": [ + { + "bbox": [ + 49, + 169, + 108, + 253 + ], + "spans": [ + { + "bbox": [ + 49, + 169, + 108, + 253 + ], + "type": "image", + "image_path": "3c34d5e10bf27da1bdb9aea82c4c5b26629fa7708943bbc0a512e5dbf77ce6d7.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 47, + 253, + 287, + 286 + ], + "lines": [ + { + "bbox": [ + 47, + 253, + 287, + 286 + ], + "spans": [ + { + "bbox": [ + 47, + 253, + 287, + 286 + ], + "type": "text", + "content": "Figure 5. Results on driving the learned avatar by the same subject under different capture conditions. Our method produces faithful expressions, multi-view consistent rendering, and good geometry." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 115, + 171, + 164, + 248 + ], + "blocks": [ + { + "bbox": [ + 115, + 171, + 164, + 248 + ], + "lines": [ + { + "bbox": [ + 115, + 171, + 164, + 248 + ], + "spans": [ + { + "bbox": [ + 115, + 171, + 164, + 248 + ], + "type": "image", + "image_path": "27d29cbf3c21bf1ea341890c4a569998d019030581e60e605b38b23539665615.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 172, + 171, + 225, + 248 + ], + "blocks": [ + { + "bbox": [ + 172, + 171, + 225, + 248 + ], + "lines": [ + { + "bbox": [ + 172, + 171, + 225, + 248 + ], + "spans": [ + { + "bbox": [ + 172, + 171, + 225, + 248 + ], + "type": "image", + "image_path": "8ada6b924f1dcda74bf7226e62c796f63d346b17aded717eb55f68e03935b669.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 233, + 171, + 282, + 248 + ], + "blocks": [ + { + "bbox": [ + 233, + 171, + 282, + 248 + ], + "lines": [ + { + "bbox": [ + 233, + 171, + 282, + 248 + ], + "spans": [ + { + "bbox": [ + 233, + 171, + 282, + 248 + ], + "type": "image", + "image_path": "36ffe8ff63775d054b498fb8300d73a3c10d6943136d7e432b12f54cafb1ff1d.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 50, + 298, + 89, + 378 + ], + "blocks": [ + { + "bbox": [ + 50, + 298, + 89, + 378 + ], + "lines": [ + { + "bbox": [ + 50, + 298, + 89, + 378 + ], + "spans": [ + { + "bbox": [ + 50, + 298, + 89, + 378 + ], + "type": "image", + "image_path": "c61ac4266f0fd60e21f5a948b9f2216adae7d0872670320746222fa55abea7d8.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 97, + 299, + 138, + 379 + ], + "blocks": [ + { + "bbox": [ + 97, + 299, + 138, + 379 + ], + "lines": [ + { + "bbox": [ + 97, + 299, + 138, + 379 + ], + "spans": [ + { + "bbox": [ + 97, + 299, + 138, + 379 + ], + "type": "image", + "image_path": "ee01942802ce4a2fa8b7d1d8606707d5645e151a99745a499fb3655d58ed92bd.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 47, + 381, + 287, + 415 + ], + "lines": [ + { + "bbox": [ + 47, + 381, + 287, + 415 + ], + "spans": [ + { + "bbox": [ + 47, + 381, + 287, + 415 + ], + "type": "text", + "content": "Figure 6. Results on expressions out of the training distribution with different amount of training data. Ours- " + }, + { + "bbox": [ + 47, + 381, + 287, + 415 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 47, + 381, + 287, + 415 + ], + "type": "text", + "content": " more robustly handles unseen expressions and degrades less with fewer training data." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 143, + 299, + 186, + 379 + ], + "blocks": [ + { + "bbox": [ + 143, + 299, + 186, + 379 + ], + "lines": [ + { + "bbox": [ + 143, + 299, + 186, + 379 + ], + "spans": [ + { + "bbox": [ + 143, + 299, + 186, + 379 + ], + "type": "image", + "image_path": "9861e135702f2237c11428c8cfd0dae1e71406bfcb88f2a17956474e7f789578.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 192, + 299, + 234, + 379 + ], + "blocks": [ + { + "bbox": [ + 192, + 299, + 234, + 379 + ], + "lines": [ + { + "bbox": [ + 192, + 299, + 234, + 379 + ], + "spans": [ + { + "bbox": [ + 192, + 299, + 234, + 379 + ], + "type": "image", + "image_path": "b5dd1b42fd6dad49ab29bba7d89539943f0859934475ec26ff93453898674288.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 239, + 299, + 286, + 379 + ], + "blocks": [ + { + "bbox": [ + 239, + 299, + 286, + 379 + ], + "lines": [ + { + "bbox": [ + 239, + 299, + 286, + 379 + ], + "spans": [ + { + "bbox": [ + 239, + 299, + 286, + 379 + ], + "type": "image", + "image_path": "71089543f7f6e7e03142148a1bfe1b01db603292f9287a5c62d6c67a15868cd8.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + } + ], + "index": 17 + }, + { + "bbox": [ + 46, + 435, + 287, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 435, + 287, + 485 + ], + "spans": [ + { + "bbox": [ + 46, + 435, + 287, + 485 + ], + "type": "text", + "content": "style, illumination, glasses. We show the driving results in Fig. 5. Our avatar faithfully reproduces the expressions of the driving frame, while also achieving multi-view consistent renderings and generating high quality geometry." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 47, + 491, + 243, + 504 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 491, + 243, + 504 + ], + "spans": [ + { + "bbox": [ + 47, + 491, + 243, + 504 + ], + "type": "text", + "content": "4.4. Ablation Study: Expression Features" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 46, + 510, + 287, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 510, + 287, + 569 + ], + "spans": [ + { + "bbox": [ + 46, + 510, + 287, + 569 + ], + "type": "text", + "content": "Learning good local features is crucial for improving model capacity and capturing high frequency details, without losing the regularization from 3DMM (Sec. 3.2). We investigate and compare three alternative approaches and our two model variations for feature learning:" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 46, + 570, + 287, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 570, + 287, + 640 + ], + "spans": [ + { + "bbox": [ + 46, + 570, + 287, + 640 + ], + "type": "text", + "content": "Static Features. We use frame-shared (hence expression-shared) vertex features " + }, + { + "bbox": [ + 46, + 570, + 287, + 640 + ], + "type": "inline_equation", + "content": "\\{z^j\\}" + }, + { + "bbox": [ + 46, + 570, + 287, + 640 + ], + "type": "text", + "content": ". As shown in Fig. 7, the expression-shared features struggle to capture strong expression-dependent variations, leading to incorrect geometry (e.g., incorrect cheek silhouette), blurry details (e.g., teeth), and inferior LPIPS scores in Tab. 2." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 46, + 642, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 287, + 713 + ], + "type": "text", + "content": "3DMM Codes. We extend Static Features by directly concatenating 3DMM expression and pose codes " + }, + { + "bbox": [ + 46, + 642, + 287, + 713 + ], + "type": "inline_equation", + "content": "(\\psi_{i},\\theta_{i})" + }, + { + "bbox": [ + 46, + 642, + 287, + 713 + ], + "type": "text", + "content": " to the vertex features " + }, + { + "bbox": [ + 46, + 642, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\{z^j\\}" + }, + { + "bbox": [ + 46, + 642, + 287, + 713 + ], + "type": "text", + "content": ". This allows the model to have more capacity to capture expression-dependent variations. Although median-level expression characteristics are recognizable (e.g., cheek silhouette in Fig. 7), the results are even" + } + ] + } + ], + "index": 23 + }, + { + "type": "table", + "bbox": [ + 309, + 70, + 544, + 216 + ], + "blocks": [ + { + "bbox": [ + 309, + 70, + 544, + 216 + ], + "lines": [ + { + "bbox": [ + 309, + 70, + 544, + 216 + ], + "spans": [ + { + "bbox": [ + 309, + 70, + 544, + 216 + ], + "type": "table", + "html": "
Subject0Subject1Subject2Subject3
Full Training Data
Static Features0.15590.15860.15520.1688
3DMM Codes0.15990.16200.17460.1738
3DMM Codes MLP0.15680.15510.15050.1686
Ours-C0.14170.14570.13830.1550
Ours-D0.14390.15230.14150.1559
First 50% Training Data
Ours-C0.20380.14830.15800.1566
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Ours-D0.17110.15110.15160.1558
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", + "image_path": "7a306dab8f7c5cb56aa4026f957119dac47ee0de3a95cb23d2ae78f32db94478.jpg" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "table_body" + } + ], + "index": 24 + }, + { + "bbox": [ + 305, + 224, + 545, + 257 + ], + "lines": [ + { + "bbox": [ + 305, + 224, + 545, + 257 + ], + "spans": [ + { + "bbox": [ + 305, + 224, + 545, + 257 + ], + "type": "text", + "content": "Table 2. Quantitative ablations with baselines in LPIPS scores (lower is better). Bold denotes the best while underline denotes the second best." + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 279, + 545, + 314 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 279, + 545, + 314 + ], + "spans": [ + { + "bbox": [ + 304, + 279, + 545, + 314 + ], + "type": "text", + "content": "blurrier for components less depend on expressions such as hairs, possibly due to the limited model capacity of shallow MLPs without spatial context." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 304, + 315, + 546, + 494 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 315, + 546, + 494 + ], + "spans": [ + { + "bbox": [ + 304, + 315, + 546, + 494 + ], + "type": "text", + "content": "3DMM Codes MLP. To further increase the model capacity, we use a more sophisticated MLP architecture inspired from Zheng et al. [54], which is a MLP-based conditional Variational AutoEncoder (cVAE), to predict the expression-dependent vertex features " + }, + { + "bbox": [ + 304, + 315, + 546, + 494 + ], + "type": "inline_equation", + "content": "\\{z_i^j\\}" + }, + { + "bbox": [ + 304, + 315, + 546, + 494 + ], + "type": "text", + "content": " from the 3DMM expression and pose codes " + }, + { + "bbox": [ + 304, + 315, + 546, + 494 + ], + "type": "inline_equation", + "content": "(\\psi_i,\\theta_i)" + }, + { + "bbox": [ + 304, + 315, + 546, + 494 + ], + "type": "text", + "content": ". More specifically, the cVAE is conditioned on 3DMM codes and vertex coordinates on neutral face mesh " + }, + { + "bbox": [ + 304, + 315, + 546, + 494 + ], + "type": "inline_equation", + "content": "V_{neutral}" + }, + { + "bbox": [ + 304, + 315, + 546, + 494 + ], + "type": "text", + "content": ". Then, the cVAE encodes the frame index into a latent code and decodes it into the vertex features " + }, + { + "bbox": [ + 304, + 315, + 546, + 494 + ], + "type": "inline_equation", + "content": "\\{z_i^j\\}" + }, + { + "bbox": [ + 304, + 315, + 546, + 494 + ], + "type": "text", + "content": ". Although the overall sharpness of results is improved (e.g., glasses and hair in Fig. 7), this model still produces blurry details such as teeth, eyes, and wrinkles. Tab. 2 further confirms that this more sophisticated MLP architecture still produces inferior results compared to CNN-based methods Ours-C and Ours-D." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 304, + 495, + 545, + 637 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 495, + 545, + 637 + ], + "spans": [ + { + "bbox": [ + 304, + 495, + 545, + 637 + ], + "type": "text", + "content": "Ours-C vs. Ours-D. By replacing MLPs with CNNs in UV space (Please refer to Sec. 3.2 for more details), both variations of Ours achieve superior rendering quality than MLP-based baselines, as shown in Fig. 7. Moreover, we find that Ours-D is more robust to expressions that are outside the training distribution compared to Ours-C (See Fig. 6). To further investigate the model robustness to out-of-training expressions, we train Ours-C and Ours-D with the first " + }, + { + "bbox": [ + 304, + 495, + 545, + 637 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 304, + 495, + 545, + 637 + ], + "type": "text", + "content": " of training frames, to simulate the scenario with less expression coverage during training. As shown in Tab. 2 and Fig. 6, Ours-D degrades less than Ours-C, indicating enhanced robustness." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 306, + 647, + 515, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 647, + 515, + 659 + ], + "spans": [ + { + "bbox": [ + 306, + 647, + 515, + 659 + ], + "type": "text", + "content": "4.5. Robustness to Expression Extrapolation" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "content": "We qualitatively test our method on expression extrapolation setting by artificially manipulating the expression code for a given test frame. In particular, we double the value of the expression code and compare the results with" + } + ] + } + ], + "index": 30 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16896" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 51, + 76, + 545, + 266 + ], + "blocks": [ + { + "bbox": [ + 51, + 76, + 545, + 266 + ], + "lines": [ + { + "bbox": [ + 51, + 76, + 545, + 266 + ], + "spans": [ + { + "bbox": [ + 51, + 76, + 545, + 266 + ], + "type": "image", + "image_path": "d7e6cecf13863a8d3c50c15853fd1d1e52f7c2b635f1648510a204579b82ec6f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 268, + 548, + 313 + ], + "lines": [ + { + "bbox": [ + 46, + 268, + 548, + 313 + ], + "spans": [ + { + "bbox": [ + 46, + 268, + 548, + 313 + ], + "type": "text", + "content": "Figure 7. Comparison between different designs for local vertex feature learning. See Sec. 4.4 for more details. \"Static feature\" struggles on capturing personalized expressions. \"3DMM Codes\" improves the personalization but suffers from overall blurriness. \"3DMM Codes MLP\" further improves the sharpness, but still cannot present the details. Overall, our convolution-based methods lead to superior renderings on areas such as cheek silhouette, glasses frames and reflections, and teeth." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 48, + 329, + 287, + 460 + ], + "blocks": [ + { + "bbox": [ + 48, + 329, + 287, + 460 + ], + "lines": [ + { + "bbox": [ + 48, + 329, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 48, + 329, + 287, + 460 + ], + "type": "image", + "image_path": "ce37a664adb733227ebf020c22a4135fb06e8620e5c28c4308eb78f4a54aa81b.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 468, + 288, + 502 + ], + "lines": [ + { + "bbox": [ + 46, + 468, + 288, + 502 + ], + "spans": [ + { + "bbox": [ + 46, + 468, + 288, + 502 + ], + "type": "text", + "content": "Figure 8. Expression extrapolation results and comparisons. Compared to other work, our method performs well on extreme and out-of-training expression." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 524, + 287, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 524, + 287, + 632 + ], + "spans": [ + { + "bbox": [ + 46, + 524, + 287, + 632 + ], + "type": "text", + "content": "prior works. As shown in Fig. 8, NHA [17] gives reasonable expression extrapolation results for both geometry and appearance. However, it cannot faithfully capture the out-of-3DMM details when compared to the ground truth. IMAvatar [53] gives reasonable geometry for extrapolation, but struggles to produce sharp extrapolated appearances. NerveFACE [15] fails in producing reasonable renderings. In contrast, our method not only faithfully captures out-of-3DMM details, but also generalizes to extrapolated expressions." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 645, + 117, + 657 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 645, + 117, + 657 + ], + "spans": [ + { + "bbox": [ + 47, + 645, + 117, + 657 + ], + "type": "text", + "content": "5. Discussion" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 665, + 288, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 712 + ], + "type": "text", + "content": "In this work, we presented a framework to learn high-quality controllable 3D head avatars from monocular RGB videos. The core of our method is a 3DMM-anchored neural radiance field decoded from features attached on 3DMM" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 333, + 547, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 333, + 547, + 430 + ], + "spans": [ + { + "bbox": [ + 304, + 333, + 547, + 430 + ], + "type": "text", + "content": "vertices. The vertex features can be either predicted from 3DMM expression and pose codes, or vertex displacements, via CNNs in UV space, where the former favors quality and the latter favors robustness. We experimentally demonstrate that it is possible to learn high-quality avatars with faithful, highly non-rigid expression deformations purely from monocular RGB videos, leading to a superior rendering quality of our method compared to other approaches." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 445, + 547, + 566 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 445, + 547, + 566 + ], + "spans": [ + { + "bbox": [ + 304, + 445, + 547, + 566 + ], + "type": "text", + "content": "Limitations. Compared to the state-of-the-art approaches, our proposed framework learns portrait avatars with superior rendering quality. Nevertheless, our method still inherits the disadvantages of NeRF [30] on time-consuming subject-specific training and slow rendering. Our method relies on the expression space of a 3DMM, thus cannot capture components that are completely missing in the 3DMM, such as the tongue. Moreover, extending our method to include the upper body or integrating into full body avatars are also interesting future directions." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 582, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 547, + 713 + ], + "type": "text", + "content": "Ethical Considerations. The rapid progress on avatar synthesis facilitates numerous downstream applications, but also raises concerns on ethical misuses. On the synthesis side, it would be ideal to actively use watermarking and avoid driving avatars with different identities. On the viewing side, forgery detection [2,25,35,36,41] is an active research field with promising progress. However, it is still hard to guarantee reliable detection of fake visual materials at the current stage. Encouraging the use of cryptographical signatures may also be a potential solution to ensure the authenticity of visual material." + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16897" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "type": "text", + "content": "[1] MetaHuman - Unreal Engine. https://www.unrealengine.com/en-US/metahuman, 2. Accessed: 2022-10-17. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 125, + 288, + 178 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 125, + 288, + 178 + ], + "spans": [ + { + "bbox": [ + 53, + 125, + 288, + 178 + ], + "type": "text", + "content": "[2] Shruti Agarwal, Hany Farid, Tarek El-Gaaly, and Ser-Nam Lim. Detecting deep-fake videos from appearance and behavior. In 2020 IEEE International Workshop on Information Forensics and Security (WIFS), pages 1-6. IEEE, 2020. 8" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 180, + 288, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 180, + 288, + 213 + ], + "spans": [ + { + "bbox": [ + 53, + 180, + 288, + 213 + ], + "type": "text", + "content": "[3] ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, and Zhixin Shu. RigNeRF: Fully Controllable Neural 3D Portraits. pages 20364-20373, 2022. 2, 3, 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 214, + 287, + 246 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 214, + 287, + 246 + ], + "spans": [ + { + "bbox": [ + 53, + 214, + 287, + 246 + ], + "type": "text", + "content": "[4] Ziqian Bai, Zhaopeng Cui, Xiaoming Liu, and Ping Tan. Riggable 3D Face Reconstruction via In-Network Optimization. pages 6216-6225, June 2021. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 247, + 288, + 280 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 247, + 288, + 280 + ], + "spans": [ + { + "bbox": [ + 53, + 247, + 288, + 280 + ], + "type": "text", + "content": "[5] Ziqian Bai, Zhaopeng Cui, Jamal Ahmed Rahim, Xiaoming Liu, and Ping Tan. Deep Facial Non-Rigid Multi-View Stereo. pages 5850–5860, 2020. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 281, + 287, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 281, + 287, + 335 + ], + "spans": [ + { + "bbox": [ + 53, + 281, + 287, + 335 + ], + "type": "text", + "content": "[6] Thabo Beeler, Fabian Hahn, Derek Bradley, Bernd Bickel, Paul Beardsley, Craig Gotsman, Robert W Sumner, and Markus Gross. High-Quality Passive Facial Performance Capture Using Anchor Frames. In ACM SIGGRAPH 2011 Papers, pages 1-10. 2011. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 336, + 288, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 336, + 288, + 380 + ], + "spans": [ + { + "bbox": [ + 53, + 336, + 288, + 380 + ], + "type": "text", + "content": "[7] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pages 187-194, 1999. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 380, + 288, + 445 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 380, + 288, + 445 + ], + "spans": [ + { + "bbox": [ + 53, + 380, + 288, + 445 + ], + "type": "text", + "content": "[8] Chen Cao, Tomas Simon, Jin Kyu Kim, Gabe Schwartz, Michael Zollhoefer, Shun-Suke Saito, Stephen Lombardi, Shih-En Wei, Danielle Belko, Shoou-I Yu, Yaser Sheikh, and Jason Saragih. Authentic Volumetric Avatars from a Phone Scan. ACM Transactions on Graphics (TOG), 41(4), 2022. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 447, + 288, + 479 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 447, + 288, + 479 + ], + "spans": [ + { + "bbox": [ + 53, + 447, + 288, + 479 + ], + "type": "text", + "content": "[9] Chen Cao, Hongzhi Wu, Yanlin Weng, Tianjia Shao, and Kun Zhou. Real-Time Facial Animation With Image-Based Dynamic Avatars. 35(4), 2016. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 480, + 288, + 534 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 480, + 288, + 534 + ], + "spans": [ + { + "bbox": [ + 48, + 480, + 288, + 534 + ], + "type": "text", + "content": "[10] Bindita Chaudhuri, Noranart Vesdapunt, Linda Shapiro, and Baoyuan Wang. Personalized Face Modeling for Improved Face Reconstruction and Motion Retargeting. In ECCV 2020: 16th European Conference on Computer Vision, pages 142-160, 2020. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 536, + 288, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 536, + 288, + 590 + ], + "spans": [ + { + "bbox": [ + 48, + 536, + 288, + 590 + ], + "type": "text", + "content": "[11] Chong Bao and Bangbang Yang, Zeng Junyi, Bao Hujun, Zhang Yinda, Cui Zhaopeng, and Zhang Guofeng. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In European Conference on Computer Vision (ECCV), 2022. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 591, + 288, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 591, + 288, + 635 + ], + "spans": [ + { + "bbox": [ + 48, + 591, + 288, + 635 + ], + "type": "text", + "content": "[12] Ruofei Du, Ming Chuang, Wayne Chang, Hugues Hoppe, and Amitabh Varshney. Montage4D: Real-time Seamless Fusion and Stylization of Multiview Video Textures. Journal of Computer Graphics Techniques, 8(1):1-34, Jan. 2019. 1" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 636, + 288, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 636, + 288, + 679 + ], + "spans": [ + { + "bbox": [ + 48, + 636, + 288, + 679 + ], + "type": "text", + "content": "[13] Ruofei Du, David Li, and Amitabh Varshney. Geollery: A Mixed Reality Social Media Platform. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, number 685 in CHI. ACM, May 2019. 1" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 681, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 681, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 681, + 288, + 713 + ], + "type": "text", + "content": "[14] Bernhard Egger, William AP Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani," + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "et al. 3D Morphable Face Models—Past, Present, and Future. 39(5):1-38, 2020. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 97, + 545, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 97, + 545, + 140 + ], + "spans": [ + { + "bbox": [ + 307, + 97, + 545, + 140 + ], + "type": "text", + "content": "[15] Guy Gafni, Justus Thies, Michael Zollhofer, and Matthias Nießner. Dynamic Neural Radiance Fields for Monocular 4D Facial Avatar Reconstruction. pages 8649-8658, 2021. 2, 3, 4, 5, 6, 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 144, + 545, + 198 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 144, + 545, + 198 + ], + "spans": [ + { + "bbox": [ + 307, + 144, + 545, + 198 + ], + "type": "text", + "content": "[16] Pablo Garrido, Michael Zollhöfer, Dan Casas, Levi Valgaerts, Kiran Varanasi, Patrick Pérez, and Christian Theobalt. Reconstruction of Personalized 3D Face Rigs From Monocular Video. ACM Transactions on Graphics (TOG), 35(3):28, 2016. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 201, + 545, + 243 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 201, + 545, + 243 + ], + "spans": [ + { + "bbox": [ + 307, + 201, + 545, + 243 + ], + "type": "text", + "content": "[17] Philip-William Grassal, Malte Prinzler, Titus Leistner, Carsten Rother, Matthias Nießner, and Justus Thies. Neural Head Avatars From Monocular RGB Videos. pages 18653–18664, 2022. 2, 5, 6, 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 247, + 545, + 344 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 247, + 545, + 344 + ], + "spans": [ + { + "bbox": [ + 307, + 247, + 545, + 344 + ], + "type": "text", + "content": "[18] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, Danhang Tang, Anastasia Tkach, Adarsh Kowdle, Emily Cooper, Ming-song Dou, Sean Fanello, Graham Fyffe, Christoph Rhemann, Jonathan Taylor, Paul Debevec, and Shahram Izadi. The Relightables: Volumetric Performance Capture of Humans With Realistic Relighting. ACM Transactions on Graphics, Nov. 2019. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 348, + 545, + 436 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 348, + 545, + 436 + ], + "spans": [ + { + "bbox": [ + 307, + 348, + 545, + 436 + ], + "type": "text", + "content": "[19] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, Danhang Tang, Anastasia Tkach, Adarsh Kowdle, Emily Cooper, Ming-song Dou, Sean Fanello, Graham Fyffe, Christoph Rhemann, Jonathan Taylor, Paul Debevec, and Shahram Izadi. The relightables: Volumetric performance capture of humans with realistic relighting. ACM Trans. Graph., 38(6), nov 2019. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 438, + 545, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 438, + 545, + 491 + ], + "spans": [ + { + "bbox": [ + 307, + 438, + 545, + 491 + ], + "type": "text", + "content": "[20] Zhenyi He, Ruofei Du, and Ken Perlin. CollaboVR: A Reconfigurable Framework for Multi-user to Communicate in Virtual Reality. In 2020 IEEE International Symposium on Mixed and Augmented Reality, ISMAR, pages 542-554. IEEE, Nov. 2020. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 495, + 545, + 539 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 495, + 545, + 539 + ], + "spans": [ + { + "bbox": [ + 307, + 495, + 545, + 539 + ], + "type": "text", + "content": "[21] Liwen Hu, Shunsuke Saito, Lingyu Wei, Koki Nagano, Jaewoo Seo, Jens Fursund, Iman Sadeghi, Carrie Sun, YenChun Chen, and Hao Li. Avatar Digitization From a Single Image for Real-Time Rendering. 36(6):1-14, 2017. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 541, + 545, + 574 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 541, + 545, + 574 + ], + "spans": [ + { + "bbox": [ + 307, + 541, + 545, + 574 + ], + "type": "text", + "content": "[22] Alexandru Eugen Ichim, Sofien Bouaziz, and Mark Pauly. Dynamic 3D Avatar Creation From Hand-Held Video Input. 34(4):1-14, 2015. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 577, + 545, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 577, + 545, + 609 + ], + "spans": [ + { + "bbox": [ + 307, + 577, + 545, + 609 + ], + "type": "text", + "content": "[23] Wei Jiang, Kwang Moo Yi, Golnoosh Samei, Oncel Tuzel, and Anurag Ranjan. NeuMan: Neural Human Radiance Field From a Single Video. 2022. 4" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 612, + 545, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 612, + 545, + 655 + ], + "spans": [ + { + "bbox": [ + 307, + 612, + 545, + 655 + ], + "type": "text", + "content": "[24] Tianye Li, Timo Bolkart, Michael. J. Black, Hao Li, and Javier Romero. Learning a Model of Facial Shape and Expression From 4D Scans. ACM Transactions on Graphics, (Proc. SIGGRAPH Asia), 36(6):194:1-194:17, 2017. 2, 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 658, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 658, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 307, + 658, + 545, + 712 + ], + "type": "text", + "content": "[25] Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. Celeb-df: A large-scale challenging dataset for deepfake forensics. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3207-3216, 2020. 8" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "16898" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 11, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "type": "text", + "content": "[26] Siyou Lin, Hongwen Zhang, Zerong Zheng, Ruizhi Shao, and Yebin Liu. Learning implicit templates for point-based clothed human modeling. In ECCV, 2022. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 107, + 287, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 107, + 287, + 150 + ], + "spans": [ + { + "bbox": [ + 49, + 107, + 287, + 150 + ], + "type": "text", + "content": "[27] Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. Advances in Neural Information Processing Systems, 33:15651-15663, 2020. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 153, + 287, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 153, + 287, + 206 + ], + "spans": [ + { + "bbox": [ + 49, + 153, + 287, + 206 + ], + "type": "text", + "content": "[28] Lingjie Liu, Marc Habermann, Viktor Rudnev, Kripasindhu Sarkar, Jiatao Gu, and Christian Theobalt. Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Control. ACM Transactions on Graphics (TOG), 40(6):1-16, 2021. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 209, + 287, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 209, + 287, + 274 + ], + "spans": [ + { + "bbox": [ + 49, + 209, + 287, + 274 + ], + "type": "text", + "content": "[29] Abhimitra Meka, Rohit Pandey, Christian Haene, Sergio Orts-Escalano, Peter Barnum, Philip David-Son, Daniel Erickson, Yinda Zhang, Jonathan Taylor, Sofien Bouaziz, et al. Deep Relightable Textures: Volumetric Performance Capture With Neural Rendering. ACM Transactions on Graphics (TOG), 39(6):1-21, 2020. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 277, + 287, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 277, + 287, + 330 + ], + "spans": [ + { + "bbox": [ + 49, + 277, + 287, + 330 + ], + "type": "text", + "content": "[30] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing Scenes As Neural Radiance Fields for View Synthesis. Communications of the ACM, 65(1):99-106, 2021. 2, 4, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 333, + 287, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 333, + 287, + 441 + ], + "spans": [ + { + "bbox": [ + 49, + 333, + 287, + 441 + ], + "type": "text", + "content": "[31] Sergio Orts-Escolano, Christoph Rhemann, Sean Fanello, Wayne Chang, Adarsh Kowdle, Yury Degtyarev, David Kim, Philip Davidson, Sameh Khamis, Mingsong Dou, Vladimir Tankovich, Charles Loop, Qin Cai, Philip Chou, Sarah Mennicken, Julien Valentin, Vivek Pradeep, Shenlong Wang, Sing Bing Kang, Pushmeet Kohli, Yuliya Lutchyn, Cem Keskin, and Shahram Izadi. Holoportation: Virtual 3D Teleportation in Real-Time. In Proceedings of the 29th Annual Symposium on User Interface Software and Technology (UIST). ACM, Oct. 2016. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 445, + 287, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 445, + 287, + 498 + ], + "spans": [ + { + "bbox": [ + 49, + 445, + 287, + 498 + ], + "type": "text", + "content": "[32] Rohit Pandey, Sergio Orts Escolano, Chloe Legendre, Christian Haene, Sofien Bouaziz, Christoph Rhemann, Paul Debevec, and Sean Fanello. Total relighting: learning to relight portraits for background replacement. ACM Transactions on Graphics (TOG), 40(4):1-21, 2021. 3, 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 49, + 501, + 287, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 501, + 287, + 555 + ], + "spans": [ + { + "bbox": [ + 49, + 501, + 287, + 555 + ], + "type": "text", + "content": "[33] Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. Nerfies: Deformable Neural Radiance Fields. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 5865-5874, 2021. 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 49, + 557, + 287, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 557, + 287, + 610 + ], + "spans": [ + { + "bbox": [ + 49, + 557, + 287, + 610 + ], + "type": "text", + "content": "[34] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural Body: Implicit Neural Representations With Structured Latent Codes for Novel View Synthesis of Dynamic Humans. pages 9054-9063, 2021. 3, 4" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 49, + 613, + 287, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 613, + 287, + 656 + ], + "spans": [ + { + "bbox": [ + 49, + 613, + 287, + 656 + ], + "type": "text", + "content": "[35] Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: A large-scale video dataset for forgery detection in human faces. arXiv preprint arXiv:1803.09179, 2018. 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 49, + 658, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 658, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 49, + 658, + 287, + 712 + ], + "type": "text", + "content": "[36] Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: Learning to detect manipulated facial images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1-11, 2019. 8" + } + ] + } + ], + "index": 10 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "type": "text", + "content": "[37] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Hao Li, and Angjoo Kanazawa. PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, Oct. 2019. 1" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 307, + 130, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 130, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 130, + 545, + 184 + ], + "type": "text", + "content": "[38] Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. Animating arbitrary objects via deep motion transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2377-2386, 2019. 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 308, + 187, + 545, + 231 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 187, + 545, + 231 + ], + "spans": [ + { + "bbox": [ + 308, + 187, + 545, + 231 + ], + "type": "text", + "content": "[39] Aliaksandr Siarohin, Stéphane Lathuilière, Sergey Tulyakov, Elisa Ricci, and Nicu Sebe. First Order Motion Model for Image Animation. In Advances in Neural Information Processing Systems (NeurIPS), volume 32, 2019. 3, 5, 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 308, + 233, + 545, + 299 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 233, + 545, + 299 + ], + "spans": [ + { + "bbox": [ + 308, + 233, + 545, + 299 + ], + "type": "text", + "content": "[40] Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. FML: Face Model Learning From Videos. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10812-10822, 2019. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 301, + 545, + 344 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 301, + 545, + 344 + ], + "spans": [ + { + "bbox": [ + 308, + 301, + 545, + 344 + ], + "type": "text", + "content": "[41] Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales, and Javier Ortega-Garcia. Deepfakes and beyond: A survey of face manipulation and fake detection. Information Fusion, 64:131-148, 2020. 8" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 347, + 545, + 368 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 347, + 545, + 368 + ], + "spans": [ + { + "bbox": [ + 308, + 347, + 545, + 368 + ], + "type": "text", + "content": "[42] Zach Waggoner. My Avatar, My Self: Identity in Video Role-Playing Games. McFarland, 2009. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 372, + 545, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 372, + 545, + 415 + ], + "spans": [ + { + "bbox": [ + 308, + 372, + 545, + 415 + ], + "type": "text", + "content": "[43] Ting-Chun Wang, Ming-Yu Liu, Andrew Tao, Guilin Liu, Jan Kautz, and Bryan Catanzaro. Few-Shot Video-to-Video Synthesis. In Advances in Neural Information Processing Systems (NeurIPS), 2019. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 418, + 545, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 418, + 545, + 449 + ], + "spans": [ + { + "bbox": [ + 308, + 418, + 545, + 449 + ], + "type": "text", + "content": "[44] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image Quality Assessment: From Error Visibility to Structural Similarity. 13(4):600-612, 2004. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 453, + 545, + 517 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 453, + 545, + 517 + ], + "spans": [ + { + "bbox": [ + 308, + 453, + 545, + 517 + ], + "type": "text", + "content": "[45] Chung-Yi Weng, Brian Curless, Pratul P Srinivasan, Jonathan T Barron, and Ira Kemelmacher-Shlizerman. Humanerf: Free-viewpoint rendering of moving people from monocular video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16210-16220, 2022. 4" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 520, + 545, + 564 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 520, + 545, + 564 + ], + "spans": [ + { + "bbox": [ + 308, + 520, + 545, + 564 + ], + "type": "text", + "content": "[46] Olivia Wiles, A Koepke, and Andrew Zisserman. X2face: A network for controlling face generation using images, audio, and pose codes. In Proceedings of the European conference on Computer Vision (ECCV), pages 670-686, 2018. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 567, + 545, + 620 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 567, + 545, + 620 + ], + "spans": [ + { + "bbox": [ + 308, + 567, + 545, + 620 + ], + "type": "text", + "content": "[47] Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In European Conference on Computer Vision, pages 597-614. Springer, 2022. 4" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 624, + 545, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 624, + 545, + 667 + ], + "spans": [ + { + "bbox": [ + 308, + 624, + 545, + 667 + ], + "type": "text", + "content": "[48] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction. pages 601-610, 2020. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "text", + "content": "[49] Egor Zakharov, Aleksei Ivakhnenko, Aliaksandra Shysheya, and Victor Lempitsky. Fast bi-layer neural synthesis of one-shot realistic head avatars. In European Conference on Computer Vision, pages 524-540. Springer, 2020. 3" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "16899" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 407 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "text", + "content": "[50] Egor Zakharov, Aliaksandra Shysheya, Egor Burkov, and Victor Lempitsky. Few-Shot Adversarial Learning of Realistic Neural Talking Head Models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9459-9468, 2019. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 129, + 287, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 129, + 287, + 183 + ], + "spans": [ + { + "bbox": [ + 48, + 129, + 287, + 183 + ], + "type": "text", + "content": "[51] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The Unreasonable Effectiveness of Deep Features As a Perceptual Metric. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 586-595, 2018. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 185, + 287, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 185, + 287, + 228 + ], + "spans": [ + { + "bbox": [ + 48, + 185, + 287, + 228 + ], + "type": "text", + "content": "[52] Jian Zhao and Hui Zhang. Thin-plate spline motion model for image animation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3657–3666, 2022. 5, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 229, + 287, + 294 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 229, + 287, + 294 + ], + "spans": [ + { + "bbox": [ + 48, + 229, + 287, + 294 + ], + "type": "text", + "content": "[53] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. Im avatar: Implicit morphable head avatars from videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13545–13555, 2022. 3, 4, 5, 6, 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 296, + 287, + 351 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 296, + 287, + 351 + ], + "spans": [ + { + "bbox": [ + 48, + 296, + 287, + 351 + ], + "type": "text", + "content": "[54] Zerong Zheng, Han Huang, Tao Yu, Hongwen Zhang, Yandong Guo, and Yebin Liu. Structured Local Radiance Fields for Human Avatar Modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15893-15903, 2022. 3, 4, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 352, + 287, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 352, + 287, + 407 + ], + "spans": [ + { + "bbox": [ + 48, + 352, + 287, + 407 + ], + "type": "text", + "content": "[55] Michael Zollhöfer, Justus Thies, Pablo Garrido, Derek Bradley, Thabo Beeler, Patrick Pérez, Marc Stamminger, Matthias Nießner, and Christian Theobalt. State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications. volume 37, pages 523-550, 2018. 2" + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "16900" + } + ] + } + ], + "index": 7 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations/471fce2b-475c-4777-b3f3-36131b107c8b_content_list.json b/2023/Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations/471fce2b-475c-4777-b3f3-36131b107c8b_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b34c57c61aa7386df0096ac8ef419f424a5ff17c --- /dev/null +++ b/2023/Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations/471fce2b-475c-4777-b3f3-36131b107c8b_content_list.json @@ -0,0 +1,1774 @@ +[ + { + "type": "text", + "text": "Learning Procedure-aware Video Representation from Instructional Videos and Their Narrations", + "text_level": 1, + "bbox": [ + 236, + 130, + 733, + 174 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yiwu Zhong $^{1*}$ , Licheng Yu $^{2}$ , Yang Bai $^{2}$ , Shangwen Li $^{2}$ , Xueting Yan $^{2\\dagger}$ , Yin Li $^{1\\dagger}$ $^{1}$ University of Wisconsin-Madison, $^{2}$ Meta AI", + "bbox": [ + 178, + 202, + 797, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "yzhong52@wisc.edu, {lichengyu, yangbai, dylanwen, xyan18}@meta.com, yin.li@wisc.edu", + "bbox": [ + 197, + 243, + 769, + 255 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 291, + 313, + 306 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The abundance of instructional videos and their narrations over the Internet offers an exciting avenue for understanding procedural activities. In this work, we propose to learn video representation that encodes both action steps and their temporal ordering, based on a largescale dataset of web instructional videos and their narrations, without using human annotations. Our method jointly learns a video representation to encode individual step concepts, and a deep probabilistic model to capture both temporal dependencies and immense individual variations in the step ordering. We empirically demonstrate that learning temporal ordering not only enables new capabilities for procedure reasoning, but also reinforces the recognition of individual steps. Our model significantly advances the state-of-the-art results on step classification $(+2.8\\% / +3.3\\%)$ on COIN / EPIC-Kitchens) and step forecasting $(+7.4\\%)$ on COIN). Moreover, our model attains promising results in zero-shot inference for step classification and forecasting, as well as in predicting diverse and plausible steps for incomplete procedures. Our code is available at https://github.com/facebookresearch/ProcedureVRL.", + "bbox": [ + 75, + 323, + 473, + 641 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 660, + 209, + 676 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Many of our daily activities (e.g. cooking or crafting) are highly structured, comprising a set of action steps conducted in a certain ordering. Yet how these activities are performed varies among individuals. Consider the example of making scrambled eggs as shown in Fig. 1. While most people tend to whisk eggs in a bowl, melt butter in a pan, and cook eggs under medium heat, expert chefs have recommended to crack eggs into the pan, add butter, and stir them under high heat. Imagine a vision model that can account for the individual variations and reason about the temporal ordering of action steps in a video, so as to infer prior missing steps, recognize the current step, and forecast", + "bbox": [ + 75, + 686, + 468, + 867 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/deb55a7fa0269ce55a71bd99c40f8df251542e94bc7bbf4564ac61c4adcfa2a6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 504, + 290, + 890, + 364 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9b380ec0b7e0c906b0ba6e7139fe316c0f5338affaa173993ea1aa306e3ef5d7.jpg", + "image_caption": [ + "Figure 1. Top: During training, our model learns from procedural videos and step descriptions to understand individual steps and capture temporal ordering and variations among steps. Bottom: Once trained, our model supports zero-shot step classification and forecasting, yielding multiple credible predictions." + ], + "image_footnote": [], + "bbox": [ + 501, + 371, + 887, + 443 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "a future step. Such a model will be immensely useful for a wide range of applications including augmented reality, virtual personal assistant, and human-robot interaction.", + "bbox": [ + 498, + 536, + 890, + 580 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Understanding complex procedural activities has been a long-standing challenge in the vision community [7, 18, 22, 39, 41, 46]. While many prior approaches learn from annotated videos following a fully supervised setting [13, 27, 69], this paradigm is difficult to scale to a plethora of activities and their variants among individuals. A promising solution is offered by the exciting advances in vision-and-language pre-training, where models learn from visual data (images or videos) and their paired text data (captions or narrations) [30, 43, 54, 70] in order to recognize a variety of concepts. This idea has recently been explored to analyze instructional videos [33, 37], yet existing methods are limited to recognize single action steps in procedural activities.", + "bbox": [ + 496, + 582, + 892, + 777 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we present a first step towards modeling temporal ordering of action steps in procedural activities by learning from instructional videos and their narrations. Our key innovation lies in the joint learning of a video representation aiming to encode individual step concepts, and a deep probabilistic model designed to capture temporal dependencies and variations among steps. The video representation, instantiated as a Transformer network, is learned by", + "bbox": [ + 496, + 780, + 893, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Work done while Yiwu Zhong was an intern at Meta.", + "bbox": [ + 94, + 875, + 380, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "†Co-corresponding authors.", + "bbox": [ + 96, + 887, + 243, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "14825", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "matching a video clip to its corresponding narration. The probabilistic model, built on a diffusion process, is tasked to predict the distribution of the video representation for a missing step, given steps in its vicinity. With the help of a pre-trained vision-and-language model [43], our model is trained using only videos and their narrations from automatic speech recognition (ASR), and thus does not require any manual annotations.", + "bbox": [ + 75, + 90, + 470, + 210 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Once learned, our model celebrates two unique benefits thanks to our model design and training framework. First, our model supports zero-shot inference given an input video, including the recognition of single steps and forecasting of future steps, and can be further fine-tuned on downstream tasks. Second, our model allows sampling multiple video representations when predicting a missing action step, with each presenting a possibly different hypothesis of the step ordering. Instead of predicting a single representation with the highest probability, sampling from a probabilistic model provides access to additional high-probability solutions that might be beneficial to prediction tasks with high ambiguity or requiring user interactions.", + "bbox": [ + 75, + 212, + 470, + 407 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We train our models on a large-scale instructional video dataset collected from YouTube (HowTo100M [38]), and evaluate them on two public benchmarks (COIN [55] and EPIC-Kitchens-100 [10]) covering a wide range of procedural videos and across the tasks of step classification and step forecasting. Through extensive experiments, we demonstrate that (1) our temporal model is highly effective in forecasting future steps, outperforming state-of-the-art methods by a large margin of $+7.4\\%$ in top-1 accuracy on COIN; (2) modeling temporal ordering reinforces video representation learning, leading to improved classification results $(+2.8\\% / + 3.3\\%)$ for step classification on COIN/EPIC-Kitchens) when probing the learned representations; (3) our training framework offers strong results for zero-shot step classification and forecasting; and (4) sampling from our probabilistic model yields diverse and plausible predictions of future steps.", + "bbox": [ + 75, + 407, + 470, + 664 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Contributions. Our work presents the first model that leverages video-and-language pre-training to capture the temporal ordering of action steps in procedural activities. Our key technical innovation lies in the design of a deep probabilistic model using a diffusion process, in tandem with video-and-language representation learning. The result is a model and a training framework that establish new state-of-the-art results on both step classification and forecasting tasks across the major benchmarks. Besides, our model is capable of generating diverse step predictions and supports zero-shot inference.", + "bbox": [ + 75, + 667, + 470, + 834 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 76, + 844, + 218, + 859 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Understanding Procedural Activities. Reasoning about procedural activities, including their action steps and the", + "bbox": [ + 75, + 869, + 470, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "temporal ordering of these steps, has been a central problem in activity recognition. While early works model temporal ordering with stochastic grammars [7, 18, 22, 39, 41, 46], more recent works consider supervised learning to localize steps and predict their ordering by learning from videos with human annotated action steps [8, 10, 13, 27, 55, 69, 71]. To alleviate the burden of costly video annotations, several works propose various forms of weakly supervised settings, with assumptions that the ordered list of steps is given without their temporal boundaries [5, 6, 67, 71], or that the key steps and their ordering remain fixed across all videos [1, 12, 13, 17, 28, 49].", + "bbox": [ + 496, + 90, + 890, + 271 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Most of prior methods focus on the tasks of step classification and localization [1,5,6,12,13,28,49,71]. Others have considered the tasks of step forecasting [48], step verification [42] and procedure planning [67]. Our work also seeks to understand procedural activities. Different from these approaches, our method focuses on learning video representation from videos and their narrations without using human annotations. The resulting video representation can be leveraged for step classification and step forecasting.", + "bbox": [ + 496, + 272, + 890, + 407 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Learning from Procedural Videos and Narrations. The success of vision-and-language pre-training has fueled a new line of research that seeks to learn concepts of individual steps from instructional videos and their narrations [19, 36, 50, 62]. For example, Miech et al. [37] propose MIL-NCE to learn representations from instructional videos [38] and their narrations extracted using ASR.", + "bbox": [ + 496, + 412, + 890, + 518 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The most relevant work is DistantSup [33], where they propose using distant supervision from a textual knowledge base (wikiHow) [26] to denoise text narrations from ASR. Specifically, DistantSup leverages a pre-trained language model [52] to link step descriptions from wikiHow to text narrations from video ASR results, and thus to create training labels for individual steps in videos. Different from [33], our method models the temporal ordering of steps in procedural activities, thus moves beyond representations of single steps to support temporal reasoning in videos. Further, our method learns from videos and narrations only, with the help of a pre-trained image-language model [43] yet without using a textual knowledge base.", + "bbox": [ + 496, + 518, + 890, + 715 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Video-and-Language Pre-Training. A relevant topic is video-and-language pre-training, aiming at learning video representation from videos and their paired natural language descriptions [3,15,16,29,31,35,54,59,63,64,66,70], often generated from ASR outputs. Despite the latest development in ASR, automatically-transcribed speech from videos can be rather noisy and lacks precise temporal alignment with the visual content. Several recent works seek to address this challenge. VideoCLIP [63] starts from the pretrained MIL-NCE model and further improves the model by retrieval augmented training with overlapped video-text pairs. Bain et al. [3] collect a less noisy dataset of video alt", + "bbox": [ + 496, + 719, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "14826", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/0daf97944a45cc64dbff9059314400d7fd8471ba4d8983b9a9282a6f4a449406.jpg", + "image_caption": [ + "Figure 2. Overview of our approach. Left panel: Our model consists of (1) a video encoder that takes a video clip and encodes it into a video embedding; (2) a transformer-based denoising model that samples noises from Gaussian distribution and generates video embeddings conditioned on the embeddings of adjacent video clips. Right panel: We leverage trained image-language model CLIP to create pseudo labels for individual video clips (a). After training, our model supports step classification given an input video clip (b), and step forecasting given a video that records previous steps (c). Note that diverse embeddings can be generated by sampling various noises." + ], + "image_footnote": [], + "bbox": [ + 84, + 90, + 651, + 330 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/4923efcea09132ad967e11187a6c0df612ced7dab2747a9dcb15be8b53eb472a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 660, + 90, + 879, + 329 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "text pairs and geared the model to match these pairs. Our work shares the key idea of learning from video and text data as prior work, and seeks to leverage external knowledge from a pre-trained image-language model [43].", + "bbox": [ + 75, + 421, + 468, + 479 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Another relevant work is MERLOT [66]. While both MERLOT and our work seek to learn video representation, our method differs from MERLOT in two folds. Our method models the sequence order of video clips for understanding procedural activities. MERLOT learns binary relative order between two given video frames for multi-modal reasoning and does not directly support action forecasting. Both methods consider a masked prediction task, yet MERLOT predicts the most likely text embeddings, while our method estimates the distribution of video representations using a deep probabilistic model.", + "bbox": [ + 75, + 482, + 468, + 648 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Diffusion Models. Diffusion models [51, 53] provide a powerful approach to characterize the probability density of high dimensional signals, and have recently demonstrated impressive results on generating high fidelity visual data, such as images [40,44,45,47], videos [21], and human body motion [57]. Our work adapts diffusion process to model the temporal ordering of steps in procedural videos. In doing so, our method not only facilitates the learning of expressive video representations for individual steps, but also enables the anticipation of future action steps.", + "bbox": [ + 75, + 650, + 468, + 801 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 76, + 814, + 166, + 829 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We consider the problem of learning video representation for understanding procedural activities from instructional videos and their narrations. An input video is represented as a sequence of $N$ clips $\\{v_{1}, v_{2}, \\ldots, v_{N}\\}$ . Each", + "bbox": [ + 75, + 839, + 468, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "$v_{i}$ captures a potential action step in the input video, and the time step $i$ records the temporal ordering of these clips. The video clips $\\{v_{i}\\}$ can be either segmented by using the timestamps of ASR outputs (as we consider during training), or densely sampled from a video following their temporal ordering (as we use during inference). During learning, we further assume that an ordered set of sentences $\\{s_1,s_2,\\dots,s_N\\}$ is associated with the video clips $\\{v_{1},v_{2},\\dots,v_{N}\\}$ , with each $s_i$ describing the action step in video clip $v_{i}$ . These sentences $\\{s_i\\}$ can be the output text from ASR, or given by matching the video clips to a text corpus using an external vision language model [43].", + "bbox": [ + 496, + 421, + 890, + 603 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Procedural-aware Video Representation. Our goal is to learn video representation that encodes both action step concepts and their temporal dependencies across a range of procedural activities. Our representation consists of (a) a video encoder $f$ that extracts a representation $x_{i}$ from an input clip $v_{i}$ (i.e., $x_{i} = f(v_{i})$ ); and (b) a probabilistic model that characterizes the conditional probability $p(x_{j} = f(v_{j}) | \\{x_{i} = f(v_{i})\\}_{i \\neq j}) \\forall j$ . This design is highly flexible and supports a number of procedural reasoning tasks. $f$ offers video representation suitable to classify individual steps in a clip. $p(x_{j} | \\{x_{i}\\}_{i \\neq j})$ models the temporal dependencies among steps, and can be used to predict the video representation of missing steps and further infer their labels.", + "bbox": [ + 496, + 608, + 890, + 804 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Method Overview. To learn our representation, we leverage a pre-trained text encoder $g$ that remains fixed during learning, and extend the idea of masked token modeling, populated in natural language processing [24]. For each input video and its narrations at training time, we randomly sample a clip $v_{j}$ from $\\{v_{1},v_{2},\\dots,v_{N}\\}$ and mask it", + "bbox": [ + 496, + 809, + 890, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "14827", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "out. We then train our model to predict the distribution of $x_{j} = f(v_{j})$ from $\\{x_{i} = f(v_{i})\\}_{i \\neq j}$ (i.e., $p(x_{j} | \\{x_{i}\\}_{i \\neq j}))$ ), align the expectation of the predicted distribution $\\mathbb{E}(x_{j})$ with the corresponding text embedding $y_{j} = g(s_{j})$ , and match all other video representations $\\{x_{i} = f(v_{i})\\}_{i \\neq j}$ to their text embeddings $\\{y_{i} = g(s_{i})\\}_{i \\neq j}$ .", + "bbox": [ + 75, + 90, + 468, + 181 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Despite the conceptual similarity, our learning is fundamentally different from masked token prediction. Our method seeks to characterize the distribution of $x_{j}$ instead of predicting the most likely $x_{j}$ , resulting in a more principled approach to capture the temporal dependencies among steps, as well as the new capability of sampling multiple high-probability solutions for $x_{j}$ . Our method is illustrated in Fig. 2. In what follows, we lay out the formulation of our model, and describe its training and inference schemes.", + "bbox": [ + 75, + 181, + 470, + 316 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.1. Modeling Action Steps and Their Ordering", + "text_level": 1, + "bbox": [ + 76, + 328, + 444, + 344 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Formally, given an input video with its clips $\\{v_{1}, v_{2}, \\ldots, v_{N}\\}$ and their narrations $\\{s_{1}, s_{2}, \\ldots, s_{N}\\}$ , our method assumes a factorization of $p(Y = \\{y_{i}\\} | X = \\{x_{i}\\})$ with video representation $x_{i} = f(v_{i})$ (learnable) and text embedding $y_{i} = f(s_{i})$ (pre-trained and fixed).", + "bbox": [ + 76, + 351, + 468, + 428 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np (Y | X) = p \\left(y _ {j} \\mid x _ {j}\\right) \\cdot p \\left(x _ {j} \\mid \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right) \\cdot \\prod_ {i} p \\left(y _ {i} \\mid x _ {i}\\right) \\quad \\forall j. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 89, + 436, + 468, + 455 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$p(y_{i}|x_{i})$ measures the alignment between a video representation $x_{i}$ and a text embedding $y_{i}$ . $p(x_{j}|\\{x_{i}\\}_{i\\neq j})$ characterizes the distribution of a video representation for a missing step given the representations of all other steps, thereby modeling the temporal ordering of steps. Note that our model is not limited to single step prediction and can be readily extended to predict multiple missing steps.", + "bbox": [ + 75, + 464, + 468, + 570 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Matching Image and Text Representations. Our model matches the video representation $x_{i}$ and text embedding $y_{i}$ in a learned vector space, such that the alignment between them can be measured by cosine similarity. We will later instantiate this definition into a more tractable form for learning. Yet it suffices to notice that $p(y_{j}|x_{j})$ does not involve additional learnable parameters given $x_{i}$ and $y_{i}$ .", + "bbox": [ + 75, + 574, + 468, + 680 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Modeling Step Ordering with Diffusion Process. The key challenge lies in the modeling of $p(x_{j}|\\{x_{i}\\}_{i\\neq j})$ , as the video representation $x_{i}$ is at least of a few hundred dimensions. To this end, we propose to model $p(x_{j}|\\{x_{i}\\}_{i\\neq j})$ using a diffusion process [51, 53] conditioned on observed video representations $\\{x_{i}\\}_{i\\neq j}$ . Here we briefly describe diffusion process in the context of our model, and refer the readers to recent surveys for more technical details [9, 65].", + "bbox": [ + 75, + 684, + 468, + 805 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Specifically, we assume a diffusion process that gradually adds noise to the input $x_{j}$ over $t \\in [0,1,\\dots,T]$ steps.", + "bbox": [ + 75, + 806, + 468, + 837 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np \\left(x _ {j} ^ {1: T} \\mid x ^ {0}\\right) = \\prod_ {t = 1} ^ {T} p \\left(x _ {j} ^ {t} \\mid x _ {j} ^ {t - 1}\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 148, + 847, + 468, + 883 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np (x _ {j} ^ {t} | x _ {j} ^ {t - 1}) = \\mathcal {N} (\\sqrt {\\alpha_ {t}} x _ {j} ^ {t - 1}, (1 - \\alpha_ {t}) I).\n$$\n", + "text_format": "latex", + "bbox": [ + 148, + 886, + 395, + 905 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\alpha_{t}$ are constant hyper-parameters. The reverse diffusion (denoising) process is parameterized with", + "bbox": [ + 500, + 90, + 890, + 121 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np \\left(x _ {j} ^ {0: T} \\mid \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right) = p \\left(x _ {j} ^ {T} \\mid \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right) \\prod_ {t = 1} ^ {T} p _ {\\theta} \\left(x _ {j} ^ {t - 1} \\mid x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right). \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 519, + 133, + 890, + 152 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "With sufficiently large $T$ , $p(x_j^T|\\{x_i\\}_{i \\neq j}) \\sim \\mathcal{N}(0, I)$ , i.e. a standard Gaussian noise that is independent of $\\{x_i\\}_{i \\neq j}$ . The denoising process is approximated by $p_\\theta(x_j^{t-1}|x_j^t, \\{x_i\\}_{i \\neq j})$ using a neural network with parameters $\\theta$ such that", + "bbox": [ + 498, + 162, + 890, + 224 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\theta} \\left(x _ {j} ^ {t - 1} \\mid x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right) = \\mathcal {N} \\left(\\mu_ {\\theta} \\left(x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right), \\Sigma_ {\\theta} \\left(x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right)\\right) \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 519, + 238, + 890, + 255 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In practice, we follow Ho et al. [20] and Tevet et al. [57] to directly predict $x_{j}^{0}$ by using a denoising model $h$ . With slight abuse of the symbols, we denote", + "bbox": [ + 498, + 266, + 890, + 311 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {x} _ {j} ^ {0} = h \\left(x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}, t\\right). \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 620, + 321, + 890, + 340 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$h$ is realized using a Transformer network with the embedding of step $t$ as part of its inputs. Once learned, one can sample from $\\mathcal{N}(0,I)$ and apply $h$ through the denoising process to predict $x_{j}$ based on $\\{x_i\\}_{i\\neq j}$ .", + "bbox": [ + 498, + 349, + 890, + 412 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Learning from Videos and Their Narrations", + "text_level": 1, + "bbox": [ + 500, + 422, + 872, + 436 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our training approximately maximizes the likelihood of Eq. 1 given a set of training videos and their narrations.", + "bbox": [ + 498, + 446, + 890, + 476 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Pseudo Labels from CLIP. It is straightforward to directly align video representations to the embeddings of their corresponding ASR text. Doing so, however, faces the challenges of low-quality ASR text and imprecise alignment between video and ASR sentences. To address these challenges, we propose to create pseudo labels by leveraging a pre-trained image-language model (e.g., CLIP [43]).", + "bbox": [ + 498, + 479, + 890, + 585 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Specifically, we first create a pool of step descriptions in the form of verb phrases (e.g., \"add water\", \"wear gloves\") parsed from ASR sentences [50], with their embeddings as $\\{y^{1:K}\\}$ . Then a trained CLIP model is applied to link each video clip with verb phrases, by matching the averaged visual features across frames with the language embeddings of verb phrases. The resulting matching scores are used as our training target.", + "bbox": [ + 498, + 587, + 890, + 707 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our pseudo labeling instantiates the matching $p(y_{i}|x_{i})$ between video representation and text embedding using", + "bbox": [ + 498, + 708, + 890, + 739 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\np \\left(y _ {i} \\mid x _ {i}\\right) = \\operatorname {s o f t m a x} \\left(\\frac {x _ {i} ^ {T} y _ {i}}{\\tau \\| x _ {i} \\| \\| y _ {i} \\|}\\right), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 581, + 750, + 890, + 780 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $y_{i}$ is selected from pool of verb phrases, i.e. $y_{i} \\in \\{y^{1:K}\\}$ , and $\\tau$ is the pre-defined temperature. The matching problem thus is converted into a \"classification\" problem, making the training feasible.", + "bbox": [ + 498, + 789, + 890, + 851 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Learning Objective and Training Loss. Our training minimizes an evidence upper bound of the negative log likelihood $-\\log p(Y|X)$ in Eq. 1. The detailed derivation of", + "bbox": [ + 498, + 854, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "14828", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "evidence upper bound is described in the Supplement. Our objective function constitutes three loss terms:", + "bbox": [ + 76, + 90, + 468, + 121 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {L} = \\mathrm {L} _ {\\mathrm {X E}} + \\mathrm {L} _ {\\mathrm {M S E}} + \\mathrm {L} _ {\\mathrm {M C}}. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 186, + 131, + 468, + 146 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The first term $\\mathrm{L}_{\\mathrm{XE}}$ seeks to match observed video representation $\\{x_{i}\\}$ to their text embeddings $\\{y_{i}\\}$ , given by", + "bbox": [ + 76, + 156, + 468, + 188 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {L} _ {\\mathrm {X} E} = \\frac {1}{N - 1} \\sum_ {i} H \\left(p _ {i} ^ {\\prime}, p \\left(y _ {i} \\mid x _ {i}\\right)\\right), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 158, + 195, + 468, + 213 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $H(\\cdot, \\cdot)$ is the cross entropy, and $p_i'$ are soft targets given by CLIP matching scores. $p(y_i|x_i)$ defined in Eq. 6 measures the similarity between $x_i$ and $y_i$ .", + "bbox": [ + 76, + 220, + 468, + 266 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The second term $\\mathrm{L}_{\\mathrm{MSE}}$ comes from the Kullback-Leibler (KL) divergence within our diffusion model, and is computed as", + "bbox": [ + 76, + 267, + 468, + 311 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {L} _ {\\text {M S E}} = \\mathbb {E} _ {x _ {j} ^ {0} \\sim p \\left(x _ {j} ^ {0} \\mid \\{x _ {i} \\} _ {i \\neq j}\\right), t \\sim [ 1, T ]} \\left[ \\| x _ {j} ^ {0} - \\hat {x} _ {j} ^ {0} \\| _ {2} ^ {2} \\right]. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 114, + 320, + 468, + 342 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Note that unlike a standard diffusion model, our model directly predicts $\\hat{x}_j^0$ . This term is applied at each step $t$ .", + "bbox": [ + 76, + 349, + 468, + 380 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The third term $\\mathrm{L}_{\\mathrm{MC}}$ is derived from matching the predicted video representation $\\hat{x}_j^0$ to its text embedding $y_{j}$", + "bbox": [ + 76, + 380, + 468, + 412 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {L} _ {\\mathrm {M C}} = \\mathbb {E} _ {x _ {j} ^ {0} \\sim p \\left(x _ {j} ^ {0} \\mid \\{x _ {i} \\} _ {i \\neq j}\\right), t \\sim [ 1, T ]} \\left[ H \\left(p _ {j} ^ {\\prime}, p \\left(y _ {j} \\mid \\hat {x} _ {j} ^ {0}\\right)\\right) \\right], \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 89, + 421, + 468, + 444 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $p_j'$ are again soft targets given by CLIP model, and $\\hat{x}_j^0$ is denoised from a sampled noise. During training, we adopt Monte Carlo estimation for $\\mathbb{E}_p\\left[-\\log p(y_j|\\hat{x}_j^0)\\right]$ , by minimizing $-\\log p(y_j|\\hat{x}_j^0)$ for each sampled $\\hat{x}_j^0$ . We attach this term at each step $t$ .", + "bbox": [ + 76, + 450, + 468, + 530 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "A critical design choice lies in $p(y_{j}|\\hat{x}_{j}^{0})$ . $p(y_{j}|\\hat{x}_{j}^{0})$ is simplified into a score function between a video representation and a finite set of text embeddings (defined using verb phrases). This allows us to reach our loss terms without worrying about global normalization constant as commonly encountered in energy-based models. Indeed, $H(p_{j}^{\\prime},p(y_{j}|\\hat{x}_{j}^{0}))$ can be interpreted as providing guidance by matching video to text embeddings. This term thus resembles the key idea of classifier guidance, which has shown to be helpful for learning diffusion models [11].", + "bbox": [ + 76, + 531, + 468, + 683 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Model Inference", + "text_level": 1, + "bbox": [ + 76, + 691, + 238, + 707 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Once trained, our model offers a procedure-aware representation with two key components. First, the video encoder $f(\\cdot)$ serves as a feature extractor for any input video clips. Second, the diffusion model, represented as its denoising model $h(\\cdot)$ , captures the temporal dependencies among steps. Our representation naturally supports a number of tasks. Here we demonstrate how our model can be used for step classification and step forecasting.", + "bbox": [ + 76, + 715, + 468, + 835 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Step Classification. An input video clip $v$ can be encoded using $f(\\cdot)$ . The video representation $x = f(v)$ can be directly compared to the text embeddings [43], so as to support zero-shot step classification. Alternative, an additional", + "bbox": [ + 76, + 839, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "classifier can be attached on top of $f$ and further fine-tuned to recognize the action step in input clip.", + "bbox": [ + 498, + 90, + 890, + 121 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Step Forecasting. A future video clip feature $x_{j}$ can be sampled from the diffusion model by drawing from a Gaussian distribution and denoising using $h(\\cdot)$ . The predicted $x_{j}$ can be further classified into action steps. This prediction can be done using again Monte Carlo estimation given by", + "bbox": [ + 498, + 126, + 890, + 202 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbb {E} _ {x _ {j} \\sim p (x _ {j} | \\{x _ {i} \\} _ {i \\neq j})} [ p (y _ {j} | x _ {j}) ]. \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 213, + 890, + 232 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Specifically, a noise $x_{T}$ is first sampled from Gaussian distribution and our denoising model gradually denoises it. At each step $t$ , the denoising model $h$ takes a noisy $x_{j}^{t}$ , predicts clip feature $\\hat{x}_{j}^{0}$ , and diffuses it to $x_{j}^{t-1}$ based on the sampled noise $x_{j}^{T}$ , as demonstrated in Eq. 2. After $T$ iterations, the predicted clip feature at $t = 0$ is used to match text embeddings. By sampling noises for multiple times, we can estimate the most likely $y_{j}$ .", + "bbox": [ + 496, + 242, + 890, + 367 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "However, sampling can be costly. In practice, to obtain top-1 prediction for missing steps, we adopt approximate inference, where the sampled noise is replaced with a fixed zero vector, corresponding to peak in the Gaussian distribution. Our empirical results validate that approximate inference achieves a very close performance as the expectation over multiple sampled noises.", + "bbox": [ + 496, + 368, + 890, + 473 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments and Results", + "text_level": 1, + "bbox": [ + 500, + 488, + 733, + 505 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we first introduce datasets, evaluation protocols and implementation details. Then we demonstrate our results on step forecasting and step classification benchmarks. Finally, we show our qualitative results and conduct ablations to study our model components.", + "bbox": [ + 496, + 513, + 890, + 589 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. For pre-training, we consider HowTo100M dataset [38] with 130K hours of YouTube tutorial videos. The videos cover various daily tasks, such as foods, housework, vehicles, etc. We use a language parser [50] to extract the verb phrases from ASR sentences of these videos and keep 9,871 most frequent verb phrases. For fine-tuning, we train our model on COIN dataset [55, 56] and EPIC-Kitchens-100 dataset [10], respectively. COIN has 476 hours of YouTube videos covering 180 tasks, such as dishes, vehicles, housework, etc. Human annotators summarize 778 unique steps in total (e.g., \"stir the egg\"), and annotate the temporal boundary and the category of each step in all videos. EPIC-Kitchens-100 dataset [10] has 100 hours of egocentric videos, capturing daily activities in kitchen. Each action in the videos is annotated with an action label and a noun label. There are 97/300 unique actions/nouns in total. We use the human annotations in COIN and EPIC-Kitchens-100 to evaluate the model performance.", + "bbox": [ + 496, + 593, + 890, + 866 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation Protocols. Our evaluation considers zero-shot and fine-tuning settings for step classification and step fore", + "bbox": [ + 500, + 869, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "14829", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/b20f5d36dd165e38db05b9f5e20b46f69c1b0fca6e3e33cb8bbc3ee613636014.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ModelPretrainingTop-1 Acc. (%)
SupervisionDatasetZero-shotFine-tuning
1SlowFast [14]Supervised: action labelsKinetics-25.6
2TimeSformer [4]Supervised: action labelsKinetics-34.7
3S3D [61]Unsupervised: ASR w. MIL-NCE [37]HT100M-28.1
4TimeSformer [4]Unsupervised: ASR w. MIL-NCE [37]HT100M-34.0
5DistantSup [33]Unsupervised: ASR + wikiHowHT100M-39.4
6Random Guess--0.1-
7CLIP [43]Unsupervised: captionsCLIP400M9.4-
8OursUnsupervised: ASRHT100M11.346.8
9Ours (oracle-5)Unsupervised: ASRHT100M14.751.8
", + "bbox": [ + 176, + 88, + 785, + 256 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Step forecasting on COIN dataset. We compare to a set of strong baselines and a oracle protocol built on our method.", + "bbox": [ + 111, + 260, + 854, + 273 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "casting on COIN and EPIC-Kitchens-100 datasets. Zero-shot setting indicates that no human annotation is used during pre-training. The pre-trained model is directly tested on the evaluation dataset. Fine-tuning setting further fine-tunes the pre-trained model using human annotations of action steps. For a fair comparison, we follow the same fine-tuning schemes as previous work in respective benchmarks.", + "bbox": [ + 75, + 287, + 470, + 393 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation Details. We adopted TimeSformer [4] as our video encoder, and used the Transformer [58] from CLIP's text encoder as denoising model. We set the maximum step $T$ to 4, maximum length of video sequence as 9, and the number of Transformer layers as 4. We used a trained CLIP model (ViT-B/16) to create pseudo labels and encode step descriptions. Following DistantSup [33], for pre-training we used SGD for 5 epochs and then AdamW [34] for 25 epochs with 128 videos in a batch. For fine-tuning, we used AdamW for 15 epochs with batch size of 64. Temperature $\\tau$ was set to 0.02. Additional implementation details can be found in Supplement.", + "bbox": [ + 75, + 397, + 470, + 580 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Step Forecasting", + "text_level": 1, + "bbox": [ + 76, + 587, + 243, + 603 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Setup. We follow the benchmark in DistantSup [33] to evaluate step forecasting on COIN, where top-1 accuracy is reported. Given a video with previous steps, the model anticipates the category of next single step (e.g., \"stir the egg\"). This task thus requires explicit modeling of the temporal ordering among steps. We only fine-tune the diffusion model while keeping the video encoder frozen.", + "bbox": [ + 75, + 609, + 468, + 715 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Results. Table 1 compares results of our method with a series of baselines. The closest competitor is DistantSup [33] in L5, which learns from ASR text and an external textual knowledge base [26] using the same video backbone (TimeSformer [4]). We also include other baselines reported in DistantSup, e.g., SlowFast [14], TimeSformer [4], and S3D [61] from L1 to L4, where models are supervised using human-annotated action labels or video ASR text. Our model significantly outperforms all baselines by at least $7.4\\%$ for the fine-tuning setting (e.g., $46.8\\%$ in L8 vs. $39.4\\%$ in L5). Further, we consider a strong baseline for the zero-shot setting by re-purposing CLIP model [43]", + "bbox": [ + 75, + 719, + 470, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "to match the input video with the descriptions of all step candidates. Comparing L7 and L8, our model outperforms this variant of CLIP by a clear margin (11.3% vs. 9.4%).", + "bbox": [ + 496, + 287, + 890, + 334 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "A unique property of our model is its ability to output multiple, potentially different predictions. We further evaluate the upper bound of our results by assuming an oracle ranking function that always selects the correction prediction from 5 outputs sampled from our model (Ours (oracle-5)). This oracle further improves the top-1 accuracy from $11.3\\%$ to $14.7\\%$ in L9, suggesting that our model is able to produce diverse predictions for step forecasting.", + "bbox": [ + 496, + 334, + 892, + 455 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Step Classification", + "text_level": 1, + "bbox": [ + 498, + 465, + 679, + 482 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Setup. Besides step ordering, we also evaluate step classification on COIN and EPIC-Kitchens-100 datasets, where a model is tasked to classify a trimmed video clip into one of the step categories. For COIN, we follow DistantSup [33] to only fine-tune the additional linear layer on top of the pretrained video encoder. For EPIC-Kitchens-100, we fully fine-tune the video encoder, following [2, 25, 33]. We report the accuracy of step classification on COIN, and that of verb, noun, and action on EPIC-Kitchens-100.", + "bbox": [ + 496, + 488, + 892, + 625 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Results. Table. 2 summarizes the results on COIN. We consider baselines as in DistantSup from L1 to L8 (e.g., SlowFast [14], VideoCLIP [63]), where models are trained using either action labels or video ASR text. To support zero-shot inference, we re-implement a model variant (DistantSup†) described in [33]. This model is pre-trained to match video embeddings with language embeddings and thus supports recognizing arbitrary step descriptions in L9. We also report the results of CLIP [43], which creates the pseudo labels for our pre-training in L10. As shown, our model consistently outperforms all the other methods by a clear margin under different settings. For example, ours outperforms CLIP by $1.8\\%$ in zero-shot setting (16.6% in L11 vs. 14.8% in L10), and outperforms DistantSup by $2.8\\%$ (56.9% in L11 vs. 54.1% in L8) in fine-tuning setting.", + "bbox": [ + 496, + 628, + 890, + 854 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table. 3 presents our results on EPIC-Kitchens-100. While TimeSformer [4] in L8 and DistantSup [33] in L9 use the same video encoder architecture as ours, our model", + "bbox": [ + 496, + 854, + 892, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "14830", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/d029b3406eece24c9c609c428e8e4efb7a0c5ada4e4857455b521d3680e3f283.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ModelPretrainingTop-1 Acc. (%)
SupervisionDatasetZero-shotFine-tuning
1TSN (RGB+Flow) [55]Supervised: action labelsKinetics-36.5*
2S3D [61]Unsupervised: ASR w. MIL-NCE [37]HT100M-37.5*
3SlowFast [14]Supervised: action labelsKinetics-32.9
4TimeSformer [4]Supervised: action labelsKinetics-48.3
5ClipBERT [29]Supervised: captionsCOCO+VG-30.8
6VideoCLIP [63]Unsupervised: ASRHT100M-39.4
7TimeSformer [4]Unsupervised: ASR w. MIL-NCE [37]HT100M-46.5
8DistantSup [33]Unsupervised: ASR + wikiHowHT100M-54.1
9DistantSup† [33]Unsupervised: ASR + wikiHowHT100M10.246.6
10CLIP [43]Unsupervised: captionsCLIP400M14.845.9
11OursUnsupervised: ASRHT100M16.656.9
", + "bbox": [ + 160, + 88, + 803, + 272 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/1c36ce09d637fc0629e1869d7b3fe07dd925d5dae12dcae34d1e48b9df97830b.jpg", + "table_caption": [ + "Table 2. Step classification on COIN dataset. DistantSup† is re-implemented based on their official code base. It is a variant reported in their paper that pre-trains the model to match language embeddings. * indicates the model is fully fine-tuned on COIN dataset." + ], + "table_footnote": [], + "table_body": "
ModelPretraining SupervisionPretraining DatasetAction (%)Verb (%)Noun (%)
1TSN [60]--33.260.246.0
2TRN [68]--35.365.945.4
3TBN [23]--36.766.047.2
4MoViNet [25]--47.772.257.3
5TSM [32]Supervised: action labelsKinetics38.367.949.0
6SlowFast [14]Supervised: action labelsKinetics38.565.650.0
7ViViT-L [2]Supervised: action labelsKinetics44.066.456.8
8TimeSformer [4]Supervised: action labelsKinetics42.366.654.4
9DistantSup [33]Unsupervised: ASR + wikiHowHT100M44.467.158.1
10OursUnsupervised: ASRHT100M47.769.560.3
", + "bbox": [ + 135, + 311, + 826, + 470 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Step classification on EPIC-Kitchens-100 dataset with fine-tuning setting. Our method outperforms the close competitors (TimeSformer, DistantSup), with results on par with even stronger backbone models (MoViNet).", + "bbox": [ + 75, + 474, + 892, + 503 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "in L10 achieves a clear gain over them, e.g., $+3.3\\% / 2.2\\%$ for action/noun. The only exception is the lower accuracy $(-2.7\\%)$ on verb when compared with MoViNet (MoViNet-A6) in L4, a heavily optimized video backbone.", + "bbox": [ + 75, + 516, + 470, + 578 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Predicting Diverse Future Steps", + "text_level": 1, + "bbox": [ + 76, + 588, + 359, + 606 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "One of the defining characteristics of our model is that it allows us to sample multiple predictions of video representation corresponding to a future step. This leads to an interesting question about the diversity of the predictions, as partially evaluated in our prior experiments. Here we present further demonstration of this capability by visualizing the step forecasting results, and more interestingly, using these results to generate future video frames.", + "bbox": [ + 75, + 612, + 468, + 733 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Fig. 3 presents the visualization for zero-shot step forecasting and key frame generation. In this setting, our model is pre-trained without any human annotation and is directly tested for step forecasting. We show multiple predictions sampled from our diffusion model. Further, we demonstrate that the text description of predicted step can be used to generate the key frames by leveraging the stable diffusion model [45]. To keep the generated images visually consistent with the input video, we let stable diffusion model take one input video frame and the description of predicted step as input and generate an image.", + "bbox": [ + 75, + 734, + 470, + 902 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "As shown in Fig. 3, our model is capable of forecasting multiple, reasonable next steps (e.g., \"flatten the dough\", \"bake pizza\"), based on which credible future frames can be generated. These results suggest that our model not only predicts meaningful video representations of individual steps, but also captures the variations in step ordering", + "bbox": [ + 496, + 516, + 893, + 609 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation Studies", + "text_level": 1, + "bbox": [ + 500, + 616, + 663, + 631 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We conduct ablation study on COIN, including step classification/forecasting with zero-shot/fine-tuning setting. Additional ablation results can be found in Supplement.", + "bbox": [ + 496, + 638, + 893, + 686 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Does modeling of temporal order help? In Table 4, we conduct a comparison on two different pre-training tasks: (1) pre-training by only matching video representations to text embeddings of the verb phrases; and (2) pre-training by our method that combines matching and temporal order modeling. In comparison to pre-training using matching only, our method significantly improves the performance for both zero-shot and fine-tuning settings and across step classification and step forecasting tasks. For example, zero-shot step classification is improved from $13.7\\%$ to $16.6\\%$ . Our results after fine-tuning attains a major gain of $+3.6\\%$ and $+1.8\\%$ for step classification and step forecasting, respectively. Importantly, our method also enables zero-shot step forecasting by predicting future video representations.", + "bbox": [ + 496, + 688, + 893, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "14831", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8edebb78e621f0deb6596584938435d870e707b6f1a671691a870c4f9190b052.jpg", + "image_caption": [ + "Model input: videos" + ], + "image_footnote": [], + "bbox": [ + 99, + 108, + 426, + 181 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/150db6b49274a701438196f77f35163587eeeaf17678abf5cbf9cf533a401992.jpg", + "image_caption": [ + "Figure 3. Visualization of zero-shot step forecasting and key frame generation. Without using any human annotation during training, our trained model is directly evaluated on COIN dataset [56]. Given a video recording previous steps (left), our model is capable of forecasting multiple reasonable predictions and each predicted step is further used for key frame generation (right). We adopt stable diffusion [45] for key frame generation, taking inputs as a text description of step and a sampled frame from input video." + ], + "image_footnote": [], + "bbox": [ + 99, + 199, + 426, + 271 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/dd54d529529683d3150e1e473c75b81b4f13b410c83b39a63cdd9c317bde711f.jpg", + "image_caption": [ + "Diverse predictions and generated key frames for next step", + "flatten the dough" + ], + "image_footnote": [], + "bbox": [ + 455, + 108, + 588, + 179 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d63f8ab5993a6912447bccc3924b3c5c64bd28f4ccaddd78e491f217bafbc86f.jpg", + "image_caption": [ + "bake pizza" + ], + "image_footnote": [], + "bbox": [ + 594, + 108, + 727, + 179 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c764b0f9f4f142f7ab56f970adcfe690b5441e3019f5a222fef89fb3e18f6791.jpg", + "image_caption": [ + "bake cookies" + ], + "image_footnote": [], + "bbox": [ + 733, + 108, + 864, + 179 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/6dd80753d9f637b270f1f69a95a12d51a1ccd0ed09fb4d02a40ec30b1f3678fa.jpg", + "image_caption": [ + "pour some salt to the garlics" + ], + "image_footnote": [], + "bbox": [ + 455, + 199, + 588, + 271 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/79d793f9ffc17a85b44817fa30b38934e5c4de3d6008de9e911d907f19d657fa.jpg", + "image_caption": [ + "put the ingredients into the bowl" + ], + "image_footnote": [], + "bbox": [ + 594, + 199, + 725, + 271 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e1ce8a8edbb31093715537ef42fbcc31c2a0bf78a63d3e081245978c58b2021a.jpg", + "image_caption": [ + "prepare seasonings and side dishes" + ], + "image_footnote": [], + "bbox": [ + 733, + 199, + 864, + 271 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/860d6379cfffe4459dec635f8905ad0ec90218b2ca46585c033d311e7d998c2f.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ModelPre-training taskOrder modelingZero-shot (top-1 acc. %)Fine-tuning (top-1 acc. %)
Step classificationStep forecastingStep classificationStep forecasting
OursMatching-13.7No zero-shot ability52.841.6
OursMatching + OrderingMask16.910.656.443.2
OursMatching + OrderingDiffusion16.611.356.946.8
", + "bbox": [ + 107, + 364, + 854, + 440 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "These results suggest that our procedure-aware pre-training can effectively facilitate the learning for both video representation and step ordering in procedure activities.", + "bbox": [ + 75, + 501, + 468, + 547 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Masked Prediction vs. Diffusion Model. We explore a model variant using the mask prediction, sharing similar spirit as the well-known Masked Language Modeling in BERT [24]. Specifically, this variant is trained to recover the video embeddings of masked video clips so that it can match to the assigned verb phrases. Our diffusion model largely outperforms the results of this variant, especially on the step forecasting $(+0.7\\%)$ and $+3.6\\%$ for zero-shot and fine-tuning, respectively, as in Table 4). This result indicates that our diffusion model is a more suitable way to capture the variation inside the step ordering.", + "bbox": [ + 75, + 556, + 468, + 724 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Approximate Inference. In Table 5, we validate that our approximate inference with a single zero-vector can achieve close empirical results as the Monte Carlo estimation (e.g., within $0.1\\%$ difference). Monte Carlo estimation computes the weighted average of multiple sampled predictions (e.g., from 5 sampled noises). We run the experiment for 5 times and the variation is small (e.g., $\\pm 0.03\\%$ ). Further, if we assume an oracle ranking function to pick the correct one from sampled predictions, our results can be further boosted by $3.4\\%$ on average, suggesting diverse predictions from our model and ample room to improve.", + "bbox": [ + 75, + 734, + 470, + 902 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/a8b41c2be5aa4e55df5d5f78e9b78f91c5b0613301eb5e95009e8511ba71fb47.jpg", + "table_caption": [ + "Table 4. Ablation study of pre-training tasks and order modeling. Our proposed order pre-training not only enables zero-shot forecasting, but also significantly improves zero-shot step classification and the fine-tuning results across evaluation tasks. Our diffusion models further improves mask modeling, especially on fine-tuning step forecasting." + ], + "table_footnote": [], + "table_body": "
ModelInference TypeTop-1 Acc. (%)
OursApproximation11.33
OursExpectation11.34 ± 0.03
OursOracle14.73 ± 0.13
", + "bbox": [ + 562, + 498, + 823, + 575 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Ablation study of inference schemes. \"Approximation\" uses a zero-vector as noise, \"Expectation\" adopts Monte Carlo estimation, and \"Oracle\" further assumes an oracle ranking function to pick the correct prediction derived from multiple noises.", + "bbox": [ + 498, + 580, + 892, + 637 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 656, + 617, + 672 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we presented a model and a training frame work for learning procedure-aware video representation from a large-scale dataset of instructional videos and their narrations, without the need for human annotations. The key strength of our model lies in the joint learning of a video encoder capturing concepts of action steps, as well as a diffusion model reasoning about the temporal dependencies among steps. We demonstrated that our model achieves strong results on step classification and forecasting in both zero-shot and fine-tuning settings and across COIN and EPIC-Kitchens-100 datasets. We believe our work provides a solid step towards understanding procedural activities. We hope that our work will shed light on the broader problem of video-language pre-training.", + "bbox": [ + 496, + 689, + 892, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "14832", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Jean-Baptiste Alayrac, Piotr Bojanowski, Nishant Agrawal, Josef Sivic, Ivan Laptev, and Simon Lacoste-Julien. Unsupervised learning from narrated instruction videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4575-4583, 2016. 2", + "[2] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6836-6846, 2021. 6, 7", + "[3] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1728-1738, 2021. 2", + "[4] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In Proceedings of the International Conference on Machine Learning (ICML), July 2021. 6, 7", + "[5] Piotr Bojanowski, Rémi Lajugie, Francis Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid, and Josef Sivic. Weakly supervised action labeling in videos under ordering constraints. In European Conference on Computer Vision, pages 628-643. Springer, 2014. 2", + "[6] Piotr Bojanowski, Rémi Lajugie, Edouard Grave, Francis Bach, Ivan Laptev, Jean Ponce, and Cordelia Schmid. Weakly-supervised alignment of video with text. In Proceedings of the IEEE international conference on computer vision, pages 4462-4470, 2015. 2", + "[7] M. Brand, N. Oliver, and A. Pentland. Coupled hidden markov models for complex action recognition. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 994-999, 1997. 1, 2", + "[8] Chien-Yi Chang, De-An Huang, Danfei Xu, Ehsan Adeli, Li Fei-Fei, and Juan Carlos Niebles. Procedure planning in instructional videos. In European Conference on Computer Vision, pages 334–350. Springer, 2020. 2", + "[9] Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. Diffusion models in vision: A survey. arXiv preprint arXiv:2209.04747, 2022. 4", + "[10] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. The EPIC-KITCHENS dataset: Collection, challenges and baselines. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(11):4125-4141, 2021. 2, 5", + "[11] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat GANs on image synthesis. Advances in Neural Information Processing Systems, 34:8780-8794, 2021. 5", + "[12] E. Elhamifar and D. Huynh. Self-supervised multi-task procedure learning from instructional videos. European Conference on Computer Vision, 2020. 2", + "[13] Ehsan Elhamifar and Zwe Naing. Unsupervised procedure learning via joint dynamic summarization. In Proceedings of" + ], + "bbox": [ + 76, + 114, + 470, + 902 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019. 1, 2", + "[14] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. SlowFast networks for video recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019. 6, 7", + "[15] Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, and Zicheng Liu. VIOLET: End-to-end video-language transformers with masked visual-token modeling. arXiv preprint arXiv:2111.12681, 2021. 2", + "[16] Deepti Ghadiyaram, Du Tran, and Dhruv Mahajan. Large-scale weakly-supervised pre-training for video action recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12046-12055, 2019. 2", + "[17] Karan Goel and Emma Brunskill. Learning procedural abstractions and evaluating discrete latent temporal structure. In International Conference on Learning Representations, 2019. 2", + "[18] Abhinav Gupta, Praveen Srinivasan, Jianbo Shi, and Larry S. Davis. Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 2012-2019, 2009. 1, 2", + "[19] Tengda Han, Weidi Xie, and Andrew Zisserman. Temporal alignment networks for long-term video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2906-2916, 2022. 2", + "[20] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. 4", + "[21] Jonathan Ho, Tim Salimans, Alexey A Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. In Advances in Neural Information Processing Systems, 2022. 3", + "[22] Y.A. Ivanov and A.F. Bobick. Recognition of visual activities and interactions by stochastic parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):852-872, 2000. 1, 2", + "[23] Evangelos Kazakos, Arsha Nagrani, Andrew Zisserman, and Dima Damen. EPIC-fusion: Audio-visual temporal binding for egocentric action recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5492–5501, 2019. 7", + "[24] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, pages 4171-4186, 2019. 3, 8", + "[25] Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, and Boqing Gong. MoViNets: Mobile video networks for efficient video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16020-16030, 2021. 6, 7", + "[26] Mahnaz Koupae and William Yang Wang. WikiHow: A large scale text summarization dataset. arXiv preprint arXiv:1810.09305, 2018. 2, 6" + ], + "bbox": [ + 501, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "14833", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[27] Hilde Kuehne, Ali Arslan, and Thomas Serre. The language of actions: Recovering the syntax and semantics of goal-directed human activities. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014. 1, 2", + "[28] Anna Kukleva, Hilde Kuehne, Fadime Sener, and Jurgen Gall. Unsupervised learning of action classes with continuous temporal embedding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12066-12074, 2019. 2", + "[29] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L. Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7331-7341, June 2021. 2, 7", + "[30] Junnan Li, Ramprasaath R Selvaraju, Akhilesh Deepak Gotmare, Shafiq Joty, Caiming Xiong, and Steven Hoi. Align before fuse: Vision and language representation learning with momentum distillation. arXiv preprint arXiv:2107.07651, 2021. 1", + "[31] Linjie Li, Yen-Chun Chen, Yu Cheng, Zhe Gan, Licheng Yu, and Jingjing Liu. HERO: Hierarchical encoder for Video+Language omni-representation pre-training. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2046-2065, Online, Nov. 2020. Association for Computational Linguistics. 2", + "[32] Ji Lin, Chuang Gan, and Song Han. TSM: Temporal shift module for efficient video understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7083-7093, 2019. 7", + "[33] Xudong Lin, Fabio Petroni, Gedas Bertasius, Marcus Rohrbach, Shih-Fu Chang, and Lorenzo Torresani. Learning to recognize procedural activities with distant supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13853-13863, 2022. 1, 2, 6, 7", + "[34] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018. 6", + "[35] Huaishao Luo, Lei Ji, Botian Shi, Haoyang Huang, Nan Duan, Tianrui Li, Jason Li, Taroon Bharti, and Ming Zhou. Univl: A unified video and language pre-training model for multimodal understanding and generation. arXiv preprint arXiv:2002.06353, 2020. 2", + "[36] Jonathan Malmaud, Jonathan Huang, Vivek Rathod, Nick Johnston, Andrew Rabinovich, and Kevin Murphy. What's cookin'? interpreting cooking videos using text, speech and vision. arXiv preprint arXiv:1503.01558, 2015. 2", + "[37] Antoine Miech, Jean-Baptiste Alayrac, Lucas Smaira, Ivan Laptev, Josef Sivic, and Andrew Zisserman. End-to-end learning of visual representations from uncurated instructional videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9879-9889, 2020. 1, 2, 6, 7", + "[38] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic." + ], + "bbox": [ + 78, + 90, + 470, + 902 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "HowTo100M: Learning a text-video embedding by watching hundred million narrated video clips. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2630-2640, 2019. 2, 5", + "[39] Ram Nevatia, Tao Zhao, and Somboon Hongeng. Hierarchical language-based representation of events in video streams. In 2003 Conference on Computer Vision and Pattern Recognition Workshop, volume 4, pages 39-39, 2003. 1, 2", + "[40] Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models. In International Conference on Machine Learning, pages 16784-16804. PMLR, 2022. 3", + "[41] Mingtao Pei, Yunde Jia, and Song-Chun Zhu. Parsing video events with goal inference and intent prediction. In 2011 International Conference on Computer Vision, pages 487-494, 2011. 1, 2", + "[42] Yicheng Qian, Weixin Luo, Dongze Lian, Xu Tang, Peilin Zhao, and Shenghua Gao. SVIP: Sequence verification for procedures in videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19890-19902, 2022. 2", + "[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (ICML), 2021. 1, 2, 3, 4, 5, 6, 7", + "[44] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125, 2022.3", + "[45] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 3, 7, 8", + "[46] M.S. Ryoo and J.K. Aggarwal. Recognition of composite human activities through context-free grammar based representation. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), volume 2, pages 1709-1718, 2006. 1, 2", + "[47] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022. 3", + "[48] Fadime Sener and Angela Yao. Zero-shot anticipation for instructional activities. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 862-871, 2019. 2", + "[49] Ozan Sener, Amir R Zamir, Silvio Savarese, and Ashutosh Saxena. Unsupervised semantic parsing of video collections. In Proceedings of the IEEE International conference on Computer Vision, pages 4480-4488, 2015. 2" + ], + "bbox": [ + 501, + 92, + 890, + 901 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "14834", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[50] Yuhan Shen, Lu Wang, and Ehsan Elhamifar. Learning to segment actions from visual and language instructions via differentiable weak sequence alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10156-10165, June 2021. 2, 4, 5", + "[51] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015. 3, 4", + "[52] Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. MPNet: Masked and permuted pre-training for language understanding. Advances in Neural Information Processing Systems, 33:16857-16867, 2020. 2", + "[53] Yang Song and Stefano Ermon. Improved techniques for training score-based generative models. Advances in neural information processing systems, 33:12438-12448, 2020. 3, 4", + "[54] Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid. VideoBERT: A joint model for video and language representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7464-7473, 2019. 1, 2", + "[55] Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, and Jie Zhou. COIN: A large-scale dataset for comprehensive instructional video analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1207-1216, 2019. 2, 5, 7", + "[56] Yansong Tang, Jiwen Lu, and Jie Zhou. Comprehensive instructional video analysis: The COIN dataset and performance evaluation. TPAMI, 2020. 5, 8", + "[57] Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H Bermano. Human motion diffusion model. In International Conference on Learning Representations, 2023. 3, 4", + "[58] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 6", + "[59] Alex Jinpeng Wang, Yixiao Ge, Rui Yan, Yuying Ge, Xudong Lin, Guanyu Cai, Jianping Wu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. All in one: Exploring unified video-language pre-training. arXiv preprint arXiv:2203.07303, 2022. 2", + "[60] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaou Tang, and Luc Van Gool. Temporal segment networks: Towards good practices for deep action recognition. In European conference on computer vision, pages 20-36. Springer, 2016. 7", + "[61] Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, and Kevin Murphy. Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification. In Proceedings of the European conference on computer vision (ECCV), pages 305–321, 2018. 6, 7", + "[62] Frank F. Xu, Lei Ji, Botian Shi, Junyi Du, Graham Neubig, Yonatan Bisk, and Nan Duan. A benchmark for structured" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "procedural knowledge extraction from cooking videos. In Proceedings of the First International Workshop on Natural Language Processing Beyond Text, pages 30-40, Online, Nov. 2020. Association for Computational Linguistics. 2", + "[63] Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. VideoCLIP: Contrastive pretraining for zero-shot video-text understanding. arXiv preprint arXiv:2109.14084, 2021. 2, 6, 7", + "[64] Jianwei Yang, Yonatan Bisk, and Jianfeng Gao. TACo: Token-aware cascade contrastive learning for video-text alignment. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11562-11572, 2021. 2", + "[65] Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. Diffusion models: A comprehensive survey of methods and applications. arXiv preprint arXiv:2209.00796, 2022. 4", + "[66] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. MERLOT: Multimodal neural script knowledge models. Advances in Neural Information Processing Systems, 34:23634-23651, 2021. 2, 3", + "[67] Henghui Zhao, Isma Hadji, Nikita Dvornik, Konstantinos G. Derpanis, Richard P. Wildes, and Allan D. Jepson. P3IV: Probabilistic procedure planning from instructional videos with weak supervision. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2928-2938, 2022. 2", + "[68] Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba. Temporal relational reasoning in videos. In Proceedings of the European conference on computer vision (ECCV), pages 803-818, 2018. 7", + "[69] Luowei Zhou, Chenliang Xu, and Jason J Corso. Towards automatic learning of procedures from web instructional videos. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018. 1, 2", + "[70] Linchao Zhu and Yi Yang. ActBERT: Learning global-local video-text representations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8746-8755, 2020. 1, 2", + "[71] Dimitri Zhukov, Jean-Baptiste Alayrac, Ramadan Gokberk Cinbis, David Fouhey, Ivan Laptev, and Josef Sivic. Cross-task weakly supervised learning from instructional videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3537-3545, 2019. 2" + ], + "bbox": [ + 501, + 92, + 890, + 753 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "14835", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations/471fce2b-475c-4777-b3f3-36131b107c8b_model.json b/2023/Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations/471fce2b-475c-4777-b3f3-36131b107c8b_model.json new file mode 100644 index 0000000000000000000000000000000000000000..81fb97709e97693376df3b9daef3a070cbce5ac4 --- /dev/null +++ b/2023/Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations/471fce2b-475c-4777-b3f3-36131b107c8b_model.json @@ -0,0 +1,2587 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.238, + 0.131, + 0.734, + 0.175 + ], + "angle": 0, + "content": "Learning Procedure-aware Video Representation from Instructional Videos and Their Narrations" + }, + { + "type": "text", + "bbox": [ + 0.179, + 0.203, + 0.798, + 0.24 + ], + "angle": 0, + "content": "Yiwu Zhong\\(^{1*}\\), Licheng Yu\\(^{2}\\), Yang Bai\\(^{2}\\), Shangwen Li\\(^{2}\\), Xueting Yan\\(^{2\\dagger}\\), Yin Li\\(^{1\\dagger}\\) \n\\(^{1}\\)University of Wisconsin-Madison, \\(^{2}\\)Meta AI" + }, + { + "type": "text", + "bbox": [ + 0.198, + 0.244, + 0.771, + 0.256 + ], + "angle": 0, + "content": "yzhong52@wisc.edu, {lichengyu, yangbai, dylanwen, xyan18}@meta.com, yin.li@wisc.edu" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.292, + 0.314, + 0.308 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.324, + 0.474, + 0.642 + ], + "angle": 0, + "content": "The abundance of instructional videos and their narrations over the Internet offers an exciting avenue for understanding procedural activities. In this work, we propose to learn video representation that encodes both action steps and their temporal ordering, based on a largescale dataset of web instructional videos and their narrations, without using human annotations. Our method jointly learns a video representation to encode individual step concepts, and a deep probabilistic model to capture both temporal dependencies and immense individual variations in the step ordering. We empirically demonstrate that learning temporal ordering not only enables new capabilities for procedure reasoning, but also reinforces the recognition of individual steps. Our model significantly advances the state-of-the-art results on step classification \\((+2.8\\% / +3.3\\%)\\) on COIN / EPIC-Kitchens) and step forecasting \\((+7.4\\%)\\) on COIN). Moreover, our model attains promising results in zero-shot inference for step classification and forecasting, as well as in predicting diverse and plausible steps for incomplete procedures. Our code is available at https://github.com/facebookresearch/ProcedureVRL." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.661, + 0.21, + 0.677 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.687, + 0.47, + 0.868 + ], + "angle": 0, + "content": "Many of our daily activities (e.g. cooking or crafting) are highly structured, comprising a set of action steps conducted in a certain ordering. Yet how these activities are performed varies among individuals. Consider the example of making scrambled eggs as shown in Fig. 1. While most people tend to whisk eggs in a bowl, melt butter in a pan, and cook eggs under medium heat, expert chefs have recommended to crack eggs into the pan, add butter, and stir them under high heat. Imagine a vision model that can account for the individual variations and reason about the temporal ordering of action steps in a video, so as to infer prior missing steps, recognize the current step, and forecast" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.291, + 0.891, + 0.366 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.502, + 0.372, + 0.888, + 0.444 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.45, + 0.893, + 0.52 + ], + "angle": 0, + "content": "Figure 1. Top: During training, our model learns from procedural videos and step descriptions to understand individual steps and capture temporal ordering and variations among steps. Bottom: Once trained, our model supports zero-shot step classification and forecasting, yielding multiple credible predictions." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.537, + 0.892, + 0.581 + ], + "angle": 0, + "content": "a future step. Such a model will be immensely useful for a wide range of applications including augmented reality, virtual personal assistant, and human-robot interaction." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.583, + 0.893, + 0.779 + ], + "angle": 0, + "content": "Understanding complex procedural activities has been a long-standing challenge in the vision community [7, 18, 22, 39, 41, 46]. While many prior approaches learn from annotated videos following a fully supervised setting [13, 27, 69], this paradigm is difficult to scale to a plethora of activities and their variants among individuals. A promising solution is offered by the exciting advances in vision-and-language pre-training, where models learn from visual data (images or videos) and their paired text data (captions or narrations) [30, 43, 54, 70] in order to recognize a variety of concepts. This idea has recently been explored to analyze instructional videos [33, 37], yet existing methods are limited to recognize single action steps in procedural activities." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.781, + 0.894, + 0.902 + ], + "angle": 0, + "content": "In this paper, we present a first step towards modeling temporal ordering of action steps in procedural activities by learning from instructional videos and their narrations. Our key innovation lies in the joint learning of a video representation aiming to encode individual step concepts, and a deep probabilistic model designed to capture temporal dependencies and variations among steps. The video representation, instantiated as a Transformer network, is learned by" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.875, + 0.382, + 0.888 + ], + "angle": 0, + "content": "*Work done while Yiwu Zhong was an intern at Meta." + }, + { + "type": "page_footnote", + "bbox": [ + 0.098, + 0.888, + 0.244, + 0.9 + ], + "angle": 0, + "content": "†Co-corresponding authors." + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.875, + 0.382, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14825" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.471, + 0.212 + ], + "angle": 0, + "content": "matching a video clip to its corresponding narration. The probabilistic model, built on a diffusion process, is tasked to predict the distribution of the video representation for a missing step, given steps in its vicinity. With the help of a pre-trained vision-and-language model [43], our model is trained using only videos and their narrations from automatic speech recognition (ASR), and thus does not require any manual annotations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.213, + 0.471, + 0.408 + ], + "angle": 0, + "content": "Once learned, our model celebrates two unique benefits thanks to our model design and training framework. First, our model supports zero-shot inference given an input video, including the recognition of single steps and forecasting of future steps, and can be further fine-tuned on downstream tasks. Second, our model allows sampling multiple video representations when predicting a missing action step, with each presenting a possibly different hypothesis of the step ordering. Instead of predicting a single representation with the highest probability, sampling from a probabilistic model provides access to additional high-probability solutions that might be beneficial to prediction tasks with high ambiguity or requiring user interactions." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.409, + 0.471, + 0.665 + ], + "angle": 0, + "content": "We train our models on a large-scale instructional video dataset collected from YouTube (HowTo100M [38]), and evaluate them on two public benchmarks (COIN [55] and EPIC-Kitchens-100 [10]) covering a wide range of procedural videos and across the tasks of step classification and step forecasting. Through extensive experiments, we demonstrate that (1) our temporal model is highly effective in forecasting future steps, outperforming state-of-the-art methods by a large margin of \\(+7.4\\%\\) in top-1 accuracy on COIN; (2) modeling temporal ordering reinforces video representation learning, leading to improved classification results \\((+2.8\\% / + 3.3\\%)\\) for step classification on COIN/EPIC-Kitchens) when probing the learned representations; (3) our training framework offers strong results for zero-shot step classification and forecasting; and (4) sampling from our probabilistic model yields diverse and plausible predictions of future steps." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.668, + 0.471, + 0.835 + ], + "angle": 0, + "content": "Contributions. Our work presents the first model that leverages video-and-language pre-training to capture the temporal ordering of action steps in procedural activities. Our key technical innovation lies in the design of a deep probabilistic model using a diffusion process, in tandem with video-and-language representation learning. The result is a model and a training framework that establish new state-of-the-art results on both step classification and forecasting tasks across the major benchmarks. Besides, our model is capable of generating diverse step predictions and supports zero-shot inference." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.845, + 0.22, + 0.86 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Understanding Procedural Activities. Reasoning about procedural activities, including their action steps and the" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.272 + ], + "angle": 0, + "content": "temporal ordering of these steps, has been a central problem in activity recognition. While early works model temporal ordering with stochastic grammars [7, 18, 22, 39, 41, 46], more recent works consider supervised learning to localize steps and predict their ordering by learning from videos with human annotated action steps [8, 10, 13, 27, 55, 69, 71]. To alleviate the burden of costly video annotations, several works propose various forms of weakly supervised settings, with assumptions that the ordered list of steps is given without their temporal boundaries [5, 6, 67, 71], or that the key steps and their ordering remain fixed across all videos [1, 12, 13, 17, 28, 49]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.273, + 0.892, + 0.409 + ], + "angle": 0, + "content": "Most of prior methods focus on the tasks of step classification and localization [1,5,6,12,13,28,49,71]. Others have considered the tasks of step forecasting [48], step verification [42] and procedure planning [67]. Our work also seeks to understand procedural activities. Different from these approaches, our method focuses on learning video representation from videos and their narrations without using human annotations. The resulting video representation can be leveraged for step classification and step forecasting." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.413, + 0.892, + 0.519 + ], + "angle": 0, + "content": "Learning from Procedural Videos and Narrations. The success of vision-and-language pre-training has fueled a new line of research that seeks to learn concepts of individual steps from instructional videos and their narrations [19, 36, 50, 62]. For example, Miech et al. [37] propose MIL-NCE to learn representations from instructional videos [38] and their narrations extracted using ASR." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.52, + 0.892, + 0.716 + ], + "angle": 0, + "content": "The most relevant work is DistantSup [33], where they propose using distant supervision from a textual knowledge base (wikiHow) [26] to denoise text narrations from ASR. Specifically, DistantSup leverages a pre-trained language model [52] to link step descriptions from wikiHow to text narrations from video ASR results, and thus to create training labels for individual steps in videos. Different from [33], our method models the temporal ordering of steps in procedural activities, thus moves beyond representations of single steps to support temporal reasoning in videos. Further, our method learns from videos and narrations only, with the help of a pre-trained image-language model [43] yet without using a textual knowledge base." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Video-and-Language Pre-Training. A relevant topic is video-and-language pre-training, aiming at learning video representation from videos and their paired natural language descriptions [3,15,16,29,31,35,54,59,63,64,66,70], often generated from ASR outputs. Despite the latest development in ASR, automatically-transcribed speech from videos can be rather noisy and lacks precise temporal alignment with the visual content. Several recent works seek to address this challenge. VideoCLIP [63] starts from the pretrained MIL-NCE model and further improves the model by retrieval augmented training with overlapped video-text pairs. Bain et al. [3] collect a less noisy dataset of video alt" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "14826" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.086, + 0.092, + 0.652, + 0.332 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.661, + 0.092, + 0.88, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.339, + 0.892, + 0.409 + ], + "angle": 0, + "content": "Figure 2. Overview of our approach. Left panel: Our model consists of (1) a video encoder that takes a video clip and encodes it into a video embedding; (2) a transformer-based denoising model that samples noises from Gaussian distribution and generates video embeddings conditioned on the embeddings of adjacent video clips. Right panel: We leverage trained image-language model CLIP to create pseudo labels for individual video clips (a). After training, our model supports step classification given an input video clip (b), and step forecasting given a video that records previous steps (c). Note that diverse embeddings can be generated by sampling various noises." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.422, + 0.469, + 0.481 + ], + "angle": 0, + "content": "text pairs and geared the model to match these pairs. Our work shares the key idea of learning from video and text data as prior work, and seeks to leverage external knowledge from a pre-trained image-language model [43]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.483, + 0.469, + 0.649 + ], + "angle": 0, + "content": "Another relevant work is MERLOT [66]. While both MERLOT and our work seek to learn video representation, our method differs from MERLOT in two folds. Our method models the sequence order of video clips for understanding procedural activities. MERLOT learns binary relative order between two given video frames for multi-modal reasoning and does not directly support action forecasting. Both methods consider a masked prediction task, yet MERLOT predicts the most likely text embeddings, while our method estimates the distribution of video representations using a deep probabilistic model." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.651, + 0.469, + 0.803 + ], + "angle": 0, + "content": "Diffusion Models. Diffusion models [51, 53] provide a powerful approach to characterize the probability density of high dimensional signals, and have recently demonstrated impressive results on generating high fidelity visual data, such as images [40,44,45,47], videos [21], and human body motion [57]. Our work adapts diffusion process to model the temporal ordering of steps in procedural videos. In doing so, our method not only facilitates the learning of expressive video representations for individual steps, but also enables the anticipation of future action steps." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.815, + 0.168, + 0.83 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.469, + 0.902 + ], + "angle": 0, + "content": "We consider the problem of learning video representation for understanding procedural activities from instructional videos and their narrations. An input video is represented as a sequence of \\(N\\) clips \\(\\{v_{1}, v_{2}, \\ldots, v_{N}\\}\\). Each" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.422, + 0.892, + 0.604 + ], + "angle": 0, + "content": "\\(v_{i}\\) captures a potential action step in the input video, and the time step \\(i\\) records the temporal ordering of these clips. The video clips \\(\\{v_{i}\\}\\) can be either segmented by using the timestamps of ASR outputs (as we consider during training), or densely sampled from a video following their temporal ordering (as we use during inference). During learning, we further assume that an ordered set of sentences \\(\\{s_1,s_2,\\dots,s_N\\}\\) is associated with the video clips \\(\\{v_{1},v_{2},\\dots,v_{N}\\}\\), with each \\(s_i\\) describing the action step in video clip \\(v_{i}\\). These sentences \\(\\{s_i\\}\\) can be the output text from ASR, or given by matching the video clips to a text corpus using an external vision language model [43]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.609, + 0.892, + 0.805 + ], + "angle": 0, + "content": "Procedural-aware Video Representation. Our goal is to learn video representation that encodes both action step concepts and their temporal dependencies across a range of procedural activities. Our representation consists of (a) a video encoder \\( f \\) that extracts a representation \\( x_{i} \\) from an input clip \\( v_{i} \\) (i.e., \\( x_{i} = f(v_{i}) \\)); and (b) a probabilistic model that characterizes the conditional probability \\( p(x_{j} = f(v_{j}) | \\{x_{i} = f(v_{i})\\}_{i \\neq j}) \\forall j \\). This design is highly flexible and supports a number of procedural reasoning tasks. \\( f \\) offers video representation suitable to classify individual steps in a clip. \\( p(x_{j} | \\{x_{i}\\}_{i \\neq j}) \\) models the temporal dependencies among steps, and can be used to predict the video representation of missing steps and further infer their labels." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Method Overview. To learn our representation, we leverage a pre-trained text encoder \\( g \\) that remains fixed during learning, and extend the idea of masked token modeling, populated in natural language processing [24]. For each input video and its narrations at training time, we randomly sample a clip \\( v_{j} \\) from \\( \\{v_{1},v_{2},\\dots,v_{N}\\} \\) and mask it" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.956 + ], + "angle": 0, + "content": "14827" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.183 + ], + "angle": 0, + "content": "out. We then train our model to predict the distribution of \\( x_{j} = f(v_{j}) \\) from \\( \\{x_{i} = f(v_{i})\\}_{i \\neq j} \\) (i.e., \\( p(x_{j} | \\{x_{i}\\}_{i \\neq j})) \\)), align the expectation of the predicted distribution \\( \\mathbb{E}(x_{j}) \\) with the corresponding text embedding \\( y_{j} = g(s_{j}) \\), and match all other video representations \\( \\{x_{i} = f(v_{i})\\}_{i \\neq j} \\) to their text embeddings \\( \\{y_{i} = g(s_{i})\\}_{i \\neq j} \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.183, + 0.471, + 0.318 + ], + "angle": 0, + "content": "Despite the conceptual similarity, our learning is fundamentally different from masked token prediction. Our method seeks to characterize the distribution of \\( x_{j} \\) instead of predicting the most likely \\( x_{j} \\), resulting in a more principled approach to capture the temporal dependencies among steps, as well as the new capability of sampling multiple high-probability solutions for \\( x_{j} \\). Our method is illustrated in Fig. 2. In what follows, we lay out the formulation of our model, and describe its training and inference schemes." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.329, + 0.445, + 0.345 + ], + "angle": 0, + "content": "3.1. Modeling Action Steps and Their Ordering" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.352, + 0.47, + 0.429 + ], + "angle": 0, + "content": "Formally, given an input video with its clips \\(\\{v_{1}, v_{2}, \\ldots, v_{N}\\}\\) and their narrations \\(\\{s_{1}, s_{2}, \\ldots, s_{N}\\}\\), our method assumes a factorization of \\(p(Y = \\{y_{i}\\} | X = \\{x_{i}\\})\\) with video representation \\(x_{i} = f(v_{i})\\) (learnable) and text embedding \\(y_{i} = f(s_{i})\\) (pre-trained and fixed)." + }, + { + "type": "equation", + "bbox": [ + 0.09, + 0.438, + 0.47, + 0.457 + ], + "angle": 0, + "content": "\\[\np (Y | X) = p \\left(y _ {j} \\mid x _ {j}\\right) \\cdot p \\left(x _ {j} \\mid \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right) \\cdot \\prod_ {i} p \\left(y _ {i} \\mid x _ {i}\\right) \\quad \\forall j. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.465, + 0.469, + 0.571 + ], + "angle": 0, + "content": "\\(p(y_{i}|x_{i})\\) measures the alignment between a video representation \\(x_{i}\\) and a text embedding \\(y_{i}\\). \\(p(x_{j}|\\{x_{i}\\}_{i\\neq j})\\) characterizes the distribution of a video representation for a missing step given the representations of all other steps, thereby modeling the temporal ordering of steps. Note that our model is not limited to single step prediction and can be readily extended to predict multiple missing steps." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.575, + 0.469, + 0.681 + ], + "angle": 0, + "content": "Matching Image and Text Representations. Our model matches the video representation \\( x_{i} \\) and text embedding \\( y_{i} \\) in a learned vector space, such that the alignment between them can be measured by cosine similarity. We will later instantiate this definition into a more tractable form for learning. Yet it suffices to notice that \\( p(y_{j}|x_{j}) \\) does not involve additional learnable parameters given \\( x_{i} \\) and \\( y_{i} \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.685, + 0.469, + 0.806 + ], + "angle": 0, + "content": "Modeling Step Ordering with Diffusion Process. The key challenge lies in the modeling of \\( p(x_{j}|\\{x_{i}\\}_{i\\neq j}) \\), as the video representation \\( x_{i} \\) is at least of a few hundred dimensions. To this end, we propose to model \\( p(x_{j}|\\{x_{i}\\}_{i\\neq j}) \\) using a diffusion process [51, 53] conditioned on observed video representations \\( \\{x_{i}\\}_{i\\neq j} \\). Here we briefly describe diffusion process in the context of our model, and refer the readers to recent surveys for more technical details [9, 65]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.807, + 0.469, + 0.838 + ], + "angle": 0, + "content": "Specifically, we assume a diffusion process that gradually adds noise to the input \\( x_{j} \\) over \\( t \\in [0,1,\\dots,T] \\) steps." + }, + { + "type": "equation", + "bbox": [ + 0.149, + 0.848, + 0.469, + 0.884 + ], + "angle": 0, + "content": "\\[\np \\left(x _ {j} ^ {1: T} \\mid x ^ {0}\\right) = \\prod_ {t = 1} ^ {T} p \\left(x _ {j} ^ {t} \\mid x _ {j} ^ {t - 1}\\right), \\tag {2}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.15, + 0.887, + 0.396, + 0.906 + ], + "angle": 0, + "content": "\\[\np (x _ {j} ^ {t} | x _ {j} ^ {t - 1}) = \\mathcal {N} (\\sqrt {\\alpha_ {t}} x _ {j} ^ {t - 1}, (1 - \\alpha_ {t}) I).\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.092, + 0.892, + 0.122 + ], + "angle": 0, + "content": "where \\(\\alpha_{t}\\) are constant hyper-parameters. The reverse diffusion (denoising) process is parameterized with" + }, + { + "type": "equation", + "bbox": [ + 0.52, + 0.135, + 0.892, + 0.153 + ], + "angle": 0, + "content": "\\[\np \\left(x _ {j} ^ {0: T} \\mid \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right) = p \\left(x _ {j} ^ {T} \\mid \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right) \\prod_ {t = 1} ^ {T} p _ {\\theta} \\left(x _ {j} ^ {t - 1} \\mid x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right). \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.163, + 0.892, + 0.225 + ], + "angle": 0, + "content": "With sufficiently large \\(T\\), \\(p(x_j^T|\\{x_i\\}_{i \\neq j}) \\sim \\mathcal{N}(0, I)\\), i.e. a standard Gaussian noise that is independent of \\(\\{x_i\\}_{i \\neq j}\\). The denoising process is approximated by \\(p_\\theta(x_j^{t-1}|x_j^t, \\{x_i\\}_{i \\neq j})\\) using a neural network with parameters \\(\\theta\\) such that" + }, + { + "type": "equation", + "bbox": [ + 0.52, + 0.239, + 0.892, + 0.256 + ], + "angle": 0, + "content": "\\[\np _ {\\theta} \\left(x _ {j} ^ {t - 1} \\mid x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right) = \\mathcal {N} \\left(\\mu_ {\\theta} \\left(x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right), \\Sigma_ {\\theta} \\left(x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right)\\right) \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.267, + 0.892, + 0.313 + ], + "angle": 0, + "content": "In practice, we follow Ho et al. [20] and Tevet et al. [57] to directly predict \\( x_{j}^{0} \\) by using a denoising model \\( h \\). With slight abuse of the symbols, we denote" + }, + { + "type": "equation", + "bbox": [ + 0.621, + 0.323, + 0.892, + 0.341 + ], + "angle": 0, + "content": "\\[\n\\hat {x} _ {j} ^ {0} = h \\left(x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}, t\\right). \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.351, + 0.892, + 0.413 + ], + "angle": 0, + "content": "\\(h\\) is realized using a Transformer network with the embedding of step \\(t\\) as part of its inputs. Once learned, one can sample from \\(\\mathcal{N}(0,I)\\) and apply \\(h\\) through the denoising process to predict \\(x_{j}\\) based on \\(\\{x_i\\}_{i\\neq j}\\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.423, + 0.874, + 0.438 + ], + "angle": 0, + "content": "3.2. Learning from Videos and Their Narrations" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.447, + 0.892, + 0.477 + ], + "angle": 0, + "content": "Our training approximately maximizes the likelihood of Eq. 1 given a set of training videos and their narrations." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.481, + 0.892, + 0.587 + ], + "angle": 0, + "content": "Pseudo Labels from CLIP. It is straightforward to directly align video representations to the embeddings of their corresponding ASR text. Doing so, however, faces the challenges of low-quality ASR text and imprecise alignment between video and ASR sentences. To address these challenges, we propose to create pseudo labels by leveraging a pre-trained image-language model (e.g., CLIP [43])." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.588, + 0.892, + 0.708 + ], + "angle": 0, + "content": "Specifically, we first create a pool of step descriptions in the form of verb phrases (e.g., \"add water\", \"wear gloves\") parsed from ASR sentences [50], with their embeddings as \\(\\{y^{1:K}\\}\\). Then a trained CLIP model is applied to link each video clip with verb phrases, by matching the averaged visual features across frames with the language embeddings of verb phrases. The resulting matching scores are used as our training target." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.709, + 0.891, + 0.74 + ], + "angle": 0, + "content": "Our pseudo labeling instantiates the matching \\( p(y_{i}|x_{i}) \\) between video representation and text embedding using" + }, + { + "type": "equation", + "bbox": [ + 0.583, + 0.75, + 0.892, + 0.781 + ], + "angle": 0, + "content": "\\[\np \\left(y _ {i} \\mid x _ {i}\\right) = \\operatorname {s o f t m a x} \\left(\\frac {x _ {i} ^ {T} y _ {i}}{\\tau \\| x _ {i} \\| \\| y _ {i} \\|}\\right), \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.79, + 0.892, + 0.852 + ], + "angle": 0, + "content": "where \\( y_{i} \\) is selected from pool of verb phrases, i.e. \\( y_{i} \\in \\{y^{1:K}\\} \\), and \\( \\tau \\) is the pre-defined temperature. The matching problem thus is converted into a \"classification\" problem, making the training feasible." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Learning Objective and Training Loss. Our training minimizes an evidence upper bound of the negative log likelihood \\(-\\log p(Y|X)\\) in Eq. 1. The detailed derivation of" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14828" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.47, + 0.122 + ], + "angle": 0, + "content": "evidence upper bound is described in the Supplement. Our objective function constitutes three loss terms:" + }, + { + "type": "equation", + "bbox": [ + 0.187, + 0.132, + 0.469, + 0.147 + ], + "angle": 0, + "content": "\\[\n\\mathrm {L} = \\mathrm {L} _ {\\mathrm {X E}} + \\mathrm {L} _ {\\mathrm {M S E}} + \\mathrm {L} _ {\\mathrm {M C}}. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.157, + 0.469, + 0.189 + ], + "angle": 0, + "content": "The first term \\(\\mathrm{L}_{\\mathrm{XE}}\\) seeks to match observed video representation \\(\\{x_{i}\\}\\) to their text embeddings \\(\\{y_{i}\\}\\), given by" + }, + { + "type": "equation", + "bbox": [ + 0.16, + 0.196, + 0.469, + 0.214 + ], + "angle": 0, + "content": "\\[\n\\mathrm {L} _ {\\mathrm {X} E} = \\frac {1}{N - 1} \\sum_ {i} H \\left(p _ {i} ^ {\\prime}, p \\left(y _ {i} \\mid x _ {i}\\right)\\right), \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.222, + 0.469, + 0.267 + ], + "angle": 0, + "content": "where \\( H(\\cdot, \\cdot) \\) is the cross entropy, and \\( p_i' \\) are soft targets given by CLIP matching scores. \\( p(y_i|x_i) \\) defined in Eq. 6 measures the similarity between \\( x_i \\) and \\( y_i \\)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.268, + 0.469, + 0.313 + ], + "angle": 0, + "content": "The second term \\(\\mathrm{L}_{\\mathrm{MSE}}\\) comes from the Kullback-Leibler (KL) divergence within our diffusion model, and is computed as" + }, + { + "type": "equation", + "bbox": [ + 0.115, + 0.321, + 0.469, + 0.343 + ], + "angle": 0, + "content": "\\[\n\\mathrm {L} _ {\\text {M S E}} = \\mathbb {E} _ {x _ {j} ^ {0} \\sim p \\left(x _ {j} ^ {0} \\mid \\{x _ {i} \\} _ {i \\neq j}\\right), t \\sim [ 1, T ]} \\left[ \\| x _ {j} ^ {0} - \\hat {x} _ {j} ^ {0} \\| _ {2} ^ {2} \\right]. \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.35, + 0.469, + 0.381 + ], + "angle": 0, + "content": "Note that unlike a standard diffusion model, our model directly predicts \\(\\hat{x}_j^0\\). This term is applied at each step \\(t\\)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.381, + 0.469, + 0.413 + ], + "angle": 0, + "content": "The third term \\(\\mathrm{L}_{\\mathrm{MC}}\\) is derived from matching the predicted video representation \\(\\hat{x}_j^0\\) to its text embedding \\(y_{j}\\)" + }, + { + "type": "equation", + "bbox": [ + 0.091, + 0.422, + 0.469, + 0.445 + ], + "angle": 0, + "content": "\\[\n\\mathrm {L} _ {\\mathrm {M C}} = \\mathbb {E} _ {x _ {j} ^ {0} \\sim p \\left(x _ {j} ^ {0} \\mid \\{x _ {i} \\} _ {i \\neq j}\\right), t \\sim [ 1, T ]} \\left[ H \\left(p _ {j} ^ {\\prime}, p \\left(y _ {j} \\mid \\hat {x} _ {j} ^ {0}\\right)\\right) \\right], \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.452, + 0.469, + 0.531 + ], + "angle": 0, + "content": "where \\( p_j' \\) are again soft targets given by CLIP model, and \\( \\hat{x}_j^0 \\) is denoised from a sampled noise. During training, we adopt Monte Carlo estimation for \\( \\mathbb{E}_p\\left[-\\log p(y_j|\\hat{x}_j^0)\\right] \\), by minimizing \\( -\\log p(y_j|\\hat{x}_j^0) \\) for each sampled \\( \\hat{x}_j^0 \\). We attach this term at each step \\( t \\)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.532, + 0.469, + 0.684 + ], + "angle": 0, + "content": "A critical design choice lies in \\( p(y_{j}|\\hat{x}_{j}^{0}) \\). \\( p(y_{j}|\\hat{x}_{j}^{0}) \\) is simplified into a score function between a video representation and a finite set of text embeddings (defined using verb phrases). This allows us to reach our loss terms without worrying about global normalization constant as commonly encountered in energy-based models. Indeed, \\( H(p_{j}^{\\prime},p(y_{j}|\\hat{x}_{j}^{0})) \\) can be interpreted as providing guidance by matching video to text embeddings. This term thus resembles the key idea of classifier guidance, which has shown to be helpful for learning diffusion models [11]." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.693, + 0.24, + 0.708 + ], + "angle": 0, + "content": "3.3. Model Inference" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.716, + 0.469, + 0.837 + ], + "angle": 0, + "content": "Once trained, our model offers a procedure-aware representation with two key components. First, the video encoder \\( f(\\cdot) \\) serves as a feature extractor for any input video clips. Second, the diffusion model, represented as its denoising model \\( h(\\cdot) \\), captures the temporal dependencies among steps. Our representation naturally supports a number of tasks. Here we demonstrate how our model can be used for step classification and step forecasting." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.84, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Step Classification. An input video clip \\( v \\) can be encoded using \\( f(\\cdot) \\). The video representation \\( x = f(v) \\) can be directly compared to the text embeddings [43], so as to support zero-shot step classification. Alternative, an additional" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.122 + ], + "angle": 0, + "content": "classifier can be attached on top of \\( f \\) and further fine-tuned to recognize the action step in input clip." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.127, + 0.892, + 0.203 + ], + "angle": 0, + "content": "Step Forecasting. A future video clip feature \\( x_{j} \\) can be sampled from the diffusion model by drawing from a Gaussian distribution and denoising using \\( h(\\cdot) \\). The predicted \\( x_{j} \\) can be further classified into action steps. This prediction can be done using again Monte Carlo estimation given by" + }, + { + "type": "equation", + "bbox": [ + 0.598, + 0.214, + 0.892, + 0.233 + ], + "angle": 0, + "content": "\\[\n\\mathbb {E} _ {x _ {j} \\sim p (x _ {j} | \\{x _ {i} \\} _ {i \\neq j})} [ p (y _ {j} | x _ {j}) ]. \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.243, + 0.892, + 0.368 + ], + "angle": 0, + "content": "Specifically, a noise \\( x_{T} \\) is first sampled from Gaussian distribution and our denoising model gradually denoises it. At each step \\( t \\), the denoising model \\( h \\) takes a noisy \\( x_{j}^{t} \\), predicts clip feature \\( \\hat{x}_{j}^{0} \\), and diffuses it to \\( x_{j}^{t-1} \\) based on the sampled noise \\( x_{j}^{T} \\), as demonstrated in Eq. 2. After \\( T \\) iterations, the predicted clip feature at \\( t = 0 \\) is used to match text embeddings. By sampling noises for multiple times, we can estimate the most likely \\( y_{j} \\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.369, + 0.892, + 0.474 + ], + "angle": 0, + "content": "However, sampling can be costly. In practice, to obtain top-1 prediction for missing steps, we adopt approximate inference, where the sampled noise is replaced with a fixed zero vector, corresponding to peak in the Gaussian distribution. Our empirical results validate that approximate inference achieves a very close performance as the expectation over multiple sampled noises." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.489, + 0.734, + 0.506 + ], + "angle": 0, + "content": "4. Experiments and Results" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.515, + 0.892, + 0.59 + ], + "angle": 0, + "content": "In this section, we first introduce datasets, evaluation protocols and implementation details. Then we demonstrate our results on step forecasting and step classification benchmarks. Finally, we show our qualitative results and conduct ablations to study our model components." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.594, + 0.892, + 0.867 + ], + "angle": 0, + "content": "Datasets. For pre-training, we consider HowTo100M dataset [38] with 130K hours of YouTube tutorial videos. The videos cover various daily tasks, such as foods, housework, vehicles, etc. We use a language parser [50] to extract the verb phrases from ASR sentences of these videos and keep 9,871 most frequent verb phrases. For fine-tuning, we train our model on COIN dataset [55, 56] and EPIC-Kitchens-100 dataset [10], respectively. COIN has 476 hours of YouTube videos covering 180 tasks, such as dishes, vehicles, housework, etc. Human annotators summarize 778 unique steps in total (e.g., \"stir the egg\"), and annotate the temporal boundary and the category of each step in all videos. EPIC-Kitchens-100 dataset [10] has 100 hours of egocentric videos, capturing daily activities in kitchen. Each action in the videos is annotated with an action label and a noun label. There are 97/300 unique actions/nouns in total. We use the human annotations in COIN and EPIC-Kitchens-100 to evaluate the model performance." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Evaluation Protocols. Our evaluation considers zero-shot and fine-tuning settings for step classification and step fore" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14829" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.178, + 0.089, + 0.787, + 0.257 + ], + "angle": 0, + "content": "
ModelPretrainingTop-1 Acc. (%)
SupervisionDatasetZero-shotFine-tuning
1SlowFast [14]Supervised: action labelsKinetics-25.6
2TimeSformer [4]Supervised: action labelsKinetics-34.7
3S3D [61]Unsupervised: ASR w. MIL-NCE [37]HT100M-28.1
4TimeSformer [4]Unsupervised: ASR w. MIL-NCE [37]HT100M-34.0
5DistantSup [33]Unsupervised: ASR + wikiHowHT100M-39.4
6Random Guess--0.1-
7CLIP [43]Unsupervised: captionsCLIP400M9.4-
8OursUnsupervised: ASRHT100M11.346.8
9Ours (oracle-5)Unsupervised: ASRHT100M14.751.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.112, + 0.261, + 0.856, + 0.275 + ], + "angle": 0, + "content": "Table 1. Step forecasting on COIN dataset. We compare to a set of strong baselines and a oracle protocol built on our method." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.289, + 0.471, + 0.395 + ], + "angle": 0, + "content": "casting on COIN and EPIC-Kitchens-100 datasets. Zero-shot setting indicates that no human annotation is used during pre-training. The pre-trained model is directly tested on the evaluation dataset. Fine-tuning setting further fine-tunes the pre-trained model using human annotations of action steps. For a fair comparison, we follow the same fine-tuning schemes as previous work in respective benchmarks." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.398, + 0.471, + 0.581 + ], + "angle": 0, + "content": "Implementation Details. We adopted TimeSformer [4] as our video encoder, and used the Transformer [58] from CLIP's text encoder as denoising model. We set the maximum step \\( T \\) to 4, maximum length of video sequence as 9, and the number of Transformer layers as 4. We used a trained CLIP model (ViT-B/16) to create pseudo labels and encode step descriptions. Following DistantSup [33], for pre-training we used SGD for 5 epochs and then AdamW [34] for 25 epochs with 128 videos in a batch. For fine-tuning, we used AdamW for 15 epochs with batch size of 64. Temperature \\( \\tau \\) was set to 0.02. Additional implementation details can be found in Supplement." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.588, + 0.245, + 0.604 + ], + "angle": 0, + "content": "4.1. Step Forecasting" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.611, + 0.47, + 0.717 + ], + "angle": 0, + "content": "Setup. We follow the benchmark in DistantSup [33] to evaluate step forecasting on COIN, where top-1 accuracy is reported. Given a video with previous steps, the model anticipates the category of next single step (e.g., \"stir the egg\"). This task thus requires explicit modeling of the temporal ordering among steps. We only fine-tune the diffusion model while keeping the video encoder frozen." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.72, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Results. Table 1 compares results of our method with a series of baselines. The closest competitor is DistantSup [33] in L5, which learns from ASR text and an external textual knowledge base [26] using the same video backbone (TimeSformer [4]). We also include other baselines reported in DistantSup, e.g., SlowFast [14], TimeSformer [4], and S3D [61] from L1 to L4, where models are supervised using human-annotated action labels or video ASR text. Our model significantly outperforms all baselines by at least \\(7.4\\%\\) for the fine-tuning setting (e.g., \\(46.8\\%\\) in L8 vs. \\(39.4\\%\\) in L5). Further, we consider a strong baseline for the zero-shot setting by re-purposing CLIP model [43]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.289, + 0.892, + 0.335 + ], + "angle": 0, + "content": "to match the input video with the descriptions of all step candidates. Comparing L7 and L8, our model outperforms this variant of CLIP by a clear margin (11.3% vs. 9.4%)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.335, + 0.893, + 0.457 + ], + "angle": 0, + "content": "A unique property of our model is its ability to output multiple, potentially different predictions. We further evaluate the upper bound of our results by assuming an oracle ranking function that always selects the correction prediction from 5 outputs sampled from our model (Ours (oracle-5)). This oracle further improves the top-1 accuracy from \\(11.3\\%\\) to \\(14.7\\%\\) in L9, suggesting that our model is able to produce diverse predictions for step forecasting." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.466, + 0.68, + 0.483 + ], + "angle": 0, + "content": "4.2. Step Classification" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.489, + 0.893, + 0.625 + ], + "angle": 0, + "content": "Setup. Besides step ordering, we also evaluate step classification on COIN and EPIC-Kitchens-100 datasets, where a model is tasked to classify a trimmed video clip into one of the step categories. For COIN, we follow DistantSup [33] to only fine-tune the additional linear layer on top of the pretrained video encoder. For EPIC-Kitchens-100, we fully fine-tune the video encoder, following [2, 25, 33]. We report the accuracy of step classification on COIN, and that of verb, noun, and action on EPIC-Kitchens-100." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.629, + 0.892, + 0.856 + ], + "angle": 0, + "content": "Results. Table. 2 summarizes the results on COIN. We consider baselines as in DistantSup from L1 to L8 (e.g., SlowFast [14], VideoCLIP [63]), where models are trained using either action labels or video ASR text. To support zero-shot inference, we re-implement a model variant (DistantSup†) described in [33]. This model is pre-trained to match video embeddings with language embeddings and thus supports recognizing arbitrary step descriptions in L9. We also report the results of CLIP [43], which creates the pseudo labels for our pre-training in L10. As shown, our model consistently outperforms all the other methods by a clear margin under different settings. For example, ours outperforms CLIP by \\(1.8\\%\\) in zero-shot setting (16.6% in L11 vs. 14.8% in L10), and outperforms DistantSup by \\(2.8\\%\\) (56.9% in L11 vs. 54.1% in L8) in fine-tuning setting." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Table. 3 presents our results on EPIC-Kitchens-100. While TimeSformer [4] in L8 and DistantSup [33] in L9 use the same video encoder architecture as ours, our model" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "14830" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.161, + 0.089, + 0.805, + 0.273 + ], + "angle": 0, + "content": "
ModelPretrainingTop-1 Acc. (%)
SupervisionDatasetZero-shotFine-tuning
1TSN (RGB+Flow) [55]Supervised: action labelsKinetics-36.5*
2S3D [61]Unsupervised: ASR w. MIL-NCE [37]HT100M-37.5*
3SlowFast [14]Supervised: action labelsKinetics-32.9
4TimeSformer [4]Supervised: action labelsKinetics-48.3
5ClipBERT [29]Supervised: captionsCOCO+VG-30.8
6VideoCLIP [63]Unsupervised: ASRHT100M-39.4
7TimeSformer [4]Unsupervised: ASR w. MIL-NCE [37]HT100M-46.5
8DistantSup [33]Unsupervised: ASR + wikiHowHT100M-54.1
9DistantSup† [33]Unsupervised: ASR + wikiHowHT100M10.246.6
10CLIP [43]Unsupervised: captionsCLIP400M14.845.9
11OursUnsupervised: ASRHT100M16.656.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.277, + 0.895, + 0.307 + ], + "angle": 0, + "content": "Table 2. Step classification on COIN dataset. DistantSup† is re-implemented based on their official code base. It is a variant reported in their paper that pre-trains the model to match language embeddings. * indicates the model is fully fine-tuned on COIN dataset." + }, + { + "type": "table", + "bbox": [ + 0.137, + 0.313, + 0.828, + 0.472 + ], + "angle": 0, + "content": "
ModelPretraining SupervisionPretraining DatasetAction (%)Verb (%)Noun (%)
1TSN [60]--33.260.246.0
2TRN [68]--35.365.945.4
3TBN [23]--36.766.047.2
4MoViNet [25]--47.772.257.3
5TSM [32]Supervised: action labelsKinetics38.367.949.0
6SlowFast [14]Supervised: action labelsKinetics38.565.650.0
7ViViT-L [2]Supervised: action labelsKinetics44.066.456.8
8TimeSformer [4]Supervised: action labelsKinetics42.366.654.4
9DistantSup [33]Unsupervised: ASR + wikiHowHT100M44.467.158.1
10OursUnsupervised: ASRHT100M47.769.560.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.475, + 0.893, + 0.504 + ], + "angle": 0, + "content": "Table 3. Step classification on EPIC-Kitchens-100 dataset with fine-tuning setting. Our method outperforms the close competitors (TimeSformer, DistantSup), with results on par with even stronger backbone models (MoViNet)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.517, + 0.471, + 0.579 + ], + "angle": 0, + "content": "in L10 achieves a clear gain over them, e.g., \\(+3.3\\% / 2.2\\%\\) for action/noun. The only exception is the lower accuracy \\((-2.7\\%)\\) on verb when compared with MoViNet (MoViNet-A6) in L4, a heavily optimized video backbone." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.589, + 0.36, + 0.607 + ], + "angle": 0, + "content": "4.3. Predicting Diverse Future Steps" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.613, + 0.47, + 0.734 + ], + "angle": 0, + "content": "One of the defining characteristics of our model is that it allows us to sample multiple predictions of video representation corresponding to a future step. This leads to an interesting question about the diversity of the predictions, as partially evaluated in our prior experiments. Here we present further demonstration of this capability by visualizing the step forecasting results, and more interestingly, using these results to generate future video frames." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Fig. 3 presents the visualization for zero-shot step forecasting and key frame generation. In this setting, our model is pre-trained without any human annotation and is directly tested for step forecasting. We show multiple predictions sampled from our diffusion model. Further, we demonstrate that the text description of predicted step can be used to generate the key frames by leveraging the stable diffusion model [45]. To keep the generated images visually consistent with the input video, we let stable diffusion model take one input video frame and the description of predicted step as input and generate an image." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.517, + 0.895, + 0.61 + ], + "angle": 0, + "content": "As shown in Fig. 3, our model is capable of forecasting multiple, reasonable next steps (e.g., \"flatten the dough\", \"bake pizza\"), based on which credible future frames can be generated. These results suggest that our model not only predicts meaningful video representations of individual steps, but also captures the variations in step ordering" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.617, + 0.665, + 0.632 + ], + "angle": 0, + "content": "4.4. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.64, + 0.894, + 0.687 + ], + "angle": 0, + "content": "We conduct ablation study on COIN, including step classification/forecasting with zero-shot/fine-tuning setting. Additional ablation results can be found in Supplement." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Does modeling of temporal order help? In Table 4, we conduct a comparison on two different pre-training tasks: (1) pre-training by only matching video representations to text embeddings of the verb phrases; and (2) pre-training by our method that combines matching and temporal order modeling. In comparison to pre-training using matching only, our method significantly improves the performance for both zero-shot and fine-tuning settings and across step classification and step forecasting tasks. For example, zero-shot step classification is improved from \\(13.7\\%\\) to \\(16.6\\%\\). Our results after fine-tuning attains a major gain of \\(+3.6\\%\\) and \\(+1.8\\%\\) for step classification and step forecasting, respectively. Importantly, our method also enables zero-shot step forecasting by predicting future video representations." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "14831" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.204, + 0.095, + 0.325, + 0.107 + ], + "angle": 0, + "content": "Model input: videos" + }, + { + "type": "image", + "bbox": [ + 0.101, + 0.109, + 0.428, + 0.182 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.101, + 0.2, + 0.428, + 0.272 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.471, + 0.095, + 0.831, + 0.107 + ], + "angle": 0, + "content": "Diverse predictions and generated key frames for next step" + }, + { + "type": "image", + "bbox": [ + 0.456, + 0.109, + 0.589, + 0.18 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.477, + 0.181, + 0.568, + 0.193 + ], + "angle": 0, + "content": "flatten the dough" + }, + { + "type": "image", + "bbox": [ + 0.595, + 0.109, + 0.728, + 0.18 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.632, + 0.181, + 0.692, + 0.192 + ], + "angle": 0, + "content": "bake pizza" + }, + { + "type": "image", + "bbox": [ + 0.734, + 0.109, + 0.865, + 0.18 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.763, + 0.181, + 0.835, + 0.191 + ], + "angle": 0, + "content": "bake cookies" + }, + { + "type": "image", + "bbox": [ + 0.456, + 0.2, + 0.589, + 0.272 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.475, + 0.273, + 0.571, + 0.296 + ], + "angle": 0, + "content": "pour some salt to the garlics" + }, + { + "type": "image", + "bbox": [ + 0.595, + 0.2, + 0.727, + 0.272 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.611, + 0.273, + 0.712, + 0.295 + ], + "angle": 0, + "content": "put the ingredients into the bowl" + }, + { + "type": "image", + "bbox": [ + 0.734, + 0.2, + 0.865, + 0.272 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.746, + 0.273, + 0.853, + 0.295 + ], + "angle": 0, + "content": "prepare seasonings and side dishes" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.301, + 0.894, + 0.358 + ], + "angle": 0, + "content": "Figure 3. Visualization of zero-shot step forecasting and key frame generation. Without using any human annotation during training, our trained model is directly evaluated on COIN dataset [56]. Given a video recording previous steps (left), our model is capable of forecasting multiple reasonable predictions and each predicted step is further used for key frame generation (right). We adopt stable diffusion [45] for key frame generation, taking inputs as a text description of step and a sampled frame from input video." + }, + { + "type": "table", + "bbox": [ + 0.109, + 0.365, + 0.855, + 0.441 + ], + "angle": 0, + "content": "
ModelPre-training taskOrder modelingZero-shot (top-1 acc. %)Fine-tuning (top-1 acc. %)
Step classificationStep forecastingStep classificationStep forecasting
OursMatching-13.7No zero-shot ability52.841.6
OursMatching + OrderingMask16.910.656.443.2
OursMatching + OrderingDiffusion16.611.356.946.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.446, + 0.893, + 0.489 + ], + "angle": 0, + "content": "Table 4. Ablation study of pre-training tasks and order modeling. Our proposed order pre-training not only enables zero-shot forecasting, but also significantly improves zero-shot step classification and the fine-tuning results across evaluation tasks. Our diffusion models further improves mask modeling, especially on fine-tuning step forecasting." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.502, + 0.47, + 0.548 + ], + "angle": 0, + "content": "These results suggest that our procedure-aware pre-training can effectively facilitate the learning for both video representation and step ordering in procedure activities." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.558, + 0.47, + 0.725 + ], + "angle": 0, + "content": "Masked Prediction vs. Diffusion Model. We explore a model variant using the mask prediction, sharing similar spirit as the well-known Masked Language Modeling in BERT [24]. Specifically, this variant is trained to recover the video embeddings of masked video clips so that it can match to the assigned verb phrases. Our diffusion model largely outperforms the results of this variant, especially on the step forecasting \\((+0.7\\%)\\) and \\(+3.6\\%\\) for zero-shot and fine-tuning, respectively, as in Table 4). This result indicates that our diffusion model is a more suitable way to capture the variation inside the step ordering." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Approximate Inference. In Table 5, we validate that our approximate inference with a single zero-vector can achieve close empirical results as the Monte Carlo estimation (e.g., within \\(0.1\\%\\) difference). Monte Carlo estimation computes the weighted average of multiple sampled predictions (e.g., from 5 sampled noises). We run the experiment for 5 times and the variation is small (e.g., \\(\\pm 0.03\\%\\)). Further, if we assume an oracle ranking function to pick the correct one from sampled predictions, our results can be further boosted by \\(3.4\\%\\) on average, suggesting diverse predictions from our model and ample room to improve." + }, + { + "type": "table", + "bbox": [ + 0.563, + 0.499, + 0.824, + 0.577 + ], + "angle": 0, + "content": "
ModelInference TypeTop-1 Acc. (%)
OursApproximation11.33
OursExpectation11.34 ± 0.03
OursOracle14.73 ± 0.13
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.581, + 0.893, + 0.638 + ], + "angle": 0, + "content": "Table 5. Ablation study of inference schemes. \"Approximation\" uses a zero-vector as noise, \"Expectation\" adopts Monte Carlo estimation, and \"Oracle\" further assumes an oracle ranking function to pick the correct prediction derived from multiple noises." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.657, + 0.619, + 0.673 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.69, + 0.893, + 0.903 + ], + "angle": 0, + "content": "In this work, we presented a model and a training frame work for learning procedure-aware video representation from a large-scale dataset of instructional videos and their narrations, without the need for human annotations. The key strength of our model lies in the joint learning of a video encoder capturing concepts of action steps, as well as a diffusion model reasoning about the temporal dependencies among steps. We demonstrated that our model achieves strong results on step classification and forecasting in both zero-shot and fine-tuning settings and across COIN and EPIC-Kitchens-100 datasets. We believe our work provides a solid step towards understanding procedural activities. We hope that our work will shed light on the broader problem of video-language pre-training." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14832" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.176, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.186 + ], + "angle": 0, + "content": "[1] Jean-Baptiste Alayrac, Piotr Bojanowski, Nishant Agrawal, Josef Sivic, Ivan Laptev, and Simon Lacoste-Julien. Unsupervised learning from narrated instruction videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4575-4583, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.187, + 0.472, + 0.257 + ], + "angle": 0, + "content": "[2] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6836-6846, 2021. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.259, + 0.472, + 0.327 + ], + "angle": 0, + "content": "[3] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1728-1738, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.329, + 0.472, + 0.386 + ], + "angle": 0, + "content": "[4] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In Proceedings of the International Conference on Machine Learning (ICML), July 2021. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.387, + 0.472, + 0.457 + ], + "angle": 0, + "content": "[5] Piotr Bojanowski, Rémi Lajugie, Francis Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid, and Josef Sivic. Weakly supervised action labeling in videos under ordering constraints. In European Conference on Computer Vision, pages 628-643. Springer, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.457, + 0.472, + 0.528 + ], + "angle": 0, + "content": "[6] Piotr Bojanowski, Rémi Lajugie, Edouard Grave, Francis Bach, Ivan Laptev, Jean Ponce, and Cordelia Schmid. Weakly-supervised alignment of video with text. In Proceedings of the IEEE international conference on computer vision, pages 4462-4470, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.529, + 0.472, + 0.585 + ], + "angle": 0, + "content": "[7] M. Brand, N. Oliver, and A. Pentland. Coupled hidden markov models for complex action recognition. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 994-999, 1997. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.587, + 0.472, + 0.643 + ], + "angle": 0, + "content": "[8] Chien-Yi Chang, De-An Huang, Danfei Xu, Ehsan Adeli, Li Fei-Fei, and Juan Carlos Niebles. Procedure planning in instructional videos. In European Conference on Computer Vision, pages 334–350. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.644, + 0.472, + 0.685 + ], + "angle": 0, + "content": "[9] Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. Diffusion models in vision: A survey. arXiv preprint arXiv:2209.04747, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.078, + 0.687, + 0.472, + 0.784 + ], + "angle": 0, + "content": "[10] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. The EPIC-KITCHENS dataset: Collection, challenges and baselines. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(11):4125-4141, 2021. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.078, + 0.785, + 0.472, + 0.828 + ], + "angle": 0, + "content": "[11] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat GANs on image synthesis. Advances in Neural Information Processing Systems, 34:8780-8794, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.078, + 0.829, + 0.472, + 0.872 + ], + "angle": 0, + "content": "[12] E. Elhamifar and D. Huynh. Self-supervised multi-task procedure learning from instructional videos. European Conference on Computer Vision, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.078, + 0.873, + 0.472, + 0.903 + ], + "angle": 0, + "content": "[13] Ehsan Elhamifar and Zwe Naing. Unsupervised procedure learning via joint dynamic summarization. In Proceedings of" + }, + { + "type": "list", + "bbox": [ + 0.078, + 0.116, + 0.472, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.122, + 0.894, + 0.178 + ], + "angle": 0, + "content": "[14] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. SlowFast networks for video recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.179, + 0.894, + 0.234 + ], + "angle": 0, + "content": "[15] Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, and Zicheng Liu. VIOLET: End-to-end video-language transformers with masked visual-token modeling. arXiv preprint arXiv:2111.12681, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.235, + 0.894, + 0.304 + ], + "angle": 0, + "content": "[16] Deepti Ghadiyaram, Du Tran, and Dhruv Mahajan. Large-scale weakly-supervised pre-training for video action recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12046-12055, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.305, + 0.894, + 0.36 + ], + "angle": 0, + "content": "[17] Karan Goel and Emma Brunskill. Learning procedural abstractions and evaluating discrete latent temporal structure. In International Conference on Learning Representations, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.362, + 0.894, + 0.433 + ], + "angle": 0, + "content": "[18] Abhinav Gupta, Praveen Srinivasan, Jianbo Shi, and Larry S. Davis. Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 2012-2019, 2009. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.434, + 0.894, + 0.49 + ], + "angle": 0, + "content": "[19] Tengda Han, Weidi Xie, and Andrew Zisserman. Temporal alignment networks for long-term video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2906-2916, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.491, + 0.894, + 0.533 + ], + "angle": 0, + "content": "[20] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.534, + 0.894, + 0.589 + ], + "angle": 0, + "content": "[21] Jonathan Ho, Tim Salimans, Alexey A Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. In Advances in Neural Information Processing Systems, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.59, + 0.894, + 0.645 + ], + "angle": 0, + "content": "[22] Y.A. Ivanov and A.F. Bobick. Recognition of visual activities and interactions by stochastic parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):852-872, 2000. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.647, + 0.894, + 0.717 + ], + "angle": 0, + "content": "[23] Evangelos Kazakos, Arsha Nagrani, Andrew Zisserman, and Dima Damen. EPIC-fusion: Audio-visual temporal binding for egocentric action recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5492–5501, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.718, + 0.894, + 0.773 + ], + "angle": 0, + "content": "[24] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, pages 4171-4186, 2019. 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.774, + 0.894, + 0.857 + ], + "angle": 0, + "content": "[25] Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, and Boqing Gong. MoViNets: Mobile video networks for efficient video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16020-16030, 2021. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.858, + 0.894, + 0.901 + ], + "angle": 0, + "content": "[26] Mahnaz Koupae and William Yang Wang. WikiHow: A large scale text summarization dataset. arXiv preprint arXiv:1810.09305, 2018. 2, 6" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "14833" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.161 + ], + "angle": 0, + "content": "[27] Hilde Kuehne, Ali Arslan, and Thomas Serre. The language of actions: Recovering the syntax and semantics of goal-directed human activities. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.162, + 0.47, + 0.233 + ], + "angle": 0, + "content": "[28] Anna Kukleva, Hilde Kuehne, Fadime Sener, and Jurgen Gall. Unsupervised learning of action classes with continuous temporal embedding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12066-12074, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.233, + 0.471, + 0.315 + ], + "angle": 0, + "content": "[29] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L. Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7331-7341, June 2021. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.316, + 0.471, + 0.384 + ], + "angle": 0, + "content": "[30] Junnan Li, Ramprasaath R Selvaraju, Akhilesh Deepak Gotmare, Shafiq Joty, Caiming Xiong, and Steven Hoi. Align before fuse: Vision and language representation learning with momentum distillation. arXiv preprint arXiv:2107.07651, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.385, + 0.471, + 0.481 + ], + "angle": 0, + "content": "[31] Linjie Li, Yen-Chun Chen, Yu Cheng, Zhe Gan, Licheng Yu, and Jingjing Liu. HERO: Hierarchical encoder for Video+Language omni-representation pre-training. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2046-2065, Online, Nov. 2020. Association for Computational Linguistics. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.483, + 0.471, + 0.538 + ], + "angle": 0, + "content": "[32] Ji Lin, Chuang Gan, and Song Han. TSM: Temporal shift module for efficient video understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7083-7093, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.539, + 0.471, + 0.621 + ], + "angle": 0, + "content": "[33] Xudong Lin, Fabio Petroni, Gedas Bertasius, Marcus Rohrbach, Shih-Fu Chang, and Lorenzo Torresani. Learning to recognize procedural activities with distant supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13853-13863, 2022. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.622, + 0.471, + 0.663 + ], + "angle": 0, + "content": "[34] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.664, + 0.471, + 0.733 + ], + "angle": 0, + "content": "[35] Huaishao Luo, Lei Ji, Botian Shi, Haoyang Huang, Nan Duan, Tianrui Li, Jason Li, Taroon Bharti, and Ming Zhou. Univl: A unified video and language pre-training model for multimodal understanding and generation. arXiv preprint arXiv:2002.06353, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.734, + 0.471, + 0.789 + ], + "angle": 0, + "content": "[36] Jonathan Malmaud, Jonathan Huang, Vivek Rathod, Nick Johnston, Andrew Rabinovich, and Kevin Murphy. What's cookin'? interpreting cooking videos using text, speech and vision. arXiv preprint arXiv:1503.01558, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.79, + 0.471, + 0.872 + ], + "angle": 0, + "content": "[37] Antoine Miech, Jean-Baptiste Alayrac, Lucas Smaira, Ivan Laptev, Josef Sivic, and Andrew Zisserman. End-to-end learning of visual representations from uncurated instructional videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9879-9889, 2020. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.471, + 0.903 + ], + "angle": 0, + "content": "[38] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic." + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.471, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.149 + ], + "angle": 0, + "content": "HowTo100M: Learning a text-video embedding by watching hundred million narrated video clips. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2630-2640, 2019. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.15, + 0.892, + 0.207 + ], + "angle": 0, + "content": "[39] Ram Nevatia, Tao Zhao, and Somboon Hongeng. Hierarchical language-based representation of events in video streams. In 2003 Conference on Computer Vision and Pattern Recognition Workshop, volume 4, pages 39-39, 2003. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.208, + 0.892, + 0.29 + ], + "angle": 0, + "content": "[40] Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models. In International Conference on Machine Learning, pages 16784-16804. PMLR, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.291, + 0.892, + 0.347 + ], + "angle": 0, + "content": "[41] Mingtao Pei, Yunde Jia, and Song-Chun Zhu. Parsing video events with goal inference and intent prediction. In 2011 International Conference on Computer Vision, pages 487-494, 2011. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.349, + 0.892, + 0.417 + ], + "angle": 0, + "content": "[42] Yicheng Qian, Weixin Luo, Dongze Lian, Xu Tang, Peilin Zhao, and Shenghua Gao. SVIP: Sequence verification for procedures in videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19890-19902, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.419, + 0.892, + 0.503 + ], + "angle": 0, + "content": "[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (ICML), 2021. 1, 2, 3, 4, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.504, + 0.892, + 0.559 + ], + "angle": 0, + "content": "[44] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125, 2022.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.561, + 0.892, + 0.631 + ], + "angle": 0, + "content": "[45] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 3, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.632, + 0.892, + 0.702 + ], + "angle": 0, + "content": "[46] M.S. Ryoo and J.K. Aggarwal. Recognition of composite human activities through context-free grammar based representation. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), volume 2, pages 1709-1718, 2006. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.703, + 0.892, + 0.788 + ], + "angle": 0, + "content": "[47] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.789, + 0.892, + 0.842 + ], + "angle": 0, + "content": "[48] Fadime Sener and Angela Yao. Zero-shot anticipation for instructional activities. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 862-871, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.845, + 0.892, + 0.902 + ], + "angle": 0, + "content": "[49] Ozan Sener, Amir R Zamir, Silvio Savarese, and Ashutosh Saxena. Unsupervised semantic parsing of video collections. In Proceedings of the IEEE International conference on Computer Vision, pages 4480-4488, 2015. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "14834" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.174 + ], + "angle": 0, + "content": "[50] Yuhan Shen, Lu Wang, and Ehsan Elhamifar. Learning to segment actions from visual and language instructions via differentiable weak sequence alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10156-10165, June 2021. 2, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.176, + 0.472, + 0.245 + ], + "angle": 0, + "content": "[51] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.247, + 0.472, + 0.302 + ], + "angle": 0, + "content": "[52] Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. MPNet: Masked and permuted pre-training for language understanding. Advances in Neural Information Processing Systems, 33:16857-16867, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.302, + 0.472, + 0.357 + ], + "angle": 0, + "content": "[53] Yang Song and Stefano Ermon. Improved techniques for training score-based generative models. Advances in neural information processing systems, 33:12438-12448, 2020. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.359, + 0.472, + 0.426 + ], + "angle": 0, + "content": "[54] Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid. VideoBERT: A joint model for video and language representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7464-7473, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.428, + 0.472, + 0.51 + ], + "angle": 0, + "content": "[55] Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, and Jie Zhou. COIN: A large-scale dataset for comprehensive instructional video analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1207-1216, 2019. 2, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.511, + 0.472, + 0.552 + ], + "angle": 0, + "content": "[56] Yansong Tang, Jiwen Lu, and Jie Zhou. Comprehensive instructional video analysis: The COIN dataset and performance evaluation. TPAMI, 2020. 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.553, + 0.472, + 0.608 + ], + "angle": 0, + "content": "[57] Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H Bermano. Human motion diffusion model. In International Conference on Learning Representations, 2023. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.609, + 0.472, + 0.664 + ], + "angle": 0, + "content": "[58] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.665, + 0.472, + 0.734 + ], + "angle": 0, + "content": "[59] Alex Jinpeng Wang, Yixiao Ge, Rui Yan, Yuying Ge, Xudong Lin, Guanyu Cai, Jianping Wu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. All in one: Exploring unified video-language pre-training. arXiv preprint arXiv:2203.07303, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.735, + 0.472, + 0.804 + ], + "angle": 0, + "content": "[60] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaou Tang, and Luc Van Gool. Temporal segment networks: Towards good practices for deep action recognition. In European conference on computer vision, pages 20-36. Springer, 2016. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.805, + 0.472, + 0.873 + ], + "angle": 0, + "content": "[61] Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, and Kevin Murphy. Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification. In Proceedings of the European conference on computer vision (ECCV), pages 305–321, 2018. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.874, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[62] Frank F. Xu, Lei Ji, Botian Shi, Junyi Du, Graham Neubig, Yonatan Bisk, and Nan Duan. A benchmark for structured" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.149 + ], + "angle": 0, + "content": "procedural knowledge extraction from cooking videos. In Proceedings of the First International Workshop on Natural Language Processing Beyond Text, pages 30-40, Online, Nov. 2020. Association for Computational Linguistics. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.15, + 0.892, + 0.219 + ], + "angle": 0, + "content": "[63] Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. VideoCLIP: Contrastive pretraining for zero-shot video-text understanding. arXiv preprint arXiv:2109.14084, 2021. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.221, + 0.892, + 0.288 + ], + "angle": 0, + "content": "[64] Jianwei Yang, Yonatan Bisk, and Jianfeng Gao. TACo: Token-aware cascade contrastive learning for video-text alignment. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11562-11572, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.29, + 0.892, + 0.359 + ], + "angle": 0, + "content": "[65] Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. Diffusion models: A comprehensive survey of methods and applications. arXiv preprint arXiv:2209.00796, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.361, + 0.892, + 0.429 + ], + "angle": 0, + "content": "[66] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. MERLOT: Multimodal neural script knowledge models. Advances in Neural Information Processing Systems, 34:23634-23651, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.431, + 0.892, + 0.513 + ], + "angle": 0, + "content": "[67] Henghui Zhao, Isma Hadji, Nikita Dvornik, Konstantinos G. Derpanis, Richard P. Wildes, and Allan D. Jepson. P3IV: Probabilistic procedure planning from instructional videos with weak supervision. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2928-2938, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.516, + 0.892, + 0.572 + ], + "angle": 0, + "content": "[68] Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba. Temporal relational reasoning in videos. In Proceedings of the European conference on computer vision (ECCV), pages 803-818, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.573, + 0.892, + 0.627 + ], + "angle": 0, + "content": "[69] Luowei Zhou, Chenliang Xu, and Jason J Corso. Towards automatic learning of procedures from web instructional videos. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.629, + 0.892, + 0.685 + ], + "angle": 0, + "content": "[70] Linchao Zhu and Yi Yang. ActBERT: Learning global-local video-text representations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8746-8755, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.686, + 0.892, + 0.755 + ], + "angle": 0, + "content": "[71] Dimitri Zhukov, Jean-Baptiste Alayrac, Ramadan Gokberk Cinbis, David Fouhey, Ivan Laptev, and Josef Sivic. Cross-task weakly supervised learning from instructional videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3537-3545, 2019. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.755 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14835" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations/471fce2b-475c-4777-b3f3-36131b107c8b_origin.pdf b/2023/Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations/471fce2b-475c-4777-b3f3-36131b107c8b_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e54be5507155fe0090958eeaab59c7e2fef4f9e3 --- /dev/null +++ b/2023/Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations/471fce2b-475c-4777-b3f3-36131b107c8b_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:20203b6255ae1bf615431952e95da5dfc97a2f65a54df79fa446a1b22dce71e3 +size 1658826 diff --git a/2023/Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations/full.md b/2023/Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations/full.md new file mode 100644 index 0000000000000000000000000000000000000000..f8ec42c2b5e109a226cdfdc06f1c1af9aac97592 --- /dev/null +++ b/2023/Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations/full.md @@ -0,0 +1,366 @@ +# Learning Procedure-aware Video Representation from Instructional Videos and Their Narrations + +Yiwu Zhong $^{1*}$ , Licheng Yu $^{2}$ , Yang Bai $^{2}$ , Shangwen Li $^{2}$ , Xueting Yan $^{2\dagger}$ , Yin Li $^{1\dagger}$ $^{1}$ University of Wisconsin-Madison, $^{2}$ Meta AI + +yzhong52@wisc.edu, {lichengyu, yangbai, dylanwen, xyan18}@meta.com, yin.li@wisc.edu + +# Abstract + +The abundance of instructional videos and their narrations over the Internet offers an exciting avenue for understanding procedural activities. In this work, we propose to learn video representation that encodes both action steps and their temporal ordering, based on a largescale dataset of web instructional videos and their narrations, without using human annotations. Our method jointly learns a video representation to encode individual step concepts, and a deep probabilistic model to capture both temporal dependencies and immense individual variations in the step ordering. We empirically demonstrate that learning temporal ordering not only enables new capabilities for procedure reasoning, but also reinforces the recognition of individual steps. Our model significantly advances the state-of-the-art results on step classification $(+2.8\% / +3.3\%)$ on COIN / EPIC-Kitchens) and step forecasting $(+7.4\%)$ on COIN). Moreover, our model attains promising results in zero-shot inference for step classification and forecasting, as well as in predicting diverse and plausible steps for incomplete procedures. Our code is available at https://github.com/facebookresearch/ProcedureVRL. + +# 1. Introduction + +Many of our daily activities (e.g. cooking or crafting) are highly structured, comprising a set of action steps conducted in a certain ordering. Yet how these activities are performed varies among individuals. Consider the example of making scrambled eggs as shown in Fig. 1. While most people tend to whisk eggs in a bowl, melt butter in a pan, and cook eggs under medium heat, expert chefs have recommended to crack eggs into the pan, add butter, and stir them under high heat. Imagine a vision model that can account for the individual variations and reason about the temporal ordering of action steps in a video, so as to infer prior missing steps, recognize the current step, and forecast + +![](images/deb55a7fa0269ce55a71bd99c40f8df251542e94bc7bbf4564ac61c4adcfa2a6.jpg) + +![](images/9b380ec0b7e0c906b0ba6e7139fe316c0f5338affaa173993ea1aa306e3ef5d7.jpg) +Figure 1. Top: During training, our model learns from procedural videos and step descriptions to understand individual steps and capture temporal ordering and variations among steps. Bottom: Once trained, our model supports zero-shot step classification and forecasting, yielding multiple credible predictions. + +a future step. Such a model will be immensely useful for a wide range of applications including augmented reality, virtual personal assistant, and human-robot interaction. + +Understanding complex procedural activities has been a long-standing challenge in the vision community [7, 18, 22, 39, 41, 46]. While many prior approaches learn from annotated videos following a fully supervised setting [13, 27, 69], this paradigm is difficult to scale to a plethora of activities and their variants among individuals. A promising solution is offered by the exciting advances in vision-and-language pre-training, where models learn from visual data (images or videos) and their paired text data (captions or narrations) [30, 43, 54, 70] in order to recognize a variety of concepts. This idea has recently been explored to analyze instructional videos [33, 37], yet existing methods are limited to recognize single action steps in procedural activities. + +In this paper, we present a first step towards modeling temporal ordering of action steps in procedural activities by learning from instructional videos and their narrations. Our key innovation lies in the joint learning of a video representation aiming to encode individual step concepts, and a deep probabilistic model designed to capture temporal dependencies and variations among steps. The video representation, instantiated as a Transformer network, is learned by + +matching a video clip to its corresponding narration. The probabilistic model, built on a diffusion process, is tasked to predict the distribution of the video representation for a missing step, given steps in its vicinity. With the help of a pre-trained vision-and-language model [43], our model is trained using only videos and their narrations from automatic speech recognition (ASR), and thus does not require any manual annotations. + +Once learned, our model celebrates two unique benefits thanks to our model design and training framework. First, our model supports zero-shot inference given an input video, including the recognition of single steps and forecasting of future steps, and can be further fine-tuned on downstream tasks. Second, our model allows sampling multiple video representations when predicting a missing action step, with each presenting a possibly different hypothesis of the step ordering. Instead of predicting a single representation with the highest probability, sampling from a probabilistic model provides access to additional high-probability solutions that might be beneficial to prediction tasks with high ambiguity or requiring user interactions. + +We train our models on a large-scale instructional video dataset collected from YouTube (HowTo100M [38]), and evaluate them on two public benchmarks (COIN [55] and EPIC-Kitchens-100 [10]) covering a wide range of procedural videos and across the tasks of step classification and step forecasting. Through extensive experiments, we demonstrate that (1) our temporal model is highly effective in forecasting future steps, outperforming state-of-the-art methods by a large margin of $+7.4\%$ in top-1 accuracy on COIN; (2) modeling temporal ordering reinforces video representation learning, leading to improved classification results $(+2.8\% / + 3.3\%)$ for step classification on COIN/EPIC-Kitchens) when probing the learned representations; (3) our training framework offers strong results for zero-shot step classification and forecasting; and (4) sampling from our probabilistic model yields diverse and plausible predictions of future steps. + +Contributions. Our work presents the first model that leverages video-and-language pre-training to capture the temporal ordering of action steps in procedural activities. Our key technical innovation lies in the design of a deep probabilistic model using a diffusion process, in tandem with video-and-language representation learning. The result is a model and a training framework that establish new state-of-the-art results on both step classification and forecasting tasks across the major benchmarks. Besides, our model is capable of generating diverse step predictions and supports zero-shot inference. + +# 2. Related Work + +Understanding Procedural Activities. Reasoning about procedural activities, including their action steps and the + +temporal ordering of these steps, has been a central problem in activity recognition. While early works model temporal ordering with stochastic grammars [7, 18, 22, 39, 41, 46], more recent works consider supervised learning to localize steps and predict their ordering by learning from videos with human annotated action steps [8, 10, 13, 27, 55, 69, 71]. To alleviate the burden of costly video annotations, several works propose various forms of weakly supervised settings, with assumptions that the ordered list of steps is given without their temporal boundaries [5, 6, 67, 71], or that the key steps and their ordering remain fixed across all videos [1, 12, 13, 17, 28, 49]. + +Most of prior methods focus on the tasks of step classification and localization [1,5,6,12,13,28,49,71]. Others have considered the tasks of step forecasting [48], step verification [42] and procedure planning [67]. Our work also seeks to understand procedural activities. Different from these approaches, our method focuses on learning video representation from videos and their narrations without using human annotations. The resulting video representation can be leveraged for step classification and step forecasting. + +Learning from Procedural Videos and Narrations. The success of vision-and-language pre-training has fueled a new line of research that seeks to learn concepts of individual steps from instructional videos and their narrations [19, 36, 50, 62]. For example, Miech et al. [37] propose MIL-NCE to learn representations from instructional videos [38] and their narrations extracted using ASR. + +The most relevant work is DistantSup [33], where they propose using distant supervision from a textual knowledge base (wikiHow) [26] to denoise text narrations from ASR. Specifically, DistantSup leverages a pre-trained language model [52] to link step descriptions from wikiHow to text narrations from video ASR results, and thus to create training labels for individual steps in videos. Different from [33], our method models the temporal ordering of steps in procedural activities, thus moves beyond representations of single steps to support temporal reasoning in videos. Further, our method learns from videos and narrations only, with the help of a pre-trained image-language model [43] yet without using a textual knowledge base. + +Video-and-Language Pre-Training. A relevant topic is video-and-language pre-training, aiming at learning video representation from videos and their paired natural language descriptions [3,15,16,29,31,35,54,59,63,64,66,70], often generated from ASR outputs. Despite the latest development in ASR, automatically-transcribed speech from videos can be rather noisy and lacks precise temporal alignment with the visual content. Several recent works seek to address this challenge. VideoCLIP [63] starts from the pretrained MIL-NCE model and further improves the model by retrieval augmented training with overlapped video-text pairs. Bain et al. [3] collect a less noisy dataset of video alt + +![](images/0daf97944a45cc64dbff9059314400d7fd8471ba4d8983b9a9282a6f4a449406.jpg) +Figure 2. Overview of our approach. Left panel: Our model consists of (1) a video encoder that takes a video clip and encodes it into a video embedding; (2) a transformer-based denoising model that samples noises from Gaussian distribution and generates video embeddings conditioned on the embeddings of adjacent video clips. Right panel: We leverage trained image-language model CLIP to create pseudo labels for individual video clips (a). After training, our model supports step classification given an input video clip (b), and step forecasting given a video that records previous steps (c). Note that diverse embeddings can be generated by sampling various noises. + +![](images/4923efcea09132ad967e11187a6c0df612ced7dab2747a9dcb15be8b53eb472a.jpg) + +text pairs and geared the model to match these pairs. Our work shares the key idea of learning from video and text data as prior work, and seeks to leverage external knowledge from a pre-trained image-language model [43]. + +Another relevant work is MERLOT [66]. While both MERLOT and our work seek to learn video representation, our method differs from MERLOT in two folds. Our method models the sequence order of video clips for understanding procedural activities. MERLOT learns binary relative order between two given video frames for multi-modal reasoning and does not directly support action forecasting. Both methods consider a masked prediction task, yet MERLOT predicts the most likely text embeddings, while our method estimates the distribution of video representations using a deep probabilistic model. + +Diffusion Models. Diffusion models [51, 53] provide a powerful approach to characterize the probability density of high dimensional signals, and have recently demonstrated impressive results on generating high fidelity visual data, such as images [40,44,45,47], videos [21], and human body motion [57]. Our work adapts diffusion process to model the temporal ordering of steps in procedural videos. In doing so, our method not only facilitates the learning of expressive video representations for individual steps, but also enables the anticipation of future action steps. + +# 3. Method + +We consider the problem of learning video representation for understanding procedural activities from instructional videos and their narrations. An input video is represented as a sequence of $N$ clips $\{v_{1}, v_{2}, \ldots, v_{N}\}$ . Each + +$v_{i}$ captures a potential action step in the input video, and the time step $i$ records the temporal ordering of these clips. The video clips $\{v_{i}\}$ can be either segmented by using the timestamps of ASR outputs (as we consider during training), or densely sampled from a video following their temporal ordering (as we use during inference). During learning, we further assume that an ordered set of sentences $\{s_1,s_2,\dots,s_N\}$ is associated with the video clips $\{v_{1},v_{2},\dots,v_{N}\}$ , with each $s_i$ describing the action step in video clip $v_{i}$ . These sentences $\{s_i\}$ can be the output text from ASR, or given by matching the video clips to a text corpus using an external vision language model [43]. + +Procedural-aware Video Representation. Our goal is to learn video representation that encodes both action step concepts and their temporal dependencies across a range of procedural activities. Our representation consists of (a) a video encoder $f$ that extracts a representation $x_{i}$ from an input clip $v_{i}$ (i.e., $x_{i} = f(v_{i})$ ); and (b) a probabilistic model that characterizes the conditional probability $p(x_{j} = f(v_{j}) | \{x_{i} = f(v_{i})\}_{i \neq j}) \forall j$ . This design is highly flexible and supports a number of procedural reasoning tasks. $f$ offers video representation suitable to classify individual steps in a clip. $p(x_{j} | \{x_{i}\}_{i \neq j})$ models the temporal dependencies among steps, and can be used to predict the video representation of missing steps and further infer their labels. + +Method Overview. To learn our representation, we leverage a pre-trained text encoder $g$ that remains fixed during learning, and extend the idea of masked token modeling, populated in natural language processing [24]. For each input video and its narrations at training time, we randomly sample a clip $v_{j}$ from $\{v_{1},v_{2},\dots,v_{N}\}$ and mask it + +out. We then train our model to predict the distribution of $x_{j} = f(v_{j})$ from $\{x_{i} = f(v_{i})\}_{i \neq j}$ (i.e., $p(x_{j} | \{x_{i}\}_{i \neq j}))$ ), align the expectation of the predicted distribution $\mathbb{E}(x_{j})$ with the corresponding text embedding $y_{j} = g(s_{j})$ , and match all other video representations $\{x_{i} = f(v_{i})\}_{i \neq j}$ to their text embeddings $\{y_{i} = g(s_{i})\}_{i \neq j}$ . + +Despite the conceptual similarity, our learning is fundamentally different from masked token prediction. Our method seeks to characterize the distribution of $x_{j}$ instead of predicting the most likely $x_{j}$ , resulting in a more principled approach to capture the temporal dependencies among steps, as well as the new capability of sampling multiple high-probability solutions for $x_{j}$ . Our method is illustrated in Fig. 2. In what follows, we lay out the formulation of our model, and describe its training and inference schemes. + +# 3.1. Modeling Action Steps and Their Ordering + +Formally, given an input video with its clips $\{v_{1}, v_{2}, \ldots, v_{N}\}$ and their narrations $\{s_{1}, s_{2}, \ldots, s_{N}\}$ , our method assumes a factorization of $p(Y = \{y_{i}\} | X = \{x_{i}\})$ with video representation $x_{i} = f(v_{i})$ (learnable) and text embedding $y_{i} = f(s_{i})$ (pre-trained and fixed). + +$$ +p (Y | X) = p \left(y _ {j} \mid x _ {j}\right) \cdot p \left(x _ {j} \mid \left\{x _ {i} \right\} _ {i \neq j}\right) \cdot \prod_ {i} p \left(y _ {i} \mid x _ {i}\right) \quad \forall j. \tag {1} +$$ + +$p(y_{i}|x_{i})$ measures the alignment between a video representation $x_{i}$ and a text embedding $y_{i}$ . $p(x_{j}|\{x_{i}\}_{i\neq j})$ characterizes the distribution of a video representation for a missing step given the representations of all other steps, thereby modeling the temporal ordering of steps. Note that our model is not limited to single step prediction and can be readily extended to predict multiple missing steps. + +Matching Image and Text Representations. Our model matches the video representation $x_{i}$ and text embedding $y_{i}$ in a learned vector space, such that the alignment between them can be measured by cosine similarity. We will later instantiate this definition into a more tractable form for learning. Yet it suffices to notice that $p(y_{j}|x_{j})$ does not involve additional learnable parameters given $x_{i}$ and $y_{i}$ . + +Modeling Step Ordering with Diffusion Process. The key challenge lies in the modeling of $p(x_{j}|\{x_{i}\}_{i\neq j})$ , as the video representation $x_{i}$ is at least of a few hundred dimensions. To this end, we propose to model $p(x_{j}|\{x_{i}\}_{i\neq j})$ using a diffusion process [51, 53] conditioned on observed video representations $\{x_{i}\}_{i\neq j}$ . Here we briefly describe diffusion process in the context of our model, and refer the readers to recent surveys for more technical details [9, 65]. + +Specifically, we assume a diffusion process that gradually adds noise to the input $x_{j}$ over $t \in [0,1,\dots,T]$ steps. + +$$ +p \left(x _ {j} ^ {1: T} \mid x ^ {0}\right) = \prod_ {t = 1} ^ {T} p \left(x _ {j} ^ {t} \mid x _ {j} ^ {t - 1}\right), \tag {2} +$$ + +$$ +p (x _ {j} ^ {t} | x _ {j} ^ {t - 1}) = \mathcal {N} (\sqrt {\alpha_ {t}} x _ {j} ^ {t - 1}, (1 - \alpha_ {t}) I). +$$ + +where $\alpha_{t}$ are constant hyper-parameters. The reverse diffusion (denoising) process is parameterized with + +$$ +p \left(x _ {j} ^ {0: T} \mid \left\{x _ {i} \right\} _ {i \neq j}\right) = p \left(x _ {j} ^ {T} \mid \left\{x _ {i} \right\} _ {i \neq j}\right) \prod_ {t = 1} ^ {T} p _ {\theta} \left(x _ {j} ^ {t - 1} \mid x _ {j} ^ {t}, \left\{x _ {i} \right\} _ {i \neq j}\right). \tag {3} +$$ + +With sufficiently large $T$ , $p(x_j^T|\{x_i\}_{i \neq j}) \sim \mathcal{N}(0, I)$ , i.e. a standard Gaussian noise that is independent of $\{x_i\}_{i \neq j}$ . The denoising process is approximated by $p_\theta(x_j^{t-1}|x_j^t, \{x_i\}_{i \neq j})$ using a neural network with parameters $\theta$ such that + +$$ +p _ {\theta} \left(x _ {j} ^ {t - 1} \mid x _ {j} ^ {t}, \left\{x _ {i} \right\} _ {i \neq j}\right) = \mathcal {N} \left(\mu_ {\theta} \left(x _ {j} ^ {t}, \left\{x _ {i} \right\} _ {i \neq j}\right), \Sigma_ {\theta} \left(x _ {j} ^ {t}, \left\{x _ {i} \right\} _ {i \neq j}\right)\right) \tag {4} +$$ + +In practice, we follow Ho et al. [20] and Tevet et al. [57] to directly predict $x_{j}^{0}$ by using a denoising model $h$ . With slight abuse of the symbols, we denote + +$$ +\hat {x} _ {j} ^ {0} = h \left(x _ {j} ^ {t}, \left\{x _ {i} \right\} _ {i \neq j}, t\right). \tag {5} +$$ + +$h$ is realized using a Transformer network with the embedding of step $t$ as part of its inputs. Once learned, one can sample from $\mathcal{N}(0,I)$ and apply $h$ through the denoising process to predict $x_{j}$ based on $\{x_i\}_{i\neq j}$ . + +# 3.2. Learning from Videos and Their Narrations + +Our training approximately maximizes the likelihood of Eq. 1 given a set of training videos and their narrations. + +Pseudo Labels from CLIP. It is straightforward to directly align video representations to the embeddings of their corresponding ASR text. Doing so, however, faces the challenges of low-quality ASR text and imprecise alignment between video and ASR sentences. To address these challenges, we propose to create pseudo labels by leveraging a pre-trained image-language model (e.g., CLIP [43]). + +Specifically, we first create a pool of step descriptions in the form of verb phrases (e.g., "add water", "wear gloves") parsed from ASR sentences [50], with their embeddings as $\{y^{1:K}\}$ . Then a trained CLIP model is applied to link each video clip with verb phrases, by matching the averaged visual features across frames with the language embeddings of verb phrases. The resulting matching scores are used as our training target. + +Our pseudo labeling instantiates the matching $p(y_{i}|x_{i})$ between video representation and text embedding using + +$$ +p \left(y _ {i} \mid x _ {i}\right) = \operatorname {s o f t m a x} \left(\frac {x _ {i} ^ {T} y _ {i}}{\tau \| x _ {i} \| \| y _ {i} \|}\right), \tag {6} +$$ + +where $y_{i}$ is selected from pool of verb phrases, i.e. $y_{i} \in \{y^{1:K}\}$ , and $\tau$ is the pre-defined temperature. The matching problem thus is converted into a "classification" problem, making the training feasible. + +Learning Objective and Training Loss. Our training minimizes an evidence upper bound of the negative log likelihood $-\log p(Y|X)$ in Eq. 1. The detailed derivation of + +evidence upper bound is described in the Supplement. Our objective function constitutes three loss terms: + +$$ +\mathrm {L} = \mathrm {L} _ {\mathrm {X E}} + \mathrm {L} _ {\mathrm {M S E}} + \mathrm {L} _ {\mathrm {M C}}. \tag {7} +$$ + +The first term $\mathrm{L}_{\mathrm{XE}}$ seeks to match observed video representation $\{x_{i}\}$ to their text embeddings $\{y_{i}\}$ , given by + +$$ +\mathrm {L} _ {\mathrm {X} E} = \frac {1}{N - 1} \sum_ {i} H \left(p _ {i} ^ {\prime}, p \left(y _ {i} \mid x _ {i}\right)\right), \tag {8} +$$ + +where $H(\cdot, \cdot)$ is the cross entropy, and $p_i'$ are soft targets given by CLIP matching scores. $p(y_i|x_i)$ defined in Eq. 6 measures the similarity between $x_i$ and $y_i$ . + +The second term $\mathrm{L}_{\mathrm{MSE}}$ comes from the Kullback-Leibler (KL) divergence within our diffusion model, and is computed as + +$$ +\mathrm {L} _ {\text {M S E}} = \mathbb {E} _ {x _ {j} ^ {0} \sim p \left(x _ {j} ^ {0} \mid \{x _ {i} \} _ {i \neq j}\right), t \sim [ 1, T ]} \left[ \| x _ {j} ^ {0} - \hat {x} _ {j} ^ {0} \| _ {2} ^ {2} \right]. \tag {9} +$$ + +Note that unlike a standard diffusion model, our model directly predicts $\hat{x}_j^0$ . This term is applied at each step $t$ . + +The third term $\mathrm{L}_{\mathrm{MC}}$ is derived from matching the predicted video representation $\hat{x}_j^0$ to its text embedding $y_{j}$ + +$$ +\mathrm {L} _ {\mathrm {M C}} = \mathbb {E} _ {x _ {j} ^ {0} \sim p \left(x _ {j} ^ {0} \mid \{x _ {i} \} _ {i \neq j}\right), t \sim [ 1, T ]} \left[ H \left(p _ {j} ^ {\prime}, p \left(y _ {j} \mid \hat {x} _ {j} ^ {0}\right)\right) \right], \tag {10} +$$ + +where $p_j'$ are again soft targets given by CLIP model, and $\hat{x}_j^0$ is denoised from a sampled noise. During training, we adopt Monte Carlo estimation for $\mathbb{E}_p\left[-\log p(y_j|\hat{x}_j^0)\right]$ , by minimizing $-\log p(y_j|\hat{x}_j^0)$ for each sampled $\hat{x}_j^0$ . We attach this term at each step $t$ . + +A critical design choice lies in $p(y_{j}|\hat{x}_{j}^{0})$ . $p(y_{j}|\hat{x}_{j}^{0})$ is simplified into a score function between a video representation and a finite set of text embeddings (defined using verb phrases). This allows us to reach our loss terms without worrying about global normalization constant as commonly encountered in energy-based models. Indeed, $H(p_{j}^{\prime},p(y_{j}|\hat{x}_{j}^{0}))$ can be interpreted as providing guidance by matching video to text embeddings. This term thus resembles the key idea of classifier guidance, which has shown to be helpful for learning diffusion models [11]. + +# 3.3. Model Inference + +Once trained, our model offers a procedure-aware representation with two key components. First, the video encoder $f(\cdot)$ serves as a feature extractor for any input video clips. Second, the diffusion model, represented as its denoising model $h(\cdot)$ , captures the temporal dependencies among steps. Our representation naturally supports a number of tasks. Here we demonstrate how our model can be used for step classification and step forecasting. + +Step Classification. An input video clip $v$ can be encoded using $f(\cdot)$ . The video representation $x = f(v)$ can be directly compared to the text embeddings [43], so as to support zero-shot step classification. Alternative, an additional + +classifier can be attached on top of $f$ and further fine-tuned to recognize the action step in input clip. + +Step Forecasting. A future video clip feature $x_{j}$ can be sampled from the diffusion model by drawing from a Gaussian distribution and denoising using $h(\cdot)$ . The predicted $x_{j}$ can be further classified into action steps. This prediction can be done using again Monte Carlo estimation given by + +$$ +\mathbb {E} _ {x _ {j} \sim p (x _ {j} | \{x _ {i} \} _ {i \neq j})} [ p (y _ {j} | x _ {j}) ]. \tag {11} +$$ + +Specifically, a noise $x_{T}$ is first sampled from Gaussian distribution and our denoising model gradually denoises it. At each step $t$ , the denoising model $h$ takes a noisy $x_{j}^{t}$ , predicts clip feature $\hat{x}_{j}^{0}$ , and diffuses it to $x_{j}^{t-1}$ based on the sampled noise $x_{j}^{T}$ , as demonstrated in Eq. 2. After $T$ iterations, the predicted clip feature at $t = 0$ is used to match text embeddings. By sampling noises for multiple times, we can estimate the most likely $y_{j}$ . + +However, sampling can be costly. In practice, to obtain top-1 prediction for missing steps, we adopt approximate inference, where the sampled noise is replaced with a fixed zero vector, corresponding to peak in the Gaussian distribution. Our empirical results validate that approximate inference achieves a very close performance as the expectation over multiple sampled noises. + +# 4. Experiments and Results + +In this section, we first introduce datasets, evaluation protocols and implementation details. Then we demonstrate our results on step forecasting and step classification benchmarks. Finally, we show our qualitative results and conduct ablations to study our model components. + +Datasets. For pre-training, we consider HowTo100M dataset [38] with 130K hours of YouTube tutorial videos. The videos cover various daily tasks, such as foods, housework, vehicles, etc. We use a language parser [50] to extract the verb phrases from ASR sentences of these videos and keep 9,871 most frequent verb phrases. For fine-tuning, we train our model on COIN dataset [55, 56] and EPIC-Kitchens-100 dataset [10], respectively. COIN has 476 hours of YouTube videos covering 180 tasks, such as dishes, vehicles, housework, etc. Human annotators summarize 778 unique steps in total (e.g., "stir the egg"), and annotate the temporal boundary and the category of each step in all videos. EPIC-Kitchens-100 dataset [10] has 100 hours of egocentric videos, capturing daily activities in kitchen. Each action in the videos is annotated with an action label and a noun label. There are 97/300 unique actions/nouns in total. We use the human annotations in COIN and EPIC-Kitchens-100 to evaluate the model performance. + +Evaluation Protocols. Our evaluation considers zero-shot and fine-tuning settings for step classification and step fore + +
ModelPretrainingTop-1 Acc. (%)
SupervisionDatasetZero-shotFine-tuning
1SlowFast [14]Supervised: action labelsKinetics-25.6
2TimeSformer [4]Supervised: action labelsKinetics-34.7
3S3D [61]Unsupervised: ASR w. MIL-NCE [37]HT100M-28.1
4TimeSformer [4]Unsupervised: ASR w. MIL-NCE [37]HT100M-34.0
5DistantSup [33]Unsupervised: ASR + wikiHowHT100M-39.4
6Random Guess--0.1-
7CLIP [43]Unsupervised: captionsCLIP400M9.4-
8OursUnsupervised: ASRHT100M11.346.8
9Ours (oracle-5)Unsupervised: ASRHT100M14.751.8
+ +Table 1. Step forecasting on COIN dataset. We compare to a set of strong baselines and a oracle protocol built on our method. + +casting on COIN and EPIC-Kitchens-100 datasets. Zero-shot setting indicates that no human annotation is used during pre-training. The pre-trained model is directly tested on the evaluation dataset. Fine-tuning setting further fine-tunes the pre-trained model using human annotations of action steps. For a fair comparison, we follow the same fine-tuning schemes as previous work in respective benchmarks. + +Implementation Details. We adopted TimeSformer [4] as our video encoder, and used the Transformer [58] from CLIP's text encoder as denoising model. We set the maximum step $T$ to 4, maximum length of video sequence as 9, and the number of Transformer layers as 4. We used a trained CLIP model (ViT-B/16) to create pseudo labels and encode step descriptions. Following DistantSup [33], for pre-training we used SGD for 5 epochs and then AdamW [34] for 25 epochs with 128 videos in a batch. For fine-tuning, we used AdamW for 15 epochs with batch size of 64. Temperature $\tau$ was set to 0.02. Additional implementation details can be found in Supplement. + +# 4.1. Step Forecasting + +Setup. We follow the benchmark in DistantSup [33] to evaluate step forecasting on COIN, where top-1 accuracy is reported. Given a video with previous steps, the model anticipates the category of next single step (e.g., "stir the egg"). This task thus requires explicit modeling of the temporal ordering among steps. We only fine-tune the diffusion model while keeping the video encoder frozen. + +Results. Table 1 compares results of our method with a series of baselines. The closest competitor is DistantSup [33] in L5, which learns from ASR text and an external textual knowledge base [26] using the same video backbone (TimeSformer [4]). We also include other baselines reported in DistantSup, e.g., SlowFast [14], TimeSformer [4], and S3D [61] from L1 to L4, where models are supervised using human-annotated action labels or video ASR text. Our model significantly outperforms all baselines by at least $7.4\%$ for the fine-tuning setting (e.g., $46.8\%$ in L8 vs. $39.4\%$ in L5). Further, we consider a strong baseline for the zero-shot setting by re-purposing CLIP model [43] + +to match the input video with the descriptions of all step candidates. Comparing L7 and L8, our model outperforms this variant of CLIP by a clear margin (11.3% vs. 9.4%). + +A unique property of our model is its ability to output multiple, potentially different predictions. We further evaluate the upper bound of our results by assuming an oracle ranking function that always selects the correction prediction from 5 outputs sampled from our model (Ours (oracle-5)). This oracle further improves the top-1 accuracy from $11.3\%$ to $14.7\%$ in L9, suggesting that our model is able to produce diverse predictions for step forecasting. + +# 4.2. Step Classification + +Setup. Besides step ordering, we also evaluate step classification on COIN and EPIC-Kitchens-100 datasets, where a model is tasked to classify a trimmed video clip into one of the step categories. For COIN, we follow DistantSup [33] to only fine-tune the additional linear layer on top of the pretrained video encoder. For EPIC-Kitchens-100, we fully fine-tune the video encoder, following [2, 25, 33]. We report the accuracy of step classification on COIN, and that of verb, noun, and action on EPIC-Kitchens-100. + +Results. Table. 2 summarizes the results on COIN. We consider baselines as in DistantSup from L1 to L8 (e.g., SlowFast [14], VideoCLIP [63]), where models are trained using either action labels or video ASR text. To support zero-shot inference, we re-implement a model variant (DistantSup†) described in [33]. This model is pre-trained to match video embeddings with language embeddings and thus supports recognizing arbitrary step descriptions in L9. We also report the results of CLIP [43], which creates the pseudo labels for our pre-training in L10. As shown, our model consistently outperforms all the other methods by a clear margin under different settings. For example, ours outperforms CLIP by $1.8\%$ in zero-shot setting (16.6% in L11 vs. 14.8% in L10), and outperforms DistantSup by $2.8\%$ (56.9% in L11 vs. 54.1% in L8) in fine-tuning setting. + +Table. 3 presents our results on EPIC-Kitchens-100. While TimeSformer [4] in L8 and DistantSup [33] in L9 use the same video encoder architecture as ours, our model + +
ModelPretrainingTop-1 Acc. (%)
SupervisionDatasetZero-shotFine-tuning
1TSN (RGB+Flow) [55]Supervised: action labelsKinetics-36.5*
2S3D [61]Unsupervised: ASR w. MIL-NCE [37]HT100M-37.5*
3SlowFast [14]Supervised: action labelsKinetics-32.9
4TimeSformer [4]Supervised: action labelsKinetics-48.3
5ClipBERT [29]Supervised: captionsCOCO+VG-30.8
6VideoCLIP [63]Unsupervised: ASRHT100M-39.4
7TimeSformer [4]Unsupervised: ASR w. MIL-NCE [37]HT100M-46.5
8DistantSup [33]Unsupervised: ASR + wikiHowHT100M-54.1
9DistantSup† [33]Unsupervised: ASR + wikiHowHT100M10.246.6
10CLIP [43]Unsupervised: captionsCLIP400M14.845.9
11OursUnsupervised: ASRHT100M16.656.9
+ +Table 2. Step classification on COIN dataset. DistantSup† is re-implemented based on their official code base. It is a variant reported in their paper that pre-trains the model to match language embeddings. * indicates the model is fully fine-tuned on COIN dataset. + +
ModelPretraining SupervisionPretraining DatasetAction (%)Verb (%)Noun (%)
1TSN [60]--33.260.246.0
2TRN [68]--35.365.945.4
3TBN [23]--36.766.047.2
4MoViNet [25]--47.772.257.3
5TSM [32]Supervised: action labelsKinetics38.367.949.0
6SlowFast [14]Supervised: action labelsKinetics38.565.650.0
7ViViT-L [2]Supervised: action labelsKinetics44.066.456.8
8TimeSformer [4]Supervised: action labelsKinetics42.366.654.4
9DistantSup [33]Unsupervised: ASR + wikiHowHT100M44.467.158.1
10OursUnsupervised: ASRHT100M47.769.560.3
+ +Table 3. Step classification on EPIC-Kitchens-100 dataset with fine-tuning setting. Our method outperforms the close competitors (TimeSformer, DistantSup), with results on par with even stronger backbone models (MoViNet). + +in L10 achieves a clear gain over them, e.g., $+3.3\% / 2.2\%$ for action/noun. The only exception is the lower accuracy $(-2.7\%)$ on verb when compared with MoViNet (MoViNet-A6) in L4, a heavily optimized video backbone. + +# 4.3. Predicting Diverse Future Steps + +One of the defining characteristics of our model is that it allows us to sample multiple predictions of video representation corresponding to a future step. This leads to an interesting question about the diversity of the predictions, as partially evaluated in our prior experiments. Here we present further demonstration of this capability by visualizing the step forecasting results, and more interestingly, using these results to generate future video frames. + +Fig. 3 presents the visualization for zero-shot step forecasting and key frame generation. In this setting, our model is pre-trained without any human annotation and is directly tested for step forecasting. We show multiple predictions sampled from our diffusion model. Further, we demonstrate that the text description of predicted step can be used to generate the key frames by leveraging the stable diffusion model [45]. To keep the generated images visually consistent with the input video, we let stable diffusion model take one input video frame and the description of predicted step as input and generate an image. + +As shown in Fig. 3, our model is capable of forecasting multiple, reasonable next steps (e.g., "flatten the dough", "bake pizza"), based on which credible future frames can be generated. These results suggest that our model not only predicts meaningful video representations of individual steps, but also captures the variations in step ordering + +# 4.4. Ablation Studies + +We conduct ablation study on COIN, including step classification/forecasting with zero-shot/fine-tuning setting. Additional ablation results can be found in Supplement. + +Does modeling of temporal order help? In Table 4, we conduct a comparison on two different pre-training tasks: (1) pre-training by only matching video representations to text embeddings of the verb phrases; and (2) pre-training by our method that combines matching and temporal order modeling. In comparison to pre-training using matching only, our method significantly improves the performance for both zero-shot and fine-tuning settings and across step classification and step forecasting tasks. For example, zero-shot step classification is improved from $13.7\%$ to $16.6\%$ . Our results after fine-tuning attains a major gain of $+3.6\%$ and $+1.8\%$ for step classification and step forecasting, respectively. Importantly, our method also enables zero-shot step forecasting by predicting future video representations. + +![](images/8edebb78e621f0deb6596584938435d870e707b6f1a671691a870c4f9190b052.jpg) +Model input: videos + +![](images/150db6b49274a701438196f77f35163587eeeaf17678abf5cbf9cf533a401992.jpg) +Figure 3. Visualization of zero-shot step forecasting and key frame generation. Without using any human annotation during training, our trained model is directly evaluated on COIN dataset [56]. Given a video recording previous steps (left), our model is capable of forecasting multiple reasonable predictions and each predicted step is further used for key frame generation (right). We adopt stable diffusion [45] for key frame generation, taking inputs as a text description of step and a sampled frame from input video. + +![](images/dd54d529529683d3150e1e473c75b81b4f13b410c83b39a63cdd9c317bde711f.jpg) +Diverse predictions and generated key frames for next step +flatten the dough + +![](images/d63f8ab5993a6912447bccc3924b3c5c64bd28f4ccaddd78e491f217bafbc86f.jpg) +bake pizza + +![](images/c764b0f9f4f142f7ab56f970adcfe690b5441e3019f5a222fef89fb3e18f6791.jpg) +bake cookies + +![](images/6dd80753d9f637b270f1f69a95a12d51a1ccd0ed09fb4d02a40ec30b1f3678fa.jpg) +pour some salt to the garlics + +![](images/79d793f9ffc17a85b44817fa30b38934e5c4de3d6008de9e911d907f19d657fa.jpg) +put the ingredients into the bowl + +![](images/e1ce8a8edbb31093715537ef42fbcc31c2a0bf78a63d3e081245978c58b2021a.jpg) +prepare seasonings and side dishes + +
ModelPre-training taskOrder modelingZero-shot (top-1 acc. %)Fine-tuning (top-1 acc. %)
Step classificationStep forecastingStep classificationStep forecasting
OursMatching-13.7No zero-shot ability52.841.6
OursMatching + OrderingMask16.910.656.443.2
OursMatching + OrderingDiffusion16.611.356.946.8
+ +These results suggest that our procedure-aware pre-training can effectively facilitate the learning for both video representation and step ordering in procedure activities. + +Masked Prediction vs. Diffusion Model. We explore a model variant using the mask prediction, sharing similar spirit as the well-known Masked Language Modeling in BERT [24]. Specifically, this variant is trained to recover the video embeddings of masked video clips so that it can match to the assigned verb phrases. Our diffusion model largely outperforms the results of this variant, especially on the step forecasting $(+0.7\%)$ and $+3.6\%$ for zero-shot and fine-tuning, respectively, as in Table 4). This result indicates that our diffusion model is a more suitable way to capture the variation inside the step ordering. + +Approximate Inference. In Table 5, we validate that our approximate inference with a single zero-vector can achieve close empirical results as the Monte Carlo estimation (e.g., within $0.1\%$ difference). Monte Carlo estimation computes the weighted average of multiple sampled predictions (e.g., from 5 sampled noises). We run the experiment for 5 times and the variation is small (e.g., $\pm 0.03\%$ ). Further, if we assume an oracle ranking function to pick the correct one from sampled predictions, our results can be further boosted by $3.4\%$ on average, suggesting diverse predictions from our model and ample room to improve. + +Table 4. Ablation study of pre-training tasks and order modeling. Our proposed order pre-training not only enables zero-shot forecasting, but also significantly improves zero-shot step classification and the fine-tuning results across evaluation tasks. Our diffusion models further improves mask modeling, especially on fine-tuning step forecasting. + +
ModelInference TypeTop-1 Acc. (%)
OursApproximation11.33
OursExpectation11.34 ± 0.03
OursOracle14.73 ± 0.13
+ +Table 5. Ablation study of inference schemes. "Approximation" uses a zero-vector as noise, "Expectation" adopts Monte Carlo estimation, and "Oracle" further assumes an oracle ranking function to pick the correct prediction derived from multiple noises. + +# 5. Conclusion + +In this work, we presented a model and a training frame work for learning procedure-aware video representation from a large-scale dataset of instructional videos and their narrations, without the need for human annotations. The key strength of our model lies in the joint learning of a video encoder capturing concepts of action steps, as well as a diffusion model reasoning about the temporal dependencies among steps. We demonstrated that our model achieves strong results on step classification and forecasting in both zero-shot and fine-tuning settings and across COIN and EPIC-Kitchens-100 datasets. We believe our work provides a solid step towards understanding procedural activities. We hope that our work will shed light on the broader problem of video-language pre-training. + +# References + +[1] Jean-Baptiste Alayrac, Piotr Bojanowski, Nishant Agrawal, Josef Sivic, Ivan Laptev, and Simon Lacoste-Julien. Unsupervised learning from narrated instruction videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4575-4583, 2016. 2 +[2] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6836-6846, 2021. 6, 7 +[3] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1728-1738, 2021. 2 +[4] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In Proceedings of the International Conference on Machine Learning (ICML), July 2021. 6, 7 +[5] Piotr Bojanowski, Rémi Lajugie, Francis Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid, and Josef Sivic. Weakly supervised action labeling in videos under ordering constraints. In European Conference on Computer Vision, pages 628-643. Springer, 2014. 2 +[6] Piotr Bojanowski, Rémi Lajugie, Edouard Grave, Francis Bach, Ivan Laptev, Jean Ponce, and Cordelia Schmid. Weakly-supervised alignment of video with text. In Proceedings of the IEEE international conference on computer vision, pages 4462-4470, 2015. 2 +[7] M. Brand, N. Oliver, and A. Pentland. Coupled hidden markov models for complex action recognition. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 994-999, 1997. 1, 2 +[8] Chien-Yi Chang, De-An Huang, Danfei Xu, Ehsan Adeli, Li Fei-Fei, and Juan Carlos Niebles. Procedure planning in instructional videos. In European Conference on Computer Vision, pages 334–350. Springer, 2020. 2 +[9] Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. Diffusion models in vision: A survey. arXiv preprint arXiv:2209.04747, 2022. 4 +[10] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. The EPIC-KITCHENS dataset: Collection, challenges and baselines. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(11):4125-4141, 2021. 2, 5 +[11] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat GANs on image synthesis. Advances in Neural Information Processing Systems, 34:8780-8794, 2021. 5 +[12] E. Elhamifar and D. Huynh. Self-supervised multi-task procedure learning from instructional videos. European Conference on Computer Vision, 2020. 2 +[13] Ehsan Elhamifar and Zwe Naing. Unsupervised procedure learning via joint dynamic summarization. In Proceedings of + +the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019. 1, 2 +[14] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. SlowFast networks for video recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019. 6, 7 +[15] Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, and Zicheng Liu. VIOLET: End-to-end video-language transformers with masked visual-token modeling. arXiv preprint arXiv:2111.12681, 2021. 2 +[16] Deepti Ghadiyaram, Du Tran, and Dhruv Mahajan. Large-scale weakly-supervised pre-training for video action recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12046-12055, 2019. 2 +[17] Karan Goel and Emma Brunskill. Learning procedural abstractions and evaluating discrete latent temporal structure. In International Conference on Learning Representations, 2019. 2 +[18] Abhinav Gupta, Praveen Srinivasan, Jianbo Shi, and Larry S. Davis. Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 2012-2019, 2009. 1, 2 +[19] Tengda Han, Weidi Xie, and Andrew Zisserman. Temporal alignment networks for long-term video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2906-2916, 2022. 2 +[20] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. 4 +[21] Jonathan Ho, Tim Salimans, Alexey A Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. In Advances in Neural Information Processing Systems, 2022. 3 +[22] Y.A. Ivanov and A.F. Bobick. Recognition of visual activities and interactions by stochastic parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):852-872, 2000. 1, 2 +[23] Evangelos Kazakos, Arsha Nagrani, Andrew Zisserman, and Dima Damen. EPIC-fusion: Audio-visual temporal binding for egocentric action recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5492–5501, 2019. 7 +[24] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, pages 4171-4186, 2019. 3, 8 +[25] Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, and Boqing Gong. MoViNets: Mobile video networks for efficient video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16020-16030, 2021. 6, 7 +[26] Mahnaz Koupae and William Yang Wang. WikiHow: A large scale text summarization dataset. arXiv preprint arXiv:1810.09305, 2018. 2, 6 + +[27] Hilde Kuehne, Ali Arslan, and Thomas Serre. The language of actions: Recovering the syntax and semantics of goal-directed human activities. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014. 1, 2 +[28] Anna Kukleva, Hilde Kuehne, Fadime Sener, and Jurgen Gall. Unsupervised learning of action classes with continuous temporal embedding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12066-12074, 2019. 2 +[29] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L. Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7331-7341, June 2021. 2, 7 +[30] Junnan Li, Ramprasaath R Selvaraju, Akhilesh Deepak Gotmare, Shafiq Joty, Caiming Xiong, and Steven Hoi. Align before fuse: Vision and language representation learning with momentum distillation. arXiv preprint arXiv:2107.07651, 2021. 1 +[31] Linjie Li, Yen-Chun Chen, Yu Cheng, Zhe Gan, Licheng Yu, and Jingjing Liu. HERO: Hierarchical encoder for Video+Language omni-representation pre-training. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2046-2065, Online, Nov. 2020. Association for Computational Linguistics. 2 +[32] Ji Lin, Chuang Gan, and Song Han. TSM: Temporal shift module for efficient video understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7083-7093, 2019. 7 +[33] Xudong Lin, Fabio Petroni, Gedas Bertasius, Marcus Rohrbach, Shih-Fu Chang, and Lorenzo Torresani. Learning to recognize procedural activities with distant supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13853-13863, 2022. 1, 2, 6, 7 +[34] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018. 6 +[35] Huaishao Luo, Lei Ji, Botian Shi, Haoyang Huang, Nan Duan, Tianrui Li, Jason Li, Taroon Bharti, and Ming Zhou. Univl: A unified video and language pre-training model for multimodal understanding and generation. arXiv preprint arXiv:2002.06353, 2020. 2 +[36] Jonathan Malmaud, Jonathan Huang, Vivek Rathod, Nick Johnston, Andrew Rabinovich, and Kevin Murphy. What's cookin'? interpreting cooking videos using text, speech and vision. arXiv preprint arXiv:1503.01558, 2015. 2 +[37] Antoine Miech, Jean-Baptiste Alayrac, Lucas Smaira, Ivan Laptev, Josef Sivic, and Andrew Zisserman. End-to-end learning of visual representations from uncurated instructional videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9879-9889, 2020. 1, 2, 6, 7 +[38] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic. + +HowTo100M: Learning a text-video embedding by watching hundred million narrated video clips. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2630-2640, 2019. 2, 5 +[39] Ram Nevatia, Tao Zhao, and Somboon Hongeng. Hierarchical language-based representation of events in video streams. In 2003 Conference on Computer Vision and Pattern Recognition Workshop, volume 4, pages 39-39, 2003. 1, 2 +[40] Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models. In International Conference on Machine Learning, pages 16784-16804. PMLR, 2022. 3 +[41] Mingtao Pei, Yunde Jia, and Song-Chun Zhu. Parsing video events with goal inference and intent prediction. In 2011 International Conference on Computer Vision, pages 487-494, 2011. 1, 2 +[42] Yicheng Qian, Weixin Luo, Dongze Lian, Xu Tang, Peilin Zhao, and Shenghua Gao. SVIP: Sequence verification for procedures in videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19890-19902, 2022. 2 +[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (ICML), 2021. 1, 2, 3, 4, 5, 6, 7 +[44] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125, 2022.3 +[45] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 3, 7, 8 +[46] M.S. Ryoo and J.K. Aggarwal. Recognition of composite human activities through context-free grammar based representation. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), volume 2, pages 1709-1718, 2006. 1, 2 +[47] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022. 3 +[48] Fadime Sener and Angela Yao. Zero-shot anticipation for instructional activities. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 862-871, 2019. 2 +[49] Ozan Sener, Amir R Zamir, Silvio Savarese, and Ashutosh Saxena. Unsupervised semantic parsing of video collections. In Proceedings of the IEEE International conference on Computer Vision, pages 4480-4488, 2015. 2 + +[50] Yuhan Shen, Lu Wang, and Ehsan Elhamifar. Learning to segment actions from visual and language instructions via differentiable weak sequence alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10156-10165, June 2021. 2, 4, 5 +[51] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015. 3, 4 +[52] Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. MPNet: Masked and permuted pre-training for language understanding. Advances in Neural Information Processing Systems, 33:16857-16867, 2020. 2 +[53] Yang Song and Stefano Ermon. Improved techniques for training score-based generative models. Advances in neural information processing systems, 33:12438-12448, 2020. 3, 4 +[54] Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid. VideoBERT: A joint model for video and language representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7464-7473, 2019. 1, 2 +[55] Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, and Jie Zhou. COIN: A large-scale dataset for comprehensive instructional video analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1207-1216, 2019. 2, 5, 7 +[56] Yansong Tang, Jiwen Lu, and Jie Zhou. Comprehensive instructional video analysis: The COIN dataset and performance evaluation. TPAMI, 2020. 5, 8 +[57] Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H Bermano. Human motion diffusion model. In International Conference on Learning Representations, 2023. 3, 4 +[58] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 6 +[59] Alex Jinpeng Wang, Yixiao Ge, Rui Yan, Yuying Ge, Xudong Lin, Guanyu Cai, Jianping Wu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. All in one: Exploring unified video-language pre-training. arXiv preprint arXiv:2203.07303, 2022. 2 +[60] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaou Tang, and Luc Van Gool. Temporal segment networks: Towards good practices for deep action recognition. In European conference on computer vision, pages 20-36. Springer, 2016. 7 +[61] Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, and Kevin Murphy. Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification. In Proceedings of the European conference on computer vision (ECCV), pages 305–321, 2018. 6, 7 +[62] Frank F. Xu, Lei Ji, Botian Shi, Junyi Du, Graham Neubig, Yonatan Bisk, and Nan Duan. A benchmark for structured + +procedural knowledge extraction from cooking videos. In Proceedings of the First International Workshop on Natural Language Processing Beyond Text, pages 30-40, Online, Nov. 2020. Association for Computational Linguistics. 2 +[63] Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. VideoCLIP: Contrastive pretraining for zero-shot video-text understanding. arXiv preprint arXiv:2109.14084, 2021. 2, 6, 7 +[64] Jianwei Yang, Yonatan Bisk, and Jianfeng Gao. TACo: Token-aware cascade contrastive learning for video-text alignment. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11562-11572, 2021. 2 +[65] Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. Diffusion models: A comprehensive survey of methods and applications. arXiv preprint arXiv:2209.00796, 2022. 4 +[66] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. MERLOT: Multimodal neural script knowledge models. Advances in Neural Information Processing Systems, 34:23634-23651, 2021. 2, 3 +[67] Henghui Zhao, Isma Hadji, Nikita Dvornik, Konstantinos G. Derpanis, Richard P. Wildes, and Allan D. Jepson. P3IV: Probabilistic procedure planning from instructional videos with weak supervision. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2928-2938, 2022. 2 +[68] Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba. Temporal relational reasoning in videos. In Proceedings of the European conference on computer vision (ECCV), pages 803-818, 2018. 7 +[69] Luowei Zhou, Chenliang Xu, and Jason J Corso. Towards automatic learning of procedures from web instructional videos. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018. 1, 2 +[70] Linchao Zhu and Yi Yang. ActBERT: Learning global-local video-text representations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8746-8755, 2020. 1, 2 +[71] Dimitri Zhukov, Jean-Baptiste Alayrac, Ramadan Gokberk Cinbis, David Fouhey, Ivan Laptev, and Josef Sivic. Cross-task weakly supervised learning from instructional videos. 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In this work, we propose to learn video representation that encodes both action steps and their temporal ordering, based on a largescale dataset of web instructional videos and their narrations, without using human annotations. Our method jointly learns a video representation to encode individual step concepts, and a deep probabilistic model to capture both temporal dependencies and immense individual variations in the step ordering. We empirically demonstrate that learning temporal ordering not only enables new capabilities for procedure reasoning, but also reinforces the recognition of individual steps. 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While most people tend to whisk eggs in a bowl, melt butter in a pan, and cook eggs under medium heat, expert chefs have recommended to crack eggs into the pan, add butter, and stir them under high heat. Imagine a vision model that can account for the individual variations and reason about the temporal ordering of action steps in a video, so as to infer prior missing steps, recognize the current step, and forecast" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 309, + 230, + 545, + 289 + ], + "blocks": [ + { + "bbox": [ + 309, + 230, + 545, + 289 + ], + "lines": [ + { + "bbox": [ + 309, + 230, + 545, + 289 + ], + "spans": [ + { + "bbox": [ + 309, + 230, + 545, + 289 + ], + "type": "image", + "image_path": "deb55a7fa0269ce55a71bd99c40f8df251542e94bc7bbf4564ac61c4adcfa2a6.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 307, + 294, + 543, + 351 + ], + "blocks": [ + { + "bbox": [ + 307, + 294, + 543, + 351 + ], + "lines": [ + { + "bbox": [ + 307, + 294, + 543, + 351 + ], + "spans": [ + { + "bbox": [ + 307, + 294, + 543, + 351 + ], + "type": "image", + "image_path": "9b380ec0b7e0c906b0ba6e7139fe316c0f5338affaa173993ea1aa306e3ef5d7.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 356, + 546, + 411 + ], + "lines": [ + { + "bbox": [ + 305, + 356, + 546, + 411 + ], + "spans": [ + { + "bbox": [ + 305, + 356, + 546, + 411 + ], + "type": "text", + "content": "Figure 1. Top: During training, our model learns from procedural videos and step descriptions to understand individual steps and capture temporal ordering and variations among steps. Bottom: Once trained, our model supports zero-shot step classification and forecasting, yielding multiple credible predictions." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 425, + 545, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 425, + 545, + 460 + ], + "spans": [ + { + "bbox": [ + 305, + 425, + 545, + 460 + ], + "type": "text", + "content": "a future step. Such a model will be immensely useful for a wide range of applications including augmented reality, virtual personal assistant, and human-robot interaction." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 461, + 546, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 461, + 546, + 616 + ], + "spans": [ + { + "bbox": [ + 304, + 461, + 546, + 616 + ], + "type": "text", + "content": "Understanding complex procedural activities has been a long-standing challenge in the vision community [7, 18, 22, 39, 41, 46]. While many prior approaches learn from annotated videos following a fully supervised setting [13, 27, 69], this paradigm is difficult to scale to a plethora of activities and their variants among individuals. A promising solution is offered by the exciting advances in vision-and-language pre-training, where models learn from visual data (images or videos) and their paired text data (captions or narrations) [30, 43, 54, 70] in order to recognize a variety of concepts. This idea has recently been explored to analyze instructional videos [33, 37], yet existing methods are limited to recognize single action steps in procedural activities." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 618, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 618, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 618, + 547, + 714 + ], + "type": "text", + "content": "In this paper, we present a first step towards modeling temporal ordering of action steps in procedural activities by learning from instructional videos and their narrations. Our key innovation lies in the joint learning of a video representation aiming to encode individual step concepts, and a deep probabilistic model designed to capture temporal dependencies and variations among steps. The video representation, instantiated as a Transformer network, is learned by" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 693, + 233, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 693, + 233, + 703 + ], + "spans": [ + { + "bbox": [ + 58, + 693, + 233, + 703 + ], + "type": "text", + "content": "*Work done while Yiwu Zhong was an intern at Meta." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 59, + 703, + 149, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 703, + 149, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 703, + 149, + 712 + ], + "type": "text", + "content": "†Co-corresponding authors." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14825" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 288, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 288, + 167 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 288, + 167 + ], + "type": "text", + "content": "matching a video clip to its corresponding narration. The probabilistic model, built on a diffusion process, is tasked to predict the distribution of the video representation for a missing step, given steps in its vicinity. With the help of a pre-trained vision-and-language model [43], our model is trained using only videos and their narrations from automatic speech recognition (ASR), and thus does not require any manual annotations." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 168, + 288, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 168, + 288, + 323 + ], + "spans": [ + { + "bbox": [ + 46, + 168, + 288, + 323 + ], + "type": "text", + "content": "Once learned, our model celebrates two unique benefits thanks to our model design and training framework. First, our model supports zero-shot inference given an input video, including the recognition of single steps and forecasting of future steps, and can be further fine-tuned on downstream tasks. Second, our model allows sampling multiple video representations when predicting a missing action step, with each presenting a possibly different hypothesis of the step ordering. Instead of predicting a single representation with the highest probability, sampling from a probabilistic model provides access to additional high-probability solutions that might be beneficial to prediction tasks with high ambiguity or requiring user interactions." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 323, + 288, + 526 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 323, + 288, + 526 + ], + "spans": [ + { + "bbox": [ + 46, + 323, + 288, + 526 + ], + "type": "text", + "content": "We train our models on a large-scale instructional video dataset collected from YouTube (HowTo100M [38]), and evaluate them on two public benchmarks (COIN [55] and EPIC-Kitchens-100 [10]) covering a wide range of procedural videos and across the tasks of step classification and step forecasting. Through extensive experiments, we demonstrate that (1) our temporal model is highly effective in forecasting future steps, outperforming state-of-the-art methods by a large margin of " + }, + { + "bbox": [ + 46, + 323, + 288, + 526 + ], + "type": "inline_equation", + "content": "+7.4\\%" + }, + { + "bbox": [ + 46, + 323, + 288, + 526 + ], + "type": "text", + "content": " in top-1 accuracy on COIN; (2) modeling temporal ordering reinforces video representation learning, leading to improved classification results " + }, + { + "bbox": [ + 46, + 323, + 288, + 526 + ], + "type": "inline_equation", + "content": "(+2.8\\% / + 3.3\\%)" + }, + { + "bbox": [ + 46, + 323, + 288, + 526 + ], + "type": "text", + "content": " for step classification on COIN/EPIC-Kitchens) when probing the learned representations; (3) our training framework offers strong results for zero-shot step classification and forecasting; and (4) sampling from our probabilistic model yields diverse and plausible predictions of future steps." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 529, + 288, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 529, + 288, + 661 + ], + "spans": [ + { + "bbox": [ + 46, + 529, + 288, + 661 + ], + "type": "text", + "content": "Contributions. Our work presents the first model that leverages video-and-language pre-training to capture the temporal ordering of action steps in procedural activities. Our key technical innovation lies in the design of a deep probabilistic model using a diffusion process, in tandem with video-and-language representation learning. The result is a model and a training framework that establish new state-of-the-art results on both step classification and forecasting tasks across the major benchmarks. Besides, our model is capable of generating diverse step predictions and supports zero-shot inference." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 669, + 134, + 681 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 669, + 134, + 681 + ], + "spans": [ + { + "bbox": [ + 47, + 669, + 134, + 681 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "type": "text", + "content": "Understanding Procedural Activities. Reasoning about procedural activities, including their action steps and the" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 72, + 545, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 215 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 215 + ], + "type": "text", + "content": "temporal ordering of these steps, has been a central problem in activity recognition. While early works model temporal ordering with stochastic grammars [7, 18, 22, 39, 41, 46], more recent works consider supervised learning to localize steps and predict their ordering by learning from videos with human annotated action steps [8, 10, 13, 27, 55, 69, 71]. To alleviate the burden of costly video annotations, several works propose various forms of weakly supervised settings, with assumptions that the ordered list of steps is given without their temporal boundaries [5, 6, 67, 71], or that the key steps and their ordering remain fixed across all videos [1, 12, 13, 17, 28, 49]." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 216, + 545, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 216, + 545, + 323 + ], + "spans": [ + { + "bbox": [ + 304, + 216, + 545, + 323 + ], + "type": "text", + "content": "Most of prior methods focus on the tasks of step classification and localization [1,5,6,12,13,28,49,71]. Others have considered the tasks of step forecasting [48], step verification [42] and procedure planning [67]. Our work also seeks to understand procedural activities. Different from these approaches, our method focuses on learning video representation from videos and their narrations without using human annotations. The resulting video representation can be leveraged for step classification and step forecasting." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "spans": [ + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "text", + "content": "Learning from Procedural Videos and Narrations. The success of vision-and-language pre-training has fueled a new line of research that seeks to learn concepts of individual steps from instructional videos and their narrations [19, 36, 50, 62]. For example, Miech et al. [37] propose MIL-NCE to learn representations from instructional videos [38] and their narrations extracted using ASR." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 411, + 545, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 411, + 545, + 567 + ], + "spans": [ + { + "bbox": [ + 304, + 411, + 545, + 567 + ], + "type": "text", + "content": "The most relevant work is DistantSup [33], where they propose using distant supervision from a textual knowledge base (wikiHow) [26] to denoise text narrations from ASR. Specifically, DistantSup leverages a pre-trained language model [52] to link step descriptions from wikiHow to text narrations from video ASR results, and thus to create training labels for individual steps in videos. Different from [33], our method models the temporal ordering of steps in procedural activities, thus moves beyond representations of single steps to support temporal reasoning in videos. Further, our method learns from videos and narrations only, with the help of a pre-trained image-language model [43] yet without using a textual knowledge base." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": "Video-and-Language Pre-Training. A relevant topic is video-and-language pre-training, aiming at learning video representation from videos and their paired natural language descriptions [3,15,16,29,31,35,54,59,63,64,66,70], often generated from ASR outputs. Despite the latest development in ASR, automatically-transcribed speech from videos can be rather noisy and lacks precise temporal alignment with the visual content. Several recent works seek to address this challenge. VideoCLIP [63] starts from the pretrained MIL-NCE model and further improves the model by retrieval augmented training with overlapped video-text pairs. Bain et al. [3] collect a less noisy dataset of video alt" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "14826" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 52, + 72, + 399, + 262 + ], + "blocks": [ + { + "bbox": [ + 52, + 72, + 399, + 262 + ], + "lines": [ + { + "bbox": [ + 52, + 72, + 399, + 262 + ], + "spans": [ + { + "bbox": [ + 52, + 72, + 399, + 262 + ], + "type": "image", + "image_path": "0daf97944a45cc64dbff9059314400d7fd8471ba4d8983b9a9282a6f4a449406.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 268, + 545, + 323 + ], + "lines": [ + { + "bbox": [ + 46, + 268, + 545, + 323 + ], + "spans": [ + { + "bbox": [ + 46, + 268, + 545, + 323 + ], + "type": "text", + "content": "Figure 2. Overview of our approach. Left panel: Our model consists of (1) a video encoder that takes a video clip and encodes it into a video embedding; (2) a transformer-based denoising model that samples noises from Gaussian distribution and generates video embeddings conditioned on the embeddings of adjacent video clips. Right panel: We leverage trained image-language model CLIP to create pseudo labels for individual video clips (a). After training, our model supports step classification given an input video clip (b), and step forecasting given a video that records previous steps (c). Note that diverse embeddings can be generated by sampling various noises." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 404, + 72, + 538, + 261 + ], + "blocks": [ + { + "bbox": [ + 404, + 72, + 538, + 261 + ], + "lines": [ + { + "bbox": [ + 404, + 72, + 538, + 261 + ], + "spans": [ + { + "bbox": [ + 404, + 72, + 538, + 261 + ], + "type": "image", + "image_path": "4923efcea09132ad967e11187a6c0df612ced7dab2747a9dcb15be8b53eb472a.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 334, + 287, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 334, + 287, + 380 + ], + "spans": [ + { + "bbox": [ + 46, + 334, + 287, + 380 + ], + "type": "text", + "content": "text pairs and geared the model to match these pairs. Our work shares the key idea of learning from video and text data as prior work, and seeks to leverage external knowledge from a pre-trained image-language model [43]." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 382, + 287, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 382, + 287, + 514 + ], + "spans": [ + { + "bbox": [ + 46, + 382, + 287, + 514 + ], + "type": "text", + "content": "Another relevant work is MERLOT [66]. While both MERLOT and our work seek to learn video representation, our method differs from MERLOT in two folds. Our method models the sequence order of video clips for understanding procedural activities. MERLOT learns binary relative order between two given video frames for multi-modal reasoning and does not directly support action forecasting. Both methods consider a masked prediction task, yet MERLOT predicts the most likely text embeddings, while our method estimates the distribution of video representations using a deep probabilistic model." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 515, + 287, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 515, + 287, + 635 + ], + "spans": [ + { + "bbox": [ + 46, + 515, + 287, + 635 + ], + "type": "text", + "content": "Diffusion Models. Diffusion models [51, 53] provide a powerful approach to characterize the probability density of high dimensional signals, and have recently demonstrated impressive results on generating high fidelity visual data, such as images [40,44,45,47], videos [21], and human body motion [57]. Our work adapts diffusion process to model the temporal ordering of steps in procedural videos. In doing so, our method not only facilitates the learning of expressive video representations for individual steps, but also enables the anticipation of future action steps." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 645, + 102, + 657 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 645, + 102, + 657 + ], + "spans": [ + { + "bbox": [ + 47, + 645, + 102, + 657 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": "We consider the problem of learning video representation for understanding procedural activities from instructional videos and their narrations. An input video is represented as a sequence of " + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": " clips " + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\{v_{1}, v_{2}, \\ldots, v_{N}\\}" + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": ". Each" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "spans": [ + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "inline_equation", + "content": "v_{i}" + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "text", + "content": " captures a potential action step in the input video, and the time step " + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "text", + "content": " records the temporal ordering of these clips. The video clips " + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "inline_equation", + "content": "\\{v_{i}\\}" + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "text", + "content": " can be either segmented by using the timestamps of ASR outputs (as we consider during training), or densely sampled from a video following their temporal ordering (as we use during inference). During learning, we further assume that an ordered set of sentences " + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "inline_equation", + "content": "\\{s_1,s_2,\\dots,s_N\\}" + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "text", + "content": " is associated with the video clips " + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "inline_equation", + "content": "\\{v_{1},v_{2},\\dots,v_{N}\\}" + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "text", + "content": ", with each " + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "inline_equation", + "content": "s_i" + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "text", + "content": " describing the action step in video clip " + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "inline_equation", + "content": "v_{i}" + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "text", + "content": ". These sentences " + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "inline_equation", + "content": "\\{s_i\\}" + }, + { + "bbox": [ + 304, + 334, + 545, + 478 + ], + "type": "text", + "content": " can be the output text from ASR, or given by matching the video clips to a text corpus using an external vision language model [43]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "spans": [ + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "text", + "content": "Procedural-aware Video Representation. Our goal is to learn video representation that encodes both action step concepts and their temporal dependencies across a range of procedural activities. Our representation consists of (a) a video encoder " + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "text", + "content": " that extracts a representation " + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "text", + "content": " from an input clip " + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "inline_equation", + "content": "v_{i}" + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "text", + "content": " (i.e., " + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "inline_equation", + "content": "x_{i} = f(v_{i})" + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "text", + "content": "); and (b) a probabilistic model that characterizes the conditional probability " + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "inline_equation", + "content": "p(x_{j} = f(v_{j}) | \\{x_{i} = f(v_{i})\\}_{i \\neq j}) \\forall j" + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "text", + "content": ". This design is highly flexible and supports a number of procedural reasoning tasks. " + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "text", + "content": " offers video representation suitable to classify individual steps in a clip. " + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "inline_equation", + "content": "p(x_{j} | \\{x_{i}\\}_{i \\neq j})" + }, + { + "bbox": [ + 304, + 482, + 545, + 637 + ], + "type": "text", + "content": " models the temporal dependencies among steps, and can be used to predict the video representation of missing steps and further infer their labels." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "type": "text", + "content": "Method Overview. To learn our representation, we leverage a pre-trained text encoder " + }, + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "type": "text", + "content": " that remains fixed during learning, and extend the idea of masked token modeling, populated in natural language processing [24]. For each input video and its narrations at training time, we randomly sample a clip " + }, + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "type": "inline_equation", + "content": "v_{j}" + }, + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\{v_{1},v_{2},\\dots,v_{N}\\}" + }, + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "type": "text", + "content": " and mask it" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14827" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "text", + "content": "out. We then train our model to predict the distribution of " + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "inline_equation", + "content": "x_{j} = f(v_{j})" + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "inline_equation", + "content": "\\{x_{i} = f(v_{i})\\}_{i \\neq j}" + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "text", + "content": " (i.e., " + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "inline_equation", + "content": "p(x_{j} | \\{x_{i}\\}_{i \\neq j}))" + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "text", + "content": "), align the expectation of the predicted distribution " + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "inline_equation", + "content": "\\mathbb{E}(x_{j})" + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "text", + "content": " with the corresponding text embedding " + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "inline_equation", + "content": "y_{j} = g(s_{j})" + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "text", + "content": ", and match all other video representations " + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "inline_equation", + "content": "\\{x_{i} = f(v_{i})\\}_{i \\neq j}" + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "text", + "content": " to their text embeddings " + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "inline_equation", + "content": "\\{y_{i} = g(s_{i})\\}_{i \\neq j}" + }, + { + "bbox": [ + 46, + 72, + 287, + 144 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 144, + 288, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 144, + 288, + 251 + ], + "spans": [ + { + "bbox": [ + 46, + 144, + 288, + 251 + ], + "type": "text", + "content": "Despite the conceptual similarity, our learning is fundamentally different from masked token prediction. Our method seeks to characterize the distribution of " + }, + { + "bbox": [ + 46, + 144, + 288, + 251 + ], + "type": "inline_equation", + "content": "x_{j}" + }, + { + "bbox": [ + 46, + 144, + 288, + 251 + ], + "type": "text", + "content": " instead of predicting the most likely " + }, + { + "bbox": [ + 46, + 144, + 288, + 251 + ], + "type": "inline_equation", + "content": "x_{j}" + }, + { + "bbox": [ + 46, + 144, + 288, + 251 + ], + "type": "text", + "content": ", resulting in a more principled approach to capture the temporal dependencies among steps, as well as the new capability of sampling multiple high-probability solutions for " + }, + { + "bbox": [ + 46, + 144, + 288, + 251 + ], + "type": "inline_equation", + "content": "x_{j}" + }, + { + "bbox": [ + 46, + 144, + 288, + 251 + ], + "type": "text", + "content": ". Our method is illustrated in Fig. 2. In what follows, we lay out the formulation of our model, and describe its training and inference schemes." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 260, + 272, + 273 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 260, + 272, + 273 + ], + "spans": [ + { + "bbox": [ + 47, + 260, + 272, + 273 + ], + "type": "text", + "content": "3.1. Modeling Action Steps and Their Ordering" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 278, + 287, + 339 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 278, + 287, + 339 + ], + "spans": [ + { + "bbox": [ + 47, + 278, + 287, + 339 + ], + "type": "text", + "content": "Formally, given an input video with its clips " + }, + { + "bbox": [ + 47, + 278, + 287, + 339 + ], + "type": "inline_equation", + "content": "\\{v_{1}, v_{2}, \\ldots, v_{N}\\}" + }, + { + "bbox": [ + 47, + 278, + 287, + 339 + ], + "type": "text", + "content": " and their narrations " + }, + { + "bbox": [ + 47, + 278, + 287, + 339 + ], + "type": "inline_equation", + "content": "\\{s_{1}, s_{2}, \\ldots, s_{N}\\}" + }, + { + "bbox": [ + 47, + 278, + 287, + 339 + ], + "type": "text", + "content": ", our method assumes a factorization of " + }, + { + "bbox": [ + 47, + 278, + 287, + 339 + ], + "type": "inline_equation", + "content": "p(Y = \\{y_{i}\\} | X = \\{x_{i}\\})" + }, + { + "bbox": [ + 47, + 278, + 287, + 339 + ], + "type": "text", + "content": " with video representation " + }, + { + "bbox": [ + 47, + 278, + 287, + 339 + ], + "type": "inline_equation", + "content": "x_{i} = f(v_{i})" + }, + { + "bbox": [ + 47, + 278, + 287, + 339 + ], + "type": "text", + "content": " (learnable) and text embedding " + }, + { + "bbox": [ + 47, + 278, + 287, + 339 + ], + "type": "inline_equation", + "content": "y_{i} = f(s_{i})" + }, + { + "bbox": [ + 47, + 278, + 287, + 339 + ], + "type": "text", + "content": " (pre-trained and fixed)." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 346, + 287, + 361 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 346, + 287, + 361 + ], + "spans": [ + { + "bbox": [ + 55, + 346, + 287, + 361 + ], + "type": "interline_equation", + "content": "p (Y | X) = p \\left(y _ {j} \\mid x _ {j}\\right) \\cdot p \\left(x _ {j} \\mid \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right) \\cdot \\prod_ {i} p \\left(y _ {i} \\mid x _ {i}\\right) \\quad \\forall j. \\tag {1}", + "image_path": "009fe4c4049c3f682165935bff8cff55a119181fec39bf59becf0c830818e80c.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 368, + 287, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 368, + 287, + 452 + ], + "spans": [ + { + "bbox": [ + 46, + 368, + 287, + 452 + ], + "type": "inline_equation", + "content": "p(y_{i}|x_{i})" + }, + { + "bbox": [ + 46, + 368, + 287, + 452 + ], + "type": "text", + "content": " measures the alignment between a video representation " + }, + { + "bbox": [ + 46, + 368, + 287, + 452 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 46, + 368, + 287, + 452 + ], + "type": "text", + "content": " and a text embedding " + }, + { + "bbox": [ + 46, + 368, + 287, + 452 + ], + "type": "inline_equation", + "content": "y_{i}" + }, + { + "bbox": [ + 46, + 368, + 287, + 452 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 46, + 368, + 287, + 452 + ], + "type": "inline_equation", + "content": "p(x_{j}|\\{x_{i}\\}_{i\\neq j})" + }, + { + "bbox": [ + 46, + 368, + 287, + 452 + ], + "type": "text", + "content": " characterizes the distribution of a video representation for a missing step given the representations of all other steps, thereby modeling the temporal ordering of steps. Note that our model is not limited to single step prediction and can be readily extended to predict multiple missing steps." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 455, + 287, + 539 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 455, + 287, + 539 + ], + "spans": [ + { + "bbox": [ + 46, + 455, + 287, + 539 + ], + "type": "text", + "content": "Matching Image and Text Representations. Our model matches the video representation " + }, + { + "bbox": [ + 46, + 455, + 287, + 539 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 46, + 455, + 287, + 539 + ], + "type": "text", + "content": " and text embedding " + }, + { + "bbox": [ + 46, + 455, + 287, + 539 + ], + "type": "inline_equation", + "content": "y_{i}" + }, + { + "bbox": [ + 46, + 455, + 287, + 539 + ], + "type": "text", + "content": " in a learned vector space, such that the alignment between them can be measured by cosine similarity. We will later instantiate this definition into a more tractable form for learning. Yet it suffices to notice that " + }, + { + "bbox": [ + 46, + 455, + 287, + 539 + ], + "type": "inline_equation", + "content": "p(y_{j}|x_{j})" + }, + { + "bbox": [ + 46, + 455, + 287, + 539 + ], + "type": "text", + "content": " does not involve additional learnable parameters given " + }, + { + "bbox": [ + 46, + 455, + 287, + 539 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 46, + 455, + 287, + 539 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 455, + 287, + 539 + ], + "type": "inline_equation", + "content": "y_{i}" + }, + { + "bbox": [ + 46, + 455, + 287, + 539 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 542, + 287, + 638 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 542, + 287, + 638 + ], + "spans": [ + { + "bbox": [ + 46, + 542, + 287, + 638 + ], + "type": "text", + "content": "Modeling Step Ordering with Diffusion Process. The key challenge lies in the modeling of " + }, + { + "bbox": [ + 46, + 542, + 287, + 638 + ], + "type": "inline_equation", + "content": "p(x_{j}|\\{x_{i}\\}_{i\\neq j})" + }, + { + "bbox": [ + 46, + 542, + 287, + 638 + ], + "type": "text", + "content": ", as the video representation " + }, + { + "bbox": [ + 46, + 542, + 287, + 638 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 46, + 542, + 287, + 638 + ], + "type": "text", + "content": " is at least of a few hundred dimensions. To this end, we propose to model " + }, + { + "bbox": [ + 46, + 542, + 287, + 638 + ], + "type": "inline_equation", + "content": "p(x_{j}|\\{x_{i}\\}_{i\\neq j})" + }, + { + "bbox": [ + 46, + 542, + 287, + 638 + ], + "type": "text", + "content": " using a diffusion process [51, 53] conditioned on observed video representations " + }, + { + "bbox": [ + 46, + 542, + 287, + 638 + ], + "type": "inline_equation", + "content": "\\{x_{i}\\}_{i\\neq j}" + }, + { + "bbox": [ + 46, + 542, + 287, + 638 + ], + "type": "text", + "content": ". Here we briefly describe diffusion process in the context of our model, and refer the readers to recent surveys for more technical details [9, 65]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 639, + 287, + 663 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 639, + 287, + 663 + ], + "spans": [ + { + "bbox": [ + 46, + 639, + 287, + 663 + ], + "type": "text", + "content": "Specifically, we assume a diffusion process that gradually adds noise to the input " + }, + { + "bbox": [ + 46, + 639, + 287, + 663 + ], + "type": "inline_equation", + "content": "x_{j}" + }, + { + "bbox": [ + 46, + 639, + 287, + 663 + ], + "type": "text", + "content": " over " + }, + { + "bbox": [ + 46, + 639, + 287, + 663 + ], + "type": "inline_equation", + "content": "t \\in [0,1,\\dots,T]" + }, + { + "bbox": [ + 46, + 639, + 287, + 663 + ], + "type": "text", + "content": " steps." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 91, + 671, + 287, + 700 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 671, + 287, + 700 + ], + "spans": [ + { + "bbox": [ + 91, + 671, + 287, + 700 + ], + "type": "interline_equation", + "content": "p \\left(x _ {j} ^ {1: T} \\mid x ^ {0}\\right) = \\prod_ {t = 1} ^ {T} p \\left(x _ {j} ^ {t} \\mid x _ {j} ^ {t - 1}\\right), \\tag {2}", + "image_path": "445db591afbc3c797ac90227367c9d0ff5aafca9430883f5418321c51b6e7c75.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 91, + 702, + 242, + 717 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 702, + 242, + 717 + ], + "spans": [ + { + "bbox": [ + 91, + 702, + 242, + 717 + ], + "type": "interline_equation", + "content": "p (x _ {j} ^ {t} | x _ {j} ^ {t - 1}) = \\mathcal {N} (\\sqrt {\\alpha_ {t}} x _ {j} ^ {t - 1}, (1 - \\alpha_ {t}) I).", + "image_path": "93128278cb6f82e1797e7e8e1da6bdcbbf9d3b5d6d053d674c7ea62ec775b6c7.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 72, + 545, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 72, + 545, + 96 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 545, + 96 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 306, + 72, + 545, + 96 + ], + "type": "inline_equation", + "content": "\\alpha_{t}" + }, + { + "bbox": [ + 306, + 72, + 545, + 96 + ], + "type": "text", + "content": " are constant hyper-parameters. The reverse diffusion (denoising) process is parameterized with" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 318, + 106, + 545, + 121 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 106, + 545, + 121 + ], + "spans": [ + { + "bbox": [ + 318, + 106, + 545, + 121 + ], + "type": "interline_equation", + "content": "p \\left(x _ {j} ^ {0: T} \\mid \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right) = p \\left(x _ {j} ^ {T} \\mid \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right) \\prod_ {t = 1} ^ {T} p _ {\\theta} \\left(x _ {j} ^ {t - 1} \\mid x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right). \\tag {3}", + "image_path": "8ba4ee7955233e5d81601e5a6d91c4bda156d3fc291897fd15da415e21c1a6e2.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 129, + 545, + 178 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 129, + 545, + 178 + ], + "spans": [ + { + "bbox": [ + 305, + 129, + 545, + 178 + ], + "type": "text", + "content": "With sufficiently large " + }, + { + "bbox": [ + 305, + 129, + 545, + 178 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 305, + 129, + 545, + 178 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 305, + 129, + 545, + 178 + ], + "type": "inline_equation", + "content": "p(x_j^T|\\{x_i\\}_{i \\neq j}) \\sim \\mathcal{N}(0, I)" + }, + { + "bbox": [ + 305, + 129, + 545, + 178 + ], + "type": "text", + "content": ", i.e. a standard Gaussian noise that is independent of " + }, + { + "bbox": [ + 305, + 129, + 545, + 178 + ], + "type": "inline_equation", + "content": "\\{x_i\\}_{i \\neq j}" + }, + { + "bbox": [ + 305, + 129, + 545, + 178 + ], + "type": "text", + "content": ". The denoising process is approximated by " + }, + { + "bbox": [ + 305, + 129, + 545, + 178 + ], + "type": "inline_equation", + "content": "p_\\theta(x_j^{t-1}|x_j^t, \\{x_i\\}_{i \\neq j})" + }, + { + "bbox": [ + 305, + 129, + 545, + 178 + ], + "type": "text", + "content": " using a neural network with parameters " + }, + { + "bbox": [ + 305, + 129, + 545, + 178 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 305, + 129, + 545, + 178 + ], + "type": "text", + "content": " such that" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 318, + 189, + 545, + 202 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 189, + 545, + 202 + ], + "spans": [ + { + "bbox": [ + 318, + 189, + 545, + 202 + ], + "type": "interline_equation", + "content": "p _ {\\theta} \\left(x _ {j} ^ {t - 1} \\mid x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right) = \\mathcal {N} \\left(\\mu_ {\\theta} \\left(x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right), \\Sigma_ {\\theta} \\left(x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}\\right)\\right) \\tag {4}", + "image_path": "4f06252cafa04c37ca1bdee59d7969281ec3b36fdcd270b0b2de97297d18b0de.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 211, + 545, + 247 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 211, + 545, + 247 + ], + "spans": [ + { + "bbox": [ + 305, + 211, + 545, + 247 + ], + "type": "text", + "content": "In practice, we follow Ho et al. [20] and Tevet et al. [57] to directly predict " + }, + { + "bbox": [ + 305, + 211, + 545, + 247 + ], + "type": "inline_equation", + "content": "x_{j}^{0}" + }, + { + "bbox": [ + 305, + 211, + 545, + 247 + ], + "type": "text", + "content": " by using a denoising model " + }, + { + "bbox": [ + 305, + 211, + 545, + 247 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 305, + 211, + 545, + 247 + ], + "type": "text", + "content": ". With slight abuse of the symbols, we denote" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 380, + 255, + 545, + 270 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 255, + 545, + 270 + ], + "spans": [ + { + "bbox": [ + 380, + 255, + 545, + 270 + ], + "type": "interline_equation", + "content": "\\hat {x} _ {j} ^ {0} = h \\left(x _ {j} ^ {t}, \\left\\{x _ {i} \\right\\} _ {i \\neq j}, t\\right). \\tag {5}", + "image_path": "2a48f83f56224fcb81558d687ca12454407a4ad39e52e26650e8f721f66652e3.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "spans": [ + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "type": "text", + "content": " is realized using a Transformer network with the embedding of step " + }, + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "type": "text", + "content": " as part of its inputs. Once learned, one can sample from " + }, + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "type": "inline_equation", + "content": "\\mathcal{N}(0,I)" + }, + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "type": "text", + "content": " and apply " + }, + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "type": "text", + "content": " through the denoising process to predict " + }, + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "type": "inline_equation", + "content": "x_{j}" + }, + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "type": "text", + "content": " based on " + }, + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "type": "inline_equation", + "content": "\\{x_i\\}_{i\\neq j}" + }, + { + "bbox": [ + 305, + 277, + 545, + 327 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 306, + 335, + 534, + 346 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 335, + 534, + 346 + ], + "spans": [ + { + "bbox": [ + 306, + 335, + 534, + 346 + ], + "type": "text", + "content": "3.2. Learning from Videos and Their Narrations" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 305, + 354, + 545, + 377 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 354, + 545, + 377 + ], + "spans": [ + { + "bbox": [ + 305, + 354, + 545, + 377 + ], + "type": "text", + "content": "Our training approximately maximizes the likelihood of Eq. 1 given a set of training videos and their narrations." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 305, + 380, + 545, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 380, + 545, + 464 + ], + "spans": [ + { + "bbox": [ + 305, + 380, + 545, + 464 + ], + "type": "text", + "content": "Pseudo Labels from CLIP. It is straightforward to directly align video representations to the embeddings of their corresponding ASR text. Doing so, however, faces the challenges of low-quality ASR text and imprecise alignment between video and ASR sentences. To address these challenges, we propose to create pseudo labels by leveraging a pre-trained image-language model (e.g., CLIP [43])." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 305, + 465, + 545, + 560 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 465, + 545, + 560 + ], + "spans": [ + { + "bbox": [ + 305, + 465, + 545, + 560 + ], + "type": "text", + "content": "Specifically, we first create a pool of step descriptions in the form of verb phrases (e.g., \"add water\", \"wear gloves\") parsed from ASR sentences [50], with their embeddings as " + }, + { + "bbox": [ + 305, + 465, + 545, + 560 + ], + "type": "inline_equation", + "content": "\\{y^{1:K}\\}" + }, + { + "bbox": [ + 305, + 465, + 545, + 560 + ], + "type": "text", + "content": ". Then a trained CLIP model is applied to link each video clip with verb phrases, by matching the averaged visual features across frames with the language embeddings of verb phrases. The resulting matching scores are used as our training target." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 305, + 561, + 545, + 586 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 561, + 545, + 586 + ], + "spans": [ + { + "bbox": [ + 305, + 561, + 545, + 586 + ], + "type": "text", + "content": "Our pseudo labeling instantiates the matching " + }, + { + "bbox": [ + 305, + 561, + 545, + 586 + ], + "type": "inline_equation", + "content": "p(y_{i}|x_{i})" + }, + { + "bbox": [ + 305, + 561, + 545, + 586 + ], + "type": "text", + "content": " between video representation and text embedding using" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 356, + 594, + 545, + 618 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 356, + 594, + 545, + 618 + ], + "spans": [ + { + "bbox": [ + 356, + 594, + 545, + 618 + ], + "type": "interline_equation", + "content": "p \\left(y _ {i} \\mid x _ {i}\\right) = \\operatorname {s o f t m a x} \\left(\\frac {x _ {i} ^ {T} y _ {i}}{\\tau \\| x _ {i} \\| \\| y _ {i} \\|}\\right), \\tag {6}", + "image_path": "60facf47956315e75955494f909351f87db0cb94413e76e38126e222a9322af7.jpg" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 305, + 625, + 545, + 674 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 625, + 545, + 674 + ], + "spans": [ + { + "bbox": [ + 305, + 625, + 545, + 674 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 625, + 545, + 674 + ], + "type": "inline_equation", + "content": "y_{i}" + }, + { + "bbox": [ + 305, + 625, + 545, + 674 + ], + "type": "text", + "content": " is selected from pool of verb phrases, i.e. " + }, + { + "bbox": [ + 305, + 625, + 545, + 674 + ], + "type": "inline_equation", + "content": "y_{i} \\in \\{y^{1:K}\\}" + }, + { + "bbox": [ + 305, + 625, + 545, + 674 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 305, + 625, + 545, + 674 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 305, + 625, + 545, + 674 + ], + "type": "text", + "content": " is the pre-defined temperature. The matching problem thus is converted into a \"classification\" problem, making the training feasible." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": "Learning Objective and Training Loss. Our training minimizes an evidence upper bound of the negative log likelihood " + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "inline_equation", + "content": "-\\log p(Y|X)" + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": " in Eq. 1. The detailed derivation of" + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14828" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "content": "evidence upper bound is described in the Supplement. Our objective function constitutes three loss terms:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 114, + 104, + 287, + 116 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 114, + 104, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 114, + 104, + 287, + 116 + ], + "type": "interline_equation", + "content": "\\mathrm {L} = \\mathrm {L} _ {\\mathrm {X E}} + \\mathrm {L} _ {\\mathrm {M S E}} + \\mathrm {L} _ {\\mathrm {M C}}. \\tag {7}", + "image_path": "3644c369d5ca7470ce87d1b8f98164b6179686f64df31d23f54035be670ea8e0.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 124, + 287, + 149 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 124, + 287, + 149 + ], + "spans": [ + { + "bbox": [ + 47, + 124, + 287, + 149 + ], + "type": "text", + "content": "The first term " + }, + { + "bbox": [ + 47, + 124, + 287, + 149 + ], + "type": "inline_equation", + "content": "\\mathrm{L}_{\\mathrm{XE}}" + }, + { + "bbox": [ + 47, + 124, + 287, + 149 + ], + "type": "text", + "content": " seeks to match observed video representation " + }, + { + "bbox": [ + 47, + 124, + 287, + 149 + ], + "type": "inline_equation", + "content": "\\{x_{i}\\}" + }, + { + "bbox": [ + 47, + 124, + 287, + 149 + ], + "type": "text", + "content": " to their text embeddings " + }, + { + "bbox": [ + 47, + 124, + 287, + 149 + ], + "type": "inline_equation", + "content": "\\{y_{i}\\}" + }, + { + "bbox": [ + 47, + 124, + 287, + 149 + ], + "type": "text", + "content": ", given by" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 97, + 155, + 287, + 169 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 155, + 287, + 169 + ], + "spans": [ + { + "bbox": [ + 97, + 155, + 287, + 169 + ], + "type": "interline_equation", + "content": "\\mathrm {L} _ {\\mathrm {X} E} = \\frac {1}{N - 1} \\sum_ {i} H \\left(p _ {i} ^ {\\prime}, p \\left(y _ {i} \\mid x _ {i}\\right)\\right), \\tag {8}", + "image_path": "037f7640ff78963bc671f1d0a747859c2bc57ed05d4ce8d9f0a5fede5e68362e.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 175, + 287, + 211 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 175, + 287, + 211 + ], + "spans": [ + { + "bbox": [ + 47, + 175, + 287, + 211 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 175, + 287, + 211 + ], + "type": "inline_equation", + "content": "H(\\cdot, \\cdot)" + }, + { + "bbox": [ + 47, + 175, + 287, + 211 + ], + "type": "text", + "content": " is the cross entropy, and " + }, + { + "bbox": [ + 47, + 175, + 287, + 211 + ], + "type": "inline_equation", + "content": "p_i'" + }, + { + "bbox": [ + 47, + 175, + 287, + 211 + ], + "type": "text", + "content": " are soft targets given by CLIP matching scores. " + }, + { + "bbox": [ + 47, + 175, + 287, + 211 + ], + "type": "inline_equation", + "content": "p(y_i|x_i)" + }, + { + "bbox": [ + 47, + 175, + 287, + 211 + ], + "type": "text", + "content": " defined in Eq. 6 measures the similarity between " + }, + { + "bbox": [ + 47, + 175, + 287, + 211 + ], + "type": "inline_equation", + "content": "x_i" + }, + { + "bbox": [ + 47, + 175, + 287, + 211 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 175, + 287, + 211 + ], + "type": "inline_equation", + "content": "y_i" + }, + { + "bbox": [ + 47, + 175, + 287, + 211 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 212, + 287, + 247 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 212, + 287, + 247 + ], + "spans": [ + { + "bbox": [ + 47, + 212, + 287, + 247 + ], + "type": "text", + "content": "The second term " + }, + { + "bbox": [ + 47, + 212, + 287, + 247 + ], + "type": "inline_equation", + "content": "\\mathrm{L}_{\\mathrm{MSE}}" + }, + { + "bbox": [ + 47, + 212, + 287, + 247 + ], + "type": "text", + "content": " comes from the Kullback-Leibler (KL) divergence within our diffusion model, and is computed as" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 70, + 254, + 287, + 271 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 254, + 287, + 271 + ], + "spans": [ + { + "bbox": [ + 70, + 254, + 287, + 271 + ], + "type": "interline_equation", + "content": "\\mathrm {L} _ {\\text {M S E}} = \\mathbb {E} _ {x _ {j} ^ {0} \\sim p \\left(x _ {j} ^ {0} \\mid \\{x _ {i} \\} _ {i \\neq j}\\right), t \\sim [ 1, T ]} \\left[ \\| x _ {j} ^ {0} - \\hat {x} _ {j} ^ {0} \\| _ {2} ^ {2} \\right]. \\tag {9}", + "image_path": "014a6f04a1299b78d754f4053548a82daf50c6b23c4d8cd77d26494c1abf05e4.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 277, + 287, + 301 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 277, + 287, + 301 + ], + "spans": [ + { + "bbox": [ + 47, + 277, + 287, + 301 + ], + "type": "text", + "content": "Note that unlike a standard diffusion model, our model directly predicts " + }, + { + "bbox": [ + 47, + 277, + 287, + 301 + ], + "type": "inline_equation", + "content": "\\hat{x}_j^0" + }, + { + "bbox": [ + 47, + 277, + 287, + 301 + ], + "type": "text", + "content": ". This term is applied at each step " + }, + { + "bbox": [ + 47, + 277, + 287, + 301 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 47, + 277, + 287, + 301 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 301, + 287, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 301, + 287, + 327 + ], + "spans": [ + { + "bbox": [ + 47, + 301, + 287, + 327 + ], + "type": "text", + "content": "The third term " + }, + { + "bbox": [ + 47, + 301, + 287, + 327 + ], + "type": "inline_equation", + "content": "\\mathrm{L}_{\\mathrm{MC}}" + }, + { + "bbox": [ + 47, + 301, + 287, + 327 + ], + "type": "text", + "content": " is derived from matching the predicted video representation " + }, + { + "bbox": [ + 47, + 301, + 287, + 327 + ], + "type": "inline_equation", + "content": "\\hat{x}_j^0" + }, + { + "bbox": [ + 47, + 301, + 287, + 327 + ], + "type": "text", + "content": " to its text embedding " + }, + { + "bbox": [ + 47, + 301, + 287, + 327 + ], + "type": "inline_equation", + "content": "y_{j}" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 55, + 334, + 287, + 352 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 334, + 287, + 352 + ], + "spans": [ + { + "bbox": [ + 55, + 334, + 287, + 352 + ], + "type": "interline_equation", + "content": "\\mathrm {L} _ {\\mathrm {M C}} = \\mathbb {E} _ {x _ {j} ^ {0} \\sim p \\left(x _ {j} ^ {0} \\mid \\{x _ {i} \\} _ {i \\neq j}\\right), t \\sim [ 1, T ]} \\left[ H \\left(p _ {j} ^ {\\prime}, p \\left(y _ {j} \\mid \\hat {x} _ {j} ^ {0}\\right)\\right) \\right], \\tag {10}", + "image_path": "cc75ac38e56387f0ccb4905fb1076daf4c39918d77be3fbff3dff441cbcd9a46.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "spans": [ + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "inline_equation", + "content": "p_j'" + }, + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "text", + "content": " are again soft targets given by CLIP model, and " + }, + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "inline_equation", + "content": "\\hat{x}_j^0" + }, + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "text", + "content": " is denoised from a sampled noise. During training, we adopt Monte Carlo estimation for " + }, + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "inline_equation", + "content": "\\mathbb{E}_p\\left[-\\log p(y_j|\\hat{x}_j^0)\\right]" + }, + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "text", + "content": ", by minimizing " + }, + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "inline_equation", + "content": "-\\log p(y_j|\\hat{x}_j^0)" + }, + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "text", + "content": " for each sampled " + }, + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "inline_equation", + "content": "\\hat{x}_j^0" + }, + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "text", + "content": ". We attach this term at each step " + }, + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 47, + 357, + 287, + 420 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 421, + 287, + 541 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 421, + 287, + 541 + ], + "spans": [ + { + "bbox": [ + 47, + 421, + 287, + 541 + ], + "type": "text", + "content": "A critical design choice lies in " + }, + { + "bbox": [ + 47, + 421, + 287, + 541 + ], + "type": "inline_equation", + "content": "p(y_{j}|\\hat{x}_{j}^{0})" + }, + { + "bbox": [ + 47, + 421, + 287, + 541 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 47, + 421, + 287, + 541 + ], + "type": "inline_equation", + "content": "p(y_{j}|\\hat{x}_{j}^{0})" + }, + { + "bbox": [ + 47, + 421, + 287, + 541 + ], + "type": "text", + "content": " is simplified into a score function between a video representation and a finite set of text embeddings (defined using verb phrases). This allows us to reach our loss terms without worrying about global normalization constant as commonly encountered in energy-based models. Indeed, " + }, + { + "bbox": [ + 47, + 421, + 287, + 541 + ], + "type": "inline_equation", + "content": "H(p_{j}^{\\prime},p(y_{j}|\\hat{x}_{j}^{0}))" + }, + { + "bbox": [ + 47, + 421, + 287, + 541 + ], + "type": "text", + "content": " can be interpreted as providing guidance by matching video to text embeddings. This term thus resembles the key idea of classifier guidance, which has shown to be helpful for learning diffusion models [11]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 548, + 146, + 560 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 548, + 146, + 560 + ], + "spans": [ + { + "bbox": [ + 47, + 548, + 146, + 560 + ], + "type": "text", + "content": "3.3. Model Inference" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 47, + 567, + 287, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 567, + 287, + 662 + ], + "spans": [ + { + "bbox": [ + 47, + 567, + 287, + 662 + ], + "type": "text", + "content": "Once trained, our model offers a procedure-aware representation with two key components. First, the video encoder " + }, + { + "bbox": [ + 47, + 567, + 287, + 662 + ], + "type": "inline_equation", + "content": "f(\\cdot)" + }, + { + "bbox": [ + 47, + 567, + 287, + 662 + ], + "type": "text", + "content": " serves as a feature extractor for any input video clips. Second, the diffusion model, represented as its denoising model " + }, + { + "bbox": [ + 47, + 567, + 287, + 662 + ], + "type": "inline_equation", + "content": "h(\\cdot)" + }, + { + "bbox": [ + 47, + 567, + 287, + 662 + ], + "type": "text", + "content": ", captures the temporal dependencies among steps. Our representation naturally supports a number of tasks. Here we demonstrate how our model can be used for step classification and step forecasting." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 47, + 665, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 665, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 665, + 287, + 713 + ], + "type": "text", + "content": "Step Classification. An input video clip " + }, + { + "bbox": [ + 47, + 665, + 287, + 713 + ], + "type": "inline_equation", + "content": "v" + }, + { + "bbox": [ + 47, + 665, + 287, + 713 + ], + "type": "text", + "content": " can be encoded using " + }, + { + "bbox": [ + 47, + 665, + 287, + 713 + ], + "type": "inline_equation", + "content": "f(\\cdot)" + }, + { + "bbox": [ + 47, + 665, + 287, + 713 + ], + "type": "text", + "content": ". The video representation " + }, + { + "bbox": [ + 47, + 665, + 287, + 713 + ], + "type": "inline_equation", + "content": "x = f(v)" + }, + { + "bbox": [ + 47, + 665, + 287, + 713 + ], + "type": "text", + "content": " can be directly compared to the text embeddings [43], so as to support zero-shot step classification. Alternative, an additional" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "text", + "content": "classifier can be attached on top of " + }, + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "text", + "content": " and further fine-tuned to recognize the action step in input clip." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 100, + 545, + 160 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 100, + 545, + 160 + ], + "spans": [ + { + "bbox": [ + 305, + 100, + 545, + 160 + ], + "type": "text", + "content": "Step Forecasting. A future video clip feature " + }, + { + "bbox": [ + 305, + 100, + 545, + 160 + ], + "type": "inline_equation", + "content": "x_{j}" + }, + { + "bbox": [ + 305, + 100, + 545, + 160 + ], + "type": "text", + "content": " can be sampled from the diffusion model by drawing from a Gaussian distribution and denoising using " + }, + { + "bbox": [ + 305, + 100, + 545, + 160 + ], + "type": "inline_equation", + "content": "h(\\cdot)" + }, + { + "bbox": [ + 305, + 100, + 545, + 160 + ], + "type": "text", + "content": ". The predicted " + }, + { + "bbox": [ + 305, + 100, + 545, + 160 + ], + "type": "inline_equation", + "content": "x_{j}" + }, + { + "bbox": [ + 305, + 100, + 545, + 160 + ], + "type": "text", + "content": " can be further classified into action steps. This prediction can be done using again Monte Carlo estimation given by" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 365, + 169, + 545, + 184 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 169, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 365, + 169, + 545, + 184 + ], + "type": "interline_equation", + "content": "\\mathbb {E} _ {x _ {j} \\sim p (x _ {j} | \\{x _ {i} \\} _ {i \\neq j})} [ p (y _ {j} | x _ {j}) ]. \\tag {11}", + "image_path": "715e8ba2e0f9796e8068c9605714d97a35273a7789e0d1c2606a064cfdea42f5.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "spans": [ + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "text", + "content": "Specifically, a noise " + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "inline_equation", + "content": "x_{T}" + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "text", + "content": " is first sampled from Gaussian distribution and our denoising model gradually denoises it. At each step " + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "text", + "content": ", the denoising model " + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "text", + "content": " takes a noisy " + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "inline_equation", + "content": "x_{j}^{t}" + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "text", + "content": ", predicts clip feature " + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "inline_equation", + "content": "\\hat{x}_{j}^{0}" + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "text", + "content": ", and diffuses it to " + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "inline_equation", + "content": "x_{j}^{t-1}" + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "text", + "content": " based on the sampled noise " + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "inline_equation", + "content": "x_{j}^{T}" + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "text", + "content": ", as demonstrated in Eq. 2. After " + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "text", + "content": " iterations, the predicted clip feature at " + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "inline_equation", + "content": "t = 0" + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "text", + "content": " is used to match text embeddings. By sampling noises for multiple times, we can estimate the most likely " + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "inline_equation", + "content": "y_{j}" + }, + { + "bbox": [ + 304, + 192, + 545, + 291 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 292, + 545, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 292, + 545, + 375 + ], + "spans": [ + { + "bbox": [ + 304, + 292, + 545, + 375 + ], + "type": "text", + "content": "However, sampling can be costly. In practice, to obtain top-1 prediction for missing steps, we adopt approximate inference, where the sampled noise is replaced with a fixed zero vector, corresponding to peak in the Gaussian distribution. Our empirical results validate that approximate inference achieves a very close performance as the expectation over multiple sampled noises." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 387, + 449, + 400 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 387, + 449, + 400 + ], + "spans": [ + { + "bbox": [ + 306, + 387, + 449, + 400 + ], + "type": "text", + "content": "4. Experiments and Results" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 407, + 545, + 467 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 407, + 545, + 467 + ], + "spans": [ + { + "bbox": [ + 304, + 407, + 545, + 467 + ], + "type": "text", + "content": "In this section, we first introduce datasets, evaluation protocols and implementation details. Then we demonstrate our results on step forecasting and step classification benchmarks. Finally, we show our qualitative results and conduct ablations to study our model components." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 470, + 545, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 470, + 545, + 686 + ], + "spans": [ + { + "bbox": [ + 304, + 470, + 545, + 686 + ], + "type": "text", + "content": "Datasets. For pre-training, we consider HowTo100M dataset [38] with 130K hours of YouTube tutorial videos. The videos cover various daily tasks, such as foods, housework, vehicles, etc. We use a language parser [50] to extract the verb phrases from ASR sentences of these videos and keep 9,871 most frequent verb phrases. For fine-tuning, we train our model on COIN dataset [55, 56] and EPIC-Kitchens-100 dataset [10], respectively. COIN has 476 hours of YouTube videos covering 180 tasks, such as dishes, vehicles, housework, etc. Human annotators summarize 778 unique steps in total (e.g., \"stir the egg\"), and annotate the temporal boundary and the category of each step in all videos. EPIC-Kitchens-100 dataset [10] has 100 hours of egocentric videos, capturing daily activities in kitchen. Each action in the videos is annotated with an action label and a noun label. There are 97/300 unique actions/nouns in total. We use the human annotations in COIN and EPIC-Kitchens-100 to evaluate the model performance." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "type": "text", + "content": "Evaluation Protocols. Our evaluation considers zero-shot and fine-tuning settings for step classification and step fore" + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14829" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 108, + 70, + 481, + 203 + ], + "blocks": [ + { + "bbox": [ + 108, + 70, + 481, + 203 + ], + "lines": [ + { + "bbox": [ + 108, + 70, + 481, + 203 + ], + "spans": [ + { + "bbox": [ + 108, + 70, + 481, + 203 + ], + "type": "table", + "html": "
ModelPretrainingTop-1 Acc. (%)
SupervisionDatasetZero-shotFine-tuning
1SlowFast [14]Supervised: action labelsKinetics-25.6
2TimeSformer [4]Supervised: action labelsKinetics-34.7
3S3D [61]Unsupervised: ASR w. MIL-NCE [37]HT100M-28.1
4TimeSformer [4]Unsupervised: ASR w. MIL-NCE [37]HT100M-34.0
5DistantSup [33]Unsupervised: ASR + wikiHowHT100M-39.4
6Random Guess--0.1-
7CLIP [43]Unsupervised: captionsCLIP400M9.4-
8OursUnsupervised: ASRHT100M11.346.8
9Ours (oracle-5)Unsupervised: ASRHT100M14.751.8
", + "image_path": "b20f5d36dd165e38db05b9f5e20b46f69c1b0fca6e3e33cb8bbc3ee613636014.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 68, + 206, + 523, + 217 + ], + "lines": [ + { + "bbox": [ + 68, + 206, + 523, + 217 + ], + "spans": [ + { + "bbox": [ + 68, + 206, + 523, + 217 + ], + "type": "text", + "content": "Table 1. Step forecasting on COIN dataset. We compare to a set of strong baselines and a oracle protocol built on our method." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 228, + 288, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 228, + 288, + 312 + ], + "spans": [ + { + "bbox": [ + 46, + 228, + 288, + 312 + ], + "type": "text", + "content": "casting on COIN and EPIC-Kitchens-100 datasets. Zero-shot setting indicates that no human annotation is used during pre-training. The pre-trained model is directly tested on the evaluation dataset. Fine-tuning setting further fine-tunes the pre-trained model using human annotations of action steps. For a fair comparison, we follow the same fine-tuning schemes as previous work in respective benchmarks." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 315, + 288, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 315, + 288, + 460 + ], + "spans": [ + { + "bbox": [ + 46, + 315, + 288, + 460 + ], + "type": "text", + "content": "Implementation Details. We adopted TimeSformer [4] as our video encoder, and used the Transformer [58] from CLIP's text encoder as denoising model. We set the maximum step " + }, + { + "bbox": [ + 46, + 315, + 288, + 460 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 46, + 315, + 288, + 460 + ], + "type": "text", + "content": " to 4, maximum length of video sequence as 9, and the number of Transformer layers as 4. We used a trained CLIP model (ViT-B/16) to create pseudo labels and encode step descriptions. Following DistantSup [33], for pre-training we used SGD for 5 epochs and then AdamW [34] for 25 epochs with 128 videos in a batch. For fine-tuning, we used AdamW for 15 epochs with batch size of 64. Temperature " + }, + { + "bbox": [ + 46, + 315, + 288, + 460 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 46, + 315, + 288, + 460 + ], + "type": "text", + "content": " was set to 0.02. Additional implementation details can be found in Supplement." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 465, + 149, + 478 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 465, + 149, + 478 + ], + "spans": [ + { + "bbox": [ + 47, + 465, + 149, + 478 + ], + "type": "text", + "content": "4.1. Step Forecasting" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 483, + 287, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 483, + 287, + 567 + ], + "spans": [ + { + "bbox": [ + 46, + 483, + 287, + 567 + ], + "type": "text", + "content": "Setup. We follow the benchmark in DistantSup [33] to evaluate step forecasting on COIN, where top-1 accuracy is reported. Given a video with previous steps, the model anticipates the category of next single step (e.g., \"stir the egg\"). This task thus requires explicit modeling of the temporal ordering among steps. We only fine-tune the diffusion model while keeping the video encoder frozen." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 570, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 570, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 570, + 288, + 714 + ], + "type": "text", + "content": "Results. Table 1 compares results of our method with a series of baselines. The closest competitor is DistantSup [33] in L5, which learns from ASR text and an external textual knowledge base [26] using the same video backbone (TimeSformer [4]). We also include other baselines reported in DistantSup, e.g., SlowFast [14], TimeSformer [4], and S3D [61] from L1 to L4, where models are supervised using human-annotated action labels or video ASR text. Our model significantly outperforms all baselines by at least " + }, + { + "bbox": [ + 46, + 570, + 288, + 714 + ], + "type": "inline_equation", + "content": "7.4\\%" + }, + { + "bbox": [ + 46, + 570, + 288, + 714 + ], + "type": "text", + "content": " for the fine-tuning setting (e.g., " + }, + { + "bbox": [ + 46, + 570, + 288, + 714 + ], + "type": "inline_equation", + "content": "46.8\\%" + }, + { + "bbox": [ + 46, + 570, + 288, + 714 + ], + "type": "text", + "content": " in L8 vs. " + }, + { + "bbox": [ + 46, + 570, + 288, + 714 + ], + "type": "inline_equation", + "content": "39.4\\%" + }, + { + "bbox": [ + 46, + 570, + 288, + 714 + ], + "type": "text", + "content": " in L5). Further, we consider a strong baseline for the zero-shot setting by re-purposing CLIP model [43]" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 228, + 545, + 265 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 228, + 545, + 265 + ], + "spans": [ + { + "bbox": [ + 304, + 228, + 545, + 265 + ], + "type": "text", + "content": "to match the input video with the descriptions of all step candidates. Comparing L7 and L8, our model outperforms this variant of CLIP by a clear margin (11.3% vs. 9.4%)." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 265, + 546, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 265, + 546, + 361 + ], + "spans": [ + { + "bbox": [ + 304, + 265, + 546, + 361 + ], + "type": "text", + "content": "A unique property of our model is its ability to output multiple, potentially different predictions. We further evaluate the upper bound of our results by assuming an oracle ranking function that always selects the correction prediction from 5 outputs sampled from our model (Ours (oracle-5)). This oracle further improves the top-1 accuracy from " + }, + { + "bbox": [ + 304, + 265, + 546, + 361 + ], + "type": "inline_equation", + "content": "11.3\\%" + }, + { + "bbox": [ + 304, + 265, + 546, + 361 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 304, + 265, + 546, + 361 + ], + "type": "inline_equation", + "content": "14.7\\%" + }, + { + "bbox": [ + 304, + 265, + 546, + 361 + ], + "type": "text", + "content": " in L9, suggesting that our model is able to produce diverse predictions for step forecasting." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 369, + 416, + 382 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 369, + 416, + 382 + ], + "spans": [ + { + "bbox": [ + 305, + 369, + 416, + 382 + ], + "type": "text", + "content": "4.2. Step Classification" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 387, + 546, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 387, + 546, + 495 + ], + "spans": [ + { + "bbox": [ + 304, + 387, + 546, + 495 + ], + "type": "text", + "content": "Setup. Besides step ordering, we also evaluate step classification on COIN and EPIC-Kitchens-100 datasets, where a model is tasked to classify a trimmed video clip into one of the step categories. For COIN, we follow DistantSup [33] to only fine-tune the additional linear layer on top of the pretrained video encoder. For EPIC-Kitchens-100, we fully fine-tune the video encoder, following [2, 25, 33]. We report the accuracy of step classification on COIN, and that of verb, noun, and action on EPIC-Kitchens-100." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 498, + 545, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 545, + 677 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 545, + 677 + ], + "type": "text", + "content": "Results. Table. 2 summarizes the results on COIN. We consider baselines as in DistantSup from L1 to L8 (e.g., SlowFast [14], VideoCLIP [63]), where models are trained using either action labels or video ASR text. To support zero-shot inference, we re-implement a model variant (DistantSup†) described in [33]. This model is pre-trained to match video embeddings with language embeddings and thus supports recognizing arbitrary step descriptions in L9. We also report the results of CLIP [43], which creates the pseudo labels for our pre-training in L10. As shown, our model consistently outperforms all the other methods by a clear margin under different settings. For example, ours outperforms CLIP by " + }, + { + "bbox": [ + 304, + 498, + 545, + 677 + ], + "type": "inline_equation", + "content": "1.8\\%" + }, + { + "bbox": [ + 304, + 498, + 545, + 677 + ], + "type": "text", + "content": " in zero-shot setting (16.6% in L11 vs. 14.8% in L10), and outperforms DistantSup by " + }, + { + "bbox": [ + 304, + 498, + 545, + 677 + ], + "type": "inline_equation", + "content": "2.8\\%" + }, + { + "bbox": [ + 304, + 498, + 545, + 677 + ], + "type": "text", + "content": " (56.9% in L11 vs. 54.1% in L8) in fine-tuning setting." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 677, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 546, + 713 + ], + "type": "text", + "content": "Table. 3 presents our results on EPIC-Kitchens-100. While TimeSformer [4] in L8 and DistantSup [33] in L9 use the same video encoder architecture as ours, our model" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "14830" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 98, + 70, + 492, + 216 + ], + "blocks": [ + { + "bbox": [ + 98, + 70, + 492, + 216 + ], + "lines": [ + { + "bbox": [ + 98, + 70, + 492, + 216 + ], + "spans": [ + { + "bbox": [ + 98, + 70, + 492, + 216 + ], + "type": "table", + "html": "
ModelPretrainingTop-1 Acc. (%)
SupervisionDatasetZero-shotFine-tuning
1TSN (RGB+Flow) [55]Supervised: action labelsKinetics-36.5*
2S3D [61]Unsupervised: ASR w. MIL-NCE [37]HT100M-37.5*
3SlowFast [14]Supervised: action labelsKinetics-32.9
4TimeSformer [4]Supervised: action labelsKinetics-48.3
5ClipBERT [29]Supervised: captionsCOCO+VG-30.8
6VideoCLIP [63]Unsupervised: ASRHT100M-39.4
7TimeSformer [4]Unsupervised: ASR w. MIL-NCE [37]HT100M-46.5
8DistantSup [33]Unsupervised: ASR + wikiHowHT100M-54.1
9DistantSup† [33]Unsupervised: ASR + wikiHowHT100M10.246.6
10CLIP [43]Unsupervised: captionsCLIP400M14.845.9
11OursUnsupervised: ASRHT100M16.656.9
", + "image_path": "d029b3406eece24c9c609c428e8e4efb7a0c5ada4e4857455b521d3680e3f283.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 83, + 247, + 506, + 373 + ], + "blocks": [ + { + "bbox": [ + 46, + 219, + 547, + 243 + ], + "lines": [ + { + "bbox": [ + 46, + 219, + 547, + 243 + ], + "spans": [ + { + "bbox": [ + 46, + 219, + 547, + 243 + ], + "type": "text", + "content": "Table 2. Step classification on COIN dataset. DistantSup† is re-implemented based on their official code base. It is a variant reported in their paper that pre-trains the model to match language embeddings. * indicates the model is fully fine-tuned on COIN dataset." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 83, + 247, + 506, + 373 + ], + "lines": [ + { + "bbox": [ + 83, + 247, + 506, + 373 + ], + "spans": [ + { + "bbox": [ + 83, + 247, + 506, + 373 + ], + "type": "table", + "html": "
ModelPretraining SupervisionPretraining DatasetAction (%)Verb (%)Noun (%)
1TSN [60]--33.260.246.0
2TRN [68]--35.365.945.4
3TBN [23]--36.766.047.2
4MoViNet [25]--47.772.257.3
5TSM [32]Supervised: action labelsKinetics38.367.949.0
6SlowFast [14]Supervised: action labelsKinetics38.565.650.0
7ViViT-L [2]Supervised: action labelsKinetics44.066.456.8
8TimeSformer [4]Supervised: action labelsKinetics42.366.654.4
9DistantSup [33]Unsupervised: ASR + wikiHowHT100M44.467.158.1
10OursUnsupervised: ASRHT100M47.769.560.3
", + "image_path": "1c36ce09d637fc0629e1869d7b3fe07dd925d5dae12dcae34d1e48b9df97830b.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 376, + 546, + 399 + ], + "lines": [ + { + "bbox": [ + 46, + 376, + 546, + 399 + ], + "spans": [ + { + "bbox": [ + 46, + 376, + 546, + 399 + ], + "type": "text", + "content": "Table 3. Step classification on EPIC-Kitchens-100 dataset with fine-tuning setting. Our method outperforms the close competitors (TimeSformer, DistantSup), with results on par with even stronger backbone models (MoViNet)." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 409, + 288, + 458 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 409, + 288, + 458 + ], + "spans": [ + { + "bbox": [ + 46, + 409, + 288, + 458 + ], + "type": "text", + "content": "in L10 achieves a clear gain over them, e.g., " + }, + { + "bbox": [ + 46, + 409, + 288, + 458 + ], + "type": "inline_equation", + "content": "+3.3\\% / 2.2\\%" + }, + { + "bbox": [ + 46, + 409, + 288, + 458 + ], + "type": "text", + "content": " for action/noun. The only exception is the lower accuracy " + }, + { + "bbox": [ + 46, + 409, + 288, + 458 + ], + "type": "inline_equation", + "content": "(-2.7\\%)" + }, + { + "bbox": [ + 46, + 409, + 288, + 458 + ], + "type": "text", + "content": " on verb when compared with MoViNet (MoViNet-A6) in L4, a heavily optimized video backbone." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 466, + 220, + 480 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 466, + 220, + 480 + ], + "spans": [ + { + "bbox": [ + 47, + 466, + 220, + 480 + ], + "type": "text", + "content": "4.3. Predicting Diverse Future Steps" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 485, + 287, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 485, + 287, + 581 + ], + "spans": [ + { + "bbox": [ + 46, + 485, + 287, + 581 + ], + "type": "text", + "content": "One of the defining characteristics of our model is that it allows us to sample multiple predictions of video representation corresponding to a future step. This leads to an interesting question about the diversity of the predictions, as partially evaluated in our prior experiments. Here we present further demonstration of this capability by visualizing the step forecasting results, and more interestingly, using these results to generate future video frames." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 582, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 288, + 715 + ], + "type": "text", + "content": "Fig. 3 presents the visualization for zero-shot step forecasting and key frame generation. In this setting, our model is pre-trained without any human annotation and is directly tested for step forecasting. We show multiple predictions sampled from our diffusion model. Further, we demonstrate that the text description of predicted step can be used to generate the key frames by leveraging the stable diffusion model [45]. To keep the generated images visually consistent with the input video, we let stable diffusion model take one input video frame and the description of predicted step as input and generate an image." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 409, + 547, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 409, + 547, + 483 + ], + "spans": [ + { + "bbox": [ + 304, + 409, + 547, + 483 + ], + "type": "text", + "content": "As shown in Fig. 3, our model is capable of forecasting multiple, reasonable next steps (e.g., \"flatten the dough\", \"bake pizza\"), based on which credible future frames can be generated. These results suggest that our model not only predicts meaningful video representations of individual steps, but also captures the variations in step ordering" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 488, + 406, + 500 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 488, + 406, + 500 + ], + "spans": [ + { + "bbox": [ + 306, + 488, + 406, + 500 + ], + "type": "text", + "content": "4.4. Ablation Studies" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 506, + 547, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 506, + 547, + 544 + ], + "spans": [ + { + "bbox": [ + 304, + 506, + 547, + 544 + ], + "type": "text", + "content": "We conduct ablation study on COIN, including step classification/forecasting with zero-shot/fine-tuning setting. Additional ablation results can be found in Supplement." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 545, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 547, + 715 + ], + "type": "text", + "content": "Does modeling of temporal order help? In Table 4, we conduct a comparison on two different pre-training tasks: (1) pre-training by only matching video representations to text embeddings of the verb phrases; and (2) pre-training by our method that combines matching and temporal order modeling. In comparison to pre-training using matching only, our method significantly improves the performance for both zero-shot and fine-tuning settings and across step classification and step forecasting tasks. For example, zero-shot step classification is improved from " + }, + { + "bbox": [ + 304, + 545, + 547, + 715 + ], + "type": "inline_equation", + "content": "13.7\\%" + }, + { + "bbox": [ + 304, + 545, + 547, + 715 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 304, + 545, + 547, + 715 + ], + "type": "inline_equation", + "content": "16.6\\%" + }, + { + "bbox": [ + 304, + 545, + 547, + 715 + ], + "type": "text", + "content": ". Our results after fine-tuning attains a major gain of " + }, + { + "bbox": [ + 304, + 545, + 547, + 715 + ], + "type": "inline_equation", + "content": "+3.6\\%" + }, + { + "bbox": [ + 304, + 545, + 547, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 545, + 547, + 715 + ], + "type": "inline_equation", + "content": "+1.8\\%" + }, + { + "bbox": [ + 304, + 545, + 547, + 715 + ], + "type": "text", + "content": " for step classification and step forecasting, respectively. 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Visualization of zero-shot step forecasting and key frame generation. Without using any human annotation during training, our trained model is directly evaluated on COIN dataset [56]. Given a video recording previous steps (left), our model is capable of forecasting multiple reasonable predictions and each predicted step is further used for key frame generation (right). 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ModelPre-training taskOrder modelingZero-shot (top-1 acc. %)Fine-tuning (top-1 acc. %)
Step classificationStep forecastingStep classificationStep forecasting
OursMatching-13.7No zero-shot ability52.841.6
OursMatching + OrderingMask16.910.656.443.2
OursMatching + OrderingDiffusion16.611.356.946.8
", + "image_path": "860d6379cfffe4459dec635f8905ad0ec90218b2ca46585c033d311e7d998c2f.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "table_body" + } + ], + "index": 17 + }, + { + "bbox": [ + 46, + 397, + 287, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 397, + 287, + 434 + ], + "spans": [ + { + "bbox": [ + 46, + 397, + 287, + 434 + ], + "type": "text", + "content": "These results suggest that our procedure-aware pre-training can effectively facilitate the learning for both video representation and step ordering in procedure activities." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 46, + 441, + 287, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 441, + 287, + 574 + ], + "spans": [ + { + "bbox": [ + 46, + 441, + 287, + 574 + ], + "type": "text", + "content": "Masked Prediction vs. Diffusion Model. We explore a model variant using the mask prediction, sharing similar spirit as the well-known Masked Language Modeling in BERT [24]. Specifically, this variant is trained to recover the video embeddings of masked video clips so that it can match to the assigned verb phrases. Our diffusion model largely outperforms the results of this variant, especially on the step forecasting " + }, + { + "bbox": [ + 46, + 441, + 287, + 574 + ], + "type": "inline_equation", + "content": "(+0.7\\%)" + }, + { + "bbox": [ + 46, + 441, + 287, + 574 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 441, + 287, + 574 + ], + "type": "inline_equation", + "content": "+3.6\\%" + }, + { + "bbox": [ + 46, + 441, + 287, + 574 + ], + "type": "text", + "content": " for zero-shot and fine-tuning, respectively, as in Table 4). This result indicates that our diffusion model is a more suitable way to capture the variation inside the step ordering." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 46, + 582, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 288, + 715 + ], + "type": "text", + "content": "Approximate Inference. In Table 5, we validate that our approximate inference with a single zero-vector can achieve close empirical results as the Monte Carlo estimation (e.g., within " + }, + { + "bbox": [ + 46, + 582, + 288, + 715 + ], + "type": "inline_equation", + "content": "0.1\\%" + }, + { + "bbox": [ + 46, + 582, + 288, + 715 + ], + "type": "text", + "content": " difference). Monte Carlo estimation computes the weighted average of multiple sampled predictions (e.g., from 5 sampled noises). We run the experiment for 5 times and the variation is small (e.g., " + }, + { + "bbox": [ + 46, + 582, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\pm 0.03\\%" + }, + { + "bbox": [ + 46, + 582, + 288, + 715 + ], + "type": "text", + "content": "). Further, if we assume an oracle ranking function to pick the correct one from sampled predictions, our results can be further boosted by " + }, + { + "bbox": [ + 46, + 582, + 288, + 715 + ], + "type": "inline_equation", + "content": "3.4\\%" + }, + { + "bbox": [ + 46, + 582, + 288, + 715 + ], + "type": "text", + "content": " on average, suggesting diverse predictions from our model and ample room to improve." + } + ] + } + ], + "index": 21 + }, + { + "type": "table", + "bbox": [ + 344, + 395, + 504, + 456 + ], + "blocks": [ + { + "bbox": [ + 46, + 353, + 546, + 387 + ], + "lines": [ + { + "bbox": [ + 46, + 353, + 546, + 387 + ], + "spans": [ + { + "bbox": [ + 46, + 353, + 546, + 387 + ], + "type": "text", + "content": "Table 4. Ablation study of pre-training tasks and order modeling. Our proposed order pre-training not only enables zero-shot forecasting, but also significantly improves zero-shot step classification and the fine-tuning results across evaluation tasks. Our diffusion models further improves mask modeling, especially on fine-tuning step forecasting." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 344, + 395, + 504, + 456 + ], + "lines": [ + { + "bbox": [ + 344, + 395, + 504, + 456 + ], + "spans": [ + { + "bbox": [ + 344, + 395, + 504, + 456 + ], + "type": "table", + "html": "
ModelInference TypeTop-1 Acc. (%)
OursApproximation11.33
OursExpectation11.34 ± 0.03
OursOracle14.73 ± 0.13
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Conclusion" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 304, + 546, + 546, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 546, + 546, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 546, + 546, + 715 + ], + "type": "text", + "content": "In this work, we presented a model and a training frame work for learning procedure-aware video representation from a large-scale dataset of instructional videos and their narrations, without the need for human annotations. The key strength of our model lies in the joint learning of a video encoder capturing concepts of action steps, as well as a diffusion model reasoning about the temporal dependencies among steps. We demonstrated that our model achieves strong results on step classification and forecasting in both zero-shot and fine-tuning settings and across COIN and EPIC-Kitchens-100 datasets. We believe our work provides a solid step towards understanding procedural activities. We hope that our work will shed light on the broader problem of video-language pre-training." + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14832" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 91, + 288, + 715 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 147 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 147 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 147 + ], + "type": "text", + "content": "[1] Jean-Baptiste Alayrac, Piotr Bojanowski, Nishant Agrawal, Josef Sivic, Ivan Laptev, and Simon Lacoste-Julien. Unsupervised learning from narrated instruction videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4575-4583, 2016. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 148, + 288, + 203 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 148, + 288, + 203 + ], + "spans": [ + { + "bbox": [ + 53, + 148, + 288, + 203 + ], + "type": "text", + "content": "[2] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6836-6846, 2021. 6, 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 205, + 288, + 258 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 205, + 288, + 258 + ], + "spans": [ + { + "bbox": [ + 53, + 205, + 288, + 258 + ], + "type": "text", + "content": "[3] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1728-1738, 2021. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 260, + 288, + 305 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 260, + 288, + 305 + ], + "spans": [ + { + "bbox": [ + 53, + 260, + 288, + 305 + ], + "type": "text", + "content": "[4] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In Proceedings of the International Conference on Machine Learning (ICML), July 2021. 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 306, + 288, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 306, + 288, + 361 + ], + "spans": [ + { + "bbox": [ + 53, + 306, + 288, + 361 + ], + "type": "text", + "content": "[5] Piotr Bojanowski, Rémi Lajugie, Francis Bach, Ivan Laptev, Jean Ponce, Cordelia Schmid, and Josef Sivic. Weakly supervised action labeling in videos under ordering constraints. In European Conference on Computer Vision, pages 628-643. Springer, 2014. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 361, + 288, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 361, + 288, + 418 + ], + "spans": [ + { + "bbox": [ + 53, + 361, + 288, + 418 + ], + "type": "text", + "content": "[6] Piotr Bojanowski, Rémi Lajugie, Edouard Grave, Francis Bach, Ivan Laptev, Jean Ponce, and Cordelia Schmid. Weakly-supervised alignment of video with text. In Proceedings of the IEEE international conference on computer vision, pages 4462-4470, 2015. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 418, + 288, + 463 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 418, + 288, + 463 + ], + "spans": [ + { + "bbox": [ + 53, + 418, + 288, + 463 + ], + "type": "text", + "content": "[7] M. Brand, N. Oliver, and A. Pentland. Coupled hidden markov models for complex action recognition. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 994-999, 1997. 1, 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 464, + 288, + 509 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 464, + 288, + 509 + ], + "spans": [ + { + "bbox": [ + 53, + 464, + 288, + 509 + ], + "type": "text", + "content": "[8] Chien-Yi Chang, De-An Huang, Danfei Xu, Ehsan Adeli, Li Fei-Fei, and Juan Carlos Niebles. Procedure planning in instructional videos. In European Conference on Computer Vision, pages 334–350. Springer, 2020. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 510, + 288, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 510, + 288, + 542 + ], + "spans": [ + { + "bbox": [ + 53, + 510, + 288, + 542 + ], + "type": "text", + "content": "[9] Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. Diffusion models in vision: A survey. arXiv preprint arXiv:2209.04747, 2022. 4" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 544, + 288, + 620 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 544, + 288, + 620 + ], + "spans": [ + { + "bbox": [ + 47, + 544, + 288, + 620 + ], + "type": "text", + "content": "[10] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. The EPIC-KITCHENS dataset: Collection, challenges and baselines. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 43(11):4125-4141, 2021. 2, 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 621, + 288, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 621, + 288, + 655 + ], + "spans": [ + { + "bbox": [ + 47, + 621, + 288, + 655 + ], + "type": "text", + "content": "[11] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat GANs on image synthesis. Advances in Neural Information Processing Systems, 34:8780-8794, 2021. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 656, + 288, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 656, + 288, + 690 + ], + "spans": [ + { + "bbox": [ + 47, + 656, + 288, + 690 + ], + "type": "text", + "content": "[12] E. Elhamifar and D. Huynh. Self-supervised multi-task procedure learning from instructional videos. European Conference on Computer Vision, 2020. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 47, + 691, + 288, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 691, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 691, + 288, + 715 + ], + "type": "text", + "content": "[13] Ehsan Elhamifar and Zwe Naing. Unsupervised procedure learning via joint dynamic summarization. In Proceedings of" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019. 1, 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 96, + 547, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 96, + 547, + 140 + ], + "spans": [ + { + "bbox": [ + 307, + 96, + 547, + 140 + ], + "type": "text", + "content": "[14] Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. SlowFast networks for video recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2019. 6, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 141, + 547, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 141, + 547, + 185 + ], + "spans": [ + { + "bbox": [ + 307, + 141, + 547, + 185 + ], + "type": "text", + "content": "[15] Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, and Zicheng Liu. VIOLET: End-to-end video-language transformers with masked visual-token modeling. arXiv preprint arXiv:2111.12681, 2021. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 186, + 547, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 547, + 240 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 547, + 240 + ], + "type": "text", + "content": "[16] Deepti Ghadiyaram, Du Tran, and Dhruv Mahajan. Large-scale weakly-supervised pre-training for video action recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 12046-12055, 2019. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 241, + 547, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 241, + 547, + 285 + ], + "spans": [ + { + "bbox": [ + 307, + 241, + 547, + 285 + ], + "type": "text", + "content": "[17] Karan Goel and Emma Brunskill. Learning procedural abstractions and evaluating discrete latent temporal structure. In International Conference on Learning Representations, 2019. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 286, + 547, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 286, + 547, + 342 + ], + "spans": [ + { + "bbox": [ + 307, + 286, + 547, + 342 + ], + "type": "text", + "content": "[18] Abhinav Gupta, Praveen Srinivasan, Jianbo Shi, and Larry S. Davis. Understanding videos, constructing plots learning a visually grounded storyline model from annotated videos. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, pages 2012-2019, 2009. 1, 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 343, + 547, + 388 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 343, + 547, + 388 + ], + "spans": [ + { + "bbox": [ + 307, + 343, + 547, + 388 + ], + "type": "text", + "content": "[19] Tengda Han, Weidi Xie, and Andrew Zisserman. Temporal alignment networks for long-term video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2906-2916, 2022. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 388, + 547, + 422 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 388, + 547, + 422 + ], + "spans": [ + { + "bbox": [ + 307, + 388, + 547, + 422 + ], + "type": "text", + "content": "[20] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems, 33:6840-6851, 2020. 4" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 422, + 547, + 466 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 422, + 547, + 466 + ], + "spans": [ + { + "bbox": [ + 307, + 422, + 547, + 466 + ], + "type": "text", + "content": "[21] Jonathan Ho, Tim Salimans, Alexey A Gritsenko, William Chan, Mohammad Norouzi, and David J Fleet. Video diffusion models. In Advances in Neural Information Processing Systems, 2022. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 467, + 547, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 467, + 547, + 510 + ], + "spans": [ + { + "bbox": [ + 307, + 467, + 547, + 510 + ], + "type": "text", + "content": "[22] Y.A. Ivanov and A.F. Bobick. Recognition of visual activities and interactions by stochastic parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):852-872, 2000. 1, 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 512, + 547, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 512, + 547, + 567 + ], + "spans": [ + { + "bbox": [ + 307, + 512, + 547, + 567 + ], + "type": "text", + "content": "[23] Evangelos Kazakos, Arsha Nagrani, Andrew Zisserman, and Dima Damen. EPIC-fusion: Audio-visual temporal binding for egocentric action recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5492–5501, 2019. 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 568, + 547, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 568, + 547, + 612 + ], + "spans": [ + { + "bbox": [ + 307, + 568, + 547, + 612 + ], + "type": "text", + "content": "[24] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, pages 4171-4186, 2019. 3, 8" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 613, + 547, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 613, + 547, + 678 + ], + "spans": [ + { + "bbox": [ + 307, + 613, + 547, + 678 + ], + "type": "text", + "content": "[25] Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, and Boqing Gong. MoViNets: Mobile video networks for efficient video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16020-16030, 2021. 6, 7" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 679, + 547, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 679, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 679, + 547, + 713 + ], + "type": "text", + "content": "[26] Mahnaz Koupae and William Yang Wang. WikiHow: A large scale text summarization dataset. arXiv preprint arXiv:1810.09305, 2018. 2, 6" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "14833" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 715 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "text", + "content": "[27] Hilde Kuehne, Ali Arslan, and Thomas Serre. The language of actions: Recovering the syntax and semantics of goal-directed human activities. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014. 1, 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 128, + 287, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 128, + 287, + 184 + ], + "spans": [ + { + "bbox": [ + 48, + 128, + 287, + 184 + ], + "type": "text", + "content": "[28] Anna Kukleva, Hilde Kuehne, Fadime Sener, and Jurgen Gall. Unsupervised learning of action classes with continuous temporal embedding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12066-12074, 2019. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 184, + 288, + 249 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 184, + 288, + 249 + ], + "spans": [ + { + "bbox": [ + 48, + 184, + 288, + 249 + ], + "type": "text", + "content": "[29] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L. Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7331-7341, June 2021. 2, 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 250, + 288, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 250, + 288, + 304 + ], + "spans": [ + { + "bbox": [ + 48, + 250, + 288, + 304 + ], + "type": "text", + "content": "[30] Junnan Li, Ramprasaath R Selvaraju, Akhilesh Deepak Gotmare, Shafiq Joty, Caiming Xiong, and Steven Hoi. Align before fuse: Vision and language representation learning with momentum distillation. arXiv preprint arXiv:2107.07651, 2021. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 304, + 288, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 304, + 288, + 380 + ], + "spans": [ + { + "bbox": [ + 48, + 304, + 288, + 380 + ], + "type": "text", + "content": "[31] Linjie Li, Yen-Chun Chen, Yu Cheng, Zhe Gan, Licheng Yu, and Jingjing Liu. HERO: Hierarchical encoder for Video+Language omni-representation pre-training. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2046-2065, Online, Nov. 2020. Association for Computational Linguistics. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 382, + 288, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 382, + 288, + 426 + ], + "spans": [ + { + "bbox": [ + 48, + 382, + 288, + 426 + ], + "type": "text", + "content": "[32] Ji Lin, Chuang Gan, and Song Han. TSM: Temporal shift module for efficient video understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7083-7093, 2019. 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 426, + 288, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 426, + 288, + 491 + ], + "spans": [ + { + "bbox": [ + 48, + 426, + 288, + 491 + ], + "type": "text", + "content": "[33] Xudong Lin, Fabio Petroni, Gedas Bertasius, Marcus Rohrbach, Shih-Fu Chang, and Lorenzo Torresani. Learning to recognize procedural activities with distant supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13853-13863, 2022. 1, 2, 6, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 492, + 288, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 492, + 288, + 525 + ], + "spans": [ + { + "bbox": [ + 48, + 492, + 288, + 525 + ], + "type": "text", + "content": "[34] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018. 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 525, + 288, + 580 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 525, + 288, + 580 + ], + "spans": [ + { + "bbox": [ + 48, + 525, + 288, + 580 + ], + "type": "text", + "content": "[35] Huaishao Luo, Lei Ji, Botian Shi, Haoyang Huang, Nan Duan, Tianrui Li, Jason Li, Taroon Bharti, and Ming Zhou. Univl: A unified video and language pre-training model for multimodal understanding and generation. arXiv preprint arXiv:2002.06353, 2020. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 581, + 288, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 581, + 288, + 624 + ], + "spans": [ + { + "bbox": [ + 48, + 581, + 288, + 624 + ], + "type": "text", + "content": "[36] Jonathan Malmaud, Jonathan Huang, Vivek Rathod, Nick Johnston, Andrew Rabinovich, and Kevin Murphy. What's cookin'? interpreting cooking videos using text, speech and vision. arXiv preprint arXiv:1503.01558, 2015. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 625, + 288, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 625, + 288, + 690 + ], + "spans": [ + { + "bbox": [ + 48, + 625, + 288, + 690 + ], + "type": "text", + "content": "[37] Antoine Miech, Jean-Baptiste Alayrac, Lucas Smaira, Ivan Laptev, Josef Sivic, and Andrew Zisserman. End-to-end learning of visual representations from uncurated instructional videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9879-9889, 2020. 1, 2, 6, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 691, + 288, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 288, + 715 + ], + "type": "text", + "content": "[38] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic." + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 714 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 118 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 118 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 118 + ], + "type": "text", + "content": "HowTo100M: Learning a text-video embedding by watching hundred million narrated video clips. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2630-2640, 2019. 2, 5" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 307, + 118, + 545, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 163 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 163 + ], + "type": "text", + "content": "[39] Ram Nevatia, Tao Zhao, and Somboon Hongeng. Hierarchical language-based representation of events in video streams. In 2003 Conference on Computer Vision and Pattern Recognition Workshop, volume 4, pages 39-39, 2003. 1, 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 164, + 545, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 164, + 545, + 229 + ], + "spans": [ + { + "bbox": [ + 307, + 164, + 545, + 229 + ], + "type": "text", + "content": "[40] Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models. In International Conference on Machine Learning, pages 16784-16804. PMLR, 2022. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 230, + 545, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 230, + 545, + 274 + ], + "spans": [ + { + "bbox": [ + 307, + 230, + 545, + 274 + ], + "type": "text", + "content": "[41] Mingtao Pei, Yunde Jia, and Song-Chun Zhu. Parsing video events with goal inference and intent prediction. In 2011 International Conference on Computer Vision, pages 487-494, 2011. 1, 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 276, + 545, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 276, + 545, + 330 + ], + "spans": [ + { + "bbox": [ + 308, + 276, + 545, + 330 + ], + "type": "text", + "content": "[42] Yicheng Qian, Weixin Luo, Dongze Lian, Xu Tang, Peilin Zhao, and Shenghua Gao. SVIP: Sequence verification for procedures in videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19890-19902, 2022. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 331, + 545, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 331, + 545, + 398 + ], + "spans": [ + { + "bbox": [ + 308, + 331, + 545, + 398 + ], + "type": "text", + "content": "[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (ICML), 2021. 1, 2, 3, 4, 5, 6, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 399, + 545, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 399, + 545, + 442 + ], + "spans": [ + { + "bbox": [ + 308, + 399, + 545, + 442 + ], + "type": "text", + "content": "[44] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125, 2022.3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 444, + 545, + 499 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 444, + 545, + 499 + ], + "spans": [ + { + "bbox": [ + 308, + 444, + 545, + 499 + ], + "type": "text", + "content": "[45] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 3, 7, 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 500, + 545, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 500, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 308, + 500, + 545, + 555 + ], + "type": "text", + "content": "[46] M.S. Ryoo and J.K. Aggarwal. Recognition of composite human activities through context-free grammar based representation. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), volume 2, pages 1709-1718, 2006. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 556, + 545, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 556, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 308, + 556, + 545, + 624 + ], + "type": "text", + "content": "[47] Chitwan Sahara, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 624, + 545, + 666 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 624, + 545, + 666 + ], + "spans": [ + { + "bbox": [ + 308, + 624, + 545, + 666 + ], + "type": "text", + "content": "[48] Fadime Sener and Angela Yao. Zero-shot anticipation for instructional activities. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 862-871, 2019. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 669, + 545, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 669, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 308, + 669, + 545, + 714 + ], + "type": "text", + "content": "[49] Ozan Sener, Amir R Zamir, Silvio Savarese, and Ashutosh Saxena. Unsupervised semantic parsing of video collections. In Proceedings of the IEEE International conference on Computer Vision, pages 4480-4488, 2015. 2" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "14834" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 137 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 288, + 137 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 288, + 137 + ], + "type": "text", + "content": "[50] Yuhan Shen, Lu Wang, and Ehsan Elhamifar. Learning to segment actions from visual and language instructions via differentiable weak sequence alignment. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10156-10165, June 2021. 2, 4, 5" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 139, + 288, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 139, + 288, + 194 + ], + "spans": [ + { + "bbox": [ + 48, + 139, + 288, + 194 + ], + "type": "text", + "content": "[51] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International Conference on Machine Learning, pages 2256-2265. PMLR, 2015. 3, 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 195, + 288, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 195, + 288, + 239 + ], + "spans": [ + { + "bbox": [ + 48, + 195, + 288, + 239 + ], + "type": "text", + "content": "[52] Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. MPNet: Masked and permuted pre-training for language understanding. Advances in Neural Information Processing Systems, 33:16857-16867, 2020. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 239, + 288, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 239, + 288, + 282 + ], + "spans": [ + { + "bbox": [ + 48, + 239, + 288, + 282 + ], + "type": "text", + "content": "[53] Yang Song and Stefano Ermon. Improved techniques for training score-based generative models. Advances in neural information processing systems, 33:12438-12448, 2020. 3, 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 284, + 288, + 337 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 284, + 288, + 337 + ], + "spans": [ + { + "bbox": [ + 48, + 284, + 288, + 337 + ], + "type": "text", + "content": "[54] Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid. VideoBERT: A joint model for video and language representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7464-7473, 2019. 1, 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 338, + 288, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 338, + 288, + 403 + ], + "spans": [ + { + "bbox": [ + 48, + 338, + 288, + 403 + ], + "type": "text", + "content": "[55] Yansong Tang, Dajun Ding, Yongming Rao, Yu Zheng, Danyang Zhang, Lili Zhao, Jiwen Lu, and Jie Zhou. COIN: A large-scale dataset for comprehensive instructional video analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1207-1216, 2019. 2, 5, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 404, + 288, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 404, + 288, + 437 + ], + "spans": [ + { + "bbox": [ + 48, + 404, + 288, + 437 + ], + "type": "text", + "content": "[56] Yansong Tang, Jiwen Lu, and Jie Zhou. Comprehensive instructional video analysis: The COIN dataset and performance evaluation. TPAMI, 2020. 5, 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 437, + 288, + 481 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 437, + 288, + 481 + ], + "spans": [ + { + "bbox": [ + 48, + 437, + 288, + 481 + ], + "type": "text", + "content": "[57] Guy Tevet, Sigal Raab, Brian Gordon, Yonatan Shafir, Daniel Cohen-Or, and Amit H Bermano. Human motion diffusion model. In International Conference on Learning Representations, 2023. 3, 4" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 482, + 288, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 482, + 288, + 525 + ], + "spans": [ + { + "bbox": [ + 48, + 482, + 288, + 525 + ], + "type": "text", + "content": "[58] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 526, + 288, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 526, + 288, + 581 + ], + "spans": [ + { + "bbox": [ + 48, + 526, + 288, + 581 + ], + "type": "text", + "content": "[59] Alex Jinpeng Wang, Yixiao Ge, Rui Yan, Yuying Ge, Xudong Lin, Guanyu Cai, Jianping Wu, Ying Shan, Xiaohu Qie, and Mike Zheng Shou. All in one: Exploring unified video-language pre-training. arXiv preprint arXiv:2203.07303, 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 582, + 288, + 636 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 582, + 288, + 636 + ], + "spans": [ + { + "bbox": [ + 48, + 582, + 288, + 636 + ], + "type": "text", + "content": "[60] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaou Tang, and Luc Van Gool. Temporal segment networks: Towards good practices for deep action recognition. In European conference on computer vision, pages 20-36. Springer, 2016. 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 637, + 288, + 691 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 637, + 288, + 691 + ], + "spans": [ + { + "bbox": [ + 48, + 637, + 288, + 691 + ], + "type": "text", + "content": "[61] Saining Xie, Chen Sun, Jonathan Huang, Zhuowen Tu, and Kevin Murphy. Rethinking spatiotemporal feature learning: Speed-accuracy trade-offs in video classification. In Proceedings of the European conference on computer vision (ECCV), pages 305–321, 2018. 6, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 692, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 692, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 692, + 288, + 713 + ], + "type": "text", + "content": "[62] Frank F. Xu, Lei Ji, Botian Shi, Junyi Du, Graham Neubig, Yonatan Bisk, and Nan Duan. A benchmark for structured" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 597 + ], + "type": "list", + "angle": 0, + "index": 24, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 118 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 118 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 118 + ], + "type": "text", + "content": "procedural knowledge extraction from cooking videos. In Proceedings of the First International Workshop on Natural Language Processing Beyond Text, pages 30-40, Online, Nov. 2020. Association for Computational Linguistics. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 118, + 545, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 173 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 173 + ], + "type": "text", + "content": "[63] Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. VideoCLIP: Contrastive pretraining for zero-shot video-text understanding. arXiv preprint arXiv:2109.14084, 2021. 2, 6, 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 175, + 545, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 175, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 307, + 175, + 545, + 228 + ], + "type": "text", + "content": "[64] Jianwei Yang, Yonatan Bisk, and Jianfeng Gao. TACo: Token-aware cascade contrastive learning for video-text alignment. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11562-11572, 2021. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 229, + 545, + 284 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 229, + 545, + 284 + ], + "spans": [ + { + "bbox": [ + 307, + 229, + 545, + 284 + ], + "type": "text", + "content": "[65] Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Bin Cui, and Ming-Hsuan Yang. Diffusion models: A comprehensive survey of methods and applications. arXiv preprint arXiv:2209.00796, 2022. 4" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 285, + 545, + 339 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 285, + 545, + 339 + ], + "spans": [ + { + "bbox": [ + 308, + 285, + 545, + 339 + ], + "type": "text", + "content": "[66] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. MERLOT: Multimodal neural script knowledge models. Advances in Neural Information Processing Systems, 34:23634-23651, 2021. 2, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 341, + 545, + 406 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 341, + 545, + 406 + ], + "spans": [ + { + "bbox": [ + 308, + 341, + 545, + 406 + ], + "type": "text", + "content": "[67] Henghui Zhao, Isma Hadji, Nikita Dvornik, Konstantinos G. Derpanis, Richard P. Wildes, and Allan D. Jepson. P3IV: Probabilistic procedure planning from instructional videos with weak supervision. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2928-2938, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 408, + 545, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 408, + 545, + 453 + ], + "spans": [ + { + "bbox": [ + 308, + 408, + 545, + 453 + ], + "type": "text", + "content": "[68] Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba. Temporal relational reasoning in videos. In Proceedings of the European conference on computer vision (ECCV), pages 803-818, 2018. 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 453, + 545, + 496 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 453, + 545, + 496 + ], + "spans": [ + { + "bbox": [ + 308, + 453, + 545, + 496 + ], + "type": "text", + "content": "[69] Luowei Zhou, Chenliang Xu, and Jason J Corso. Towards automatic learning of procedures from web instructional videos. In Thirty-Second AAAI Conference on Artificial Intelligence, 2018. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 498, + 545, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 498, + 545, + 542 + ], + "spans": [ + { + "bbox": [ + 308, + 498, + 545, + 542 + ], + "type": "text", + "content": "[70] Linchao Zhu and Yi Yang. ActBERT: Learning global-local video-text representations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8746-8755, 2020. 1, 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 543, + 545, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 543, + 545, + 597 + ], + "spans": [ + { + "bbox": [ + 308, + 543, + 545, + 597 + ], + "type": "text", + "content": "[71] Dimitri Zhukov, Jean-Baptiste Alayrac, Ramadan Gokberk Cinbis, David Fouhey, Ivan Laptev, and Josef Sivic. Cross-task weakly supervised learning from instructional videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3537-3545, 2019. 2" + } + ] + } + ], + "index": 23 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "14835" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Rotation-Equivariant Features for Visual Correspondence/3ac6cb22-128e-410d-8dc0-595aae3be692_content_list.json b/2023/Learning Rotation-Equivariant Features for Visual Correspondence/3ac6cb22-128e-410d-8dc0-595aae3be692_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..7d4012a44433f4cb4e6fb3b38d4cfdd2c0c20766 --- /dev/null +++ b/2023/Learning Rotation-Equivariant Features for Visual Correspondence/3ac6cb22-128e-410d-8dc0-595aae3be692_content_list.json @@ -0,0 +1,1460 @@ +[ + { + "type": "text", + "text": "Learning Rotation-Equivariant Features for Visual Correspondence", + "text_level": 1, + "bbox": [ + 161, + 180, + 811, + 201 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Jongmin Lee Byungjin Kim Seungwook Kim Minsu Cho", + "bbox": [ + 196, + 224, + 769, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Pohang University of Science and Technology (POSTECH), South Korea", + "bbox": [ + 211, + 243, + 759, + 260 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "http://cylab.postech.ac.kr/research/RELF", + "bbox": [ + 314, + 262, + 650, + 274 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 248, + 304, + 324, + 319 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Extracting discriminative local features that are invariant to imaging variations is an integral part of establishing correspondences between images. In this work, we introduce a self-supervised learning framework to extract discriminative rotation-invariant descriptors using group-equivariant CNNs. Thanks to employing group-equivariant CNNs, our method effectively learns to obtain rotation-equivariant features and their orientations explicitly, without having to perform sophisticated data augmentations. The resultant features and their orientations are further processed by group aligning, a novel invariant mapping technique that shifts the group-equivariant features by their orientations along the group dimension. Our group aligning technique achieves rotation-invariance without any collapse of the group dimension and thus eschews loss of discriminability. The proposed method is trained end-to-end in a self-supervised manner, where we use an orientation alignment loss for the orientation estimation and a contrastive descriptor loss for robust local descriptors to geometric/photometric variations. Our method demonstrates state-of-the-art matching accuracy among existing rotation-invariant descriptors under varying rotation and also shows competitive results when transferred to the task of keypoint matching and camera pose estimation.", + "bbox": [ + 99, + 333, + 473, + 648 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 100, + 676, + 223, + 691 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Extracting local descriptors is an essential step for visual correspondence across images, which is used for a wide range of computer vision problems such as visual localization [29, 47, 48], simultaneous localization and mapping [7, 8, 39], and 3D reconstruction [1, 16, 17, 49, 66]. To establish reliable visual correspondences, the properties of invariance and discriminativeness are required for local descriptors; the descriptors need to be invariant to geometric/photometric variations of images while being discriminative enough to distinguish true matches from false ones. Since the remarkable success of deep learning for visual", + "bbox": [ + 99, + 701, + 470, + 845 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "recognition, deep neural networks have also been adopted to learn local descriptors, showing enhanced performances on visual correspondence [44, 45, 64]. Learning rotation-invariant local descriptors, however, remains challenging; the classical techniques [11, 27, 46] for rotation-invariant descriptors, which are used for shallow gradient-based feature maps, cannot be applied to feature maps from standard deep neural networks, in which rotation of input induces unpredictable feature variations. Achieving rotation invariance without sacrificing discriminativeness is particularly important for local descriptors as rotation is one of the most frequent imaging variations in reality.", + "bbox": [ + 497, + 305, + 870, + 464 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this work, we propose a self-supervised approach to obtain rotation-invariant and discriminative local descriptors by leveraging rotation-equivariant CNNs. First, we use group-equivariant CNNs [60] to jointly extract rotation-equivariant local features and their orientations from an image. To extract reliable orientations, we use an orientation alignment loss [21, 23, 63], which trains the network to predict the dominant orientation robustly against other imaging variations, including illumination or viewpoint changes. Using group-equivariant CNNs enables the local features to be empowered with explicitly encoded rotation equivariance without having to perform rigorous data augmentations [58, 60]. Second, to obtain discriminative rotation-invariant descriptors from rotation-equivariant features, we propose group-aligning that shifts the group-equivariant features by their dominant orientation along their group dimension. Conventional methods to yield invariant features from group-equivariant features collapse the group dimension by group-pooling, e.g., max-pooling or bilinear-pooling [26], resulting in a drop in feature discriminability and quality. In contrast, our group-aligning preserves the group dimension, achieving rotation-invariance while eschewing loss of discriminability. Furthermore, by preserving the group dimension, we can obtain multiple descriptors by performing group-aligning using multiple orientation candidates, which improves the matching performance by compensating for potential errors in dominant orientation prediction. Finally, we evaluate our rotation-invariant descriptors against existing local descriptors, and", + "bbox": [ + 497, + 467, + 870, + 846 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 95, + 8, + 171, + 46 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 228, + 5, + 818, + 48 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "21887", + "bbox": [ + 463, + 876, + 504, + 887 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "our group-aligning scheme against group-pooling methods on various image matching benchmarks to demonstrate the efficacy of our method.", + "bbox": [ + 99, + 146, + 470, + 185 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The contribution of our paper is fourfold:", + "bbox": [ + 117, + 186, + 376, + 199 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose to extract discriminative rotation-invariant local descriptors to tackle the task of visual correspondence by utilizing rotation-equivariant CNNs.", + "- We propose group-aligning, a method to shift a group-equivariant descriptor in the group dimension by its dominant orientation to obtain a rotation-invariant descriptor without having to collapse the group information to preserve feature discriminability.", + "- We use self-supervisory losses of orientation alignment loss for orientation estimation, and a contrastive descriptor loss for robust local descriptor extraction.", + "- We demonstrate state-of-the-art performances under varying rotations on the Roto-360 dataset and show competitive transferability on the HPatches dataset [2] and the MVS dataset [53]." + ], + "bbox": [ + 115, + 205, + 470, + 422 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related work", + "text_level": 1, + "bbox": [ + 100, + 432, + 230, + 446 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Classical invariant local descriptors. Classical methods to extract invariant local descriptors first aggregate image gradients to obtain a rotation-equivariant representation, i.e., histogram, from which the estimated dominant orientation is subtracted to obtain rotation-invariant features [27, 46]. Several studies [4, 11, 59] suggest extracting local descriptors by invariant mapping of the order-based gradient histogram of a patch. However, these classical methods for shallow gradient-based feature maps cannot be applied to deep feature maps from standard neural networks, in which rotation induces unpredictable feature variations. Therefore, we propose a deep end-to-end pipeline to obtain orientation-normalized local descriptors by utilizing rotation-equivariant CNNs [60] with additional losses.", + "bbox": [ + 99, + 454, + 470, + 636 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Learning-based invariant local descriptors. A branch of learning-based methods learns to obtain invariant local descriptors in an explicit manner. GIFT [26] constructs group-equivariant features by rotating or rescaling the images, and then collapses the group dimension using bilinear pooling to obtain invariant local descriptors. However, their groups are limited to non-cyclic discrete rotations ranging from $-90^{\\circ}$ to $90^{\\circ}$ . Furthermore, their reliance on data augmentation implies a lower sampling efficiency compared to group-equivariant networks. LISRD [42] jointly learns meta descriptors with different levels of regional variations and selects the most appropriate level of invariance given the context. Another branch of learning methods aims to learn the invariance implicitly using descriptor similarity losses from the image pair using camera pose or homography supervision. These methods are either patch-based [10, 36, 54, 56]", + "bbox": [ + 99, + 637, + 473, + 845 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "or image-based [8, 9, 22, 24, 28, 37, 40, 44, 50, 57]. While these methods may be robust to rotation, they cannot be said to be equivariant or invariant to rotation. We construct group-equivariant local features using the steerable networks [60], which explicitly encodes cyclic rotational equivariance to the features without having to rely on data augmentation. We can then yield rotation-invariant features by group-aligning that shifts the group-equivariant features along the group dimension by their dominant orientations, preserving feature discriminability.", + "bbox": [ + 497, + 146, + 870, + 277 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Equivariant representation learning. There has been a constant pursuit to learn equivariant representations by explicitly incorporating group equivariance into the model architecture design [30-32, 51, 60, 65]. For example, G-CNNs [6] use group equivariant convolutions that reduce sample complexity by exploiting symmetries on discrete isometric groups; SFCNNs [61] and H-Nets [62] extract features from more diverse groups and continuous domains by using harmonics as filters. There are also studies that focus on scale-equivariant representation learning [3, 21, 52]. [12, 18, 23, 38, 43] leverage equivariant neural networks to tackle vision tasks e.g., keypoint detection. In this work, we also propose to use equivariant neural networks to facilitate the learning of discriminative rotation-invariant descriptors. We guide the readers to section 1 of the supplementary material for a brief introduction to group equivariance.", + "bbox": [ + 497, + 277, + 870, + 486 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Rotation-equivariant features, Rotation-invariant descriptors", + "text_level": 1, + "bbox": [ + 497, + 500, + 868, + 532 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this section, we first draw the line between the terms feature and descriptor which will be used throughout this paper. The goal of our work is to learn to extract rotation-equivariant local features from our rotation-equivariant backbone network, and then to align them by their dominant orientation to finally yield rotation-invariant descriptors. In the subsequent subsections, we elaborate on the process of rotation-equivariant feature extraction from steerable CNNs (Sec. 3.1), assignment of equivariant features to keypoints (Sec. 3.2), how group-aligning is performed to yield rotation-invariant yet discriminative descriptors (Sec. 3.3), how we formulate our orientation alignment loss (Sec.3.4) and contrastive descriptor loss (Sec.3.5) to train our network to extract descriptors which are robust to not only rotation but also other imaging transformations, and finally how we obtain scale-invariant descriptors at test time using image pyramids (Sec.3.6). Figure 1 shows the overall architecture of our method.", + "bbox": [ + 497, + 539, + 868, + 774 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3.1. Rotation-equivariant feature extraction", + "text_level": 1, + "bbox": [ + 499, + 785, + 820, + 799 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "As the feature extractor, we use ReResNet18 [12], which has the same structure as ResNet18 [15] but is constructed using rotation-equivariant convolutional layers [60]. The", + "bbox": [ + 497, + 806, + 870, + 846 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "21888", + "bbox": [ + 463, + 876, + 505, + 887 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/59ded6a0b8cf8aa0c87e941fdeee5b71bcdcd73df6adbf3f8f65cc360cab88d7.jpg", + "image_caption": [ + "Figure 1. Overview of the proposed pipeline. An input image is forwarded through the equivariant networks to yield equivariant feature maps from multiple intermediate layers, encoding both low-level geometry and high-level semantic information. The feature maps are bilinearly interpolated to have equal spatial dimensions to be concatenated together. We use the first channel of the feature map $\\mathbf{F}$ as the orientation histogram map $\\mathbf{O}$ to predict the dominant orientations, which are used to shift the group-equivariant representation along the group dimension to yield discriminative rotation-invariant descriptors. To learn to extract accurate dominant orientation $\\hat{\\theta}$ , we use the orientation alignment loss $\\mathcal{L}^{\\mathrm{ori}}$ . To obtain descriptors robust to illumination and geometric changes, we use a contrastive descriptor loss $\\mathcal{L}^{\\mathrm{desc}}$ using the ground-truth homography $\\mathcal{H}_{\\mathrm{GT}}$ ." + ], + "image_footnote": [], + "bbox": [ + 105, + 143, + 867, + 301 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "layer acts on a cyclic group $G_N$ and is equivariant for all translations and $N$ discrete rotations. At the first layer, the scalar field of the input image is lifted to the vector field of the group representation [60]. We leverage feature pyramids from the intermediate layers of the ReResNet18 backbone to construct output features as follows:", + "bbox": [ + 99, + 409, + 473, + 486 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {F} = \\bigoplus_ {i \\in l} \\eta (\\mathbf {f} _ {i}), \\quad \\mathbf {f} _ {i} = [ \\Pi_ {j = 1} ^ {i} L _ {j} ] (I), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 494, + 470, + 522 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathbf{f}_i\\in \\mathbb{R}^{C_i\\times |G|\\times H_i\\times W_i}$ is an intermediate feature from $L_{i},L_{i}$ is the $i$ -th layer of the equivariant network, $\\eta$ denotes bilinear interpolation to $H\\times W$ , and $\\bigoplus$ denotes concatenation along the $C$ dimension. We utilize the multi-layer feature maps to exploit the low-level geometry information and high-level semantics in the local descriptors [13,19,35]. The output features $\\mathbf{F}\\in \\mathbb{R}^{C\\times |G|\\times H\\times W}$ contains rotationequivariant features with multiple layers containing different semantics and receptive fields. We set $H = H_{1}$ and $W = W_{1}$ , which are $\\frac{1}{2}$ of the input image size.", + "bbox": [ + 99, + 528, + 473, + 663 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Assigning local features to keypoints", + "text_level": 1, + "bbox": [ + 100, + 668, + 400, + 682 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "During training, we extract $K$ keypoints from the source image using Harris corner detection [14]. We then use the ground-truth homography $\\mathcal{H}_{\\mathrm{GT}}$ to obtain ground-truth keypoint correspondences. Also, we allocate a local feature $\\mathbf{p} \\in \\mathbb{R}^{C \\times |G| \\times K}$ to each keypoint, using the interpolated location of the equivariant feature map $\\mathbf{F}$ . We experiment our descriptor with SIFT [27], LF-Net [40], SuperPoint [8], and KeyNet [20] as the keypoint detector during inference time.", + "bbox": [ + 99, + 687, + 470, + 793 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.3. Group aligning for invariant mapping", + "text_level": 1, + "bbox": [ + 100, + 799, + 410, + 814 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To transform the rotation-equivariant feature to a rotation-invariant descriptor, we propose group aligning, an", + "bbox": [ + 99, + 819, + 472, + 846 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "operation to shift the group-equivariant feature in the $G$ -dimension using the dominant orientation $\\hat{\\theta}$ . Unlike existing methods that use group pooling, e.g., average pooling or max pooling, which collapses the group dimension, group aligning preserves the rich group information. Figure 2 illustrates the difference between group pooling and group aligning on an equivariant representation.", + "bbox": [ + 497, + 409, + 870, + 500 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Estimating the dominant orientation and the shifting value. We obtain the orientation histogram map $\\mathbf{O} \\in \\mathbb{R}^{|G| \\times H \\times W} = \\mathbf{F}_0$ by selecting the first channel of the rotation-equivariant tensor $\\mathbf{F}$ as an orientation histogram map. Note that the first channels of each group action are simultaneously used as the channels of the descriptors and to construct the orientation histogram. The histogram-based representation of $\\mathbf{O}$ provides richer information than directly regressing the dominant orientation, as the orientation histogram enables predicting multiple (i.e., top- $k$ ) candidates as the dominant orientation. We first select an orientation vector $\\mathbf{o} \\in \\mathbb{R}^{|G|}$ of a keypoint from the orientation histogram map $\\mathbf{O}$ using the coordinates of the keypoint. Next, we estimate the dominant orientation value $\\hat{\\theta}$ from the orientation vector $\\mathbf{o}$ by selecting the index of the maximum score, $\\hat{\\theta} = \\frac{360}{|G|} \\arg \\max_g \\mathbf{o}$ . Using the dominant orientation value $\\hat{\\theta}$ , we obtain the shifting value $\\hat{\\Delta} = \\frac{|G|}{360} \\hat{\\theta}$ in $G$ -dim. At training time, we use the ground-truth rotation $\\theta_{\\mathrm{GT}}$ instead of the predicted dominant orientation value $\\hat{\\theta}$ to generate the shifting value $\\Delta_{\\mathrm{GT}}$ .", + "bbox": [ + 497, + 501, + 868, + 766 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Group aligning. Given a keypoint-allocated feature tensor $\\mathbf{p} \\in \\mathbb{R}^{C \\times |G|}$ from the equivariant representation $\\mathbf{F}$ , we obtain the rotation-invariant local descriptor $\\mathbf{d} \\in \\mathbb{R}^{C|G|}$ by group aligning using $\\Delta$ . After computing the dominant orientation $\\hat{\\theta}$ and the shifting value $\\hat{\\Delta}$ from $\\mathbf{o}$ , we obtain the orientation-normalized descriptor $\\mathbf{d}' \\in \\mathbb{R}^{C|G|}$ by shifting $\\mathbf{p}$", + "bbox": [ + 497, + 767, + 870, + 846 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "21889", + "bbox": [ + 463, + 876, + 505, + 887 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/4cfedb4a049db1906ac7b2e9eb74b61d750496c6ef061521fe5b9617b405d2e1.jpg", + "image_caption": [ + "(a) Group pooling", + "(b) Group aligning" + ], + "image_footnote": [], + "bbox": [ + 112, + 144, + 453, + 336 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "in the $G$ -dimension by $-\\hat{\\Delta}$ and flattening the descriptor to a vector. We use cyclic shifting in consideration of the cyclic property of rotation. We finally obtain the L2-normalized descriptor $\\mathbf{d}$ from the orientation-normalized descriptor $\\mathbf{d}'$ such that $||\\mathbf{d}||^2 = 1$ . Formally, this process can be defined as:", + "bbox": [ + 99, + 446, + 470, + 521 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {p} _ {:, i} ^ {\\prime} = T _ {r} ^ {\\prime} \\left(\\mathbf {p} _ {:, i}, \\hat {\\Delta}\\right) = \\mathbf {p} _ {:, (i + \\hat {\\Delta}) \\bmod | G |},\n$$\n", + "text_format": "latex", + "bbox": [ + 166, + 522, + 401, + 540 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {d} _ {| G |: | G | (i + 1)} ^ {\\prime} = \\mathbf {p} _ {i} ^ {\\prime}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 278, + 541, + 468, + 560 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {d} = \\frac {\\mathbf {d} ^ {\\prime}}{| | \\mathbf {d} ^ {\\prime} | | _ {2}},\n$$\n", + "text_format": "latex", + "bbox": [ + 324, + 560, + 401, + 589 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $T_r'$ is shifting operator in vector space, and $\\mathbf{p}'$ is a group-aligned descriptor before flattening. This shifting by $\\hat{\\Delta}$ aligns all the descriptors in the direction of their dominant orientations, creating orientation-normalized descriptors. This process is conceptually similar to subtracting the dominant orientation value of the orientation histogram in the classical descriptor SIFT [27], but we apply this concept to the equivariant neural features. The proposed group aligning preserves the group information, so our invariant descriptors have more representative power than the existing group-pooled descriptors which collapse the group dimension for invariance.", + "bbox": [ + 99, + 593, + 470, + 750 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Orientation alignment loss", + "text_level": 1, + "bbox": [ + 100, + 758, + 327, + 774 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To learn to obtain the dominant orientations from the orientation vectors, we use an orientation alignment loss [21, 23, 63] to supervise the orientation histograms in $\\mathbf{O}$ to be rotation equivariant under the photometric/geometric transformations. Figure 3 shows the illustration of orientation", + "bbox": [ + 99, + 779, + 470, + 845 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/913e20d0650200e75b53cd63aebac1942d4d6a1b149e593e091cd94587a6b1f4.jpg", + "image_caption": [ + "Figure 2. Difference between group pooling and group aligning. In group pooling, the group dimension is collapsed to yield an invariant descriptor $(\\mathbb{R}^{C\\times |G|}\\to \\mathbb{R}^C)$ . In group aligning, the entire feature is cyclically shifted in the group dimension to obtain an invariant descriptor $(\\mathbb{R}^{C\\times |G|}\\to \\mathbb{R}^{C|G|})$ while preserving the group information and discriminability.", + "Figure 3. Illustration of orientation alignment loss. Given two rotation-equivariant tensors $\\mathbf{p}^{\\mathrm{A}},\\mathbf{p}^{\\mathrm{B}}\\in \\mathbb{R}^{C\\times |G|}$ obtained from two different rotated versions of the same image, we apply cyclic shift on one of the descriptors in the group dimension using the GT difference in rotation. The orientation alignment loss supervises the output orientation vectors of the two descriptors to be the same." + ], + "image_footnote": [], + "bbox": [ + 504, + 144, + 867, + 333 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "alignment loss. The cyclic shift of an orientation histogram map at the training time is formulated as follows:", + "bbox": [ + 497, + 435, + 868, + 461 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nT _ {r} ^ {\\prime} \\left(\\mathbf {O} _ {i}, \\Delta_ {\\mathrm {G T}}\\right) = \\mathbf {O} _ {\\left(i + \\Delta_ {\\mathrm {G T}}\\right) \\bmod | G |}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 574, + 473, + 868, + 489 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\Delta_{\\mathrm{GT}} = \\frac{|G|}{360}\\theta_{\\mathrm{GT}}$ is the shifting value calculated from the ground-truth rotation $\\theta_{\\mathrm{GT}}$ . We formulate the orientation alignment loss in the form of a cross-entropy as follows:", + "bbox": [ + 497, + 500, + 868, + 541 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} ^ {\\mathrm {o r i}} \\left(\\mathbf {O} ^ {\\mathrm {A}}, \\mathbf {O} ^ {\\mathrm {B}}, \\Delta_ {\\mathrm {G T}}\\right) = \\\\ - \\sum_ {k \\in K} \\sum_ {g \\in G} \\sigma \\left(\\mathbf {O} _ {g, k} ^ {\\mathrm {A}}\\right) \\log \\left(\\sigma \\left(T _ {r} ^ {\\prime} \\left(\\mathbf {O} _ {g, k} ^ {\\mathrm {B}}, \\Delta_ {\\mathrm {G T}}\\right)\\right)\\right), \\tag {4} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 519, + 549, + 868, + 598 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{O}^{\\mathrm{A}}$ is the source orientation histogram map and $\\mathbf{O}^{\\mathrm{B}}$ is the target orientation histogram map obtained from a synthetically warped source image, $\\sigma$ is a softmax function applied to the $G$ -dimension of the orientation histogram map to represent the orientation vector as a probability distribution for the cross-entropy loss to be applicable. Using Equation 4, the network learns to predict the characteristic orientations robustly against different imaging variations, such as photometric transformations and geometric transformations beyond rotation, as these transformations cannot be handled by equivariance to discrete rotations alone. Note that it is not straightforward to define the characteristic orientation of a keypoint to provide strong supervision. However, we facilitate the learning of characteristic orientations by formulating it as a self-supervised learning framework, leveraging the known relative orientation between two keypoint orientation histogram maps obtained from differently rotated versions of the same image.", + "bbox": [ + 497, + 608, + 870, + 845 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "21890", + "bbox": [ + 463, + 876, + 505, + 887 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/d01f4b550fc7aecc1c1cbf6f99f9cf3ffa96df0f877698726ff7b52cdb034cfc.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MMApred.
@1px
Align97.5484.90
Avg33.7233.72
Max57.9257.92
None23.9723.97
Bilinear43.6026.42
", + "bbox": [ + 196, + 144, + 376, + 238 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.5. Contrastive descriptor loss", + "text_level": 1, + "bbox": [ + 100, + 321, + 329, + 337 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We propose to use a descriptor similarity loss motivated by contrastive learning [5] to further empower the descriptors to be robust against variations apart from rotation, e.g., illumination or viewpoint. The descriptor loss is formulated in a contrastive manner as follows:", + "bbox": [ + 99, + 343, + 470, + 408 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} ^ {\\text {d e s c}} \\left(\\mathbf {D} ^ {\\mathrm {A}}, \\mathbf {D} ^ {\\mathrm {B}}\\right) = \\\\ \\sum_ {\\left(\\mathbf {d} _ {i} ^ {\\mathrm {A}}, \\mathbf {d} _ {i} ^ {\\mathrm {B}}\\right) \\in \\left(\\mathbf {D} ^ {\\mathrm {A}}, \\mathbf {D} ^ {\\mathrm {B}}\\right)} - \\log \\frac {\\exp \\left(\\operatorname {s i m} \\left(\\mathbf {d} _ {i} ^ {\\mathrm {A}} , \\mathbf {d} _ {i} ^ {\\mathrm {B}}\\right) / \\tau\\right)}{\\sum_ {k \\in K \\backslash i} \\exp \\left(\\operatorname {s i m} \\left(\\mathbf {d} _ {i} ^ {\\mathrm {A}} , \\mathbf {d} _ {k} ^ {\\mathrm {B}}\\right)\\right) / \\tau}, \\tag {5} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 100, + 415, + 473, + 480 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where sim is cosine similarity and $\\tau$ is the softmax temperature. Unlike the triplet loss with one hard negative sample, the contrastive loss can optimize the distance for all negative pairs. This contrastive loss with InfoNCE [41] maximizes the mutual information between the encoded features and effectively reduces the low-level noise. Our overall self-supervised loss is formulated as $\\mathcal{L} = \\alpha \\mathcal{L}^{\\mathrm{ori}} + \\mathcal{L}^{\\mathrm{desc}}$ , where $\\alpha$ is a balancing term.", + "bbox": [ + 99, + 482, + 470, + 586 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.6. Scale robustness", + "text_level": 1, + "bbox": [ + 100, + 595, + 253, + 606 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "While we employ a rotation-equivariant network, it does not ensure that the descriptors are robust to scale changes. Thus, at inference time, we construct an image pyramid using a scale factor of $2^{1/4}$ from a maximum of 1,024 pixels to a minimum of 256 pixels as in R2D2 [44]. After constructing the scale-wise descriptors $\\in \\mathbb{R}^{S \\times C|G| \\times K}$ with $S$ varying scales, we finally generate the scale-invariant local descriptors $\\in \\mathbb{R}^{C|G| \\times K}$ by max-pooling in the scale dimension inspired by scale-space maxima as in SIFT [27,33], for improved robustness to scale changes.", + "bbox": [ + 99, + 615, + 470, + 745 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiment", + "text_level": 1, + "bbox": [ + 100, + 758, + 218, + 771 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Implementation details. We use rotation-equivariant ResNet-18 (ReResNet-18) [12] implemented using the rotation-equivariant layers of $E(2)$ -CNN [60] as our backbone. We remove the first maxpool layer to preserve the spatial size, so that the spatial resolution of the rotation-", + "bbox": [ + 99, + 780, + 470, + 846 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/0c0a8fd0faadb110a9b27575c37eeae2e81afd55a1cd3365432d34506bc559b7.jpg", + "table_caption": [ + "Table 1. Evaluation with GT keypoint pairs on Roto-360 without training. 'Align' uses GT rotation difference to apply group-aligning to demonstrate the upper-bound. 'None' does not use pooling nor aligning, demonstrating the lower-bound. We use an average of 111 keypoint pairs extracted using SuperPoint [8]." + ], + "table_footnote": [], + "table_body": "
MMApred.
@10px@5px@3px
Align93.0891.3590.18688.3
Avg85.8482.1281.05705.9
Max82.6178.0077.79686.0
None19.6818.8118.57349.1
Bilinear42.6941.0340.51332.5
", + "bbox": [ + 534, + 144, + 833, + 238 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 2. Evaluation with predicted keypoint pairs on Roto-360 with training. 'Max' and 'Avg' collapses the group dimension of the features through max pooling or average pooling. 'pred.' denotes the average number of predicted matches. We use an average of 1161 keypoint pairs extracted using SuperPoint [8].", + "bbox": [ + 497, + 239, + 870, + 301 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "equivariant feature $\\mathbf{F}$ is $H = \\frac{H'}{2}$ and $W = \\frac{W'}{2}$ , where $H'$ and $W'$ are the height and width of an input image. We use 16 for the order of cyclic group $G$ . We use a batch size of 8, a learning rate of $10^{-4}$ , and a weight decay of 0.1. We train our model for 12 epochs with 1,000 iterations using a machine with an Intel i7-8700 CPU and an NVIDIA GeForce RTX 3090 GPU. We use the temperature $\\tau$ of $\\mathcal{L}^{\\mathrm{desc}}$ as 0.07. The loss balancing factor $\\alpha$ is 10. The final output descriptor size is 1,024, with $C = 64$ , $|G| = 16$ . We use SuperPoint [8] as the keypoint detector to evaluate our method except Table 4. For all descriptors, we use the mutual nearest neighbour matcher to predict the correspondences.", + "bbox": [ + 497, + 320, + 870, + 480 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets and metrics", + "text_level": 1, + "bbox": [ + 499, + 486, + 685, + 500 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We use a synthetic training dataset to train our model in a self-supervised manner. We evaluate our model on the Roto-360 dataset and show the transferability on real image benchmarks, i.e., HPatches [2] and MVS [53] datasets.", + "bbox": [ + 497, + 505, + 868, + 558 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training dataset. We generate a synthetic dataset for self-supervised training from the MS-COCO dataset [25]. We warp images with random homographies for geometric robustness and transform the colors by jitter, noise, and blur for photometric robustness. As we need the ground-truth rotation $\\theta_{\\mathrm{GT}}$ for our orientation alignment loss, we decompose the synthetic homography $\\mathcal{H}$ as follows: $\\theta_{\\mathrm{GT}} = \\arctan \\left(\\frac{\\mathcal{H}_{21}}{\\mathcal{H}_{11}}\\right)$ , where we assume that a $3\\times 3$ homography matrix $\\mathcal{H}$ with no significant tilt can be approximated to an affine matrix. We sample $K = 512$ keypoints for an image using Harris corner detector [14], obtaining 512 corresponding keypoint pairs for each image pair using homography and rotation. Note that this dataset generation protocol is the same as that of GIFT [26] for a fair comparison.", + "bbox": [ + 497, + 558, + 868, + 739 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Roto-360 is an evaluation dataset that consists of 360 image pairs with in-plane rotation ranging from $0^{\\circ}$ to $350^{\\circ}$ at $10^{\\circ}$ intervals, created using ten randomly sampled images from HPatches [2]. Roto-360 is more suitable to evaluate the rotation invariance of our descriptors, as the extreme rotation (ER) dataset [26] only covers $180^{\\circ}$ , and includes photometric variations. We use mean matching accuracy (MMA) as the evaluation metric with pixel thresholds of 3/5/10 pixels", + "bbox": [ + 497, + 739, + 870, + 846 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "21891", + "bbox": [ + 463, + 876, + 502, + 887 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/f60bbd6a8720487386384f93178500a42246f0a50312043656d85b31e42b5520.jpg", + "table_caption": [], + "table_footnote": [ + "Table 3. Comparison to existing local descriptors on Roto-360. We use mutual nearest matching for all methods to establish matches between images. 'total.' and 'pred.' denotes the average number of detected keypoints and predicted matches, respectively. 'ours*' denotes selecting multiple candidate descriptors based on the ratio of max value in the orientation histogram. We use SuperPoint keypoint detector [8] same to the GIFT descriptor [26]." + ], + "table_body": "
MethodMMApred.total.
@10px@5px@3px
SIFT [27]78.8678.5978.23774.11500.0
ORB [46]86.7885.2978.73607.61005.2
SuperPoint [8]22.8522.1021.83462.61161.0
LF-Net [40]75.0574.3072.61386.71024.0
RF-Net [50]15.6415.1814.581602.55000.0
D2-Net [9]15.569.305.21386.91474.5
R2D2 [44]15.8014.9713.50197.91500.0
GIFT [26]42.3542.0541.59589.21161.0
LISRD [42]16.9616.0415.64323.61781.1
ASLFeat [28]19.3416.3813.131366.96764.2
DISK [57]13.2212.4312.04359.12048.0
PosFeat [24]13.7611.799.82717.27623.5
ours93.0891.3590.18688.31161.0
ours*94.3592.8291.691333.02340.4
", + "bbox": [ + 105, + 144, + 460, + 328 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "and the number of predicted matches following [9, 34].", + "bbox": [ + 99, + 447, + 442, + 461 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "HPatches [2] has 57 scenes with illumination variations and 59 scenes with viewpoint variations. Each scene contains five image pairs with ground-truth planar homography. We use the same evaluation metrics to Roto-360 to show the transferability of our local descriptors.", + "bbox": [ + 99, + 461, + 470, + 527 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "MVS dataset [53] has six image sequences of outdoor scenes with GT camera poses. We evaluate the relative pose estimation accuracy at $5^{\\circ} / 10^{\\circ} / 20^{\\circ}$ angular difference thresholds.", + "bbox": [ + 99, + 527, + 470, + 579 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Comparison to other invariant mappings", + "text_level": 1, + "bbox": [ + 100, + 589, + 431, + 604 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1 compares group aligning to various group pooling methods on the Roto-360 dataset using ground-truth keypoint pairs, i.e., no keypoint deviation, without training. The purpose is to compare the invariant mapping operations only while keeping the backbone network and the number of keypoints fixed. We use $\\Delta_{\\mathrm{GT}}$ to shift the equivariant features, and group aligning shows almost perfect keypoint correspondences with $97.54\\%$ matching accuracy. Group pooling, such as max pooling or average pooling, significantly reduces discriminative power compared to group aligning. The results show that group aligning shows the best results, proving that leveraging the full group-equivariant features instead of collapsing the groups shows higher discriminability. Note that the bilinear pooling [26] does not guarantee the rotation-invariant matching.", + "bbox": [ + 99, + 610, + 470, + 805 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2 compares the proposed group aligning to the existing group pooling methods on the Roto-360 dataset, this time with predicted keypoint pairs and with training. Note", + "bbox": [ + 99, + 806, + 470, + 846 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/823125de5034ee352e8772a1e4550191920a58425c79d0d06d871c90f15d3237.jpg", + "table_caption": [], + "table_footnote": [ + "Table 4. Comparison to existing local descriptors when using the same keypoint detector on Roto-360. Results in bold indicate the best result, and underlined results indicate the second best." + ], + "table_body": "
Det.Desc.MMApred.total.
@10px@5px@3px
SIFT [27]SIFT [27]78.8678.5978.23774.11500
GIFT [26]37.9736.8236.09531.21500
ours84.6779.8577.96558.31500
ours*84.9180.0978.18759.82219
LF-Net [40]LF-Net [40]75.0574.3072.61386.71024
GIFT [26]35.5633.8232.29426.31024
ours79.9071.6367.39431.81024
ours*80.3271.9967.62591.41503
SuperPoint [8]SuperPoint [8]22.8522.1021.83462.61161
GIFT [26]42.3542.0541.59589.21161
ours93.0891.3590.18688.31161
ours*94.3592.8291.691333234
KeyNet [20]HyNet [55]24.4322.8220.64288.7995
GIFT [26]34.0832.3129.17275.7995
ours72.9561.3641.33257.2995
ours*72.4860.6940.95356.61484
", + "bbox": [ + 504, + 144, + 868, + 319 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "that while other methods are trained only with $\\mathcal{L}^{\\mathrm{desc}}$ , our method is trained also with $\\mathcal{L}^{\\mathrm{ori}}$ to facilitate group aligning. While the number of predicted matches is the highest for average pooling, the MMA results are significantly higher for group aligning, which shows group-aligned descriptors have a higher precision. Overall, incorporating group aligning demonstrates the best results in terms of MMA compared to average pooling, max pooling or bilinear pooling [26]. Note that pooling or aligning the group-equivariant features to obtain invariant descriptors shows consistent improvements over not pooling nor aligning the group-equivariant features.", + "bbox": [ + 497, + 380, + 870, + 536 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Comparison to existing local descriptors", + "text_level": 1, + "bbox": [ + 499, + 549, + 823, + 564 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3 shows the matching accuracy compared to existing local descriptors on the Roto-360 dataset. We evaluate the descriptors using their own keypoint detectors [8, 9, 27, 39, 40, 44, 50], or combined with off-the-shelf detectors [24, 26, 42]. While the classical methods [27, 46] achieve better matching accuracy than the existing learning-based methods, our method achieves the best results overall. This is because the learning-based methods learn only a limited degree of invariance in a data-driven manner without guaranteeing full invariance to rotation by design.", + "bbox": [ + 497, + 570, + 868, + 701 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 4 shows the performance of our method in comparison to existing local descriptors when using the same keypoint detector, where our method shows consistent performance improvement. In particular, our rotation-invariant descriptor shows consistently higher matching accuracy than GIFT [26], which is a representative learning-based group-invariant descriptor. While our model shows a lower MMA than the LF-Net [40] descriptor when using the LF-Net detector at 5px and 3px thresholds, we conjecture that this is due to the better integrity of the detector and descriptor of LF-Net due to their joint training scheme.", + "bbox": [ + 497, + 701, + 870, + 846 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "21892", + "bbox": [ + 463, + 876, + 505, + 887 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/77e9b256cbac4ed532bf876b59f6f95af26029f1cbe0c77445fa0864daee7f1a.jpg", + "image_caption": [ + "Figure 4. An example of multiple descriptor extraction. The distribution is an orientation histogram $\\mathbf{o} \\in \\mathbb{R}^{16}$ , and the scores are confidence values for each bin from group-equivariant features. Arrows indicate the orientation candidates for multiple descriptor extraction. The example shows selecting three orientations to obtain three candidate descriptors for a feature point, which is possible as we predict a score for each orientation." + ], + "image_footnote": [], + "bbox": [ + 109, + 143, + 458, + 223 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "These results show that our descriptors obtained using the proposed group aligning show the highest matching accuracy under rotation changes compared to existing methods. The improvement of our method is also attributed to the usage of rotation-equivariant networks, which have a higher sampling efficiency, i.e., do not require intensive rotation augmentations to learn rotation invariance.", + "bbox": [ + 99, + 332, + 470, + 423 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Multiple descriptor extraction using orientation candidates. Group aligning can extract multiple descriptors with different alignments by using multiple orientation candidates, denoted by 'ours*', whose scores are at least $60\\%$ of the maximum score in the orientation histogram. When there is a single keypoint position with $k$ descriptors that are differently aligned, we treat it as if there are $k$ detected keypoints. Multiple descriptor extraction compensates for incorrect orientation predictions and further enhances matching accuracy. Figure 4 illustrates an example of multiple descriptor extraction with a score ratio threshold of 0.6.", + "bbox": [ + 99, + 423, + 470, + 567 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Consistency of matching accuracy with respect to rotation changes. Figure 5 illustrates how the matching accuracy changes with respect to varying degrees of rotation. Our method shows the highest consistency, proving the enhanced invariance of descriptors obtained using group aligning against different rotations. While MMA of SIFT [27] and ORB [46] are high at the upright rotations, they tend to fluctuate significantly with varying rotations. The existing learning-based group-invariant descriptor, GIFT [26], fails to find correspondences beyond $60^{\\circ}$ .", + "bbox": [ + 99, + 567, + 470, + 699 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Transferability to real image benchmarks", + "text_level": 1, + "bbox": [ + 100, + 707, + 435, + 722 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 5 shows the matching performance of local descriptors on HPatches illumination/viewpoint [2] and pose estimation [53]. Our model shows the highest performance overall on the HPatches dataset. The performance gain of ours becomes smaller compared to the Roto-360 dataset due to the absence of extreme rotations in HPatches. While GIFT shows a higher performance under illumination changes that only contain identity mappings, ours†, which uses a larger backbone network (ReWRN), improves", + "bbox": [ + 99, + 726, + 470, + 846 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c5dc650cbf561ee4a2d0989159ae043610775796b40aed15ed3a436ac89c6f1c.jpg", + "image_caption": [ + "Figure 5. Matching accuracies according to varying degree of rotations on Roto-360." + ], + "image_footnote": [], + "bbox": [ + 504, + 143, + 860, + 305 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/ed7488127ec34117c6bf31008dfa4090bed516da8e90c576ff865dda1044275e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodHP-allHP-illuHP-viewPose
@5px@3px@5px@3px@5px@3px20°10°5°
SIFT [27]51.3646.3249.0844.6253.5747.960.020.000.00
ORB [46]52.2247.4050.8546.2953.5548.470.060.000.00
SuperPoint [8]69.7161.7574.6367.5364.9656.170.200.070.01
LF-Net [40]56.4552.2262.2157.6350.8847.000.060.030.01
RF-Net [50]59.0854.4261.6357.4656.6251.490.100.040.01
D2-Net [9]50.1832.5463.8044.0937.0221.380.110.050.01
GIFT [26]76.0367.3179.7171.8972.4862.880.600.280.09
LISRD [42]62.1656.1270.0963.6454.5048.850.050.020.00
oursavgpool64.1057.9462.2856.2765.8559.550.270.100.05
oursmaxpool61.5755.8159.6653.9163.4257.640.270.110.03
oursthinelunpool [26]45.5941.9045.1341.5746.0342.220.350.170.09
oursthinelunpool† [26]58.7253.7757.3252.6760.0654.830.240.110.03
oursgroupalign70.6963.4270.3962.8870.9763.950.580.260.12
oursgroupalign*73.9266.3773.1365.3374.6967.380.560.300.12
oursgroupalign†78.0069.7077.9469.3578.0670.030.560.330.14
", + "bbox": [ + 502, + 353, + 870, + 489 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 5. Evaluation with predicted keypoint pairs on real image benchmarks. The first group of methods includes existing local feature extraction methods. The second group of methods includes comparisons to other group pooling methods by replacing our group aligning with them. 'ours*' denotes the extraction of multiple descriptors using the orientation candidates, whose scores are at least $60\\%$ of the maximum score in the orientation histogram. 'ours†' denotes our method using the rotation-equivariant WideResNet16-8 (ReWRN) backbone for feature extraction. We use SuperPoint [8] keypoint detector to evaluate ours.", + "bbox": [ + 497, + 491, + 870, + 612 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "matching accuracy by $7.15\\%$ p at 3px and $5.58\\%$ p at 5px, and ours* improves by $4.5\\%$ p at 3px, $2.21\\%$ p at 5px under viewpoint changes compared to GIFT [26]. It should be noted that the core difference between oursbilinearpool and GIFT is the usage of explicit rotation-equivariant CNNs [60], which clearly shows that bilinear pooling is not well-compatible with the equivariant CNNs in comparison to group aligning. Using the same network with bilinear pooling (oursbilinearpool†) proposed in [26] shows significantly lower results compared to oursgroupalign†.", + "bbox": [ + 497, + 635, + 868, + 767 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In the MVS dataset [53] to evaluate relative camera pose estimation, our model shows a higher performance than GIFT at finer error thresholds of $10^{\\circ}$ and $5^{\\circ}$ . This shows that our model can find more precise correspondences under 3D viewpoint changes. Overall, these results show that our descriptors using rotation-equivariant representation ex", + "bbox": [ + 497, + 767, + 870, + 846 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "21893", + "bbox": [ + 463, + 876, + 504, + 887 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/4e7afd4ebb64086f254f880d5555e18e6bad4a89cf9892fe98cb97178d4a031c.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
HP-allRoto-360params. (millions)
@5px@3px@5px@3px
ours (proposed |G| = 16)70.6963.4291.3590.180.62M
w/o orientation loss66.4158.6185.2983.260.62M
w/o descriptor loss27.4924.8325.6424.980.62M
w/o image scale pyramid68.7762.2591.4790.430.62M
w/o equivariant backbone47.2542.528.658.5111.18M
|G| = 6463.9657.3585.1283.320.16M
|G| = 3668.1760.9587.7885.890.26M
|G| = 3269.4462.0889.1087.310.31M
|G| = 2469.7262.2190.2788.340.39M
|G| = 865.7458.9287.1685.571.24M
", + "bbox": [ + 104, + 143, + 468, + 264 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 6. Ablation test on HPatches and Roto-360. 'params.' denotes the number of model parameters.", + "bbox": [ + 100, + 266, + 468, + 289 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "hibit strong transferability to the real-world examples.", + "bbox": [ + 99, + 321, + 436, + 334 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.5. Ablation study and design choice", + "text_level": 1, + "bbox": [ + 100, + 340, + 374, + 356 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 6 shows the results of ablation studies on the HPatches and Roto-360 datasets. The matching accuracy drops when either the orientation alignment loss or the contrastive descriptor loss is not used. Specifically, even when using the ground truth rotation difference for group alignment, not using the descriptor loss results in lower performance, highlighting the importance of robustness to other sorts of variations, e.g., illumination or viewpoint. Not using the image pyramid at inference time results in a slight drop in HPatches, but the performance on Roto-360 remains nearly unchanged. When training without equivariant layers, ResNet-18 with conventional convolutional layers was used - this results in a drastic drop in performance especially on Roto-360, with a rapid increase in the number of model parameters. This demonstrates the significance of high sample efficiency of group-equivariant layers.", + "bbox": [ + 99, + 362, + 472, + 570 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We also demonstrate the effect of the order of cyclic group $G$ on the performance of our method in the second group of Table 6. We fix the computational cost $C \\times |G| = 1,024$ , and vary the order of group to show the parameter efficiency of the group equivariant networks. Our design choice $|G| = 16$ yields the best results, and the performance drops gracefully as $G$ increases. This is because with a higher order of groups, the precision of dominant orientation estimation is likely to decrease, leading to lower results. Reducing the order of group to $|G| = 8$ reduces the MMA in both benchmarks as well, which we suspect is because the range of rotation covered by one group action becomes too wide, leading to increased approximation errors.", + "bbox": [ + 99, + 570, + 473, + 741 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.6. Qualitative results", + "text_level": 1, + "bbox": [ + 100, + 747, + 268, + 760 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Figure 6 visualizes the consistency of dominant orientation estimation. From the source (left) and target (middle) images, we estimate the dominant orientation for the same set of predicted keypoints. We use the ground truth rotation to align the estimated orientation and the target image for better visibility (right). The green and red arrows (middle,", + "bbox": [ + 99, + 767, + 470, + 846 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/545b64514cf1d004fca964542115e7d2201376f603c064821ae1fc77ce9d822d.jpg", + "image_caption": [ + "Figure 6. Visualization of consistency of dominant orientation estimation. Best viewed in electronics and colour." + ], + "image_footnote": [], + "bbox": [ + 517, + 143, + 852, + 415 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "right) represent the consistent and inconsistent orientation predictions with respect to the initial estimations (left) at a $30^{\\circ}$ threshold. The numbers on the left represent the number of consistent estimations/number of detected keypoints. Compared to LF-Net [40] and RF-Net [50], our method predicts more consistent dominant orientations of keypoints.", + "bbox": [ + 497, + 477, + 870, + 555 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 499, + 572, + 611, + 586 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have proposed a self-supervised rotation-equivariant network for visual correspondence to improve the discriminability of local descriptors. Our invariant mapping called group-aligning shifts the rotation-equivariant features along the group dimension based on the orientation value to produce rotation-invariant descriptors while preserving the feature discriminability, without collapsing the group dimension. Our method achieves state-of-the-art performance in obtaining rotation-invariant descriptors, which are transferable to tasks such as keypoint matching and camera pose estimation. We believe that our approach can be further extended to other geometric transformation groups, and will motivate group-equivariant learning for practical applications of computer vision.", + "bbox": [ + 497, + 596, + 870, + 779 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement. This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-TF2103-02 and also by the NRF grant (NRF-2021R1A2C3012728) funded by the Korea government (MSIT).", + "bbox": [ + 497, + 780, + 870, + 846 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "21894", + "bbox": [ + 463, + 876, + 505, + 887 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 102, + 144, + 191, + 157 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Sameer Agarwal, Yasutaka Furukawa, Noah Snavely, Ian Simon, Brian Curless, Steven M Seitz, and Richard Szeliski. Building rome in a day. Communications of the ACM, 54(10):105-112, 2011. 1", + "[2] Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk. Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5173-5182, 2017. 2, 5, 6, 7", + "[3] Axel Barroso-Laguna, Yurun Tian, and Krystian Mikolajczyk. Scalenet: A shallow architecture for scale estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12808-12818, 2022. 2", + "[4] Fabio Bellavia and Carlo Colombo. Rethinking the sgloh descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4):931-944, 2017. 2", + "[5] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 5", + "[6] Taco Cohen and Max Welling. Group equivariant convolutional networks. In International conference on machine learning, pages 2990-2999. PMLR, 2016. 2", + "[7] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Toward geometric deep slam. arXiv preprint arXiv:1707.07410, 2017. 1", + "[8] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. In CVPR Deep Learning for Visual SLAM Workshop, 2018. 1, 2, 3, 5, 6, 7", + "[9] Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, and Torsten Sattler. D2-net: A trainable cnn for joint description and detection of local features. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pages 8092-8101, 2019. 2, 6, 7", + "[10] Patrick Ebel, Anastasiia Mishchuk, Kwang Moo Yi, Pascal Fua, and Eduard Trulls. Beyond cartesian representations for local descriptors. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 253-262, 2019. 2", + "[11] Bin Fan, Fuchao Wu, and Zhanyi Hu. Rotationally invariant descriptors using intensity order pooling. IEEE transactions on pattern analysis and machine intelligence, 34(10):2031-2045, 2011. 1, 2", + "[12] Jiaming Han, Jian Ding, Nan Xue, and Gui-Song Xia. Redet: A rotation-equivariant detector for aerial object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2786-2795, 2021. 2, 5", + "[13] Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik. Hypercolumns for object segmentation and fine-grained localization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 447-456, 2015. 3" + ], + "bbox": [ + 102, + 167, + 470, + 844 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] Chris Harris, Mike Stephens, et al. A combined corner and edge detector. In *Alvey vision conference*, volume 15, pages 10-5244. CiteSeer, 1988. 3, 5", + "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 2", + "[16] Jared Heinly, Johannes L. Schonberger, Enrique Dunn, and Jan-Michael Frahm. Reconstructing the world* in six days. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3287-3295, 2015. 1", + "[17] Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, and Eduard Trulls. Image matching across wide baselines: From paper to practice. International Journal of Computer Vision, 129(2):517-547, 2021. 1", + "[18] Seungwook Kim, Yoonwoo Jeong, Chunghyun Park, Jaesik Park, and Minsu Cho. SeLCA: Self-supervised learning of canonical axis. In NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022. 2", + "[19] Seungwook Kim, Juhong Min, and Minsu Cho. Transformer: Match-to-match attention for semantic correspondence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8697-8707, 2022. 3", + "[20] Axel Barroso Laguna and Krystian Mikolajczyk. Key. net: Keypoint detection by handcrafted and learned cnn filters revisited. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 3, 6", + "[21] Jongmin Lee, Yoonwoo Jeong, and Minsu Cho. Self-supervised learning of image scale and orientation. In 31st British Machine Vision Conference 2021, BMVC 2021, Virtual Event, UK. BMVA Press, 2021. 1, 2, 4", + "[22] Jongmin Lee, Yoonwoo Jeong, Seungwook Kim, Juhong Min, and Minsu Cho. Learning to distill convolutional features into compact local descriptors. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 898-908, 2021. 2", + "[23] Jongmin Lee, Byungjin Kim, and Minsu Cho. Self-supervised equivariant learning for oriented keypoint detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4847-4857, 2022. 1, 2, 4", + "[24] Kunhong Li, Longguang Wang, Li Liu, Qing Ran, Kai Xu, and Yulan Guo. Decoupling makes weakly supervised local feature better. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15838-15848, 2022. 2, 6", + "[25] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 5", + "[26] Yuan Liu, Zehong Shen, Zhixuan Lin, Sida Peng, Hujun Bao, and Xiaowei Zhou. Gift: Learning transformation-invariant dense visual descriptors via group cnns. Advances in Neural Information Processing Systems, 32:6992-7003, 2019. 1, 2, 5, 6, 7" + ], + "bbox": [ + 502, + 147, + 870, + 844 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "21895", + "bbox": [ + 463, + 876, + 504, + 887 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[27] David G Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91-110, 2004. 1, 2, 3, 4, 5, 6, 7", + "[28] Zixin Luo, Lei Zhou, Xuyang Bai, Hongkai Chen, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, and Long Quan. Aslfeat: Learning local features of accurate shape and localization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6589-6598, 2020. 2, 6", + "[29] Simon Lynen, Bernhard Zeisl, Dror Aiger, Michael Bosse, Joel Hesch, Marc Pollefeys, Roland Siegwart, and Torsten Sattler. Large-scale, real-time visual-inertial localization revisited. The International Journal of Robotics Research, 39(9):1061-1084, 2020. 1", + "[30] Diego Marcos, Michele Volpi, Nikos Komodakis, and Devis Tuia. Rotation equivariant vector field networks. In Proceedings of the IEEE International Conference on Computer Vision, pages 5048-5057, 2017. 2", + "[31] Roland Memisevic. On multi-view feature learning. In ICML, 2012. 2", + "[32] Roland Memisevic and Geoffrey E Hinton. Learning to represent spatial transformations with factored higher-order boltzmann machines. Neural computation, 22(6):1473-1492, 2010. 2", + "[33] Krystian Mikolajczyk and Cordelia Schmid. Scale & affine invariant interest point detectors. International journal of computer vision, 60(1):63-86, 2004. 5", + "[34] Krystian Molkajczyk and Cordelia Schmid. A performance evaluation of local descriptors. IEEE transactions on pattern analysis and machine intelligence, 27(10):1615-1630, 2005. 6", + "[35] Juhong Min, Jongmin Lee, Jean Ponce, and Minsu Cho. Hyperpixel flow: Semantic correspondence with multi-layer neural features. In ICCV, 2019. 3", + "[36] Anastasiia Mishchuk, Dmytro Mishkin, Filip Radenovic, and Jiri Matas. Working hard to know your neighbor's margins: Local descriptor learning loss. In Advances in Neural Information Processing Systems, pages 4826-4837, 2017. 2", + "[37] Dmytro Mishkin, Filip Radenovic, and Jiri Matas. Repeatability is not enough: Learning affine regions via discriminability. In Proceedings of the European Conference on Computer Vision (ECCV), pages 284-300, 2018. 2", + "[38] Daniel Moyer, Esra Abaci Turk, P Ellen Grant, William M Wells, and Polina Golland. Equivariant filters for efficient tracking in 3d imaging. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 193-202. Springer, 2021. 2", + "[39] Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics, 31(5):1147-1163, 2015. 1, 6", + "[40] Yuki Ono, Eduard Trulls, Pascal Fua, and Kwang Moo Yi. Lf-net: learning local features from images. In Advances in neural information processing systems, pages 6234-6244, 2018. 2, 3, 6, 7, 8", + "[41] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 5" + ], + "bbox": [ + 102, + 147, + 470, + 845 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[42] Rémi Pautrat, Viktor Larsson, Martin R Oswald, and Marc Pollefeys. Online invariance selection for local feature descriptors. In European Conference on Computer Vision, pages 707-724. Springer, 2020. 2, 6, 7", + "[43] Nicolas Pielawski, Elisabeth Wetzer, Johan Överstedt, Ji-ahao Lu, Carolina Wahlby, Joakim Lindblad, and Nataša Sladoje. CoMIR: Contrastive multimodal image representation for registration. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 18433–18444. Curran Associates, Inc., 2020. 2", + "[44] Jerome Revaud, Cesar De Souza, Martin Humenberger, and Philippe Weinzaepfel. R2d2: Reliable and repeatable detector and descriptor. Advances in neural information processing systems, 32:12405-12415, 2019. 1, 2, 5, 6", + "[45] Jérôme Revaud, Vincent Leroy, Philippe Weinzaepfel, and Boris Chidlovskii. Pump: Pyramidal and uniqueness matching priors for unsupervised learning of local descriptors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3926-3936, 2022. 1", + "[46] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In 2011 International conference on computer vision, pages 2564-2571. IEEE, 2011. 1, 2, 6, 7", + "[47] Torsten Sattler, Bastian Leibe, and Leif Kobbelt. Improving image-based localization by active correspondence search. In European conference on computer vision, pages 752-765. Springer, 2012. 1", + "[48] Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenberg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, et al. Benchmarking 6dof outdoor visual localization in changing conditions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8601-8610, 2018. 1", + "[49] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4104-4113, 2016. 1", + "[50] Xuelun Shen, Cheng Wang, Xin Li, Zenglei Yu, Jonathan Li, Chenglu Wen, Ming Cheng, and Zijian He. Rf-net: An end-to-end image matching network based on receptive field. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8132-8140, 2019. 2, 6, 7, 8", + "[51] Kihyuk Sohn and Honglak Lee. Learning invariant representations with local transformations. In ICML, 2012. 2", + "[52] Ivan Sosnovik, Artem Moskalev, and Arnold Smeulders. How to transform kernels for scale-convolutions. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1092-1097, 2021. 2", + "[53] Christoph Strecha, Wolfgang Von Hansen, Luc Van Gool, Pascal Fua, and Ulrich Thoennessen. On benchmarking camera calibration and multi-view stereo for high resolution imagery. In 2008 IEEE conference on computer vision and pattern recognition, pages 1-8. IEEE, 2008. 2, 5, 6, 7", + "[54] Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, and Krystian Mikolajczyk. Hynet: Learning local descriptor with hybrid similarity measure and triplet loss. Ad" + ], + "bbox": [ + 502, + 147, + 868, + 845 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "21896", + "bbox": [ + 463, + 876, + 504, + 887 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "vances in Neural Information Processing Systems, 33:7401-7412, 2020. 2", + "[55] Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, and Krystian Mikolajczyk. Hynet: Learning local descriptor with hybrid similarity measure and triplet loss. In NeurIPS, 2020. 6", + "[56] Yurun Tian, Xin Yu, Bin Fan, Fuchao Wu, Huub Heijnen, and Vassileios Balntas. Sosnet: Second order similarity regularization for local descriptor learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11016-11025, 2019. 2", + "[57] Michal Jan Tyszkiewicz, Pascal Fua, and Eduard Trulls. Disk: learning local features with policy gradient. Advances in Neural Information Processing Systems, 33, 2020. 2, 6", + "[58] Rui Wang, Robin Walters, and Rose Yu. Data augmentation vs. equivariant networks: A theory of generalization on dynamics forecasting. arXiv preprint arXiv:2206.09450, 2022. 1", + "[59] Zhenhua Wang, Bin Fan, Gang Wang, and Fuchao Wu. Exploring local and overall ordinal information for robust feature description. IEEE transactions on pattern analysis and machine intelligence, 38(11):2198-2211, 2015. 2", + "[60] Maurice Weiler and Gabriele Cesa. General e (2)-equivariant steerable cnns. Advances in Neural Information Processing Systems, 32:14334-14345, 2019. 1, 2, 3, 5, 7", + "[61] Maurice Weiler, Fred A Hamprecht, and Martin Storath. Learning steerable filters for rotation equivariant cnns. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 849-858, 2018. 2", + "[62] Daniel E Worrall, Stephan J Garbin, Daniyar Turmukhambetov, and Gabriel J Brostow. Harmonic networks: Deep translation and rotation equivariance. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5028-5037, 2017. 2", + "[63] Pei Yan, Yihua Tan, Shengzhou Xiong, Yuan Tai, and Yansheng Li. Learning soft estimator of keypoint scale and orientation with probabilistic covariant loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19406-19415, 2022. 1, 4", + "[64] Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, and Pascal Fua. Lift: Learned invariant feature transform. In European conference on computer vision, pages 467-483. Springer, 2016. 1", + "[65] Yanzhao Zhou, Qixiang Ye, Qiang Qiu, and Jianbin Jiao. Oriented response networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 519-528, 2017. 2", + "[66] Siyu Zhu, Runze Zhang, Lei Zhou, Tianwei Shen, Tian Fang, Ping Tan, and Long Quan. Very large-scale global sfm by distributed motion averaging. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4568-4577, 2018. 1" + ], + "bbox": [ + 102, + 148, + 470, + 781 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "21897", + "bbox": [ + 465, + 876, + 504, + 887 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Learning Rotation-Equivariant Features for Visual Correspondence/3ac6cb22-128e-410d-8dc0-595aae3be692_model.json b/2023/Learning Rotation-Equivariant Features for Visual Correspondence/3ac6cb22-128e-410d-8dc0-595aae3be692_model.json new file mode 100644 index 0000000000000000000000000000000000000000..3e59f3814e78d12e50849bb31a575e7635fa4ec5 --- /dev/null +++ b/2023/Learning Rotation-Equivariant Features for Visual Correspondence/3ac6cb22-128e-410d-8dc0-595aae3be692_model.json @@ -0,0 +1,2224 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.096, + 0.009, + 0.172, + 0.047 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.23, + 0.006, + 0.819, + 0.049 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.163, + 0.181, + 0.812, + 0.202 + ], + "angle": 0, + "content": "Learning Rotation-Equivariant Features for Visual Correspondence" + }, + { + "type": "text", + "bbox": [ + 0.197, + 0.225, + 0.77, + 0.241 + ], + "angle": 0, + "content": "Jongmin Lee Byungjin Kim Seungwook Kim Minsu Cho" + }, + { + "type": "text", + "bbox": [ + 0.212, + 0.244, + 0.76, + 0.261 + ], + "angle": 0, + "content": "Pohang University of Science and Technology (POSTECH), South Korea" + }, + { + "type": "text", + "bbox": [ + 0.315, + 0.263, + 0.651, + 0.275 + ], + "angle": 0, + "content": "http://cylab.postech.ac.kr/research/RELF" + }, + { + "type": "title", + "bbox": [ + 0.249, + 0.305, + 0.326, + 0.32 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.334, + 0.474, + 0.649 + ], + "angle": 0, + "content": "Extracting discriminative local features that are invariant to imaging variations is an integral part of establishing correspondences between images. In this work, we introduce a self-supervised learning framework to extract discriminative rotation-invariant descriptors using group-equivariant CNNs. Thanks to employing group-equivariant CNNs, our method effectively learns to obtain rotation-equivariant features and their orientations explicitly, without having to perform sophisticated data augmentations. The resultant features and their orientations are further processed by group aligning, a novel invariant mapping technique that shifts the group-equivariant features by their orientations along the group dimension. Our group aligning technique achieves rotation-invariance without any collapse of the group dimension and thus eschews loss of discriminability. The proposed method is trained end-to-end in a self-supervised manner, where we use an orientation alignment loss for the orientation estimation and a contrastive descriptor loss for robust local descriptors to geometric/photometric variations. Our method demonstrates state-of-the-art matching accuracy among existing rotation-invariant descriptors under varying rotation and also shows competitive results when transferred to the task of keypoint matching and camera pose estimation." + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.678, + 0.225, + 0.692 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.702, + 0.472, + 0.846 + ], + "angle": 0, + "content": "Extracting local descriptors is an essential step for visual correspondence across images, which is used for a wide range of computer vision problems such as visual localization [29, 47, 48], simultaneous localization and mapping [7, 8, 39], and 3D reconstruction [1, 16, 17, 49, 66]. To establish reliable visual correspondences, the properties of invariance and discriminativeness are required for local descriptors; the descriptors need to be invariant to geometric/photometric variations of images while being discriminative enough to distinguish true matches from false ones. Since the remarkable success of deep learning for visual" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.306, + 0.871, + 0.465 + ], + "angle": 0, + "content": "recognition, deep neural networks have also been adopted to learn local descriptors, showing enhanced performances on visual correspondence [44, 45, 64]. Learning rotation-invariant local descriptors, however, remains challenging; the classical techniques [11, 27, 46] for rotation-invariant descriptors, which are used for shallow gradient-based feature maps, cannot be applied to feature maps from standard deep neural networks, in which rotation of input induces unpredictable feature variations. Achieving rotation invariance without sacrificing discriminativeness is particularly important for local descriptors as rotation is one of the most frequent imaging variations in reality." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.468, + 0.871, + 0.847 + ], + "angle": 0, + "content": "In this work, we propose a self-supervised approach to obtain rotation-invariant and discriminative local descriptors by leveraging rotation-equivariant CNNs. First, we use group-equivariant CNNs [60] to jointly extract rotation-equivariant local features and their orientations from an image. To extract reliable orientations, we use an orientation alignment loss [21, 23, 63], which trains the network to predict the dominant orientation robustly against other imaging variations, including illumination or viewpoint changes. Using group-equivariant CNNs enables the local features to be empowered with explicitly encoded rotation equivariance without having to perform rigorous data augmentations [58, 60]. Second, to obtain discriminative rotation-invariant descriptors from rotation-equivariant features, we propose group-aligning that shifts the group-equivariant features by their dominant orientation along their group dimension. Conventional methods to yield invariant features from group-equivariant features collapse the group dimension by group-pooling, e.g., max-pooling or bilinear-pooling [26], resulting in a drop in feature discriminability and quality. In contrast, our group-aligning preserves the group dimension, achieving rotation-invariance while eschewing loss of discriminability. Furthermore, by preserving the group dimension, we can obtain multiple descriptors by performing group-aligning using multiple orientation candidates, which improves the matching performance by compensating for potential errors in dominant orientation prediction. Finally, we evaluate our rotation-invariant descriptors against existing local descriptors, and" + }, + { + "type": "page_number", + "bbox": [ + 0.465, + 0.877, + 0.505, + 0.888 + ], + "angle": 0, + "content": "21887" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.1, + 0.147, + 0.471, + 0.186 + ], + "angle": 0, + "content": "our group-aligning scheme against group-pooling methods on various image matching benchmarks to demonstrate the efficacy of our method." + }, + { + "type": "text", + "bbox": [ + 0.118, + 0.187, + 0.378, + 0.2 + ], + "angle": 0, + "content": "The contribution of our paper is fourfold:" + }, + { + "type": "text", + "bbox": [ + 0.117, + 0.206, + 0.47, + 0.245 + ], + "angle": 0, + "content": "- We propose to extract discriminative rotation-invariant local descriptors to tackle the task of visual correspondence by utilizing rotation-equivariant CNNs." + }, + { + "type": "text", + "bbox": [ + 0.117, + 0.252, + 0.471, + 0.317 + ], + "angle": 0, + "content": "- We propose group-aligning, a method to shift a group-equivariant descriptor in the group dimension by its dominant orientation to obtain a rotation-invariant descriptor without having to collapse the group information to preserve feature discriminability." + }, + { + "type": "text", + "bbox": [ + 0.117, + 0.325, + 0.471, + 0.364 + ], + "angle": 0, + "content": "- We use self-supervisory losses of orientation alignment loss for orientation estimation, and a contrastive descriptor loss for robust local descriptor extraction." + }, + { + "type": "text", + "bbox": [ + 0.117, + 0.371, + 0.471, + 0.423 + ], + "angle": 0, + "content": "- We demonstrate state-of-the-art performances under varying rotations on the Roto-360 dataset and show competitive transferability on the HPatches dataset [2] and the MVS dataset [53]." + }, + { + "type": "list", + "bbox": [ + 0.117, + 0.206, + 0.471, + 0.423 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.101, + 0.433, + 0.231, + 0.447 + ], + "angle": 0, + "content": "2. Related work" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.455, + 0.471, + 0.637 + ], + "angle": 0, + "content": "Classical invariant local descriptors. Classical methods to extract invariant local descriptors first aggregate image gradients to obtain a rotation-equivariant representation, i.e., histogram, from which the estimated dominant orientation is subtracted to obtain rotation-invariant features [27, 46]. Several studies [4, 11, 59] suggest extracting local descriptors by invariant mapping of the order-based gradient histogram of a patch. However, these classical methods for shallow gradient-based feature maps cannot be applied to deep feature maps from standard neural networks, in which rotation induces unpredictable feature variations. Therefore, we propose a deep end-to-end pipeline to obtain orientation-normalized local descriptors by utilizing rotation-equivariant CNNs [60] with additional losses." + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.638, + 0.474, + 0.846 + ], + "angle": 0, + "content": "Learning-based invariant local descriptors. A branch of learning-based methods learns to obtain invariant local descriptors in an explicit manner. GIFT [26] constructs group-equivariant features by rotating or rescaling the images, and then collapses the group dimension using bilinear pooling to obtain invariant local descriptors. However, their groups are limited to non-cyclic discrete rotations ranging from \\(-90^{\\circ}\\) to \\(90^{\\circ}\\). Furthermore, their reliance on data augmentation implies a lower sampling efficiency compared to group-equivariant networks. LISRD [42] jointly learns meta descriptors with different levels of regional variations and selects the most appropriate level of invariance given the context. Another branch of learning methods aims to learn the invariance implicitly using descriptor similarity losses from the image pair using camera pose or homography supervision. These methods are either patch-based [10, 36, 54, 56]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.147, + 0.871, + 0.278 + ], + "angle": 0, + "content": "or image-based [8, 9, 22, 24, 28, 37, 40, 44, 50, 57]. While these methods may be robust to rotation, they cannot be said to be equivariant or invariant to rotation. We construct group-equivariant local features using the steerable networks [60], which explicitly encodes cyclic rotational equivariance to the features without having to rely on data augmentation. We can then yield rotation-invariant features by group-aligning that shifts the group-equivariant features along the group dimension by their dominant orientations, preserving feature discriminability." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.279, + 0.871, + 0.488 + ], + "angle": 0, + "content": "Equivariant representation learning. There has been a constant pursuit to learn equivariant representations by explicitly incorporating group equivariance into the model architecture design [30-32, 51, 60, 65]. For example, G-CNNs [6] use group equivariant convolutions that reduce sample complexity by exploiting symmetries on discrete isometric groups; SFCNNs [61] and H-Nets [62] extract features from more diverse groups and continuous domains by using harmonics as filters. There are also studies that focus on scale-equivariant representation learning [3, 21, 52]. [12, 18, 23, 38, 43] leverage equivariant neural networks to tackle vision tasks e.g., keypoint detection. In this work, we also propose to use equivariant neural networks to facilitate the learning of discriminative rotation-invariant descriptors. We guide the readers to section 1 of the supplementary material for a brief introduction to group equivariance." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.501, + 0.87, + 0.533 + ], + "angle": 0, + "content": "3. Rotation-equivariant features, Rotation-invariant descriptors" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.54, + 0.87, + 0.775 + ], + "angle": 0, + "content": "In this section, we first draw the line between the terms feature and descriptor which will be used throughout this paper. The goal of our work is to learn to extract rotation-equivariant local features from our rotation-equivariant backbone network, and then to align them by their dominant orientation to finally yield rotation-invariant descriptors. In the subsequent subsections, we elaborate on the process of rotation-equivariant feature extraction from steerable CNNs (Sec. 3.1), assignment of equivariant features to keypoints (Sec. 3.2), how group-aligning is performed to yield rotation-invariant yet discriminative descriptors (Sec. 3.3), how we formulate our orientation alignment loss (Sec.3.4) and contrastive descriptor loss (Sec.3.5) to train our network to extract descriptors which are robust to not only rotation but also other imaging transformations, and finally how we obtain scale-invariant descriptors at test time using image pyramids (Sec.3.6). Figure 1 shows the overall architecture of our method." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.786, + 0.821, + 0.8 + ], + "angle": 0, + "content": "3.1. Rotation-equivariant feature extraction" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.807, + 0.871, + 0.847 + ], + "angle": 0, + "content": "As the feature extractor, we use ReResNet18 [12], which has the same structure as ResNet18 [15] but is constructed using rotation-equivariant convolutional layers [60]. The" + }, + { + "type": "page_number", + "bbox": [ + 0.465, + 0.877, + 0.506, + 0.888 + ], + "angle": 0, + "content": "21888" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.107, + 0.144, + 0.868, + 0.302 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.1, + 0.303, + 0.871, + 0.388 + ], + "angle": 0, + "content": "Figure 1. Overview of the proposed pipeline. An input image is forwarded through the equivariant networks to yield equivariant feature maps from multiple intermediate layers, encoding both low-level geometry and high-level semantic information. The feature maps are bilinearly interpolated to have equal spatial dimensions to be concatenated together. We use the first channel of the feature map \\(\\mathbf{F}\\) as the orientation histogram map \\(\\mathbf{O}\\) to predict the dominant orientations, which are used to shift the group-equivariant representation along the group dimension to yield discriminative rotation-invariant descriptors. To learn to extract accurate dominant orientation \\(\\hat{\\theta}\\), we use the orientation alignment loss \\(\\mathcal{L}^{\\mathrm{ori}}\\). To obtain descriptors robust to illumination and geometric changes, we use a contrastive descriptor loss \\(\\mathcal{L}^{\\mathrm{desc}}\\) using the ground-truth homography \\(\\mathcal{H}_{\\mathrm{GT}}\\)." + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.41, + 0.474, + 0.488 + ], + "angle": 0, + "content": "layer acts on a cyclic group \\( G_N \\) and is equivariant for all translations and \\( N \\) discrete rotations. At the first layer, the scalar field of the input image is lifted to the vector field of the group representation [60]. We leverage feature pyramids from the intermediate layers of the ReResNet18 backbone to construct output features as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.173, + 0.495, + 0.472, + 0.523 + ], + "angle": 0, + "content": "\\[\n\\mathbf {F} = \\bigoplus_ {i \\in l} \\eta (\\mathbf {f} _ {i}), \\quad \\mathbf {f} _ {i} = [ \\Pi_ {j = 1} ^ {i} L _ {j} ] (I), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.529, + 0.474, + 0.664 + ], + "angle": 0, + "content": "where \\(\\mathbf{f}_i\\in \\mathbb{R}^{C_i\\times |G|\\times H_i\\times W_i}\\) is an intermediate feature from \\(L_{i},L_{i}\\) is the \\(i\\) -th layer of the equivariant network, \\(\\eta\\) denotes bilinear interpolation to \\(H\\times W\\) , and \\(\\bigoplus\\) denotes concatenation along the \\(C\\) dimension. We utilize the multi-layer feature maps to exploit the low-level geometry information and high-level semantics in the local descriptors [13,19,35]. The output features \\(\\mathbf{F}\\in \\mathbb{R}^{C\\times |G|\\times H\\times W}\\) contains rotationequivariant features with multiple layers containing different semantics and receptive fields. We set \\(H = H_{1}\\) and \\(W = W_{1}\\) , which are \\(\\frac{1}{2}\\) of the input image size." + }, + { + "type": "title", + "bbox": [ + 0.101, + 0.669, + 0.4, + 0.683 + ], + "angle": 0, + "content": "3.2. Assigning local features to keypoints" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.688, + 0.472, + 0.794 + ], + "angle": 0, + "content": "During training, we extract \\(K\\) keypoints from the source image using Harris corner detection [14]. We then use the ground-truth homography \\(\\mathcal{H}_{\\mathrm{GT}}\\) to obtain ground-truth keypoint correspondences. Also, we allocate a local feature \\(\\mathbf{p} \\in \\mathbb{R}^{C \\times |G| \\times K}\\) to each keypoint, using the interpolated location of the equivariant feature map \\(\\mathbf{F}\\). We experiment our descriptor with SIFT [27], LF-Net [40], SuperPoint [8], and KeyNet [20] as the keypoint detector during inference time." + }, + { + "type": "title", + "bbox": [ + 0.101, + 0.8, + 0.411, + 0.815 + ], + "angle": 0, + "content": "3.3. Group aligning for invariant mapping" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.82, + 0.473, + 0.847 + ], + "angle": 0, + "content": "To transform the rotation-equivariant feature to a rotation-invariant descriptor, we propose group aligning, an" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.41, + 0.871, + 0.501 + ], + "angle": 0, + "content": "operation to shift the group-equivariant feature in the \\(G\\)-dimension using the dominant orientation \\(\\hat{\\theta}\\). Unlike existing methods that use group pooling, e.g., average pooling or max pooling, which collapses the group dimension, group aligning preserves the rich group information. Figure 2 illustrates the difference between group pooling and group aligning on an equivariant representation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.502, + 0.87, + 0.767 + ], + "angle": 0, + "content": "Estimating the dominant orientation and the shifting value. We obtain the orientation histogram map \\(\\mathbf{O} \\in \\mathbb{R}^{|G| \\times H \\times W} = \\mathbf{F}_0\\) by selecting the first channel of the rotation-equivariant tensor \\(\\mathbf{F}\\) as an orientation histogram map. Note that the first channels of each group action are simultaneously used as the channels of the descriptors and to construct the orientation histogram. The histogram-based representation of \\(\\mathbf{O}\\) provides richer information than directly regressing the dominant orientation, as the orientation histogram enables predicting multiple (i.e., top-\\(k\\)) candidates as the dominant orientation. We first select an orientation vector \\(\\mathbf{o} \\in \\mathbb{R}^{|G|}\\) of a keypoint from the orientation histogram map \\(\\mathbf{O}\\) using the coordinates of the keypoint. Next, we estimate the dominant orientation value \\(\\hat{\\theta}\\) from the orientation vector \\(\\mathbf{o}\\) by selecting the index of the maximum score, \\(\\hat{\\theta} = \\frac{360}{|G|} \\arg \\max_g \\mathbf{o}\\). Using the dominant orientation value \\(\\hat{\\theta}\\), we obtain the shifting value \\(\\hat{\\Delta} = \\frac{|G|}{360} \\hat{\\theta}\\) in \\(G\\)-dim. At training time, we use the ground-truth rotation \\(\\theta_{\\mathrm{GT}}\\) instead of the predicted dominant orientation value \\(\\hat{\\theta}\\) to generate the shifting value \\(\\Delta_{\\mathrm{GT}}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.768, + 0.871, + 0.847 + ], + "angle": 0, + "content": "Group aligning. Given a keypoint-allocated feature tensor \\(\\mathbf{p} \\in \\mathbb{R}^{C \\times |G|}\\) from the equivariant representation \\(\\mathbf{F}\\), we obtain the rotation-invariant local descriptor \\(\\mathbf{d} \\in \\mathbb{R}^{C|G|}\\) by group aligning using \\(\\Delta\\). After computing the dominant orientation \\(\\hat{\\theta}\\) and the shifting value \\(\\hat{\\Delta}\\) from \\(\\mathbf{o}\\), we obtain the orientation-normalized descriptor \\(\\mathbf{d}' \\in \\mathbb{R}^{C|G|}\\) by shifting \\(\\mathbf{p}\\)" + }, + { + "type": "page_number", + "bbox": [ + 0.465, + 0.877, + 0.506, + 0.888 + ], + "angle": 0, + "content": "21889" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.114, + 0.145, + 0.454, + 0.337 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.131, + 0.34, + 0.245, + 0.352 + ], + "angle": 0, + "content": "(a) Group pooling" + }, + { + "type": "image_caption", + "bbox": [ + 0.31, + 0.34, + 0.43, + 0.352 + ], + "angle": 0, + "content": "(b) Group aligning" + }, + { + "type": "image_caption", + "bbox": [ + 0.1, + 0.353, + 0.471, + 0.424 + ], + "angle": 0, + "content": "Figure 2. Difference between group pooling and group aligning. In group pooling, the group dimension is collapsed to yield an invariant descriptor \\((\\mathbb{R}^{C\\times |G|}\\to \\mathbb{R}^C)\\). In group aligning, the entire feature is cyclically shifted in the group dimension to obtain an invariant descriptor \\((\\mathbb{R}^{C\\times |G|}\\to \\mathbb{R}^{C|G|})\\) while preserving the group information and discriminability." + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.447, + 0.472, + 0.522 + ], + "angle": 0, + "content": "in the \\(G\\)-dimension by \\(-\\hat{\\Delta}\\) and flattening the descriptor to a vector. We use cyclic shifting in consideration of the cyclic property of rotation. We finally obtain the L2-normalized descriptor \\(\\mathbf{d}\\) from the orientation-normalized descriptor \\(\\mathbf{d}'\\) such that \\(||\\mathbf{d}||^2 = 1\\). Formally, this process can be defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.168, + 0.523, + 0.402, + 0.541 + ], + "angle": 0, + "content": "\\[\n\\mathbf {p} _ {:, i} ^ {\\prime} = T _ {r} ^ {\\prime} \\left(\\mathbf {p} _ {:, i}, \\hat {\\Delta}\\right) = \\mathbf {p} _ {:, (i + \\hat {\\Delta}) \\bmod | G |},\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.28, + 0.542, + 0.47, + 0.561 + ], + "angle": 0, + "content": "\\[\n\\mathbf {d} _ {| G |: | G | (i + 1)} ^ {\\prime} = \\mathbf {p} _ {i} ^ {\\prime}, \\tag {2}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.326, + 0.561, + 0.402, + 0.59 + ], + "angle": 0, + "content": "\\[\n\\mathbf {d} = \\frac {\\mathbf {d} ^ {\\prime}}{| | \\mathbf {d} ^ {\\prime} | | _ {2}},\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.595, + 0.472, + 0.751 + ], + "angle": 0, + "content": "where \\( T_r' \\) is shifting operator in vector space, and \\( \\mathbf{p}' \\) is a group-aligned descriptor before flattening. This shifting by \\( \\hat{\\Delta} \\) aligns all the descriptors in the direction of their dominant orientations, creating orientation-normalized descriptors. This process is conceptually similar to subtracting the dominant orientation value of the orientation histogram in the classical descriptor SIFT [27], but we apply this concept to the equivariant neural features. The proposed group aligning preserves the group information, so our invariant descriptors have more representative power than the existing group-pooled descriptors which collapse the group dimension for invariance." + }, + { + "type": "title", + "bbox": [ + 0.101, + 0.76, + 0.328, + 0.775 + ], + "angle": 0, + "content": "3.4. Orientation alignment loss" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.78, + 0.471, + 0.846 + ], + "angle": 0, + "content": "To learn to obtain the dominant orientations from the orientation vectors, we use an orientation alignment loss [21, 23, 63] to supervise the orientation histograms in \\(\\mathbf{O}\\) to be rotation equivariant under the photometric/geometric transformations. Figure 3 shows the illustration of orientation" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.145, + 0.868, + 0.334 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.341, + 0.871, + 0.414 + ], + "angle": 0, + "content": "Figure 3. Illustration of orientation alignment loss. Given two rotation-equivariant tensors \\(\\mathbf{p}^{\\mathrm{A}},\\mathbf{p}^{\\mathrm{B}}\\in \\mathbb{R}^{C\\times |G|}\\) obtained from two different rotated versions of the same image, we apply cyclic shift on one of the descriptors in the group dimension using the GT difference in rotation. The orientation alignment loss supervises the output orientation vectors of the two descriptors to be the same." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.437, + 0.87, + 0.463 + ], + "angle": 0, + "content": "alignment loss. The cyclic shift of an orientation histogram map at the training time is formulated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.575, + 0.474, + 0.87, + 0.49 + ], + "angle": 0, + "content": "\\[\nT _ {r} ^ {\\prime} \\left(\\mathbf {O} _ {i}, \\Delta_ {\\mathrm {G T}}\\right) = \\mathbf {O} _ {\\left(i + \\Delta_ {\\mathrm {G T}}\\right) \\bmod | G |}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.5, + 0.87, + 0.542 + ], + "angle": 0, + "content": "where \\(\\Delta_{\\mathrm{GT}} = \\frac{|G|}{360}\\theta_{\\mathrm{GT}}\\) is the shifting value calculated from the ground-truth rotation \\(\\theta_{\\mathrm{GT}}\\). We formulate the orientation alignment loss in the form of a cross-entropy as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.52, + 0.551, + 0.869, + 0.599 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} ^ {\\mathrm {o r i}} \\left(\\mathbf {O} ^ {\\mathrm {A}}, \\mathbf {O} ^ {\\mathrm {B}}, \\Delta_ {\\mathrm {G T}}\\right) = \\\\ - \\sum_ {k \\in K} \\sum_ {g \\in G} \\sigma \\left(\\mathbf {O} _ {g, k} ^ {\\mathrm {A}}\\right) \\log \\left(\\sigma \\left(T _ {r} ^ {\\prime} \\left(\\mathbf {O} _ {g, k} ^ {\\mathrm {B}}, \\Delta_ {\\mathrm {G T}}\\right)\\right)\\right), \\tag {4} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.609, + 0.871, + 0.846 + ], + "angle": 0, + "content": "where \\(\\mathbf{O}^{\\mathrm{A}}\\) is the source orientation histogram map and \\(\\mathbf{O}^{\\mathrm{B}}\\) is the target orientation histogram map obtained from a synthetically warped source image, \\(\\sigma\\) is a softmax function applied to the \\(G\\)-dimension of the orientation histogram map to represent the orientation vector as a probability distribution for the cross-entropy loss to be applicable. Using Equation 4, the network learns to predict the characteristic orientations robustly against different imaging variations, such as photometric transformations and geometric transformations beyond rotation, as these transformations cannot be handled by equivariance to discrete rotations alone. Note that it is not straightforward to define the characteristic orientation of a keypoint to provide strong supervision. However, we facilitate the learning of characteristic orientations by formulating it as a self-supervised learning framework, leveraging the known relative orientation between two keypoint orientation histogram maps obtained from differently rotated versions of the same image." + }, + { + "type": "page_number", + "bbox": [ + 0.465, + 0.877, + 0.506, + 0.888 + ], + "angle": 0, + "content": "21890" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.197, + 0.145, + 0.378, + 0.239 + ], + "angle": 0, + "content": "
MMApred.
@1px
Align97.5484.90
Avg33.7233.72
Max57.9257.92
None23.9723.97
Bilinear43.6026.42
" + }, + { + "type": "table_caption", + "bbox": [ + 0.1, + 0.24, + 0.471, + 0.302 + ], + "angle": 0, + "content": "Table 1. Evaluation with GT keypoint pairs on Roto-360 without training. 'Align' uses GT rotation difference to apply group-aligning to demonstrate the upper-bound. 'None' does not use pooling nor aligning, demonstrating the lower-bound. We use an average of 111 keypoint pairs extracted using SuperPoint [8]." + }, + { + "type": "title", + "bbox": [ + 0.101, + 0.323, + 0.33, + 0.338 + ], + "angle": 0, + "content": "3.5. Contrastive descriptor loss" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.344, + 0.471, + 0.409 + ], + "angle": 0, + "content": "We propose to use a descriptor similarity loss motivated by contrastive learning [5] to further empower the descriptors to be robust against variations apart from rotation, e.g., illumination or viewpoint. The descriptor loss is formulated in a contrastive manner as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.101, + 0.416, + 0.474, + 0.482 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} ^ {\\text {d e s c}} \\left(\\mathbf {D} ^ {\\mathrm {A}}, \\mathbf {D} ^ {\\mathrm {B}}\\right) = \\\\ \\sum_ {\\left(\\mathbf {d} _ {i} ^ {\\mathrm {A}}, \\mathbf {d} _ {i} ^ {\\mathrm {B}}\\right) \\in \\left(\\mathbf {D} ^ {\\mathrm {A}}, \\mathbf {D} ^ {\\mathrm {B}}\\right)} - \\log \\frac {\\exp \\left(\\operatorname {s i m} \\left(\\mathbf {d} _ {i} ^ {\\mathrm {A}} , \\mathbf {d} _ {i} ^ {\\mathrm {B}}\\right) / \\tau\\right)}{\\sum_ {k \\in K \\backslash i} \\exp \\left(\\operatorname {s i m} \\left(\\mathbf {d} _ {i} ^ {\\mathrm {A}} , \\mathbf {d} _ {k} ^ {\\mathrm {B}}\\right)\\right) / \\tau}, \\tag {5} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.483, + 0.471, + 0.587 + ], + "angle": 0, + "content": "where sim is cosine similarity and \\(\\tau\\) is the softmax temperature. Unlike the triplet loss with one hard negative sample, the contrastive loss can optimize the distance for all negative pairs. This contrastive loss with InfoNCE [41] maximizes the mutual information between the encoded features and effectively reduces the low-level noise. Our overall self-supervised loss is formulated as \\(\\mathcal{L} = \\alpha \\mathcal{L}^{\\mathrm{ori}} + \\mathcal{L}^{\\mathrm{desc}}\\), where \\(\\alpha\\) is a balancing term." + }, + { + "type": "title", + "bbox": [ + 0.101, + 0.596, + 0.254, + 0.608 + ], + "angle": 0, + "content": "3.6. Scale robustness" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.616, + 0.471, + 0.747 + ], + "angle": 0, + "content": "While we employ a rotation-equivariant network, it does not ensure that the descriptors are robust to scale changes. Thus, at inference time, we construct an image pyramid using a scale factor of \\(2^{1/4}\\) from a maximum of 1,024 pixels to a minimum of 256 pixels as in R2D2 [44]. After constructing the scale-wise descriptors \\(\\in \\mathbb{R}^{S \\times C|G| \\times K}\\) with \\(S\\) varying scales, we finally generate the scale-invariant local descriptors \\(\\in \\mathbb{R}^{C|G| \\times K}\\) by max-pooling in the scale dimension inspired by scale-space maxima as in SIFT [27,33], for improved robustness to scale changes." + }, + { + "type": "title", + "bbox": [ + 0.101, + 0.759, + 0.219, + 0.773 + ], + "angle": 0, + "content": "4. Experiment" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.781, + 0.471, + 0.847 + ], + "angle": 0, + "content": "Implementation details. We use rotation-equivariant ResNet-18 (ReResNet-18) [12] implemented using the rotation-equivariant layers of \\( E(2) \\)-CNN [60] as our backbone. We remove the first maxpool layer to preserve the spatial size, so that the spatial resolution of the rotation-" + }, + { + "type": "table", + "bbox": [ + 0.536, + 0.145, + 0.835, + 0.239 + ], + "angle": 0, + "content": "
MMApred.
@10px@5px@3px
Align93.0891.3590.18688.3
Avg85.8482.1281.05705.9
Max82.6178.0077.79686.0
None19.6818.8118.57349.1
Bilinear42.6941.0340.51332.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.24, + 0.871, + 0.302 + ], + "angle": 0, + "content": "Table 2. Evaluation with predicted keypoint pairs on Roto-360 with training. 'Max' and 'Avg' collapses the group dimension of the features through max pooling or average pooling. 'pred.' denotes the average number of predicted matches. We use an average of 1161 keypoint pairs extracted using SuperPoint [8]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.321, + 0.871, + 0.481 + ], + "angle": 0, + "content": "equivariant feature \\(\\mathbf{F}\\) is \\(H = \\frac{H'}{2}\\) and \\(W = \\frac{W'}{2}\\), where \\(H'\\) and \\(W'\\) are the height and width of an input image. We use 16 for the order of cyclic group \\(G\\). We use a batch size of 8, a learning rate of \\(10^{-4}\\), and a weight decay of 0.1. We train our model for 12 epochs with 1,000 iterations using a machine with an Intel i7-8700 CPU and an NVIDIA GeForce RTX 3090 GPU. We use the temperature \\(\\tau\\) of \\(\\mathcal{L}^{\\mathrm{desc}}\\) as 0.07. The loss balancing factor \\(\\alpha\\) is 10. The final output descriptor size is 1,024, with \\(C = 64\\), \\(|G| = 16\\). We use SuperPoint [8] as the keypoint detector to evaluate our method except Table 4. For all descriptors, we use the mutual nearest neighbour matcher to predict the correspondences." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.487, + 0.686, + 0.5 + ], + "angle": 0, + "content": "4.1. Datasets and metrics" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.507, + 0.87, + 0.559 + ], + "angle": 0, + "content": "We use a synthetic training dataset to train our model in a self-supervised manner. We evaluate our model on the Roto-360 dataset and show the transferability on real image benchmarks, i.e., HPatches [2] and MVS [53] datasets." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.559, + 0.87, + 0.741 + ], + "angle": 0, + "content": "Training dataset. We generate a synthetic dataset for self-supervised training from the MS-COCO dataset [25]. We warp images with random homographies for geometric robustness and transform the colors by jitter, noise, and blur for photometric robustness. As we need the ground-truth rotation \\(\\theta_{\\mathrm{GT}}\\) for our orientation alignment loss, we decompose the synthetic homography \\(\\mathcal{H}\\) as follows: \\(\\theta_{\\mathrm{GT}} = \\arctan \\left(\\frac{\\mathcal{H}_{21}}{\\mathcal{H}_{11}}\\right)\\), where we assume that a \\(3\\times 3\\) homography matrix \\(\\mathcal{H}\\) with no significant tilt can be approximated to an affine matrix. We sample \\(K = 512\\) keypoints for an image using Harris corner detector [14], obtaining 512 corresponding keypoint pairs for each image pair using homography and rotation. Note that this dataset generation protocol is the same as that of GIFT [26] for a fair comparison." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.741, + 0.871, + 0.847 + ], + "angle": 0, + "content": "Roto-360 is an evaluation dataset that consists of 360 image pairs with in-plane rotation ranging from \\(0^{\\circ}\\) to \\(350^{\\circ}\\) at \\(10^{\\circ}\\) intervals, created using ten randomly sampled images from HPatches [2]. Roto-360 is more suitable to evaluate the rotation invariance of our descriptors, as the extreme rotation (ER) dataset [26] only covers \\(180^{\\circ}\\), and includes photometric variations. We use mean matching accuracy (MMA) as the evaluation metric with pixel thresholds of 3/5/10 pixels" + }, + { + "type": "page_number", + "bbox": [ + 0.465, + 0.877, + 0.504, + 0.888 + ], + "angle": 0, + "content": "21891" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.107, + 0.146, + 0.462, + 0.329 + ], + "angle": 0, + "content": "
MethodMMApred.total.
@10px@5px@3px
SIFT [27]78.8678.5978.23774.11500.0
ORB [46]86.7885.2978.73607.61005.2
SuperPoint [8]22.8522.1021.83462.61161.0
LF-Net [40]75.0574.3072.61386.71024.0
RF-Net [50]15.6415.1814.581602.55000.0
D2-Net [9]15.569.305.21386.91474.5
R2D2 [44]15.8014.9713.50197.91500.0
GIFT [26]42.3542.0541.59589.21161.0
LISRD [42]16.9616.0415.64323.61781.1
ASLFeat [28]19.3416.3813.131366.96764.2
DISK [57]13.2212.4312.04359.12048.0
PosFeat [24]13.7611.799.82717.27623.5
ours93.0891.3590.18688.31161.0
ours*94.3592.8291.691333.02340.4
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.1, + 0.33, + 0.472, + 0.415 + ], + "angle": 0, + "content": "Table 3. Comparison to existing local descriptors on Roto-360. We use mutual nearest matching for all methods to establish matches between images. 'total.' and 'pred.' denotes the average number of detected keypoints and predicted matches, respectively. 'ours*' denotes selecting multiple candidate descriptors based on the ratio of max value in the orientation histogram. We use SuperPoint keypoint detector [8] same to the GIFT descriptor [26]." + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.448, + 0.443, + 0.462 + ], + "angle": 0, + "content": "and the number of predicted matches following [9, 34]." + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.462, + 0.472, + 0.528 + ], + "angle": 0, + "content": "HPatches [2] has 57 scenes with illumination variations and 59 scenes with viewpoint variations. Each scene contains five image pairs with ground-truth planar homography. We use the same evaluation metrics to Roto-360 to show the transferability of our local descriptors." + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.528, + 0.472, + 0.58 + ], + "angle": 0, + "content": "MVS dataset [53] has six image sequences of outdoor scenes with GT camera poses. We evaluate the relative pose estimation accuracy at \\(5^{\\circ} / 10^{\\circ} / 20^{\\circ}\\) angular difference thresholds." + }, + { + "type": "title", + "bbox": [ + 0.101, + 0.59, + 0.433, + 0.605 + ], + "angle": 0, + "content": "4.2. Comparison to other invariant mappings" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.611, + 0.472, + 0.806 + ], + "angle": 0, + "content": "Table 1 compares group aligning to various group pooling methods on the Roto-360 dataset using ground-truth keypoint pairs, i.e., no keypoint deviation, without training. The purpose is to compare the invariant mapping operations only while keeping the backbone network and the number of keypoints fixed. We use \\(\\Delta_{\\mathrm{GT}}\\) to shift the equivariant features, and group aligning shows almost perfect keypoint correspondences with \\(97.54\\%\\) matching accuracy. Group pooling, such as max pooling or average pooling, significantly reduces discriminative power compared to group aligning. The results show that group aligning shows the best results, proving that leveraging the full group-equivariant features instead of collapsing the groups shows higher discriminability. Note that the bilinear pooling [26] does not guarantee the rotation-invariant matching." + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.807, + 0.472, + 0.847 + ], + "angle": 0, + "content": "Table 2 compares the proposed group aligning to the existing group pooling methods on the Roto-360 dataset, this time with predicted keypoint pairs and with training. Note" + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.145, + 0.869, + 0.32 + ], + "angle": 0, + "content": "
Det.Desc.MMApred.total.
@10px@5px@3px
SIFT [27]SIFT [27]78.8678.5978.23774.11500
GIFT [26]37.9736.8236.09531.21500
ours84.6779.8577.96558.31500
ours*84.9180.0978.18759.82219
LF-Net [40]LF-Net [40]75.0574.3072.61386.71024
GIFT [26]35.5633.8232.29426.31024
ours79.9071.6367.39431.81024
ours*80.3271.9967.62591.41503
SuperPoint [8]SuperPoint [8]22.8522.1021.83462.61161
GIFT [26]42.3542.0541.59589.21161
ours93.0891.3590.18688.31161
ours*94.3592.8291.691333234
KeyNet [20]HyNet [55]24.4322.8220.64288.7995
GIFT [26]34.0832.3129.17275.7995
ours72.9561.3641.33257.2995
ours*72.4860.6940.95356.61484
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.499, + 0.32, + 0.871, + 0.356 + ], + "angle": 0, + "content": "Table 4. Comparison to existing local descriptors when using the same keypoint detector on Roto-360. Results in bold indicate the best result, and underlined results indicate the second best." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.381, + 0.871, + 0.538 + ], + "angle": 0, + "content": "that while other methods are trained only with \\(\\mathcal{L}^{\\mathrm{desc}}\\), our method is trained also with \\(\\mathcal{L}^{\\mathrm{ori}}\\) to facilitate group aligning. While the number of predicted matches is the highest for average pooling, the MMA results are significantly higher for group aligning, which shows group-aligned descriptors have a higher precision. Overall, incorporating group aligning demonstrates the best results in terms of MMA compared to average pooling, max pooling or bilinear pooling [26]. Note that pooling or aligning the group-equivariant features to obtain invariant descriptors shows consistent improvements over not pooling nor aligning the group-equivariant features." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.55, + 0.825, + 0.565 + ], + "angle": 0, + "content": "4.3. Comparison to existing local descriptors" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.571, + 0.87, + 0.702 + ], + "angle": 0, + "content": "Table 3 shows the matching accuracy compared to existing local descriptors on the Roto-360 dataset. We evaluate the descriptors using their own keypoint detectors [8, 9, 27, 39, 40, 44, 50], or combined with off-the-shelf detectors [24, 26, 42]. While the classical methods [27, 46] achieve better matching accuracy than the existing learning-based methods, our method achieves the best results overall. This is because the learning-based methods learn only a limited degree of invariance in a data-driven manner without guaranteeing full invariance to rotation by design." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.702, + 0.871, + 0.847 + ], + "angle": 0, + "content": "Table 4 shows the performance of our method in comparison to existing local descriptors when using the same keypoint detector, where our method shows consistent performance improvement. In particular, our rotation-invariant descriptor shows consistently higher matching accuracy than GIFT [26], which is a representative learning-based group-invariant descriptor. While our model shows a lower MMA than the LF-Net [40] descriptor when using the LF-Net detector at 5px and 3px thresholds, we conjecture that this is due to the better integrity of the detector and descriptor of LF-Net due to their joint training scheme." + }, + { + "type": "page_number", + "bbox": [ + 0.465, + 0.877, + 0.506, + 0.888 + ], + "angle": 0, + "content": "21892" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.11, + 0.144, + 0.46, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.1, + 0.226, + 0.472, + 0.309 + ], + "angle": 0, + "content": "Figure 4. An example of multiple descriptor extraction. The distribution is an orientation histogram \\(\\mathbf{o} \\in \\mathbb{R}^{16}\\), and the scores are confidence values for each bin from group-equivariant features. Arrows indicate the orientation candidates for multiple descriptor extraction. The example shows selecting three orientations to obtain three candidate descriptors for a feature point, which is possible as we predict a score for each orientation." + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.333, + 0.472, + 0.424 + ], + "angle": 0, + "content": "These results show that our descriptors obtained using the proposed group aligning show the highest matching accuracy under rotation changes compared to existing methods. The improvement of our method is also attributed to the usage of rotation-equivariant networks, which have a higher sampling efficiency, i.e., do not require intensive rotation augmentations to learn rotation invariance." + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.424, + 0.472, + 0.568 + ], + "angle": 0, + "content": "Multiple descriptor extraction using orientation candidates. Group aligning can extract multiple descriptors with different alignments by using multiple orientation candidates, denoted by 'ours*', whose scores are at least \\(60\\%\\) of the maximum score in the orientation histogram. When there is a single keypoint position with \\(k\\) descriptors that are differently aligned, we treat it as if there are \\(k\\) detected keypoints. Multiple descriptor extraction compensates for incorrect orientation predictions and further enhances matching accuracy. Figure 4 illustrates an example of multiple descriptor extraction with a score ratio threshold of 0.6." + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.568, + 0.472, + 0.7 + ], + "angle": 0, + "content": "Consistency of matching accuracy with respect to rotation changes. Figure 5 illustrates how the matching accuracy changes with respect to varying degrees of rotation. Our method shows the highest consistency, proving the enhanced invariance of descriptors obtained using group aligning against different rotations. While MMA of SIFT [27] and ORB [46] are high at the upright rotations, they tend to fluctuate significantly with varying rotations. The existing learning-based group-invariant descriptor, GIFT [26], fails to find correspondences beyond \\(60^{\\circ}\\)." + }, + { + "type": "title", + "bbox": [ + 0.101, + 0.708, + 0.436, + 0.723 + ], + "angle": 0, + "content": "4.4. Transferability to real image benchmarks" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.728, + 0.472, + 0.847 + ], + "angle": 0, + "content": "Table 5 shows the matching performance of local descriptors on HPatches illumination/viewpoint [2] and pose estimation [53]. Our model shows the highest performance overall on the HPatches dataset. The performance gain of ours becomes smaller compared to the Roto-360 dataset due to the absence of extreme rotations in HPatches. While GIFT shows a higher performance under illumination changes that only contain identity mappings, ours†, which uses a larger backbone network (ReWRN), improves" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.144, + 0.861, + 0.306 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.306, + 0.871, + 0.331 + ], + "angle": 0, + "content": "Figure 5. Matching accuracies according to varying degree of rotations on Roto-360." + }, + { + "type": "table", + "bbox": [ + 0.504, + 0.355, + 0.871, + 0.49 + ], + "angle": 0, + "content": "
MethodHP-allHP-illuHP-viewPose
@5px@3px@5px@3px@5px@3px20°10°5°
SIFT [27]51.3646.3249.0844.6253.5747.960.020.000.00
ORB [46]52.2247.4050.8546.2953.5548.470.060.000.00
SuperPoint [8]69.7161.7574.6367.5364.9656.170.200.070.01
LF-Net [40]56.4552.2262.2157.6350.8847.000.060.030.01
RF-Net [50]59.0854.4261.6357.4656.6251.490.100.040.01
D2-Net [9]50.1832.5463.8044.0937.0221.380.110.050.01
GIFT [26]76.0367.3179.7171.8972.4862.880.600.280.09
LISRD [42]62.1656.1270.0963.6454.5048.850.050.020.00
oursavgpool64.1057.9462.2856.2765.8559.550.270.100.05
oursmaxpool61.5755.8159.6653.9163.4257.640.270.110.03
oursthinelunpool [26]45.5941.9045.1341.5746.0342.220.350.170.09
oursthinelunpool† [26]58.7253.7757.3252.6760.0654.830.240.110.03
oursgroupalign70.6963.4270.3962.8870.9763.950.580.260.12
oursgroupalign*73.9266.3773.1365.3374.6967.380.560.300.12
oursgroupalign†78.0069.7077.9469.3578.0670.030.560.330.14
" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.492, + 0.871, + 0.613 + ], + "angle": 0, + "content": "Table 5. Evaluation with predicted keypoint pairs on real image benchmarks. The first group of methods includes existing local feature extraction methods. The second group of methods includes comparisons to other group pooling methods by replacing our group aligning with them. 'ours*' denotes the extraction of multiple descriptors using the orientation candidates, whose scores are at least \\(60\\%\\) of the maximum score in the orientation histogram. 'ours†' denotes our method using the rotation-equivariant WideResNet16-8 (ReWRN) backbone for feature extraction. We use SuperPoint [8] keypoint detector to evaluate ours." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.636, + 0.87, + 0.768 + ], + "angle": 0, + "content": "matching accuracy by \\(7.15\\%\\) p at 3px and \\(5.58\\%\\) p at 5px, and ours* improves by \\(4.5\\%\\) p at 3px, \\(2.21\\%\\) p at 5px under viewpoint changes compared to GIFT [26]. It should be noted that the core difference between oursbilinearpool and GIFT is the usage of explicit rotation-equivariant CNNs [60], which clearly shows that bilinear pooling is not well-compatible with the equivariant CNNs in comparison to group aligning. Using the same network with bilinear pooling (oursbilinearpool†) proposed in [26] shows significantly lower results compared to oursgroupalign†." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.768, + 0.871, + 0.847 + ], + "angle": 0, + "content": "In the MVS dataset [53] to evaluate relative camera pose estimation, our model shows a higher performance than GIFT at finer error thresholds of \\(10^{\\circ}\\) and \\(5^{\\circ}\\). This shows that our model can find more precise correspondences under 3D viewpoint changes. Overall, these results show that our descriptors using rotation-equivariant representation ex" + }, + { + "type": "page_number", + "bbox": [ + 0.465, + 0.877, + 0.505, + 0.888 + ], + "angle": 0, + "content": "21893" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.105, + 0.144, + 0.47, + 0.266 + ], + "angle": 0, + "content": "
HP-allRoto-360params. (millions)
@5px@3px@5px@3px
ours (proposed |G| = 16)70.6963.4291.3590.180.62M
w/o orientation loss66.4158.6185.2983.260.62M
w/o descriptor loss27.4924.8325.6424.980.62M
w/o image scale pyramid68.7762.2591.4790.430.62M
w/o equivariant backbone47.2542.528.658.5111.18M
|G| = 6463.9657.3585.1283.320.16M
|G| = 3668.1760.9587.7885.890.26M
|G| = 3269.4462.0889.1087.310.31M
|G| = 2469.7262.2190.2788.340.39M
|G| = 865.7458.9287.1685.571.24M
" + }, + { + "type": "table_caption", + "bbox": [ + 0.101, + 0.267, + 0.469, + 0.29 + ], + "angle": 0, + "content": "Table 6. Ablation test on HPatches and Roto-360. 'params.' denotes the number of model parameters." + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.323, + 0.438, + 0.336 + ], + "angle": 0, + "content": "hibit strong transferability to the real-world examples." + }, + { + "type": "title", + "bbox": [ + 0.101, + 0.342, + 0.375, + 0.357 + ], + "angle": 0, + "content": "4.5. Ablation study and design choice" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.363, + 0.473, + 0.571 + ], + "angle": 0, + "content": "Table 6 shows the results of ablation studies on the HPatches and Roto-360 datasets. The matching accuracy drops when either the orientation alignment loss or the contrastive descriptor loss is not used. Specifically, even when using the ground truth rotation difference for group alignment, not using the descriptor loss results in lower performance, highlighting the importance of robustness to other sorts of variations, e.g., illumination or viewpoint. Not using the image pyramid at inference time results in a slight drop in HPatches, but the performance on Roto-360 remains nearly unchanged. When training without equivariant layers, ResNet-18 with conventional convolutional layers was used - this results in a drastic drop in performance especially on Roto-360, with a rapid increase in the number of model parameters. This demonstrates the significance of high sample efficiency of group-equivariant layers." + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.571, + 0.474, + 0.742 + ], + "angle": 0, + "content": "We also demonstrate the effect of the order of cyclic group \\( G \\) on the performance of our method in the second group of Table 6. We fix the computational cost \\( C \\times |G| = 1,024 \\), and vary the order of group to show the parameter efficiency of the group equivariant networks. Our design choice \\( |G| = 16 \\) yields the best results, and the performance drops gracefully as \\( G \\) increases. This is because with a higher order of groups, the precision of dominant orientation estimation is likely to decrease, leading to lower results. Reducing the order of group to \\( |G| = 8 \\) reduces the MMA in both benchmarks as well, which we suspect is because the range of rotation covered by one group action becomes too wide, leading to increased approximation errors." + }, + { + "type": "title", + "bbox": [ + 0.101, + 0.748, + 0.27, + 0.761 + ], + "angle": 0, + "content": "4.6. Qualitative results" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.768, + 0.472, + 0.847 + ], + "angle": 0, + "content": "Figure 6 visualizes the consistency of dominant orientation estimation. From the source (left) and target (middle) images, we estimate the dominant orientation for the same set of predicted keypoints. We use the ground truth rotation to align the estimated orientation and the target image for better visibility (right). The green and red arrows (middle," + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.144, + 0.853, + 0.416 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.416, + 0.871, + 0.44 + ], + "angle": 0, + "content": "Figure 6. Visualization of consistency of dominant orientation estimation. Best viewed in electronics and colour." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.478, + 0.871, + 0.557 + ], + "angle": 0, + "content": "right) represent the consistent and inconsistent orientation predictions with respect to the initial estimations (left) at a \\(30^{\\circ}\\) threshold. The numbers on the left represent the number of consistent estimations/number of detected keypoints. Compared to LF-Net [40] and RF-Net [50], our method predicts more consistent dominant orientations of keypoints." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.573, + 0.613, + 0.587 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.597, + 0.871, + 0.78 + ], + "angle": 0, + "content": "We have proposed a self-supervised rotation-equivariant network for visual correspondence to improve the discriminability of local descriptors. Our invariant mapping called group-aligning shifts the rotation-equivariant features along the group dimension based on the orientation value to produce rotation-invariant descriptors while preserving the feature discriminability, without collapsing the group dimension. Our method achieves state-of-the-art performance in obtaining rotation-invariant descriptors, which are transferable to tasks such as keypoint matching and camera pose estimation. We believe that our approach can be further extended to other geometric transformation groups, and will motivate group-equivariant learning for practical applications of computer vision." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.781, + 0.871, + 0.847 + ], + "angle": 0, + "content": "Acknowledgement. This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-TF2103-02 and also by the NRF grant (NRF-2021R1A2C3012728) funded by the Korea government (MSIT)." + }, + { + "type": "page_number", + "bbox": [ + 0.465, + 0.877, + 0.506, + 0.888 + ], + "angle": 0, + "content": "21894" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.103, + 0.146, + 0.192, + 0.159 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.168, + 0.471, + 0.215 + ], + "angle": 0, + "content": "[1] Sameer Agarwal, Yasutaka Furukawa, Noah Snavely, Ian Simon, Brian Curless, Steven M Seitz, and Richard Szeliski. Building rome in a day. Communications of the ACM, 54(10):105-112, 2011. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.217, + 0.472, + 0.276 + ], + "angle": 0, + "content": "[2] Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk. Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5173-5182, 2017. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.278, + 0.472, + 0.336 + ], + "angle": 0, + "content": "[3] Axel Barroso-Laguna, Yurun Tian, and Krystian Mikolajczyk. Scalenet: A shallow architecture for scale estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12808-12818, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.338, + 0.471, + 0.373 + ], + "angle": 0, + "content": "[4] Fabio Bellavia and Carlo Colombo. Rethinking the sgloh descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4):931-944, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.375, + 0.471, + 0.422 + ], + "angle": 0, + "content": "[5] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.423, + 0.471, + 0.459 + ], + "angle": 0, + "content": "[6] Taco Cohen and Max Welling. Group equivariant convolutional networks. In International conference on machine learning, pages 2990-2999. PMLR, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.46, + 0.471, + 0.495 + ], + "angle": 0, + "content": "[7] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Toward geometric deep slam. arXiv preprint arXiv:1707.07410, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.496, + 0.472, + 0.543 + ], + "angle": 0, + "content": "[8] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. In CVPR Deep Learning for Visual SLAM Workshop, 2018. 1, 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.545, + 0.472, + 0.615 + ], + "angle": 0, + "content": "[9] Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, and Torsten Sattler. D2-net: A trainable cnn for joint description and detection of local features. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pages 8092-8101, 2019. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.617, + 0.472, + 0.675 + ], + "angle": 0, + "content": "[10] Patrick Ebel, Anastasiia Mishchuk, Kwang Moo Yi, Pascal Fua, and Eduard Trulls. Beyond cartesian representations for local descriptors. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 253-262, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.678, + 0.472, + 0.724 + ], + "angle": 0, + "content": "[11] Bin Fan, Fuchao Wu, and Zhanyi Hu. Rotationally invariant descriptors using intensity order pooling. IEEE transactions on pattern analysis and machine intelligence, 34(10):2031-2045, 2011. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.726, + 0.472, + 0.784 + ], + "angle": 0, + "content": "[12] Jiaming Han, Jian Ding, Nan Xue, and Gui-Song Xia. Redet: A rotation-equivariant detector for aerial object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2786-2795, 2021. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.786, + 0.472, + 0.845 + ], + "angle": 0, + "content": "[13] Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik. Hypercolumns for object segmentation and fine-grained localization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 447-456, 2015. 3" + }, + { + "type": "list", + "bbox": [ + 0.104, + 0.168, + 0.472, + 0.845 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.148, + 0.869, + 0.184 + ], + "angle": 0, + "content": "[14] Chris Harris, Mike Stephens, et al. A combined corner and edge detector. In *Alvey vision conference*, volume 15, pages 10-5244. CiteSeer, 1988. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.185, + 0.87, + 0.233 + ], + "angle": 0, + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.234, + 0.87, + 0.28 + ], + "angle": 0, + "content": "[16] Jared Heinly, Johannes L. Schonberger, Enrique Dunn, and Jan-Michael Frahm. Reconstructing the world* in six days. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3287-3295, 2015. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.281, + 0.87, + 0.34 + ], + "angle": 0, + "content": "[17] Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, and Eduard Trulls. Image matching across wide baselines: From paper to practice. International Journal of Computer Vision, 129(2):517-547, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.342, + 0.871, + 0.389 + ], + "angle": 0, + "content": "[18] Seungwook Kim, Yoonwoo Jeong, Chunghyun Park, Jaesik Park, and Minsu Cho. SeLCA: Self-supervised learning of canonical axis. In NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.39, + 0.87, + 0.449 + ], + "angle": 0, + "content": "[19] Seungwook Kim, Juhong Min, and Minsu Cho. Transformer: Match-to-match attention for semantic correspondence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8697-8707, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.45, + 0.87, + 0.497 + ], + "angle": 0, + "content": "[20] Axel Barroso Laguna and Krystian Mikolajczyk. Key. net: Keypoint detection by handcrafted and learned cnn filters revisited. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.498, + 0.87, + 0.545 + ], + "angle": 0, + "content": "[21] Jongmin Lee, Yoonwoo Jeong, and Minsu Cho. Self-supervised learning of image scale and orientation. In 31st British Machine Vision Conference 2021, BMVC 2021, Virtual Event, UK. BMVA Press, 2021. 1, 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.546, + 0.87, + 0.605 + ], + "angle": 0, + "content": "[22] Jongmin Lee, Yoonwoo Jeong, Seungwook Kim, Juhong Min, and Minsu Cho. Learning to distill convolutional features into compact local descriptors. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 898-908, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.607, + 0.87, + 0.664 + ], + "angle": 0, + "content": "[23] Jongmin Lee, Byungjin Kim, and Minsu Cho. Self-supervised equivariant learning for oriented keypoint detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4847-4857, 2022. 1, 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.666, + 0.87, + 0.725 + ], + "angle": 0, + "content": "[24] Kunhong Li, Longguang Wang, Li Liu, Qing Ran, Kai Xu, and Yulan Guo. Decoupling makes weakly supervised local feature better. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15838-15848, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.726, + 0.87, + 0.786 + ], + "angle": 0, + "content": "[25] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.787, + 0.87, + 0.845 + ], + "angle": 0, + "content": "[26] Yuan Liu, Zehong Shen, Zhixuan Lin, Sida Peng, Hujun Bao, and Xiaowei Zhou. Gift: Learning transformation-invariant dense visual descriptors via group cnns. Advances in Neural Information Processing Systems, 32:6992-7003, 2019. 1, 2, 5, 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.148, + 0.871, + 0.845 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.465, + 0.877, + 0.505, + 0.888 + ], + "angle": 0, + "content": "21895" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.148, + 0.471, + 0.184 + ], + "angle": 0, + "content": "[27] David G Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91-110, 2004. 1, 2, 3, 4, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.185, + 0.472, + 0.256 + ], + "angle": 0, + "content": "[28] Zixin Luo, Lei Zhou, Xuyang Bai, Hongkai Chen, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, and Long Quan. Aslfeat: Learning local features of accurate shape and localization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6589-6598, 2020. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.257, + 0.472, + 0.316 + ], + "angle": 0, + "content": "[29] Simon Lynen, Bernhard Zeisl, Dror Aiger, Michael Bosse, Joel Hesch, Marc Pollefeys, Roland Siegwart, and Torsten Sattler. Large-scale, real-time visual-inertial localization revisited. The International Journal of Robotics Research, 39(9):1061-1084, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.317, + 0.472, + 0.364 + ], + "angle": 0, + "content": "[30] Diego Marcos, Michele Volpi, Nikos Komodakis, and Devis Tuia. Rotation equivariant vector field networks. In Proceedings of the IEEE International Conference on Computer Vision, pages 5048-5057, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.365, + 0.472, + 0.388 + ], + "angle": 0, + "content": "[31] Roland Memisevic. On multi-view feature learning. In ICML, 2012. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.389, + 0.472, + 0.436 + ], + "angle": 0, + "content": "[32] Roland Memisevic and Geoffrey E Hinton. Learning to represent spatial transformations with factored higher-order boltzmann machines. Neural computation, 22(6):1473-1492, 2010. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.437, + 0.472, + 0.472 + ], + "angle": 0, + "content": "[33] Krystian Mikolajczyk and Cordelia Schmid. Scale & affine invariant interest point detectors. International journal of computer vision, 60(1):63-86, 2004. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.473, + 0.472, + 0.52 + ], + "angle": 0, + "content": "[34] Krystian Molkajczyk and Cordelia Schmid. A performance evaluation of local descriptors. IEEE transactions on pattern analysis and machine intelligence, 27(10):1615-1630, 2005. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.521, + 0.472, + 0.557 + ], + "angle": 0, + "content": "[35] Juhong Min, Jongmin Lee, Jean Ponce, and Minsu Cho. Hyperpixel flow: Semantic correspondence with multi-layer neural features. In ICCV, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.558, + 0.472, + 0.606 + ], + "angle": 0, + "content": "[36] Anastasiia Mishchuk, Dmytro Mishkin, Filip Radenovic, and Jiri Matas. Working hard to know your neighbor's margins: Local descriptor learning loss. In Advances in Neural Information Processing Systems, pages 4826-4837, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.606, + 0.472, + 0.653 + ], + "angle": 0, + "content": "[37] Dmytro Mishkin, Filip Radenovic, and Jiri Matas. Repeatability is not enough: Learning affine regions via discriminability. In Proceedings of the European Conference on Computer Vision (ECCV), pages 284-300, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.654, + 0.472, + 0.713 + ], + "angle": 0, + "content": "[38] Daniel Moyer, Esra Abaci Turk, P Ellen Grant, William M Wells, and Polina Golland. Equivariant filters for efficient tracking in 3d imaging. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 193-202. Springer, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.714, + 0.472, + 0.761 + ], + "angle": 0, + "content": "[39] Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics, 31(5):1147-1163, 2015. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.762, + 0.472, + 0.809 + ], + "angle": 0, + "content": "[40] Yuki Ono, Eduard Trulls, Pascal Fua, and Kwang Moo Yi. Lf-net: learning local features from images. In Advances in neural information processing systems, pages 6234-6244, 2018. 2, 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.103, + 0.81, + 0.472, + 0.846 + ], + "angle": 0, + "content": "[41] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 5" + }, + { + "type": "list", + "bbox": [ + 0.103, + 0.148, + 0.472, + 0.846 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.148, + 0.869, + 0.196 + ], + "angle": 0, + "content": "[42] Rémi Pautrat, Viktor Larsson, Martin R Oswald, and Marc Pollefeys. Online invariance selection for local feature descriptors. In European Conference on Computer Vision, pages 707-724. Springer, 2020. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.198, + 0.87, + 0.281 + ], + "angle": 0, + "content": "[43] Nicolas Pielawski, Elisabeth Wetzer, Johan Överstedt, Ji-ahao Lu, Carolina Wahlby, Joakim Lindblad, and Nataša Sladoje. CoMIR: Contrastive multimodal image representation for registration. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 18433–18444. Curran Associates, Inc., 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.282, + 0.87, + 0.33 + ], + "angle": 0, + "content": "[44] Jerome Revaud, Cesar De Souza, Martin Humenberger, and Philippe Weinzaepfel. R2d2: Reliable and repeatable detector and descriptor. Advances in neural information processing systems, 32:12405-12415, 2019. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.331, + 0.87, + 0.392 + ], + "angle": 0, + "content": "[45] Jérôme Revaud, Vincent Leroy, Philippe Weinzaepfel, and Boris Chidlovskii. Pump: Pyramidal and uniqueness matching priors for unsupervised learning of local descriptors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3926-3936, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.393, + 0.87, + 0.44 + ], + "angle": 0, + "content": "[46] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In 2011 International conference on computer vision, pages 2564-2571. IEEE, 2011. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.442, + 0.87, + 0.49 + ], + "angle": 0, + "content": "[47] Torsten Sattler, Bastian Leibe, and Leif Kobbelt. Improving image-based localization by active correspondence search. In European conference on computer vision, pages 752-765. Springer, 2012. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.491, + 0.87, + 0.563 + ], + "angle": 0, + "content": "[48] Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenberg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, et al. Benchmarking 6dof outdoor visual localization in changing conditions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8601-8610, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.565, + 0.87, + 0.611 + ], + "angle": 0, + "content": "[49] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4104-4113, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.612, + 0.87, + 0.673 + ], + "angle": 0, + "content": "[50] Xuelun Shen, Cheng Wang, Xin Li, Zenglei Yu, Jonathan Li, Chenglu Wen, Ming Cheng, and Zijian He. Rf-net: An end-to-end image matching network based on receptive field. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8132-8140, 2019. 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.674, + 0.87, + 0.698 + ], + "angle": 0, + "content": "[51] Kihyuk Sohn and Honglak Lee. Learning invariant representations with local transformations. In ICML, 2012. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.699, + 0.87, + 0.748 + ], + "angle": 0, + "content": "[52] Ivan Sosnovik, Artem Moskalev, and Arnold Smeulders. How to transform kernels for scale-convolutions. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1092-1097, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.749, + 0.87, + 0.808 + ], + "angle": 0, + "content": "[53] Christoph Strecha, Wolfgang Von Hansen, Luc Van Gool, Pascal Fua, and Ulrich Thoennessen. On benchmarking camera calibration and multi-view stereo for high resolution imagery. In 2008 IEEE conference on computer vision and pattern recognition, pages 1-8. IEEE, 2008. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.81, + 0.87, + 0.846 + ], + "angle": 0, + "content": "[54] Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, and Krystian Mikolajczyk. Hynet: Learning local descriptor with hybrid similarity measure and triplet loss. Ad" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.148, + 0.87, + 0.846 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.465, + 0.877, + 0.505, + 0.888 + ], + "angle": 0, + "content": "21896" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.134, + 0.149, + 0.471, + 0.171 + ], + "angle": 0, + "content": "vances in Neural Information Processing Systems, 33:7401-7412, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.174, + 0.471, + 0.221 + ], + "angle": 0, + "content": "[55] Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, and Krystian Mikolajczyk. Hynet: Learning local descriptor with hybrid similarity measure and triplet loss. In NeurIPS, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.222, + 0.472, + 0.282 + ], + "angle": 0, + "content": "[56] Yurun Tian, Xin Yu, Bin Fan, Fuchao Wu, Huub Heijnen, and Vassileios Balntas. Sosnet: Second order similarity regularization for local descriptor learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11016-11025, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.283, + 0.471, + 0.32 + ], + "angle": 0, + "content": "[57] Michal Jan Tyszkiewicz, Pascal Fua, and Eduard Trulls. Disk: learning local features with policy gradient. Advances in Neural Information Processing Systems, 33, 2020. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.32, + 0.471, + 0.367 + ], + "angle": 0, + "content": "[58] Rui Wang, Robin Walters, and Rose Yu. Data augmentation vs. equivariant networks: A theory of generalization on dynamics forecasting. arXiv preprint arXiv:2206.09450, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.369, + 0.471, + 0.416 + ], + "angle": 0, + "content": "[59] Zhenhua Wang, Bin Fan, Gang Wang, and Fuchao Wu. Exploring local and overall ordinal information for robust feature description. IEEE transactions on pattern analysis and machine intelligence, 38(11):2198-2211, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.418, + 0.471, + 0.453 + ], + "angle": 0, + "content": "[60] Maurice Weiler and Gabriele Cesa. General e (2)-equivariant steerable cnns. Advances in Neural Information Processing Systems, 32:14334-14345, 2019. 1, 2, 3, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.455, + 0.471, + 0.503 + ], + "angle": 0, + "content": "[61] Maurice Weiler, Fred A Hamprecht, and Martin Storath. Learning steerable filters for rotation equivariant cnns. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 849-858, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.504, + 0.471, + 0.563 + ], + "angle": 0, + "content": "[62] Daniel E Worrall, Stephan J Garbin, Daniyar Turmukhambetov, and Gabriel J Brostow. Harmonic networks: Deep translation and rotation equivariance. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5028-5037, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.565, + 0.471, + 0.624 + ], + "angle": 0, + "content": "[63] Pei Yan, Yihua Tan, Shengzhou Xiong, Yuan Tai, and Yansheng Li. Learning soft estimator of keypoint scale and orientation with probabilistic covariant loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19406-19415, 2022. 1, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.625, + 0.471, + 0.672 + ], + "angle": 0, + "content": "[64] Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, and Pascal Fua. Lift: Learned invariant feature transform. In European conference on computer vision, pages 467-483. Springer, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.674, + 0.471, + 0.722 + ], + "angle": 0, + "content": "[65] Yanzhao Zhou, Qixiang Ye, Qiang Qiu, and Jianbin Jiao. Oriented response networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 519-528, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.104, + 0.723, + 0.471, + 0.782 + ], + "angle": 0, + "content": "[66] Siyu Zhu, Runze Zhang, Lei Zhou, Tianwei Shen, Tian Fang, Ping Tan, and Long Quan. Very large-scale global sfm by distributed motion averaging. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4568-4577, 2018. 1" + }, + { + "type": "list", + "bbox": [ + 0.104, + 0.149, + 0.472, + 0.782 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.466, + 0.877, + 0.505, + 0.888 + ], + "angle": 0, + "content": "21897" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Rotation-Equivariant Features for Visual Correspondence/3ac6cb22-128e-410d-8dc0-595aae3be692_origin.pdf b/2023/Learning Rotation-Equivariant Features for Visual Correspondence/3ac6cb22-128e-410d-8dc0-595aae3be692_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..5a4a5c04fa455ea7dbd107d3d0644d5a3991d1ba --- /dev/null +++ b/2023/Learning Rotation-Equivariant Features for Visual Correspondence/3ac6cb22-128e-410d-8dc0-595aae3be692_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:872153e11c0c27717ceddfe3967ebb24a8d1a5b253f0422f9fe16e2ed3af757a +size 918464 diff --git a/2023/Learning Rotation-Equivariant Features for Visual Correspondence/full.md b/2023/Learning Rotation-Equivariant Features for Visual Correspondence/full.md new file mode 100644 index 0000000000000000000000000000000000000000..db5fbc0e65e8b43630b452713502f4cd1ee6c024 --- /dev/null +++ b/2023/Learning Rotation-Equivariant Features for Visual Correspondence/full.md @@ -0,0 +1,302 @@ +# Learning Rotation-Equivariant Features for Visual Correspondence + +Jongmin Lee Byungjin Kim Seungwook Kim Minsu Cho + +Pohang University of Science and Technology (POSTECH), South Korea + +http://cylab.postech.ac.kr/research/RELF + +# Abstract + +Extracting discriminative local features that are invariant to imaging variations is an integral part of establishing correspondences between images. In this work, we introduce a self-supervised learning framework to extract discriminative rotation-invariant descriptors using group-equivariant CNNs. Thanks to employing group-equivariant CNNs, our method effectively learns to obtain rotation-equivariant features and their orientations explicitly, without having to perform sophisticated data augmentations. The resultant features and their orientations are further processed by group aligning, a novel invariant mapping technique that shifts the group-equivariant features by their orientations along the group dimension. Our group aligning technique achieves rotation-invariance without any collapse of the group dimension and thus eschews loss of discriminability. The proposed method is trained end-to-end in a self-supervised manner, where we use an orientation alignment loss for the orientation estimation and a contrastive descriptor loss for robust local descriptors to geometric/photometric variations. Our method demonstrates state-of-the-art matching accuracy among existing rotation-invariant descriptors under varying rotation and also shows competitive results when transferred to the task of keypoint matching and camera pose estimation. + +# 1. Introduction + +Extracting local descriptors is an essential step for visual correspondence across images, which is used for a wide range of computer vision problems such as visual localization [29, 47, 48], simultaneous localization and mapping [7, 8, 39], and 3D reconstruction [1, 16, 17, 49, 66]. To establish reliable visual correspondences, the properties of invariance and discriminativeness are required for local descriptors; the descriptors need to be invariant to geometric/photometric variations of images while being discriminative enough to distinguish true matches from false ones. Since the remarkable success of deep learning for visual + +recognition, deep neural networks have also been adopted to learn local descriptors, showing enhanced performances on visual correspondence [44, 45, 64]. Learning rotation-invariant local descriptors, however, remains challenging; the classical techniques [11, 27, 46] for rotation-invariant descriptors, which are used for shallow gradient-based feature maps, cannot be applied to feature maps from standard deep neural networks, in which rotation of input induces unpredictable feature variations. Achieving rotation invariance without sacrificing discriminativeness is particularly important for local descriptors as rotation is one of the most frequent imaging variations in reality. + +In this work, we propose a self-supervised approach to obtain rotation-invariant and discriminative local descriptors by leveraging rotation-equivariant CNNs. First, we use group-equivariant CNNs [60] to jointly extract rotation-equivariant local features and their orientations from an image. To extract reliable orientations, we use an orientation alignment loss [21, 23, 63], which trains the network to predict the dominant orientation robustly against other imaging variations, including illumination or viewpoint changes. Using group-equivariant CNNs enables the local features to be empowered with explicitly encoded rotation equivariance without having to perform rigorous data augmentations [58, 60]. Second, to obtain discriminative rotation-invariant descriptors from rotation-equivariant features, we propose group-aligning that shifts the group-equivariant features by their dominant orientation along their group dimension. Conventional methods to yield invariant features from group-equivariant features collapse the group dimension by group-pooling, e.g., max-pooling or bilinear-pooling [26], resulting in a drop in feature discriminability and quality. In contrast, our group-aligning preserves the group dimension, achieving rotation-invariance while eschewing loss of discriminability. Furthermore, by preserving the group dimension, we can obtain multiple descriptors by performing group-aligning using multiple orientation candidates, which improves the matching performance by compensating for potential errors in dominant orientation prediction. Finally, we evaluate our rotation-invariant descriptors against existing local descriptors, and + +our group-aligning scheme against group-pooling methods on various image matching benchmarks to demonstrate the efficacy of our method. + +The contribution of our paper is fourfold: + +- We propose to extract discriminative rotation-invariant local descriptors to tackle the task of visual correspondence by utilizing rotation-equivariant CNNs. +- We propose group-aligning, a method to shift a group-equivariant descriptor in the group dimension by its dominant orientation to obtain a rotation-invariant descriptor without having to collapse the group information to preserve feature discriminability. +- We use self-supervisory losses of orientation alignment loss for orientation estimation, and a contrastive descriptor loss for robust local descriptor extraction. +- We demonstrate state-of-the-art performances under varying rotations on the Roto-360 dataset and show competitive transferability on the HPatches dataset [2] and the MVS dataset [53]. + +# 2. Related work + +Classical invariant local descriptors. Classical methods to extract invariant local descriptors first aggregate image gradients to obtain a rotation-equivariant representation, i.e., histogram, from which the estimated dominant orientation is subtracted to obtain rotation-invariant features [27, 46]. Several studies [4, 11, 59] suggest extracting local descriptors by invariant mapping of the order-based gradient histogram of a patch. However, these classical methods for shallow gradient-based feature maps cannot be applied to deep feature maps from standard neural networks, in which rotation induces unpredictable feature variations. Therefore, we propose a deep end-to-end pipeline to obtain orientation-normalized local descriptors by utilizing rotation-equivariant CNNs [60] with additional losses. + +Learning-based invariant local descriptors. A branch of learning-based methods learns to obtain invariant local descriptors in an explicit manner. GIFT [26] constructs group-equivariant features by rotating or rescaling the images, and then collapses the group dimension using bilinear pooling to obtain invariant local descriptors. However, their groups are limited to non-cyclic discrete rotations ranging from $-90^{\circ}$ to $90^{\circ}$ . Furthermore, their reliance on data augmentation implies a lower sampling efficiency compared to group-equivariant networks. LISRD [42] jointly learns meta descriptors with different levels of regional variations and selects the most appropriate level of invariance given the context. Another branch of learning methods aims to learn the invariance implicitly using descriptor similarity losses from the image pair using camera pose or homography supervision. These methods are either patch-based [10, 36, 54, 56] + +or image-based [8, 9, 22, 24, 28, 37, 40, 44, 50, 57]. While these methods may be robust to rotation, they cannot be said to be equivariant or invariant to rotation. We construct group-equivariant local features using the steerable networks [60], which explicitly encodes cyclic rotational equivariance to the features without having to rely on data augmentation. We can then yield rotation-invariant features by group-aligning that shifts the group-equivariant features along the group dimension by their dominant orientations, preserving feature discriminability. + +Equivariant representation learning. There has been a constant pursuit to learn equivariant representations by explicitly incorporating group equivariance into the model architecture design [30-32, 51, 60, 65]. For example, G-CNNs [6] use group equivariant convolutions that reduce sample complexity by exploiting symmetries on discrete isometric groups; SFCNNs [61] and H-Nets [62] extract features from more diverse groups and continuous domains by using harmonics as filters. There are also studies that focus on scale-equivariant representation learning [3, 21, 52]. [12, 18, 23, 38, 43] leverage equivariant neural networks to tackle vision tasks e.g., keypoint detection. In this work, we also propose to use equivariant neural networks to facilitate the learning of discriminative rotation-invariant descriptors. We guide the readers to section 1 of the supplementary material for a brief introduction to group equivariance. + +# 3. Rotation-equivariant features, Rotation-invariant descriptors + +In this section, we first draw the line between the terms feature and descriptor which will be used throughout this paper. The goal of our work is to learn to extract rotation-equivariant local features from our rotation-equivariant backbone network, and then to align them by their dominant orientation to finally yield rotation-invariant descriptors. In the subsequent subsections, we elaborate on the process of rotation-equivariant feature extraction from steerable CNNs (Sec. 3.1), assignment of equivariant features to keypoints (Sec. 3.2), how group-aligning is performed to yield rotation-invariant yet discriminative descriptors (Sec. 3.3), how we formulate our orientation alignment loss (Sec.3.4) and contrastive descriptor loss (Sec.3.5) to train our network to extract descriptors which are robust to not only rotation but also other imaging transformations, and finally how we obtain scale-invariant descriptors at test time using image pyramids (Sec.3.6). Figure 1 shows the overall architecture of our method. + +# 3.1. Rotation-equivariant feature extraction + +As the feature extractor, we use ReResNet18 [12], which has the same structure as ResNet18 [15] but is constructed using rotation-equivariant convolutional layers [60]. The + +![](images/59ded6a0b8cf8aa0c87e941fdeee5b71bcdcd73df6adbf3f8f65cc360cab88d7.jpg) +Figure 1. Overview of the proposed pipeline. An input image is forwarded through the equivariant networks to yield equivariant feature maps from multiple intermediate layers, encoding both low-level geometry and high-level semantic information. The feature maps are bilinearly interpolated to have equal spatial dimensions to be concatenated together. We use the first channel of the feature map $\mathbf{F}$ as the orientation histogram map $\mathbf{O}$ to predict the dominant orientations, which are used to shift the group-equivariant representation along the group dimension to yield discriminative rotation-invariant descriptors. To learn to extract accurate dominant orientation $\hat{\theta}$ , we use the orientation alignment loss $\mathcal{L}^{\mathrm{ori}}$ . To obtain descriptors robust to illumination and geometric changes, we use a contrastive descriptor loss $\mathcal{L}^{\mathrm{desc}}$ using the ground-truth homography $\mathcal{H}_{\mathrm{GT}}$ . + +layer acts on a cyclic group $G_N$ and is equivariant for all translations and $N$ discrete rotations. At the first layer, the scalar field of the input image is lifted to the vector field of the group representation [60]. We leverage feature pyramids from the intermediate layers of the ReResNet18 backbone to construct output features as follows: + +$$ +\mathbf {F} = \bigoplus_ {i \in l} \eta (\mathbf {f} _ {i}), \quad \mathbf {f} _ {i} = [ \Pi_ {j = 1} ^ {i} L _ {j} ] (I), \tag {1} +$$ + +where $\mathbf{f}_i\in \mathbb{R}^{C_i\times |G|\times H_i\times W_i}$ is an intermediate feature from $L_{i},L_{i}$ is the $i$ -th layer of the equivariant network, $\eta$ denotes bilinear interpolation to $H\times W$ , and $\bigoplus$ denotes concatenation along the $C$ dimension. We utilize the multi-layer feature maps to exploit the low-level geometry information and high-level semantics in the local descriptors [13,19,35]. The output features $\mathbf{F}\in \mathbb{R}^{C\times |G|\times H\times W}$ contains rotationequivariant features with multiple layers containing different semantics and receptive fields. We set $H = H_{1}$ and $W = W_{1}$ , which are $\frac{1}{2}$ of the input image size. + +# 3.2. Assigning local features to keypoints + +During training, we extract $K$ keypoints from the source image using Harris corner detection [14]. We then use the ground-truth homography $\mathcal{H}_{\mathrm{GT}}$ to obtain ground-truth keypoint correspondences. Also, we allocate a local feature $\mathbf{p} \in \mathbb{R}^{C \times |G| \times K}$ to each keypoint, using the interpolated location of the equivariant feature map $\mathbf{F}$ . We experiment our descriptor with SIFT [27], LF-Net [40], SuperPoint [8], and KeyNet [20] as the keypoint detector during inference time. + +# 3.3. Group aligning for invariant mapping + +To transform the rotation-equivariant feature to a rotation-invariant descriptor, we propose group aligning, an + +operation to shift the group-equivariant feature in the $G$ -dimension using the dominant orientation $\hat{\theta}$ . Unlike existing methods that use group pooling, e.g., average pooling or max pooling, which collapses the group dimension, group aligning preserves the rich group information. Figure 2 illustrates the difference between group pooling and group aligning on an equivariant representation. + +Estimating the dominant orientation and the shifting value. We obtain the orientation histogram map $\mathbf{O} \in \mathbb{R}^{|G| \times H \times W} = \mathbf{F}_0$ by selecting the first channel of the rotation-equivariant tensor $\mathbf{F}$ as an orientation histogram map. Note that the first channels of each group action are simultaneously used as the channels of the descriptors and to construct the orientation histogram. The histogram-based representation of $\mathbf{O}$ provides richer information than directly regressing the dominant orientation, as the orientation histogram enables predicting multiple (i.e., top- $k$ ) candidates as the dominant orientation. We first select an orientation vector $\mathbf{o} \in \mathbb{R}^{|G|}$ of a keypoint from the orientation histogram map $\mathbf{O}$ using the coordinates of the keypoint. Next, we estimate the dominant orientation value $\hat{\theta}$ from the orientation vector $\mathbf{o}$ by selecting the index of the maximum score, $\hat{\theta} = \frac{360}{|G|} \arg \max_g \mathbf{o}$ . Using the dominant orientation value $\hat{\theta}$ , we obtain the shifting value $\hat{\Delta} = \frac{|G|}{360} \hat{\theta}$ in $G$ -dim. At training time, we use the ground-truth rotation $\theta_{\mathrm{GT}}$ instead of the predicted dominant orientation value $\hat{\theta}$ to generate the shifting value $\Delta_{\mathrm{GT}}$ . + +Group aligning. Given a keypoint-allocated feature tensor $\mathbf{p} \in \mathbb{R}^{C \times |G|}$ from the equivariant representation $\mathbf{F}$ , we obtain the rotation-invariant local descriptor $\mathbf{d} \in \mathbb{R}^{C|G|}$ by group aligning using $\Delta$ . After computing the dominant orientation $\hat{\theta}$ and the shifting value $\hat{\Delta}$ from $\mathbf{o}$ , we obtain the orientation-normalized descriptor $\mathbf{d}' \in \mathbb{R}^{C|G|}$ by shifting $\mathbf{p}$ + +![](images/4cfedb4a049db1906ac7b2e9eb74b61d750496c6ef061521fe5b9617b405d2e1.jpg) +(a) Group pooling +(b) Group aligning + +in the $G$ -dimension by $-\hat{\Delta}$ and flattening the descriptor to a vector. We use cyclic shifting in consideration of the cyclic property of rotation. We finally obtain the L2-normalized descriptor $\mathbf{d}$ from the orientation-normalized descriptor $\mathbf{d}'$ such that $||\mathbf{d}||^2 = 1$ . Formally, this process can be defined as: + +$$ +\mathbf {p} _ {:, i} ^ {\prime} = T _ {r} ^ {\prime} \left(\mathbf {p} _ {:, i}, \hat {\Delta}\right) = \mathbf {p} _ {:, (i + \hat {\Delta}) \bmod | G |}, +$$ + +$$ +\mathbf {d} _ {| G |: | G | (i + 1)} ^ {\prime} = \mathbf {p} _ {i} ^ {\prime}, \tag {2} +$$ + +$$ +\mathbf {d} = \frac {\mathbf {d} ^ {\prime}}{| | \mathbf {d} ^ {\prime} | | _ {2}}, +$$ + +where $T_r'$ is shifting operator in vector space, and $\mathbf{p}'$ is a group-aligned descriptor before flattening. This shifting by $\hat{\Delta}$ aligns all the descriptors in the direction of their dominant orientations, creating orientation-normalized descriptors. This process is conceptually similar to subtracting the dominant orientation value of the orientation histogram in the classical descriptor SIFT [27], but we apply this concept to the equivariant neural features. The proposed group aligning preserves the group information, so our invariant descriptors have more representative power than the existing group-pooled descriptors which collapse the group dimension for invariance. + +# 3.4. Orientation alignment loss + +To learn to obtain the dominant orientations from the orientation vectors, we use an orientation alignment loss [21, 23, 63] to supervise the orientation histograms in $\mathbf{O}$ to be rotation equivariant under the photometric/geometric transformations. Figure 3 shows the illustration of orientation + +![](images/913e20d0650200e75b53cd63aebac1942d4d6a1b149e593e091cd94587a6b1f4.jpg) +Figure 2. Difference between group pooling and group aligning. In group pooling, the group dimension is collapsed to yield an invariant descriptor $(\mathbb{R}^{C\times |G|}\to \mathbb{R}^C)$ . In group aligning, the entire feature is cyclically shifted in the group dimension to obtain an invariant descriptor $(\mathbb{R}^{C\times |G|}\to \mathbb{R}^{C|G|})$ while preserving the group information and discriminability. +Figure 3. Illustration of orientation alignment loss. Given two rotation-equivariant tensors $\mathbf{p}^{\mathrm{A}},\mathbf{p}^{\mathrm{B}}\in \mathbb{R}^{C\times |G|}$ obtained from two different rotated versions of the same image, we apply cyclic shift on one of the descriptors in the group dimension using the GT difference in rotation. The orientation alignment loss supervises the output orientation vectors of the two descriptors to be the same. + +alignment loss. The cyclic shift of an orientation histogram map at the training time is formulated as follows: + +$$ +T _ {r} ^ {\prime} \left(\mathbf {O} _ {i}, \Delta_ {\mathrm {G T}}\right) = \mathbf {O} _ {\left(i + \Delta_ {\mathrm {G T}}\right) \bmod | G |}, \tag {3} +$$ + +where $\Delta_{\mathrm{GT}} = \frac{|G|}{360}\theta_{\mathrm{GT}}$ is the shifting value calculated from the ground-truth rotation $\theta_{\mathrm{GT}}$ . We formulate the orientation alignment loss in the form of a cross-entropy as follows: + +$$ +\begin{array}{l} \mathcal {L} ^ {\mathrm {o r i}} \left(\mathbf {O} ^ {\mathrm {A}}, \mathbf {O} ^ {\mathrm {B}}, \Delta_ {\mathrm {G T}}\right) = \\ - \sum_ {k \in K} \sum_ {g \in G} \sigma \left(\mathbf {O} _ {g, k} ^ {\mathrm {A}}\right) \log \left(\sigma \left(T _ {r} ^ {\prime} \left(\mathbf {O} _ {g, k} ^ {\mathrm {B}}, \Delta_ {\mathrm {G T}}\right)\right)\right), \tag {4} \\ \end{array} +$$ + +where $\mathbf{O}^{\mathrm{A}}$ is the source orientation histogram map and $\mathbf{O}^{\mathrm{B}}$ is the target orientation histogram map obtained from a synthetically warped source image, $\sigma$ is a softmax function applied to the $G$ -dimension of the orientation histogram map to represent the orientation vector as a probability distribution for the cross-entropy loss to be applicable. Using Equation 4, the network learns to predict the characteristic orientations robustly against different imaging variations, such as photometric transformations and geometric transformations beyond rotation, as these transformations cannot be handled by equivariance to discrete rotations alone. Note that it is not straightforward to define the characteristic orientation of a keypoint to provide strong supervision. However, we facilitate the learning of characteristic orientations by formulating it as a self-supervised learning framework, leveraging the known relative orientation between two keypoint orientation histogram maps obtained from differently rotated versions of the same image. + +
MMApred.
@1px
Align97.5484.90
Avg33.7233.72
Max57.9257.92
None23.9723.97
Bilinear43.6026.42
+ +# 3.5. Contrastive descriptor loss + +We propose to use a descriptor similarity loss motivated by contrastive learning [5] to further empower the descriptors to be robust against variations apart from rotation, e.g., illumination or viewpoint. The descriptor loss is formulated in a contrastive manner as follows: + +$$ +\begin{array}{l} \mathcal {L} ^ {\text {d e s c}} \left(\mathbf {D} ^ {\mathrm {A}}, \mathbf {D} ^ {\mathrm {B}}\right) = \\ \sum_ {\left(\mathbf {d} _ {i} ^ {\mathrm {A}}, \mathbf {d} _ {i} ^ {\mathrm {B}}\right) \in \left(\mathbf {D} ^ {\mathrm {A}}, \mathbf {D} ^ {\mathrm {B}}\right)} - \log \frac {\exp \left(\operatorname {s i m} \left(\mathbf {d} _ {i} ^ {\mathrm {A}} , \mathbf {d} _ {i} ^ {\mathrm {B}}\right) / \tau\right)}{\sum_ {k \in K \backslash i} \exp \left(\operatorname {s i m} \left(\mathbf {d} _ {i} ^ {\mathrm {A}} , \mathbf {d} _ {k} ^ {\mathrm {B}}\right)\right) / \tau}, \tag {5} \\ \end{array} +$$ + +where sim is cosine similarity and $\tau$ is the softmax temperature. Unlike the triplet loss with one hard negative sample, the contrastive loss can optimize the distance for all negative pairs. This contrastive loss with InfoNCE [41] maximizes the mutual information between the encoded features and effectively reduces the low-level noise. Our overall self-supervised loss is formulated as $\mathcal{L} = \alpha \mathcal{L}^{\mathrm{ori}} + \mathcal{L}^{\mathrm{desc}}$ , where $\alpha$ is a balancing term. + +# 3.6. Scale robustness + +While we employ a rotation-equivariant network, it does not ensure that the descriptors are robust to scale changes. Thus, at inference time, we construct an image pyramid using a scale factor of $2^{1/4}$ from a maximum of 1,024 pixels to a minimum of 256 pixels as in R2D2 [44]. After constructing the scale-wise descriptors $\in \mathbb{R}^{S \times C|G| \times K}$ with $S$ varying scales, we finally generate the scale-invariant local descriptors $\in \mathbb{R}^{C|G| \times K}$ by max-pooling in the scale dimension inspired by scale-space maxima as in SIFT [27,33], for improved robustness to scale changes. + +# 4. Experiment + +Implementation details. We use rotation-equivariant ResNet-18 (ReResNet-18) [12] implemented using the rotation-equivariant layers of $E(2)$ -CNN [60] as our backbone. We remove the first maxpool layer to preserve the spatial size, so that the spatial resolution of the rotation- + +Table 1. Evaluation with GT keypoint pairs on Roto-360 without training. 'Align' uses GT rotation difference to apply group-aligning to demonstrate the upper-bound. 'None' does not use pooling nor aligning, demonstrating the lower-bound. We use an average of 111 keypoint pairs extracted using SuperPoint [8]. + +
MMApred.
@10px@5px@3px
Align93.0891.3590.18688.3
Avg85.8482.1281.05705.9
Max82.6178.0077.79686.0
None19.6818.8118.57349.1
Bilinear42.6941.0340.51332.5
+ +Table 2. Evaluation with predicted keypoint pairs on Roto-360 with training. 'Max' and 'Avg' collapses the group dimension of the features through max pooling or average pooling. 'pred.' denotes the average number of predicted matches. We use an average of 1161 keypoint pairs extracted using SuperPoint [8]. + +equivariant feature $\mathbf{F}$ is $H = \frac{H'}{2}$ and $W = \frac{W'}{2}$ , where $H'$ and $W'$ are the height and width of an input image. We use 16 for the order of cyclic group $G$ . We use a batch size of 8, a learning rate of $10^{-4}$ , and a weight decay of 0.1. We train our model for 12 epochs with 1,000 iterations using a machine with an Intel i7-8700 CPU and an NVIDIA GeForce RTX 3090 GPU. We use the temperature $\tau$ of $\mathcal{L}^{\mathrm{desc}}$ as 0.07. The loss balancing factor $\alpha$ is 10. The final output descriptor size is 1,024, with $C = 64$ , $|G| = 16$ . We use SuperPoint [8] as the keypoint detector to evaluate our method except Table 4. For all descriptors, we use the mutual nearest neighbour matcher to predict the correspondences. + +# 4.1. Datasets and metrics + +We use a synthetic training dataset to train our model in a self-supervised manner. We evaluate our model on the Roto-360 dataset and show the transferability on real image benchmarks, i.e., HPatches [2] and MVS [53] datasets. + +Training dataset. We generate a synthetic dataset for self-supervised training from the MS-COCO dataset [25]. We warp images with random homographies for geometric robustness and transform the colors by jitter, noise, and blur for photometric robustness. As we need the ground-truth rotation $\theta_{\mathrm{GT}}$ for our orientation alignment loss, we decompose the synthetic homography $\mathcal{H}$ as follows: $\theta_{\mathrm{GT}} = \arctan \left(\frac{\mathcal{H}_{21}}{\mathcal{H}_{11}}\right)$ , where we assume that a $3\times 3$ homography matrix $\mathcal{H}$ with no significant tilt can be approximated to an affine matrix. We sample $K = 512$ keypoints for an image using Harris corner detector [14], obtaining 512 corresponding keypoint pairs for each image pair using homography and rotation. Note that this dataset generation protocol is the same as that of GIFT [26] for a fair comparison. + +Roto-360 is an evaluation dataset that consists of 360 image pairs with in-plane rotation ranging from $0^{\circ}$ to $350^{\circ}$ at $10^{\circ}$ intervals, created using ten randomly sampled images from HPatches [2]. Roto-360 is more suitable to evaluate the rotation invariance of our descriptors, as the extreme rotation (ER) dataset [26] only covers $180^{\circ}$ , and includes photometric variations. We use mean matching accuracy (MMA) as the evaluation metric with pixel thresholds of 3/5/10 pixels + +
MethodMMApred.total.
@10px@5px@3px
SIFT [27]78.8678.5978.23774.11500.0
ORB [46]86.7885.2978.73607.61005.2
SuperPoint [8]22.8522.1021.83462.61161.0
LF-Net [40]75.0574.3072.61386.71024.0
RF-Net [50]15.6415.1814.581602.55000.0
D2-Net [9]15.569.305.21386.91474.5
R2D2 [44]15.8014.9713.50197.91500.0
GIFT [26]42.3542.0541.59589.21161.0
LISRD [42]16.9616.0415.64323.61781.1
ASLFeat [28]19.3416.3813.131366.96764.2
DISK [57]13.2212.4312.04359.12048.0
PosFeat [24]13.7611.799.82717.27623.5
ours93.0891.3590.18688.31161.0
ours*94.3592.8291.691333.02340.4
+ +Table 3. Comparison to existing local descriptors on Roto-360. We use mutual nearest matching for all methods to establish matches between images. 'total.' and 'pred.' denotes the average number of detected keypoints and predicted matches, respectively. 'ours*' denotes selecting multiple candidate descriptors based on the ratio of max value in the orientation histogram. We use SuperPoint keypoint detector [8] same to the GIFT descriptor [26]. + +and the number of predicted matches following [9, 34]. + +HPatches [2] has 57 scenes with illumination variations and 59 scenes with viewpoint variations. Each scene contains five image pairs with ground-truth planar homography. We use the same evaluation metrics to Roto-360 to show the transferability of our local descriptors. + +MVS dataset [53] has six image sequences of outdoor scenes with GT camera poses. We evaluate the relative pose estimation accuracy at $5^{\circ} / 10^{\circ} / 20^{\circ}$ angular difference thresholds. + +# 4.2. Comparison to other invariant mappings + +Table 1 compares group aligning to various group pooling methods on the Roto-360 dataset using ground-truth keypoint pairs, i.e., no keypoint deviation, without training. The purpose is to compare the invariant mapping operations only while keeping the backbone network and the number of keypoints fixed. We use $\Delta_{\mathrm{GT}}$ to shift the equivariant features, and group aligning shows almost perfect keypoint correspondences with $97.54\%$ matching accuracy. Group pooling, such as max pooling or average pooling, significantly reduces discriminative power compared to group aligning. The results show that group aligning shows the best results, proving that leveraging the full group-equivariant features instead of collapsing the groups shows higher discriminability. Note that the bilinear pooling [26] does not guarantee the rotation-invariant matching. + +Table 2 compares the proposed group aligning to the existing group pooling methods on the Roto-360 dataset, this time with predicted keypoint pairs and with training. Note + +
Det.Desc.MMApred.total.
@10px@5px@3px
SIFT [27]SIFT [27]78.8678.5978.23774.11500
GIFT [26]37.9736.8236.09531.21500
ours84.6779.8577.96558.31500
ours*84.9180.0978.18759.82219
LF-Net [40]LF-Net [40]75.0574.3072.61386.71024
GIFT [26]35.5633.8232.29426.31024
ours79.9071.6367.39431.81024
ours*80.3271.9967.62591.41503
SuperPoint [8]SuperPoint [8]22.8522.1021.83462.61161
GIFT [26]42.3542.0541.59589.21161
ours93.0891.3590.18688.31161
ours*94.3592.8291.691333234
KeyNet [20]HyNet [55]24.4322.8220.64288.7995
GIFT [26]34.0832.3129.17275.7995
ours72.9561.3641.33257.2995
ours*72.4860.6940.95356.61484
+ +Table 4. Comparison to existing local descriptors when using the same keypoint detector on Roto-360. Results in bold indicate the best result, and underlined results indicate the second best. + +that while other methods are trained only with $\mathcal{L}^{\mathrm{desc}}$ , our method is trained also with $\mathcal{L}^{\mathrm{ori}}$ to facilitate group aligning. While the number of predicted matches is the highest for average pooling, the MMA results are significantly higher for group aligning, which shows group-aligned descriptors have a higher precision. Overall, incorporating group aligning demonstrates the best results in terms of MMA compared to average pooling, max pooling or bilinear pooling [26]. Note that pooling or aligning the group-equivariant features to obtain invariant descriptors shows consistent improvements over not pooling nor aligning the group-equivariant features. + +# 4.3. Comparison to existing local descriptors + +Table 3 shows the matching accuracy compared to existing local descriptors on the Roto-360 dataset. We evaluate the descriptors using their own keypoint detectors [8, 9, 27, 39, 40, 44, 50], or combined with off-the-shelf detectors [24, 26, 42]. While the classical methods [27, 46] achieve better matching accuracy than the existing learning-based methods, our method achieves the best results overall. This is because the learning-based methods learn only a limited degree of invariance in a data-driven manner without guaranteeing full invariance to rotation by design. + +Table 4 shows the performance of our method in comparison to existing local descriptors when using the same keypoint detector, where our method shows consistent performance improvement. In particular, our rotation-invariant descriptor shows consistently higher matching accuracy than GIFT [26], which is a representative learning-based group-invariant descriptor. While our model shows a lower MMA than the LF-Net [40] descriptor when using the LF-Net detector at 5px and 3px thresholds, we conjecture that this is due to the better integrity of the detector and descriptor of LF-Net due to their joint training scheme. + +![](images/77e9b256cbac4ed532bf876b59f6f95af26029f1cbe0c77445fa0864daee7f1a.jpg) +Figure 4. An example of multiple descriptor extraction. The distribution is an orientation histogram $\mathbf{o} \in \mathbb{R}^{16}$ , and the scores are confidence values for each bin from group-equivariant features. Arrows indicate the orientation candidates for multiple descriptor extraction. The example shows selecting three orientations to obtain three candidate descriptors for a feature point, which is possible as we predict a score for each orientation. + +These results show that our descriptors obtained using the proposed group aligning show the highest matching accuracy under rotation changes compared to existing methods. The improvement of our method is also attributed to the usage of rotation-equivariant networks, which have a higher sampling efficiency, i.e., do not require intensive rotation augmentations to learn rotation invariance. + +Multiple descriptor extraction using orientation candidates. Group aligning can extract multiple descriptors with different alignments by using multiple orientation candidates, denoted by 'ours*', whose scores are at least $60\%$ of the maximum score in the orientation histogram. When there is a single keypoint position with $k$ descriptors that are differently aligned, we treat it as if there are $k$ detected keypoints. Multiple descriptor extraction compensates for incorrect orientation predictions and further enhances matching accuracy. Figure 4 illustrates an example of multiple descriptor extraction with a score ratio threshold of 0.6. + +Consistency of matching accuracy with respect to rotation changes. Figure 5 illustrates how the matching accuracy changes with respect to varying degrees of rotation. Our method shows the highest consistency, proving the enhanced invariance of descriptors obtained using group aligning against different rotations. While MMA of SIFT [27] and ORB [46] are high at the upright rotations, they tend to fluctuate significantly with varying rotations. The existing learning-based group-invariant descriptor, GIFT [26], fails to find correspondences beyond $60^{\circ}$ . + +# 4.4. Transferability to real image benchmarks + +Table 5 shows the matching performance of local descriptors on HPatches illumination/viewpoint [2] and pose estimation [53]. Our model shows the highest performance overall on the HPatches dataset. The performance gain of ours becomes smaller compared to the Roto-360 dataset due to the absence of extreme rotations in HPatches. While GIFT shows a higher performance under illumination changes that only contain identity mappings, ours†, which uses a larger backbone network (ReWRN), improves + +![](images/c5dc650cbf561ee4a2d0989159ae043610775796b40aed15ed3a436ac89c6f1c.jpg) +Figure 5. Matching accuracies according to varying degree of rotations on Roto-360. + +
MethodHP-allHP-illuHP-viewPose
@5px@3px@5px@3px@5px@3px20°10°5°
SIFT [27]51.3646.3249.0844.6253.5747.960.020.000.00
ORB [46]52.2247.4050.8546.2953.5548.470.060.000.00
SuperPoint [8]69.7161.7574.6367.5364.9656.170.200.070.01
LF-Net [40]56.4552.2262.2157.6350.8847.000.060.030.01
RF-Net [50]59.0854.4261.6357.4656.6251.490.100.040.01
D2-Net [9]50.1832.5463.8044.0937.0221.380.110.050.01
GIFT [26]76.0367.3179.7171.8972.4862.880.600.280.09
LISRD [42]62.1656.1270.0963.6454.5048.850.050.020.00
oursavgpool64.1057.9462.2856.2765.8559.550.270.100.05
oursmaxpool61.5755.8159.6653.9163.4257.640.270.110.03
oursthinelunpool [26]45.5941.9045.1341.5746.0342.220.350.170.09
oursthinelunpool† [26]58.7253.7757.3252.6760.0654.830.240.110.03
oursgroupalign70.6963.4270.3962.8870.9763.950.580.260.12
oursgroupalign*73.9266.3773.1365.3374.6967.380.560.300.12
oursgroupalign†78.0069.7077.9469.3578.0670.030.560.330.14
+ +Table 5. Evaluation with predicted keypoint pairs on real image benchmarks. The first group of methods includes existing local feature extraction methods. The second group of methods includes comparisons to other group pooling methods by replacing our group aligning with them. 'ours*' denotes the extraction of multiple descriptors using the orientation candidates, whose scores are at least $60\%$ of the maximum score in the orientation histogram. 'ours†' denotes our method using the rotation-equivariant WideResNet16-8 (ReWRN) backbone for feature extraction. We use SuperPoint [8] keypoint detector to evaluate ours. + +matching accuracy by $7.15\%$ p at 3px and $5.58\%$ p at 5px, and ours* improves by $4.5\%$ p at 3px, $2.21\%$ p at 5px under viewpoint changes compared to GIFT [26]. It should be noted that the core difference between oursbilinearpool and GIFT is the usage of explicit rotation-equivariant CNNs [60], which clearly shows that bilinear pooling is not well-compatible with the equivariant CNNs in comparison to group aligning. Using the same network with bilinear pooling (oursbilinearpool†) proposed in [26] shows significantly lower results compared to oursgroupalign†. + +In the MVS dataset [53] to evaluate relative camera pose estimation, our model shows a higher performance than GIFT at finer error thresholds of $10^{\circ}$ and $5^{\circ}$ . This shows that our model can find more precise correspondences under 3D viewpoint changes. Overall, these results show that our descriptors using rotation-equivariant representation ex + +
HP-allRoto-360params. (millions)
@5px@3px@5px@3px
ours (proposed |G| = 16)70.6963.4291.3590.180.62M
w/o orientation loss66.4158.6185.2983.260.62M
w/o descriptor loss27.4924.8325.6424.980.62M
w/o image scale pyramid68.7762.2591.4790.430.62M
w/o equivariant backbone47.2542.528.658.5111.18M
|G| = 6463.9657.3585.1283.320.16M
|G| = 3668.1760.9587.7885.890.26M
|G| = 3269.4462.0889.1087.310.31M
|G| = 2469.7262.2190.2788.340.39M
|G| = 865.7458.9287.1685.571.24M
+ +Table 6. Ablation test on HPatches and Roto-360. 'params.' denotes the number of model parameters. + +hibit strong transferability to the real-world examples. + +# 4.5. Ablation study and design choice + +Table 6 shows the results of ablation studies on the HPatches and Roto-360 datasets. The matching accuracy drops when either the orientation alignment loss or the contrastive descriptor loss is not used. Specifically, even when using the ground truth rotation difference for group alignment, not using the descriptor loss results in lower performance, highlighting the importance of robustness to other sorts of variations, e.g., illumination or viewpoint. Not using the image pyramid at inference time results in a slight drop in HPatches, but the performance on Roto-360 remains nearly unchanged. When training without equivariant layers, ResNet-18 with conventional convolutional layers was used - this results in a drastic drop in performance especially on Roto-360, with a rapid increase in the number of model parameters. This demonstrates the significance of high sample efficiency of group-equivariant layers. + +We also demonstrate the effect of the order of cyclic group $G$ on the performance of our method in the second group of Table 6. We fix the computational cost $C \times |G| = 1,024$ , and vary the order of group to show the parameter efficiency of the group equivariant networks. Our design choice $|G| = 16$ yields the best results, and the performance drops gracefully as $G$ increases. This is because with a higher order of groups, the precision of dominant orientation estimation is likely to decrease, leading to lower results. Reducing the order of group to $|G| = 8$ reduces the MMA in both benchmarks as well, which we suspect is because the range of rotation covered by one group action becomes too wide, leading to increased approximation errors. + +# 4.6. Qualitative results + +Figure 6 visualizes the consistency of dominant orientation estimation. From the source (left) and target (middle) images, we estimate the dominant orientation for the same set of predicted keypoints. We use the ground truth rotation to align the estimated orientation and the target image for better visibility (right). The green and red arrows (middle, + +![](images/545b64514cf1d004fca964542115e7d2201376f603c064821ae1fc77ce9d822d.jpg) +Figure 6. Visualization of consistency of dominant orientation estimation. Best viewed in electronics and colour. + +right) represent the consistent and inconsistent orientation predictions with respect to the initial estimations (left) at a $30^{\circ}$ threshold. The numbers on the left represent the number of consistent estimations/number of detected keypoints. Compared to LF-Net [40] and RF-Net [50], our method predicts more consistent dominant orientations of keypoints. + +# 5. Conclusion + +We have proposed a self-supervised rotation-equivariant network for visual correspondence to improve the discriminability of local descriptors. Our invariant mapping called group-aligning shifts the rotation-equivariant features along the group dimension based on the orientation value to produce rotation-invariant descriptors while preserving the feature discriminability, without collapsing the group dimension. Our method achieves state-of-the-art performance in obtaining rotation-invariant descriptors, which are transferable to tasks such as keypoint matching and camera pose estimation. We believe that our approach can be further extended to other geometric transformation groups, and will motivate group-equivariant learning for practical applications of computer vision. + +Acknowledgement. This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-TF2103-02 and also by the NRF grant (NRF-2021R1A2C3012728) funded by the Korea government (MSIT). + +# References + +[1] Sameer Agarwal, Yasutaka Furukawa, Noah Snavely, Ian Simon, Brian Curless, Steven M Seitz, and Richard Szeliski. Building rome in a day. Communications of the ACM, 54(10):105-112, 2011. 1 +[2] Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk. Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5173-5182, 2017. 2, 5, 6, 7 +[3] Axel Barroso-Laguna, Yurun Tian, and Krystian Mikolajczyk. Scalenet: A shallow architecture for scale estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12808-12818, 2022. 2 +[4] Fabio Bellavia and Carlo Colombo. Rethinking the sgloh descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4):931-944, 2017. 2 +[5] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 5 +[6] Taco Cohen and Max Welling. Group equivariant convolutional networks. In International conference on machine learning, pages 2990-2999. PMLR, 2016. 2 +[7] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Toward geometric deep slam. arXiv preprint arXiv:1707.07410, 2017. 1 +[8] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. In CVPR Deep Learning for Visual SLAM Workshop, 2018. 1, 2, 3, 5, 6, 7 +[9] Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, and Torsten Sattler. D2-net: A trainable cnn for joint description and detection of local features. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pages 8092-8101, 2019. 2, 6, 7 +[10] Patrick Ebel, Anastasiia Mishchuk, Kwang Moo Yi, Pascal Fua, and Eduard Trulls. Beyond cartesian representations for local descriptors. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 253-262, 2019. 2 +[11] Bin Fan, Fuchao Wu, and Zhanyi Hu. Rotationally invariant descriptors using intensity order pooling. IEEE transactions on pattern analysis and machine intelligence, 34(10):2031-2045, 2011. 1, 2 +[12] Jiaming Han, Jian Ding, Nan Xue, and Gui-Song Xia. Redet: A rotation-equivariant detector for aerial object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2786-2795, 2021. 2, 5 +[13] Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik. Hypercolumns for object segmentation and fine-grained localization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 447-456, 2015. 3 + +[14] Chris Harris, Mike Stephens, et al. A combined corner and edge detector. In *Alvey vision conference*, volume 15, pages 10-5244. CiteSeer, 1988. 3, 5 +[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 2 +[16] Jared Heinly, Johannes L. Schonberger, Enrique Dunn, and Jan-Michael Frahm. Reconstructing the world* in six days. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3287-3295, 2015. 1 +[17] Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, and Eduard Trulls. Image matching across wide baselines: From paper to practice. International Journal of Computer Vision, 129(2):517-547, 2021. 1 +[18] Seungwook Kim, Yoonwoo Jeong, Chunghyun Park, Jaesik Park, and Minsu Cho. SeLCA: Self-supervised learning of canonical axis. In NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022. 2 +[19] Seungwook Kim, Juhong Min, and Minsu Cho. Transformer: Match-to-match attention for semantic correspondence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8697-8707, 2022. 3 +[20] Axel Barroso Laguna and Krystian Mikolajczyk. Key. net: Keypoint detection by handcrafted and learned cnn filters revisited. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 3, 6 +[21] Jongmin Lee, Yoonwoo Jeong, and Minsu Cho. Self-supervised learning of image scale and orientation. In 31st British Machine Vision Conference 2021, BMVC 2021, Virtual Event, UK. BMVA Press, 2021. 1, 2, 4 +[22] Jongmin Lee, Yoonwoo Jeong, Seungwook Kim, Juhong Min, and Minsu Cho. Learning to distill convolutional features into compact local descriptors. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 898-908, 2021. 2 +[23] Jongmin Lee, Byungjin Kim, and Minsu Cho. Self-supervised equivariant learning for oriented keypoint detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4847-4857, 2022. 1, 2, 4 +[24] Kunhong Li, Longguang Wang, Li Liu, Qing Ran, Kai Xu, and Yulan Guo. Decoupling makes weakly supervised local feature better. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15838-15848, 2022. 2, 6 +[25] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 5 +[26] Yuan Liu, Zehong Shen, Zhixuan Lin, Sida Peng, Hujun Bao, and Xiaowei Zhou. Gift: Learning transformation-invariant dense visual descriptors via group cnns. Advances in Neural Information Processing Systems, 32:6992-7003, 2019. 1, 2, 5, 6, 7 + +[27] David G Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91-110, 2004. 1, 2, 3, 4, 5, 6, 7 +[28] Zixin Luo, Lei Zhou, Xuyang Bai, Hongkai Chen, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, and Long Quan. Aslfeat: Learning local features of accurate shape and localization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6589-6598, 2020. 2, 6 +[29] Simon Lynen, Bernhard Zeisl, Dror Aiger, Michael Bosse, Joel Hesch, Marc Pollefeys, Roland Siegwart, and Torsten Sattler. Large-scale, real-time visual-inertial localization revisited. The International Journal of Robotics Research, 39(9):1061-1084, 2020. 1 +[30] Diego Marcos, Michele Volpi, Nikos Komodakis, and Devis Tuia. Rotation equivariant vector field networks. In Proceedings of the IEEE International Conference on Computer Vision, pages 5048-5057, 2017. 2 +[31] Roland Memisevic. On multi-view feature learning. In ICML, 2012. 2 +[32] Roland Memisevic and Geoffrey E Hinton. Learning to represent spatial transformations with factored higher-order boltzmann machines. Neural computation, 22(6):1473-1492, 2010. 2 +[33] Krystian Mikolajczyk and Cordelia Schmid. Scale & affine invariant interest point detectors. International journal of computer vision, 60(1):63-86, 2004. 5 +[34] Krystian Molkajczyk and Cordelia Schmid. A performance evaluation of local descriptors. IEEE transactions on pattern analysis and machine intelligence, 27(10):1615-1630, 2005. 6 +[35] Juhong Min, Jongmin Lee, Jean Ponce, and Minsu Cho. Hyperpixel flow: Semantic correspondence with multi-layer neural features. In ICCV, 2019. 3 +[36] Anastasiia Mishchuk, Dmytro Mishkin, Filip Radenovic, and Jiri Matas. Working hard to know your neighbor's margins: Local descriptor learning loss. In Advances in Neural Information Processing Systems, pages 4826-4837, 2017. 2 +[37] Dmytro Mishkin, Filip Radenovic, and Jiri Matas. Repeatability is not enough: Learning affine regions via discriminability. In Proceedings of the European Conference on Computer Vision (ECCV), pages 284-300, 2018. 2 +[38] Daniel Moyer, Esra Abaci Turk, P Ellen Grant, William M Wells, and Polina Golland. Equivariant filters for efficient tracking in 3d imaging. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 193-202. Springer, 2021. 2 +[39] Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics, 31(5):1147-1163, 2015. 1, 6 +[40] Yuki Ono, Eduard Trulls, Pascal Fua, and Kwang Moo Yi. Lf-net: learning local features from images. In Advances in neural information processing systems, pages 6234-6244, 2018. 2, 3, 6, 7, 8 +[41] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 5 + +[42] Rémi Pautrat, Viktor Larsson, Martin R Oswald, and Marc Pollefeys. Online invariance selection for local feature descriptors. In European Conference on Computer Vision, pages 707-724. Springer, 2020. 2, 6, 7 +[43] Nicolas Pielawski, Elisabeth Wetzer, Johan Överstedt, Ji-ahao Lu, Carolina Wahlby, Joakim Lindblad, and Nataša Sladoje. CoMIR: Contrastive multimodal image representation for registration. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 18433–18444. Curran Associates, Inc., 2020. 2 +[44] Jerome Revaud, Cesar De Souza, Martin Humenberger, and Philippe Weinzaepfel. R2d2: Reliable and repeatable detector and descriptor. Advances in neural information processing systems, 32:12405-12415, 2019. 1, 2, 5, 6 +[45] Jérôme Revaud, Vincent Leroy, Philippe Weinzaepfel, and Boris Chidlovskii. Pump: Pyramidal and uniqueness matching priors for unsupervised learning of local descriptors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3926-3936, 2022. 1 +[46] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In 2011 International conference on computer vision, pages 2564-2571. IEEE, 2011. 1, 2, 6, 7 +[47] Torsten Sattler, Bastian Leibe, and Leif Kobbelt. Improving image-based localization by active correspondence search. In European conference on computer vision, pages 752-765. Springer, 2012. 1 +[48] Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenberg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, et al. Benchmarking 6dof outdoor visual localization in changing conditions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8601-8610, 2018. 1 +[49] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4104-4113, 2016. 1 +[50] Xuelun Shen, Cheng Wang, Xin Li, Zenglei Yu, Jonathan Li, Chenglu Wen, Ming Cheng, and Zijian He. Rf-net: An end-to-end image matching network based on receptive field. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8132-8140, 2019. 2, 6, 7, 8 +[51] Kihyuk Sohn and Honglak Lee. Learning invariant representations with local transformations. In ICML, 2012. 2 +[52] Ivan Sosnovik, Artem Moskalev, and Arnold Smeulders. How to transform kernels for scale-convolutions. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1092-1097, 2021. 2 +[53] Christoph Strecha, Wolfgang Von Hansen, Luc Van Gool, Pascal Fua, and Ulrich Thoennessen. On benchmarking camera calibration and multi-view stereo for high resolution imagery. In 2008 IEEE conference on computer vision and pattern recognition, pages 1-8. IEEE, 2008. 2, 5, 6, 7 +[54] Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, and Krystian Mikolajczyk. Hynet: Learning local descriptor with hybrid similarity measure and triplet loss. Ad + +vances in Neural Information Processing Systems, 33:7401-7412, 2020. 2 +[55] Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, and Krystian Mikolajczyk. Hynet: Learning local descriptor with hybrid similarity measure and triplet loss. In NeurIPS, 2020. 6 +[56] Yurun Tian, Xin Yu, Bin Fan, Fuchao Wu, Huub Heijnen, and Vassileios Balntas. Sosnet: Second order similarity regularization for local descriptor learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11016-11025, 2019. 2 +[57] Michal Jan Tyszkiewicz, Pascal Fua, and Eduard Trulls. Disk: learning local features with policy gradient. Advances in Neural Information Processing Systems, 33, 2020. 2, 6 +[58] Rui Wang, Robin Walters, and Rose Yu. Data augmentation vs. equivariant networks: A theory of generalization on dynamics forecasting. arXiv preprint arXiv:2206.09450, 2022. 1 +[59] Zhenhua Wang, Bin Fan, Gang Wang, and Fuchao Wu. Exploring local and overall ordinal information for robust feature description. IEEE transactions on pattern analysis and machine intelligence, 38(11):2198-2211, 2015. 2 +[60] Maurice Weiler and Gabriele Cesa. General e (2)-equivariant steerable cnns. Advances in Neural Information Processing Systems, 32:14334-14345, 2019. 1, 2, 3, 5, 7 +[61] Maurice Weiler, Fred A Hamprecht, and Martin Storath. Learning steerable filters for rotation equivariant cnns. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 849-858, 2018. 2 +[62] Daniel E Worrall, Stephan J Garbin, Daniyar Turmukhambetov, and Gabriel J Brostow. Harmonic networks: Deep translation and rotation equivariance. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5028-5037, 2017. 2 +[63] Pei Yan, Yihua Tan, Shengzhou Xiong, Yuan Tai, and Yansheng Li. Learning soft estimator of keypoint scale and orientation with probabilistic covariant loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19406-19415, 2022. 1, 4 +[64] Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, and Pascal Fua. Lift: Learned invariant feature transform. In European conference on computer vision, pages 467-483. Springer, 2016. 1 +[65] Yanzhao Zhou, Qixiang Ye, Qiang Qiu, and Jianbin Jiao. Oriented response networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 519-528, 2017. 2 +[66] Siyu Zhu, Runze Zhang, Lei Zhou, Tianwei Shen, Tian Fang, Ping Tan, and Long Quan. Very large-scale global sfm by distributed motion averaging. 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In this work, we introduce a self-supervised learning framework to extract discriminative rotation-invariant descriptors using group-equivariant CNNs. Thanks to employing group-equivariant CNNs, our method effectively learns to obtain rotation-equivariant features and their orientations explicitly, without having to perform sophisticated data augmentations. The resultant features and their orientations are further processed by group aligning, a novel invariant mapping technique that shifts the group-equivariant features by their orientations along the group dimension. Our group aligning technique achieves rotation-invariance without any collapse of the group dimension and thus eschews loss of discriminability. The proposed method is trained end-to-end in a self-supervised manner, where we use an orientation alignment loss for the orientation estimation and a contrastive descriptor loss for robust local descriptors to geometric/photometric variations. Our method demonstrates state-of-the-art matching accuracy among existing rotation-invariant descriptors under varying rotation and also shows competitive results when transferred to the task of keypoint matching and camera pose estimation." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 60, + 570, + 133, + 582 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 570, + 133, + 582 + ], + "spans": [ + { + "bbox": [ + 60, + 570, + 133, + 582 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 59, + 591, + 280, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 591, + 280, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 591, + 280, + 712 + ], + "type": "text", + "content": "Extracting local descriptors is an essential step for visual correspondence across images, which is used for a wide range of computer vision problems such as visual localization [29, 47, 48], simultaneous localization and mapping [7, 8, 39], and 3D reconstruction [1, 16, 17, 49, 66]. To establish reliable visual correspondences, the properties of invariance and discriminativeness are required for local descriptors; the descriptors need to be invariant to geometric/photometric variations of images while being discriminative enough to distinguish true matches from false ones. Since the remarkable success of deep learning for visual" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 296, + 257, + 518, + 391 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 257, + 518, + 391 + ], + "spans": [ + { + "bbox": [ + 296, + 257, + 518, + 391 + ], + "type": "text", + "content": "recognition, deep neural networks have also been adopted to learn local descriptors, showing enhanced performances on visual correspondence [44, 45, 64]. Learning rotation-invariant local descriptors, however, remains challenging; the classical techniques [11, 27, 46] for rotation-invariant descriptors, which are used for shallow gradient-based feature maps, cannot be applied to feature maps from standard deep neural networks, in which rotation of input induces unpredictable feature variations. Achieving rotation invariance without sacrificing discriminativeness is particularly important for local descriptors as rotation is one of the most frequent imaging variations in reality." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 394, + 518, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 394, + 518, + 713 + ], + "spans": [ + { + "bbox": [ + 296, + 394, + 518, + 713 + ], + "type": "text", + "content": "In this work, we propose a self-supervised approach to obtain rotation-invariant and discriminative local descriptors by leveraging rotation-equivariant CNNs. First, we use group-equivariant CNNs [60] to jointly extract rotation-equivariant local features and their orientations from an image. To extract reliable orientations, we use an orientation alignment loss [21, 23, 63], which trains the network to predict the dominant orientation robustly against other imaging variations, including illumination or viewpoint changes. Using group-equivariant CNNs enables the local features to be empowered with explicitly encoded rotation equivariance without having to perform rigorous data augmentations [58, 60]. Second, to obtain discriminative rotation-invariant descriptors from rotation-equivariant features, we propose group-aligning that shifts the group-equivariant features by their dominant orientation along their group dimension. Conventional methods to yield invariant features from group-equivariant features collapse the group dimension by group-pooling, e.g., max-pooling or bilinear-pooling [26], resulting in a drop in feature discriminability and quality. In contrast, our group-aligning preserves the group dimension, achieving rotation-invariance while eschewing loss of discriminability. Furthermore, by preserving the group dimension, we can obtain multiple descriptors by performing group-aligning using multiple orientation candidates, which improves the matching performance by compensating for potential errors in dominant orientation prediction. Finally, we evaluate our rotation-invariant descriptors against existing local descriptors, and" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 57, + 7, + 102, + 39 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 7, + 102, + 39 + ], + "spans": [ + { + "bbox": [ + 57, + 7, + 102, + 39 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 136, + 5, + 487, + 41 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 136, + 5, + 487, + 41 + ], + "spans": [ + { + "bbox": [ + 136, + 5, + 487, + 41 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 276, + 738, + 300, + 747 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 276, + 738, + 300, + 747 + ], + "spans": [ + { + "bbox": [ + 276, + 738, + 300, + 747 + ], + "type": "text", + "content": "21887" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 59, + 123, + 280, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 123, + 280, + 156 + ], + "spans": [ + { + "bbox": [ + 59, + 123, + 280, + 156 + ], + "type": "text", + "content": "our group-aligning scheme against group-pooling methods on various image matching benchmarks to demonstrate the efficacy of our method." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 70, + 157, + 224, + 168 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 157, + 224, + 168 + ], + "spans": [ + { + "bbox": [ + 70, + 157, + 224, + 168 + ], + "type": "text", + "content": "The contribution of our paper is fourfold:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 69, + 173, + 280, + 356 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 69, + 173, + 279, + 206 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 173, + 279, + 206 + ], + "spans": [ + { + "bbox": [ + 69, + 173, + 279, + 206 + ], + "type": "text", + "content": "- We propose to extract discriminative rotation-invariant local descriptors to tackle the task of visual correspondence by utilizing rotation-equivariant CNNs." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 69, + 212, + 280, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 212, + 280, + 266 + ], + "spans": [ + { + "bbox": [ + 69, + 212, + 280, + 266 + ], + "type": "text", + "content": "- We propose group-aligning, a method to shift a group-equivariant descriptor in the group dimension by its dominant orientation to obtain a rotation-invariant descriptor without having to collapse the group information to preserve feature discriminability." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 69, + 273, + 280, + 306 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 273, + 280, + 306 + ], + "spans": [ + { + "bbox": [ + 69, + 273, + 280, + 306 + ], + "type": "text", + "content": "- We use self-supervisory losses of orientation alignment loss for orientation estimation, and a contrastive descriptor loss for robust local descriptor extraction." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 69, + 312, + 280, + 356 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 312, + 280, + 356 + ], + "spans": [ + { + "bbox": [ + 69, + 312, + 280, + 356 + ], + "type": "text", + "content": "- We demonstrate state-of-the-art performances under varying rotations on the Roto-360 dataset and show competitive transferability on the HPatches dataset [2] and the MVS dataset [53]." + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 60, + 364, + 137, + 376 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 364, + 137, + 376 + ], + "spans": [ + { + "bbox": [ + 60, + 364, + 137, + 376 + ], + "type": "text", + "content": "2. Related work" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 59, + 383, + 280, + 536 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 383, + 280, + 536 + ], + "spans": [ + { + "bbox": [ + 59, + 383, + 280, + 536 + ], + "type": "text", + "content": "Classical invariant local descriptors. Classical methods to extract invariant local descriptors first aggregate image gradients to obtain a rotation-equivariant representation, i.e., histogram, from which the estimated dominant orientation is subtracted to obtain rotation-invariant features [27, 46]. Several studies [4, 11, 59] suggest extracting local descriptors by invariant mapping of the order-based gradient histogram of a patch. However, these classical methods for shallow gradient-based feature maps cannot be applied to deep feature maps from standard neural networks, in which rotation induces unpredictable feature variations. Therefore, we propose a deep end-to-end pipeline to obtain orientation-normalized local descriptors by utilizing rotation-equivariant CNNs [60] with additional losses." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 59, + 537, + 282, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 537, + 282, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 537, + 282, + 712 + ], + "type": "text", + "content": "Learning-based invariant local descriptors. A branch of learning-based methods learns to obtain invariant local descriptors in an explicit manner. GIFT [26] constructs group-equivariant features by rotating or rescaling the images, and then collapses the group dimension using bilinear pooling to obtain invariant local descriptors. However, their groups are limited to non-cyclic discrete rotations ranging from " + }, + { + "bbox": [ + 59, + 537, + 282, + 712 + ], + "type": "inline_equation", + "content": "-90^{\\circ}" + }, + { + "bbox": [ + 59, + 537, + 282, + 712 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 59, + 537, + 282, + 712 + ], + "type": "inline_equation", + "content": "90^{\\circ}" + }, + { + "bbox": [ + 59, + 537, + 282, + 712 + ], + "type": "text", + "content": ". Furthermore, their reliance on data augmentation implies a lower sampling efficiency compared to group-equivariant networks. LISRD [42] jointly learns meta descriptors with different levels of regional variations and selects the most appropriate level of invariance given the context. Another branch of learning methods aims to learn the invariance implicitly using descriptor similarity losses from the image pair using camera pose or homography supervision. These methods are either patch-based [10, 36, 54, 56]" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 296, + 123, + 518, + 234 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 123, + 518, + 234 + ], + "spans": [ + { + "bbox": [ + 296, + 123, + 518, + 234 + ], + "type": "text", + "content": "or image-based [8, 9, 22, 24, 28, 37, 40, 44, 50, 57]. While these methods may be robust to rotation, they cannot be said to be equivariant or invariant to rotation. We construct group-equivariant local features using the steerable networks [60], which explicitly encodes cyclic rotational equivariance to the features without having to rely on data augmentation. We can then yield rotation-invariant features by group-aligning that shifts the group-equivariant features along the group dimension by their dominant orientations, preserving feature discriminability." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 234, + 518, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 234, + 518, + 410 + ], + "spans": [ + { + "bbox": [ + 296, + 234, + 518, + 410 + ], + "type": "text", + "content": "Equivariant representation learning. There has been a constant pursuit to learn equivariant representations by explicitly incorporating group equivariance into the model architecture design [30-32, 51, 60, 65]. For example, G-CNNs [6] use group equivariant convolutions that reduce sample complexity by exploiting symmetries on discrete isometric groups; SFCNNs [61] and H-Nets [62] extract features from more diverse groups and continuous domains by using harmonics as filters. There are also studies that focus on scale-equivariant representation learning [3, 21, 52]. [12, 18, 23, 38, 43] leverage equivariant neural networks to tackle vision tasks e.g., keypoint detection. In this work, we also propose to use equivariant neural networks to facilitate the learning of discriminative rotation-invariant descriptors. We guide the readers to section 1 of the supplementary material for a brief introduction to group equivariance." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 421, + 517, + 448 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 421, + 517, + 448 + ], + "spans": [ + { + "bbox": [ + 296, + 421, + 517, + 448 + ], + "type": "text", + "content": "3. Rotation-equivariant features, Rotation-invariant descriptors" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 454, + 517, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 454, + 517, + 652 + ], + "spans": [ + { + "bbox": [ + 296, + 454, + 517, + 652 + ], + "type": "text", + "content": "In this section, we first draw the line between the terms feature and descriptor which will be used throughout this paper. The goal of our work is to learn to extract rotation-equivariant local features from our rotation-equivariant backbone network, and then to align them by their dominant orientation to finally yield rotation-invariant descriptors. In the subsequent subsections, we elaborate on the process of rotation-equivariant feature extraction from steerable CNNs (Sec. 3.1), assignment of equivariant features to keypoints (Sec. 3.2), how group-aligning is performed to yield rotation-invariant yet discriminative descriptors (Sec. 3.3), how we formulate our orientation alignment loss (Sec.3.4) and contrastive descriptor loss (Sec.3.5) to train our network to extract descriptors which are robust to not only rotation but also other imaging transformations, and finally how we obtain scale-invariant descriptors at test time using image pyramids (Sec.3.6). Figure 1 shows the overall architecture of our method." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 297, + 661, + 488, + 673 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 661, + 488, + 673 + ], + "spans": [ + { + "bbox": [ + 297, + 661, + 488, + 673 + ], + "type": "text", + "content": "3.1. Rotation-equivariant feature extraction" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 296, + 679, + 518, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 679, + 518, + 713 + ], + "spans": [ + { + "bbox": [ + 296, + 679, + 518, + 713 + ], + "type": "text", + "content": "As the feature extractor, we use ReResNet18 [12], which has the same structure as ResNet18 [15] but is constructed using rotation-equivariant convolutional layers [60]. The" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "spans": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "type": "text", + "content": "21888" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 121, + 516, + 254 + ], + "blocks": [ + { + "bbox": [ + 63, + 121, + 516, + 254 + ], + "lines": [ + { + "bbox": [ + 63, + 121, + 516, + 254 + ], + "spans": [ + { + "bbox": [ + 63, + 121, + 516, + 254 + ], + "type": "image", + "image_path": "59ded6a0b8cf8aa0c87e941fdeee5b71bcdcd73df6adbf3f8f65cc360cab88d7.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "lines": [ + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "spans": [ + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "type": "text", + "content": "Figure 1. Overview of the proposed pipeline. An input image is forwarded through the equivariant networks to yield equivariant feature maps from multiple intermediate layers, encoding both low-level geometry and high-level semantic information. The feature maps are bilinearly interpolated to have equal spatial dimensions to be concatenated together. We use the first channel of the feature map " + }, + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "type": "inline_equation", + "content": "\\mathbf{F}" + }, + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "type": "text", + "content": " as the orientation histogram map " + }, + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "type": "inline_equation", + "content": "\\mathbf{O}" + }, + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "type": "text", + "content": " to predict the dominant orientations, which are used to shift the group-equivariant representation along the group dimension to yield discriminative rotation-invariant descriptors. To learn to extract accurate dominant orientation " + }, + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "type": "inline_equation", + "content": "\\hat{\\theta}" + }, + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "type": "text", + "content": ", we use the orientation alignment loss " + }, + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "type": "inline_equation", + "content": "\\mathcal{L}^{\\mathrm{ori}}" + }, + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "type": "text", + "content": ". To obtain descriptors robust to illumination and geometric changes, we use a contrastive descriptor loss " + }, + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "type": "inline_equation", + "content": "\\mathcal{L}^{\\mathrm{desc}}" + }, + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "type": "text", + "content": " using the ground-truth homography " + }, + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "type": "inline_equation", + "content": "\\mathcal{H}_{\\mathrm{GT}}" + }, + { + "bbox": [ + 59, + 255, + 518, + 326 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 59, + 345, + 282, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 345, + 282, + 410 + ], + "spans": [ + { + "bbox": [ + 59, + 345, + 282, + 410 + ], + "type": "text", + "content": "layer acts on a cyclic group " + }, + { + "bbox": [ + 59, + 345, + 282, + 410 + ], + "type": "inline_equation", + "content": "G_N" + }, + { + "bbox": [ + 59, + 345, + 282, + 410 + ], + "type": "text", + "content": " and is equivariant for all translations and " + }, + { + "bbox": [ + 59, + 345, + 282, + 410 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 59, + 345, + 282, + 410 + ], + "type": "text", + "content": " discrete rotations. At the first layer, the scalar field of the input image is lifted to the vector field of the group representation [60]. We leverage feature pyramids from the intermediate layers of the ReResNet18 backbone to construct output features as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 102, + 416, + 280, + 440 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 102, + 416, + 280, + 440 + ], + "spans": [ + { + "bbox": [ + 102, + 416, + 280, + 440 + ], + "type": "interline_equation", + "content": "\\mathbf {F} = \\bigoplus_ {i \\in l} \\eta (\\mathbf {f} _ {i}), \\quad \\mathbf {f} _ {i} = [ \\Pi_ {j = 1} ^ {i} L _ {j} ] (I), \\tag {1}", + "image_path": "adb647f739c1973a480aa12e08c25f4a2246c2cb53868ff32631c78e40050265.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "spans": [ + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_i\\in \\mathbb{R}^{C_i\\times |G|\\times H_i\\times W_i}" + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "text", + "content": " is an intermediate feature from " + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "inline_equation", + "content": "L_{i},L_{i}" + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "text", + "content": " is the " + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "text", + "content": " -th layer of the equivariant network, " + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "inline_equation", + "content": "\\eta" + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "text", + "content": " denotes bilinear interpolation to " + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "inline_equation", + "content": "H\\times W" + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "text", + "content": " , and " + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "inline_equation", + "content": "\\bigoplus" + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "text", + "content": " denotes concatenation along the " + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "text", + "content": " dimension. We utilize the multi-layer feature maps to exploit the low-level geometry information and high-level semantics in the local descriptors [13,19,35]. The output features " + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "inline_equation", + "content": "\\mathbf{F}\\in \\mathbb{R}^{C\\times |G|\\times H\\times W}" + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "text", + "content": " contains rotationequivariant features with multiple layers containing different semantics and receptive fields. We set " + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "inline_equation", + "content": "H = H_{1}" + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "inline_equation", + "content": "W = W_{1}" + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "text", + "content": " , which are " + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "inline_equation", + "content": "\\frac{1}{2}" + }, + { + "bbox": [ + 59, + 445, + 282, + 559 + ], + "type": "text", + "content": " of the input image size." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 60, + 563, + 238, + 575 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 563, + 238, + 575 + ], + "spans": [ + { + "bbox": [ + 60, + 563, + 238, + 575 + ], + "type": "text", + "content": "3.2. Assigning local features to keypoints" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 59, + 579, + 280, + 668 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 579, + 280, + 668 + ], + "spans": [ + { + "bbox": [ + 59, + 579, + 280, + 668 + ], + "type": "text", + "content": "During training, we extract " + }, + { + "bbox": [ + 59, + 579, + 280, + 668 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 59, + 579, + 280, + 668 + ], + "type": "text", + "content": " keypoints from the source image using Harris corner detection [14]. We then use the ground-truth homography " + }, + { + "bbox": [ + 59, + 579, + 280, + 668 + ], + "type": "inline_equation", + "content": "\\mathcal{H}_{\\mathrm{GT}}" + }, + { + "bbox": [ + 59, + 579, + 280, + 668 + ], + "type": "text", + "content": " to obtain ground-truth keypoint correspondences. Also, we allocate a local feature " + }, + { + "bbox": [ + 59, + 579, + 280, + 668 + ], + "type": "inline_equation", + "content": "\\mathbf{p} \\in \\mathbb{R}^{C \\times |G| \\times K}" + }, + { + "bbox": [ + 59, + 579, + 280, + 668 + ], + "type": "text", + "content": " to each keypoint, using the interpolated location of the equivariant feature map " + }, + { + "bbox": [ + 59, + 579, + 280, + 668 + ], + "type": "inline_equation", + "content": "\\mathbf{F}" + }, + { + "bbox": [ + 59, + 579, + 280, + 668 + ], + "type": "text", + "content": ". We experiment our descriptor with SIFT [27], LF-Net [40], SuperPoint [8], and KeyNet [20] as the keypoint detector during inference time." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 60, + 673, + 244, + 686 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 673, + 244, + 686 + ], + "spans": [ + { + "bbox": [ + 60, + 673, + 244, + 686 + ], + "type": "text", + "content": "3.3. Group aligning for invariant mapping" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 59, + 690, + 281, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 690, + 281, + 713 + ], + "spans": [ + { + "bbox": [ + 59, + 690, + 281, + 713 + ], + "type": "text", + "content": "To transform the rotation-equivariant feature to a rotation-invariant descriptor, we propose group aligning, an" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 296, + 345, + 518, + 421 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 345, + 518, + 421 + ], + "spans": [ + { + "bbox": [ + 296, + 345, + 518, + 421 + ], + "type": "text", + "content": "operation to shift the group-equivariant feature in the " + }, + { + "bbox": [ + 296, + 345, + 518, + 421 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 296, + 345, + 518, + 421 + ], + "type": "text", + "content": "-dimension using the dominant orientation " + }, + { + "bbox": [ + 296, + 345, + 518, + 421 + ], + "type": "inline_equation", + "content": "\\hat{\\theta}" + }, + { + "bbox": [ + 296, + 345, + 518, + 421 + ], + "type": "text", + "content": ". Unlike existing methods that use group pooling, e.g., average pooling or max pooling, which collapses the group dimension, group aligning preserves the rich group information. Figure 2 illustrates the difference between group pooling and group aligning on an equivariant representation." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "spans": [ + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": "Estimating the dominant orientation and the shifting value. We obtain the orientation histogram map " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "\\mathbf{O} \\in \\mathbb{R}^{|G| \\times H \\times W} = \\mathbf{F}_0" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": " by selecting the first channel of the rotation-equivariant tensor " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "\\mathbf{F}" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": " as an orientation histogram map. Note that the first channels of each group action are simultaneously used as the channels of the descriptors and to construct the orientation histogram. The histogram-based representation of " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "\\mathbf{O}" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": " provides richer information than directly regressing the dominant orientation, as the orientation histogram enables predicting multiple (i.e., top-" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": ") candidates as the dominant orientation. We first select an orientation vector " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "\\mathbf{o} \\in \\mathbb{R}^{|G|}" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": " of a keypoint from the orientation histogram map " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "\\mathbf{O}" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": " using the coordinates of the keypoint. Next, we estimate the dominant orientation value " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "\\hat{\\theta}" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": " from the orientation vector " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "\\mathbf{o}" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": " by selecting the index of the maximum score, " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "\\hat{\\theta} = \\frac{360}{|G|} \\arg \\max_g \\mathbf{o}" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": ". Using the dominant orientation value " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "\\hat{\\theta}" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": ", we obtain the shifting value " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "\\hat{\\Delta} = \\frac{|G|}{360} \\hat{\\theta}" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": "-dim. At training time, we use the ground-truth rotation " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "\\theta_{\\mathrm{GT}}" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": " instead of the predicted dominant orientation value " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "\\hat{\\theta}" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": " to generate the shifting value " + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "inline_equation", + "content": "\\Delta_{\\mathrm{GT}}" + }, + { + "bbox": [ + 296, + 422, + 517, + 645 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "spans": [ + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "content": "Group aligning. Given a keypoint-allocated feature tensor " + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{p} \\in \\mathbb{R}^{C \\times |G|}" + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "content": " from the equivariant representation " + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{F}" + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "content": ", we obtain the rotation-invariant local descriptor " + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{d} \\in \\mathbb{R}^{C|G|}" + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "content": " by group aligning using " + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "content": ". After computing the dominant orientation " + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "inline_equation", + "content": "\\hat{\\theta}" + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "content": " and the shifting value " + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "inline_equation", + "content": "\\hat{\\Delta}" + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{o}" + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "content": ", we obtain the orientation-normalized descriptor " + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{d}' \\in \\mathbb{R}^{C|G|}" + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "content": " by shifting " + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{p}" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "spans": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "type": "text", + "content": "21889" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 67, + 122, + 270, + 283 + ], + "blocks": [ + { + "bbox": [ + 67, + 122, + 270, + 283 + ], + "lines": [ + { + "bbox": [ + 67, + 122, + 270, + 283 + ], + "spans": [ + { + "bbox": [ + 67, + 122, + 270, + 283 + ], + "type": "image", + "image_path": "4cfedb4a049db1906ac7b2e9eb74b61d750496c6ef061521fe5b9617b405d2e1.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 77, + 286, + 145, + 296 + ], + "lines": [ + { + "bbox": [ + 77, + 286, + 145, + 296 + ], + "spans": [ + { + "bbox": [ + 77, + 286, + 145, + 296 + ], + "type": "text", + "content": "(a) Group pooling" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 184, + 286, + 255, + 296 + ], + "lines": [ + { + "bbox": [ + 184, + 286, + 255, + 296 + ], + "spans": [ + { + "bbox": [ + 184, + 286, + 255, + 296 + ], + "type": "text", + "content": "(b) Group aligning" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 59, + 376, + 280, + 439 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 376, + 280, + 439 + ], + "spans": [ + { + "bbox": [ + 59, + 376, + 280, + 439 + ], + "type": "text", + "content": "in the " + }, + { + "bbox": [ + 59, + 376, + 280, + 439 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 59, + 376, + 280, + 439 + ], + "type": "text", + "content": "-dimension by " + }, + { + "bbox": [ + 59, + 376, + 280, + 439 + ], + "type": "inline_equation", + "content": "-\\hat{\\Delta}" + }, + { + "bbox": [ + 59, + 376, + 280, + 439 + ], + "type": "text", + "content": " and flattening the descriptor to a vector. We use cyclic shifting in consideration of the cyclic property of rotation. We finally obtain the L2-normalized descriptor " + }, + { + "bbox": [ + 59, + 376, + 280, + 439 + ], + "type": "inline_equation", + "content": "\\mathbf{d}" + }, + { + "bbox": [ + 59, + 376, + 280, + 439 + ], + "type": "text", + "content": " from the orientation-normalized descriptor " + }, + { + "bbox": [ + 59, + 376, + 280, + 439 + ], + "type": "inline_equation", + "content": "\\mathbf{d}'" + }, + { + "bbox": [ + 59, + 376, + 280, + 439 + ], + "type": "text", + "content": " such that " + }, + { + "bbox": [ + 59, + 376, + 280, + 439 + ], + "type": "inline_equation", + "content": "||\\mathbf{d}||^2 = 1" + }, + { + "bbox": [ + 59, + 376, + 280, + 439 + ], + "type": "text", + "content": ". Formally, this process can be defined as:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 99, + 440, + 239, + 455 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 99, + 440, + 239, + 455 + ], + "spans": [ + { + "bbox": [ + 99, + 440, + 239, + 455 + ], + "type": "interline_equation", + "content": "\\mathbf {p} _ {:, i} ^ {\\prime} = T _ {r} ^ {\\prime} \\left(\\mathbf {p} _ {:, i}, \\hat {\\Delta}\\right) = \\mathbf {p} _ {:, (i + \\hat {\\Delta}) \\bmod | G |},", + "image_path": "aa9f9a25ee777cc428b8a6f9d6f1cf514de6118087f7584fdd3b79e614686086.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 166, + 456, + 279, + 472 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 166, + 456, + 279, + 472 + ], + "spans": [ + { + "bbox": [ + 166, + 456, + 279, + 472 + ], + "type": "interline_equation", + "content": "\\mathbf {d} _ {| G |: | G | (i + 1)} ^ {\\prime} = \\mathbf {p} _ {i} ^ {\\prime}, \\tag {2}", + "image_path": "0dbf8fb2f039cd4b4dda696decc7426a4d0130247a90434a7916e629e7d97773.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 193, + 472, + 239, + 496 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 193, + 472, + 239, + 496 + ], + "spans": [ + { + "bbox": [ + 193, + 472, + 239, + 496 + ], + "type": "interline_equation", + "content": "\\mathbf {d} = \\frac {\\mathbf {d} ^ {\\prime}}{| | \\mathbf {d} ^ {\\prime} | | _ {2}},", + "image_path": "c46b91b627598929ab934f437614185cb79f4b18d64997d047992c6a8de31505.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 59, + 500, + 280, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 500, + 280, + 632 + ], + "spans": [ + { + "bbox": [ + 59, + 500, + 280, + 632 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 59, + 500, + 280, + 632 + ], + "type": "inline_equation", + "content": "T_r'" + }, + { + "bbox": [ + 59, + 500, + 280, + 632 + ], + "type": "text", + "content": " is shifting operator in vector space, and " + }, + { + "bbox": [ + 59, + 500, + 280, + 632 + ], + "type": "inline_equation", + "content": "\\mathbf{p}'" + }, + { + "bbox": [ + 59, + 500, + 280, + 632 + ], + "type": "text", + "content": " is a group-aligned descriptor before flattening. This shifting by " + }, + { + "bbox": [ + 59, + 500, + 280, + 632 + ], + "type": "inline_equation", + "content": "\\hat{\\Delta}" + }, + { + "bbox": [ + 59, + 500, + 280, + 632 + ], + "type": "text", + "content": " aligns all the descriptors in the direction of their dominant orientations, creating orientation-normalized descriptors. This process is conceptually similar to subtracting the dominant orientation value of the orientation histogram in the classical descriptor SIFT [27], but we apply this concept to the equivariant neural features. The proposed group aligning preserves the group information, so our invariant descriptors have more representative power than the existing group-pooled descriptors which collapse the group dimension for invariance." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 60, + 639, + 195, + 652 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 639, + 195, + 652 + ], + "spans": [ + { + "bbox": [ + 60, + 639, + 195, + 652 + ], + "type": "text", + "content": "3.4. Orientation alignment loss" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 59, + 656, + 280, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 656, + 280, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 656, + 280, + 712 + ], + "type": "text", + "content": "To learn to obtain the dominant orientations from the orientation vectors, we use an orientation alignment loss [21, 23, 63] to supervise the orientation histograms in " + }, + { + "bbox": [ + 59, + 656, + 280, + 712 + ], + "type": "inline_equation", + "content": "\\mathbf{O}" + }, + { + "bbox": [ + 59, + 656, + 280, + 712 + ], + "type": "text", + "content": " to be rotation equivariant under the photometric/geometric transformations. Figure 3 shows the illustration of orientation" + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 300, + 122, + 516, + 281 + ], + "blocks": [ + { + "bbox": [ + 59, + 297, + 280, + 357 + ], + "lines": [ + { + "bbox": [ + 59, + 297, + 280, + 357 + ], + "spans": [ + { + "bbox": [ + 59, + 297, + 280, + 357 + ], + "type": "text", + "content": "Figure 2. Difference between group pooling and group aligning. In group pooling, the group dimension is collapsed to yield an invariant descriptor " + }, + { + "bbox": [ + 59, + 297, + 280, + 357 + ], + "type": "inline_equation", + "content": "(\\mathbb{R}^{C\\times |G|}\\to \\mathbb{R}^C)" + }, + { + "bbox": [ + 59, + 297, + 280, + 357 + ], + "type": "text", + "content": ". In group aligning, the entire feature is cyclically shifted in the group dimension to obtain an invariant descriptor " + }, + { + "bbox": [ + 59, + 297, + 280, + 357 + ], + "type": "inline_equation", + "content": "(\\mathbb{R}^{C\\times |G|}\\to \\mathbb{R}^{C|G|})" + }, + { + "bbox": [ + 59, + 297, + 280, + 357 + ], + "type": "text", + "content": " while preserving the group information and discriminability." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 300, + 122, + 516, + 281 + ], + "lines": [ + { + "bbox": [ + 300, + 122, + 516, + 281 + ], + "spans": [ + { + "bbox": [ + 300, + 122, + 516, + 281 + ], + "type": "image", + "image_path": "913e20d0650200e75b53cd63aebac1942d4d6a1b149e593e091cd94587a6b1f4.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 296, + 287, + 518, + 348 + ], + "lines": [ + { + "bbox": [ + 296, + 287, + 518, + 348 + ], + "spans": [ + { + "bbox": [ + 296, + 287, + 518, + 348 + ], + "type": "text", + "content": "Figure 3. Illustration of orientation alignment loss. Given two rotation-equivariant tensors " + }, + { + "bbox": [ + 296, + 287, + 518, + 348 + ], + "type": "inline_equation", + "content": "\\mathbf{p}^{\\mathrm{A}},\\mathbf{p}^{\\mathrm{B}}\\in \\mathbb{R}^{C\\times |G|}" + }, + { + "bbox": [ + 296, + 287, + 518, + 348 + ], + "type": "text", + "content": " obtained from two different rotated versions of the same image, we apply cyclic shift on one of the descriptors in the group dimension using the GT difference in rotation. The orientation alignment loss supervises the output orientation vectors of the two descriptors to be the same." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 367, + 517, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 367, + 517, + 389 + ], + "spans": [ + { + "bbox": [ + 296, + 367, + 517, + 389 + ], + "type": "text", + "content": "alignment loss. The cyclic shift of an orientation histogram map at the training time is formulated as follows:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 342, + 399, + 517, + 412 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 342, + 399, + 517, + 412 + ], + "spans": [ + { + "bbox": [ + 342, + 399, + 517, + 412 + ], + "type": "interline_equation", + "content": "T _ {r} ^ {\\prime} \\left(\\mathbf {O} _ {i}, \\Delta_ {\\mathrm {G T}}\\right) = \\mathbf {O} _ {\\left(i + \\Delta_ {\\mathrm {G T}}\\right) \\bmod | G |}, \\tag {3}", + "image_path": "81e11959cf46c11b39b622e5555c51da90e5d6289d0b4a3795715b9017fef0ce.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 296, + 421, + 517, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 421, + 517, + 456 + ], + "spans": [ + { + "bbox": [ + 296, + 421, + 517, + 456 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 296, + 421, + 517, + 456 + ], + "type": "inline_equation", + "content": "\\Delta_{\\mathrm{GT}} = \\frac{|G|}{360}\\theta_{\\mathrm{GT}}" + }, + { + "bbox": [ + 296, + 421, + 517, + 456 + ], + "type": "text", + "content": " is the shifting value calculated from the ground-truth rotation " + }, + { + "bbox": [ + 296, + 421, + 517, + 456 + ], + "type": "inline_equation", + "content": "\\theta_{\\mathrm{GT}}" + }, + { + "bbox": [ + 296, + 421, + 517, + 456 + ], + "type": "text", + "content": ". We formulate the orientation alignment loss in the form of a cross-entropy as follows:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 309, + 463, + 517, + 504 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 309, + 463, + 517, + 504 + ], + "spans": [ + { + "bbox": [ + 309, + 463, + 517, + 504 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} ^ {\\mathrm {o r i}} \\left(\\mathbf {O} ^ {\\mathrm {A}}, \\mathbf {O} ^ {\\mathrm {B}}, \\Delta_ {\\mathrm {G T}}\\right) = \\\\ - \\sum_ {k \\in K} \\sum_ {g \\in G} \\sigma \\left(\\mathbf {O} _ {g, k} ^ {\\mathrm {A}}\\right) \\log \\left(\\sigma \\left(T _ {r} ^ {\\prime} \\left(\\mathbf {O} _ {g, k} ^ {\\mathrm {B}}, \\Delta_ {\\mathrm {G T}}\\right)\\right)\\right), \\tag {4} \\\\ \\end{array}", + "image_path": "00946eae5d3246b26ba16dfdc84baa917249cb48136fa7e8073d0e512d264b0b.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 296, + 512, + 518, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 512, + 518, + 712 + ], + "spans": [ + { + "bbox": [ + 296, + 512, + 518, + 712 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 296, + 512, + 518, + 712 + ], + "type": "inline_equation", + "content": "\\mathbf{O}^{\\mathrm{A}}" + }, + { + "bbox": [ + 296, + 512, + 518, + 712 + ], + "type": "text", + "content": " is the source orientation histogram map and " + }, + { + "bbox": [ + 296, + 512, + 518, + 712 + ], + "type": "inline_equation", + "content": "\\mathbf{O}^{\\mathrm{B}}" + }, + { + "bbox": [ + 296, + 512, + 518, + 712 + ], + "type": "text", + "content": " is the target orientation histogram map obtained from a synthetically warped source image, " + }, + { + "bbox": [ + 296, + 512, + 518, + 712 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 296, + 512, + 518, + 712 + ], + "type": "text", + "content": " is a softmax function applied to the " + }, + { + "bbox": [ + 296, + 512, + 518, + 712 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 296, + 512, + 518, + 712 + ], + "type": "text", + "content": "-dimension of the orientation histogram map to represent the orientation vector as a probability distribution for the cross-entropy loss to be applicable. Using Equation 4, the network learns to predict the characteristic orientations robustly against different imaging variations, such as photometric transformations and geometric transformations beyond rotation, as these transformations cannot be handled by equivariance to discrete rotations alone. Note that it is not straightforward to define the characteristic orientation of a keypoint to provide strong supervision. However, we facilitate the learning of characteristic orientations by formulating it as a self-supervised learning framework, leveraging the known relative orientation between two keypoint orientation histogram maps obtained from differently rotated versions of the same image." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "spans": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "type": "text", + "content": "21890" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 117, + 122, + 224, + 201 + ], + "blocks": [ + { + "bbox": [ + 117, + 122, + 224, + 201 + ], + "lines": [ + { + "bbox": [ + 117, + 122, + 224, + 201 + ], + "spans": [ + { + "bbox": [ + 117, + 122, + 224, + 201 + ], + "type": "table", + "html": "
MMApred.
@1px
Align97.5484.90
Avg33.7233.72
Max57.9257.92
None23.9723.97
Bilinear43.6026.42
", + "image_path": "d01f4b550fc7aecc1c1cbf6f99f9cf3ffa96df0f877698726ff7b52cdb034cfc.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 60, + 271, + 196, + 284 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 271, + 196, + 284 + ], + "spans": [ + { + "bbox": [ + 60, + 271, + 196, + 284 + ], + "type": "text", + "content": "3.5. Contrastive descriptor loss" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 59, + 289, + 280, + 344 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 289, + 280, + 344 + ], + "spans": [ + { + "bbox": [ + 59, + 289, + 280, + 344 + ], + "type": "text", + "content": "We propose to use a descriptor similarity loss motivated by contrastive learning [5] to further empower the descriptors to be robust against variations apart from rotation, e.g., illumination or viewpoint. The descriptor loss is formulated in a contrastive manner as follows:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 60, + 350, + 282, + 405 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 350, + 282, + 405 + ], + "spans": [ + { + "bbox": [ + 60, + 350, + 282, + 405 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} ^ {\\text {d e s c}} \\left(\\mathbf {D} ^ {\\mathrm {A}}, \\mathbf {D} ^ {\\mathrm {B}}\\right) = \\\\ \\sum_ {\\left(\\mathbf {d} _ {i} ^ {\\mathrm {A}}, \\mathbf {d} _ {i} ^ {\\mathrm {B}}\\right) \\in \\left(\\mathbf {D} ^ {\\mathrm {A}}, \\mathbf {D} ^ {\\mathrm {B}}\\right)} - \\log \\frac {\\exp \\left(\\operatorname {s i m} \\left(\\mathbf {d} _ {i} ^ {\\mathrm {A}} , \\mathbf {d} _ {i} ^ {\\mathrm {B}}\\right) / \\tau\\right)}{\\sum_ {k \\in K \\backslash i} \\exp \\left(\\operatorname {s i m} \\left(\\mathbf {d} _ {i} ^ {\\mathrm {A}} , \\mathbf {d} _ {k} ^ {\\mathrm {B}}\\right)\\right) / \\tau}, \\tag {5} \\\\ \\end{array}", + "image_path": "4ef8c2fc5b4264b73fc070816064d5e1d11a68e3b4a89fa87a2d3e65f2fb21fb.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 59, + 406, + 280, + 494 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 406, + 280, + 494 + ], + "spans": [ + { + "bbox": [ + 59, + 406, + 280, + 494 + ], + "type": "text", + "content": "where sim is cosine similarity and " + }, + { + "bbox": [ + 59, + 406, + 280, + 494 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 59, + 406, + 280, + 494 + ], + "type": "text", + "content": " is the softmax temperature. Unlike the triplet loss with one hard negative sample, the contrastive loss can optimize the distance for all negative pairs. This contrastive loss with InfoNCE [41] maximizes the mutual information between the encoded features and effectively reduces the low-level noise. Our overall self-supervised loss is formulated as " + }, + { + "bbox": [ + 59, + 406, + 280, + 494 + ], + "type": "inline_equation", + "content": "\\mathcal{L} = \\alpha \\mathcal{L}^{\\mathrm{ori}} + \\mathcal{L}^{\\mathrm{desc}}" + }, + { + "bbox": [ + 59, + 406, + 280, + 494 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 59, + 406, + 280, + 494 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 59, + 406, + 280, + 494 + ], + "type": "text", + "content": " is a balancing term." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 60, + 501, + 151, + 511 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 501, + 151, + 511 + ], + "spans": [ + { + "bbox": [ + 60, + 501, + 151, + 511 + ], + "type": "text", + "content": "3.6. Scale robustness" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 59, + 518, + 280, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 518, + 280, + 628 + ], + "spans": [ + { + "bbox": [ + 59, + 518, + 280, + 628 + ], + "type": "text", + "content": "While we employ a rotation-equivariant network, it does not ensure that the descriptors are robust to scale changes. Thus, at inference time, we construct an image pyramid using a scale factor of " + }, + { + "bbox": [ + 59, + 518, + 280, + 628 + ], + "type": "inline_equation", + "content": "2^{1/4}" + }, + { + "bbox": [ + 59, + 518, + 280, + 628 + ], + "type": "text", + "content": " from a maximum of 1,024 pixels to a minimum of 256 pixels as in R2D2 [44]. After constructing the scale-wise descriptors " + }, + { + "bbox": [ + 59, + 518, + 280, + 628 + ], + "type": "inline_equation", + "content": "\\in \\mathbb{R}^{S \\times C|G| \\times K}" + }, + { + "bbox": [ + 59, + 518, + 280, + 628 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 59, + 518, + 280, + 628 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 59, + 518, + 280, + 628 + ], + "type": "text", + "content": " varying scales, we finally generate the scale-invariant local descriptors " + }, + { + "bbox": [ + 59, + 518, + 280, + 628 + ], + "type": "inline_equation", + "content": "\\in \\mathbb{R}^{C|G| \\times K}" + }, + { + "bbox": [ + 59, + 518, + 280, + 628 + ], + "type": "text", + "content": " by max-pooling in the scale dimension inspired by scale-space maxima as in SIFT [27,33], for improved robustness to scale changes." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 60, + 639, + 130, + 650 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 639, + 130, + 650 + ], + "spans": [ + { + "bbox": [ + 60, + 639, + 130, + 650 + ], + "type": "text", + "content": "4. Experiment" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 59, + 657, + 280, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 657, + 280, + 713 + ], + "spans": [ + { + "bbox": [ + 59, + 657, + 280, + 713 + ], + "type": "text", + "content": "Implementation details. We use rotation-equivariant ResNet-18 (ReResNet-18) [12] implemented using the rotation-equivariant layers of " + }, + { + "bbox": [ + 59, + 657, + 280, + 713 + ], + "type": "inline_equation", + "content": "E(2)" + }, + { + "bbox": [ + 59, + 657, + 280, + 713 + ], + "type": "text", + "content": "-CNN [60] as our backbone. We remove the first maxpool layer to preserve the spatial size, so that the spatial resolution of the rotation-" + } + ] + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 318, + 122, + 496, + 201 + ], + "blocks": [ + { + "bbox": [ + 59, + 202, + 280, + 254 + ], + "lines": [ + { + "bbox": [ + 59, + 202, + 280, + 254 + ], + "spans": [ + { + "bbox": [ + 59, + 202, + 280, + 254 + ], + "type": "text", + "content": "Table 1. Evaluation with GT keypoint pairs on Roto-360 without training. 'Align' uses GT rotation difference to apply group-aligning to demonstrate the upper-bound. 'None' does not use pooling nor aligning, demonstrating the lower-bound. We use an average of 111 keypoint pairs extracted using SuperPoint [8]." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 318, + 122, + 496, + 201 + ], + "lines": [ + { + "bbox": [ + 318, + 122, + 496, + 201 + ], + "spans": [ + { + "bbox": [ + 318, + 122, + 496, + 201 + ], + "type": "table", + "html": "
MMApred.
@10px@5px@3px
Align93.0891.3590.18688.3
Avg85.8482.1281.05705.9
Max82.6178.0077.79686.0
None19.6818.8118.57349.1
Bilinear42.6941.0340.51332.5
", + "image_path": "0c0a8fd0faadb110a9b27575c37eeae2e81afd55a1cd3365432d34506bc559b7.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 202, + 518, + 254 + ], + "lines": [ + { + "bbox": [ + 296, + 202, + 518, + 254 + ], + "spans": [ + { + "bbox": [ + 296, + 202, + 518, + 254 + ], + "type": "text", + "content": "Table 2. Evaluation with predicted keypoint pairs on Roto-360 with training. 'Max' and 'Avg' collapses the group dimension of the features through max pooling or average pooling. 'pred.' denotes the average number of predicted matches. We use an average of 1161 keypoint pairs extracted using SuperPoint [8]." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "spans": [ + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "content": "equivariant feature " + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "inline_equation", + "content": "\\mathbf{F}" + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "content": " is " + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "inline_equation", + "content": "H = \\frac{H'}{2}" + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "inline_equation", + "content": "W = \\frac{W'}{2}" + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "inline_equation", + "content": "H'" + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "inline_equation", + "content": "W'" + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "content": " are the height and width of an input image. We use 16 for the order of cyclic group " + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "content": ". We use a batch size of 8, a learning rate of " + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "inline_equation", + "content": "10^{-4}" + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "content": ", and a weight decay of 0.1. We train our model for 12 epochs with 1,000 iterations using a machine with an Intel i7-8700 CPU and an NVIDIA GeForce RTX 3090 GPU. We use the temperature " + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "inline_equation", + "content": "\\mathcal{L}^{\\mathrm{desc}}" + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "content": " as 0.07. The loss balancing factor " + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "content": " is 10. The final output descriptor size is 1,024, with " + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "inline_equation", + "content": "C = 64" + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "inline_equation", + "content": "|G| = 16" + }, + { + "bbox": [ + 296, + 270, + 518, + 405 + ], + "type": "text", + "content": ". We use SuperPoint [8] as the keypoint detector to evaluate our method except Table 4. For all descriptors, we use the mutual nearest neighbour matcher to predict the correspondences." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 297, + 410, + 408, + 421 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 410, + 408, + 421 + ], + "spans": [ + { + "bbox": [ + 297, + 410, + 408, + 421 + ], + "type": "text", + "content": "4.1. Datasets and metrics" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 296, + 426, + 517, + 470 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 426, + 517, + 470 + ], + "spans": [ + { + "bbox": [ + 296, + 426, + 517, + 470 + ], + "type": "text", + "content": "We use a synthetic training dataset to train our model in a self-supervised manner. We evaluate our model on the Roto-360 dataset and show the transferability on real image benchmarks, i.e., HPatches [2] and MVS [53] datasets." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "spans": [ + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "text", + "content": "Training dataset. We generate a synthetic dataset for self-supervised training from the MS-COCO dataset [25]. We warp images with random homographies for geometric robustness and transform the colors by jitter, noise, and blur for photometric robustness. As we need the ground-truth rotation " + }, + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "inline_equation", + "content": "\\theta_{\\mathrm{GT}}" + }, + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "text", + "content": " for our orientation alignment loss, we decompose the synthetic homography " + }, + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "inline_equation", + "content": "\\mathcal{H}" + }, + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "text", + "content": " as follows: " + }, + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "inline_equation", + "content": "\\theta_{\\mathrm{GT}} = \\arctan \\left(\\frac{\\mathcal{H}_{21}}{\\mathcal{H}_{11}}\\right)" + }, + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "text", + "content": ", where we assume that a " + }, + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "inline_equation", + "content": "3\\times 3" + }, + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "text", + "content": " homography matrix " + }, + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "inline_equation", + "content": "\\mathcal{H}" + }, + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "text", + "content": " with no significant tilt can be approximated to an affine matrix. We sample " + }, + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "inline_equation", + "content": "K = 512" + }, + { + "bbox": [ + 296, + 470, + 517, + 623 + ], + "type": "text", + "content": " keypoints for an image using Harris corner detector [14], obtaining 512 corresponding keypoint pairs for each image pair using homography and rotation. Note that this dataset generation protocol is the same as that of GIFT [26] for a fair comparison." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 296, + 623, + 518, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 623, + 518, + 713 + ], + "spans": [ + { + "bbox": [ + 296, + 623, + 518, + 713 + ], + "type": "text", + "content": "Roto-360 is an evaluation dataset that consists of 360 image pairs with in-plane rotation ranging from " + }, + { + "bbox": [ + 296, + 623, + 518, + 713 + ], + "type": "inline_equation", + "content": "0^{\\circ}" + }, + { + "bbox": [ + 296, + 623, + 518, + 713 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 296, + 623, + 518, + 713 + ], + "type": "inline_equation", + "content": "350^{\\circ}" + }, + { + "bbox": [ + 296, + 623, + 518, + 713 + ], + "type": "text", + "content": " at " + }, + { + "bbox": [ + 296, + 623, + 518, + 713 + ], + "type": "inline_equation", + "content": "10^{\\circ}" + }, + { + "bbox": [ + 296, + 623, + 518, + 713 + ], + "type": "text", + "content": " intervals, created using ten randomly sampled images from HPatches [2]. Roto-360 is more suitable to evaluate the rotation invariance of our descriptors, as the extreme rotation (ER) dataset [26] only covers " + }, + { + "bbox": [ + 296, + 623, + 518, + 713 + ], + "type": "inline_equation", + "content": "180^{\\circ}" + }, + { + "bbox": [ + 296, + 623, + 518, + 713 + ], + "type": "text", + "content": ", and includes photometric variations. We use mean matching accuracy (MMA) as the evaluation metric with pixel thresholds of 3/5/10 pixels" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 276, + 738, + 299, + 747 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 276, + 738, + 299, + 747 + ], + "spans": [ + { + "bbox": [ + 276, + 738, + 299, + 747 + ], + "type": "text", + "content": "21891" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 63, + 122, + 274, + 277 + ], + "blocks": [ + { + "bbox": [ + 63, + 122, + 274, + 277 + ], + "lines": [ + { + "bbox": [ + 63, + 122, + 274, + 277 + ], + "spans": [ + { + "bbox": [ + 63, + 122, + 274, + 277 + ], + "type": "table", + "html": "
MethodMMApred.total.
@10px@5px@3px
SIFT [27]78.8678.5978.23774.11500.0
ORB [46]86.7885.2978.73607.61005.2
SuperPoint [8]22.8522.1021.83462.61161.0
LF-Net [40]75.0574.3072.61386.71024.0
RF-Net [50]15.6415.1814.581602.55000.0
D2-Net [9]15.569.305.21386.91474.5
R2D2 [44]15.8014.9713.50197.91500.0
GIFT [26]42.3542.0541.59589.21161.0
LISRD [42]16.9616.0415.64323.61781.1
ASLFeat [28]19.3416.3813.131366.96764.2
DISK [57]13.2212.4312.04359.12048.0
PosFeat [24]13.7611.799.82717.27623.5
ours93.0891.3590.18688.31161.0
ours*94.3592.8291.691333.02340.4
", + "image_path": "f60bbd6a8720487386384f93178500a42246f0a50312043656d85b31e42b5520.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 59, + 277, + 280, + 349 + ], + "lines": [ + { + "bbox": [ + 59, + 277, + 280, + 349 + ], + "spans": [ + { + "bbox": [ + 59, + 277, + 280, + 349 + ], + "type": "text", + "content": "Table 3. Comparison to existing local descriptors on Roto-360. We use mutual nearest matching for all methods to establish matches between images. 'total.' and 'pred.' denotes the average number of detected keypoints and predicted matches, respectively. 'ours*' denotes selecting multiple candidate descriptors based on the ratio of max value in the orientation histogram. We use SuperPoint keypoint detector [8] same to the GIFT descriptor [26]." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 0 + }, + { + "bbox": [ + 59, + 377, + 263, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 377, + 263, + 389 + ], + "spans": [ + { + "bbox": [ + 59, + 377, + 263, + 389 + ], + "type": "text", + "content": "and the number of predicted matches following [9, 34]." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 59, + 389, + 280, + 444 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 389, + 280, + 444 + ], + "spans": [ + { + "bbox": [ + 59, + 389, + 280, + 444 + ], + "type": "text", + "content": "HPatches [2] has 57 scenes with illumination variations and 59 scenes with viewpoint variations. Each scene contains five image pairs with ground-truth planar homography. We use the same evaluation metrics to Roto-360 to show the transferability of our local descriptors." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 59, + 444, + 280, + 488 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 444, + 280, + 488 + ], + "spans": [ + { + "bbox": [ + 59, + 444, + 280, + 488 + ], + "type": "text", + "content": "MVS dataset [53] has six image sequences of outdoor scenes with GT camera poses. We evaluate the relative pose estimation accuracy at " + }, + { + "bbox": [ + 59, + 444, + 280, + 488 + ], + "type": "inline_equation", + "content": "5^{\\circ} / 10^{\\circ} / 20^{\\circ}" + }, + { + "bbox": [ + 59, + 444, + 280, + 488 + ], + "type": "text", + "content": " angular difference thresholds." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 60, + 496, + 257, + 509 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 496, + 257, + 509 + ], + "spans": [ + { + "bbox": [ + 60, + 496, + 257, + 509 + ], + "type": "text", + "content": "4.2. Comparison to other invariant mappings" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 59, + 514, + 280, + 678 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 514, + 280, + 678 + ], + "spans": [ + { + "bbox": [ + 59, + 514, + 280, + 678 + ], + "type": "text", + "content": "Table 1 compares group aligning to various group pooling methods on the Roto-360 dataset using ground-truth keypoint pairs, i.e., no keypoint deviation, without training. The purpose is to compare the invariant mapping operations only while keeping the backbone network and the number of keypoints fixed. We use " + }, + { + "bbox": [ + 59, + 514, + 280, + 678 + ], + "type": "inline_equation", + "content": "\\Delta_{\\mathrm{GT}}" + }, + { + "bbox": [ + 59, + 514, + 280, + 678 + ], + "type": "text", + "content": " to shift the equivariant features, and group aligning shows almost perfect keypoint correspondences with " + }, + { + "bbox": [ + 59, + 514, + 280, + 678 + ], + "type": "inline_equation", + "content": "97.54\\%" + }, + { + "bbox": [ + 59, + 514, + 280, + 678 + ], + "type": "text", + "content": " matching accuracy. Group pooling, such as max pooling or average pooling, significantly reduces discriminative power compared to group aligning. The results show that group aligning shows the best results, proving that leveraging the full group-equivariant features instead of collapsing the groups shows higher discriminability. Note that the bilinear pooling [26] does not guarantee the rotation-invariant matching." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 59, + 679, + 280, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 679, + 280, + 713 + ], + "spans": [ + { + "bbox": [ + 59, + 679, + 280, + 713 + ], + "type": "text", + "content": "Table 2 compares the proposed group aligning to the existing group pooling methods on the Roto-360 dataset, this time with predicted keypoint pairs and with training. Note" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 300, + 122, + 517, + 269 + ], + "blocks": [ + { + "bbox": [ + 300, + 122, + 517, + 269 + ], + "lines": [ + { + "bbox": [ + 300, + 122, + 517, + 269 + ], + "spans": [ + { + "bbox": [ + 300, + 122, + 517, + 269 + ], + "type": "table", + "html": "
Det.Desc.MMApred.total.
@10px@5px@3px
SIFT [27]SIFT [27]78.8678.5978.23774.11500
GIFT [26]37.9736.8236.09531.21500
ours84.6779.8577.96558.31500
ours*84.9180.0978.18759.82219
LF-Net [40]LF-Net [40]75.0574.3072.61386.71024
GIFT [26]35.5633.8232.29426.31024
ours79.9071.6367.39431.81024
ours*80.3271.9967.62591.41503
SuperPoint [8]SuperPoint [8]22.8522.1021.83462.61161
GIFT [26]42.3542.0541.59589.21161
ours93.0891.3590.18688.31161
ours*94.3592.8291.691333234
KeyNet [20]HyNet [55]24.4322.8220.64288.7995
GIFT [26]34.0832.3129.17275.7995
ours72.9561.3641.33257.2995
ours*72.4860.6940.95356.61484
", + "image_path": "823125de5034ee352e8772a1e4550191920a58425c79d0d06d871c90f15d3237.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 296, + 269, + 518, + 299 + ], + "lines": [ + { + "bbox": [ + 296, + 269, + 518, + 299 + ], + "spans": [ + { + "bbox": [ + 296, + 269, + 518, + 299 + ], + "type": "text", + "content": "Table 4. Comparison to existing local descriptors when using the same keypoint detector on Roto-360. Results in bold indicate the best result, and underlined results indicate the second best." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 8 + }, + { + "bbox": [ + 296, + 320, + 518, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 320, + 518, + 452 + ], + "spans": [ + { + "bbox": [ + 296, + 320, + 518, + 452 + ], + "type": "text", + "content": "that while other methods are trained only with " + }, + { + "bbox": [ + 296, + 320, + 518, + 452 + ], + "type": "inline_equation", + "content": "\\mathcal{L}^{\\mathrm{desc}}" + }, + { + "bbox": [ + 296, + 320, + 518, + 452 + ], + "type": "text", + "content": ", our method is trained also with " + }, + { + "bbox": [ + 296, + 320, + 518, + 452 + ], + "type": "inline_equation", + "content": "\\mathcal{L}^{\\mathrm{ori}}" + }, + { + "bbox": [ + 296, + 320, + 518, + 452 + ], + "type": "text", + "content": " to facilitate group aligning. While the number of predicted matches is the highest for average pooling, the MMA results are significantly higher for group aligning, which shows group-aligned descriptors have a higher precision. Overall, incorporating group aligning demonstrates the best results in terms of MMA compared to average pooling, max pooling or bilinear pooling [26]. Note that pooling or aligning the group-equivariant features to obtain invariant descriptors shows consistent improvements over not pooling nor aligning the group-equivariant features." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 297, + 463, + 490, + 475 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 463, + 490, + 475 + ], + "spans": [ + { + "bbox": [ + 297, + 463, + 490, + 475 + ], + "type": "text", + "content": "4.3. Comparison to existing local descriptors" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 480, + 517, + 591 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 480, + 517, + 591 + ], + "spans": [ + { + "bbox": [ + 296, + 480, + 517, + 591 + ], + "type": "text", + "content": "Table 3 shows the matching accuracy compared to existing local descriptors on the Roto-360 dataset. We evaluate the descriptors using their own keypoint detectors [8, 9, 27, 39, 40, 44, 50], or combined with off-the-shelf detectors [24, 26, 42]. While the classical methods [27, 46] achieve better matching accuracy than the existing learning-based methods, our method achieves the best results overall. This is because the learning-based methods learn only a limited degree of invariance in a data-driven manner without guaranteeing full invariance to rotation by design." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 591, + 518, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 591, + 518, + 713 + ], + "spans": [ + { + "bbox": [ + 296, + 591, + 518, + 713 + ], + "type": "text", + "content": "Table 4 shows the performance of our method in comparison to existing local descriptors when using the same keypoint detector, where our method shows consistent performance improvement. In particular, our rotation-invariant descriptor shows consistently higher matching accuracy than GIFT [26], which is a representative learning-based group-invariant descriptor. While our model shows a lower MMA than the LF-Net [40] descriptor when using the LF-Net detector at 5px and 3px thresholds, we conjecture that this is due to the better integrity of the detector and descriptor of LF-Net due to their joint training scheme." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "spans": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "type": "text", + "content": "21892" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 121, + 273, + 188 + ], + "blocks": [ + { + "bbox": [ + 65, + 121, + 273, + 188 + ], + "lines": [ + { + "bbox": [ + 65, + 121, + 273, + 188 + ], + "spans": [ + { + "bbox": [ + 65, + 121, + 273, + 188 + ], + "type": "image", + "image_path": "77e9b256cbac4ed532bf876b59f6f95af26029f1cbe0c77445fa0864daee7f1a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 59, + 190, + 280, + 260 + ], + "lines": [ + { + "bbox": [ + 59, + 190, + 280, + 260 + ], + "spans": [ + { + "bbox": [ + 59, + 190, + 280, + 260 + ], + "type": "text", + "content": "Figure 4. An example of multiple descriptor extraction. The distribution is an orientation histogram " + }, + { + "bbox": [ + 59, + 190, + 280, + 260 + ], + "type": "inline_equation", + "content": "\\mathbf{o} \\in \\mathbb{R}^{16}" + }, + { + "bbox": [ + 59, + 190, + 280, + 260 + ], + "type": "text", + "content": ", and the scores are confidence values for each bin from group-equivariant features. Arrows indicate the orientation candidates for multiple descriptor extraction. The example shows selecting three orientations to obtain three candidate descriptors for a feature point, which is possible as we predict a score for each orientation." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 59, + 280, + 280, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 280, + 280, + 357 + ], + "spans": [ + { + "bbox": [ + 59, + 280, + 280, + 357 + ], + "type": "text", + "content": "These results show that our descriptors obtained using the proposed group aligning show the highest matching accuracy under rotation changes compared to existing methods. The improvement of our method is also attributed to the usage of rotation-equivariant networks, which have a higher sampling efficiency, i.e., do not require intensive rotation augmentations to learn rotation invariance." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 59, + 357, + 280, + 478 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 357, + 280, + 478 + ], + "spans": [ + { + "bbox": [ + 59, + 357, + 280, + 478 + ], + "type": "text", + "content": "Multiple descriptor extraction using orientation candidates. Group aligning can extract multiple descriptors with different alignments by using multiple orientation candidates, denoted by 'ours*', whose scores are at least " + }, + { + "bbox": [ + 59, + 357, + 280, + 478 + ], + "type": "inline_equation", + "content": "60\\%" + }, + { + "bbox": [ + 59, + 357, + 280, + 478 + ], + "type": "text", + "content": " of the maximum score in the orientation histogram. When there is a single keypoint position with " + }, + { + "bbox": [ + 59, + 357, + 280, + 478 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 59, + 357, + 280, + 478 + ], + "type": "text", + "content": " descriptors that are differently aligned, we treat it as if there are " + }, + { + "bbox": [ + 59, + 357, + 280, + 478 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 59, + 357, + 280, + 478 + ], + "type": "text", + "content": " detected keypoints. Multiple descriptor extraction compensates for incorrect orientation predictions and further enhances matching accuracy. Figure 4 illustrates an example of multiple descriptor extraction with a score ratio threshold of 0.6." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 59, + 478, + 280, + 589 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 478, + 280, + 589 + ], + "spans": [ + { + "bbox": [ + 59, + 478, + 280, + 589 + ], + "type": "text", + "content": "Consistency of matching accuracy with respect to rotation changes. Figure 5 illustrates how the matching accuracy changes with respect to varying degrees of rotation. Our method shows the highest consistency, proving the enhanced invariance of descriptors obtained using group aligning against different rotations. While MMA of SIFT [27] and ORB [46] are high at the upright rotations, they tend to fluctuate significantly with varying rotations. The existing learning-based group-invariant descriptor, GIFT [26], fails to find correspondences beyond " + }, + { + "bbox": [ + 59, + 478, + 280, + 589 + ], + "type": "inline_equation", + "content": "60^{\\circ}" + }, + { + "bbox": [ + 59, + 478, + 280, + 589 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 60, + 596, + 259, + 608 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 596, + 259, + 608 + ], + "spans": [ + { + "bbox": [ + 60, + 596, + 259, + 608 + ], + "type": "text", + "content": "4.4. Transferability to real image benchmarks" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 59, + 612, + 280, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 612, + 280, + 713 + ], + "spans": [ + { + "bbox": [ + 59, + 612, + 280, + 713 + ], + "type": "text", + "content": "Table 5 shows the matching performance of local descriptors on HPatches illumination/viewpoint [2] and pose estimation [53]. Our model shows the highest performance overall on the HPatches dataset. The performance gain of ours becomes smaller compared to the Roto-360 dataset due to the absence of extreme rotations in HPatches. While GIFT shows a higher performance under illumination changes that only contain identity mappings, ours†, which uses a larger backbone network (ReWRN), improves" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 300, + 121, + 512, + 257 + ], + "blocks": [ + { + "bbox": [ + 300, + 121, + 512, + 257 + ], + "lines": [ + { + "bbox": [ + 300, + 121, + 512, + 257 + ], + "spans": [ + { + "bbox": [ + 300, + 121, + 512, + 257 + ], + "type": "image", + "image_path": "c5dc650cbf561ee4a2d0989159ae043610775796b40aed15ed3a436ac89c6f1c.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 297, + 257, + 518, + 278 + ], + "lines": [ + { + "bbox": [ + 297, + 257, + 518, + 278 + ], + "spans": [ + { + "bbox": [ + 297, + 257, + 518, + 278 + ], + "type": "text", + "content": "Figure 5. Matching accuracies according to varying degree of rotations on Roto-360." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 299, + 298, + 518, + 412 + ], + "blocks": [ + { + "bbox": [ + 299, + 298, + 518, + 412 + ], + "lines": [ + { + "bbox": [ + 299, + 298, + 518, + 412 + ], + "spans": [ + { + "bbox": [ + 299, + 298, + 518, + 412 + ], + "type": "table", + "html": "
MethodHP-allHP-illuHP-viewPose
@5px@3px@5px@3px@5px@3px20°10°5°
SIFT [27]51.3646.3249.0844.6253.5747.960.020.000.00
ORB [46]52.2247.4050.8546.2953.5548.470.060.000.00
SuperPoint [8]69.7161.7574.6367.5364.9656.170.200.070.01
LF-Net [40]56.4552.2262.2157.6350.8847.000.060.030.01
RF-Net [50]59.0854.4261.6357.4656.6251.490.100.040.01
D2-Net [9]50.1832.5463.8044.0937.0221.380.110.050.01
GIFT [26]76.0367.3179.7171.8972.4862.880.600.280.09
LISRD [42]62.1656.1270.0963.6454.5048.850.050.020.00
oursavgpool64.1057.9462.2856.2765.8559.550.270.100.05
oursmaxpool61.5755.8159.6653.9163.4257.640.270.110.03
oursthinelunpool [26]45.5941.9045.1341.5746.0342.220.350.170.09
oursthinelunpool† [26]58.7253.7757.3252.6760.0654.830.240.110.03
oursgroupalign70.6963.4270.3962.8870.9763.950.580.260.12
oursgroupalign*73.9266.3773.1365.3374.6967.380.560.300.12
oursgroupalign†78.0069.7077.9469.3578.0670.030.560.330.14
", + "image_path": "ed7488127ec34117c6bf31008dfa4090bed516da8e90c576ff865dda1044275e.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 296, + 414, + 518, + 516 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 414, + 518, + 516 + ], + "spans": [ + { + "bbox": [ + 296, + 414, + 518, + 516 + ], + "type": "text", + "content": "Table 5. Evaluation with predicted keypoint pairs on real image benchmarks. The first group of methods includes existing local feature extraction methods. The second group of methods includes comparisons to other group pooling methods by replacing our group aligning with them. 'ours*' denotes the extraction of multiple descriptors using the orientation candidates, whose scores are at least " + }, + { + "bbox": [ + 296, + 414, + 518, + 516 + ], + "type": "inline_equation", + "content": "60\\%" + }, + { + "bbox": [ + 296, + 414, + 518, + 516 + ], + "type": "text", + "content": " of the maximum score in the orientation histogram. 'ours†' denotes our method using the rotation-equivariant WideResNet16-8 (ReWRN) backbone for feature extraction. We use SuperPoint [8] keypoint detector to evaluate ours." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 535, + 517, + 646 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 535, + 517, + 646 + ], + "spans": [ + { + "bbox": [ + 296, + 535, + 517, + 646 + ], + "type": "text", + "content": "matching accuracy by " + }, + { + "bbox": [ + 296, + 535, + 517, + 646 + ], + "type": "inline_equation", + "content": "7.15\\%" + }, + { + "bbox": [ + 296, + 535, + 517, + 646 + ], + "type": "text", + "content": " p at 3px and " + }, + { + "bbox": [ + 296, + 535, + 517, + 646 + ], + "type": "inline_equation", + "content": "5.58\\%" + }, + { + "bbox": [ + 296, + 535, + 517, + 646 + ], + "type": "text", + "content": " p at 5px, and ours* improves by " + }, + { + "bbox": [ + 296, + 535, + 517, + 646 + ], + "type": "inline_equation", + "content": "4.5\\%" + }, + { + "bbox": [ + 296, + 535, + 517, + 646 + ], + "type": "text", + "content": " p at 3px, " + }, + { + "bbox": [ + 296, + 535, + 517, + 646 + ], + "type": "inline_equation", + "content": "2.21\\%" + }, + { + "bbox": [ + 296, + 535, + 517, + 646 + ], + "type": "text", + "content": " p at 5px under viewpoint changes compared to GIFT [26]. It should be noted that the core difference between oursbilinearpool and GIFT is the usage of explicit rotation-equivariant CNNs [60], which clearly shows that bilinear pooling is not well-compatible with the equivariant CNNs in comparison to group aligning. Using the same network with bilinear pooling (oursbilinearpool†) proposed in [26] shows significantly lower results compared to oursgroupalign†." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "spans": [ + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "content": "In the MVS dataset [53] to evaluate relative camera pose estimation, our model shows a higher performance than GIFT at finer error thresholds of " + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "inline_equation", + "content": "10^{\\circ}" + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "inline_equation", + "content": "5^{\\circ}" + }, + { + "bbox": [ + 296, + 646, + 518, + 713 + ], + "type": "text", + "content": ". This shows that our model can find more precise correspondences under 3D viewpoint changes. Overall, these results show that our descriptors using rotation-equivariant representation ex" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 276, + 738, + 300, + 747 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 276, + 738, + 300, + 747 + ], + "spans": [ + { + "bbox": [ + 276, + 738, + 300, + 747 + ], + "type": "text", + "content": "21893" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 62, + 121, + 279, + 223 + ], + "blocks": [ + { + "bbox": [ + 62, + 121, + 279, + 223 + ], + "lines": [ + { + "bbox": [ + 62, + 121, + 279, + 223 + ], + "spans": [ + { + "bbox": [ + 62, + 121, + 279, + 223 + ], + "type": "table", + "html": "
HP-allRoto-360params. (millions)
@5px@3px@5px@3px
ours (proposed |G| = 16)70.6963.4291.3590.180.62M
w/o orientation loss66.4158.6185.2983.260.62M
w/o descriptor loss27.4924.8325.6424.980.62M
w/o image scale pyramid68.7762.2591.4790.430.62M
w/o equivariant backbone47.2542.528.658.5111.18M
|G| = 6463.9657.3585.1283.320.16M
|G| = 3668.1760.9587.7885.890.26M
|G| = 3269.4462.0889.1087.310.31M
|G| = 2469.7262.2190.2788.340.39M
|G| = 865.7458.9287.1685.571.24M
", + "image_path": "4e7afd4ebb64086f254f880d5555e18e6bad4a89cf9892fe98cb97178d4a031c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 60, + 224, + 279, + 244 + ], + "lines": [ + { + "bbox": [ + 60, + 224, + 279, + 244 + ], + "spans": [ + { + "bbox": [ + 60, + 224, + 279, + 244 + ], + "type": "text", + "content": "Table 6. Ablation test on HPatches and Roto-360. 'params.' denotes the number of model parameters." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 59, + 271, + 260, + 282 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 271, + 260, + 282 + ], + "spans": [ + { + "bbox": [ + 59, + 271, + 260, + 282 + ], + "type": "text", + "content": "hibit strong transferability to the real-world examples." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 60, + 287, + 223, + 300 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 287, + 223, + 300 + ], + "spans": [ + { + "bbox": [ + 60, + 287, + 223, + 300 + ], + "type": "text", + "content": "4.5. Ablation study and design choice" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 59, + 305, + 281, + 480 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 305, + 281, + 480 + ], + "spans": [ + { + "bbox": [ + 59, + 305, + 281, + 480 + ], + "type": "text", + "content": "Table 6 shows the results of ablation studies on the HPatches and Roto-360 datasets. The matching accuracy drops when either the orientation alignment loss or the contrastive descriptor loss is not used. Specifically, even when using the ground truth rotation difference for group alignment, not using the descriptor loss results in lower performance, highlighting the importance of robustness to other sorts of variations, e.g., illumination or viewpoint. Not using the image pyramid at inference time results in a slight drop in HPatches, but the performance on Roto-360 remains nearly unchanged. When training without equivariant layers, ResNet-18 with conventional convolutional layers was used - this results in a drastic drop in performance especially on Roto-360, with a rapid increase in the number of model parameters. This demonstrates the significance of high sample efficiency of group-equivariant layers." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 59, + 480, + 282, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 480, + 282, + 624 + ], + "spans": [ + { + "bbox": [ + 59, + 480, + 282, + 624 + ], + "type": "text", + "content": "We also demonstrate the effect of the order of cyclic group " + }, + { + "bbox": [ + 59, + 480, + 282, + 624 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 59, + 480, + 282, + 624 + ], + "type": "text", + "content": " on the performance of our method in the second group of Table 6. We fix the computational cost " + }, + { + "bbox": [ + 59, + 480, + 282, + 624 + ], + "type": "inline_equation", + "content": "C \\times |G| = 1,024" + }, + { + "bbox": [ + 59, + 480, + 282, + 624 + ], + "type": "text", + "content": ", and vary the order of group to show the parameter efficiency of the group equivariant networks. Our design choice " + }, + { + "bbox": [ + 59, + 480, + 282, + 624 + ], + "type": "inline_equation", + "content": "|G| = 16" + }, + { + "bbox": [ + 59, + 480, + 282, + 624 + ], + "type": "text", + "content": " yields the best results, and the performance drops gracefully as " + }, + { + "bbox": [ + 59, + 480, + 282, + 624 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 59, + 480, + 282, + 624 + ], + "type": "text", + "content": " increases. This is because with a higher order of groups, the precision of dominant orientation estimation is likely to decrease, leading to lower results. Reducing the order of group to " + }, + { + "bbox": [ + 59, + 480, + 282, + 624 + ], + "type": "inline_equation", + "content": "|G| = 8" + }, + { + "bbox": [ + 59, + 480, + 282, + 624 + ], + "type": "text", + "content": " reduces the MMA in both benchmarks as well, which we suspect is because the range of rotation covered by one group action becomes too wide, leading to increased approximation errors." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 60, + 629, + 160, + 640 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 629, + 160, + 640 + ], + "spans": [ + { + "bbox": [ + 60, + 629, + 160, + 640 + ], + "type": "text", + "content": "4.6. Qualitative results" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 59, + 646, + 280, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 646, + 280, + 713 + ], + "spans": [ + { + "bbox": [ + 59, + 646, + 280, + 713 + ], + "type": "text", + "content": "Figure 6 visualizes the consistency of dominant orientation estimation. From the source (left) and target (middle) images, we estimate the dominant orientation for the same set of predicted keypoints. We use the ground truth rotation to align the estimated orientation and the target image for better visibility (right). The green and red arrows (middle," + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 308, + 121, + 507, + 350 + ], + "blocks": [ + { + "bbox": [ + 308, + 121, + 507, + 350 + ], + "lines": [ + { + "bbox": [ + 308, + 121, + 507, + 350 + ], + "spans": [ + { + "bbox": [ + 308, + 121, + 507, + 350 + ], + "type": "image", + "image_path": "545b64514cf1d004fca964542115e7d2201376f603c064821ae1fc77ce9d822d.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 297, + 350, + 518, + 370 + ], + "lines": [ + { + "bbox": [ + 297, + 350, + 518, + 370 + ], + "spans": [ + { + "bbox": [ + 297, + 350, + 518, + 370 + ], + "type": "text", + "content": "Figure 6. Visualization of consistency of dominant orientation estimation. Best viewed in electronics and colour." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 296, + 402, + 518, + 468 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 402, + 518, + 468 + ], + "spans": [ + { + "bbox": [ + 296, + 402, + 518, + 468 + ], + "type": "text", + "content": "right) represent the consistent and inconsistent orientation predictions with respect to the initial estimations (left) at a " + }, + { + "bbox": [ + 296, + 402, + 518, + 468 + ], + "type": "inline_equation", + "content": "30^{\\circ}" + }, + { + "bbox": [ + 296, + 402, + 518, + 468 + ], + "type": "text", + "content": " threshold. The numbers on the left represent the number of consistent estimations/number of detected keypoints. Compared to LF-Net [40] and RF-Net [50], our method predicts more consistent dominant orientations of keypoints." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 297, + 482, + 364, + 494 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 482, + 364, + 494 + ], + "spans": [ + { + "bbox": [ + 297, + 482, + 364, + 494 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 502, + 518, + 656 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 502, + 518, + 656 + ], + "spans": [ + { + "bbox": [ + 296, + 502, + 518, + 656 + ], + "type": "text", + "content": "We have proposed a self-supervised rotation-equivariant network for visual correspondence to improve the discriminability of local descriptors. Our invariant mapping called group-aligning shifts the rotation-equivariant features along the group dimension based on the orientation value to produce rotation-invariant descriptors while preserving the feature discriminability, without collapsing the group dimension. Our method achieves state-of-the-art performance in obtaining rotation-invariant descriptors, which are transferable to tasks such as keypoint matching and camera pose estimation. We believe that our approach can be further extended to other geometric transformation groups, and will motivate group-equivariant learning for practical applications of computer vision." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 657, + 518, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 657, + 518, + 713 + ], + "spans": [ + { + "bbox": [ + 296, + 657, + 518, + 713 + ], + "type": "text", + "content": "Acknowledgement. This work was supported by Samsung Research Funding & Incubation Center of Samsung Electronics under Project Number SRFC-TF2103-02 and also by the NRF grant (NRF-2021R1A2C3012728) funded by the Korea government (MSIT)." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "spans": [ + { + "bbox": [ + 276, + 738, + 301, + 747 + ], + "type": "text", + "content": "21894" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 122, + 114, + 133 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 122, + 114, + 133 + ], + "spans": [ + { + "bbox": [ + 61, + 122, + 114, + 133 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 141, + 280, + 711 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 65, + 141, + 280, + 181 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 141, + 280, + 181 + ], + "spans": [ + { + "bbox": [ + 65, + 141, + 280, + 181 + ], + "type": "text", + "content": "[1] Sameer Agarwal, Yasutaka Furukawa, Noah Snavely, Ian Simon, Brian Curless, Steven M Seitz, and Richard Szeliski. Building rome in a day. Communications of the ACM, 54(10):105-112, 2011. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 65, + 182, + 280, + 232 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 182, + 280, + 232 + ], + "spans": [ + { + "bbox": [ + 65, + 182, + 280, + 232 + ], + "type": "text", + "content": "[2] Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk. Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5173-5182, 2017. 2, 5, 6, 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 65, + 234, + 280, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 234, + 280, + 282 + ], + "spans": [ + { + "bbox": [ + 65, + 234, + 280, + 282 + ], + "type": "text", + "content": "[3] Axel Barroso-Laguna, Yurun Tian, and Krystian Mikolajczyk. Scalenet: A shallow architecture for scale estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12808-12818, 2022. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 65, + 284, + 280, + 314 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 284, + 280, + 314 + ], + "spans": [ + { + "bbox": [ + 65, + 284, + 280, + 314 + ], + "type": "text", + "content": "[4] Fabio Bellavia and Carlo Colombo. Rethinking the sgloh descriptor. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4):931-944, 2017. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 65, + 315, + 280, + 355 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 315, + 280, + 355 + ], + "spans": [ + { + "bbox": [ + 65, + 315, + 280, + 355 + ], + "type": "text", + "content": "[5] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 65, + 356, + 280, + 386 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 356, + 280, + 386 + ], + "spans": [ + { + "bbox": [ + 65, + 356, + 280, + 386 + ], + "type": "text", + "content": "[6] Taco Cohen and Max Welling. Group equivariant convolutional networks. In International conference on machine learning, pages 2990-2999. PMLR, 2016. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 65, + 387, + 280, + 416 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 387, + 280, + 416 + ], + "spans": [ + { + "bbox": [ + 65, + 387, + 280, + 416 + ], + "type": "text", + "content": "[7] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Toward geometric deep slam. arXiv preprint arXiv:1707.07410, 2017. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 65, + 417, + 280, + 457 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 417, + 280, + 457 + ], + "spans": [ + { + "bbox": [ + 65, + 417, + 280, + 457 + ], + "type": "text", + "content": "[8] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. In CVPR Deep Learning for Visual SLAM Workshop, 2018. 1, 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 65, + 458, + 280, + 517 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 458, + 280, + 517 + ], + "spans": [ + { + "bbox": [ + 65, + 458, + 280, + 517 + ], + "type": "text", + "content": "[9] Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, and Torsten Sattler. D2-net: A trainable cnn for joint description and detection of local features. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition, pages 8092-8101, 2019. 2, 6, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 61, + 519, + 280, + 568 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 519, + 280, + 568 + ], + "spans": [ + { + "bbox": [ + 61, + 519, + 280, + 568 + ], + "type": "text", + "content": "[10] Patrick Ebel, Anastasiia Mishchuk, Kwang Moo Yi, Pascal Fua, and Eduard Trulls. Beyond cartesian representations for local descriptors. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 253-262, 2019. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 61, + 570, + 280, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 570, + 280, + 609 + ], + "spans": [ + { + "bbox": [ + 61, + 570, + 280, + 609 + ], + "type": "text", + "content": "[11] Bin Fan, Fuchao Wu, and Zhanyi Hu. Rotationally invariant descriptors using intensity order pooling. IEEE transactions on pattern analysis and machine intelligence, 34(10):2031-2045, 2011. 1, 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 61, + 611, + 280, + 660 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 611, + 280, + 660 + ], + "spans": [ + { + "bbox": [ + 61, + 611, + 280, + 660 + ], + "type": "text", + "content": "[12] Jiaming Han, Jian Ding, Nan Xue, and Gui-Song Xia. Redet: A rotation-equivariant detector for aerial object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2786-2795, 2021. 2, 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 61, + 661, + 280, + 711 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 661, + 280, + 711 + ], + "spans": [ + { + "bbox": [ + 61, + 661, + 280, + 711 + ], + "type": "text", + "content": "[13] Bharath Hariharan, Pablo Arbeláez, Ross Girshick, and Jitendra Malik. Hypercolumns for object segmentation and fine-grained localization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 447-456, 2015. 3" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 299, + 124, + 518, + 711 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 299, + 124, + 517, + 154 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 124, + 517, + 154 + ], + "spans": [ + { + "bbox": [ + 299, + 124, + 517, + 154 + ], + "type": "text", + "content": "[14] Chris Harris, Mike Stephens, et al. A combined corner and edge detector. In *Alvey vision conference*, volume 15, pages 10-5244. CiteSeer, 1988. 3, 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 299, + 155, + 517, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 155, + 517, + 196 + ], + "spans": [ + { + "bbox": [ + 299, + 155, + 517, + 196 + ], + "type": "text", + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 299, + 197, + 517, + 235 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 197, + 517, + 235 + ], + "spans": [ + { + "bbox": [ + 299, + 197, + 517, + 235 + ], + "type": "text", + "content": "[16] Jared Heinly, Johannes L. Schonberger, Enrique Dunn, and Jan-Michael Frahm. Reconstructing the world* in six days. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3287-3295, 2015. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 299, + 236, + 517, + 286 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 236, + 517, + 286 + ], + "spans": [ + { + "bbox": [ + 299, + 236, + 517, + 286 + ], + "type": "text", + "content": "[17] Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, and Eduard Trulls. Image matching across wide baselines: From paper to practice. International Journal of Computer Vision, 129(2):517-547, 2021. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 299, + 287, + 518, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 287, + 518, + 327 + ], + "spans": [ + { + "bbox": [ + 299, + 287, + 518, + 327 + ], + "type": "text", + "content": "[18] Seungwook Kim, Yoonwoo Jeong, Chunghyun Park, Jaesik Park, and Minsu Cho. SeLCA: Self-supervised learning of canonical axis. In NeurIPS 2022 Workshop on Symmetry and Geometry in Neural Representations, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 299, + 328, + 517, + 378 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 328, + 517, + 378 + ], + "spans": [ + { + "bbox": [ + 299, + 328, + 517, + 378 + ], + "type": "text", + "content": "[19] Seungwook Kim, Juhong Min, and Minsu Cho. Transformer: Match-to-match attention for semantic correspondence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8697-8707, 2022. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 299, + 378, + 517, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 378, + 517, + 418 + ], + "spans": [ + { + "bbox": [ + 299, + 378, + 517, + 418 + ], + "type": "text", + "content": "[20] Axel Barroso Laguna and Krystian Mikolajczyk. Key. net: Keypoint detection by handcrafted and learned cnn filters revisited. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 3, 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 299, + 419, + 517, + 458 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 419, + 517, + 458 + ], + "spans": [ + { + "bbox": [ + 299, + 419, + 517, + 458 + ], + "type": "text", + "content": "[21] Jongmin Lee, Yoonwoo Jeong, and Minsu Cho. Self-supervised learning of image scale and orientation. In 31st British Machine Vision Conference 2021, BMVC 2021, Virtual Event, UK. BMVA Press, 2021. 1, 2, 4" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 299, + 459, + 517, + 509 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 459, + 517, + 509 + ], + "spans": [ + { + "bbox": [ + 299, + 459, + 517, + 509 + ], + "type": "text", + "content": "[22] Jongmin Lee, Yoonwoo Jeong, Seungwook Kim, Juhong Min, and Minsu Cho. Learning to distill convolutional features into compact local descriptors. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 898-908, 2021. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 299, + 511, + 517, + 559 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 511, + 517, + 559 + ], + "spans": [ + { + "bbox": [ + 299, + 511, + 517, + 559 + ], + "type": "text", + "content": "[23] Jongmin Lee, Byungjin Kim, and Minsu Cho. Self-supervised equivariant learning for oriented keypoint detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4847-4857, 2022. 1, 2, 4" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 299, + 560, + 517, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 560, + 517, + 610 + ], + "spans": [ + { + "bbox": [ + 299, + 560, + 517, + 610 + ], + "type": "text", + "content": "[24] Kunhong Li, Longguang Wang, Li Liu, Qing Ran, Kai Xu, and Yulan Guo. Decoupling makes weakly supervised local feature better. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15838-15848, 2022. 2, 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 299, + 611, + 517, + 661 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 611, + 517, + 661 + ], + "spans": [ + { + "bbox": [ + 299, + 611, + 517, + 661 + ], + "type": "text", + "content": "[25] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 299, + 662, + 517, + 711 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 662, + 517, + 711 + ], + "spans": [ + { + "bbox": [ + 299, + 662, + 517, + 711 + ], + "type": "text", + "content": "[26] Yuan Liu, Zehong Shen, Zhixuan Lin, Sida Peng, Hujun Bao, and Xiaowei Zhou. Gift: Learning transformation-invariant dense visual descriptors via group cnns. Advances in Neural Information Processing Systems, 32:6992-7003, 2019. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 276, + 738, + 300, + 747 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 276, + 738, + 300, + 747 + ], + "spans": [ + { + "bbox": [ + 276, + 738, + 300, + 747 + ], + "type": "text", + "content": "21895" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 124, + 280, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 61, + 124, + 280, + 154 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 124, + 280, + 154 + ], + "spans": [ + { + "bbox": [ + 61, + 124, + 280, + 154 + ], + "type": "text", + "content": "[27] David G Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91-110, 2004. 1, 2, 3, 4, 5, 6, 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 155, + 280, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 155, + 280, + 215 + ], + "spans": [ + { + "bbox": [ + 61, + 155, + 280, + 215 + ], + "type": "text", + "content": "[28] Zixin Luo, Lei Zhou, Xuyang Bai, Hongkai Chen, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, and Long Quan. Aslfeat: Learning local features of accurate shape and localization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6589-6598, 2020. 2, 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 216, + 280, + 266 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 216, + 280, + 266 + ], + "spans": [ + { + "bbox": [ + 61, + 216, + 280, + 266 + ], + "type": "text", + "content": "[29] Simon Lynen, Bernhard Zeisl, Dror Aiger, Michael Bosse, Joel Hesch, Marc Pollefeys, Roland Siegwart, and Torsten Sattler. Large-scale, real-time visual-inertial localization revisited. The International Journal of Robotics Research, 39(9):1061-1084, 2020. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 266, + 280, + 306 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 266, + 280, + 306 + ], + "spans": [ + { + "bbox": [ + 61, + 266, + 280, + 306 + ], + "type": "text", + "content": "[30] Diego Marcos, Michele Volpi, Nikos Komodakis, and Devis Tuia. Rotation equivariant vector field networks. In Proceedings of the IEEE International Conference on Computer Vision, pages 5048-5057, 2017. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 307, + 280, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 307, + 280, + 326 + ], + "spans": [ + { + "bbox": [ + 61, + 307, + 280, + 326 + ], + "type": "text", + "content": "[31] Roland Memisevic. On multi-view feature learning. In ICML, 2012. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 327, + 280, + 367 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 327, + 280, + 367 + ], + "spans": [ + { + "bbox": [ + 61, + 327, + 280, + 367 + ], + "type": "text", + "content": "[32] Roland Memisevic and Geoffrey E Hinton. Learning to represent spatial transformations with factored higher-order boltzmann machines. Neural computation, 22(6):1473-1492, 2010. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 367, + 280, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 367, + 280, + 397 + ], + "spans": [ + { + "bbox": [ + 61, + 367, + 280, + 397 + ], + "type": "text", + "content": "[33] Krystian Mikolajczyk and Cordelia Schmid. Scale & affine invariant interest point detectors. International journal of computer vision, 60(1):63-86, 2004. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 398, + 280, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 398, + 280, + 437 + ], + "spans": [ + { + "bbox": [ + 61, + 398, + 280, + 437 + ], + "type": "text", + "content": "[34] Krystian Molkajczyk and Cordelia Schmid. A performance evaluation of local descriptors. IEEE transactions on pattern analysis and machine intelligence, 27(10):1615-1630, 2005. 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 61, + 438, + 280, + 468 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 438, + 280, + 468 + ], + "spans": [ + { + "bbox": [ + 61, + 438, + 280, + 468 + ], + "type": "text", + "content": "[35] Juhong Min, Jongmin Lee, Jean Ponce, and Minsu Cho. Hyperpixel flow: Semantic correspondence with multi-layer neural features. In ICCV, 2019. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 61, + 469, + 280, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 469, + 280, + 510 + ], + "spans": [ + { + "bbox": [ + 61, + 469, + 280, + 510 + ], + "type": "text", + "content": "[36] Anastasiia Mishchuk, Dmytro Mishkin, Filip Radenovic, and Jiri Matas. Working hard to know your neighbor's margins: Local descriptor learning loss. In Advances in Neural Information Processing Systems, pages 4826-4837, 2017. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 61, + 510, + 280, + 549 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 510, + 280, + 549 + ], + "spans": [ + { + "bbox": [ + 61, + 510, + 280, + 549 + ], + "type": "text", + "content": "[37] Dmytro Mishkin, Filip Radenovic, and Jiri Matas. Repeatability is not enough: Learning affine regions via discriminability. In Proceedings of the European Conference on Computer Vision (ECCV), pages 284-300, 2018. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 61, + 550, + 280, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 550, + 280, + 600 + ], + "spans": [ + { + "bbox": [ + 61, + 550, + 280, + 600 + ], + "type": "text", + "content": "[38] Daniel Moyer, Esra Abaci Turk, P Ellen Grant, William M Wells, and Polina Golland. Equivariant filters for efficient tracking in 3d imaging. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 193-202. Springer, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 61, + 601, + 280, + 640 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 601, + 280, + 640 + ], + "spans": [ + { + "bbox": [ + 61, + 601, + 280, + 640 + ], + "type": "text", + "content": "[39] Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics, 31(5):1147-1163, 2015. 1, 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 61, + 641, + 280, + 681 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 641, + 280, + 681 + ], + "spans": [ + { + "bbox": [ + 61, + 641, + 280, + 681 + ], + "type": "text", + "content": "[40] Yuki Ono, Eduard Trulls, Pascal Fua, and Kwang Moo Yi. Lf-net: learning local features from images. In Advances in neural information processing systems, pages 6234-6244, 2018. 2, 3, 6, 7, 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 61, + 682, + 280, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 682, + 280, + 712 + ], + "spans": [ + { + "bbox": [ + 61, + 682, + 280, + 712 + ], + "type": "text", + "content": "[41] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 5" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 299, + 124, + 517, + 712 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 299, + 124, + 517, + 165 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 124, + 517, + 165 + ], + "spans": [ + { + "bbox": [ + 299, + 124, + 517, + 165 + ], + "type": "text", + "content": "[42] Rémi Pautrat, Viktor Larsson, Martin R Oswald, and Marc Pollefeys. Online invariance selection for local feature descriptors. In European Conference on Computer Vision, pages 707-724. Springer, 2020. 2, 6, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 299, + 166, + 517, + 236 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 166, + 517, + 236 + ], + "spans": [ + { + "bbox": [ + 299, + 166, + 517, + 236 + ], + "type": "text", + "content": "[43] Nicolas Pielawski, Elisabeth Wetzer, Johan Överstedt, Ji-ahao Lu, Carolina Wahlby, Joakim Lindblad, and Nataša Sladoje. CoMIR: Contrastive multimodal image representation for registration. In H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, editors, Advances in Neural Information Processing Systems, volume 33, pages 18433–18444. Curran Associates, Inc., 2020. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 299, + 237, + 517, + 277 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 237, + 517, + 277 + ], + "spans": [ + { + "bbox": [ + 299, + 237, + 517, + 277 + ], + "type": "text", + "content": "[44] Jerome Revaud, Cesar De Souza, Martin Humenberger, and Philippe Weinzaepfel. R2d2: Reliable and repeatable detector and descriptor. Advances in neural information processing systems, 32:12405-12415, 2019. 1, 2, 5, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 299, + 278, + 517, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 278, + 517, + 330 + ], + "spans": [ + { + "bbox": [ + 299, + 278, + 517, + 330 + ], + "type": "text", + "content": "[45] Jérôme Revaud, Vincent Leroy, Philippe Weinzaepfel, and Boris Chidlovskii. Pump: Pyramidal and uniqueness matching priors for unsupervised learning of local descriptors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3926-3936, 2022. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 299, + 330, + 517, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 330, + 517, + 370 + ], + "spans": [ + { + "bbox": [ + 299, + 330, + 517, + 370 + ], + "type": "text", + "content": "[46] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In 2011 International conference on computer vision, pages 2564-2571. IEEE, 2011. 1, 2, 6, 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 299, + 372, + 517, + 412 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 372, + 517, + 412 + ], + "spans": [ + { + "bbox": [ + 299, + 372, + 517, + 412 + ], + "type": "text", + "content": "[47] Torsten Sattler, Bastian Leibe, and Leif Kobbelt. Improving image-based localization by active correspondence search. In European conference on computer vision, pages 752-765. Springer, 2012. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 299, + 413, + 517, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 413, + 517, + 474 + ], + "spans": [ + { + "bbox": [ + 299, + 413, + 517, + 474 + ], + "type": "text", + "content": "[48] Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenberg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, et al. Benchmarking 6dof outdoor visual localization in changing conditions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8601-8610, 2018. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 299, + 475, + 517, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 475, + 517, + 514 + ], + "spans": [ + { + "bbox": [ + 299, + 475, + 517, + 514 + ], + "type": "text", + "content": "[49] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4104-4113, 2016. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 299, + 515, + 517, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 515, + 517, + 566 + ], + "spans": [ + { + "bbox": [ + 299, + 515, + 517, + 566 + ], + "type": "text", + "content": "[50] Xuelun Shen, Cheng Wang, Xin Li, Zenglei Yu, Jonathan Li, Chenglu Wen, Ming Cheng, and Zijian He. Rf-net: An end-to-end image matching network based on receptive field. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8132-8140, 2019. 2, 6, 7, 8" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 299, + 567, + 517, + 587 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 567, + 517, + 587 + ], + "spans": [ + { + "bbox": [ + 299, + 567, + 517, + 587 + ], + "type": "text", + "content": "[51] Kihyuk Sohn and Honglak Lee. Learning invariant representations with local transformations. In ICML, 2012. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 299, + 588, + 517, + 629 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 588, + 517, + 629 + ], + "spans": [ + { + "bbox": [ + 299, + 588, + 517, + 629 + ], + "type": "text", + "content": "[52] Ivan Sosnovik, Artem Moskalev, and Arnold Smeulders. How to transform kernels for scale-convolutions. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1092-1097, 2021. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 299, + 630, + 517, + 680 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 630, + 517, + 680 + ], + "spans": [ + { + "bbox": [ + 299, + 630, + 517, + 680 + ], + "type": "text", + "content": "[53] Christoph Strecha, Wolfgang Von Hansen, Luc Van Gool, Pascal Fua, and Ulrich Thoennessen. On benchmarking camera calibration and multi-view stereo for high resolution imagery. In 2008 IEEE conference on computer vision and pattern recognition, pages 1-8. IEEE, 2008. 2, 5, 6, 7" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 299, + 682, + 517, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 682, + 517, + 712 + ], + "spans": [ + { + "bbox": [ + 299, + 682, + 517, + 712 + ], + "type": "text", + "content": "[54] Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, and Krystian Mikolajczyk. Hynet: Learning local descriptor with hybrid similarity measure and triplet loss. Ad" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 276, + 738, + 300, + 747 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 276, + 738, + 300, + 747 + ], + "spans": [ + { + "bbox": [ + 276, + 738, + 300, + 747 + ], + "type": "text", + "content": "21896" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 125, + 280, + 658 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 79, + 125, + 280, + 143 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 125, + 280, + 143 + ], + "spans": [ + { + "bbox": [ + 79, + 125, + 280, + 143 + ], + "type": "text", + "content": "vances in Neural Information Processing Systems, 33:7401-7412, 2020. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 61, + 146, + 280, + 186 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 146, + 280, + 186 + ], + "spans": [ + { + "bbox": [ + 61, + 146, + 280, + 186 + ], + "type": "text", + "content": "[55] Yurun Tian, Axel Barroso Laguna, Tony Ng, Vassileios Balntas, and Krystian Mikolajczyk. Hynet: Learning local descriptor with hybrid similarity measure and triplet loss. In NeurIPS, 2020. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 186, + 280, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 186, + 280, + 237 + ], + "spans": [ + { + "bbox": [ + 61, + 186, + 280, + 237 + ], + "type": "text", + "content": "[56] Yurun Tian, Xin Yu, Bin Fan, Fuchao Wu, Huub Heijnen, and Vassileios Balntas. Sosnet: Second order similarity regularization for local descriptor learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11016-11025, 2019. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 238, + 280, + 269 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 238, + 280, + 269 + ], + "spans": [ + { + "bbox": [ + 61, + 238, + 280, + 269 + ], + "type": "text", + "content": "[57] Michal Jan Tyszkiewicz, Pascal Fua, and Eduard Trulls. Disk: learning local features with policy gradient. Advances in Neural Information Processing Systems, 33, 2020. 2, 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 269, + 280, + 309 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 269, + 280, + 309 + ], + "spans": [ + { + "bbox": [ + 61, + 269, + 280, + 309 + ], + "type": "text", + "content": "[58] Rui Wang, Robin Walters, and Rose Yu. Data augmentation vs. equivariant networks: A theory of generalization on dynamics forecasting. arXiv preprint arXiv:2206.09450, 2022. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 310, + 280, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 310, + 280, + 350 + ], + "spans": [ + { + "bbox": [ + 61, + 310, + 280, + 350 + ], + "type": "text", + "content": "[59] Zhenhua Wang, Bin Fan, Gang Wang, and Fuchao Wu. Exploring local and overall ordinal information for robust feature description. IEEE transactions on pattern analysis and machine intelligence, 38(11):2198-2211, 2015. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 351, + 280, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 351, + 280, + 381 + ], + "spans": [ + { + "bbox": [ + 61, + 351, + 280, + 381 + ], + "type": "text", + "content": "[60] Maurice Weiler and Gabriele Cesa. General e (2)-equivariant steerable cnns. Advances in Neural Information Processing Systems, 32:14334-14345, 2019. 1, 2, 3, 5, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 383, + 280, + 423 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 383, + 280, + 423 + ], + "spans": [ + { + "bbox": [ + 61, + 383, + 280, + 423 + ], + "type": "text", + "content": "[61] Maurice Weiler, Fred A Hamprecht, and Martin Storath. Learning steerable filters for rotation equivariant cnns. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 849-858, 2018. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 61, + 424, + 280, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 424, + 280, + 474 + ], + "spans": [ + { + "bbox": [ + 61, + 424, + 280, + 474 + ], + "type": "text", + "content": "[62] Daniel E Worrall, Stephan J Garbin, Daniyar Turmukhambetov, and Gabriel J Brostow. Harmonic networks: Deep translation and rotation equivariance. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5028-5037, 2017. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 61, + 475, + 280, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 475, + 280, + 525 + ], + "spans": [ + { + "bbox": [ + 61, + 475, + 280, + 525 + ], + "type": "text", + "content": "[63] Pei Yan, Yihua Tan, Shengzhou Xiong, Yuan Tai, and Yansheng Li. Learning soft estimator of keypoint scale and orientation with probabilistic covariant loss. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 19406-19415, 2022. 1, 4" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 61, + 526, + 280, + 565 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 526, + 280, + 565 + ], + "spans": [ + { + "bbox": [ + 61, + 526, + 280, + 565 + ], + "type": "text", + "content": "[64] Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, and Pascal Fua. Lift: Learned invariant feature transform. In European conference on computer vision, pages 467-483. Springer, 2016. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 61, + 567, + 280, + 607 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 567, + 280, + 607 + ], + "spans": [ + { + "bbox": [ + 61, + 567, + 280, + 607 + ], + "type": "text", + "content": "[65] Yanzhao Zhou, Qixiang Ye, Qiang Qiu, and Jianbin Jiao. Oriented response networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 519-528, 2017. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 61, + 608, + 280, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 608, + 280, + 658 + ], + "spans": [ + { + "bbox": [ + 61, + 608, + 280, + 658 + ], + "type": "text", + "content": "[66] Siyu Zhu, Runze Zhang, Lei Zhou, Tianwei Shen, Tian Fang, Ping Tan, and Long Quan. Very large-scale global sfm by distributed motion averaging. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4568-4577, 2018. 1" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 277, + 738, + 300, + 747 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 277, + 738, + 300, + 747 + ], + "spans": [ + { + "bbox": [ + 277, + 738, + 300, + 747 + ], + "type": "text", + "content": "21897" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Sample Relationship for Exposure Correction/328dc83f-b0c4-4ce9-90e2-03cb25bedb51_content_list.json b/2023/Learning Sample Relationship for Exposure Correction/328dc83f-b0c4-4ce9-90e2-03cb25bedb51_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..726e861269da14272339fbd26d86d6c6f3462e4d --- /dev/null +++ b/2023/Learning Sample Relationship for Exposure Correction/328dc83f-b0c4-4ce9-90e2-03cb25bedb51_content_list.json @@ -0,0 +1,2118 @@ +[ + { + "type": "text", + "text": "Learning Sample Relationship for Exposure Correction", + "text_level": 1, + "bbox": [ + 204, + 130, + 767, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Jie Huang,\\* Feng Zhao,\\* Man Zhou\\*,\\* Jie Xiao, Naishan Zheng, Kaiwen Zheng, Zhiwei Xiong University of Science and Technology of China", + "bbox": [ + 122, + 180, + 846, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{hj0117,manman,ustchbxj,nszheng,kezh}@mail.ustc.edu.cn，{fzhao956,zwxiong}@ustc.edu.cn", + "bbox": [ + 106, + 218, + 864, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 268, + 313, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network. As well recognized, the optimization flow is the opposite. Despite great advancement, existing exposure correction methods are usually trained with a mini-batch of both underexposure and overexposure mixed samples and have not explored the relationship between them to solve the optimization inconsistency.", + "bbox": [ + 75, + 301, + 470, + 422 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we introduce a new perspective to conjunct their optimization processes by correlating and constraining the relationship of correction procedure in a minibatch. The core designs of our framework consist of two steps: 1) formulating the exposure relationship of samples across the batch dimension via a context-irrelevant pretext task. 2) delivering the above sample relationship design as the regularization term within the loss function to promote optimization consistency. The proposed sample relationship design as a general term can be easily integrated into existing exposure correction methods without any computational burden in inference time. Extensive experiments over multiple representative exposure correction benchmarks demonstrate consistent performance gains by introducing our sample relationship design.", + "bbox": [ + 73, + 424, + 470, + 650 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 683, + 209, + 698 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The images captured under non-ideal illumination conditions, i.e., underexposure or overexposure scenes, usually suffer from unpleasant visual effects and thus count against the down-streaming vision tasks. To this end, exposure correction techniques have been developed, which aim to correct both underexposure and overexposure images to the normal exposure automatically. It is recognized that a single algorithm is challenging to take for exposure correction since the mapping flows of correcting underexposure and overexposure are quite different [12].", + "bbox": [ + 75, + 709, + 468, + 861 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recent years have witnessed explosive advancement only on the single underexposure correction, including conventional methods that rely on manually designed strategies [2, 8, 10, 20, 28, 31], and deep-learning-driven methods that account for the powerful learning capability of complicated neural networks [32-34, 39], where deep-learning methods have achieved improvement in restoring corruptions [25, 35-37]. Seldom efforts have been devoted to both underexposure and overexposure scenes within a single algorithm for meeting the practical application. Very recently, some promising works [1, 12, 14, 24] attempt to solve the above issue. Both of them follow the common principle of alleviating the optimization process inconsistency by conjugating their exposure representations in spatial domain [12, 14, 24, 29] or in frequency transform domain [13].", + "bbox": [ + 500, + 268, + 893, + 497 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In fact, most above exposure correction approaches are trained with a mini-batch that contains both underexposure and overexposure mixed samples (see Fig. 1). Within the mini-batch, the optimization process of a single network is the opposite. On the other hand, correlating the relationship of samples across the mini-batch could conjunct their optimization processes [11]. Therefore, by constraining the relationship across the batch dimension, the adverse effect of opposite optimization in the mini-batch could be relieved.", + "bbox": [ + 496, + 502, + 893, + 638 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To this end, in this paper, we introduce a new perspective that conjuncts the optimization processes across the batch dimension via sample relationship learning and further improves the optimization processes of exposure correction. To achieve this, we construct an exposure-relationship learning (ERL) framework consisting of two steps (see Fig. 2). In the first step (see Fig. 3), we devise a batch-correlation module (BCM) that captures the relationship of samples across the batch dimension. To enable such a relationship focusing on the exposure-related representations, we train BCM via a pretext task that excludes context information correlation. Then, in the second step (see Fig. 4), we deliver the above sample relationship as the additional training regularization term within the loss function of exposure correction algorithms, where the relationship of corrected results is optimized on the trained BCM. In this way, the optimization processes within a mini-batch", + "bbox": [ + 496, + 643, + 893, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Equal contributions.", + "bbox": [ + 94, + 875, + 212, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding authors.", + "bbox": [ + 96, + 887, + 225, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "9904", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/c6efd2a6c8579cb7c4627113e69250de3bbf173b327694ddac8c0ec082440f10.jpg", + "image_caption": [ + "Figure 1. The illustration of optimization flow of underexposure and overexposure correction. As can be seen, the corrected results of underexposure samples are still obviously darker than normal exposure, while the corrected overexposure samples behave oppositely. This demonstrates the corrected results of underexposure and overexposure are optimized to approach their corresponding ground truth in the opposite direction." + ], + "image_footnote": [], + "bbox": [ + 99, + 85, + 459, + 267 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "are conjunct and the adverse effect of inconsistency optimization can be reduced.", + "bbox": [ + 75, + 393, + 468, + 422 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our proposed ERL framework is general and could be integrated with existing exposure correction approaches. The above sample relationship regularization is only adopted during the training procedure and does not introduce any computation burden in inference time. Extensive experiments on exposure correction datasets demonstrate consistent gains by applying our ERL framework. Moreover, it can also be extended to the mixed image enhancement task, demonstrating the extensive capability of our method.", + "bbox": [ + 75, + 422, + 470, + 559 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We summarize the main contributions of this work as:", + "bbox": [ + 96, + 560, + 454, + 574 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- This work is the first time to solve inconsistency optimization of exposure correction from a new perspective of batch dimension. By exploring the relationship of samples within a mini-batch, their optimization processes are conjunct to relieve the adverse inconsistency optimization effect.", + "- We propose an exposure relationship learning (ERL) framework to correlate and constrain the relationship of corrected samples across the mini-batch. The learned sample relationship acts as an additional regularization term within the loss function to assist the model optimization.", + "- Our ERL framework is general and can be integrated into the existing exposure correction methods without introducing any computation burden during inference.", + "- Extensive experiments over multiple exposure correction datasets demonstrate consistent performance gains by introducing our sample relationship learning mechanism." + ], + "bbox": [ + 94, + 583, + 470, + 898 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 89, + 642, + 104 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Conventional Methods", + "text_level": 1, + "bbox": [ + 500, + 114, + 709, + 130 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In the past decades, various methods have been proposed for correcting the exposure of images with poor illumination. Conventional works mainly rely on manually designed models and strategies. A line of work is based on histogram-based techniques [2,3,15,16], which improve the lightness and contrast of an improper exposure image. While another line of work is based on Retinex theory [18], which decomposes an image into the reflectance component and the illumination component. By enhancing the illumination component that improves the lightness, while regularizing the reflectance component that suppresses the noises, these methods have achieved prominent effect for exposure correction [8, 10, 20, 30].", + "bbox": [ + 496, + 138, + 893, + 335 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Learning-based Methods", + "text_level": 1, + "bbox": [ + 500, + 347, + 728, + 363 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In recent years, deep-learning-based methods have attracted more attention [41-44] which is also adopted for adjusting images with poor illumination, which are learned to correct exposures in a data-driven manner. Some of these approaches are also based on the Retinex theory [30, 32, 33, 38, 39]. As a representative, RetinexNet [32] and KIND [39] decompose an image into illumination and reflectance components, which enhances the illumination and the reflectance components to approach corresponding components of ground truth. Alternatively, another kind of components decomposed method is designed to divide the representation into different frequency bands and then enhance them progressively [21, 27, 34], such as DRBN [34]. Additionally, a few methods are also developed based on a self-supervised manner [9, 22, 23, 40]. For instance, Zero-DCE [9] formulates exposure adjustment as a task of image-specific curve estimation with a deep network. However, most of these methods are not dedicated to correcting various exposures, limiting their wide-range applications under various light conditions.", + "bbox": [ + 496, + 371, + 892, + 672 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "More recently, a few works have been developed for correcting both underexposure and overexposure images. This is challenging since the optimization flows of underexposure and overexposure correction are opposite. As a pioneer work, MSEC [1] proposes to correct varieties of exposures with a Laplacian pyramid architecture, which restores lightness and details in a coarse-to-fine manner. To ease the correction of wide-range exposures with inconsistency optimization flows, Huang et al. proposed to narrow the distribution of exposure representation with exposure normalization [12]. Similarly, CMEC [24] proposes to map different exposures to an exposure-invariant space with the assistance of a transformer for exposure correction, while ECLNet [14] derives exposure-consistency representations with the bilateral activation mechanism to as", + "bbox": [ + 496, + 674, + 893, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "9905", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/029bddfbf071450ff343de885e31100f97ea8c33f2fe8483b5ed0c91deff6e55.jpg", + "image_caption": [ + "Figure 2. The overview of our proposed ERL framework, consisting of two steps. In the first step (Sec. 3.2), we formulate the exposure relationship of samples across the batch dimension on the BCM via a pretext task. In the second step (Sec. 3.3), the above-trained BCM serves as a regularization function to constrain the sample relationship of exposure correction." + ], + "image_footnote": [], + "bbox": [ + 101, + 85, + 433, + 284 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "sist exposure correction. To solve this problem in other viewpoints, Wang et al. proposed a local color distribution operator for exposure correction to meet the problem of non-uniform illumination problem [29], while FECNet [13] corrects lightness and structures progressively in a Fourier-based perspective. Although these algorithms have definitely achieved remarkable progress, there remains room for improving the optimization process across the batch dimension that contains both underexposure and overexposure corrections. In this work, we introduce a new perspective that explores the sample relationships within a minibatch to conjunct their optimization processes, which reduces the adverse effect of opposite optimization flows.", + "bbox": [ + 75, + 383, + 468, + 580 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 76, + 595, + 168, + 611 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we first present an overview of the exposure relationship learning framework. We then introduce the proposed BCM in detail via its pretext task learning that captures sample relationships. Finally, we detail the above sample relationship as the regularization term to train exposure correction approaches.", + "bbox": [ + 75, + 622, + 468, + 713 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Motivation and Overview", + "text_level": 1, + "bbox": [ + 76, + 724, + 308, + 739 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given images with incorrect exposure settings, the exposure correction task aims to correct underexposure and its adverse overexposure images to normal exposure in a single network. Previous works are usually trained with a mini-batch containing both underexposure and overexposure samples, which enables the network to learn to correct various exposures simultaneously. However, the large discrepancies between underexposure and overexposure corrections would inevitably lead to opposite optimization flows across the batch dimension. As depicted in", + "bbox": [ + 75, + 750, + 470, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Fig. 1, the underexposure and overexposure corrected results across the mini-batch are optimized oppositely. Such a large inconsistency optimization leads to a sub-optimal optimization process, which has rarely been explored for exposure correction.", + "bbox": [ + 496, + 90, + 890, + 165 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To this end, we start a new perspective for exposure correction that conjuncts the optimization of samples within a mini-batch to reduce the adverse effect of inconsistency optimization, which is built upon learning the sample relationship [11]. To achieve this, we correlate and constrain the relationship of correction procedures on the corrected results and design an ERL framework with a two-step mechanism (see Fig. 2). In the first step, it correlates the exposure relationship of samples within a mini-batch by employing the BCM. To exclude the influence of context information on the relationship, the BCM is trained with a context-irrelevant pretext task. While in the second step, the above sample relationship built upon BCM acts as an additional regularization term within the loss function of exposure correction methods, which constrains the relationship of samples. In this way, their optimization processes across the batch dimension are conjunct during training, and it would not introduce any computation burden in inference time.", + "bbox": [ + 496, + 167, + 892, + 439 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Sample Relationship Correlation", + "text_level": 1, + "bbox": [ + 500, + 446, + 790, + 462 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As the first step of the ERL framework, we propose to correlate the sample relationship across the batch dimension, which is implemented by BCM via a context-irrelevant task.", + "bbox": [ + 496, + 469, + 890, + 527 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Batch-correlation module (BCM). We design the BCM to capture the sample relationship in feature space. Its architecture is shown in Fig. 3, which consists of a feature encoder $f_{e}$ with several CNN-based layers, and a sample relationship part with a transformer encoder $t_{e}$ . In addition, the decoder $f_{d}$ consists of several CNN-based layers are set to follow the transformer encoder that reconstructs the input image, which assists the training of BCM (see Sec. 3.3).", + "bbox": [ + 496, + 529, + 892, + 650 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We then describe the workflow of BCM that captures sample relationships as follows. Let $I \\in R^{B \\times 3 \\times H \\times W}$ denote a sequence of input images in a mini-batch, where $B$ indicates the length of the sequence, and $H$ and $W$ are the spatial dimensions. The CNN-based encoder $f_{e}$ first extracts feature from images as $F \\in R^{B \\times C \\times H \\times W}$ , where $C$ is the channel dimension. Then these features are resized to the resolution of $16 \\times 16$ , which can filter out most content-relevant information since low-resolution contains less content. After that, these features are reshaped to the dimension of $R^{B \\times C'}$ that transfers the dimension of the feature as $C'$ . Finally, we correlate the relationship of these features in the batch dimension with the transformer encoder $t_{e}$ , and obtain the features $F' \\in R^{B \\times C'}$ that are correlated across the batch dimension. The whole above process is expressed as:", + "bbox": [ + 496, + 650, + 893, + 876 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nF ^ {\\prime} = t _ {e} \\left(f _ {e} (I)\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 640, + 883, + 890, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "9906", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/db96c5ebc260a9161c92617cfc12c2cf0d7e29dbea938b4e09ff67170fd36628.jpg", + "image_caption": [ + "Figure 3. The illustration of our proposed BCM, which correlates the exposure relationship of samples across the batch dimension. Specifically, it consists of an encoder part $f_{e}$ that extracts features and a decoder part $f_{d}$ that reconstructs the image, both of which have been trained in a self-reconstruction manner. We then apply a transformer encoder $t_{e}$ between the encoder and decoder parts to explore the sample relationship. Furthermore, we train the BCM with a pretext task that reconstructs the content-related phase information, leading to it focusing more on correlating exposure-related information." + ], + "image_footnote": [], + "bbox": [ + 112, + 90, + 851, + 299 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where the resize and reshape operations are omitted here.", + "bbox": [ + 76, + 380, + 454, + 395 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We further detail how the transformer encoder $t_e$ explores the sample relationship across the batch dimension. Transformer usually contains a multihead self-attention layer that models the relationship of spatial and channel dimensions [7]. By transposing the feature into the dimension that the transformer layer can operate on the batch dimension, the self-attention mechanism of the multi-head self-attention layer could cross attention samples in a minibatch, thus correlating their relationships.", + "bbox": [ + 75, + 397, + 467, + 532 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Train the BCM via a context-irrelevant task. In preliminary, the encoder and decoder parts of the BCM are first trained in a self-reconstruction manner that can reconstruct input images of themselves, then these parts are fixed and a transformer encoder $t_e$ is incorporated between them to construct BCM. Thus, when training BCM, only the transformer encoder $t_e$ is trained to learn the sample relationship.", + "bbox": [ + 75, + 535, + 467, + 641 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Since the optimization processes of the correction are mainly affected by the exposure condition, we hope the sample relationship learning correlates more with exposure-related components. To this end, the context relationship across the batch dimension should not be focused, and we now need to construct a pretext task that trains BCM to exclude the influences of context relationships.", + "bbox": [ + 75, + 642, + 467, + 747 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As shown in Fig. 3, we train a context-irrelevant pretext task on the BCM via a self-reconstruction manner. For a sample correlating with other samples, it is hard to reconstruct itself since the information of other samples is introduced. Therefore, we construct the pretext task to reconstruct content-relevant information, which suppresses the correlation of the content component, thus driving the content-irrelevant information to be more correlated across the mini-batch. Specifically, a mini-batch containing normal exposure samples $I \\in R^{B \\times 3 \\times H \\times W}$ are sent to the", + "bbox": [ + 75, + 750, + 467, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "BCM, which are substantially derived correlated feature $F'$ as depicted in Eq. 1. Then $F'$ are reshaped and resized to the dimension of $F' \\in R^{B \\times C \\times H \\times W}$ , which are further fused with $F$ . Finally, the decoder $f_d$ reconstructs the integrated features to the image space as $I_{rec}$ . This procedure is expressed as:", + "bbox": [ + 498, + 380, + 890, + 469 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nI ^ {\\prime} = f _ {d} \\left(W \\left(\\operatorname {c a t} \\left([ F, F ^ {\\prime} ]\\right)\\right)\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 604, + 470, + 890, + 488 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $W$ denotes the $1 \\times 1$ convolution operation, and $[\\cdot]$ means the concatenate operation.", + "bbox": [ + 498, + 494, + 888, + 525 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We then introduce a phase component constraint as the learning objective of the BCM. Based on FECNet [13], the amplitude component reflects the exposure-related information, while the phase component corresponds more to content information. Therefore, we construct the learning objective $\\mathcal{L}_{pre}$ to constrain the phase component of the reconstructed images $I^{\\prime}$ to be closed with that of the input images $I$ :", + "bbox": [ + 496, + 526, + 890, + 643 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {p r e} = \\left| \\left| \\mathcal {P} (\\mathcal {F} (I)) - \\mathcal {P} (\\mathcal {F} \\left(I ^ {\\prime}\\right)) \\right| \\right| _ {1}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 578, + 646, + 890, + 664 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $||\\cdot ||_1$ denotes the mean absolutely error, $\\mathcal{F}$ is the Fourier transform operation, and $\\mathcal{P}(\\cdot)$ represents getting the phase component. Based on this regularization, the BCM reduces the content information correlation across the minibatch which would put a negative effect on content reconstruction, thus leading to BCM focusing more on correlating exposure-related information.", + "bbox": [ + 496, + 670, + 890, + 776 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Sample Relationship Constraining", + "text_level": 1, + "bbox": [ + 500, + 786, + 799, + 803 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Deliver the sample relationship as regularization. After correlating the sample relationship across batch dimensions, we further deliver the sample relationship as an additional regularization term that conjuncts the optimization within a mini-batch. For implementation, when training the exposure correction network, we constrain the relationship", + "bbox": [ + 496, + 809, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "9907", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/81ba7f3e753176eebd6111590d9215c517099ae237bb8dd516f88b40785174f6.jpg", + "image_caption": [ + "Figure 4. The illustration of delivering the sample relationship as regularization for exposure correction. We employ the encoder and transformer parts of the trained BCM as the regularization function, and integrate this regularization term $\\mathcal{L}_r$ with the conventional loss functions $\\mathcal{L}_c$ of exposure correction methods to optimize the exposure correction model." + ], + "image_footnote": [], + "bbox": [ + 109, + 89, + 460, + 282 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "of the corrected samples to be closed to that of the ground truth.", + "bbox": [ + 75, + 388, + 468, + 417 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Specifically, we employ the trained BCM to construct the sample relationship within a mini-batch, where we discard its decoder part and deploy the correlated feature $F'$ as the sample relationships representation. To this end, we apply the sample relationship regularization on the $F'$ during the training of exposure correction, and its learning objective $\\mathcal{L}_r$ is expressed as:", + "bbox": [ + 75, + 417, + 472, + 525 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {r} = \\left| \\left| F _ {r e s} ^ {\\prime} - F _ {g t} ^ {\\prime} \\right| \\right| _ {1} \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 200, + 532, + 468, + 553 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $F_{res}^{\\prime}$ and $F_{gt}^{\\prime}$ denote the sample relationships representation of corrected results and ground truth, respectively.", + "bbox": [ + 75, + 560, + 468, + 590 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Integrate the above regularization term to existing methods. The above sample relationship regularization term is general and could be plugged into the training paradigm of existing exposure correction algorithms as an additional optimization objective. As shown in Fig. 4, the total learning objective for training existing exposure correction method is the combination of the conventional loss $\\mathcal{L}_c$ and the relationship regularization term $\\mathcal{L}_r$ :", + "bbox": [ + 75, + 590, + 470, + 712 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {c} + \\alpha \\mathcal {L} _ {r} \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 218, + 722, + 468, + 738 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\alpha$ is the weight factor and we set it to 0.1 empirically, and the selection of $\\alpha$ will be discussed in the ablation study. The conventional loss denotes the original loss function employed in these exposure correction methods.", + "bbox": [ + 75, + 747, + 468, + 809 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Discussion", + "text_level": 1, + "bbox": [ + 76, + 816, + 194, + 832 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To better understand how the proposed ERL framework conjunct the optimization processes by modeling sample relationships across the batch dimension, we provide an intuitive explanation as demonstrated in Fig. 5. Given the", + "bbox": [ + 75, + 839, + 470, + 902 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/cc63628cc676eccc4ff80aaf87ed0b61871a3dcfff001cce08be1f2886dd7ad5.jpg", + "image_caption": [ + "Figure 5. With the regularization of sample relationship, besides being optimized to the ground truth $Y_{i}$ , the corrected results $X_{i}$ are also implicitly constrained with other samples to keep their relationships to be closed with those of the ground truth." + ], + "image_footnote": [], + "bbox": [ + 522, + 92, + 879, + 241 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "corrected results $X(X = X_{1},\\dots,X_{i},\\dots X_{N})$ and their corresponding ground truth $Y(Y = Y_{1},\\dots,Y_{i},\\dots Y_{N})$ in a minibatch, when the network is only optimized with the conventional loss, the sample $X_{i}$ is only optimized to approach the corresponding ground truth $Y_{i}$ , and their optimization flows are separated and often opposite in exposure correction.", + "bbox": [ + 496, + 306, + 890, + 397 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "While with the employ of sample relationship constraining, the corrected result $X_{i}$ are further constrained to other corrected results $X_{j}$ (where $j \\neq i$ ) in a mini-batch, which drives their relationships to be closed to those of $Y_{i}$ and $Y_{j}$ (where $j \\neq i$ ). To this end, the optimization flow of $X_{i}$ is also affected by other samples, which could lead to a more consistent exposure correction effect across the mini-batch. In CMEC [24], it models consistency learning of different exposures that regularize the underexposure and overexposure representations to be similar in the network. Similarly, we consider our sample relationship regularization is another form of consistency learning, and it implicitly regularizes the results' exposure representation to be correlated and constrained in a mini-batch.", + "bbox": [ + 496, + 398, + 892, + 609 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 625, + 633, + 642 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Settings", + "text_level": 1, + "bbox": [ + 500, + 650, + 705, + 666 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. To evaluate the performance of our proposed method, we conduct experiments on two representative datasets, including the MSEC dataset proposed in MSEC [1] and SICE dataset [5] proposed in SICE. The MSEC dataset is rendered from the MIT-Adobe FiveK dataset [4], consisting of five exposure levels, where its retouched version by experts of middle exposure is set as ground truth. Finally, it consists of 17675 images for training, 750 images for validation, and 5905 images for testing. While for the SICE dataset, the middle exposure subset of this dataset is selected as the ground truth, and the second and last second exposure subsets are set as the underexposed and overexposed images, respectively. Finally, the SICE dataset contains 512 images for training and 30 images for testing.", + "bbox": [ + 496, + 674, + 890, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "9908", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/52af340b28e5149b37f1718b426aa7533c3955a7d83bbbd0cb91a588d585f167.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodMSECSICE#Param
UnderOverAverageUnderOverAverage
PSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIM
HE [26]16.520.691816.530.699116.530.695914.690.565112.870.499113.780.5376-
CLAHE [28]16.770.621114.450.584215.380.599012.690.503710.210.484711.450.4942-
RetinexNet [32]12.130.620910.470.595311.140.604812.940.517112.870.525212.900.52120.84M
URetinexNet [33]13.850.73719.810.673311.420.698817.390.64487.400.454312.400.54961.32M
Zero-DCE [9]14.550.588710.400.514212.060.544116.920.63307.110.429212.020.53110.079M
Zero-DCE++ [19]13.820.58879.740.514211.370.558311.930.47556.880.40889.410.44220.010M
DPED [17]20.060.682613.140.581215.910.621916.830.61337.990.430012.410.52170.39M
KIND [39]15.510.711511.660.730013.200.742413.430.48377.850.477910.640.48088.54M
SID [6]19.370.810318.830.805519.040.807419.510.663516.790.644418.150.65407.40M
RUAS [22]13.430.68076.390.46559.200.551516.630.55894.540.319610.590.43930.0014M
SCI [23]9.9650.66815.8370.51907.490.578617.860.64014.450.362912.490.50510.0003M
MSEC [1]20.520.812919.790.815620.080.821019.620.651217.590.656018.580.65367.04M
CMEC [24]22.230.814022.750.833622.540.825717.680.659218.170.681117.930.67025.40M
SID-ENC [12]22.590.842322.360.851922.450.848121.360.665219.380.684320.370.67487.45M
LCDPNet [29]22.350.865022.170.847622.300.855217.450.562217.040.646317.250.60430.96M
DRBN [34]19.740.829019.370.832119.520.830917.960.676717.330.682817.650.67980.53M
DRBN+ERL19.910.830519.600.838419.730.835518.090.673517.930.686618.010.67960.53M
DRBN-ENC [12]22.720.854422.110.852122.350.853021.890.707119.090.722920.490.71500.58M
DRBN-ENC+ERL22.920.870422.450.872422.670.871622.060.705319.500.720520.780.71290.58M
ECLNet [14]22.370.856622.700.867322.570.863122.050.689319.250.687220.650.68610.018M
ECLNet+ERL22.900.862422.580.867622.700.865522.140.690819.470.698220.810.69450.018M
FECNet [13]22.960.859823.220.874823.120.868822.010.673719.910.696120.960.68490.15M
FECNet+ERL23.100.863923.180.875923.150.871122.350.667120.100.689121.220.67810.15M
", + "bbox": [ + 76, + 88, + 916, + 425 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Quantitative results of different methods on the MSEC and the SICE datasets in terms of PSNR and SSIM. Bold denotes the results that are improved by incorporating ERL. The underline denotes the best result.", + "bbox": [ + 75, + 426, + 890, + 455 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation details. We implement our proposed model with PyTorch on a single NVIDIA GTX 3090 GPU. Firstly, we train the BCM on the normal exposure images for 100 epochs that captures the exposure sample relationship. Then, to couple the above sample relationship as additional regularization of existing methods, we select several methods as equipped methods, including DRBN [34], DRBN-ENC-4 [12], ECLNet [14] and FECNet [13]. After the integration, we add $+$ between their names and ERL to denote the improved method (such as DRBN+ERL).", + "bbox": [ + 75, + 463, + 468, + 613 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "During training, we employ an Adam optimizer with $\\beta_{1} = 0.9$ , $\\beta_{2} = 0.99$ to update the model. We set the training settings following the original settings of the above-equipped methods, including the update of the learning rate, the number of training epochs, and the patch size. All of the loss functions for exposure correction are optimized in an end-to-end manner. During the evaluation, we implement Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) for numerical evaluation.", + "bbox": [ + 75, + 616, + 470, + 752 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Comparison with State-of-the-Art Methods", + "text_level": 1, + "bbox": [ + 76, + 768, + 444, + 786 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To evaluate the performance with existing exposure correction methods, several methods are adopted as a comparison, including two traditional methods HE [26] and CLAHE [28]. Additionally, several deep-learning-based methods are involved as well, including RetinexNet [32], DPED [17], SID [6], KinD [39], DRBN [34], URetinexNet [33], Zero-DCE [9], Zero", + "bbox": [ + 75, + 794, + 468, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "DCE++ [19], RUAS [22] and SCI [23]. We further include several specific approaches for exposure correction: MSEC [1], CMEC [24], SID-ENC [12], DRBN-ENC [12], ECLNet [14], LCDPNet [29] and FECNet [13].", + "bbox": [ + 496, + 463, + 890, + 525 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Quantitative results. The quantitative results are shown in Table 1. For the MSEC dataset, following MSEC, we average the results of the exposures of the first two levels and the remaining levels of exposure as underexposure and overexposure results, respectively. As can be observed, recently proposed exposure correction approaches (such as FECNet and ECLNet) achieve excellent performance on the two datasets. With the incorporation of the ERL framework, their overall quantitative results are further improved on most subsets and metrics. It substantiates the effectiveness and flexibility of our proposed sample relationship regularization. Note that the introducing of our sample relationship regularization would not increase parameters or computation costs. In addition, the effectiveness of our framework can also be verified to improve the performance during the training procedure as shown in Fig. 7. As can be seen, our ERL framework improves performance during most training time with higher PSNR, demonstrating it improves optimization processes and thus lead to better performance.", + "bbox": [ + 496, + 531, + 892, + 818 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Qualitative results. We provide visual comparisons on the MSEC dataset in Fig. 6, and visual comparisons on the SICE dataset in Fig. 8, respectively. As can be seen, with the employ of the sample relationship regularization term, the global lightness or local structures and details of the en", + "bbox": [ + 496, + 825, + 893, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "9909", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/0979bd5e0353b6f62f9de29605f4f42305c847960f8d2d3df904782d3e8602d4.jpg", + "image_caption": [ + "(a) Input" + ], + "image_footnote": [], + "bbox": [ + 83, + 94, + 217, + 162 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/58caa6ef28c7b8e82a58ca1edd1188a5eeed0c90ce1fcdd8d9971cf49146a885.jpg", + "image_caption": [ + "(b) CMEC" + ], + "image_footnote": [], + "bbox": [ + 218, + 94, + 354, + 162 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/87ffc6498418e8d66dc49ae455658678323899156130448da6f41d63b61efaea.jpg", + "image_caption": [ + "(c) LCDPNet" + ], + "image_footnote": [], + "bbox": [ + 357, + 94, + 491, + 162 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/1342ad0bdf359bf4894a3fc1d136659e4d825dfc2b32e95e83a92e366c2b29d2.jpg", + "image_caption": [ + "(d) DRBN" + ], + "image_footnote": [], + "bbox": [ + 493, + 94, + 630, + 162 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ff8663777e94a7fb0cb15e96bd43296e0d18b3e5a8b0ed3f38f9cdd9e3dccd81.jpg", + "image_caption": [ + "(e) ECLNet" + ], + "image_footnote": [], + "bbox": [ + 633, + 94, + 767, + 162 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/080aac085b77b397b9958b98e6c9e0caf781c915c1db245e06716fad438346a7.jpg", + "image_caption": [ + "(f) FECNet" + ], + "image_footnote": [], + "bbox": [ + 771, + 94, + 905, + 164 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b13da5b011a47e18075c76c5e1eb3b49b15965ad17b74258a744d1d7e211f0fc.jpg", + "image_caption": [ + "(g) GT" + ], + "image_footnote": [], + "bbox": [ + 83, + 178, + 217, + 247 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8ddfdd70f959d5826097b6bacf3305f6c91db1492377fbc3486b83a37e72b019.jpg", + "image_caption": [ + "(h) MSEC" + ], + "image_footnote": [], + "bbox": [ + 220, + 178, + 354, + 247 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c620484564779e30309bdb972b4eb0609a73de391b7efdc0aee099965677b883.jpg", + "image_caption": [ + "(i) Zero-DCE++" + ], + "image_footnote": [], + "bbox": [ + 357, + 178, + 491, + 247 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/11b7df0cfb140ec053e212b8b10c6c0dde92c87e29ac0a911c20fff07462c5b0.jpg", + "image_caption": [ + "(j) DRBN+ERL" + ], + "image_footnote": [], + "bbox": [ + 493, + 178, + 630, + 247 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/afab3043001feecc701381e1c927f4d7cd28a383c092983b0c62102fcccb283e.jpg", + "image_caption": [ + "(k) ECLNet+ERL" + ], + "image_footnote": [], + "bbox": [ + 633, + 178, + 767, + 247 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a0f1cdf2019f475518d258807ae69be7d79b0241ef2f6b85979aa0e31512136d.jpg", + "image_caption": [ + "(1) FECNet+ERL" + ], + "image_footnote": [], + "bbox": [ + 771, + 178, + 905, + 247 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/20570e6847526d8476b46b6469ed9c29d55dd710bb9ac76a6663f7423aa16da2.jpg", + "image_caption": [ + "(m) Input" + ], + "image_footnote": [], + "bbox": [ + 83, + 262, + 217, + 332 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/cbbc3250d2995f5f90de8ad1b164e2dfd247830e24747bfe1c788db5356b6ce3.jpg", + "image_caption": [ + "(n)CMEC" + ], + "image_footnote": [], + "bbox": [ + 220, + 261, + 354, + 333 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c0993c0c0537b8935590ac19d1a903dbdf642f8170a4836fa4cb8c9b368bc7f0.jpg", + "image_caption": [ + "(o) LCDPNet" + ], + "image_footnote": [], + "bbox": [ + 357, + 262, + 491, + 332 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/308783b573562e5edb3f35adc4137371e4e22e2a952e98b6ffa7f9c5381659aa.jpg", + "image_caption": [ + "(p) DRBN" + ], + "image_footnote": [], + "bbox": [ + 493, + 262, + 630, + 332 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2a0d9ef2923ebd91c047b869618f31b88508fdd860fcef8df96812aac0e52107.jpg", + "image_caption": [ + "(q) ECLNet" + ], + "image_footnote": [], + "bbox": [ + 633, + 262, + 767, + 332 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/31bba62bf91a5bea6c0f63f9562a63c5d059c7dcc51dfd8c1faf2e97963b03f7.jpg", + "image_caption": [ + "(r) DRBN-ENC" + ], + "image_footnote": [], + "bbox": [ + 771, + 262, + 905, + 332 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c75fce3402dfff7f7f843a8f6ade9cbabb8c5895b8d12b36c7ab4d3950491a10.jpg", + "image_caption": [ + "(s)GT", + "Figure 6. Visualization results on the MSEC dataset of (top) underexposure correction and (bottom) overexposure correction." + ], + "image_footnote": [], + "bbox": [ + 83, + 347, + 217, + 416 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8b85905fad8960ad6ad74b9b9e8550922bc0609490439324c95490d1e4f59979.jpg", + "image_caption": [ + "(t)MSEC" + ], + "image_footnote": [], + "bbox": [ + 220, + 345, + 354, + 416 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/af23a38afff6b5ee3c0bd798a7679aaf3aeb9a0c089f13bd2eca649b2f2def20.jpg", + "image_caption": [ + "(u) Zero-DCE++" + ], + "image_footnote": [], + "bbox": [ + 357, + 345, + 491, + 416 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/3f6bc920d7a38fefe25f8fa96498fa3cef7857fd76e40252f92462c10d28ddf1.jpg", + "image_caption": [ + "(v) DRBN+ERL" + ], + "image_footnote": [], + "bbox": [ + 493, + 345, + 630, + 416 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f0e0e3f027220b76eaf556091e654ad5544b1c6e916cbe661a7b395cb8535921.jpg", + "image_caption": [ + "(w) ECLNet+ERL" + ], + "image_footnote": [], + "bbox": [ + 633, + 345, + 767, + 416 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/dcaac92df05cb0125c7b44e257b72ec577483c7bf968a1f226f3f334cebea4dc.jpg", + "image_caption": [ + "(x) DRBN-ENC+ERL" + ], + "image_footnote": [], + "bbox": [ + 771, + 345, + 905, + 416 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/6db3db19a5505207ee73dcbb5901e6af830e44cb2e5cc1dfe938fbfea506aabd.jpg", + "image_caption": [ + "(a) Training of ECLNet", + "Figure 7. Graph of PSNR with/without ERL framework during the training process of the ECLNet and DRBN-ENC." + ], + "image_footnote": [], + "bbox": [ + 89, + 460, + 272, + 564 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5cd94a664ee1b21caf78cafec95d31f9ba93bba8baeb58d0dc531c7d45065849.jpg", + "image_caption": [ + "(b) Training of DRBN-ENC" + ], + "image_footnote": [], + "bbox": [ + 279, + 460, + 462, + 564 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "hanced results are further improved. Therefore, these results prove our method could improve existing methods to generate better visual results. We provide more visual results in the supplementary materials.", + "bbox": [ + 75, + 619, + 467, + 681 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation Studies", + "text_level": 1, + "bbox": [ + 76, + 694, + 240, + 708 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We conduct ablation studies on the SICE dataset to further investigate the effectiveness of our proposed sample relationship modeling under different configurations, and we set the baseline as training the exposure correction method ECLNet on its original settings.", + "bbox": [ + 75, + 718, + 467, + 792 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Investigation of the BCM. To validate the effectiveness of the BCM design, we perform ablations with different configurations of the BCM and present the results in Table 2. As can be seen, without the employ of the pretext task (which means the $t_e$ in the BCM is randomly initialized) or training the pretext task using pixel $L_1$ loss, the performance can also be improved compared with the base-", + "bbox": [ + 75, + 795, + 467, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/2ec2462f97485fa9e1f6a1a70fcd177e913200906eb109d3e05b6ab73ab38ab3.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
OptionBaselinew/o pretext taskwith L1with Lpre
PSNR20.6520.7620.7220.81
SSIM0.68610.69120.68970.6945
", + "bbox": [ + 500, + 450, + 890, + 500 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2. Ablation study of investigating the configuration of the BCM based on the ECLNet in SICE dataset.", + "bbox": [ + 500, + 502, + 890, + 530 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "line. This demonstrates that the sample relationship regularization mechanism itself is effective for improving performance, while the designing of our pretext task can further elevate the exposure correction effect.", + "bbox": [ + 496, + 542, + 890, + 602 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Investigation of weight coefficient. To investigate the influences of the weight coefficient $\\alpha$ on sample relationship regularization term, we perform experiment experiments with setting different coefficients $\\alpha$ . As shown in Fig. 9 (a), larger $\\alpha$ could lead to a slight performance decrease. Overall, the performance remains stable and is robust to the weight coefficient changes.", + "bbox": [ + 496, + 604, + 890, + 709 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Investigation of batch size. We perform ablation studies to investigate the batch size of sample relationship regularization for optimizing exposure correction in Fig. 9 (b). The results show that setting the batch size of 4 improves more performance than other settings, while a larger batch size can not improve performance significantly. This may be caused by the sample relationship of large batch size being hard to optimize due to the high variance.", + "bbox": [ + 496, + 710, + 890, + 832 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Extension on the Mixed Enhancement Task", + "text_level": 1, + "bbox": [ + 500, + 845, + 867, + 859 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To further demonstrate the extensive ability of our method, we further train and test the method on an image", + "bbox": [ + 500, + 869, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "9910", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7745b297b343bbeb81220584128a9df517dc8e125bec11ad0f60342dde602f43.jpg", + "image_caption": [ + "(a) Input" + ], + "image_footnote": [], + "bbox": [ + 83, + 95, + 217, + 164 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/5e8528feaff35955b983805f25767989f6d715c37292361f78e35ac3b86a3715.jpg", + "image_caption": [ + "(b) CMEC" + ], + "image_footnote": [], + "bbox": [ + 220, + 94, + 354, + 164 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/444ba7282e7795579f21d94c6ad10f2e39e3827a3b60568c0e486f957017e3b7.jpg", + "image_caption": [ + "(c) LCDPNet" + ], + "image_footnote": [], + "bbox": [ + 359, + 94, + 493, + 162 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/be187d5ac3faa6febfdc79ceb8f6c9fce53c4699b5da2f0f3fa689d2e54b4cb3.jpg", + "image_caption": [ + "(d) DRBN" + ], + "image_footnote": [], + "bbox": [ + 496, + 94, + 630, + 162 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/8b618325fbd19c5f6931e584d94093ad99346caf797da99e59e9028da4dbc7c2.jpg", + "image_caption": [ + "(e) ECLNet" + ], + "image_footnote": [], + "bbox": [ + 633, + 94, + 767, + 162 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/b32c1a5f3004f6bc25415356ccfe06cdbf72e70195742d739e733925c5b342b1.jpg", + "image_caption": [ + "(f) FECNet" + ], + "image_footnote": [], + "bbox": [ + 771, + 94, + 903, + 164 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/5b06570c1fee61b47fc8356e7e70e69b284e6e51b0a38382116301ba23f5b4ca.jpg", + "image_caption": [ + "(g) GT" + ], + "image_footnote": [], + "bbox": [ + 83, + 176, + 217, + 246 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/47fe4356dce6cb812ce1162ebe1fbfdbe0e27ae8c150b380a0d02f6646928e65.jpg", + "image_caption": [ + "(h) MSEC" + ], + "image_footnote": [], + "bbox": [ + 220, + 176, + 354, + 246 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/61dee80c3456635996ac56c318c2e84509b24f547467eccc37817fd5a637ed69.jpg", + "image_caption": [ + "(i) Zero-DCE++" + ], + "image_footnote": [], + "bbox": [ + 357, + 176, + 493, + 246 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/2a0c61a4574544e24c78076a64eb8e1f9358e8880cf82cb5e67330065a2b31a0.jpg", + "image_caption": [ + "(j) DRBN+ERL" + ], + "image_footnote": [], + "bbox": [ + 496, + 176, + 630, + 246 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c9989fee82b63a7f8d64f2e205f07d3cf8d38a0ebe2b0f3ff0e4657ff4361257.jpg", + "image_caption": [ + "(k) ECLNet+ERL" + ], + "image_footnote": [], + "bbox": [ + 633, + 176, + 767, + 246 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/9cdb0220c6c4d7324bef65581acb444d7753dc574a32dc038124bf1b192b1723.jpg", + "image_caption": [ + "(1) FECNet+ERL" + ], + "image_footnote": [], + "bbox": [ + 771, + 176, + 903, + 246 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e189b62415a8913da81b2a27100f1a48a7e123dd94fe177b7ab73b19978b6598.jpg", + "image_caption": [ + "(m) Input" + ], + "image_footnote": [], + "bbox": [ + 83, + 258, + 217, + 329 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/5df5c791f4215085d66756d2aacbf287a12e4adb9ef9a6f129a65827c911c8fb.jpg", + "image_caption": [ + "(n) CMEC" + ], + "image_footnote": [], + "bbox": [ + 220, + 258, + 354, + 329 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/3ee582e7761064db0707ee6bd38f12b183ccbafb8ef385133cf1ebedd6ded2bb.jpg", + "image_caption": [ + "(o) LCDPNet" + ], + "image_footnote": [], + "bbox": [ + 357, + 258, + 493, + 329 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/b4727c1bbe0ddb2047b297a8515aace31931cd8165891895605399b929f7a131.jpg", + "image_caption": [ + "(p) DRBN" + ], + "image_footnote": [], + "bbox": [ + 496, + 258, + 630, + 329 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/632ec40ba0e638ffefa5d04285679ef2e3489bb84c19f15a4d93617f13e26af7.jpg", + "image_caption": [ + "(q) ECLNet" + ], + "image_footnote": [], + "bbox": [ + 633, + 258, + 767, + 329 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/7c9473306b7ced920957010c66b1185ff2cb2c6e07ee166fba0a3bcb807de976.jpg", + "image_caption": [ + "(r) DRBN-ENC" + ], + "image_footnote": [], + "bbox": [ + 771, + 258, + 903, + 329 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/af3a368111c9ddebb5d7e287c737d2734631e3e1057e6da2c67aacfa2a7644f5.jpg", + "image_caption": [ + "(s) GT", + "Figure 8. Visualization results on the SICE dataset of (top) underexposure correction and (bottom) overexposure correction." + ], + "image_footnote": [], + "bbox": [ + 83, + 342, + 217, + 412 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/7b6a26ed1a4a1104479cbd98affbe20c169f9f5cf063e4dda1968cee11dcdd63.jpg", + "image_caption": [ + "(t) MSEC" + ], + "image_footnote": [], + "bbox": [ + 220, + 342, + 354, + 412 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/78bf177f2c24c214fb3d0dcd221df85c498450b4c12cac4d1920b8b0dd10c739.jpg", + "image_caption": [ + "(u) Zero-DCE++" + ], + "image_footnote": [], + "bbox": [ + 357, + 342, + 493, + 412 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/b055f5959d3926aedc5d44da9a83f38309a397771757ad8cedbfb60126b61df0.jpg", + "image_caption": [ + "(v) DRBN+ERL" + ], + "image_footnote": [], + "bbox": [ + 496, + 342, + 630, + 412 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/30fe0ca7ecae2c01003e63cc4754aa28e6c8bb746d67e87e78cf12e695c52e96.jpg", + "image_caption": [ + "(w) ECLNet+ERL" + ], + "image_footnote": [], + "bbox": [ + 633, + 342, + 767, + 412 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/7d4e9bdee93e61e979978d79f7452f893af376bd6d0845e9408a3d2a2bb5d066.jpg", + "image_caption": [ + "(x) DRBN-ENC+ERL" + ], + "image_footnote": [], + "bbox": [ + 771, + 342, + 903, + 412 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/5c23ba8c5b8b048e93406c4566ba88f450f6ba35ba5d29a44d3e3e9648ba91ce.jpg", + "image_caption": [ + "(a) Ablation of weight coefficient $\\alpha$", + "Figure 9. Ablation study for investigating the factors of (a) the weight coefficient $\\alpha$ and (b) the batch size of our method based on the ECLNet network." + ], + "image_footnote": [], + "bbox": [ + 86, + 455, + 274, + 563 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/a2762782fdccca3cd0f87d7587504acf86e165d9b187f629f0f3e19611521960.jpg", + "image_caption": [ + "(b) Ablation of batch size" + ], + "image_footnote": [], + "bbox": [ + 279, + 455, + 465, + 563 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/b28bc09bbe5ae9f87e129ff90aa4e67a875e92ec886c77eeb13cb5673b0c9948.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodLOLSICE (over)FiveKAverage
DRBN19.39/0.81719.46/0.72921.77/0.85920.21/0.801
DRBN+ERL19.84/0.82419.34/0.73022.14/0.87320.44/0.809
ECLNet21.54/0.80619.98/0.72823.54/0.85121.69/0.795
ECLNet+ERL22.01/0.82719.73/0.71423.71/0.85321.82/0.798
FECNet22.03/0.83619.78/0.71823.82/0.84921.88/0.800
FECNet+ERL21.08/0.82920.57/0.72224.18/0.86421.94/0.805
", + "bbox": [ + 78, + 637, + 485, + 734 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 3. Quantitative results of applying our framework on exposure correction methods for multiple enhancement tasks. Bold denotes the results that are improved by incorporating the ERL.", + "bbox": [ + 76, + 738, + 468, + 780 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "mixed enhancement task containing different enhancement tasks, where the optimization flows of them are quite different. Specifically, we blend the LOL dataset [32] designed for low-light enhancement, MIT-FiveK dataset [4] collected for image retouching, and the overexposure subset from the SICE dataset to build a Task-mix dataset. As shown in Table 3, with the equipped with our sample relationship regu", + "bbox": [ + 75, + 794, + 470, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "larization, the overall performance is improved. We provide visual results in the supplementary materials.", + "bbox": [ + 500, + 449, + 890, + 481 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 494, + 617, + 508 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, for the first time, we explore the sample relationship within a mini-batch to meet the optimization inconsistency problem of exposure correction. Thus, we introduce a framework that conjunct the optimization process of exposure correction with two steps. The first step correlates the exposure relationship of samples via a context-irrelevant pretext task, while the second step constrains the relationship of corrected results by delivering the above sample relationship as a regularization term within the loss function. Our sample relationship design is compatible with existing exposure correction approaches that serve as a general regularization term. Extensive experiments demonstrate our framework consistently boosts representative methods' performance on exposure correction datasets. Although the performance improvement is not very significant in some cases, we hope our approach would shed light on investigating the sample relationship for improving the performance of low-level vision problems in the future.", + "bbox": [ + 496, + 518, + 890, + 790 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments. This work was supported by the JKW Research Funds under Grant 20-163-14-LZ-001-004-01, and the Anhui Provincial Natural Science Foundation under Grant 2108085UD12. We acknowledge the support of GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC.", + "bbox": [ + 496, + 809, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "9911", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Mahmoud Afifi, Konstantinos G Derpanis, Bjorn Ommer, and Michael S Brown. Learning multi-scale photo exposure correction. In Proceedings of the IEEE International Conference on Computer Vision, 2021. 1, 2, 5, 6", + "[2] Tarik Arici, Salih Dikbas, and Yucel Altunbasak. A histogram modification framework and its application for image contrast enhancement. IEEE Transactions on Image Processing, 18(9):1921-1935, 2009. 1, 2", + "[3] Tarik Arici, Salih Dikbas, and Yucel Altunbasak. A histogram modification framework and its application for image contrast enhancement. IEEE Transactions on image processing, 18(9):1921-1935, 2009. 2", + "[4] Vladimir Bychkovsky, Sylvain Paris, Eric Chan, and Frédo Durand. Learning photographic global tonal adjustment with a database of input/output image pairs. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 97-104, 2011. 5, 8", + "[5] Jianrui Cai, Shuhang Gu, and Lei Zhang. Learning a deep single image contrast enhancer from multi-exposure images. IEEE Transactions on Image Processing, 27(4):2049-2062, 2018. 5", + "[6] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. arXiv preprint arXiv:1805.01934, 2018. 6", + "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 4", + "[8] Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, and Xinghao Ding. A weighted variational model for simultaneous reflectance and illumination estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2782-2790, 2016. 1, 2", + "[9] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1780-1789, 2020. 2, 6", + "[10] Xiaojie Guo, Yu Li, and Haibin Ling. LIME: Low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing, 26(2):982-993, 2016. 1, 2", + "[11] Zhi Hou, Baosheng Yu, and Dacheng Tao. Batchformer: Learning to explore sample relationships for robust representation learning. In CVPR, 2022. 1, 3", + "[12] Jie Huang, Yajing Liu, Xueyang Fu, Man Zhou, Yang Wang, Feng Zhao, and Zhiwei Xiong. Exposure normalization and compensation for multiple-exposure correction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6043-6052, 2022. 1, 2, 6", + "[13] Jie Huang, Yajing Liu, Feng Zhao, Keyu Yan, Jinghao Zhang, Yukun Huang, Man Zhou, and Zhiwei Xiong. Deep fourier-based exposure correction network with spatial fre" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "quency interaction. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 1, 3, 4, 6", + "[14] Jie Huang, Man Zhou, Yajing Liu, Mingde Yao, Feng Zhao, and Zhiwei Xiong. Exposure-consistency representation learning for exposure correction. In Proceedings of the 30th ACM International Conference on Multimedia, page 6309-6317, 2022. 1, 2, 6", + "[15] Haidi Ibrahim and Nicholas Sia Pik Kong. Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(4):1752-1758, 2007. 2", + "[16] Haidi Ibrahim and Nicholas Sia Pik Kong. Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(4):1752-1758, 2007. 2", + "[17] Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, and Luc Van Gool. Dslr-quality photos on mobile devices with deep convolutional networks. In ICCV, pages 3277-3285, 2017. 6", + "[18] Edwin H Land. The Retinex theory of color vision. Scientific American, 237(6):108-129, 1977. 2", + "[19] Chongyi Li, Chunle Guo, and Chen Change Loy. Learning to enhance low-light image via zero-reference deep curve estimation. arXiv preprint arXiv:2103.00860, 2021. 6", + "[20] Mading Li, Jiaying Liu, Wenhan Yang, Xiaoyan Sun, and Zongming Guo. Structure-revealing low-light image enhancement via robust Retinex model. IEEE Transactions on Image Processing, 27(6):2828-2841, 2018. 1, 2", + "[21] Seokjae Lim and Wonjun Kim. DSLR: Deep stacked laplacian restorer for low-light image enhancement. IEEE Transactions on Multimedia, 23:4272-4284, 2020. 2", + "[22] Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 10561-10570, 2021. 2, 6", + "[23] Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. Toward fast, flexible, and robust low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5637-5646, June 2022. 2, 6", + "[24] Ntumba Elie Nsamp, Zhongyun Hu, and Qing Wang. Learning exposure correction via consistency modeling. In Proceedings of the British Machine Vision Conference (BMVC), pages 1-12, 2018. 1, 2, 5, 6", + "[25] Zhihong Pan, Baopu Li, Dongliang He, Mingde Yao, Wenhao Wu, Tianwei Lin, Xin Li, and Errui Ding. Towards bidirectional arbitrary image rescaling: Joint optimization and cycle idempotence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17389-17398, 2022. 1", + "[26] Ioannis Pitas. Digital image processing algorithms and applications. John Wiley & Sons, 2000. 6", + "[27] Xutong Ren, Mading Li, Wen-Huang Cheng, and Jiaying Liu. Joint enhancement and denoising method via sequential decomposition. In Proceedings of the IEEE International Symposium on Circuits and Systems, pages 1-5. IEEE, 2018. 2" + ], + "bbox": [ + 503, + 92, + 890, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "9912", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[28] Ali M Reza. Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. IEEE Transactions on Multimedia, pages 35-44, 2004. 1, 6", + "[29] Haoyuan Wang, Ke Xu, and Rynson W.H. Lau. Local color distributions prior for image enhancement. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 1, 3, 6", + "[30] Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, and Jiaya Jia. Underexposed photo enhancement using deep illumination estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6849-6857, 2019. 2", + "[31] Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Transactions on Image Processing, 22(9):3538-3548, 2013. 1", + "[32] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep Retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560, 2018. 1, 2, 6, 8", + "[33] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5901-5910, June 2022. 1, 2, 6", + "[34] Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3063-3072, 2020. 1, 2, 6", + "[35] Mingde Yao, Dongliang He, Xin Li, Zhihong Pan, and Zhiwei Xiong. Bidirectional translation between uhd-hdr and hd-sdr videos. IEEE Transactions on Multimedia, 2023. 1", + "[36] Mingde Yao, Zhiwei Xiong, Lizhi Wang, Dong Liu, and Xuejin Chen. Spectral-depth imaging with deep learning based reconstruction. Optics express, 27(26):38312-38325, 2019. 1", + "[37] Wenlong Zhang, Yihao Liu, Chao Dong, and Yu Qiao. RankSRGAN: Generative adversarial networks with ranker for image super-resolution. In Proceedings of the IEEE International Conference on Computer Vision, pages 3096-3105, 2019. 1", + "[38] Yonghua Zhang, Xiaojie Guo, Jiayi Ma, Wei Liu, and Jiawan Zhang. Beyond brightening low-light images. International Journal of Computer Vision, 129(4):1013-1037, 2021. 2", + "[39] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In Proceedings of the ACM International Conference on Multimedia, pages 1632-1640, 2019. 1, 2, 6", + "[40] Anqi Zhu, Lin Zhang, Ying Shen, Yong Ma, Shengjie Zhao, and Yicong Zhou. Zero-shot restoration of underexposed images via robust Retinex decomposition. In Proceedings of the IEEE International Conference on Multimedia and Expo, pages 1-6, 2020. 2" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[41] Yurui Zhu, Xueyang Fu, Chengzhi Cao, Xi Wang, Qibin Sun, and Zheng-Jun Zha. Single image shadow detection via complementary mechanism. In Proceedings of the 30th ACM International Conference on Multimedia, pages 6717-6726, 2022. 2", + "[42] Yurui Zhu, Xueyang Fu, and Aiping Liu. Learning dual transformation networks for image contrast enhancement. IEEE Signal Processing Letters, 27:1999-2003, 2020. 2", + "[43] Yurui Zhu, Xi Wang, Xueyang Fu, and Xiaowei Hu. Enhanced coarse-to-fine network for image restoration from under-display cameras. In Computer Vision-ECCV 2022 Workshops: Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part V, pages 130-146. Springer, 2023. 2", + "[44] Yurui Zhu, Zeyu Xiao, Yanchi Fang, Xueyang Fu, Zhiwei Xiong, and Zheng-Jun Zha. Efficient model-driven network for shadow removal. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 3635-3643, 2022. 2" + ], + "bbox": [ + 501, + 90, + 890, + 342 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "9913", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Sample Relationship for Exposure Correction/328dc83f-b0c4-4ce9-90e2-03cb25bedb51_model.json b/2023/Learning Sample Relationship for Exposure Correction/328dc83f-b0c4-4ce9-90e2-03cb25bedb51_model.json new file mode 100644 index 0000000000000000000000000000000000000000..dbae9ed262fbd004e7cb791717a0a3dcc74b9535 --- /dev/null +++ b/2023/Learning Sample Relationship for Exposure Correction/328dc83f-b0c4-4ce9-90e2-03cb25bedb51_model.json @@ -0,0 +1,3036 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.205, + 0.131, + 0.768, + 0.154 + ], + "angle": 0, + "content": "Learning Sample Relationship for Exposure Correction" + }, + { + "type": "text", + "bbox": [ + 0.123, + 0.181, + 0.847, + 0.217 + ], + "angle": 0, + "content": "Jie Huang,\\* Feng Zhao,\\* Man Zhou\\*,\\* Jie Xiao, Naishan Zheng, Kaiwen Zheng, Zhiwei Xiong University of Science and Technology of China" + }, + { + "type": "text", + "bbox": [ + 0.107, + 0.219, + 0.865, + 0.235 + ], + "angle": 0, + "content": "{hj0117,manman,ustchbxj,nszheng,kezh}@mail.ustc.edu.cn，{fzhao956,zwxiong}@ustc.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.269, + 0.314, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.303, + 0.471, + 0.424 + ], + "angle": 0, + "content": "Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network. As well recognized, the optimization flow is the opposite. Despite great advancement, existing exposure correction methods are usually trained with a mini-batch of both underexposure and overexposure mixed samples and have not explored the relationship between them to solve the optimization inconsistency." + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.425, + 0.471, + 0.651 + ], + "angle": 0, + "content": "In this paper, we introduce a new perspective to conjunct their optimization processes by correlating and constraining the relationship of correction procedure in a minibatch. The core designs of our framework consist of two steps: 1) formulating the exposure relationship of samples across the batch dimension via a context-irrelevant pretext task. 2) delivering the above sample relationship design as the regularization term within the loss function to promote optimization consistency. The proposed sample relationship design as a general term can be easily integrated into existing exposure correction methods without any computational burden in inference time. Extensive experiments over multiple representative exposure correction benchmarks demonstrate consistent performance gains by introducing our sample relationship design." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.684, + 0.21, + 0.699 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.71, + 0.47, + 0.862 + ], + "angle": 0, + "content": "The images captured under non-ideal illumination conditions, i.e., underexposure or overexposure scenes, usually suffer from unpleasant visual effects and thus count against the down-streaming vision tasks. To this end, exposure correction techniques have been developed, which aim to correct both underexposure and overexposure images to the normal exposure automatically. It is recognized that a single algorithm is challenging to take for exposure correction since the mapping flows of correcting underexposure and overexposure are quite different [12]." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.27, + 0.895, + 0.498 + ], + "angle": 0, + "content": "Recent years have witnessed explosive advancement only on the single underexposure correction, including conventional methods that rely on manually designed strategies [2, 8, 10, 20, 28, 31], and deep-learning-driven methods that account for the powerful learning capability of complicated neural networks [32-34, 39], where deep-learning methods have achieved improvement in restoring corruptions [25, 35-37]. Seldom efforts have been devoted to both underexposure and overexposure scenes within a single algorithm for meeting the practical application. Very recently, some promising works [1, 12, 14, 24] attempt to solve the above issue. Both of them follow the common principle of alleviating the optimization process inconsistency by conjugating their exposure representations in spatial domain [12, 14, 24, 29] or in frequency transform domain [13]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.503, + 0.895, + 0.64 + ], + "angle": 0, + "content": "In fact, most above exposure correction approaches are trained with a mini-batch that contains both underexposure and overexposure mixed samples (see Fig. 1). Within the mini-batch, the optimization process of a single network is the opposite. On the other hand, correlating the relationship of samples across the mini-batch could conjunct their optimization processes [11]. Therefore, by constraining the relationship across the batch dimension, the adverse effect of opposite optimization in the mini-batch could be relieved." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.644, + 0.895, + 0.902 + ], + "angle": 0, + "content": "To this end, in this paper, we introduce a new perspective that conjuncts the optimization processes across the batch dimension via sample relationship learning and further improves the optimization processes of exposure correction. To achieve this, we construct an exposure-relationship learning (ERL) framework consisting of two steps (see Fig. 2). In the first step (see Fig. 3), we devise a batch-correlation module (BCM) that captures the relationship of samples across the batch dimension. To enable such a relationship focusing on the exposure-related representations, we train BCM via a pretext task that excludes context information correlation. Then, in the second step (see Fig. 4), we deliver the above sample relationship as the additional training regularization term within the loss function of exposure correction algorithms, where the relationship of corrected results is optimized on the trained BCM. In this way, the optimization processes within a mini-batch" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.875, + 0.213, + 0.888 + ], + "angle": 0, + "content": "*Equal contributions." + }, + { + "type": "page_footnote", + "bbox": [ + 0.097, + 0.888, + 0.227, + 0.9 + ], + "angle": 0, + "content": "† Corresponding authors." + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.875, + 0.227, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9904" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.1, + 0.087, + 0.46, + 0.268 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.279, + 0.473, + 0.378 + ], + "angle": 0, + "content": "Figure 1. The illustration of optimization flow of underexposure and overexposure correction. As can be seen, the corrected results of underexposure samples are still obviously darker than normal exposure, while the corrected overexposure samples behave oppositely. This demonstrates the corrected results of underexposure and overexposure are optimized to approach their corresponding ground truth in the opposite direction." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.394, + 0.47, + 0.424 + ], + "angle": 0, + "content": "are conjunct and the adverse effect of inconsistency optimization can be reduced." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.424, + 0.471, + 0.56 + ], + "angle": 0, + "content": "Our proposed ERL framework is general and could be integrated with existing exposure correction approaches. The above sample relationship regularization is only adopted during the training procedure and does not introduce any computation burden in inference time. Extensive experiments on exposure correction datasets demonstrate consistent gains by applying our ERL framework. Moreover, it can also be extended to the mixed image enhancement task, demonstrating the extensive capability of our method." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.561, + 0.455, + 0.575 + ], + "angle": 0, + "content": "We summarize the main contributions of this work as:" + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.584, + 0.47, + 0.676 + ], + "angle": 0, + "content": "- This work is the first time to solve inconsistency optimization of exposure correction from a new perspective of batch dimension. By exploring the relationship of samples within a mini-batch, their optimization processes are conjunct to relieve the adverse inconsistency optimization effect." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.685, + 0.471, + 0.777 + ], + "angle": 0, + "content": "- We propose an exposure relationship learning (ERL) framework to correlate and constrain the relationship of corrected samples across the mini-batch. The learned sample relationship acts as an additional regularization term within the loss function to assist the model optimization." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.785, + 0.471, + 0.832 + ], + "angle": 0, + "content": "- Our ERL framework is general and can be integrated into the existing exposure correction methods without introducing any computation burden during inference." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.841, + 0.471, + 0.9 + ], + "angle": 0, + "content": "- Extensive experiments over multiple exposure correction datasets demonstrate consistent performance gains by introducing our sample relationship learning mechanism." + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.584, + 0.471, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.09, + 0.643, + 0.106 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.115, + 0.71, + 0.131 + ], + "angle": 0, + "content": "2.1. Conventional Methods" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.14, + 0.894, + 0.337 + ], + "angle": 0, + "content": "In the past decades, various methods have been proposed for correcting the exposure of images with poor illumination. Conventional works mainly rely on manually designed models and strategies. A line of work is based on histogram-based techniques [2,3,15,16], which improve the lightness and contrast of an improper exposure image. While another line of work is based on Retinex theory [18], which decomposes an image into the reflectance component and the illumination component. By enhancing the illumination component that improves the lightness, while regularizing the reflectance component that suppresses the noises, these methods have achieved prominent effect for exposure correction [8, 10, 20, 30]." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.348, + 0.729, + 0.364 + ], + "angle": 0, + "content": "2.2. Learning-based Methods" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.372, + 0.893, + 0.674 + ], + "angle": 0, + "content": "In recent years, deep-learning-based methods have attracted more attention [41-44] which is also adopted for adjusting images with poor illumination, which are learned to correct exposures in a data-driven manner. Some of these approaches are also based on the Retinex theory [30, 32, 33, 38, 39]. As a representative, RetinexNet [32] and KIND [39] decompose an image into illumination and reflectance components, which enhances the illumination and the reflectance components to approach corresponding components of ground truth. Alternatively, another kind of components decomposed method is designed to divide the representation into different frequency bands and then enhance them progressively [21, 27, 34], such as DRBN [34]. Additionally, a few methods are also developed based on a self-supervised manner [9, 22, 23, 40]. For instance, Zero-DCE [9] formulates exposure adjustment as a task of image-specific curve estimation with a deep network. However, most of these methods are not dedicated to correcting various exposures, limiting their wide-range applications under various light conditions." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.675, + 0.895, + 0.901 + ], + "angle": 0, + "content": "More recently, a few works have been developed for correcting both underexposure and overexposure images. This is challenging since the optimization flows of underexposure and overexposure correction are opposite. As a pioneer work, MSEC [1] proposes to correct varieties of exposures with a Laplacian pyramid architecture, which restores lightness and details in a coarse-to-fine manner. To ease the correction of wide-range exposures with inconsistency optimization flows, Huang et al. proposed to narrow the distribution of exposure representation with exposure normalization [12]. Similarly, CMEC [24] proposes to map different exposures to an exposure-invariant space with the assistance of a transformer for exposure correction, while ECLNet [14] derives exposure-consistency representations with the bilateral activation mechanism to as" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9905" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.102, + 0.086, + 0.434, + 0.285 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.286, + 0.471, + 0.37 + ], + "angle": 0, + "content": "Figure 2. The overview of our proposed ERL framework, consisting of two steps. In the first step (Sec. 3.2), we formulate the exposure relationship of samples across the batch dimension on the BCM via a pretext task. In the second step (Sec. 3.3), the above-trained BCM serves as a regularization function to constrain the sample relationship of exposure correction." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.384, + 0.47, + 0.581 + ], + "angle": 0, + "content": "sist exposure correction. To solve this problem in other viewpoints, Wang et al. proposed a local color distribution operator for exposure correction to meet the problem of non-uniform illumination problem [29], while FECNet [13] corrects lightness and structures progressively in a Fourier-based perspective. Although these algorithms have definitely achieved remarkable progress, there remains room for improving the optimization process across the batch dimension that contains both underexposure and overexposure corrections. In this work, we introduce a new perspective that explores the sample relationships within a minibatch to conjunct their optimization processes, which reduces the adverse effect of opposite optimization flows." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.597, + 0.169, + 0.612 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.623, + 0.47, + 0.714 + ], + "angle": 0, + "content": "In this section, we first present an overview of the exposure relationship learning framework. We then introduce the proposed BCM in detail via its pretext task learning that captures sample relationships. Finally, we detail the above sample relationship as the regularization term to train exposure correction approaches." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.726, + 0.31, + 0.74 + ], + "angle": 0, + "content": "3.1. Motivation and Overview" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Given images with incorrect exposure settings, the exposure correction task aims to correct underexposure and its adverse overexposure images to normal exposure in a single network. Previous works are usually trained with a mini-batch containing both underexposure and overexposure samples, which enables the network to learn to correct various exposures simultaneously. However, the large discrepancies between underexposure and overexposure corrections would inevitably lead to opposite optimization flows across the batch dimension. As depicted in" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.166 + ], + "angle": 0, + "content": "Fig. 1, the underexposure and overexposure corrected results across the mini-batch are optimized oppositely. Such a large inconsistency optimization leads to a sub-optimal optimization process, which has rarely been explored for exposure correction." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.168, + 0.893, + 0.44 + ], + "angle": 0, + "content": "To this end, we start a new perspective for exposure correction that conjuncts the optimization of samples within a mini-batch to reduce the adverse effect of inconsistency optimization, which is built upon learning the sample relationship [11]. To achieve this, we correlate and constrain the relationship of correction procedures on the corrected results and design an ERL framework with a two-step mechanism (see Fig. 2). In the first step, it correlates the exposure relationship of samples within a mini-batch by employing the BCM. To exclude the influence of context information on the relationship, the BCM is trained with a context-irrelevant pretext task. While in the second step, the above sample relationship built upon BCM acts as an additional regularization term within the loss function of exposure correction methods, which constrains the relationship of samples. In this way, their optimization processes across the batch dimension are conjunct during training, and it would not introduce any computation burden in inference time." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.447, + 0.791, + 0.463 + ], + "angle": 0, + "content": "3.2. Sample Relationship Correlation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.47, + 0.892, + 0.529 + ], + "angle": 0, + "content": "As the first step of the ERL framework, we propose to correlate the sample relationship across the batch dimension, which is implemented by BCM via a context-irrelevant task." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.53, + 0.893, + 0.651 + ], + "angle": 0, + "content": "Batch-correlation module (BCM). We design the BCM to capture the sample relationship in feature space. Its architecture is shown in Fig. 3, which consists of a feature encoder \\( f_{e} \\) with several CNN-based layers, and a sample relationship part with a transformer encoder \\( t_{e} \\). In addition, the decoder \\( f_{d} \\) consists of several CNN-based layers are set to follow the transformer encoder that reconstructs the input image, which assists the training of BCM (see Sec. 3.3)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.651, + 0.894, + 0.877 + ], + "angle": 0, + "content": "We then describe the workflow of BCM that captures sample relationships as follows. Let \\( I \\in R^{B \\times 3 \\times H \\times W} \\) denote a sequence of input images in a mini-batch, where \\( B \\) indicates the length of the sequence, and \\( H \\) and \\( W \\) are the spatial dimensions. The CNN-based encoder \\( f_{e} \\) first extracts feature from images as \\( F \\in R^{B \\times C \\times H \\times W} \\), where \\( C \\) is the channel dimension. Then these features are resized to the resolution of \\( 16 \\times 16 \\), which can filter out most content-relevant information since low-resolution contains less content. After that, these features are reshaped to the dimension of \\( R^{B \\times C'} \\) that transfers the dimension of the feature as \\( C' \\). Finally, we correlate the relationship of these features in the batch dimension with the transformer encoder \\( t_{e} \\), and obtain the features \\( F' \\in R^{B \\times C'} \\) that are correlated across the batch dimension. The whole above process is expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.641, + 0.885, + 0.892, + 0.903 + ], + "angle": 0, + "content": "\\[\nF ^ {\\prime} = t _ {e} \\left(f _ {e} (I)\\right), \\tag {1}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9906" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.113, + 0.092, + 0.852, + 0.3 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.303, + 0.892, + 0.372 + ], + "angle": 0, + "content": "Figure 3. The illustration of our proposed BCM, which correlates the exposure relationship of samples across the batch dimension. Specifically, it consists of an encoder part \\( f_{e} \\) that extracts features and a decoder part \\( f_{d} \\) that reconstructs the image, both of which have been trained in a self-reconstruction manner. We then apply a transformer encoder \\( t_{e} \\) between the encoder and decoder parts to explore the sample relationship. Furthermore, we train the BCM with a pretext task that reconstructs the content-related phase information, leading to it focusing more on correlating exposure-related information." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.381, + 0.455, + 0.396 + ], + "angle": 0, + "content": "where the resize and reshape operations are omitted here." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.398, + 0.468, + 0.534 + ], + "angle": 0, + "content": "We further detail how the transformer encoder \\( t_e \\) explores the sample relationship across the batch dimension. Transformer usually contains a multihead self-attention layer that models the relationship of spatial and channel dimensions [7]. By transposing the feature into the dimension that the transformer layer can operate on the batch dimension, the self-attention mechanism of the multi-head self-attention layer could cross attention samples in a minibatch, thus correlating their relationships." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.536, + 0.468, + 0.642 + ], + "angle": 0, + "content": "Train the BCM via a context-irrelevant task. In preliminary, the encoder and decoder parts of the BCM are first trained in a self-reconstruction manner that can reconstruct input images of themselves, then these parts are fixed and a transformer encoder \\( t_e \\) is incorporated between them to construct BCM. Thus, when training BCM, only the transformer encoder \\( t_e \\) is trained to learn the sample relationship." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.643, + 0.468, + 0.748 + ], + "angle": 0, + "content": "Since the optimization processes of the correction are mainly affected by the exposure condition, we hope the sample relationship learning correlates more with exposure-related components. To this end, the context relationship across the batch dimension should not be focused, and we now need to construct a pretext task that trains BCM to exclude the influences of context relationships." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.468, + 0.901 + ], + "angle": 0, + "content": "As shown in Fig. 3, we train a context-irrelevant pretext task on the BCM via a self-reconstruction manner. For a sample correlating with other samples, it is hard to reconstruct itself since the information of other samples is introduced. Therefore, we construct the pretext task to reconstruct content-relevant information, which suppresses the correlation of the content component, thus driving the content-irrelevant information to be more correlated across the mini-batch. Specifically, a mini-batch containing normal exposure samples \\( I \\in R^{B \\times 3 \\times H \\times W} \\) are sent to the" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.381, + 0.892, + 0.47 + ], + "angle": 0, + "content": "BCM, which are substantially derived correlated feature \\( F' \\) as depicted in Eq. 1. Then \\( F' \\) are reshaped and resized to the dimension of \\( F' \\in R^{B \\times C \\times H \\times W} \\), which are further fused with \\( F \\). Finally, the decoder \\( f_d \\) reconstructs the integrated features to the image space as \\( I_{rec} \\). This procedure is expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.605, + 0.472, + 0.891, + 0.489 + ], + "angle": 0, + "content": "\\[\nI ^ {\\prime} = f _ {d} \\left(W \\left(\\operatorname {c a t} \\left([ F, F ^ {\\prime} ]\\right)\\right)\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.495, + 0.89, + 0.526 + ], + "angle": 0, + "content": "where \\(W\\) denotes the \\(1 \\times 1\\) convolution operation, and \\([\\cdot]\\) means the concatenate operation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.527, + 0.892, + 0.645 + ], + "angle": 0, + "content": "We then introduce a phase component constraint as the learning objective of the BCM. Based on FECNet [13], the amplitude component reflects the exposure-related information, while the phase component corresponds more to content information. Therefore, we construct the learning objective \\(\\mathcal{L}_{pre}\\) to constrain the phase component of the reconstructed images \\(I^{\\prime}\\) to be closed with that of the input images \\(I\\):" + }, + { + "type": "equation", + "bbox": [ + 0.579, + 0.647, + 0.891, + 0.665 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {p r e} = \\left| \\left| \\mathcal {P} (\\mathcal {F} (I)) - \\mathcal {P} (\\mathcal {F} \\left(I ^ {\\prime}\\right)) \\right| \\right| _ {1}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.671, + 0.892, + 0.777 + ], + "angle": 0, + "content": "where \\(||\\cdot ||_1\\) denotes the mean absolutely error, \\(\\mathcal{F}\\) is the Fourier transform operation, and \\(\\mathcal{P}(\\cdot)\\) represents getting the phase component. Based on this regularization, the BCM reduces the content information correlation across the minibatch which would put a negative effect on content reconstruction, thus leading to BCM focusing more on correlating exposure-related information." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.787, + 0.8, + 0.804 + ], + "angle": 0, + "content": "3.3. Sample Relationship Constraining" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Deliver the sample relationship as regularization. After correlating the sample relationship across batch dimensions, we further deliver the sample relationship as an additional regularization term that conjuncts the optimization within a mini-batch. For implementation, when training the exposure correction network, we constrain the relationship" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9907" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.11, + 0.09, + 0.462, + 0.284 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.289, + 0.473, + 0.375 + ], + "angle": 0, + "content": "Figure 4. The illustration of delivering the sample relationship as regularization for exposure correction. We employ the encoder and transformer parts of the trained BCM as the regularization function, and integrate this regularization term \\(\\mathcal{L}_r\\) with the conventional loss functions \\(\\mathcal{L}_c\\) of exposure correction methods to optimize the exposure correction model." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.389, + 0.47, + 0.418 + ], + "angle": 0, + "content": "of the corrected samples to be closed to that of the ground truth." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.419, + 0.473, + 0.526 + ], + "angle": 0, + "content": "Specifically, we employ the trained BCM to construct the sample relationship within a mini-batch, where we discard its decoder part and deploy the correlated feature \\( F' \\) as the sample relationships representation. To this end, we apply the sample relationship regularization on the \\( F' \\) during the training of exposure correction, and its learning objective \\( \\mathcal{L}_r \\) is expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.201, + 0.534, + 0.47, + 0.554 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {r} = \\left| \\left| F _ {r e s} ^ {\\prime} - F _ {g t} ^ {\\prime} \\right| \\right| _ {1} \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.561, + 0.47, + 0.592 + ], + "angle": 0, + "content": "where \\(F_{res}^{\\prime}\\) and \\(F_{gt}^{\\prime}\\) denote the sample relationships representation of corrected results and ground truth, respectively." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.592, + 0.471, + 0.713 + ], + "angle": 0, + "content": "Integrate the above regularization term to existing methods. The above sample relationship regularization term is general and could be plugged into the training paradigm of existing exposure correction algorithms as an additional optimization objective. As shown in Fig. 4, the total learning objective for training existing exposure correction method is the combination of the conventional loss \\(\\mathcal{L}_c\\) and the relationship regularization term \\(\\mathcal{L}_r\\):" + }, + { + "type": "equation", + "bbox": [ + 0.22, + 0.723, + 0.47, + 0.739 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathcal {L} _ {c} + \\alpha \\mathcal {L} _ {r} \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.748, + 0.47, + 0.81 + ], + "angle": 0, + "content": "where \\(\\alpha\\) is the weight factor and we set it to 0.1 empirically, and the selection of \\(\\alpha\\) will be discussed in the ablation study. The conventional loss denotes the original loss function employed in these exposure correction methods." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.818, + 0.196, + 0.833 + ], + "angle": 0, + "content": "3.4. Discussion" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.903 + ], + "angle": 0, + "content": "To better understand how the proposed ERL framework conjunct the optimization processes by modeling sample relationships across the batch dimension, we provide an intuitive explanation as demonstrated in Fig. 5. Given the" + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.093, + 0.88, + 0.242 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.242, + 0.895, + 0.299 + ], + "angle": 0, + "content": "Figure 5. With the regularization of sample relationship, besides being optimized to the ground truth \\( Y_{i} \\), the corrected results \\( X_{i} \\) are also implicitly constrained with other samples to keep their relationships to be closed with those of the ground truth." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.307, + 0.892, + 0.398 + ], + "angle": 0, + "content": "corrected results \\( X(X = X_{1},\\dots,X_{i},\\dots X_{N}) \\) and their corresponding ground truth \\( Y(Y = Y_{1},\\dots,Y_{i},\\dots Y_{N}) \\) in a minibatch, when the network is only optimized with the conventional loss, the sample \\( X_{i} \\) is only optimized to approach the corresponding ground truth \\( Y_{i} \\), and their optimization flows are separated and often opposite in exposure correction." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.399, + 0.893, + 0.61 + ], + "angle": 0, + "content": "While with the employ of sample relationship constraining, the corrected result \\( X_{i} \\) are further constrained to other corrected results \\( X_{j} \\) (where \\( j \\neq i \\)) in a mini-batch, which drives their relationships to be closed to those of \\( Y_{i} \\) and \\( Y_{j} \\) (where \\( j \\neq i \\)). To this end, the optimization flow of \\( X_{i} \\) is also affected by other samples, which could lead to a more consistent exposure correction effect across the mini-batch. In CMEC [24], it models consistency learning of different exposures that regularize the underexposure and overexposure representations to be similar in the network. Similarly, we consider our sample relationship regularization is another form of consistency learning, and it implicitly regularizes the results' exposure representation to be correlated and constrained in a mini-batch." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.626, + 0.634, + 0.643 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.651, + 0.707, + 0.667 + ], + "angle": 0, + "content": "4.1. Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.675, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Datasets. To evaluate the performance of our proposed method, we conduct experiments on two representative datasets, including the MSEC dataset proposed in MSEC [1] and SICE dataset [5] proposed in SICE. The MSEC dataset is rendered from the MIT-Adobe FiveK dataset [4], consisting of five exposure levels, where its retouched version by experts of middle exposure is set as ground truth. Finally, it consists of 17675 images for training, 750 images for validation, and 5905 images for testing. While for the SICE dataset, the middle exposure subset of this dataset is selected as the ground truth, and the second and last second exposure subsets are set as the underexposed and overexposed images, respectively. Finally, the SICE dataset contains 512 images for training and 30 images for testing." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9908" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.078, + 0.089, + 0.917, + 0.426 + ], + "angle": 0, + "content": "
MethodMSECSICE#Param
UnderOverAverageUnderOverAverage
PSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIM
HE [26]16.520.691816.530.699116.530.695914.690.565112.870.499113.780.5376-
CLAHE [28]16.770.621114.450.584215.380.599012.690.503710.210.484711.450.4942-
RetinexNet [32]12.130.620910.470.595311.140.604812.940.517112.870.525212.900.52120.84M
URetinexNet [33]13.850.73719.810.673311.420.698817.390.64487.400.454312.400.54961.32M
Zero-DCE [9]14.550.588710.400.514212.060.544116.920.63307.110.429212.020.53110.079M
Zero-DCE++ [19]13.820.58879.740.514211.370.558311.930.47556.880.40889.410.44220.010M
DPED [17]20.060.682613.140.581215.910.621916.830.61337.990.430012.410.52170.39M
KIND [39]15.510.711511.660.730013.200.742413.430.48377.850.477910.640.48088.54M
SID [6]19.370.810318.830.805519.040.807419.510.663516.790.644418.150.65407.40M
RUAS [22]13.430.68076.390.46559.200.551516.630.55894.540.319610.590.43930.0014M
SCI [23]9.9650.66815.8370.51907.490.578617.860.64014.450.362912.490.50510.0003M
MSEC [1]20.520.812919.790.815620.080.821019.620.651217.590.656018.580.65367.04M
CMEC [24]22.230.814022.750.833622.540.825717.680.659218.170.681117.930.67025.40M
SID-ENC [12]22.590.842322.360.851922.450.848121.360.665219.380.684320.370.67487.45M
LCDPNet [29]22.350.865022.170.847622.300.855217.450.562217.040.646317.250.60430.96M
DRBN [34]19.740.829019.370.832119.520.830917.960.676717.330.682817.650.67980.53M
DRBN+ERL19.910.830519.600.838419.730.835518.090.673517.930.686618.010.67960.53M
DRBN-ENC [12]22.720.854422.110.852122.350.853021.890.707119.090.722920.490.71500.58M
DRBN-ENC+ERL22.920.870422.450.872422.670.871622.060.705319.500.720520.780.71290.58M
ECLNet [14]22.370.856622.700.867322.570.863122.050.689319.250.687220.650.68610.018M
ECLNet+ERL22.900.862422.580.867622.700.865522.140.690819.470.698220.810.69450.018M
FECNet [13]22.960.859823.220.874823.120.868822.010.673719.910.696120.960.68490.15M
FECNet+ERL23.100.863923.180.875923.150.871122.350.667120.100.689121.220.67810.15M
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.428, + 0.892, + 0.456 + ], + "angle": 0, + "content": "Table 1. Quantitative results of different methods on the MSEC and the SICE datasets in terms of PSNR and SSIM. Bold denotes the results that are improved by incorporating ERL. The underline denotes the best result." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.464, + 0.47, + 0.614 + ], + "angle": 0, + "content": "Implementation details. We implement our proposed model with PyTorch on a single NVIDIA GTX 3090 GPU. Firstly, we train the BCM on the normal exposure images for 100 epochs that captures the exposure sample relationship. Then, to couple the above sample relationship as additional regularization of existing methods, we select several methods as equipped methods, including DRBN [34], DRBN-ENC-4 [12], ECLNet [14] and FECNet [13]. After the integration, we add \\(+\\) between their names and ERL to denote the improved method (such as DRBN+ERL)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.617, + 0.471, + 0.753 + ], + "angle": 0, + "content": "During training, we employ an Adam optimizer with \\(\\beta_{1} = 0.9\\), \\(\\beta_{2} = 0.99\\) to update the model. We set the training settings following the original settings of the above-equipped methods, including the update of the learning rate, the number of training epochs, and the patch size. All of the loss functions for exposure correction are optimized in an end-to-end manner. During the evaluation, we implement Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) for numerical evaluation." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.77, + 0.446, + 0.787 + ], + "angle": 0, + "content": "4.2. Comparison with State-of-the-Art Methods" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.795, + 0.47, + 0.903 + ], + "angle": 0, + "content": "To evaluate the performance with existing exposure correction methods, several methods are adopted as a comparison, including two traditional methods HE [26] and CLAHE [28]. Additionally, several deep-learning-based methods are involved as well, including RetinexNet [32], DPED [17], SID [6], KinD [39], DRBN [34], URetinexNet [33], Zero-DCE [9], Zero" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.464, + 0.892, + 0.526 + ], + "angle": 0, + "content": "DCE++ [19], RUAS [22] and SCI [23]. We further include several specific approaches for exposure correction: MSEC [1], CMEC [24], SID-ENC [12], DRBN-ENC [12], ECLNet [14], LCDPNet [29] and FECNet [13]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.532, + 0.893, + 0.819 + ], + "angle": 0, + "content": "Quantitative results. The quantitative results are shown in Table 1. For the MSEC dataset, following MSEC, we average the results of the exposures of the first two levels and the remaining levels of exposure as underexposure and overexposure results, respectively. As can be observed, recently proposed exposure correction approaches (such as FECNet and ECLNet) achieve excellent performance on the two datasets. With the incorporation of the ERL framework, their overall quantitative results are further improved on most subsets and metrics. It substantiates the effectiveness and flexibility of our proposed sample relationship regularization. Note that the introducing of our sample relationship regularization would not increase parameters or computation costs. In addition, the effectiveness of our framework can also be verified to improve the performance during the training procedure as shown in Fig. 7. As can be seen, our ERL framework improves performance during most training time with higher PSNR, demonstrating it improves optimization processes and thus lead to better performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.826, + 0.894, + 0.903 + ], + "angle": 0, + "content": "Qualitative results. We provide visual comparisons on the MSEC dataset in Fig. 6, and visual comparisons on the SICE dataset in Fig. 8, respectively. As can be seen, with the employ of the sample relationship regularization term, the global lightness or local structures and details of the en" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9909" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.095, + 0.218, + 0.164 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.126, + 0.165, + 0.175, + 0.176 + ], + "angle": 0, + "content": "(a) Input" + }, + { + "type": "image", + "bbox": [ + 0.22, + 0.095, + 0.355, + 0.164 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.258, + 0.165, + 0.317, + 0.175 + ], + "angle": 0, + "content": "(b) CMEC" + }, + { + "type": "image", + "bbox": [ + 0.358, + 0.095, + 0.493, + 0.164 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.388, + 0.165, + 0.462, + 0.175 + ], + "angle": 0, + "content": "(c) LCDPNet" + }, + { + "type": "image", + "bbox": [ + 0.495, + 0.095, + 0.631, + 0.164 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.534, + 0.165, + 0.591, + 0.175 + ], + "angle": 0, + "content": "(d) DRBN" + }, + { + "type": "image", + "bbox": [ + 0.634, + 0.095, + 0.769, + 0.164 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.668, + 0.165, + 0.732, + 0.175 + ], + "angle": 0, + "content": "(e) ECLNet" + }, + { + "type": "image", + "bbox": [ + 0.772, + 0.095, + 0.906, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.806, + 0.165, + 0.869, + 0.175 + ], + "angle": 0, + "content": "(f) FECNet" + }, + { + "type": "image", + "bbox": [ + 0.084, + 0.179, + 0.218, + 0.248 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.131, + 0.248, + 0.169, + 0.259 + ], + "angle": 0, + "content": "(g) GT" + }, + { + "type": "image", + "bbox": [ + 0.221, + 0.179, + 0.355, + 0.248 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.258, + 0.248, + 0.316, + 0.259 + ], + "angle": 0, + "content": "(h) MSEC" + }, + { + "type": "image", + "bbox": [ + 0.358, + 0.179, + 0.493, + 0.248 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.38, + 0.248, + 0.47, + 0.259 + ], + "angle": 0, + "content": "(i) Zero-DCE++" + }, + { + "type": "image", + "bbox": [ + 0.495, + 0.179, + 0.631, + 0.248 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.518, + 0.248, + 0.606, + 0.259 + ], + "angle": 0, + "content": "(j) DRBN+ERL" + }, + { + "type": "image", + "bbox": [ + 0.634, + 0.179, + 0.768, + 0.248 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.651, + 0.248, + 0.749, + 0.259 + ], + "angle": 0, + "content": "(k) ECLNet+ERL" + }, + { + "type": "image", + "bbox": [ + 0.772, + 0.179, + 0.906, + 0.248 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.791, + 0.248, + 0.885, + 0.258 + ], + "angle": 0, + "content": "(1) FECNet+ERL" + }, + { + "type": "image", + "bbox": [ + 0.084, + 0.263, + 0.218, + 0.333 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.125, + 0.333, + 0.177, + 0.343 + ], + "angle": 0, + "content": "(m) Input" + }, + { + "type": "image", + "bbox": [ + 0.221, + 0.262, + 0.355, + 0.334 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.258, + 0.334, + 0.317, + 0.344 + ], + "angle": 0, + "content": "(n)CMEC" + }, + { + "type": "image", + "bbox": [ + 0.358, + 0.263, + 0.493, + 0.333 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.388, + 0.333, + 0.462, + 0.343 + ], + "angle": 0, + "content": "(o) LCDPNet" + }, + { + "type": "image", + "bbox": [ + 0.495, + 0.263, + 0.631, + 0.333 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.534, + 0.333, + 0.591, + 0.343 + ], + "angle": 0, + "content": "(p) DRBN" + }, + { + "type": "image", + "bbox": [ + 0.634, + 0.263, + 0.768, + 0.333 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.668, + 0.333, + 0.733, + 0.343 + ], + "angle": 0, + "content": "(q) ECLNet" + }, + { + "type": "image", + "bbox": [ + 0.772, + 0.263, + 0.906, + 0.333 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.794, + 0.333, + 0.882, + 0.343 + ], + "angle": 0, + "content": "(r) DRBN-ENC" + }, + { + "type": "image", + "bbox": [ + 0.084, + 0.348, + 0.218, + 0.417 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.131, + 0.417, + 0.168, + 0.426 + ], + "angle": 0, + "content": "(s)GT" + }, + { + "type": "image", + "bbox": [ + 0.221, + 0.347, + 0.355, + 0.417 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.26, + 0.417, + 0.315, + 0.426 + ], + "angle": 0, + "content": "(t)MSEC" + }, + { + "type": "image", + "bbox": [ + 0.358, + 0.347, + 0.493, + 0.417 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.379, + 0.417, + 0.471, + 0.427 + ], + "angle": 0, + "content": "(u) Zero-DCE++" + }, + { + "type": "image", + "bbox": [ + 0.495, + 0.347, + 0.631, + 0.417 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.517, + 0.417, + 0.607, + 0.427 + ], + "angle": 0, + "content": "(v) DRBN+ERL" + }, + { + "type": "image", + "bbox": [ + 0.634, + 0.347, + 0.769, + 0.417 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.65, + 0.417, + 0.75, + 0.427 + ], + "angle": 0, + "content": "(w) ECLNet+ERL" + }, + { + "type": "image", + "bbox": [ + 0.772, + 0.347, + 0.906, + 0.417 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.777, + 0.417, + 0.898, + 0.427 + ], + "angle": 0, + "content": "(x) DRBN-ENC+ERL" + }, + { + "type": "image_caption", + "bbox": [ + 0.115, + 0.431, + 0.853, + 0.446 + ], + "angle": 0, + "content": "Figure 6. Visualization results on the MSEC dataset of (top) underexposure correction and (bottom) overexposure correction." + }, + { + "type": "image", + "bbox": [ + 0.09, + 0.461, + 0.274, + 0.565 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.116, + 0.567, + 0.243, + 0.579 + ], + "angle": 0, + "content": "(a) Training of ECLNet" + }, + { + "type": "image", + "bbox": [ + 0.28, + 0.461, + 0.464, + 0.565 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.293, + 0.567, + 0.445, + 0.579 + ], + "angle": 0, + "content": "(b) Training of DRBN-ENC" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.583, + 0.468, + 0.61 + ], + "angle": 0, + "content": "Figure 7. Graph of PSNR with/without ERL framework during the training process of the ECLNet and DRBN-ENC." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.621, + 0.468, + 0.682 + ], + "angle": 0, + "content": "hanced results are further improved. Therefore, these results prove our method could improve existing methods to generate better visual results. We provide more visual results in the supplementary materials." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.695, + 0.241, + 0.709 + ], + "angle": 0, + "content": "4.3. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.719, + 0.468, + 0.794 + ], + "angle": 0, + "content": "We conduct ablation studies on the SICE dataset to further investigate the effectiveness of our proposed sample relationship modeling under different configurations, and we set the baseline as training the exposure correction method ECLNet on its original settings." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.796, + 0.468, + 0.901 + ], + "angle": 0, + "content": "Investigation of the BCM. To validate the effectiveness of the BCM design, we perform ablations with different configurations of the BCM and present the results in Table 2. As can be seen, without the employ of the pretext task (which means the \\( t_e \\) in the BCM is randomly initialized) or training the pretext task using pixel \\( L_1 \\) loss, the performance can also be improved compared with the base-" + }, + { + "type": "table", + "bbox": [ + 0.501, + 0.451, + 0.892, + 0.5 + ], + "angle": 0, + "content": "
OptionBaselinew/o pretext taskwith L1with Lpre
PSNR20.6520.7620.7220.81
SSIM0.68610.69120.68970.6945
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.503, + 0.892, + 0.531 + ], + "angle": 0, + "content": "Table 2. Ablation study of investigating the configuration of the BCM based on the ECLNet in SICE dataset." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.543, + 0.892, + 0.603 + ], + "angle": 0, + "content": "line. This demonstrates that the sample relationship regularization mechanism itself is effective for improving performance, while the designing of our pretext task can further elevate the exposure correction effect." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.605, + 0.892, + 0.71 + ], + "angle": 0, + "content": "Investigation of weight coefficient. To investigate the influences of the weight coefficient \\(\\alpha\\) on sample relationship regularization term, we perform experiment experiments with setting different coefficients \\(\\alpha\\). As shown in Fig. 9 (a), larger \\(\\alpha\\) could lead to a slight performance decrease. Overall, the performance remains stable and is robust to the weight coefficient changes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.712, + 0.892, + 0.833 + ], + "angle": 0, + "content": "Investigation of batch size. We perform ablation studies to investigate the batch size of sample relationship regularization for optimizing exposure correction in Fig. 9 (b). The results show that setting the batch size of 4 improves more performance than other settings, while a larger batch size can not improve performance significantly. This may be caused by the sample relationship of large batch size being hard to optimize due to the high variance." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.846, + 0.868, + 0.861 + ], + "angle": 0, + "content": "4.4. Extension on the Mixed Enhancement Task" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "To further demonstrate the extensive ability of our method, we further train and test the method on an image" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9910" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.096, + 0.218, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.126, + 0.165, + 0.175, + 0.176 + ], + "angle": 0, + "content": "(a) Input" + }, + { + "type": "image", + "bbox": [ + 0.221, + 0.095, + 0.356, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.258, + 0.166, + 0.317, + 0.175 + ], + "angle": 0, + "content": "(b) CMEC" + }, + { + "type": "image", + "bbox": [ + 0.36, + 0.095, + 0.494, + 0.164 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.388, + 0.165, + 0.462, + 0.175 + ], + "angle": 0, + "content": "(c) LCDPNet" + }, + { + "type": "image", + "bbox": [ + 0.497, + 0.095, + 0.632, + 0.164 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.534, + 0.165, + 0.591, + 0.175 + ], + "angle": 0, + "content": "(d) DRBN" + }, + { + "type": "image", + "bbox": [ + 0.635, + 0.095, + 0.769, + 0.164 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.668, + 0.165, + 0.732, + 0.175 + ], + "angle": 0, + "content": "(e) ECLNet" + }, + { + "type": "image", + "bbox": [ + 0.772, + 0.095, + 0.905, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.806, + 0.165, + 0.869, + 0.175 + ], + "angle": 0, + "content": "(f) FECNet" + }, + { + "type": "image", + "bbox": [ + 0.084, + 0.178, + 0.218, + 0.247 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.131, + 0.248, + 0.169, + 0.258 + ], + "angle": 0, + "content": "(g) GT" + }, + { + "type": "image", + "bbox": [ + 0.222, + 0.178, + 0.355, + 0.247 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.259, + 0.248, + 0.316, + 0.258 + ], + "angle": 0, + "content": "(h) MSEC" + }, + { + "type": "image", + "bbox": [ + 0.358, + 0.178, + 0.494, + 0.247 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.38, + 0.248, + 0.469, + 0.258 + ], + "angle": 0, + "content": "(i) Zero-DCE++" + }, + { + "type": "image", + "bbox": [ + 0.497, + 0.178, + 0.631, + 0.247 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.518, + 0.248, + 0.606, + 0.258 + ], + "angle": 0, + "content": "(j) DRBN+ERL" + }, + { + "type": "image", + "bbox": [ + 0.635, + 0.178, + 0.768, + 0.247 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.651, + 0.248, + 0.749, + 0.258 + ], + "angle": 0, + "content": "(k) ECLNet+ERL" + }, + { + "type": "image", + "bbox": [ + 0.772, + 0.178, + 0.905, + 0.247 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.791, + 0.248, + 0.885, + 0.258 + ], + "angle": 0, + "content": "(1) FECNet+ERL" + }, + { + "type": "image", + "bbox": [ + 0.084, + 0.26, + 0.218, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.124, + 0.331, + 0.177, + 0.341 + ], + "angle": 0, + "content": "(m) Input" + }, + { + "type": "image", + "bbox": [ + 0.222, + 0.26, + 0.355, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.259, + 0.331, + 0.317, + 0.34 + ], + "angle": 0, + "content": "(n) CMEC" + }, + { + "type": "image", + "bbox": [ + 0.358, + 0.26, + 0.494, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.388, + 0.331, + 0.462, + 0.34 + ], + "angle": 0, + "content": "(o) LCDPNet" + }, + { + "type": "image", + "bbox": [ + 0.497, + 0.26, + 0.631, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.534, + 0.331, + 0.591, + 0.341 + ], + "angle": 0, + "content": "(p) DRBN" + }, + { + "type": "image", + "bbox": [ + 0.635, + 0.26, + 0.768, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.668, + 0.331, + 0.733, + 0.341 + ], + "angle": 0, + "content": "(q) ECLNet" + }, + { + "type": "image", + "bbox": [ + 0.772, + 0.26, + 0.905, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.794, + 0.331, + 0.882, + 0.341 + ], + "angle": 0, + "content": "(r) DRBN-ENC" + }, + { + "type": "image", + "bbox": [ + 0.084, + 0.343, + 0.218, + 0.414 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.131, + 0.415, + 0.168, + 0.424 + ], + "angle": 0, + "content": "(s) GT" + }, + { + "type": "image", + "bbox": [ + 0.222, + 0.343, + 0.355, + 0.414 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.26, + 0.415, + 0.315, + 0.424 + ], + "angle": 0, + "content": "(t) MSEC" + }, + { + "type": "image", + "bbox": [ + 0.358, + 0.343, + 0.494, + 0.414 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.379, + 0.415, + 0.471, + 0.424 + ], + "angle": 0, + "content": "(u) Zero-DCE++" + }, + { + "type": "image", + "bbox": [ + 0.497, + 0.343, + 0.631, + 0.414 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.517, + 0.415, + 0.607, + 0.424 + ], + "angle": 0, + "content": "(v) DRBN+ERL" + }, + { + "type": "image", + "bbox": [ + 0.635, + 0.343, + 0.768, + 0.414 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.65, + 0.415, + 0.75, + 0.424 + ], + "angle": 0, + "content": "(w) ECLNet+ERL" + }, + { + "type": "image", + "bbox": [ + 0.772, + 0.343, + 0.905, + 0.414 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.778, + 0.415, + 0.898, + 0.424 + ], + "angle": 0, + "content": "(x) DRBN-ENC+ERL" + }, + { + "type": "image_caption", + "bbox": [ + 0.12, + 0.428, + 0.848, + 0.442 + ], + "angle": 0, + "content": "Figure 8. Visualization results on the SICE dataset of (top) underexposure correction and (bottom) overexposure correction." + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.456, + 0.275, + 0.564 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.084, + 0.565, + 0.274, + 0.576 + ], + "angle": 0, + "content": "(a) Ablation of weight coefficient \\(\\alpha\\)" + }, + { + "type": "image", + "bbox": [ + 0.28, + 0.456, + 0.466, + 0.564 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.3, + 0.566, + 0.437, + 0.576 + ], + "angle": 0, + "content": "(b) Ablation of batch size" + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.585, + 0.47, + 0.626 + ], + "angle": 0, + "content": "Figure 9. Ablation study for investigating the factors of (a) the weight coefficient \\(\\alpha\\) and (b) the batch size of our method based on the ECLNet network." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.638, + 0.486, + 0.736 + ], + "angle": 0, + "content": "
MethodLOLSICE (over)FiveKAverage
DRBN19.39/0.81719.46/0.72921.77/0.85920.21/0.801
DRBN+ERL19.84/0.82419.34/0.73022.14/0.87320.44/0.809
ECLNet21.54/0.80619.98/0.72823.54/0.85121.69/0.795
ECLNet+ERL22.01/0.82719.73/0.71423.71/0.85321.82/0.798
FECNet22.03/0.83619.78/0.71823.82/0.84921.88/0.800
FECNet+ERL21.08/0.82920.57/0.72224.18/0.86421.94/0.805
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.739, + 0.47, + 0.781 + ], + "angle": 0, + "content": "Table 3. Quantitative results of applying our framework on exposure correction methods for multiple enhancement tasks. Bold denotes the results that are improved by incorporating the ERL." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.795, + 0.471, + 0.903 + ], + "angle": 0, + "content": "mixed enhancement task containing different enhancement tasks, where the optimization flows of them are quite different. Specifically, we blend the LOL dataset [32] designed for low-light enhancement, MIT-FiveK dataset [4] collected for image retouching, and the overexposure subset from the SICE dataset to build a Task-mix dataset. As shown in Table 3, with the equipped with our sample relationship regu" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.45, + 0.892, + 0.482 + ], + "angle": 0, + "content": "larization, the overall performance is improved. We provide visual results in the supplementary materials." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.495, + 0.619, + 0.51 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.52, + 0.892, + 0.791 + ], + "angle": 0, + "content": "In this paper, for the first time, we explore the sample relationship within a mini-batch to meet the optimization inconsistency problem of exposure correction. Thus, we introduce a framework that conjunct the optimization process of exposure correction with two steps. The first step correlates the exposure relationship of samples via a context-irrelevant pretext task, while the second step constrains the relationship of corrected results by delivering the above sample relationship as a regularization term within the loss function. Our sample relationship design is compatible with existing exposure correction approaches that serve as a general regularization term. Extensive experiments demonstrate our framework consistently boosts representative methods' performance on exposure correction datasets. Although the performance improvement is not very significant in some cases, we hope our approach would shed light on investigating the sample relationship for improving the performance of low-level vision problems in the future." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Acknowledgments. This work was supported by the JKW Research Funds under Grant 20-163-14-LZ-001-004-01, and the Anhui Provincial Natural Science Foundation under Grant 2108085UD12. We acknowledge the support of GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "9911" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.176, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.171 + ], + "angle": 0, + "content": "[1] Mahmoud Afifi, Konstantinos G Derpanis, Bjorn Ommer, and Michael S Brown. Learning multi-scale photo exposure correction. In Proceedings of the IEEE International Conference on Computer Vision, 2021. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.173, + 0.472, + 0.228 + ], + "angle": 0, + "content": "[2] Tarik Arici, Salih Dikbas, and Yucel Altunbasak. A histogram modification framework and its application for image contrast enhancement. IEEE Transactions on Image Processing, 18(9):1921-1935, 2009. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.231, + 0.471, + 0.286 + ], + "angle": 0, + "content": "[3] Tarik Arici, Salih Dikbas, and Yucel Altunbasak. A histogram modification framework and its application for image contrast enhancement. IEEE Transactions on image processing, 18(9):1921-1935, 2009. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.288, + 0.472, + 0.357 + ], + "angle": 0, + "content": "[4] Vladimir Bychkovsky, Sylvain Paris, Eric Chan, and Frédo Durand. Learning photographic global tonal adjustment with a database of input/output image pairs. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 97-104, 2011. 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.36, + 0.47, + 0.413 + ], + "angle": 0, + "content": "[5] Jianrui Cai, Shuhang Gu, and Lei Zhang. Learning a deep single image contrast enhancer from multi-exposure images. IEEE Transactions on Image Processing, 27(4):2049-2062, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.417, + 0.47, + 0.457 + ], + "angle": 0, + "content": "[6] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. arXiv preprint arXiv:1805.01934, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.46, + 0.47, + 0.543 + ], + "angle": 0, + "content": "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.546, + 0.47, + 0.614 + ], + "angle": 0, + "content": "[8] Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, and Xinghao Ding. A weighted variational model for simultaneous reflectance and illumination estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2782-2790, 2016. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.616, + 0.47, + 0.685 + ], + "angle": 0, + "content": "[9] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1780-1789, 2020. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.688, + 0.47, + 0.741 + ], + "angle": 0, + "content": "[10] Xiaojie Guo, Yu Li, and Haibin Ling. LIME: Low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing, 26(2):982-993, 2016. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.745, + 0.469, + 0.786 + ], + "angle": 0, + "content": "[11] Zhi Hou, Baosheng Yu, and Dacheng Tao. Batchformer: Learning to explore sample relationships for robust representation learning. In CVPR, 2022. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.789, + 0.47, + 0.857 + ], + "angle": 0, + "content": "[12] Jie Huang, Yajing Liu, Xueyang Fu, Man Zhou, Yang Wang, Feng Zhao, and Zhiwei Xiong. Exposure normalization and compensation for multiple-exposure correction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6043-6052, 2022. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.86, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[13] Jie Huang, Yajing Liu, Feng Zhao, Keyu Yan, Jinghao Zhang, Yukun Huang, Man Zhou, and Zhiwei Xiong. Deep fourier-based exposure correction network with spatial fre" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.116, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "quency interaction. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 1, 3, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.892, + 0.188 + ], + "angle": 0, + "content": "[14] Jie Huang, Man Zhou, Yajing Liu, Mingde Yao, Feng Zhao, and Zhiwei Xiong. Exposure-consistency representation learning for exposure correction. In Proceedings of the 30th ACM International Conference on Multimedia, page 6309-6317, 2022. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.191, + 0.892, + 0.244 + ], + "angle": 0, + "content": "[15] Haidi Ibrahim and Nicholas Sia Pik Kong. Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(4):1752-1758, 2007. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.247, + 0.892, + 0.3 + ], + "angle": 0, + "content": "[16] Haidi Ibrahim and Nicholas Sia Pik Kong. Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(4):1752-1758, 2007. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.302, + 0.892, + 0.356 + ], + "angle": 0, + "content": "[17] Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, and Luc Van Gool. Dslr-quality photos on mobile devices with deep convolutional networks. In ICCV, pages 3277-3285, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.358, + 0.892, + 0.384 + ], + "angle": 0, + "content": "[18] Edwin H Land. The Retinex theory of color vision. Scientific American, 237(6):108-129, 1977. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.386, + 0.892, + 0.427 + ], + "angle": 0, + "content": "[19] Chongyi Li, Chunle Guo, and Chen Change Loy. Learning to enhance low-light image via zero-reference deep curve estimation. arXiv preprint arXiv:2103.00860, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.429, + 0.892, + 0.482 + ], + "angle": 0, + "content": "[20] Mading Li, Jiaying Liu, Wenhan Yang, Xiaoyan Sun, and Zongming Guo. Structure-revealing low-light image enhancement via robust Retinex model. IEEE Transactions on Image Processing, 27(6):2828-2841, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.484, + 0.892, + 0.524 + ], + "angle": 0, + "content": "[21] Seokjae Lim and Wonjun Kim. DSLR: Deep stacked laplacian restorer for low-light image enhancement. IEEE Transactions on Multimedia, 23:4272-4284, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.526, + 0.892, + 0.594 + ], + "angle": 0, + "content": "[22] Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 10561-10570, 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.596, + 0.892, + 0.663 + ], + "angle": 0, + "content": "[23] Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. Toward fast, flexible, and robust low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5637-5646, June 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.665, + 0.892, + 0.719 + ], + "angle": 0, + "content": "[24] Ntumba Elie Nsamp, Zhongyun Hu, and Qing Wang. Learning exposure correction via consistency modeling. In Proceedings of the British Machine Vision Conference (BMVC), pages 1-12, 2018. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.721, + 0.892, + 0.802 + ], + "angle": 0, + "content": "[25] Zhihong Pan, Baopu Li, Dongliang He, Mingde Yao, Wenhao Wu, Tianwei Lin, Xin Li, and Errui Ding. Towards bidirectional arbitrary image rescaling: Joint optimization and cycle idempotence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17389-17398, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.804, + 0.892, + 0.831 + ], + "angle": 0, + "content": "[26] Ioannis Pitas. Digital image processing algorithms and applications. John Wiley & Sons, 2000. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.833, + 0.892, + 0.899 + ], + "angle": 0, + "content": "[27] Xutong Ren, Mading Li, Wen-Huang Cheng, and Jiaying Liu. Joint enhancement and denoising method via sequential decomposition. In Proceedings of the IEEE International Symposium on Circuits and Systems, pages 1-5. IEEE, 2018. 2" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "9912" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "[28] Ali M Reza. Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. IEEE Transactions on Multimedia, pages 35-44, 2004. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.151, + 0.472, + 0.207 + ], + "angle": 0, + "content": "[29] Haoyuan Wang, Ke Xu, and Rynson W.H. Lau. Local color distributions prior for image enhancement. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 1, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.209, + 0.471, + 0.278 + ], + "angle": 0, + "content": "[30] Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, and Jiaya Jia. Underexposed photo enhancement using deep illumination estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6849-6857, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.281, + 0.47, + 0.336 + ], + "angle": 0, + "content": "[31] Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Transactions on Image Processing, 22(9):3538-3548, 2013. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.339, + 0.469, + 0.38 + ], + "angle": 0, + "content": "[32] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep Retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560, 2018. 1, 2, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.383, + 0.47, + 0.466 + ], + "angle": 0, + "content": "[33] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5901-5910, June 2022. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.469, + 0.47, + 0.551 + ], + "angle": 0, + "content": "[34] Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3063-3072, 2020. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.554, + 0.469, + 0.596 + ], + "angle": 0, + "content": "[35] Mingde Yao, Dongliang He, Xin Li, Zhihong Pan, and Zhiwei Xiong. Bidirectional translation between uhd-hdr and hd-sdr videos. IEEE Transactions on Multimedia, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.599, + 0.469, + 0.653 + ], + "angle": 0, + "content": "[36] Mingde Yao, Zhiwei Xiong, Lizhi Wang, Dong Liu, and Xuejin Chen. Spectral-depth imaging with deep learning based reconstruction. Optics express, 27(26):38312-38325, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.657, + 0.469, + 0.725 + ], + "angle": 0, + "content": "[37] Wenlong Zhang, Yihao Liu, Chao Dong, and Yu Qiao. RankSRGAN: Generative adversarial networks with ranker for image super-resolution. In Proceedings of the IEEE International Conference on Computer Vision, pages 3096-3105, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.729, + 0.469, + 0.771 + ], + "angle": 0, + "content": "[38] Yonghua Zhang, Xiaojie Guo, Jiayi Ma, Wei Liu, and Jiawan Zhang. Beyond brightening low-light images. International Journal of Computer Vision, 129(4):1013-1037, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.774, + 0.469, + 0.829 + ], + "angle": 0, + "content": "[39] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In Proceedings of the ACM International Conference on Multimedia, pages 1632-1640, 2019. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.832, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[40] Anqi Zhu, Lin Zhang, Ying Shen, Yong Ma, Shengjie Zhao, and Yicong Zhou. Zero-shot restoration of underexposed images via robust Retinex decomposition. In Proceedings of the IEEE International Conference on Multimedia and Expo, pages 1-6, 2020. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.092, + 0.892, + 0.16 + ], + "angle": 0, + "content": "[41] Yurui Zhu, Xueyang Fu, Chengzhi Cao, Xi Wang, Qibin Sun, and Zheng-Jun Zha. Single image shadow detection via complementary mechanism. In Proceedings of the 30th ACM International Conference on Multimedia, pages 6717-6726, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.164, + 0.892, + 0.205 + ], + "angle": 0, + "content": "[42] Yurui Zhu, Xueyang Fu, and Aiping Liu. Learning dual transformation networks for image contrast enhancement. IEEE Signal Processing Letters, 27:1999-2003, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.207, + 0.892, + 0.275 + ], + "angle": 0, + "content": "[43] Yurui Zhu, Xi Wang, Xueyang Fu, and Xiaowei Hu. Enhanced coarse-to-fine network for image restoration from under-display cameras. In Computer Vision-ECCV 2022 Workshops: Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part V, pages 130-146. Springer, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.277, + 0.892, + 0.343 + ], + "angle": 0, + "content": "[44] Yurui Zhu, Zeyu Xiao, Yanchi Fang, Xueyang Fu, Zhiwei Xiong, and Zheng-Jun Zha. Efficient model-driven network for shadow removal. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 3635-3643, 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.092, + 0.892, + 0.343 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.515, + 0.956 + ], + "angle": 0, + "content": "9913" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Sample Relationship for Exposure Correction/328dc83f-b0c4-4ce9-90e2-03cb25bedb51_origin.pdf b/2023/Learning Sample Relationship for Exposure Correction/328dc83f-b0c4-4ce9-90e2-03cb25bedb51_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..08c9fb3c5405b30eec950c3f5ce3b23e427f679f --- /dev/null +++ b/2023/Learning Sample Relationship for Exposure Correction/328dc83f-b0c4-4ce9-90e2-03cb25bedb51_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6d72718dc3eb601ce893c08902ba25f6d6f06f8c49434aca9f5c744c4be6bd2d +size 2465998 diff --git a/2023/Learning Sample Relationship for Exposure Correction/full.md b/2023/Learning Sample Relationship for Exposure Correction/full.md new file mode 100644 index 0000000000000000000000000000000000000000..8efabcd0fbd7cb2cfeb6e83416959972ac5b1e95 --- /dev/null +++ b/2023/Learning Sample Relationship for Exposure Correction/full.md @@ -0,0 +1,412 @@ +# Learning Sample Relationship for Exposure Correction + +Jie Huang,\* Feng Zhao,\* Man Zhou\*,\* Jie Xiao, Naishan Zheng, Kaiwen Zheng, Zhiwei Xiong University of Science and Technology of China + +{hj0117,manman,ustchbxj,nszheng,kezh}@mail.ustc.edu.cn，{fzhao956,zwxiong}@ustc.edu.cn + +# Abstract + +Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network. As well recognized, the optimization flow is the opposite. Despite great advancement, existing exposure correction methods are usually trained with a mini-batch of both underexposure and overexposure mixed samples and have not explored the relationship between them to solve the optimization inconsistency. + +In this paper, we introduce a new perspective to conjunct their optimization processes by correlating and constraining the relationship of correction procedure in a minibatch. The core designs of our framework consist of two steps: 1) formulating the exposure relationship of samples across the batch dimension via a context-irrelevant pretext task. 2) delivering the above sample relationship design as the regularization term within the loss function to promote optimization consistency. The proposed sample relationship design as a general term can be easily integrated into existing exposure correction methods without any computational burden in inference time. Extensive experiments over multiple representative exposure correction benchmarks demonstrate consistent performance gains by introducing our sample relationship design. + +# 1. Introduction + +The images captured under non-ideal illumination conditions, i.e., underexposure or overexposure scenes, usually suffer from unpleasant visual effects and thus count against the down-streaming vision tasks. To this end, exposure correction techniques have been developed, which aim to correct both underexposure and overexposure images to the normal exposure automatically. It is recognized that a single algorithm is challenging to take for exposure correction since the mapping flows of correcting underexposure and overexposure are quite different [12]. + +Recent years have witnessed explosive advancement only on the single underexposure correction, including conventional methods that rely on manually designed strategies [2, 8, 10, 20, 28, 31], and deep-learning-driven methods that account for the powerful learning capability of complicated neural networks [32-34, 39], where deep-learning methods have achieved improvement in restoring corruptions [25, 35-37]. Seldom efforts have been devoted to both underexposure and overexposure scenes within a single algorithm for meeting the practical application. Very recently, some promising works [1, 12, 14, 24] attempt to solve the above issue. Both of them follow the common principle of alleviating the optimization process inconsistency by conjugating their exposure representations in spatial domain [12, 14, 24, 29] or in frequency transform domain [13]. + +In fact, most above exposure correction approaches are trained with a mini-batch that contains both underexposure and overexposure mixed samples (see Fig. 1). Within the mini-batch, the optimization process of a single network is the opposite. On the other hand, correlating the relationship of samples across the mini-batch could conjunct their optimization processes [11]. Therefore, by constraining the relationship across the batch dimension, the adverse effect of opposite optimization in the mini-batch could be relieved. + +To this end, in this paper, we introduce a new perspective that conjuncts the optimization processes across the batch dimension via sample relationship learning and further improves the optimization processes of exposure correction. To achieve this, we construct an exposure-relationship learning (ERL) framework consisting of two steps (see Fig. 2). In the first step (see Fig. 3), we devise a batch-correlation module (BCM) that captures the relationship of samples across the batch dimension. To enable such a relationship focusing on the exposure-related representations, we train BCM via a pretext task that excludes context information correlation. Then, in the second step (see Fig. 4), we deliver the above sample relationship as the additional training regularization term within the loss function of exposure correction algorithms, where the relationship of corrected results is optimized on the trained BCM. In this way, the optimization processes within a mini-batch + +![](images/c6efd2a6c8579cb7c4627113e69250de3bbf173b327694ddac8c0ec082440f10.jpg) +Figure 1. The illustration of optimization flow of underexposure and overexposure correction. As can be seen, the corrected results of underexposure samples are still obviously darker than normal exposure, while the corrected overexposure samples behave oppositely. This demonstrates the corrected results of underexposure and overexposure are optimized to approach their corresponding ground truth in the opposite direction. + +are conjunct and the adverse effect of inconsistency optimization can be reduced. + +Our proposed ERL framework is general and could be integrated with existing exposure correction approaches. The above sample relationship regularization is only adopted during the training procedure and does not introduce any computation burden in inference time. Extensive experiments on exposure correction datasets demonstrate consistent gains by applying our ERL framework. Moreover, it can also be extended to the mixed image enhancement task, demonstrating the extensive capability of our method. + +We summarize the main contributions of this work as: + +- This work is the first time to solve inconsistency optimization of exposure correction from a new perspective of batch dimension. By exploring the relationship of samples within a mini-batch, their optimization processes are conjunct to relieve the adverse inconsistency optimization effect. +- We propose an exposure relationship learning (ERL) framework to correlate and constrain the relationship of corrected samples across the mini-batch. The learned sample relationship acts as an additional regularization term within the loss function to assist the model optimization. +- Our ERL framework is general and can be integrated into the existing exposure correction methods without introducing any computation burden during inference. +- Extensive experiments over multiple exposure correction datasets demonstrate consistent performance gains by introducing our sample relationship learning mechanism. + +# 2. Related Work + +# 2.1. Conventional Methods + +In the past decades, various methods have been proposed for correcting the exposure of images with poor illumination. Conventional works mainly rely on manually designed models and strategies. A line of work is based on histogram-based techniques [2,3,15,16], which improve the lightness and contrast of an improper exposure image. While another line of work is based on Retinex theory [18], which decomposes an image into the reflectance component and the illumination component. By enhancing the illumination component that improves the lightness, while regularizing the reflectance component that suppresses the noises, these methods have achieved prominent effect for exposure correction [8, 10, 20, 30]. + +# 2.2. Learning-based Methods + +In recent years, deep-learning-based methods have attracted more attention [41-44] which is also adopted for adjusting images with poor illumination, which are learned to correct exposures in a data-driven manner. Some of these approaches are also based on the Retinex theory [30, 32, 33, 38, 39]. As a representative, RetinexNet [32] and KIND [39] decompose an image into illumination and reflectance components, which enhances the illumination and the reflectance components to approach corresponding components of ground truth. Alternatively, another kind of components decomposed method is designed to divide the representation into different frequency bands and then enhance them progressively [21, 27, 34], such as DRBN [34]. Additionally, a few methods are also developed based on a self-supervised manner [9, 22, 23, 40]. For instance, Zero-DCE [9] formulates exposure adjustment as a task of image-specific curve estimation with a deep network. However, most of these methods are not dedicated to correcting various exposures, limiting their wide-range applications under various light conditions. + +More recently, a few works have been developed for correcting both underexposure and overexposure images. This is challenging since the optimization flows of underexposure and overexposure correction are opposite. As a pioneer work, MSEC [1] proposes to correct varieties of exposures with a Laplacian pyramid architecture, which restores lightness and details in a coarse-to-fine manner. To ease the correction of wide-range exposures with inconsistency optimization flows, Huang et al. proposed to narrow the distribution of exposure representation with exposure normalization [12]. Similarly, CMEC [24] proposes to map different exposures to an exposure-invariant space with the assistance of a transformer for exposure correction, while ECLNet [14] derives exposure-consistency representations with the bilateral activation mechanism to as + +![](images/029bddfbf071450ff343de885e31100f97ea8c33f2fe8483b5ed0c91deff6e55.jpg) +Figure 2. The overview of our proposed ERL framework, consisting of two steps. In the first step (Sec. 3.2), we formulate the exposure relationship of samples across the batch dimension on the BCM via a pretext task. In the second step (Sec. 3.3), the above-trained BCM serves as a regularization function to constrain the sample relationship of exposure correction. + +sist exposure correction. To solve this problem in other viewpoints, Wang et al. proposed a local color distribution operator for exposure correction to meet the problem of non-uniform illumination problem [29], while FECNet [13] corrects lightness and structures progressively in a Fourier-based perspective. Although these algorithms have definitely achieved remarkable progress, there remains room for improving the optimization process across the batch dimension that contains both underexposure and overexposure corrections. In this work, we introduce a new perspective that explores the sample relationships within a minibatch to conjunct their optimization processes, which reduces the adverse effect of opposite optimization flows. + +# 3. Method + +In this section, we first present an overview of the exposure relationship learning framework. We then introduce the proposed BCM in detail via its pretext task learning that captures sample relationships. Finally, we detail the above sample relationship as the regularization term to train exposure correction approaches. + +# 3.1. Motivation and Overview + +Given images with incorrect exposure settings, the exposure correction task aims to correct underexposure and its adverse overexposure images to normal exposure in a single network. Previous works are usually trained with a mini-batch containing both underexposure and overexposure samples, which enables the network to learn to correct various exposures simultaneously. However, the large discrepancies between underexposure and overexposure corrections would inevitably lead to opposite optimization flows across the batch dimension. As depicted in + +Fig. 1, the underexposure and overexposure corrected results across the mini-batch are optimized oppositely. Such a large inconsistency optimization leads to a sub-optimal optimization process, which has rarely been explored for exposure correction. + +To this end, we start a new perspective for exposure correction that conjuncts the optimization of samples within a mini-batch to reduce the adverse effect of inconsistency optimization, which is built upon learning the sample relationship [11]. To achieve this, we correlate and constrain the relationship of correction procedures on the corrected results and design an ERL framework with a two-step mechanism (see Fig. 2). In the first step, it correlates the exposure relationship of samples within a mini-batch by employing the BCM. To exclude the influence of context information on the relationship, the BCM is trained with a context-irrelevant pretext task. While in the second step, the above sample relationship built upon BCM acts as an additional regularization term within the loss function of exposure correction methods, which constrains the relationship of samples. In this way, their optimization processes across the batch dimension are conjunct during training, and it would not introduce any computation burden in inference time. + +# 3.2. Sample Relationship Correlation + +As the first step of the ERL framework, we propose to correlate the sample relationship across the batch dimension, which is implemented by BCM via a context-irrelevant task. + +Batch-correlation module (BCM). We design the BCM to capture the sample relationship in feature space. Its architecture is shown in Fig. 3, which consists of a feature encoder $f_{e}$ with several CNN-based layers, and a sample relationship part with a transformer encoder $t_{e}$ . In addition, the decoder $f_{d}$ consists of several CNN-based layers are set to follow the transformer encoder that reconstructs the input image, which assists the training of BCM (see Sec. 3.3). + +We then describe the workflow of BCM that captures sample relationships as follows. Let $I \in R^{B \times 3 \times H \times W}$ denote a sequence of input images in a mini-batch, where $B$ indicates the length of the sequence, and $H$ and $W$ are the spatial dimensions. The CNN-based encoder $f_{e}$ first extracts feature from images as $F \in R^{B \times C \times H \times W}$ , where $C$ is the channel dimension. Then these features are resized to the resolution of $16 \times 16$ , which can filter out most content-relevant information since low-resolution contains less content. After that, these features are reshaped to the dimension of $R^{B \times C'}$ that transfers the dimension of the feature as $C'$ . Finally, we correlate the relationship of these features in the batch dimension with the transformer encoder $t_{e}$ , and obtain the features $F' \in R^{B \times C'}$ that are correlated across the batch dimension. The whole above process is expressed as: + +$$ +F ^ {\prime} = t _ {e} \left(f _ {e} (I)\right), \tag {1} +$$ + +![](images/db96c5ebc260a9161c92617cfc12c2cf0d7e29dbea938b4e09ff67170fd36628.jpg) +Figure 3. The illustration of our proposed BCM, which correlates the exposure relationship of samples across the batch dimension. Specifically, it consists of an encoder part $f_{e}$ that extracts features and a decoder part $f_{d}$ that reconstructs the image, both of which have been trained in a self-reconstruction manner. We then apply a transformer encoder $t_{e}$ between the encoder and decoder parts to explore the sample relationship. Furthermore, we train the BCM with a pretext task that reconstructs the content-related phase information, leading to it focusing more on correlating exposure-related information. + +where the resize and reshape operations are omitted here. + +We further detail how the transformer encoder $t_e$ explores the sample relationship across the batch dimension. Transformer usually contains a multihead self-attention layer that models the relationship of spatial and channel dimensions [7]. By transposing the feature into the dimension that the transformer layer can operate on the batch dimension, the self-attention mechanism of the multi-head self-attention layer could cross attention samples in a minibatch, thus correlating their relationships. + +Train the BCM via a context-irrelevant task. In preliminary, the encoder and decoder parts of the BCM are first trained in a self-reconstruction manner that can reconstruct input images of themselves, then these parts are fixed and a transformer encoder $t_e$ is incorporated between them to construct BCM. Thus, when training BCM, only the transformer encoder $t_e$ is trained to learn the sample relationship. + +Since the optimization processes of the correction are mainly affected by the exposure condition, we hope the sample relationship learning correlates more with exposure-related components. To this end, the context relationship across the batch dimension should not be focused, and we now need to construct a pretext task that trains BCM to exclude the influences of context relationships. + +As shown in Fig. 3, we train a context-irrelevant pretext task on the BCM via a self-reconstruction manner. For a sample correlating with other samples, it is hard to reconstruct itself since the information of other samples is introduced. Therefore, we construct the pretext task to reconstruct content-relevant information, which suppresses the correlation of the content component, thus driving the content-irrelevant information to be more correlated across the mini-batch. Specifically, a mini-batch containing normal exposure samples $I \in R^{B \times 3 \times H \times W}$ are sent to the + +BCM, which are substantially derived correlated feature $F'$ as depicted in Eq. 1. Then $F'$ are reshaped and resized to the dimension of $F' \in R^{B \times C \times H \times W}$ , which are further fused with $F$ . Finally, the decoder $f_d$ reconstructs the integrated features to the image space as $I_{rec}$ . This procedure is expressed as: + +$$ +I ^ {\prime} = f _ {d} \left(W \left(\operatorname {c a t} \left([ F, F ^ {\prime} ]\right)\right)\right), \tag {2} +$$ + +where $W$ denotes the $1 \times 1$ convolution operation, and $[\cdot]$ means the concatenate operation. + +We then introduce a phase component constraint as the learning objective of the BCM. Based on FECNet [13], the amplitude component reflects the exposure-related information, while the phase component corresponds more to content information. Therefore, we construct the learning objective $\mathcal{L}_{pre}$ to constrain the phase component of the reconstructed images $I^{\prime}$ to be closed with that of the input images $I$ : + +$$ +\mathcal {L} _ {p r e} = \left| \left| \mathcal {P} (\mathcal {F} (I)) - \mathcal {P} (\mathcal {F} \left(I ^ {\prime}\right)) \right| \right| _ {1}, \tag {3} +$$ + +where $||\cdot ||_1$ denotes the mean absolutely error, $\mathcal{F}$ is the Fourier transform operation, and $\mathcal{P}(\cdot)$ represents getting the phase component. Based on this regularization, the BCM reduces the content information correlation across the minibatch which would put a negative effect on content reconstruction, thus leading to BCM focusing more on correlating exposure-related information. + +# 3.3. Sample Relationship Constraining + +Deliver the sample relationship as regularization. After correlating the sample relationship across batch dimensions, we further deliver the sample relationship as an additional regularization term that conjuncts the optimization within a mini-batch. For implementation, when training the exposure correction network, we constrain the relationship + +![](images/81ba7f3e753176eebd6111590d9215c517099ae237bb8dd516f88b40785174f6.jpg) +Figure 4. The illustration of delivering the sample relationship as regularization for exposure correction. We employ the encoder and transformer parts of the trained BCM as the regularization function, and integrate this regularization term $\mathcal{L}_r$ with the conventional loss functions $\mathcal{L}_c$ of exposure correction methods to optimize the exposure correction model. + +of the corrected samples to be closed to that of the ground truth. + +Specifically, we employ the trained BCM to construct the sample relationship within a mini-batch, where we discard its decoder part and deploy the correlated feature $F'$ as the sample relationships representation. To this end, we apply the sample relationship regularization on the $F'$ during the training of exposure correction, and its learning objective $\mathcal{L}_r$ is expressed as: + +$$ +\mathcal {L} _ {r} = \left| \left| F _ {r e s} ^ {\prime} - F _ {g t} ^ {\prime} \right| \right| _ {1} \tag {4} +$$ + +where $F_{res}^{\prime}$ and $F_{gt}^{\prime}$ denote the sample relationships representation of corrected results and ground truth, respectively. + +Integrate the above regularization term to existing methods. The above sample relationship regularization term is general and could be plugged into the training paradigm of existing exposure correction algorithms as an additional optimization objective. As shown in Fig. 4, the total learning objective for training existing exposure correction method is the combination of the conventional loss $\mathcal{L}_c$ and the relationship regularization term $\mathcal{L}_r$ : + +$$ +\mathcal {L} = \mathcal {L} _ {c} + \alpha \mathcal {L} _ {r} \tag {5} +$$ + +where $\alpha$ is the weight factor and we set it to 0.1 empirically, and the selection of $\alpha$ will be discussed in the ablation study. The conventional loss denotes the original loss function employed in these exposure correction methods. + +# 3.4. Discussion + +To better understand how the proposed ERL framework conjunct the optimization processes by modeling sample relationships across the batch dimension, we provide an intuitive explanation as demonstrated in Fig. 5. Given the + +![](images/cc63628cc676eccc4ff80aaf87ed0b61871a3dcfff001cce08be1f2886dd7ad5.jpg) +Figure 5. With the regularization of sample relationship, besides being optimized to the ground truth $Y_{i}$ , the corrected results $X_{i}$ are also implicitly constrained with other samples to keep their relationships to be closed with those of the ground truth. + +corrected results $X(X = X_{1},\dots,X_{i},\dots X_{N})$ and their corresponding ground truth $Y(Y = Y_{1},\dots,Y_{i},\dots Y_{N})$ in a minibatch, when the network is only optimized with the conventional loss, the sample $X_{i}$ is only optimized to approach the corresponding ground truth $Y_{i}$ , and their optimization flows are separated and often opposite in exposure correction. + +While with the employ of sample relationship constraining, the corrected result $X_{i}$ are further constrained to other corrected results $X_{j}$ (where $j \neq i$ ) in a mini-batch, which drives their relationships to be closed to those of $Y_{i}$ and $Y_{j}$ (where $j \neq i$ ). To this end, the optimization flow of $X_{i}$ is also affected by other samples, which could lead to a more consistent exposure correction effect across the mini-batch. In CMEC [24], it models consistency learning of different exposures that regularize the underexposure and overexposure representations to be similar in the network. Similarly, we consider our sample relationship regularization is another form of consistency learning, and it implicitly regularizes the results' exposure representation to be correlated and constrained in a mini-batch. + +# 4. Experiments + +# 4.1. Experimental Settings + +Datasets. To evaluate the performance of our proposed method, we conduct experiments on two representative datasets, including the MSEC dataset proposed in MSEC [1] and SICE dataset [5] proposed in SICE. The MSEC dataset is rendered from the MIT-Adobe FiveK dataset [4], consisting of five exposure levels, where its retouched version by experts of middle exposure is set as ground truth. Finally, it consists of 17675 images for training, 750 images for validation, and 5905 images for testing. While for the SICE dataset, the middle exposure subset of this dataset is selected as the ground truth, and the second and last second exposure subsets are set as the underexposed and overexposed images, respectively. Finally, the SICE dataset contains 512 images for training and 30 images for testing. + +
MethodMSECSICE#Param
UnderOverAverageUnderOverAverage
PSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIM
HE [26]16.520.691816.530.699116.530.695914.690.565112.870.499113.780.5376-
CLAHE [28]16.770.621114.450.584215.380.599012.690.503710.210.484711.450.4942-
RetinexNet [32]12.130.620910.470.595311.140.604812.940.517112.870.525212.900.52120.84M
URetinexNet [33]13.850.73719.810.673311.420.698817.390.64487.400.454312.400.54961.32M
Zero-DCE [9]14.550.588710.400.514212.060.544116.920.63307.110.429212.020.53110.079M
Zero-DCE++ [19]13.820.58879.740.514211.370.558311.930.47556.880.40889.410.44220.010M
DPED [17]20.060.682613.140.581215.910.621916.830.61337.990.430012.410.52170.39M
KIND [39]15.510.711511.660.730013.200.742413.430.48377.850.477910.640.48088.54M
SID [6]19.370.810318.830.805519.040.807419.510.663516.790.644418.150.65407.40M
RUAS [22]13.430.68076.390.46559.200.551516.630.55894.540.319610.590.43930.0014M
SCI [23]9.9650.66815.8370.51907.490.578617.860.64014.450.362912.490.50510.0003M
MSEC [1]20.520.812919.790.815620.080.821019.620.651217.590.656018.580.65367.04M
CMEC [24]22.230.814022.750.833622.540.825717.680.659218.170.681117.930.67025.40M
SID-ENC [12]22.590.842322.360.851922.450.848121.360.665219.380.684320.370.67487.45M
LCDPNet [29]22.350.865022.170.847622.300.855217.450.562217.040.646317.250.60430.96M
DRBN [34]19.740.829019.370.832119.520.830917.960.676717.330.682817.650.67980.53M
DRBN+ERL19.910.830519.600.838419.730.835518.090.673517.930.686618.010.67960.53M
DRBN-ENC [12]22.720.854422.110.852122.350.853021.890.707119.090.722920.490.71500.58M
DRBN-ENC+ERL22.920.870422.450.872422.670.871622.060.705319.500.720520.780.71290.58M
ECLNet [14]22.370.856622.700.867322.570.863122.050.689319.250.687220.650.68610.018M
ECLNet+ERL22.900.862422.580.867622.700.865522.140.690819.470.698220.810.69450.018M
FECNet [13]22.960.859823.220.874823.120.868822.010.673719.910.696120.960.68490.15M
FECNet+ERL23.100.863923.180.875923.150.871122.350.667120.100.689121.220.67810.15M
+ +Table 1. Quantitative results of different methods on the MSEC and the SICE datasets in terms of PSNR and SSIM. Bold denotes the results that are improved by incorporating ERL. The underline denotes the best result. + +Implementation details. We implement our proposed model with PyTorch on a single NVIDIA GTX 3090 GPU. Firstly, we train the BCM on the normal exposure images for 100 epochs that captures the exposure sample relationship. Then, to couple the above sample relationship as additional regularization of existing methods, we select several methods as equipped methods, including DRBN [34], DRBN-ENC-4 [12], ECLNet [14] and FECNet [13]. After the integration, we add $+$ between their names and ERL to denote the improved method (such as DRBN+ERL). + +During training, we employ an Adam optimizer with $\beta_{1} = 0.9$ , $\beta_{2} = 0.99$ to update the model. We set the training settings following the original settings of the above-equipped methods, including the update of the learning rate, the number of training epochs, and the patch size. All of the loss functions for exposure correction are optimized in an end-to-end manner. During the evaluation, we implement Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) for numerical evaluation. + +# 4.2. Comparison with State-of-the-Art Methods + +To evaluate the performance with existing exposure correction methods, several methods are adopted as a comparison, including two traditional methods HE [26] and CLAHE [28]. Additionally, several deep-learning-based methods are involved as well, including RetinexNet [32], DPED [17], SID [6], KinD [39], DRBN [34], URetinexNet [33], Zero-DCE [9], Zero + +DCE++ [19], RUAS [22] and SCI [23]. We further include several specific approaches for exposure correction: MSEC [1], CMEC [24], SID-ENC [12], DRBN-ENC [12], ECLNet [14], LCDPNet [29] and FECNet [13]. + +Quantitative results. The quantitative results are shown in Table 1. For the MSEC dataset, following MSEC, we average the results of the exposures of the first two levels and the remaining levels of exposure as underexposure and overexposure results, respectively. As can be observed, recently proposed exposure correction approaches (such as FECNet and ECLNet) achieve excellent performance on the two datasets. With the incorporation of the ERL framework, their overall quantitative results are further improved on most subsets and metrics. It substantiates the effectiveness and flexibility of our proposed sample relationship regularization. Note that the introducing of our sample relationship regularization would not increase parameters or computation costs. In addition, the effectiveness of our framework can also be verified to improve the performance during the training procedure as shown in Fig. 7. As can be seen, our ERL framework improves performance during most training time with higher PSNR, demonstrating it improves optimization processes and thus lead to better performance. + +Qualitative results. We provide visual comparisons on the MSEC dataset in Fig. 6, and visual comparisons on the SICE dataset in Fig. 8, respectively. As can be seen, with the employ of the sample relationship regularization term, the global lightness or local structures and details of the en + +![](images/0979bd5e0353b6f62f9de29605f4f42305c847960f8d2d3df904782d3e8602d4.jpg) +(a) Input + +![](images/58caa6ef28c7b8e82a58ca1edd1188a5eeed0c90ce1fcdd8d9971cf49146a885.jpg) +(b) CMEC + +![](images/87ffc6498418e8d66dc49ae455658678323899156130448da6f41d63b61efaea.jpg) +(c) LCDPNet + +![](images/1342ad0bdf359bf4894a3fc1d136659e4d825dfc2b32e95e83a92e366c2b29d2.jpg) +(d) DRBN + +![](images/ff8663777e94a7fb0cb15e96bd43296e0d18b3e5a8b0ed3f38f9cdd9e3dccd81.jpg) +(e) ECLNet + +![](images/080aac085b77b397b9958b98e6c9e0caf781c915c1db245e06716fad438346a7.jpg) +(f) FECNet + +![](images/b13da5b011a47e18075c76c5e1eb3b49b15965ad17b74258a744d1d7e211f0fc.jpg) +(g) GT + +![](images/8ddfdd70f959d5826097b6bacf3305f6c91db1492377fbc3486b83a37e72b019.jpg) +(h) MSEC + +![](images/c620484564779e30309bdb972b4eb0609a73de391b7efdc0aee099965677b883.jpg) +(i) Zero-DCE++ + +![](images/11b7df0cfb140ec053e212b8b10c6c0dde92c87e29ac0a911c20fff07462c5b0.jpg) +(j) DRBN+ERL + +![](images/afab3043001feecc701381e1c927f4d7cd28a383c092983b0c62102fcccb283e.jpg) +(k) ECLNet+ERL + +![](images/a0f1cdf2019f475518d258807ae69be7d79b0241ef2f6b85979aa0e31512136d.jpg) +(1) FECNet+ERL + +![](images/20570e6847526d8476b46b6469ed9c29d55dd710bb9ac76a6663f7423aa16da2.jpg) +(m) Input + +![](images/cbbc3250d2995f5f90de8ad1b164e2dfd247830e24747bfe1c788db5356b6ce3.jpg) +(n)CMEC + +![](images/c0993c0c0537b8935590ac19d1a903dbdf642f8170a4836fa4cb8c9b368bc7f0.jpg) +(o) LCDPNet + +![](images/308783b573562e5edb3f35adc4137371e4e22e2a952e98b6ffa7f9c5381659aa.jpg) +(p) DRBN + +![](images/2a0d9ef2923ebd91c047b869618f31b88508fdd860fcef8df96812aac0e52107.jpg) +(q) ECLNet + +![](images/31bba62bf91a5bea6c0f63f9562a63c5d059c7dcc51dfd8c1faf2e97963b03f7.jpg) +(r) DRBN-ENC + +![](images/c75fce3402dfff7f7f843a8f6ade9cbabb8c5895b8d12b36c7ab4d3950491a10.jpg) +(s)GT +Figure 6. Visualization results on the MSEC dataset of (top) underexposure correction and (bottom) overexposure correction. + +![](images/8b85905fad8960ad6ad74b9b9e8550922bc0609490439324c95490d1e4f59979.jpg) +(t)MSEC + +![](images/af23a38afff6b5ee3c0bd798a7679aaf3aeb9a0c089f13bd2eca649b2f2def20.jpg) +(u) Zero-DCE++ + +![](images/3f6bc920d7a38fefe25f8fa96498fa3cef7857fd76e40252f92462c10d28ddf1.jpg) +(v) DRBN+ERL + +![](images/f0e0e3f027220b76eaf556091e654ad5544b1c6e916cbe661a7b395cb8535921.jpg) +(w) ECLNet+ERL + +![](images/dcaac92df05cb0125c7b44e257b72ec577483c7bf968a1f226f3f334cebea4dc.jpg) +(x) DRBN-ENC+ERL + +![](images/6db3db19a5505207ee73dcbb5901e6af830e44cb2e5cc1dfe938fbfea506aabd.jpg) +(a) Training of ECLNet +Figure 7. Graph of PSNR with/without ERL framework during the training process of the ECLNet and DRBN-ENC. + +![](images/5cd94a664ee1b21caf78cafec95d31f9ba93bba8baeb58d0dc531c7d45065849.jpg) +(b) Training of DRBN-ENC + +hanced results are further improved. Therefore, these results prove our method could improve existing methods to generate better visual results. We provide more visual results in the supplementary materials. + +# 4.3. Ablation Studies + +We conduct ablation studies on the SICE dataset to further investigate the effectiveness of our proposed sample relationship modeling under different configurations, and we set the baseline as training the exposure correction method ECLNet on its original settings. + +Investigation of the BCM. To validate the effectiveness of the BCM design, we perform ablations with different configurations of the BCM and present the results in Table 2. As can be seen, without the employ of the pretext task (which means the $t_e$ in the BCM is randomly initialized) or training the pretext task using pixel $L_1$ loss, the performance can also be improved compared with the base- + +
OptionBaselinew/o pretext taskwith L1with Lpre
PSNR20.6520.7620.7220.81
SSIM0.68610.69120.68970.6945
+ +Table 2. Ablation study of investigating the configuration of the BCM based on the ECLNet in SICE dataset. + +line. This demonstrates that the sample relationship regularization mechanism itself is effective for improving performance, while the designing of our pretext task can further elevate the exposure correction effect. + +Investigation of weight coefficient. To investigate the influences of the weight coefficient $\alpha$ on sample relationship regularization term, we perform experiment experiments with setting different coefficients $\alpha$ . As shown in Fig. 9 (a), larger $\alpha$ could lead to a slight performance decrease. Overall, the performance remains stable and is robust to the weight coefficient changes. + +Investigation of batch size. We perform ablation studies to investigate the batch size of sample relationship regularization for optimizing exposure correction in Fig. 9 (b). The results show that setting the batch size of 4 improves more performance than other settings, while a larger batch size can not improve performance significantly. This may be caused by the sample relationship of large batch size being hard to optimize due to the high variance. + +# 4.4. Extension on the Mixed Enhancement Task + +To further demonstrate the extensive ability of our method, we further train and test the method on an image + +![](images/7745b297b343bbeb81220584128a9df517dc8e125bec11ad0f60342dde602f43.jpg) +(a) Input + +![](images/5e8528feaff35955b983805f25767989f6d715c37292361f78e35ac3b86a3715.jpg) +(b) CMEC + +![](images/444ba7282e7795579f21d94c6ad10f2e39e3827a3b60568c0e486f957017e3b7.jpg) +(c) LCDPNet + +![](images/be187d5ac3faa6febfdc79ceb8f6c9fce53c4699b5da2f0f3fa689d2e54b4cb3.jpg) +(d) DRBN + +![](images/8b618325fbd19c5f6931e584d94093ad99346caf797da99e59e9028da4dbc7c2.jpg) +(e) ECLNet + +![](images/b32c1a5f3004f6bc25415356ccfe06cdbf72e70195742d739e733925c5b342b1.jpg) +(f) FECNet + +![](images/5b06570c1fee61b47fc8356e7e70e69b284e6e51b0a38382116301ba23f5b4ca.jpg) +(g) GT + +![](images/47fe4356dce6cb812ce1162ebe1fbfdbe0e27ae8c150b380a0d02f6646928e65.jpg) +(h) MSEC + +![](images/61dee80c3456635996ac56c318c2e84509b24f547467eccc37817fd5a637ed69.jpg) +(i) Zero-DCE++ + +![](images/2a0c61a4574544e24c78076a64eb8e1f9358e8880cf82cb5e67330065a2b31a0.jpg) +(j) DRBN+ERL + +![](images/c9989fee82b63a7f8d64f2e205f07d3cf8d38a0ebe2b0f3ff0e4657ff4361257.jpg) +(k) ECLNet+ERL + +![](images/9cdb0220c6c4d7324bef65581acb444d7753dc574a32dc038124bf1b192b1723.jpg) +(1) FECNet+ERL + +![](images/e189b62415a8913da81b2a27100f1a48a7e123dd94fe177b7ab73b19978b6598.jpg) +(m) Input + +![](images/5df5c791f4215085d66756d2aacbf287a12e4adb9ef9a6f129a65827c911c8fb.jpg) +(n) CMEC + +![](images/3ee582e7761064db0707ee6bd38f12b183ccbafb8ef385133cf1ebedd6ded2bb.jpg) +(o) LCDPNet + +![](images/b4727c1bbe0ddb2047b297a8515aace31931cd8165891895605399b929f7a131.jpg) +(p) DRBN + +![](images/632ec40ba0e638ffefa5d04285679ef2e3489bb84c19f15a4d93617f13e26af7.jpg) +(q) ECLNet + +![](images/7c9473306b7ced920957010c66b1185ff2cb2c6e07ee166fba0a3bcb807de976.jpg) +(r) DRBN-ENC + +![](images/af3a368111c9ddebb5d7e287c737d2734631e3e1057e6da2c67aacfa2a7644f5.jpg) +(s) GT +Figure 8. Visualization results on the SICE dataset of (top) underexposure correction and (bottom) overexposure correction. + +![](images/7b6a26ed1a4a1104479cbd98affbe20c169f9f5cf063e4dda1968cee11dcdd63.jpg) +(t) MSEC + +![](images/78bf177f2c24c214fb3d0dcd221df85c498450b4c12cac4d1920b8b0dd10c739.jpg) +(u) Zero-DCE++ + +![](images/b055f5959d3926aedc5d44da9a83f38309a397771757ad8cedbfb60126b61df0.jpg) +(v) DRBN+ERL + +![](images/30fe0ca7ecae2c01003e63cc4754aa28e6c8bb746d67e87e78cf12e695c52e96.jpg) +(w) ECLNet+ERL + +![](images/7d4e9bdee93e61e979978d79f7452f893af376bd6d0845e9408a3d2a2bb5d066.jpg) +(x) DRBN-ENC+ERL + +![](images/5c23ba8c5b8b048e93406c4566ba88f450f6ba35ba5d29a44d3e3e9648ba91ce.jpg) +(a) Ablation of weight coefficient $\alpha$ +Figure 9. Ablation study for investigating the factors of (a) the weight coefficient $\alpha$ and (b) the batch size of our method based on the ECLNet network. + +![](images/a2762782fdccca3cd0f87d7587504acf86e165d9b187f629f0f3e19611521960.jpg) +(b) Ablation of batch size + +
MethodLOLSICE (over)FiveKAverage
DRBN19.39/0.81719.46/0.72921.77/0.85920.21/0.801
DRBN+ERL19.84/0.82419.34/0.73022.14/0.87320.44/0.809
ECLNet21.54/0.80619.98/0.72823.54/0.85121.69/0.795
ECLNet+ERL22.01/0.82719.73/0.71423.71/0.85321.82/0.798
FECNet22.03/0.83619.78/0.71823.82/0.84921.88/0.800
FECNet+ERL21.08/0.82920.57/0.72224.18/0.86421.94/0.805
+ +Table 3. Quantitative results of applying our framework on exposure correction methods for multiple enhancement tasks. Bold denotes the results that are improved by incorporating the ERL. + +mixed enhancement task containing different enhancement tasks, where the optimization flows of them are quite different. Specifically, we blend the LOL dataset [32] designed for low-light enhancement, MIT-FiveK dataset [4] collected for image retouching, and the overexposure subset from the SICE dataset to build a Task-mix dataset. As shown in Table 3, with the equipped with our sample relationship regu + +larization, the overall performance is improved. We provide visual results in the supplementary materials. + +# 5. Conclusion + +In this paper, for the first time, we explore the sample relationship within a mini-batch to meet the optimization inconsistency problem of exposure correction. Thus, we introduce a framework that conjunct the optimization process of exposure correction with two steps. The first step correlates the exposure relationship of samples via a context-irrelevant pretext task, while the second step constrains the relationship of corrected results by delivering the above sample relationship as a regularization term within the loss function. Our sample relationship design is compatible with existing exposure correction approaches that serve as a general regularization term. Extensive experiments demonstrate our framework consistently boosts representative methods' performance on exposure correction datasets. Although the performance improvement is not very significant in some cases, we hope our approach would shed light on investigating the sample relationship for improving the performance of low-level vision problems in the future. + +Acknowledgments. This work was supported by the JKW Research Funds under Grant 20-163-14-LZ-001-004-01, and the Anhui Provincial Natural Science Foundation under Grant 2108085UD12. We acknowledge the support of GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC. + +# References + +[1] Mahmoud Afifi, Konstantinos G Derpanis, Bjorn Ommer, and Michael S Brown. Learning multi-scale photo exposure correction. In Proceedings of the IEEE International Conference on Computer Vision, 2021. 1, 2, 5, 6 +[2] Tarik Arici, Salih Dikbas, and Yucel Altunbasak. A histogram modification framework and its application for image contrast enhancement. IEEE Transactions on Image Processing, 18(9):1921-1935, 2009. 1, 2 +[3] Tarik Arici, Salih Dikbas, and Yucel Altunbasak. A histogram modification framework and its application for image contrast enhancement. IEEE Transactions on image processing, 18(9):1921-1935, 2009. 2 +[4] Vladimir Bychkovsky, Sylvain Paris, Eric Chan, and Frédo Durand. Learning photographic global tonal adjustment with a database of input/output image pairs. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 97-104, 2011. 5, 8 +[5] Jianrui Cai, Shuhang Gu, and Lei Zhang. Learning a deep single image contrast enhancer from multi-exposure images. IEEE Transactions on Image Processing, 27(4):2049-2062, 2018. 5 +[6] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. arXiv preprint arXiv:1805.01934, 2018. 6 +[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 4 +[8] Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, and Xinghao Ding. A weighted variational model for simultaneous reflectance and illumination estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2782-2790, 2016. 1, 2 +[9] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1780-1789, 2020. 2, 6 +[10] Xiaojie Guo, Yu Li, and Haibin Ling. LIME: Low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing, 26(2):982-993, 2016. 1, 2 +[11] Zhi Hou, Baosheng Yu, and Dacheng Tao. Batchformer: Learning to explore sample relationships for robust representation learning. In CVPR, 2022. 1, 3 +[12] Jie Huang, Yajing Liu, Xueyang Fu, Man Zhou, Yang Wang, Feng Zhao, and Zhiwei Xiong. Exposure normalization and compensation for multiple-exposure correction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6043-6052, 2022. 1, 2, 6 +[13] Jie Huang, Yajing Liu, Feng Zhao, Keyu Yan, Jinghao Zhang, Yukun Huang, Man Zhou, and Zhiwei Xiong. Deep fourier-based exposure correction network with spatial fre + +quency interaction. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 1, 3, 4, 6 +[14] Jie Huang, Man Zhou, Yajing Liu, Mingde Yao, Feng Zhao, and Zhiwei Xiong. Exposure-consistency representation learning for exposure correction. In Proceedings of the 30th ACM International Conference on Multimedia, page 6309-6317, 2022. 1, 2, 6 +[15] Haidi Ibrahim and Nicholas Sia Pik Kong. Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(4):1752-1758, 2007. 2 +[16] Haidi Ibrahim and Nicholas Sia Pik Kong. Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(4):1752-1758, 2007. 2 +[17] Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, and Luc Van Gool. Dslr-quality photos on mobile devices with deep convolutional networks. In ICCV, pages 3277-3285, 2017. 6 +[18] Edwin H Land. The Retinex theory of color vision. Scientific American, 237(6):108-129, 1977. 2 +[19] Chongyi Li, Chunle Guo, and Chen Change Loy. Learning to enhance low-light image via zero-reference deep curve estimation. arXiv preprint arXiv:2103.00860, 2021. 6 +[20] Mading Li, Jiaying Liu, Wenhan Yang, Xiaoyan Sun, and Zongming Guo. Structure-revealing low-light image enhancement via robust Retinex model. IEEE Transactions on Image Processing, 27(6):2828-2841, 2018. 1, 2 +[21] Seokjae Lim and Wonjun Kim. DSLR: Deep stacked laplacian restorer for low-light image enhancement. IEEE Transactions on Multimedia, 23:4272-4284, 2020. 2 +[22] Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 10561-10570, 2021. 2, 6 +[23] Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. Toward fast, flexible, and robust low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5637-5646, June 2022. 2, 6 +[24] Ntumba Elie Nsamp, Zhongyun Hu, and Qing Wang. Learning exposure correction via consistency modeling. In Proceedings of the British Machine Vision Conference (BMVC), pages 1-12, 2018. 1, 2, 5, 6 +[25] Zhihong Pan, Baopu Li, Dongliang He, Mingde Yao, Wenhao Wu, Tianwei Lin, Xin Li, and Errui Ding. Towards bidirectional arbitrary image rescaling: Joint optimization and cycle idempotence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17389-17398, 2022. 1 +[26] Ioannis Pitas. Digital image processing algorithms and applications. John Wiley & Sons, 2000. 6 +[27] Xutong Ren, Mading Li, Wen-Huang Cheng, and Jiaying Liu. Joint enhancement and denoising method via sequential decomposition. In Proceedings of the IEEE International Symposium on Circuits and Systems, pages 1-5. IEEE, 2018. 2 + +[28] Ali M Reza. Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. IEEE Transactions on Multimedia, pages 35-44, 2004. 1, 6 +[29] Haoyuan Wang, Ke Xu, and Rynson W.H. Lau. Local color distributions prior for image enhancement. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 1, 3, 6 +[30] Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, and Jiaya Jia. Underexposed photo enhancement using deep illumination estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6849-6857, 2019. 2 +[31] Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Transactions on Image Processing, 22(9):3538-3548, 2013. 1 +[32] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep Retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560, 2018. 1, 2, 6, 8 +[33] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5901-5910, June 2022. 1, 2, 6 +[34] Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3063-3072, 2020. 1, 2, 6 +[35] Mingde Yao, Dongliang He, Xin Li, Zhihong Pan, and Zhiwei Xiong. Bidirectional translation between uhd-hdr and hd-sdr videos. IEEE Transactions on Multimedia, 2023. 1 +[36] Mingde Yao, Zhiwei Xiong, Lizhi Wang, Dong Liu, and Xuejin Chen. Spectral-depth imaging with deep learning based reconstruction. Optics express, 27(26):38312-38325, 2019. 1 +[37] Wenlong Zhang, Yihao Liu, Chao Dong, and Yu Qiao. RankSRGAN: Generative adversarial networks with ranker for image super-resolution. In Proceedings of the IEEE International Conference on Computer Vision, pages 3096-3105, 2019. 1 +[38] Yonghua Zhang, Xiaojie Guo, Jiayi Ma, Wei Liu, and Jiawan Zhang. Beyond brightening low-light images. International Journal of Computer Vision, 129(4):1013-1037, 2021. 2 +[39] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In Proceedings of the ACM International Conference on Multimedia, pages 1632-1640, 2019. 1, 2, 6 +[40] Anqi Zhu, Lin Zhang, Ying Shen, Yong Ma, Shengjie Zhao, and Yicong Zhou. Zero-shot restoration of underexposed images via robust Retinex decomposition. In Proceedings of the IEEE International Conference on Multimedia and Expo, pages 1-6, 2020. 2 + +[41] Yurui Zhu, Xueyang Fu, Chengzhi Cao, Xi Wang, Qibin Sun, and Zheng-Jun Zha. Single image shadow detection via complementary mechanism. In Proceedings of the 30th ACM International Conference on Multimedia, pages 6717-6726, 2022. 2 +[42] Yurui Zhu, Xueyang Fu, and Aiping Liu. Learning dual transformation networks for image contrast enhancement. IEEE Signal Processing Letters, 27:1999-2003, 2020. 2 +[43] Yurui Zhu, Xi Wang, Xueyang Fu, and Xiaowei Hu. Enhanced coarse-to-fine network for image restoration from under-display cameras. In Computer Vision-ECCV 2022 Workshops: Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part V, pages 130-146. Springer, 2023. 2 +[44] Yurui Zhu, Zeyu Xiao, Yanchi Fang, Xueyang Fu, Zhiwei Xiong, and Zheng-Jun Zha. Efficient model-driven network for shadow removal. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 3635-3643, 2022. 2 \ No newline at end of file diff --git a/2023/Learning Sample Relationship for Exposure Correction/images.zip b/2023/Learning Sample Relationship for Exposure Correction/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..4abb5477f4f7d4e516886899474e66ece8ba0e83 --- /dev/null +++ b/2023/Learning Sample Relationship for Exposure Correction/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8abd762ad63fec74bc6faacb5ad50c208ec6e26d7b60a6196b0e4a793feae2b3 +size 901889 diff --git a/2023/Learning Sample Relationship for Exposure Correction/layout.json b/2023/Learning Sample Relationship for Exposure Correction/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..3e801103127de1b51129c2d1c0dcb83ed85f194e --- /dev/null +++ b/2023/Learning Sample Relationship for Exposure Correction/layout.json @@ -0,0 +1,11168 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 125, + 103, + 470, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 125, + 103, + 470, + 121 + ], + "spans": [ + { + "bbox": [ + 125, + 103, + 470, + 121 + ], + "type": "text", + "content": "Learning Sample Relationship for Exposure Correction" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 75, + 143, + 518, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 143, + 518, + 171 + ], + "spans": [ + { + "bbox": [ + 75, + 143, + 518, + 171 + ], + "type": "text", + "content": "Jie Huang,\\* Feng Zhao,\\* Man Zhou\\*,\\* Jie Xiao, Naishan Zheng, Kaiwen Zheng, Zhiwei Xiong University of Science and Technology of China" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 65, + 173, + 529, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 173, + 529, + 186 + ], + "spans": [ + { + "bbox": [ + 65, + 173, + 529, + 186 + ], + "type": "text", + "content": "{hj0117,manman,ustchbxj,nszheng,kezh}@mail.ustc.edu.cn，{fzhao956,zwxiong}@ustc.edu.cn" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "spans": [ + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 239, + 288, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 239, + 288, + 335 + ], + "spans": [ + { + "bbox": [ + 46, + 239, + 288, + 335 + ], + "type": "text", + "content": "Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network. As well recognized, the optimization flow is the opposite. Despite great advancement, existing exposure correction methods are usually trained with a mini-batch of both underexposure and overexposure mixed samples and have not explored the relationship between them to solve the optimization inconsistency." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 45, + 336, + 288, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 336, + 288, + 515 + ], + "spans": [ + { + "bbox": [ + 45, + 336, + 288, + 515 + ], + "type": "text", + "content": "In this paper, we introduce a new perspective to conjunct their optimization processes by correlating and constraining the relationship of correction procedure in a minibatch. The core designs of our framework consist of two steps: 1) formulating the exposure relationship of samples across the batch dimension via a context-irrelevant pretext task. 2) delivering the above sample relationship design as the regularization term within the loss function to promote optimization consistency. The proposed sample relationship design as a general term can be easily integrated into existing exposure correction methods without any computational burden in inference time. Extensive experiments over multiple representative exposure correction benchmarks demonstrate consistent performance gains by introducing our sample relationship design." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 541, + 128, + 553 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 541, + 128, + 553 + ], + "spans": [ + { + "bbox": [ + 47, + 541, + 128, + 553 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 562, + 287, + 682 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 562, + 287, + 682 + ], + "spans": [ + { + "bbox": [ + 46, + 562, + 287, + 682 + ], + "type": "text", + "content": "The images captured under non-ideal illumination conditions, i.e., underexposure or overexposure scenes, usually suffer from unpleasant visual effects and thus count against the down-streaming vision tasks. To this end, exposure correction techniques have been developed, which aim to correct both underexposure and overexposure images to the normal exposure automatically. It is recognized that a single algorithm is challenging to take for exposure correction since the mapping flows of correcting underexposure and overexposure are quite different [12]." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 213, + 547, + 394 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 213, + 547, + 394 + ], + "spans": [ + { + "bbox": [ + 306, + 213, + 547, + 394 + ], + "type": "text", + "content": "Recent years have witnessed explosive advancement only on the single underexposure correction, including conventional methods that rely on manually designed strategies [2, 8, 10, 20, 28, 31], and deep-learning-driven methods that account for the powerful learning capability of complicated neural networks [32-34, 39], where deep-learning methods have achieved improvement in restoring corruptions [25, 35-37]. Seldom efforts have been devoted to both underexposure and overexposure scenes within a single algorithm for meeting the practical application. Very recently, some promising works [1, 12, 14, 24] attempt to solve the above issue. Both of them follow the common principle of alleviating the optimization process inconsistency by conjugating their exposure representations in spatial domain [12, 14, 24, 29] or in frequency transform domain [13]." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 398, + 547, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 398, + 547, + 506 + ], + "spans": [ + { + "bbox": [ + 304, + 398, + 547, + 506 + ], + "type": "text", + "content": "In fact, most above exposure correction approaches are trained with a mini-batch that contains both underexposure and overexposure mixed samples (see Fig. 1). Within the mini-batch, the optimization process of a single network is the opposite. On the other hand, correlating the relationship of samples across the mini-batch could conjunct their optimization processes [11]. Therefore, by constraining the relationship across the batch dimension, the adverse effect of opposite optimization in the mini-batch could be relieved." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 510, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 510, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 510, + 547, + 714 + ], + "type": "text", + "content": "To this end, in this paper, we introduce a new perspective that conjuncts the optimization processes across the batch dimension via sample relationship learning and further improves the optimization processes of exposure correction. To achieve this, we construct an exposure-relationship learning (ERL) framework consisting of two steps (see Fig. 2). In the first step (see Fig. 3), we devise a batch-correlation module (BCM) that captures the relationship of samples across the batch dimension. To enable such a relationship focusing on the exposure-related representations, we train BCM via a pretext task that excludes context information correlation. Then, in the second step (see Fig. 4), we deliver the above sample relationship as the additional training regularization term within the loss function of exposure correction algorithms, where the relationship of corrected results is optimized on the trained BCM. In this way, the optimization processes within a mini-batch" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 693, + 130, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 693, + 130, + 703 + ], + "spans": [ + { + "bbox": [ + 58, + 693, + 130, + 703 + ], + "type": "text", + "content": "*Equal contributions." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 59, + 703, + 138, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 703, + 138, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 703, + 138, + 712 + ], + "type": "text", + "content": "† Corresponding authors." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9904" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 61, + 68, + 281, + 212 + ], + "blocks": [ + { + "bbox": [ + 61, + 68, + 281, + 212 + ], + "lines": [ + { + "bbox": [ + 61, + 68, + 281, + 212 + ], + "spans": [ + { + "bbox": [ + 61, + 68, + 281, + 212 + ], + "type": "image", + "image_path": "c6efd2a6c8579cb7c4627113e69250de3bbf173b327694ddac8c0ec082440f10.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 220, + 289, + 299 + ], + "lines": [ + { + "bbox": [ + 46, + 220, + 289, + 299 + ], + "spans": [ + { + "bbox": [ + 46, + 220, + 289, + 299 + ], + "type": "text", + "content": "Figure 1. The illustration of optimization flow of underexposure and overexposure correction. As can be seen, the corrected results of underexposure samples are still obviously darker than normal exposure, while the corrected overexposure samples behave oppositely. This demonstrates the corrected results of underexposure and overexposure are optimized to approach their corresponding ground truth in the opposite direction." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 312, + 287, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 312, + 287, + 335 + ], + "spans": [ + { + "bbox": [ + 46, + 312, + 287, + 335 + ], + "type": "text", + "content": "are conjunct and the adverse effect of inconsistency optimization can be reduced." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 335, + 288, + 443 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 335, + 288, + 443 + ], + "spans": [ + { + "bbox": [ + 46, + 335, + 288, + 443 + ], + "type": "text", + "content": "Our proposed ERL framework is general and could be integrated with existing exposure correction approaches. The above sample relationship regularization is only adopted during the training procedure and does not introduce any computation burden in inference time. Extensive experiments on exposure correction datasets demonstrate consistent gains by applying our ERL framework. Moreover, it can also be extended to the mixed image enhancement task, demonstrating the extensive capability of our method." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 59, + 444, + 278, + 455 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 444, + 278, + 455 + ], + "spans": [ + { + "bbox": [ + 59, + 444, + 278, + 455 + ], + "type": "text", + "content": "We summarize the main contributions of this work as:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 58, + 462, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 59, + 462, + 287, + 535 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 462, + 287, + 535 + ], + "spans": [ + { + "bbox": [ + 59, + 462, + 287, + 535 + ], + "type": "text", + "content": "- This work is the first time to solve inconsistency optimization of exposure correction from a new perspective of batch dimension. By exploring the relationship of samples within a mini-batch, their optimization processes are conjunct to relieve the adverse inconsistency optimization effect." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 58, + 542, + 288, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 542, + 288, + 615 + ], + "spans": [ + { + "bbox": [ + 58, + 542, + 288, + 615 + ], + "type": "text", + "content": "- We propose an exposure relationship learning (ERL) framework to correlate and constrain the relationship of corrected samples across the mini-batch. The learned sample relationship acts as an additional regularization term within the loss function to assist the model optimization." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 58, + 621, + 288, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 621, + 288, + 658 + ], + "spans": [ + { + "bbox": [ + 58, + 621, + 288, + 658 + ], + "type": "text", + "content": "- Our ERL framework is general and can be integrated into the existing exposure correction methods without introducing any computation burden during inference." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 58, + 666, + 288, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 666, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 666, + 288, + 712 + ], + "type": "text", + "content": "- Extensive experiments over multiple exposure correction datasets demonstrate consistent performance gains by introducing our sample relationship learning mechanism." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 71, + 393, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 71, + 393, + 83 + ], + "spans": [ + { + "bbox": [ + 306, + 71, + 393, + 83 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 91, + 434, + 103 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 91, + 434, + 103 + ], + "spans": [ + { + "bbox": [ + 306, + 91, + 434, + 103 + ], + "type": "text", + "content": "2.1. Conventional Methods" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 110, + 547, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 110, + 547, + 266 + ], + "spans": [ + { + "bbox": [ + 304, + 110, + 547, + 266 + ], + "type": "text", + "content": "In the past decades, various methods have been proposed for correcting the exposure of images with poor illumination. Conventional works mainly rely on manually designed models and strategies. A line of work is based on histogram-based techniques [2,3,15,16], which improve the lightness and contrast of an improper exposure image. While another line of work is based on Retinex theory [18], which decomposes an image into the reflectance component and the illumination component. By enhancing the illumination component that improves the lightness, while regularizing the reflectance component that suppresses the noises, these methods have achieved prominent effect for exposure correction [8, 10, 20, 30]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 275, + 446, + 288 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 275, + 446, + 288 + ], + "spans": [ + { + "bbox": [ + 306, + 275, + 446, + 288 + ], + "type": "text", + "content": "2.2. Learning-based Methods" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 294, + 546, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 294, + 546, + 533 + ], + "spans": [ + { + "bbox": [ + 304, + 294, + 546, + 533 + ], + "type": "text", + "content": "In recent years, deep-learning-based methods have attracted more attention [41-44] which is also adopted for adjusting images with poor illumination, which are learned to correct exposures in a data-driven manner. Some of these approaches are also based on the Retinex theory [30, 32, 33, 38, 39]. As a representative, RetinexNet [32] and KIND [39] decompose an image into illumination and reflectance components, which enhances the illumination and the reflectance components to approach corresponding components of ground truth. Alternatively, another kind of components decomposed method is designed to divide the representation into different frequency bands and then enhance them progressively [21, 27, 34], such as DRBN [34]. Additionally, a few methods are also developed based on a self-supervised manner [9, 22, 23, 40]. For instance, Zero-DCE [9] formulates exposure adjustment as a task of image-specific curve estimation with a deep network. However, most of these methods are not dedicated to correcting various exposures, limiting their wide-range applications under various light conditions." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 534, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 534, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 534, + 547, + 713 + ], + "type": "text", + "content": "More recently, a few works have been developed for correcting both underexposure and overexposure images. This is challenging since the optimization flows of underexposure and overexposure correction are opposite. As a pioneer work, MSEC [1] proposes to correct varieties of exposures with a Laplacian pyramid architecture, which restores lightness and details in a coarse-to-fine manner. To ease the correction of wide-range exposures with inconsistency optimization flows, Huang et al. proposed to narrow the distribution of exposure representation with exposure normalization [12]. Similarly, CMEC [24] proposes to map different exposures to an exposure-invariant space with the assistance of a transformer for exposure correction, while ECLNet [14] derives exposure-consistency representations with the bilateral activation mechanism to as" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9905" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 62, + 68, + 265, + 225 + ], + "blocks": [ + { + "bbox": [ + 62, + 68, + 265, + 225 + ], + "lines": [ + { + "bbox": [ + 62, + 68, + 265, + 225 + ], + "spans": [ + { + "bbox": [ + 62, + 68, + 265, + 225 + ], + "type": "image", + "image_path": "029bddfbf071450ff343de885e31100f97ea8c33f2fe8483b5ed0c91deff6e55.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 226, + 288, + 293 + ], + "lines": [ + { + "bbox": [ + 46, + 226, + 288, + 293 + ], + "spans": [ + { + "bbox": [ + 46, + 226, + 288, + 293 + ], + "type": "text", + "content": "Figure 2. The overview of our proposed ERL framework, consisting of two steps. In the first step (Sec. 3.2), we formulate the exposure relationship of samples across the batch dimension on the BCM via a pretext task. In the second step (Sec. 3.3), the above-trained BCM serves as a regularization function to constrain the sample relationship of exposure correction." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 304, + 287, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 304, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 46, + 304, + 287, + 460 + ], + "type": "text", + "content": "sist exposure correction. To solve this problem in other viewpoints, Wang et al. proposed a local color distribution operator for exposure correction to meet the problem of non-uniform illumination problem [29], while FECNet [13] corrects lightness and structures progressively in a Fourier-based perspective. Although these algorithms have definitely achieved remarkable progress, there remains room for improving the optimization process across the batch dimension that contains both underexposure and overexposure corrections. In this work, we introduce a new perspective that explores the sample relationships within a minibatch to conjunct their optimization processes, which reduces the adverse effect of opposite optimization flows." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 472, + 103, + 484 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 472, + 103, + 484 + ], + "spans": [ + { + "bbox": [ + 47, + 472, + 103, + 484 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 493, + 287, + 565 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 493, + 287, + 565 + ], + "spans": [ + { + "bbox": [ + 46, + 493, + 287, + 565 + ], + "type": "text", + "content": "In this section, we first present an overview of the exposure relationship learning framework. We then introduce the proposed BCM in detail via its pretext task learning that captures sample relationships. Finally, we detail the above sample relationship as the regularization term to train exposure correction approaches." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 574, + 189, + 586 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 574, + 189, + 586 + ], + "spans": [ + { + "bbox": [ + 47, + 574, + 189, + 586 + ], + "type": "text", + "content": "3.1. Motivation and Overview" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": "Given images with incorrect exposure settings, the exposure correction task aims to correct underexposure and its adverse overexposure images to normal exposure in a single network. Previous works are usually trained with a mini-batch containing both underexposure and overexposure samples, which enables the network to learn to correct various exposures simultaneously. However, the large discrepancies between underexposure and overexposure corrections would inevitably lead to opposite optimization flows across the batch dimension. As depicted in" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 72, + 545, + 131 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 131 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 131 + ], + "type": "text", + "content": "Fig. 1, the underexposure and overexposure corrected results across the mini-batch are optimized oppositely. Such a large inconsistency optimization leads to a sub-optimal optimization process, which has rarely been explored for exposure correction." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 133, + 546, + 348 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 133, + 546, + 348 + ], + "spans": [ + { + "bbox": [ + 304, + 133, + 546, + 348 + ], + "type": "text", + "content": "To this end, we start a new perspective for exposure correction that conjuncts the optimization of samples within a mini-batch to reduce the adverse effect of inconsistency optimization, which is built upon learning the sample relationship [11]. To achieve this, we correlate and constrain the relationship of correction procedures on the corrected results and design an ERL framework with a two-step mechanism (see Fig. 2). In the first step, it correlates the exposure relationship of samples within a mini-batch by employing the BCM. To exclude the influence of context information on the relationship, the BCM is trained with a context-irrelevant pretext task. While in the second step, the above sample relationship built upon BCM acts as an additional regularization term within the loss function of exposure correction methods, which constrains the relationship of samples. In this way, their optimization processes across the batch dimension are conjunct during training, and it would not introduce any computation burden in inference time." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 354, + 484, + 366 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 354, + 484, + 366 + ], + "spans": [ + { + "bbox": [ + 306, + 354, + 484, + 366 + ], + "type": "text", + "content": "3.2. Sample Relationship Correlation" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 372, + 545, + 418 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 372, + 545, + 418 + ], + "spans": [ + { + "bbox": [ + 304, + 372, + 545, + 418 + ], + "type": "text", + "content": "As the first step of the ERL framework, we propose to correlate the sample relationship across the batch dimension, which is implemented by BCM via a context-irrelevant task." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 419, + 546, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 419, + 546, + 515 + ], + "spans": [ + { + "bbox": [ + 304, + 419, + 546, + 515 + ], + "type": "text", + "content": "Batch-correlation module (BCM). We design the BCM to capture the sample relationship in feature space. Its architecture is shown in Fig. 3, which consists of a feature encoder " + }, + { + "bbox": [ + 304, + 419, + 546, + 515 + ], + "type": "inline_equation", + "content": "f_{e}" + }, + { + "bbox": [ + 304, + 419, + 546, + 515 + ], + "type": "text", + "content": " with several CNN-based layers, and a sample relationship part with a transformer encoder " + }, + { + "bbox": [ + 304, + 419, + 546, + 515 + ], + "type": "inline_equation", + "content": "t_{e}" + }, + { + "bbox": [ + 304, + 419, + 546, + 515 + ], + "type": "text", + "content": ". In addition, the decoder " + }, + { + "bbox": [ + 304, + 419, + 546, + 515 + ], + "type": "inline_equation", + "content": "f_{d}" + }, + { + "bbox": [ + 304, + 419, + 546, + 515 + ], + "type": "text", + "content": " consists of several CNN-based layers are set to follow the transformer encoder that reconstructs the input image, which assists the training of BCM (see Sec. 3.3)." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "spans": [ + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "content": "We then describe the workflow of BCM that captures sample relationships as follows. Let " + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "inline_equation", + "content": "I \\in R^{B \\times 3 \\times H \\times W}" + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "content": " denote a sequence of input images in a mini-batch, where " + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "content": " indicates the length of the sequence, and " + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "content": " are the spatial dimensions. The CNN-based encoder " + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "inline_equation", + "content": "f_{e}" + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "content": " first extracts feature from images as " + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "inline_equation", + "content": "F \\in R^{B \\times C \\times H \\times W}" + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "content": " is the channel dimension. Then these features are resized to the resolution of " + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "inline_equation", + "content": "16 \\times 16" + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "content": ", which can filter out most content-relevant information since low-resolution contains less content. After that, these features are reshaped to the dimension of " + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "inline_equation", + "content": "R^{B \\times C'}" + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "content": " that transfers the dimension of the feature as " + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "inline_equation", + "content": "C'" + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "content": ". Finally, we correlate the relationship of these features in the batch dimension with the transformer encoder " + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "inline_equation", + "content": "t_{e}" + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "content": ", and obtain the features " + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "inline_equation", + "content": "F' \\in R^{B \\times C'}" + }, + { + "bbox": [ + 304, + 515, + 547, + 694 + ], + "type": "text", + "content": " that are correlated across the batch dimension. The whole above process is expressed as:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 392, + 700, + 545, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 392, + 700, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 392, + 700, + 545, + 715 + ], + "type": "interline_equation", + "content": "F ^ {\\prime} = t _ {e} \\left(f _ {e} (I)\\right), \\tag {1}", + "image_path": "13849b1e92f3321d05d1c1b64d2ea77af8ded2cc52dfcea6c6b276fa5306c140.jpg" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9906" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 69, + 72, + 521, + 237 + ], + "blocks": [ + { + "bbox": [ + 69, + 72, + 521, + 237 + ], + "lines": [ + { + "bbox": [ + 69, + 72, + 521, + 237 + ], + "spans": [ + { + "bbox": [ + 69, + 72, + 521, + 237 + ], + "type": "image", + "image_path": "db96c5ebc260a9161c92617cfc12c2cf0d7e29dbea938b4e09ff67170fd36628.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 239, + 545, + 294 + ], + "lines": [ + { + "bbox": [ + 46, + 239, + 545, + 294 + ], + "spans": [ + { + "bbox": [ + 46, + 239, + 545, + 294 + ], + "type": "text", + "content": "Figure 3. The illustration of our proposed BCM, which correlates the exposure relationship of samples across the batch dimension. Specifically, it consists of an encoder part " + }, + { + "bbox": [ + 46, + 239, + 545, + 294 + ], + "type": "inline_equation", + "content": "f_{e}" + }, + { + "bbox": [ + 46, + 239, + 545, + 294 + ], + "type": "text", + "content": " that extracts features and a decoder part " + }, + { + "bbox": [ + 46, + 239, + 545, + 294 + ], + "type": "inline_equation", + "content": "f_{d}" + }, + { + "bbox": [ + 46, + 239, + 545, + 294 + ], + "type": "text", + "content": " that reconstructs the image, both of which have been trained in a self-reconstruction manner. We then apply a transformer encoder " + }, + { + "bbox": [ + 46, + 239, + 545, + 294 + ], + "type": "inline_equation", + "content": "t_{e}" + }, + { + "bbox": [ + 46, + 239, + 545, + 294 + ], + "type": "text", + "content": " between the encoder and decoder parts to explore the sample relationship. Furthermore, we train the BCM with a pretext task that reconstructs the content-related phase information, leading to it focusing more on correlating exposure-related information." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 301, + 278, + 313 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 301, + 278, + 313 + ], + "spans": [ + { + "bbox": [ + 47, + 301, + 278, + 313 + ], + "type": "text", + "content": "where the resize and reshape operations are omitted here." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 315, + 286, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 315, + 286, + 422 + ], + "spans": [ + { + "bbox": [ + 46, + 315, + 286, + 422 + ], + "type": "text", + "content": "We further detail how the transformer encoder " + }, + { + "bbox": [ + 46, + 315, + 286, + 422 + ], + "type": "inline_equation", + "content": "t_e" + }, + { + "bbox": [ + 46, + 315, + 286, + 422 + ], + "type": "text", + "content": " explores the sample relationship across the batch dimension. Transformer usually contains a multihead self-attention layer that models the relationship of spatial and channel dimensions [7]. By transposing the feature into the dimension that the transformer layer can operate on the batch dimension, the self-attention mechanism of the multi-head self-attention layer could cross attention samples in a minibatch, thus correlating their relationships." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 424, + 286, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 424, + 286, + 508 + ], + "spans": [ + { + "bbox": [ + 46, + 424, + 286, + 508 + ], + "type": "text", + "content": "Train the BCM via a context-irrelevant task. In preliminary, the encoder and decoder parts of the BCM are first trained in a self-reconstruction manner that can reconstruct input images of themselves, then these parts are fixed and a transformer encoder " + }, + { + "bbox": [ + 46, + 424, + 286, + 508 + ], + "type": "inline_equation", + "content": "t_e" + }, + { + "bbox": [ + 46, + 424, + 286, + 508 + ], + "type": "text", + "content": " is incorporated between them to construct BCM. Thus, when training BCM, only the transformer encoder " + }, + { + "bbox": [ + 46, + 424, + 286, + 508 + ], + "type": "inline_equation", + "content": "t_e" + }, + { + "bbox": [ + 46, + 424, + 286, + 508 + ], + "type": "text", + "content": " is trained to learn the sample relationship." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 509, + 286, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 509, + 286, + 592 + ], + "spans": [ + { + "bbox": [ + 46, + 509, + 286, + 592 + ], + "type": "text", + "content": "Since the optimization processes of the correction are mainly affected by the exposure condition, we hope the sample relationship learning correlates more with exposure-related components. To this end, the context relationship across the batch dimension should not be focused, and we now need to construct a pretext task that trains BCM to exclude the influences of context relationships." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 594, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 286, + 713 + ], + "type": "text", + "content": "As shown in Fig. 3, we train a context-irrelevant pretext task on the BCM via a self-reconstruction manner. For a sample correlating with other samples, it is hard to reconstruct itself since the information of other samples is introduced. Therefore, we construct the pretext task to reconstruct content-relevant information, which suppresses the correlation of the content component, thus driving the content-irrelevant information to be more correlated across the mini-batch. Specifically, a mini-batch containing normal exposure samples " + }, + { + "bbox": [ + 46, + 594, + 286, + 713 + ], + "type": "inline_equation", + "content": "I \\in R^{B \\times 3 \\times H \\times W}" + }, + { + "bbox": [ + 46, + 594, + 286, + 713 + ], + "type": "text", + "content": " are sent to the" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "spans": [ + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "text", + "content": "BCM, which are substantially derived correlated feature " + }, + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "inline_equation", + "content": "F'" + }, + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "text", + "content": " as depicted in Eq. 1. Then " + }, + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "inline_equation", + "content": "F'" + }, + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "text", + "content": " are reshaped and resized to the dimension of " + }, + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "inline_equation", + "content": "F' \\in R^{B \\times C \\times H \\times W}" + }, + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "text", + "content": ", which are further fused with " + }, + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "inline_equation", + "content": "F" + }, + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "text", + "content": ". Finally, the decoder " + }, + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "inline_equation", + "content": "f_d" + }, + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "text", + "content": " reconstructs the integrated features to the image space as " + }, + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "inline_equation", + "content": "I_{rec}" + }, + { + "bbox": [ + 305, + 301, + 545, + 372 + ], + "type": "text", + "content": ". This procedure is expressed as:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 370, + 373, + 545, + 387 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 370, + 373, + 545, + 387 + ], + "spans": [ + { + "bbox": [ + 370, + 373, + 545, + 387 + ], + "type": "interline_equation", + "content": "I ^ {\\prime} = f _ {d} \\left(W \\left(\\operatorname {c a t} \\left([ F, F ^ {\\prime} ]\\right)\\right)\\right), \\tag {2}", + "image_path": "1583e23d2998849ea26fd10d6fee1ad6ca86c0ee02d26eefab009708569d21bf.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 392, + 544, + 416 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 392, + 544, + 416 + ], + "spans": [ + { + "bbox": [ + 305, + 392, + 544, + 416 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 392, + 544, + 416 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 305, + 392, + 544, + 416 + ], + "type": "text", + "content": " denotes the " + }, + { + "bbox": [ + 305, + 392, + 544, + 416 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 305, + 392, + 544, + 416 + ], + "type": "text", + "content": " convolution operation, and " + }, + { + "bbox": [ + 305, + 392, + 544, + 416 + ], + "type": "inline_equation", + "content": "[\\cdot]" + }, + { + "bbox": [ + 305, + 392, + 544, + 416 + ], + "type": "text", + "content": " means the concatenate operation." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 417, + 545, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 417, + 545, + 510 + ], + "spans": [ + { + "bbox": [ + 304, + 417, + 545, + 510 + ], + "type": "text", + "content": "We then introduce a phase component constraint as the learning objective of the BCM. Based on FECNet [13], the amplitude component reflects the exposure-related information, while the phase component corresponds more to content information. Therefore, we construct the learning objective " + }, + { + "bbox": [ + 304, + 417, + 545, + 510 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{pre}" + }, + { + "bbox": [ + 304, + 417, + 545, + 510 + ], + "type": "text", + "content": " to constrain the phase component of the reconstructed images " + }, + { + "bbox": [ + 304, + 417, + 545, + 510 + ], + "type": "inline_equation", + "content": "I^{\\prime}" + }, + { + "bbox": [ + 304, + 417, + 545, + 510 + ], + "type": "text", + "content": " to be closed with that of the input images " + }, + { + "bbox": [ + 304, + 417, + 545, + 510 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 304, + 417, + 545, + 510 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 354, + 512, + 545, + 526 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 354, + 512, + 545, + 526 + ], + "spans": [ + { + "bbox": [ + 354, + 512, + 545, + 526 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {p r e} = \\left| \\left| \\mathcal {P} (\\mathcal {F} (I)) - \\mathcal {P} (\\mathcal {F} \\left(I ^ {\\prime}\\right)) \\right| \\right| _ {1}, \\tag {3}", + "image_path": "02823f23b71b29e353edf315140b7e59db5f28a9f699d86e46109d7b39460d12.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 531, + 545, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 531, + 545, + 615 + ], + "spans": [ + { + "bbox": [ + 304, + 531, + 545, + 615 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 531, + 545, + 615 + ], + "type": "inline_equation", + "content": "||\\cdot ||_1" + }, + { + "bbox": [ + 304, + 531, + 545, + 615 + ], + "type": "text", + "content": " denotes the mean absolutely error, " + }, + { + "bbox": [ + 304, + 531, + 545, + 615 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 304, + 531, + 545, + 615 + ], + "type": "text", + "content": " is the Fourier transform operation, and " + }, + { + "bbox": [ + 304, + 531, + 545, + 615 + ], + "type": "inline_equation", + "content": "\\mathcal{P}(\\cdot)" + }, + { + "bbox": [ + 304, + 531, + 545, + 615 + ], + "type": "text", + "content": " represents getting the phase component. Based on this regularization, the BCM reduces the content information correlation across the minibatch which would put a negative effect on content reconstruction, thus leading to BCM focusing more on correlating exposure-related information." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 623, + 489, + 636 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 623, + 489, + 636 + ], + "spans": [ + { + "bbox": [ + 306, + 623, + 489, + 636 + ], + "type": "text", + "content": "3.3. Sample Relationship Constraining" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "type": "text", + "content": "Deliver the sample relationship as regularization. After correlating the sample relationship across batch dimensions, we further deliver the sample relationship as an additional regularization term that conjuncts the optimization within a mini-batch. For implementation, when training the exposure correction network, we constrain the relationship" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9907" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 67, + 71, + 282, + 224 + ], + "blocks": [ + { + "bbox": [ + 67, + 71, + 282, + 224 + ], + "lines": [ + { + "bbox": [ + 67, + 71, + 282, + 224 + ], + "spans": [ + { + "bbox": [ + 67, + 71, + 282, + 224 + ], + "type": "image", + "image_path": "81ba7f3e753176eebd6111590d9215c517099ae237bb8dd516f88b40785174f6.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 228, + 289, + 297 + ], + "lines": [ + { + "bbox": [ + 46, + 228, + 289, + 297 + ], + "spans": [ + { + "bbox": [ + 46, + 228, + 289, + 297 + ], + "type": "text", + "content": "Figure 4. The illustration of delivering the sample relationship as regularization for exposure correction. We employ the encoder and transformer parts of the trained BCM as the regularization function, and integrate this regularization term " + }, + { + "bbox": [ + 46, + 228, + 289, + 297 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_r" + }, + { + "bbox": [ + 46, + 228, + 289, + 297 + ], + "type": "text", + "content": " with the conventional loss functions " + }, + { + "bbox": [ + 46, + 228, + 289, + 297 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_c" + }, + { + "bbox": [ + 46, + 228, + 289, + 297 + ], + "type": "text", + "content": " of exposure correction methods to optimize the exposure correction model." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 308, + 287, + 331 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 308, + 287, + 331 + ], + "spans": [ + { + "bbox": [ + 46, + 308, + 287, + 331 + ], + "type": "text", + "content": "of the corrected samples to be closed to that of the ground truth." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 331, + 289, + 416 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 331, + 289, + 416 + ], + "spans": [ + { + "bbox": [ + 46, + 331, + 289, + 416 + ], + "type": "text", + "content": "Specifically, we employ the trained BCM to construct the sample relationship within a mini-batch, where we discard its decoder part and deploy the correlated feature " + }, + { + "bbox": [ + 46, + 331, + 289, + 416 + ], + "type": "inline_equation", + "content": "F'" + }, + { + "bbox": [ + 46, + 331, + 289, + 416 + ], + "type": "text", + "content": " as the sample relationships representation. To this end, we apply the sample relationship regularization on the " + }, + { + "bbox": [ + 46, + 331, + 289, + 416 + ], + "type": "inline_equation", + "content": "F'" + }, + { + "bbox": [ + 46, + 331, + 289, + 416 + ], + "type": "text", + "content": " during the training of exposure correction, and its learning objective " + }, + { + "bbox": [ + 46, + 331, + 289, + 416 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_r" + }, + { + "bbox": [ + 46, + 331, + 289, + 416 + ], + "type": "text", + "content": " is expressed as:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 123, + 422, + 287, + 438 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 123, + 422, + 287, + 438 + ], + "spans": [ + { + "bbox": [ + 123, + 422, + 287, + 438 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {r} = \\left| \\left| F _ {r e s} ^ {\\prime} - F _ {g t} ^ {\\prime} \\right| \\right| _ {1} \\tag {4}", + "image_path": "864cf979b45477aaae4f5430131f53bfc5eea92f468ef4d242e7042d44159cff.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 444, + 287, + 468 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 444, + 287, + 468 + ], + "spans": [ + { + "bbox": [ + 46, + 444, + 287, + 468 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 444, + 287, + 468 + ], + "type": "inline_equation", + "content": "F_{res}^{\\prime}" + }, + { + "bbox": [ + 46, + 444, + 287, + 468 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 444, + 287, + 468 + ], + "type": "inline_equation", + "content": "F_{gt}^{\\prime}" + }, + { + "bbox": [ + 46, + 444, + 287, + 468 + ], + "type": "text", + "content": " denote the sample relationships representation of corrected results and ground truth, respectively." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 468, + 288, + 564 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 468, + 288, + 564 + ], + "spans": [ + { + "bbox": [ + 46, + 468, + 288, + 564 + ], + "type": "text", + "content": "Integrate the above regularization term to existing methods. The above sample relationship regularization term is general and could be plugged into the training paradigm of existing exposure correction algorithms as an additional optimization objective. As shown in Fig. 4, the total learning objective for training existing exposure correction method is the combination of the conventional loss " + }, + { + "bbox": [ + 46, + 468, + 288, + 564 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_c" + }, + { + "bbox": [ + 46, + 468, + 288, + 564 + ], + "type": "text", + "content": " and the relationship regularization term " + }, + { + "bbox": [ + 46, + 468, + 288, + 564 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_r" + }, + { + "bbox": [ + 46, + 468, + 288, + 564 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 134, + 572, + 287, + 585 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 134, + 572, + 287, + 585 + ], + "spans": [ + { + "bbox": [ + 134, + 572, + 287, + 585 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathcal {L} _ {c} + \\alpha \\mathcal {L} _ {r} \\tag {5}", + "image_path": "360fe0120e0194016dab9cd41e99c9f0a985a4fc49f44b43a88569fcd090a95a.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 592, + 287, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 592, + 287, + 641 + ], + "spans": [ + { + "bbox": [ + 46, + 592, + 287, + 641 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 592, + 287, + 641 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 46, + 592, + 287, + 641 + ], + "type": "text", + "content": " is the weight factor and we set it to 0.1 empirically, and the selection of " + }, + { + "bbox": [ + 46, + 592, + 287, + 641 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 46, + 592, + 287, + 641 + ], + "type": "text", + "content": " will be discussed in the ablation study. The conventional loss denotes the original loss function employed in these exposure correction methods." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 647, + 119, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 647, + 119, + 659 + ], + "spans": [ + { + "bbox": [ + 47, + 647, + 119, + 659 + ], + "type": "text", + "content": "3.4. Discussion" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "content": "To better understand how the proposed ERL framework conjunct the optimization processes by modeling sample relationships across the batch dimension, we provide an intuitive explanation as demonstrated in Fig. 5. Given the" + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 320, + 73, + 538, + 191 + ], + "blocks": [ + { + "bbox": [ + 320, + 73, + 538, + 191 + ], + "lines": [ + { + "bbox": [ + 320, + 73, + 538, + 191 + ], + "spans": [ + { + "bbox": [ + 320, + 73, + 538, + 191 + ], + "type": "image", + "image_path": "cc63628cc676eccc4ff80aaf87ed0b61871a3dcfff001cce08be1f2886dd7ad5.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 191, + 547, + 236 + ], + "lines": [ + { + "bbox": [ + 304, + 191, + 547, + 236 + ], + "spans": [ + { + "bbox": [ + 304, + 191, + 547, + 236 + ], + "type": "text", + "content": "Figure 5. With the regularization of sample relationship, besides being optimized to the ground truth " + }, + { + "bbox": [ + 304, + 191, + 547, + 236 + ], + "type": "inline_equation", + "content": "Y_{i}" + }, + { + "bbox": [ + 304, + 191, + 547, + 236 + ], + "type": "text", + "content": ", the corrected results " + }, + { + "bbox": [ + 304, + 191, + 547, + 236 + ], + "type": "inline_equation", + "content": "X_{i}" + }, + { + "bbox": [ + 304, + 191, + 547, + 236 + ], + "type": "text", + "content": " are also implicitly constrained with other samples to keep their relationships to be closed with those of the ground truth." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 243, + 545, + 315 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 243, + 545, + 315 + ], + "spans": [ + { + "bbox": [ + 304, + 243, + 545, + 315 + ], + "type": "text", + "content": "corrected results " + }, + { + "bbox": [ + 304, + 243, + 545, + 315 + ], + "type": "inline_equation", + "content": "X(X = X_{1},\\dots,X_{i},\\dots X_{N})" + }, + { + "bbox": [ + 304, + 243, + 545, + 315 + ], + "type": "text", + "content": " and their corresponding ground truth " + }, + { + "bbox": [ + 304, + 243, + 545, + 315 + ], + "type": "inline_equation", + "content": "Y(Y = Y_{1},\\dots,Y_{i},\\dots Y_{N})" + }, + { + "bbox": [ + 304, + 243, + 545, + 315 + ], + "type": "text", + "content": " in a minibatch, when the network is only optimized with the conventional loss, the sample " + }, + { + "bbox": [ + 304, + 243, + 545, + 315 + ], + "type": "inline_equation", + "content": "X_{i}" + }, + { + "bbox": [ + 304, + 243, + 545, + 315 + ], + "type": "text", + "content": " is only optimized to approach the corresponding ground truth " + }, + { + "bbox": [ + 304, + 243, + 545, + 315 + ], + "type": "inline_equation", + "content": "Y_{i}" + }, + { + "bbox": [ + 304, + 243, + 545, + 315 + ], + "type": "text", + "content": ", and their optimization flows are separated and often opposite in exposure correction." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "spans": [ + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "text", + "content": "While with the employ of sample relationship constraining, the corrected result " + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "inline_equation", + "content": "X_{i}" + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "text", + "content": " are further constrained to other corrected results " + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "inline_equation", + "content": "X_{j}" + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "text", + "content": " (where " + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "inline_equation", + "content": "j \\neq i" + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "text", + "content": ") in a mini-batch, which drives their relationships to be closed to those of " + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "inline_equation", + "content": "Y_{i}" + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "inline_equation", + "content": "Y_{j}" + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "text", + "content": " (where " + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "inline_equation", + "content": "j \\neq i" + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "text", + "content": "). To this end, the optimization flow of " + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "inline_equation", + "content": "X_{i}" + }, + { + "bbox": [ + 304, + 316, + 546, + 483 + ], + "type": "text", + "content": " is also affected by other samples, which could lead to a more consistent exposure correction effect across the mini-batch. In CMEC [24], it models consistency learning of different exposures that regularize the underexposure and overexposure representations to be similar in the network. Similarly, we consider our sample relationship regularization is another form of consistency learning, and it implicitly regularizes the results' exposure representation to be correlated and constrained in a mini-batch." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 495, + 388, + 509 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 495, + 388, + 509 + ], + "spans": [ + { + "bbox": [ + 306, + 495, + 388, + 509 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 515, + 432, + 528 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 515, + 432, + 528 + ], + "spans": [ + { + "bbox": [ + 306, + 515, + 432, + 528 + ], + "type": "text", + "content": "4.1. Experimental Settings" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 534, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 534, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 534, + 545, + 714 + ], + "type": "text", + "content": "Datasets. To evaluate the performance of our proposed method, we conduct experiments on two representative datasets, including the MSEC dataset proposed in MSEC [1] and SICE dataset [5] proposed in SICE. The MSEC dataset is rendered from the MIT-Adobe FiveK dataset [4], consisting of five exposure levels, where its retouched version by experts of middle exposure is set as ground truth. Finally, it consists of 17675 images for training, 750 images for validation, and 5905 images for testing. While for the SICE dataset, the middle exposure subset of this dataset is selected as the ground truth, and the second and last second exposure subsets are set as the underexposed and overexposed images, respectively. Finally, the SICE dataset contains 512 images for training and 30 images for testing." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9908" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 47, + 70, + 561, + 337 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 561, + 337 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 561, + 337 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 561, + 337 + ], + "type": "table", + "html": "
MethodMSECSICE#Param
UnderOverAverageUnderOverAverage
PSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIM
HE [26]16.520.691816.530.699116.530.695914.690.565112.870.499113.780.5376-
CLAHE [28]16.770.621114.450.584215.380.599012.690.503710.210.484711.450.4942-
RetinexNet [32]12.130.620910.470.595311.140.604812.940.517112.870.525212.900.52120.84M
URetinexNet [33]13.850.73719.810.673311.420.698817.390.64487.400.454312.400.54961.32M
Zero-DCE [9]14.550.588710.400.514212.060.544116.920.63307.110.429212.020.53110.079M
Zero-DCE++ [19]13.820.58879.740.514211.370.558311.930.47556.880.40889.410.44220.010M
DPED [17]20.060.682613.140.581215.910.621916.830.61337.990.430012.410.52170.39M
KIND [39]15.510.711511.660.730013.200.742413.430.48377.850.477910.640.48088.54M
SID [6]19.370.810318.830.805519.040.807419.510.663516.790.644418.150.65407.40M
RUAS [22]13.430.68076.390.46559.200.551516.630.55894.540.319610.590.43930.0014M
SCI [23]9.9650.66815.8370.51907.490.578617.860.64014.450.362912.490.50510.0003M
MSEC [1]20.520.812919.790.815620.080.821019.620.651217.590.656018.580.65367.04M
CMEC [24]22.230.814022.750.833622.540.825717.680.659218.170.681117.930.67025.40M
SID-ENC [12]22.590.842322.360.851922.450.848121.360.665219.380.684320.370.67487.45M
LCDPNet [29]22.350.865022.170.847622.300.855217.450.562217.040.646317.250.60430.96M
DRBN [34]19.740.829019.370.832119.520.830917.960.676717.330.682817.650.67980.53M
DRBN+ERL19.910.830519.600.838419.730.835518.090.673517.930.686618.010.67960.53M
DRBN-ENC [12]22.720.854422.110.852122.350.853021.890.707119.090.722920.490.71500.58M
DRBN-ENC+ERL22.920.870422.450.872422.670.871622.060.705319.500.720520.780.71290.58M
ECLNet [14]22.370.856622.700.867322.570.863122.050.689319.250.687220.650.68610.018M
ECLNet+ERL22.900.862422.580.867622.700.865522.140.690819.470.698220.810.69450.018M
FECNet [13]22.960.859823.220.874823.120.868822.010.673719.910.696120.960.68490.15M
FECNet+ERL23.100.863923.180.875923.150.871122.350.667120.100.689121.220.67810.15M
", + "image_path": "52af340b28e5149b37f1718b426aa7533c3955a7d83bbbd0cb91a588d585f167.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 338, + 545, + 361 + ], + "lines": [ + { + "bbox": [ + 46, + 338, + 545, + 361 + ], + "spans": [ + { + "bbox": [ + 46, + 338, + 545, + 361 + ], + "type": "text", + "content": "Table 1. Quantitative results of different methods on the MSEC and the SICE datasets in terms of PSNR and SSIM. Bold denotes the results that are improved by incorporating ERL. The underline denotes the best result." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 367, + 287, + 486 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 367, + 287, + 486 + ], + "spans": [ + { + "bbox": [ + 46, + 367, + 287, + 486 + ], + "type": "text", + "content": "Implementation details. We implement our proposed model with PyTorch on a single NVIDIA GTX 3090 GPU. Firstly, we train the BCM on the normal exposure images for 100 epochs that captures the exposure sample relationship. Then, to couple the above sample relationship as additional regularization of existing methods, we select several methods as equipped methods, including DRBN [34], DRBN-ENC-4 [12], ECLNet [14] and FECNet [13]. After the integration, we add " + }, + { + "bbox": [ + 46, + 367, + 287, + 486 + ], + "type": "inline_equation", + "content": "+" + }, + { + "bbox": [ + 46, + 367, + 287, + 486 + ], + "type": "text", + "content": " between their names and ERL to denote the improved method (such as DRBN+ERL)." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 488, + 288, + 596 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 488, + 288, + 596 + ], + "spans": [ + { + "bbox": [ + 46, + 488, + 288, + 596 + ], + "type": "text", + "content": "During training, we employ an Adam optimizer with " + }, + { + "bbox": [ + 46, + 488, + 288, + 596 + ], + "type": "inline_equation", + "content": "\\beta_{1} = 0.9" + }, + { + "bbox": [ + 46, + 488, + 288, + 596 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 488, + 288, + 596 + ], + "type": "inline_equation", + "content": "\\beta_{2} = 0.99" + }, + { + "bbox": [ + 46, + 488, + 288, + 596 + ], + "type": "text", + "content": " to update the model. We set the training settings following the original settings of the above-equipped methods, including the update of the learning rate, the number of training epochs, and the patch size. All of the loss functions for exposure correction are optimized in an end-to-end manner. During the evaluation, we implement Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) for numerical evaluation." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 609, + 272, + 623 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 609, + 272, + 623 + ], + "spans": [ + { + "bbox": [ + 47, + 609, + 272, + 623 + ], + "type": "text", + "content": "4.2. Comparison with State-of-the-Art Methods" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 629, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 629, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 629, + 287, + 715 + ], + "type": "text", + "content": "To evaluate the performance with existing exposure correction methods, several methods are adopted as a comparison, including two traditional methods HE [26] and CLAHE [28]. Additionally, several deep-learning-based methods are involved as well, including RetinexNet [32], DPED [17], SID [6], KinD [39], DRBN [34], URetinexNet [33], Zero-DCE [9], Zero" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 367, + 545, + 416 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 367, + 545, + 416 + ], + "spans": [ + { + "bbox": [ + 304, + 367, + 545, + 416 + ], + "type": "text", + "content": "DCE++ [19], RUAS [22] and SCI [23]. We further include several specific approaches for exposure correction: MSEC [1], CMEC [24], SID-ENC [12], DRBN-ENC [12], ECLNet [14], LCDPNet [29] and FECNet [13]." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 421, + 546, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 421, + 546, + 648 + ], + "spans": [ + { + "bbox": [ + 304, + 421, + 546, + 648 + ], + "type": "text", + "content": "Quantitative results. The quantitative results are shown in Table 1. For the MSEC dataset, following MSEC, we average the results of the exposures of the first two levels and the remaining levels of exposure as underexposure and overexposure results, respectively. As can be observed, recently proposed exposure correction approaches (such as FECNet and ECLNet) achieve excellent performance on the two datasets. With the incorporation of the ERL framework, their overall quantitative results are further improved on most subsets and metrics. It substantiates the effectiveness and flexibility of our proposed sample relationship regularization. Note that the introducing of our sample relationship regularization would not increase parameters or computation costs. In addition, the effectiveness of our framework can also be verified to improve the performance during the training procedure as shown in Fig. 7. As can be seen, our ERL framework improves performance during most training time with higher PSNR, demonstrating it improves optimization processes and thus lead to better performance." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 654, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 654, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 654, + 547, + 715 + ], + "type": "text", + "content": "Qualitative results. We provide visual comparisons on the MSEC dataset in Fig. 6, and visual comparisons on the SICE dataset in Fig. 8, respectively. 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Therefore, these results prove our method could improve existing methods to generate better visual results. We provide more visual results in the supplementary materials." + } + ] + } + ], + "index": 54 + }, + { + "bbox": [ + 47, + 550, + 147, + 561 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 550, + 147, + 561 + ], + "spans": [ + { + "bbox": [ + 47, + 550, + 147, + 561 + ], + "type": "text", + "content": "4.3. Ablation Studies" + } + ] + } + ], + "index": 55 + }, + { + "bbox": [ + 46, + 569, + 286, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 569, + 286, + 628 + ], + "spans": [ + { + "bbox": [ + 46, + 569, + 286, + 628 + ], + "type": "text", + "content": "We conduct ablation studies on the SICE dataset to further investigate the effectiveness of our proposed sample relationship modeling under different configurations, and we set the baseline as training the exposure correction method ECLNet on its original settings." + } + ] + } + ], + "index": 56 + }, + { + "bbox": [ + 46, + 630, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 630, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 630, + 286, + 713 + ], + "type": "text", + "content": "Investigation of the BCM. To validate the effectiveness of the BCM design, we perform ablations with different configurations of the BCM and present the results in Table 2. As can be seen, without the employ of the pretext task (which means the " + }, + { + "bbox": [ + 46, + 630, + 286, + 713 + ], + "type": "inline_equation", + "content": "t_e" + }, + { + "bbox": [ + 46, + 630, + 286, + 713 + ], + "type": "text", + "content": " in the BCM is randomly initialized) or training the pretext task using pixel " + }, + { + "bbox": [ + 46, + 630, + 286, + 713 + ], + "type": "inline_equation", + "content": "L_1" + }, + { + "bbox": [ + 46, + 630, + 286, + 713 + ], + "type": "text", + "content": " loss, the performance can also be improved compared with the base-" + } + ] + } + ], + "index": 57 + }, + { + "type": "table", + "bbox": [ + 306, + 357, + 545, + 396 + ], + "blocks": [ + { + "bbox": [ + 306, + 357, + 545, + 396 + ], + "lines": [ + { + "bbox": [ + 306, + 357, + 545, + 396 + ], + "spans": [ + { + "bbox": [ + 306, + 357, + 545, + 396 + ], + "type": "table", + "html": "
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This demonstrates that the sample relationship regularization mechanism itself is effective for improving performance, while the designing of our pretext task can further elevate the exposure correction effect." + } + ] + } + ], + "index": 60 + }, + { + "bbox": [ + 304, + 479, + 545, + 562 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 479, + 545, + 562 + ], + "spans": [ + { + "bbox": [ + 304, + 479, + 545, + 562 + ], + "type": "text", + "content": "Investigation of weight coefficient. To investigate the influences of the weight coefficient " + }, + { + "bbox": [ + 304, + 479, + 545, + 562 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 304, + 479, + 545, + 562 + ], + "type": "text", + "content": " on sample relationship regularization term, we perform experiment experiments with setting different coefficients " + }, + { + "bbox": [ + 304, + 479, + 545, + 562 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 304, + 479, + 545, + 562 + ], + "type": "text", + "content": ". As shown in Fig. 9 (a), larger " + }, + { + "bbox": [ + 304, + 479, + 545, + 562 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 304, + 479, + 545, + 562 + ], + "type": "text", + "content": " could lead to a slight performance decrease. Overall, the performance remains stable and is robust to the weight coefficient changes." + } + ] + } + ], + "index": 61 + }, + { + "bbox": [ + 304, + 563, + 545, + 659 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 563, + 545, + 659 + ], + "spans": [ + { + "bbox": [ + 304, + 563, + 545, + 659 + ], + "type": "text", + "content": "Investigation of batch size. We perform ablation studies to investigate the batch size of sample relationship regularization for optimizing exposure correction in Fig. 9 (b). The results show that setting the batch size of 4 improves more performance than other settings, while a larger batch size can not improve performance significantly. This may be caused by the sample relationship of large batch size being hard to optimize due to the high variance." + } + ] + } + ], + "index": 62 + }, + { + "bbox": [ + 306, + 670, + 531, + 681 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 670, + 531, + 681 + ], + "spans": [ + { + "bbox": [ + 306, + 670, + 531, + 681 + ], + "type": "text", + "content": "4.4. 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MethodLOLSICE (over)FiveKAverage
DRBN19.39/0.81719.46/0.72921.77/0.85920.21/0.801
DRBN+ERL19.84/0.82419.34/0.73022.14/0.87320.44/0.809
ECLNet21.54/0.80619.98/0.72823.54/0.85121.69/0.795
ECLNet+ERL22.01/0.82719.73/0.71423.71/0.85321.82/0.798
FECNet22.03/0.83619.78/0.71823.82/0.84921.88/0.800
FECNet+ERL21.08/0.82920.57/0.72224.18/0.86421.94/0.805
", + "image_path": "b28bc09bbe5ae9f87e129ff90aa4e67a875e92ec886c77eeb13cb5673b0c9948.jpg" + } + ] + } + ], + "index": 54, + "angle": 0, + "type": "table_body" + } + ], + "index": 54 + }, + { + "bbox": [ + 47, + 585, + 287, + 618 + ], + "lines": [ + { + "bbox": [ + 47, + 585, + 287, + 618 + ], + "spans": [ + { + "bbox": [ + 47, + 585, + 287, + 618 + ], + "type": "text", + "content": "Table 3. Quantitative results of applying our framework on exposure correction methods for multiple enhancement tasks. Bold denotes the results that are improved by incorporating the ERL." + } + ] + } + ], + "index": 55, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "type": "text", + "content": "mixed enhancement task containing different enhancement tasks, where the optimization flows of them are quite different. Specifically, we blend the LOL dataset [32] designed for low-light enhancement, MIT-FiveK dataset [4] collected for image retouching, and the overexposure subset from the SICE dataset to build a Task-mix dataset. As shown in Table 3, with the equipped with our sample relationship regu" + } + ] + } + ], + "index": 56 + }, + { + "bbox": [ + 306, + 356, + 545, + 381 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 356, + 545, + 381 + ], + "spans": [ + { + "bbox": [ + 306, + 356, + 545, + 381 + ], + "type": "text", + "content": "larization, the overall performance is improved. We provide visual results in the supplementary materials." + } + ] + } + ], + "index": 57 + }, + { + "bbox": [ + 306, + 392, + 378, + 403 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 392, + 378, + 403 + ], + "spans": [ + { + "bbox": [ + 306, + 392, + 378, + 403 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 58 + }, + { + "bbox": [ + 304, + 411, + 545, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 411, + 545, + 626 + ], + "spans": [ + { + "bbox": [ + 304, + 411, + 545, + 626 + ], + "type": "text", + "content": "In this paper, for the first time, we explore the sample relationship within a mini-batch to meet the optimization inconsistency problem of exposure correction. Thus, we introduce a framework that conjunct the optimization process of exposure correction with two steps. The first step correlates the exposure relationship of samples via a context-irrelevant pretext task, while the second step constrains the relationship of corrected results by delivering the above sample relationship as a regularization term within the loss function. Our sample relationship design is compatible with existing exposure correction approaches that serve as a general regularization term. Extensive experiments demonstrate our framework consistently boosts representative methods' performance on exposure correction datasets. Although the performance improvement is not very significant in some cases, we hope our approach would shed light on investigating the sample relationship for improving the performance of low-level vision problems in the future." + } + ] + } + ], + "index": 59 + }, + { + "bbox": [ + 304, + 641, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 641, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 641, + 545, + 712 + ], + "type": "text", + "content": "Acknowledgments. This work was supported by the JKW Research Funds under Grant 20-163-14-LZ-001-004-01, and the Anhui Provincial Natural Science Foundation under Grant 2108085UD12. We acknowledge the support of GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC." + } + ] + } + ], + "index": 60 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "9911" + } + ] + } + ], + "index": 61 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "text", + "content": "[1] Mahmoud Afifi, Konstantinos G Derpanis, Bjorn Ommer, and Michael S Brown. Learning multi-scale photo exposure correction. In Proceedings of the IEEE International Conference on Computer Vision, 2021. 1, 2, 5, 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 137, + 288, + 180 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 137, + 288, + 180 + ], + "spans": [ + { + "bbox": [ + 53, + 137, + 288, + 180 + ], + "type": "text", + "content": "[2] Tarik Arici, Salih Dikbas, and Yucel Altunbasak. A histogram modification framework and its application for image contrast enhancement. IEEE Transactions on Image Processing, 18(9):1921-1935, 2009. 1, 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 182, + 288, + 226 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 182, + 288, + 226 + ], + "spans": [ + { + "bbox": [ + 53, + 182, + 288, + 226 + ], + "type": "text", + "content": "[3] Tarik Arici, Salih Dikbas, and Yucel Altunbasak. A histogram modification framework and its application for image contrast enhancement. IEEE Transactions on image processing, 18(9):1921-1935, 2009. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 228, + 288, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 228, + 288, + 282 + ], + "spans": [ + { + "bbox": [ + 53, + 228, + 288, + 282 + ], + "type": "text", + "content": "[4] Vladimir Bychkovsky, Sylvain Paris, Eric Chan, and Frédo Durand. Learning photographic global tonal adjustment with a database of input/output image pairs. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 97-104, 2011. 5, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 285, + 287, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 285, + 287, + 327 + ], + "spans": [ + { + "bbox": [ + 53, + 285, + 287, + 327 + ], + "type": "text", + "content": "[5] Jianrui Cai, Shuhang Gu, and Lei Zhang. Learning a deep single image contrast enhancer from multi-exposure images. IEEE Transactions on Image Processing, 27(4):2049-2062, 2018. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 330, + 287, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 330, + 287, + 361 + ], + "spans": [ + { + "bbox": [ + 53, + 330, + 287, + 361 + ], + "type": "text", + "content": "[6] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. arXiv preprint arXiv:1805.01934, 2018. 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 364, + 287, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 364, + 287, + 430 + ], + "spans": [ + { + "bbox": [ + 53, + 364, + 287, + 430 + ], + "type": "text", + "content": "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 4" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 432, + 287, + 486 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 432, + 287, + 486 + ], + "spans": [ + { + "bbox": [ + 53, + 432, + 287, + 486 + ], + "type": "text", + "content": "[8] Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, and Xinghao Ding. A weighted variational model for simultaneous reflectance and illumination estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2782-2790, 2016. 1, 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 487, + 287, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 487, + 287, + 542 + ], + "spans": [ + { + "bbox": [ + 53, + 487, + 287, + 542 + ], + "type": "text", + "content": "[9] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1780-1789, 2020. 2, 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 544, + 287, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 544, + 287, + 586 + ], + "spans": [ + { + "bbox": [ + 48, + 544, + 287, + 586 + ], + "type": "text", + "content": "[10] Xiaojie Guo, Yu Li, and Haibin Ling. LIME: Low-light image enhancement via illumination map estimation. IEEE Transactions on Image Processing, 26(2):982-993, 2016. 1, 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 590, + 287, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 590, + 287, + 622 + ], + "spans": [ + { + "bbox": [ + 48, + 590, + 287, + 622 + ], + "type": "text", + "content": "[11] Zhi Hou, Baosheng Yu, and Dacheng Tao. Batchformer: Learning to explore sample relationships for robust representation learning. In CVPR, 2022. 1, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 624, + 287, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 287, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 287, + 678 + ], + "type": "text", + "content": "[12] Jie Huang, Yajing Liu, Xueyang Fu, Man Zhou, Yang Wang, Feng Zhao, and Zhiwei Xiong. Exposure normalization and compensation for multiple-exposure correction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6043-6052, 2022. 1, 2, 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 681, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 681, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 681, + 287, + 713 + ], + "type": "text", + "content": "[13] Jie Huang, Yajing Liu, Feng Zhao, Keyu Yan, Jinghao Zhang, Yukun Huang, Man Zhou, and Zhiwei Xiong. Deep fourier-based exposure correction network with spatial fre" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "quency interaction. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 1, 3, 4, 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 96, + 545, + 148 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 545, + 148 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 545, + 148 + ], + "type": "text", + "content": "[14] Jie Huang, Man Zhou, Yajing Liu, Mingde Yao, Feng Zhao, and Zhiwei Xiong. Exposure-consistency representation learning for exposure correction. In Proceedings of the 30th ACM International Conference on Multimedia, page 6309-6317, 2022. 1, 2, 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 151, + 545, + 193 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 151, + 545, + 193 + ], + "spans": [ + { + "bbox": [ + 308, + 151, + 545, + 193 + ], + "type": "text", + "content": "[15] Haidi Ibrahim and Nicholas Sia Pik Kong. Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(4):1752-1758, 2007. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 195, + 545, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 195, + 545, + 237 + ], + "spans": [ + { + "bbox": [ + 308, + 195, + 545, + 237 + ], + "type": "text", + "content": "[16] Haidi Ibrahim and Nicholas Sia Pik Kong. Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Transactions on Consumer Electronics, 53(4):1752-1758, 2007. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 239, + 545, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 239, + 545, + 281 + ], + "spans": [ + { + "bbox": [ + 308, + 239, + 545, + 281 + ], + "type": "text", + "content": "[17] Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, and Luc Van Gool. Dslr-quality photos on mobile devices with deep convolutional networks. In ICCV, pages 3277-3285, 2017. 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 283, + 545, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 283, + 545, + 304 + ], + "spans": [ + { + "bbox": [ + 308, + 283, + 545, + 304 + ], + "type": "text", + "content": "[18] Edwin H Land. The Retinex theory of color vision. Scientific American, 237(6):108-129, 1977. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 305, + 545, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 305, + 545, + 338 + ], + "spans": [ + { + "bbox": [ + 308, + 305, + 545, + 338 + ], + "type": "text", + "content": "[19] Chongyi Li, Chunle Guo, and Chen Change Loy. Learning to enhance low-light image via zero-reference deep curve estimation. arXiv preprint arXiv:2103.00860, 2021. 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 339, + 545, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 339, + 545, + 381 + ], + "spans": [ + { + "bbox": [ + 308, + 339, + 545, + 381 + ], + "type": "text", + "content": "[20] Mading Li, Jiaying Liu, Wenhan Yang, Xiaoyan Sun, and Zongming Guo. Structure-revealing low-light image enhancement via robust Retinex model. IEEE Transactions on Image Processing, 27(6):2828-2841, 2018. 1, 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 383, + 545, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 383, + 545, + 415 + ], + "spans": [ + { + "bbox": [ + 308, + 383, + 545, + 415 + ], + "type": "text", + "content": "[21] Seokjae Lim and Wonjun Kim. DSLR: Deep stacked laplacian restorer for low-light image enhancement. IEEE Transactions on Multimedia, 23:4272-4284, 2020. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 416, + 545, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 416, + 545, + 470 + ], + "spans": [ + { + "bbox": [ + 308, + 416, + 545, + 470 + ], + "type": "text", + "content": "[22] Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 10561-10570, 2021. 2, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 472, + 545, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 472, + 545, + 525 + ], + "spans": [ + { + "bbox": [ + 308, + 472, + 545, + 525 + ], + "type": "text", + "content": "[23] Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. Toward fast, flexible, and robust low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5637-5646, June 2022. 2, 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 526, + 545, + 569 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 526, + 545, + 569 + ], + "spans": [ + { + "bbox": [ + 308, + 526, + 545, + 569 + ], + "type": "text", + "content": "[24] Ntumba Elie Nsamp, Zhongyun Hu, and Qing Wang. Learning exposure correction via consistency modeling. In Proceedings of the British Machine Vision Conference (BMVC), pages 1-12, 2018. 1, 2, 5, 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 571, + 545, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 571, + 545, + 635 + ], + "spans": [ + { + "bbox": [ + 308, + 571, + 545, + 635 + ], + "type": "text", + "content": "[25] Zhihong Pan, Baopu Li, Dongliang He, Mingde Yao, Wenhao Wu, Tianwei Lin, Xin Li, and Errui Ding. Towards bidirectional arbitrary image rescaling: Joint optimization and cycle idempotence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17389-17398, 2022. 1" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 636, + 545, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 636, + 545, + 658 + ], + "spans": [ + { + "bbox": [ + 308, + 636, + 545, + 658 + ], + "type": "text", + "content": "[26] Ioannis Pitas. Digital image processing algorithms and applications. John Wiley & Sons, 2000. 6" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 659, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 659, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 308, + 659, + 545, + 712 + ], + "type": "text", + "content": "[27] Xutong Ren, Mading Li, Wen-Huang Cheng, and Jiaying Liu. Joint enhancement and denoising method via sequential decomposition. In Proceedings of the IEEE International Symposium on Circuits and Systems, pages 1-5. IEEE, 2018. 2" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "9912" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "text", + "content": "[28] Ali M Reza. Realization of the contrast limited adaptive histogram equalization (clahe) for real-time image enhancement. IEEE Transactions on Multimedia, pages 35-44, 2004. 1, 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 119, + 288, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 119, + 288, + 163 + ], + "spans": [ + { + "bbox": [ + 48, + 119, + 288, + 163 + ], + "type": "text", + "content": "[29] Haoyuan Wang, Ke Xu, and Rynson W.H. Lau. Local color distributions prior for image enhancement. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 1, 3, 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 165, + 288, + 220 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 165, + 288, + 220 + ], + "spans": [ + { + "bbox": [ + 48, + 165, + 288, + 220 + ], + "type": "text", + "content": "[30] Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, and Jiaya Jia. Underexposed photo enhancement using deep illumination estimation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6849-6857, 2019. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 222, + 287, + 266 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 222, + 287, + 266 + ], + "spans": [ + { + "bbox": [ + 48, + 222, + 287, + 266 + ], + "type": "text", + "content": "[31] Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Transactions on Image Processing, 22(9):3538-3548, 2013. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 268, + 287, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 268, + 287, + 300 + ], + "spans": [ + { + "bbox": [ + 48, + 268, + 287, + 300 + ], + "type": "text", + "content": "[32] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep Retinex decomposition for low-light enhancement. arXiv preprint arXiv:1808.04560, 2018. 1, 2, 6, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 303, + 287, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 303, + 287, + 369 + ], + "spans": [ + { + "bbox": [ + 48, + 303, + 287, + 369 + ], + "type": "text", + "content": "[33] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5901-5910, June 2022. 1, 2, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 371, + 287, + 436 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 371, + 287, + 436 + ], + "spans": [ + { + "bbox": [ + 48, + 371, + 287, + 436 + ], + "type": "text", + "content": "[34] Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3063-3072, 2020. 1, 2, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 438, + 287, + 472 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 438, + 287, + 472 + ], + "spans": [ + { + "bbox": [ + 48, + 438, + 287, + 472 + ], + "type": "text", + "content": "[35] Mingde Yao, Dongliang He, Xin Li, Zhihong Pan, and Zhiwei Xiong. Bidirectional translation between uhd-hdr and hd-sdr videos. IEEE Transactions on Multimedia, 2023. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 474, + 287, + 517 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 474, + 287, + 517 + ], + "spans": [ + { + "bbox": [ + 48, + 474, + 287, + 517 + ], + "type": "text", + "content": "[36] Mingde Yao, Zhiwei Xiong, Lizhi Wang, Dong Liu, and Xuejin Chen. Spectral-depth imaging with deep learning based reconstruction. Optics express, 27(26):38312-38325, 2019. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 520, + 287, + 574 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 520, + 287, + 574 + ], + "spans": [ + { + "bbox": [ + 48, + 520, + 287, + 574 + ], + "type": "text", + "content": "[37] Wenlong Zhang, Yihao Liu, Chao Dong, and Yu Qiao. RankSRGAN: Generative adversarial networks with ranker for image super-resolution. In Proceedings of the IEEE International Conference on Computer Vision, pages 3096-3105, 2019. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 577, + 287, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 577, + 287, + 610 + ], + "spans": [ + { + "bbox": [ + 48, + 577, + 287, + 610 + ], + "type": "text", + "content": "[38] Yonghua Zhang, Xiaojie Guo, Jiayi Ma, Wei Liu, and Jiawan Zhang. Beyond brightening low-light images. International Journal of Computer Vision, 129(4):1013-1037, 2021. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 613, + 287, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 613, + 287, + 656 + ], + "spans": [ + { + "bbox": [ + 48, + 613, + 287, + 656 + ], + "type": "text", + "content": "[39] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In Proceedings of the ACM International Conference on Multimedia, pages 1632-1640, 2019. 1, 2, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 658, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 287, + 713 + ], + "type": "text", + "content": "[40] Anqi Zhu, Lin Zhang, Ying Shen, Yong Ma, Shengjie Zhao, and Yicong Zhou. Zero-shot restoration of underexposed images via robust Retinex decomposition. In Proceedings of the IEEE International Conference on Multimedia and Expo, pages 1-6, 2020. 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 72, + 545, + 271 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 307, + 72, + 545, + 126 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 72, + 545, + 126 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 545, + 126 + ], + "type": "text", + "content": "[41] Yurui Zhu, Xueyang Fu, Chengzhi Cao, Xi Wang, Qibin Sun, and Zheng-Jun Zha. Single image shadow detection via complementary mechanism. In Proceedings of the 30th ACM International Conference on Multimedia, pages 6717-6726, 2022. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 129, + 545, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 545, + 162 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 545, + 162 + ], + "type": "text", + "content": "[42] Yurui Zhu, Xueyang Fu, and Aiping Liu. Learning dual transformation networks for image contrast enhancement. IEEE Signal Processing Letters, 27:1999-2003, 2020. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 163, + 545, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 163, + 545, + 217 + ], + "spans": [ + { + "bbox": [ + 307, + 163, + 545, + 217 + ], + "type": "text", + "content": "[43] Yurui Zhu, Xi Wang, Xueyang Fu, and Xiaowei Hu. Enhanced coarse-to-fine network for image restoration from under-display cameras. In Computer Vision-ECCV 2022 Workshops: Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part V, pages 130-146. Springer, 2023. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 219, + 545, + 271 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 219, + 545, + 271 + ], + "spans": [ + { + "bbox": [ + 307, + 219, + 545, + 271 + ], + "type": "text", + "content": "[44] Yurui Zhu, Zeyu Xiao, Yanchi Fang, Xueyang Fu, Zhiwei Xiong, and Zheng-Jun Zha. Efficient model-driven network for shadow removal. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 36, pages 3635-3643, 2022. 2" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "9913" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Semantic Relationship Among Instances for Image-Text Matching/5b93c863-5d73-452f-a25d-f402de582d22_content_list.json b/2023/Learning Semantic Relationship Among Instances for Image-Text Matching/5b93c863-5d73-452f-a25d-f402de582d22_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..aaee2498f187b422431e60bc2c4f2ad9571f8c46 --- /dev/null +++ b/2023/Learning Semantic Relationship Among Instances for Image-Text Matching/5b93c863-5d73-452f-a25d-f402de582d22_content_list.json @@ -0,0 +1,1738 @@ +[ + { + "type": "text", + "text": "Learning Semantic Relationship among Instances for Image-Text Matching", + "text_level": 1, + "bbox": [ + 106, + 130, + 866, + 154 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Zheren Fu $^{1}$ , Zhendong Mao $^{1,2,*}$ , Yan Song $^{1}$ , Yongdong Zhang $^{1,2}$", + "bbox": [ + 228, + 179, + 738, + 199 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1University of Science and Technology of China, Hefei, China", + "bbox": [ + 238, + 199, + 730, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China", + "bbox": [ + 106, + 215, + 861, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "fzr@mail.ustc.edu.cn, {zdmao, songyan, zhyd73}@ustc.edu.cn", + "bbox": [ + 225, + 236, + 738, + 251 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 286, + 313, + 301 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Image-text matching, a bridge connecting image and language, is an important task, which generally learns a holistic cross-modal embedding to achieve a high-quality semantic alignment between the two modalities. However, previous studies only focus on capturing fragment-level relation within a sample from a particular modality, e.g., salient regions in an image or text words in a sentence, where they usually pay less attention to capturing instance-level interactions among samples and modalities, e.g., multiple images and texts. In this paper, we argue that sample relations could help learn subtle differences for hard negative instances, and thus transfer shared knowledge for infrequent samples should be promising in obtaining better holistic embeddings. Therefore, we propose a novel hierarchical relation modeling framework (HREM), which explicitly capture both fragment- and instance-level relations to learn discriminative and robust cross-modal embeddings. Extensive experiments on Flickr30K and MS-COCO show our proposed method outperforms the state-of-the-art ones by $4\\% -10\\%$ in terms of rSum. Our code is available at https://github.com/ CrossmodalGroup/HREM.", + "bbox": [ + 75, + 319, + 473, + 650 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 681, + 209, + 698 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Image-text matching bridges the semantic gap between visual and textual modalities and is a fundamental task for various multi-modal learning applications, such as cross-modal retrieval [22] and text-to-image synthesis [17]. The critical challenge is accurately and efficiently learning cross-modal embeddings and their similarities for images and texts, to achieve a high-quality semantic alignment. In general, existing image-text matching methods can be classified into two paradigms. The first embedding-based matching [4, 10, 20, 35] separately encodes the whole images and texts into a holistic embedding space, then globally", + "bbox": [ + 75, + 708, + 468, + 875 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e6a83aa3df497260d8f28612bc27123557c7fe9067d6e3c47d502be5d4994ccc.jpg", + "image_caption": [ + "(a) Pipeline of embedding-based method" + ], + "image_footnote": [], + "bbox": [ + 503, + 285, + 890, + 359 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/36d78871bb4465a4f45b9822c3c5c83257225f7fc8ba45a5109acbe8b606bd0b.jpg", + "image_caption": [ + "(b) Semantic ambiguity" + ], + "image_footnote": [], + "bbox": [ + 501, + 373, + 694, + 508 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/83426cd2cd16b2eb6393988ae8260dcb9a4499364c0d86cd074ce0e00f45797a.jpg", + "image_caption": [ + "(c) Semantic scarcity", + "Figure 1. Illustration of our motivation. Sample relation modeling improves the holistic representation of cross-modal learning. Colors and shapes indicate different modalities and image-text pairs, respectively. Orange elements mark effective interactions: (a) The pipeline of the previous and our work, we add the cross-modal relation interaction between samples. (b) For the identical theme of \"surfer with surfboard\", specific behaviors exist subtle differences, like \"hold/squat/ride on the surfboard\" and \"stare/break/wipe out the wave\". Our method distinguishes these hard negative samples from semantic ambiguities. (c) For similar themes under \"man play a ball\", corresponding behaviors usually are semantic similar, like \"play the hockey/cricket/polo\" all need to \"hit the ball\" with \"sticks/bats\". Our method improves learning embeddings on these infrequent samples with semantic scarcities for themselves." + ], + "image_footnote": [], + "bbox": [ + 699, + 373, + 888, + 510 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "measures the semantic similarity of the two modalities. The second score-based matching [3,7,19,27] applies the cross-modal interaction between visual and textual local features, then learns a cumulative similarity score.", + "bbox": [ + 496, + 761, + 890, + 821 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recently, embedding-based methods have served as the mainstream solution owing to both accuracy and efficiency in image-text matching, which contains two steps as shown in Fig. 1 (a): (1) Capturing the intra-modal relation between visual fragments (e.g., regional features) or textual", + "bbox": [ + 496, + 824, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author.", + "bbox": [ + 94, + 887, + 220, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "15159", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "fragments (e.g., word features) independently, then enhancing the semantic representation of local features. (2) Aggregating relation-enhanced local features of two modalities into the holistic embedding space. For the first step, most use the graph neural network [5, 20, 21] or attention module [35, 45, 46] to capture semantic relations and enhance the local features of two modalities, respectively. Some work further exploits the spatial relation [45] for visual regions or grammatical relation [28] for textual words. For the second step, they design pooling functions [4] or sequence encoders [21] to aggregate local features and get holistic embeddings. Existing embedding-based methods follow the principle of separately encoding images and texts by two branches as Fig. 1 (a). In each branch, these methods only focus on the fragment-level relation modeling and local features interaction within one sample, e.g., the region features inside one image (or the word features inside one text). In this way, the instance-level relation modeling and global embeddings interaction among different samples and modalities, e.g., holistic embeddings of multiple images and texts, are entirely overlooked.", + "bbox": [ + 76, + 90, + 472, + 407 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Consequently, existing embedding-based methods directly use global embeddings to compute loss function, $e.g.$ , hard negative triplet loss [10] on random mini-batch, which is insufficient to exploit the manifold structure of holistic embedding space [39]. First, they fail to learn subtle semantic discrepancies among different samples (e.g., similar behaviors with an identical theme as shown in Fig. 1 (b)), then can not distinguish hard negative samples with semantic ambiguities because of the heterogeneity of visual and textual semantics. Second, they are unable to transfer shared knowledge from diverse samples (e.g., different samples that contain similar behaviors with similar themes as shown in Fig. 1 (c)), then can not effectively learn on these infrequent samples with semantic scarcities. Therefore, it is expected that a framework should precisely capture the sample relationship to learn better cross-modal embeddings, while does not break the principle of embedding-based methods, i.e., independently encodes embeddings without modality interaction at the inference stage.", + "bbox": [ + 76, + 412, + 472, + 700 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In doing so, we propose a Hierarchical RElation Modeling framework (HREM) that, for the first time to our knowledge, explicitly captures both fragment-level and instance-level relations to learn holistic embeddings jointly. Therefore, HREM learns not only contextual semantics among intra-modal fragments to enhance local features, but also the associated semantics among inter-modal instances to distinguish hard negative samples and improve learning on infrequent samples. As illustrated in Fig. 1 (a) and Fig. 2, we propose a novel step (i.e., the \"stage-three\") to exactly capture the semantic relationship of cross-modal samples. First, we propose a novel cross-embedding association graph, which explicitly identifies the connection", + "bbox": [ + 76, + 704, + 472, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "relation and learns the relevance relation between batch samples with fragment-level semantic matching. Next, we propose two relation interaction mechanisms, which explore inter-modal and intra-modal relations synchronously or asynchronously with our improved attention modules to obtain enhanced embeddings. Consequently, HREM only needs to capture the instance-level relation for training, then encode multi-modal embeddings independently at the inference stage, to achieve high accuracy and efficiency for image-text matching.", + "bbox": [ + 496, + 90, + 893, + 242 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To summarize, the major contributions are as follows: (1) We propose a hierarchical relation modeling framework (HREM) for image-text matching. To the best of our knowledge, this is the first work that explicitly captures both fragment-level relations within modality and instance-level relations across modalities. (2) We propose a novel cross-embedding association graph by identifying the connection relation and learning the relevance relation. (3) We propose two relation interaction mechanisms to learn the relation-enhanced embeddings. (4) HREM outperforms all state-of-the-art methods for image-text retrieval on two widely used benchmarks, Flickr30K and MS-COCO, by $4\\% - 10\\%$ rSum.", + "bbox": [ + 496, + 243, + 893, + 425 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 441, + 640, + 458 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Image-Text Matching", + "text_level": 1, + "bbox": [ + 500, + 467, + 702, + 484 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "According to how the cross-modal interaction is implemented, image-text matching methods are divided into two categories, embedding-based and score-based matching. **Embedding-based.** It independently encodes images and sentences into a holistic embedding space by two branches, where the semantic similarity is calculated by cosine similarity [10]. Existing work usually utilizes the GCN [20, 44] or self-attention layer [45, 46] to capture semantic relations between fragments inside one sample and enhance contextual semantics of local features, then propose a particular aggregator to learn global embeddings. For example, VSRN [20] proposes a semantic reasoning network to learn local visual features with key scene concepts. CVSE [43] proposes a consensus-aware module to integrate commonsense knowledge into local features for two modalities. GPO [4] presents a generalized pooling function to project local features into the global embedding. MV [24] proposes a multi-view encoder to learn multiple embeddings for one image and models intra-class variations.", + "bbox": [ + 496, + 491, + 893, + 779 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Score-based. It conducts fine-grained cross-modal interaction and semantic alignment between local fragments, then calculates a cumulative similarity score. [3, 7, 19, 51]. For example, SCAN [19] proposes an attention mechanism for cross-modal interaction between visual and textual fragments. IMRAM [3] proposes an iterative network for multiple steps of cross-modal interaction. NAAF [51] measures the similarity and dissimilarity degrees via two matching", + "bbox": [ + 496, + 779, + 893, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "15160", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/dac8f35c27f3fcf6cfac49c419e7776c070e93f67731a7d941abc2d3a5b7cbc9.jpg", + "image_caption": [ + "Figure 2. Overview of our hierarchical relation modeling framework (HREM). Given $N$ image-text pairs $(N = 3$ in this Figure), we first capture the fragment-level relations and learn the relation-enhanced local features for each image or text independently, then aggregate local features by pooling operation to get global embeddings $\\{\\pmb{v}_i,\\pmb{u}_i\\}_{i = 1}^N$ . Next, we propose a novel cross-embedding association graph to capture instance-level relations by identifying the connection relation and learning the relevance relation between samples. Finally, we propose two cross-modal relation interaction mechanisms to get relation-enhanced embeddings and compute the final loss function." + ], + "image_footnote": [], + "bbox": [ + 78, + 88, + 893, + 308 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "mechanisms to infer the overall similarity jointly.", + "bbox": [ + 76, + 415, + 403, + 430 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Nonetheless, existing embedding-based methods only capture the relation between local fragments within each sample. In addition, we explore the relation across different samples and modalities to learn better holistic embeddings.", + "bbox": [ + 75, + 431, + 470, + 492 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Sample Relationship Learning", + "text_level": 1, + "bbox": [ + 76, + 503, + 348, + 518 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Among data scarcity and diversity, sample relations have been explicitly explored for representation enhancement [23, 49]. A simple way is to generate input as new samples from existing samples [13], such as mix-up [49], and cutmix [48]. Some work designs the delicate objective function for output, such as various metric learning losses [12, 14]. Another way is using the knowledge distillation between samples, such as transferring invariant knowledge for zero-shot learning [32] and domain generalization [33]. Recently, some methods focus on the view of batch interaction [8, 9, 16]. IBC [39] constructs a fully connected graph for mini-batch samples and classifies each sample employing a message passing network. HIST [25] constructs a hypergraph to formulate higher-order relations between samples. However, these methods study the primary vision task, e.g., classification. We capture the sample relationship for cross-modal learning and bridge semantic discrepancies between visual and textual modalities.", + "bbox": [ + 75, + 526, + 472, + 797 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. The Proposed Method", + "text_level": 1, + "bbox": [ + 76, + 814, + 287, + 830 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The overview of HREM is depicted in Fig. 2. We first introduce feature extraction in Sec. 3.1 and fragment-level relation modeling in Sec. 3.2. Then we introduce instance-level relation modeling in Sec. 3.3. Finally, we describe the", + "bbox": [ + 75, + 839, + 470, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "optimization in Sec. 3.4 and discussion in Sec. 3.5.", + "bbox": [ + 498, + 415, + 839, + 430 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Feature Extraction", + "text_level": 1, + "bbox": [ + 498, + 438, + 683, + 453 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Visual Representation. Given an image $I$ , we use the bottom-up-attention network [1] to extract the salient regions by the Faster-RCNN [37] and get the region features by the pre-trained ResNet-101 [15]. Then we add a fully-connect (FC) layer to map each region to a $d$ -dimensional local feature. We denote as $R = \\{r_1, \\dots, r_{n_r}\\} \\in \\mathbb{R}^{n_r \\times d}$ , which is the visual fragments and local features for the image $I$ , $n_r$ is the number of region features.", + "bbox": [ + 496, + 460, + 890, + 582 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Textual Representation. Given a sentence $T$ , we use the sequence models, bi-directional gated recurrent unit (Bi-GRU) [38], or pre-trained BERT [6] to extract the set of word features. We also add an FC layer to keep the same dimension with images. We denote as $C = \\{c_1, \\dots, c_{n_c}\\} \\in \\mathbb{R}^{n_c \\times d}$ , which is the textual fragments and local features for the text, $n_c$ is the number of word features.", + "bbox": [ + 496, + 583, + 892, + 688 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Fragment-level Relation Modeling", + "text_level": 1, + "bbox": [ + 498, + 696, + 802, + 713 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To capture contextual information between fragments and enhance local features of two modalities introduced by Sec. 3.1, we propose fragment-level relation modeling for visual regions and textual words, respectively.", + "bbox": [ + 496, + 719, + 890, + 779 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Visual Regions. We construct a semantic relation graph between visual regions within one image, and propose the relation interaction module to learn the contextual semantics for region features. The graph nodes are region features $R$ , and edges are their semantic relation. Specifically, we use the graph attention network [42], e.g., self-attention layer [41], to capture the semantic relation and learn relation-enhanced local features. It first maps original", + "bbox": [ + 496, + 780, + 892, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "15161", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "features to queries and key-value pairs, then use a weighted sum of the values as outputs, where the weighting depends on scaled dot-product between queries and keys:", + "bbox": [ + 75, + 90, + 472, + 137 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nA t t \\left(\\boldsymbol {Q}, \\boldsymbol {K}, \\boldsymbol {V}\\right) = \\operatorname {s o f t m a x} \\left(\\frac {\\boldsymbol {Q} \\boldsymbol {K} ^ {T}}{\\sqrt {d _ {k}}}\\right) \\boldsymbol {V}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 133, + 148, + 468, + 184 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $Att$ is called the scaled dot-product attention [29], $d_{k}$ is the dimension of input features. In this case, we build up a fully-connected graph, and the semantic relation is implicitly expressed in the attention weight. After initial region features $R = \\{r_1,\\dots ,r_{n_r}\\} \\in \\mathbb{R}^{n_r\\times d}$ pass through the self-attention layer with the feed-forward network to learn contextual information, we obtain the relation-enhanced region features $R^{V} = \\{r_{1}^{V},\\dots ,r_{n_{r}}^{V}\\} \\in \\mathbb{R}^{n_{r}\\times d}$ .", + "bbox": [ + 75, + 188, + 468, + 309 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, we aggregate initial and enhanced region features to get the global visual embedding $\\pmb{v} \\in \\mathbb{R}^d$ , by using the maximum pooling and average pooling, with the parameter $\\beta$ to control the ratio of two representations.", + "bbox": [ + 75, + 310, + 468, + 369 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {v} = \\beta \\cdot \\operatorname {M a x P o o l} (R) + (1 - \\beta) \\cdot \\operatorname {A v g P o o l} \\left(R ^ {V}\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 93, + 381, + 468, + 398 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Textual Words. Similarly, we construct a semantic relation graph between textual words within one sentence, and use the relation interaction module to enhance contextual information for the word features. The nodes are word features $C = \\{c_{1},\\dots ,c_{n_{c}}\\} \\in \\mathbb{R}^{n_{c}\\times d}$ and the edges are their semantic relation. We also use the self-attention layer to implement, hence we get relation-enhanced word features $C^U = \\{c_1^U,\\dots ,c_{n_c}^U\\} \\in \\mathbb{R}^{n_c\\times d}$ , then aggregate them to get the global textual embedding $\\pmb {u}\\in \\mathbb{R}^{d}$ , like Eq. (2).", + "bbox": [ + 75, + 411, + 468, + 547 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {u} = \\beta \\cdot \\operatorname {M a x P o o l} (C) + (1 - \\beta) \\cdot \\operatorname {A v g P o o l} \\left(C ^ {U}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 91, + 564, + 468, + 580 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Instance-level Relation Modeling", + "text_level": 1, + "bbox": [ + 76, + 590, + 366, + 607 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We propose the instance-level relation modeling for multiple images and texts to learn better cross-modal embeddings obtained by Sec. 3.2. Given $N$ image-text pairs and their embeddings $\\{\\pmb{v}_i,\\pmb{u}_i\\}_{i = 1}^N$ , we propose a novel cross-embedding association graph $\\mathcal{G}(\\mathcal{V},\\mathcal{E})$ , where the nodes are the embeddings $\\mathcal{V} = \\{\\pmb{v}_1,\\dots,\\pmb{v}_N,\\pmb{u}_1\\dots,\\pmb{u}_N\\} \\in \\mathbb{R}^{2N\\times d}$ , the edges $\\mathcal{E}$ are pairwise semantic relations.", + "bbox": [ + 75, + 614, + 468, + 720 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3.1 Cross-Embedding Association Graph", + "text_level": 1, + "bbox": [ + 76, + 739, + 390, + 755 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The critical challenge is how to construct the pairwise relation accurately. Without loss of generality, we divide the relation into two parts: connection and relevance.", + "bbox": [ + 75, + 763, + 468, + 808 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We use the matrix $\\mathbf{A} \\in \\mathbb{R}^{2N \\times 2N}$ to represent the connection relation, i.e., whether there exists an associated edge between nodes. We use the matrix $S \\in \\mathbb{R}^{2N \\times 2N}$ to represent the relevance relation, i.e., the degree of semantic association between nodes. Further, we divide these matrices into two patterns and four blocks: intra-modal rela", + "bbox": [ + 75, + 809, + 468, + 898 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "tion (Image-to-Image $I\\to I$ , Text-to-Text $T\\rightarrow T$ ) and intermodal relation (Image-to-Text $I\\rightarrow T$ , Text-to-Image $T\\rightarrow I$ ), the shape of each block is equal to $\\mathbb{R}^{N\\times N}$", + "bbox": [ + 496, + 90, + 890, + 137 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {A} = \\left[\\begin{array}{l l}\\boldsymbol {A} _ {I \\rightarrow I}&\\boldsymbol {A} _ {I \\rightarrow T}\\\\\\boldsymbol {A} _ {T \\rightarrow I}&\\boldsymbol {A} _ {T \\rightarrow T}\\end{array}\\right], \\boldsymbol {S} = \\left[\\begin{array}{l l}\\boldsymbol {S} _ {I \\rightarrow I}&\\boldsymbol {S} _ {I \\rightarrow T}\\\\\\boldsymbol {S} _ {T \\rightarrow I}&\\boldsymbol {S} _ {T \\rightarrow T}\\end{array}\\right], \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 511, + 150, + 890, + 185 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Connection Relation Identifying. The simple idea is to build a fully connected graph like Sec. 3.2 and previous work [5,45] that elements of $A$ are all 1, However, the fullyconnection easily likely leads to over-smoothing and noisy association [39] for nodes. We need to identify the effective connection and filter out unassociated semantics.", + "bbox": [ + 496, + 186, + 890, + 275 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Therefore, we construct the neighbor space of embedding to identify the connection relation. If two embedding nodes are close, their semantic information usually overlaps, they probably have a semantic connection [25].", + "bbox": [ + 496, + 277, + 890, + 338 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\left(\\boldsymbol {A} _ {I \\rightarrow I}\\right) _ {i j} = 1 \\text {i f} \\boldsymbol {v} _ {j} \\in \\mathcal {N} _ {\\text {i n t r a}} \\left(\\boldsymbol {v} _ {i}\\right) \\text {e l s e} 0, \\\\ \\left(\\boldsymbol {A} _ {T \\rightarrow T}\\right) _ {i j} = 1 \\text {i f} \\boldsymbol {u} _ {j} \\in \\mathcal {N} _ {\\text {i n t r a}} \\left(\\boldsymbol {u} _ {i}\\right) \\text {e l s e} 0, \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 557, + 351, + 834, + 387 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathcal{N}_{\\text {intra }}$ is the neighbor space of embeddings for intramodal connection. We select the first $\\tau N$ ( $\\tau$ is range from 0 to 1) nearest single-modal samples ranked by the embedding similarity to construct $\\mathcal{N}_{\\text {intra }}$ in Eq. (5). Correspondingly, we define $\\mathcal{N}_{\\text {inter }}$ for inter-modal connection:", + "bbox": [ + 496, + 398, + 890, + 474 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\left(\\boldsymbol {A} _ {I \\rightarrow T}\\right) _ {i j} = 1 \\text {i f} \\boldsymbol {u} _ {j} \\in \\mathcal {N} _ {\\text {i n t e r}} \\left(\\boldsymbol {v} _ {i}\\right) \\text {e l s e} 0, \\\\ \\left(\\boldsymbol {A} _ {T \\rightarrow I}\\right) _ {i j} = 1 \\text {i f} \\boldsymbol {v} _ {j} \\in \\mathcal {N} _ {\\text {i n t e r}} \\left(\\boldsymbol {u} _ {i}\\right) \\text {e l s e} 0, \\tag {6} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 557, + 488, + 888, + 523 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Since the heterogeneity between visual and textual semantics, directly using global embeddings is insufficient to identify the inter-modal connection relation. Following score-based methods [19, 40], we use the fine-grained matching of fragments and local features to measure the inter-modal neighbor space. As shown in Fig. 3, given an image-text pair and its region-word similarity matrix. We first pick up the most matching textual word (or visual region) for each region (or each word), then average these matched scores to express the overall matching values $p$ .", + "bbox": [ + 496, + 537, + 890, + 688 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} p _ {I \\rightarrow T} = \\frac {1}{n _ {r}} \\sum_ {\\substack {m = 1\\\\n}} ^ {n _ {r}} \\max _ {n \\in [ 1, n _ {c} ]} \\left(\\boldsymbol {r} _ {m} ^ {\\mathrm {T}} \\boldsymbol {c} _ {n}\\right), \\tag{7} \\\\ p _ {T \\rightarrow I} = \\frac {1}{n _ {c}} \\sum_ {m = 1} ^ {n _ {c}} \\max _ {n \\in [ 1, n _ {r} ]} \\left(\\boldsymbol {c} _ {m} ^ {\\mathrm {T}} \\boldsymbol {r} _ {n}\\right), \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 580, + 700, + 890, + 782 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where we select the first $\\tau N$ nearest cross-modal samples ranked by the fragment-level matching values Eq. (7) to construct the inter-modal neighbor space $\\mathcal{N}_{inter}$ in Eq (6).", + "bbox": [ + 496, + 794, + 890, + 840 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Relevance Relation Learning. The relevance relation is the degree of semantic association between two connected nodes. Existing work [16, 39] uses the global embedding similarity to approximate, which is insufficient to bridge the", + "bbox": [ + 496, + 840, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "15162", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/14b908b03158b1f247a87bcbfda75507562bdb66ae08668c35a6e93deaa7a8ac.jpg", + "image_caption": [ + "Figure 3. Graph construction of inter-modal relation with fragment-level matching. For every image-text pair, we use corresponding modules to get connection relation $a_{I \\to T} (a_{T \\to I})$ and relevance relation $s_{I \\to T} (s_{T \\to I})$ , which are elements of connection matrix $A_{I \\to T} (A_{T \\to I})$ and relevance matrix $S_{I \\to T} (S_{T \\to I})$ ." + ], + "image_footnote": [], + "bbox": [ + 78, + 88, + 472, + 236 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "semantic discrepancy between visual and textual space [19]. In contrast, we explicitly learn the cross-modal relevance relation. As shown in Fig. 3, we employ fine-grained matching like Eq. (7). After picking up the max matching of row-wise and column-wise, we select the top- $K$ scores and concatenate them as the matching vectors $\\mathbf{q}$ .", + "bbox": [ + 75, + 340, + 472, + 434 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\boldsymbol {q} _ {I \\rightarrow T} = T o p K (\\{\\max _ {n \\in [ 1, n _ {c} ]} \\left(\\boldsymbol {r} _ {m} ^ {\\mathrm {T}} \\boldsymbol {c} _ {n}\\right) \\} _ {m = 1} ^ {n _ {r}}), \\\\ \\boldsymbol {q} _ {T \\rightarrow I} = \\operatorname {T o p K} \\left(\\left. \\left\\{\\max _ {n \\in [ 1, n _ {r} ]} \\left(\\boldsymbol {c} _ {m} ^ {\\mathrm {T}} \\boldsymbol {r} _ {n}\\right) \\right\\} _ {m = 1} ^ {n _ {c}}\\right), \\tag {8} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 137, + 445, + 468, + 500 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\pmb{q} \\in \\mathbb{R}^{K}$ , then we use an MLP and add the overall matching values to learn the inter-modal relevance (scalar).", + "bbox": [ + 76, + 510, + 468, + 542 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} s _ {I \\rightarrow T} = M L P \\left(\\boldsymbol {q} _ {I \\rightarrow T}\\right) + p _ {I \\rightarrow T}, \\\\ s _ {T \\rightarrow I} = M L P \\left(\\boldsymbol {q} _ {T \\rightarrow I}\\right) + p _ {T \\rightarrow I}, \\tag {9} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 169, + 563, + 468, + 598 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For different image-text pairs, we compute the corresponding relevance values, which are the elements of the inter-modal relevance matrix $S_{I\\rightarrow T}$ and $S_{T\\rightarrow I}$ . As for the intra-modal relevance relation, we can use the global embeddings to compute the relevance matrix.", + "bbox": [ + 75, + 606, + 468, + 681 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\left(\\boldsymbol {S} _ {T \\rightarrow T}\\right) _ {i j} = e ^ {- \\frac {\\left\\| \\boldsymbol {v} _ {i} - \\boldsymbol {v} _ {j} \\right\\| _ {2} ^ {2}}{\\sigma}}, \\left(\\boldsymbol {S} _ {I \\rightarrow I}\\right) _ {i j} = e ^ {- \\frac {\\left\\| \\boldsymbol {u} _ {i} - \\boldsymbol {u} _ {j} \\right\\| _ {2} ^ {2}}{\\sigma}}, \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 89, + 695, + 468, + 720 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\sigma$ is a positive scalar to control the relevance values ( $\\sigma = 1$ for simplicity). Besides, we add a relevance regularization loss to ensure the learned inter-modal relevance matrix does not crash during training [18].", + "bbox": [ + 75, + 722, + 468, + 782 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {r e g} = \\mathcal {L} _ {k l} \\left(\\boldsymbol {S} _ {I \\rightarrow T}, \\boldsymbol {S} _ {I \\rightarrow T} ^ {g}\\right) + \\mathcal {L} _ {k l} \\left(\\boldsymbol {S} _ {T \\rightarrow I}, \\boldsymbol {S} _ {T \\rightarrow I} ^ {g}\\right), \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 799, + 468, + 816 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{kl}$ is the KL-divergence loss with softmax function, $(S_{I\\rightarrow T})_{ij}^{g} = e^{-\\frac{\\|v_{i} - u_{j}\\|_{2}^{2}}{\\sigma}},(S_{T\\rightarrow I})_{ij}^{g} = e^{-\\frac{\\|u_{i} - v_{j}\\|_{2}^{2}}{\\sigma}}$ are computed by the global embeddings like Eq. (10). We hope the learned inter-modal relevance relation is close to the semantic relation of global embeddings.", + "bbox": [ + 76, + 818, + 468, + 901 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/5efd157389196476d3990f0556f0bbfc5cf58a6ac621dea7130f703f88487160.jpg", + "image_caption": [ + "(a) Standalone mechanism. Explore inter-modal and intra-modal relationship between visual and textual embeddings asynchronously." + ], + "image_footnote": [], + "bbox": [ + 501, + 90, + 880, + 250 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/dd4f045719afe396d7496afcff522dbaf42ec0dc4761b831a1a0ddc3e627c199.jpg", + "image_caption": [ + "(b) Fusion mechanism. Explore inter/intra-modal relation synchronously.", + "Figure 4. Two relation interaction mechanisms based on how to explore the inter-modal and intra-modal relations. The connection matrix $A$ and the relevance matrix $S$ are applied to the attention modules wholly or separately, as Eq. (12)." + ], + "image_footnote": [], + "bbox": [ + 503, + 284, + 877, + 364 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3.2 Relation Interaction Mechanisms", + "text_level": 1, + "bbox": [ + 500, + 472, + 785, + 486 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "After constructing our cross-embedding association graph in Sec. 3.3.1, we design two relation interaction mechanisms to capture the semantic relations between images and texts, where the embeddings are updated by the information interaction process, as shown in Fig. 4.", + "bbox": [ + 498, + 496, + 890, + 571 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Fusion Mechanism. We concatenate the visual and textual embeddings as input, as shown in Fig. 4b. The inter-modal and intra-modal relation interactions are conducted synchronously. The embeddings first pass through the multi-head self-attention module for attention diversity. Besides, we adopt the feed-forward network module for relation reasoning, which a multi-layer perceptron implements [41]. It is similar to the fragment-level interaction modules in Sec. 3.2. We also add the residual connection [15] and layer normalization [2] after them.", + "bbox": [ + 496, + 571, + 892, + 720 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The connection matrix $\\mathbf{A}$ is an attention mask matrix for the attention module, where the zero positions are not allowed to attend while the non-zero positions will be unchanged [41]. The relevance matrix $\\mathbf{S}$ is an extra attention weight matrix as the explicit relation modeling, we use $\\lambda$ to balance $\\mathbf{S}$ with the original attention weight matrix. Therefore, we revise the basic attention formula Eq. (1) to:", + "bbox": [ + 496, + 722, + 893, + 829 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {A t t} \\left(\\boldsymbol {Q} \\boldsymbol {K} \\boldsymbol {V}; \\boldsymbol {A}, \\boldsymbol {S}\\right) = \\underset {\\text {s . t . m a s k} (\\boldsymbol {A})} {\\operatorname {s o f t m a x}} \\left(\\frac {\\boldsymbol {Q} \\boldsymbol {K} ^ {T}}{\\sqrt {d _ {k}}} + \\lambda \\boldsymbol {S}\\right) \\boldsymbol {V}, \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 513, + 849, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "15163", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/649065e98d19144a8ed3d556e7e875ceb85e86ddc5de0bf49624a4a6d88e90e6.jpg", + "image_caption": [ + "Figure 5. The comparison between accuracy and speed for cross-modal retrieval. We perform all methods (Region+BiGRU based) on the whole Flickr30K test-set with one RTX3090 GPU." + ], + "image_footnote": [], + "bbox": [ + 81, + 90, + 464, + 215 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Standalone Mechanism. As shown in Fig. 4a, the visual and textual embeddings are sent into two branches and get relation interaction. The embeddings first get inter-modal relation interaction by the multi-head cross-attention module, where $Q$ and $K, V$ come from two modalities. Then they get intra-modal relation interaction by the multi-head self-attention module, where $Q, K, V$ come from the same modality. Finally, the enhanced embeddings are output by following the feed-forward network module.", + "bbox": [ + 75, + 296, + 467, + 431 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The connection matrix $\\mathbf{A}$ and relevance matrix $S$ first are divided into pre-defined four blocks as Eq. (4), then each block is applied to corresponding modules as Eq. (12). Specifically, the inter-modal relation parts perform on the first cross-attention module, and the intra-modal relation parts act on the second self-attention module.", + "bbox": [ + 75, + 433, + 467, + 523 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "After the $L$ layers of the relation interaction mechanism, we get the final relation-enhanced embeddings of two modalities, $\\{\\overline{\\pmb{v}}_1,\\dots ,\\overline{\\pmb{v}}_N\\}$ and $\\{\\overline{\\pmb{u}}_1\\dots ,\\overline{\\pmb{u}}_N\\}$ .", + "bbox": [ + 75, + 525, + 467, + 571 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3.4. Optimization", + "text_level": 1, + "bbox": [ + 76, + 582, + 215, + 598 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Neighbor Batch Sampling. To ensure effective relation interaction in Sec. 3.3, we propose a neighbor sampling to replace random sampling for batches at the later training. We use the k-means clustering [30] on visual embeddings, then randomly choose $P$ clusters and select $K$ images from each cluster, batch size $N = P \\times K$ . Finally, we select one positive text for each image to get $N$ image-text pairs.", + "bbox": [ + 75, + 606, + 467, + 712 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Objective Function. We use the triplet loss [10], the similarity score is the cosine similarity between visual embedding $\\mathbf{v}$ and textual embedding $\\mathbf{u}$ , $s(\\mathbf{v},\\mathbf{u}) = \\frac{\\mathbf{v}^\\top\\mathbf{u}}{\\|\\mathbf{v}\\|\\cdot\\|\\mathbf{u}\\|}$ .", + "bbox": [ + 75, + 713, + 467, + 765 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\left[ \\alpha - s (\\boldsymbol {v}, \\boldsymbol {u}) + s (\\boldsymbol {v}, \\boldsymbol {u} ^ {-}) \\right] _ {+} + \\left[ \\alpha - s (\\boldsymbol {v}, \\boldsymbol {u}) + s (\\boldsymbol {v} ^ {-}, \\boldsymbol {u}) \\right] _ {+}, \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 76, + 777, + 467, + 808 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\alpha$ represents a margin parameter, $[x]_{+} = \\max (x,0)$ $(\\pmb {v},\\pmb {u})$ is a positive image-text pair, and $(\\pmb {v},\\pmb{u}^{-}),(v^{-},\\pmb {u})$ are negative image-text pairs in the batch. We use the distance-weighted sampling [31] for hard negative mining.", + "bbox": [ + 76, + 809, + 467, + 869 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We not only use the relation-enhanced embeddings to compute matching loss as Eq. (13), but also add the ini", + "bbox": [ + 75, + 869, + 467, + 900 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/8bf24753b89b22b43351fe3799c09d3f5e9e2bf6d5200e59a8e267e467bdf1b5.jpg", + "table_caption": [ + "Table 1. Comparisons of image-text retrieval on MS-COCO 5K test-set. Region represents using region features [1] for images. BiGRU [38] and BERT [6] represent using their word features for texts. $E$ and $S$ indicate embedding-based and score-based methods, respectively. \\* shows the ensemble results of two models." + ], + "table_footnote": [], + "table_body": "
TypeMethodMS-COCO 5K
IMG → TEXTTEXT → IMGrSum
R@1R@5R@10R@1R@5R@10
Region + BiGRU
SIMRAM2020 [3]53.783.291.039.769.179.8416.5
UARDA*2022 [50]56.283.891.340.669.580.9422.3
NAAF*2022 [51]58.985.292.042.570.981.4430.9
EGPO2021 [4]56.683.691.439.369.981.1421.9
CGMN2022 [5]53.481.389.641.271.982.4419.8
MV2022 [24]56.784.191.440.370.681.6424.6
HREM (Standalone)58.485.592.439.870.581.0427.6
HREM (Fusion)58.985.392.140.070.681.2428.1
HREM (Full)*60.686.492.541.371.982.4435.1
Region + BERT
SSSAMT2021 [11]57.784.290.840.870.580.5424.5
DIME*2021 [36]59.385.491.943.173.083.1435.8
DCPA2022 [40]53.582.490.240.471.082.0419.5
EDSRAN*2021 [45]55.383.590.941.772.782.8426.9
GPO2021 [4]58.385.392.342.472.783.2434.3
VSRN++2022 [21]54.782.990.942.072.282.7425.4
HREM (Standalone)61.887.093.244.073.783.4443.1
HREM (Fusion)62.387.693.443.973.683.3444.1
HREM (Full)*64.088.593.745.475.184.3450.9
", + "bbox": [ + 504, + 170, + 888, + 395 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "tial embeddings for matching loss to keep the embedding consistency, since we need to encode embeddings directly without sample interaction at the inference stage.", + "bbox": [ + 498, + 422, + 890, + 468 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {c r o s s}} = \\mathcal {L} (\\overline {{\\boldsymbol {v}}}, \\overline {{\\boldsymbol {u}}}) + \\left[ \\mathcal {L} (\\boldsymbol {v}, \\boldsymbol {u}) + \\mathcal {L} (\\overline {{\\boldsymbol {v}}}, \\boldsymbol {u}) + \\mathcal {L} (\\boldsymbol {v}, \\overline {{\\boldsymbol {u}}}) \\right], \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 506, + 498, + 890, + 515 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Finally, we combine the cross-embedding matching loss Eq. (14) with the relevance regularization loss Eq. (11).", + "bbox": [ + 498, + 529, + 890, + 560 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3.5. Discussion", + "text_level": 1, + "bbox": [ + 500, + 573, + 617, + 587 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Inference Stage. Since we may not have the batch data in the actual application, our framework can encode the cross-modal embeddings without sample interaction at the inference stage. The instance-level relation modeling is only for training. Intuitively, when we train the embedding encoding network and the sample interaction network together with the end-to-end manner and the consistent loss in Sec. 3.4, the encoding network will also be improved with the helpful supervision of embedding interaction.", + "bbox": [ + 496, + 597, + 890, + 733 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Time Complexity. Two matching methods have different time complexity in cross-modal retrieval. Given $N$ image-text pairs, separate encoding makes the time complexity of embedding-based methods to be $O(2N)$ , while cross-modal interaction makes score-based to be $O(N^2)$ . Given one query and the set to be retrieved $N$ samples, the time complexity of query retrieval is $O(1)$ for embedding-based, but is $O(N)$ for score-based. Hence score-based methods usually sacrifice the retrieval speed for the performance boost. However, our method can achieve both highly accurate and efficient retrieval, as shown in Fig. 5.", + "bbox": [ + 496, + 734, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "15164", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/9a382dd26604e18f47eaa918a2e056ee2bf166f089105f7d18a9d5d4c83c4fb5.jpg", + "table_caption": [ + "Table 2. Comparisons of image-text retrieval performances on Flickr30K and MS-COCO 1K test-set. Region represents using Faster-RCNN [37] to extract region features [1] for images. BiGRU [38] and BERT [6] represent using them to extract word features for texts. We list the existing state-of-the-art embedding-based image-text matching methods. * indicates the ensemble results of two models." + ], + "table_footnote": [], + "table_body": "
MethodFlickr30K 1KMS-COCO 1K
IMG → TEXTTEXT → IMGrSumIMG → TEXTTEXT → IMGrSum
R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10
Region + BiGRU
VSRN*2019 [20]71.390.696.054.781.888.2482.676.294.898.262.889.795.1516.8
CVSE2020 [43]73.592.195.852.980.487.8482.474.895.198.359.989.495.2512.7
GPO2021 [4]76.594.297.756.483.489.9498.178.596.098.761.790.395.6520.8
MV2022 [24]79.094.997.759.184.690.6505.878.795.798.762.790.495.7521.9
HREM (Fusion)79.594.397.459.385.191.2506.880.096.098.762.790.195.4522.8
HREM (Full)*81.496.598.560.985.691.3514.381.296.598.963.790.796.0527.1
Region + BERT
CAMERA*2020 [35]78.095.197.960.385.991.7508.977.596.398.863.490.995.8522.7
DSRAN*2021 [45]77.895.197.659.286.091.9507.678.395.798.464.590.895.8523.5
GPO2021 [4]81.795.497.661.485.991.5513.579.796.498.964.891.496.3527.5
VSRN++2022 [21]79.294.697.560.685.691.4508.977.996.098.564.191.096.1523.6
HREM (Fusion)83.396.098.163.587.192.4520.481.196.698.966.191.696.5530.7
HREM (Full)*84.096.198.664.488.093.1524.282.996.999.067.192.096.6534.6
", + "bbox": [ + 80, + 142, + 890, + 375 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 396, + 209, + 412 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 76, + 420, + 267, + 436 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Datasets & Metrics. We choose the typical Flickr30K [47] and MS-COCO [26] datasets, where each image is associated with five texts. Flickr30K contains 29,000, 1,000, and 1,014 training, testing, and validation images, respectively. MS-COCO contains 82,738, 5,000, and 5,000 training, testing, and validation images, respectively. The results of MS-COCO are tested on averaging over 5-folds of 1K test images and on the entire 5K test images. Following [4], We evaluate performances by the metric, $R@K$ and $rSum$ .", + "bbox": [ + 75, + 444, + 468, + 580 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Implementation Details. The embedding dimension $d = 1024$ as previous work [4]. We use the pre-extracted region features [1] for images, and we use the BiGRU [38] with GloVe [34] or BERT-base [6] to extract textual features. The batch size $N = 128$ for Flickr30K and $N = 256$ for MS-COCO. The layer of interaction mechanism $L = 1$ , the hyper-parameters as $\\beta = 0.8$ , $\\tau = 0.5$ , $\\lambda = 1.5$ , $K = 10$ , and the margin of triplet loss $\\alpha = 0.2$ .", + "bbox": [ + 75, + 580, + 468, + 702 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.2. Comparison with State-of-the-art Methods", + "text_level": 1, + "bbox": [ + 76, + 710, + 441, + 727 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We follow the standard evaluation protocols [51] on two datasets. We first perform two proposed interaction mechanisms individually, then report the ensemble results.", + "bbox": [ + 75, + 734, + 468, + 779 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "On Flickr30K. Quantitative results on Flickr30K 1K test-set are shown in Tab. 2, where our proposed method outperforms all state-of-the-art embedding-based imagetext matching methods [20, 21, 24, 35, 45] with impressive margins for the R@K and rSum. Furthermore, our method has the coincident superiority on different textual encoders [6, 38] and still gets the best on the ensemble results.", + "bbox": [ + 75, + 779, + 470, + 883 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "On MS-COCO. Performances on MS-COCO 1K and", + "bbox": [ + 96, + 885, + 470, + 898 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ecf9dace5631dd2475e4bf13f020bd28f1431f64470ccd2109b7b606ca9f1703.jpg", + "image_caption": [ + "(a) $\\lambda$" + ], + "image_footnote": [], + "bbox": [ + 501, + 395, + 633, + 473 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c80d01a5cc187d9c768679899aec530cde95d6e5681bbe7bb32f706d61abc896.jpg", + "image_caption": [ + "(b) $K$" + ], + "image_footnote": [], + "bbox": [ + 638, + 395, + 761, + 473 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/afb59d42c1da43919b964d82eaa10f87f43fe510cde8bb88475478b061102d10.jpg", + "image_caption": [ + "(c) $\\tau$" + ], + "image_footnote": [], + "bbox": [ + 764, + 395, + 888, + 473 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/26de47d68ee660acb49b6183a9d33c84463bcd10984d4c378b9ba23173e67723.jpg", + "image_caption": [ + "Figure 6. Effect of hyper-parameters. (a) The weight of relevance matrix $\\lambda$ for Eq. (12), (b) The number of local matching $K$ for Eq. (8), (c) The threshold of neighbor space $\\tau$ for Eq. (5, 6).", + "Figure 7. The curve of triplet loss with of hard negative mining. The loss values are computed by enhanced and initial embeddings, which are the first and second item of Eq. (14), respectively." + ], + "image_footnote": [], + "bbox": [ + 501, + 556, + 694, + 646 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c3457c8d9d4064a82cd7d3916966f118d74a31124d5f35afd1a1c40e59262d26.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 710, + 556, + 888, + 646 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5K test-set are shown in Tab. 1 and Tab. 2. Our method performs best compared with existing state-of-the-art methods [4,35,45] on the 1K test set. Furthermore, on the more extensive database of 5K test set, our method outperforms previous work [21,50,51] with larger performance gaps, both embedding-based and score-based methods, which shows the superiority of our method more convincingly.", + "bbox": [ + 496, + 727, + 890, + 834 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation Study & Robustness Analysis", + "text_level": 1, + "bbox": [ + 500, + 845, + 831, + 863 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "By default, we perform the experiments on our framework of fusion mechanism with Region+BERT settings.", + "bbox": [ + 500, + 869, + 890, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "15165", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/e7b69bedc5327f69c44f066d7f892a0fe705dddcb25b88ac276c0937a0b41a9c.jpg", + "table_caption": [ + "Table 3. Comparison of different module ablation for our framework. $\\checkmark$ means retaining it (or otherwise removing it).", + "(a) The ablation study of hierarchical relation modeling on Flickr30K" + ], + "table_footnote": [], + "table_body": "
Fragment-level VisualTextualInstance-levelIMG → TEXTTEXT → IMG
Intra-modalInter-modalR@1R@5R@1TR@5
✓✓81.594.962.386.2
✓✓✓81.895.162.486.2
✓✓✓83.195.963.287.0
✓✓80.194.860.985.3
✓✓✓80.594.761.285.2
✓✓✓82.295.562.686.4
✓✓✓✓83.396.063.587.1
", + "bbox": [ + 81, + 143, + 467, + 243 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/6f2c0f35a5d8777845e82742d60633593d10a1e83324ab6784a165eee92fbd6e.jpg", + "table_caption": [ + "(b) The ablation study of instance-level relation modeling on Flickr30K" + ], + "table_footnote": [], + "table_body": "
MethodsIMG → TEXTTEXT → IMG
R@1R@5R@1TR@5
w/o connection matrix A81.495.161.586.3
w/o relevance matrix S81.795.361.986.5
w/o consistency Lcross81.695.661.886.7
w/o regularization Lreg82.895.862.986.9
w/o neighbor batch sampling82.895.963.187.0
HREM83.396.063.587.1
", + "bbox": [ + 81, + 262, + 467, + 375 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Hyper-parameters. Fig. 6 shows the effect of three critical parameters, which demonstrates the significance of capturing sample relations and the effectiveness of our proposed modules. $\\lambda$ is the attention coefficient for the relevance matrix, thus it should be large enough to provide extra fine-grained supervision, $K$ is related to the number of words in variable sentences, thus it cannot be too large to adapt short texts. $\\tau$ represents the ratio of connected samples in the batch, it will not be meaningful if close to 0 or 1. Finally, the performances are relatively stable when all parameter values change in a proper range, our method is insensitive to hyper-parameter selection.", + "bbox": [ + 75, + 410, + 468, + 590 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Hierarchical Relation. To better verify the effectiveness of hierarchical relation modeling, we provide the ablation study in Tab. 3a. It shows that both instance-level and fragment-level relation modeling help improve the learning of cross-modal embeddings. First, capturing the instance-level relation, especially the inter-modal relation, is more critical than the intra-modal and fragment-level relation. Besides, capturing fragment-level relations for the textual modality seems redundant. We believe that word features have already learned contextual semantics by the textual encoders, e.g., BiGRU [38] and BERT [6]. However, exploring the semantic relations for region features are meaningful as previous work [20, 21, 46]. Finally, modeling all hierarchical relations is also essential for the optimal result.", + "bbox": [ + 75, + 595, + 467, + 805 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Instance-level Relation. Tab. 3b shows the ablation of our instance-level relation modeling. First, the connection and relevance matrices are significant in explicitly capturing the cross-modal relation. And the cross-embedding matching loss $\\mathcal{L}_{cross}$ is indispensable to keep the embedding consistency at the inference stage. Besides, the regularization", + "bbox": [ + 75, + 810, + 468, + 901 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/c31502b568b8f8122f736415e311b538636bcb79f5620f43e2fb0a60be705205.jpg", + "table_caption": [ + "Table 4. R@1 comparison on hard samples with semantic ambiguity and infrequent samples with semantic scarcity in different proportions, selected by the percentile rank of average embedding similarity between themselves and other samples in the dataset. $\\checkmark$ means training with sample relation modeling." + ], + "table_footnote": [], + "table_body": "
Samples TypePercentile RankSample InteractionFlickr30KMS-COCO 5K
I → TT → II → TT → I
Hard5%✓74.632.036.48.5
76.034.840.49.2
10%✓72.038.839.212.3
75.040.444.613.6
20%✓74.545.143.618.5
78.047.149.319.5
Infrequent5%✓79.238.450.816.7
84.040.859.219.4
10%✓78.041.253.019.8
81.243.661.022.6
20%✓75.046.654.823.9
82.048.461.026.2
", + "bbox": [ + 504, + 170, + 888, + 348 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "loss $\\mathcal{L}_{reg}$ can ensure the training stability to improve performances. Finally, the neighbor batch sampling adapts to mine more potential connected samples from mini-batch.", + "bbox": [ + 498, + 371, + 890, + 416 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Special Samples. Tab. 4 shows the performance comparison of hard samples with semantic ambiguity and infrequent samples with semantic scarcity in the test set (hard and infrequent samples will get high average similarity with other negative samples in datasets). We find our method can improve these special samples significantly. Fig. 7 shows the curve of triplet loss with hard negative mining, the values of enhanced embeddings are lower than the initial, proving the capacity for recognizing hard samples.", + "bbox": [ + 496, + 417, + 890, + 551 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Retrieval Speed. We show the trade-off between performance and computation for cross-modal retrieval in Fig. 5. Although our method belongs to the embedding-based image-text matching methods [4,20], achieves both high accuracy and efficiency, and is more than 10 times faster than the latest score-based methods [7, 19, 51] on Flickr30K.", + "bbox": [ + 496, + 551, + 890, + 643 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 656, + 617, + 671 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This paper proposes a novel hierarchical relation modeling framework (HREM) for image-text matching. HREM not only captures fragment-level relations within the single modality and sample, but also effectively exploits instance-level relations across different modalities and samples to learn better holistic embeddings. Based on our design, HREM encodes embedding without interaction on sample or modality at the inference stage, thus achieving high efficiency on cross-modal retrieval. Extensive experiments on two benchmarks show the superiority of our method.", + "bbox": [ + 496, + 681, + 890, + 832 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Acknowledgements", + "text_level": 1, + "bbox": [ + 500, + 845, + 692, + 862 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work is supported by the National Natural Science Foundation of China under Grant 62222212, 62121002.", + "bbox": [ + 500, + 869, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "15166", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. Bottom-up and top-down attention for image captioning and visual question answering. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6077-6086, 2018. 3, 6, 7", + "[2] Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. ArXiv, abs/1607.06450, 2016. 5", + "[3] Hui Chen, Guiguang Ding, Xudong Liu, Zijia Lin, Ji Liu, and Jungong Han. Imram: Iterative matching with recurrent attention memory for cross-modal image-text retrieval. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12652-12660, 2020. 1, 2, 6", + "[4] Jiacheng Chen, Hexiang Hu, Hao Wu, Yuning Jiang, and Chang Lian Wang. Learning the best pooling strategy for visual semantic embedding. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15784-15793, 2021. 1, 2, 6, 7, 8", + "[5] Yuhao Cheng, Xiaoguang Zhu, Jiuchao Qian, Fei Wen, and Peilin Liu. Cross-modal graph matching network for imagetext retrieval. ACM Trans. Multimedia Comput. Commun. Appl., 18(4), mar 2022. 2, 4, 6", + "[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv, abs/1810.04805, 2019. 3, 6, 7, 8", + "[7] Haiwen Diao, Ying Zhang, Lingyun Ma, and Huchuan Lu. Similarity reasoning and filtration for image-text matching. ArXiv, abs/2101.01368, 2021. 1, 2, 8", + "[8] Ismail Elezi, Jenny Seidenschwarz, Laurin Wagner, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, and Laura Leal-Taixe. The group loss: A deeper look into group loss for deep metric learning. IEEE transactions on pattern analysis and machine intelligence, PP, 2022. 3", + "[9] Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, and Laura Leal-Taixe. The group loss for deep metric learning. ArXiv, abs/1912.00385, 2020. 3", + "[10] Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, and Sanja Fidler. Vse++: Improving visual-semantic embeddings with hard negatives. In BMVC, 2018. 1, 2, 6", + "[11] Zhihao Fan, Zhongyu Wei, Zejun Li, Siyuan Wang, Hajun Shan, Xuanjing Huang, and Jianqing Fan. Constructing phrase-level semantic labels to form multi-grained supervision for image-text retrieval. ArXiv, abs/2109.05523, 2021. 6", + "[12] Zheren Fu, Yan Li, Zhendong Mao, Quan Wang, and Yongdong Zhang. Deep metric learning with self-supervised ranking. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. 3", + "[13] Zheren Fu, Zhendong Mao, Bo Hu, An-An Liu, and Yongdong Zhang. Intra-class adaptive augmentation with neighbor correction for deep metric learning. IEEE Transactions on Multimedia, pages 1-14, 2022. 3", + "[14] Zheren Fu, Zhendong Mao, Chenggang Clarence Yan, An an Liu, Hongtao Xie, and Yongdong Zhang. Self-supervised" + ], + "bbox": [ + 78, + 114, + 470, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "synthesis ranking for deep metric learning. IEEE Transactions on Circuits and Systems for Video Technology, 32:4736-4750, 2022. 3", + "[15] Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016. 3, 5", + "[16] Zhi Hou, Baosheng Yu, and Dacheng Tao. Batchformer: Learning to explore sample relationships for robust representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7256-7266, 2022. 3, 4", + "[17] Mengqi Huang, Zhendong Mao, Penghui Wang, Quang Wang, and Yongdong Zhang. Dse-gan: Dynamic semantic evolution generative adversarial network for text-to-image generation. Proceedings of the 30th ACM International Conference on Multimedia, 2022. 1", + "[18] Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak. Self-taught metric learning without labels. CVPR, 2022. 5", + "[19] Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu, and Xiaodong He. Stacked cross attention for image-text matching. In Proceedings of the European Conference on Computer Vision (ECCV), pages 201-216, 2018. 1, 2, 4, 5, 8", + "[20] Kunpeng Li, Yulun Zhang, K. Li, Yuanyuan Li, and Yun Raymond Fu. Visual semantic reasoning for image-text matching. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 4653-4661, 2019. 1, 2, 7, 8", + "[21] Kunpeng Li, Yulun Zhang, Kai Li, Yuanyuan Li, and Yun Raymond Fu. Image-text embedding learning via visual and textual semantic reasoning. IEEE transactions on pattern analysis and machine intelligence, PP, 2022. 2, 6, 7, 8", + "[22] Pandeng Li, Hongtao Xie, Jiannan Ge, Lei Zhang, Shaobo Min, and Yongdong Zhang. Dual-stream knowledge-preserving hashing for unsupervised video retrieval. In European Conference on Computer Vision, pages 181-197. Springer, 2022. 1", + "[23] Shuang Li, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Feng Qiao, and Xinjing Cheng. Metasaug: Meta semantic augmentation for long-tailed visual recognition. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5208-5217, 2021. 3", + "[24] Zheng Li, Caili Guo, Zerun Feng, Jenq-Neng Hwang, and Xijun Xue. Multi-view visual semantic embedding. In IJ-CAI, 2022. 2, 6, 7", + "[25] Jongin Lim, Sangdoo Yun, Seulki Park, and Jin Young Choi. Hypergraph-induced semantic tuplet loss for deep metric learning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 212-222, 2022. 3, 4", + "[26] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 7", + "[27] Chunxiao Liu, Zhendong Mao, Tianzhu Zhang, Hongtao Xie, Bin Wang, and Yongdong Zhang. Graph structured network for image-text matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10921-10930, 2020. 1" + ], + "bbox": [ + 503, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "15167", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[28] Xin Liu, Yi He, Yiu ming Cheung, Xing Xu, and N. Wang. Learning relationship-enhanced semantic graph for fine-grained image-text matching. IEEE transactions on cybernetics, PP, 2022. 2", + "[29] Thang Luong, Hieu Pham, and Christopher D. Manning. Effective approaches to attention-based neural machine translation. ArXiv, abs/1508.04025, 2015. 4", + "[30] J MacQueen. Classification and analysis of multivariate observations. In 5th Berkeley Symp. Math. Statist. Probability, pages 281-297, 1967. 6", + "[31] R. Manmatha, Chaoxia Wu, Alex Smola, and Philipp Krahenbihl. Sampling matters in deep embedding learning. 2017 IEEE International Conference on Computer Vision (ICCV), pages 2859-2867, 2017. 6", + "[32] Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari, and Zeynep Akata. Learning graph embeddings for compositional zero-shot learning. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 953-962, 2021. 3", + "[33] Xingchao Peng, Zijun Huang, Xineng Sun, and Kate Saenko. Domain agnostic learning with disentangled representations. ArXiv, abs/1904.12347, 2019. 3", + "[34] Jeffrey Pennington, Richard Socher, and Christopher D. Manning. Glove: Global vectors for word representation. In EMNLP, 2014. 7", + "[35] Leigang Qu, Meng Liu, Da Cao, Liqiang Nie, and Qi Tian. Context-aware multi-view summarization network for image-text matching. Proceedings of the 28th ACM International Conference on Multimedia, 2020. 1, 2, 7", + "[36] Leigang Qu, Meng Liu, Jianlong Wu, Zan Gao, and Liqiang Nie. Dynamic modality interaction modeling for image-text retrieval. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021. 6", + "[37] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39:1137-1149, 2015. 3, 7", + "[38] Mike Schuster and Kuldip K. Paliwal. Bidirectional recurrent neural networks. IEEE Trans. Signal Process., 45:2673-2681, 1997. 3, 6, 7, 8", + "[39] Jenny Seidenschwarz, Ismail Elezi, and Laura Leal-Taix'e. Learning intra-batch connections for deep metric learning. In ICML, 2021. 2, 3, 4", + "[40] Zhan Shi, Tianzhu Zhang, Xiaoyan Wei, Feng Wu, and Yongdong Zhang. Decoupled cross-modal phrase-attention network for image-sentence matching. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, PP, 2022. 4, 6", + "[41] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3, 5", + "[42] Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio', and Yoshua Bengio. Graph attention networks. ArXiv, abs/1710.10903, 2018. 3" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[43] Haoran Wang, Ying Zhang, Zhong Ji, Yanwei Pang, and Lingyun Ma. Consensus-aware visual-semantic embedding for image-text matching. In ECCV, 2020. 2, 7", + "[44] Sijin Wang, Ruiping Wang, Ziwei Yao, S. Shan, and Xilin Chen. Cross-modal scene graph matching for relationship-aware image-text retrieval. 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1497-1506, 2020. 2", + "[45] Keyu Wen, Xiaodong Gu, and Qingrong Cheng. Learning dual semantic relations with graph attention for image-text matching. IEEE Transactions on Circuits and Systems for Video Technology, 31:2866-2879, 2021. 2, 4, 6, 7", + "[46] Yiling Wu, Shuhui Wang, Guoli Song, and Qingming Huang. Learning fragment self-attention embeddings for image-text matching. Proceedings of the 27th ACM International Conference on Multimedia, 2019. 2, 8", + "[47] Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. Transactions of the Association for Computational Linguistics, 2:67-78, 2014. 7", + "[48] Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Young Joon Yoo. Cutmix: Regularization strategy to train strong classifiers with localizable features. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 6022-6031, 2019. 3", + "[49] Hongyi Zhang, Moustapha Cisse, Yann Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. ArXiv, abs/1710.09412, 2018. 3", + "[50] Kun Zhang, Zhendong Mao, Anan Liu, and Yongdong Zhang. Unified adaptive relevance distinguishable attention network for image-text matching. IEEE Transactions on Multimedia, pages 1-1, 2022. 6, 7", + "[51] Kun Zhang, Zhendong Mao, Quan Wang, and Yongdong Zhang. Negative-aware attention framework for image-text matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15661-15670, 2022. 2, 6, 7, 8" + ], + "bbox": [ + 501, + 92, + 890, + 626 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "15168", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Semantic Relationship Among Instances for Image-Text Matching/5b93c863-5d73-452f-a25d-f402de582d22_model.json b/2023/Learning Semantic Relationship Among Instances for Image-Text Matching/5b93c863-5d73-452f-a25d-f402de582d22_model.json new file mode 100644 index 0000000000000000000000000000000000000000..b8d4da070a1e5af11aea2b7b6f15e1c04050eb1f --- /dev/null +++ b/2023/Learning Semantic Relationship Among Instances for Image-Text Matching/5b93c863-5d73-452f-a25d-f402de582d22_model.json @@ -0,0 +1,2387 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.107, + 0.131, + 0.867, + 0.155 + ], + "angle": 0, + "content": "Learning Semantic Relationship among Instances for Image-Text Matching" + }, + { + "type": "text", + "bbox": [ + 0.229, + 0.18, + 0.74, + 0.2 + ], + "angle": 0, + "content": "Zheren Fu\\(^{1}\\), Zhendong Mao\\(^{1,2,*}\\), Yan Song\\(^{1}\\), Yongdong Zhang\\(^{1,2}\\)" + }, + { + "type": "text", + "bbox": [ + 0.239, + 0.2, + 0.732, + 0.217 + ], + "angle": 0, + "content": "1University of Science and Technology of China, Hefei, China" + }, + { + "type": "text", + "bbox": [ + 0.107, + 0.217, + 0.862, + 0.235 + ], + "angle": 0, + "content": "\\(^{2}\\)Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China" + }, + { + "type": "text", + "bbox": [ + 0.226, + 0.237, + 0.74, + 0.252 + ], + "angle": 0, + "content": "fzr@mail.ustc.edu.cn, {zdmao, songyan, zhyd73}@ustc.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.287, + 0.314, + 0.303 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.32, + 0.474, + 0.651 + ], + "angle": 0, + "content": "Image-text matching, a bridge connecting image and language, is an important task, which generally learns a holistic cross-modal embedding to achieve a high-quality semantic alignment between the two modalities. However, previous studies only focus on capturing fragment-level relation within a sample from a particular modality, e.g., salient regions in an image or text words in a sentence, where they usually pay less attention to capturing instance-level interactions among samples and modalities, e.g., multiple images and texts. In this paper, we argue that sample relations could help learn subtle differences for hard negative instances, and thus transfer shared knowledge for infrequent samples should be promising in obtaining better holistic embeddings. Therefore, we propose a novel hierarchical relation modeling framework (HREM), which explicitly capture both fragment- and instance-level relations to learn discriminative and robust cross-modal embeddings. Extensive experiments on Flickr30K and MS-COCO show our proposed method outperforms the state-of-the-art ones by \\(4\\% -10\\%\\) in terms of rSum. Our code is available at https://github.com/ CrossmodalGroup/HREM." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.683, + 0.21, + 0.699 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.709, + 0.47, + 0.876 + ], + "angle": 0, + "content": "Image-text matching bridges the semantic gap between visual and textual modalities and is a fundamental task for various multi-modal learning applications, such as cross-modal retrieval [22] and text-to-image synthesis [17]. The critical challenge is accurately and efficiently learning cross-modal embeddings and their similarities for images and texts, to achieve a high-quality semantic alignment. In general, existing image-text matching methods can be classified into two paradigms. The first embedding-based matching [4, 10, 20, 35] separately encodes the whole images and texts into a holistic embedding space, then globally" + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.286, + 0.892, + 0.361 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.604, + 0.363, + 0.788, + 0.373 + ], + "angle": 0, + "content": "(a) Pipeline of embedding-based method" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.374, + 0.695, + 0.51 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.551, + 0.512, + 0.657, + 0.523 + ], + "angle": 0, + "content": "(b) Semantic ambiguity" + }, + { + "type": "image", + "bbox": [ + 0.7, + 0.374, + 0.889, + 0.511 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.741, + 0.512, + 0.836, + 0.522 + ], + "angle": 0, + "content": "(c) Semantic scarcity" + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.534, + 0.895, + 0.729 + ], + "angle": 0, + "content": "Figure 1. Illustration of our motivation. Sample relation modeling improves the holistic representation of cross-modal learning. Colors and shapes indicate different modalities and image-text pairs, respectively. Orange elements mark effective interactions: (a) The pipeline of the previous and our work, we add the cross-modal relation interaction between samples. (b) For the identical theme of \"surfer with surfboard\", specific behaviors exist subtle differences, like \"hold/squat/ride on the surfboard\" and \"stare/break/wipe out the wave\". Our method distinguishes these hard negative samples from semantic ambiguities. (c) For similar themes under \"man play a ball\", corresponding behaviors usually are semantic similar, like \"play the hockey/cricket/polo\" all need to \"hit the ball\" with \"sticks/bats\". Our method improves learning embeddings on these infrequent samples with semantic scarcities for themselves." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.762, + 0.892, + 0.822 + ], + "angle": 0, + "content": "measures the semantic similarity of the two modalities. The second score-based matching [3,7,19,27] applies the cross-modal interaction between visual and textual local features, then learns a cumulative similarity score." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Recently, embedding-based methods have served as the mainstream solution owing to both accuracy and efficiency in image-text matching, which contains two steps as shown in Fig. 1 (a): (1) Capturing the intra-modal relation between visual fragments (e.g., regional features) or textual" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.888, + 0.222, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "15159" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.473, + 0.409 + ], + "angle": 0, + "content": "fragments (e.g., word features) independently, then enhancing the semantic representation of local features. (2) Aggregating relation-enhanced local features of two modalities into the holistic embedding space. For the first step, most use the graph neural network [5, 20, 21] or attention module [35, 45, 46] to capture semantic relations and enhance the local features of two modalities, respectively. Some work further exploits the spatial relation [45] for visual regions or grammatical relation [28] for textual words. For the second step, they design pooling functions [4] or sequence encoders [21] to aggregate local features and get holistic embeddings. Existing embedding-based methods follow the principle of separately encoding images and texts by two branches as Fig. 1 (a). In each branch, these methods only focus on the fragment-level relation modeling and local features interaction within one sample, e.g., the region features inside one image (or the word features inside one text). In this way, the instance-level relation modeling and global embeddings interaction among different samples and modalities, e.g., holistic embeddings of multiple images and texts, are entirely overlooked." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.413, + 0.473, + 0.702 + ], + "angle": 0, + "content": "Consequently, existing embedding-based methods directly use global embeddings to compute loss function, \\( e.g. \\), hard negative triplet loss [10] on random mini-batch, which is insufficient to exploit the manifold structure of holistic embedding space [39]. First, they fail to learn subtle semantic discrepancies among different samples (e.g., similar behaviors with an identical theme as shown in Fig. 1 (b)), then can not distinguish hard negative samples with semantic ambiguities because of the heterogeneity of visual and textual semantics. Second, they are unable to transfer shared knowledge from diverse samples (e.g., different samples that contain similar behaviors with similar themes as shown in Fig. 1 (c)), then can not effectively learn on these infrequent samples with semantic scarcities. Therefore, it is expected that a framework should precisely capture the sample relationship to learn better cross-modal embeddings, while does not break the principle of embedding-based methods, i.e., independently encodes embeddings without modality interaction at the inference stage." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.705, + 0.473, + 0.903 + ], + "angle": 0, + "content": "In doing so, we propose a Hierarchical RElation Modeling framework (HREM) that, for the first time to our knowledge, explicitly captures both fragment-level and instance-level relations to learn holistic embeddings jointly. Therefore, HREM learns not only contextual semantics among intra-modal fragments to enhance local features, but also the associated semantics among inter-modal instances to distinguish hard negative samples and improve learning on infrequent samples. As illustrated in Fig. 1 (a) and Fig. 2, we propose a novel step (i.e., the \"stage-three\") to exactly capture the semantic relationship of cross-modal samples. First, we propose a novel cross-embedding association graph, which explicitly identifies the connection" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.243 + ], + "angle": 0, + "content": "relation and learns the relevance relation between batch samples with fragment-level semantic matching. Next, we propose two relation interaction mechanisms, which explore inter-modal and intra-modal relations synchronously or asynchronously with our improved attention modules to obtain enhanced embeddings. Consequently, HREM only needs to capture the instance-level relation for training, then encode multi-modal embeddings independently at the inference stage, to achieve high accuracy and efficiency for image-text matching." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.244, + 0.895, + 0.426 + ], + "angle": 0, + "content": "To summarize, the major contributions are as follows: (1) We propose a hierarchical relation modeling framework (HREM) for image-text matching. To the best of our knowledge, this is the first work that explicitly captures both fragment-level relations within modality and instance-level relations across modalities. (2) We propose a novel cross-embedding association graph by identifying the connection relation and learning the relevance relation. (3) We propose two relation interaction mechanisms to learn the relation-enhanced embeddings. (4) HREM outperforms all state-of-the-art methods for image-text retrieval on two widely used benchmarks, Flickr30K and MS-COCO, by \\(4\\% - 10\\%\\) rSum." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.442, + 0.642, + 0.459 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.468, + 0.704, + 0.485 + ], + "angle": 0, + "content": "2.1. Image-Text Matching" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.492, + 0.895, + 0.78 + ], + "angle": 0, + "content": "According to how the cross-modal interaction is implemented, image-text matching methods are divided into two categories, embedding-based and score-based matching. **Embedding-based.** It independently encodes images and sentences into a holistic embedding space by two branches, where the semantic similarity is calculated by cosine similarity [10]. Existing work usually utilizes the GCN [20, 44] or self-attention layer [45, 46] to capture semantic relations between fragments inside one sample and enhance contextual semantics of local features, then propose a particular aggregator to learn global embeddings. For example, VSRN [20] proposes a semantic reasoning network to learn local visual features with key scene concepts. CVSE [43] proposes a consensus-aware module to integrate commonsense knowledge into local features for two modalities. GPO [4] presents a generalized pooling function to project local features into the global embedding. MV [24] proposes a multi-view encoder to learn multiple embeddings for one image and models intra-class variations." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Score-based. It conducts fine-grained cross-modal interaction and semantic alignment between local fragments, then calculates a cumulative similarity score. [3, 7, 19, 51]. For example, SCAN [19] proposes an attention mechanism for cross-modal interaction between visual and textual fragments. IMRAM [3] proposes an iterative network for multiple steps of cross-modal interaction. NAAF [51] measures the similarity and dissimilarity degrees via two matching" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "15160" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.08, + 0.089, + 0.895, + 0.309 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.319, + 0.895, + 0.391 + ], + "angle": 0, + "content": "Figure 2. Overview of our hierarchical relation modeling framework (HREM). Given \\(N\\) image-text pairs \\((N = 3\\) in this Figure), we first capture the fragment-level relations and learn the relation-enhanced local features for each image or text independently, then aggregate local features by pooling operation to get global embeddings \\(\\{\\pmb{v}_i,\\pmb{u}_i\\}_{i = 1}^N\\). Next, we propose a novel cross-embedding association graph to capture instance-level relations by identifying the connection relation and learning the relevance relation between samples. Finally, we propose two cross-modal relation interaction mechanisms to get relation-enhanced embeddings and compute the final loss function." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.416, + 0.405, + 0.431 + ], + "angle": 0, + "content": "mechanisms to infer the overall similarity jointly." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.432, + 0.471, + 0.493 + ], + "angle": 0, + "content": "Nonetheless, existing embedding-based methods only capture the relation between local fragments within each sample. In addition, we explore the relation across different samples and modalities to learn better holistic embeddings." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.504, + 0.349, + 0.52 + ], + "angle": 0, + "content": "2.2. Sample Relationship Learning" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.527, + 0.473, + 0.799 + ], + "angle": 0, + "content": "Among data scarcity and diversity, sample relations have been explicitly explored for representation enhancement [23, 49]. A simple way is to generate input as new samples from existing samples [13], such as mix-up [49], and cutmix [48]. Some work designs the delicate objective function for output, such as various metric learning losses [12, 14]. Another way is using the knowledge distillation between samples, such as transferring invariant knowledge for zero-shot learning [32] and domain generalization [33]. Recently, some methods focus on the view of batch interaction [8, 9, 16]. IBC [39] constructs a fully connected graph for mini-batch samples and classifies each sample employing a message passing network. HIST [25] constructs a hypergraph to formulate higher-order relations between samples. However, these methods study the primary vision task, e.g., classification. We capture the sample relationship for cross-modal learning and bridge semantic discrepancies between visual and textual modalities." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.815, + 0.289, + 0.831 + ], + "angle": 0, + "content": "3. The Proposed Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.902 + ], + "angle": 0, + "content": "The overview of HREM is depicted in Fig. 2. We first introduce feature extraction in Sec. 3.1 and fragment-level relation modeling in Sec. 3.2. Then we introduce instance-level relation modeling in Sec. 3.3. Finally, we describe the" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.416, + 0.84, + 0.431 + ], + "angle": 0, + "content": "optimization in Sec. 3.4 and discussion in Sec. 3.5." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.439, + 0.684, + 0.454 + ], + "angle": 0, + "content": "3.1. Feature Extraction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.462, + 0.892, + 0.583 + ], + "angle": 0, + "content": "Visual Representation. Given an image \\(I\\), we use the bottom-up-attention network [1] to extract the salient regions by the Faster-RCNN [37] and get the region features by the pre-trained ResNet-101 [15]. Then we add a fully-connect (FC) layer to map each region to a \\(d\\)-dimensional local feature. We denote as \\(R = \\{r_1, \\dots, r_{n_r}\\} \\in \\mathbb{R}^{n_r \\times d}\\), which is the visual fragments and local features for the image \\(I\\), \\(n_r\\) is the number of region features." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.584, + 0.893, + 0.689 + ], + "angle": 0, + "content": "Textual Representation. Given a sentence \\( T \\), we use the sequence models, bi-directional gated recurrent unit (Bi-GRU) [38], or pre-trained BERT [6] to extract the set of word features. We also add an FC layer to keep the same dimension with images. We denote as \\( C = \\{c_1, \\dots, c_{n_c}\\} \\in \\mathbb{R}^{n_c \\times d} \\), which is the textual fragments and local features for the text, \\( n_c \\) is the number of word features." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.697, + 0.803, + 0.714 + ], + "angle": 0, + "content": "3.2. Fragment-level Relation Modeling" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.892, + 0.78 + ], + "angle": 0, + "content": "To capture contextual information between fragments and enhance local features of two modalities introduced by Sec. 3.1, we propose fragment-level relation modeling for visual regions and textual words, respectively." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.781, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Visual Regions. We construct a semantic relation graph between visual regions within one image, and propose the relation interaction module to learn the contextual semantics for region features. The graph nodes are region features \\( R \\), and edges are their semantic relation. Specifically, we use the graph attention network [42], e.g., self-attention layer [41], to capture the semantic relation and learn relation-enhanced local features. It first maps original" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "15161" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.138 + ], + "angle": 0, + "content": "features to queries and key-value pairs, then use a weighted sum of the values as outputs, where the weighting depends on scaled dot-product between queries and keys:" + }, + { + "type": "equation", + "bbox": [ + 0.135, + 0.149, + 0.47, + 0.185 + ], + "angle": 0, + "content": "\\[\nA t t \\left(\\boldsymbol {Q}, \\boldsymbol {K}, \\boldsymbol {V}\\right) = \\operatorname {s o f t m a x} \\left(\\frac {\\boldsymbol {Q} \\boldsymbol {K} ^ {T}}{\\sqrt {d _ {k}}}\\right) \\boldsymbol {V}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.189, + 0.47, + 0.31 + ], + "angle": 0, + "content": "where \\( Att \\) is called the scaled dot-product attention [29], \\( d_{k} \\) is the dimension of input features. In this case, we build up a fully-connected graph, and the semantic relation is implicitly expressed in the attention weight. After initial region features \\( R = \\{r_1,\\dots ,r_{n_r}\\} \\in \\mathbb{R}^{n_r\\times d} \\) pass through the self-attention layer with the feed-forward network to learn contextual information, we obtain the relation-enhanced region features \\( R^{V} = \\{r_{1}^{V},\\dots ,r_{n_{r}}^{V}\\} \\in \\mathbb{R}^{n_{r}\\times d} \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.311, + 0.469, + 0.371 + ], + "angle": 0, + "content": "Finally, we aggregate initial and enhanced region features to get the global visual embedding \\(\\pmb{v} \\in \\mathbb{R}^d\\), by using the maximum pooling and average pooling, with the parameter \\(\\beta\\) to control the ratio of two representations." + }, + { + "type": "equation", + "bbox": [ + 0.094, + 0.382, + 0.47, + 0.4 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {v} = \\beta \\cdot \\operatorname {M a x P o o l} (R) + (1 - \\beta) \\cdot \\operatorname {A v g P o o l} \\left(R ^ {V}\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.412, + 0.47, + 0.548 + ], + "angle": 0, + "content": "Textual Words. Similarly, we construct a semantic relation graph between textual words within one sentence, and use the relation interaction module to enhance contextual information for the word features. The nodes are word features \\( C = \\{c_{1},\\dots ,c_{n_{c}}\\} \\in \\mathbb{R}^{n_{c}\\times d} \\) and the edges are their semantic relation. We also use the self-attention layer to implement, hence we get relation-enhanced word features \\( C^U = \\{c_1^U,\\dots ,c_{n_c}^U\\} \\in \\mathbb{R}^{n_c\\times d} \\), then aggregate them to get the global textual embedding \\( \\pmb {u}\\in \\mathbb{R}^{d} \\), like Eq. (2)." + }, + { + "type": "equation", + "bbox": [ + 0.093, + 0.565, + 0.469, + 0.582 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {u} = \\beta \\cdot \\operatorname {M a x P o o l} (C) + (1 - \\beta) \\cdot \\operatorname {A v g P o o l} \\left(C ^ {U}\\right), \\tag {3}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.591, + 0.367, + 0.608 + ], + "angle": 0, + "content": "3.3. Instance-level Relation Modeling" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.615, + 0.469, + 0.721 + ], + "angle": 0, + "content": "We propose the instance-level relation modeling for multiple images and texts to learn better cross-modal embeddings obtained by Sec. 3.2. Given \\(N\\) image-text pairs and their embeddings \\(\\{\\pmb{v}_i,\\pmb{u}_i\\}_{i = 1}^N\\), we propose a novel cross-embedding association graph \\(\\mathcal{G}(\\mathcal{V},\\mathcal{E})\\), where the nodes are the embeddings \\(\\mathcal{V} = \\{\\pmb{v}_1,\\dots,\\pmb{v}_N,\\pmb{u}_1\\dots,\\pmb{u}_N\\} \\in \\mathbb{R}^{2N\\times d}\\), the edges \\(\\mathcal{E}\\) are pairwise semantic relations." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.74, + 0.392, + 0.756 + ], + "angle": 0, + "content": "3.3.1 Cross-Embedding Association Graph" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.765, + 0.469, + 0.809 + ], + "angle": 0, + "content": "The critical challenge is how to construct the pairwise relation accurately. Without loss of generality, we divide the relation into two parts: connection and relevance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.469, + 0.9 + ], + "angle": 0, + "content": "We use the matrix \\( \\mathbf{A} \\in \\mathbb{R}^{2N \\times 2N} \\) to represent the connection relation, i.e., whether there exists an associated edge between nodes. We use the matrix \\( S \\in \\mathbb{R}^{2N \\times 2N} \\) to represent the relevance relation, i.e., the degree of semantic association between nodes. Further, we divide these matrices into two patterns and four blocks: intra-modal rela" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.138 + ], + "angle": 0, + "content": "tion (Image-to-Image \\(I\\to I\\) , Text-to-Text \\(T\\rightarrow T\\) ) and intermodal relation (Image-to-Text \\(I\\rightarrow T\\) , Text-to-Image \\(T\\rightarrow I\\) ), the shape of each block is equal to \\(\\mathbb{R}^{N\\times N}\\)" + }, + { + "type": "equation", + "bbox": [ + 0.512, + 0.151, + 0.892, + 0.186 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {A} = \\left[\\begin{array}{l l}\\boldsymbol {A} _ {I \\rightarrow I}&\\boldsymbol {A} _ {I \\rightarrow T}\\\\\\boldsymbol {A} _ {T \\rightarrow I}&\\boldsymbol {A} _ {T \\rightarrow T}\\end{array}\\right], \\boldsymbol {S} = \\left[\\begin{array}{l l}\\boldsymbol {S} _ {I \\rightarrow I}&\\boldsymbol {S} _ {I \\rightarrow T}\\\\\\boldsymbol {S} _ {T \\rightarrow I}&\\boldsymbol {S} _ {T \\rightarrow T}\\end{array}\\right], \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.187, + 0.892, + 0.276 + ], + "angle": 0, + "content": "Connection Relation Identifying. The simple idea is to build a fully connected graph like Sec. 3.2 and previous work [5,45] that elements of \\( A \\) are all 1, However, the fullyconnection easily likely leads to over-smoothing and noisy association [39] for nodes. We need to identify the effective connection and filter out unassociated semantics." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.278, + 0.892, + 0.339 + ], + "angle": 0, + "content": "Therefore, we construct the neighbor space of embedding to identify the connection relation. If two embedding nodes are close, their semantic information usually overlaps, they probably have a semantic connection [25]." + }, + { + "type": "equation", + "bbox": [ + 0.558, + 0.352, + 0.836, + 0.388 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\left(\\boldsymbol {A} _ {I \\rightarrow I}\\right) _ {i j} = 1 \\text {i f} \\boldsymbol {v} _ {j} \\in \\mathcal {N} _ {\\text {i n t r a}} \\left(\\boldsymbol {v} _ {i}\\right) \\text {e l s e} 0, \\\\ \\left(\\boldsymbol {A} _ {T \\rightarrow T}\\right) _ {i j} = 1 \\text {i f} \\boldsymbol {u} _ {j} \\in \\mathcal {N} _ {\\text {i n t r a}} \\left(\\boldsymbol {u} _ {i}\\right) \\text {e l s e} 0, \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.4, + 0.892, + 0.476 + ], + "angle": 0, + "content": "where \\(\\mathcal{N}_{\\text {intra }}\\) is the neighbor space of embeddings for intramodal connection. We select the first \\(\\tau N\\) (\\(\\tau\\) is range from 0 to 1) nearest single-modal samples ranked by the embedding similarity to construct \\(\\mathcal{N}_{\\text {intra }}\\) in Eq. (5). Correspondingly, we define \\(\\mathcal{N}_{\\text {inter }}\\) for inter-modal connection:" + }, + { + "type": "equation", + "bbox": [ + 0.558, + 0.489, + 0.89, + 0.525 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\left(\\boldsymbol {A} _ {I \\rightarrow T}\\right) _ {i j} = 1 \\text {i f} \\boldsymbol {u} _ {j} \\in \\mathcal {N} _ {\\text {i n t e r}} \\left(\\boldsymbol {v} _ {i}\\right) \\text {e l s e} 0, \\\\ \\left(\\boldsymbol {A} _ {T \\rightarrow I}\\right) _ {i j} = 1 \\text {i f} \\boldsymbol {v} _ {j} \\in \\mathcal {N} _ {\\text {i n t e r}} \\left(\\boldsymbol {u} _ {i}\\right) \\text {e l s e} 0, \\tag {6} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.538, + 0.892, + 0.689 + ], + "angle": 0, + "content": "Since the heterogeneity between visual and textual semantics, directly using global embeddings is insufficient to identify the inter-modal connection relation. Following score-based methods [19, 40], we use the fine-grained matching of fragments and local features to measure the inter-modal neighbor space. As shown in Fig. 3, given an image-text pair and its region-word similarity matrix. We first pick up the most matching textual word (or visual region) for each region (or each word), then average these matched scores to express the overall matching values \\( p \\)." + }, + { + "type": "equation", + "bbox": [ + 0.581, + 0.702, + 0.892, + 0.783 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} p _ {I \\rightarrow T} = \\frac {1}{n _ {r}} \\sum_ {\\substack {m = 1\\\\n}} ^ {n _ {r}} \\max _ {n \\in [ 1, n _ {c} ]} \\left(\\boldsymbol {r} _ {m} ^ {\\mathrm {T}} \\boldsymbol {c} _ {n}\\right), \\tag{7} \\\\ p _ {T \\rightarrow I} = \\frac {1}{n _ {c}} \\sum_ {m = 1} ^ {n _ {c}} \\max _ {n \\in [ 1, n _ {r} ]} \\left(\\boldsymbol {c} _ {m} ^ {\\mathrm {T}} \\boldsymbol {r} _ {n}\\right), \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.795, + 0.892, + 0.841 + ], + "angle": 0, + "content": "where we select the first \\(\\tau N\\) nearest cross-modal samples ranked by the fragment-level matching values Eq. (7) to construct the inter-modal neighbor space \\(\\mathcal{N}_{inter}\\) in Eq (6)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.841, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Relevance Relation Learning. The relevance relation is the degree of semantic association between two connected nodes. Existing work [16, 39] uses the global embedding similarity to approximate, which is insufficient to bridge the" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15162" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.08, + 0.089, + 0.473, + 0.237 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.247, + 0.47, + 0.318 + ], + "angle": 0, + "content": "Figure 3. Graph construction of inter-modal relation with fragment-level matching. For every image-text pair, we use corresponding modules to get connection relation \\( a_{I \\to T} (a_{T \\to I}) \\) and relevance relation \\( s_{I \\to T} (s_{T \\to I}) \\), which are elements of connection matrix \\( A_{I \\to T} (A_{T \\to I}) \\) and relevance matrix \\( S_{I \\to T} (S_{T \\to I}) \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.342, + 0.473, + 0.435 + ], + "angle": 0, + "content": "semantic discrepancy between visual and textual space [19]. In contrast, we explicitly learn the cross-modal relevance relation. As shown in Fig. 3, we employ fine-grained matching like Eq. (7). After picking up the max matching of row-wise and column-wise, we select the top-\\(K\\) scores and concatenate them as the matching vectors \\(\\mathbf{q}\\)." + }, + { + "type": "equation", + "bbox": [ + 0.138, + 0.446, + 0.469, + 0.501 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\boldsymbol {q} _ {I \\rightarrow T} = T o p K (\\{\\max _ {n \\in [ 1, n _ {c} ]} \\left(\\boldsymbol {r} _ {m} ^ {\\mathrm {T}} \\boldsymbol {c} _ {n}\\right) \\} _ {m = 1} ^ {n _ {r}}), \\\\ \\boldsymbol {q} _ {T \\rightarrow I} = \\operatorname {T o p K} \\left(\\left. \\left\\{\\max _ {n \\in [ 1, n _ {r} ]} \\left(\\boldsymbol {c} _ {m} ^ {\\mathrm {T}} \\boldsymbol {r} _ {n}\\right) \\right\\} _ {m = 1} ^ {n _ {c}}\\right), \\tag {8} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.511, + 0.47, + 0.543 + ], + "angle": 0, + "content": "where \\(\\pmb{q} \\in \\mathbb{R}^{K}\\), then we use an MLP and add the overall matching values to learn the inter-modal relevance (scalar)." + }, + { + "type": "equation", + "bbox": [ + 0.171, + 0.564, + 0.469, + 0.599 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} s _ {I \\rightarrow T} = M L P \\left(\\boldsymbol {q} _ {I \\rightarrow T}\\right) + p _ {I \\rightarrow T}, \\\\ s _ {T \\rightarrow I} = M L P \\left(\\boldsymbol {q} _ {T \\rightarrow I}\\right) + p _ {T \\rightarrow I}, \\tag {9} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.607, + 0.47, + 0.683 + ], + "angle": 0, + "content": "For different image-text pairs, we compute the corresponding relevance values, which are the elements of the inter-modal relevance matrix \\( S_{I\\rightarrow T} \\) and \\( S_{T\\rightarrow I} \\). As for the intra-modal relevance relation, we can use the global embeddings to compute the relevance matrix." + }, + { + "type": "equation", + "bbox": [ + 0.091, + 0.696, + 0.469, + 0.722 + ], + "angle": 0, + "content": "\\[\n\\left(\\boldsymbol {S} _ {T \\rightarrow T}\\right) _ {i j} = e ^ {- \\frac {\\left\\| \\boldsymbol {v} _ {i} - \\boldsymbol {v} _ {j} \\right\\| _ {2} ^ {2}}{\\sigma}}, \\left(\\boldsymbol {S} _ {I \\rightarrow I}\\right) _ {i j} = e ^ {- \\frac {\\left\\| \\boldsymbol {u} _ {i} - \\boldsymbol {u} _ {j} \\right\\| _ {2} ^ {2}}{\\sigma}}, \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.723, + 0.47, + 0.784 + ], + "angle": 0, + "content": "where \\(\\sigma\\) is a positive scalar to control the relevance values (\\(\\sigma = 1\\) for simplicity). Besides, we add a relevance regularization loss to ensure the learned inter-modal relevance matrix does not crash during training [18]." + }, + { + "type": "equation", + "bbox": [ + 0.101, + 0.8, + 0.47, + 0.818 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {r e g} = \\mathcal {L} _ {k l} \\left(\\boldsymbol {S} _ {I \\rightarrow T}, \\boldsymbol {S} _ {I \\rightarrow T} ^ {g}\\right) + \\mathcal {L} _ {k l} \\left(\\boldsymbol {S} _ {T \\rightarrow I}, \\boldsymbol {S} _ {T \\rightarrow I} ^ {g}\\right), \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.819, + 0.469, + 0.902 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{kl}\\) is the KL-divergence loss with softmax function, \\((S_{I\\rightarrow T})_{ij}^{g} = e^{-\\frac{\\|v_{i} - u_{j}\\|_{2}^{2}}{\\sigma}},(S_{T\\rightarrow I})_{ij}^{g} = e^{-\\frac{\\|u_{i} - v_{j}\\|_{2}^{2}}{\\sigma}}\\) are computed by the global embeddings like Eq. (10). We hope the learned inter-modal relevance relation is close to the semantic relation of global embeddings." + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.091, + 0.882, + 0.251 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.256, + 0.892, + 0.281 + ], + "angle": 0, + "content": "(a) Standalone mechanism. Explore inter-modal and intra-modal relationship between visual and textual embeddings asynchronously." + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.285, + 0.878, + 0.365 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.503, + 0.366, + 0.888, + 0.38 + ], + "angle": 0, + "content": "(b) Fusion mechanism. Explore inter/intra-modal relation synchronously." + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.391, + 0.893, + 0.448 + ], + "angle": 0, + "content": "Figure 4. Two relation interaction mechanisms based on how to explore the inter-modal and intra-modal relations. The connection matrix \\( A \\) and the relevance matrix \\( S \\) are applied to the attention modules wholly or separately, as Eq. (12)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.473, + 0.787, + 0.487 + ], + "angle": 0, + "content": "3.3.2 Relation Interaction Mechanisms" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.497, + 0.892, + 0.572 + ], + "angle": 0, + "content": "After constructing our cross-embedding association graph in Sec. 3.3.1, we design two relation interaction mechanisms to capture the semantic relations between images and texts, where the embeddings are updated by the information interaction process, as shown in Fig. 4." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.573, + 0.893, + 0.722 + ], + "angle": 0, + "content": "Fusion Mechanism. We concatenate the visual and textual embeddings as input, as shown in Fig. 4b. The inter-modal and intra-modal relation interactions are conducted synchronously. The embeddings first pass through the multi-head self-attention module for attention diversity. Besides, we adopt the feed-forward network module for relation reasoning, which a multi-layer perceptron implements [41]. It is similar to the fragment-level interaction modules in Sec. 3.2. We also add the residual connection [15] and layer normalization [2] after them." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.723, + 0.894, + 0.83 + ], + "angle": 0, + "content": "The connection matrix \\( \\mathbf{A} \\) is an attention mask matrix for the attention module, where the zero positions are not allowed to attend while the non-zero positions will be unchanged [41]. The relevance matrix \\( \\mathbf{S} \\) is an extra attention weight matrix as the explicit relation modeling, we use \\( \\lambda \\) to balance \\( \\mathbf{S} \\) with the original attention weight matrix. Therefore, we revise the basic attention formula Eq. (1) to:" + }, + { + "type": "equation", + "bbox": [ + 0.514, + 0.851, + 0.892, + 0.901 + ], + "angle": 0, + "content": "\\[\n\\operatorname {A t t} \\left(\\boldsymbol {Q} \\boldsymbol {K} \\boldsymbol {V}; \\boldsymbol {A}, \\boldsymbol {S}\\right) = \\underset {\\text {s . t . m a s k} (\\boldsymbol {A})} {\\operatorname {s o f t m a x}} \\left(\\frac {\\boldsymbol {Q} \\boldsymbol {K} ^ {T}}{\\sqrt {d _ {k}}} + \\lambda \\boldsymbol {S}\\right) \\boldsymbol {V}, \\tag {12}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15163" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.092, + 0.465, + 0.216 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.227, + 0.468, + 0.268 + ], + "angle": 0, + "content": "Figure 5. The comparison between accuracy and speed for cross-modal retrieval. We perform all methods (Region+BiGRU based) on the whole Flickr30K test-set with one RTX3090 GPU." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.297, + 0.468, + 0.432 + ], + "angle": 0, + "content": "Standalone Mechanism. As shown in Fig. 4a, the visual and textual embeddings are sent into two branches and get relation interaction. The embeddings first get inter-modal relation interaction by the multi-head cross-attention module, where \\( Q \\) and \\( K, V \\) come from two modalities. Then they get intra-modal relation interaction by the multi-head self-attention module, where \\( Q, K, V \\) come from the same modality. Finally, the enhanced embeddings are output by following the feed-forward network module." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.434, + 0.468, + 0.524 + ], + "angle": 0, + "content": "The connection matrix \\( \\mathbf{A} \\) and relevance matrix \\( S \\) first are divided into pre-defined four blocks as Eq. (4), then each block is applied to corresponding modules as Eq. (12). Specifically, the inter-modal relation parts perform on the first cross-attention module, and the intra-modal relation parts act on the second self-attention module." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.526, + 0.468, + 0.572 + ], + "angle": 0, + "content": "After the \\(L\\) layers of the relation interaction mechanism, we get the final relation-enhanced embeddings of two modalities, \\(\\{\\overline{\\pmb{v}}_1,\\dots ,\\overline{\\pmb{v}}_N\\}\\) and \\(\\{\\overline{\\pmb{u}}_1\\dots ,\\overline{\\pmb{u}}_N\\}\\)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.583, + 0.216, + 0.599 + ], + "angle": 0, + "content": "3.4. Optimization" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.607, + 0.468, + 0.713 + ], + "angle": 0, + "content": "Neighbor Batch Sampling. To ensure effective relation interaction in Sec. 3.3, we propose a neighbor sampling to replace random sampling for batches at the later training. We use the k-means clustering [30] on visual embeddings, then randomly choose \\( P \\) clusters and select \\( K \\) images from each cluster, batch size \\( N = P \\times K \\). Finally, we select one positive text for each image to get \\( N \\) image-text pairs." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.714, + 0.468, + 0.766 + ], + "angle": 0, + "content": "Objective Function. We use the triplet loss [10], the similarity score is the cosine similarity between visual embedding \\(\\mathbf{v}\\) and textual embedding \\(\\mathbf{u}\\), \\(s(\\mathbf{v},\\mathbf{u}) = \\frac{\\mathbf{v}^\\top\\mathbf{u}}{\\|\\mathbf{v}\\|\\cdot\\|\\mathbf{u}\\|}\\)." + }, + { + "type": "equation", + "bbox": [ + 0.077, + 0.779, + 0.468, + 0.809 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\left[ \\alpha - s (\\boldsymbol {v}, \\boldsymbol {u}) + s (\\boldsymbol {v}, \\boldsymbol {u} ^ {-}) \\right] _ {+} + \\left[ \\alpha - s (\\boldsymbol {v}, \\boldsymbol {u}) + s (\\boldsymbol {v} ^ {-}, \\boldsymbol {u}) \\right] _ {+}, \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.81, + 0.468, + 0.87 + ], + "angle": 0, + "content": "where \\(\\alpha\\) represents a margin parameter, \\([x]_{+} = \\max (x,0)\\) \\((\\pmb {v},\\pmb {u})\\) is a positive image-text pair, and \\((\\pmb {v},\\pmb{u}^{-}),(v^{-},\\pmb {u})\\) are negative image-text pairs in the batch. We use the distance-weighted sampling [31] for hard negative mining." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.468, + 0.901 + ], + "angle": 0, + "content": "We not only use the relation-enhanced embeddings to compute matching loss as Eq. (13), but also add the ini" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.09, + 0.892, + 0.159 + ], + "angle": 0, + "content": "Table 1. Comparisons of image-text retrieval on MS-COCO 5K test-set. Region represents using region features [1] for images. BiGRU [38] and BERT [6] represent using their word features for texts. \\(E\\) and \\(S\\) indicate embedding-based and score-based methods, respectively. \\* shows the ensemble results of two models." + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.171, + 0.89, + 0.396 + ], + "angle": 0, + "content": "
TypeMethodMS-COCO 5K
IMG → TEXTTEXT → IMGrSum
R@1R@5R@10R@1R@5R@10
Region + BiGRU
SIMRAM2020 [3]53.783.291.039.769.179.8416.5
UARDA*2022 [50]56.283.891.340.669.580.9422.3
NAAF*2022 [51]58.985.292.042.570.981.4430.9
EGPO2021 [4]56.683.691.439.369.981.1421.9
CGMN2022 [5]53.481.389.641.271.982.4419.8
MV2022 [24]56.784.191.440.370.681.6424.6
HREM (Standalone)58.485.592.439.870.581.0427.6
HREM (Fusion)58.985.392.140.070.681.2428.1
HREM (Full)*60.686.492.541.371.982.4435.1
Region + BERT
SSSAMT2021 [11]57.784.290.840.870.580.5424.5
DIME*2021 [36]59.385.491.943.173.083.1435.8
DCPA2022 [40]53.582.490.240.471.082.0419.5
EDSRAN*2021 [45]55.383.590.941.772.782.8426.9
GPO2021 [4]58.385.392.342.472.783.2434.3
VSRN++2022 [21]54.782.990.942.072.282.7425.4
HREM (Standalone)61.887.093.244.073.783.4443.1
HREM (Fusion)62.387.693.443.973.683.3444.1
HREM (Full)*64.088.593.745.475.184.3450.9
" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.424, + 0.892, + 0.469 + ], + "angle": 0, + "content": "tial embeddings for matching loss to keep the embedding consistency, since we need to encode embeddings directly without sample interaction at the inference stage." + }, + { + "type": "equation", + "bbox": [ + 0.508, + 0.499, + 0.891, + 0.516 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {c r o s s}} = \\mathcal {L} (\\overline {{\\boldsymbol {v}}}, \\overline {{\\boldsymbol {u}}}) + \\left[ \\mathcal {L} (\\boldsymbol {v}, \\boldsymbol {u}) + \\mathcal {L} (\\overline {{\\boldsymbol {v}}}, \\boldsymbol {u}) + \\mathcal {L} (\\boldsymbol {v}, \\overline {{\\boldsymbol {u}}}) \\right], \\tag {14}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.53, + 0.891, + 0.561 + ], + "angle": 0, + "content": "Finally, we combine the cross-embedding matching loss Eq. (14) with the relevance regularization loss Eq. (11)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.574, + 0.618, + 0.588 + ], + "angle": 0, + "content": "3.5. Discussion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.598, + 0.892, + 0.734 + ], + "angle": 0, + "content": "Inference Stage. Since we may not have the batch data in the actual application, our framework can encode the cross-modal embeddings without sample interaction at the inference stage. The instance-level relation modeling is only for training. Intuitively, when we train the embedding encoding network and the sample interaction network together with the end-to-end manner and the consistent loss in Sec. 3.4, the encoding network will also be improved with the helpful supervision of embedding interaction." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Time Complexity. Two matching methods have different time complexity in cross-modal retrieval. Given \\(N\\) image-text pairs, separate encoding makes the time complexity of embedding-based methods to be \\(O(2N)\\), while cross-modal interaction makes score-based to be \\(O(N^2)\\). Given one query and the set to be retrieved \\(N\\) samples, the time complexity of query retrieval is \\(O(1)\\) for embedding-based, but is \\(O(N)\\) for score-based. Hence score-based methods usually sacrifice the retrieval speed for the performance boost. However, our method can achieve both highly accurate and efficient retrieval, as shown in Fig. 5." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15164" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.089, + 0.895, + 0.133 + ], + "angle": 0, + "content": "Table 2. Comparisons of image-text retrieval performances on Flickr30K and MS-COCO 1K test-set. Region represents using Faster-RCNN [37] to extract region features [1] for images. BiGRU [38] and BERT [6] represent using them to extract word features for texts. We list the existing state-of-the-art embedding-based image-text matching methods. * indicates the ensemble results of two models." + }, + { + "type": "table", + "bbox": [ + 0.081, + 0.143, + 0.892, + 0.375 + ], + "angle": 0, + "content": "
MethodFlickr30K 1KMS-COCO 1K
IMG → TEXTTEXT → IMGrSumIMG → TEXTTEXT → IMGrSum
R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10
Region + BiGRU
VSRN*2019 [20]71.390.696.054.781.888.2482.676.294.898.262.889.795.1516.8
CVSE2020 [43]73.592.195.852.980.487.8482.474.895.198.359.989.495.2512.7
GPO2021 [4]76.594.297.756.483.489.9498.178.596.098.761.790.395.6520.8
MV2022 [24]79.094.997.759.184.690.6505.878.795.798.762.790.495.7521.9
HREM (Fusion)79.594.397.459.385.191.2506.880.096.098.762.790.195.4522.8
HREM (Full)*81.496.598.560.985.691.3514.381.296.598.963.790.796.0527.1
Region + BERT
CAMERA*2020 [35]78.095.197.960.385.991.7508.977.596.398.863.490.995.8522.7
DSRAN*2021 [45]77.895.197.659.286.091.9507.678.395.798.464.590.895.8523.5
GPO2021 [4]81.795.497.661.485.991.5513.579.796.498.964.891.496.3527.5
VSRN++2022 [21]79.294.697.560.685.691.4508.977.996.098.564.191.096.1523.6
HREM (Fusion)83.396.098.163.587.192.4520.481.196.698.966.191.696.5530.7
HREM (Full)*84.096.198.664.488.093.1524.282.996.999.067.192.096.6534.6
" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.397, + 0.21, + 0.414 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.421, + 0.268, + 0.437 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.445, + 0.47, + 0.581 + ], + "angle": 0, + "content": "Datasets & Metrics. We choose the typical Flickr30K [47] and MS-COCO [26] datasets, where each image is associated with five texts. Flickr30K contains 29,000, 1,000, and 1,014 training, testing, and validation images, respectively. MS-COCO contains 82,738, 5,000, and 5,000 training, testing, and validation images, respectively. The results of MS-COCO are tested on averaging over 5-folds of 1K test images and on the entire 5K test images. Following [4], We evaluate performances by the metric, \\( R@K \\) and \\( rSum \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.581, + 0.47, + 0.703 + ], + "angle": 0, + "content": "Implementation Details. The embedding dimension \\( d = 1024 \\) as previous work [4]. We use the pre-extracted region features [1] for images, and we use the BiGRU [38] with GloVe [34] or BERT-base [6] to extract textual features. The batch size \\( N = 128 \\) for Flickr30K and \\( N = 256 \\) for MS-COCO. The layer of interaction mechanism \\( L = 1 \\), the hyper-parameters as \\( \\beta = 0.8 \\), \\( \\tau = 0.5 \\), \\( \\lambda = 1.5 \\), \\( K = 10 \\), and the margin of triplet loss \\( \\alpha = 0.2 \\)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.711, + 0.442, + 0.728 + ], + "angle": 0, + "content": "4.2. Comparison with State-of-the-art Methods" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.47, + 0.78 + ], + "angle": 0, + "content": "We follow the standard evaluation protocols [51] on two datasets. We first perform two proposed interaction mechanisms individually, then report the ensemble results." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.471, + 0.885 + ], + "angle": 0, + "content": "On Flickr30K. Quantitative results on Flickr30K 1K test-set are shown in Tab. 2, where our proposed method outperforms all state-of-the-art embedding-based imagetext matching methods [20, 21, 24, 35, 45] with impressive margins for the R@K and rSum. Furthermore, our method has the coincident superiority on different textual encoders [6, 38] and still gets the best on the ensemble results." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.886, + 0.471, + 0.9 + ], + "angle": 0, + "content": "On MS-COCO. Performances on MS-COCO 1K and" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.396, + 0.635, + 0.474 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.554, + 0.474, + 0.585, + 0.487 + ], + "angle": 0, + "content": "(a) \\(\\lambda\\)" + }, + { + "type": "image", + "bbox": [ + 0.639, + 0.396, + 0.762, + 0.474 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.679, + 0.474, + 0.715, + 0.487 + ], + "angle": 0, + "content": "(b) \\(K\\)" + }, + { + "type": "image", + "bbox": [ + 0.765, + 0.396, + 0.889, + 0.474 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.817, + 0.474, + 0.847, + 0.487 + ], + "angle": 0, + "content": "(c) \\(\\tau\\)" + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.498, + 0.892, + 0.542 + ], + "angle": 0, + "content": "Figure 6. Effect of hyper-parameters. (a) The weight of relevance matrix \\(\\lambda\\) for Eq. (12), (b) The number of local matching \\(K\\) for Eq. (8), (c) The threshold of neighbor space \\(\\tau\\) for Eq. (5, 6)." + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.557, + 0.695, + 0.647 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.711, + 0.557, + 0.889, + 0.647 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.658, + 0.892, + 0.701 + ], + "angle": 0, + "content": "Figure 7. The curve of triplet loss with of hard negative mining. The loss values are computed by enhanced and initial embeddings, which are the first and second item of Eq. (14), respectively." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.728, + 0.892, + 0.835 + ], + "angle": 0, + "content": "5K test-set are shown in Tab. 1 and Tab. 2. Our method performs best compared with existing state-of-the-art methods [4,35,45] on the 1K test set. Furthermore, on the more extensive database of 5K test set, our method outperforms previous work [21,50,51] with larger performance gaps, both embedding-based and score-based methods, which shows the superiority of our method more convincingly." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.847, + 0.833, + 0.864 + ], + "angle": 0, + "content": "4.3. Ablation Study & Robustness Analysis" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.892, + 0.902 + ], + "angle": 0, + "content": "By default, we perform the experiments on our framework of fusion mechanism with Region+BERT settings." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15165" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.09, + 0.47, + 0.118 + ], + "angle": 0, + "content": "Table 3. Comparison of different module ablation for our framework. \\(\\checkmark\\) means retaining it (or otherwise removing it)." + }, + { + "type": "table_caption", + "bbox": [ + 0.089, + 0.129, + 0.458, + 0.142 + ], + "angle": 0, + "content": "(a) The ablation study of hierarchical relation modeling on Flickr30K" + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.145, + 0.468, + 0.244 + ], + "angle": 0, + "content": "
Fragment-level VisualTextualInstance-levelIMG → TEXTTEXT → IMG
Intra-modalInter-modalR@1R@5R@1TR@5
✓✓81.594.962.386.2
✓✓✓81.895.162.486.2
✓✓✓83.195.963.287.0
✓✓80.194.860.985.3
✓✓✓80.594.761.285.2
✓✓✓82.295.562.686.4
✓✓✓✓83.396.063.587.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.086, + 0.247, + 0.465, + 0.26 + ], + "angle": 0, + "content": "(b) The ablation study of instance-level relation modeling on Flickr30K" + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.263, + 0.468, + 0.375 + ], + "angle": 0, + "content": "
MethodsIMG → TEXTTEXT → IMG
R@1R@5R@1TR@5
w/o connection matrix A81.495.161.586.3
w/o relevance matrix S81.795.361.986.5
w/o consistency Lcross81.695.661.886.7
w/o regularization Lreg82.895.862.986.9
w/o neighbor batch sampling82.895.963.187.0
HREM83.396.063.587.1
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.411, + 0.47, + 0.592 + ], + "angle": 0, + "content": "Hyper-parameters. Fig. 6 shows the effect of three critical parameters, which demonstrates the significance of capturing sample relations and the effectiveness of our proposed modules. \\(\\lambda\\) is the attention coefficient for the relevance matrix, thus it should be large enough to provide extra fine-grained supervision, \\(K\\) is related to the number of words in variable sentences, thus it cannot be too large to adapt short texts. \\(\\tau\\) represents the ratio of connected samples in the batch, it will not be meaningful if close to 0 or 1. Finally, the performances are relatively stable when all parameter values change in a proper range, our method is insensitive to hyper-parameter selection." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.596, + 0.468, + 0.806 + ], + "angle": 0, + "content": "Hierarchical Relation. To better verify the effectiveness of hierarchical relation modeling, we provide the ablation study in Tab. 3a. It shows that both instance-level and fragment-level relation modeling help improve the learning of cross-modal embeddings. First, capturing the instance-level relation, especially the inter-modal relation, is more critical than the intra-modal and fragment-level relation. Besides, capturing fragment-level relations for the textual modality seems redundant. We believe that word features have already learned contextual semantics by the textual encoders, e.g., BiGRU [38] and BERT [6]. However, exploring the semantic relations for region features are meaningful as previous work [20, 21, 46]. Finally, modeling all hierarchical relations is also essential for the optimal result." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Instance-level Relation. Tab. 3b shows the ablation of our instance-level relation modeling. First, the connection and relevance matrices are significant in explicitly capturing the cross-modal relation. And the cross-embedding matching loss \\(\\mathcal{L}_{cross}\\) is indispensable to keep the embedding consistency at the inference stage. Besides, the regularization" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.09, + 0.892, + 0.159 + ], + "angle": 0, + "content": "Table 4. R@1 comparison on hard samples with semantic ambiguity and infrequent samples with semantic scarcity in different proportions, selected by the percentile rank of average embedding similarity between themselves and other samples in the dataset. \\(\\checkmark\\) means training with sample relation modeling." + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.171, + 0.89, + 0.349 + ], + "angle": 0, + "content": "
Samples TypePercentile RankSample InteractionFlickr30KMS-COCO 5K
I → TT → II → TT → I
Hard5%✓74.632.036.48.5
76.034.840.49.2
10%✓72.038.839.212.3
75.040.444.613.6
20%✓74.545.143.618.5
78.047.149.319.5
Infrequent5%✓79.238.450.816.7
84.040.859.219.4
10%✓78.041.253.019.8
81.243.661.022.6
20%✓75.046.654.823.9
82.048.461.026.2
" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.372, + 0.892, + 0.417 + ], + "angle": 0, + "content": "loss \\(\\mathcal{L}_{reg}\\) can ensure the training stability to improve performances. Finally, the neighbor batch sampling adapts to mine more potential connected samples from mini-batch." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.418, + 0.892, + 0.553 + ], + "angle": 0, + "content": "Special Samples. Tab. 4 shows the performance comparison of hard samples with semantic ambiguity and infrequent samples with semantic scarcity in the test set (hard and infrequent samples will get high average similarity with other negative samples in datasets). We find our method can improve these special samples significantly. Fig. 7 shows the curve of triplet loss with hard negative mining, the values of enhanced embeddings are lower than the initial, proving the capacity for recognizing hard samples." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.553, + 0.892, + 0.644 + ], + "angle": 0, + "content": "Retrieval Speed. We show the trade-off between performance and computation for cross-modal retrieval in Fig. 5. Although our method belongs to the embedding-based image-text matching methods [4,20], achieves both high accuracy and efficiency, and is more than 10 times faster than the latest score-based methods [7, 19, 51] on Flickr30K." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.657, + 0.619, + 0.672 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.682, + 0.892, + 0.833 + ], + "angle": 0, + "content": "This paper proposes a novel hierarchical relation modeling framework (HREM) for image-text matching. HREM not only captures fragment-level relations within the single modality and sample, but also effectively exploits instance-level relations across different modalities and samples to learn better holistic embeddings. Based on our design, HREM encodes embedding without interaction on sample or modality at the inference stage, thus achieving high efficiency on cross-modal retrieval. Extensive experiments on two benchmarks show the superiority of our method." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.846, + 0.693, + 0.863 + ], + "angle": 0, + "content": "6. Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.892, + 0.9 + ], + "angle": 0, + "content": "This work is supported by the National Natural Science Foundation of China under Grant 62222212, 62121002." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15166" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.176, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.198 + ], + "angle": 0, + "content": "[1] Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. Bottom-up and top-down attention for image captioning and visual question answering. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6077-6086, 2018. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.2, + 0.471, + 0.226 + ], + "angle": 0, + "content": "[2] Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. ArXiv, abs/1607.06450, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.228, + 0.472, + 0.31 + ], + "angle": 0, + "content": "[3] Hui Chen, Guiguang Ding, Xudong Liu, Zijia Lin, Ji Liu, and Jungong Han. Imram: Iterative matching with recurrent attention memory for cross-modal image-text retrieval. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12652-12660, 2020. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.312, + 0.471, + 0.38 + ], + "angle": 0, + "content": "[4] Jiacheng Chen, Hexiang Hu, Hao Wu, Yuning Jiang, and Chang Lian Wang. Learning the best pooling strategy for visual semantic embedding. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15784-15793, 2021. 1, 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.382, + 0.47, + 0.436 + ], + "angle": 0, + "content": "[5] Yuhao Cheng, Xiaoguang Zhu, Jiuchao Qian, Fei Wen, and Peilin Liu. Cross-modal graph matching network for imagetext retrieval. ACM Trans. Multimedia Comput. Commun. Appl., 18(4), mar 2022. 2, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.438, + 0.47, + 0.492 + ], + "angle": 0, + "content": "[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv, abs/1810.04805, 2019. 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.494, + 0.469, + 0.535 + ], + "angle": 0, + "content": "[7] Haiwen Diao, Ying Zhang, Lingyun Ma, and Huchuan Lu. Similarity reasoning and filtration for image-text matching. ArXiv, abs/2101.01368, 2021. 1, 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.537, + 0.47, + 0.606 + ], + "angle": 0, + "content": "[8] Ismail Elezi, Jenny Seidenschwarz, Laurin Wagner, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, and Laura Leal-Taixe. The group loss: A deeper look into group loss for deep metric learning. IEEE transactions on pattern analysis and machine intelligence, PP, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.607, + 0.47, + 0.648 + ], + "angle": 0, + "content": "[9] Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, and Laura Leal-Taixe. The group loss for deep metric learning. ArXiv, abs/1912.00385, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.649, + 0.469, + 0.69 + ], + "angle": 0, + "content": "[10] Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, and Sanja Fidler. Vse++: Improving visual-semantic embeddings with hard negatives. In BMVC, 2018. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.691, + 0.47, + 0.759 + ], + "angle": 0, + "content": "[11] Zhihao Fan, Zhongyu Wei, Zejun Li, Siyuan Wang, Hajun Shan, Xuanjing Huang, and Jianqing Fan. Constructing phrase-level semantic labels to form multi-grained supervision for image-text retrieval. ArXiv, abs/2109.05523, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.761, + 0.469, + 0.816 + ], + "angle": 0, + "content": "[12] Zheren Fu, Yan Li, Zhendong Mao, Quan Wang, and Yongdong Zhang. Deep metric learning with self-supervised ranking. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.818, + 0.47, + 0.872 + ], + "angle": 0, + "content": "[13] Zheren Fu, Zhendong Mao, Bo Hu, An-An Liu, and Yongdong Zhang. Intra-class adaptive augmentation with neighbor correction for deep metric learning. IEEE Transactions on Multimedia, pages 1-14, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.874, + 0.47, + 0.902 + ], + "angle": 0, + "content": "[14] Zheren Fu, Zhendong Mao, Chenggang Clarence Yan, An an Liu, Hongtao Xie, and Yongdong Zhang. Self-supervised" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.115, + 0.472, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.134 + ], + "angle": 0, + "content": "synthesis ranking for deep metric learning. IEEE Transactions on Circuits and Systems for Video Technology, 32:4736-4750, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.136, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[15] Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.192, + 0.892, + 0.259 + ], + "angle": 0, + "content": "[16] Zhi Hou, Baosheng Yu, and Dacheng Tao. Batchformer: Learning to explore sample relationships for robust representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7256-7266, 2022. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.261, + 0.892, + 0.328 + ], + "angle": 0, + "content": "[17] Mengqi Huang, Zhendong Mao, Penghui Wang, Quang Wang, and Yongdong Zhang. Dse-gan: Dynamic semantic evolution generative adversarial network for text-to-image generation. Proceedings of the 30th ACM International Conference on Multimedia, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.33, + 0.892, + 0.357 + ], + "angle": 0, + "content": "[18] Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak. Self-taught metric learning without labels. CVPR, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.359, + 0.892, + 0.413 + ], + "angle": 0, + "content": "[19] Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu, and Xiaodong He. Stacked cross attention for image-text matching. In Proceedings of the European Conference on Computer Vision (ECCV), pages 201-216, 2018. 1, 2, 4, 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.415, + 0.892, + 0.469 + ], + "angle": 0, + "content": "[20] Kunpeng Li, Yulun Zhang, K. Li, Yuanyuan Li, and Yun Raymond Fu. Visual semantic reasoning for image-text matching. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 4653-4661, 2019. 1, 2, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.471, + 0.892, + 0.537 + ], + "angle": 0, + "content": "[21] Kunpeng Li, Yulun Zhang, Kai Li, Yuanyuan Li, and Yun Raymond Fu. Image-text embedding learning via visual and textual semantic reasoning. IEEE transactions on pattern analysis and machine intelligence, PP, 2022. 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.539, + 0.892, + 0.607 + ], + "angle": 0, + "content": "[22] Pandeng Li, Hongtao Xie, Jiannan Ge, Lei Zhang, Shaobo Min, and Yongdong Zhang. Dual-stream knowledge-preserving hashing for unsupervised video retrieval. In European Conference on Computer Vision, pages 181-197. Springer, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.609, + 0.892, + 0.678 + ], + "angle": 0, + "content": "[23] Shuang Li, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Feng Qiao, and Xinjing Cheng. Metasaug: Meta semantic augmentation for long-tailed visual recognition. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5208-5217, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.679, + 0.892, + 0.718 + ], + "angle": 0, + "content": "[24] Zheng Li, Caili Guo, Zerun Feng, Jenq-Neng Hwang, and Xijun Xue. Multi-view visual semantic embedding. In IJ-CAI, 2022. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.721, + 0.892, + 0.776 + ], + "angle": 0, + "content": "[25] Jongin Lim, Sangdoo Yun, Seulki Park, and Jin Young Choi. Hypergraph-induced semantic tuplet loss for deep metric learning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 212-222, 2022. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.777, + 0.892, + 0.83 + ], + "angle": 0, + "content": "[26] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.832, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[27] Chunxiao Liu, Zhendong Mao, Tianzhu Zhang, Hongtao Xie, Bin Wang, and Yongdong Zhang. Graph structured network for image-text matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10921-10930, 2020. 1" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15167" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "[28] Xin Liu, Yi He, Yiu ming Cheung, Xing Xu, and N. Wang. Learning relationship-enhanced semantic graph for fine-grained image-text matching. IEEE transactions on cybernetics, PP, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.151, + 0.47, + 0.192 + ], + "angle": 0, + "content": "[29] Thang Luong, Hieu Pham, and Christopher D. Manning. Effective approaches to attention-based neural machine translation. ArXiv, abs/1508.04025, 2015. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.194, + 0.469, + 0.235 + ], + "angle": 0, + "content": "[30] J MacQueen. Classification and analysis of multivariate observations. In 5th Berkeley Symp. Math. Statist. Probability, pages 281-297, 1967. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.238, + 0.469, + 0.293 + ], + "angle": 0, + "content": "[31] R. Manmatha, Chaoxia Wu, Alex Smola, and Philipp Krahenbihl. Sampling matters in deep embedding learning. 2017 IEEE International Conference on Computer Vision (ICCV), pages 2859-2867, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.296, + 0.469, + 0.365 + ], + "angle": 0, + "content": "[32] Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari, and Zeynep Akata. Learning graph embeddings for compositional zero-shot learning. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 953-962, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.368, + 0.469, + 0.408 + ], + "angle": 0, + "content": "[33] Xingchao Peng, Zijun Huang, Xineng Sun, and Kate Saenko. Domain agnostic learning with disentangled representations. ArXiv, abs/1904.12347, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.412, + 0.469, + 0.452 + ], + "angle": 0, + "content": "[34] Jeffrey Pennington, Richard Socher, and Christopher D. Manning. Glove: Global vectors for word representation. In EMNLP, 2014. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.455, + 0.469, + 0.51 + ], + "angle": 0, + "content": "[35] Leigang Qu, Meng Liu, Da Cao, Liqiang Nie, and Qi Tian. Context-aware multi-view summarization network for image-text matching. Proceedings of the 28th ACM International Conference on Multimedia, 2020. 1, 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.513, + 0.469, + 0.581 + ], + "angle": 0, + "content": "[36] Leigang Qu, Meng Liu, Jianlong Wu, Zan Gao, and Liqiang Nie. Dynamic modality interaction modeling for image-text retrieval. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.584, + 0.469, + 0.64 + ], + "angle": 0, + "content": "[37] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39:1137-1149, 2015. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.643, + 0.469, + 0.683 + ], + "angle": 0, + "content": "[38] Mike Schuster and Kuldip K. Paliwal. Bidirectional recurrent neural networks. IEEE Trans. Signal Process., 45:2673-2681, 1997. 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.686, + 0.469, + 0.727 + ], + "angle": 0, + "content": "[39] Jenny Seidenschwarz, Ismail Elezi, and Laura Leal-Taix'e. Learning intra-batch connections for deep metric learning. In ICML, 2021. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.73, + 0.469, + 0.798 + ], + "angle": 0, + "content": "[40] Zhan Shi, Tianzhu Zhang, Xiaoyan Wei, Feng Wu, and Yongdong Zhang. Decoupled cross-modal phrase-attention network for image-sentence matching. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, PP, 2022. 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.802, + 0.469, + 0.857 + ], + "angle": 0, + "content": "[41] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[42] Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio', and Yoshua Bengio. Graph attention networks. ArXiv, abs/1710.10903, 2018. 3" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.134 + ], + "angle": 0, + "content": "[43] Haoran Wang, Ying Zhang, Zhong Ji, Yanwei Pang, and Lingyun Ma. Consensus-aware visual-semantic embedding for image-text matching. In ECCV, 2020. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.892, + 0.204 + ], + "angle": 0, + "content": "[44] Sijin Wang, Ruiping Wang, Ziwei Yao, S. Shan, and Xilin Chen. Cross-modal scene graph matching for relationship-aware image-text retrieval. 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1497-1506, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.206, + 0.892, + 0.261 + ], + "angle": 0, + "content": "[45] Keyu Wen, Xiaodong Gu, and Qingrong Cheng. Learning dual semantic relations with graph attention for image-text matching. IEEE Transactions on Circuits and Systems for Video Technology, 31:2866-2879, 2021. 2, 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.263, + 0.892, + 0.317 + ], + "angle": 0, + "content": "[46] Yiling Wu, Shuhui Wang, Guoli Song, and Qingming Huang. Learning fragment self-attention embeddings for image-text matching. Proceedings of the 27th ACM International Conference on Multimedia, 2019. 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.32, + 0.892, + 0.387 + ], + "angle": 0, + "content": "[47] Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. Transactions of the Association for Computational Linguistics, 2:67-78, 2014. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.39, + 0.892, + 0.459 + ], + "angle": 0, + "content": "[48] Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Young Joon Yoo. Cutmix: Regularization strategy to train strong classifiers with localizable features. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 6022-6031, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.461, + 0.892, + 0.5 + ], + "angle": 0, + "content": "[49] Hongyi Zhang, Moustapha Cisse, Yann Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. ArXiv, abs/1710.09412, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.503, + 0.892, + 0.558 + ], + "angle": 0, + "content": "[50] Kun Zhang, Zhendong Mao, Anan Liu, and Yongdong Zhang. Unified adaptive relevance distinguishable attention network for image-text matching. IEEE Transactions on Multimedia, pages 1-1, 2022. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.56, + 0.892, + 0.627 + ], + "angle": 0, + "content": "[51] Kun Zhang, Zhendong Mao, Quan Wang, and Yongdong Zhang. Negative-aware attention framework for image-text matching. 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However, previous studies only focus on capturing fragment-level relation within a sample from a particular modality, e.g., salient regions in an image or text words in a sentence, where they usually pay less attention to capturing instance-level interactions among samples and modalities, e.g., multiple images and texts. In this paper, we argue that sample relations could help learn subtle differences for hard negative instances, and thus transfer shared knowledge for infrequent samples should be promising in obtaining better holistic embeddings. Therefore, we propose a novel hierarchical relation modeling framework (HREM), which explicitly capture both fragment- and instance-level relations to learn discriminative and robust cross-modal embeddings. Extensive experiments on Flickr30K and MS-COCO show our proposed method outperforms the state-of-the-art ones by $4\% -10\%$ in terms of rSum. Our code is available at https://github.com/ CrossmodalGroup/HREM. + +# 1. Introduction + +Image-text matching bridges the semantic gap between visual and textual modalities and is a fundamental task for various multi-modal learning applications, such as cross-modal retrieval [22] and text-to-image synthesis [17]. The critical challenge is accurately and efficiently learning cross-modal embeddings and their similarities for images and texts, to achieve a high-quality semantic alignment. In general, existing image-text matching methods can be classified into two paradigms. The first embedding-based matching [4, 10, 20, 35] separately encodes the whole images and texts into a holistic embedding space, then globally + +![](images/e6a83aa3df497260d8f28612bc27123557c7fe9067d6e3c47d502be5d4994ccc.jpg) +(a) Pipeline of embedding-based method + +![](images/36d78871bb4465a4f45b9822c3c5c83257225f7fc8ba45a5109acbe8b606bd0b.jpg) +(b) Semantic ambiguity + +![](images/83426cd2cd16b2eb6393988ae8260dcb9a4499364c0d86cd074ce0e00f45797a.jpg) +(c) Semantic scarcity +Figure 1. Illustration of our motivation. Sample relation modeling improves the holistic representation of cross-modal learning. Colors and shapes indicate different modalities and image-text pairs, respectively. Orange elements mark effective interactions: (a) The pipeline of the previous and our work, we add the cross-modal relation interaction between samples. (b) For the identical theme of "surfer with surfboard", specific behaviors exist subtle differences, like "hold/squat/ride on the surfboard" and "stare/break/wipe out the wave". Our method distinguishes these hard negative samples from semantic ambiguities. (c) For similar themes under "man play a ball", corresponding behaviors usually are semantic similar, like "play the hockey/cricket/polo" all need to "hit the ball" with "sticks/bats". Our method improves learning embeddings on these infrequent samples with semantic scarcities for themselves. + +measures the semantic similarity of the two modalities. The second score-based matching [3,7,19,27] applies the cross-modal interaction between visual and textual local features, then learns a cumulative similarity score. + +Recently, embedding-based methods have served as the mainstream solution owing to both accuracy and efficiency in image-text matching, which contains two steps as shown in Fig. 1 (a): (1) Capturing the intra-modal relation between visual fragments (e.g., regional features) or textual + +fragments (e.g., word features) independently, then enhancing the semantic representation of local features. (2) Aggregating relation-enhanced local features of two modalities into the holistic embedding space. For the first step, most use the graph neural network [5, 20, 21] or attention module [35, 45, 46] to capture semantic relations and enhance the local features of two modalities, respectively. Some work further exploits the spatial relation [45] for visual regions or grammatical relation [28] for textual words. For the second step, they design pooling functions [4] or sequence encoders [21] to aggregate local features and get holistic embeddings. Existing embedding-based methods follow the principle of separately encoding images and texts by two branches as Fig. 1 (a). In each branch, these methods only focus on the fragment-level relation modeling and local features interaction within one sample, e.g., the region features inside one image (or the word features inside one text). In this way, the instance-level relation modeling and global embeddings interaction among different samples and modalities, e.g., holistic embeddings of multiple images and texts, are entirely overlooked. + +Consequently, existing embedding-based methods directly use global embeddings to compute loss function, $e.g.$ , hard negative triplet loss [10] on random mini-batch, which is insufficient to exploit the manifold structure of holistic embedding space [39]. First, they fail to learn subtle semantic discrepancies among different samples (e.g., similar behaviors with an identical theme as shown in Fig. 1 (b)), then can not distinguish hard negative samples with semantic ambiguities because of the heterogeneity of visual and textual semantics. Second, they are unable to transfer shared knowledge from diverse samples (e.g., different samples that contain similar behaviors with similar themes as shown in Fig. 1 (c)), then can not effectively learn on these infrequent samples with semantic scarcities. Therefore, it is expected that a framework should precisely capture the sample relationship to learn better cross-modal embeddings, while does not break the principle of embedding-based methods, i.e., independently encodes embeddings without modality interaction at the inference stage. + +In doing so, we propose a Hierarchical RElation Modeling framework (HREM) that, for the first time to our knowledge, explicitly captures both fragment-level and instance-level relations to learn holistic embeddings jointly. Therefore, HREM learns not only contextual semantics among intra-modal fragments to enhance local features, but also the associated semantics among inter-modal instances to distinguish hard negative samples and improve learning on infrequent samples. As illustrated in Fig. 1 (a) and Fig. 2, we propose a novel step (i.e., the "stage-three") to exactly capture the semantic relationship of cross-modal samples. First, we propose a novel cross-embedding association graph, which explicitly identifies the connection + +relation and learns the relevance relation between batch samples with fragment-level semantic matching. Next, we propose two relation interaction mechanisms, which explore inter-modal and intra-modal relations synchronously or asynchronously with our improved attention modules to obtain enhanced embeddings. Consequently, HREM only needs to capture the instance-level relation for training, then encode multi-modal embeddings independently at the inference stage, to achieve high accuracy and efficiency for image-text matching. + +To summarize, the major contributions are as follows: (1) We propose a hierarchical relation modeling framework (HREM) for image-text matching. To the best of our knowledge, this is the first work that explicitly captures both fragment-level relations within modality and instance-level relations across modalities. (2) We propose a novel cross-embedding association graph by identifying the connection relation and learning the relevance relation. (3) We propose two relation interaction mechanisms to learn the relation-enhanced embeddings. (4) HREM outperforms all state-of-the-art methods for image-text retrieval on two widely used benchmarks, Flickr30K and MS-COCO, by $4\% - 10\%$ rSum. + +# 2. Related Work + +# 2.1. Image-Text Matching + +According to how the cross-modal interaction is implemented, image-text matching methods are divided into two categories, embedding-based and score-based matching. **Embedding-based.** It independently encodes images and sentences into a holistic embedding space by two branches, where the semantic similarity is calculated by cosine similarity [10]. Existing work usually utilizes the GCN [20, 44] or self-attention layer [45, 46] to capture semantic relations between fragments inside one sample and enhance contextual semantics of local features, then propose a particular aggregator to learn global embeddings. For example, VSRN [20] proposes a semantic reasoning network to learn local visual features with key scene concepts. CVSE [43] proposes a consensus-aware module to integrate commonsense knowledge into local features for two modalities. GPO [4] presents a generalized pooling function to project local features into the global embedding. MV [24] proposes a multi-view encoder to learn multiple embeddings for one image and models intra-class variations. + +Score-based. It conducts fine-grained cross-modal interaction and semantic alignment between local fragments, then calculates a cumulative similarity score. [3, 7, 19, 51]. For example, SCAN [19] proposes an attention mechanism for cross-modal interaction between visual and textual fragments. IMRAM [3] proposes an iterative network for multiple steps of cross-modal interaction. NAAF [51] measures the similarity and dissimilarity degrees via two matching + +![](images/dac8f35c27f3fcf6cfac49c419e7776c070e93f67731a7d941abc2d3a5b7cbc9.jpg) +Figure 2. Overview of our hierarchical relation modeling framework (HREM). Given $N$ image-text pairs $(N = 3$ in this Figure), we first capture the fragment-level relations and learn the relation-enhanced local features for each image or text independently, then aggregate local features by pooling operation to get global embeddings $\{\pmb{v}_i,\pmb{u}_i\}_{i = 1}^N$ . Next, we propose a novel cross-embedding association graph to capture instance-level relations by identifying the connection relation and learning the relevance relation between samples. Finally, we propose two cross-modal relation interaction mechanisms to get relation-enhanced embeddings and compute the final loss function. + +mechanisms to infer the overall similarity jointly. + +Nonetheless, existing embedding-based methods only capture the relation between local fragments within each sample. In addition, we explore the relation across different samples and modalities to learn better holistic embeddings. + +# 2.2. Sample Relationship Learning + +Among data scarcity and diversity, sample relations have been explicitly explored for representation enhancement [23, 49]. A simple way is to generate input as new samples from existing samples [13], such as mix-up [49], and cutmix [48]. Some work designs the delicate objective function for output, such as various metric learning losses [12, 14]. Another way is using the knowledge distillation between samples, such as transferring invariant knowledge for zero-shot learning [32] and domain generalization [33]. Recently, some methods focus on the view of batch interaction [8, 9, 16]. IBC [39] constructs a fully connected graph for mini-batch samples and classifies each sample employing a message passing network. HIST [25] constructs a hypergraph to formulate higher-order relations between samples. However, these methods study the primary vision task, e.g., classification. We capture the sample relationship for cross-modal learning and bridge semantic discrepancies between visual and textual modalities. + +# 3. The Proposed Method + +The overview of HREM is depicted in Fig. 2. We first introduce feature extraction in Sec. 3.1 and fragment-level relation modeling in Sec. 3.2. Then we introduce instance-level relation modeling in Sec. 3.3. Finally, we describe the + +optimization in Sec. 3.4 and discussion in Sec. 3.5. + +# 3.1. Feature Extraction + +Visual Representation. Given an image $I$ , we use the bottom-up-attention network [1] to extract the salient regions by the Faster-RCNN [37] and get the region features by the pre-trained ResNet-101 [15]. Then we add a fully-connect (FC) layer to map each region to a $d$ -dimensional local feature. We denote as $R = \{r_1, \dots, r_{n_r}\} \in \mathbb{R}^{n_r \times d}$ , which is the visual fragments and local features for the image $I$ , $n_r$ is the number of region features. + +Textual Representation. Given a sentence $T$ , we use the sequence models, bi-directional gated recurrent unit (Bi-GRU) [38], or pre-trained BERT [6] to extract the set of word features. We also add an FC layer to keep the same dimension with images. We denote as $C = \{c_1, \dots, c_{n_c}\} \in \mathbb{R}^{n_c \times d}$ , which is the textual fragments and local features for the text, $n_c$ is the number of word features. + +# 3.2. Fragment-level Relation Modeling + +To capture contextual information between fragments and enhance local features of two modalities introduced by Sec. 3.1, we propose fragment-level relation modeling for visual regions and textual words, respectively. + +Visual Regions. We construct a semantic relation graph between visual regions within one image, and propose the relation interaction module to learn the contextual semantics for region features. The graph nodes are region features $R$ , and edges are their semantic relation. Specifically, we use the graph attention network [42], e.g., self-attention layer [41], to capture the semantic relation and learn relation-enhanced local features. It first maps original + +features to queries and key-value pairs, then use a weighted sum of the values as outputs, where the weighting depends on scaled dot-product between queries and keys: + +$$ +A t t \left(\boldsymbol {Q}, \boldsymbol {K}, \boldsymbol {V}\right) = \operatorname {s o f t m a x} \left(\frac {\boldsymbol {Q} \boldsymbol {K} ^ {T}}{\sqrt {d _ {k}}}\right) \boldsymbol {V}, \tag {1} +$$ + +where $Att$ is called the scaled dot-product attention [29], $d_{k}$ is the dimension of input features. In this case, we build up a fully-connected graph, and the semantic relation is implicitly expressed in the attention weight. After initial region features $R = \{r_1,\dots ,r_{n_r}\} \in \mathbb{R}^{n_r\times d}$ pass through the self-attention layer with the feed-forward network to learn contextual information, we obtain the relation-enhanced region features $R^{V} = \{r_{1}^{V},\dots ,r_{n_{r}}^{V}\} \in \mathbb{R}^{n_{r}\times d}$ . + +Finally, we aggregate initial and enhanced region features to get the global visual embedding $\pmb{v} \in \mathbb{R}^d$ , by using the maximum pooling and average pooling, with the parameter $\beta$ to control the ratio of two representations. + +$$ +\boldsymbol {v} = \beta \cdot \operatorname {M a x P o o l} (R) + (1 - \beta) \cdot \operatorname {A v g P o o l} \left(R ^ {V}\right), \tag {2} +$$ + +Textual Words. Similarly, we construct a semantic relation graph between textual words within one sentence, and use the relation interaction module to enhance contextual information for the word features. The nodes are word features $C = \{c_{1},\dots ,c_{n_{c}}\} \in \mathbb{R}^{n_{c}\times d}$ and the edges are their semantic relation. We also use the self-attention layer to implement, hence we get relation-enhanced word features $C^U = \{c_1^U,\dots ,c_{n_c}^U\} \in \mathbb{R}^{n_c\times d}$ , then aggregate them to get the global textual embedding $\pmb {u}\in \mathbb{R}^{d}$ , like Eq. (2). + +$$ +\boldsymbol {u} = \beta \cdot \operatorname {M a x P o o l} (C) + (1 - \beta) \cdot \operatorname {A v g P o o l} \left(C ^ {U}\right), \tag {3} +$$ + +# 3.3. Instance-level Relation Modeling + +We propose the instance-level relation modeling for multiple images and texts to learn better cross-modal embeddings obtained by Sec. 3.2. Given $N$ image-text pairs and their embeddings $\{\pmb{v}_i,\pmb{u}_i\}_{i = 1}^N$ , we propose a novel cross-embedding association graph $\mathcal{G}(\mathcal{V},\mathcal{E})$ , where the nodes are the embeddings $\mathcal{V} = \{\pmb{v}_1,\dots,\pmb{v}_N,\pmb{u}_1\dots,\pmb{u}_N\} \in \mathbb{R}^{2N\times d}$ , the edges $\mathcal{E}$ are pairwise semantic relations. + +# 3.3.1 Cross-Embedding Association Graph + +The critical challenge is how to construct the pairwise relation accurately. Without loss of generality, we divide the relation into two parts: connection and relevance. + +We use the matrix $\mathbf{A} \in \mathbb{R}^{2N \times 2N}$ to represent the connection relation, i.e., whether there exists an associated edge between nodes. We use the matrix $S \in \mathbb{R}^{2N \times 2N}$ to represent the relevance relation, i.e., the degree of semantic association between nodes. Further, we divide these matrices into two patterns and four blocks: intra-modal rela + +tion (Image-to-Image $I\to I$ , Text-to-Text $T\rightarrow T$ ) and intermodal relation (Image-to-Text $I\rightarrow T$ , Text-to-Image $T\rightarrow I$ ), the shape of each block is equal to $\mathbb{R}^{N\times N}$ + +$$ +\boldsymbol {A} = \left[\begin{array}{l l}\boldsymbol {A} _ {I \rightarrow I}&\boldsymbol {A} _ {I \rightarrow T}\\\boldsymbol {A} _ {T \rightarrow I}&\boldsymbol {A} _ {T \rightarrow T}\end{array}\right], \boldsymbol {S} = \left[\begin{array}{l l}\boldsymbol {S} _ {I \rightarrow I}&\boldsymbol {S} _ {I \rightarrow T}\\\boldsymbol {S} _ {T \rightarrow I}&\boldsymbol {S} _ {T \rightarrow T}\end{array}\right], \tag {4} +$$ + +Connection Relation Identifying. The simple idea is to build a fully connected graph like Sec. 3.2 and previous work [5,45] that elements of $A$ are all 1, However, the fullyconnection easily likely leads to over-smoothing and noisy association [39] for nodes. We need to identify the effective connection and filter out unassociated semantics. + +Therefore, we construct the neighbor space of embedding to identify the connection relation. If two embedding nodes are close, their semantic information usually overlaps, they probably have a semantic connection [25]. + +$$ +\begin{array}{l} \left(\boldsymbol {A} _ {I \rightarrow I}\right) _ {i j} = 1 \text {i f} \boldsymbol {v} _ {j} \in \mathcal {N} _ {\text {i n t r a}} \left(\boldsymbol {v} _ {i}\right) \text {e l s e} 0, \\ \left(\boldsymbol {A} _ {T \rightarrow T}\right) _ {i j} = 1 \text {i f} \boldsymbol {u} _ {j} \in \mathcal {N} _ {\text {i n t r a}} \left(\boldsymbol {u} _ {i}\right) \text {e l s e} 0, \\ \end{array} +$$ + +where $\mathcal{N}_{\text {intra }}$ is the neighbor space of embeddings for intramodal connection. We select the first $\tau N$ ( $\tau$ is range from 0 to 1) nearest single-modal samples ranked by the embedding similarity to construct $\mathcal{N}_{\text {intra }}$ in Eq. (5). Correspondingly, we define $\mathcal{N}_{\text {inter }}$ for inter-modal connection: + +$$ +\begin{array}{l} \left(\boldsymbol {A} _ {I \rightarrow T}\right) _ {i j} = 1 \text {i f} \boldsymbol {u} _ {j} \in \mathcal {N} _ {\text {i n t e r}} \left(\boldsymbol {v} _ {i}\right) \text {e l s e} 0, \\ \left(\boldsymbol {A} _ {T \rightarrow I}\right) _ {i j} = 1 \text {i f} \boldsymbol {v} _ {j} \in \mathcal {N} _ {\text {i n t e r}} \left(\boldsymbol {u} _ {i}\right) \text {e l s e} 0, \tag {6} \\ \end{array} +$$ + +Since the heterogeneity between visual and textual semantics, directly using global embeddings is insufficient to identify the inter-modal connection relation. Following score-based methods [19, 40], we use the fine-grained matching of fragments and local features to measure the inter-modal neighbor space. As shown in Fig. 3, given an image-text pair and its region-word similarity matrix. We first pick up the most matching textual word (or visual region) for each region (or each word), then average these matched scores to express the overall matching values $p$ . + +$$ +\begin{array}{l} p _ {I \rightarrow T} = \frac {1}{n _ {r}} \sum_ {\substack {m = 1\\n}} ^ {n _ {r}} \max _ {n \in [ 1, n _ {c} ]} \left(\boldsymbol {r} _ {m} ^ {\mathrm {T}} \boldsymbol {c} _ {n}\right), \tag{7} \\ p _ {T \rightarrow I} = \frac {1}{n _ {c}} \sum_ {m = 1} ^ {n _ {c}} \max _ {n \in [ 1, n _ {r} ]} \left(\boldsymbol {c} _ {m} ^ {\mathrm {T}} \boldsymbol {r} _ {n}\right), \\ \end{array} +$$ + +where we select the first $\tau N$ nearest cross-modal samples ranked by the fragment-level matching values Eq. (7) to construct the inter-modal neighbor space $\mathcal{N}_{inter}$ in Eq (6). + +Relevance Relation Learning. The relevance relation is the degree of semantic association between two connected nodes. Existing work [16, 39] uses the global embedding similarity to approximate, which is insufficient to bridge the + +![](images/14b908b03158b1f247a87bcbfda75507562bdb66ae08668c35a6e93deaa7a8ac.jpg) +Figure 3. Graph construction of inter-modal relation with fragment-level matching. For every image-text pair, we use corresponding modules to get connection relation $a_{I \to T} (a_{T \to I})$ and relevance relation $s_{I \to T} (s_{T \to I})$ , which are elements of connection matrix $A_{I \to T} (A_{T \to I})$ and relevance matrix $S_{I \to T} (S_{T \to I})$ . + +semantic discrepancy between visual and textual space [19]. In contrast, we explicitly learn the cross-modal relevance relation. As shown in Fig. 3, we employ fine-grained matching like Eq. (7). After picking up the max matching of row-wise and column-wise, we select the top- $K$ scores and concatenate them as the matching vectors $\mathbf{q}$ . + +$$ +\begin{array}{l} \boldsymbol {q} _ {I \rightarrow T} = T o p K (\{\max _ {n \in [ 1, n _ {c} ]} \left(\boldsymbol {r} _ {m} ^ {\mathrm {T}} \boldsymbol {c} _ {n}\right) \} _ {m = 1} ^ {n _ {r}}), \\ \boldsymbol {q} _ {T \rightarrow I} = \operatorname {T o p K} \left(\left. \left\{\max _ {n \in [ 1, n _ {r} ]} \left(\boldsymbol {c} _ {m} ^ {\mathrm {T}} \boldsymbol {r} _ {n}\right) \right\} _ {m = 1} ^ {n _ {c}}\right), \tag {8} \\ \end{array} +$$ + +where $\pmb{q} \in \mathbb{R}^{K}$ , then we use an MLP and add the overall matching values to learn the inter-modal relevance (scalar). + +$$ +\begin{array}{l} s _ {I \rightarrow T} = M L P \left(\boldsymbol {q} _ {I \rightarrow T}\right) + p _ {I \rightarrow T}, \\ s _ {T \rightarrow I} = M L P \left(\boldsymbol {q} _ {T \rightarrow I}\right) + p _ {T \rightarrow I}, \tag {9} \\ \end{array} +$$ + +For different image-text pairs, we compute the corresponding relevance values, which are the elements of the inter-modal relevance matrix $S_{I\rightarrow T}$ and $S_{T\rightarrow I}$ . As for the intra-modal relevance relation, we can use the global embeddings to compute the relevance matrix. + +$$ +\left(\boldsymbol {S} _ {T \rightarrow T}\right) _ {i j} = e ^ {- \frac {\left\| \boldsymbol {v} _ {i} - \boldsymbol {v} _ {j} \right\| _ {2} ^ {2}}{\sigma}}, \left(\boldsymbol {S} _ {I \rightarrow I}\right) _ {i j} = e ^ {- \frac {\left\| \boldsymbol {u} _ {i} - \boldsymbol {u} _ {j} \right\| _ {2} ^ {2}}{\sigma}}, \tag {10} +$$ + +where $\sigma$ is a positive scalar to control the relevance values ( $\sigma = 1$ for simplicity). Besides, we add a relevance regularization loss to ensure the learned inter-modal relevance matrix does not crash during training [18]. + +$$ +\mathcal {L} _ {r e g} = \mathcal {L} _ {k l} \left(\boldsymbol {S} _ {I \rightarrow T}, \boldsymbol {S} _ {I \rightarrow T} ^ {g}\right) + \mathcal {L} _ {k l} \left(\boldsymbol {S} _ {T \rightarrow I}, \boldsymbol {S} _ {T \rightarrow I} ^ {g}\right), \tag {11} +$$ + +where $\mathcal{L}_{kl}$ is the KL-divergence loss with softmax function, $(S_{I\rightarrow T})_{ij}^{g} = e^{-\frac{\|v_{i} - u_{j}\|_{2}^{2}}{\sigma}},(S_{T\rightarrow I})_{ij}^{g} = e^{-\frac{\|u_{i} - v_{j}\|_{2}^{2}}{\sigma}}$ are computed by the global embeddings like Eq. (10). We hope the learned inter-modal relevance relation is close to the semantic relation of global embeddings. + +![](images/5efd157389196476d3990f0556f0bbfc5cf58a6ac621dea7130f703f88487160.jpg) +(a) Standalone mechanism. Explore inter-modal and intra-modal relationship between visual and textual embeddings asynchronously. + +![](images/dd4f045719afe396d7496afcff522dbaf42ec0dc4761b831a1a0ddc3e627c199.jpg) +(b) Fusion mechanism. Explore inter/intra-modal relation synchronously. +Figure 4. Two relation interaction mechanisms based on how to explore the inter-modal and intra-modal relations. The connection matrix $A$ and the relevance matrix $S$ are applied to the attention modules wholly or separately, as Eq. (12). + +# 3.3.2 Relation Interaction Mechanisms + +After constructing our cross-embedding association graph in Sec. 3.3.1, we design two relation interaction mechanisms to capture the semantic relations between images and texts, where the embeddings are updated by the information interaction process, as shown in Fig. 4. + +Fusion Mechanism. We concatenate the visual and textual embeddings as input, as shown in Fig. 4b. The inter-modal and intra-modal relation interactions are conducted synchronously. The embeddings first pass through the multi-head self-attention module for attention diversity. Besides, we adopt the feed-forward network module for relation reasoning, which a multi-layer perceptron implements [41]. It is similar to the fragment-level interaction modules in Sec. 3.2. We also add the residual connection [15] and layer normalization [2] after them. + +The connection matrix $\mathbf{A}$ is an attention mask matrix for the attention module, where the zero positions are not allowed to attend while the non-zero positions will be unchanged [41]. The relevance matrix $\mathbf{S}$ is an extra attention weight matrix as the explicit relation modeling, we use $\lambda$ to balance $\mathbf{S}$ with the original attention weight matrix. Therefore, we revise the basic attention formula Eq. (1) to: + +$$ +\operatorname {A t t} \left(\boldsymbol {Q} \boldsymbol {K} \boldsymbol {V}; \boldsymbol {A}, \boldsymbol {S}\right) = \underset {\text {s . t . m a s k} (\boldsymbol {A})} {\operatorname {s o f t m a x}} \left(\frac {\boldsymbol {Q} \boldsymbol {K} ^ {T}}{\sqrt {d _ {k}}} + \lambda \boldsymbol {S}\right) \boldsymbol {V}, \tag {12} +$$ + +![](images/649065e98d19144a8ed3d556e7e875ceb85e86ddc5de0bf49624a4a6d88e90e6.jpg) +Figure 5. The comparison between accuracy and speed for cross-modal retrieval. We perform all methods (Region+BiGRU based) on the whole Flickr30K test-set with one RTX3090 GPU. + +Standalone Mechanism. As shown in Fig. 4a, the visual and textual embeddings are sent into two branches and get relation interaction. The embeddings first get inter-modal relation interaction by the multi-head cross-attention module, where $Q$ and $K, V$ come from two modalities. Then they get intra-modal relation interaction by the multi-head self-attention module, where $Q, K, V$ come from the same modality. Finally, the enhanced embeddings are output by following the feed-forward network module. + +The connection matrix $\mathbf{A}$ and relevance matrix $S$ first are divided into pre-defined four blocks as Eq. (4), then each block is applied to corresponding modules as Eq. (12). Specifically, the inter-modal relation parts perform on the first cross-attention module, and the intra-modal relation parts act on the second self-attention module. + +After the $L$ layers of the relation interaction mechanism, we get the final relation-enhanced embeddings of two modalities, $\{\overline{\pmb{v}}_1,\dots ,\overline{\pmb{v}}_N\}$ and $\{\overline{\pmb{u}}_1\dots ,\overline{\pmb{u}}_N\}$ . + +# 3.4. Optimization + +Neighbor Batch Sampling. To ensure effective relation interaction in Sec. 3.3, we propose a neighbor sampling to replace random sampling for batches at the later training. We use the k-means clustering [30] on visual embeddings, then randomly choose $P$ clusters and select $K$ images from each cluster, batch size $N = P \times K$ . Finally, we select one positive text for each image to get $N$ image-text pairs. + +Objective Function. We use the triplet loss [10], the similarity score is the cosine similarity between visual embedding $\mathbf{v}$ and textual embedding $\mathbf{u}$ , $s(\mathbf{v},\mathbf{u}) = \frac{\mathbf{v}^\top\mathbf{u}}{\|\mathbf{v}\|\cdot\|\mathbf{u}\|}$ . + +$$ +\mathcal {L} = \left[ \alpha - s (\boldsymbol {v}, \boldsymbol {u}) + s (\boldsymbol {v}, \boldsymbol {u} ^ {-}) \right] _ {+} + \left[ \alpha - s (\boldsymbol {v}, \boldsymbol {u}) + s (\boldsymbol {v} ^ {-}, \boldsymbol {u}) \right] _ {+}, \tag {13} +$$ + +where $\alpha$ represents a margin parameter, $[x]_{+} = \max (x,0)$ $(\pmb {v},\pmb {u})$ is a positive image-text pair, and $(\pmb {v},\pmb{u}^{-}),(v^{-},\pmb {u})$ are negative image-text pairs in the batch. We use the distance-weighted sampling [31] for hard negative mining. + +We not only use the relation-enhanced embeddings to compute matching loss as Eq. (13), but also add the ini + +Table 1. Comparisons of image-text retrieval on MS-COCO 5K test-set. Region represents using region features [1] for images. BiGRU [38] and BERT [6] represent using their word features for texts. $E$ and $S$ indicate embedding-based and score-based methods, respectively. \* shows the ensemble results of two models. + +
TypeMethodMS-COCO 5K
IMG → TEXTTEXT → IMGrSum
R@1R@5R@10R@1R@5R@10
Region + BiGRU
SIMRAM2020 [3]53.783.291.039.769.179.8416.5
UARDA*2022 [50]56.283.891.340.669.580.9422.3
NAAF*2022 [51]58.985.292.042.570.981.4430.9
EGPO2021 [4]56.683.691.439.369.981.1421.9
CGMN2022 [5]53.481.389.641.271.982.4419.8
MV2022 [24]56.784.191.440.370.681.6424.6
HREM (Standalone)58.485.592.439.870.581.0427.6
HREM (Fusion)58.985.392.140.070.681.2428.1
HREM (Full)*60.686.492.541.371.982.4435.1
Region + BERT
SSSAMT2021 [11]57.784.290.840.870.580.5424.5
DIME*2021 [36]59.385.491.943.173.083.1435.8
DCPA2022 [40]53.582.490.240.471.082.0419.5
EDSRAN*2021 [45]55.383.590.941.772.782.8426.9
GPO2021 [4]58.385.392.342.472.783.2434.3
VSRN++2022 [21]54.782.990.942.072.282.7425.4
HREM (Standalone)61.887.093.244.073.783.4443.1
HREM (Fusion)62.387.693.443.973.683.3444.1
HREM (Full)*64.088.593.745.475.184.3450.9
+ +tial embeddings for matching loss to keep the embedding consistency, since we need to encode embeddings directly without sample interaction at the inference stage. + +$$ +\mathcal {L} _ {\text {c r o s s}} = \mathcal {L} (\overline {{\boldsymbol {v}}}, \overline {{\boldsymbol {u}}}) + \left[ \mathcal {L} (\boldsymbol {v}, \boldsymbol {u}) + \mathcal {L} (\overline {{\boldsymbol {v}}}, \boldsymbol {u}) + \mathcal {L} (\boldsymbol {v}, \overline {{\boldsymbol {u}}}) \right], \tag {14} +$$ + +Finally, we combine the cross-embedding matching loss Eq. (14) with the relevance regularization loss Eq. (11). + +# 3.5. Discussion + +Inference Stage. Since we may not have the batch data in the actual application, our framework can encode the cross-modal embeddings without sample interaction at the inference stage. The instance-level relation modeling is only for training. Intuitively, when we train the embedding encoding network and the sample interaction network together with the end-to-end manner and the consistent loss in Sec. 3.4, the encoding network will also be improved with the helpful supervision of embedding interaction. + +Time Complexity. Two matching methods have different time complexity in cross-modal retrieval. Given $N$ image-text pairs, separate encoding makes the time complexity of embedding-based methods to be $O(2N)$ , while cross-modal interaction makes score-based to be $O(N^2)$ . Given one query and the set to be retrieved $N$ samples, the time complexity of query retrieval is $O(1)$ for embedding-based, but is $O(N)$ for score-based. Hence score-based methods usually sacrifice the retrieval speed for the performance boost. However, our method can achieve both highly accurate and efficient retrieval, as shown in Fig. 5. + +Table 2. Comparisons of image-text retrieval performances on Flickr30K and MS-COCO 1K test-set. Region represents using Faster-RCNN [37] to extract region features [1] for images. BiGRU [38] and BERT [6] represent using them to extract word features for texts. We list the existing state-of-the-art embedding-based image-text matching methods. * indicates the ensemble results of two models. + +
MethodFlickr30K 1KMS-COCO 1K
IMG → TEXTTEXT → IMGrSumIMG → TEXTTEXT → IMGrSum
R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10
Region + BiGRU
VSRN*2019 [20]71.390.696.054.781.888.2482.676.294.898.262.889.795.1516.8
CVSE2020 [43]73.592.195.852.980.487.8482.474.895.198.359.989.495.2512.7
GPO2021 [4]76.594.297.756.483.489.9498.178.596.098.761.790.395.6520.8
MV2022 [24]79.094.997.759.184.690.6505.878.795.798.762.790.495.7521.9
HREM (Fusion)79.594.397.459.385.191.2506.880.096.098.762.790.195.4522.8
HREM (Full)*81.496.598.560.985.691.3514.381.296.598.963.790.796.0527.1
Region + BERT
CAMERA*2020 [35]78.095.197.960.385.991.7508.977.596.398.863.490.995.8522.7
DSRAN*2021 [45]77.895.197.659.286.091.9507.678.395.798.464.590.895.8523.5
GPO2021 [4]81.795.497.661.485.991.5513.579.796.498.964.891.496.3527.5
VSRN++2022 [21]79.294.697.560.685.691.4508.977.996.098.564.191.096.1523.6
HREM (Fusion)83.396.098.163.587.192.4520.481.196.698.966.191.696.5530.7
HREM (Full)*84.096.198.664.488.093.1524.282.996.999.067.192.096.6534.6
+ +# 4. Experiments + +# 4.1. Experimental Setup + +Datasets & Metrics. We choose the typical Flickr30K [47] and MS-COCO [26] datasets, where each image is associated with five texts. Flickr30K contains 29,000, 1,000, and 1,014 training, testing, and validation images, respectively. MS-COCO contains 82,738, 5,000, and 5,000 training, testing, and validation images, respectively. The results of MS-COCO are tested on averaging over 5-folds of 1K test images and on the entire 5K test images. Following [4], We evaluate performances by the metric, $R@K$ and $rSum$ . + +Implementation Details. The embedding dimension $d = 1024$ as previous work [4]. We use the pre-extracted region features [1] for images, and we use the BiGRU [38] with GloVe [34] or BERT-base [6] to extract textual features. The batch size $N = 128$ for Flickr30K and $N = 256$ for MS-COCO. The layer of interaction mechanism $L = 1$ , the hyper-parameters as $\beta = 0.8$ , $\tau = 0.5$ , $\lambda = 1.5$ , $K = 10$ , and the margin of triplet loss $\alpha = 0.2$ . + +# 4.2. Comparison with State-of-the-art Methods + +We follow the standard evaluation protocols [51] on two datasets. We first perform two proposed interaction mechanisms individually, then report the ensemble results. + +On Flickr30K. Quantitative results on Flickr30K 1K test-set are shown in Tab. 2, where our proposed method outperforms all state-of-the-art embedding-based imagetext matching methods [20, 21, 24, 35, 45] with impressive margins for the R@K and rSum. Furthermore, our method has the coincident superiority on different textual encoders [6, 38] and still gets the best on the ensemble results. + +On MS-COCO. Performances on MS-COCO 1K and + +![](images/ecf9dace5631dd2475e4bf13f020bd28f1431f64470ccd2109b7b606ca9f1703.jpg) +(a) $\lambda$ + +![](images/c80d01a5cc187d9c768679899aec530cde95d6e5681bbe7bb32f706d61abc896.jpg) +(b) $K$ + +![](images/afb59d42c1da43919b964d82eaa10f87f43fe510cde8bb88475478b061102d10.jpg) +(c) $\tau$ + +![](images/26de47d68ee660acb49b6183a9d33c84463bcd10984d4c378b9ba23173e67723.jpg) +Figure 6. Effect of hyper-parameters. (a) The weight of relevance matrix $\lambda$ for Eq. (12), (b) The number of local matching $K$ for Eq. (8), (c) The threshold of neighbor space $\tau$ for Eq. (5, 6). +Figure 7. The curve of triplet loss with of hard negative mining. The loss values are computed by enhanced and initial embeddings, which are the first and second item of Eq. (14), respectively. + +![](images/c3457c8d9d4064a82cd7d3916966f118d74a31124d5f35afd1a1c40e59262d26.jpg) + +5K test-set are shown in Tab. 1 and Tab. 2. Our method performs best compared with existing state-of-the-art methods [4,35,45] on the 1K test set. Furthermore, on the more extensive database of 5K test set, our method outperforms previous work [21,50,51] with larger performance gaps, both embedding-based and score-based methods, which shows the superiority of our method more convincingly. + +# 4.3. Ablation Study & Robustness Analysis + +By default, we perform the experiments on our framework of fusion mechanism with Region+BERT settings. + +Table 3. Comparison of different module ablation for our framework. $\checkmark$ means retaining it (or otherwise removing it). +(a) The ablation study of hierarchical relation modeling on Flickr30K + +
Fragment-level VisualTextualInstance-levelIMG → TEXTTEXT → IMG
Intra-modalInter-modalR@1R@5R@1TR@5
✓✓81.594.962.386.2
✓✓✓81.895.162.486.2
✓✓✓83.195.963.287.0
✓✓80.194.860.985.3
✓✓✓80.594.761.285.2
✓✓✓82.295.562.686.4
✓✓✓✓83.396.063.587.1
+ +(b) The ablation study of instance-level relation modeling on Flickr30K + +
MethodsIMG → TEXTTEXT → IMG
R@1R@5R@1TR@5
w/o connection matrix A81.495.161.586.3
w/o relevance matrix S81.795.361.986.5
w/o consistency Lcross81.695.661.886.7
w/o regularization Lreg82.895.862.986.9
w/o neighbor batch sampling82.895.963.187.0
HREM83.396.063.587.1
+ +Hyper-parameters. Fig. 6 shows the effect of three critical parameters, which demonstrates the significance of capturing sample relations and the effectiveness of our proposed modules. $\lambda$ is the attention coefficient for the relevance matrix, thus it should be large enough to provide extra fine-grained supervision, $K$ is related to the number of words in variable sentences, thus it cannot be too large to adapt short texts. $\tau$ represents the ratio of connected samples in the batch, it will not be meaningful if close to 0 or 1. Finally, the performances are relatively stable when all parameter values change in a proper range, our method is insensitive to hyper-parameter selection. + +Hierarchical Relation. To better verify the effectiveness of hierarchical relation modeling, we provide the ablation study in Tab. 3a. It shows that both instance-level and fragment-level relation modeling help improve the learning of cross-modal embeddings. First, capturing the instance-level relation, especially the inter-modal relation, is more critical than the intra-modal and fragment-level relation. Besides, capturing fragment-level relations for the textual modality seems redundant. We believe that word features have already learned contextual semantics by the textual encoders, e.g., BiGRU [38] and BERT [6]. However, exploring the semantic relations for region features are meaningful as previous work [20, 21, 46]. Finally, modeling all hierarchical relations is also essential for the optimal result. + +Instance-level Relation. Tab. 3b shows the ablation of our instance-level relation modeling. First, the connection and relevance matrices are significant in explicitly capturing the cross-modal relation. And the cross-embedding matching loss $\mathcal{L}_{cross}$ is indispensable to keep the embedding consistency at the inference stage. Besides, the regularization + +Table 4. R@1 comparison on hard samples with semantic ambiguity and infrequent samples with semantic scarcity in different proportions, selected by the percentile rank of average embedding similarity between themselves and other samples in the dataset. $\checkmark$ means training with sample relation modeling. + +
Samples TypePercentile RankSample InteractionFlickr30KMS-COCO 5K
I → TT → II → TT → I
Hard5%✓74.632.036.48.5
76.034.840.49.2
10%✓72.038.839.212.3
75.040.444.613.6
20%✓74.545.143.618.5
78.047.149.319.5
Infrequent5%✓79.238.450.816.7
84.040.859.219.4
10%✓78.041.253.019.8
81.243.661.022.6
20%✓75.046.654.823.9
82.048.461.026.2
+ +loss $\mathcal{L}_{reg}$ can ensure the training stability to improve performances. Finally, the neighbor batch sampling adapts to mine more potential connected samples from mini-batch. + +Special Samples. Tab. 4 shows the performance comparison of hard samples with semantic ambiguity and infrequent samples with semantic scarcity in the test set (hard and infrequent samples will get high average similarity with other negative samples in datasets). We find our method can improve these special samples significantly. Fig. 7 shows the curve of triplet loss with hard negative mining, the values of enhanced embeddings are lower than the initial, proving the capacity for recognizing hard samples. + +Retrieval Speed. We show the trade-off between performance and computation for cross-modal retrieval in Fig. 5. Although our method belongs to the embedding-based image-text matching methods [4,20], achieves both high accuracy and efficiency, and is more than 10 times faster than the latest score-based methods [7, 19, 51] on Flickr30K. + +# 5. Conclusion + +This paper proposes a novel hierarchical relation modeling framework (HREM) for image-text matching. HREM not only captures fragment-level relations within the single modality and sample, but also effectively exploits instance-level relations across different modalities and samples to learn better holistic embeddings. Based on our design, HREM encodes embedding without interaction on sample or modality at the inference stage, thus achieving high efficiency on cross-modal retrieval. Extensive experiments on two benchmarks show the superiority of our method. + +# 6. Acknowledgements + +This work is supported by the National Natural Science Foundation of China under Grant 62222212, 62121002. + +# References + +[1] Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. Bottom-up and top-down attention for image captioning and visual question answering. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6077-6086, 2018. 3, 6, 7 +[2] Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. ArXiv, abs/1607.06450, 2016. 5 +[3] Hui Chen, Guiguang Ding, Xudong Liu, Zijia Lin, Ji Liu, and Jungong Han. Imram: Iterative matching with recurrent attention memory for cross-modal image-text retrieval. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12652-12660, 2020. 1, 2, 6 +[4] Jiacheng Chen, Hexiang Hu, Hao Wu, Yuning Jiang, and Chang Lian Wang. Learning the best pooling strategy for visual semantic embedding. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15784-15793, 2021. 1, 2, 6, 7, 8 +[5] Yuhao Cheng, Xiaoguang Zhu, Jiuchao Qian, Fei Wen, and Peilin Liu. Cross-modal graph matching network for imagetext retrieval. ACM Trans. Multimedia Comput. Commun. Appl., 18(4), mar 2022. 2, 4, 6 +[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv, abs/1810.04805, 2019. 3, 6, 7, 8 +[7] Haiwen Diao, Ying Zhang, Lingyun Ma, and Huchuan Lu. Similarity reasoning and filtration for image-text matching. ArXiv, abs/2101.01368, 2021. 1, 2, 8 +[8] Ismail Elezi, Jenny Seidenschwarz, Laurin Wagner, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, and Laura Leal-Taixe. The group loss: A deeper look into group loss for deep metric learning. IEEE transactions on pattern analysis and machine intelligence, PP, 2022. 3 +[9] Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, and Laura Leal-Taixe. The group loss for deep metric learning. ArXiv, abs/1912.00385, 2020. 3 +[10] Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, and Sanja Fidler. Vse++: Improving visual-semantic embeddings with hard negatives. In BMVC, 2018. 1, 2, 6 +[11] Zhihao Fan, Zhongyu Wei, Zejun Li, Siyuan Wang, Hajun Shan, Xuanjing Huang, and Jianqing Fan. Constructing phrase-level semantic labels to form multi-grained supervision for image-text retrieval. ArXiv, abs/2109.05523, 2021. 6 +[12] Zheren Fu, Yan Li, Zhendong Mao, Quan Wang, and Yongdong Zhang. Deep metric learning with self-supervised ranking. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. 3 +[13] Zheren Fu, Zhendong Mao, Bo Hu, An-An Liu, and Yongdong Zhang. Intra-class adaptive augmentation with neighbor correction for deep metric learning. IEEE Transactions on Multimedia, pages 1-14, 2022. 3 +[14] Zheren Fu, Zhendong Mao, Chenggang Clarence Yan, An an Liu, Hongtao Xie, and Yongdong Zhang. Self-supervised + +synthesis ranking for deep metric learning. IEEE Transactions on Circuits and Systems for Video Technology, 32:4736-4750, 2022. 3 +[15] Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016. 3, 5 +[16] Zhi Hou, Baosheng Yu, and Dacheng Tao. Batchformer: Learning to explore sample relationships for robust representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7256-7266, 2022. 3, 4 +[17] Mengqi Huang, Zhendong Mao, Penghui Wang, Quang Wang, and Yongdong Zhang. Dse-gan: Dynamic semantic evolution generative adversarial network for text-to-image generation. Proceedings of the 30th ACM International Conference on Multimedia, 2022. 1 +[18] Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak. Self-taught metric learning without labels. CVPR, 2022. 5 +[19] Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu, and Xiaodong He. Stacked cross attention for image-text matching. In Proceedings of the European Conference on Computer Vision (ECCV), pages 201-216, 2018. 1, 2, 4, 5, 8 +[20] Kunpeng Li, Yulun Zhang, K. Li, Yuanyuan Li, and Yun Raymond Fu. Visual semantic reasoning for image-text matching. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 4653-4661, 2019. 1, 2, 7, 8 +[21] Kunpeng Li, Yulun Zhang, Kai Li, Yuanyuan Li, and Yun Raymond Fu. Image-text embedding learning via visual and textual semantic reasoning. IEEE transactions on pattern analysis and machine intelligence, PP, 2022. 2, 6, 7, 8 +[22] Pandeng Li, Hongtao Xie, Jiannan Ge, Lei Zhang, Shaobo Min, and Yongdong Zhang. Dual-stream knowledge-preserving hashing for unsupervised video retrieval. In European Conference on Computer Vision, pages 181-197. Springer, 2022. 1 +[23] Shuang Li, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Feng Qiao, and Xinjing Cheng. Metasaug: Meta semantic augmentation for long-tailed visual recognition. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5208-5217, 2021. 3 +[24] Zheng Li, Caili Guo, Zerun Feng, Jenq-Neng Hwang, and Xijun Xue. Multi-view visual semantic embedding. In IJ-CAI, 2022. 2, 6, 7 +[25] Jongin Lim, Sangdoo Yun, Seulki Park, and Jin Young Choi. Hypergraph-induced semantic tuplet loss for deep metric learning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 212-222, 2022. 3, 4 +[26] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 7 +[27] Chunxiao Liu, Zhendong Mao, Tianzhu Zhang, Hongtao Xie, Bin Wang, and Yongdong Zhang. Graph structured network for image-text matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10921-10930, 2020. 1 + +[28] Xin Liu, Yi He, Yiu ming Cheung, Xing Xu, and N. Wang. Learning relationship-enhanced semantic graph for fine-grained image-text matching. IEEE transactions on cybernetics, PP, 2022. 2 +[29] Thang Luong, Hieu Pham, and Christopher D. Manning. Effective approaches to attention-based neural machine translation. ArXiv, abs/1508.04025, 2015. 4 +[30] J MacQueen. Classification and analysis of multivariate observations. In 5th Berkeley Symp. Math. Statist. Probability, pages 281-297, 1967. 6 +[31] R. Manmatha, Chaoxia Wu, Alex Smola, and Philipp Krahenbihl. Sampling matters in deep embedding learning. 2017 IEEE International Conference on Computer Vision (ICCV), pages 2859-2867, 2017. 6 +[32] Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari, and Zeynep Akata. Learning graph embeddings for compositional zero-shot learning. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 953-962, 2021. 3 +[33] Xingchao Peng, Zijun Huang, Xineng Sun, and Kate Saenko. Domain agnostic learning with disentangled representations. ArXiv, abs/1904.12347, 2019. 3 +[34] Jeffrey Pennington, Richard Socher, and Christopher D. Manning. Glove: Global vectors for word representation. In EMNLP, 2014. 7 +[35] Leigang Qu, Meng Liu, Da Cao, Liqiang Nie, and Qi Tian. Context-aware multi-view summarization network for image-text matching. Proceedings of the 28th ACM International Conference on Multimedia, 2020. 1, 2, 7 +[36] Leigang Qu, Meng Liu, Jianlong Wu, Zan Gao, and Liqiang Nie. Dynamic modality interaction modeling for image-text retrieval. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021. 6 +[37] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39:1137-1149, 2015. 3, 7 +[38] Mike Schuster and Kuldip K. Paliwal. Bidirectional recurrent neural networks. IEEE Trans. Signal Process., 45:2673-2681, 1997. 3, 6, 7, 8 +[39] Jenny Seidenschwarz, Ismail Elezi, and Laura Leal-Taix'e. Learning intra-batch connections for deep metric learning. In ICML, 2021. 2, 3, 4 +[40] Zhan Shi, Tianzhu Zhang, Xiaoyan Wei, Feng Wu, and Yongdong Zhang. Decoupled cross-modal phrase-attention network for image-sentence matching. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, PP, 2022. 4, 6 +[41] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3, 5 +[42] Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio', and Yoshua Bengio. Graph attention networks. ArXiv, abs/1710.10903, 2018. 3 + +[43] Haoran Wang, Ying Zhang, Zhong Ji, Yanwei Pang, and Lingyun Ma. Consensus-aware visual-semantic embedding for image-text matching. In ECCV, 2020. 2, 7 +[44] Sijin Wang, Ruiping Wang, Ziwei Yao, S. Shan, and Xilin Chen. Cross-modal scene graph matching for relationship-aware image-text retrieval. 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1497-1506, 2020. 2 +[45] Keyu Wen, Xiaodong Gu, and Qingrong Cheng. Learning dual semantic relations with graph attention for image-text matching. IEEE Transactions on Circuits and Systems for Video Technology, 31:2866-2879, 2021. 2, 4, 6, 7 +[46] Yiling Wu, Shuhui Wang, Guoli Song, and Qingming Huang. Learning fragment self-attention embeddings for image-text matching. Proceedings of the 27th ACM International Conference on Multimedia, 2019. 2, 8 +[47] Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. Transactions of the Association for Computational Linguistics, 2:67-78, 2014. 7 +[48] Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Young Joon Yoo. Cutmix: Regularization strategy to train strong classifiers with localizable features. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 6022-6031, 2019. 3 +[49] Hongyi Zhang, Moustapha Cisse, Yann Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. ArXiv, abs/1710.09412, 2018. 3 +[50] Kun Zhang, Zhendong Mao, Anan Liu, and Yongdong Zhang. Unified adaptive relevance distinguishable attention network for image-text matching. IEEE Transactions on Multimedia, pages 1-1, 2022. 6, 7 +[51] Kun Zhang, Zhendong Mao, Quan Wang, and Yongdong Zhang. Negative-aware attention framework for image-text matching. 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However, previous studies only focus on capturing fragment-level relation within a sample from a particular modality, e.g., salient regions in an image or text words in a sentence, where they usually pay less attention to capturing instance-level interactions among samples and modalities, e.g., multiple images and texts. In this paper, we argue that sample relations could help learn subtle differences for hard negative instances, and thus transfer shared knowledge for infrequent samples should be promising in obtaining better holistic embeddings. Therefore, we propose a novel hierarchical relation modeling framework (HREM), which explicitly capture both fragment- and instance-level relations to learn discriminative and robust cross-modal embeddings. 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Introduction" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 561, + 287, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 561, + 287, + 693 + ], + "spans": [ + { + "bbox": [ + 46, + 561, + 287, + 693 + ], + "type": "text", + "content": "Image-text matching bridges the semantic gap between visual and textual modalities and is a fundamental task for various multi-modal learning applications, such as cross-modal retrieval [22] and text-to-image synthesis [17]. The critical challenge is accurately and efficiently learning cross-modal embeddings and their similarities for images and texts, to achieve a high-quality semantic alignment. In general, existing image-text matching methods can be classified into two paradigms. The first embedding-based matching [4, 10, 20, 35] separately encodes the whole images and texts into a holistic embedding space, then globally" + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 308, + 226, + 545, + 285 + ], + "blocks": [ + { + "bbox": [ + 308, + 226, + 545, + 285 + ], + "lines": [ + { + "bbox": [ + 308, + 226, + 545, + 285 + ], + "spans": [ + { + "bbox": [ + 308, + 226, + 545, + 285 + ], + "type": "image", + "image_path": "e6a83aa3df497260d8f28612bc27123557c7fe9067d6e3c47d502be5d4994ccc.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 369, + 287, + 482, + 295 + ], + "lines": [ + { + "bbox": [ + 369, + 287, + 482, + 295 + ], + "spans": [ + { + "bbox": [ + 369, + 287, + 482, + 295 + ], + "type": "text", + "content": "(a) Pipeline of embedding-based method" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 307, + 296, + 425, + 403 + ], + "blocks": [ + { + "bbox": [ + 307, + 296, + 425, + 403 + ], + "lines": [ + { + "bbox": [ + 307, + 296, + 425, + 403 + ], + "spans": [ + { + "bbox": [ + 307, + 296, + 425, + 403 + ], + "type": "image", + "image_path": "36d78871bb4465a4f45b9822c3c5c83257225f7fc8ba45a5109acbe8b606bd0b.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 337, + 405, + 402, + 414 + ], + "lines": [ + { + "bbox": [ + 337, + 405, + 402, + 414 + ], + "spans": [ + { + "bbox": [ + 337, + 405, + 402, + 414 + ], + "type": "text", + "content": "(b) Semantic ambiguity" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 428, + 296, + 544, + 404 + ], + "blocks": [ + { + "bbox": [ + 428, + 296, + 544, + 404 + ], + "lines": [ + { + "bbox": [ + 428, + 296, + 544, + 404 + ], + "spans": [ + { + "bbox": [ + 428, + 296, + 544, + 404 + ], + "type": "image", + "image_path": "83426cd2cd16b2eb6393988ae8260dcb9a4499364c0d86cd074ce0e00f45797a.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 453, + 405, + 511, + 413 + ], + "lines": [ + { + "bbox": [ + 453, + 405, + 511, + 413 + ], + "spans": [ + { + "bbox": [ + 453, + 405, + 511, + 413 + ], + "type": "text", + "content": "(c) Semantic scarcity" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 304, + 422, + 547, + 577 + ], + "lines": [ + { + "bbox": [ + 304, + 422, + 547, + 577 + ], + "spans": [ + { + "bbox": [ + 304, + 422, + 547, + 577 + ], + "type": "text", + "content": "Figure 1. Illustration of our motivation. Sample relation modeling improves the holistic representation of cross-modal learning. Colors and shapes indicate different modalities and image-text pairs, respectively. Orange elements mark effective interactions: (a) The pipeline of the previous and our work, we add the cross-modal relation interaction between samples. (b) For the identical theme of \"surfer with surfboard\", specific behaviors exist subtle differences, like \"hold/squat/ride on the surfboard\" and \"stare/break/wipe out the wave\". Our method distinguishes these hard negative samples from semantic ambiguities. (c) For similar themes under \"man play a ball\", corresponding behaviors usually are semantic similar, like \"play the hockey/cricket/polo\" all need to \"hit the ball\" with \"sticks/bats\". Our method improves learning embeddings on these infrequent samples with semantic scarcities for themselves." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 603, + 545, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 603, + 545, + 651 + ], + "spans": [ + { + "bbox": [ + 304, + 603, + 545, + 651 + ], + "type": "text", + "content": "measures the semantic similarity of the two modalities. The second score-based matching [3,7,19,27] applies the cross-modal interaction between visual and textual local features, then learns a cumulative similarity score." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "content": "Recently, embedding-based methods have served as the mainstream solution owing to both accuracy and efficiency in image-text matching, which contains two steps as shown in Fig. 1 (a): (1) Capturing the intra-modal relation between visual fragments (e.g., regional features) or textual" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 703, + 135, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 703, + 135, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 703, + 135, + 712 + ], + "type": "text", + "content": "*Corresponding author." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "15159" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 289, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 289, + 323 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 289, + 323 + ], + "type": "text", + "content": "fragments (e.g., word features) independently, then enhancing the semantic representation of local features. (2) Aggregating relation-enhanced local features of two modalities into the holistic embedding space. For the first step, most use the graph neural network [5, 20, 21] or attention module [35, 45, 46] to capture semantic relations and enhance the local features of two modalities, respectively. Some work further exploits the spatial relation [45] for visual regions or grammatical relation [28] for textual words. For the second step, they design pooling functions [4] or sequence encoders [21] to aggregate local features and get holistic embeddings. Existing embedding-based methods follow the principle of separately encoding images and texts by two branches as Fig. 1 (a). In each branch, these methods only focus on the fragment-level relation modeling and local features interaction within one sample, e.g., the region features inside one image (or the word features inside one text). In this way, the instance-level relation modeling and global embeddings interaction among different samples and modalities, e.g., holistic embeddings of multiple images and texts, are entirely overlooked." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 327, + 289, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 327, + 289, + 555 + ], + "spans": [ + { + "bbox": [ + 47, + 327, + 289, + 555 + ], + "type": "text", + "content": "Consequently, existing embedding-based methods directly use global embeddings to compute loss function, " + }, + { + "bbox": [ + 47, + 327, + 289, + 555 + ], + "type": "inline_equation", + "content": "e.g." + }, + { + "bbox": [ + 47, + 327, + 289, + 555 + ], + "type": "text", + "content": ", hard negative triplet loss [10] on random mini-batch, which is insufficient to exploit the manifold structure of holistic embedding space [39]. First, they fail to learn subtle semantic discrepancies among different samples (e.g., similar behaviors with an identical theme as shown in Fig. 1 (b)), then can not distinguish hard negative samples with semantic ambiguities because of the heterogeneity of visual and textual semantics. Second, they are unable to transfer shared knowledge from diverse samples (e.g., different samples that contain similar behaviors with similar themes as shown in Fig. 1 (c)), then can not effectively learn on these infrequent samples with semantic scarcities. Therefore, it is expected that a framework should precisely capture the sample relationship to learn better cross-modal embeddings, while does not break the principle of embedding-based methods, i.e., independently encodes embeddings without modality interaction at the inference stage." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 558, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 558, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 558, + 289, + 715 + ], + "type": "text", + "content": "In doing so, we propose a Hierarchical RElation Modeling framework (HREM) that, for the first time to our knowledge, explicitly captures both fragment-level and instance-level relations to learn holistic embeddings jointly. Therefore, HREM learns not only contextual semantics among intra-modal fragments to enhance local features, but also the associated semantics among inter-modal instances to distinguish hard negative samples and improve learning on infrequent samples. As illustrated in Fig. 1 (a) and Fig. 2, we propose a novel step (i.e., the \"stage-three\") to exactly capture the semantic relationship of cross-modal samples. First, we propose a novel cross-embedding association graph, which explicitly identifies the connection" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "type": "text", + "content": "relation and learns the relevance relation between batch samples with fragment-level semantic matching. Next, we propose two relation interaction mechanisms, which explore inter-modal and intra-modal relations synchronously or asynchronously with our improved attention modules to obtain enhanced embeddings. Consequently, HREM only needs to capture the instance-level relation for training, then encode multi-modal embeddings independently at the inference stage, to achieve high accuracy and efficiency for image-text matching." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 193, + 547, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 193, + 547, + 337 + ], + "spans": [ + { + "bbox": [ + 304, + 193, + 547, + 337 + ], + "type": "text", + "content": "To summarize, the major contributions are as follows: (1) We propose a hierarchical relation modeling framework (HREM) for image-text matching. To the best of our knowledge, this is the first work that explicitly captures both fragment-level relations within modality and instance-level relations across modalities. (2) We propose a novel cross-embedding association graph by identifying the connection relation and learning the relevance relation. (3) We propose two relation interaction mechanisms to learn the relation-enhanced embeddings. (4) HREM outperforms all state-of-the-art methods for image-text retrieval on two widely used benchmarks, Flickr30K and MS-COCO, by " + }, + { + "bbox": [ + 304, + 193, + 547, + 337 + ], + "type": "inline_equation", + "content": "4\\% - 10\\%" + }, + { + "bbox": [ + 304, + 193, + 547, + 337 + ], + "type": "text", + "content": " rSum." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 306, + 350, + 392, + 363 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 350, + 392, + 363 + ], + "spans": [ + { + "bbox": [ + 306, + 350, + 392, + 363 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 306, + 370, + 430, + 384 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 370, + 430, + 384 + ], + "spans": [ + { + "bbox": [ + 306, + 370, + 430, + 384 + ], + "type": "text", + "content": "2.1. Image-Text Matching" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 389, + 547, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 389, + 547, + 617 + ], + "spans": [ + { + "bbox": [ + 304, + 389, + 547, + 617 + ], + "type": "text", + "content": "According to how the cross-modal interaction is implemented, image-text matching methods are divided into two categories, embedding-based and score-based matching. **Embedding-based.** It independently encodes images and sentences into a holistic embedding space by two branches, where the semantic similarity is calculated by cosine similarity [10]. Existing work usually utilizes the GCN [20, 44] or self-attention layer [45, 46] to capture semantic relations between fragments inside one sample and enhance contextual semantics of local features, then propose a particular aggregator to learn global embeddings. For example, VSRN [20] proposes a semantic reasoning network to learn local visual features with key scene concepts. CVSE [43] proposes a consensus-aware module to integrate commonsense knowledge into local features for two modalities. GPO [4] presents a generalized pooling function to project local features into the global embedding. MV [24] proposes a multi-view encoder to learn multiple embeddings for one image and models intra-class variations." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 617, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 547, + 715 + ], + "type": "text", + "content": "Score-based. It conducts fine-grained cross-modal interaction and semantic alignment between local fragments, then calculates a cumulative similarity score. [3, 7, 19, 51]. For example, SCAN [19] proposes an attention mechanism for cross-modal interaction between visual and textual fragments. IMRAM [3] proposes an iterative network for multiple steps of cross-modal interaction. NAAF [51] measures the similarity and dissimilarity degrees via two matching" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "15160" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 70, + 547, + 244 + ], + "blocks": [ + { + "bbox": [ + 48, + 70, + 547, + 244 + ], + "lines": [ + { + "bbox": [ + 48, + 70, + 547, + 244 + ], + "spans": [ + { + "bbox": [ + 48, + 70, + 547, + 244 + ], + "type": "image", + "image_path": "dac8f35c27f3fcf6cfac49c419e7776c070e93f67731a7d941abc2d3a5b7cbc9.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 252, + 547, + 309 + ], + "lines": [ + { + "bbox": [ + 46, + 252, + 547, + 309 + ], + "spans": [ + { + "bbox": [ + 46, + 252, + 547, + 309 + ], + "type": "text", + "content": "Figure 2. Overview of our hierarchical relation modeling framework (HREM). Given " + }, + { + "bbox": [ + 46, + 252, + 547, + 309 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 252, + 547, + 309 + ], + "type": "text", + "content": " image-text pairs " + }, + { + "bbox": [ + 46, + 252, + 547, + 309 + ], + "type": "inline_equation", + "content": "(N = 3" + }, + { + "bbox": [ + 46, + 252, + 547, + 309 + ], + "type": "text", + "content": " in this Figure), we first capture the fragment-level relations and learn the relation-enhanced local features for each image or text independently, then aggregate local features by pooling operation to get global embeddings " + }, + { + "bbox": [ + 46, + 252, + 547, + 309 + ], + "type": "inline_equation", + "content": "\\{\\pmb{v}_i,\\pmb{u}_i\\}_{i = 1}^N" + }, + { + "bbox": [ + 46, + 252, + 547, + 309 + ], + "type": "text", + "content": ". Next, we propose a novel cross-embedding association graph to capture instance-level relations by identifying the connection relation and learning the relevance relation between samples. Finally, we propose two cross-modal relation interaction mechanisms to get relation-enhanced embeddings and compute the final loss function." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 329, + 247, + 341 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 329, + 247, + 341 + ], + "spans": [ + { + "bbox": [ + 47, + 329, + 247, + 341 + ], + "type": "text", + "content": "mechanisms to infer the overall similarity jointly." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 342, + 288, + 390 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 342, + 288, + 390 + ], + "spans": [ + { + "bbox": [ + 46, + 342, + 288, + 390 + ], + "type": "text", + "content": "Nonetheless, existing embedding-based methods only capture the relation between local fragments within each sample. In addition, we explore the relation across different samples and modalities to learn better holistic embeddings." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 399, + 213, + 411 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 399, + 213, + 411 + ], + "spans": [ + { + "bbox": [ + 47, + 399, + 213, + 411 + ], + "type": "text", + "content": "2.2. Sample Relationship Learning" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 417, + 289, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 417, + 289, + 632 + ], + "spans": [ + { + "bbox": [ + 46, + 417, + 289, + 632 + ], + "type": "text", + "content": "Among data scarcity and diversity, sample relations have been explicitly explored for representation enhancement [23, 49]. A simple way is to generate input as new samples from existing samples [13], such as mix-up [49], and cutmix [48]. Some work designs the delicate objective function for output, such as various metric learning losses [12, 14]. Another way is using the knowledge distillation between samples, such as transferring invariant knowledge for zero-shot learning [32] and domain generalization [33]. Recently, some methods focus on the view of batch interaction [8, 9, 16]. IBC [39] constructs a fully connected graph for mini-batch samples and classifies each sample employing a message passing network. HIST [25] constructs a hypergraph to formulate higher-order relations between samples. However, these methods study the primary vision task, e.g., classification. We capture the sample relationship for cross-modal learning and bridge semantic discrepancies between visual and textual modalities." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 645, + 176, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 645, + 176, + 658 + ], + "spans": [ + { + "bbox": [ + 47, + 645, + 176, + 658 + ], + "type": "text", + "content": "3. The Proposed Method" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "text", + "content": "The overview of HREM is depicted in Fig. 2. We first introduce feature extraction in Sec. 3.1 and fragment-level relation modeling in Sec. 3.2. Then we introduce instance-level relation modeling in Sec. 3.3. Finally, we describe the" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 329, + 514, + 341 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 329, + 514, + 341 + ], + "spans": [ + { + "bbox": [ + 305, + 329, + 514, + 341 + ], + "type": "text", + "content": "optimization in Sec. 3.4 and discussion in Sec. 3.5." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 347, + 418, + 359 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 347, + 418, + 359 + ], + "spans": [ + { + "bbox": [ + 305, + 347, + 418, + 359 + ], + "type": "text", + "content": "3.1. Feature Extraction" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 365, + 545, + 461 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 365, + 545, + 461 + ], + "spans": [ + { + "bbox": [ + 304, + 365, + 545, + 461 + ], + "type": "text", + "content": "Visual Representation. Given an image " + }, + { + "bbox": [ + 304, + 365, + 545, + 461 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 304, + 365, + 545, + 461 + ], + "type": "text", + "content": ", we use the bottom-up-attention network [1] to extract the salient regions by the Faster-RCNN [37] and get the region features by the pre-trained ResNet-101 [15]. Then we add a fully-connect (FC) layer to map each region to a " + }, + { + "bbox": [ + 304, + 365, + 545, + 461 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 304, + 365, + 545, + 461 + ], + "type": "text", + "content": "-dimensional local feature. We denote as " + }, + { + "bbox": [ + 304, + 365, + 545, + 461 + ], + "type": "inline_equation", + "content": "R = \\{r_1, \\dots, r_{n_r}\\} \\in \\mathbb{R}^{n_r \\times d}" + }, + { + "bbox": [ + 304, + 365, + 545, + 461 + ], + "type": "text", + "content": ", which is the visual fragments and local features for the image " + }, + { + "bbox": [ + 304, + 365, + 545, + 461 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 304, + 365, + 545, + 461 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 365, + 545, + 461 + ], + "type": "inline_equation", + "content": "n_r" + }, + { + "bbox": [ + 304, + 365, + 545, + 461 + ], + "type": "text", + "content": " is the number of region features." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 462, + 546, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 462, + 546, + 545 + ], + "spans": [ + { + "bbox": [ + 304, + 462, + 546, + 545 + ], + "type": "text", + "content": "Textual Representation. Given a sentence " + }, + { + "bbox": [ + 304, + 462, + 546, + 545 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 462, + 546, + 545 + ], + "type": "text", + "content": ", we use the sequence models, bi-directional gated recurrent unit (Bi-GRU) [38], or pre-trained BERT [6] to extract the set of word features. We also add an FC layer to keep the same dimension with images. We denote as " + }, + { + "bbox": [ + 304, + 462, + 546, + 545 + ], + "type": "inline_equation", + "content": "C = \\{c_1, \\dots, c_{n_c}\\} \\in \\mathbb{R}^{n_c \\times d}" + }, + { + "bbox": [ + 304, + 462, + 546, + 545 + ], + "type": "text", + "content": ", which is the textual fragments and local features for the text, " + }, + { + "bbox": [ + 304, + 462, + 546, + 545 + ], + "type": "inline_equation", + "content": "n_c" + }, + { + "bbox": [ + 304, + 462, + 546, + 545 + ], + "type": "text", + "content": " is the number of word features." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 552, + 491, + 565 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 552, + 491, + 565 + ], + "spans": [ + { + "bbox": [ + 305, + 552, + 491, + 565 + ], + "type": "text", + "content": "3.2. Fragment-level Relation Modeling" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 570, + 545, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 545, + 617 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 545, + 617 + ], + "type": "text", + "content": "To capture contextual information between fragments and enhance local features of two modalities introduced by Sec. 3.1, we propose fragment-level relation modeling for visual regions and textual words, respectively." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "text", + "content": "Visual Regions. We construct a semantic relation graph between visual regions within one image, and propose the relation interaction module to learn the contextual semantics for region features. The graph nodes are region features " + }, + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "text", + "content": ", and edges are their semantic relation. Specifically, we use the graph attention network [42], e.g., self-attention layer [41], to capture the semantic relation and learn relation-enhanced local features. It first maps original" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15161" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 109 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 109 + ], + "type": "text", + "content": "features to queries and key-value pairs, then use a weighted sum of the values as outputs, where the weighting depends on scaled dot-product between queries and keys:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 82, + 118, + 287, + 146 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 118, + 287, + 146 + ], + "spans": [ + { + "bbox": [ + 82, + 118, + 287, + 146 + ], + "type": "interline_equation", + "content": "A t t \\left(\\boldsymbol {Q}, \\boldsymbol {K}, \\boldsymbol {V}\\right) = \\operatorname {s o f t m a x} \\left(\\frac {\\boldsymbol {Q} \\boldsymbol {K} ^ {T}}{\\sqrt {d _ {k}}}\\right) \\boldsymbol {V}, \\tag {1}", + "image_path": "3704ed599daae7de8e1d48e753275d3324d6e0df874edcdc9c90172a190b4db0.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 149, + 287, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 149, + 287, + 245 + ], + "spans": [ + { + "bbox": [ + 46, + 149, + 287, + 245 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 149, + 287, + 245 + ], + "type": "inline_equation", + "content": "Att" + }, + { + "bbox": [ + 46, + 149, + 287, + 245 + ], + "type": "text", + "content": " is called the scaled dot-product attention [29], " + }, + { + "bbox": [ + 46, + 149, + 287, + 245 + ], + "type": "inline_equation", + "content": "d_{k}" + }, + { + "bbox": [ + 46, + 149, + 287, + 245 + ], + "type": "text", + "content": " is the dimension of input features. In this case, we build up a fully-connected graph, and the semantic relation is implicitly expressed in the attention weight. After initial region features " + }, + { + "bbox": [ + 46, + 149, + 287, + 245 + ], + "type": "inline_equation", + "content": "R = \\{r_1,\\dots ,r_{n_r}\\} \\in \\mathbb{R}^{n_r\\times d}" + }, + { + "bbox": [ + 46, + 149, + 287, + 245 + ], + "type": "text", + "content": " pass through the self-attention layer with the feed-forward network to learn contextual information, we obtain the relation-enhanced region features " + }, + { + "bbox": [ + 46, + 149, + 287, + 245 + ], + "type": "inline_equation", + "content": "R^{V} = \\{r_{1}^{V},\\dots ,r_{n_{r}}^{V}\\} \\in \\mathbb{R}^{n_{r}\\times d}" + }, + { + "bbox": [ + 46, + 149, + 287, + 245 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 246, + 287, + 293 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 246, + 287, + 293 + ], + "spans": [ + { + "bbox": [ + 46, + 246, + 287, + 293 + ], + "type": "text", + "content": "Finally, we aggregate initial and enhanced region features to get the global visual embedding " + }, + { + "bbox": [ + 46, + 246, + 287, + 293 + ], + "type": "inline_equation", + "content": "\\pmb{v} \\in \\mathbb{R}^d" + }, + { + "bbox": [ + 46, + 246, + 287, + 293 + ], + "type": "text", + "content": ", by using the maximum pooling and average pooling, with the parameter " + }, + { + "bbox": [ + 46, + 246, + 287, + 293 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 46, + 246, + 287, + 293 + ], + "type": "text", + "content": " to control the ratio of two representations." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 57, + 302, + 287, + 316 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 302, + 287, + 316 + ], + "spans": [ + { + "bbox": [ + 57, + 302, + 287, + 316 + ], + "type": "interline_equation", + "content": "\\boldsymbol {v} = \\beta \\cdot \\operatorname {M a x P o o l} (R) + (1 - \\beta) \\cdot \\operatorname {A v g P o o l} \\left(R ^ {V}\\right), \\tag {2}", + "image_path": "446444f6b8d55d2e7b5682f8cc78176b9929baabf5104c57491ce5819da255cf.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 326, + 287, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 326, + 287, + 434 + ], + "spans": [ + { + "bbox": [ + 46, + 326, + 287, + 434 + ], + "type": "text", + "content": "Textual Words. Similarly, we construct a semantic relation graph between textual words within one sentence, and use the relation interaction module to enhance contextual information for the word features. The nodes are word features " + }, + { + "bbox": [ + 46, + 326, + 287, + 434 + ], + "type": "inline_equation", + "content": "C = \\{c_{1},\\dots ,c_{n_{c}}\\} \\in \\mathbb{R}^{n_{c}\\times d}" + }, + { + "bbox": [ + 46, + 326, + 287, + 434 + ], + "type": "text", + "content": " and the edges are their semantic relation. We also use the self-attention layer to implement, hence we get relation-enhanced word features " + }, + { + "bbox": [ + 46, + 326, + 287, + 434 + ], + "type": "inline_equation", + "content": "C^U = \\{c_1^U,\\dots ,c_{n_c}^U\\} \\in \\mathbb{R}^{n_c\\times d}" + }, + { + "bbox": [ + 46, + 326, + 287, + 434 + ], + "type": "text", + "content": ", then aggregate them to get the global textual embedding " + }, + { + "bbox": [ + 46, + 326, + 287, + 434 + ], + "type": "inline_equation", + "content": "\\pmb {u}\\in \\mathbb{R}^{d}" + }, + { + "bbox": [ + 46, + 326, + 287, + 434 + ], + "type": "text", + "content": ", like Eq. (2)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 447, + 287, + 460 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 447, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 56, + 447, + 287, + 460 + ], + "type": "interline_equation", + "content": "\\boldsymbol {u} = \\beta \\cdot \\operatorname {M a x P o o l} (C) + (1 - \\beta) \\cdot \\operatorname {A v g P o o l} \\left(C ^ {U}\\right), \\tag {3}", + "image_path": "c014fc23e6c62bbdc865dbba2f32c79bc87d709c3a8f0f0aa1583f7ae991976b.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 468, + 224, + 481 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 468, + 224, + 481 + ], + "spans": [ + { + "bbox": [ + 47, + 468, + 224, + 481 + ], + "type": "text", + "content": "3.3. Instance-level Relation Modeling" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 487, + 287, + 571 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 487, + 287, + 571 + ], + "spans": [ + { + "bbox": [ + 46, + 487, + 287, + 571 + ], + "type": "text", + "content": "We propose the instance-level relation modeling for multiple images and texts to learn better cross-modal embeddings obtained by Sec. 3.2. Given " + }, + { + "bbox": [ + 46, + 487, + 287, + 571 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 487, + 287, + 571 + ], + "type": "text", + "content": " image-text pairs and their embeddings " + }, + { + "bbox": [ + 46, + 487, + 287, + 571 + ], + "type": "inline_equation", + "content": "\\{\\pmb{v}_i,\\pmb{u}_i\\}_{i = 1}^N" + }, + { + "bbox": [ + 46, + 487, + 287, + 571 + ], + "type": "text", + "content": ", we propose a novel cross-embedding association graph " + }, + { + "bbox": [ + 46, + 487, + 287, + 571 + ], + "type": "inline_equation", + "content": "\\mathcal{G}(\\mathcal{V},\\mathcal{E})" + }, + { + "bbox": [ + 46, + 487, + 287, + 571 + ], + "type": "text", + "content": ", where the nodes are the embeddings " + }, + { + "bbox": [ + 46, + 487, + 287, + 571 + ], + "type": "inline_equation", + "content": "\\mathcal{V} = \\{\\pmb{v}_1,\\dots,\\pmb{v}_N,\\pmb{u}_1\\dots,\\pmb{u}_N\\} \\in \\mathbb{R}^{2N\\times d}" + }, + { + "bbox": [ + 46, + 487, + 287, + 571 + ], + "type": "text", + "content": ", the edges " + }, + { + "bbox": [ + 46, + 487, + 287, + 571 + ], + "type": "inline_equation", + "content": "\\mathcal{E}" + }, + { + "bbox": [ + 46, + 487, + 287, + 571 + ], + "type": "text", + "content": " are pairwise semantic relations." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 586, + 239, + 598 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 586, + 239, + 598 + ], + "spans": [ + { + "bbox": [ + 47, + 586, + 239, + 598 + ], + "type": "text", + "content": "3.3.1 Cross-Embedding Association Graph" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 605, + 287, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 287, + 640 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 287, + 640 + ], + "type": "text", + "content": "The critical challenge is how to construct the pairwise relation accurately. Without loss of generality, we divide the relation into two parts: connection and relevance." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 641, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 287, + 712 + ], + "type": "text", + "content": "We use the matrix " + }, + { + "bbox": [ + 46, + 641, + 287, + 712 + ], + "type": "inline_equation", + "content": "\\mathbf{A} \\in \\mathbb{R}^{2N \\times 2N}" + }, + { + "bbox": [ + 46, + 641, + 287, + 712 + ], + "type": "text", + "content": " to represent the connection relation, i.e., whether there exists an associated edge between nodes. We use the matrix " + }, + { + "bbox": [ + 46, + 641, + 287, + 712 + ], + "type": "inline_equation", + "content": "S \\in \\mathbb{R}^{2N \\times 2N}" + }, + { + "bbox": [ + 46, + 641, + 287, + 712 + ], + "type": "text", + "content": " to represent the relevance relation, i.e., the degree of semantic association between nodes. Further, we divide these matrices into two patterns and four blocks: intra-modal rela" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 72, + 545, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 109 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 109 + ], + "type": "text", + "content": "tion (Image-to-Image " + }, + { + "bbox": [ + 304, + 72, + 545, + 109 + ], + "type": "inline_equation", + "content": "I\\to I" + }, + { + "bbox": [ + 304, + 72, + 545, + 109 + ], + "type": "text", + "content": " , Text-to-Text " + }, + { + "bbox": [ + 304, + 72, + 545, + 109 + ], + "type": "inline_equation", + "content": "T\\rightarrow T" + }, + { + "bbox": [ + 304, + 72, + 545, + 109 + ], + "type": "text", + "content": " ) and intermodal relation (Image-to-Text " + }, + { + "bbox": [ + 304, + 72, + 545, + 109 + ], + "type": "inline_equation", + "content": "I\\rightarrow T" + }, + { + "bbox": [ + 304, + 72, + 545, + 109 + ], + "type": "text", + "content": " , Text-to-Image " + }, + { + "bbox": [ + 304, + 72, + 545, + 109 + ], + "type": "inline_equation", + "content": "T\\rightarrow I" + }, + { + "bbox": [ + 304, + 72, + 545, + 109 + ], + "type": "text", + "content": " ), the shape of each block is equal to " + }, + { + "bbox": [ + 304, + 72, + 545, + 109 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{N\\times N}" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 119, + 545, + 147 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 119, + 545, + 147 + ], + "spans": [ + { + "bbox": [ + 313, + 119, + 545, + 147 + ], + "type": "interline_equation", + "content": "\\boldsymbol {A} = \\left[\\begin{array}{l l}\\boldsymbol {A} _ {I \\rightarrow I}&\\boldsymbol {A} _ {I \\rightarrow T}\\\\\\boldsymbol {A} _ {T \\rightarrow I}&\\boldsymbol {A} _ {T \\rightarrow T}\\end{array}\\right], \\boldsymbol {S} = \\left[\\begin{array}{l l}\\boldsymbol {S} _ {I \\rightarrow I}&\\boldsymbol {S} _ {I \\rightarrow T}\\\\\\boldsymbol {S} _ {T \\rightarrow I}&\\boldsymbol {S} _ {T \\rightarrow T}\\end{array}\\right], \\tag {4}", + "image_path": "43a05f32284f568c1014172018ca3272866e61af65fb9e63e2f0a4886c9c54d9.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 148, + 545, + 218 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 148, + 545, + 218 + ], + "spans": [ + { + "bbox": [ + 304, + 148, + 545, + 218 + ], + "type": "text", + "content": "Connection Relation Identifying. The simple idea is to build a fully connected graph like Sec. 3.2 and previous work [5,45] that elements of " + }, + { + "bbox": [ + 304, + 148, + 545, + 218 + ], + "type": "inline_equation", + "content": "A" + }, + { + "bbox": [ + 304, + 148, + 545, + 218 + ], + "type": "text", + "content": " are all 1, However, the fullyconnection easily likely leads to over-smoothing and noisy association [39] for nodes. We need to identify the effective connection and filter out unassociated semantics." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 220, + 545, + 268 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 220, + 545, + 268 + ], + "spans": [ + { + "bbox": [ + 304, + 220, + 545, + 268 + ], + "type": "text", + "content": "Therefore, we construct the neighbor space of embedding to identify the connection relation. If two embedding nodes are close, their semantic information usually overlaps, they probably have a semantic connection [25]." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 341, + 278, + 511, + 307 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 341, + 278, + 511, + 307 + ], + "spans": [ + { + "bbox": [ + 341, + 278, + 511, + 307 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\left(\\boldsymbol {A} _ {I \\rightarrow I}\\right) _ {i j} = 1 \\text {i f} \\boldsymbol {v} _ {j} \\in \\mathcal {N} _ {\\text {i n t r a}} \\left(\\boldsymbol {v} _ {i}\\right) \\text {e l s e} 0, \\\\ \\left(\\boldsymbol {A} _ {T \\rightarrow T}\\right) _ {i j} = 1 \\text {i f} \\boldsymbol {u} _ {j} \\in \\mathcal {N} _ {\\text {i n t r a}} \\left(\\boldsymbol {u} _ {i}\\right) \\text {e l s e} 0, \\\\ \\end{array}", + "image_path": "ee49c3e955796d5d02f9d2b0d2831461f5e5716e3f298bff39ac776f7a7493e1.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 316, + 545, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 316, + 545, + 376 + ], + "spans": [ + { + "bbox": [ + 304, + 316, + 545, + 376 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 316, + 545, + 376 + ], + "type": "inline_equation", + "content": "\\mathcal{N}_{\\text {intra }}" + }, + { + "bbox": [ + 304, + 316, + 545, + 376 + ], + "type": "text", + "content": " is the neighbor space of embeddings for intramodal connection. We select the first " + }, + { + "bbox": [ + 304, + 316, + 545, + 376 + ], + "type": "inline_equation", + "content": "\\tau N" + }, + { + "bbox": [ + 304, + 316, + 545, + 376 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 304, + 316, + 545, + 376 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 304, + 316, + 545, + 376 + ], + "type": "text", + "content": " is range from 0 to 1) nearest single-modal samples ranked by the embedding similarity to construct " + }, + { + "bbox": [ + 304, + 316, + 545, + 376 + ], + "type": "inline_equation", + "content": "\\mathcal{N}_{\\text {intra }}" + }, + { + "bbox": [ + 304, + 316, + 545, + 376 + ], + "type": "text", + "content": " in Eq. (5). Correspondingly, we define " + }, + { + "bbox": [ + 304, + 316, + 545, + 376 + ], + "type": "inline_equation", + "content": "\\mathcal{N}_{\\text {inter }}" + }, + { + "bbox": [ + 304, + 316, + 545, + 376 + ], + "type": "text", + "content": " for inter-modal connection:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 341, + 387, + 544, + 415 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 341, + 387, + 544, + 415 + ], + "spans": [ + { + "bbox": [ + 341, + 387, + 544, + 415 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\left(\\boldsymbol {A} _ {I \\rightarrow T}\\right) _ {i j} = 1 \\text {i f} \\boldsymbol {u} _ {j} \\in \\mathcal {N} _ {\\text {i n t e r}} \\left(\\boldsymbol {v} _ {i}\\right) \\text {e l s e} 0, \\\\ \\left(\\boldsymbol {A} _ {T \\rightarrow I}\\right) _ {i j} = 1 \\text {i f} \\boldsymbol {v} _ {j} \\in \\mathcal {N} _ {\\text {i n t e r}} \\left(\\boldsymbol {u} _ {i}\\right) \\text {e l s e} 0, \\tag {6} \\\\ \\end{array}", + "image_path": "e97688a892f9f7b40f9ceca1073585c66840f0d0c8c2891ca7abdcba6994ddc6.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 426, + 545, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 426, + 545, + 545 + ], + "spans": [ + { + "bbox": [ + 304, + 426, + 545, + 545 + ], + "type": "text", + "content": "Since the heterogeneity between visual and textual semantics, directly using global embeddings is insufficient to identify the inter-modal connection relation. Following score-based methods [19, 40], we use the fine-grained matching of fragments and local features to measure the inter-modal neighbor space. As shown in Fig. 3, given an image-text pair and its region-word similarity matrix. We first pick up the most matching textual word (or visual region) for each region (or each word), then average these matched scores to express the overall matching values " + }, + { + "bbox": [ + 304, + 426, + 545, + 545 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 304, + 426, + 545, + 545 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 355, + 555, + 545, + 620 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 355, + 555, + 545, + 620 + ], + "spans": [ + { + "bbox": [ + 355, + 555, + 545, + 620 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} p _ {I \\rightarrow T} = \\frac {1}{n _ {r}} \\sum_ {\\substack {m = 1\\\\n}} ^ {n _ {r}} \\max _ {n \\in [ 1, n _ {c} ]} \\left(\\boldsymbol {r} _ {m} ^ {\\mathrm {T}} \\boldsymbol {c} _ {n}\\right), \\tag{7} \\\\ p _ {T \\rightarrow I} = \\frac {1}{n _ {c}} \\sum_ {m = 1} ^ {n _ {c}} \\max _ {n \\in [ 1, n _ {r} ]} \\left(\\boldsymbol {c} _ {m} ^ {\\mathrm {T}} \\boldsymbol {r} _ {n}\\right), \\\\ \\end{array}", + "image_path": "e9ea2e210ce9285372db348a79aeb1051617335d4f3173d1047ac1562f98b0ae.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "spans": [ + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "text", + "content": "where we select the first " + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "inline_equation", + "content": "\\tau N" + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "text", + "content": " nearest cross-modal samples ranked by the fragment-level matching values Eq. (7) to construct the inter-modal neighbor space " + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "inline_equation", + "content": "\\mathcal{N}_{inter}" + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "text", + "content": " in Eq (6)." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "type": "text", + "content": "Relevance Relation Learning. The relevance relation is the degree of semantic association between two connected nodes. Existing work [16, 39] uses the global embedding similarity to approximate, which is insufficient to bridge the" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15162" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 70, + 289, + 187 + ], + "blocks": [ + { + "bbox": [ + 48, + 70, + 289, + 187 + ], + "lines": [ + { + "bbox": [ + 48, + 70, + 289, + 187 + ], + "spans": [ + { + "bbox": [ + 48, + 70, + 289, + 187 + ], + "type": "image", + "image_path": "14b908b03158b1f247a87bcbfda75507562bdb66ae08668c35a6e93deaa7a8ac.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 195, + 287, + 251 + ], + "lines": [ + { + "bbox": [ + 46, + 195, + 287, + 251 + ], + "spans": [ + { + "bbox": [ + 46, + 195, + 287, + 251 + ], + "type": "text", + "content": "Figure 3. Graph construction of inter-modal relation with fragment-level matching. For every image-text pair, we use corresponding modules to get connection relation " + }, + { + "bbox": [ + 46, + 195, + 287, + 251 + ], + "type": "inline_equation", + "content": "a_{I \\to T} (a_{T \\to I})" + }, + { + "bbox": [ + 46, + 195, + 287, + 251 + ], + "type": "text", + "content": " and relevance relation " + }, + { + "bbox": [ + 46, + 195, + 287, + 251 + ], + "type": "inline_equation", + "content": "s_{I \\to T} (s_{T \\to I})" + }, + { + "bbox": [ + 46, + 195, + 287, + 251 + ], + "type": "text", + "content": ", which are elements of connection matrix " + }, + { + "bbox": [ + 46, + 195, + 287, + 251 + ], + "type": "inline_equation", + "content": "A_{I \\to T} (A_{T \\to I})" + }, + { + "bbox": [ + 46, + 195, + 287, + 251 + ], + "type": "text", + "content": " and relevance matrix " + }, + { + "bbox": [ + 46, + 195, + 287, + 251 + ], + "type": "inline_equation", + "content": "S_{I \\to T} (S_{T \\to I})" + }, + { + "bbox": [ + 46, + 195, + 287, + 251 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 270, + 289, + 344 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 270, + 289, + 344 + ], + "spans": [ + { + "bbox": [ + 46, + 270, + 289, + 344 + ], + "type": "text", + "content": "semantic discrepancy between visual and textual space [19]. In contrast, we explicitly learn the cross-modal relevance relation. As shown in Fig. 3, we employ fine-grained matching like Eq. (7). After picking up the max matching of row-wise and column-wise, we select the top-" + }, + { + "bbox": [ + 46, + 270, + 289, + 344 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 270, + 289, + 344 + ], + "type": "text", + "content": " scores and concatenate them as the matching vectors " + }, + { + "bbox": [ + 46, + 270, + 289, + 344 + ], + "type": "inline_equation", + "content": "\\mathbf{q}" + }, + { + "bbox": [ + 46, + 270, + 289, + 344 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 84, + 353, + 287, + 396 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 84, + 353, + 287, + 396 + ], + "spans": [ + { + "bbox": [ + 84, + 353, + 287, + 396 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\boldsymbol {q} _ {I \\rightarrow T} = T o p K (\\{\\max _ {n \\in [ 1, n _ {c} ]} \\left(\\boldsymbol {r} _ {m} ^ {\\mathrm {T}} \\boldsymbol {c} _ {n}\\right) \\} _ {m = 1} ^ {n _ {r}}), \\\\ \\boldsymbol {q} _ {T \\rightarrow I} = \\operatorname {T o p K} \\left(\\left. \\left\\{\\max _ {n \\in [ 1, n _ {r} ]} \\left(\\boldsymbol {c} _ {m} ^ {\\mathrm {T}} \\boldsymbol {r} _ {n}\\right) \\right\\} _ {m = 1} ^ {n _ {c}}\\right), \\tag {8} \\\\ \\end{array}", + "image_path": "73e3079657530e2379014c1c633ebea8f3a709a4b0974d77dce9076c71405b0a.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 404, + 287, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 404, + 287, + 430 + ], + "spans": [ + { + "bbox": [ + 47, + 404, + 287, + 430 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 404, + 287, + 430 + ], + "type": "inline_equation", + "content": "\\pmb{q} \\in \\mathbb{R}^{K}" + }, + { + "bbox": [ + 47, + 404, + 287, + 430 + ], + "type": "text", + "content": ", then we use an MLP and add the overall matching values to learn the inter-modal relevance (scalar)." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 104, + 446, + 287, + 474 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 446, + 287, + 474 + ], + "spans": [ + { + "bbox": [ + 104, + 446, + 287, + 474 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} s _ {I \\rightarrow T} = M L P \\left(\\boldsymbol {q} _ {I \\rightarrow T}\\right) + p _ {I \\rightarrow T}, \\\\ s _ {T \\rightarrow I} = M L P \\left(\\boldsymbol {q} _ {T \\rightarrow I}\\right) + p _ {T \\rightarrow I}, \\tag {9} \\\\ \\end{array}", + "image_path": "b724c113a7be324b0619925e81f140e7f423621f618002848e260f55b9482470.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 480, + 287, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 480, + 287, + 540 + ], + "spans": [ + { + "bbox": [ + 46, + 480, + 287, + 540 + ], + "type": "text", + "content": "For different image-text pairs, we compute the corresponding relevance values, which are the elements of the inter-modal relevance matrix " + }, + { + "bbox": [ + 46, + 480, + 287, + 540 + ], + "type": "inline_equation", + "content": "S_{I\\rightarrow T}" + }, + { + "bbox": [ + 46, + 480, + 287, + 540 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 480, + 287, + 540 + ], + "type": "inline_equation", + "content": "S_{T\\rightarrow I}" + }, + { + "bbox": [ + 46, + 480, + 287, + 540 + ], + "type": "text", + "content": ". As for the intra-modal relevance relation, we can use the global embeddings to compute the relevance matrix." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 551, + 287, + 571 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 551, + 287, + 571 + ], + "spans": [ + { + "bbox": [ + 55, + 551, + 287, + 571 + ], + "type": "interline_equation", + "content": "\\left(\\boldsymbol {S} _ {T \\rightarrow T}\\right) _ {i j} = e ^ {- \\frac {\\left\\| \\boldsymbol {v} _ {i} - \\boldsymbol {v} _ {j} \\right\\| _ {2} ^ {2}}{\\sigma}}, \\left(\\boldsymbol {S} _ {I \\rightarrow I}\\right) _ {i j} = e ^ {- \\frac {\\left\\| \\boldsymbol {u} _ {i} - \\boldsymbol {u} _ {j} \\right\\| _ {2} ^ {2}}{\\sigma}}, \\tag {10}", + "image_path": "f23f83345eaf720857e4e5a9794a441a36abb253b87b00cf96946fa1d4bc49f8.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 572, + 287, + 620 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 572, + 287, + 620 + ], + "spans": [ + { + "bbox": [ + 46, + 572, + 287, + 620 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 572, + 287, + 620 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 46, + 572, + 287, + 620 + ], + "type": "text", + "content": " is a positive scalar to control the relevance values (" + }, + { + "bbox": [ + 46, + 572, + 287, + 620 + ], + "type": "inline_equation", + "content": "\\sigma = 1" + }, + { + "bbox": [ + 46, + 572, + 287, + 620 + ], + "type": "text", + "content": " for simplicity). Besides, we add a relevance regularization loss to ensure the learned inter-modal relevance matrix does not crash during training [18]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 61, + 633, + 287, + 647 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 633, + 287, + 647 + ], + "spans": [ + { + "bbox": [ + 61, + 633, + 287, + 647 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {r e g} = \\mathcal {L} _ {k l} \\left(\\boldsymbol {S} _ {I \\rightarrow T}, \\boldsymbol {S} _ {I \\rightarrow T} ^ {g}\\right) + \\mathcal {L} _ {k l} \\left(\\boldsymbol {S} _ {T \\rightarrow I}, \\boldsymbol {S} _ {T \\rightarrow I} ^ {g}\\right), \\tag {11}", + "image_path": "7cb7ff142176456b482381a7a3ab407341b208d75cff91f37b95b0740d6b6da4.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 648, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 648, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 648, + 287, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 648, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{kl}" + }, + { + "bbox": [ + 47, + 648, + 287, + 714 + ], + "type": "text", + "content": " is the KL-divergence loss with softmax function, " + }, + { + "bbox": [ + 47, + 648, + 287, + 714 + ], + "type": "inline_equation", + "content": "(S_{I\\rightarrow T})_{ij}^{g} = e^{-\\frac{\\|v_{i} - u_{j}\\|_{2}^{2}}{\\sigma}},(S_{T\\rightarrow I})_{ij}^{g} = e^{-\\frac{\\|u_{i} - v_{j}\\|_{2}^{2}}{\\sigma}}" + }, + { + "bbox": [ + 47, + 648, + 287, + 714 + ], + "type": "text", + "content": " are computed by the global embeddings like Eq. (10). We hope the learned inter-modal relevance relation is close to the semantic relation of global embeddings." + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 307, + 72, + 539, + 198 + ], + "blocks": [ + { + "bbox": [ + 307, + 72, + 539, + 198 + ], + "lines": [ + { + "bbox": [ + 307, + 72, + 539, + 198 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 539, + 198 + ], + "type": "image", + "image_path": "5efd157389196476d3990f0556f0bbfc5cf58a6ac621dea7130f703f88487160.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 306, + 202, + 545, + 222 + ], + "lines": [ + { + "bbox": [ + 306, + 202, + 545, + 222 + ], + "spans": [ + { + "bbox": [ + 306, + 202, + 545, + 222 + ], + "type": "text", + "content": "(a) Standalone mechanism. Explore inter-modal and intra-modal relationship between visual and textual embeddings asynchronously." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 308, + 225, + 537, + 289 + ], + "blocks": [ + { + "bbox": [ + 308, + 225, + 537, + 289 + ], + "lines": [ + { + "bbox": [ + 308, + 225, + 537, + 289 + ], + "spans": [ + { + "bbox": [ + 308, + 225, + 537, + 289 + ], + "type": "image", + "image_path": "dd4f045719afe396d7496afcff522dbaf42ec0dc4761b831a1a0ddc3e627c199.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 307, + 289, + 543, + 300 + ], + "lines": [ + { + "bbox": [ + 307, + 289, + 543, + 300 + ], + "spans": [ + { + "bbox": [ + 307, + 289, + 543, + 300 + ], + "type": "text", + "content": "(b) Fusion mechanism. Explore inter/intra-modal relation synchronously." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 305, + 309, + 546, + 354 + ], + "lines": [ + { + "bbox": [ + 305, + 309, + 546, + 354 + ], + "spans": [ + { + "bbox": [ + 305, + 309, + 546, + 354 + ], + "type": "text", + "content": "Figure 4. Two relation interaction mechanisms based on how to explore the inter-modal and intra-modal relations. The connection matrix " + }, + { + "bbox": [ + 305, + 309, + 546, + 354 + ], + "type": "inline_equation", + "content": "A" + }, + { + "bbox": [ + 305, + 309, + 546, + 354 + ], + "type": "text", + "content": " and the relevance matrix " + }, + { + "bbox": [ + 305, + 309, + 546, + 354 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 305, + 309, + 546, + 354 + ], + "type": "text", + "content": " are applied to the attention modules wholly or separately, as Eq. (12)." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 374, + 481, + 385 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 374, + 481, + 385 + ], + "spans": [ + { + "bbox": [ + 306, + 374, + 481, + 385 + ], + "type": "text", + "content": "3.3.2 Relation Interaction Mechanisms" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 393, + 545, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 393, + 545, + 453 + ], + "spans": [ + { + "bbox": [ + 305, + 393, + 545, + 453 + ], + "type": "text", + "content": "After constructing our cross-embedding association graph in Sec. 3.3.1, we design two relation interaction mechanisms to capture the semantic relations between images and texts, where the embeddings are updated by the information interaction process, as shown in Fig. 4." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 453, + 546, + 571 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 453, + 546, + 571 + ], + "spans": [ + { + "bbox": [ + 304, + 453, + 546, + 571 + ], + "type": "text", + "content": "Fusion Mechanism. We concatenate the visual and textual embeddings as input, as shown in Fig. 4b. The inter-modal and intra-modal relation interactions are conducted synchronously. The embeddings first pass through the multi-head self-attention module for attention diversity. Besides, we adopt the feed-forward network module for relation reasoning, which a multi-layer perceptron implements [41]. It is similar to the fragment-level interaction modules in Sec. 3.2. We also add the residual connection [15] and layer normalization [2] after them." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 572, + 547, + 657 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 572, + 547, + 657 + ], + "spans": [ + { + "bbox": [ + 304, + 572, + 547, + 657 + ], + "type": "text", + "content": "The connection matrix " + }, + { + "bbox": [ + 304, + 572, + 547, + 657 + ], + "type": "inline_equation", + "content": "\\mathbf{A}" + }, + { + "bbox": [ + 304, + 572, + 547, + 657 + ], + "type": "text", + "content": " is an attention mask matrix for the attention module, where the zero positions are not allowed to attend while the non-zero positions will be unchanged [41]. The relevance matrix " + }, + { + "bbox": [ + 304, + 572, + 547, + 657 + ], + "type": "inline_equation", + "content": "\\mathbf{S}" + }, + { + "bbox": [ + 304, + 572, + 547, + 657 + ], + "type": "text", + "content": " is an extra attention weight matrix as the explicit relation modeling, we use " + }, + { + "bbox": [ + 304, + 572, + 547, + 657 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 304, + 572, + 547, + 657 + ], + "type": "text", + "content": " to balance " + }, + { + "bbox": [ + 304, + 572, + 547, + 657 + ], + "type": "inline_equation", + "content": "\\mathbf{S}" + }, + { + "bbox": [ + 304, + 572, + 547, + 657 + ], + "type": "text", + "content": " with the original attention weight matrix. Therefore, we revise the basic attention formula Eq. (1) to:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 314, + 673, + 545, + 713 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 673, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 314, + 673, + 545, + 713 + ], + "type": "interline_equation", + "content": "\\operatorname {A t t} \\left(\\boldsymbol {Q} \\boldsymbol {K} \\boldsymbol {V}; \\boldsymbol {A}, \\boldsymbol {S}\\right) = \\underset {\\text {s . t . m a s k} (\\boldsymbol {A})} {\\operatorname {s o f t m a x}} \\left(\\frac {\\boldsymbol {Q} \\boldsymbol {K} ^ {T}}{\\sqrt {d _ {k}}} + \\lambda \\boldsymbol {S}\\right) \\boldsymbol {V}, \\tag {12}", + "image_path": "446ae5b1aaba59679e6f5301ea166afeb65a42b8e31e08e9bb1020d662007930.jpg" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15163" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 72, + 284, + 171 + ], + "blocks": [ + { + "bbox": [ + 50, + 72, + 284, + 171 + ], + "lines": [ + { + "bbox": [ + 50, + 72, + 284, + 171 + ], + "spans": [ + { + "bbox": [ + 50, + 72, + 284, + 171 + ], + "type": "image", + "image_path": "649065e98d19144a8ed3d556e7e875ceb85e86ddc5de0bf49624a4a6d88e90e6.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 179, + 286, + 212 + ], + "lines": [ + { + "bbox": [ + 46, + 179, + 286, + 212 + ], + "spans": [ + { + "bbox": [ + 46, + 179, + 286, + 212 + ], + "type": "text", + "content": "Figure 5. The comparison between accuracy and speed for cross-modal retrieval. We perform all methods (Region+BiGRU based) on the whole Flickr30K test-set with one RTX3090 GPU." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 235, + 286, + 342 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 235, + 286, + 342 + ], + "spans": [ + { + "bbox": [ + 46, + 235, + 286, + 342 + ], + "type": "text", + "content": "Standalone Mechanism. As shown in Fig. 4a, the visual and textual embeddings are sent into two branches and get relation interaction. The embeddings first get inter-modal relation interaction by the multi-head cross-attention module, where " + }, + { + "bbox": [ + 46, + 235, + 286, + 342 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 46, + 235, + 286, + 342 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 235, + 286, + 342 + ], + "type": "inline_equation", + "content": "K, V" + }, + { + "bbox": [ + 46, + 235, + 286, + 342 + ], + "type": "text", + "content": " come from two modalities. Then they get intra-modal relation interaction by the multi-head self-attention module, where " + }, + { + "bbox": [ + 46, + 235, + 286, + 342 + ], + "type": "inline_equation", + "content": "Q, K, V" + }, + { + "bbox": [ + 46, + 235, + 286, + 342 + ], + "type": "text", + "content": " come from the same modality. Finally, the enhanced embeddings are output by following the feed-forward network module." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 343, + 286, + 415 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 343, + 286, + 415 + ], + "spans": [ + { + "bbox": [ + 46, + 343, + 286, + 415 + ], + "type": "text", + "content": "The connection matrix " + }, + { + "bbox": [ + 46, + 343, + 286, + 415 + ], + "type": "inline_equation", + "content": "\\mathbf{A}" + }, + { + "bbox": [ + 46, + 343, + 286, + 415 + ], + "type": "text", + "content": " and relevance matrix " + }, + { + "bbox": [ + 46, + 343, + 286, + 415 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 46, + 343, + 286, + 415 + ], + "type": "text", + "content": " first are divided into pre-defined four blocks as Eq. (4), then each block is applied to corresponding modules as Eq. (12). Specifically, the inter-modal relation parts perform on the first cross-attention module, and the intra-modal relation parts act on the second self-attention module." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 416, + 286, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 416, + 286, + 453 + ], + "spans": [ + { + "bbox": [ + 46, + 416, + 286, + 453 + ], + "type": "text", + "content": "After the " + }, + { + "bbox": [ + 46, + 416, + 286, + 453 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 46, + 416, + 286, + 453 + ], + "type": "text", + "content": " layers of the relation interaction mechanism, we get the final relation-enhanced embeddings of two modalities, " + }, + { + "bbox": [ + 46, + 416, + 286, + 453 + ], + "type": "inline_equation", + "content": "\\{\\overline{\\pmb{v}}_1,\\dots ,\\overline{\\pmb{v}}_N\\}" + }, + { + "bbox": [ + 46, + 416, + 286, + 453 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 416, + 286, + 453 + ], + "type": "inline_equation", + "content": "\\{\\overline{\\pmb{u}}_1\\dots ,\\overline{\\pmb{u}}_N\\}" + }, + { + "bbox": [ + 46, + 416, + 286, + 453 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 461, + 132, + 474 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 461, + 132, + 474 + ], + "spans": [ + { + "bbox": [ + 47, + 461, + 132, + 474 + ], + "type": "text", + "content": "3.4. Optimization" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 480, + 286, + 564 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 480, + 286, + 564 + ], + "spans": [ + { + "bbox": [ + 46, + 480, + 286, + 564 + ], + "type": "text", + "content": "Neighbor Batch Sampling. To ensure effective relation interaction in Sec. 3.3, we propose a neighbor sampling to replace random sampling for batches at the later training. We use the k-means clustering [30] on visual embeddings, then randomly choose " + }, + { + "bbox": [ + 46, + 480, + 286, + 564 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 46, + 480, + 286, + 564 + ], + "type": "text", + "content": " clusters and select " + }, + { + "bbox": [ + 46, + 480, + 286, + 564 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 480, + 286, + 564 + ], + "type": "text", + "content": " images from each cluster, batch size " + }, + { + "bbox": [ + 46, + 480, + 286, + 564 + ], + "type": "inline_equation", + "content": "N = P \\times K" + }, + { + "bbox": [ + 46, + 480, + 286, + 564 + ], + "type": "text", + "content": ". Finally, we select one positive text for each image to get " + }, + { + "bbox": [ + 46, + 480, + 286, + 564 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 480, + 286, + 564 + ], + "type": "text", + "content": " image-text pairs." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 565, + 286, + 606 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 565, + 286, + 606 + ], + "spans": [ + { + "bbox": [ + 46, + 565, + 286, + 606 + ], + "type": "text", + "content": "Objective Function. We use the triplet loss [10], the similarity score is the cosine similarity between visual embedding " + }, + { + "bbox": [ + 46, + 565, + 286, + 606 + ], + "type": "inline_equation", + "content": "\\mathbf{v}" + }, + { + "bbox": [ + 46, + 565, + 286, + 606 + ], + "type": "text", + "content": " and textual embedding " + }, + { + "bbox": [ + 46, + 565, + 286, + 606 + ], + "type": "inline_equation", + "content": "\\mathbf{u}" + }, + { + "bbox": [ + 46, + 565, + 286, + 606 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 565, + 286, + 606 + ], + "type": "inline_equation", + "content": "s(\\mathbf{v},\\mathbf{u}) = \\frac{\\mathbf{v}^\\top\\mathbf{u}}{\\|\\mathbf{v}\\|\\cdot\\|\\mathbf{u}\\|}" + }, + { + "bbox": [ + 46, + 565, + 286, + 606 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 616, + 286, + 640 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 616, + 286, + 640 + ], + "spans": [ + { + "bbox": [ + 47, + 616, + 286, + 640 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\left[ \\alpha - s (\\boldsymbol {v}, \\boldsymbol {u}) + s (\\boldsymbol {v}, \\boldsymbol {u} ^ {-}) \\right] _ {+} + \\left[ \\alpha - s (\\boldsymbol {v}, \\boldsymbol {u}) + s (\\boldsymbol {v} ^ {-}, \\boldsymbol {u}) \\right] _ {+}, \\tag {13}", + "image_path": "ecdf226e1308a9e5db98aabe56116ce547869da7408bb74f7ad23d3d3e9b8c23.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 641, + 286, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 641, + 286, + 689 + ], + "spans": [ + { + "bbox": [ + 47, + 641, + 286, + 689 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 641, + 286, + 689 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 47, + 641, + 286, + 689 + ], + "type": "text", + "content": " represents a margin parameter, " + }, + { + "bbox": [ + 47, + 641, + 286, + 689 + ], + "type": "inline_equation", + "content": "[x]_{+} = \\max (x,0)" + }, + { + "bbox": [ + 47, + 641, + 286, + 689 + ], + "type": "inline_equation", + "content": "(\\pmb {v},\\pmb {u})" + }, + { + "bbox": [ + 47, + 641, + 286, + 689 + ], + "type": "text", + "content": " is a positive image-text pair, and " + }, + { + "bbox": [ + 47, + 641, + 286, + 689 + ], + "type": "inline_equation", + "content": "(\\pmb {v},\\pmb{u}^{-}),(v^{-},\\pmb {u})" + }, + { + "bbox": [ + 47, + 641, + 286, + 689 + ], + "type": "text", + "content": " are negative image-text pairs in the batch. We use the distance-weighted sampling [31] for hard negative mining." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 689, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 286, + 713 + ], + "type": "text", + "content": "We not only use the relation-enhanced embeddings to compute matching loss as Eq. (13), but also add the ini" + } + ] + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 309, + 135, + 544, + 313 + ], + "blocks": [ + { + "bbox": [ + 305, + 71, + 545, + 125 + ], + "lines": [ + { + "bbox": [ + 305, + 71, + 545, + 125 + ], + "spans": [ + { + "bbox": [ + 305, + 71, + 545, + 125 + ], + "type": "text", + "content": "Table 1. Comparisons of image-text retrieval on MS-COCO 5K test-set. Region represents using region features [1] for images. BiGRU [38] and BERT [6] represent using their word features for texts. " + }, + { + "bbox": [ + 305, + 71, + 545, + 125 + ], + "type": "inline_equation", + "content": "E" + }, + { + "bbox": [ + 305, + 71, + 545, + 125 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 71, + 545, + 125 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 305, + 71, + 545, + 125 + ], + "type": "text", + "content": " indicate embedding-based and score-based methods, respectively. \\* shows the ensemble results of two models." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 135, + 544, + 313 + ], + "lines": [ + { + "bbox": [ + 309, + 135, + 544, + 313 + ], + "spans": [ + { + "bbox": [ + 309, + 135, + 544, + 313 + ], + "type": "table", + "html": "
TypeMethodMS-COCO 5K
IMG → TEXTTEXT → IMGrSum
R@1R@5R@10R@1R@5R@10
Region + BiGRU
SIMRAM2020 [3]53.783.291.039.769.179.8416.5
UARDA*2022 [50]56.283.891.340.669.580.9422.3
NAAF*2022 [51]58.985.292.042.570.981.4430.9
EGPO2021 [4]56.683.691.439.369.981.1421.9
CGMN2022 [5]53.481.389.641.271.982.4419.8
MV2022 [24]56.784.191.440.370.681.6424.6
HREM (Standalone)58.485.592.439.870.581.0427.6
HREM (Fusion)58.985.392.140.070.681.2428.1
HREM (Full)*60.686.492.541.371.982.4435.1
Region + BERT
SSSAMT2021 [11]57.784.290.840.870.580.5424.5
DIME*2021 [36]59.385.491.943.173.083.1435.8
DCPA2022 [40]53.582.490.240.471.082.0419.5
EDSRAN*2021 [45]55.383.590.941.772.782.8426.9
GPO2021 [4]58.385.392.342.472.783.2434.3
VSRN++2022 [21]54.782.990.942.072.282.7425.4
HREM (Standalone)61.887.093.244.073.783.4443.1
HREM (Fusion)62.387.693.443.973.683.3444.1
HREM (Full)*64.088.593.745.475.184.3450.9
", + "image_path": "8bf24753b89b22b43351fe3799c09d3f5e9e2bf6d5200e59a8e267e467bdf1b5.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 335, + 545, + 371 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 335, + 545, + 371 + ], + "spans": [ + { + "bbox": [ + 305, + 335, + 545, + 371 + ], + "type": "text", + "content": "tial embeddings for matching loss to keep the embedding consistency, since we need to encode embeddings directly without sample interaction at the inference stage." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 310, + 395, + 545, + 408 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 310, + 395, + 545, + 408 + ], + "spans": [ + { + "bbox": [ + 310, + 395, + 545, + 408 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {c r o s s}} = \\mathcal {L} (\\overline {{\\boldsymbol {v}}}, \\overline {{\\boldsymbol {u}}}) + \\left[ \\mathcal {L} (\\boldsymbol {v}, \\boldsymbol {u}) + \\mathcal {L} (\\overline {{\\boldsymbol {v}}}, \\boldsymbol {u}) + \\mathcal {L} (\\boldsymbol {v}, \\overline {{\\boldsymbol {u}}}) \\right], \\tag {14}", + "image_path": "743e9e2c8414041f354cde6be852df2ddb45f84a64e5315f3958de7573cd1940.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 419, + 545, + 444 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 419, + 545, + 444 + ], + "spans": [ + { + "bbox": [ + 305, + 419, + 545, + 444 + ], + "type": "text", + "content": "Finally, we combine the cross-embedding matching loss Eq. (14) with the relevance regularization loss Eq. (11)." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 454, + 378, + 465 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 454, + 378, + 465 + ], + "spans": [ + { + "bbox": [ + 306, + 454, + 378, + 465 + ], + "type": "text", + "content": "3.5. Discussion" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 473, + 545, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 473, + 545, + 581 + ], + "spans": [ + { + "bbox": [ + 304, + 473, + 545, + 581 + ], + "type": "text", + "content": "Inference Stage. Since we may not have the batch data in the actual application, our framework can encode the cross-modal embeddings without sample interaction at the inference stage. The instance-level relation modeling is only for training. Intuitively, when we train the embedding encoding network and the sample interaction network together with the end-to-end manner and the consistent loss in Sec. 3.4, the encoding network will also be improved with the helpful supervision of embedding interaction." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "content": "Time Complexity. Two matching methods have different time complexity in cross-modal retrieval. Given " + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "content": " image-text pairs, separate encoding makes the time complexity of embedding-based methods to be " + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "inline_equation", + "content": "O(2N)" + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "content": ", while cross-modal interaction makes score-based to be " + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "inline_equation", + "content": "O(N^2)" + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "content": ". Given one query and the set to be retrieved " + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "content": " samples, the time complexity of query retrieval is " + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "inline_equation", + "content": "O(1)" + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "content": " for embedding-based, but is " + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "inline_equation", + "content": "O(N)" + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "content": " for score-based. Hence score-based methods usually sacrifice the retrieval speed for the performance boost. However, our method can achieve both highly accurate and efficient retrieval, as shown in Fig. 5." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15164" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 49, + 113, + 545, + 297 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 547, + 105 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 547, + 105 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 547, + 105 + ], + "type": "text", + "content": "Table 2. Comparisons of image-text retrieval performances on Flickr30K and MS-COCO 1K test-set. Region represents using Faster-RCNN [37] to extract region features [1] for images. BiGRU [38] and BERT [6] represent using them to extract word features for texts. We list the existing state-of-the-art embedding-based image-text matching methods. * indicates the ensemble results of two models." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 49, + 113, + 545, + 297 + ], + "lines": [ + { + "bbox": [ + 49, + 113, + 545, + 297 + ], + "spans": [ + { + "bbox": [ + 49, + 113, + 545, + 297 + ], + "type": "table", + "html": "
MethodFlickr30K 1KMS-COCO 1K
IMG → TEXTTEXT → IMGrSumIMG → TEXTTEXT → IMGrSum
R@1R@5R@10R@1R@5R@10R@1R@5R@10R@1R@5R@10
Region + BiGRU
VSRN*2019 [20]71.390.696.054.781.888.2482.676.294.898.262.889.795.1516.8
CVSE2020 [43]73.592.195.852.980.487.8482.474.895.198.359.989.495.2512.7
GPO2021 [4]76.594.297.756.483.489.9498.178.596.098.761.790.395.6520.8
MV2022 [24]79.094.997.759.184.690.6505.878.795.798.762.790.495.7521.9
HREM (Fusion)79.594.397.459.385.191.2506.880.096.098.762.790.195.4522.8
HREM (Full)*81.496.598.560.985.691.3514.381.296.598.963.790.796.0527.1
Region + BERT
CAMERA*2020 [35]78.095.197.960.385.991.7508.977.596.398.863.490.995.8522.7
DSRAN*2021 [45]77.895.197.659.286.091.9507.678.395.798.464.590.895.8523.5
GPO2021 [4]81.795.497.661.485.991.5513.579.796.498.964.891.496.3527.5
VSRN++2022 [21]79.294.697.560.685.691.4508.977.996.098.564.191.096.1523.6
HREM (Fusion)83.396.098.163.587.192.4520.481.196.698.966.191.696.5530.7
HREM (Full)*84.096.198.664.488.093.1524.282.996.999.067.192.096.6534.6
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Experimental Setup" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 352, + 287, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 352, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 46, + 352, + 287, + 460 + ], + "type": "text", + "content": "Datasets & Metrics. We choose the typical Flickr30K [47] and MS-COCO [26] datasets, where each image is associated with five texts. Flickr30K contains 29,000, 1,000, and 1,014 training, testing, and validation images, respectively. MS-COCO contains 82,738, 5,000, and 5,000 training, testing, and validation images, respectively. The results of MS-COCO are tested on averaging over 5-folds of 1K test images and on the entire 5K test images. Following [4], We evaluate performances by the metric, " + }, + { + "bbox": [ + 46, + 352, + 287, + 460 + ], + "type": "inline_equation", + "content": "R@K" + }, + { + "bbox": [ + 46, + 352, + 287, + 460 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 352, + 287, + 460 + ], + "type": "inline_equation", + "content": "rSum" + }, + { + "bbox": [ + 46, + 352, + 287, + 460 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "spans": [ + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "content": "Implementation Details. The embedding dimension " + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "inline_equation", + "content": "d = 1024" + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "content": " as previous work [4]. We use the pre-extracted region features [1] for images, and we use the BiGRU [38] with GloVe [34] or BERT-base [6] to extract textual features. The batch size " + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "inline_equation", + "content": "N = 128" + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "content": " for Flickr30K and " + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "inline_equation", + "content": "N = 256" + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "content": " for MS-COCO. The layer of interaction mechanism " + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "inline_equation", + "content": "L = 1" + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "content": ", the hyper-parameters as " + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "inline_equation", + "content": "\\beta = 0.8" + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "inline_equation", + "content": "\\tau = 0.5" + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "inline_equation", + "content": "\\lambda = 1.5" + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "inline_equation", + "content": "K = 10" + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "content": ", and the margin of triplet loss " + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "inline_equation", + "content": "\\alpha = 0.2" + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 563, + 270, + 576 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 563, + 270, + 576 + ], + "spans": [ + { + "bbox": [ + 47, + 563, + 270, + 576 + ], + "type": "text", + "content": "4.2. Comparison with State-of-the-art Methods" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "text", + "content": "We follow the standard evaluation protocols [51] on two datasets. We first perform two proposed interaction mechanisms individually, then report the ensemble results." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 617, + 288, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 288, + 700 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 288, + 700 + ], + "type": "text", + "content": "On Flickr30K. Quantitative results on Flickr30K 1K test-set are shown in Tab. 2, where our proposed method outperforms all state-of-the-art embedding-based imagetext matching methods [20, 21, 24, 35, 45] with impressive margins for the R@K and rSum. Furthermore, our method has the coincident superiority on different textual encoders [6, 38] and still gets the best on the ensemble results." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 59, + 701, + 288, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 701, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 701, + 288, + 712 + ], + "type": "text", + "content": "On MS-COCO. Performances on MS-COCO 1K and" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 307, + 313, + 388, + 375 + ], + "blocks": [ + { + "bbox": [ + 307, + 313, + 388, + 375 + ], + "lines": [ + { + "bbox": [ + 307, + 313, + 388, + 375 + ], + "spans": [ + { + "bbox": [ + 307, + 313, + 388, + 375 + ], + "type": "image", + "image_path": "ecf9dace5631dd2475e4bf13f020bd28f1431f64470ccd2109b7b606ca9f1703.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 339, + 375, + 358, + 385 + ], + "lines": [ + { + "bbox": [ + 339, + 375, + 358, + 385 + ], + "spans": [ + { + "bbox": [ + 339, + 375, + 358, + 385 + ], + "type": "text", + "content": "(a) " + }, + { + "bbox": [ + 339, + 375, + 358, + 385 + ], + "type": "inline_equation", + "content": "\\lambda" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 391, + 313, + 466, + 375 + ], + "blocks": [ + { + "bbox": [ + 391, + 313, + 466, + 375 + ], + "lines": [ + { + "bbox": [ + 391, + 313, + 466, + 375 + ], + "spans": [ + { + "bbox": [ + 391, + 313, + 466, + 375 + ], + "type": "image", + "image_path": "c80d01a5cc187d9c768679899aec530cde95d6e5681bbe7bb32f706d61abc896.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 415, + 375, + 437, + 385 + ], + "lines": [ + { + "bbox": [ + 415, + 375, + 437, + 385 + ], + "spans": [ + { + "bbox": [ + 415, + 375, + 437, + 385 + ], + "type": "text", + "content": "(b) " + }, + { + "bbox": [ + 415, + 375, + 437, + 385 + ], + "type": "inline_equation", + "content": "K" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 468, + 313, + 544, + 375 + ], + "blocks": [ + { + "bbox": [ + 468, + 313, + 544, + 375 + ], + "lines": [ + { + "bbox": [ + 468, + 313, + 544, + 375 + ], + "spans": [ + { + "bbox": [ + 468, + 313, + 544, + 375 + ], + "type": "image", + "image_path": "afb59d42c1da43919b964d82eaa10f87f43fe510cde8bb88475478b061102d10.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 500, + 375, + 518, + 385 + ], + "lines": [ + { + "bbox": [ + 500, + 375, + 518, + 385 + ], + "spans": [ + { + "bbox": [ + 500, + 375, + 518, + 385 + ], + "type": "text", + "content": "(c) " + }, + { + "bbox": [ + 500, + 375, + 518, + 385 + ], + "type": "inline_equation", + "content": "\\tau" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 307, + 441, + 425, + 512 + ], + "blocks": [ + { + "bbox": [ + 305, + 394, + 545, + 429 + ], + "lines": [ + { + "bbox": [ + 305, + 394, + 545, + 429 + ], + "spans": [ + { + "bbox": [ + 305, + 394, + 545, + 429 + ], + "type": "text", + "content": "Figure 6. Effect of hyper-parameters. (a) The weight of relevance matrix " + }, + { + "bbox": [ + 305, + 394, + 545, + 429 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 305, + 394, + 545, + 429 + ], + "type": "text", + "content": " for Eq. (12), (b) The number of local matching " + }, + { + "bbox": [ + 305, + 394, + 545, + 429 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 305, + 394, + 545, + 429 + ], + "type": "text", + "content": " for Eq. (8), (c) The threshold of neighbor space " + }, + { + "bbox": [ + 305, + 394, + 545, + 429 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 305, + 394, + 545, + 429 + ], + "type": "text", + "content": " for Eq. (5, 6)." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 307, + 441, + 425, + 512 + ], + "lines": [ + { + "bbox": [ + 307, + 441, + 425, + 512 + ], + "spans": [ + { + "bbox": [ + 307, + 441, + 425, + 512 + ], + "type": "image", + "image_path": "26de47d68ee660acb49b6183a9d33c84463bcd10984d4c378b9ba23173e67723.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 521, + 545, + 555 + ], + "lines": [ + { + "bbox": [ + 305, + 521, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 305, + 521, + 545, + 555 + ], + "type": "text", + "content": "Figure 7. The curve of triplet loss with of hard negative mining. The loss values are computed by enhanced and initial embeddings, which are the first and second item of Eq. (14), respectively." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 435, + 441, + 544, + 512 + ], + "blocks": [ + { + "bbox": [ + 435, + 441, + 544, + 512 + ], + "lines": [ + { + "bbox": [ + 435, + 441, + 544, + 512 + ], + "spans": [ + { + "bbox": [ + 435, + 441, + 544, + 512 + ], + "type": "image", + "image_path": "c3457c8d9d4064a82cd7d3916966f118d74a31124d5f35afd1a1c40e59262d26.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 576, + 545, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 576, + 545, + 661 + ], + "spans": [ + { + "bbox": [ + 304, + 576, + 545, + 661 + ], + "type": "text", + "content": "5K test-set are shown in Tab. 1 and Tab. 2. Our method performs best compared with existing state-of-the-art methods [4,35,45] on the 1K test set. Furthermore, on the more extensive database of 5K test set, our method outperforms previous work [21,50,51] with larger performance gaps, both embedding-based and score-based methods, which shows the superiority of our method more convincingly." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 306, + 670, + 509, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 670, + 509, + 684 + ], + "spans": [ + { + "bbox": [ + 306, + 670, + 509, + 684 + ], + "type": "text", + "content": "4.3. Ablation Study & Robustness Analysis" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 306, + 689, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 545, + 714 + ], + "type": "text", + "content": "By default, we perform the experiments on our framework of fusion mechanism with Region+BERT settings." + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15165" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 50, + 114, + 286, + 193 + ], + "blocks": [ + { + "bbox": [ + 47, + 71, + 287, + 93 + ], + "lines": [ + { + "bbox": [ + 47, + 71, + 287, + 93 + ], + "spans": [ + { + "bbox": [ + 47, + 71, + 287, + 93 + ], + "type": "text", + "content": "Table 3. Comparison of different module ablation for our framework. " + }, + { + "bbox": [ + 47, + 71, + 287, + 93 + ], + "type": "inline_equation", + "content": "\\checkmark" + }, + { + "bbox": [ + 47, + 71, + 287, + 93 + ], + "type": "text", + "content": " means retaining it (or otherwise removing it)." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 54, + 102, + 280, + 112 + ], + "lines": [ + { + "bbox": [ + 54, + 102, + 280, + 112 + ], + "spans": [ + { + "bbox": [ + 54, + 102, + 280, + 112 + ], + "type": "text", + "content": "(a) The ablation study of hierarchical relation modeling on Flickr30K" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 114, + 286, + 193 + ], + "lines": [ + { + "bbox": [ + 50, + 114, + 286, + 193 + ], + "spans": [ + { + "bbox": [ + 50, + 114, + 286, + 193 + ], + "type": "table", + "html": "
Fragment-level VisualTextualInstance-levelIMG → TEXTTEXT → IMG
Intra-modalInter-modalR@1R@5R@1TR@5
✓✓81.594.962.386.2
✓✓✓81.895.162.486.2
✓✓✓83.195.963.287.0
✓✓80.194.860.985.3
✓✓✓80.594.761.285.2
✓✓✓82.295.562.686.4
✓✓✓✓83.396.063.587.1
", + "image_path": "e7b69bedc5327f69c44f066d7f892a0fe705dddcb25b88ac276c0937a0b41a9c.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 50, + 208, + 286, + 297 + ], + "blocks": [ + { + "bbox": [ + 52, + 195, + 284, + 205 + ], + "lines": [ + { + "bbox": [ + 52, + 195, + 284, + 205 + ], + "spans": [ + { + "bbox": [ + 52, + 195, + 284, + 205 + ], + "type": "text", + "content": "(b) The ablation study of instance-level relation modeling on Flickr30K" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 208, + 286, + 297 + ], + "lines": [ + { + "bbox": [ + 50, + 208, + 286, + 297 + ], + "spans": [ + { + "bbox": [ + 50, + 208, + 286, + 297 + ], + "type": "table", + "html": "
MethodsIMG → TEXTTEXT → IMG
R@1R@5R@1TR@5
w/o connection matrix A81.495.161.586.3
w/o relevance matrix S81.795.361.986.5
w/o consistency Lcross81.695.661.886.7
w/o regularization Lreg82.895.862.986.9
w/o neighbor batch sampling82.895.963.187.0
HREM83.396.063.587.1
", + "image_path": "6f2c0f35a5d8777845e82742d60633593d10a1e83324ab6784a165eee92fbd6e.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 325, + 287, + 468 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 325, + 287, + 468 + ], + "spans": [ + { + "bbox": [ + 46, + 325, + 287, + 468 + ], + "type": "text", + "content": "Hyper-parameters. Fig. 6 shows the effect of three critical parameters, which demonstrates the significance of capturing sample relations and the effectiveness of our proposed modules. " + }, + { + "bbox": [ + 46, + 325, + 287, + 468 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 325, + 287, + 468 + ], + "type": "text", + "content": " is the attention coefficient for the relevance matrix, thus it should be large enough to provide extra fine-grained supervision, " + }, + { + "bbox": [ + 46, + 325, + 287, + 468 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 325, + 287, + 468 + ], + "type": "text", + "content": " is related to the number of words in variable sentences, thus it cannot be too large to adapt short texts. " + }, + { + "bbox": [ + 46, + 325, + 287, + 468 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 46, + 325, + 287, + 468 + ], + "type": "text", + "content": " represents the ratio of connected samples in the batch, it will not be meaningful if close to 0 or 1. Finally, the performances are relatively stable when all parameter values change in a proper range, our method is insensitive to hyper-parameter selection." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 472, + 286, + 638 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 472, + 286, + 638 + ], + "spans": [ + { + "bbox": [ + 46, + 472, + 286, + 638 + ], + "type": "text", + "content": "Hierarchical Relation. To better verify the effectiveness of hierarchical relation modeling, we provide the ablation study in Tab. 3a. It shows that both instance-level and fragment-level relation modeling help improve the learning of cross-modal embeddings. First, capturing the instance-level relation, especially the inter-modal relation, is more critical than the intra-modal and fragment-level relation. Besides, capturing fragment-level relations for the textual modality seems redundant. We believe that word features have already learned contextual semantics by the textual encoders, e.g., BiGRU [38] and BERT [6]. However, exploring the semantic relations for region features are meaningful as previous work [20, 21, 46]. Finally, modeling all hierarchical relations is also essential for the optimal result." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 642, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 287, + 714 + ], + "type": "text", + "content": "Instance-level Relation. Tab. 3b shows the ablation of our instance-level relation modeling. First, the connection and relevance matrices are significant in explicitly capturing the cross-modal relation. And the cross-embedding matching loss " + }, + { + "bbox": [ + 46, + 642, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{cross}" + }, + { + "bbox": [ + 46, + 642, + 287, + 714 + ], + "type": "text", + "content": " is indispensable to keep the embedding consistency at the inference stage. Besides, the regularization" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 309, + 135, + 544, + 276 + ], + "blocks": [ + { + "bbox": [ + 305, + 71, + 545, + 125 + ], + "lines": [ + { + "bbox": [ + 305, + 71, + 545, + 125 + ], + "spans": [ + { + "bbox": [ + 305, + 71, + 545, + 125 + ], + "type": "text", + "content": "Table 4. R@1 comparison on hard samples with semantic ambiguity and infrequent samples with semantic scarcity in different proportions, selected by the percentile rank of average embedding similarity between themselves and other samples in the dataset. " + }, + { + "bbox": [ + 305, + 71, + 545, + 125 + ], + "type": "inline_equation", + "content": "\\checkmark" + }, + { + "bbox": [ + 305, + 71, + 545, + 125 + ], + "type": "text", + "content": " means training with sample relation modeling." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 135, + 544, + 276 + ], + "lines": [ + { + "bbox": [ + 309, + 135, + 544, + 276 + ], + "spans": [ + { + "bbox": [ + 309, + 135, + 544, + 276 + ], + "type": "table", + "html": "
Samples TypePercentile RankSample InteractionFlickr30KMS-COCO 5K
I → TT → II → TT → I
Hard5%✓74.632.036.48.5
76.034.840.49.2
10%✓72.038.839.212.3
75.040.444.613.6
20%✓74.545.143.618.5
78.047.149.319.5
Infrequent5%✓79.238.450.816.7
84.040.859.219.4
10%✓78.041.253.019.8
81.243.661.022.6
20%✓75.046.654.823.9
82.048.461.026.2
", + "image_path": "c31502b568b8f8122f736415e311b538636bcb79f5620f43e2fb0a60be705205.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 294, + 545, + 330 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 294, + 545, + 330 + ], + "spans": [ + { + "bbox": [ + 305, + 294, + 545, + 330 + ], + "type": "text", + "content": "loss " + }, + { + "bbox": [ + 305, + 294, + 545, + 330 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{reg}" + }, + { + "bbox": [ + 305, + 294, + 545, + 330 + ], + "type": "text", + "content": " can ensure the training stability to improve performances. Finally, the neighbor batch sampling adapts to mine more potential connected samples from mini-batch." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 331, + 545, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 331, + 545, + 437 + ], + "spans": [ + { + "bbox": [ + 304, + 331, + 545, + 437 + ], + "type": "text", + "content": "Special Samples. Tab. 4 shows the performance comparison of hard samples with semantic ambiguity and infrequent samples with semantic scarcity in the test set (hard and infrequent samples will get high average similarity with other negative samples in datasets). We find our method can improve these special samples significantly. Fig. 7 shows the curve of triplet loss with hard negative mining, the values of enhanced embeddings are lower than the initial, proving the capacity for recognizing hard samples." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 437, + 545, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 437, + 545, + 510 + ], + "spans": [ + { + "bbox": [ + 304, + 437, + 545, + 510 + ], + "type": "text", + "content": "Retrieval Speed. We show the trade-off between performance and computation for cross-modal retrieval in Fig. 5. Although our method belongs to the embedding-based image-text matching methods [4,20], achieves both high accuracy and efficiency, and is more than 10 times faster than the latest score-based methods [7, 19, 51] on Flickr30K." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 520, + 378, + 532 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 520, + 378, + 532 + ], + "spans": [ + { + "bbox": [ + 306, + 520, + 378, + 532 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 540, + 545, + 659 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 540, + 545, + 659 + ], + "spans": [ + { + "bbox": [ + 304, + 540, + 545, + 659 + ], + "type": "text", + "content": "This paper proposes a novel hierarchical relation modeling framework (HREM) for image-text matching. HREM not only captures fragment-level relations within the single modality and sample, but also effectively exploits instance-level relations across different modalities and samples to learn better holistic embeddings. Based on our design, HREM encodes embedding without interaction on sample or modality at the inference stage, thus achieving high efficiency on cross-modal retrieval. Extensive experiments on two benchmarks show the superiority of our method." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 670, + 424, + 683 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 670, + 424, + 683 + ], + "spans": [ + { + "bbox": [ + 306, + 670, + 424, + 683 + ], + "type": "text", + "content": "6. Acknowledgements" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 689, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 545, + 712 + ], + "type": "text", + "content": "This work is supported by the National Natural Science Foundation of China under Grant 62222212, 62121002." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15166" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 156 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 156 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 156 + ], + "type": "text", + "content": "[1] Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. Bottom-up and top-down attention for image captioning and visual question answering. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6077-6086, 2018. 3, 6, 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 158, + 288, + 178 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 158, + 288, + 178 + ], + "spans": [ + { + "bbox": [ + 53, + 158, + 288, + 178 + ], + "type": "text", + "content": "[2] Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. ArXiv, abs/1607.06450, 2016. 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 180, + 288, + 245 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 180, + 288, + 245 + ], + "spans": [ + { + "bbox": [ + 53, + 180, + 288, + 245 + ], + "type": "text", + "content": "[3] Hui Chen, Guiguang Ding, Xudong Liu, Zijia Lin, Ji Liu, and Jungong Han. Imram: Iterative matching with recurrent attention memory for cross-modal image-text retrieval. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 12652-12660, 2020. 1, 2, 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 247, + 288, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 247, + 288, + 300 + ], + "spans": [ + { + "bbox": [ + 53, + 247, + 288, + 300 + ], + "type": "text", + "content": "[4] Jiacheng Chen, Hexiang Hu, Hao Wu, Yuning Jiang, and Chang Lian Wang. Learning the best pooling strategy for visual semantic embedding. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15784-15793, 2021. 1, 2, 6, 7, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 302, + 287, + 345 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 302, + 287, + 345 + ], + "spans": [ + { + "bbox": [ + 53, + 302, + 287, + 345 + ], + "type": "text", + "content": "[5] Yuhao Cheng, Xiaoguang Zhu, Jiuchao Qian, Fei Wen, and Peilin Liu. Cross-modal graph matching network for imagetext retrieval. ACM Trans. Multimedia Comput. Commun. Appl., 18(4), mar 2022. 2, 4, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 346, + 287, + 389 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 346, + 287, + 389 + ], + "spans": [ + { + "bbox": [ + 53, + 346, + 287, + 389 + ], + "type": "text", + "content": "[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv, abs/1810.04805, 2019. 3, 6, 7, 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 391, + 287, + 423 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 391, + 287, + 423 + ], + "spans": [ + { + "bbox": [ + 53, + 391, + 287, + 423 + ], + "type": "text", + "content": "[7] Haiwen Diao, Ying Zhang, Lingyun Ma, and Huchuan Lu. Similarity reasoning and filtration for image-text matching. ArXiv, abs/2101.01368, 2021. 1, 2, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 425, + 287, + 479 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 425, + 287, + 479 + ], + "spans": [ + { + "bbox": [ + 53, + 425, + 287, + 479 + ], + "type": "text", + "content": "[8] Ismail Elezi, Jenny Seidenschwarz, Laurin Wagner, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, and Laura Leal-Taixe. The group loss: A deeper look into group loss for deep metric learning. IEEE transactions on pattern analysis and machine intelligence, PP, 2022. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 480, + 287, + 513 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 480, + 287, + 513 + ], + "spans": [ + { + "bbox": [ + 53, + 480, + 287, + 513 + ], + "type": "text", + "content": "[9] Ismail Elezi, Sebastiano Vascon, Alessandro Torcinovich, Marcello Pelillo, and Laura Leal-Taixe. The group loss for deep metric learning. ArXiv, abs/1912.00385, 2020. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 514, + 287, + 546 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 514, + 287, + 546 + ], + "spans": [ + { + "bbox": [ + 48, + 514, + 287, + 546 + ], + "type": "text", + "content": "[10] Fartash Faghri, David J. Fleet, Jamie Ryan Kiros, and Sanja Fidler. Vse++: Improving visual-semantic embeddings with hard negatives. In BMVC, 2018. 1, 2, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 547, + 287, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 547, + 287, + 601 + ], + "spans": [ + { + "bbox": [ + 48, + 547, + 287, + 601 + ], + "type": "text", + "content": "[11] Zhihao Fan, Zhongyu Wei, Zejun Li, Siyuan Wang, Hajun Shan, Xuanjing Huang, and Jianqing Fan. Constructing phrase-level semantic labels to form multi-grained supervision for image-text retrieval. ArXiv, abs/2109.05523, 2021. 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 602, + 287, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 602, + 287, + 646 + ], + "spans": [ + { + "bbox": [ + 48, + 602, + 287, + 646 + ], + "type": "text", + "content": "[12] Zheren Fu, Yan Li, Zhendong Mao, Quan Wang, and Yongdong Zhang. Deep metric learning with self-supervised ranking. In Proceedings of the 35th AAAI Conference on Artificial Intelligence, 2021. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 647, + 287, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 287, + 690 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 287, + 690 + ], + "type": "text", + "content": "[13] Zheren Fu, Zhendong Mao, Bo Hu, An-An Liu, and Yongdong Zhang. Intra-class adaptive augmentation with neighbor correction for deep metric learning. IEEE Transactions on Multimedia, pages 1-14, 2022. 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 692, + 287, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 692, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 48, + 692, + 287, + 714 + ], + "type": "text", + "content": "[14] Zheren Fu, Zhendong Mao, Chenggang Clarence Yan, An an Liu, Hongtao Xie, and Yongdong Zhang. Self-supervised" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 106 + ], + "type": "text", + "content": "synthesis ranking for deep metric learning. IEEE Transactions on Circuits and Systems for Video Technology, 32:4736-4750, 2022. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 107, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 107, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 308, + 107, + 545, + 150 + ], + "type": "text", + "content": "[15] Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016. 3, 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 152, + 545, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 152, + 545, + 205 + ], + "spans": [ + { + "bbox": [ + 308, + 152, + 545, + 205 + ], + "type": "text", + "content": "[16] Zhi Hou, Baosheng Yu, and Dacheng Tao. Batchformer: Learning to explore sample relationships for robust representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7256-7266, 2022. 3, 4" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 206, + 545, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 206, + 545, + 259 + ], + "spans": [ + { + "bbox": [ + 308, + 206, + 545, + 259 + ], + "type": "text", + "content": "[17] Mengqi Huang, Zhendong Mao, Penghui Wang, Quang Wang, and Yongdong Zhang. Dse-gan: Dynamic semantic evolution generative adversarial network for text-to-image generation. Proceedings of the 30th ACM International Conference on Multimedia, 2022. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 261, + 545, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 261, + 545, + 282 + ], + "spans": [ + { + "bbox": [ + 308, + 261, + 545, + 282 + ], + "type": "text", + "content": "[18] Sungyeon Kim, Dongwon Kim, Minsu Cho, and Suha Kwak. Self-taught metric learning without labels. CVPR, 2022. 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 284, + 545, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 284, + 545, + 327 + ], + "spans": [ + { + "bbox": [ + 308, + 284, + 545, + 327 + ], + "type": "text", + "content": "[19] Kuang-Huei Lee, Xi Chen, Gang Hua, Houdong Hu, and Xiaodong He. Stacked cross attention for image-text matching. In Proceedings of the European Conference on Computer Vision (ECCV), pages 201-216, 2018. 1, 2, 4, 5, 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 328, + 545, + 371 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 328, + 545, + 371 + ], + "spans": [ + { + "bbox": [ + 308, + 328, + 545, + 371 + ], + "type": "text", + "content": "[20] Kunpeng Li, Yulun Zhang, K. Li, Yuanyuan Li, and Yun Raymond Fu. Visual semantic reasoning for image-text matching. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 4653-4661, 2019. 1, 2, 7, 8" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 373, + 545, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 373, + 545, + 425 + ], + "spans": [ + { + "bbox": [ + 308, + 373, + 545, + 425 + ], + "type": "text", + "content": "[21] Kunpeng Li, Yulun Zhang, Kai Li, Yuanyuan Li, and Yun Raymond Fu. Image-text embedding learning via visual and textual semantic reasoning. IEEE transactions on pattern analysis and machine intelligence, PP, 2022. 2, 6, 7, 8" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 426, + 545, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 426, + 545, + 480 + ], + "spans": [ + { + "bbox": [ + 308, + 426, + 545, + 480 + ], + "type": "text", + "content": "[22] Pandeng Li, Hongtao Xie, Jiannan Ge, Lei Zhang, Shaobo Min, and Yongdong Zhang. Dual-stream knowledge-preserving hashing for unsupervised video retrieval. In European Conference on Computer Vision, pages 181-197. Springer, 2022. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 482, + 545, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 482, + 545, + 536 + ], + "spans": [ + { + "bbox": [ + 308, + 482, + 545, + 536 + ], + "type": "text", + "content": "[23] Shuang Li, Kaixiong Gong, Chi Harold Liu, Yulin Wang, Feng Qiao, and Xinjing Cheng. Metasaug: Meta semantic augmentation for long-tailed visual recognition. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5208-5217, 2021. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 537, + 545, + 568 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 537, + 545, + 568 + ], + "spans": [ + { + "bbox": [ + 308, + 537, + 545, + 568 + ], + "type": "text", + "content": "[24] Zheng Li, Caili Guo, Zerun Feng, Jenq-Neng Hwang, and Xijun Xue. Multi-view visual semantic embedding. In IJ-CAI, 2022. 2, 6, 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 571, + 545, + 614 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 571, + 545, + 614 + ], + "spans": [ + { + "bbox": [ + 308, + 571, + 545, + 614 + ], + "type": "text", + "content": "[25] Jongin Lim, Sangdoo Yun, Seulki Park, and Jin Young Choi. Hypergraph-induced semantic tuplet loss for deep metric learning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 212-222, 2022. 3, 4" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 615, + 545, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 615, + 545, + 657 + ], + "spans": [ + { + "bbox": [ + 308, + 615, + 545, + 657 + ], + "type": "text", + "content": "[26] Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C. Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 7" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 658, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 658, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 658, + 545, + 713 + ], + "type": "text", + "content": "[27] Chunxiao Liu, Zhendong Mao, Tianzhu Zhang, Hongtao Xie, Bin Wang, and Yongdong Zhang. Graph structured network for image-text matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10921-10930, 2020. 1" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "15167" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "text", + "content": "[28] Xin Liu, Yi He, Yiu ming Cheung, Xing Xu, and N. Wang. Learning relationship-enhanced semantic graph for fine-grained image-text matching. IEEE transactions on cybernetics, PP, 2022. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 119, + 287, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 119, + 287, + 152 + ], + "spans": [ + { + "bbox": [ + 49, + 119, + 287, + 152 + ], + "type": "text", + "content": "[29] Thang Luong, Hieu Pham, and Christopher D. Manning. Effective approaches to attention-based neural machine translation. ArXiv, abs/1508.04025, 2015. 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 153, + 287, + 186 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 153, + 287, + 186 + ], + "spans": [ + { + "bbox": [ + 48, + 153, + 287, + 186 + ], + "type": "text", + "content": "[30] J MacQueen. Classification and analysis of multivariate observations. In 5th Berkeley Symp. Math. Statist. Probability, pages 281-297, 1967. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 188, + 287, + 232 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 188, + 287, + 232 + ], + "spans": [ + { + "bbox": [ + 48, + 188, + 287, + 232 + ], + "type": "text", + "content": "[31] R. Manmatha, Chaoxia Wu, Alex Smola, and Philipp Krahenbihl. Sampling matters in deep embedding learning. 2017 IEEE International Conference on Computer Vision (ICCV), pages 2859-2867, 2017. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 234, + 287, + 289 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 234, + 287, + 289 + ], + "spans": [ + { + "bbox": [ + 48, + 234, + 287, + 289 + ], + "type": "text", + "content": "[32] Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari, and Zeynep Akata. Learning graph embeddings for compositional zero-shot learning. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 953-962, 2021. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 291, + 287, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 291, + 287, + 323 + ], + "spans": [ + { + "bbox": [ + 48, + 291, + 287, + 323 + ], + "type": "text", + "content": "[33] Xingchao Peng, Zijun Huang, Xineng Sun, and Kate Saenko. Domain agnostic learning with disentangled representations. ArXiv, abs/1904.12347, 2019. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 326, + 287, + 357 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 326, + 287, + 357 + ], + "spans": [ + { + "bbox": [ + 48, + 326, + 287, + 357 + ], + "type": "text", + "content": "[34] Jeffrey Pennington, Richard Socher, and Christopher D. Manning. Glove: Global vectors for word representation. In EMNLP, 2014. 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 360, + 287, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 360, + 287, + 403 + ], + "spans": [ + { + "bbox": [ + 48, + 360, + 287, + 403 + ], + "type": "text", + "content": "[35] Leigang Qu, Meng Liu, Da Cao, Liqiang Nie, and Qi Tian. Context-aware multi-view summarization network for image-text matching. Proceedings of the 28th ACM International Conference on Multimedia, 2020. 1, 2, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 406, + 287, + 460 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 406, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 48, + 406, + 287, + 460 + ], + "type": "text", + "content": "[36] Leigang Qu, Meng Liu, Jianlong Wu, Zan Gao, and Liqiang Nie. Dynamic modality interaction modeling for image-text retrieval. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 462, + 287, + 506 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 462, + 287, + 506 + ], + "spans": [ + { + "bbox": [ + 48, + 462, + 287, + 506 + ], + "type": "text", + "content": "[37] Shaoqing Ren, Kaiming He, Ross B. Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39:1137-1149, 2015. 3, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 509, + 287, + 540 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 509, + 287, + 540 + ], + "spans": [ + { + "bbox": [ + 48, + 509, + 287, + 540 + ], + "type": "text", + "content": "[38] Mike Schuster and Kuldip K. Paliwal. Bidirectional recurrent neural networks. IEEE Trans. Signal Process., 45:2673-2681, 1997. 3, 6, 7, 8" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 543, + 287, + 575 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 543, + 287, + 575 + ], + "spans": [ + { + "bbox": [ + 48, + 543, + 287, + 575 + ], + "type": "text", + "content": "[39] Jenny Seidenschwarz, Ismail Elezi, and Laura Leal-Taix'e. Learning intra-batch connections for deep metric learning. In ICML, 2021. 2, 3, 4" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 578, + 287, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 578, + 287, + 632 + ], + "spans": [ + { + "bbox": [ + 48, + 578, + 287, + 632 + ], + "type": "text", + "content": "[40] Zhan Shi, Tianzhu Zhang, Xiaoyan Wei, Feng Wu, and Yongdong Zhang. Decoupled cross-modal phrase-attention network for image-sentence matching. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, PP, 2022. 4, 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 635, + 287, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 635, + 287, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 635, + 287, + 678 + ], + "type": "text", + "content": "[41] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3, 5" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 680, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 287, + 712 + ], + "type": "text", + "content": "[42] Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio', and Yoshua Bengio. Graph attention networks. ArXiv, abs/1710.10903, 2018. 3" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 496 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "text", + "content": "[43] Haoran Wang, Ying Zhang, Zhong Ji, Yanwei Pang, and Lingyun Ma. Consensus-aware visual-semantic embedding for image-text matching. In ECCV, 2020. 2, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 107, + 545, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 545, + 161 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 545, + 161 + ], + "type": "text", + "content": "[44] Sijin Wang, Ruiping Wang, Ziwei Yao, S. Shan, and Xilin Chen. Cross-modal scene graph matching for relationship-aware image-text retrieval. 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1497-1506, 2020. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 163, + 545, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 163, + 545, + 206 + ], + "spans": [ + { + "bbox": [ + 307, + 163, + 545, + 206 + ], + "type": "text", + "content": "[45] Keyu Wen, Xiaodong Gu, and Qingrong Cheng. Learning dual semantic relations with graph attention for image-text matching. IEEE Transactions on Circuits and Systems for Video Technology, 31:2866-2879, 2021. 2, 4, 6, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 208, + 545, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 208, + 545, + 251 + ], + "spans": [ + { + "bbox": [ + 307, + 208, + 545, + 251 + ], + "type": "text", + "content": "[46] Yiling Wu, Shuhui Wang, Guoli Song, and Qingming Huang. Learning fragment self-attention embeddings for image-text matching. Proceedings of the 27th ACM International Conference on Multimedia, 2019. 2, 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 253, + 545, + 306 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 253, + 545, + 306 + ], + "spans": [ + { + "bbox": [ + 307, + 253, + 545, + 306 + ], + "type": "text", + "content": "[47] Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. Transactions of the Association for Computational Linguistics, 2:67-78, 2014. 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 308, + 545, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 308, + 545, + 363 + ], + "spans": [ + { + "bbox": [ + 307, + 308, + 545, + 363 + ], + "type": "text", + "content": "[48] Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Young Joon Yoo. Cutmix: Regularization strategy to train strong classifiers with localizable features. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 6022-6031, 2019. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 365, + 545, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 365, + 545, + 396 + ], + "spans": [ + { + "bbox": [ + 307, + 365, + 545, + 396 + ], + "type": "text", + "content": "[49] Hongyi Zhang, Moustapha Cisse, Yann Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. ArXiv, abs/1710.09412, 2018. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 398, + 545, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 398, + 545, + 441 + ], + "spans": [ + { + "bbox": [ + 307, + 398, + 545, + 441 + ], + "type": "text", + "content": "[50] Kun Zhang, Zhendong Mao, Anan Liu, and Yongdong Zhang. Unified adaptive relevance distinguishable attention network for image-text matching. IEEE Transactions on Multimedia, pages 1-1, 2022. 6, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 443, + 545, + 496 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 443, + 545, + 496 + ], + "spans": [ + { + "bbox": [ + 307, + 443, + 545, + 496 + ], + "type": "text", + "content": "[51] Kun Zhang, Zhendong Mao, Quan Wang, and Yongdong Zhang. Negative-aware attention framework for image-text matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15661-15670, 2022. 2, 6, 7, 8" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "15168" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Semantic-Aware Disentangled Representation for Flexible 3D Human Body Editing/ba9dabaf-077e-4e19-9ae1-c1f2c2a30c7c_content_list.json b/2023/Learning Semantic-Aware Disentangled Representation for Flexible 3D Human Body Editing/ba9dabaf-077e-4e19-9ae1-c1f2c2a30c7c_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..c82e429232800b67c6b82600cbcb96bfd1297d11 --- /dev/null +++ b/2023/Learning Semantic-Aware Disentangled Representation for Flexible 3D Human Body Editing/ba9dabaf-077e-4e19-9ae1-c1f2c2a30c7c_content_list.json @@ -0,0 +1,1746 @@ +[ + { + "type": "text", + "text": "Learning Semantic-Aware Disentangled Representation for Flexible 3D Human Body Editing", + "text_level": 1, + "bbox": [ + 204, + 130, + 767, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Xiaokun Sun $^{1}$ Qiao Feng $^{1}$ Xiongzheng Li $^{1}$ Jinsong Zhang $^{1}$ Yu-Kun Lai $^{2}$ Jingyu Yang $^{1}$ Kun Li $^{1*}$ $^{1}$ Tianjin University, China $^{2}$ Cardiff University, United Kingdom", + "bbox": [ + 107, + 202, + 867, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$\\{s x k_{-}26,f e n g q i a o,l x z,j i n s z h a n g,y j y,\\text{lik}\\} @t j u.$ edu.cn LaiY4@cardiff.ac.uk", + "bbox": [ + 166, + 241, + 797, + 257 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/09a40f280b371bb4f14776ffe09dc743714193e967d147e1836b503307f8685c.jpg", + "image_caption": [ + "Figure 1. SemanticHuman. We present a semantic-aware and editable human body representation with high reconstruction precision in an unsupervised setting. The key idea is to employ a part-aware skeleton-separated decoupling strategy to learn a geometrically meaningful latent space with fine-grained semantics, which benefits part-level personalized human body editing. After embedding a human body into the bone code $Z_{b}$ and shape code $Z_{s}$ (left), we can flexibly edit human body attributes by modifying the corresponding latent codes (right)." + ], + "image_footnote": [], + "bbox": [ + 138, + 272, + 830, + 479 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 547, + 313, + 561 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "3D human body representation learning has received increasing attention in recent years. However, existing works cannot flexibly, controllably and accurately represent human bodies, limited by coarse semantics and unsatisfactory representation capability, particularly in the absence of supervised data. In this paper, we propose a human body representation with fine-grained semantics and high reconstruction-accuracy in an unsupervised setting. Specifically, we establish a correspondence between latent vectors and geometric measures of body parts by designing a part-aware skeleton-separated decoupling strategy, which facilitates controllable editing of human bodies by modifying the corresponding latent codes. With the help of a bone-guided auto-encoder and an orientation-adaptive weighting strategy, our representation can be trained in an unsupervised manner. With the geometrically meaningful latent space, it can be applied to a wide range of applications, from human body editing to latent code interpolation and shape style transfer. Experimental results on public datasets demonstrate the accurate reconstruction and flexible editing abilities of the proposed method. The code will be available", + "bbox": [ + 75, + 561, + 472, + 878 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "at http://cic.tju.edu.cn/faculty/likun/projects/SemanticHuman.", + "bbox": [ + 500, + 549, + 893, + 580 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 500, + 589, + 632, + 604 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Learning low-dimensional representations of human bodies plays an important role in various applications including human body reconstruction [4, 19, 32, 37], generation [7, 30, 31] and editing [35, 36, 39]. Existing methods [2, 18, 22, 25, 29] are either too semantically coarse to enable personalized human body editing, or suffer from poor reconstruction performance due to limited representation capability. This paper aims to develop a fine-grained semantic-aware human body representation with flexible representation ability.", + "bbox": [ + 496, + 614, + 890, + 765 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Since human bodies are rich in variations of poses and shapes, traditional linear models [1, 25, 29, 35, 36] cannot handle complex nonlinear structures of human body meshes accurately. Therefore, parametric models have been proposed for better representation. The landmark works SCAPE [2] and SMPL [22] represent human bodies by the shape and pose parameters. However, the semantics of their shape parameters are not sufficiently precise, making it impossible to flexibly edit the body shape. Furthermore, the", + "bbox": [ + 496, + 766, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 94, + 888, + 220, + 901 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "16985", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "representation ability of these methods is limited by the linear shape space of human body shapes, and hence their reconstruction accuracy is often unsatisfactory.", + "bbox": [ + 75, + 90, + 468, + 136 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "With the success of deep learning, the encoder-decoder architecture has demonstrated excellent representation capability [7, 10, 13, 14, 26]. Such methods improve the reconstruction precision by constructing different convolution-like operators for feature extraction on irregular meshes. However, these works lack disentangled representation and fail to obtain promising results for geometrically complex human body parts. Several works [3, 9, 11, 18, 38] pursue the disentanglement of latent representations, i.e., each latent code has clear semantics. But these methods either require paired supervised data or have poor performance on the reconstruction, which significantly affects their generalization and robustness. In addition, the semantics of the above representations are coarse, which only enables person-level attribute transfer and cannot be applied to part-level flexible editing.", + "bbox": [ + 75, + 138, + 468, + 380 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we aim to build a human body representation with fine-grained semantics and high reconstruction accuracy in an unsupervised setting, which needs to overcome two main challenges. First, how to disentangle the human body to reconstruct precise semantics is a key but difficult problem. Although it is straightforward to decompose a human body into articulated parts for part-level editing, the hidden space of each part is still coupled. Secondly, providing paired supervised data requires a lot of manual effort, and it is very challenging to make the representation disentangled without sacrificing reconstruction accuracy in an unsupervised manner.", + "bbox": [ + 75, + 382, + 468, + 561 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address these challenges, we propose SemanticHuman, an editable human body representation with fine-grained semantics and high reconstruction-precision, which facilitates controllable human body editing without paired supervised data. To reconstruct fine-grained semantics, we design a part-aware skeleton-separated decoupling strategy with anatomical priors of the human body. Specifically, we disentangle body part variations into bone-related variations (e.g., length and orientation variations) and bone-independent variations (e.g., circumference variations). In contrast to the previous pose and shape disentanglement on the entire person [2, 18, 22], this part-aware skeleton-separated decoupling strategy establishes a correspondence between latent vectors and geometric properties of body parts, which benefits part-level controllable editing.", + "bbox": [ + 75, + 566, + 468, + 792 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To ensure high reconstruction accuracy and fine-grained semantics of the representation by unsupervised learning, we propose a bone-guided autoencoder architecture and an orientation-adaptive geometry-preserving loss. The bone-guided auto-encoder fuses the geometric features of body parts with their joint information to achieve accurate and efficient modeling of human bodies. Besides,", + "bbox": [ + 75, + 795, + 468, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "an orientation-adaptive weighting strategy is introduced to compute the geometry-preserving loss, which can provide effective geometric regularization for unsupervised disentanglement and part-level editing. Experimental results on two public datasets with different mesh connectivities demonstrate the high reconstruction-precision and controllable editing capability of the proposed method. An example is given in Fig. 1. The code will be available at http://cic.tju.edu.cn/faculty/likun/projects/SemanticHuman.", + "bbox": [ + 496, + 90, + 890, + 241 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our main contributions are summarized as follows:", + "bbox": [ + 517, + 242, + 857, + 257 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a semantic-aware and editable human body representation with fine-grained representation ability. The latent space of our approach facilitates personalized editing of human bodies by modifying their latent vectors.", + "- We propose a part-aware skeleton-separated decoupling strategy exploiting structural priors of the human body to learn geometrically meaningful latent codes with fine-grained semantics.", + "- We propose a bone-guided auto-encoder architecture and an orientation-adaptive geometry-preserving loss to ensure the robust and effective disentanglement of the representation learned without supervision." + ], + "bbox": [ + 517, + 267, + 890, + 484 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 497, + 640, + 513 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Classical Human Parametric Models. Since human bodies are geometric structures with strong priors, it is straightforward to use a statistical parametric model to represent human bodies. SCAPE [2] is one of pioneering works, which models variations between different human bodies as shape-related and pose-related deformations. SMPL [22] represents the human body more accurately and robustly based on vertices instead of triangle deformations. The SMPL has been extended to represent animals [40], hands [28], and a combination of hands and faces [24].", + "bbox": [ + 496, + 522, + 890, + 672 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Deep Learning for Mesh Analysis. Traditional convolutional neural networks cannot be directly applied to meshes with irregular structures. A series of investigations [6, 8, 16, 17, 21, 33] has been devoted to constructing convolution-like operators on irregular meshes. Ranjan et al. [26] learn nonlinear representations of human faces by applying spectral convolutions to meshes. Additionally, Bouritsas et al. [7] and Gong et al. [14] propose to analyze per-vertex spatial features using spiral convolution. Chen et al. [10] and Gao et al. [13] improve the robustness and efficiency by adopting attention mechanisms in spatial aggregation. Recent works [30, 31] analyze meshes based on as-consistent-as-possible (ACAP) features [12] rather than Euclidean coordinates to learn large-scale deformations on meshes. However, these methods cannot provide explicit", + "bbox": [ + 496, + 674, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "16986", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/720b324976466e256ae74042d3556ed3ee73527b7d40073492a5774ac404cfd3.jpg", + "image_caption": [ + "Figure 2. SemanticHuman consists of a reconstruction branch, a disentanglement branch, and an editing branch. (a) The encoder $E$ maps a mesh $x_{1}$ into the bone code $Z_{b_{1}} = \\{z_{b_{1}}^{1},\\dots,z_{b_{1}}^{K}\\}$ and shape code $Z_{s_1} = \\{z_{s_1}^1,\\dots,z_{s_1}^K\\}$ , where $K$ is the number of parts, and the decoder $D$ aims to recover the original mesh $D(Z_{b_1},Z_{s_1})$ . (b) With the help of $L_{dis}$ , the swapped codes $(Z_{b_2},Z_{s_1})$ will be fed into $D$ to generate the target mesh $D(Z_{b_2},Z_{s_1})$ retaining the skeleton of $x_{2}$ and shape features of $x_{1}$ . (c) By introducing $L_{edit}$ , we force the generated mesh from the scaled codes $(Z_{b_1},\\alpha Z_{s_1})$ to deform as desired, where $\\alpha$ is a scale factor." + ], + "image_footnote": [], + "bbox": [ + 81, + 88, + 468, + 391 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "semantics and fail to handle complex geometries. We alleviate these problems by introducing an autoencoder framework that carries geometric priors of human bodies.", + "bbox": [ + 76, + 537, + 468, + 580 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Disentangled Representation for Human Bodies. Jiang et al. [18] present a shape and pose disentangled human body representation based on a deep hierarchical neural network, achieving superior reconstruction accuracy. But this method depends on a strong data constraint: each posed mesh must have a paired mesh in a neutral pose. Inspired by the unsupervised disentangled generative model [3], various novel loss functions [9, 11, 38] have been proposed to preserve shape or pose in unsupervised disentanglement. Nevertheless, the supervision provided by these losses is not robust enough, and hence the reconstruction performance of such methods is unsatisfactory. By decoupling pose and shape on the whole human body, these disentangled representations can provide only coarse semantics for latent codes, and thus cannot support part-level human body editing.", + "bbox": [ + 76, + 582, + 468, + 824 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Different from prior works, we propose a part-aware skeleton-separated disentanglement strategy, which not only provides precise semantics but also benefits the design of effective information-preserving losses for unsupervised learning.", + "bbox": [ + 76, + 825, + 468, + 901 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/e5f4ec492c88af3f824f792214a7f2f39b8a5024bb87ac3d0f2cb070a5576293.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 517, + 88, + 678, + 210 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/3da256418e9350cafa00980129551c684ccdc87ea7f9e0124b3d51bdc14f539c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 661, + 88, + 867, + 210 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/0d7aa9764ac3bfdfd5d1c2c5ebe3b3d8102a414b9a8fae6a452516d7e75b1e73.jpg", + "image_caption": [ + "Figure 3. The part-aware skeleton-separated decoupling strategy: (a) anatomical human body parts, (b) human body bones and joints, (c) overview of decoupling strategy, and (d) angles between lines and $\\sigma_b^k$ ." + ], + "image_footnote": [], + "bbox": [ + 517, + 212, + 736, + 325 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/d64b976bdc07959b12ccd330d717c4eba766fab42a5a407c2c27284e0cf9e3f4.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 751, + 212, + 823, + 325 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 500, + 393, + 589, + 410 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Overview", + "text_level": 1, + "bbox": [ + 500, + 419, + 609, + 434 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given a set of human meshes with consistent connectivity, our goal is to learn a latent representation that has both fine-grained semantics and high reconstruction-precision in an unsupervised setting. In previous unsupervised decoupling works [3, 9, 11, 38], it is a common way to design novel loss functions capturing the shape or pose information to make representations disentangled. Nonetheless, limited by this traditional disentanglement idea focusing on the whole body, these methods are semantically coarse and not robust enough. In contrast, we design a part-aware skeleton-separated decoupling strategy (Sec. 3.2), which not only provides fine-grained semantics for flexible and precise editing of human attributes (e.g., circumference, bone orientation and length) but also facilitates the construction of robust information-preserving losses to achieve unsupervised learning.", + "bbox": [ + 496, + 443, + 890, + 684 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Based on this decoupling strategy, we introduce a bone-guided encoder-decoder framework (Sec. 3.3) and exploit three losses (Sec. 3.4) to achieve three core tasks by unsupervised learning: accurate geometric reconstruction (Sec. 3.4.1), unsupervised disentanglement (Sec. 3.4.2) and part-level shape editing (Sec. 3.4.3). An overview of our method is illustrated in Fig. 2, three flows of our pipeline complete the corresponding tasks with the help of specific losses.", + "bbox": [ + 496, + 685, + 890, + 820 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Part-Level Bone and Shape Disentanglement", + "text_level": 1, + "bbox": [ + 500, + 830, + 879, + 848 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We leverage an observation that a human body is composed of $K = 17$ anatomical components each containing a bone defined by joints, as shown in Figs. 3 (a) and (b). In", + "bbox": [ + 498, + 854, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "16987", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/d62004f3706a16fb23dad22e6b8cc3b8c6e3f251de686dc669c12811d5197995.jpg", + "image_caption": [ + "Figure 4. Details of our bone-guided autoencoder." + ], + "image_footnote": [], + "bbox": [ + 158, + 90, + 810, + 220 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "particular, for geometrically simple human body parts such as the waist, arms, and legs, their shapes can be approximated as cylinders with bones as axes, i.e., the geometry of these parts can be modeled as variations along their bone orientations $o_{b}$ , and along their orthogonal directions $o_{b\\perp}$ which are indicated by solid arrows and hollow arrows in Fig. 3 (b).", + "bbox": [ + 75, + 246, + 472, + 351 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Inspired by this observation, we propose a part-aware skeleton-separated decoupling strategy. An overview of our decoupling strategy is shown in Fig. 3 (c). Specifically, we separate body part variations into bone-related variations (e.g., length and orientation variations) and bone-independent shape variations (e.g., size and style variations), which are represented by the bone embedding $z_{b}^{k}$ and shape embedding $z_{s}^{k}$ for the $k$ -th part, respectively. This part-aware skeleton-separated decoupling strategy establishes a correspondence between latent codes and geometric properties of body parts, which provides fine-grained semantics and enables part-level personalized human body editing.", + "bbox": [ + 75, + 353, + 473, + 547 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Bone-Guided Autoencoder", + "text_level": 1, + "bbox": [ + 76, + 559, + 318, + 574 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The basic architecture of our framework is shown in Fig. 4. All three tasks are based on this architecture. Given a human body mesh $x$ , the bone branch and shape branch of encoder $E$ embed the mesh into the bone code $Z_{b} = \\{z_{b}^{1},\\dots,z_{b}^{K}\\}$ and shape code $Z_{s} = \\{z_{s}^{1},\\dots,z_{s}^{K}\\}$ , respectively, where $z_{b}^{k}$ and $z_{s}^{k}$ are localized latent codes for the $k$ -th body part.", + "bbox": [ + 76, + 582, + 468, + 686 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In particular, the bone branch infers localized bone codes $\\{z_b^1,\\dots,z_b^K\\}$ with global information (e.g., length and orientation) about each body part from joints $B$ predicted by a linear regressor $J(\\cdot)$ [22]. Besides, the shape branch takes mesh $x$ as input with a hierarchical spiral convolution encoder for learning geometric features in multiple scales. Geometric features belonging to each part are subsequently fed into the corresponding fully connected layer according to the part labels of vertices to obtain localized bone codes $\\{z_s^1,\\dots,z_s^K\\}$ containing regional geometric details. Finally, the decoder with a similar structure to the shape branch accurately and efficiently reconstructs the original mesh $D(Z_{b},Z_{s})$ by integrating local and global information of each body part.", + "bbox": [ + 76, + 688, + 472, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Losses for Unsupervised Learning", + "text_level": 1, + "bbox": [ + 498, + 246, + 799, + 263 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "With the above framework, we utilize three losses to achieve the corresponding tasks in an unsupervised setting, and the full objective function is defined as:", + "bbox": [ + 496, + 268, + 892, + 314 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {f u l l}} = \\mathcal {L} _ {\\text {r e c}} + \\mathcal {L} _ {\\text {d i s}} + \\mathcal {L} _ {\\text {e d i t}}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 594, + 319, + 890, + 335 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{rec}$ is a geometric reconstruction loss for accurate human body reconstruction, $\\mathcal{L}_{dis}$ is a disentanglement loss to ensure the bone and shape disentanglement of body parts, and $\\mathcal{L}_{edit}$ is introduced to enable part-level shape editing. The losses and training flows will be described in detail in the following sections.", + "bbox": [ + 496, + 340, + 893, + 431 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4.1 Accurate Geometric Reconstruction", + "text_level": 1, + "bbox": [ + 498, + 438, + 805, + 452 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As shown in Fig. 2 (a), in order to reconstruct the original mesh as accurately as possible, we adopt the geometric reconstruction loss as follows:", + "bbox": [ + 496, + 460, + 892, + 506 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {r e c}} = \\mathcal {L} _ {\\text {v e r t}} + \\lambda_ {\\text {e d g e}} \\cdot \\mathcal {L} _ {\\text {e d g e}}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 593, + 513, + 890, + 529 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\lambda_{edge}$ is the weight of edge regularization. The vertex loss forces the reconstructed mesh $D(E(x))$ to be as close as possible to the original mesh $x$ with the supervision of vertex-wise L1 distance, which is calculated as:", + "bbox": [ + 496, + 534, + 893, + 592 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {v e r t}} = \\| x - D (E (x)) \\| _ {1}. \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 598, + 890, + 614 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "However, only using this loss to supervise vertex positions cannot avoid producing over-length edges, which seriously affects the smoothness and reasonableness of results. Inspired by [15,34], we address this problem by introducing an edge length regularization $L_{edge}$ , which is formulated as:", + "bbox": [ + 496, + 619, + 890, + 696 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {e d g e}} = \\sum_ {p} \\sum_ {v \\in N (p)} \\| p - v \\| _ {2} ^ {2}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 593, + 699, + 890, + 733 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $N(p)$ is the set of 1-ring neighbors of vertex $p$ . This loss ensures the smoothness of output meshes by enforcing their surface to be tight.", + "bbox": [ + 496, + 733, + 890, + 779 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4.2 Unsupervised Disentanglement", + "text_level": 1, + "bbox": [ + 498, + 786, + 769, + 801 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "After the reconstruction flow in Sec. 3.4.1, our representation is already capable of accurate reconstruction, but its latent space is still entangled. Based on the part-aware skeleton-separated decoupling strategy (Sec. 3.2), we use $\\mathcal{L}_{dis}$ to ensure the bone and shape disentanglement of body parts, which can be defined as:", + "bbox": [ + 496, + 810, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "16988", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {d i s} = \\mathcal {L} _ {d i s - b} + \\lambda_ {d i s - s} \\cdot \\mathcal {L} _ {d i s - s}. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 166, + 90, + 468, + 106 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To achieve unsupervised disentanglement, given two human bodies $x_{1}$ and $x_{2}$ , $x_{swp}$ denotes the generated mesh $D(Z_{b_2}, Z_{s_1})$ , which is constructed with the bone latent code from $x_{2}$ and shape latent code from $x_{1}$ , as shown in Fig. 2 (b). Overall, $\\mathcal{L}_{dis\\_b}$ and $\\mathcal{L}_{dis\\_s}$ are used to preserve bone information belonging to parts of $x_{2}$ and geometric features along $o_{b\\perp}$ belonging to parts of $x_{1}$ , respectively. In this way, our method achieves decoupling via unsupervised learning. $x_{swp}$ should have close joint positions as $x_{2}$ as these are largely determined by the bones, so $L_{dis\\_b}$ is defined as", + "bbox": [ + 76, + 112, + 468, + 263 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {d i s} b} = \\| J \\left(x _ {2}\\right) - J \\left(x _ {s w p}\\right) \\| _ {1}, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 166, + 268, + 468, + 286 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $J(x)$ is the vector containing joint positions for $x$ obtained through a joint regressor.", + "bbox": [ + 76, + 291, + 468, + 321 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "However, preserving geometric features is a challenging issue. An intuitive idea to solve this issue is to use the matrix of pairwise Euclidean distances between all vertices in each part to capture regional shape information, which can be defined as:", + "bbox": [ + 76, + 321, + 468, + 395 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {d i s} - s} = \\sum_ {k = 1} ^ {K} \\| D _ {e} \\left(x _ {1} ^ {k}\\right) - D _ {e} \\left(x _ {\\operatorname {s w p}} ^ {k}\\right) \\| _ {1}, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 143, + 397, + 468, + 438 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $x^{k}$ is the $k$ -th part-mesh of $x$ , and $D_{e}(x^{k})$ denotes the matrix of pairwise Euclidean distances between all vertices in $x^{k}$ . $D_{e}(x^{k})$ is of size $n(x^{k}) \\times n(x^{k})$ , where $n(x^{k})$ is the number of vertices of the $k$ -th part. Nevertheless, the bone length is coupled with the Euclidean distance matrix, which leads to incomplete decoupling and affects editing precision (Sec. 4.3).", + "bbox": [ + 76, + 440, + 468, + 545 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To alleviate this problem, we propose an orientation-adaptive weighting (OAW) strategy to enforce the representation to focus on shape variations along $o_{b\\perp}$ . For each body part $x^{k}$ , we first construct a pairwise angle matrix of size $n(x^{k}) \\times n(x^{k})$ by joining every pair of vertices to form a line, and working out the angle $A(x^{k})$ (in degrees) the line makes with the bone orientation $o_{b}^{k}$ , as illustrated in Fig. 3 (d). As can be seen, the larger the angle, the more significant the line contributes to the shape variation along $o_{b\\perp}$ . We apply the following thresholding and normalization $f$ to obtain the weight matrix $W(x^{k})$ also of the same size by suppressing small angles, and setting the maximum weight to one:", + "bbox": [ + 76, + 547, + 468, + 739 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nW \\left(x ^ {k}\\right) = f \\left(A \\left(x ^ {k}\\right)\\right), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 200, + 739, + 467, + 757 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $f(\\cdot)$ is an element-wise mapping function defined as", + "bbox": [ + 76, + 762, + 465, + 777 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nf (a) = \\left\\{ \\begin{array}{l l} a / 9 0, & \\text {i f} a > \\sigma \\\\ 0, & \\text {o t h e r w i s e .} \\end{array} \\right. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 782, + 468, + 823 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The weighted pairwise distance matrix is then defined as", + "bbox": [ + 76, + 827, + 450, + 840 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nD _ {e} ^ {w} \\left(x ^ {k}\\right) = W \\left(x ^ {k}\\right) \\otimes D _ {e} \\left(x ^ {k}\\right), \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 845, + 467, + 864 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\otimes$ is element-wise matrix multiplication. This orientation-adaptive weighting strategy enforces the bone", + "bbox": [ + 76, + 869, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "length information to be separated from the Euclidean distance matrix as much as possible. Nonetheless, this strategy also ignores some useful geometric information along $o_b$ , which may result in unreasonable mesh deformations when editing bone orientation in large scale (Sec. 4.3). To address this problem, we further impose part-level volume regularization penalizing the unreasonable shape variations. Ideally, when the shape along $o_{b\\perp}$ is retained, the volume of a body part should change in proportion to the bone length, so we introduce a volume constraint, which provides strong geometric supervision to achieve natural and reasonable editing results, which can be calculated as:", + "bbox": [ + 496, + 90, + 890, + 272 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {v o l} = \\sum_ {k = 1} ^ {K} \\| v \\left(x _ {1} ^ {k}\\right) / l \\left(x _ {1} ^ {k}\\right) - v \\left(x _ {s w p} ^ {k}\\right) / l \\left(x _ {s w p} ^ {k}\\right) \\| _ {1}, \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 273, + 890, + 316 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $v(\\cdot)$ is a function that calculates the volume of a mesh part according to the tetrahedral volume formula, and $l(\\cdot)$ is a function that measures the length of a mesh part between the two joints. We can then rewrite $\\mathcal{L}_{dis\\_s}$ :", + "bbox": [ + 496, + 319, + 890, + 378 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {d i s} - s} = \\sum_ {k = 1} ^ {K} \\| D _ {e} ^ {w} \\left(x _ {1} ^ {k}\\right) - D _ {e} ^ {w} \\left(x _ {\\text {s w p}} ^ {k}\\right) \\| _ {1} + \\mathcal {L} _ {\\text {v o l}}. \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 524, + 383, + 890, + 425 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4.3 Part-Level Shape Editing", + "text_level": 1, + "bbox": [ + 500, + 436, + 730, + 452 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "With the constraints in Sec. 3.4.2, our autoencoder learns a bone and shape disentangled representation for body parts, and hence we can directly control bone-related attributes (e.g., bone orientation and length) of parts by modifying their joints. Nevertheless, we cannot flexibly edit other bone-independent shape features (e.g., circumference) as desired. To address this issue, we propose an editing flow shown in Fig. 2 (c), which achieves part-level shape editing by forcing the generated mesh from the scaled codes $(Z_{b_1},\\alpha Z_{s_1})$ to deform as desired with $L_{edit}$ :", + "bbox": [ + 496, + 460, + 890, + 612 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {e d i t}} = \\mathcal {L} _ {\\text {e d i t} b} + \\lambda_ {\\text {e d i t} s} \\cdot \\mathcal {L} _ {\\text {e d i t} s} + \\lambda_ {\\text {n o r m}} \\cdot \\mathcal {L} _ {\\text {n o r m}}, \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 506, + 616, + 890, + 633 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {e d i t} - s} = \\sum_ {k = 1} ^ {K} \\| \\alpha D _ {e} ^ {w} \\left(x _ {1} ^ {k}\\right) - D _ {e} ^ {w} \\left(x _ {s c a} ^ {k}\\right) \\| _ {1}, \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 557, + 638, + 890, + 679 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {e d i t} - b} = \\left\\| J \\left(x _ {1}\\right) - J \\left(x _ {s c a}\\right) \\right\\| _ {1}, \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 589, + 684, + 890, + 700 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\alpha$ is a scalar uniformly sampled in $(\\alpha_{\\mathrm{min}},\\alpha_{\\mathrm{max}})$ during training, and $x_{sca}$ denotes the generated mesh $D(Z_{b_1},\\alpha Z_{s_1})$ from the scaled codes, i.e., the edited mesh $x_{sca}$ has bone code from $x_{1}$ , and shape code from $x_{1}$ with flexible scaling to change body shape.", + "bbox": [ + 500, + 704, + 890, + 779 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In particular, $\\mathcal{L}_{edit\\_s}$ constrains the $k$ -th part of $x_{sca}$ reconstructed from the scaled shape codes $\\alpha Z_{s_1}$ to have the shape described by $\\alpha D_e^W (x_1^k)$ . As $D_e^W (\\cdot)$ is designed to largely capture the pairwise distances along $o_{b\\perp}^{k}$ , i.e. reflecting shape size and style for the part. Elements in the matrix therefore should be scaled accordingly when the part is scaled along $o_{b\\perp}^{k}$ . Besides, $\\mathcal{L}_{edit\\_b}$ is introduced to preserve bone information during the editing process.", + "bbox": [ + 496, + 780, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "16989", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "However, the editing flow easily causes training collapses. We solve this problem by imposing a vector norm regularization $\\mathcal{L}_{norm}$ to establish a mapping between the norm of localized shape codes and the circumference of body parts, which not only benefits converge, but also allows users to edit part shape size on a unified scale. This regularization can be defined as:", + "bbox": [ + 75, + 90, + 470, + 196 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {n o r m}} = \\frac {1}{K} \\sum_ {k = 1} ^ {K} \\left| \\| z _ {s _ {1}} ^ {k} \\| _ {2} - \\operatorname {c i r c} \\left(x _ {1} ^ {k}\\right) \\right|, \\tag {16}\n$$\n", + "text_format": "latex", + "bbox": [ + 142, + 198, + 468, + 237 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\text{circ}(\\cdot)$ is a function that measures the circumference of a part using the identified landmarks. $\\mathcal{L}_{edit}$ successfully allows shape codes' norm and direction to represent shape size and style respectively, which not only enables flexible part-level editing but also ensures the continuity of the learned latent space to a certain extent.", + "bbox": [ + 75, + 238, + 468, + 329 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3.5. Implementation Details", + "text_level": 1, + "bbox": [ + 76, + 337, + 294, + 353 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For the spiral convolution encoder, we use a framework similar to [7]. Specifically, it consists of four spiral convolution layers and downsampling layers, and the structure of the decoder is a mirror of the encoder, except that the downsampling layers are replaced by the upsampling layers. Our algorithm is implemented in PyTorch [23]. All the training and test experiments are carried out on a PC with an RTX 3090 GPU. We train our network for 300 epochs with a learning rate of $1 \\times 10^{-3}$ , a learning rate decay of 0.99 after each epoch and the Adam optimizer [20]. The entire training time takes around 24 hours. We use 16-dimensional latent codes (8 for bones and 8 for shape) to embed each part. More implementation details can be found in the supplementary material.", + "bbox": [ + 75, + 359, + 470, + 571 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 580, + 209, + 598 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Datasets", + "text_level": 1, + "bbox": [ + 76, + 604, + 179, + 619 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "DFAUST. The dynamic human body dataset with the same mesh connectivity as SMPL [22] from Bogo et al. [5], captures 14 different body motion sequences (e.g., hips, running, and jumping) for each of the 10 human subjects. We evenly extract one-twentieth of the original dataset for the convenience of training. Then we randomly split the extracted data into a test set of 182 meshes and a training set of 1936 meshes.", + "bbox": [ + 75, + 628, + 468, + 748 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "SPRING. The large human body dataset with the same mesh connectivity as SCAPE [2] from Yang et al. [35], consists of $3000+$ subject meshes with a rough A-pose registered from the CAESAR dataset [27] using a non-rigid deformation algorithm. For the subsequent experiments, the SPRING dataset is randomly split into 2743 training and 305 test meshes.", + "bbox": [ + 75, + 750, + 468, + 853 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Data Preprocessing. For the training data, our method requires a joint regressor, body part semantics of each vertex, and landmarks for calculating circumferences. Since", + "bbox": [ + 75, + 854, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/f870d9efdd0a08e8db6d243668fa62f39aa3302dc3de13ffd06a3cd07c1a218b.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodDFAUSTSPRING
EavdParam(M)EavdParam(M)
COMA [26]6.067.546.046.84
Neural3DMM [7]5.4930.356.1127.56
Spiralplus [14]5.3515.154.9913.75
Pai3DMM [13]5.7615.184.4513.78
Deep3DMM [10]9.918.3510.887.84
Unsup [38]10.1812.89--
Ours4.701.594.331.47
", + "bbox": [ + 504, + 88, + 883, + 239 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/9d2fecc76d177d54f809178ab877eb817df3ae59dc0f23d17989cfcda8465927.jpg", + "table_caption": [ + "Table 1. Quantitative reconstruction results on DFAUST [5] and SPRING [35] datasets. - : not supported for this dataset. Param(M) shows the number of learnable parameters in millions." + ], + "table_footnote": [], + "table_body": "
MethodDHNN
EavdParam(M)
DHNN [18]3.1691.63
Ours3.961.47
", + "bbox": [ + 573, + 292, + 815, + 371 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Quantitative reconstruction results on DHNN [18]. $\\mathbf{{Param}\\left( M\\right) }$ shows the number of learnable parameters in millions.", + "bbox": [ + 498, + 371, + 888, + 398 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "the mesh connectivity of datasets is consistent (which is required by nearly all 3D human representation learning works), the SMPL model only needs to be registered once to meet the requirements. More details about data preprocessing are given in the supplementary material.", + "bbox": [ + 498, + 409, + 890, + 486 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Comparison", + "text_level": 1, + "bbox": [ + 500, + 497, + 632, + 513 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In the following, we evaluate the representation ability (reconstruction precision) and editing capacity (semantics) of our approach on the DFAUST and SPRING datasets.", + "bbox": [ + 498, + 521, + 890, + 566 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Reconstruction Experiments. We first compare four kinds of methods to validate the reconstruction precision of our representation: the spectral-based approach (COMA [26]), the spiral-based methods (Neural3DMM [7] and Spiralplus [14]), the attention-based approaches (Pai3DMM [13] and Deep3DMM [10]), and the disentanglement representation works (DHNN [18] and Unsup [38]). For a fair comparison, we use the official implementation of the compared methods with the same latent space dimension. Note that DHNN [18] did not release the training code, so we train and test our model on its dataset for a fair comparison. we utilize the average point-wise Euclidean distance $E_{\\text{avd}}$ (in millimeters) between corresponding vertices in the input and its reconstruction as metrics.", + "bbox": [ + 498, + 566, + 892, + 777 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As shown in Tab. 1, our proposed approach shows excellent representation capability with a small model size. Fig. 5 visualizes some reconstruction results and their error maps. It can be observed that our approach outperforms other methods in reconstruction accuracy, especially for complex geometric details (e.g., faces and hands), which demonstrates the effectiveness of our bone-guided autoencoder.", + "bbox": [ + 496, + 779, + 890, + 898 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "16990", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/70f8f53cf971f3f8dfedcd6304c9abba18928de929760778c9e11a0436545e8e.jpg", + "image_caption": [ + "Figure 5. Qualitative reconstruction results on DFAUST [5] and SPRING [35]. The per-vertex Euclidean distance error is color-coded on the reconstructed meshes for visual inspection. Since Unsup [38] has a data constraint that the same subject in different poses should be given, it cannot be trained on the SPRING dataset. Note that our representation is trained without the need of data constraint, which is used in the compared methods." + ], + "image_footnote": [], + "bbox": [ + 114, + 88, + 854, + 256 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/634c30d0315aa63ed2cd067badf22ed533be694bb8571a67cf460b8f2b133e84.jpg", + "image_caption": [ + "Figure 6. Qualitative editing results by Unsup [38] (first row), HBR [36] (second and third rows) and our method. We show the reconstructed bodies and edited bodies on the left and right of the source mesh." + ], + "image_footnote": [], + "bbox": [ + 78, + 318, + 468, + 493 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/8eb46ccd3e0b430dbc9c3c0a0ac9e13a34b8a3d3945decba278aa81bbd3206e7.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodBone OrientationBone LengthShape Size
EjointEcricEjointEcricEjointEcric
Unsup [38]36.7914.16----
HBR [36]--38.3715.2712.8718.78
Ours3.269.751.575.920.4517.64
", + "bbox": [ + 78, + 554, + 468, + 647 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Quantitative editing results. - : not supported for this task.", + "bbox": [ + 76, + 648, + 467, + 662 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In addition, Tab. 2 gives the quantitative results on the DHNN dataset [18]. It is important to note that the compared method [18] uses a training data assumption (i.e., each posed mesh has a paired mesh in a neutral pose) for shape and pose disentanglement, but our method does not make use of this constraint. Under this unfair condition, the reconstruction accuracy of our method is only slightly lower than DHNN [18], and our model is more lightweight and semantically finer. Please refer to our supplementary material for more experimental results and details.", + "bbox": [ + 75, + 672, + 468, + 823 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Editing Experiments. We also demonstrate the flexible editing ability of our model on three editing tasks: editing bone orientation and bone length, and editing part shape size. Since bone orientation is approximately equivalent to pose, we compare with the unsupervised pose-and-shape", + "bbox": [ + 75, + 824, + 468, + 902 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "disentanglement work Unsup [38] on the task of editing bone orientation on the DFAUST dataset. In contrast, bone length and shape size are shape information, so we compare with the Human Body Reshaping work HBR [36] on the task of editing bone length and shape size on the SPRING dataset. As there is no ground truth for these editing tasks, how to measure the editing performance is a problem. We utilize the joint and circumference errors $E_{joint}$ , $E_{circ}$ (in millimeters) to evaluate the accuracy of editing bone and shape.", + "bbox": [ + 496, + 323, + 890, + 473 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Specifically, for each test mesh, we randomly select editing targets and then calculate $E_{joint}$ , $E_{circ}$ between the target attribute value and the actual attribute value of the edited human body, extracted using $J(\\cdot)$ and $circ(\\cdot)$ , which can be defined as:", + "bbox": [ + 496, + 474, + 890, + 547 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\nE _ {j o i n t} = \\left\\| T _ {j o i n t} - J \\left(x _ {\\text {e d i t e d}}\\right) \\right\\| _ {2}, \\tag {17}\n$$\n", + "text_format": "latex", + "bbox": [ + 581, + 551, + 890, + 569 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\nE _ {c i r c} = \\frac {1}{K} \\sum_ {k = 1} ^ {K} \\left| T _ {c i r c} ^ {k} - \\operatorname {c i r c} \\left(x _ {\\text {e d i t e d}} ^ {k}\\right) \\right|, \\tag {18}\n$$\n", + "text_format": "latex", + "bbox": [ + 562, + 570, + 890, + 611 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "where $T_{joint}$ and $T_{circ}$ are the editing targets of joint positions and circumference.", + "bbox": [ + 496, + 612, + 888, + 641 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Tab. 3 gives the quantitative editing results. Our method not only achieves the best performance on all the editing tasks but also preserves other unedited attributes well. Some visual results are shown in Fig. 6. Compared with Unsup [38] and HBR [36], our method can edit the human body more accurately, reasonably and flexibly. Since the attribute error does not always reflect the editing performance of models, we also design a user study for better evaluation (see the supplementary material).", + "bbox": [ + 496, + 642, + 890, + 779 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation Study", + "text_level": 1, + "bbox": [ + 500, + 786, + 653, + 801 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effect of Orientation-Adaptive Weighting Strategy. We validate the effectiveness of the orientation-adaptive weighting strategy by ablating it during training. As shown in Tab. 4, introducing this strategy helps our representation to focus on geometric features along $o_{b\\perp}$ , leading to higher editing accuracy and more complete decoupling.", + "bbox": [ + 496, + 809, + 890, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "16991", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9c6ef8d2d4fa819ab3f320c56a2e5536000c8a9d9d1057e4a6871f023f96e62b.jpg", + "image_caption": [ + "Figure 7. Qualitative results of volume regularization ablation study." + ], + "image_footnote": [], + "bbox": [ + 116, + 89, + 429, + 215 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/09c61590abf58a847f1f19c4bc7e37a7c32f0a5bb8450fca448b6a2d658e30f7.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodReconstructionEditing
DFA. SPR. MeanBone Ori. \\(E_{joint}\\)Bone Length\\(E_{joint}\\)Shape Size\\(E_{joint}\\)Mean
Full4.704.334.523.269.751.575.920.4517.646.43
w/o OAW4.664.394.533.0213.661.5810.950.6120.408.37
w/o \\(L_{edge}\\)5.235.015.123.209.761.528.370.5329.488.81
w/o \\(L_{dis}\\)4.814.884.85----1.5617.50-
w/o \\(L_{edit}\\)4.874.434.653.0010.191.888.01---
", + "bbox": [ + 78, + 258, + 470, + 361 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 4. Quantitative ablation study for reconstruction and editing (in mm). - : not supported for this task.", + "bbox": [ + 76, + 363, + 468, + 391 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effect of Other Losses. To evaluate the impact of $\\mathcal{L}_{\\text {edge }}$ , $\\mathcal{L}_{\\text {dis }}$ and $\\mathcal{L}_{\\text {edit }}$ , we remove them from the supervision one by one during training. As compared in Tab. 4, $\\mathcal{L}_{\\text {edge }}$ effectively reduces the reconstruction error, and the use of $\\mathcal{L}_{\\text {dis }}$ and $\\mathcal{L}_{\\text {edit }}$ is required to enable flexible bone and part shape editing. The ablation study proves that all losses are necessary.", + "bbox": [ + 75, + 402, + 468, + 508 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effect of Part-Level Volume Regularization. To analyze the impact of volume regularization, we remove it during the training process. Fig. 7 shows the comparison results of edited bodies. It can be seen that our volume constraint provides strong geometric supervision, resulting in more natural and reasonable editing results.", + "bbox": [ + 75, + 513, + 468, + 604 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.4. Applications", + "text_level": 1, + "bbox": [ + 76, + 627, + 210, + 643 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Latent Space Interpolation. Our representation decouples the bone and shape of each part, which allows us to interpolate meshes by linearly interpolating the bone and shape latent codes. Fig. 8 shows reasonable and meaningful results of such interpolation. It is worth noting that, thanks to the latent space with fine-grained semantics, we can perform mesh interpolation at the component level (see Figs. 8 (b) and (c)).", + "bbox": [ + 75, + 654, + 468, + 775 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Shape Style Transfer. As mentioned in Sec. 3.4.3, we successfully make the norm and direction of shape vectors represent shape size and style, respectively. Thus, we can transfer shape style by swapping the direction of the shape vectors. Some results are shown in Fig. 9. It can be easily observed that our approach changes the shape style of the source mesh naturally and preserves its other attributes (e.g., bone and circumference).", + "bbox": [ + 75, + 779, + 468, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/8489b0839c6c91ef0b84fc6ce0d461382a421f6a1725908b2236e0592645637f.jpg", + "image_caption": [ + "Figure 8. Interpolation results on the bone-and-shape disentangled space of (a) the whole body, (b) upper body and (c) lower body, where the highlighted bodies are the input reconstructed bodies." + ], + "image_footnote": [], + "bbox": [ + 519, + 88, + 640, + 210 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/28229fbe5847bc2bdbf2056bfa71090b833a3163b03431f83b08794831078531.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 640, + 89, + 751, + 210 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4f37b10db4116c77a4a114dd6d531a08a4990f6b5692b2a3891deb12b1569e0d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 751, + 89, + 866, + 210 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/06533bc6b0bb07b660e506c371160cd5497ac282b7e06d8e995558d7cde0af0b.jpg", + "image_caption": [ + "Figure 9. Results of shape style transfer by linearly interpolating the direction of shape codes." + ], + "image_footnote": [], + "bbox": [ + 519, + 267, + 867, + 406 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion and Discussion", + "text_level": 1, + "bbox": [ + 500, + 457, + 746, + 472 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Conclusion. In this paper, we propose a human body representation with fine-grained semantics and high reconstruction-accuracy in an unsupervised setting. The key idea is to exploit a part-aware skeleton-separated decoupling strategy to establish a correspondence between latent vectors and geometric properties of body parts, which benefits personalized editing of human bodies by modifying the corresponding latent codes. Based on this disentanglement strategy, we propose a bone-guided autoencoder and well-designed losses to learn representation in an unsupervised manner. At last, with the geometrically meaningful latent space, the application can be extended from human body editing to latent code interpolation and shape style transfer.", + "bbox": [ + 496, + 491, + 890, + 688 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations. Since our decoupling strategy is based on the cylinder assumption (i.e., the shape of body parts can be approximated as cylinders with bones as axes), we cannot controllably edit the bone length and circumference of parts (e.g., faces, hands, and feet) whose geometry is too complex to meet this assumption. Additionally, editing bone orientation with our method may fail when the target orientation is uncommon in the training data. In further work, we will dig deeper into the prior knowledge about the human body to improve the generalization capability of our representation.", + "bbox": [ + 496, + 695, + 890, + 847 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement. This work was supported in part by the National Natural Science Foundation of China (62122058 and 62171317).", + "bbox": [ + 498, + 854, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "16992", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Brett Allen, Brian Curless, and Zoran Popovic. The space of human body shapes: reconstruction and parameterization from range scans. ACM Trans. Graph., 2003. 1", + "[2] Dragomir Anguelov, Praveen Srinivasan, Daphne Koller, Sebastian Thrun, Jim Rodgers, and James Davis. SCAPE: shape completion and animation of people. ACM Trans. Graph., 2005. 1, 2, 6", + "[3] Tristan Aumentado-Armstrong, Stavros Tsogkas, Allan Jepson, and Sven Dickinson. Geometric disentanglement for generative latent shape models. In Int. Conf. Comput. Vis., 2019. 2, 3", + "[4] Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler, Javier Romero, and Michael J. Black. Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image. In Eur. Conf. Comput. Vis., 2016. 1", + "[5] Federica Bogo, Javier Romero, Gerard Pons-Moll, and Michael J Black. Dynamic FAUST: Registering human bodies in motion. In IEEE Conf. Comput. Vis. Pattern Recog., 2017. 6, 7", + "[6] Davide Boscaini, Jonathan Masci, Simone Melzi, Michael M Bronstein, Umberto Castellani, and Pierre Vandergheynst. Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks. In Comput. Graph. Forum, 2015. 2", + "[7] Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Michael Bronstein, and Stefanos Zafeiriou. Neural 3D morphable models: Spiral convolutional networks for 3d shape representation learning and generation. In Int. Conf. Comput. Vis., 2019. 1, 2, 6", + "[8] Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203, 2013. 2", + "[9] Haoyu Chen, Hao Tang, Henglin Shi, Wei Peng, Nicu Sebe, and Guoying Zhao. Intrinsic-extrinsic preserved GANs for unsupervised 3D pose transfer. In Int. Conf. Comput. Vis., 2021. 2, 3", + "[10] Zhixiang Chen and Tae-Kyun Kim. Learning feature aggregation for deep 3D morphable models. In IEEE Conf. Comput. Vis. Pattern Recog., 2021. 2, 6", + "[11] Luca Cosmo, Antonio Norelli, Oshri Halimi, Ron Kimmel, and Emanuele Rodola. LIMP: Learning latent shape representations with metric preservation priors. In Eur. Conf. Comput. Vis., 2020. 2, 3", + "[12] Lin Gao, Yu-Kun Lai, Jie Yang, Ling-Xiao Zhang, Shihong Xia, and Leif Kobbelt. Sparse data driven mesh deformation. IEEE Trans. Vis. Comput. Graph., 2019. 2", + "[13] Zhongpai Gao, Junchi Yan, Guangtao Zhai, Juyong Zhang, Yiyan Yang, and Xiaokang Yang. Learning local neighboring structure for robust 3D shape representation. In AAAI, 2021. 2, 6", + "[14] Shunwang Gong, Lei Chen, Michael Bronstein, and Stefanos Zafeiriou. SpiralNet++: A fast and highly efficient mesh convolution operator. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2019, 2, 6" + ], + "bbox": [ + 78, + 114, + 468, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[15] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. 3D-CODED: 3D correspondences by deep deformation. In Eur. Conf. Comput. Vis., 2018. 4", + "[16] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Adv. Neural Inform. Process. Syst., 2017. 2", + "[17] Mikael Henaff, Joan Bruna, and Yann LeCun. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163, 2015. 2", + "[18] Boyi Jiang, Juyong Zhang, Jianfei Cai, and Jianmin Zheng. Disentangled human body embedding based on deep hierarchical neural network. IEEE Trans. Vis. Comput. Graph., 2020. 1, 2, 3, 6, 7", + "[19] Angjoo Kanazawa, Michael J. Black, David W. Jacobs, and Jitendra Malik. End-to-end recovery of human shape and pose. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 1", + "[20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. 2015. 6", + "[21] Linjie Li, Zhe Gan, Yu Cheng, and Jingjing Liu. Relation-aware graph attention network for visual question answering. In Int. Conf. Comput. Vis., 2019. 2", + "[22] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. SMPL: A skinned multiperson linear model. ACM Trans. Graph., 2015. 1, 2, 4, 6", + "[23] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. PyTorch: An imperative style, high-performance deep learning library. 2019. 6", + "[24] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3D hands, face, and body from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., 2019. 2", + "[25] Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt, and Bernt Schiele. Building statistical shape spaces for 3D human modeling. Pattern Recognition, 2017. 1", + "[26] Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J Black. Generating 3D faces using convolutional mesh autoencoders. In Eur. Conf. Comput. Vis., 2018. 2, 6", + "[27] Kathleen M Robinette, Hans Daanen, and Eric Paquet. The caesar project: a 3-d surface anthropometry survey. In International Conference on 3-D Digital Imaging and Modeling, 1999. 6", + "[28] Javier Romero, Dimitrios Tzionas, and Michael J Black. Embodied hands: Modeling and capturing hands and bodies together. ACM Trans. Graph., 2017. 2", + "[29] Hyewon Seo and Nadia Magnenat-Thalmann. An automatic modeling of human bodies from sizing parameters. In Proceedings of the Symposium on Interactive 3D Graphics, 2003. 1", + "[30] Qingyang Tan, Lin Gao, Yu-Kun Lai, and Shihong Xia. Variational autoencoders for deforming 3D mesh models. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 1, 2" + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "16993", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[31] Qingyang Tan, Lin Gao, Yu-Kun Lai, Jie Yang, and Shihong Xia. Mesh-based autoencoders for localized deformation component analysis. In AAAI, 2018. 1, 2", + "[32] Yating Tian, Hongwen Zhang, Yebin Liu, and Limin Wang. Recovering 3D human mesh from monocular images: A survey. arXiv preprint arXiv:2203.01923, 2022. 1", + "[33] Nitika Verma, Edmond Boyer, and Jakob Verbeek. Dynamic filters in graph convolutional networks. arXiv preprint arXiv:1706.05206, 2017. 2", + "[34] Jiashun Wang, Chao Wen, Yanwei Fu, Haitao Lin, Tianyun Zou, Xiangyang Xue, and Yinda Zhang. Neural pose transfer by spatially adaptive instance normalization. In IEEE Conf. Comput. Vis. Pattern Recog., 2020. 4", + "[35] Yipin Yang, Yao Yu, Yu Zhou, Sidan Du, James Davis, and Ruigang Yang. Semantic parametric reshaping of human body models. In International Conference on 3D Vision, 2014. 1, 6, 7", + "[36] Yanhong Zeng, Jianlong Fu, and Hongyang Chao. 3D human body reshaping with anthropometric modeling. In International Conference on Internet Multimedia Computing and Service, 2017. 1, 7", + "[37] Hongwen Zhang, Yating Tian, Xinchi Zhou, Wanli Ouyang, Yebin Liu, Limin Wang, and Zhenan Sun. PyMAF: 3D human pose and shape regression with pyramidal mesh alignment feedback loop. In Int. Conf. Comput. Vis., 2021. 1", + "[38] Keyang Zhou, Bharat Lal Bhatnagar, and Gerard Pons-Moll. Unsupervised shape and pose disentanglement for 3D meshes. In Eur. Conf. Comput. Vis., 2020. 2, 3, 6, 7", + "[39] Shizhe Zhou, Hongbo Fu, Ligang Liu, Daniel Cohen-Or, and Xiaoguang Han. Parametric reshaping of human bodies in images. ACM Trans. Graph., 2010. 1", + "[40] Silvia Zuffi, Angjoo Kanazawa, David W Jacobs, and Michael J Black. 3D menagerie: Modeling the 3D shape and pose of animals. In IEEE Conf. Comput. Vis. Pattern Recog., 2017. 2" + ], + "bbox": [ + 78, + 90, + 467, + 587 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "16994", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Semantic-Aware Disentangled Representation for Flexible 3D Human Body Editing/ba9dabaf-077e-4e19-9ae1-c1f2c2a30c7c_model.json b/2023/Learning Semantic-Aware Disentangled Representation for Flexible 3D Human Body Editing/ba9dabaf-077e-4e19-9ae1-c1f2c2a30c7c_model.json new file mode 100644 index 0000000000000000000000000000000000000000..4f4087b45f23a5fe42a6ba340461ea390bf6bb23 --- /dev/null +++ b/2023/Learning Semantic-Aware Disentangled Representation for Flexible 3D Human Body Editing/ba9dabaf-077e-4e19-9ae1-c1f2c2a30c7c_model.json @@ -0,0 +1,2200 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.205, + 0.131, + 0.768, + 0.177 + ], + "angle": 0, + "content": "Learning Semantic-Aware Disentangled Representation for Flexible 3D Human Body Editing" + }, + { + "type": "text", + "bbox": [ + 0.109, + 0.203, + 0.868, + 0.24 + ], + "angle": 0, + "content": "Xiaokun Sun\\(^{1}\\) Qiao Feng\\(^{1}\\) Xiongzheng Li\\(^{1}\\) Jinsong Zhang\\(^{1}\\) Yu-Kun Lai\\(^{2}\\) Jingyu Yang\\(^{1}\\) Kun Li\\(^{1*}\\) \n\\(^{1}\\)Tianjin University, China \\(^{2}\\)Cardiff University, United Kingdom" + }, + { + "type": "text", + "bbox": [ + 0.168, + 0.242, + 0.798, + 0.258 + ], + "angle": 0, + "content": "\\(\\{s x k_{-}26,f e n g q i a o,l x z,j i n s z h a n g,y j y,\\text{lik}\\} @t j u.\\) edu.cn LaiY4@cardiff.ac.uk" + }, + { + "type": "image", + "bbox": [ + 0.139, + 0.273, + 0.831, + 0.48 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.48, + 0.895, + 0.537 + ], + "angle": 0, + "content": "Figure 1. SemanticHuman. We present a semantic-aware and editable human body representation with high reconstruction precision in an unsupervised setting. The key idea is to employ a part-aware skeleton-separated decoupling strategy to learn a geometrically meaningful latent space with fine-grained semantics, which benefits part-level personalized human body editing. After embedding a human body into the bone code \\( Z_{b} \\) and shape code \\( Z_{s} \\) (left), we can flexibly edit human body attributes by modifying the corresponding latent codes (right)." + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.549, + 0.314, + 0.563 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.563, + 0.473, + 0.88 + ], + "angle": 0, + "content": "3D human body representation learning has received increasing attention in recent years. However, existing works cannot flexibly, controllably and accurately represent human bodies, limited by coarse semantics and unsatisfactory representation capability, particularly in the absence of supervised data. In this paper, we propose a human body representation with fine-grained semantics and high reconstruction-accuracy in an unsupervised setting. Specifically, we establish a correspondence between latent vectors and geometric measures of body parts by designing a part-aware skeleton-separated decoupling strategy, which facilitates controllable editing of human bodies by modifying the corresponding latent codes. With the help of a bone-guided auto-encoder and an orientation-adaptive weighting strategy, our representation can be trained in an unsupervised manner. With the geometrically meaningful latent space, it can be applied to a wide range of applications, from human body editing to latent code interpolation and shape style transfer. Experimental results on public datasets demonstrate the accurate reconstruction and flexible editing abilities of the proposed method. The code will be available" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.55, + 0.894, + 0.581 + ], + "angle": 0, + "content": "at http://cic.tju.edu.cn/faculty/likun/projects/SemanticHuman." + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.59, + 0.633, + 0.605 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.615, + 0.892, + 0.766 + ], + "angle": 0, + "content": "Learning low-dimensional representations of human bodies plays an important role in various applications including human body reconstruction [4, 19, 32, 37], generation [7, 30, 31] and editing [35, 36, 39]. Existing methods [2, 18, 22, 25, 29] are either too semantically coarse to enable personalized human body editing, or suffer from poor reconstruction performance due to limited representation capability. This paper aims to develop a fine-grained semantic-aware human body representation with flexible representation ability." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.767, + 0.895, + 0.904 + ], + "angle": 0, + "content": "Since human bodies are rich in variations of poses and shapes, traditional linear models [1, 25, 29, 35, 36] cannot handle complex nonlinear structures of human body meshes accurately. Therefore, parametric models have been proposed for better representation. The landmark works SCAPE [2] and SMPL [22] represent human bodies by the shape and pose parameters. However, the semantics of their shape parameters are not sufficiently precise, making it impossible to flexibly edit the body shape. Furthermore, the" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.889, + 0.221, + 0.902 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16985" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.137 + ], + "angle": 0, + "content": "representation ability of these methods is limited by the linear shape space of human body shapes, and hence their reconstruction accuracy is often unsatisfactory." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.139, + 0.47, + 0.381 + ], + "angle": 0, + "content": "With the success of deep learning, the encoder-decoder architecture has demonstrated excellent representation capability [7, 10, 13, 14, 26]. Such methods improve the reconstruction precision by constructing different convolution-like operators for feature extraction on irregular meshes. However, these works lack disentangled representation and fail to obtain promising results for geometrically complex human body parts. Several works [3, 9, 11, 18, 38] pursue the disentanglement of latent representations, i.e., each latent code has clear semantics. But these methods either require paired supervised data or have poor performance on the reconstruction, which significantly affects their generalization and robustness. In addition, the semantics of the above representations are coarse, which only enables person-level attribute transfer and cannot be applied to part-level flexible editing." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.383, + 0.47, + 0.563 + ], + "angle": 0, + "content": "In this paper, we aim to build a human body representation with fine-grained semantics and high reconstruction accuracy in an unsupervised setting, which needs to overcome two main challenges. First, how to disentangle the human body to reconstruct precise semantics is a key but difficult problem. Although it is straightforward to decompose a human body into articulated parts for part-level editing, the hidden space of each part is still coupled. Secondly, providing paired supervised data requires a lot of manual effort, and it is very challenging to make the representation disentangled without sacrificing reconstruction accuracy in an unsupervised manner." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.567, + 0.47, + 0.793 + ], + "angle": 0, + "content": "To address these challenges, we propose SemanticHuman, an editable human body representation with fine-grained semantics and high reconstruction-precision, which facilitates controllable human body editing without paired supervised data. To reconstruct fine-grained semantics, we design a part-aware skeleton-separated decoupling strategy with anatomical priors of the human body. Specifically, we disentangle body part variations into bone-related variations (e.g., length and orientation variations) and bone-independent variations (e.g., circumference variations). In contrast to the previous pose and shape disentanglement on the entire person [2, 18, 22], this part-aware skeleton-separated decoupling strategy establishes a correspondence between latent vectors and geometric properties of body parts, which benefits part-level controllable editing." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.796, + 0.47, + 0.901 + ], + "angle": 0, + "content": "To ensure high reconstruction accuracy and fine-grained semantics of the representation by unsupervised learning, we propose a bone-guided autoencoder architecture and an orientation-adaptive geometry-preserving loss. The bone-guided auto-encoder fuses the geometric features of body parts with their joint information to achieve accurate and efficient modeling of human bodies. Besides," + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.242 + ], + "angle": 0, + "content": "an orientation-adaptive weighting strategy is introduced to compute the geometry-preserving loss, which can provide effective geometric regularization for unsupervised disentanglement and part-level editing. Experimental results on two public datasets with different mesh connectivities demonstrate the high reconstruction-precision and controllable editing capability of the proposed method. An example is given in Fig. 1. The code will be available at http://cic.tju.edu.cn/faculty/likun/projects/SemanticHuman." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.243, + 0.858, + 0.258 + ], + "angle": 0, + "content": "Our main contributions are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.268, + 0.892, + 0.342 + ], + "angle": 0, + "content": "- We propose a semantic-aware and editable human body representation with fine-grained representation ability. The latent space of our approach facilitates personalized editing of human bodies by modifying their latent vectors." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.354, + 0.892, + 0.413 + ], + "angle": 0, + "content": "- We propose a part-aware skeleton-separated decoupling strategy exploiting structural priors of the human body to learn geometrically meaningful latent codes with fine-grained semantics." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.424, + 0.892, + 0.485 + ], + "angle": 0, + "content": "- We propose a bone-guided auto-encoder architecture and an orientation-adaptive geometry-preserving loss to ensure the robust and effective disentanglement of the representation learned without supervision." + }, + { + "type": "list", + "bbox": [ + 0.519, + 0.268, + 0.892, + 0.485 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.498, + 0.641, + 0.514 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.523, + 0.892, + 0.674 + ], + "angle": 0, + "content": "Classical Human Parametric Models. Since human bodies are geometric structures with strong priors, it is straightforward to use a statistical parametric model to represent human bodies. SCAPE [2] is one of pioneering works, which models variations between different human bodies as shape-related and pose-related deformations. SMPL [22] represents the human body more accurately and robustly based on vertices instead of triangle deformations. The SMPL has been extended to represent animals [40], hands [28], and a combination of hands and faces [24]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.675, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Deep Learning for Mesh Analysis. Traditional convolutional neural networks cannot be directly applied to meshes with irregular structures. A series of investigations [6, 8, 16, 17, 21, 33] has been devoted to constructing convolution-like operators on irregular meshes. Ranjan et al. [26] learn nonlinear representations of human faces by applying spectral convolutions to meshes. Additionally, Bouritsas et al. [7] and Gong et al. [14] propose to analyze per-vertex spatial features using spiral convolution. Chen et al. [10] and Gao et al. [13] improve the robustness and efficiency by adopting attention mechanisms in spatial aggregation. Recent works [30, 31] analyze meshes based on as-consistent-as-possible (ACAP) features [12] rather than Euclidean coordinates to learn large-scale deformations on meshes. However, these methods cannot provide explicit" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "16986" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.083, + 0.089, + 0.47, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.392, + 0.47, + 0.531 + ], + "angle": 0, + "content": "Figure 2. SemanticHuman consists of a reconstruction branch, a disentanglement branch, and an editing branch. (a) The encoder \\( E \\) maps a mesh \\( x_{1} \\) into the bone code \\( Z_{b_{1}} = \\{z_{b_{1}}^{1},\\dots,z_{b_{1}}^{K}\\} \\) and shape code \\( Z_{s_1} = \\{z_{s_1}^1,\\dots,z_{s_1}^K\\} \\), where \\( K \\) is the number of parts, and the decoder \\( D \\) aims to recover the original mesh \\( D(Z_{b_1},Z_{s_1}) \\). (b) With the help of \\( L_{dis} \\), the swapped codes \\( (Z_{b_2},Z_{s_1}) \\) will be fed into \\( D \\) to generate the target mesh \\( D(Z_{b_2},Z_{s_1}) \\) retaining the skeleton of \\( x_{2} \\) and shape features of \\( x_{1} \\). (c) By introducing \\( L_{edit} \\), we force the generated mesh from the scaled codes \\( (Z_{b_1},\\alpha Z_{s_1}) \\) to deform as desired, where \\( \\alpha \\) is a scale factor." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.538, + 0.47, + 0.582 + ], + "angle": 0, + "content": "semantics and fail to handle complex geometries. We alleviate these problems by introducing an autoencoder framework that carries geometric priors of human bodies." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.583, + 0.47, + 0.825 + ], + "angle": 0, + "content": "Disentangled Representation for Human Bodies. Jiang et al. [18] present a shape and pose disentangled human body representation based on a deep hierarchical neural network, achieving superior reconstruction accuracy. But this method depends on a strong data constraint: each posed mesh must have a paired mesh in a neutral pose. Inspired by the unsupervised disentangled generative model [3], various novel loss functions [9, 11, 38] have been proposed to preserve shape or pose in unsupervised disentanglement. Nevertheless, the supervision provided by these losses is not robust enough, and hence the reconstruction performance of such methods is unsatisfactory. By decoupling pose and shape on the whole human body, these disentangled representations can provide only coarse semantics for latent codes, and thus cannot support part-level human body editing." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.826, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Different from prior works, we propose a part-aware skeleton-separated disentanglement strategy, which not only provides precise semantics but also benefits the design of effective information-preserving losses for unsupervised learning." + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.089, + 0.679, + 0.212 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.663, + 0.089, + 0.868, + 0.212 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.519, + 0.213, + 0.738, + 0.326 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.752, + 0.213, + 0.824, + 0.326 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.325, + 0.892, + 0.38 + ], + "angle": 0, + "content": "Figure 3. The part-aware skeleton-separated decoupling strategy: (a) anatomical human body parts, (b) human body bones and joints, (c) overview of decoupling strategy, and (d) angles between lines and \\( \\sigma_b^k \\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.395, + 0.591, + 0.411 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.42, + 0.611, + 0.435 + ], + "angle": 0, + "content": "3.1. Overview" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.444, + 0.892, + 0.685 + ], + "angle": 0, + "content": "Given a set of human meshes with consistent connectivity, our goal is to learn a latent representation that has both fine-grained semantics and high reconstruction-precision in an unsupervised setting. In previous unsupervised decoupling works [3, 9, 11, 38], it is a common way to design novel loss functions capturing the shape or pose information to make representations disentangled. Nonetheless, limited by this traditional disentanglement idea focusing on the whole body, these methods are semantically coarse and not robust enough. In contrast, we design a part-aware skeleton-separated decoupling strategy (Sec. 3.2), which not only provides fine-grained semantics for flexible and precise editing of human attributes (e.g., circumference, bone orientation and length) but also facilitates the construction of robust information-preserving losses to achieve unsupervised learning." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.686, + 0.892, + 0.821 + ], + "angle": 0, + "content": "Based on this decoupling strategy, we introduce a bone-guided encoder-decoder framework (Sec. 3.3) and exploit three losses (Sec. 3.4) to achieve three core tasks by unsupervised learning: accurate geometric reconstruction (Sec. 3.4.1), unsupervised disentanglement (Sec. 3.4.2) and part-level shape editing (Sec. 3.4.3). An overview of our method is illustrated in Fig. 2, three flows of our pipeline complete the corresponding tasks with the help of specific losses." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.832, + 0.88, + 0.849 + ], + "angle": 0, + "content": "3.2. Part-Level Bone and Shape Disentanglement" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "We leverage an observation that a human body is composed of \\( K = 17 \\) anatomical components each containing a bone defined by joints, as shown in Figs. 3 (a) and (b). In" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16987" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.16, + 0.091, + 0.812, + 0.221 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.334, + 0.224, + 0.635, + 0.239 + ], + "angle": 0, + "content": "Figure 4. Details of our bone-guided autoencoder." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.247, + 0.473, + 0.352 + ], + "angle": 0, + "content": "particular, for geometrically simple human body parts such as the waist, arms, and legs, their shapes can be approximated as cylinders with bones as axes, i.e., the geometry of these parts can be modeled as variations along their bone orientations \\( o_{b} \\), and along their orthogonal directions \\( o_{b\\perp} \\) which are indicated by solid arrows and hollow arrows in Fig. 3 (b)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.354, + 0.474, + 0.549 + ], + "angle": 0, + "content": "Inspired by this observation, we propose a part-aware skeleton-separated decoupling strategy. An overview of our decoupling strategy is shown in Fig. 3 (c). Specifically, we separate body part variations into bone-related variations (e.g., length and orientation variations) and bone-independent shape variations (e.g., size and style variations), which are represented by the bone embedding \\( z_{b}^{k} \\) and shape embedding \\( z_{s}^{k} \\) for the \\( k \\)-th part, respectively. This part-aware skeleton-separated decoupling strategy establishes a correspondence between latent codes and geometric properties of body parts, which provides fine-grained semantics and enables part-level personalized human body editing." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.56, + 0.32, + 0.575 + ], + "angle": 0, + "content": "3.3. Bone-Guided Autoencoder" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.583, + 0.47, + 0.688 + ], + "angle": 0, + "content": "The basic architecture of our framework is shown in Fig. 4. All three tasks are based on this architecture. Given a human body mesh \\( x \\), the bone branch and shape branch of encoder \\( E \\) embed the mesh into the bone code \\( Z_{b} = \\{z_{b}^{1},\\dots,z_{b}^{K}\\} \\) and shape code \\( Z_{s} = \\{z_{s}^{1},\\dots,z_{s}^{K}\\} \\), respectively, where \\( z_{b}^{k} \\) and \\( z_{s}^{k} \\) are localized latent codes for the \\( k \\)-th body part." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.689, + 0.473, + 0.902 + ], + "angle": 0, + "content": "In particular, the bone branch infers localized bone codes \\(\\{z_b^1,\\dots,z_b^K\\}\\) with global information (e.g., length and orientation) about each body part from joints \\(B\\) predicted by a linear regressor \\(J(\\cdot)\\) [22]. Besides, the shape branch takes mesh \\(x\\) as input with a hierarchical spiral convolution encoder for learning geometric features in multiple scales. Geometric features belonging to each part are subsequently fed into the corresponding fully connected layer according to the part labels of vertices to obtain localized bone codes \\(\\{z_s^1,\\dots,z_s^K\\}\\) containing regional geometric details. Finally, the decoder with a similar structure to the shape branch accurately and efficiently reconstructs the original mesh \\(D(Z_{b},Z_{s})\\) by integrating local and global information of each body part." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.247, + 0.8, + 0.264 + ], + "angle": 0, + "content": "3.4. Losses for Unsupervised Learning" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.269, + 0.893, + 0.315 + ], + "angle": 0, + "content": "With the above framework, we utilize three losses to achieve the corresponding tasks in an unsupervised setting, and the full objective function is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.595, + 0.32, + 0.892, + 0.337 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {f u l l}} = \\mathcal {L} _ {\\text {r e c}} + \\mathcal {L} _ {\\text {d i s}} + \\mathcal {L} _ {\\text {e d i t}}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.341, + 0.894, + 0.432 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{rec}\\) is a geometric reconstruction loss for accurate human body reconstruction, \\(\\mathcal{L}_{dis}\\) is a disentanglement loss to ensure the bone and shape disentanglement of body parts, and \\(\\mathcal{L}_{edit}\\) is introduced to enable part-level shape editing. The losses and training flows will be described in detail in the following sections." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.439, + 0.807, + 0.453 + ], + "angle": 0, + "content": "3.4.1 Accurate Geometric Reconstruction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.462, + 0.893, + 0.507 + ], + "angle": 0, + "content": "As shown in Fig. 2 (a), in order to reconstruct the original mesh as accurately as possible, we adopt the geometric reconstruction loss as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.594, + 0.514, + 0.892, + 0.53 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {r e c}} = \\mathcal {L} _ {\\text {v e r t}} + \\lambda_ {\\text {e d g e}} \\cdot \\mathcal {L} _ {\\text {e d g e}}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.535, + 0.894, + 0.593 + ], + "angle": 0, + "content": "where \\(\\lambda_{edge}\\) is the weight of edge regularization. The vertex loss forces the reconstructed mesh \\(D(E(x))\\) to be as close as possible to the original mesh \\(x\\) with the supervision of vertex-wise L1 distance, which is calculated as:" + }, + { + "type": "equation", + "bbox": [ + 0.604, + 0.599, + 0.892, + 0.616 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {v e r t}} = \\| x - D (E (x)) \\| _ {1}. \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.621, + 0.892, + 0.697 + ], + "angle": 0, + "content": "However, only using this loss to supervise vertex positions cannot avoid producing over-length edges, which seriously affects the smoothness and reasonableness of results. Inspired by [15,34], we address this problem by introducing an edge length regularization \\( L_{edge} \\), which is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.594, + 0.7, + 0.892, + 0.734 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {e d g e}} = \\sum_ {p} \\sum_ {v \\in N (p)} \\| p - v \\| _ {2} ^ {2}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.734, + 0.892, + 0.78 + ], + "angle": 0, + "content": "where \\( N(p) \\) is the set of 1-ring neighbors of vertex \\( p \\). This loss ensures the smoothness of output meshes by enforcing their surface to be tight." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.787, + 0.771, + 0.803 + ], + "angle": 0, + "content": "3.4.2 Unsupervised Disentanglement" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.901 + ], + "angle": 0, + "content": "After the reconstruction flow in Sec. 3.4.1, our representation is already capable of accurate reconstruction, but its latent space is still entangled. Based on the part-aware skeleton-separated decoupling strategy (Sec. 3.2), we use \\(\\mathcal{L}_{dis}\\) to ensure the bone and shape disentanglement of body parts, which can be defined as:" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16988" + } + ], + [ + { + "type": "equation", + "bbox": [ + 0.168, + 0.092, + 0.469, + 0.107 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {d i s} = \\mathcal {L} _ {d i s - b} + \\lambda_ {d i s - s} \\cdot \\mathcal {L} _ {d i s - s}. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.113, + 0.47, + 0.265 + ], + "angle": 0, + "content": "To achieve unsupervised disentanglement, given two human bodies \\( x_{1} \\) and \\( x_{2} \\), \\( x_{swp} \\) denotes the generated mesh \\( D(Z_{b_2}, Z_{s_1}) \\), which is constructed with the bone latent code from \\( x_{2} \\) and shape latent code from \\( x_{1} \\), as shown in Fig. 2 (b). Overall, \\( \\mathcal{L}_{dis\\_b} \\) and \\( \\mathcal{L}_{dis\\_s} \\) are used to preserve bone information belonging to parts of \\( x_{2} \\) and geometric features along \\( o_{b\\perp} \\) belonging to parts of \\( x_{1} \\), respectively. In this way, our method achieves decoupling via unsupervised learning. \\( x_{swp} \\) should have close joint positions as \\( x_{2} \\) as these are largely determined by the bones, so \\( L_{dis\\_b} \\) is defined as" + }, + { + "type": "equation", + "bbox": [ + 0.168, + 0.27, + 0.469, + 0.287 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {d i s} b} = \\| J \\left(x _ {2}\\right) - J \\left(x _ {s w p}\\right) \\| _ {1}, \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.292, + 0.469, + 0.322 + ], + "angle": 0, + "content": "where \\( J(x) \\) is the vector containing joint positions for \\( x \\) obtained through a joint regressor." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.323, + 0.469, + 0.396 + ], + "angle": 0, + "content": "However, preserving geometric features is a challenging issue. An intuitive idea to solve this issue is to use the matrix of pairwise Euclidean distances between all vertices in each part to capture regional shape information, which can be defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.144, + 0.398, + 0.469, + 0.439 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {d i s} - s} = \\sum_ {k = 1} ^ {K} \\| D _ {e} \\left(x _ {1} ^ {k}\\right) - D _ {e} \\left(x _ {\\operatorname {s w p}} ^ {k}\\right) \\| _ {1}, \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.441, + 0.469, + 0.546 + ], + "angle": 0, + "content": "where \\( x^{k} \\) is the \\( k \\)-th part-mesh of \\( x \\), and \\( D_{e}(x^{k}) \\) denotes the matrix of pairwise Euclidean distances between all vertices in \\( x^{k} \\). \\( D_{e}(x^{k}) \\) is of size \\( n(x^{k}) \\times n(x^{k}) \\), where \\( n(x^{k}) \\) is the number of vertices of the \\( k \\)-th part. Nevertheless, the bone length is coupled with the Euclidean distance matrix, which leads to incomplete decoupling and affects editing precision (Sec. 4.3)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.549, + 0.469, + 0.741 + ], + "angle": 0, + "content": "To alleviate this problem, we propose an orientation-adaptive weighting (OAW) strategy to enforce the representation to focus on shape variations along \\(o_{b\\perp}\\). For each body part \\(x^{k}\\), we first construct a pairwise angle matrix of size \\(n(x^{k}) \\times n(x^{k})\\) by joining every pair of vertices to form a line, and working out the angle \\(A(x^{k})\\) (in degrees) the line makes with the bone orientation \\(o_{b}^{k}\\), as illustrated in Fig. 3 (d). As can be seen, the larger the angle, the more significant the line contributes to the shape variation along \\(o_{b\\perp}\\). We apply the following thresholding and normalization \\(f\\) to obtain the weight matrix \\(W(x^{k})\\) also of the same size by suppressing small angles, and setting the maximum weight to one:" + }, + { + "type": "equation", + "bbox": [ + 0.202, + 0.741, + 0.468, + 0.758 + ], + "angle": 0, + "content": "\\[\nW \\left(x ^ {k}\\right) = f \\left(A \\left(x ^ {k}\\right)\\right), \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.763, + 0.466, + 0.778 + ], + "angle": 0, + "content": "where \\( f(\\cdot) \\) is an element-wise mapping function defined as" + }, + { + "type": "equation", + "bbox": [ + 0.173, + 0.783, + 0.469, + 0.824 + ], + "angle": 0, + "content": "\\[\nf (a) = \\left\\{ \\begin{array}{l l} a / 9 0, & \\text {i f} a > \\sigma \\\\ 0, & \\text {o t h e r w i s e .} \\end{array} \\right. \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.828, + 0.452, + 0.842 + ], + "angle": 0, + "content": "The weighted pairwise distance matrix is then defined as" + }, + { + "type": "equation", + "bbox": [ + 0.172, + 0.847, + 0.468, + 0.865 + ], + "angle": 0, + "content": "\\[\nD _ {e} ^ {w} \\left(x ^ {k}\\right) = W \\left(x ^ {k}\\right) \\otimes D _ {e} \\left(x ^ {k}\\right), \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.469, + 0.901 + ], + "angle": 0, + "content": "where \\(\\otimes\\) is element-wise matrix multiplication. This orientation-adaptive weighting strategy enforces the bone" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.273 + ], + "angle": 0, + "content": "length information to be separated from the Euclidean distance matrix as much as possible. Nonetheless, this strategy also ignores some useful geometric information along \\( o_b \\), which may result in unreasonable mesh deformations when editing bone orientation in large scale (Sec. 4.3). To address this problem, we further impose part-level volume regularization penalizing the unreasonable shape variations. Ideally, when the shape along \\( o_{b\\perp} \\) is retained, the volume of a body part should change in proportion to the bone length, so we introduce a volume constraint, which provides strong geometric supervision to achieve natural and reasonable editing results, which can be calculated as:" + }, + { + "type": "equation", + "bbox": [ + 0.519, + 0.275, + 0.891, + 0.317 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {v o l} = \\sum_ {k = 1} ^ {K} \\| v \\left(x _ {1} ^ {k}\\right) / l \\left(x _ {1} ^ {k}\\right) - v \\left(x _ {s w p} ^ {k}\\right) / l \\left(x _ {s w p} ^ {k}\\right) \\| _ {1}, \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.32, + 0.892, + 0.38 + ], + "angle": 0, + "content": "where \\( v(\\cdot) \\) is a function that calculates the volume of a mesh part according to the tetrahedral volume formula, and \\( l(\\cdot) \\) is a function that measures the length of a mesh part between the two joints. We can then rewrite \\( \\mathcal{L}_{dis\\_s} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.525, + 0.385, + 0.891, + 0.426 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {d i s} - s} = \\sum_ {k = 1} ^ {K} \\| D _ {e} ^ {w} \\left(x _ {1} ^ {k}\\right) - D _ {e} ^ {w} \\left(x _ {\\text {s w p}} ^ {k}\\right) \\| _ {1} + \\mathcal {L} _ {\\text {v o l}}. \\tag {12}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.438, + 0.731, + 0.453 + ], + "angle": 0, + "content": "3.4.3 Part-Level Shape Editing" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.461, + 0.892, + 0.613 + ], + "angle": 0, + "content": "With the constraints in Sec. 3.4.2, our autoencoder learns a bone and shape disentangled representation for body parts, and hence we can directly control bone-related attributes (e.g., bone orientation and length) of parts by modifying their joints. Nevertheless, we cannot flexibly edit other bone-independent shape features (e.g., circumference) as desired. To address this issue, we propose an editing flow shown in Fig. 2 (c), which achieves part-level shape editing by forcing the generated mesh from the scaled codes \\((Z_{b_1},\\alpha Z_{s_1})\\) to deform as desired with \\(L_{edit}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.508, + 0.617, + 0.891, + 0.634 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {e d i t}} = \\mathcal {L} _ {\\text {e d i t} b} + \\lambda_ {\\text {e d i t} s} \\cdot \\mathcal {L} _ {\\text {e d i t} s} + \\lambda_ {\\text {n o r m}} \\cdot \\mathcal {L} _ {\\text {n o r m}}, \\tag {13}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.558, + 0.639, + 0.891, + 0.68 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {e d i t} - s} = \\sum_ {k = 1} ^ {K} \\| \\alpha D _ {e} ^ {w} \\left(x _ {1} ^ {k}\\right) - D _ {e} ^ {w} \\left(x _ {s c a} ^ {k}\\right) \\| _ {1}, \\tag {14}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.591, + 0.685, + 0.891, + 0.702 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {e d i t} - b} = \\left\\| J \\left(x _ {1}\\right) - J \\left(x _ {s c a}\\right) \\right\\| _ {1}, \\tag {15}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.705, + 0.891, + 0.78 + ], + "angle": 0, + "content": "where \\(\\alpha\\) is a scalar uniformly sampled in \\((\\alpha_{\\mathrm{min}},\\alpha_{\\mathrm{max}})\\) during training, and \\(x_{sca}\\) denotes the generated mesh \\(D(Z_{b_1},\\alpha Z_{s_1})\\) from the scaled codes, i.e., the edited mesh \\(x_{sca}\\) has bone code from \\(x_{1}\\), and shape code from \\(x_{1}\\) with flexible scaling to change body shape." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.781, + 0.892, + 0.901 + ], + "angle": 0, + "content": "In particular, \\(\\mathcal{L}_{edit\\_s}\\) constrains the \\(k\\)-th part of \\(x_{sca}\\) reconstructed from the scaled shape codes \\(\\alpha Z_{s_1}\\) to have the shape described by \\(\\alpha D_e^W (x_1^k)\\). As \\(D_e^W (\\cdot)\\) is designed to largely capture the pairwise distances along \\(o_{b\\perp}^{k}\\), i.e. reflecting shape size and style for the part. Elements in the matrix therefore should be scaled accordingly when the part is scaled along \\(o_{b\\perp}^{k}\\). Besides, \\(\\mathcal{L}_{edit\\_b}\\) is introduced to preserve bone information during the editing process." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16989" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.471, + 0.198 + ], + "angle": 0, + "content": "However, the editing flow easily causes training collapses. We solve this problem by imposing a vector norm regularization \\(\\mathcal{L}_{norm}\\) to establish a mapping between the norm of localized shape codes and the circumference of body parts, which not only benefits converge, but also allows users to edit part shape size on a unified scale. This regularization can be defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.143, + 0.199, + 0.47, + 0.238 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {n o r m}} = \\frac {1}{K} \\sum_ {k = 1} ^ {K} \\left| \\| z _ {s _ {1}} ^ {k} \\| _ {2} - \\operatorname {c i r c} \\left(x _ {1} ^ {k}\\right) \\right|, \\tag {16}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.239, + 0.47, + 0.33 + ], + "angle": 0, + "content": "where \\( \\text{circ}(\\cdot) \\) is a function that measures the circumference of a part using the identified landmarks. \\( \\mathcal{L}_{edit} \\) successfully allows shape codes' norm and direction to represent shape size and style respectively, which not only enables flexible part-level editing but also ensures the continuity of the learned latent space to a certain extent." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.338, + 0.295, + 0.354 + ], + "angle": 0, + "content": "3.5. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.361, + 0.471, + 0.573 + ], + "angle": 0, + "content": "For the spiral convolution encoder, we use a framework similar to [7]. Specifically, it consists of four spiral convolution layers and downsampling layers, and the structure of the decoder is a mirror of the encoder, except that the downsampling layers are replaced by the upsampling layers. Our algorithm is implemented in PyTorch [23]. All the training and test experiments are carried out on a PC with an RTX 3090 GPU. We train our network for 300 epochs with a learning rate of \\(1 \\times 10^{-3}\\), a learning rate decay of 0.99 after each epoch and the Adam optimizer [20]. The entire training time takes around 24 hours. We use 16-dimensional latent codes (8 for bones and 8 for shape) to embed each part. More implementation details can be found in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.582, + 0.21, + 0.599 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.606, + 0.181, + 0.621 + ], + "angle": 0, + "content": "4.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.629, + 0.47, + 0.749 + ], + "angle": 0, + "content": "DFAUST. The dynamic human body dataset with the same mesh connectivity as SMPL [22] from Bogo et al. [5], captures 14 different body motion sequences (e.g., hips, running, and jumping) for each of the 10 human subjects. We evenly extract one-twentieth of the original dataset for the convenience of training. Then we randomly split the extracted data into a test set of 182 meshes and a training set of 1936 meshes." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.47, + 0.854 + ], + "angle": 0, + "content": "SPRING. The large human body dataset with the same mesh connectivity as SCAPE [2] from Yang et al. [35], consists of \\(3000+\\) subject meshes with a rough A-pose registered from the CAESAR dataset [27] using a non-rigid deformation algorithm. For the subsequent experiments, the SPRING dataset is randomly split into 2743 training and 305 test meshes." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Data Preprocessing. For the training data, our method requires a joint regressor, body part semantics of each vertex, and landmarks for calculating circumferences. Since" + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.089, + 0.885, + 0.24 + ], + "angle": 0, + "content": "
MethodDFAUSTSPRING
EavdParam(M)EavdParam(M)
COMA [26]6.067.546.046.84
Neural3DMM [7]5.4930.356.1127.56
Spiralplus [14]5.3515.154.9913.75
Pai3DMM [13]5.7615.184.4513.78
Deep3DMM [10]9.918.3510.887.84
Unsup [38]10.1812.89--
Ours4.701.594.331.47
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.242, + 0.892, + 0.283 + ], + "angle": 0, + "content": "Table 1. Quantitative reconstruction results on DFAUST [5] and SPRING [35] datasets. - : not supported for this dataset. Param(M) shows the number of learnable parameters in millions." + }, + { + "type": "table", + "bbox": [ + 0.574, + 0.294, + 0.816, + 0.372 + ], + "angle": 0, + "content": "
MethodDHNN
EavdParam(M)
DHNN [18]3.1691.63
Ours3.961.47
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.372, + 0.89, + 0.4 + ], + "angle": 0, + "content": "Table 2. Quantitative reconstruction results on DHNN [18]. \\( \\mathbf{{Param}\\left( M\\right) } \\) shows the number of learnable parameters in millions." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.41, + 0.892, + 0.487 + ], + "angle": 0, + "content": "the mesh connectivity of datasets is consistent (which is required by nearly all 3D human representation learning works), the SMPL model only needs to be registered once to meet the requirements. More details about data preprocessing are given in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.498, + 0.633, + 0.515 + ], + "angle": 0, + "content": "4.2. Comparison" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.522, + 0.892, + 0.567 + ], + "angle": 0, + "content": "In the following, we evaluate the representation ability (reconstruction precision) and editing capacity (semantics) of our approach on the DFAUST and SPRING datasets." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.568, + 0.893, + 0.779 + ], + "angle": 0, + "content": "Reconstruction Experiments. We first compare four kinds of methods to validate the reconstruction precision of our representation: the spectral-based approach (COMA [26]), the spiral-based methods (Neural3DMM [7] and Spiralplus [14]), the attention-based approaches (Pai3DMM [13] and Deep3DMM [10]), and the disentanglement representation works (DHNN [18] and Unsup [38]). For a fair comparison, we use the official implementation of the compared methods with the same latent space dimension. Note that DHNN [18] did not release the training code, so we train and test our model on its dataset for a fair comparison. we utilize the average point-wise Euclidean distance \\(E_{\\text{avd}}\\) (in millimeters) between corresponding vertices in the input and its reconstruction as metrics." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.892, + 0.9 + ], + "angle": 0, + "content": "As shown in Tab. 1, our proposed approach shows excellent representation capability with a small model size. Fig. 5 visualizes some reconstruction results and their error maps. It can be observed that our approach outperforms other methods in reconstruction accuracy, especially for complex geometric details (e.g., faces and hands), which demonstrates the effectiveness of our bone-guided autoencoder." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "16990" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.115, + 0.089, + 0.855, + 0.257 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.259, + 0.895, + 0.316 + ], + "angle": 0, + "content": "Figure 5. Qualitative reconstruction results on DFAUST [5] and SPRING [35]. The per-vertex Euclidean distance error is color-coded on the reconstructed meshes for visual inspection. Since Unsup [38] has a data constraint that the same subject in different poses should be given, it cannot be trained on the SPRING dataset. Note that our representation is trained without the need of data constraint, which is used in the compared methods." + }, + { + "type": "image", + "bbox": [ + 0.08, + 0.319, + 0.47, + 0.494 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.494, + 0.47, + 0.548 + ], + "angle": 0, + "content": "Figure 6. Qualitative editing results by Unsup [38] (first row), HBR [36] (second and third rows) and our method. We show the reconstructed bodies and edited bodies on the left and right of the source mesh." + }, + { + "type": "table", + "bbox": [ + 0.08, + 0.555, + 0.469, + 0.648 + ], + "angle": 0, + "content": "
MethodBone OrientationBone LengthShape Size
EjointEcricEjointEcricEjointEcric
Unsup [38]36.7914.16----
HBR [36]--38.3715.2712.8718.78
Ours3.269.751.575.920.4517.64
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.649, + 0.468, + 0.663 + ], + "angle": 0, + "content": "Table 3. Quantitative editing results. - : not supported for this task." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.674, + 0.469, + 0.824 + ], + "angle": 0, + "content": "In addition, Tab. 2 gives the quantitative results on the DHNN dataset [18]. It is important to note that the compared method [18] uses a training data assumption (i.e., each posed mesh has a paired mesh in a neutral pose) for shape and pose disentanglement, but our method does not make use of this constraint. Under this unfair condition, the reconstruction accuracy of our method is only slightly lower than DHNN [18], and our model is more lightweight and semantically finer. Please refer to our supplementary material for more experimental results and details." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.469, + 0.903 + ], + "angle": 0, + "content": "Editing Experiments. We also demonstrate the flexible editing ability of our model on three editing tasks: editing bone orientation and bone length, and editing part shape size. Since bone orientation is approximately equivalent to pose, we compare with the unsupervised pose-and-shape" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.324, + 0.892, + 0.474 + ], + "angle": 0, + "content": "disentanglement work Unsup [38] on the task of editing bone orientation on the DFAUST dataset. In contrast, bone length and shape size are shape information, so we compare with the Human Body Reshaping work HBR [36] on the task of editing bone length and shape size on the SPRING dataset. As there is no ground truth for these editing tasks, how to measure the editing performance is a problem. We utilize the joint and circumference errors \\( E_{joint} \\), \\( E_{circ} \\) (in millimeters) to evaluate the accuracy of editing bone and shape." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.475, + 0.892, + 0.548 + ], + "angle": 0, + "content": "Specifically, for each test mesh, we randomly select editing targets and then calculate \\( E_{joint} \\), \\( E_{circ} \\) between the target attribute value and the actual attribute value of the edited human body, extracted using \\( J(\\cdot) \\) and \\( circ(\\cdot) \\), which can be defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.582, + 0.552, + 0.891, + 0.57 + ], + "angle": 0, + "content": "\\[\nE _ {j o i n t} = \\left\\| T _ {j o i n t} - J \\left(x _ {\\text {e d i t e d}}\\right) \\right\\| _ {2}, \\tag {17}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.563, + 0.571, + 0.891, + 0.612 + ], + "angle": 0, + "content": "\\[\nE _ {c i r c} = \\frac {1}{K} \\sum_ {k = 1} ^ {K} \\left| T _ {c i r c} ^ {k} - \\operatorname {c i r c} \\left(x _ {\\text {e d i t e d}} ^ {k}\\right) \\right|, \\tag {18}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.613, + 0.89, + 0.642 + ], + "angle": 0, + "content": "where \\( T_{joint} \\) and \\( T_{circ} \\) are the editing targets of joint positions and circumference." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.643, + 0.892, + 0.78 + ], + "angle": 0, + "content": "Tab. 3 gives the quantitative editing results. Our method not only achieves the best performance on all the editing tasks but also preserves other unedited attributes well. Some visual results are shown in Fig. 6. Compared with Unsup [38] and HBR [36], our method can edit the human body more accurately, reasonably and flexibly. Since the attribute error does not always reflect the editing performance of models, we also design a user study for better evaluation (see the supplementary material)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.787, + 0.654, + 0.803 + ], + "angle": 0, + "content": "4.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Effect of Orientation-Adaptive Weighting Strategy. We validate the effectiveness of the orientation-adaptive weighting strategy by ablating it during training. As shown in Tab. 4, introducing this strategy helps our representation to focus on geometric features along \\(o_{b\\perp}\\), leading to higher editing accuracy and more complete decoupling." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "16991" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.117, + 0.09, + 0.43, + 0.217 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.219, + 0.47, + 0.247 + ], + "angle": 0, + "content": "Figure 7. Qualitative results of volume regularization ablation study." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.26, + 0.472, + 0.362 + ], + "angle": 0, + "content": "
MethodReconstructionEditing
DFA. SPR. MeanBone Ori. \\(E_{joint}\\)Bone Length\\(E_{joint}\\)Shape Size\\(E_{joint}\\)Mean
Full4.704.334.523.269.751.575.920.4517.646.43
w/o OAW4.664.394.533.0213.661.5810.950.6120.408.37
w/o \\(L_{edge}\\)5.235.015.123.209.761.528.370.5329.488.81
w/o \\(L_{dis}\\)4.814.884.85----1.5617.50-
w/o \\(L_{edit}\\)4.874.434.653.0010.191.888.01---
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.364, + 0.47, + 0.392 + ], + "angle": 0, + "content": "Table 4. Quantitative ablation study for reconstruction and editing (in mm). - : not supported for this task." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.404, + 0.469, + 0.51 + ], + "angle": 0, + "content": "Effect of Other Losses. To evaluate the impact of \\(\\mathcal{L}_{\\text {edge }}\\), \\(\\mathcal{L}_{\\text {dis }}\\) and \\(\\mathcal{L}_{\\text {edit }}\\), we remove them from the supervision one by one during training. As compared in Tab. 4, \\(\\mathcal{L}_{\\text {edge }}\\) effectively reduces the reconstruction error, and the use of \\(\\mathcal{L}_{\\text {dis }}\\) and \\(\\mathcal{L}_{\\text {edit }}\\) is required to enable flexible bone and part shape editing. The ablation study proves that all losses are necessary." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.514, + 0.47, + 0.605 + ], + "angle": 0, + "content": "Effect of Part-Level Volume Regularization. To analyze the impact of volume regularization, we remove it during the training process. Fig. 7 shows the comparison results of edited bodies. It can be seen that our volume constraint provides strong geometric supervision, resulting in more natural and reasonable editing results." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.628, + 0.212, + 0.644 + ], + "angle": 0, + "content": "4.4. Applications" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.655, + 0.469, + 0.776 + ], + "angle": 0, + "content": "Latent Space Interpolation. Our representation decouples the bone and shape of each part, which allows us to interpolate meshes by linearly interpolating the bone and shape latent codes. Fig. 8 shows reasonable and meaningful results of such interpolation. It is worth noting that, thanks to the latent space with fine-grained semantics, we can perform mesh interpolation at the component level (see Figs. 8 (b) and (c))." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Shape Style Transfer. As mentioned in Sec. 3.4.3, we successfully make the norm and direction of shape vectors represent shape size and style, respectively. Thus, we can transfer shape style by swapping the direction of the shape vectors. Some results are shown in Fig. 9. It can be easily observed that our approach changes the shape style of the source mesh naturally and preserves its other attributes (e.g., bone and circumference)." + }, + { + "type": "image", + "bbox": [ + 0.52, + 0.089, + 0.642, + 0.212 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.642, + 0.09, + 0.753, + 0.212 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.753, + 0.09, + 0.867, + 0.212 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.213, + 0.892, + 0.254 + ], + "angle": 0, + "content": "Figure 8. Interpolation results on the bone-and-shape disentangled space of (a) the whole body, (b) upper body and (c) lower body, where the highlighted bodies are the input reconstructed bodies." + }, + { + "type": "image", + "bbox": [ + 0.521, + 0.268, + 0.868, + 0.407 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.41, + 0.892, + 0.437 + ], + "angle": 0, + "content": "Figure 9. Results of shape style transfer by linearly interpolating the direction of shape codes." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.458, + 0.747, + 0.473 + ], + "angle": 0, + "content": "5. Conclusion and Discussion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.492, + 0.892, + 0.689 + ], + "angle": 0, + "content": "Conclusion. In this paper, we propose a human body representation with fine-grained semantics and high reconstruction-accuracy in an unsupervised setting. The key idea is to exploit a part-aware skeleton-separated decoupling strategy to establish a correspondence between latent vectors and geometric properties of body parts, which benefits personalized editing of human bodies by modifying the corresponding latent codes. Based on this disentanglement strategy, we propose a bone-guided autoencoder and well-designed losses to learn representation in an unsupervised manner. At last, with the geometrically meaningful latent space, the application can be extended from human body editing to latent code interpolation and shape style transfer." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.696, + 0.892, + 0.848 + ], + "angle": 0, + "content": "Limitations. Since our decoupling strategy is based on the cylinder assumption (i.e., the shape of body parts can be approximated as cylinders with bones as axes), we cannot controllably edit the bone length and circumference of parts (e.g., faces, hands, and feet) whose geometry is too complex to meet this assumption. Additionally, editing bone orientation with our method may fail when the target orientation is uncommon in the training data. In further work, we will dig deeper into the prior knowledge about the human body to improve the generalization capability of our representation." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Acknowledgement. This work was supported in part by the National Natural Science Foundation of China (62122058 and 62171317)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16992" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.469, + 0.158 + ], + "angle": 0, + "content": "[1] Brett Allen, Brian Curless, and Zoran Popovic. The space of human body shapes: reconstruction and parameterization from range scans. ACM Trans. Graph., 2003. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.16, + 0.469, + 0.214 + ], + "angle": 0, + "content": "[2] Dragomir Anguelov, Praveen Srinivasan, Daphne Koller, Sebastian Thrun, Jim Rodgers, and James Davis. SCAPE: shape completion and animation of people. ACM Trans. Graph., 2005. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.217, + 0.469, + 0.271 + ], + "angle": 0, + "content": "[3] Tristan Aumentado-Armstrong, Stavros Tsogkas, Allan Jepson, and Sven Dickinson. Geometric disentanglement for generative latent shape models. In Int. Conf. Comput. Vis., 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.274, + 0.469, + 0.329 + ], + "angle": 0, + "content": "[4] Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler, Javier Romero, and Michael J. Black. Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image. In Eur. Conf. Comput. Vis., 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.331, + 0.469, + 0.385 + ], + "angle": 0, + "content": "[5] Federica Bogo, Javier Romero, Gerard Pons-Moll, and Michael J Black. Dynamic FAUST: Registering human bodies in motion. In IEEE Conf. Comput. Vis. Pattern Recog., 2017. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.388, + 0.469, + 0.457 + ], + "angle": 0, + "content": "[6] Davide Boscaini, Jonathan Masci, Simone Melzi, Michael M Bronstein, Umberto Castellani, and Pierre Vandergheynst. Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks. In Comput. Graph. Forum, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.46, + 0.469, + 0.528 + ], + "angle": 0, + "content": "[7] Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Michael Bronstein, and Stefanos Zafeiriou. Neural 3D morphable models: Spiral convolutional networks for 3d shape representation learning and generation. In Int. Conf. Comput. Vis., 2019. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.531, + 0.469, + 0.572 + ], + "angle": 0, + "content": "[8] Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.574, + 0.469, + 0.627 + ], + "angle": 0, + "content": "[9] Haoyu Chen, Hao Tang, Henglin Shi, Wei Peng, Nicu Sebe, and Guoying Zhao. Intrinsic-extrinsic preserved GANs for unsupervised 3D pose transfer. In Int. Conf. Comput. Vis., 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.631, + 0.469, + 0.672 + ], + "angle": 0, + "content": "[10] Zhixiang Chen and Tae-Kyun Kim. Learning feature aggregation for deep 3D morphable models. In IEEE Conf. Comput. Vis. Pattern Recog., 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.674, + 0.469, + 0.729 + ], + "angle": 0, + "content": "[11] Luca Cosmo, Antonio Norelli, Oshri Halimi, Ron Kimmel, and Emanuele Rodola. LIMP: Learning latent shape representations with metric preservation priors. In Eur. Conf. Comput. Vis., 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.732, + 0.469, + 0.773 + ], + "angle": 0, + "content": "[12] Lin Gao, Yu-Kun Lai, Jie Yang, Ling-Xiao Zhang, Shihong Xia, and Leif Kobbelt. Sparse data driven mesh deformation. IEEE Trans. Vis. Comput. Graph., 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.775, + 0.469, + 0.829 + ], + "angle": 0, + "content": "[13] Zhongpai Gao, Junchi Yan, Guangtao Zhai, Juyong Zhang, Yiyan Yang, and Xiaokang Yang. Learning local neighboring structure for robust 3D shape representation. In AAAI, 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.832, + 0.469, + 0.899 + ], + "angle": 0, + "content": "[14] Shunwang Gong, Lei Chen, Michael Bronstein, and Stefanos Zafeiriou. SpiralNet++: A fast and highly efficient mesh convolution operator. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2019, 2, 6" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.469, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[15] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. 3D-CODED: 3D correspondences by deep deformation. In Eur. Conf. Comput. Vis., 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[16] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Adv. Neural Inform. Process. Syst., 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.193, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[17] Mikael Henaff, Joan Bruna, and Yann LeCun. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.235, + 0.892, + 0.289 + ], + "angle": 0, + "content": "[18] Boyi Jiang, Juyong Zhang, Jianfei Cai, and Jianmin Zheng. Disentangled human body embedding based on deep hierarchical neural network. IEEE Trans. Vis. Comput. Graph., 2020. 1, 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.292, + 0.892, + 0.333 + ], + "angle": 0, + "content": "[19] Angjoo Kanazawa, Michael J. Black, David W. Jacobs, and Jitendra Malik. End-to-end recovery of human shape and pose. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.335, + 0.892, + 0.362 + ], + "angle": 0, + "content": "[20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.364, + 0.892, + 0.404 + ], + "angle": 0, + "content": "[21] Linjie Li, Zhe Gan, Yu Cheng, and Jingjing Liu. Relation-aware graph attention network for visual question answering. In Int. Conf. Comput. Vis., 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.407, + 0.892, + 0.46 + ], + "angle": 0, + "content": "[22] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. SMPL: A skinned multiperson linear model. ACM Trans. Graph., 2015. 1, 2, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.463, + 0.892, + 0.531 + ], + "angle": 0, + "content": "[23] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. PyTorch: An imperative style, high-performance deep learning library. 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.533, + 0.892, + 0.602 + ], + "angle": 0, + "content": "[24] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3D hands, face, and body from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.604, + 0.892, + 0.657 + ], + "angle": 0, + "content": "[25] Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt, and Bernt Schiele. Building statistical shape spaces for 3D human modeling. Pattern Recognition, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.661, + 0.892, + 0.702 + ], + "angle": 0, + "content": "[26] Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J Black. Generating 3D faces using convolutional mesh autoencoders. In Eur. Conf. Comput. Vis., 2018. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.704, + 0.892, + 0.757 + ], + "angle": 0, + "content": "[27] Kathleen M Robinette, Hans Daanen, and Eric Paquet. The caesar project: a 3-d surface anthropometry survey. In International Conference on 3-D Digital Imaging and Modeling, 1999. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.76, + 0.892, + 0.801 + ], + "angle": 0, + "content": "[28] Javier Romero, Dimitrios Tzionas, and Michael J Black. Embodied hands: Modeling and capturing hands and bodies together. ACM Trans. Graph., 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.804, + 0.892, + 0.857 + ], + "angle": 0, + "content": "[29] Hyewon Seo and Nadia Magnenat-Thalmann. An automatic modeling of human bodies from sizing parameters. In Proceedings of the Symposium on Interactive 3D Graphics, 2003. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.859, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[30] Qingyang Tan, Lin Gao, Yu-Kun Lai, and Shihong Xia. Variational autoencoders for deforming 3D mesh models. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16993" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.468, + 0.134 + ], + "angle": 0, + "content": "[31] Qingyang Tan, Lin Gao, Yu-Kun Lai, Jie Yang, and Shihong Xia. Mesh-based autoencoders for localized deformation component analysis. In AAAI, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.136, + 0.468, + 0.177 + ], + "angle": 0, + "content": "[32] Yating Tian, Hongwen Zhang, Yebin Liu, and Limin Wang. Recovering 3D human mesh from monocular images: A survey. arXiv preprint arXiv:2203.01923, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.179, + 0.468, + 0.219 + ], + "angle": 0, + "content": "[33] Nitika Verma, Edmond Boyer, and Jakob Verbeek. Dynamic filters in graph convolutional networks. arXiv preprint arXiv:1706.05206, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.221, + 0.468, + 0.276 + ], + "angle": 0, + "content": "[34] Jiashun Wang, Chao Wen, Yanwei Fu, Haitao Lin, Tianyun Zou, Xiangyang Xue, and Yinda Zhang. Neural pose transfer by spatially adaptive instance normalization. In IEEE Conf. Comput. Vis. Pattern Recog., 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.278, + 0.468, + 0.332 + ], + "angle": 0, + "content": "[35] Yipin Yang, Yao Yu, Yu Zhou, Sidan Du, James Davis, and Ruigang Yang. Semantic parametric reshaping of human body models. In International Conference on 3D Vision, 2014. 1, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.334, + 0.468, + 0.389 + ], + "angle": 0, + "content": "[36] Yanhong Zeng, Jianlong Fu, and Hongyang Chao. 3D human body reshaping with anthropometric modeling. In International Conference on Internet Multimedia Computing and Service, 2017. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.391, + 0.468, + 0.446 + ], + "angle": 0, + "content": "[37] Hongwen Zhang, Yating Tian, Xinchi Zhou, Wanli Ouyang, Yebin Liu, Limin Wang, and Zhenan Sun. PyMAF: 3D human pose and shape regression with pyramidal mesh alignment feedback loop. In Int. Conf. Comput. Vis., 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.448, + 0.468, + 0.489 + ], + "angle": 0, + "content": "[38] Keyang Zhou, Bharat Lal Bhatnagar, and Gerard Pons-Moll. Unsupervised shape and pose disentanglement for 3D meshes. In Eur. Conf. Comput. Vis., 2020. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.49, + 0.468, + 0.532 + ], + "angle": 0, + "content": "[39] Shizhe Zhou, Hongbo Fu, Ligang Liu, Daniel Cohen-Or, and Xiaoguang Han. Parametric reshaping of human bodies in images. ACM Trans. Graph., 2010. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.533, + 0.468, + 0.588 + ], + "angle": 0, + "content": "[40] Silvia Zuffi, Angjoo Kanazawa, David W Jacobs, and Michael J Black. 3D menagerie: Modeling the 3D shape and pose of animals. In IEEE Conf. Comput. Vis. 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SemanticHuman. We present a semantic-aware and editable human body representation with high reconstruction precision in an unsupervised setting. The key idea is to employ a part-aware skeleton-separated decoupling strategy to learn a geometrically meaningful latent space with fine-grained semantics, which benefits part-level personalized human body editing. After embedding a human body into the bone code $Z_{b}$ and shape code $Z_{s}$ (left), we can flexibly edit human body attributes by modifying the corresponding latent codes (right). + +# Abstract + +3D human body representation learning has received increasing attention in recent years. However, existing works cannot flexibly, controllably and accurately represent human bodies, limited by coarse semantics and unsatisfactory representation capability, particularly in the absence of supervised data. In this paper, we propose a human body representation with fine-grained semantics and high reconstruction-accuracy in an unsupervised setting. Specifically, we establish a correspondence between latent vectors and geometric measures of body parts by designing a part-aware skeleton-separated decoupling strategy, which facilitates controllable editing of human bodies by modifying the corresponding latent codes. With the help of a bone-guided auto-encoder and an orientation-adaptive weighting strategy, our representation can be trained in an unsupervised manner. With the geometrically meaningful latent space, it can be applied to a wide range of applications, from human body editing to latent code interpolation and shape style transfer. Experimental results on public datasets demonstrate the accurate reconstruction and flexible editing abilities of the proposed method. The code will be available + +at http://cic.tju.edu.cn/faculty/likun/projects/SemanticHuman. + +# 1. Introduction + +Learning low-dimensional representations of human bodies plays an important role in various applications including human body reconstruction [4, 19, 32, 37], generation [7, 30, 31] and editing [35, 36, 39]. Existing methods [2, 18, 22, 25, 29] are either too semantically coarse to enable personalized human body editing, or suffer from poor reconstruction performance due to limited representation capability. This paper aims to develop a fine-grained semantic-aware human body representation with flexible representation ability. + +Since human bodies are rich in variations of poses and shapes, traditional linear models [1, 25, 29, 35, 36] cannot handle complex nonlinear structures of human body meshes accurately. Therefore, parametric models have been proposed for better representation. The landmark works SCAPE [2] and SMPL [22] represent human bodies by the shape and pose parameters. However, the semantics of their shape parameters are not sufficiently precise, making it impossible to flexibly edit the body shape. Furthermore, the + +representation ability of these methods is limited by the linear shape space of human body shapes, and hence their reconstruction accuracy is often unsatisfactory. + +With the success of deep learning, the encoder-decoder architecture has demonstrated excellent representation capability [7, 10, 13, 14, 26]. Such methods improve the reconstruction precision by constructing different convolution-like operators for feature extraction on irregular meshes. However, these works lack disentangled representation and fail to obtain promising results for geometrically complex human body parts. Several works [3, 9, 11, 18, 38] pursue the disentanglement of latent representations, i.e., each latent code has clear semantics. But these methods either require paired supervised data or have poor performance on the reconstruction, which significantly affects their generalization and robustness. In addition, the semantics of the above representations are coarse, which only enables person-level attribute transfer and cannot be applied to part-level flexible editing. + +In this paper, we aim to build a human body representation with fine-grained semantics and high reconstruction accuracy in an unsupervised setting, which needs to overcome two main challenges. First, how to disentangle the human body to reconstruct precise semantics is a key but difficult problem. Although it is straightforward to decompose a human body into articulated parts for part-level editing, the hidden space of each part is still coupled. Secondly, providing paired supervised data requires a lot of manual effort, and it is very challenging to make the representation disentangled without sacrificing reconstruction accuracy in an unsupervised manner. + +To address these challenges, we propose SemanticHuman, an editable human body representation with fine-grained semantics and high reconstruction-precision, which facilitates controllable human body editing without paired supervised data. To reconstruct fine-grained semantics, we design a part-aware skeleton-separated decoupling strategy with anatomical priors of the human body. Specifically, we disentangle body part variations into bone-related variations (e.g., length and orientation variations) and bone-independent variations (e.g., circumference variations). In contrast to the previous pose and shape disentanglement on the entire person [2, 18, 22], this part-aware skeleton-separated decoupling strategy establishes a correspondence between latent vectors and geometric properties of body parts, which benefits part-level controllable editing. + +To ensure high reconstruction accuracy and fine-grained semantics of the representation by unsupervised learning, we propose a bone-guided autoencoder architecture and an orientation-adaptive geometry-preserving loss. The bone-guided auto-encoder fuses the geometric features of body parts with their joint information to achieve accurate and efficient modeling of human bodies. Besides, + +an orientation-adaptive weighting strategy is introduced to compute the geometry-preserving loss, which can provide effective geometric regularization for unsupervised disentanglement and part-level editing. Experimental results on two public datasets with different mesh connectivities demonstrate the high reconstruction-precision and controllable editing capability of the proposed method. An example is given in Fig. 1. The code will be available at http://cic.tju.edu.cn/faculty/likun/projects/SemanticHuman. + +Our main contributions are summarized as follows: + +- We propose a semantic-aware and editable human body representation with fine-grained representation ability. The latent space of our approach facilitates personalized editing of human bodies by modifying their latent vectors. +- We propose a part-aware skeleton-separated decoupling strategy exploiting structural priors of the human body to learn geometrically meaningful latent codes with fine-grained semantics. +- We propose a bone-guided auto-encoder architecture and an orientation-adaptive geometry-preserving loss to ensure the robust and effective disentanglement of the representation learned without supervision. + +# 2. Related Work + +Classical Human Parametric Models. Since human bodies are geometric structures with strong priors, it is straightforward to use a statistical parametric model to represent human bodies. SCAPE [2] is one of pioneering works, which models variations between different human bodies as shape-related and pose-related deformations. SMPL [22] represents the human body more accurately and robustly based on vertices instead of triangle deformations. The SMPL has been extended to represent animals [40], hands [28], and a combination of hands and faces [24]. + +Deep Learning for Mesh Analysis. Traditional convolutional neural networks cannot be directly applied to meshes with irregular structures. A series of investigations [6, 8, 16, 17, 21, 33] has been devoted to constructing convolution-like operators on irregular meshes. Ranjan et al. [26] learn nonlinear representations of human faces by applying spectral convolutions to meshes. Additionally, Bouritsas et al. [7] and Gong et al. [14] propose to analyze per-vertex spatial features using spiral convolution. Chen et al. [10] and Gao et al. [13] improve the robustness and efficiency by adopting attention mechanisms in spatial aggregation. Recent works [30, 31] analyze meshes based on as-consistent-as-possible (ACAP) features [12] rather than Euclidean coordinates to learn large-scale deformations on meshes. However, these methods cannot provide explicit + +![](images/720b324976466e256ae74042d3556ed3ee73527b7d40073492a5774ac404cfd3.jpg) +Figure 2. SemanticHuman consists of a reconstruction branch, a disentanglement branch, and an editing branch. (a) The encoder $E$ maps a mesh $x_{1}$ into the bone code $Z_{b_{1}} = \{z_{b_{1}}^{1},\dots,z_{b_{1}}^{K}\}$ and shape code $Z_{s_1} = \{z_{s_1}^1,\dots,z_{s_1}^K\}$ , where $K$ is the number of parts, and the decoder $D$ aims to recover the original mesh $D(Z_{b_1},Z_{s_1})$ . (b) With the help of $L_{dis}$ , the swapped codes $(Z_{b_2},Z_{s_1})$ will be fed into $D$ to generate the target mesh $D(Z_{b_2},Z_{s_1})$ retaining the skeleton of $x_{2}$ and shape features of $x_{1}$ . (c) By introducing $L_{edit}$ , we force the generated mesh from the scaled codes $(Z_{b_1},\alpha Z_{s_1})$ to deform as desired, where $\alpha$ is a scale factor. + +semantics and fail to handle complex geometries. We alleviate these problems by introducing an autoencoder framework that carries geometric priors of human bodies. + +Disentangled Representation for Human Bodies. Jiang et al. [18] present a shape and pose disentangled human body representation based on a deep hierarchical neural network, achieving superior reconstruction accuracy. But this method depends on a strong data constraint: each posed mesh must have a paired mesh in a neutral pose. Inspired by the unsupervised disentangled generative model [3], various novel loss functions [9, 11, 38] have been proposed to preserve shape or pose in unsupervised disentanglement. Nevertheless, the supervision provided by these losses is not robust enough, and hence the reconstruction performance of such methods is unsatisfactory. By decoupling pose and shape on the whole human body, these disentangled representations can provide only coarse semantics for latent codes, and thus cannot support part-level human body editing. + +Different from prior works, we propose a part-aware skeleton-separated disentanglement strategy, which not only provides precise semantics but also benefits the design of effective information-preserving losses for unsupervised learning. + +![](images/e5f4ec492c88af3f824f792214a7f2f39b8a5024bb87ac3d0f2cb070a5576293.jpg) + +![](images/3da256418e9350cafa00980129551c684ccdc87ea7f9e0124b3d51bdc14f539c.jpg) + +![](images/0d7aa9764ac3bfdfd5d1c2c5ebe3b3d8102a414b9a8fae6a452516d7e75b1e73.jpg) +Figure 3. The part-aware skeleton-separated decoupling strategy: (a) anatomical human body parts, (b) human body bones and joints, (c) overview of decoupling strategy, and (d) angles between lines and $\sigma_b^k$ . + +![](images/d64b976bdc07959b12ccd330d717c4eba766fab42a5a407c2c27284e0cf9e3f4.jpg) + +# 3. Method + +# 3.1. Overview + +Given a set of human meshes with consistent connectivity, our goal is to learn a latent representation that has both fine-grained semantics and high reconstruction-precision in an unsupervised setting. In previous unsupervised decoupling works [3, 9, 11, 38], it is a common way to design novel loss functions capturing the shape or pose information to make representations disentangled. Nonetheless, limited by this traditional disentanglement idea focusing on the whole body, these methods are semantically coarse and not robust enough. In contrast, we design a part-aware skeleton-separated decoupling strategy (Sec. 3.2), which not only provides fine-grained semantics for flexible and precise editing of human attributes (e.g., circumference, bone orientation and length) but also facilitates the construction of robust information-preserving losses to achieve unsupervised learning. + +Based on this decoupling strategy, we introduce a bone-guided encoder-decoder framework (Sec. 3.3) and exploit three losses (Sec. 3.4) to achieve three core tasks by unsupervised learning: accurate geometric reconstruction (Sec. 3.4.1), unsupervised disentanglement (Sec. 3.4.2) and part-level shape editing (Sec. 3.4.3). An overview of our method is illustrated in Fig. 2, three flows of our pipeline complete the corresponding tasks with the help of specific losses. + +# 3.2. Part-Level Bone and Shape Disentanglement + +We leverage an observation that a human body is composed of $K = 17$ anatomical components each containing a bone defined by joints, as shown in Figs. 3 (a) and (b). In + +![](images/d62004f3706a16fb23dad22e6b8cc3b8c6e3f251de686dc669c12811d5197995.jpg) +Figure 4. Details of our bone-guided autoencoder. + +particular, for geometrically simple human body parts such as the waist, arms, and legs, their shapes can be approximated as cylinders with bones as axes, i.e., the geometry of these parts can be modeled as variations along their bone orientations $o_{b}$ , and along their orthogonal directions $o_{b\perp}$ which are indicated by solid arrows and hollow arrows in Fig. 3 (b). + +Inspired by this observation, we propose a part-aware skeleton-separated decoupling strategy. An overview of our decoupling strategy is shown in Fig. 3 (c). Specifically, we separate body part variations into bone-related variations (e.g., length and orientation variations) and bone-independent shape variations (e.g., size and style variations), which are represented by the bone embedding $z_{b}^{k}$ and shape embedding $z_{s}^{k}$ for the $k$ -th part, respectively. This part-aware skeleton-separated decoupling strategy establishes a correspondence between latent codes and geometric properties of body parts, which provides fine-grained semantics and enables part-level personalized human body editing. + +# 3.3. Bone-Guided Autoencoder + +The basic architecture of our framework is shown in Fig. 4. All three tasks are based on this architecture. Given a human body mesh $x$ , the bone branch and shape branch of encoder $E$ embed the mesh into the bone code $Z_{b} = \{z_{b}^{1},\dots,z_{b}^{K}\}$ and shape code $Z_{s} = \{z_{s}^{1},\dots,z_{s}^{K}\}$ , respectively, where $z_{b}^{k}$ and $z_{s}^{k}$ are localized latent codes for the $k$ -th body part. + +In particular, the bone branch infers localized bone codes $\{z_b^1,\dots,z_b^K\}$ with global information (e.g., length and orientation) about each body part from joints $B$ predicted by a linear regressor $J(\cdot)$ [22]. Besides, the shape branch takes mesh $x$ as input with a hierarchical spiral convolution encoder for learning geometric features in multiple scales. Geometric features belonging to each part are subsequently fed into the corresponding fully connected layer according to the part labels of vertices to obtain localized bone codes $\{z_s^1,\dots,z_s^K\}$ containing regional geometric details. Finally, the decoder with a similar structure to the shape branch accurately and efficiently reconstructs the original mesh $D(Z_{b},Z_{s})$ by integrating local and global information of each body part. + +# 3.4. Losses for Unsupervised Learning + +With the above framework, we utilize three losses to achieve the corresponding tasks in an unsupervised setting, and the full objective function is defined as: + +$$ +\mathcal {L} _ {\text {f u l l}} = \mathcal {L} _ {\text {r e c}} + \mathcal {L} _ {\text {d i s}} + \mathcal {L} _ {\text {e d i t}}, \tag {1} +$$ + +where $\mathcal{L}_{rec}$ is a geometric reconstruction loss for accurate human body reconstruction, $\mathcal{L}_{dis}$ is a disentanglement loss to ensure the bone and shape disentanglement of body parts, and $\mathcal{L}_{edit}$ is introduced to enable part-level shape editing. The losses and training flows will be described in detail in the following sections. + +# 3.4.1 Accurate Geometric Reconstruction + +As shown in Fig. 2 (a), in order to reconstruct the original mesh as accurately as possible, we adopt the geometric reconstruction loss as follows: + +$$ +\mathcal {L} _ {\text {r e c}} = \mathcal {L} _ {\text {v e r t}} + \lambda_ {\text {e d g e}} \cdot \mathcal {L} _ {\text {e d g e}}, \tag {2} +$$ + +where $\lambda_{edge}$ is the weight of edge regularization. The vertex loss forces the reconstructed mesh $D(E(x))$ to be as close as possible to the original mesh $x$ with the supervision of vertex-wise L1 distance, which is calculated as: + +$$ +\mathcal {L} _ {\text {v e r t}} = \| x - D (E (x)) \| _ {1}. \tag {3} +$$ + +However, only using this loss to supervise vertex positions cannot avoid producing over-length edges, which seriously affects the smoothness and reasonableness of results. Inspired by [15,34], we address this problem by introducing an edge length regularization $L_{edge}$ , which is formulated as: + +$$ +\mathcal {L} _ {\text {e d g e}} = \sum_ {p} \sum_ {v \in N (p)} \| p - v \| _ {2} ^ {2}, \tag {4} +$$ + +where $N(p)$ is the set of 1-ring neighbors of vertex $p$ . This loss ensures the smoothness of output meshes by enforcing their surface to be tight. + +# 3.4.2 Unsupervised Disentanglement + +After the reconstruction flow in Sec. 3.4.1, our representation is already capable of accurate reconstruction, but its latent space is still entangled. Based on the part-aware skeleton-separated decoupling strategy (Sec. 3.2), we use $\mathcal{L}_{dis}$ to ensure the bone and shape disentanglement of body parts, which can be defined as: + +$$ +\mathcal {L} _ {d i s} = \mathcal {L} _ {d i s - b} + \lambda_ {d i s - s} \cdot \mathcal {L} _ {d i s - s}. \tag {5} +$$ + +To achieve unsupervised disentanglement, given two human bodies $x_{1}$ and $x_{2}$ , $x_{swp}$ denotes the generated mesh $D(Z_{b_2}, Z_{s_1})$ , which is constructed with the bone latent code from $x_{2}$ and shape latent code from $x_{1}$ , as shown in Fig. 2 (b). Overall, $\mathcal{L}_{dis\_b}$ and $\mathcal{L}_{dis\_s}$ are used to preserve bone information belonging to parts of $x_{2}$ and geometric features along $o_{b\perp}$ belonging to parts of $x_{1}$ , respectively. In this way, our method achieves decoupling via unsupervised learning. $x_{swp}$ should have close joint positions as $x_{2}$ as these are largely determined by the bones, so $L_{dis\_b}$ is defined as + +$$ +\mathcal {L} _ {\text {d i s} b} = \| J \left(x _ {2}\right) - J \left(x _ {s w p}\right) \| _ {1}, \tag {6} +$$ + +where $J(x)$ is the vector containing joint positions for $x$ obtained through a joint regressor. + +However, preserving geometric features is a challenging issue. An intuitive idea to solve this issue is to use the matrix of pairwise Euclidean distances between all vertices in each part to capture regional shape information, which can be defined as: + +$$ +\mathcal {L} _ {\text {d i s} - s} = \sum_ {k = 1} ^ {K} \| D _ {e} \left(x _ {1} ^ {k}\right) - D _ {e} \left(x _ {\operatorname {s w p}} ^ {k}\right) \| _ {1}, \tag {7} +$$ + +where $x^{k}$ is the $k$ -th part-mesh of $x$ , and $D_{e}(x^{k})$ denotes the matrix of pairwise Euclidean distances between all vertices in $x^{k}$ . $D_{e}(x^{k})$ is of size $n(x^{k}) \times n(x^{k})$ , where $n(x^{k})$ is the number of vertices of the $k$ -th part. Nevertheless, the bone length is coupled with the Euclidean distance matrix, which leads to incomplete decoupling and affects editing precision (Sec. 4.3). + +To alleviate this problem, we propose an orientation-adaptive weighting (OAW) strategy to enforce the representation to focus on shape variations along $o_{b\perp}$ . For each body part $x^{k}$ , we first construct a pairwise angle matrix of size $n(x^{k}) \times n(x^{k})$ by joining every pair of vertices to form a line, and working out the angle $A(x^{k})$ (in degrees) the line makes with the bone orientation $o_{b}^{k}$ , as illustrated in Fig. 3 (d). As can be seen, the larger the angle, the more significant the line contributes to the shape variation along $o_{b\perp}$ . We apply the following thresholding and normalization $f$ to obtain the weight matrix $W(x^{k})$ also of the same size by suppressing small angles, and setting the maximum weight to one: + +$$ +W \left(x ^ {k}\right) = f \left(A \left(x ^ {k}\right)\right), \tag {8} +$$ + +where $f(\cdot)$ is an element-wise mapping function defined as + +$$ +f (a) = \left\{ \begin{array}{l l} a / 9 0, & \text {i f} a > \sigma \\ 0, & \text {o t h e r w i s e .} \end{array} \right. \tag {9} +$$ + +The weighted pairwise distance matrix is then defined as + +$$ +D _ {e} ^ {w} \left(x ^ {k}\right) = W \left(x ^ {k}\right) \otimes D _ {e} \left(x ^ {k}\right), \tag {10} +$$ + +where $\otimes$ is element-wise matrix multiplication. This orientation-adaptive weighting strategy enforces the bone + +length information to be separated from the Euclidean distance matrix as much as possible. Nonetheless, this strategy also ignores some useful geometric information along $o_b$ , which may result in unreasonable mesh deformations when editing bone orientation in large scale (Sec. 4.3). To address this problem, we further impose part-level volume regularization penalizing the unreasonable shape variations. Ideally, when the shape along $o_{b\perp}$ is retained, the volume of a body part should change in proportion to the bone length, so we introduce a volume constraint, which provides strong geometric supervision to achieve natural and reasonable editing results, which can be calculated as: + +$$ +\mathcal {L} _ {v o l} = \sum_ {k = 1} ^ {K} \| v \left(x _ {1} ^ {k}\right) / l \left(x _ {1} ^ {k}\right) - v \left(x _ {s w p} ^ {k}\right) / l \left(x _ {s w p} ^ {k}\right) \| _ {1}, \tag {11} +$$ + +where $v(\cdot)$ is a function that calculates the volume of a mesh part according to the tetrahedral volume formula, and $l(\cdot)$ is a function that measures the length of a mesh part between the two joints. We can then rewrite $\mathcal{L}_{dis\_s}$ : + +$$ +\mathcal {L} _ {\text {d i s} - s} = \sum_ {k = 1} ^ {K} \| D _ {e} ^ {w} \left(x _ {1} ^ {k}\right) - D _ {e} ^ {w} \left(x _ {\text {s w p}} ^ {k}\right) \| _ {1} + \mathcal {L} _ {\text {v o l}}. \tag {12} +$$ + +# 3.4.3 Part-Level Shape Editing + +With the constraints in Sec. 3.4.2, our autoencoder learns a bone and shape disentangled representation for body parts, and hence we can directly control bone-related attributes (e.g., bone orientation and length) of parts by modifying their joints. Nevertheless, we cannot flexibly edit other bone-independent shape features (e.g., circumference) as desired. To address this issue, we propose an editing flow shown in Fig. 2 (c), which achieves part-level shape editing by forcing the generated mesh from the scaled codes $(Z_{b_1},\alpha Z_{s_1})$ to deform as desired with $L_{edit}$ : + +$$ +\mathcal {L} _ {\text {e d i t}} = \mathcal {L} _ {\text {e d i t} b} + \lambda_ {\text {e d i t} s} \cdot \mathcal {L} _ {\text {e d i t} s} + \lambda_ {\text {n o r m}} \cdot \mathcal {L} _ {\text {n o r m}}, \tag {13} +$$ + +$$ +\mathcal {L} _ {\text {e d i t} - s} = \sum_ {k = 1} ^ {K} \| \alpha D _ {e} ^ {w} \left(x _ {1} ^ {k}\right) - D _ {e} ^ {w} \left(x _ {s c a} ^ {k}\right) \| _ {1}, \tag {14} +$$ + +$$ +\mathcal {L} _ {\text {e d i t} - b} = \left\| J \left(x _ {1}\right) - J \left(x _ {s c a}\right) \right\| _ {1}, \tag {15} +$$ + +where $\alpha$ is a scalar uniformly sampled in $(\alpha_{\mathrm{min}},\alpha_{\mathrm{max}})$ during training, and $x_{sca}$ denotes the generated mesh $D(Z_{b_1},\alpha Z_{s_1})$ from the scaled codes, i.e., the edited mesh $x_{sca}$ has bone code from $x_{1}$ , and shape code from $x_{1}$ with flexible scaling to change body shape. + +In particular, $\mathcal{L}_{edit\_s}$ constrains the $k$ -th part of $x_{sca}$ reconstructed from the scaled shape codes $\alpha Z_{s_1}$ to have the shape described by $\alpha D_e^W (x_1^k)$ . As $D_e^W (\cdot)$ is designed to largely capture the pairwise distances along $o_{b\perp}^{k}$ , i.e. reflecting shape size and style for the part. Elements in the matrix therefore should be scaled accordingly when the part is scaled along $o_{b\perp}^{k}$ . Besides, $\mathcal{L}_{edit\_b}$ is introduced to preserve bone information during the editing process. + +However, the editing flow easily causes training collapses. We solve this problem by imposing a vector norm regularization $\mathcal{L}_{norm}$ to establish a mapping between the norm of localized shape codes and the circumference of body parts, which not only benefits converge, but also allows users to edit part shape size on a unified scale. This regularization can be defined as: + +$$ +\mathcal {L} _ {\text {n o r m}} = \frac {1}{K} \sum_ {k = 1} ^ {K} \left| \| z _ {s _ {1}} ^ {k} \| _ {2} - \operatorname {c i r c} \left(x _ {1} ^ {k}\right) \right|, \tag {16} +$$ + +where $\text{circ}(\cdot)$ is a function that measures the circumference of a part using the identified landmarks. $\mathcal{L}_{edit}$ successfully allows shape codes' norm and direction to represent shape size and style respectively, which not only enables flexible part-level editing but also ensures the continuity of the learned latent space to a certain extent. + +# 3.5. Implementation Details + +For the spiral convolution encoder, we use a framework similar to [7]. Specifically, it consists of four spiral convolution layers and downsampling layers, and the structure of the decoder is a mirror of the encoder, except that the downsampling layers are replaced by the upsampling layers. Our algorithm is implemented in PyTorch [23]. All the training and test experiments are carried out on a PC with an RTX 3090 GPU. We train our network for 300 epochs with a learning rate of $1 \times 10^{-3}$ , a learning rate decay of 0.99 after each epoch and the Adam optimizer [20]. The entire training time takes around 24 hours. We use 16-dimensional latent codes (8 for bones and 8 for shape) to embed each part. More implementation details can be found in the supplementary material. + +# 4. Experiments + +# 4.1. Datasets + +DFAUST. The dynamic human body dataset with the same mesh connectivity as SMPL [22] from Bogo et al. [5], captures 14 different body motion sequences (e.g., hips, running, and jumping) for each of the 10 human subjects. We evenly extract one-twentieth of the original dataset for the convenience of training. Then we randomly split the extracted data into a test set of 182 meshes and a training set of 1936 meshes. + +SPRING. The large human body dataset with the same mesh connectivity as SCAPE [2] from Yang et al. [35], consists of $3000+$ subject meshes with a rough A-pose registered from the CAESAR dataset [27] using a non-rigid deformation algorithm. For the subsequent experiments, the SPRING dataset is randomly split into 2743 training and 305 test meshes. + +Data Preprocessing. For the training data, our method requires a joint regressor, body part semantics of each vertex, and landmarks for calculating circumferences. Since + +
MethodDFAUSTSPRING
EavdParam(M)EavdParam(M)
COMA [26]6.067.546.046.84
Neural3DMM [7]5.4930.356.1127.56
Spiralplus [14]5.3515.154.9913.75
Pai3DMM [13]5.7615.184.4513.78
Deep3DMM [10]9.918.3510.887.84
Unsup [38]10.1812.89--
Ours4.701.594.331.47
+ +Table 1. Quantitative reconstruction results on DFAUST [5] and SPRING [35] datasets. - : not supported for this dataset. Param(M) shows the number of learnable parameters in millions. + +
MethodDHNN
EavdParam(M)
DHNN [18]3.1691.63
Ours3.961.47
+ +Table 2. Quantitative reconstruction results on DHNN [18]. $\mathbf{{Param}\left( M\right) }$ shows the number of learnable parameters in millions. + +the mesh connectivity of datasets is consistent (which is required by nearly all 3D human representation learning works), the SMPL model only needs to be registered once to meet the requirements. More details about data preprocessing are given in the supplementary material. + +# 4.2. Comparison + +In the following, we evaluate the representation ability (reconstruction precision) and editing capacity (semantics) of our approach on the DFAUST and SPRING datasets. + +Reconstruction Experiments. We first compare four kinds of methods to validate the reconstruction precision of our representation: the spectral-based approach (COMA [26]), the spiral-based methods (Neural3DMM [7] and Spiralplus [14]), the attention-based approaches (Pai3DMM [13] and Deep3DMM [10]), and the disentanglement representation works (DHNN [18] and Unsup [38]). For a fair comparison, we use the official implementation of the compared methods with the same latent space dimension. Note that DHNN [18] did not release the training code, so we train and test our model on its dataset for a fair comparison. we utilize the average point-wise Euclidean distance $E_{\text{avd}}$ (in millimeters) between corresponding vertices in the input and its reconstruction as metrics. + +As shown in Tab. 1, our proposed approach shows excellent representation capability with a small model size. Fig. 5 visualizes some reconstruction results and their error maps. It can be observed that our approach outperforms other methods in reconstruction accuracy, especially for complex geometric details (e.g., faces and hands), which demonstrates the effectiveness of our bone-guided autoencoder. + +![](images/70f8f53cf971f3f8dfedcd6304c9abba18928de929760778c9e11a0436545e8e.jpg) +Figure 5. Qualitative reconstruction results on DFAUST [5] and SPRING [35]. The per-vertex Euclidean distance error is color-coded on the reconstructed meshes for visual inspection. Since Unsup [38] has a data constraint that the same subject in different poses should be given, it cannot be trained on the SPRING dataset. Note that our representation is trained without the need of data constraint, which is used in the compared methods. + +![](images/634c30d0315aa63ed2cd067badf22ed533be694bb8571a67cf460b8f2b133e84.jpg) +Figure 6. Qualitative editing results by Unsup [38] (first row), HBR [36] (second and third rows) and our method. We show the reconstructed bodies and edited bodies on the left and right of the source mesh. + +
MethodBone OrientationBone LengthShape Size
EjointEcricEjointEcricEjointEcric
Unsup [38]36.7914.16----
HBR [36]--38.3715.2712.8718.78
Ours3.269.751.575.920.4517.64
+ +Table 3. Quantitative editing results. - : not supported for this task. + +In addition, Tab. 2 gives the quantitative results on the DHNN dataset [18]. It is important to note that the compared method [18] uses a training data assumption (i.e., each posed mesh has a paired mesh in a neutral pose) for shape and pose disentanglement, but our method does not make use of this constraint. Under this unfair condition, the reconstruction accuracy of our method is only slightly lower than DHNN [18], and our model is more lightweight and semantically finer. Please refer to our supplementary material for more experimental results and details. + +Editing Experiments. We also demonstrate the flexible editing ability of our model on three editing tasks: editing bone orientation and bone length, and editing part shape size. Since bone orientation is approximately equivalent to pose, we compare with the unsupervised pose-and-shape + +disentanglement work Unsup [38] on the task of editing bone orientation on the DFAUST dataset. In contrast, bone length and shape size are shape information, so we compare with the Human Body Reshaping work HBR [36] on the task of editing bone length and shape size on the SPRING dataset. As there is no ground truth for these editing tasks, how to measure the editing performance is a problem. We utilize the joint and circumference errors $E_{joint}$ , $E_{circ}$ (in millimeters) to evaluate the accuracy of editing bone and shape. + +Specifically, for each test mesh, we randomly select editing targets and then calculate $E_{joint}$ , $E_{circ}$ between the target attribute value and the actual attribute value of the edited human body, extracted using $J(\cdot)$ and $circ(\cdot)$ , which can be defined as: + +$$ +E _ {j o i n t} = \left\| T _ {j o i n t} - J \left(x _ {\text {e d i t e d}}\right) \right\| _ {2}, \tag {17} +$$ + +$$ +E _ {c i r c} = \frac {1}{K} \sum_ {k = 1} ^ {K} \left| T _ {c i r c} ^ {k} - \operatorname {c i r c} \left(x _ {\text {e d i t e d}} ^ {k}\right) \right|, \tag {18} +$$ + +where $T_{joint}$ and $T_{circ}$ are the editing targets of joint positions and circumference. + +Tab. 3 gives the quantitative editing results. Our method not only achieves the best performance on all the editing tasks but also preserves other unedited attributes well. Some visual results are shown in Fig. 6. Compared with Unsup [38] and HBR [36], our method can edit the human body more accurately, reasonably and flexibly. Since the attribute error does not always reflect the editing performance of models, we also design a user study for better evaluation (see the supplementary material). + +# 4.3. Ablation Study + +Effect of Orientation-Adaptive Weighting Strategy. We validate the effectiveness of the orientation-adaptive weighting strategy by ablating it during training. As shown in Tab. 4, introducing this strategy helps our representation to focus on geometric features along $o_{b\perp}$ , leading to higher editing accuracy and more complete decoupling. + +![](images/9c6ef8d2d4fa819ab3f320c56a2e5536000c8a9d9d1057e4a6871f023f96e62b.jpg) +Figure 7. Qualitative results of volume regularization ablation study. + +
MethodReconstructionEditing
DFA. SPR. MeanBone Ori. \(E_{joint}\)Bone Length\(E_{joint}\)Shape Size\(E_{joint}\)Mean
Full4.704.334.523.269.751.575.920.4517.646.43
w/o OAW4.664.394.533.0213.661.5810.950.6120.408.37
w/o \(L_{edge}\)5.235.015.123.209.761.528.370.5329.488.81
w/o \(L_{dis}\)4.814.884.85----1.5617.50-
w/o \(L_{edit}\)4.874.434.653.0010.191.888.01---
+ +Table 4. Quantitative ablation study for reconstruction and editing (in mm). - : not supported for this task. + +Effect of Other Losses. To evaluate the impact of $\mathcal{L}_{\text {edge }}$ , $\mathcal{L}_{\text {dis }}$ and $\mathcal{L}_{\text {edit }}$ , we remove them from the supervision one by one during training. As compared in Tab. 4, $\mathcal{L}_{\text {edge }}$ effectively reduces the reconstruction error, and the use of $\mathcal{L}_{\text {dis }}$ and $\mathcal{L}_{\text {edit }}$ is required to enable flexible bone and part shape editing. The ablation study proves that all losses are necessary. + +Effect of Part-Level Volume Regularization. To analyze the impact of volume regularization, we remove it during the training process. Fig. 7 shows the comparison results of edited bodies. It can be seen that our volume constraint provides strong geometric supervision, resulting in more natural and reasonable editing results. + +# 4.4. Applications + +Latent Space Interpolation. Our representation decouples the bone and shape of each part, which allows us to interpolate meshes by linearly interpolating the bone and shape latent codes. Fig. 8 shows reasonable and meaningful results of such interpolation. It is worth noting that, thanks to the latent space with fine-grained semantics, we can perform mesh interpolation at the component level (see Figs. 8 (b) and (c)). + +Shape Style Transfer. As mentioned in Sec. 3.4.3, we successfully make the norm and direction of shape vectors represent shape size and style, respectively. Thus, we can transfer shape style by swapping the direction of the shape vectors. Some results are shown in Fig. 9. It can be easily observed that our approach changes the shape style of the source mesh naturally and preserves its other attributes (e.g., bone and circumference). + +![](images/8489b0839c6c91ef0b84fc6ce0d461382a421f6a1725908b2236e0592645637f.jpg) +Figure 8. Interpolation results on the bone-and-shape disentangled space of (a) the whole body, (b) upper body and (c) lower body, where the highlighted bodies are the input reconstructed bodies. + +![](images/28229fbe5847bc2bdbf2056bfa71090b833a3163b03431f83b08794831078531.jpg) + +![](images/4f37b10db4116c77a4a114dd6d531a08a4990f6b5692b2a3891deb12b1569e0d.jpg) + +![](images/06533bc6b0bb07b660e506c371160cd5497ac282b7e06d8e995558d7cde0af0b.jpg) +Figure 9. Results of shape style transfer by linearly interpolating the direction of shape codes. + +# 5. Conclusion and Discussion + +Conclusion. In this paper, we propose a human body representation with fine-grained semantics and high reconstruction-accuracy in an unsupervised setting. The key idea is to exploit a part-aware skeleton-separated decoupling strategy to establish a correspondence between latent vectors and geometric properties of body parts, which benefits personalized editing of human bodies by modifying the corresponding latent codes. Based on this disentanglement strategy, we propose a bone-guided autoencoder and well-designed losses to learn representation in an unsupervised manner. At last, with the geometrically meaningful latent space, the application can be extended from human body editing to latent code interpolation and shape style transfer. + +Limitations. Since our decoupling strategy is based on the cylinder assumption (i.e., the shape of body parts can be approximated as cylinders with bones as axes), we cannot controllably edit the bone length and circumference of parts (e.g., faces, hands, and feet) whose geometry is too complex to meet this assumption. Additionally, editing bone orientation with our method may fail when the target orientation is uncommon in the training data. In further work, we will dig deeper into the prior knowledge about the human body to improve the generalization capability of our representation. + +Acknowledgement. This work was supported in part by the National Natural Science Foundation of China (62122058 and 62171317). + +# References + +[1] Brett Allen, Brian Curless, and Zoran Popovic. The space of human body shapes: reconstruction and parameterization from range scans. ACM Trans. Graph., 2003. 1 +[2] Dragomir Anguelov, Praveen Srinivasan, Daphne Koller, Sebastian Thrun, Jim Rodgers, and James Davis. SCAPE: shape completion and animation of people. ACM Trans. Graph., 2005. 1, 2, 6 +[3] Tristan Aumentado-Armstrong, Stavros Tsogkas, Allan Jepson, and Sven Dickinson. Geometric disentanglement for generative latent shape models. In Int. Conf. Comput. Vis., 2019. 2, 3 +[4] Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler, Javier Romero, and Michael J. Black. Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image. In Eur. Conf. Comput. Vis., 2016. 1 +[5] Federica Bogo, Javier Romero, Gerard Pons-Moll, and Michael J Black. Dynamic FAUST: Registering human bodies in motion. In IEEE Conf. Comput. Vis. Pattern Recog., 2017. 6, 7 +[6] Davide Boscaini, Jonathan Masci, Simone Melzi, Michael M Bronstein, Umberto Castellani, and Pierre Vandergheynst. Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks. In Comput. Graph. Forum, 2015. 2 +[7] Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Michael Bronstein, and Stefanos Zafeiriou. Neural 3D morphable models: Spiral convolutional networks for 3d shape representation learning and generation. In Int. Conf. Comput. Vis., 2019. 1, 2, 6 +[8] Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203, 2013. 2 +[9] Haoyu Chen, Hao Tang, Henglin Shi, Wei Peng, Nicu Sebe, and Guoying Zhao. Intrinsic-extrinsic preserved GANs for unsupervised 3D pose transfer. In Int. Conf. Comput. Vis., 2021. 2, 3 +[10] Zhixiang Chen and Tae-Kyun Kim. Learning feature aggregation for deep 3D morphable models. In IEEE Conf. Comput. Vis. Pattern Recog., 2021. 2, 6 +[11] Luca Cosmo, Antonio Norelli, Oshri Halimi, Ron Kimmel, and Emanuele Rodola. LIMP: Learning latent shape representations with metric preservation priors. In Eur. Conf. Comput. Vis., 2020. 2, 3 +[12] Lin Gao, Yu-Kun Lai, Jie Yang, Ling-Xiao Zhang, Shihong Xia, and Leif Kobbelt. Sparse data driven mesh deformation. IEEE Trans. Vis. Comput. Graph., 2019. 2 +[13] Zhongpai Gao, Junchi Yan, Guangtao Zhai, Juyong Zhang, Yiyan Yang, and Xiaokang Yang. Learning local neighboring structure for robust 3D shape representation. In AAAI, 2021. 2, 6 +[14] Shunwang Gong, Lei Chen, Michael Bronstein, and Stefanos Zafeiriou. SpiralNet++: A fast and highly efficient mesh convolution operator. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2019, 2, 6 + +[15] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. 3D-CODED: 3D correspondences by deep deformation. In Eur. Conf. Comput. Vis., 2018. 4 +[16] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Adv. Neural Inform. Process. Syst., 2017. 2 +[17] Mikael Henaff, Joan Bruna, and Yann LeCun. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163, 2015. 2 +[18] Boyi Jiang, Juyong Zhang, Jianfei Cai, and Jianmin Zheng. Disentangled human body embedding based on deep hierarchical neural network. IEEE Trans. Vis. Comput. Graph., 2020. 1, 2, 3, 6, 7 +[19] Angjoo Kanazawa, Michael J. Black, David W. Jacobs, and Jitendra Malik. End-to-end recovery of human shape and pose. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 1 +[20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. 2015. 6 +[21] Linjie Li, Zhe Gan, Yu Cheng, and Jingjing Liu. Relation-aware graph attention network for visual question answering. In Int. Conf. Comput. Vis., 2019. 2 +[22] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. SMPL: A skinned multiperson linear model. ACM Trans. Graph., 2015. 1, 2, 4, 6 +[23] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. PyTorch: An imperative style, high-performance deep learning library. 2019. 6 +[24] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3D hands, face, and body from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., 2019. 2 +[25] Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt, and Bernt Schiele. Building statistical shape spaces for 3D human modeling. Pattern Recognition, 2017. 1 +[26] Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J Black. Generating 3D faces using convolutional mesh autoencoders. In Eur. Conf. Comput. Vis., 2018. 2, 6 +[27] Kathleen M Robinette, Hans Daanen, and Eric Paquet. The caesar project: a 3-d surface anthropometry survey. In International Conference on 3-D Digital Imaging and Modeling, 1999. 6 +[28] Javier Romero, Dimitrios Tzionas, and Michael J Black. Embodied hands: Modeling and capturing hands and bodies together. ACM Trans. Graph., 2017. 2 +[29] Hyewon Seo and Nadia Magnenat-Thalmann. An automatic modeling of human bodies from sizing parameters. In Proceedings of the Symposium on Interactive 3D Graphics, 2003. 1 +[30] Qingyang Tan, Lin Gao, Yu-Kun Lai, and Shihong Xia. Variational autoencoders for deforming 3D mesh models. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 1, 2 + +[31] Qingyang Tan, Lin Gao, Yu-Kun Lai, Jie Yang, and Shihong Xia. Mesh-based autoencoders for localized deformation component analysis. In AAAI, 2018. 1, 2 +[32] Yating Tian, Hongwen Zhang, Yebin Liu, and Limin Wang. Recovering 3D human mesh from monocular images: A survey. arXiv preprint arXiv:2203.01923, 2022. 1 +[33] Nitika Verma, Edmond Boyer, and Jakob Verbeek. Dynamic filters in graph convolutional networks. arXiv preprint arXiv:1706.05206, 2017. 2 +[34] Jiashun Wang, Chao Wen, Yanwei Fu, Haitao Lin, Tianyun Zou, Xiangyang Xue, and Yinda Zhang. Neural pose transfer by spatially adaptive instance normalization. In IEEE Conf. Comput. Vis. Pattern Recog., 2020. 4 +[35] Yipin Yang, Yao Yu, Yu Zhou, Sidan Du, James Davis, and Ruigang Yang. Semantic parametric reshaping of human body models. In International Conference on 3D Vision, 2014. 1, 6, 7 +[36] Yanhong Zeng, Jianlong Fu, and Hongyang Chao. 3D human body reshaping with anthropometric modeling. In International Conference on Internet Multimedia Computing and Service, 2017. 1, 7 +[37] Hongwen Zhang, Yating Tian, Xinchi Zhou, Wanli Ouyang, Yebin Liu, Limin Wang, and Zhenan Sun. PyMAF: 3D human pose and shape regression with pyramidal mesh alignment feedback loop. In Int. Conf. Comput. Vis., 2021. 1 +[38] Keyang Zhou, Bharat Lal Bhatnagar, and Gerard Pons-Moll. Unsupervised shape and pose disentanglement for 3D meshes. In Eur. Conf. Comput. Vis., 2020. 2, 3, 6, 7 +[39] Shizhe Zhou, Hongbo Fu, Ligang Liu, Daniel Cohen-Or, and Xiaoguang Han. Parametric reshaping of human bodies in images. ACM Trans. Graph., 2010. 1 +[40] Silvia Zuffi, Angjoo Kanazawa, David W Jacobs, and Michael J Black. 3D menagerie: Modeling the 3D shape and pose of animals. In IEEE Conf. Comput. Vis. 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SemanticHuman. We present a semantic-aware and editable human body representation with high reconstruction precision in an unsupervised setting. The key idea is to employ a part-aware skeleton-separated decoupling strategy to learn a geometrically meaningful latent space with fine-grained semantics, which benefits part-level personalized human body editing. After embedding a human body into the bone code " + }, + { + "bbox": [ + 46, + 380, + 547, + 425 + ], + "type": "inline_equation", + "content": "Z_{b}" + }, + { + "bbox": [ + 46, + 380, + 547, + 425 + ], + "type": "text", + "content": " and shape code " + }, + { + "bbox": [ + 46, + 380, + 547, + 425 + ], + "type": "inline_equation", + "content": "Z_{s}" + }, + { + "bbox": [ + 46, + 380, + 547, + 425 + ], + "type": "text", + "content": " (left), we can flexibly edit human body attributes by modifying the corresponding latent codes (right)." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "bbox": [ + 143, + 434, + 192, + 445 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 434, + 192, + 445 + ], + "spans": [ + { + "bbox": [ + 143, + 434, + 192, + 445 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 445, + 289, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 445, + 289, + 696 + ], + "spans": [ + { + "bbox": [ + 46, + 445, + 289, + 696 + ], + "type": "text", + "content": "3D human body representation learning has received increasing attention in recent years. However, existing works cannot flexibly, controllably and accurately represent human bodies, limited by coarse semantics and unsatisfactory representation capability, particularly in the absence of supervised data. In this paper, we propose a human body representation with fine-grained semantics and high reconstruction-accuracy in an unsupervised setting. Specifically, we establish a correspondence between latent vectors and geometric measures of body parts by designing a part-aware skeleton-separated decoupling strategy, which facilitates controllable editing of human bodies by modifying the corresponding latent codes. With the help of a bone-guided auto-encoder and an orientation-adaptive weighting strategy, our representation can be trained in an unsupervised manner. With the geometrically meaningful latent space, it can be applied to a wide range of applications, from human body editing to latent code interpolation and shape style transfer. Experimental results on public datasets demonstrate the accurate reconstruction and flexible editing abilities of the proposed method. The code will be available" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 435, + 547, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 435, + 547, + 460 + ], + "spans": [ + { + "bbox": [ + 306, + 435, + 547, + 460 + ], + "type": "text", + "content": "at http://cic.tju.edu.cn/faculty/likun/projects/SemanticHuman." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 467, + 387, + 479 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 467, + 387, + 479 + ], + "spans": [ + { + "bbox": [ + 306, + 467, + 387, + 479 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 487, + 545, + 606 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 487, + 545, + 606 + ], + "spans": [ + { + "bbox": [ + 304, + 487, + 545, + 606 + ], + "type": "text", + "content": "Learning low-dimensional representations of human bodies plays an important role in various applications including human body reconstruction [4, 19, 32, 37], generation [7, 30, 31] and editing [35, 36, 39]. Existing methods [2, 18, 22, 25, 29] are either too semantically coarse to enable personalized human body editing, or suffer from poor reconstruction performance due to limited representation capability. This paper aims to develop a fine-grained semantic-aware human body representation with flexible representation ability." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 607, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 607, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 607, + 547, + 715 + ], + "type": "text", + "content": "Since human bodies are rich in variations of poses and shapes, traditional linear models [1, 25, 29, 35, 36] cannot handle complex nonlinear structures of human body meshes accurately. Therefore, parametric models have been proposed for better representation. The landmark works SCAPE [2] and SMPL [22] represent human bodies by the shape and pose parameters. However, the semantics of their shape parameters are not sufficiently precise, making it impossible to flexibly edit the body shape. Furthermore, the" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 704, + 135, + 714 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 704, + 135, + 714 + ], + "spans": [ + { + "bbox": [ + 58, + 704, + 135, + 714 + ], + "type": "text", + "content": "*Corresponding author" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16985" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 108 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 108 + ], + "type": "text", + "content": "representation ability of these methods is limited by the linear shape space of human body shapes, and hence their reconstruction accuracy is often unsatisfactory." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 110, + 287, + 301 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 110, + 287, + 301 + ], + "spans": [ + { + "bbox": [ + 46, + 110, + 287, + 301 + ], + "type": "text", + "content": "With the success of deep learning, the encoder-decoder architecture has demonstrated excellent representation capability [7, 10, 13, 14, 26]. Such methods improve the reconstruction precision by constructing different convolution-like operators for feature extraction on irregular meshes. However, these works lack disentangled representation and fail to obtain promising results for geometrically complex human body parts. Several works [3, 9, 11, 18, 38] pursue the disentanglement of latent representations, i.e., each latent code has clear semantics. But these methods either require paired supervised data or have poor performance on the reconstruction, which significantly affects their generalization and robustness. In addition, the semantics of the above representations are coarse, which only enables person-level attribute transfer and cannot be applied to part-level flexible editing." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 303, + 287, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 303, + 287, + 445 + ], + "spans": [ + { + "bbox": [ + 46, + 303, + 287, + 445 + ], + "type": "text", + "content": "In this paper, we aim to build a human body representation with fine-grained semantics and high reconstruction accuracy in an unsupervised setting, which needs to overcome two main challenges. First, how to disentangle the human body to reconstruct precise semantics is a key but difficult problem. Although it is straightforward to decompose a human body into articulated parts for part-level editing, the hidden space of each part is still coupled. Secondly, providing paired supervised data requires a lot of manual effort, and it is very challenging to make the representation disentangled without sacrificing reconstruction accuracy in an unsupervised manner." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 449, + 287, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 449, + 287, + 628 + ], + "spans": [ + { + "bbox": [ + 46, + 449, + 287, + 628 + ], + "type": "text", + "content": "To address these challenges, we propose SemanticHuman, an editable human body representation with fine-grained semantics and high reconstruction-precision, which facilitates controllable human body editing without paired supervised data. To reconstruct fine-grained semantics, we design a part-aware skeleton-separated decoupling strategy with anatomical priors of the human body. Specifically, we disentangle body part variations into bone-related variations (e.g., length and orientation variations) and bone-independent variations (e.g., circumference variations). In contrast to the previous pose and shape disentanglement on the entire person [2, 18, 22], this part-aware skeleton-separated decoupling strategy establishes a correspondence between latent vectors and geometric properties of body parts, which benefits part-level controllable editing." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 630, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 630, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 630, + 287, + 713 + ], + "type": "text", + "content": "To ensure high reconstruction accuracy and fine-grained semantics of the representation by unsupervised learning, we propose a bone-guided autoencoder architecture and an orientation-adaptive geometry-preserving loss. The bone-guided auto-encoder fuses the geometric features of body parts with their joint information to achieve accurate and efficient modeling of human bodies. Besides," + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 72, + 545, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 191 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 191 + ], + "type": "text", + "content": "an orientation-adaptive weighting strategy is introduced to compute the geometry-preserving loss, which can provide effective geometric regularization for unsupervised disentanglement and part-level editing. Experimental results on two public datasets with different mesh connectivities demonstrate the high reconstruction-precision and controllable editing capability of the proposed method. An example is given in Fig. 1. The code will be available at http://cic.tju.edu.cn/faculty/likun/projects/SemanticHuman." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 317, + 192, + 525, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 192, + 525, + 204 + ], + "spans": [ + { + "bbox": [ + 317, + 192, + 525, + 204 + ], + "type": "text", + "content": "Our main contributions are summarized as follows:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 317, + 212, + 545, + 384 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 317, + 212, + 545, + 270 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 212, + 545, + 270 + ], + "spans": [ + { + "bbox": [ + 317, + 212, + 545, + 270 + ], + "type": "text", + "content": "- We propose a semantic-aware and editable human body representation with fine-grained representation ability. The latent space of our approach facilitates personalized editing of human bodies by modifying their latent vectors." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 317, + 280, + 545, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 280, + 545, + 327 + ], + "spans": [ + { + "bbox": [ + 317, + 280, + 545, + 327 + ], + "type": "text", + "content": "- We propose a part-aware skeleton-separated decoupling strategy exploiting structural priors of the human body to learn geometrically meaningful latent codes with fine-grained semantics." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 317, + 335, + 545, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 335, + 545, + 384 + ], + "spans": [ + { + "bbox": [ + 317, + 335, + 545, + 384 + ], + "type": "text", + "content": "- We propose a bone-guided auto-encoder architecture and an orientation-adaptive geometry-preserving loss to ensure the robust and effective disentanglement of the representation learned without supervision." + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 394, + 392, + 407 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 394, + 392, + 407 + ], + "spans": [ + { + "bbox": [ + 306, + 394, + 392, + 407 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 414, + 545, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 414, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 304, + 414, + 545, + 533 + ], + "type": "text", + "content": "Classical Human Parametric Models. Since human bodies are geometric structures with strong priors, it is straightforward to use a statistical parametric model to represent human bodies. SCAPE [2] is one of pioneering works, which models variations between different human bodies as shape-related and pose-related deformations. SMPL [22] represents the human body more accurately and robustly based on vertices instead of triangle deformations. The SMPL has been extended to represent animals [40], hands [28], and a combination of hands and faces [24]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 534, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 534, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 534, + 545, + 713 + ], + "type": "text", + "content": "Deep Learning for Mesh Analysis. Traditional convolutional neural networks cannot be directly applied to meshes with irregular structures. A series of investigations [6, 8, 16, 17, 21, 33] has been devoted to constructing convolution-like operators on irregular meshes. Ranjan et al. [26] learn nonlinear representations of human faces by applying spectral convolutions to meshes. Additionally, Bouritsas et al. [7] and Gong et al. [14] propose to analyze per-vertex spatial features using spiral convolution. Chen et al. [10] and Gao et al. [13] improve the robustness and efficiency by adopting attention mechanisms in spatial aggregation. Recent works [30, 31] analyze meshes based on as-consistent-as-possible (ACAP) features [12] rather than Euclidean coordinates to learn large-scale deformations on meshes. However, these methods cannot provide explicit" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "16986" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 70, + 287, + 310 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 287, + 310 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 287, + 310 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 287, + 310 + ], + "type": "image", + "image_path": "720b324976466e256ae74042d3556ed3ee73527b7d40073492a5774ac404cfd3.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "lines": [ + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "spans": [ + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": "Figure 2. SemanticHuman consists of a reconstruction branch, a disentanglement branch, and an editing branch. (a) The encoder " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "E" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": " maps a mesh " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "x_{1}" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": " into the bone code " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "Z_{b_{1}} = \\{z_{b_{1}}^{1},\\dots,z_{b_{1}}^{K}\\}" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": " and shape code " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "Z_{s_1} = \\{z_{s_1}^1,\\dots,z_{s_1}^K\\}" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": " is the number of parts, and the decoder " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": " aims to recover the original mesh " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "D(Z_{b_1},Z_{s_1})" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": ". (b) With the help of " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "L_{dis}" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": ", the swapped codes " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "(Z_{b_2},Z_{s_1})" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": " will be fed into " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": " to generate the target mesh " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "D(Z_{b_2},Z_{s_1})" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": " retaining the skeleton of " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "x_{2}" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": " and shape features of " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "x_{1}" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": ". (c) By introducing " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "L_{edit}" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": ", we force the generated mesh from the scaled codes " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "(Z_{b_1},\\alpha Z_{s_1})" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": " to deform as desired, where " + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 47, + 310, + 287, + 420 + ], + "type": "text", + "content": " is a scale factor." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 426, + 287, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 426, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 47, + 426, + 287, + 460 + ], + "type": "text", + "content": "semantics and fail to handle complex geometries. We alleviate these problems by introducing an autoencoder framework that carries geometric priors of human bodies." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 461, + 287, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 461, + 287, + 653 + ], + "spans": [ + { + "bbox": [ + 47, + 461, + 287, + 653 + ], + "type": "text", + "content": "Disentangled Representation for Human Bodies. Jiang et al. [18] present a shape and pose disentangled human body representation based on a deep hierarchical neural network, achieving superior reconstruction accuracy. But this method depends on a strong data constraint: each posed mesh must have a paired mesh in a neutral pose. Inspired by the unsupervised disentangled generative model [3], various novel loss functions [9, 11, 38] have been proposed to preserve shape or pose in unsupervised disentanglement. Nevertheless, the supervision provided by these losses is not robust enough, and hence the reconstruction performance of such methods is unsatisfactory. By decoupling pose and shape on the whole human body, these disentangled representations can provide only coarse semantics for latent codes, and thus cannot support part-level human body editing." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 654, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 654, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 654, + 287, + 714 + ], + "type": "text", + "content": "Different from prior works, we propose a part-aware skeleton-separated disentanglement strategy, which not only provides precise semantics but also benefits the design of effective information-preserving losses for unsupervised learning." + } + ] + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 317, + 70, + 415, + 167 + ], + "blocks": [ + { + "bbox": [ + 317, + 70, + 415, + 167 + ], + "lines": [ + { + "bbox": [ + 317, + 70, + 415, + 167 + ], + "spans": [ + { + "bbox": [ + 317, + 70, + 415, + 167 + ], + "type": "image", + "image_path": "e5f4ec492c88af3f824f792214a7f2f39b8a5024bb87ac3d0f2cb070a5576293.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 405, + 70, + 531, + 167 + ], + "blocks": [ + { + "bbox": [ + 405, + 70, + 531, + 167 + ], + "lines": [ + { + "bbox": [ + 405, + 70, + 531, + 167 + ], + "spans": [ + { + "bbox": [ + 405, + 70, + 531, + 167 + ], + "type": "image", + "image_path": "3da256418e9350cafa00980129551c684ccdc87ea7f9e0124b3d51bdc14f539c.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 317, + 168, + 451, + 258 + ], + "blocks": [ + { + "bbox": [ + 317, + 168, + 451, + 258 + ], + "lines": [ + { + "bbox": [ + 317, + 168, + 451, + 258 + ], + "spans": [ + { + "bbox": [ + 317, + 168, + 451, + 258 + ], + "type": "image", + "image_path": "0d7aa9764ac3bfdfd5d1c2c5ebe3b3d8102a414b9a8fae6a452516d7e75b1e73.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 257, + 545, + 300 + ], + "lines": [ + { + "bbox": [ + 305, + 257, + 545, + 300 + ], + "spans": [ + { + "bbox": [ + 305, + 257, + 545, + 300 + ], + "type": "text", + "content": "Figure 3. The part-aware skeleton-separated decoupling strategy: (a) anatomical human body parts, (b) human body bones and joints, (c) overview of decoupling strategy, and (d) angles between lines and " + }, + { + "bbox": [ + 305, + 257, + 545, + 300 + ], + "type": "inline_equation", + "content": "\\sigma_b^k" + }, + { + "bbox": [ + 305, + 257, + 545, + 300 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 460, + 168, + 504, + 258 + ], + "blocks": [ + { + "bbox": [ + 460, + 168, + 504, + 258 + ], + "lines": [ + { + "bbox": [ + 460, + 168, + 504, + 258 + ], + "spans": [ + { + "bbox": [ + 460, + 168, + 504, + 258 + ], + "type": "image", + "image_path": "d64b976bdc07959b12ccd330d717c4eba766fab42a5a407c2c27284e0cf9e3f4.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 312, + 361, + 325 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 312, + 361, + 325 + ], + "spans": [ + { + "bbox": [ + 306, + 312, + 361, + 325 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 332, + 373, + 344 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 332, + 373, + 344 + ], + "spans": [ + { + "bbox": [ + 306, + 332, + 373, + 344 + ], + "type": "text", + "content": "3.1. Overview" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 351, + 545, + 542 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 351, + 545, + 542 + ], + "spans": [ + { + "bbox": [ + 304, + 351, + 545, + 542 + ], + "type": "text", + "content": "Given a set of human meshes with consistent connectivity, our goal is to learn a latent representation that has both fine-grained semantics and high reconstruction-precision in an unsupervised setting. In previous unsupervised decoupling works [3, 9, 11, 38], it is a common way to design novel loss functions capturing the shape or pose information to make representations disentangled. Nonetheless, limited by this traditional disentanglement idea focusing on the whole body, these methods are semantically coarse and not robust enough. In contrast, we design a part-aware skeleton-separated decoupling strategy (Sec. 3.2), which not only provides fine-grained semantics for flexible and precise editing of human attributes (e.g., circumference, bone orientation and length) but also facilitates the construction of robust information-preserving losses to achieve unsupervised learning." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 543, + 545, + 650 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 543, + 545, + 650 + ], + "spans": [ + { + "bbox": [ + 304, + 543, + 545, + 650 + ], + "type": "text", + "content": "Based on this decoupling strategy, we introduce a bone-guided encoder-decoder framework (Sec. 3.3) and exploit three losses (Sec. 3.4) to achieve three core tasks by unsupervised learning: accurate geometric reconstruction (Sec. 3.4.1), unsupervised disentanglement (Sec. 3.4.2) and part-level shape editing (Sec. 3.4.3). An overview of our method is illustrated in Fig. 2, three flows of our pipeline complete the corresponding tasks with the help of specific losses." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 658, + 538, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 658, + 538, + 672 + ], + "spans": [ + { + "bbox": [ + 306, + 658, + 538, + 672 + ], + "type": "text", + "content": "3.2. Part-Level Bone and Shape Disentanglement" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": "We leverage an observation that a human body is composed of " + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "inline_equation", + "content": "K = 17" + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": " anatomical components each containing a bone defined by joints, as shown in Figs. 3 (a) and (b). In" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16987" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 97, + 72, + 496, + 175 + ], + "blocks": [ + { + "bbox": [ + 97, + 72, + 496, + 175 + ], + "lines": [ + { + "bbox": [ + 97, + 72, + 496, + 175 + ], + "spans": [ + { + "bbox": [ + 97, + 72, + 496, + 175 + ], + "type": "image", + "image_path": "d62004f3706a16fb23dad22e6b8cc3b8c6e3f251de686dc669c12811d5197995.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 204, + 177, + 388, + 189 + ], + "lines": [ + { + "bbox": [ + 204, + 177, + 388, + 189 + ], + "spans": [ + { + "bbox": [ + 204, + 177, + 388, + 189 + ], + "type": "text", + "content": "Figure 4. Details of our bone-guided autoencoder." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 195, + 289, + 278 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 195, + 289, + 278 + ], + "spans": [ + { + "bbox": [ + 46, + 195, + 289, + 278 + ], + "type": "text", + "content": "particular, for geometrically simple human body parts such as the waist, arms, and legs, their shapes can be approximated as cylinders with bones as axes, i.e., the geometry of these parts can be modeled as variations along their bone orientations " + }, + { + "bbox": [ + 46, + 195, + 289, + 278 + ], + "type": "inline_equation", + "content": "o_{b}" + }, + { + "bbox": [ + 46, + 195, + 289, + 278 + ], + "type": "text", + "content": ", and along their orthogonal directions " + }, + { + "bbox": [ + 46, + 195, + 289, + 278 + ], + "type": "inline_equation", + "content": "o_{b\\perp}" + }, + { + "bbox": [ + 46, + 195, + 289, + 278 + ], + "type": "text", + "content": " which are indicated by solid arrows and hollow arrows in Fig. 3 (b)." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 280, + 290, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 280, + 290, + 434 + ], + "spans": [ + { + "bbox": [ + 46, + 280, + 290, + 434 + ], + "type": "text", + "content": "Inspired by this observation, we propose a part-aware skeleton-separated decoupling strategy. An overview of our decoupling strategy is shown in Fig. 3 (c). Specifically, we separate body part variations into bone-related variations (e.g., length and orientation variations) and bone-independent shape variations (e.g., size and style variations), which are represented by the bone embedding " + }, + { + "bbox": [ + 46, + 280, + 290, + 434 + ], + "type": "inline_equation", + "content": "z_{b}^{k}" + }, + { + "bbox": [ + 46, + 280, + 290, + 434 + ], + "type": "text", + "content": " and shape embedding " + }, + { + "bbox": [ + 46, + 280, + 290, + 434 + ], + "type": "inline_equation", + "content": "z_{s}^{k}" + }, + { + "bbox": [ + 46, + 280, + 290, + 434 + ], + "type": "text", + "content": " for the " + }, + { + "bbox": [ + 46, + 280, + 290, + 434 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 280, + 290, + 434 + ], + "type": "text", + "content": "-th part, respectively. This part-aware skeleton-separated decoupling strategy establishes a correspondence between latent codes and geometric properties of body parts, which provides fine-grained semantics and enables part-level personalized human body editing." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 443, + 195, + 455 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 443, + 195, + 455 + ], + "spans": [ + { + "bbox": [ + 47, + 443, + 195, + 455 + ], + "type": "text", + "content": "3.3. Bone-Guided Autoencoder" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "spans": [ + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "text", + "content": "The basic architecture of our framework is shown in Fig. 4. All three tasks are based on this architecture. Given a human body mesh " + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "text", + "content": ", the bone branch and shape branch of encoder " + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "inline_equation", + "content": "E" + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "text", + "content": " embed the mesh into the bone code " + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "inline_equation", + "content": "Z_{b} = \\{z_{b}^{1},\\dots,z_{b}^{K}\\}" + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "text", + "content": " and shape code " + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "inline_equation", + "content": "Z_{s} = \\{z_{s}^{1},\\dots,z_{s}^{K}\\}" + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "text", + "content": ", respectively, where " + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "inline_equation", + "content": "z_{b}^{k}" + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "inline_equation", + "content": "z_{s}^{k}" + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "text", + "content": " are localized latent codes for the " + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 47, + 461, + 287, + 544 + ], + "type": "text", + "content": "-th body part." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "text", + "content": "In particular, the bone branch infers localized bone codes " + }, + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "inline_equation", + "content": "\\{z_b^1,\\dots,z_b^K\\}" + }, + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "text", + "content": " with global information (e.g., length and orientation) about each body part from joints " + }, + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "text", + "content": " predicted by a linear regressor " + }, + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "inline_equation", + "content": "J(\\cdot)" + }, + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "text", + "content": " [22]. Besides, the shape branch takes mesh " + }, + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "text", + "content": " as input with a hierarchical spiral convolution encoder for learning geometric features in multiple scales. Geometric features belonging to each part are subsequently fed into the corresponding fully connected layer according to the part labels of vertices to obtain localized bone codes " + }, + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "inline_equation", + "content": "\\{z_s^1,\\dots,z_s^K\\}" + }, + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "text", + "content": " containing regional geometric details. Finally, the decoder with a similar structure to the shape branch accurately and efficiently reconstructs the original mesh " + }, + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "inline_equation", + "content": "D(Z_{b},Z_{s})" + }, + { + "bbox": [ + 47, + 545, + 289, + 714 + ], + "type": "text", + "content": " by integrating local and global information of each body part." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 195, + 489, + 209 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 195, + 489, + 209 + ], + "spans": [ + { + "bbox": [ + 305, + 195, + 489, + 209 + ], + "type": "text", + "content": "3.4. Losses for Unsupervised Learning" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 213, + 546, + 249 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 213, + 546, + 249 + ], + "spans": [ + { + "bbox": [ + 304, + 213, + 546, + 249 + ], + "type": "text", + "content": "With the above framework, we utilize three losses to achieve the corresponding tasks in an unsupervised setting, and the full objective function is defined as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 364, + 253, + 545, + 266 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 364, + 253, + 545, + 266 + ], + "spans": [ + { + "bbox": [ + 364, + 253, + 545, + 266 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {f u l l}} = \\mathcal {L} _ {\\text {r e c}} + \\mathcal {L} _ {\\text {d i s}} + \\mathcal {L} _ {\\text {e d i t}}, \\tag {1}", + "image_path": "aff552916a472071477dc05b71ced15051a7798c7445d16e58374bcd882ccbc5.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 270, + 547, + 342 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 270, + 547, + 342 + ], + "spans": [ + { + "bbox": [ + 304, + 270, + 547, + 342 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 270, + 547, + 342 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{rec}" + }, + { + "bbox": [ + 304, + 270, + 547, + 342 + ], + "type": "text", + "content": " is a geometric reconstruction loss for accurate human body reconstruction, " + }, + { + "bbox": [ + 304, + 270, + 547, + 342 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{dis}" + }, + { + "bbox": [ + 304, + 270, + 547, + 342 + ], + "type": "text", + "content": " is a disentanglement loss to ensure the bone and shape disentanglement of body parts, and " + }, + { + "bbox": [ + 304, + 270, + 547, + 342 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{edit}" + }, + { + "bbox": [ + 304, + 270, + 547, + 342 + ], + "type": "text", + "content": " is introduced to enable part-level shape editing. The losses and training flows will be described in detail in the following sections." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 347, + 493, + 358 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 347, + 493, + 358 + ], + "spans": [ + { + "bbox": [ + 305, + 347, + 493, + 358 + ], + "type": "text", + "content": "3.4.1 Accurate Geometric Reconstruction" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 365, + 546, + 401 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 365, + 546, + 401 + ], + "spans": [ + { + "bbox": [ + 304, + 365, + 546, + 401 + ], + "type": "text", + "content": "As shown in Fig. 2 (a), in order to reconstruct the original mesh as accurately as possible, we adopt the geometric reconstruction loss as follows:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 363, + 407, + 545, + 419 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 363, + 407, + 545, + 419 + ], + "spans": [ + { + "bbox": [ + 363, + 407, + 545, + 419 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {r e c}} = \\mathcal {L} _ {\\text {v e r t}} + \\lambda_ {\\text {e d g e}} \\cdot \\mathcal {L} _ {\\text {e d g e}}, \\tag {2}", + "image_path": "1e9c777b7ae791da6a3ef52ac56a97f7aa1970a6fcbace73bc3c36daa4a68787.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 423, + 547, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 423, + 547, + 469 + ], + "spans": [ + { + "bbox": [ + 304, + 423, + 547, + 469 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 423, + 547, + 469 + ], + "type": "inline_equation", + "content": "\\lambda_{edge}" + }, + { + "bbox": [ + 304, + 423, + 547, + 469 + ], + "type": "text", + "content": " is the weight of edge regularization. The vertex loss forces the reconstructed mesh " + }, + { + "bbox": [ + 304, + 423, + 547, + 469 + ], + "type": "inline_equation", + "content": "D(E(x))" + }, + { + "bbox": [ + 304, + 423, + 547, + 469 + ], + "type": "text", + "content": " to be as close as possible to the original mesh " + }, + { + "bbox": [ + 304, + 423, + 547, + 469 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 304, + 423, + 547, + 469 + ], + "type": "text", + "content": " with the supervision of vertex-wise L1 distance, which is calculated as:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 369, + 474, + 545, + 487 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 474, + 545, + 487 + ], + "spans": [ + { + "bbox": [ + 369, + 474, + 545, + 487 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {v e r t}} = \\| x - D (E (x)) \\| _ {1}. \\tag {3}", + "image_path": "ae1445a1a994e409641d55a816a5489c6858d897f3119302c4c0b400b97eb81d.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 491, + 545, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 491, + 545, + 552 + ], + "spans": [ + { + "bbox": [ + 304, + 491, + 545, + 552 + ], + "type": "text", + "content": "However, only using this loss to supervise vertex positions cannot avoid producing over-length edges, which seriously affects the smoothness and reasonableness of results. Inspired by [15,34], we address this problem by introducing an edge length regularization " + }, + { + "bbox": [ + 304, + 491, + 545, + 552 + ], + "type": "inline_equation", + "content": "L_{edge}" + }, + { + "bbox": [ + 304, + 491, + 545, + 552 + ], + "type": "text", + "content": ", which is formulated as:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 363, + 554, + 545, + 581 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 363, + 554, + 545, + 581 + ], + "spans": [ + { + "bbox": [ + 363, + 554, + 545, + 581 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {e d g e}} = \\sum_ {p} \\sum_ {v \\in N (p)} \\| p - v \\| _ {2} ^ {2}, \\tag {4}", + "image_path": "556c07d7b281ab1205fbead00d93f3886e1b9e8b171ab2249339edee153a92aa.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 581, + 545, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 581, + 545, + 617 + ], + "spans": [ + { + "bbox": [ + 304, + 581, + 545, + 617 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 581, + 545, + 617 + ], + "type": "inline_equation", + "content": "N(p)" + }, + { + "bbox": [ + 304, + 581, + 545, + 617 + ], + "type": "text", + "content": " is the set of 1-ring neighbors of vertex " + }, + { + "bbox": [ + 304, + 581, + 545, + 617 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 304, + 581, + 545, + 617 + ], + "type": "text", + "content": ". This loss ensures the smoothness of output meshes by enforcing their surface to be tight." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 305, + 623, + 471, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 623, + 471, + 635 + ], + "spans": [ + { + "bbox": [ + 305, + 623, + 471, + 635 + ], + "type": "text", + "content": "3.4.2 Unsupervised Disentanglement" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": "After the reconstruction flow in Sec. 3.4.1, our representation is already capable of accurate reconstruction, but its latent space is still entangled. Based on the part-aware skeleton-separated decoupling strategy (Sec. 3.2), we use " + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{dis}" + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": " to ensure the bone and shape disentanglement of body parts, which can be defined as:" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16988" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 102, + 72, + 287, + 84 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 102, + 72, + 287, + 84 + ], + "spans": [ + { + "bbox": [ + 102, + 72, + 287, + 84 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {d i s} = \\mathcal {L} _ {d i s - b} + \\lambda_ {d i s - s} \\cdot \\mathcal {L} _ {d i s - s}. \\tag {5}", + "image_path": "858acc1d2c955f48353600b8a801b8ccdac7cc6746e4d58dc15ba2f52afe23e8.jpg" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "spans": [ + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": "To achieve unsupervised disentanglement, given two human bodies " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "x_{1}" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "x_{2}" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "x_{swp}" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": " denotes the generated mesh " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "D(Z_{b_2}, Z_{s_1})" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": ", which is constructed with the bone latent code from " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "x_{2}" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": " and shape latent code from " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "x_{1}" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": ", as shown in Fig. 2 (b). Overall, " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{dis\\_b}" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{dis\\_s}" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": " are used to preserve bone information belonging to parts of " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "x_{2}" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": " and geometric features along " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "o_{b\\perp}" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": " belonging to parts of " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "x_{1}" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": ", respectively. In this way, our method achieves decoupling via unsupervised learning. " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "x_{swp}" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": " should have close joint positions as " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "x_{2}" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": " as these are largely determined by the bones, so " + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "inline_equation", + "content": "L_{dis\\_b}" + }, + { + "bbox": [ + 47, + 89, + 287, + 209 + ], + "type": "text", + "content": " is defined as" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 102, + 213, + 287, + 227 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 102, + 213, + 287, + 227 + ], + "spans": [ + { + "bbox": [ + 102, + 213, + 287, + 227 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {d i s} b} = \\| J \\left(x _ {2}\\right) - J \\left(x _ {s w p}\\right) \\| _ {1}, \\tag {6}", + "image_path": "93697959fcd93f61173dc3b31533443f5651f9c9f2b637b17dec16508417127a.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 231, + 287, + 255 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 231, + 287, + 255 + ], + "spans": [ + { + "bbox": [ + 47, + 231, + 287, + 255 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 231, + 287, + 255 + ], + "type": "inline_equation", + "content": "J(x)" + }, + { + "bbox": [ + 47, + 231, + 287, + 255 + ], + "type": "text", + "content": " is the vector containing joint positions for " + }, + { + "bbox": [ + 47, + 231, + 287, + 255 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 47, + 231, + 287, + 255 + ], + "type": "text", + "content": " obtained through a joint regressor." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 255, + 287, + 313 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 255, + 287, + 313 + ], + "spans": [ + { + "bbox": [ + 47, + 255, + 287, + 313 + ], + "type": "text", + "content": "However, preserving geometric features is a challenging issue. An intuitive idea to solve this issue is to use the matrix of pairwise Euclidean distances between all vertices in each part to capture regional shape information, which can be defined as:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 88, + 315, + 287, + 347 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 88, + 315, + 287, + 347 + ], + "spans": [ + { + "bbox": [ + 88, + 315, + 287, + 347 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {d i s} - s} = \\sum_ {k = 1} ^ {K} \\| D _ {e} \\left(x _ {1} ^ {k}\\right) - D _ {e} \\left(x _ {\\operatorname {s w p}} ^ {k}\\right) \\| _ {1}, \\tag {7}", + "image_path": "f87d699d257606e552d8873b1b3500d3bac4ee639cfd55e05a5dc60bde676e75.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "spans": [ + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "inline_equation", + "content": "x^{k}" + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "text", + "content": " is the " + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "text", + "content": "-th part-mesh of " + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "inline_equation", + "content": "D_{e}(x^{k})" + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "text", + "content": " denotes the matrix of pairwise Euclidean distances between all vertices in " + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "inline_equation", + "content": "x^{k}" + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "inline_equation", + "content": "D_{e}(x^{k})" + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "text", + "content": " is of size " + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "inline_equation", + "content": "n(x^{k}) \\times n(x^{k})" + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "inline_equation", + "content": "n(x^{k})" + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "text", + "content": " is the number of vertices of the " + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 47, + 349, + 287, + 432 + ], + "type": "text", + "content": "-th part. Nevertheless, the bone length is coupled with the Euclidean distance matrix, which leads to incomplete decoupling and affects editing precision (Sec. 4.3)." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "spans": [ + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "text", + "content": "To alleviate this problem, we propose an orientation-adaptive weighting (OAW) strategy to enforce the representation to focus on shape variations along " + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "inline_equation", + "content": "o_{b\\perp}" + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "text", + "content": ". For each body part " + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "inline_equation", + "content": "x^{k}" + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "text", + "content": ", we first construct a pairwise angle matrix of size " + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "inline_equation", + "content": "n(x^{k}) \\times n(x^{k})" + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "text", + "content": " by joining every pair of vertices to form a line, and working out the angle " + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "inline_equation", + "content": "A(x^{k})" + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "text", + "content": " (in degrees) the line makes with the bone orientation " + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "inline_equation", + "content": "o_{b}^{k}" + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "text", + "content": ", as illustrated in Fig. 3 (d). As can be seen, the larger the angle, the more significant the line contributes to the shape variation along " + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "inline_equation", + "content": "o_{b\\perp}" + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "text", + "content": ". We apply the following thresholding and normalization " + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "text", + "content": " to obtain the weight matrix " + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "inline_equation", + "content": "W(x^{k})" + }, + { + "bbox": [ + 47, + 434, + 287, + 586 + ], + "type": "text", + "content": " also of the same size by suppressing small angles, and setting the maximum weight to one:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 123, + 586, + 286, + 600 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 123, + 586, + 286, + 600 + ], + "spans": [ + { + "bbox": [ + 123, + 586, + 286, + 600 + ], + "type": "interline_equation", + "content": "W \\left(x ^ {k}\\right) = f \\left(A \\left(x ^ {k}\\right)\\right), \\tag {8}", + "image_path": "ca1ca35393a8b55abe31bbe7607703556983d2732d27b8f41dc673a10b04f3f1.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 604, + 285, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 604, + 285, + 616 + ], + "spans": [ + { + "bbox": [ + 47, + 604, + 285, + 616 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 604, + 285, + 616 + ], + "type": "inline_equation", + "content": "f(\\cdot)" + }, + { + "bbox": [ + 47, + 604, + 285, + 616 + ], + "type": "text", + "content": " is an element-wise mapping function defined as" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 620, + 287, + 652 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 620, + 287, + 652 + ], + "spans": [ + { + "bbox": [ + 105, + 620, + 287, + 652 + ], + "type": "interline_equation", + "content": "f (a) = \\left\\{ \\begin{array}{l l} a / 9 0, & \\text {i f} a > \\sigma \\\\ 0, & \\text {o t h e r w i s e .} \\end{array} \\right. \\tag {9}", + "image_path": "a8168bf18ad4d56e1c7dbc05a2e92a33510f9e9c3c1b3c5c34ae457a5e0f5c16.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 655, + 276, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 655, + 276, + 666 + ], + "spans": [ + { + "bbox": [ + 47, + 655, + 276, + 666 + ], + "type": "text", + "content": "The weighted pairwise distance matrix is then defined as" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 670, + 286, + 685 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 670, + 286, + 685 + ], + "spans": [ + { + "bbox": [ + 105, + 670, + 286, + 685 + ], + "type": "interline_equation", + "content": "D _ {e} ^ {w} \\left(x ^ {k}\\right) = W \\left(x ^ {k}\\right) \\otimes D _ {e} \\left(x ^ {k}\\right), \\tag {10}", + "image_path": "81ad52109d0bb3865e89f4212c89031a72b3634c7eda0166a949c28f17e3b351.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\otimes" + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": " is element-wise matrix multiplication. This orientation-adaptive weighting strategy enforces the bone" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 72, + 545, + 216 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 216 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 216 + ], + "type": "text", + "content": "length information to be separated from the Euclidean distance matrix as much as possible. Nonetheless, this strategy also ignores some useful geometric information along " + }, + { + "bbox": [ + 304, + 72, + 545, + 216 + ], + "type": "inline_equation", + "content": "o_b" + }, + { + "bbox": [ + 304, + 72, + 545, + 216 + ], + "type": "text", + "content": ", which may result in unreasonable mesh deformations when editing bone orientation in large scale (Sec. 4.3). To address this problem, we further impose part-level volume regularization penalizing the unreasonable shape variations. Ideally, when the shape along " + }, + { + "bbox": [ + 304, + 72, + 545, + 216 + ], + "type": "inline_equation", + "content": "o_{b\\perp}" + }, + { + "bbox": [ + 304, + 72, + 545, + 216 + ], + "type": "text", + "content": " is retained, the volume of a body part should change in proportion to the bone length, so we introduce a volume constraint, which provides strong geometric supervision to achieve natural and reasonable editing results, which can be calculated as:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 317, + 217, + 545, + 251 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 217, + 545, + 251 + ], + "spans": [ + { + "bbox": [ + 317, + 217, + 545, + 251 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {v o l} = \\sum_ {k = 1} ^ {K} \\| v \\left(x _ {1} ^ {k}\\right) / l \\left(x _ {1} ^ {k}\\right) - v \\left(x _ {s w p} ^ {k}\\right) / l \\left(x _ {s w p} ^ {k}\\right) \\| _ {1}, \\tag {11}", + "image_path": "28f5556e54ddc406736d0a6d579c0a4d9a6a5c1fd16a7a644178918a769e1257.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 253, + 545, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 253, + 545, + 300 + ], + "spans": [ + { + "bbox": [ + 304, + 253, + 545, + 300 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 253, + 545, + 300 + ], + "type": "inline_equation", + "content": "v(\\cdot)" + }, + { + "bbox": [ + 304, + 253, + 545, + 300 + ], + "type": "text", + "content": " is a function that calculates the volume of a mesh part according to the tetrahedral volume formula, and " + }, + { + "bbox": [ + 304, + 253, + 545, + 300 + ], + "type": "inline_equation", + "content": "l(\\cdot)" + }, + { + "bbox": [ + 304, + 253, + 545, + 300 + ], + "type": "text", + "content": " is a function that measures the length of a mesh part between the two joints. We can then rewrite " + }, + { + "bbox": [ + 304, + 253, + 545, + 300 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{dis\\_s}" + }, + { + "bbox": [ + 304, + 253, + 545, + 300 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 321, + 304, + 545, + 337 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 304, + 545, + 337 + ], + "spans": [ + { + "bbox": [ + 321, + 304, + 545, + 337 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {d i s} - s} = \\sum_ {k = 1} ^ {K} \\| D _ {e} ^ {w} \\left(x _ {1} ^ {k}\\right) - D _ {e} ^ {w} \\left(x _ {\\text {s w p}} ^ {k}\\right) \\| _ {1} + \\mathcal {L} _ {\\text {v o l}}. \\tag {12}", + "image_path": "81168b08955b8d302b999a776088d5524a137bc0a7ccb5aa2e036cda183a95e8.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 306, + 346, + 447, + 358 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 346, + 447, + 358 + ], + "spans": [ + { + "bbox": [ + 306, + 346, + 447, + 358 + ], + "type": "text", + "content": "3.4.3 Part-Level Shape Editing" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 365, + 545, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 365, + 545, + 485 + ], + "spans": [ + { + "bbox": [ + 304, + 365, + 545, + 485 + ], + "type": "text", + "content": "With the constraints in Sec. 3.4.2, our autoencoder learns a bone and shape disentangled representation for body parts, and hence we can directly control bone-related attributes (e.g., bone orientation and length) of parts by modifying their joints. Nevertheless, we cannot flexibly edit other bone-independent shape features (e.g., circumference) as desired. To address this issue, we propose an editing flow shown in Fig. 2 (c), which achieves part-level shape editing by forcing the generated mesh from the scaled codes " + }, + { + "bbox": [ + 304, + 365, + 545, + 485 + ], + "type": "inline_equation", + "content": "(Z_{b_1},\\alpha Z_{s_1})" + }, + { + "bbox": [ + 304, + 365, + 545, + 485 + ], + "type": "text", + "content": " to deform as desired with " + }, + { + "bbox": [ + 304, + 365, + 545, + 485 + ], + "type": "inline_equation", + "content": "L_{edit}" + }, + { + "bbox": [ + 304, + 365, + 545, + 485 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 310, + 488, + 545, + 502 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 310, + 488, + 545, + 502 + ], + "spans": [ + { + "bbox": [ + 310, + 488, + 545, + 502 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {e d i t}} = \\mathcal {L} _ {\\text {e d i t} b} + \\lambda_ {\\text {e d i t} s} \\cdot \\mathcal {L} _ {\\text {e d i t} s} + \\lambda_ {\\text {n o r m}} \\cdot \\mathcal {L} _ {\\text {n o r m}}, \\tag {13}", + "image_path": "7c1cf90e25f0d410ab3923456ee1d1109fdd87e1231d1f2cd0a3e86463f840ff.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 341, + 506, + 545, + 538 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 341, + 506, + 545, + 538 + ], + "spans": [ + { + "bbox": [ + 341, + 506, + 545, + 538 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {e d i t} - s} = \\sum_ {k = 1} ^ {K} \\| \\alpha D _ {e} ^ {w} \\left(x _ {1} ^ {k}\\right) - D _ {e} ^ {w} \\left(x _ {s c a} ^ {k}\\right) \\| _ {1}, \\tag {14}", + "image_path": "1e15d6432f0b18ec9cbfa80eace7fa81274ea6c3b7d0ad47cd384f2326d7359c.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 361, + 542, + 545, + 555 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 361, + 542, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 361, + 542, + 545, + 555 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {e d i t} - b} = \\left\\| J \\left(x _ {1}\\right) - J \\left(x _ {s c a}\\right) \\right\\| _ {1}, \\tag {15}", + "image_path": "2e5f2a22cd9c64f87b8aa4649f9fc7884711bb87c3b31f3db58f55f3a78b728f.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "spans": [ + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "text", + "content": " is a scalar uniformly sampled in " + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "inline_equation", + "content": "(\\alpha_{\\mathrm{min}},\\alpha_{\\mathrm{max}})" + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "text", + "content": " during training, and " + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "inline_equation", + "content": "x_{sca}" + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "text", + "content": " denotes the generated mesh " + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "inline_equation", + "content": "D(Z_{b_1},\\alpha Z_{s_1})" + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "text", + "content": " from the scaled codes, i.e., the edited mesh " + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "inline_equation", + "content": "x_{sca}" + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "text", + "content": " has bone code from " + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "inline_equation", + "content": "x_{1}" + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "text", + "content": ", and shape code from " + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "inline_equation", + "content": "x_{1}" + }, + { + "bbox": [ + 306, + 558, + 545, + 617 + ], + "type": "text", + "content": " with flexible scaling to change body shape." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "text", + "content": "In particular, " + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{edit\\_s}" + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "text", + "content": " constrains the " + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "text", + "content": "-th part of " + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "inline_equation", + "content": "x_{sca}" + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "text", + "content": " reconstructed from the scaled shape codes " + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\alpha Z_{s_1}" + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "text", + "content": " to have the shape described by " + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\alpha D_e^W (x_1^k)" + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "text", + "content": ". As " + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "inline_equation", + "content": "D_e^W (\\cdot)" + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "text", + "content": " is designed to largely capture the pairwise distances along " + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "inline_equation", + "content": "o_{b\\perp}^{k}" + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "text", + "content": ", i.e. reflecting shape size and style for the part. Elements in the matrix therefore should be scaled accordingly when the part is scaled along " + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "inline_equation", + "content": "o_{b\\perp}^{k}" + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "text", + "content": ". Besides, " + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{edit\\_b}" + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "text", + "content": " is introduced to preserve bone information during the editing process." + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "16989" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 288, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 288, + 156 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 288, + 156 + ], + "type": "text", + "content": "However, the editing flow easily causes training collapses. We solve this problem by imposing a vector norm regularization " + }, + { + "bbox": [ + 46, + 72, + 288, + 156 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{norm}" + }, + { + "bbox": [ + 46, + 72, + 288, + 156 + ], + "type": "text", + "content": " to establish a mapping between the norm of localized shape codes and the circumference of body parts, which not only benefits converge, but also allows users to edit part shape size on a unified scale. This regularization can be defined as:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 87, + 157, + 287, + 188 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 157, + 287, + 188 + ], + "spans": [ + { + "bbox": [ + 87, + 157, + 287, + 188 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {n o r m}} = \\frac {1}{K} \\sum_ {k = 1} ^ {K} \\left| \\| z _ {s _ {1}} ^ {k} \\| _ {2} - \\operatorname {c i r c} \\left(x _ {1} ^ {k}\\right) \\right|, \\tag {16}", + "image_path": "2e801c4cfcd84c340f721ade6be45594c644883da1b1876aae7e3d9b0eee1c46.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 189, + 287, + 261 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 189, + 287, + 261 + ], + "spans": [ + { + "bbox": [ + 46, + 189, + 287, + 261 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 189, + 287, + 261 + ], + "type": "inline_equation", + "content": "\\text{circ}(\\cdot)" + }, + { + "bbox": [ + 46, + 189, + 287, + 261 + ], + "type": "text", + "content": " is a function that measures the circumference of a part using the identified landmarks. " + }, + { + "bbox": [ + 46, + 189, + 287, + 261 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{edit}" + }, + { + "bbox": [ + 46, + 189, + 287, + 261 + ], + "type": "text", + "content": " successfully allows shape codes' norm and direction to represent shape size and style respectively, which not only enables flexible part-level editing but also ensures the continuity of the learned latent space to a certain extent." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 267, + 180, + 280 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 267, + 180, + 280 + ], + "spans": [ + { + "bbox": [ + 47, + 267, + 180, + 280 + ], + "type": "text", + "content": "3.5. Implementation Details" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 285, + 288, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 285, + 288, + 453 + ], + "spans": [ + { + "bbox": [ + 46, + 285, + 288, + 453 + ], + "type": "text", + "content": "For the spiral convolution encoder, we use a framework similar to [7]. Specifically, it consists of four spiral convolution layers and downsampling layers, and the structure of the decoder is a mirror of the encoder, except that the downsampling layers are replaced by the upsampling layers. Our algorithm is implemented in PyTorch [23]. All the training and test experiments are carried out on a PC with an RTX 3090 GPU. We train our network for 300 epochs with a learning rate of " + }, + { + "bbox": [ + 46, + 285, + 288, + 453 + ], + "type": "inline_equation", + "content": "1 \\times 10^{-3}" + }, + { + "bbox": [ + 46, + 285, + 288, + 453 + ], + "type": "text", + "content": ", a learning rate decay of 0.99 after each epoch and the Adam optimizer [20]. The entire training time takes around 24 hours. We use 16-dimensional latent codes (8 for bones and 8 for shape) to embed each part. More implementation details can be found in the supplementary material." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 460, + 128, + 474 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 460, + 128, + 474 + ], + "spans": [ + { + "bbox": [ + 47, + 460, + 128, + 474 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 479, + 110, + 491 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 479, + 110, + 491 + ], + "spans": [ + { + "bbox": [ + 47, + 479, + 110, + 491 + ], + "type": "text", + "content": "4.1. Datasets" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 498, + 287, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 498, + 287, + 593 + ], + "spans": [ + { + "bbox": [ + 46, + 498, + 287, + 593 + ], + "type": "text", + "content": "DFAUST. The dynamic human body dataset with the same mesh connectivity as SMPL [22] from Bogo et al. [5], captures 14 different body motion sequences (e.g., hips, running, and jumping) for each of the 10 human subjects. We evenly extract one-twentieth of the original dataset for the convenience of training. Then we randomly split the extracted data into a test set of 182 meshes and a training set of 1936 meshes." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 594, + 287, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 287, + 676 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 287, + 676 + ], + "type": "text", + "content": "SPRING. The large human body dataset with the same mesh connectivity as SCAPE [2] from Yang et al. [35], consists of " + }, + { + "bbox": [ + 46, + 594, + 287, + 676 + ], + "type": "inline_equation", + "content": "3000+" + }, + { + "bbox": [ + 46, + 594, + 287, + 676 + ], + "type": "text", + "content": " subject meshes with a rough A-pose registered from the CAESAR dataset [27] using a non-rigid deformation algorithm. For the subsequent experiments, the SPRING dataset is randomly split into 2743 training and 305 test meshes." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 677, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 287, + 713 + ], + "type": "text", + "content": "Data Preprocessing. For the training data, our method requires a joint regressor, body part semantics of each vertex, and landmarks for calculating circumferences. Since" + } + ] + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 309, + 70, + 541, + 190 + ], + "blocks": [ + { + "bbox": [ + 309, + 70, + 541, + 190 + ], + "lines": [ + { + "bbox": [ + 309, + 70, + 541, + 190 + ], + "spans": [ + { + "bbox": [ + 309, + 70, + 541, + 190 + ], + "type": "table", + "html": "
MethodDFAUSTSPRING
EavdParam(M)EavdParam(M)
COMA [26]6.067.546.046.84
Neural3DMM [7]5.4930.356.1127.56
Spiralplus [14]5.3515.154.9913.75
Pai3DMM [13]5.7615.184.4513.78
Deep3DMM [10]9.918.3510.887.84
Unsup [38]10.1812.89--
Ours4.701.594.331.47
", + "image_path": "f870d9efdd0a08e8db6d243668fa62f39aa3302dc3de13ffd06a3cd07c1a218b.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 351, + 232, + 499, + 294 + ], + "blocks": [ + { + "bbox": [ + 305, + 191, + 545, + 224 + ], + "lines": [ + { + "bbox": [ + 305, + 191, + 545, + 224 + ], + "spans": [ + { + "bbox": [ + 305, + 191, + 545, + 224 + ], + "type": "text", + "content": "Table 1. Quantitative reconstruction results on DFAUST [5] and SPRING [35] datasets. - : not supported for this dataset. Param(M) shows the number of learnable parameters in millions." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 351, + 232, + 499, + 294 + ], + "lines": [ + { + "bbox": [ + 351, + 232, + 499, + 294 + ], + "spans": [ + { + "bbox": [ + 351, + 232, + 499, + 294 + ], + "type": "table", + "html": "
MethodDHNN
EavdParam(M)
DHNN [18]3.1691.63
Ours3.961.47
", + "image_path": "9d2fecc76d177d54f809178ab877eb817df3ae59dc0f23d17989cfcda8465927.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 294, + 544, + 316 + ], + "lines": [ + { + "bbox": [ + 305, + 294, + 544, + 316 + ], + "spans": [ + { + "bbox": [ + 305, + 294, + 544, + 316 + ], + "type": "text", + "content": "Table 2. Quantitative reconstruction results on DHNN [18]. " + }, + { + "bbox": [ + 305, + 294, + 544, + 316 + ], + "type": "inline_equation", + "content": "\\mathbf{{Param}\\left( M\\right) }" + }, + { + "bbox": [ + 305, + 294, + 544, + 316 + ], + "type": "text", + "content": " shows the number of learnable parameters in millions." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 305, + 324, + 545, + 385 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 324, + 545, + 385 + ], + "spans": [ + { + "bbox": [ + 305, + 324, + 545, + 385 + ], + "type": "text", + "content": "the mesh connectivity of datasets is consistent (which is required by nearly all 3D human representation learning works), the SMPL model only needs to be registered once to meet the requirements. More details about data preprocessing are given in the supplementary material." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 394, + 387, + 407 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 394, + 387, + 407 + ], + "spans": [ + { + "bbox": [ + 306, + 394, + 387, + 407 + ], + "type": "text", + "content": "4.2. Comparison" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 413, + 545, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 413, + 545, + 449 + ], + "spans": [ + { + "bbox": [ + 305, + 413, + 545, + 449 + ], + "type": "text", + "content": "In the following, we evaluate the representation ability (reconstruction precision) and editing capacity (semantics) of our approach on the DFAUST and SPRING datasets." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 449, + 546, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 449, + 546, + 616 + ], + "spans": [ + { + "bbox": [ + 305, + 449, + 546, + 616 + ], + "type": "text", + "content": "Reconstruction Experiments. We first compare four kinds of methods to validate the reconstruction precision of our representation: the spectral-based approach (COMA [26]), the spiral-based methods (Neural3DMM [7] and Spiralplus [14]), the attention-based approaches (Pai3DMM [13] and Deep3DMM [10]), and the disentanglement representation works (DHNN [18] and Unsup [38]). For a fair comparison, we use the official implementation of the compared methods with the same latent space dimension. Note that DHNN [18] did not release the training code, so we train and test our model on its dataset for a fair comparison. we utilize the average point-wise Euclidean distance " + }, + { + "bbox": [ + 305, + 449, + 546, + 616 + ], + "type": "inline_equation", + "content": "E_{\\text{avd}}" + }, + { + "bbox": [ + 305, + 449, + 546, + 616 + ], + "type": "text", + "content": " (in millimeters) between corresponding vertices in the input and its reconstruction as metrics." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 617, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 545, + 712 + ], + "type": "text", + "content": "As shown in Tab. 1, our proposed approach shows excellent representation capability with a small model size. Fig. 5 visualizes some reconstruction results and their error maps. It can be observed that our approach outperforms other methods in reconstruction accuracy, especially for complex geometric details (e.g., faces and hands), which demonstrates the effectiveness of our bone-guided autoencoder." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "16990" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 70, + 70, + 523, + 203 + ], + "blocks": [ + { + "bbox": [ + 70, + 70, + 523, + 203 + ], + "lines": [ + { + "bbox": [ + 70, + 70, + 523, + 203 + ], + "spans": [ + { + "bbox": [ + 70, + 70, + 523, + 203 + ], + "type": "image", + "image_path": "70f8f53cf971f3f8dfedcd6304c9abba18928de929760778c9e11a0436545e8e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 205, + 547, + 250 + ], + "lines": [ + { + "bbox": [ + 46, + 205, + 547, + 250 + ], + "spans": [ + { + "bbox": [ + 46, + 205, + 547, + 250 + ], + "type": "text", + "content": "Figure 5. Qualitative reconstruction results on DFAUST [5] and SPRING [35]. The per-vertex Euclidean distance error is color-coded on the reconstructed meshes for visual inspection. Since Unsup [38] has a data constraint that the same subject in different poses should be given, it cannot be trained on the SPRING dataset. Note that our representation is trained without the need of data constraint, which is used in the compared methods." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 48, + 252, + 287, + 391 + ], + "blocks": [ + { + "bbox": [ + 48, + 252, + 287, + 391 + ], + "lines": [ + { + "bbox": [ + 48, + 252, + 287, + 391 + ], + "spans": [ + { + "bbox": [ + 48, + 252, + 287, + 391 + ], + "type": "image", + "image_path": "634c30d0315aa63ed2cd067badf22ed533be694bb8571a67cf460b8f2b133e84.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 391, + 287, + 434 + ], + "lines": [ + { + "bbox": [ + 46, + 391, + 287, + 434 + ], + "spans": [ + { + "bbox": [ + 46, + 391, + 287, + 434 + ], + "type": "text", + "content": "Figure 6. Qualitative editing results by Unsup [38] (first row), HBR [36] (second and third rows) and our method. We show the reconstructed bodies and edited bodies on the left and right of the source mesh." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 48, + 439, + 287, + 513 + ], + "blocks": [ + { + "bbox": [ + 48, + 439, + 287, + 513 + ], + "lines": [ + { + "bbox": [ + 48, + 439, + 287, + 513 + ], + "spans": [ + { + "bbox": [ + 48, + 439, + 287, + 513 + ], + "type": "table", + "html": "
MethodBone OrientationBone LengthShape Size
EjointEcricEjointEcricEjointEcric
Unsup [38]36.7914.16----
HBR [36]--38.3715.2712.8718.78
Ours3.269.751.575.920.4517.64
", + "image_path": "8eb46ccd3e0b430dbc9c3c0a0ac9e13a34b8a3d3945decba278aa81bbd3206e7.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 514, + 286, + 525 + ], + "lines": [ + { + "bbox": [ + 47, + 514, + 286, + 525 + ], + "spans": [ + { + "bbox": [ + 47, + 514, + 286, + 525 + ], + "type": "text", + "content": "Table 3. Quantitative editing results. - : not supported for this task." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 533, + 287, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 533, + 287, + 652 + ], + "spans": [ + { + "bbox": [ + 46, + 533, + 287, + 652 + ], + "type": "text", + "content": "In addition, Tab. 2 gives the quantitative results on the DHNN dataset [18]. It is important to note that the compared method [18] uses a training data assumption (i.e., each posed mesh has a paired mesh in a neutral pose) for shape and pose disentanglement, but our method does not make use of this constraint. Under this unfair condition, the reconstruction accuracy of our method is only slightly lower than DHNN [18], and our model is more lightweight and semantically finer. Please refer to our supplementary material for more experimental results and details." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "text", + "content": "Editing Experiments. We also demonstrate the flexible editing ability of our model on three editing tasks: editing bone orientation and bone length, and editing part shape size. Since bone orientation is approximately equivalent to pose, we compare with the unsupervised pose-and-shape" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 256, + 545, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 256, + 545, + 375 + ], + "spans": [ + { + "bbox": [ + 304, + 256, + 545, + 375 + ], + "type": "text", + "content": "disentanglement work Unsup [38] on the task of editing bone orientation on the DFAUST dataset. In contrast, bone length and shape size are shape information, so we compare with the Human Body Reshaping work HBR [36] on the task of editing bone length and shape size on the SPRING dataset. As there is no ground truth for these editing tasks, how to measure the editing performance is a problem. We utilize the joint and circumference errors " + }, + { + "bbox": [ + 304, + 256, + 545, + 375 + ], + "type": "inline_equation", + "content": "E_{joint}" + }, + { + "bbox": [ + 304, + 256, + 545, + 375 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 256, + 545, + 375 + ], + "type": "inline_equation", + "content": "E_{circ}" + }, + { + "bbox": [ + 304, + 256, + 545, + 375 + ], + "type": "text", + "content": " (in millimeters) to evaluate the accuracy of editing bone and shape." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 376, + 545, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 376, + 545, + 434 + ], + "spans": [ + { + "bbox": [ + 304, + 376, + 545, + 434 + ], + "type": "text", + "content": "Specifically, for each test mesh, we randomly select editing targets and then calculate " + }, + { + "bbox": [ + 304, + 376, + 545, + 434 + ], + "type": "inline_equation", + "content": "E_{joint}" + }, + { + "bbox": [ + 304, + 376, + 545, + 434 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 376, + 545, + 434 + ], + "type": "inline_equation", + "content": "E_{circ}" + }, + { + "bbox": [ + 304, + 376, + 545, + 434 + ], + "type": "text", + "content": " between the target attribute value and the actual attribute value of the edited human body, extracted using " + }, + { + "bbox": [ + 304, + 376, + 545, + 434 + ], + "type": "inline_equation", + "content": "J(\\cdot)" + }, + { + "bbox": [ + 304, + 376, + 545, + 434 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 376, + 545, + 434 + ], + "type": "inline_equation", + "content": "circ(\\cdot)" + }, + { + "bbox": [ + 304, + 376, + 545, + 434 + ], + "type": "text", + "content": ", which can be defined as:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 356, + 437, + 545, + 451 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 356, + 437, + 545, + 451 + ], + "spans": [ + { + "bbox": [ + 356, + 437, + 545, + 451 + ], + "type": "interline_equation", + "content": "E _ {j o i n t} = \\left\\| T _ {j o i n t} - J \\left(x _ {\\text {e d i t e d}}\\right) \\right\\| _ {2}, \\tag {17}", + "image_path": "2069934bac81cb6e53553a0000528a64890077d2ead3663734cda05b11ae31c2.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 344, + 452, + 545, + 484 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 344, + 452, + 545, + 484 + ], + "spans": [ + { + "bbox": [ + 344, + 452, + 545, + 484 + ], + "type": "interline_equation", + "content": "E _ {c i r c} = \\frac {1}{K} \\sum_ {k = 1} ^ {K} \\left| T _ {c i r c} ^ {k} - \\operatorname {c i r c} \\left(x _ {\\text {e d i t e d}} ^ {k}\\right) \\right|, \\tag {18}", + "image_path": "2697e6131873a4c76ea3f1104f6d140ec83477ff6acc8430145351c10fcf6ee7.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 485, + 544, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 485, + 544, + 508 + ], + "spans": [ + { + "bbox": [ + 304, + 485, + 544, + 508 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 485, + 544, + 508 + ], + "type": "inline_equation", + "content": "T_{joint}" + }, + { + "bbox": [ + 304, + 485, + 544, + 508 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 485, + 544, + 508 + ], + "type": "inline_equation", + "content": "T_{circ}" + }, + { + "bbox": [ + 304, + 485, + 544, + 508 + ], + "type": "text", + "content": " are the editing targets of joint positions and circumference." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 509, + 545, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 509, + 545, + 617 + ], + "spans": [ + { + "bbox": [ + 304, + 509, + 545, + 617 + ], + "type": "text", + "content": "Tab. 3 gives the quantitative editing results. Our method not only achieves the best performance on all the editing tasks but also preserves other unedited attributes well. Some visual results are shown in Fig. 6. Compared with Unsup [38] and HBR [36], our method can edit the human body more accurately, reasonably and flexibly. Since the attribute error does not always reflect the editing performance of models, we also design a user study for better evaluation (see the supplementary material)." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 623, + 400, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 623, + 400, + 635 + ], + "spans": [ + { + "bbox": [ + 306, + 623, + 400, + 635 + ], + "type": "text", + "content": "4.3. Ablation Study" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "type": "text", + "content": "Effect of Orientation-Adaptive Weighting Strategy. We validate the effectiveness of the orientation-adaptive weighting strategy by ablating it during training. As shown in Tab. 4, introducing this strategy helps our representation to focus on geometric features along " + }, + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "type": "inline_equation", + "content": "o_{b\\perp}" + }, + { + "bbox": [ + 304, + 641, + 545, + 714 + ], + "type": "text", + "content": ", leading to higher editing accuracy and more complete decoupling." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16991" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 71, + 71, + 263, + 171 + ], + "blocks": [ + { + "bbox": [ + 71, + 71, + 263, + 171 + ], + "lines": [ + { + "bbox": [ + 71, + 71, + 263, + 171 + ], + "spans": [ + { + "bbox": [ + 71, + 71, + 263, + 171 + ], + "type": "image", + "image_path": "9c6ef8d2d4fa819ab3f320c56a2e5536000c8a9d9d1057e4a6871f023f96e62b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 47, + 173, + 287, + 195 + ], + "lines": [ + { + "bbox": [ + 47, + 173, + 287, + 195 + ], + "spans": [ + { + "bbox": [ + 47, + 173, + 287, + 195 + ], + "type": "text", + "content": "Figure 7. Qualitative results of volume regularization ablation study." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 48, + 205, + 288, + 286 + ], + "blocks": [ + { + "bbox": [ + 48, + 205, + 288, + 286 + ], + "lines": [ + { + "bbox": [ + 48, + 205, + 288, + 286 + ], + "spans": [ + { + "bbox": [ + 48, + 205, + 288, + 286 + ], + "type": "table", + "html": "
MethodReconstructionEditing
DFA. SPR. MeanBone Ori. \\(E_{joint}\\)Bone Length\\(E_{joint}\\)Shape Size\\(E_{joint}\\)Mean
Full4.704.334.523.269.751.575.920.4517.646.43
w/o OAW4.664.394.533.0213.661.5810.950.6120.408.37
w/o \\(L_{edge}\\)5.235.015.123.209.761.528.370.5329.488.81
w/o \\(L_{dis}\\)4.814.884.85----1.5617.50-
w/o \\(L_{edit}\\)4.874.434.653.0010.191.888.01---
", + "image_path": "09c61590abf58a847f1f19c4bc7e37a7c32f0a5bb8450fca448b6a2d658e30f7.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 288, + 287, + 310 + ], + "lines": [ + { + "bbox": [ + 47, + 288, + 287, + 310 + ], + "spans": [ + { + "bbox": [ + 47, + 288, + 287, + 310 + ], + "type": "text", + "content": "Table 4. Quantitative ablation study for reconstruction and editing (in mm). - : not supported for this task." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "spans": [ + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "text", + "content": "Effect of Other Losses. To evaluate the impact of " + }, + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text {edge }}" + }, + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text {dis }}" + }, + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text {edit }}" + }, + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "text", + "content": ", we remove them from the supervision one by one during training. As compared in Tab. 4, " + }, + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text {edge }}" + }, + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "text", + "content": " effectively reduces the reconstruction error, and the use of " + }, + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text {dis }}" + }, + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text {edit }}" + }, + { + "bbox": [ + 46, + 319, + 287, + 403 + ], + "type": "text", + "content": " is required to enable flexible bone and part shape editing. The ablation study proves that all losses are necessary." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 407, + 287, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 407, + 287, + 479 + ], + "spans": [ + { + "bbox": [ + 46, + 407, + 287, + 479 + ], + "type": "text", + "content": "Effect of Part-Level Volume Regularization. To analyze the impact of volume regularization, we remove it during the training process. Fig. 7 shows the comparison results of edited bodies. It can be seen that our volume constraint provides strong geometric supervision, resulting in more natural and reasonable editing results." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 497, + 129, + 510 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 497, + 129, + 510 + ], + "spans": [ + { + "bbox": [ + 47, + 497, + 129, + 510 + ], + "type": "text", + "content": "4.4. Applications" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 518, + 287, + 614 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 518, + 287, + 614 + ], + "spans": [ + { + "bbox": [ + 46, + 518, + 287, + 614 + ], + "type": "text", + "content": "Latent Space Interpolation. Our representation decouples the bone and shape of each part, which allows us to interpolate meshes by linearly interpolating the bone and shape latent codes. Fig. 8 shows reasonable and meaningful results of such interpolation. It is worth noting that, thanks to the latent space with fine-grained semantics, we can perform mesh interpolation at the component level (see Figs. 8 (b) and (c))." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 617, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 287, + 713 + ], + "type": "text", + "content": "Shape Style Transfer. As mentioned in Sec. 3.4.3, we successfully make the norm and direction of shape vectors represent shape size and style, respectively. Thus, we can transfer shape style by swapping the direction of the shape vectors. Some results are shown in Fig. 9. It can be easily observed that our approach changes the shape style of the source mesh naturally and preserves its other attributes (e.g., bone and circumference)." + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 318, + 70, + 392, + 167 + ], + "blocks": [ + { + "bbox": [ + 318, + 70, + 392, + 167 + ], + "lines": [ + { + "bbox": [ + 318, + 70, + 392, + 167 + ], + "spans": [ + { + "bbox": [ + 318, + 70, + 392, + 167 + ], + "type": "image", + "image_path": "8489b0839c6c91ef0b84fc6ce0d461382a421f6a1725908b2236e0592645637f.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 306, + 168, + 545, + 201 + ], + "lines": [ + { + "bbox": [ + 306, + 168, + 545, + 201 + ], + "spans": [ + { + "bbox": [ + 306, + 168, + 545, + 201 + ], + "type": "text", + "content": "Figure 8. Interpolation results on the bone-and-shape disentangled space of (a) the whole body, (b) upper body and (c) lower body, where the highlighted bodies are the input reconstructed bodies." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 392, + 71, + 460, + 167 + ], + "blocks": [ + { + "bbox": [ + 392, + 71, + 460, + 167 + ], + "lines": [ + { + "bbox": [ + 392, + 71, + 460, + 167 + ], + "spans": [ + { + "bbox": [ + 392, + 71, + 460, + 167 + ], + "type": "image", + "image_path": "28229fbe5847bc2bdbf2056bfa71090b833a3163b03431f83b08794831078531.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 460, + 71, + 530, + 167 + ], + "blocks": [ + { + "bbox": [ + 460, + 71, + 530, + 167 + ], + "lines": [ + { + "bbox": [ + 460, + 71, + 530, + 167 + ], + "spans": [ + { + "bbox": [ + 460, + 71, + 530, + 167 + ], + "type": "image", + "image_path": "4f37b10db4116c77a4a114dd6d531a08a4990f6b5692b2a3891deb12b1569e0d.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 318, + 212, + 531, + 322 + ], + "blocks": [ + { + "bbox": [ + 318, + 212, + 531, + 322 + ], + "lines": [ + { + "bbox": [ + 318, + 212, + 531, + 322 + ], + "spans": [ + { + "bbox": [ + 318, + 212, + 531, + 322 + ], + "type": "image", + "image_path": "06533bc6b0bb07b660e506c371160cd5497ac282b7e06d8e995558d7cde0af0b.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 306, + 324, + 545, + 346 + ], + "lines": [ + { + "bbox": [ + 306, + 324, + 545, + 346 + ], + "spans": [ + { + "bbox": [ + 306, + 324, + 545, + 346 + ], + "type": "text", + "content": "Figure 9. Results of shape style transfer by linearly interpolating the direction of shape codes." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 362, + 457, + 374 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 362, + 457, + 374 + ], + "spans": [ + { + "bbox": [ + 306, + 362, + 457, + 374 + ], + "type": "text", + "content": "5. Conclusion and Discussion" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 389, + 545, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 389, + 545, + 545 + ], + "spans": [ + { + "bbox": [ + 304, + 389, + 545, + 545 + ], + "type": "text", + "content": "Conclusion. In this paper, we propose a human body representation with fine-grained semantics and high reconstruction-accuracy in an unsupervised setting. The key idea is to exploit a part-aware skeleton-separated decoupling strategy to establish a correspondence between latent vectors and geometric properties of body parts, which benefits personalized editing of human bodies by modifying the corresponding latent codes. Based on this disentanglement strategy, we propose a bone-guided autoencoder and well-designed losses to learn representation in an unsupervised manner. At last, with the geometrically meaningful latent space, the application can be extended from human body editing to latent code interpolation and shape style transfer." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 551, + 545, + 671 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 551, + 545, + 671 + ], + "spans": [ + { + "bbox": [ + 304, + 551, + 545, + 671 + ], + "type": "text", + "content": "Limitations. Since our decoupling strategy is based on the cylinder assumption (i.e., the shape of body parts can be approximated as cylinders with bones as axes), we cannot controllably edit the bone length and circumference of parts (e.g., faces, hands, and feet) whose geometry is too complex to meet this assumption. Additionally, editing bone orientation with our method may fail when the target orientation is uncommon in the training data. In further work, we will dig deeper into the prior knowledge about the human body to improve the generalization capability of our representation." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": "Acknowledgement. This work was supported in part by the National Natural Science Foundation of China (62122058 and 62171317)." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16992" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 125 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 125 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 125 + ], + "type": "text", + "content": "[1] Brett Allen, Brian Curless, and Zoran Popovic. The space of human body shapes: reconstruction and parameterization from range scans. ACM Trans. Graph., 2003. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 126, + 287, + 169 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 126, + 287, + 169 + ], + "spans": [ + { + "bbox": [ + 53, + 126, + 287, + 169 + ], + "type": "text", + "content": "[2] Dragomir Anguelov, Praveen Srinivasan, Daphne Koller, Sebastian Thrun, Jim Rodgers, and James Davis. SCAPE: shape completion and animation of people. ACM Trans. Graph., 2005. 1, 2, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 171, + 287, + 214 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 171, + 287, + 214 + ], + "spans": [ + { + "bbox": [ + 53, + 171, + 287, + 214 + ], + "type": "text", + "content": "[3] Tristan Aumentado-Armstrong, Stavros Tsogkas, Allan Jepson, and Sven Dickinson. Geometric disentanglement for generative latent shape models. In Int. Conf. Comput. Vis., 2019. 2, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 217, + 287, + 260 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 217, + 287, + 260 + ], + "spans": [ + { + "bbox": [ + 53, + 217, + 287, + 260 + ], + "type": "text", + "content": "[4] Federica Bogo, Angjoo Kanazawa, Christoph Lassner, Peter Gehler, Javier Romero, and Michael J. Black. Keep it SMPL: Automatic estimation of 3D human pose and shape from a single image. In Eur. Conf. Comput. Vis., 2016. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 262, + 287, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 262, + 287, + 304 + ], + "spans": [ + { + "bbox": [ + 53, + 262, + 287, + 304 + ], + "type": "text", + "content": "[5] Federica Bogo, Javier Romero, Gerard Pons-Moll, and Michael J Black. Dynamic FAUST: Registering human bodies in motion. In IEEE Conf. Comput. Vis. Pattern Recog., 2017. 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 307, + 287, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 307, + 287, + 361 + ], + "spans": [ + { + "bbox": [ + 53, + 307, + 287, + 361 + ], + "type": "text", + "content": "[6] Davide Boscaini, Jonathan Masci, Simone Melzi, Michael M Bronstein, Umberto Castellani, and Pierre Vandergheynst. Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks. In Comput. Graph. Forum, 2015. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 364, + 287, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 364, + 287, + 418 + ], + "spans": [ + { + "bbox": [ + 53, + 364, + 287, + 418 + ], + "type": "text", + "content": "[7] Giorgos Bouritsas, Sergiy Bokhnyak, Stylianos Ploumpis, Michael Bronstein, and Stefanos Zafeiriou. Neural 3D morphable models: Spiral convolutional networks for 3d shape representation learning and generation. In Int. Conf. Comput. Vis., 2019. 1, 2, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 420, + 287, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 420, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 53, + 420, + 287, + 453 + ], + "type": "text", + "content": "[8] Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203, 2013. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 454, + 287, + 496 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 454, + 287, + 496 + ], + "spans": [ + { + "bbox": [ + 53, + 454, + 287, + 496 + ], + "type": "text", + "content": "[9] Haoyu Chen, Hao Tang, Henglin Shi, Wei Peng, Nicu Sebe, and Guoying Zhao. Intrinsic-extrinsic preserved GANs for unsupervised 3D pose transfer. In Int. Conf. Comput. Vis., 2021. 2, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 499, + 287, + 532 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 499, + 287, + 532 + ], + "spans": [ + { + "bbox": [ + 48, + 499, + 287, + 532 + ], + "type": "text", + "content": "[10] Zhixiang Chen and Tae-Kyun Kim. Learning feature aggregation for deep 3D morphable models. In IEEE Conf. Comput. Vis. Pattern Recog., 2021. 2, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 533, + 287, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 533, + 287, + 577 + ], + "spans": [ + { + "bbox": [ + 48, + 533, + 287, + 577 + ], + "type": "text", + "content": "[11] Luca Cosmo, Antonio Norelli, Oshri Halimi, Ron Kimmel, and Emanuele Rodola. LIMP: Learning latent shape representations with metric preservation priors. In Eur. Conf. Comput. Vis., 2020. 2, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 579, + 287, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 579, + 287, + 612 + ], + "spans": [ + { + "bbox": [ + 48, + 579, + 287, + 612 + ], + "type": "text", + "content": "[12] Lin Gao, Yu-Kun Lai, Jie Yang, Ling-Xiao Zhang, Shihong Xia, and Leif Kobbelt. Sparse data driven mesh deformation. IEEE Trans. Vis. Comput. Graph., 2019. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 613, + 287, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 613, + 287, + 656 + ], + "spans": [ + { + "bbox": [ + 48, + 613, + 287, + 656 + ], + "type": "text", + "content": "[13] Zhongpai Gao, Junchi Yan, Guangtao Zhai, Juyong Zhang, Yiyan Yang, and Xiaokang Yang. Learning local neighboring structure for robust 3D shape representation. In AAAI, 2021. 2, 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "type": "text", + "content": "[14] Shunwang Gong, Lei Chen, Michael Bronstein, and Stefanos Zafeiriou. SpiralNet++: A fast and highly efficient mesh convolution operator. In Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2019, 2, 6" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 32, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "text", + "content": "[15] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. 3D-CODED: 3D correspondences by deep deformation. In Eur. Conf. Comput. Vis., 2018. 4" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 118, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 150 + ], + "type": "text", + "content": "[16] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Adv. Neural Inform. Process. Syst., 2017. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 152, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 184 + ], + "type": "text", + "content": "[17] Mikael Henaff, Joan Bruna, and Yann LeCun. Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163, 2015. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 186, + 545, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 545, + 228 + ], + "type": "text", + "content": "[18] Boyi Jiang, Juyong Zhang, Jianfei Cai, and Jianmin Zheng. Disentangled human body embedding based on deep hierarchical neural network. IEEE Trans. Vis. Comput. Graph., 2020. 1, 2, 3, 6, 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 231, + 545, + 263 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 231, + 545, + 263 + ], + "spans": [ + { + "bbox": [ + 307, + 231, + 545, + 263 + ], + "type": "text", + "content": "[19] Angjoo Kanazawa, Michael J. Black, David W. Jacobs, and Jitendra Malik. End-to-end recovery of human shape and pose. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 265, + 545, + 286 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 265, + 545, + 286 + ], + "spans": [ + { + "bbox": [ + 307, + 265, + 545, + 286 + ], + "type": "text", + "content": "[20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. 2015. 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 288, + 545, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 288, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 307, + 288, + 545, + 319 + ], + "type": "text", + "content": "[21] Linjie Li, Zhe Gan, Yu Cheng, and Jingjing Liu. Relation-aware graph attention network for visual question answering. In Int. Conf. Comput. Vis., 2019. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 322, + 545, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 322, + 545, + 364 + ], + "spans": [ + { + "bbox": [ + 307, + 322, + 545, + 364 + ], + "type": "text", + "content": "[22] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. SMPL: A skinned multiperson linear model. ACM Trans. Graph., 2015. 1, 2, 4, 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 366, + 545, + 420 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 366, + 545, + 420 + ], + "spans": [ + { + "bbox": [ + 307, + 366, + 545, + 420 + ], + "type": "text", + "content": "[23] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. PyTorch: An imperative style, high-performance deep learning library. 2019. 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 422, + 545, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 422, + 545, + 476 + ], + "spans": [ + { + "bbox": [ + 307, + 422, + 545, + 476 + ], + "type": "text", + "content": "[24] Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed AA Osman, Dimitrios Tzionas, and Michael J Black. Expressive body capture: 3D hands, face, and body from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., 2019. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 478, + 545, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 478, + 545, + 520 + ], + "spans": [ + { + "bbox": [ + 307, + 478, + 545, + 520 + ], + "type": "text", + "content": "[25] Leonid Pishchulin, Stefanie Wuhrer, Thomas Helten, Christian Theobalt, and Bernt Schiele. Building statistical shape spaces for 3D human modeling. Pattern Recognition, 2017. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 523, + 545, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 523, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 307, + 523, + 545, + 555 + ], + "type": "text", + "content": "[26] Anurag Ranjan, Timo Bolkart, Soubhik Sanyal, and Michael J Black. Generating 3D faces using convolutional mesh autoencoders. In Eur. Conf. Comput. Vis., 2018. 2, 6" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 557, + 545, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 557, + 545, + 599 + ], + "spans": [ + { + "bbox": [ + 307, + 557, + 545, + 599 + ], + "type": "text", + "content": "[27] Kathleen M Robinette, Hans Daanen, and Eric Paquet. The caesar project: a 3-d surface anthropometry survey. In International Conference on 3-D Digital Imaging and Modeling, 1999. 6" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 601, + 545, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 601, + 545, + 634 + ], + "spans": [ + { + "bbox": [ + 307, + 601, + 545, + 634 + ], + "type": "text", + "content": "[28] Javier Romero, Dimitrios Tzionas, and Michael J Black. Embodied hands: Modeling and capturing hands and bodies together. ACM Trans. Graph., 2017. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 307, + 636, + 545, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 636, + 545, + 678 + ], + "spans": [ + { + "bbox": [ + 307, + 636, + 545, + 678 + ], + "type": "text", + "content": "[29] Hyewon Seo and Nadia Magnenat-Thalmann. An automatic modeling of human bodies from sizing parameters. In Proceedings of the Symposium on Interactive 3D Graphics, 2003. 1" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 307, + 680, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 680, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 680, + 545, + 713 + ], + "type": "text", + "content": "[30] Qingyang Tan, Lin Gao, Yu-Kun Lai, and Shihong Xia. Variational autoencoders for deforming 3D mesh models. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 1, 2" + } + ] + } + ], + "index": 31 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16993" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 286, + 465 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 48, + 72, + 286, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 286, + 106 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 286, + 106 + ], + "type": "text", + "content": "[31] Qingyang Tan, Lin Gao, Yu-Kun Lai, Jie Yang, and Shihong Xia. Mesh-based autoencoders for localized deformation component analysis. In AAAI, 2018. 1, 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 107, + 286, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 107, + 286, + 140 + ], + "spans": [ + { + "bbox": [ + 49, + 107, + 286, + 140 + ], + "type": "text", + "content": "[32] Yating Tian, Hongwen Zhang, Yebin Liu, and Limin Wang. Recovering 3D human mesh from monocular images: A survey. arXiv preprint arXiv:2203.01923, 2022. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 141, + 286, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 141, + 286, + 173 + ], + "spans": [ + { + "bbox": [ + 49, + 141, + 286, + 173 + ], + "type": "text", + "content": "[33] Nitika Verma, Edmond Boyer, and Jakob Verbeek. Dynamic filters in graph convolutional networks. arXiv preprint arXiv:1706.05206, 2017. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 175, + 286, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 175, + 286, + 218 + ], + "spans": [ + { + "bbox": [ + 49, + 175, + 286, + 218 + ], + "type": "text", + "content": "[34] Jiashun Wang, Chao Wen, Yanwei Fu, Haitao Lin, Tianyun Zou, Xiangyang Xue, and Yinda Zhang. Neural pose transfer by spatially adaptive instance normalization. In IEEE Conf. Comput. Vis. Pattern Recog., 2020. 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 220, + 286, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 220, + 286, + 262 + ], + "spans": [ + { + "bbox": [ + 49, + 220, + 286, + 262 + ], + "type": "text", + "content": "[35] Yipin Yang, Yao Yu, Yu Zhou, Sidan Du, James Davis, and Ruigang Yang. Semantic parametric reshaping of human body models. In International Conference on 3D Vision, 2014. 1, 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 264, + 286, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 264, + 286, + 308 + ], + "spans": [ + { + "bbox": [ + 49, + 264, + 286, + 308 + ], + "type": "text", + "content": "[36] Yanhong Zeng, Jianlong Fu, and Hongyang Chao. 3D human body reshaping with anthropometric modeling. In International Conference on Internet Multimedia Computing and Service, 2017. 1, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 309, + 286, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 309, + 286, + 353 + ], + "spans": [ + { + "bbox": [ + 49, + 309, + 286, + 353 + ], + "type": "text", + "content": "[37] Hongwen Zhang, Yating Tian, Xinchi Zhou, Wanli Ouyang, Yebin Liu, Limin Wang, and Zhenan Sun. PyMAF: 3D human pose and shape regression with pyramidal mesh alignment feedback loop. In Int. Conf. Comput. Vis., 2021. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 49, + 354, + 286, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 354, + 286, + 387 + ], + "spans": [ + { + "bbox": [ + 49, + 354, + 286, + 387 + ], + "type": "text", + "content": "[38] Keyang Zhou, Bharat Lal Bhatnagar, and Gerard Pons-Moll. Unsupervised shape and pose disentanglement for 3D meshes. In Eur. Conf. Comput. Vis., 2020. 2, 3, 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 49, + 388, + 286, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 388, + 286, + 421 + ], + "spans": [ + { + "bbox": [ + 49, + 388, + 286, + 421 + ], + "type": "text", + "content": "[39] Shizhe Zhou, Hongbo Fu, Ligang Liu, Daniel Cohen-Or, and Xiaoguang Han. Parametric reshaping of human bodies in images. ACM Trans. Graph., 2010. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 49, + 422, + 286, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 422, + 286, + 465 + ], + "spans": [ + { + "bbox": [ + 49, + 422, + 286, + 465 + ], + "type": "text", + "content": "[40] Silvia Zuffi, Angjoo Kanazawa, David W Jacobs, and Michael J Black. 3D menagerie: Modeling the 3D shape and pose of animals. In IEEE Conf. Comput. Vis. 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Motivation and superiority. (a) The enhancement results (bottom row) without semantic priors show color deviations (e.g., the black car turns gray). (b) Our SKF provides remarkable performance boost on LOL/LOL-v2 datasets in terms of PSNR/SSIM." + ], + "image_footnote": [], + "bbox": [ + 78, + 297, + 480, + 441 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/37ac32e4fcb36b221a276f8bdf22c0d536c464b6dcf1feb1a166cc79b869a813.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 503, + 297, + 687, + 441 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/0a7689bd731a2f436d1bab409d4865cccf297b0e501011386061ec20cf17d89c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 705, + 296, + 888, + 441 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 479, + 313, + 494 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement", + "bbox": [ + 75, + 497, + 473, + 845 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 852, + 209, + 866 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In real world, low-light imaging is fairly common due to unavoidable environmental or technical constraints such as", + "bbox": [ + 76, + 869, + 468, + 898 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "insufficient illumination and limited exposure time. Low-light images not only have poor visibility for human perception, but also are unsuitable for subsequent multimedia computing and downstream vision tasks designed for high-quality images [4, 9, 36]. Thus, low-light image enhancement (LLIE) is proposed to reveal buried details in low-light images and avoid degraded performance in subsequent vision tasks. Mainstream traditional methods for LLIE include Histogram Equalization-based methods [2] and Retinex model-based methods [18].", + "bbox": [ + 496, + 479, + 892, + 632 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recently, many deep learning-based LLIE methods have proposed, such as end-to-end frameworks [5,7,34,45,46,48] and Retinex-based frameworks [29, 41, 43, 44, 49, 53, 54]. Benefiting from their ability in modeling the mapping between the low-light and high-quality image, deep LLIE methods commonly achieve better results than traditional approaches. However, existing methods typically improve low-light images globally and uniformly, without taking into account the semantic information of different regions, which is crucial for enhancement. As shown in Fig. 1(a), a network that lacks the utilization of semantic priors can easily deviate from a region's original hue [22]. Furthermore, studies have demonstrated the significance of incorporating semantic priors into low-light enhancement. Fan et al. [8] utilize semantic map as prior and incorporated it into the feature representation space, thereby enhancing image quality. Rather than relying on optimizing intermediate fea", + "bbox": [ + 496, + 643, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "1662", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "tures, Zheng et al. [58] adopt a novel loss to guarantee the semantic consistency of the enhanced images. These methods successfully combine the semantic priors with LLIE task, demonstrating the superiority of semantic constraints and guidance. However, their methods fail to fully exploit the knowledge that semantic segmentation networks can provide, limiting the performance gain by semantic priors. Furthermore, the interaction between segmentation and enhancement is designed for specific methods, limiting the possibility of incorporating semantic guidance into LLIE task. Hence, we wonder two questions: 1. How can we obtain various and available semantic knowledge? 2. How does semantic knowledge contribute to image quality improvement in LLIE task?", + "bbox": [ + 75, + 90, + 472, + 303 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We attempt to answer the first question. First, a semantic segmentation network pre-trained on large-scale datasets is introduced as a semantic knowledge bank (SKB). The SKB can provide richer and more diverse semantic priors to improve the capability of enhancement networks. Second, according to previous works [8, 19, 58], the available priors provided by the SKB primarily consist of intermediate features and semantic maps. Once training a LLIE model, the SKB yields above semantic priors and guides the enhancement process. The priors can not only refine image features by employing techniques like affinity matrices, spatial feature transformations [40], and attention mechanisms, but also guide the design of objective functions by explicitly incorporating regional information into LLIE task [26].", + "bbox": [ + 75, + 305, + 472, + 517 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Then we try to answer the second question. We design a series of novel methods to integrate semantic knowledge into LLIE task based on the above answers, formulating in a novel semantic-aware knowledge-guided framework (SKF). First, we use the High-Resolution Network [38] (HRNet) pre-trained on the PASCAL-Context dataset [35] as the previously mentioned SKB. In order to make use of intermediate features, we develop a semantic-aware embedding (SE) module. It computes the similarity between the reference and target features and employs cross-modal interactions between heterogeneous representations. As a result, we quantify the semantic awareness of image features as a form of attention and embed semantic consistency in enhancement network.", + "bbox": [ + 75, + 520, + 472, + 731 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Second, some methods [20, 55] propose to optimize image enhancement using color histogram in order to preserve the color consistency of the image rather than simply enhancing the brightness globally. The color histogram, on the other hand, is still a global statistical feature that cannot guarantee local consistency. Hence, we propose a semantic-guided color histogram (SCH) loss to refine color consistency. Here, we intend to make use of local geometric information derived from the scene semantics and global color information derived from the content. In addition to guarantee original color of the enhanced image, it can also", + "bbox": [ + 75, + 734, + 472, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "add spatial information to the color histogram, performing a more nuanced color recovery.", + "bbox": [ + 496, + 90, + 890, + 121 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Third, existing loss functions are not well aligned with human perception and fail to capture an image's intrinsic signal structure, resulting in unpleasant visual results. To improve visual quality, EnlightenGAN [16] employs global and local image-content consistency and randomly chooses the local patch. However, the discriminator do not know where the regions are likely to be 'fake'. Thus, we propose a semantic-guided adversarial (SA) loss. Specifically, the ability of the discriminator is improved by using segmentation map to determine the fake areas, which can improve the image quality further.", + "bbox": [ + 496, + 121, + 893, + 287 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The main contributions of our work are as follows:", + "bbox": [ + 517, + 287, + 854, + 301 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a semantic-aware knowledge-guided framework (SKF) to boost the performance of existing methods by jointly maintaining color consistency and improving image quality.", + "- We propose three key techniques to take full advantage of semantic priors provided by semantic knowledge bank (SKB): semantic-aware embedding (SE) module, semantic-guided color histogram (SCH) loss, and semantic-guided adversarial (SA) loss.", + "- We conduct experiments on LOL/LOL-v2 datasets and unpaired datasets. The experimental results demonstrate large performance improvements by our SKF, verifying its effectiveness in resolving the LLIE task." + ], + "bbox": [ + 517, + 304, + 890, + 506 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 510, + 640, + 526 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Low-light Image Enhancement", + "text_level": 1, + "bbox": [ + 500, + 532, + 774, + 547 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Traditional methods. Traditional methods for low-light enhancement include Histogram Equalization-based methods [2] and Retinex model-based methods [18]. The former improve low-light images by extending the dynamic range. The latter decompose a low-light image into reflection and illumination maps and the reflection component is treated as the enhanced image. Such model-based methods require explicit priors to fit data well, but designing proper priors for various scenes is difficult [44].", + "bbox": [ + 496, + 551, + 890, + 688 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Learning-based methods. Recent deep learning-based methods show promising results [15, 29, 43, 44, 53, 54, 56]. We can further divide existing designs into Retinex-based methods and end-to-end methods. Retinex-based methods use deep network to decompose and enhance an image. Wei et al. proposed a two-stage Retinex-based method called Retinex-Net [43]. Inspired by Retinex-Net, Zhang et al. proposed two refined methods, called KinD [54] and KinD++ [53]. Recently, Wu et al. [44] proposed a novel deep unfolding Retinex-based network to further integrate the strengths of model-based and learning-based methods.", + "bbox": [ + 496, + 689, + 890, + 854 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In comparison to Retinex-based method, end-to-end methods directly learning an enhanced result [5-7, 27, 32, 34, 37, 41, 45, 46, 51, 57, 59]. Lore et al. [30] made the", + "bbox": [ + 496, + 854, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "1663", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/61757e88f6d3669113f6efd48f1724280811759bf86dbf4bff6cab4a2c32f22c.jpg", + "image_caption": [ + "Figure 2. Overview of our Semantic-aware Knowledge-guided Framework (SKF). With a pre-trained Segmentation Net, our SKF utilizes semantic priors to improve the enhancement process in two aspects: (a) In feature-level, the multi-scale semantic-aware embedding modules enable cross-modal interactions between semantic features and image features in representation space. (b) In loss-level, the semantic segmentation result is introduced into the computation of color histogram loss and adversarial loss as a guidance." + ], + "image_footnote": [], + "bbox": [ + 81, + 89, + 893, + 375 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "first attempt by proposing a deep autoencoder named LowLight Net (LLNet). Later on, various end-to-end methods are proposed. Physics-based concepts, e.g. Laplacian pyramid [27], local parametric filter [34], Lagrange multiplier [57], De-Bayer-Filter [5], normalization flow [41] and wavelet transform [7], are proposed to improve model interpretability and lead to visually pleasing results. In [16, 17, 48], adversarial learning is introduced to capture the visual properties. In [11], the light enhancement is creatively formulated as a task of image-specific curve estimation using zero-shot learning. In [20, 47, 55], 3D lookup table and color histogram are utilized to preserve the color consistency. However, existing designs focus on optimizing enhancement process, while ignoring the semantic information of different regions. In contrast, we design a SKF with three key techniques to explore the potential of semantic priors and thus produce visually pleasing enhanced results.", + "bbox": [ + 75, + 440, + 472, + 698 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Semantic-Guided Methods", + "text_level": 1, + "bbox": [ + 76, + 710, + 318, + 724 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Recently, semantic-guided methods prove the reliability of semantic priors. These methods could be divided into two kinds: loss-level semantic-guided methods and feature-level semantic-guided methods.", + "bbox": [ + 75, + 731, + 468, + 792 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Loss-level semantic-guided methods. In order to make use of semantic priors, some works focus on utilizing semantic-aware losses as extra objective functions of the original vision tasks. In image denoising [28], image super-resolution [1], low-light image enhancement [58], researchers directly utilized semantic segmentation loss as an extra constraint to guide the training process. Furthermore,", + "bbox": [ + 75, + 795, + 470, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Liang et al. [26] better maintained the details of the images by using a semantic brightness consistency loss.", + "bbox": [ + 496, + 440, + 890, + 470 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Feature-level semantic-guided methods. In comparison to loss-level semantic-guided methods, feature-level semantic-guided methods concentrate on extracting intermediate features from semantic segmentation network and introduce semantic priors in feature representation space to combine with image features. Similar works have been done on image restoration [23], image deraining [24], image super-resolution [40], low-light image enhancement [8], depth estimation [10, 19].", + "bbox": [ + 496, + 470, + 892, + 607 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Existing semantic-guided methods are limited by the insufficient interaction between semantic priors and original tasks. Hence, we propose a semantic-aware framework to fully exploit semantic information both on loss-level and feature-level, including two semantic-guided losses and a semantic-aware embedding module. Specifically, comparing to semantic-guided methods in LLIE task [8, 26, 58], our SKF is appealing in acting as a general framework.", + "bbox": [ + 496, + 607, + 890, + 729 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 500, + 738, + 591, + 753 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Motivation and Overview", + "text_level": 1, + "bbox": [ + 500, + 760, + 732, + 773 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Illumination enhancement is the process of making an underexposed image look better by adjusting the lighting, eliminating noise, and restoring lost details. Semantic priors can provide a wealth of information for improving the enhancement performance. Specifically, semantic priors can help reformulate the existing LLIE methods as a region-aware enhancement framework. In particular, the novel model will blur noises on smooth regions in a simple way,", + "bbox": [ + 496, + 780, + 890, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "1664", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "such as skies, whereas being careful on regions with rich details, such as indoor scene. Furthermore, combining with semantic prior, the color consistency of enhanced image will be carefully preserved. A network that lacks access to semantic priors can easily deviate from a region's original hue [22]. Existing low-light enhancement methods, however, ignore the importance of semantic information and thus have limited capability.", + "bbox": [ + 75, + 90, + 472, + 210 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this paper, we propose a novel SKF, jointly optimizing image features, maintaining regional color consistency and improving image quality. As shown in Fig. 2, semantic priors are provided by SKB and integrated into LLIE task by three key components: SE module, SCH loss and SA loss.", + "bbox": [ + 75, + 212, + 468, + 287 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Problem definition of semantic-aware LLIE. Given a low-light image $I_{l}\\in \\mathbb{R}^{W\\times H\\times 3}$ with width $W$ and height $H$ . Combining with semantic segmentation, the LLIE process can be modeled as two functions, first:", + "bbox": [ + 76, + 287, + 470, + 347 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nM = \\mathbf {F} _ {\\text {s e g m e n t}} \\left(I _ {l}; \\theta_ {s}\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 192, + 354, + 468, + 371 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $M$ is the semantic prior, including segmentation result and intermediate features with multi-scale dimensions. $\\mathbf{F}_{segment}$ represents the pre-trained semantic segmentation network, acting as the SKB, and $\\theta_{s}$ is frozen in training stage. Then $M$ is used as input:", + "bbox": [ + 75, + 375, + 470, + 450 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\widehat {I _ {h}} = \\mathbf {F} _ {\\text {e n h a n c e}} \\left(I _ {l}, M; \\theta_ {e}\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 181, + 454, + 468, + 472 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\widehat{I_h} \\in \\mathbb{R}^{W \\times H \\times 3}$ is the enhanced result and $\\mathbf{F}_{\\text{enhance}}$ represents the enhancement network. During training stage, $\\theta_e$ will be updated by minimizing the objective function with the guidance of $M$ while $\\theta_s$ is fixed:", + "bbox": [ + 75, + 478, + 468, + 542 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\widehat {\\theta} _ {e} = \\operatorname {a r g m i n} \\mathcal {L} \\left(\\widehat {I _ {h}}, I _ {h}, M\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 176, + 549, + 468, + 569 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $I_h \\in \\mathbb{R}^{W \\times H \\times 3}$ is the ground truth, $\\mathcal{L}(\\widehat{I_h}, I_h, M)$ is the objective function of semantic-aware LLIE.", + "bbox": [ + 75, + 570, + 468, + 602 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Semantic-Aware Embedding Module", + "text_level": 1, + "bbox": [ + 76, + 607, + 397, + 625 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "When refining image features with the help of semantic priors, another challenge should be particularly considered is the discrepancy between the two sources. To alleviate this issue, we propose the SE module to refine the image feature maps, as illustrated in Fig. 3. The SE modules are like bridges between Segmentation Net and Enhancement Net (see Fig. 2), establishing connections between two heterogeneous tasks.", + "bbox": [ + 75, + 628, + 468, + 748 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In our framework, we choose HRNet [38] as the SKB due to its exceptional performance and make some task-specific modifications. Besides the semantic map, we utilize output features before the representation head as multiscale semantic priors. For further illustration, three SE modules are shown in Fig. 2, thus we take three semantic/image features $(F_s^b / F_i^b, b = 0,1,2)$ with three spatial resolutions $(H / 2^{4 - b}, W / 2^{4 - b})$ , where $H$ and $W$ are the height and width of the input image. The SE module performs a pixel-wise interaction between $F_s^b$ and $F_i^b$ , and gives", + "bbox": [ + 75, + 750, + 470, + 902 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/d6712df58507d67af038079659715bffde80688d16bccc1e40e882c1de38c525.jpg", + "image_caption": [ + "Figure 3. Architecture of the semantic-aware embedding (SE) module. At the $b^{th}$ decoder layer, SE module transforms the image feature map $F_{i}^{b}$ with the semantic feature map $F_{s}^{b}$ and produces the refined output feature $F_{o}^{b}$ ." + ], + "image_footnote": [], + "bbox": [ + 501, + 87, + 890, + 231 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "the final refined feature map $F_{o}^{b}$ . Details of the learning process are provided below.", + "bbox": [ + 496, + 297, + 890, + 327 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The SE module computes the semantic awareness of the image features through cross-modal similarity and produces a semantic-aware map. We first apply convolution layers to transform $F_{s}^{b}$ and $F_{i}^{b}$ to the same dimension. Next, inspired by Restormer [50], we adopt a transposed-attention mechanism to compute the attention map with a low computational cost. Hence, the semantic-aware attention map is described as follows:", + "bbox": [ + 496, + 328, + 890, + 446 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nA ^ {b} = \\operatorname {S o f t m a x} \\left(W _ {k} \\left(F _ {i} ^ {b}\\right) \\times W _ {q} \\left(F _ {s} ^ {b}\\right) / \\sqrt {C}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 544, + 450, + 890, + 474 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $W_{k}(\\cdot)$ and $W_{q}(\\cdot)$ are convolution layers, $LN$ is layer normalization, $C$ is channel of features. Here, $A^{b} \\in \\mathbb{R}^{C \\times C}$ indicates the semantic-aware attention map, which represents the interrelationship between $F_{i}^{b}$ and $F_{s}^{b}$ . Then we use $A^{b}$ to fabricate image feature $F_{i}^{b}$ as follows:", + "bbox": [ + 496, + 478, + 890, + 555 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nF _ {o} ^ {b} = F N \\left(W _ {v} \\left(F _ {i} ^ {b}\\right) \\times A ^ {b} + F _ {i} ^ {b}\\right), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 586, + 556, + 890, + 575 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $FN$ denotes feed-forward network, $F_{o}^{b}$ is the final refined feature map of $b^{th}$ SE module and becomes the input of $(b + 1)^{th}$ layer of the Enhancement Net decoder.", + "bbox": [ + 496, + 580, + 890, + 628 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Semantic-Guided Color Histogram Loss", + "text_level": 1, + "bbox": [ + 498, + 635, + 843, + 652 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Color histogram carries crucial underlying image statistics and is profitable for learning color representations. DCC-Net [55] uses PCE modules with affinity matrix to match the color histogram and content in feature-level, therefore retaining color consistency of enhanced image. However, color histogram describes a global statistic information, differences in color characteristics between various instances are eliminated. Thus, we propose an intuitive way to achieve local color adjustment, i.e., semantic-guided color histogram (SCH) loss, as shown in Fig. 2. It focuses on adjusting color histogram of each instance, thereby retaining more detailed color information.", + "bbox": [ + 496, + 659, + 890, + 839 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The semantic map is firstly used to divide enhanced result into image patches with different instance labels. Each patch includes a single instance with the same label. Hence, the process of producing patches are defined as follows:", + "bbox": [ + 496, + 839, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "1665", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nP = \\left\\{P ^ {0}, P ^ {1}, \\dots , P ^ {\\text {c l a s s}} \\right\\}, \\quad P ^ {c} = I _ {\\text {o u t}} \\odot I _ {\\text {s e g}} ^ {c}, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 89, + 468, + 109 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\odot$ is the dot product, $I_{out}$ denotes the enhanced result, $I_{seg}^{c}$ denotes the $c^{th}$ channel of the one-hot semantic map, $P^c\\in \\mathbb{R}^{W\\times H\\times 3}$ denotes the $c^{th}$ image patch, $P$ denotes the group of all the patches.", + "bbox": [ + 76, + 114, + 468, + 176 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Due to the discrete nature of color histogram, we approximate the differentiable version inspired by Kernel Density Estimation [3] for model training. Considering the prediction error of the semantic result, pixels that are close to the boundary are not considered. We refine the patch group $P$ to $P^{\\prime}$ without edge pixels, mitigating effects of misclassification. In the case of R channel of the $c^{th}$ image patch $P^{c^{\\prime}}(R)$ , the estimation process is defined as follows:", + "bbox": [ + 76, + 176, + 468, + 297 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nx _ {i j} ^ {h} = x _ {j} - \\frac {i - 0 . 5}{2 5 5}, \\quad x _ {i j} ^ {l} = x _ {j} - \\frac {i + 0 . 5}{2 5 5}, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 127, + 303, + 468, + 334 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $x_{j}$ denotes $j^{th}$ pixel in $P^{c'}(R)$ , $i \\in [0,255]$ denotes pixel intensity. $x_{ij}^{h}$ and $x_{ij}^{l}$ represent higher anchor and lower anchor respectively, which are key variables to estimate histogram as follows:", + "bbox": [ + 76, + 339, + 468, + 400 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} H _ {i} ^ {c} = \\sum_ {j} \\left(S i g m o i d \\left(\\alpha \\cdot x _ {i j} ^ {h}\\right) - S i g m o i d \\left(\\alpha \\cdot x _ {i j} ^ {l}\\right)\\right), (8) \\\\ H ^ {c} = \\left\\{i, H _ {i} ^ {c} \\right\\} _ {i = 0} ^ {2 5 5}, (9) \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 76, + 406, + 468, + 450 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $H^{c}$ denotes the differentiable histogram of $P^{c'}(R)$ , $H_{i}^{c}$ denotes estimated number of pixels with intensity value $i$ . $\\alpha$ is a scaling factor, we experimentally set it to 400 for better estimation. The difference between results of two Sigmoid $(\\cdot)$ denotes the contribution of $x_{j}$ to the number of pixels with intensity value $i$ . Specifically, when $x_{j}$ exactly equal to $i$ , the difference is 1, i.e., $x_{j}$ adds 1 to $H_{i}^{c}$ .", + "bbox": [ + 76, + 455, + 468, + 561 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, we apply $l_{1}$ loss to constrain the estimated differentiable histogram. Therefore, the SCH loss can be described as follows:", + "bbox": [ + 76, + 561, + 468, + 604 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {S C H} = \\sum_ {c} \\| H ^ {c} (\\hat {I _ {h}}) - H ^ {c} (I _ {h}) \\| _ {1}, \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 145, + 611, + 468, + 642 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\hat{I}_h$ and $I_h$ denote output and groundtruth respectively, $H^c (\\cdot)$ denotes the process of histogram estimation.", + "bbox": [ + 76, + 648, + 468, + 679 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Semantic-Guided Adversarial Loss", + "text_level": 1, + "bbox": [ + 76, + 685, + 382, + 698 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Global and local discriminator is used to encourage more realistic results in image inpainting tasks [14, 25]. EnlightenGAN [16] employs this idea as well, but the local patches are selected randomly instead of focusing on fake regions. Therefore we introduce semantic information to guide the discriminator to focus on intriguing regions. To achieve this, we further refine the global and local adversarial loss function respectively by the segmentation map $I_{seg}$ and image patches $P'$ mentioned in Sec. 3.3. Finally, we propose the semantic-guided adversarial (SA) loss.", + "bbox": [ + 76, + 704, + 468, + 854 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For the local adversarial loss, we first use refined patch group $P^{\\prime}$ as candidate fake patches of the output $I_{out}$ . Then, we compare the discriminating result of image patches", + "bbox": [ + 76, + 854, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "among $P^{\\prime}$ , the worst patch is most likely to be 'fake' and can be chosen to update parameters of both discriminator and generator. Hence, the discriminator plausibly uses the semantic priors to find the target fake region $x_{f} \\sim p_{\\text{fake}}$ by itself. While the real patches $x_{r} \\sim p_{\\text{real}}$ are still randomly cropped from real images each time. The local adversarial loss function is defined as:", + "bbox": [ + 496, + 90, + 890, + 195 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\text {l o c a l}} = \\min _ {G} \\max _ {D} \\mathbb {E} _ {x _ {r} \\sim p _ {\\text {r e a l}}} M S E (D \\left(x _ {r}\\right), 0) \\tag {11} \\\\ + \\mathbb {E} _ {x _ {f} \\sim p _ {f a k e}} M S E (D (x _ {f}), 1), \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 527, + 196, + 890, + 239 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nx _ {f} = P ^ {t}, D \\left(P ^ {t}\\right) = \\min \\left(D \\left(P ^ {0}\\right), \\dots , D \\left(P ^ {\\text {c l a s s}}\\right)\\right), \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 514, + 251, + 890, + 268 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $MSE(\\cdot)$ denotes the mean squared error and $P^t$ denotes the target fake patch.", + "bbox": [ + 498, + 271, + 890, + 301 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For the global adversarial loss, we adopt a simple design to achieve semantic-aware guidance when discriminating a fake sample. We concatenate $I_{out}$ and $I_{seg}^{\\prime}$ , which is the output feature before Softmax, as a new $x_{f}$ . The images $x_{r}$ with real distribution are randomly sampled. Finally, the global adversarial loss function is defined as:", + "bbox": [ + 498, + 301, + 890, + 391 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\text {g l o b a l}} = \\min _ {G} \\max _ {D} \\mathbb {E} _ {x _ {r} \\sim p _ {\\text {r e a l}}} M S E (D \\left(x _ {r}\\right), 0) \\tag {13} \\\\ + \\mathbb {E} _ {x _ {f} \\sim p _ {f a k e}} M S E (D (x _ {f}, I _ {s e g} ^ {\\prime}), 1), \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 524, + 395, + 890, + 438 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Therefore, the SA loss can be defined as:", + "bbox": [ + 519, + 441, + 790, + 455 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {S A} = \\mathcal {L} _ {\\text {g l o b a l}} + \\mathcal {L} _ {\\text {l o c a l}}, \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 612, + 460, + 890, + 477 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We define the original loss function of Enhancement Net as $\\mathcal{L}_{\\text{recon}}$ , which may be $l_{1}$ loss, MSE loss, SSIM loss, etc., or their combination according to original setting of each selected method. Thus, the overall loss function of our SKF can be formulated as follows:", + "bbox": [ + 496, + 479, + 890, + 554 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {a l l}} = \\mathcal {L} _ {\\text {r e c o n}} + \\lambda_ {S C H} \\mathcal {L} _ {S C H} + \\lambda_ {S A} \\mathcal {L} _ {S A}, \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 539, + 559, + 890, + 575 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\lambda$ s are weights to balance the loss terms.", + "bbox": [ + 519, + 578, + 828, + 592 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 599, + 632, + 616 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Settings", + "text_level": 1, + "bbox": [ + 500, + 622, + 705, + 638 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. We evaluate the proposed framework on several datasets from various scenes, including LOL [43], LOL-v2 [49], MEF [31], LIME [12], NPE [39] and DICM [21]. The LOL dataset [43] is a real captured dataset including 485 low/normal light image pairs for training and 15 pairs for testing. The LOL-v2 dataset [49] is the real part of LOL-v2, which is larger and more diverse than LOL, including 689 low/normal light pairs for training and 100 pairs for testing. The MEF (17 images), LIME (10 images), NPE (85 images) and DICM (64 images) are real captured datasets including unpaired images.", + "bbox": [ + 496, + 643, + 890, + 809 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Metrics. To evaluate the performance of different LLIE methods with and without our SKF, we use both full-reference and non-reference image quality evaluation metrics. For LOL/LOL-v2 datasets, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) [42], learned perceptual image patch similarity (LPIPS) [52], natural image", + "bbox": [ + 496, + 810, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "1666", + "bbox": [ + 483, + 945, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/8ab4141c50c7c146edc741333b8d9a54f03a135b8d2f1bdcb24447adc66d6c94.jpg", + "table_caption": [ + "Table 1. Quantitative comparison on the LOL [43] and LOL-v2 [49] datasets. $\\uparrow (\\downarrow)$ denotes that, larger (smaller) values lead to better quality. $+\\left(-\\right)$ denotes the improvement (reduction) of performance, corresponding to $\\uparrow (\\downarrow)$ . The bold denotes the best." + ], + "table_footnote": [], + "table_body": "
MethodLOLLOL-v2Param(M)
PSNR ↑SSIM ↑LPIPS ↓NIQE ↓PSNR ↑SSIM ↑LPIPS ↓NIQE ↓
LIME [13] TIP'1616.7600.5600.350-15.2400.470---
Zero-DCE [11] CVPR'2014.8610.5620.3357.76718.0590.5800.3138.0580.33
EnlightGAN [16] TIP'2117.4830.6520.3224.68418.6400.6770.3095.0898.64
ISSR [8] MM'2018.8460.7880.2435.24916.9940.7980.2065.17912.12
MIRNet [51] PAMI'2224.1400.8420.1314.20320.3570.7820.3175.0945.90
RetinexNet [43] BMVC'1816.7700.4620.4748.87318.3710.7230.3655.8490.62
RetinexNet-SKF(Ours)20.418 (+3.648)0.711 (+0.249)0.216 (+0.258)4.211 (+4.662)19.849 (+1.478)0.719 (-0.004)0.255 (+0.110)4.233 (+1.616)0.66
KinD [54] MM'1920.8700.7990.2075.18917.5440.6690.3756.8498.03
KinD-SKF(Ours)21.913 (+1.043)0.835 (+0.036)0.143 (+0.064)5.031 (+0.158)19.821 (+2.277)0.833 (+0.164)0.201 (+0.174)4.778 (+2.071)8.50
DRBN [48] CVPR'2019.8600.8340.1554.79320.1300.8300.1474.9612.21
DRBN-SKF(Ours)22.837 (+2.977)0.841 (+0.007)0.138 (+0.017)4.464 (+0.329)22.441 (+2.311)0.871 (+0.041)0.132 (+0.015)4.460 (+0.501)2.43
KinD++ [53] ICV'2018.9700.8040.1754.76019.0870.8170.1805.0869.63
KinD+-+SKF(Ours)20.363 (+1.393)0.805 (+0.001)0.201 (-0.026)4.142 (+0.618)19.779 (+0.692)0.837 (+0.020)0.178 (+0.002)4.179 (+0.907)10.21
HWMNet [7] ICIP'2224.2400.8520.1145.14120.9280.7980.3595.97066.56
HWMNet(SKF(Ours)25.086 (+0.846)0.860 (+0.008)0.108 (+0.006)4.346 (+0.795)22.490 (+1.562)0.836 (+0.038)0.175 (+0.184)4.683 (+1.288)69.98
SNR-LLIE-Net [46] CVPR'2224.6080.8400.1515.17921.4790.8480.1574.62339.13
SNR-LLIE-Net-SKF(Ours)25.031 (+0.552)0.855 (+0.015)0.113 (+0.038)4.722 (+0.457)21.927 (+0.448)0.842 (-0.006)0.160 (-0.003)3.963 (+0.660)39.44
LLFlow-S [41] AAAT'2224.0600.8600.1365.41225.9220.8600.1736.1504.97
LLFlow-S-SKF(Ours)25.942 (+1.882)0.865 (+0.005)0.125 (+0.011)5.606 (-0.194)28.107 (+2.185)0.884 (+0.024)0.133 (+0.040)5.415 (+0.735)5.26
LLFlow-L [41] AAAT'2224.9990.8700.1175.58226.2000.8880.1375.40637.68
LLFlow-L-SKF(Ours)26.798 (+1.799)0.879 (+0.009)0.105 (+0.012)5.589 (-0.007)28.451 (+2.251)0.905 (+0.017)0.112 (+0.025)5.725 (-0.319)39.91
", + "bbox": [ + 80, + 128, + 888, + 436 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "quality evaluator (NIQE) [33] are employed. For the MEF, LIME, NPE and DICM datasets without paired data, only NIQE is used, as there is no ground-truth.", + "bbox": [ + 75, + 450, + 468, + 497 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Compared Methods. To verify the effectiveness of our designs, we compare our method with a rich collection of SOTA methods for LLIE, including LIME [13], RetinexNet [43], KinD [54], DRBN [48], KinD++ [53], Zero-DCE [11], ISSR [8], EnlightGAN [16], MIRNet [51], HWMNet [7], SNR-LLIE-Net [46], LLFlow [41]. To demonstrate the superiority of our method faithfully, we reasonably select several methods as the baseline networks. Specifically, both the most representative methods including RetinexNet, KinD and KinD++, and three latest methods including HWMNet, SNR-LLIE-Net and LLFlow are selected. Thus, our methods are denoted as RetinexNet-SKF, KinD-SKF, DRBN-SKF, KinD++-SKF, HWMNet-SKF, SNR-LLIE-Net-SKF, LLFlow-S-SKF and LLFlow-L-SKF (small and large version of LLFlow respectively).", + "bbox": [ + 75, + 512, + 470, + 739 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation Details. We conduct our experiments on NVIDIA 3090 GPU and NVIDIA A100 GPU, which are based on the released code of the baseline networks with the same training settings. Specifically, only the last subnets of Retinex-SKF, KinD-SKF and $\\mathrm{KinD} + +$ -SKF are trained with SCH loss and SA loss, while the other subnets are trained with the original loss functions. Furthermore, we do not apply SA loss to LLFlow because there is no enhanced output in training stage. Additionally, SE modules are reasonably located in decoders of all the baseline networks.", + "bbox": [ + 75, + 755, + 470, + 906 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Quantitative Evaluation", + "text_level": 1, + "bbox": [ + 500, + 450, + 720, + 465 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Quantitative results on LOL and LOL-v2 datasets. The evaluation results are shown in Table 1. We can observe that our SKF achieves consistent and significant performance gain over each baseline method. Specifically, our SKF provides an average improvement of 1.750 dB/1.611 dB on LOL/LOL-v2 datasets respectively and this is achieved by introducing the capability of suppressing noise and artifacts and preserving color consistency. Notably, our LLFlow-L-SKF earns PSNR values of 26.798 dB/28.451 dB on LOL/LOL-v2 datasets, establishing a new SOTA. Furthermore, SSIM values achieve similar performance as well. Our SKF yields better SSIM values by an average of 0.041/0.037 on LOL/LOL-v2 datasets, which illustrates that our SKF helps baseline methods restore the luminance and contrast and preserve the structural information with details. Besides, the substantial gain of LPIPS and NIQE provided by our SKF reasonably indicates that human intuition is more closely matched by introducing semantic priors from our designs.", + "bbox": [ + 496, + 473, + 890, + 760 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Quantitative results on MEF, LIME, NPE and DICM datasets. The evaluation results on the MEF, LIME, NPE and DICM datasets are described in Table 2. In general, each method with SKF obtain better NIQE results than baseline on all six datasets except three worse cases of DRBN-SKF and HWMNet-SKF. The RetinexNet-SKF performs the best with NIQE of 3.632 on MEF dataset, while the KinD++-SKF achieves the best performance on other five datasets. Overall, it is notable that our SKF yields an", + "bbox": [ + 496, + 763, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "1667", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/ed1d47dd0d14d20b361f651701641f6ee8f6ef4389a89bea35bf19f993d08ee3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 80, + 89, + 893, + 204 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/40a80b14bd31e010f61b7aa0b0a47a27ab4d0bfe409f256613da8a3bc5a3b950.jpg", + "image_caption": [ + "Figure 4. Visual comparison of baseline methods with and without SKF on LOL dataset. Our SKF enables baseline methods produce images with less noise, more color information and realistic details." + ], + "image_footnote": [], + "bbox": [ + 80, + 210, + 893, + 325 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/916ec48415c2acfa7e5d30b193b4c721062cee7fefefb9eabb27c55863cf4480.jpg", + "image_caption": [ + "Figure 5. Visual comparison of baseline methods with and without SKF on LIME dataset." + ], + "image_footnote": [], + "bbox": [ + 78, + 359, + 890, + 435 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/be6b5503960a8f09ed34acdb0b895736aa76ec5caec88232d443000394c83c65.jpg", + "table_caption": [ + "Table 2. Quantitative comparison on the LOL [43], LOL-v2 [49], MEF [31], LIME [12], NPE [39] and DICM [21] datasets in terms of NIQE, where smaller values lead to better quality." + ], + "table_footnote": [], + "table_body": "
MethodLOLLOL-v2MEFLIMENPEDICM
Input6.74886.79114.26504.43804.31244.2550
RetinexNet [43] BMVC'186.87315.84884.14904.42004.50084.5912
RetinexNet-SKF(Ours)4.21184.23313.63214.07794.01523.6945
KinD [54] MM'195.18916.84904.13444.64184.68963.9371
KinD-SKF(Ours)5.03064.77833.94604.36073.87213.7909
DRBN [48] CVPR'204.79304.96124.09564.40193.92054.0433
DRBN-SKF(Ours)4.46364.45994.08944.33924.01923.8541
KinD++ [53] IUCV'204.76025.08563.74984.37563.98483.7076
KinD+-+SKF(Ours)4.14154.17853.76453.98923.82013.5382
HWMNet [7] ICIP'225.14075.97024.21754.35494.06833.9196
HWMNet-SKF(Ours)4.34604.68264.03124.36993.99424.0760
", + "bbox": [ + 81, + 500, + 468, + 662 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "average gain of 0.519 on NIQE across all the methods and datasets. The better NIQE shows that the methods with our SKF can produce images with more natural textures and become more effective for restoring low-light images.", + "bbox": [ + 75, + 667, + 468, + 729 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Qualitative Evaluation", + "text_level": 1, + "bbox": [ + 76, + 733, + 287, + 748 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The qualitative evaluations on LOL and LIME datasets are shown in Figs. 4 and 5 respectively. As indicated by Fig. 4, our SKF can improve the enhancement capability of baseline methods and generate images with more pleasing perceptual quality. Specifically, the results of RetinexNet are unreal due to the obvious color gap and serious noise, which can be mitigated by our SKF. Compared to results of KinD and $\\mathrm{KinD + + }$ , KinD-SKF and $\\mathrm{KinD + + - }$ SKF resolve the issue of inconsistent lighting and strange white artifacts. For other results, more consistent color and natu", + "bbox": [ + 75, + 750, + 470, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ral details recovery for desk, wall and clothes are achieved by our SKF.", + "bbox": [ + 496, + 455, + 890, + 484 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We further exhibit the visual enhancement results on the LIME dataset in Fig. 5. It can be observed that the methods with our SKF suppress the unnatural halo around the lamp and restore naturalistic color and details. Hence, methods with our SKF yield more visually pleasing results as compared to baselines, supporting our method's excellent performance in quantitative evaluation. More visualization results are provided in the supplementary material.", + "bbox": [ + 496, + 486, + 892, + 608 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation Study", + "text_level": 1, + "bbox": [ + 500, + 611, + 653, + 627 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We conduct ablation studies on LOL dataset to prove the effectiveness of our SKF from various aspects.", + "bbox": [ + 496, + 628, + 890, + 657 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "SCH loss, SA loss and SE module. As shown in Table 3, we conduct experiments of KinD++-SKF, DRBN-SKF and HWMNet-SKF. The addition of SCH loss and SE module improves the PSNR by an average of $0.243\\mathrm{~dB}$ and $0.841\\mathrm{~dB}$ over the baseline respectively. Simultaneously applying SCH loss and SE module further improves the baseline method by yielding an average gain of $1.741\\mathrm{~dB}$ over the baseline. This verifies that more beneficial semantic-aware priors are integrated into enhancement process. Despite that adding SA loss causes minor drops in some full-reference metrics, average gain of 0.292 with NIQE are obtained across all the cases. Therefore, the baseline methods are refined through semantic-aware knowledge by each component, and the total framework leads to a significant performance boost. Additionally, results in Fig. 6 demonstrate that model with SCH loss and SE module can pre", + "bbox": [ + 496, + 659, + 892, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "1668", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/aea890da05f70b663f7c72603eb4c708fc007e6154449199c01018ed455448e4.jpg", + "image_caption": [ + "Figure 6. Visual comparison of DRBN-SKF for investigating the contribution of key techniques of our SKF." + ], + "image_footnote": [], + "bbox": [ + 80, + 89, + 890, + 170 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/249599e330fbbcd5ff023f59abf064a75975ede14eb6b2e13c2654dcbd0ed1a4.jpg", + "table_caption": [ + "Table 3. Ablation study of KinD++-SKF, DRBN-SKF and HWMNet-SKF for investigating the contribution of key techniques of our SKF." + ], + "table_footnote": [], + "table_body": "
SCH lossSA lossSE moduleKinD++-SKFDRBN-SKFHWMNet-SKF
PSNR↑SSIM↑LPIPS↓NIQE↓PSNR↑SSIM↑LPIPS↓NIQE↓PSNR↑SSIM↑LPIPS↓NIQE↓
✓18.9700.8040.1754.76019.8600.8340.1554.79324.2400.8520.1145.141
19.1700.8060.1704.75920.0400.8350.1544.79324.5900.8590.1125.023
✓19.3850.8000.1894.39220.0700.8340.1494.70124.3050.8530.1114.712
✓19.7810.8080.1814.71221.3340.8370.1434.67824.4770.8590.1114.988
✓✓20.6200.8150.1764.53622.5500.8360.1504.58125.1230.8600.1114.711
✓✓✓20.3630.8050.2014.14222.8370.8410.1384.46425.0860.8600.1084.346
", + "bbox": [ + 86, + 215, + 885, + 314 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/aaf094dc4b16aefce41aa93203f71256c03d36b8f3887de0bdbf3d08021561a7.jpg", + "table_caption": [ + "Table 4. Ablation study of HWMNet-SKF for investigating the effect of semantic priors in the loss function." + ], + "table_footnote": [], + "table_body": "
LschLSAPSNR↑SSIM↑LPIPS↓NIQE↓
w/o Sw/ Sw/o SAw/o Sw/ S
24.2400.8520.1145.141
✓✓24.4770.8590.1114.988
✓✓24.5680.8570.1134.613
✓✓24.6680.8590.1114.567
✓✓25.1230.8600.1084.711
✓✓25.0400.8590.1114.546
✓✓25.0860.8600.1084.311
", + "bbox": [ + 86, + 353, + 460, + 452 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/34381dcdcc3df71fea7f99df02add858f8180bf4249ba3c79ccb7802b301fc5c.jpg", + "table_caption": [ + "Table 5. Ablation study for investigating whether the performance improvement comes from semantic priors or more parameters." + ], + "table_footnote": [], + "table_body": "
MethodPSNR ↑SSIM ↑LPIPS ↓Param(M)
HWMNetBaseline24.2400.8520.11466.56
Large24.4450.8530.11569.99
w/ SKF25.0860.8600.10869.98
LLFlow-SBaseline24.0600.8600.1364.97
Large24.1670.8580.1375.38
w/ SKF25.9420.8650.1255.26
LLFlow-LBaseline24.9990.8700.11737.68
Large25.2920.8730.11340.55
w/ SKF26.7980.8790.10539.91
", + "bbox": [ + 109, + 488, + 439, + 609 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "serve color consistency and details and the SA loss reduces fake regions by producing more natural textures.", + "bbox": [ + 76, + 614, + 467, + 643 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Semantic-guided losses. Table 4 lists the results of different settings of losses. The w/o S and w/ S denote calculating the global histogram and our semantic-guided histogram respectively. For SA loss, the w/o SA, w/o S and w/ S denote without SA loss, classic global and local adversarial loss like EnlightGAN [16] and our SA loss. First, the HWMNet-SKF with SCH loss presents better performance, achieving average margin of $0.512\\mathrm{dB}$ improvement on PSNR, indicating the significant capability of SCH loss in preserving color consistency. Furthermore, the average gain of 0.271 on NIQE by adding classic adversarial loss can be attributed to the capability of discriminator to improve visual quality. Finally, our SA loss provides favorable gain of 0.411 on NIQE over the baseline and faithfully demonstrates that the semantic priors help find out fake regions and thus produce more natural images.", + "bbox": [ + 75, + 643, + 467, + 883 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Superiority of semantic priors. We choose HWMNet", + "bbox": [ + 96, + 885, + 467, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "SKF, LLFlow-S-SKF and LLFlow-L-SKF to investigate whether the improvement of performance benefits from semantic priors provided by our SKF or the more parameters of our SE modules. As shown in Table 5, Baseline, Large and w/ SKF denote the original model, original model with more layers or channels and original model with our SKF. Our methods achieve significant improvement by average margin of $1.272\\mathrm{dB}$ on PSNR comparing to large versions with similar number of parameters. Hence, we prove the superiority of the semantic priors instead of extra parameters.", + "bbox": [ + 496, + 321, + 890, + 473 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 491, + 617, + 507 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work has proposed a novel framework for semantic-aware image enhancement, named SKF. The SKF incorporates semantic priors into Enhancement Net to preserve color consistency and visual details by SE module, SCH loss and SA loss. SE module allows image features to perceive rich and spatial information by semantic feature representations. SCH loss offers effective semantic-aware regional constrain for preserving color consistency. SA loss combines global and local adversarial loss and semantic priors to seek target fake region and generates natural results. Extensive experiments show that our SKF achieves superior performance in the case of all six baseline methods, and the LLFlow-L-SKF outperforms all the competitors. However, the improvement is limited when dealing with unknown category, inducing more possibility when improving the capability of identifying unknown instance by SKB. Furthermore, we will also explore the potential of our SKF in other low-level vision tasks.", + "bbox": [ + 496, + 516, + 890, + 787 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements: This work was supported in part by the National Natural Science Foundation of China under grant 62102069, U20B2063 and 62220106008, the Sichuan Science and Technology Program under grant 2022YFG0032, and the China Academy of Space Technology (CAST) Innovation Program. We are also sponsored by CAAI-Huawei MindSpore Open Fund.", + "bbox": [ + 496, + 795, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "1669", + "bbox": [ + 483, + 945, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Andreas Aakerberg, Anders S Johansen, Kamal Nasrollahi, and Thomas B Moeslund. Semantic segmentation guided real-world super-resolution. In WACV, pages 449-458, 2022. 3", + "[2] Mohammad Abdullah-Al-Wadud, Md Hasanul Kabir, M Ali Akber Dewan, and Oksam Chae. A dynamic histogram equalization for image contrast enhancement. TCE, 53:593-600, 2007. 1, 2", + "[3] Mor Avi-Aharon, Assaf Arbelle, and Tammy Riklin Raviv. DeepHist: Differentiable joint and color histogram layers for image-to-image translation. arXiv preprint arXiv:2005.03995, 2020. 5", + "[4] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. ArcFace: Additive angular margin loss for deep face recognition. In CVPR, pages 4690-4699, 2019. 1", + "[5] Xingbo Dong, Wanyan Xu, Zhihui Miao, Lan Ma, Chao Zhang, Jiewen Yang, Zhe Jin, Andrew Beng Jin Teoh, and Jiajun Shen. Abandoning the bayer-filter to see in the dark. In CVPR, pages 17431-17440, 2022. 1, 2, 3", + "[6] Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Burst image restoration and enhancement. In CVPR, pages 5759-5768, 2022. 2", + "[7] Chi-Mao Fan, Tsung-Jung Liu, and Kuan-Hsien Liu. Half wavelet attention on M-Net+ for low-light image enhancement. arXiv preprint arXiv:2203.01296, 2022. 1, 2, 3, 6, 7", + "[8] Minhao Fan, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Integrating semantic segmentation and Retinex model for low-light image enhancement. In ACMMM, pages 2317-2325, 2020. 1, 2, 3, 6", + "[9] Jun Fu, Jing Liu, Hajjie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In CVPR, pages 3146-3154, 2019. 1", + "[10] Vitor Guizilini, Rui Hou, Jie Li, Rares Ambrus, and Adrien Gaidon. Semantically-guided representation learning for self-supervised monocular depth. In ICLR, 2019. 3", + "[11] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In CVPR, pages 1780-1789, 2020. 3, 6", + "[12] Xiaojie Guo. LIME: A method for low-light image enhancement. In ACMMM, pages 87-91, 2016. 5, 7", + "[13] Xiaojie Guo, Yu Li, and Haibin Ling. LIME: Low-light image enhancement via illumination map estimation. TIP, 26:982-993, 2016. 6", + "[14] Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. Globally and locally consistent image completion. ACM Trans. Graph., 36:1-14, 2017. 5", + "[15] Kui Jiang, Zhongyuan Wang, Zheng Wang, Chen Chen, Peng Yi, Tao Lu, and Chia-Wen Lin. Degrade is upgrade: Learning degradation for low-light image enhancement. In AAAI, volume 36, pages 1078-1086, 2022. 2", + "[16] Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang" + ], + "bbox": [ + 78, + 114, + 468, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Wang. EnlightenGAN: Deep light enhancement without paired supervision. TIP, 30:2340-2349, 2021. 2, 3, 5, 6, 8", + "[17] Yeying Jin, Wenhan Yang, and Robby T Tan. Unsupervised night image enhancement: when layer decomposition meets light-effects suppression. arXiv preprint arXiv:2207.10564, 2022.3", + "[18] Daniel J Jobson, Zia-ur Rahman, and Glenn A Woodell. Properties and performance of a center/surround Retinex. TIP, 6:451–462, 1997. 1, 2", + "[19] Hyunyoung Jung, Eunhyeok Park, and Sungjoo Yoo. Fine-grained semantics-aware representation enhancement for self-supervised monocular depth estimation. In ICCV, pages 12642-12652, 2021. 2, 3", + "[20] Bomi Kim, Sunhyeok Lee, Nahyun Kim, Donggon Jang, and Dae-Shik Kim. Learning color representations for low-light image enhancement. In WACV, pages 1455-1463, 2022. 2, 3", + "[21] Chulwoo Lee, Chul Lee, and Chang-Su Kim. Contrast enhancement based on layered difference representation of 2D histograms. TIP, 22:5372-5384, 2013. 5, 7", + "[22] Chongyi Li, Chunle Guo, Ling-Hao Han, Jun Jiang, Ming-Ming Cheng, Jinwei Gu, and Chen Change Loy. Low-light image and video enhancement using deep learning: A survey. TPAMI, 2021. 1, 4", + "[23] Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo, and Lei Zhang. Blind face restoration via deep multi-scale component dictionaries. In ECCV, pages 399-415. Springer, 2020. 3", + "[24] Yi Li, Yi Chang, Changfeng Yu, and Luxin Yan. Close the loop: A unified bottom-up and top-down paradigm for joint image deraining and segmentation. In AAAI, 2022. 3", + "[25] Yijun Li, Sifei Liu, Jimei Yang, and Ming-Hsuan Yang. Generative face completion. In CVPR, pages 3911-3919, 2017. 5", + "[26] Dong Liang, Ling Li, Mingqiang Wei, Shuo Yang, Liyan Zhang, Wenhan Yang, Yun Du, and Huiyu Zhou. Semantically contrastive learning for low-light image enhancement. In AAAI, pages 1555-1563, 2022. 2, 3", + "[27] Seokjae Lim and Wonjun Kim. DSLR: Deep stacked Laplacian restorer for low-light image enhancement. TMM, 23:4272-4284, 2020. 2, 3", + "[28] Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, and Thomas S. Huang. When image denoising meets high-level vision tasks: A deep learning approach. In IJCAI, 2018. 3", + "[29] Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In CVPR, pages 10561-10570, 2021. 1, 2", + "[30] Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. LL-Net: A deep autoencoder approach to natural low-light image enhancement. PR, 61:650–662, 2017. 2", + "[31] Kede Ma, Kai Zeng, and Zhou Wang. Perceptual quality assessment for multi-exposure image fusion. TIP, 24:3345-3356, 2015. 5, 7", + "[32] Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. Toward fast, flexible, and robust low-light image enhancement. In CVPR, pages 5637-5646, 2022. 2" + ], + "bbox": [ + 503, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "1670", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[33] Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a 'completely blind' image quality analyzer. SPL, 20:209-212, 2012. 6", + "[34] Sean Moran, Pierre Marza, Steven McDonagh, Sarah Parisot, and Gregory Slabaugh. DeepLPF: Deep local parametric filters for image enhancement. In CVPR, pages 12826-12835, 2020. 1, 2, 3", + "[35] Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic segmentation in the wild. In CVPR, pages 891–898, 2014. 2", + "[36] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS, 28, 2015. 1", + "[37] Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li. MAXIM: Multi-axis mlp for image processing. In CVPR, pages 5769-5780, 2022. 2", + "[38] Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al. Deep high-resolution representation learning for visual recognition. TPAMI, 43:3349-3364, 2020. 2, 4", + "[39] Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non-uniform illumination images. TIP, 22:3538-3548, 2013. 5, 7", + "[40] Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Recovering realistic texture in image super-resolution by deep spatial feature transform. In CVPR, pages 606-615, 2018. 2, 3", + "[41] Yufei Wang, Renjie Wan, Wenhan Yang, Haoliang Li, LapPui Chau, and Alex Kot. Low-light image enhancement with normalizing flow. In AAAI, volume 36, pages 2604-2612, 2022. 1, 2, 3, 6", + "[42] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: From error visibility to structural similarity. TIP, 13:600-612, 2004. 5", + "[43] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retina decomposition for low-light enhancement. In BMVC, 2018. 1, 2, 5, 6, 7", + "[44] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. URetinex-Net: Retinex-based deep unfolding network for low-light image enhancement. In CVPR, pages 5901–5910, 2022. 1, 2", + "[45] Ke Xu, Xin Yang, Baocai Yin, and Rynson WH Lau. Learning to restore low-light images via decomposition-and-enhancement. In CVPR, pages 2281-2290, 2020. 1, 2", + "[46] Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. SNR-aware low-light image enhancement. In CVPR, pages 17714-17724, 2022. 1, 2, 6", + "[47] Canqian Yang, Meiguang Jin, Xu Jia, Yi Xu, and Ying Chen. AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement. In CVPR, pages 17522-17531, 2022. 3", + "[48] Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to perceptual quality: A semi-" + ], + "bbox": [ + 78, + 90, + 468, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "supervised approach for low-light image enhancement. In CVPR, pages 3063-3072, 2020. 1, 3, 6, 7", + "[49] Wenhan Yang, Wenjing Wang, Haofeng Huang, Shiqi Wang, and Jiaying Liu. Sparse gradient regularized deep Retinex network for robust low-light image enhancement. TIP, 30:2072-2086, 2021. 1, 5, 6, 7", + "[50] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, pages 5728-5739, 2022. 4", + "[51] Syed Waqas Zamir, Aditya Arora, Salman Hameed Khan, Hayat Munawar, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Learning Enriched Features for Fast Image Restoration and Enhancement. TPAMI, 2022. 2, 6", + "[52] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 5", + "[53] Yonghua Zhang, Xiaojie Guo, Jiayi Ma, Wei Liu, and Jiawan Zhang. Beyond brightening low-light images. IJCV, 129:1013-1037, 2021. 1, 2, 6, 7", + "[54] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In ACMMM, pages 1632-1640, 2019. 1, 2, 6, 7", + "[55] Zhao Zhang, Huan Zheng, Richang Hong, Mingliang Xu, Shuicheng Yan, and Meng Wang. Deep color consistent network for low-light image enhancement. In CVPR, pages 1899-1908, 2022. 2, 3, 4", + "[56] Zunjin Zhao, Bangshu Xiong, Lei Wang, Qiaofeng Ou, Lei Yu, and Fa Kuang. RetinexDIP: A unified deep framework for low-light image enhancement. TCSVT, 32:1076-1088, 2021. 2", + "[57] Chuanjun Zheng, Daming Shi, and Wentian Shi. Adaptive unfolding total variation network for low-light image enhancement. In ICCV, pages 4439-4448, 2021. 2, 3", + "[58] Shen Zheng and Gaurav Gupta. Semantic-guided zero-shot learning for low-light image/video enhancement. In WACV, pages 581-590, 2022. 2, 3", + "[59] Minfeng Zhu, Pingbo Pan, Wei Chen, and Yi Yang. EEMEFN: Low-light image enhancement via edge-enhanced multi-exposure fusion network. In AAAI, volume 34, pages 13106-13113, 2020. 2" + ], + "bbox": [ + 501, + 92, + 890, + 686 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "1671", + "bbox": [ + 483, + 944, + 513, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement/23000cae-300d-4cde-a471-90bcf65ddfb8_model.json b/2023/Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement/23000cae-300d-4cde-a471-90bcf65ddfb8_model.json new file mode 100644 index 0000000000000000000000000000000000000000..47f2e2b1de44ffe524e8e675e3effadd79e6c694 --- /dev/null +++ b/2023/Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement/23000cae-300d-4cde-a471-90bcf65ddfb8_model.json @@ -0,0 +1,2321 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.0, + 0.812, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.224, + 0.131, + 0.748, + 0.176 + ], + "angle": 0, + "content": "Learning Semantic-Aware Knowledge Guidance for Low-Light Image Enhancement" + }, + { + "type": "text", + "bbox": [ + 0.104, + 0.203, + 0.865, + 0.257 + ], + "angle": 0, + "content": "Yuhui Wu\\(^{1}\\), Chen Pan\\(^{1}\\), Guoqing Wang\\(^{1*}\\), Yang Yang\\(^{1}\\), Jiwei Wei\\(^{1}\\), Chongyi Li\\(^{2}\\), Heng Tao Shen\\(^{1}\\) \n\\(^{1}\\)Center for Future Media, University of Electronic Science and Technology of China, China \n\\(^{2}\\)S-Lab, Nanyang Technological University, Singapore" + }, + { + "type": "text", + "bbox": [ + 0.19, + 0.26, + 0.775, + 0.275 + ], + "angle": 0, + "content": "wuyuhui132@gmail.com; panchen0103@163.com; gqwang0420@hotmail.com;" + }, + { + "type": "text", + "bbox": [ + 0.092, + 0.278, + 0.872, + 0.292 + ], + "angle": 0, + "content": "dlyyang@gmail.com; mathematic6@gmail.com; chongyi.li@ntu.edu.sg; shenhengtao@hotmail.com" + }, + { + "type": "image", + "bbox": [ + 0.08, + 0.298, + 0.481, + 0.442 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.298, + 0.688, + 0.442 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.706, + 0.297, + 0.889, + 0.442 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.447, + 0.893, + 0.474 + ], + "angle": 0, + "content": "Figure 1. Motivation and superiority. (a) The enhancement results (bottom row) without semantic priors show color deviations (e.g., the black car turns gray). (b) Our SKF provides remarkable performance boost on LOL/LOL-v2 datasets in terms of PSNR/SSIM." + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.481, + 0.314, + 0.495 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.498, + 0.474, + 0.846 + ], + "angle": 0, + "content": "Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement" + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.853, + 0.21, + 0.867 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.47, + 0.9 + ], + "angle": 0, + "content": "In real world, low-light imaging is fairly common due to unavoidable environmental or technical constraints such as" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.481, + 0.893, + 0.633 + ], + "angle": 0, + "content": "insufficient illumination and limited exposure time. Low-light images not only have poor visibility for human perception, but also are unsuitable for subsequent multimedia computing and downstream vision tasks designed for high-quality images [4, 9, 36]. Thus, low-light image enhancement (LLIE) is proposed to reveal buried details in low-light images and avoid degraded performance in subsequent vision tasks. Mainstream traditional methods for LLIE include Histogram Equalization-based methods [2] and Retinex model-based methods [18]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.644, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Recently, many deep learning-based LLIE methods have proposed, such as end-to-end frameworks [5,7,34,45,46,48] and Retinex-based frameworks [29, 41, 43, 44, 49, 53, 54]. Benefiting from their ability in modeling the mapping between the low-light and high-quality image, deep LLIE methods commonly achieve better results than traditional approaches. However, existing methods typically improve low-light images globally and uniformly, without taking into account the semantic information of different regions, which is crucial for enhancement. As shown in Fig. 1(a), a network that lacks the utilization of semantic priors can easily deviate from a region's original hue [22]. Furthermore, studies have demonstrated the significance of incorporating semantic priors into low-light enhancement. Fan et al. [8] utilize semantic map as prior and incorporated it into the feature representation space, thereby enhancing image quality. Rather than relying on optimizing intermediate fea" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1662" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.304 + ], + "angle": 0, + "content": "tures, Zheng et al. [58] adopt a novel loss to guarantee the semantic consistency of the enhanced images. These methods successfully combine the semantic priors with LLIE task, demonstrating the superiority of semantic constraints and guidance. However, their methods fail to fully exploit the knowledge that semantic segmentation networks can provide, limiting the performance gain by semantic priors. Furthermore, the interaction between segmentation and enhancement is designed for specific methods, limiting the possibility of incorporating semantic guidance into LLIE task. Hence, we wonder two questions: 1. How can we obtain various and available semantic knowledge? 2. How does semantic knowledge contribute to image quality improvement in LLIE task?" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.306, + 0.473, + 0.518 + ], + "angle": 0, + "content": "We attempt to answer the first question. First, a semantic segmentation network pre-trained on large-scale datasets is introduced as a semantic knowledge bank (SKB). The SKB can provide richer and more diverse semantic priors to improve the capability of enhancement networks. Second, according to previous works [8, 19, 58], the available priors provided by the SKB primarily consist of intermediate features and semantic maps. Once training a LLIE model, the SKB yields above semantic priors and guides the enhancement process. The priors can not only refine image features by employing techniques like affinity matrices, spatial feature transformations [40], and attention mechanisms, but also guide the design of objective functions by explicitly incorporating regional information into LLIE task [26]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.521, + 0.473, + 0.732 + ], + "angle": 0, + "content": "Then we try to answer the second question. We design a series of novel methods to integrate semantic knowledge into LLIE task based on the above answers, formulating in a novel semantic-aware knowledge-guided framework (SKF). First, we use the High-Resolution Network [38] (HRNet) pre-trained on the PASCAL-Context dataset [35] as the previously mentioned SKB. In order to make use of intermediate features, we develop a semantic-aware embedding (SE) module. It computes the similarity between the reference and target features and employs cross-modal interactions between heterogeneous representations. As a result, we quantify the semantic awareness of image features as a form of attention and embed semantic consistency in enhancement network." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Second, some methods [20, 55] propose to optimize image enhancement using color histogram in order to preserve the color consistency of the image rather than simply enhancing the brightness globally. The color histogram, on the other hand, is still a global statistical feature that cannot guarantee local consistency. Hence, we propose a semantic-guided color histogram (SCH) loss to refine color consistency. Here, we intend to make use of local geometric information derived from the scene semantics and global color information derived from the content. In addition to guarantee original color of the enhanced image, it can also" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.122 + ], + "angle": 0, + "content": "add spatial information to the color histogram, performing a more nuanced color recovery." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.122, + 0.894, + 0.288 + ], + "angle": 0, + "content": "Third, existing loss functions are not well aligned with human perception and fail to capture an image's intrinsic signal structure, resulting in unpleasant visual results. To improve visual quality, EnlightenGAN [16] employs global and local image-content consistency and randomly chooses the local patch. However, the discriminator do not know where the regions are likely to be 'fake'. Thus, we propose a semantic-guided adversarial (SA) loss. Specifically, the ability of the discriminator is improved by using segmentation map to determine the fake areas, which can improve the image quality further." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.289, + 0.855, + 0.302 + ], + "angle": 0, + "content": "The main contributions of our work are as follows:" + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.305, + 0.892, + 0.367 + ], + "angle": 0, + "content": "- We propose a semantic-aware knowledge-guided framework (SKF) to boost the performance of existing methods by jointly maintaining color consistency and improving image quality." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.368, + 0.892, + 0.444 + ], + "angle": 0, + "content": "- We propose three key techniques to take full advantage of semantic priors provided by semantic knowledge bank (SKB): semantic-aware embedding (SE) module, semantic-guided color histogram (SCH) loss, and semantic-guided adversarial (SA) loss." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.446, + 0.892, + 0.507 + ], + "angle": 0, + "content": "- We conduct experiments on LOL/LOL-v2 datasets and unpaired datasets. The experimental results demonstrate large performance improvements by our SKF, verifying its effectiveness in resolving the LLIE task." + }, + { + "type": "list", + "bbox": [ + 0.519, + 0.305, + 0.892, + 0.507 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.511, + 0.642, + 0.527 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.533, + 0.776, + 0.549 + ], + "angle": 0, + "content": "2.1. Low-light Image Enhancement" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.553, + 0.892, + 0.689 + ], + "angle": 0, + "content": "Traditional methods. Traditional methods for low-light enhancement include Histogram Equalization-based methods [2] and Retinex model-based methods [18]. The former improve low-light images by extending the dynamic range. The latter decompose a low-light image into reflection and illumination maps and the reflection component is treated as the enhanced image. Such model-based methods require explicit priors to fit data well, but designing proper priors for various scenes is difficult [44]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.69, + 0.892, + 0.856 + ], + "angle": 0, + "content": "Learning-based methods. Recent deep learning-based methods show promising results [15, 29, 43, 44, 53, 54, 56]. We can further divide existing designs into Retinex-based methods and end-to-end methods. Retinex-based methods use deep network to decompose and enhance an image. Wei et al. proposed a two-stage Retinex-based method called Retinex-Net [43]. Inspired by Retinex-Net, Zhang et al. proposed two refined methods, called KinD [54] and KinD++ [53]. Recently, Wu et al. [44] proposed a novel deep unfolding Retinex-based network to further integrate the strengths of model-based and learning-based methods." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "In comparison to Retinex-based method, end-to-end methods directly learning an enhanced result [5-7, 27, 32, 34, 37, 41, 45, 46, 51, 57, 59]. Lore et al. [30] made the" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1663" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.09, + 0.895, + 0.376 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.38, + 0.895, + 0.437 + ], + "angle": 0, + "content": "Figure 2. Overview of our Semantic-aware Knowledge-guided Framework (SKF). With a pre-trained Segmentation Net, our SKF utilizes semantic priors to improve the enhancement process in two aspects: (a) In feature-level, the multi-scale semantic-aware embedding modules enable cross-modal interactions between semantic features and image features in representation space. (b) In loss-level, the semantic segmentation result is introduced into the computation of color histogram loss and adversarial loss as a guidance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.441, + 0.473, + 0.699 + ], + "angle": 0, + "content": "first attempt by proposing a deep autoencoder named LowLight Net (LLNet). Later on, various end-to-end methods are proposed. Physics-based concepts, e.g. Laplacian pyramid [27], local parametric filter [34], Lagrange multiplier [57], De-Bayer-Filter [5], normalization flow [41] and wavelet transform [7], are proposed to improve model interpretability and lead to visually pleasing results. In [16, 17, 48], adversarial learning is introduced to capture the visual properties. In [11], the light enhancement is creatively formulated as a task of image-specific curve estimation using zero-shot learning. In [20, 47, 55], 3D lookup table and color histogram are utilized to preserve the color consistency. However, existing designs focus on optimizing enhancement process, while ignoring the semantic information of different regions. In contrast, we design a SKF with three key techniques to explore the potential of semantic priors and thus produce visually pleasing enhanced results." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.711, + 0.32, + 0.725 + ], + "angle": 0, + "content": "2.2. Semantic-Guided Methods" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.732, + 0.47, + 0.793 + ], + "angle": 0, + "content": "Recently, semantic-guided methods prove the reliability of semantic priors. These methods could be divided into two kinds: loss-level semantic-guided methods and feature-level semantic-guided methods." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.796, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Loss-level semantic-guided methods. In order to make use of semantic priors, some works focus on utilizing semantic-aware losses as extra objective functions of the original vision tasks. In image denoising [28], image super-resolution [1], low-light image enhancement [58], researchers directly utilized semantic segmentation loss as an extra constraint to guide the training process. Furthermore," + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.441, + 0.892, + 0.472 + ], + "angle": 0, + "content": "Liang et al. [26] better maintained the details of the images by using a semantic brightness consistency loss." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.472, + 0.893, + 0.608 + ], + "angle": 0, + "content": "Feature-level semantic-guided methods. In comparison to loss-level semantic-guided methods, feature-level semantic-guided methods concentrate on extracting intermediate features from semantic segmentation network and introduce semantic priors in feature representation space to combine with image features. Similar works have been done on image restoration [23], image deraining [24], image super-resolution [40], low-light image enhancement [8], depth estimation [10, 19]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.608, + 0.892, + 0.73 + ], + "angle": 0, + "content": "Existing semantic-guided methods are limited by the insufficient interaction between semantic priors and original tasks. Hence, we propose a semantic-aware framework to fully exploit semantic information both on loss-level and feature-level, including two semantic-guided losses and a semantic-aware embedding module. Specifically, comparing to semantic-guided methods in LLIE task [8, 26, 58], our SKF is appealing in acting as a general framework." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.739, + 0.593, + 0.754 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.761, + 0.733, + 0.775 + ], + "angle": 0, + "content": "3.1. Motivation and Overview" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.781, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Illumination enhancement is the process of making an underexposed image look better by adjusting the lighting, eliminating noise, and restoring lost details. Semantic priors can provide a wealth of information for improving the enhancement performance. Specifically, semantic priors can help reformulate the existing LLIE methods as a region-aware enhancement framework. In particular, the novel model will blur noises on smooth regions in a simple way," + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1664" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.212 + ], + "angle": 0, + "content": "such as skies, whereas being careful on regions with rich details, such as indoor scene. Furthermore, combining with semantic prior, the color consistency of enhanced image will be carefully preserved. A network that lacks access to semantic priors can easily deviate from a region's original hue [22]. Existing low-light enhancement methods, however, ignore the importance of semantic information and thus have limited capability." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.213, + 0.47, + 0.288 + ], + "angle": 0, + "content": "In this paper, we propose a novel SKF, jointly optimizing image features, maintaining regional color consistency and improving image quality. As shown in Fig. 2, semantic priors are provided by SKB and integrated into LLIE task by three key components: SE module, SCH loss and SA loss." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.288, + 0.471, + 0.348 + ], + "angle": 0, + "content": "Problem definition of semantic-aware LLIE. Given a low-light image \\(I_{l}\\in \\mathbb{R}^{W\\times H\\times 3}\\) with width \\(W\\) and height \\(H\\). Combining with semantic segmentation, the LLIE process can be modeled as two functions, first:" + }, + { + "type": "equation", + "bbox": [ + 0.194, + 0.355, + 0.47, + 0.372 + ], + "angle": 0, + "content": "\\[\nM = \\mathbf {F} _ {\\text {s e g m e n t}} \\left(I _ {l}; \\theta_ {s}\\right), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.375, + 0.471, + 0.451 + ], + "angle": 0, + "content": "where \\(M\\) is the semantic prior, including segmentation result and intermediate features with multi-scale dimensions. \\(\\mathbf{F}_{segment}\\) represents the pre-trained semantic segmentation network, acting as the SKB, and \\(\\theta_{s}\\) is frozen in training stage. Then \\(M\\) is used as input:" + }, + { + "type": "equation", + "bbox": [ + 0.182, + 0.455, + 0.469, + 0.473 + ], + "angle": 0, + "content": "\\[\n\\widehat {I _ {h}} = \\mathbf {F} _ {\\text {e n h a n c e}} \\left(I _ {l}, M; \\theta_ {e}\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.479, + 0.47, + 0.543 + ], + "angle": 0, + "content": "where \\(\\widehat{I_h} \\in \\mathbb{R}^{W \\times H \\times 3}\\) is the enhanced result and \\(\\mathbf{F}_{\\text{enhance}}\\) represents the enhancement network. During training stage, \\(\\theta_e\\) will be updated by minimizing the objective function with the guidance of \\(M\\) while \\(\\theta_s\\) is fixed:" + }, + { + "type": "equation", + "bbox": [ + 0.178, + 0.55, + 0.469, + 0.57 + ], + "angle": 0, + "content": "\\[\n\\widehat {\\theta} _ {e} = \\operatorname {a r g m i n} \\mathcal {L} \\left(\\widehat {I _ {h}}, I _ {h}, M\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.571, + 0.469, + 0.603 + ], + "angle": 0, + "content": "where \\(I_h \\in \\mathbb{R}^{W \\times H \\times 3}\\) is the ground truth, \\(\\mathcal{L}(\\widehat{I_h}, I_h, M)\\) is the objective function of semantic-aware LLIE." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.608, + 0.398, + 0.625 + ], + "angle": 0, + "content": "3.2. Semantic-Aware Embedding Module" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.629, + 0.47, + 0.749 + ], + "angle": 0, + "content": "When refining image features with the help of semantic priors, another challenge should be particularly considered is the discrepancy between the two sources. To alleviate this issue, we propose the SE module to refine the image feature maps, as illustrated in Fig. 3. The SE modules are like bridges between Segmentation Net and Enhancement Net (see Fig. 2), establishing connections between two heterogeneous tasks." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.471, + 0.903 + ], + "angle": 0, + "content": "In our framework, we choose HRNet [38] as the SKB due to its exceptional performance and make some task-specific modifications. Besides the semantic map, we utilize output features before the representation head as multiscale semantic priors. For further illustration, three SE modules are shown in Fig. 2, thus we take three semantic/image features \\((F_s^b / F_i^b, b = 0,1,2)\\) with three spatial resolutions \\((H / 2^{4 - b}, W / 2^{4 - b})\\), where \\(H\\) and \\(W\\) are the height and width of the input image. The SE module performs a pixel-wise interaction between \\(F_s^b\\) and \\(F_i^b\\), and gives" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.088, + 0.892, + 0.232 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.236, + 0.892, + 0.293 + ], + "angle": 0, + "content": "Figure 3. Architecture of the semantic-aware embedding (SE) module. At the \\( b^{th} \\) decoder layer, SE module transforms the image feature map \\( F_{i}^{b} \\) with the semantic feature map \\( F_{s}^{b} \\) and produces the refined output feature \\( F_{o}^{b} \\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.298, + 0.892, + 0.328 + ], + "angle": 0, + "content": "the final refined feature map \\( F_{o}^{b} \\). Details of the learning process are provided below." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.329, + 0.892, + 0.448 + ], + "angle": 0, + "content": "The SE module computes the semantic awareness of the image features through cross-modal similarity and produces a semantic-aware map. We first apply convolution layers to transform \\( F_{s}^{b} \\) and \\( F_{i}^{b} \\) to the same dimension. Next, inspired by Restormer [50], we adopt a transposed-attention mechanism to compute the attention map with a low computational cost. Hence, the semantic-aware attention map is described as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.545, + 0.451, + 0.892, + 0.476 + ], + "angle": 0, + "content": "\\[\nA ^ {b} = \\operatorname {S o f t m a x} \\left(W _ {k} \\left(F _ {i} ^ {b}\\right) \\times W _ {q} \\left(F _ {s} ^ {b}\\right) / \\sqrt {C}\\right), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.479, + 0.892, + 0.556 + ], + "angle": 0, + "content": "where \\( W_{k}(\\cdot) \\) and \\( W_{q}(\\cdot) \\) are convolution layers, \\( LN \\) is layer normalization, \\( C \\) is channel of features. Here, \\( A^{b} \\in \\mathbb{R}^{C \\times C} \\) indicates the semantic-aware attention map, which represents the interrelationship between \\( F_{i}^{b} \\) and \\( F_{s}^{b} \\). Then we use \\( A^{b} \\) to fabricate image feature \\( F_{i}^{b} \\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.587, + 0.558, + 0.892, + 0.577 + ], + "angle": 0, + "content": "\\[\nF _ {o} ^ {b} = F N \\left(W _ {v} \\left(F _ {i} ^ {b}\\right) \\times A ^ {b} + F _ {i} ^ {b}\\right), \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.581, + 0.892, + 0.629 + ], + "angle": 0, + "content": "where \\(FN\\) denotes feed-forward network, \\(F_{o}^{b}\\) is the final refined feature map of \\(b^{th}\\) SE module and becomes the input of \\((b + 1)^{th}\\) layer of the Enhancement Net decoder." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.636, + 0.844, + 0.653 + ], + "angle": 0, + "content": "3.3. Semantic-Guided Color Histogram Loss" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.66, + 0.892, + 0.84 + ], + "angle": 0, + "content": "Color histogram carries crucial underlying image statistics and is profitable for learning color representations. DCC-Net [55] uses PCE modules with affinity matrix to match the color histogram and content in feature-level, therefore retaining color consistency of enhanced image. However, color histogram describes a global statistic information, differences in color characteristics between various instances are eliminated. Thus, we propose an intuitive way to achieve local color adjustment, i.e., semantic-guided color histogram (SCH) loss, as shown in Fig. 2. It focuses on adjusting color histogram of each instance, thereby retaining more detailed color information." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.892, + 0.901 + ], + "angle": 0, + "content": "The semantic map is firstly used to divide enhanced result into image patches with different instance labels. Each patch includes a single instance with the same label. Hence, the process of producing patches are defined as follows:" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1665" + } + ], + [ + { + "type": "equation", + "bbox": [ + 0.101, + 0.09, + 0.47, + 0.11 + ], + "angle": 0, + "content": "\\[\nP = \\left\\{P ^ {0}, P ^ {1}, \\dots , P ^ {\\text {c l a s s}} \\right\\}, \\quad P ^ {c} = I _ {\\text {o u t}} \\odot I _ {\\text {s e g}} ^ {c}, \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.115, + 0.469, + 0.177 + ], + "angle": 0, + "content": "where \\(\\odot\\) is the dot product, \\(I_{out}\\) denotes the enhanced result, \\(I_{seg}^{c}\\) denotes the \\(c^{th}\\) channel of the one-hot semantic map, \\(P^c\\in \\mathbb{R}^{W\\times H\\times 3}\\) denotes the \\(c^{th}\\) image patch, \\(P\\) denotes the group of all the patches." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.178, + 0.47, + 0.298 + ], + "angle": 0, + "content": "Due to the discrete nature of color histogram, we approximate the differentiable version inspired by Kernel Density Estimation [3] for model training. Considering the prediction error of the semantic result, pixels that are close to the boundary are not considered. We refine the patch group \\(P\\) to \\(P^{\\prime}\\) without edge pixels, mitigating effects of misclassification. In the case of R channel of the \\(c^{th}\\) image patch \\(P^{c^{\\prime}}(R)\\), the estimation process is defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.128, + 0.304, + 0.469, + 0.335 + ], + "angle": 0, + "content": "\\[\nx _ {i j} ^ {h} = x _ {j} - \\frac {i - 0 . 5}{2 5 5}, \\quad x _ {i j} ^ {l} = x _ {j} - \\frac {i + 0 . 5}{2 5 5}, \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.34, + 0.469, + 0.401 + ], + "angle": 0, + "content": "where \\( x_{j} \\) denotes \\( j^{th} \\) pixel in \\( P^{c'}(R) \\), \\( i \\in [0,255] \\) denotes pixel intensity. \\( x_{ij}^{h} \\) and \\( x_{ij}^{l} \\) represent higher anchor and lower anchor respectively, which are key variables to estimate histogram as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.078, + 0.407, + 0.469, + 0.452 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} H _ {i} ^ {c} = \\sum_ {j} \\left(S i g m o i d \\left(\\alpha \\cdot x _ {i j} ^ {h}\\right) - S i g m o i d \\left(\\alpha \\cdot x _ {i j} ^ {l}\\right)\\right), (8) \\\\ H ^ {c} = \\left\\{i, H _ {i} ^ {c} \\right\\} _ {i = 0} ^ {2 5 5}, (9) \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.456, + 0.469, + 0.563 + ], + "angle": 0, + "content": "where \\(H^{c}\\) denotes the differentiable histogram of \\(P^{c'}(R)\\), \\(H_{i}^{c}\\) denotes estimated number of pixels with intensity value \\(i\\). \\(\\alpha\\) is a scaling factor, we experimentally set it to 400 for better estimation. The difference between results of two Sigmoid \\((\\cdot)\\) denotes the contribution of \\(x_{j}\\) to the number of pixels with intensity value \\(i\\). Specifically, when \\(x_{j}\\) exactly equal to \\(i\\), the difference is 1, i.e., \\(x_{j}\\) adds 1 to \\(H_{i}^{c}\\)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.563, + 0.469, + 0.606 + ], + "angle": 0, + "content": "Finally, we apply \\( l_{1} \\) loss to constrain the estimated differentiable histogram. Therefore, the SCH loss can be described as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.146, + 0.612, + 0.469, + 0.643 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {S C H} = \\sum_ {c} \\| H ^ {c} (\\hat {I _ {h}}) - H ^ {c} (I _ {h}) \\| _ {1}, \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.649, + 0.469, + 0.68 + ], + "angle": 0, + "content": "where \\(\\hat{I}_h\\) and \\(I_h\\) denote output and groundtruth respectively, \\(H^c (\\cdot)\\) denotes the process of histogram estimation." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.686, + 0.383, + 0.699 + ], + "angle": 0, + "content": "3.4. Semantic-Guided Adversarial Loss" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.705, + 0.469, + 0.855 + ], + "angle": 0, + "content": "Global and local discriminator is used to encourage more realistic results in image inpainting tasks [14, 25]. EnlightenGAN [16] employs this idea as well, but the local patches are selected randomly instead of focusing on fake regions. Therefore we introduce semantic information to guide the discriminator to focus on intriguing regions. To achieve this, we further refine the global and local adversarial loss function respectively by the segmentation map \\( I_{seg} \\) and image patches \\( P' \\) mentioned in Sec. 3.3. Finally, we propose the semantic-guided adversarial (SA) loss." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.856, + 0.469, + 0.901 + ], + "angle": 0, + "content": "For the local adversarial loss, we first use refined patch group \\(P^{\\prime}\\) as candidate fake patches of the output \\(I_{out}\\). Then, we compare the discriminating result of image patches" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.196 + ], + "angle": 0, + "content": "among \\(P^{\\prime}\\), the worst patch is most likely to be 'fake' and can be chosen to update parameters of both discriminator and generator. Hence, the discriminator plausibly uses the semantic priors to find the target fake region \\(x_{f} \\sim p_{\\text{fake}}\\) by itself. While the real patches \\(x_{r} \\sim p_{\\text{real}}\\) are still randomly cropped from real images each time. The local adversarial loss function is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.529, + 0.197, + 0.892, + 0.24 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {\\text {l o c a l}} = \\min _ {G} \\max _ {D} \\mathbb {E} _ {x _ {r} \\sim p _ {\\text {r e a l}}} M S E (D \\left(x _ {r}\\right), 0) \\tag {11} \\\\ + \\mathbb {E} _ {x _ {f} \\sim p _ {f a k e}} M S E (D (x _ {f}), 1), \\\\ \\end{array}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.515, + 0.252, + 0.892, + 0.27 + ], + "angle": 0, + "content": "\\[\nx _ {f} = P ^ {t}, D \\left(P ^ {t}\\right) = \\min \\left(D \\left(P ^ {0}\\right), \\dots , D \\left(P ^ {\\text {c l a s s}}\\right)\\right), \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.272, + 0.891, + 0.302 + ], + "angle": 0, + "content": "where \\( MSE(\\cdot) \\) denotes the mean squared error and \\( P^t \\) denotes the target fake patch." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.303, + 0.892, + 0.392 + ], + "angle": 0, + "content": "For the global adversarial loss, we adopt a simple design to achieve semantic-aware guidance when discriminating a fake sample. We concatenate \\( I_{out} \\) and \\( I_{seg}^{\\prime} \\), which is the output feature before Softmax, as a new \\( x_{f} \\). The images \\( x_{r} \\) with real distribution are randomly sampled. Finally, the global adversarial loss function is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.526, + 0.396, + 0.892, + 0.439 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {\\text {g l o b a l}} = \\min _ {G} \\max _ {D} \\mathbb {E} _ {x _ {r} \\sim p _ {\\text {r e a l}}} M S E (D \\left(x _ {r}\\right), 0) \\tag {13} \\\\ + \\mathbb {E} _ {x _ {f} \\sim p _ {f a k e}} M S E (D (x _ {f}, I _ {s e g} ^ {\\prime}), 1), \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.52, + 0.443, + 0.792, + 0.457 + ], + "angle": 0, + "content": "Therefore, the SA loss can be defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.613, + 0.462, + 0.891, + 0.478 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {S A} = \\mathcal {L} _ {\\text {g l o b a l}} + \\mathcal {L} _ {\\text {l o c a l}}, \\tag {14}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.481, + 0.892, + 0.555 + ], + "angle": 0, + "content": "We define the original loss function of Enhancement Net as \\(\\mathcal{L}_{\\text{recon}}\\), which may be \\(l_{1}\\) loss, MSE loss, SSIM loss, etc., or their combination according to original setting of each selected method. Thus, the overall loss function of our SKF can be formulated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.54, + 0.56, + 0.891, + 0.576 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {a l l}} = \\mathcal {L} _ {\\text {r e c o n}} + \\lambda_ {S C H} \\mathcal {L} _ {S C H} + \\lambda_ {S A} \\mathcal {L} _ {S A}, \\tag {15}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.52, + 0.579, + 0.83, + 0.593 + ], + "angle": 0, + "content": "where \\(\\lambda\\)s are weights to balance the loss terms." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.6, + 0.633, + 0.617 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.623, + 0.706, + 0.639 + ], + "angle": 0, + "content": "4.1. Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.645, + 0.892, + 0.81 + ], + "angle": 0, + "content": "Datasets. We evaluate the proposed framework on several datasets from various scenes, including LOL [43], LOL-v2 [49], MEF [31], LIME [12], NPE [39] and DICM [21]. The LOL dataset [43] is a real captured dataset including 485 low/normal light image pairs for training and 15 pairs for testing. The LOL-v2 dataset [49] is the real part of LOL-v2, which is larger and more diverse than LOL, including 689 low/normal light pairs for training and 100 pairs for testing. The MEF (17 images), LIME (10 images), NPE (85 images) and DICM (64 images) are real captured datasets including unpaired images." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Metrics. To evaluate the performance of different LLIE methods with and without our SKF, we use both full-reference and non-reference image quality evaluation metrics. For LOL/LOL-v2 datasets, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) [42], learned perceptual image patch similarity (LPIPS) [52], natural image" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "1666" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.089, + 0.892, + 0.119 + ], + "angle": 0, + "content": "Table 1. Quantitative comparison on the LOL [43] and LOL-v2 [49] datasets. \\(\\uparrow (\\downarrow)\\) denotes that, larger (smaller) values lead to better quality. \\(+\\left(-\\right)\\) denotes the improvement (reduction) of performance, corresponding to \\(\\uparrow (\\downarrow)\\). The bold denotes the best." + }, + { + "type": "table", + "bbox": [ + 0.081, + 0.129, + 0.889, + 0.438 + ], + "angle": 0, + "content": "
MethodLOLLOL-v2Param(M)
PSNR ↑SSIM ↑LPIPS ↓NIQE ↓PSNR ↑SSIM ↑LPIPS ↓NIQE ↓
LIME [13] TIP'1616.7600.5600.350-15.2400.470---
Zero-DCE [11] CVPR'2014.8610.5620.3357.76718.0590.5800.3138.0580.33
EnlightGAN [16] TIP'2117.4830.6520.3224.68418.6400.6770.3095.0898.64
ISSR [8] MM'2018.8460.7880.2435.24916.9940.7980.2065.17912.12
MIRNet [51] PAMI'2224.1400.8420.1314.20320.3570.7820.3175.0945.90
RetinexNet [43] BMVC'1816.7700.4620.4748.87318.3710.7230.3655.8490.62
RetinexNet-SKF(Ours)20.418 (+3.648)0.711 (+0.249)0.216 (+0.258)4.211 (+4.662)19.849 (+1.478)0.719 (-0.004)0.255 (+0.110)4.233 (+1.616)0.66
KinD [54] MM'1920.8700.7990.2075.18917.5440.6690.3756.8498.03
KinD-SKF(Ours)21.913 (+1.043)0.835 (+0.036)0.143 (+0.064)5.031 (+0.158)19.821 (+2.277)0.833 (+0.164)0.201 (+0.174)4.778 (+2.071)8.50
DRBN [48] CVPR'2019.8600.8340.1554.79320.1300.8300.1474.9612.21
DRBN-SKF(Ours)22.837 (+2.977)0.841 (+0.007)0.138 (+0.017)4.464 (+0.329)22.441 (+2.311)0.871 (+0.041)0.132 (+0.015)4.460 (+0.501)2.43
KinD++ [53] ICV'2018.9700.8040.1754.76019.0870.8170.1805.0869.63
KinD+-+SKF(Ours)20.363 (+1.393)0.805 (+0.001)0.201 (-0.026)4.142 (+0.618)19.779 (+0.692)0.837 (+0.020)0.178 (+0.002)4.179 (+0.907)10.21
HWMNet [7] ICIP'2224.2400.8520.1145.14120.9280.7980.3595.97066.56
HWMNet(SKF(Ours)25.086 (+0.846)0.860 (+0.008)0.108 (+0.006)4.346 (+0.795)22.490 (+1.562)0.836 (+0.038)0.175 (+0.184)4.683 (+1.288)69.98
SNR-LLIE-Net [46] CVPR'2224.6080.8400.1515.17921.4790.8480.1574.62339.13
SNR-LLIE-Net-SKF(Ours)25.031 (+0.552)0.855 (+0.015)0.113 (+0.038)4.722 (+0.457)21.927 (+0.448)0.842 (-0.006)0.160 (-0.003)3.963 (+0.660)39.44
LLFlow-S [41] AAAT'2224.0600.8600.1365.41225.9220.8600.1736.1504.97
LLFlow-S-SKF(Ours)25.942 (+1.882)0.865 (+0.005)0.125 (+0.011)5.606 (-0.194)28.107 (+2.185)0.884 (+0.024)0.133 (+0.040)5.415 (+0.735)5.26
LLFlow-L [41] AAAT'2224.9990.8700.1175.58226.2000.8880.1375.40637.68
LLFlow-L-SKF(Ours)26.798 (+1.799)0.879 (+0.009)0.105 (+0.012)5.589 (-0.007)28.451 (+2.251)0.905 (+0.017)0.112 (+0.025)5.725 (-0.319)39.91
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.452, + 0.47, + 0.498 + ], + "angle": 0, + "content": "quality evaluator (NIQE) [33] are employed. For the MEF, LIME, NPE and DICM datasets without paired data, only NIQE is used, as there is no ground-truth." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.513, + 0.471, + 0.741 + ], + "angle": 0, + "content": "Compared Methods. To verify the effectiveness of our designs, we compare our method with a rich collection of SOTA methods for LLIE, including LIME [13], RetinexNet [43], KinD [54], DRBN [48], KinD++ [53], Zero-DCE [11], ISSR [8], EnlightGAN [16], MIRNet [51], HWMNet [7], SNR-LLIE-Net [46], LLFlow [41]. To demonstrate the superiority of our method faithfully, we reasonably select several methods as the baseline networks. Specifically, both the most representative methods including RetinexNet, KinD and KinD++, and three latest methods including HWMNet, SNR-LLIE-Net and LLFlow are selected. Thus, our methods are denoted as RetinexNet-SKF, KinD-SKF, DRBN-SKF, KinD++-SKF, HWMNet-SKF, SNR-LLIE-Net-SKF, LLFlow-S-SKF and LLFlow-L-SKF (small and large version of LLFlow respectively)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.756, + 0.471, + 0.907 + ], + "angle": 0, + "content": "Implementation Details. We conduct our experiments on NVIDIA 3090 GPU and NVIDIA A100 GPU, which are based on the released code of the baseline networks with the same training settings. Specifically, only the last subnets of Retinex-SKF, KinD-SKF and \\(\\mathrm{KinD} + +\\)-SKF are trained with SCH loss and SA loss, while the other subnets are trained with the original loss functions. Furthermore, we do not apply SA loss to LLFlow because there is no enhanced output in training stage. Additionally, SE modules are reasonably located in decoders of all the baseline networks." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.451, + 0.722, + 0.467 + ], + "angle": 0, + "content": "4.2. Quantitative Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.474, + 0.892, + 0.761 + ], + "angle": 0, + "content": "Quantitative results on LOL and LOL-v2 datasets. The evaluation results are shown in Table 1. We can observe that our SKF achieves consistent and significant performance gain over each baseline method. Specifically, our SKF provides an average improvement of 1.750 dB/1.611 dB on LOL/LOL-v2 datasets respectively and this is achieved by introducing the capability of suppressing noise and artifacts and preserving color consistency. Notably, our LLFlow-L-SKF earns PSNR values of 26.798 dB/28.451 dB on LOL/LOL-v2 datasets, establishing a new SOTA. Furthermore, SSIM values achieve similar performance as well. Our SKF yields better SSIM values by an average of 0.041/0.037 on LOL/LOL-v2 datasets, which illustrates that our SKF helps baseline methods restore the luminance and contrast and preserve the structural information with details. Besides, the substantial gain of LPIPS and NIQE provided by our SKF reasonably indicates that human intuition is more closely matched by introducing semantic priors from our designs." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Quantitative results on MEF, LIME, NPE and DICM datasets. The evaluation results on the MEF, LIME, NPE and DICM datasets are described in Table 2. In general, each method with SKF obtain better NIQE results than baseline on all six datasets except three worse cases of DRBN-SKF and HWMNet-SKF. The RetinexNet-SKF performs the best with NIQE of 3.632 on MEF dataset, while the KinD++-SKF achieves the best performance on other five datasets. Overall, it is notable that our SKF yields an" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1667" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.09, + 0.894, + 0.205 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.081, + 0.211, + 0.894, + 0.326 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.328, + 0.893, + 0.357 + ], + "angle": 0, + "content": "Figure 4. Visual comparison of baseline methods with and without SKF on LOL dataset. Our SKF enables baseline methods produce images with less noise, more color information and realistic details." + }, + { + "type": "image", + "bbox": [ + 0.08, + 0.361, + 0.892, + 0.436 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.217, + 0.438, + 0.751, + 0.451 + ], + "angle": 0, + "content": "Figure 5. Visual comparison of baseline methods with and without SKF on LIME dataset." + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.455, + 0.47, + 0.497 + ], + "angle": 0, + "content": "Table 2. Quantitative comparison on the LOL [43], LOL-v2 [49], MEF [31], LIME [12], NPE [39] and DICM [21] datasets in terms of NIQE, where smaller values lead to better quality." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.501, + 0.47, + 0.664 + ], + "angle": 0, + "content": "
MethodLOLLOL-v2MEFLIMENPEDICM
Input6.74886.79114.26504.43804.31244.2550
RetinexNet [43] BMVC'186.87315.84884.14904.42004.50084.5912
RetinexNet-SKF(Ours)4.21184.23313.63214.07794.01523.6945
KinD [54] MM'195.18916.84904.13444.64184.68963.9371
KinD-SKF(Ours)5.03064.77833.94604.36073.87213.7909
DRBN [48] CVPR'204.79304.96124.09564.40193.92054.0433
DRBN-SKF(Ours)4.46364.45994.08944.33924.01923.8541
KinD++ [53] IUCV'204.76025.08563.74984.37563.98483.7076
KinD+-+SKF(Ours)4.14154.17853.76453.98923.82013.5382
HWMNet [7] ICIP'225.14075.97024.21754.35494.06833.9196
HWMNet-SKF(Ours)4.34604.68264.03124.36993.99424.0760
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.669, + 0.47, + 0.73 + ], + "angle": 0, + "content": "average gain of 0.519 on NIQE across all the methods and datasets. The better NIQE shows that the methods with our SKF can produce images with more natural textures and become more effective for restoring low-light images." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.734, + 0.288, + 0.749 + ], + "angle": 0, + "content": "4.3. Qualitative Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.471, + 0.901 + ], + "angle": 0, + "content": "The qualitative evaluations on LOL and LIME datasets are shown in Figs. 4 and 5 respectively. As indicated by Fig. 4, our SKF can improve the enhancement capability of baseline methods and generate images with more pleasing perceptual quality. Specifically, the results of RetinexNet are unreal due to the obvious color gap and serious noise, which can be mitigated by our SKF. Compared to results of KinD and \\(\\mathrm{KinD + + }\\), KinD-SKF and \\(\\mathrm{KinD + + - }\\) SKF resolve the issue of inconsistent lighting and strange white artifacts. For other results, more consistent color and natu" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.456, + 0.892, + 0.486 + ], + "angle": 0, + "content": "ral details recovery for desk, wall and clothes are achieved by our SKF." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.487, + 0.893, + 0.609 + ], + "angle": 0, + "content": "We further exhibit the visual enhancement results on the LIME dataset in Fig. 5. It can be observed that the methods with our SKF suppress the unnatural halo around the lamp and restore naturalistic color and details. Hence, methods with our SKF yield more visually pleasing results as compared to baselines, supporting our method's excellent performance in quantitative evaluation. More visualization results are provided in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.612, + 0.655, + 0.628 + ], + "angle": 0, + "content": "4.4. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.629, + 0.892, + 0.658 + ], + "angle": 0, + "content": "We conduct ablation studies on LOL dataset to prove the effectiveness of our SKF from various aspects." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.66, + 0.893, + 0.901 + ], + "angle": 0, + "content": "SCH loss, SA loss and SE module. As shown in Table 3, we conduct experiments of KinD++-SKF, DRBN-SKF and HWMNet-SKF. The addition of SCH loss and SE module improves the PSNR by an average of \\(0.243\\mathrm{~dB}\\) and \\(0.841\\mathrm{~dB}\\) over the baseline respectively. Simultaneously applying SCH loss and SE module further improves the baseline method by yielding an average gain of \\(1.741\\mathrm{~dB}\\) over the baseline. This verifies that more beneficial semantic-aware priors are integrated into enhancement process. Despite that adding SA loss causes minor drops in some full-reference metrics, average gain of 0.292 with NIQE are obtained across all the cases. Therefore, the baseline methods are refined through semantic-aware knowledge by each component, and the total framework leads to a significant performance boost. Additionally, results in Fig. 6 demonstrate that model with SCH loss and SE module can pre" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1668" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.09, + 0.891, + 0.171 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.165, + 0.176, + 0.804, + 0.19 + ], + "angle": 0, + "content": "Figure 6. Visual comparison of DRBN-SKF for investigating the contribution of key techniques of our SKF." + }, + { + "type": "table_caption", + "bbox": [ + 0.078, + 0.198, + 0.891, + 0.212 + ], + "angle": 0, + "content": "Table 3. Ablation study of KinD++-SKF, DRBN-SKF and HWMNet-SKF for investigating the contribution of key techniques of our SKF." + }, + { + "type": "table", + "bbox": [ + 0.088, + 0.216, + 0.887, + 0.315 + ], + "angle": 0, + "content": "
SCH lossSA lossSE moduleKinD++-SKFDRBN-SKFHWMNet-SKF
PSNR↑SSIM↑LPIPS↓NIQE↓PSNR↑SSIM↑LPIPS↓NIQE↓PSNR↑SSIM↑LPIPS↓NIQE↓
✓18.9700.8040.1754.76019.8600.8340.1554.79324.2400.8520.1145.141
19.1700.8060.1704.75920.0400.8350.1544.79324.5900.8590.1125.023
✓19.3850.8000.1894.39220.0700.8340.1494.70124.3050.8530.1114.712
✓19.7810.8080.1814.71221.3340.8370.1434.67824.4770.8590.1114.988
✓✓20.6200.8150.1764.53622.5500.8360.1504.58125.1230.8600.1114.711
✓✓✓20.3630.8050.2014.14222.8370.8410.1384.46425.0860.8600.1084.346
" + }, + { + "type": "table_caption", + "bbox": [ + 0.078, + 0.32, + 0.468, + 0.348 + ], + "angle": 0, + "content": "Table 4. Ablation study of HWMNet-SKF for investigating the effect of semantic priors in the loss function." + }, + { + "type": "table", + "bbox": [ + 0.088, + 0.354, + 0.462, + 0.453 + ], + "angle": 0, + "content": "
LschLSAPSNR↑SSIM↑LPIPS↓NIQE↓
w/o Sw/ Sw/o SAw/o Sw/ S
24.2400.8520.1145.141
✓✓24.4770.8590.1114.988
✓✓24.5680.8570.1134.613
✓✓24.6680.8590.1114.567
✓✓25.1230.8600.1084.711
✓✓25.0400.8590.1114.546
✓✓25.0860.8600.1084.311
" + }, + { + "type": "table_caption", + "bbox": [ + 0.078, + 0.456, + 0.468, + 0.484 + ], + "angle": 0, + "content": "Table 5. Ablation study for investigating whether the performance improvement comes from semantic priors or more parameters." + }, + { + "type": "table", + "bbox": [ + 0.11, + 0.489, + 0.441, + 0.61 + ], + "angle": 0, + "content": "
MethodPSNR ↑SSIM ↑LPIPS ↓Param(M)
HWMNetBaseline24.2400.8520.11466.56
Large24.4450.8530.11569.99
w/ SKF25.0860.8600.10869.98
LLFlow-SBaseline24.0600.8600.1364.97
Large24.1670.8580.1375.38
w/ SKF25.9420.8650.1255.26
LLFlow-LBaseline24.9990.8700.11737.68
Large25.2920.8730.11340.55
w/ SKF26.7980.8790.10539.91
" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.615, + 0.468, + 0.644 + ], + "angle": 0, + "content": "serve color consistency and details and the SA loss reduces fake regions by producing more natural textures." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.645, + 0.468, + 0.885 + ], + "angle": 0, + "content": "Semantic-guided losses. Table 4 lists the results of different settings of losses. The w/o S and w/ S denote calculating the global histogram and our semantic-guided histogram respectively. For SA loss, the w/o SA, w/o S and w/ S denote without SA loss, classic global and local adversarial loss like EnlightGAN [16] and our SA loss. First, the HWMNet-SKF with SCH loss presents better performance, achieving average margin of \\(0.512\\mathrm{dB}\\) improvement on PSNR, indicating the significant capability of SCH loss in preserving color consistency. Furthermore, the average gain of 0.271 on NIQE by adding classic adversarial loss can be attributed to the capability of discriminator to improve visual quality. Finally, our SA loss provides favorable gain of 0.411 on NIQE over the baseline and faithfully demonstrates that the semantic priors help find out fake regions and thus produce more natural images." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.886, + 0.468, + 0.901 + ], + "angle": 0, + "content": "Superiority of semantic priors. We choose HWMNet" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.322, + 0.892, + 0.474 + ], + "angle": 0, + "content": "SKF, LLFlow-S-SKF and LLFlow-L-SKF to investigate whether the improvement of performance benefits from semantic priors provided by our SKF or the more parameters of our SE modules. As shown in Table 5, Baseline, Large and w/ SKF denote the original model, original model with more layers or channels and original model with our SKF. Our methods achieve significant improvement by average margin of \\(1.272\\mathrm{dB}\\) on PSNR comparing to large versions with similar number of parameters. Hence, we prove the superiority of the semantic priors instead of extra parameters." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.492, + 0.619, + 0.508 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.517, + 0.892, + 0.789 + ], + "angle": 0, + "content": "This work has proposed a novel framework for semantic-aware image enhancement, named SKF. The SKF incorporates semantic priors into Enhancement Net to preserve color consistency and visual details by SE module, SCH loss and SA loss. SE module allows image features to perceive rich and spatial information by semantic feature representations. SCH loss offers effective semantic-aware regional constrain for preserving color consistency. SA loss combines global and local adversarial loss and semantic priors to seek target fake region and generates natural results. Extensive experiments show that our SKF achieves superior performance in the case of all six baseline methods, and the LLFlow-L-SKF outperforms all the competitors. However, the improvement is limited when dealing with unknown category, inducing more possibility when improving the capability of identifying unknown instance by SKB. Furthermore, we will also explore the potential of our SKF in other low-level vision tasks." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Acknowledgements: This work was supported in part by the National Natural Science Foundation of China under grant 62102069, U20B2063 and 62220106008, the Sichuan Science and Technology Program under grant 2022YFG0032, and the China Academy of Space Technology (CAST) Innovation Program. We are also sponsored by CAAI-Huawei MindSpore Open Fund." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1669" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.175, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.169 + ], + "angle": 0, + "content": "[1] Andreas Aakerberg, Anders S Johansen, Kamal Nasrollahi, and Thomas B Moeslund. Semantic segmentation guided real-world super-resolution. In WACV, pages 449-458, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.173, + 0.47, + 0.227 + ], + "angle": 0, + "content": "[2] Mohammad Abdullah-Al-Wadud, Md Hasanul Kabir, M Ali Akber Dewan, and Oksam Chae. A dynamic histogram equalization for image contrast enhancement. TCE, 53:593-600, 2007. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.23, + 0.47, + 0.284 + ], + "angle": 0, + "content": "[3] Mor Avi-Aharon, Assaf Arbelle, and Tammy Riklin Raviv. DeepHist: Differentiable joint and color histogram layers for image-to-image translation. arXiv preprint arXiv:2005.03995, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.287, + 0.468, + 0.327 + ], + "angle": 0, + "content": "[4] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. ArcFace: Additive angular margin loss for deep face recognition. In CVPR, pages 4690-4699, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.33, + 0.468, + 0.385 + ], + "angle": 0, + "content": "[5] Xingbo Dong, Wanyan Xu, Zhihui Miao, Lan Ma, Chao Zhang, Jiewen Yang, Zhe Jin, Andrew Beng Jin Teoh, and Jiajun Shen. Abandoning the bayer-filter to see in the dark. In CVPR, pages 17431-17440, 2022. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.387, + 0.468, + 0.441 + ], + "angle": 0, + "content": "[6] Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Burst image restoration and enhancement. In CVPR, pages 5759-5768, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.444, + 0.468, + 0.497 + ], + "angle": 0, + "content": "[7] Chi-Mao Fan, Tsung-Jung Liu, and Kuan-Hsien Liu. Half wavelet attention on M-Net+ for low-light image enhancement. arXiv preprint arXiv:2203.01296, 2022. 1, 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.501, + 0.468, + 0.555 + ], + "angle": 0, + "content": "[8] Minhao Fan, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Integrating semantic segmentation and Retinex model for low-light image enhancement. In ACMMM, pages 2317-2325, 2020. 1, 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.558, + 0.468, + 0.599 + ], + "angle": 0, + "content": "[9] Jun Fu, Jing Liu, Hajjie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In CVPR, pages 3146-3154, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.601, + 0.468, + 0.642 + ], + "angle": 0, + "content": "[10] Vitor Guizilini, Rui Hou, Jie Li, Rares Ambrus, and Adrien Gaidon. Semantically-guided representation learning for self-supervised monocular depth. In ICLR, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.644, + 0.468, + 0.699 + ], + "angle": 0, + "content": "[11] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In CVPR, pages 1780-1789, 2020. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.701, + 0.468, + 0.728 + ], + "angle": 0, + "content": "[12] Xiaojie Guo. LIME: A method for low-light image enhancement. In ACMMM, pages 87-91, 2016. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.731, + 0.468, + 0.77 + ], + "angle": 0, + "content": "[13] Xiaojie Guo, Yu Li, and Haibin Ling. LIME: Low-light image enhancement via illumination map estimation. TIP, 26:982-993, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.773, + 0.468, + 0.814 + ], + "angle": 0, + "content": "[14] Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. Globally and locally consistent image completion. ACM Trans. Graph., 36:1-14, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.817, + 0.468, + 0.871 + ], + "angle": 0, + "content": "[15] Kui Jiang, Zhongyuan Wang, Zheng Wang, Chen Chen, Peng Yi, Tao Lu, and Chia-Wen Lin. Degrade is upgrade: Learning degradation for low-light image enhancement. In AAAI, volume 36, pages 1078-1086, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.468, + 0.902 + ], + "angle": 0, + "content": "[16] Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.47, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.133 + ], + "angle": 0, + "content": "Wang. EnlightenGAN: Deep light enhancement without paired supervision. TIP, 30:2340-2349, 2021. 2, 3, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.135, + 0.892, + 0.188 + ], + "angle": 0, + "content": "[17] Yeying Jin, Wenhan Yang, and Robby T Tan. Unsupervised night image enhancement: when layer decomposition meets light-effects suppression. arXiv preprint arXiv:2207.10564, 2022.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.19, + 0.892, + 0.231 + ], + "angle": 0, + "content": "[18] Daniel J Jobson, Zia-ur Rahman, and Glenn A Woodell. Properties and performance of a center/surround Retinex. TIP, 6:451–462, 1997. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.233, + 0.892, + 0.287 + ], + "angle": 0, + "content": "[19] Hyunyoung Jung, Eunhyeok Park, and Sungjoo Yoo. Fine-grained semantics-aware representation enhancement for self-supervised monocular depth estimation. In ICCV, pages 12642-12652, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.288, + 0.892, + 0.342 + ], + "angle": 0, + "content": "[20] Bomi Kim, Sunhyeok Lee, Nahyun Kim, Donggon Jang, and Dae-Shik Kim. Learning color representations for low-light image enhancement. In WACV, pages 1455-1463, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.344, + 0.892, + 0.384 + ], + "angle": 0, + "content": "[21] Chulwoo Lee, Chul Lee, and Chang-Su Kim. Contrast enhancement based on layered difference representation of 2D histograms. TIP, 22:5372-5384, 2013. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.385, + 0.892, + 0.44 + ], + "angle": 0, + "content": "[22] Chongyi Li, Chunle Guo, Ling-Hao Han, Jun Jiang, Ming-Ming Cheng, Jinwei Gu, and Chen Change Loy. Low-light image and video enhancement using deep learning: A survey. TPAMI, 2021. 1, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.442, + 0.892, + 0.496 + ], + "angle": 0, + "content": "[23] Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo, and Lei Zhang. Blind face restoration via deep multi-scale component dictionaries. In ECCV, pages 399-415. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.498, + 0.892, + 0.537 + ], + "angle": 0, + "content": "[24] Yi Li, Yi Chang, Changfeng Yu, and Luxin Yan. Close the loop: A unified bottom-up and top-down paradigm for joint image deraining and segmentation. In AAAI, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.539, + 0.892, + 0.578 + ], + "angle": 0, + "content": "[25] Yijun Li, Sifei Liu, Jimei Yang, and Ming-Hsuan Yang. Generative face completion. In CVPR, pages 3911-3919, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.581, + 0.892, + 0.635 + ], + "angle": 0, + "content": "[26] Dong Liang, Ling Li, Mingqiang Wei, Shuo Yang, Liyan Zhang, Wenhan Yang, Yun Du, and Huiyu Zhou. Semantically contrastive learning for low-light image enhancement. In AAAI, pages 1555-1563, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.637, + 0.892, + 0.676 + ], + "angle": 0, + "content": "[27] Seokjae Lim and Wonjun Kim. DSLR: Deep stacked Laplacian restorer for low-light image enhancement. TMM, 23:4272-4284, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.679, + 0.892, + 0.719 + ], + "angle": 0, + "content": "[28] Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, and Thomas S. Huang. When image denoising meets high-level vision tasks: A deep learning approach. In IJCAI, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.721, + 0.892, + 0.775 + ], + "angle": 0, + "content": "[29] Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In CVPR, pages 10561-10570, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.776, + 0.892, + 0.816 + ], + "angle": 0, + "content": "[30] Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. LL-Net: A deep autoencoder approach to natural low-light image enhancement. PR, 61:650–662, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.818, + 0.892, + 0.858 + ], + "angle": 0, + "content": "[31] Kede Ma, Kai Zeng, and Zhou Wang. Perceptual quality assessment for multi-exposure image fusion. TIP, 24:3345-3356, 2015. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.86, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[32] Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. Toward fast, flexible, and robust low-light image enhancement. In CVPR, pages 5637-5646, 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "1670" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.133 + ], + "angle": 0, + "content": "[33] Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a 'completely blind' image quality analyzer. SPL, 20:209-212, 2012. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.136, + 0.47, + 0.19 + ], + "angle": 0, + "content": "[34] Sean Moran, Pierre Marza, Steven McDonagh, Sarah Parisot, and Gregory Slabaugh. DeepLPF: Deep local parametric filters for image enhancement. In CVPR, pages 12826-12835, 2020. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.192, + 0.469, + 0.26 + ], + "angle": 0, + "content": "[35] Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic segmentation in the wild. In CVPR, pages 891–898, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.263, + 0.469, + 0.304 + ], + "angle": 0, + "content": "[36] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS, 28, 2015. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.305, + 0.469, + 0.359 + ], + "angle": 0, + "content": "[37] Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li. MAXIM: Multi-axis mlp for image processing. In CVPR, pages 5769-5780, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.362, + 0.469, + 0.43 + ], + "angle": 0, + "content": "[38] Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al. Deep high-resolution representation learning for visual recognition. TPAMI, 43:3349-3364, 2020. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.433, + 0.469, + 0.473 + ], + "angle": 0, + "content": "[39] Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non-uniform illumination images. TIP, 22:3538-3548, 2013. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.476, + 0.469, + 0.53 + ], + "angle": 0, + "content": "[40] Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Recovering realistic texture in image super-resolution by deep spatial feature transform. In CVPR, pages 606-615, 2018. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.532, + 0.469, + 0.586 + ], + "angle": 0, + "content": "[41] Yufei Wang, Renjie Wan, Wenhan Yang, Haoliang Li, LapPui Chau, and Alex Kot. Low-light image enhancement with normalizing flow. In AAAI, volume 36, pages 2604-2612, 2022. 1, 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.589, + 0.469, + 0.63 + ], + "angle": 0, + "content": "[42] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: From error visibility to structural similarity. TIP, 13:600-612, 2004. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.632, + 0.469, + 0.672 + ], + "angle": 0, + "content": "[43] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retina decomposition for low-light enhancement. In BMVC, 2018. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.675, + 0.469, + 0.729 + ], + "angle": 0, + "content": "[44] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. URetinex-Net: Retinex-based deep unfolding network for low-light image enhancement. In CVPR, pages 5901–5910, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.732, + 0.469, + 0.772 + ], + "angle": 0, + "content": "[45] Ke Xu, Xin Yang, Baocai Yin, and Rynson WH Lau. Learning to restore low-light images via decomposition-and-enhancement. In CVPR, pages 2281-2290, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.775, + 0.469, + 0.814 + ], + "angle": 0, + "content": "[46] Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. SNR-aware low-light image enhancement. In CVPR, pages 17714-17724, 2022. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.817, + 0.469, + 0.87 + ], + "angle": 0, + "content": "[47] Canqian Yang, Meiguang Jin, Xu Jia, Yi Xu, and Ying Chen. AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement. In CVPR, pages 17522-17531, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[48] Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to perceptual quality: A semi-" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "supervised approach for low-light image enhancement. In CVPR, pages 3063-3072, 2020. 1, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.122, + 0.892, + 0.176 + ], + "angle": 0, + "content": "[49] Wenhan Yang, Wenjing Wang, Haofeng Huang, Shiqi Wang, and Jiaying Liu. Sparse gradient regularized deep Retinex network for robust low-light image enhancement. TIP, 30:2072-2086, 2021. 1, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.179, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[50] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, pages 5728-5739, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.235, + 0.892, + 0.289 + ], + "angle": 0, + "content": "[51] Syed Waqas Zamir, Aditya Arora, Salman Hameed Khan, Hayat Munawar, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Learning Enriched Features for Fast Image Restoration and Enhancement. TPAMI, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.292, + 0.892, + 0.345 + ], + "angle": 0, + "content": "[52] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.348, + 0.892, + 0.388 + ], + "angle": 0, + "content": "[53] Yonghua Zhang, Xiaojie Guo, Jiayi Ma, Wei Liu, and Jiawan Zhang. Beyond brightening low-light images. IJCV, 129:1013-1037, 2021. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.391, + 0.892, + 0.432 + ], + "angle": 0, + "content": "[54] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In ACMMM, pages 1632-1640, 2019. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.434, + 0.892, + 0.488 + ], + "angle": 0, + "content": "[55] Zhao Zhang, Huan Zheng, Richang Hong, Mingliang Xu, Shuicheng Yan, and Meng Wang. Deep color consistent network for low-light image enhancement. In CVPR, pages 1899-1908, 2022. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.49, + 0.892, + 0.543 + ], + "angle": 0, + "content": "[56] Zunjin Zhao, Bangshu Xiong, Lei Wang, Qiaofeng Ou, Lei Yu, and Fa Kuang. RetinexDIP: A unified deep framework for low-light image enhancement. TCSVT, 32:1076-1088, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.547, + 0.892, + 0.588 + ], + "angle": 0, + "content": "[57] Chuanjun Zheng, Daming Shi, and Wentian Shi. Adaptive unfolding total variation network for low-light image enhancement. In ICCV, pages 4439-4448, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.59, + 0.892, + 0.631 + ], + "angle": 0, + "content": "[58] Shen Zheng and Gaurav Gupta. Semantic-guided zero-shot learning for low-light image/video enhancement. In WACV, pages 581-590, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.633, + 0.892, + 0.687 + ], + "angle": 0, + "content": "[59] Minfeng Zhu, Pingbo Pan, Wei Chen, and Yi Yang. EEMEFN: Low-light image enhancement via edge-enhanced multi-exposure fusion network. 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Motivation and superiority. (a) The enhancement results (bottom row) without semantic priors show color deviations (e.g., the black car turns gray). (b) Our SKF provides remarkable performance boost on LOL/LOL-v2 datasets in terms of PSNR/SSIM. + +![](images/37ac32e4fcb36b221a276f8bdf22c0d536c464b6dcf1feb1a166cc79b869a813.jpg) + +![](images/0a7689bd731a2f436d1bab409d4865cccf297b0e501011386061ec20cf17d89c.jpg) + +# Abstract + +Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement + +# 1. Introduction + +In real world, low-light imaging is fairly common due to unavoidable environmental or technical constraints such as + +insufficient illumination and limited exposure time. Low-light images not only have poor visibility for human perception, but also are unsuitable for subsequent multimedia computing and downstream vision tasks designed for high-quality images [4, 9, 36]. Thus, low-light image enhancement (LLIE) is proposed to reveal buried details in low-light images and avoid degraded performance in subsequent vision tasks. Mainstream traditional methods for LLIE include Histogram Equalization-based methods [2] and Retinex model-based methods [18]. + +Recently, many deep learning-based LLIE methods have proposed, such as end-to-end frameworks [5,7,34,45,46,48] and Retinex-based frameworks [29, 41, 43, 44, 49, 53, 54]. Benefiting from their ability in modeling the mapping between the low-light and high-quality image, deep LLIE methods commonly achieve better results than traditional approaches. However, existing methods typically improve low-light images globally and uniformly, without taking into account the semantic information of different regions, which is crucial for enhancement. As shown in Fig. 1(a), a network that lacks the utilization of semantic priors can easily deviate from a region's original hue [22]. Furthermore, studies have demonstrated the significance of incorporating semantic priors into low-light enhancement. Fan et al. [8] utilize semantic map as prior and incorporated it into the feature representation space, thereby enhancing image quality. Rather than relying on optimizing intermediate fea + +tures, Zheng et al. [58] adopt a novel loss to guarantee the semantic consistency of the enhanced images. These methods successfully combine the semantic priors with LLIE task, demonstrating the superiority of semantic constraints and guidance. However, their methods fail to fully exploit the knowledge that semantic segmentation networks can provide, limiting the performance gain by semantic priors. Furthermore, the interaction between segmentation and enhancement is designed for specific methods, limiting the possibility of incorporating semantic guidance into LLIE task. Hence, we wonder two questions: 1. How can we obtain various and available semantic knowledge? 2. How does semantic knowledge contribute to image quality improvement in LLIE task? + +We attempt to answer the first question. First, a semantic segmentation network pre-trained on large-scale datasets is introduced as a semantic knowledge bank (SKB). The SKB can provide richer and more diverse semantic priors to improve the capability of enhancement networks. Second, according to previous works [8, 19, 58], the available priors provided by the SKB primarily consist of intermediate features and semantic maps. Once training a LLIE model, the SKB yields above semantic priors and guides the enhancement process. The priors can not only refine image features by employing techniques like affinity matrices, spatial feature transformations [40], and attention mechanisms, but also guide the design of objective functions by explicitly incorporating regional information into LLIE task [26]. + +Then we try to answer the second question. We design a series of novel methods to integrate semantic knowledge into LLIE task based on the above answers, formulating in a novel semantic-aware knowledge-guided framework (SKF). First, we use the High-Resolution Network [38] (HRNet) pre-trained on the PASCAL-Context dataset [35] as the previously mentioned SKB. In order to make use of intermediate features, we develop a semantic-aware embedding (SE) module. It computes the similarity between the reference and target features and employs cross-modal interactions between heterogeneous representations. As a result, we quantify the semantic awareness of image features as a form of attention and embed semantic consistency in enhancement network. + +Second, some methods [20, 55] propose to optimize image enhancement using color histogram in order to preserve the color consistency of the image rather than simply enhancing the brightness globally. The color histogram, on the other hand, is still a global statistical feature that cannot guarantee local consistency. Hence, we propose a semantic-guided color histogram (SCH) loss to refine color consistency. Here, we intend to make use of local geometric information derived from the scene semantics and global color information derived from the content. In addition to guarantee original color of the enhanced image, it can also + +add spatial information to the color histogram, performing a more nuanced color recovery. + +Third, existing loss functions are not well aligned with human perception and fail to capture an image's intrinsic signal structure, resulting in unpleasant visual results. To improve visual quality, EnlightenGAN [16] employs global and local image-content consistency and randomly chooses the local patch. However, the discriminator do not know where the regions are likely to be 'fake'. Thus, we propose a semantic-guided adversarial (SA) loss. Specifically, the ability of the discriminator is improved by using segmentation map to determine the fake areas, which can improve the image quality further. + +The main contributions of our work are as follows: + +- We propose a semantic-aware knowledge-guided framework (SKF) to boost the performance of existing methods by jointly maintaining color consistency and improving image quality. +- We propose three key techniques to take full advantage of semantic priors provided by semantic knowledge bank (SKB): semantic-aware embedding (SE) module, semantic-guided color histogram (SCH) loss, and semantic-guided adversarial (SA) loss. +- We conduct experiments on LOL/LOL-v2 datasets and unpaired datasets. The experimental results demonstrate large performance improvements by our SKF, verifying its effectiveness in resolving the LLIE task. + +# 2. Related Work + +# 2.1. Low-light Image Enhancement + +Traditional methods. Traditional methods for low-light enhancement include Histogram Equalization-based methods [2] and Retinex model-based methods [18]. The former improve low-light images by extending the dynamic range. The latter decompose a low-light image into reflection and illumination maps and the reflection component is treated as the enhanced image. Such model-based methods require explicit priors to fit data well, but designing proper priors for various scenes is difficult [44]. + +Learning-based methods. Recent deep learning-based methods show promising results [15, 29, 43, 44, 53, 54, 56]. We can further divide existing designs into Retinex-based methods and end-to-end methods. Retinex-based methods use deep network to decompose and enhance an image. Wei et al. proposed a two-stage Retinex-based method called Retinex-Net [43]. Inspired by Retinex-Net, Zhang et al. proposed two refined methods, called KinD [54] and KinD++ [53]. Recently, Wu et al. [44] proposed a novel deep unfolding Retinex-based network to further integrate the strengths of model-based and learning-based methods. + +In comparison to Retinex-based method, end-to-end methods directly learning an enhanced result [5-7, 27, 32, 34, 37, 41, 45, 46, 51, 57, 59]. Lore et al. [30] made the + +![](images/61757e88f6d3669113f6efd48f1724280811759bf86dbf4bff6cab4a2c32f22c.jpg) +Figure 2. Overview of our Semantic-aware Knowledge-guided Framework (SKF). With a pre-trained Segmentation Net, our SKF utilizes semantic priors to improve the enhancement process in two aspects: (a) In feature-level, the multi-scale semantic-aware embedding modules enable cross-modal interactions between semantic features and image features in representation space. (b) In loss-level, the semantic segmentation result is introduced into the computation of color histogram loss and adversarial loss as a guidance. + +first attempt by proposing a deep autoencoder named LowLight Net (LLNet). Later on, various end-to-end methods are proposed. Physics-based concepts, e.g. Laplacian pyramid [27], local parametric filter [34], Lagrange multiplier [57], De-Bayer-Filter [5], normalization flow [41] and wavelet transform [7], are proposed to improve model interpretability and lead to visually pleasing results. In [16, 17, 48], adversarial learning is introduced to capture the visual properties. In [11], the light enhancement is creatively formulated as a task of image-specific curve estimation using zero-shot learning. In [20, 47, 55], 3D lookup table and color histogram are utilized to preserve the color consistency. However, existing designs focus on optimizing enhancement process, while ignoring the semantic information of different regions. In contrast, we design a SKF with three key techniques to explore the potential of semantic priors and thus produce visually pleasing enhanced results. + +# 2.2. Semantic-Guided Methods + +Recently, semantic-guided methods prove the reliability of semantic priors. These methods could be divided into two kinds: loss-level semantic-guided methods and feature-level semantic-guided methods. + +Loss-level semantic-guided methods. In order to make use of semantic priors, some works focus on utilizing semantic-aware losses as extra objective functions of the original vision tasks. In image denoising [28], image super-resolution [1], low-light image enhancement [58], researchers directly utilized semantic segmentation loss as an extra constraint to guide the training process. Furthermore, + +Liang et al. [26] better maintained the details of the images by using a semantic brightness consistency loss. + +Feature-level semantic-guided methods. In comparison to loss-level semantic-guided methods, feature-level semantic-guided methods concentrate on extracting intermediate features from semantic segmentation network and introduce semantic priors in feature representation space to combine with image features. Similar works have been done on image restoration [23], image deraining [24], image super-resolution [40], low-light image enhancement [8], depth estimation [10, 19]. + +Existing semantic-guided methods are limited by the insufficient interaction between semantic priors and original tasks. Hence, we propose a semantic-aware framework to fully exploit semantic information both on loss-level and feature-level, including two semantic-guided losses and a semantic-aware embedding module. Specifically, comparing to semantic-guided methods in LLIE task [8, 26, 58], our SKF is appealing in acting as a general framework. + +# 3. Method + +# 3.1. Motivation and Overview + +Illumination enhancement is the process of making an underexposed image look better by adjusting the lighting, eliminating noise, and restoring lost details. Semantic priors can provide a wealth of information for improving the enhancement performance. Specifically, semantic priors can help reformulate the existing LLIE methods as a region-aware enhancement framework. In particular, the novel model will blur noises on smooth regions in a simple way, + +such as skies, whereas being careful on regions with rich details, such as indoor scene. Furthermore, combining with semantic prior, the color consistency of enhanced image will be carefully preserved. A network that lacks access to semantic priors can easily deviate from a region's original hue [22]. Existing low-light enhancement methods, however, ignore the importance of semantic information and thus have limited capability. + +In this paper, we propose a novel SKF, jointly optimizing image features, maintaining regional color consistency and improving image quality. As shown in Fig. 2, semantic priors are provided by SKB and integrated into LLIE task by three key components: SE module, SCH loss and SA loss. + +Problem definition of semantic-aware LLIE. Given a low-light image $I_{l}\in \mathbb{R}^{W\times H\times 3}$ with width $W$ and height $H$ . Combining with semantic segmentation, the LLIE process can be modeled as two functions, first: + +$$ +M = \mathbf {F} _ {\text {s e g m e n t}} \left(I _ {l}; \theta_ {s}\right), \tag {1} +$$ + +where $M$ is the semantic prior, including segmentation result and intermediate features with multi-scale dimensions. $\mathbf{F}_{segment}$ represents the pre-trained semantic segmentation network, acting as the SKB, and $\theta_{s}$ is frozen in training stage. Then $M$ is used as input: + +$$ +\widehat {I _ {h}} = \mathbf {F} _ {\text {e n h a n c e}} \left(I _ {l}, M; \theta_ {e}\right), \tag {2} +$$ + +where $\widehat{I_h} \in \mathbb{R}^{W \times H \times 3}$ is the enhanced result and $\mathbf{F}_{\text{enhance}}$ represents the enhancement network. During training stage, $\theta_e$ will be updated by minimizing the objective function with the guidance of $M$ while $\theta_s$ is fixed: + +$$ +\widehat {\theta} _ {e} = \operatorname {a r g m i n} \mathcal {L} \left(\widehat {I _ {h}}, I _ {h}, M\right), \tag {3} +$$ + +where $I_h \in \mathbb{R}^{W \times H \times 3}$ is the ground truth, $\mathcal{L}(\widehat{I_h}, I_h, M)$ is the objective function of semantic-aware LLIE. + +# 3.2. Semantic-Aware Embedding Module + +When refining image features with the help of semantic priors, another challenge should be particularly considered is the discrepancy between the two sources. To alleviate this issue, we propose the SE module to refine the image feature maps, as illustrated in Fig. 3. The SE modules are like bridges between Segmentation Net and Enhancement Net (see Fig. 2), establishing connections between two heterogeneous tasks. + +In our framework, we choose HRNet [38] as the SKB due to its exceptional performance and make some task-specific modifications. Besides the semantic map, we utilize output features before the representation head as multiscale semantic priors. For further illustration, three SE modules are shown in Fig. 2, thus we take three semantic/image features $(F_s^b / F_i^b, b = 0,1,2)$ with three spatial resolutions $(H / 2^{4 - b}, W / 2^{4 - b})$ , where $H$ and $W$ are the height and width of the input image. The SE module performs a pixel-wise interaction between $F_s^b$ and $F_i^b$ , and gives + +![](images/d6712df58507d67af038079659715bffde80688d16bccc1e40e882c1de38c525.jpg) +Figure 3. Architecture of the semantic-aware embedding (SE) module. At the $b^{th}$ decoder layer, SE module transforms the image feature map $F_{i}^{b}$ with the semantic feature map $F_{s}^{b}$ and produces the refined output feature $F_{o}^{b}$ . + +the final refined feature map $F_{o}^{b}$ . Details of the learning process are provided below. + +The SE module computes the semantic awareness of the image features through cross-modal similarity and produces a semantic-aware map. We first apply convolution layers to transform $F_{s}^{b}$ and $F_{i}^{b}$ to the same dimension. Next, inspired by Restormer [50], we adopt a transposed-attention mechanism to compute the attention map with a low computational cost. Hence, the semantic-aware attention map is described as follows: + +$$ +A ^ {b} = \operatorname {S o f t m a x} \left(W _ {k} \left(F _ {i} ^ {b}\right) \times W _ {q} \left(F _ {s} ^ {b}\right) / \sqrt {C}\right), \tag {4} +$$ + +where $W_{k}(\cdot)$ and $W_{q}(\cdot)$ are convolution layers, $LN$ is layer normalization, $C$ is channel of features. Here, $A^{b} \in \mathbb{R}^{C \times C}$ indicates the semantic-aware attention map, which represents the interrelationship between $F_{i}^{b}$ and $F_{s}^{b}$ . Then we use $A^{b}$ to fabricate image feature $F_{i}^{b}$ as follows: + +$$ +F _ {o} ^ {b} = F N \left(W _ {v} \left(F _ {i} ^ {b}\right) \times A ^ {b} + F _ {i} ^ {b}\right), \tag {5} +$$ + +where $FN$ denotes feed-forward network, $F_{o}^{b}$ is the final refined feature map of $b^{th}$ SE module and becomes the input of $(b + 1)^{th}$ layer of the Enhancement Net decoder. + +# 3.3. Semantic-Guided Color Histogram Loss + +Color histogram carries crucial underlying image statistics and is profitable for learning color representations. DCC-Net [55] uses PCE modules with affinity matrix to match the color histogram and content in feature-level, therefore retaining color consistency of enhanced image. However, color histogram describes a global statistic information, differences in color characteristics between various instances are eliminated. Thus, we propose an intuitive way to achieve local color adjustment, i.e., semantic-guided color histogram (SCH) loss, as shown in Fig. 2. It focuses on adjusting color histogram of each instance, thereby retaining more detailed color information. + +The semantic map is firstly used to divide enhanced result into image patches with different instance labels. Each patch includes a single instance with the same label. Hence, the process of producing patches are defined as follows: + +$$ +P = \left\{P ^ {0}, P ^ {1}, \dots , P ^ {\text {c l a s s}} \right\}, \quad P ^ {c} = I _ {\text {o u t}} \odot I _ {\text {s e g}} ^ {c}, \tag {6} +$$ + +where $\odot$ is the dot product, $I_{out}$ denotes the enhanced result, $I_{seg}^{c}$ denotes the $c^{th}$ channel of the one-hot semantic map, $P^c\in \mathbb{R}^{W\times H\times 3}$ denotes the $c^{th}$ image patch, $P$ denotes the group of all the patches. + +Due to the discrete nature of color histogram, we approximate the differentiable version inspired by Kernel Density Estimation [3] for model training. Considering the prediction error of the semantic result, pixels that are close to the boundary are not considered. We refine the patch group $P$ to $P^{\prime}$ without edge pixels, mitigating effects of misclassification. In the case of R channel of the $c^{th}$ image patch $P^{c^{\prime}}(R)$ , the estimation process is defined as follows: + +$$ +x _ {i j} ^ {h} = x _ {j} - \frac {i - 0 . 5}{2 5 5}, \quad x _ {i j} ^ {l} = x _ {j} - \frac {i + 0 . 5}{2 5 5}, \tag {7} +$$ + +where $x_{j}$ denotes $j^{th}$ pixel in $P^{c'}(R)$ , $i \in [0,255]$ denotes pixel intensity. $x_{ij}^{h}$ and $x_{ij}^{l}$ represent higher anchor and lower anchor respectively, which are key variables to estimate histogram as follows: + +$$ +\begin{array}{l} H _ {i} ^ {c} = \sum_ {j} \left(S i g m o i d \left(\alpha \cdot x _ {i j} ^ {h}\right) - S i g m o i d \left(\alpha \cdot x _ {i j} ^ {l}\right)\right), (8) \\ H ^ {c} = \left\{i, H _ {i} ^ {c} \right\} _ {i = 0} ^ {2 5 5}, (9) \\ \end{array} +$$ + +where $H^{c}$ denotes the differentiable histogram of $P^{c'}(R)$ , $H_{i}^{c}$ denotes estimated number of pixels with intensity value $i$ . $\alpha$ is a scaling factor, we experimentally set it to 400 for better estimation. The difference between results of two Sigmoid $(\cdot)$ denotes the contribution of $x_{j}$ to the number of pixels with intensity value $i$ . Specifically, when $x_{j}$ exactly equal to $i$ , the difference is 1, i.e., $x_{j}$ adds 1 to $H_{i}^{c}$ . + +Finally, we apply $l_{1}$ loss to constrain the estimated differentiable histogram. Therefore, the SCH loss can be described as follows: + +$$ +\mathcal {L} _ {S C H} = \sum_ {c} \| H ^ {c} (\hat {I _ {h}}) - H ^ {c} (I _ {h}) \| _ {1}, \tag {10} +$$ + +where $\hat{I}_h$ and $I_h$ denote output and groundtruth respectively, $H^c (\cdot)$ denotes the process of histogram estimation. + +# 3.4. Semantic-Guided Adversarial Loss + +Global and local discriminator is used to encourage more realistic results in image inpainting tasks [14, 25]. EnlightenGAN [16] employs this idea as well, but the local patches are selected randomly instead of focusing on fake regions. Therefore we introduce semantic information to guide the discriminator to focus on intriguing regions. To achieve this, we further refine the global and local adversarial loss function respectively by the segmentation map $I_{seg}$ and image patches $P'$ mentioned in Sec. 3.3. Finally, we propose the semantic-guided adversarial (SA) loss. + +For the local adversarial loss, we first use refined patch group $P^{\prime}$ as candidate fake patches of the output $I_{out}$ . Then, we compare the discriminating result of image patches + +among $P^{\prime}$ , the worst patch is most likely to be 'fake' and can be chosen to update parameters of both discriminator and generator. Hence, the discriminator plausibly uses the semantic priors to find the target fake region $x_{f} \sim p_{\text{fake}}$ by itself. While the real patches $x_{r} \sim p_{\text{real}}$ are still randomly cropped from real images each time. The local adversarial loss function is defined as: + +$$ +\begin{array}{l} \mathcal {L} _ {\text {l o c a l}} = \min _ {G} \max _ {D} \mathbb {E} _ {x _ {r} \sim p _ {\text {r e a l}}} M S E (D \left(x _ {r}\right), 0) \tag {11} \\ + \mathbb {E} _ {x _ {f} \sim p _ {f a k e}} M S E (D (x _ {f}), 1), \\ \end{array} +$$ + +$$ +x _ {f} = P ^ {t}, D \left(P ^ {t}\right) = \min \left(D \left(P ^ {0}\right), \dots , D \left(P ^ {\text {c l a s s}}\right)\right), \tag {12} +$$ + +where $MSE(\cdot)$ denotes the mean squared error and $P^t$ denotes the target fake patch. + +For the global adversarial loss, we adopt a simple design to achieve semantic-aware guidance when discriminating a fake sample. We concatenate $I_{out}$ and $I_{seg}^{\prime}$ , which is the output feature before Softmax, as a new $x_{f}$ . The images $x_{r}$ with real distribution are randomly sampled. Finally, the global adversarial loss function is defined as: + +$$ +\begin{array}{l} \mathcal {L} _ {\text {g l o b a l}} = \min _ {G} \max _ {D} \mathbb {E} _ {x _ {r} \sim p _ {\text {r e a l}}} M S E (D \left(x _ {r}\right), 0) \tag {13} \\ + \mathbb {E} _ {x _ {f} \sim p _ {f a k e}} M S E (D (x _ {f}, I _ {s e g} ^ {\prime}), 1), \\ \end{array} +$$ + +Therefore, the SA loss can be defined as: + +$$ +\mathcal {L} _ {S A} = \mathcal {L} _ {\text {g l o b a l}} + \mathcal {L} _ {\text {l o c a l}}, \tag {14} +$$ + +We define the original loss function of Enhancement Net as $\mathcal{L}_{\text{recon}}$ , which may be $l_{1}$ loss, MSE loss, SSIM loss, etc., or their combination according to original setting of each selected method. Thus, the overall loss function of our SKF can be formulated as follows: + +$$ +\mathcal {L} _ {\text {a l l}} = \mathcal {L} _ {\text {r e c o n}} + \lambda_ {S C H} \mathcal {L} _ {S C H} + \lambda_ {S A} \mathcal {L} _ {S A}, \tag {15} +$$ + +where $\lambda$ s are weights to balance the loss terms. + +# 4. Experiments + +# 4.1. Experimental Settings + +Datasets. We evaluate the proposed framework on several datasets from various scenes, including LOL [43], LOL-v2 [49], MEF [31], LIME [12], NPE [39] and DICM [21]. The LOL dataset [43] is a real captured dataset including 485 low/normal light image pairs for training and 15 pairs for testing. The LOL-v2 dataset [49] is the real part of LOL-v2, which is larger and more diverse than LOL, including 689 low/normal light pairs for training and 100 pairs for testing. The MEF (17 images), LIME (10 images), NPE (85 images) and DICM (64 images) are real captured datasets including unpaired images. + +Metrics. To evaluate the performance of different LLIE methods with and without our SKF, we use both full-reference and non-reference image quality evaluation metrics. For LOL/LOL-v2 datasets, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) [42], learned perceptual image patch similarity (LPIPS) [52], natural image + +Table 1. Quantitative comparison on the LOL [43] and LOL-v2 [49] datasets. $\uparrow (\downarrow)$ denotes that, larger (smaller) values lead to better quality. $+\left(-\right)$ denotes the improvement (reduction) of performance, corresponding to $\uparrow (\downarrow)$ . The bold denotes the best. + +
MethodLOLLOL-v2Param(M)
PSNR ↑SSIM ↑LPIPS ↓NIQE ↓PSNR ↑SSIM ↑LPIPS ↓NIQE ↓
LIME [13] TIP'1616.7600.5600.350-15.2400.470---
Zero-DCE [11] CVPR'2014.8610.5620.3357.76718.0590.5800.3138.0580.33
EnlightGAN [16] TIP'2117.4830.6520.3224.68418.6400.6770.3095.0898.64
ISSR [8] MM'2018.8460.7880.2435.24916.9940.7980.2065.17912.12
MIRNet [51] PAMI'2224.1400.8420.1314.20320.3570.7820.3175.0945.90
RetinexNet [43] BMVC'1816.7700.4620.4748.87318.3710.7230.3655.8490.62
RetinexNet-SKF(Ours)20.418 (+3.648)0.711 (+0.249)0.216 (+0.258)4.211 (+4.662)19.849 (+1.478)0.719 (-0.004)0.255 (+0.110)4.233 (+1.616)0.66
KinD [54] MM'1920.8700.7990.2075.18917.5440.6690.3756.8498.03
KinD-SKF(Ours)21.913 (+1.043)0.835 (+0.036)0.143 (+0.064)5.031 (+0.158)19.821 (+2.277)0.833 (+0.164)0.201 (+0.174)4.778 (+2.071)8.50
DRBN [48] CVPR'2019.8600.8340.1554.79320.1300.8300.1474.9612.21
DRBN-SKF(Ours)22.837 (+2.977)0.841 (+0.007)0.138 (+0.017)4.464 (+0.329)22.441 (+2.311)0.871 (+0.041)0.132 (+0.015)4.460 (+0.501)2.43
KinD++ [53] ICV'2018.9700.8040.1754.76019.0870.8170.1805.0869.63
KinD+-+SKF(Ours)20.363 (+1.393)0.805 (+0.001)0.201 (-0.026)4.142 (+0.618)19.779 (+0.692)0.837 (+0.020)0.178 (+0.002)4.179 (+0.907)10.21
HWMNet [7] ICIP'2224.2400.8520.1145.14120.9280.7980.3595.97066.56
HWMNet(SKF(Ours)25.086 (+0.846)0.860 (+0.008)0.108 (+0.006)4.346 (+0.795)22.490 (+1.562)0.836 (+0.038)0.175 (+0.184)4.683 (+1.288)69.98
SNR-LLIE-Net [46] CVPR'2224.6080.8400.1515.17921.4790.8480.1574.62339.13
SNR-LLIE-Net-SKF(Ours)25.031 (+0.552)0.855 (+0.015)0.113 (+0.038)4.722 (+0.457)21.927 (+0.448)0.842 (-0.006)0.160 (-0.003)3.963 (+0.660)39.44
LLFlow-S [41] AAAT'2224.0600.8600.1365.41225.9220.8600.1736.1504.97
LLFlow-S-SKF(Ours)25.942 (+1.882)0.865 (+0.005)0.125 (+0.011)5.606 (-0.194)28.107 (+2.185)0.884 (+0.024)0.133 (+0.040)5.415 (+0.735)5.26
LLFlow-L [41] AAAT'2224.9990.8700.1175.58226.2000.8880.1375.40637.68
LLFlow-L-SKF(Ours)26.798 (+1.799)0.879 (+0.009)0.105 (+0.012)5.589 (-0.007)28.451 (+2.251)0.905 (+0.017)0.112 (+0.025)5.725 (-0.319)39.91
+ +quality evaluator (NIQE) [33] are employed. For the MEF, LIME, NPE and DICM datasets without paired data, only NIQE is used, as there is no ground-truth. + +Compared Methods. To verify the effectiveness of our designs, we compare our method with a rich collection of SOTA methods for LLIE, including LIME [13], RetinexNet [43], KinD [54], DRBN [48], KinD++ [53], Zero-DCE [11], ISSR [8], EnlightGAN [16], MIRNet [51], HWMNet [7], SNR-LLIE-Net [46], LLFlow [41]. To demonstrate the superiority of our method faithfully, we reasonably select several methods as the baseline networks. Specifically, both the most representative methods including RetinexNet, KinD and KinD++, and three latest methods including HWMNet, SNR-LLIE-Net and LLFlow are selected. Thus, our methods are denoted as RetinexNet-SKF, KinD-SKF, DRBN-SKF, KinD++-SKF, HWMNet-SKF, SNR-LLIE-Net-SKF, LLFlow-S-SKF and LLFlow-L-SKF (small and large version of LLFlow respectively). + +Implementation Details. We conduct our experiments on NVIDIA 3090 GPU and NVIDIA A100 GPU, which are based on the released code of the baseline networks with the same training settings. Specifically, only the last subnets of Retinex-SKF, KinD-SKF and $\mathrm{KinD} + +$ -SKF are trained with SCH loss and SA loss, while the other subnets are trained with the original loss functions. Furthermore, we do not apply SA loss to LLFlow because there is no enhanced output in training stage. Additionally, SE modules are reasonably located in decoders of all the baseline networks. + +# 4.2. Quantitative Evaluation + +Quantitative results on LOL and LOL-v2 datasets. The evaluation results are shown in Table 1. We can observe that our SKF achieves consistent and significant performance gain over each baseline method. Specifically, our SKF provides an average improvement of 1.750 dB/1.611 dB on LOL/LOL-v2 datasets respectively and this is achieved by introducing the capability of suppressing noise and artifacts and preserving color consistency. Notably, our LLFlow-L-SKF earns PSNR values of 26.798 dB/28.451 dB on LOL/LOL-v2 datasets, establishing a new SOTA. Furthermore, SSIM values achieve similar performance as well. Our SKF yields better SSIM values by an average of 0.041/0.037 on LOL/LOL-v2 datasets, which illustrates that our SKF helps baseline methods restore the luminance and contrast and preserve the structural information with details. Besides, the substantial gain of LPIPS and NIQE provided by our SKF reasonably indicates that human intuition is more closely matched by introducing semantic priors from our designs. + +Quantitative results on MEF, LIME, NPE and DICM datasets. The evaluation results on the MEF, LIME, NPE and DICM datasets are described in Table 2. In general, each method with SKF obtain better NIQE results than baseline on all six datasets except three worse cases of DRBN-SKF and HWMNet-SKF. The RetinexNet-SKF performs the best with NIQE of 3.632 on MEF dataset, while the KinD++-SKF achieves the best performance on other five datasets. Overall, it is notable that our SKF yields an + +![](images/ed1d47dd0d14d20b361f651701641f6ee8f6ef4389a89bea35bf19f993d08ee3.jpg) + +![](images/40a80b14bd31e010f61b7aa0b0a47a27ab4d0bfe409f256613da8a3bc5a3b950.jpg) +Figure 4. Visual comparison of baseline methods with and without SKF on LOL dataset. Our SKF enables baseline methods produce images with less noise, more color information and realistic details. + +![](images/916ec48415c2acfa7e5d30b193b4c721062cee7fefefb9eabb27c55863cf4480.jpg) +Figure 5. Visual comparison of baseline methods with and without SKF on LIME dataset. + +Table 2. Quantitative comparison on the LOL [43], LOL-v2 [49], MEF [31], LIME [12], NPE [39] and DICM [21] datasets in terms of NIQE, where smaller values lead to better quality. + +
MethodLOLLOL-v2MEFLIMENPEDICM
Input6.74886.79114.26504.43804.31244.2550
RetinexNet [43] BMVC'186.87315.84884.14904.42004.50084.5912
RetinexNet-SKF(Ours)4.21184.23313.63214.07794.01523.6945
KinD [54] MM'195.18916.84904.13444.64184.68963.9371
KinD-SKF(Ours)5.03064.77833.94604.36073.87213.7909
DRBN [48] CVPR'204.79304.96124.09564.40193.92054.0433
DRBN-SKF(Ours)4.46364.45994.08944.33924.01923.8541
KinD++ [53] IUCV'204.76025.08563.74984.37563.98483.7076
KinD+-+SKF(Ours)4.14154.17853.76453.98923.82013.5382
HWMNet [7] ICIP'225.14075.97024.21754.35494.06833.9196
HWMNet-SKF(Ours)4.34604.68264.03124.36993.99424.0760
+ +average gain of 0.519 on NIQE across all the methods and datasets. The better NIQE shows that the methods with our SKF can produce images with more natural textures and become more effective for restoring low-light images. + +# 4.3. Qualitative Evaluation + +The qualitative evaluations on LOL and LIME datasets are shown in Figs. 4 and 5 respectively. As indicated by Fig. 4, our SKF can improve the enhancement capability of baseline methods and generate images with more pleasing perceptual quality. Specifically, the results of RetinexNet are unreal due to the obvious color gap and serious noise, which can be mitigated by our SKF. Compared to results of KinD and $\mathrm{KinD + + }$ , KinD-SKF and $\mathrm{KinD + + - }$ SKF resolve the issue of inconsistent lighting and strange white artifacts. For other results, more consistent color and natu + +ral details recovery for desk, wall and clothes are achieved by our SKF. + +We further exhibit the visual enhancement results on the LIME dataset in Fig. 5. It can be observed that the methods with our SKF suppress the unnatural halo around the lamp and restore naturalistic color and details. Hence, methods with our SKF yield more visually pleasing results as compared to baselines, supporting our method's excellent performance in quantitative evaluation. More visualization results are provided in the supplementary material. + +# 4.4. Ablation Study + +We conduct ablation studies on LOL dataset to prove the effectiveness of our SKF from various aspects. + +SCH loss, SA loss and SE module. As shown in Table 3, we conduct experiments of KinD++-SKF, DRBN-SKF and HWMNet-SKF. The addition of SCH loss and SE module improves the PSNR by an average of $0.243\mathrm{~dB}$ and $0.841\mathrm{~dB}$ over the baseline respectively. Simultaneously applying SCH loss and SE module further improves the baseline method by yielding an average gain of $1.741\mathrm{~dB}$ over the baseline. This verifies that more beneficial semantic-aware priors are integrated into enhancement process. Despite that adding SA loss causes minor drops in some full-reference metrics, average gain of 0.292 with NIQE are obtained across all the cases. Therefore, the baseline methods are refined through semantic-aware knowledge by each component, and the total framework leads to a significant performance boost. Additionally, results in Fig. 6 demonstrate that model with SCH loss and SE module can pre + +![](images/aea890da05f70b663f7c72603eb4c708fc007e6154449199c01018ed455448e4.jpg) +Figure 6. Visual comparison of DRBN-SKF for investigating the contribution of key techniques of our SKF. + +Table 3. Ablation study of KinD++-SKF, DRBN-SKF and HWMNet-SKF for investigating the contribution of key techniques of our SKF. + +
SCH lossSA lossSE moduleKinD++-SKFDRBN-SKFHWMNet-SKF
PSNR↑SSIM↑LPIPS↓NIQE↓PSNR↑SSIM↑LPIPS↓NIQE↓PSNR↑SSIM↑LPIPS↓NIQE↓
✓18.9700.8040.1754.76019.8600.8340.1554.79324.2400.8520.1145.141
19.1700.8060.1704.75920.0400.8350.1544.79324.5900.8590.1125.023
✓19.3850.8000.1894.39220.0700.8340.1494.70124.3050.8530.1114.712
✓19.7810.8080.1814.71221.3340.8370.1434.67824.4770.8590.1114.988
✓✓20.6200.8150.1764.53622.5500.8360.1504.58125.1230.8600.1114.711
✓✓✓20.3630.8050.2014.14222.8370.8410.1384.46425.0860.8600.1084.346
+ +Table 4. Ablation study of HWMNet-SKF for investigating the effect of semantic priors in the loss function. + +
LschLSAPSNR↑SSIM↑LPIPS↓NIQE↓
w/o Sw/ Sw/o SAw/o Sw/ S
24.2400.8520.1145.141
✓✓24.4770.8590.1114.988
✓✓24.5680.8570.1134.613
✓✓24.6680.8590.1114.567
✓✓25.1230.8600.1084.711
✓✓25.0400.8590.1114.546
✓✓25.0860.8600.1084.311
+ +Table 5. Ablation study for investigating whether the performance improvement comes from semantic priors or more parameters. + +
MethodPSNR ↑SSIM ↑LPIPS ↓Param(M)
HWMNetBaseline24.2400.8520.11466.56
Large24.4450.8530.11569.99
w/ SKF25.0860.8600.10869.98
LLFlow-SBaseline24.0600.8600.1364.97
Large24.1670.8580.1375.38
w/ SKF25.9420.8650.1255.26
LLFlow-LBaseline24.9990.8700.11737.68
Large25.2920.8730.11340.55
w/ SKF26.7980.8790.10539.91
+ +serve color consistency and details and the SA loss reduces fake regions by producing more natural textures. + +Semantic-guided losses. Table 4 lists the results of different settings of losses. The w/o S and w/ S denote calculating the global histogram and our semantic-guided histogram respectively. For SA loss, the w/o SA, w/o S and w/ S denote without SA loss, classic global and local adversarial loss like EnlightGAN [16] and our SA loss. First, the HWMNet-SKF with SCH loss presents better performance, achieving average margin of $0.512\mathrm{dB}$ improvement on PSNR, indicating the significant capability of SCH loss in preserving color consistency. Furthermore, the average gain of 0.271 on NIQE by adding classic adversarial loss can be attributed to the capability of discriminator to improve visual quality. Finally, our SA loss provides favorable gain of 0.411 on NIQE over the baseline and faithfully demonstrates that the semantic priors help find out fake regions and thus produce more natural images. + +Superiority of semantic priors. We choose HWMNet + +SKF, LLFlow-S-SKF and LLFlow-L-SKF to investigate whether the improvement of performance benefits from semantic priors provided by our SKF or the more parameters of our SE modules. As shown in Table 5, Baseline, Large and w/ SKF denote the original model, original model with more layers or channels and original model with our SKF. Our methods achieve significant improvement by average margin of $1.272\mathrm{dB}$ on PSNR comparing to large versions with similar number of parameters. Hence, we prove the superiority of the semantic priors instead of extra parameters. + +# 5. Conclusion + +This work has proposed a novel framework for semantic-aware image enhancement, named SKF. The SKF incorporates semantic priors into Enhancement Net to preserve color consistency and visual details by SE module, SCH loss and SA loss. SE module allows image features to perceive rich and spatial information by semantic feature representations. SCH loss offers effective semantic-aware regional constrain for preserving color consistency. SA loss combines global and local adversarial loss and semantic priors to seek target fake region and generates natural results. Extensive experiments show that our SKF achieves superior performance in the case of all six baseline methods, and the LLFlow-L-SKF outperforms all the competitors. However, the improvement is limited when dealing with unknown category, inducing more possibility when improving the capability of identifying unknown instance by SKB. Furthermore, we will also explore the potential of our SKF in other low-level vision tasks. + +Acknowledgements: This work was supported in part by the National Natural Science Foundation of China under grant 62102069, U20B2063 and 62220106008, the Sichuan Science and Technology Program under grant 2022YFG0032, and the China Academy of Space Technology (CAST) Innovation Program. We are also sponsored by CAAI-Huawei MindSpore Open Fund. + +# References + +[1] Andreas Aakerberg, Anders S Johansen, Kamal Nasrollahi, and Thomas B Moeslund. Semantic segmentation guided real-world super-resolution. In WACV, pages 449-458, 2022. 3 +[2] Mohammad Abdullah-Al-Wadud, Md Hasanul Kabir, M Ali Akber Dewan, and Oksam Chae. A dynamic histogram equalization for image contrast enhancement. TCE, 53:593-600, 2007. 1, 2 +[3] Mor Avi-Aharon, Assaf Arbelle, and Tammy Riklin Raviv. DeepHist: Differentiable joint and color histogram layers for image-to-image translation. arXiv preprint arXiv:2005.03995, 2020. 5 +[4] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. ArcFace: Additive angular margin loss for deep face recognition. In CVPR, pages 4690-4699, 2019. 1 +[5] Xingbo Dong, Wanyan Xu, Zhihui Miao, Lan Ma, Chao Zhang, Jiewen Yang, Zhe Jin, Andrew Beng Jin Teoh, and Jiajun Shen. Abandoning the bayer-filter to see in the dark. In CVPR, pages 17431-17440, 2022. 1, 2, 3 +[6] Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Burst image restoration and enhancement. In CVPR, pages 5759-5768, 2022. 2 +[7] Chi-Mao Fan, Tsung-Jung Liu, and Kuan-Hsien Liu. Half wavelet attention on M-Net+ for low-light image enhancement. arXiv preprint arXiv:2203.01296, 2022. 1, 2, 3, 6, 7 +[8] Minhao Fan, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Integrating semantic segmentation and Retinex model for low-light image enhancement. In ACMMM, pages 2317-2325, 2020. 1, 2, 3, 6 +[9] Jun Fu, Jing Liu, Hajjie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In CVPR, pages 3146-3154, 2019. 1 +[10] Vitor Guizilini, Rui Hou, Jie Li, Rares Ambrus, and Adrien Gaidon. Semantically-guided representation learning for self-supervised monocular depth. In ICLR, 2019. 3 +[11] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In CVPR, pages 1780-1789, 2020. 3, 6 +[12] Xiaojie Guo. LIME: A method for low-light image enhancement. In ACMMM, pages 87-91, 2016. 5, 7 +[13] Xiaojie Guo, Yu Li, and Haibin Ling. LIME: Low-light image enhancement via illumination map estimation. TIP, 26:982-993, 2016. 6 +[14] Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. Globally and locally consistent image completion. ACM Trans. Graph., 36:1-14, 2017. 5 +[15] Kui Jiang, Zhongyuan Wang, Zheng Wang, Chen Chen, Peng Yi, Tao Lu, and Chia-Wen Lin. Degrade is upgrade: Learning degradation for low-light image enhancement. In AAAI, volume 36, pages 1078-1086, 2022. 2 +[16] Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang + +Wang. EnlightenGAN: Deep light enhancement without paired supervision. TIP, 30:2340-2349, 2021. 2, 3, 5, 6, 8 +[17] Yeying Jin, Wenhan Yang, and Robby T Tan. Unsupervised night image enhancement: when layer decomposition meets light-effects suppression. arXiv preprint arXiv:2207.10564, 2022.3 +[18] Daniel J Jobson, Zia-ur Rahman, and Glenn A Woodell. Properties and performance of a center/surround Retinex. TIP, 6:451–462, 1997. 1, 2 +[19] Hyunyoung Jung, Eunhyeok Park, and Sungjoo Yoo. Fine-grained semantics-aware representation enhancement for self-supervised monocular depth estimation. In ICCV, pages 12642-12652, 2021. 2, 3 +[20] Bomi Kim, Sunhyeok Lee, Nahyun Kim, Donggon Jang, and Dae-Shik Kim. Learning color representations for low-light image enhancement. In WACV, pages 1455-1463, 2022. 2, 3 +[21] Chulwoo Lee, Chul Lee, and Chang-Su Kim. Contrast enhancement based on layered difference representation of 2D histograms. TIP, 22:5372-5384, 2013. 5, 7 +[22] Chongyi Li, Chunle Guo, Ling-Hao Han, Jun Jiang, Ming-Ming Cheng, Jinwei Gu, and Chen Change Loy. Low-light image and video enhancement using deep learning: A survey. TPAMI, 2021. 1, 4 +[23] Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo, and Lei Zhang. Blind face restoration via deep multi-scale component dictionaries. In ECCV, pages 399-415. Springer, 2020. 3 +[24] Yi Li, Yi Chang, Changfeng Yu, and Luxin Yan. Close the loop: A unified bottom-up and top-down paradigm for joint image deraining and segmentation. In AAAI, 2022. 3 +[25] Yijun Li, Sifei Liu, Jimei Yang, and Ming-Hsuan Yang. Generative face completion. In CVPR, pages 3911-3919, 2017. 5 +[26] Dong Liang, Ling Li, Mingqiang Wei, Shuo Yang, Liyan Zhang, Wenhan Yang, Yun Du, and Huiyu Zhou. Semantically contrastive learning for low-light image enhancement. In AAAI, pages 1555-1563, 2022. 2, 3 +[27] Seokjae Lim and Wonjun Kim. DSLR: Deep stacked Laplacian restorer for low-light image enhancement. TMM, 23:4272-4284, 2020. 2, 3 +[28] Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, and Thomas S. Huang. When image denoising meets high-level vision tasks: A deep learning approach. In IJCAI, 2018. 3 +[29] Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In CVPR, pages 10561-10570, 2021. 1, 2 +[30] Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. LL-Net: A deep autoencoder approach to natural low-light image enhancement. PR, 61:650–662, 2017. 2 +[31] Kede Ma, Kai Zeng, and Zhou Wang. Perceptual quality assessment for multi-exposure image fusion. TIP, 24:3345-3356, 2015. 5, 7 +[32] Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. Toward fast, flexible, and robust low-light image enhancement. In CVPR, pages 5637-5646, 2022. 2 + +[33] Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a 'completely blind' image quality analyzer. SPL, 20:209-212, 2012. 6 +[34] Sean Moran, Pierre Marza, Steven McDonagh, Sarah Parisot, and Gregory Slabaugh. DeepLPF: Deep local parametric filters for image enhancement. In CVPR, pages 12826-12835, 2020. 1, 2, 3 +[35] Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic segmentation in the wild. In CVPR, pages 891–898, 2014. 2 +[36] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS, 28, 2015. 1 +[37] Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li. MAXIM: Multi-axis mlp for image processing. In CVPR, pages 5769-5780, 2022. 2 +[38] Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al. Deep high-resolution representation learning for visual recognition. TPAMI, 43:3349-3364, 2020. 2, 4 +[39] Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non-uniform illumination images. TIP, 22:3538-3548, 2013. 5, 7 +[40] Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Recovering realistic texture in image super-resolution by deep spatial feature transform. In CVPR, pages 606-615, 2018. 2, 3 +[41] Yufei Wang, Renjie Wan, Wenhan Yang, Haoliang Li, LapPui Chau, and Alex Kot. Low-light image enhancement with normalizing flow. In AAAI, volume 36, pages 2604-2612, 2022. 1, 2, 3, 6 +[42] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: From error visibility to structural similarity. TIP, 13:600-612, 2004. 5 +[43] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retina decomposition for low-light enhancement. In BMVC, 2018. 1, 2, 5, 6, 7 +[44] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. URetinex-Net: Retinex-based deep unfolding network for low-light image enhancement. In CVPR, pages 5901–5910, 2022. 1, 2 +[45] Ke Xu, Xin Yang, Baocai Yin, and Rynson WH Lau. Learning to restore low-light images via decomposition-and-enhancement. In CVPR, pages 2281-2290, 2020. 1, 2 +[46] Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. SNR-aware low-light image enhancement. In CVPR, pages 17714-17724, 2022. 1, 2, 6 +[47] Canqian Yang, Meiguang Jin, Xu Jia, Yi Xu, and Ying Chen. AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement. In CVPR, pages 17522-17531, 2022. 3 +[48] Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to perceptual quality: A semi- + +supervised approach for low-light image enhancement. In CVPR, pages 3063-3072, 2020. 1, 3, 6, 7 +[49] Wenhan Yang, Wenjing Wang, Haofeng Huang, Shiqi Wang, and Jiaying Liu. Sparse gradient regularized deep Retinex network for robust low-light image enhancement. TIP, 30:2072-2086, 2021. 1, 5, 6, 7 +[50] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, pages 5728-5739, 2022. 4 +[51] Syed Waqas Zamir, Aditya Arora, Salman Hameed Khan, Hayat Munawar, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Learning Enriched Features for Fast Image Restoration and Enhancement. TPAMI, 2022. 2, 6 +[52] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 5 +[53] Yonghua Zhang, Xiaojie Guo, Jiayi Ma, Wei Liu, and Jiawan Zhang. Beyond brightening low-light images. IJCV, 129:1013-1037, 2021. 1, 2, 6, 7 +[54] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In ACMMM, pages 1632-1640, 2019. 1, 2, 6, 7 +[55] Zhao Zhang, Huan Zheng, Richang Hong, Mingliang Xu, Shuicheng Yan, and Meng Wang. Deep color consistent network for low-light image enhancement. In CVPR, pages 1899-1908, 2022. 2, 3, 4 +[56] Zunjin Zhao, Bangshu Xiong, Lei Wang, Qiaofeng Ou, Lei Yu, and Fa Kuang. RetinexDIP: A unified deep framework for low-light image enhancement. TCSVT, 32:1076-1088, 2021. 2 +[57] Chuanjun Zheng, Daming Shi, and Wentian Shi. Adaptive unfolding total variation network for low-light image enhancement. In ICCV, pages 4439-4448, 2021. 2, 3 +[58] Shen Zheng and Gaurav Gupta. Semantic-guided zero-shot learning for low-light image/video enhancement. In WACV, pages 581-590, 2022. 2, 3 +[59] Minfeng Zhu, Pingbo Pan, Wei Chen, and Yi Yang. EEMEFN: Low-light image enhancement via edge-enhanced multi-exposure fusion network. 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Motivation and superiority. (a) The enhancement results (bottom row) without semantic priors show color deviations (e.g., the black car turns gray). (b) Our SKF provides remarkable performance boost on LOL/LOL-v2 datasets in terms of PSNR/SSIM." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 308, + 236, + 421, + 350 + ], + "blocks": [ + { + "bbox": [ + 308, + 236, + 421, + 350 + ], + "lines": [ + { + "bbox": [ + 308, + 236, + 421, + 350 + ], + "spans": [ + { + "bbox": [ + 308, + 236, + 421, + 350 + ], + "type": "image", + "image_path": "37ac32e4fcb36b221a276f8bdf22c0d536c464b6dcf1feb1a166cc79b869a813.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 432, + 235, + 544, + 350 + ], + "blocks": [ + { + "bbox": [ + 432, + 235, + 544, + 350 + ], + "lines": [ + { + "bbox": [ + 432, + 235, + 544, + 350 + ], + "spans": [ + { + "bbox": [ + 432, + 235, + 544, + 350 + ], + "type": "image", + "image_path": "0a7689bd731a2f436d1bab409d4865cccf297b0e501011386061ec20cf17d89c.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 143, + 380, + 192, + 392 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 380, + 192, + 392 + ], + "spans": [ + { + "bbox": [ + 143, + 380, + 192, + 392 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 394, + 290, + 670 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 394, + 290, + 670 + ], + "spans": [ + { + "bbox": [ + 46, + 394, + 290, + 670 + ], + "type": "text", + "content": "Low-light image enhancement (LLIE) investigates how to improve illumination and produce normal-light images. The majority of existing methods improve low-light images via a global and uniform manner, without taking into account the semantic information of different regions. Without semantic priors, a network may easily deviate from a region's original color. To address this issue, we propose a novel semantic-aware knowledge-guided framework (SKF) that can assist a low-light enhancement model in learning rich and diverse priors encapsulated in a semantic segmentation model. We concentrate on incorporating semantic knowledge from three key aspects: a semantic-aware embedding module that wisely integrates semantic priors in feature representation space, a semantic-guided color histogram loss that preserves color consistency of various instances, and a semantic-guided adversarial loss that produces more natural textures by semantic priors. Our SKF is appealing in acting as a general framework in LLIE task. Extensive experiments show that models equipped with the SKF significantly outperform the baselines on multiple datasets and our SKF generalizes to different models and scenes well. The code is available at Semantic-Aware-Low-Light-Image-Enhancement" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 675, + 128, + 686 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 675, + 128, + 686 + ], + "spans": [ + { + "bbox": [ + 47, + 675, + 128, + 686 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 47, + 689, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 712 + ], + "type": "text", + "content": "In real world, low-light imaging is fairly common due to unavoidable environmental or technical constraints such as" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 380, + 546, + 501 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 380, + 546, + 501 + ], + "spans": [ + { + "bbox": [ + 304, + 380, + 546, + 501 + ], + "type": "text", + "content": "insufficient illumination and limited exposure time. Low-light images not only have poor visibility for human perception, but also are unsuitable for subsequent multimedia computing and downstream vision tasks designed for high-quality images [4, 9, 36]. Thus, low-light image enhancement (LLIE) is proposed to reveal buried details in low-light images and avoid degraded performance in subsequent vision tasks. Mainstream traditional methods for LLIE include Histogram Equalization-based methods [2] and Retinex model-based methods [18]." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 510, + 547, + 715 + ], + "type": "text", + "content": "Recently, many deep learning-based LLIE methods have proposed, such as end-to-end frameworks [5,7,34,45,46,48] and Retinex-based frameworks [29, 41, 43, 44, 49, 53, 54]. Benefiting from their ability in modeling the mapping between the low-light and high-quality image, deep LLIE methods commonly achieve better results than traditional approaches. However, existing methods typically improve low-light images globally and uniformly, without taking into account the semantic information of different regions, which is crucial for enhancement. As shown in Fig. 1(a), a network that lacks the utilization of semantic priors can easily deviate from a region's original hue [22]. Furthermore, studies have demonstrated the significance of incorporating semantic priors into low-light enhancement. Fan et al. [8] utilize semantic map as prior and incorporated it into the feature representation space, thereby enhancing image quality. Rather than relying on optimizing intermediate fea" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1662" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "type": "text", + "content": "tures, Zheng et al. [58] adopt a novel loss to guarantee the semantic consistency of the enhanced images. These methods successfully combine the semantic priors with LLIE task, demonstrating the superiority of semantic constraints and guidance. However, their methods fail to fully exploit the knowledge that semantic segmentation networks can provide, limiting the performance gain by semantic priors. Furthermore, the interaction between segmentation and enhancement is designed for specific methods, limiting the possibility of incorporating semantic guidance into LLIE task. Hence, we wonder two questions: 1. How can we obtain various and available semantic knowledge? 2. How does semantic knowledge contribute to image quality improvement in LLIE task?" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 242, + 289, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 242, + 289, + 410 + ], + "spans": [ + { + "bbox": [ + 46, + 242, + 289, + 410 + ], + "type": "text", + "content": "We attempt to answer the first question. First, a semantic segmentation network pre-trained on large-scale datasets is introduced as a semantic knowledge bank (SKB). The SKB can provide richer and more diverse semantic priors to improve the capability of enhancement networks. Second, according to previous works [8, 19, 58], the available priors provided by the SKB primarily consist of intermediate features and semantic maps. Once training a LLIE model, the SKB yields above semantic priors and guides the enhancement process. The priors can not only refine image features by employing techniques like affinity matrices, spatial feature transformations [40], and attention mechanisms, but also guide the design of objective functions by explicitly incorporating regional information into LLIE task [26]." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 412, + 289, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 412, + 289, + 579 + ], + "spans": [ + { + "bbox": [ + 46, + 412, + 289, + 579 + ], + "type": "text", + "content": "Then we try to answer the second question. We design a series of novel methods to integrate semantic knowledge into LLIE task based on the above answers, formulating in a novel semantic-aware knowledge-guided framework (SKF). First, we use the High-Resolution Network [38] (HRNet) pre-trained on the PASCAL-Context dataset [35] as the previously mentioned SKB. In order to make use of intermediate features, we develop a semantic-aware embedding (SE) module. It computes the similarity between the reference and target features and employs cross-modal interactions between heterogeneous representations. As a result, we quantify the semantic awareness of image features as a form of attention and embed semantic consistency in enhancement network." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 582, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 289, + 715 + ], + "type": "text", + "content": "Second, some methods [20, 55] propose to optimize image enhancement using color histogram in order to preserve the color consistency of the image rather than simply enhancing the brightness globally. The color histogram, on the other hand, is still a global statistical feature that cannot guarantee local consistency. Hence, we propose a semantic-guided color histogram (SCH) loss to refine color consistency. Here, we intend to make use of local geometric information derived from the scene semantics and global color information derived from the content. In addition to guarantee original color of the enhanced image, it can also" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 72, + 545, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 96 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 96 + ], + "type": "text", + "content": "add spatial information to the color histogram, performing a more nuanced color recovery." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 96, + 547, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 96, + 547, + 228 + ], + "spans": [ + { + "bbox": [ + 304, + 96, + 547, + 228 + ], + "type": "text", + "content": "Third, existing loss functions are not well aligned with human perception and fail to capture an image's intrinsic signal structure, resulting in unpleasant visual results. To improve visual quality, EnlightenGAN [16] employs global and local image-content consistency and randomly chooses the local patch. However, the discriminator do not know where the regions are likely to be 'fake'. Thus, we propose a semantic-guided adversarial (SA) loss. Specifically, the ability of the discriminator is improved by using segmentation map to determine the fake areas, which can improve the image quality further." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 317, + 228, + 523, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 228, + 523, + 239 + ], + "spans": [ + { + "bbox": [ + 317, + 228, + 523, + 239 + ], + "type": "text", + "content": "The main contributions of our work are as follows:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 317, + 241, + 545, + 401 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 317, + 241, + 545, + 290 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 241, + 545, + 290 + ], + "spans": [ + { + "bbox": [ + 317, + 241, + 545, + 290 + ], + "type": "text", + "content": "- We propose a semantic-aware knowledge-guided framework (SKF) to boost the performance of existing methods by jointly maintaining color consistency and improving image quality." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 317, + 291, + 545, + 351 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 291, + 545, + 351 + ], + "spans": [ + { + "bbox": [ + 317, + 291, + 545, + 351 + ], + "type": "text", + "content": "- We propose three key techniques to take full advantage of semantic priors provided by semantic knowledge bank (SKB): semantic-aware embedding (SE) module, semantic-guided color histogram (SCH) loss, and semantic-guided adversarial (SA) loss." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 317, + 353, + 545, + 401 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 353, + 545, + 401 + ], + "spans": [ + { + "bbox": [ + 317, + 353, + 545, + 401 + ], + "type": "text", + "content": "- We conduct experiments on LOL/LOL-v2 datasets and unpaired datasets. The experimental results demonstrate large performance improvements by our SKF, verifying its effectiveness in resolving the LLIE task." + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 404, + 392, + 417 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 404, + 392, + 417 + ], + "spans": [ + { + "bbox": [ + 306, + 404, + 392, + 417 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 422, + 474, + 434 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 422, + 474, + 434 + ], + "spans": [ + { + "bbox": [ + 306, + 422, + 474, + 434 + ], + "type": "text", + "content": "2.1. Low-light Image Enhancement" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 437, + 545, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 437, + 545, + 545 + ], + "spans": [ + { + "bbox": [ + 304, + 437, + 545, + 545 + ], + "type": "text", + "content": "Traditional methods. Traditional methods for low-light enhancement include Histogram Equalization-based methods [2] and Retinex model-based methods [18]. The former improve low-light images by extending the dynamic range. The latter decompose a low-light image into reflection and illumination maps and the reflection component is treated as the enhanced image. Such model-based methods require explicit priors to fit data well, but designing proper priors for various scenes is difficult [44]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 546, + 545, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 546, + 545, + 677 + ], + "spans": [ + { + "bbox": [ + 304, + 546, + 545, + 677 + ], + "type": "text", + "content": "Learning-based methods. Recent deep learning-based methods show promising results [15, 29, 43, 44, 53, 54, 56]. We can further divide existing designs into Retinex-based methods and end-to-end methods. Retinex-based methods use deep network to decompose and enhance an image. Wei et al. proposed a two-stage Retinex-based method called Retinex-Net [43]. Inspired by Retinex-Net, Zhang et al. proposed two refined methods, called KinD [54] and KinD++ [53]. Recently, Wu et al. [44] proposed a novel deep unfolding Retinex-based network to further integrate the strengths of model-based and learning-based methods." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "type": "text", + "content": "In comparison to Retinex-based method, end-to-end methods directly learning an enhanced result [5-7, 27, 32, 34, 37, 41, 45, 46, 51, 57, 59]. Lore et al. [30] made the" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1663" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 71, + 547, + 297 + ], + "blocks": [ + { + "bbox": [ + 50, + 71, + 547, + 297 + ], + "lines": [ + { + "bbox": [ + 50, + 71, + 547, + 297 + ], + "spans": [ + { + "bbox": [ + 50, + 71, + 547, + 297 + ], + "type": "image", + "image_path": "61757e88f6d3669113f6efd48f1724280811759bf86dbf4bff6cab4a2c32f22c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 300, + 547, + 346 + ], + "lines": [ + { + "bbox": [ + 46, + 300, + 547, + 346 + ], + "spans": [ + { + "bbox": [ + 46, + 300, + 547, + 346 + ], + "type": "text", + "content": "Figure 2. Overview of our Semantic-aware Knowledge-guided Framework (SKF). With a pre-trained Segmentation Net, our SKF utilizes semantic priors to improve the enhancement process in two aspects: (a) In feature-level, the multi-scale semantic-aware embedding modules enable cross-modal interactions between semantic features and image features in representation space. (b) In loss-level, the semantic segmentation result is introduced into the computation of color histogram loss and adversarial loss as a guidance." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 349, + 289, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 349, + 289, + 553 + ], + "spans": [ + { + "bbox": [ + 46, + 349, + 289, + 553 + ], + "type": "text", + "content": "first attempt by proposing a deep autoencoder named LowLight Net (LLNet). Later on, various end-to-end methods are proposed. Physics-based concepts, e.g. Laplacian pyramid [27], local parametric filter [34], Lagrange multiplier [57], De-Bayer-Filter [5], normalization flow [41] and wavelet transform [7], are proposed to improve model interpretability and lead to visually pleasing results. In [16, 17, 48], adversarial learning is introduced to capture the visual properties. In [11], the light enhancement is creatively formulated as a task of image-specific curve estimation using zero-shot learning. In [20, 47, 55], 3D lookup table and color histogram are utilized to preserve the color consistency. However, existing designs focus on optimizing enhancement process, while ignoring the semantic information of different regions. In contrast, we design a SKF with three key techniques to explore the potential of semantic priors and thus produce visually pleasing enhanced results." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 563, + 195, + 574 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 563, + 195, + 574 + ], + "spans": [ + { + "bbox": [ + 47, + 563, + 195, + 574 + ], + "type": "text", + "content": "2.2. Semantic-Guided Methods" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 579, + 287, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 579, + 287, + 628 + ], + "spans": [ + { + "bbox": [ + 46, + 579, + 287, + 628 + ], + "type": "text", + "content": "Recently, semantic-guided methods prove the reliability of semantic priors. These methods could be divided into two kinds: loss-level semantic-guided methods and feature-level semantic-guided methods." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "type": "text", + "content": "Loss-level semantic-guided methods. In order to make use of semantic priors, some works focus on utilizing semantic-aware losses as extra objective functions of the original vision tasks. In image denoising [28], image super-resolution [1], low-light image enhancement [58], researchers directly utilized semantic segmentation loss as an extra constraint to guide the training process. Furthermore," + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 349, + 545, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 349, + 545, + 373 + ], + "spans": [ + { + "bbox": [ + 304, + 349, + 545, + 373 + ], + "type": "text", + "content": "Liang et al. [26] better maintained the details of the images by using a semantic brightness consistency loss." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 373, + 546, + 481 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 373, + 546, + 481 + ], + "spans": [ + { + "bbox": [ + 304, + 373, + 546, + 481 + ], + "type": "text", + "content": "Feature-level semantic-guided methods. In comparison to loss-level semantic-guided methods, feature-level semantic-guided methods concentrate on extracting intermediate features from semantic segmentation network and introduce semantic priors in feature representation space to combine with image features. Similar works have been done on image restoration [23], image deraining [24], image super-resolution [40], low-light image enhancement [8], depth estimation [10, 19]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 481, + 545, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 481, + 545, + 578 + ], + "spans": [ + { + "bbox": [ + 304, + 481, + 545, + 578 + ], + "type": "text", + "content": "Existing semantic-guided methods are limited by the insufficient interaction between semantic priors and original tasks. Hence, we propose a semantic-aware framework to fully exploit semantic information both on loss-level and feature-level, including two semantic-guided losses and a semantic-aware embedding module. Specifically, comparing to semantic-guided methods in LLIE task [8, 26, 58], our SKF is appealing in acting as a general framework." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 585, + 362, + 597 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 585, + 362, + 597 + ], + "spans": [ + { + "bbox": [ + 306, + 585, + 362, + 597 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 602, + 448, + 613 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 602, + 448, + 613 + ], + "spans": [ + { + "bbox": [ + 306, + 602, + 448, + 613 + ], + "type": "text", + "content": "3.1. Motivation and Overview" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 618, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 618, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 618, + 545, + 714 + ], + "type": "text", + "content": "Illumination enhancement is the process of making an underexposed image look better by adjusting the lighting, eliminating noise, and restoring lost details. Semantic priors can provide a wealth of information for improving the enhancement performance. Specifically, semantic priors can help reformulate the existing LLIE methods as a region-aware enhancement framework. In particular, the novel model will blur noises on smooth regions in a simple way," + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1664" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 167 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 167 + ], + "type": "text", + "content": "such as skies, whereas being careful on regions with rich details, such as indoor scene. Furthermore, combining with semantic prior, the color consistency of enhanced image will be carefully preserved. A network that lacks access to semantic priors can easily deviate from a region's original hue [22]. Existing low-light enhancement methods, however, ignore the importance of semantic information and thus have limited capability." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 168, + 287, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 168, + 287, + 228 + ], + "spans": [ + { + "bbox": [ + 46, + 168, + 287, + 228 + ], + "type": "text", + "content": "In this paper, we propose a novel SKF, jointly optimizing image features, maintaining regional color consistency and improving image quality. As shown in Fig. 2, semantic priors are provided by SKB and integrated into LLIE task by three key components: SE module, SCH loss and SA loss." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 228, + 288, + 275 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 228, + 288, + 275 + ], + "spans": [ + { + "bbox": [ + 47, + 228, + 288, + 275 + ], + "type": "text", + "content": "Problem definition of semantic-aware LLIE. Given a low-light image " + }, + { + "bbox": [ + 47, + 228, + 288, + 275 + ], + "type": "inline_equation", + "content": "I_{l}\\in \\mathbb{R}^{W\\times H\\times 3}" + }, + { + "bbox": [ + 47, + 228, + 288, + 275 + ], + "type": "text", + "content": " with width " + }, + { + "bbox": [ + 47, + 228, + 288, + 275 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 47, + 228, + 288, + 275 + ], + "type": "text", + "content": " and height " + }, + { + "bbox": [ + 47, + 228, + 288, + 275 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 47, + 228, + 288, + 275 + ], + "type": "text", + "content": ". Combining with semantic segmentation, the LLIE process can be modeled as two functions, first:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 118, + 281, + 287, + 294 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 118, + 281, + 287, + 294 + ], + "spans": [ + { + "bbox": [ + 118, + 281, + 287, + 294 + ], + "type": "interline_equation", + "content": "M = \\mathbf {F} _ {\\text {s e g m e n t}} \\left(I _ {l}; \\theta_ {s}\\right), \\tag {1}", + "image_path": "762f2fa048449bb0f35a8714fb504e0111d68d0aa244ca2dcab275980850ee5e.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 297, + 288, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 297, + 288, + 357 + ], + "spans": [ + { + "bbox": [ + 46, + 297, + 288, + 357 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 297, + 288, + 357 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 297, + 288, + 357 + ], + "type": "text", + "content": " is the semantic prior, including segmentation result and intermediate features with multi-scale dimensions. " + }, + { + "bbox": [ + 46, + 297, + 288, + 357 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{segment}" + }, + { + "bbox": [ + 46, + 297, + 288, + 357 + ], + "type": "text", + "content": " represents the pre-trained semantic segmentation network, acting as the SKB, and " + }, + { + "bbox": [ + 46, + 297, + 288, + 357 + ], + "type": "inline_equation", + "content": "\\theta_{s}" + }, + { + "bbox": [ + 46, + 297, + 288, + 357 + ], + "type": "text", + "content": " is frozen in training stage. Then " + }, + { + "bbox": [ + 46, + 297, + 288, + 357 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 297, + 288, + 357 + ], + "type": "text", + "content": " is used as input:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 111, + 360, + 287, + 374 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 360, + 287, + 374 + ], + "spans": [ + { + "bbox": [ + 111, + 360, + 287, + 374 + ], + "type": "interline_equation", + "content": "\\widehat {I _ {h}} = \\mathbf {F} _ {\\text {e n h a n c e}} \\left(I _ {l}, M; \\theta_ {e}\\right), \\tag {2}", + "image_path": "f940564fe5b3534a688cc88f9dcab442e41464c8ca4518791c52a3cbff6541a7.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 379, + 287, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 379, + 287, + 430 + ], + "spans": [ + { + "bbox": [ + 46, + 379, + 287, + 430 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 379, + 287, + 430 + ], + "type": "inline_equation", + "content": "\\widehat{I_h} \\in \\mathbb{R}^{W \\times H \\times 3}" + }, + { + "bbox": [ + 46, + 379, + 287, + 430 + ], + "type": "text", + "content": " is the enhanced result and " + }, + { + "bbox": [ + 46, + 379, + 287, + 430 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{\\text{enhance}}" + }, + { + "bbox": [ + 46, + 379, + 287, + 430 + ], + "type": "text", + "content": " represents the enhancement network. During training stage, " + }, + { + "bbox": [ + 46, + 379, + 287, + 430 + ], + "type": "inline_equation", + "content": "\\theta_e" + }, + { + "bbox": [ + 46, + 379, + 287, + 430 + ], + "type": "text", + "content": " will be updated by minimizing the objective function with the guidance of " + }, + { + "bbox": [ + 46, + 379, + 287, + 430 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 379, + 287, + 430 + ], + "type": "text", + "content": " while " + }, + { + "bbox": [ + 46, + 379, + 287, + 430 + ], + "type": "inline_equation", + "content": "\\theta_s" + }, + { + "bbox": [ + 46, + 379, + 287, + 430 + ], + "type": "text", + "content": " is fixed:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 108, + 435, + 287, + 451 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 108, + 435, + 287, + 451 + ], + "spans": [ + { + "bbox": [ + 108, + 435, + 287, + 451 + ], + "type": "interline_equation", + "content": "\\widehat {\\theta} _ {e} = \\operatorname {a r g m i n} \\mathcal {L} \\left(\\widehat {I _ {h}}, I _ {h}, M\\right), \\tag {3}", + "image_path": "9812fee6b289f145aba0ed810b9853d628f7f5042378e1b1a02f76fed1aa6377.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 452, + 287, + 477 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 452, + 287, + 477 + ], + "spans": [ + { + "bbox": [ + 46, + 452, + 287, + 477 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 452, + 287, + 477 + ], + "type": "inline_equation", + "content": "I_h \\in \\mathbb{R}^{W \\times H \\times 3}" + }, + { + "bbox": [ + 46, + 452, + 287, + 477 + ], + "type": "text", + "content": " is the ground truth, " + }, + { + "bbox": [ + 46, + 452, + 287, + 477 + ], + "type": "inline_equation", + "content": "\\mathcal{L}(\\widehat{I_h}, I_h, M)" + }, + { + "bbox": [ + 46, + 452, + 287, + 477 + ], + "type": "text", + "content": " is the objective function of semantic-aware LLIE." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 481, + 243, + 495 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 481, + 243, + 495 + ], + "spans": [ + { + "bbox": [ + 47, + 481, + 243, + 495 + ], + "type": "text", + "content": "3.2. Semantic-Aware Embedding Module" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 498, + 287, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 498, + 287, + 593 + ], + "spans": [ + { + "bbox": [ + 46, + 498, + 287, + 593 + ], + "type": "text", + "content": "When refining image features with the help of semantic priors, another challenge should be particularly considered is the discrepancy between the two sources. To alleviate this issue, we propose the SE module to refine the image feature maps, as illustrated in Fig. 3. The SE modules are like bridges between Segmentation Net and Enhancement Net (see Fig. 2), establishing connections between two heterogeneous tasks." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": "In our framework, we choose HRNet [38] as the SKB due to its exceptional performance and make some task-specific modifications. Besides the semantic map, we utilize output features before the representation head as multiscale semantic priors. For further illustration, three SE modules are shown in Fig. 2, thus we take three semantic/image features " + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "inline_equation", + "content": "(F_s^b / F_i^b, b = 0,1,2)" + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": " with three spatial resolutions " + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "inline_equation", + "content": "(H / 2^{4 - b}, W / 2^{4 - b})" + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": " are the height and width of the input image. The SE module performs a pixel-wise interaction between " + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "inline_equation", + "content": "F_s^b" + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "inline_equation", + "content": "F_i^b" + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": ", and gives" + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 307, + 69, + 545, + 183 + ], + "blocks": [ + { + "bbox": [ + 307, + 69, + 545, + 183 + ], + "lines": [ + { + "bbox": [ + 307, + 69, + 545, + 183 + ], + "spans": [ + { + "bbox": [ + 307, + 69, + 545, + 183 + ], + "type": "image", + "image_path": "d6712df58507d67af038079659715bffde80688d16bccc1e40e882c1de38c525.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 186, + 545, + 232 + ], + "lines": [ + { + "bbox": [ + 304, + 186, + 545, + 232 + ], + "spans": [ + { + "bbox": [ + 304, + 186, + 545, + 232 + ], + "type": "text", + "content": "Figure 3. Architecture of the semantic-aware embedding (SE) module. At the " + }, + { + "bbox": [ + 304, + 186, + 545, + 232 + ], + "type": "inline_equation", + "content": "b^{th}" + }, + { + "bbox": [ + 304, + 186, + 545, + 232 + ], + "type": "text", + "content": " decoder layer, SE module transforms the image feature map " + }, + { + "bbox": [ + 304, + 186, + 545, + 232 + ], + "type": "inline_equation", + "content": "F_{i}^{b}" + }, + { + "bbox": [ + 304, + 186, + 545, + 232 + ], + "type": "text", + "content": " with the semantic feature map " + }, + { + "bbox": [ + 304, + 186, + 545, + 232 + ], + "type": "inline_equation", + "content": "F_{s}^{b}" + }, + { + "bbox": [ + 304, + 186, + 545, + 232 + ], + "type": "text", + "content": " and produces the refined output feature " + }, + { + "bbox": [ + 304, + 186, + 545, + 232 + ], + "type": "inline_equation", + "content": "F_{o}^{b}" + }, + { + "bbox": [ + 304, + 186, + 545, + 232 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 236, + 545, + 259 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 236, + 545, + 259 + ], + "spans": [ + { + "bbox": [ + 304, + 236, + 545, + 259 + ], + "type": "text", + "content": "the final refined feature map " + }, + { + "bbox": [ + 304, + 236, + 545, + 259 + ], + "type": "inline_equation", + "content": "F_{o}^{b}" + }, + { + "bbox": [ + 304, + 236, + 545, + 259 + ], + "type": "text", + "content": ". Details of the learning process are provided below." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 260, + 545, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 260, + 545, + 354 + ], + "spans": [ + { + "bbox": [ + 304, + 260, + 545, + 354 + ], + "type": "text", + "content": "The SE module computes the semantic awareness of the image features through cross-modal similarity and produces a semantic-aware map. We first apply convolution layers to transform " + }, + { + "bbox": [ + 304, + 260, + 545, + 354 + ], + "type": "inline_equation", + "content": "F_{s}^{b}" + }, + { + "bbox": [ + 304, + 260, + 545, + 354 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 260, + 545, + 354 + ], + "type": "inline_equation", + "content": "F_{i}^{b}" + }, + { + "bbox": [ + 304, + 260, + 545, + 354 + ], + "type": "text", + "content": " to the same dimension. Next, inspired by Restormer [50], we adopt a transposed-attention mechanism to compute the attention map with a low computational cost. Hence, the semantic-aware attention map is described as follows:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 333, + 357, + 545, + 376 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 357, + 545, + 376 + ], + "spans": [ + { + "bbox": [ + 333, + 357, + 545, + 376 + ], + "type": "interline_equation", + "content": "A ^ {b} = \\operatorname {S o f t m a x} \\left(W _ {k} \\left(F _ {i} ^ {b}\\right) \\times W _ {q} \\left(F _ {s} ^ {b}\\right) / \\sqrt {C}\\right), \\tag {4}", + "image_path": "f6e8634afaa1e3e3cb47feb71c0a24e97351475c0afa6ba627db656682974d46.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "spans": [ + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "inline_equation", + "content": "W_{k}(\\cdot)" + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "inline_equation", + "content": "W_{q}(\\cdot)" + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "text", + "content": " are convolution layers, " + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "inline_equation", + "content": "LN" + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "text", + "content": " is layer normalization, " + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "text", + "content": " is channel of features. Here, " + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "inline_equation", + "content": "A^{b} \\in \\mathbb{R}^{C \\times C}" + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "text", + "content": " indicates the semantic-aware attention map, which represents the interrelationship between " + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "inline_equation", + "content": "F_{i}^{b}" + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "inline_equation", + "content": "F_{s}^{b}" + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "text", + "content": ". Then we use " + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "inline_equation", + "content": "A^{b}" + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "text", + "content": " to fabricate image feature " + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "inline_equation", + "content": "F_{i}^{b}" + }, + { + "bbox": [ + 304, + 379, + 545, + 440 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 359, + 441, + 545, + 456 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 359, + 441, + 545, + 456 + ], + "spans": [ + { + "bbox": [ + 359, + 441, + 545, + 456 + ], + "type": "interline_equation", + "content": "F _ {o} ^ {b} = F N \\left(W _ {v} \\left(F _ {i} ^ {b}\\right) \\times A ^ {b} + F _ {i} ^ {b}\\right), \\tag {5}", + "image_path": "0944f88495862b0a1c4798f511dbc2cba72de2e6fc62572fc574421f9923ff93.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 460, + 545, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 460, + 545, + 498 + ], + "spans": [ + { + "bbox": [ + 304, + 460, + 545, + 498 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 460, + 545, + 498 + ], + "type": "inline_equation", + "content": "FN" + }, + { + "bbox": [ + 304, + 460, + 545, + 498 + ], + "type": "text", + "content": " denotes feed-forward network, " + }, + { + "bbox": [ + 304, + 460, + 545, + 498 + ], + "type": "inline_equation", + "content": "F_{o}^{b}" + }, + { + "bbox": [ + 304, + 460, + 545, + 498 + ], + "type": "text", + "content": " is the final refined feature map of " + }, + { + "bbox": [ + 304, + 460, + 545, + 498 + ], + "type": "inline_equation", + "content": "b^{th}" + }, + { + "bbox": [ + 304, + 460, + 545, + 498 + ], + "type": "text", + "content": " SE module and becomes the input of " + }, + { + "bbox": [ + 304, + 460, + 545, + 498 + ], + "type": "inline_equation", + "content": "(b + 1)^{th}" + }, + { + "bbox": [ + 304, + 460, + 545, + 498 + ], + "type": "text", + "content": " layer of the Enhancement Net decoder." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 305, + 503, + 516, + 517 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 503, + 516, + 517 + ], + "spans": [ + { + "bbox": [ + 305, + 503, + 516, + 517 + ], + "type": "text", + "content": "3.3. Semantic-Guided Color Histogram Loss" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 522, + 545, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 522, + 545, + 665 + ], + "spans": [ + { + "bbox": [ + 304, + 522, + 545, + 665 + ], + "type": "text", + "content": "Color histogram carries crucial underlying image statistics and is profitable for learning color representations. DCC-Net [55] uses PCE modules with affinity matrix to match the color histogram and content in feature-level, therefore retaining color consistency of enhanced image. However, color histogram describes a global statistic information, differences in color characteristics between various instances are eliminated. Thus, we propose an intuitive way to achieve local color adjustment, i.e., semantic-guided color histogram (SCH) loss, as shown in Fig. 2. It focuses on adjusting color histogram of each instance, thereby retaining more detailed color information." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "content": "The semantic map is firstly used to divide enhanced result into image patches with different instance labels. Each patch includes a single instance with the same label. Hence, the process of producing patches are defined as follows:" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1665" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 71, + 287, + 87 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 71, + 287, + 87 + ], + "spans": [ + { + "bbox": [ + 61, + 71, + 287, + 87 + ], + "type": "interline_equation", + "content": "P = \\left\\{P ^ {0}, P ^ {1}, \\dots , P ^ {\\text {c l a s s}} \\right\\}, \\quad P ^ {c} = I _ {\\text {o u t}} \\odot I _ {\\text {s e g}} ^ {c}, \\tag {6}", + "image_path": "fc1fc45a1edf54e60d24374c1e4ebad22296628957a66a143d195979dc26faaf.jpg" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "spans": [ + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "inline_equation", + "content": "\\odot" + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "text", + "content": " is the dot product, " + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "inline_equation", + "content": "I_{out}" + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "text", + "content": " denotes the enhanced result, " + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "inline_equation", + "content": "I_{seg}^{c}" + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "text", + "content": " denotes the " + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "inline_equation", + "content": "c^{th}" + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "text", + "content": " channel of the one-hot semantic map, " + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "inline_equation", + "content": "P^c\\in \\mathbb{R}^{W\\times H\\times 3}" + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "text", + "content": " denotes the " + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "inline_equation", + "content": "c^{th}" + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "text", + "content": " image patch, " + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 47, + 91, + 287, + 140 + ], + "type": "text", + "content": " denotes the group of all the patches." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 140, + 287, + 236 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 140, + 287, + 236 + ], + "spans": [ + { + "bbox": [ + 47, + 140, + 287, + 236 + ], + "type": "text", + "content": "Due to the discrete nature of color histogram, we approximate the differentiable version inspired by Kernel Density Estimation [3] for model training. Considering the prediction error of the semantic result, pixels that are close to the boundary are not considered. We refine the patch group " + }, + { + "bbox": [ + 47, + 140, + 287, + 236 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 47, + 140, + 287, + 236 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 47, + 140, + 287, + 236 + ], + "type": "inline_equation", + "content": "P^{\\prime}" + }, + { + "bbox": [ + 47, + 140, + 287, + 236 + ], + "type": "text", + "content": " without edge pixels, mitigating effects of misclassification. In the case of R channel of the " + }, + { + "bbox": [ + 47, + 140, + 287, + 236 + ], + "type": "inline_equation", + "content": "c^{th}" + }, + { + "bbox": [ + 47, + 140, + 287, + 236 + ], + "type": "text", + "content": " image patch " + }, + { + "bbox": [ + 47, + 140, + 287, + 236 + ], + "type": "inline_equation", + "content": "P^{c^{\\prime}}(R)" + }, + { + "bbox": [ + 47, + 140, + 287, + 236 + ], + "type": "text", + "content": ", the estimation process is defined as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 78, + 240, + 287, + 265 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 78, + 240, + 287, + 265 + ], + "spans": [ + { + "bbox": [ + 78, + 240, + 287, + 265 + ], + "type": "interline_equation", + "content": "x _ {i j} ^ {h} = x _ {j} - \\frac {i - 0 . 5}{2 5 5}, \\quad x _ {i j} ^ {l} = x _ {j} - \\frac {i + 0 . 5}{2 5 5}, \\tag {7}", + "image_path": "bfe7fecf2de9e768a07c3653dedf0a2dd9260be902ddb25109639aa3fa7a55f0.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "spans": [ + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "inline_equation", + "content": "x_{j}" + }, + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "text", + "content": " denotes " + }, + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "inline_equation", + "content": "j^{th}" + }, + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "text", + "content": " pixel in " + }, + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "inline_equation", + "content": "P^{c'}(R)" + }, + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "inline_equation", + "content": "i \\in [0,255]" + }, + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "text", + "content": " denotes pixel intensity. " + }, + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "inline_equation", + "content": "x_{ij}^{h}" + }, + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "inline_equation", + "content": "x_{ij}^{l}" + }, + { + "bbox": [ + 47, + 269, + 287, + 317 + ], + "type": "text", + "content": " represent higher anchor and lower anchor respectively, which are key variables to estimate histogram as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 322, + 287, + 357 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 322, + 287, + 357 + ], + "spans": [ + { + "bbox": [ + 47, + 322, + 287, + 357 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} H _ {i} ^ {c} = \\sum_ {j} \\left(S i g m o i d \\left(\\alpha \\cdot x _ {i j} ^ {h}\\right) - S i g m o i d \\left(\\alpha \\cdot x _ {i j} ^ {l}\\right)\\right), (8) \\\\ H ^ {c} = \\left\\{i, H _ {i} ^ {c} \\right\\} _ {i = 0} ^ {2 5 5}, (9) \\\\ \\end{array}", + "image_path": "15d576a963b540823cc4a4a27f52ab64212f8d062b388e2af05c91a71a387342.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "spans": [ + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "inline_equation", + "content": "H^{c}" + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "content": " denotes the differentiable histogram of " + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "inline_equation", + "content": "P^{c'}(R)" + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "inline_equation", + "content": "H_{i}^{c}" + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "content": " denotes estimated number of pixels with intensity value " + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "content": " is a scaling factor, we experimentally set it to 400 for better estimation. The difference between results of two Sigmoid " + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "inline_equation", + "content": "(\\cdot)" + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "content": " denotes the contribution of " + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "inline_equation", + "content": "x_{j}" + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "content": " to the number of pixels with intensity value " + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "content": ". Specifically, when " + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "inline_equation", + "content": "x_{j}" + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "content": " exactly equal to " + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "content": ", the difference is 1, i.e., " + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "inline_equation", + "content": "x_{j}" + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "content": " adds 1 to " + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "inline_equation", + "content": "H_{i}^{c}" + }, + { + "bbox": [ + 47, + 361, + 287, + 445 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 445, + 287, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 445, + 287, + 479 + ], + "spans": [ + { + "bbox": [ + 47, + 445, + 287, + 479 + ], + "type": "text", + "content": "Finally, we apply " + }, + { + "bbox": [ + 47, + 445, + 287, + 479 + ], + "type": "inline_equation", + "content": "l_{1}" + }, + { + "bbox": [ + 47, + 445, + 287, + 479 + ], + "type": "text", + "content": " loss to constrain the estimated differentiable histogram. Therefore, the SCH loss can be described as follows:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 89, + 484, + 287, + 509 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 89, + 484, + 287, + 509 + ], + "spans": [ + { + "bbox": [ + 89, + 484, + 287, + 509 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {S C H} = \\sum_ {c} \\| H ^ {c} (\\hat {I _ {h}}) - H ^ {c} (I _ {h}) \\| _ {1}, \\tag {10}", + "image_path": "4bd7a335d095e0bcf4c7f0c5904968c659e52de33ad014b56ba896905ca3d5ce.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 514, + 287, + 538 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 514, + 287, + 538 + ], + "spans": [ + { + "bbox": [ + 47, + 514, + 287, + 538 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 514, + 287, + 538 + ], + "type": "inline_equation", + "content": "\\hat{I}_h" + }, + { + "bbox": [ + 47, + 514, + 287, + 538 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 514, + 287, + 538 + ], + "type": "inline_equation", + "content": "I_h" + }, + { + "bbox": [ + 47, + 514, + 287, + 538 + ], + "type": "text", + "content": " denote output and groundtruth respectively, " + }, + { + "bbox": [ + 47, + 514, + 287, + 538 + ], + "type": "inline_equation", + "content": "H^c (\\cdot)" + }, + { + "bbox": [ + 47, + 514, + 287, + 538 + ], + "type": "text", + "content": " denotes the process of histogram estimation." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 543, + 234, + 553 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 543, + 234, + 553 + ], + "spans": [ + { + "bbox": [ + 47, + 543, + 234, + 553 + ], + "type": "text", + "content": "3.4. Semantic-Guided Adversarial Loss" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 558, + 287, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 558, + 287, + 677 + ], + "spans": [ + { + "bbox": [ + 47, + 558, + 287, + 677 + ], + "type": "text", + "content": "Global and local discriminator is used to encourage more realistic results in image inpainting tasks [14, 25]. EnlightenGAN [16] employs this idea as well, but the local patches are selected randomly instead of focusing on fake regions. Therefore we introduce semantic information to guide the discriminator to focus on intriguing regions. To achieve this, we further refine the global and local adversarial loss function respectively by the segmentation map " + }, + { + "bbox": [ + 47, + 558, + 287, + 677 + ], + "type": "inline_equation", + "content": "I_{seg}" + }, + { + "bbox": [ + 47, + 558, + 287, + 677 + ], + "type": "text", + "content": " and image patches " + }, + { + "bbox": [ + 47, + 558, + 287, + 677 + ], + "type": "inline_equation", + "content": "P'" + }, + { + "bbox": [ + 47, + 558, + 287, + 677 + ], + "type": "text", + "content": " mentioned in Sec. 3.3. Finally, we propose the semantic-guided adversarial (SA) loss." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "type": "text", + "content": "For the local adversarial loss, we first use refined patch group " + }, + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "type": "inline_equation", + "content": "P^{\\prime}" + }, + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "type": "text", + "content": " as candidate fake patches of the output " + }, + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "type": "inline_equation", + "content": "I_{out}" + }, + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "type": "text", + "content": ". Then, we compare the discriminating result of image patches" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 72, + 545, + 155 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 155 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 155 + ], + "type": "text", + "content": "among " + }, + { + "bbox": [ + 304, + 72, + 545, + 155 + ], + "type": "inline_equation", + "content": "P^{\\prime}" + }, + { + "bbox": [ + 304, + 72, + 545, + 155 + ], + "type": "text", + "content": ", the worst patch is most likely to be 'fake' and can be chosen to update parameters of both discriminator and generator. Hence, the discriminator plausibly uses the semantic priors to find the target fake region " + }, + { + "bbox": [ + 304, + 72, + 545, + 155 + ], + "type": "inline_equation", + "content": "x_{f} \\sim p_{\\text{fake}}" + }, + { + "bbox": [ + 304, + 72, + 545, + 155 + ], + "type": "text", + "content": " by itself. While the real patches " + }, + { + "bbox": [ + 304, + 72, + 545, + 155 + ], + "type": "inline_equation", + "content": "x_{r} \\sim p_{\\text{real}}" + }, + { + "bbox": [ + 304, + 72, + 545, + 155 + ], + "type": "text", + "content": " are still randomly cropped from real images each time. The local adversarial loss function is defined as:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 323, + 156, + 545, + 190 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 323, + 156, + 545, + 190 + ], + "spans": [ + { + "bbox": [ + 323, + 156, + 545, + 190 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {\\text {l o c a l}} = \\min _ {G} \\max _ {D} \\mathbb {E} _ {x _ {r} \\sim p _ {\\text {r e a l}}} M S E (D \\left(x _ {r}\\right), 0) \\tag {11} \\\\ + \\mathbb {E} _ {x _ {f} \\sim p _ {f a k e}} M S E (D (x _ {f}), 1), \\\\ \\end{array}", + "image_path": "fdbc55c4344340ba8ea4936df41f63d140299996332f0d95758cafce2debd772.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 315, + 199, + 545, + 213 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 199, + 545, + 213 + ], + "spans": [ + { + "bbox": [ + 315, + 199, + 545, + 213 + ], + "type": "interline_equation", + "content": "x _ {f} = P ^ {t}, D \\left(P ^ {t}\\right) = \\min \\left(D \\left(P ^ {0}\\right), \\dots , D \\left(P ^ {\\text {c l a s s}}\\right)\\right), \\tag {12}", + "image_path": "8675196136d564a640f3e7298b5f0d5472c00ac40800dad755eb834a47fd6f1c.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 215, + 545, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 215, + 545, + 239 + ], + "spans": [ + { + "bbox": [ + 305, + 215, + 545, + 239 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 215, + 545, + 239 + ], + "type": "inline_equation", + "content": "MSE(\\cdot)" + }, + { + "bbox": [ + 305, + 215, + 545, + 239 + ], + "type": "text", + "content": " denotes the mean squared error and " + }, + { + "bbox": [ + 305, + 215, + 545, + 239 + ], + "type": "inline_equation", + "content": "P^t" + }, + { + "bbox": [ + 305, + 215, + 545, + 239 + ], + "type": "text", + "content": " denotes the target fake patch." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 239, + 545, + 310 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 239, + 545, + 310 + ], + "spans": [ + { + "bbox": [ + 305, + 239, + 545, + 310 + ], + "type": "text", + "content": "For the global adversarial loss, we adopt a simple design to achieve semantic-aware guidance when discriminating a fake sample. We concatenate " + }, + { + "bbox": [ + 305, + 239, + 545, + 310 + ], + "type": "inline_equation", + "content": "I_{out}" + }, + { + "bbox": [ + 305, + 239, + 545, + 310 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 239, + 545, + 310 + ], + "type": "inline_equation", + "content": "I_{seg}^{\\prime}" + }, + { + "bbox": [ + 305, + 239, + 545, + 310 + ], + "type": "text", + "content": ", which is the output feature before Softmax, as a new " + }, + { + "bbox": [ + 305, + 239, + 545, + 310 + ], + "type": "inline_equation", + "content": "x_{f}" + }, + { + "bbox": [ + 305, + 239, + 545, + 310 + ], + "type": "text", + "content": ". The images " + }, + { + "bbox": [ + 305, + 239, + 545, + 310 + ], + "type": "inline_equation", + "content": "x_{r}" + }, + { + "bbox": [ + 305, + 239, + 545, + 310 + ], + "type": "text", + "content": " with real distribution are randomly sampled. Finally, the global adversarial loss function is defined as:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 321, + 313, + 545, + 347 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 313, + 545, + 347 + ], + "spans": [ + { + "bbox": [ + 321, + 313, + 545, + 347 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {\\text {g l o b a l}} = \\min _ {G} \\max _ {D} \\mathbb {E} _ {x _ {r} \\sim p _ {\\text {r e a l}}} M S E (D \\left(x _ {r}\\right), 0) \\tag {13} \\\\ + \\mathbb {E} _ {x _ {f} \\sim p _ {f a k e}} M S E (D (x _ {f}, I _ {s e g} ^ {\\prime}), 1), \\\\ \\end{array}", + "image_path": "e62ba08260e53c17bf131ece0c0adb8cf4c7eb5ed1d2200fee66315ccca4084f.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 318, + 350, + 484, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 350, + 484, + 361 + ], + "spans": [ + { + "bbox": [ + 318, + 350, + 484, + 361 + ], + "type": "text", + "content": "Therefore, the SA loss can be defined as:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 375, + 365, + 545, + 378 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 375, + 365, + 545, + 378 + ], + "spans": [ + { + "bbox": [ + 375, + 365, + 545, + 378 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {S A} = \\mathcal {L} _ {\\text {g l o b a l}} + \\mathcal {L} _ {\\text {l o c a l}}, \\tag {14}", + "image_path": "c7608a5d0c6521a8c358a0701358db434361abef3426568cc2c3a809942a3b43.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 380, + 545, + 439 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 380, + 545, + 439 + ], + "spans": [ + { + "bbox": [ + 304, + 380, + 545, + 439 + ], + "type": "text", + "content": "We define the original loss function of Enhancement Net as " + }, + { + "bbox": [ + 304, + 380, + 545, + 439 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text{recon}}" + }, + { + "bbox": [ + 304, + 380, + 545, + 439 + ], + "type": "text", + "content": ", which may be " + }, + { + "bbox": [ + 304, + 380, + 545, + 439 + ], + "type": "inline_equation", + "content": "l_{1}" + }, + { + "bbox": [ + 304, + 380, + 545, + 439 + ], + "type": "text", + "content": " loss, MSE loss, SSIM loss, etc., or their combination according to original setting of each selected method. Thus, the overall loss function of our SKF can be formulated as follows:" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 330, + 443, + 545, + 456 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 330, + 443, + 545, + 456 + ], + "spans": [ + { + "bbox": [ + 330, + 443, + 545, + 456 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {a l l}} = \\mathcal {L} _ {\\text {r e c o n}} + \\lambda_ {S C H} \\mathcal {L} _ {S C H} + \\lambda_ {S A} \\mathcal {L} _ {S A}, \\tag {15}", + "image_path": "d2658e60876912a2079ad323dfd6d983cc59c1963ee8e2ef4cbb5ebde1263ef6.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 318, + 458, + 507, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 458, + 507, + 469 + ], + "spans": [ + { + "bbox": [ + 318, + 458, + 507, + 469 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 318, + 458, + 507, + 469 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 318, + 458, + 507, + 469 + ], + "type": "text", + "content": "s are weights to balance the loss terms." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 306, + 475, + 387, + 488 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 475, + 387, + 488 + ], + "spans": [ + { + "bbox": [ + 306, + 475, + 387, + 488 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 306, + 493, + 432, + 506 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 493, + 432, + 506 + ], + "spans": [ + { + "bbox": [ + 306, + 493, + 432, + 506 + ], + "type": "text", + "content": "4.1. Experimental Settings" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 304, + 510, + 545, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 510, + 545, + 641 + ], + "spans": [ + { + "bbox": [ + 304, + 510, + 545, + 641 + ], + "type": "text", + "content": "Datasets. We evaluate the proposed framework on several datasets from various scenes, including LOL [43], LOL-v2 [49], MEF [31], LIME [12], NPE [39] and DICM [21]. The LOL dataset [43] is a real captured dataset including 485 low/normal light image pairs for training and 15 pairs for testing. The LOL-v2 dataset [49] is the real part of LOL-v2, which is larger and more diverse than LOL, including 689 low/normal light pairs for training and 100 pairs for testing. The MEF (17 images), LIME (10 images), NPE (85 images) and DICM (64 images) are real captured datasets including unpaired images." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": "Metrics. To evaluate the performance of different LLIE methods with and without our SKF, we use both full-reference and non-reference image quality evaluation metrics. For LOL/LOL-v2 datasets, peak signal-to-noise ratio (PSNR), structural similarity (SSIM) [42], learned perceptual image patch similarity (LPIPS) [52], natural image" + } + ] + } + ], + "index": 27 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "text", + "content": "1666" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 49, + 102, + 544, + 346 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "type": "text", + "content": "Table 1. Quantitative comparison on the LOL [43] and LOL-v2 [49] datasets. " + }, + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "type": "inline_equation", + "content": "\\uparrow (\\downarrow)" + }, + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "type": "text", + "content": " denotes that, larger (smaller) values lead to better quality. " + }, + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "type": "inline_equation", + "content": "+\\left(-\\right)" + }, + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "type": "text", + "content": " denotes the improvement (reduction) of performance, corresponding to " + }, + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "type": "inline_equation", + "content": "\\uparrow (\\downarrow)" + }, + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "type": "text", + "content": ". The bold denotes the best." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 49, + 102, + 544, + 346 + ], + "lines": [ + { + "bbox": [ + 49, + 102, + 544, + 346 + ], + "spans": [ + { + "bbox": [ + 49, + 102, + 544, + 346 + ], + "type": "table", + "html": "
MethodLOLLOL-v2Param(M)
PSNR ↑SSIM ↑LPIPS ↓NIQE ↓PSNR ↑SSIM ↑LPIPS ↓NIQE ↓
LIME [13] TIP'1616.7600.5600.350-15.2400.470---
Zero-DCE [11] CVPR'2014.8610.5620.3357.76718.0590.5800.3138.0580.33
EnlightGAN [16] TIP'2117.4830.6520.3224.68418.6400.6770.3095.0898.64
ISSR [8] MM'2018.8460.7880.2435.24916.9940.7980.2065.17912.12
MIRNet [51] PAMI'2224.1400.8420.1314.20320.3570.7820.3175.0945.90
RetinexNet [43] BMVC'1816.7700.4620.4748.87318.3710.7230.3655.8490.62
RetinexNet-SKF(Ours)20.418 (+3.648)0.711 (+0.249)0.216 (+0.258)4.211 (+4.662)19.849 (+1.478)0.719 (-0.004)0.255 (+0.110)4.233 (+1.616)0.66
KinD [54] MM'1920.8700.7990.2075.18917.5440.6690.3756.8498.03
KinD-SKF(Ours)21.913 (+1.043)0.835 (+0.036)0.143 (+0.064)5.031 (+0.158)19.821 (+2.277)0.833 (+0.164)0.201 (+0.174)4.778 (+2.071)8.50
DRBN [48] CVPR'2019.8600.8340.1554.79320.1300.8300.1474.9612.21
DRBN-SKF(Ours)22.837 (+2.977)0.841 (+0.007)0.138 (+0.017)4.464 (+0.329)22.441 (+2.311)0.871 (+0.041)0.132 (+0.015)4.460 (+0.501)2.43
KinD++ [53] ICV'2018.9700.8040.1754.76019.0870.8170.1805.0869.63
KinD+-+SKF(Ours)20.363 (+1.393)0.805 (+0.001)0.201 (-0.026)4.142 (+0.618)19.779 (+0.692)0.837 (+0.020)0.178 (+0.002)4.179 (+0.907)10.21
HWMNet [7] ICIP'2224.2400.8520.1145.14120.9280.7980.3595.97066.56
HWMNet(SKF(Ours)25.086 (+0.846)0.860 (+0.008)0.108 (+0.006)4.346 (+0.795)22.490 (+1.562)0.836 (+0.038)0.175 (+0.184)4.683 (+1.288)69.98
SNR-LLIE-Net [46] CVPR'2224.6080.8400.1515.17921.4790.8480.1574.62339.13
SNR-LLIE-Net-SKF(Ours)25.031 (+0.552)0.855 (+0.015)0.113 (+0.038)4.722 (+0.457)21.927 (+0.448)0.842 (-0.006)0.160 (-0.003)3.963 (+0.660)39.44
LLFlow-S [41] AAAT'2224.0600.8600.1365.41225.9220.8600.1736.1504.97
LLFlow-S-SKF(Ours)25.942 (+1.882)0.865 (+0.005)0.125 (+0.011)5.606 (-0.194)28.107 (+2.185)0.884 (+0.024)0.133 (+0.040)5.415 (+0.735)5.26
LLFlow-L [41] AAAT'2224.9990.8700.1175.58226.2000.8880.1375.40637.68
LLFlow-L-SKF(Ours)26.798 (+1.799)0.879 (+0.009)0.105 (+0.012)5.589 (-0.007)28.451 (+2.251)0.905 (+0.017)0.112 (+0.025)5.725 (-0.319)39.91
", + "image_path": "8ab4141c50c7c146edc741333b8d9a54f03a135b8d2f1bdcb24447adc66d6c94.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 357, + 287, + 394 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 357, + 287, + 394 + ], + "spans": [ + { + "bbox": [ + 46, + 357, + 287, + 394 + ], + "type": "text", + "content": "quality evaluator (NIQE) [33] are employed. For the MEF, LIME, NPE and DICM datasets without paired data, only NIQE is used, as there is no ground-truth." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 406, + 288, + 586 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 406, + 288, + 586 + ], + "spans": [ + { + "bbox": [ + 46, + 406, + 288, + 586 + ], + "type": "text", + "content": "Compared Methods. To verify the effectiveness of our designs, we compare our method with a rich collection of SOTA methods for LLIE, including LIME [13], RetinexNet [43], KinD [54], DRBN [48], KinD++ [53], Zero-DCE [11], ISSR [8], EnlightGAN [16], MIRNet [51], HWMNet [7], SNR-LLIE-Net [46], LLFlow [41]. To demonstrate the superiority of our method faithfully, we reasonably select several methods as the baseline networks. Specifically, both the most representative methods including RetinexNet, KinD and KinD++, and three latest methods including HWMNet, SNR-LLIE-Net and LLFlow are selected. Thus, our methods are denoted as RetinexNet-SKF, KinD-SKF, DRBN-SKF, KinD++-SKF, HWMNet-SKF, SNR-LLIE-Net-SKF, LLFlow-S-SKF and LLFlow-L-SKF (small and large version of LLFlow respectively)." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 598, + 288, + 718 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 598, + 288, + 718 + ], + "spans": [ + { + "bbox": [ + 46, + 598, + 288, + 718 + ], + "type": "text", + "content": "Implementation Details. We conduct our experiments on NVIDIA 3090 GPU and NVIDIA A100 GPU, which are based on the released code of the baseline networks with the same training settings. Specifically, only the last subnets of Retinex-SKF, KinD-SKF and " + }, + { + "bbox": [ + 46, + 598, + 288, + 718 + ], + "type": "inline_equation", + "content": "\\mathrm{KinD} + +" + }, + { + "bbox": [ + 46, + 598, + 288, + 718 + ], + "type": "text", + "content": "-SKF are trained with SCH loss and SA loss, while the other subnets are trained with the original loss functions. Furthermore, we do not apply SA loss to LLFlow because there is no enhanced output in training stage. Additionally, SE modules are reasonably located in decoders of all the baseline networks." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 306, + 357, + 441, + 369 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 357, + 441, + 369 + ], + "spans": [ + { + "bbox": [ + 306, + 357, + 441, + 369 + ], + "type": "text", + "content": "4.2. Quantitative Evaluation" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 375, + 545, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 375, + 545, + 602 + ], + "spans": [ + { + "bbox": [ + 304, + 375, + 545, + 602 + ], + "type": "text", + "content": "Quantitative results on LOL and LOL-v2 datasets. The evaluation results are shown in Table 1. We can observe that our SKF achieves consistent and significant performance gain over each baseline method. Specifically, our SKF provides an average improvement of 1.750 dB/1.611 dB on LOL/LOL-v2 datasets respectively and this is achieved by introducing the capability of suppressing noise and artifacts and preserving color consistency. Notably, our LLFlow-L-SKF earns PSNR values of 26.798 dB/28.451 dB on LOL/LOL-v2 datasets, establishing a new SOTA. Furthermore, SSIM values achieve similar performance as well. Our SKF yields better SSIM values by an average of 0.041/0.037 on LOL/LOL-v2 datasets, which illustrates that our SKF helps baseline methods restore the luminance and contrast and preserve the structural information with details. Besides, the substantial gain of LPIPS and NIQE provided by our SKF reasonably indicates that human intuition is more closely matched by introducing semantic priors from our designs." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 605, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 545, + 713 + ], + "type": "text", + "content": "Quantitative results on MEF, LIME, NPE and DICM datasets. The evaluation results on the MEF, LIME, NPE and DICM datasets are described in Table 2. In general, each method with SKF obtain better NIQE results than baseline on all six datasets except three worse cases of DRBN-SKF and HWMNet-SKF. The RetinexNet-SKF performs the best with NIQE of 3.632 on MEF dataset, while the KinD++-SKF achieves the best performance on other five datasets. Overall, it is notable that our SKF yields an" + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1667" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 71, + 547, + 162 + ], + "blocks": [ + { + "bbox": [ + 49, + 71, + 547, + 162 + ], + "lines": [ + { + "bbox": [ + 49, + 71, + 547, + 162 + ], + "spans": [ + { + "bbox": [ + 49, + 71, + 547, + 162 + ], + "type": "image", + "image_path": "ed1d47dd0d14d20b361f651701641f6ee8f6ef4389a89bea35bf19f993d08ee3.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 49, + 167, + 547, + 258 + ], + "blocks": [ + { + "bbox": [ + 49, + 167, + 547, + 258 + ], + "lines": [ + { + "bbox": [ + 49, + 167, + 547, + 258 + ], + "spans": [ + { + "bbox": [ + 49, + 167, + 547, + 258 + ], + "type": "image", + "image_path": "40a80b14bd31e010f61b7aa0b0a47a27ab4d0bfe409f256613da8a3bc5a3b950.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 259, + 546, + 282 + ], + "lines": [ + { + "bbox": [ + 46, + 259, + 546, + 282 + ], + "spans": [ + { + "bbox": [ + 46, + 259, + 546, + 282 + ], + "type": "text", + "content": "Figure 4. Visual comparison of baseline methods with and without SKF on LOL dataset. Our SKF enables baseline methods produce images with less noise, more color information and realistic details." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 48, + 285, + 545, + 345 + ], + "blocks": [ + { + "bbox": [ + 48, + 285, + 545, + 345 + ], + "lines": [ + { + "bbox": [ + 48, + 285, + 545, + 345 + ], + "spans": [ + { + "bbox": [ + 48, + 285, + 545, + 345 + ], + "type": "image", + "image_path": "916ec48415c2acfa7e5d30b193b4c721062cee7fefefb9eabb27c55863cf4480.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 132, + 346, + 459, + 357 + ], + "lines": [ + { + "bbox": [ + 132, + 346, + 459, + 357 + ], + "spans": [ + { + "bbox": [ + 132, + 346, + 459, + 357 + ], + "type": "text", + "content": "Figure 5. Visual comparison of baseline methods with and without SKF on LIME dataset." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 50, + 396, + 287, + 525 + ], + "blocks": [ + { + "bbox": [ + 46, + 360, + 287, + 393 + ], + "lines": [ + { + "bbox": [ + 46, + 360, + 287, + 393 + ], + "spans": [ + { + "bbox": [ + 46, + 360, + 287, + 393 + ], + "type": "text", + "content": "Table 2. Quantitative comparison on the LOL [43], LOL-v2 [49], MEF [31], LIME [12], NPE [39] and DICM [21] datasets in terms of NIQE, where smaller values lead to better quality." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 396, + 287, + 525 + ], + "lines": [ + { + "bbox": [ + 50, + 396, + 287, + 525 + ], + "spans": [ + { + "bbox": [ + 50, + 396, + 287, + 525 + ], + "type": "table", + "html": "
MethodLOLLOL-v2MEFLIMENPEDICM
Input6.74886.79114.26504.43804.31244.2550
RetinexNet [43] BMVC'186.87315.84884.14904.42004.50084.5912
RetinexNet-SKF(Ours)4.21184.23313.63214.07794.01523.6945
KinD [54] MM'195.18916.84904.13444.64184.68963.9371
KinD-SKF(Ours)5.03064.77833.94604.36073.87213.7909
DRBN [48] CVPR'204.79304.96124.09564.40193.92054.0433
DRBN-SKF(Ours)4.46364.45994.08944.33924.01923.8541
KinD++ [53] IUCV'204.76025.08563.74984.37563.98483.7076
KinD+-+SKF(Ours)4.14154.17853.76453.98923.82013.5382
HWMNet [7] ICIP'225.14075.97024.21754.35494.06833.9196
HWMNet-SKF(Ours)4.34604.68264.03124.36993.99424.0760
", + "image_path": "be6b5503960a8f09ed34acdb0b895736aa76ec5caec88232d443000394c83c65.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 529, + 287, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 529, + 287, + 578 + ], + "spans": [ + { + "bbox": [ + 46, + 529, + 287, + 578 + ], + "type": "text", + "content": "average gain of 0.519 on NIQE across all the methods and datasets. The better NIQE shows that the methods with our SKF can produce images with more natural textures and become more effective for restoring low-light images." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 581, + 176, + 593 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 581, + 176, + 593 + ], + "spans": [ + { + "bbox": [ + 47, + 581, + 176, + 593 + ], + "type": "text", + "content": "4.3. Qualitative Evaluation" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": "The qualitative evaluations on LOL and LIME datasets are shown in Figs. 4 and 5 respectively. As indicated by Fig. 4, our SKF can improve the enhancement capability of baseline methods and generate images with more pleasing perceptual quality. Specifically, the results of RetinexNet are unreal due to the obvious color gap and serious noise, which can be mitigated by our SKF. Compared to results of KinD and " + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{KinD + + }" + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": ", KinD-SKF and " + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{KinD + + - }" + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": " SKF resolve the issue of inconsistent lighting and strange white artifacts. For other results, more consistent color and natu" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 361, + 545, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 361, + 545, + 384 + ], + "spans": [ + { + "bbox": [ + 304, + 361, + 545, + 384 + ], + "type": "text", + "content": "ral details recovery for desk, wall and clothes are achieved by our SKF." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 385, + 546, + 482 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 385, + 546, + 482 + ], + "spans": [ + { + "bbox": [ + 304, + 385, + 546, + 482 + ], + "type": "text", + "content": "We further exhibit the visual enhancement results on the LIME dataset in Fig. 5. It can be observed that the methods with our SKF suppress the unnatural halo around the lamp and restore naturalistic color and details. Hence, methods with our SKF yield more visually pleasing results as compared to baselines, supporting our method's excellent performance in quantitative evaluation. More visualization results are provided in the supplementary material." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 484, + 400, + 497 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 484, + 400, + 497 + ], + "spans": [ + { + "bbox": [ + 306, + 484, + 400, + 497 + ], + "type": "text", + "content": "4.4. Ablation Study" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 498, + 545, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 545, + 521 + ], + "type": "text", + "content": "We conduct ablation studies on LOL dataset to prove the effectiveness of our SKF from various aspects." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 522, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 522, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 522, + 546, + 713 + ], + "type": "text", + "content": "SCH loss, SA loss and SE module. As shown in Table 3, we conduct experiments of KinD++-SKF, DRBN-SKF and HWMNet-SKF. The addition of SCH loss and SE module improves the PSNR by an average of " + }, + { + "bbox": [ + 304, + 522, + 546, + 713 + ], + "type": "inline_equation", + "content": "0.243\\mathrm{~dB}" + }, + { + "bbox": [ + 304, + 522, + 546, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 522, + 546, + 713 + ], + "type": "inline_equation", + "content": "0.841\\mathrm{~dB}" + }, + { + "bbox": [ + 304, + 522, + 546, + 713 + ], + "type": "text", + "content": " over the baseline respectively. Simultaneously applying SCH loss and SE module further improves the baseline method by yielding an average gain of " + }, + { + "bbox": [ + 304, + 522, + 546, + 713 + ], + "type": "inline_equation", + "content": "1.741\\mathrm{~dB}" + }, + { + "bbox": [ + 304, + 522, + 546, + 713 + ], + "type": "text", + "content": " over the baseline. This verifies that more beneficial semantic-aware priors are integrated into enhancement process. Despite that adding SA loss causes minor drops in some full-reference metrics, average gain of 0.292 with NIQE are obtained across all the cases. Therefore, the baseline methods are refined through semantic-aware knowledge by each component, and the total framework leads to a significant performance boost. Additionally, results in Fig. 6 demonstrate that model with SCH loss and SE module can pre" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1668" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 71, + 545, + 135 + ], + "blocks": [ + { + "bbox": [ + 49, + 71, + 545, + 135 + ], + "lines": [ + { + "bbox": [ + 49, + 71, + 545, + 135 + ], + "spans": [ + { + "bbox": [ + 49, + 71, + 545, + 135 + ], + "type": "image", + "image_path": "aea890da05f70b663f7c72603eb4c708fc007e6154449199c01018ed455448e4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 100, + 139, + 492, + 150 + ], + "lines": [ + { + "bbox": [ + 100, + 139, + 492, + 150 + ], + "spans": [ + { + "bbox": [ + 100, + 139, + 492, + 150 + ], + "type": "text", + "content": "Figure 6. Visual comparison of DRBN-SKF for investigating the contribution of key techniques of our SKF." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 53, + 171, + 542, + 249 + ], + "blocks": [ + { + "bbox": [ + 47, + 156, + 545, + 167 + ], + "lines": [ + { + "bbox": [ + 47, + 156, + 545, + 167 + ], + "spans": [ + { + "bbox": [ + 47, + 156, + 545, + 167 + ], + "type": "text", + "content": "Table 3. Ablation study of KinD++-SKF, DRBN-SKF and HWMNet-SKF for investigating the contribution of key techniques of our SKF." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 53, + 171, + 542, + 249 + ], + "lines": [ + { + "bbox": [ + 53, + 171, + 542, + 249 + ], + "spans": [ + { + "bbox": [ + 53, + 171, + 542, + 249 + ], + "type": "table", + "html": "
SCH lossSA lossSE moduleKinD++-SKFDRBN-SKFHWMNet-SKF
PSNR↑SSIM↑LPIPS↓NIQE↓PSNR↑SSIM↑LPIPS↓NIQE↓PSNR↑SSIM↑LPIPS↓NIQE↓
✓18.9700.8040.1754.76019.8600.8340.1554.79324.2400.8520.1145.141
19.1700.8060.1704.75920.0400.8350.1544.79324.5900.8590.1125.023
✓19.3850.8000.1894.39220.0700.8340.1494.70124.3050.8530.1114.712
✓19.7810.8080.1814.71221.3340.8370.1434.67824.4770.8590.1114.988
✓✓20.6200.8150.1764.53622.5500.8360.1504.58125.1230.8600.1114.711
✓✓✓20.3630.8050.2014.14222.8370.8410.1384.46425.0860.8600.1084.346
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LschLSAPSNR↑SSIM↑LPIPS↓NIQE↓
w/o Sw/ Sw/o SAw/o Sw/ S
24.2400.8520.1145.141
✓✓24.4770.8590.1114.988
✓✓24.5680.8570.1134.613
✓✓24.6680.8590.1114.567
✓✓25.1230.8600.1084.711
✓✓25.0400.8590.1114.546
✓✓25.0860.8600.1084.311
", + "image_path": "aaf094dc4b16aefce41aa93203f71256c03d36b8f3887de0bdbf3d08021561a7.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 67, + 387, + 269, + 483 + ], + "blocks": [ + { + "bbox": [ + 47, + 361, + 286, + 383 + ], + "lines": [ + { + "bbox": [ + 47, + 361, + 286, + 383 + ], + "spans": [ + { + "bbox": [ + 47, + 361, + 286, + 383 + ], + "type": "text", + "content": "Table 5. Ablation study for investigating whether the performance improvement comes from semantic priors or more parameters." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 67, + 387, + 269, + 483 + ], + "lines": [ + { + "bbox": [ + 67, + 387, + 269, + 483 + ], + "spans": [ + { + "bbox": [ + 67, + 387, + 269, + 483 + ], + "type": "table", + "html": "
MethodPSNR ↑SSIM ↑LPIPS ↓Param(M)
HWMNetBaseline24.2400.8520.11466.56
Large24.4450.8530.11569.99
w/ SKF25.0860.8600.10869.98
LLFlow-SBaseline24.0600.8600.1364.97
Large24.1670.8580.1375.38
w/ SKF25.9420.8650.1255.26
LLFlow-LBaseline24.9990.8700.11737.68
Large25.2920.8730.11340.55
w/ SKF26.7980.8790.10539.91
", + "image_path": "34381dcdcc3df71fea7f99df02add858f8180bf4249ba3c79ccb7802b301fc5c.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 487, + 286, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 487, + 286, + 510 + ], + "spans": [ + { + "bbox": [ + 47, + 487, + 286, + 510 + ], + "type": "text", + "content": "serve color consistency and details and the SA loss reduces fake regions by producing more natural textures." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 510, + 286, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 510, + 286, + 700 + ], + "spans": [ + { + "bbox": [ + 46, + 510, + 286, + 700 + ], + "type": "text", + "content": "Semantic-guided losses. Table 4 lists the results of different settings of losses. The w/o S and w/ S denote calculating the global histogram and our semantic-guided histogram respectively. For SA loss, the w/o SA, w/o S and w/ S denote without SA loss, classic global and local adversarial loss like EnlightGAN [16] and our SA loss. First, the HWMNet-SKF with SCH loss presents better performance, achieving average margin of " + }, + { + "bbox": [ + 46, + 510, + 286, + 700 + ], + "type": "inline_equation", + "content": "0.512\\mathrm{dB}" + }, + { + "bbox": [ + 46, + 510, + 286, + 700 + ], + "type": "text", + "content": " improvement on PSNR, indicating the significant capability of SCH loss in preserving color consistency. Furthermore, the average gain of 0.271 on NIQE by adding classic adversarial loss can be attributed to the capability of discriminator to improve visual quality. Finally, our SA loss provides favorable gain of 0.411 on NIQE over the baseline and faithfully demonstrates that the semantic priors help find out fake regions and thus produce more natural images." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 59, + 701, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 701, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 59, + 701, + 286, + 713 + ], + "type": "text", + "content": "Superiority of semantic priors. We choose HWMNet" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 255, + 545, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 255, + 545, + 375 + ], + "spans": [ + { + "bbox": [ + 304, + 255, + 545, + 375 + ], + "type": "text", + "content": "SKF, LLFlow-S-SKF and LLFlow-L-SKF to investigate whether the improvement of performance benefits from semantic priors provided by our SKF or the more parameters of our SE modules. As shown in Table 5, Baseline, Large and w/ SKF denote the original model, original model with more layers or channels and original model with our SKF. Our methods achieve significant improvement by average margin of " + }, + { + "bbox": [ + 304, + 255, + 545, + 375 + ], + "type": "inline_equation", + "content": "1.272\\mathrm{dB}" + }, + { + "bbox": [ + 304, + 255, + 545, + 375 + ], + "type": "text", + "content": " on PSNR comparing to large versions with similar number of parameters. Hence, we prove the superiority of the semantic priors instead of extra parameters." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 389, + 378, + 402 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 389, + 378, + 402 + ], + "spans": [ + { + "bbox": [ + 306, + 389, + 378, + 402 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 409, + 545, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 409, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 304, + 409, + 545, + 624 + ], + "type": "text", + "content": "This work has proposed a novel framework for semantic-aware image enhancement, named SKF. The SKF incorporates semantic priors into Enhancement Net to preserve color consistency and visual details by SE module, SCH loss and SA loss. SE module allows image features to perceive rich and spatial information by semantic feature representations. SCH loss offers effective semantic-aware regional constrain for preserving color consistency. SA loss combines global and local adversarial loss and semantic priors to seek target fake region and generates natural results. Extensive experiments show that our SKF achieves superior performance in the case of all six baseline methods, and the LLFlow-L-SKF outperforms all the competitors. However, the improvement is limited when dealing with unknown category, inducing more possibility when improving the capability of identifying unknown instance by SKB. Furthermore, we will also explore the potential of our SKF in other low-level vision tasks." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": "Acknowledgements: This work was supported in part by the National Natural Science Foundation of China under grant 62102069, U20B2063 and 62220106008, the Sichuan Science and Technology Program under grant 2022YFG0032, and the China Academy of Space Technology (CAST) Innovation Program. We are also sponsored by CAAI-Huawei MindSpore Open Fund." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "text", + "content": "1669" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 287, + 714 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 133 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 133 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 133 + ], + "type": "text", + "content": "[1] Andreas Aakerberg, Anders S Johansen, Kamal Nasrollahi, and Thomas B Moeslund. Semantic segmentation guided real-world super-resolution. In WACV, pages 449-458, 2022. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 137, + 287, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 137, + 287, + 179 + ], + "spans": [ + { + "bbox": [ + 53, + 137, + 287, + 179 + ], + "type": "text", + "content": "[2] Mohammad Abdullah-Al-Wadud, Md Hasanul Kabir, M Ali Akber Dewan, and Oksam Chae. A dynamic histogram equalization for image contrast enhancement. TCE, 53:593-600, 2007. 1, 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 182, + 287, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 182, + 287, + 224 + ], + "spans": [ + { + "bbox": [ + 53, + 182, + 287, + 224 + ], + "type": "text", + "content": "[3] Mor Avi-Aharon, Assaf Arbelle, and Tammy Riklin Raviv. DeepHist: Differentiable joint and color histogram layers for image-to-image translation. arXiv preprint arXiv:2005.03995, 2020. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 227, + 286, + 258 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 227, + 286, + 258 + ], + "spans": [ + { + "bbox": [ + 53, + 227, + 286, + 258 + ], + "type": "text", + "content": "[4] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. ArcFace: Additive angular margin loss for deep face recognition. In CVPR, pages 4690-4699, 2019. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 261, + 286, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 261, + 286, + 304 + ], + "spans": [ + { + "bbox": [ + 53, + 261, + 286, + 304 + ], + "type": "text", + "content": "[5] Xingbo Dong, Wanyan Xu, Zhihui Miao, Lan Ma, Chao Zhang, Jiewen Yang, Zhe Jin, Andrew Beng Jin Teoh, and Jiajun Shen. Abandoning the bayer-filter to see in the dark. In CVPR, pages 17431-17440, 2022. 1, 2, 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 306, + 286, + 349 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 306, + 286, + 349 + ], + "spans": [ + { + "bbox": [ + 53, + 306, + 286, + 349 + ], + "type": "text", + "content": "[6] Akshay Dudhane, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Burst image restoration and enhancement. In CVPR, pages 5759-5768, 2022. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 351, + 286, + 393 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 351, + 286, + 393 + ], + "spans": [ + { + "bbox": [ + 53, + 351, + 286, + 393 + ], + "type": "text", + "content": "[7] Chi-Mao Fan, Tsung-Jung Liu, and Kuan-Hsien Liu. Half wavelet attention on M-Net+ for low-light image enhancement. arXiv preprint arXiv:2203.01296, 2022. 1, 2, 3, 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 396, + 286, + 439 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 396, + 286, + 439 + ], + "spans": [ + { + "bbox": [ + 53, + 396, + 286, + 439 + ], + "type": "text", + "content": "[8] Minhao Fan, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Integrating semantic segmentation and Retinex model for low-light image enhancement. In ACMMM, pages 2317-2325, 2020. 1, 2, 3, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 441, + 286, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 441, + 286, + 474 + ], + "spans": [ + { + "bbox": [ + 53, + 441, + 286, + 474 + ], + "type": "text", + "content": "[9] Jun Fu, Jing Liu, Hajjie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In CVPR, pages 3146-3154, 2019. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 475, + 286, + 508 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 475, + 286, + 508 + ], + "spans": [ + { + "bbox": [ + 48, + 475, + 286, + 508 + ], + "type": "text", + "content": "[10] Vitor Guizilini, Rui Hou, Jie Li, Rares Ambrus, and Adrien Gaidon. Semantically-guided representation learning for self-supervised monocular depth. In ICLR, 2019. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 510, + 286, + 553 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 510, + 286, + 553 + ], + "spans": [ + { + "bbox": [ + 48, + 510, + 286, + 553 + ], + "type": "text", + "content": "[11] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In CVPR, pages 1780-1789, 2020. 3, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 555, + 286, + 576 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 555, + 286, + 576 + ], + "spans": [ + { + "bbox": [ + 48, + 555, + 286, + 576 + ], + "type": "text", + "content": "[12] Xiaojie Guo. LIME: A method for low-light image enhancement. In ACMMM, pages 87-91, 2016. 5, 7" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 578, + 286, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 578, + 286, + 609 + ], + "spans": [ + { + "bbox": [ + 48, + 578, + 286, + 609 + ], + "type": "text", + "content": "[13] Xiaojie Guo, Yu Li, and Haibin Ling. LIME: Low-light image enhancement via illumination map estimation. TIP, 26:982-993, 2016. 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 612, + 286, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 612, + 286, + 644 + ], + "spans": [ + { + "bbox": [ + 48, + 612, + 286, + 644 + ], + "type": "text", + "content": "[14] Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. Globally and locally consistent image completion. ACM Trans. Graph., 36:1-14, 2017. 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 647, + 286, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 286, + 689 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 286, + 689 + ], + "type": "text", + "content": "[15] Kui Jiang, Zhongyuan Wang, Zheng Wang, Chen Chen, Peng Yi, Tao Lu, and Chia-Wen Lin. Degrade is upgrade: Learning degradation for low-light image enhancement. In AAAI, volume 36, pages 1078-1086, 2022. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 691, + 286, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 286, + 714 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 286, + 714 + ], + "type": "text", + "content": "[16] Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 35, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 105 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 105 + ], + "type": "text", + "content": "Wang. EnlightenGAN: Deep light enhancement without paired supervision. TIP, 30:2340-2349, 2021. 2, 3, 5, 6, 8" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 106, + 545, + 148 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 106, + 545, + 148 + ], + "spans": [ + { + "bbox": [ + 308, + 106, + 545, + 148 + ], + "type": "text", + "content": "[17] Yeying Jin, Wenhan Yang, and Robby T Tan. Unsupervised night image enhancement: when layer decomposition meets light-effects suppression. arXiv preprint arXiv:2207.10564, 2022.3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 150, + 545, + 182 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 150, + 545, + 182 + ], + "spans": [ + { + "bbox": [ + 308, + 150, + 545, + 182 + ], + "type": "text", + "content": "[18] Daniel J Jobson, Zia-ur Rahman, and Glenn A Woodell. Properties and performance of a center/surround Retinex. TIP, 6:451–462, 1997. 1, 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 184, + 545, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 184, + 545, + 227 + ], + "spans": [ + { + "bbox": [ + 308, + 184, + 545, + 227 + ], + "type": "text", + "content": "[19] Hyunyoung Jung, Eunhyeok Park, and Sungjoo Yoo. Fine-grained semantics-aware representation enhancement for self-supervised monocular depth estimation. In ICCV, pages 12642-12652, 2021. 2, 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 228, + 545, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 228, + 545, + 270 + ], + "spans": [ + { + "bbox": [ + 308, + 228, + 545, + 270 + ], + "type": "text", + "content": "[20] Bomi Kim, Sunhyeok Lee, Nahyun Kim, Donggon Jang, and Dae-Shik Kim. Learning color representations for low-light image enhancement. In WACV, pages 1455-1463, 2022. 2, 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 272, + 545, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 272, + 545, + 304 + ], + "spans": [ + { + "bbox": [ + 308, + 272, + 545, + 304 + ], + "type": "text", + "content": "[21] Chulwoo Lee, Chul Lee, and Chang-Su Kim. Contrast enhancement based on layered difference representation of 2D histograms. TIP, 22:5372-5384, 2013. 5, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 304, + 545, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 304, + 545, + 348 + ], + "spans": [ + { + "bbox": [ + 308, + 304, + 545, + 348 + ], + "type": "text", + "content": "[22] Chongyi Li, Chunle Guo, Ling-Hao Han, Jun Jiang, Ming-Ming Cheng, Jinwei Gu, and Chen Change Loy. Low-light image and video enhancement using deep learning: A survey. TPAMI, 2021. 1, 4" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 350, + 545, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 350, + 545, + 392 + ], + "spans": [ + { + "bbox": [ + 308, + 350, + 545, + 392 + ], + "type": "text", + "content": "[23] Xiaoming Li, Chaofeng Chen, Shangchen Zhou, Xianhui Lin, Wangmeng Zuo, and Lei Zhang. Blind face restoration via deep multi-scale component dictionaries. In ECCV, pages 399-415. Springer, 2020. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 394, + 545, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 394, + 545, + 425 + ], + "spans": [ + { + "bbox": [ + 308, + 394, + 545, + 425 + ], + "type": "text", + "content": "[24] Yi Li, Yi Chang, Changfeng Yu, and Luxin Yan. Close the loop: A unified bottom-up and top-down paradigm for joint image deraining and segmentation. In AAAI, 2022. 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 426, + 545, + 457 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 426, + 545, + 457 + ], + "spans": [ + { + "bbox": [ + 308, + 426, + 545, + 457 + ], + "type": "text", + "content": "[25] Yijun Li, Sifei Liu, Jimei Yang, and Ming-Hsuan Yang. Generative face completion. In CVPR, pages 3911-3919, 2017. 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 460, + 545, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 460, + 545, + 502 + ], + "spans": [ + { + "bbox": [ + 308, + 460, + 545, + 502 + ], + "type": "text", + "content": "[26] Dong Liang, Ling Li, Mingqiang Wei, Shuo Yang, Liyan Zhang, Wenhan Yang, Yun Du, and Huiyu Zhou. Semantically contrastive learning for low-light image enhancement. In AAAI, pages 1555-1563, 2022. 2, 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 504, + 545, + 535 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 504, + 545, + 535 + ], + "spans": [ + { + "bbox": [ + 308, + 504, + 545, + 535 + ], + "type": "text", + "content": "[27] Seokjae Lim and Wonjun Kim. DSLR: Deep stacked Laplacian restorer for low-light image enhancement. TMM, 23:4272-4284, 2020. 2, 3" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 537, + 545, + 569 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 537, + 545, + 569 + ], + "spans": [ + { + "bbox": [ + 308, + 537, + 545, + 569 + ], + "type": "text", + "content": "[28] Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, and Thomas S. Huang. When image denoising meets high-level vision tasks: A deep learning approach. In IJCAI, 2018. 3" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 571, + 545, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 571, + 545, + 613 + ], + "spans": [ + { + "bbox": [ + 308, + 571, + 545, + 613 + ], + "type": "text", + "content": "[29] Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In CVPR, pages 10561-10570, 2021. 1, 2" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 308, + 614, + 545, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 614, + 545, + 646 + ], + "spans": [ + { + "bbox": [ + 308, + 614, + 545, + 646 + ], + "type": "text", + "content": "[30] Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. LL-Net: A deep autoencoder approach to natural low-light image enhancement. PR, 61:650–662, 2017. 2" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 308, + 647, + 545, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 647, + 545, + 679 + ], + "spans": [ + { + "bbox": [ + 308, + 647, + 545, + 679 + ], + "type": "text", + "content": "[31] Kede Ma, Kai Zeng, and Zhou Wang. Perceptual quality assessment for multi-exposure image fusion. TIP, 24:3345-3356, 2015. 5, 7" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "type": "text", + "content": "[32] Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. Toward fast, flexible, and robust low-light image enhancement. In CVPR, pages 5637-5646, 2022. 2" + } + ] + } + ], + "index": 34 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1670" + } + ] + } + ], + "index": 36 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 105 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 105 + ], + "type": "text", + "content": "[33] Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a 'completely blind' image quality analyzer. SPL, 20:209-212, 2012. 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 107, + 287, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 107, + 287, + 150 + ], + "spans": [ + { + "bbox": [ + 48, + 107, + 287, + 150 + ], + "type": "text", + "content": "[34] Sean Moran, Pierre Marza, Steven McDonagh, Sarah Parisot, and Gregory Slabaugh. DeepLPF: Deep local parametric filters for image enhancement. In CVPR, pages 12826-12835, 2020. 1, 2, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 152, + 287, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 152, + 287, + 205 + ], + "spans": [ + { + "bbox": [ + 48, + 152, + 287, + 205 + ], + "type": "text", + "content": "[35] Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic segmentation in the wild. In CVPR, pages 891–898, 2014. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 208, + 287, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 208, + 287, + 240 + ], + "spans": [ + { + "bbox": [ + 48, + 208, + 287, + 240 + ], + "type": "text", + "content": "[36] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster R-CNN: Towards real-time object detection with region proposal networks. NIPS, 28, 2015. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 241, + 287, + 284 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 241, + 287, + 284 + ], + "spans": [ + { + "bbox": [ + 48, + 241, + 287, + 284 + ], + "type": "text", + "content": "[37] Zhengzhong Tu, Hossein Talebi, Han Zhang, Feng Yang, Peyman Milanfar, Alan Bovik, and Yinxiao Li. MAXIM: Multi-axis mlp for image processing. In CVPR, pages 5769-5780, 2022. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 286, + 287, + 340 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 286, + 287, + 340 + ], + "spans": [ + { + "bbox": [ + 48, + 286, + 287, + 340 + ], + "type": "text", + "content": "[38] Jingdong Wang, Ke Sun, Tianheng Cheng, Borui Jiang, Chaorui Deng, Yang Zhao, Dong Liu, Yadong Mu, Mingkui Tan, Xinggang Wang, et al. Deep high-resolution representation learning for visual recognition. TPAMI, 43:3349-3364, 2020. 2, 4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 342, + 287, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 342, + 287, + 374 + ], + "spans": [ + { + "bbox": [ + 48, + 342, + 287, + 374 + ], + "type": "text", + "content": "[39] Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non-uniform illumination images. TIP, 22:3538-3548, 2013. 5, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 376, + 287, + 419 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 376, + 287, + 419 + ], + "spans": [ + { + "bbox": [ + 48, + 376, + 287, + 419 + ], + "type": "text", + "content": "[40] Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Recovering realistic texture in image super-resolution by deep spatial feature transform. In CVPR, pages 606-615, 2018. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 421, + 287, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 421, + 287, + 464 + ], + "spans": [ + { + "bbox": [ + 48, + 421, + 287, + 464 + ], + "type": "text", + "content": "[41] Yufei Wang, Renjie Wan, Wenhan Yang, Haoliang Li, LapPui Chau, and Alex Kot. Low-light image enhancement with normalizing flow. In AAAI, volume 36, pages 2604-2612, 2022. 1, 2, 3, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 466, + 287, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 466, + 287, + 498 + ], + "spans": [ + { + "bbox": [ + 48, + 466, + 287, + 498 + ], + "type": "text", + "content": "[42] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: From error visibility to structural similarity. TIP, 13:600-612, 2004. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 500, + 287, + 532 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 500, + 287, + 532 + ], + "spans": [ + { + "bbox": [ + 48, + 500, + 287, + 532 + ], + "type": "text", + "content": "[43] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retina decomposition for low-light enhancement. In BMVC, 2018. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 534, + 287, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 534, + 287, + 577 + ], + "spans": [ + { + "bbox": [ + 48, + 534, + 287, + 577 + ], + "type": "text", + "content": "[44] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. URetinex-Net: Retinex-based deep unfolding network for low-light image enhancement. In CVPR, pages 5901–5910, 2022. 1, 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 579, + 287, + 611 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 579, + 287, + 611 + ], + "spans": [ + { + "bbox": [ + 48, + 579, + 287, + 611 + ], + "type": "text", + "content": "[45] Ke Xu, Xin Yang, Baocai Yin, and Rynson WH Lau. Learning to restore low-light images via decomposition-and-enhancement. In CVPR, pages 2281-2290, 2020. 1, 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 613, + 287, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 613, + 287, + 644 + ], + "spans": [ + { + "bbox": [ + 48, + 613, + 287, + 644 + ], + "type": "text", + "content": "[46] Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. SNR-aware low-light image enhancement. In CVPR, pages 17714-17724, 2022. 1, 2, 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 647, + 287, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 287, + 689 + ], + "type": "text", + "content": "[47] Canqian Yang, Meiguang Jin, Xu Jia, Yi Xu, and Ying Chen. AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancement. In CVPR, pages 17522-17531, 2022. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "text", + "content": "[48] Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to perceptual quality: A semi-" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 544 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "text", + "content": "supervised approach for low-light image enhancement. In CVPR, pages 3063-3072, 2020. 1, 3, 6, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 96, + 545, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 96, + 545, + 139 + ], + "spans": [ + { + "bbox": [ + 307, + 96, + 545, + 139 + ], + "type": "text", + "content": "[49] Wenhan Yang, Wenjing Wang, Haofeng Huang, Shiqi Wang, and Jiaying Liu. Sparse gradient regularized deep Retinex network for robust low-light image enhancement. TIP, 30:2072-2086, 2021. 1, 5, 6, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 141, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 141, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 141, + 545, + 184 + ], + "type": "text", + "content": "[50] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, pages 5728-5739, 2022. 4" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 186, + 545, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 545, + 228 + ], + "type": "text", + "content": "[51] Syed Waqas Zamir, Aditya Arora, Salman Hameed Khan, Hayat Munawar, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Learning Enriched Features for Fast Image Restoration and Enhancement. TPAMI, 2022. 2, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 231, + 545, + 273 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 231, + 545, + 273 + ], + "spans": [ + { + "bbox": [ + 307, + 231, + 545, + 273 + ], + "type": "text", + "content": "[52] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 275, + 545, + 307 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 275, + 545, + 307 + ], + "spans": [ + { + "bbox": [ + 307, + 275, + 545, + 307 + ], + "type": "text", + "content": "[53] Yonghua Zhang, Xiaojie Guo, Jiayi Ma, Wei Liu, and Jiawan Zhang. Beyond brightening low-light images. IJCV, 129:1013-1037, 2021. 1, 2, 6, 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 309, + 545, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 309, + 545, + 342 + ], + "spans": [ + { + "bbox": [ + 307, + 309, + 545, + 342 + ], + "type": "text", + "content": "[54] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In ACMMM, pages 1632-1640, 2019. 1, 2, 6, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 343, + 545, + 386 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 343, + 545, + 386 + ], + "spans": [ + { + "bbox": [ + 307, + 343, + 545, + 386 + ], + "type": "text", + "content": "[55] Zhao Zhang, Huan Zheng, Richang Hong, Mingliang Xu, Shuicheng Yan, and Meng Wang. Deep color consistent network for low-light image enhancement. In CVPR, pages 1899-1908, 2022. 2, 3, 4" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 388, + 545, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 388, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 307, + 388, + 545, + 430 + ], + "type": "text", + "content": "[56] Zunjin Zhao, Bangshu Xiong, Lei Wang, Qiaofeng Ou, Lei Yu, and Fa Kuang. RetinexDIP: A unified deep framework for low-light image enhancement. TCSVT, 32:1076-1088, 2021. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 433, + 545, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 433, + 545, + 465 + ], + "spans": [ + { + "bbox": [ + 307, + 433, + 545, + 465 + ], + "type": "text", + "content": "[57] Chuanjun Zheng, Daming Shi, and Wentian Shi. Adaptive unfolding total variation network for low-light image enhancement. In ICCV, pages 4439-4448, 2021. 2, 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 467, + 545, + 499 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 467, + 545, + 499 + ], + "spans": [ + { + "bbox": [ + 307, + 467, + 545, + 499 + ], + "type": "text", + "content": "[58] Shen Zheng and Gaurav Gupta. Semantic-guided zero-shot learning for low-light image/video enhancement. In WACV, pages 581-590, 2022. 2, 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 501, + 545, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 501, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 307, + 501, + 545, + 544 + ], + "type": "text", + "content": "[59] Minfeng Zhu, Pingbo Pan, Wei Chen, and Yi Yang. EEMEFN: Low-light image enhancement via edge-enhanced multi-exposure fusion network. 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A situation hyper-graph is a representation that describes situations as scene sub-graphs for video frames and hyper-edges for connected sub-graphs and has been proposed to capture all such information in a compact structured form. In this work, we propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs, coined Situation Hyper-Graph based Video Question Answering (SHG-VQA). To this end, we train a situation hyper-graph decoder to implicitly identify graph representations with actions and object/human-object relationships from the input video clip. and to use cross-attention between the predicted situation hyper-graphs and the question embedding to predict the correct answer. The proposed method is trained in an end-to-end manner and optimized by a VQA loss with the cross-entropy function and a Hungarian matching loss for the situation graph prediction. The effectiveness of the proposed architecture is extensively evaluated on two challenging benchmarks: AGQA and STAR. Our results show that learning the underlying situation hypergraphs helps the system to significantly improve its performance for novel challenges of video question-answering tasks1.", + "bbox": [ + 75, + 335, + 473, + 728 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 746, + 207, + 762 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Video question answering in real-world scenarios is a challenging task as it requires focusing on several factors including the perception of the current scene, language understanding, situated reasoning, and future prediction. Visual perception in the reasoning task requires capturing various aspects of visual understanding, e.g., detecting a diverse set", + "bbox": [ + 75, + 772, + 470, + 863 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/b12d3ee4e4edbfe888ae8a98da74e8d3f6644ede2494554e1408cd9c27ff643a.jpg", + "image_caption": [ + "Figure 1. The situation hyper-graph for a video is composed of situations with entities and their relationships (shown as subgraphs in the pink box). These situations may evolve over time. Temporal actions act as hyper-edges connecting these situations into one situation hyper-graph. Learning situation graphs, as well as temporal actions, is vital for reasoning-based video question answering." + ], + "image_footnote": [], + "bbox": [ + 501, + 305, + 893, + 465 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "of entities, recognizing their interactions, as well as understanding the changing dynamics between these entities over time. Similarly, linguistic understanding has its challenges as some question or answer concepts may not be present in the input text or video.", + "bbox": [ + 496, + 594, + 893, + 669 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Visual question answering, as well as its extension over time, video question answering, have both benefited from representing knowledge in graph structures, e.g., scene graphs [20, 35], spatio-temporal graphs [4, 55], and knowledge graphs [36, 45]. Another approach in this direction is the re-introduction of the concept of \"situation cognition\" embodied in \"situation hyper-graphs\" [47]. This adds the computation of actions to the graphs that capture the interaction between entities. In this case, situations are represented by hyper-graphs that join atomic entities and relations (e.g., agents, objects, and relationships) with their actions (Fig. 1). This is an ambitious task for existing systems as it is impractical to encapsulate all possible interactions in the real-world context.", + "bbox": [ + 496, + 671, + 895, + 881 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recent work [23] shows that transformers are capable", + "bbox": [ + 517, + 885, + 890, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "$^{1}$ Code will be available at https://github.com/aurooj/SHG-VQA", + "bbox": [ + 75, + 875, + 470, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "14879", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "of learning graphs without adapting graph-specific details in the architectures achieving competitive or even better performance than sophisticated graph-specific models. Our work supports this idea by implicitly learning the underlying hyper-graphs of a video. Thus, it requires no graph computation for inference and uses decoder's output directly for cross attention module. More precisely, we propose to learn situation hyper-graphs, namely framewise actor-object and object-object relations as well as their respective actions, from the input video directly without the need for explicit object detection or other required prior knowledge. While the actions capture events across transitions over multiple frames, such as Drinking from a bottle, the relationship encoding actually considers all possible combinations of static, single frame actor-object, and object-object relationships as unique classes, e.g., in the form of person - hold - bottle or bottle - stands on - table, thus serving as an object and relation classifier. Leveraging this setup allows us to streamline the spatio-temporal graph learning as a set prediction task for predicting relationship predicates and actions in a Situation hyper-graph Decoder block. To train the Situation Graph Decoder, we use a bipartite matching loss between the predicted set and ground truth hyper-graph tokens. The output of the situation graph decoder is a set of action and relationship tokens, which are then combined with the embedding of the associated question to derive the final answer. An overview of the proposed architecture is given in Fig. 2. Note that, compared to other works targeting video scene graph generation, e.g., those listed in [63], we are less focused on learning the best possible scene graph, but rather on learning the representation of the scene which best supports the question answering task. Thus, while capturing the essence of a scene, as well as the transition from one scene to the other, we are not only optimizing the scene graph accuracy but also considering the VQA loss.", + "bbox": [ + 76, + 90, + 472, + 619 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We evaluate the proposed method on two challenging video question answering benchmarks: a) STAR [47], featuring four different question types, interaction, sequence, prediction, and feasibility based on a subset of the real-world Charades dataset [44]; and b) Action Genome QA (AGQA) [11] dataset which tests vision focused reasoning skills based on novel compositions, novel reasoning steps, and indirect references. Compared to other VQA datasets, these datasets provide dense ground truth hyper-graph information for each video, which allows us to learn the respective embedding. Our results show that the proposed hyper-graph encoding significantly improves VQA performance as it has the ability to infer correct answers from spatio-temporal graphs from the input video. Our ablations further reveal that achieving high-quality graphs can be critical for VQA performance.", + "bbox": [ + 76, + 619, + 472, + 859 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contributions to this paper are as follows:", + "bbox": [ + 96, + 861, + 400, + 875 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- We introduce a novel architecture that enables the com", + "bbox": [ + 94, + 885, + 470, + 898 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "putation of situation hyper-graphs from video data to solve the complex reasoning task of video question-answering;", + "bbox": [ + 529, + 90, + 893, + 137 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a situation hyper-graph decoder module to decode the atomic actions and object/actor-object relationships and model the hyper-graph learning as a transformer-based set prediction task and use a set prediction loss function to predict actions and relationships between entities in the input video;", + "- We use the resulting high-level embedding information as sole visual information for the reasoning and show that this is sufficient for an effective VQA system." + ], + "bbox": [ + 517, + 148, + 893, + 297 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 316, + 640, + 330 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Video Question Answering: Video question answering is an active area of research with efforts in insightful directions [37,62], such as attention [16, 18, 29], cross-modal interactions [25, 41, 52, 56, 59, 60], hierarchical learning [5, 24, 38], and so on. A few other algorithms classes include modular networks [5, 24, 49], symbolic reasoning [47, 53, 54], and memory networks [8, 10]. Several video QA benchmarks are introduced to evaluate this task from varying perspectives entailing description [34, 51, 57, 61], temporal reasoning [17, 53, 64], causal structures [48, 53], visual-language comprehension [26, 46, 52], relational reasoning [46], and measuring social intelligence [58]. STAR [47] benchmark goes one step further providing a benchmark to perform diagnostic study at additional fronts such as predicting future interactions and feasibility of next possible actions in the unseen future. Existing approaches on the benchmark either rely on object features [40] as nodes explicitly modeling their interactions through a message passing mechanism [15, 47], or benefit from efficient input sampling strategies during training to learn robust visual representations [24, 25]. We, however, take a different approach and focus on inferring the underlying semantic graph structure in the video and use it for QA reasoning. Our method is independent of using pretrained object detectors and uses a simple approach to learn to infer the sets of relationship predicates as well as actions for each frame. The model is trained end-to-end with the frozen backbone.", + "bbox": [ + 496, + 340, + 895, + 750 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Graph-based VQA: Another related line of work is graph-based VQA methods [22, 27]. Some of them work on object features extracted from a pretrained detector (e.g., Faster-RCNN [40]) [5, 15, 31, 42, 49], while some operate on frame-level [19, 25, 56] (e.g., ResNet), clip-level features [38, 41, 52](e.g., C3D, S3D) or transformer-based backbones [7, 32, 39]. We focus on predicting atomic actions, and relationship triplets from the frame-level or clip-level features directly instead of an explicit graph.", + "bbox": [ + 496, + 763, + 895, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "14880", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/d54a2ede95f3e54d7ea1d91a60e1202e72d987e549c794f69e9e021fb14447e4.jpg", + "image_caption": [ + "Figure 2. The SHG-VQA architecture: we start with encoding the input video into spatio-temporal features using a pre-trained backbone. These video features are flattened into a sequence of tokens of length $T \\times h \\times w$ and position encoded to be further processed through a visual encoder. These encoded tokens are input to the action decoder to predict the set of atomic actions from the input action queries as well as to the relationship decoder which takes relationship queries as input along with the video tokens. The action decoder and relationship decoder output the situation graph embeddings. In the text branch, the question and the answer choices are composed into a sequence and passed through a text transformer to obtain encoded word embeddings; for open-ended VQA, only the question is passed to the text transformer. The generated hyper-graph along with the encoded text tokens are then used as input to a cross-attentional transformer and the combined representations are used to predict the correct answer with a classifier. Section 3.4 describes the losses and training objectives." + ], + "image_footnote": [], + "bbox": [ + 83, + 90, + 890, + 310 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Scene Graph Generation: The proposed formulation of situation hyper-graph deviates from existing scene-graph generation approaches [2, 47, 63] as we do not require object detections as input or object level supervision, nor do we model it in an explicit graph structure. Our goal is simple: given an input video, predict all object-relation-object and actor-relation-object triplets as well as associated actions for each video frame. Our intuition is that forcing the model to predict these predicates will drive the system to learn a latent graph structure for visual input. The predicted situation hyper-graph is treated as an abstract video representation and used for VQA reasoning in the next step.", + "bbox": [ + 75, + 458, + 472, + 640 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. The SHG-VQA Model", + "text_level": 1, + "bbox": [ + 76, + 660, + 290, + 678 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Situation video question answering has three essential steps 1) the visual recognition capacities of visual entities, their relationships, actions, and how these transition over time, 2) the language understanding capacity to the questions, 3) the question-guided reasoning process over the representation learned in the first step. Sub-optimal performance at any of these steps will affect the overall task performance. A key problem here is that capturing the visual structures directly from raw data, e.g., in the form of features often results in a rather noisy signal and does not provide suitable input for high-level language-guided reasoning. To overcome this mismatch, we propose to learn the implicit structure of the visual input as an intermediate step between learning the video representation and question-based reasoning. Forcing the model", + "bbox": [ + 75, + 688, + 472, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "to learn to predict this implicit structure (actions, relations between entities) not only improves the video representation but also acts as a lightweight, high-level representation of the video content and can be used for the VQA task. We illustrate our architecture in Fig. 2.", + "bbox": [ + 496, + 458, + 893, + 532 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Input Processing", + "text_level": 1, + "bbox": [ + 500, + 544, + 666, + 559 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given the input video and the question, we encode the inputs as described below:", + "bbox": [ + 496, + 566, + 890, + 595 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Question Encoder: The question is first tokenized into word tokens using a wordPiece tokenizer. These word tokens along with the special class token $[CLS]$ are the inputs of an embedding layer. The output word embeddings from this layer are input to a transformer encoder that encodes each word using multi-head self-attention between different words at each encoder layer.", + "bbox": [ + 496, + 598, + 890, + 703 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Video Encoder: Let $V \\in \\mathbb{R}^{T \\times H \\times W \\times 3}$ be the input video clip, where $T$ is the clip length with 3 color channels, height $H$ and width $W$ . First, we extract video features $x_{V} \\in \\mathbb{R}^{T \\times h \\times w \\times d_{x}}$ using a convolutional backbone with $d_{x}$ being the feature dimension, $h$ and $w$ are the reduced feature's height and width. As transformers process sequential input data, the video features $x_{V}$ are flattened into a sequence (of size $\\mathbb{R}^{Thw \\times d_x}$ ) and reduced to dimension $d$ through a linear layer. Then, we append a trainable vector of dimension $d$ for a special class token [VIS] to this sequence of video features at index 0. These features are combined with position encodings and input to a transformer-based video encoder $V_{e}$ .", + "bbox": [ + 496, + 703, + 893, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "14881", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The output features of the video are forwarded to the action decoder and relationship decoder to infer the situation graph capturing the action information, as well as the entities and their relationships. The output of the last layer of both decoders is then combined and augmented with frame positions, forming the final hyper-graph embedding. We further attach a randomly initialized class token $[HG]$ . These situation graph embeddings are then input to a multi-layered cross-attentional transformer encoder for more fine-grained interaction between the question words and semantic knowledge extracted from the video in the form of a graph. The output features corresponding to $[CLS]$ and $[HG]$ tokens are input to an answer classifier to produce an answer.", + "bbox": [ + 75, + 90, + 472, + 287 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Situation hyper-graph Generation", + "text_level": 1, + "bbox": [ + 76, + 300, + 375, + 316 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Real-world video understanding relies on the scene understanding including changing relationships between the objects over time and the evolving actions. Therefore, we represent the given video as a \"situation graph\" denoted by $G$ which describes actions and relationships between objects. Let $G = (V, E)$ , where $V$ is the set of vertices representing all possible entities in the dataset, and $E$ is the set of edges representing all possible relationships between each pair of entities. Given an input video, we want to learn a situation graph $g_{t} = (v_{t}, e_{t})$ , $v_{t} \\in V$ , $e_{t} \\in E$ for each time step $t \\in \\{1, 2, \\dots, T\\}$ in the video, that captures the entities (objects, actors), their relationships as well as the associated actions that are present in that frame. The hyper-graph for one video is thus represented by the set of situation graphs $\\mathcal{G} = \\{g_{1}, \\dots, g_{T}\\}$ . An action may comprise multiple relationships and objects. For each frame, we further have a set of actions $A_{t} = a_{1}, a_{2}, \\dots, a_{N}$ . The set of actions for the full video is then given as $\\mathcal{A} = \\{A_{1}, \\dots, A_{T}\\}$ . Note that the actions are predicted in addition to the graph structure and that both will be merged in a situation hyper-graph embedding in a separate step after the decoder block. Rather than predicting the set of vertices and edges in the graph $g_{t}$ , we propose to predict the graph structure by formulating this graph prediction as follows:", + "bbox": [ + 75, + 325, + 473, + 686 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Let $x_{V_e} \\in \\mathbb{R}^{T' \\times h \\times w \\times d}$ be the encoded video features, where $T'$ denotes the temporal length of encoded features, $h$ and $w$ are spatial dimensions, and $d$ is the feature's dimension. A relationship predicate $p$ describes interactions between entities by triplet tokens object-relation-object or actor-relation-object. We propose to predict the set of relationship predicates $R$ and the set of atomic actions $A$ occurring in each video frame. This is intuitive because the ability to predict the atomic actions and relations between entities for each time step benefits the high level reasoning tasks such as video question answering in this work. Therefore, for each time step $t \\in \\{1,2,\\dots,T\\}$ in the video, we predict the set of relationship predicates denoted by $R_t$ in each video frame where $R_t = \\{p_1,p_2,\\dots,p_M\\}$ and $p_i$ is the", + "bbox": [ + 75, + 686, + 470, + 901 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/f103a63b4502d1faa176348019aa0e11b89742f5de852ae8463e0ce093435a92.jpg", + "image_caption": [ + "Figure 3. Situation hyper-graph embeddings: We start with decoded queries from action and relationship decoder. Then we add type encoding vectors $[ACT]$ and $[REL]$ for actions and relationships, attention masks, and an embedding vector for the situation ID $(t \\in \\{1, \\dots, T\\})$ . The sums are input to the cross-attentional module. See section 3.2.2 for details)." + ], + "image_footnote": [], + "bbox": [ + 501, + 88, + 893, + 319 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$i^{th}$ predicate between two entities, representing the vertices $v_{x}$ and $v_{y}$ (object-object or actor-object) and the relation resp. edge $e_i$ represented as $< v_x,e_i,v_y>$ , $M = |R_t|$ is the relationship set size. Additionally, we predict the set of $N$ actions $A_{t}$ for each time step $t$ where $A_{t} = \\{a_{1},a_{2},\\dots,a_{N}\\}$ and $a_{j}$ is the $j^{th}$ action occurring at time step $t$ . $N = |A_t|$ is the actions set size for each step $t$ . $M$ and $N$ are hyperparameters in our system.", + "bbox": [ + 496, + 425, + 893, + 546 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To obtain actions and relation predicates from the video, we use a transformer decoder that takes video features as memory to learn action queries and relation queries.", + "bbox": [ + 496, + 547, + 893, + 594 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2.1 Prediction Head", + "text_level": 1, + "bbox": [ + 500, + 602, + 668, + 616 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The decoded output embeddings for action and relationship queries are input to respective prediction heads. Prediction heads use a 2-layer feed forward network (FFN) with GELU activation and LayerNorm.", + "bbox": [ + 496, + 627, + 893, + 686 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Considering that actions and relationships at a given time step are permutation-invariant, we use optimal bipartite matching between predicted and ground truth actions/relationships. An optimal bipartite matching between the predicted classes and ground-truth labels is the one with the minimum matching cost. Once the optimal matching pairs have been obtained, we use a Hungarian loss function to optimize for ground truth classes [1]. See section 3.4 for details of the loss function.", + "bbox": [ + 496, + 688, + 893, + 823 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2.2 Situation hyper-graph Embedding", + "text_level": 1, + "bbox": [ + 500, + 845, + 794, + 861 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For the decoded queries of actions and relationships (referred as graph token embeddings), situation hyper-graph", + "bbox": [ + 496, + 869, + 893, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "14882", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/ec66a1bdbec07a50d4fb341509d717a53684787acbb9e3b49ad19de795d1f325.jpg", + "table_caption": [ + "Table 1. Results on AGQA dataset for different question types. The best results are shown in bold font. Numbers are reported in percentages." + ], + "table_footnote": [], + "table_body": "
MethodReasoningSemanticStructureOverall
obj-relrel-actionobj-actionsuperlativesequencingexistsdurationactivityobjrelactionquerycomparechooselogicverifybinaryopenall
PSAC [30]37.8449.9550.0033.2049.7849.9445.214.1437.9749.9546.8531.6349.4946.5649.9649.9048.8731.6340.18
HME [9]37.4249.9049.9733.2149.7749.9647.035.4337.5549.9947.5831.0149.7146.4249.8749.9648.9131.0139.89
HCRN [24]40.3349.8649.8533.5549.7050.0143.845.5240.3349.9646.4136.3449.2243.4250.0250.0147.9736.3442.11
SHG-VQA46.4260.6764.6338.8362.1756.0648.1510.1247.6156.1953.8343.4260.6847.7652.8656.6355.0443.4249.20
", + "bbox": [ + 84, + 114, + 885, + 199 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "embeddings are constructed in order to be used with question features for video question answering. First, these action and relationship graph embeddings are combined for each time step $t$ representing a situation at $t$ . Then, we add token type embedding $[ACT]$ for actions and $[REL]$ for relations to their respective graph embeddings. A situation ID (or frame position $t$ ) embedding is also added to these embeddings. An additional attention mask is used to differentiate actual tokens and padded tokens (no-class token $\\phi$ ) at training time. At inference time, no attention mask is used as we do not employ any information about the graph at test time. Finally, we add a special class token $[HG]$ to this sequence of features. See Fig. 3 for visualization.", + "bbox": [ + 75, + 223, + 473, + 420 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Cross-attentional Transformer Module", + "text_level": 1, + "bbox": [ + 76, + 428, + 413, + 444 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The situation hyper-graph embeddings obtained at previous step (section 3.2.2) are input along with the question to a cross-attentional transformer module which allows fine-grained computation between the question features and the graph features. A standard co-attentional transformer module is used for cross-attention between the two sequential feature inputs. The feature outputs corresponding to the $[HG]$ token and $[CLS]$ token from the cross-attentional transformer block are fed to a feed-forward network (FFN) for answer prediction.", + "bbox": [ + 75, + 452, + 473, + 602 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Learning Objective", + "text_level": 1, + "bbox": [ + 76, + 611, + 264, + 628 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The SHG-VQA model is trained with the following training objective i.e.,", + "bbox": [ + 75, + 635, + 472, + 665 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL = L _ {a c t} + L _ {r e l} + L _ {v q a} \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 189, + 676, + 468, + 694 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $L_{act}$ and $L_{rel}$ are the set prediction loss terms for predicting the action set and relationship set for the video, and $L_{vqa}$ is the cross-entropy loss over the predicted situation graph and question.", + "bbox": [ + 76, + 704, + 472, + 765 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Actions and relationships set prediction loss: The situation graph prediction module infers fixed sets of sizes $|N| \\times T$ actions and $|M| \\times T$ relationships for $T$ -length video clip in a single pass through the action decoder and relationship decoder respectively. We modify the set prediction loss used in [1] as follows. Let $A$ be the set of ground-truth actions and $\\hat{A} = \\{\\hat{a}_i\\}_{i=1}^{|N| \\times T}$ be the predicted set of actions. In a scenario where $\\hat{A}$ is larger than the set of actions present", + "bbox": [ + 75, + 776, + 473, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "in the video, a special class $\\phi$ (no class) is padded to the ground truth set $A$ . We obtain a bipartite matching between ground-truth and predicted set for each timestep $t$ as follows:", + "bbox": [ + 496, + 223, + 893, + 270 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\sigma} _ {a} = \\sum_ {t} ^ {T} \\operatorname {a r g m i n} _ {\\sigma_ {t} \\in \\zeta_ {| N |}} \\sum_ {i} ^ {| N |} \\mathcal {L} _ {\\text {m a t c h}} \\left(a _ {t _ {i}}, \\hat {a} _ {\\sigma_ {t} (i)}\\right) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 522, + 292, + 893, + 335 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Where, $\\sigma_{t}$ is a permutation of N elements for frame t, $\\mathcal{L}_{\\text{match}}(a_{ti}, \\hat{a}_{\\sigma_t(i)})$ is a pair-wise matching cost between $i^{th}$ ground-truth action label in $t^{th}$ frame i.e., $a_{t_i}$ and a predicted action label at index $\\sigma_t(i)$ . This optimal assignment for each step $t$ is computed using the Hungarian algorithm and the cost is summed over all $T$ steps. The proposed set prediction loss takes into account only the class predictions for all video frames with no bounding box ground truths being used, different from the original set prediction loss used in [1] for object detection in images. Let $\\hat{p}(c_{t(i)})$ be the class probability for the action prediction at $\\sigma_t(i)$ , $\\mathcal{L}_{\\text{match}}(a_{t_i}, \\hat{a}_{\\sigma_t(i)})$ would be $-\\mathbb{1}_{\\{c_{t(i)} \\neq \\phi\\}} \\hat{p}_{\\sigma_t(i)}(c_{t(i)})$ . After we obtain a one-to-one optimal matching between ground-truth and predicted set items without duplicates at each time step, we can compute the loss between the matched pairs using a Hungarian loss as follows:", + "bbox": [ + 496, + 345, + 893, + 588 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {a c t}} (a, \\hat {a}) = \\sum_ {i = 1} ^ {| N | \\times T} - \\log \\hat {p} _ {\\hat {\\sigma} (i)} \\left(c _ {i}\\right) \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 578, + 595, + 890, + 640 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Likewise, $R$ is the set of ground-truth relations and $\\hat{R} = \\{\\hat{p_i}\\}_{i = 1}^{|M|\\times T}$ denotes the predicted relationships. $L_{rel}$ is formulated as follows:", + "bbox": [ + 500, + 648, + 893, + 696 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\sigma} _ {p} = \\sum_ {t} ^ {T} \\operatorname {a r g m i n} _ {\\sigma_ {t} \\in \\zeta_ {| M |}} \\sum_ {i} ^ {| M |} \\mathcal {L} _ {\\text {m a t c h}} \\left(p _ {t _ {i}}, \\hat {p} _ {\\sigma_ {t} (i)}\\right) \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 521, + 720, + 893, + 763 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {r e l} (p, \\hat {p}) = \\sum_ {i = 1} ^ {| M | \\times T} - \\log \\hat {p} _ {\\hat {\\sigma} (i)} (c _ {i}) \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 578, + 775, + 893, + 819 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\hat{\\sigma}(i)$ is the optimal matching obtained in the previous step for each frame. The inferred graph is input to the cross-attentional transformer module along with the question and the answer choices as explained in section 3.2.2. The proposed network is trained in an end-to-end manner.", + "bbox": [ + 496, + 824, + 893, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "14883", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/c92e0f178205b9d0f9fcb54015a3a56c35479cef94478792ef29e873db6bdd07.jpg", + "table_caption": [ + "Table 2. Results on AGQA's novel compositions test metric." + ], + "table_footnote": [], + "table_body": "
MethodSequencingSuperlativeDurationObj-relationOverall
BOAllBOAllBOAllBOAllBOAll
PSAC [30]49.1929.3340.9645.2317.7633.3247.8934.8442.0643.760.0124.2846.4919.3434.71
HME [9]49.3328.0640.5344.0613.830.9548.4534.7242.3139.580.0021.9645.4217.1733.15
HCRN [24]48.3130.0040.7345.1217.3033.0646.1539.1143.0137.152.8621.8844.8820.1234.13
SHG-VQA50.8838.5945.7951.1423.6439.2551.8449.2150.6639.736.2324.8249.0726.6839.37
", + "bbox": [ + 129, + 114, + 841, + 199 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/8c49ed809474c3254a6e3ab1bf47529ef9745e7b5207123fa169dbe95eb8814e.jpg", + "table_caption": [ + "Table 3. Evaluation on AGQA's more compositional steps." + ], + "table_footnote": [], + "table_body": "
More Compositional StepsBinaryOpenAll
PSAC [30]47.6514.8147.19
HME [9]48.0920.9847.72
HCRN [24]46.9623.7046.63
SHG-VQA47.1322.6646.97
", + "bbox": [ + 143, + 247, + 401, + 308 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/b50e2b572029c54e5abd18b698d8924e14b863a3facf8398aacc557e4a2aa8e4.jpg", + "table_caption": [ + "Table 4. Evaluation on AGQA's indirect references test metric." + ], + "table_footnote": [], + "table_body": "
MethodObjectActionTemporal
BOAllBOAllBOAll
PrecisionPSAC63.6953.7756.6461.0152.4653.2457.5253.7454.39
HME62.9552.3155.3958.2148.1249.0455.9952.4253.04
HCRN54.0667.2463.4353.8764.4363.4752.3566.8464.34
SHG-VQA76.9386.2781.5979.5587.4084.9069.0887.3582.87
RecallPSAC45.0627.3638.8040.9122.1825.9635.1326.8430.64
HME46.0326.8039.2341.3221.7125.6737.9626.5931.80
HCRN44.8435.4641.5244.0130.4333.1735.1134.3834.71
SHG-VQA53.8643.9849.8554.1637.8343.1052.2143.5746.78
", + "bbox": [ + 78, + 339, + 475, + 459 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 474, + 209, + 491 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Datasets", + "text_level": 1, + "bbox": [ + 76, + 500, + 179, + 515 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "AGQA Benchmark [11]: The Action Genome Question Answering benchmark is a visual dataset comprising 192M hand-crafted questions about 9.6K videos from the Charades dataset [43]. In addition to VQA accuracy, AGQA presents three testing metrics for testing the VQA methods: indirect references, novel compositions, and more compositional steps. We use the AGQA 2.0 Balanced dataset, which consists of 2.27M question-answer pairs as a result of balancing the original dataset using stricter procedures to reduce as much language bias as possible. Of the 2.27M questions, there are approximately 1.6M training questions and 669K test questions [12]. To have a standard train-val-test setup for our experiments, we randomly sampled $10\\%$ QA pairs from training data for validation of hyperparameters.", + "bbox": [ + 75, + 525, + 470, + 737 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "STAR Benchmark [47]: The STAR dataset provides 60K situated reasoning questions based on 22K trimmed situation video clips, also based on the Charades dataset [43]. They further provide $\\sim 144\\mathrm{K}$ ground-truth situation graphs including 111 actions, 37 unique objects, and 24 relationships. The dataset is split into training, validation, and test sets where test evaluation can be done on the evaluation server a limited number of times. We perform ablations and analysis on the validation set and report test set results to compare with the baselines in Table 5.", + "bbox": [ + 75, + 750, + 470, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Implementations", + "text_level": 1, + "bbox": [ + 500, + 222, + 666, + 239 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "On the VQA task, we report accuracy; we also report mAP for situation hyper-graph predictions. For AGQA's indirect references testing metric, precision and recall are reported. For STAR benchmark, we follow the same training protocol as [47] and train SHG-VQA from scratch on each question type separately unless specified otherwise. Training details are shared in the supplementary document.", + "bbox": [ + 496, + 250, + 890, + 354 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Visual Embeddings: The video frames are resized to size $224 \\times 224$ with a clip length of 16 frames. We use RandAugment [3] for data augmentation during training. The video clip is input to a pretrained convolutional network with freeze weights to obtain video features of size $16 \\times 7 \\times 7 \\times 2048$ . A 2-layer 3D convolutional block with the kernel of size $5 \\times 3 \\times 3$ further processes the zero-padded extracted features yielding features of size $8 \\times 7 \\times 7 \\times d$ . The spatio-temporal dimensions are then flattened to obtain a sequence of length $Thw = 392$ $d$ -dimensional tokens where $d = 768$ . The input encoders as well as situation hyper-graph decoders use $L = 5$ transformer encoder layers with non-shared weights.", + "bbox": [ + 496, + 359, + 892, + 542 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Query embeddings for action and relationship predicates: The features output from the video encoder is then input to a situation hyper-graph decoder comprising transformer-based action and relationship decoders. Our best model uses $M = 8$ relation queries and $N = 3$ action queries for each situation; $T$ is set to 16. AGQA has 157 total raw actions, 36 unique objects, and 44 unique relationships which obtain 456 relationship triplets $< v_{x}, e_{i}, v_{y} >$ in the training and validation set. For STAR dataset, there are 37 objects, and 24 relationships yielding 563 unique relation predicates; it also has 111 action classes.", + "bbox": [ + 496, + 547, + 893, + 712 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Situation graph embeddings: The decoded situation graph queries are input to a cross-attentional transformer. The input situation graph embedding is a sum of 4 different encoding types: 1) decoded query embeddings for predicted actions and relationship predicates, 2) situation IDs denoting the situation (or frame) number for each query, 3) attention mask set to 1 for actual tokens and 0 for padded tokens, and 4) token type embeddings to distinguish between action tokens and relationship tokens as shown in Fig. 3. Our network uses a 2-layer co-attentional module [33] for cross-attention.", + "bbox": [ + 496, + 719, + 893, + 885 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "14884", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/62b33b892546a31cdc5d55cb61aa59de447d268681f827a71f6c314b3917247d.jpg", + "table_caption": [ + "Table 5. Results on STAR dataset. Best results are shown in bold font and second best results are underlined. Numbers are reported for VQA accuracy in percentages." + ], + "table_footnote": [], + "table_body": "
Method(test)BackboneObj.Hyper.Question TypeOverall
Inter.Seq.Pred.Feas.
Q-type (Random) [21]-XX25.0624.9324.7924.8124.89
Q-type (Frequent) [21]-XX19.0919.4512.9018.3117.44
Blind Model (LSTM) [14]GloVeXX32.2432.1728.5628.4130.34
Blind Model (BERT) [6]BERTXX32.6834.2129.9829.2631.53
CNN-LSTM [53]ResNext101-K400XX33.2532.6730.6930.4331.76
CNN-BERT [28]ResNext101-K400XX33.5937.1630.9530.8433.14
LCGN [15]ResNext101-K400✓X39.0137.9728.8126.9833.19
HRCN [24]ResNext101-K400✓X39.1038.1728.7527.2733.32
ClipBERT [25]ResNext101-K400XX39.8143.5932.2431.4236.70
NS-SR [47]ResNext101-K400✓✓30.8831.7630.2329.7330.65
SHG-VQA (Ours)SlowR50-K400X✓47.9842.0335.3432.5239.47
SHG-VQA (Ours)ResNext101-ImageNet1KX✓45.842.7734.6429.9138.28
", + "bbox": [ + 174, + 128, + 792, + 305 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "AGQA: For AGQA, we train our model with SlowR50 backbone and report results for VQA accuracy on the test set. We also report our model's generalization capability to indirect references. Furthermore, we train our network to report its generalization to novel compositions and to more compositional steps. For more compositional steps, we train our network with randomly sampled 100K QA pairs.", + "bbox": [ + 75, + 316, + 472, + 421 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "STAR: Following [47], we train the model for each question type separately to compare with the baselines. However, this protocol is expensive in terms of time and resources. To address the matter of limited resources, we also tried combining all questions together after the questions filtering from interaction and sequence types and train a single model instead of multiple trainings. We use this training regime to study models ablations.", + "bbox": [ + 75, + 422, + 472, + 542 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5. Results and Analysis", + "text_level": 1, + "bbox": [ + 76, + 549, + 274, + 566 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.1. Comparison to State-of-the-Art", + "text_level": 1, + "bbox": [ + 76, + 574, + 354, + 590 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "AGQA: We compare our method with the existing state-of-the-art methods on AGQA benchmark. The best baseline on AGQA is HCRN [24] for overall accuracy. HCRN uses appearance features from ResNet101 as well as motion features from ResNext101-Kinetics400 backbones. Our model outperforms HCRN by a significant margin of $7.09\\%$ (HCRN: $42.11\\%$ vs. SHG-VQA: $49.20\\%$ ) in terms of overall accuracy (see Table 1). We observe the biggest improvement of $14.63\\%$ absolute points on object-action reasoning questions compared to the best model in that category i.e., PSAC [30]: $50.00\\%$ vs. SHG-VQA: $64.63\\%$ . We further report results on the three novel testing metrics as follows:", + "bbox": [ + 75, + 598, + 470, + 777 + ], + "page_idx": 6 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "a) Novel Compositions: For novel composition at test time, we observe an overall gain of $4.70\\%$ when compared to the best contender, i.e., PSAC [30]. For open-ended questions, we outperform HCRN by $6.56\\%$ (see Table 2);", + "b) Indirect References: When tested for indirect references questions in Table 4, we outperform all baselines by an absolute $7.83\\% -16.62\\%$ in terms of recall; For precision, we also gain similar improvements;" + ], + "bbox": [ + 75, + 779, + 470, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "c) Compositional Steps: Our model is trained on only $15\\%$ (100K QA pairs) of the training data and is still able to perform on par with the baselines (trained on 1.6M QA pairs) achieving second best results for each category of more compositional steps (table 3).", + "bbox": [ + 496, + 316, + 893, + 392 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "STAR: For STAR dataset, we first compare the proposed architecture to other state-of-the-art works in the field using SlowR50 [13] video backbone and a ResNext101 [50] as a frame-level backbone to evaluate accuracy based on 3D video and 2D image architectures. It shows that for both settings, SHG-VQA significantly outperforms other baseline methods even with weaker backbones (see Table 5). Concretely, we obtain an absolute gain of $8.53\\%$ over NSSR [47], $5.86\\%$ compared to HCRN [24], $5.99\\%$ improvement over LCGN [15], and $\\sim 2.5\\%$ over ClipBERT [25] which is a SOTA model in terms of overall VQA accuracy. We notice the substantial gain of $7.86\\%$ for interaction questions which test the understanding of interactions between entities in a situation. Prediction is the next category of questions that benefits the most with $2.48\\%$ improvement.", + "bbox": [ + 496, + 393, + 895, + 621 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.2. Ablation and Hyperparameter Analysis", + "text_level": 1, + "bbox": [ + 498, + 632, + 841, + 648 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We perform our ablation studies on the STAR benchmark as discussed below:", + "bbox": [ + 496, + 657, + 890, + 686 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Impact of situation graphs quality: To assess the effect of situation graphs' quality on VQA accuracy, we train a baseline version of our system, where the model is trained on ground truth situation graphs for VQA tasks only (Table 6). Since the ground truth situation graphs are not available for the test set, we can only compare SHG-VQA and this baseline on the validation set. As expected, when taking ground truth situation graphs as input, the performance is significantly improved for interaction $(\\mathrm{GT} = 91.9\\%$ vs. predicted $= 46.78\\%)$ and sequence $(\\mathrm{GT} = 80.5\\%$ vs. predicted $= 42.52\\%$ questions. However, for the questions about the unseen part of the video, the model with ground truth graph tokens still struggles despite better performance compared to SHG-VQA: predic", + "bbox": [ + 496, + 688, + 895, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "14885", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/dc1ab2022e594aa10a48c74d7649a4a68d0f6c7fc4ba5c76b82b853e7a3e9a91.jpg", + "image_caption": [ + "Figure 4. Qualitative example for using frame-wise set prediction loss. Col. 1 shows the frame, col. 2 shows the ground-truth situation graph, col. 3 shows predicted graphs when trained with set prediction loss for the full video, and col. 4 shows the predicted graph when the model is trained by matching each timestep $t$ . The edges show the person-object relationship labels along with the number of times it was predicted." + ], + "image_footnote": [], + "bbox": [ + 84, + 90, + 467, + 176 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/23bdfaf7611cac4c0fbe60b5e54babb46a1533058b25d0db158454a9642f05c7.jpg", + "table_caption": [ + "Table 6. Impact of hyper-graphs (HG) quality. Results shown for STAR val set with SlowR50 backbone." + ], + "table_footnote": [], + "table_body": "
MethodInteractionSequencePredictionFeasibilityOverall
Predicted hyper-graphs47.0842.5237.8233.6140.26
GT hyper-graphs91.980.541.2235.4262.46
", + "bbox": [ + 89, + 344, + 457, + 388 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "tion (GT=41.22% vs. predicted=37.82%) and feasibility (GT=35.42% vs. predicted=33.61%). We also report the mAP scores for the prediction of action and relationship predicates using our best model in Table 7. We obtain an overall mAP of 87.63 for actions and 72.9 for relationships respectively.", + "bbox": [ + 75, + 416, + 467, + 507 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Input to cross-attentional transformer: To evaluate the choice of input to the cross-attentional transformer, we experiment with three settings: a) question and video embeddings, b) question and situation graphs embeddings, c) question, situation graphs, and video embeddings. We observe no gain in the overall VQA accuracy when adding video embeddings to the cross-attentional transformer and get our best results with (b) (table 8).", + "bbox": [ + 75, + 507, + 467, + 626 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Situation hyper-graph components: To study the impact of different components of situation graphs, we train our system with only action predicate tokens with objective $L_{act} + L_{vqa}$ , only relationship predicates (with $L_{rel} + L_{vqa}$ ), and the full model (eq. 1). The action predicates are more effective compared to the relationship predicates. When compared in terms of predicate classification, we observe high accuracy for action predicates. Nonetheless, using the full situation graphs perform better than omitting actions or relationship prediction task (table 9).", + "bbox": [ + 75, + 628, + 467, + 777 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Number of queries: Number of action queries $M$ and relationship queries $N$ is a hyperparameter for SHG-VQA. We report the performance of the SHG-VQA with a varying number of queries for actions and relationships at each timestep in Table 9. We report our best results with $M = 3$ and $N = 8$ . Additional results are reported in the supplementary document.", + "bbox": [ + 75, + 777, + 467, + 883 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Frame-wise set prediction loss: Using the notion of time $t$", + "bbox": [ + 76, + 885, + 467, + 900 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/ae66477072ebacdfe10b83a110a4382eb796ea27fb90a97c26537ab6fb3db361.jpg", + "table_caption": [ + "Table 7. Predicate classification results for situation hyper-graphs in terms of mAP for STAR validation set from SHG-VQA with SlowR50 backbone. Numbers are reported in percentages." + ], + "table_footnote": [], + "table_body": "
InteractSequencePredictionFeasibilityOverall
Actions84.7789.4385.3091.4687.63
Relationships72.8373.570.0572.8272.9
", + "bbox": [ + 540, + 142, + 849, + 186 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/e426f6814154be3427834fa4a7689046066953e56de681611698c0b06155924f.jpg", + "table_caption": [ + "Table 8. Results for cross-attention input. Results shown for STAR test set with SlowR50 backbone." + ], + "table_footnote": [], + "table_body": "
MethodInteractionSequencePredictionFeasibilityOverall
Q + V33.2835.6027.9326.4330.81
Q + HG47.9842.0335.3432.5239.47
Q + V + HG45.4544.1934.2232.8739.18
", + "bbox": [ + 535, + 232, + 854, + 285 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/24fd31d879f45a0add99bd54898f2cae0ef7387508ccb0cf64c808039bafe616.jpg", + "table_caption": [ + "Table 9. Model variations on STAR validation set with a single model using SlowR50 backbone for all question types. Best results are shown in bold font and second best results are underlined. Numbers are reported for VQA accuracy in percentages." + ], + "table_footnote": [], + "table_body": "
Method (val)InteractionSequencePredictionFeasibilityOverall
hyper-graph components
Action only – Act=340.9438.0835.5830.5638.68
Relation. only – Rel=835.9435.7934.7827.8635.16
Both – Act=3, Rel=842.9338.2036.0630.5639.20
Number of queries
Act=2, Rel=840.3240.1338.7829.7338.34
Act=3, Rel=842.9338.2036.0630.5639.20
Act=4, Rel=838.4035.7935.5830.3537.06
Act=3, Rel=1241.3240.8039.4231.3939.90
Act=4, Rel=1240.4039.0536.8629.1138.39
", + "bbox": [ + 506, + 359, + 883, + 484 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "in the set prediction loss alleviates the problem of duplicate predictions within each situation. See Fig. 4 for qualitative examples of produced hyper-graphs with and without this loss. Extra examples are in the supplementary document.", + "bbox": [ + 498, + 497, + 890, + 558 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 566, + 617, + 583 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We presented a novel approach to model situation graph prediction as an underlying sub-task for video question answering. The proposed method predicts a situation hypergraph structure composed of existing actions and relationships in the input video. The input question can then reason over the predicted graph to solve VQA. We show the impact of the proposed approach by evaluating on two video question-answering benchmarks and achieving significant performance gains overall baseline methods. Our method demonstrates promise for further research in this direction to improve VQA systems even further.", + "bbox": [ + 496, + 593, + 893, + 758 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements. Aisha Urooj is supported by the ARO grant W911NF-19-1-0356. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. This work was partially supported under NSF grant CNS-2050731, Research Experience for Undergraduates in Computer Vision. Dr. Gan is supported by the MIT-IBM Watson AI Lab, DSO grant DSOCO21072, and gift funding from MERL, Cisco, Sony, and Amazon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ARO, IARPA, DOI/IBC, or the U.S. Government.", + "bbox": [ + 496, + 763, + 893, + 896 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "14886", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 4, 5", + "[2] Xiaojun Chang, Pengzhen Ren, Pengfei Xu, Zhihui Li, Xiaojiang Chen, and Alexander G Hauptmann. A comprehensive survey of scene graphs: Generation and application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. 3", + "[3] Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 702-703, 2020. 6", + "[4] Vinay Damodaran, Sharanya Chakravarthy, Akshay Kumar, Anjana Umapathy, Teruko Mitamura, Yuta Nakashima, Noa Garcia, and Chenhui Chu. Understanding the role of scene graphs in visual question answering. arXiv preprint arXiv:2101.05479, 2021. 1", + "[5] Long Hoang Dang, Thao Minh Le, Vuong Le, and Truyen Tran. Hierarchical object-oriented spatio-temporal reasoning for video question answering. arXiv preprint arXiv:2106.13432, 2021. 2", + "[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 7", + "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 2", + "[8] Chenyou Fan, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, and Heng Huang. Heterogeneous memory enhanced multimodal attention model for video question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1999-2007, 2019. 2", + "[9] Chenyou Fan, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, and Heng Huang. Heterogeneous memory enhanced multimodal attention model for video question answering. CoRR, abs/1904.04357, 2019. 5, 6", + "[10] Jiyang Gao, Runzhou Ge, Kan Chen, and Ram Nevatia. Motion-appearance co-memory networks for video question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6576–6585, 2018. 2", + "[11] Madeleine Grunde-McLaughlin, Ranjay Krishna, and Maneesh Agrawala. Agqa: A benchmark for compositional spatio-temporal reasoning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11287-11297, 2021. 2, 6" + ], + "bbox": [ + 78, + 116, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[12] Madeleine Grunde-McLaughlin, Ranjay Krishna, and Maneesh Agrawala. Agqa 2.0: An updated benchmark for compositional spatio-temporal reasoning, 2022. 6", + "[13] Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh. Learning spatio-temporal features with 3d residual networks for action recognition. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 3154-3160, 2017. 7", + "[14] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. *Neural computation*, 9(8):1735–1780, 1997. 7", + "[15] Ronghang Hu, Anna Rohrbach, Trevor Darrell, and Kate Saenko. Language-conditioned graph networks for relational reasoning. In Proceedings of the IEEE International Conference on Computer Vision, pages 10294-10303, 2019. 2, 7", + "[16] Yunseok Jang, Yale Song, Chris Dongjoo Kim, Youngjae Yu, Youngjin Kim, and Gunhee Kim. Video question answering with spatio-temporal reasoning. International Journal of Computer Vision, 127(10):1385-1412, 2019. 2", + "[17] Yunseok Jang, Yale Song, Youngjae Yu, Youngjin Kim, and Gunhee Kim. Tgif-qa: Toward spatio-temporal reasoning in visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2758–2766, 2017. 2", + "[18] Jianwen Jiang, Ziqiang Chen, Haojie Lin, Xibin Zhao, and Yue Gao. Divide and conquer: Question-guided spatiotemporal contextual attention for video question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11101-11108, 2020. 2", + "[19] Pin Jiang and Yahong Han. Reasoning with heterogeneous graph alignment for video question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11109-11116, 2020. 2", + "[20] Juanzi Li Jiaxin Shi, Hanwang Zhang. Explainable and explicit visual reasoning over scene graphs. In CVPR, 2019. 1", + "[21] Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In CVPR, 2017. 7", + "[22] Yash Kant, Dhruv Batra, Peter Anderson, Alexander Schwing, Devi Parikh, Jiasen Lu, and Harsh Agrawal. Spatially aware multimodal transformers for textvqa. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IX 16, pages 715-732. Springer, 2020. 2", + "[23] Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong. Pure transformers are powerful graph learners. arXiv preprint arXiv:2207.02505, 2022.1", + "[24] Thao Minh Le, Vuong Le, Svetha Venkatesh, and Truyen Tran. Hierarchical conditional relation networks for video question answering. CoRR, abs/2002.10698, 2020. 2, 5, 6, 7", + "[25] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7331-7341, 2021. 2, 7" + ], + "bbox": [ + 501, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "14887", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[26] Jie Lei, Licheng Yu, Mohit Bansal, and Tamara L Berg. Tvqa: Localized, compositional video question answering. In EMNLP, 2018. 2", + "[27] Linjie Li, Zhe Gan, Yu Cheng, and Jingjing Liu. Relation-aware graph attention network for visual question answering. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10313-10322, 2019. 2", + "[28] Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. Visualbert: A simple and perform-. mant baseline for vision and language. arXiv preprint arXiv:1908.03557, 2019. 7", + "[29] Xiangpeng Li, Jingkuan Song, Lianli Gao, Xianglong Liu, Wenbing Huang, Xiangnan He, and Chuang Gan. Beyond rms: Positional self-attention with co-attention for video question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 8658–8665, 2019. 2", + "[30] Xiangpeng Li, Jingkuan Song, Lianli Gao, Xianglong Liu, Wenbing Huang, Xiangnan He, and Chuang Gan. Beyond rnns: Positional self-attention with co-attention for video question answering. In AAAI, 2019. 5, 6, 7", + "[31] Fei Liu, Jing Liu, Weining Wang, and Hanqing Lu. Hair: Hierarchical visual-semantic relational reasoning for video question answering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1698–1707, 2021. 2", + "[32] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. arXiv preprint arXiv:2106.13230, 2021. 2", + "[33] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. arXiv preprint arXiv:1908.02265, 2019. 6", + "[34] Tegan Maharaj, Nicolas Ballas, Anna Rohrbach, Aaron Courville, and Christopher Pal. A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6884–6893, 2017. 2", + "[35] Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B Tenenbaum, and Jiajun Wu. The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision. In International Conference on Learning Representations, 2018. 1", + "[36] Kenneth Marino, Xinlei Chen, Devi Parikh, Abhinav Gupta, and Marcus Rohrbach. Krisp: Integrating implicit and symbolic knowledge for open-domain knowledge-based vqa. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14111-14121, 2021. 1", + "[37] Devshree Patel, Ratnam Parikh, and Yesha Shastri. Recent advances in video question answering: A review of datasets and methods. In International Conference on Pattern Recognition, pages 339-356. Springer, 2021. 2", + "[38] Liang Peng, Shuangji Yang, Yi Bin, and Guoqing Wang. Progressive graph attention network for video question answering. In Proceedings of the 29th ACM International Conference on Multimedia, pages 2871-2879, 2021. 2" + ], + "bbox": [ + 78, + 90, + 470, + 901 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[39] Tianwen Qian, Jingjing Chen, Shaoxiang Chen, Bo Wu, and Yu-Gang Jiang. Scene graph refinement network for visual question answering. IEEE Transactions on Multimedia, 2022. 2", + "[40] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 2", + "[41] Ahjeong Seo, Gi-Cheon Kang, Joonhan Park, and Byoung-Tak Zhang. Attend what you need: Motion-appearance synergistic networks for video question answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6167–6177, Online, Aug. 2021. Association for Computational Linguistics. 2", + "[42] Paul Hongsuck Seo, Arsha Nagrani, and Cordelia Schmid. Look before you speak: Visually contextualized utterances. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16877-16887, 2021. 2", + "[43] Gunnar A Sigurdsson, Gül Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, and Abhinav Gupta. Hollywood in homes: Crowdsourcing data collection for activity understanding. In European Conference on Computer Vision, pages 510-526. Springer, 2016. 6", + "[44] Gunnar A. Sigurdsson, Gúl Varol, X. Wang, Ali Farhadi, Ivan Laptev, and Abhinav Kumar Gupta. Hollywood in homes: Crowdsourcing data collection for activity understanding. ECCV, 2016. 2", + "[45] Ajeet Kumar Singh, Anand Mishra, Shashank Shekhar, and Anirban Chakraborty. From strings to things: Knowledge-enabled vqa model that can read and reason. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4602-4612, 2019. 1", + "[46] Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. Movieqa: Understanding stories in movies through question-answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4631–4640, 2016. 2", + "[47] Bo Wu, Shoubin Yu, Zhenfang Chen, Joshua B Tenenbaum, and Chuang Gan. Star: A benchmark for situated reasoning in real-world videos. In Thirty-fifth Conference on Neural Information Processing Systems, 2021. 1, 2, 3, 6, 7", + "[48] Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question-answering to explaining temporal actions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9777–9786, 2021. 2", + "[49] Junbin Xiao, Angela Yao, Zhiyuan Liu, Yicong Li, Wei Ji, and Tat-Seng Chua. Video as conditional graph hierarchy for multi-granular question answering. arXiv preprint arXiv:2112.06197, 2021. 2", + "[50] Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017. 7" + ], + "bbox": [ + 501, + 90, + 893, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "14888", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[51] Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang. Video question answering via gradually refined attention over appearance and motion. In Proceedings of the 25th ACM international conference on Multimedia, pages 1645–1653, 2017. 2", + "[52] Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, and Cordelia Schmid. Just ask: Learning to answer questions from millions of narrated videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1686-1697, 2021. 2", + "[53] Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B Tenenbaum. Clevrer: Collision events for video representation and reasoning. ICLR, 2020. 2, 7", + "[54] Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Joshua B Tenenbaum. Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding. In Advances in Neural Information Processing Systems (NIPS), 2018. 2", + "[55] Fei Yu, Jiji Tang, Weichong Yin, Yu Sun, Hao Tian, Hua Wu, and Haifeng Wang. Ernie-vil: Knowledge enhanced vision-language representations through scene graph. arXiv preprint arXiv:2006.16934, 2020. 1", + "[56] Weijiang Yu, Haoteng Zheng, Mengfei Li, Lei Ji, Lijun Wu, Nong Xiao, and Nan Duan. Learning from inside: Self-driven siamese sampling and reasoning for video question answering. Advances in Neural Information Processing Systems, 34, 2021. 2", + "[57] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 9127–9134, 2019. 2", + "[58] Amir Zadeh, Michael Chan, Paul Pu Liang, Edmund Tong, and Louis-Philippe Morency. Social-iq: A question answering benchmark for artificial social intelligence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8807–8817, 2019. 2", + "[59] Rowan Zellers, Jiasen Lu, Ximing Lu, Youngjae Yu, Yanpeng Zhao, Mohammadreza Salehi, Aditya Kusupati, Jack Hessel, Ali Farhadi, and Yejin Choi. Merlot reserve: Neural script knowledge through vision and language and sound. arXiv preprint arXiv:2201.02639, 2022. 2", + "[60] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. Advances in Neural Information Processing Systems, 34, 2021. 2", + "[61] Kuo-Hao Zeng, Tseng-Hung Chen, Ching-Yao Chuang, Yuan-Hong Liao, Juan Carlos Niebles, and Min Sun. Leveraging video descriptions to learn video question answering. CoRR, abs/1611.04021, 2016. 2", + "[62] Yaoyao Zhong, Wei Ji, Junbin Xiao, Yicong Li, Weihong Deng, and Tat-Seng Chua. Video question answering: Datasets, algorithms and challenges, 2022. 2", + "[63] Guangming Zhu, Liang Zhang, Youliang Jiang, Yixuan Dang, Haoran Hou, Peiyi Shen, Mingtao Feng, Xia Zhao, Qiguang Miao, Syed Afaq Ali Shah, and Mohammed Bennamoun." + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Scene graph generation: A comprehensive survey. CoRR, abs/2201.00443, 2022. 2, 3", + "[64] Linchao Zhu, Zhongwen Xu, Yi Yang, and Alexander G Hauptmann. Uncovering the temporal context for video question answering. International Journal of Computer Vision, 124(3):409-421, 2017. 2" + ], + "bbox": [ + 501, + 92, + 893, + 176 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "14889", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Learning Situation Hyper-Graphs for Video Question Answering/cab54a6b-1009-4dbc-b1ca-0ed1a0963ee9_model.json b/2023/Learning Situation Hyper-Graphs for Video Question Answering/cab54a6b-1009-4dbc-b1ca-0ed1a0963ee9_model.json new file mode 100644 index 0000000000000000000000000000000000000000..4175b0ed9dbaaf6aa9673683c53cacc907799e35 --- /dev/null +++ b/2023/Learning Situation Hyper-Graphs for Video Question Answering/cab54a6b-1009-4dbc-b1ca-0ed1a0963ee9_model.json @@ -0,0 +1,2213 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.157, + 0.131, + 0.815, + 0.154 + ], + "angle": 0, + "content": "Learning Situation Hyper-Graphs for Video Question Answering" + }, + { + "type": "text", + "bbox": [ + 0.223, + 0.18, + 0.744, + 0.217 + ], + "angle": 0, + "content": "Aisha Urooj Khan\\(^{1,3}\\), Hilde Kuehne\\(^{2,4}\\), Bo Wu\\(^{2}\\), Kim Chheu\\(^{5}\\), Walid Bousselham\\(^{4}\\), Chuang Gan\\(^{2,6}\\), Niels Lobo\\(^{1}\\), Mubarak Shah\\(^{1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.126, + 0.233, + 0.844, + 0.27 + ], + "angle": 0, + "content": "\\(^{1}\\) CRCV, University of Central Florida, \\(^{2}\\) MIT-IBM Watson AI Lab, \\(^{3}\\) Mayo Clinic, AZ \n\\(^{4}\\) Goethe University Frankfurt Germany, \\(^{5}\\) Western Michigan University, \\(^{6}\\) UMass Amherst" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.305, + 0.313, + 0.32 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.337, + 0.474, + 0.729 + ], + "angle": 0, + "content": "Answering questions about complex situations in videos requires not only capturing the presence of actors, objects, and their relations but also the evolution of these relationships over time. A situation hyper-graph is a representation that describes situations as scene sub-graphs for video frames and hyper-edges for connected sub-graphs and has been proposed to capture all such information in a compact structured form. In this work, we propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs, coined Situation Hyper-Graph based Video Question Answering (SHG-VQA). To this end, we train a situation hyper-graph decoder to implicitly identify graph representations with actions and object/human-object relationships from the input video clip. and to use cross-attention between the predicted situation hyper-graphs and the question embedding to predict the correct answer. The proposed method is trained in an end-to-end manner and optimized by a VQA loss with the cross-entropy function and a Hungarian matching loss for the situation graph prediction. The effectiveness of the proposed architecture is extensively evaluated on two challenging benchmarks: AGQA and STAR. Our results show that learning the underlying situation hypergraphs helps the system to significantly improve its performance for novel challenges of video question-answering tasks1." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.747, + 0.208, + 0.763 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.773, + 0.471, + 0.864 + ], + "angle": 0, + "content": "Video question answering in real-world scenarios is a challenging task as it requires focusing on several factors including the perception of the current scene, language understanding, situated reasoning, and future prediction. Visual perception in the reasoning task requires capturing various aspects of visual understanding, e.g., detecting a diverse set" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.306, + 0.895, + 0.466 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.48, + 0.895, + 0.565 + ], + "angle": 0, + "content": "Figure 1. The situation hyper-graph for a video is composed of situations with entities and their relationships (shown as subgraphs in the pink box). These situations may evolve over time. Temporal actions act as hyper-edges connecting these situations into one situation hyper-graph. Learning situation graphs, as well as temporal actions, is vital for reasoning-based video question answering." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.595, + 0.895, + 0.67 + ], + "angle": 0, + "content": "of entities, recognizing their interactions, as well as understanding the changing dynamics between these entities over time. Similarly, linguistic understanding has its challenges as some question or answer concepts may not be present in the input text or video." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.672, + 0.896, + 0.882 + ], + "angle": 0, + "content": "Visual question answering, as well as its extension over time, video question answering, have both benefited from representing knowledge in graph structures, e.g., scene graphs [20, 35], spatio-temporal graphs [4, 55], and knowledge graphs [36, 45]. Another approach in this direction is the re-introduction of the concept of \"situation cognition\" embodied in \"situation hyper-graphs\" [47]. This adds the computation of actions to the graphs that capture the interaction between entities. In this case, situations are represented by hyper-graphs that join atomic entities and relations (e.g., agents, objects, and relationships) with their actions (Fig. 1). This is an ambitious task for existing systems as it is impractical to encapsulate all possible interactions in the real-world context." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.886, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Recent work [23] shows that transformers are capable" + }, + { + "type": "page_footnote", + "bbox": [ + 0.076, + 0.875, + 0.471, + 0.9 + ], + "angle": 0, + "content": "\\(^{1}\\)Code will be available at https://github.com/aurooj/SHG-VQA" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14879" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.473, + 0.62 + ], + "angle": 0, + "content": "of learning graphs without adapting graph-specific details in the architectures achieving competitive or even better performance than sophisticated graph-specific models. Our work supports this idea by implicitly learning the underlying hyper-graphs of a video. Thus, it requires no graph computation for inference and uses decoder's output directly for cross attention module. More precisely, we propose to learn situation hyper-graphs, namely framewise actor-object and object-object relations as well as their respective actions, from the input video directly without the need for explicit object detection or other required prior knowledge. While the actions capture events across transitions over multiple frames, such as Drinking from a bottle, the relationship encoding actually considers all possible combinations of static, single frame actor-object, and object-object relationships as unique classes, e.g., in the form of person - hold - bottle or bottle - stands on - table, thus serving as an object and relation classifier. Leveraging this setup allows us to streamline the spatio-temporal graph learning as a set prediction task for predicting relationship predicates and actions in a Situation hyper-graph Decoder block. To train the Situation Graph Decoder, we use a bipartite matching loss between the predicted set and ground truth hyper-graph tokens. The output of the situation graph decoder is a set of action and relationship tokens, which are then combined with the embedding of the associated question to derive the final answer. An overview of the proposed architecture is given in Fig. 2. Note that, compared to other works targeting video scene graph generation, e.g., those listed in [63], we are less focused on learning the best possible scene graph, but rather on learning the representation of the scene which best supports the question answering task. Thus, while capturing the essence of a scene, as well as the transition from one scene to the other, we are not only optimizing the scene graph accuracy but also considering the VQA loss." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.621, + 0.473, + 0.861 + ], + "angle": 0, + "content": "We evaluate the proposed method on two challenging video question answering benchmarks: a) STAR [47], featuring four different question types, interaction, sequence, prediction, and feasibility based on a subset of the real-world Charades dataset [44]; and b) Action Genome QA (AGQA) [11] dataset which tests vision focused reasoning skills based on novel compositions, novel reasoning steps, and indirect references. Compared to other VQA datasets, these datasets provide dense ground truth hyper-graph information for each video, which allows us to learn the respective embedding. Our results show that the proposed hyper-graph encoding significantly improves VQA performance as it has the ability to infer correct answers from spatio-temporal graphs from the input video. Our ablations further reveal that achieving high-quality graphs can be critical for VQA performance." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.862, + 0.401, + 0.876 + ], + "angle": 0, + "content": "Our contributions to this paper are as follows:" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.886, + 0.472, + 0.9 + ], + "angle": 0, + "content": "- We introduce a novel architecture that enables the com" + }, + { + "type": "text", + "bbox": [ + 0.531, + 0.092, + 0.895, + 0.138 + ], + "angle": 0, + "content": "putation of situation hyper-graphs from video data to solve the complex reasoning task of video question-answering;" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.149, + 0.894, + 0.24 + ], + "angle": 0, + "content": "- We propose a situation hyper-graph decoder module to decode the atomic actions and object/actor-object relationships and model the hyper-graph learning as a transformer-based set prediction task and use a set prediction loss function to predict actions and relationships between entities in the input video;" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.252, + 0.895, + 0.298 + ], + "angle": 0, + "content": "- We use the resulting high-level embedding information as sole visual information for the reasoning and show that this is sufficient for an effective VQA system." + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.149, + 0.895, + 0.298 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.317, + 0.642, + 0.332 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.342, + 0.896, + 0.751 + ], + "angle": 0, + "content": "Video Question Answering: Video question answering is an active area of research with efforts in insightful directions [37,62], such as attention [16, 18, 29], cross-modal interactions [25, 41, 52, 56, 59, 60], hierarchical learning [5, 24, 38], and so on. A few other algorithms classes include modular networks [5, 24, 49], symbolic reasoning [47, 53, 54], and memory networks [8, 10]. Several video QA benchmarks are introduced to evaluate this task from varying perspectives entailing description [34, 51, 57, 61], temporal reasoning [17, 53, 64], causal structures [48, 53], visual-language comprehension [26, 46, 52], relational reasoning [46], and measuring social intelligence [58]. STAR [47] benchmark goes one step further providing a benchmark to perform diagnostic study at additional fronts such as predicting future interactions and feasibility of next possible actions in the unseen future. Existing approaches on the benchmark either rely on object features [40] as nodes explicitly modeling their interactions through a message passing mechanism [15, 47], or benefit from efficient input sampling strategies during training to learn robust visual representations [24, 25]. We, however, take a different approach and focus on inferring the underlying semantic graph structure in the video and use it for QA reasoning. Our method is independent of using pretrained object detectors and uses a simple approach to learn to infer the sets of relationship predicates as well as actions for each frame. The model is trained end-to-end with the frozen backbone." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.897, + 0.903 + ], + "angle": 0, + "content": "Graph-based VQA: Another related line of work is graph-based VQA methods [22, 27]. Some of them work on object features extracted from a pretrained detector (e.g., Faster-RCNN [40]) [5, 15, 31, 42, 49], while some operate on frame-level [19, 25, 56] (e.g., ResNet), clip-level features [38, 41, 52](e.g., C3D, S3D) or transformer-based backbones [7, 32, 39]. We focus on predicting atomic actions, and relationship triplets from the frame-level or clip-level features directly instead of an explicit graph." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "14880" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.092, + 0.891, + 0.311 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.32, + 0.896, + 0.435 + ], + "angle": 0, + "content": "Figure 2. The SHG-VQA architecture: we start with encoding the input video into spatio-temporal features using a pre-trained backbone. These video features are flattened into a sequence of tokens of length \\( T \\times h \\times w \\) and position encoded to be further processed through a visual encoder. These encoded tokens are input to the action decoder to predict the set of atomic actions from the input action queries as well as to the relationship decoder which takes relationship queries as input along with the video tokens. The action decoder and relationship decoder output the situation graph embeddings. In the text branch, the question and the answer choices are composed into a sequence and passed through a text transformer to obtain encoded word embeddings; for open-ended VQA, only the question is passed to the text transformer. The generated hyper-graph along with the encoded text tokens are then used as input to a cross-attentional transformer and the combined representations are used to predict the correct answer with a classifier. Section 3.4 describes the losses and training objectives." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.459, + 0.473, + 0.641 + ], + "angle": 0, + "content": "Scene Graph Generation: The proposed formulation of situation hyper-graph deviates from existing scene-graph generation approaches [2, 47, 63] as we do not require object detections as input or object level supervision, nor do we model it in an explicit graph structure. Our goal is simple: given an input video, predict all object-relation-object and actor-relation-object triplets as well as associated actions for each video frame. Our intuition is that forcing the model to predict these predicates will drive the system to learn a latent graph structure for visual input. The predicted situation hyper-graph is treated as an abstract video representation and used for VQA reasoning in the next step." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.661, + 0.291, + 0.679 + ], + "angle": 0, + "content": "3. The SHG-VQA Model" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.689, + 0.473, + 0.901 + ], + "angle": 0, + "content": "Situation video question answering has three essential steps 1) the visual recognition capacities of visual entities, their relationships, actions, and how these transition over time, 2) the language understanding capacity to the questions, 3) the question-guided reasoning process over the representation learned in the first step. Sub-optimal performance at any of these steps will affect the overall task performance. A key problem here is that capturing the visual structures directly from raw data, e.g., in the form of features often results in a rather noisy signal and does not provide suitable input for high-level language-guided reasoning. To overcome this mismatch, we propose to learn the implicit structure of the visual input as an intermediate step between learning the video representation and question-based reasoning. Forcing the model" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.459, + 0.895, + 0.534 + ], + "angle": 0, + "content": "to learn to predict this implicit structure (actions, relations between entities) not only improves the video representation but also acts as a lightweight, high-level representation of the video content and can be used for the VQA task. We illustrate our architecture in Fig. 2." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.545, + 0.667, + 0.56 + ], + "angle": 0, + "content": "3.1. Input Processing" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.568, + 0.892, + 0.597 + ], + "angle": 0, + "content": "Given the input video and the question, we encode the inputs as described below:" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.599, + 0.892, + 0.704 + ], + "angle": 0, + "content": "Question Encoder: The question is first tokenized into word tokens using a wordPiece tokenizer. These word tokens along with the special class token \\([CLS]\\) are the inputs of an embedding layer. The output word embeddings from this layer are input to a transformer encoder that encodes each word using multi-head self-attention between different words at each encoder layer." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.704, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Video Encoder: Let \\( V \\in \\mathbb{R}^{T \\times H \\times W \\times 3} \\) be the input video clip, where \\( T \\) is the clip length with 3 color channels, height \\( H \\) and width \\( W \\). First, we extract video features \\( x_{V} \\in \\mathbb{R}^{T \\times h \\times w \\times d_{x}} \\) using a convolutional backbone with \\( d_{x} \\) being the feature dimension, \\( h \\) and \\( w \\) are the reduced feature's height and width. As transformers process sequential input data, the video features \\( x_{V} \\) are flattened into a sequence (of size \\( \\mathbb{R}^{Thw \\times d_x} \\)) and reduced to dimension \\( d \\) through a linear layer. Then, we append a trainable vector of dimension \\( d \\) for a special class token [VIS] to this sequence of video features at index 0. These features are combined with position encodings and input to a transformer-based video encoder \\( V_{e} \\)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.956 + ], + "angle": 0, + "content": "14881" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.288 + ], + "angle": 0, + "content": "The output features of the video are forwarded to the action decoder and relationship decoder to infer the situation graph capturing the action information, as well as the entities and their relationships. The output of the last layer of both decoders is then combined and augmented with frame positions, forming the final hyper-graph embedding. We further attach a randomly initialized class token \\([HG]\\). These situation graph embeddings are then input to a multi-layered cross-attentional transformer encoder for more fine-grained interaction between the question words and semantic knowledge extracted from the video in the form of a graph. The output features corresponding to \\([CLS]\\) and \\([HG]\\) tokens are input to an answer classifier to produce an answer." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.301, + 0.376, + 0.318 + ], + "angle": 0, + "content": "3.2. Situation hyper-graph Generation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.326, + 0.474, + 0.688 + ], + "angle": 0, + "content": "Real-world video understanding relies on the scene understanding including changing relationships between the objects over time and the evolving actions. Therefore, we represent the given video as a \"situation graph\" denoted by \\( G \\) which describes actions and relationships between objects. Let \\( G = (V, E) \\), where \\( V \\) is the set of vertices representing all possible entities in the dataset, and \\( E \\) is the set of edges representing all possible relationships between each pair of entities. Given an input video, we want to learn a situation graph \\( g_{t} = (v_{t}, e_{t}) \\), \\( v_{t} \\in V \\), \\( e_{t} \\in E \\) for each time step \\( t \\in \\{1, 2, \\dots, T\\} \\) in the video, that captures the entities (objects, actors), their relationships as well as the associated actions that are present in that frame. The hyper-graph for one video is thus represented by the set of situation graphs \\( \\mathcal{G} = \\{g_{1}, \\dots, g_{T}\\} \\). An action may comprise multiple relationships and objects. For each frame, we further have a set of actions \\( A_{t} = a_{1}, a_{2}, \\dots, a_{N} \\). The set of actions for the full video is then given as \\( \\mathcal{A} = \\{A_{1}, \\dots, A_{T}\\} \\). Note that the actions are predicted in addition to the graph structure and that both will be merged in a situation hyper-graph embedding in a separate step after the decoder block. Rather than predicting the set of vertices and edges in the graph \\( g_{t} \\), we propose to predict the graph structure by formulating this graph prediction as follows:" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.688, + 0.472, + 0.902 + ], + "angle": 0, + "content": "Let \\( x_{V_e} \\in \\mathbb{R}^{T' \\times h \\times w \\times d} \\) be the encoded video features, where \\( T' \\) denotes the temporal length of encoded features, \\( h \\) and \\( w \\) are spatial dimensions, and \\( d \\) is the feature's dimension. A relationship predicate \\( p \\) describes interactions between entities by triplet tokens object-relation-object or actor-relation-object. We propose to predict the set of relationship predicates \\( R \\) and the set of atomic actions \\( A \\) occurring in each video frame. This is intuitive because the ability to predict the atomic actions and relations between entities for each time step benefits the high level reasoning tasks such as video question answering in this work. Therefore, for each time step \\( t \\in \\{1,2,\\dots,T\\} \\) in the video, we predict the set of relationship predicates denoted by \\( R_t \\) in each video frame where \\( R_t = \\{p_1,p_2,\\dots,p_M\\} \\) and \\( p_i \\) is the" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.089, + 0.895, + 0.32 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.328, + 0.895, + 0.412 + ], + "angle": 0, + "content": "Figure 3. Situation hyper-graph embeddings: We start with decoded queries from action and relationship decoder. Then we add type encoding vectors \\([ACT]\\) and \\([REL]\\) for actions and relationships, attention masks, and an embedding vector for the situation ID \\((t \\in \\{1, \\dots, T\\})\\). The sums are input to the cross-attentional module. See section 3.2.2 for details)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.426, + 0.895, + 0.547 + ], + "angle": 0, + "content": "\\(i^{th}\\) predicate between two entities, representing the vertices \\(v_{x}\\) and \\(v_{y}\\) (object-object or actor-object) and the relation resp. edge \\(e_i\\) represented as \\(< v_x,e_i,v_y>\\), \\(M = |R_t|\\) is the relationship set size. Additionally, we predict the set of \\(N\\) actions \\(A_{t}\\) for each time step \\(t\\) where \\(A_{t} = \\{a_{1},a_{2},\\dots,a_{N}\\}\\) and \\(a_{j}\\) is the \\(j^{th}\\) action occurring at time step \\(t\\). \\(N = |A_t|\\) is the actions set size for each step \\(t\\). \\(M\\) and \\(N\\) are hyperparameters in our system." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.548, + 0.895, + 0.595 + ], + "angle": 0, + "content": "To obtain actions and relation predicates from the video, we use a transformer decoder that takes video features as memory to learn action queries and relation queries." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.603, + 0.669, + 0.617 + ], + "angle": 0, + "content": "3.2.1 Prediction Head" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.628, + 0.894, + 0.688 + ], + "angle": 0, + "content": "The decoded output embeddings for action and relationship queries are input to respective prediction heads. Prediction heads use a 2-layer feed forward network (FFN) with GELU activation and LayerNorm." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.895, + 0.824 + ], + "angle": 0, + "content": "Considering that actions and relationships at a given time step are permutation-invariant, we use optimal bipartite matching between predicted and ground truth actions/relationships. An optimal bipartite matching between the predicted classes and ground-truth labels is the one with the minimum matching cost. Once the optimal matching pairs have been obtained, we use a Hungarian loss function to optimize for ground truth classes [1]. See section 3.4 for details of the loss function." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.846, + 0.795, + 0.862 + ], + "angle": 0, + "content": "3.2.2 Situation hyper-graph Embedding" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.871, + 0.895, + 0.902 + ], + "angle": 0, + "content": "For the decoded queries of actions and relationships (referred as graph token embeddings), situation hyper-graph" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14882" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.089, + 0.892, + 0.105 + ], + "angle": 0, + "content": "Table 1. Results on AGQA dataset for different question types. The best results are shown in bold font. Numbers are reported in percentages." + }, + { + "type": "table", + "bbox": [ + 0.086, + 0.115, + 0.887, + 0.2 + ], + "angle": 0, + "content": "
MethodReasoningSemanticStructureOverall
obj-relrel-actionobj-actionsuperlativesequencingexistsdurationactivityobjrelactionquerycomparechooselogicverifybinaryopenall
PSAC [30]37.8449.9550.0033.2049.7849.9445.214.1437.9749.9546.8531.6349.4946.5649.9649.9048.8731.6340.18
HME [9]37.4249.9049.9733.2149.7749.9647.035.4337.5549.9947.5831.0149.7146.4249.8749.9648.9131.0139.89
HCRN [24]40.3349.8649.8533.5549.7050.0143.845.5240.3349.9646.4136.3449.2243.4250.0250.0147.9736.3442.11
SHG-VQA46.4260.6764.6338.8362.1756.0648.1510.1247.6156.1953.8343.4260.6847.7652.8656.6355.0443.4249.20
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.224, + 0.474, + 0.421 + ], + "angle": 0, + "content": "embeddings are constructed in order to be used with question features for video question answering. First, these action and relationship graph embeddings are combined for each time step \\( t \\) representing a situation at \\( t \\). Then, we add token type embedding \\( [ACT] \\) for actions and \\( [REL] \\) for relations to their respective graph embeddings. A situation ID (or frame position \\( t \\)) embedding is also added to these embeddings. An additional attention mask is used to differentiate actual tokens and padded tokens (no-class token \\( \\phi \\)) at training time. At inference time, no attention mask is used as we do not employ any information about the graph at test time. Finally, we add a special class token \\( [HG] \\) to this sequence of features. See Fig. 3 for visualization." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.429, + 0.414, + 0.445 + ], + "angle": 0, + "content": "3.3. Cross-attentional Transformer Module" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.453, + 0.474, + 0.603 + ], + "angle": 0, + "content": "The situation hyper-graph embeddings obtained at previous step (section 3.2.2) are input along with the question to a cross-attentional transformer module which allows fine-grained computation between the question features and the graph features. A standard co-attentional transformer module is used for cross-attention between the two sequential feature inputs. The feature outputs corresponding to the \\([HG]\\) token and \\([CLS]\\) token from the cross-attentional transformer block are fed to a feed-forward network (FFN) for answer prediction." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.612, + 0.265, + 0.629 + ], + "angle": 0, + "content": "3.4. Learning Objective" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.636, + 0.473, + 0.666 + ], + "angle": 0, + "content": "The SHG-VQA model is trained with the following training objective i.e.," + }, + { + "type": "equation", + "bbox": [ + 0.19, + 0.678, + 0.47, + 0.695 + ], + "angle": 0, + "content": "\\[\nL = L _ {a c t} + L _ {r e l} + L _ {v q a} \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.705, + 0.473, + 0.766 + ], + "angle": 0, + "content": "where \\( L_{act} \\) and \\( L_{rel} \\) are the set prediction loss terms for predicting the action set and relationship set for the video, and \\( L_{vqa} \\) is the cross-entropy loss over the predicted situation graph and question." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.777, + 0.474, + 0.903 + ], + "angle": 0, + "content": "Actions and relationships set prediction loss: The situation graph prediction module infers fixed sets of sizes \\( |N| \\times T \\) actions and \\( |M| \\times T \\) relationships for \\( T \\)-length video clip in a single pass through the action decoder and relationship decoder respectively. We modify the set prediction loss used in [1] as follows. Let \\( A \\) be the set of ground-truth actions and \\( \\hat{A} = \\{\\hat{a}_i\\}_{i=1}^{|N| \\times T} \\) be the predicted set of actions. In a scenario where \\( \\hat{A} \\) is larger than the set of actions present" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.224, + 0.895, + 0.271 + ], + "angle": 0, + "content": "in the video, a special class \\(\\phi\\) (no class) is padded to the ground truth set \\(A\\). We obtain a bipartite matching between ground-truth and predicted set for each timestep \\(t\\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.523, + 0.293, + 0.895, + 0.337 + ], + "angle": 0, + "content": "\\[\n\\hat {\\sigma} _ {a} = \\sum_ {t} ^ {T} \\operatorname {a r g m i n} _ {\\sigma_ {t} \\in \\zeta_ {| N |}} \\sum_ {i} ^ {| N |} \\mathcal {L} _ {\\text {m a t c h}} \\left(a _ {t _ {i}}, \\hat {a} _ {\\sigma_ {t} (i)}\\right) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.346, + 0.895, + 0.589 + ], + "angle": 0, + "content": "Where, \\(\\sigma_{t}\\) is a permutation of N elements for frame t, \\(\\mathcal{L}_{\\text{match}}(a_{ti}, \\hat{a}_{\\sigma_t(i)})\\) is a pair-wise matching cost between \\(i^{th}\\) ground-truth action label in \\(t^{th}\\) frame i.e., \\(a_{t_i}\\) and a predicted action label at index \\(\\sigma_t(i)\\). This optimal assignment for each step \\(t\\) is computed using the Hungarian algorithm and the cost is summed over all \\(T\\) steps. The proposed set prediction loss takes into account only the class predictions for all video frames with no bounding box ground truths being used, different from the original set prediction loss used in [1] for object detection in images. Let \\(\\hat{p}(c_{t(i)})\\) be the class probability for the action prediction at \\(\\sigma_t(i)\\), \\(\\mathcal{L}_{\\text{match}}(a_{t_i}, \\hat{a}_{\\sigma_t(i)})\\) would be \\(-\\mathbb{1}_{\\{c_{t(i)} \\neq \\phi\\}} \\hat{p}_{\\sigma_t(i)}(c_{t(i)})\\). After we obtain a one-to-one optimal matching between ground-truth and predicted set items without duplicates at each time step, we can compute the loss between the matched pairs using a Hungarian loss as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.58, + 0.597, + 0.892, + 0.641 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {a c t}} (a, \\hat {a}) = \\sum_ {i = 1} ^ {| N | \\times T} - \\log \\hat {p} _ {\\hat {\\sigma} (i)} \\left(c _ {i}\\right) \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.65, + 0.894, + 0.697 + ], + "angle": 0, + "content": "Likewise, \\(R\\) is the set of ground-truth relations and \\(\\hat{R} = \\{\\hat{p_i}\\}_{i = 1}^{|M|\\times T}\\) denotes the predicted relationships. \\(L_{rel}\\) is formulated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.522, + 0.721, + 0.895, + 0.765 + ], + "angle": 0, + "content": "\\[\n\\hat {\\sigma} _ {p} = \\sum_ {t} ^ {T} \\operatorname {a r g m i n} _ {\\sigma_ {t} \\in \\zeta_ {| M |}} \\sum_ {i} ^ {| M |} \\mathcal {L} _ {\\text {m a t c h}} \\left(p _ {t _ {i}}, \\hat {p} _ {\\sigma_ {t} (i)}\\right) \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.58, + 0.776, + 0.895, + 0.82 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {r e l} (p, \\hat {p}) = \\sum_ {i = 1} ^ {| M | \\times T} - \\log \\hat {p} _ {\\hat {\\sigma} (i)} (c _ {i}) \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.895, + 0.903 + ], + "angle": 0, + "content": "where \\(\\hat{\\sigma}(i)\\) is the optimal matching obtained in the previous step for each frame. The inferred graph is input to the cross-attentional transformer module along with the question and the answer choices as explained in section 3.2.2. The proposed network is trained in an end-to-end manner." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14883" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.306, + 0.09, + 0.665, + 0.104 + ], + "angle": 0, + "content": "Table 2. Results on AGQA's novel compositions test metric." + }, + { + "type": "table", + "bbox": [ + 0.13, + 0.115, + 0.842, + 0.2 + ], + "angle": 0, + "content": "
MethodSequencingSuperlativeDurationObj-relationOverall
BOAllBOAllBOAllBOAllBOAll
PSAC [30]49.1929.3340.9645.2317.7633.3247.8934.8442.0643.760.0124.2846.4919.3434.71
HME [9]49.3328.0640.5344.0613.830.9548.4534.7242.3139.580.0021.9645.4217.1733.15
HCRN [24]48.3130.0040.7345.1217.3033.0646.1539.1143.0137.152.8621.8844.8820.1234.13
SHG-VQA50.8838.5945.7951.1423.6439.2551.8449.2150.6639.736.2324.8249.0726.6839.37
" + }, + { + "type": "table_caption", + "bbox": [ + 0.099, + 0.222, + 0.447, + 0.236 + ], + "angle": 0, + "content": "Table 3. Evaluation on AGQA's more compositional steps." + }, + { + "type": "table", + "bbox": [ + 0.145, + 0.248, + 0.402, + 0.309 + ], + "angle": 0, + "content": "
More Compositional StepsBinaryOpenAll
PSAC [30]47.6514.8147.19
HME [9]48.0920.9847.72
HCRN [24]46.9623.7046.63
SHG-VQA47.1322.6646.97
" + }, + { + "type": "table_caption", + "bbox": [ + 0.087, + 0.314, + 0.459, + 0.327 + ], + "angle": 0, + "content": "Table 4. Evaluation on AGQA's indirect references test metric." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.34, + 0.477, + 0.46 + ], + "angle": 0, + "content": "
MethodObjectActionTemporal
BOAllBOAllBOAll
PrecisionPSAC63.6953.7756.6461.0152.4653.2457.5253.7454.39
HME62.9552.3155.3958.2148.1249.0455.9952.4253.04
HCRN54.0667.2463.4353.8764.4363.4752.3566.8464.34
SHG-VQA76.9386.2781.5979.5587.4084.9069.0887.3582.87
RecallPSAC45.0627.3638.8040.9122.1825.9635.1326.8430.64
HME46.0326.8039.2341.3221.7125.6737.9626.5931.80
HCRN44.8435.4641.5244.0130.4333.1735.1134.3834.71
SHG-VQA53.8643.9849.8554.1637.8343.1052.2143.5746.78
" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.475, + 0.21, + 0.492 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.501, + 0.18, + 0.516 + ], + "angle": 0, + "content": "4.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.526, + 0.471, + 0.738 + ], + "angle": 0, + "content": "AGQA Benchmark [11]: The Action Genome Question Answering benchmark is a visual dataset comprising 192M hand-crafted questions about 9.6K videos from the Charades dataset [43]. In addition to VQA accuracy, AGQA presents three testing metrics for testing the VQA methods: indirect references, novel compositions, and more compositional steps. We use the AGQA 2.0 Balanced dataset, which consists of 2.27M question-answer pairs as a result of balancing the original dataset using stricter procedures to reduce as much language bias as possible. Of the 2.27M questions, there are approximately 1.6M training questions and 669K test questions [12]. To have a standard train-val-test setup for our experiments, we randomly sampled \\(10\\%\\) QA pairs from training data for validation of hyperparameters." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.471, + 0.901 + ], + "angle": 0, + "content": "STAR Benchmark [47]: The STAR dataset provides 60K situated reasoning questions based on 22K trimmed situation video clips, also based on the Charades dataset [43]. They further provide \\(\\sim 144\\mathrm{K}\\) ground-truth situation graphs including 111 actions, 37 unique objects, and 24 relationships. The dataset is split into training, validation, and test sets where test evaluation can be done on the evaluation server a limited number of times. We perform ablations and analysis on the validation set and report test set results to compare with the baselines in Table 5." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.223, + 0.667, + 0.24 + ], + "angle": 0, + "content": "4.2. Implementations" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.25, + 0.892, + 0.356 + ], + "angle": 0, + "content": "On the VQA task, we report accuracy; we also report mAP for situation hyper-graph predictions. For AGQA's indirect references testing metric, precision and recall are reported. For STAR benchmark, we follow the same training protocol as [47] and train SHG-VQA from scratch on each question type separately unless specified otherwise. Training details are shared in the supplementary document." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.361, + 0.893, + 0.543 + ], + "angle": 0, + "content": "Visual Embeddings: The video frames are resized to size \\(224 \\times 224\\) with a clip length of 16 frames. We use RandAugment [3] for data augmentation during training. The video clip is input to a pretrained convolutional network with freeze weights to obtain video features of size \\(16 \\times 7 \\times 7 \\times 2048\\). A 2-layer 3D convolutional block with the kernel of size \\(5 \\times 3 \\times 3\\) further processes the zero-padded extracted features yielding features of size \\(8 \\times 7 \\times 7 \\times d\\). The spatio-temporal dimensions are then flattened to obtain a sequence of length \\(Thw = 392\\) \\(d\\)-dimensional tokens where \\(d = 768\\). The input encoders as well as situation hyper-graph decoders use \\(L = 5\\) transformer encoder layers with non-shared weights." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.548, + 0.895, + 0.713 + ], + "angle": 0, + "content": "Query embeddings for action and relationship predicates: The features output from the video encoder is then input to a situation hyper-graph decoder comprising transformer-based action and relationship decoders. Our best model uses \\( M = 8 \\) relation queries and \\( N = 3 \\) action queries for each situation; \\( T \\) is set to 16. AGQA has 157 total raw actions, 36 unique objects, and 44 unique relationships which obtain 456 relationship triplets \\( < v_{x}, e_{i}, v_{y} > \\) in the training and validation set. For STAR dataset, there are 37 objects, and 24 relationships yielding 563 unique relation predicates; it also has 111 action classes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.895, + 0.886 + ], + "angle": 0, + "content": "Situation graph embeddings: The decoded situation graph queries are input to a cross-attentional transformer. The input situation graph embedding is a sum of 4 different encoding types: 1) decoded query embeddings for predicted actions and relationship predicates, 2) situation IDs denoting the situation (or frame) number for each query, 3) attention mask set to 1 for actual tokens and 0 for padded tokens, and 4) token type embeddings to distinguish between action tokens and relationship tokens as shown in Fig. 3. Our network uses a 2-layer co-attentional module [33] for cross-attention." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "14884" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.089, + 0.895, + 0.121 + ], + "angle": 0, + "content": "Table 5. Results on STAR dataset. Best results are shown in bold font and second best results are underlined. Numbers are reported for VQA accuracy in percentages." + }, + { + "type": "table", + "bbox": [ + 0.175, + 0.129, + 0.794, + 0.306 + ], + "angle": 0, + "content": "
Method(test)BackboneObj.Hyper.Question TypeOverall
Inter.Seq.Pred.Feas.
Q-type (Random) [21]-XX25.0624.9324.7924.8124.89
Q-type (Frequent) [21]-XX19.0919.4512.9018.3117.44
Blind Model (LSTM) [14]GloVeXX32.2432.1728.5628.4130.34
Blind Model (BERT) [6]BERTXX32.6834.2129.9829.2631.53
CNN-LSTM [53]ResNext101-K400XX33.2532.6730.6930.4331.76
CNN-BERT [28]ResNext101-K400XX33.5937.1630.9530.8433.14
LCGN [15]ResNext101-K400✓X39.0137.9728.8126.9833.19
HRCN [24]ResNext101-K400✓X39.1038.1728.7527.2733.32
ClipBERT [25]ResNext101-K400XX39.8143.5932.2431.4236.70
NS-SR [47]ResNext101-K400✓✓30.8831.7630.2329.7330.65
SHG-VQA (Ours)SlowR50-K400X✓47.9842.0335.3432.5239.47
SHG-VQA (Ours)ResNext101-ImageNet1KX✓45.842.7734.6429.9138.28
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.317, + 0.473, + 0.422 + ], + "angle": 0, + "content": "AGQA: For AGQA, we train our model with SlowR50 backbone and report results for VQA accuracy on the test set. We also report our model's generalization capability to indirect references. Furthermore, we train our network to report its generalization to novel compositions and to more compositional steps. For more compositional steps, we train our network with randomly sampled 100K QA pairs." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.423, + 0.473, + 0.543 + ], + "angle": 0, + "content": "STAR: Following [47], we train the model for each question type separately to compare with the baselines. However, this protocol is expensive in terms of time and resources. To address the matter of limited resources, we also tried combining all questions together after the questions filtering from interaction and sequence types and train a single model instead of multiple trainings. We use this training regime to study models ablations." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.55, + 0.276, + 0.567 + ], + "angle": 0, + "content": "5. Results and Analysis" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.575, + 0.356, + 0.592 + ], + "angle": 0, + "content": "5.1. Comparison to State-of-the-Art" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.599, + 0.472, + 0.779 + ], + "angle": 0, + "content": "AGQA: We compare our method with the existing state-of-the-art methods on AGQA benchmark. The best baseline on AGQA is HCRN [24] for overall accuracy. HCRN uses appearance features from ResNet101 as well as motion features from ResNext101-Kinetics400 backbones. Our model outperforms HCRN by a significant margin of \\(7.09\\%\\) (HCRN: \\(42.11\\%\\) vs. SHG-VQA: \\(49.20\\%\\)) in terms of overall accuracy (see Table 1). We observe the biggest improvement of \\(14.63\\%\\) absolute points on object-action reasoning questions compared to the best model in that category i.e., PSAC [30]: \\(50.00\\%\\) vs. SHG-VQA: \\(64.63\\%\\). We further report results on the three novel testing metrics as follows:" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.472, + 0.84 + ], + "angle": 0, + "content": "a) Novel Compositions: For novel composition at test time, we observe an overall gain of \\(4.70\\%\\) when compared to the best contender, i.e., PSAC [30]. For open-ended questions, we outperform HCRN by \\(6.56\\%\\) (see Table 2);" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.472, + 0.902 + ], + "angle": 0, + "content": "b) Indirect References: When tested for indirect references questions in Table 4, we outperform all baselines by an absolute \\(7.83\\% -16.62\\%\\) in terms of recall; For precision, we also gain similar improvements;" + }, + { + "type": "list", + "bbox": [ + 0.076, + 0.78, + 0.472, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.317, + 0.895, + 0.393 + ], + "angle": 0, + "content": "c) Compositional Steps: Our model is trained on only \\(15\\%\\) (100K QA pairs) of the training data and is still able to perform on par with the baselines (trained on 1.6M QA pairs) achieving second best results for each category of more compositional steps (table 3)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.394, + 0.897, + 0.622 + ], + "angle": 0, + "content": "STAR: For STAR dataset, we first compare the proposed architecture to other state-of-the-art works in the field using SlowR50 [13] video backbone and a ResNext101 [50] as a frame-level backbone to evaluate accuracy based on 3D video and 2D image architectures. It shows that for both settings, SHG-VQA significantly outperforms other baseline methods even with weaker backbones (see Table 5). Concretely, we obtain an absolute gain of \\(8.53\\%\\) over NSSR [47], \\(5.86\\%\\) compared to HCRN [24], \\(5.99\\%\\) improvement over LCGN [15], and \\(\\sim 2.5\\%\\) over ClipBERT [25] which is a SOTA model in terms of overall VQA accuracy. We notice the substantial gain of \\(7.86\\%\\) for interaction questions which test the understanding of interactions between entities in a situation. Prediction is the next category of questions that benefits the most with \\(2.48\\%\\) improvement." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.633, + 0.842, + 0.65 + ], + "angle": 0, + "content": "5.2. Ablation and Hyperparameter Analysis" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.658, + 0.892, + 0.687 + ], + "angle": 0, + "content": "We perform our ablation studies on the STAR benchmark as discussed below:" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.897, + 0.903 + ], + "angle": 0, + "content": "Impact of situation graphs quality: To assess the effect of situation graphs' quality on VQA accuracy, we train a baseline version of our system, where the model is trained on ground truth situation graphs for VQA tasks only (Table 6). Since the ground truth situation graphs are not available for the test set, we can only compare SHG-VQA and this baseline on the validation set. As expected, when taking ground truth situation graphs as input, the performance is significantly improved for interaction \\((\\mathrm{GT} = 91.9\\%\\) vs. predicted \\(= 46.78\\%)\\) and sequence \\((\\mathrm{GT} = 80.5\\%\\) vs. predicted \\(= 42.52\\%\\) questions. However, for the questions about the unseen part of the video, the model with ground truth graph tokens still struggles despite better performance compared to SHG-VQA: predic" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14885" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.086, + 0.092, + 0.468, + 0.177 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.191, + 0.47, + 0.288 + ], + "angle": 0, + "content": "Figure 4. Qualitative example for using frame-wise set prediction loss. Col. 1 shows the frame, col. 2 shows the ground-truth situation graph, col. 3 shows predicted graphs when trained with set prediction loss for the full video, and col. 4 shows the predicted graph when the model is trained by matching each timestep \\( t \\). The edges show the person-object relationship labels along with the number of times it was predicted." + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.306, + 0.468, + 0.333 + ], + "angle": 0, + "content": "Table 6. Impact of hyper-graphs (HG) quality. Results shown for STAR val set with SlowR50 backbone." + }, + { + "type": "table", + "bbox": [ + 0.09, + 0.345, + 0.458, + 0.389 + ], + "angle": 0, + "content": "
MethodInteractionSequencePredictionFeasibilityOverall
Predicted hyper-graphs47.0842.5237.8233.6140.26
GT hyper-graphs91.980.541.2235.4262.46
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.417, + 0.468, + 0.508 + ], + "angle": 0, + "content": "tion (GT=41.22% vs. predicted=37.82%) and feasibility (GT=35.42% vs. predicted=33.61%). We also report the mAP scores for the prediction of action and relationship predicates using our best model in Table 7. We obtain an overall mAP of 87.63 for actions and 72.9 for relationships respectively." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.508, + 0.468, + 0.627 + ], + "angle": 0, + "content": "Input to cross-attentional transformer: To evaluate the choice of input to the cross-attentional transformer, we experiment with three settings: a) question and video embeddings, b) question and situation graphs embeddings, c) question, situation graphs, and video embeddings. We observe no gain in the overall VQA accuracy when adding video embeddings to the cross-attentional transformer and get our best results with (b) (table 8)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.629, + 0.468, + 0.779 + ], + "angle": 0, + "content": "Situation hyper-graph components: To study the impact of different components of situation graphs, we train our system with only action predicate tokens with objective \\( L_{act} + L_{vqa} \\), only relationship predicates (with \\( L_{rel} + L_{vqa} \\)), and the full model (eq. 1). The action predicates are more effective compared to the relationship predicates. When compared in terms of predicate classification, we observe high accuracy for action predicates. Nonetheless, using the full situation graphs perform better than omitting actions or relationship prediction task (table 9)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.779, + 0.468, + 0.884 + ], + "angle": 0, + "content": "Number of queries: Number of action queries \\( M \\) and relationship queries \\( N \\) is a hyperparameter for SHG-VQA. We report the performance of the SHG-VQA with a varying number of queries for actions and relationships at each timestep in Table 9. We report our best results with \\( M = 3 \\) and \\( N = 8 \\). Additional results are reported in the supplementary document." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.886, + 0.468, + 0.901 + ], + "angle": 0, + "content": "Frame-wise set prediction loss: Using the notion of time \\( t \\)" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.09, + 0.892, + 0.132 + ], + "angle": 0, + "content": "Table 7. Predicate classification results for situation hyper-graphs in terms of mAP for STAR validation set from SHG-VQA with SlowR50 backbone. Numbers are reported in percentages." + }, + { + "type": "table", + "bbox": [ + 0.541, + 0.143, + 0.85, + 0.187 + ], + "angle": 0, + "content": "
InteractSequencePredictionFeasibilityOverall
Actions84.7789.4385.3091.4687.63
Relationships72.8373.570.0572.8272.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.194, + 0.892, + 0.221 + ], + "angle": 0, + "content": "Table 8. Results for cross-attention input. Results shown for STAR test set with SlowR50 backbone." + }, + { + "type": "table", + "bbox": [ + 0.536, + 0.233, + 0.855, + 0.286 + ], + "angle": 0, + "content": "
MethodInteractionSequencePredictionFeasibilityOverall
Q + V33.2835.6027.9326.4330.81
Q + HG47.9842.0335.3432.5239.47
Q + V + HG45.4544.1934.2232.8739.18
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.293, + 0.894, + 0.349 + ], + "angle": 0, + "content": "Table 9. Model variations on STAR validation set with a single model using SlowR50 backbone for all question types. Best results are shown in bold font and second best results are underlined. Numbers are reported for VQA accuracy in percentages." + }, + { + "type": "table", + "bbox": [ + 0.507, + 0.36, + 0.884, + 0.485 + ], + "angle": 0, + "content": "
Method (val)InteractionSequencePredictionFeasibilityOverall
hyper-graph components
Action only – Act=340.9438.0835.5830.5638.68
Relation. only – Rel=835.9435.7934.7827.8635.16
Both – Act=3, Rel=842.9338.2036.0630.5639.20
Number of queries
Act=2, Rel=840.3240.1338.7829.7338.34
Act=3, Rel=842.9338.2036.0630.5639.20
Act=4, Rel=838.4035.7935.5830.3537.06
Act=3, Rel=1241.3240.8039.4231.3939.90
Act=4, Rel=1240.4039.0536.8629.1138.39
" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.498, + 0.892, + 0.559 + ], + "angle": 0, + "content": "in the set prediction loss alleviates the problem of duplicate predictions within each situation. See Fig. 4 for qualitative examples of produced hyper-graphs with and without this loss. Extra examples are in the supplementary document." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.568, + 0.618, + 0.584 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.594, + 0.894, + 0.76 + ], + "angle": 0, + "content": "We presented a novel approach to model situation graph prediction as an underlying sub-task for video question answering. The proposed method predicts a situation hypergraph structure composed of existing actions and relationships in the input video. The input question can then reason over the predicted graph to solve VQA. We show the impact of the proposed approach by evaluating on two video question-answering benchmarks and achieving significant performance gains overall baseline methods. Our method demonstrates promise for further research in this direction to improve VQA systems even further." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.764, + 0.894, + 0.897 + ], + "angle": 0, + "content": "Acknowledgements. Aisha Urooj is supported by the ARO grant W911NF-19-1-0356. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. This work was partially supported under NSF grant CNS-2050731, Research Experience for Undergraduates in Computer Vision. Dr. Gan is supported by the MIT-IBM Watson AI Lab, DSO grant DSOCO21072, and gift funding from MERL, Cisco, Sony, and Amazon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ARO, IARPA, DOI/IBC, or the U.S. Government." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14886" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.117, + 0.472, + 0.186 + ], + "angle": 0, + "content": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.19, + 0.472, + 0.259 + ], + "angle": 0, + "content": "[2] Xiaojun Chang, Pengzhen Ren, Pengfei Xu, Zhihui Li, Xiaojiang Chen, and Alexander G Hauptmann. A comprehensive survey of scene graphs: Generation and application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.263, + 0.472, + 0.333 + ], + "angle": 0, + "content": "[3] Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 702-703, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.336, + 0.472, + 0.405 + ], + "angle": 0, + "content": "[4] Vinay Damodaran, Sharanya Chakravarthy, Akshay Kumar, Anjana Umapathy, Teruko Mitamura, Yuta Nakashima, Noa Garcia, and Chenhui Chu. Understanding the role of scene graphs in visual question answering. arXiv preprint arXiv:2101.05479, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.409, + 0.472, + 0.464 + ], + "angle": 0, + "content": "[5] Long Hoang Dang, Thao Minh Le, Vuong Le, and Truyen Tran. Hierarchical object-oriented spatio-temporal reasoning for video question answering. arXiv preprint arXiv:2106.13432, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.468, + 0.472, + 0.523 + ], + "angle": 0, + "content": "[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.526, + 0.472, + 0.61 + ], + "angle": 0, + "content": "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.613, + 0.472, + 0.695 + ], + "angle": 0, + "content": "[8] Chenyou Fan, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, and Heng Huang. Heterogeneous memory enhanced multimodal attention model for video question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1999-2007, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.7, + 0.472, + 0.755 + ], + "angle": 0, + "content": "[9] Chenyou Fan, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, and Heng Huang. Heterogeneous memory enhanced multimodal attention model for video question answering. CoRR, abs/1904.04357, 2019. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.758, + 0.472, + 0.827 + ], + "angle": 0, + "content": "[10] Jiyang Gao, Runzhou Ge, Kan Chen, and Ram Nevatia. Motion-appearance co-memory networks for video question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6576–6585, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.831, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[11] Madeleine Grunde-McLaughlin, Ranjay Krishna, and Maneesh Agrawala. Agqa: A benchmark for compositional spatio-temporal reasoning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11287-11297, 2021. 2, 6" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.117, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.135 + ], + "angle": 0, + "content": "[12] Madeleine Grunde-McLaughlin, Ranjay Krishna, and Maneesh Agrawala. Agqa 2.0: An updated benchmark for compositional spatio-temporal reasoning, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.894, + 0.203 + ], + "angle": 0, + "content": "[13] Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh. Learning spatio-temporal features with 3d residual networks for action recognition. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 3154-3160, 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.205, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[14] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. *Neural computation*, 9(8):1735–1780, 1997. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.234, + 0.894, + 0.3 + ], + "angle": 0, + "content": "[15] Ronghang Hu, Anna Rohrbach, Trevor Darrell, and Kate Saenko. Language-conditioned graph networks for relational reasoning. In Proceedings of the IEEE International Conference on Computer Vision, pages 10294-10303, 2019. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.302, + 0.894, + 0.357 + ], + "angle": 0, + "content": "[16] Yunseok Jang, Yale Song, Chris Dongjoo Kim, Youngjae Yu, Youngjin Kim, and Gunhee Kim. Video question answering with spatio-temporal reasoning. International Journal of Computer Vision, 127(10):1385-1412, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.359, + 0.894, + 0.427 + ], + "angle": 0, + "content": "[17] Yunseok Jang, Yale Song, Youngjae Yu, Youngjin Kim, and Gunhee Kim. Tgif-qa: Toward spatio-temporal reasoning in visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2758–2766, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.428, + 0.894, + 0.497 + ], + "angle": 0, + "content": "[18] Jianwen Jiang, Ziqiang Chen, Haojie Lin, Xibin Zhao, and Yue Gao. Divide and conquer: Question-guided spatiotemporal contextual attention for video question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11101-11108, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.498, + 0.894, + 0.552 + ], + "angle": 0, + "content": "[19] Pin Jiang and Yahong Han. Reasoning with heterogeneous graph alignment for video question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11109-11116, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.554, + 0.894, + 0.593 + ], + "angle": 0, + "content": "[20] Juanzi Li Jiaxin Shi, Hanwang Zhang. Explainable and explicit visual reasoning over scene graphs. In CVPR, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.595, + 0.894, + 0.65 + ], + "angle": 0, + "content": "[21] Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In CVPR, 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.651, + 0.894, + 0.734 + ], + "angle": 0, + "content": "[22] Yash Kant, Dhruv Batra, Peter Anderson, Alexander Schwing, Devi Parikh, Jiasen Lu, and Harsh Agrawal. Spatially aware multimodal transformers for textvqa. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IX 16, pages 715-732. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.735, + 0.894, + 0.789 + ], + "angle": 0, + "content": "[23] Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong. Pure transformers are powerful graph learners. arXiv preprint arXiv:2207.02505, 2022.1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.79, + 0.894, + 0.831 + ], + "angle": 0, + "content": "[24] Thao Minh Le, Vuong Le, Svetha Venkatesh, and Truyen Tran. Hierarchical conditional relation networks for video question answering. CoRR, abs/2002.10698, 2020. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.832, + 0.894, + 0.901 + ], + "angle": 0, + "content": "[25] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7331-7341, 2021. 2, 7" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.956 + ], + "angle": 0, + "content": "14887" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.134 + ], + "angle": 0, + "content": "[26] Jie Lei, Licheng Yu, Mohit Bansal, and Tamara L Berg. Tvqa: Localized, compositional video question answering. In EMNLP, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.134, + 0.472, + 0.19 + ], + "angle": 0, + "content": "[27] Linjie Li, Zhe Gan, Yu Cheng, and Jingjing Liu. Relation-aware graph attention network for visual question answering. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10313-10322, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.19, + 0.472, + 0.246 + ], + "angle": 0, + "content": "[28] Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. Visualbert: A simple and perform-. mant baseline for vision and language. arXiv preprint arXiv:1908.03557, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.246, + 0.472, + 0.328 + ], + "angle": 0, + "content": "[29] Xiangpeng Li, Jingkuan Song, Lianli Gao, Xianglong Liu, Wenbing Huang, Xiangnan He, and Chuang Gan. Beyond rms: Positional self-attention with co-attention for video question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 8658–8665, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.328, + 0.472, + 0.386 + ], + "angle": 0, + "content": "[30] Xiangpeng Li, Jingkuan Song, Lianli Gao, Xianglong Liu, Wenbing Huang, Xiangnan He, and Chuang Gan. Beyond rnns: Positional self-attention with co-attention for video question answering. In AAAI, 2019. 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.386, + 0.472, + 0.455 + ], + "angle": 0, + "content": "[31] Fei Liu, Jing Liu, Weining Wang, and Hanqing Lu. Hair: Hierarchical visual-semantic relational reasoning for video question answering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1698–1707, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.455, + 0.472, + 0.497 + ], + "angle": 0, + "content": "[32] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. arXiv preprint arXiv:2106.13230, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.497, + 0.472, + 0.552 + ], + "angle": 0, + "content": "[33] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. arXiv preprint arXiv:1908.02265, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.552, + 0.472, + 0.636 + ], + "angle": 0, + "content": "[34] Tegan Maharaj, Nicolas Ballas, Anna Rohrbach, Aaron Courville, and Christopher Pal. A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6884–6893, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.636, + 0.472, + 0.707 + ], + "angle": 0, + "content": "[35] Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B Tenenbaum, and Jiajun Wu. The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision. In International Conference on Learning Representations, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.707, + 0.472, + 0.788 + ], + "angle": 0, + "content": "[36] Kenneth Marino, Xinlei Chen, Devi Parikh, Abhinav Gupta, and Marcus Rohrbach. Krisp: Integrating implicit and symbolic knowledge for open-domain knowledge-based vqa. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14111-14121, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.788, + 0.472, + 0.845 + ], + "angle": 0, + "content": "[37] Devshree Patel, Ratnam Parikh, and Yesha Shastri. Recent advances in video question answering: A review of datasets and methods. In International Conference on Pattern Recognition, pages 339-356. Springer, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.845, + 0.472, + 0.902 + ], + "angle": 0, + "content": "[38] Liang Peng, Shuangji Yang, Yi Bin, and Guoqing Wang. Progressive graph attention network for video question answering. In Proceedings of the 29th ACM International Conference on Multimedia, pages 2871-2879, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.092, + 0.895, + 0.147 + ], + "angle": 0, + "content": "[39] Tianwen Qian, Jingjing Chen, Shaoxiang Chen, Bo Wu, and Yu-Gang Jiang. Scene graph refinement network for visual question answering. IEEE Transactions on Multimedia, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.895, + 0.205 + ], + "angle": 0, + "content": "[40] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.205, + 0.895, + 0.316 + ], + "angle": 0, + "content": "[41] Ahjeong Seo, Gi-Cheon Kang, Joonhan Park, and Byoung-Tak Zhang. Attend what you need: Motion-appearance synergistic networks for video question answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6167–6177, Online, Aug. 2021. Association for Computational Linguistics. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.316, + 0.895, + 0.383 + ], + "angle": 0, + "content": "[42] Paul Hongsuck Seo, Arsha Nagrani, and Cordelia Schmid. Look before you speak: Visually contextualized utterances. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16877-16887, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.384, + 0.895, + 0.455 + ], + "angle": 0, + "content": "[43] Gunnar A Sigurdsson, Gül Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, and Abhinav Gupta. Hollywood in homes: Crowdsourcing data collection for activity understanding. In European Conference on Computer Vision, pages 510-526. Springer, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.455, + 0.895, + 0.511 + ], + "angle": 0, + "content": "[44] Gunnar A. Sigurdsson, Gúl Varol, X. Wang, Ali Farhadi, Ivan Laptev, and Abhinav Kumar Gupta. Hollywood in homes: Crowdsourcing data collection for activity understanding. ECCV, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.511, + 0.895, + 0.58 + ], + "angle": 0, + "content": "[45] Ajeet Kumar Singh, Anand Mishra, Shashank Shekhar, and Anirban Chakraborty. From strings to things: Knowledge-enabled vqa model that can read and reason. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4602-4612, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.58, + 0.895, + 0.651 + ], + "angle": 0, + "content": "[46] Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. Movieqa: Understanding stories in movies through question-answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4631–4640, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.651, + 0.895, + 0.707 + ], + "angle": 0, + "content": "[47] Bo Wu, Shoubin Yu, Zhenfang Chen, Joshua B Tenenbaum, and Chuang Gan. Star: A benchmark for situated reasoning in real-world videos. In Thirty-fifth Conference on Neural Information Processing Systems, 2021. 1, 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.707, + 0.895, + 0.774 + ], + "angle": 0, + "content": "[48] Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question-answering to explaining temporal actions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9777–9786, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.774, + 0.895, + 0.831 + ], + "angle": 0, + "content": "[49] Junbin Xiao, Angela Yao, Zhiyuan Liu, Yicong Li, Wei Ji, and Tat-Seng Chua. Video as conditional graph hierarchy for multi-granular question answering. arXiv preprint arXiv:2112.06197, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.831, + 0.895, + 0.9 + ], + "angle": 0, + "content": "[50] Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017. 7" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.092, + 0.895, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "14888" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.161 + ], + "angle": 0, + "content": "[51] Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang. Video question answering via gradually refined attention over appearance and motion. In Proceedings of the 25th ACM international conference on Multimedia, pages 1645–1653, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.164, + 0.472, + 0.232 + ], + "angle": 0, + "content": "[52] Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, and Cordelia Schmid. Just ask: Learning to answer questions from millions of narrated videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1686-1697, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.234, + 0.472, + 0.289 + ], + "angle": 0, + "content": "[53] Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B Tenenbaum. Clevrer: Collision events for video representation and reasoning. ICLR, 2020. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.292, + 0.472, + 0.361 + ], + "angle": 0, + "content": "[54] Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Joshua B Tenenbaum. Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding. In Advances in Neural Information Processing Systems (NIPS), 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.362, + 0.472, + 0.417 + ], + "angle": 0, + "content": "[55] Fei Yu, Jiji Tang, Weichong Yin, Yu Sun, Hao Tian, Hua Wu, and Haifeng Wang. Ernie-vil: Knowledge enhanced vision-language representations through scene graph. arXiv preprint arXiv:2006.16934, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.42, + 0.472, + 0.487 + ], + "angle": 0, + "content": "[56] Weijiang Yu, Haoteng Zheng, Mengfei Li, Lei Ji, Lijun Wu, Nong Xiao, and Nan Duan. Learning from inside: Self-driven siamese sampling and reasoning for video question answering. Advances in Neural Information Processing Systems, 34, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.49, + 0.472, + 0.559 + ], + "angle": 0, + "content": "[57] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 9127–9134, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.562, + 0.472, + 0.63 + ], + "angle": 0, + "content": "[58] Amir Zadeh, Michael Chan, Paul Pu Liang, Edmund Tong, and Louis-Philippe Morency. Social-iq: A question answering benchmark for artificial social intelligence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8807–8817, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.632, + 0.472, + 0.701 + ], + "angle": 0, + "content": "[59] Rowan Zellers, Jiasen Lu, Ximing Lu, Youngjae Yu, Yanpeng Zhao, Mohammadreza Salehi, Aditya Kusupati, Jack Hessel, Ali Farhadi, and Yejin Choi. Merlot reserve: Neural script knowledge through vision and language and sound. arXiv preprint arXiv:2201.02639, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.703, + 0.472, + 0.758 + ], + "angle": 0, + "content": "[60] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. Advances in Neural Information Processing Systems, 34, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.76, + 0.472, + 0.814 + ], + "angle": 0, + "content": "[61] Kuo-Hao Zeng, Tseng-Hung Chen, Ching-Yao Chuang, Yuan-Hong Liao, Juan Carlos Niebles, and Min Sun. Leveraging video descriptions to learn video question answering. CoRR, abs/1611.04021, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.817, + 0.472, + 0.857 + ], + "angle": 0, + "content": "[62] Yaoyao Zhong, Wei Ji, Junbin Xiao, Yicong Li, Weihong Deng, and Tat-Seng Chua. Video question answering: Datasets, algorithms and challenges, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.86, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[63] Guangming Zhu, Liang Zhang, Youliang Jiang, Yixuan Dang, Haoran Hou, Peiyi Shen, Mingtao Feng, Xia Zhao, Qiguang Miao, Syed Afaq Ali Shah, and Mohammed Bennamoun." + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "Scene graph generation: A comprehensive survey. CoRR, abs/2201.00443, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.122, + 0.894, + 0.177 + ], + "angle": 0, + "content": "[64] Linchao Zhu, Zhongwen Xu, Yi Yang, and Alexander G Hauptmann. Uncovering the temporal context for video question answering. International Journal of Computer Vision, 124(3):409-421, 2017. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.177 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.956 + ], + "angle": 0, + "content": "14889" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Situation Hyper-Graphs for Video Question Answering/cab54a6b-1009-4dbc-b1ca-0ed1a0963ee9_origin.pdf b/2023/Learning Situation Hyper-Graphs for Video Question Answering/cab54a6b-1009-4dbc-b1ca-0ed1a0963ee9_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..d2adf02ebc739460936d168460ae38d3b18bce4a --- /dev/null +++ b/2023/Learning Situation Hyper-Graphs for Video Question Answering/cab54a6b-1009-4dbc-b1ca-0ed1a0963ee9_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:434046f5ee9a09c34fca25936e38cf9d5b9d35450da0cec0b6b9373c0c641592 +size 3209072 diff --git a/2023/Learning Situation Hyper-Graphs for Video Question Answering/full.md b/2023/Learning Situation Hyper-Graphs for Video Question Answering/full.md new file mode 100644 index 0000000000000000000000000000000000000000..b3628ebd439a057aa5b140b1052c249ee3e34464 --- /dev/null +++ b/2023/Learning Situation Hyper-Graphs for Video Question Answering/full.md @@ -0,0 +1,299 @@ +# Learning Situation Hyper-Graphs for Video Question Answering + +Aisha Urooj Khan $^{1,3}$ , Hilde Kuehne $^{2,4}$ , Bo Wu $^{2}$ , Kim Chheu $^{5}$ , Walid Bousselham $^{4}$ , Chuang Gan $^{2,6}$ , Niels Lobo $^{1}$ , Mubarak Shah $^{1}$ + +$^{1}$ CRCV, University of Central Florida, $^{2}$ MIT-IBM Watson AI Lab, $^{3}$ Mayo Clinic, AZ + $^{4}$ Goethe University Frankfurt Germany, $^{5}$ Western Michigan University, $^{6}$ UMass Amherst + +# Abstract + +Answering questions about complex situations in videos requires not only capturing the presence of actors, objects, and their relations but also the evolution of these relationships over time. A situation hyper-graph is a representation that describes situations as scene sub-graphs for video frames and hyper-edges for connected sub-graphs and has been proposed to capture all such information in a compact structured form. In this work, we propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs, coined Situation Hyper-Graph based Video Question Answering (SHG-VQA). To this end, we train a situation hyper-graph decoder to implicitly identify graph representations with actions and object/human-object relationships from the input video clip. and to use cross-attention between the predicted situation hyper-graphs and the question embedding to predict the correct answer. The proposed method is trained in an end-to-end manner and optimized by a VQA loss with the cross-entropy function and a Hungarian matching loss for the situation graph prediction. The effectiveness of the proposed architecture is extensively evaluated on two challenging benchmarks: AGQA and STAR. Our results show that learning the underlying situation hypergraphs helps the system to significantly improve its performance for novel challenges of video question-answering tasks1. + +# 1. Introduction + +Video question answering in real-world scenarios is a challenging task as it requires focusing on several factors including the perception of the current scene, language understanding, situated reasoning, and future prediction. Visual perception in the reasoning task requires capturing various aspects of visual understanding, e.g., detecting a diverse set + +![](images/b12d3ee4e4edbfe888ae8a98da74e8d3f6644ede2494554e1408cd9c27ff643a.jpg) +Figure 1. The situation hyper-graph for a video is composed of situations with entities and their relationships (shown as subgraphs in the pink box). These situations may evolve over time. Temporal actions act as hyper-edges connecting these situations into one situation hyper-graph. Learning situation graphs, as well as temporal actions, is vital for reasoning-based video question answering. + +of entities, recognizing their interactions, as well as understanding the changing dynamics between these entities over time. Similarly, linguistic understanding has its challenges as some question or answer concepts may not be present in the input text or video. + +Visual question answering, as well as its extension over time, video question answering, have both benefited from representing knowledge in graph structures, e.g., scene graphs [20, 35], spatio-temporal graphs [4, 55], and knowledge graphs [36, 45]. Another approach in this direction is the re-introduction of the concept of "situation cognition" embodied in "situation hyper-graphs" [47]. This adds the computation of actions to the graphs that capture the interaction between entities. In this case, situations are represented by hyper-graphs that join atomic entities and relations (e.g., agents, objects, and relationships) with their actions (Fig. 1). This is an ambitious task for existing systems as it is impractical to encapsulate all possible interactions in the real-world context. + +Recent work [23] shows that transformers are capable + +of learning graphs without adapting graph-specific details in the architectures achieving competitive or even better performance than sophisticated graph-specific models. Our work supports this idea by implicitly learning the underlying hyper-graphs of a video. Thus, it requires no graph computation for inference and uses decoder's output directly for cross attention module. More precisely, we propose to learn situation hyper-graphs, namely framewise actor-object and object-object relations as well as their respective actions, from the input video directly without the need for explicit object detection or other required prior knowledge. While the actions capture events across transitions over multiple frames, such as Drinking from a bottle, the relationship encoding actually considers all possible combinations of static, single frame actor-object, and object-object relationships as unique classes, e.g., in the form of person - hold - bottle or bottle - stands on - table, thus serving as an object and relation classifier. Leveraging this setup allows us to streamline the spatio-temporal graph learning as a set prediction task for predicting relationship predicates and actions in a Situation hyper-graph Decoder block. To train the Situation Graph Decoder, we use a bipartite matching loss between the predicted set and ground truth hyper-graph tokens. The output of the situation graph decoder is a set of action and relationship tokens, which are then combined with the embedding of the associated question to derive the final answer. An overview of the proposed architecture is given in Fig. 2. Note that, compared to other works targeting video scene graph generation, e.g., those listed in [63], we are less focused on learning the best possible scene graph, but rather on learning the representation of the scene which best supports the question answering task. Thus, while capturing the essence of a scene, as well as the transition from one scene to the other, we are not only optimizing the scene graph accuracy but also considering the VQA loss. + +We evaluate the proposed method on two challenging video question answering benchmarks: a) STAR [47], featuring four different question types, interaction, sequence, prediction, and feasibility based on a subset of the real-world Charades dataset [44]; and b) Action Genome QA (AGQA) [11] dataset which tests vision focused reasoning skills based on novel compositions, novel reasoning steps, and indirect references. Compared to other VQA datasets, these datasets provide dense ground truth hyper-graph information for each video, which allows us to learn the respective embedding. Our results show that the proposed hyper-graph encoding significantly improves VQA performance as it has the ability to infer correct answers from spatio-temporal graphs from the input video. Our ablations further reveal that achieving high-quality graphs can be critical for VQA performance. + +Our contributions to this paper are as follows: + +- We introduce a novel architecture that enables the com + +putation of situation hyper-graphs from video data to solve the complex reasoning task of video question-answering; + +- We propose a situation hyper-graph decoder module to decode the atomic actions and object/actor-object relationships and model the hyper-graph learning as a transformer-based set prediction task and use a set prediction loss function to predict actions and relationships between entities in the input video; +- We use the resulting high-level embedding information as sole visual information for the reasoning and show that this is sufficient for an effective VQA system. + +# 2. Related Work + +Video Question Answering: Video question answering is an active area of research with efforts in insightful directions [37,62], such as attention [16, 18, 29], cross-modal interactions [25, 41, 52, 56, 59, 60], hierarchical learning [5, 24, 38], and so on. A few other algorithms classes include modular networks [5, 24, 49], symbolic reasoning [47, 53, 54], and memory networks [8, 10]. Several video QA benchmarks are introduced to evaluate this task from varying perspectives entailing description [34, 51, 57, 61], temporal reasoning [17, 53, 64], causal structures [48, 53], visual-language comprehension [26, 46, 52], relational reasoning [46], and measuring social intelligence [58]. STAR [47] benchmark goes one step further providing a benchmark to perform diagnostic study at additional fronts such as predicting future interactions and feasibility of next possible actions in the unseen future. Existing approaches on the benchmark either rely on object features [40] as nodes explicitly modeling their interactions through a message passing mechanism [15, 47], or benefit from efficient input sampling strategies during training to learn robust visual representations [24, 25]. We, however, take a different approach and focus on inferring the underlying semantic graph structure in the video and use it for QA reasoning. Our method is independent of using pretrained object detectors and uses a simple approach to learn to infer the sets of relationship predicates as well as actions for each frame. The model is trained end-to-end with the frozen backbone. + +Graph-based VQA: Another related line of work is graph-based VQA methods [22, 27]. Some of them work on object features extracted from a pretrained detector (e.g., Faster-RCNN [40]) [5, 15, 31, 42, 49], while some operate on frame-level [19, 25, 56] (e.g., ResNet), clip-level features [38, 41, 52](e.g., C3D, S3D) or transformer-based backbones [7, 32, 39]. We focus on predicting atomic actions, and relationship triplets from the frame-level or clip-level features directly instead of an explicit graph. + +![](images/d54a2ede95f3e54d7ea1d91a60e1202e72d987e549c794f69e9e021fb14447e4.jpg) +Figure 2. The SHG-VQA architecture: we start with encoding the input video into spatio-temporal features using a pre-trained backbone. These video features are flattened into a sequence of tokens of length $T \times h \times w$ and position encoded to be further processed through a visual encoder. These encoded tokens are input to the action decoder to predict the set of atomic actions from the input action queries as well as to the relationship decoder which takes relationship queries as input along with the video tokens. The action decoder and relationship decoder output the situation graph embeddings. In the text branch, the question and the answer choices are composed into a sequence and passed through a text transformer to obtain encoded word embeddings; for open-ended VQA, only the question is passed to the text transformer. The generated hyper-graph along with the encoded text tokens are then used as input to a cross-attentional transformer and the combined representations are used to predict the correct answer with a classifier. Section 3.4 describes the losses and training objectives. + +Scene Graph Generation: The proposed formulation of situation hyper-graph deviates from existing scene-graph generation approaches [2, 47, 63] as we do not require object detections as input or object level supervision, nor do we model it in an explicit graph structure. Our goal is simple: given an input video, predict all object-relation-object and actor-relation-object triplets as well as associated actions for each video frame. Our intuition is that forcing the model to predict these predicates will drive the system to learn a latent graph structure for visual input. The predicted situation hyper-graph is treated as an abstract video representation and used for VQA reasoning in the next step. + +# 3. The SHG-VQA Model + +Situation video question answering has three essential steps 1) the visual recognition capacities of visual entities, their relationships, actions, and how these transition over time, 2) the language understanding capacity to the questions, 3) the question-guided reasoning process over the representation learned in the first step. Sub-optimal performance at any of these steps will affect the overall task performance. A key problem here is that capturing the visual structures directly from raw data, e.g., in the form of features often results in a rather noisy signal and does not provide suitable input for high-level language-guided reasoning. To overcome this mismatch, we propose to learn the implicit structure of the visual input as an intermediate step between learning the video representation and question-based reasoning. Forcing the model + +to learn to predict this implicit structure (actions, relations between entities) not only improves the video representation but also acts as a lightweight, high-level representation of the video content and can be used for the VQA task. We illustrate our architecture in Fig. 2. + +# 3.1. Input Processing + +Given the input video and the question, we encode the inputs as described below: + +Question Encoder: The question is first tokenized into word tokens using a wordPiece tokenizer. These word tokens along with the special class token $[CLS]$ are the inputs of an embedding layer. The output word embeddings from this layer are input to a transformer encoder that encodes each word using multi-head self-attention between different words at each encoder layer. + +Video Encoder: Let $V \in \mathbb{R}^{T \times H \times W \times 3}$ be the input video clip, where $T$ is the clip length with 3 color channels, height $H$ and width $W$ . First, we extract video features $x_{V} \in \mathbb{R}^{T \times h \times w \times d_{x}}$ using a convolutional backbone with $d_{x}$ being the feature dimension, $h$ and $w$ are the reduced feature's height and width. As transformers process sequential input data, the video features $x_{V}$ are flattened into a sequence (of size $\mathbb{R}^{Thw \times d_x}$ ) and reduced to dimension $d$ through a linear layer. Then, we append a trainable vector of dimension $d$ for a special class token [VIS] to this sequence of video features at index 0. These features are combined with position encodings and input to a transformer-based video encoder $V_{e}$ . + +The output features of the video are forwarded to the action decoder and relationship decoder to infer the situation graph capturing the action information, as well as the entities and their relationships. The output of the last layer of both decoders is then combined and augmented with frame positions, forming the final hyper-graph embedding. We further attach a randomly initialized class token $[HG]$ . These situation graph embeddings are then input to a multi-layered cross-attentional transformer encoder for more fine-grained interaction between the question words and semantic knowledge extracted from the video in the form of a graph. The output features corresponding to $[CLS]$ and $[HG]$ tokens are input to an answer classifier to produce an answer. + +# 3.2. Situation hyper-graph Generation + +Real-world video understanding relies on the scene understanding including changing relationships between the objects over time and the evolving actions. Therefore, we represent the given video as a "situation graph" denoted by $G$ which describes actions and relationships between objects. Let $G = (V, E)$ , where $V$ is the set of vertices representing all possible entities in the dataset, and $E$ is the set of edges representing all possible relationships between each pair of entities. Given an input video, we want to learn a situation graph $g_{t} = (v_{t}, e_{t})$ , $v_{t} \in V$ , $e_{t} \in E$ for each time step $t \in \{1, 2, \dots, T\}$ in the video, that captures the entities (objects, actors), their relationships as well as the associated actions that are present in that frame. The hyper-graph for one video is thus represented by the set of situation graphs $\mathcal{G} = \{g_{1}, \dots, g_{T}\}$ . An action may comprise multiple relationships and objects. For each frame, we further have a set of actions $A_{t} = a_{1}, a_{2}, \dots, a_{N}$ . The set of actions for the full video is then given as $\mathcal{A} = \{A_{1}, \dots, A_{T}\}$ . Note that the actions are predicted in addition to the graph structure and that both will be merged in a situation hyper-graph embedding in a separate step after the decoder block. Rather than predicting the set of vertices and edges in the graph $g_{t}$ , we propose to predict the graph structure by formulating this graph prediction as follows: + +Let $x_{V_e} \in \mathbb{R}^{T' \times h \times w \times d}$ be the encoded video features, where $T'$ denotes the temporal length of encoded features, $h$ and $w$ are spatial dimensions, and $d$ is the feature's dimension. A relationship predicate $p$ describes interactions between entities by triplet tokens object-relation-object or actor-relation-object. We propose to predict the set of relationship predicates $R$ and the set of atomic actions $A$ occurring in each video frame. This is intuitive because the ability to predict the atomic actions and relations between entities for each time step benefits the high level reasoning tasks such as video question answering in this work. Therefore, for each time step $t \in \{1,2,\dots,T\}$ in the video, we predict the set of relationship predicates denoted by $R_t$ in each video frame where $R_t = \{p_1,p_2,\dots,p_M\}$ and $p_i$ is the + +![](images/f103a63b4502d1faa176348019aa0e11b89742f5de852ae8463e0ce093435a92.jpg) +Figure 3. Situation hyper-graph embeddings: We start with decoded queries from action and relationship decoder. Then we add type encoding vectors $[ACT]$ and $[REL]$ for actions and relationships, attention masks, and an embedding vector for the situation ID $(t \in \{1, \dots, T\})$ . The sums are input to the cross-attentional module. See section 3.2.2 for details). + +$i^{th}$ predicate between two entities, representing the vertices $v_{x}$ and $v_{y}$ (object-object or actor-object) and the relation resp. edge $e_i$ represented as $< v_x,e_i,v_y>$ , $M = |R_t|$ is the relationship set size. Additionally, we predict the set of $N$ actions $A_{t}$ for each time step $t$ where $A_{t} = \{a_{1},a_{2},\dots,a_{N}\}$ and $a_{j}$ is the $j^{th}$ action occurring at time step $t$ . $N = |A_t|$ is the actions set size for each step $t$ . $M$ and $N$ are hyperparameters in our system. + +To obtain actions and relation predicates from the video, we use a transformer decoder that takes video features as memory to learn action queries and relation queries. + +# 3.2.1 Prediction Head + +The decoded output embeddings for action and relationship queries are input to respective prediction heads. Prediction heads use a 2-layer feed forward network (FFN) with GELU activation and LayerNorm. + +Considering that actions and relationships at a given time step are permutation-invariant, we use optimal bipartite matching between predicted and ground truth actions/relationships. An optimal bipartite matching between the predicted classes and ground-truth labels is the one with the minimum matching cost. Once the optimal matching pairs have been obtained, we use a Hungarian loss function to optimize for ground truth classes [1]. See section 3.4 for details of the loss function. + +# 3.2.2 Situation hyper-graph Embedding + +For the decoded queries of actions and relationships (referred as graph token embeddings), situation hyper-graph + +Table 1. Results on AGQA dataset for different question types. The best results are shown in bold font. Numbers are reported in percentages. + +
MethodReasoningSemanticStructureOverall
obj-relrel-actionobj-actionsuperlativesequencingexistsdurationactivityobjrelactionquerycomparechooselogicverifybinaryopenall
PSAC [30]37.8449.9550.0033.2049.7849.9445.214.1437.9749.9546.8531.6349.4946.5649.9649.9048.8731.6340.18
HME [9]37.4249.9049.9733.2149.7749.9647.035.4337.5549.9947.5831.0149.7146.4249.8749.9648.9131.0139.89
HCRN [24]40.3349.8649.8533.5549.7050.0143.845.5240.3349.9646.4136.3449.2243.4250.0250.0147.9736.3442.11
SHG-VQA46.4260.6764.6338.8362.1756.0648.1510.1247.6156.1953.8343.4260.6847.7652.8656.6355.0443.4249.20
+ +embeddings are constructed in order to be used with question features for video question answering. First, these action and relationship graph embeddings are combined for each time step $t$ representing a situation at $t$ . Then, we add token type embedding $[ACT]$ for actions and $[REL]$ for relations to their respective graph embeddings. A situation ID (or frame position $t$ ) embedding is also added to these embeddings. An additional attention mask is used to differentiate actual tokens and padded tokens (no-class token $\phi$ ) at training time. At inference time, no attention mask is used as we do not employ any information about the graph at test time. Finally, we add a special class token $[HG]$ to this sequence of features. See Fig. 3 for visualization. + +# 3.3. Cross-attentional Transformer Module + +The situation hyper-graph embeddings obtained at previous step (section 3.2.2) are input along with the question to a cross-attentional transformer module which allows fine-grained computation between the question features and the graph features. A standard co-attentional transformer module is used for cross-attention between the two sequential feature inputs. The feature outputs corresponding to the $[HG]$ token and $[CLS]$ token from the cross-attentional transformer block are fed to a feed-forward network (FFN) for answer prediction. + +# 3.4. Learning Objective + +The SHG-VQA model is trained with the following training objective i.e., + +$$ +L = L _ {a c t} + L _ {r e l} + L _ {v q a} \tag {1} +$$ + +where $L_{act}$ and $L_{rel}$ are the set prediction loss terms for predicting the action set and relationship set for the video, and $L_{vqa}$ is the cross-entropy loss over the predicted situation graph and question. + +Actions and relationships set prediction loss: The situation graph prediction module infers fixed sets of sizes $|N| \times T$ actions and $|M| \times T$ relationships for $T$ -length video clip in a single pass through the action decoder and relationship decoder respectively. We modify the set prediction loss used in [1] as follows. Let $A$ be the set of ground-truth actions and $\hat{A} = \{\hat{a}_i\}_{i=1}^{|N| \times T}$ be the predicted set of actions. In a scenario where $\hat{A}$ is larger than the set of actions present + +in the video, a special class $\phi$ (no class) is padded to the ground truth set $A$ . We obtain a bipartite matching between ground-truth and predicted set for each timestep $t$ as follows: + +$$ +\hat {\sigma} _ {a} = \sum_ {t} ^ {T} \operatorname {a r g m i n} _ {\sigma_ {t} \in \zeta_ {| N |}} \sum_ {i} ^ {| N |} \mathcal {L} _ {\text {m a t c h}} \left(a _ {t _ {i}}, \hat {a} _ {\sigma_ {t} (i)}\right) \tag {2} +$$ + +Where, $\sigma_{t}$ is a permutation of N elements for frame t, $\mathcal{L}_{\text{match}}(a_{ti}, \hat{a}_{\sigma_t(i)})$ is a pair-wise matching cost between $i^{th}$ ground-truth action label in $t^{th}$ frame i.e., $a_{t_i}$ and a predicted action label at index $\sigma_t(i)$ . This optimal assignment for each step $t$ is computed using the Hungarian algorithm and the cost is summed over all $T$ steps. The proposed set prediction loss takes into account only the class predictions for all video frames with no bounding box ground truths being used, different from the original set prediction loss used in [1] for object detection in images. Let $\hat{p}(c_{t(i)})$ be the class probability for the action prediction at $\sigma_t(i)$ , $\mathcal{L}_{\text{match}}(a_{t_i}, \hat{a}_{\sigma_t(i)})$ would be $-\mathbb{1}_{\{c_{t(i)} \neq \phi\}} \hat{p}_{\sigma_t(i)}(c_{t(i)})$ . After we obtain a one-to-one optimal matching between ground-truth and predicted set items without duplicates at each time step, we can compute the loss between the matched pairs using a Hungarian loss as follows: + +$$ +\mathcal {L} _ {\text {a c t}} (a, \hat {a}) = \sum_ {i = 1} ^ {| N | \times T} - \log \hat {p} _ {\hat {\sigma} (i)} \left(c _ {i}\right) \tag {3} +$$ + +Likewise, $R$ is the set of ground-truth relations and $\hat{R} = \{\hat{p_i}\}_{i = 1}^{|M|\times T}$ denotes the predicted relationships. $L_{rel}$ is formulated as follows: + +$$ +\hat {\sigma} _ {p} = \sum_ {t} ^ {T} \operatorname {a r g m i n} _ {\sigma_ {t} \in \zeta_ {| M |}} \sum_ {i} ^ {| M |} \mathcal {L} _ {\text {m a t c h}} \left(p _ {t _ {i}}, \hat {p} _ {\sigma_ {t} (i)}\right) \tag {4} +$$ + +$$ +\mathcal {L} _ {r e l} (p, \hat {p}) = \sum_ {i = 1} ^ {| M | \times T} - \log \hat {p} _ {\hat {\sigma} (i)} (c _ {i}) \tag {5} +$$ + +where $\hat{\sigma}(i)$ is the optimal matching obtained in the previous step for each frame. The inferred graph is input to the cross-attentional transformer module along with the question and the answer choices as explained in section 3.2.2. The proposed network is trained in an end-to-end manner. + +Table 2. Results on AGQA's novel compositions test metric. + +
MethodSequencingSuperlativeDurationObj-relationOverall
BOAllBOAllBOAllBOAllBOAll
PSAC [30]49.1929.3340.9645.2317.7633.3247.8934.8442.0643.760.0124.2846.4919.3434.71
HME [9]49.3328.0640.5344.0613.830.9548.4534.7242.3139.580.0021.9645.4217.1733.15
HCRN [24]48.3130.0040.7345.1217.3033.0646.1539.1143.0137.152.8621.8844.8820.1234.13
SHG-VQA50.8838.5945.7951.1423.6439.2551.8449.2150.6639.736.2324.8249.0726.6839.37
+ +Table 3. Evaluation on AGQA's more compositional steps. + +
More Compositional StepsBinaryOpenAll
PSAC [30]47.6514.8147.19
HME [9]48.0920.9847.72
HCRN [24]46.9623.7046.63
SHG-VQA47.1322.6646.97
+ +Table 4. Evaluation on AGQA's indirect references test metric. + +
MethodObjectActionTemporal
BOAllBOAllBOAll
PrecisionPSAC63.6953.7756.6461.0152.4653.2457.5253.7454.39
HME62.9552.3155.3958.2148.1249.0455.9952.4253.04
HCRN54.0667.2463.4353.8764.4363.4752.3566.8464.34
SHG-VQA76.9386.2781.5979.5587.4084.9069.0887.3582.87
RecallPSAC45.0627.3638.8040.9122.1825.9635.1326.8430.64
HME46.0326.8039.2341.3221.7125.6737.9626.5931.80
HCRN44.8435.4641.5244.0130.4333.1735.1134.3834.71
SHG-VQA53.8643.9849.8554.1637.8343.1052.2143.5746.78
+ +# 4. Experiments + +# 4.1. Datasets + +AGQA Benchmark [11]: The Action Genome Question Answering benchmark is a visual dataset comprising 192M hand-crafted questions about 9.6K videos from the Charades dataset [43]. In addition to VQA accuracy, AGQA presents three testing metrics for testing the VQA methods: indirect references, novel compositions, and more compositional steps. We use the AGQA 2.0 Balanced dataset, which consists of 2.27M question-answer pairs as a result of balancing the original dataset using stricter procedures to reduce as much language bias as possible. Of the 2.27M questions, there are approximately 1.6M training questions and 669K test questions [12]. To have a standard train-val-test setup for our experiments, we randomly sampled $10\%$ QA pairs from training data for validation of hyperparameters. + +STAR Benchmark [47]: The STAR dataset provides 60K situated reasoning questions based on 22K trimmed situation video clips, also based on the Charades dataset [43]. They further provide $\sim 144\mathrm{K}$ ground-truth situation graphs including 111 actions, 37 unique objects, and 24 relationships. The dataset is split into training, validation, and test sets where test evaluation can be done on the evaluation server a limited number of times. We perform ablations and analysis on the validation set and report test set results to compare with the baselines in Table 5. + +# 4.2. Implementations + +On the VQA task, we report accuracy; we also report mAP for situation hyper-graph predictions. For AGQA's indirect references testing metric, precision and recall are reported. For STAR benchmark, we follow the same training protocol as [47] and train SHG-VQA from scratch on each question type separately unless specified otherwise. Training details are shared in the supplementary document. + +Visual Embeddings: The video frames are resized to size $224 \times 224$ with a clip length of 16 frames. We use RandAugment [3] for data augmentation during training. The video clip is input to a pretrained convolutional network with freeze weights to obtain video features of size $16 \times 7 \times 7 \times 2048$ . A 2-layer 3D convolutional block with the kernel of size $5 \times 3 \times 3$ further processes the zero-padded extracted features yielding features of size $8 \times 7 \times 7 \times d$ . The spatio-temporal dimensions are then flattened to obtain a sequence of length $Thw = 392$ $d$ -dimensional tokens where $d = 768$ . The input encoders as well as situation hyper-graph decoders use $L = 5$ transformer encoder layers with non-shared weights. + +Query embeddings for action and relationship predicates: The features output from the video encoder is then input to a situation hyper-graph decoder comprising transformer-based action and relationship decoders. Our best model uses $M = 8$ relation queries and $N = 3$ action queries for each situation; $T$ is set to 16. AGQA has 157 total raw actions, 36 unique objects, and 44 unique relationships which obtain 456 relationship triplets $< v_{x}, e_{i}, v_{y} >$ in the training and validation set. For STAR dataset, there are 37 objects, and 24 relationships yielding 563 unique relation predicates; it also has 111 action classes. + +Situation graph embeddings: The decoded situation graph queries are input to a cross-attentional transformer. The input situation graph embedding is a sum of 4 different encoding types: 1) decoded query embeddings for predicted actions and relationship predicates, 2) situation IDs denoting the situation (or frame) number for each query, 3) attention mask set to 1 for actual tokens and 0 for padded tokens, and 4) token type embeddings to distinguish between action tokens and relationship tokens as shown in Fig. 3. Our network uses a 2-layer co-attentional module [33] for cross-attention. + +Table 5. Results on STAR dataset. Best results are shown in bold font and second best results are underlined. Numbers are reported for VQA accuracy in percentages. + +
Method(test)BackboneObj.Hyper.Question TypeOverall
Inter.Seq.Pred.Feas.
Q-type (Random) [21]-XX25.0624.9324.7924.8124.89
Q-type (Frequent) [21]-XX19.0919.4512.9018.3117.44
Blind Model (LSTM) [14]GloVeXX32.2432.1728.5628.4130.34
Blind Model (BERT) [6]BERTXX32.6834.2129.9829.2631.53
CNN-LSTM [53]ResNext101-K400XX33.2532.6730.6930.4331.76
CNN-BERT [28]ResNext101-K400XX33.5937.1630.9530.8433.14
LCGN [15]ResNext101-K400✓X39.0137.9728.8126.9833.19
HRCN [24]ResNext101-K400✓X39.1038.1728.7527.2733.32
ClipBERT [25]ResNext101-K400XX39.8143.5932.2431.4236.70
NS-SR [47]ResNext101-K400✓✓30.8831.7630.2329.7330.65
SHG-VQA (Ours)SlowR50-K400X✓47.9842.0335.3432.5239.47
SHG-VQA (Ours)ResNext101-ImageNet1KX✓45.842.7734.6429.9138.28
+ +AGQA: For AGQA, we train our model with SlowR50 backbone and report results for VQA accuracy on the test set. We also report our model's generalization capability to indirect references. Furthermore, we train our network to report its generalization to novel compositions and to more compositional steps. For more compositional steps, we train our network with randomly sampled 100K QA pairs. + +STAR: Following [47], we train the model for each question type separately to compare with the baselines. However, this protocol is expensive in terms of time and resources. To address the matter of limited resources, we also tried combining all questions together after the questions filtering from interaction and sequence types and train a single model instead of multiple trainings. We use this training regime to study models ablations. + +# 5. Results and Analysis + +# 5.1. Comparison to State-of-the-Art + +AGQA: We compare our method with the existing state-of-the-art methods on AGQA benchmark. The best baseline on AGQA is HCRN [24] for overall accuracy. HCRN uses appearance features from ResNet101 as well as motion features from ResNext101-Kinetics400 backbones. Our model outperforms HCRN by a significant margin of $7.09\%$ (HCRN: $42.11\%$ vs. SHG-VQA: $49.20\%$ ) in terms of overall accuracy (see Table 1). We observe the biggest improvement of $14.63\%$ absolute points on object-action reasoning questions compared to the best model in that category i.e., PSAC [30]: $50.00\%$ vs. SHG-VQA: $64.63\%$ . We further report results on the three novel testing metrics as follows: + +a) Novel Compositions: For novel composition at test time, we observe an overall gain of $4.70\%$ when compared to the best contender, i.e., PSAC [30]. For open-ended questions, we outperform HCRN by $6.56\%$ (see Table 2); +b) Indirect References: When tested for indirect references questions in Table 4, we outperform all baselines by an absolute $7.83\% -16.62\%$ in terms of recall; For precision, we also gain similar improvements; + +c) Compositional Steps: Our model is trained on only $15\%$ (100K QA pairs) of the training data and is still able to perform on par with the baselines (trained on 1.6M QA pairs) achieving second best results for each category of more compositional steps (table 3). + +STAR: For STAR dataset, we first compare the proposed architecture to other state-of-the-art works in the field using SlowR50 [13] video backbone and a ResNext101 [50] as a frame-level backbone to evaluate accuracy based on 3D video and 2D image architectures. It shows that for both settings, SHG-VQA significantly outperforms other baseline methods even with weaker backbones (see Table 5). Concretely, we obtain an absolute gain of $8.53\%$ over NSSR [47], $5.86\%$ compared to HCRN [24], $5.99\%$ improvement over LCGN [15], and $\sim 2.5\%$ over ClipBERT [25] which is a SOTA model in terms of overall VQA accuracy. We notice the substantial gain of $7.86\%$ for interaction questions which test the understanding of interactions between entities in a situation. Prediction is the next category of questions that benefits the most with $2.48\%$ improvement. + +# 5.2. Ablation and Hyperparameter Analysis + +We perform our ablation studies on the STAR benchmark as discussed below: + +Impact of situation graphs quality: To assess the effect of situation graphs' quality on VQA accuracy, we train a baseline version of our system, where the model is trained on ground truth situation graphs for VQA tasks only (Table 6). Since the ground truth situation graphs are not available for the test set, we can only compare SHG-VQA and this baseline on the validation set. As expected, when taking ground truth situation graphs as input, the performance is significantly improved for interaction $(\mathrm{GT} = 91.9\%$ vs. predicted $= 46.78\%)$ and sequence $(\mathrm{GT} = 80.5\%$ vs. predicted $= 42.52\%$ questions. However, for the questions about the unseen part of the video, the model with ground truth graph tokens still struggles despite better performance compared to SHG-VQA: predic + +![](images/dc1ab2022e594aa10a48c74d7649a4a68d0f6c7fc4ba5c76b82b853e7a3e9a91.jpg) +Figure 4. Qualitative example for using frame-wise set prediction loss. Col. 1 shows the frame, col. 2 shows the ground-truth situation graph, col. 3 shows predicted graphs when trained with set prediction loss for the full video, and col. 4 shows the predicted graph when the model is trained by matching each timestep $t$ . The edges show the person-object relationship labels along with the number of times it was predicted. + +Table 6. Impact of hyper-graphs (HG) quality. Results shown for STAR val set with SlowR50 backbone. + +
MethodInteractionSequencePredictionFeasibilityOverall
Predicted hyper-graphs47.0842.5237.8233.6140.26
GT hyper-graphs91.980.541.2235.4262.46
+ +tion (GT=41.22% vs. predicted=37.82%) and feasibility (GT=35.42% vs. predicted=33.61%). We also report the mAP scores for the prediction of action and relationship predicates using our best model in Table 7. We obtain an overall mAP of 87.63 for actions and 72.9 for relationships respectively. + +Input to cross-attentional transformer: To evaluate the choice of input to the cross-attentional transformer, we experiment with three settings: a) question and video embeddings, b) question and situation graphs embeddings, c) question, situation graphs, and video embeddings. We observe no gain in the overall VQA accuracy when adding video embeddings to the cross-attentional transformer and get our best results with (b) (table 8). + +Situation hyper-graph components: To study the impact of different components of situation graphs, we train our system with only action predicate tokens with objective $L_{act} + L_{vqa}$ , only relationship predicates (with $L_{rel} + L_{vqa}$ ), and the full model (eq. 1). The action predicates are more effective compared to the relationship predicates. When compared in terms of predicate classification, we observe high accuracy for action predicates. Nonetheless, using the full situation graphs perform better than omitting actions or relationship prediction task (table 9). + +Number of queries: Number of action queries $M$ and relationship queries $N$ is a hyperparameter for SHG-VQA. We report the performance of the SHG-VQA with a varying number of queries for actions and relationships at each timestep in Table 9. We report our best results with $M = 3$ and $N = 8$ . Additional results are reported in the supplementary document. + +Frame-wise set prediction loss: Using the notion of time $t$ + +Table 7. Predicate classification results for situation hyper-graphs in terms of mAP for STAR validation set from SHG-VQA with SlowR50 backbone. Numbers are reported in percentages. + +
InteractSequencePredictionFeasibilityOverall
Actions84.7789.4385.3091.4687.63
Relationships72.8373.570.0572.8272.9
+ +Table 8. Results for cross-attention input. Results shown for STAR test set with SlowR50 backbone. + +
MethodInteractionSequencePredictionFeasibilityOverall
Q + V33.2835.6027.9326.4330.81
Q + HG47.9842.0335.3432.5239.47
Q + V + HG45.4544.1934.2232.8739.18
+ +Table 9. Model variations on STAR validation set with a single model using SlowR50 backbone for all question types. Best results are shown in bold font and second best results are underlined. Numbers are reported for VQA accuracy in percentages. + +
Method (val)InteractionSequencePredictionFeasibilityOverall
hyper-graph components
Action only – Act=340.9438.0835.5830.5638.68
Relation. only – Rel=835.9435.7934.7827.8635.16
Both – Act=3, Rel=842.9338.2036.0630.5639.20
Number of queries
Act=2, Rel=840.3240.1338.7829.7338.34
Act=3, Rel=842.9338.2036.0630.5639.20
Act=4, Rel=838.4035.7935.5830.3537.06
Act=3, Rel=1241.3240.8039.4231.3939.90
Act=4, Rel=1240.4039.0536.8629.1138.39
+ +in the set prediction loss alleviates the problem of duplicate predictions within each situation. See Fig. 4 for qualitative examples of produced hyper-graphs with and without this loss. Extra examples are in the supplementary document. + +# 6. Conclusion + +We presented a novel approach to model situation graph prediction as an underlying sub-task for video question answering. The proposed method predicts a situation hypergraph structure composed of existing actions and relationships in the input video. The input question can then reason over the predicted graph to solve VQA. We show the impact of the proposed approach by evaluating on two video question-answering benchmarks and achieving significant performance gains overall baseline methods. Our method demonstrates promise for further research in this direction to improve VQA systems even further. + +Acknowledgements. Aisha Urooj is supported by the ARO grant W911NF-19-1-0356. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. This work was partially supported under NSF grant CNS-2050731, Research Experience for Undergraduates in Computer Vision. Dr. Gan is supported by the MIT-IBM Watson AI Lab, DSO grant DSOCO21072, and gift funding from MERL, Cisco, Sony, and Amazon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ARO, IARPA, DOI/IBC, or the U.S. Government. + +# References + +[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 4, 5 +[2] Xiaojun Chang, Pengzhen Ren, Pengfei Xu, Zhihui Li, Xiaojiang Chen, and Alexander G Hauptmann. A comprehensive survey of scene graphs: Generation and application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. 3 +[3] Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 702-703, 2020. 6 +[4] Vinay Damodaran, Sharanya Chakravarthy, Akshay Kumar, Anjana Umapathy, Teruko Mitamura, Yuta Nakashima, Noa Garcia, and Chenhui Chu. Understanding the role of scene graphs in visual question answering. arXiv preprint arXiv:2101.05479, 2021. 1 +[5] Long Hoang Dang, Thao Minh Le, Vuong Le, and Truyen Tran. Hierarchical object-oriented spatio-temporal reasoning for video question answering. arXiv preprint arXiv:2106.13432, 2021. 2 +[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 7 +[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 2 +[8] Chenyou Fan, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, and Heng Huang. Heterogeneous memory enhanced multimodal attention model for video question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1999-2007, 2019. 2 +[9] Chenyou Fan, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, and Heng Huang. Heterogeneous memory enhanced multimodal attention model for video question answering. CoRR, abs/1904.04357, 2019. 5, 6 +[10] Jiyang Gao, Runzhou Ge, Kan Chen, and Ram Nevatia. Motion-appearance co-memory networks for video question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6576–6585, 2018. 2 +[11] Madeleine Grunde-McLaughlin, Ranjay Krishna, and Maneesh Agrawala. Agqa: A benchmark for compositional spatio-temporal reasoning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11287-11297, 2021. 2, 6 + +[12] Madeleine Grunde-McLaughlin, Ranjay Krishna, and Maneesh Agrawala. Agqa 2.0: An updated benchmark for compositional spatio-temporal reasoning, 2022. 6 +[13] Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh. Learning spatio-temporal features with 3d residual networks for action recognition. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 3154-3160, 2017. 7 +[14] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. *Neural computation*, 9(8):1735–1780, 1997. 7 +[15] Ronghang Hu, Anna Rohrbach, Trevor Darrell, and Kate Saenko. Language-conditioned graph networks for relational reasoning. In Proceedings of the IEEE International Conference on Computer Vision, pages 10294-10303, 2019. 2, 7 +[16] Yunseok Jang, Yale Song, Chris Dongjoo Kim, Youngjae Yu, Youngjin Kim, and Gunhee Kim. Video question answering with spatio-temporal reasoning. International Journal of Computer Vision, 127(10):1385-1412, 2019. 2 +[17] Yunseok Jang, Yale Song, Youngjae Yu, Youngjin Kim, and Gunhee Kim. Tgif-qa: Toward spatio-temporal reasoning in visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2758–2766, 2017. 2 +[18] Jianwen Jiang, Ziqiang Chen, Haojie Lin, Xibin Zhao, and Yue Gao. Divide and conquer: Question-guided spatiotemporal contextual attention for video question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11101-11108, 2020. 2 +[19] Pin Jiang and Yahong Han. Reasoning with heterogeneous graph alignment for video question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11109-11116, 2020. 2 +[20] Juanzi Li Jiaxin Shi, Hanwang Zhang. Explainable and explicit visual reasoning over scene graphs. In CVPR, 2019. 1 +[21] Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In CVPR, 2017. 7 +[22] Yash Kant, Dhruv Batra, Peter Anderson, Alexander Schwing, Devi Parikh, Jiasen Lu, and Harsh Agrawal. Spatially aware multimodal transformers for textvqa. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IX 16, pages 715-732. Springer, 2020. 2 +[23] Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong. Pure transformers are powerful graph learners. arXiv preprint arXiv:2207.02505, 2022.1 +[24] Thao Minh Le, Vuong Le, Svetha Venkatesh, and Truyen Tran. Hierarchical conditional relation networks for video question answering. CoRR, abs/2002.10698, 2020. 2, 5, 6, 7 +[25] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7331-7341, 2021. 2, 7 + +[26] Jie Lei, Licheng Yu, Mohit Bansal, and Tamara L Berg. Tvqa: Localized, compositional video question answering. In EMNLP, 2018. 2 +[27] Linjie Li, Zhe Gan, Yu Cheng, and Jingjing Liu. Relation-aware graph attention network for visual question answering. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10313-10322, 2019. 2 +[28] Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. Visualbert: A simple and perform-. mant baseline for vision and language. arXiv preprint arXiv:1908.03557, 2019. 7 +[29] Xiangpeng Li, Jingkuan Song, Lianli Gao, Xianglong Liu, Wenbing Huang, Xiangnan He, and Chuang Gan. Beyond rms: Positional self-attention with co-attention for video question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 8658–8665, 2019. 2 +[30] Xiangpeng Li, Jingkuan Song, Lianli Gao, Xianglong Liu, Wenbing Huang, Xiangnan He, and Chuang Gan. Beyond rnns: Positional self-attention with co-attention for video question answering. In AAAI, 2019. 5, 6, 7 +[31] Fei Liu, Jing Liu, Weining Wang, and Hanqing Lu. Hair: Hierarchical visual-semantic relational reasoning for video question answering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1698–1707, 2021. 2 +[32] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. arXiv preprint arXiv:2106.13230, 2021. 2 +[33] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. arXiv preprint arXiv:1908.02265, 2019. 6 +[34] Tegan Maharaj, Nicolas Ballas, Anna Rohrbach, Aaron Courville, and Christopher Pal. A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6884–6893, 2017. 2 +[35] Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B Tenenbaum, and Jiajun Wu. The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision. In International Conference on Learning Representations, 2018. 1 +[36] Kenneth Marino, Xinlei Chen, Devi Parikh, Abhinav Gupta, and Marcus Rohrbach. Krisp: Integrating implicit and symbolic knowledge for open-domain knowledge-based vqa. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14111-14121, 2021. 1 +[37] Devshree Patel, Ratnam Parikh, and Yesha Shastri. Recent advances in video question answering: A review of datasets and methods. In International Conference on Pattern Recognition, pages 339-356. Springer, 2021. 2 +[38] Liang Peng, Shuangji Yang, Yi Bin, and Guoqing Wang. Progressive graph attention network for video question answering. In Proceedings of the 29th ACM International Conference on Multimedia, pages 2871-2879, 2021. 2 + +[39] Tianwen Qian, Jingjing Chen, Shaoxiang Chen, Bo Wu, and Yu-Gang Jiang. Scene graph refinement network for visual question answering. IEEE Transactions on Multimedia, 2022. 2 +[40] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 2 +[41] Ahjeong Seo, Gi-Cheon Kang, Joonhan Park, and Byoung-Tak Zhang. Attend what you need: Motion-appearance synergistic networks for video question answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6167–6177, Online, Aug. 2021. Association for Computational Linguistics. 2 +[42] Paul Hongsuck Seo, Arsha Nagrani, and Cordelia Schmid. Look before you speak: Visually contextualized utterances. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16877-16887, 2021. 2 +[43] Gunnar A Sigurdsson, Gül Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, and Abhinav Gupta. Hollywood in homes: Crowdsourcing data collection for activity understanding. In European Conference on Computer Vision, pages 510-526. Springer, 2016. 6 +[44] Gunnar A. Sigurdsson, Gúl Varol, X. Wang, Ali Farhadi, Ivan Laptev, and Abhinav Kumar Gupta. Hollywood in homes: Crowdsourcing data collection for activity understanding. ECCV, 2016. 2 +[45] Ajeet Kumar Singh, Anand Mishra, Shashank Shekhar, and Anirban Chakraborty. From strings to things: Knowledge-enabled vqa model that can read and reason. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4602-4612, 2019. 1 +[46] Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. Movieqa: Understanding stories in movies through question-answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4631–4640, 2016. 2 +[47] Bo Wu, Shoubin Yu, Zhenfang Chen, Joshua B Tenenbaum, and Chuang Gan. Star: A benchmark for situated reasoning in real-world videos. In Thirty-fifth Conference on Neural Information Processing Systems, 2021. 1, 2, 3, 6, 7 +[48] Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question-answering to explaining temporal actions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9777–9786, 2021. 2 +[49] Junbin Xiao, Angela Yao, Zhiyuan Liu, Yicong Li, Wei Ji, and Tat-Seng Chua. Video as conditional graph hierarchy for multi-granular question answering. arXiv preprint arXiv:2112.06197, 2021. 2 +[50] Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017. 7 + +[51] Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang. Video question answering via gradually refined attention over appearance and motion. In Proceedings of the 25th ACM international conference on Multimedia, pages 1645–1653, 2017. 2 +[52] Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, and Cordelia Schmid. Just ask: Learning to answer questions from millions of narrated videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1686-1697, 2021. 2 +[53] Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B Tenenbaum. Clevrer: Collision events for video representation and reasoning. ICLR, 2020. 2, 7 +[54] Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Joshua B Tenenbaum. Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding. In Advances in Neural Information Processing Systems (NIPS), 2018. 2 +[55] Fei Yu, Jiji Tang, Weichong Yin, Yu Sun, Hao Tian, Hua Wu, and Haifeng Wang. Ernie-vil: Knowledge enhanced vision-language representations through scene graph. arXiv preprint arXiv:2006.16934, 2020. 1 +[56] Weijiang Yu, Haoteng Zheng, Mengfei Li, Lei Ji, Lijun Wu, Nong Xiao, and Nan Duan. Learning from inside: Self-driven siamese sampling and reasoning for video question answering. Advances in Neural Information Processing Systems, 34, 2021. 2 +[57] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 9127–9134, 2019. 2 +[58] Amir Zadeh, Michael Chan, Paul Pu Liang, Edmund Tong, and Louis-Philippe Morency. Social-iq: A question answering benchmark for artificial social intelligence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8807–8817, 2019. 2 +[59] Rowan Zellers, Jiasen Lu, Ximing Lu, Youngjae Yu, Yanpeng Zhao, Mohammadreza Salehi, Aditya Kusupati, Jack Hessel, Ali Farhadi, and Yejin Choi. Merlot reserve: Neural script knowledge through vision and language and sound. arXiv preprint arXiv:2201.02639, 2022. 2 +[60] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. Advances in Neural Information Processing Systems, 34, 2021. 2 +[61] Kuo-Hao Zeng, Tseng-Hung Chen, Ching-Yao Chuang, Yuan-Hong Liao, Juan Carlos Niebles, and Min Sun. Leveraging video descriptions to learn video question answering. CoRR, abs/1611.04021, 2016. 2 +[62] Yaoyao Zhong, Wei Ji, Junbin Xiao, Yicong Li, Weihong Deng, and Tat-Seng Chua. Video question answering: Datasets, algorithms and challenges, 2022. 2 +[63] Guangming Zhu, Liang Zhang, Youliang Jiang, Yixuan Dang, Haoran Hou, Peiyi Shen, Mingtao Feng, Xia Zhao, Qiguang Miao, Syed Afaq Ali Shah, and Mohammed Bennamoun. + +Scene graph generation: A comprehensive survey. CoRR, abs/2201.00443, 2022. 2, 3 +[64] Linchao Zhu, Zhongwen Xu, Yi Yang, and Alexander G Hauptmann. Uncovering the temporal context for video question answering. 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A situation hyper-graph is a representation that describes situations as scene sub-graphs for video frames and hyper-edges for connected sub-graphs and has been proposed to capture all such information in a compact structured form. In this work, we propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs, coined Situation Hyper-Graph based Video Question Answering (SHG-VQA). To this end, we train a situation hyper-graph decoder to implicitly identify graph representations with actions and object/human-object relationships from the input video clip. and to use cross-attention between the predicted situation hyper-graphs and the question embedding to predict the correct answer. The proposed method is trained in an end-to-end manner and optimized by a VQA loss with the cross-entropy function and a Hungarian matching loss for the situation graph prediction. The effectiveness of the proposed architecture is extensively evaluated on two challenging benchmarks: AGQA and STAR. Our results show that learning the underlying situation hypergraphs helps the system to significantly improve its performance for novel challenges of video question-answering tasks1." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 591, + 127, + 604 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 591, + 127, + 604 + ], + "spans": [ + { + "bbox": [ + 47, + 591, + 127, + 604 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 612, + 288, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 612, + 288, + 684 + ], + "spans": [ + { + "bbox": [ + 46, + 612, + 288, + 684 + ], + "type": "text", + "content": "Video question answering in real-world scenarios is a challenging task as it requires focusing on several factors including the perception of the current scene, language understanding, situated reasoning, and future prediction. Visual perception in the reasoning task requires capturing various aspects of visual understanding, e.g., detecting a diverse set" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 307, + 242, + 547, + 369 + ], + "blocks": [ + { + "bbox": [ + 307, + 242, + 547, + 369 + ], + "lines": [ + { + "bbox": [ + 307, + 242, + 547, + 369 + ], + "spans": [ + { + "bbox": [ + 307, + 242, + 547, + 369 + ], + "type": "image", + "image_path": "b12d3ee4e4edbfe888ae8a98da74e8d3f6644ede2494554e1408cd9c27ff643a.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 380, + 547, + 447 + ], + "lines": [ + { + "bbox": [ + 305, + 380, + 547, + 447 + ], + "spans": [ + { + "bbox": [ + 305, + 380, + 547, + 447 + ], + "type": "text", + "content": "Figure 1. The situation hyper-graph for a video is composed of situations with entities and their relationships (shown as subgraphs in the pink box). These situations may evolve over time. Temporal actions act as hyper-edges connecting these situations into one situation hyper-graph. Learning situation graphs, as well as temporal actions, is vital for reasoning-based video question answering." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 471, + 547, + 530 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 471, + 547, + 530 + ], + "spans": [ + { + "bbox": [ + 304, + 471, + 547, + 530 + ], + "type": "text", + "content": "of entities, recognizing their interactions, as well as understanding the changing dynamics between these entities over time. Similarly, linguistic understanding has its challenges as some question or answer concepts may not be present in the input text or video." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 532, + 548, + 698 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 532, + 548, + 698 + ], + "spans": [ + { + "bbox": [ + 304, + 532, + 548, + 698 + ], + "type": "text", + "content": "Visual question answering, as well as its extension over time, video question answering, have both benefited from representing knowledge in graph structures, e.g., scene graphs [20, 35], spatio-temporal graphs [4, 55], and knowledge graphs [36, 45]. Another approach in this direction is the re-introduction of the concept of \"situation cognition\" embodied in \"situation hyper-graphs\" [47]. This adds the computation of actions to the graphs that capture the interaction between entities. In this case, situations are represented by hyper-graphs that join atomic entities and relations (e.g., agents, objects, and relationships) with their actions (Fig. 1). This is an ambitious task for existing systems as it is impractical to encapsulate all possible interactions in the real-world context." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 317, + 701, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 701, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 701, + 545, + 713 + ], + "type": "text", + "content": "Recent work [23] shows that transformers are capable" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 693, + 288, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 693, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 693, + 288, + 712 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 46, + 693, + 288, + 712 + ], + "type": "text", + "content": "Code will be available at https://github.com/aurooj/SHG-VQA" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14879" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 289, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 289, + 491 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 289, + 491 + ], + "type": "text", + "content": "of learning graphs without adapting graph-specific details in the architectures achieving competitive or even better performance than sophisticated graph-specific models. Our work supports this idea by implicitly learning the underlying hyper-graphs of a video. Thus, it requires no graph computation for inference and uses decoder's output directly for cross attention module. More precisely, we propose to learn situation hyper-graphs, namely framewise actor-object and object-object relations as well as their respective actions, from the input video directly without the need for explicit object detection or other required prior knowledge. While the actions capture events across transitions over multiple frames, such as Drinking from a bottle, the relationship encoding actually considers all possible combinations of static, single frame actor-object, and object-object relationships as unique classes, e.g., in the form of person - hold - bottle or bottle - stands on - table, thus serving as an object and relation classifier. Leveraging this setup allows us to streamline the spatio-temporal graph learning as a set prediction task for predicting relationship predicates and actions in a Situation hyper-graph Decoder block. To train the Situation Graph Decoder, we use a bipartite matching loss between the predicted set and ground truth hyper-graph tokens. The output of the situation graph decoder is a set of action and relationship tokens, which are then combined with the embedding of the associated question to derive the final answer. An overview of the proposed architecture is given in Fig. 2. Note that, compared to other works targeting video scene graph generation, e.g., those listed in [63], we are less focused on learning the best possible scene graph, but rather on learning the representation of the scene which best supports the question answering task. Thus, while capturing the essence of a scene, as well as the transition from one scene to the other, we are not only optimizing the scene graph accuracy but also considering the VQA loss." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 491, + 289, + 681 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 491, + 289, + 681 + ], + "spans": [ + { + "bbox": [ + 47, + 491, + 289, + 681 + ], + "type": "text", + "content": "We evaluate the proposed method on two challenging video question answering benchmarks: a) STAR [47], featuring four different question types, interaction, sequence, prediction, and feasibility based on a subset of the real-world Charades dataset [44]; and b) Action Genome QA (AGQA) [11] dataset which tests vision focused reasoning skills based on novel compositions, novel reasoning steps, and indirect references. Compared to other VQA datasets, these datasets provide dense ground truth hyper-graph information for each video, which allows us to learn the respective embedding. Our results show that the proposed hyper-graph encoding significantly improves VQA performance as it has the ability to infer correct answers from spatio-temporal graphs from the input video. Our ablations further reveal that achieving high-quality graphs can be critical for VQA performance." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 59, + 682, + 245, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 682, + 245, + 693 + ], + "spans": [ + { + "bbox": [ + 59, + 682, + 245, + 693 + ], + "type": "text", + "content": "Our contributions to this paper are as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 58, + 701, + 288, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 701, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 701, + 288, + 712 + ], + "type": "text", + "content": "- We introduce a novel architecture that enables the com" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 324, + 72, + 547, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 72, + 547, + 109 + ], + "spans": [ + { + "bbox": [ + 324, + 72, + 547, + 109 + ], + "type": "text", + "content": "putation of situation hyper-graphs from video data to solve the complex reasoning task of video question-answering;" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 317, + 118, + 547, + 236 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 317, + 118, + 547, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 118, + 547, + 190 + ], + "spans": [ + { + "bbox": [ + 317, + 118, + 547, + 190 + ], + "type": "text", + "content": "- We propose a situation hyper-graph decoder module to decode the atomic actions and object/actor-object relationships and model the hyper-graph learning as a transformer-based set prediction task and use a set prediction loss function to predict actions and relationships between entities in the input video;" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 317, + 199, + 547, + 236 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 199, + 547, + 236 + ], + "spans": [ + { + "bbox": [ + 317, + 199, + 547, + 236 + ], + "type": "text", + "content": "- We use the resulting high-level embedding information as sole visual information for the reasoning and show that this is sufficient for an effective VQA system." + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 251, + 392, + 262 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 251, + 392, + 262 + ], + "spans": [ + { + "bbox": [ + 306, + 251, + 392, + 262 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 270, + 548, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 270, + 548, + 594 + ], + "spans": [ + { + "bbox": [ + 304, + 270, + 548, + 594 + ], + "type": "text", + "content": "Video Question Answering: Video question answering is an active area of research with efforts in insightful directions [37,62], such as attention [16, 18, 29], cross-modal interactions [25, 41, 52, 56, 59, 60], hierarchical learning [5, 24, 38], and so on. A few other algorithms classes include modular networks [5, 24, 49], symbolic reasoning [47, 53, 54], and memory networks [8, 10]. Several video QA benchmarks are introduced to evaluate this task from varying perspectives entailing description [34, 51, 57, 61], temporal reasoning [17, 53, 64], causal structures [48, 53], visual-language comprehension [26, 46, 52], relational reasoning [46], and measuring social intelligence [58]. STAR [47] benchmark goes one step further providing a benchmark to perform diagnostic study at additional fronts such as predicting future interactions and feasibility of next possible actions in the unseen future. Existing approaches on the benchmark either rely on object features [40] as nodes explicitly modeling their interactions through a message passing mechanism [15, 47], or benefit from efficient input sampling strategies during training to learn robust visual representations [24, 25]. We, however, take a different approach and focus on inferring the underlying semantic graph structure in the video and use it for QA reasoning. Our method is independent of using pretrained object detectors and uses a simple approach to learn to infer the sets of relationship predicates as well as actions for each frame. The model is trained end-to-end with the frozen backbone." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 605, + 548, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 548, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 548, + 715 + ], + "type": "text", + "content": "Graph-based VQA: Another related line of work is graph-based VQA methods [22, 27]. Some of them work on object features extracted from a pretrained detector (e.g., Faster-RCNN [40]) [5, 15, 31, 42, 49], while some operate on frame-level [19, 25, 56] (e.g., ResNet), clip-level features [38, 41, 52](e.g., C3D, S3D) or transformer-based backbones [7, 32, 39]. We focus on predicting atomic actions, and relationship triplets from the frame-level or clip-level features directly instead of an explicit graph." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "14880" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 51, + 72, + 545, + 246 + ], + "blocks": [ + { + "bbox": [ + 51, + 72, + 545, + 246 + ], + "lines": [ + { + "bbox": [ + 51, + 72, + 545, + 246 + ], + "spans": [ + { + "bbox": [ + 51, + 72, + 545, + 246 + ], + "type": "image", + "image_path": "d54a2ede95f3e54d7ea1d91a60e1202e72d987e549c794f69e9e021fb14447e4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 253, + 548, + 344 + ], + "lines": [ + { + "bbox": [ + 46, + 253, + 548, + 344 + ], + "spans": [ + { + "bbox": [ + 46, + 253, + 548, + 344 + ], + "type": "text", + "content": "Figure 2. The SHG-VQA architecture: we start with encoding the input video into spatio-temporal features using a pre-trained backbone. These video features are flattened into a sequence of tokens of length " + }, + { + "bbox": [ + 46, + 253, + 548, + 344 + ], + "type": "inline_equation", + "content": "T \\times h \\times w" + }, + { + "bbox": [ + 46, + 253, + 548, + 344 + ], + "type": "text", + "content": " and position encoded to be further processed through a visual encoder. These encoded tokens are input to the action decoder to predict the set of atomic actions from the input action queries as well as to the relationship decoder which takes relationship queries as input along with the video tokens. The action decoder and relationship decoder output the situation graph embeddings. In the text branch, the question and the answer choices are composed into a sequence and passed through a text transformer to obtain encoded word embeddings; for open-ended VQA, only the question is passed to the text transformer. The generated hyper-graph along with the encoded text tokens are then used as input to a cross-attentional transformer and the combined representations are used to predict the correct answer with a classifier. Section 3.4 describes the losses and training objectives." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 363, + 289, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 363, + 289, + 507 + ], + "spans": [ + { + "bbox": [ + 46, + 363, + 289, + 507 + ], + "type": "text", + "content": "Scene Graph Generation: The proposed formulation of situation hyper-graph deviates from existing scene-graph generation approaches [2, 47, 63] as we do not require object detections as input or object level supervision, nor do we model it in an explicit graph structure. Our goal is simple: given an input video, predict all object-relation-object and actor-relation-object triplets as well as associated actions for each video frame. Our intuition is that forcing the model to predict these predicates will drive the system to learn a latent graph structure for visual input. The predicted situation hyper-graph is treated as an abstract video representation and used for VQA reasoning in the next step." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 523, + 178, + 537 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 523, + 178, + 537 + ], + "spans": [ + { + "bbox": [ + 47, + 523, + 178, + 537 + ], + "type": "text", + "content": "3. The SHG-VQA Model" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "text", + "content": "Situation video question answering has three essential steps 1) the visual recognition capacities of visual entities, their relationships, actions, and how these transition over time, 2) the language understanding capacity to the questions, 3) the question-guided reasoning process over the representation learned in the first step. Sub-optimal performance at any of these steps will affect the overall task performance. A key problem here is that capturing the visual structures directly from raw data, e.g., in the form of features often results in a rather noisy signal and does not provide suitable input for high-level language-guided reasoning. To overcome this mismatch, we propose to learn the implicit structure of the visual input as an intermediate step between learning the video representation and question-based reasoning. Forcing the model" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 363, + 547, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 363, + 547, + 422 + ], + "spans": [ + { + "bbox": [ + 304, + 363, + 547, + 422 + ], + "type": "text", + "content": "to learn to predict this implicit structure (actions, relations between entities) not only improves the video representation but also acts as a lightweight, high-level representation of the video content and can be used for the VQA task. We illustrate our architecture in Fig. 2." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 306, + 431, + 408, + 443 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 431, + 408, + 443 + ], + "spans": [ + { + "bbox": [ + 306, + 431, + 408, + 443 + ], + "type": "text", + "content": "3.1. Input Processing" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 449, + 545, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 449, + 545, + 472 + ], + "spans": [ + { + "bbox": [ + 304, + 449, + 545, + 472 + ], + "type": "text", + "content": "Given the input video and the question, we encode the inputs as described below:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 474, + 545, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 474, + 545, + 557 + ], + "spans": [ + { + "bbox": [ + 304, + 474, + 545, + 557 + ], + "type": "text", + "content": "Question Encoder: The question is first tokenized into word tokens using a wordPiece tokenizer. These word tokens along with the special class token " + }, + { + "bbox": [ + 304, + 474, + 545, + 557 + ], + "type": "inline_equation", + "content": "[CLS]" + }, + { + "bbox": [ + 304, + 474, + 545, + 557 + ], + "type": "text", + "content": " are the inputs of an embedding layer. The output word embeddings from this layer are input to a transformer encoder that encodes each word using multi-head self-attention between different words at each encoder layer." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": "Video Encoder: Let " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "V \\in \\mathbb{R}^{T \\times H \\times W \\times 3}" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " be the input video clip, where " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " is the clip length with 3 color channels, height " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " and width " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": ". First, we extract video features " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "x_{V} \\in \\mathbb{R}^{T \\times h \\times w \\times d_{x}}" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " using a convolutional backbone with " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "d_{x}" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " being the feature dimension, " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " are the reduced feature's height and width. As transformers process sequential input data, the video features " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "x_{V}" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " are flattened into a sequence (of size " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{Thw \\times d_x}" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": ") and reduced to dimension " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " through a linear layer. Then, we append a trainable vector of dimension " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " for a special class token [VIS] to this sequence of video features at index 0. These features are combined with position encodings and input to a transformer-based video encoder " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "V_{e}" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14881" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "content": "The output features of the video are forwarded to the action decoder and relationship decoder to infer the situation graph capturing the action information, as well as the entities and their relationships. The output of the last layer of both decoders is then combined and augmented with frame positions, forming the final hyper-graph embedding. We further attach a randomly initialized class token " + }, + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "inline_equation", + "content": "[HG]" + }, + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "content": ". These situation graph embeddings are then input to a multi-layered cross-attentional transformer encoder for more fine-grained interaction between the question words and semantic knowledge extracted from the video in the form of a graph. The output features corresponding to " + }, + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "inline_equation", + "content": "[CLS]" + }, + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "inline_equation", + "content": "[HG]" + }, + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "content": " tokens are input to an answer classifier to produce an answer." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 238, + 230, + 251 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 238, + 230, + 251 + ], + "spans": [ + { + "bbox": [ + 47, + 238, + 230, + 251 + ], + "type": "text", + "content": "3.2. Situation hyper-graph Generation" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "spans": [ + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "content": "Real-world video understanding relies on the scene understanding including changing relationships between the objects over time and the evolving actions. Therefore, we represent the given video as a \"situation graph\" denoted by " + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "content": " which describes actions and relationships between objects. Let " + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "inline_equation", + "content": "G = (V, E)" + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "content": " is the set of vertices representing all possible entities in the dataset, and " + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "inline_equation", + "content": "E" + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "content": " is the set of edges representing all possible relationships between each pair of entities. Given an input video, we want to learn a situation graph " + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "inline_equation", + "content": "g_{t} = (v_{t}, e_{t})" + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "inline_equation", + "content": "v_{t} \\in V" + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "inline_equation", + "content": "e_{t} \\in E" + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "content": " for each time step " + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "inline_equation", + "content": "t \\in \\{1, 2, \\dots, T\\}" + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "content": " in the video, that captures the entities (objects, actors), their relationships as well as the associated actions that are present in that frame. The hyper-graph for one video is thus represented by the set of situation graphs " + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "inline_equation", + "content": "\\mathcal{G} = \\{g_{1}, \\dots, g_{T}\\}" + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "content": ". An action may comprise multiple relationships and objects. For each frame, we further have a set of actions " + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "inline_equation", + "content": "A_{t} = a_{1}, a_{2}, \\dots, a_{N}" + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "content": ". The set of actions for the full video is then given as " + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "inline_equation", + "content": "\\mathcal{A} = \\{A_{1}, \\dots, A_{T}\\}" + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "content": ". Note that the actions are predicted in addition to the graph structure and that both will be merged in a situation hyper-graph embedding in a separate step after the decoder block. Rather than predicting the set of vertices and edges in the graph " + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "inline_equation", + "content": "g_{t}" + }, + { + "bbox": [ + 46, + 258, + 290, + 544 + ], + "type": "text", + "content": ", we propose to predict the graph structure by formulating this graph prediction as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "content": "Let " + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "inline_equation", + "content": "x_{V_e} \\in \\mathbb{R}^{T' \\times h \\times w \\times d}" + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "content": " be the encoded video features, where " + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "inline_equation", + "content": "T'" + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "content": " denotes the temporal length of encoded features, " + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "content": " are spatial dimensions, and " + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "content": " is the feature's dimension. A relationship predicate " + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "content": " describes interactions between entities by triplet tokens object-relation-object or actor-relation-object. We propose to predict the set of relationship predicates " + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "content": " and the set of atomic actions " + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "inline_equation", + "content": "A" + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "content": " occurring in each video frame. This is intuitive because the ability to predict the atomic actions and relations between entities for each time step benefits the high level reasoning tasks such as video question answering in this work. Therefore, for each time step " + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "inline_equation", + "content": "t \\in \\{1,2,\\dots,T\\}" + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "content": " in the video, we predict the set of relationship predicates denoted by " + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "inline_equation", + "content": "R_t" + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "content": " in each video frame where " + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "inline_equation", + "content": "R_t = \\{p_1,p_2,\\dots,p_M\\}" + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "inline_equation", + "content": "p_i" + }, + { + "bbox": [ + 46, + 544, + 288, + 714 + ], + "type": "text", + "content": " is the" + } + ] + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 307, + 70, + 547, + 253 + ], + "blocks": [ + { + "bbox": [ + 307, + 70, + 547, + 253 + ], + "lines": [ + { + "bbox": [ + 307, + 70, + 547, + 253 + ], + "spans": [ + { + "bbox": [ + 307, + 70, + 547, + 253 + ], + "type": "image", + "image_path": "f103a63b4502d1faa176348019aa0e11b89742f5de852ae8463e0ce093435a92.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 259, + 547, + 326 + ], + "lines": [ + { + "bbox": [ + 304, + 259, + 547, + 326 + ], + "spans": [ + { + "bbox": [ + 304, + 259, + 547, + 326 + ], + "type": "text", + "content": "Figure 3. Situation hyper-graph embeddings: We start with decoded queries from action and relationship decoder. Then we add type encoding vectors " + }, + { + "bbox": [ + 304, + 259, + 547, + 326 + ], + "type": "inline_equation", + "content": "[ACT]" + }, + { + "bbox": [ + 304, + 259, + 547, + 326 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 259, + 547, + 326 + ], + "type": "inline_equation", + "content": "[REL]" + }, + { + "bbox": [ + 304, + 259, + 547, + 326 + ], + "type": "text", + "content": " for actions and relationships, attention masks, and an embedding vector for the situation ID " + }, + { + "bbox": [ + 304, + 259, + 547, + 326 + ], + "type": "inline_equation", + "content": "(t \\in \\{1, \\dots, T\\})" + }, + { + "bbox": [ + 304, + 259, + 547, + 326 + ], + "type": "text", + "content": ". The sums are input to the cross-attentional module. See section 3.2.2 for details)." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "spans": [ + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "i^{th}" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " predicate between two entities, representing the vertices " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "v_{x}" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "v_{y}" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " (object-object or actor-object) and the relation resp. edge " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "e_i" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " represented as " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "< v_x,e_i,v_y>" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "M = |R_t|" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " is the relationship set size. Additionally, we predict the set of " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " actions " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "A_{t}" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " for each time step " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "A_{t} = \\{a_{1},a_{2},\\dots,a_{N}\\}" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "a_{j}" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " is the " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "j^{th}" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " action occurring at time step " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "N = |A_t|" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " is the actions set size for each step " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 304, + 337, + 547, + 433 + ], + "type": "text", + "content": " are hyperparameters in our system." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 434, + 547, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 434, + 547, + 471 + ], + "spans": [ + { + "bbox": [ + 304, + 434, + 547, + 471 + ], + "type": "text", + "content": "To obtain actions and relation predicates from the video, we use a transformer decoder that takes video features as memory to learn action queries and relation queries." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 477, + 409, + 488 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 477, + 409, + 488 + ], + "spans": [ + { + "bbox": [ + 306, + 477, + 409, + 488 + ], + "type": "text", + "content": "3.2.1 Prediction Head" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 497, + 547, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 497, + 547, + 544 + ], + "spans": [ + { + "bbox": [ + 304, + 497, + 547, + 544 + ], + "type": "text", + "content": "The decoded output embeddings for action and relationship queries are input to respective prediction heads. Prediction heads use a 2-layer feed forward network (FFN) with GELU activation and LayerNorm." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 545, + 547, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 547, + 652 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 547, + 652 + ], + "type": "text", + "content": "Considering that actions and relationships at a given time step are permutation-invariant, we use optimal bipartite matching between predicted and ground truth actions/relationships. An optimal bipartite matching between the predicted classes and ground-truth labels is the one with the minimum matching cost. Once the optimal matching pairs have been obtained, we use a Hungarian loss function to optimize for ground truth classes [1]. See section 3.4 for details of the loss function." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 670, + 486, + 682 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 670, + 486, + 682 + ], + "spans": [ + { + "bbox": [ + 306, + 670, + 486, + 682 + ], + "type": "text", + "content": "3.2.2 Situation hyper-graph Embedding" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 689, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 689, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 689, + 547, + 714 + ], + "type": "text", + "content": "For the decoded queries of actions and relationships (referred as graph token embeddings), situation hyper-graph" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14882" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 52, + 91, + 542, + 158 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 545, + 83 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 545, + 83 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 545, + 83 + ], + "type": "text", + "content": "Table 1. Results on AGQA dataset for different question types. The best results are shown in bold font. Numbers are reported in percentages." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 52, + 91, + 542, + 158 + ], + "lines": [ + { + "bbox": [ + 52, + 91, + 542, + 158 + ], + "spans": [ + { + "bbox": [ + 52, + 91, + 542, + 158 + ], + "type": "table", + "html": "
MethodReasoningSemanticStructureOverall
obj-relrel-actionobj-actionsuperlativesequencingexistsdurationactivityobjrelactionquerycomparechooselogicverifybinaryopenall
PSAC [30]37.8449.9550.0033.2049.7849.9445.214.1437.9749.9546.8531.6349.4946.5649.9649.9048.8731.6340.18
HME [9]37.4249.9049.9733.2149.7749.9647.035.4337.5549.9947.5831.0149.7146.4249.8749.9648.9131.0139.89
HCRN [24]40.3349.8649.8533.5549.7050.0143.845.5240.3349.9646.4136.3449.2243.4250.0250.0147.9736.3442.11
SHG-VQA46.4260.6764.6338.8362.1756.0648.1510.1247.6156.1953.8343.4260.6847.7652.8656.6355.0443.4249.20
", + "image_path": "ec66a1bdbec07a50d4fb341509d717a53684787acbb9e3b49ad19de795d1f325.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "spans": [ + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "text", + "content": "embeddings are constructed in order to be used with question features for video question answering. First, these action and relationship graph embeddings are combined for each time step " + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "text", + "content": " representing a situation at " + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "text", + "content": ". Then, we add token type embedding " + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "inline_equation", + "content": "[ACT]" + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "text", + "content": " for actions and " + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "inline_equation", + "content": "[REL]" + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "text", + "content": " for relations to their respective graph embeddings. A situation ID (or frame position " + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "text", + "content": ") embedding is also added to these embeddings. An additional attention mask is used to differentiate actual tokens and padded tokens (no-class token " + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "text", + "content": ") at training time. At inference time, no attention mask is used as we do not employ any information about the graph at test time. Finally, we add a special class token " + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "inline_equation", + "content": "[HG]" + }, + { + "bbox": [ + 46, + 177, + 290, + 333 + ], + "type": "text", + "content": " to this sequence of features. See Fig. 3 for visualization." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 339, + 253, + 352 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 339, + 253, + 352 + ], + "spans": [ + { + "bbox": [ + 47, + 339, + 253, + 352 + ], + "type": "text", + "content": "3.3. Cross-attentional Transformer Module" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 358, + 290, + 477 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 358, + 290, + 477 + ], + "spans": [ + { + "bbox": [ + 46, + 358, + 290, + 477 + ], + "type": "text", + "content": "The situation hyper-graph embeddings obtained at previous step (section 3.2.2) are input along with the question to a cross-attentional transformer module which allows fine-grained computation between the question features and the graph features. A standard co-attentional transformer module is used for cross-attention between the two sequential feature inputs. The feature outputs corresponding to the " + }, + { + "bbox": [ + 46, + 358, + 290, + 477 + ], + "type": "inline_equation", + "content": "[HG]" + }, + { + "bbox": [ + 46, + 358, + 290, + 477 + ], + "type": "text", + "content": " token and " + }, + { + "bbox": [ + 46, + 358, + 290, + 477 + ], + "type": "inline_equation", + "content": "[CLS]" + }, + { + "bbox": [ + 46, + 358, + 290, + 477 + ], + "type": "text", + "content": " token from the cross-attentional transformer block are fed to a feed-forward network (FFN) for answer prediction." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 484, + 162, + 498 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 484, + 162, + 498 + ], + "spans": [ + { + "bbox": [ + 47, + 484, + 162, + 498 + ], + "type": "text", + "content": "3.4. Learning Objective" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 503, + 289, + 527 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 503, + 289, + 527 + ], + "spans": [ + { + "bbox": [ + 46, + 503, + 289, + 527 + ], + "type": "text", + "content": "The SHG-VQA model is trained with the following training objective i.e.," + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 116, + 536, + 287, + 550 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 536, + 287, + 550 + ], + "spans": [ + { + "bbox": [ + 116, + 536, + 287, + 550 + ], + "type": "interline_equation", + "content": "L = L _ {a c t} + L _ {r e l} + L _ {v q a} \\tag {1}", + "image_path": "8a66716d5c855730038ab06f36f5f8fc2da14850a1ff6107886e9c0563ab1612.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 558, + 289, + 606 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 558, + 289, + 606 + ], + "spans": [ + { + "bbox": [ + 47, + 558, + 289, + 606 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 558, + 289, + 606 + ], + "type": "inline_equation", + "content": "L_{act}" + }, + { + "bbox": [ + 47, + 558, + 289, + 606 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 558, + 289, + 606 + ], + "type": "inline_equation", + "content": "L_{rel}" + }, + { + "bbox": [ + 47, + 558, + 289, + 606 + ], + "type": "text", + "content": " are the set prediction loss terms for predicting the action set and relationship set for the video, and " + }, + { + "bbox": [ + 47, + 558, + 289, + 606 + ], + "type": "inline_equation", + "content": "L_{vqa}" + }, + { + "bbox": [ + 47, + 558, + 289, + 606 + ], + "type": "text", + "content": " is the cross-entropy loss over the predicted situation graph and question." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "text", + "content": "Actions and relationships set prediction loss: The situation graph prediction module infers fixed sets of sizes " + }, + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "inline_equation", + "content": "|N| \\times T" + }, + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "text", + "content": " actions and " + }, + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "inline_equation", + "content": "|M| \\times T" + }, + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "text", + "content": " relationships for " + }, + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "text", + "content": "-length video clip in a single pass through the action decoder and relationship decoder respectively. We modify the set prediction loss used in [1] as follows. Let " + }, + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "inline_equation", + "content": "A" + }, + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "text", + "content": " be the set of ground-truth actions and " + }, + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "inline_equation", + "content": "\\hat{A} = \\{\\hat{a}_i\\}_{i=1}^{|N| \\times T}" + }, + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "text", + "content": " be the predicted set of actions. In a scenario where " + }, + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "inline_equation", + "content": "\\hat{A}" + }, + { + "bbox": [ + 46, + 615, + 290, + 715 + ], + "type": "text", + "content": " is larger than the set of actions present" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 177, + 547, + 214 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 177, + 547, + 214 + ], + "spans": [ + { + "bbox": [ + 304, + 177, + 547, + 214 + ], + "type": "text", + "content": "in the video, a special class " + }, + { + "bbox": [ + 304, + 177, + 547, + 214 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 304, + 177, + 547, + 214 + ], + "type": "text", + "content": " (no class) is padded to the ground truth set " + }, + { + "bbox": [ + 304, + 177, + 547, + 214 + ], + "type": "inline_equation", + "content": "A" + }, + { + "bbox": [ + 304, + 177, + 547, + 214 + ], + "type": "text", + "content": ". We obtain a bipartite matching between ground-truth and predicted set for each timestep " + }, + { + "bbox": [ + 304, + 177, + 547, + 214 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 177, + 547, + 214 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 320, + 232, + 547, + 266 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 232, + 547, + 266 + ], + "spans": [ + { + "bbox": [ + 320, + 232, + 547, + 266 + ], + "type": "interline_equation", + "content": "\\hat {\\sigma} _ {a} = \\sum_ {t} ^ {T} \\operatorname {a r g m i n} _ {\\sigma_ {t} \\in \\zeta_ {| N |}} \\sum_ {i} ^ {| N |} \\mathcal {L} _ {\\text {m a t c h}} \\left(a _ {t _ {i}}, \\hat {a} _ {\\sigma_ {t} (i)}\\right) \\tag {2}", + "image_path": "26dac9c067cb7b85f69f9261563916f83dbab8d8ca3f36fdea45737c4a94842f.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "spans": [ + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "content": "Where, " + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "inline_equation", + "content": "\\sigma_{t}" + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "content": " is a permutation of N elements for frame t, " + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text{match}}(a_{ti}, \\hat{a}_{\\sigma_t(i)})" + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "content": " is a pair-wise matching cost between " + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "inline_equation", + "content": "i^{th}" + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "content": " ground-truth action label in " + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "inline_equation", + "content": "t^{th}" + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "content": " frame i.e., " + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "inline_equation", + "content": "a_{t_i}" + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "content": " and a predicted action label at index " + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "inline_equation", + "content": "\\sigma_t(i)" + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "content": ". This optimal assignment for each step " + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "content": " is computed using the Hungarian algorithm and the cost is summed over all " + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "content": " steps. The proposed set prediction loss takes into account only the class predictions for all video frames with no bounding box ground truths being used, different from the original set prediction loss used in [1] for object detection in images. Let " + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "inline_equation", + "content": "\\hat{p}(c_{t(i)})" + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "content": " be the class probability for the action prediction at " + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "inline_equation", + "content": "\\sigma_t(i)" + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text{match}}(a_{t_i}, \\hat{a}_{\\sigma_t(i)})" + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "content": " would be " + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "inline_equation", + "content": "-\\mathbb{1}_{\\{c_{t(i)} \\neq \\phi\\}} \\hat{p}_{\\sigma_t(i)}(c_{t(i)})" + }, + { + "bbox": [ + 304, + 274, + 547, + 466 + ], + "type": "text", + "content": ". After we obtain a one-to-one optimal matching between ground-truth and predicted set items without duplicates at each time step, we can compute the loss between the matched pairs using a Hungarian loss as follows:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 354, + 472, + 545, + 507 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 354, + 472, + 545, + 507 + ], + "spans": [ + { + "bbox": [ + 354, + 472, + 545, + 507 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {a c t}} (a, \\hat {a}) = \\sum_ {i = 1} ^ {| N | \\times T} - \\log \\hat {p} _ {\\hat {\\sigma} (i)} \\left(c _ {i}\\right) \\tag {3}", + "image_path": "283f02e0156d55a338e643c7c8b807bfaf2b03f3c9c9540efffe752d4de8f1e5.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 514, + 547, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 514, + 547, + 552 + ], + "spans": [ + { + "bbox": [ + 306, + 514, + 547, + 552 + ], + "type": "text", + "content": "Likewise, " + }, + { + "bbox": [ + 306, + 514, + 547, + 552 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 306, + 514, + 547, + 552 + ], + "type": "text", + "content": " is the set of ground-truth relations and " + }, + { + "bbox": [ + 306, + 514, + 547, + 552 + ], + "type": "inline_equation", + "content": "\\hat{R} = \\{\\hat{p_i}\\}_{i = 1}^{|M|\\times T}" + }, + { + "bbox": [ + 306, + 514, + 547, + 552 + ], + "type": "text", + "content": " denotes the predicted relationships. " + }, + { + "bbox": [ + 306, + 514, + 547, + 552 + ], + "type": "inline_equation", + "content": "L_{rel}" + }, + { + "bbox": [ + 306, + 514, + 547, + 552 + ], + "type": "text", + "content": " is formulated as follows:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 319, + 571, + 547, + 605 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 319, + 571, + 547, + 605 + ], + "spans": [ + { + "bbox": [ + 319, + 571, + 547, + 605 + ], + "type": "interline_equation", + "content": "\\hat {\\sigma} _ {p} = \\sum_ {t} ^ {T} \\operatorname {a r g m i n} _ {\\sigma_ {t} \\in \\zeta_ {| M |}} \\sum_ {i} ^ {| M |} \\mathcal {L} _ {\\text {m a t c h}} \\left(p _ {t _ {i}}, \\hat {p} _ {\\sigma_ {t} (i)}\\right) \\tag {4}", + "image_path": "b8e9b0b372dc437caa69ab10b6d77a7da1b3fa245e8e7f5bd58cafea64138b24.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 354, + 614, + 547, + 649 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 354, + 614, + 547, + 649 + ], + "spans": [ + { + "bbox": [ + 354, + 614, + 547, + 649 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {r e l} (p, \\hat {p}) = \\sum_ {i = 1} ^ {| M | \\times T} - \\log \\hat {p} _ {\\hat {\\sigma} (i)} (c _ {i}) \\tag {5}", + "image_path": "e9bececa840e837a57926e145ac2d0372c0f54e38debf3e11b2642097d1fdd78.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "inline_equation", + "content": "\\hat{\\sigma}(i)" + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "content": " is the optimal matching obtained in the previous step for each frame. The inferred graph is input to the cross-attentional transformer module along with the question and the answer choices as explained in section 3.2.2. The proposed network is trained in an end-to-end manner." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14883" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 79, + 91, + 515, + 158 + ], + "blocks": [ + { + "bbox": [ + 187, + 71, + 406, + 82 + ], + "lines": [ + { + "bbox": [ + 187, + 71, + 406, + 82 + ], + "spans": [ + { + "bbox": [ + 187, + 71, + 406, + 82 + ], + "type": "text", + "content": "Table 2. Results on AGQA's novel compositions test metric." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 79, + 91, + 515, + 158 + ], + "lines": [ + { + "bbox": [ + 79, + 91, + 515, + 158 + ], + "spans": [ + { + "bbox": [ + 79, + 91, + 515, + 158 + ], + "type": "table", + "html": "
MethodSequencingSuperlativeDurationObj-relationOverall
BOAllBOAllBOAllBOAllBOAll
PSAC [30]49.1929.3340.9645.2317.7633.3247.8934.8442.0643.760.0124.2846.4919.3434.71
HME [9]49.3328.0640.5344.0613.830.9548.4534.7242.3139.580.0021.9645.4217.1733.15
HCRN [24]48.3130.0040.7345.1217.3033.0646.1539.1143.0137.152.8621.8844.8820.1234.13
SHG-VQA50.8838.5945.7951.1423.6439.2551.8449.2150.6639.736.2324.8249.0726.6839.37
", + "image_path": "c92e0f178205b9d0f9fcb54015a3a56c35479cef94478792ef29e873db6bdd07.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 88, + 196, + 246, + 244 + ], + "blocks": [ + { + "bbox": [ + 60, + 175, + 273, + 186 + ], + "lines": [ + { + "bbox": [ + 60, + 175, + 273, + 186 + ], + "spans": [ + { + "bbox": [ + 60, + 175, + 273, + 186 + ], + "type": "text", + "content": "Table 3. Evaluation on AGQA's more compositional steps." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 88, + 196, + 246, + 244 + ], + "lines": [ + { + "bbox": [ + 88, + 196, + 246, + 244 + ], + "spans": [ + { + "bbox": [ + 88, + 196, + 246, + 244 + ], + "type": "table", + "html": "
More Compositional StepsBinaryOpenAll
PSAC [30]47.6514.8147.19
HME [9]48.0920.9847.72
HCRN [24]46.9623.7046.63
SHG-VQA47.1322.6646.97
", + "image_path": "8c49ed809474c3254a6e3ab1bf47529ef9745e7b5207123fa169dbe95eb8814e.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 48, + 269, + 291, + 364 + ], + "blocks": [ + { + "bbox": [ + 53, + 248, + 280, + 258 + ], + "lines": [ + { + "bbox": [ + 53, + 248, + 280, + 258 + ], + "spans": [ + { + "bbox": [ + 53, + 248, + 280, + 258 + ], + "type": "text", + "content": "Table 4. Evaluation on AGQA's indirect references test metric." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 269, + 291, + 364 + ], + "lines": [ + { + "bbox": [ + 48, + 269, + 291, + 364 + ], + "spans": [ + { + "bbox": [ + 48, + 269, + 291, + 364 + ], + "type": "table", + "html": "
MethodObjectActionTemporal
BOAllBOAllBOAll
PrecisionPSAC63.6953.7756.6461.0152.4653.2457.5253.7454.39
HME62.9552.3155.3958.2148.1249.0455.9952.4253.04
HCRN54.0667.2463.4353.8764.4363.4752.3566.8464.34
SHG-VQA76.9386.2781.5979.5587.4084.9069.0887.3582.87
RecallPSAC45.0627.3638.8040.9122.1825.9635.1326.8430.64
HME46.0326.8039.2341.3221.7125.6737.9626.5931.80
HCRN44.8435.4641.5244.0130.4333.1735.1134.3834.71
SHG-VQA53.8643.9849.8554.1637.8343.1052.2143.5746.78
", + "image_path": "b50e2b572029c54e5abd18b698d8924e14b863a3facf8398aacc557e4a2aa8e4.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 376, + 128, + 389 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 376, + 128, + 389 + ], + "spans": [ + { + "bbox": [ + 47, + 376, + 128, + 389 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 396, + 110, + 408 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 396, + 110, + 408 + ], + "spans": [ + { + "bbox": [ + 47, + 396, + 110, + 408 + ], + "type": "text", + "content": "4.1. Datasets" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 416, + 288, + 584 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 416, + 288, + 584 + ], + "spans": [ + { + "bbox": [ + 46, + 416, + 288, + 584 + ], + "type": "text", + "content": "AGQA Benchmark [11]: The Action Genome Question Answering benchmark is a visual dataset comprising 192M hand-crafted questions about 9.6K videos from the Charades dataset [43]. In addition to VQA accuracy, AGQA presents three testing metrics for testing the VQA methods: indirect references, novel compositions, and more compositional steps. We use the AGQA 2.0 Balanced dataset, which consists of 2.27M question-answer pairs as a result of balancing the original dataset using stricter procedures to reduce as much language bias as possible. Of the 2.27M questions, there are approximately 1.6M training questions and 669K test questions [12]. To have a standard train-val-test setup for our experiments, we randomly sampled " + }, + { + "bbox": [ + 46, + 416, + 288, + 584 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 416, + 288, + 584 + ], + "type": "text", + "content": " QA pairs from training data for validation of hyperparameters." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": "STAR Benchmark [47]: The STAR dataset provides 60K situated reasoning questions based on 22K trimmed situation video clips, also based on the Charades dataset [43]. They further provide " + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "inline_equation", + "content": "\\sim 144\\mathrm{K}" + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": " ground-truth situation graphs including 111 actions, 37 unique objects, and 24 relationships. The dataset is split into training, validation, and test sets where test evaluation can be done on the evaluation server a limited number of times. We perform ablations and analysis on the validation set and report test set results to compare with the baselines in Table 5." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 176, + 408, + 190 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 176, + 408, + 190 + ], + "spans": [ + { + "bbox": [ + 306, + 176, + 408, + 190 + ], + "type": "text", + "content": "4.2. Implementations" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 198, + 545, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 198, + 545, + 281 + ], + "spans": [ + { + "bbox": [ + 304, + 198, + 545, + 281 + ], + "type": "text", + "content": "On the VQA task, we report accuracy; we also report mAP for situation hyper-graph predictions. For AGQA's indirect references testing metric, precision and recall are reported. For STAR benchmark, we follow the same training protocol as [47] and train SHG-VQA from scratch on each question type separately unless specified otherwise. Training details are shared in the supplementary document." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "spans": [ + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "text", + "content": "Visual Embeddings: The video frames are resized to size " + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "inline_equation", + "content": "224 \\times 224" + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "text", + "content": " with a clip length of 16 frames. We use RandAugment [3] for data augmentation during training. The video clip is input to a pretrained convolutional network with freeze weights to obtain video features of size " + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "inline_equation", + "content": "16 \\times 7 \\times 7 \\times 2048" + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "text", + "content": ". A 2-layer 3D convolutional block with the kernel of size " + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "inline_equation", + "content": "5 \\times 3 \\times 3" + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "text", + "content": " further processes the zero-padded extracted features yielding features of size " + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "inline_equation", + "content": "8 \\times 7 \\times 7 \\times d" + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "text", + "content": ". The spatio-temporal dimensions are then flattened to obtain a sequence of length " + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "inline_equation", + "content": "Thw = 392" + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "text", + "content": "-dimensional tokens where " + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "inline_equation", + "content": "d = 768" + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "text", + "content": ". The input encoders as well as situation hyper-graph decoders use " + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "inline_equation", + "content": "L = 5" + }, + { + "bbox": [ + 304, + 285, + 546, + 430 + ], + "type": "text", + "content": " transformer encoder layers with non-shared weights." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 434, + 547, + 564 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 434, + 547, + 564 + ], + "spans": [ + { + "bbox": [ + 304, + 434, + 547, + 564 + ], + "type": "text", + "content": "Query embeddings for action and relationship predicates: The features output from the video encoder is then input to a situation hyper-graph decoder comprising transformer-based action and relationship decoders. Our best model uses " + }, + { + "bbox": [ + 304, + 434, + 547, + 564 + ], + "type": "inline_equation", + "content": "M = 8" + }, + { + "bbox": [ + 304, + 434, + 547, + 564 + ], + "type": "text", + "content": " relation queries and " + }, + { + "bbox": [ + 304, + 434, + 547, + 564 + ], + "type": "inline_equation", + "content": "N = 3" + }, + { + "bbox": [ + 304, + 434, + 547, + 564 + ], + "type": "text", + "content": " action queries for each situation; " + }, + { + "bbox": [ + 304, + 434, + 547, + 564 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 434, + 547, + 564 + ], + "type": "text", + "content": " is set to 16. AGQA has 157 total raw actions, 36 unique objects, and 44 unique relationships which obtain 456 relationship triplets " + }, + { + "bbox": [ + 304, + 434, + 547, + 564 + ], + "type": "inline_equation", + "content": "< v_{x}, e_{i}, v_{y} >" + }, + { + "bbox": [ + 304, + 434, + 547, + 564 + ], + "type": "text", + "content": " in the training and validation set. For STAR dataset, there are 37 objects, and 24 relationships yielding 563 unique relation predicates; it also has 111 action classes." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 570, + 547, + 701 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 547, + 701 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 547, + 701 + ], + "type": "text", + "content": "Situation graph embeddings: The decoded situation graph queries are input to a cross-attentional transformer. The input situation graph embedding is a sum of 4 different encoding types: 1) decoded query embeddings for predicted actions and relationship predicates, 2) situation IDs denoting the situation (or frame) number for each query, 3) attention mask set to 1 for actual tokens and 0 for padded tokens, and 4) token type embeddings to distinguish between action tokens and relationship tokens as shown in Fig. 3. Our network uses a 2-layer co-attentional module [33] for cross-attention." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "14884" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 107, + 102, + 485, + 242 + ], + "blocks": [ + { + "bbox": [ + 46, + 70, + 547, + 95 + ], + "lines": [ + { + "bbox": [ + 46, + 70, + 547, + 95 + ], + "spans": [ + { + "bbox": [ + 46, + 70, + 547, + 95 + ], + "type": "text", + "content": "Table 5. Results on STAR dataset. Best results are shown in bold font and second best results are underlined. Numbers are reported for VQA accuracy in percentages." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 107, + 102, + 485, + 242 + ], + "lines": [ + { + "bbox": [ + 107, + 102, + 485, + 242 + ], + "spans": [ + { + "bbox": [ + 107, + 102, + 485, + 242 + ], + "type": "table", + "html": "
Method(test)BackboneObj.Hyper.Question TypeOverall
Inter.Seq.Pred.Feas.
Q-type (Random) [21]-XX25.0624.9324.7924.8124.89
Q-type (Frequent) [21]-XX19.0919.4512.9018.3117.44
Blind Model (LSTM) [14]GloVeXX32.2432.1728.5628.4130.34
Blind Model (BERT) [6]BERTXX32.6834.2129.9829.2631.53
CNN-LSTM [53]ResNext101-K400XX33.2532.6730.6930.4331.76
CNN-BERT [28]ResNext101-K400XX33.5937.1630.9530.8433.14
LCGN [15]ResNext101-K400✓X39.0137.9728.8126.9833.19
HRCN [24]ResNext101-K400✓X39.1038.1728.7527.2733.32
ClipBERT [25]ResNext101-K400XX39.8143.5932.2431.4236.70
NS-SR [47]ResNext101-K400✓✓30.8831.7630.2329.7330.65
SHG-VQA (Ours)SlowR50-K400X✓47.9842.0335.3432.5239.47
SHG-VQA (Ours)ResNext101-ImageNet1KX✓45.842.7734.6429.9138.28
", + "image_path": "62b33b892546a31cdc5d55cb61aa59de447d268681f827a71f6c314b3917247d.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 251, + 289, + 334 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 251, + 289, + 334 + ], + "spans": [ + { + "bbox": [ + 46, + 251, + 289, + 334 + ], + "type": "text", + "content": "AGQA: For AGQA, we train our model with SlowR50 backbone and report results for VQA accuracy on the test set. We also report our model's generalization capability to indirect references. Furthermore, we train our network to report its generalization to novel compositions and to more compositional steps. For more compositional steps, we train our network with randomly sampled 100K QA pairs." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 335, + 289, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 335, + 289, + 430 + ], + "spans": [ + { + "bbox": [ + 46, + 335, + 289, + 430 + ], + "type": "text", + "content": "STAR: Following [47], we train the model for each question type separately to compare with the baselines. However, this protocol is expensive in terms of time and resources. To address the matter of limited resources, we also tried combining all questions together after the questions filtering from interaction and sequence types and train a single model instead of multiple trainings. We use this training regime to study models ablations." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 435, + 168, + 449 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 435, + 168, + 449 + ], + "spans": [ + { + "bbox": [ + 47, + 435, + 168, + 449 + ], + "type": "text", + "content": "5. Results and Analysis" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 455, + 217, + 468 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 455, + 217, + 468 + ], + "spans": [ + { + "bbox": [ + 47, + 455, + 217, + 468 + ], + "type": "text", + "content": "5.1. Comparison to State-of-the-Art" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "spans": [ + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "text", + "content": "AGQA: We compare our method with the existing state-of-the-art methods on AGQA benchmark. The best baseline on AGQA is HCRN [24] for overall accuracy. HCRN uses appearance features from ResNet101 as well as motion features from ResNext101-Kinetics400 backbones. Our model outperforms HCRN by a significant margin of " + }, + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "inline_equation", + "content": "7.09\\%" + }, + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "text", + "content": " (HCRN: " + }, + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "inline_equation", + "content": "42.11\\%" + }, + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "text", + "content": " vs. SHG-VQA: " + }, + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "inline_equation", + "content": "49.20\\%" + }, + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "text", + "content": ") in terms of overall accuracy (see Table 1). We observe the biggest improvement of " + }, + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "inline_equation", + "content": "14.63\\%" + }, + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "text", + "content": " absolute points on object-action reasoning questions compared to the best model in that category i.e., PSAC [30]: " + }, + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "inline_equation", + "content": "50.00\\%" + }, + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "text", + "content": " vs. SHG-VQA: " + }, + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "inline_equation", + "content": "64.63\\%" + }, + { + "bbox": [ + 46, + 474, + 288, + 616 + ], + "type": "text", + "content": ". We further report results on the three novel testing metrics as follows:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 617, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 46, + 617, + 288, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 288, + 665 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 288, + 665 + ], + "type": "text", + "content": "a) Novel Compositions: For novel composition at test time, we observe an overall gain of " + }, + { + "bbox": [ + 46, + 617, + 288, + 665 + ], + "type": "inline_equation", + "content": "4.70\\%" + }, + { + "bbox": [ + 46, + 617, + 288, + 665 + ], + "type": "text", + "content": " when compared to the best contender, i.e., PSAC [30]. For open-ended questions, we outperform HCRN by " + }, + { + "bbox": [ + 46, + 617, + 288, + 665 + ], + "type": "inline_equation", + "content": "6.56\\%" + }, + { + "bbox": [ + 46, + 617, + 288, + 665 + ], + "type": "text", + "content": " (see Table 2);" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "text", + "content": "b) Indirect References: When tested for indirect references questions in Table 4, we outperform all baselines by an absolute " + }, + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "inline_equation", + "content": "7.83\\% -16.62\\%" + }, + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "text", + "content": " in terms of recall; For precision, we also gain similar improvements;" + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 304, + 251, + 547, + 311 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 251, + 547, + 311 + ], + "spans": [ + { + "bbox": [ + 304, + 251, + 547, + 311 + ], + "type": "text", + "content": "c) Compositional Steps: Our model is trained on only " + }, + { + "bbox": [ + 304, + 251, + 547, + 311 + ], + "type": "inline_equation", + "content": "15\\%" + }, + { + "bbox": [ + 304, + 251, + 547, + 311 + ], + "type": "text", + "content": " (100K QA pairs) of the training data and is still able to perform on par with the baselines (trained on 1.6M QA pairs) achieving second best results for each category of more compositional steps (table 3)." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "spans": [ + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "text", + "content": "STAR: For STAR dataset, we first compare the proposed architecture to other state-of-the-art works in the field using SlowR50 [13] video backbone and a ResNext101 [50] as a frame-level backbone to evaluate accuracy based on 3D video and 2D image architectures. It shows that for both settings, SHG-VQA significantly outperforms other baseline methods even with weaker backbones (see Table 5). Concretely, we obtain an absolute gain of " + }, + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "inline_equation", + "content": "8.53\\%" + }, + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "text", + "content": " over NSSR [47], " + }, + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "inline_equation", + "content": "5.86\\%" + }, + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "text", + "content": " compared to HCRN [24], " + }, + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "inline_equation", + "content": "5.99\\%" + }, + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "text", + "content": " improvement over LCGN [15], and " + }, + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "inline_equation", + "content": "\\sim 2.5\\%" + }, + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "text", + "content": " over ClipBERT [25] which is a SOTA model in terms of overall VQA accuracy. We notice the substantial gain of " + }, + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "inline_equation", + "content": "7.86\\%" + }, + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "text", + "content": " for interaction questions which test the understanding of interactions between entities in a situation. Prediction is the next category of questions that benefits the most with " + }, + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "inline_equation", + "content": "2.48\\%" + }, + { + "bbox": [ + 304, + 312, + 548, + 492 + ], + "type": "text", + "content": " improvement." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 501, + 515, + 514 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 501, + 515, + 514 + ], + "spans": [ + { + "bbox": [ + 305, + 501, + 515, + 514 + ], + "type": "text", + "content": "5.2. Ablation and Hyperparameter Analysis" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 521, + 545, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 545, + 544 + ], + "type": "text", + "content": "We perform our ablation studies on the STAR benchmark as discussed below:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 545, + 548, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 548, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 548, + 715 + ], + "type": "text", + "content": "Impact of situation graphs quality: To assess the effect of situation graphs' quality on VQA accuracy, we train a baseline version of our system, where the model is trained on ground truth situation graphs for VQA tasks only (Table 6). Since the ground truth situation graphs are not available for the test set, we can only compare SHG-VQA and this baseline on the validation set. As expected, when taking ground truth situation graphs as input, the performance is significantly improved for interaction " + }, + { + "bbox": [ + 304, + 545, + 548, + 715 + ], + "type": "inline_equation", + "content": "(\\mathrm{GT} = 91.9\\%" + }, + { + "bbox": [ + 304, + 545, + 548, + 715 + ], + "type": "text", + "content": " vs. predicted " + }, + { + "bbox": [ + 304, + 545, + 548, + 715 + ], + "type": "inline_equation", + "content": "= 46.78\\%)" + }, + { + "bbox": [ + 304, + 545, + 548, + 715 + ], + "type": "text", + "content": " and sequence " + }, + { + "bbox": [ + 304, + 545, + 548, + 715 + ], + "type": "inline_equation", + "content": "(\\mathrm{GT} = 80.5\\%" + }, + { + "bbox": [ + 304, + 545, + 548, + 715 + ], + "type": "text", + "content": " vs. predicted " + }, + { + "bbox": [ + 304, + 545, + 548, + 715 + ], + "type": "inline_equation", + "content": "= 42.52\\%" + }, + { + "bbox": [ + 304, + 545, + 548, + 715 + ], + "type": "text", + "content": " questions. However, for the questions about the unseen part of the video, the model with ground truth graph tokens still struggles despite better performance compared to SHG-VQA: predic" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14885" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 52, + 72, + 286, + 140 + ], + "blocks": [ + { + "bbox": [ + 52, + 72, + 286, + 140 + ], + "lines": [ + { + "bbox": [ + 52, + 72, + 286, + 140 + ], + "spans": [ + { + "bbox": [ + 52, + 72, + 286, + 140 + ], + "type": "image", + "image_path": "dc1ab2022e594aa10a48c74d7649a4a68d0f6c7fc4ba5c76b82b853e7a3e9a91.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 151, + 287, + 228 + ], + "lines": [ + { + "bbox": [ + 46, + 151, + 287, + 228 + ], + "spans": [ + { + "bbox": [ + 46, + 151, + 287, + 228 + ], + "type": "text", + "content": "Figure 4. Qualitative example for using frame-wise set prediction loss. Col. 1 shows the frame, col. 2 shows the ground-truth situation graph, col. 3 shows predicted graphs when trained with set prediction loss for the full video, and col. 4 shows the predicted graph when the model is trained by matching each timestep " + }, + { + "bbox": [ + 46, + 151, + 287, + 228 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 151, + 287, + 228 + ], + "type": "text", + "content": ". The edges show the person-object relationship labels along with the number of times it was predicted." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 55, + 273, + 280, + 308 + ], + "blocks": [ + { + "bbox": [ + 47, + 242, + 286, + 263 + ], + "lines": [ + { + "bbox": [ + 47, + 242, + 286, + 263 + ], + "spans": [ + { + "bbox": [ + 47, + 242, + 286, + 263 + ], + "type": "text", + "content": "Table 6. Impact of hyper-graphs (HG) quality. Results shown for STAR val set with SlowR50 backbone." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 55, + 273, + 280, + 308 + ], + "lines": [ + { + "bbox": [ + 55, + 273, + 280, + 308 + ], + "spans": [ + { + "bbox": [ + 55, + 273, + 280, + 308 + ], + "type": "table", + "html": "
MethodInteractionSequencePredictionFeasibilityOverall
Predicted hyper-graphs47.0842.5237.8233.6140.26
GT hyper-graphs91.980.541.2235.4262.46
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We obtain an overall mAP of 87.63 for actions and 72.9 for relationships respectively." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 402, + 286, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 402, + 286, + 496 + ], + "spans": [ + { + "bbox": [ + 46, + 402, + 286, + 496 + ], + "type": "text", + "content": "Input to cross-attentional transformer: To evaluate the choice of input to the cross-attentional transformer, we experiment with three settings: a) question and video embeddings, b) question and situation graphs embeddings, c) question, situation graphs, and video embeddings. We observe no gain in the overall VQA accuracy when adding video embeddings to the cross-attentional transformer and get our best results with (b) (table 8)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 498, + 286, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 498, + 286, + 616 + ], + "spans": [ + { + "bbox": [ + 46, + 498, + 286, + 616 + ], + "type": "text", + "content": "Situation hyper-graph components: To study the impact of different components of situation graphs, we train our system with only action predicate tokens with objective " + }, + { + "bbox": [ + 46, + 498, + 286, + 616 + ], + "type": "inline_equation", + "content": "L_{act} + L_{vqa}" + }, + { + "bbox": [ + 46, + 498, + 286, + 616 + ], + "type": "text", + "content": ", only relationship predicates (with " + }, + { + "bbox": [ + 46, + 498, + 286, + 616 + ], + "type": "inline_equation", + "content": "L_{rel} + L_{vqa}" + }, + { + "bbox": [ + 46, + 498, + 286, + 616 + ], + "type": "text", + "content": "), and the full model (eq. 1). The action predicates are more effective compared to the relationship predicates. When compared in terms of predicate classification, we observe high accuracy for action predicates. Nonetheless, using the full situation graphs perform better than omitting actions or relationship prediction task (table 9)." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 616, + 286, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 616, + 286, + 700 + ], + "spans": [ + { + "bbox": [ + 46, + 616, + 286, + 700 + ], + "type": "text", + "content": "Number of queries: Number of action queries " + }, + { + "bbox": [ + 46, + 616, + 286, + 700 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 616, + 286, + 700 + ], + "type": "text", + "content": " and relationship queries " + }, + { + "bbox": [ + 46, + 616, + 286, + 700 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 616, + 286, + 700 + ], + "type": "text", + "content": " is a hyperparameter for SHG-VQA. We report the performance of the SHG-VQA with a varying number of queries for actions and relationships at each timestep in Table 9. We report our best results with " + }, + { + "bbox": [ + 46, + 616, + 286, + 700 + ], + "type": "inline_equation", + "content": "M = 3" + }, + { + "bbox": [ + 46, + 616, + 286, + 700 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 616, + 286, + 700 + ], + "type": "inline_equation", + "content": "N = 8" + }, + { + "bbox": [ + 46, + 616, + 286, + 700 + ], + "type": "text", + "content": ". Additional results are reported in the supplementary document." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 701, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 701, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 701, + 286, + 713 + ], + "type": "text", + "content": "Frame-wise set prediction loss: Using the notion of time " + }, + { + "bbox": [ + 47, + 701, + 286, + 713 + ], + "type": "inline_equation", + "content": "t" + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 331, + 113, + 520, + 148 + ], + "blocks": [ + { + "bbox": [ + 305, + 71, + 545, + 104 + ], + "lines": [ + { + "bbox": [ + 305, + 71, + 545, + 104 + ], + "spans": [ + { + "bbox": [ + 305, + 71, + 545, + 104 + ], + "type": "text", + "content": "Table 7. Predicate classification results for situation hyper-graphs in terms of mAP for STAR validation set from SHG-VQA with SlowR50 backbone. Numbers are reported in percentages." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 331, + 113, + 520, + 148 + ], + "lines": [ + { + "bbox": [ + 331, + 113, + 520, + 148 + ], + "spans": [ + { + "bbox": [ + 331, + 113, + 520, + 148 + ], + "type": "table", + "html": "
InteractSequencePredictionFeasibilityOverall
Actions84.7789.4385.3091.4687.63
Relationships72.8373.570.0572.8272.9
", + "image_path": "ae66477072ebacdfe10b83a110a4382eb796ea27fb90a97c26537ab6fb3db361.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 328, + 184, + 523, + 226 + ], + "blocks": [ + { + "bbox": [ + 305, + 153, + 545, + 175 + ], + "lines": [ + { + "bbox": [ + 305, + 153, + 545, + 175 + ], + "spans": [ + { + "bbox": [ + 305, + 153, + 545, + 175 + ], + "type": "text", + "content": "Table 8. Results for cross-attention input. Results shown for STAR test set with SlowR50 backbone." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 328, + 184, + 523, + 226 + ], + "lines": [ + { + "bbox": [ + 328, + 184, + 523, + 226 + ], + "spans": [ + { + "bbox": [ + 328, + 184, + 523, + 226 + ], + "type": "table", + "html": "
MethodInteractionSequencePredictionFeasibilityOverall
Q + V33.2835.6027.9326.4330.81
Q + HG47.9842.0335.3432.5239.47
Q + V + HG45.4544.1934.2232.8739.18
", + "image_path": "e426f6814154be3427834fa4a7689046066953e56de681611698c0b06155924f.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "type": "table", + "bbox": [ + 310, + 285, + 541, + 384 + ], + "blocks": [ + { + "bbox": [ + 305, + 232, + 547, + 276 + ], + "lines": [ + { + "bbox": [ + 305, + 232, + 547, + 276 + ], + "spans": [ + { + "bbox": [ + 305, + 232, + 547, + 276 + ], + "type": "text", + "content": "Table 9. Model variations on STAR validation set with a single model using SlowR50 backbone for all question types. Best results are shown in bold font and second best results are underlined. Numbers are reported for VQA accuracy in percentages." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 310, + 285, + 541, + 384 + ], + "lines": [ + { + "bbox": [ + 310, + 285, + 541, + 384 + ], + "spans": [ + { + "bbox": [ + 310, + 285, + 541, + 384 + ], + "type": "table", + "html": "
Method (val)InteractionSequencePredictionFeasibilityOverall
hyper-graph components
Action only – Act=340.9438.0835.5830.5638.68
Relation. only – Rel=835.9435.7934.7827.8635.16
Both – Act=3, Rel=842.9338.2036.0630.5639.20
Number of queries
Act=2, Rel=840.3240.1338.7829.7338.34
Act=3, Rel=842.9338.2036.0630.5639.20
Act=4, Rel=838.4035.7935.5830.3537.06
Act=3, Rel=1241.3240.8039.4231.3939.90
Act=4, Rel=1240.4039.0536.8629.1138.39
", + "image_path": "24fd31d879f45a0add99bd54898f2cae0ef7387508ccb0cf64c808039bafe616.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_body" + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 394, + 545, + 442 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 394, + 545, + 442 + ], + "spans": [ + { + "bbox": [ + 305, + 394, + 545, + 442 + ], + "type": "text", + "content": "in the set prediction loss alleviates the problem of duplicate predictions within each situation. See Fig. 4 for qualitative examples of produced hyper-graphs with and without this loss. Extra examples are in the supplementary document." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 449, + 378, + 462 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 449, + 378, + 462 + ], + "spans": [ + { + "bbox": [ + 306, + 449, + 378, + 462 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 470, + 547, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 470, + 547, + 601 + ], + "spans": [ + { + "bbox": [ + 304, + 470, + 547, + 601 + ], + "type": "text", + "content": "We presented a novel approach to model situation graph prediction as an underlying sub-task for video question answering. The proposed method predicts a situation hypergraph structure composed of existing actions and relationships in the input video. The input question can then reason over the predicted graph to solve VQA. We show the impact of the proposed approach by evaluating on two video question-answering benchmarks and achieving significant performance gains overall baseline methods. Our method demonstrates promise for further research in this direction to improve VQA systems even further." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 605, + 547, + 710 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 547, + 710 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 547, + 710 + ], + "type": "text", + "content": "Acknowledgements. Aisha Urooj is supported by the ARO grant W911NF-19-1-0356. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. This work was partially supported under NSF grant CNS-2050731, Research Experience for Undergraduates in Computer Vision. Dr. Gan is supported by the MIT-IBM Watson AI Lab, DSO grant DSOCO21072, and gift funding from MERL, Cisco, Sony, and Amazon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of ARO, IARPA, DOI/IBC, or the U.S. Government." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "14886" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 92, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 53, + 92, + 288, + 147 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 92, + 288, + 147 + ], + "spans": [ + { + "bbox": [ + 53, + 92, + 288, + 147 + ], + "type": "text", + "content": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 4, 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 150, + 288, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 150, + 288, + 205 + ], + "spans": [ + { + "bbox": [ + 53, + 150, + 288, + 205 + ], + "type": "text", + "content": "[2] Xiaojun Chang, Pengzhen Ren, Pengfei Xu, Zhihui Li, Xiaojiang Chen, and Alexander G Hauptmann. A comprehensive survey of scene graphs: Generation and application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 208, + 288, + 263 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 208, + 288, + 263 + ], + "spans": [ + { + "bbox": [ + 53, + 208, + 288, + 263 + ], + "type": "text", + "content": "[3] Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le. Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 702-703, 2020. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 266, + 288, + 320 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 266, + 288, + 320 + ], + "spans": [ + { + "bbox": [ + 53, + 266, + 288, + 320 + ], + "type": "text", + "content": "[4] Vinay Damodaran, Sharanya Chakravarthy, Akshay Kumar, Anjana Umapathy, Teruko Mitamura, Yuta Nakashima, Noa Garcia, and Chenhui Chu. Understanding the role of scene graphs in visual question answering. arXiv preprint arXiv:2101.05479, 2021. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 323, + 288, + 367 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 323, + 288, + 367 + ], + "spans": [ + { + "bbox": [ + 53, + 323, + 288, + 367 + ], + "type": "text", + "content": "[5] Long Hoang Dang, Thao Minh Le, Vuong Le, and Truyen Tran. Hierarchical object-oriented spatio-temporal reasoning for video question answering. arXiv preprint arXiv:2106.13432, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 370, + 288, + 414 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 370, + 288, + 414 + ], + "spans": [ + { + "bbox": [ + 53, + 370, + 288, + 414 + ], + "type": "text", + "content": "[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 416, + 288, + 483 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 416, + 288, + 483 + ], + "spans": [ + { + "bbox": [ + 53, + 416, + 288, + 483 + ], + "type": "text", + "content": "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 485, + 288, + 550 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 485, + 288, + 550 + ], + "spans": [ + { + "bbox": [ + 53, + 485, + 288, + 550 + ], + "type": "text", + "content": "[8] Chenyou Fan, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, and Heng Huang. Heterogeneous memory enhanced multimodal attention model for video question answering. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1999-2007, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 554, + 288, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 554, + 288, + 597 + ], + "spans": [ + { + "bbox": [ + 53, + 554, + 288, + 597 + ], + "type": "text", + "content": "[9] Chenyou Fan, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, and Heng Huang. Heterogeneous memory enhanced multimodal attention model for video question answering. CoRR, abs/1904.04357, 2019. 5, 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 600, + 288, + 654 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 600, + 288, + 654 + ], + "spans": [ + { + "bbox": [ + 48, + 600, + 288, + 654 + ], + "type": "text", + "content": "[10] Jiyang Gao, Runzhou Ge, Kan Chen, and Ram Nevatia. Motion-appearance co-memory networks for video question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6576–6585, 2018. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 658, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 288, + 713 + ], + "type": "text", + "content": "[11] Madeleine Grunde-McLaughlin, Ranjay Krishna, and Maneesh Agrawala. Agqa: A benchmark for compositional spatio-temporal reasoning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11287-11297, 2021. 2, 6" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 307, + 73, + 547, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 547, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 547, + 106 + ], + "type": "text", + "content": "[12] Madeleine Grunde-McLaughlin, Ranjay Krishna, and Maneesh Agrawala. Agqa 2.0: An updated benchmark for compositional spatio-temporal reasoning, 2022. 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 307, + 107, + 547, + 160 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 547, + 160 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 547, + 160 + ], + "type": "text", + "content": "[13] Kensho Hara, Hirokatsu Kataoka, and Yutaka Satoh. Learning spatio-temporal features with 3d residual networks for action recognition. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 3154-3160, 2017. 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 162, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 162, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 162, + 545, + 184 + ], + "type": "text", + "content": "[14] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. *Neural computation*, 9(8):1735–1780, 1997. 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 185, + 547, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 185, + 547, + 237 + ], + "spans": [ + { + "bbox": [ + 308, + 185, + 547, + 237 + ], + "type": "text", + "content": "[15] Ronghang Hu, Anna Rohrbach, Trevor Darrell, and Kate Saenko. Language-conditioned graph networks for relational reasoning. In Proceedings of the IEEE International Conference on Computer Vision, pages 10294-10303, 2019. 2, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 239, + 547, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 239, + 547, + 282 + ], + "spans": [ + { + "bbox": [ + 308, + 239, + 547, + 282 + ], + "type": "text", + "content": "[16] Yunseok Jang, Yale Song, Chris Dongjoo Kim, Youngjae Yu, Youngjin Kim, and Gunhee Kim. Video question answering with spatio-temporal reasoning. International Journal of Computer Vision, 127(10):1385-1412, 2019. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 284, + 547, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 284, + 547, + 338 + ], + "spans": [ + { + "bbox": [ + 308, + 284, + 547, + 338 + ], + "type": "text", + "content": "[17] Yunseok Jang, Yale Song, Youngjae Yu, Youngjin Kim, and Gunhee Kim. Tgif-qa: Toward spatio-temporal reasoning in visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2758–2766, 2017. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 338, + 547, + 393 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 338, + 547, + 393 + ], + "spans": [ + { + "bbox": [ + 308, + 338, + 547, + 393 + ], + "type": "text", + "content": "[18] Jianwen Jiang, Ziqiang Chen, Haojie Lin, Xibin Zhao, and Yue Gao. Divide and conquer: Question-guided spatiotemporal contextual attention for video question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11101-11108, 2020. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 394, + 547, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 394, + 547, + 437 + ], + "spans": [ + { + "bbox": [ + 308, + 394, + 547, + 437 + ], + "type": "text", + "content": "[19] Pin Jiang and Yahong Han. Reasoning with heterogeneous graph alignment for video question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11109-11116, 2020. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 438, + 547, + 469 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 438, + 547, + 469 + ], + "spans": [ + { + "bbox": [ + 308, + 438, + 547, + 469 + ], + "type": "text", + "content": "[20] Juanzi Li Jiaxin Shi, Hanwang Zhang. Explainable and explicit visual reasoning over scene graphs. In CVPR, 2019. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 471, + 547, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 471, + 547, + 514 + ], + "spans": [ + { + "bbox": [ + 308, + 471, + 547, + 514 + ], + "type": "text", + "content": "[21] Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In CVPR, 2017. 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 515, + 547, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 515, + 547, + 581 + ], + "spans": [ + { + "bbox": [ + 308, + 515, + 547, + 581 + ], + "type": "text", + "content": "[22] Yash Kant, Dhruv Batra, Peter Anderson, Alexander Schwing, Devi Parikh, Jiasen Lu, and Harsh Agrawal. Spatially aware multimodal transformers for textvqa. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part IX 16, pages 715-732. Springer, 2020. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 582, + 547, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 582, + 547, + 624 + ], + "spans": [ + { + "bbox": [ + 308, + 582, + 547, + 624 + ], + "type": "text", + "content": "[23] Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong. Pure transformers are powerful graph learners. arXiv preprint arXiv:2207.02505, 2022.1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 625, + 547, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 625, + 547, + 658 + ], + "spans": [ + { + "bbox": [ + 308, + 625, + 547, + 658 + ], + "type": "text", + "content": "[24] Thao Minh Le, Vuong Le, Svetha Venkatesh, and Truyen Tran. Hierarchical conditional relation networks for video question answering. CoRR, abs/2002.10698, 2020. 2, 5, 6, 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 658, + 547, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 658, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 658, + 547, + 713 + ], + "type": "text", + "content": "[25] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7331-7341, 2021. 2, 7" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14887" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "type": "text", + "content": "[26] Jie Lei, Licheng Yu, Mohit Bansal, and Tamara L Berg. Tvqa: Localized, compositional video question answering. In EMNLP, 2018. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 106, + 288, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 106, + 288, + 150 + ], + "spans": [ + { + "bbox": [ + 48, + 106, + 288, + 150 + ], + "type": "text", + "content": "[27] Linjie Li, Zhe Gan, Yu Cheng, and Jingjing Liu. Relation-aware graph attention network for visual question answering. In Proceedings of the IEEE/CVF international conference on computer vision, pages 10313-10322, 2019. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 150, + 288, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 150, + 288, + 194 + ], + "spans": [ + { + "bbox": [ + 48, + 150, + 288, + 194 + ], + "type": "text", + "content": "[28] Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang. Visualbert: A simple and perform-. mant baseline for vision and language. arXiv preprint arXiv:1908.03557, 2019. 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 194, + 288, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 194, + 288, + 259 + ], + "spans": [ + { + "bbox": [ + 48, + 194, + 288, + 259 + ], + "type": "text", + "content": "[29] Xiangpeng Li, Jingkuan Song, Lianli Gao, Xianglong Liu, Wenbing Huang, Xiangnan He, and Chuang Gan. Beyond rms: Positional self-attention with co-attention for video question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 8658–8665, 2019. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 259, + 288, + 305 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 259, + 288, + 305 + ], + "spans": [ + { + "bbox": [ + 48, + 259, + 288, + 305 + ], + "type": "text", + "content": "[30] Xiangpeng Li, Jingkuan Song, Lianli Gao, Xianglong Liu, Wenbing Huang, Xiangnan He, and Chuang Gan. Beyond rnns: Positional self-attention with co-attention for video question answering. In AAAI, 2019. 5, 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 305, + 288, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 305, + 288, + 360 + ], + "spans": [ + { + "bbox": [ + 48, + 305, + 288, + 360 + ], + "type": "text", + "content": "[31] Fei Liu, Jing Liu, Weining Wang, and Hanqing Lu. Hair: Hierarchical visual-semantic relational reasoning for video question answering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1698–1707, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 360, + 288, + 393 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 360, + 288, + 393 + ], + "spans": [ + { + "bbox": [ + 48, + 360, + 288, + 393 + ], + "type": "text", + "content": "[32] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. arXiv preprint arXiv:2106.13230, 2021. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 393, + 288, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 393, + 288, + 437 + ], + "spans": [ + { + "bbox": [ + 48, + 393, + 288, + 437 + ], + "type": "text", + "content": "[33] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. arXiv preprint arXiv:1908.02265, 2019. 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 437, + 288, + 503 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 437, + 288, + 503 + ], + "spans": [ + { + "bbox": [ + 48, + 437, + 288, + 503 + ], + "type": "text", + "content": "[34] Tegan Maharaj, Nicolas Ballas, Anna Rohrbach, Aaron Courville, and Christopher Pal. A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6884–6893, 2017. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 503, + 288, + 559 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 503, + 288, + 559 + ], + "spans": [ + { + "bbox": [ + 48, + 503, + 288, + 559 + ], + "type": "text", + "content": "[35] Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B Tenenbaum, and Jiajun Wu. The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision. In International Conference on Learning Representations, 2018. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 559, + 288, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 559, + 288, + 624 + ], + "spans": [ + { + "bbox": [ + 48, + 559, + 288, + 624 + ], + "type": "text", + "content": "[36] Kenneth Marino, Xinlei Chen, Devi Parikh, Abhinav Gupta, and Marcus Rohrbach. Krisp: Integrating implicit and symbolic knowledge for open-domain knowledge-based vqa. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14111-14121, 2021. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 624, + 288, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 288, + 669 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 288, + 669 + ], + "type": "text", + "content": "[37] Devshree Patel, Ratnam Parikh, and Yesha Shastri. Recent advances in video question answering: A review of datasets and methods. In International Conference on Pattern Recognition, pages 339-356. Springer, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 669, + 288, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 669, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 48, + 669, + 288, + 714 + ], + "type": "text", + "content": "[38] Liang Peng, Shuangji Yang, Yi Bin, and Guoqing Wang. Progressive graph attention network for video question answering. In Proceedings of the 29th ACM International Conference on Multimedia, pages 2871-2879, 2021. 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 72, + 547, + 712 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 307, + 72, + 547, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 72, + 547, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 547, + 116 + ], + "type": "text", + "content": "[39] Tianwen Qian, Jingjing Chen, Shaoxiang Chen, Bo Wu, and Yu-Gang Jiang. Scene graph refinement network for visual question answering. IEEE Transactions on Multimedia, 2022. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 118, + 547, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 547, + 162 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 547, + 162 + ], + "type": "text", + "content": "[40] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 162, + 547, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 162, + 547, + 250 + ], + "spans": [ + { + "bbox": [ + 307, + 162, + 547, + 250 + ], + "type": "text", + "content": "[41] Ahjeong Seo, Gi-Cheon Kang, Joonhan Park, and Byoung-Tak Zhang. Attend what you need: Motion-appearance synergistic networks for video question answering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6167–6177, Online, Aug. 2021. Association for Computational Linguistics. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 250, + 547, + 303 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 250, + 547, + 303 + ], + "spans": [ + { + "bbox": [ + 307, + 250, + 547, + 303 + ], + "type": "text", + "content": "[42] Paul Hongsuck Seo, Arsha Nagrani, and Cordelia Schmid. Look before you speak: Visually contextualized utterances. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16877-16887, 2021. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 304, + 547, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 304, + 547, + 360 + ], + "spans": [ + { + "bbox": [ + 307, + 304, + 547, + 360 + ], + "type": "text", + "content": "[43] Gunnar A Sigurdsson, Gül Varol, Xiaolong Wang, Ali Farhadi, Ivan Laptev, and Abhinav Gupta. Hollywood in homes: Crowdsourcing data collection for activity understanding. In European Conference on Computer Vision, pages 510-526. Springer, 2016. 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 360, + 547, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 360, + 547, + 404 + ], + "spans": [ + { + "bbox": [ + 307, + 360, + 547, + 404 + ], + "type": "text", + "content": "[44] Gunnar A. Sigurdsson, Gúl Varol, X. Wang, Ali Farhadi, Ivan Laptev, and Abhinav Kumar Gupta. Hollywood in homes: Crowdsourcing data collection for activity understanding. ECCV, 2016. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 404, + 547, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 404, + 547, + 459 + ], + "spans": [ + { + "bbox": [ + 307, + 404, + 547, + 459 + ], + "type": "text", + "content": "[45] Ajeet Kumar Singh, Anand Mishra, Shashank Shekhar, and Anirban Chakraborty. From strings to things: Knowledge-enabled vqa model that can read and reason. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4602-4612, 2019. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 459, + 547, + 515 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 459, + 547, + 515 + ], + "spans": [ + { + "bbox": [ + 307, + 459, + 547, + 515 + ], + "type": "text", + "content": "[46] Makarand Tapaswi, Yukun Zhu, Rainer Stiefelhagen, Antonio Torralba, Raquel Urtasun, and Sanja Fidler. Movieqa: Understanding stories in movies through question-answering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4631–4640, 2016. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 515, + 547, + 559 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 515, + 547, + 559 + ], + "spans": [ + { + "bbox": [ + 307, + 515, + 547, + 559 + ], + "type": "text", + "content": "[47] Bo Wu, Shoubin Yu, Zhenfang Chen, Joshua B Tenenbaum, and Chuang Gan. Star: A benchmark for situated reasoning in real-world videos. In Thirty-fifth Conference on Neural Information Processing Systems, 2021. 1, 2, 3, 6, 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 559, + 547, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 559, + 547, + 613 + ], + "spans": [ + { + "bbox": [ + 307, + 559, + 547, + 613 + ], + "type": "text", + "content": "[48] Junbin Xiao, Xindi Shang, Angela Yao, and Tat-Seng Chua. Next-qa: Next phase of question-answering to explaining temporal actions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9777–9786, 2021. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 613, + 547, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 613, + 547, + 658 + ], + "spans": [ + { + "bbox": [ + 307, + 613, + 547, + 658 + ], + "type": "text", + "content": "[49] Junbin Xiao, Angela Yao, Zhiyuan Liu, Yicong Li, Wei Ji, and Tat-Seng Chua. Video as conditional graph hierarchy for multi-granular question answering. arXiv preprint arXiv:2112.06197, 2021. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 658, + 547, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 658, + 547, + 712 + ], + "spans": [ + { + "bbox": [ + 307, + 658, + 547, + 712 + ], + "type": "text", + "content": "[50] Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017. 7" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "14888" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 288, + 127 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 288, + 127 + ], + "type": "text", + "content": "[51] Dejing Xu, Zhou Zhao, Jun Xiao, Fei Wu, Hanwang Zhang, Xiangnan He, and Yueting Zhuang. Video question answering via gradually refined attention over appearance and motion. In Proceedings of the 25th ACM international conference on Multimedia, pages 1645–1653, 2017. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 129, + 288, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 129, + 288, + 183 + ], + "spans": [ + { + "bbox": [ + 48, + 129, + 288, + 183 + ], + "type": "text", + "content": "[52] Antoine Yang, Antoine Miech, Josef Sivic, Ivan Laptev, and Cordelia Schmid. Just ask: Learning to answer questions from millions of narrated videos. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1686-1697, 2021. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 185, + 288, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 185, + 288, + 228 + ], + "spans": [ + { + "bbox": [ + 48, + 185, + 288, + 228 + ], + "type": "text", + "content": "[53] Kexin Yi, Chuang Gan, Yunzhu Li, Pushmeet Kohli, Jiajun Wu, Antonio Torralba, and Joshua B Tenenbaum. Clevrer: Collision events for video representation and reasoning. ICLR, 2020. 2, 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 231, + 288, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 231, + 288, + 285 + ], + "spans": [ + { + "bbox": [ + 48, + 231, + 288, + 285 + ], + "type": "text", + "content": "[54] Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Joshua B Tenenbaum. Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding. In Advances in Neural Information Processing Systems (NIPS), 2018. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 286, + 288, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 286, + 288, + 330 + ], + "spans": [ + { + "bbox": [ + 48, + 286, + 288, + 330 + ], + "type": "text", + "content": "[55] Fei Yu, Jiji Tang, Weichong Yin, Yu Sun, Hao Tian, Hua Wu, and Haifeng Wang. Ernie-vil: Knowledge enhanced vision-language representations through scene graph. arXiv preprint arXiv:2006.16934, 2020. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 332, + 288, + 385 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 332, + 288, + 385 + ], + "spans": [ + { + "bbox": [ + 48, + 332, + 288, + 385 + ], + "type": "text", + "content": "[56] Weijiang Yu, Haoteng Zheng, Mengfei Li, Lei Ji, Lijun Wu, Nong Xiao, and Nan Duan. Learning from inside: Self-driven siamese sampling and reasoning for video question answering. Advances in Neural Information Processing Systems, 34, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 388, + 288, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 388, + 288, + 442 + ], + "spans": [ + { + "bbox": [ + 48, + 388, + 288, + 442 + ], + "type": "text", + "content": "[57] Zhou Yu, Dejing Xu, Jun Yu, Ting Yu, Zhou Zhao, Yueting Zhuang, and Dacheng Tao. Activitynet-qa: A dataset for understanding complex web videos via question answering. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 9127–9134, 2019. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 445, + 288, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 445, + 288, + 498 + ], + "spans": [ + { + "bbox": [ + 48, + 445, + 288, + 498 + ], + "type": "text", + "content": "[58] Amir Zadeh, Michael Chan, Paul Pu Liang, Edmund Tong, and Louis-Philippe Morency. Social-iq: A question answering benchmark for artificial social intelligence. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8807–8817, 2019. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 500, + 288, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 500, + 288, + 555 + ], + "spans": [ + { + "bbox": [ + 48, + 500, + 288, + 555 + ], + "type": "text", + "content": "[59] Rowan Zellers, Jiasen Lu, Ximing Lu, Youngjae Yu, Yanpeng Zhao, Mohammadreza Salehi, Aditya Kusupati, Jack Hessel, Ali Farhadi, and Yejin Choi. Merlot reserve: Neural script knowledge through vision and language and sound. arXiv preprint arXiv:2201.02639, 2022. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 556, + 288, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 556, + 288, + 600 + ], + "spans": [ + { + "bbox": [ + 48, + 556, + 288, + 600 + ], + "type": "text", + "content": "[60] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. Advances in Neural Information Processing Systems, 34, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 601, + 288, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 601, + 288, + 644 + ], + "spans": [ + { + "bbox": [ + 48, + 601, + 288, + 644 + ], + "type": "text", + "content": "[61] Kuo-Hao Zeng, Tseng-Hung Chen, Ching-Yao Chuang, Yuan-Hong Liao, Juan Carlos Niebles, and Min Sun. Leveraging video descriptions to learn video question answering. CoRR, abs/1611.04021, 2016. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 647, + 288, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 288, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 288, + 678 + ], + "type": "text", + "content": "[62] Yaoyao Zhong, Wei Ji, Junbin Xiao, Yicong Li, Weihong Deng, and Tat-Seng Chua. Video question answering: Datasets, algorithms and challenges, 2022. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 681, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 681, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 681, + 288, + 713 + ], + "type": "text", + "content": "[63] Guangming Zhu, Liang Zhang, Youliang Jiang, Yixuan Dang, Haoran Hou, Peiyi Shen, Mingtao Feng, Xia Zhao, Qiguang Miao, Syed Afaq Ali Shah, and Mohammed Bennamoun." + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 140 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "text", + "content": "Scene graph generation: A comprehensive survey. CoRR, abs/2201.00443, 2022. 2, 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 96, + 547, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 96, + 547, + 140 + ], + "spans": [ + { + "bbox": [ + 307, + 96, + 547, + 140 + ], + "type": "text", + "content": "[64] Linchao Zhu, Zhongwen Xu, Yi Yang, and Alexander G Hauptmann. Uncovering the temporal context for video question answering. International Journal of Computer Vision, 124(3):409-421, 2017. 2" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "14889" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/7f46a567-7a9f-43dd-9afb-c2991cdfbe26_content_list.json b/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/7f46a567-7a9f-43dd-9afb-c2991cdfbe26_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b9de45e5c98073f9371c98f07c8e7390aeb9fda1 --- /dev/null +++ b/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/7f46a567-7a9f-43dd-9afb-c2991cdfbe26_content_list.json @@ -0,0 +1,1613 @@ +[ + { + "type": "text", + "text": "Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution", + "text_level": 1, + "bbox": [ + 91, + 128, + 879, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yunfan Lu $^{1}$ * Zipeng Wang $^{1}$ * Minjie Liu $^{1}$ † Hongjian Wang $^{2}$ † Lin Wang $^{1,3‡}$ $^{1}$ AI Thrust, HKUST(GZ) $^{2}$ Shenzhen International Graduate School, Tsinghua University \n $^{3}$ Dept. of Computer Science and Engineering, HKUST", + "bbox": [ + 143, + 202, + 867, + 256 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{ylu066, zwang253, m1iu942}@connect.hkust-gz.edu.cn, whj20@mails.tsinghua.edu.cn, linwang@ust.hk", + "bbox": [ + 86, + 258, + 916, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 308, + 313, + 325 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video superresolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the Spatial-Temporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https://vlis2022.github.io/cvpr23/egvsr.", + "bbox": [ + 75, + 340, + 473, + 763 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 768, + 209, + 786 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Video super-resolution (VSR) is a task of recovering high-resolution (HR) frames from successive multiple low-resolution (LR) frames. Unlike LR videos, HR videos contain more visual information, e.g., edge and texture, which", + "bbox": [ + 75, + 794, + 468, + 854 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/1bdcd320c38cce6b2c2752c8cda7ff21014fc319f2245e931868b14c45d970cc.jpg", + "image_caption": [ + "Figure 1. (a) Our method learns implicit neural representations (INR) from the queried spatial-temporal coordinates (STF) and temporal features (TF) from RGB frames and events. (b) An example of VSR with random scale factors, $e.g., 6.5$ , by our method." + ], + "image_footnote": [], + "bbox": [ + 504, + 309, + 890, + 503 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "can be very helpful for many tasks, e.g., metaverse [52], surveillance [15] and entertainment [4]. However, VSR is a highly ill-posed problem owing to the loss of both spatial and temporal information, especially in the real-world scenes [5, 6, 26, 45]. Recently, deep learning-based algorithms have been successfully applied to learn the intra-frame correlation and temporal consistency from the LR frames to recover HR frames, e.g., DUF [19], EDVR [48], RBPN [14], BasicVSR [5], BasicVSR++ [6]. However, due to the lack of inter-frame information, these methods are hampered by the limitations of modeling the spatial and temporal dependencies and may fail to recover HR frames in complex scenes.", + "bbox": [ + 496, + 579, + 892, + 775 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Event cameras are bio-inspired sensors that can asynchronously detect the per-pixel intensity changes and generate event streams with low latency (1us) and high dynamic range (HDR) compared with the conventional frame-based cameras (140dB vs. 60dB) [35, 51]. This has sparked extensive research in reconstructing image/video from events [12, 29, 42, 44, 46, 53]. However, the reconstructed results are less plausible due to the loss of visual", + "bbox": [ + 496, + 780, + 893, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*These authors are co-first authors.", + "bbox": [ + 93, + 862, + 282, + 875 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "These authors are co-second authors.", + "bbox": [ + 96, + 875, + 297, + 886 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "$\\ddagger$ Corresponding author", + "bbox": [ + 96, + 887, + 220, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "1557", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "details, e.g., structures, and textures. As a result, a recent work has utilized events for guiding VSR [18], trying to 'inject' energy from the event-based to the frame-based cameras. It leverages the high-frequency event data to synthesize neighboring frames, so as to find correspondences between consecutive frames. However, it only treats video frames in discrete ways with 2D arrays of pixels and up-samples them at a fixed up-scaling factor e.g., $\\times 2$ or $\\times 4$ . This causes inconvenience and inflexibility in the applications of SR videos, which often require arbitrary resolutions, i.e., random scales.", + "bbox": [ + 75, + 90, + 472, + 257 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recently, some works tried to learn continuous image representations with arbitrary resolutions, e.g., LIIF [7], taking 2D queried coordinates and 2D features as input to learn an implicit neural representation (INR). VideoINR [8], on the other hand, decodes the LR videos into arbitrary spatial resolutions and frame rates by learning from the spatial coordinates and temporal coordinates, respectively. However, it is still unclear how to leverage events to guide learning spatial-temporal INRs for VSR. This is hampered by two challenges. Firstly, although event data can benefit VSR with its high-frequency temporal and spatial information, the large modality gap between the events and video frames makes it challenging to use INRs to represent the 3D spatial-temporal coordinates with event data. Moreover, there lacks HR real-world dataset with spatially well-aligned events and frames.", + "bbox": [ + 75, + 261, + 472, + 503 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantage of the high-temporal resolution property of events. Accordingly, we propose a novel framework that subtly incorporates the spatial-temporal interpolation from events to VSR in a unified framework, as shown in Fig. 1. Our key idea is to learn INRs from the queried spatial-temporal coordinates and features from both the RGB frames and events. Our framework mainly includes three parts. The Spatial-Temporal Fusion (STF) branch learns the spatial-temporal information from events and RGB frames (Sec. 3.2). The shallow feature fusion and deep feature fusion are employed to narrow the modality gap and fuse the events and RGB frames into 3D global spatial-temporal representations. Then, the Temporal Filter (TF) branch further unlocks more explicit motion information from events. It learns the 2D event features from events nearing the queried timestamp (Sec. 3.3). With the features from the STF and TF branches, the Spatial-Temporal Implicit Representation (STIR) module decodes the features and recovers SR frames with arbitrary spatial resolutions (Sec. 3.4). That is, given the arbitrary queried coordinates, we apply 3D sampling and 2D sampling to the fused 3D features and event data separately. Finally, the sampling features are added and fed into a decoder, and generate targeted SR frames. In addition, we collect a real-world dataset with a spatial reso", + "bbox": [ + 75, + 508, + 472, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "lution of $3264 \\times 2248$ , in which the events and RGB frames are spatially aligned. Extensive experiments on two real-world datasets show that our method surpasses the existing methods by 1.3 dB.", + "bbox": [ + 496, + 90, + 890, + 151 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In summary, the main contributions of this paper are five-fold: (I) Our work serves as the first attempt to address a non-trivial problem of learning INRs from events and RGB frames for VSR at random scales. (II) We propose the STF branch and the TF branch to model the spatial and temporal dependencies from events and RGB frames. (III) We propose the STIR module to reconstruct RGB frames with arbitrary spatial resolutions. (IV) We collect a high-quality real-world dataset with spatially aligned events and RGB frames. (V) Our method significantly surpasses the existing methods and achieves SR with random-scales, e.g., 6.5.", + "bbox": [ + 496, + 152, + 893, + 319 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 330, + 640, + 347 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Event-guided Video and Image SR Recently, event data has shown the potential to guide the image or video SR. eSL-Net [43] and EvIntSR [13] focus on employing events to guide the single image SR. Specifically, eSL-Net [43] feeds both the events and LR image to a sparse learning framework to recover an SR image. EvIntSR [13] first reconstructs the latent frames from the events and LR image, which are then fused to reconstruct the SR image. Differently, event-guided VSR takes consecutive frames and events as inputs and models both the spatial and temporal information. A recent work [18] proposed a two-stage method by 1) utilizing events to interpolate the LR video to get a high-frequency video and 2) rebuilding HR key frames. However, it encodes video frames in discrete ways and only up-samples videos at a fixed upscale factor, e.g., $\\times 2$ . We make the first attempt to achieve VSR at random scales by learning the spatial-temporal implicit representations from events and video frames.", + "bbox": [ + 496, + 356, + 890, + 628 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Video Super-Resolution (VSR) The dominant research for VSR mainly focuses on designing learning pipelines [24], concerning the feature learning, frame alignment and multiframe fusion [3, 16, 22, 23, 30, 40, 47, 49, 50]. For example, Bao et.al [2] employed the motion compensation to achieve the frame alignment, while EDVR [48] proposes the deformable convolutions after extracting the features from the input frames. To address the feature propagation and alignment problem effectively, BasicVSR [5] and BasicVSR++ [6] proposed a succinct pipeline based on the bidirectional propagation and optical flow. To better exploit the temporal information of video frames, RBPN [14] extracts and propagates the spatial and temporal information of consecutive frames in a recurrent back-projection manner. Inspired by the random image upsampling, VideoINR [9] represents the frames with implicit representations and thus makes it possible to learn random-scale VSR. However, these works focus on a single modality. Differently, in our work, we", + "bbox": [ + 496, + 628, + 893, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "1558", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/921c0601a1d7398c258274f3bc5ed699088a2da7e2465a531632983ea9f73024.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 86, + 95, + 885, + 280 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/e5c0a2145cfacea2326b51ae590f21abfb41243f43870ce72b3e1c40d37fc886.jpg", + "image_caption": [ + "Figure 2. Overview of our proposed framework. Our method consists of three parts, the Spatial-Temporal Fusion (STF), the Temporal Filter (TF) and Spatial-Temporal Implicit Representation (STIR). These three parts are shown in (a) (b) (c) of this figure, respectively. Details of STF, TF and STIR are described in this Sec.3.2, Sec.3.3 and Sec.3.4, respectively." + ], + "image_footnote": [], + "bbox": [ + 86, + 282, + 885, + 373 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "make the first attempt to address a novel problem of achieving VSR at random scales by taking advantage of the high-temporal resolution property of events.", + "bbox": [ + 75, + 439, + 468, + 484 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Implicit Neural Representation (INR) It also called the coordinate-based representation, aims at parameterizing signals, e.g., images and audio, in a continuous way via neural networks [36]. INR has been widely applied to 3D scene representation [28,33] and generative models [17,32], etc. Recently, INRs have been extensively studied for image and video SR. For instance, LIIF [7] achieves image SR with random scales given the 2D queried coordinates and 2D features. JIIF [39] uses HR images to guide the interpolation weights and values of LR depth maps. VideoINR [8] decodes the LR and low-frame-rate videos into an arbitrary spatial resolution and frame rate with three INRs. It adopts two INR functions to learn the spatial coordinates and the temporal coordinates, respectively. These two INR functions are then used to generate a motion flow field, which is applied back to warp the encoded features. Then, the warped features are decoded to recover the SR frame by the spatial INR function. Differently, considering the large modality gap between the events and video frames, we accordingly propose a novel INR module to directly represent the 3D spatial-temporal coordinates from event data.", + "bbox": [ + 75, + 484, + 470, + 803 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. The Proposed Framework", + "text_level": 1, + "bbox": [ + 76, + 813, + 320, + 830 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Event Representation As event streams are sparse points, we first describe how to stack them into the fixed-size representations as inputs of our framework. Events are produced by detecting variations in the log intensity of each pixel. An", + "bbox": [ + 75, + 839, + 468, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "event $e = (x,y,t,p)$ is triggered and recorded when the logarithmic brightness change exceeds a certain threshold $\\theta$ at pixel $(x,y)$ . This process can be described as Eq.1, where $\\Delta L = \\log (I_t^l + n) - \\log (I_{t - \\Delta t}^l + n)$ , $n$ is noise, $I_t^l$ and $I_{t - \\Delta t}^l$ are intensity values in linear domain at timestamps $t$ and $t - \\Delta t$ , respectively.", + "bbox": [ + 498, + 439, + 892, + 532 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\np = \\left\\{ \\begin{array}{l} + 1, \\Delta L > \\theta \\\\ - 1, \\Delta L < - \\theta \\end{array} \\right. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 622, + 541, + 890, + 583 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "According to Eq.1, the relation between frames $I_{t_0}^l (x,y)$ and $I_{t_1}^l (x,y)$ at timestamps $t_0$ and $t_1$ can be formulated as Eq.2, where $p$ is the polarity of event at pixel $(x,y)$ .", + "bbox": [ + 498, + 589, + 890, + 636 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nI _ {t _ {1}} ^ {l} (x, y) = I _ {t _ {0}} ^ {l} (x, y) \\times \\exp \\left(\\theta \\int_ {t _ {0}} ^ {t _ {1}} p d t\\right) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 560, + 646, + 890, + 681 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Events record the intensity changes with higher temporal resolution than frames, which is advantageous for VSR task [18]. We split events into $M$ moment segments [42] with a shape of $H \\times W \\times M \\times 2$ as the input to our framework. Each segment keeps events that take place within time window. The window size is set to be a small constant to preserve the temporal information of events.", + "bbox": [ + 496, + 686, + 890, + 792 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Overview", + "text_level": 1, + "bbox": [ + 500, + 801, + 609, + 815 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The overall framework of our method is depicted in Fig.2, which consists of three parts: (I) spatial-temporal fusion (STF) branch, (II) temporal filter (TF) branch, and (III) spatial-temporal implicit representation (STIR) module. The input of our framework are spatially aligned", + "bbox": [ + 496, + 824, + 892, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "1559", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "RGB video frames $V = \\{V_0\\ldots V_i\\ldots V_n\\}$ and events $E \\in R^{H\\times W\\times M\\times 2}$ , where $H\\times W$ denotes the frame size. The output of our framework is a super-resolved video frame $I_{s,t}^{SR}$ with the up-sampling scale $s$ and the timestamp $t$ . Note that the values of $s$ and $t$ can be freely adjusted. In practice, $s$ is a real number greater than 1, and $t$ can take the timestamps of all frames. The spatial resolution of the SR frame $I_{s,t}^{SR}$ is $sW\\times sH$ .", + "bbox": [ + 76, + 90, + 468, + 212 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our framework consists of three major components. The STF branch learns the holistic spatial-temporal information from events and RGB frames (Sec.3.2). Then, the TF branch unlocks more explicit motion information from events. It learns 2D event features from events nearing the queried timestamp $t$ (Sec.3.3). Lastly, the STIR module decodes the features and recovers SR frames with arbitrary spatial resolutions (Sec.3.4). We now describe these components in detail in the following sections.", + "bbox": [ + 75, + 213, + 470, + 349 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Spatial-Temporal Fusion (STF) Branch", + "text_level": 1, + "bbox": [ + 76, + 361, + 413, + 378 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This module aims to extract spatial and temporal information from events $E$ and RGB frames $V$ to obtain a global spatial-temporal representation $F_{ST}$ . The representation is a 3D feature map of $H\\times W\\times T\\times C$ , where $T$ is the temporal dimension, and $C$ is the number of representation channels. The output $F_{ST}$ of STF branch $f_{STF}$ can be described as:", + "bbox": [ + 75, + 385, + 468, + 476 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nF _ {S T} = f _ {S T F} (V, E) \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 204, + 483, + 468, + 500 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Specifically, as depicted in Fig. 2(a), we first employ two $1 \\times 1$ convolutional layers to obtain the initial frame feature map $F_0^f$ and the event feature map $F_0^e$ with same dimension. As stated in [13], shallow features preserve sharper details and local structural information while deeper features preserve more semantic information. Thus, we design fusion blocks to aggregate both the shallow and deep features.", + "bbox": [ + 75, + 503, + 468, + 609 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Shallow feature fusion: As explored in [27], the residual architecture improves the model's capacity for representation and lessens the gradient vanishing issue. For this reason, we first employ two high preserving blocks (HPB) as the basic feature extractors to extract two shallow feature maps $F_{l}^{f}$ and $F_{l}^{e}$ from the initial feature maps $F_{0}^{f}$ and $F_{0}^{e}$ , respectively. After that, $F_{l}^{f}$ and $F_{l}^{e}$ are concatenated and then fed into a transformer-based fusion model to obtain a fused feature map so as to bridge the modality gap. The intuition behind this is that transformer has a larger perception field [1, 25, 25, 31, 41], which can be potentially used to model the global spatial and temporal dependencies from the frames and events. Then, the fused feature map is split into two parts in the channel dimension and added to $F_{l}^{f}$ and $F_{l}^{e}$ as the input for deep feature fusion.", + "bbox": [ + 75, + 609, + 468, + 838 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Deep feature fusion: After the shallow feature fusion, we again use two HPBs to extract deep features, which are then added and passed to the transformer-based fusion model to attain a 3D feature map $F_{ST}$ . Through shallow and deep", + "bbox": [ + 75, + 839, + 468, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "feature fusion, we can better learn the temporal and spatial information from the events and frames.", + "bbox": [ + 498, + 90, + 890, + 121 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Temporal Filter (TF) Branch", + "text_level": 1, + "bbox": [ + 500, + 131, + 759, + 148 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Through the STF branch, the spatial-temporal feature information from both the frames and events can be effectively learned. However, STF branch is insufficient to take full advantage of the high temporal resolution of event data. Therefore, we design the TF branch to explore more detailed motion information solely from events, which turns out to be effective in further enhancing the VSR performance, as demonstrated in our experiments (See Table 5).", + "bbox": [ + 496, + 155, + 890, + 276 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Intuitively, we design the TF branch $f_{TF}$ to capture the detailed motion information from events $E_{t,\\Delta t}$ near the key frame at timestamp $t$ , where $\\Delta t$ is a small time interval. TF branch first selects events from $t - \\Delta t$ to $t + \\Delta t$ (Fig. 2(c)). The selected events are interpolated and sent to three convolutional layers to learn the temporal features. Overall, the output $F_{T}$ of TF branch $f_{TF}$ can be described as:", + "bbox": [ + 496, + 277, + 890, + 382 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nF _ {T} = f _ {T F} \\left(E _ {t, \\Delta t}\\right) \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 630, + 385, + 890, + 402 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In Sec.4.4, we show that STF branch can capture pixel intensities, while the TF branch can capture the motion details, e.g., edges and corners.", + "bbox": [ + 496, + 405, + 890, + 450 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Spatial-Temporal Implicit Representation", + "text_level": 1, + "bbox": [ + 498, + 460, + 856, + 478 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this section, our goal is to learn continuous INRs for VSR based on the spatial-temporal feature map $F_{ST}$ and temporal feature map $F_{T}$ . The INRs are then used to decode coordinates at time $t$ with scale factor $s$ into RGB values. In this paper, we introduce the Spatial-Temporal Implicit Representation (STIR) module to accomplish the spatial-temporal VSR, as shown in Eq.5. We employ a simple-yet-effective 3D feature sampling and trilinear interpolation scheme to upsample $F_{ST}$ and $F_{T}$ to a desired resolution. A decoder, parameterized as a multi-layer CNN, is used to convert the interpolated features into RGB values. Fig. 2(c) depicts the detailed design of STIR. The output SR frame $I_{t,s}^{SR}$ of the STIR $f_{STIR}$ can be formulated as:", + "bbox": [ + 496, + 484, + 890, + 681 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nI _ {t, s} ^ {S R} = f _ {S T I R} \\left(F _ {S T}, F _ {T}\\right), \\forall s, t \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 589, + 681, + 890, + 700 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3D Feature Sampling: Here, we aim at generating a coordinate to make a query in grid form $F_{ST}$ . We uniformly sample a 3D coordinate grid, which can be expressed as $C_{t,s}$ with the dimension of $sH \\times sW \\times 3$ . Formally, for any query $q$ , the corresponding element $p_q$ in the 3D coordinate grid $C_{t,s}$ can be described as $p_q = (x_q, y_q, t_q)$ , where $x_q \\in [0, H], y_q \\in [0, W], t_q \\in [t_s, t_e]$ , $t_s$ is the start time and $t_e$ is the end time of input. For each coordinate $p_q = (x_q, y_q, t_q)$ , we choose features of the nearest eight points around this coordinate in the 3D spatial-temporal feature $F_{ST}$ for interpolation.", + "bbox": [ + 496, + 704, + 890, + 869 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Feature Interpolation: Then, we compute the feature of a queried coordinate $p_{q}$ by using 3D interpolation techniques,", + "bbox": [ + 498, + 869, + 890, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "1560", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "such as trilinear interpolation. Inspired by the representation theory [37], complex signals in the low-dimensional space e.g., images, can be transformed as linear representations in the high-dimensional space e.g., features. From this theory, we can observe that the spatial-temporal feature $F_{ST}$ is indeed the high-dimensional feature representation for the low-dimensional image. We use linear interpolation to obtain features at the queried coordinate $p_q$ . In the experiments of Table 6, we have compared several interpolation methods, e.g., nearest sampling, and the results show that linear interpolation shows the best performance.", + "bbox": [ + 75, + 90, + 472, + 257 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In summary, for any scale $s$ and timestamp $t$ , the feature interpolation (i.e., 3D to 2D) process can be formulated by Eq. 6, given the 3D coordinate grid $C_{t,s}$ and spatial-temporal feature $F_{ST}$ .", + "bbox": [ + 75, + 258, + 470, + 318 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nF _ {S F} = f _ {\\text {s a m p l e}} \\left(F _ {S T}, C _ {t, s}\\right) \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 179, + 321, + 468, + 339 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Implicit Representation Decoding: Finally, the sampled 2D feature map $F_{SF}$ and the temporal feature map $F_{T}$ are added together and fed into the decoder. For simplicity and efficiency, we design three-layers CNN structure as the decoder. This is supported by the empirical experiment in Table 6, showing that a sample CNN block can achieve good results with low complexity.", + "bbox": [ + 75, + 342, + 472, + 449 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.5. Loss Function", + "text_level": 1, + "bbox": [ + 76, + 460, + 223, + 474 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We employ the Charbonnier loss [21] as our VSR supervision loss $\\mathcal{L}_{SR}$ between the ground truth (GT) HR frame $I_{t,s}^{HR}$ and the output SR frame $I_{t,s}^{SR}$ at timestamp $t$ with upsampling scale $s$ , as shown in Eq. 7, where $\\epsilon$ is $1e - 3$ .", + "bbox": [ + 76, + 484, + 468, + 545 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In training, the value range of $t$ is all timestamps of key frames $\\{t_0, t_1 \\dots t_n\\}$ . $s$ could be a real number in the range [1.0, $s_{max}$ ], where $s_{max}$ is the maximum up-sampling scale during training depending on the resolution of training data. The loss function $\\mathcal{L}$ is shown in Eq. 8. For example, when the resolution of input LR frame is set to be $128 \\times 128$ and the GT HR frame is $1024 \\times 1024$ , the $s_{max}$ is 8.", + "bbox": [ + 75, + 546, + 470, + 652 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {S R} = \\sqrt {\\left(I _ {t , s} ^ {S R} - I _ {t , s} ^ {H R}\\right) ^ {2} + \\epsilon^ {2}} \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 169, + 654, + 468, + 679 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\sum_ {t \\in t _ {0}, t _ {1} \\dots t _ {n}} \\left(\\sum_ {s \\in [ 1. 0, s _ {\\max } ]} \\mathcal {L} _ {S R} \\left(I _ {t, s} ^ {S R}, I _ {t, s} ^ {H R}\\right)\\right) \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 104, + 681, + 468, + 729 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.6. Real-world Dataset Collection", + "text_level": 1, + "bbox": [ + 76, + 739, + 344, + 753 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Existing datasets, e.g., CED [35], suffer from the limited resolution $(346 \\times 260)$ and severe noise, as shown in Fig.7. Although CED dataset provides the active pixel sensor (APS) frames, they are in low quality because they are simply demosaiced by OpenCV [34] from RAW data. Therefore, collecting HR and high-quality datasets with spatially aligned frames and events is important to inspire more research for the event-guided VSR problem. In this paper, we collected a new real-world dataset, called ALPIX-", + "bbox": [ + 75, + 763, + 472, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "VSR, using a ALPIX-Eiger event camera1. The camera outputs well aligned RGB frames and events. The RGB frames enjoy a resolution of $3264 \\times 2448$ and are generated by a carefully designed image signal processor(ISP) from RAW data with the Quad Bayer pattern [10], and the events have a resolution with $1632 \\times 1224$ .", + "bbox": [ + 496, + 90, + 890, + 180 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our ALPIX-VSR dataset consists of 26 video sequences with 5388 frames and well-aligned events in total. These sequences include diverse scenes, e.g., streets, buildings, flowers, textures, and machines. To avoid motion blur and low-light noise, we collect the dataset in bright indoor and sunny outdoor scenes. For more details about our real-world dataset, please refer to the supplementary material.", + "bbox": [ + 496, + 181, + 892, + 287 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 498, + 300, + 633, + 316 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experiments Setting", + "text_level": 1, + "bbox": [ + 498, + 325, + 692, + 340 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Implementation Details and Datasets: For all experiments, we use the Adam optimizer [20] with a learning rate of $1e - 4$ for CED dataset and $5e - 5$ for our ALPIX-VSR dataset. We train our framework for 100 epochs with a batch size of 2 using two NVIDIA RTX A30 GPU cards.", + "bbox": [ + 496, + 348, + 890, + 422 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation Metrics: We statistically assess the effectiveness of our approach using the peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM).", + "bbox": [ + 496, + 422, + 890, + 468 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets: We use the CED [35] and the ALPIX-VSR dataset for experiments. 1) CED Dataset. It includes a collection of color events and video sequences in many scenes, e.g., indoor, outdoor, driving, human, calibration. The resolution of the frames and events is $346 \\times 260$ . We follow the setting of E-VSR [18] to preprocess this dataset. Note that the RGB frames provided by CED are obtained by demosaicing [34] from raw frames and suffer from severe noise. 2) ALPIX-VSR Dataset. We select 20 videos for training and 6 videos for testing. The training and testing sets include 4212 and 1176 frames with aligned events, respectively. Note that we apply data augmentation strategy, such as random crop, to ALPIX-VSR dataset for all compared methods to avoid memory overflow during training.", + "bbox": [ + 496, + 469, + 892, + 680 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Comparison with SoTA Methods", + "text_level": 1, + "bbox": [ + 498, + 689, + 790, + 705 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We compare our method with six SoTA methods under three VSR settings: (I) one SoTA event-guided, fixed-scale VSR method E-VSR [18] (II) one SoTA method of frame-based arbitrary-scale VSR method VideoINR [8] (III) five SoTA methods of frame-based, fixed-scale VSR methods: BasicVSR++ [6], RBPN [14], SOF [45], TDAN [40], DUF [19]. We report $\\times 2$ and $\\times 4$ super-resolution results of our method and all 6 comparison methods on CED dataset. We also compare our method with E-VSR and BasicVSR++ on our ALPIX-VSR dataset. Moreover, we compare our method with VideoINR on out-of-distribution", + "bbox": [ + 496, + 712, + 890, + 878 + ], + "page_idx": 4 + }, + { + "type": "page_footnote", + "text": "1https://www.alpsentek.com/product", + "bbox": [ + 517, + 886, + 715, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "1561", + "bbox": [ + 483, + 944, + 513, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/4ce67f57ab0d49282f967e472b658d79da0508147839a0e148a3b89378fb16e3.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Clip NameDUF [19]*TDAN [40]SOF [45]RBPN [14]VideoINR [8]*E-VSR [18]Ours
people_dynamic<wave>32.02 / 0.933335.83 / 0.954033.32 / 0.936040.07 / 0.986827.47 / 0.822941.08 / 0.989138.78 / 0.9794
indoors_foosball_230.55 / 0.926232.12 / 0.933930.86 / 0.925334.15 / 0.973926.03 / 0.776634.77 / 0.977538.68 / 0.9750
simple_wires_230.08 / 0.938731.57 / 0.946630.12 / 0.932633.83 / 0.973926.77 / 0.832134.44 / 0.977338.67 / 0.9815
people_dynamic_dancing31.64 / 0.936935.73 / 0.956632.93 / 0.938839.56 / 0.986927.36 / 0.820240.49 / 0.989139.06 / 0.9798
people_dynamic_jumping31.57 / 0.933435.42 / 0.953632.79 / 0.934739.44 / 0.985927.24 / 0.818340.32 / 0.988038.93 / 0.9792
simple_fruit_fast37.46 / 0.944237.75 / 0.944037.22 / 0.939040.33 / 0.978227.21 / 0.845640.80 / 0.980141.96 / 0.9821
outdoor_jumping_infrared_225.33 / 0.816228.91 / 0.906226.67 / 0.874630.36 / 0.964826.88 / 0.822630.70 / 0.969838.03 / 0.9755
simple_carpet_fast31.43 / 0.881132.54 / 0.900631.83 / 0.877434.91 / 0.950224.21 / 0.590935.16 / 0.953636.14 / 0.9635
people_dynamic_armroll31.38 / 0.931135.55 / 0.954132.79 / 0.934540.05 / 0.987827.26 / 0.819341.00 / 0.989838.84 / 0.9787
indoors_kitchen_229.92 / 0.927330.67 / 0.932329.61 / 0.919231.51 / 0.955126.44 / 0.750231.79 / 0.958637.68 / 0.9726
people_dynamic_sitting30.62 / 0.933135.09 / 0.956132.13 / 0.936739.03 / 0.986227.63 / 0.823039.97 / 0.988438.86 / 0.9810
average PSNR/SSIM31.09 / 0.918333.74 / 0.939831.84 / 0.922636.66 / 0.975426.77 / 0.793837.32 / 0.978338.69 / 0.9771
", + "bbox": [ + 81, + 75, + 890, + 255 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Quantitative results (PSNR/SSIM) of the proposed our framework and other methods on the CED dataset for $\\times 2$ . Because the official training code is not available, * denoted values were acquired from the pre-trained model that the authors have released.", + "bbox": [ + 76, + 257, + 892, + 286 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/b49eb1d1e8221412ef24c5df280981e7edf79124595238555ef3c631f04f32af.jpg", + "image_caption": [ + "Figure 3. Visual results of $\\times 4$ VSR on the CED dataset. Our method recovers more details, e.g., textual, edge than the SoTA event-guided VSR method E-VSR [18] and the random up-sampling VSR method Video INR [8]." + ], + "image_footnote": [], + "bbox": [ + 78, + 299, + 890, + 638 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "scales to demonstrate our method's ability for arbitrary-scale VSR. Note that E-VSR only supports $\\times 2$ and $\\times 4$ SR, and BasicVSR++ only supports $\\times 4$ SR.", + "bbox": [ + 75, + 695, + 468, + 741 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation on CED Dataset Table 1 and Table 2 present the quantitative results for $\\times 2$ and $\\times 4$ VSR, respectively. Our model clearly outperforms other methods in terms of PSNR, and shows a comparable performance in SSIM with E-VSR. The qualitative results in Fig. 3 demonstrate that our model is capable of recovering fine details, like sharp edges and detailed textures. We can see that event-guided methods (E-VSR and our method) yield better performances than the frame-based counterparts, showing the complementary effects of event data for VSR. Furthermore,", + "bbox": [ + 75, + 750, + 472, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "VideoINR performs noticeably worse than other methods, indicating that frame-based implicit neural representations struggle with the low-resolution and high-noise input. Contrarily, our approach benefits from additional event information and generates satisfactory INRs.", + "bbox": [ + 496, + 695, + 890, + 771 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2 shows quantitative results in $\\times 4$ SR on the CED dataset, where our model achieves SoTA performance while remaining lightweight and efficient. Specifically, our model only accounts for one-200th of the E-VSR. BasicVSR++, the SoTA method of frame-based VSR, fails to perform well on CED dataset as the upsampling scale increases, which demonstrates its less robustness on the highly noisy dataset.", + "bbox": [ + 496, + 775, + 892, + 881 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation on the ALPIX-VSR Dataset The quantitative", + "bbox": [ + 500, + 885, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "1562", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/98be5bcb3365d831b5d5d2d3fbde8299a1b3f7423c27936cc30c0815e2b78895.jpg", + "image_caption": [ + "Figure 4. Results of $\\times 4$ VSR on the ALPIX-VSR dataset." + ], + "image_footnote": [], + "bbox": [ + 91, + 87, + 877, + 266 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9cf36e686681e31c54d97a6595ef16c27e9c41e2755b2b6e3c470b7ed21388f3.jpg", + "image_caption": [ + "Figure 5. Results of $\\times 8$ VSR on the ALPIX-VSR dataset." + ], + "image_footnote": [], + "bbox": [ + 86, + 282, + 879, + 448 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/2c2ed9518d0e6853c3991798721c9c9a3999f6a26515b611dda868e46fb84ea7.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodsModel Size(M)PSNRSSIM
DUF [19]1.9024.430.8177
TDAN [40]1.9727.880.8231
SOF [45]1.0027.000.8050
RBPN [14]12.1829.800.8975
BasicVSR++ [6]7.3014.760.1641
VideoINR [8]*11.3125.530.7871
E-VSR [18]412.4230.150.9052
Ours2.4531.120.9211
", + "bbox": [ + 78, + 474, + 460, + 587 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/048904a958bdfeabdad5558f77e063c06e97b3355d1253ec8a38394bd06eb6ec.jpg", + "table_caption": [ + "Table 2. Quantitative results on CED dataset for $\\times 4$ . * denotes the values obtained from the official pre-trained models." + ], + "table_footnote": [], + "table_body": "
MethodsPSNRSSIM
×2E-VSR36.100.9761
Ours38.250.9822
×4E-VSR32.540.9163
BasicVSR++35.300.9353
Ours37.120.9503
×6VideoINR*31.150.9084
Ours31.850.9267
×8VideoINR*28.110.8625
Ours28.530.8901
", + "bbox": [ + 81, + 616, + 459, + 744 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "and qualitative results are shown in Table 3 and Fig. 5. We present comparison results of our method with BasicVSR++ and E-VSR on our collected real-world dataset.", + "bbox": [ + 76, + 805, + 468, + 849 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Random Scale Up-sampling", + "text_level": 1, + "bbox": [ + 76, + 853, + 328, + 869 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Results of random scale upsampling are shown in Table 3. We upsample the video frames to $\\times 2$ , $\\times 4$ , $\\times 6$", + "bbox": [ + 76, + 869, + 468, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/04ac9cdb9fb803f4edb05a94de8d54131d937dba31e67b846532cbf88c1dd429.jpg", + "table_caption": [ + "Table 3. Quantitative comparison (PSNR/SSIM) of our methods and other methods on the ALPIX-VSR dataset. * denotes the values obtained from the official pre-trained models." + ], + "table_footnote": [], + "table_body": "
×1.8×2.6×5.6
Ours39.2508 / 0.980337.3408 / 0.958931.2549 / 0.9135
×6.6×7.1×7.8
Ours28.3182 / 0.877228.3188 / 0.8776228.3198 / 0.8783
", + "bbox": [ + 500, + 476, + 895, + 534 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Quantitative results(PSNR/SSIM) of random-scale comparison on theALPIX-VSR dataset.", + "bbox": [ + 500, + 537, + 890, + 566 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": ", $\\times 8$ , and compare with the SoTA models, E-VSR and BasicVSR++. Though these two models are strictly constrained to a specific upsampling scale, our method presents superior performance on these settings.", + "bbox": [ + 498, + 575, + 890, + 637 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We also compare our method with VideoINR, the SoTA random-scale VSR method quantitatively in $\\times 6$ , $\\times 8$ . Results in Table 3 show that our model surpasses VideoINR in all evaluated scales of $\\times 6$ , $\\times 8$ . Furthermore, to evaluate the performance of our method on arbitrary scales, we conduct experiments of 6 random-chosen float scales. The results are shown in Table 4 and indicate our model's robustness across arbitrary random scales. It is easy to find that values of performance, e.g., PSNR, SSIM, and upsampling scales are not strictly monotonic.", + "bbox": [ + 496, + 638, + 892, + 789 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation Studies and Discussion", + "text_level": 1, + "bbox": [ + 500, + 803, + 782, + 816 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The following ablation experiments investigate the importance of each of our proposed modules. As it takes more than 120 hours to train a model on the complete CED dataset, we uniformly select $1/5$ of CED as the dataset for ablation experiments.", + "bbox": [ + 496, + 827, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "1563", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/c762fa683464dc23dacab932a1e2fc0648080d35fe71a993f6a38087372ba5b0.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
TF BranchShallow Feature FusionModel Size(M)PSNRSSIM
1ww2.451338.140.9820
2w/ow2.448237.960.9812
3ww/o1.736037.760.9729
", + "bbox": [ + 81, + 88, + 467, + 160 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/033e0889743c00645aee2e1d1c00257acbc76b3c70a9f0e5a3c1ca02660c4414.jpg", + "table_caption": [ + "Table 5. Ablation of the TF branch and shallow feature fusion." + ], + "table_footnote": [], + "table_body": "
InterpolationDecoderLF ChannelsPSNRSSIM
1LinearCNN1638.140.9820
2NearestCNN1628.910.9131
3LinearSIREN1610.300.2997
4LinearMLP1637.940.9811
5LinearCNN836.820.9763
6LinearCNN2438.250.9825
", + "bbox": [ + 81, + 183, + 467, + 277 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Efficiency of TF branch and Shallow Feature Fusion: Table 5 validates the contribution of TF branch and shallow feature fusion in STF branch. The removal of TF branch reduces both PSNR and SSIM scores, where PSNR drops by $0.18dB$ . In Fig. 6, we use PCA [11] to visualize the output of TF branch. As can be seen, $F_{T}$ focuses on edge and corner information, which is very helpful for VSR, especially texture recovery. We also find that removing shallow feature fusion results in a large performance drop (PSNR drops by $0.38dB$ and SSIM drops by nearly $0.1dB$ ). This finding indicates that fusion on shallow features is critical since shallow features carry rich local structure information which may be missing in deep features.", + "bbox": [ + 75, + 325, + 468, + 521 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Feature Interpolation: We apply a feature-based interpolation strategy, i.e. interpolating features near a coordinate and sending the interpolated feature into the decoder to reconstruct HR frames. Such strategy has been studied in a prior work on implicit neural representation learning for 3D objects [38] and shown to be able to recover clearer details and sharper edges. We further study the influence of interpolation methods, as shown in Table 6. The trilinear manner of interpolation yields better PSNR and SSIM scores compared with the nearest interpolation.", + "bbox": [ + 75, + 523, + 468, + 672 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Feature Decoder: We also compare the performance of different decoder designs, including MLP, SIREN [36] and CNN. Table 6 shows that decoding with CNN has the best performance among these methods, while non-convergence occurs with SIREN. We argue the reason CNN performs well is that decoding in STIR is only a dimensional reduction process, so no complicated design is required.", + "bbox": [ + 75, + 674, + 468, + 779 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Robustness to Noise: In comparison to BasicVSR++, our method not only performs SR but also removes noise on the CED dataset, as shown in Fig.7. Note that BasicVSR++ is a SoTA frame-based method in VSR task. We analyze that the poor performance of BasicVSR++ on CED is caused by the dependence for only frame modality and excessive emphasis on the frame's high-frequency information. High frequencies are often present in the image as edges, cor", + "bbox": [ + 75, + 779, + 468, + 900 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/30f6ee7449bb99141dd4ba2564bce4902b80fd3622946667a83fa3131e7ca336.jpg", + "table_caption": [ + "Table 6. Impacts of interpolation methods, decoder designs and channel size of the spatial-temporal features $F_{ST}$ ." + ], + "table_footnote": [], + "table_body": "
PSNRSSIM
3 to 138.140.9820
5 to 338.040.9818
", + "bbox": [ + 504, + 88, + 885, + 128 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 7. Ablation for the number of input and output frames.", + "bbox": [ + 503, + 132, + 887, + 146 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d82853ecb9cfd4000c76ca4f3d5bbfe667ca1779a1100c2e9588e2994e81bdcf.jpg", + "image_caption": [ + "Figure 6. Feature visualization of $F_{SF}$ (Eq. 6) and $F_{T}$ (Eq. 4)." + ], + "image_footnote": [], + "bbox": [ + 501, + 154, + 890, + 279 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e11c66dd2545f1e24007106b812eaff1990d8536fbdb6a1a23500f357e9e9ecd.jpg", + "image_caption": [ + "(a) Basic VSR++" + ], + "image_footnote": [], + "bbox": [ + 517, + 301, + 635, + 414 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/584f2cfeec7857800f686884586fcc498de46dc46ea844d959812eb1d21d98ab.jpg", + "image_caption": [ + "(b) Ours" + ], + "image_footnote": [], + "bbox": [ + 637, + 301, + 751, + 414 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/3e26b06353930604d5d7a160e15ee132f574621030c6ee77db67998ced9d2f35.jpg", + "image_caption": [ + "(c) GT", + "Figure 7. Comparison of noise removal capacity of BasicVSR++ [6] with our methods, with respect to HR GT." + ], + "image_footnote": [], + "bbox": [ + 753, + 301, + 869, + 415 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "ners and noise. Therefore BasicVSR++ is easily affected by serious noise. In comparison to BasicVSR++, our framework has more robustness. Benefiting from the guidance of events, e.g., edges, corners, our method can effectively reduce the adverse effects of noise on frames.", + "bbox": [ + 496, + 476, + 892, + 551 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 564, + 617, + 579 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we proposed a novel framework which jointly learns INRs from RGB frames and events, and enables arbitrary scale VSR. Our method effectively uses high temporal resolution property of events to complement RGB frames with STF and TF. A simple yet effective STIR is used to recover frames at arbitrary scales. Extensive experiments on two real-world datasets validate our method enjoys better performance over current related SoTA methods with significantly lower model size.", + "bbox": [ + 496, + 590, + 890, + 726 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitation and Future Work: In this work we tackle the problem of spatial VSR. Since our work learns INRs of videos, it naturally enables frame interpolation, which will be explored in our future work. We hope our work can shed light on more research about enhancing implicit neural representations with non-photorealistic data.", + "bbox": [ + 496, + 726, + 890, + 816 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Acknowledgment", + "text_level": 1, + "bbox": [ + 500, + 830, + 669, + 847 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work was supported by the Research Project Fund of AlpsenTek and the National Natural Science Foundation of China (NSFC) under Grant No. NSFC22FYT45.", + "bbox": [ + 496, + 854, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "1564", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6836-6846, 2021.", + "[2] Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang. Memc-net: Motion estimation and motion compensation driven neural network for video interpolation and enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3):933-948, 2021.", + "[3] Jose Caballero, Christian Ledig, Andrew Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, and Wenzhe Shi. Real-time video super-resolution with spatio-temporal networks and motion compensation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4778-4787, 2017.", + "[4] Daniel Canedo and António JR Neves. Facial expression recognition using computer vision: A systematic review. Applied Sciences, 9(21):4678, 2019.", + "[5] Kelvin C.K. Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Basicvsr: The search for essential components in video super-resolution and beyond. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2021.", + "[6] Kelvin C.K. Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improving video superresolution with enhanced propagation and alignment. 2022.", + "[7] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8628-8638, 2021.", + "[8] Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang, Humphrey Shi, and Xiaolong Wang. Videoirr: Learning video implicit neural representation for continuous space-time super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2047-2057, 2022.", + "[9] Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang, Humphrey Shi, and Xiaolong Wang. Videoirr: Learning video implicit neural representation for continuous space-time super-resolution. 2022.", + "[10] Minhyeok Cho, Haechang Lee, Hyunwoo Je, Kijeong Kim, Dongil Ryu, Jinsu Kim, Jonghyun Bae, and Albert No. Pynet-qxq: A distilled pynet for qxq bayer pattern demosaicing in cmos image sensor. arXiv preprint arXiv:2203.04314, 2022.", + "[11] Andreas Daffertshofer, Claudine JC Lamoth, Onno G Meijer, and Peter J Beek. Pca in studying coordination and variability: a tutorial. Clinical biomechanics, 19(4):415-428, 2004.", + "[12] Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J Davison, Jörg Conradt, Kostas Daniilidis, et al. Event-based vision: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(1):154-180, 2020." + ], + "bbox": [ + 78, + 114, + 470, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Jin Han, Yixin Yang, Chu Zhou, Chao Xu, and Boxin Shi. Evintsr-net: Event guided multiple latent frames reconstruction and super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4882-4891, 2021.", + "[14] Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita. Recurrent back-projection network for video superresolution. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3892-3901, 2019.", + "[15] Earnest Paul Ijjina, Dhananjai Chand, Savyasachi Gupta, and K Goutham. Computer vision-based accident detection in traffic surveillance. In 2019 10th International conference on computing, communication and networking technologies (ICCCNT), pages 1-6. IEEE, 2019.", + "[16] Takashi Isobe, Songjiang Li, Xu Jia, Shanxin Yuan, Gregory Slabaugh, Chunjing Xu, Ya-Li Li, Shengjin Wang, and Qi Tian. Video super-resolution with temporal group attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8008-8017, 2020.", + "[17] Ajay Jain, Ben Mildenhall, Jonathan T Barron, Pieter Abbeel, and Ben Poole. Zero-shot text-guided object generation with dream fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 867-876, 2022.", + "[18] Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, and Dacheng Tao. Turning frequency to resolution: Video super-resolution via event cameras. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7772-7781, 2021.", + "[19] Younghyun Jo, Seoung Wug Oh, Jaeyeon Kang, and Seon Joo Kim. Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3224-3232, 2018.", + "[20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.", + "[21] Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE transactions on pattern analysis and machine intelligence, 41(11):2599-2613, 2018.", + "[22] Sheng Li, Fengxiang He, Bo Du, Lefei Zhang, Yonghao Xu, and Dacheng Tao. Fast spatio-temporal residual network for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10522-10531, 2019.", + "[23] Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, Xinchao Wang, and Thomas S Huang. Learning temporal dynamics for video super-resolution: A deep learning approach. IEEE Transactions on Image Processing, 27(7):3432-3445, 2018.", + "[24] Hongying Liu, Zhubo Ruan, Peng Zhao, Chao Dong, Fanhua Shang, Yuanyuan Liu, Linlin Yang, and Radu Timofte. Video super-resolution based on deep learning: a comprehensive survey. In Artif Intell Rev., 2022." + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "1565", + "bbox": [ + 483, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[25] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3202-3211, 2022.", + "[26] Yunfan Lu, Yiqi Lin, Hao Wu, Yunhao Luo, Xu Zheng, and Lin Wang. All one needs to know about priors for deep image restoration and enhancement: A survey. arXiv preprint arXiv:2206.02070, 2022.", + "[27] Zhisheng Lu, Juncheng Li, Hong Liu, Chaoyan Huang, Linlin Zhang, and Tieyong Zeng. Transformer for single image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 457-466, 2022.", + "[28] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021.", + "[29] Mohammad Mostafavi, Yeongwoo Nam, Jonghyun Choi, and Kuk-Jin Yoon. E2sri: Learning to super-resolve intensity images from events. IEEE transactions on pattern analysis and machine intelligence, 44(10):6890-6909, 2021.", + "[30] Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. Ntire 2019 challenge on video deblurring and superresolution: Dataset and study. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019.", + "[31] Daniel Neimark, Omri Bar, Maya Zohar, and Dotan Asselmann. Video transformer network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3163-3172, 2021.", + "[32] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional generative neural feature fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11453-11464, 2021.", + "[33] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019.", + "[34] Vadim Pisarevsky. Introduction to opencv. *Agenda*, 42:433–434, 2010.", + "[35] Cedric Scheerlinck, Henri Rebecq, Timo Stoffregen, Nick Barnes, Robert Mahony, and Davide Scaramuzza. Ced: Color event camera dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0-0, 2019.", + "[36] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462-7473, 2020.", + "[37] Alex J Smola and Bernhard Schölkopf. A tutorial on support vector regression. Statistics and computing, 14(3):199-222, 2004.", + "[38] Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In Proceedings of the IEEE/CVF Conference" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "on Computer Vision and Pattern Recognition, pages 5459-5469, 2022.", + "[39] Jiaxiang Tang, Xiaokang Chen, and Gang Zeng. Joint implicit image function for guided depth super-resolution. In Proceedings of the 29th ACM International Conference on Multimedia, pages 4390-4399, 2021.", + "[40] Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu. Tdan: Temporally-deformable alignment network for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3360-3369, 2020.", + "[41] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017.", + "[42] Bishan Wang, Jingwei He, Lei Yu, Gui-Song Xia, and Wen Yang. Event enhanced high-quality image recovery. In European Conference on Computer Vision, pages 155-171. Springer, 2020.", + "[43] Bishan Wang, Jingwei He, Lei Yu, Gui-Song Xia, and Wen Yang. Event enhanced high-quality image recovery. In European Conference on Computer Vision, pages 155-171. Springer, 2020.", + "[44] Lin Wang, Yujeong Chae, Sung-Hoon Yoon, Tae-Kyun Kim, and Kuk-Jin Yoon. Evdistill: Asynchronous events to end-task learning via bidirectional reconstruction-guided cross-modal knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 608-619, 2021.", + "[45] Longguang Wang, Yulan Guo, Li Liu, Zaiping Lin, Xinpu Deng, and Wei An. Deep video super-resolution using hr optical flow estimation. IEEE Transactions on Image Processing, 29:4323-4336, 2020.", + "[46] Lin Wang, Tae-Kyun Kim, and Kuk-Jin Yoon. Eventsr: From asynchronous events to image reconstruction, restoration, and super-resolution via end-to-end adversarial learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8315-8325, 2020.", + "[47] Wei Wang, Haochen Zhang, Zehuan Yuan, and Changhu Wang. Unsupervised real-world super-resolution: A domain adaptation perspective. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4318-4327, 2021.", + "[48] Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong, and Chen Change Loy. Basicsr. https://github.com/xinntao/BasicSR, 2020.", + "[49] Xi Yang, Wangmeng Xiang, Hui Zeng, and Lei Zhang. Realworld video super-resolution: A benchmark dataset and a decomposition based learning scheme. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4781-4790, 2021.", + "[50] Huanjing Yue, Zhiming Zhang, and Jingyu Yang. Realrawvsr: Real-world raw video super-resolution with a benchmark dataset. arXiv preprint arXiv:2209.12475, 2022.", + "[51] Xu Zheng, Yexin Liu, Yunfan Lu, Tongyan Hua, Tianbo Pan, Weiming Zhang, Dacheng Tao, and Lin Wang. Deep learning for event-based vision: A comprehensive survey and benchmarks. arXiv preprint arXiv:2302.08890, 2023." + ], + "bbox": [ + 503, + 92, + 890, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "1566", + "bbox": [ + 483, + 945, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[52] Pengyuan Zhou, Jinjing Zhu, Yiting Wang, Yunfan Lu, Zixiang Wei, Haolin Shi, Yuchen Ding, Yu Gao, Qinglong Huang, Yan Shi, et al. Vetaverse: Technologies, applications, and visions toward the intersection of meta-verse, vehicles, and transportation systems. arXiv preprint arXiv:2210.15109, 2022.", + "[53] Yunhao Zou, Yinqiang Zheng, Tsuyoshi Takatani, and Ying Fu. Learning to reconstruct high speed and high dynamic range videos from events. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2024-2033, 2021." + ], + "bbox": [ + 78, + 90, + 468, + 246 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "1567", + "bbox": [ + 483, + 945, + 514, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/7f46a567-7a9f-43dd-9afb-c2991cdfbe26_model.json b/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/7f46a567-7a9f-43dd-9afb-c2991cdfbe26_model.json new file mode 100644 index 0000000000000000000000000000000000000000..37321b902caa976c4bc74f4154f12f175d79cc8a --- /dev/null +++ b/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/7f46a567-7a9f-43dd-9afb-c2991cdfbe26_model.json @@ -0,0 +1,2235 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.093, + 0.13, + 0.88, + 0.177 + ], + "angle": 0, + "content": "Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution" + }, + { + "type": "text", + "bbox": [ + 0.144, + 0.203, + 0.868, + 0.257 + ], + "angle": 0, + "content": "Yunfan Lu\\(^{1}\\) * Zipeng Wang\\(^{1}\\) * Minjie Liu\\(^{1}\\) † Hongjian Wang\\(^{2}\\) † Lin Wang\\(^{1,3‡}\\) \n\\(^{1}\\)AI Thrust, HKUST(GZ) \\(^{2}\\)Shenzhen International Graduate School, Tsinghua University \n\\(^{3}\\)Dept. of Computer Science and Engineering, HKUST" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.26, + 0.917, + 0.275 + ], + "angle": 0, + "content": "{ylu066, zwang253, m1iu942}@connect.hkust-gz.edu.cn, whj20@mails.tsinghua.edu.cn, linwang@ust.hk" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.309, + 0.314, + 0.327 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.341, + 0.474, + 0.765 + ], + "angle": 0, + "content": "Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video superresolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the Spatial-Temporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https://vlis2022.github.io/cvpr23/egvsr." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.77, + 0.21, + 0.787 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.795, + 0.47, + 0.856 + ], + "angle": 0, + "content": "Video super-resolution (VSR) is a task of recovering high-resolution (HR) frames from successive multiple low-resolution (LR) frames. Unlike LR videos, HR videos contain more visual information, e.g., edge and texture, which" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.31, + 0.891, + 0.504 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.508, + 0.895, + 0.566 + ], + "angle": 0, + "content": "Figure 1. (a) Our method learns implicit neural representations (INR) from the queried spatial-temporal coordinates (STF) and temporal features (TF) from RGB frames and events. (b) An example of VSR with random scale factors, \\( e.g., 6.5 \\), by our method." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.58, + 0.893, + 0.776 + ], + "angle": 0, + "content": "can be very helpful for many tasks, e.g., metaverse [52], surveillance [15] and entertainment [4]. However, VSR is a highly ill-posed problem owing to the loss of both spatial and temporal information, especially in the real-world scenes [5, 6, 26, 45]. Recently, deep learning-based algorithms have been successfully applied to learn the intra-frame correlation and temporal consistency from the LR frames to recover HR frames, e.g., DUF [19], EDVR [48], RBPN [14], BasicVSR [5], BasicVSR++ [6]. However, due to the lack of inter-frame information, these methods are hampered by the limitations of modeling the spatial and temporal dependencies and may fail to recover HR frames in complex scenes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.781, + 0.895, + 0.902 + ], + "angle": 0, + "content": "Event cameras are bio-inspired sensors that can asynchronously detect the per-pixel intensity changes and generate event streams with low latency (1us) and high dynamic range (HDR) compared with the conventional frame-based cameras (140dB vs. 60dB) [35, 51]. This has sparked extensive research in reconstructing image/video from events [12, 29, 42, 44, 46, 53]. However, the reconstructed results are less plausible due to the loss of visual" + }, + { + "type": "page_footnote", + "bbox": [ + 0.094, + 0.863, + 0.283, + 0.875 + ], + "angle": 0, + "content": "*These authors are co-first authors." + }, + { + "type": "page_footnote", + "bbox": [ + 0.097, + 0.876, + 0.298, + 0.887 + ], + "angle": 0, + "content": "These authors are co-second authors." + }, + { + "type": "page_footnote", + "bbox": [ + 0.097, + 0.888, + 0.221, + 0.9 + ], + "angle": 0, + "content": "\\(\\ddagger\\) Corresponding author" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.863, + 0.298, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1557" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.258 + ], + "angle": 0, + "content": "details, e.g., structures, and textures. As a result, a recent work has utilized events for guiding VSR [18], trying to 'inject' energy from the event-based to the frame-based cameras. It leverages the high-frequency event data to synthesize neighboring frames, so as to find correspondences between consecutive frames. However, it only treats video frames in discrete ways with 2D arrays of pixels and up-samples them at a fixed up-scaling factor e.g., \\(\\times 2\\) or \\(\\times 4\\). This causes inconvenience and inflexibility in the applications of SR videos, which often require arbitrary resolutions, i.e., random scales." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.262, + 0.473, + 0.504 + ], + "angle": 0, + "content": "Recently, some works tried to learn continuous image representations with arbitrary resolutions, e.g., LIIF [7], taking 2D queried coordinates and 2D features as input to learn an implicit neural representation (INR). VideoINR [8], on the other hand, decodes the LR videos into arbitrary spatial resolutions and frame rates by learning from the spatial coordinates and temporal coordinates, respectively. However, it is still unclear how to leverage events to guide learning spatial-temporal INRs for VSR. This is hampered by two challenges. Firstly, although event data can benefit VSR with its high-frequency temporal and spatial information, the large modality gap between the events and video frames makes it challenging to use INRs to represent the 3D spatial-temporal coordinates with event data. Moreover, there lacks HR real-world dataset with spatially well-aligned events and frames." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.509, + 0.473, + 0.903 + ], + "angle": 0, + "content": "In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantage of the high-temporal resolution property of events. Accordingly, we propose a novel framework that subtly incorporates the spatial-temporal interpolation from events to VSR in a unified framework, as shown in Fig. 1. Our key idea is to learn INRs from the queried spatial-temporal coordinates and features from both the RGB frames and events. Our framework mainly includes three parts. The Spatial-Temporal Fusion (STF) branch learns the spatial-temporal information from events and RGB frames (Sec. 3.2). The shallow feature fusion and deep feature fusion are employed to narrow the modality gap and fuse the events and RGB frames into 3D global spatial-temporal representations. Then, the Temporal Filter (TF) branch further unlocks more explicit motion information from events. It learns the 2D event features from events nearing the queried timestamp (Sec. 3.3). With the features from the STF and TF branches, the Spatial-Temporal Implicit Representation (STIR) module decodes the features and recovers SR frames with arbitrary spatial resolutions (Sec. 3.4). That is, given the arbitrary queried coordinates, we apply 3D sampling and 2D sampling to the fused 3D features and event data separately. Finally, the sampling features are added and fed into a decoder, and generate targeted SR frames. In addition, we collect a real-world dataset with a spatial reso" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.152 + ], + "angle": 0, + "content": "lution of \\(3264 \\times 2248\\), in which the events and RGB frames are spatially aligned. Extensive experiments on two real-world datasets show that our method surpasses the existing methods by 1.3 dB." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.153, + 0.895, + 0.32 + ], + "angle": 0, + "content": "In summary, the main contributions of this paper are five-fold: (I) Our work serves as the first attempt to address a non-trivial problem of learning INRs from events and RGB frames for VSR at random scales. (II) We propose the STF branch and the TF branch to model the spatial and temporal dependencies from events and RGB frames. (III) We propose the STIR module to reconstruct RGB frames with arbitrary spatial resolutions. (IV) We collect a high-quality real-world dataset with spatially aligned events and RGB frames. (V) Our method significantly surpasses the existing methods and achieves SR with random-scales, e.g., 6.5." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.332, + 0.642, + 0.348 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.357, + 0.892, + 0.629 + ], + "angle": 0, + "content": "Event-guided Video and Image SR Recently, event data has shown the potential to guide the image or video SR. eSL-Net [43] and EvIntSR [13] focus on employing events to guide the single image SR. Specifically, eSL-Net [43] feeds both the events and LR image to a sparse learning framework to recover an SR image. EvIntSR [13] first reconstructs the latent frames from the events and LR image, which are then fused to reconstruct the SR image. Differently, event-guided VSR takes consecutive frames and events as inputs and models both the spatial and temporal information. A recent work [18] proposed a two-stage method by 1) utilizing events to interpolate the LR video to get a high-frequency video and 2) rebuilding HR key frames. However, it encodes video frames in discrete ways and only up-samples videos at a fixed upscale factor, e.g., \\(\\times 2\\). We make the first attempt to achieve VSR at random scales by learning the spatial-temporal implicit representations from events and video frames." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.63, + 0.895, + 0.902 + ], + "angle": 0, + "content": "Video Super-Resolution (VSR) The dominant research for VSR mainly focuses on designing learning pipelines [24], concerning the feature learning, frame alignment and multiframe fusion [3, 16, 22, 23, 30, 40, 47, 49, 50]. For example, Bao et.al [2] employed the motion compensation to achieve the frame alignment, while EDVR [48] proposes the deformable convolutions after extracting the features from the input frames. To address the feature propagation and alignment problem effectively, BasicVSR [5] and BasicVSR++ [6] proposed a succinct pipeline based on the bidirectional propagation and optical flow. To better exploit the temporal information of video frames, RBPN [14] extracts and propagates the spatial and temporal information of consecutive frames in a recurrent back-projection manner. Inspired by the random image upsampling, VideoINR [9] represents the frames with implicit representations and thus makes it possible to learn random-scale VSR. However, these works focus on a single modality. Differently, in our work, we" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1558" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.088, + 0.096, + 0.887, + 0.281 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.283, + 0.887, + 0.374 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.383, + 0.893, + 0.427 + ], + "angle": 0, + "content": "Figure 2. Overview of our proposed framework. Our method consists of three parts, the Spatial-Temporal Fusion (STF), the Temporal Filter (TF) and Spatial-Temporal Implicit Representation (STIR). These three parts are shown in (a) (b) (c) of this figure, respectively. Details of STF, TF and STIR are described in this Sec.3.2, Sec.3.3 and Sec.3.4, respectively." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.44, + 0.47, + 0.485 + ], + "angle": 0, + "content": "make the first attempt to address a novel problem of achieving VSR at random scales by taking advantage of the high-temporal resolution property of events." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.486, + 0.471, + 0.804 + ], + "angle": 0, + "content": "Implicit Neural Representation (INR) It also called the coordinate-based representation, aims at parameterizing signals, e.g., images and audio, in a continuous way via neural networks [36]. INR has been widely applied to 3D scene representation [28,33] and generative models [17,32], etc. Recently, INRs have been extensively studied for image and video SR. For instance, LIIF [7] achieves image SR with random scales given the 2D queried coordinates and 2D features. JIIF [39] uses HR images to guide the interpolation weights and values of LR depth maps. VideoINR [8] decodes the LR and low-frame-rate videos into an arbitrary spatial resolution and frame rate with three INRs. It adopts two INR functions to learn the spatial coordinates and the temporal coordinates, respectively. These two INR functions are then used to generate a motion flow field, which is applied back to warp the encoded features. Then, the warped features are decoded to recover the SR frame by the spatial INR function. Differently, considering the large modality gap between the events and video frames, we accordingly propose a novel INR module to directly represent the 3D spatial-temporal coordinates from event data." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.814, + 0.321, + 0.831 + ], + "angle": 0, + "content": "3. The Proposed Framework" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.47, + 0.903 + ], + "angle": 0, + "content": "Event Representation As event streams are sparse points, we first describe how to stack them into the fixed-size representations as inputs of our framework. Events are produced by detecting variations in the log intensity of each pixel. An" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.44, + 0.893, + 0.533 + ], + "angle": 0, + "content": "event \\( e = (x,y,t,p) \\) is triggered and recorded when the logarithmic brightness change exceeds a certain threshold \\( \\theta \\) at pixel \\( (x,y) \\). This process can be described as Eq.1, where \\( \\Delta L = \\log (I_t^l + n) - \\log (I_{t - \\Delta t}^l + n) \\), \\( n \\) is noise, \\( I_t^l \\) and \\( I_{t - \\Delta t}^l \\) are intensity values in linear domain at timestamps \\( t \\) and \\( t - \\Delta t \\), respectively." + }, + { + "type": "equation", + "bbox": [ + 0.623, + 0.542, + 0.892, + 0.584 + ], + "angle": 0, + "content": "\\[\np = \\left\\{ \\begin{array}{l} + 1, \\Delta L > \\theta \\\\ - 1, \\Delta L < - \\theta \\end{array} \\right. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.59, + 0.892, + 0.637 + ], + "angle": 0, + "content": "According to Eq.1, the relation between frames \\( I_{t_0}^l (x,y) \\) and \\( I_{t_1}^l (x,y) \\) at timestamps \\( t_0 \\) and \\( t_1 \\) can be formulated as Eq.2, where \\( p \\) is the polarity of event at pixel \\( (x,y) \\)." + }, + { + "type": "equation", + "bbox": [ + 0.562, + 0.647, + 0.892, + 0.682 + ], + "angle": 0, + "content": "\\[\nI _ {t _ {1}} ^ {l} (x, y) = I _ {t _ {0}} ^ {l} (x, y) \\times \\exp \\left(\\theta \\int_ {t _ {0}} ^ {t _ {1}} p d t\\right) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.687, + 0.892, + 0.793 + ], + "angle": 0, + "content": "Events record the intensity changes with higher temporal resolution than frames, which is advantageous for VSR task [18]. We split events into \\( M \\) moment segments [42] with a shape of \\( H \\times W \\times M \\times 2 \\) as the input to our framework. Each segment keeps events that take place within time window. The window size is set to be a small constant to preserve the temporal information of events." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.802, + 0.61, + 0.816 + ], + "angle": 0, + "content": "3.1. Overview" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.893, + 0.902 + ], + "angle": 0, + "content": "The overall framework of our method is depicted in Fig.2, which consists of three parts: (I) spatial-temporal fusion (STF) branch, (II) temporal filter (TF) branch, and (III) spatial-temporal implicit representation (STIR) module. The input of our framework are spatially aligned" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1559" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.091, + 0.47, + 0.213 + ], + "angle": 0, + "content": "RGB video frames \\( V = \\{V_0\\ldots V_i\\ldots V_n\\} \\) and events \\( E \\in R^{H\\times W\\times M\\times 2} \\), where \\( H\\times W \\) denotes the frame size. The output of our framework is a super-resolved video frame \\( I_{s,t}^{SR} \\) with the up-sampling scale \\( s \\) and the timestamp \\( t \\). Note that the values of \\( s \\) and \\( t \\) can be freely adjusted. In practice, \\( s \\) is a real number greater than 1, and \\( t \\) can take the timestamps of all frames. The spatial resolution of the SR frame \\( I_{s,t}^{SR} \\) is \\( sW\\times sH \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.214, + 0.471, + 0.35 + ], + "angle": 0, + "content": "Our framework consists of three major components. The STF branch learns the holistic spatial-temporal information from events and RGB frames (Sec.3.2). Then, the TF branch unlocks more explicit motion information from events. It learns 2D event features from events nearing the queried timestamp \\( t \\) (Sec.3.3). Lastly, the STIR module decodes the features and recovers SR frames with arbitrary spatial resolutions (Sec.3.4). We now describe these components in detail in the following sections." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.362, + 0.415, + 0.379 + ], + "angle": 0, + "content": "3.2. Spatial-Temporal Fusion (STF) Branch" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.386, + 0.47, + 0.477 + ], + "angle": 0, + "content": "This module aims to extract spatial and temporal information from events \\(E\\) and RGB frames \\(V\\) to obtain a global spatial-temporal representation \\(F_{ST}\\). The representation is a 3D feature map of \\(H\\times W\\times T\\times C\\), where \\(T\\) is the temporal dimension, and \\(C\\) is the number of representation channels. The output \\(F_{ST}\\) of STF branch \\(f_{STF}\\) can be described as:" + }, + { + "type": "equation", + "bbox": [ + 0.205, + 0.484, + 0.469, + 0.5 + ], + "angle": 0, + "content": "\\[\nF _ {S T} = f _ {S T F} (V, E) \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.504, + 0.47, + 0.61 + ], + "angle": 0, + "content": "Specifically, as depicted in Fig. 2(a), we first employ two \\(1 \\times 1\\) convolutional layers to obtain the initial frame feature map \\(F_0^f\\) and the event feature map \\(F_0^e\\) with same dimension. As stated in [13], shallow features preserve sharper details and local structural information while deeper features preserve more semantic information. Thus, we design fusion blocks to aggregate both the shallow and deep features." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.611, + 0.47, + 0.839 + ], + "angle": 0, + "content": "Shallow feature fusion: As explored in [27], the residual architecture improves the model's capacity for representation and lessens the gradient vanishing issue. For this reason, we first employ two high preserving blocks (HPB) as the basic feature extractors to extract two shallow feature maps \\( F_{l}^{f} \\) and \\( F_{l}^{e} \\) from the initial feature maps \\( F_{0}^{f} \\) and \\( F_{0}^{e} \\), respectively. After that, \\( F_{l}^{f} \\) and \\( F_{l}^{e} \\) are concatenated and then fed into a transformer-based fusion model to obtain a fused feature map so as to bridge the modality gap. The intuition behind this is that transformer has a larger perception field [1, 25, 25, 31, 41], which can be potentially used to model the global spatial and temporal dependencies from the frames and events. Then, the fused feature map is split into two parts in the channel dimension and added to \\( F_{l}^{f} \\) and \\( F_{l}^{e} \\) as the input for deep feature fusion." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Deep feature fusion: After the shallow feature fusion, we again use two HPBs to extract deep features, which are then added and passed to the transformer-based fusion model to attain a 3D feature map \\( F_{ST} \\). Through shallow and deep" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.122 + ], + "angle": 0, + "content": "feature fusion, we can better learn the temporal and spatial information from the events and frames." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.132, + 0.76, + 0.149 + ], + "angle": 0, + "content": "3.3. Temporal Filter (TF) Branch" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.156, + 0.892, + 0.277 + ], + "angle": 0, + "content": "Through the STF branch, the spatial-temporal feature information from both the frames and events can be effectively learned. However, STF branch is insufficient to take full advantage of the high temporal resolution of event data. Therefore, we design the TF branch to explore more detailed motion information solely from events, which turns out to be effective in further enhancing the VSR performance, as demonstrated in our experiments (See Table 5)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.278, + 0.892, + 0.383 + ], + "angle": 0, + "content": "Intuitively, we design the TF branch \\( f_{TF} \\) to capture the detailed motion information from events \\( E_{t,\\Delta t} \\) near the key frame at timestamp \\( t \\), where \\( \\Delta t \\) is a small time interval. TF branch first selects events from \\( t - \\Delta t \\) to \\( t + \\Delta t \\) (Fig. 2(c)). The selected events are interpolated and sent to three convolutional layers to learn the temporal features. Overall, the output \\( F_{T} \\) of TF branch \\( f_{TF} \\) can be described as:" + }, + { + "type": "equation", + "bbox": [ + 0.631, + 0.386, + 0.892, + 0.403 + ], + "angle": 0, + "content": "\\[\nF _ {T} = f _ {T F} \\left(E _ {t, \\Delta t}\\right) \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.406, + 0.892, + 0.452 + ], + "angle": 0, + "content": "In Sec.4.4, we show that STF branch can capture pixel intensities, while the TF branch can capture the motion details, e.g., edges and corners." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.462, + 0.857, + 0.479 + ], + "angle": 0, + "content": "3.4. Spatial-Temporal Implicit Representation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.485, + 0.892, + 0.682 + ], + "angle": 0, + "content": "In this section, our goal is to learn continuous INRs for VSR based on the spatial-temporal feature map \\( F_{ST} \\) and temporal feature map \\( F_{T} \\). The INRs are then used to decode coordinates at time \\( t \\) with scale factor \\( s \\) into RGB values. In this paper, we introduce the Spatial-Temporal Implicit Representation (STIR) module to accomplish the spatial-temporal VSR, as shown in Eq.5. We employ a simple-yet-effective 3D feature sampling and trilinear interpolation scheme to upsample \\( F_{ST} \\) and \\( F_{T} \\) to a desired resolution. A decoder, parameterized as a multi-layer CNN, is used to convert the interpolated features into RGB values. Fig. 2(c) depicts the detailed design of STIR. The output SR frame \\( I_{t,s}^{SR} \\) of the STIR \\( f_{STIR} \\) can be formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.591, + 0.683, + 0.892, + 0.702 + ], + "angle": 0, + "content": "\\[\nI _ {t, s} ^ {S R} = f _ {S T I R} \\left(F _ {S T}, F _ {T}\\right), \\forall s, t \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.892, + 0.87 + ], + "angle": 0, + "content": "3D Feature Sampling: Here, we aim at generating a coordinate to make a query in grid form \\( F_{ST} \\). We uniformly sample a 3D coordinate grid, which can be expressed as \\( C_{t,s} \\) with the dimension of \\( sH \\times sW \\times 3 \\). Formally, for any query \\( q \\), the corresponding element \\( p_q \\) in the 3D coordinate grid \\( C_{t,s} \\) can be described as \\( p_q = (x_q, y_q, t_q) \\), where \\( x_q \\in [0, H], y_q \\in [0, W], t_q \\in [t_s, t_e] \\), \\( t_s \\) is the start time and \\( t_e \\) is the end time of input. For each coordinate \\( p_q = (x_q, y_q, t_q) \\), we choose features of the nearest eight points around this coordinate in the 3D spatial-temporal feature \\( F_{ST} \\) for interpolation." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Feature Interpolation: Then, we compute the feature of a queried coordinate \\( p_{q} \\) by using 3D interpolation techniques," + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1560" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.258 + ], + "angle": 0, + "content": "such as trilinear interpolation. Inspired by the representation theory [37], complex signals in the low-dimensional space e.g., images, can be transformed as linear representations in the high-dimensional space e.g., features. From this theory, we can observe that the spatial-temporal feature \\( F_{ST} \\) is indeed the high-dimensional feature representation for the low-dimensional image. We use linear interpolation to obtain features at the queried coordinate \\( p_q \\). In the experiments of Table 6, we have compared several interpolation methods, e.g., nearest sampling, and the results show that linear interpolation shows the best performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.259, + 0.472, + 0.319 + ], + "angle": 0, + "content": "In summary, for any scale \\( s \\) and timestamp \\( t \\), the feature interpolation (i.e., 3D to 2D) process can be formulated by Eq. 6, given the 3D coordinate grid \\( C_{t,s} \\) and spatial-temporal feature \\( F_{ST} \\)." + }, + { + "type": "equation", + "bbox": [ + 0.181, + 0.323, + 0.47, + 0.34 + ], + "angle": 0, + "content": "\\[\nF _ {S F} = f _ {\\text {s a m p l e}} \\left(F _ {S T}, C _ {t, s}\\right) \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.343, + 0.473, + 0.45 + ], + "angle": 0, + "content": "Implicit Representation Decoding: Finally, the sampled 2D feature map \\( F_{SF} \\) and the temporal feature map \\( F_{T} \\) are added together and fed into the decoder. For simplicity and efficiency, we design three-layers CNN structure as the decoder. This is supported by the empirical experiment in Table 6, showing that a sample CNN block can achieve good results with low complexity." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.461, + 0.224, + 0.475 + ], + "angle": 0, + "content": "3.5. Loss Function" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.485, + 0.47, + 0.546 + ], + "angle": 0, + "content": "We employ the Charbonnier loss [21] as our VSR supervision loss \\(\\mathcal{L}_{SR}\\) between the ground truth (GT) HR frame \\(I_{t,s}^{HR}\\) and the output SR frame \\(I_{t,s}^{SR}\\) at timestamp \\(t\\) with upsampling scale \\(s\\), as shown in Eq. 7, where \\(\\epsilon\\) is \\(1e - 3\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.547, + 0.472, + 0.653 + ], + "angle": 0, + "content": "In training, the value range of \\( t \\) is all timestamps of key frames \\( \\{t_0, t_1 \\dots t_n\\} \\). \\( s \\) could be a real number in the range [1.0, \\( s_{max} \\)], where \\( s_{max} \\) is the maximum up-sampling scale during training depending on the resolution of training data. The loss function \\( \\mathcal{L} \\) is shown in Eq. 8. For example, when the resolution of input LR frame is set to be \\( 128 \\times 128 \\) and the GT HR frame is \\( 1024 \\times 1024 \\), the \\( s_{max} \\) is 8." + }, + { + "type": "equation", + "bbox": [ + 0.17, + 0.655, + 0.47, + 0.68 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {S R} = \\sqrt {\\left(I _ {t , s} ^ {S R} - I _ {t , s} ^ {H R}\\right) ^ {2} + \\epsilon^ {2}} \\tag {7}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.105, + 0.682, + 0.47, + 0.731 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\sum_ {t \\in t _ {0}, t _ {1} \\dots t _ {n}} \\left(\\sum_ {s \\in [ 1. 0, s _ {\\max } ]} \\mathcal {L} _ {S R} \\left(I _ {t, s} ^ {S R}, I _ {t, s} ^ {H R}\\right)\\right) \\tag {8}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.741, + 0.345, + 0.755 + ], + "angle": 0, + "content": "3.6. Real-world Dataset Collection" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.765, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Existing datasets, e.g., CED [35], suffer from the limited resolution \\((346 \\times 260)\\) and severe noise, as shown in Fig.7. Although CED dataset provides the active pixel sensor (APS) frames, they are in low quality because they are simply demosaiced by OpenCV [34] from RAW data. Therefore, collecting HR and high-quality datasets with spatially aligned frames and events is important to inspire more research for the event-guided VSR problem. In this paper, we collected a new real-world dataset, called ALPIX-" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.181 + ], + "angle": 0, + "content": "VSR, using a ALPIX-Eiger event camera1. The camera outputs well aligned RGB frames and events. The RGB frames enjoy a resolution of \\(3264 \\times 2448\\) and are generated by a carefully designed image signal processor(ISP) from RAW data with the Quad Bayer pattern [10], and the events have a resolution with \\(1632 \\times 1224\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.182, + 0.893, + 0.289 + ], + "angle": 0, + "content": "Our ALPIX-VSR dataset consists of 26 video sequences with 5388 frames and well-aligned events in total. These sequences include diverse scenes, e.g., streets, buildings, flowers, textures, and machines. To avoid motion blur and low-light noise, we collect the dataset in bright indoor and sunny outdoor scenes. For more details about our real-world dataset, please refer to the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.301, + 0.634, + 0.318 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.326, + 0.694, + 0.342 + ], + "angle": 0, + "content": "4.1. Experiments Setting" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.349, + 0.892, + 0.424 + ], + "angle": 0, + "content": "Implementation Details and Datasets: For all experiments, we use the Adam optimizer [20] with a learning rate of \\(1e - 4\\) for CED dataset and \\(5e - 5\\) for our ALPIX-VSR dataset. We train our framework for 100 epochs with a batch size of 2 using two NVIDIA RTX A30 GPU cards." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.424, + 0.892, + 0.469 + ], + "angle": 0, + "content": "Evaluation Metrics: We statistically assess the effectiveness of our approach using the peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.47, + 0.893, + 0.681 + ], + "angle": 0, + "content": "Datasets: We use the CED [35] and the ALPIX-VSR dataset for experiments. 1) CED Dataset. It includes a collection of color events and video sequences in many scenes, e.g., indoor, outdoor, driving, human, calibration. The resolution of the frames and events is \\(346 \\times 260\\). We follow the setting of E-VSR [18] to preprocess this dataset. Note that the RGB frames provided by CED are obtained by demosaicing [34] from raw frames and suffer from severe noise. 2) ALPIX-VSR Dataset. We select 20 videos for training and 6 videos for testing. The training and testing sets include 4212 and 1176 frames with aligned events, respectively. Note that we apply data augmentation strategy, such as random crop, to ALPIX-VSR dataset for all compared methods to avoid memory overflow during training." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.69, + 0.791, + 0.706 + ], + "angle": 0, + "content": "4.2. Comparison with SoTA Methods" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.713, + 0.892, + 0.879 + ], + "angle": 0, + "content": "We compare our method with six SoTA methods under three VSR settings: (I) one SoTA event-guided, fixed-scale VSR method E-VSR [18] (II) one SoTA method of frame-based arbitrary-scale VSR method VideoINR [8] (III) five SoTA methods of frame-based, fixed-scale VSR methods: BasicVSR++ [6], RBPN [14], SOF [45], TDAN [40], DUF [19]. We report \\(\\times 2\\) and \\(\\times 4\\) super-resolution results of our method and all 6 comparison methods on CED dataset. We also compare our method with E-VSR and BasicVSR++ on our ALPIX-VSR dataset. Moreover, we compare our method with VideoINR on out-of-distribution" + }, + { + "type": "page_footnote", + "bbox": [ + 0.518, + 0.887, + 0.716, + 0.902 + ], + "angle": 0, + "content": "1https://www.alpsentek.com/product" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "1561" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.082, + 0.077, + 0.891, + 0.256 + ], + "angle": 0, + "content": "
Clip NameDUF [19]*TDAN [40]SOF [45]RBPN [14]VideoINR [8]*E-VSR [18]Ours
people_dynamic<wave>32.02 / 0.933335.83 / 0.954033.32 / 0.936040.07 / 0.986827.47 / 0.822941.08 / 0.989138.78 / 0.9794
indoors_foosball_230.55 / 0.926232.12 / 0.933930.86 / 0.925334.15 / 0.973926.03 / 0.776634.77 / 0.977538.68 / 0.9750
simple_wires_230.08 / 0.938731.57 / 0.946630.12 / 0.932633.83 / 0.973926.77 / 0.832134.44 / 0.977338.67 / 0.9815
people_dynamic_dancing31.64 / 0.936935.73 / 0.956632.93 / 0.938839.56 / 0.986927.36 / 0.820240.49 / 0.989139.06 / 0.9798
people_dynamic_jumping31.57 / 0.933435.42 / 0.953632.79 / 0.934739.44 / 0.985927.24 / 0.818340.32 / 0.988038.93 / 0.9792
simple_fruit_fast37.46 / 0.944237.75 / 0.944037.22 / 0.939040.33 / 0.978227.21 / 0.845640.80 / 0.980141.96 / 0.9821
outdoor_jumping_infrared_225.33 / 0.816228.91 / 0.906226.67 / 0.874630.36 / 0.964826.88 / 0.822630.70 / 0.969838.03 / 0.9755
simple_carpet_fast31.43 / 0.881132.54 / 0.900631.83 / 0.877434.91 / 0.950224.21 / 0.590935.16 / 0.953636.14 / 0.9635
people_dynamic_armroll31.38 / 0.931135.55 / 0.954132.79 / 0.934540.05 / 0.987827.26 / 0.819341.00 / 0.989838.84 / 0.9787
indoors_kitchen_229.92 / 0.927330.67 / 0.932329.61 / 0.919231.51 / 0.955126.44 / 0.750231.79 / 0.958637.68 / 0.9726
people_dynamic_sitting30.62 / 0.933135.09 / 0.956132.13 / 0.936739.03 / 0.986227.63 / 0.823039.97 / 0.988438.86 / 0.9810
average PSNR/SSIM31.09 / 0.918333.74 / 0.939831.84 / 0.922636.66 / 0.975426.77 / 0.793837.32 / 0.978338.69 / 0.9771
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.258, + 0.893, + 0.287 + ], + "angle": 0, + "content": "Table 1. Quantitative results (PSNR/SSIM) of the proposed our framework and other methods on the CED dataset for \\( \\times 2 \\). Because the official training code is not available, * denoted values were acquired from the pre-trained model that the authors have released." + }, + { + "type": "image", + "bbox": [ + 0.079, + 0.3, + 0.891, + 0.639 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.651, + 0.893, + 0.68 + ], + "angle": 0, + "content": "Figure 3. Visual results of \\(\\times 4\\) VSR on the CED dataset. Our method recovers more details, e.g., textual, edge than the SoTA event-guided VSR method E-VSR [18] and the random up-sampling VSR method Video INR [8]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.696, + 0.47, + 0.742 + ], + "angle": 0, + "content": "scales to demonstrate our method's ability for arbitrary-scale VSR. Note that E-VSR only supports \\(\\times 2\\) and \\(\\times 4\\) SR, and BasicVSR++ only supports \\(\\times 4\\) SR." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Evaluation on CED Dataset Table 1 and Table 2 present the quantitative results for \\(\\times 2\\) and \\(\\times 4\\) VSR, respectively. Our model clearly outperforms other methods in terms of PSNR, and shows a comparable performance in SSIM with E-VSR. The qualitative results in Fig. 3 demonstrate that our model is capable of recovering fine details, like sharp edges and detailed textures. We can see that event-guided methods (E-VSR and our method) yield better performances than the frame-based counterparts, showing the complementary effects of event data for VSR. Furthermore," + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.696, + 0.892, + 0.772 + ], + "angle": 0, + "content": "VideoINR performs noticeably worse than other methods, indicating that frame-based implicit neural representations struggle with the low-resolution and high-noise input. Contrarily, our approach benefits from additional event information and generates satisfactory INRs." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.776, + 0.893, + 0.882 + ], + "angle": 0, + "content": "Table 2 shows quantitative results in \\(\\times 4\\) SR on the CED dataset, where our model achieves SoTA performance while remaining lightweight and efficient. Specifically, our model only accounts for one-200th of the E-VSR. BasicVSR++, the SoTA method of frame-based VSR, fails to perform well on CED dataset as the upsampling scale increases, which demonstrates its less robustness on the highly noisy dataset." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.886, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Evaluation on the ALPIX-VSR Dataset The quantitative" + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1562" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.088, + 0.878, + 0.267 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.312, + 0.269, + 0.657, + 0.282 + ], + "angle": 0, + "content": "Figure 4. Results of \\(\\times 4\\) VSR on the ALPIX-VSR dataset." + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.283, + 0.88, + 0.449 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.312, + 0.45, + 0.657, + 0.463 + ], + "angle": 0, + "content": "Figure 5. Results of \\(\\times 8\\) VSR on the ALPIX-VSR dataset." + }, + { + "type": "table", + "bbox": [ + 0.08, + 0.475, + 0.462, + 0.588 + ], + "angle": 0, + "content": "
MethodsModel Size(M)PSNRSSIM
DUF [19]1.9024.430.8177
TDAN [40]1.9727.880.8231
SOF [45]1.0027.000.8050
RBPN [14]12.1829.800.8975
BasicVSR++ [6]7.3014.760.1641
VideoINR [8]*11.3125.530.7871
E-VSR [18]412.4230.150.9052
Ours2.4531.120.9211
" + }, + { + "type": "table_caption", + "bbox": [ + 0.078, + 0.589, + 0.469, + 0.616 + ], + "angle": 0, + "content": "Table 2. Quantitative results on CED dataset for \\( \\times 4 \\). * denotes the values obtained from the official pre-trained models." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.617, + 0.46, + 0.746 + ], + "angle": 0, + "content": "
MethodsPSNRSSIM
×2E-VSR36.100.9761
Ours38.250.9822
×4E-VSR32.540.9163
BasicVSR++35.300.9353
Ours37.120.9503
×6VideoINR*31.150.9084
Ours31.850.9267
×8VideoINR*28.110.8625
Ours28.530.8901
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.75, + 0.47, + 0.793 + ], + "angle": 0, + "content": "Table 3. Quantitative comparison (PSNR/SSIM) of our methods and other methods on the ALPIX-VSR dataset. * denotes the values obtained from the official pre-trained models." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.806, + 0.469, + 0.851 + ], + "angle": 0, + "content": "and qualitative results are shown in Table 3 and Fig. 5. We present comparison results of our method with BasicVSR++ and E-VSR on our collected real-world dataset." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.854, + 0.329, + 0.87 + ], + "angle": 0, + "content": "4.3. Random Scale Up-sampling" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Results of random scale upsampling are shown in Table 3. We upsample the video frames to \\(\\times 2\\), \\(\\times 4\\), \\(\\times 6\\)" + }, + { + "type": "table", + "bbox": [ + 0.501, + 0.477, + 0.896, + 0.535 + ], + "angle": 0, + "content": "
×1.8×2.6×5.6
Ours39.2508 / 0.980337.3408 / 0.958931.2549 / 0.9135
×6.6×7.1×7.8
Ours28.3182 / 0.877228.3188 / 0.8776228.3198 / 0.8783
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.539, + 0.892, + 0.567 + ], + "angle": 0, + "content": "Table 4. Quantitative results(PSNR/SSIM) of random-scale comparison on theALPIX-VSR dataset." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.577, + 0.892, + 0.638 + ], + "angle": 0, + "content": ", \\(\\times 8\\), and compare with the SoTA models, E-VSR and BasicVSR++. Though these two models are strictly constrained to a specific upsampling scale, our method presents superior performance on these settings." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.639, + 0.893, + 0.79 + ], + "angle": 0, + "content": "We also compare our method with VideoINR, the SoTA random-scale VSR method quantitatively in \\(\\times 6\\), \\(\\times 8\\). Results in Table 3 show that our model surpasses VideoINR in all evaluated scales of \\(\\times 6\\), \\(\\times 8\\). Furthermore, to evaluate the performance of our method on arbitrary scales, we conduct experiments of 6 random-chosen float scales. The results are shown in Table 4 and indicate our model's robustness across arbitrary random scales. It is easy to find that values of performance, e.g., PSNR, SSIM, and upsampling scales are not strictly monotonic." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.804, + 0.783, + 0.818 + ], + "angle": 0, + "content": "4.4. Ablation Studies and Discussion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.828, + 0.892, + 0.901 + ], + "angle": 0, + "content": "The following ablation experiments investigate the importance of each of our proposed modules. As it takes more than 120 hours to train a model on the complete CED dataset, we uniformly select \\(1/5\\) of CED as the dataset for ablation experiments." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1563" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.082, + 0.089, + 0.468, + 0.161 + ], + "angle": 0, + "content": "
TF BranchShallow Feature FusionModel Size(M)PSNRSSIM
1ww2.451338.140.9820
2w/ow2.448237.960.9812
3ww/o1.736037.760.9729
" + }, + { + "type": "table_caption", + "bbox": [ + 0.087, + 0.163, + 0.459, + 0.176 + ], + "angle": 0, + "content": "Table 5. Ablation of the TF branch and shallow feature fusion." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.184, + 0.468, + 0.278 + ], + "angle": 0, + "content": "
InterpolationDecoderLF ChannelsPSNRSSIM
1LinearCNN1638.140.9820
2NearestCNN1628.910.9131
3LinearSIREN1610.300.2997
4LinearMLP1637.940.9811
5LinearCNN836.820.9763
6LinearCNN2438.250.9825
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.28, + 0.469, + 0.308 + ], + "angle": 0, + "content": "Table 6. Impacts of interpolation methods, decoder designs and channel size of the spatial-temporal features \\( F_{ST} \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.327, + 0.469, + 0.522 + ], + "angle": 0, + "content": "Efficiency of TF branch and Shallow Feature Fusion: Table 5 validates the contribution of TF branch and shallow feature fusion in STF branch. The removal of TF branch reduces both PSNR and SSIM scores, where PSNR drops by \\(0.18dB\\). In Fig. 6, we use PCA [11] to visualize the output of TF branch. As can be seen, \\(F_{T}\\) focuses on edge and corner information, which is very helpful for VSR, especially texture recovery. We also find that removing shallow feature fusion results in a large performance drop (PSNR drops by \\(0.38dB\\) and SSIM drops by nearly \\(0.1dB\\)). This finding indicates that fusion on shallow features is critical since shallow features carry rich local structure information which may be missing in deep features." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.524, + 0.469, + 0.674 + ], + "angle": 0, + "content": "Feature Interpolation: We apply a feature-based interpolation strategy, i.e. interpolating features near a coordinate and sending the interpolated feature into the decoder to reconstruct HR frames. Such strategy has been studied in a prior work on implicit neural representation learning for 3D objects [38] and shown to be able to recover clearer details and sharper edges. We further study the influence of interpolation methods, as shown in Table 6. The trilinear manner of interpolation yields better PSNR and SSIM scores compared with the nearest interpolation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.675, + 0.469, + 0.78 + ], + "angle": 0, + "content": "Feature Decoder: We also compare the performance of different decoder designs, including MLP, SIREN [36] and CNN. Table 6 shows that decoding with CNN has the best performance among these methods, while non-convergence occurs with SIREN. We argue the reason CNN performs well is that decoding in STIR is only a dimensional reduction process, so no complicated design is required." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Robustness to Noise: In comparison to BasicVSR++, our method not only performs SR but also removes noise on the CED dataset, as shown in Fig.7. Note that BasicVSR++ is a SoTA frame-based method in VSR task. We analyze that the poor performance of BasicVSR++ on CED is caused by the dependence for only frame modality and excessive emphasis on the frame's high-frequency information. High frequencies are often present in the image as edges, cor" + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.089, + 0.886, + 0.129 + ], + "angle": 0, + "content": "
PSNRSSIM
3 to 138.140.9820
5 to 338.040.9818
" + }, + { + "type": "table_caption", + "bbox": [ + 0.504, + 0.133, + 0.888, + 0.147 + ], + "angle": 0, + "content": "Table 7. Ablation for the number of input and output frames." + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.155, + 0.892, + 0.28 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.505, + 0.281, + 0.885, + 0.296 + ], + "angle": 0, + "content": "Figure 6. Feature visualization of \\( F_{SF} \\) (Eq. 6) and \\( F_{T} \\) (Eq. 4)." + }, + { + "type": "image", + "bbox": [ + 0.519, + 0.303, + 0.636, + 0.415 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.534, + 0.418, + 0.625, + 0.429 + ], + "angle": 0, + "content": "(a) Basic VSR++" + }, + { + "type": "image", + "bbox": [ + 0.638, + 0.303, + 0.753, + 0.415 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.671, + 0.418, + 0.718, + 0.429 + ], + "angle": 0, + "content": "(b) Ours" + }, + { + "type": "image", + "bbox": [ + 0.754, + 0.303, + 0.87, + 0.416 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.792, + 0.418, + 0.829, + 0.429 + ], + "angle": 0, + "content": "(c) GT" + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.436, + 0.892, + 0.464 + ], + "angle": 0, + "content": "Figure 7. Comparison of noise removal capacity of BasicVSR++ [6] with our methods, with respect to HR GT." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.477, + 0.893, + 0.552 + ], + "angle": 0, + "content": "ners and noise. Therefore BasicVSR++ is easily affected by serious noise. In comparison to BasicVSR++, our framework has more robustness. Benefiting from the guidance of events, e.g., edges, corners, our method can effectively reduce the adverse effects of noise on frames." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.565, + 0.619, + 0.58 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.591, + 0.892, + 0.727 + ], + "angle": 0, + "content": "In this paper, we proposed a novel framework which jointly learns INRs from RGB frames and events, and enables arbitrary scale VSR. Our method effectively uses high temporal resolution property of events to complement RGB frames with STF and TF. A simple yet effective STIR is used to recover frames at arbitrary scales. Extensive experiments on two real-world datasets validate our method enjoys better performance over current related SoTA methods with significantly lower model size." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.727, + 0.892, + 0.817 + ], + "angle": 0, + "content": "Limitation and Future Work: In this work we tackle the problem of spatial VSR. Since our work learns INRs of videos, it naturally enables frame interpolation, which will be explored in our future work. We hope our work can shed light on more research about enhancing implicit neural representations with non-photorealistic data." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.831, + 0.671, + 0.848 + ], + "angle": 0, + "content": "6. Acknowledgment" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.892, + 0.9 + ], + "angle": 0, + "content": "This work was supported by the Research Project Fund of AlpsenTek and the National Natural Science Foundation of China (NSFC) under Grant No. NSFC22FYT45." + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1564" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.184 + ], + "angle": 0, + "content": "[1] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6836-6846, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.187, + 0.472, + 0.257 + ], + "angle": 0, + "content": "[2] Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang. Memc-net: Motion estimation and motion compensation driven neural network for video interpolation and enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3):933-948, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.259, + 0.47, + 0.342 + ], + "angle": 0, + "content": "[3] Jose Caballero, Christian Ledig, Andrew Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, and Wenzhe Shi. Real-time video super-resolution with spatio-temporal networks and motion compensation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4778-4787, 2017." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.344, + 0.47, + 0.385 + ], + "angle": 0, + "content": "[4] Daniel Canedo and António JR Neves. Facial expression recognition using computer vision: A systematic review. Applied Sciences, 9(21):4678, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.388, + 0.47, + 0.457 + ], + "angle": 0, + "content": "[5] Kelvin C.K. Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Basicvsr: The search for essential components in video super-resolution and beyond. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.459, + 0.469, + 0.5 + ], + "angle": 0, + "content": "[6] Kelvin C.K. Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improving video superresolution with enhanced propagation and alignment. 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.502, + 0.469, + 0.571 + ], + "angle": 0, + "content": "[7] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8628-8638, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.574, + 0.469, + 0.657 + ], + "angle": 0, + "content": "[8] Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang, Humphrey Shi, and Xiaolong Wang. Videoirr: Learning video implicit neural representation for continuous space-time super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2047-2057, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.659, + 0.469, + 0.715 + ], + "angle": 0, + "content": "[9] Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang, Humphrey Shi, and Xiaolong Wang. Videoirr: Learning video implicit neural representation for continuous space-time super-resolution. 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.717, + 0.469, + 0.784 + ], + "angle": 0, + "content": "[10] Minhyeok Cho, Haechang Lee, Hyunwoo Je, Kijeong Kim, Dongil Ryu, Jinsu Kim, Jonghyun Bae, and Albert No. Pynet-qxq: A distilled pynet for qxq bayer pattern demosaicing in cmos image sensor. arXiv preprint arXiv:2203.04314, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.788, + 0.469, + 0.83 + ], + "angle": 0, + "content": "[11] Andreas Daffertshofer, Claudine JC Lamoth, Onno G Meijer, and Peter J Beek. Pca in studying coordination and variability: a tutorial. Clinical biomechanics, 19(4):415-428, 2004." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.832, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[12] Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J Davison, Jörg Conradt, Kostas Daniilidis, et al. Event-based vision: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(1):154-180, 2020." + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.472, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.162 + ], + "angle": 0, + "content": "[13] Jin Han, Yixin Yang, Chu Zhou, Chao Xu, and Boxin Shi. Evintsr-net: Event guided multiple latent frames reconstruction and super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4882-4891, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.164, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[14] Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita. Recurrent back-projection network for video superresolution. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3892-3901, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.235, + 0.892, + 0.304 + ], + "angle": 0, + "content": "[15] Earnest Paul Ijjina, Dhananjai Chand, Savyasachi Gupta, and K Goutham. Computer vision-based accident detection in traffic surveillance. In 2019 10th International conference on computing, communication and networking technologies (ICCCNT), pages 1-6. IEEE, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.305, + 0.892, + 0.375 + ], + "angle": 0, + "content": "[16] Takashi Isobe, Songjiang Li, Xu Jia, Shanxin Yuan, Gregory Slabaugh, Chunjing Xu, Ya-Li Li, Shengjin Wang, and Qi Tian. Video super-resolution with temporal group attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8008-8017, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.377, + 0.892, + 0.445 + ], + "angle": 0, + "content": "[17] Ajay Jain, Ben Mildenhall, Jonathan T Barron, Pieter Abbeel, and Ben Poole. Zero-shot text-guided object generation with dream fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 867-876, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.447, + 0.892, + 0.517 + ], + "angle": 0, + "content": "[18] Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, and Dacheng Tao. Turning frequency to resolution: Video super-resolution via event cameras. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7772-7781, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.519, + 0.892, + 0.588 + ], + "angle": 0, + "content": "[19] Younghyun Jo, Seoung Wug Oh, Jaeyeon Kang, and Seon Joo Kim. Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3224-3232, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.589, + 0.892, + 0.63 + ], + "angle": 0, + "content": "[20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.632, + 0.892, + 0.701 + ], + "angle": 0, + "content": "[21] Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE transactions on pattern analysis and machine intelligence, 41(11):2599-2613, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.703, + 0.892, + 0.773 + ], + "angle": 0, + "content": "[22] Sheng Li, Fengxiang He, Bo Du, Lefei Zhang, Yonghao Xu, and Dacheng Tao. Fast spatio-temporal residual network for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10522-10531, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.775, + 0.892, + 0.844 + ], + "angle": 0, + "content": "[23] Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, Xinchao Wang, and Thomas S Huang. Learning temporal dynamics for video super-resolution: A deep learning approach. IEEE Transactions on Image Processing, 27(7):3432-3445, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.845, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[24] Hongying Liu, Zhubo Ruan, Peng Zhao, Chao Dong, Fanhua Shang, Yuanyuan Liu, Linlin Yang, and Radu Timofte. Video super-resolution based on deep learning: a comprehensive survey. In Artif Intell Rev., 2022." + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1565" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.148 + ], + "angle": 0, + "content": "[25] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3202-3211, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.149, + 0.471, + 0.205 + ], + "angle": 0, + "content": "[26] Yunfan Lu, Yiqi Lin, Hao Wu, Yunhao Luo, Xu Zheng, and Lin Wang. All one needs to know about priors for deep image restoration and enhancement: A survey. arXiv preprint arXiv:2206.02070, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.207, + 0.471, + 0.275 + ], + "angle": 0, + "content": "[27] Zhisheng Lu, Juncheng Li, Hong Liu, Chaoyan Huang, Linlin Zhang, and Tieyong Zeng. Transformer for single image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 457-466, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.277, + 0.471, + 0.332 + ], + "angle": 0, + "content": "[28] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.334, + 0.471, + 0.389 + ], + "angle": 0, + "content": "[29] Mohammad Mostafavi, Yeongwoo Nam, Jonghyun Choi, and Kuk-Jin Yoon. E2sri: Learning to super-resolve intensity images from events. IEEE transactions on pattern analysis and machine intelligence, 44(10):6890-6909, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.391, + 0.471, + 0.474 + ], + "angle": 0, + "content": "[30] Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. Ntire 2019 challenge on video deblurring and superresolution: Dataset and study. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.476, + 0.471, + 0.532 + ], + "angle": 0, + "content": "[31] Daniel Neimark, Omri Bar, Maya Zohar, and Dotan Asselmann. Video transformer network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3163-3172, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.533, + 0.471, + 0.588 + ], + "angle": 0, + "content": "[32] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional generative neural feature fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11453-11464, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.589, + 0.471, + 0.658 + ], + "angle": 0, + "content": "[33] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.66, + 0.471, + 0.687 + ], + "angle": 0, + "content": "[34] Vadim Pisarevsky. Introduction to opencv. *Agenda*, 42:433–434, 2010." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.689, + 0.471, + 0.758 + ], + "angle": 0, + "content": "[35] Cedric Scheerlinck, Henri Rebecq, Timo Stoffregen, Nick Barnes, Robert Mahony, and Davide Scaramuzza. Ced: Color event camera dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0-0, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.76, + 0.471, + 0.815 + ], + "angle": 0, + "content": "[36] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462-7473, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.817, + 0.471, + 0.856 + ], + "angle": 0, + "content": "[37] Alex J Smola and Bernhard Schölkopf. A tutorial on support vector regression. Statistics and computing, 14(3):199-222, 2004." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[38] Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In Proceedings of the IEEE/CVF Conference" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.471, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "on Computer Vision and Pattern Recognition, pages 5459-5469, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.121, + 0.892, + 0.176 + ], + "angle": 0, + "content": "[39] Jiaxiang Tang, Xiaokang Chen, and Gang Zeng. Joint implicit image function for guided depth super-resolution. In Proceedings of the 29th ACM International Conference on Multimedia, pages 4390-4399, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.177, + 0.892, + 0.245 + ], + "angle": 0, + "content": "[40] Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu. Tdan: Temporally-deformable alignment network for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3360-3369, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.247, + 0.892, + 0.302 + ], + "angle": 0, + "content": "[41] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.303, + 0.892, + 0.357 + ], + "angle": 0, + "content": "[42] Bishan Wang, Jingwei He, Lei Yu, Gui-Song Xia, and Wen Yang. Event enhanced high-quality image recovery. In European Conference on Computer Vision, pages 155-171. Springer, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.359, + 0.892, + 0.413 + ], + "angle": 0, + "content": "[43] Bishan Wang, Jingwei He, Lei Yu, Gui-Song Xia, and Wen Yang. Event enhanced high-quality image recovery. In European Conference on Computer Vision, pages 155-171. Springer, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.414, + 0.892, + 0.497 + ], + "angle": 0, + "content": "[44] Lin Wang, Yujeong Chae, Sung-Hoon Yoon, Tae-Kyun Kim, and Kuk-Jin Yoon. Evdistill: Asynchronous events to end-task learning via bidirectional reconstruction-guided cross-modal knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 608-619, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.498, + 0.892, + 0.552 + ], + "angle": 0, + "content": "[45] Longguang Wang, Yulan Guo, Li Liu, Zaiping Lin, Xinpu Deng, and Wei An. Deep video super-resolution using hr optical flow estimation. IEEE Transactions on Image Processing, 29:4323-4336, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.554, + 0.892, + 0.622 + ], + "angle": 0, + "content": "[46] Lin Wang, Tae-Kyun Kim, and Kuk-Jin Yoon. Eventsr: From asynchronous events to image reconstruction, restoration, and super-resolution via end-to-end adversarial learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8315-8325, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.623, + 0.892, + 0.691 + ], + "angle": 0, + "content": "[47] Wei Wang, Haochen Zhang, Zehuan Yuan, and Changhu Wang. Unsupervised real-world super-resolution: A domain adaptation perspective. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4318-4327, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.692, + 0.892, + 0.733 + ], + "angle": 0, + "content": "[48] Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong, and Chen Change Loy. Basicsr. https://github.com/xinntao/BasicSR, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.735, + 0.892, + 0.803 + ], + "angle": 0, + "content": "[49] Xi Yang, Wangmeng Xiang, Hui Zeng, and Lei Zhang. Realworld video super-resolution: A benchmark dataset and a decomposition based learning scheme. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4781-4790, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.804, + 0.892, + 0.845 + ], + "angle": 0, + "content": "[50] Huanjing Yue, Zhiming Zhang, and Jingyu Yang. Realrawvsr: Real-world raw video super-resolution with a benchmark dataset. arXiv preprint arXiv:2209.12475, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.846, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[51] Xu Zheng, Yexin Liu, Yunfan Lu, Tongyan Hua, Tianbo Pan, Weiming Zhang, Dacheng Tao, and Lin Wang. Deep learning for event-based vision: A comprehensive survey and benchmarks. arXiv preprint arXiv:2302.08890, 2023." + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "1566" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.174 + ], + "angle": 0, + "content": "[52] Pengyuan Zhou, Jinjing Zhu, Yiting Wang, Yunfan Lu, Zixiang Wei, Haolin Shi, Yuchen Ding, Yu Gao, Qinglong Huang, Yan Shi, et al. Vetaverse: Technologies, applications, and visions toward the intersection of meta-verse, vehicles, and transportation systems. arXiv preprint arXiv:2210.15109, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.178, + 0.47, + 0.247 + ], + "angle": 0, + "content": "[53] Yunhao Zou, Yinqiang Zheng, Tsuyoshi Takatani, and Ying Fu. Learning to reconstruct high speed and high dynamic range videos from events. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2024-2033, 2021." + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.247 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.484, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "1567" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/7f46a567-7a9f-43dd-9afb-c2991cdfbe26_origin.pdf b/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/7f46a567-7a9f-43dd-9afb-c2991cdfbe26_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..5d04e65bf625baf0092b79d5e56078dc2ba98a76 --- /dev/null +++ b/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/7f46a567-7a9f-43dd-9afb-c2991cdfbe26_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:989c32be7b81d7199100138d76c91c5ff509187a36d390658a66102ab3c49d0d +size 4736077 diff --git a/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/full.md b/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/full.md new file mode 100644 index 0000000000000000000000000000000000000000..62fcc489b1f58da932306c2ef295ad21f1db0a56 --- /dev/null +++ b/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/full.md @@ -0,0 +1,313 @@ +# Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution + +Yunfan Lu $^{1}$ * Zipeng Wang $^{1}$ * Minjie Liu $^{1}$ † Hongjian Wang $^{2}$ † Lin Wang $^{1,3‡}$ $^{1}$ AI Thrust, HKUST(GZ) $^{2}$ Shenzhen International Graduate School, Tsinghua University + $^{3}$ Dept. of Computer Science and Engineering, HKUST + +{ylu066, zwang253, m1iu942}@connect.hkust-gz.edu.cn, whj20@mails.tsinghua.edu.cn, linwang@ust.hk + +# Abstract + +Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video superresolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the Spatial-Temporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https://vlis2022.github.io/cvpr23/egvsr. + +# 1. Introduction + +Video super-resolution (VSR) is a task of recovering high-resolution (HR) frames from successive multiple low-resolution (LR) frames. Unlike LR videos, HR videos contain more visual information, e.g., edge and texture, which + +![](images/1bdcd320c38cce6b2c2752c8cda7ff21014fc319f2245e931868b14c45d970cc.jpg) +Figure 1. (a) Our method learns implicit neural representations (INR) from the queried spatial-temporal coordinates (STF) and temporal features (TF) from RGB frames and events. (b) An example of VSR with random scale factors, $e.g., 6.5$ , by our method. + +can be very helpful for many tasks, e.g., metaverse [52], surveillance [15] and entertainment [4]. However, VSR is a highly ill-posed problem owing to the loss of both spatial and temporal information, especially in the real-world scenes [5, 6, 26, 45]. Recently, deep learning-based algorithms have been successfully applied to learn the intra-frame correlation and temporal consistency from the LR frames to recover HR frames, e.g., DUF [19], EDVR [48], RBPN [14], BasicVSR [5], BasicVSR++ [6]. However, due to the lack of inter-frame information, these methods are hampered by the limitations of modeling the spatial and temporal dependencies and may fail to recover HR frames in complex scenes. + +Event cameras are bio-inspired sensors that can asynchronously detect the per-pixel intensity changes and generate event streams with low latency (1us) and high dynamic range (HDR) compared with the conventional frame-based cameras (140dB vs. 60dB) [35, 51]. This has sparked extensive research in reconstructing image/video from events [12, 29, 42, 44, 46, 53]. However, the reconstructed results are less plausible due to the loss of visual + +details, e.g., structures, and textures. As a result, a recent work has utilized events for guiding VSR [18], trying to 'inject' energy from the event-based to the frame-based cameras. It leverages the high-frequency event data to synthesize neighboring frames, so as to find correspondences between consecutive frames. However, it only treats video frames in discrete ways with 2D arrays of pixels and up-samples them at a fixed up-scaling factor e.g., $\times 2$ or $\times 4$ . This causes inconvenience and inflexibility in the applications of SR videos, which often require arbitrary resolutions, i.e., random scales. + +Recently, some works tried to learn continuous image representations with arbitrary resolutions, e.g., LIIF [7], taking 2D queried coordinates and 2D features as input to learn an implicit neural representation (INR). VideoINR [8], on the other hand, decodes the LR videos into arbitrary spatial resolutions and frame rates by learning from the spatial coordinates and temporal coordinates, respectively. However, it is still unclear how to leverage events to guide learning spatial-temporal INRs for VSR. This is hampered by two challenges. Firstly, although event data can benefit VSR with its high-frequency temporal and spatial information, the large modality gap between the events and video frames makes it challenging to use INRs to represent the 3D spatial-temporal coordinates with event data. Moreover, there lacks HR real-world dataset with spatially well-aligned events and frames. + +In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantage of the high-temporal resolution property of events. Accordingly, we propose a novel framework that subtly incorporates the spatial-temporal interpolation from events to VSR in a unified framework, as shown in Fig. 1. Our key idea is to learn INRs from the queried spatial-temporal coordinates and features from both the RGB frames and events. Our framework mainly includes three parts. The Spatial-Temporal Fusion (STF) branch learns the spatial-temporal information from events and RGB frames (Sec. 3.2). The shallow feature fusion and deep feature fusion are employed to narrow the modality gap and fuse the events and RGB frames into 3D global spatial-temporal representations. Then, the Temporal Filter (TF) branch further unlocks more explicit motion information from events. It learns the 2D event features from events nearing the queried timestamp (Sec. 3.3). With the features from the STF and TF branches, the Spatial-Temporal Implicit Representation (STIR) module decodes the features and recovers SR frames with arbitrary spatial resolutions (Sec. 3.4). That is, given the arbitrary queried coordinates, we apply 3D sampling and 2D sampling to the fused 3D features and event data separately. Finally, the sampling features are added and fed into a decoder, and generate targeted SR frames. In addition, we collect a real-world dataset with a spatial reso + +lution of $3264 \times 2248$ , in which the events and RGB frames are spatially aligned. Extensive experiments on two real-world datasets show that our method surpasses the existing methods by 1.3 dB. + +In summary, the main contributions of this paper are five-fold: (I) Our work serves as the first attempt to address a non-trivial problem of learning INRs from events and RGB frames for VSR at random scales. (II) We propose the STF branch and the TF branch to model the spatial and temporal dependencies from events and RGB frames. (III) We propose the STIR module to reconstruct RGB frames with arbitrary spatial resolutions. (IV) We collect a high-quality real-world dataset with spatially aligned events and RGB frames. (V) Our method significantly surpasses the existing methods and achieves SR with random-scales, e.g., 6.5. + +# 2. Related Work + +Event-guided Video and Image SR Recently, event data has shown the potential to guide the image or video SR. eSL-Net [43] and EvIntSR [13] focus on employing events to guide the single image SR. Specifically, eSL-Net [43] feeds both the events and LR image to a sparse learning framework to recover an SR image. EvIntSR [13] first reconstructs the latent frames from the events and LR image, which are then fused to reconstruct the SR image. Differently, event-guided VSR takes consecutive frames and events as inputs and models both the spatial and temporal information. A recent work [18] proposed a two-stage method by 1) utilizing events to interpolate the LR video to get a high-frequency video and 2) rebuilding HR key frames. However, it encodes video frames in discrete ways and only up-samples videos at a fixed upscale factor, e.g., $\times 2$ . We make the first attempt to achieve VSR at random scales by learning the spatial-temporal implicit representations from events and video frames. + +Video Super-Resolution (VSR) The dominant research for VSR mainly focuses on designing learning pipelines [24], concerning the feature learning, frame alignment and multiframe fusion [3, 16, 22, 23, 30, 40, 47, 49, 50]. For example, Bao et.al [2] employed the motion compensation to achieve the frame alignment, while EDVR [48] proposes the deformable convolutions after extracting the features from the input frames. To address the feature propagation and alignment problem effectively, BasicVSR [5] and BasicVSR++ [6] proposed a succinct pipeline based on the bidirectional propagation and optical flow. To better exploit the temporal information of video frames, RBPN [14] extracts and propagates the spatial and temporal information of consecutive frames in a recurrent back-projection manner. Inspired by the random image upsampling, VideoINR [9] represents the frames with implicit representations and thus makes it possible to learn random-scale VSR. However, these works focus on a single modality. Differently, in our work, we + +![](images/921c0601a1d7398c258274f3bc5ed699088a2da7e2465a531632983ea9f73024.jpg) + +![](images/e5c0a2145cfacea2326b51ae590f21abfb41243f43870ce72b3e1c40d37fc886.jpg) +Figure 2. Overview of our proposed framework. Our method consists of three parts, the Spatial-Temporal Fusion (STF), the Temporal Filter (TF) and Spatial-Temporal Implicit Representation (STIR). These three parts are shown in (a) (b) (c) of this figure, respectively. Details of STF, TF and STIR are described in this Sec.3.2, Sec.3.3 and Sec.3.4, respectively. + +make the first attempt to address a novel problem of achieving VSR at random scales by taking advantage of the high-temporal resolution property of events. + +Implicit Neural Representation (INR) It also called the coordinate-based representation, aims at parameterizing signals, e.g., images and audio, in a continuous way via neural networks [36]. INR has been widely applied to 3D scene representation [28,33] and generative models [17,32], etc. Recently, INRs have been extensively studied for image and video SR. For instance, LIIF [7] achieves image SR with random scales given the 2D queried coordinates and 2D features. JIIF [39] uses HR images to guide the interpolation weights and values of LR depth maps. VideoINR [8] decodes the LR and low-frame-rate videos into an arbitrary spatial resolution and frame rate with three INRs. It adopts two INR functions to learn the spatial coordinates and the temporal coordinates, respectively. These two INR functions are then used to generate a motion flow field, which is applied back to warp the encoded features. Then, the warped features are decoded to recover the SR frame by the spatial INR function. Differently, considering the large modality gap between the events and video frames, we accordingly propose a novel INR module to directly represent the 3D spatial-temporal coordinates from event data. + +# 3. The Proposed Framework + +Event Representation As event streams are sparse points, we first describe how to stack them into the fixed-size representations as inputs of our framework. Events are produced by detecting variations in the log intensity of each pixel. An + +event $e = (x,y,t,p)$ is triggered and recorded when the logarithmic brightness change exceeds a certain threshold $\theta$ at pixel $(x,y)$ . This process can be described as Eq.1, where $\Delta L = \log (I_t^l + n) - \log (I_{t - \Delta t}^l + n)$ , $n$ is noise, $I_t^l$ and $I_{t - \Delta t}^l$ are intensity values in linear domain at timestamps $t$ and $t - \Delta t$ , respectively. + +$$ +p = \left\{ \begin{array}{l} + 1, \Delta L > \theta \\ - 1, \Delta L < - \theta \end{array} \right. \tag {1} +$$ + +According to Eq.1, the relation between frames $I_{t_0}^l (x,y)$ and $I_{t_1}^l (x,y)$ at timestamps $t_0$ and $t_1$ can be formulated as Eq.2, where $p$ is the polarity of event at pixel $(x,y)$ . + +$$ +I _ {t _ {1}} ^ {l} (x, y) = I _ {t _ {0}} ^ {l} (x, y) \times \exp \left(\theta \int_ {t _ {0}} ^ {t _ {1}} p d t\right) \tag {2} +$$ + +Events record the intensity changes with higher temporal resolution than frames, which is advantageous for VSR task [18]. We split events into $M$ moment segments [42] with a shape of $H \times W \times M \times 2$ as the input to our framework. Each segment keeps events that take place within time window. The window size is set to be a small constant to preserve the temporal information of events. + +# 3.1. Overview + +The overall framework of our method is depicted in Fig.2, which consists of three parts: (I) spatial-temporal fusion (STF) branch, (II) temporal filter (TF) branch, and (III) spatial-temporal implicit representation (STIR) module. The input of our framework are spatially aligned + +RGB video frames $V = \{V_0\ldots V_i\ldots V_n\}$ and events $E \in R^{H\times W\times M\times 2}$ , where $H\times W$ denotes the frame size. The output of our framework is a super-resolved video frame $I_{s,t}^{SR}$ with the up-sampling scale $s$ and the timestamp $t$ . Note that the values of $s$ and $t$ can be freely adjusted. In practice, $s$ is a real number greater than 1, and $t$ can take the timestamps of all frames. The spatial resolution of the SR frame $I_{s,t}^{SR}$ is $sW\times sH$ . + +Our framework consists of three major components. The STF branch learns the holistic spatial-temporal information from events and RGB frames (Sec.3.2). Then, the TF branch unlocks more explicit motion information from events. It learns 2D event features from events nearing the queried timestamp $t$ (Sec.3.3). Lastly, the STIR module decodes the features and recovers SR frames with arbitrary spatial resolutions (Sec.3.4). We now describe these components in detail in the following sections. + +# 3.2. Spatial-Temporal Fusion (STF) Branch + +This module aims to extract spatial and temporal information from events $E$ and RGB frames $V$ to obtain a global spatial-temporal representation $F_{ST}$ . The representation is a 3D feature map of $H\times W\times T\times C$ , where $T$ is the temporal dimension, and $C$ is the number of representation channels. The output $F_{ST}$ of STF branch $f_{STF}$ can be described as: + +$$ +F _ {S T} = f _ {S T F} (V, E) \tag {3} +$$ + +Specifically, as depicted in Fig. 2(a), we first employ two $1 \times 1$ convolutional layers to obtain the initial frame feature map $F_0^f$ and the event feature map $F_0^e$ with same dimension. As stated in [13], shallow features preserve sharper details and local structural information while deeper features preserve more semantic information. Thus, we design fusion blocks to aggregate both the shallow and deep features. + +Shallow feature fusion: As explored in [27], the residual architecture improves the model's capacity for representation and lessens the gradient vanishing issue. For this reason, we first employ two high preserving blocks (HPB) as the basic feature extractors to extract two shallow feature maps $F_{l}^{f}$ and $F_{l}^{e}$ from the initial feature maps $F_{0}^{f}$ and $F_{0}^{e}$ , respectively. After that, $F_{l}^{f}$ and $F_{l}^{e}$ are concatenated and then fed into a transformer-based fusion model to obtain a fused feature map so as to bridge the modality gap. The intuition behind this is that transformer has a larger perception field [1, 25, 25, 31, 41], which can be potentially used to model the global spatial and temporal dependencies from the frames and events. Then, the fused feature map is split into two parts in the channel dimension and added to $F_{l}^{f}$ and $F_{l}^{e}$ as the input for deep feature fusion. + +Deep feature fusion: After the shallow feature fusion, we again use two HPBs to extract deep features, which are then added and passed to the transformer-based fusion model to attain a 3D feature map $F_{ST}$ . Through shallow and deep + +feature fusion, we can better learn the temporal and spatial information from the events and frames. + +# 3.3. Temporal Filter (TF) Branch + +Through the STF branch, the spatial-temporal feature information from both the frames and events can be effectively learned. However, STF branch is insufficient to take full advantage of the high temporal resolution of event data. Therefore, we design the TF branch to explore more detailed motion information solely from events, which turns out to be effective in further enhancing the VSR performance, as demonstrated in our experiments (See Table 5). + +Intuitively, we design the TF branch $f_{TF}$ to capture the detailed motion information from events $E_{t,\Delta t}$ near the key frame at timestamp $t$ , where $\Delta t$ is a small time interval. TF branch first selects events from $t - \Delta t$ to $t + \Delta t$ (Fig. 2(c)). The selected events are interpolated and sent to three convolutional layers to learn the temporal features. Overall, the output $F_{T}$ of TF branch $f_{TF}$ can be described as: + +$$ +F _ {T} = f _ {T F} \left(E _ {t, \Delta t}\right) \tag {4} +$$ + +In Sec.4.4, we show that STF branch can capture pixel intensities, while the TF branch can capture the motion details, e.g., edges and corners. + +# 3.4. Spatial-Temporal Implicit Representation + +In this section, our goal is to learn continuous INRs for VSR based on the spatial-temporal feature map $F_{ST}$ and temporal feature map $F_{T}$ . The INRs are then used to decode coordinates at time $t$ with scale factor $s$ into RGB values. In this paper, we introduce the Spatial-Temporal Implicit Representation (STIR) module to accomplish the spatial-temporal VSR, as shown in Eq.5. We employ a simple-yet-effective 3D feature sampling and trilinear interpolation scheme to upsample $F_{ST}$ and $F_{T}$ to a desired resolution. A decoder, parameterized as a multi-layer CNN, is used to convert the interpolated features into RGB values. Fig. 2(c) depicts the detailed design of STIR. The output SR frame $I_{t,s}^{SR}$ of the STIR $f_{STIR}$ can be formulated as: + +$$ +I _ {t, s} ^ {S R} = f _ {S T I R} \left(F _ {S T}, F _ {T}\right), \forall s, t \tag {5} +$$ + +3D Feature Sampling: Here, we aim at generating a coordinate to make a query in grid form $F_{ST}$ . We uniformly sample a 3D coordinate grid, which can be expressed as $C_{t,s}$ with the dimension of $sH \times sW \times 3$ . Formally, for any query $q$ , the corresponding element $p_q$ in the 3D coordinate grid $C_{t,s}$ can be described as $p_q = (x_q, y_q, t_q)$ , where $x_q \in [0, H], y_q \in [0, W], t_q \in [t_s, t_e]$ , $t_s$ is the start time and $t_e$ is the end time of input. For each coordinate $p_q = (x_q, y_q, t_q)$ , we choose features of the nearest eight points around this coordinate in the 3D spatial-temporal feature $F_{ST}$ for interpolation. + +Feature Interpolation: Then, we compute the feature of a queried coordinate $p_{q}$ by using 3D interpolation techniques, + +such as trilinear interpolation. Inspired by the representation theory [37], complex signals in the low-dimensional space e.g., images, can be transformed as linear representations in the high-dimensional space e.g., features. From this theory, we can observe that the spatial-temporal feature $F_{ST}$ is indeed the high-dimensional feature representation for the low-dimensional image. We use linear interpolation to obtain features at the queried coordinate $p_q$ . In the experiments of Table 6, we have compared several interpolation methods, e.g., nearest sampling, and the results show that linear interpolation shows the best performance. + +In summary, for any scale $s$ and timestamp $t$ , the feature interpolation (i.e., 3D to 2D) process can be formulated by Eq. 6, given the 3D coordinate grid $C_{t,s}$ and spatial-temporal feature $F_{ST}$ . + +$$ +F _ {S F} = f _ {\text {s a m p l e}} \left(F _ {S T}, C _ {t, s}\right) \tag {6} +$$ + +Implicit Representation Decoding: Finally, the sampled 2D feature map $F_{SF}$ and the temporal feature map $F_{T}$ are added together and fed into the decoder. For simplicity and efficiency, we design three-layers CNN structure as the decoder. This is supported by the empirical experiment in Table 6, showing that a sample CNN block can achieve good results with low complexity. + +# 3.5. Loss Function + +We employ the Charbonnier loss [21] as our VSR supervision loss $\mathcal{L}_{SR}$ between the ground truth (GT) HR frame $I_{t,s}^{HR}$ and the output SR frame $I_{t,s}^{SR}$ at timestamp $t$ with upsampling scale $s$ , as shown in Eq. 7, where $\epsilon$ is $1e - 3$ . + +In training, the value range of $t$ is all timestamps of key frames $\{t_0, t_1 \dots t_n\}$ . $s$ could be a real number in the range [1.0, $s_{max}$ ], where $s_{max}$ is the maximum up-sampling scale during training depending on the resolution of training data. The loss function $\mathcal{L}$ is shown in Eq. 8. For example, when the resolution of input LR frame is set to be $128 \times 128$ and the GT HR frame is $1024 \times 1024$ , the $s_{max}$ is 8. + +$$ +\mathcal {L} _ {S R} = \sqrt {\left(I _ {t , s} ^ {S R} - I _ {t , s} ^ {H R}\right) ^ {2} + \epsilon^ {2}} \tag {7} +$$ + +$$ +\mathcal {L} = \sum_ {t \in t _ {0}, t _ {1} \dots t _ {n}} \left(\sum_ {s \in [ 1. 0, s _ {\max } ]} \mathcal {L} _ {S R} \left(I _ {t, s} ^ {S R}, I _ {t, s} ^ {H R}\right)\right) \tag {8} +$$ + +# 3.6. Real-world Dataset Collection + +Existing datasets, e.g., CED [35], suffer from the limited resolution $(346 \times 260)$ and severe noise, as shown in Fig.7. Although CED dataset provides the active pixel sensor (APS) frames, they are in low quality because they are simply demosaiced by OpenCV [34] from RAW data. Therefore, collecting HR and high-quality datasets with spatially aligned frames and events is important to inspire more research for the event-guided VSR problem. In this paper, we collected a new real-world dataset, called ALPIX- + +VSR, using a ALPIX-Eiger event camera1. The camera outputs well aligned RGB frames and events. The RGB frames enjoy a resolution of $3264 \times 2448$ and are generated by a carefully designed image signal processor(ISP) from RAW data with the Quad Bayer pattern [10], and the events have a resolution with $1632 \times 1224$ . + +Our ALPIX-VSR dataset consists of 26 video sequences with 5388 frames and well-aligned events in total. These sequences include diverse scenes, e.g., streets, buildings, flowers, textures, and machines. To avoid motion blur and low-light noise, we collect the dataset in bright indoor and sunny outdoor scenes. For more details about our real-world dataset, please refer to the supplementary material. + +# 4. Experiments + +# 4.1. Experiments Setting + +Implementation Details and Datasets: For all experiments, we use the Adam optimizer [20] with a learning rate of $1e - 4$ for CED dataset and $5e - 5$ for our ALPIX-VSR dataset. We train our framework for 100 epochs with a batch size of 2 using two NVIDIA RTX A30 GPU cards. + +Evaluation Metrics: We statistically assess the effectiveness of our approach using the peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM). + +Datasets: We use the CED [35] and the ALPIX-VSR dataset for experiments. 1) CED Dataset. It includes a collection of color events and video sequences in many scenes, e.g., indoor, outdoor, driving, human, calibration. The resolution of the frames and events is $346 \times 260$ . We follow the setting of E-VSR [18] to preprocess this dataset. Note that the RGB frames provided by CED are obtained by demosaicing [34] from raw frames and suffer from severe noise. 2) ALPIX-VSR Dataset. We select 20 videos for training and 6 videos for testing. The training and testing sets include 4212 and 1176 frames with aligned events, respectively. Note that we apply data augmentation strategy, such as random crop, to ALPIX-VSR dataset for all compared methods to avoid memory overflow during training. + +# 4.2. Comparison with SoTA Methods + +We compare our method with six SoTA methods under three VSR settings: (I) one SoTA event-guided, fixed-scale VSR method E-VSR [18] (II) one SoTA method of frame-based arbitrary-scale VSR method VideoINR [8] (III) five SoTA methods of frame-based, fixed-scale VSR methods: BasicVSR++ [6], RBPN [14], SOF [45], TDAN [40], DUF [19]. We report $\times 2$ and $\times 4$ super-resolution results of our method and all 6 comparison methods on CED dataset. We also compare our method with E-VSR and BasicVSR++ on our ALPIX-VSR dataset. Moreover, we compare our method with VideoINR on out-of-distribution + +
Clip NameDUF [19]*TDAN [40]SOF [45]RBPN [14]VideoINR [8]*E-VSR [18]Ours
people_dynamic<wave>32.02 / 0.933335.83 / 0.954033.32 / 0.936040.07 / 0.986827.47 / 0.822941.08 / 0.989138.78 / 0.9794
indoors_foosball_230.55 / 0.926232.12 / 0.933930.86 / 0.925334.15 / 0.973926.03 / 0.776634.77 / 0.977538.68 / 0.9750
simple_wires_230.08 / 0.938731.57 / 0.946630.12 / 0.932633.83 / 0.973926.77 / 0.832134.44 / 0.977338.67 / 0.9815
people_dynamic_dancing31.64 / 0.936935.73 / 0.956632.93 / 0.938839.56 / 0.986927.36 / 0.820240.49 / 0.989139.06 / 0.9798
people_dynamic_jumping31.57 / 0.933435.42 / 0.953632.79 / 0.934739.44 / 0.985927.24 / 0.818340.32 / 0.988038.93 / 0.9792
simple_fruit_fast37.46 / 0.944237.75 / 0.944037.22 / 0.939040.33 / 0.978227.21 / 0.845640.80 / 0.980141.96 / 0.9821
outdoor_jumping_infrared_225.33 / 0.816228.91 / 0.906226.67 / 0.874630.36 / 0.964826.88 / 0.822630.70 / 0.969838.03 / 0.9755
simple_carpet_fast31.43 / 0.881132.54 / 0.900631.83 / 0.877434.91 / 0.950224.21 / 0.590935.16 / 0.953636.14 / 0.9635
people_dynamic_armroll31.38 / 0.931135.55 / 0.954132.79 / 0.934540.05 / 0.987827.26 / 0.819341.00 / 0.989838.84 / 0.9787
indoors_kitchen_229.92 / 0.927330.67 / 0.932329.61 / 0.919231.51 / 0.955126.44 / 0.750231.79 / 0.958637.68 / 0.9726
people_dynamic_sitting30.62 / 0.933135.09 / 0.956132.13 / 0.936739.03 / 0.986227.63 / 0.823039.97 / 0.988438.86 / 0.9810
average PSNR/SSIM31.09 / 0.918333.74 / 0.939831.84 / 0.922636.66 / 0.975426.77 / 0.793837.32 / 0.978338.69 / 0.9771
+ +Table 1. Quantitative results (PSNR/SSIM) of the proposed our framework and other methods on the CED dataset for $\times 2$ . Because the official training code is not available, * denoted values were acquired from the pre-trained model that the authors have released. + +![](images/b49eb1d1e8221412ef24c5df280981e7edf79124595238555ef3c631f04f32af.jpg) +Figure 3. Visual results of $\times 4$ VSR on the CED dataset. Our method recovers more details, e.g., textual, edge than the SoTA event-guided VSR method E-VSR [18] and the random up-sampling VSR method Video INR [8]. + +scales to demonstrate our method's ability for arbitrary-scale VSR. Note that E-VSR only supports $\times 2$ and $\times 4$ SR, and BasicVSR++ only supports $\times 4$ SR. + +Evaluation on CED Dataset Table 1 and Table 2 present the quantitative results for $\times 2$ and $\times 4$ VSR, respectively. Our model clearly outperforms other methods in terms of PSNR, and shows a comparable performance in SSIM with E-VSR. The qualitative results in Fig. 3 demonstrate that our model is capable of recovering fine details, like sharp edges and detailed textures. We can see that event-guided methods (E-VSR and our method) yield better performances than the frame-based counterparts, showing the complementary effects of event data for VSR. Furthermore, + +VideoINR performs noticeably worse than other methods, indicating that frame-based implicit neural representations struggle with the low-resolution and high-noise input. Contrarily, our approach benefits from additional event information and generates satisfactory INRs. + +Table 2 shows quantitative results in $\times 4$ SR on the CED dataset, where our model achieves SoTA performance while remaining lightweight and efficient. Specifically, our model only accounts for one-200th of the E-VSR. BasicVSR++, the SoTA method of frame-based VSR, fails to perform well on CED dataset as the upsampling scale increases, which demonstrates its less robustness on the highly noisy dataset. + +Evaluation on the ALPIX-VSR Dataset The quantitative + +![](images/98be5bcb3365d831b5d5d2d3fbde8299a1b3f7423c27936cc30c0815e2b78895.jpg) +Figure 4. Results of $\times 4$ VSR on the ALPIX-VSR dataset. + +![](images/9cf36e686681e31c54d97a6595ef16c27e9c41e2755b2b6e3c470b7ed21388f3.jpg) +Figure 5. Results of $\times 8$ VSR on the ALPIX-VSR dataset. + +
MethodsModel Size(M)PSNRSSIM
DUF [19]1.9024.430.8177
TDAN [40]1.9727.880.8231
SOF [45]1.0027.000.8050
RBPN [14]12.1829.800.8975
BasicVSR++ [6]7.3014.760.1641
VideoINR [8]*11.3125.530.7871
E-VSR [18]412.4230.150.9052
Ours2.4531.120.9211
+ +Table 2. Quantitative results on CED dataset for $\times 4$ . * denotes the values obtained from the official pre-trained models. + +
MethodsPSNRSSIM
×2E-VSR36.100.9761
Ours38.250.9822
×4E-VSR32.540.9163
BasicVSR++35.300.9353
Ours37.120.9503
×6VideoINR*31.150.9084
Ours31.850.9267
×8VideoINR*28.110.8625
Ours28.530.8901
+ +and qualitative results are shown in Table 3 and Fig. 5. We present comparison results of our method with BasicVSR++ and E-VSR on our collected real-world dataset. + +# 4.3. Random Scale Up-sampling + +Results of random scale upsampling are shown in Table 3. We upsample the video frames to $\times 2$ , $\times 4$ , $\times 6$ + +Table 3. Quantitative comparison (PSNR/SSIM) of our methods and other methods on the ALPIX-VSR dataset. * denotes the values obtained from the official pre-trained models. + +
×1.8×2.6×5.6
Ours39.2508 / 0.980337.3408 / 0.958931.2549 / 0.9135
×6.6×7.1×7.8
Ours28.3182 / 0.877228.3188 / 0.8776228.3198 / 0.8783
+ +Table 4. Quantitative results(PSNR/SSIM) of random-scale comparison on theALPIX-VSR dataset. + +, $\times 8$ , and compare with the SoTA models, E-VSR and BasicVSR++. Though these two models are strictly constrained to a specific upsampling scale, our method presents superior performance on these settings. + +We also compare our method with VideoINR, the SoTA random-scale VSR method quantitatively in $\times 6$ , $\times 8$ . Results in Table 3 show that our model surpasses VideoINR in all evaluated scales of $\times 6$ , $\times 8$ . Furthermore, to evaluate the performance of our method on arbitrary scales, we conduct experiments of 6 random-chosen float scales. The results are shown in Table 4 and indicate our model's robustness across arbitrary random scales. It is easy to find that values of performance, e.g., PSNR, SSIM, and upsampling scales are not strictly monotonic. + +# 4.4. Ablation Studies and Discussion + +The following ablation experiments investigate the importance of each of our proposed modules. As it takes more than 120 hours to train a model on the complete CED dataset, we uniformly select $1/5$ of CED as the dataset for ablation experiments. + +
TF BranchShallow Feature FusionModel Size(M)PSNRSSIM
1ww2.451338.140.9820
2w/ow2.448237.960.9812
3ww/o1.736037.760.9729
+ +Table 5. Ablation of the TF branch and shallow feature fusion. + +
InterpolationDecoderLF ChannelsPSNRSSIM
1LinearCNN1638.140.9820
2NearestCNN1628.910.9131
3LinearSIREN1610.300.2997
4LinearMLP1637.940.9811
5LinearCNN836.820.9763
6LinearCNN2438.250.9825
+ +Efficiency of TF branch and Shallow Feature Fusion: Table 5 validates the contribution of TF branch and shallow feature fusion in STF branch. The removal of TF branch reduces both PSNR and SSIM scores, where PSNR drops by $0.18dB$ . In Fig. 6, we use PCA [11] to visualize the output of TF branch. As can be seen, $F_{T}$ focuses on edge and corner information, which is very helpful for VSR, especially texture recovery. We also find that removing shallow feature fusion results in a large performance drop (PSNR drops by $0.38dB$ and SSIM drops by nearly $0.1dB$ ). This finding indicates that fusion on shallow features is critical since shallow features carry rich local structure information which may be missing in deep features. + +Feature Interpolation: We apply a feature-based interpolation strategy, i.e. interpolating features near a coordinate and sending the interpolated feature into the decoder to reconstruct HR frames. Such strategy has been studied in a prior work on implicit neural representation learning for 3D objects [38] and shown to be able to recover clearer details and sharper edges. We further study the influence of interpolation methods, as shown in Table 6. The trilinear manner of interpolation yields better PSNR and SSIM scores compared with the nearest interpolation. + +Feature Decoder: We also compare the performance of different decoder designs, including MLP, SIREN [36] and CNN. Table 6 shows that decoding with CNN has the best performance among these methods, while non-convergence occurs with SIREN. We argue the reason CNN performs well is that decoding in STIR is only a dimensional reduction process, so no complicated design is required. + +Robustness to Noise: In comparison to BasicVSR++, our method not only performs SR but also removes noise on the CED dataset, as shown in Fig.7. Note that BasicVSR++ is a SoTA frame-based method in VSR task. We analyze that the poor performance of BasicVSR++ on CED is caused by the dependence for only frame modality and excessive emphasis on the frame's high-frequency information. High frequencies are often present in the image as edges, cor + +Table 6. Impacts of interpolation methods, decoder designs and channel size of the spatial-temporal features $F_{ST}$ . + +
PSNRSSIM
3 to 138.140.9820
5 to 338.040.9818
+ +Table 7. Ablation for the number of input and output frames. + +![](images/d82853ecb9cfd4000c76ca4f3d5bbfe667ca1779a1100c2e9588e2994e81bdcf.jpg) +Figure 6. Feature visualization of $F_{SF}$ (Eq. 6) and $F_{T}$ (Eq. 4). + +![](images/e11c66dd2545f1e24007106b812eaff1990d8536fbdb6a1a23500f357e9e9ecd.jpg) +(a) Basic VSR++ + +![](images/584f2cfeec7857800f686884586fcc498de46dc46ea844d959812eb1d21d98ab.jpg) +(b) Ours + +![](images/3e26b06353930604d5d7a160e15ee132f574621030c6ee77db67998ced9d2f35.jpg) +(c) GT +Figure 7. Comparison of noise removal capacity of BasicVSR++ [6] with our methods, with respect to HR GT. + +ners and noise. Therefore BasicVSR++ is easily affected by serious noise. In comparison to BasicVSR++, our framework has more robustness. Benefiting from the guidance of events, e.g., edges, corners, our method can effectively reduce the adverse effects of noise on frames. + +# 5. Conclusion + +In this paper, we proposed a novel framework which jointly learns INRs from RGB frames and events, and enables arbitrary scale VSR. Our method effectively uses high temporal resolution property of events to complement RGB frames with STF and TF. A simple yet effective STIR is used to recover frames at arbitrary scales. Extensive experiments on two real-world datasets validate our method enjoys better performance over current related SoTA methods with significantly lower model size. + +Limitation and Future Work: In this work we tackle the problem of spatial VSR. Since our work learns INRs of videos, it naturally enables frame interpolation, which will be explored in our future work. We hope our work can shed light on more research about enhancing implicit neural representations with non-photorealistic data. + +# 6. Acknowledgment + +This work was supported by the Research Project Fund of AlpsenTek and the National Natural Science Foundation of China (NSFC) under Grant No. NSFC22FYT45. + +# References + +[1] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6836-6846, 2021. +[2] Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang. Memc-net: Motion estimation and motion compensation driven neural network for video interpolation and enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3):933-948, 2021. +[3] Jose Caballero, Christian Ledig, Andrew Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, and Wenzhe Shi. Real-time video super-resolution with spatio-temporal networks and motion compensation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4778-4787, 2017. +[4] Daniel Canedo and António JR Neves. Facial expression recognition using computer vision: A systematic review. Applied Sciences, 9(21):4678, 2019. +[5] Kelvin C.K. Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Basicvsr: The search for essential components in video super-resolution and beyond. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2021. +[6] Kelvin C.K. Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improving video superresolution with enhanced propagation and alignment. 2022. +[7] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8628-8638, 2021. +[8] Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang, Humphrey Shi, and Xiaolong Wang. Videoirr: Learning video implicit neural representation for continuous space-time super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2047-2057, 2022. +[9] Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang, Humphrey Shi, and Xiaolong Wang. Videoirr: Learning video implicit neural representation for continuous space-time super-resolution. 2022. +[10] Minhyeok Cho, Haechang Lee, Hyunwoo Je, Kijeong Kim, Dongil Ryu, Jinsu Kim, Jonghyun Bae, and Albert No. Pynet-qxq: A distilled pynet for qxq bayer pattern demosaicing in cmos image sensor. arXiv preprint arXiv:2203.04314, 2022. +[11] Andreas Daffertshofer, Claudine JC Lamoth, Onno G Meijer, and Peter J Beek. Pca in studying coordination and variability: a tutorial. Clinical biomechanics, 19(4):415-428, 2004. +[12] Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J Davison, Jörg Conradt, Kostas Daniilidis, et al. Event-based vision: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(1):154-180, 2020. + +[13] Jin Han, Yixin Yang, Chu Zhou, Chao Xu, and Boxin Shi. Evintsr-net: Event guided multiple latent frames reconstruction and super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4882-4891, 2021. +[14] Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita. Recurrent back-projection network for video superresolution. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3892-3901, 2019. +[15] Earnest Paul Ijjina, Dhananjai Chand, Savyasachi Gupta, and K Goutham. Computer vision-based accident detection in traffic surveillance. In 2019 10th International conference on computing, communication and networking technologies (ICCCNT), pages 1-6. IEEE, 2019. +[16] Takashi Isobe, Songjiang Li, Xu Jia, Shanxin Yuan, Gregory Slabaugh, Chunjing Xu, Ya-Li Li, Shengjin Wang, and Qi Tian. Video super-resolution with temporal group attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8008-8017, 2020. +[17] Ajay Jain, Ben Mildenhall, Jonathan T Barron, Pieter Abbeel, and Ben Poole. Zero-shot text-guided object generation with dream fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 867-876, 2022. +[18] Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, and Dacheng Tao. Turning frequency to resolution: Video super-resolution via event cameras. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7772-7781, 2021. +[19] Younghyun Jo, Seoung Wug Oh, Jaeyeon Kang, and Seon Joo Kim. Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3224-3232, 2018. +[20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. +[21] Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE transactions on pattern analysis and machine intelligence, 41(11):2599-2613, 2018. +[22] Sheng Li, Fengxiang He, Bo Du, Lefei Zhang, Yonghao Xu, and Dacheng Tao. Fast spatio-temporal residual network for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10522-10531, 2019. +[23] Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, Xinchao Wang, and Thomas S Huang. Learning temporal dynamics for video super-resolution: A deep learning approach. IEEE Transactions on Image Processing, 27(7):3432-3445, 2018. +[24] Hongying Liu, Zhubo Ruan, Peng Zhao, Chao Dong, Fanhua Shang, Yuanyuan Liu, Linlin Yang, and Radu Timofte. Video super-resolution based on deep learning: a comprehensive survey. In Artif Intell Rev., 2022. + +[25] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3202-3211, 2022. +[26] Yunfan Lu, Yiqi Lin, Hao Wu, Yunhao Luo, Xu Zheng, and Lin Wang. All one needs to know about priors for deep image restoration and enhancement: A survey. arXiv preprint arXiv:2206.02070, 2022. +[27] Zhisheng Lu, Juncheng Li, Hong Liu, Chaoyan Huang, Linlin Zhang, and Tieyong Zeng. Transformer for single image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 457-466, 2022. +[28] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. +[29] Mohammad Mostafavi, Yeongwoo Nam, Jonghyun Choi, and Kuk-Jin Yoon. E2sri: Learning to super-resolve intensity images from events. IEEE transactions on pattern analysis and machine intelligence, 44(10):6890-6909, 2021. +[30] Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. Ntire 2019 challenge on video deblurring and superresolution: Dataset and study. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019. +[31] Daniel Neimark, Omri Bar, Maya Zohar, and Dotan Asselmann. Video transformer network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3163-3172, 2021. +[32] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional generative neural feature fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11453-11464, 2021. +[33] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019. +[34] Vadim Pisarevsky. Introduction to opencv. *Agenda*, 42:433–434, 2010. +[35] Cedric Scheerlinck, Henri Rebecq, Timo Stoffregen, Nick Barnes, Robert Mahony, and Davide Scaramuzza. Ced: Color event camera dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0-0, 2019. +[36] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462-7473, 2020. +[37] Alex J Smola and Bernhard Schölkopf. A tutorial on support vector regression. Statistics and computing, 14(3):199-222, 2004. +[38] Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In Proceedings of the IEEE/CVF Conference + +on Computer Vision and Pattern Recognition, pages 5459-5469, 2022. +[39] Jiaxiang Tang, Xiaokang Chen, and Gang Zeng. Joint implicit image function for guided depth super-resolution. In Proceedings of the 29th ACM International Conference on Multimedia, pages 4390-4399, 2021. +[40] Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu. Tdan: Temporally-deformable alignment network for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3360-3369, 2020. +[41] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. +[42] Bishan Wang, Jingwei He, Lei Yu, Gui-Song Xia, and Wen Yang. Event enhanced high-quality image recovery. In European Conference on Computer Vision, pages 155-171. Springer, 2020. +[43] Bishan Wang, Jingwei He, Lei Yu, Gui-Song Xia, and Wen Yang. Event enhanced high-quality image recovery. In European Conference on Computer Vision, pages 155-171. Springer, 2020. +[44] Lin Wang, Yujeong Chae, Sung-Hoon Yoon, Tae-Kyun Kim, and Kuk-Jin Yoon. Evdistill: Asynchronous events to end-task learning via bidirectional reconstruction-guided cross-modal knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 608-619, 2021. +[45] Longguang Wang, Yulan Guo, Li Liu, Zaiping Lin, Xinpu Deng, and Wei An. Deep video super-resolution using hr optical flow estimation. IEEE Transactions on Image Processing, 29:4323-4336, 2020. +[46] Lin Wang, Tae-Kyun Kim, and Kuk-Jin Yoon. Eventsr: From asynchronous events to image reconstruction, restoration, and super-resolution via end-to-end adversarial learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8315-8325, 2020. +[47] Wei Wang, Haochen Zhang, Zehuan Yuan, and Changhu Wang. Unsupervised real-world super-resolution: A domain adaptation perspective. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4318-4327, 2021. +[48] Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong, and Chen Change Loy. Basicsr. https://github.com/xinntao/BasicSR, 2020. +[49] Xi Yang, Wangmeng Xiang, Hui Zeng, and Lei Zhang. Realworld video super-resolution: A benchmark dataset and a decomposition based learning scheme. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4781-4790, 2021. +[50] Huanjing Yue, Zhiming Zhang, and Jingyu Yang. Realrawvsr: Real-world raw video super-resolution with a benchmark dataset. arXiv preprint arXiv:2209.12475, 2022. +[51] Xu Zheng, Yexin Liu, Yunfan Lu, Tongyan Hua, Tianbo Pan, Weiming Zhang, Dacheng Tao, and Lin Wang. Deep learning for event-based vision: A comprehensive survey and benchmarks. arXiv preprint arXiv:2302.08890, 2023. + +[52] Pengyuan Zhou, Jinjing Zhu, Yiting Wang, Yunfan Lu, Zixiang Wei, Haolin Shi, Yuchen Ding, Yu Gao, Qinglong Huang, Yan Shi, et al. Vetaverse: Technologies, applications, and visions toward the intersection of meta-verse, vehicles, and transportation systems. arXiv preprint arXiv:2210.15109, 2022. +[53] Yunhao Zou, Yinqiang Zheng, Tsuyoshi Takatani, and Ying Fu. Learning to reconstruct high speed and high dynamic range videos from events. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2024-2033, 2021. \ No newline at end of file diff --git a/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/images.zip b/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..19deb17189088533f43b40f5ea645edb224d7e46 --- /dev/null +++ b/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2265ff20bfca37d4961ddfffc7c50162c4b65643f62eb2d82e5ed892c4ca0c9d +size 892638 diff --git a/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/layout.json b/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..3159bf10585be61a6018b2778669a894227c18f7 --- /dev/null +++ b/2023/Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution/layout.json @@ -0,0 +1,9861 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 56, + 102, + 538, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 102, + 538, + 140 + ], + "spans": [ + { + "bbox": [ + 56, + 102, + 538, + 140 + ], + "type": "text", + "content": "Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "spans": [ + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "text", + "content": "Yunfan Lu" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "text", + "content": " * Zipeng Wang" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "text", + "content": " * Minjie Liu" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "text", + "content": " † Hongjian Wang" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "text", + "content": " † Lin Wang" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "inline_equation", + "content": "^{1,3‡}" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "text", + "content": "AI Thrust, HKUST(GZ) " + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "text", + "content": "Shenzhen International Graduate School, Tsinghua University \n" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 88, + 160, + 531, + 203 + ], + "type": "text", + "content": "Dept. of Computer Science and Engineering, HKUST" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 205, + 561, + 217 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 205, + 561, + 217 + ], + "spans": [ + { + "bbox": [ + 53, + 205, + 561, + 217 + ], + "type": "text", + "content": "{ylu066, zwang253, m1iu942}@connect.hkust-gz.edu.cn, whj20@mails.tsinghua.edu.cn, linwang@ust.hk" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 143, + 244, + 192, + 258 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 244, + 192, + 258 + ], + "spans": [ + { + "bbox": [ + 143, + 244, + 192, + 258 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 270, + 290, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 270, + 290, + 605 + ], + "spans": [ + { + "bbox": [ + 46, + 270, + 290, + 605 + ], + "type": "text", + "content": "Event cameras sense the intensity changes asynchronously and produce event streams with high dynamic range and low latency. This has inspired research endeavors utilizing events to guide the challenging video superresolution (VSR) task. In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantages of the high temporal resolution property of events. This is hampered by the difficulties of representing the spatial-temporal information of events when guiding VSR. To this end, we propose a novel framework that incorporates the spatial-temporal interpolation of events to VSR in a unified framework. Our key idea is to learn implicit neural representations from queried spatial-temporal coordinates and features from both RGB frames and events. Our method contains three parts. Specifically, the Spatial-Temporal Fusion (STF) module first learns the 3D features from events and RGB frames. Then, the Temporal Filter (TF) module unlocks more explicit motion information from the events near the queried timestamp and generates the 2D features. Lastly, the Spatial-Temporal Implicit Representation (STIR) module recovers the SR frame in arbitrary resolutions from the outputs of these two modules. In addition, we collect a real-world dataset with spatially aligned events and RGB frames. Extensive experiments show that our method significantly surpasses the prior-arts and achieves VSR with random scales, e.g., 6.5. Code and dataset are available at https://vlis2022.github.io/cvpr23/egvsr." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 609, + 128, + 623 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 609, + 128, + 623 + ], + "spans": [ + { + "bbox": [ + 47, + 609, + 128, + 623 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 629, + 287, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 629, + 287, + 677 + ], + "spans": [ + { + "bbox": [ + 46, + 629, + 287, + 677 + ], + "type": "text", + "content": "Video super-resolution (VSR) is a task of recovering high-resolution (HR) frames from successive multiple low-resolution (LR) frames. Unlike LR videos, HR videos contain more visual information, e.g., edge and texture, which" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 309, + 245, + 545, + 399 + ], + "blocks": [ + { + "bbox": [ + 309, + 245, + 545, + 399 + ], + "lines": [ + { + "bbox": [ + 309, + 245, + 545, + 399 + ], + "spans": [ + { + "bbox": [ + 309, + 245, + 545, + 399 + ], + "type": "image", + "image_path": "1bdcd320c38cce6b2c2752c8cda7ff21014fc319f2245e931868b14c45d970cc.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 402, + 547, + 448 + ], + "lines": [ + { + "bbox": [ + 305, + 402, + 547, + 448 + ], + "spans": [ + { + "bbox": [ + 305, + 402, + 547, + 448 + ], + "type": "text", + "content": "Figure 1. (a) Our method learns implicit neural representations (INR) from the queried spatial-temporal coordinates (STF) and temporal features (TF) from RGB frames and events. (b) An example of VSR with random scale factors, " + }, + { + "bbox": [ + 305, + 402, + 547, + 448 + ], + "type": "inline_equation", + "content": "e.g., 6.5" + }, + { + "bbox": [ + 305, + 402, + 547, + 448 + ], + "type": "text", + "content": ", by our method." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 459, + 546, + 614 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 459, + 546, + 614 + ], + "spans": [ + { + "bbox": [ + 304, + 459, + 546, + 614 + ], + "type": "text", + "content": "can be very helpful for many tasks, e.g., metaverse [52], surveillance [15] and entertainment [4]. However, VSR is a highly ill-posed problem owing to the loss of both spatial and temporal information, especially in the real-world scenes [5, 6, 26, 45]. Recently, deep learning-based algorithms have been successfully applied to learn the intra-frame correlation and temporal consistency from the LR frames to recover HR frames, e.g., DUF [19], EDVR [48], RBPN [14], BasicVSR [5], BasicVSR++ [6]. However, due to the lack of inter-frame information, these methods are hampered by the limitations of modeling the spatial and temporal dependencies and may fail to recover HR frames in complex scenes." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 618, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 618, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 618, + 547, + 714 + ], + "type": "text", + "content": "Event cameras are bio-inspired sensors that can asynchronously detect the per-pixel intensity changes and generate event streams with low latency (1us) and high dynamic range (HDR) compared with the conventional frame-based cameras (140dB vs. 60dB) [35, 51]. This has sparked extensive research in reconstructing image/video from events [12, 29, 42, 44, 46, 53]. However, the reconstructed results are less plausible due to the loss of visual" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 683, + 173, + 693 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 683, + 173, + 693 + ], + "spans": [ + { + "bbox": [ + 57, + 683, + 173, + 693 + ], + "type": "text", + "content": "*These authors are co-first authors." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 59, + 693, + 182, + 702 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 693, + 182, + 702 + ], + "spans": [ + { + "bbox": [ + 59, + 693, + 182, + 702 + ], + "type": "text", + "content": "These authors are co-second authors." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 59, + 703, + 135, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 703, + 135, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 703, + 135, + 712 + ], + "type": "inline_equation", + "content": "\\ddagger" + }, + { + "bbox": [ + 59, + 703, + 135, + 712 + ], + "type": "text", + "content": " Corresponding author" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "1557" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "content": "details, e.g., structures, and textures. As a result, a recent work has utilized events for guiding VSR [18], trying to 'inject' energy from the event-based to the frame-based cameras. It leverages the high-frequency event data to synthesize neighboring frames, so as to find correspondences between consecutive frames. However, it only treats video frames in discrete ways with 2D arrays of pixels and up-samples them at a fixed up-scaling factor e.g., " + }, + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "inline_equation", + "content": "\\times 2" + }, + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "content": " or " + }, + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "content": ". This causes inconvenience and inflexibility in the applications of SR videos, which often require arbitrary resolutions, i.e., random scales." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 207, + 289, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 207, + 289, + 399 + ], + "spans": [ + { + "bbox": [ + 46, + 207, + 289, + 399 + ], + "type": "text", + "content": "Recently, some works tried to learn continuous image representations with arbitrary resolutions, e.g., LIIF [7], taking 2D queried coordinates and 2D features as input to learn an implicit neural representation (INR). VideoINR [8], on the other hand, decodes the LR videos into arbitrary spatial resolutions and frame rates by learning from the spatial coordinates and temporal coordinates, respectively. However, it is still unclear how to leverage events to guide learning spatial-temporal INRs for VSR. This is hampered by two challenges. Firstly, although event data can benefit VSR with its high-frequency temporal and spatial information, the large modality gap between the events and video frames makes it challenging to use INRs to represent the 3D spatial-temporal coordinates with event data. Moreover, there lacks HR real-world dataset with spatially well-aligned events and frames." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 403, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 403, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 403, + 289, + 715 + ], + "type": "text", + "content": "In this paper, we make the first attempt to address a novel problem of achieving VSR at random scales by taking advantage of the high-temporal resolution property of events. Accordingly, we propose a novel framework that subtly incorporates the spatial-temporal interpolation from events to VSR in a unified framework, as shown in Fig. 1. Our key idea is to learn INRs from the queried spatial-temporal coordinates and features from both the RGB frames and events. Our framework mainly includes three parts. The Spatial-Temporal Fusion (STF) branch learns the spatial-temporal information from events and RGB frames (Sec. 3.2). The shallow feature fusion and deep feature fusion are employed to narrow the modality gap and fuse the events and RGB frames into 3D global spatial-temporal representations. Then, the Temporal Filter (TF) branch further unlocks more explicit motion information from events. It learns the 2D event features from events nearing the queried timestamp (Sec. 3.3). With the features from the STF and TF branches, the Spatial-Temporal Implicit Representation (STIR) module decodes the features and recovers SR frames with arbitrary spatial resolutions (Sec. 3.4). That is, given the arbitrary queried coordinates, we apply 3D sampling and 2D sampling to the fused 3D features and event data separately. Finally, the sampling features are added and fed into a decoder, and generate targeted SR frames. In addition, we collect a real-world dataset with a spatial reso" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "text", + "content": "lution of " + }, + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "3264 \\times 2248" + }, + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "text", + "content": ", in which the events and RGB frames are spatially aligned. Extensive experiments on two real-world datasets show that our method surpasses the existing methods by 1.3 dB." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 121, + 547, + 253 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 121, + 547, + 253 + ], + "spans": [ + { + "bbox": [ + 304, + 121, + 547, + 253 + ], + "type": "text", + "content": "In summary, the main contributions of this paper are five-fold: (I) Our work serves as the first attempt to address a non-trivial problem of learning INRs from events and RGB frames for VSR at random scales. (II) We propose the STF branch and the TF branch to model the spatial and temporal dependencies from events and RGB frames. (III) We propose the STIR module to reconstruct RGB frames with arbitrary spatial resolutions. (IV) We collect a high-quality real-world dataset with spatially aligned events and RGB frames. (V) Our method significantly surpasses the existing methods and achieves SR with random-scales, e.g., 6.5." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 306, + 262, + 392, + 275 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 262, + 392, + 275 + ], + "spans": [ + { + "bbox": [ + 306, + 262, + 392, + 275 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 282, + 545, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 282, + 545, + 498 + ], + "spans": [ + { + "bbox": [ + 304, + 282, + 545, + 498 + ], + "type": "text", + "content": "Event-guided Video and Image SR Recently, event data has shown the potential to guide the image or video SR. eSL-Net [43] and EvIntSR [13] focus on employing events to guide the single image SR. Specifically, eSL-Net [43] feeds both the events and LR image to a sparse learning framework to recover an SR image. EvIntSR [13] first reconstructs the latent frames from the events and LR image, which are then fused to reconstruct the SR image. Differently, event-guided VSR takes consecutive frames and events as inputs and models both the spatial and temporal information. A recent work [18] proposed a two-stage method by 1) utilizing events to interpolate the LR video to get a high-frequency video and 2) rebuilding HR key frames. However, it encodes video frames in discrete ways and only up-samples videos at a fixed upscale factor, e.g., " + }, + { + "bbox": [ + 304, + 282, + 545, + 498 + ], + "type": "inline_equation", + "content": "\\times 2" + }, + { + "bbox": [ + 304, + 282, + 545, + 498 + ], + "type": "text", + "content": ". We make the first attempt to achieve VSR at random scales by learning the spatial-temporal implicit representations from events and video frames." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 498, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 547, + 714 + ], + "type": "text", + "content": "Video Super-Resolution (VSR) The dominant research for VSR mainly focuses on designing learning pipelines [24], concerning the feature learning, frame alignment and multiframe fusion [3, 16, 22, 23, 30, 40, 47, 49, 50]. For example, Bao et.al [2] employed the motion compensation to achieve the frame alignment, while EDVR [48] proposes the deformable convolutions after extracting the features from the input frames. To address the feature propagation and alignment problem effectively, BasicVSR [5] and BasicVSR++ [6] proposed a succinct pipeline based on the bidirectional propagation and optical flow. To better exploit the temporal information of video frames, RBPN [14] extracts and propagates the spatial and temporal information of consecutive frames in a recurrent back-projection manner. Inspired by the random image upsampling, VideoINR [9] represents the frames with implicit representations and thus makes it possible to learn random-scale VSR. However, these works focus on a single modality. Differently, in our work, we" + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1558" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 76, + 542, + 222 + ], + "blocks": [ + { + "bbox": [ + 53, + 76, + 542, + 222 + ], + "lines": [ + { + "bbox": [ + 53, + 76, + 542, + 222 + ], + "spans": [ + { + "bbox": [ + 53, + 76, + 542, + 222 + ], + "type": "image", + "image_path": "921c0601a1d7398c258274f3bc5ed699088a2da7e2465a531632983ea9f73024.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 53, + 224, + 542, + 296 + ], + "blocks": [ + { + "bbox": [ + 53, + 224, + 542, + 296 + ], + "lines": [ + { + "bbox": [ + 53, + 224, + 542, + 296 + ], + "spans": [ + { + "bbox": [ + 53, + 224, + 542, + 296 + ], + "type": "image", + "image_path": "e5c0a2145cfacea2326b51ae590f21abfb41243f43870ce72b3e1c40d37fc886.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 303, + 546, + 338 + ], + "lines": [ + { + "bbox": [ + 46, + 303, + 546, + 338 + ], + "spans": [ + { + "bbox": [ + 46, + 303, + 546, + 338 + ], + "type": "text", + "content": "Figure 2. Overview of our proposed framework. Our method consists of three parts, the Spatial-Temporal Fusion (STF), the Temporal Filter (TF) and Spatial-Temporal Implicit Representation (STIR). These three parts are shown in (a) (b) (c) of this figure, respectively. Details of STF, TF and STIR are described in this Sec.3.2, Sec.3.3 and Sec.3.4, respectively." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 348, + 287, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 348, + 287, + 384 + ], + "spans": [ + { + "bbox": [ + 46, + 348, + 287, + 384 + ], + "type": "text", + "content": "make the first attempt to address a novel problem of achieving VSR at random scales by taking advantage of the high-temporal resolution property of events." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 384, + 288, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 384, + 288, + 636 + ], + "spans": [ + { + "bbox": [ + 46, + 384, + 288, + 636 + ], + "type": "text", + "content": "Implicit Neural Representation (INR) It also called the coordinate-based representation, aims at parameterizing signals, e.g., images and audio, in a continuous way via neural networks [36]. INR has been widely applied to 3D scene representation [28,33] and generative models [17,32], etc. Recently, INRs have been extensively studied for image and video SR. For instance, LIIF [7] achieves image SR with random scales given the 2D queried coordinates and 2D features. JIIF [39] uses HR images to guide the interpolation weights and values of LR depth maps. VideoINR [8] decodes the LR and low-frame-rate videos into an arbitrary spatial resolution and frame rate with three INRs. It adopts two INR functions to learn the spatial coordinates and the temporal coordinates, respectively. These two INR functions are then used to generate a motion flow field, which is applied back to warp the encoded features. Then, the warped features are decoded to recover the SR frame by the spatial INR function. Differently, considering the large modality gap between the events and video frames, we accordingly propose a novel INR module to directly represent the 3D spatial-temporal coordinates from event data." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 644, + 196, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 644, + 196, + 658 + ], + "spans": [ + { + "bbox": [ + 47, + 644, + 196, + 658 + ], + "type": "text", + "content": "3. The Proposed Framework" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 665, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 715 + ], + "type": "text", + "content": "Event Representation As event streams are sparse points, we first describe how to stack them into the fixed-size representations as inputs of our framework. Events are produced by detecting variations in the log intensity of each pixel. An" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "spans": [ + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "text", + "content": "event " + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "inline_equation", + "content": "e = (x,y,t,p)" + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "text", + "content": " is triggered and recorded when the logarithmic brightness change exceeds a certain threshold " + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "text", + "content": " at pixel " + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "inline_equation", + "content": "(x,y)" + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "text", + "content": ". This process can be described as Eq.1, where " + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "inline_equation", + "content": "\\Delta L = \\log (I_t^l + n) - \\log (I_{t - \\Delta t}^l + n)" + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "text", + "content": " is noise, " + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "inline_equation", + "content": "I_t^l" + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "inline_equation", + "content": "I_{t - \\Delta t}^l" + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "text", + "content": " are intensity values in linear domain at timestamps " + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "inline_equation", + "content": "t - \\Delta t" + }, + { + "bbox": [ + 305, + 348, + 546, + 422 + ], + "type": "text", + "content": ", respectively." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 381, + 429, + 545, + 462 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 381, + 429, + 545, + 462 + ], + "spans": [ + { + "bbox": [ + 381, + 429, + 545, + 462 + ], + "type": "interline_equation", + "content": "p = \\left\\{ \\begin{array}{l} + 1, \\Delta L > \\theta \\\\ - 1, \\Delta L < - \\theta \\end{array} \\right. \\tag {1}", + "image_path": "2e889401bdf4cdfecf42b19599e083f9672c8338c43681004d38996317be2e34.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "spans": [ + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "text", + "content": "According to Eq.1, the relation between frames " + }, + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "inline_equation", + "content": "I_{t_0}^l (x,y)" + }, + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "inline_equation", + "content": "I_{t_1}^l (x,y)" + }, + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "text", + "content": " at timestamps " + }, + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "inline_equation", + "content": "t_0" + }, + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "inline_equation", + "content": "t_1" + }, + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "text", + "content": " can be formulated as Eq.2, where " + }, + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "text", + "content": " is the polarity of event at pixel " + }, + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "inline_equation", + "content": "(x,y)" + }, + { + "bbox": [ + 305, + 467, + 545, + 504 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 343, + 512, + 545, + 540 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 512, + 545, + 540 + ], + "spans": [ + { + "bbox": [ + 343, + 512, + 545, + 540 + ], + "type": "interline_equation", + "content": "I _ {t _ {1}} ^ {l} (x, y) = I _ {t _ {0}} ^ {l} (x, y) \\times \\exp \\left(\\theta \\int_ {t _ {0}} ^ {t _ {1}} p d t\\right) \\tag {2}", + "image_path": "bf473c14f381a2102cd71b9f06889c0675af91543ebca7b8a8eb5e444595f9b2.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "spans": [ + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "text", + "content": "Events record the intensity changes with higher temporal resolution than frames, which is advantageous for VSR task [18]. We split events into " + }, + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "text", + "content": " moment segments [42] with a shape of " + }, + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "inline_equation", + "content": "H \\times W \\times M \\times 2" + }, + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "text", + "content": " as the input to our framework. Each segment keeps events that take place within time window. The window size is set to be a small constant to preserve the temporal information of events." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 635, + 373, + 646 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 635, + 373, + 646 + ], + "spans": [ + { + "bbox": [ + 306, + 635, + 373, + 646 + ], + "type": "text", + "content": "3.1. Overview" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 653, + 546, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 546, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 546, + 714 + ], + "type": "text", + "content": "The overall framework of our method is depicted in Fig.2, which consists of three parts: (I) spatial-temporal fusion (STF) branch, (II) temporal filter (TF) branch, and (III) spatial-temporal implicit representation (STIR) module. The input of our framework are spatially aligned" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1559" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "content": "RGB video frames " + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "inline_equation", + "content": "V = \\{V_0\\ldots V_i\\ldots V_n\\}" + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "content": " and events " + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "inline_equation", + "content": "E \\in R^{H\\times W\\times M\\times 2}" + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "inline_equation", + "content": "H\\times W" + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "content": " denotes the frame size. The output of our framework is a super-resolved video frame " + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "inline_equation", + "content": "I_{s,t}^{SR}" + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "content": " with the up-sampling scale " + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "content": " and the timestamp " + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "content": ". Note that the values of " + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "content": " can be freely adjusted. In practice, " + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "content": " is a real number greater than 1, and " + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "content": " can take the timestamps of all frames. The spatial resolution of the SR frame " + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "inline_equation", + "content": "I_{s,t}^{SR}" + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "content": " is " + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "inline_equation", + "content": "sW\\times sH" + }, + { + "bbox": [ + 47, + 72, + 287, + 168 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 169, + 288, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 169, + 288, + 277 + ], + "spans": [ + { + "bbox": [ + 46, + 169, + 288, + 277 + ], + "type": "text", + "content": "Our framework consists of three major components. The STF branch learns the holistic spatial-temporal information from events and RGB frames (Sec.3.2). Then, the TF branch unlocks more explicit motion information from events. It learns 2D event features from events nearing the queried timestamp " + }, + { + "bbox": [ + 46, + 169, + 288, + 277 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 169, + 288, + 277 + ], + "type": "text", + "content": " (Sec.3.3). Lastly, the STIR module decodes the features and recovers SR frames with arbitrary spatial resolutions (Sec.3.4). We now describe these components in detail in the following sections." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 286, + 253, + 300 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 286, + 253, + 300 + ], + "spans": [ + { + "bbox": [ + 47, + 286, + 253, + 300 + ], + "type": "text", + "content": "3.2. Spatial-Temporal Fusion (STF) Branch" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "spans": [ + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "text", + "content": "This module aims to extract spatial and temporal information from events " + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "inline_equation", + "content": "E" + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "text", + "content": " and RGB frames " + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "text", + "content": " to obtain a global spatial-temporal representation " + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "inline_equation", + "content": "F_{ST}" + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "text", + "content": ". The representation is a 3D feature map of " + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "inline_equation", + "content": "H\\times W\\times T\\times C" + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "text", + "content": " is the temporal dimension, and " + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "text", + "content": " is the number of representation channels. The output " + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "inline_equation", + "content": "F_{ST}" + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "text", + "content": " of STF branch " + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "inline_equation", + "content": "f_{STF}" + }, + { + "bbox": [ + 46, + 305, + 287, + 377 + ], + "type": "text", + "content": " can be described as:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 125, + 383, + 287, + 396 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 125, + 383, + 287, + 396 + ], + "spans": [ + { + "bbox": [ + 125, + 383, + 287, + 396 + ], + "type": "interline_equation", + "content": "F _ {S T} = f _ {S T F} (V, E) \\tag {3}", + "image_path": "1a02681c22c33d1827e61274e5af03b9cd31ba328bd70c8b06669c8aef81d7aa.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 399, + 287, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 399, + 287, + 483 + ], + "spans": [ + { + "bbox": [ + 46, + 399, + 287, + 483 + ], + "type": "text", + "content": "Specifically, as depicted in Fig. 2(a), we first employ two " + }, + { + "bbox": [ + 46, + 399, + 287, + 483 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 46, + 399, + 287, + 483 + ], + "type": "text", + "content": " convolutional layers to obtain the initial frame feature map " + }, + { + "bbox": [ + 46, + 399, + 287, + 483 + ], + "type": "inline_equation", + "content": "F_0^f" + }, + { + "bbox": [ + 46, + 399, + 287, + 483 + ], + "type": "text", + "content": " and the event feature map " + }, + { + "bbox": [ + 46, + 399, + 287, + 483 + ], + "type": "inline_equation", + "content": "F_0^e" + }, + { + "bbox": [ + 46, + 399, + 287, + 483 + ], + "type": "text", + "content": " with same dimension. As stated in [13], shallow features preserve sharper details and local structural information while deeper features preserve more semantic information. Thus, we design fusion blocks to aggregate both the shallow and deep features." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "spans": [ + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "text", + "content": "Shallow feature fusion: As explored in [27], the residual architecture improves the model's capacity for representation and lessens the gradient vanishing issue. For this reason, we first employ two high preserving blocks (HPB) as the basic feature extractors to extract two shallow feature maps " + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "inline_equation", + "content": "F_{l}^{f}" + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "inline_equation", + "content": "F_{l}^{e}" + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "text", + "content": " from the initial feature maps " + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "inline_equation", + "content": "F_{0}^{f}" + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "inline_equation", + "content": "F_{0}^{e}" + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "text", + "content": ", respectively. After that, " + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "inline_equation", + "content": "F_{l}^{f}" + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "inline_equation", + "content": "F_{l}^{e}" + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "text", + "content": " are concatenated and then fed into a transformer-based fusion model to obtain a fused feature map so as to bridge the modality gap. The intuition behind this is that transformer has a larger perception field [1, 25, 25, 31, 41], which can be potentially used to model the global spatial and temporal dependencies from the frames and events. Then, the fused feature map is split into two parts in the channel dimension and added to " + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "inline_equation", + "content": "F_{l}^{f}" + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "inline_equation", + "content": "F_{l}^{e}" + }, + { + "bbox": [ + 46, + 483, + 287, + 664 + ], + "type": "text", + "content": " as the input for deep feature fusion." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": "Deep feature fusion: After the shallow feature fusion, we again use two HPBs to extract deep features, which are then added and passed to the transformer-based fusion model to attain a 3D feature map " + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "inline_equation", + "content": "F_{ST}" + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": ". Through shallow and deep" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "text", + "content": "feature fusion, we can better learn the temporal and spatial information from the events and frames." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 104, + 465, + 118 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 104, + 465, + 118 + ], + "spans": [ + { + "bbox": [ + 306, + 104, + 465, + 118 + ], + "type": "text", + "content": "3.3. Temporal Filter (TF) Branch" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 123, + 545, + 219 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 123, + 545, + 219 + ], + "spans": [ + { + "bbox": [ + 304, + 123, + 545, + 219 + ], + "type": "text", + "content": "Through the STF branch, the spatial-temporal feature information from both the frames and events can be effectively learned. However, STF branch is insufficient to take full advantage of the high temporal resolution of event data. Therefore, we design the TF branch to explore more detailed motion information solely from events, which turns out to be effective in further enhancing the VSR performance, as demonstrated in our experiments (See Table 5)." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "spans": [ + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "text", + "content": "Intuitively, we design the TF branch " + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "inline_equation", + "content": "f_{TF}" + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "text", + "content": " to capture the detailed motion information from events " + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "inline_equation", + "content": "E_{t,\\Delta t}" + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "text", + "content": " near the key frame at timestamp " + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "inline_equation", + "content": "\\Delta t" + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "text", + "content": " is a small time interval. TF branch first selects events from " + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "inline_equation", + "content": "t - \\Delta t" + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "inline_equation", + "content": "t + \\Delta t" + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "text", + "content": " (Fig. 2(c)). The selected events are interpolated and sent to three convolutional layers to learn the temporal features. Overall, the output " + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "inline_equation", + "content": "F_{T}" + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "text", + "content": " of TF branch " + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "inline_equation", + "content": "f_{TF}" + }, + { + "bbox": [ + 304, + 220, + 545, + 303 + ], + "type": "text", + "content": " can be described as:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 386, + 305, + 545, + 319 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 386, + 305, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 386, + 305, + 545, + 319 + ], + "type": "interline_equation", + "content": "F _ {T} = f _ {T F} \\left(E _ {t, \\Delta t}\\right) \\tag {4}", + "image_path": "c9ed06e97427f9d56e965b9a81a6d50e0f78e25eed23fb5e609f00dac388ad15.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 321, + 545, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 321, + 545, + 357 + ], + "spans": [ + { + "bbox": [ + 304, + 321, + 545, + 357 + ], + "type": "text", + "content": "In Sec.4.4, we show that STF branch can capture pixel intensities, while the TF branch can capture the motion details, e.g., edges and corners." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 365, + 524, + 379 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 365, + 524, + 379 + ], + "spans": [ + { + "bbox": [ + 305, + 365, + 524, + 379 + ], + "type": "text", + "content": "3.4. Spatial-Temporal Implicit Representation" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "spans": [ + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "text", + "content": "In this section, our goal is to learn continuous INRs for VSR based on the spatial-temporal feature map " + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "inline_equation", + "content": "F_{ST}" + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "text", + "content": " and temporal feature map " + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "inline_equation", + "content": "F_{T}" + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "text", + "content": ". The INRs are then used to decode coordinates at time " + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "text", + "content": " with scale factor " + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "text", + "content": " into RGB values. In this paper, we introduce the Spatial-Temporal Implicit Representation (STIR) module to accomplish the spatial-temporal VSR, as shown in Eq.5. We employ a simple-yet-effective 3D feature sampling and trilinear interpolation scheme to upsample " + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "inline_equation", + "content": "F_{ST}" + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "inline_equation", + "content": "F_{T}" + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "text", + "content": " to a desired resolution. A decoder, parameterized as a multi-layer CNN, is used to convert the interpolated features into RGB values. Fig. 2(c) depicts the detailed design of STIR. The output SR frame " + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "inline_equation", + "content": "I_{t,s}^{SR}" + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "text", + "content": " of the STIR " + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "inline_equation", + "content": "f_{STIR}" + }, + { + "bbox": [ + 304, + 384, + 545, + 540 + ], + "type": "text", + "content": " can be formulated as:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 361, + 540, + 545, + 555 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 361, + 540, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 361, + 540, + 545, + 555 + ], + "type": "interline_equation", + "content": "I _ {t, s} ^ {S R} = f _ {S T I R} \\left(F _ {S T}, F _ {T}\\right), \\forall s, t \\tag {5}", + "image_path": "5aac9cd9a64f3d7533cd9f8b31872e12cae914c52c6d6528069ba877ea6b1373.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "content": "3D Feature Sampling: Here, we aim at generating a coordinate to make a query in grid form " + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "inline_equation", + "content": "F_{ST}" + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "content": ". We uniformly sample a 3D coordinate grid, which can be expressed as " + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "inline_equation", + "content": "C_{t,s}" + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "content": " with the dimension of " + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "inline_equation", + "content": "sH \\times sW \\times 3" + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "content": ". Formally, for any query " + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "content": ", the corresponding element " + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "inline_equation", + "content": "p_q" + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "content": " in the 3D coordinate grid " + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "inline_equation", + "content": "C_{t,s}" + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "content": " can be described as " + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "inline_equation", + "content": "p_q = (x_q, y_q, t_q)" + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "inline_equation", + "content": "x_q \\in [0, H], y_q \\in [0, W], t_q \\in [t_s, t_e]" + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "inline_equation", + "content": "t_s" + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "content": " is the start time and " + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "inline_equation", + "content": "t_e" + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "content": " is the end time of input. For each coordinate " + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "inline_equation", + "content": "p_q = (x_q, y_q, t_q)" + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "content": ", we choose features of the nearest eight points around this coordinate in the 3D spatial-temporal feature " + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "inline_equation", + "content": "F_{ST}" + }, + { + "bbox": [ + 304, + 558, + 545, + 689 + ], + "type": "text", + "content": " for interpolation." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "type": "text", + "content": "Feature Interpolation: Then, we compute the feature of a queried coordinate " + }, + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "type": "inline_equation", + "content": "p_{q}" + }, + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "type": "text", + "content": " by using 3D interpolation techniques," + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1560" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "content": "such as trilinear interpolation. Inspired by the representation theory [37], complex signals in the low-dimensional space e.g., images, can be transformed as linear representations in the high-dimensional space e.g., features. From this theory, we can observe that the spatial-temporal feature " + }, + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "inline_equation", + "content": "F_{ST}" + }, + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "content": " is indeed the high-dimensional feature representation for the low-dimensional image. We use linear interpolation to obtain features at the queried coordinate " + }, + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "inline_equation", + "content": "p_q" + }, + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "content": ". In the experiments of Table 6, we have compared several interpolation methods, e.g., nearest sampling, and the results show that linear interpolation shows the best performance." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 205, + 288, + 252 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 205, + 288, + 252 + ], + "spans": [ + { + "bbox": [ + 46, + 205, + 288, + 252 + ], + "type": "text", + "content": "In summary, for any scale " + }, + { + "bbox": [ + 46, + 205, + 288, + 252 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 46, + 205, + 288, + 252 + ], + "type": "text", + "content": " and timestamp " + }, + { + "bbox": [ + 46, + 205, + 288, + 252 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 205, + 288, + 252 + ], + "type": "text", + "content": ", the feature interpolation (i.e., 3D to 2D) process can be formulated by Eq. 6, given the 3D coordinate grid " + }, + { + "bbox": [ + 46, + 205, + 288, + 252 + ], + "type": "inline_equation", + "content": "C_{t,s}" + }, + { + "bbox": [ + 46, + 205, + 288, + 252 + ], + "type": "text", + "content": " and spatial-temporal feature " + }, + { + "bbox": [ + 46, + 205, + 288, + 252 + ], + "type": "inline_equation", + "content": "F_{ST}" + }, + { + "bbox": [ + 46, + 205, + 288, + 252 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 110, + 255, + 287, + 269 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 110, + 255, + 287, + 269 + ], + "spans": [ + { + "bbox": [ + 110, + 255, + 287, + 269 + ], + "type": "interline_equation", + "content": "F _ {S F} = f _ {\\text {s a m p l e}} \\left(F _ {S T}, C _ {t, s}\\right) \\tag {6}", + "image_path": "628c8b79de84ed845ffd9e01c9b764f1dd2f81a1da24cd0e9299686c9e732b66.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 271, + 289, + 356 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 271, + 289, + 356 + ], + "spans": [ + { + "bbox": [ + 46, + 271, + 289, + 356 + ], + "type": "text", + "content": "Implicit Representation Decoding: Finally, the sampled 2D feature map " + }, + { + "bbox": [ + 46, + 271, + 289, + 356 + ], + "type": "inline_equation", + "content": "F_{SF}" + }, + { + "bbox": [ + 46, + 271, + 289, + 356 + ], + "type": "text", + "content": " and the temporal feature map " + }, + { + "bbox": [ + 46, + 271, + 289, + 356 + ], + "type": "inline_equation", + "content": "F_{T}" + }, + { + "bbox": [ + 46, + 271, + 289, + 356 + ], + "type": "text", + "content": " are added together and fed into the decoder. For simplicity and efficiency, we design three-layers CNN structure as the decoder. This is supported by the empirical experiment in Table 6, showing that a sample CNN block can achieve good results with low complexity." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 365, + 137, + 376 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 365, + 137, + 376 + ], + "spans": [ + { + "bbox": [ + 47, + 365, + 137, + 376 + ], + "type": "text", + "content": "3.5. Loss Function" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "spans": [ + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "text", + "content": "We employ the Charbonnier loss [21] as our VSR supervision loss " + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{SR}" + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "text", + "content": " between the ground truth (GT) HR frame " + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "inline_equation", + "content": "I_{t,s}^{HR}" + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "text", + "content": " and the output SR frame " + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "inline_equation", + "content": "I_{t,s}^{SR}" + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "text", + "content": " at timestamp " + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "text", + "content": " with upsampling scale " + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "text", + "content": ", as shown in Eq. 7, where " + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "text", + "content": " is " + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "inline_equation", + "content": "1e - 3" + }, + { + "bbox": [ + 47, + 384, + 287, + 432 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "spans": [ + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "text", + "content": "In training, the value range of " + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "text", + "content": " is all timestamps of key frames " + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "inline_equation", + "content": "\\{t_0, t_1 \\dots t_n\\}" + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "text", + "content": " could be a real number in the range [1.0, " + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "inline_equation", + "content": "s_{max}" + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "text", + "content": "], where " + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "inline_equation", + "content": "s_{max}" + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "text", + "content": " is the maximum up-sampling scale during training depending on the resolution of training data. The loss function " + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "text", + "content": " is shown in Eq. 8. For example, when the resolution of input LR frame is set to be " + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "inline_equation", + "content": "128 \\times 128" + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "text", + "content": " and the GT HR frame is " + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "inline_equation", + "content": "1024 \\times 1024" + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "text", + "content": ", the " + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "inline_equation", + "content": "s_{max}" + }, + { + "bbox": [ + 46, + 433, + 288, + 517 + ], + "type": "text", + "content": " is 8." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 104, + 518, + 287, + 538 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 518, + 287, + 538 + ], + "spans": [ + { + "bbox": [ + 104, + 518, + 287, + 538 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {S R} = \\sqrt {\\left(I _ {t , s} ^ {S R} - I _ {t , s} ^ {H R}\\right) ^ {2} + \\epsilon^ {2}} \\tag {7}", + "image_path": "b5e6bc30190c39eddcd62ca017dec4218861149d5a2b78e2f8945005f4c8c126.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 64, + 540, + 287, + 578 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 540, + 287, + 578 + ], + "spans": [ + { + "bbox": [ + 64, + 540, + 287, + 578 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\sum_ {t \\in t _ {0}, t _ {1} \\dots t _ {n}} \\left(\\sum_ {s \\in [ 1. 0, s _ {\\max } ]} \\mathcal {L} _ {S R} \\left(I _ {t, s} ^ {S R}, I _ {t, s} ^ {H R}\\right)\\right) \\tag {8}", + "image_path": "e1e8baa39d7bced1e6bee9941cb6329c12ae1e7b64577bea07277445a3c5eb8a.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 586, + 211, + 597 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 586, + 211, + 597 + ], + "spans": [ + { + "bbox": [ + 47, + 586, + 211, + 597 + ], + "type": "text", + "content": "3.6. Real-world Dataset Collection" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 605, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 289, + 715 + ], + "type": "text", + "content": "Existing datasets, e.g., CED [35], suffer from the limited resolution " + }, + { + "bbox": [ + 46, + 605, + 289, + 715 + ], + "type": "inline_equation", + "content": "(346 \\times 260)" + }, + { + "bbox": [ + 46, + 605, + 289, + 715 + ], + "type": "text", + "content": " and severe noise, as shown in Fig.7. Although CED dataset provides the active pixel sensor (APS) frames, they are in low quality because they are simply demosaiced by OpenCV [34] from RAW data. Therefore, collecting HR and high-quality datasets with spatially aligned frames and events is important to inspire more research for the event-guided VSR problem. In this paper, we collected a new real-world dataset, called ALPIX-" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "text", + "content": "VSR, using a ALPIX-Eiger event camera1. The camera outputs well aligned RGB frames and events. The RGB frames enjoy a resolution of " + }, + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "inline_equation", + "content": "3264 \\times 2448" + }, + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "text", + "content": " and are generated by a carefully designed image signal processor(ISP) from RAW data with the Quad Bayer pattern [10], and the events have a resolution with " + }, + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "inline_equation", + "content": "1632 \\times 1224" + }, + { + "bbox": [ + 304, + 72, + 545, + 143 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 144, + 546, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 144, + 546, + 228 + ], + "spans": [ + { + "bbox": [ + 304, + 144, + 546, + 228 + ], + "type": "text", + "content": "Our ALPIX-VSR dataset consists of 26 video sequences with 5388 frames and well-aligned events in total. These sequences include diverse scenes, e.g., streets, buildings, flowers, textures, and machines. To avoid motion blur and low-light noise, we collect the dataset in bright indoor and sunny outdoor scenes. For more details about our real-world dataset, please refer to the supplementary material." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 238, + 388, + 251 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 238, + 388, + 251 + ], + "spans": [ + { + "bbox": [ + 305, + 238, + 388, + 251 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 258, + 424, + 270 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 258, + 424, + 270 + ], + "spans": [ + { + "bbox": [ + 305, + 258, + 424, + 270 + ], + "type": "text", + "content": "4.1. Experiments Setting" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 276, + 545, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 276, + 545, + 335 + ], + "spans": [ + { + "bbox": [ + 304, + 276, + 545, + 335 + ], + "type": "text", + "content": "Implementation Details and Datasets: For all experiments, we use the Adam optimizer [20] with a learning rate of " + }, + { + "bbox": [ + 304, + 276, + 545, + 335 + ], + "type": "inline_equation", + "content": "1e - 4" + }, + { + "bbox": [ + 304, + 276, + 545, + 335 + ], + "type": "text", + "content": " for CED dataset and " + }, + { + "bbox": [ + 304, + 276, + 545, + 335 + ], + "type": "inline_equation", + "content": "5e - 5" + }, + { + "bbox": [ + 304, + 276, + 545, + 335 + ], + "type": "text", + "content": " for our ALPIX-VSR dataset. We train our framework for 100 epochs with a batch size of 2 using two NVIDIA RTX A30 GPU cards." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 335, + 545, + 371 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 335, + 545, + 371 + ], + "spans": [ + { + "bbox": [ + 304, + 335, + 545, + 371 + ], + "type": "text", + "content": "Evaluation Metrics: We statistically assess the effectiveness of our approach using the peak-signal-to-noise ratio (PSNR) and structural similarity (SSIM)." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 372, + 546, + 539 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 372, + 546, + 539 + ], + "spans": [ + { + "bbox": [ + 304, + 372, + 546, + 539 + ], + "type": "text", + "content": "Datasets: We use the CED [35] and the ALPIX-VSR dataset for experiments. 1) CED Dataset. It includes a collection of color events and video sequences in many scenes, e.g., indoor, outdoor, driving, human, calibration. The resolution of the frames and events is " + }, + { + "bbox": [ + 304, + 372, + 546, + 539 + ], + "type": "inline_equation", + "content": "346 \\times 260" + }, + { + "bbox": [ + 304, + 372, + 546, + 539 + ], + "type": "text", + "content": ". We follow the setting of E-VSR [18] to preprocess this dataset. Note that the RGB frames provided by CED are obtained by demosaicing [34] from raw frames and suffer from severe noise. 2) ALPIX-VSR Dataset. We select 20 videos for training and 6 videos for testing. The training and testing sets include 4212 and 1176 frames with aligned events, respectively. Note that we apply data augmentation strategy, such as random crop, to ALPIX-VSR dataset for all compared methods to avoid memory overflow during training." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 546, + 484, + 559 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 546, + 484, + 559 + ], + "spans": [ + { + "bbox": [ + 305, + 546, + 484, + 559 + ], + "type": "text", + "content": "4.2. Comparison with SoTA Methods" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 564, + 545, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 564, + 545, + 696 + ], + "spans": [ + { + "bbox": [ + 304, + 564, + 545, + 696 + ], + "type": "text", + "content": "We compare our method with six SoTA methods under three VSR settings: (I) one SoTA event-guided, fixed-scale VSR method E-VSR [18] (II) one SoTA method of frame-based arbitrary-scale VSR method VideoINR [8] (III) five SoTA methods of frame-based, fixed-scale VSR methods: BasicVSR++ [6], RBPN [14], SOF [45], TDAN [40], DUF [19]. We report " + }, + { + "bbox": [ + 304, + 564, + 545, + 696 + ], + "type": "inline_equation", + "content": "\\times 2" + }, + { + "bbox": [ + 304, + 564, + 545, + 696 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 564, + 545, + 696 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 304, + 564, + 545, + 696 + ], + "type": "text", + "content": " super-resolution results of our method and all 6 comparison methods on CED dataset. We also compare our method with E-VSR and BasicVSR++ on our ALPIX-VSR dataset. Moreover, we compare our method with VideoINR on out-of-distribution" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 317, + 702, + 438, + 714 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 702, + 438, + 714 + ], + "spans": [ + { + "bbox": [ + 317, + 702, + 438, + 714 + ], + "type": "text", + "content": "1https://www.alpsentek.com/product" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 296, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 314, + 757 + ], + "type": "text", + "content": "1561" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 50, + 60, + 545, + 202 + ], + "blocks": [ + { + "bbox": [ + 50, + 60, + 545, + 202 + ], + "lines": [ + { + "bbox": [ + 50, + 60, + 545, + 202 + ], + "spans": [ + { + "bbox": [ + 50, + 60, + 545, + 202 + ], + "type": "table", + "html": "
Clip NameDUF [19]*TDAN [40]SOF [45]RBPN [14]VideoINR [8]*E-VSR [18]Ours
people_dynamic<wave>32.02 / 0.933335.83 / 0.954033.32 / 0.936040.07 / 0.986827.47 / 0.822941.08 / 0.989138.78 / 0.9794
indoors_foosball_230.55 / 0.926232.12 / 0.933930.86 / 0.925334.15 / 0.973926.03 / 0.776634.77 / 0.977538.68 / 0.9750
simple_wires_230.08 / 0.938731.57 / 0.946630.12 / 0.932633.83 / 0.973926.77 / 0.832134.44 / 0.977338.67 / 0.9815
people_dynamic_dancing31.64 / 0.936935.73 / 0.956632.93 / 0.938839.56 / 0.986927.36 / 0.820240.49 / 0.989139.06 / 0.9798
people_dynamic_jumping31.57 / 0.933435.42 / 0.953632.79 / 0.934739.44 / 0.985927.24 / 0.818340.32 / 0.988038.93 / 0.9792
simple_fruit_fast37.46 / 0.944237.75 / 0.944037.22 / 0.939040.33 / 0.978227.21 / 0.845640.80 / 0.980141.96 / 0.9821
outdoor_jumping_infrared_225.33 / 0.816228.91 / 0.906226.67 / 0.874630.36 / 0.964826.88 / 0.822630.70 / 0.969838.03 / 0.9755
simple_carpet_fast31.43 / 0.881132.54 / 0.900631.83 / 0.877434.91 / 0.950224.21 / 0.590935.16 / 0.953636.14 / 0.9635
people_dynamic_armroll31.38 / 0.931135.55 / 0.954132.79 / 0.934540.05 / 0.987827.26 / 0.819341.00 / 0.989838.84 / 0.9787
indoors_kitchen_229.92 / 0.927330.67 / 0.932329.61 / 0.919231.51 / 0.955126.44 / 0.750231.79 / 0.958637.68 / 0.9726
people_dynamic_sitting30.62 / 0.933135.09 / 0.956132.13 / 0.936739.03 / 0.986227.63 / 0.823039.97 / 0.988438.86 / 0.9810
average PSNR/SSIM31.09 / 0.918333.74 / 0.939831.84 / 0.922636.66 / 0.975426.77 / 0.793837.32 / 0.978338.69 / 0.9771
", + "image_path": "4ce67f57ab0d49282f967e472b658d79da0508147839a0e148a3b89378fb16e3.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 204, + 546, + 227 + ], + "lines": [ + { + "bbox": [ + 47, + 204, + 546, + 227 + ], + "spans": [ + { + "bbox": [ + 47, + 204, + 546, + 227 + ], + "type": "text", + "content": "Table 1. Quantitative results (PSNR/SSIM) of the proposed our framework and other methods on the CED dataset for " + }, + { + "bbox": [ + 47, + 204, + 546, + 227 + ], + "type": "inline_equation", + "content": "\\times 2" + }, + { + "bbox": [ + 47, + 204, + 546, + 227 + ], + "type": "text", + "content": ". Because the official training code is not available, * denoted values were acquired from the pre-trained model that the authors have released." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 48, + 237, + 545, + 506 + ], + "blocks": [ + { + "bbox": [ + 48, + 237, + 545, + 506 + ], + "lines": [ + { + "bbox": [ + 48, + 237, + 545, + 506 + ], + "spans": [ + { + "bbox": [ + 48, + 237, + 545, + 506 + ], + "type": "image", + "image_path": "b49eb1d1e8221412ef24c5df280981e7edf79124595238555ef3c631f04f32af.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 515, + 546, + 538 + ], + "lines": [ + { + "bbox": [ + 46, + 515, + 546, + 538 + ], + "spans": [ + { + "bbox": [ + 46, + 515, + 546, + 538 + ], + "type": "text", + "content": "Figure 3. Visual results of " + }, + { + "bbox": [ + 46, + 515, + 546, + 538 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 46, + 515, + 546, + 538 + ], + "type": "text", + "content": " VSR on the CED dataset. Our method recovers more details, e.g., textual, edge than the SoTA event-guided VSR method E-VSR [18] and the random up-sampling VSR method Video INR [8]." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 551, + 287, + 587 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 551, + 287, + 587 + ], + "spans": [ + { + "bbox": [ + 46, + 551, + 287, + 587 + ], + "type": "text", + "content": "scales to demonstrate our method's ability for arbitrary-scale VSR. Note that E-VSR only supports " + }, + { + "bbox": [ + 46, + 551, + 287, + 587 + ], + "type": "inline_equation", + "content": "\\times 2" + }, + { + "bbox": [ + 46, + 551, + 287, + 587 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 551, + 287, + 587 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 46, + 551, + 287, + 587 + ], + "type": "text", + "content": " SR, and BasicVSR++ only supports " + }, + { + "bbox": [ + 46, + 551, + 287, + 587 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 46, + 551, + 287, + 587 + ], + "type": "text", + "content": " SR." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "content": "Evaluation on CED Dataset Table 1 and Table 2 present the quantitative results for " + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "inline_equation", + "content": "\\times 2" + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "content": " VSR, respectively. Our model clearly outperforms other methods in terms of PSNR, and shows a comparable performance in SSIM with E-VSR. The qualitative results in Fig. 3 demonstrate that our model is capable of recovering fine details, like sharp edges and detailed textures. We can see that event-guided methods (E-VSR and our method) yield better performances than the frame-based counterparts, showing the complementary effects of event data for VSR. Furthermore," + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 551, + 545, + 611 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 551, + 545, + 611 + ], + "spans": [ + { + "bbox": [ + 304, + 551, + 545, + 611 + ], + "type": "text", + "content": "VideoINR performs noticeably worse than other methods, indicating that frame-based implicit neural representations struggle with the low-resolution and high-noise input. Contrarily, our approach benefits from additional event information and generates satisfactory INRs." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 614, + 546, + 698 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 614, + 546, + 698 + ], + "spans": [ + { + "bbox": [ + 304, + 614, + 546, + 698 + ], + "type": "text", + "content": "Table 2 shows quantitative results in " + }, + { + "bbox": [ + 304, + 614, + 546, + 698 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 304, + 614, + 546, + 698 + ], + "type": "text", + "content": " SR on the CED dataset, where our model achieves SoTA performance while remaining lightweight and efficient. Specifically, our model only accounts for one-200th of the E-VSR. BasicVSR++, the SoTA method of frame-based VSR, fails to perform well on CED dataset as the upsampling scale increases, which demonstrates its less robustness on the highly noisy dataset." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 701, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 701, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 701, + 545, + 713 + ], + "type": "text", + "content": "Evaluation on the ALPIX-VSR Dataset The quantitative" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1562" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 69, + 537, + 211 + ], + "blocks": [ + { + "bbox": [ + 56, + 69, + 537, + 211 + ], + "lines": [ + { + "bbox": [ + 56, + 69, + 537, + 211 + ], + "spans": [ + { + "bbox": [ + 56, + 69, + 537, + 211 + ], + "type": "image", + "image_path": "98be5bcb3365d831b5d5d2d3fbde8299a1b3f7423c27936cc30c0815e2b78895.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 190, + 213, + 402, + 223 + ], + "lines": [ + { + "bbox": [ + 190, + 213, + 402, + 223 + ], + "spans": [ + { + "bbox": [ + 190, + 213, + 402, + 223 + ], + "type": "text", + "content": "Figure 4. Results of " + }, + { + "bbox": [ + 190, + 213, + 402, + 223 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 190, + 213, + 402, + 223 + ], + "type": "text", + "content": " VSR on the ALPIX-VSR dataset." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 53, + 224, + 538, + 355 + ], + "blocks": [ + { + "bbox": [ + 53, + 224, + 538, + 355 + ], + "lines": [ + { + "bbox": [ + 53, + 224, + 538, + 355 + ], + "spans": [ + { + "bbox": [ + 53, + 224, + 538, + 355 + ], + "type": "image", + "image_path": "9cf36e686681e31c54d97a6595ef16c27e9c41e2755b2b6e3c470b7ed21388f3.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 190, + 356, + 402, + 366 + ], + "lines": [ + { + "bbox": [ + 190, + 356, + 402, + 366 + ], + "spans": [ + { + "bbox": [ + 190, + 356, + 402, + 366 + ], + "type": "text", + "content": "Figure 5. Results of " + }, + { + "bbox": [ + 190, + 356, + 402, + 366 + ], + "type": "inline_equation", + "content": "\\times 8" + }, + { + "bbox": [ + 190, + 356, + 402, + 366 + ], + "type": "text", + "content": " VSR on the ALPIX-VSR dataset." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 48, + 376, + 282, + 465 + ], + "blocks": [ + { + "bbox": [ + 48, + 376, + 282, + 465 + ], + "lines": [ + { + "bbox": [ + 48, + 376, + 282, + 465 + ], + "spans": [ + { + "bbox": [ + 48, + 376, + 282, + 465 + ], + "type": "table", + "html": "
MethodsModel Size(M)PSNRSSIM
DUF [19]1.9024.430.8177
TDAN [40]1.9727.880.8231
SOF [45]1.0027.000.8050
RBPN [14]12.1829.800.8975
BasicVSR++ [6]7.3014.760.1641
VideoINR [8]*11.3125.530.7871
E-VSR [18]412.4230.150.9052
Ours2.4531.120.9211
", + "image_path": "2c2ed9518d0e6853c3991798721c9c9a3999f6a26515b611dda868e46fb84ea7.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 50, + 488, + 281, + 590 + ], + "blocks": [ + { + "bbox": [ + 47, + 466, + 287, + 487 + ], + "lines": [ + { + "bbox": [ + 47, + 466, + 287, + 487 + ], + "spans": [ + { + "bbox": [ + 47, + 466, + 287, + 487 + ], + "type": "text", + "content": "Table 2. Quantitative results on CED dataset for " + }, + { + "bbox": [ + 47, + 466, + 287, + 487 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 47, + 466, + 287, + 487 + ], + "type": "text", + "content": ". * denotes the values obtained from the official pre-trained models." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 488, + 281, + 590 + ], + "lines": [ + { + "bbox": [ + 50, + 488, + 281, + 590 + ], + "spans": [ + { + "bbox": [ + 50, + 488, + 281, + 590 + ], + "type": "table", + "html": "
MethodsPSNRSSIM
×2E-VSR36.100.9761
Ours38.250.9822
×4E-VSR32.540.9163
BasicVSR++35.300.9353
Ours37.120.9503
×6VideoINR*31.150.9084
Ours31.850.9267
×8VideoINR*28.110.8625
Ours28.530.8901
", + "image_path": "048904a958bdfeabdad5558f77e063c06e97b3355d1253ec8a38394bd06eb6ec.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 638, + 287, + 673 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 638, + 287, + 673 + ], + "spans": [ + { + "bbox": [ + 47, + 638, + 287, + 673 + ], + "type": "text", + "content": "and qualitative results are shown in Table 3 and Fig. 5. We present comparison results of our method with BasicVSR++ and E-VSR on our collected real-world dataset." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 676, + 201, + 689 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 676, + 201, + 689 + ], + "spans": [ + { + "bbox": [ + 47, + 676, + 201, + 689 + ], + "type": "text", + "content": "4.3. Random Scale Up-sampling" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": "Results of random scale upsampling are shown in Table 3. We upsample the video frames to " + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\times 2" + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\times 6" + } + ] + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 306, + 377, + 548, + 423 + ], + "blocks": [ + { + "bbox": [ + 47, + 594, + 287, + 628 + ], + "lines": [ + { + "bbox": [ + 47, + 594, + 287, + 628 + ], + "spans": [ + { + "bbox": [ + 47, + 594, + 287, + 628 + ], + "type": "text", + "content": "Table 3. Quantitative comparison (PSNR/SSIM) of our methods and other methods on the ALPIX-VSR dataset. * denotes the values obtained from the official pre-trained models." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 306, + 377, + 548, + 423 + ], + "lines": [ + { + "bbox": [ + 306, + 377, + 548, + 423 + ], + "spans": [ + { + "bbox": [ + 306, + 377, + 548, + 423 + ], + "type": "table", + "html": "
×1.8×2.6×5.6
Ours39.2508 / 0.980337.3408 / 0.958931.2549 / 0.9135
×6.6×7.1×7.8
Ours28.3182 / 0.877228.3188 / 0.8776228.3198 / 0.8783
", + "image_path": "04ac9cdb9fb803f4edb05a94de8d54131d937dba31e67b846532cbf88c1dd429.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 426, + 545, + 449 + ], + "lines": [ + { + "bbox": [ + 306, + 426, + 545, + 449 + ], + "spans": [ + { + "bbox": [ + 306, + 426, + 545, + 449 + ], + "type": "text", + "content": "Table 4. Quantitative results(PSNR/SSIM) of random-scale comparison on theALPIX-VSR dataset." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 305, + 456, + 545, + 505 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 456, + 545, + 505 + ], + "spans": [ + { + "bbox": [ + 305, + 456, + 545, + 505 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 305, + 456, + 545, + 505 + ], + "type": "inline_equation", + "content": "\\times 8" + }, + { + "bbox": [ + 305, + 456, + 545, + 505 + ], + "type": "text", + "content": ", and compare with the SoTA models, E-VSR and BasicVSR++. Though these two models are strictly constrained to a specific upsampling scale, our method presents superior performance on these settings." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 506, + 546, + 625 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 506, + 546, + 625 + ], + "spans": [ + { + "bbox": [ + 304, + 506, + 546, + 625 + ], + "type": "text", + "content": "We also compare our method with VideoINR, the SoTA random-scale VSR method quantitatively in " + }, + { + "bbox": [ + 304, + 506, + 546, + 625 + ], + "type": "inline_equation", + "content": "\\times 6" + }, + { + "bbox": [ + 304, + 506, + 546, + 625 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 506, + 546, + 625 + ], + "type": "inline_equation", + "content": "\\times 8" + }, + { + "bbox": [ + 304, + 506, + 546, + 625 + ], + "type": "text", + "content": ". Results in Table 3 show that our model surpasses VideoINR in all evaluated scales of " + }, + { + "bbox": [ + 304, + 506, + 546, + 625 + ], + "type": "inline_equation", + "content": "\\times 6" + }, + { + "bbox": [ + 304, + 506, + 546, + 625 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 506, + 546, + 625 + ], + "type": "inline_equation", + "content": "\\times 8" + }, + { + "bbox": [ + 304, + 506, + 546, + 625 + ], + "type": "text", + "content": ". Furthermore, to evaluate the performance of our method on arbitrary scales, we conduct experiments of 6 random-chosen float scales. The results are shown in Table 4 and indicate our model's robustness across arbitrary random scales. It is easy to find that values of performance, e.g., PSNR, SSIM, and upsampling scales are not strictly monotonic." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 636, + 479, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 636, + 479, + 647 + ], + "spans": [ + { + "bbox": [ + 306, + 636, + 479, + 647 + ], + "type": "text", + "content": "4.4. Ablation Studies and Discussion" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 655, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 655, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 655, + 545, + 713 + ], + "type": "text", + "content": "The following ablation experiments investigate the importance of each of our proposed modules. As it takes more than 120 hours to train a model on the complete CED dataset, we uniformly select " + }, + { + "bbox": [ + 304, + 655, + 545, + 713 + ], + "type": "inline_equation", + "content": "1/5" + }, + { + "bbox": [ + 304, + 655, + 545, + 713 + ], + "type": "text", + "content": " of CED as the dataset for ablation experiments." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1563" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 50, + 70, + 286, + 127 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 286, + 127 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 286, + 127 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 286, + 127 + ], + "type": "table", + "html": "
TF BranchShallow Feature FusionModel Size(M)PSNRSSIM
1ww2.451338.140.9820
2w/ow2.448237.960.9812
3ww/o1.736037.760.9729
", + "image_path": "c762fa683464dc23dacab932a1e2fc0648080d35fe71a993f6a38087372ba5b0.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 50, + 145, + 286, + 220 + ], + "blocks": [ + { + "bbox": [ + 53, + 129, + 280, + 139 + ], + "lines": [ + { + "bbox": [ + 53, + 129, + 280, + 139 + ], + "spans": [ + { + "bbox": [ + 53, + 129, + 280, + 139 + ], + "type": "text", + "content": "Table 5. Ablation of the TF branch and shallow feature fusion." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 145, + 286, + 220 + ], + "lines": [ + { + "bbox": [ + 50, + 145, + 286, + 220 + ], + "spans": [ + { + "bbox": [ + 50, + 145, + 286, + 220 + ], + "type": "table", + "html": "
InterpolationDecoderLF ChannelsPSNRSSIM
1LinearCNN1638.140.9820
2NearestCNN1628.910.9131
3LinearSIREN1610.300.2997
4LinearMLP1637.940.9811
5LinearCNN836.820.9763
6LinearCNN2438.250.9825
", + "image_path": "033e0889743c00645aee2e1d1c00257acbc76b3c70a9f0e5a3c1ca02660c4414.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 258, + 287, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 258, + 287, + 413 + ], + "spans": [ + { + "bbox": [ + 46, + 258, + 287, + 413 + ], + "type": "text", + "content": "Efficiency of TF branch and Shallow Feature Fusion: Table 5 validates the contribution of TF branch and shallow feature fusion in STF branch. The removal of TF branch reduces both PSNR and SSIM scores, where PSNR drops by " + }, + { + "bbox": [ + 46, + 258, + 287, + 413 + ], + "type": "inline_equation", + "content": "0.18dB" + }, + { + "bbox": [ + 46, + 258, + 287, + 413 + ], + "type": "text", + "content": ". In Fig. 6, we use PCA [11] to visualize the output of TF branch. As can be seen, " + }, + { + "bbox": [ + 46, + 258, + 287, + 413 + ], + "type": "inline_equation", + "content": "F_{T}" + }, + { + "bbox": [ + 46, + 258, + 287, + 413 + ], + "type": "text", + "content": " focuses on edge and corner information, which is very helpful for VSR, especially texture recovery. We also find that removing shallow feature fusion results in a large performance drop (PSNR drops by " + }, + { + "bbox": [ + 46, + 258, + 287, + 413 + ], + "type": "inline_equation", + "content": "0.38dB" + }, + { + "bbox": [ + 46, + 258, + 287, + 413 + ], + "type": "text", + "content": " and SSIM drops by nearly " + }, + { + "bbox": [ + 46, + 258, + 287, + 413 + ], + "type": "inline_equation", + "content": "0.1dB" + }, + { + "bbox": [ + 46, + 258, + 287, + 413 + ], + "type": "text", + "content": "). This finding indicates that fusion on shallow features is critical since shallow features carry rich local structure information which may be missing in deep features." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 415, + 287, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 415, + 287, + 533 + ], + "spans": [ + { + "bbox": [ + 46, + 415, + 287, + 533 + ], + "type": "text", + "content": "Feature Interpolation: We apply a feature-based interpolation strategy, i.e. interpolating features near a coordinate and sending the interpolated feature into the decoder to reconstruct HR frames. Such strategy has been studied in a prior work on implicit neural representation learning for 3D objects [38] and shown to be able to recover clearer details and sharper edges. We further study the influence of interpolation methods, as shown in Table 6. The trilinear manner of interpolation yields better PSNR and SSIM scores compared with the nearest interpolation." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 534, + 287, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 534, + 287, + 617 + ], + "spans": [ + { + "bbox": [ + 46, + 534, + 287, + 617 + ], + "type": "text", + "content": "Feature Decoder: We also compare the performance of different decoder designs, including MLP, SIREN [36] and CNN. Table 6 shows that decoding with CNN has the best performance among these methods, while non-convergence occurs with SIREN. We argue the reason CNN performs well is that decoding in STIR is only a dimensional reduction process, so no complicated design is required." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 617, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 287, + 713 + ], + "type": "text", + "content": "Robustness to Noise: In comparison to BasicVSR++, our method not only performs SR but also removes noise on the CED dataset, as shown in Fig.7. Note that BasicVSR++ is a SoTA frame-based method in VSR task. We analyze that the poor performance of BasicVSR++ on CED is caused by the dependence for only frame modality and excessive emphasis on the frame's high-frequency information. High frequencies are often present in the image as edges, cor" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 309, + 70, + 542, + 102 + ], + "blocks": [ + { + "bbox": [ + 47, + 221, + 287, + 243 + ], + "lines": [ + { + "bbox": [ + 47, + 221, + 287, + 243 + ], + "spans": [ + { + "bbox": [ + 47, + 221, + 287, + 243 + ], + "type": "text", + "content": "Table 6. Impacts of interpolation methods, decoder designs and channel size of the spatial-temporal features " + }, + { + "bbox": [ + 47, + 221, + 287, + 243 + ], + "type": "inline_equation", + "content": "F_{ST}" + }, + { + "bbox": [ + 47, + 221, + 287, + 243 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 70, + 542, + 102 + ], + "lines": [ + { + "bbox": [ + 309, + 70, + 542, + 102 + ], + "spans": [ + { + "bbox": [ + 309, + 70, + 542, + 102 + ], + "type": "table", + "html": "
PSNRSSIM
3 to 138.140.9820
5 to 338.040.9818
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Ablation for the number of input and output frames." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 307, + 122, + 545, + 221 + ], + "blocks": [ + { + "bbox": [ + 307, + 122, + 545, + 221 + ], + "lines": [ + { + "bbox": [ + 307, + 122, + 545, + 221 + ], + "spans": [ + { + "bbox": [ + 307, + 122, + 545, + 221 + ], + "type": "image", + "image_path": "d82853ecb9cfd4000c76ca4f3d5bbfe667ca1779a1100c2e9588e2994e81bdcf.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 309, + 222, + 541, + 234 + ], + "lines": [ + { + "bbox": [ + 309, + 222, + 541, + 234 + ], + "spans": [ + { + "bbox": [ + 309, + 222, + 541, + 234 + ], + "type": "text", + "content": "Figure 6. Feature visualization of " + }, + { + "bbox": [ + 309, + 222, + 541, + 234 + ], + "type": "inline_equation", + "content": "F_{SF}" + }, + { + "bbox": [ + 309, + 222, + 541, + 234 + ], + "type": "text", + "content": " (Eq. 6) and " + }, + { + "bbox": [ + 309, + 222, + 541, + 234 + ], + "type": "inline_equation", + "content": "F_{T}" + }, + { + "bbox": [ + 309, + 222, + 541, + 234 + ], + "type": "text", + "content": " (Eq. 4)." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 317, + 239, + 389, + 328 + ], + "blocks": [ + { + "bbox": [ + 317, + 239, + 389, + 328 + ], + "lines": [ + { + "bbox": [ + 317, + 239, + 389, + 328 + ], + "spans": [ + { + "bbox": [ + 317, + 239, + 389, + 328 + ], + "type": "image", + "image_path": "e11c66dd2545f1e24007106b812eaff1990d8536fbdb6a1a23500f357e9e9ecd.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 326, + 331, + 382, + 339 + ], + "lines": [ + { + "bbox": [ + 326, + 331, + 382, + 339 + ], + "spans": [ + { + "bbox": [ + 326, + 331, + 382, + 339 + ], + "type": "text", + "content": "(a) Basic VSR++" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 390, + 239, + 460, + 328 + ], + "blocks": [ + { + "bbox": [ + 390, + 239, + 460, + 328 + ], + "lines": [ + { + "bbox": [ + 390, + 239, + 460, + 328 + ], + "spans": [ + { + "bbox": [ + 390, + 239, + 460, + 328 + ], + "type": "image", + "image_path": "584f2cfeec7857800f686884586fcc498de46dc46ea844d959812eb1d21d98ab.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 410, + 331, + 439, + 339 + ], + "lines": [ + { + "bbox": [ + 410, + 331, + 439, + 339 + ], + "spans": [ + { + "bbox": [ + 410, + 331, + 439, + 339 + ], + "type": "text", + "content": "(b) Ours" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 461, + 239, + 532, + 329 + ], + "blocks": [ + { + "bbox": [ + 461, + 239, + 532, + 329 + ], + "lines": [ + { + "bbox": [ + 461, + 239, + 532, + 329 + ], + "spans": [ + { + "bbox": [ + 461, + 239, + 532, + 329 + ], + "type": "image", + "image_path": "3e26b06353930604d5d7a160e15ee132f574621030c6ee77db67998ced9d2f35.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 484, + 331, + 507, + 339 + ], + "lines": [ + { + "bbox": [ + 484, + 331, + 507, + 339 + ], + "spans": [ + { + "bbox": [ + 484, + 331, + 507, + 339 + ], + "type": "text", + "content": "(c) GT" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 306, + 345, + 545, + 367 + ], + "lines": [ + { + "bbox": [ + 306, + 345, + 545, + 367 + ], + "spans": [ + { + "bbox": [ + 306, + 345, + 545, + 367 + ], + "type": "text", + "content": "Figure 7. Comparison of noise removal capacity of BasicVSR++ [6] with our methods, with respect to HR GT." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 377, + 546, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 377, + 546, + 437 + ], + "spans": [ + { + "bbox": [ + 304, + 377, + 546, + 437 + ], + "type": "text", + "content": "ners and noise. Therefore BasicVSR++ is easily affected by serious noise. In comparison to BasicVSR++, our framework has more robustness. Benefiting from the guidance of events, e.g., edges, corners, our method can effectively reduce the adverse effects of noise on frames." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 447, + 378, + 459 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 447, + 378, + 459 + ], + "spans": [ + { + "bbox": [ + 306, + 447, + 378, + 459 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 468, + 545, + 575 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 468, + 545, + 575 + ], + "spans": [ + { + "bbox": [ + 304, + 468, + 545, + 575 + ], + "type": "text", + "content": "In this paper, we proposed a novel framework which jointly learns INRs from RGB frames and events, and enables arbitrary scale VSR. Our method effectively uses high temporal resolution property of events to complement RGB frames with STF and TF. A simple yet effective STIR is used to recover frames at arbitrary scales. Extensive experiments on two real-world datasets validate our method enjoys better performance over current related SoTA methods with significantly lower model size." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 575, + 545, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 575, + 545, + 647 + ], + "spans": [ + { + "bbox": [ + 304, + 575, + 545, + 647 + ], + "type": "text", + "content": "Limitation and Future Work: In this work we tackle the problem of spatial VSR. Since our work learns INRs of videos, it naturally enables frame interpolation, which will be explored in our future work. We hope our work can shed light on more research about enhancing implicit neural representations with non-photorealistic data." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 306, + 658, + 410, + 671 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 658, + 410, + 671 + ], + "spans": [ + { + "bbox": [ + 306, + 658, + 410, + 671 + ], + "type": "text", + "content": "6. Acknowledgment" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 677, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 545, + 712 + ], + "type": "text", + "content": "This work was supported by the Research Project Fund of AlpsenTek and the National Natural Science Foundation of China (NSFC) under Grant No. NSFC22FYT45." + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1564" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "text", + "content": "[1] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6836-6846, 2021." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 148, + 288, + 203 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 148, + 288, + 203 + ], + "spans": [ + { + "bbox": [ + 53, + 148, + 288, + 203 + ], + "type": "text", + "content": "[2] Wenbo Bao, Wei-Sheng Lai, Xiaoyun Zhang, Zhiyong Gao, and Ming-Hsuan Yang. Memc-net: Motion estimation and motion compensation driven neural network for video interpolation and enhancement. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3):933-948, 2021." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 205, + 287, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 205, + 287, + 270 + ], + "spans": [ + { + "bbox": [ + 53, + 205, + 287, + 270 + ], + "type": "text", + "content": "[3] Jose Caballero, Christian Ledig, Andrew Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, and Wenzhe Shi. Real-time video super-resolution with spatio-temporal networks and motion compensation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4778-4787, 2017." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 272, + 287, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 272, + 287, + 304 + ], + "spans": [ + { + "bbox": [ + 53, + 272, + 287, + 304 + ], + "type": "text", + "content": "[4] Daniel Canedo and António JR Neves. Facial expression recognition using computer vision: A systematic review. Applied Sciences, 9(21):4678, 2019." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 307, + 287, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 307, + 287, + 361 + ], + "spans": [ + { + "bbox": [ + 53, + 307, + 287, + 361 + ], + "type": "text", + "content": "[5] Kelvin C.K. Chan, Xintao Wang, Ke Yu, Chao Dong, and Chen Change Loy. Basicvsr: The search for essential components in video super-resolution and beyond. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2021." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 363, + 287, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 363, + 287, + 396 + ], + "spans": [ + { + "bbox": [ + 53, + 363, + 287, + 396 + ], + "type": "text", + "content": "[6] Kelvin C.K. Chan, Shangchen Zhou, Xiangyu Xu, and Chen Change Loy. Basicvsr++: Improving video superresolution with enhanced propagation and alignment. 2022." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 397, + 287, + 452 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 397, + 287, + 452 + ], + "spans": [ + { + "bbox": [ + 53, + 397, + 287, + 452 + ], + "type": "text", + "content": "[7] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8628-8638, 2021." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 454, + 287, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 454, + 287, + 520 + ], + "spans": [ + { + "bbox": [ + 53, + 454, + 287, + 520 + ], + "type": "text", + "content": "[8] Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang, Humphrey Shi, and Xiaolong Wang. Videoirr: Learning video implicit neural representation for continuous space-time super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2047-2057, 2022." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 521, + 287, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 521, + 287, + 566 + ], + "spans": [ + { + "bbox": [ + 53, + 521, + 287, + 566 + ], + "type": "text", + "content": "[9] Zeyuan Chen, Yinbo Chen, Jingwen Liu, Xingqian Xu, Vidit Goel, Zhangyang Wang, Humphrey Shi, and Xiaolong Wang. Videoirr: Learning video implicit neural representation for continuous space-time super-resolution. 2022." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 567, + 287, + 620 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 567, + 287, + 620 + ], + "spans": [ + { + "bbox": [ + 48, + 567, + 287, + 620 + ], + "type": "text", + "content": "[10] Minhyeok Cho, Haechang Lee, Hyunwoo Je, Kijeong Kim, Dongil Ryu, Jinsu Kim, Jonghyun Bae, and Albert No. Pynet-qxq: A distilled pynet for qxq bayer pattern demosaicing in cmos image sensor. arXiv preprint arXiv:2203.04314, 2022." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 624, + 287, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 287, + 657 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 287, + 657 + ], + "type": "text", + "content": "[11] Andreas Daffertshofer, Claudine JC Lamoth, Onno G Meijer, and Peter J Beek. Pca in studying coordination and variability: a tutorial. Clinical biomechanics, 19(4):415-428, 2004." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "type": "text", + "content": "[12] Guillermo Gallego, Tobi Delbrück, Garrick Orchard, Chiara Bartolozzi, Brian Taba, Andrea Censi, Stefan Leutenegger, Andrew J Davison, Jörg Conradt, Kostas Daniilidis, et al. Event-based vision: A survey. IEEE transactions on pattern analysis and machine intelligence, 44(1):154-180, 2020." + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "type": "text", + "content": "[13] Jin Han, Yixin Yang, Chu Zhou, Chao Xu, and Boxin Shi. Evintsr-net: Event guided multiple latent frames reconstruction and super-resolution. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4882-4891, 2021." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 129, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 545, + 184 + ], + "type": "text", + "content": "[14] Muhammad Haris, Gregory Shakhnarovich, and Norimichi Ukita. Recurrent back-projection network for video superresolution. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3892-3901, 2019." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 186, + 545, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 545, + 240 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 545, + 240 + ], + "type": "text", + "content": "[15] Earnest Paul Ijjina, Dhananjai Chand, Savyasachi Gupta, and K Goutham. Computer vision-based accident detection in traffic surveillance. In 2019 10th International conference on computing, communication and networking technologies (ICCCNT), pages 1-6. IEEE, 2019." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 241, + 545, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 241, + 545, + 297 + ], + "spans": [ + { + "bbox": [ + 307, + 241, + 545, + 297 + ], + "type": "text", + "content": "[16] Takashi Isobe, Songjiang Li, Xu Jia, Shanxin Yuan, Gregory Slabaugh, Chunjing Xu, Ya-Li Li, Shengjin Wang, and Qi Tian. Video super-resolution with temporal group attention. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8008-8017, 2020." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 298, + 545, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 298, + 545, + 352 + ], + "spans": [ + { + "bbox": [ + 307, + 298, + 545, + 352 + ], + "type": "text", + "content": "[17] Ajay Jain, Ben Mildenhall, Jonathan T Barron, Pieter Abbeel, and Ben Poole. Zero-shot text-guided object generation with dream fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 867-876, 2022." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 354, + 545, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 354, + 545, + 409 + ], + "spans": [ + { + "bbox": [ + 307, + 354, + 545, + 409 + ], + "type": "text", + "content": "[18] Yongcheng Jing, Yiding Yang, Xinchao Wang, Mingli Song, and Dacheng Tao. Turning frequency to resolution: Video super-resolution via event cameras. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7772-7781, 2021." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 411, + 545, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 411, + 545, + 465 + ], + "spans": [ + { + "bbox": [ + 307, + 411, + 545, + 465 + ], + "type": "text", + "content": "[19] Younghyun Jo, Seoung Wug Oh, Jaeyeon Kang, and Seon Joo Kim. Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3224-3232, 2018." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 466, + 545, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 466, + 545, + 498 + ], + "spans": [ + { + "bbox": [ + 307, + 466, + 545, + 498 + ], + "type": "text", + "content": "[20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 500, + 545, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 500, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 307, + 500, + 545, + 555 + ], + "type": "text", + "content": "[21] Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang. Fast and accurate image super-resolution with deep laplacian pyramid networks. IEEE transactions on pattern analysis and machine intelligence, 41(11):2599-2613, 2018." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 556, + 545, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 556, + 545, + 612 + ], + "spans": [ + { + "bbox": [ + 307, + 556, + 545, + 612 + ], + "type": "text", + "content": "[22] Sheng Li, Fengxiang He, Bo Du, Lefei Zhang, Yonghao Xu, and Dacheng Tao. Fast spatio-temporal residual network for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10522-10531, 2019." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 613, + 545, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 613, + 545, + 668 + ], + "spans": [ + { + "bbox": [ + 307, + 613, + 545, + 668 + ], + "type": "text", + "content": "[23] Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, Xinchao Wang, and Thomas S Huang. Learning temporal dynamics for video super-resolution: A deep learning approach. IEEE Transactions on Image Processing, 27(7):3432-3445, 2018." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 669, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 669, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 307, + 669, + 545, + 712 + ], + "type": "text", + "content": "[24] Hongying Liu, Zhubo Ruan, Peng Zhao, Chao Dong, Fanhua Shang, Yuanyuan Liu, Linlin Yang, and Radu Timofte. Video super-resolution based on deep learning: a comprehensive survey. In Artif Intell Rev., 2022." + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 748, + 315, + 757 + ], + "type": "text", + "content": "1565" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 117 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 117 + ], + "type": "text", + "content": "[25] Ze Liu, Jia Ning, Yue Cao, Yixuan Wei, Zheng Zhang, Stephen Lin, and Han Hu. Video swim transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3202-3211, 2022." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 118, + 288, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 118, + 288, + 162 + ], + "spans": [ + { + "bbox": [ + 49, + 118, + 288, + 162 + ], + "type": "text", + "content": "[26] Yunfan Lu, Yiqi Lin, Hao Wu, Yunhao Luo, Xu Zheng, and Lin Wang. All one needs to know about priors for deep image restoration and enhancement: A survey. arXiv preprint arXiv:2206.02070, 2022." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 163, + 288, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 163, + 288, + 217 + ], + "spans": [ + { + "bbox": [ + 48, + 163, + 288, + 217 + ], + "type": "text", + "content": "[27] Zhisheng Lu, Juncheng Li, Hong Liu, Chaoyan Huang, Linlin Zhang, and Tieyong Zeng. Transformer for single image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 457-466, 2022." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 219, + 288, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 219, + 288, + 262 + ], + "spans": [ + { + "bbox": [ + 48, + 219, + 288, + 262 + ], + "type": "text", + "content": "[28] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 264, + 288, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 264, + 288, + 308 + ], + "spans": [ + { + "bbox": [ + 48, + 264, + 288, + 308 + ], + "type": "text", + "content": "[29] Mohammad Mostafavi, Yeongwoo Nam, Jonghyun Choi, and Kuk-Jin Yoon. E2sri: Learning to super-resolve intensity images from events. IEEE transactions on pattern analysis and machine intelligence, 44(10):6890-6909, 2021." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 309, + 288, + 375 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 309, + 288, + 375 + ], + "spans": [ + { + "bbox": [ + 48, + 309, + 288, + 375 + ], + "type": "text", + "content": "[30] Seungjun Nah, Sungyong Baik, Seokil Hong, Gyeongsik Moon, Sanghyun Son, Radu Timofte, and Kyoung Mu Lee. Ntire 2019 challenge on video deblurring and superresolution: Dataset and study. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0–0, 2019." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 376, + 288, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 376, + 288, + 421 + ], + "spans": [ + { + "bbox": [ + 48, + 376, + 288, + 421 + ], + "type": "text", + "content": "[31] Daniel Neimark, Omri Bar, Maya Zohar, and Dotan Asselmann. Video transformer network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3163-3172, 2021." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 422, + 288, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 422, + 288, + 465 + ], + "spans": [ + { + "bbox": [ + 48, + 422, + 288, + 465 + ], + "type": "text", + "content": "[32] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional generative neural feature fields. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11453-11464, 2021." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 466, + 288, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 466, + 288, + 521 + ], + "spans": [ + { + "bbox": [ + 48, + 466, + 288, + 521 + ], + "type": "text", + "content": "[33] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 522, + 288, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 522, + 288, + 544 + ], + "spans": [ + { + "bbox": [ + 48, + 522, + 288, + 544 + ], + "type": "text", + "content": "[34] Vadim Pisarevsky. Introduction to opencv. *Agenda*, 42:433–434, 2010." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 545, + 288, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 545, + 288, + 600 + ], + "spans": [ + { + "bbox": [ + 48, + 545, + 288, + 600 + ], + "type": "text", + "content": "[35] Cedric Scheerlinck, Henri Rebecq, Timo Stoffregen, Nick Barnes, Robert Mahony, and Davide Scaramuzza. Ced: Color event camera dataset. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0-0, 2019." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 601, + 288, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 601, + 288, + 645 + ], + "spans": [ + { + "bbox": [ + 48, + 601, + 288, + 645 + ], + "type": "text", + "content": "[36] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. Advances in Neural Information Processing Systems, 33:7462-7473, 2020." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 647, + 288, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 288, + 677 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 288, + 677 + ], + "type": "text", + "content": "[37] Alex J Smola and Bernhard Schölkopf. A tutorial on support vector regression. Statistics and computing, 14(3):199-222, 2004." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 680, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 288, + 713 + ], + "type": "text", + "content": "[38] Cheng Sun, Min Sun, and Hwann-Tzong Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In Proceedings of the IEEE/CVF Conference" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "text", + "content": "on Computer Vision and Pattern Recognition, pages 5459-5469, 2022." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 95, + 545, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 95, + 545, + 139 + ], + "spans": [ + { + "bbox": [ + 308, + 95, + 545, + 139 + ], + "type": "text", + "content": "[39] Jiaxiang Tang, Xiaokang Chen, and Gang Zeng. Joint implicit image function for guided depth super-resolution. In Proceedings of the 29th ACM International Conference on Multimedia, pages 4390-4399, 2021." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 140, + 545, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 140, + 545, + 194 + ], + "spans": [ + { + "bbox": [ + 308, + 140, + 545, + 194 + ], + "type": "text", + "content": "[40] Yapeng Tian, Yulun Zhang, Yun Fu, and Chenliang Xu. Tdan: Temporally-deformable alignment network for video super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3360-3369, 2020." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 195, + 545, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 195, + 545, + 239 + ], + "spans": [ + { + "bbox": [ + 308, + 195, + 545, + 239 + ], + "type": "text", + "content": "[41] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 239, + 545, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 239, + 545, + 282 + ], + "spans": [ + { + "bbox": [ + 308, + 239, + 545, + 282 + ], + "type": "text", + "content": "[42] Bishan Wang, Jingwei He, Lei Yu, Gui-Song Xia, and Wen Yang. Event enhanced high-quality image recovery. In European Conference on Computer Vision, pages 155-171. Springer, 2020." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 284, + 545, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 284, + 545, + 327 + ], + "spans": [ + { + "bbox": [ + 308, + 284, + 545, + 327 + ], + "type": "text", + "content": "[43] Bishan Wang, Jingwei He, Lei Yu, Gui-Song Xia, and Wen Yang. Event enhanced high-quality image recovery. In European Conference on Computer Vision, pages 155-171. Springer, 2020." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 327, + 545, + 393 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 327, + 545, + 393 + ], + "spans": [ + { + "bbox": [ + 308, + 327, + 545, + 393 + ], + "type": "text", + "content": "[44] Lin Wang, Yujeong Chae, Sung-Hoon Yoon, Tae-Kyun Kim, and Kuk-Jin Yoon. Evdistill: Asynchronous events to end-task learning via bidirectional reconstruction-guided cross-modal knowledge distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 608-619, 2021." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 394, + 545, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 394, + 545, + 437 + ], + "spans": [ + { + "bbox": [ + 308, + 394, + 545, + 437 + ], + "type": "text", + "content": "[45] Longguang Wang, Yulan Guo, Li Liu, Zaiping Lin, Xinpu Deng, and Wei An. Deep video super-resolution using hr optical flow estimation. IEEE Transactions on Image Processing, 29:4323-4336, 2020." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 438, + 545, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 438, + 545, + 492 + ], + "spans": [ + { + "bbox": [ + 308, + 438, + 545, + 492 + ], + "type": "text", + "content": "[46] Lin Wang, Tae-Kyun Kim, and Kuk-Jin Yoon. Eventsr: From asynchronous events to image reconstruction, restoration, and super-resolution via end-to-end adversarial learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8315-8325, 2020." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 493, + 545, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 493, + 545, + 547 + ], + "spans": [ + { + "bbox": [ + 308, + 493, + 545, + 547 + ], + "type": "text", + "content": "[47] Wei Wang, Haochen Zhang, Zehuan Yuan, and Changhu Wang. Unsupervised real-world super-resolution: A domain adaptation perspective. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4318-4327, 2021." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 548, + 545, + 580 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 548, + 545, + 580 + ], + "spans": [ + { + "bbox": [ + 308, + 548, + 545, + 580 + ], + "type": "text", + "content": "[48] Xintao Wang, Ke Yu, Kelvin C.K. Chan, Chao Dong, and Chen Change Loy. Basicsr. https://github.com/xinntao/BasicSR, 2020." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 582, + 545, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 582, + 545, + 635 + ], + "spans": [ + { + "bbox": [ + 308, + 582, + 545, + 635 + ], + "type": "text", + "content": "[49] Xi Yang, Wangmeng Xiang, Hui Zeng, and Lei Zhang. Realworld video super-resolution: A benchmark dataset and a decomposition based learning scheme. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 4781-4790, 2021." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 636, + 545, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 636, + 545, + 669 + ], + "spans": [ + { + "bbox": [ + 308, + 636, + 545, + 669 + ], + "type": "text", + "content": "[50] Huanjing Yue, Zhiming Zhang, and Jingyu Yang. Realrawvsr: Real-world raw video super-resolution with a benchmark dataset. arXiv preprint arXiv:2209.12475, 2022." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "text", + "content": "[51] Xu Zheng, Yexin Liu, Yunfan Lu, Tongyan Hua, Tianbo Pan, Weiming Zhang, Dacheng Tao, and Lin Wang. Deep learning for event-based vision: A comprehensive survey and benchmarks. arXiv preprint arXiv:2302.08890, 2023." + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "text", + "content": "1566" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 195 + ], + "type": "list", + "angle": 0, + "index": 2, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 137 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 137 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 137 + ], + "type": "text", + "content": "[52] Pengyuan Zhou, Jinjing Zhu, Yiting Wang, Yunfan Lu, Zixiang Wei, Haolin Shi, Yuchen Ding, Yu Gao, Qinglong Huang, Yan Shi, et al. Vetaverse: Technologies, applications, and visions toward the intersection of meta-verse, vehicles, and transportation systems. arXiv preprint arXiv:2210.15109, 2022." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 140, + 287, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 140, + 287, + 195 + ], + "spans": [ + { + "bbox": [ + 48, + 140, + 287, + 195 + ], + "type": "text", + "content": "[53] Yunhao Zou, Yinqiang Zheng, Tsuyoshi Takatani, and Ying Fu. Learning to reconstruct high speed and high dynamic range videos from events. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2024-2033, 2021." + } + ] + } + ], + "index": 1 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 296, + 749, + 315, + 757 + ], + "type": "text", + "content": "1567" + } + ] + } + ], + "index": 3 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Steerable Function for Efficient Image Resampling/067ca41f-5208-49cd-b2f0-82f2850ff9d3_content_list.json b/2023/Learning Steerable Function for Efficient Image Resampling/067ca41f-5208-49cd-b2f0-82f2850ff9d3_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..9b8be8bd6937e96ae81a2154c27dcc78b691dc4b --- /dev/null +++ b/2023/Learning Steerable Function for Efficient Image Resampling/067ca41f-5208-49cd-b2f0-82f2850ff9d3_content_list.json @@ -0,0 +1,2908 @@ +[ + { + "type": "text", + "text": "Learning Steerable Function for Efficient Image Resampling", + "text_level": 1, + "bbox": [ + 179, + 130, + 789, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Jiacheng Li $^{1*}$ Chang Chen $^{2*}$ Wei Huang $^{1}$ \nZhiqiang Lang $^{2}$ Fenglong Song $^{2}$ Youliang Yan $^{2}$ Zhiwei Xiong $^{1\\dagger}$ $^{1}$ University of Science and Technology of China ${}^{2}$ Huawei Noah's Ark Lab", + "bbox": [ + 181, + 179, + 787, + 233 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{jcee,weih527}@mail.ustc.edu.cn zwxiong@ustc.edu.cn {chenchang25,langzhiqiang,songfenglong,yanyouliang}@huawei.com", + "bbox": [ + 209, + 236, + 754, + 268 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 304, + 312, + 319 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Image resampling is a basic technique that is widely employed in daily applications. Existing deep neural networks (DNNs) have made impressive progress in resampling performance. Yet these methods are still not the perfect substitute for interpolation, due to the issues of efficiency and continuous resampling. In this work, we propose a novel method of Learning Resampling Function (termed LeRF), which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption of interpolation methods. Specifically, LeRF assigns spatially-varying steerable resampling functions to input image pixels and learns to predict the hyper-parameters that determine the orientations of these resampling functions with a neural network. To achieve highly efficient inference, we adopt look-up tables (LUTs) to accelerate the inference of the learned neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures. Extensive experiments show that our method runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly, e.g., up to 3dB PSNR gain over bicubic for $\\times 2$ upsampling on Manga109.", + "bbox": [ + 75, + 335, + 473, + 670 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 699, + 207, + 714 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Due to the rapid growth of visual data, there is a strong demand for digital image processing. Image resampling, one of the most common techniques, aims to obtain another image by generating new pixels following a geometric transformation rule from existing pixels in a given image [8]. Common transformations include upsampling (i.e., single image super-resolution), downsampling, affine transformation, etc. Image resampling enjoys various applications, ranging from photo editing, optical distortion com", + "bbox": [ + 75, + 726, + 468, + 863 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/5400022abe66cf2906d4dac8f37792dbd54c428e10f6f244f74d7fcbb2a915cf.jpg", + "image_caption": [ + "Figure 1. LeRF assigns steerable resampling functions to input pixels, and learns to predict the hyper-parameters that determine the orientations of these continuous functions for resampling under arbitrary transformations." + ], + "image_footnote": [], + "bbox": [ + 506, + 304, + 893, + 523 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "pensation [10], online content streaming [35], and visual special effects production [38].", + "bbox": [ + 498, + 597, + 890, + 627 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recently, deep neural networks (DNNs) have made impressive progress in the field of image resampling [9, 12, 17, 27, 39, 40], thanks to the learning-from-data paradigm that obtains powerful structural priors from large-scale datasets. Despite the superior performance that DNN-based methods have achieved, long-lived interpolation methods like bicubic [16] are still preferred choices in most cases.", + "bbox": [ + 496, + 628, + 890, + 733 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We attribute this phenomenon to the following two reasons: 1) Interpolation is simple and highly efficient, resulting in less dependency and thus the practicality to be deployed on a variety of devices, ranging from IoT devices to gaming workstations. 2) Interpolation supports arbitrary transformations. It assumes a continuous resampling function for a local area, resulting in the versatility in applying to not only homographic transformations like upsampling and downsampling, but also general warping. Although recent DNN-based methods explore beyond fixed-scale upsampling [5, 12, 27, 39, 43, 48], an efficient and continuous", + "bbox": [ + 496, + 734, + 892, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Equal contribution. † Corresponding author. This work was done when Jiacheng Li was a research intern at Huawei Noah's Ark Lab.", + "bbox": [ + 76, + 875, + 468, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "5866", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "solution that matches interpolation remains less explored.", + "bbox": [ + 76, + 90, + 455, + 104 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this work, we aim to fill this blank research area by taking a middle way between DNN-based methods and interpolation methods. We propose a novel method of Learning Resampling Function (termed LeRF), where parameterized continuous functions for resampling different structures are learned from data. Specifically, as illustrated in Fig. 1, we assign spatially-varying steerable resampling functions to image pixels, whose orientations are parameterized with several hyper-parameters. Then, we train a neural network to predict these hyper-parameters for each pixel in an input image, thus defining the resampling function for that pixel location. Finally, we obtain the output image by interpolating an input image with these locally adapted resampling functions. LeRF takes advantage of both the structural priors learned by DNN and the locally continuous assumption of interpolation methods. Furthermore, we present an efficient implementation, where the inference of the learned neural network is accelerated with look-up tables (LUTs) [15,22,23,29]. We further design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures in images.", + "bbox": [ + 75, + 106, + 468, + 422 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We examine the advantages and generalization ability of LeRF in various image resampling tasks, including arbitrary-scale upsampling, homographic transformation, and general warping. In particular, as illustrated in Fig. 2, at a similar running time, our method outperforms popular interpolation methods significantly in upsampling, which demonstrates the superiority of LeRF in terms of performance and efficiency.", + "bbox": [ + 75, + 422, + 468, + 542 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Contributions of this paper are summarized as follows:", + "bbox": [ + 96, + 544, + 459, + 558 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1) We propose LeRF, a novel method for continuous resampling. We assign spatially-varying steerable resampling functions to image pixels, where we train a neural network to predict the hyper-parameters that determine the orientations of these resampling functions.", + "2) We present an efficient implementation of LeRF by adopting look-up tables to accelerate the inference of the trained neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures.", + "3) Extensive experiments demonstrate that our method operates as efficient as interpolation, generalizes well to arbitrary transformations, and obtains significantly better performance over interpolation." + ], + "bbox": [ + 76, + 559, + 468, + 770 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 76, + 784, + 225, + 800 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Image interpolation for resampling. Interpolation, the most common solution for image resampling, assumes a locally continuous intensity surface and approximates it with a fixed resampling function, such as nearest (nearest), linear (bilinear), cubic (bicubic) [16], and windowed sinc (lanczos). It predicts resampling weights with the as", + "bbox": [ + 75, + 809, + 468, + 900 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/0f687746b33e4873affbc51e5bb45701943afdc2fcbe9d795a398c45890da4ef.jpg", + "image_caption": [ + "Figure 2. Performance-efficiency trade-off of arbitrary-scale upsampling methods. PSNR values are obtained on Set14 for $\\times 4$ upsampling. The running time is evaluated on mobile and desktop devices, respectively." + ], + "image_footnote": [], + "bbox": [ + 517, + 88, + 861, + 258 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "sumed resampling function, and then aggregates input pixels with these resampling weights to obtain the target pixel. This assumption allows for continuous resampling under arbitrary transformations, yet leads to blurry results due to ignoring different local structures [34]. We follow the same assumption on local continuity, but our method deals with different structures with adapted resampling functions instead of fixed ones.", + "bbox": [ + 496, + 325, + 890, + 445 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Adaptive image resampling. To integrate local structural information into the resampling process, many methods are proposed, including edge-directed interpolation [2, 24, 44, 52] and kernel regression [21, 41, 51]. Different from these methods that rely on hand-designed rules, our method adopts a neural network to learn structural priors and integrate them into the resampling functions in a data-driven way. Another line of work achieves adaptive resampling by combining interpolation methods with adaptive filtering [11, 14, 17, 37]. Among them, RAISR achieves superresolution by predicting adaptive filters for each pixel from a hash table and then applying the predicted adaptive filters to pre-upsampled images [11, 37]. This kind of method operates on the pre-resampled results of common interpolation methods (usually bicubic) with a fixed transformation (e.g., $\\times 2$ upsampling), thus lacking the generalization ability to be extended to unseen arbitrary transformations.", + "bbox": [ + 496, + 446, + 892, + 702 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "DNN-based image resampling. With the rise of deep neural networks, impressive progress has been made in image resampling, such as upsampling [3,4,9,17,25,27,36,46,47, 49, 54], downsampling [40, 42], and homographic transformation [39]. For arbitrary-scale upsampling, Meta-SR [12] and LIIF [5] are two representative works. Meta-SR predicts the resampling weights from upsampling scales with a weight prediction network, similar to kernel prediction networks [26, 32], and aggregates deep features to get the final upsampled image [12]. Following works improve the process with support for homographic transformations [39] and scale-aware feature adaptation [43]. On the other hand, LIIF models the aggregation step with a neural implicit rep", + "bbox": [ + 496, + 704, + 892, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "5867", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/739a7144b63699f17db3e1c72812626ca6f985a4009a82b3b0dbd092dada55f4.jpg", + "image_caption": [ + "(a) Interpolation" + ], + "image_footnote": [], + "bbox": [ + 133, + 95, + 465, + 236 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/6ad6f74b164bcc6b4445606f311733dfb99fbf006eaeda65cbdb74127c30946c.jpg", + "image_caption": [ + "(b) LeRF (Ours)", + "Figure 3. Comparison between interpolation and LeRF. (a) Interpolation assumes a spatial-invariant fixed function $\\Phi(\\cdot)$ to predict resampling weights $W$ . (b) LeRF learns spatially-varying steerable function $\\Phi_{\\Theta}$ with a DNN $f(\\cdot)$ ." + ], + "image_footnote": [], + "bbox": [ + 513, + 93, + 851, + 244 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "resentation, predicting the target pixels from deep latent features and target coordinates [5]. Following works improve the implicit representation by integrating frequency analysis [20] and modulation-based transformer [48]. Different from these works, we utilize explicit local functions, resulting in advantages in efficiency and interpretability.", + "bbox": [ + 75, + 313, + 468, + 404 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Efficient super-resolution with look-up table. A look-up table is composed of index-value pairs, which can be efficiently retrieved through memory access. It is widely applied in the image signal processing pipeline [18, 50]. Recently, SR-LUT has been introduced to accelerate the inference of a fixed-scale super-resolution network by traversing all possible low-resolution (LR) patches, pre-computing all corresponding high-resolution (HR) patches, and saving them as index-value pairs [15]. At inference, the computations in the super-resolution network are replaced with retrieving values from LUT, leading to inference acceleration. Different from existing LUT-based methods [15,22,23,29], which adopt different groups of LUTs under different upsampling scales, our LUTs store the same group of hyperparameters across arbitrary transformations, thus achieving continuous resampling at high efficiency.", + "bbox": [ + 75, + 405, + 470, + 647 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Learning Resampling Function", + "text_level": 1, + "bbox": [ + 76, + 664, + 362, + 681 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Formulation", + "text_level": 1, + "bbox": [ + 76, + 690, + 210, + 705 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Typically, resampling through interpolation can be implemented in the following steps.", + "bbox": [ + 75, + 715, + 468, + 744 + ], + "page_idx": 2 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "① Obtain relative offsets: the target coordinates after transformation, such as upsampling, are projected back to the coordinate space of the input image, and the relative spatial offsets between target pixels and source pixels in their support patches are obtained.", + "$②$ Predict resampling weights: based on the relative spatial offsets, the resampling weights, i.e., resampling kernels, are predicted for each pixel in the support patch.", + "③ Aggregate pixels: the source pixels are aggregated through weighted summation to obtain the target pixel" + ], + "bbox": [ + 75, + 747, + 470, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "value. The above process can be formulated as", + "bbox": [ + 500, + 313, + 810, + 328 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {I} _ {q} = \\sum_ {p \\in \\Omega} W _ {p \\rightarrow q} I _ {p} \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 632, + 338, + 890, + 371 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\hat{I}_q$ is the interpolated pixel value at the target coordinate, $I_p$ the pixel value at the source coordinate, $\\Omega$ the support patch, and $W_{p\\rightarrow q}$ the weight from $p$ to $q$ .", + "bbox": [ + 498, + 378, + 890, + 426 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As illustrated in Fig. 3(a), an isotropic resampling function $\\Phi(\\cdot)$ , e.g. cubic interpolation, is assumed to predict resampling weights from relative offsets, which can be formulated as", + "bbox": [ + 498, + 426, + 890, + 484 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nW _ {p \\rightarrow q} = \\Phi \\left(d _ {p \\rightarrow q}\\right) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 630, + 497, + 890, + 513 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $d_{p\\rightarrow q}$ is the relative offset from $p$ to $q$ , which can be obtained based on the geometric transformation $\\mathcal{T}$ . Thanks to the continuity of the resampling function $\\Phi$ , arbitrary transformations from $I_{in}$ to $I_{out}$ can be achieved. The assumption of local continuity contributes to the versatility and high efficiency of interpolation. But the spatially-invariant resampling function leads to blurry results due to the ignorance of local structures.", + "bbox": [ + 496, + 518, + 890, + 640 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. The Steerable Resampling Function", + "text_level": 1, + "bbox": [ + 500, + 648, + 810, + 666 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this work, we propose a novel method of learning resampling function that adapts the resampling functions to local structures in a data-driven way. Different from the fixed resampling function in interpolation, we assume a kind of steerable resampling function $\\Phi_{\\Theta}$ , parameterized by $\\Theta$ . Specifically, we utilize the anisotropic gaussian,", + "bbox": [ + 496, + 672, + 890, + 763 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\Phi_ {\\boldsymbol {\\Sigma}} (\\boldsymbol {x} - \\boldsymbol {\\mu}) = \\frac {1}{2 \\pi | \\boldsymbol {\\Sigma} | ^ {\\frac {1}{2}}} \\exp \\left\\{- \\frac {1}{2} (\\boldsymbol {x} - \\boldsymbol {\\mu}) ^ {T} \\boldsymbol {\\Sigma} ^ {- 1} (\\boldsymbol {x} - \\boldsymbol {\\mu}) \\right\\} \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 506, + 772, + 890, + 819 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $(\\pmb{x} - \\pmb{\\mu})$ are the relative offsets between the target and source pixels, $|\\cdot|$ denotes the determinant of the covariance matrix $\\pmb{\\Sigma}$ . We parameterize $\\pmb{\\Sigma}$ as the following,", + "bbox": [ + 498, + 816, + 890, + 862 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\Sigma} = \\left( \\begin{array}{c c} \\sigma_ {X} ^ {2} & \\rho \\sigma_ {X} \\sigma_ {Y} \\\\ \\rho \\sigma_ {X} \\sigma_ {Y} & \\sigma_ {Y} ^ {2} \\end{array} \\right) \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 871, + 890, + 905 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "5868", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/9a251886bcfa5b106679e059b54006d9d4e53dcfafe709b64a2310fddacf151d.jpg", + "image_caption": [ + "(a) LUT acceleration of DNN" + ], + "image_footnote": [], + "bbox": [ + 122, + 88, + 269, + 224 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/b205e45c59e500cd364aceaf7e850535a109099ad6d863319bcf7f515e8cbc96.jpg", + "image_caption": [ + "(b) Ensemble strategy" + ], + "image_footnote": [], + "bbox": [ + 341, + 85, + 537, + 242 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/595ebafb9afdfe7837dda1b2d6054f44980c965018c9635de4fc0d7046a1d166.jpg", + "image_caption": [ + "(c) Edge-sensitive indexing patterns" + ], + "image_footnote": [], + "bbox": [ + 588, + 89, + 851, + 239 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/958264d104cfa9b2e391f45c1884e23db817388dbe9a9b12442a683446dbe7ee.jpg", + "image_caption": [ + "Figure 5. LUT acceleration of DNN and our adaptations. (a) A learned neural network can be accelerated by traversing all possible inputs $i_{*}$ , pre-computing all corresponding outputs $v_{*}$ , and saving them to LUTs [15,22,23,29]. (b) In the proposed directional ensemble strategy, only predictions along the same direction are averaged, instead of all directions in rotation ensemble [15]. (c) We introduce edge-sensitive indexing patterns to better capture edge orientations. The pixels covered by directional ensemble are depicted with dashed boxes.", + "Figure 4. Visualization of parameterized resampling functions." + ], + "image_footnote": [], + "bbox": [ + 99, + 340, + 439, + 409 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\rho, \\sigma_{X}$ , and $\\sigma_{Y}$ are hyper-parameters, and thus the resampling function becomes $\\Phi_{(\\rho, \\sigma_{X}, \\sigma_{Y})}$ . From the perspective of statistics, $\\rho$ can be interpreted as the correlation between 2D variables and $\\sigma_{X}, \\sigma_{Y}$ the standard deviations.", + "bbox": [ + 75, + 449, + 468, + 508 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As illustrated in Fig. 4, our resampling function is steerable, where different orientations and shapes can be obtained by tuning hyper-parameters $(\\rho, \\sigma_{X}, \\sigma_{Y})$ , showing its modeling capacity for a variety of local structures. Other non-steerable functions, such as Keys cubic [16] are also applicable as resampling functions. Details of the comparison are provided in Table 3 and Fig. 10.", + "bbox": [ + 75, + 508, + 468, + 614 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "But, direct optimization of the above hyper-parameters leads to unstable gradients and divergence. Thus, we modify the formulation by omitting the determinant multiplier and predicting a group of $(\\rho, \\frac{1}{\\sigma_X}, \\frac{1}{\\sigma_Y})$ for stable training. In the following section, we introduce the learning process of these hyper-parameters and our efficient implementation.", + "bbox": [ + 75, + 616, + 468, + 705 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Learning with DNN and LUT Acceleration", + "text_level": 1, + "bbox": [ + 76, + 717, + 442, + 732 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As shown in Fig. 3(b), we adopt a deep neural network to learn structural priors from an external dataset for predicting hyper-parameters $\\Theta$ in resampling functions. The training loss function can be formulated as", + "bbox": [ + 75, + 739, + 468, + 801 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {q} = \\left\\| I _ {q} - \\sum_ {p \\in \\Omega} \\Phi_ {\\Theta} \\left(d _ {p \\rightarrow q}\\right) I _ {p} \\right\\| ^ {2}, \\quad \\Theta = f (\\mathcal {N} _ {p}), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 101, + 811, + 468, + 845 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $I_q$ is the ground truth pixel value at location $q$ , $f(\\cdot)$ is a trainable DNN, and $\\mathcal{N}_p$ denotes the surrounding pixels of $I_p$ (not necessarily the same as the support patch $\\Omega$ ).", + "bbox": [ + 76, + 854, + 468, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Furthermore, to match the efficiency of interpolation, we accelerate the inference of the trained DNN by adopting look-up tables [15, 22, 23, 29]. As shown in Fig. 5(a), for each pair in the LUT to accelerate DNN, its index $i_{*}$ is a combination of pixels (i.e., $\\mathcal{N}_p$ ), and its value $v_{*}$ is a group of corresponding hyper-parameters for that pixel combination (i.e., $\\Theta$ ). This way, hyper-parameters can be retrieved directly from the saved values in LUTs, skipping computations in DNN and thus resulting in high efficiency.", + "bbox": [ + 496, + 343, + 890, + 479 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Different from existing LUT-based methods, whose LUT values are image pixels, our LUTs store hyper-parameters that reflect structural characteristics. Thus, to better extract structural priors, we propose the following adaptations.", + "bbox": [ + 496, + 481, + 890, + 542 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Directional ensemble strategy. We propose a directional ensemble (DE) strategy to replace the rotation ensemble (RE) strategy in existing LUT-based methods [15, 22, 23]. As illustrated in Fig. 5(b), in RE, the predictions are averaged across all directions, while the proposed DE strategy only ensembles the predictions with the same direction (i.e., $180^{\\circ}$ rotational symmetry, instead of $90^{\\circ}$ in RE). This enables the learning of $\\rho$ , which determines the orientation of the steerable resampling function (see Table 5 and Fig. 11).", + "bbox": [ + 496, + 546, + 892, + 681 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Edge-sensitive indexing patterns. As illustrated in Fig. 5(c), we include patterns \"C\" and \"X\", alongside the default \"S\" pattern in SR-LUT to better capture edges of different orientations. For example, The \"C\" and \"C\" patterns are sensitive to vertical and horizontal edges, respectively. We validate their effectiveness in Table 5. Correspondingly, as shown in Fig. 5(a), our DNN follows a multibranch design, and each branch is accelerated by a LUT.", + "bbox": [ + 496, + 686, + 890, + 806 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Pre-filtering stage. Inspired by DNN-based resampling methods [12, 43, 48], we pre-filter the image with a neural network, which can also be accelerated by LUTs with the proposed edge-sensitive indexing patterns. This prefiltering stage helps enhance image content and contributes to better performance (see Table 5 and Fig. 11).", + "bbox": [ + 496, + 810, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "5869", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/10a5fd10412b4c32f8fb7d2b646bc0374fe8491366a50ac1fa0712faf88ade31.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodSet5Set14BSDS100Urban100Manga109
×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4
Nearest31.3431.0730.8429.6329.1528.8428.5727.7028.9928.7228.4027.6226.2125.9125.6224.7828.5928.3628.1426.87
Bilinear34.9933.1932.2331.4931.6830.2629.2428.7030.9229.6628.6728.2028.2426.9125.9625.4632.4530.3329.1628.28
Bicubic36.7634.6833.6432.7033.0731.4530.3229.6232.1430.6729.5428.9329.5027.9526.8726.2234.7632.1330.8129.61
Lanczos236.8334.7433.7032.7433.1331.5030.3629.6532.1930.7129.5828.9529.5528.0026.9126.2534.8732.2230.8929.66
Lanczos337.6135.3134.2333.2433.7531.9730.7630.0232.7431.1129.8929.2330.1228.4227.2526.5536.1233.0631.6330.28
RAISR* [37]35.5035.4935.5733.3831.8431.6731.7130.2230.8730.6830.6629.4228.7728.6028.6427.0133.8133.7433.8830.61
SR-LUT* [15]37.7435.5234.4733.4533.5331.7930.5929.8633.0631.4030.1529.4530.2428.5527.3926.6935.3132.3030.9629.58
LeRF (Ours)38.3036.6035.7134.7434.5933.0631.9831.1033.7632.0830.8330.0931.8630.0828.8627.9936.5734.7933.8832.67
Meta-SR [12]41.29-38.12-37.47-33.99-35.79-32.32-35.85-32.98-42.92-39.22-
LIIF [5]41.2238.9938.0836.9937.4435.3133.9632.9535.7533.6832.2831.4536.7034.0832.8431.7042.7740.1939.1337.69
MethodSet5Set14BSDS100Urban100Manga109
×2.0 ×3.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0
Nearest28.8727.9126.8826.2527.0726.0825.3324.7427.1226.1725.5725.0324.2523.3422.6822.1726.1225.0424.0623.43
Bilinear30.4329.5328.2727.5527.9427.0426.1625.5127.6026.7726.1125.5324.8123.9923.2622.6827.2126.1624.9524.19
Bicubic31.4130.3929.1228.4228.7027.6326.7526.0928.1827.2026.5325.9525.4324.4523.7123.1428.2026.9525.6724.90
Lanczos231.4430.4129.1428.4428.7227.6426.7726.1028.2027.2126.5425.9625.4524.4723.7323.1528.2326.9725.7024.92
Lanczos331.8530.7929.4928.7829.0427.9127.0126.3128.4327.3926.7026.1025.7124.6823.9223.3228.7027.3826.0225.21
RAISR* [37]32.3531.8729.9129.6529.0428.6227.1026.8628.2427.8426.6826.4225.9225.5024.0823.8929.3028.7326.4026.12
SR-LUT* [15]32.0431.0029.7029.0028.8827.8426.9526.3028.6227.5426.8426.2125.8324.7824.0023.3928.0326.7425.3924.72
LeRF (Ours)33.1732.0230.8630.1530.0628.8428.0527.3529.1528.0027.3126.7026.9025.6824.8824.2330.8629.4828.1027.25
Meta-SR [12]-34.71-32.48-30.56-28.83-29.26-27.73-28.91-26.69-34.37-31.32
LIIF [5]35.6334.5933.1732.3731.6830.3929.4528.6530.4729.2428.4227.7330.2328.8027.5526.6635.7334.1732.2931.19
", + "bbox": [ + 76, + 88, + 890, + 459 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments and Results", + "text_level": 1, + "bbox": [ + 76, + 522, + 312, + 539 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Settings", + "text_level": 1, + "bbox": [ + 76, + 551, + 284, + 568 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets and metrics. We train LeRF on the DIV2K dataset [1], which is widely used in image resampling tasks. We train LeRF on the $\\times 4$ downsampled data pairs, and apply the obtained model to resampling for arbitrary transformations. For arbitrary-scale upsampling, we evaluate LeRF with 5 benchmark datasets: Set5, Set14, BSDS100 [30], Urban100 [13], and Manga109 [31]. We select representative symmetric or asymmetric upsampling scales for evaluation and apply bicubic interpolation as the degradation model to obtain the LR images. For performance evaluation, we report PSNR and SSIM [45] for fidelity, and LPIPS [53] for perceptual quality. For efficiency evaluation, we report running time, theoretical multiply-accumulate operations (MACs), and storage requirements to evaluate the performance-efficiency trade-off.", + "bbox": [ + 75, + 578, + 468, + 805 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Comparison methods. The main competitors to the proposed method are interpolation methods, including nearest neighbor, bilinear, bicubic, lanczos2, and lanczos3. Furthermore, we combine fixed-scale super-resolution methods with bicubic interpolation as additional baselines for arbitrary-scale upsampling (RAISR* and SR-LUT*).", + "bbox": [ + 75, + 810, + 470, + 902 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/07441248b2ec43768e2f0c9376ec31c153243121992f41d0c742676b535b77c5.jpg", + "table_caption": [ + "Table 1. Quantitative comparison in PSNR for arbitrary-scale upsampling. $\\frac{\\times r_h}{\\times r_w}$ denotes upsampling $r_h$ times along the short side and $r_w$ times along the long side. SSIM and LPIPS results are in the supplementary document. * denotes that we combine fixed-scale super-resolution methods with bicubic to achieve arbitrary-scale upsampling. The best and second best results are highlighted and underlined." + ], + "table_footnote": [], + "table_body": "
MethodRunTime (ms)MACsStorage SizePSNR/SSIM (dB)/-
Nearest11--24.74/0.6553
Bilinear3114.74M-25.51/0.6824
Bicubic12651.61M-26.09/0.7056
Lanczos2494110.59M-26.10/0.7058
Lanczos3914165.89M-26.31/0.7130
RAISR* [37]3,744-3.19MB26.86/0.7357
SR-LUT* [15]13753.33M81.56KB26.30/0.7256
LeRF (Ours)11057.94M1.67MB27.35/0.7475
MetaSR [12]10,2601.68T85.59MB28.83/0.7876
LIIF [5]67,0802.54T255.76MB28.65/0.7878
", + "bbox": [ + 500, + 521, + 890, + 688 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 2. Efficiency comparison of running time, MACs, storage requirements, and performance (Set14) for producing a $1280 \\times 720$ HD image through $4 \\times$ upsampling. For interpolation methods, SR-LUT*, and LeRF, the running time is evaluated on CPU of a mobile phone and averaged across 10 trials, while for RAISR* and DNNs, that is evaluated on CPU of a desktop computer.", + "bbox": [ + 496, + 694, + 893, + 779 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Specifically, since RAISR [37] lacks generalization ability across different upsampling scales, we fuse the results of $\\times 2$ , $\\times 3$ , and $\\times 4$ models by choosing the model with the closest integer upsampling scales (e.g., $\\times 3$ model for $\\frac{\\times 2.0}{\\times 3.0}$ ). We retrain SR-LUT [15] on the mixed-scale data of $\\times 2$ , $\\times 3$ , and $\\times 4$ downsampled data pairs jointly with bicubic interpolation to achieve arbitrary-scale upsampling. Finally, we", + "bbox": [ + 496, + 794, + 893, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "5870", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/fbb0cca2ddcf8dacae44cd84066c190a3ee59c182bf6dc77d530e294a93fa789.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 84, + 88, + 184, + 164 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/bac27e9cea9804a3004e79870601ce321fcb5a7cdf2c651924753a750db492cc.jpg", + "image_caption": [ + "$\\frac{\\times 1.5}{\\times 2.0}$ upsampling" + ], + "image_footnote": [], + "bbox": [ + 86, + 189, + 183, + 263 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/847a1d81c14673e7bd5a22c60ac229e2697a365e25a992340d2f4beace4472a5.jpg", + "image_caption": [ + "$\\times 2.0$ $\\times 2.4$", + "$\\frac{\\times 3.0}{\\times 3.0}$ upsampling $\\frac{\\times 4.0}{\\times 4.0}$ upsampling" + ], + "image_footnote": [], + 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"images/d3d4c98605f47cfdfaa9f20bf9f91a5ca382009b949c46308b475f44e0e5073c.jpg", + "image_caption": [ + "Bilinear (23.46/0.8875)" + ], + "image_footnote": [], + "bbox": [ + 292, + 189, + 388, + 263 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/e3d110872ba52b704b5b16d2ee6a30fdbbd8721318ddf2a1648ea789a8c7ef8d.jpg", + "image_caption": [ + "Bicubic (24.88/0.9174)" + ], + "image_footnote": [], + "bbox": [ + 390, + 189, + 486, + 263 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/eee0aa067990bc0962dd95f6f4eb82bb3095a349a8950c8888b17eb157d127b4.jpg", + "image_caption": [ + "Lanczos3 (25.63/0.9275)" + ], + "image_footnote": [], + "bbox": [ + 486, + 189, + 583, + 263 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/2f31818cf5ca815131c0ac7e1dbbf398a63bd34568e0ca0aba30e84c2457e69e.jpg", + "image_caption": [ + "RAISR\\* (26.62/0.9362)" + ], + "image_footnote": [], + "bbox": [ + 584, + 189, + 679, + 263 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/3b40d174a9d0ba0ad4729c19a53c7a4140bdec1b451746d32cdc17047fc5d447.jpg", + "image_caption": [ + "SR-LUT\\* (25.51/0.9305)" + ], + "image_footnote": [], + "bbox": [ + 681, + 189, + 776, + 263 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/a77b07e9b32ba9c34fe651ef87e0659ae8c29f062ec2c6424eee67c9f5c5f5b4.jpg", + "image_caption": [ + "LeRF (Ours) (28.36/0.9577)" + ], + "image_footnote": [], + "bbox": [ + 777, + 189, + 872, + 263 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/5768528ba2a993209dde411abd89f623447d1e230153acf747b72da8fbdb6a5b.jpg", + "image_caption": [ + "GT (PSNR/SSIM)" + ], + "image_footnote": [], + "bbox": [ + 196, + 265, + 292, + 362 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/47aa598f5d10910653ab906c5303122a719c9aac53b519d0f66bc0232ecd5950.jpg", + "image_caption": [ + "Bilinear (29.83/0.9553)" + ], + "image_footnote": [], + "bbox": [ + 292, + 265, + 388, + 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"bbox": [ + 681, + 265, + 776, + 362 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/f7abf73ba09258bc94bd0c8d3a68db3695a3fc9ca97d5c6ee7275f9aee7e4ef5.jpg", + "image_caption": [ + "LeRF (Ours) (35.28/0.9787)" + ], + "image_footnote": [], + "bbox": [ + 777, + 265, + 872, + 287 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/5a3288e7b8ac4e9cb3f9f54224664b3ec8d05826e572237e9bea1a89b32ba969.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 777, + 289, + 872, + 362 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/1caffee726ba8edbf3c22bfc7bfd5d96191cb008d8ffdac1ed4dbfb912e5eb84.jpg", + "image_caption": [ + "GT (PSNR/SSIM)", + "GT (PSNR/SSIM)", + "Figure 6. Qualitative comparison for arbitrary-scale upsampling. From top to bottom, the example images are: UltraEleven (Manga109), MukoukizuNoChonbo (Manga109), 126007 (BSDS100), and img092 (Ubran100). Best view in color and on screen." + ], + "image_footnote": [], + "bbox": [ + 196, + 387, + 292, + 462 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/68552918029e4a1671f2941f1b64a461c959bd3473a6b64980d5312467119506.jpg", + "image_caption": [ + "Bilinear (28.14/0.8051)", + "Bilinear (16.39/0.4217)" + ], + "image_footnote": [], + "bbox": [ + 292, + 387, + 388, + 462 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/506335ae24d05e4676cab68f29dbec211b29f83a2c90edbc406aabe5a1dee5b2.jpg", + "image_caption": [ + "Bicubic (28.57/0.8191)", + "Bicubic (16.58/0.4402)" + ], + "image_footnote": [], + "bbox": [ + 390, + 387, + 486, + 462 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/f132b203b81bbac2be5efc062854a9b3e1bb254471aa330ef5928adbed1eae9a.jpg", + "image_caption": [ + "Lanczos3 (28.76/0.8244)", + "Lanczos3 (16.63/0.4429)" + ], + "image_footnote": [], + "bbox": [ + 486, + 387, + 583, + 462 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/6fbd1845cd5f75dbecdd9c12e63629af8113324dcefa6326eefc5cd1d7308d51.jpg", + "image_caption": [ + "RAISR\\* (29.14/0.8396)", + "RAISR* (16.97/0.4896)" + ], + "image_footnote": [], + "bbox": [ + 584, + 387, + 679, + 462 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/753a69bf1dcd346a7a5542011d8052886d910a9e81659c6c2ce63512a5e37338.jpg", + "image_caption": [ + "SR-LUT\\* (28.98/0.8355)", + "SR-LUT\\* (16.76/0.4619)" + ], + "image_footnote": [], + "bbox": [ + 681, + 387, + 776, + 462 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/5ec66c53217a0a818c4bee75ca3900ad72e3b572c15c228bc034610e6f9b329d.jpg", + "image_caption": [ + "LeRF (Ours) (29.54/0.8440)", + "LeRF (Ours) (17.30/0.5175)" + ], + "image_footnote": [], + "bbox": [ + 777, + 387, + 872, + 462 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "include DNN-based arbitrary-scale super-resolution methods (Meta-SR [12] and LIIF [5]) for reference.", + "bbox": [ + 75, + 547, + 467, + 577 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation details. Our method is trained with the Adam optimizer [19] in the cosine annealing schedule [28]. We train LeRF with the MSE loss function for $5 \\times 10^{4}$ iterations at a batch size of 32. Except for the indexing patterns, each branch in our DNN keeps the same architecture as the network in SR-LUT. To obtain a fair comparison in running time tests, we implement LeRF and interpolation methods on the Android platform under the same parallel API, with the only difference being the resampling functions and LUT retrieval in our method. Our method is implemented in MindSpore [33] and the code is available at https://gitee.com/mindspore/models/tree/master/research/cv/lerf.", + "bbox": [ + 75, + 579, + 468, + 773 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Performance Evaluation", + "text_level": 1, + "bbox": [ + 76, + 786, + 299, + 800 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Quantitative comparison. We list the quantitative comparisons with other methods in Table 1. As can be seen, our method achieves significantly better performance than interpolation methods. For example, it exceeds bicubic interpolation up to 3dB PSNR when upsampling $\\frac{\\times 2.0}{\\times 2.0}$ on the Manga109 dataset.", + "bbox": [ + 75, + 810, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Qualitative comparison. In Fig. 6, we compare the visual quality of LeRF with interpolation methods, RAISR*, and SR-LUT*. As can be seen, our method obtains better visual quality across various local structures and textures. Specifically, since the resampling functions are adapted to local structures, LeRF is capable of retaining clearer textures and avoiding blurry boundaries.", + "bbox": [ + 496, + 547, + 890, + 652 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Visualization of learned resampling functions. We further visualize the intermediate results of the proposed method in Fig. 7. As can be seen, the hyper-parameter $\\rho$ clearly distinguishes the orientations of edges (red vs. blue), and $\\sigma_{X}$ and $\\sigma_{Y}$ capture the horizontal and vertical lines, respectively. In addition, in the second row of each example, we visualize the resampling functions defined by the predicted hyper-parameters. The shapes of resampling functions are well adapted to corners, flat surfaces, and edges with various orientations. This validates the effectiveness of extracting structural priors in a data-driven way.", + "bbox": [ + 496, + 654, + 890, + 820 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Efficiency Evaluation", + "text_level": 1, + "bbox": [ + 500, + 830, + 699, + 847 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To evaluate the efficiency, we conduct running time tests, estimate the theoretical MACs, and report storage requirements for LeRF and comparison methods. We list the de", + "bbox": [ + 496, + 854, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "5871", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/25d0ea535b8a4b274814b0d991799b120d79acf0294f4e5cc5b35d9b3295a3ac.jpg", + "image_caption": [ + "Input" + ], + "image_footnote": [], + "bbox": [ + 80, + 90, + 156, + 179 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f205677316aad2f457657dc7555cf7f2bfbdfc4e1cb6173740d0399a2fb3083d.jpg", + "image_caption": [ + "P" + ], + "image_footnote": [], + "bbox": [ + 171, + 90, + 264, + 179 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/dc8cde45ecc5beb589cfd950ba89c394e24541b9648408ae43a2ab6e89ddb87d.jpg", + "image_caption": [ + "1/σx" + ], + "image_footnote": [], + "bbox": [ + 279, + 90, + 370, + 179 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c6adc01ba1066d31045d106fc626e2550a9abce15952092883ccbab491304fd7.jpg", + "image_caption": [ + "1/σY" + ], + "image_footnote": [], + "bbox": [ + 375, + 90, + 467, + 179 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d9944ab0b96fbbcc8482565d854c0732ca97a3d965c59d5d97a19d254f6246b7.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 81, + 191, + 145, + 241 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/98017ebe726af1c326bd1fc5f9f0916aa0d1c99ce79786defeaaa0da2398a0d5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 160, + 191, + 222, + 241 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2d5af6d83456f6e5d657be3906fb08fa9aa2f6aefc529a8764f419ffdf2ca88a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 236, + 191, + 300, + 241 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8fad0b8b4779677d7f2780884fecf575c7b5daa5112e037c80f767aab63adf45.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 315, + 191, + 377, + 241 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7e8ff22d641486427709dc45a2937b3349cd61be8c8f0f8137234d1c02ebadbb.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 393, + 191, + 457, + 241 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a9908942c66178dbe0406a9c2f72ff67a08ddcbb5863be8a86c1335a300b4ffb.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 80, + 242, + 156, + 329 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7f7e7979155b6475598db9427e5a0aab99982ac5264732bae66c1e4b0929c58e.jpg", + "image_caption": [ + "P" + ], + "image_footnote": [], + "bbox": [ + 161, + 242, + 264, + 329 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f82ba91eb3022b9a971d59078688151ecf18c4e73e99f2f5311b2153fc964db6.jpg", + "image_caption": [ + "1/σx" + ], + "image_footnote": [], + "bbox": [ + 281, + 242, + 362, + 329 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ee46b67a89c2936786208518b38e851f8327d260261cd1c2bddf879e2d922773.jpg", + "image_caption": [ + "$1 / \\sigma_{\\mathrm{V}}$" + ], + "image_footnote": [], + "bbox": [ + 375, + 242, + 459, + 329 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/844a49504f0a75addc6ef152269032383cacc760b7c2f35b12a6bcbd5be2f548.jpg", + "image_caption": [ + "Input", + "Figure 7. Visualization of the pixel-wise hyper-parameters and the corresponding resampling functions. The shapes of the predicted resampling functions are well adapted to local structures." + ], + "image_footnote": [], + "bbox": [ + 81, + 340, + 145, + 392 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/6c3b0a1de921a506b23cff8fa5e9e340e1f533823ad51a1b604ed677bb6b7574.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 160, + 342, + 223, + 392 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f15f54019ad9585a9331d219543a633cc751af2dbb1dd8809fd641df34fd5fed.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 236, + 342, + 300, + 392 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/63836fb9e59b60e2a17ded7db047029ee03e8a6b7b1658e0a17c8d6db5b2f807.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 315, + 342, + 379, + 392 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9ac47137cd2f72ce898a71f7b891eb2ce1486090d3d14d58d33e66ffaccd9eba.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 395, + 342, + 457, + 392 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "tailed comparison in Table 2. As can be seen, the proposed LeRF achieves comparable or better running time compared to the popular interpolation methods, showing its role as a superior competitor with significantly better performance. On the other hand, compared to DNN-based methods, LeRF shows a clear advantage in efficiency. Furthermore, our method requires far less extra storage as well as fewer software dependencies, demonstrating its practicality for being deployed on various devices.", + "bbox": [ + 75, + 458, + 468, + 592 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Generalization Evaluation", + "text_level": 1, + "bbox": [ + 76, + 603, + 313, + 617 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In Fig. 8, we evaluate the generalization ability of LeRF to arbitrary transformations and compare it with widely applied interpolation methods. As can be seen, the learned resampling functions generalize well to unseen deformations. LeRF generates sharper edges and retains more texture details, leading to more visually pleasing results than popular interpolation methods.", + "bbox": [ + 75, + 626, + 468, + 731 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5. Ablation Analysis", + "text_level": 1, + "bbox": [ + 76, + 741, + 250, + 757 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Analysis of GT-optimized resampling functions. To analyze the optimal shapes of resampling functions across different transformations, we obtain hyper-parameters in resampling functions via per-image optimization. In Fig. 9, we visualize the clustering results on Set14 of these GT-optimized resampling functions across different upsampling scales. 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Visual comparison of LeRF with interpolation methods under general homographic transformations (downsampling, rotation, and sheering) and arbitrary warping (according to a barrel-shaped distortion and optical flow)." + ], + "image_footnote": [], + "bbox": [ + 503, + 470, + 566, + 501 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/557b80883555f163bad46a5ff3b8f9bbd99fd9fd4e227c3839f19e5280bd95a5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 570, + 470, + 616, + 500 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/273e6ff9bf7a131874c309763b24ddb1e1fa52c0850211ede43cdc982f71188b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 622, + 470, + 694, + 500 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/81a69e6e31ba8988cc488bb043e86df8cf84eaec096c0b329f0c49fac510f557.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 700, + 470, + 763, + 500 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d251d0723ae96a0d6389dafde9a57cfc87cbe9bf1d0821b79ad8be3d86dcfbe9.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 769, + 470, + 812, + 500 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/11ff05c979e8e57023c8e3653abdc48c88b84a16b0b394bc487d6e9bae83f29b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 816, + 470, + 890, + 500 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/18dcfcb4091f34bbaf48770f9e47172bfee3dfd0b2af264c5877b5d03001d8a2.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 503, + 506, + 566, + 535 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/688e6fd3293b9449bf8500a7f7d48734c04993a5d21099455b44bce1645f94f8.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 570, + 506, + 616, + 534 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/14f9fce41a5f0f5de6e3c98f151cdb1c88ffd19b1dfcbb2788d14bab78d03d5a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 622, + 506, + 694, + 534 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b2801d6c32c3933a85fb416f560451063fa36446c6b0132a40b6e488f23f0349.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 700, + 506, + 763, + 534 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4ef274c74beec19d1bdbf3633c6a61c0d02077aa1764bf47a8e0708437684c3c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 769, + 506, + 812, + 534 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4a67af9d6f29a72969aa13c23951c15e4882c18b98a802e5d8321b74daedd971.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 816, + 506, + 880, + 534 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/eb8a62d6e81d52c153cf9363f51853bbb3e35e5bc9836be2923779e99b881780.jpg", + "image_caption": [ + "Figure 9. Visualization of GT-optimized resampling function clustering centroids on Set14 for $\\times 2$ , $\\times 3$ , and $\\times 4$ upsampling, respectively. They show consistency across different upsampling scales." + ], + "image_footnote": [], + "bbox": [ + 503, + 539, + 566, + 569 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e1353e4ca09e76c82198061d338e8b7ef6a2338ff70b381b3f55972f0301e75c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 570, + 539, + 616, + 569 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/dafb3805b80a4dc32d974c0944b7303b52dc2ae0503707cb8a6db514144b6869.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 622, + 539, + 694, + 569 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/3def002723fd43864d802e021e244cb9932027241364f6e5695b43fd81bd36ee.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 700, + 539, + 763, + 569 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8c580592dc9d445aece62540c10f04ad550a9363551dd09991e724987788d6b3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 769, + 539, + 812, + 569 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e42cdb7fc4f385c634382cef6103c1e7fdae8756a824ab6e62654c751e9b2df3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 816, + 539, + 880, + 569 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The effectiveness of the steerable resampling function. We conduct the following ablation experiments on the Set5 benchmark to analyze the design of the steerable resampling function. 1) Fixed gaussian: We freeze the hyperparameters in the anisotropic gaussian. 2) Learned Keys cubic: We utilize another non-steerable resampling function, the isotropic Keys cubic function [16], which has only one hyper-parameter that controls the sharpness of the one-dimension piecewise cubic. The experimental results are listed in Table 3, and visual results are shown in Fig. 10. As can be seen, non-steerable resampling functions like Keys cubic produce zig-zag artifacts and inferior performance, showing the effectiveness of our learned steerable resampling functions.", + "bbox": [ + 498, + 643, + 890, + 853 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Analysis of training data. By default, LeRF is trained with $\\times 4$ downsampled data pairs. We retrain our method on $\\times 2$ data pairs and mixed data pairs with both symmetric and", + "bbox": [ + 500, + 854, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "5872", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/d0af40ed0f04f4e05d45466a0fb3d67f5074c7708e4d59ab2192094777bacb8d.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MACsStorage×2×3×4
Ours57.94M1.67MB35.7132.0230.15
fixed gaussian Φ(0,1,1)45.04M244.69KB30.7527.7026.31
learned Keys cubic126.94M489.38KB34.6831.1729.08
", + "bbox": [ + 78, + 88, + 468, + 152 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/fe07a24bd1d1d1a90a2254bb413664d10845963498029349e94a79aabf1db23d.jpg", + "image_caption": [ + "Figure 10. The non-steerable Keys cubic function produces artifacts along diagonal edges." + ], + "image_footnote": [], + "bbox": [ + 78, + 204, + 202, + 303 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/546df072c7988b673fddf5e972254588cd926e310df0ada09385c4121f03fc4f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 212, + 203, + 267, + 304 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/1d33e7d3bb57e58c3786f9bff0ba3bdf0483e388bd534c1a3dd1547b0275506d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 276, + 203, + 401, + 303 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c866bc4844594cea9128c9eac77a2f7afe3d52c4bdcd5dd4721042bab8d8e6d7.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 416, + 203, + 468, + 304 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/835a4a0118a063a31282efb550ca2e74452c4a66f9da20daabce3ae94ba72873.jpg", + "table_caption": [ + "Table 3. Ablation experiments on Set5 on the the design of the steerable resampling function." + ], + "table_footnote": [], + "table_body": "
MACsStorage×2×3×4
Ours (×4 data)57.94M1.67MB35.7132.0230.15
×2 data training57.94M1.67MB35.6732.1129.91
mixed-scale training57.94M1.67MB35.6732.2630.06
", + "bbox": [ + 86, + 372, + 460, + 438 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "asymmetric upsampling scales. As listed in Table 4, training on mixed data pairs yields similar performance, showing the robustness and generalization ability of our method, which is consistent with the clustering results in Fig. 9.", + "bbox": [ + 76, + 474, + 468, + 535 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The effectiveness of the LUT acceleration and the proposed adaptations. As listed in Table 5, we conduct the following ablation experiments to analyze the design of the LUT acceleration and our adaptations. 1) Without LUT acceleration: Compared with the learned network, the LUT acceleration saves a lot of computations at a very small performance cost. 2) Without DE: The visualization of the learned hyper-parameter $\\rho$ in Fig. 11 shows that RE makes an isotropic assumption and thus lacks the ability to learn edge orientations, resulting in a performance drop. 3) Without edge-sensitive patterns: LeRF with \"SCX\" indexing patterns outperforms the variant with only the default \"S\" pattern, showing the ability of edge-sensitive indexing patterns to better capture edge structures. 4) Without prefiltering or pre-filtering only: As shown in Fig. 11 and Table 4, the pre-filtering stage enhances edges and contributes to better performance, and pre-filtering only is suboptimal due to the lack of adaptivity of resampling functions.", + "bbox": [ + 76, + 535, + 468, + 808 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.6. Limitation", + "text_level": 1, + "bbox": [ + 76, + 816, + 194, + 830 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In most cases, our method produces better results compared with interpolation. But, it may fail under certain circumstances. In Fig. 12, we show its limitation in down-sampling, where aliasing artifacts appear in regions with", + "bbox": [ + 76, + 839, + 468, + 901 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/ff5b1eb1dd72d5bd1040b34568c1a89afad4fa9919d104c823b8b5b3073e426e.jpg", + "table_caption": [ + "Table 4. Ablation experiments on Set5 on data pairs for training." + ], + "table_footnote": [], + "table_body": "
MACsStorage×2×3×4
Ours57.94M1.67MB35.7132.0230.15
w/o LUT acceleration21.75G1.59MB36.1132.2330.28
w/o DE57.94M978.76KB34.4431.5029.47
w/o “CX” pattern49.65M326.25KB34.9031.7929.70
w/o pre-filtering53.45M1.43MB34.4331.4129.33
pre-filtering only56.10M244.69KB34.6931.1629.15
", + "bbox": [ + 501, + 88, + 890, + 188 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Ablation experiments on Set5 on the LUT acceleration and our adaptations.", + "bbox": [ + 498, + 199, + 890, + 227 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/56664dfaea8f834a5d7167ffdd54893035ca8ef713a2634578f97ff9007ea22a.jpg", + "image_caption": [ + "Input" + ], + "image_footnote": [], + "bbox": [ + 501, + 233, + 591, + 339 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/66c7abd973aebfbb76c96ce2b42b62574946d5a92b988b156ab1287ea408c7c9.jpg", + "image_caption": [ + "RE $\\rho$" + ], + "image_footnote": [], + "bbox": [ + 598, + 233, + 691, + 338 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/a8dacb2d12919453e3b5d20079476ed9dbafe25596ff21328d1780a2d8e220ac.jpg", + "image_caption": [ + "DE $\\rho$" + ], + "image_footnote": [], + "bbox": [ + 697, + 233, + 792, + 338 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0c90ab218f09fb9d0cd08b67600a67b4b2ee4679dbe55aa92286b9578950284c.jpg", + "image_caption": [ + "DE Output" + ], + "image_footnote": [], + "bbox": [ + 800, + 233, + 890, + 338 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/2764194bb7f0a06c9289c68c61c0ac17f9f15f8be0b971e5342bdc0eb0af347f.jpg", + "image_caption": [ + "Input" + ], + "image_footnote": [], + "bbox": [ + 501, + 358, + 591, + 426 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/2900e459c23302ab321901fbe3c285a4fbeeefe48e28be57d1c75545723a1a44.jpg", + "image_caption": [ + "Pre-filtered" + ], + "image_footnote": [], + "bbox": [ + 599, + 358, + 691, + 426 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/1b27b2086df73880f0499edbc4d5b79b481c359022312e9bcd1acccd6e44eb10.jpg", + "image_caption": [ + "Input" + ], + "image_footnote": [], + "bbox": [ + 699, + 358, + 790, + 426 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/43f3fb468d40bd05d54adc7fff6ccf2369e07416d3517e7f8c971528944a3339.jpg", + "image_caption": [ + "Pre-filtered" + ], + "image_footnote": [], + "bbox": [ + 799, + 358, + 890, + 426 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c0f00a75a738abd987ff410563934b02a24a7ace6df7df0357647b6f021066d8.jpg", + "image_caption": [ + "Figure 11. Upper: Replacing RE with DE enables the learning of edge orientations. Lower: The pre-filtering stage enhances edges.", + "HR", + "Figure 12. Limitation. LeRF produces aliasing artifacts for image downsampling in regions with highly dense textures." + ], + "image_footnote": [], + "bbox": [ + 501, + 492, + 573, + 547 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/f6837b0ef69dae3b95e1a10bc562588b1d20e7b0581deed576cb8048afc5b4a2.jpg", + "image_caption": [ + "Bilinear" + ], + "image_footnote": [], + "bbox": [ + 580, + 492, + 651, + 547 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/1a23f327d492a42139ea8a6e07a1f697ffd7d7cb3409aa736233b18920bd32de.jpg", + "image_caption": [ + "Bicubic" + ], + "image_footnote": [], + "bbox": [ + 660, + 492, + 732, + 547 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/00bff936ff00952bc1c9afae37e5e9960650c3567d155a37d176b1f4e8e33e03.jpg", + "image_caption": [ + "Lanczos3" + ], + "image_footnote": [], + "bbox": [ + 738, + 492, + 812, + 547 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/57940cd2c684f594fb665f23fcfc87ccbb90f120af7fd64a3b5b45faad4a47c4.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 818, + 492, + 890, + 547 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "highly dense textures. It is probably caused by a lack of training on downsampled data. Moreover, replacing the pre-filtering stage with anti-aliasing filters may mitigate this problem [6, 7].", + "bbox": [ + 496, + 621, + 890, + 681 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion Remarks", + "text_level": 1, + "bbox": [ + 500, + 695, + 699, + 710 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we propose LeRF, a novel method for continuous resampling by integrating learned structural priors into the steerable resampling function. We show its superior performance, high efficiency, and versatility for arbitrary transformations. In the future, we plan to extend this idea to other modalities, e.g., video and point clouds.", + "bbox": [ + 496, + 720, + 890, + 811 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement", + "text_level": 1, + "bbox": [ + 500, + 825, + 658, + 842 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work was supported in part by the National Natural Science Foundation of China under Grants 62131003 and 62021001. We acknowledge the support of MindSpore.", + "bbox": [ + 496, + 849, + 890, + 896 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "5873", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Eirikur Agustsson and Radu Timofte. NTIRE 2017 challenge on single image super-resolution: Dataset and study. In CVPR Workshops, 2017. 5", + "[2] Jan P. Allebach and Ping Wah Wong. Edge-directed interpolation. In ICIP, 1996. 2", + "[3] Chang Chen, Xinmei Tian, Feng Wu, and Zhiwei Xiong. UDNet: Up-down network for compact and efficient feature representation in image super-resolution. In ICCV Workshops, 2017. 2", + "[4] Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha, and Feng Wu. Camera lens super-resolution. In CVPR, 2019. 2", + "[5] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In CVPR, 2021. 1, 2, 3, 5, 6", + "[6] Robert L. Cook. Stochastic sampling in computer graphics. ACM Trans. Graph., 5(1):51-72, 1986. 8", + "[7] Franklin C. Crow. The aliasing problem in computer-generated shaded images. Commun. ACM, 20(11):799-805, 1977. 8", + "[8] Neil Anthony Dodgson. Image resampling. Technical report, University of Cambridge, Computer Laboratory, 1992. 1", + "[9] Chao Dong, Chen Change Loy, Kaiming He, and Xiaou Tang. Learning a deep convolutional network for image super-resolution. In ECCV, 2014. 1, 2", + "[10] Irvine C. Gardner and Francis E. Washer. Lenses of extremely wide angle for airplane mapping. J. Opt. Soc. Am., 38(5):421-431, 1948. 1", + "[11] Pascal Getreuer, Ignacio Garcia-Dorado, John Isidoro, Sungjoon Choi, Frank Ong, and Peyman Milanfar. BLADE: filter learning for general purpose computational photography. In ICCP, 2018. 2", + "[12] Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Tieniu Tan, and Jian Sun. Meta-SR: A magnification-arbitrary network for super-resolution. In CVPR, 2019. 1, 2, 4, 5, 6", + "[13] Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja. Single image super-resolution from transformed self-exemplars. In CVPR, 2015. 5", + "[14] Xu Jia, Hong Chang, and Tinne Tuytelaars. Super-resolution with deep adaptive image resampling. arxiv, 1712.06463, 2017.2", + "[15] Younghyun Jo and Seon Joo Kim. Practical single-image super-resolution using look-up table. In CVPR, 2021. 2, 3, 4, 5", + "[16] Robert G. Keys. Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust., 29:1153-1160, 1981. 1, 2, 4, 7", + "[17] Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. Accurate image super-resolution using very deep convolutional networks. In CVPR, 2016. 1, 2", + "[18] Seon Joo Kim, Hai Ting Lin, Zheng Lu, Sabine Süssstrunk, Stephen Lin, and Michael S. Brown. A new in-camera imaging model for color computer vision and its application. IEEE Trans. Pattern Anal. Mach. Intell., 34(12):2289-2302, 2012. 3" + ], + "bbox": [ + 78, + 114, + 467, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[19] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 6", + "[20] Jaewon Lee and Kyong Hwan Jin. Local texture estimator for implicit representation function. In CVPR, 2022. 3", + "[21] Yeon Ju Lee and Jungho Yoon. Nonlinear image upsampling method based on radial basis function interpolation. IEEE Trans. Image Process., 19(10):2682-2692, 2010. 2", + "[22] Jiacheng Li, Chang Chen, Zhen Cheng, and Zhiwei Xiong. MuLUT: Cooperating multiple look-up tables for efficient image super-resolution. In ECCV, 2022. 2, 3, 4", + "[23] Jiacheng Li, Chang Chen, Zhen Cheng, and Zhiwei Xiong. Toward DNN of LUTs: Learning efficient image restoration with multiple look-up tables. arxiv, 2303.14506, 2023. 2, 3, 4", + "[24] Xin Li and Michael T. Orchard. New edge-directed interpolation. IEEE Trans. Image Process., 10(10):1521-1527, 2001. 2", + "[25] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. SwinIR: Image restoration using swim transformer. In ICCV Workshops, 2021. 2", + "[26] Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, and Radu Timofte. Flow-based kernel prior with application to blind super-resolution. In CVPR, 2021. 2", + "[27] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Young Mu Lee. Enhanced deep residual networks for single image super-resolution. In CVPR Workshops, 2017. 1, 2", + "[28] Ilya Loshchilov and Frank Hutter. SGDR: stochastic gradient descent with warm restarts. In *ICLR*, 2017. 6", + "[29] Cheng Ma, Jingyi Zhang, Jie Zhou, and Jiwen Lu. Learning series-parallel lookup tables for efficient image superresolution. In ECCV, 2022. 2, 3, 4", + "[30] David R. Martin, Charless C. Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, 2001. 5", + "[31] Yusuke Matsui, Kota Ito, Yuji Aramaki, Azuma Fujimoto, Toru Ogawa, Toshihiko Yamasaki, and Kiyoharu Aizawa. Sketch-based manga retrieval using manga109 dataset. Multim. Tools Appl., 76(20):21811-21838, 2017. 5", + "[32] Ben Mildenhall, Jonathan T. Barron, Jiawen Chen, Dillon Sharlet, Ren Ng, and Robert Carroll. Burst denoising with kernel prediction networks. In CVPR, 2018. 2", + "[33] MindSpore. https://www.mindspore.cn, 2023.6", + "[34] Don P. Mitchell and Arun N. Netravali. Reconstruction filters in computer-graphics. In SIGGRAPH, 1988. 2", + "[35] Marta Mrak, Mislav Grgic, and Murat Kunt. High-Quality Visual Experience: Creation, Processing and Interactivity of High-Resolution and High-Dimensional Video Signals. Signals and Communication Technology. Springer Berlin Heidelberg, 2016. 1", + "[36] Zhihong Pan, Baopu Li, Dongliang He, Mingde Yao, Wenhao Wu, Tianwei Lin, Xin Li, and Errui Ding. Towards bidirectional arbitrary image rescaling: Joint optimization and cycle idempotence. In CVPR, 2022. 2", + "[37] Yaniv Romano, John Isidoro, and Peyman Milanfar. RAISR: rapid and accurate image super resolution. IEEE Trans. Computational Imaging, 3(1):110-125, 2017. 2, 5" + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "5874", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[38] Thomas G. Smith and George Lucas. Industrial Light & Magic: The Art of Special Effects. Virgin, 1986. 1", + "[39] Sanghyun Son and Kyoung Mu Lee. SRWarp: Generalized image super-resolution under arbitrary transformation. In CVPR, 2021. 1, 2", + "[40] Wanjie Sun and Zhenzhong Chen. Learned image downscaling for upscaling using content adaptive resampler. IEEE Trans. Image Process., 29:4027-4040, 2020. 1, 2", + "[41] Hiroyuki Takeda, Sina Farsiu, and Peyman Milanfar. Kernel regression for image processing and reconstruction. IEEE Trans. Image Process., 16(2):349-366, 2007. 2", + "[42] Hossein Talebi and Peyman Milanfar. Learning to resize images for computer vision tasks. In ICCV, 2021. 2", + "[43] Longguang Wang, Yingqian Wang, Zaiping Lin, Jungang Yang, Wei An, and Yulan Guo. Learning A single network for scale-arbitrary super-resolution. In ICCV, 2021. 1, 2, 4", + "[44] Qing Wang and Rabab Kreidieh Ward. A new orientation-adaptive interpolation method. IEEE Trans. Image Process., 16(4):889-900, 2007. 2", + "[45] Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600-612, 2004. 5", + "[46] Bartlomiej Wronski, Ignacio Garcia-Dorado, Manfred Ernst, Damien Kelly, Michael Krainin, Chia-Kai Liang, Marc Levoy, and Peyman Milanfar. Handheld multi-frame superresolution. ACM Trans. Graph., 38(4):28:1-28:18, 2019. 2", + "[47] Zeyu Xiao, Xueyang Fu, Jie Huang, Zhen Cheng, and Zhiwei Xiong. Space-time distillation for video super-resolution. In CVPR, 2021. 2", + "[48] Jingyu Yang, Sheng Shen, Huanjing Yue, and Kun Li. Implicit transformer network for screen content image continuous super-resolution. In NeurIPS, 2021. 1, 3, 4", + "[49] Mingde Yao, Dongliang He, Xin Li, Zhihong Pan, and Zhiwei Xiong. Bidirectional translation between uhd-hdr and hd-sdr videos. IEEE Trans. Multimedia, 2023. 2", + "[50] Hui Zeng, Jianrui Cai, Lida Li, Zisheng Cao, and Lei Zhang. Learning image-adaptive 3d lookup tables for high performance photo enhancement in real-time. IEEE Trans. Pattern Anal. Mach. Intell., 44(4):2058-2073, 2022. 3", + "[51] Kaibing Zhang, Xinbo Gao, Dacheng Tao, and Xuelong Li. Single image super-resolution with non-local means and steering kernel regression. IEEE Trans. Image Process., 21(11):4544-4556, 2012. 2", + "[52] Lei Zhang and Xiaolin Wu. An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans. Image Process., 15(8):2226-2238, 2006. 2", + "[53] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 5", + "[54] Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. In ECCV, 2018. 2" + ], + "bbox": [ + 78, + 90, + 468, + 844 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "5875", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Steerable Function for Efficient Image Resampling/067ca41f-5208-49cd-b2f0-82f2850ff9d3_model.json b/2023/Learning Steerable Function for Efficient Image Resampling/067ca41f-5208-49cd-b2f0-82f2850ff9d3_model.json new file mode 100644 index 0000000000000000000000000000000000000000..3e4113fa38586a3e945554e90b460960cdf2ee3c --- /dev/null +++ b/2023/Learning Steerable Function for Efficient Image Resampling/067ca41f-5208-49cd-b2f0-82f2850ff9d3_model.json @@ -0,0 +1,3960 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.18, + 0.131, + 0.79, + 0.154 + ], + "angle": 0, + "content": "Learning Steerable Function for Efficient Image Resampling" + }, + { + "type": "text", + "bbox": [ + 0.182, + 0.18, + 0.788, + 0.234 + ], + "angle": 0, + "content": "Jiacheng Li\\(^{1*}\\) Chang Chen\\(^{2*}\\) Wei Huang\\(^{1}\\) \nZhiqiang Lang\\(^{2}\\) Fenglong Song\\(^{2}\\) Youliang Yan\\(^{2}\\) Zhiwei Xiong\\(^{1\\dagger}\\) \n\\(^{1}\\)University of Science and Technology of China \\({}^{2}\\)Huawei Noah's Ark Lab" + }, + { + "type": "text", + "bbox": [ + 0.21, + 0.237, + 0.756, + 0.27 + ], + "angle": 0, + "content": "{jcee,weih527}@mail.ustc.edu.cn zwxiong@ustc.edu.cn {chenchang25,langzhiqiang,songfenglong,yanyouliang}@huawei.com" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.305, + 0.313, + 0.32 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.337, + 0.474, + 0.671 + ], + "angle": 0, + "content": "Image resampling is a basic technique that is widely employed in daily applications. Existing deep neural networks (DNNs) have made impressive progress in resampling performance. Yet these methods are still not the perfect substitute for interpolation, due to the issues of efficiency and continuous resampling. In this work, we propose a novel method of Learning Resampling Function (termed LeRF), which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption of interpolation methods. Specifically, LeRF assigns spatially-varying steerable resampling functions to input image pixels and learns to predict the hyper-parameters that determine the orientations of these resampling functions with a neural network. To achieve highly efficient inference, we adopt look-up tables (LUTs) to accelerate the inference of the learned neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures. Extensive experiments show that our method runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly, e.g., up to 3dB PSNR gain over bicubic for \\(\\times 2\\) upsampling on Manga109." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.7, + 0.208, + 0.715 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.727, + 0.47, + 0.864 + ], + "angle": 0, + "content": "Due to the rapid growth of visual data, there is a strong demand for digital image processing. Image resampling, one of the most common techniques, aims to obtain another image by generating new pixels following a geometric transformation rule from existing pixels in a given image [8]. Common transformations include upsampling (i.e., single image super-resolution), downsampling, affine transformation, etc. Image resampling enjoys various applications, ranging from photo editing, optical distortion com" + }, + { + "type": "image", + "bbox": [ + 0.507, + 0.305, + 0.895, + 0.525 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.535, + 0.892, + 0.59 + ], + "angle": 0, + "content": "Figure 1. LeRF assigns steerable resampling functions to input pixels, and learns to predict the hyper-parameters that determine the orientations of these continuous functions for resampling under arbitrary transformations." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.598, + 0.891, + 0.628 + ], + "angle": 0, + "content": "pensation [10], online content streaming [35], and visual special effects production [38]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.629, + 0.892, + 0.734 + ], + "angle": 0, + "content": "Recently, deep neural networks (DNNs) have made impressive progress in the field of image resampling [9, 12, 17, 27, 39, 40], thanks to the learning-from-data paradigm that obtains powerful structural priors from large-scale datasets. Despite the superior performance that DNN-based methods have achieved, long-lived interpolation methods like bicubic [16] are still preferred choices in most cases." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.893, + 0.901 + ], + "angle": 0, + "content": "We attribute this phenomenon to the following two reasons: 1) Interpolation is simple and highly efficient, resulting in less dependency and thus the practicality to be deployed on a variety of devices, ranging from IoT devices to gaming workstations. 2) Interpolation supports arbitrary transformations. It assumes a continuous resampling function for a local area, resulting in the versatility in applying to not only homographic transformations like upsampling and downsampling, but also general warping. Although recent DNN-based methods explore beyond fixed-scale upsampling [5, 12, 27, 39, 43, 48], an efficient and continuous" + }, + { + "type": "page_footnote", + "bbox": [ + 0.077, + 0.875, + 0.469, + 0.9 + ], + "angle": 0, + "content": "*Equal contribution. † Corresponding author. This work was done when Jiacheng Li was a research intern at Huawei Noah's Ark Lab." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5866" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.457, + 0.106 + ], + "angle": 0, + "content": "solution that matches interpolation remains less explored." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.107, + 0.47, + 0.424 + ], + "angle": 0, + "content": "In this work, we aim to fill this blank research area by taking a middle way between DNN-based methods and interpolation methods. We propose a novel method of Learning Resampling Function (termed LeRF), where parameterized continuous functions for resampling different structures are learned from data. Specifically, as illustrated in Fig. 1, we assign spatially-varying steerable resampling functions to image pixels, whose orientations are parameterized with several hyper-parameters. Then, we train a neural network to predict these hyper-parameters for each pixel in an input image, thus defining the resampling function for that pixel location. Finally, we obtain the output image by interpolating an input image with these locally adapted resampling functions. LeRF takes advantage of both the structural priors learned by DNN and the locally continuous assumption of interpolation methods. Furthermore, we present an efficient implementation, where the inference of the learned neural network is accelerated with look-up tables (LUTs) [15,22,23,29]. We further design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures in images." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.424, + 0.469, + 0.544 + ], + "angle": 0, + "content": "We examine the advantages and generalization ability of LeRF in various image resampling tasks, including arbitrary-scale upsampling, homographic transformation, and general warping. In particular, as illustrated in Fig. 2, at a similar running time, our method outperforms popular interpolation methods significantly in upsampling, which demonstrates the superiority of LeRF in terms of performance and efficiency." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.545, + 0.46, + 0.559 + ], + "angle": 0, + "content": "Contributions of this paper are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.56, + 0.469, + 0.635 + ], + "angle": 0, + "content": "1) We propose LeRF, a novel method for continuous resampling. We assign spatially-varying steerable resampling functions to image pixels, where we train a neural network to predict the hyper-parameters that determine the orientations of these resampling functions." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.636, + 0.469, + 0.71 + ], + "angle": 0, + "content": "2) We present an efficient implementation of LeRF by adopting look-up tables to accelerate the inference of the trained neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.711, + 0.469, + 0.771 + ], + "angle": 0, + "content": "3) Extensive experiments demonstrate that our method operates as efficient as interpolation, generalizes well to arbitrary transformations, and obtains significantly better performance over interpolation." + }, + { + "type": "list", + "bbox": [ + 0.077, + 0.56, + 0.469, + 0.771 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.785, + 0.227, + 0.801 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Image interpolation for resampling. Interpolation, the most common solution for image resampling, assumes a locally continuous intensity surface and approximates it with a fixed resampling function, such as nearest (nearest), linear (bilinear), cubic (bicubic) [16], and windowed sinc (lanczos). It predicts resampling weights with the as" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.089, + 0.862, + 0.26 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.263, + 0.892, + 0.319 + ], + "angle": 0, + "content": "Figure 2. Performance-efficiency trade-off of arbitrary-scale upsampling methods. PSNR values are obtained on Set14 for \\(\\times 4\\) upsampling. The running time is evaluated on mobile and desktop devices, respectively." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.326, + 0.892, + 0.446 + ], + "angle": 0, + "content": "sumed resampling function, and then aggregates input pixels with these resampling weights to obtain the target pixel. This assumption allows for continuous resampling under arbitrary transformations, yet leads to blurry results due to ignoring different local structures [34]. We follow the same assumption on local continuity, but our method deals with different structures with adapted resampling functions instead of fixed ones." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.448, + 0.893, + 0.703 + ], + "angle": 0, + "content": "Adaptive image resampling. To integrate local structural information into the resampling process, many methods are proposed, including edge-directed interpolation [2, 24, 44, 52] and kernel regression [21, 41, 51]. Different from these methods that rely on hand-designed rules, our method adopts a neural network to learn structural priors and integrate them into the resampling functions in a data-driven way. Another line of work achieves adaptive resampling by combining interpolation methods with adaptive filtering [11, 14, 17, 37]. Among them, RAISR achieves superresolution by predicting adaptive filters for each pixel from a hash table and then applying the predicted adaptive filters to pre-upsampled images [11, 37]. This kind of method operates on the pre-resampled results of common interpolation methods (usually bicubic) with a fixed transformation (e.g., \\(\\times 2\\) upsampling), thus lacking the generalization ability to be extended to unseen arbitrary transformations." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.893, + 0.901 + ], + "angle": 0, + "content": "DNN-based image resampling. With the rise of deep neural networks, impressive progress has been made in image resampling, such as upsampling [3,4,9,17,25,27,36,46,47, 49, 54], downsampling [40, 42], and homographic transformation [39]. For arbitrary-scale upsampling, Meta-SR [12] and LIIF [5] are two representative works. Meta-SR predicts the resampling weights from upsampling scales with a weight prediction network, similar to kernel prediction networks [26, 32], and aggregates deep features to get the final upsampled image [12]. Following works improve the process with support for homographic transformations [39] and scale-aware feature adaptation [43]. On the other hand, LIIF models the aggregation step with a neural implicit rep" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "5867" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.135, + 0.096, + 0.466, + 0.237 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.239, + 0.246, + 0.33, + 0.26 + ], + "angle": 0, + "content": "(a) Interpolation" + }, + { + "type": "image", + "bbox": [ + 0.514, + 0.094, + 0.852, + 0.245 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.624, + 0.247, + 0.714, + 0.26 + ], + "angle": 0, + "content": "(b) LeRF (Ours)" + }, + { + "type": "image_caption", + "bbox": [ + 0.075, + 0.277, + 0.895, + 0.307 + ], + "angle": 0, + "content": "Figure 3. Comparison between interpolation and LeRF. (a) Interpolation assumes a spatial-invariant fixed function \\(\\Phi(\\cdot)\\) to predict resampling weights \\(W\\). (b) LeRF learns spatially-varying steerable function \\(\\Phi_{\\Theta}\\) with a DNN \\(f(\\cdot)\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.314, + 0.47, + 0.405 + ], + "angle": 0, + "content": "resentation, predicting the target pixels from deep latent features and target coordinates [5]. Following works improve the implicit representation by integrating frequency analysis [20] and modulation-based transformer [48]. Different from these works, we utilize explicit local functions, resulting in advantages in efficiency and interpretability." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.406, + 0.471, + 0.648 + ], + "angle": 0, + "content": "Efficient super-resolution with look-up table. A look-up table is composed of index-value pairs, which can be efficiently retrieved through memory access. It is widely applied in the image signal processing pipeline [18, 50]. Recently, SR-LUT has been introduced to accelerate the inference of a fixed-scale super-resolution network by traversing all possible low-resolution (LR) patches, pre-computing all corresponding high-resolution (HR) patches, and saving them as index-value pairs [15]. At inference, the computations in the super-resolution network are replaced with retrieving values from LUT, leading to inference acceleration. Different from existing LUT-based methods [15,22,23,29], which adopt different groups of LUTs under different upsampling scales, our LUTs store the same group of hyperparameters across arbitrary transformations, thus achieving continuous resampling at high efficiency." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.665, + 0.364, + 0.683 + ], + "angle": 0, + "content": "3. Learning Resampling Function" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.691, + 0.212, + 0.706 + ], + "angle": 0, + "content": "3.1. Formulation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.716, + 0.47, + 0.746 + ], + "angle": 0, + "content": "Typically, resampling through interpolation can be implemented in the following steps." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.748, + 0.47, + 0.822 + ], + "angle": 0, + "content": "① Obtain relative offsets: the target coordinates after transformation, such as upsampling, are projected back to the coordinate space of the input image, and the relative spatial offsets between target pixels and source pixels in their support patches are obtained." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.824, + 0.47, + 0.87 + ], + "angle": 0, + "content": "\\(②\\) Predict resampling weights: based on the relative spatial offsets, the resampling weights, i.e., resampling kernels, are predicted for each pixel in the support patch." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.471, + 0.902 + ], + "angle": 0, + "content": "③ Aggregate pixels: the source pixels are aggregated through weighted summation to obtain the target pixel" + }, + { + "type": "list", + "bbox": [ + 0.076, + 0.748, + 0.471, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.314, + 0.811, + 0.329 + ], + "angle": 0, + "content": "value. The above process can be formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.633, + 0.339, + 0.892, + 0.372 + ], + "angle": 0, + "content": "\\[\n\\hat {I} _ {q} = \\sum_ {p \\in \\Omega} W _ {p \\rightarrow q} I _ {p} \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.38, + 0.892, + 0.427 + ], + "angle": 0, + "content": "where \\(\\hat{I}_q\\) is the interpolated pixel value at the target coordinate, \\(I_p\\) the pixel value at the source coordinate, \\(\\Omega\\) the support patch, and \\(W_{p\\rightarrow q}\\) the weight from \\(p\\) to \\(q\\)." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.427, + 0.892, + 0.485 + ], + "angle": 0, + "content": "As illustrated in Fig. 3(a), an isotropic resampling function \\(\\Phi(\\cdot)\\), e.g. cubic interpolation, is assumed to predict resampling weights from relative offsets, which can be formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.631, + 0.498, + 0.892, + 0.515 + ], + "angle": 0, + "content": "\\[\nW _ {p \\rightarrow q} = \\Phi \\left(d _ {p \\rightarrow q}\\right) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.52, + 0.892, + 0.641 + ], + "angle": 0, + "content": "where \\(d_{p\\rightarrow q}\\) is the relative offset from \\(p\\) to \\(q\\), which can be obtained based on the geometric transformation \\(\\mathcal{T}\\). Thanks to the continuity of the resampling function \\(\\Phi\\), arbitrary transformations from \\(I_{in}\\) to \\(I_{out}\\) can be achieved. The assumption of local continuity contributes to the versatility and high efficiency of interpolation. But the spatially-invariant resampling function leads to blurry results due to the ignorance of local structures." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.65, + 0.812, + 0.667 + ], + "angle": 0, + "content": "3.2. The Steerable Resampling Function" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.673, + 0.892, + 0.765 + ], + "angle": 0, + "content": "In this work, we propose a novel method of learning resampling function that adapts the resampling functions to local structures in a data-driven way. Different from the fixed resampling function in interpolation, we assume a kind of steerable resampling function \\(\\Phi_{\\Theta}\\), parameterized by \\(\\Theta\\). Specifically, we utilize the anisotropic gaussian," + }, + { + "type": "equation", + "bbox": [ + 0.507, + 0.773, + 0.892, + 0.82 + ], + "angle": 0, + "content": "\\[\n\\Phi_ {\\boldsymbol {\\Sigma}} (\\boldsymbol {x} - \\boldsymbol {\\mu}) = \\frac {1}{2 \\pi | \\boldsymbol {\\Sigma} | ^ {\\frac {1}{2}}} \\exp \\left\\{- \\frac {1}{2} (\\boldsymbol {x} - \\boldsymbol {\\mu}) ^ {T} \\boldsymbol {\\Sigma} ^ {- 1} (\\boldsymbol {x} - \\boldsymbol {\\mu}) \\right\\} \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.818, + 0.892, + 0.863 + ], + "angle": 0, + "content": "where \\((\\pmb{x} - \\pmb{\\mu})\\) are the relative offsets between the target and source pixels, \\(|\\cdot|\\) denotes the determinant of the covariance matrix \\(\\pmb{\\Sigma}\\). We parameterize \\(\\pmb{\\Sigma}\\) as the following," + }, + { + "type": "equation", + "bbox": [ + 0.604, + 0.872, + 0.892, + 0.906 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\Sigma} = \\left( \\begin{array}{c c} \\sigma_ {X} ^ {2} & \\rho \\sigma_ {X} \\sigma_ {Y} \\\\ \\rho \\sigma_ {X} \\sigma_ {Y} & \\sigma_ {Y} ^ {2} \\end{array} \\right) \\tag {4}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "5868" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.123, + 0.089, + 0.27, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.128, + 0.247, + 0.303, + 0.261 + ], + "angle": 0, + "content": "(a) LUT acceleration of DNN" + }, + { + "type": "image", + "bbox": [ + 0.342, + 0.087, + 0.538, + 0.243 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.367, + 0.247, + 0.496, + 0.262 + ], + "angle": 0, + "content": "(b) Ensemble strategy" + }, + { + "type": "image", + "bbox": [ + 0.589, + 0.09, + 0.852, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.623, + 0.247, + 0.832, + 0.262 + ], + "angle": 0, + "content": "(c) Edge-sensitive indexing patterns" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.279, + 0.895, + 0.336 + ], + "angle": 0, + "content": "Figure 5. LUT acceleration of DNN and our adaptations. (a) A learned neural network can be accelerated by traversing all possible inputs \\( i_{*} \\), pre-computing all corresponding outputs \\( v_{*} \\), and saving them to LUTs [15,22,23,29]. (b) In the proposed directional ensemble strategy, only predictions along the same direction are averaged, instead of all directions in rotation ensemble [15]. (c) We introduce edge-sensitive indexing patterns to better capture edge orientations. The pixels covered by directional ensemble are depicted with dashed boxes." + }, + { + "type": "image", + "bbox": [ + 0.1, + 0.342, + 0.441, + 0.41 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.086, + 0.415, + 0.46, + 0.429 + ], + "angle": 0, + "content": "Figure 4. Visualization of parameterized resampling functions." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.45, + 0.469, + 0.51 + ], + "angle": 0, + "content": "where \\(\\rho, \\sigma_{X}\\), and \\(\\sigma_{Y}\\) are hyper-parameters, and thus the resampling function becomes \\(\\Phi_{(\\rho, \\sigma_{X}, \\sigma_{Y})}\\). From the perspective of statistics, \\(\\rho\\) can be interpreted as the correlation between 2D variables and \\(\\sigma_{X}, \\sigma_{Y}\\) the standard deviations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.51, + 0.469, + 0.616 + ], + "angle": 0, + "content": "As illustrated in Fig. 4, our resampling function is steerable, where different orientations and shapes can be obtained by tuning hyper-parameters \\((\\rho, \\sigma_{X}, \\sigma_{Y})\\), showing its modeling capacity for a variety of local structures. Other non-steerable functions, such as Keys cubic [16] are also applicable as resampling functions. Details of the comparison are provided in Table 3 and Fig. 10." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.617, + 0.47, + 0.707 + ], + "angle": 0, + "content": "But, direct optimization of the above hyper-parameters leads to unstable gradients and divergence. Thus, we modify the formulation by omitting the determinant multiplier and predicting a group of \\((\\rho, \\frac{1}{\\sigma_X}, \\frac{1}{\\sigma_Y})\\) for stable training. In the following section, we introduce the learning process of these hyper-parameters and our efficient implementation." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.718, + 0.443, + 0.733 + ], + "angle": 0, + "content": "3.3. Learning with DNN and LUT Acceleration" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.741, + 0.469, + 0.802 + ], + "angle": 0, + "content": "As shown in Fig. 3(b), we adopt a deep neural network to learn structural priors from an external dataset for predicting hyper-parameters \\(\\Theta\\) in resampling functions. The training loss function can be formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.102, + 0.813, + 0.469, + 0.847 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {q} = \\left\\| I _ {q} - \\sum_ {p \\in \\Omega} \\Phi_ {\\Theta} \\left(d _ {p \\rightarrow q}\\right) I _ {p} \\right\\| ^ {2}, \\quad \\Theta = f (\\mathcal {N} _ {p}), \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.856, + 0.469, + 0.902 + ], + "angle": 0, + "content": "where \\(I_q\\) is the ground truth pixel value at location \\(q\\), \\(f(\\cdot)\\) is a trainable DNN, and \\(\\mathcal{N}_p\\) denotes the surrounding pixels of \\(I_p\\) (not necessarily the same as the support patch \\(\\Omega\\))." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.344, + 0.892, + 0.48 + ], + "angle": 0, + "content": "Furthermore, to match the efficiency of interpolation, we accelerate the inference of the trained DNN by adopting look-up tables [15, 22, 23, 29]. As shown in Fig. 5(a), for each pair in the LUT to accelerate DNN, its index \\( i_{*} \\) is a combination of pixels (i.e., \\( \\mathcal{N}_p \\)), and its value \\( v_{*} \\) is a group of corresponding hyper-parameters for that pixel combination (i.e., \\( \\Theta \\)). This way, hyper-parameters can be retrieved directly from the saved values in LUTs, skipping computations in DNN and thus resulting in high efficiency." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.482, + 0.892, + 0.544 + ], + "angle": 0, + "content": "Different from existing LUT-based methods, whose LUT values are image pixels, our LUTs store hyper-parameters that reflect structural characteristics. Thus, to better extract structural priors, we propose the following adaptations." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.547, + 0.893, + 0.683 + ], + "angle": 0, + "content": "Directional ensemble strategy. We propose a directional ensemble (DE) strategy to replace the rotation ensemble (RE) strategy in existing LUT-based methods [15, 22, 23]. As illustrated in Fig. 5(b), in RE, the predictions are averaged across all directions, while the proposed DE strategy only ensembles the predictions with the same direction (i.e., \\(180^{\\circ}\\) rotational symmetry, instead of \\(90^{\\circ}\\) in RE). This enables the learning of \\(\\rho\\), which determines the orientation of the steerable resampling function (see Table 5 and Fig. 11)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.687, + 0.892, + 0.807 + ], + "angle": 0, + "content": "Edge-sensitive indexing patterns. As illustrated in Fig. 5(c), we include patterns \"C\" and \"X\", alongside the default \"S\" pattern in SR-LUT to better capture edges of different orientations. For example, The \"C\" and \"C\" patterns are sensitive to vertical and horizontal edges, respectively. We validate their effectiveness in Table 5. Correspondingly, as shown in Fig. 5(a), our DNN follows a multibranch design, and each branch is accelerated by a LUT." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Pre-filtering stage. Inspired by DNN-based resampling methods [12, 43, 48], we pre-filter the image with a neural network, which can also be accelerated by LUTs with the proposed edge-sensitive indexing patterns. This prefiltering stage helps enhance image content and contributes to better performance (see Table 5 and Fig. 11)." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "5869" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.078, + 0.089, + 0.892, + 0.46 + ], + "angle": 0, + "content": "
MethodSet5Set14BSDS100Urban100Manga109
×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4
Nearest31.3431.0730.8429.6329.1528.8428.5727.7028.9928.7228.4027.6226.2125.9125.6224.7828.5928.3628.1426.87
Bilinear34.9933.1932.2331.4931.6830.2629.2428.7030.9229.6628.6728.2028.2426.9125.9625.4632.4530.3329.1628.28
Bicubic36.7634.6833.6432.7033.0731.4530.3229.6232.1430.6729.5428.9329.5027.9526.8726.2234.7632.1330.8129.61
Lanczos236.8334.7433.7032.7433.1331.5030.3629.6532.1930.7129.5828.9529.5528.0026.9126.2534.8732.2230.8929.66
Lanczos337.6135.3134.2333.2433.7531.9730.7630.0232.7431.1129.8929.2330.1228.4227.2526.5536.1233.0631.6330.28
RAISR* [37]35.5035.4935.5733.3831.8431.6731.7130.2230.8730.6830.6629.4228.7728.6028.6427.0133.8133.7433.8830.61
SR-LUT* [15]37.7435.5234.4733.4533.5331.7930.5929.8633.0631.4030.1529.4530.2428.5527.3926.6935.3132.3030.9629.58
LeRF (Ours)38.3036.6035.7134.7434.5933.0631.9831.1033.7632.0830.8330.0931.8630.0828.8627.9936.5734.7933.8832.67
Meta-SR [12]41.29-38.12-37.47-33.99-35.79-32.32-35.85-32.98-42.92-39.22-
LIIF [5]41.2238.9938.0836.9937.4435.3133.9632.9535.7533.6832.2831.4536.7034.0832.8431.7042.7740.1939.1337.69
MethodSet5Set14BSDS100Urban100Manga109
×2.0 ×3.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0
Nearest28.8727.9126.8826.2527.0726.0825.3324.7427.1226.1725.5725.0324.2523.3422.6822.1726.1225.0424.0623.43
Bilinear30.4329.5328.2727.5527.9427.0426.1625.5127.6026.7726.1125.5324.8123.9923.2622.6827.2126.1624.9524.19
Bicubic31.4130.3929.1228.4228.7027.6326.7526.0928.1827.2026.5325.9525.4324.4523.7123.1428.2026.9525.6724.90
Lanczos231.4430.4129.1428.4428.7227.6426.7726.1028.2027.2126.5425.9625.4524.4723.7323.1528.2326.9725.7024.92
Lanczos331.8530.7929.4928.7829.0427.9127.0126.3128.4327.3926.7026.1025.7124.6823.9223.3228.7027.3826.0225.21
RAISR* [37]32.3531.8729.9129.6529.0428.6227.1026.8628.2427.8426.6826.4225.9225.5024.0823.8929.3028.7326.4026.12
SR-LUT* [15]32.0431.0029.7029.0028.8827.8426.9526.3028.6227.5426.8426.2125.8324.7824.0023.3928.0326.7425.3924.72
LeRF (Ours)33.1732.0230.8630.1530.0628.8428.0527.3529.1528.0027.3126.7026.9025.6824.8824.2330.8629.4828.1027.25
Meta-SR [12]-34.71-32.48-30.56-28.83-29.26-27.73-28.91-26.69-34.37-31.32
LIIF [5]35.6334.5933.1732.3731.6830.3929.4528.6530.4729.2428.4227.7330.2328.8027.5526.6635.7334.1732.2931.19
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.469, + 0.894, + 0.514 + ], + "angle": 0, + "content": "Table 1. Quantitative comparison in PSNR for arbitrary-scale upsampling. \\(\\frac{\\times r_h}{\\times r_w}\\) denotes upsampling \\(r_h\\) times along the short side and \\(r_w\\) times along the long side. SSIM and LPIPS results are in the supplementary document. * denotes that we combine fixed-scale super-resolution methods with bicubic to achieve arbitrary-scale upsampling. The best and second best results are highlighted and underlined." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.523, + 0.313, + 0.54 + ], + "angle": 0, + "content": "4. Experiments and Results" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.552, + 0.285, + 0.569 + ], + "angle": 0, + "content": "4.1. Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.579, + 0.47, + 0.806 + ], + "angle": 0, + "content": "Datasets and metrics. We train LeRF on the DIV2K dataset [1], which is widely used in image resampling tasks. We train LeRF on the \\(\\times 4\\) downsampled data pairs, and apply the obtained model to resampling for arbitrary transformations. For arbitrary-scale upsampling, we evaluate LeRF with 5 benchmark datasets: Set5, Set14, BSDS100 [30], Urban100 [13], and Manga109 [31]. We select representative symmetric or asymmetric upsampling scales for evaluation and apply bicubic interpolation as the degradation model to obtain the LR images. For performance evaluation, we report PSNR and SSIM [45] for fidelity, and LPIPS [53] for perceptual quality. For efficiency evaluation, we report running time, theoretical multiply-accumulate operations (MACs), and storage requirements to evaluate the performance-efficiency trade-off." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Comparison methods. The main competitors to the proposed method are interpolation methods, including nearest neighbor, bilinear, bicubic, lanczos2, and lanczos3. Furthermore, we combine fixed-scale super-resolution methods with bicubic interpolation as additional baselines for arbitrary-scale upsampling (RAISR* and SR-LUT*)." + }, + { + "type": "table", + "bbox": [ + 0.501, + 0.522, + 0.892, + 0.689 + ], + "angle": 0, + "content": "
MethodRunTime (ms)MACsStorage SizePSNR/SSIM (dB)/-
Nearest11--24.74/0.6553
Bilinear3114.74M-25.51/0.6824
Bicubic12651.61M-26.09/0.7056
Lanczos2494110.59M-26.10/0.7058
Lanczos3914165.89M-26.31/0.7130
RAISR* [37]3,744-3.19MB26.86/0.7357
SR-LUT* [15]13753.33M81.56KB26.30/0.7256
LeRF (Ours)11057.94M1.67MB27.35/0.7475
MetaSR [12]10,2601.68T85.59MB28.83/0.7876
LIIF [5]67,0802.54T255.76MB28.65/0.7878
" + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.695, + 0.894, + 0.78 + ], + "angle": 0, + "content": "Table 2. Efficiency comparison of running time, MACs, storage requirements, and performance (Set14) for producing a \\(1280 \\times 720\\) HD image through \\(4 \\times\\) upsampling. For interpolation methods, SR-LUT*, and LeRF, the running time is evaluated on CPU of a mobile phone and averaged across 10 trials, while for RAISR* and DNNs, that is evaluated on CPU of a desktop computer." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.795, + 0.894, + 0.903 + ], + "angle": 0, + "content": "Specifically, since RAISR [37] lacks generalization ability across different upsampling scales, we fuse the results of \\(\\times 2\\), \\(\\times 3\\), and \\(\\times 4\\) models by choosing the model with the closest integer upsampling scales (e.g., \\(\\times 3\\) model for \\(\\frac{\\times 2.0}{\\times 3.0}\\)). We retrain SR-LUT [15] on the mixed-scale data of \\(\\times 2\\), \\(\\times 3\\), and \\(\\times 4\\) downsampled data pairs jointly with bicubic interpolation to achieve arbitrary-scale upsampling. Finally, we" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "5870" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.086, + 0.089, + 0.185, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.1, + 0.167, + 0.163, + 0.19 + ], + "angle": 0, + "content": "\\(\\frac{\\times 1.5}{\\times 2.0}\\) upsampling" + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.19, + 0.184, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.124, + 0.266, + 0.15, + 0.288 + ], + "angle": 0, + "content": "\\(\\times 2.0\\) \\(\\times 2.4\\)" + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.288, + 0.184, + 0.363 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.1, + 0.365, + 0.162, + 0.49 + ], + "angle": 0, + "content": "\\(\\frac{\\times 3.0}{\\times 3.0}\\) upsampling \\(\\frac{\\times 4.0}{\\times 4.0}\\) upsampling" + }, + { + "type": "image", + "bbox": [ + 0.197, + 0.091, + 0.293, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.209, + 0.168, + 0.279, + 0.19 + ], + "angle": 0, + "content": "GT (PSNR/SSIM)" + }, + { + "type": "image", + "bbox": [ + 0.293, + 0.091, + 0.389, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.306, + 0.168, + 0.377, + 0.19 + ], + "angle": 0, + "content": "Bilinear (23.46/0.8875)" + }, + { + "type": "image", + "bbox": [ + 0.391, + 0.091, + 0.487, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.4, + 0.168, + 0.474, + 0.19 + ], + "angle": 0, + "content": "Bicubic (24.88/0.9174)" + }, + { + "type": "image", + "bbox": [ + 0.488, + 0.091, + 0.584, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.168, + 0.571, + 0.19 + ], + "angle": 0, + "content": "Lanczos3 (25.63/0.9275)" + }, + { + "type": "image", + "bbox": [ + 0.585, + 0.091, + 0.681, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.597, + 0.168, + 0.668, + 0.19 + ], + "angle": 0, + "content": "RAISR\\* (26.62/0.9362)" + }, + { + "type": "image", + "bbox": [ + 0.682, + 0.091, + 0.777, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.695, + 0.168, + 0.765, + 0.19 + ], + "angle": 0, + "content": "SR-LUT\\* (25.51/0.9305)" + }, + { + "type": "image", + "bbox": [ + 0.779, + 0.091, + 0.874, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.795, + 0.168, + 0.862, + 0.19 + ], + "angle": 0, + "content": "LeRF (Ours) (28.36/0.9577)" + }, + { + "type": "image", + "bbox": [ + 0.197, + 0.19, + 0.293, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.209, + 0.267, + 0.279, + 0.289 + ], + "angle": 0, + "content": "GT (PSNR/SSIM)" + }, + { + "type": "image", + "bbox": [ + 0.293, + 0.19, + 0.389, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.306, + 0.267, + 0.377, + 0.29 + ], + "angle": 0, + "content": "Bilinear (29.83/0.9553)" + }, + { + "type": "image", + "bbox": [ + 0.391, + 0.19, + 0.487, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.419, + 0.267, + 0.474, + 0.29 + ], + "angle": 0, + "content": "Bicubic .29/0.9641)" + }, + { + "type": "image", + "bbox": [ + 0.488, + 0.19, + 0.584, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.267, + 0.571, + 0.29 + ], + "angle": 0, + "content": "Lanczos3 (32.06/0.9666)" + }, + { + "type": "image", + "bbox": [ + 0.585, + 0.19, + 0.681, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.598, + 0.267, + 0.668, + 0.29 + ], + "angle": 0, + "content": 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], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.795, + 0.465, + 0.862, + 0.488 + ], + "angle": 0, + "content": "LeRF (Ours) (17.30/0.5175)" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.498, + 0.891, + 0.526 + ], + "angle": 0, + "content": "Figure 6. Qualitative comparison for arbitrary-scale upsampling. From top to bottom, the example images are: UltraEleven (Manga109), MukoukizuNoChonbo (Manga109), 126007 (BSDS100), and img092 (Ubran100). Best view in color and on screen." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.549, + 0.468, + 0.578 + ], + "angle": 0, + "content": "include DNN-based arbitrary-scale super-resolution methods (Meta-SR [12] and LIIF [5]) for reference." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.58, + 0.469, + 0.775 + ], + "angle": 0, + "content": "Implementation details. Our method is trained with the Adam optimizer [19] in the cosine annealing schedule [28]. We train LeRF with the MSE loss function for \\(5 \\times 10^{4}\\) iterations at a batch size of 32. Except for the indexing patterns, each branch in our DNN keeps the same architecture as the network in SR-LUT. To obtain a fair comparison in running time tests, we implement LeRF and interpolation methods on the Android platform under the same parallel API, with the only difference being the resampling functions and LUT retrieval in our method. Our method is implemented in MindSpore [33] and the code is available at https://gitee.com/mindspore/models/tree/master/research/cv/lerf." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.787, + 0.3, + 0.801 + ], + "angle": 0, + "content": "4.2. Performance Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Quantitative comparison. We list the quantitative comparisons with other methods in Table 1. As can be seen, our method achieves significantly better performance than interpolation methods. For example, it exceeds bicubic interpolation up to 3dB PSNR when upsampling \\(\\frac{\\times 2.0}{\\times 2.0}\\) on the Manga109 dataset." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.549, + 0.892, + 0.654 + ], + "angle": 0, + "content": "Qualitative comparison. In Fig. 6, we compare the visual quality of LeRF with interpolation methods, RAISR*, and SR-LUT*. As can be seen, our method obtains better visual quality across various local structures and textures. Specifically, since the resampling functions are adapted to local structures, LeRF is capable of retaining clearer textures and avoiding blurry boundaries." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.655, + 0.892, + 0.821 + ], + "angle": 0, + "content": "Visualization of learned resampling functions. We further visualize the intermediate results of the proposed method in Fig. 7. As can be seen, the hyper-parameter \\(\\rho\\) clearly distinguishes the orientations of edges (red vs. blue), and \\(\\sigma_{X}\\) and \\(\\sigma_{Y}\\) capture the horizontal and vertical lines, respectively. In addition, in the second row of each example, we visualize the resampling functions defined by the predicted hyper-parameters. The shapes of resampling functions are well adapted to corners, flat surfaces, and edges with various orientations. This validates the effectiveness of extracting structural priors in a data-driven way." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.832, + 0.7, + 0.848 + ], + "angle": 0, + "content": "4.3. Efficiency Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.891, + 0.901 + ], + "angle": 0, + "content": "To evaluate the efficiency, we conduct running time tests, estimate the theoretical MACs, and report storage requirements for LeRF and comparison methods. We list the de" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "5871" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.091, + 0.157, + 0.18 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.104, + 0.181, + 0.132, + 0.191 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image", + "bbox": [ + 0.172, + 0.091, + 0.265, + 0.18 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.205, + 0.182, + 0.217, + 0.191 + ], + "angle": 0, + "content": "P" + }, + { + "type": "image", + "bbox": [ + 0.28, + 0.092, + 0.371, + 0.18 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.306, + 0.181, + 0.332, + 0.191 + ], + "angle": 0, + "content": "1/σx" + }, + { + "type": "image", + "bbox": [ + 0.376, + 0.092, + 0.468, + 0.18 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.403, + 0.181, + 0.428, + 0.191 + ], + "angle": 0, + "content": "1/σY" + }, + { + "type": "image", + "bbox": [ + 0.082, + 0.192, + 0.147, + 0.242 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.161, + 0.192, + 0.223, + 0.242 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.238, + 0.192, + 0.301, + 0.242 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.316, + 0.192, + 0.379, + 0.242 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.395, + 0.192, + 0.458, + 0.242 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.081, + 0.243, + 0.157, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.105, + 0.331, + 0.133, + 0.341 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image", + "bbox": [ + 0.162, + 0.243, + 0.265, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.207, + 0.332, + 0.217, + 0.341 + ], + "angle": 0, + "content": "P" + }, + { + "type": "image", + "bbox": [ + 0.282, + 0.243, + 0.364, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.306, + 0.331, + 0.332, + 0.341 + ], + "angle": 0, + "content": "1/σx" + }, + { + "type": "image", + "bbox": [ + 0.377, + 0.243, + 0.46, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.404, + 0.331, + 0.43, + 0.341 + ], + "angle": 0, + "content": "\\(1 / \\sigma_{\\mathrm{V}}\\)" + }, + { + "type": "image", + "bbox": [ + 0.082, + 0.342, + 0.147, + 0.393 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.161, + 0.343, + 0.225, + 0.393 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.238, + 0.343, + 0.302, + 0.393 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.316, + 0.343, + 0.38, + 0.393 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.396, + 0.343, + 0.459, + 0.393 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.404, + 0.47, + 0.447 + ], + "angle": 0, + "content": "Figure 7. Visualization of the pixel-wise hyper-parameters and the corresponding resampling functions. The shapes of the predicted resampling functions are well adapted to local structures." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.459, + 0.47, + 0.593 + ], + "angle": 0, + "content": "tailed comparison in Table 2. As can be seen, the proposed LeRF achieves comparable or better running time compared to the popular interpolation methods, showing its role as a superior competitor with significantly better performance. On the other hand, compared to DNN-based methods, LeRF shows a clear advantage in efficiency. Furthermore, our method requires far less extra storage as well as fewer software dependencies, demonstrating its practicality for being deployed on various devices." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.604, + 0.315, + 0.618 + ], + "angle": 0, + "content": "4.4. Generalization Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.627, + 0.469, + 0.732 + ], + "angle": 0, + "content": "In Fig. 8, we evaluate the generalization ability of LeRF to arbitrary transformations and compare it with widely applied interpolation methods. As can be seen, the learned resampling functions generalize well to unseen deformations. LeRF generates sharper edges and retains more texture details, leading to more visually pleasing results than popular interpolation methods." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.742, + 0.25, + 0.758 + ], + "angle": 0, + "content": "4.5. Ablation Analysis" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.765, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Analysis of GT-optimized resampling functions. To analyze the optimal shapes of resampling functions across different transformations, we obtain hyper-parameters in resampling functions via per-image optimization. In Fig. 9, we visualize the clustering results on Set14 of these GT-optimized resampling functions across different upsampling scales. As can be seen, the GT-optimized cluster centroids are very similar across different upsampling scales, explaining the generalization ability of our method." + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.088, + 0.574, + 0.143 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.577, + 0.103, + 0.597, + 0.125 + ], + "angle": 0, + "content": "#" + }, + { + "type": "image", + "bbox": [ + 0.618, + 0.101, + 0.654, + 0.129 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.671, + 0.089, + 0.743, + 0.143 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.746, + 0.09, + 0.816, + 0.143 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.82, + 0.09, + 0.892, + 0.143 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.144, + 0.574, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.577, + 0.144, + 0.668, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.672, + 0.145, + 0.743, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.747, + 0.145, + 0.816, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.82, + 0.145, + 0.892, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.519, + 0.207, + 0.558, + 0.247 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.574, + 0.2, + 0.666, + 0.255 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.672, + 0.2, + 0.743, + 0.255 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.747, + 0.2, + 0.816, + 0.255 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.82, + 0.2, + 0.892, + 0.255 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.256, + 0.574, + 0.312 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.577, + 0.256, + 0.669, + 0.312 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.672, + 0.256, + 0.743, + 0.312 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.747, + 0.256, + 0.816, + 0.312 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.82, + 0.256, + 0.892, + 0.312 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.313, + 0.574, + 0.368 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.577, + 0.313, + 0.668, + 0.368 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.672, + 0.313, + 0.743, + 0.368 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.747, + 0.313, + 0.816, + 0.368 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.82, + 0.313, + 0.892, + 0.368 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.523, + 0.371, + 0.555, + 0.383 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image_caption", + "bbox": [ + 0.576, + 0.371, + 0.662, + 0.382 + ], + "angle": 0, + "content": "Transformation" + }, + { + "type": "image_caption", + "bbox": [ + 0.686, + 0.371, + 0.732, + 0.381 + ], + "angle": 0, + "content": "Bilinear" + }, + { + "type": "image_caption", + "bbox": [ + 0.759, + 0.371, + 0.804, + 0.381 + ], + "angle": 0, + "content": "Bicubic" + }, + { + "type": "image_caption", + "bbox": [ + 0.84, + 0.371, + 0.868, + 0.381 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.401, + 0.892, + 0.457 + ], + "angle": 0, + "content": "Figure 8. Visual comparison of LeRF with interpolation methods under general homographic transformations (downsampling, rotation, and sheering) and arbitrary warping (according to a barrel-shaped distortion and optical flow)." + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.471, + 0.568, + 0.502 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.571, + 0.471, + 0.617, + 0.501 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.623, + 0.471, + 0.696, + 0.501 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.701, + 0.471, + 0.764, + 0.501 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.77, + 0.471, + 0.813, + 0.501 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.818, + 0.471, + 0.891, + 0.501 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.507, + 0.567, + 0.536 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.571, + 0.507, + 0.617, + 0.535 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.623, + 0.507, + 0.696, + 0.535 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.701, + 0.507, + 0.764, + 0.535 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.77, + 0.507, + 0.813, + 0.535 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.818, + 0.507, + 0.882, + 0.535 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.54, + 0.568, + 0.57 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.571, + 0.54, + 0.617, + 0.57 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.623, + 0.54, + 0.696, + 0.57 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.701, + 0.54, + 0.764, + 0.57 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.77, + 0.54, + 0.813, + 0.57 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.818, + 0.54, + 0.882, + 0.57 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.583, + 0.891, + 0.625 + ], + "angle": 0, + "content": "Figure 9. Visualization of GT-optimized resampling function clustering centroids on Set14 for \\(\\times 2\\), \\(\\times 3\\), and \\(\\times 4\\) upsampling, respectively. They show consistency across different upsampling scales." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.644, + 0.892, + 0.854 + ], + "angle": 0, + "content": "The effectiveness of the steerable resampling function. We conduct the following ablation experiments on the Set5 benchmark to analyze the design of the steerable resampling function. 1) Fixed gaussian: We freeze the hyperparameters in the anisotropic gaussian. 2) Learned Keys cubic: We utilize another non-steerable resampling function, the isotropic Keys cubic function [16], which has only one hyper-parameter that controls the sharpness of the one-dimension piecewise cubic. The experimental results are listed in Table 3, and visual results are shown in Fig. 10. As can be seen, non-steerable resampling functions like Keys cubic produce zig-zag artifacts and inferior performance, showing the effectiveness of our learned steerable resampling functions." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Analysis of training data. By default, LeRF is trained with \\(\\times 4\\) downsampled data pairs. We retrain our method on \\(\\times 2\\) data pairs and mixed data pairs with both symmetric and" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5872" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.08, + 0.089, + 0.47, + 0.153 + ], + "angle": 0, + "content": "
MACsStorage×2×3×4
Ours57.94M1.67MB35.7132.0230.15
fixed gaussian Φ(0,1,1)45.04M244.69KB30.7527.7026.31
learned Keys cubic126.94M489.38KB34.6831.1729.08
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.163, + 0.47, + 0.192 + ], + "angle": 0, + "content": "Table 3. Ablation experiments on Set5 on the the design of the steerable resampling function." + }, + { + "type": "image", + "bbox": [ + 0.079, + 0.205, + 0.204, + 0.304 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.214, + 0.204, + 0.268, + 0.305 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.277, + 0.204, + 0.403, + 0.304 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.417, + 0.204, + 0.47, + 0.305 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.33, + 0.469, + 0.359 + ], + "angle": 0, + "content": "Figure 10. The non-steerable Keys cubic function produces artifacts along diagonal edges." + }, + { + "type": "table", + "bbox": [ + 0.088, + 0.373, + 0.462, + 0.439 + ], + "angle": 0, + "content": "
MACsStorage×2×3×4
Ours (×4 data)57.94M1.67MB35.7132.0230.15
×2 data training57.94M1.67MB35.6732.1129.91
mixed-scale training57.94M1.67MB35.6732.2630.06
" + }, + { + "type": "table_caption", + "bbox": [ + 0.082, + 0.449, + 0.465, + 0.463 + ], + "angle": 0, + "content": "Table 4. Ablation experiments on Set5 on data pairs for training." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.475, + 0.469, + 0.536 + ], + "angle": 0, + "content": "asymmetric upsampling scales. As listed in Table 4, training on mixed data pairs yields similar performance, showing the robustness and generalization ability of our method, which is consistent with the clustering results in Fig. 9." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.536, + 0.47, + 0.809 + ], + "angle": 0, + "content": "The effectiveness of the LUT acceleration and the proposed adaptations. As listed in Table 5, we conduct the following ablation experiments to analyze the design of the LUT acceleration and our adaptations. 1) Without LUT acceleration: Compared with the learned network, the LUT acceleration saves a lot of computations at a very small performance cost. 2) Without DE: The visualization of the learned hyper-parameter \\(\\rho\\) in Fig. 11 shows that RE makes an isotropic assumption and thus lacks the ability to learn edge orientations, resulting in a performance drop. 3) Without edge-sensitive patterns: LeRF with \"SCX\" indexing patterns outperforms the variant with only the default \"S\" pattern, showing the ability of edge-sensitive indexing patterns to better capture edge structures. 4) Without prefiltering or pre-filtering only: As shown in Fig. 11 and Table 4, the pre-filtering stage enhances edges and contributes to better performance, and pre-filtering only is suboptimal due to the lack of adaptivity of resampling functions." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.817, + 0.196, + 0.832 + ], + "angle": 0, + "content": "4.6. Limitation" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.84, + 0.469, + 0.902 + ], + "angle": 0, + "content": "In most cases, our method produces better results compared with interpolation. But, it may fail under certain circumstances. In Fig. 12, we show its limitation in down-sampling, where aliasing artifacts appear in regions with" + }, + { + "type": "table", + "bbox": [ + 0.502, + 0.089, + 0.892, + 0.189 + ], + "angle": 0, + "content": "
MACsStorage×2×3×4
Ours57.94M1.67MB35.7132.0230.15
w/o LUT acceleration21.75G1.59MB36.1132.2330.28
w/o DE57.94M978.76KB34.4431.5029.47
w/o “CX” pattern49.65M326.25KB34.9031.7929.70
w/o pre-filtering53.45M1.43MB34.4331.4129.33
pre-filtering only56.10M244.69KB34.6931.1629.15
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.2, + 0.892, + 0.228 + ], + "angle": 0, + "content": "Table 5. Ablation experiments on Set5 on the LUT acceleration and our adaptations." + }, + { + "type": "image", + "bbox": [ + 0.502, + 0.234, + 0.593, + 0.34 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.533, + 0.34, + 0.562, + 0.351 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image", + "bbox": [ + 0.599, + 0.234, + 0.692, + 0.339 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.631, + 0.34, + 0.661, + 0.351 + ], + "angle": 0, + "content": "RE \\(\\rho\\)" + }, + { + "type": "image", + "bbox": [ + 0.699, + 0.234, + 0.793, + 0.339 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.729, + 0.34, + 0.759, + 0.351 + ], + "angle": 0, + "content": "DE \\(\\rho\\)" + }, + { + "type": "image", + "bbox": [ + 0.801, + 0.234, + 0.892, + 0.339 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.818, + 0.34, + 0.876, + 0.351 + ], + "angle": 0, + "content": "DE Output" + }, + { + "type": "image", + "bbox": [ + 0.502, + 0.359, + 0.593, + 0.428 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.534, + 0.429, + 0.562, + 0.44 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image", + "bbox": [ + 0.601, + 0.359, + 0.692, + 0.428 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.615, + 0.429, + 0.675, + 0.44 + ], + "angle": 0, + "content": "Pre-filtered" + }, + { + "type": "image", + "bbox": [ + 0.7, + 0.359, + 0.791, + 0.428 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.731, + 0.429, + 0.76, + 0.44 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image", + "bbox": [ + 0.8, + 0.359, + 0.892, + 0.428 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.815, + 0.429, + 0.875, + 0.439 + ], + "angle": 0, + "content": "Pre-filtered" + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.458, + 0.892, + 0.486 + ], + "angle": 0, + "content": "Figure 11. Upper: Replacing RE with DE enables the learning of edge orientations. Lower: The pre-filtering stage enhances edges." + }, + { + "type": "image", + "bbox": [ + 0.502, + 0.493, + 0.574, + 0.549 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.528, + 0.552, + 0.548, + 0.562 + ], + "angle": 0, + "content": "HR" + }, + { + "type": "image", + "bbox": [ + 0.581, + 0.493, + 0.653, + 0.549 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.593, + 0.552, + 0.638, + 0.563 + ], + "angle": 0, + "content": "Bilinear" + }, + { + "type": "image", + "bbox": [ + 0.661, + 0.493, + 0.733, + 0.549 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.674, + 0.552, + 0.718, + 0.563 + ], + "angle": 0, + "content": "Bicubic" + }, + { + "type": "image", + "bbox": [ + 0.74, + 0.493, + 0.813, + 0.549 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.747, + 0.552, + 0.8, + 0.563 + ], + "angle": 0, + "content": "Lanczos3" + }, + { + "type": "image", + "bbox": [ + 0.82, + 0.493, + 0.892, + 0.549 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.841, + 0.552, + 0.869, + 0.563 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.581, + 0.892, + 0.611 + ], + "angle": 0, + "content": "Figure 12. Limitation. LeRF produces aliasing artifacts for image downsampling in regions with highly dense textures." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.622, + 0.892, + 0.683 + ], + "angle": 0, + "content": "highly dense textures. It is probably caused by a lack of training on downsampled data. Moreover, replacing the pre-filtering stage with anti-aliasing filters may mitigate this problem [6, 7]." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.696, + 0.7, + 0.712 + ], + "angle": 0, + "content": "5. Conclusion Remarks" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.722, + 0.892, + 0.813 + ], + "angle": 0, + "content": "In this work, we propose LeRF, a novel method for continuous resampling by integrating learned structural priors into the steerable resampling function. We show its superior performance, high efficiency, and versatility for arbitrary transformations. In the future, we plan to extend this idea to other modalities, e.g., video and point clouds." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.826, + 0.66, + 0.843 + ], + "angle": 0, + "content": "Acknowledgement" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.851, + 0.892, + 0.897 + ], + "angle": 0, + "content": "This work was supported in part by the National Natural Science Foundation of China under Grants 62131003 and 62021001. We acknowledge the support of MindSpore." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.515, + 0.957 + ], + "angle": 0, + "content": "5873" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.175, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.468, + 0.157 + ], + "angle": 0, + "content": "[1] Eirikur Agustsson and Radu Timofte. NTIRE 2017 challenge on single image super-resolution: Dataset and study. In CVPR Workshops, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.159, + 0.468, + 0.185 + ], + "angle": 0, + "content": "[2] Jan P. Allebach and Ping Wah Wong. Edge-directed interpolation. In ICIP, 1996. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.188, + 0.468, + 0.242 + ], + "angle": 0, + "content": "[3] Chang Chen, Xinmei Tian, Feng Wu, and Zhiwei Xiong. UDNet: Up-down network for compact and efficient feature representation in image super-resolution. In ICCV Workshops, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.245, + 0.468, + 0.284 + ], + "angle": 0, + "content": "[4] Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha, and Feng Wu. Camera lens super-resolution. In CVPR, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.287, + 0.468, + 0.327 + ], + "angle": 0, + "content": "[5] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In CVPR, 2021. 1, 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.331, + 0.468, + 0.357 + ], + "angle": 0, + "content": "[6] Robert L. Cook. Stochastic sampling in computer graphics. ACM Trans. Graph., 5(1):51-72, 1986. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.36, + 0.468, + 0.399 + ], + "angle": 0, + "content": "[7] Franklin C. Crow. The aliasing problem in computer-generated shaded images. Commun. ACM, 20(11):799-805, 1977. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.403, + 0.468, + 0.43 + ], + "angle": 0, + "content": "[8] Neil Anthony Dodgson. Image resampling. Technical report, University of Cambridge, Computer Laboratory, 1992. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.432, + 0.468, + 0.471 + ], + "angle": 0, + "content": "[9] Chao Dong, Chen Change Loy, Kaiming He, and Xiaou Tang. Learning a deep convolutional network for image super-resolution. In ECCV, 2014. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.475, + 0.468, + 0.514 + ], + "angle": 0, + "content": "[10] Irvine C. Gardner and Francis E. Washer. Lenses of extremely wide angle for airplane mapping. J. Opt. Soc. Am., 38(5):421-431, 1948. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.518, + 0.468, + 0.572 + ], + "angle": 0, + "content": "[11] Pascal Getreuer, Ignacio Garcia-Dorado, John Isidoro, Sungjoon Choi, Frank Ong, and Peyman Milanfar. BLADE: filter learning for general purpose computational photography. In ICCP, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.575, + 0.468, + 0.614 + ], + "angle": 0, + "content": "[12] Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Tieniu Tan, and Jian Sun. Meta-SR: A magnification-arbitrary network for super-resolution. In CVPR, 2019. 1, 2, 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.617, + 0.468, + 0.657 + ], + "angle": 0, + "content": "[13] Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja. Single image super-resolution from transformed self-exemplars. In CVPR, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.66, + 0.468, + 0.699 + ], + "angle": 0, + "content": "[14] Xu Jia, Hong Chang, and Tinne Tuytelaars. Super-resolution with deep adaptive image resampling. arxiv, 1712.06463, 2017.2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.704, + 0.468, + 0.743 + ], + "angle": 0, + "content": "[15] Younghyun Jo and Seon Joo Kim. Practical single-image super-resolution using look-up table. In CVPR, 2021. 2, 3, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.746, + 0.468, + 0.786 + ], + "angle": 0, + "content": "[16] Robert G. Keys. Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust., 29:1153-1160, 1981. 1, 2, 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.789, + 0.468, + 0.829 + ], + "angle": 0, + "content": "[17] Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. Accurate image super-resolution using very deep convolutional networks. In CVPR, 2016. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.832, + 0.468, + 0.899 + ], + "angle": 0, + "content": "[18] Seon Joo Kim, Hai Ting Lin, Zheng Lu, Sabine Süssstrunk, Stephen Lin, and Michael S. Brown. A new in-camera imaging model for color computer vision and its application. IEEE Trans. Pattern Anal. Mach. Intell., 34(12):2289-2302, 2012. 3" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.468, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "[19] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.122, + 0.892, + 0.148 + ], + "angle": 0, + "content": "[20] Jaewon Lee and Kyong Hwan Jin. Local texture estimator for implicit representation function. In CVPR, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.151, + 0.892, + 0.191 + ], + "angle": 0, + "content": "[21] Yeon Ju Lee and Jungho Yoon. Nonlinear image upsampling method based on radial basis function interpolation. IEEE Trans. Image Process., 19(10):2682-2692, 2010. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.193, + 0.892, + 0.234 + ], + "angle": 0, + "content": "[22] Jiacheng Li, Chang Chen, Zhen Cheng, and Zhiwei Xiong. MuLUT: Cooperating multiple look-up tables for efficient image super-resolution. In ECCV, 2022. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.236, + 0.892, + 0.288 + ], + "angle": 0, + "content": "[23] Jiacheng Li, Chang Chen, Zhen Cheng, and Zhiwei Xiong. Toward DNN of LUTs: Learning efficient image restoration with multiple look-up tables. arxiv, 2303.14506, 2023. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.292, + 0.892, + 0.331 + ], + "angle": 0, + "content": "[24] Xin Li and Michael T. Orchard. New edge-directed interpolation. IEEE Trans. Image Process., 10(10):1521-1527, 2001. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.334, + 0.892, + 0.375 + ], + "angle": 0, + "content": "[25] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. SwinIR: Image restoration using swim transformer. In ICCV Workshops, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.378, + 0.892, + 0.417 + ], + "angle": 0, + "content": "[26] Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, and Radu Timofte. Flow-based kernel prior with application to blind super-resolution. In CVPR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.42, + 0.892, + 0.461 + ], + "angle": 0, + "content": "[27] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Young Mu Lee. Enhanced deep residual networks for single image super-resolution. In CVPR Workshops, 2017. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.463, + 0.892, + 0.489 + ], + "angle": 0, + "content": "[28] Ilya Loshchilov and Frank Hutter. SGDR: stochastic gradient descent with warm restarts. In *ICLR*, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.491, + 0.892, + 0.531 + ], + "angle": 0, + "content": "[29] Cheng Ma, Jingyi Zhang, Jie Zhou, and Jiwen Lu. Learning series-parallel lookup tables for efficient image superresolution. In ECCV, 2022. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.534, + 0.892, + 0.588 + ], + "angle": 0, + "content": "[30] David R. Martin, Charless C. Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, 2001. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.591, + 0.892, + 0.645 + ], + "angle": 0, + "content": "[31] Yusuke Matsui, Kota Ito, Yuji Aramaki, Azuma Fujimoto, Toru Ogawa, Toshihiko Yamasaki, and Kiyoharu Aizawa. Sketch-based manga retrieval using manga109 dataset. Multim. Tools Appl., 76(20):21811-21838, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.647, + 0.892, + 0.687 + ], + "angle": 0, + "content": "[32] Ben Mildenhall, Jonathan T. Barron, Jiawen Chen, Dillon Sharlet, Ren Ng, and Robert Carroll. Burst denoising with kernel prediction networks. In CVPR, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.689, + 0.821, + 0.703 + ], + "angle": 0, + "content": "[33] MindSpore. https://www.mindspore.cn, 2023.6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.705, + 0.892, + 0.731 + ], + "angle": 0, + "content": "[34] Don P. Mitchell and Arun N. Netravali. Reconstruction filters in computer-graphics. In SIGGRAPH, 1988. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.733, + 0.892, + 0.801 + ], + "angle": 0, + "content": "[35] Marta Mrak, Mislav Grgic, and Murat Kunt. High-Quality Visual Experience: Creation, Processing and Interactivity of High-Resolution and High-Dimensional Video Signals. Signals and Communication Technology. Springer Berlin Heidelberg, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.804, + 0.892, + 0.857 + ], + "angle": 0, + "content": "[36] Zhihong Pan, Baopu Li, Dongliang He, Mingde Yao, Wenhao Wu, Tianwei Lin, Xin Li, and Errui Ding. Towards bidirectional arbitrary image rescaling: Joint optimization and cycle idempotence. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.86, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[37] Yaniv Romano, John Isidoro, and Peyman Milanfar. RAISR: rapid and accurate image super resolution. IEEE Trans. Computational Imaging, 3(1):110-125, 2017. 2, 5" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "5874" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.468, + 0.12 + ], + "angle": 0, + "content": "[38] Thomas G. Smith and George Lucas. Industrial Light & Magic: The Art of Special Effects. Virgin, 1986. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.122, + 0.468, + 0.162 + ], + "angle": 0, + "content": "[39] Sanghyun Son and Kyoung Mu Lee. SRWarp: Generalized image super-resolution under arbitrary transformation. In CVPR, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.165, + 0.469, + 0.205 + ], + "angle": 0, + "content": "[40] Wanjie Sun and Zhenzhong Chen. Learned image downscaling for upscaling using content adaptive resampler. IEEE Trans. Image Process., 29:4027-4040, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.208, + 0.469, + 0.248 + ], + "angle": 0, + "content": "[41] Hiroyuki Takeda, Sina Farsiu, and Peyman Milanfar. Kernel regression for image processing and reconstruction. IEEE Trans. Image Process., 16(2):349-366, 2007. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.251, + 0.468, + 0.277 + ], + "angle": 0, + "content": "[42] Hossein Talebi and Peyman Milanfar. Learning to resize images for computer vision tasks. In ICCV, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.28, + 0.469, + 0.32 + ], + "angle": 0, + "content": "[43] Longguang Wang, Yingqian Wang, Zaiping Lin, Jungang Yang, Wei An, and Yulan Guo. Learning A single network for scale-arbitrary super-resolution. In ICCV, 2021. 1, 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.322, + 0.468, + 0.362 + ], + "angle": 0, + "content": "[44] Qing Wang and Rabab Kreidieh Ward. A new orientation-adaptive interpolation method. IEEE Trans. Image Process., 16(4):889-900, 2007. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.365, + 0.468, + 0.419 + ], + "angle": 0, + "content": "[45] Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600-612, 2004. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.422, + 0.468, + 0.476 + ], + "angle": 0, + "content": "[46] Bartlomiej Wronski, Ignacio Garcia-Dorado, Manfred Ernst, Damien Kelly, Michael Krainin, Chia-Kai Liang, Marc Levoy, and Peyman Milanfar. Handheld multi-frame superresolution. ACM Trans. Graph., 38(4):28:1-28:18, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.478, + 0.468, + 0.518 + ], + "angle": 0, + "content": "[47] Zeyu Xiao, Xueyang Fu, Jie Huang, Zhen Cheng, and Zhiwei Xiong. Space-time distillation for video super-resolution. In CVPR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.521, + 0.468, + 0.561 + ], + "angle": 0, + "content": "[48] Jingyu Yang, Sheng Shen, Huanjing Yue, and Kun Li. Implicit transformer network for screen content image continuous super-resolution. In NeurIPS, 2021. 1, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.564, + 0.468, + 0.604 + ], + "angle": 0, + "content": "[49] Mingde Yao, Dongliang He, Xin Li, Zhihong Pan, and Zhiwei Xiong. Bidirectional translation between uhd-hdr and hd-sdr videos. IEEE Trans. Multimedia, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.607, + 0.468, + 0.66 + ], + "angle": 0, + "content": "[50] Hui Zeng, Jianrui Cai, Lida Li, Zisheng Cao, and Lei Zhang. Learning image-adaptive 3d lookup tables for high performance photo enhancement in real-time. IEEE Trans. Pattern Anal. Mach. Intell., 44(4):2058-2073, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.663, + 0.468, + 0.717 + ], + "angle": 0, + "content": "[51] Kaibing Zhang, Xinbo Gao, Dacheng Tao, and Xuelong Li. Single image super-resolution with non-local means and steering kernel regression. IEEE Trans. Image Process., 21(11):4544-4556, 2012. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.72, + 0.468, + 0.76 + ], + "angle": 0, + "content": "[52] Lei Zhang and Xiaolin Wu. An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans. Image Process., 15(8):2226-2238, 2006. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.763, + 0.468, + 0.803 + ], + "angle": 0, + "content": "[53] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.806, + 0.468, + 0.845 + ], + "angle": 0, + "content": "[54] Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. 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Existing deep neural networks (DNNs) have made impressive progress in resampling performance. Yet these methods are still not the perfect substitute for interpolation, due to the issues of efficiency and continuous resampling. In this work, we propose a novel method of Learning Resampling Function (termed LeRF), which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption of interpolation methods. Specifically, LeRF assigns spatially-varying steerable resampling functions to input image pixels and learns to predict the hyper-parameters that determine the orientations of these resampling functions with a neural network. To achieve highly efficient inference, we adopt look-up tables (LUTs) to accelerate the inference of the learned neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures. Extensive experiments show that our method runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly, e.g., up to 3dB PSNR gain over bicubic for $\times 2$ upsampling on Manga109. + +# 1. Introduction + +Due to the rapid growth of visual data, there is a strong demand for digital image processing. Image resampling, one of the most common techniques, aims to obtain another image by generating new pixels following a geometric transformation rule from existing pixels in a given image [8]. Common transformations include upsampling (i.e., single image super-resolution), downsampling, affine transformation, etc. Image resampling enjoys various applications, ranging from photo editing, optical distortion com + +![](images/5400022abe66cf2906d4dac8f37792dbd54c428e10f6f244f74d7fcbb2a915cf.jpg) +Figure 1. LeRF assigns steerable resampling functions to input pixels, and learns to predict the hyper-parameters that determine the orientations of these continuous functions for resampling under arbitrary transformations. + +pensation [10], online content streaming [35], and visual special effects production [38]. + +Recently, deep neural networks (DNNs) have made impressive progress in the field of image resampling [9, 12, 17, 27, 39, 40], thanks to the learning-from-data paradigm that obtains powerful structural priors from large-scale datasets. Despite the superior performance that DNN-based methods have achieved, long-lived interpolation methods like bicubic [16] are still preferred choices in most cases. + +We attribute this phenomenon to the following two reasons: 1) Interpolation is simple and highly efficient, resulting in less dependency and thus the practicality to be deployed on a variety of devices, ranging from IoT devices to gaming workstations. 2) Interpolation supports arbitrary transformations. It assumes a continuous resampling function for a local area, resulting in the versatility in applying to not only homographic transformations like upsampling and downsampling, but also general warping. Although recent DNN-based methods explore beyond fixed-scale upsampling [5, 12, 27, 39, 43, 48], an efficient and continuous + +solution that matches interpolation remains less explored. + +In this work, we aim to fill this blank research area by taking a middle way between DNN-based methods and interpolation methods. We propose a novel method of Learning Resampling Function (termed LeRF), where parameterized continuous functions for resampling different structures are learned from data. Specifically, as illustrated in Fig. 1, we assign spatially-varying steerable resampling functions to image pixels, whose orientations are parameterized with several hyper-parameters. Then, we train a neural network to predict these hyper-parameters for each pixel in an input image, thus defining the resampling function for that pixel location. Finally, we obtain the output image by interpolating an input image with these locally adapted resampling functions. LeRF takes advantage of both the structural priors learned by DNN and the locally continuous assumption of interpolation methods. Furthermore, we present an efficient implementation, where the inference of the learned neural network is accelerated with look-up tables (LUTs) [15,22,23,29]. We further design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures in images. + +We examine the advantages and generalization ability of LeRF in various image resampling tasks, including arbitrary-scale upsampling, homographic transformation, and general warping. In particular, as illustrated in Fig. 2, at a similar running time, our method outperforms popular interpolation methods significantly in upsampling, which demonstrates the superiority of LeRF in terms of performance and efficiency. + +Contributions of this paper are summarized as follows: + +1) We propose LeRF, a novel method for continuous resampling. We assign spatially-varying steerable resampling functions to image pixels, where we train a neural network to predict the hyper-parameters that determine the orientations of these resampling functions. +2) We present an efficient implementation of LeRF by adopting look-up tables to accelerate the inference of the trained neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures. +3) Extensive experiments demonstrate that our method operates as efficient as interpolation, generalizes well to arbitrary transformations, and obtains significantly better performance over interpolation. + +# 2. Related Works + +Image interpolation for resampling. Interpolation, the most common solution for image resampling, assumes a locally continuous intensity surface and approximates it with a fixed resampling function, such as nearest (nearest), linear (bilinear), cubic (bicubic) [16], and windowed sinc (lanczos). It predicts resampling weights with the as + +![](images/0f687746b33e4873affbc51e5bb45701943afdc2fcbe9d795a398c45890da4ef.jpg) +Figure 2. Performance-efficiency trade-off of arbitrary-scale upsampling methods. PSNR values are obtained on Set14 for $\times 4$ upsampling. The running time is evaluated on mobile and desktop devices, respectively. + +sumed resampling function, and then aggregates input pixels with these resampling weights to obtain the target pixel. This assumption allows for continuous resampling under arbitrary transformations, yet leads to blurry results due to ignoring different local structures [34]. We follow the same assumption on local continuity, but our method deals with different structures with adapted resampling functions instead of fixed ones. + +Adaptive image resampling. To integrate local structural information into the resampling process, many methods are proposed, including edge-directed interpolation [2, 24, 44, 52] and kernel regression [21, 41, 51]. Different from these methods that rely on hand-designed rules, our method adopts a neural network to learn structural priors and integrate them into the resampling functions in a data-driven way. Another line of work achieves adaptive resampling by combining interpolation methods with adaptive filtering [11, 14, 17, 37]. Among them, RAISR achieves superresolution by predicting adaptive filters for each pixel from a hash table and then applying the predicted adaptive filters to pre-upsampled images [11, 37]. This kind of method operates on the pre-resampled results of common interpolation methods (usually bicubic) with a fixed transformation (e.g., $\times 2$ upsampling), thus lacking the generalization ability to be extended to unseen arbitrary transformations. + +DNN-based image resampling. With the rise of deep neural networks, impressive progress has been made in image resampling, such as upsampling [3,4,9,17,25,27,36,46,47, 49, 54], downsampling [40, 42], and homographic transformation [39]. For arbitrary-scale upsampling, Meta-SR [12] and LIIF [5] are two representative works. Meta-SR predicts the resampling weights from upsampling scales with a weight prediction network, similar to kernel prediction networks [26, 32], and aggregates deep features to get the final upsampled image [12]. Following works improve the process with support for homographic transformations [39] and scale-aware feature adaptation [43]. On the other hand, LIIF models the aggregation step with a neural implicit rep + +![](images/739a7144b63699f17db3e1c72812626ca6f985a4009a82b3b0dbd092dada55f4.jpg) +(a) Interpolation + +![](images/6ad6f74b164bcc6b4445606f311733dfb99fbf006eaeda65cbdb74127c30946c.jpg) +(b) LeRF (Ours) +Figure 3. Comparison between interpolation and LeRF. (a) Interpolation assumes a spatial-invariant fixed function $\Phi(\cdot)$ to predict resampling weights $W$ . (b) LeRF learns spatially-varying steerable function $\Phi_{\Theta}$ with a DNN $f(\cdot)$ . + +resentation, predicting the target pixels from deep latent features and target coordinates [5]. Following works improve the implicit representation by integrating frequency analysis [20] and modulation-based transformer [48]. Different from these works, we utilize explicit local functions, resulting in advantages in efficiency and interpretability. + +Efficient super-resolution with look-up table. A look-up table is composed of index-value pairs, which can be efficiently retrieved through memory access. It is widely applied in the image signal processing pipeline [18, 50]. Recently, SR-LUT has been introduced to accelerate the inference of a fixed-scale super-resolution network by traversing all possible low-resolution (LR) patches, pre-computing all corresponding high-resolution (HR) patches, and saving them as index-value pairs [15]. At inference, the computations in the super-resolution network are replaced with retrieving values from LUT, leading to inference acceleration. Different from existing LUT-based methods [15,22,23,29], which adopt different groups of LUTs under different upsampling scales, our LUTs store the same group of hyperparameters across arbitrary transformations, thus achieving continuous resampling at high efficiency. + +# 3. Learning Resampling Function + +# 3.1. Formulation + +Typically, resampling through interpolation can be implemented in the following steps. + +① Obtain relative offsets: the target coordinates after transformation, such as upsampling, are projected back to the coordinate space of the input image, and the relative spatial offsets between target pixels and source pixels in their support patches are obtained. +$②$ Predict resampling weights: based on the relative spatial offsets, the resampling weights, i.e., resampling kernels, are predicted for each pixel in the support patch. +③ Aggregate pixels: the source pixels are aggregated through weighted summation to obtain the target pixel + +value. The above process can be formulated as + +$$ +\hat {I} _ {q} = \sum_ {p \in \Omega} W _ {p \rightarrow q} I _ {p} \tag {1} +$$ + +where $\hat{I}_q$ is the interpolated pixel value at the target coordinate, $I_p$ the pixel value at the source coordinate, $\Omega$ the support patch, and $W_{p\rightarrow q}$ the weight from $p$ to $q$ . + +As illustrated in Fig. 3(a), an isotropic resampling function $\Phi(\cdot)$ , e.g. cubic interpolation, is assumed to predict resampling weights from relative offsets, which can be formulated as + +$$ +W _ {p \rightarrow q} = \Phi \left(d _ {p \rightarrow q}\right) \tag {2} +$$ + +where $d_{p\rightarrow q}$ is the relative offset from $p$ to $q$ , which can be obtained based on the geometric transformation $\mathcal{T}$ . Thanks to the continuity of the resampling function $\Phi$ , arbitrary transformations from $I_{in}$ to $I_{out}$ can be achieved. The assumption of local continuity contributes to the versatility and high efficiency of interpolation. But the spatially-invariant resampling function leads to blurry results due to the ignorance of local structures. + +# 3.2. The Steerable Resampling Function + +In this work, we propose a novel method of learning resampling function that adapts the resampling functions to local structures in a data-driven way. Different from the fixed resampling function in interpolation, we assume a kind of steerable resampling function $\Phi_{\Theta}$ , parameterized by $\Theta$ . Specifically, we utilize the anisotropic gaussian, + +$$ +\Phi_ {\boldsymbol {\Sigma}} (\boldsymbol {x} - \boldsymbol {\mu}) = \frac {1}{2 \pi | \boldsymbol {\Sigma} | ^ {\frac {1}{2}}} \exp \left\{- \frac {1}{2} (\boldsymbol {x} - \boldsymbol {\mu}) ^ {T} \boldsymbol {\Sigma} ^ {- 1} (\boldsymbol {x} - \boldsymbol {\mu}) \right\} \tag {3} +$$ + +where $(\pmb{x} - \pmb{\mu})$ are the relative offsets between the target and source pixels, $|\cdot|$ denotes the determinant of the covariance matrix $\pmb{\Sigma}$ . We parameterize $\pmb{\Sigma}$ as the following, + +$$ +\boldsymbol {\Sigma} = \left( \begin{array}{c c} \sigma_ {X} ^ {2} & \rho \sigma_ {X} \sigma_ {Y} \\ \rho \sigma_ {X} \sigma_ {Y} & \sigma_ {Y} ^ {2} \end{array} \right) \tag {4} +$$ + +![](images/9a251886bcfa5b106679e059b54006d9d4e53dcfafe709b64a2310fddacf151d.jpg) +(a) LUT acceleration of DNN + +![](images/b205e45c59e500cd364aceaf7e850535a109099ad6d863319bcf7f515e8cbc96.jpg) +(b) Ensemble strategy + +![](images/595ebafb9afdfe7837dda1b2d6054f44980c965018c9635de4fc0d7046a1d166.jpg) +(c) Edge-sensitive indexing patterns + +![](images/958264d104cfa9b2e391f45c1884e23db817388dbe9a9b12442a683446dbe7ee.jpg) +Figure 5. LUT acceleration of DNN and our adaptations. (a) A learned neural network can be accelerated by traversing all possible inputs $i_{*}$ , pre-computing all corresponding outputs $v_{*}$ , and saving them to LUTs [15,22,23,29]. (b) In the proposed directional ensemble strategy, only predictions along the same direction are averaged, instead of all directions in rotation ensemble [15]. (c) We introduce edge-sensitive indexing patterns to better capture edge orientations. The pixels covered by directional ensemble are depicted with dashed boxes. +Figure 4. Visualization of parameterized resampling functions. + +where $\rho, \sigma_{X}$ , and $\sigma_{Y}$ are hyper-parameters, and thus the resampling function becomes $\Phi_{(\rho, \sigma_{X}, \sigma_{Y})}$ . From the perspective of statistics, $\rho$ can be interpreted as the correlation between 2D variables and $\sigma_{X}, \sigma_{Y}$ the standard deviations. + +As illustrated in Fig. 4, our resampling function is steerable, where different orientations and shapes can be obtained by tuning hyper-parameters $(\rho, \sigma_{X}, \sigma_{Y})$ , showing its modeling capacity for a variety of local structures. Other non-steerable functions, such as Keys cubic [16] are also applicable as resampling functions. Details of the comparison are provided in Table 3 and Fig. 10. + +But, direct optimization of the above hyper-parameters leads to unstable gradients and divergence. Thus, we modify the formulation by omitting the determinant multiplier and predicting a group of $(\rho, \frac{1}{\sigma_X}, \frac{1}{\sigma_Y})$ for stable training. In the following section, we introduce the learning process of these hyper-parameters and our efficient implementation. + +# 3.3. Learning with DNN and LUT Acceleration + +As shown in Fig. 3(b), we adopt a deep neural network to learn structural priors from an external dataset for predicting hyper-parameters $\Theta$ in resampling functions. The training loss function can be formulated as + +$$ +\mathcal {L} _ {q} = \left\| I _ {q} - \sum_ {p \in \Omega} \Phi_ {\Theta} \left(d _ {p \rightarrow q}\right) I _ {p} \right\| ^ {2}, \quad \Theta = f (\mathcal {N} _ {p}), \tag {5} +$$ + +where $I_q$ is the ground truth pixel value at location $q$ , $f(\cdot)$ is a trainable DNN, and $\mathcal{N}_p$ denotes the surrounding pixels of $I_p$ (not necessarily the same as the support patch $\Omega$ ). + +Furthermore, to match the efficiency of interpolation, we accelerate the inference of the trained DNN by adopting look-up tables [15, 22, 23, 29]. As shown in Fig. 5(a), for each pair in the LUT to accelerate DNN, its index $i_{*}$ is a combination of pixels (i.e., $\mathcal{N}_p$ ), and its value $v_{*}$ is a group of corresponding hyper-parameters for that pixel combination (i.e., $\Theta$ ). This way, hyper-parameters can be retrieved directly from the saved values in LUTs, skipping computations in DNN and thus resulting in high efficiency. + +Different from existing LUT-based methods, whose LUT values are image pixels, our LUTs store hyper-parameters that reflect structural characteristics. Thus, to better extract structural priors, we propose the following adaptations. + +Directional ensemble strategy. We propose a directional ensemble (DE) strategy to replace the rotation ensemble (RE) strategy in existing LUT-based methods [15, 22, 23]. As illustrated in Fig. 5(b), in RE, the predictions are averaged across all directions, while the proposed DE strategy only ensembles the predictions with the same direction (i.e., $180^{\circ}$ rotational symmetry, instead of $90^{\circ}$ in RE). This enables the learning of $\rho$ , which determines the orientation of the steerable resampling function (see Table 5 and Fig. 11). + +Edge-sensitive indexing patterns. As illustrated in Fig. 5(c), we include patterns "C" and "X", alongside the default "S" pattern in SR-LUT to better capture edges of different orientations. For example, The "C" and "C" patterns are sensitive to vertical and horizontal edges, respectively. We validate their effectiveness in Table 5. Correspondingly, as shown in Fig. 5(a), our DNN follows a multibranch design, and each branch is accelerated by a LUT. + +Pre-filtering stage. Inspired by DNN-based resampling methods [12, 43, 48], we pre-filter the image with a neural network, which can also be accelerated by LUTs with the proposed edge-sensitive indexing patterns. This prefiltering stage helps enhance image content and contributes to better performance (see Table 5 and Fig. 11). + +
MethodSet5Set14BSDS100Urban100Manga109
×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4
Nearest31.3431.0730.8429.6329.1528.8428.5727.7028.9928.7228.4027.6226.2125.9125.6224.7828.5928.3628.1426.87
Bilinear34.9933.1932.2331.4931.6830.2629.2428.7030.9229.6628.6728.2028.2426.9125.9625.4632.4530.3329.1628.28
Bicubic36.7634.6833.6432.7033.0731.4530.3229.6232.1430.6729.5428.9329.5027.9526.8726.2234.7632.1330.8129.61
Lanczos236.8334.7433.7032.7433.1331.5030.3629.6532.1930.7129.5828.9529.5528.0026.9126.2534.8732.2230.8929.66
Lanczos337.6135.3134.2333.2433.7531.9730.7630.0232.7431.1129.8929.2330.1228.4227.2526.5536.1233.0631.6330.28
RAISR* [37]35.5035.4935.5733.3831.8431.6731.7130.2230.8730.6830.6629.4228.7728.6028.6427.0133.8133.7433.8830.61
SR-LUT* [15]37.7435.5234.4733.4533.5331.7930.5929.8633.0631.4030.1529.4530.2428.5527.3926.6935.3132.3030.9629.58
LeRF (Ours)38.3036.6035.7134.7434.5933.0631.9831.1033.7632.0830.8330.0931.8630.0828.8627.9936.5734.7933.8832.67
Meta-SR [12]41.29-38.12-37.47-33.99-35.79-32.32-35.85-32.98-42.92-39.22-
LIIF [5]41.2238.9938.0836.9937.4435.3133.9632.9535.7533.6832.2831.4536.7034.0832.8431.7042.7740.1939.1337.69
MethodSet5Set14BSDS100Urban100Manga109
×2.0 ×3.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0
Nearest28.8727.9126.8826.2527.0726.0825.3324.7427.1226.1725.5725.0324.2523.3422.6822.1726.1225.0424.0623.43
Bilinear30.4329.5328.2727.5527.9427.0426.1625.5127.6026.7726.1125.5324.8123.9923.2622.6827.2126.1624.9524.19
Bicubic31.4130.3929.1228.4228.7027.6326.7526.0928.1827.2026.5325.9525.4324.4523.7123.1428.2026.9525.6724.90
Lanczos231.4430.4129.1428.4428.7227.6426.7726.1028.2027.2126.5425.9625.4524.4723.7323.1528.2326.9725.7024.92
Lanczos331.8530.7929.4928.7829.0427.9127.0126.3128.4327.3926.7026.1025.7124.6823.9223.3228.7027.3826.0225.21
RAISR* [37]32.3531.8729.9129.6529.0428.6227.1026.8628.2427.8426.6826.4225.9225.5024.0823.8929.3028.7326.4026.12
SR-LUT* [15]32.0431.0029.7029.0028.8827.8426.9526.3028.6227.5426.8426.2125.8324.7824.0023.3928.0326.7425.3924.72
LeRF (Ours)33.1732.0230.8630.1530.0628.8428.0527.3529.1528.0027.3126.7026.9025.6824.8824.2330.8629.4828.1027.25
Meta-SR [12]-34.71-32.48-30.56-28.83-29.26-27.73-28.91-26.69-34.37-31.32
LIIF [5]35.6334.5933.1732.3731.6830.3929.4528.6530.4729.2428.4227.7330.2328.8027.5526.6635.7334.1732.2931.19
+ +# 4. Experiments and Results + +# 4.1. Experimental Settings + +Datasets and metrics. We train LeRF on the DIV2K dataset [1], which is widely used in image resampling tasks. We train LeRF on the $\times 4$ downsampled data pairs, and apply the obtained model to resampling for arbitrary transformations. For arbitrary-scale upsampling, we evaluate LeRF with 5 benchmark datasets: Set5, Set14, BSDS100 [30], Urban100 [13], and Manga109 [31]. We select representative symmetric or asymmetric upsampling scales for evaluation and apply bicubic interpolation as the degradation model to obtain the LR images. For performance evaluation, we report PSNR and SSIM [45] for fidelity, and LPIPS [53] for perceptual quality. For efficiency evaluation, we report running time, theoretical multiply-accumulate operations (MACs), and storage requirements to evaluate the performance-efficiency trade-off. + +Comparison methods. The main competitors to the proposed method are interpolation methods, including nearest neighbor, bilinear, bicubic, lanczos2, and lanczos3. Furthermore, we combine fixed-scale super-resolution methods with bicubic interpolation as additional baselines for arbitrary-scale upsampling (RAISR* and SR-LUT*). + +Table 1. Quantitative comparison in PSNR for arbitrary-scale upsampling. $\frac{\times r_h}{\times r_w}$ denotes upsampling $r_h$ times along the short side and $r_w$ times along the long side. SSIM and LPIPS results are in the supplementary document. * denotes that we combine fixed-scale super-resolution methods with bicubic to achieve arbitrary-scale upsampling. The best and second best results are highlighted and underlined. + +
MethodRunTime (ms)MACsStorage SizePSNR/SSIM (dB)/-
Nearest11--24.74/0.6553
Bilinear3114.74M-25.51/0.6824
Bicubic12651.61M-26.09/0.7056
Lanczos2494110.59M-26.10/0.7058
Lanczos3914165.89M-26.31/0.7130
RAISR* [37]3,744-3.19MB26.86/0.7357
SR-LUT* [15]13753.33M81.56KB26.30/0.7256
LeRF (Ours)11057.94M1.67MB27.35/0.7475
MetaSR [12]10,2601.68T85.59MB28.83/0.7876
LIIF [5]67,0802.54T255.76MB28.65/0.7878
+ +Table 2. Efficiency comparison of running time, MACs, storage requirements, and performance (Set14) for producing a $1280 \times 720$ HD image through $4 \times$ upsampling. For interpolation methods, SR-LUT*, and LeRF, the running time is evaluated on CPU of a mobile phone and averaged across 10 trials, while for RAISR* and DNNs, that is evaluated on CPU of a desktop computer. + +Specifically, since RAISR [37] lacks generalization ability across different upsampling scales, we fuse the results of $\times 2$ , $\times 3$ , and $\times 4$ models by choosing the model with the closest integer upsampling scales (e.g., $\times 3$ model for $\frac{\times 2.0}{\times 3.0}$ ). We retrain SR-LUT [15] on the mixed-scale data of $\times 2$ , $\times 3$ , and $\times 4$ downsampled data pairs jointly with bicubic interpolation to achieve arbitrary-scale upsampling. Finally, we + +![](images/fbb0cca2ddcf8dacae44cd84066c190a3ee59c182bf6dc77d530e294a93fa789.jpg) + +![](images/bac27e9cea9804a3004e79870601ce321fcb5a7cdf2c651924753a750db492cc.jpg) +$\frac{\times 1.5}{\times 2.0}$ upsampling + +![](images/847a1d81c14673e7bd5a22c60ac229e2697a365e25a992340d2f4beace4472a5.jpg) +$\times 2.0$ $\times 2.4$ +$\frac{\times 3.0}{\times 3.0}$ upsampling $\frac{\times 4.0}{\times 4.0}$ upsampling + +![](images/7333c374180ff596db917805fefe371ede68f2d3f796b059387d762432cf67d3.jpg) + +![](images/b7420831d29f555dcd34789f8032688c6e8d3f76c86d3bc980e2f7c26c7895d2.jpg) + +![](images/bf9d74a1f498b67f09fba5872a200ea93addcc3c969b7d9ab2ba526d1c4cae78.jpg) + +![](images/521c0d5bddaf53e1530e7ff3cf2780c1ce65c095569f5a5fc80ccf3a79f297d3.jpg) + +![](images/4a3ecfeb6fedcf537c84c5364fcd089a7a73ed6540c085d2d0b74039f3d70537.jpg) + +![](images/dcd2a896bd60c2437cee0c877fd5b76dcc78e13353561894bc689fe545a845b8.jpg) + +![](images/15c976390ced659037a7f017a8fc08dacc87f0031fd55d805b484370a02f6040.jpg) + +![](images/12829ce90481ac3af031723dd1d95c57b7ce62fbb1d14ef5cde5a37a78e6424b.jpg) +GT (PSNR/SSIM) + +![](images/d3d4c98605f47cfdfaa9f20bf9f91a5ca382009b949c46308b475f44e0e5073c.jpg) +Bilinear (23.46/0.8875) + +![](images/e3d110872ba52b704b5b16d2ee6a30fdbbd8721318ddf2a1648ea789a8c7ef8d.jpg) +Bicubic (24.88/0.9174) + +![](images/eee0aa067990bc0962dd95f6f4eb82bb3095a349a8950c8888b17eb157d127b4.jpg) +Lanczos3 (25.63/0.9275) + +![](images/2f31818cf5ca815131c0ac7e1dbbf398a63bd34568e0ca0aba30e84c2457e69e.jpg) +RAISR\* (26.62/0.9362) + +![](images/3b40d174a9d0ba0ad4729c19a53c7a4140bdec1b451746d32cdc17047fc5d447.jpg) +SR-LUT\* (25.51/0.9305) + +![](images/a77b07e9b32ba9c34fe651ef87e0659ae8c29f062ec2c6424eee67c9f5c5f5b4.jpg) +LeRF (Ours) (28.36/0.9577) + +![](images/5768528ba2a993209dde411abd89f623447d1e230153acf747b72da8fbdb6a5b.jpg) +GT (PSNR/SSIM) + +![](images/47aa598f5d10910653ab906c5303122a719c9aac53b519d0f66bc0232ecd5950.jpg) +Bilinear (29.83/0.9553) + +![](images/3f292e586c14a9a08bc2fd76f91577f667856f89a67ecf3ab97a82caaab0ca1a.jpg) +Bicubic .29/0.9641) + +![](images/45daf9efea4f30af223f268140d75d4e04759e199608970b7dea27d4cdd300cb.jpg) +Lanczos3 (32.06/0.9666) + +![](images/102218e997307ef4eba1ccf244ef1c6db0edae9ad9a1638a32f12141f28d64c2.jpg) +RAISR\* (32.34/0.9681) + +![](images/ac0897a937cb54539f2fc9eefab19165b4523de9237a5f55cdcd1034fd59c7fa.jpg) +SR-LUT\* (31.98/0.9664) + +![](images/f7abf73ba09258bc94bd0c8d3a68db3695a3fc9ca97d5c6ee7275f9aee7e4ef5.jpg) +LeRF (Ours) (35.28/0.9787) + +![](images/5a3288e7b8ac4e9cb3f9f54224664b3ec8d05826e572237e9bea1a89b32ba969.jpg) + +![](images/1caffee726ba8edbf3c22bfc7bfd5d96191cb008d8ffdac1ed4dbfb912e5eb84.jpg) +GT (PSNR/SSIM) +GT (PSNR/SSIM) +Figure 6. Qualitative comparison for arbitrary-scale upsampling. From top to bottom, the example images are: UltraEleven (Manga109), MukoukizuNoChonbo (Manga109), 126007 (BSDS100), and img092 (Ubran100). Best view in color and on screen. + +![](images/68552918029e4a1671f2941f1b64a461c959bd3473a6b64980d5312467119506.jpg) +Bilinear (28.14/0.8051) +Bilinear (16.39/0.4217) + +![](images/506335ae24d05e4676cab68f29dbec211b29f83a2c90edbc406aabe5a1dee5b2.jpg) +Bicubic (28.57/0.8191) +Bicubic (16.58/0.4402) + +![](images/f132b203b81bbac2be5efc062854a9b3e1bb254471aa330ef5928adbed1eae9a.jpg) +Lanczos3 (28.76/0.8244) +Lanczos3 (16.63/0.4429) + +![](images/6fbd1845cd5f75dbecdd9c12e63629af8113324dcefa6326eefc5cd1d7308d51.jpg) +RAISR\* (29.14/0.8396) +RAISR* (16.97/0.4896) + +![](images/753a69bf1dcd346a7a5542011d8052886d910a9e81659c6c2ce63512a5e37338.jpg) +SR-LUT\* (28.98/0.8355) +SR-LUT\* (16.76/0.4619) + +![](images/5ec66c53217a0a818c4bee75ca3900ad72e3b572c15c228bc034610e6f9b329d.jpg) +LeRF (Ours) (29.54/0.8440) +LeRF (Ours) (17.30/0.5175) + +include DNN-based arbitrary-scale super-resolution methods (Meta-SR [12] and LIIF [5]) for reference. + +Implementation details. Our method is trained with the Adam optimizer [19] in the cosine annealing schedule [28]. We train LeRF with the MSE loss function for $5 \times 10^{4}$ iterations at a batch size of 32. Except for the indexing patterns, each branch in our DNN keeps the same architecture as the network in SR-LUT. To obtain a fair comparison in running time tests, we implement LeRF and interpolation methods on the Android platform under the same parallel API, with the only difference being the resampling functions and LUT retrieval in our method. Our method is implemented in MindSpore [33] and the code is available at https://gitee.com/mindspore/models/tree/master/research/cv/lerf. + +# 4.2. Performance Evaluation + +Quantitative comparison. We list the quantitative comparisons with other methods in Table 1. As can be seen, our method achieves significantly better performance than interpolation methods. For example, it exceeds bicubic interpolation up to 3dB PSNR when upsampling $\frac{\times 2.0}{\times 2.0}$ on the Manga109 dataset. + +Qualitative comparison. In Fig. 6, we compare the visual quality of LeRF with interpolation methods, RAISR*, and SR-LUT*. As can be seen, our method obtains better visual quality across various local structures and textures. Specifically, since the resampling functions are adapted to local structures, LeRF is capable of retaining clearer textures and avoiding blurry boundaries. + +Visualization of learned resampling functions. We further visualize the intermediate results of the proposed method in Fig. 7. As can be seen, the hyper-parameter $\rho$ clearly distinguishes the orientations of edges (red vs. blue), and $\sigma_{X}$ and $\sigma_{Y}$ capture the horizontal and vertical lines, respectively. In addition, in the second row of each example, we visualize the resampling functions defined by the predicted hyper-parameters. The shapes of resampling functions are well adapted to corners, flat surfaces, and edges with various orientations. This validates the effectiveness of extracting structural priors in a data-driven way. + +# 4.3. Efficiency Evaluation + +To evaluate the efficiency, we conduct running time tests, estimate the theoretical MACs, and report storage requirements for LeRF and comparison methods. We list the de + +![](images/25d0ea535b8a4b274814b0d991799b120d79acf0294f4e5cc5b35d9b3295a3ac.jpg) +Input + +![](images/f205677316aad2f457657dc7555cf7f2bfbdfc4e1cb6173740d0399a2fb3083d.jpg) +P + +![](images/dc8cde45ecc5beb589cfd950ba89c394e24541b9648408ae43a2ab6e89ddb87d.jpg) +1/σx + +![](images/c6adc01ba1066d31045d106fc626e2550a9abce15952092883ccbab491304fd7.jpg) +1/σY + +![](images/d9944ab0b96fbbcc8482565d854c0732ca97a3d965c59d5d97a19d254f6246b7.jpg) + +![](images/98017ebe726af1c326bd1fc5f9f0916aa0d1c99ce79786defeaaa0da2398a0d5.jpg) + +![](images/2d5af6d83456f6e5d657be3906fb08fa9aa2f6aefc529a8764f419ffdf2ca88a.jpg) + +![](images/8fad0b8b4779677d7f2780884fecf575c7b5daa5112e037c80f767aab63adf45.jpg) + +![](images/7e8ff22d641486427709dc45a2937b3349cd61be8c8f0f8137234d1c02ebadbb.jpg) + +![](images/a9908942c66178dbe0406a9c2f72ff67a08ddcbb5863be8a86c1335a300b4ffb.jpg) + +![](images/7f7e7979155b6475598db9427e5a0aab99982ac5264732bae66c1e4b0929c58e.jpg) +P + +![](images/f82ba91eb3022b9a971d59078688151ecf18c4e73e99f2f5311b2153fc964db6.jpg) +1/σx + +![](images/ee46b67a89c2936786208518b38e851f8327d260261cd1c2bddf879e2d922773.jpg) +$1 / \sigma_{\mathrm{V}}$ + +![](images/844a49504f0a75addc6ef152269032383cacc760b7c2f35b12a6bcbd5be2f548.jpg) +Input +Figure 7. Visualization of the pixel-wise hyper-parameters and the corresponding resampling functions. The shapes of the predicted resampling functions are well adapted to local structures. + +![](images/6c3b0a1de921a506b23cff8fa5e9e340e1f533823ad51a1b604ed677bb6b7574.jpg) + +![](images/f15f54019ad9585a9331d219543a633cc751af2dbb1dd8809fd641df34fd5fed.jpg) + +![](images/63836fb9e59b60e2a17ded7db047029ee03e8a6b7b1658e0a17c8d6db5b2f807.jpg) + +![](images/9ac47137cd2f72ce898a71f7b891eb2ce1486090d3d14d58d33e66ffaccd9eba.jpg) + +tailed comparison in Table 2. As can be seen, the proposed LeRF achieves comparable or better running time compared to the popular interpolation methods, showing its role as a superior competitor with significantly better performance. On the other hand, compared to DNN-based methods, LeRF shows a clear advantage in efficiency. Furthermore, our method requires far less extra storage as well as fewer software dependencies, demonstrating its practicality for being deployed on various devices. + +# 4.4. Generalization Evaluation + +In Fig. 8, we evaluate the generalization ability of LeRF to arbitrary transformations and compare it with widely applied interpolation methods. As can be seen, the learned resampling functions generalize well to unseen deformations. LeRF generates sharper edges and retains more texture details, leading to more visually pleasing results than popular interpolation methods. + +# 4.5. Ablation Analysis + +Analysis of GT-optimized resampling functions. To analyze the optimal shapes of resampling functions across different transformations, we obtain hyper-parameters in resampling functions via per-image optimization. In Fig. 9, we visualize the clustering results on Set14 of these GT-optimized resampling functions across different upsampling scales. As can be seen, the GT-optimized cluster centroids are very similar across different upsampling scales, explaining the generalization ability of our method. + +![](images/b6135982d31276aa8bfffad76013a2ed199daf64adcd6b55af69c58c8243a5ff.jpg) +# + +![](images/493ad016f78b4a5cac2ae0bfea7c38735730f2cbc7689dc8c3a6aef42abed73d.jpg) + +![](images/76d751c0151994875ffe5039d3ad5921b5b145e6c7aa6430901fea03d7542f37.jpg) + +![](images/719d1f62c5232159aeb55d68cb26e26bb20e4a2ceccc4519d02c9b4dca5e6fa8.jpg) + +![](images/bf5647bceef1a92735d9dd3674bcebac1100b2e182964cc437ba7da6312f2fcd.jpg) + +![](images/914868f3fe1f64b8e8d0952887d58dfd24aea04f884a0c61d838c77ddc3b0c6f.jpg) + +![](images/a22e920c59f69a5accc57cbf06c0807fdeb8226c9b42859d39f5c419969f66bd.jpg) + +![](images/f8f6bc07981ad7741b39fd083c20298098ca5d143009098bf1f31c9716eca4da.jpg) + +![](images/dc53b5e0c7e6f1546eb3488027e2b480ab81d3eb9873a628246ab1045680f5a7.jpg) + +![](images/5d123b9c435774fe5b172fc5bc1abdb722660679987bb4626ad8240d912e34b9.jpg) + +![](images/9dd00d2c930bb2d609595c59af10ac92287b43af6fbc7ffe66c71b3a29b693b9.jpg) + +![](images/eabe7aadc5bca6e173479c5a811c8a95a6a43e82d8686eb00b3fc5579e279834.jpg) + +![](images/48a851054ca4b4304661f010653cf28f5182f3b350c8ecc2941788017c2b3af4.jpg) + +![](images/3d07cabe1a024a4fe4211b5a33d8d310aaa9f32ee342eb93f0ec5efcd3f3a973.jpg) + +![](images/873b824a4f7a05b44d29f060b102bef45ddf85e17bcb2da1467ec7614799fac9.jpg) + +![](images/41da28d59b9d81b70f3a368dd324801bf2671a84d32d192b397aaf3e29143b17.jpg) + +![](images/a448d1bb02a621da90478b91e3a791d67af42a2f553ae120e547920de16f1af8.jpg) + +![](images/dee7629f531c6fe8c87e54ae0de4c5bd3f94848c9f935cfabe8db88964eb1838.jpg) + +![](images/83c2e440523437840e4843d57cb1360e21a6cc952c260cca607702bb56c2dbbc.jpg) + +![](images/4fcabf24581237b71a99a1d3d11a2b0ab84d6c9bfd3bd43b7ce9722732fee69c.jpg) + +![](images/38a701899a0aaa1e1138365a6128067c8b89a416d676a4352970754465e66bfa.jpg) +Input + +![](images/eb360b6884db40fa0db1ed83ceacaaa1dfc05f3edd86da8836e61a50e9ce4d23.jpg) +Transformation + +![](images/113b71b01eaf2c5ce987f09f80d3397d0debbfe1ed3391568c4bafa95f3102cd.jpg) +Bilinear + +![](images/64716cf768f0f3a46cfe9bacb6b7093204b9e5d9b5c7eb91e34df99bc77bda0b.jpg) +Bicubic + +![](images/ee8aa99873f108bcc4cbfb60216dde6b23af1941caafb6f4693c814f88144e33.jpg) +Ours + +![](images/7a8dd1ebdbaf582a88dccc3e9dc6133b571524b4cd283a2c9ff6a572c503016b.jpg) +Figure 8. Visual comparison of LeRF with interpolation methods under general homographic transformations (downsampling, rotation, and sheering) and arbitrary warping (according to a barrel-shaped distortion and optical flow). + +![](images/557b80883555f163bad46a5ff3b8f9bbd99fd9fd4e227c3839f19e5280bd95a5.jpg) + +![](images/273e6ff9bf7a131874c309763b24ddb1e1fa52c0850211ede43cdc982f71188b.jpg) + +![](images/81a69e6e31ba8988cc488bb043e86df8cf84eaec096c0b329f0c49fac510f557.jpg) + +![](images/d251d0723ae96a0d6389dafde9a57cfc87cbe9bf1d0821b79ad8be3d86dcfbe9.jpg) + +![](images/11ff05c979e8e57023c8e3653abdc48c88b84a16b0b394bc487d6e9bae83f29b.jpg) + +![](images/18dcfcb4091f34bbaf48770f9e47172bfee3dfd0b2af264c5877b5d03001d8a2.jpg) + +![](images/688e6fd3293b9449bf8500a7f7d48734c04993a5d21099455b44bce1645f94f8.jpg) + +![](images/14f9fce41a5f0f5de6e3c98f151cdb1c88ffd19b1dfcbb2788d14bab78d03d5a.jpg) + +![](images/b2801d6c32c3933a85fb416f560451063fa36446c6b0132a40b6e488f23f0349.jpg) + +![](images/4ef274c74beec19d1bdbf3633c6a61c0d02077aa1764bf47a8e0708437684c3c.jpg) + +![](images/4a67af9d6f29a72969aa13c23951c15e4882c18b98a802e5d8321b74daedd971.jpg) + +![](images/eb8a62d6e81d52c153cf9363f51853bbb3e35e5bc9836be2923779e99b881780.jpg) +Figure 9. Visualization of GT-optimized resampling function clustering centroids on Set14 for $\times 2$ , $\times 3$ , and $\times 4$ upsampling, respectively. They show consistency across different upsampling scales. + +![](images/e1353e4ca09e76c82198061d338e8b7ef6a2338ff70b381b3f55972f0301e75c.jpg) + +![](images/dafb3805b80a4dc32d974c0944b7303b52dc2ae0503707cb8a6db514144b6869.jpg) + +![](images/3def002723fd43864d802e021e244cb9932027241364f6e5695b43fd81bd36ee.jpg) + +![](images/8c580592dc9d445aece62540c10f04ad550a9363551dd09991e724987788d6b3.jpg) + +![](images/e42cdb7fc4f385c634382cef6103c1e7fdae8756a824ab6e62654c751e9b2df3.jpg) + +The effectiveness of the steerable resampling function. We conduct the following ablation experiments on the Set5 benchmark to analyze the design of the steerable resampling function. 1) Fixed gaussian: We freeze the hyperparameters in the anisotropic gaussian. 2) Learned Keys cubic: We utilize another non-steerable resampling function, the isotropic Keys cubic function [16], which has only one hyper-parameter that controls the sharpness of the one-dimension piecewise cubic. The experimental results are listed in Table 3, and visual results are shown in Fig. 10. As can be seen, non-steerable resampling functions like Keys cubic produce zig-zag artifacts and inferior performance, showing the effectiveness of our learned steerable resampling functions. + +Analysis of training data. By default, LeRF is trained with $\times 4$ downsampled data pairs. We retrain our method on $\times 2$ data pairs and mixed data pairs with both symmetric and + +
MACsStorage×2×3×4
Ours57.94M1.67MB35.7132.0230.15
fixed gaussian Φ(0,1,1)45.04M244.69KB30.7527.7026.31
learned Keys cubic126.94M489.38KB34.6831.1729.08
+ +![](images/fe07a24bd1d1d1a90a2254bb413664d10845963498029349e94a79aabf1db23d.jpg) +Figure 10. The non-steerable Keys cubic function produces artifacts along diagonal edges. + +![](images/546df072c7988b673fddf5e972254588cd926e310df0ada09385c4121f03fc4f.jpg) + +![](images/1d33e7d3bb57e58c3786f9bff0ba3bdf0483e388bd534c1a3dd1547b0275506d.jpg) + +![](images/c866bc4844594cea9128c9eac77a2f7afe3d52c4bdcd5dd4721042bab8d8e6d7.jpg) + +Table 3. Ablation experiments on Set5 on the the design of the steerable resampling function. + +
MACsStorage×2×3×4
Ours (×4 data)57.94M1.67MB35.7132.0230.15
×2 data training57.94M1.67MB35.6732.1129.91
mixed-scale training57.94M1.67MB35.6732.2630.06
+ +asymmetric upsampling scales. As listed in Table 4, training on mixed data pairs yields similar performance, showing the robustness and generalization ability of our method, which is consistent with the clustering results in Fig. 9. + +The effectiveness of the LUT acceleration and the proposed adaptations. As listed in Table 5, we conduct the following ablation experiments to analyze the design of the LUT acceleration and our adaptations. 1) Without LUT acceleration: Compared with the learned network, the LUT acceleration saves a lot of computations at a very small performance cost. 2) Without DE: The visualization of the learned hyper-parameter $\rho$ in Fig. 11 shows that RE makes an isotropic assumption and thus lacks the ability to learn edge orientations, resulting in a performance drop. 3) Without edge-sensitive patterns: LeRF with "SCX" indexing patterns outperforms the variant with only the default "S" pattern, showing the ability of edge-sensitive indexing patterns to better capture edge structures. 4) Without prefiltering or pre-filtering only: As shown in Fig. 11 and Table 4, the pre-filtering stage enhances edges and contributes to better performance, and pre-filtering only is suboptimal due to the lack of adaptivity of resampling functions. + +# 4.6. Limitation + +In most cases, our method produces better results compared with interpolation. But, it may fail under certain circumstances. In Fig. 12, we show its limitation in down-sampling, where aliasing artifacts appear in regions with + +Table 4. Ablation experiments on Set5 on data pairs for training. + +
MACsStorage×2×3×4
Ours57.94M1.67MB35.7132.0230.15
w/o LUT acceleration21.75G1.59MB36.1132.2330.28
w/o DE57.94M978.76KB34.4431.5029.47
w/o “CX” pattern49.65M326.25KB34.9031.7929.70
w/o pre-filtering53.45M1.43MB34.4331.4129.33
pre-filtering only56.10M244.69KB34.6931.1629.15
+ +Table 5. Ablation experiments on Set5 on the LUT acceleration and our adaptations. + +![](images/56664dfaea8f834a5d7167ffdd54893035ca8ef713a2634578f97ff9007ea22a.jpg) +Input + +![](images/66c7abd973aebfbb76c96ce2b42b62574946d5a92b988b156ab1287ea408c7c9.jpg) +RE $\rho$ + +![](images/a8dacb2d12919453e3b5d20079476ed9dbafe25596ff21328d1780a2d8e220ac.jpg) +DE $\rho$ + +![](images/0c90ab218f09fb9d0cd08b67600a67b4b2ee4679dbe55aa92286b9578950284c.jpg) +DE Output + +![](images/2764194bb7f0a06c9289c68c61c0ac17f9f15f8be0b971e5342bdc0eb0af347f.jpg) +Input + +![](images/2900e459c23302ab321901fbe3c285a4fbeeefe48e28be57d1c75545723a1a44.jpg) +Pre-filtered + +![](images/1b27b2086df73880f0499edbc4d5b79b481c359022312e9bcd1acccd6e44eb10.jpg) +Input + +![](images/43f3fb468d40bd05d54adc7fff6ccf2369e07416d3517e7f8c971528944a3339.jpg) +Pre-filtered + +![](images/c0f00a75a738abd987ff410563934b02a24a7ace6df7df0357647b6f021066d8.jpg) +Figure 11. Upper: Replacing RE with DE enables the learning of edge orientations. Lower: The pre-filtering stage enhances edges. +HR +Figure 12. Limitation. LeRF produces aliasing artifacts for image downsampling in regions with highly dense textures. + +![](images/f6837b0ef69dae3b95e1a10bc562588b1d20e7b0581deed576cb8048afc5b4a2.jpg) +Bilinear + +![](images/1a23f327d492a42139ea8a6e07a1f697ffd7d7cb3409aa736233b18920bd32de.jpg) +Bicubic + +![](images/00bff936ff00952bc1c9afae37e5e9960650c3567d155a37d176b1f4e8e33e03.jpg) +Lanczos3 + +![](images/57940cd2c684f594fb665f23fcfc87ccbb90f120af7fd64a3b5b45faad4a47c4.jpg) +Ours + +highly dense textures. It is probably caused by a lack of training on downsampled data. Moreover, replacing the pre-filtering stage with anti-aliasing filters may mitigate this problem [6, 7]. + +# 5. Conclusion Remarks + +In this work, we propose LeRF, a novel method for continuous resampling by integrating learned structural priors into the steerable resampling function. We show its superior performance, high efficiency, and versatility for arbitrary transformations. In the future, we plan to extend this idea to other modalities, e.g., video and point clouds. + +# Acknowledgement + +This work was supported in part by the National Natural Science Foundation of China under Grants 62131003 and 62021001. We acknowledge the support of MindSpore. + +# References + +[1] Eirikur Agustsson and Radu Timofte. NTIRE 2017 challenge on single image super-resolution: Dataset and study. In CVPR Workshops, 2017. 5 +[2] Jan P. Allebach and Ping Wah Wong. Edge-directed interpolation. In ICIP, 1996. 2 +[3] Chang Chen, Xinmei Tian, Feng Wu, and Zhiwei Xiong. UDNet: Up-down network for compact and efficient feature representation in image super-resolution. In ICCV Workshops, 2017. 2 +[4] Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha, and Feng Wu. Camera lens super-resolution. In CVPR, 2019. 2 +[5] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In CVPR, 2021. 1, 2, 3, 5, 6 +[6] Robert L. Cook. Stochastic sampling in computer graphics. ACM Trans. Graph., 5(1):51-72, 1986. 8 +[7] Franklin C. Crow. The aliasing problem in computer-generated shaded images. Commun. ACM, 20(11):799-805, 1977. 8 +[8] Neil Anthony Dodgson. Image resampling. Technical report, University of Cambridge, Computer Laboratory, 1992. 1 +[9] Chao Dong, Chen Change Loy, Kaiming He, and Xiaou Tang. Learning a deep convolutional network for image super-resolution. In ECCV, 2014. 1, 2 +[10] Irvine C. Gardner and Francis E. Washer. Lenses of extremely wide angle for airplane mapping. J. Opt. Soc. Am., 38(5):421-431, 1948. 1 +[11] Pascal Getreuer, Ignacio Garcia-Dorado, John Isidoro, Sungjoon Choi, Frank Ong, and Peyman Milanfar. BLADE: filter learning for general purpose computational photography. In ICCP, 2018. 2 +[12] Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Tieniu Tan, and Jian Sun. Meta-SR: A magnification-arbitrary network for super-resolution. In CVPR, 2019. 1, 2, 4, 5, 6 +[13] Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja. Single image super-resolution from transformed self-exemplars. In CVPR, 2015. 5 +[14] Xu Jia, Hong Chang, and Tinne Tuytelaars. Super-resolution with deep adaptive image resampling. arxiv, 1712.06463, 2017.2 +[15] Younghyun Jo and Seon Joo Kim. Practical single-image super-resolution using look-up table. In CVPR, 2021. 2, 3, 4, 5 +[16] Robert G. Keys. Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust., 29:1153-1160, 1981. 1, 2, 4, 7 +[17] Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. Accurate image super-resolution using very deep convolutional networks. In CVPR, 2016. 1, 2 +[18] Seon Joo Kim, Hai Ting Lin, Zheng Lu, Sabine Süssstrunk, Stephen Lin, and Michael S. Brown. A new in-camera imaging model for color computer vision and its application. IEEE Trans. Pattern Anal. Mach. Intell., 34(12):2289-2302, 2012. 3 + +[19] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 6 +[20] Jaewon Lee and Kyong Hwan Jin. Local texture estimator for implicit representation function. In CVPR, 2022. 3 +[21] Yeon Ju Lee and Jungho Yoon. Nonlinear image upsampling method based on radial basis function interpolation. IEEE Trans. Image Process., 19(10):2682-2692, 2010. 2 +[22] Jiacheng Li, Chang Chen, Zhen Cheng, and Zhiwei Xiong. MuLUT: Cooperating multiple look-up tables for efficient image super-resolution. In ECCV, 2022. 2, 3, 4 +[23] Jiacheng Li, Chang Chen, Zhen Cheng, and Zhiwei Xiong. Toward DNN of LUTs: Learning efficient image restoration with multiple look-up tables. arxiv, 2303.14506, 2023. 2, 3, 4 +[24] Xin Li and Michael T. Orchard. New edge-directed interpolation. IEEE Trans. Image Process., 10(10):1521-1527, 2001. 2 +[25] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. SwinIR: Image restoration using swim transformer. In ICCV Workshops, 2021. 2 +[26] Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, and Radu Timofte. Flow-based kernel prior with application to blind super-resolution. In CVPR, 2021. 2 +[27] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Young Mu Lee. Enhanced deep residual networks for single image super-resolution. In CVPR Workshops, 2017. 1, 2 +[28] Ilya Loshchilov and Frank Hutter. SGDR: stochastic gradient descent with warm restarts. In *ICLR*, 2017. 6 +[29] Cheng Ma, Jingyi Zhang, Jie Zhou, and Jiwen Lu. Learning series-parallel lookup tables for efficient image superresolution. In ECCV, 2022. 2, 3, 4 +[30] David R. Martin, Charless C. Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, 2001. 5 +[31] Yusuke Matsui, Kota Ito, Yuji Aramaki, Azuma Fujimoto, Toru Ogawa, Toshihiko Yamasaki, and Kiyoharu Aizawa. Sketch-based manga retrieval using manga109 dataset. Multim. Tools Appl., 76(20):21811-21838, 2017. 5 +[32] Ben Mildenhall, Jonathan T. Barron, Jiawen Chen, Dillon Sharlet, Ren Ng, and Robert Carroll. Burst denoising with kernel prediction networks. In CVPR, 2018. 2 +[33] MindSpore. https://www.mindspore.cn, 2023.6 +[34] Don P. Mitchell and Arun N. Netravali. Reconstruction filters in computer-graphics. In SIGGRAPH, 1988. 2 +[35] Marta Mrak, Mislav Grgic, and Murat Kunt. High-Quality Visual Experience: Creation, Processing and Interactivity of High-Resolution and High-Dimensional Video Signals. Signals and Communication Technology. Springer Berlin Heidelberg, 2016. 1 +[36] Zhihong Pan, Baopu Li, Dongliang He, Mingde Yao, Wenhao Wu, Tianwei Lin, Xin Li, and Errui Ding. Towards bidirectional arbitrary image rescaling: Joint optimization and cycle idempotence. In CVPR, 2022. 2 +[37] Yaniv Romano, John Isidoro, and Peyman Milanfar. RAISR: rapid and accurate image super resolution. IEEE Trans. Computational Imaging, 3(1):110-125, 2017. 2, 5 + +[38] Thomas G. Smith and George Lucas. Industrial Light & Magic: The Art of Special Effects. Virgin, 1986. 1 +[39] Sanghyun Son and Kyoung Mu Lee. SRWarp: Generalized image super-resolution under arbitrary transformation. In CVPR, 2021. 1, 2 +[40] Wanjie Sun and Zhenzhong Chen. Learned image downscaling for upscaling using content adaptive resampler. IEEE Trans. Image Process., 29:4027-4040, 2020. 1, 2 +[41] Hiroyuki Takeda, Sina Farsiu, and Peyman Milanfar. Kernel regression for image processing and reconstruction. IEEE Trans. Image Process., 16(2):349-366, 2007. 2 +[42] Hossein Talebi and Peyman Milanfar. Learning to resize images for computer vision tasks. In ICCV, 2021. 2 +[43] Longguang Wang, Yingqian Wang, Zaiping Lin, Jungang Yang, Wei An, and Yulan Guo. Learning A single network for scale-arbitrary super-resolution. In ICCV, 2021. 1, 2, 4 +[44] Qing Wang and Rabab Kreidieh Ward. A new orientation-adaptive interpolation method. IEEE Trans. Image Process., 16(4):889-900, 2007. 2 +[45] Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600-612, 2004. 5 +[46] Bartlomiej Wronski, Ignacio Garcia-Dorado, Manfred Ernst, Damien Kelly, Michael Krainin, Chia-Kai Liang, Marc Levoy, and Peyman Milanfar. Handheld multi-frame superresolution. ACM Trans. Graph., 38(4):28:1-28:18, 2019. 2 +[47] Zeyu Xiao, Xueyang Fu, Jie Huang, Zhen Cheng, and Zhiwei Xiong. Space-time distillation for video super-resolution. In CVPR, 2021. 2 +[48] Jingyu Yang, Sheng Shen, Huanjing Yue, and Kun Li. Implicit transformer network for screen content image continuous super-resolution. In NeurIPS, 2021. 1, 3, 4 +[49] Mingde Yao, Dongliang He, Xin Li, Zhihong Pan, and Zhiwei Xiong. Bidirectional translation between uhd-hdr and hd-sdr videos. IEEE Trans. Multimedia, 2023. 2 +[50] Hui Zeng, Jianrui Cai, Lida Li, Zisheng Cao, and Lei Zhang. Learning image-adaptive 3d lookup tables for high performance photo enhancement in real-time. IEEE Trans. Pattern Anal. Mach. Intell., 44(4):2058-2073, 2022. 3 +[51] Kaibing Zhang, Xinbo Gao, Dacheng Tao, and Xuelong Li. Single image super-resolution with non-local means and steering kernel regression. IEEE Trans. Image Process., 21(11):4544-4556, 2012. 2 +[52] Lei Zhang and Xiaolin Wu. An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans. Image Process., 15(8):2226-2238, 2006. 2 +[53] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 5 +[54] Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. In ECCV, 2018. 2 \ No newline at end of file diff --git a/2023/Learning Steerable Function for Efficient Image Resampling/images.zip b/2023/Learning Steerable Function for Efficient Image Resampling/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..bc746a636829d4cada6ee70eddf7f91094200f5f --- /dev/null +++ b/2023/Learning Steerable Function for Efficient Image Resampling/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b31e09188f01e076656a756de1b3cbf335921d4fb7f9f87a7e4536b73258aeae +size 1039746 diff --git a/2023/Learning Steerable Function for Efficient Image Resampling/layout.json b/2023/Learning Steerable Function for Efficient Image Resampling/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..e01c18ac501c72829d4455886804a1c5f7bda6d4 --- /dev/null +++ b/2023/Learning Steerable Function for Efficient Image Resampling/layout.json @@ -0,0 +1,15071 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 110, + 103, + 483, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 110, + 103, + 483, + 121 + ], + "spans": [ + { + "bbox": [ + 110, + 103, + 483, + 121 + ], + "type": "text", + "content": "Learning Steerable Function for Efficient Image Resampling" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "spans": [ + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "text", + "content": "Jiacheng Li" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "inline_equation", + "content": "^{1*}" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "text", + "content": " Chang Chen" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "inline_equation", + "content": "^{2*}" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "text", + "content": " Wei Huang" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "text", + "content": " \nZhiqiang Lang" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "text", + "content": " Fenglong Song" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "text", + "content": " Youliang Yan" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "text", + "content": " Zhiwei Xiong" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "inline_equation", + "content": "^{1\\dagger}" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "text", + "content": "University of Science and Technology of China " + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "inline_equation", + "content": "{}^{2}" + }, + { + "bbox": [ + 111, + 142, + 482, + 185 + ], + "type": "text", + "content": "Huawei Noah's Ark Lab" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 128, + 187, + 462, + 213 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 128, + 187, + 462, + 213 + ], + "spans": [ + { + "bbox": [ + 128, + 187, + 462, + 213 + ], + "type": "text", + "content": "{jcee,weih527}@mail.ustc.edu.cn zwxiong@ustc.edu.cn {chenchang25,langzhiqiang,songfenglong,yanyouliang}@huawei.com" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 143, + 241, + 191, + 253 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 241, + 191, + 253 + ], + "spans": [ + { + "bbox": [ + 143, + 241, + 191, + 253 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 266, + 290, + 531 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 266, + 290, + 531 + ], + "spans": [ + { + "bbox": [ + 46, + 266, + 290, + 531 + ], + "type": "text", + "content": "Image resampling is a basic technique that is widely employed in daily applications. Existing deep neural networks (DNNs) have made impressive progress in resampling performance. Yet these methods are still not the perfect substitute for interpolation, due to the issues of efficiency and continuous resampling. In this work, we propose a novel method of Learning Resampling Function (termed LeRF), which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption of interpolation methods. Specifically, LeRF assigns spatially-varying steerable resampling functions to input image pixels and learns to predict the hyper-parameters that determine the orientations of these resampling functions with a neural network. To achieve highly efficient inference, we adopt look-up tables (LUTs) to accelerate the inference of the learned neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures. Extensive experiments show that our method runs as fast as interpolation, generalizes well to arbitrary transformations, and outperforms interpolation significantly, e.g., up to 3dB PSNR gain over bicubic for " + }, + { + "bbox": [ + 46, + 266, + 290, + 531 + ], + "type": "inline_equation", + "content": "\\times 2" + }, + { + "bbox": [ + 46, + 266, + 290, + 531 + ], + "type": "text", + "content": " upsampling on Manga109." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 554, + 127, + 566 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 554, + 127, + 566 + ], + "spans": [ + { + "bbox": [ + 47, + 554, + 127, + 566 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 575, + 287, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 575, + 287, + 684 + ], + "spans": [ + { + "bbox": [ + 46, + 575, + 287, + 684 + ], + "type": "text", + "content": "Due to the rapid growth of visual data, there is a strong demand for digital image processing. Image resampling, one of the most common techniques, aims to obtain another image by generating new pixels following a geometric transformation rule from existing pixels in a given image [8]. Common transformations include upsampling (i.e., single image super-resolution), downsampling, affine transformation, etc. Image resampling enjoys various applications, ranging from photo editing, optical distortion com" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 310, + 241, + 547, + 415 + ], + "blocks": [ + { + "bbox": [ + 310, + 241, + 547, + 415 + ], + "lines": [ + { + "bbox": [ + 310, + 241, + 547, + 415 + ], + "spans": [ + { + "bbox": [ + 310, + 241, + 547, + 415 + ], + "type": "image", + "image_path": "5400022abe66cf2906d4dac8f37792dbd54c428e10f6f244f74d7fcbb2a915cf.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 423, + 545, + 467 + ], + "lines": [ + { + "bbox": [ + 305, + 423, + 545, + 467 + ], + "spans": [ + { + "bbox": [ + 305, + 423, + 545, + 467 + ], + "type": "text", + "content": "Figure 1. LeRF assigns steerable resampling functions to input pixels, and learns to predict the hyper-parameters that determine the orientations of these continuous functions for resampling under arbitrary transformations." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 473, + 545, + 497 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 473, + 545, + 497 + ], + "spans": [ + { + "bbox": [ + 305, + 473, + 545, + 497 + ], + "type": "text", + "content": "pensation [10], online content streaming [35], and visual special effects production [38]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 498, + 545, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 545, + 581 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 545, + 581 + ], + "type": "text", + "content": "Recently, deep neural networks (DNNs) have made impressive progress in the field of image resampling [9, 12, 17, 27, 39, 40], thanks to the learning-from-data paradigm that obtains powerful structural priors from large-scale datasets. Despite the superior performance that DNN-based methods have achieved, long-lived interpolation methods like bicubic [16] are still preferred choices in most cases." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 582, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 546, + 713 + ], + "type": "text", + "content": "We attribute this phenomenon to the following two reasons: 1) Interpolation is simple and highly efficient, resulting in less dependency and thus the practicality to be deployed on a variety of devices, ranging from IoT devices to gaming workstations. 2) Interpolation supports arbitrary transformations. It assumes a continuous resampling function for a local area, resulting in the versatility in applying to not only homographic transformations like upsampling and downsampling, but also general warping. Although recent DNN-based methods explore beyond fixed-scale upsampling [5, 12, 27, 39, 43, 48], an efficient and continuous" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 693, + 287, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 693, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 47, + 693, + 287, + 712 + ], + "type": "text", + "content": "*Equal contribution. † Corresponding author. This work was done when Jiacheng Li was a research intern at Huawei Noah's Ark Lab." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5866" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 279, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 279, + 83 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 279, + 83 + ], + "type": "text", + "content": "solution that matches interpolation remains less explored." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 84, + 287, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 84, + 287, + 335 + ], + "spans": [ + { + "bbox": [ + 46, + 84, + 287, + 335 + ], + "type": "text", + "content": "In this work, we aim to fill this blank research area by taking a middle way between DNN-based methods and interpolation methods. We propose a novel method of Learning Resampling Function (termed LeRF), where parameterized continuous functions for resampling different structures are learned from data. Specifically, as illustrated in Fig. 1, we assign spatially-varying steerable resampling functions to image pixels, whose orientations are parameterized with several hyper-parameters. Then, we train a neural network to predict these hyper-parameters for each pixel in an input image, thus defining the resampling function for that pixel location. Finally, we obtain the output image by interpolating an input image with these locally adapted resampling functions. LeRF takes advantage of both the structural priors learned by DNN and the locally continuous assumption of interpolation methods. Furthermore, we present an efficient implementation, where the inference of the learned neural network is accelerated with look-up tables (LUTs) [15,22,23,29]. We further design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures in images." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 335, + 287, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 335, + 287, + 430 + ], + "spans": [ + { + "bbox": [ + 46, + 335, + 287, + 430 + ], + "type": "text", + "content": "We examine the advantages and generalization ability of LeRF in various image resampling tasks, including arbitrary-scale upsampling, homographic transformation, and general warping. In particular, as illustrated in Fig. 2, at a similar running time, our method outperforms popular interpolation methods significantly in upsampling, which demonstrates the superiority of LeRF in terms of performance and efficiency." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 59, + 431, + 281, + 442 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 431, + 281, + 442 + ], + "spans": [ + { + "bbox": [ + 59, + 431, + 281, + 442 + ], + "type": "text", + "content": "Contributions of this paper are summarized as follows:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 443, + 287, + 610 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 47, + 443, + 287, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 443, + 287, + 502 + ], + "spans": [ + { + "bbox": [ + 47, + 443, + 287, + 502 + ], + "type": "text", + "content": "1) We propose LeRF, a novel method for continuous resampling. We assign spatially-varying steerable resampling functions to image pixels, where we train a neural network to predict the hyper-parameters that determine the orientations of these resampling functions." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 503, + 287, + 562 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 503, + 287, + 562 + ], + "spans": [ + { + "bbox": [ + 47, + 503, + 287, + 562 + ], + "type": "text", + "content": "2) We present an efficient implementation of LeRF by adopting look-up tables to accelerate the inference of the trained neural network. Furthermore, we design a directional ensemble strategy and edge-sensitive indexing patterns to better capture local structures." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 563, + 287, + 610 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 563, + 287, + 610 + ], + "spans": [ + { + "bbox": [ + 47, + 563, + 287, + 610 + ], + "type": "text", + "content": "3) Extensive experiments demonstrate that our method operates as efficient as interpolation, generalizes well to arbitrary transformations, and obtains significantly better performance over interpolation." + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 47, + 621, + 138, + 634 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 621, + 138, + 634 + ], + "spans": [ + { + "bbox": [ + 47, + 621, + 138, + 634 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 641, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 287, + 713 + ], + "type": "text", + "content": "Image interpolation for resampling. Interpolation, the most common solution for image resampling, assumes a locally continuous intensity surface and approximates it with a fixed resampling function, such as nearest (nearest), linear (bilinear), cubic (bicubic) [16], and windowed sinc (lanczos). It predicts resampling weights with the as" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 317, + 70, + 527, + 205 + ], + "blocks": [ + { + "bbox": [ + 317, + 70, + 527, + 205 + ], + "lines": [ + { + "bbox": [ + 317, + 70, + 527, + 205 + ], + "spans": [ + { + "bbox": [ + 317, + 70, + 527, + 205 + ], + "type": "image", + "image_path": "0f687746b33e4873affbc51e5bb45701943afdc2fcbe9d795a398c45890da4ef.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 208, + 545, + 252 + ], + "lines": [ + { + "bbox": [ + 305, + 208, + 545, + 252 + ], + "spans": [ + { + "bbox": [ + 305, + 208, + 545, + 252 + ], + "type": "text", + "content": "Figure 2. Performance-efficiency trade-off of arbitrary-scale upsampling methods. PSNR values are obtained on Set14 for " + }, + { + "bbox": [ + 305, + 208, + 545, + 252 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 305, + 208, + 545, + 252 + ], + "type": "text", + "content": " upsampling. The running time is evaluated on mobile and desktop devices, respectively." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 258, + 545, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 258, + 545, + 353 + ], + "spans": [ + { + "bbox": [ + 304, + 258, + 545, + 353 + ], + "type": "text", + "content": "sumed resampling function, and then aggregates input pixels with these resampling weights to obtain the target pixel. This assumption allows for continuous resampling under arbitrary transformations, yet leads to blurry results due to ignoring different local structures [34]. We follow the same assumption on local continuity, but our method deals with different structures with adapted resampling functions instead of fixed ones." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 354, + 546, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 354, + 546, + 556 + ], + "spans": [ + { + "bbox": [ + 304, + 354, + 546, + 556 + ], + "type": "text", + "content": "Adaptive image resampling. To integrate local structural information into the resampling process, many methods are proposed, including edge-directed interpolation [2, 24, 44, 52] and kernel regression [21, 41, 51]. Different from these methods that rely on hand-designed rules, our method adopts a neural network to learn structural priors and integrate them into the resampling functions in a data-driven way. Another line of work achieves adaptive resampling by combining interpolation methods with adaptive filtering [11, 14, 17, 37]. Among them, RAISR achieves superresolution by predicting adaptive filters for each pixel from a hash table and then applying the predicted adaptive filters to pre-upsampled images [11, 37]. This kind of method operates on the pre-resampled results of common interpolation methods (usually bicubic) with a fixed transformation (e.g., " + }, + { + "bbox": [ + 304, + 354, + 546, + 556 + ], + "type": "inline_equation", + "content": "\\times 2" + }, + { + "bbox": [ + 304, + 354, + 546, + 556 + ], + "type": "text", + "content": " upsampling), thus lacking the generalization ability to be extended to unseen arbitrary transformations." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 558, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 546, + 713 + ], + "type": "text", + "content": "DNN-based image resampling. With the rise of deep neural networks, impressive progress has been made in image resampling, such as upsampling [3,4,9,17,25,27,36,46,47, 49, 54], downsampling [40, 42], and homographic transformation [39]. For arbitrary-scale upsampling, Meta-SR [12] and LIIF [5] are two representative works. Meta-SR predicts the resampling weights from upsampling scales with a weight prediction network, similar to kernel prediction networks [26, 32], and aggregates deep features to get the final upsampled image [12]. Following works improve the process with support for homographic transformations [39] and scale-aware feature adaptation [43]. On the other hand, LIIF models the aggregation step with a neural implicit rep" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5867" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 82, + 76, + 285, + 187 + ], + "blocks": [ + { + "bbox": [ + 82, + 76, + 285, + 187 + ], + "lines": [ + { + "bbox": [ + 82, + 76, + 285, + 187 + ], + "spans": [ + { + "bbox": [ + 82, + 76, + 285, + 187 + ], + "type": "image", + "image_path": "739a7144b63699f17db3e1c72812626ca6f985a4009a82b3b0dbd092dada55f4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 146, + 194, + 201, + 205 + ], + "lines": [ + { + "bbox": [ + 146, + 194, + 201, + 205 + ], + "spans": [ + { + "bbox": [ + 146, + 194, + 201, + 205 + ], + "type": "text", + "content": "(a) Interpolation" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 314, + 74, + 521, + 194 + ], + "blocks": [ + { + "bbox": [ + 314, + 74, + 521, + 194 + ], + "lines": [ + { + "bbox": [ + 314, + 74, + 521, + 194 + ], + "spans": [ + { + "bbox": [ + 314, + 74, + 521, + 194 + ], + "type": "image", + "image_path": "6ad6f74b164bcc6b4445606f311733dfb99fbf006eaeda65cbdb74127c30946c.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 381, + 195, + 436, + 205 + ], + "lines": [ + { + "bbox": [ + 381, + 195, + 436, + 205 + ], + "spans": [ + { + "bbox": [ + 381, + 195, + 436, + 205 + ], + "type": "text", + "content": "(b) LeRF (Ours)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 45, + 219, + 547, + 243 + ], + "lines": [ + { + "bbox": [ + 45, + 219, + 547, + 243 + ], + "spans": [ + { + "bbox": [ + 45, + 219, + 547, + 243 + ], + "type": "text", + "content": "Figure 3. Comparison between interpolation and LeRF. (a) Interpolation assumes a spatial-invariant fixed function " + }, + { + "bbox": [ + 45, + 219, + 547, + 243 + ], + "type": "inline_equation", + "content": "\\Phi(\\cdot)" + }, + { + "bbox": [ + 45, + 219, + 547, + 243 + ], + "type": "text", + "content": " to predict resampling weights " + }, + { + "bbox": [ + 45, + 219, + 547, + 243 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 45, + 219, + 547, + 243 + ], + "type": "text", + "content": ". (b) LeRF learns spatially-varying steerable function " + }, + { + "bbox": [ + 45, + 219, + 547, + 243 + ], + "type": "inline_equation", + "content": "\\Phi_{\\Theta}" + }, + { + "bbox": [ + 45, + 219, + 547, + 243 + ], + "type": "text", + "content": " with a DNN " + }, + { + "bbox": [ + 45, + 219, + 547, + 243 + ], + "type": "inline_equation", + "content": "f(\\cdot)" + }, + { + "bbox": [ + 45, + 219, + 547, + 243 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 248, + 287, + 320 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 248, + 287, + 320 + ], + "spans": [ + { + "bbox": [ + 46, + 248, + 287, + 320 + ], + "type": "text", + "content": "resentation, predicting the target pixels from deep latent features and target coordinates [5]. Following works improve the implicit representation by integrating frequency analysis [20] and modulation-based transformer [48]. Different from these works, we utilize explicit local functions, resulting in advantages in efficiency and interpretability." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 321, + 288, + 513 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 321, + 288, + 513 + ], + "spans": [ + { + "bbox": [ + 46, + 321, + 288, + 513 + ], + "type": "text", + "content": "Efficient super-resolution with look-up table. A look-up table is composed of index-value pairs, which can be efficiently retrieved through memory access. It is widely applied in the image signal processing pipeline [18, 50]. Recently, SR-LUT has been introduced to accelerate the inference of a fixed-scale super-resolution network by traversing all possible low-resolution (LR) patches, pre-computing all corresponding high-resolution (HR) patches, and saving them as index-value pairs [15]. At inference, the computations in the super-resolution network are replaced with retrieving values from LUT, leading to inference acceleration. Different from existing LUT-based methods [15,22,23,29], which adopt different groups of LUTs under different upsampling scales, our LUTs store the same group of hyperparameters across arbitrary transformations, thus achieving continuous resampling at high efficiency." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 526, + 222, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 526, + 222, + 540 + ], + "spans": [ + { + "bbox": [ + 47, + 526, + 222, + 540 + ], + "type": "text", + "content": "3. Learning Resampling Function" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 547, + 129, + 559 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 547, + 129, + 559 + ], + "spans": [ + { + "bbox": [ + 47, + 547, + 129, + 559 + ], + "type": "text", + "content": "3.1. Formulation" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 567, + 287, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 567, + 287, + 590 + ], + "spans": [ + { + "bbox": [ + 46, + 567, + 287, + 590 + ], + "type": "text", + "content": "Typically, resampling through interpolation can be implemented in the following steps." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 592, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 46, + 592, + 287, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 592, + 287, + 651 + ], + "spans": [ + { + "bbox": [ + 46, + 592, + 287, + 651 + ], + "type": "text", + "content": "① Obtain relative offsets: the target coordinates after transformation, such as upsampling, are projected back to the coordinate space of the input image, and the relative spatial offsets between target pixels and source pixels in their support patches are obtained." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 652, + 287, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 652, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 46, + 652, + 287, + 689 + ], + "type": "inline_equation", + "content": "②" + }, + { + "bbox": [ + 46, + 652, + 287, + 689 + ], + "type": "text", + "content": " Predict resampling weights: based on the relative spatial offsets, the resampling weights, i.e., resampling kernels, are predicted for each pixel in the support patch." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 689, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 288, + 714 + ], + "type": "text", + "content": "③ Aggregate pixels: the source pixels are aggregated through weighted summation to obtain the target pixel" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 248, + 496, + 260 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 248, + 496, + 260 + ], + "spans": [ + { + "bbox": [ + 306, + 248, + 496, + 260 + ], + "type": "text", + "content": "value. The above process can be formulated as" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 387, + 268, + 545, + 294 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 387, + 268, + 545, + 294 + ], + "spans": [ + { + "bbox": [ + 387, + 268, + 545, + 294 + ], + "type": "interline_equation", + "content": "\\hat {I} _ {q} = \\sum_ {p \\in \\Omega} W _ {p \\rightarrow q} I _ {p} \\tag {1}", + "image_path": "00372e6f60680a146128b9eff299a453886a7fd59f264715252ab676dfb46651.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "spans": [ + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "inline_equation", + "content": "\\hat{I}_q" + }, + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "text", + "content": " is the interpolated pixel value at the target coordinate, " + }, + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "inline_equation", + "content": "I_p" + }, + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "text", + "content": " the pixel value at the source coordinate, " + }, + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "text", + "content": " the support patch, and " + }, + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "inline_equation", + "content": "W_{p\\rightarrow q}" + }, + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "text", + "content": " the weight from " + }, + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 305, + 300, + 545, + 338 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 338, + 545, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 338, + 545, + 384 + ], + "spans": [ + { + "bbox": [ + 305, + 338, + 545, + 384 + ], + "type": "text", + "content": "As illustrated in Fig. 3(a), an isotropic resampling function " + }, + { + "bbox": [ + 305, + 338, + 545, + 384 + ], + "type": "inline_equation", + "content": "\\Phi(\\cdot)" + }, + { + "bbox": [ + 305, + 338, + 545, + 384 + ], + "type": "text", + "content": ", e.g. cubic interpolation, is assumed to predict resampling weights from relative offsets, which can be formulated as" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 386, + 394, + 545, + 407 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 386, + 394, + 545, + 407 + ], + "spans": [ + { + "bbox": [ + 386, + 394, + 545, + 407 + ], + "type": "interline_equation", + "content": "W _ {p \\rightarrow q} = \\Phi \\left(d _ {p \\rightarrow q}\\right) \\tag {2}", + "image_path": "7b50c35d351b60729fc21899401ecf1fc6982489d2cf820fd66e583899d17a4b.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "spans": [ + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "inline_equation", + "content": "d_{p\\rightarrow q}" + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "text", + "content": " is the relative offset from " + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "text", + "content": ", which can be obtained based on the geometric transformation " + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "text", + "content": ". Thanks to the continuity of the resampling function " + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "inline_equation", + "content": "\\Phi" + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "text", + "content": ", arbitrary transformations from " + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "inline_equation", + "content": "I_{in}" + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "inline_equation", + "content": "I_{out}" + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "text", + "content": " can be achieved. The assumption of local continuity contributes to the versatility and high efficiency of interpolation. But the spatially-invariant resampling function leads to blurry results due to the ignorance of local structures." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 514, + 496, + 528 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 514, + 496, + 528 + ], + "spans": [ + { + "bbox": [ + 306, + 514, + 496, + 528 + ], + "type": "text", + "content": "3.2. The Steerable Resampling Function" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 533, + 545, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 533, + 545, + 605 + ], + "spans": [ + { + "bbox": [ + 304, + 533, + 545, + 605 + ], + "type": "text", + "content": "In this work, we propose a novel method of learning resampling function that adapts the resampling functions to local structures in a data-driven way. Different from the fixed resampling function in interpolation, we assume a kind of steerable resampling function " + }, + { + "bbox": [ + 304, + 533, + 545, + 605 + ], + "type": "inline_equation", + "content": "\\Phi_{\\Theta}" + }, + { + "bbox": [ + 304, + 533, + 545, + 605 + ], + "type": "text", + "content": ", parameterized by " + }, + { + "bbox": [ + 304, + 533, + 545, + 605 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 304, + 533, + 545, + 605 + ], + "type": "text", + "content": ". Specifically, we utilize the anisotropic gaussian," + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 310, + 612, + 545, + 649 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 310, + 612, + 545, + 649 + ], + "spans": [ + { + "bbox": [ + 310, + 612, + 545, + 649 + ], + "type": "interline_equation", + "content": "\\Phi_ {\\boldsymbol {\\Sigma}} (\\boldsymbol {x} - \\boldsymbol {\\mu}) = \\frac {1}{2 \\pi | \\boldsymbol {\\Sigma} | ^ {\\frac {1}{2}}} \\exp \\left\\{- \\frac {1}{2} (\\boldsymbol {x} - \\boldsymbol {\\mu}) ^ {T} \\boldsymbol {\\Sigma} ^ {- 1} (\\boldsymbol {x} - \\boldsymbol {\\mu}) \\right\\} \\tag {3}", + "image_path": "62be19d63f3c247e61bdd03782ab103fe90ad655f35bed30913a7a80c266ab7f.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 305, + 647, + 545, + 683 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 647, + 545, + 683 + ], + "spans": [ + { + "bbox": [ + 305, + 647, + 545, + 683 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 647, + 545, + 683 + ], + "type": "inline_equation", + "content": "(\\pmb{x} - \\pmb{\\mu})" + }, + { + "bbox": [ + 305, + 647, + 545, + 683 + ], + "type": "text", + "content": " are the relative offsets between the target and source pixels, " + }, + { + "bbox": [ + 305, + 647, + 545, + 683 + ], + "type": "inline_equation", + "content": "|\\cdot|" + }, + { + "bbox": [ + 305, + 647, + 545, + 683 + ], + "type": "text", + "content": " denotes the determinant of the covariance matrix " + }, + { + "bbox": [ + 305, + 647, + 545, + 683 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}" + }, + { + "bbox": [ + 305, + 647, + 545, + 683 + ], + "type": "text", + "content": ". We parameterize " + }, + { + "bbox": [ + 305, + 647, + 545, + 683 + ], + "type": "inline_equation", + "content": "\\pmb{\\Sigma}" + }, + { + "bbox": [ + 305, + 647, + 545, + 683 + ], + "type": "text", + "content": " as the following," + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 369, + 690, + 545, + 717 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 690, + 545, + 717 + ], + "spans": [ + { + "bbox": [ + 369, + 690, + 545, + 717 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\Sigma} = \\left( \\begin{array}{c c} \\sigma_ {X} ^ {2} & \\rho \\sigma_ {X} \\sigma_ {Y} \\\\ \\rho \\sigma_ {X} \\sigma_ {Y} & \\sigma_ {Y} ^ {2} \\end{array} \\right) \\tag {4}", + "image_path": "a9a62153523ab475d75c6a6c0d93e88a7c58d5aa7bde8b7833cac23bef4aded6.jpg" + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5868" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 75, + 70, + 165, + 178 + ], + "blocks": [ + { + "bbox": [ + 75, + 70, + 165, + 178 + ], + "lines": [ + { + "bbox": [ + 75, + 70, + 165, + 178 + ], + "spans": [ + { + "bbox": [ + 75, + 70, + 165, + 178 + ], + "type": "image", + "image_path": "9a251886bcfa5b106679e059b54006d9d4e53dcfafe709b64a2310fddacf151d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 78, + 195, + 185, + 206 + ], + "lines": [ + { + "bbox": [ + 78, + 195, + 185, + 206 + ], + "spans": [ + { + "bbox": [ + 78, + 195, + 185, + 206 + ], + "type": "text", + "content": "(a) LUT acceleration of DNN" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 209, + 68, + 329, + 192 + ], + "blocks": [ + { + "bbox": [ + 209, + 68, + 329, + 192 + ], + "lines": [ + { + "bbox": [ + 209, + 68, + 329, + 192 + ], + "spans": [ + { + "bbox": [ + 209, + 68, + 329, + 192 + ], + "type": "image", + "image_path": "b205e45c59e500cd364aceaf7e850535a109099ad6d863319bcf7f515e8cbc96.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 224, + 195, + 303, + 207 + ], + "lines": [ + { + "bbox": [ + 224, + 195, + 303, + 207 + ], + "spans": [ + { + "bbox": [ + 224, + 195, + 303, + 207 + ], + "type": "text", + "content": "(b) Ensemble strategy" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 360, + 71, + 521, + 190 + ], + "blocks": [ + { + "bbox": [ + 360, + 71, + 521, + 190 + ], + "lines": [ + { + "bbox": [ + 360, + 71, + 521, + 190 + ], + "spans": [ + { + "bbox": [ + 360, + 71, + 521, + 190 + ], + "type": "image", + "image_path": "595ebafb9afdfe7837dda1b2d6054f44980c965018c9635de4fc0d7046a1d166.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 381, + 195, + 509, + 207 + ], + "lines": [ + { + "bbox": [ + 381, + 195, + 509, + 207 + ], + "spans": [ + { + "bbox": [ + 381, + 195, + 509, + 207 + ], + "type": "text", + "content": "(c) Edge-sensitive indexing patterns" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 61, + 270, + 269, + 324 + ], + "blocks": [ + { + "bbox": [ + 46, + 220, + 547, + 266 + ], + "lines": [ + { + "bbox": [ + 46, + 220, + 547, + 266 + ], + "spans": [ + { + "bbox": [ + 46, + 220, + 547, + 266 + ], + "type": "text", + "content": "Figure 5. LUT acceleration of DNN and our adaptations. (a) A learned neural network can be accelerated by traversing all possible inputs " + }, + { + "bbox": [ + 46, + 220, + 547, + 266 + ], + "type": "inline_equation", + "content": "i_{*}" + }, + { + "bbox": [ + 46, + 220, + 547, + 266 + ], + "type": "text", + "content": ", pre-computing all corresponding outputs " + }, + { + "bbox": [ + 46, + 220, + 547, + 266 + ], + "type": "inline_equation", + "content": "v_{*}" + }, + { + "bbox": [ + 46, + 220, + 547, + 266 + ], + "type": "text", + "content": ", and saving them to LUTs [15,22,23,29]. (b) In the proposed directional ensemble strategy, only predictions along the same direction are averaged, instead of all directions in rotation ensemble [15]. (c) We introduce edge-sensitive indexing patterns to better capture edge orientations. The pixels covered by directional ensemble are depicted with dashed boxes." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 61, + 270, + 269, + 324 + ], + "lines": [ + { + "bbox": [ + 61, + 270, + 269, + 324 + ], + "spans": [ + { + "bbox": [ + 61, + 270, + 269, + 324 + ], + "type": "image", + "image_path": "958264d104cfa9b2e391f45c1884e23db817388dbe9a9b12442a683446dbe7ee.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 52, + 328, + 281, + 339 + ], + "lines": [ + { + "bbox": [ + 52, + 328, + 281, + 339 + ], + "spans": [ + { + "bbox": [ + 52, + 328, + 281, + 339 + ], + "type": "text", + "content": "Figure 4. Visualization of parameterized resampling functions." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 356, + 287, + 403 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 356, + 287, + 403 + ], + "spans": [ + { + "bbox": [ + 46, + 356, + 287, + 403 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 356, + 287, + 403 + ], + "type": "inline_equation", + "content": "\\rho, \\sigma_{X}" + }, + { + "bbox": [ + 46, + 356, + 287, + 403 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 356, + 287, + 403 + ], + "type": "inline_equation", + "content": "\\sigma_{Y}" + }, + { + "bbox": [ + 46, + 356, + 287, + 403 + ], + "type": "text", + "content": " are hyper-parameters, and thus the resampling function becomes " + }, + { + "bbox": [ + 46, + 356, + 287, + 403 + ], + "type": "inline_equation", + "content": "\\Phi_{(\\rho, \\sigma_{X}, \\sigma_{Y})}" + }, + { + "bbox": [ + 46, + 356, + 287, + 403 + ], + "type": "text", + "content": ". From the perspective of statistics, " + }, + { + "bbox": [ + 46, + 356, + 287, + 403 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 46, + 356, + 287, + 403 + ], + "type": "text", + "content": " can be interpreted as the correlation between 2D variables and " + }, + { + "bbox": [ + 46, + 356, + 287, + 403 + ], + "type": "inline_equation", + "content": "\\sigma_{X}, \\sigma_{Y}" + }, + { + "bbox": [ + 46, + 356, + 287, + 403 + ], + "type": "text", + "content": " the standard deviations." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 403, + 287, + 487 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 403, + 287, + 487 + ], + "spans": [ + { + "bbox": [ + 46, + 403, + 287, + 487 + ], + "type": "text", + "content": "As illustrated in Fig. 4, our resampling function is steerable, where different orientations and shapes can be obtained by tuning hyper-parameters " + }, + { + "bbox": [ + 46, + 403, + 287, + 487 + ], + "type": "inline_equation", + "content": "(\\rho, \\sigma_{X}, \\sigma_{Y})" + }, + { + "bbox": [ + 46, + 403, + 287, + 487 + ], + "type": "text", + "content": ", showing its modeling capacity for a variety of local structures. Other non-steerable functions, such as Keys cubic [16] are also applicable as resampling functions. Details of the comparison are provided in Table 3 and Fig. 10." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 488, + 287, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 488, + 287, + 559 + ], + "spans": [ + { + "bbox": [ + 46, + 488, + 287, + 559 + ], + "type": "text", + "content": "But, direct optimization of the above hyper-parameters leads to unstable gradients and divergence. Thus, we modify the formulation by omitting the determinant multiplier and predicting a group of " + }, + { + "bbox": [ + 46, + 488, + 287, + 559 + ], + "type": "inline_equation", + "content": "(\\rho, \\frac{1}{\\sigma_X}, \\frac{1}{\\sigma_Y})" + }, + { + "bbox": [ + 46, + 488, + 287, + 559 + ], + "type": "text", + "content": " for stable training. In the following section, we introduce the learning process of these hyper-parameters and our efficient implementation." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 568, + 271, + 580 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 568, + 271, + 580 + ], + "spans": [ + { + "bbox": [ + 47, + 568, + 271, + 580 + ], + "type": "text", + "content": "3.3. Learning with DNN and LUT Acceleration" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 586, + 287, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 586, + 287, + 635 + ], + "spans": [ + { + "bbox": [ + 46, + 586, + 287, + 635 + ], + "type": "text", + "content": "As shown in Fig. 3(b), we adopt a deep neural network to learn structural priors from an external dataset for predicting hyper-parameters " + }, + { + "bbox": [ + 46, + 586, + 287, + 635 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 46, + 586, + 287, + 635 + ], + "type": "text", + "content": " in resampling functions. The training loss function can be formulated as" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 62, + 643, + 287, + 670 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 643, + 287, + 670 + ], + "spans": [ + { + "bbox": [ + 62, + 643, + 287, + 670 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {q} = \\left\\| I _ {q} - \\sum_ {p \\in \\Omega} \\Phi_ {\\Theta} \\left(d _ {p \\rightarrow q}\\right) I _ {p} \\right\\| ^ {2}, \\quad \\Theta = f (\\mathcal {N} _ {p}), \\tag {5}", + "image_path": "d33d75878cbdf2a0640d3c8849f90c2309f8da1c07d201826afd1299c7ea7d0d.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "inline_equation", + "content": "I_q" + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "text", + "content": " is the ground truth pixel value at location " + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "inline_equation", + "content": "f(\\cdot)" + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "text", + "content": " is a trainable DNN, and " + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{N}_p" + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "text", + "content": " denotes the surrounding pixels of " + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "inline_equation", + "content": "I_p" + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "text", + "content": " (not necessarily the same as the support patch " + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 272, + 545, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 272, + 545, + 380 + ], + "spans": [ + { + "bbox": [ + 304, + 272, + 545, + 380 + ], + "type": "text", + "content": "Furthermore, to match the efficiency of interpolation, we accelerate the inference of the trained DNN by adopting look-up tables [15, 22, 23, 29]. As shown in Fig. 5(a), for each pair in the LUT to accelerate DNN, its index " + }, + { + "bbox": [ + 304, + 272, + 545, + 380 + ], + "type": "inline_equation", + "content": "i_{*}" + }, + { + "bbox": [ + 304, + 272, + 545, + 380 + ], + "type": "text", + "content": " is a combination of pixels (i.e., " + }, + { + "bbox": [ + 304, + 272, + 545, + 380 + ], + "type": "inline_equation", + "content": "\\mathcal{N}_p" + }, + { + "bbox": [ + 304, + 272, + 545, + 380 + ], + "type": "text", + "content": "), and its value " + }, + { + "bbox": [ + 304, + 272, + 545, + 380 + ], + "type": "inline_equation", + "content": "v_{*}" + }, + { + "bbox": [ + 304, + 272, + 545, + 380 + ], + "type": "text", + "content": " is a group of corresponding hyper-parameters for that pixel combination (i.e., " + }, + { + "bbox": [ + 304, + 272, + 545, + 380 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 304, + 272, + 545, + 380 + ], + "type": "text", + "content": "). This way, hyper-parameters can be retrieved directly from the saved values in LUTs, skipping computations in DNN and thus resulting in high efficiency." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 381, + 545, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 381, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 304, + 381, + 545, + 430 + ], + "type": "text", + "content": "Different from existing LUT-based methods, whose LUT values are image pixels, our LUTs store hyper-parameters that reflect structural characteristics. Thus, to better extract structural priors, we propose the following adaptations." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 433, + 546, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 433, + 546, + 540 + ], + "spans": [ + { + "bbox": [ + 304, + 433, + 546, + 540 + ], + "type": "text", + "content": "Directional ensemble strategy. We propose a directional ensemble (DE) strategy to replace the rotation ensemble (RE) strategy in existing LUT-based methods [15, 22, 23]. As illustrated in Fig. 5(b), in RE, the predictions are averaged across all directions, while the proposed DE strategy only ensembles the predictions with the same direction (i.e., " + }, + { + "bbox": [ + 304, + 433, + 546, + 540 + ], + "type": "inline_equation", + "content": "180^{\\circ}" + }, + { + "bbox": [ + 304, + 433, + 546, + 540 + ], + "type": "text", + "content": " rotational symmetry, instead of " + }, + { + "bbox": [ + 304, + 433, + 546, + 540 + ], + "type": "inline_equation", + "content": "90^{\\circ}" + }, + { + "bbox": [ + 304, + 433, + 546, + 540 + ], + "type": "text", + "content": " in RE). This enables the learning of " + }, + { + "bbox": [ + 304, + 433, + 546, + 540 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 304, + 433, + 546, + 540 + ], + "type": "text", + "content": ", which determines the orientation of the steerable resampling function (see Table 5 and Fig. 11)." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 544, + 545, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 544, + 545, + 639 + ], + "spans": [ + { + "bbox": [ + 304, + 544, + 545, + 639 + ], + "type": "text", + "content": "Edge-sensitive indexing patterns. As illustrated in Fig. 5(c), we include patterns \"C\" and \"X\", alongside the default \"S\" pattern in SR-LUT to better capture edges of different orientations. For example, The \"C\" and \"C\" patterns are sensitive to vertical and horizontal edges, respectively. We validate their effectiveness in Table 5. Correspondingly, as shown in Fig. 5(a), our DNN follows a multibranch design, and each branch is accelerated by a LUT." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": "Pre-filtering stage. Inspired by DNN-based resampling methods [12, 43, 48], we pre-filter the image with a neural network, which can also be accelerated by LUTs with the proposed edge-sensitive indexing patterns. This prefiltering stage helps enhance image content and contributes to better performance (see Table 5 and Fig. 11)." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5869" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 47, + 70, + 545, + 364 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 545, + 364 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 545, + 364 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 545, + 364 + ], + "type": "table", + "html": "
MethodSet5Set14BSDS100Urban100Manga109
×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×1.5 ×1.5×1.5 ×2.0×2.0 ×2.0×2.0 ×2.4
Nearest31.3431.0730.8429.6329.1528.8428.5727.7028.9928.7228.4027.6226.2125.9125.6224.7828.5928.3628.1426.87
Bilinear34.9933.1932.2331.4931.6830.2629.2428.7030.9229.6628.6728.2028.2426.9125.9625.4632.4530.3329.1628.28
Bicubic36.7634.6833.6432.7033.0731.4530.3229.6232.1430.6729.5428.9329.5027.9526.8726.2234.7632.1330.8129.61
Lanczos236.8334.7433.7032.7433.1331.5030.3629.6532.1930.7129.5828.9529.5528.0026.9126.2534.8732.2230.8929.66
Lanczos337.6135.3134.2333.2433.7531.9730.7630.0232.7431.1129.8929.2330.1228.4227.2526.5536.1233.0631.6330.28
RAISR* [37]35.5035.4935.5733.3831.8431.6731.7130.2230.8730.6830.6629.4228.7728.6028.6427.0133.8133.7433.8830.61
SR-LUT* [15]37.7435.5234.4733.4533.5331.7930.5929.8633.0631.4030.1529.4530.2428.5527.3926.6935.3132.3030.9629.58
LeRF (Ours)38.3036.6035.7134.7434.5933.0631.9831.1033.7632.0830.8330.0931.8630.0828.8627.9936.5734.7933.8832.67
Meta-SR [12]41.29-38.12-37.47-33.99-35.79-32.32-35.85-32.98-42.92-39.22-
LIIF [5]41.2238.9938.0836.9937.4435.3133.9632.9535.7533.6832.2831.4536.7034.0832.8431.7042.7740.1939.1337.69
MethodSet5Set14BSDS100Urban100Manga109
×2.0 ×3.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0×2.0 ×3.0×3.0 ×3.0×3.0 ×4.0×4.0 ×4.0
Nearest28.8727.9126.8826.2527.0726.0825.3324.7427.1226.1725.5725.0324.2523.3422.6822.1726.1225.0424.0623.43
Bilinear30.4329.5328.2727.5527.9427.0426.1625.5127.6026.7726.1125.5324.8123.9923.2622.6827.2126.1624.9524.19
Bicubic31.4130.3929.1228.4228.7027.6326.7526.0928.1827.2026.5325.9525.4324.4523.7123.1428.2026.9525.6724.90
Lanczos231.4430.4129.1428.4428.7227.6426.7726.1028.2027.2126.5425.9625.4524.4723.7323.1528.2326.9725.7024.92
Lanczos331.8530.7929.4928.7829.0427.9127.0126.3128.4327.3926.7026.1025.7124.6823.9223.3228.7027.3826.0225.21
RAISR* [37]32.3531.8729.9129.6529.0428.6227.1026.8628.2427.8426.6826.4225.9225.5024.0823.8929.3028.7326.4026.12
SR-LUT* [15]32.0431.0029.7029.0028.8827.8426.9526.3028.6227.5426.8426.2125.8324.7824.0023.3928.0326.7425.3924.72
LeRF (Ours)33.1732.0230.8630.1530.0628.8428.0527.3529.1528.0027.3126.7026.9025.6824.8824.2330.8629.4828.1027.25
Meta-SR [12]-34.71-32.48-30.56-28.83-29.26-27.73-28.91-26.69-34.37-31.32
LIIF [5]35.6334.5933.1732.3731.6830.3929.4528.6530.4729.2428.4227.7330.2328.8027.5526.6635.7334.1732.2931.19
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MethodRunTime (ms)MACsStorage SizePSNR/SSIM (dB)/-
Nearest11--24.74/0.6553
Bilinear3114.74M-25.51/0.6824
Bicubic12651.61M-26.09/0.7056
Lanczos2494110.59M-26.10/0.7058
Lanczos3914165.89M-26.31/0.7130
RAISR* [37]3,744-3.19MB26.86/0.7357
SR-LUT* [15]13753.33M81.56KB26.30/0.7256
LeRF (Ours)11057.94M1.67MB27.35/0.7475
MetaSR [12]10,2601.68T85.59MB28.83/0.7876
LIIF [5]67,0802.54T255.76MB28.65/0.7878
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Our method is trained with the Adam optimizer [19] in the cosine annealing schedule [28]. We train LeRF with the MSE loss function for " + }, + { + "bbox": [ + 46, + 459, + 287, + 613 + ], + "type": "inline_equation", + "content": "5 \\times 10^{4}" + }, + { + "bbox": [ + 46, + 459, + 287, + 613 + ], + "type": "text", + "content": " iterations at a batch size of 32. Except for the indexing patterns, each branch in our DNN keeps the same architecture as the network in SR-LUT. To obtain a fair comparison in running time tests, we implement LeRF and interpolation methods on the Android platform under the same parallel API, with the only difference being the resampling functions and LUT retrieval in our method. Our method is implemented in MindSpore [33] and the code is available at https://gitee.com/mindspore/models/tree/master/research/cv/lerf." + } + ] + } + ], + "index": 65 + }, + { + "bbox": [ + 47, + 623, + 183, + 634 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 623, + 183, + 634 + ], + "spans": [ + { + "bbox": [ + 47, + 623, + 183, + 634 + ], + "type": "text", + "content": "4.2. Performance Evaluation" + } + ] + } + ], + "index": 66 + }, + { + "bbox": [ + 46, + 642, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 287, + 713 + ], + "type": "text", + "content": "Quantitative comparison. We list the quantitative comparisons with other methods in Table 1. As can be seen, our method achieves significantly better performance than interpolation methods. For example, it exceeds bicubic interpolation up to 3dB PSNR when upsampling " + }, + { + "bbox": [ + 46, + 642, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\frac{\\times 2.0}{\\times 2.0}" + }, + { + "bbox": [ + 46, + 642, + 287, + 713 + ], + "type": "text", + "content": " on the Manga109 dataset." + } + ] + } + ], + "index": 67 + }, + { + "bbox": [ + 304, + 434, + 545, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 434, + 545, + 517 + ], + "spans": [ + { + "bbox": [ + 304, + 434, + 545, + 517 + ], + "type": "text", + "content": "Qualitative comparison. In Fig. 6, we compare the visual quality of LeRF with interpolation methods, RAISR*, and SR-LUT*. As can be seen, our method obtains better visual quality across various local structures and textures. Specifically, since the resampling functions are adapted to local structures, LeRF is capable of retaining clearer textures and avoiding blurry boundaries." + } + ] + } + ], + "index": 68 + }, + { + "bbox": [ + 304, + 518, + 545, + 650 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 518, + 545, + 650 + ], + "spans": [ + { + "bbox": [ + 304, + 518, + 545, + 650 + ], + "type": "text", + "content": "Visualization of learned resampling functions. We further visualize the intermediate results of the proposed method in Fig. 7. As can be seen, the hyper-parameter " + }, + { + "bbox": [ + 304, + 518, + 545, + 650 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 304, + 518, + 545, + 650 + ], + "type": "text", + "content": " clearly distinguishes the orientations of edges (red vs. blue), and " + }, + { + "bbox": [ + 304, + 518, + 545, + 650 + ], + "type": "inline_equation", + "content": "\\sigma_{X}" + }, + { + "bbox": [ + 304, + 518, + 545, + 650 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 518, + 545, + 650 + ], + "type": "inline_equation", + "content": "\\sigma_{Y}" + }, + { + "bbox": [ + 304, + 518, + 545, + 650 + ], + "type": "text", + "content": " capture the horizontal and vertical lines, respectively. In addition, in the second row of each example, we visualize the resampling functions defined by the predicted hyper-parameters. The shapes of resampling functions are well adapted to corners, flat surfaces, and edges with various orientations. This validates the effectiveness of extracting structural priors in a data-driven way." + } + ] + } + ], + "index": 69 + }, + { + "bbox": [ + 306, + 658, + 428, + 671 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 658, + 428, + 671 + ], + "spans": [ + { + "bbox": [ + 306, + 658, + 428, + 671 + ], + "type": "text", + "content": "4.3. Efficiency Evaluation" + } + ] + } + ], + "index": 70 + }, + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "type": "text", + "content": "To evaluate the efficiency, we conduct running time tests, estimate the theoretical MACs, and report storage requirements for LeRF and comparison methods. 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Visualization of the pixel-wise hyper-parameters and the corresponding resampling functions. 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As can be seen, the proposed LeRF achieves comparable or better running time compared to the popular interpolation methods, showing its role as a superior competitor with significantly better performance. On the other hand, compared to DNN-based methods, LeRF shows a clear advantage in efficiency. Furthermore, our method requires far less extra storage as well as fewer software dependencies, demonstrating its practicality for being deployed on various devices." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 47, + 478, + 192, + 489 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 478, + 192, + 489 + ], + "spans": [ + { + "bbox": [ + 47, + 478, + 192, + 489 + ], + "type": "text", + "content": "4.4. Generalization Evaluation" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 46, + 496, + 287, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 496, + 287, + 579 + ], + "spans": [ + { + "bbox": [ + 46, + 496, + 287, + 579 + ], + "type": "text", + "content": "In Fig. 8, we evaluate the generalization ability of LeRF to arbitrary transformations and compare it with widely applied interpolation methods. As can be seen, the learned resampling functions generalize well to unseen deformations. LeRF generates sharper edges and retains more texture details, leading to more visually pleasing results than popular interpolation methods." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 47, + 587, + 153, + 600 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 587, + 153, + 600 + ], + "spans": [ + { + "bbox": [ + 47, + 587, + 153, + 600 + ], + "type": "text", + "content": "4.5. 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We conduct the following ablation experiments on the Set5 benchmark to analyze the design of the steerable resampling function. 1) Fixed gaussian: We freeze the hyperparameters in the anisotropic gaussian. 2) Learned Keys cubic: We utilize another non-steerable resampling function, the isotropic Keys cubic function [16], which has only one hyper-parameter that controls the sharpness of the one-dimension piecewise cubic. The experimental results are listed in Table 3, and visual results are shown in Fig. 10. As can be seen, non-steerable resampling functions like Keys cubic produce zig-zag artifacts and inferior performance, showing the effectiveness of our learned steerable resampling functions." + } + ] + } + ], + "index": 83 + }, + { + "bbox": [ + 306, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 677, + 545, + 713 + ], + "type": "text", + "content": "Analysis of training data. By default, LeRF is trained with " + }, + { + "bbox": [ + 306, + 677, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\times 4" + }, + { + "bbox": [ + 306, + 677, + 545, + 713 + ], + "type": "text", + "content": " downsampled data pairs. 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Ours57.94M1.67MB35.7132.0230.15
fixed gaussian Φ(0,1,1)45.04M244.69KB30.7527.7026.31
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Ours (×4 data)57.94M1.67MB35.7132.0230.15
×2 data training57.94M1.67MB35.6732.1129.91
mixed-scale training57.94M1.67MB35.6732.2630.06
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MACsStorage×2×3×4
Ours57.94M1.67MB35.7132.0230.15
w/o LUT acceleration21.75G1.59MB36.1132.2330.28
w/o DE57.94M978.76KB34.4431.5029.47
w/o “CX” pattern49.65M326.25KB34.9031.7929.70
w/o pre-filtering53.45M1.43MB34.4331.4129.33
pre-filtering only56.10M244.69KB34.6931.1629.15
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It is probably caused by a lack of training on downsampled data. Moreover, replacing the pre-filtering stage with anti-aliasing filters may mitigate this problem [6, 7]." + } + ] + } + ], + "index": 43 + }, + { + "bbox": [ + 306, + 551, + 428, + 563 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 551, + 428, + 563 + ], + "spans": [ + { + "bbox": [ + 306, + 551, + 428, + 563 + ], + "type": "text", + "content": "5. Conclusion Remarks" + } + ] + } + ], + "index": 44 + }, + { + "bbox": [ + 304, + 571, + 545, + 643 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 571, + 545, + 643 + ], + "spans": [ + { + "bbox": [ + 304, + 571, + 545, + 643 + ], + "type": "text", + "content": "In this work, we propose LeRF, a novel method for continuous resampling by integrating learned structural priors into the steerable resampling function. We show its superior performance, high efficiency, and versatility for arbitrary transformations. In the future, we plan to extend this idea to other modalities, e.g., video and point clouds." + } + ] + } + ], + "index": 45 + }, + { + "bbox": [ + 306, + 654, + 403, + 667 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 654, + 403, + 667 + ], + "spans": [ + { + "bbox": [ + 306, + 654, + 403, + 667 + ], + "type": "text", + "content": "Acknowledgement" + } + ] + } + ], + "index": 46 + }, + { + "bbox": [ + 304, + 673, + 545, + 710 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 673, + 545, + 710 + ], + "spans": [ + { + "bbox": [ + 304, + 673, + 545, + 710 + ], + "type": "text", + "content": "This work was supported in part by the National Natural Science Foundation of China under Grants 62131003 and 62021001. We acknowledge the support of MindSpore." + } + ] + } + ], + "index": 47 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5873" + } + ] + } + ], + "index": 48 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 286, + 712 + ], + "type": "list", + "angle": 0, + "index": 19, + "blocks": [ + { + "bbox": [ + 53, + 91, + 286, + 124 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 286, + 124 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 286, + 124 + ], + "type": "text", + "content": "[1] Eirikur Agustsson and Radu Timofte. NTIRE 2017 challenge on single image super-resolution: Dataset and study. In CVPR Workshops, 2017. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 125, + 286, + 146 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 125, + 286, + 146 + ], + "spans": [ + { + "bbox": [ + 53, + 125, + 286, + 146 + ], + "type": "text", + "content": "[2] Jan P. Allebach and Ping Wah Wong. Edge-directed interpolation. In ICIP, 1996. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 148, + 286, + 191 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 148, + 286, + 191 + ], + "spans": [ + { + "bbox": [ + 53, + 148, + 286, + 191 + ], + "type": "text", + "content": "[3] Chang Chen, Xinmei Tian, Feng Wu, and Zhiwei Xiong. UDNet: Up-down network for compact and efficient feature representation in image super-resolution. In ICCV Workshops, 2017. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 194, + 286, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 194, + 286, + 224 + ], + "spans": [ + { + "bbox": [ + 53, + 194, + 286, + 224 + ], + "type": "text", + "content": "[4] Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha, and Feng Wu. Camera lens super-resolution. In CVPR, 2019. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 227, + 286, + 258 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 227, + 286, + 258 + ], + "spans": [ + { + "bbox": [ + 53, + 227, + 286, + 258 + ], + "type": "text", + "content": "[5] Yinbo Chen, Sifei Liu, and Xiaolong Wang. Learning continuous image representation with local implicit image function. In CVPR, 2021. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 262, + 286, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 262, + 286, + 282 + ], + "spans": [ + { + "bbox": [ + 53, + 262, + 286, + 282 + ], + "type": "text", + "content": "[6] Robert L. Cook. Stochastic sampling in computer graphics. ACM Trans. Graph., 5(1):51-72, 1986. 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 285, + 286, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 285, + 286, + 316 + ], + "spans": [ + { + "bbox": [ + 53, + 285, + 286, + 316 + ], + "type": "text", + "content": "[7] Franklin C. Crow. The aliasing problem in computer-generated shaded images. Commun. ACM, 20(11):799-805, 1977. 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 319, + 286, + 340 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 319, + 286, + 340 + ], + "spans": [ + { + "bbox": [ + 53, + 319, + 286, + 340 + ], + "type": "text", + "content": "[8] Neil Anthony Dodgson. Image resampling. Technical report, University of Cambridge, Computer Laboratory, 1992. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 342, + 286, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 342, + 286, + 373 + ], + "spans": [ + { + "bbox": [ + 53, + 342, + 286, + 373 + ], + "type": "text", + "content": "[9] Chao Dong, Chen Change Loy, Kaiming He, and Xiaou Tang. Learning a deep convolutional network for image super-resolution. In ECCV, 2014. 1, 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 376, + 286, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 376, + 286, + 407 + ], + "spans": [ + { + "bbox": [ + 48, + 376, + 286, + 407 + ], + "type": "text", + "content": "[10] Irvine C. Gardner and Francis E. Washer. Lenses of extremely wide angle for airplane mapping. J. Opt. Soc. Am., 38(5):421-431, 1948. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 410, + 286, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 410, + 286, + 453 + ], + "spans": [ + { + "bbox": [ + 48, + 410, + 286, + 453 + ], + "type": "text", + "content": "[11] Pascal Getreuer, Ignacio Garcia-Dorado, John Isidoro, Sungjoon Choi, Frank Ong, and Peyman Milanfar. BLADE: filter learning for general purpose computational photography. In ICCP, 2018. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 455, + 286, + 486 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 455, + 286, + 486 + ], + "spans": [ + { + "bbox": [ + 48, + 455, + 286, + 486 + ], + "type": "text", + "content": "[12] Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Tieniu Tan, and Jian Sun. Meta-SR: A magnification-arbitrary network for super-resolution. In CVPR, 2019. 1, 2, 4, 5, 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 488, + 286, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 488, + 286, + 520 + ], + "spans": [ + { + "bbox": [ + 48, + 488, + 286, + 520 + ], + "type": "text", + "content": "[13] Jia-Bin Huang, Abhishek Singh, and Narendra Ahuja. Single image super-resolution from transformed self-exemplars. In CVPR, 2015. 5" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 522, + 286, + 553 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 522, + 286, + 553 + ], + "spans": [ + { + "bbox": [ + 48, + 522, + 286, + 553 + ], + "type": "text", + "content": "[14] Xu Jia, Hong Chang, and Tinne Tuytelaars. Super-resolution with deep adaptive image resampling. arxiv, 1712.06463, 2017.2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 557, + 286, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 557, + 286, + 588 + ], + "spans": [ + { + "bbox": [ + 48, + 557, + 286, + 588 + ], + "type": "text", + "content": "[15] Younghyun Jo and Seon Joo Kim. Practical single-image super-resolution using look-up table. In CVPR, 2021. 2, 3, 4, 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 590, + 286, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 590, + 286, + 622 + ], + "spans": [ + { + "bbox": [ + 48, + 590, + 286, + 622 + ], + "type": "text", + "content": "[16] Robert G. Keys. Cubic convolution interpolation for digital image processing. IEEE Trans. Acoust., 29:1153-1160, 1981. 1, 2, 4, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 48, + 624, + 286, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 286, + 656 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 286, + 656 + ], + "type": "text", + "content": "[17] Jiwon Kim, Jung Kwon Lee, and Kyoung Mu Lee. Accurate image super-resolution using very deep convolutional networks. In CVPR, 2016. 1, 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 48, + 658, + 286, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 286, + 712 + ], + "type": "text", + "content": "[18] Seon Joo Kim, Hai Ting Lin, Zheng Lu, Sabine Süssstrunk, Stephen Lin, and Michael S. Brown. A new in-camera imaging model for color computer vision and its application. IEEE Trans. Pattern Anal. Mach. Intell., 34(12):2289-2302, 2012. 3" + } + ] + } + ], + "index": 18 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 39, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "type": "text", + "content": "[19] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 96, + 545, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 96, + 545, + 117 + ], + "spans": [ + { + "bbox": [ + 307, + 96, + 545, + 117 + ], + "type": "text", + "content": "[20] Jaewon Lee and Kyong Hwan Jin. Local texture estimator for implicit representation function. In CVPR, 2022. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 119, + 545, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 119, + 545, + 151 + ], + "spans": [ + { + "bbox": [ + 307, + 119, + 545, + 151 + ], + "type": "text", + "content": "[21] Yeon Ju Lee and Jungho Yoon. Nonlinear image upsampling method based on radial basis function interpolation. IEEE Trans. Image Process., 19(10):2682-2692, 2010. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 152, + 545, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 185 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 185 + ], + "type": "text", + "content": "[22] Jiacheng Li, Chang Chen, Zhen Cheng, and Zhiwei Xiong. MuLUT: Cooperating multiple look-up tables for efficient image super-resolution. In ECCV, 2022. 2, 3, 4" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 186, + 545, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 545, + 228 + ], + "type": "text", + "content": "[23] Jiacheng Li, Chang Chen, Zhen Cheng, and Zhiwei Xiong. Toward DNN of LUTs: Learning efficient image restoration with multiple look-up tables. arxiv, 2303.14506, 2023. 2, 3, 4" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 231, + 545, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 231, + 545, + 262 + ], + "spans": [ + { + "bbox": [ + 307, + 231, + 545, + 262 + ], + "type": "text", + "content": "[24] Xin Li and Michael T. Orchard. New edge-directed interpolation. IEEE Trans. Image Process., 10(10):1521-1527, 2001. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 264, + 545, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 264, + 545, + 297 + ], + "spans": [ + { + "bbox": [ + 307, + 264, + 545, + 297 + ], + "type": "text", + "content": "[25] Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. SwinIR: Image restoration using swim transformer. In ICCV Workshops, 2021. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 299, + 545, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 299, + 545, + 330 + ], + "spans": [ + { + "bbox": [ + 307, + 299, + 545, + 330 + ], + "type": "text", + "content": "[26] Jingyun Liang, Kai Zhang, Shuhang Gu, Luc Van Gool, and Radu Timofte. Flow-based kernel prior with application to blind super-resolution. In CVPR, 2021. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 332, + 545, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 332, + 545, + 365 + ], + "spans": [ + { + "bbox": [ + 307, + 332, + 545, + 365 + ], + "type": "text", + "content": "[27] Bee Lim, Sanghyun Son, Heewon Kim, Seungjun Nah, and Young Mu Lee. Enhanced deep residual networks for single image super-resolution. In CVPR Workshops, 2017. 1, 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 366, + 545, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 366, + 545, + 387 + ], + "spans": [ + { + "bbox": [ + 307, + 366, + 545, + 387 + ], + "type": "text", + "content": "[28] Ilya Loshchilov and Frank Hutter. SGDR: stochastic gradient descent with warm restarts. In *ICLR*, 2017. 6" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 307, + 388, + 545, + 420 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 388, + 545, + 420 + ], + "spans": [ + { + "bbox": [ + 307, + 388, + 545, + 420 + ], + "type": "text", + "content": "[29] Cheng Ma, Jingyi Zhang, Jie Zhou, and Jiwen Lu. Learning series-parallel lookup tables for efficient image superresolution. In ECCV, 2022. 2, 3, 4" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 307, + 422, + 545, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 422, + 545, + 465 + ], + "spans": [ + { + "bbox": [ + 307, + 422, + 545, + 465 + ], + "type": "text", + "content": "[30] David R. Martin, Charless C. Fowlkes, Doron Tal, and Jitendra Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In ICCV, 2001. 5" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 307, + 468, + 545, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 468, + 545, + 510 + ], + "spans": [ + { + "bbox": [ + 307, + 468, + 545, + 510 + ], + "type": "text", + "content": "[31] Yusuke Matsui, Kota Ito, Yuji Aramaki, Azuma Fujimoto, Toru Ogawa, Toshihiko Yamasaki, and Kiyoharu Aizawa. Sketch-based manga retrieval using manga109 dataset. Multim. Tools Appl., 76(20):21811-21838, 2017. 5" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 307, + 512, + 545, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 512, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 307, + 512, + 545, + 544 + ], + "type": "text", + "content": "[32] Ben Mildenhall, Jonathan T. Barron, Jiawen Chen, Dillon Sharlet, Ren Ng, and Robert Carroll. Burst denoising with kernel prediction networks. In CVPR, 2018. 2" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 307, + 545, + 502, + 556 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 545, + 502, + 556 + ], + "spans": [ + { + "bbox": [ + 307, + 545, + 502, + 556 + ], + "type": "text", + "content": "[33] MindSpore. https://www.mindspore.cn, 2023.6" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 307, + 558, + 545, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 558, + 545, + 578 + ], + "spans": [ + { + "bbox": [ + 307, + 558, + 545, + 578 + ], + "type": "text", + "content": "[34] Don P. Mitchell and Arun N. Netravali. Reconstruction filters in computer-graphics. In SIGGRAPH, 1988. 2" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 307, + 580, + 545, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 580, + 545, + 634 + ], + "spans": [ + { + "bbox": [ + 307, + 580, + 545, + 634 + ], + "type": "text", + "content": "[35] Marta Mrak, Mislav Grgic, and Murat Kunt. High-Quality Visual Experience: Creation, Processing and Interactivity of High-Resolution and High-Dimensional Video Signals. Signals and Communication Technology. Springer Berlin Heidelberg, 2016. 1" + } + ] + } + ], + "index": 36 + }, + { + "bbox": [ + 307, + 636, + 545, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 636, + 545, + 678 + ], + "spans": [ + { + "bbox": [ + 307, + 636, + 545, + 678 + ], + "type": "text", + "content": "[36] Zhihong Pan, Baopu Li, Dongliang He, Mingde Yao, Wenhao Wu, Tianwei Lin, Xin Li, and Errui Ding. Towards bidirectional arbitrary image rescaling: Joint optimization and cycle idempotence. In CVPR, 2022. 2" + } + ] + } + ], + "index": 37 + }, + { + "bbox": [ + 307, + 681, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 681, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 307, + 681, + 545, + 712 + ], + "type": "text", + "content": "[37] Yaniv Romano, John Isidoro, and Peyman Milanfar. RAISR: rapid and accurate image super resolution. IEEE Trans. Computational Imaging, 3(1):110-125, 2017. 2, 5" + } + ] + } + ], + "index": 38 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5874" + } + ] + } + ], + "index": 40 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 669 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 48, + 72, + 286, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 286, + 95 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 286, + 95 + ], + "type": "text", + "content": "[38] Thomas G. Smith and George Lucas. Industrial Light & Magic: The Art of Special Effects. Virgin, 1986. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 96, + 286, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 96, + 286, + 128 + ], + "spans": [ + { + "bbox": [ + 49, + 96, + 286, + 128 + ], + "type": "text", + "content": "[39] Sanghyun Son and Kyoung Mu Lee. SRWarp: Generalized image super-resolution under arbitrary transformation. In CVPR, 2021. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 130, + 287, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 130, + 287, + 162 + ], + "spans": [ + { + "bbox": [ + 48, + 130, + 287, + 162 + ], + "type": "text", + "content": "[40] Wanjie Sun and Zhenzhong Chen. Learned image downscaling for upscaling using content adaptive resampler. IEEE Trans. Image Process., 29:4027-4040, 2020. 1, 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 164, + 287, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 164, + 287, + 196 + ], + "spans": [ + { + "bbox": [ + 48, + 164, + 287, + 196 + ], + "type": "text", + "content": "[41] Hiroyuki Takeda, Sina Farsiu, and Peyman Milanfar. Kernel regression for image processing and reconstruction. IEEE Trans. Image Process., 16(2):349-366, 2007. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 198, + 286, + 219 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 198, + 286, + 219 + ], + "spans": [ + { + "bbox": [ + 48, + 198, + 286, + 219 + ], + "type": "text", + "content": "[42] Hossein Talebi and Peyman Milanfar. Learning to resize images for computer vision tasks. In ICCV, 2021. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 221, + 287, + 253 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 221, + 287, + 253 + ], + "spans": [ + { + "bbox": [ + 48, + 221, + 287, + 253 + ], + "type": "text", + "content": "[43] Longguang Wang, Yingqian Wang, Zaiping Lin, Jungang Yang, Wei An, and Yulan Guo. Learning A single network for scale-arbitrary super-resolution. In ICCV, 2021. 1, 2, 4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 255, + 286, + 286 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 255, + 286, + 286 + ], + "spans": [ + { + "bbox": [ + 48, + 255, + 286, + 286 + ], + "type": "text", + "content": "[44] Qing Wang and Rabab Kreidieh Ward. A new orientation-adaptive interpolation method. IEEE Trans. Image Process., 16(4):889-900, 2007. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 289, + 286, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 289, + 286, + 331 + ], + "spans": [ + { + "bbox": [ + 48, + 289, + 286, + 331 + ], + "type": "text", + "content": "[45] Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600-612, 2004. 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 334, + 286, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 334, + 286, + 376 + ], + "spans": [ + { + "bbox": [ + 48, + 334, + 286, + 376 + ], + "type": "text", + "content": "[46] Bartlomiej Wronski, Ignacio Garcia-Dorado, Manfred Ernst, Damien Kelly, Michael Krainin, Chia-Kai Liang, Marc Levoy, and Peyman Milanfar. Handheld multi-frame superresolution. ACM Trans. Graph., 38(4):28:1-28:18, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 378, + 286, + 410 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 378, + 286, + 410 + ], + "spans": [ + { + "bbox": [ + 48, + 378, + 286, + 410 + ], + "type": "text", + "content": "[47] Zeyu Xiao, Xueyang Fu, Jie Huang, Zhen Cheng, and Zhiwei Xiong. Space-time distillation for video super-resolution. In CVPR, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 412, + 286, + 444 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 412, + 286, + 444 + ], + "spans": [ + { + "bbox": [ + 48, + 412, + 286, + 444 + ], + "type": "text", + "content": "[48] Jingyu Yang, Sheng Shen, Huanjing Yue, and Kun Li. Implicit transformer network for screen content image continuous super-resolution. In NeurIPS, 2021. 1, 3, 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 446, + 286, + 478 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 446, + 286, + 478 + ], + "spans": [ + { + "bbox": [ + 48, + 446, + 286, + 478 + ], + "type": "text", + "content": "[49] Mingde Yao, Dongliang He, Xin Li, Zhihong Pan, and Zhiwei Xiong. Bidirectional translation between uhd-hdr and hd-sdr videos. IEEE Trans. Multimedia, 2023. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 480, + 286, + 522 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 480, + 286, + 522 + ], + "spans": [ + { + "bbox": [ + 48, + 480, + 286, + 522 + ], + "type": "text", + "content": "[50] Hui Zeng, Jianrui Cai, Lida Li, Zisheng Cao, and Lei Zhang. Learning image-adaptive 3d lookup tables for high performance photo enhancement in real-time. IEEE Trans. Pattern Anal. Mach. Intell., 44(4):2058-2073, 2022. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 525, + 286, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 525, + 286, + 567 + ], + "spans": [ + { + "bbox": [ + 48, + 525, + 286, + 567 + ], + "type": "text", + "content": "[51] Kaibing Zhang, Xinbo Gao, Dacheng Tao, and Xuelong Li. Single image super-resolution with non-local means and steering kernel regression. IEEE Trans. Image Process., 21(11):4544-4556, 2012. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 570, + 286, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 570, + 286, + 601 + ], + "spans": [ + { + "bbox": [ + 48, + 570, + 286, + 601 + ], + "type": "text", + "content": "[52] Lei Zhang and Xiaolin Wu. An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans. Image Process., 15(8):2226-2238, 2006. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 604, + 286, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 604, + 286, + 635 + ], + "spans": [ + { + "bbox": [ + 48, + 604, + 286, + 635 + ], + "type": "text", + "content": "[53] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 638, + 286, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 638, + 286, + 669 + ], + "spans": [ + { + "bbox": [ + 48, + 638, + 286, + 669 + ], + "type": "text", + "content": "[54] Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, and Yun Fu. Image super-resolution using very deep residual channel attention networks. 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Lau†", + "bbox": [ + 540, + 180, + 697, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1City University of Hong Kong", + "bbox": [ + 217, + 198, + 465, + 217 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ East China Normal University", + "bbox": [ + 504, + 199, + 751, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "jiayinlin5-c@my.cityu.edu.hk, xtan@cs.ecnu.edu.cn, Rynson.Lau@cityu.edu.hk", + "bbox": [ + 155, + 219, + 808, + 233 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 268, + 313, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Detecting mirrors from static images has received significant research interest recently. However, detecting mirrors over dynamic scenes is still under-explored due to the lack of a high-quality dataset and an effective method for video mirror detection (VMD). To the best of our knowledge, this is the first work to address the VMD problem from a deep-learning-based perspective. Our observation is that there are often correspondences between the contents inside (reflected) and outside (real) of a mirror, but such correspondences may not always appear in every frame, e.g., due to the change of camera pose. This inspires us to propose a video mirror detection method, named VMD-Net, that can tolerate spatially missing correspondences by considering the mirror correspondences at both the intra-frame level as well as inter-frame level via a dual correspondence module that looks over multiple frames spatially and temporally for correlating correspondences. We further propose a first large-scale dataset for VMD (named VMD-D), which contains 14,987 image frames from 269 videos with corresponding manually annotated masks. Experimental results show that the proposed method outperforms SOTA methods from relevant fields. To enable real-time VMD, our method efficiently utilizes the backbone features by removing the redundant multi-level module design and gets rid of post-processing of the output maps commonly used in existing methods, making it very efficient and practical for real-time video-based applications. Code, dataset, and models are available at https://jiaying.link/cvpr2023-vmd/", + "bbox": [ + 75, + 301, + 473, + 724 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 757, + 209, + 772 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Mirrors appear everywhere. They can adversely affect the performance of computer vision tasks (e.g., depth estimation [35], vision-and-language navigation [2], semantic segmentation [49]), due to their fundamental property that they reflect objects from their surroundings. Thus, it is nec", + "bbox": [ + 75, + 784, + 468, + 859 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/fe267c940ac5f0c9c457d83d02d468f7ecbbdb2ea59451cb41eaecb937b6dd80.jpg", + "image_caption": [], + "image_footnote": [ + "$\\longleftrightarrow$ intra-frame correspondence" + ], + "bbox": [ + 504, + 267, + 602, + 354 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e143b424250c1210f09ee8fc7195cb524b3d19488e571b1adb2c0b43fe90b35e.jpg", + "image_caption": [ + "Image" + ], + "image_footnote": [], + "bbox": [ + 506, + 356, + 602, + 398 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/90f6aa71a75f7786b969931a6c7ffdbb16dfd4e9530ab5c1d1ed6a96a4548a46.jpg", + "image_caption": [], + "image_footnote": [ + "$\\longleftrightarrow$ inter-frame correspondence" + ], + "bbox": [ + 602, + 268, + 696, + 354 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/488d1f0843d954001c4a77250c09edb9ec09b71bbb73a69574e230731fde806a.jpg", + "image_caption": [ + "VCNet" + ], + "image_footnote": [], + "bbox": [ + 604, + 356, + 696, + 398 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d10b9ed4d1043d555f5231d1b4f5363b3102446b4977c2f3b39461a2c80f4a7d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 697, + 268, + 792, + 354 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d162cc780ea794ed715315456249e0afd4cc6e1f404d975602ca160143597ee6.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 697, + 356, + 792, + 398 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/98422925ad2bc4b9548cd312569560292c72d855e6cfe067de68e5a547162104.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 792, + 268, + 888, + 354 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/8ca154b8aba7e1efa499f7e8e83c8ae3fb6c222f4c73283270ed44efa532e23e.jpg", + "image_caption": [ + "GT", + "Figure 1. Although state-of-the-art single-image mirror detection method VCNet [36] performs well on a single image (e.g., the first row) by using implicitly intra-frame correspondence, it may fail when the intra-frame cue is weak or even absent in some video frames (e.g., the second and third rows). The lack in exploiting inter-frame information causes the current mirror detection methods to produce inaccurate and inconsistent results when applied to VMD. In contrast, our method can perform well in both situations by utilizing the proposed dual correspondence module to exploit intra-frame (spatial) and inter-frame (temporal) correspondences." + ], + "image_footnote": [], + "bbox": [ + 794, + 356, + 887, + 398 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "essary to build a robust computer vision model that can distinguish mirrors from their surrounding objects correctly.", + "bbox": [ + 496, + 622, + 890, + 652 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Existing single-image mirror detection methods exploit different cues, such as context contrast [42], explicitly correspondences [22], semantics association [14], and chirality and implicit correspondences [36], to detect mirrors from single RGB input images. Despite these recent efforts being put into the mirror detection problem, none of them focuses on detecting mirrors from videos. However, a lot of real-world computer vision applications are video-based (e.g., robotic navigation, autonomous driving, and surveillance), rather than image-based. Hence, solving the video mirror detection (VMD) problem can benefit these applications.", + "bbox": [ + 496, + 655, + 892, + 821 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we aim to address the VMD problem. There are two major challenges with this problem. First, to the best of our knowledge, there are no existing large-scale datasets that can be used for training and evaluation on the VMD problem. Second, existing mirror detection methods,", + "bbox": [ + 496, + 824, + 893, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Joint first authors.", + "bbox": [ + 94, + 875, + 199, + 886 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding author.", + "bbox": [ + 96, + 887, + 220, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "9109", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6e6f76ed23fbd1d35779951e2604a1b50fbd5389acef935999ef34337eae9a33.jpg", + "image_caption": [ + "Figure 2. Quantitative comparison on the performance and efficiency between existing mirror detection methods and our method for VMD. All models are trained/tested on the proposed VMD-D dataset, under a single RTX 3090 GPU. Our model has $\\sim 5$ times smaller network parameters and runs $\\sim 18\\times$ faster than the state-of-the-art image-based mirror detection method, VCNet [36], and still outperforms it by a large margin." + ], + "image_footnote": [], + "bbox": [ + 94, + 92, + 449, + 294 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "which are all developed for the image-based mirror detection task, are all based on static cues. None of them take advantages of the dynamic nature of videos in the VMD problem. Figure 1 shows that the current state-of-the-art mirror detection method, VCNet [36], may fail when correspondences are missing in some challenging frames (e.g., second and third rows) due to, for example, the change of camera pose, even though it may perform well in some easy cases (e.g., the first row). Besides, as the image-based mirror detection task is already very challenging, existing methods for this task often adopt heavy network design and time-consuming post-processing techniques [19] to improve their results. Figure 2 shows that existing image-based mirror detection models run at about 1fps, even on one of the latest GPUs, which cannot support real-time VMD. All these drawbacks motivate us to develop a large-scale dataset and an effective/efficient method for video mirror detection.", + "bbox": [ + 75, + 441, + 467, + 699 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we address the VMD problem in two ways. First, we construct the first large-scale video mirror detection benchmark dataset (VMD-D). It contains 14,987 image frames in 269 videos, coming from diverse scenes. The constructed VMD-D dataset provides large-scale and high-diversity data for training and evaluation on the VMD problem. Second, we propose an effective and efficient method, called VMD-Net, for the VMD problem. The proposed method exploits multi-frame correspondences at both intra-frame (spatial) and inter-frame (temporal) levels. Compared with state-of-the-art image-based mirror detection methods, which typically adopt heavy pipelines, our method uses a light-weight network architecture without the need for any", + "bbox": [ + 75, + 704, + 467, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "post-processing techniques. As a result, our method is efficient for real-time applications. In particular, our method has $\\sim 5$ times fewer network parameters and runs $\\sim 18\\times$ faster than the latest state-of-the-art image-based mirror detection method, VCNet [36]. We conduct comprehensive experiments to demonstrate the effectiveness and efficiency of our proposed method. Experimental results show that our method outperforms state-of-the-art methods from relevant tasks on the proposed large-scale VMD-D dataset.", + "bbox": [ + 496, + 90, + 890, + 226 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our key contributions can be summarized as follows:", + "bbox": [ + 517, + 228, + 870, + 242 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We construct the first large-scale video mirror detection dataset, called VMD-D. The new dataset contains 14,988 image frames from 269 videos with precise annotated masks.", + "- We propose a novel network, called VMD-Net, to exploit both intra-frame and inter-frame correspondences via a dual correspondence (DC) module. This DC module allows VMD-Net to tolerate occasionally missing correspondences in the temporal dimension.", + "- Extensive evaluations show that our method outperforms existing state-of-the-art methods from relevant tasks on our proposed VMD-D dataset." + ], + "bbox": [ + 517, + 255, + 890, + 460 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 477, + 638, + 492 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Image-based Mirror Detection. Recently, Yang et al. [42] propose the first mirror detection dataset and the first mirror detection network to detect mirrors by modeling contextual contrasted information. Lin et al. [22] then propose a more diverse and larger mirror detection benchmark, and a correspondence-aware method for mirror detection, which correlates the contents inside and outside of the mirror. Mei et al. [27] further consider using RGB-D data for detecting mirrors, and construct the first RGB-D dataset for mirror detection. Tan et al. [35] address the depth refinement problem on mirror surfaces by proposing another RGB-D mirror dataset and a detect-and-refine method for mirror detection. Most recently, Tan et al. [36] propose an image-based method for mirror detection based on the chirality cue and implicit correspondences.", + "bbox": [ + 496, + 502, + 890, + 729 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Despite their success, none of these methods focuses on the VMD problem. They also have high computational costs, thus not practical for real-time applications. In this paper, we aim to address the VMD problem by constructing a benchmark dataset and proposing an efficient/effective method for the problem.", + "bbox": [ + 496, + 729, + 890, + 821 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Video Object Segmentation Video object segmentation (VOS) aims to segment the target object(s) from the input videos. Currently, it can be categorized into two main individual tasks: the unsupervised video object segmentation (UVOS) [32, 38-40, 43, 45] and the semi-supervised video", + "bbox": [ + 496, + 824, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "9110", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/608afd6ead44b8fe5a3f75bbc23a151b587390f8cfe4f364a3b36ed79bcec810.jpg", + "image_caption": [ + "Figure 3. Snapshots of the proposed Video Mirror Detection dataset, VMD-D, with pixel-level annotations." + ], + "image_footnote": [], + "bbox": [ + 78, + 88, + 893, + 224 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "object segmentation (SVOS) [7, 8, 23-25]. The main difference between UVOS and SVOS is that UVOS does not require annotation for the first frame of the input video during the inference phase, while SVOS needs to do it. Thus, UVOS is more related to the video mirror detection problem since we do not use the first-frame ground truth mirror label to infer the location of mirrors in the rest of the video frames. UVOS focuses on automatically detecting the primary foreground object without using the ground truth mask of the first frame during testing. Early UVOS methods use handcrafted features [30, 34] to detect foreground objects in the input videos. Recent CNN-based UVOS models [38-40] focus on exploring different cues like memory [38], graph neural network [39] and visual attention [40] for better performance. The latest UVOS methods [32, 45] take optical flow as auxiliary information to help segment videos, thanks to the rapid evolution on optical flow estimation [37].", + "bbox": [ + 75, + 276, + 472, + 547 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "However, UVOS methods cannot directly address the video mirror detection problem, since mirrors are not always the primary foreground objects while having changing appearance patterns during motion.", + "bbox": [ + 76, + 560, + 468, + 621 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Video Salient Object Detection Video salient object detection (VSOD) is related to the UVOS task. It aims to automatically detect the most visually distinctive objects from an input video without indicating where the salient object is in the first frame. Unlike the methods for single-image salient object detection [29, 48], VSOD methods require building a model with a thorough understanding of dynamic attention. Most earlier VSOD methods detect the salient objects using handcrafted features [4, 15, 16]. With the help of deep neural networks, recent VSOD methods achieve great progress. Fan et al. [11] take attention shift into account. Gu et al. [12] exploit self-attention models to detect salient objects from videos efficiently. Zhang et al. [46] proposes a dynamic strategy of context fusion for VSOD.", + "bbox": [ + 75, + 632, + 468, + 843 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Similar to UVOS, the methods for video salient object detection are not directly applicable to video mirror detection since mirrors are not always distinctive.", + "bbox": [ + 76, + 854, + 468, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Video Mirror Detection Dataset (VMD-D)", + "text_level": 1, + "bbox": [ + 498, + 273, + 874, + 292 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To facilitate research on the video mirror detection problem, we first contribute a large-scale video mirror detection dataset (named VMD-D). It consists of 269 videos in 14,988 image frames with corresponding precise annotations from diverse scenes. Figure 3 shows some example video frames in our proposed VMD-D dataset. The details of our VMD are discussed below.", + "bbox": [ + 496, + 300, + 890, + 405 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Dataset Construction", + "text_level": 1, + "bbox": [ + 500, + 415, + 699, + 429 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To construct the first large-scale video mirror detection dataset, we use a smartphone to collect high-resolution videos (e.g., $1920 \\times 1080$ resolution) with mirrors in daily-life scenes. Following a common practice [6] used to construct datasets for video-based problems, we manually trim the collected videos to make sure that each video frame has at least one mirror region. After that, we obtain 269 video clips and then randomly split them into a training set with 143 videos (containing 7,835 images) and a test set with 126 videos (containing 7,152 images). We then label pixel-level mirror masks by annotators. The total duration of our videos is 502 seconds for covering long-temporal scenarios. The frame rate is 30 fps for all collected videos.", + "bbox": [ + 496, + 438, + 890, + 633 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Dataset Analysis", + "text_level": 1, + "bbox": [ + 500, + 643, + 663, + 660 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Table 1 shows the comparison of different datasets from the relevant areas, including image mirror detection [22,42] (top group), video object segmentation [20,28,33] (second group), and video shadow detection [6] (third group), and ours. Our dataset reflects good video quality (i.e., high-resolution videos) and quantity (i.e., a large number of annotated frames), and is practical for being the first step for large-scale video mirror detection.", + "bbox": [ + 496, + 666, + 890, + 787 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Area Distribution. Figure 4(a) shows the ratio of mirror area over the image area (mirror area distribution). We can see that our dataset contains mirrors covering a wide range of area ratios. We also note that it consists of a lot of small mirrors $(< 0.1)$ , which makes our dataset very challenging.", + "bbox": [ + 496, + 791, + 890, + 867 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Contrast Distribution. We analyze the contrasts between the mirror regions and non-mirror regions by computing $\\chi^2$", + "bbox": [ + 498, + 869, + 890, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "9111", + "bbox": [ + 480, + 944, + 513, + 955 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/1ce96c81a6077923144114a0b1d867f1a5b07c3d13eb00307bc585ba632f6b3c.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Dataset#Videos#Annos.#Time (s).#FPSMax Reso.
MSD [42]-4,018--640×512
PMD [22]-6,461--4000×3000
FBMS [28]59720--640×480
STV2 [20]14947--640×360
DAVIS [33]503455144241920×1080
ViSha [6]12011,685390301280×720
Ours26915,066502301920×1080
", + "bbox": [ + 76, + 88, + 480, + 234 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Table 1. Comparison of different video datasets for relevant tasks.", + "bbox": [ + 76, + 246, + 468, + 261 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/274d704530ba4f4a196d77643f9043116634ff641d60432573bbf3415b669e54.jpg", + "image_caption": [ + "(a) mirror area distribution", + "Figure 4. Statistics of our VMD dataset." + ], + "image_footnote": [], + "bbox": [ + 81, + 277, + 272, + 388 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/835d306de4d703ee7e7553390ac3f97becb10176a599739047e59897a981e570.jpg", + "image_caption": [ + "(b) color contrast distribution" + ], + "image_footnote": [], + "bbox": [ + 277, + 276, + 465, + 388 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "distance between their RGB histograms. We also compare the distribution between MSD [42] and PMD [22], as shown in Figure 4(b). In general, VMD-D has more images with extremely low color contrasts ( $< 0.1$ ) compared with existing mirror datasets MSD and PMD. This indicates our images are collected from very diverse scenes, making them more challenging to detect.", + "bbox": [ + 75, + 458, + 468, + 565 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Method", + "text_level": 1, + "bbox": [ + 76, + 579, + 168, + 594 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Overview", + "text_level": 1, + "bbox": [ + 76, + 604, + 187, + 619 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We observe that there are often spatial correspondences between the contents inside (reflected) and outside (real) of a mirror. However, such spatial correspondences may not always appear in every frame. The lack in exploiting temporal correspondence may cause current image-based mirror detection methods to fail in the VMD task. Hence, in this paper, we propose a method that is able to tolerate temporally missing correspondences by considering mirror correspondences at both the intra-frame level (spatial) as well as the inter-frame level (temporal).", + "bbox": [ + 75, + 628, + 468, + 779 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Figure 5 shows the overall structure of our proposed method VMD-Net. The key idea of our network design is to leverage the intra-frame and the inter-frame correspondences for video mirror detection. Our VMD-Net takes three images from the same video clips as inputs. The first two images $I_{t}$ and $I_{t + 1}$ are from adjacent video frames, while the third image $I_{n}$ is randomly selected from other frames. We first apply a shared backbone network ResNext", + "bbox": [ + 75, + 780, + 470, + 901 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/cb999da9f8d988e65ef969195cb7ce6aa822226f9ce7995e5b4c1499ffb91a92.jpg", + "image_caption": [ + "Figure 5. The framework of our proposed method. $I_{t}, I_{t + 1}, I_{n}$ are input images. In particular, $I_{t}$ and $I_{t + 1}$ are two adjacent video frames, and $I_{n}$ is a video frame randomly selected from the same video of $I_{t}$ . $P_{t}, P_{t + 1}, P_{n}$ are intermediate maps and the final outputs maps are $M_{t}, M_{t + 1}, M_{n}$ for the corresponding input images." + ], + "image_footnote": [], + "bbox": [ + 535, + 93, + 916, + 349 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "101 [41] to extract multi-scale features from the input images. Unlike existing single-image mirror detection methods, which make full usage of the features at all stages (i.e., from the $1^{st}$ to the $5^{th}$ scales), for each input image $i$ , our VMD-Net only utilizes the low-level features at the $2^{nd}$ scale (denoted as $\\mathbf{F}_{low}^{i}$ ) and the high-level features at the $5^{th}$ scale. Following the design of DeepLabV3 [5], the high-level features from the backbone network are then fed to an atrous spatial pyramid pooling module to obtain enhanced semantic features (denoted as $\\mathbf{F}_{high}^{i}$ ). Taking image $I_{t}$ as an example, we then assign a dual correspondence module to both low-level features $\\mathbf{F}_{low}^{t}$ and high-level features $\\mathbf{F}_{high}^{t}$ to produce the intermediate map $P_{t}$ and the final output $M_{t}$ . Similarly, the dual correspondence module takes the low-level features and high-level features from $I_{t+1}, I_{n}$ to produce the intermediate maps $P_{t+1}, P_{n}$ and the final outputs $M_{t+1}, M_{n}$ .", + "bbox": [ + 496, + 458, + 892, + 717 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Dual Correspondence (DC) Module", + "text_level": 1, + "bbox": [ + 500, + 726, + 808, + 742 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Figure 6 shows the structure of the proposed DC module. It consists of two stages. The first stage of the DC module aims to learn intra-frame correspondences for all input images and the short-term inter-frame correspondences inside mirrors in the adjacent input image $I_{t}$ and $I_{t + 1}$ . The second stage of the DC module focuses on extracting long-term inter-frame correspondences across all input images to enhance the learning of temporal correspondence. This enables our VMD-Net to exploit correspondences at both intra-frame and inter-frame levels at different tempo", + "bbox": [ + 496, + 750, + 893, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "9112", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/2f4ba6477c79b1ffe7d114d9c9a75f0e811b45e842dfaf76537b484e8d2eb047.jpg", + "image_caption": [ + "Figure 6. Our DC module. It consists of two stages. The first stage in the DC module aims to extract intra-frame and short-term temporal correspondences from two adjacent video frames $I_{t}$ , $I_{t+1}$ , while the second stage in the DC module focuses on learning long-term temporal correspondences from all input frames $I_{t}$ , $I_{t+1}$ , $I_{n}$ ." + ], + "image_footnote": [], + "bbox": [ + 125, + 88, + 883, + 372 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "ral scales.", + "bbox": [ + 76, + 454, + 147, + 468 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Stage 1. Our DC module takes the low-level features $\\mathbf{F}_{low}$ and high-level features $\\mathbf{F}_{high}$ as input. In the first stage, we first extract the intra-frame correspondences from the high-level features $\\mathbf{F}_{high}$ of each image by the global relation (GR) block proposed in [22]. The GR block can effectively and efficiently extract the correspondence between the content inside and outside of the mirrors by modeling spatial corresponding relations in a single image. The high-level correspondence-aware features from the randomly selected frame $I_{n}$ are directly concatenated with the corresponding low-level features $\\mathbf{F}_{low}^{n}$ in the decoder to output an intermediate map $P_{n}$ , while the high-level correspondence-aware features extracted from the two adjacent frames $I_{t}$ and $I_{t + 1}$ are forwarded to a cross-attention (Cross-Att.) module to learn short-term temporal correspondences. To compute short-term temporal correspondences between $I_{t}$ and $I_{t + 1}$ , we also feed their low-level features $\\mathbf{F}_{low}^{t},\\mathbf{F}_{low}^{t + 1}$ to another cross-attention module.", + "bbox": [ + 75, + 473, + 473, + 744 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our cross-attention module is inspired by [17], but we extend it from self-attention (single-input and single-out features) to cross-attention with two-input and two-output features and also enhance its ability to model spatial correspondences. Our module first applies two convolutional layers with $1 \\times 1$ filters to the two input features $\\mathbf{F}_t$ and $\\mathbf{F}_{t+1}$ and further generates a spatially enhanced affinity matrix $A^{se} \\in \\mathbb{R}^{(2H + 2W - 1) \\times (W \\times H)}$ . Different from the original affinity matrix $A \\in \\mathbb{R}^{(H + W - 1) \\times (W \\times H)}$ in [17], which is computed by extracting local contextual information in the", + "bbox": [ + 75, + 750, + 472, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "horizontal and vertical directions of the input features, our spatially enhanced affinity operation also computes global contextual information by taking the diagonal items of the input features into our design. Formally, our cross-attention process can be described as:", + "bbox": [ + 496, + 454, + 893, + 530 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nK = \\operatorname {C o n v} _ {1 \\times 1} (\\mathbf {F} _ {t}); Q = \\operatorname {C o n v} _ {1 \\times 1} (\\mathbf {F} _ {t + 1}), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 562, + 542, + 890, + 559 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nA ^ {s e} = \\mathbb {A} ^ {s e} (K, Q), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 552, + 561, + 890, + 578 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nV = \\operatorname {C o n v} _ {1 \\times 1} \\left(\\mathbf {F} _ {t}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 563, + 580, + 890, + 597 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n(2 H + 2 W - 1) \\times (W \\times H)\n$$\n", + "text_format": "latex", + "bbox": [ + 653, + 599, + 787, + 612 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {C} ^ {t} = K + \\omega_ {t} \\quad \\sum_ {i} A ^ {s e} V, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 558, + 612, + 890, + 643 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n(2 H + 2 W - 1) \\times (W \\times H)\n$$\n", + "text_format": "latex", + "bbox": [ + 668, + 646, + 802, + 659 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {C} ^ {t + 1} = Q + \\omega_ {t + 1} \\quad \\sum_ {i} \\quad A ^ {s e} V, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 542, + 659, + 890, + 689 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathbf{C}$ is the output of the cross-attention module. $\\omega_{t}$ and $\\omega_{t + 1}$ are learnable parameters. $\\mathbb{A}^{se}$ is our spatially enhanced affinity operation. $\\mathbf{C}^t$ and $\\mathbf{C}^{t + 1}$ are the output correspondence-aware features. We apply the cross-attention module to both $\\mathbf{F}_{low}^{t},\\mathbf{F}_{low}^{t + 1}$ and $\\mathbf{F}_{high}^{t},\\mathbf{F}_{high}^{t + 1}$ to obtain correspondence-aware features $\\mathbf{C}_{low}^{t},\\mathbf{C}_{low}^{t + 1}$ and $\\mathbf{C}_{high}^{t},\\mathbf{C}_{high}^{t + 1}$ . We then concatenate the correspondence features from the same input image but at different levels. The concatenated features $[\\mathbf{C}_{low};\\mathbf{C}_{high}]$ are forwarded to a decoder to obtain an intermediate prediction $P_{t}$ for $I_{t}$ . Similarly, we obtain the intermediate predictions $P_{t + 1}$ and $P_{n}$ for $I_{t + 1}$ and $I_{n}$ .", + "bbox": [ + 496, + 700, + 890, + 883 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Stage 2. The second stage of our DC module takes all in", + "bbox": [ + 517, + 885, + 890, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "9113", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "intermediate prediction maps $P_{t}, P_{t + 1}, P_{n}$ and high-level features $\\mathbf{F}_{high}^{t}, \\mathbf{F}_{high}^{t + 1}, \\mathbf{F}_{high}^{n}$ as inputs. The input features and prediction maps are forwarded to a reverse cross-attention module. This module aims at explicitly exploiting the correspondence between the contents inside and outside of the mirrors in different frames for long-range temporal correspondences. In particular, we need to explicitly model the correspondences between input frame $I_{t}$ and the randomly selected frame $I_{n}$ . To do this, we first multiply $F_{high}$ by their corresponding intermediate prediction map $P$ , which are normalized by a sigmoid function. We then reverse $P$ to obtain the reversed prediction map $\\hat{P}$ , which indicates the potential non-mirror regions. We also notice that the mirrors will potentially flip the real object horizontally in its content. Thus, we conduct a horizontal flip to the input non-mirror features to model the potential relation of mirror reflection. Similarly, we compute temporal correspondences between $I_{t}$ and $I_{t + 1}$ with this strategy. In the second stage of the DC module, we also use our cross-attention module to extract the inter-frame correspondences. The process of our reverse cross-attention module can be formulated as follows:", + "bbox": [ + 75, + 90, + 472, + 421 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\Omega_ {n, -} = \\mathbb {C} \\mathbb {A} \\left(P _ {n} \\mathbf {F} _ {h i g h} ^ {n}, \\left(\\hat {P} _ {t} \\mathbf {F} _ {h i g h} ^ {t}\\right) ^ {\\top}\\right), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 150, + 431, + 468, + 450 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\Omega_ {t, -} = \\mathbb {C} \\mathbb {A} \\left(P _ {t} \\mathbf {F} _ {h i g h} ^ {t}, \\left(\\hat {P} _ {t + 1} \\mathbf {F} _ {h i g h} ^ {t + 1}\\right) ^ {\\top}\\right), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 453, + 468, + 472 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\Omega_ {t + 1, -} = \\mathbb {C A} \\left(P _ {t + 1} \\mathbf {F} _ {h i g h} ^ {t + 1}, \\left(\\hat {P} _ {t} \\mathbf {F} _ {h i g h} ^ {t}\\right) ^ {\\top}\\right), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 138, + 474, + 468, + 494 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\mathbb{C}\\mathbb{A}$ is our cross-attention module. $\\top$ is a horizontal flip operation. $\\Omega$ is the final output feature. Note that we omit the second output of the cross-attention modules used in the $2^{nd}$ stage for convenience. We then forward the output features to individual decoders to obtain the final output predictions $M_{t}, M_{t+1}, M_{n}$ for the input images $I_{t}, I_{t+1}, I_{n}$ , respectively.", + "bbox": [ + 75, + 503, + 468, + 612 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Loss Functions", + "text_level": 1, + "bbox": [ + 76, + 619, + 230, + 633 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We adopt the Lovász-Softmax loss function [3] to supervise our network training. The final loss function is:", + "bbox": [ + 76, + 643, + 468, + 672 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\ni \\in \\{t, t + 1, n \\}\n$$\n", + "text_format": "latex", + "bbox": [ + 161, + 681, + 235, + 695 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\sum_ {i} \\quad \\mathcal {L} _ {h} \\left(P _ {i}, G _ {i}\\right) + \\mathcal {L} _ {h} \\left(M _ {i}, G _ {i}\\right), \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 130, + 695, + 468, + 723 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_h(\\cdot ,\\cdot)$ denotes the lovasz-hinge loss. $P_{i},M_{i},G_{i}$ are the intermediate output from the DC module, the final output of our network, and the ground truth label of image $I_{i}$ , respectively.", + "bbox": [ + 76, + 733, + 468, + 794 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 76, + 806, + 209, + 823 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Experimental Settings and Evaluation Metrics", + "text_level": 1, + "bbox": [ + 76, + 830, + 468, + 848 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We have implemented VMD-Net in Pytorch [31] and trained it on a PC with an RTX3090 GPU card. During training, we resize the input images to $384 \\times 384$ . We", + "bbox": [ + 76, + 854, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "use ResNext-101 [41] pre-trained on ImageNet [9] as our backbone network to extract image features. We adopt Adam [18] as the optimizer with a momentum of 0.9 and a weight decay of $5 \\times 10^{-4}$ . The base learning rate, batch size, and the number of training epochs are 0.0001, 8, and 15, respectively. We use the cosine learning rate decay with 3-epoch warm-up period to adjust the learning rate during training. Note that we do NOT apply any post-processing techniques to refine our predictions.", + "bbox": [ + 496, + 90, + 890, + 226 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We employ four popular metrics, intersection over union (IoU), pixel accuracy, F-measure $(F_{\\beta})$ [1], and mean absolute error (MAE) to evaluate the performance of tested methods quantitatively. $F_{\\beta}$ is defined as:", + "bbox": [ + 496, + 227, + 890, + 287 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nF _ {\\beta} = \\frac {1 + \\beta^ {2} (P r e c i s i o n \\times R e c a l l)}{\\beta^ {2} P r e c i s i o n + R e c a l l},\n$$\n", + "text_format": "latex", + "bbox": [ + 568, + 297, + 821, + 330 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\beta$ is set to 0.3 as suggested in [1]. It evaluates the overall performance between precision and recall.", + "bbox": [ + 496, + 339, + 890, + 369 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Comparison to the State-of-the-art Methods", + "text_level": 1, + "bbox": [ + 498, + 378, + 872, + 395 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Due to the lack of methods for video mirror detection, we compare our method with 14 state-of-the-art methods from relevant fields, including GateNet [48] and MINet [29] for salient object detection; PCSA [13] for video salient object detection; DeepLabV3 [5], PSPNet [47] and OCRNet [44] for semantic segmentation; TVSD [6], STICT [26] and ScCor [10] for video shadow detection; HFAN [32] for video object segmentation; GlassNet [21] for glass surface detection; MirrorNet [42], PMDNet [22] and VCNet [36] for single-image mirror detection. We train and test all baseline methods on our video mirror detection dataset VMD-D using their released codes on the same platform. Table 2 shows the quantitative results. Our method achieves the best performance with a large margin on all four metrics.", + "bbox": [ + 496, + 402, + 890, + 613 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Figure 7 visually compares the results of our method with the selected state-of-the-art methods from relevant fields. We can see that when compared with image-based mirror detection methods PMDNet [22] and VCNet [36], which produce inaccurate and incoherent results both in a short-term temporal interval (e.g., the 1st and 2nd rows; the 3rd and the 4th rows) and a long-term temporal interval (e.g., the 5th and 6th rows; the 7th and 8th rows), our method can precisely predict the mirror regions by exploiting inter-frame and intra-frame correspondences in both situations. We attribute the superior performances of our method to the design of the DC module, which enables the modeling of spatial, short-range, and long-range temporal correspondences.", + "bbox": [ + 496, + 613, + 890, + 824 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In particular, we also compare the efficiency of our method with most existing mirror detection methods. Table 3 shows the quantitative results in the number of network parameters and FPS. Our model has significantly fewer network parameters and runs much faster than the", + "bbox": [ + 496, + 825, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "9114", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/6e366e2bc0b144a3b11f6776b9c0d4095662d666e94105f3fdfccfe4ea34c2a9.jpg", + "image_caption": [ + "Image" + ], + "image_footnote": [], + "bbox": [ + 80, + 88, + 158, + 369 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d7536aefa4255608c739b362633283dabdb74412d45ac51a0e0505d2eaa2566d.jpg", + "image_caption": [ + "GateNet [48]" + ], + "image_footnote": [], + "bbox": [ + 161, + 88, + 238, + 369 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/38182c110535ca90dcc3a8b6062186d54615245eb6e65fedf4d153d418b879a8.jpg", + "image_caption": [ + "DeepLabV3 [5]" + ], + "image_footnote": [], + "bbox": [ + 241, + 88, + 320, + 369 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/413d9889243360f56b9bff4f1924e1ab4a869bc3b8ea2d0650d3c99cffdb3237.jpg", + "image_caption": [ + "Sc-Cor [10]" + ], + "image_footnote": [], + "bbox": [ + 321, + 88, + 401, + 369 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a1904b63ae3983b8b2262988015e806a8f563d9516dce46bac7527234475e547.jpg", + "image_caption": [ + "HFAN [32]" + ], + "image_footnote": [], + "bbox": [ + 403, + 88, + 482, + 369 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d46deeb0889bf0b9ce4936d4f2e69f2f2215d7ef4f552602d2a975f32cd9f074.jpg", + "image_caption": [ + "GlassNet [21]" + ], + "image_footnote": [], + "bbox": [ + 483, + 88, + 565, + 369 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5c51e4b630bfca49a822d16e6fb50f8131ced043dd107bef3a815965b9b48588.jpg", + "image_caption": [ + "PMDNet [22]", + "Figure 7. Visual comparison between the proposed VMD-Net and selected state-of-the-art methods from relevant fields." + ], + "image_footnote": [], + "bbox": [ + 566, + 88, + 645, + 369 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/dab159e1040c06f076b5e4b22710cf39dabcee505b973d713a4e5b957a8dd6f0.jpg", + "image_caption": [ + "VCNet [36]" + ], + "image_footnote": [], + "bbox": [ + 647, + 88, + 727, + 369 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/47d26960f1beafd2187a4757f4e1721bbeb6ef85eb33fb613879df86dfb0a1fa.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 728, + 88, + 808, + 369 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/16ba031f45415b3013ff0aa344b448d2849fc0bc28c7d194232eee91deabcc02.jpg", + "image_caption": [ + "Ground Truth" + ], + "image_footnote": [], + "bbox": [ + 810, + 88, + 892, + 369 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/5be865ff16e6101b4fc4ad0bf169af8993539514453180ab4c83d9c7f83c2127.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodsIoU↑Accuracy↑Fβ↑MAE↓
GateNet [48]0.4290.8510.6650.153
MINet [29]0.4120.8540.6760.148
PCSA [13]0.1930.8030.4640.198
DeepLabV3 [5]0.4810.8460.6810.157
PSPNet [47]0.4640.8500.6650.152
OCRNet [44]0.3940.7860.6400.175
TVSD [6]0.4800.8750.7460.125
STICT [26]0.1640.8090.5300.198
Sc-Cor [10]0.5120.8630.6960.137
HFAN [32]0.4590.8760.7060.124
GlassNet [21]0.5520.8640.7180.137
MirrorNet [42]0.5050.8550.6810.145
PMDNet [22]0.5320.8720.7490.128
VCNet [36]0.5390.8770.7490.123
Ours0.5670.8950.7870.105
", + "bbox": [ + 84, + 435, + 460, + 736 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "state-of-the-art image-based mirror detection method, VCNet [36]. Specifically, our model has $\\sim 5$ times fewer network parameters and runs $\\sim 18\\times$ faster than VCNet [36]. Furthermore, our model outperforms VCNet [36] by a large margin. These results demonstrate the superior practicality and suitability of our model for the VMD problem.", + "bbox": [ + 75, + 809, + 467, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/c79eb1378bcfc266a42df047b50e7fcc1e1c1a46c2d8d05582ac450b07a580c4.jpg", + "table_caption": [ + "Table 2. Quantitative comparison between the proposed VMD-Net and 14 state-of-the-art methods from relevant fields. The best results are shown in bold." + ], + "table_footnote": [], + "table_body": "
MethodsParams. (M) ↓FPS ↑Fβ ↑
MirrorNet [42]121.771.160.681
PMDNet [22]147.661.280.749
VCNet [36]333.170.890.749
Ours62.2417.060.787
", + "bbox": [ + 529, + 435, + 862, + 534 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Quantitative comparison on the performance and efficiency between existing mirror detection methods and our method for VMD. All models are trained/tested on the proposed VMD-D dataset, on a single RTX 3090 GPU.", + "bbox": [ + 498, + 544, + 890, + 599 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Ablation Study", + "text_level": 1, + "bbox": [ + 500, + 632, + 651, + 648 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We have conducted ablation experiments to verify the effectiveness of our design. First, we replace the proposed DC module with a convolution layer and restrict the network to take a single image as input to form the image-based baseline (\"I-Base\") for comparison. We then modify I-Base to take three images as input as our video-based baseline (\"V-Base\"). Our method (\"Ours\") is V-Base with the full DC module (i.e., DC with its first stage and its second stage). To test the effectiveness of each stage in the proposed DC module, we also construct two ablated models, which remove the first stage (\"Ours w/o DC $1^{st}$ ) or the second stage (\"Ours w/o DC $2^{nd}$ ) in the proposed DC module. Note that for \"Ours w/o DC $1^{st}$ \" ablated model, since the second stage of the DC module requires intermediate prediction maps $P_{t}, P_{t+1}, P_{n}$ to compute the reverse attention, we directly concatenate the low-level features $\\mathbf{F}_{low}$ with high", + "bbox": [ + 496, + 657, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "9115", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/dbd22f17cd7e38f4a054fe9a4db4f764e4e027a8145f94801ed578d937462792.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
AblationIoU↑Accuracy↑Fβ↑MAE↓
I-Base0.4240.8540.6860.146
V-Base0.4460.8660.7190.134
Ours w/o DC 1st0.5240.8760.7430.124
Ours w/o DC 2nd0.5330.8780.7360.122
Ours (Isame only)0.5250.8710.7400.123
Ours (Iadj only)0.5390.8760.7450.124
Ours0.5670.8950.7870.105
", + "bbox": [ + 78, + 88, + 467, + 232 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 4. Ablation Study. \"Ours (I_same only)\" / \"Ours (I_adj only)\" is the ablation model that takes three same/adjacent image frames as input, respectively. DC $1^{st}$ and DC $2^{nd}$ are the first and the second stage of the DC module, respectively.", + "bbox": [ + 75, + 242, + 468, + 299 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "level features $\\mathbf{F}_{high}$ , and then forward them to a decoder to predict $P_{t}, P_{t+1}, P_{n}$ without computing intra-frame correspondences and short-term inter-frame correspondences.", + "bbox": [ + 75, + 356, + 468, + 402 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 4 shows the performances of different ablated models. As shown in the last row, our final proposed network with the full DC module outperforms other baselines on all metrics. In particular, even the ablated models with only a single-stage DC module ( $3^{rd}$ row and $4^{th}$ row) can perform better than the baseline networks without the full DC module (\"I-Base\" and \"V-Base\"). This indicates the importance of correspondence learning for the VMD task. Also, the effective combination of the two stages in the DC module (\"Ours\") can boost the overall performance compared with the single-stage DC module. This shows that learning \"dual\" correspondences outperforms learning \"sole\" correspondences in our network.", + "bbox": [ + 75, + 417, + 468, + 612 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To further investigate the role of different types of correspondences in our network, we replace the input images (i.e., two adjacent frames and one randomly selected frame) with three adjacent frames $(\\mathrm{I}_{adj}$ only) or three video frames from the same frame $(\\mathrm{I}_{same}$ only). The \" $\\mathrm{I}_{adj}$ only\" model focuses on short-term temporal correspondences while the \" $\\mathrm{I}_{same}$ only\" model can only learn intra-frame correspondences, due to their limited input information. The results are listed in the last three rows and show that our method, which exploits intra-frame as well as both short-term and long-term temporal correspondences, outperforms these ablated models. Figure 8 shows the visual results of different ablated models. Our method with sufficient learning of spatial and temporal correspondences can locate the mirrors precisely in both frames. In particular, the results in the last row show that our method can still detect the mirrors by exploiting long-range temporal correspondences even though intra-frame correspondences are missing in the image.", + "bbox": [ + 75, + 628, + 468, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/f9a5e454da86e710564810891805bf0d004ef6c9901aa68273cedb5882b332ac.jpg", + "image_caption": [ + "Figure 8. Visual comparison of different ablated models." + ], + "image_footnote": [], + "bbox": [ + 504, + 87, + 890, + 205 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 256, + 625, + 272 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we have investigated the video mirror detection (VMD) problem. To the best of our knowledge, we are the first to address the VMD problem from a deep-learning-based perspective. We have constructed the first large-scale video mirror dataset (VMD-D). It contains 14,988 image frames from 269 videos with corresponding masks. We have also proposed a novel network, called VMD-Net, to leverage both intra-frame and inter-frame correspondences for video mirror detection. Experimental results show that our VMD-Net outperforms state-of-the-art methods from relevant tasks on our VMD-D dataset.", + "bbox": [ + 496, + 282, + 890, + 446 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Our method does have limitations. Figure 9 shows that our method tends to produce results with coarse boundaries. Unlike existing image-based mirror detection methods [22, 36], which often explicitly adopt edge supervision in their network training, our method does not leverage such auxiliary information since we do not plan to focus this incremental cue in this first work for VMD. As a future work, we are working on improving our method by leveraging additional information like boundaries to help detect mirrors more precisely in videos.", + "bbox": [ + 496, + 449, + 890, + 601 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/b243456be669f2b37db6f66f2fd00aad37c6af126285fcdb0bd1d6d24014f812.jpg", + "image_caption": [ + "Figure 9. Failure cases. Our method may not be good at boundary extraction, due to the lack of explicit supervision of edges." + ], + "image_footnote": [], + "bbox": [ + 504, + 614, + 887, + 742 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 500, + 814, + 658, + 830 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Xin Tan is sponsored by Shanghai Sailing Program (23YF1410500). This work is in part supported by two SRG grants from City University of Hong Kong (Ref: 7005674 and 7005843).", + "bbox": [ + 498, + 839, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "9116", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Radhakrishna Achanta, Sheila Hemami, Francisco Estrada, and Sabine Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009. 6", + "[2] Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian Reid, Stephen Gould, and Anton Van Den Hengel. Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments. In CVPR, pages 3674-3683, 2018. 1", + "[3] Maxim Berman, Amal Rannen Triki, and Matthew B Blaschko. The lovasz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In CVPR, pages 4413–4421, 2018. 6", + "[4] Chenglizhao Chen, Shuai Li, Yongguang Wang, Hong Qin, and Aimin Hao. Video saliency detection via spatial-temporal fusion and low-rank coherency diffusion. IEEE Transactions on Image Processing, 26(7):3156-3170, 2017. 3", + "[5] Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017. 4, 6, 7", + "[6] Zhihao Chen, Liang Wan, Lei Zhu, Jia Shen, Huazhu Fu, Wennan Liu, and Jing Qin. Triple-cooperative video shadow detection. In CVPR, pages 2715-2724, 2021. 3, 4, 6, 7", + "[7] Ho Kei Cheng and Alexander G Schwing. Xmem: Long-term video object segmentation with an atkinson-shiffrin memory model. In ECCV, pages 640-658. Springer, 2022. 3", + "[8] Suhwan Cho, Heansung Lee, Minhyeok Lee, Chaewon Park, Sungjun Jang, Minjung Kim, and Sangyoun Lee. Tackling background distraction in video object segmentation. In ECCV, pages 446-462. Springer, 2022. 3", + "[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248-255. IEEE, 2009. 6", + "[10] Xinpeng Ding, Jingwen Yang, Xiaowei Hu, and Xiaomeng Li. Learning shadow correspondence for video shadow detection. In ECCV, pages 705-722. Springer, 2022. 6, 7", + "[11] Deng-Ping Fan, Wenguan Wang, Ming-Ming Cheng, and Jianbing Shen. Shifting more attention to video salient object detection. In CVPR, pages 8554-8564, 2019. 3", + "[12] Yuchao Gu, Lijuan Wang, Ziqin Wang, Yun Liu, Ming-Ming Cheng, and Shao-Ping Lu. Pyramid constrained self-attention network for fast video salient object detection. In AAAI, 2020. 3", + "[13] Yuchao Gu, Lijuan Wang, Ziqin Wang, Yun Liu, Ming-Ming Cheng, and Shao-Ping Lu. Pyramid constrained self-attention network for fast video salient object detection. In AAAI, 2020. 6, 7", + "[14] Huankang Guan, Jiaying Lin, and Rynson W.H. Lau. Learning semantic associations for mirror detection. In CVPR, pages 5941-5950, June 2022. 1", + "[15] Chenlei Guo, Qi Ma, and Liming Zhang. Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In CVPR. IEEE, 2008. 3", + "[16] Fang Guo, Wenguan Wang, Jianbing Shen, Ling Shao, Jian Yang, Dacheng Tao, and Yuan Yan Tang. Video saliency" + ], + "bbox": [ + 78, + 114, + 470, + 902 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "detection using object proposals. IEEE transactions on cybernetics, 48(11):3159-3170, 2017. 3", + "[17] Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu. Ccnet: Criss-cross attention for semantic segmentation. In ICCV, pages 603-612, 2019. 5", + "[18] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6", + "[19] Philipp Krahenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. NeurIPS, 24, 2011. 2", + "[20] Fuxin Li, Taeyoung Kim, Ahmad Humayun, David Tsai, and James M Rehg. Video segmentation by tracking many figure-ground segments. In ICCV, pages 2192-2199, 2013. 3, 4", + "[21] Jiaying Lin, Zebang He, and Rynson WH Lau. Rich context aggregation with reflection prior for glass surface detection. In CVPR, pages 13415-13424, 2021. 6, 7", + "[22] Jiaying Lin, Guodong Wang, and Rynson W. H. Lau. Progressive mirror detection. In CVPR, 2020. 1, 2, 3, 4, 5, 6, 7, 8", + "[23] Zhihui Lin, Tianyu Yang, Maomao Li, Ziyu Wang, Chun Yuan, Wenhao Jiang, and Wei Liu. Swem: Towards real-time video object segmentation with sequential weighted expectation-maximization. In CVPR, pages 1362-1372, 2022. 3", + "[24] Yong Liu, Ran Yu, Jiahao Wang, Xinyuan Zhao, Yitong Wang, Yansong Tang, and Yujiu Yang. Global spectral filter memory network for video object segmentation. In ECCV, pages 648-665. Springer, 2022. 3", + "[25] Yong Liu, Ran Yu, Fei Yin, Xinyuan Zhao, Wei Zhao, Weihao Xia, and Yujiu Yang. Learning quality-aware dynamic memory for video object segmentation. In European Conference on Computer Vision, pages 468-486. Springer, 2022. 3", + "[26] Xiao Lu, Yihong Cao, Sheng Liu, Chengjiang Long, Zipei Chen, Xuanyu Zhou, Yimin Yang, and Chunxia Xiao. Video shadow detection via spatio-temporal interpolation consistency training. In CVPR, pages 3116-3125, 2022. 6, 7", + "[27] Haiyang Mei, Bo Dong, Wen Dong, Pieter Peers, Xin Yang, Qiang Zhang, and Xiaopeng Wei. Depth-aware mirror segmentation. In CVPR, pages 3044-3053, 2021. 2", + "[28] Peter Ochs, Jitendra Malik, and Thomas Brox. Segmentation of moving objects by long term video analysis. IEEE transactions on pattern analysis and machine intelligence, 36(6):1187-1200, 2013. 3, 4", + "[29] Youwei Pang, Xiaoji Zhao, Lihe Zhang, and Hutchuan Lu. Multi-scale interactive network for salient object detection. In CVPR, pages 9413-9422, 2020. 3, 6, 7", + "[30] Anestis Papazoglou and Vittorio Ferrari. Fast object segmentation in unconstrained video. In ICCV, pages 1777-1784, 2013. 3", + "[31] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner," + ], + "bbox": [ + 501, + 92, + 893, + 902 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "9117", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. 6", + "[32] Gensheng Pei, Fumin Shen, Yazhou Yao, Guo-Sen Xie, Zhenmin Tang, and Jinhui Tang. Hierarchical feature alignment network for unsupervised video object segmentation. In ECCV, pages 596-613. Springer, 2022. 2, 3, 6, 7", + "[33] F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung. A benchmark dataset and evaluation methodology for video object segmentation. In CVPR, 2016. 3, 4", + "[34] Federico Perazzi, Oliver Wang, Markus Gross, and Alexander Sorkine-Hornung. Fully connected object proposals for video segmentation. In ICCV, pages 3227-3234, 2015. 3", + "[35] Jiaqi Tan, Weijie Lin, Angel X Chang, and Manolis Savva. Mirror3d: Depth refinement for mirror surfaces. In CVPR, pages 15990-15999, 2021. 1, 2", + "[36] Xin Tan, Jiaying Lin, Ke Xu, Chen Pan, Lizhuang Ma, and Rynson W. H. Lau. Mirror detection with the visual chirality cue. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 1, 2, 6, 7, 8", + "[37] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In ECCV, pages 402-419. Springer, 2020. 3", + "[38] Pavel Tokmakov, Karteek Alahari, and Cordelia Schmid. Learning video object segmentation with visual memory. In ICCV, pages 4481-4490, 2017. 2, 3", + "[39] Wenguan Wang, Xiankai Lu, Jianbing Shen, David J Crandall, and Ling Shao. Zero-shot video object segmentation via attentive graph neural networks. In ICCV, pages 9236-9245, 2019. 2, 3", + "[40] Wenguan Wang, Hongmei Song, Shuyang Zhao, Jianbing Shen, Sanyuan Zhao, Steven CH Hoi, and Haibin Ling. Learning unsupervised video object segmentation through visual attention. In CVPR, pages 3064-3074, 2019. 2, 3", + "[41] Saining Xie, Ross Girshick, Piotr Dólár, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In CVPR, pages 1492–1500, 2017. 4, 6", + "[42] Xin Yang, Haiyang Mei, Ke Xu, Xiaopeng Wei, Baocai Yin, and Rynson W.H. Lau. Where is my mirror? In ICCV, 2019. 1, 2, 3, 4, 6, 7", + "[43] Zhao Yang, Qiang Wang, Luca Bertinetto, Weiming Hu, Song Bai, and Philip HS Torr. Anchor diffusion for unsupervised video object segmentation. In ICCV, pages 931-940, 2019. 2", + "[44] Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object-contextual representations for semantic segmentation. In ECCV, pages 173-190. Springer, 2020. 6, 7", + "[45] Kaihua Zhang, Zicheng Zhao, Dong Liu, Qingshan Liu, and Bo Liu. Deep transport network for unsupervised video object segmentation. In ICCV, pages 8781-8790, 2021. 2, 3", + "[46] Miao Zhang, Jie Liu, Yifei Wang, Yongri Piao, Shunyu Yao, Wei Ji, Jingjing Li, Huchuan Lu, and Zhongxuan Luo. Dynamic context-sensitive filtering network for video salient object detection. In ICCV, pages 1553-1563, 2021. 3" + ], + "bbox": [ + 78, + 90, + 468, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[47] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In CVPR, pages 2881-2890, 2017. 6, 7", + "[48] Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu, and Lei Zhang. Suppress and balance: A simple gated network for salient object detection. In ECCV, pages 35-51. Springer, 2020. 3, 6, 7", + "[49] Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Scene parsing through ade20k dataset. In CVPR, pages 633-641, 2017. 1" + ], + "bbox": [ + 501, + 92, + 890, + 233 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "9118", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning To Detect Mirrors From Videos via Dual Correspondences/1cca248f-f36a-45a2-94fd-cecf65153065_model.json b/2023/Learning To Detect Mirrors From Videos via Dual Correspondences/1cca248f-f36a-45a2-94fd-cecf65153065_model.json new file mode 100644 index 0000000000000000000000000000000000000000..5ab1394d47f5ea2ec3c223a55a75d3b9ffeb1334 --- /dev/null +++ b/2023/Learning To Detect Mirrors From Videos via Dual Correspondences/1cca248f-f36a-45a2-94fd-cecf65153065_model.json @@ -0,0 +1,2475 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.148, + 0.131, + 0.823, + 0.152 + ], + "angle": 0, + "content": "Learning to Detect Mirrors from Videos via Dual Correspondences" + }, + { + "type": "text", + "bbox": [ + 0.277, + 0.18, + 0.385, + 0.199 + ], + "angle": 0, + "content": "Jiaying Lin\\(^{1*}\\)" + }, + { + "type": "text", + "bbox": [ + 0.418, + 0.181, + 0.499, + 0.198 + ], + "angle": 0, + "content": "Xin Tan\\*" + }, + { + "type": "text", + "bbox": [ + 0.541, + 0.181, + 0.699, + 0.199 + ], + "angle": 0, + "content": "Rynson W.H. Lau†" + }, + { + "type": "text", + "bbox": [ + 0.218, + 0.199, + 0.466, + 0.218 + ], + "angle": 0, + "content": "1City University of Hong Kong" + }, + { + "type": "text", + "bbox": [ + 0.506, + 0.2, + 0.753, + 0.217 + ], + "angle": 0, + "content": "\\(^{2}\\)East China Normal University" + }, + { + "type": "text", + "bbox": [ + 0.156, + 0.22, + 0.81, + 0.234 + ], + "angle": 0, + "content": "jiayinlin5-c@my.cityu.edu.hk, xtan@cs.ecnu.edu.cn, Rynson.Lau@cityu.edu.hk" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.269, + 0.314, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.302, + 0.474, + 0.726 + ], + "angle": 0, + "content": "Detecting mirrors from static images has received significant research interest recently. However, detecting mirrors over dynamic scenes is still under-explored due to the lack of a high-quality dataset and an effective method for video mirror detection (VMD). To the best of our knowledge, this is the first work to address the VMD problem from a deep-learning-based perspective. Our observation is that there are often correspondences between the contents inside (reflected) and outside (real) of a mirror, but such correspondences may not always appear in every frame, e.g., due to the change of camera pose. This inspires us to propose a video mirror detection method, named VMD-Net, that can tolerate spatially missing correspondences by considering the mirror correspondences at both the intra-frame level as well as inter-frame level via a dual correspondence module that looks over multiple frames spatially and temporally for correlating correspondences. We further propose a first large-scale dataset for VMD (named VMD-D), which contains 14,987 image frames from 269 videos with corresponding manually annotated masks. Experimental results show that the proposed method outperforms SOTA methods from relevant fields. To enable real-time VMD, our method efficiently utilizes the backbone features by removing the redundant multi-level module design and gets rid of post-processing of the output maps commonly used in existing methods, making it very efficient and practical for real-time video-based applications. Code, dataset, and models are available at https://jiaying.link/cvpr2023-vmd/" + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.758, + 0.21, + 0.773 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.785, + 0.47, + 0.86 + ], + "angle": 0, + "content": "Mirrors appear everywhere. They can adversely affect the performance of computer vision tasks (e.g., depth estimation [35], vision-and-language navigation [2], semantic segmentation [49]), due to their fundamental property that they reflect objects from their surroundings. Thus, it is nec" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.268, + 0.603, + 0.356 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.508, + 0.357, + 0.603, + 0.4 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.536, + 0.404, + 0.574, + 0.418 + ], + "angle": 0, + "content": "Image" + }, + { + "type": "image", + "bbox": [ + 0.603, + 0.269, + 0.697, + 0.356 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.605, + 0.357, + 0.697, + 0.4 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.627, + 0.404, + 0.674, + 0.417 + ], + "angle": 0, + "content": "VCNet" + }, + { + "type": "image", + "bbox": [ + 0.698, + 0.269, + 0.793, + 0.356 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.699, + 0.357, + 0.793, + 0.4 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.729, + 0.404, + 0.762, + 0.417 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image", + "bbox": [ + 0.794, + 0.269, + 0.89, + 0.356 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.795, + 0.357, + 0.888, + 0.4 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.829, + 0.404, + 0.852, + 0.416 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image_footnote", + "bbox": [ + 0.507, + 0.426, + 0.698, + 0.439 + ], + "angle": 0, + "content": "\\(\\longleftrightarrow\\) intra-frame correspondence" + }, + { + "type": "image_footnote", + "bbox": [ + 0.703, + 0.426, + 0.893, + 0.439 + ], + "angle": 0, + "content": "\\(\\longleftrightarrow\\) inter-frame correspondence" + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.451, + 0.895, + 0.592 + ], + "angle": 0, + "content": "Figure 1. Although state-of-the-art single-image mirror detection method VCNet [36] performs well on a single image (e.g., the first row) by using implicitly intra-frame correspondence, it may fail when the intra-frame cue is weak or even absent in some video frames (e.g., the second and third rows). The lack in exploiting inter-frame information causes the current mirror detection methods to produce inaccurate and inconsistent results when applied to VMD. In contrast, our method can perform well in both situations by utilizing the proposed dual correspondence module to exploit intra-frame (spatial) and inter-frame (temporal) correspondences." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.623, + 0.892, + 0.654 + ], + "angle": 0, + "content": "essary to build a robust computer vision model that can distinguish mirrors from their surrounding objects correctly." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.656, + 0.893, + 0.822 + ], + "angle": 0, + "content": "Existing single-image mirror detection methods exploit different cues, such as context contrast [42], explicitly correspondences [22], semantics association [14], and chirality and implicit correspondences [36], to detect mirrors from single RGB input images. Despite these recent efforts being put into the mirror detection problem, none of them focuses on detecting mirrors from videos. However, a lot of real-world computer vision applications are video-based (e.g., robotic navigation, autonomous driving, and surveillance), rather than image-based. Hence, solving the video mirror detection (VMD) problem can benefit these applications." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.894, + 0.901 + ], + "angle": 0, + "content": "In this paper, we aim to address the VMD problem. There are two major challenges with this problem. First, to the best of our knowledge, there are no existing large-scale datasets that can be used for training and evaluation on the VMD problem. Second, existing mirror detection methods," + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.875, + 0.2, + 0.887 + ], + "angle": 0, + "content": "*Joint first authors." + }, + { + "type": "page_footnote", + "bbox": [ + 0.097, + 0.888, + 0.222, + 0.9 + ], + "angle": 0, + "content": "† Corresponding author." + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.875, + 0.222, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9109" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.096, + 0.093, + 0.45, + 0.295 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.312, + 0.47, + 0.409 + ], + "angle": 0, + "content": "Figure 2. Quantitative comparison on the performance and efficiency between existing mirror detection methods and our method for VMD. All models are trained/tested on the proposed VMD-D dataset, under a single RTX 3090 GPU. Our model has \\(\\sim 5\\) times smaller network parameters and runs \\(\\sim 18\\times\\) faster than the state-of-the-art image-based mirror detection method, VCNet [36], and still outperforms it by a large margin." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.443, + 0.468, + 0.7 + ], + "angle": 0, + "content": "which are all developed for the image-based mirror detection task, are all based on static cues. None of them take advantages of the dynamic nature of videos in the VMD problem. Figure 1 shows that the current state-of-the-art mirror detection method, VCNet [36], may fail when correspondences are missing in some challenging frames (e.g., second and third rows) due to, for example, the change of camera pose, even though it may perform well in some easy cases (e.g., the first row). Besides, as the image-based mirror detection task is already very challenging, existing methods for this task often adopt heavy network design and time-consuming post-processing techniques [19] to improve their results. Figure 2 shows that existing image-based mirror detection models run at about 1fps, even on one of the latest GPUs, which cannot support real-time VMD. All these drawbacks motivate us to develop a large-scale dataset and an effective/efficient method for video mirror detection." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.705, + 0.468, + 0.901 + ], + "angle": 0, + "content": "In this paper, we address the VMD problem in two ways. First, we construct the first large-scale video mirror detection benchmark dataset (VMD-D). It contains 14,987 image frames in 269 videos, coming from diverse scenes. The constructed VMD-D dataset provides large-scale and high-diversity data for training and evaluation on the VMD problem. Second, we propose an effective and efficient method, called VMD-Net, for the VMD problem. The proposed method exploits multi-frame correspondences at both intra-frame (spatial) and inter-frame (temporal) levels. Compared with state-of-the-art image-based mirror detection methods, which typically adopt heavy pipelines, our method uses a light-weight network architecture without the need for any" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.227 + ], + "angle": 0, + "content": "post-processing techniques. As a result, our method is efficient for real-time applications. In particular, our method has \\(\\sim 5\\) times fewer network parameters and runs \\(\\sim 18\\times\\) faster than the latest state-of-the-art image-based mirror detection method, VCNet [36]. We conduct comprehensive experiments to demonstrate the effectiveness and efficiency of our proposed method. Experimental results show that our method outperforms state-of-the-art methods from relevant tasks on the proposed large-scale VMD-D dataset." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.229, + 0.871, + 0.243 + ], + "angle": 0, + "content": "Our key contributions can be summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.256, + 0.891, + 0.313 + ], + "angle": 0, + "content": "- We construct the first large-scale video mirror detection dataset, called VMD-D. The new dataset contains 14,988 image frames from 269 videos with precise annotated masks." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.329, + 0.891, + 0.403 + ], + "angle": 0, + "content": "- We propose a novel network, called VMD-Net, to exploit both intra-frame and inter-frame correspondences via a dual correspondence (DC) module. This DC module allows VMD-Net to tolerate occasionally missing correspondences in the temporal dimension." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.417, + 0.891, + 0.461 + ], + "angle": 0, + "content": "- Extensive evaluations show that our method outperforms existing state-of-the-art methods from relevant tasks on our proposed VMD-D dataset." + }, + { + "type": "list", + "bbox": [ + 0.519, + 0.256, + 0.891, + 0.461 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.478, + 0.64, + 0.493 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.503, + 0.892, + 0.73 + ], + "angle": 0, + "content": "Image-based Mirror Detection. Recently, Yang et al. [42] propose the first mirror detection dataset and the first mirror detection network to detect mirrors by modeling contextual contrasted information. Lin et al. [22] then propose a more diverse and larger mirror detection benchmark, and a correspondence-aware method for mirror detection, which correlates the contents inside and outside of the mirror. Mei et al. [27] further consider using RGB-D data for detecting mirrors, and construct the first RGB-D dataset for mirror detection. Tan et al. [35] address the depth refinement problem on mirror surfaces by proposing another RGB-D mirror dataset and a detect-and-refine method for mirror detection. Most recently, Tan et al. [36] propose an image-based method for mirror detection based on the chirality cue and implicit correspondences." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.731, + 0.892, + 0.822 + ], + "angle": 0, + "content": "Despite their success, none of these methods focuses on the VMD problem. They also have high computational costs, thus not practical for real-time applications. In this paper, we aim to address the VMD problem by constructing a benchmark dataset and proposing an efficient/effective method for the problem." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Video Object Segmentation Video object segmentation (VOS) aims to segment the target object(s) from the input videos. Currently, it can be categorized into two main individual tasks: the unsupervised video object segmentation (UVOS) [32, 38-40, 43, 45] and the semi-supervised video" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9110" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.08, + 0.089, + 0.895, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.166, + 0.236, + 0.802, + 0.251 + ], + "angle": 0, + "content": "Figure 3. Snapshots of the proposed Video Mirror Detection dataset, VMD-D, with pixel-level annotations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.277, + 0.473, + 0.549 + ], + "angle": 0, + "content": "object segmentation (SVOS) [7, 8, 23-25]. The main difference between UVOS and SVOS is that UVOS does not require annotation for the first frame of the input video during the inference phase, while SVOS needs to do it. Thus, UVOS is more related to the video mirror detection problem since we do not use the first-frame ground truth mirror label to infer the location of mirrors in the rest of the video frames. UVOS focuses on automatically detecting the primary foreground object without using the ground truth mask of the first frame during testing. Early UVOS methods use handcrafted features [30, 34] to detect foreground objects in the input videos. Recent CNN-based UVOS models [38-40] focus on exploring different cues like memory [38], graph neural network [39] and visual attention [40] for better performance. The latest UVOS methods [32, 45] take optical flow as auxiliary information to help segment videos, thanks to the rapid evolution on optical flow estimation [37]." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.561, + 0.47, + 0.622 + ], + "angle": 0, + "content": "However, UVOS methods cannot directly address the video mirror detection problem, since mirrors are not always the primary foreground objects while having changing appearance patterns during motion." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.633, + 0.47, + 0.844 + ], + "angle": 0, + "content": "Video Salient Object Detection Video salient object detection (VSOD) is related to the UVOS task. It aims to automatically detect the most visually distinctive objects from an input video without indicating where the salient object is in the first frame. Unlike the methods for single-image salient object detection [29, 48], VSOD methods require building a model with a thorough understanding of dynamic attention. Most earlier VSOD methods detect the salient objects using handcrafted features [4, 15, 16]. With the help of deep neural networks, recent VSOD methods achieve great progress. Fan et al. [11] take attention shift into account. Gu et al. [12] exploit self-attention models to detect salient objects from videos efficiently. Zhang et al. [46] proposes a dynamic strategy of context fusion for VSOD." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.856, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Similar to UVOS, the methods for video salient object detection are not directly applicable to video mirror detection since mirrors are not always distinctive." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.275, + 0.875, + 0.293 + ], + "angle": 0, + "content": "3. Video Mirror Detection Dataset (VMD-D)" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.301, + 0.892, + 0.406 + ], + "angle": 0, + "content": "To facilitate research on the video mirror detection problem, we first contribute a large-scale video mirror detection dataset (named VMD-D). It consists of 269 videos in 14,988 image frames with corresponding precise annotations from diverse scenes. Figure 3 shows some example video frames in our proposed VMD-D dataset. The details of our VMD are discussed below." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.416, + 0.7, + 0.43 + ], + "angle": 0, + "content": "3.1. Dataset Construction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.439, + 0.892, + 0.635 + ], + "angle": 0, + "content": "To construct the first large-scale video mirror detection dataset, we use a smartphone to collect high-resolution videos (e.g., \\(1920 \\times 1080\\) resolution) with mirrors in daily-life scenes. Following a common practice [6] used to construct datasets for video-based problems, we manually trim the collected videos to make sure that each video frame has at least one mirror region. After that, we obtain 269 video clips and then randomly split them into a training set with 143 videos (containing 7,835 images) and a test set with 126 videos (containing 7,152 images). We then label pixel-level mirror masks by annotators. The total duration of our videos is 502 seconds for covering long-temporal scenarios. The frame rate is 30 fps for all collected videos." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.644, + 0.665, + 0.661 + ], + "angle": 0, + "content": "3.2. Dataset Analysis" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.667, + 0.892, + 0.789 + ], + "angle": 0, + "content": "Table 1 shows the comparison of different datasets from the relevant areas, including image mirror detection [22,42] (top group), video object segmentation [20,28,33] (second group), and video shadow detection [6] (third group), and ours. Our dataset reflects good video quality (i.e., high-resolution videos) and quantity (i.e., a large number of annotated frames), and is practical for being the first step for large-scale video mirror detection." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.792, + 0.892, + 0.868 + ], + "angle": 0, + "content": "Area Distribution. Figure 4(a) shows the ratio of mirror area over the image area (mirror area distribution). We can see that our dataset contains mirrors covering a wide range of area ratios. We also note that it consists of a lot of small mirrors \\((< 0.1)\\), which makes our dataset very challenging." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Contrast Distribution. We analyze the contrasts between the mirror regions and non-mirror regions by computing \\(\\chi^2\\)" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "9111" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.078, + 0.089, + 0.482, + 0.236 + ], + "angle": 0, + "content": "
Dataset#Videos#Annos.#Time (s).#FPSMax Reso.
MSD [42]-4,018--640×512
PMD [22]-6,461--4000×3000
FBMS [28]59720--640×480
STV2 [20]14947--640×360
DAVIS [33]503455144241920×1080
ViSha [6]12011,685390301280×720
Ours26915,066502301920×1080
" + }, + { + "type": "table_caption", + "bbox": [ + 0.078, + 0.247, + 0.47, + 0.262 + ], + "angle": 0, + "content": "Table 1. Comparison of different video datasets for relevant tasks." + }, + { + "type": "image", + "bbox": [ + 0.082, + 0.279, + 0.273, + 0.389 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.106, + 0.393, + 0.248, + 0.404 + ], + "angle": 0, + "content": "(a) mirror area distribution" + }, + { + "type": "image", + "bbox": [ + 0.278, + 0.277, + 0.466, + 0.389 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.293, + 0.393, + 0.449, + 0.404 + ], + "angle": 0, + "content": "(b) color contrast distribution" + }, + { + "type": "image_caption", + "bbox": [ + 0.153, + 0.417, + 0.393, + 0.431 + ], + "angle": 0, + "content": "Figure 4. Statistics of our VMD dataset." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.459, + 0.47, + 0.566 + ], + "angle": 0, + "content": "distance between their RGB histograms. We also compare the distribution between MSD [42] and PMD [22], as shown in Figure 4(b). In general, VMD-D has more images with extremely low color contrasts (\\(< 0.1\\)) compared with existing mirror datasets MSD and PMD. This indicates our images are collected from very diverse scenes, making them more challenging to detect." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.58, + 0.169, + 0.595 + ], + "angle": 0, + "content": "4. Method" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.605, + 0.188, + 0.62 + ], + "angle": 0, + "content": "4.1. Overview" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.629, + 0.47, + 0.78 + ], + "angle": 0, + "content": "We observe that there are often spatial correspondences between the contents inside (reflected) and outside (real) of a mirror. However, such spatial correspondences may not always appear in every frame. The lack in exploiting temporal correspondence may cause current image-based mirror detection methods to fail in the VMD task. Hence, in this paper, we propose a method that is able to tolerate temporally missing correspondences by considering mirror correspondences at both the intra-frame level (spatial) as well as the inter-frame level (temporal)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.781, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Figure 5 shows the overall structure of our proposed method VMD-Net. The key idea of our network design is to leverage the intra-frame and the inter-frame correspondences for video mirror detection. Our VMD-Net takes three images from the same video clips as inputs. The first two images \\( I_{t} \\) and \\( I_{t + 1} \\) are from adjacent video frames, while the third image \\( I_{n} \\) is randomly selected from other frames. We first apply a shared backbone network ResNext" + }, + { + "type": "image", + "bbox": [ + 0.537, + 0.094, + 0.918, + 0.35 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.362, + 0.895, + 0.435 + ], + "angle": 0, + "content": "Figure 5. The framework of our proposed method. \\( I_{t}, I_{t + 1}, I_{n} \\) are input images. In particular, \\( I_{t} \\) and \\( I_{t + 1} \\) are two adjacent video frames, and \\( I_{n} \\) is a video frame randomly selected from the same video of \\( I_{t} \\). \\( P_{t}, P_{t + 1}, P_{n} \\) are intermediate maps and the final outputs maps are \\( M_{t}, M_{t + 1}, M_{n} \\) for the corresponding input images." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.459, + 0.893, + 0.718 + ], + "angle": 0, + "content": "101 [41] to extract multi-scale features from the input images. Unlike existing single-image mirror detection methods, which make full usage of the features at all stages (i.e., from the \\( 1^{st} \\) to the \\( 5^{th} \\) scales), for each input image \\( i \\), our VMD-Net only utilizes the low-level features at the \\( 2^{nd} \\) scale (denoted as \\( \\mathbf{F}_{low}^{i} \\)) and the high-level features at the \\( 5^{th} \\) scale. Following the design of DeepLabV3 [5], the high-level features from the backbone network are then fed to an atrous spatial pyramid pooling module to obtain enhanced semantic features (denoted as \\( \\mathbf{F}_{high}^{i} \\)). Taking image \\( I_{t} \\) as an example, we then assign a dual correspondence module to both low-level features \\( \\mathbf{F}_{low}^{t} \\) and high-level features \\( \\mathbf{F}_{high}^{t} \\) to produce the intermediate map \\( P_{t} \\) and the final output \\( M_{t} \\). Similarly, the dual correspondence module takes the low-level features and high-level features from \\( I_{t+1}, I_{n} \\) to produce the intermediate maps \\( P_{t+1}, P_{n} \\) and the final outputs \\( M_{t+1}, M_{n} \\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.727, + 0.81, + 0.743 + ], + "angle": 0, + "content": "4.2. Dual Correspondence (DC) Module" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.894, + 0.903 + ], + "angle": 0, + "content": "Figure 6 shows the structure of the proposed DC module. It consists of two stages. The first stage of the DC module aims to learn intra-frame correspondences for all input images and the short-term inter-frame correspondences inside mirrors in the adjacent input image \\( I_{t} \\) and \\( I_{t + 1} \\). The second stage of the DC module focuses on extracting long-term inter-frame correspondences across all input images to enhance the learning of temporal correspondence. This enables our VMD-Net to exploit correspondences at both intra-frame and inter-frame levels at different tempo" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9112" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.127, + 0.089, + 0.885, + 0.373 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.386, + 0.895, + 0.43 + ], + "angle": 0, + "content": "Figure 6. Our DC module. It consists of two stages. The first stage in the DC module aims to extract intra-frame and short-term temporal correspondences from two adjacent video frames \\( I_{t} \\), \\( I_{t+1} \\), while the second stage in the DC module focuses on learning long-term temporal correspondences from all input frames \\( I_{t} \\), \\( I_{t+1} \\), \\( I_{n} \\)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.455, + 0.148, + 0.469 + ], + "angle": 0, + "content": "ral scales." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.474, + 0.474, + 0.746 + ], + "angle": 0, + "content": "Stage 1. Our DC module takes the low-level features \\(\\mathbf{F}_{low}\\) and high-level features \\(\\mathbf{F}_{high}\\) as input. In the first stage, we first extract the intra-frame correspondences from the high-level features \\(\\mathbf{F}_{high}\\) of each image by the global relation (GR) block proposed in [22]. The GR block can effectively and efficiently extract the correspondence between the content inside and outside of the mirrors by modeling spatial corresponding relations in a single image. The high-level correspondence-aware features from the randomly selected frame \\(I_{n}\\) are directly concatenated with the corresponding low-level features \\(\\mathbf{F}_{low}^{n}\\) in the decoder to output an intermediate map \\(P_{n}\\), while the high-level correspondence-aware features extracted from the two adjacent frames \\(I_{t}\\) and \\(I_{t + 1}\\) are forwarded to a cross-attention (Cross-Att.) module to learn short-term temporal correspondences. To compute short-term temporal correspondences between \\(I_{t}\\) and \\(I_{t + 1}\\), we also feed their low-level features \\(\\mathbf{F}_{low}^{t},\\mathbf{F}_{low}^{t + 1}\\) to another cross-attention module." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Our cross-attention module is inspired by [17], but we extend it from self-attention (single-input and single-out features) to cross-attention with two-input and two-output features and also enhance its ability to model spatial correspondences. Our module first applies two convolutional layers with \\( 1 \\times 1 \\) filters to the two input features \\( \\mathbf{F}_t \\) and \\( \\mathbf{F}_{t+1} \\) and further generates a spatially enhanced affinity matrix \\( A^{se} \\in \\mathbb{R}^{(2H + 2W - 1) \\times (W \\times H)} \\). Different from the original affinity matrix \\( A \\in \\mathbb{R}^{(H + W - 1) \\times (W \\times H)} \\) in [17], which is computed by extracting local contextual information in the" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.455, + 0.895, + 0.531 + ], + "angle": 0, + "content": "horizontal and vertical directions of the input features, our spatially enhanced affinity operation also computes global contextual information by taking the diagonal items of the input features into our design. Formally, our cross-attention process can be described as:" + }, + { + "type": "equation", + "bbox": [ + 0.563, + 0.544, + 0.891, + 0.56 + ], + "angle": 0, + "content": "\\[\nK = \\operatorname {C o n v} _ {1 \\times 1} (\\mathbf {F} _ {t}); Q = \\operatorname {C o n v} _ {1 \\times 1} (\\mathbf {F} _ {t + 1}), \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.553, + 0.563, + 0.891, + 0.579 + ], + "angle": 0, + "content": "\\[\nA ^ {s e} = \\mathbb {A} ^ {s e} (K, Q), \\tag {2}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.565, + 0.582, + 0.891, + 0.598 + ], + "angle": 0, + "content": "\\[\nV = \\operatorname {C o n v} _ {1 \\times 1} \\left(\\mathbf {F} _ {t}\\right), \\tag {3}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.654, + 0.601, + 0.789, + 0.613 + ], + "angle": 0, + "content": "\\[\n(2 H + 2 W - 1) \\times (W \\times H)\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.56, + 0.613, + 0.891, + 0.644 + ], + "angle": 0, + "content": "\\[\n\\mathbf {C} ^ {t} = K + \\omega_ {t} \\quad \\sum_ {i} A ^ {s e} V, \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.669, + 0.647, + 0.803, + 0.66 + ], + "angle": 0, + "content": "\\[\n(2 H + 2 W - 1) \\times (W \\times H)\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.544, + 0.66, + 0.891, + 0.69 + ], + "angle": 0, + "content": "\\[\n\\mathbf {C} ^ {t + 1} = Q + \\omega_ {t + 1} \\quad \\sum_ {i} \\quad A ^ {s e} V, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.701, + 0.892, + 0.885 + ], + "angle": 0, + "content": "where \\(\\mathbf{C}\\) is the output of the cross-attention module. \\(\\omega_{t}\\) and \\(\\omega_{t + 1}\\) are learnable parameters. \\(\\mathbb{A}^{se}\\) is our spatially enhanced affinity operation. \\(\\mathbf{C}^t\\) and \\(\\mathbf{C}^{t + 1}\\) are the output correspondence-aware features. We apply the cross-attention module to both \\(\\mathbf{F}_{low}^{t},\\mathbf{F}_{low}^{t + 1}\\) and \\(\\mathbf{F}_{high}^{t},\\mathbf{F}_{high}^{t + 1}\\) to obtain correspondence-aware features \\(\\mathbf{C}_{low}^{t},\\mathbf{C}_{low}^{t + 1}\\) and \\(\\mathbf{C}_{high}^{t},\\mathbf{C}_{high}^{t + 1}\\). We then concatenate the correspondence features from the same input image but at different levels. The concatenated features \\([\\mathbf{C}_{low};\\mathbf{C}_{high}]\\) are forwarded to a decoder to obtain an intermediate prediction \\(P_{t}\\) for \\(I_{t}\\). Similarly, we obtain the intermediate predictions \\(P_{t + 1}\\) and \\(P_{n}\\) for \\(I_{t + 1}\\) and \\(I_{n}\\)." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.886, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Stage 2. The second stage of our DC module takes all in" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9113" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.422 + ], + "angle": 0, + "content": "intermediate prediction maps \\( P_{t}, P_{t + 1}, P_{n} \\) and high-level features \\( \\mathbf{F}_{high}^{t}, \\mathbf{F}_{high}^{t + 1}, \\mathbf{F}_{high}^{n} \\) as inputs. The input features and prediction maps are forwarded to a reverse cross-attention module. This module aims at explicitly exploiting the correspondence between the contents inside and outside of the mirrors in different frames for long-range temporal correspondences. In particular, we need to explicitly model the correspondences between input frame \\( I_{t} \\) and the randomly selected frame \\( I_{n} \\). To do this, we first multiply \\( F_{high} \\) by their corresponding intermediate prediction map \\( P \\), which are normalized by a sigmoid function. We then reverse \\( P \\) to obtain the reversed prediction map \\( \\hat{P} \\), which indicates the potential non-mirror regions. We also notice that the mirrors will potentially flip the real object horizontally in its content. Thus, we conduct a horizontal flip to the input non-mirror features to model the potential relation of mirror reflection. Similarly, we compute temporal correspondences between \\( I_{t} \\) and \\( I_{t + 1} \\) with this strategy. In the second stage of the DC module, we also use our cross-attention module to extract the inter-frame correspondences. The process of our reverse cross-attention module can be formulated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.151, + 0.432, + 0.47, + 0.452 + ], + "angle": 0, + "content": "\\[\n\\Omega_ {n, -} = \\mathbb {C} \\mathbb {A} \\left(P _ {n} \\mathbf {F} _ {h i g h} ^ {n}, \\left(\\hat {P} _ {t} \\mathbf {F} _ {h i g h} ^ {t}\\right) ^ {\\top}\\right), \\tag {6}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.155, + 0.454, + 0.469, + 0.473 + ], + "angle": 0, + "content": "\\[\n\\Omega_ {t, -} = \\mathbb {C} \\mathbb {A} \\left(P _ {t} \\mathbf {F} _ {h i g h} ^ {t}, \\left(\\hat {P} _ {t + 1} \\mathbf {F} _ {h i g h} ^ {t + 1}\\right) ^ {\\top}\\right), \\tag {7}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.139, + 0.476, + 0.469, + 0.496 + ], + "angle": 0, + "content": "\\[\n\\Omega_ {t + 1, -} = \\mathbb {C A} \\left(P _ {t + 1} \\mathbf {F} _ {h i g h} ^ {t + 1}, \\left(\\hat {P} _ {t} \\mathbf {F} _ {h i g h} ^ {t}\\right) ^ {\\top}\\right), \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.505, + 0.47, + 0.613 + ], + "angle": 0, + "content": "where \\(\\mathbb{C}\\mathbb{A}\\) is our cross-attention module. \\(\\top\\) is a horizontal flip operation. \\(\\Omega\\) is the final output feature. Note that we omit the second output of the cross-attention modules used in the \\(2^{nd}\\) stage for convenience. We then forward the output features to individual decoders to obtain the final output predictions \\(M_{t}, M_{t+1}, M_{n}\\) for the input images \\(I_{t}, I_{t+1}, I_{n}\\), respectively." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.62, + 0.231, + 0.635 + ], + "angle": 0, + "content": "4.3. Loss Functions" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.644, + 0.469, + 0.674 + ], + "angle": 0, + "content": "We adopt the Lovász-Softmax loss function [3] to supervise our network training. The final loss function is:" + }, + { + "type": "equation", + "bbox": [ + 0.162, + 0.683, + 0.236, + 0.696 + ], + "angle": 0, + "content": "\\[\ni \\in \\{t, t + 1, n \\}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.132, + 0.696, + 0.469, + 0.724 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\sum_ {i} \\quad \\mathcal {L} _ {h} \\left(P _ {i}, G _ {i}\\right) + \\mathcal {L} _ {h} \\left(M _ {i}, G _ {i}\\right), \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.734, + 0.469, + 0.795 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_h(\\cdot ,\\cdot)\\) denotes the lovasz-hinge loss. \\(P_{i},M_{i},G_{i}\\) are the intermediate output from the DC module, the final output of our network, and the ground truth label of image \\(I_{i}\\), respectively." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.808, + 0.21, + 0.824 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.832, + 0.469, + 0.849 + ], + "angle": 0, + "content": "5.1. Experimental Settings and Evaluation Metrics" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.856, + 0.469, + 0.901 + ], + "angle": 0, + "content": "We have implemented VMD-Net in Pytorch [31] and trained it on a PC with an RTX3090 GPU card. During training, we resize the input images to \\(384 \\times 384\\). We" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.227 + ], + "angle": 0, + "content": "use ResNext-101 [41] pre-trained on ImageNet [9] as our backbone network to extract image features. We adopt Adam [18] as the optimizer with a momentum of 0.9 and a weight decay of \\(5 \\times 10^{-4}\\). The base learning rate, batch size, and the number of training epochs are 0.0001, 8, and 15, respectively. We use the cosine learning rate decay with 3-epoch warm-up period to adjust the learning rate during training. Note that we do NOT apply any post-processing techniques to refine our predictions." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.228, + 0.892, + 0.288 + ], + "angle": 0, + "content": "We employ four popular metrics, intersection over union (IoU), pixel accuracy, F-measure \\((F_{\\beta})\\) [1], and mean absolute error (MAE) to evaluate the performance of tested methods quantitatively. \\(F_{\\beta}\\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.57, + 0.299, + 0.822, + 0.331 + ], + "angle": 0, + "content": "\\[\nF _ {\\beta} = \\frac {1 + \\beta^ {2} (P r e c i s i o n \\times R e c a l l)}{\\beta^ {2} P r e c i s i o n + R e c a l l},\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.34, + 0.892, + 0.371 + ], + "angle": 0, + "content": "where \\(\\beta\\) is set to 0.3 as suggested in [1]. It evaluates the overall performance between precision and recall." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.38, + 0.874, + 0.396 + ], + "angle": 0, + "content": "5.2. Comparison to the State-of-the-art Methods" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.403, + 0.892, + 0.614 + ], + "angle": 0, + "content": "Due to the lack of methods for video mirror detection, we compare our method with 14 state-of-the-art methods from relevant fields, including GateNet [48] and MINet [29] for salient object detection; PCSA [13] for video salient object detection; DeepLabV3 [5], PSPNet [47] and OCRNet [44] for semantic segmentation; TVSD [6], STICT [26] and ScCor [10] for video shadow detection; HFAN [32] for video object segmentation; GlassNet [21] for glass surface detection; MirrorNet [42], PMDNet [22] and VCNet [36] for single-image mirror detection. We train and test all baseline methods on our video mirror detection dataset VMD-D using their released codes on the same platform. Table 2 shows the quantitative results. Our method achieves the best performance with a large margin on all four metrics." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.614, + 0.892, + 0.825 + ], + "angle": 0, + "content": "Figure 7 visually compares the results of our method with the selected state-of-the-art methods from relevant fields. We can see that when compared with image-based mirror detection methods PMDNet [22] and VCNet [36], which produce inaccurate and incoherent results both in a short-term temporal interval (e.g., the 1st and 2nd rows; the 3rd and the 4th rows) and a long-term temporal interval (e.g., the 5th and 6th rows; the 7th and 8th rows), our method can precisely predict the mirror regions by exploiting inter-frame and intra-frame correspondences in both situations. We attribute the superior performances of our method to the design of the DC module, which enables the modeling of spatial, short-range, and long-range temporal correspondences." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.826, + 0.892, + 0.901 + ], + "angle": 0, + "content": "In particular, we also compare the efficiency of our method with most existing mirror detection methods. Table 3 shows the quantitative results in the number of network parameters and FPS. Our model has significantly fewer network parameters and runs much faster than the" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9114" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.089, + 0.159, + 0.371 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.104, + 0.376, + 0.135, + 0.387 + ], + "angle": 0, + "content": "Image" + }, + { + "type": "image", + "bbox": [ + 0.162, + 0.089, + 0.24, + 0.371 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.171, + 0.376, + 0.232, + 0.387 + ], + "angle": 0, + "content": "GateNet [48]" + }, + { + "type": "image", + "bbox": [ + 0.242, + 0.089, + 0.321, + 0.371 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.247, + 0.376, + 0.319, + 0.387 + ], + "angle": 0, + "content": "DeepLabV3 [5]" + }, + { + "type": "image", + "bbox": [ + 0.323, + 0.089, + 0.403, + 0.371 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.335, + 0.376, + 0.392, + 0.387 + ], + "angle": 0, + "content": "Sc-Cor [10]" + }, + { + "type": "image", + "bbox": [ + 0.405, + 0.089, + 0.483, + 0.371 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.418, + 0.376, + 0.472, + 0.387 + ], + "angle": 0, + "content": "HFAN [32]" + }, + { + "type": "image", + "bbox": [ + 0.485, + 0.089, + 0.566, + 0.371 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.494, + 0.376, + 0.559, + 0.387 + ], + "angle": 0, + "content": "GlassNet [21]" + }, + { + "type": "image", + "bbox": [ + 0.567, + 0.089, + 0.646, + 0.371 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.576, + 0.376, + 0.64, + 0.387 + ], + "angle": 0, + "content": "PMDNet [22]" + }, + { + "type": "image", + "bbox": [ + 0.648, + 0.089, + 0.728, + 0.371 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.661, + 0.376, + 0.716, + 0.387 + ], + "angle": 0, + "content": "VCNet [36]" + }, + { + "type": "image", + "bbox": [ + 0.73, + 0.089, + 0.81, + 0.371 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.758, + 0.376, + 0.782, + 0.387 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image", + "bbox": [ + 0.812, + 0.089, + 0.893, + 0.371 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.819, + 0.376, + 0.884, + 0.387 + ], + "angle": 0, + "content": "Ground Truth" + }, + { + "type": "image_caption", + "bbox": [ + 0.13, + 0.4, + 0.838, + 0.412 + ], + "angle": 0, + "content": "Figure 7. Visual comparison between the proposed VMD-Net and selected state-of-the-art methods from relevant fields." + }, + { + "type": "table", + "bbox": [ + 0.086, + 0.436, + 0.461, + 0.737 + ], + "angle": 0, + "content": "
MethodsIoU↑Accuracy↑Fβ↑MAE↓
GateNet [48]0.4290.8510.6650.153
MINet [29]0.4120.8540.6760.148
PCSA [13]0.1930.8030.4640.198
DeepLabV3 [5]0.4810.8460.6810.157
PSPNet [47]0.4640.8500.6650.152
OCRNet [44]0.3940.7860.6400.175
TVSD [6]0.4800.8750.7460.125
STICT [26]0.1640.8090.5300.198
Sc-Cor [10]0.5120.8630.6960.137
HFAN [32]0.4590.8760.7060.124
GlassNet [21]0.5520.8640.7180.137
MirrorNet [42]0.5050.8550.6810.145
PMDNet [22]0.5320.8720.7490.128
VCNet [36]0.5390.8770.7490.123
Ours0.5670.8950.7870.105
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.749, + 0.47, + 0.79 + ], + "angle": 0, + "content": "Table 2. Quantitative comparison between the proposed VMD-Net and 14 state-of-the-art methods from relevant fields. The best results are shown in bold." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.468, + 0.901 + ], + "angle": 0, + "content": "state-of-the-art image-based mirror detection method, VCNet [36]. Specifically, our model has \\(\\sim 5\\) times fewer network parameters and runs \\(\\sim 18\\times\\) faster than VCNet [36]. Furthermore, our model outperforms VCNet [36] by a large margin. These results demonstrate the superior practicality and suitability of our model for the VMD problem." + }, + { + "type": "table", + "bbox": [ + 0.53, + 0.436, + 0.864, + 0.535 + ], + "angle": 0, + "content": "
MethodsParams. (M) ↓FPS ↑Fβ ↑
MirrorNet [42]121.771.160.681
PMDNet [22]147.661.280.749
VCNet [36]333.170.890.749
Ours62.2417.060.787
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.545, + 0.892, + 0.601 + ], + "angle": 0, + "content": "Table 3. Quantitative comparison on the performance and efficiency between existing mirror detection methods and our method for VMD. All models are trained/tested on the proposed VMD-D dataset, on a single RTX 3090 GPU." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.633, + 0.653, + 0.649 + ], + "angle": 0, + "content": "5.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.892, + 0.901 + ], + "angle": 0, + "content": "We have conducted ablation experiments to verify the effectiveness of our design. First, we replace the proposed DC module with a convolution layer and restrict the network to take a single image as input to form the image-based baseline (\"I-Base\") for comparison. We then modify I-Base to take three images as input as our video-based baseline (\"V-Base\"). Our method (\"Ours\") is V-Base with the full DC module (i.e., DC with its first stage and its second stage). To test the effectiveness of each stage in the proposed DC module, we also construct two ablated models, which remove the first stage (\"Ours w/o DC \\(1^{st}\\)) or the second stage (\"Ours w/o DC \\(2^{nd}\\)) in the proposed DC module. Note that for \"Ours w/o DC \\(1^{st}\\)\" ablated model, since the second stage of the DC module requires intermediate prediction maps \\(P_{t}, P_{t+1}, P_{n}\\) to compute the reverse attention, we directly concatenate the low-level features \\(\\mathbf{F}_{low}\\) with high" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9115" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.08, + 0.089, + 0.468, + 0.233 + ], + "angle": 0, + "content": "
AblationIoU↑Accuracy↑Fβ↑MAE↓
I-Base0.4240.8540.6860.146
V-Base0.4460.8660.7190.134
Ours w/o DC 1st0.5240.8760.7430.124
Ours w/o DC 2nd0.5330.8780.7360.122
Ours (Isame only)0.5250.8710.7400.123
Ours (Iadj only)0.5390.8760.7450.124
Ours0.5670.8950.7870.105
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.243, + 0.47, + 0.3 + ], + "angle": 0, + "content": "Table 4. Ablation Study. \"Ours (I_same only)\" / \"Ours (I_adj only)\" is the ablation model that takes three same/adjacent image frames as input, respectively. DC \\(1^{st}\\) and DC \\(2^{nd}\\) are the first and the second stage of the DC module, respectively." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.357, + 0.469, + 0.403 + ], + "angle": 0, + "content": "level features \\(\\mathbf{F}_{high}\\), and then forward them to a decoder to predict \\(P_{t}, P_{t+1}, P_{n}\\) without computing intra-frame correspondences and short-term inter-frame correspondences." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.418, + 0.47, + 0.613 + ], + "angle": 0, + "content": "Table 4 shows the performances of different ablated models. As shown in the last row, our final proposed network with the full DC module outperforms other baselines on all metrics. In particular, even the ablated models with only a single-stage DC module (\\(3^{rd}\\) row and \\(4^{th}\\) row) can perform better than the baseline networks without the full DC module (\"I-Base\" and \"V-Base\"). This indicates the importance of correspondence learning for the VMD task. Also, the effective combination of the two stages in the DC module (\"Ours\") can boost the overall performance compared with the single-stage DC module. This shows that learning \"dual\" correspondences outperforms learning \"sole\" correspondences in our network." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.629, + 0.47, + 0.901 + ], + "angle": 0, + "content": "To further investigate the role of different types of correspondences in our network, we replace the input images (i.e., two adjacent frames and one randomly selected frame) with three adjacent frames \\((\\mathrm{I}_{adj}\\) only) or three video frames from the same frame \\((\\mathrm{I}_{same}\\) only). The \" \\(\\mathrm{I}_{adj}\\) only\" model focuses on short-term temporal correspondences while the \" \\(\\mathrm{I}_{same}\\) only\" model can only learn intra-frame correspondences, due to their limited input information. The results are listed in the last three rows and show that our method, which exploits intra-frame as well as both short-term and long-term temporal correspondences, outperforms these ablated models. Figure 8 shows the visual results of different ablated models. Our method with sufficient learning of spatial and temporal correspondences can locate the mirrors precisely in both frames. In particular, the results in the last row show that our method can still detect the mirrors by exploiting long-range temporal correspondences even though intra-frame correspondences are missing in the image." + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.088, + 0.891, + 0.207 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.527, + 0.217, + 0.863, + 0.231 + ], + "angle": 0, + "content": "Figure 8. Visual comparison of different ablated models." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.257, + 0.627, + 0.273 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.283, + 0.892, + 0.448 + ], + "angle": 0, + "content": "In this paper, we have investigated the video mirror detection (VMD) problem. To the best of our knowledge, we are the first to address the VMD problem from a deep-learning-based perspective. We have constructed the first large-scale video mirror dataset (VMD-D). It contains 14,988 image frames from 269 videos with corresponding masks. We have also proposed a novel network, called VMD-Net, to leverage both intra-frame and inter-frame correspondences for video mirror detection. Experimental results show that our VMD-Net outperforms state-of-the-art methods from relevant tasks on our VMD-D dataset." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.45, + 0.892, + 0.602 + ], + "angle": 0, + "content": "Our method does have limitations. Figure 9 shows that our method tends to produce results with coarse boundaries. Unlike existing image-based mirror detection methods [22, 36], which often explicitly adopt edge supervision in their network training, our method does not leverage such auxiliary information since we do not plan to focus this incremental cue in this first work for VMD. As a future work, we are working on improving our method by leveraging additional information like boundaries to help detect mirrors more precisely in videos." + }, + { + "type": "image", + "bbox": [ + 0.506, + 0.615, + 0.888, + 0.743 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.754, + 0.891, + 0.782 + ], + "angle": 0, + "content": "Figure 9. Failure cases. Our method may not be good at boundary extraction, due to the lack of explicit supervision of edges." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.815, + 0.659, + 0.831 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.84, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Xin Tan is sponsored by Shanghai Sailing Program (23YF1410500). This work is in part supported by two SRG grants from City University of Hong Kong (Ref: 7005674 and 7005843)." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9116" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.176, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.156 + ], + "angle": 0, + "content": "[1] Radhakrishna Achanta, Sheila Hemami, Francisco Estrada, and Sabine Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.157, + 0.472, + 0.227 + ], + "angle": 0, + "content": "[2] Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian Reid, Stephen Gould, and Anton Van Den Hengel. Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments. In CVPR, pages 3674-3683, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.228, + 0.47, + 0.282 + ], + "angle": 0, + "content": "[3] Maxim Berman, Amal Rannen Triki, and Matthew B Blaschko. The lovasz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In CVPR, pages 4413–4421, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.284, + 0.47, + 0.352 + ], + "angle": 0, + "content": "[4] Chenglizhao Chen, Shuai Li, Yongguang Wang, Hong Qin, and Aimin Hao. Video saliency detection via spatial-temporal fusion and low-rank coherency diffusion. IEEE Transactions on Image Processing, 26(7):3156-3170, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.353, + 0.47, + 0.408 + ], + "angle": 0, + "content": "[5] Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017. 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.41, + 0.47, + 0.451 + ], + "angle": 0, + "content": "[6] Zhihao Chen, Liang Wan, Lei Zhu, Jia Shen, Huazhu Fu, Wennan Liu, and Jing Qin. Triple-cooperative video shadow detection. In CVPR, pages 2715-2724, 2021. 3, 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.452, + 0.47, + 0.493 + ], + "angle": 0, + "content": "[7] Ho Kei Cheng and Alexander G Schwing. Xmem: Long-term video object segmentation with an atkinson-shiffrin memory model. In ECCV, pages 640-658. Springer, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.494, + 0.47, + 0.55 + ], + "angle": 0, + "content": "[8] Suhwan Cho, Heansung Lee, Minhyeok Lee, Chaewon Park, Sungjun Jang, Minjung Kim, and Sangyoun Lee. Tackling background distraction in video object segmentation. In ECCV, pages 446-462. Springer, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.55, + 0.47, + 0.592 + ], + "angle": 0, + "content": "[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248-255. IEEE, 2009. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.592, + 0.47, + 0.634 + ], + "angle": 0, + "content": "[10] Xinpeng Ding, Jingwen Yang, Xiaowei Hu, and Xiaomeng Li. Learning shadow correspondence for video shadow detection. In ECCV, pages 705-722. Springer, 2022. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.634, + 0.47, + 0.676 + ], + "angle": 0, + "content": "[11] Deng-Ping Fan, Wenguan Wang, Ming-Ming Cheng, and Jianbing Shen. Shifting more attention to video salient object detection. In CVPR, pages 8554-8564, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.677, + 0.47, + 0.731 + ], + "angle": 0, + "content": "[12] Yuchao Gu, Lijuan Wang, Ziqin Wang, Yun Liu, Ming-Ming Cheng, and Shao-Ping Lu. Pyramid constrained self-attention network for fast video salient object detection. In AAAI, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.732, + 0.47, + 0.788 + ], + "angle": 0, + "content": "[13] Yuchao Gu, Lijuan Wang, Ziqin Wang, Yun Liu, Ming-Ming Cheng, and Shao-Ping Lu. Pyramid constrained self-attention network for fast video salient object detection. In AAAI, 2020. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.789, + 0.47, + 0.83 + ], + "angle": 0, + "content": "[14] Huankang Guan, Jiaying Lin, and Rynson W.H. Lau. Learning semantic associations for mirror detection. In CVPR, pages 5941-5950, June 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.831, + 0.47, + 0.872 + ], + "angle": 0, + "content": "[15] Chenlei Guo, Qi Ma, and Liming Zhang. Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In CVPR. IEEE, 2008. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.873, + 0.47, + 0.903 + ], + "angle": 0, + "content": "[16] Fang Guo, Wenguan Wang, Jianbing Shen, Ling Shao, Jian Yang, Dacheng Tao, and Yuan Yan Tang. Video saliency" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.115, + 0.472, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "detection using object proposals. IEEE transactions on cybernetics, 48(11):3159-3170, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.122, + 0.894, + 0.176 + ], + "angle": 0, + "content": "[17] Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu. Ccnet: Criss-cross attention for semantic segmentation. In ICCV, pages 603-612, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.179, + 0.894, + 0.219 + ], + "angle": 0, + "content": "[18] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.221, + 0.892, + 0.262 + ], + "angle": 0, + "content": "[19] Philipp Krahenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. NeurIPS, 24, 2011. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.263, + 0.892, + 0.305 + ], + "angle": 0, + "content": "[20] Fuxin Li, Taeyoung Kim, Ahmad Humayun, David Tsai, and James M Rehg. Video segmentation by tracking many figure-ground segments. In ICCV, pages 2192-2199, 2013. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.306, + 0.892, + 0.348 + ], + "angle": 0, + "content": "[21] Jiaying Lin, Zebang He, and Rynson WH Lau. Rich context aggregation with reflection prior for glass surface detection. In CVPR, pages 13415-13424, 2021. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.349, + 0.892, + 0.39 + ], + "angle": 0, + "content": "[22] Jiaying Lin, Guodong Wang, and Rynson W. H. Lau. Progressive mirror detection. In CVPR, 2020. 1, 2, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.392, + 0.892, + 0.46 + ], + "angle": 0, + "content": "[23] Zhihui Lin, Tianyu Yang, Maomao Li, Ziyu Wang, Chun Yuan, Wenhao Jiang, and Wei Liu. Swem: Towards real-time video object segmentation with sequential weighted expectation-maximization. In CVPR, pages 1362-1372, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.462, + 0.892, + 0.518 + ], + "angle": 0, + "content": "[24] Yong Liu, Ran Yu, Jiahao Wang, Xinyuan Zhao, Yitong Wang, Yansong Tang, and Yujiu Yang. Global spectral filter memory network for video object segmentation. In ECCV, pages 648-665. Springer, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.519, + 0.892, + 0.587 + ], + "angle": 0, + "content": "[25] Yong Liu, Ran Yu, Fei Yin, Xinyuan Zhao, Wei Zhao, Weihao Xia, and Yujiu Yang. Learning quality-aware dynamic memory for video object segmentation. In European Conference on Computer Vision, pages 468-486. Springer, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.589, + 0.892, + 0.646 + ], + "angle": 0, + "content": "[26] Xiao Lu, Yihong Cao, Sheng Liu, Chengjiang Long, Zipei Chen, Xuanyu Zhou, Yimin Yang, and Chunxia Xiao. Video shadow detection via spatio-temporal interpolation consistency training. In CVPR, pages 3116-3125, 2022. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.647, + 0.892, + 0.688 + ], + "angle": 0, + "content": "[27] Haiyang Mei, Bo Dong, Wen Dong, Pieter Peers, Xin Yang, Qiang Zhang, and Xiaopeng Wei. Depth-aware mirror segmentation. In CVPR, pages 3044-3053, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.689, + 0.892, + 0.745 + ], + "angle": 0, + "content": "[28] Peter Ochs, Jitendra Malik, and Thomas Brox. Segmentation of moving objects by long term video analysis. IEEE transactions on pattern analysis and machine intelligence, 36(6):1187-1200, 2013. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.746, + 0.892, + 0.788 + ], + "angle": 0, + "content": "[29] Youwei Pang, Xiaoji Zhao, Lihe Zhang, and Hutchuan Lu. Multi-scale interactive network for salient object detection. In CVPR, pages 9413-9422, 2020. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.789, + 0.892, + 0.829 + ], + "angle": 0, + "content": "[30] Anestis Papazoglou and Vittorio Ferrari. Fast object segmentation in unconstrained video. In ICCV, pages 1777-1784, 2013. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.831, + 0.892, + 0.903 + ], + "angle": 0, + "content": "[31] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner," + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9117" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.092, + 0.47, + 0.175 + ], + "angle": 0, + "content": "Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.177, + 0.47, + 0.232 + ], + "angle": 0, + "content": "[32] Gensheng Pei, Fumin Shen, Yazhou Yao, Guo-Sen Xie, Zhenmin Tang, and Jinhui Tang. Hierarchical feature alignment network for unsupervised video object segmentation. In ECCV, pages 596-613. Springer, 2022. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.233, + 0.47, + 0.287 + ], + "angle": 0, + "content": "[33] F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung. A benchmark dataset and evaluation methodology for video object segmentation. In CVPR, 2016. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.288, + 0.47, + 0.328 + ], + "angle": 0, + "content": "[34] Federico Perazzi, Oliver Wang, Markus Gross, and Alexander Sorkine-Hornung. Fully connected object proposals for video segmentation. In ICCV, pages 3227-3234, 2015. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.33, + 0.47, + 0.371 + ], + "angle": 0, + "content": "[35] Jiaqi Tan, Weijie Lin, Angel X Chang, and Manolis Savva. Mirror3d: Depth refinement for mirror surfaces. In CVPR, pages 15990-15999, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.372, + 0.47, + 0.426 + ], + "angle": 0, + "content": "[36] Xin Tan, Jiaying Lin, Ke Xu, Chen Pan, Lizhuang Ma, and Rynson W. H. Lau. Mirror detection with the visual chirality cue. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 1, 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.428, + 0.47, + 0.468 + ], + "angle": 0, + "content": "[37] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In ECCV, pages 402-419. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.469, + 0.47, + 0.51 + ], + "angle": 0, + "content": "[38] Pavel Tokmakov, Karteek Alahari, and Cordelia Schmid. Learning video object segmentation with visual memory. In ICCV, pages 4481-4490, 2017. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.511, + 0.47, + 0.566 + ], + "angle": 0, + "content": "[39] Wenguan Wang, Xiankai Lu, Jianbing Shen, David J Crandall, and Ling Shao. Zero-shot video object segmentation via attentive graph neural networks. In ICCV, pages 9236-9245, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.567, + 0.47, + 0.622 + ], + "angle": 0, + "content": "[40] Wenguan Wang, Hongmei Song, Shuyang Zhao, Jianbing Shen, Sanyuan Zhao, Steven CH Hoi, and Haibin Ling. Learning unsupervised video object segmentation through visual attention. In CVPR, pages 3064-3074, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.623, + 0.47, + 0.664 + ], + "angle": 0, + "content": "[41] Saining Xie, Ross Girshick, Piotr Dólár, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In CVPR, pages 1492–1500, 2017. 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.665, + 0.47, + 0.705 + ], + "angle": 0, + "content": "[42] Xin Yang, Haiyang Mei, Ke Xu, Xiaopeng Wei, Baocai Yin, and Rynson W.H. Lau. Where is my mirror? In ICCV, 2019. 1, 2, 3, 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.707, + 0.47, + 0.76 + ], + "angle": 0, + "content": "[43] Zhao Yang, Qiang Wang, Luca Bertinetto, Weiming Hu, Song Bai, and Philip HS Torr. Anchor diffusion for unsupervised video object segmentation. In ICCV, pages 931-940, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.761, + 0.47, + 0.803 + ], + "angle": 0, + "content": "[44] Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object-contextual representations for semantic segmentation. In ECCV, pages 173-190. Springer, 2020. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.805, + 0.47, + 0.845 + ], + "angle": 0, + "content": "[45] Kaihua Zhang, Zicheng Zhao, Dong Liu, Qingshan Liu, and Bo Liu. Deep transport network for unsupervised video object segmentation. In ICCV, pages 8781-8790, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.847, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[46] Miao Zhang, Jie Liu, Yifei Wang, Yongri Piao, Shunyu Yao, Wei Ji, Jingjing Li, Huchuan Lu, and Zhongxuan Luo. Dynamic context-sensitive filtering network for video salient object detection. In ICCV, pages 1553-1563, 2021. 3" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.135 + ], + "angle": 0, + "content": "[47] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In CVPR, pages 2881-2890, 2017. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[48] Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu, and Lei Zhang. Suppress and balance: A simple gated network for salient object detection. In ECCV, pages 35-51. Springer, 2020. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.192, + 0.892, + 0.234 + ], + "angle": 0, + "content": "[49] Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Scene parsing through ade20k dataset. In CVPR, pages 633-641, 2017. 1" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.234 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9118" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Detect Mirrors From Videos via Dual Correspondences/1cca248f-f36a-45a2-94fd-cecf65153065_origin.pdf b/2023/Learning To Detect Mirrors From Videos via Dual Correspondences/1cca248f-f36a-45a2-94fd-cecf65153065_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..73e580eb88e755f5ca5869c720c3b883c0fdf9dc --- /dev/null +++ b/2023/Learning To Detect Mirrors From Videos via Dual Correspondences/1cca248f-f36a-45a2-94fd-cecf65153065_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4b8c8d55ceb52b09046fce444da3254711c022fd26dc113b234917ab56fb349a +size 2594383 diff --git a/2023/Learning To Detect Mirrors From Videos via Dual Correspondences/full.md b/2023/Learning To Detect Mirrors From Videos via Dual Correspondences/full.md new file mode 100644 index 0000000000000000000000000000000000000000..e0d2d792aab29ab384aec70dd3fa2fd52075ace9 --- /dev/null +++ b/2023/Learning To Detect Mirrors From Videos via Dual Correspondences/full.md @@ -0,0 +1,355 @@ +# Learning to Detect Mirrors from Videos via Dual Correspondences + +Jiaying Lin $^{1*}$ + +Xin Tan\* + +Rynson W.H. Lau† + +1City University of Hong Kong + +$^{2}$ East China Normal University + +jiayinlin5-c@my.cityu.edu.hk, xtan@cs.ecnu.edu.cn, Rynson.Lau@cityu.edu.hk + +# Abstract + +Detecting mirrors from static images has received significant research interest recently. However, detecting mirrors over dynamic scenes is still under-explored due to the lack of a high-quality dataset and an effective method for video mirror detection (VMD). To the best of our knowledge, this is the first work to address the VMD problem from a deep-learning-based perspective. Our observation is that there are often correspondences between the contents inside (reflected) and outside (real) of a mirror, but such correspondences may not always appear in every frame, e.g., due to the change of camera pose. This inspires us to propose a video mirror detection method, named VMD-Net, that can tolerate spatially missing correspondences by considering the mirror correspondences at both the intra-frame level as well as inter-frame level via a dual correspondence module that looks over multiple frames spatially and temporally for correlating correspondences. We further propose a first large-scale dataset for VMD (named VMD-D), which contains 14,987 image frames from 269 videos with corresponding manually annotated masks. Experimental results show that the proposed method outperforms SOTA methods from relevant fields. To enable real-time VMD, our method efficiently utilizes the backbone features by removing the redundant multi-level module design and gets rid of post-processing of the output maps commonly used in existing methods, making it very efficient and practical for real-time video-based applications. Code, dataset, and models are available at https://jiaying.link/cvpr2023-vmd/ + +# 1. Introduction + +Mirrors appear everywhere. They can adversely affect the performance of computer vision tasks (e.g., depth estimation [35], vision-and-language navigation [2], semantic segmentation [49]), due to their fundamental property that they reflect objects from their surroundings. Thus, it is nec + +![](images/fe267c940ac5f0c9c457d83d02d468f7ecbbdb2ea59451cb41eaecb937b6dd80.jpg) +$\longleftrightarrow$ intra-frame correspondence + +![](images/e143b424250c1210f09ee8fc7195cb524b3d19488e571b1adb2c0b43fe90b35e.jpg) +Image + +![](images/90f6aa71a75f7786b969931a6c7ffdbb16dfd4e9530ab5c1d1ed6a96a4548a46.jpg) +$\longleftrightarrow$ inter-frame correspondence + +![](images/488d1f0843d954001c4a77250c09edb9ec09b71bbb73a69574e230731fde806a.jpg) +VCNet + +![](images/d10b9ed4d1043d555f5231d1b4f5363b3102446b4977c2f3b39461a2c80f4a7d.jpg) + +![](images/d162cc780ea794ed715315456249e0afd4cc6e1f404d975602ca160143597ee6.jpg) +Ours + +![](images/98422925ad2bc4b9548cd312569560292c72d855e6cfe067de68e5a547162104.jpg) + +![](images/8ca154b8aba7e1efa499f7e8e83c8ae3fb6c222f4c73283270ed44efa532e23e.jpg) +GT +Figure 1. Although state-of-the-art single-image mirror detection method VCNet [36] performs well on a single image (e.g., the first row) by using implicitly intra-frame correspondence, it may fail when the intra-frame cue is weak or even absent in some video frames (e.g., the second and third rows). The lack in exploiting inter-frame information causes the current mirror detection methods to produce inaccurate and inconsistent results when applied to VMD. In contrast, our method can perform well in both situations by utilizing the proposed dual correspondence module to exploit intra-frame (spatial) and inter-frame (temporal) correspondences. + +essary to build a robust computer vision model that can distinguish mirrors from their surrounding objects correctly. + +Existing single-image mirror detection methods exploit different cues, such as context contrast [42], explicitly correspondences [22], semantics association [14], and chirality and implicit correspondences [36], to detect mirrors from single RGB input images. Despite these recent efforts being put into the mirror detection problem, none of them focuses on detecting mirrors from videos. However, a lot of real-world computer vision applications are video-based (e.g., robotic navigation, autonomous driving, and surveillance), rather than image-based. Hence, solving the video mirror detection (VMD) problem can benefit these applications. + +In this paper, we aim to address the VMD problem. There are two major challenges with this problem. First, to the best of our knowledge, there are no existing large-scale datasets that can be used for training and evaluation on the VMD problem. Second, existing mirror detection methods, + +![](images/6e6f76ed23fbd1d35779951e2604a1b50fbd5389acef935999ef34337eae9a33.jpg) +Figure 2. Quantitative comparison on the performance and efficiency between existing mirror detection methods and our method for VMD. All models are trained/tested on the proposed VMD-D dataset, under a single RTX 3090 GPU. Our model has $\sim 5$ times smaller network parameters and runs $\sim 18\times$ faster than the state-of-the-art image-based mirror detection method, VCNet [36], and still outperforms it by a large margin. + +which are all developed for the image-based mirror detection task, are all based on static cues. None of them take advantages of the dynamic nature of videos in the VMD problem. Figure 1 shows that the current state-of-the-art mirror detection method, VCNet [36], may fail when correspondences are missing in some challenging frames (e.g., second and third rows) due to, for example, the change of camera pose, even though it may perform well in some easy cases (e.g., the first row). Besides, as the image-based mirror detection task is already very challenging, existing methods for this task often adopt heavy network design and time-consuming post-processing techniques [19] to improve their results. Figure 2 shows that existing image-based mirror detection models run at about 1fps, even on one of the latest GPUs, which cannot support real-time VMD. All these drawbacks motivate us to develop a large-scale dataset and an effective/efficient method for video mirror detection. + +In this paper, we address the VMD problem in two ways. First, we construct the first large-scale video mirror detection benchmark dataset (VMD-D). It contains 14,987 image frames in 269 videos, coming from diverse scenes. The constructed VMD-D dataset provides large-scale and high-diversity data for training and evaluation on the VMD problem. Second, we propose an effective and efficient method, called VMD-Net, for the VMD problem. The proposed method exploits multi-frame correspondences at both intra-frame (spatial) and inter-frame (temporal) levels. Compared with state-of-the-art image-based mirror detection methods, which typically adopt heavy pipelines, our method uses a light-weight network architecture without the need for any + +post-processing techniques. As a result, our method is efficient for real-time applications. In particular, our method has $\sim 5$ times fewer network parameters and runs $\sim 18\times$ faster than the latest state-of-the-art image-based mirror detection method, VCNet [36]. We conduct comprehensive experiments to demonstrate the effectiveness and efficiency of our proposed method. Experimental results show that our method outperforms state-of-the-art methods from relevant tasks on the proposed large-scale VMD-D dataset. + +Our key contributions can be summarized as follows: + +- We construct the first large-scale video mirror detection dataset, called VMD-D. The new dataset contains 14,988 image frames from 269 videos with precise annotated masks. +- We propose a novel network, called VMD-Net, to exploit both intra-frame and inter-frame correspondences via a dual correspondence (DC) module. This DC module allows VMD-Net to tolerate occasionally missing correspondences in the temporal dimension. +- Extensive evaluations show that our method outperforms existing state-of-the-art methods from relevant tasks on our proposed VMD-D dataset. + +# 2. Related Work + +Image-based Mirror Detection. Recently, Yang et al. [42] propose the first mirror detection dataset and the first mirror detection network to detect mirrors by modeling contextual contrasted information. Lin et al. [22] then propose a more diverse and larger mirror detection benchmark, and a correspondence-aware method for mirror detection, which correlates the contents inside and outside of the mirror. Mei et al. [27] further consider using RGB-D data for detecting mirrors, and construct the first RGB-D dataset for mirror detection. Tan et al. [35] address the depth refinement problem on mirror surfaces by proposing another RGB-D mirror dataset and a detect-and-refine method for mirror detection. Most recently, Tan et al. [36] propose an image-based method for mirror detection based on the chirality cue and implicit correspondences. + +Despite their success, none of these methods focuses on the VMD problem. They also have high computational costs, thus not practical for real-time applications. In this paper, we aim to address the VMD problem by constructing a benchmark dataset and proposing an efficient/effective method for the problem. + +Video Object Segmentation Video object segmentation (VOS) aims to segment the target object(s) from the input videos. Currently, it can be categorized into two main individual tasks: the unsupervised video object segmentation (UVOS) [32, 38-40, 43, 45] and the semi-supervised video + +![](images/608afd6ead44b8fe5a3f75bbc23a151b587390f8cfe4f364a3b36ed79bcec810.jpg) +Figure 3. Snapshots of the proposed Video Mirror Detection dataset, VMD-D, with pixel-level annotations. + +object segmentation (SVOS) [7, 8, 23-25]. The main difference between UVOS and SVOS is that UVOS does not require annotation for the first frame of the input video during the inference phase, while SVOS needs to do it. Thus, UVOS is more related to the video mirror detection problem since we do not use the first-frame ground truth mirror label to infer the location of mirrors in the rest of the video frames. UVOS focuses on automatically detecting the primary foreground object without using the ground truth mask of the first frame during testing. Early UVOS methods use handcrafted features [30, 34] to detect foreground objects in the input videos. Recent CNN-based UVOS models [38-40] focus on exploring different cues like memory [38], graph neural network [39] and visual attention [40] for better performance. The latest UVOS methods [32, 45] take optical flow as auxiliary information to help segment videos, thanks to the rapid evolution on optical flow estimation [37]. + +However, UVOS methods cannot directly address the video mirror detection problem, since mirrors are not always the primary foreground objects while having changing appearance patterns during motion. + +Video Salient Object Detection Video salient object detection (VSOD) is related to the UVOS task. It aims to automatically detect the most visually distinctive objects from an input video without indicating where the salient object is in the first frame. Unlike the methods for single-image salient object detection [29, 48], VSOD methods require building a model with a thorough understanding of dynamic attention. Most earlier VSOD methods detect the salient objects using handcrafted features [4, 15, 16]. With the help of deep neural networks, recent VSOD methods achieve great progress. Fan et al. [11] take attention shift into account. Gu et al. [12] exploit self-attention models to detect salient objects from videos efficiently. Zhang et al. [46] proposes a dynamic strategy of context fusion for VSOD. + +Similar to UVOS, the methods for video salient object detection are not directly applicable to video mirror detection since mirrors are not always distinctive. + +# 3. Video Mirror Detection Dataset (VMD-D) + +To facilitate research on the video mirror detection problem, we first contribute a large-scale video mirror detection dataset (named VMD-D). It consists of 269 videos in 14,988 image frames with corresponding precise annotations from diverse scenes. Figure 3 shows some example video frames in our proposed VMD-D dataset. The details of our VMD are discussed below. + +# 3.1. Dataset Construction + +To construct the first large-scale video mirror detection dataset, we use a smartphone to collect high-resolution videos (e.g., $1920 \times 1080$ resolution) with mirrors in daily-life scenes. Following a common practice [6] used to construct datasets for video-based problems, we manually trim the collected videos to make sure that each video frame has at least one mirror region. After that, we obtain 269 video clips and then randomly split them into a training set with 143 videos (containing 7,835 images) and a test set with 126 videos (containing 7,152 images). We then label pixel-level mirror masks by annotators. The total duration of our videos is 502 seconds for covering long-temporal scenarios. The frame rate is 30 fps for all collected videos. + +# 3.2. Dataset Analysis + +Table 1 shows the comparison of different datasets from the relevant areas, including image mirror detection [22,42] (top group), video object segmentation [20,28,33] (second group), and video shadow detection [6] (third group), and ours. Our dataset reflects good video quality (i.e., high-resolution videos) and quantity (i.e., a large number of annotated frames), and is practical for being the first step for large-scale video mirror detection. + +Area Distribution. Figure 4(a) shows the ratio of mirror area over the image area (mirror area distribution). We can see that our dataset contains mirrors covering a wide range of area ratios. We also note that it consists of a lot of small mirrors $(< 0.1)$ , which makes our dataset very challenging. + +Contrast Distribution. We analyze the contrasts between the mirror regions and non-mirror regions by computing $\chi^2$ + +
Dataset#Videos#Annos.#Time (s).#FPSMax Reso.
MSD [42]-4,018--640×512
PMD [22]-6,461--4000×3000
FBMS [28]59720--640×480
STV2 [20]14947--640×360
DAVIS [33]503455144241920×1080
ViSha [6]12011,685390301280×720
Ours26915,066502301920×1080
+ +Table 1. Comparison of different video datasets for relevant tasks. + +![](images/274d704530ba4f4a196d77643f9043116634ff641d60432573bbf3415b669e54.jpg) +(a) mirror area distribution +Figure 4. Statistics of our VMD dataset. + +![](images/835d306de4d703ee7e7553390ac3f97becb10176a599739047e59897a981e570.jpg) +(b) color contrast distribution + +distance between their RGB histograms. We also compare the distribution between MSD [42] and PMD [22], as shown in Figure 4(b). In general, VMD-D has more images with extremely low color contrasts ( $< 0.1$ ) compared with existing mirror datasets MSD and PMD. This indicates our images are collected from very diverse scenes, making them more challenging to detect. + +# 4. Method + +# 4.1. Overview + +We observe that there are often spatial correspondences between the contents inside (reflected) and outside (real) of a mirror. However, such spatial correspondences may not always appear in every frame. The lack in exploiting temporal correspondence may cause current image-based mirror detection methods to fail in the VMD task. Hence, in this paper, we propose a method that is able to tolerate temporally missing correspondences by considering mirror correspondences at both the intra-frame level (spatial) as well as the inter-frame level (temporal). + +Figure 5 shows the overall structure of our proposed method VMD-Net. The key idea of our network design is to leverage the intra-frame and the inter-frame correspondences for video mirror detection. Our VMD-Net takes three images from the same video clips as inputs. The first two images $I_{t}$ and $I_{t + 1}$ are from adjacent video frames, while the third image $I_{n}$ is randomly selected from other frames. We first apply a shared backbone network ResNext + +![](images/cb999da9f8d988e65ef969195cb7ce6aa822226f9ce7995e5b4c1499ffb91a92.jpg) +Figure 5. The framework of our proposed method. $I_{t}, I_{t + 1}, I_{n}$ are input images. In particular, $I_{t}$ and $I_{t + 1}$ are two adjacent video frames, and $I_{n}$ is a video frame randomly selected from the same video of $I_{t}$ . $P_{t}, P_{t + 1}, P_{n}$ are intermediate maps and the final outputs maps are $M_{t}, M_{t + 1}, M_{n}$ for the corresponding input images. + +101 [41] to extract multi-scale features from the input images. Unlike existing single-image mirror detection methods, which make full usage of the features at all stages (i.e., from the $1^{st}$ to the $5^{th}$ scales), for each input image $i$ , our VMD-Net only utilizes the low-level features at the $2^{nd}$ scale (denoted as $\mathbf{F}_{low}^{i}$ ) and the high-level features at the $5^{th}$ scale. Following the design of DeepLabV3 [5], the high-level features from the backbone network are then fed to an atrous spatial pyramid pooling module to obtain enhanced semantic features (denoted as $\mathbf{F}_{high}^{i}$ ). Taking image $I_{t}$ as an example, we then assign a dual correspondence module to both low-level features $\mathbf{F}_{low}^{t}$ and high-level features $\mathbf{F}_{high}^{t}$ to produce the intermediate map $P_{t}$ and the final output $M_{t}$ . Similarly, the dual correspondence module takes the low-level features and high-level features from $I_{t+1}, I_{n}$ to produce the intermediate maps $P_{t+1}, P_{n}$ and the final outputs $M_{t+1}, M_{n}$ . + +# 4.2. Dual Correspondence (DC) Module + +Figure 6 shows the structure of the proposed DC module. It consists of two stages. The first stage of the DC module aims to learn intra-frame correspondences for all input images and the short-term inter-frame correspondences inside mirrors in the adjacent input image $I_{t}$ and $I_{t + 1}$ . The second stage of the DC module focuses on extracting long-term inter-frame correspondences across all input images to enhance the learning of temporal correspondence. This enables our VMD-Net to exploit correspondences at both intra-frame and inter-frame levels at different tempo + +![](images/2f4ba6477c79b1ffe7d114d9c9a75f0e811b45e842dfaf76537b484e8d2eb047.jpg) +Figure 6. Our DC module. It consists of two stages. The first stage in the DC module aims to extract intra-frame and short-term temporal correspondences from two adjacent video frames $I_{t}$ , $I_{t+1}$ , while the second stage in the DC module focuses on learning long-term temporal correspondences from all input frames $I_{t}$ , $I_{t+1}$ , $I_{n}$ . + +ral scales. + +Stage 1. Our DC module takes the low-level features $\mathbf{F}_{low}$ and high-level features $\mathbf{F}_{high}$ as input. In the first stage, we first extract the intra-frame correspondences from the high-level features $\mathbf{F}_{high}$ of each image by the global relation (GR) block proposed in [22]. The GR block can effectively and efficiently extract the correspondence between the content inside and outside of the mirrors by modeling spatial corresponding relations in a single image. The high-level correspondence-aware features from the randomly selected frame $I_{n}$ are directly concatenated with the corresponding low-level features $\mathbf{F}_{low}^{n}$ in the decoder to output an intermediate map $P_{n}$ , while the high-level correspondence-aware features extracted from the two adjacent frames $I_{t}$ and $I_{t + 1}$ are forwarded to a cross-attention (Cross-Att.) module to learn short-term temporal correspondences. To compute short-term temporal correspondences between $I_{t}$ and $I_{t + 1}$ , we also feed their low-level features $\mathbf{F}_{low}^{t},\mathbf{F}_{low}^{t + 1}$ to another cross-attention module. + +Our cross-attention module is inspired by [17], but we extend it from self-attention (single-input and single-out features) to cross-attention with two-input and two-output features and also enhance its ability to model spatial correspondences. Our module first applies two convolutional layers with $1 \times 1$ filters to the two input features $\mathbf{F}_t$ and $\mathbf{F}_{t+1}$ and further generates a spatially enhanced affinity matrix $A^{se} \in \mathbb{R}^{(2H + 2W - 1) \times (W \times H)}$ . Different from the original affinity matrix $A \in \mathbb{R}^{(H + W - 1) \times (W \times H)}$ in [17], which is computed by extracting local contextual information in the + +horizontal and vertical directions of the input features, our spatially enhanced affinity operation also computes global contextual information by taking the diagonal items of the input features into our design. Formally, our cross-attention process can be described as: + +$$ +K = \operatorname {C o n v} _ {1 \times 1} (\mathbf {F} _ {t}); Q = \operatorname {C o n v} _ {1 \times 1} (\mathbf {F} _ {t + 1}), \tag {1} +$$ + +$$ +A ^ {s e} = \mathbb {A} ^ {s e} (K, Q), \tag {2} +$$ + +$$ +V = \operatorname {C o n v} _ {1 \times 1} \left(\mathbf {F} _ {t}\right), \tag {3} +$$ + +$$ +(2 H + 2 W - 1) \times (W \times H) +$$ + +$$ +\mathbf {C} ^ {t} = K + \omega_ {t} \quad \sum_ {i} A ^ {s e} V, \tag {4} +$$ + +$$ +(2 H + 2 W - 1) \times (W \times H) +$$ + +$$ +\mathbf {C} ^ {t + 1} = Q + \omega_ {t + 1} \quad \sum_ {i} \quad A ^ {s e} V, \tag {5} +$$ + +where $\mathbf{C}$ is the output of the cross-attention module. $\omega_{t}$ and $\omega_{t + 1}$ are learnable parameters. $\mathbb{A}^{se}$ is our spatially enhanced affinity operation. $\mathbf{C}^t$ and $\mathbf{C}^{t + 1}$ are the output correspondence-aware features. We apply the cross-attention module to both $\mathbf{F}_{low}^{t},\mathbf{F}_{low}^{t + 1}$ and $\mathbf{F}_{high}^{t},\mathbf{F}_{high}^{t + 1}$ to obtain correspondence-aware features $\mathbf{C}_{low}^{t},\mathbf{C}_{low}^{t + 1}$ and $\mathbf{C}_{high}^{t},\mathbf{C}_{high}^{t + 1}$ . We then concatenate the correspondence features from the same input image but at different levels. The concatenated features $[\mathbf{C}_{low};\mathbf{C}_{high}]$ are forwarded to a decoder to obtain an intermediate prediction $P_{t}$ for $I_{t}$ . Similarly, we obtain the intermediate predictions $P_{t + 1}$ and $P_{n}$ for $I_{t + 1}$ and $I_{n}$ . + +Stage 2. The second stage of our DC module takes all in + +intermediate prediction maps $P_{t}, P_{t + 1}, P_{n}$ and high-level features $\mathbf{F}_{high}^{t}, \mathbf{F}_{high}^{t + 1}, \mathbf{F}_{high}^{n}$ as inputs. The input features and prediction maps are forwarded to a reverse cross-attention module. This module aims at explicitly exploiting the correspondence between the contents inside and outside of the mirrors in different frames for long-range temporal correspondences. In particular, we need to explicitly model the correspondences between input frame $I_{t}$ and the randomly selected frame $I_{n}$ . To do this, we first multiply $F_{high}$ by their corresponding intermediate prediction map $P$ , which are normalized by a sigmoid function. We then reverse $P$ to obtain the reversed prediction map $\hat{P}$ , which indicates the potential non-mirror regions. We also notice that the mirrors will potentially flip the real object horizontally in its content. Thus, we conduct a horizontal flip to the input non-mirror features to model the potential relation of mirror reflection. Similarly, we compute temporal correspondences between $I_{t}$ and $I_{t + 1}$ with this strategy. In the second stage of the DC module, we also use our cross-attention module to extract the inter-frame correspondences. The process of our reverse cross-attention module can be formulated as follows: + +$$ +\Omega_ {n, -} = \mathbb {C} \mathbb {A} \left(P _ {n} \mathbf {F} _ {h i g h} ^ {n}, \left(\hat {P} _ {t} \mathbf {F} _ {h i g h} ^ {t}\right) ^ {\top}\right), \tag {6} +$$ + +$$ +\Omega_ {t, -} = \mathbb {C} \mathbb {A} \left(P _ {t} \mathbf {F} _ {h i g h} ^ {t}, \left(\hat {P} _ {t + 1} \mathbf {F} _ {h i g h} ^ {t + 1}\right) ^ {\top}\right), \tag {7} +$$ + +$$ +\Omega_ {t + 1, -} = \mathbb {C A} \left(P _ {t + 1} \mathbf {F} _ {h i g h} ^ {t + 1}, \left(\hat {P} _ {t} \mathbf {F} _ {h i g h} ^ {t}\right) ^ {\top}\right), \tag {8} +$$ + +where $\mathbb{C}\mathbb{A}$ is our cross-attention module. $\top$ is a horizontal flip operation. $\Omega$ is the final output feature. Note that we omit the second output of the cross-attention modules used in the $2^{nd}$ stage for convenience. We then forward the output features to individual decoders to obtain the final output predictions $M_{t}, M_{t+1}, M_{n}$ for the input images $I_{t}, I_{t+1}, I_{n}$ , respectively. + +# 4.3. Loss Functions + +We adopt the Lovász-Softmax loss function [3] to supervise our network training. The final loss function is: + +$$ +i \in \{t, t + 1, n \} +$$ + +$$ +\mathcal {L} = \sum_ {i} \quad \mathcal {L} _ {h} \left(P _ {i}, G _ {i}\right) + \mathcal {L} _ {h} \left(M _ {i}, G _ {i}\right), \tag {9} +$$ + +where $\mathcal{L}_h(\cdot ,\cdot)$ denotes the lovasz-hinge loss. $P_{i},M_{i},G_{i}$ are the intermediate output from the DC module, the final output of our network, and the ground truth label of image $I_{i}$ , respectively. + +# 5. Experiments + +# 5.1. Experimental Settings and Evaluation Metrics + +We have implemented VMD-Net in Pytorch [31] and trained it on a PC with an RTX3090 GPU card. During training, we resize the input images to $384 \times 384$ . We + +use ResNext-101 [41] pre-trained on ImageNet [9] as our backbone network to extract image features. We adopt Adam [18] as the optimizer with a momentum of 0.9 and a weight decay of $5 \times 10^{-4}$ . The base learning rate, batch size, and the number of training epochs are 0.0001, 8, and 15, respectively. We use the cosine learning rate decay with 3-epoch warm-up period to adjust the learning rate during training. Note that we do NOT apply any post-processing techniques to refine our predictions. + +We employ four popular metrics, intersection over union (IoU), pixel accuracy, F-measure $(F_{\beta})$ [1], and mean absolute error (MAE) to evaluate the performance of tested methods quantitatively. $F_{\beta}$ is defined as: + +$$ +F _ {\beta} = \frac {1 + \beta^ {2} (P r e c i s i o n \times R e c a l l)}{\beta^ {2} P r e c i s i o n + R e c a l l}, +$$ + +where $\beta$ is set to 0.3 as suggested in [1]. It evaluates the overall performance between precision and recall. + +# 5.2. Comparison to the State-of-the-art Methods + +Due to the lack of methods for video mirror detection, we compare our method with 14 state-of-the-art methods from relevant fields, including GateNet [48] and MINet [29] for salient object detection; PCSA [13] for video salient object detection; DeepLabV3 [5], PSPNet [47] and OCRNet [44] for semantic segmentation; TVSD [6], STICT [26] and ScCor [10] for video shadow detection; HFAN [32] for video object segmentation; GlassNet [21] for glass surface detection; MirrorNet [42], PMDNet [22] and VCNet [36] for single-image mirror detection. We train and test all baseline methods on our video mirror detection dataset VMD-D using their released codes on the same platform. Table 2 shows the quantitative results. Our method achieves the best performance with a large margin on all four metrics. + +Figure 7 visually compares the results of our method with the selected state-of-the-art methods from relevant fields. We can see that when compared with image-based mirror detection methods PMDNet [22] and VCNet [36], which produce inaccurate and incoherent results both in a short-term temporal interval (e.g., the 1st and 2nd rows; the 3rd and the 4th rows) and a long-term temporal interval (e.g., the 5th and 6th rows; the 7th and 8th rows), our method can precisely predict the mirror regions by exploiting inter-frame and intra-frame correspondences in both situations. We attribute the superior performances of our method to the design of the DC module, which enables the modeling of spatial, short-range, and long-range temporal correspondences. + +In particular, we also compare the efficiency of our method with most existing mirror detection methods. Table 3 shows the quantitative results in the number of network parameters and FPS. Our model has significantly fewer network parameters and runs much faster than the + +![](images/6e366e2bc0b144a3b11f6776b9c0d4095662d666e94105f3fdfccfe4ea34c2a9.jpg) +Image + +![](images/d7536aefa4255608c739b362633283dabdb74412d45ac51a0e0505d2eaa2566d.jpg) +GateNet [48] + +![](images/38182c110535ca90dcc3a8b6062186d54615245eb6e65fedf4d153d418b879a8.jpg) +DeepLabV3 [5] + +![](images/413d9889243360f56b9bff4f1924e1ab4a869bc3b8ea2d0650d3c99cffdb3237.jpg) +Sc-Cor [10] + +![](images/a1904b63ae3983b8b2262988015e806a8f563d9516dce46bac7527234475e547.jpg) +HFAN [32] + +![](images/d46deeb0889bf0b9ce4936d4f2e69f2f2215d7ef4f552602d2a975f32cd9f074.jpg) +GlassNet [21] + +![](images/5c51e4b630bfca49a822d16e6fb50f8131ced043dd107bef3a815965b9b48588.jpg) +PMDNet [22] +Figure 7. Visual comparison between the proposed VMD-Net and selected state-of-the-art methods from relevant fields. + +![](images/dab159e1040c06f076b5e4b22710cf39dabcee505b973d713a4e5b957a8dd6f0.jpg) +VCNet [36] + +![](images/47d26960f1beafd2187a4757f4e1721bbeb6ef85eb33fb613879df86dfb0a1fa.jpg) +Ours + +![](images/16ba031f45415b3013ff0aa344b448d2849fc0bc28c7d194232eee91deabcc02.jpg) +Ground Truth + +
MethodsIoU↑Accuracy↑Fβ↑MAE↓
GateNet [48]0.4290.8510.6650.153
MINet [29]0.4120.8540.6760.148
PCSA [13]0.1930.8030.4640.198
DeepLabV3 [5]0.4810.8460.6810.157
PSPNet [47]0.4640.8500.6650.152
OCRNet [44]0.3940.7860.6400.175
TVSD [6]0.4800.8750.7460.125
STICT [26]0.1640.8090.5300.198
Sc-Cor [10]0.5120.8630.6960.137
HFAN [32]0.4590.8760.7060.124
GlassNet [21]0.5520.8640.7180.137
MirrorNet [42]0.5050.8550.6810.145
PMDNet [22]0.5320.8720.7490.128
VCNet [36]0.5390.8770.7490.123
Ours0.5670.8950.7870.105
+ +state-of-the-art image-based mirror detection method, VCNet [36]. Specifically, our model has $\sim 5$ times fewer network parameters and runs $\sim 18\times$ faster than VCNet [36]. Furthermore, our model outperforms VCNet [36] by a large margin. These results demonstrate the superior practicality and suitability of our model for the VMD problem. + +Table 2. Quantitative comparison between the proposed VMD-Net and 14 state-of-the-art methods from relevant fields. The best results are shown in bold. + +
MethodsParams. (M) ↓FPS ↑Fβ ↑
MirrorNet [42]121.771.160.681
PMDNet [22]147.661.280.749
VCNet [36]333.170.890.749
Ours62.2417.060.787
+ +Table 3. Quantitative comparison on the performance and efficiency between existing mirror detection methods and our method for VMD. All models are trained/tested on the proposed VMD-D dataset, on a single RTX 3090 GPU. + +# 5.3. Ablation Study + +We have conducted ablation experiments to verify the effectiveness of our design. First, we replace the proposed DC module with a convolution layer and restrict the network to take a single image as input to form the image-based baseline ("I-Base") for comparison. We then modify I-Base to take three images as input as our video-based baseline ("V-Base"). Our method ("Ours") is V-Base with the full DC module (i.e., DC with its first stage and its second stage). To test the effectiveness of each stage in the proposed DC module, we also construct two ablated models, which remove the first stage ("Ours w/o DC $1^{st}$ ) or the second stage ("Ours w/o DC $2^{nd}$ ) in the proposed DC module. Note that for "Ours w/o DC $1^{st}$ " ablated model, since the second stage of the DC module requires intermediate prediction maps $P_{t}, P_{t+1}, P_{n}$ to compute the reverse attention, we directly concatenate the low-level features $\mathbf{F}_{low}$ with high + +
AblationIoU↑Accuracy↑Fβ↑MAE↓
I-Base0.4240.8540.6860.146
V-Base0.4460.8660.7190.134
Ours w/o DC 1st0.5240.8760.7430.124
Ours w/o DC 2nd0.5330.8780.7360.122
Ours (Isame only)0.5250.8710.7400.123
Ours (Iadj only)0.5390.8760.7450.124
Ours0.5670.8950.7870.105
+ +Table 4. Ablation Study. "Ours (I_same only)" / "Ours (I_adj only)" is the ablation model that takes three same/adjacent image frames as input, respectively. DC $1^{st}$ and DC $2^{nd}$ are the first and the second stage of the DC module, respectively. + +level features $\mathbf{F}_{high}$ , and then forward them to a decoder to predict $P_{t}, P_{t+1}, P_{n}$ without computing intra-frame correspondences and short-term inter-frame correspondences. + +Table 4 shows the performances of different ablated models. As shown in the last row, our final proposed network with the full DC module outperforms other baselines on all metrics. In particular, even the ablated models with only a single-stage DC module ( $3^{rd}$ row and $4^{th}$ row) can perform better than the baseline networks without the full DC module ("I-Base" and "V-Base"). This indicates the importance of correspondence learning for the VMD task. Also, the effective combination of the two stages in the DC module ("Ours") can boost the overall performance compared with the single-stage DC module. This shows that learning "dual" correspondences outperforms learning "sole" correspondences in our network. + +To further investigate the role of different types of correspondences in our network, we replace the input images (i.e., two adjacent frames and one randomly selected frame) with three adjacent frames $(\mathrm{I}_{adj}$ only) or three video frames from the same frame $(\mathrm{I}_{same}$ only). The " $\mathrm{I}_{adj}$ only" model focuses on short-term temporal correspondences while the " $\mathrm{I}_{same}$ only" model can only learn intra-frame correspondences, due to their limited input information. The results are listed in the last three rows and show that our method, which exploits intra-frame as well as both short-term and long-term temporal correspondences, outperforms these ablated models. Figure 8 shows the visual results of different ablated models. Our method with sufficient learning of spatial and temporal correspondences can locate the mirrors precisely in both frames. In particular, the results in the last row show that our method can still detect the mirrors by exploiting long-range temporal correspondences even though intra-frame correspondences are missing in the image. + +![](images/f9a5e454da86e710564810891805bf0d004ef6c9901aa68273cedb5882b332ac.jpg) +Figure 8. Visual comparison of different ablated models. + +# 6. Conclusion + +In this paper, we have investigated the video mirror detection (VMD) problem. To the best of our knowledge, we are the first to address the VMD problem from a deep-learning-based perspective. We have constructed the first large-scale video mirror dataset (VMD-D). It contains 14,988 image frames from 269 videos with corresponding masks. We have also proposed a novel network, called VMD-Net, to leverage both intra-frame and inter-frame correspondences for video mirror detection. Experimental results show that our VMD-Net outperforms state-of-the-art methods from relevant tasks on our VMD-D dataset. + +Our method does have limitations. Figure 9 shows that our method tends to produce results with coarse boundaries. Unlike existing image-based mirror detection methods [22, 36], which often explicitly adopt edge supervision in their network training, our method does not leverage such auxiliary information since we do not plan to focus this incremental cue in this first work for VMD. As a future work, we are working on improving our method by leveraging additional information like boundaries to help detect mirrors more precisely in videos. + +![](images/b243456be669f2b37db6f66f2fd00aad37c6af126285fcdb0bd1d6d24014f812.jpg) +Figure 9. Failure cases. Our method may not be good at boundary extraction, due to the lack of explicit supervision of edges. + +# Acknowledgments + +Xin Tan is sponsored by Shanghai Sailing Program (23YF1410500). This work is in part supported by two SRG grants from City University of Hong Kong (Ref: 7005674 and 7005843). + +# References + +[1] Radhakrishna Achanta, Sheila Hemami, Francisco Estrada, and Sabine Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009. 6 +[2] Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian Reid, Stephen Gould, and Anton Van Den Hengel. Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments. In CVPR, pages 3674-3683, 2018. 1 +[3] Maxim Berman, Amal Rannen Triki, and Matthew B Blaschko. The lovasz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In CVPR, pages 4413–4421, 2018. 6 +[4] Chenglizhao Chen, Shuai Li, Yongguang Wang, Hong Qin, and Aimin Hao. Video saliency detection via spatial-temporal fusion and low-rank coherency diffusion. IEEE Transactions on Image Processing, 26(7):3156-3170, 2017. 3 +[5] Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017. 4, 6, 7 +[6] Zhihao Chen, Liang Wan, Lei Zhu, Jia Shen, Huazhu Fu, Wennan Liu, and Jing Qin. Triple-cooperative video shadow detection. In CVPR, pages 2715-2724, 2021. 3, 4, 6, 7 +[7] Ho Kei Cheng and Alexander G Schwing. Xmem: Long-term video object segmentation with an atkinson-shiffrin memory model. In ECCV, pages 640-658. Springer, 2022. 3 +[8] Suhwan Cho, Heansung Lee, Minhyeok Lee, Chaewon Park, Sungjun Jang, Minjung Kim, and Sangyoun Lee. Tackling background distraction in video object segmentation. In ECCV, pages 446-462. Springer, 2022. 3 +[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248-255. IEEE, 2009. 6 +[10] Xinpeng Ding, Jingwen Yang, Xiaowei Hu, and Xiaomeng Li. Learning shadow correspondence for video shadow detection. In ECCV, pages 705-722. Springer, 2022. 6, 7 +[11] Deng-Ping Fan, Wenguan Wang, Ming-Ming Cheng, and Jianbing Shen. Shifting more attention to video salient object detection. In CVPR, pages 8554-8564, 2019. 3 +[12] Yuchao Gu, Lijuan Wang, Ziqin Wang, Yun Liu, Ming-Ming Cheng, and Shao-Ping Lu. Pyramid constrained self-attention network for fast video salient object detection. In AAAI, 2020. 3 +[13] Yuchao Gu, Lijuan Wang, Ziqin Wang, Yun Liu, Ming-Ming Cheng, and Shao-Ping Lu. Pyramid constrained self-attention network for fast video salient object detection. In AAAI, 2020. 6, 7 +[14] Huankang Guan, Jiaying Lin, and Rynson W.H. Lau. Learning semantic associations for mirror detection. In CVPR, pages 5941-5950, June 2022. 1 +[15] Chenlei Guo, Qi Ma, and Liming Zhang. Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In CVPR. IEEE, 2008. 3 +[16] Fang Guo, Wenguan Wang, Jianbing Shen, Ling Shao, Jian Yang, Dacheng Tao, and Yuan Yan Tang. Video saliency + +detection using object proposals. IEEE transactions on cybernetics, 48(11):3159-3170, 2017. 3 +[17] Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu. Ccnet: Criss-cross attention for semantic segmentation. In ICCV, pages 603-612, 2019. 5 +[18] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6 +[19] Philipp Krahenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. NeurIPS, 24, 2011. 2 +[20] Fuxin Li, Taeyoung Kim, Ahmad Humayun, David Tsai, and James M Rehg. Video segmentation by tracking many figure-ground segments. In ICCV, pages 2192-2199, 2013. 3, 4 +[21] Jiaying Lin, Zebang He, and Rynson WH Lau. Rich context aggregation with reflection prior for glass surface detection. In CVPR, pages 13415-13424, 2021. 6, 7 +[22] Jiaying Lin, Guodong Wang, and Rynson W. H. Lau. Progressive mirror detection. In CVPR, 2020. 1, 2, 3, 4, 5, 6, 7, 8 +[23] Zhihui Lin, Tianyu Yang, Maomao Li, Ziyu Wang, Chun Yuan, Wenhao Jiang, and Wei Liu. Swem: Towards real-time video object segmentation with sequential weighted expectation-maximization. In CVPR, pages 1362-1372, 2022. 3 +[24] Yong Liu, Ran Yu, Jiahao Wang, Xinyuan Zhao, Yitong Wang, Yansong Tang, and Yujiu Yang. Global spectral filter memory network for video object segmentation. In ECCV, pages 648-665. Springer, 2022. 3 +[25] Yong Liu, Ran Yu, Fei Yin, Xinyuan Zhao, Wei Zhao, Weihao Xia, and Yujiu Yang. Learning quality-aware dynamic memory for video object segmentation. In European Conference on Computer Vision, pages 468-486. Springer, 2022. 3 +[26] Xiao Lu, Yihong Cao, Sheng Liu, Chengjiang Long, Zipei Chen, Xuanyu Zhou, Yimin Yang, and Chunxia Xiao. Video shadow detection via spatio-temporal interpolation consistency training. In CVPR, pages 3116-3125, 2022. 6, 7 +[27] Haiyang Mei, Bo Dong, Wen Dong, Pieter Peers, Xin Yang, Qiang Zhang, and Xiaopeng Wei. Depth-aware mirror segmentation. In CVPR, pages 3044-3053, 2021. 2 +[28] Peter Ochs, Jitendra Malik, and Thomas Brox. Segmentation of moving objects by long term video analysis. IEEE transactions on pattern analysis and machine intelligence, 36(6):1187-1200, 2013. 3, 4 +[29] Youwei Pang, Xiaoji Zhao, Lihe Zhang, and Hutchuan Lu. Multi-scale interactive network for salient object detection. In CVPR, pages 9413-9422, 2020. 3, 6, 7 +[30] Anestis Papazoglou and Vittorio Ferrari. Fast object segmentation in unconstrained video. In ICCV, pages 1777-1784, 2013. 3 +[31] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, + +Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. 6 +[32] Gensheng Pei, Fumin Shen, Yazhou Yao, Guo-Sen Xie, Zhenmin Tang, and Jinhui Tang. Hierarchical feature alignment network for unsupervised video object segmentation. In ECCV, pages 596-613. Springer, 2022. 2, 3, 6, 7 +[33] F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung. A benchmark dataset and evaluation methodology for video object segmentation. In CVPR, 2016. 3, 4 +[34] Federico Perazzi, Oliver Wang, Markus Gross, and Alexander Sorkine-Hornung. Fully connected object proposals for video segmentation. In ICCV, pages 3227-3234, 2015. 3 +[35] Jiaqi Tan, Weijie Lin, Angel X Chang, and Manolis Savva. Mirror3d: Depth refinement for mirror surfaces. In CVPR, pages 15990-15999, 2021. 1, 2 +[36] Xin Tan, Jiaying Lin, Ke Xu, Chen Pan, Lizhuang Ma, and Rynson W. H. Lau. Mirror detection with the visual chirality cue. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 1, 2, 6, 7, 8 +[37] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In ECCV, pages 402-419. Springer, 2020. 3 +[38] Pavel Tokmakov, Karteek Alahari, and Cordelia Schmid. Learning video object segmentation with visual memory. In ICCV, pages 4481-4490, 2017. 2, 3 +[39] Wenguan Wang, Xiankai Lu, Jianbing Shen, David J Crandall, and Ling Shao. Zero-shot video object segmentation via attentive graph neural networks. In ICCV, pages 9236-9245, 2019. 2, 3 +[40] Wenguan Wang, Hongmei Song, Shuyang Zhao, Jianbing Shen, Sanyuan Zhao, Steven CH Hoi, and Haibin Ling. Learning unsupervised video object segmentation through visual attention. In CVPR, pages 3064-3074, 2019. 2, 3 +[41] Saining Xie, Ross Girshick, Piotr Dólár, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In CVPR, pages 1492–1500, 2017. 4, 6 +[42] Xin Yang, Haiyang Mei, Ke Xu, Xiaopeng Wei, Baocai Yin, and Rynson W.H. Lau. Where is my mirror? In ICCV, 2019. 1, 2, 3, 4, 6, 7 +[43] Zhao Yang, Qiang Wang, Luca Bertinetto, Weiming Hu, Song Bai, and Philip HS Torr. Anchor diffusion for unsupervised video object segmentation. In ICCV, pages 931-940, 2019. 2 +[44] Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object-contextual representations for semantic segmentation. In ECCV, pages 173-190. Springer, 2020. 6, 7 +[45] Kaihua Zhang, Zicheng Zhao, Dong Liu, Qingshan Liu, and Bo Liu. Deep transport network for unsupervised video object segmentation. In ICCV, pages 8781-8790, 2021. 2, 3 +[46] Miao Zhang, Jie Liu, Yifei Wang, Yongri Piao, Shunyu Yao, Wei Ji, Jingjing Li, Huchuan Lu, and Zhongxuan Luo. Dynamic context-sensitive filtering network for video salient object detection. In ICCV, pages 1553-1563, 2021. 3 + +[47] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In CVPR, pages 2881-2890, 2017. 6, 7 +[48] Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu, and Lei Zhang. Suppress and balance: A simple gated network for salient object detection. In ECCV, pages 35-51. Springer, 2020. 3, 6, 7 +[49] Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Scene parsing through ade20k dataset. 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However, detecting mirrors over dynamic scenes is still under-explored due to the lack of a high-quality dataset and an effective method for video mirror detection (VMD). To the best of our knowledge, this is the first work to address the VMD problem from a deep-learning-based perspective. Our observation is that there are often correspondences between the contents inside (reflected) and outside (real) of a mirror, but such correspondences may not always appear in every frame, e.g., due to the change of camera pose. This inspires us to propose a video mirror detection method, named VMD-Net, that can tolerate spatially missing correspondences by considering the mirror correspondences at both the intra-frame level as well as inter-frame level via a dual correspondence module that looks over multiple frames spatially and temporally for correlating correspondences. 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Although state-of-the-art single-image mirror detection method VCNet [36] performs well on a single image (e.g., the first row) by using implicitly intra-frame correspondence, it may fail when the intra-frame cue is weak or even absent in some video frames (e.g., the second and third rows). The lack in exploiting inter-frame information causes the current mirror detection methods to produce inaccurate and inconsistent results when applied to VMD. In contrast, our method can perform well in both situations by utilizing the proposed dual correspondence module to exploit intra-frame (spatial) and inter-frame (temporal) correspondences." + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_caption" + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 493, + 545, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 493, + 545, + 517 + ], + "spans": [ + { + "bbox": [ + 304, + 493, + 545, + 517 + ], + "type": "text", + "content": "essary to build a robust computer vision model that can distinguish mirrors from their surrounding objects correctly." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 304, + 519, + 546, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 519, + 546, + 651 + ], + "spans": [ + { + "bbox": [ + 304, + 519, + 546, + 651 + ], + "type": "text", + "content": "Existing single-image mirror detection methods exploit different cues, such as context contrast [42], explicitly correspondences [22], semantics association [14], and chirality and implicit correspondences [36], to detect mirrors from single RGB input images. Despite these recent efforts being put into the mirror detection problem, none of them focuses on detecting mirrors from videos. However, a lot of real-world computer vision applications are video-based (e.g., robotic navigation, autonomous driving, and surveillance), rather than image-based. Hence, solving the video mirror detection (VMD) problem can benefit these applications." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 304, + 653, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 547, + 713 + ], + "type": "text", + "content": "In this paper, we aim to address the VMD problem. There are two major challenges with this problem. First, to the best of our knowledge, there are no existing large-scale datasets that can be used for training and evaluation on the VMD problem. Second, existing mirror detection methods," + } + ] + } + ], + "index": 30 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 693, + 122, + 702 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 693, + 122, + 702 + ], + "spans": [ + { + "bbox": [ + 58, + 693, + 122, + 702 + ], + "type": "text", + "content": "*Joint first authors." + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 59, + 703, + 135, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 703, + 135, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 703, + 135, + 712 + ], + "type": "text", + "content": "† Corresponding author." + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9109" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 58, + 73, + 275, + 233 + ], + "blocks": [ + { + "bbox": [ + 58, + 73, + 275, + 233 + ], + "lines": [ + { + "bbox": [ + 58, + 73, + 275, + 233 + ], + "spans": [ + { + "bbox": [ + 58, + 73, + 275, + 233 + ], + "type": "image", + "image_path": "6e6f76ed23fbd1d35779951e2604a1b50fbd5389acef935999ef34337eae9a33.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 247, + 287, + 323 + ], + "lines": [ + { + "bbox": [ + 46, + 247, + 287, + 323 + ], + "spans": [ + { + "bbox": [ + 46, + 247, + 287, + 323 + ], + "type": "text", + "content": "Figure 2. Quantitative comparison on the performance and efficiency between existing mirror detection methods and our method for VMD. All models are trained/tested on the proposed VMD-D dataset, under a single RTX 3090 GPU. Our model has " + }, + { + "bbox": [ + 46, + 247, + 287, + 323 + ], + "type": "inline_equation", + "content": "\\sim 5" + }, + { + "bbox": [ + 46, + 247, + 287, + 323 + ], + "type": "text", + "content": " times smaller network parameters and runs " + }, + { + "bbox": [ + 46, + 247, + 287, + 323 + ], + "type": "inline_equation", + "content": "\\sim 18\\times" + }, + { + "bbox": [ + 46, + 247, + 287, + 323 + ], + "type": "text", + "content": " faster than the state-of-the-art image-based mirror detection method, VCNet [36], and still outperforms it by a large margin." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 350, + 286, + 554 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 350, + 286, + 554 + ], + "spans": [ + { + "bbox": [ + 46, + 350, + 286, + 554 + ], + "type": "text", + "content": "which are all developed for the image-based mirror detection task, are all based on static cues. None of them take advantages of the dynamic nature of videos in the VMD problem. Figure 1 shows that the current state-of-the-art mirror detection method, VCNet [36], may fail when correspondences are missing in some challenging frames (e.g., second and third rows) due to, for example, the change of camera pose, even though it may perform well in some easy cases (e.g., the first row). Besides, as the image-based mirror detection task is already very challenging, existing methods for this task often adopt heavy network design and time-consuming post-processing techniques [19] to improve their results. Figure 2 shows that existing image-based mirror detection models run at about 1fps, even on one of the latest GPUs, which cannot support real-time VMD. All these drawbacks motivate us to develop a large-scale dataset and an effective/efficient method for video mirror detection." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 558, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 558, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 558, + 286, + 713 + ], + "type": "text", + "content": "In this paper, we address the VMD problem in two ways. First, we construct the first large-scale video mirror detection benchmark dataset (VMD-D). It contains 14,987 image frames in 269 videos, coming from diverse scenes. The constructed VMD-D dataset provides large-scale and high-diversity data for training and evaluation on the VMD problem. Second, we propose an effective and efficient method, called VMD-Net, for the VMD problem. The proposed method exploits multi-frame correspondences at both intra-frame (spatial) and inter-frame (temporal) levels. Compared with state-of-the-art image-based mirror detection methods, which typically adopt heavy pipelines, our method uses a light-weight network architecture without the need for any" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "type": "text", + "content": "post-processing techniques. As a result, our method is efficient for real-time applications. In particular, our method has " + }, + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "type": "inline_equation", + "content": "\\sim 5" + }, + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "type": "text", + "content": " times fewer network parameters and runs " + }, + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "type": "inline_equation", + "content": "\\sim 18\\times" + }, + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "type": "text", + "content": " faster than the latest state-of-the-art image-based mirror detection method, VCNet [36]. We conduct comprehensive experiments to demonstrate the effectiveness and efficiency of our proposed method. Experimental results show that our method outperforms state-of-the-art methods from relevant tasks on the proposed large-scale VMD-D dataset." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 317, + 181, + 533, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 181, + 533, + 192 + ], + "spans": [ + { + "bbox": [ + 317, + 181, + 533, + 192 + ], + "type": "text", + "content": "Our key contributions can be summarized as follows:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 317, + 202, + 545, + 365 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 317, + 202, + 545, + 247 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 202, + 545, + 247 + ], + "spans": [ + { + "bbox": [ + 317, + 202, + 545, + 247 + ], + "type": "text", + "content": "- We construct the first large-scale video mirror detection dataset, called VMD-D. The new dataset contains 14,988 image frames from 269 videos with precise annotated masks." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 317, + 260, + 545, + 319 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 260, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 317, + 260, + 545, + 319 + ], + "type": "text", + "content": "- We propose a novel network, called VMD-Net, to exploit both intra-frame and inter-frame correspondences via a dual correspondence (DC) module. This DC module allows VMD-Net to tolerate occasionally missing correspondences in the temporal dimension." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 317, + 330, + 545, + 365 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 330, + 545, + 365 + ], + "spans": [ + { + "bbox": [ + 317, + 330, + 545, + 365 + ], + "type": "text", + "content": "- Extensive evaluations show that our method outperforms existing state-of-the-art methods from relevant tasks on our proposed VMD-D dataset." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 378, + 391, + 390 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 378, + 391, + 390 + ], + "spans": [ + { + "bbox": [ + 306, + 378, + 391, + 390 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 398, + 545, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 398, + 545, + 578 + ], + "spans": [ + { + "bbox": [ + 304, + 398, + 545, + 578 + ], + "type": "text", + "content": "Image-based Mirror Detection. Recently, Yang et al. [42] propose the first mirror detection dataset and the first mirror detection network to detect mirrors by modeling contextual contrasted information. Lin et al. [22] then propose a more diverse and larger mirror detection benchmark, and a correspondence-aware method for mirror detection, which correlates the contents inside and outside of the mirror. Mei et al. [27] further consider using RGB-D data for detecting mirrors, and construct the first RGB-D dataset for mirror detection. Tan et al. [35] address the depth refinement problem on mirror surfaces by proposing another RGB-D mirror dataset and a detect-and-refine method for mirror detection. Most recently, Tan et al. [36] propose an image-based method for mirror detection based on the chirality cue and implicit correspondences." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 578, + 545, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 578, + 545, + 651 + ], + "spans": [ + { + "bbox": [ + 304, + 578, + 545, + 651 + ], + "type": "text", + "content": "Despite their success, none of these methods focuses on the VMD problem. They also have high computational costs, thus not practical for real-time applications. In this paper, we aim to address the VMD problem by constructing a benchmark dataset and proposing an efficient/effective method for the problem." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "content": "Video Object Segmentation Video object segmentation (VOS) aims to segment the target object(s) from the input videos. Currently, it can be categorized into two main individual tasks: the unsupervised video object segmentation (UVOS) [32, 38-40, 43, 45] and the semi-supervised video" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9110" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 70, + 547, + 178 + ], + "blocks": [ + { + "bbox": [ + 48, + 70, + 547, + 178 + ], + "lines": [ + { + "bbox": [ + 48, + 70, + 547, + 178 + ], + "spans": [ + { + "bbox": [ + 48, + 70, + 547, + 178 + ], + "type": "image", + "image_path": "608afd6ead44b8fe5a3f75bbc23a151b587390f8cfe4f364a3b36ed79bcec810.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 101, + 186, + 490, + 198 + ], + "lines": [ + { + "bbox": [ + 101, + 186, + 490, + 198 + ], + "spans": [ + { + "bbox": [ + 101, + 186, + 490, + 198 + ], + "type": "text", + "content": "Figure 3. Snapshots of the proposed Video Mirror Detection dataset, VMD-D, with pixel-level annotations." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 219, + 289, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 219, + 289, + 434 + ], + "spans": [ + { + "bbox": [ + 46, + 219, + 289, + 434 + ], + "type": "text", + "content": "object segmentation (SVOS) [7, 8, 23-25]. The main difference between UVOS and SVOS is that UVOS does not require annotation for the first frame of the input video during the inference phase, while SVOS needs to do it. Thus, UVOS is more related to the video mirror detection problem since we do not use the first-frame ground truth mirror label to infer the location of mirrors in the rest of the video frames. UVOS focuses on automatically detecting the primary foreground object without using the ground truth mask of the first frame during testing. Early UVOS methods use handcrafted features [30, 34] to detect foreground objects in the input videos. Recent CNN-based UVOS models [38-40] focus on exploring different cues like memory [38], graph neural network [39] and visual attention [40] for better performance. The latest UVOS methods [32, 45] take optical flow as auxiliary information to help segment videos, thanks to the rapid evolution on optical flow estimation [37]." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 444, + 287, + 492 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 444, + 287, + 492 + ], + "spans": [ + { + "bbox": [ + 47, + 444, + 287, + 492 + ], + "type": "text", + "content": "However, UVOS methods cannot directly address the video mirror detection problem, since mirrors are not always the primary foreground objects while having changing appearance patterns during motion." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 501, + 287, + 668 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 501, + 287, + 668 + ], + "spans": [ + { + "bbox": [ + 46, + 501, + 287, + 668 + ], + "type": "text", + "content": "Video Salient Object Detection Video salient object detection (VSOD) is related to the UVOS task. It aims to automatically detect the most visually distinctive objects from an input video without indicating where the salient object is in the first frame. Unlike the methods for single-image salient object detection [29, 48], VSOD methods require building a model with a thorough understanding of dynamic attention. Most earlier VSOD methods detect the salient objects using handcrafted features [4, 15, 16]. With the help of deep neural networks, recent VSOD methods achieve great progress. Fan et al. [11] take attention shift into account. Gu et al. [12] exploit self-attention models to detect salient objects from videos efficiently. Zhang et al. [46] proposes a dynamic strategy of context fusion for VSOD." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "type": "text", + "content": "Similar to UVOS, the methods for video salient object detection are not directly applicable to video mirror detection since mirrors are not always distinctive." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 217, + 535, + 232 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 217, + 535, + 232 + ], + "spans": [ + { + "bbox": [ + 305, + 217, + 535, + 232 + ], + "type": "text", + "content": "3. Video Mirror Detection Dataset (VMD-D)" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 238, + 545, + 321 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 238, + 545, + 321 + ], + "spans": [ + { + "bbox": [ + 304, + 238, + 545, + 321 + ], + "type": "text", + "content": "To facilitate research on the video mirror detection problem, we first contribute a large-scale video mirror detection dataset (named VMD-D). It consists of 269 videos in 14,988 image frames with corresponding precise annotations from diverse scenes. Figure 3 shows some example video frames in our proposed VMD-D dataset. The details of our VMD are discussed below." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 329, + 428, + 340 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 329, + 428, + 340 + ], + "spans": [ + { + "bbox": [ + 306, + 329, + 428, + 340 + ], + "type": "text", + "content": "3.1. Dataset Construction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 347, + 545, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 347, + 545, + 502 + ], + "spans": [ + { + "bbox": [ + 304, + 347, + 545, + 502 + ], + "type": "text", + "content": "To construct the first large-scale video mirror detection dataset, we use a smartphone to collect high-resolution videos (e.g., " + }, + { + "bbox": [ + 304, + 347, + 545, + 502 + ], + "type": "inline_equation", + "content": "1920 \\times 1080" + }, + { + "bbox": [ + 304, + 347, + 545, + 502 + ], + "type": "text", + "content": " resolution) with mirrors in daily-life scenes. Following a common practice [6] used to construct datasets for video-based problems, we manually trim the collected videos to make sure that each video frame has at least one mirror region. After that, we obtain 269 video clips and then randomly split them into a training set with 143 videos (containing 7,835 images) and a test set with 126 videos (containing 7,152 images). We then label pixel-level mirror masks by annotators. The total duration of our videos is 502 seconds for covering long-temporal scenarios. The frame rate is 30 fps for all collected videos." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 510, + 406, + 523 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 510, + 406, + 523 + ], + "spans": [ + { + "bbox": [ + 306, + 510, + 406, + 523 + ], + "type": "text", + "content": "3.2. Dataset Analysis" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 528, + 545, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 528, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 304, + 528, + 545, + 624 + ], + "type": "text", + "content": "Table 1 shows the comparison of different datasets from the relevant areas, including image mirror detection [22,42] (top group), video object segmentation [20,28,33] (second group), and video shadow detection [6] (third group), and ours. Our dataset reflects good video quality (i.e., high-resolution videos) and quantity (i.e., a large number of annotated frames), and is practical for being the first step for large-scale video mirror detection." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 627, + 545, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 627, + 545, + 687 + ], + "spans": [ + { + "bbox": [ + 304, + 627, + 545, + 687 + ], + "type": "text", + "content": "Area Distribution. Figure 4(a) shows the ratio of mirror area over the image area (mirror area distribution). We can see that our dataset contains mirrors covering a wide range of area ratios. We also note that it consists of a lot of small mirrors " + }, + { + "bbox": [ + 304, + 627, + 545, + 687 + ], + "type": "inline_equation", + "content": "(< 0.1)" + }, + { + "bbox": [ + 304, + 627, + 545, + 687 + ], + "type": "text", + "content": ", which makes our dataset very challenging." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "type": "text", + "content": "Contrast Distribution. We analyze the contrasts between the mirror regions and non-mirror regions by computing " + }, + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\chi^2" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "type": "text", + "content": "9111" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 47, + 70, + 294, + 186 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 294, + 186 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 294, + 186 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 294, + 186 + ], + "type": "table", + "html": "
Dataset#Videos#Annos.#Time (s).#FPSMax Reso.
MSD [42]-4,018--640×512
PMD [22]-6,461--4000×3000
FBMS [28]59720--640×480
STV2 [20]14947--640×360
DAVIS [33]503455144241920×1080
ViSha [6]12011,685390301280×720
Ours26915,066502301920×1080
", + "image_path": "1ce96c81a6077923144114a0b1d867f1a5b07c3d13eb00307bc585ba632f6b3c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 195, + 287, + 207 + ], + "lines": [ + { + "bbox": [ + 47, + 195, + 287, + 207 + ], + "spans": [ + { + "bbox": [ + 47, + 195, + 287, + 207 + ], + "type": "text", + "content": "Table 1. Comparison of different video datasets for relevant tasks." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 50, + 220, + 167, + 308 + ], + "blocks": [ + { + "bbox": [ + 50, + 220, + 167, + 308 + ], + "lines": [ + { + "bbox": [ + 50, + 220, + 167, + 308 + ], + "spans": [ + { + "bbox": [ + 50, + 220, + 167, + 308 + ], + "type": "image", + "image_path": "274d704530ba4f4a196d77643f9043116634ff641d60432573bbf3415b669e54.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 64, + 311, + 151, + 319 + ], + "lines": [ + { + "bbox": [ + 64, + 311, + 151, + 319 + ], + "spans": [ + { + "bbox": [ + 64, + 311, + 151, + 319 + ], + "type": "text", + "content": "(a) mirror area distribution" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 93, + 330, + 240, + 341 + ], + "lines": [ + { + "bbox": [ + 93, + 330, + 240, + 341 + ], + "spans": [ + { + "bbox": [ + 93, + 330, + 240, + 341 + ], + "type": "text", + "content": "Figure 4. Statistics of our VMD dataset." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 170, + 219, + 285, + 308 + ], + "blocks": [ + { + "bbox": [ + 170, + 219, + 285, + 308 + ], + "lines": [ + { + "bbox": [ + 170, + 219, + 285, + 308 + ], + "spans": [ + { + "bbox": [ + 170, + 219, + 285, + 308 + ], + "type": "image", + "image_path": "835d306de4d703ee7e7553390ac3f97becb10176a599739047e59897a981e570.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 179, + 311, + 274, + 319 + ], + "lines": [ + { + "bbox": [ + 179, + 311, + 274, + 319 + ], + "spans": [ + { + "bbox": [ + 179, + 311, + 274, + 319 + ], + "type": "text", + "content": "(b) color contrast distribution" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 363, + 287, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 363, + 287, + 448 + ], + "spans": [ + { + "bbox": [ + 46, + 363, + 287, + 448 + ], + "type": "text", + "content": "distance between their RGB histograms. We also compare the distribution between MSD [42] and PMD [22], as shown in Figure 4(b). In general, VMD-D has more images with extremely low color contrasts (" + }, + { + "bbox": [ + 46, + 363, + 287, + 448 + ], + "type": "inline_equation", + "content": "< 0.1" + }, + { + "bbox": [ + 46, + 363, + 287, + 448 + ], + "type": "text", + "content": ") compared with existing mirror datasets MSD and PMD. This indicates our images are collected from very diverse scenes, making them more challenging to detect." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 459, + 103, + 471 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 459, + 103, + 471 + ], + "spans": [ + { + "bbox": [ + 47, + 459, + 103, + 471 + ], + "type": "text", + "content": "4. Method" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 479, + 115, + 491 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 479, + 115, + 491 + ], + "spans": [ + { + "bbox": [ + 47, + 479, + 115, + 491 + ], + "type": "text", + "content": "4.1. Overview" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 498, + 287, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 498, + 287, + 617 + ], + "spans": [ + { + "bbox": [ + 46, + 498, + 287, + 617 + ], + "type": "text", + "content": "We observe that there are often spatial correspondences between the contents inside (reflected) and outside (real) of a mirror. However, such spatial correspondences may not always appear in every frame. The lack in exploiting temporal correspondence may cause current image-based mirror detection methods to fail in the VMD task. Hence, in this paper, we propose a method that is able to tolerate temporally missing correspondences by considering mirror correspondences at both the intra-frame level (spatial) as well as the inter-frame level (temporal)." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "text", + "content": "Figure 5 shows the overall structure of our proposed method VMD-Net. The key idea of our network design is to leverage the intra-frame and the inter-frame correspondences for video mirror detection. Our VMD-Net takes three images from the same video clips as inputs. The first two images " + }, + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "inline_equation", + "content": "I_{t + 1}" + }, + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "text", + "content": " are from adjacent video frames, while the third image " + }, + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "inline_equation", + "content": "I_{n}" + }, + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "text", + "content": " is randomly selected from other frames. We first apply a shared backbone network ResNext" + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 328, + 74, + 561, + 277 + ], + "blocks": [ + { + "bbox": [ + 328, + 74, + 561, + 277 + ], + "lines": [ + { + "bbox": [ + 328, + 74, + 561, + 277 + ], + "spans": [ + { + "bbox": [ + 328, + 74, + 561, + 277 + ], + "type": "image", + "image_path": "cb999da9f8d988e65ef969195cb7ce6aa822226f9ce7995e5b4c1499ffb91a92.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "lines": [ + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "spans": [ + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "text", + "content": "Figure 5. The framework of our proposed method. " + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "inline_equation", + "content": "I_{t}, I_{t + 1}, I_{n}" + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "text", + "content": " are input images. In particular, " + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "inline_equation", + "content": "I_{t + 1}" + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "text", + "content": " are two adjacent video frames, and " + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "inline_equation", + "content": "I_{n}" + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "text", + "content": " is a video frame randomly selected from the same video of " + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "inline_equation", + "content": "P_{t}, P_{t + 1}, P_{n}" + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "text", + "content": " are intermediate maps and the final outputs maps are " + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "inline_equation", + "content": "M_{t}, M_{t + 1}, M_{n}" + }, + { + "bbox": [ + 304, + 286, + 547, + 344 + ], + "type": "text", + "content": " for the corresponding input images." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "spans": [ + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": "101 [41] to extract multi-scale features from the input images. Unlike existing single-image mirror detection methods, which make full usage of the features at all stages (i.e., from the " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "1^{st}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": " to the " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "5^{th}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": " scales), for each input image " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": ", our VMD-Net only utilizes the low-level features at the " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "2^{nd}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": " scale (denoted as " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{low}^{i}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": ") and the high-level features at the " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "5^{th}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": " scale. Following the design of DeepLabV3 [5], the high-level features from the backbone network are then fed to an atrous spatial pyramid pooling module to obtain enhanced semantic features (denoted as " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{high}^{i}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": "). Taking image " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": " as an example, we then assign a dual correspondence module to both low-level features " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{low}^{t}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": " and high-level features " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{high}^{t}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": " to produce the intermediate map " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "P_{t}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": " and the final output " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "M_{t}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": ". Similarly, the dual correspondence module takes the low-level features and high-level features from " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "I_{t+1}, I_{n}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": " to produce the intermediate maps " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "P_{t+1}, P_{n}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": " and the final outputs " + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "inline_equation", + "content": "M_{t+1}, M_{n}" + }, + { + "bbox": [ + 304, + 363, + 546, + 568 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 575, + 495, + 588 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 575, + 495, + 588 + ], + "spans": [ + { + "bbox": [ + 306, + 575, + 495, + 588 + ], + "type": "text", + "content": "4.2. Dual Correspondence (DC) Module" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "type": "text", + "content": "Figure 6 shows the structure of the proposed DC module. It consists of two stages. The first stage of the DC module aims to learn intra-frame correspondences for all input images and the short-term inter-frame correspondences inside mirrors in the adjacent input image " + }, + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "type": "inline_equation", + "content": "I_{t + 1}" + }, + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "type": "text", + "content": ". The second stage of the DC module focuses on extracting long-term inter-frame correspondences across all input images to enhance the learning of temporal correspondence. This enables our VMD-Net to exploit correspondences at both intra-frame and inter-frame levels at different tempo" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9112" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 77, + 70, + 541, + 295 + ], + "blocks": [ + { + "bbox": [ + 77, + 70, + 541, + 295 + ], + "lines": [ + { + "bbox": [ + 77, + 70, + 541, + 295 + ], + "spans": [ + { + "bbox": [ + 77, + 70, + 541, + 295 + ], + "type": "image", + "image_path": "2f4ba6477c79b1ffe7d114d9c9a75f0e811b45e842dfaf76537b484e8d2eb047.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "lines": [ + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "spans": [ + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "type": "text", + "content": "Figure 6. Our DC module. It consists of two stages. The first stage in the DC module aims to extract intra-frame and short-term temporal correspondences from two adjacent video frames " + }, + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "type": "inline_equation", + "content": "I_{t+1}" + }, + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "type": "text", + "content": ", while the second stage in the DC module focuses on learning long-term temporal correspondences from all input frames " + }, + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "type": "inline_equation", + "content": "I_{t+1}" + }, + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "type": "inline_equation", + "content": "I_{n}" + }, + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 360, + 90, + 371 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 360, + 90, + 371 + ], + "spans": [ + { + "bbox": [ + 47, + 360, + 90, + 371 + ], + "type": "text", + "content": "ral scales." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "spans": [ + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "text", + "content": "Stage 1. Our DC module takes the low-level features " + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{low}" + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "text", + "content": " and high-level features " + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{high}" + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "text", + "content": " as input. In the first stage, we first extract the intra-frame correspondences from the high-level features " + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{high}" + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "text", + "content": " of each image by the global relation (GR) block proposed in [22]. The GR block can effectively and efficiently extract the correspondence between the content inside and outside of the mirrors by modeling spatial corresponding relations in a single image. The high-level correspondence-aware features from the randomly selected frame " + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "inline_equation", + "content": "I_{n}" + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "text", + "content": " are directly concatenated with the corresponding low-level features " + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{low}^{n}" + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "text", + "content": " in the decoder to output an intermediate map " + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "inline_equation", + "content": "P_{n}" + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "text", + "content": ", while the high-level correspondence-aware features extracted from the two adjacent frames " + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "inline_equation", + "content": "I_{t + 1}" + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "text", + "content": " are forwarded to a cross-attention (Cross-Att.) module to learn short-term temporal correspondences. To compute short-term temporal correspondences between " + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "inline_equation", + "content": "I_{t + 1}" + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "text", + "content": ", we also feed their low-level features " + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{low}^{t},\\mathbf{F}_{low}^{t + 1}" + }, + { + "bbox": [ + 46, + 375, + 290, + 590 + ], + "type": "text", + "content": " to another cross-attention module." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "content": "Our cross-attention module is inspired by [17], but we extend it from self-attention (single-input and single-out features) to cross-attention with two-input and two-output features and also enhance its ability to model spatial correspondences. Our module first applies two convolutional layers with " + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "content": " filters to the two input features " + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_t" + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{t+1}" + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "content": " and further generates a spatially enhanced affinity matrix " + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "inline_equation", + "content": "A^{se} \\in \\mathbb{R}^{(2H + 2W - 1) \\times (W \\times H)}" + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "content": ". Different from the original affinity matrix " + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "inline_equation", + "content": "A \\in \\mathbb{R}^{(H + W - 1) \\times (W \\times H)}" + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "content": " in [17], which is computed by extracting local contextual information in the" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 360, + 547, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 360, + 547, + 420 + ], + "spans": [ + { + "bbox": [ + 304, + 360, + 547, + 420 + ], + "type": "text", + "content": "horizontal and vertical directions of the input features, our spatially enhanced affinity operation also computes global contextual information by taking the diagonal items of the input features into our design. Formally, our cross-attention process can be described as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 344, + 430, + 545, + 443 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 344, + 430, + 545, + 443 + ], + "spans": [ + { + "bbox": [ + 344, + 430, + 545, + 443 + ], + "type": "interline_equation", + "content": "K = \\operatorname {C o n v} _ {1 \\times 1} (\\mathbf {F} _ {t}); Q = \\operatorname {C o n v} _ {1 \\times 1} (\\mathbf {F} _ {t + 1}), \\tag {1}", + "image_path": "0c18a69983a319ff8efff76dcab250b05f5912731d76e32fc052ba70c9d8bbe0.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 338, + 445, + 545, + 458 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 338, + 445, + 545, + 458 + ], + "spans": [ + { + "bbox": [ + 338, + 445, + 545, + 458 + ], + "type": "interline_equation", + "content": "A ^ {s e} = \\mathbb {A} ^ {s e} (K, Q), \\tag {2}", + "image_path": "9083695dcbf73b0da36617f452cedb9a3f343b6c9ab844c6bbfdce7652403b01.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 345, + 460, + 545, + 473 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 460, + 545, + 473 + ], + "spans": [ + { + "bbox": [ + 345, + 460, + 545, + 473 + ], + "type": "interline_equation", + "content": "V = \\operatorname {C o n v} _ {1 \\times 1} \\left(\\mathbf {F} _ {t}\\right), \\tag {3}", + "image_path": "c4002dca338392b1965709406db76c8958b8730cf70ca76995a80c94c96afcff.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 400, + 475, + 482, + 485 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 400, + 475, + 482, + 485 + ], + "spans": [ + { + "bbox": [ + 400, + 475, + 482, + 485 + ], + "type": "interline_equation", + "content": "(2 H + 2 W - 1) \\times (W \\times H)", + "image_path": "34480cce048674bb37eaac7147d30a1652c12b55678f4db951c29aa78c8ae250.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 342, + 485, + 545, + 510 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 342, + 485, + 545, + 510 + ], + "spans": [ + { + "bbox": [ + 342, + 485, + 545, + 510 + ], + "type": "interline_equation", + "content": "\\mathbf {C} ^ {t} = K + \\omega_ {t} \\quad \\sum_ {i} A ^ {s e} V, \\tag {4}", + "image_path": "5eedd3a448991261b418aa876667bc5581f2d70e2bb8bb1b60cc57e36de72244.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 409, + 512, + 491, + 522 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 409, + 512, + 491, + 522 + ], + "spans": [ + { + "bbox": [ + 409, + 512, + 491, + 522 + ], + "type": "interline_equation", + "content": "(2 H + 2 W - 1) \\times (W \\times H)", + "image_path": "aa25a809b578439956545fdb869f7821ee9a7a70c4e673a0242e15141307647e.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 332, + 522, + 545, + 546 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 332, + 522, + 545, + 546 + ], + "spans": [ + { + "bbox": [ + 332, + 522, + 545, + 546 + ], + "type": "interline_equation", + "content": "\\mathbf {C} ^ {t + 1} = Q + \\omega_ {t + 1} \\quad \\sum_ {i} \\quad A ^ {s e} V, \\tag {5}", + "image_path": "c5e42ef72378949c62bf6186326b2cdde165ad0644a62c247980072d6e95acf0.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "spans": [ + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "\\mathbf{C}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " is the output of the cross-attention module. " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "\\omega_{t}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "\\omega_{t + 1}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " are learnable parameters. " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "\\mathbb{A}^{se}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " is our spatially enhanced affinity operation. " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "\\mathbf{C}^t" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "\\mathbf{C}^{t + 1}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " are the output correspondence-aware features. We apply the cross-attention module to both " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{low}^{t},\\mathbf{F}_{low}^{t + 1}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{high}^{t},\\mathbf{F}_{high}^{t + 1}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " to obtain correspondence-aware features " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{low}^{t},\\mathbf{C}_{low}^{t + 1}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_{high}^{t},\\mathbf{C}_{high}^{t + 1}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": ". We then concatenate the correspondence features from the same input image but at different levels. The concatenated features " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "[\\mathbf{C}_{low};\\mathbf{C}_{high}]" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " are forwarded to a decoder to obtain an intermediate prediction " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "P_{t}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": ". Similarly, we obtain the intermediate predictions " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "P_{t + 1}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "P_{n}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "I_{t + 1}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "inline_equation", + "content": "I_{n}" + }, + { + "bbox": [ + 304, + 555, + 545, + 700 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 317, + 701, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 701, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 317, + 701, + 545, + 714 + ], + "type": "text", + "content": "Stage 2. The second stage of our DC module takes all in" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9113" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "text", + "content": "intermediate prediction maps " + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "inline_equation", + "content": "P_{t}, P_{t + 1}, P_{n}" + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "text", + "content": " and high-level features " + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{high}^{t}, \\mathbf{F}_{high}^{t + 1}, \\mathbf{F}_{high}^{n}" + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "text", + "content": " as inputs. The input features and prediction maps are forwarded to a reverse cross-attention module. This module aims at explicitly exploiting the correspondence between the contents inside and outside of the mirrors in different frames for long-range temporal correspondences. In particular, we need to explicitly model the correspondences between input frame " + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "text", + "content": " and the randomly selected frame " + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "inline_equation", + "content": "I_{n}" + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "text", + "content": ". To do this, we first multiply " + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "inline_equation", + "content": "F_{high}" + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "text", + "content": " by their corresponding intermediate prediction map " + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "text", + "content": ", which are normalized by a sigmoid function. We then reverse " + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "text", + "content": " to obtain the reversed prediction map " + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "inline_equation", + "content": "\\hat{P}" + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "text", + "content": ", which indicates the potential non-mirror regions. We also notice that the mirrors will potentially flip the real object horizontally in its content. Thus, we conduct a horizontal flip to the input non-mirror features to model the potential relation of mirror reflection. Similarly, we compute temporal correspondences between " + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "inline_equation", + "content": "I_{t + 1}" + }, + { + "bbox": [ + 46, + 72, + 289, + 334 + ], + "type": "text", + "content": " with this strategy. In the second stage of the DC module, we also use our cross-attention module to extract the inter-frame correspondences. The process of our reverse cross-attention module can be formulated as follows:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 92, + 342, + 287, + 357 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 92, + 342, + 287, + 357 + ], + "spans": [ + { + "bbox": [ + 92, + 342, + 287, + 357 + ], + "type": "interline_equation", + "content": "\\Omega_ {n, -} = \\mathbb {C} \\mathbb {A} \\left(P _ {n} \\mathbf {F} _ {h i g h} ^ {n}, \\left(\\hat {P} _ {t} \\mathbf {F} _ {h i g h} ^ {t}\\right) ^ {\\top}\\right), \\tag {6}", + "image_path": "0e24cc7b02ed163332bb866da89985c363ce4c76db227afbbfc7b6b433daec63.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 94, + 359, + 287, + 374 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 359, + 287, + 374 + ], + "spans": [ + { + "bbox": [ + 94, + 359, + 287, + 374 + ], + "type": "interline_equation", + "content": "\\Omega_ {t, -} = \\mathbb {C} \\mathbb {A} \\left(P _ {t} \\mathbf {F} _ {h i g h} ^ {t}, \\left(\\hat {P} _ {t + 1} \\mathbf {F} _ {h i g h} ^ {t + 1}\\right) ^ {\\top}\\right), \\tag {7}", + "image_path": "67707be797bc9754e379f70a6cc8a1cbb1460a79e960fab544cd016ea3cf501e.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 85, + 376, + 287, + 392 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 376, + 287, + 392 + ], + "spans": [ + { + "bbox": [ + 85, + 376, + 287, + 392 + ], + "type": "interline_equation", + "content": "\\Omega_ {t + 1, -} = \\mathbb {C A} \\left(P _ {t + 1} \\mathbf {F} _ {h i g h} ^ {t + 1}, \\left(\\hat {P} _ {t} \\mathbf {F} _ {h i g h} ^ {t}\\right) ^ {\\top}\\right), \\tag {8}", + "image_path": "9e170bacdafab664bf187653c6236d5bbb0aceb3a4429e5347d3c33a5c2e7deb.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "spans": [ + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "inline_equation", + "content": "\\mathbb{C}\\mathbb{A}" + }, + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "text", + "content": " is our cross-attention module. " + }, + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "inline_equation", + "content": "\\top" + }, + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "text", + "content": " is a horizontal flip operation. " + }, + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "text", + "content": " is the final output feature. Note that we omit the second output of the cross-attention modules used in the " + }, + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "inline_equation", + "content": "2^{nd}" + }, + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "text", + "content": " stage for convenience. We then forward the output features to individual decoders to obtain the final output predictions " + }, + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "inline_equation", + "content": "M_{t}, M_{t+1}, M_{n}" + }, + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "text", + "content": " for the input images " + }, + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "inline_equation", + "content": "I_{t}, I_{t+1}, I_{n}" + }, + { + "bbox": [ + 46, + 399, + 287, + 485 + ], + "type": "text", + "content": ", respectively." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 491, + 141, + 502 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 491, + 141, + 502 + ], + "spans": [ + { + "bbox": [ + 47, + 491, + 141, + 502 + ], + "type": "text", + "content": "4.3. Loss Functions" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 510, + 287, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 510, + 287, + 533 + ], + "spans": [ + { + "bbox": [ + 47, + 510, + 287, + 533 + ], + "type": "text", + "content": "We adopt the Lovász-Softmax loss function [3] to supervise our network training. The final loss function is:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 99, + 540, + 144, + 551 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 99, + 540, + 144, + 551 + ], + "spans": [ + { + "bbox": [ + 99, + 540, + 144, + 551 + ], + "type": "interline_equation", + "content": "i \\in \\{t, t + 1, n \\}", + "image_path": "e01f82fbdc2acd856f4ec34438c50a63b1ead08abbfc54d12db0dd53df2c6363.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 80, + 551, + 287, + 573 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 80, + 551, + 287, + 573 + ], + "spans": [ + { + "bbox": [ + 80, + 551, + 287, + 573 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\sum_ {i} \\quad \\mathcal {L} _ {h} \\left(P _ {i}, G _ {i}\\right) + \\mathcal {L} _ {h} \\left(M _ {i}, G _ {i}\\right), \\tag {9}", + "image_path": "a9fb310b9229e5fa98da72ca5266a941c854455cc99f382109d95c2d17d9aa3c.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 581, + 287, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 581, + 287, + 629 + ], + "spans": [ + { + "bbox": [ + 47, + 581, + 287, + 629 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 581, + 287, + 629 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_h(\\cdot ,\\cdot)" + }, + { + "bbox": [ + 47, + 581, + 287, + 629 + ], + "type": "text", + "content": " denotes the lovasz-hinge loss. " + }, + { + "bbox": [ + 47, + 581, + 287, + 629 + ], + "type": "inline_equation", + "content": "P_{i},M_{i},G_{i}" + }, + { + "bbox": [ + 47, + 581, + 287, + 629 + ], + "type": "text", + "content": " are the intermediate output from the DC module, the final output of our network, and the ground truth label of image " + }, + { + "bbox": [ + 47, + 581, + 287, + 629 + ], + "type": "inline_equation", + "content": "I_{i}" + }, + { + "bbox": [ + 47, + 581, + 287, + 629 + ], + "type": "text", + "content": ", respectively." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 639, + 128, + 652 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 639, + 128, + 652 + ], + "spans": [ + { + "bbox": [ + 47, + 639, + 128, + 652 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 658, + 287, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 658, + 287, + 672 + ], + "spans": [ + { + "bbox": [ + 47, + 658, + 287, + 672 + ], + "type": "text", + "content": "5.1. Experimental Settings and Evaluation Metrics" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "type": "text", + "content": "We have implemented VMD-Net in Pytorch [31] and trained it on a PC with an RTX3090 GPU card. During training, we resize the input images to " + }, + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "type": "inline_equation", + "content": "384 \\times 384" + }, + { + "bbox": [ + 47, + 677, + 287, + 713 + ], + "type": "text", + "content": ". We" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "type": "text", + "content": "use ResNext-101 [41] pre-trained on ImageNet [9] as our backbone network to extract image features. We adopt Adam [18] as the optimizer with a momentum of 0.9 and a weight decay of " + }, + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "type": "inline_equation", + "content": "5 \\times 10^{-4}" + }, + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "type": "text", + "content": ". The base learning rate, batch size, and the number of training epochs are 0.0001, 8, and 15, respectively. We use the cosine learning rate decay with 3-epoch warm-up period to adjust the learning rate during training. Note that we do NOT apply any post-processing techniques to refine our predictions." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 180, + 545, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 180, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 304, + 180, + 545, + 228 + ], + "type": "text", + "content": "We employ four popular metrics, intersection over union (IoU), pixel accuracy, F-measure " + }, + { + "bbox": [ + 304, + 180, + 545, + 228 + ], + "type": "inline_equation", + "content": "(F_{\\beta})" + }, + { + "bbox": [ + 304, + 180, + 545, + 228 + ], + "type": "text", + "content": " [1], and mean absolute error (MAE) to evaluate the performance of tested methods quantitatively. " + }, + { + "bbox": [ + 304, + 180, + 545, + 228 + ], + "type": "inline_equation", + "content": "F_{\\beta}" + }, + { + "bbox": [ + 304, + 180, + 545, + 228 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 348, + 236, + 503, + 262 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 348, + 236, + 503, + 262 + ], + "spans": [ + { + "bbox": [ + 348, + 236, + 503, + 262 + ], + "type": "interline_equation", + "content": "F _ {\\beta} = \\frac {1 + \\beta^ {2} (P r e c i s i o n \\times R e c a l l)}{\\beta^ {2} P r e c i s i o n + R e c a l l},", + "image_path": "a8bbb387abcc665d780070eab05a2581aa2f07a6bbb50578146e6f7ce77f6f6e.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 269, + 545, + 293 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 269, + 545, + 293 + ], + "spans": [ + { + "bbox": [ + 304, + 269, + 545, + 293 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 269, + 545, + 293 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 304, + 269, + 545, + 293 + ], + "type": "text", + "content": " is set to 0.3 as suggested in [1]. It evaluates the overall performance between precision and recall." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 300, + 534, + 313 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 300, + 534, + 313 + ], + "spans": [ + { + "bbox": [ + 305, + 300, + 534, + 313 + ], + "type": "text", + "content": "5.2. Comparison to the State-of-the-art Methods" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 319, + 545, + 486 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 319, + 545, + 486 + ], + "spans": [ + { + "bbox": [ + 304, + 319, + 545, + 486 + ], + "type": "text", + "content": "Due to the lack of methods for video mirror detection, we compare our method with 14 state-of-the-art methods from relevant fields, including GateNet [48] and MINet [29] for salient object detection; PCSA [13] for video salient object detection; DeepLabV3 [5], PSPNet [47] and OCRNet [44] for semantic segmentation; TVSD [6], STICT [26] and ScCor [10] for video shadow detection; HFAN [32] for video object segmentation; GlassNet [21] for glass surface detection; MirrorNet [42], PMDNet [22] and VCNet [36] for single-image mirror detection. We train and test all baseline methods on our video mirror detection dataset VMD-D using their released codes on the same platform. Table 2 shows the quantitative results. Our method achieves the best performance with a large margin on all four metrics." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 486, + 545, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 486, + 545, + 653 + ], + "spans": [ + { + "bbox": [ + 304, + 486, + 545, + 653 + ], + "type": "text", + "content": "Figure 7 visually compares the results of our method with the selected state-of-the-art methods from relevant fields. We can see that when compared with image-based mirror detection methods PMDNet [22] and VCNet [36], which produce inaccurate and incoherent results both in a short-term temporal interval (e.g., the 1st and 2nd rows; the 3rd and the 4th rows) and a long-term temporal interval (e.g., the 5th and 6th rows; the 7th and 8th rows), our method can precisely predict the mirror regions by exploiting inter-frame and intra-frame correspondences in both situations. We attribute the superior performances of our method to the design of the DC module, which enables the modeling of spatial, short-range, and long-range temporal correspondences." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "content": "In particular, we also compare the efficiency of our method with most existing mirror detection methods. Table 3 shows the quantitative results in the number of network parameters and FPS. 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MethodsIoU↑Accuracy↑Fβ↑MAE↓
GateNet [48]0.4290.8510.6650.153
MINet [29]0.4120.8540.6760.148
PCSA [13]0.1930.8030.4640.198
DeepLabV3 [5]0.4810.8460.6810.157
PSPNet [47]0.4640.8500.6650.152
OCRNet [44]0.3940.7860.6400.175
TVSD [6]0.4800.8750.7460.125
STICT [26]0.1640.8090.5300.198
Sc-Cor [10]0.5120.8630.6960.137
HFAN [32]0.4590.8760.7060.124
GlassNet [21]0.5520.8640.7180.137
MirrorNet [42]0.5050.8550.6810.145
PMDNet [22]0.5320.8720.7490.128
VCNet [36]0.5390.8770.7490.123
Ours0.5670.8950.7870.105
", + "image_path": "5be865ff16e6101b4fc4ad0bf169af8993539514453180ab4c83d9c7f83c2127.jpg" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "table_body" + } + ], + "index": 21 + }, + { + "bbox": [ + 46, + 641, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 286, + 713 + ], + "type": "text", + "content": "state-of-the-art image-based mirror detection method, VCNet [36]. Specifically, our model has " + }, + { + "bbox": [ + 46, + 641, + 286, + 713 + ], + "type": "inline_equation", + "content": "\\sim 5" + }, + { + "bbox": [ + 46, + 641, + 286, + 713 + ], + "type": "text", + "content": " times fewer network parameters and runs " + }, + { + "bbox": [ + 46, + 641, + 286, + 713 + ], + "type": "inline_equation", + "content": "\\sim 18\\times" + }, + { + "bbox": [ + 46, + 641, + 286, + 713 + ], + "type": "text", + "content": " faster than VCNet [36]. Furthermore, our model outperforms VCNet [36] by a large margin. These results demonstrate the superior practicality and suitability of our model for the VMD problem." + } + ] + } + ], + "index": 23 + }, + { + "type": "table", + "bbox": [ + 324, + 345, + 528, + 423 + ], + "blocks": [ + { + "bbox": [ + 46, + 593, + 287, + 625 + ], + "lines": [ + { + "bbox": [ + 46, + 593, + 287, + 625 + ], + "spans": [ + { + "bbox": [ + 46, + 593, + 287, + 625 + ], + "type": "text", + "content": "Table 2. Quantitative comparison between the proposed VMD-Net and 14 state-of-the-art methods from relevant fields. The best results are shown in bold." + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 324, + 345, + 528, + 423 + ], + "lines": [ + { + "bbox": [ + 324, + 345, + 528, + 423 + ], + "spans": [ + { + "bbox": [ + 324, + 345, + 528, + 423 + ], + "type": "table", + "html": "
MethodsParams. (M) ↓FPS ↑Fβ ↑
MirrorNet [42]121.771.160.681
PMDNet [22]147.661.280.749
VCNet [36]333.170.890.749
Ours62.2417.060.787
", + "image_path": "c79eb1378bcfc266a42df047b50e7fcc1e1c1a46c2d8d05582ac450b07a580c4.jpg" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "table_body" + } + ], + "index": 24 + }, + { + "bbox": [ + 305, + 431, + 545, + 475 + ], + "lines": [ + { + "bbox": [ + 305, + 431, + 545, + 475 + ], + "spans": [ + { + "bbox": [ + 305, + 431, + 545, + 475 + ], + "type": "text", + "content": "Table 3. Quantitative comparison on the performance and efficiency between existing mirror detection methods and our method for VMD. All models are trained/tested on the proposed VMD-D dataset, on a single RTX 3090 GPU." + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 306, + 501, + 399, + 514 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 501, + 399, + 514 + ], + "spans": [ + { + "bbox": [ + 306, + 501, + 399, + 514 + ], + "type": "text", + "content": "5.3. Ablation Study" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "text", + "content": "We have conducted ablation experiments to verify the effectiveness of our design. First, we replace the proposed DC module with a convolution layer and restrict the network to take a single image as input to form the image-based baseline (\"I-Base\") for comparison. We then modify I-Base to take three images as input as our video-based baseline (\"V-Base\"). Our method (\"Ours\") is V-Base with the full DC module (i.e., DC with its first stage and its second stage). To test the effectiveness of each stage in the proposed DC module, we also construct two ablated models, which remove the first stage (\"Ours w/o DC " + }, + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "inline_equation", + "content": "1^{st}" + }, + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "text", + "content": ") or the second stage (\"Ours w/o DC " + }, + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "inline_equation", + "content": "2^{nd}" + }, + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "text", + "content": ") in the proposed DC module. Note that for \"Ours w/o DC " + }, + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "inline_equation", + "content": "1^{st}" + }, + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "text", + "content": "\" ablated model, since the second stage of the DC module requires intermediate prediction maps " + }, + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "inline_equation", + "content": "P_{t}, P_{t+1}, P_{n}" + }, + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "text", + "content": " to compute the reverse attention, we directly concatenate the low-level features " + }, + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{low}" + }, + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "text", + "content": " with high" + } + ] + } + ], + "index": 27 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9115" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 48, + 70, + 286, + 184 + ], + "blocks": [ + { + "bbox": [ + 48, + 70, + 286, + 184 + ], + "lines": [ + { + "bbox": [ + 48, + 70, + 286, + 184 + ], + "spans": [ + { + "bbox": [ + 48, + 70, + 286, + 184 + ], + "type": "table", + "html": "
AblationIoU↑Accuracy↑Fβ↑MAE↓
I-Base0.4240.8540.6860.146
V-Base0.4460.8660.7190.134
Ours w/o DC 1st0.5240.8760.7430.124
Ours w/o DC 2nd0.5330.8780.7360.122
Ours (Isame only)0.5250.8710.7400.123
Ours (Iadj only)0.5390.8760.7450.124
Ours0.5670.8950.7870.105
", + "image_path": "dbd22f17cd7e38f4a054fe9a4db4f764e4e027a8145f94801ed578d937462792.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 192, + 287, + 237 + ], + "lines": [ + { + "bbox": [ + 46, + 192, + 287, + 237 + ], + "spans": [ + { + "bbox": [ + 46, + 192, + 287, + 237 + ], + "type": "text", + "content": "Table 4. Ablation Study. \"Ours (I_same only)\" / \"Ours (I_adj only)\" is the ablation model that takes three same/adjacent image frames as input, respectively. DC " + }, + { + "bbox": [ + 46, + 192, + 287, + 237 + ], + "type": "inline_equation", + "content": "1^{st}" + }, + { + "bbox": [ + 46, + 192, + 287, + 237 + ], + "type": "text", + "content": " and DC " + }, + { + "bbox": [ + 46, + 192, + 287, + 237 + ], + "type": "inline_equation", + "content": "2^{nd}" + }, + { + "bbox": [ + 46, + 192, + 287, + 237 + ], + "type": "text", + "content": " are the first and the second stage of the DC module, respectively." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 282, + 287, + 319 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 282, + 287, + 319 + ], + "spans": [ + { + "bbox": [ + 46, + 282, + 287, + 319 + ], + "type": "text", + "content": "level features " + }, + { + "bbox": [ + 46, + 282, + 287, + 319 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_{high}" + }, + { + "bbox": [ + 46, + 282, + 287, + 319 + ], + "type": "text", + "content": ", and then forward them to a decoder to predict " + }, + { + "bbox": [ + 46, + 282, + 287, + 319 + ], + "type": "inline_equation", + "content": "P_{t}, P_{t+1}, P_{n}" + }, + { + "bbox": [ + 46, + 282, + 287, + 319 + ], + "type": "text", + "content": " without computing intra-frame correspondences and short-term inter-frame correspondences." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 331, + 287, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 331, + 287, + 485 + ], + "spans": [ + { + "bbox": [ + 46, + 331, + 287, + 485 + ], + "type": "text", + "content": "Table 4 shows the performances of different ablated models. As shown in the last row, our final proposed network with the full DC module outperforms other baselines on all metrics. In particular, even the ablated models with only a single-stage DC module (" + }, + { + "bbox": [ + 46, + 331, + 287, + 485 + ], + "type": "inline_equation", + "content": "3^{rd}" + }, + { + "bbox": [ + 46, + 331, + 287, + 485 + ], + "type": "text", + "content": " row and " + }, + { + "bbox": [ + 46, + 331, + 287, + 485 + ], + "type": "inline_equation", + "content": "4^{th}" + }, + { + "bbox": [ + 46, + 331, + 287, + 485 + ], + "type": "text", + "content": " row) can perform better than the baseline networks without the full DC module (\"I-Base\" and \"V-Base\"). This indicates the importance of correspondence learning for the VMD task. Also, the effective combination of the two stages in the DC module (\"Ours\") can boost the overall performance compared with the single-stage DC module. This shows that learning \"dual\" correspondences outperforms learning \"sole\" correspondences in our network." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 498, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 498, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 498, + 287, + 713 + ], + "type": "text", + "content": "To further investigate the role of different types of correspondences in our network, we replace the input images (i.e., two adjacent frames and one randomly selected frame) with three adjacent frames " + }, + { + "bbox": [ + 46, + 498, + 287, + 713 + ], + "type": "inline_equation", + "content": "(\\mathrm{I}_{adj}" + }, + { + "bbox": [ + 46, + 498, + 287, + 713 + ], + "type": "text", + "content": " only) or three video frames from the same frame " + }, + { + "bbox": [ + 46, + 498, + 287, + 713 + ], + "type": "inline_equation", + "content": "(\\mathrm{I}_{same}" + }, + { + "bbox": [ + 46, + 498, + 287, + 713 + ], + "type": "text", + "content": " only). The \" " + }, + { + "bbox": [ + 46, + 498, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{I}_{adj}" + }, + { + "bbox": [ + 46, + 498, + 287, + 713 + ], + "type": "text", + "content": " only\" model focuses on short-term temporal correspondences while the \" " + }, + { + "bbox": [ + 46, + 498, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{I}_{same}" + }, + { + "bbox": [ + 46, + 498, + 287, + 713 + ], + "type": "text", + "content": " only\" model can only learn intra-frame correspondences, due to their limited input information. The results are listed in the last three rows and show that our method, which exploits intra-frame as well as both short-term and long-term temporal correspondences, outperforms these ablated models. Figure 8 shows the visual results of different ablated models. Our method with sufficient learning of spatial and temporal correspondences can locate the mirrors precisely in both frames. In particular, the results in the last row show that our method can still detect the mirrors by exploiting long-range temporal correspondences even though intra-frame correspondences are missing in the image." + } + ] + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 309, + 69, + 545, + 163 + ], + "blocks": [ + { + "bbox": [ + 309, + 69, + 545, + 163 + ], + "lines": [ + { + "bbox": [ + 309, + 69, + 545, + 163 + ], + "spans": [ + { + "bbox": [ + 309, + 69, + 545, + 163 + ], + "type": "image", + "image_path": "f9a5e454da86e710564810891805bf0d004ef6c9901aa68273cedb5882b332ac.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 322, + 171, + 528, + 182 + ], + "lines": [ + { + "bbox": [ + 322, + 171, + 528, + 182 + ], + "spans": [ + { + "bbox": [ + 322, + 171, + 528, + 182 + ], + "type": "text", + "content": "Figure 8. Visual comparison of different ablated models." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "bbox": [ + 306, + 203, + 383, + 216 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 203, + 383, + 216 + ], + "spans": [ + { + "bbox": [ + 306, + 203, + 383, + 216 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 224, + 545, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 224, + 545, + 354 + ], + "spans": [ + { + "bbox": [ + 304, + 224, + 545, + 354 + ], + "type": "text", + "content": "In this paper, we have investigated the video mirror detection (VMD) problem. To the best of our knowledge, we are the first to address the VMD problem from a deep-learning-based perspective. We have constructed the first large-scale video mirror dataset (VMD-D). It contains 14,988 image frames from 269 videos with corresponding masks. We have also proposed a novel network, called VMD-Net, to leverage both intra-frame and inter-frame correspondences for video mirror detection. Experimental results show that our VMD-Net outperforms state-of-the-art methods from relevant tasks on our VMD-D dataset." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 356, + 545, + 476 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 356, + 545, + 476 + ], + "spans": [ + { + "bbox": [ + 304, + 356, + 545, + 476 + ], + "type": "text", + "content": "Our method does have limitations. Figure 9 shows that our method tends to produce results with coarse boundaries. Unlike existing image-based mirror detection methods [22, 36], which often explicitly adopt edge supervision in their network training, our method does not leverage such auxiliary information since we do not plan to focus this incremental cue in this first work for VMD. As a future work, we are working on improving our method by leveraging additional information like boundaries to help detect mirrors more precisely in videos." + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 309, + 487, + 543, + 588 + ], + "blocks": [ + { + "bbox": [ + 309, + 487, + 543, + 588 + ], + "lines": [ + { + "bbox": [ + 309, + 487, + 543, + 588 + ], + "spans": [ + { + "bbox": [ + 309, + 487, + 543, + 588 + ], + "type": "image", + "image_path": "b243456be669f2b37db6f66f2fd00aad37c6af126285fcdb0bd1d6d24014f812.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 597, + 545, + 619 + ], + "lines": [ + { + "bbox": [ + 305, + 597, + 545, + 619 + ], + "spans": [ + { + "bbox": [ + 305, + 597, + 545, + 619 + ], + "type": "text", + "content": "Figure 9. Failure cases. Our method may not be good at boundary extraction, due to the lack of explicit supervision of edges." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 645, + 403, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 645, + 403, + 658 + ], + "spans": [ + { + "bbox": [ + 306, + 645, + 403, + 658 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 665, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 665, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 305, + 665, + 545, + 712 + ], + "type": "text", + "content": "Xin Tan is sponsored by Shanghai Sailing Program (23YF1410500). This work is in part supported by two SRG grants from City University of Hong Kong (Ref: 7005674 and 7005843)." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9116" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 715 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "type": "text", + "content": "[1] Radhakrishna Achanta, Sheila Hemami, Francisco Estrada, and Sabine Susstrunk. Frequency-tuned salient region detection. In CVPR, 2009. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 124, + 288, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 124, + 288, + 179 + ], + "spans": [ + { + "bbox": [ + 53, + 124, + 288, + 179 + ], + "type": "text", + "content": "[2] Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian Reid, Stephen Gould, and Anton Van Den Hengel. Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments. In CVPR, pages 3674-3683, 2018. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 180, + 287, + 223 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 180, + 287, + 223 + ], + "spans": [ + { + "bbox": [ + 53, + 180, + 287, + 223 + ], + "type": "text", + "content": "[3] Maxim Berman, Amal Rannen Triki, and Matthew B Blaschko. The lovasz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In CVPR, pages 4413–4421, 2018. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 224, + 287, + 278 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 224, + 287, + 278 + ], + "spans": [ + { + "bbox": [ + 53, + 224, + 287, + 278 + ], + "type": "text", + "content": "[4] Chenglizhao Chen, Shuai Li, Yongguang Wang, Hong Qin, and Aimin Hao. Video saliency detection via spatial-temporal fusion and low-rank coherency diffusion. IEEE Transactions on Image Processing, 26(7):3156-3170, 2017. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 279, + 287, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 279, + 287, + 323 + ], + "spans": [ + { + "bbox": [ + 53, + 279, + 287, + 323 + ], + "type": "text", + "content": "[5] Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017. 4, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 324, + 287, + 357 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 324, + 287, + 357 + ], + "spans": [ + { + "bbox": [ + 53, + 324, + 287, + 357 + ], + "type": "text", + "content": "[6] Zhihao Chen, Liang Wan, Lei Zhu, Jia Shen, Huazhu Fu, Wennan Liu, and Jing Qin. Triple-cooperative video shadow detection. In CVPR, pages 2715-2724, 2021. 3, 4, 6, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 357, + 287, + 390 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 357, + 287, + 390 + ], + "spans": [ + { + "bbox": [ + 53, + 357, + 287, + 390 + ], + "type": "text", + "content": "[7] Ho Kei Cheng and Alexander G Schwing. Xmem: Long-term video object segmentation with an atkinson-shiffrin memory model. In ECCV, pages 640-658. Springer, 2022. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 391, + 287, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 391, + 287, + 435 + ], + "spans": [ + { + "bbox": [ + 53, + 391, + 287, + 435 + ], + "type": "text", + "content": "[8] Suhwan Cho, Heansung Lee, Minhyeok Lee, Chaewon Park, Sungjun Jang, Minjung Kim, and Sangyoun Lee. Tackling background distraction in video object segmentation. In ECCV, pages 446-462. Springer, 2022. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 435, + 287, + 468 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 435, + 287, + 468 + ], + "spans": [ + { + "bbox": [ + 53, + 435, + 287, + 468 + ], + "type": "text", + "content": "[9] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248-255. IEEE, 2009. 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 468, + 287, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 468, + 287, + 502 + ], + "spans": [ + { + "bbox": [ + 48, + 468, + 287, + 502 + ], + "type": "text", + "content": "[10] Xinpeng Ding, Jingwen Yang, Xiaowei Hu, and Xiaomeng Li. Learning shadow correspondence for video shadow detection. In ECCV, pages 705-722. Springer, 2022. 6, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 502, + 287, + 535 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 502, + 287, + 535 + ], + "spans": [ + { + "bbox": [ + 48, + 502, + 287, + 535 + ], + "type": "text", + "content": "[11] Deng-Ping Fan, Wenguan Wang, Ming-Ming Cheng, and Jianbing Shen. Shifting more attention to video salient object detection. In CVPR, pages 8554-8564, 2019. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 536, + 287, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 536, + 287, + 578 + ], + "spans": [ + { + "bbox": [ + 48, + 536, + 287, + 578 + ], + "type": "text", + "content": "[12] Yuchao Gu, Lijuan Wang, Ziqin Wang, Yun Liu, Ming-Ming Cheng, and Shao-Ping Lu. Pyramid constrained self-attention network for fast video salient object detection. In AAAI, 2020. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 579, + 287, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 579, + 287, + 624 + ], + "spans": [ + { + "bbox": [ + 48, + 579, + 287, + 624 + ], + "type": "text", + "content": "[13] Yuchao Gu, Lijuan Wang, Ziqin Wang, Yun Liu, Ming-Ming Cheng, and Shao-Ping Lu. Pyramid constrained self-attention network for fast video salient object detection. In AAAI, 2020. 6, 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 624, + 287, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 287, + 657 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 287, + 657 + ], + "type": "text", + "content": "[14] Huankang Guan, Jiaying Lin, and Rynson W.H. Lau. Learning semantic associations for mirror detection. In CVPR, pages 5941-5950, June 2022. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 658, + 287, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 287, + 690 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 287, + 690 + ], + "type": "text", + "content": "[15] Chenlei Guo, Qi Ma, and Liming Zhang. Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In CVPR. IEEE, 2008. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 691, + 287, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 287, + 715 + ], + "type": "text", + "content": "[16] Fang Guo, Wenguan Wang, Jianbing Shen, Ling Shao, Jian Yang, Dacheng Tao, and Yuan Yan Tang. Video saliency" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 715 + ], + "type": "list", + "angle": 0, + "index": 34, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "detection using object proposals. IEEE transactions on cybernetics, 48(11):3159-3170, 2017. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 96, + 547, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 96, + 547, + 139 + ], + "spans": [ + { + "bbox": [ + 307, + 96, + 547, + 139 + ], + "type": "text", + "content": "[17] Zilong Huang, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu. Ccnet: Criss-cross attention for semantic segmentation. In ICCV, pages 603-612, 2019. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 141, + 547, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 141, + 547, + 173 + ], + "spans": [ + { + "bbox": [ + 307, + 141, + 547, + 173 + ], + "type": "text", + "content": "[18] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 175, + 545, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 175, + 545, + 207 + ], + "spans": [ + { + "bbox": [ + 307, + 175, + 545, + 207 + ], + "type": "text", + "content": "[19] Philipp Krahenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. NeurIPS, 24, 2011. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 208, + 545, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 208, + 545, + 241 + ], + "spans": [ + { + "bbox": [ + 307, + 208, + 545, + 241 + ], + "type": "text", + "content": "[20] Fuxin Li, Taeyoung Kim, Ahmad Humayun, David Tsai, and James M Rehg. Video segmentation by tracking many figure-ground segments. In ICCV, pages 2192-2199, 2013. 3, 4" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 242, + 545, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 242, + 545, + 275 + ], + "spans": [ + { + "bbox": [ + 307, + 242, + 545, + 275 + ], + "type": "text", + "content": "[21] Jiaying Lin, Zebang He, and Rynson WH Lau. Rich context aggregation with reflection prior for glass surface detection. In CVPR, pages 13415-13424, 2021. 6, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 276, + 545, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 276, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 307, + 276, + 545, + 308 + ], + "type": "text", + "content": "[22] Jiaying Lin, Guodong Wang, and Rynson W. H. Lau. Progressive mirror detection. In CVPR, 2020. 1, 2, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 310, + 545, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 310, + 545, + 364 + ], + "spans": [ + { + "bbox": [ + 307, + 310, + 545, + 364 + ], + "type": "text", + "content": "[23] Zhihui Lin, Tianyu Yang, Maomao Li, Ziyu Wang, Chun Yuan, Wenhao Jiang, and Wei Liu. Swem: Towards real-time video object segmentation with sequential weighted expectation-maximization. In CVPR, pages 1362-1372, 2022. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 365, + 545, + 410 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 365, + 545, + 410 + ], + "spans": [ + { + "bbox": [ + 307, + 365, + 545, + 410 + ], + "type": "text", + "content": "[24] Yong Liu, Ran Yu, Jiahao Wang, Xinyuan Zhao, Yitong Wang, Yansong Tang, and Yujiu Yang. Global spectral filter memory network for video object segmentation. In ECCV, pages 648-665. Springer, 2022. 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 411, + 545, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 411, + 545, + 464 + ], + "spans": [ + { + "bbox": [ + 307, + 411, + 545, + 464 + ], + "type": "text", + "content": "[25] Yong Liu, Ran Yu, Fei Yin, Xinyuan Zhao, Wei Zhao, Weihao Xia, and Yujiu Yang. Learning quality-aware dynamic memory for video object segmentation. In European Conference on Computer Vision, pages 468-486. Springer, 2022. 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 466, + 545, + 511 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 466, + 545, + 511 + ], + "spans": [ + { + "bbox": [ + 307, + 466, + 545, + 511 + ], + "type": "text", + "content": "[26] Xiao Lu, Yihong Cao, Sheng Liu, Chengjiang Long, Zipei Chen, Xuanyu Zhou, Yimin Yang, and Chunxia Xiao. Video shadow detection via spatio-temporal interpolation consistency training. In CVPR, pages 3116-3125, 2022. 6, 7" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 512, + 545, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 512, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 307, + 512, + 545, + 544 + ], + "type": "text", + "content": "[27] Haiyang Mei, Bo Dong, Wen Dong, Pieter Peers, Xin Yang, Qiang Zhang, and Xiaopeng Wei. Depth-aware mirror segmentation. In CVPR, pages 3044-3053, 2021. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 307, + 545, + 545, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 545, + 545, + 590 + ], + "spans": [ + { + "bbox": [ + 307, + 545, + 545, + 590 + ], + "type": "text", + "content": "[28] Peter Ochs, Jitendra Malik, and Thomas Brox. Segmentation of moving objects by long term video analysis. IEEE transactions on pattern analysis and machine intelligence, 36(6):1187-1200, 2013. 3, 4" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 307, + 590, + 545, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 590, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 307, + 590, + 545, + 624 + ], + "type": "text", + "content": "[29] Youwei Pang, Xiaoji Zhao, Lihe Zhang, and Hutchuan Lu. Multi-scale interactive network for salient object detection. In CVPR, pages 9413-9422, 2020. 3, 6, 7" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 307, + 624, + 545, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 624, + 545, + 656 + ], + "spans": [ + { + "bbox": [ + 307, + 624, + 545, + 656 + ], + "type": "text", + "content": "[30] Anestis Papazoglou and Vittorio Ferrari. Fast object segmentation in unconstrained video. In ICCV, pages 1777-1784, 2013. 3" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 307, + 658, + 545, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 658, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 307, + 658, + 545, + 715 + ], + "type": "text", + "content": "[31] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner," + } + ] + } + ], + "index": 33 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9117" + } + ] + } + ], + "index": 35 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 66, + 72, + 287, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 72, + 287, + 138 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 287, + 138 + ], + "type": "text", + "content": "Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems 32, pages 8024-8035. Curran Associates, Inc., 2019. 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 140, + 287, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 140, + 287, + 183 + ], + "spans": [ + { + "bbox": [ + 48, + 140, + 287, + 183 + ], + "type": "text", + "content": "[32] Gensheng Pei, Fumin Shen, Yazhou Yao, Guo-Sen Xie, Zhenmin Tang, and Jinhui Tang. Hierarchical feature alignment network for unsupervised video object segmentation. In ECCV, pages 596-613. Springer, 2022. 2, 3, 6, 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 184, + 287, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 184, + 287, + 227 + ], + "spans": [ + { + "bbox": [ + 48, + 184, + 287, + 227 + ], + "type": "text", + "content": "[33] F. Perazzi, J. Pont-Tuset, B. McWilliams, L. Van Gool, M. Gross, and A. Sorkine-Hornung. A benchmark dataset and evaluation methodology for video object segmentation. In CVPR, 2016. 3, 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 228, + 287, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 228, + 287, + 259 + ], + "spans": [ + { + "bbox": [ + 48, + 228, + 287, + 259 + ], + "type": "text", + "content": "[34] Federico Perazzi, Oliver Wang, Markus Gross, and Alexander Sorkine-Hornung. Fully connected object proposals for video segmentation. In ICCV, pages 3227-3234, 2015. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 261, + 287, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 261, + 287, + 293 + ], + "spans": [ + { + "bbox": [ + 48, + 261, + 287, + 293 + ], + "type": "text", + "content": "[35] Jiaqi Tan, Weijie Lin, Angel X Chang, and Manolis Savva. Mirror3d: Depth refinement for mirror surfaces. In CVPR, pages 15990-15999, 2021. 1, 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 294, + 287, + 337 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 294, + 287, + 337 + ], + "spans": [ + { + "bbox": [ + 48, + 294, + 287, + 337 + ], + "type": "text", + "content": "[36] Xin Tan, Jiaying Lin, Ke Xu, Chen Pan, Lizhuang Ma, and Rynson W. H. Lau. Mirror detection with the visual chirality cue. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 1, 2, 6, 7, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 338, + 287, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 338, + 287, + 370 + ], + "spans": [ + { + "bbox": [ + 48, + 338, + 287, + 370 + ], + "type": "text", + "content": "[37] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In ECCV, pages 402-419. Springer, 2020. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 371, + 287, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 371, + 287, + 403 + ], + "spans": [ + { + "bbox": [ + 48, + 371, + 287, + 403 + ], + "type": "text", + "content": "[38] Pavel Tokmakov, Karteek Alahari, and Cordelia Schmid. Learning video object segmentation with visual memory. In ICCV, pages 4481-4490, 2017. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 404, + 287, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 404, + 287, + 448 + ], + "spans": [ + { + "bbox": [ + 48, + 404, + 287, + 448 + ], + "type": "text", + "content": "[39] Wenguan Wang, Xiankai Lu, Jianbing Shen, David J Crandall, and Ling Shao. Zero-shot video object segmentation via attentive graph neural networks. In ICCV, pages 9236-9245, 2019. 2, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 449, + 287, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 449, + 287, + 492 + ], + "spans": [ + { + "bbox": [ + 48, + 449, + 287, + 492 + ], + "type": "text", + "content": "[40] Wenguan Wang, Hongmei Song, Shuyang Zhao, Jianbing Shen, Sanyuan Zhao, Steven CH Hoi, and Haibin Ling. Learning unsupervised video object segmentation through visual attention. In CVPR, pages 3064-3074, 2019. 2, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 493, + 287, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 493, + 287, + 525 + ], + "spans": [ + { + "bbox": [ + 48, + 493, + 287, + 525 + ], + "type": "text", + "content": "[41] Saining Xie, Ross Girshick, Piotr Dólár, Zhuowen Tu, and Kaiming He. Aggregated residual transformations for deep neural networks. In CVPR, pages 1492–1500, 2017. 4, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 526, + 287, + 558 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 526, + 287, + 558 + ], + "spans": [ + { + "bbox": [ + 48, + 526, + 287, + 558 + ], + "type": "text", + "content": "[42] Xin Yang, Haiyang Mei, Ke Xu, Xiaopeng Wei, Baocai Yin, and Rynson W.H. Lau. Where is my mirror? In ICCV, 2019. 1, 2, 3, 4, 6, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 559, + 287, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 559, + 287, + 601 + ], + "spans": [ + { + "bbox": [ + 48, + 559, + 287, + 601 + ], + "type": "text", + "content": "[43] Zhao Yang, Qiang Wang, Luca Bertinetto, Weiming Hu, Song Bai, and Philip HS Torr. Anchor diffusion for unsupervised video object segmentation. In ICCV, pages 931-940, 2019. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 602, + 287, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 602, + 287, + 635 + ], + "spans": [ + { + "bbox": [ + 48, + 602, + 287, + 635 + ], + "type": "text", + "content": "[44] Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object-contextual representations for semantic segmentation. In ECCV, pages 173-190. Springer, 2020. 6, 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 637, + 287, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 637, + 287, + 669 + ], + "spans": [ + { + "bbox": [ + 48, + 637, + 287, + 669 + ], + "type": "text", + "content": "[45] Kaihua Zhang, Zicheng Zhao, Dong Liu, Qingshan Liu, and Bo Liu. Deep transport network for unsupervised video object segmentation. In ICCV, pages 8781-8790, 2021. 2, 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "type": "text", + "content": "[46] Miao Zhang, Jie Liu, Yifei Wang, Yongri Piao, Shunyu Yao, Wei Ji, Jingjing Li, Huchuan Lu, and Zhongxuan Luo. Dynamic context-sensitive filtering network for video salient object detection. In ICCV, pages 1553-1563, 2021. 3" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 185 + ], + "type": "list", + "angle": 0, + "index": 20, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "text", + "content": "[47] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In CVPR, pages 2881-2890, 2017. 6, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "type": "text", + "content": "[48] Xiaoqi Zhao, Youwei Pang, Lihe Zhang, Huchuan Lu, and Lei Zhang. Suppress and balance: A simple gated network for salient object detection. In ECCV, pages 35-51. Springer, 2020. 3, 6, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 152, + 545, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 185 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 185 + ], + "type": "text", + "content": "[49] Bolei Zhou, Hang Zhao, Xavier Puig, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Scene parsing through ade20k dataset. In CVPR, pages 633-641, 2017. 1" + } + ] + } + ], + "index": 19 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "9118" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/5ac75f03-1745-4705-ac4d-6c3f7fb370ae_content_list.json b/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/5ac75f03-1745-4705-ac4d-6c3f7fb370ae_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..0542032dca8ed83968c6f138a13cd1dc0bd5605c --- /dev/null +++ b/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/5ac75f03-1745-4705-ac4d-6c3f7fb370ae_content_list.json @@ -0,0 +1,1641 @@ +[ + { + "type": "text", + "text": "Learning to Detect and Segment for Open Vocabulary Object Detection", + "text_level": 1, + "bbox": [ + 125, + 130, + 843, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Tao Wang Sichuan University", + "bbox": [ + 408, + 181, + 560, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "twangnh@gmail.com", + "bbox": [ + 405, + 219, + 558, + 233 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 268, + 313, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Open vocabulary object detection has been greatly advanced by the recent development of vision-language pretrained model, which helps recognize novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask prediction. In this work, we propose CondHead, a principled dynamic network design to better generalize the box regression and mask segmentation for open vocabulary setting. The core idea is to conditionally parameterize the network heads on semantic embedding and thus the model is guided with class-specific knowledge to better detect novel categories. Specifically, CondHead is composed of two streams of network heads, the dynamically aggregated head and dynamically generated head. The former is instantiated with a set of static heads that are conditionally aggregated, these heads are optimized as experts and are expected to learn sophisticated prediction. The latter is instantiated with dynamically generated parameters and encodes general class-specific information. With such a conditional design, the detection model is bridged by the semantic embedding to offer strongly generalizable class-wise box and mask prediction. Our method brings significant improvement to the state-of-the-art open vocabulary object detection methods with very minor overhead, e.g., it surpasses a RegionClip model by 3.0 detection AP on novel categories, with only $1.1\\%$ more computation.", + "bbox": [ + 76, + 301, + 473, + 694 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 726, + 209, + 742 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Given the semantic object categories of interest, object detection aims at localizing each object instance from the input images. The prior research efforts mainly focus on the close-set setting, where the images containing the interested object categories are annotated and used to train a detector. The obtained detector only recognizes object categories that are annotated in the training set. In such a setting, more data needs to be collected and annotated if novel category1 needs", + "bbox": [ + 75, + 752, + 468, + 875 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/78c4e153e7d309ccd977fb3631f3380c2a2a6351ae87ae73defb90a5bea702ce.jpg", + "image_caption": [ + "Figure 1. Illustration of our main intuition. Given the object proposals, the bounding box regression and mask segmentation learned from some object categories could generalize to the target category. For example, the knowledge learned from a chicken could help detect and segment the long thin feet and the small head of an ibis (upper row). Similarly for the hairbrush, the knowledge learned from the toothbrush could better handle the extreme aspect ratio and occlusion from the hand (lower row)." + ], + "image_footnote": [], + "bbox": [ + 504, + 268, + 890, + 506 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "to be detected. However, data collection and annotation are very costly for object detection, which raises a significant challenge for traditional object detection methods.", + "bbox": [ + 496, + 671, + 892, + 715 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To address the challenge, the open vocabulary object detection methods are widely explored recently, these methods [1, 7, 9, 11, 14, 21, 23, 30-32] aim at generalizing object detection on novel categories by only training on a set of labeled categories. The core of these methods is transferring the strong image-text aligned features [16, 22] to classify objects of novel categories. To achieve bounding box regression and mask segmentation on novel categories, they simply employ the class-agnostic network heads. Although class agnostic heads can be readily applied to novel target object categories, they offer limited capacity to learn category-specific knowledge like object", + "bbox": [ + 496, + 719, + 893, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "we treat category and class interchangeably in this paper", + "bbox": [ + 94, + 886, + 401, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "7051", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/5cf8b6e1b98cb76b27ab34fcd582ae0dceb133e3d77b45f122441240fa44935c.jpg", + "image_caption": [ + "Figure 2. Overview of CondHead. To detect objects of novel categories, we aim at conditionally parameterizing the bounding box regression and mask segmentation based on the semantic embedding, which is strongly correlated with the visual feature and provides effective class-specific cues to refine the box and predict the mask." + ], + "image_footnote": [], + "bbox": [ + 83, + 88, + 890, + 234 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "shape, and thus provide sub-optimal performance. On the other hand, training class-wise heads is not feasible as we do not have bounding box and mask annotation for the target categories. As shown in Figure 1, our intuition is that the class-wise knowledge could naturally generalize across object categories, and may be leveraged to achieve much higher quality box regression and mask segmentation on the target categories, in a category-specific manner. However, we find a brute force way of training class-wise heads on the base categories and manually gathering the class-specific prediction with closet semantic similarity during inference provides limited gain. The reason is that there still remains gap between the base and target categories, such as appearance and context.", + "bbox": [ + 75, + 311, + 472, + 523 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Motivated by the strong semantic-visual aligned representation [7, 11, 31, 32] in open vocabulary object detection, we propose to exploit the semantic embedding as a conditional prior to parameterize class-wise bounding box regression and mask segmentation. Such conditioning is learned on base categories and easily generalizes to novel categories with the semantic embedding. Our method, named CondHead, is based on dynamic parameter generation of neural networks [5, 6, 15, 17, 29]. To achieve strong efficiency, it exploits both large complex network head for their representative power and small light network head for their efficiency. The complex head is employed by conditional weight aggregation over a set of static heads. The light head is employed by dynamically basing its parameters on the semantic embedding. The final prediction is obtained by combining the predictions from the two stream results. Through optimization on the base categories, the set of static heads are expected to learn sophisticated expert knowledge to cope with complex shapes and appearance, the dynamic head is endowed with general class-specific knowledge such as color and context.", + "bbox": [ + 75, + 530, + 472, + 848 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our CondHead is flexible regarding the choice of semantic-visual representation. The stronger quality of the aligned representation is expected to bring higher perfor", + "bbox": [ + 76, + 854, + 470, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "mance, as the conditional knowledge from the semantic embedding could generalize better to the target visual features. This is demonstrated by the clear growth of improvement over three baselines with increasing quality of pretrained semantic-visual encoder networks, OVR-CNN [31], ViLD [11] and RegionCLIP [32], on both COCO [19] and LVIS [12] datasets. Remarkably, CondHead brings an average 2.8 improvement w.r.t both box and mask AP for the strong RegionCLIP baseline, with only about $1\\%$ more computation. We also demonstrate intriguing qualitative results, showing how the semantic conditioning positively affects the regression and segmentation tasks.", + "bbox": [ + 496, + 311, + 890, + 493 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contributions are three-fold. 1) To the best of our knowledge, we are the first to leverage semantic-visual aligned representation for open vocabulary box regression and mask segmentation. 2) We design a differentiable semantic-conditioned head design to efficiently bridge the strong category-specific prediction learned on base categories to the target novel categories. 3) We extensively validate the proposed method on various benchmark datasets and conduct thorough ablation and analysis to understand how the semantic conditioning helps detect and segment the novel categories.", + "bbox": [ + 496, + 494, + 890, + 660 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 676, + 640, + 691 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Detecting Objects with Zero-shot and Open Vocabulary Setting Despite the remarkable success of fully-supervised object detection [2, 3, 10, 20, 24-26, 36], the instance-level annotation for novel object categories is costly. Recent years, open vocabulary object detection emerges as an effective solution, which aims at detecting novel categories without corresponding annotation. The representative methods employ region-based alignment, e.g., OVR-CNN [31], ViLD [11], RegionCLIP [32] and DetPro [7]. They are based on the two-stage object detection framework [26] and aim to match the object region feature with the generalizable semantic category embedding. They mainly differ in the learning of semantic-visual representa", + "bbox": [ + 496, + 704, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "7052", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "tion. Specifically, OVR-CNN [31] optimizes the semantic-visual grounding objective based multimodal transformer and applies the learned vision to language projection to facilitate novel category recognition. ViLD [11] transfers the stronger visual-semantic representation from large-scale vision-language pre-training [22]. Based on ViLD, Det-Pro [7] introduces a learnable prompt mechanism [33] to enhance the semantic representation and achieve higher performance. RegionCLIP [32] instead focuses on more effective region-wise pre-training to obtain more discriminative representation. In addition to pursuing more stronger region feature alignment, concurrent works [9, 14, 21, 30] explore other aspects of open vocabulary detection. For example, Gao et al. [9] and Huynh et al. [14] develop pseudo-labeling approach to generate pseudo ground-truth labels for object detection and instance segmentation. Instead of labeling perspective, some works focus on transformer architecture design for open vocabulary detection. OV-DETR [30] proposes a binary matching objective to enable query matching with novel objects on the end-to-end DETR framework DETR [3]. Minderer et al. [21] directly transfers pre-trained vision transformers to simplify open vocabulary detection.", + "bbox": [ + 76, + 90, + 472, + 438 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The prior works generally focus on the recognition of open vocabulary objects, while the localization or segmentation are typically conducted in a class-agnostic fashion. In this work, we explore this orthogonal perspective and aim at developing effective methods for more accurate box regression and mask segmentation.", + "bbox": [ + 76, + 440, + 470, + 531 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Dynamic Network Design Traditional neural network architectures employ static weights, i.e., the weights are independent of input signal and are fixed after training. Such static neural networks are limited at flexibility, in dealing with diverse input signals like images. Some previous works explore the idea of dynamic instantiation of networks to improve the flexibility. For example, Jia et al. [17] proposes to actively predict the convolution filters based on the input. In addition to explicit network parameter generation, Jaderberg et al. [15] introduces a dynamic parametric transformation (STN) to adaptively transform the image feature map, recovering affine distortions in the input and thus facilitating recognition. STN [15] operates on global image space, the Deformable Convolution [6,35] instead works in local scale. It learns spatial offsets to adjust the sampling locations of convolution kernels. The dynamic designs like STN [15] and Deformable Convolution [6,35] introduce explicit control over the networks. Some recent works introduce implicit dynamics to achieve strong network capacity. Notably, [5,29] develop conditionally parameterized convolution filters to increase the network capacity. Specifically, the convolution kernel is instantiated by dynamically aggregating a set of base filters.", + "bbox": [ + 76, + 535, + 472, + 883 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our work is related to the above dynamic network ar", + "bbox": [ + 96, + 885, + 468, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "chitecture designs, in that we also aim at achieving input-conditioned processing, to better adapt to the input data. Unlike prior works, we focus on the object instance-level design of dynamic processing. Moreover, instead of operating on the same visual signal, we instead introduce semantic signal for the dynamic network design.", + "bbox": [ + 496, + 90, + 890, + 183 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 498, + 195, + 635, + 213 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Problem Definition", + "text_level": 1, + "bbox": [ + 500, + 220, + 683, + 234 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given the bounding box annotation on a set of base object categories $\\mathbb{F} = \\{C_1,C_2,\\dots C_N\\}$ , open vocabulary object detection aims at training on the base data and generalizing to any open set of target object categories $\\mathbb{F}^* = \\{C_1,C_2,\\dots C_M\\}$ . Open vocabulary instance segmentation is also expected if the mask annotation is given.", + "bbox": [ + 496, + 243, + 890, + 334 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The state-of-the-art two-stage methods [7, 11, 31, 32] can be simplified as a region-semantic alignment framework. The model is initialized with an image encoder $(\\mathcal{T}_i)$ and a language encoder $(\\mathcal{T}_l)$ , which extract image feature and semantic embedding: $\\mathbf{F} = \\mathcal{T}_i(I)$ , $\\mathbf{s}_n = \\mathcal{T}_l(C_n)$ , here $I$ denotes the input image. These encoders [16, 22] are typically pre-trained on large-scale image-caption [4, 22, 28] data and thus extract strongly correlated visual-semantic representations. With region proposal network [26] trained on the base categories to extract object proposals, the open-vocabulary detection is trained in a region-wise manner.", + "bbox": [ + 496, + 335, + 892, + 500 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Concretely, given an object proposal bounding box $\\mathbf{p} = (x_1, y_1, x_2, y_2)$ , feature pooling is conducted on the image feature map $\\mathbf{F}$ to obtain the region feature $\\mathbf{f}$ . The object proposal box is then matched with the ground-truth object instance set $\\aleph$ w.r.t IOU (Intersection Over Union) metric:", + "bbox": [ + 498, + 501, + 890, + 575 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n(\\mathbf {b}, c) = \\underset {(\\mathbf {b} ^ {\\prime}, c ^ {\\prime}) \\in \\aleph} {\\arg \\max } \\operatorname {I O U} (\\mathbf {p}, \\mathbf {b} ^ {\\prime}) \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 598, + 588, + 890, + 616 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathbf{b} = (x_1^*, y_1^*, x_2^*, y_2^*)$ means the obtained ground-truth bounding box and $c$ is corresponding object category. Then, optimization is conducted to further minimize the alignment between ground-truth semantic embedding $\\mathbf{s}_c$ and region feature $\\mathbf{f}$ , e.g., through similarity-based cross entropy loss [11].", + "bbox": [ + 496, + 627, + 890, + 718 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To generalize the bounding box regression, prior works employ class-agnostic network head $\\mathcal{B}$ with parameter $\\theta$ and optimize it on the base category data:", + "bbox": [ + 496, + 718, + 890, + 763 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\theta} \\mathcal {L} _ {\\mathcal {B}} \\left(\\mathcal {B} _ {\\theta}, \\mathcal {X}\\right) = \\mathcal {L} _ {\\mathcal {B}} \\left(\\mathcal {B} _ {\\theta} (\\boldsymbol {p}, \\boldsymbol {f}), \\boldsymbol {b}\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 571, + 775, + 890, + 797 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathcal{X} = (\\mathbf{p},\\mathbf{f},\\mathbf{b})$ denotes the tuple data of proposal, region feature and bounding box. $\\mathcal{L}_{\\mathcal{B}}$ is typically defined as L1 or L2 error between the regressed bounding box and ground-truth box.", + "bbox": [ + 496, + 809, + 890, + 869 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To learn instance segmentation, the mask region feature $\\mathbf{v}$ is pooled with the refined bounding box from the box", + "bbox": [ + 498, + 869, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "7053", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "head, Then the class-agnostic mask segmentation network head $\\mathcal{M}$ with parameter $\\vartheta$ is trained by:", + "bbox": [ + 75, + 90, + 468, + 121 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\vartheta} \\mathcal {L} _ {\\mathcal {M}} \\left(\\mathcal {M} _ {\\vartheta}, \\mathcal {Y}\\right) = \\mathcal {L} _ {\\mathcal {M}} \\left(\\mathcal {M} _ {\\vartheta} (\\boldsymbol {v}), \\boldsymbol {m}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 138, + 133, + 468, + 155 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{m}$ denotes the corresponding ground-truth mask segmentation and $\\mathcal{V} = (\\mathbf{v},\\mathbf{m})$ denotes the paired region feature and mask. $\\mathcal{L}_{\\mathcal{M}}$ is typically defined as pixel-wise segmentation error between the predicted mask and ground-truth.", + "bbox": [ + 75, + 166, + 468, + 227 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Although the class-agnostic bounding box regression and mask segmentation heads readily generalize to the target novel object categories, they offer sub-optimal results due to the less representative capability. Since the semantic embeddings are strongly correlated with the visual features, we propose to achieve generalizable class-wise prediction by conditioning the network parameters on the base category semantic embeddings during training:", + "bbox": [ + 75, + 227, + 468, + 349 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\alpha} \\mathcal {L} _ {\\mathcal {B}} \\left(\\mathcal {B} _ {\\theta \\left(\\mathbf {s} _ {c}\\right)}, \\mathcal {X}\\right) \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 207, + 376, + 468, + 398 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\beta} \\mathcal {L} _ {\\mathcal {M}} \\left(\\mathcal {M} _ {\\vartheta \\left(\\mathbf {s} _ {c}\\right)}, \\mathcal {Y}\\right) \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 200, + 422, + 468, + 445 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Where $\\alpha$ and $\\beta$ are parameters of the conditioning function $\\theta(\\cdot)$ and $\\vartheta(\\cdot)$ . Through this approach, the model learns to generalize regression and segmentation to novel categories in a class-wise manner, by predicting corresponding parameters $\\theta(\\mathbf{s}_m)$ and $\\vartheta(\\mathbf{s}_m)$ during inference.", + "bbox": [ + 75, + 457, + 468, + 534 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. CondHead Formulation", + "text_level": 1, + "bbox": [ + 76, + 542, + 295, + 556 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our proposed CondHead aims to bridge the gap between base and target categories, with the semantic conditioning mechanism. We instantiate the conditioning function with neural networks to achieve flexible optimization and inference. To construct such conditioning framework, we observe the challenges in network capacity: 1) More complex head architecture (e.g., head with several hidden layers) offers stronger representative ability, but it requires large number of parameters, which are not efficient to generate and hard to optimize. For example, a hidden fully layer with input and output dimension of 256 involves more than 60k scalar weights. 2) Less complex head architecture (e.g., head with a single layer) is easier to optimize and can be efficiently generated, but provides limited capacity.", + "bbox": [ + 75, + 566, + 468, + 777 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To address the challenge, we design a dual conditioning framework, to leverage both the complex heads and light heads. The framework is composed of dynamically aggregated head and dynamically generated head.", + "bbox": [ + 73, + 777, + 468, + 839 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Dynamically Aggregated Head To leverage the large network capacity of complex heads, we propose to generate dynamic weights to aggregate a set of complex heads $\\{\\mathcal{B}_1,\\mathcal{B}_2,\\dots,\\mathcal{B}_H\\}$ , with associated parameters", + "bbox": [ + 75, + 839, + 468, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$\\{\\theta_{1}, \\theta_{2}, \\dots, \\theta_{H}\\}$ . These heads act as experts that are good at refining bounding box and predicting mask segmentation for objects categories with certain shapes or appearance. Specifically, the aggregation weights is generated by:", + "bbox": [ + 498, + 90, + 890, + 152 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {w} = \\mathcal {A} _ {\\phi} (\\mathbf {s} _ {c}) \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 651, + 164, + 890, + 180 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{w} \\in \\mathbb{R}^H$ , and $\\mathcal{A}$ is a small neural network with parameter $\\phi$ . To regularize the weight aggregation space as a convex hull for optimization, the weight $\\mathbf{w}$ is then normalized with Softmax function:", + "bbox": [ + 498, + 193, + 890, + 253 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nw _ {h} = \\frac {e ^ {w _ {h}}}{\\sum_ {h ^ {\\prime} = 1} ^ {H} e ^ {w _ {h ^ {\\prime}}}} \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 629, + 262, + 890, + 297 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "which ensures the weights sums to 1 and each element is larger than 0. With the normalized aggregation weight, the expert heads are then combined to a single head $\\hat{\\mathcal{B}}$ with parameter $\\hat{\\theta}$ :", + "bbox": [ + 498, + 306, + 890, + 367 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\theta} = \\sum_ {h = 1} ^ {H} w _ {h} * \\theta_ {h} \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 638, + 377, + 890, + 419 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Similarly, for a set of mask heads $\\{\\mathcal{M}_1,\\mathcal{M}_2,\\dots,\\mathcal{M}_H\\}$ , with associated parameters $\\{\\vartheta_1,\\vartheta_2,\\dots,\\vartheta_H\\}$ , the weight generation and aggregation can be conducted to obtain a single mask head $\\hat{\\mathcal{M}}$ with parameter $\\hat{\\vartheta}$ . When the dynamic aggregation is applied to both box regression and mask segmentation, separate weight generation networks are used, as box regression and mask segmentation may not share the same attention across the expert models.", + "bbox": [ + 498, + 429, + 890, + 551 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Dynamically Generated Head To introduce stronger conditioning on the semantic embedding, we propose to directly generate the network parameter. With bounding box regression head as an example, the parameter is first generated as:", + "bbox": [ + 498, + 551, + 890, + 627 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\dot {\\theta} = \\mathcal {D} _ {\\varphi} (\\mathbf {s} _ {c}) \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 653, + 638, + 890, + 657 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "the parameter $\\dot{\\theta}$ is used to instantiate a new box head $\\dot{B}$ . Since $\\dot{\\theta}$ is directly generated from the semantic embedding, it easily encodes general class-specific information like aspect ratio and color. Similarly, a dynamic mask segmentation head $\\dot{\\mathcal{M}}$ can be obtained with generated parameter $\\dot{\\vartheta}$ .", + "bbox": [ + 498, + 670, + 890, + 747 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Combining the Dynamic Predictions We then combine the above dynamically aggregated heads $\\hat{B},\\hat{\\mathcal{M}}$ and dynamically generated heads $\\hat{B},\\hat{\\mathcal{M}}$ to obtain the final result. We consider simple weighted averaging on their prediction:", + "bbox": [ + 498, + 748, + 890, + 809 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\bar {\\mathcal {B}} _ {\\theta \\left(\\mathbf {s} _ {c}\\right)} (\\cdot) = \\lambda * \\hat {\\mathcal {B}} (\\cdot) + (1 - \\lambda) * \\dot {\\mathcal {B}} (\\cdot) \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 568, + 835, + 890, + 853 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\overline {{\\mathcal {M}}} _ {\\vartheta \\left(\\mathbf {s} _ {c}\\right)} (\\cdot) = \\mu * \\hat {\\mathcal {M}} (\\cdot) + (1 - \\mu) * \\dot {\\mathcal {M}} (\\cdot) \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 555, + 878, + 890, + 897 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "7054", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$\\overline{\\mathcal{B}}$ and $\\overline{\\mathcal{M}}$ are for the box regression and mask segmentation head respectively. $\\lambda$ and $\\mu$ are hyper-parameters.", + "bbox": [ + 75, + 90, + 468, + 121 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "After optimization on the base category data with equation 4 and equation 5, $\\overline{B}$ and $\\overline{\\mathcal{M}}$ are employed during inference on the target categories by conditioning on the target semantic embedding as $\\overline{\\mathcal{B}}_{\\theta (\\mathbf{s}_m)}(\\cdot)$ and $\\overline{\\mathcal{M}}_{\\vartheta (\\mathbf{s}_m)}(\\cdot)$", + "bbox": [ + 75, + 121, + 468, + 183 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Optimizing the Expert Heads with Temperature Annealing During the early optimization stage, the expert heads within the dynamically aggregated head all require training signals to initiate the learning of basic regression and segmentation capability. While in the later optimization stage, the experts are expected to provide specialized capability in dealing with different objects. We thus facilitate the optimization of the expert heads by a temperature annealing Softmax strategy. Concretely, the normalization of aggregation weights is performed with temperature $\\tau$", + "bbox": [ + 75, + 184, + 470, + 335 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nw _ {h} = \\frac {e ^ {w _ {h} / \\tau}}{\\sum_ {h ^ {\\prime} = 1} ^ {H} e ^ {w _ {h ^ {\\prime}} / \\tau}} \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 199, + 345, + 468, + 386 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "During the early training stages, the temperature is set as a large value and with the progress of training to provide nearly uniform gradients for all the expert heads, the temperature is gradually annealed to a smaller value to achieve the desired specialized learning.", + "bbox": [ + 75, + 393, + 468, + 470 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Relation to Close Works", + "text_level": 1, + "bbox": [ + 76, + 479, + 302, + 496 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Conditionally Parameterized Convolution CondHead is closely related to Dynamic Convolution [5] and CondConv [29]. They differ from CondHead in motivation and application. They aim to improve the representation capacity of convolution kernels through conditional parameterization, the conditioning is performed on the visual features of each network layer. Moreover, they are applied to the image recognition task. While CondHead aims to bridge the perception gap between the base training object categories and target novel object categories by conditioning on the strongly visual-aligned semantic embedding. CondHead is applied to the more challenging bounding box regression and mask segmentation task, which is not explored before.", + "bbox": [ + 75, + 506, + 468, + 702 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Partially Supervised Instance Segmentation Recent works [13, 18] explore partially supervised instance segmentation, where the base categories have both box and mask annotation while the novel categories have only box annotation. $\\mathbf{Mask}^{X}$ R-CNN [13] propose a parameterized transformation function to transfer the box regression weights to mask segmentation weights. Shapemask [18] explores strong shape priors to achieve better class-agnostic object segmentation, which generalizes better to novel categories without mask annotation. Our focused open vocabulary object detection and instance segmentation are much more challenging than the partially supervised instance segmentation, which is based on good detection quality and", + "bbox": [ + 75, + 704, + 470, + 901 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/5a03f682cb75c507fc3328f474ef5b43bb37a28e452c288041f87e692bf0a39f.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Head ArchitectureA D {Bh} {Mh} B M
2fc2fc2fc3conv1fc
", + "bbox": [ + 540, + 89, + 854, + 128 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 1. Architectural instantiate of CondHead. fc and conv denote fully connected and convolutional networks, respectively, with a hidden dimension of 256. The digit means the number of layers.", + "bbox": [ + 498, + 140, + 893, + 196 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "strong object region representation. $\\mathbf{Mask}^X$ R-CNN [13] cannot be applied here as box annotation is not available. Shapemask [18] could be applied here, we compare to it for instance segmentation and evaluate an augmented version of CondHead with Shapemask.", + "bbox": [ + 498, + 220, + 893, + 297 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 309, + 633, + 325 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We study four questions in experiments. 1) Is CondHead able to improve the performance of open vocabulary object detection? is it efficient? 2) How does the quality of semantic-visual representation affects the performance? 3) How does CondHead improves the box regression and mask segmentation? 4) How does each component and hyperparameter take effect? Throughout the experiments, unless otherwise stated, we adopt the architectural instantiation shown in Table 1.", + "bbox": [ + 498, + 335, + 893, + 470 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets and Setup", + "text_level": 1, + "bbox": [ + 500, + 479, + 684, + 496 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We adopt object detection benchmark datasets COCO [19] and LVIS [12] to evaluate our method. Following prior works [11, 31], we manually split the datasets into base and target categories2. We also evaluate the generalization through inference on two other common object benchmarks, PASCAL VOC [8] and Objects365 [27].", + "bbox": [ + 498, + 503, + 892, + 608 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Setup Since the prior works mainly focus on the classification of novel object categories, we evaluate CondHead by adopting the prior state-of-the-art open vocabulary methods and treating them as baselines. We employ the following methods with increasing quality of semantic-visual alignment:", + "bbox": [ + 498, + 609, + 893, + 699 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- OVR-CNN [31] proposes to pre-train the multi-modal encoder with image-caption pair data. The pre-trained rich visual-semantic representation is then transferred to recognize the objects with open vocabulary.", + "- ViLD [11] achieves much higher performance than OVR-CNN by transferring the stronger visualsemantic representation of CLIP [22], which was pretrained on a much larger scale of image-caption data.", + "- RegionCLIP [32] further improves the alignment between the semantic and visual embeddings by conduct" + ], + "bbox": [ + 517, + 708, + 890, + 878 + ], + "page_idx": 4 + }, + { + "type": "page_footnote", + "text": "we use the pre-processed data from [31] and [32].", + "bbox": [ + 517, + 886, + 787, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "7055", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/8079b354d003ede105077328ed35d8c551420afcbfdd19606c9a09397131415a.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodObject DetectionInstance Segmentation
NovelBaseAllNovelBaseAll
DELO [34] (CVPR20)3.4113.813.0---
PL [23] (AAAI20)4.1235.927.9---
OVR-CNN [31] (CVPR21)22.846.039.9---
OVR-CNN*22.645.939.820.142.336.5
CondHead (Ours)24.0 (+1.4)46.5 (+0.6)40.6 (+0.8)21.4 (+1.3)42.9 (+0.6)37.3 (+0.8)
ViLD [16] (ICLR22)27.659.551.3---
ViLD*27.759.751.424.156.748.2
CondHead (Ours)29.8 (+2.1)60.8 (+1.1)52.7 (+1.3)25.9 (+1.8)57.9 (+1.2)49.5 (+1.3)
RegionCLIP [32] (CVPR22)31.457.150.4---
RegionCLIP*31.356.549.927.554.147.1
CondHead (Ours)33.7 (+2.4)58.0 (+1.5)51.7 (+1.8)29.7 (+2.2)55.8 (+1.7)49.0 (+1.9)
", + "bbox": [ + 125, + 88, + 844, + 296 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/363f43c9ff50a5dd64232bc3502df7259c2dd7e926d0e82a102ae82a4af0001d.jpg", + "table_caption": [ + "Table 2. Open vocabulary object detection results on COCO. * denotes our re-evaluation by adding the class-agnostic mask head." + ], + "table_footnote": [], + "table_body": "
MethodBackboneObject DetectionInstance Segmentation
APrAPcAPfAPAPrAPcAPfAP
SupervisedRN50-FPN3.322.534.523.34.222.832.721.8
Supervised +RFS [12]11.623.532.524.312.823.331.023.1
ViLD [11]16.726.534.227.816.624.630.325.5
ViLD*RN50-FPN16.627.033.027.516.925.029.125.2
CondHead (Ours)18.8 (+2.2)28.3 (+1.3)33.7 (+0.7)28.8 (+1.3)19.1 (+2.2)26.2 (+1.1)29.9 (+0.8)26.4 (+1.2)
RegionCLIP [32]17.127.434.028.2----
RegionCLIP*RN50-C417.027.234.328.217.426.031.626.7
CondHead (Ours)19.9 (+2.9)28.6 (+1.4)35.2 (+0.9)29.7 (+1.5)20.0 (+2.6)27.3 (+1.3)32.2 (+0.6)27.9 (+1.2)
ViLD [11]19.827.134.528.7----
ViLD*RN152-FPN20.027.034.028.519.825.132.126.9
CondHead (Ours)21.9 (+1.9)28.4 (+1.4)34.6 (+0.6)29.7 (+1.2)21.6 (+1.8)26.2 (+1.1)33.0 (+0.9)28.1 (+1.2)
RegionCLIP [32]22.032.136.932.3----
RegionCLIP*RN50x4-C422.131.837.032.221.830.235.130.7
CondHead (Ours)25.1 (+3.0)33.4 (+1.6)37.8 (+0.8)33.7 (+1.5)24.4 (+2.6)31.6 (+1.4)35.9 (+0.8)32.0 (+1.3)
", + "bbox": [ + 91, + 335, + 879, + 590 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. Open vocabulary object detection result on LVIS. * denotes our re-evaluation by adding the mask head. Supervised and Supervised + RFS are baselines that have access to the training data of target categories. RFS means the repeat factor sampling method [12].", + "bbox": [ + 75, + 599, + 893, + 630 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "ing region-wise pre-training. The improved representation further improves the recognition of novel objects and thus brings significant performance gain.", + "bbox": [ + 107, + 656, + 468, + 702 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Results", + "text_level": 1, + "bbox": [ + 76, + 710, + 171, + 724 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Results on COCO We re-evaluate the baseline methods by adding class-agnostic mask segmentation heads. Then we replace the bounding box regression and mask segmentation heads with the proposed CondHead and re-run the experiments. As shown in Table 2, our method surpasses all baseline methods, especially on the novel object category set. We observe the improvements grow with the three baselines, e.g., the object detection AP improvements are 1.4 with OVR-CNN, 2.1 with ViLD, and 2.4 with RegionCLIP. A similar observation holds for the instance mask segmentation task. This phenomenon verifies that CondHead can", + "bbox": [ + 75, + 734, + 470, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "leverage semantic embeddings that better align with visual representations to achieve stronger bounding box regression and mask segmentation ability.", + "bbox": [ + 496, + 656, + 890, + 700 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Results on LVIS Following the evaluation on COCO, We then evaluate CondHead on LVIS dataset. As shown in Table 3, our method brings about 2.0-3.0 absolute improvement on the target novel object categories (i.e., $\\mathrm{AP}_r$ ), for both bounding box detection and instance segmentation. The improvement holds for stronger backbone networks. We also observe a similar growth of improvement as COCO, i.e., the RegionCLIP method gains more improvement with CondHead than that of ViLD. This is likely because RegionCLIP provides stronger semantic-visual alignment with its region-wise pre-training, and thus CondHead better generalizes to the target categories.", + "bbox": [ + 496, + 702, + 892, + 883 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Cross Dataset Evaluation We further evaluate CondHead", + "bbox": [ + 500, + 885, + 890, + 898 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "7056", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/b5257e66e9f55273f7c79a42839ea74e8750386cded33de116f37a706fbf7901.jpg", + "image_caption": [ + "Figure 3. Qualitative comparison with baseline ViLD [11]. The bounding box regression and mask segmentation results are overlaid on the images (Yellow: Proposals. Green: Regressed bounding box. Blue: segmentation mask). Best viewed with zoom-in." + ], + "image_footnote": [], + "bbox": [ + 102, + 85, + 877, + 291 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/9d3ecc8fc32cd86fa2eb9248fe1dace2e9a02e3f40206d47a2f4de2551cfcb9f.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodPASCAL VOCCOCOObjects365
AP50AP75APAP50AP75APAP50AP75
Supervised78.549.046.567.650.925.638.628.0
ViLD [11]72.256.736.655.639.811.818.212.6
CondHead (Ours)74.6 (+2.4)58.5 (+1.8)39.1 (+2.5)59.1 (+3.5)42.2 (+2.4)13.2 (+1.4)20.4 (+2.2)14.2 (+1.6)
", + "bbox": [ + 142, + 340, + 831, + 428 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "with the cross dataset scenarios, where the open vocabulary object detector is expected to generalize on datasets that are different from the one used for training. We simply replace the vocabulary of a trained model with target dataset vocabulary to evaluate the performance. As shown in Table 4, we observe the improvement of CondHead transfers well. For example, 3.5, 2.4, and 2.2 absolute improvements in $\\mathrm{AP}_50$ for COCO, PASCAL VOC, and Objects365 respectively.", + "bbox": [ + 75, + 472, + 472, + 593 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Qualitative Results To demonstrate how CondHead improves bounding box regression and mask segmentation, we visualize the example qualitative results and compare that with the baseline method. As shown in Figure 3, CondHead is better at refining the proposal box and segmenting the target objects. For example, the baseline ViLD wrongly regresses the target chessboard to the table, while CondHead correctly predicts the chessboard (Figure 3 first column). In addition, CondHead better segments the challenging sweat pants while ViLD predicts inferior segmentation mask (Figure 3 last column).", + "bbox": [ + 75, + 594, + 468, + 760 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Analysis", + "text_level": 1, + "bbox": [ + 76, + 768, + 181, + 787 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The Effects of Language Descriptions We examine how CondHead is affected by language descriptions. As shown in Figure 4, when the input text is for a wrong category label, the box regression deteriorates, e.g., the proposal for sunflower is misguided to predict the carrot. On the other hand, it is interesting that when supplied with manually tuned descriptions, the detection and segmentation qual-", + "bbox": [ + 75, + 794, + 470, + 902 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ity is improved, e.g., when employing snowboard and motorcycle as descriptions, the snowmobile is predicted better. This intriguing observation suggests CondHead learns strong category-conditioned prediction and a better performance could be achieved by simply tuning the semantic descriptions during inference.", + "bbox": [ + 496, + 472, + 893, + 566 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/6fb4b1a0f9048136f339b7919d0fe840be2d7be850e1210e37320710d1ab6827.jpg", + "table_caption": [ + "Table 4. Cross-dataset evaluation results. The model is trained on LVIS and directly evaluated on the target datasets." + ], + "table_footnote": [], + "table_body": "
APbrrAPm
ViLD*16.616.9
ClassWise17.1 (+0.5)17.3 (+0.4)
CondHead18.8 (+2.2)19.1 (+2.2)
RegionCLIP*17.017.4
ClassWise17.3 (+0.3)17.9 (+0.5)
CondHead17.9 (+2.9)20.0 (+2.6)
", + "bbox": [ + 553, + 575, + 841, + 693 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 5. The results of naive class-wise transfer experiment (discussed in Section 1) on LVIS. $(\\mathrm{AP}_r^b$ : box AP, $\\mathrm{AP}_r^{m}$ : mask AP).", + "bbox": [ + 498, + 703, + 890, + 733 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Naive Class-wise Transfer We also establish another intuitive experiment as discussed in Section 1 to demonstrate the effectiveness of the proposed method. Since our intuition (Figure 1) is that the class-specific knowledge could be shared across categories, we train the class-wise heads on the base categories and then perform inference on the target categories in a semantically guided way. This is achieved by gathering the class-specific predictions with semantic similarity to the base categories. We use cosine similarity on the semantic embedding and select the category with the high-", + "bbox": [ + 496, + 750, + 893, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "7057", + "bbox": [ + 482, + 944, + 514, + 957 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/cfcf29db7d1427f74e5af8982e52ca697427eb9b99550df4412c98323ff382d9.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 99, + 88, + 251, + 196 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/fe8df09e92eaf2c295803411f1e29694f00cbfb35ff8c09774c3de0f2c598047.jpg", + "image_caption": [ + "A photo of car", + "A photo of car" + ], + "image_footnote": [], + "bbox": [ + 99, + 196, + 251, + 294 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/a12c4624403f721b0c4fda2e4c2580e2f9326b5f6c2ba70bc1d61e700c8815a7.jpg", + "image_caption": [ + "A flower photo of sunflow" + ], + "image_footnote": [], + "bbox": [ + 253, + 88, + 406, + 196 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/051fea0c8a128d8496b8d5212acb4e6ab047cb6bd125de515f7f173d124f882d.jpg", + "image_caption": [ + "A snowboard photo", + "Figure 4. Effect of tuning language descriptions. We select some intriguing examples for which tuning the input language descriptions could deteriorate or help object detection. Yellow: Proposals. Green: Regressed bounding box." + ], + "image_footnote": [], + "bbox": [ + 253, + 196, + 406, + 295 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "A photo of snowmobile", + "bbox": [ + 251, + 296, + 393, + 306 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/06f2e27c7f1dad5ff32277f5ada817b5805875611b4840f325b30a2bdbcdd6e3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 408, + 88, + 560, + 196 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0542d248cea4dd9e8b94e2518de0a8b131604e1f9c07bec344792ae745811f75.jpg", + "image_caption": [ + "er A photo of flower, a sun" + ], + "image_footnote": [], + "bbox": [ + 563, + 88, + 715, + 196 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/8351c28e794c42a5f36dfe8cf1507517043f8ae3a4a31af1f584f41028f1b351.jpg", + "image_caption": [ + "Ground Truth" + ], + "image_footnote": [], + "bbox": [ + 718, + 88, + 870, + 196 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "A photo of snowmobile", + "bbox": [ + 251, + 296, + 393, + 306 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/2c9aa922a2d0fe6ca74cfe9b7fcf94783dd76f7fe7a860944bde574918a0abd6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 408, + 213, + 560, + 294 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/9e514e38b0ab34426910bf9703376040443ad4edcca44f63dd64ec64414e3f5f.jpg", + "image_caption": [ + "A snowboard photo of motorcycle" + ], + "image_footnote": [], + "bbox": [ + 563, + 213, + 715, + 294 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/30b91c4314d581be481d1afa6d9996d86a4756298b7c0e1ea4625d26fd1e4ba0.jpg", + "image_caption": [ + "Ground Truth" + ], + "image_footnote": [], + "bbox": [ + 718, + 213, + 870, + 294 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "est similarity. As shown in Table 5, we train the ClassWise baseline on both ViLD and RegionCLIP. ClassWise method provides a small improvement to class-agnostic heads compared to CondHead, demonstrating that CondHead better generalizes to the target categories.", + "bbox": [ + 75, + 359, + 468, + 436 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/6de006e649d05821ccc2b5e272d2d6b3bc5636e378d5f2c1f9abe2b53e6edaa7.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
VSHCHCHSRSHCHCHS
APm r16.919.619.120.417.420.320.021.0
Δ-2.72.23.5-2.92.63.6
", + "bbox": [ + 107, + 450, + 439, + 506 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Augmenting CondHead with Shape Priors As discussed in Section 3.3, we compare to Shapemask [18] which introduces explicit class-agnostic shape priors. We then explore an augmented version of CondHead by integrating the Shapemask, the semantic embedding is utilized to estimate the initial shape prior, then the dynamic expert aggregation is conducted on the latter coarse mask estimation and refinement. Please refer to the supplementary for more implementation details. As shown in Table 6, Shapemask brings comparable improvement to CondHead, this is attributed to its strong prior and complex multi-stage mask refinement capability. Further integrating Shapemask into CondHead offers larger gain, e.g., 3.5 and 3.6 AP improvement for the two baselines. This implies CondHead can be improved with explicit shape priors.", + "bbox": [ + 75, + 580, + 468, + 808 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Component Analysis As shown in Table 7, we perform an ablation study by applying CondHead on the box and mask heads independently, we find they improve box AP and mask AP independently. We also observe CondHead introduces a very small computation overhead. To understand how the number of expert heads and aggregation", + "bbox": [ + 75, + 810, + 470, + 901 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/ec99e495c12ff702920a65cd8662c59a126d08d4c67873c61e8fe7f76aafadee.jpg", + "table_caption": [ + "Table 6. Results with Shapemask. V and R denote ViLD and RegionCLIP, SH, CH, and $\\mathrm{CH}_S$ denote Shapemask head, CondHead and CondHead augmented with Shapemask, respectively." + ], + "table_footnote": [], + "table_body": "
CondBoxCondMaskAPr bAPr mFLOPs
17.017.4193.5
✓19.617.8194.1
✓✓19.920.0195.8
", + "bbox": [ + 524, + 357, + 867, + 428 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 7. Component analysis. CondBox and CondMask mean applying CondHead on box and mask heads, respectively.", + "bbox": [ + 500, + 438, + 890, + 468 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/7b7d8771bc60b38f483550f60139b029cac4a9205f5268eaa2342fe489e228a5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 517, + 484, + 696, + 564 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/231e2d80b1cac3e4eaf28f708c58b3199fda0ffd423a029f354f5100bafbaa91.jpg", + "image_caption": [ + "Figure 5. Component analysis. Effect of expert number, $\\lambda$ and $\\mu$ ." + ], + "image_footnote": [], + "bbox": [ + 700, + 487, + 870, + 568 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "hyper-parameter affects CondHead, a detailed analysis is conducted as shown in Figure 5. We find the performance gain is diminishing beyond 40 expert heads and the hyperparameters $\\lambda$ and $\\mu$ work best at between 0.5 and 0.7. More analysis experiments are presented in the supplementary.", + "bbox": [ + 496, + 614, + 890, + 691 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 708, + 617, + 723 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We introduce a conditionally parameterized neural network design to improve open vocabulary bounding box regression and mask segmentation, which is not explored before. Specifically, we leverage the pre-trained semantic embedding to guide the parameterization of the box and mask heads. The semantic embedding is strongly aligned with visual representation and thus provides effective cues for refining the bounding box and segmenting the objects. Our method named CondHead is extensively validated on different datasets and setups. We hope our findings provide insights for future work on open vocabulary detection.", + "bbox": [ + 496, + 734, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "7058", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, and Ajay Divakaran. Zero-shot object detection. In Proceedings of the European Conference on Computer Vision (ECCV), pages 384-400, 2018. 1", + "[2] Zhaowei Cai and Nuno Vasconcelos. Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6154-6162, 2018. 2", + "[3] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. arXiv preprint arXiv:2005.12872, 2020. 2, 3", + "[4] Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dólár, and C Lawrence Zitnick. Microsoft coco captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325, 2015. 3", + "[5] Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Lu Yuan, and Zicheng Liu. Dynamic convolution: Attention over convolution kernels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11030-11039, 2020. 2, 3, 5", + "[6] Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764-773, 2017. 2, 3", + "[7] Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, and Guoqi Li. Learning to prompt for open-vocabulary object detection with vision-language model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14084-14093, 2022. 1, 2, 3", + "[8] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010. 5", + "[9] Mingfei Gao, Chen Xing, Juan Carlos Niebles, Junnan Li, Ran Xu, Wenhao Liu, and Caiming Xiong. Open vocabulary object detection with pseudo bounding-box labels. 1, 3", + "[10] Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440-1448, 2015. 2", + "[11] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. arXiv preprint arXiv:2104.13921, 2021. 1, 2, 3, 5, 6, 7", + "[12] Agrim Gupta, Piotr Dollar, and Ross Girshick. LVIS: A dataset for large vocabulary instance segmentation. In CVPR, 2019. 2, 5, 6", + "[13] Ronghang Hu, Piotr Dólar, Kaiming He, Trevor Darrell, and Ross Girshick. Learning to segment every thing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4233-4241, 2018. 5", + "[14] Dat Huynh, Jason Kuen, Zhe Lin, Jiumiang Gu, and Ehsan Elhamifar. Open-vocabulary instance segmentation via robust cross-modal pseudo-labeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7020-7031, 2022. 1, 3" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[15] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. Advances in neural information processing systems, 28, 2015. 2, 3", + "[16] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 3, 6", + "[17] Xu Jia, Bert De Brabandere, Tinne Tuytelaars, and Luc V Gool. Dynamic filter networks. Advances in neural information processing systems, 29, 2016. 2, 3", + "[18] Weicheng Kuo, Anelia Angelova, Jitendra Malik, and Tsung-Yi Lin. Shapemask: Learning to segment novel objects by refining shape priors. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9207-9216, 2019. 5, 8", + "[19] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 2, 5", + "[20] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. Ssd: Single shot multibox detector. In European conference on computer vision, pages 21-37. Springer, 2016. 2", + "[21] Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, et al. Simple open-vocabulary object detection with vision transformers. arXiv preprint arXiv:2205.06230, 2022. 1, 3", + "[22] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 3, 5", + "[23] Shafin Rahman, Salman Khan, and Nick Barnes. Improved visual-semantic alignment for zero-shot object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11932–11939, 2020. 1, 6", + "[24] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779-788, 2016. 2", + "[25] Joseph Redmon and Ali Farhadi. Yolo9000: better, faster, stronger. arXiv preprint, 2017. 2", + "[26] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91-99, 2015. 2, 3", + "[27] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 8430-8439, 2019. 5" + ], + "bbox": [ + 501, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "7059", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[28] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2556-2565, 2018. 3", + "[29] Brandon Yang, Gabriel Bender, Quoc V Le, and Jiquan Ngiam. Condconv: Conditionally parameterized convolutions for efficient inference. Advances in Neural Information Processing Systems, 32, 2019. 2, 3, 5", + "[30] Yuhang Zang, Wei Li, Kaiyang Zhou, Chen Huang, and Chen Change Loy. Open-vocabulary detr with conditional matching. arXiv preprint arXiv:2203.11876, 2022. 1, 3", + "[31] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14393-14402, 2021. 1, 2, 3, 5, 6", + "[32] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al. Regionclip: Region-based language-image pretraining. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16793-16803, 2022. 1, 2, 3, 5, 6", + "[33] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9):2337-2348, 2022. 3", + "[34] Pengkai Zhu, Hanxiao Wang, and Venkatesh Saligrama. Don't even look once: Synthesizing features for zero-shot detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11693-11702, 2020. 6", + "[35] Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. Deformable convnets v2: More deformable, better results. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9308-9316, 2019. 3", + "[36] Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transformers for end-to-end object detection. The International Conference on Learning Representations, 2020. 2" + ], + "bbox": [ + 78, + 90, + 468, + 667 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "7060", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/5ac75f03-1745-4705-ac4d-6c3f7fb370ae_model.json b/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/5ac75f03-1745-4705-ac4d-6c3f7fb370ae_model.json new file mode 100644 index 0000000000000000000000000000000000000000..20b225e14b9fddbb571355522ac43ccca866cfc5 --- /dev/null +++ b/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/5ac75f03-1745-4705-ac4d-6c3f7fb370ae_model.json @@ -0,0 +1,2112 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.126, + 0.131, + 0.844, + 0.153 + ], + "angle": 0, + "content": "Learning to Detect and Segment for Open Vocabulary Object Detection" + }, + { + "type": "text", + "bbox": [ + 0.41, + 0.182, + 0.562, + 0.217 + ], + "angle": 0, + "content": "Tao Wang Sichuan University" + }, + { + "type": "text", + "bbox": [ + 0.406, + 0.22, + 0.56, + 0.234 + ], + "angle": 0, + "content": "twangnh@gmail.com" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.269, + 0.314, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.302, + 0.474, + 0.695 + ], + "angle": 0, + "content": "Open vocabulary object detection has been greatly advanced by the recent development of vision-language pretrained model, which helps recognize novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask prediction. In this work, we propose CondHead, a principled dynamic network design to better generalize the box regression and mask segmentation for open vocabulary setting. The core idea is to conditionally parameterize the network heads on semantic embedding and thus the model is guided with class-specific knowledge to better detect novel categories. Specifically, CondHead is composed of two streams of network heads, the dynamically aggregated head and dynamically generated head. The former is instantiated with a set of static heads that are conditionally aggregated, these heads are optimized as experts and are expected to learn sophisticated prediction. The latter is instantiated with dynamically generated parameters and encodes general class-specific information. With such a conditional design, the detection model is bridged by the semantic embedding to offer strongly generalizable class-wise box and mask prediction. Our method brings significant improvement to the state-of-the-art open vocabulary object detection methods with very minor overhead, e.g., it surpasses a RegionClip model by 3.0 detection AP on novel categories, with only \\(1.1\\%\\) more computation." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.727, + 0.21, + 0.743 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.753, + 0.47, + 0.875 + ], + "angle": 0, + "content": "Given the semantic object categories of interest, object detection aims at localizing each object instance from the input images. The prior research efforts mainly focus on the close-set setting, where the images containing the interested object categories are annotated and used to train a detector. The obtained detector only recognizes object categories that are annotated in the training set. In such a setting, more data needs to be collected and annotated if novel category1 needs" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.27, + 0.891, + 0.507 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.528, + 0.895, + 0.64 + ], + "angle": 0, + "content": "Figure 1. Illustration of our main intuition. Given the object proposals, the bounding box regression and mask segmentation learned from some object categories could generalize to the target category. For example, the knowledge learned from a chicken could help detect and segment the long thin feet and the small head of an ibis (upper row). Similarly for the hairbrush, the knowledge learned from the toothbrush could better handle the extreme aspect ratio and occlusion from the hand (lower row)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.672, + 0.893, + 0.717 + ], + "angle": 0, + "content": "to be detected. However, data collection and annotation are very costly for object detection, which raises a significant challenge for traditional object detection methods." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.895, + 0.902 + ], + "angle": 0, + "content": "To address the challenge, the open vocabulary object detection methods are widely explored recently, these methods [1, 7, 9, 11, 14, 21, 23, 30-32] aim at generalizing object detection on novel categories by only training on a set of labeled categories. The core of these methods is transferring the strong image-text aligned features [16, 22] to classify objects of novel categories. To achieve bounding box regression and mask segmentation on novel categories, they simply employ the class-agnostic network heads. Although class agnostic heads can be readily applied to novel target object categories, they offer limited capacity to learn category-specific knowledge like object" + }, + { + "type": "page_footnote", + "bbox": [ + 0.095, + 0.887, + 0.403, + 0.901 + ], + "angle": 0, + "content": "we treat category and class interchangeably in this paper" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "7051" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.089, + 0.891, + 0.236 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.244, + 0.894, + 0.288 + ], + "angle": 0, + "content": "Figure 2. Overview of CondHead. To detect objects of novel categories, we aim at conditionally parameterizing the bounding box regression and mask segmentation based on the semantic embedding, which is strongly correlated with the visual feature and provides effective class-specific cues to refine the box and predict the mask." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.313, + 0.473, + 0.525 + ], + "angle": 0, + "content": "shape, and thus provide sub-optimal performance. On the other hand, training class-wise heads is not feasible as we do not have bounding box and mask annotation for the target categories. As shown in Figure 1, our intuition is that the class-wise knowledge could naturally generalize across object categories, and may be leveraged to achieve much higher quality box regression and mask segmentation on the target categories, in a category-specific manner. However, we find a brute force way of training class-wise heads on the base categories and manually gathering the class-specific prediction with closet semantic similarity during inference provides limited gain. The reason is that there still remains gap between the base and target categories, such as appearance and context." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.531, + 0.473, + 0.849 + ], + "angle": 0, + "content": "Motivated by the strong semantic-visual aligned representation [7, 11, 31, 32] in open vocabulary object detection, we propose to exploit the semantic embedding as a conditional prior to parameterize class-wise bounding box regression and mask segmentation. Such conditioning is learned on base categories and easily generalizes to novel categories with the semantic embedding. Our method, named CondHead, is based on dynamic parameter generation of neural networks [5, 6, 15, 17, 29]. To achieve strong efficiency, it exploits both large complex network head for their representative power and small light network head for their efficiency. The complex head is employed by conditional weight aggregation over a set of static heads. The light head is employed by dynamically basing its parameters on the semantic embedding. The final prediction is obtained by combining the predictions from the two stream results. Through optimization on the base categories, the set of static heads are expected to learn sophisticated expert knowledge to cope with complex shapes and appearance, the dynamic head is endowed with general class-specific knowledge such as color and context." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.856, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Our CondHead is flexible regarding the choice of semantic-visual representation. The stronger quality of the aligned representation is expected to bring higher perfor" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.313, + 0.892, + 0.494 + ], + "angle": 0, + "content": "mance, as the conditional knowledge from the semantic embedding could generalize better to the target visual features. This is demonstrated by the clear growth of improvement over three baselines with increasing quality of pretrained semantic-visual encoder networks, OVR-CNN [31], ViLD [11] and RegionCLIP [32], on both COCO [19] and LVIS [12] datasets. Remarkably, CondHead brings an average 2.8 improvement w.r.t both box and mask AP for the strong RegionCLIP baseline, with only about \\(1\\%\\) more computation. We also demonstrate intriguing qualitative results, showing how the semantic conditioning positively affects the regression and segmentation tasks." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.495, + 0.892, + 0.661 + ], + "angle": 0, + "content": "Our contributions are three-fold. 1) To the best of our knowledge, we are the first to leverage semantic-visual aligned representation for open vocabulary box regression and mask segmentation. 2) We design a differentiable semantic-conditioned head design to efficiently bridge the strong category-specific prediction learned on base categories to the target novel categories. 3) We extensively validate the proposed method on various benchmark datasets and conduct thorough ablation and analysis to understand how the semantic conditioning helps detect and segment the novel categories." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.677, + 0.642, + 0.693 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Detecting Objects with Zero-shot and Open Vocabulary Setting Despite the remarkable success of fully-supervised object detection [2, 3, 10, 20, 24-26, 36], the instance-level annotation for novel object categories is costly. Recent years, open vocabulary object detection emerges as an effective solution, which aims at detecting novel categories without corresponding annotation. The representative methods employ region-based alignment, e.g., OVR-CNN [31], ViLD [11], RegionCLIP [32] and DetPro [7]. They are based on the two-stage object detection framework [26] and aim to match the object region feature with the generalizable semantic category embedding. They mainly differ in the learning of semantic-visual representa" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7052" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.473, + 0.439 + ], + "angle": 0, + "content": "tion. Specifically, OVR-CNN [31] optimizes the semantic-visual grounding objective based multimodal transformer and applies the learned vision to language projection to facilitate novel category recognition. ViLD [11] transfers the stronger visual-semantic representation from large-scale vision-language pre-training [22]. Based on ViLD, Det-Pro [7] introduces a learnable prompt mechanism [33] to enhance the semantic representation and achieve higher performance. RegionCLIP [32] instead focuses on more effective region-wise pre-training to obtain more discriminative representation. In addition to pursuing more stronger region feature alignment, concurrent works [9, 14, 21, 30] explore other aspects of open vocabulary detection. For example, Gao et al. [9] and Huynh et al. [14] develop pseudo-labeling approach to generate pseudo ground-truth labels for object detection and instance segmentation. Instead of labeling perspective, some works focus on transformer architecture design for open vocabulary detection. OV-DETR [30] proposes a binary matching objective to enable query matching with novel objects on the end-to-end DETR framework DETR [3]. Minderer et al. [21] directly transfers pre-trained vision transformers to simplify open vocabulary detection." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.441, + 0.472, + 0.532 + ], + "angle": 0, + "content": "The prior works generally focus on the recognition of open vocabulary objects, while the localization or segmentation are typically conducted in a class-agnostic fashion. In this work, we explore this orthogonal perspective and aim at developing effective methods for more accurate box regression and mask segmentation." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.536, + 0.473, + 0.884 + ], + "angle": 0, + "content": "Dynamic Network Design Traditional neural network architectures employ static weights, i.e., the weights are independent of input signal and are fixed after training. Such static neural networks are limited at flexibility, in dealing with diverse input signals like images. Some previous works explore the idea of dynamic instantiation of networks to improve the flexibility. For example, Jia et al. [17] proposes to actively predict the convolution filters based on the input. In addition to explicit network parameter generation, Jaderberg et al. [15] introduces a dynamic parametric transformation (STN) to adaptively transform the image feature map, recovering affine distortions in the input and thus facilitating recognition. STN [15] operates on global image space, the Deformable Convolution [6,35] instead works in local scale. It learns spatial offsets to adjust the sampling locations of convolution kernels. The dynamic designs like STN [15] and Deformable Convolution [6,35] introduce explicit control over the networks. Some recent works introduce implicit dynamics to achieve strong network capacity. Notably, [5,29] develop conditionally parameterized convolution filters to increase the network capacity. Specifically, the convolution kernel is instantiated by dynamically aggregating a set of base filters." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.886, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Our work is related to the above dynamic network ar" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.184 + ], + "angle": 0, + "content": "chitecture designs, in that we also aim at achieving input-conditioned processing, to better adapt to the input data. Unlike prior works, we focus on the object instance-level design of dynamic processing. Moreover, instead of operating on the same visual signal, we instead introduce semantic signal for the dynamic network design." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.196, + 0.637, + 0.214 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.221, + 0.684, + 0.236 + ], + "angle": 0, + "content": "3.1. Problem Definition" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.244, + 0.892, + 0.335 + ], + "angle": 0, + "content": "Given the bounding box annotation on a set of base object categories \\(\\mathbb{F} = \\{C_1,C_2,\\dots C_N\\}\\), open vocabulary object detection aims at training on the base data and generalizing to any open set of target object categories \\(\\mathbb{F}^* = \\{C_1,C_2,\\dots C_M\\}\\). Open vocabulary instance segmentation is also expected if the mask annotation is given." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.336, + 0.893, + 0.501 + ], + "angle": 0, + "content": "The state-of-the-art two-stage methods [7, 11, 31, 32] can be simplified as a region-semantic alignment framework. The model is initialized with an image encoder \\((\\mathcal{T}_i)\\) and a language encoder \\((\\mathcal{T}_l)\\), which extract image feature and semantic embedding: \\(\\mathbf{F} = \\mathcal{T}_i(I)\\), \\(\\mathbf{s}_n = \\mathcal{T}_l(C_n)\\), here \\(I\\) denotes the input image. These encoders [16, 22] are typically pre-trained on large-scale image-caption [4, 22, 28] data and thus extract strongly correlated visual-semantic representations. With region proposal network [26] trained on the base categories to extract object proposals, the open-vocabulary detection is trained in a region-wise manner." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.502, + 0.892, + 0.577 + ], + "angle": 0, + "content": "Concretely, given an object proposal bounding box \\(\\mathbf{p} = (x_1, y_1, x_2, y_2)\\), feature pooling is conducted on the image feature map \\(\\mathbf{F}\\) to obtain the region feature \\(\\mathbf{f}\\). The object proposal box is then matched with the ground-truth object instance set \\(\\aleph\\) w.r.t IOU (Intersection Over Union) metric:" + }, + { + "type": "equation", + "bbox": [ + 0.599, + 0.589, + 0.891, + 0.617 + ], + "angle": 0, + "content": "\\[\n(\\mathbf {b}, c) = \\underset {(\\mathbf {b} ^ {\\prime}, c ^ {\\prime}) \\in \\aleph} {\\arg \\max } \\operatorname {I O U} (\\mathbf {p}, \\mathbf {b} ^ {\\prime}) \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.628, + 0.892, + 0.719 + ], + "angle": 0, + "content": "where \\(\\mathbf{b} = (x_1^*, y_1^*, x_2^*, y_2^*)\\) means the obtained ground-truth bounding box and \\(c\\) is corresponding object category. Then, optimization is conducted to further minimize the alignment between ground-truth semantic embedding \\(\\mathbf{s}_c\\) and region feature \\(\\mathbf{f}\\), e.g., through similarity-based cross entropy loss [11]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.719, + 0.892, + 0.765 + ], + "angle": 0, + "content": "To generalize the bounding box regression, prior works employ class-agnostic network head \\(\\mathcal{B}\\) with parameter \\(\\theta\\) and optimize it on the base category data:" + }, + { + "type": "equation", + "bbox": [ + 0.573, + 0.776, + 0.891, + 0.798 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\theta} \\mathcal {L} _ {\\mathcal {B}} \\left(\\mathcal {B} _ {\\theta}, \\mathcal {X}\\right) = \\mathcal {L} _ {\\mathcal {B}} \\left(\\mathcal {B} _ {\\theta} (\\boldsymbol {p}, \\boldsymbol {f}), \\boldsymbol {b}\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.892, + 0.87 + ], + "angle": 0, + "content": "where \\(\\mathcal{X} = (\\mathbf{p},\\mathbf{f},\\mathbf{b})\\) denotes the tuple data of proposal, region feature and bounding box. \\(\\mathcal{L}_{\\mathcal{B}}\\) is typically defined as L1 or L2 error between the regressed bounding box and ground-truth box." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "To learn instance segmentation, the mask region feature \\(\\mathbf{v}\\) is pooled with the refined bounding box from the box" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7053" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.122 + ], + "angle": 0, + "content": "head, Then the class-agnostic mask segmentation network head \\(\\mathcal{M}\\) with parameter \\(\\vartheta\\) is trained by:" + }, + { + "type": "equation", + "bbox": [ + 0.14, + 0.134, + 0.469, + 0.156 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\vartheta} \\mathcal {L} _ {\\mathcal {M}} \\left(\\mathcal {M} _ {\\vartheta}, \\mathcal {Y}\\right) = \\mathcal {L} _ {\\mathcal {M}} \\left(\\mathcal {M} _ {\\vartheta} (\\boldsymbol {v}), \\boldsymbol {m}\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.167, + 0.469, + 0.228 + ], + "angle": 0, + "content": "where \\(\\mathbf{m}\\) denotes the corresponding ground-truth mask segmentation and \\(\\mathcal{V} = (\\mathbf{v},\\mathbf{m})\\) denotes the paired region feature and mask. \\(\\mathcal{L}_{\\mathcal{M}}\\) is typically defined as pixel-wise segmentation error between the predicted mask and ground-truth." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.228, + 0.469, + 0.35 + ], + "angle": 0, + "content": "Although the class-agnostic bounding box regression and mask segmentation heads readily generalize to the target novel object categories, they offer sub-optimal results due to the less representative capability. Since the semantic embeddings are strongly correlated with the visual features, we propose to achieve generalizable class-wise prediction by conditioning the network parameters on the base category semantic embeddings during training:" + }, + { + "type": "equation", + "bbox": [ + 0.208, + 0.377, + 0.469, + 0.399 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\alpha} \\mathcal {L} _ {\\mathcal {B}} \\left(\\mathcal {B} _ {\\theta \\left(\\mathbf {s} _ {c}\\right)}, \\mathcal {X}\\right) \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.201, + 0.424, + 0.469, + 0.446 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\beta} \\mathcal {L} _ {\\mathcal {M}} \\left(\\mathcal {M} _ {\\vartheta \\left(\\mathbf {s} _ {c}\\right)}, \\mathcal {Y}\\right) \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.458, + 0.469, + 0.535 + ], + "angle": 0, + "content": "Where \\(\\alpha\\) and \\(\\beta\\) are parameters of the conditioning function \\(\\theta(\\cdot)\\) and \\(\\vartheta(\\cdot)\\). Through this approach, the model learns to generalize regression and segmentation to novel categories in a class-wise manner, by predicting corresponding parameters \\(\\theta(\\mathbf{s}_m)\\) and \\(\\vartheta(\\mathbf{s}_m)\\) during inference." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.543, + 0.297, + 0.558 + ], + "angle": 0, + "content": "3.2. CondHead Formulation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.567, + 0.469, + 0.779 + ], + "angle": 0, + "content": "Our proposed CondHead aims to bridge the gap between base and target categories, with the semantic conditioning mechanism. We instantiate the conditioning function with neural networks to achieve flexible optimization and inference. To construct such conditioning framework, we observe the challenges in network capacity: 1) More complex head architecture (e.g., head with several hidden layers) offers stronger representative ability, but it requires large number of parameters, which are not efficient to generate and hard to optimize. For example, a hidden fully layer with input and output dimension of 256 involves more than 60k scalar weights. 2) Less complex head architecture (e.g., head with a single layer) is easier to optimize and can be efficiently generated, but provides limited capacity." + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.779, + 0.469, + 0.84 + ], + "angle": 0, + "content": "To address the challenge, we design a dual conditioning framework, to leverage both the complex heads and light heads. The framework is composed of dynamically aggregated head and dynamically generated head." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.469, + 0.902 + ], + "angle": 0, + "content": "Dynamically Aggregated Head To leverage the large network capacity of complex heads, we propose to generate dynamic weights to aggregate a set of complex heads \\(\\{\\mathcal{B}_1,\\mathcal{B}_2,\\dots,\\mathcal{B}_H\\}\\), with associated parameters" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.153 + ], + "angle": 0, + "content": "\\(\\{\\theta_{1}, \\theta_{2}, \\dots, \\theta_{H}\\}\\). These heads act as experts that are good at refining bounding box and predicting mask segmentation for objects categories with certain shapes or appearance. Specifically, the aggregation weights is generated by:" + }, + { + "type": "equation", + "bbox": [ + 0.653, + 0.165, + 0.891, + 0.181 + ], + "angle": 0, + "content": "\\[\n\\mathbf {w} = \\mathcal {A} _ {\\phi} (\\mathbf {s} _ {c}) \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.194, + 0.892, + 0.254 + ], + "angle": 0, + "content": "where \\(\\mathbf{w} \\in \\mathbb{R}^H\\), and \\(\\mathcal{A}\\) is a small neural network with parameter \\(\\phi\\). To regularize the weight aggregation space as a convex hull for optimization, the weight \\(\\mathbf{w}\\) is then normalized with Softmax function:" + }, + { + "type": "equation", + "bbox": [ + 0.63, + 0.263, + 0.891, + 0.299 + ], + "angle": 0, + "content": "\\[\nw _ {h} = \\frac {e ^ {w _ {h}}}{\\sum_ {h ^ {\\prime} = 1} ^ {H} e ^ {w _ {h ^ {\\prime}}}} \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.308, + 0.892, + 0.368 + ], + "angle": 0, + "content": "which ensures the weights sums to 1 and each element is larger than 0. With the normalized aggregation weight, the expert heads are then combined to a single head \\(\\hat{\\mathcal{B}}\\) with parameter \\(\\hat{\\theta}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.639, + 0.378, + 0.891, + 0.42 + ], + "angle": 0, + "content": "\\[\n\\hat {\\theta} = \\sum_ {h = 1} ^ {H} w _ {h} * \\theta_ {h} \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.43, + 0.892, + 0.552 + ], + "angle": 0, + "content": "Similarly, for a set of mask heads \\(\\{\\mathcal{M}_1,\\mathcal{M}_2,\\dots,\\mathcal{M}_H\\}\\), with associated parameters \\(\\{\\vartheta_1,\\vartheta_2,\\dots,\\vartheta_H\\}\\), the weight generation and aggregation can be conducted to obtain a single mask head \\(\\hat{\\mathcal{M}}\\) with parameter \\(\\hat{\\vartheta}\\). When the dynamic aggregation is applied to both box regression and mask segmentation, separate weight generation networks are used, as box regression and mask segmentation may not share the same attention across the expert models." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.553, + 0.892, + 0.628 + ], + "angle": 0, + "content": "Dynamically Generated Head To introduce stronger conditioning on the semantic embedding, we propose to directly generate the network parameter. With bounding box regression head as an example, the parameter is first generated as:" + }, + { + "type": "equation", + "bbox": [ + 0.654, + 0.639, + 0.891, + 0.658 + ], + "angle": 0, + "content": "\\[\n\\dot {\\theta} = \\mathcal {D} _ {\\varphi} (\\mathbf {s} _ {c}) \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.671, + 0.892, + 0.748 + ], + "angle": 0, + "content": "the parameter \\(\\dot{\\theta}\\) is used to instantiate a new box head \\(\\dot{B}\\). Since \\(\\dot{\\theta}\\) is directly generated from the semantic embedding, it easily encodes general class-specific information like aspect ratio and color. Similarly, a dynamic mask segmentation head \\(\\dot{\\mathcal{M}}\\) can be obtained with generated parameter \\(\\dot{\\vartheta}\\)." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.749, + 0.892, + 0.81 + ], + "angle": 0, + "content": "Combining the Dynamic Predictions We then combine the above dynamically aggregated heads \\(\\hat{B},\\hat{\\mathcal{M}}\\) and dynamically generated heads \\(\\hat{B},\\hat{\\mathcal{M}}\\) to obtain the final result. We consider simple weighted averaging on their prediction:" + }, + { + "type": "equation", + "bbox": [ + 0.57, + 0.836, + 0.891, + 0.854 + ], + "angle": 0, + "content": "\\[\n\\bar {\\mathcal {B}} _ {\\theta \\left(\\mathbf {s} _ {c}\\right)} (\\cdot) = \\lambda * \\hat {\\mathcal {B}} (\\cdot) + (1 - \\lambda) * \\dot {\\mathcal {B}} (\\cdot) \\tag {10}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.557, + 0.879, + 0.891, + 0.898 + ], + "angle": 0, + "content": "\\[\n\\overline {{\\mathcal {M}}} _ {\\vartheta \\left(\\mathbf {s} _ {c}\\right)} (\\cdot) = \\mu * \\hat {\\mathcal {M}} (\\cdot) + (1 - \\mu) * \\dot {\\mathcal {M}} (\\cdot) \\tag {11}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7054" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.091, + 0.47, + 0.122 + ], + "angle": 0, + "content": "\\(\\overline{\\mathcal{B}}\\) and \\(\\overline{\\mathcal{M}}\\) are for the box regression and mask segmentation head respectively. \\(\\lambda\\) and \\(\\mu\\) are hyper-parameters." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.122, + 0.469, + 0.184 + ], + "angle": 0, + "content": "After optimization on the base category data with equation 4 and equation 5, \\(\\overline{B}\\) and \\(\\overline{\\mathcal{M}}\\) are employed during inference on the target categories by conditioning on the target semantic embedding as \\(\\overline{\\mathcal{B}}_{\\theta (\\mathbf{s}_m)}(\\cdot)\\) and \\(\\overline{\\mathcal{M}}_{\\vartheta (\\mathbf{s}_m)}(\\cdot)\\)" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.185, + 0.471, + 0.336 + ], + "angle": 0, + "content": "Optimizing the Expert Heads with Temperature Annealing During the early optimization stage, the expert heads within the dynamically aggregated head all require training signals to initiate the learning of basic regression and segmentation capability. While in the later optimization stage, the experts are expected to provide specialized capability in dealing with different objects. We thus facilitate the optimization of the expert heads by a temperature annealing Softmax strategy. Concretely, the normalization of aggregation weights is performed with temperature \\(\\tau\\)" + }, + { + "type": "equation", + "bbox": [ + 0.2, + 0.347, + 0.47, + 0.387 + ], + "angle": 0, + "content": "\\[\nw _ {h} = \\frac {e ^ {w _ {h} / \\tau}}{\\sum_ {h ^ {\\prime} = 1} ^ {H} e ^ {w _ {h ^ {\\prime}} / \\tau}} \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.395, + 0.47, + 0.472 + ], + "angle": 0, + "content": "During the early training stages, the temperature is set as a large value and with the progress of training to provide nearly uniform gradients for all the expert heads, the temperature is gradually annealed to a smaller value to achieve the desired specialized learning." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.481, + 0.303, + 0.497 + ], + "angle": 0, + "content": "3.3. Relation to Close Works" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.507, + 0.47, + 0.703 + ], + "angle": 0, + "content": "Conditionally Parameterized Convolution CondHead is closely related to Dynamic Convolution [5] and CondConv [29]. They differ from CondHead in motivation and application. They aim to improve the representation capacity of convolution kernels through conditional parameterization, the conditioning is performed on the visual features of each network layer. Moreover, they are applied to the image recognition task. While CondHead aims to bridge the perception gap between the base training object categories and target novel object categories by conditioning on the strongly visual-aligned semantic embedding. CondHead is applied to the more challenging bounding box regression and mask segmentation task, which is not explored before." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.705, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Partially Supervised Instance Segmentation Recent works [13, 18] explore partially supervised instance segmentation, where the base categories have both box and mask annotation while the novel categories have only box annotation. \\(\\mathbf{Mask}^{X}\\) R-CNN [13] propose a parameterized transformation function to transfer the box regression weights to mask segmentation weights. Shapemask [18] explores strong shape priors to achieve better class-agnostic object segmentation, which generalizes better to novel categories without mask annotation. Our focused open vocabulary object detection and instance segmentation are much more challenging than the partially supervised instance segmentation, which is based on good detection quality and" + }, + { + "type": "table", + "bbox": [ + 0.541, + 0.09, + 0.855, + 0.13 + ], + "angle": 0, + "content": "
Head ArchitectureA D {Bh} {Mh} B M
2fc2fc2fc3conv1fc
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.141, + 0.894, + 0.198 + ], + "angle": 0, + "content": "Table 1. Architectural instantiate of CondHead. fc and conv denote fully connected and convolutional networks, respectively, with a hidden dimension of 256. The digit means the number of layers." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.221, + 0.894, + 0.299 + ], + "angle": 0, + "content": "strong object region representation. \\(\\mathbf{Mask}^X\\) R-CNN [13] cannot be applied here as box annotation is not available. Shapemask [18] could be applied here, we compare to it for instance segmentation and evaluate an augmented version of CondHead with Shapemask." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.31, + 0.634, + 0.327 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.336, + 0.894, + 0.471 + ], + "angle": 0, + "content": "We study four questions in experiments. 1) Is CondHead able to improve the performance of open vocabulary object detection? is it efficient? 2) How does the quality of semantic-visual representation affects the performance? 3) How does CondHead improves the box regression and mask segmentation? 4) How does each component and hyperparameter take effect? Throughout the experiments, unless otherwise stated, we adopt the architectural instantiation shown in Table 1." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.48, + 0.685, + 0.497 + ], + "angle": 0, + "content": "4.1. Datasets and Setup" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.504, + 0.893, + 0.609 + ], + "angle": 0, + "content": "We adopt object detection benchmark datasets COCO [19] and LVIS [12] to evaluate our method. Following prior works [11, 31], we manually split the datasets into base and target categories2. We also evaluate the generalization through inference on two other common object benchmarks, PASCAL VOC [8] and Objects365 [27]." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.61, + 0.894, + 0.7 + ], + "angle": 0, + "content": "Setup Since the prior works mainly focus on the classification of novel object categories, we evaluate CondHead by adopting the prior state-of-the-art open vocabulary methods and treating them as baselines. We employ the following methods with increasing quality of semantic-visual alignment:" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.709, + 0.892, + 0.771 + ], + "angle": 0, + "content": "- OVR-CNN [31] proposes to pre-train the multi-modal encoder with image-caption pair data. The pre-trained rich visual-semantic representation is then transferred to recognize the objects with open vocabulary." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.779, + 0.892, + 0.84 + ], + "angle": 0, + "content": "- ViLD [11] achieves much higher performance than OVR-CNN by transferring the stronger visualsemantic representation of CLIP [22], which was pretrained on a much larger scale of image-caption data." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.848, + 0.892, + 0.88 + ], + "angle": 0, + "content": "- RegionCLIP [32] further improves the alignment between the semantic and visual embeddings by conduct" + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.709, + 0.892, + 0.88 + ], + "angle": 0, + "content": null + }, + { + "type": "page_footnote", + "bbox": [ + 0.518, + 0.887, + 0.789, + 0.901 + ], + "angle": 0, + "content": "we use the pre-processed data from [31] and [32]." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7055" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.126, + 0.089, + 0.846, + 0.297 + ], + "angle": 0, + "content": "
MethodObject DetectionInstance Segmentation
NovelBaseAllNovelBaseAll
DELO [34] (CVPR20)3.4113.813.0---
PL [23] (AAAI20)4.1235.927.9---
OVR-CNN [31] (CVPR21)22.846.039.9---
OVR-CNN*22.645.939.820.142.336.5
CondHead (Ours)24.0 (+1.4)46.5 (+0.6)40.6 (+0.8)21.4 (+1.3)42.9 (+0.6)37.3 (+0.8)
ViLD [16] (ICLR22)27.659.551.3---
ViLD*27.759.751.424.156.748.2
CondHead (Ours)29.8 (+2.1)60.8 (+1.1)52.7 (+1.3)25.9 (+1.8)57.9 (+1.2)49.5 (+1.3)
RegionCLIP [32] (CVPR22)31.457.150.4---
RegionCLIP*31.356.549.927.554.147.1
CondHead (Ours)33.7 (+2.4)58.0 (+1.5)51.7 (+1.8)29.7 (+2.2)55.8 (+1.7)49.0 (+1.9)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.092, + 0.308, + 0.877, + 0.323 + ], + "angle": 0, + "content": "Table 2. Open vocabulary object detection results on COCO. * denotes our re-evaluation by adding the class-agnostic mask head." + }, + { + "type": "table", + "bbox": [ + 0.092, + 0.337, + 0.88, + 0.591 + ], + "angle": 0, + "content": "
MethodBackboneObject DetectionInstance Segmentation
APrAPcAPfAPAPrAPcAPfAP
SupervisedRN50-FPN3.322.534.523.34.222.832.721.8
Supervised +RFS [12]11.623.532.524.312.823.331.023.1
ViLD [11]16.726.534.227.816.624.630.325.5
ViLD*RN50-FPN16.627.033.027.516.925.029.125.2
CondHead (Ours)18.8 (+2.2)28.3 (+1.3)33.7 (+0.7)28.8 (+1.3)19.1 (+2.2)26.2 (+1.1)29.9 (+0.8)26.4 (+1.2)
RegionCLIP [32]17.127.434.028.2----
RegionCLIP*RN50-C417.027.234.328.217.426.031.626.7
CondHead (Ours)19.9 (+2.9)28.6 (+1.4)35.2 (+0.9)29.7 (+1.5)20.0 (+2.6)27.3 (+1.3)32.2 (+0.6)27.9 (+1.2)
ViLD [11]19.827.134.528.7----
ViLD*RN152-FPN20.027.034.028.519.825.132.126.9
CondHead (Ours)21.9 (+1.9)28.4 (+1.4)34.6 (+0.6)29.7 (+1.2)21.6 (+1.8)26.2 (+1.1)33.0 (+0.9)28.1 (+1.2)
RegionCLIP [32]22.032.136.932.3----
RegionCLIP*RN50x4-C422.131.837.032.221.830.235.130.7
CondHead (Ours)25.1 (+3.0)33.4 (+1.6)37.8 (+0.8)33.7 (+1.5)24.4 (+2.6)31.6 (+1.4)35.9 (+0.8)32.0 (+1.3)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.601, + 0.895, + 0.631 + ], + "angle": 0, + "content": "Table 3. Open vocabulary object detection result on LVIS. * denotes our re-evaluation by adding the mask head. Supervised and Supervised + RFS are baselines that have access to the training data of target categories. RFS means the repeat factor sampling method [12]." + }, + { + "type": "text", + "bbox": [ + 0.109, + 0.657, + 0.47, + 0.703 + ], + "angle": 0, + "content": "ing region-wise pre-training. The improved representation further improves the recognition of novel objects and thus brings significant performance gain." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.712, + 0.172, + 0.726 + ], + "angle": 0, + "content": "4.2. Results" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Results on COCO We re-evaluate the baseline methods by adding class-agnostic mask segmentation heads. Then we replace the bounding box regression and mask segmentation heads with the proposed CondHead and re-run the experiments. As shown in Table 2, our method surpasses all baseline methods, especially on the novel object category set. We observe the improvements grow with the three baselines, e.g., the object detection AP improvements are 1.4 with OVR-CNN, 2.1 with ViLD, and 2.4 with RegionCLIP. A similar observation holds for the instance mask segmentation task. This phenomenon verifies that CondHead can" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.657, + 0.892, + 0.701 + ], + "angle": 0, + "content": "leverage semantic embeddings that better align with visual representations to achieve stronger bounding box regression and mask segmentation ability." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.703, + 0.893, + 0.884 + ], + "angle": 0, + "content": "Results on LVIS Following the evaluation on COCO, We then evaluate CondHead on LVIS dataset. As shown in Table 3, our method brings about 2.0-3.0 absolute improvement on the target novel object categories (i.e., \\(\\mathrm{AP}_r\\)), for both bounding box detection and instance segmentation. The improvement holds for stronger backbone networks. We also observe a similar growth of improvement as COCO, i.e., the RegionCLIP method gains more improvement with CondHead than that of ViLD. This is likely because RegionCLIP provides stronger semantic-visual alignment with its region-wise pre-training, and thus CondHead better generalizes to the target categories." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.886, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Cross Dataset Evaluation We further evaluate CondHead" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7056" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.104, + 0.087, + 0.878, + 0.292 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.305, + 0.895, + 0.337 + ], + "angle": 0, + "content": "Figure 3. Qualitative comparison with baseline ViLD [11]. The bounding box regression and mask segmentation results are overlaid on the images (Yellow: Proposals. Green: Regressed bounding box. Blue: segmentation mask). Best viewed with zoom-in." + }, + { + "type": "table", + "bbox": [ + 0.143, + 0.342, + 0.832, + 0.429 + ], + "angle": 0, + "content": "
MethodPASCAL VOCCOCOObjects365
AP50AP75APAP50AP75APAP50AP75
Supervised78.549.046.567.650.925.638.628.0
ViLD [11]72.256.736.655.639.811.818.212.6
CondHead (Ours)74.6 (+2.4)58.5 (+1.8)39.1 (+2.5)59.1 (+3.5)42.2 (+2.4)13.2 (+1.4)20.4 (+2.2)14.2 (+1.6)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.141, + 0.438, + 0.829, + 0.455 + ], + "angle": 0, + "content": "Table 4. Cross-dataset evaluation results. The model is trained on LVIS and directly evaluated on the target datasets." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.473, + 0.473, + 0.594 + ], + "angle": 0, + "content": "with the cross dataset scenarios, where the open vocabulary object detector is expected to generalize on datasets that are different from the one used for training. We simply replace the vocabulary of a trained model with target dataset vocabulary to evaluate the performance. As shown in Table 4, we observe the improvement of CondHead transfers well. For example, 3.5, 2.4, and 2.2 absolute improvements in \\(\\mathrm{AP}_50\\) for COCO, PASCAL VOC, and Objects365 respectively." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.595, + 0.47, + 0.761 + ], + "angle": 0, + "content": "Qualitative Results To demonstrate how CondHead improves bounding box regression and mask segmentation, we visualize the example qualitative results and compare that with the baseline method. As shown in Figure 3, CondHead is better at refining the proposal box and segmenting the target objects. For example, the baseline ViLD wrongly regresses the target chessboard to the table, while CondHead correctly predicts the chessboard (Figure 3 first column). In addition, CondHead better segments the challenging sweat pants while ViLD predicts inferior segmentation mask (Figure 3 last column)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.77, + 0.182, + 0.788 + ], + "angle": 0, + "content": "4.3. Analysis" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.795, + 0.471, + 0.903 + ], + "angle": 0, + "content": "The Effects of Language Descriptions We examine how CondHead is affected by language descriptions. As shown in Figure 4, when the input text is for a wrong category label, the box regression deteriorates, e.g., the proposal for sunflower is misguided to predict the carrot. On the other hand, it is interesting that when supplied with manually tuned descriptions, the detection and segmentation qual-" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.473, + 0.895, + 0.567 + ], + "angle": 0, + "content": "ity is improved, e.g., when employing snowboard and motorcycle as descriptions, the snowmobile is predicted better. This intriguing observation suggests CondHead learns strong category-conditioned prediction and a better performance could be achieved by simply tuning the semantic descriptions during inference." + }, + { + "type": "table", + "bbox": [ + 0.555, + 0.576, + 0.842, + 0.694 + ], + "angle": 0, + "content": "
APbrrAPm
ViLD*16.616.9
ClassWise17.1 (+0.5)17.3 (+0.4)
CondHead18.8 (+2.2)19.1 (+2.2)
RegionCLIP*17.017.4
ClassWise17.3 (+0.3)17.9 (+0.5)
CondHead17.9 (+2.9)20.0 (+2.6)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.704, + 0.892, + 0.734 + ], + "angle": 0, + "content": "Table 5. The results of naive class-wise transfer experiment (discussed in Section 1) on LVIS. \\((\\mathrm{AP}_r^b\\) : box AP, \\(\\mathrm{AP}_r^{m}\\) : mask AP)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Naive Class-wise Transfer We also establish another intuitive experiment as discussed in Section 1 to demonstrate the effectiveness of the proposed method. Since our intuition (Figure 1) is that the class-specific knowledge could be shared across categories, we train the class-wise heads on the base categories and then perform inference on the target categories in a semantically guided way. This is achieved by gathering the class-specific predictions with semantic similarity to the base categories. We use cosine similarity on the semantic embedding and select the category with the high-" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.958 + ], + "angle": 0, + "content": "7057" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.1, + 0.089, + 0.252, + 0.197 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.1, + 0.198, + 0.252, + 0.295 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.102, + 0.297, + 0.188, + 0.308 + ], + "angle": 0, + "content": "A photo of car" + }, + { + "type": "image", + "bbox": [ + 0.254, + 0.089, + 0.408, + 0.197 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.254, + 0.198, + 0.407, + 0.296 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.253, + 0.297, + 0.394, + 0.308 + ], + "angle": 0, + "content": "A photo of snowmobile" + }, + { + "type": "image", + "bbox": [ + 0.41, + 0.089, + 0.561, + 0.197 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.408, + 0.198, + 0.56, + 0.209 + ], + "angle": 0, + "content": "A flower photo of sunflow" + }, + { + "type": "image", + "bbox": [ + 0.564, + 0.089, + 0.716, + 0.197 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.564, + 0.198, + 0.716, + 0.208 + ], + "angle": 0, + "content": "er A photo of flower, a sun" + }, + { + "type": "image", + "bbox": [ + 0.719, + 0.089, + 0.871, + 0.197 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.784, + 0.198, + 0.865, + 0.208 + ], + "angle": 0, + "content": "Ground Truth" + }, + { + "type": "image_caption", + "bbox": [ + 0.1, + 0.298, + 0.188, + 0.308 + ], + "angle": 0, + "content": "A photo of car" + }, + { + "type": "image_caption", + "bbox": [ + 0.253, + 0.297, + 0.394, + 0.308 + ], + "angle": 0, + "content": "A photo of snowmobile" + }, + { + "type": "image", + "bbox": [ + 0.41, + 0.214, + 0.561, + 0.295 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.41, + 0.297, + 0.528, + 0.308 + ], + "angle": 0, + "content": "A snowboard photo" + }, + { + "type": "image", + "bbox": [ + 0.564, + 0.214, + 0.716, + 0.295 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.575, + 0.297, + 0.778, + 0.308 + ], + "angle": 0, + "content": "A snowboard photo of motorcycle" + }, + { + "type": "image", + "bbox": [ + 0.719, + 0.214, + 0.871, + 0.295 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.79, + 0.297, + 0.871, + 0.308 + ], + "angle": 0, + "content": "Ground Truth" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.318, + 0.892, + 0.347 + ], + "angle": 0, + "content": "Figure 4. Effect of tuning language descriptions. We select some intriguing examples for which tuning the input language descriptions could deteriorate or help object detection. Yellow: Proposals. Green: Regressed bounding box." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.36, + 0.47, + 0.437 + ], + "angle": 0, + "content": "est similarity. As shown in Table 5, we train the ClassWise baseline on both ViLD and RegionCLIP. ClassWise method provides a small improvement to class-agnostic heads compared to CondHead, demonstrating that CondHead better generalizes to the target categories." + }, + { + "type": "table", + "bbox": [ + 0.108, + 0.452, + 0.441, + 0.507 + ], + "angle": 0, + "content": "
VSHCHCHSRSHCHCHS
APm r16.919.619.120.417.420.320.021.0
Δ-2.72.23.5-2.92.63.6
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.518, + 0.47, + 0.561 + ], + "angle": 0, + "content": "Table 6. Results with Shapemask. V and R denote ViLD and RegionCLIP, SH, CH, and \\(\\mathrm{CH}_S\\) denote Shapemask head, CondHead and CondHead augmented with Shapemask, respectively." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.581, + 0.47, + 0.809 + ], + "angle": 0, + "content": "Augmenting CondHead with Shape Priors As discussed in Section 3.3, we compare to Shapemask [18] which introduces explicit class-agnostic shape priors. We then explore an augmented version of CondHead by integrating the Shapemask, the semantic embedding is utilized to estimate the initial shape prior, then the dynamic expert aggregation is conducted on the latter coarse mask estimation and refinement. Please refer to the supplementary for more implementation details. As shown in Table 6, Shapemask brings comparable improvement to CondHead, this is attributed to its strong prior and complex multi-stage mask refinement capability. Further integrating Shapemask into CondHead offers larger gain, e.g., 3.5 and 3.6 AP improvement for the two baselines. This implies CondHead can be improved with explicit shape priors." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Component Analysis As shown in Table 7, we perform an ablation study by applying CondHead on the box and mask heads independently, we find they improve box AP and mask AP independently. We also observe CondHead introduces a very small computation overhead. To understand how the number of expert heads and aggregation" + }, + { + "type": "table", + "bbox": [ + 0.526, + 0.358, + 0.869, + 0.429 + ], + "angle": 0, + "content": "
CondBoxCondMaskAPr bAPr mFLOPs
17.017.4193.5
✓19.617.8194.1
✓✓19.920.0195.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.439, + 0.892, + 0.469 + ], + "angle": 0, + "content": "Table 7. Component analysis. CondBox and CondMask mean applying CondHead on box and mask heads, respectively." + }, + { + "type": "image", + "bbox": [ + 0.519, + 0.486, + 0.697, + 0.565 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.701, + 0.488, + 0.871, + 0.569 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.502, + 0.579, + 0.889, + 0.594 + ], + "angle": 0, + "content": "Figure 5. Component analysis. Effect of expert number, \\(\\lambda\\) and \\(\\mu\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.616, + 0.892, + 0.692 + ], + "angle": 0, + "content": "hyper-parameter affects CondHead, a detailed analysis is conducted as shown in Figure 5. We find the performance gain is diminishing beyond 40 expert heads and the hyperparameters \\(\\lambda\\) and \\(\\mu\\) work best at between 0.5 and 0.7. More analysis experiments are presented in the supplementary." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.709, + 0.619, + 0.724 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.892, + 0.901 + ], + "angle": 0, + "content": "We introduce a conditionally parameterized neural network design to improve open vocabulary bounding box regression and mask segmentation, which is not explored before. Specifically, we leverage the pre-trained semantic embedding to guide the parameterization of the box and mask heads. The semantic embedding is strongly aligned with visual representation and thus provides effective cues for refining the bounding box and segmenting the objects. Our method named CondHead is extensively validated on different datasets and setups. We hope our findings provide insights for future work on open vocabulary detection." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7058" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.171 + ], + "angle": 0, + "content": "[1] Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, and Ajay Divakaran. Zero-shot object detection. In Proceedings of the European Conference on Computer Vision (ECCV), pages 384-400, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.172, + 0.472, + 0.227 + ], + "angle": 0, + "content": "[2] Zhaowei Cai and Nuno Vasconcelos. Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6154-6162, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.228, + 0.471, + 0.282 + ], + "angle": 0, + "content": "[3] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. arXiv preprint arXiv:2005.12872, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.284, + 0.471, + 0.338 + ], + "angle": 0, + "content": "[4] Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dólár, and C Lawrence Zitnick. Microsoft coco captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325, 2015. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.34, + 0.471, + 0.409 + ], + "angle": 0, + "content": "[5] Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Lu Yuan, and Zicheng Liu. Dynamic convolution: Attention over convolution kernels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11030-11039, 2020. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.41, + 0.471, + 0.466 + ], + "angle": 0, + "content": "[6] Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764-773, 2017. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.467, + 0.472, + 0.535 + ], + "angle": 0, + "content": "[7] Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, and Guoqi Li. Learning to prompt for open-vocabulary object detection with vision-language model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14084-14093, 2022. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.536, + 0.471, + 0.591 + ], + "angle": 0, + "content": "[8] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.593, + 0.471, + 0.635 + ], + "angle": 0, + "content": "[9] Mingfei Gao, Chen Xing, Juan Carlos Niebles, Junnan Li, Ran Xu, Wenhao Liu, and Caiming Xiong. Open vocabulary object detection with pseudo bounding-box labels. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.636, + 0.47, + 0.675 + ], + "angle": 0, + "content": "[10] Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440-1448, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.677, + 0.47, + 0.732 + ], + "angle": 0, + "content": "[11] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. arXiv preprint arXiv:2104.13921, 2021. 1, 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.734, + 0.47, + 0.773 + ], + "angle": 0, + "content": "[12] Agrim Gupta, Piotr Dollar, and Ross Girshick. LVIS: A dataset for large vocabulary instance segmentation. In CVPR, 2019. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.776, + 0.47, + 0.831 + ], + "angle": 0, + "content": "[13] Ronghang Hu, Piotr Dólar, Kaiming He, Trevor Darrell, and Ross Girshick. Learning to segment every thing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4233-4241, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.832, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[14] Dat Huynh, Jason Kuen, Zhe Lin, Jiumiang Gu, and Ehsan Elhamifar. Open-vocabulary instance segmentation via robust cross-modal pseudo-labeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7020-7031, 2022. 1, 3" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.135 + ], + "angle": 0, + "content": "[15] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. Advances in neural information processing systems, 28, 2015. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.137, + 0.894, + 0.219 + ], + "angle": 0, + "content": "[16] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.221, + 0.892, + 0.262 + ], + "angle": 0, + "content": "[17] Xu Jia, Bert De Brabandere, Tinne Tuytelaars, and Luc V Gool. Dynamic filter networks. Advances in neural information processing systems, 29, 2016. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.264, + 0.892, + 0.332 + ], + "angle": 0, + "content": "[18] Weicheng Kuo, Anelia Angelova, Jitendra Malik, and Tsung-Yi Lin. Shapemask: Learning to segment novel objects by refining shape priors. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9207-9216, 2019. 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.334, + 0.892, + 0.403 + ], + "angle": 0, + "content": "[19] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.406, + 0.892, + 0.462 + ], + "angle": 0, + "content": "[20] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. Ssd: Single shot multibox detector. In European conference on computer vision, pages 21-37. Springer, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.463, + 0.892, + 0.545 + ], + "angle": 0, + "content": "[21] Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, et al. Simple open-vocabulary object detection with vision transformers. arXiv preprint arXiv:2205.06230, 2022. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.547, + 0.892, + 0.63 + ], + "angle": 0, + "content": "[22] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.632, + 0.892, + 0.687 + ], + "angle": 0, + "content": "[23] Shafin Rahman, Salman Khan, and Nick Barnes. Improved visual-semantic alignment for zero-shot object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11932–11939, 2020. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.689, + 0.892, + 0.744 + ], + "angle": 0, + "content": "[24] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779-788, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.746, + 0.892, + 0.773 + ], + "angle": 0, + "content": "[25] Joseph Redmon and Ali Farhadi. Yolo9000: better, faster, stronger. arXiv preprint, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.775, + 0.892, + 0.83 + ], + "angle": 0, + "content": "[26] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91-99, 2015. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.832, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[27] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 8430-8439, 2019. 5" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "7059" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.174 + ], + "angle": 0, + "content": "[28] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2556-2565, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.178, + 0.469, + 0.232 + ], + "angle": 0, + "content": "[29] Brandon Yang, Gabriel Bender, Quoc V Le, and Jiquan Ngiam. Condconv: Conditionally parameterized convolutions for efficient inference. Advances in Neural Information Processing Systems, 32, 2019. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.234, + 0.469, + 0.275 + ], + "angle": 0, + "content": "[30] Yuhang Zang, Wei Li, Kaiyang Zhou, Chen Huang, and Chen Change Loy. Open-vocabulary detr with conditional matching. arXiv preprint arXiv:2203.11876, 2022. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.277, + 0.469, + 0.344 + ], + "angle": 0, + "content": "[31] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14393-14402, 2021. 1, 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.347, + 0.469, + 0.429 + ], + "angle": 0, + "content": "[32] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al. Regionclip: Region-based language-image pretraining. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16793-16803, 2022. 1, 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.432, + 0.469, + 0.485 + ], + "angle": 0, + "content": "[33] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9):2337-2348, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.488, + 0.469, + 0.555 + ], + "angle": 0, + "content": "[34] Pengkai Zhu, Hanxiao Wang, and Venkatesh Saligrama. Don't even look once: Synthesizing features for zero-shot detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11693-11702, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.558, + 0.469, + 0.613 + ], + "angle": 0, + "content": "[35] Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. Deformable convnets v2: More deformable, better results. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9308-9316, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.615, + 0.469, + 0.669 + ], + "angle": 0, + "content": "[36] Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transformers for end-to-end object detection. The International Conference on Learning Representations, 2020. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.669 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.956 + ], + "angle": 0, + "content": "7060" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/5ac75f03-1745-4705-ac4d-6c3f7fb370ae_origin.pdf b/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/5ac75f03-1745-4705-ac4d-6c3f7fb370ae_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..a00a20a40a188c370562c68af5ad16822c511944 --- /dev/null +++ b/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/5ac75f03-1745-4705-ac4d-6c3f7fb370ae_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a4c6b08b7cff073e75b0924e56dc51d3eea36d422ae909e43cc32256100b9b9e +size 3772760 diff --git a/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/full.md b/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/full.md new file mode 100644 index 0000000000000000000000000000000000000000..b765dda1ccaf4d730175ae74dc3e2fa8e38f2975 --- /dev/null +++ b/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/full.md @@ -0,0 +1,319 @@ +# Learning to Detect and Segment for Open Vocabulary Object Detection + +Tao Wang Sichuan University + +twangnh@gmail.com + +# Abstract + +Open vocabulary object detection has been greatly advanced by the recent development of vision-language pretrained model, which helps recognize novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask prediction. In this work, we propose CondHead, a principled dynamic network design to better generalize the box regression and mask segmentation for open vocabulary setting. The core idea is to conditionally parameterize the network heads on semantic embedding and thus the model is guided with class-specific knowledge to better detect novel categories. Specifically, CondHead is composed of two streams of network heads, the dynamically aggregated head and dynamically generated head. The former is instantiated with a set of static heads that are conditionally aggregated, these heads are optimized as experts and are expected to learn sophisticated prediction. The latter is instantiated with dynamically generated parameters and encodes general class-specific information. With such a conditional design, the detection model is bridged by the semantic embedding to offer strongly generalizable class-wise box and mask prediction. Our method brings significant improvement to the state-of-the-art open vocabulary object detection methods with very minor overhead, e.g., it surpasses a RegionClip model by 3.0 detection AP on novel categories, with only $1.1\%$ more computation. + +# 1. Introduction + +Given the semantic object categories of interest, object detection aims at localizing each object instance from the input images. The prior research efforts mainly focus on the close-set setting, where the images containing the interested object categories are annotated and used to train a detector. The obtained detector only recognizes object categories that are annotated in the training set. In such a setting, more data needs to be collected and annotated if novel category1 needs + +![](images/78c4e153e7d309ccd977fb3631f3380c2a2a6351ae87ae73defb90a5bea702ce.jpg) +Figure 1. Illustration of our main intuition. Given the object proposals, the bounding box regression and mask segmentation learned from some object categories could generalize to the target category. For example, the knowledge learned from a chicken could help detect and segment the long thin feet and the small head of an ibis (upper row). Similarly for the hairbrush, the knowledge learned from the toothbrush could better handle the extreme aspect ratio and occlusion from the hand (lower row). + +to be detected. However, data collection and annotation are very costly for object detection, which raises a significant challenge for traditional object detection methods. + +To address the challenge, the open vocabulary object detection methods are widely explored recently, these methods [1, 7, 9, 11, 14, 21, 23, 30-32] aim at generalizing object detection on novel categories by only training on a set of labeled categories. The core of these methods is transferring the strong image-text aligned features [16, 22] to classify objects of novel categories. To achieve bounding box regression and mask segmentation on novel categories, they simply employ the class-agnostic network heads. Although class agnostic heads can be readily applied to novel target object categories, they offer limited capacity to learn category-specific knowledge like object + +![](images/5cf8b6e1b98cb76b27ab34fcd582ae0dceb133e3d77b45f122441240fa44935c.jpg) +Figure 2. Overview of CondHead. To detect objects of novel categories, we aim at conditionally parameterizing the bounding box regression and mask segmentation based on the semantic embedding, which is strongly correlated with the visual feature and provides effective class-specific cues to refine the box and predict the mask. + +shape, and thus provide sub-optimal performance. On the other hand, training class-wise heads is not feasible as we do not have bounding box and mask annotation for the target categories. As shown in Figure 1, our intuition is that the class-wise knowledge could naturally generalize across object categories, and may be leveraged to achieve much higher quality box regression and mask segmentation on the target categories, in a category-specific manner. However, we find a brute force way of training class-wise heads on the base categories and manually gathering the class-specific prediction with closet semantic similarity during inference provides limited gain. The reason is that there still remains gap between the base and target categories, such as appearance and context. + +Motivated by the strong semantic-visual aligned representation [7, 11, 31, 32] in open vocabulary object detection, we propose to exploit the semantic embedding as a conditional prior to parameterize class-wise bounding box regression and mask segmentation. Such conditioning is learned on base categories and easily generalizes to novel categories with the semantic embedding. Our method, named CondHead, is based on dynamic parameter generation of neural networks [5, 6, 15, 17, 29]. To achieve strong efficiency, it exploits both large complex network head for their representative power and small light network head for their efficiency. The complex head is employed by conditional weight aggregation over a set of static heads. The light head is employed by dynamically basing its parameters on the semantic embedding. The final prediction is obtained by combining the predictions from the two stream results. Through optimization on the base categories, the set of static heads are expected to learn sophisticated expert knowledge to cope with complex shapes and appearance, the dynamic head is endowed with general class-specific knowledge such as color and context. + +Our CondHead is flexible regarding the choice of semantic-visual representation. The stronger quality of the aligned representation is expected to bring higher perfor + +mance, as the conditional knowledge from the semantic embedding could generalize better to the target visual features. This is demonstrated by the clear growth of improvement over three baselines with increasing quality of pretrained semantic-visual encoder networks, OVR-CNN [31], ViLD [11] and RegionCLIP [32], on both COCO [19] and LVIS [12] datasets. Remarkably, CondHead brings an average 2.8 improvement w.r.t both box and mask AP for the strong RegionCLIP baseline, with only about $1\%$ more computation. We also demonstrate intriguing qualitative results, showing how the semantic conditioning positively affects the regression and segmentation tasks. + +Our contributions are three-fold. 1) To the best of our knowledge, we are the first to leverage semantic-visual aligned representation for open vocabulary box regression and mask segmentation. 2) We design a differentiable semantic-conditioned head design to efficiently bridge the strong category-specific prediction learned on base categories to the target novel categories. 3) We extensively validate the proposed method on various benchmark datasets and conduct thorough ablation and analysis to understand how the semantic conditioning helps detect and segment the novel categories. + +# 2. Related Work + +Detecting Objects with Zero-shot and Open Vocabulary Setting Despite the remarkable success of fully-supervised object detection [2, 3, 10, 20, 24-26, 36], the instance-level annotation for novel object categories is costly. Recent years, open vocabulary object detection emerges as an effective solution, which aims at detecting novel categories without corresponding annotation. The representative methods employ region-based alignment, e.g., OVR-CNN [31], ViLD [11], RegionCLIP [32] and DetPro [7]. They are based on the two-stage object detection framework [26] and aim to match the object region feature with the generalizable semantic category embedding. They mainly differ in the learning of semantic-visual representa + +tion. Specifically, OVR-CNN [31] optimizes the semantic-visual grounding objective based multimodal transformer and applies the learned vision to language projection to facilitate novel category recognition. ViLD [11] transfers the stronger visual-semantic representation from large-scale vision-language pre-training [22]. Based on ViLD, Det-Pro [7] introduces a learnable prompt mechanism [33] to enhance the semantic representation and achieve higher performance. RegionCLIP [32] instead focuses on more effective region-wise pre-training to obtain more discriminative representation. In addition to pursuing more stronger region feature alignment, concurrent works [9, 14, 21, 30] explore other aspects of open vocabulary detection. For example, Gao et al. [9] and Huynh et al. [14] develop pseudo-labeling approach to generate pseudo ground-truth labels for object detection and instance segmentation. Instead of labeling perspective, some works focus on transformer architecture design for open vocabulary detection. OV-DETR [30] proposes a binary matching objective to enable query matching with novel objects on the end-to-end DETR framework DETR [3]. Minderer et al. [21] directly transfers pre-trained vision transformers to simplify open vocabulary detection. + +The prior works generally focus on the recognition of open vocabulary objects, while the localization or segmentation are typically conducted in a class-agnostic fashion. In this work, we explore this orthogonal perspective and aim at developing effective methods for more accurate box regression and mask segmentation. + +Dynamic Network Design Traditional neural network architectures employ static weights, i.e., the weights are independent of input signal and are fixed after training. Such static neural networks are limited at flexibility, in dealing with diverse input signals like images. Some previous works explore the idea of dynamic instantiation of networks to improve the flexibility. For example, Jia et al. [17] proposes to actively predict the convolution filters based on the input. In addition to explicit network parameter generation, Jaderberg et al. [15] introduces a dynamic parametric transformation (STN) to adaptively transform the image feature map, recovering affine distortions in the input and thus facilitating recognition. STN [15] operates on global image space, the Deformable Convolution [6,35] instead works in local scale. It learns spatial offsets to adjust the sampling locations of convolution kernels. The dynamic designs like STN [15] and Deformable Convolution [6,35] introduce explicit control over the networks. Some recent works introduce implicit dynamics to achieve strong network capacity. Notably, [5,29] develop conditionally parameterized convolution filters to increase the network capacity. Specifically, the convolution kernel is instantiated by dynamically aggregating a set of base filters. + +Our work is related to the above dynamic network ar + +chitecture designs, in that we also aim at achieving input-conditioned processing, to better adapt to the input data. Unlike prior works, we focus on the object instance-level design of dynamic processing. Moreover, instead of operating on the same visual signal, we instead introduce semantic signal for the dynamic network design. + +# 3. Methodology + +# 3.1. Problem Definition + +Given the bounding box annotation on a set of base object categories $\mathbb{F} = \{C_1,C_2,\dots C_N\}$ , open vocabulary object detection aims at training on the base data and generalizing to any open set of target object categories $\mathbb{F}^* = \{C_1,C_2,\dots C_M\}$ . Open vocabulary instance segmentation is also expected if the mask annotation is given. + +The state-of-the-art two-stage methods [7, 11, 31, 32] can be simplified as a region-semantic alignment framework. The model is initialized with an image encoder $(\mathcal{T}_i)$ and a language encoder $(\mathcal{T}_l)$ , which extract image feature and semantic embedding: $\mathbf{F} = \mathcal{T}_i(I)$ , $\mathbf{s}_n = \mathcal{T}_l(C_n)$ , here $I$ denotes the input image. These encoders [16, 22] are typically pre-trained on large-scale image-caption [4, 22, 28] data and thus extract strongly correlated visual-semantic representations. With region proposal network [26] trained on the base categories to extract object proposals, the open-vocabulary detection is trained in a region-wise manner. + +Concretely, given an object proposal bounding box $\mathbf{p} = (x_1, y_1, x_2, y_2)$ , feature pooling is conducted on the image feature map $\mathbf{F}$ to obtain the region feature $\mathbf{f}$ . The object proposal box is then matched with the ground-truth object instance set $\aleph$ w.r.t IOU (Intersection Over Union) metric: + +$$ +(\mathbf {b}, c) = \underset {(\mathbf {b} ^ {\prime}, c ^ {\prime}) \in \aleph} {\arg \max } \operatorname {I O U} (\mathbf {p}, \mathbf {b} ^ {\prime}) \tag {1} +$$ + +where $\mathbf{b} = (x_1^*, y_1^*, x_2^*, y_2^*)$ means the obtained ground-truth bounding box and $c$ is corresponding object category. Then, optimization is conducted to further minimize the alignment between ground-truth semantic embedding $\mathbf{s}_c$ and region feature $\mathbf{f}$ , e.g., through similarity-based cross entropy loss [11]. + +To generalize the bounding box regression, prior works employ class-agnostic network head $\mathcal{B}$ with parameter $\theta$ and optimize it on the base category data: + +$$ +\min _ {\theta} \mathcal {L} _ {\mathcal {B}} \left(\mathcal {B} _ {\theta}, \mathcal {X}\right) = \mathcal {L} _ {\mathcal {B}} \left(\mathcal {B} _ {\theta} (\boldsymbol {p}, \boldsymbol {f}), \boldsymbol {b}\right), \tag {2} +$$ + +where $\mathcal{X} = (\mathbf{p},\mathbf{f},\mathbf{b})$ denotes the tuple data of proposal, region feature and bounding box. $\mathcal{L}_{\mathcal{B}}$ is typically defined as L1 or L2 error between the regressed bounding box and ground-truth box. + +To learn instance segmentation, the mask region feature $\mathbf{v}$ is pooled with the refined bounding box from the box + +head, Then the class-agnostic mask segmentation network head $\mathcal{M}$ with parameter $\vartheta$ is trained by: + +$$ +\min _ {\vartheta} \mathcal {L} _ {\mathcal {M}} \left(\mathcal {M} _ {\vartheta}, \mathcal {Y}\right) = \mathcal {L} _ {\mathcal {M}} \left(\mathcal {M} _ {\vartheta} (\boldsymbol {v}), \boldsymbol {m}\right), \tag {3} +$$ + +where $\mathbf{m}$ denotes the corresponding ground-truth mask segmentation and $\mathcal{V} = (\mathbf{v},\mathbf{m})$ denotes the paired region feature and mask. $\mathcal{L}_{\mathcal{M}}$ is typically defined as pixel-wise segmentation error between the predicted mask and ground-truth. + +Although the class-agnostic bounding box regression and mask segmentation heads readily generalize to the target novel object categories, they offer sub-optimal results due to the less representative capability. Since the semantic embeddings are strongly correlated with the visual features, we propose to achieve generalizable class-wise prediction by conditioning the network parameters on the base category semantic embeddings during training: + +$$ +\min _ {\alpha} \mathcal {L} _ {\mathcal {B}} \left(\mathcal {B} _ {\theta \left(\mathbf {s} _ {c}\right)}, \mathcal {X}\right) \tag {4} +$$ + +$$ +\min _ {\beta} \mathcal {L} _ {\mathcal {M}} \left(\mathcal {M} _ {\vartheta \left(\mathbf {s} _ {c}\right)}, \mathcal {Y}\right) \tag {5} +$$ + +Where $\alpha$ and $\beta$ are parameters of the conditioning function $\theta(\cdot)$ and $\vartheta(\cdot)$ . Through this approach, the model learns to generalize regression and segmentation to novel categories in a class-wise manner, by predicting corresponding parameters $\theta(\mathbf{s}_m)$ and $\vartheta(\mathbf{s}_m)$ during inference. + +# 3.2. CondHead Formulation + +Our proposed CondHead aims to bridge the gap between base and target categories, with the semantic conditioning mechanism. We instantiate the conditioning function with neural networks to achieve flexible optimization and inference. To construct such conditioning framework, we observe the challenges in network capacity: 1) More complex head architecture (e.g., head with several hidden layers) offers stronger representative ability, but it requires large number of parameters, which are not efficient to generate and hard to optimize. For example, a hidden fully layer with input and output dimension of 256 involves more than 60k scalar weights. 2) Less complex head architecture (e.g., head with a single layer) is easier to optimize and can be efficiently generated, but provides limited capacity. + +To address the challenge, we design a dual conditioning framework, to leverage both the complex heads and light heads. The framework is composed of dynamically aggregated head and dynamically generated head. + +Dynamically Aggregated Head To leverage the large network capacity of complex heads, we propose to generate dynamic weights to aggregate a set of complex heads $\{\mathcal{B}_1,\mathcal{B}_2,\dots,\mathcal{B}_H\}$ , with associated parameters + +$\{\theta_{1}, \theta_{2}, \dots, \theta_{H}\}$ . These heads act as experts that are good at refining bounding box and predicting mask segmentation for objects categories with certain shapes or appearance. Specifically, the aggregation weights is generated by: + +$$ +\mathbf {w} = \mathcal {A} _ {\phi} (\mathbf {s} _ {c}) \tag {6} +$$ + +where $\mathbf{w} \in \mathbb{R}^H$ , and $\mathcal{A}$ is a small neural network with parameter $\phi$ . To regularize the weight aggregation space as a convex hull for optimization, the weight $\mathbf{w}$ is then normalized with Softmax function: + +$$ +w _ {h} = \frac {e ^ {w _ {h}}}{\sum_ {h ^ {\prime} = 1} ^ {H} e ^ {w _ {h ^ {\prime}}}} \tag {7} +$$ + +which ensures the weights sums to 1 and each element is larger than 0. With the normalized aggregation weight, the expert heads are then combined to a single head $\hat{\mathcal{B}}$ with parameter $\hat{\theta}$ : + +$$ +\hat {\theta} = \sum_ {h = 1} ^ {H} w _ {h} * \theta_ {h} \tag {8} +$$ + +Similarly, for a set of mask heads $\{\mathcal{M}_1,\mathcal{M}_2,\dots,\mathcal{M}_H\}$ , with associated parameters $\{\vartheta_1,\vartheta_2,\dots,\vartheta_H\}$ , the weight generation and aggregation can be conducted to obtain a single mask head $\hat{\mathcal{M}}$ with parameter $\hat{\vartheta}$ . When the dynamic aggregation is applied to both box regression and mask segmentation, separate weight generation networks are used, as box regression and mask segmentation may not share the same attention across the expert models. + +Dynamically Generated Head To introduce stronger conditioning on the semantic embedding, we propose to directly generate the network parameter. With bounding box regression head as an example, the parameter is first generated as: + +$$ +\dot {\theta} = \mathcal {D} _ {\varphi} (\mathbf {s} _ {c}) \tag {9} +$$ + +the parameter $\dot{\theta}$ is used to instantiate a new box head $\dot{B}$ . Since $\dot{\theta}$ is directly generated from the semantic embedding, it easily encodes general class-specific information like aspect ratio and color. Similarly, a dynamic mask segmentation head $\dot{\mathcal{M}}$ can be obtained with generated parameter $\dot{\vartheta}$ . + +Combining the Dynamic Predictions We then combine the above dynamically aggregated heads $\hat{B},\hat{\mathcal{M}}$ and dynamically generated heads $\hat{B},\hat{\mathcal{M}}$ to obtain the final result. We consider simple weighted averaging on their prediction: + +$$ +\bar {\mathcal {B}} _ {\theta \left(\mathbf {s} _ {c}\right)} (\cdot) = \lambda * \hat {\mathcal {B}} (\cdot) + (1 - \lambda) * \dot {\mathcal {B}} (\cdot) \tag {10} +$$ + +$$ +\overline {{\mathcal {M}}} _ {\vartheta \left(\mathbf {s} _ {c}\right)} (\cdot) = \mu * \hat {\mathcal {M}} (\cdot) + (1 - \mu) * \dot {\mathcal {M}} (\cdot) \tag {11} +$$ + +$\overline{\mathcal{B}}$ and $\overline{\mathcal{M}}$ are for the box regression and mask segmentation head respectively. $\lambda$ and $\mu$ are hyper-parameters. + +After optimization on the base category data with equation 4 and equation 5, $\overline{B}$ and $\overline{\mathcal{M}}$ are employed during inference on the target categories by conditioning on the target semantic embedding as $\overline{\mathcal{B}}_{\theta (\mathbf{s}_m)}(\cdot)$ and $\overline{\mathcal{M}}_{\vartheta (\mathbf{s}_m)}(\cdot)$ + +Optimizing the Expert Heads with Temperature Annealing During the early optimization stage, the expert heads within the dynamically aggregated head all require training signals to initiate the learning of basic regression and segmentation capability. While in the later optimization stage, the experts are expected to provide specialized capability in dealing with different objects. We thus facilitate the optimization of the expert heads by a temperature annealing Softmax strategy. Concretely, the normalization of aggregation weights is performed with temperature $\tau$ + +$$ +w _ {h} = \frac {e ^ {w _ {h} / \tau}}{\sum_ {h ^ {\prime} = 1} ^ {H} e ^ {w _ {h ^ {\prime}} / \tau}} \tag {12} +$$ + +During the early training stages, the temperature is set as a large value and with the progress of training to provide nearly uniform gradients for all the expert heads, the temperature is gradually annealed to a smaller value to achieve the desired specialized learning. + +# 3.3. Relation to Close Works + +Conditionally Parameterized Convolution CondHead is closely related to Dynamic Convolution [5] and CondConv [29]. They differ from CondHead in motivation and application. They aim to improve the representation capacity of convolution kernels through conditional parameterization, the conditioning is performed on the visual features of each network layer. Moreover, they are applied to the image recognition task. While CondHead aims to bridge the perception gap between the base training object categories and target novel object categories by conditioning on the strongly visual-aligned semantic embedding. CondHead is applied to the more challenging bounding box regression and mask segmentation task, which is not explored before. + +Partially Supervised Instance Segmentation Recent works [13, 18] explore partially supervised instance segmentation, where the base categories have both box and mask annotation while the novel categories have only box annotation. $\mathbf{Mask}^{X}$ R-CNN [13] propose a parameterized transformation function to transfer the box regression weights to mask segmentation weights. Shapemask [18] explores strong shape priors to achieve better class-agnostic object segmentation, which generalizes better to novel categories without mask annotation. Our focused open vocabulary object detection and instance segmentation are much more challenging than the partially supervised instance segmentation, which is based on good detection quality and + +
Head ArchitectureA D {Bh} {Mh} B M
2fc2fc2fc3conv1fc
+ +Table 1. Architectural instantiate of CondHead. fc and conv denote fully connected and convolutional networks, respectively, with a hidden dimension of 256. The digit means the number of layers. + +strong object region representation. $\mathbf{Mask}^X$ R-CNN [13] cannot be applied here as box annotation is not available. Shapemask [18] could be applied here, we compare to it for instance segmentation and evaluate an augmented version of CondHead with Shapemask. + +# 4. Experiments + +We study four questions in experiments. 1) Is CondHead able to improve the performance of open vocabulary object detection? is it efficient? 2) How does the quality of semantic-visual representation affects the performance? 3) How does CondHead improves the box regression and mask segmentation? 4) How does each component and hyperparameter take effect? Throughout the experiments, unless otherwise stated, we adopt the architectural instantiation shown in Table 1. + +# 4.1. Datasets and Setup + +We adopt object detection benchmark datasets COCO [19] and LVIS [12] to evaluate our method. Following prior works [11, 31], we manually split the datasets into base and target categories2. We also evaluate the generalization through inference on two other common object benchmarks, PASCAL VOC [8] and Objects365 [27]. + +Setup Since the prior works mainly focus on the classification of novel object categories, we evaluate CondHead by adopting the prior state-of-the-art open vocabulary methods and treating them as baselines. We employ the following methods with increasing quality of semantic-visual alignment: + +- OVR-CNN [31] proposes to pre-train the multi-modal encoder with image-caption pair data. The pre-trained rich visual-semantic representation is then transferred to recognize the objects with open vocabulary. +- ViLD [11] achieves much higher performance than OVR-CNN by transferring the stronger visualsemantic representation of CLIP [22], which was pretrained on a much larger scale of image-caption data. +- RegionCLIP [32] further improves the alignment between the semantic and visual embeddings by conduct + +
MethodObject DetectionInstance Segmentation
NovelBaseAllNovelBaseAll
DELO [34] (CVPR20)3.4113.813.0---
PL [23] (AAAI20)4.1235.927.9---
OVR-CNN [31] (CVPR21)22.846.039.9---
OVR-CNN*22.645.939.820.142.336.5
CondHead (Ours)24.0 (+1.4)46.5 (+0.6)40.6 (+0.8)21.4 (+1.3)42.9 (+0.6)37.3 (+0.8)
ViLD [16] (ICLR22)27.659.551.3---
ViLD*27.759.751.424.156.748.2
CondHead (Ours)29.8 (+2.1)60.8 (+1.1)52.7 (+1.3)25.9 (+1.8)57.9 (+1.2)49.5 (+1.3)
RegionCLIP [32] (CVPR22)31.457.150.4---
RegionCLIP*31.356.549.927.554.147.1
CondHead (Ours)33.7 (+2.4)58.0 (+1.5)51.7 (+1.8)29.7 (+2.2)55.8 (+1.7)49.0 (+1.9)
+ +Table 2. Open vocabulary object detection results on COCO. * denotes our re-evaluation by adding the class-agnostic mask head. + +
MethodBackboneObject DetectionInstance Segmentation
APrAPcAPfAPAPrAPcAPfAP
SupervisedRN50-FPN3.322.534.523.34.222.832.721.8
Supervised +RFS [12]11.623.532.524.312.823.331.023.1
ViLD [11]16.726.534.227.816.624.630.325.5
ViLD*RN50-FPN16.627.033.027.516.925.029.125.2
CondHead (Ours)18.8 (+2.2)28.3 (+1.3)33.7 (+0.7)28.8 (+1.3)19.1 (+2.2)26.2 (+1.1)29.9 (+0.8)26.4 (+1.2)
RegionCLIP [32]17.127.434.028.2----
RegionCLIP*RN50-C417.027.234.328.217.426.031.626.7
CondHead (Ours)19.9 (+2.9)28.6 (+1.4)35.2 (+0.9)29.7 (+1.5)20.0 (+2.6)27.3 (+1.3)32.2 (+0.6)27.9 (+1.2)
ViLD [11]19.827.134.528.7----
ViLD*RN152-FPN20.027.034.028.519.825.132.126.9
CondHead (Ours)21.9 (+1.9)28.4 (+1.4)34.6 (+0.6)29.7 (+1.2)21.6 (+1.8)26.2 (+1.1)33.0 (+0.9)28.1 (+1.2)
RegionCLIP [32]22.032.136.932.3----
RegionCLIP*RN50x4-C422.131.837.032.221.830.235.130.7
CondHead (Ours)25.1 (+3.0)33.4 (+1.6)37.8 (+0.8)33.7 (+1.5)24.4 (+2.6)31.6 (+1.4)35.9 (+0.8)32.0 (+1.3)
+ +Table 3. Open vocabulary object detection result on LVIS. * denotes our re-evaluation by adding the mask head. Supervised and Supervised + RFS are baselines that have access to the training data of target categories. RFS means the repeat factor sampling method [12]. + +ing region-wise pre-training. The improved representation further improves the recognition of novel objects and thus brings significant performance gain. + +# 4.2. Results + +Results on COCO We re-evaluate the baseline methods by adding class-agnostic mask segmentation heads. Then we replace the bounding box regression and mask segmentation heads with the proposed CondHead and re-run the experiments. As shown in Table 2, our method surpasses all baseline methods, especially on the novel object category set. We observe the improvements grow with the three baselines, e.g., the object detection AP improvements are 1.4 with OVR-CNN, 2.1 with ViLD, and 2.4 with RegionCLIP. A similar observation holds for the instance mask segmentation task. This phenomenon verifies that CondHead can + +leverage semantic embeddings that better align with visual representations to achieve stronger bounding box regression and mask segmentation ability. + +Results on LVIS Following the evaluation on COCO, We then evaluate CondHead on LVIS dataset. As shown in Table 3, our method brings about 2.0-3.0 absolute improvement on the target novel object categories (i.e., $\mathrm{AP}_r$ ), for both bounding box detection and instance segmentation. The improvement holds for stronger backbone networks. We also observe a similar growth of improvement as COCO, i.e., the RegionCLIP method gains more improvement with CondHead than that of ViLD. This is likely because RegionCLIP provides stronger semantic-visual alignment with its region-wise pre-training, and thus CondHead better generalizes to the target categories. + +Cross Dataset Evaluation We further evaluate CondHead + +![](images/b5257e66e9f55273f7c79a42839ea74e8750386cded33de116f37a706fbf7901.jpg) +Figure 3. Qualitative comparison with baseline ViLD [11]. The bounding box regression and mask segmentation results are overlaid on the images (Yellow: Proposals. Green: Regressed bounding box. Blue: segmentation mask). Best viewed with zoom-in. + +
MethodPASCAL VOCCOCOObjects365
AP50AP75APAP50AP75APAP50AP75
Supervised78.549.046.567.650.925.638.628.0
ViLD [11]72.256.736.655.639.811.818.212.6
CondHead (Ours)74.6 (+2.4)58.5 (+1.8)39.1 (+2.5)59.1 (+3.5)42.2 (+2.4)13.2 (+1.4)20.4 (+2.2)14.2 (+1.6)
+ +with the cross dataset scenarios, where the open vocabulary object detector is expected to generalize on datasets that are different from the one used for training. We simply replace the vocabulary of a trained model with target dataset vocabulary to evaluate the performance. As shown in Table 4, we observe the improvement of CondHead transfers well. For example, 3.5, 2.4, and 2.2 absolute improvements in $\mathrm{AP}_50$ for COCO, PASCAL VOC, and Objects365 respectively. + +Qualitative Results To demonstrate how CondHead improves bounding box regression and mask segmentation, we visualize the example qualitative results and compare that with the baseline method. As shown in Figure 3, CondHead is better at refining the proposal box and segmenting the target objects. For example, the baseline ViLD wrongly regresses the target chessboard to the table, while CondHead correctly predicts the chessboard (Figure 3 first column). In addition, CondHead better segments the challenging sweat pants while ViLD predicts inferior segmentation mask (Figure 3 last column). + +# 4.3. Analysis + +The Effects of Language Descriptions We examine how CondHead is affected by language descriptions. As shown in Figure 4, when the input text is for a wrong category label, the box regression deteriorates, e.g., the proposal for sunflower is misguided to predict the carrot. On the other hand, it is interesting that when supplied with manually tuned descriptions, the detection and segmentation qual- + +ity is improved, e.g., when employing snowboard and motorcycle as descriptions, the snowmobile is predicted better. This intriguing observation suggests CondHead learns strong category-conditioned prediction and a better performance could be achieved by simply tuning the semantic descriptions during inference. + +Table 4. Cross-dataset evaluation results. The model is trained on LVIS and directly evaluated on the target datasets. + +
APbrrAPm
ViLD*16.616.9
ClassWise17.1 (+0.5)17.3 (+0.4)
CondHead18.8 (+2.2)19.1 (+2.2)
RegionCLIP*17.017.4
ClassWise17.3 (+0.3)17.9 (+0.5)
CondHead17.9 (+2.9)20.0 (+2.6)
+ +Table 5. The results of naive class-wise transfer experiment (discussed in Section 1) on LVIS. $(\mathrm{AP}_r^b$ : box AP, $\mathrm{AP}_r^{m}$ : mask AP). + +Naive Class-wise Transfer We also establish another intuitive experiment as discussed in Section 1 to demonstrate the effectiveness of the proposed method. Since our intuition (Figure 1) is that the class-specific knowledge could be shared across categories, we train the class-wise heads on the base categories and then perform inference on the target categories in a semantically guided way. This is achieved by gathering the class-specific predictions with semantic similarity to the base categories. We use cosine similarity on the semantic embedding and select the category with the high- + +![](images/cfcf29db7d1427f74e5af8982e52ca697427eb9b99550df4412c98323ff382d9.jpg) + +![](images/fe8df09e92eaf2c295803411f1e29694f00cbfb35ff8c09774c3de0f2c598047.jpg) +A photo of car +A photo of car + +![](images/a12c4624403f721b0c4fda2e4c2580e2f9326b5f6c2ba70bc1d61e700c8815a7.jpg) +A flower photo of sunflow + +![](images/051fea0c8a128d8496b8d5212acb4e6ab047cb6bd125de515f7f173d124f882d.jpg) +A snowboard photo +Figure 4. Effect of tuning language descriptions. We select some intriguing examples for which tuning the input language descriptions could deteriorate or help object detection. Yellow: Proposals. Green: Regressed bounding box. + +A photo of snowmobile + +![](images/06f2e27c7f1dad5ff32277f5ada817b5805875611b4840f325b30a2bdbcdd6e3.jpg) + +![](images/0542d248cea4dd9e8b94e2518de0a8b131604e1f9c07bec344792ae745811f75.jpg) +er A photo of flower, a sun + +![](images/8351c28e794c42a5f36dfe8cf1507517043f8ae3a4a31af1f584f41028f1b351.jpg) +Ground Truth + +A photo of snowmobile + +![](images/2c9aa922a2d0fe6ca74cfe9b7fcf94783dd76f7fe7a860944bde574918a0abd6.jpg) + +![](images/9e514e38b0ab34426910bf9703376040443ad4edcca44f63dd64ec64414e3f5f.jpg) +A snowboard photo of motorcycle + +![](images/30b91c4314d581be481d1afa6d9996d86a4756298b7c0e1ea4625d26fd1e4ba0.jpg) +Ground Truth + +est similarity. As shown in Table 5, we train the ClassWise baseline on both ViLD and RegionCLIP. ClassWise method provides a small improvement to class-agnostic heads compared to CondHead, demonstrating that CondHead better generalizes to the target categories. + +
VSHCHCHSRSHCHCHS
APm r16.919.619.120.417.420.320.021.0
Δ-2.72.23.5-2.92.63.6
+ +Augmenting CondHead with Shape Priors As discussed in Section 3.3, we compare to Shapemask [18] which introduces explicit class-agnostic shape priors. We then explore an augmented version of CondHead by integrating the Shapemask, the semantic embedding is utilized to estimate the initial shape prior, then the dynamic expert aggregation is conducted on the latter coarse mask estimation and refinement. Please refer to the supplementary for more implementation details. As shown in Table 6, Shapemask brings comparable improvement to CondHead, this is attributed to its strong prior and complex multi-stage mask refinement capability. Further integrating Shapemask into CondHead offers larger gain, e.g., 3.5 and 3.6 AP improvement for the two baselines. This implies CondHead can be improved with explicit shape priors. + +Component Analysis As shown in Table 7, we perform an ablation study by applying CondHead on the box and mask heads independently, we find they improve box AP and mask AP independently. We also observe CondHead introduces a very small computation overhead. To understand how the number of expert heads and aggregation + +Table 6. Results with Shapemask. V and R denote ViLD and RegionCLIP, SH, CH, and $\mathrm{CH}_S$ denote Shapemask head, CondHead and CondHead augmented with Shapemask, respectively. + +
CondBoxCondMaskAPr bAPr mFLOPs
17.017.4193.5
✓19.617.8194.1
✓✓19.920.0195.8
+ +Table 7. Component analysis. CondBox and CondMask mean applying CondHead on box and mask heads, respectively. + +![](images/7b7d8771bc60b38f483550f60139b029cac4a9205f5268eaa2342fe489e228a5.jpg) + +![](images/231e2d80b1cac3e4eaf28f708c58b3199fda0ffd423a029f354f5100bafbaa91.jpg) +Figure 5. Component analysis. Effect of expert number, $\lambda$ and $\mu$ . + +hyper-parameter affects CondHead, a detailed analysis is conducted as shown in Figure 5. We find the performance gain is diminishing beyond 40 expert heads and the hyperparameters $\lambda$ and $\mu$ work best at between 0.5 and 0.7. More analysis experiments are presented in the supplementary. + +# 5. Conclusion + +We introduce a conditionally parameterized neural network design to improve open vocabulary bounding box regression and mask segmentation, which is not explored before. Specifically, we leverage the pre-trained semantic embedding to guide the parameterization of the box and mask heads. The semantic embedding is strongly aligned with visual representation and thus provides effective cues for refining the bounding box and segmenting the objects. Our method named CondHead is extensively validated on different datasets and setups. We hope our findings provide insights for future work on open vocabulary detection. + +# References + +[1] Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, and Ajay Divakaran. Zero-shot object detection. In Proceedings of the European Conference on Computer Vision (ECCV), pages 384-400, 2018. 1 +[2] Zhaowei Cai and Nuno Vasconcelos. Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6154-6162, 2018. 2 +[3] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. arXiv preprint arXiv:2005.12872, 2020. 2, 3 +[4] Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dólár, and C Lawrence Zitnick. Microsoft coco captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325, 2015. 3 +[5] Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Lu Yuan, and Zicheng Liu. Dynamic convolution: Attention over convolution kernels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11030-11039, 2020. 2, 3, 5 +[6] Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764-773, 2017. 2, 3 +[7] Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, and Guoqi Li. Learning to prompt for open-vocabulary object detection with vision-language model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14084-14093, 2022. 1, 2, 3 +[8] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010. 5 +[9] Mingfei Gao, Chen Xing, Juan Carlos Niebles, Junnan Li, Ran Xu, Wenhao Liu, and Caiming Xiong. Open vocabulary object detection with pseudo bounding-box labels. 1, 3 +[10] Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440-1448, 2015. 2 +[11] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. arXiv preprint arXiv:2104.13921, 2021. 1, 2, 3, 5, 6, 7 +[12] Agrim Gupta, Piotr Dollar, and Ross Girshick. LVIS: A dataset for large vocabulary instance segmentation. In CVPR, 2019. 2, 5, 6 +[13] Ronghang Hu, Piotr Dólar, Kaiming He, Trevor Darrell, and Ross Girshick. Learning to segment every thing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4233-4241, 2018. 5 +[14] Dat Huynh, Jason Kuen, Zhe Lin, Jiumiang Gu, and Ehsan Elhamifar. Open-vocabulary instance segmentation via robust cross-modal pseudo-labeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7020-7031, 2022. 1, 3 + +[15] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. Advances in neural information processing systems, 28, 2015. 2, 3 +[16] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 3, 6 +[17] Xu Jia, Bert De Brabandere, Tinne Tuytelaars, and Luc V Gool. Dynamic filter networks. Advances in neural information processing systems, 29, 2016. 2, 3 +[18] Weicheng Kuo, Anelia Angelova, Jitendra Malik, and Tsung-Yi Lin. Shapemask: Learning to segment novel objects by refining shape priors. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9207-9216, 2019. 5, 8 +[19] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 2, 5 +[20] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. Ssd: Single shot multibox detector. In European conference on computer vision, pages 21-37. Springer, 2016. 2 +[21] Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, et al. Simple open-vocabulary object detection with vision transformers. arXiv preprint arXiv:2205.06230, 2022. 1, 3 +[22] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 3, 5 +[23] Shafin Rahman, Salman Khan, and Nick Barnes. Improved visual-semantic alignment for zero-shot object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11932–11939, 2020. 1, 6 +[24] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779-788, 2016. 2 +[25] Joseph Redmon and Ali Farhadi. Yolo9000: better, faster, stronger. arXiv preprint, 2017. 2 +[26] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91-99, 2015. 2, 3 +[27] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 8430-8439, 2019. 5 + +[28] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2556-2565, 2018. 3 +[29] Brandon Yang, Gabriel Bender, Quoc V Le, and Jiquan Ngiam. Condconv: Conditionally parameterized convolutions for efficient inference. Advances in Neural Information Processing Systems, 32, 2019. 2, 3, 5 +[30] Yuhang Zang, Wei Li, Kaiyang Zhou, Chen Huang, and Chen Change Loy. Open-vocabulary detr with conditional matching. arXiv preprint arXiv:2203.11876, 2022. 1, 3 +[31] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14393-14402, 2021. 1, 2, 3, 5, 6 +[32] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al. Regionclip: Region-based language-image pretraining. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16793-16803, 2022. 1, 2, 3, 5, 6 +[33] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9):2337-2348, 2022. 3 +[34] Pengkai Zhu, Hanxiao Wang, and Venkatesh Saligrama. Don't even look once: Synthesizing features for zero-shot detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11693-11702, 2020. 6 +[35] Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. Deformable convnets v2: More deformable, better results. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9308-9316, 2019. 3 +[36] Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transformers for end-to-end object detection. The International Conference on Learning Representations, 2020. 2 \ No newline at end of file diff --git a/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/images.zip b/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..331a4d2755476d61c8b3fea37422d58acf7c119c --- /dev/null +++ b/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:15379892863764c974aa7e70f73ea7335af6ccd2607a46e8d7ad0e20869f4e43 +size 687592 diff --git a/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/layout.json b/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..03a8f36e00608dc0a5dbffc15939036aae91f0db --- /dev/null +++ b/2023/Learning To Detect and Segment for Open Vocabulary Object Detection/layout.json @@ -0,0 +1,7993 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 77, + 103, + 516, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 103, + 516, + 121 + ], + "spans": [ + { + "bbox": [ + 77, + 103, + 516, + 121 + ], + "type": "text", + "content": "Learning to Detect and Segment for Open Vocabulary Object Detection" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 250, + 144, + 343, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 250, + 144, + 343, + 171 + ], + "spans": [ + { + "bbox": [ + 250, + 144, + 343, + 171 + ], + "type": "text", + "content": "Tao Wang Sichuan University" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 248, + 174, + 342, + 185 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 248, + 174, + 342, + 185 + ], + "spans": [ + { + "bbox": [ + 248, + 174, + 342, + 185 + ], + "type": "text", + "content": "twangnh@gmail.com" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "spans": [ + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 239, + 290, + 550 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 239, + 290, + 550 + ], + "spans": [ + { + "bbox": [ + 47, + 239, + 290, + 550 + ], + "type": "text", + "content": "Open vocabulary object detection has been greatly advanced by the recent development of vision-language pretrained model, which helps recognize novel objects with only semantic categories. The prior works mainly focus on knowledge transferring to the object proposal classification and employ class-agnostic box and mask prediction. In this work, we propose CondHead, a principled dynamic network design to better generalize the box regression and mask segmentation for open vocabulary setting. The core idea is to conditionally parameterize the network heads on semantic embedding and thus the model is guided with class-specific knowledge to better detect novel categories. Specifically, CondHead is composed of two streams of network heads, the dynamically aggregated head and dynamically generated head. The former is instantiated with a set of static heads that are conditionally aggregated, these heads are optimized as experts and are expected to learn sophisticated prediction. The latter is instantiated with dynamically generated parameters and encodes general class-specific information. With such a conditional design, the detection model is bridged by the semantic embedding to offer strongly generalizable class-wise box and mask prediction. Our method brings significant improvement to the state-of-the-art open vocabulary object detection methods with very minor overhead, e.g., it surpasses a RegionClip model by 3.0 detection AP on novel categories, with only " + }, + { + "bbox": [ + 47, + 239, + 290, + 550 + ], + "type": "inline_equation", + "content": "1.1\\%" + }, + { + "bbox": [ + 47, + 239, + 290, + 550 + ], + "type": "text", + "content": " more computation." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 575, + 128, + 588 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 575, + 128, + 588 + ], + "spans": [ + { + "bbox": [ + 47, + 575, + 128, + 588 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 596, + 287, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 596, + 287, + 693 + ], + "spans": [ + { + "bbox": [ + 46, + 596, + 287, + 693 + ], + "type": "text", + "content": "Given the semantic object categories of interest, object detection aims at localizing each object instance from the input images. The prior research efforts mainly focus on the close-set setting, where the images containing the interested object categories are annotated and used to train a detector. The obtained detector only recognizes object categories that are annotated in the training set. In such a setting, more data needs to be collected and annotated if novel category1 needs" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 309, + 213, + 545, + 401 + ], + "blocks": [ + { + "bbox": [ + 309, + 213, + 545, + 401 + ], + "lines": [ + { + "bbox": [ + 309, + 213, + 545, + 401 + ], + "spans": [ + { + "bbox": [ + 309, + 213, + 545, + 401 + ], + "type": "image", + "image_path": "78c4e153e7d309ccd977fb3631f3380c2a2a6351ae87ae73defb90a5bea702ce.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 418, + 547, + 506 + ], + "lines": [ + { + "bbox": [ + 304, + 418, + 547, + 506 + ], + "spans": [ + { + "bbox": [ + 304, + 418, + 547, + 506 + ], + "type": "text", + "content": "Figure 1. Illustration of our main intuition. Given the object proposals, the bounding box regression and mask segmentation learned from some object categories could generalize to the target category. For example, the knowledge learned from a chicken could help detect and segment the long thin feet and the small head of an ibis (upper row). Similarly for the hairbrush, the knowledge learned from the toothbrush could better handle the extreme aspect ratio and occlusion from the hand (lower row)." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 532, + 546, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 532, + 546, + 567 + ], + "spans": [ + { + "bbox": [ + 304, + 532, + 546, + 567 + ], + "type": "text", + "content": "to be detected. However, data collection and annotation are very costly for object detection, which raises a significant challenge for traditional object detection methods." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": "To address the challenge, the open vocabulary object detection methods are widely explored recently, these methods [1, 7, 9, 11, 14, 21, 23, 30-32] aim at generalizing object detection on novel categories by only training on a set of labeled categories. The core of these methods is transferring the strong image-text aligned features [16, 22] to classify objects of novel categories. To achieve bounding box regression and mask segmentation on novel categories, they simply employ the class-agnostic network heads. Although class agnostic heads can be readily applied to novel target object categories, they offer limited capacity to learn category-specific knowledge like object" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 702, + 246, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 246, + 713 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 246, + 713 + ], + "type": "text", + "content": "we treat category and class interchangeably in this paper" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "7051" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 51, + 70, + 545, + 186 + ], + "blocks": [ + { + "bbox": [ + 51, + 70, + 545, + 186 + ], + "lines": [ + { + "bbox": [ + 51, + 70, + 545, + 186 + ], + "spans": [ + { + "bbox": [ + 51, + 70, + 545, + 186 + ], + "type": "image", + "image_path": "5cf8b6e1b98cb76b27ab34fcd582ae0dceb133e3d77b45f122441240fa44935c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 193, + 547, + 228 + ], + "lines": [ + { + "bbox": [ + 46, + 193, + 547, + 228 + ], + "spans": [ + { + "bbox": [ + 46, + 193, + 547, + 228 + ], + "type": "text", + "content": "Figure 2. Overview of CondHead. To detect objects of novel categories, we aim at conditionally parameterizing the bounding box regression and mask segmentation based on the semantic embedding, which is strongly correlated with the visual feature and provides effective class-specific cues to refine the box and predict the mask." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 247, + 289, + 415 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 247, + 289, + 415 + ], + "spans": [ + { + "bbox": [ + 46, + 247, + 289, + 415 + ], + "type": "text", + "content": "shape, and thus provide sub-optimal performance. On the other hand, training class-wise heads is not feasible as we do not have bounding box and mask annotation for the target categories. As shown in Figure 1, our intuition is that the class-wise knowledge could naturally generalize across object categories, and may be leveraged to achieve much higher quality box regression and mask segmentation on the target categories, in a category-specific manner. However, we find a brute force way of training class-wise heads on the base categories and manually gathering the class-specific prediction with closet semantic similarity during inference provides limited gain. The reason is that there still remains gap between the base and target categories, such as appearance and context." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 420, + 289, + 672 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 420, + 289, + 672 + ], + "spans": [ + { + "bbox": [ + 46, + 420, + 289, + 672 + ], + "type": "text", + "content": "Motivated by the strong semantic-visual aligned representation [7, 11, 31, 32] in open vocabulary object detection, we propose to exploit the semantic embedding as a conditional prior to parameterize class-wise bounding box regression and mask segmentation. Such conditioning is learned on base categories and easily generalizes to novel categories with the semantic embedding. Our method, named CondHead, is based on dynamic parameter generation of neural networks [5, 6, 15, 17, 29]. To achieve strong efficiency, it exploits both large complex network head for their representative power and small light network head for their efficiency. The complex head is employed by conditional weight aggregation over a set of static heads. The light head is employed by dynamically basing its parameters on the semantic embedding. The final prediction is obtained by combining the predictions from the two stream results. Through optimization on the base categories, the set of static heads are expected to learn sophisticated expert knowledge to cope with complex shapes and appearance, the dynamic head is endowed with general class-specific knowledge such as color and context." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 677, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 677, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 677, + 288, + 715 + ], + "type": "text", + "content": "Our CondHead is flexible regarding the choice of semantic-visual representation. The stronger quality of the aligned representation is expected to bring higher perfor" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 247, + 545, + 391 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 247, + 545, + 391 + ], + "spans": [ + { + "bbox": [ + 304, + 247, + 545, + 391 + ], + "type": "text", + "content": "mance, as the conditional knowledge from the semantic embedding could generalize better to the target visual features. This is demonstrated by the clear growth of improvement over three baselines with increasing quality of pretrained semantic-visual encoder networks, OVR-CNN [31], ViLD [11] and RegionCLIP [32], on both COCO [19] and LVIS [12] datasets. Remarkably, CondHead brings an average 2.8 improvement w.r.t both box and mask AP for the strong RegionCLIP baseline, with only about " + }, + { + "bbox": [ + 304, + 247, + 545, + 391 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 304, + 247, + 545, + 391 + ], + "type": "text", + "content": " more computation. We also demonstrate intriguing qualitative results, showing how the semantic conditioning positively affects the regression and segmentation tasks." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 392, + 545, + 523 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 392, + 545, + 523 + ], + "spans": [ + { + "bbox": [ + 304, + 392, + 545, + 523 + ], + "type": "text", + "content": "Our contributions are three-fold. 1) To the best of our knowledge, we are the first to leverage semantic-visual aligned representation for open vocabulary box regression and mask segmentation. 2) We design a differentiable semantic-conditioned head design to efficiently bridge the strong category-specific prediction learned on base categories to the target novel categories. 3) We extensively validate the proposed method on various benchmark datasets and conduct thorough ablation and analysis to understand how the semantic conditioning helps detect and segment the novel categories." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 536, + 392, + 548 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 536, + 392, + 548 + ], + "spans": [ + { + "bbox": [ + 306, + 536, + 392, + 548 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 558, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 545, + 713 + ], + "type": "text", + "content": "Detecting Objects with Zero-shot and Open Vocabulary Setting Despite the remarkable success of fully-supervised object detection [2, 3, 10, 20, 24-26, 36], the instance-level annotation for novel object categories is costly. Recent years, open vocabulary object detection emerges as an effective solution, which aims at detecting novel categories without corresponding annotation. The representative methods employ region-based alignment, e.g., OVR-CNN [31], ViLD [11], RegionCLIP [32] and DetPro [7]. They are based on the two-stage object detection framework [26] and aim to match the object region feature with the generalizable semantic category embedding. They mainly differ in the learning of semantic-visual representa" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7052" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 289, + 347 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 289, + 347 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 289, + 347 + ], + "type": "text", + "content": "tion. Specifically, OVR-CNN [31] optimizes the semantic-visual grounding objective based multimodal transformer and applies the learned vision to language projection to facilitate novel category recognition. ViLD [11] transfers the stronger visual-semantic representation from large-scale vision-language pre-training [22]. Based on ViLD, Det-Pro [7] introduces a learnable prompt mechanism [33] to enhance the semantic representation and achieve higher performance. RegionCLIP [32] instead focuses on more effective region-wise pre-training to obtain more discriminative representation. In addition to pursuing more stronger region feature alignment, concurrent works [9, 14, 21, 30] explore other aspects of open vocabulary detection. For example, Gao et al. [9] and Huynh et al. [14] develop pseudo-labeling approach to generate pseudo ground-truth labels for object detection and instance segmentation. Instead of labeling perspective, some works focus on transformer architecture design for open vocabulary detection. OV-DETR [30] proposes a binary matching objective to enable query matching with novel objects on the end-to-end DETR framework DETR [3]. Minderer et al. [21] directly transfers pre-trained vision transformers to simplify open vocabulary detection." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 349, + 288, + 421 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 349, + 288, + 421 + ], + "spans": [ + { + "bbox": [ + 47, + 349, + 288, + 421 + ], + "type": "text", + "content": "The prior works generally focus on the recognition of open vocabulary objects, while the localization or segmentation are typically conducted in a class-agnostic fashion. In this work, we explore this orthogonal perspective and aim at developing effective methods for more accurate box regression and mask segmentation." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 424, + 289, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 424, + 289, + 700 + ], + "spans": [ + { + "bbox": [ + 47, + 424, + 289, + 700 + ], + "type": "text", + "content": "Dynamic Network Design Traditional neural network architectures employ static weights, i.e., the weights are independent of input signal and are fixed after training. Such static neural networks are limited at flexibility, in dealing with diverse input signals like images. Some previous works explore the idea of dynamic instantiation of networks to improve the flexibility. For example, Jia et al. [17] proposes to actively predict the convolution filters based on the input. In addition to explicit network parameter generation, Jaderberg et al. [15] introduces a dynamic parametric transformation (STN) to adaptively transform the image feature map, recovering affine distortions in the input and thus facilitating recognition. STN [15] operates on global image space, the Deformable Convolution [6,35] instead works in local scale. It learns spatial offsets to adjust the sampling locations of convolution kernels. The dynamic designs like STN [15] and Deformable Convolution [6,35] introduce explicit control over the networks. Some recent works introduce implicit dynamics to achieve strong network capacity. Notably, [5,29] develop conditionally parameterized convolution filters to increase the network capacity. Specifically, the convolution kernel is instantiated by dynamically aggregating a set of base filters." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 59, + 701, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 701, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 59, + 701, + 287, + 713 + ], + "type": "text", + "content": "Our work is related to the above dynamic network ar" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 72, + 545, + 145 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 145 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 145 + ], + "type": "text", + "content": "chitecture designs, in that we also aim at achieving input-conditioned processing, to better adapt to the input data. Unlike prior works, we focus on the object instance-level design of dynamic processing. Moreover, instead of operating on the same visual signal, we instead introduce semantic signal for the dynamic network design." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 305, + 155, + 389, + 169 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 155, + 389, + 169 + ], + "spans": [ + { + "bbox": [ + 305, + 155, + 389, + 169 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 306, + 175, + 418, + 186 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 175, + 418, + 186 + ], + "spans": [ + { + "bbox": [ + 306, + 175, + 418, + 186 + ], + "type": "text", + "content": "3.1. Problem Definition" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 193, + 545, + 265 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 193, + 545, + 265 + ], + "spans": [ + { + "bbox": [ + 304, + 193, + 545, + 265 + ], + "type": "text", + "content": "Given the bounding box annotation on a set of base object categories " + }, + { + "bbox": [ + 304, + 193, + 545, + 265 + ], + "type": "inline_equation", + "content": "\\mathbb{F} = \\{C_1,C_2,\\dots C_N\\}" + }, + { + "bbox": [ + 304, + 193, + 545, + 265 + ], + "type": "text", + "content": ", open vocabulary object detection aims at training on the base data and generalizing to any open set of target object categories " + }, + { + "bbox": [ + 304, + 193, + 545, + 265 + ], + "type": "inline_equation", + "content": "\\mathbb{F}^* = \\{C_1,C_2,\\dots C_M\\}" + }, + { + "bbox": [ + 304, + 193, + 545, + 265 + ], + "type": "text", + "content": ". Open vocabulary instance segmentation is also expected if the mask annotation is given." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 266, + 546, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 266, + 546, + 396 + ], + "spans": [ + { + "bbox": [ + 304, + 266, + 546, + 396 + ], + "type": "text", + "content": "The state-of-the-art two-stage methods [7, 11, 31, 32] can be simplified as a region-semantic alignment framework. The model is initialized with an image encoder " + }, + { + "bbox": [ + 304, + 266, + 546, + 396 + ], + "type": "inline_equation", + "content": "(\\mathcal{T}_i)" + }, + { + "bbox": [ + 304, + 266, + 546, + 396 + ], + "type": "text", + "content": " and a language encoder " + }, + { + "bbox": [ + 304, + 266, + 546, + 396 + ], + "type": "inline_equation", + "content": "(\\mathcal{T}_l)" + }, + { + "bbox": [ + 304, + 266, + 546, + 396 + ], + "type": "text", + "content": ", which extract image feature and semantic embedding: " + }, + { + "bbox": [ + 304, + 266, + 546, + 396 + ], + "type": "inline_equation", + "content": "\\mathbf{F} = \\mathcal{T}_i(I)" + }, + { + "bbox": [ + 304, + 266, + 546, + 396 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 266, + 546, + 396 + ], + "type": "inline_equation", + "content": "\\mathbf{s}_n = \\mathcal{T}_l(C_n)" + }, + { + "bbox": [ + 304, + 266, + 546, + 396 + ], + "type": "text", + "content": ", here " + }, + { + "bbox": [ + 304, + 266, + 546, + 396 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 304, + 266, + 546, + 396 + ], + "type": "text", + "content": " denotes the input image. These encoders [16, 22] are typically pre-trained on large-scale image-caption [4, 22, 28] data and thus extract strongly correlated visual-semantic representations. With region proposal network [26] trained on the base categories to extract object proposals, the open-vocabulary detection is trained in a region-wise manner." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 397, + 545, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 397, + 545, + 456 + ], + "spans": [ + { + "bbox": [ + 305, + 397, + 545, + 456 + ], + "type": "text", + "content": "Concretely, given an object proposal bounding box " + }, + { + "bbox": [ + 305, + 397, + 545, + 456 + ], + "type": "inline_equation", + "content": "\\mathbf{p} = (x_1, y_1, x_2, y_2)" + }, + { + "bbox": [ + 305, + 397, + 545, + 456 + ], + "type": "text", + "content": ", feature pooling is conducted on the image feature map " + }, + { + "bbox": [ + 305, + 397, + 545, + 456 + ], + "type": "inline_equation", + "content": "\\mathbf{F}" + }, + { + "bbox": [ + 305, + 397, + 545, + 456 + ], + "type": "text", + "content": " to obtain the region feature " + }, + { + "bbox": [ + 305, + 397, + 545, + 456 + ], + "type": "inline_equation", + "content": "\\mathbf{f}" + }, + { + "bbox": [ + 305, + 397, + 545, + 456 + ], + "type": "text", + "content": ". The object proposal box is then matched with the ground-truth object instance set " + }, + { + "bbox": [ + 305, + 397, + 545, + 456 + ], + "type": "inline_equation", + "content": "\\aleph" + }, + { + "bbox": [ + 305, + 397, + 545, + 456 + ], + "type": "text", + "content": " w.r.t IOU (Intersection Over Union) metric:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 366, + 466, + 545, + 488 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 366, + 466, + 545, + 488 + ], + "spans": [ + { + "bbox": [ + 366, + 466, + 545, + 488 + ], + "type": "interline_equation", + "content": "(\\mathbf {b}, c) = \\underset {(\\mathbf {b} ^ {\\prime}, c ^ {\\prime}) \\in \\aleph} {\\arg \\max } \\operatorname {I O U} (\\mathbf {p}, \\mathbf {b} ^ {\\prime}) \\tag {1}", + "image_path": "69db0430c2a7305017238fb62ab4a0a0257f211155180ccdf4336ff8a539f61a.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 497, + 545, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 497, + 545, + 569 + ], + "spans": [ + { + "bbox": [ + 304, + 497, + 545, + 569 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 497, + 545, + 569 + ], + "type": "inline_equation", + "content": "\\mathbf{b} = (x_1^*, y_1^*, x_2^*, y_2^*)" + }, + { + "bbox": [ + 304, + 497, + 545, + 569 + ], + "type": "text", + "content": " means the obtained ground-truth bounding box and " + }, + { + "bbox": [ + 304, + 497, + 545, + 569 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 304, + 497, + 545, + 569 + ], + "type": "text", + "content": " is corresponding object category. Then, optimization is conducted to further minimize the alignment between ground-truth semantic embedding " + }, + { + "bbox": [ + 304, + 497, + 545, + 569 + ], + "type": "inline_equation", + "content": "\\mathbf{s}_c" + }, + { + "bbox": [ + 304, + 497, + 545, + 569 + ], + "type": "text", + "content": " and region feature " + }, + { + "bbox": [ + 304, + 497, + 545, + 569 + ], + "type": "inline_equation", + "content": "\\mathbf{f}" + }, + { + "bbox": [ + 304, + 497, + 545, + 569 + ], + "type": "text", + "content": ", e.g., through similarity-based cross entropy loss [11]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 569, + 545, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 569, + 545, + 605 + ], + "spans": [ + { + "bbox": [ + 304, + 569, + 545, + 605 + ], + "type": "text", + "content": "To generalize the bounding box regression, prior works employ class-agnostic network head " + }, + { + "bbox": [ + 304, + 569, + 545, + 605 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 304, + 569, + 545, + 605 + ], + "type": "text", + "content": " with parameter " + }, + { + "bbox": [ + 304, + 569, + 545, + 605 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 304, + 569, + 545, + 605 + ], + "type": "text", + "content": " and optimize it on the base category data:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 350, + 614, + 545, + 632 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 350, + 614, + 545, + 632 + ], + "spans": [ + { + "bbox": [ + 350, + 614, + 545, + 632 + ], + "type": "interline_equation", + "content": "\\min _ {\\theta} \\mathcal {L} _ {\\mathcal {B}} \\left(\\mathcal {B} _ {\\theta}, \\mathcal {X}\\right) = \\mathcal {L} _ {\\mathcal {B}} \\left(\\mathcal {B} _ {\\theta} (\\boldsymbol {p}, \\boldsymbol {f}), \\boldsymbol {b}\\right), \\tag {2}", + "image_path": "2f14d5e337ef6a983cbb3f30683966b2cbd948dbd29960e1e40d7c410afeac1b.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 641, + 545, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 641, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 304, + 641, + 545, + 689 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 641, + 545, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{X} = (\\mathbf{p},\\mathbf{f},\\mathbf{b})" + }, + { + "bbox": [ + 304, + 641, + 545, + 689 + ], + "type": "text", + "content": " denotes the tuple data of proposal, region feature and bounding box. " + }, + { + "bbox": [ + 304, + 641, + 545, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{B}}" + }, + { + "bbox": [ + 304, + 641, + 545, + 689 + ], + "type": "text", + "content": " is typically defined as L1 or L2 error between the regressed bounding box and ground-truth box." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": "To learn instance segmentation, the mask region feature " + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{v}" + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": " is pooled with the refined bounding box from the box" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7053" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "text", + "content": "head, Then the class-agnostic mask segmentation network head " + }, + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "text", + "content": " with parameter " + }, + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "inline_equation", + "content": "\\vartheta" + }, + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "text", + "content": " is trained by:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 85, + 106, + 287, + 123 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 106, + 287, + 123 + ], + "spans": [ + { + "bbox": [ + 85, + 106, + 287, + 123 + ], + "type": "interline_equation", + "content": "\\min _ {\\vartheta} \\mathcal {L} _ {\\mathcal {M}} \\left(\\mathcal {M} _ {\\vartheta}, \\mathcal {Y}\\right) = \\mathcal {L} _ {\\mathcal {M}} \\left(\\mathcal {M} _ {\\vartheta} (\\boldsymbol {v}), \\boldsymbol {m}\\right), \\tag {3}", + "image_path": "f5bd76674d65acd93470d335a6b872821114e270ef4536ae2ed1b939c9041097.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 132, + 287, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 132, + 287, + 180 + ], + "spans": [ + { + "bbox": [ + 46, + 132, + 287, + 180 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 132, + 287, + 180 + ], + "type": "inline_equation", + "content": "\\mathbf{m}" + }, + { + "bbox": [ + 46, + 132, + 287, + 180 + ], + "type": "text", + "content": " denotes the corresponding ground-truth mask segmentation and " + }, + { + "bbox": [ + 46, + 132, + 287, + 180 + ], + "type": "inline_equation", + "content": "\\mathcal{V} = (\\mathbf{v},\\mathbf{m})" + }, + { + "bbox": [ + 46, + 132, + 287, + 180 + ], + "type": "text", + "content": " denotes the paired region feature and mask. " + }, + { + "bbox": [ + 46, + 132, + 287, + 180 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{M}}" + }, + { + "bbox": [ + 46, + 132, + 287, + 180 + ], + "type": "text", + "content": " is typically defined as pixel-wise segmentation error between the predicted mask and ground-truth." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 180, + 287, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 180, + 287, + 277 + ], + "spans": [ + { + "bbox": [ + 46, + 180, + 287, + 277 + ], + "type": "text", + "content": "Although the class-agnostic bounding box regression and mask segmentation heads readily generalize to the target novel object categories, they offer sub-optimal results due to the less representative capability. Since the semantic embeddings are strongly correlated with the visual features, we propose to achieve generalizable class-wise prediction by conditioning the network parameters on the base category semantic embeddings during training:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 127, + 298, + 287, + 316 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 127, + 298, + 287, + 316 + ], + "spans": [ + { + "bbox": [ + 127, + 298, + 287, + 316 + ], + "type": "interline_equation", + "content": "\\min _ {\\alpha} \\mathcal {L} _ {\\mathcal {B}} \\left(\\mathcal {B} _ {\\theta \\left(\\mathbf {s} _ {c}\\right)}, \\mathcal {X}\\right) \\tag {4}", + "image_path": "5534484dc942b8da8555c23d3881d3cc1855c7f2aa1db32d79f38df21c9e6fdb.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 123, + 335, + 287, + 353 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 123, + 335, + 287, + 353 + ], + "spans": [ + { + "bbox": [ + 123, + 335, + 287, + 353 + ], + "type": "interline_equation", + "content": "\\min _ {\\beta} \\mathcal {L} _ {\\mathcal {M}} \\left(\\mathcal {M} _ {\\vartheta \\left(\\mathbf {s} _ {c}\\right)}, \\mathcal {Y}\\right) \\tag {5}", + "image_path": "04b3bccc53c421cd70ca2b09917be00a6189264dd8cb27390b6ab69a56217878.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "spans": [ + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "text", + "content": "Where " + }, + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "text", + "content": " are parameters of the conditioning function " + }, + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "inline_equation", + "content": "\\theta(\\cdot)" + }, + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "inline_equation", + "content": "\\vartheta(\\cdot)" + }, + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "text", + "content": ". Through this approach, the model learns to generalize regression and segmentation to novel categories in a class-wise manner, by predicting corresponding parameters " + }, + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "inline_equation", + "content": "\\theta(\\mathbf{s}_m)" + }, + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "inline_equation", + "content": "\\vartheta(\\mathbf{s}_m)" + }, + { + "bbox": [ + 46, + 362, + 287, + 423 + ], + "type": "text", + "content": " during inference." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 430, + 181, + 441 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 430, + 181, + 441 + ], + "spans": [ + { + "bbox": [ + 47, + 430, + 181, + 441 + ], + "type": "text", + "content": "3.2. CondHead Formulation" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 449, + 287, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 449, + 287, + 616 + ], + "spans": [ + { + "bbox": [ + 46, + 449, + 287, + 616 + ], + "type": "text", + "content": "Our proposed CondHead aims to bridge the gap between base and target categories, with the semantic conditioning mechanism. We instantiate the conditioning function with neural networks to achieve flexible optimization and inference. To construct such conditioning framework, we observe the challenges in network capacity: 1) More complex head architecture (e.g., head with several hidden layers) offers stronger representative ability, but it requires large number of parameters, which are not efficient to generate and hard to optimize. For example, a hidden fully layer with input and output dimension of 256 involves more than 60k scalar weights. 2) Less complex head architecture (e.g., head with a single layer) is easier to optimize and can be efficiently generated, but provides limited capacity." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 45, + 616, + 287, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 616, + 287, + 665 + ], + "spans": [ + { + "bbox": [ + 45, + 616, + 287, + 665 + ], + "type": "text", + "content": "To address the challenge, we design a dual conditioning framework, to leverage both the complex heads and light heads. The framework is composed of dynamically aggregated head and dynamically generated head." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": "Dynamically Aggregated Head To leverage the large network capacity of complex heads, we propose to generate dynamic weights to aggregate a set of complex heads " + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\{\\mathcal{B}_1,\\mathcal{B}_2,\\dots,\\mathcal{B}_H\\}" + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": ", with associated parameters" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 72, + 545, + 121 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 121 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 121 + ], + "type": "inline_equation", + "content": "\\{\\theta_{1}, \\theta_{2}, \\dots, \\theta_{H}\\}" + }, + { + "bbox": [ + 305, + 72, + 545, + 121 + ], + "type": "text", + "content": ". These heads act as experts that are good at refining bounding box and predicting mask segmentation for objects categories with certain shapes or appearance. Specifically, the aggregation weights is generated by:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 399, + 130, + 545, + 143 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 399, + 130, + 545, + 143 + ], + "spans": [ + { + "bbox": [ + 399, + 130, + 545, + 143 + ], + "type": "interline_equation", + "content": "\\mathbf {w} = \\mathcal {A} _ {\\phi} (\\mathbf {s} _ {c}) \\tag {6}", + "image_path": "a0474c70559d6102b8d8052f899e41e8635637fe20b5ff8ea758c32a8d3d3104.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 153, + 545, + 201 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 153, + 545, + 201 + ], + "spans": [ + { + "bbox": [ + 305, + 153, + 545, + 201 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 153, + 545, + 201 + ], + "type": "inline_equation", + "content": "\\mathbf{w} \\in \\mathbb{R}^H" + }, + { + "bbox": [ + 305, + 153, + 545, + 201 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 305, + 153, + 545, + 201 + ], + "type": "inline_equation", + "content": "\\mathcal{A}" + }, + { + "bbox": [ + 305, + 153, + 545, + 201 + ], + "type": "text", + "content": " is a small neural network with parameter " + }, + { + "bbox": [ + 305, + 153, + 545, + 201 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 305, + 153, + 545, + 201 + ], + "type": "text", + "content": ". To regularize the weight aggregation space as a convex hull for optimization, the weight " + }, + { + "bbox": [ + 305, + 153, + 545, + 201 + ], + "type": "inline_equation", + "content": "\\mathbf{w}" + }, + { + "bbox": [ + 305, + 153, + 545, + 201 + ], + "type": "text", + "content": " is then normalized with Softmax function:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 385, + 208, + 545, + 236 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 385, + 208, + 545, + 236 + ], + "spans": [ + { + "bbox": [ + 385, + 208, + 545, + 236 + ], + "type": "interline_equation", + "content": "w _ {h} = \\frac {e ^ {w _ {h}}}{\\sum_ {h ^ {\\prime} = 1} ^ {H} e ^ {w _ {h ^ {\\prime}}}} \\tag {7}", + "image_path": "71a2b455aee7689a3daca5546f18b4ea3c442ba1a8e07737efa9e4837a31db14.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 243, + 545, + 291 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 243, + 545, + 291 + ], + "spans": [ + { + "bbox": [ + 305, + 243, + 545, + 291 + ], + "type": "text", + "content": "which ensures the weights sums to 1 and each element is larger than 0. With the normalized aggregation weight, the expert heads are then combined to a single head " + }, + { + "bbox": [ + 305, + 243, + 545, + 291 + ], + "type": "inline_equation", + "content": "\\hat{\\mathcal{B}}" + }, + { + "bbox": [ + 305, + 243, + 545, + 291 + ], + "type": "text", + "content": " with parameter " + }, + { + "bbox": [ + 305, + 243, + 545, + 291 + ], + "type": "inline_equation", + "content": "\\hat{\\theta}" + }, + { + "bbox": [ + 305, + 243, + 545, + 291 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 391, + 299, + 545, + 332 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 391, + 299, + 545, + 332 + ], + "spans": [ + { + "bbox": [ + 391, + 299, + 545, + 332 + ], + "type": "interline_equation", + "content": "\\hat {\\theta} = \\sum_ {h = 1} ^ {H} w _ {h} * \\theta_ {h} \\tag {8}", + "image_path": "c1a1ac88ca291397ce8480c1122eca71fb6683379cebf9c2283576c8addf6733.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 340, + 545, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 340, + 545, + 437 + ], + "spans": [ + { + "bbox": [ + 305, + 340, + 545, + 437 + ], + "type": "text", + "content": "Similarly, for a set of mask heads " + }, + { + "bbox": [ + 305, + 340, + 545, + 437 + ], + "type": "inline_equation", + "content": "\\{\\mathcal{M}_1,\\mathcal{M}_2,\\dots,\\mathcal{M}_H\\}" + }, + { + "bbox": [ + 305, + 340, + 545, + 437 + ], + "type": "text", + "content": ", with associated parameters " + }, + { + "bbox": [ + 305, + 340, + 545, + 437 + ], + "type": "inline_equation", + "content": "\\{\\vartheta_1,\\vartheta_2,\\dots,\\vartheta_H\\}" + }, + { + "bbox": [ + 305, + 340, + 545, + 437 + ], + "type": "text", + "content": ", the weight generation and aggregation can be conducted to obtain a single mask head " + }, + { + "bbox": [ + 305, + 340, + 545, + 437 + ], + "type": "inline_equation", + "content": "\\hat{\\mathcal{M}}" + }, + { + "bbox": [ + 305, + 340, + 545, + 437 + ], + "type": "text", + "content": " with parameter " + }, + { + "bbox": [ + 305, + 340, + 545, + 437 + ], + "type": "inline_equation", + "content": "\\hat{\\vartheta}" + }, + { + "bbox": [ + 305, + 340, + 545, + 437 + ], + "type": "text", + "content": ". When the dynamic aggregation is applied to both box regression and mask segmentation, separate weight generation networks are used, as box regression and mask segmentation may not share the same attention across the expert models." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 437, + 545, + 497 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 437, + 545, + 497 + ], + "spans": [ + { + "bbox": [ + 305, + 437, + 545, + 497 + ], + "type": "text", + "content": "Dynamically Generated Head To introduce stronger conditioning on the semantic embedding, we propose to directly generate the network parameter. With bounding box regression head as an example, the parameter is first generated as:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 400, + 506, + 545, + 521 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 400, + 506, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 400, + 506, + 545, + 521 + ], + "type": "interline_equation", + "content": "\\dot {\\theta} = \\mathcal {D} _ {\\varphi} (\\mathbf {s} _ {c}) \\tag {9}", + "image_path": "45900effc00c1298ecca6d08795789c8a69064f23cd7a1fe87211198d409ae43.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 305, + 531, + 545, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 531, + 545, + 592 + ], + "spans": [ + { + "bbox": [ + 305, + 531, + 545, + 592 + ], + "type": "text", + "content": "the parameter " + }, + { + "bbox": [ + 305, + 531, + 545, + 592 + ], + "type": "inline_equation", + "content": "\\dot{\\theta}" + }, + { + "bbox": [ + 305, + 531, + 545, + 592 + ], + "type": "text", + "content": " is used to instantiate a new box head " + }, + { + "bbox": [ + 305, + 531, + 545, + 592 + ], + "type": "inline_equation", + "content": "\\dot{B}" + }, + { + "bbox": [ + 305, + 531, + 545, + 592 + ], + "type": "text", + "content": ". Since " + }, + { + "bbox": [ + 305, + 531, + 545, + 592 + ], + "type": "inline_equation", + "content": "\\dot{\\theta}" + }, + { + "bbox": [ + 305, + 531, + 545, + 592 + ], + "type": "text", + "content": " is directly generated from the semantic embedding, it easily encodes general class-specific information like aspect ratio and color. Similarly, a dynamic mask segmentation head " + }, + { + "bbox": [ + 305, + 531, + 545, + 592 + ], + "type": "inline_equation", + "content": "\\dot{\\mathcal{M}}" + }, + { + "bbox": [ + 305, + 531, + 545, + 592 + ], + "type": "text", + "content": " can be obtained with generated parameter " + }, + { + "bbox": [ + 305, + 531, + 545, + 592 + ], + "type": "inline_equation", + "content": "\\dot{\\vartheta}" + }, + { + "bbox": [ + 305, + 531, + 545, + 592 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 305, + 593, + 545, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 593, + 545, + 641 + ], + "spans": [ + { + "bbox": [ + 305, + 593, + 545, + 641 + ], + "type": "text", + "content": "Combining the Dynamic Predictions We then combine the above dynamically aggregated heads " + }, + { + "bbox": [ + 305, + 593, + 545, + 641 + ], + "type": "inline_equation", + "content": "\\hat{B},\\hat{\\mathcal{M}}" + }, + { + "bbox": [ + 305, + 593, + 545, + 641 + ], + "type": "text", + "content": " and dynamically generated heads " + }, + { + "bbox": [ + 305, + 593, + 545, + 641 + ], + "type": "inline_equation", + "content": "\\hat{B},\\hat{\\mathcal{M}}" + }, + { + "bbox": [ + 305, + 593, + 545, + 641 + ], + "type": "text", + "content": " to obtain the final result. We consider simple weighted averaging on their prediction:" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 348, + 662, + 545, + 676 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 348, + 662, + 545, + 676 + ], + "spans": [ + { + "bbox": [ + 348, + 662, + 545, + 676 + ], + "type": "interline_equation", + "content": "\\bar {\\mathcal {B}} _ {\\theta \\left(\\mathbf {s} _ {c}\\right)} (\\cdot) = \\lambda * \\hat {\\mathcal {B}} (\\cdot) + (1 - \\lambda) * \\dot {\\mathcal {B}} (\\cdot) \\tag {10}", + "image_path": "b68c18d07ca30b7a2ec4c68372989ac9103fc6f702645496882b22ce026e82ce.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 340, + 696, + 545, + 711 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 340, + 696, + 545, + 711 + ], + "spans": [ + { + "bbox": [ + 340, + 696, + 545, + 711 + ], + "type": "interline_equation", + "content": "\\overline {{\\mathcal {M}}} _ {\\vartheta \\left(\\mathbf {s} _ {c}\\right)} (\\cdot) = \\mu * \\hat {\\mathcal {M}} (\\cdot) + (1 - \\mu) * \\dot {\\mathcal {M}} (\\cdot) \\tag {11}", + "image_path": "17d6eaa87859b732a95c5c7b1f2f03ecf634c6cdc9105eb4de65de21e9077598.jpg" + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7054" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "inline_equation", + "content": "\\overline{\\mathcal{B}}" + }, + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "inline_equation", + "content": "\\overline{\\mathcal{M}}" + }, + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "text", + "content": " are for the box regression and mask segmentation head respectively. " + }, + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "text", + "content": " are hyper-parameters." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 96, + 287, + 145 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 96, + 287, + 145 + ], + "spans": [ + { + "bbox": [ + 46, + 96, + 287, + 145 + ], + "type": "text", + "content": "After optimization on the base category data with equation 4 and equation 5, " + }, + { + "bbox": [ + 46, + 96, + 287, + 145 + ], + "type": "inline_equation", + "content": "\\overline{B}" + }, + { + "bbox": [ + 46, + 96, + 287, + 145 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 96, + 287, + 145 + ], + "type": "inline_equation", + "content": "\\overline{\\mathcal{M}}" + }, + { + "bbox": [ + 46, + 96, + 287, + 145 + ], + "type": "text", + "content": " are employed during inference on the target categories by conditioning on the target semantic embedding as " + }, + { + "bbox": [ + 46, + 96, + 287, + 145 + ], + "type": "inline_equation", + "content": "\\overline{\\mathcal{B}}_{\\theta (\\mathbf{s}_m)}(\\cdot)" + }, + { + "bbox": [ + 46, + 96, + 287, + 145 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 96, + 287, + 145 + ], + "type": "inline_equation", + "content": "\\overline{\\mathcal{M}}_{\\vartheta (\\mathbf{s}_m)}(\\cdot)" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 146, + 288, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 146, + 288, + 266 + ], + "spans": [ + { + "bbox": [ + 46, + 146, + 288, + 266 + ], + "type": "text", + "content": "Optimizing the Expert Heads with Temperature Annealing During the early optimization stage, the expert heads within the dynamically aggregated head all require training signals to initiate the learning of basic regression and segmentation capability. While in the later optimization stage, the experts are expected to provide specialized capability in dealing with different objects. We thus facilitate the optimization of the expert heads by a temperature annealing Softmax strategy. Concretely, the normalization of aggregation weights is performed with temperature " + }, + { + "bbox": [ + 46, + 146, + 288, + 266 + ], + "type": "inline_equation", + "content": "\\tau" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 122, + 274, + 287, + 306 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 122, + 274, + 287, + 306 + ], + "spans": [ + { + "bbox": [ + 122, + 274, + 287, + 306 + ], + "type": "interline_equation", + "content": "w _ {h} = \\frac {e ^ {w _ {h} / \\tau}}{\\sum_ {h ^ {\\prime} = 1} ^ {H} e ^ {w _ {h ^ {\\prime}} / \\tau}} \\tag {12}", + "image_path": "fa38132afa7170e8b8bae7f08c581a76a4761b5c3050e14e01c6da01c41e5c1e.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 312, + 287, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 312, + 287, + 373 + ], + "spans": [ + { + "bbox": [ + 46, + 312, + 287, + 373 + ], + "type": "text", + "content": "During the early training stages, the temperature is set as a large value and with the progress of training to provide nearly uniform gradients for all the expert heads, the temperature is gradually annealed to a smaller value to achieve the desired specialized learning." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 380, + 185, + 393 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 380, + 185, + 393 + ], + "spans": [ + { + "bbox": [ + 47, + 380, + 185, + 393 + ], + "type": "text", + "content": "3.3. Relation to Close Works" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 401, + 287, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 401, + 287, + 556 + ], + "spans": [ + { + "bbox": [ + 46, + 401, + 287, + 556 + ], + "type": "text", + "content": "Conditionally Parameterized Convolution CondHead is closely related to Dynamic Convolution [5] and CondConv [29]. They differ from CondHead in motivation and application. They aim to improve the representation capacity of convolution kernels through conditional parameterization, the conditioning is performed on the visual features of each network layer. Moreover, they are applied to the image recognition task. While CondHead aims to bridge the perception gap between the base training object categories and target novel object categories by conditioning on the strongly visual-aligned semantic embedding. CondHead is applied to the more challenging bounding box regression and mask segmentation task, which is not explored before." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 558, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 558, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 558, + 288, + 714 + ], + "type": "text", + "content": "Partially Supervised Instance Segmentation Recent works [13, 18] explore partially supervised instance segmentation, where the base categories have both box and mask annotation while the novel categories have only box annotation. " + }, + { + "bbox": [ + 46, + 558, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{Mask}^{X}" + }, + { + "bbox": [ + 46, + 558, + 288, + 714 + ], + "type": "text", + "content": " R-CNN [13] propose a parameterized transformation function to transfer the box regression weights to mask segmentation weights. Shapemask [18] explores strong shape priors to achieve better class-agnostic object segmentation, which generalizes better to novel categories without mask annotation. Our focused open vocabulary object detection and instance segmentation are much more challenging than the partially supervised instance segmentation, which is based on good detection quality and" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 331, + 71, + 523, + 102 + ], + "blocks": [ + { + "bbox": [ + 331, + 71, + 523, + 102 + ], + "lines": [ + { + "bbox": [ + 331, + 71, + 523, + 102 + ], + "spans": [ + { + "bbox": [ + 331, + 71, + 523, + 102 + ], + "type": "table", + "html": "
Head ArchitectureA D {Bh} {Mh} B M
2fc2fc2fc3conv1fc
", + "image_path": "5a03f682cb75c507fc3328f474ef5b43bb37a28e452c288041f87e692bf0a39f.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 111, + 547, + 156 + ], + "lines": [ + { + "bbox": [ + 305, + 111, + 547, + 156 + ], + "spans": [ + { + "bbox": [ + 305, + 111, + 547, + 156 + ], + "type": "text", + "content": "Table 1. Architectural instantiate of CondHead. fc and conv denote fully connected and convolutional networks, respectively, with a hidden dimension of 256. The digit means the number of layers." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 305, + 175, + 547, + 236 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 175, + 547, + 236 + ], + "spans": [ + { + "bbox": [ + 305, + 175, + 547, + 236 + ], + "type": "text", + "content": "strong object region representation. " + }, + { + "bbox": [ + 305, + 175, + 547, + 236 + ], + "type": "inline_equation", + "content": "\\mathbf{Mask}^X" + }, + { + "bbox": [ + 305, + 175, + 547, + 236 + ], + "type": "text", + "content": " R-CNN [13] cannot be applied here as box annotation is not available. Shapemask [18] could be applied here, we compare to it for instance segmentation and evaluate an augmented version of CondHead with Shapemask." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 245, + 388, + 258 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 245, + 388, + 258 + ], + "spans": [ + { + "bbox": [ + 306, + 245, + 388, + 258 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 266, + 547, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 266, + 547, + 373 + ], + "spans": [ + { + "bbox": [ + 305, + 266, + 547, + 373 + ], + "type": "text", + "content": "We study four questions in experiments. 1) Is CondHead able to improve the performance of open vocabulary object detection? is it efficient? 2) How does the quality of semantic-visual representation affects the performance? 3) How does CondHead improves the box regression and mask segmentation? 4) How does each component and hyperparameter take effect? Throughout the experiments, unless otherwise stated, we adopt the architectural instantiation shown in Table 1." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 380, + 419, + 393 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 380, + 419, + 393 + ], + "spans": [ + { + "bbox": [ + 306, + 380, + 419, + 393 + ], + "type": "text", + "content": "4.1. Datasets and Setup" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 399, + 546, + 482 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 399, + 546, + 482 + ], + "spans": [ + { + "bbox": [ + 305, + 399, + 546, + 482 + ], + "type": "text", + "content": "We adopt object detection benchmark datasets COCO [19] and LVIS [12] to evaluate our method. Following prior works [11, 31], we manually split the datasets into base and target categories2. We also evaluate the generalization through inference on two other common object benchmarks, PASCAL VOC [8] and Objects365 [27]." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 483, + 547, + 554 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 483, + 547, + 554 + ], + "spans": [ + { + "bbox": [ + 305, + 483, + 547, + 554 + ], + "type": "text", + "content": "Setup Since the prior works mainly focus on the classification of novel object categories, we evaluate CondHead by adopting the prior state-of-the-art open vocabulary methods and treating them as baselines. We employ the following methods with increasing quality of semantic-visual alignment:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 561, + 545, + 696 + ], + "type": "list", + "angle": 0, + "index": 19, + "blocks": [ + { + "bbox": [ + 317, + 561, + 545, + 610 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 561, + 545, + 610 + ], + "spans": [ + { + "bbox": [ + 317, + 561, + 545, + 610 + ], + "type": "text", + "content": "- OVR-CNN [31] proposes to pre-train the multi-modal encoder with image-caption pair data. The pre-trained rich visual-semantic representation is then transferred to recognize the objects with open vocabulary." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 616, + 545, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 616, + 545, + 665 + ], + "spans": [ + { + "bbox": [ + 317, + 616, + 545, + 665 + ], + "type": "text", + "content": "- ViLD [11] achieves much higher performance than OVR-CNN by transferring the stronger visualsemantic representation of CLIP [22], which was pretrained on a much larger scale of image-caption data." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 671, + 545, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 671, + 545, + 696 + ], + "spans": [ + { + "bbox": [ + 317, + 671, + 545, + 696 + ], + "type": "text", + "content": "- RegionCLIP [32] further improves the alignment between the semantic and visual embeddings by conduct" + } + ] + } + ], + "index": 18 + } + ], + "sub_type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 317, + 702, + 482, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 702, + 482, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 702, + 482, + 713 + ], + "type": "text", + "content": "we use the pre-processed data from [31] and [32]." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7055" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 77, + 70, + 517, + 235 + ], + "blocks": [ + { + "bbox": [ + 77, + 70, + 517, + 235 + ], + "lines": [ + { + "bbox": [ + 77, + 70, + 517, + 235 + ], + "spans": [ + { + "bbox": [ + 77, + 70, + 517, + 235 + ], + "type": "table", + "html": "
MethodObject DetectionInstance Segmentation
NovelBaseAllNovelBaseAll
DELO [34] (CVPR20)3.4113.813.0---
PL [23] (AAAI20)4.1235.927.9---
OVR-CNN [31] (CVPR21)22.846.039.9---
OVR-CNN*22.645.939.820.142.336.5
CondHead (Ours)24.0 (+1.4)46.5 (+0.6)40.6 (+0.8)21.4 (+1.3)42.9 (+0.6)37.3 (+0.8)
ViLD [16] (ICLR22)27.659.551.3---
ViLD*27.759.751.424.156.748.2
CondHead (Ours)29.8 (+2.1)60.8 (+1.1)52.7 (+1.3)25.9 (+1.8)57.9 (+1.2)49.5 (+1.3)
RegionCLIP [32] (CVPR22)31.457.150.4---
RegionCLIP*31.356.549.927.554.147.1
CondHead (Ours)33.7 (+2.4)58.0 (+1.5)51.7 (+1.8)29.7 (+2.2)55.8 (+1.7)49.0 (+1.9)
", + "image_path": "8079b354d003ede105077328ed35d8c551420afcbfdd19606c9a09397131415a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 56, + 266, + 538, + 468 + ], + "blocks": [ + { + "bbox": [ + 56, + 243, + 536, + 255 + ], + "lines": [ + { + "bbox": [ + 56, + 243, + 536, + 255 + ], + "spans": [ + { + "bbox": [ + 56, + 243, + 536, + 255 + ], + "type": "text", + "content": "Table 2. Open vocabulary object detection results on COCO. * denotes our re-evaluation by adding the class-agnostic mask head." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 266, + 538, + 468 + ], + "lines": [ + { + "bbox": [ + 56, + 266, + 538, + 468 + ], + "spans": [ + { + "bbox": [ + 56, + 266, + 538, + 468 + ], + "type": "table", + "html": "
MethodBackboneObject DetectionInstance Segmentation
APrAPcAPfAPAPrAPcAPfAP
SupervisedRN50-FPN3.322.534.523.34.222.832.721.8
Supervised +RFS [12]11.623.532.524.312.823.331.023.1
ViLD [11]16.726.534.227.816.624.630.325.5
ViLD*RN50-FPN16.627.033.027.516.925.029.125.2
CondHead (Ours)18.8 (+2.2)28.3 (+1.3)33.7 (+0.7)28.8 (+1.3)19.1 (+2.2)26.2 (+1.1)29.9 (+0.8)26.4 (+1.2)
RegionCLIP [32]17.127.434.028.2----
RegionCLIP*RN50-C417.027.234.328.217.426.031.626.7
CondHead (Ours)19.9 (+2.9)28.6 (+1.4)35.2 (+0.9)29.7 (+1.5)20.0 (+2.6)27.3 (+1.3)32.2 (+0.6)27.9 (+1.2)
ViLD [11]19.827.134.528.7----
ViLD*RN152-FPN20.027.034.028.519.825.132.126.9
CondHead (Ours)21.9 (+1.9)28.4 (+1.4)34.6 (+0.6)29.7 (+1.2)21.6 (+1.8)26.2 (+1.1)33.0 (+0.9)28.1 (+1.2)
RegionCLIP [32]22.032.136.932.3----
RegionCLIP*RN50x4-C422.131.837.032.221.830.235.130.7
CondHead (Ours)25.1 (+3.0)33.4 (+1.6)37.8 (+0.8)33.7 (+1.5)24.4 (+2.6)31.6 (+1.4)35.9 (+0.8)32.0 (+1.3)
", + "image_path": "363f43c9ff50a5dd64232bc3502df7259c2dd7e926d0e82a102ae82a4af0001d.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 475, + 547, + 499 + ], + "lines": [ + { + "bbox": [ + 46, + 475, + 547, + 499 + ], + "spans": [ + { + "bbox": [ + 46, + 475, + 547, + 499 + ], + "type": "text", + "content": "Table 3. Open vocabulary object detection result on LVIS. * denotes our re-evaluation by adding the mask head. Supervised and Supervised + RFS are baselines that have access to the training data of target categories. RFS means the repeat factor sampling method [12]." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 66, + 520, + 287, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 520, + 287, + 556 + ], + "spans": [ + { + "bbox": [ + 66, + 520, + 287, + 556 + ], + "type": "text", + "content": "ing region-wise pre-training. The improved representation further improves the recognition of novel objects and thus brings significant performance gain." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 563, + 105, + 574 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 563, + 105, + 574 + ], + "spans": [ + { + "bbox": [ + 47, + 563, + 105, + 574 + ], + "type": "text", + "content": "4.2. Results" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 582, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 288, + 713 + ], + "type": "text", + "content": "Results on COCO We re-evaluate the baseline methods by adding class-agnostic mask segmentation heads. Then we replace the bounding box regression and mask segmentation heads with the proposed CondHead and re-run the experiments. As shown in Table 2, our method surpasses all baseline methods, especially on the novel object category set. We observe the improvements grow with the three baselines, e.g., the object detection AP improvements are 1.4 with OVR-CNN, 2.1 with ViLD, and 2.4 with RegionCLIP. A similar observation holds for the instance mask segmentation task. This phenomenon verifies that CondHead can" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 520, + 545, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 520, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 304, + 520, + 545, + 555 + ], + "type": "text", + "content": "leverage semantic embeddings that better align with visual representations to achieve stronger bounding box regression and mask segmentation ability." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 556, + 546, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 556, + 546, + 700 + ], + "spans": [ + { + "bbox": [ + 304, + 556, + 546, + 700 + ], + "type": "text", + "content": "Results on LVIS Following the evaluation on COCO, We then evaluate CondHead on LVIS dataset. As shown in Table 3, our method brings about 2.0-3.0 absolute improvement on the target novel object categories (i.e., " + }, + { + "bbox": [ + 304, + 556, + 546, + 700 + ], + "type": "inline_equation", + "content": "\\mathrm{AP}_r" + }, + { + "bbox": [ + 304, + 556, + 546, + 700 + ], + "type": "text", + "content": "), for both bounding box detection and instance segmentation. The improvement holds for stronger backbone networks. We also observe a similar growth of improvement as COCO, i.e., the RegionCLIP method gains more improvement with CondHead than that of ViLD. This is likely because RegionCLIP provides stronger semantic-visual alignment with its region-wise pre-training, and thus CondHead better generalizes to the target categories." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 701, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 701, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 306, + 701, + 545, + 712 + ], + "type": "text", + "content": "Cross Dataset Evaluation We further evaluate CondHead" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7056" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 68, + 537, + 231 + ], + "blocks": [ + { + "bbox": [ + 63, + 68, + 537, + 231 + ], + "lines": [ + { + "bbox": [ + 63, + 68, + 537, + 231 + ], + "spans": [ + { + "bbox": [ + 63, + 68, + 537, + 231 + ], + "type": "image", + "image_path": "b5257e66e9f55273f7c79a42839ea74e8750386cded33de116f37a706fbf7901.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 241, + 547, + 266 + ], + "lines": [ + { + "bbox": [ + 46, + 241, + 547, + 266 + ], + "spans": [ + { + "bbox": [ + 46, + 241, + 547, + 266 + ], + "type": "text", + "content": "Figure 3. Qualitative comparison with baseline ViLD [11]. The bounding box regression and mask segmentation results are overlaid on the images (Yellow: Proposals. Green: Regressed bounding box. Blue: segmentation mask). Best viewed with zoom-in." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 87, + 270, + 509, + 339 + ], + "blocks": [ + { + "bbox": [ + 87, + 270, + 509, + 339 + ], + "lines": [ + { + "bbox": [ + 87, + 270, + 509, + 339 + ], + "spans": [ + { + "bbox": [ + 87, + 270, + 509, + 339 + ], + "type": "table", + "html": "
MethodPASCAL VOCCOCOObjects365
AP50AP75APAP50AP75APAP50AP75
Supervised78.549.046.567.650.925.638.628.0
ViLD [11]72.256.736.655.639.811.818.212.6
CondHead (Ours)74.6 (+2.4)58.5 (+1.8)39.1 (+2.5)59.1 (+3.5)42.2 (+2.4)13.2 (+1.4)20.4 (+2.2)14.2 (+1.6)
", + "image_path": "9d3ecc8fc32cd86fa2eb9248fe1dace2e9a02e3f40206d47a2f4de2551cfcb9f.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 374, + 289, + 470 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 374, + 289, + 470 + ], + "spans": [ + { + "bbox": [ + 46, + 374, + 289, + 470 + ], + "type": "text", + "content": "with the cross dataset scenarios, where the open vocabulary object detector is expected to generalize on datasets that are different from the one used for training. We simply replace the vocabulary of a trained model with target dataset vocabulary to evaluate the performance. As shown in Table 4, we observe the improvement of CondHead transfers well. For example, 3.5, 2.4, and 2.2 absolute improvements in " + }, + { + "bbox": [ + 46, + 374, + 289, + 470 + ], + "type": "inline_equation", + "content": "\\mathrm{AP}_50" + }, + { + "bbox": [ + 46, + 374, + 289, + 470 + ], + "type": "text", + "content": " for COCO, PASCAL VOC, and Objects365 respectively." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 471, + 287, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 471, + 287, + 602 + ], + "spans": [ + { + "bbox": [ + 46, + 471, + 287, + 602 + ], + "type": "text", + "content": "Qualitative Results To demonstrate how CondHead improves bounding box regression and mask segmentation, we visualize the example qualitative results and compare that with the baseline method. As shown in Figure 3, CondHead is better at refining the proposal box and segmenting the target objects. For example, the baseline ViLD wrongly regresses the target chessboard to the table, while CondHead correctly predicts the chessboard (Figure 3 first column). In addition, CondHead better segments the challenging sweat pants while ViLD predicts inferior segmentation mask (Figure 3 last column)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 609, + 111, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 609, + 111, + 624 + ], + "spans": [ + { + "bbox": [ + 47, + 609, + 111, + 624 + ], + "type": "text", + "content": "4.3. Analysis" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "type": "text", + "content": "The Effects of Language Descriptions We examine how CondHead is affected by language descriptions. As shown in Figure 4, when the input text is for a wrong category label, the box regression deteriorates, e.g., the proposal for sunflower is misguided to predict the carrot. On the other hand, it is interesting that when supplied with manually tuned descriptions, the detection and segmentation qual-" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 374, + 547, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 374, + 547, + 449 + ], + "spans": [ + { + "bbox": [ + 304, + 374, + 547, + 449 + ], + "type": "text", + "content": "ity is improved, e.g., when employing snowboard and motorcycle as descriptions, the snowmobile is predicted better. This intriguing observation suggests CondHead learns strong category-conditioned prediction and a better performance could be achieved by simply tuning the semantic descriptions during inference." + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 339, + 456, + 515, + 549 + ], + "blocks": [ + { + "bbox": [ + 86, + 346, + 507, + 360 + ], + "lines": [ + { + "bbox": [ + 86, + 346, + 507, + 360 + ], + "spans": [ + { + "bbox": [ + 86, + 346, + 507, + 360 + ], + "type": "text", + "content": "Table 4. Cross-dataset evaluation results. The model is trained on LVIS and directly evaluated on the target datasets." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 339, + 456, + 515, + 549 + ], + "lines": [ + { + "bbox": [ + 339, + 456, + 515, + 549 + ], + "spans": [ + { + "bbox": [ + 339, + 456, + 515, + 549 + ], + "type": "table", + "html": "
APbrrAPm
ViLD*16.616.9
ClassWise17.1 (+0.5)17.3 (+0.4)
CondHead18.8 (+2.2)19.1 (+2.2)
RegionCLIP*17.017.4
ClassWise17.3 (+0.3)17.9 (+0.5)
CondHead17.9 (+2.9)20.0 (+2.6)
", + "image_path": "6fb4b1a0f9048136f339b7919d0fe840be2d7be850e1210e37320710d1ab6827.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 557, + 545, + 581 + ], + "lines": [ + { + "bbox": [ + 305, + 557, + 545, + 581 + ], + "spans": [ + { + "bbox": [ + 305, + 557, + 545, + 581 + ], + "type": "text", + "content": "Table 5. The results of naive class-wise transfer experiment (discussed in Section 1) on LVIS. " + }, + { + "bbox": [ + 305, + 557, + 545, + 581 + ], + "type": "inline_equation", + "content": "(\\mathrm{AP}_r^b" + }, + { + "bbox": [ + 305, + 557, + 545, + 581 + ], + "type": "text", + "content": " : box AP, " + }, + { + "bbox": [ + 305, + 557, + 545, + 581 + ], + "type": "inline_equation", + "content": "\\mathrm{AP}_r^{m}" + }, + { + "bbox": [ + 305, + 557, + 545, + 581 + ], + "type": "text", + "content": " : mask AP)." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "type": "text", + "content": "Naive Class-wise Transfer We also establish another intuitive experiment as discussed in Section 1 to demonstrate the effectiveness of the proposed method. Since our intuition (Figure 1) is that the class-specific knowledge could be shared across categories, we train the class-wise heads on the base categories and then perform inference on the target categories in a semantically guided way. This is achieved by gathering the class-specific predictions with semantic similarity to the base categories. 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As shown in Table 5, we train the ClassWise baseline on both ViLD and RegionCLIP. ClassWise method provides a small improvement to class-agnostic heads compared to CondHead, demonstrating that CondHead better generalizes to the target categories." + } + ] + } + ], + "index": 21 + }, + { + "type": "table", + "bbox": [ + 66, + 357, + 269, + 401 + ], + "blocks": [ + { + "bbox": [ + 66, + 357, + 269, + 401 + ], + "lines": [ + { + "bbox": [ + 66, + 357, + 269, + 401 + ], + "spans": [ + { + "bbox": [ + 66, + 357, + 269, + 401 + ], + "type": "table", + "html": "
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CondBoxCondMaskAPr bAPr mFLOPs
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CondBox and CondMask mean applying CondHead on box and mask heads, respectively." + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 317, + 384, + 426, + 447 + ], + "blocks": [ + { + "bbox": [ + 317, + 384, + 426, + 447 + ], + "lines": [ + { + "bbox": [ + 317, + 384, + 426, + 447 + ], + "spans": [ + { + "bbox": [ + 317, + 384, + 426, + 447 + ], + "type": "image", + "image_path": "7b7d8771bc60b38f483550f60139b029cac4a9205f5268eaa2342fe489e228a5.jpg" + } + ] + } + ], + "index": 28, + "angle": 0, + "type": "image_body" + } + ], + "index": 28 + }, + { + "type": "image", + "bbox": [ + 429, + 386, + 533, + 450 + ], + "blocks": [ + { + "bbox": [ + 429, + 386, + 533, + 450 + ], + "lines": [ + { + "bbox": [ + 429, + 386, + 533, + 450 + ], + "spans": [ + { + "bbox": [ + 429, + 386, + 533, + 450 + ], + "type": "image", + "image_path": "231e2d80b1cac3e4eaf28f708c58b3199fda0ffd423a029f354f5100bafbaa91.jpg" + } + ] + } + ], + "index": 29, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 307, + 458, + 544, + 470 + ], + "lines": [ + { + "bbox": [ + 307, + 458, + 544, + 470 + ], + "spans": [ + { + "bbox": [ + 307, + 458, + 544, + 470 + ], + "type": "text", + "content": "Figure 5. Component analysis. Effect of expert number, " + }, + { + "bbox": [ + 307, + 458, + 544, + 470 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 307, + 458, + 544, + 470 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 307, + 458, + 544, + 470 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 307, + 458, + 544, + 470 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 30, + "angle": 0, + "type": "image_caption" + } + ], + "index": 29 + }, + { + "bbox": [ + 304, + 487, + 545, + 548 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 487, + 545, + 548 + ], + "spans": [ + { + "bbox": [ + 304, + 487, + 545, + 548 + ], + "type": "text", + "content": "hyper-parameter affects CondHead, a detailed analysis is conducted as shown in Figure 5. We find the performance gain is diminishing beyond 40 expert heads and the hyperparameters " + }, + { + "bbox": [ + 304, + 487, + 545, + 548 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 304, + 487, + 545, + 548 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 487, + 545, + 548 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 304, + 487, + 545, + 548 + ], + "type": "text", + "content": " work best at between 0.5 and 0.7. More analysis experiments are presented in the supplementary." + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 306, + 561, + 378, + 573 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 561, + 378, + 573 + ], + "spans": [ + { + "bbox": [ + 306, + 561, + 378, + 573 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "content": "We introduce a conditionally parameterized neural network design to improve open vocabulary bounding box regression and mask segmentation, which is not explored before. Specifically, we leverage the pre-trained semantic embedding to guide the parameterization of the box and mask heads. The semantic embedding is strongly aligned with visual representation and thus provides effective cues for refining the bounding box and segmenting the objects. Our method named CondHead is extensively validated on different datasets and setups. We hope our findings provide insights for future work on open vocabulary detection." + } + ] + } + ], + "index": 33 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7058" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "text", + "content": "[1] Ankan Bansal, Karan Sikka, Gaurav Sharma, Rama Chellappa, and Ajay Divakaran. Zero-shot object detection. In Proceedings of the European Conference on Computer Vision (ECCV), pages 384-400, 2018. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 136, + 288, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 136, + 288, + 179 + ], + "spans": [ + { + "bbox": [ + 53, + 136, + 288, + 179 + ], + "type": "text", + "content": "[2] Zhaowei Cai and Nuno Vasconcelos. Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6154-6162, 2018. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 180, + 288, + 223 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 180, + 288, + 223 + ], + "spans": [ + { + "bbox": [ + 53, + 180, + 288, + 223 + ], + "type": "text", + "content": "[3] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. arXiv preprint arXiv:2005.12872, 2020. 2, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 224, + 288, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 224, + 288, + 267 + ], + "spans": [ + { + "bbox": [ + 53, + 224, + 288, + 267 + ], + "type": "text", + "content": "[4] Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dólár, and C Lawrence Zitnick. Microsoft coco captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325, 2015. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 269, + 288, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 269, + 288, + 323 + ], + "spans": [ + { + "bbox": [ + 53, + 269, + 288, + 323 + ], + "type": "text", + "content": "[5] Yinpeng Chen, Xiyang Dai, Mengchen Liu, Dongdong Chen, Lu Yuan, and Zicheng Liu. Dynamic convolution: Attention over convolution kernels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11030-11039, 2020. 2, 3, 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 324, + 288, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 324, + 288, + 369 + ], + "spans": [ + { + "bbox": [ + 53, + 324, + 288, + 369 + ], + "type": "text", + "content": "[6] Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764-773, 2017. 2, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 369, + 288, + 423 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 369, + 288, + 423 + ], + "spans": [ + { + "bbox": [ + 53, + 369, + 288, + 423 + ], + "type": "text", + "content": "[7] Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, and Guoqi Li. Learning to prompt for open-vocabulary object detection with vision-language model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14084-14093, 2022. 1, 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 424, + 288, + 468 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 424, + 288, + 468 + ], + "spans": [ + { + "bbox": [ + 53, + 424, + 288, + 468 + ], + "type": "text", + "content": "[8] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 469, + 288, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 469, + 288, + 502 + ], + "spans": [ + { + "bbox": [ + 53, + 469, + 288, + 502 + ], + "type": "text", + "content": "[9] Mingfei Gao, Chen Xing, Juan Carlos Niebles, Junnan Li, Ran Xu, Wenhao Liu, and Caiming Xiong. Open vocabulary object detection with pseudo bounding-box labels. 1, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 503, + 287, + 534 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 503, + 287, + 534 + ], + "spans": [ + { + "bbox": [ + 48, + 503, + 287, + 534 + ], + "type": "text", + "content": "[10] Ross Girshick. Fast r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 1440-1448, 2015. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 536, + 287, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 536, + 287, + 579 + ], + "spans": [ + { + "bbox": [ + 48, + 536, + 287, + 579 + ], + "type": "text", + "content": "[11] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. arXiv preprint arXiv:2104.13921, 2021. 1, 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 581, + 287, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 581, + 287, + 612 + ], + "spans": [ + { + "bbox": [ + 48, + 581, + 287, + 612 + ], + "type": "text", + "content": "[12] Agrim Gupta, Piotr Dollar, and Ross Girshick. LVIS: A dataset for large vocabulary instance segmentation. In CVPR, 2019. 2, 5, 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 614, + 287, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 614, + 287, + 658 + ], + "spans": [ + { + "bbox": [ + 48, + 614, + 287, + 658 + ], + "type": "text", + "content": "[13] Ronghang Hu, Piotr Dólar, Kaiming He, Trevor Darrell, and Ross Girshick. Learning to segment every thing. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4233-4241, 2018. 5" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 658, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 287, + 713 + ], + "type": "text", + "content": "[14] Dat Huynh, Jason Kuen, Zhe Lin, Jiumiang Gu, and Ehsan Elhamifar. Open-vocabulary instance segmentation via robust cross-modal pseudo-labeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7020-7031, 2022. 1, 3" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "text", + "content": "[15] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. Advances in neural information processing systems, 28, 2015. 2, 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 108, + 547, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 108, + 547, + 173 + ], + "spans": [ + { + "bbox": [ + 307, + 108, + 547, + 173 + ], + "type": "text", + "content": "[16] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 3, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 175, + 545, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 175, + 545, + 207 + ], + "spans": [ + { + "bbox": [ + 307, + 175, + 545, + 207 + ], + "type": "text", + "content": "[17] Xu Jia, Bert De Brabandere, Tinne Tuytelaars, and Luc V Gool. Dynamic filter networks. Advances in neural information processing systems, 29, 2016. 2, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 209, + 545, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 209, + 545, + 262 + ], + "spans": [ + { + "bbox": [ + 308, + 209, + 545, + 262 + ], + "type": "text", + "content": "[18] Weicheng Kuo, Anelia Angelova, Jitendra Malik, and Tsung-Yi Lin. Shapemask: Learning to segment novel objects by refining shape priors. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9207-9216, 2019. 5, 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 264, + 545, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 264, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 308, + 264, + 545, + 319 + ], + "type": "text", + "content": "[19] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 2, 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 321, + 545, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 321, + 545, + 365 + ], + "spans": [ + { + "bbox": [ + 308, + 321, + 545, + 365 + ], + "type": "text", + "content": "[20] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg. Ssd: Single shot multibox detector. In European conference on computer vision, pages 21-37. Springer, 2016. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 366, + 545, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 366, + 545, + 431 + ], + "spans": [ + { + "bbox": [ + 308, + 366, + 545, + 431 + ], + "type": "text", + "content": "[21] Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, et al. Simple open-vocabulary object detection with vision transformers. arXiv preprint arXiv:2205.06230, 2022. 1, 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 433, + 545, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 433, + 545, + 498 + ], + "spans": [ + { + "bbox": [ + 308, + 433, + 545, + 498 + ], + "type": "text", + "content": "[22] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 3, 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 500, + 545, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 500, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 308, + 500, + 545, + 544 + ], + "type": "text", + "content": "[23] Shafin Rahman, Salman Khan, and Nick Barnes. Improved visual-semantic alignment for zero-shot object detection. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11932–11939, 2020. 1, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 545, + 545, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 545, + 545, + 589 + ], + "spans": [ + { + "bbox": [ + 308, + 545, + 545, + 589 + ], + "type": "text", + "content": "[24] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779-788, 2016. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 590, + 545, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 590, + 545, + 612 + ], + "spans": [ + { + "bbox": [ + 308, + 590, + 545, + 612 + ], + "type": "text", + "content": "[25] Joseph Redmon and Ali Farhadi. Yolo9000: better, faster, stronger. arXiv preprint, 2017. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 613, + 545, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 613, + 545, + 657 + ], + "spans": [ + { + "bbox": [ + 308, + 613, + 545, + 657 + ], + "type": "text", + "content": "[26] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems, pages 91-99, 2015. 2, 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 658, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 658, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 658, + 545, + 713 + ], + "type": "text", + "content": "[27] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 8430-8439, 2019. 5" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7059" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 529 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 137 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 137 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 137 + ], + "type": "text", + "content": "[28] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2556-2565, 2018. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 140, + 287, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 140, + 287, + 183 + ], + "spans": [ + { + "bbox": [ + 49, + 140, + 287, + 183 + ], + "type": "text", + "content": "[29] Brandon Yang, Gabriel Bender, Quoc V Le, and Jiquan Ngiam. Condconv: Conditionally parameterized convolutions for efficient inference. Advances in Neural Information Processing Systems, 32, 2019. 2, 3, 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 185, + 287, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 185, + 287, + 217 + ], + "spans": [ + { + "bbox": [ + 49, + 185, + 287, + 217 + ], + "type": "text", + "content": "[30] Yuhang Zang, Wei Li, Kaiyang Zhou, Chen Huang, and Chen Change Loy. Open-vocabulary detr with conditional matching. arXiv preprint arXiv:2203.11876, 2022. 1, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 219, + 287, + 272 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 219, + 287, + 272 + ], + "spans": [ + { + "bbox": [ + 49, + 219, + 287, + 272 + ], + "type": "text", + "content": "[31] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14393-14402, 2021. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 274, + 287, + 339 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 274, + 287, + 339 + ], + "spans": [ + { + "bbox": [ + 49, + 274, + 287, + 339 + ], + "type": "text", + "content": "[32] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al. Regionclip: Region-based language-image pretraining. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16793-16803, 2022. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 342, + 287, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 342, + 287, + 384 + ], + "spans": [ + { + "bbox": [ + 49, + 342, + 287, + 384 + ], + "type": "text", + "content": "[33] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9):2337-2348, 2022. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 386, + 287, + 439 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 386, + 287, + 439 + ], + "spans": [ + { + "bbox": [ + 49, + 386, + 287, + 439 + ], + "type": "text", + "content": "[34] Pengkai Zhu, Hanxiao Wang, and Venkatesh Saligrama. Don't even look once: Synthesizing features for zero-shot detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11693-11702, 2020. 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 49, + 441, + 287, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 441, + 287, + 485 + ], + "spans": [ + { + "bbox": [ + 49, + 441, + 287, + 485 + ], + "type": "text", + "content": "[35] Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. Deformable convnets v2: More deformable, better results. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9308-9316, 2019. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 49, + 487, + 287, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 487, + 287, + 529 + ], + "spans": [ + { + "bbox": [ + 49, + 487, + 287, + 529 + ], + "type": "text", + "content": "[36] Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transformers for end-to-end object detection. The International Conference on Learning Representations, 2020. 2" + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "7060" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning To Dub Movies via Hierarchical Prosody Models/a85158d4-00c7-4fae-9712-36af3c2f8ea8_content_list.json b/2023/Learning To Dub Movies via Hierarchical Prosody Models/a85158d4-00c7-4fae-9712-36af3c2f8ea8_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b8a2125c269b118dc6b480be7a0c3b167cb83f93 --- /dev/null +++ b/2023/Learning To Dub Movies via Hierarchical Prosody Models/a85158d4-00c7-4fae-9712-36af3c2f8ea8_content_list.json @@ -0,0 +1,1740 @@ +[ + { + "type": "text", + "text": "Learning to Dub Movies via Hierarchical Prosody Models", + "text_level": 1, + "bbox": [ + 192, + 130, + 777, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Gaoxiang Cong $^{1}$ Liang Li $^{2,3\\dagger}$ Yuankai Qi $^{4}$ Zheng-Jun Zha $^{5}$ Qi Wu $^{4}$ Wenyu Wang $^{1}$ Bin Jiang $^{1}$ Ming-Hsuan Yang $^{6,7}$ Qingming Huang $^{2}$", + "bbox": [ + 137, + 178, + 828, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Shandong University $^{2}$ Institute of Computing Technology, Chinese Academy of Sciences $^{3}$ Lishui Institute of Hangzhou Dianzi University", + "bbox": [ + 125, + 215, + 844, + 251 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{4}$ Australian Institute for Machine Learning, University of Adelaide", + "bbox": [ + 220, + 251, + 748, + 268 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{5}$ University of Science and Technology of China $^{6}$ University of California $^{7}$ Yonsei University", + "bbox": [ + 106, + 268, + 861, + 287 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 321, + 313, + 338 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Given a piece of text, a video clip and a reference audio, the movie dubbing (also known as visual voice clone, V2C) task aims to generate speeches that match the speaker's emotion presented in the video using the desired speaker voice as reference. V2C is more challenging than conventional text-to-speech tasks as it additionally requires the generated speech to exactly match the varying emotions and speaking speed presented in the video. Unlike previous works, we propose a novel movie dubbing architecture to tackle these problems via hierarchical prosody modeling, which bridges the visual information to corresponding speech prosody from three aspects: lip, face, and scene. Specifically, we align lip movement to the speech duration, and convey facial expression to speech energy and pitch via attention mechanism based on valence and arousal representations inspired by the psychology findings. Moreover, we design an emotion booster to capture the atmosphere from global video scenes. All these embeddings are used together to generate mel-spectrogram, which is then converted into speech waves by an existing vocoder. Extensive experimental results on the V2C and Chem benchmark datasets demonstrate the favourable performance of the proposed method. The code and trained models will be made available at https://github.com/GalaxyCong/HPMDubbing.", + "bbox": [ + 76, + 353, + 473, + 731 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 760, + 209, + 776 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Movie dubbing, also known as visual voice clone (V2C) [9], aims to convert a paragraph of text to a speech with both desired voice specified by reference audio and desired emotion and speed presented in the reference video as shown in the top panel of Figure 1. V2C is more challenging than other speech synthesis tasks in two aspects: first,", + "bbox": [ + 76, + 785, + 468, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/49237742c5ba9740a90e5b7990a4b1b1fdda9e72f5e8341acf93b0d4d54b0e6d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 506, + 321, + 888, + 422 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/590ed0ce8c7446d507fe98c68f38e05f7b27adffb3d42c5340f53b914380413a.jpg", + "image_caption": [ + "(a) Visual Voice Cloning (V2C)", + "(b) Hierarchical Prosody Modeling", + "Figure 1. (a) Illustration of the V2C tasks. (b) To generate natural speech with proper emotions, we align the phonemes with lip motion, estimate pitch and energy based on facial expression's arousal and valence, and predict global emotion from video scenes." + ], + "image_footnote": [], + "bbox": [ + 506, + 436, + 887, + 560 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "it requires synchronization between lip motion and generated speech; second, it requires proper prosodic variations of the generated speech to reflect the speaker's emotion in the video (i.e., the movie's plot). These pose significant challenges to existing voice cloning methods.", + "bbox": [ + 496, + 672, + 890, + 748 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Although significant progress has been made, existing methods do not handle the challenges in V2C well. Specifically, text-based dubbing methods [46-48, 54] construct speeches from given text conditioned on the different speaker embedding but do not consider audio-visual synchronization. On the other hand, lip-referred dubbing schemes [18, 32, 55] predict mel-spectrograms directly from a sequence of lip movements typically by encoder-decoder models. Due to high error rates in generated words, these methods can hardly guarantee high-quality results. Further", + "bbox": [ + 496, + 750, + 892, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding author.", + "bbox": [ + 94, + 886, + 220, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "14687", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "more, video-and-text based dubbing methods [17, 20, 32] focus on inferring speaker characters (e.g., age and gender). However, these visual references usually do not convey targeted emotion well as intended in V2C.", + "bbox": [ + 75, + 90, + 468, + 151 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "An ideal dub should align well with the target character so that the audiences feel it is the character speaking instead of the dubber [7]. Thus, a professional dubber usually has a keen sense of observing the unique characteristics of the subject and acts on voice accordingly. In this work, we address these issues with a hierarchical dubbing architecture to synthesize speech. Unlike previous methods, our model connects video representations to speech counterparts at three levels: lip, face, and scene, as shown in Figure 1.", + "bbox": [ + 75, + 152, + 470, + 305 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we propose a hierarchical prosody modeling for movie dubbing, which could keep the audio-visual sync and synthesis speech with proper prosody following the movie's plot. Specifically, we first design a duration alignment module that controls speech speed by learning temporal correspondence via multi-head attention over phonemes and lip motion. Second, we propose an affective-display based Prosody Adaptor (PA), which learns affective psychology computing conditioned on facial expression and is supervised by corresponding energy and pitch in the target voice. In particular, we introduce arousal and valence features extracted from facial regions as emotion representations. This is inspired by the affective computing method [51], which analyses the facial affect relying on dimensional measures, namely valence (how positive the emotional display is) and arousal (how calming or exciting the expression looks). Third, we exploit a scene-atmosphere based emotion booster, which fuses the global video representation with the above adapted hidden sequence and is supervised by the emotive state of the whole voice. The outputs of these three modules are fed into a transformer-based decoder, which converts the speech-related representations into mel-spectrogram. Finally, we output the target speech waves from the mel-spectrogram via a powerful vocoder.", + "bbox": [ + 75, + 306, + 470, + 669 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The contributions of this paper are summarized below:", + "bbox": [ + 96, + 671, + 457, + 686 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a novel hierarchical movie dubbing architecture to better synthesize speech with proper prosody by associating them with visual counterparts: lips, facial expressions, and surrounding scenes.", + "- We design an affective display-based prosody adaptor to predict the energy and pitch of speech from the arousal and valence fluctuations of facial regions in videos, which provides a fine-grained alignment with speakers' emotions.", + "- Extensive experimental results demonstrate the proposed method performs well against state-of-the-art models on two benchmark datasets." + ], + "bbox": [ + 96, + 695, + 467, + 898 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 89, + 640, + 104 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Text to Speech Synthesis. Over the recent years, numerous TTS models [2, 29, 40, 41, 47, 48, 54] have been proposed for generating high-quality natural speech conditioned on given text. Tacotron [54] is an end-to-end generative TTS model that synthesizes speech directly from characters. Then, Tacotron2 [29] replaces the RNN structures by introducing the attention mechanism to improve training efficiency and solve the long dependency issue. Furthermore, FastSpeech [47] and Fastspeech2 [46] exploit the Feed-Forward Transformer (FFT) to generate mel-spectrogram from phoneme sequences. Despite the impressive voice generated, these methods cannot provide the audio with desired emotion and audio-visual sync for movie dubbing.", + "bbox": [ + 496, + 114, + 890, + 311 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Lip to Speech Synthesis. This task aims to reconstruct speech based on the lip motions alone [3,25]. Lip2Wav [42] is a sequence-to-sequence architecture focusing on learning mappings between lip and speech for individual speakers. Recently, [15, 18, 49, 55] improve the architecture and training methods, and provide the possibility of unconstrained speech synthesis in the wild. However, lip-to-speech is incompetent for movie dubbing because the word error rate is still high [1, 3, 11, 13, 16]. In this work, we focus on reconstructing accurate speech from lip motions and generating the desired emotion and identity with proper prosody.", + "bbox": [ + 496, + 316, + 890, + 484 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Talking Heads. Numerous methods have been developed for audio-visual translation [58] or speaking style transfer [57] by reconstructing the visual content in video [8, 30, 31, 50, 53, 59, 61–63]. Wav2Lip [43] uses an expert lip-syncs discriminator to morph lip movements of arbitrary identities. Recently, Papantoniou et al. [38] develop a Neural Emotion Director (NED) to manipulate emotions while preserving speech-related lip movements. However, these methods cannot adapt to the movie dubbing task because they emphasize using generative models to readjust the facial regions instead of reconstructing the desired speech.", + "bbox": [ + 496, + 487, + 890, + 655 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Visual Voice Cloning. Movie dubbing, also known as visual voice clone, aims to convert scripts to speech with both desired voice identity and emotion based on the reference audio and video. To control the speed of generated speech, Neural Dubber [20] exploits a text-video aligner by using scaled dot-product attention mechanism. VTTS [17] uses multi-source attention to fuse the triplets feature and outputs the mel-spectrogram via an RNN-based decoder. Since explicit emotion categories [28] do not exist in these methods, Chen et al. [9] develops a V2C model on a more challenging Densiny Animation dataset, which concentrates on emotional dubbing for movie characters. Although the V2C considers emotion labels, the adopted global video representation negatively affects the fine-grained emotional expression and makes it challenging to render correct prosody corresponding to plot developments. To solve this issue, we", + "bbox": [ + 496, + 657, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "14688", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/ddac5c75842e9b374005ec2daed22d9bc18b8fe847feef9e7e4cfc59c9d4ea4f.jpg", + "image_caption": [ + "Figure 2. Architecture of the proposed hierarchical modular network for movie dubbing, which consists of four main components: Duration Aligner (Sec. 3.1), which learns to predict speech duration based on aligning lip movement and text phoneme; Prosody Adaptor (Sec. 3.2), which predicts energy and pitch from facial arousal and valence, respectively; Atmosphere Booster (Sec. 3.3), which learns a global emotion embedding from a video scene level; and Mel-Generator (Sec. 3.4), which generates mel-spectrograms from embeddings obtained by the aforementioned three modules. The mel-spectrograms are finally converted to audio by a widely adopted vocoder." + ], + "image_footnote": [], + "bbox": [ + 81, + 89, + 890, + 398 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "propose a hierarchical movie dubbing architecture to better synthesize speech with proper prosody and emotion.", + "bbox": [ + 75, + 501, + 470, + 532 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 76, + 547, + 169, + 565 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The main architecture of the proposed model is shown in Fig. 2. First, we use a phoneme encoder [9] to convert the input text $Z_{text}$ to a series of phoneme embeddings $\\mathcal{O} = \\{\\mathbf{o}_1,\\dots,\\mathbf{o}_L\\}$ and use a speaker encoder $F_{spk}$ [9] to capture the voice characteristics $\\mathcal{U}$ from different speakers. Then, taking phonemes and lip regions as input, the duration aligner module uses a multi-head attention mechanism to learn to associate phonemes with related lip movements. Next, the affective display-based prosody adaptor module learns to predict the energy and pitch of the desired speech based on arousal and valence features extracted from facial expressions, respectively. And then, the scene atmosphere booster encodes a global representation of emotion of the entire video content. All the outputs of the above three modules are combined to generate mel-spectrograms, which are finally transformed to a waveform $Y_{voice}$ using a adopted vocoder. We detail each module below.", + "bbox": [ + 75, + 575, + 472, + 833 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Duration Aligner", + "text_level": 1, + "bbox": [ + 76, + 845, + 246, + 862 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The duration aligner contains three steps: (1) extracting the lip features from movie; (2) aligning the phonemes of", + "bbox": [ + 75, + 869, + 470, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "text with the lips; (3) expanding the fused phoneme-lip representation to the desired mel-spectrogram length.", + "bbox": [ + 496, + 500, + 890, + 532 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Extracting lip feature. Let $D_w$ , $D_h$ and $D_c$ be the width, height and number of channels of the video frames, respectively. We first extract lip regions $\\mathbf{x_m} \\in \\mathbb{R}^{T_v \\times D_w \\times D_h \\times D_c}$ from the given video using mouth region pre-processing from [20, 33-36]. Then we exploit the LipEncoder to obtain the lip movement representation:", + "bbox": [ + 496, + 534, + 892, + 625 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {E} _ {\\mathrm {l i p}} = \\operatorname {L i p E n c o d e r} \\left(\\mathbf {x} _ {\\mathbf {m}}\\right) \\in \\mathbb {R} ^ {T _ {v} \\times D _ {m}}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 630, + 890, + 650 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $T_{v}$ denotes the number of video frames, and $D_{m}$ is the hidden dimension of the dynamic lip feature. The LipEncoder consists of several feed-forward transformer blocks that are suitable for capturing both long-term and short-term dynamics lip movement features.", + "bbox": [ + 496, + 655, + 890, + 731 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Aligning text with lips. Inspired by the success of attention mechanism for cross-modality alignment [10, 14, 19, 26, 27, 44, 45, 64], we adopt multi-head attention to learn the alignment between the text phoneme and the lip movement sequence. We use lip embedding as a query to compute the attention on text phonemes. The larger the attention, the more related between a lip embedding and a text phoneme. Due to variations of mouth shapes and pronunciations, the multi-head attention mechanism is suitable for learning their alignments from different aspects. The text-video context sequence $\\mathbf{E}_{\\mathrm{lip},\\mathrm{txt}} = [\\alpha_{\\mathrm{lip},\\mathrm{txt}}^{1},\\dots,\\alpha_{\\mathrm{lip},\\mathrm{txt}}^{n}]\\in$", + "bbox": [ + 496, + 734, + 893, + 904 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "14689", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "$\\mathbb{R}^{T_v\\times D_m}$ is a concatenation of outputs of $n$ attention heads. Concretely, the $k$ -th head's output $\\alpha_{lip,txt}^k$ is obtained by:", + "bbox": [ + 76, + 89, + 467, + 125 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\alpha_ {l i p, t x t} ^ {k} = \\operatorname {s o f t m a x} \\left(\\frac {\\mathrm {Q} ^ {\\top} \\mathrm {K}}{\\sqrt {d _ {k}}} + \\mathrm {M} _ {\\mathrm {t}}\\right) \\mathrm {V} ^ {\\top}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 176, + 133, + 468, + 165 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {Q} = \\mathrm {W} _ {j} ^ {Q} E _ {l i p} ^ {\\top}, \\mathrm {K} = \\mathrm {W} _ {j} ^ {K} \\mathcal {O} ^ {\\top}, \\mathrm {V} = \\mathrm {W} _ {j} ^ {V} \\mathcal {O} ^ {\\top},\n$$\n", + "text_format": "latex", + "bbox": [ + 135, + 166, + 411, + 184 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{W}_j^*$ is a learnable parameter matrix, $d_k$ is the embedding dimension of $\\alpha_{lip,txt}^k$ , and $\\mathbf{M_t}$ is a mask matrix indicating whether a token can be attended. The aligned representation $E_{lip,txt}$ is later expanded to the length of the desired mel-spectrogram.", + "bbox": [ + 76, + 193, + 468, + 270 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Expanding to the desired length. According to the findings in [20], in an audio-visual clip, the length of a melspectrograms sequence is $n$ times that of a video frame sequence because they are temporally synchronized in audio and visual modalities. The number $n$ is computed as:", + "bbox": [ + 76, + 275, + 468, + 349 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nn = \\frac {T _ {m e l}}{T _ {v}} = \\frac {s r / h s}{F P S} \\in \\mathbb {N} ^ {+}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 179, + 361, + 468, + 393 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $FPS$ denotes the Frames per Second of the video, $sr$ denotes the sampling rate of the audio, and $hs$ denotes hop size when transforming the raw waveform into melspectrograms. Phoneme-lip feature $E_{lip,txt}$ is simply duplicated $n$ times in [20] as the final phoneme-lip representation, which lacks flexibility. Instead, we propose to use transposed convolutions to learn the expansion of $E_{lip,txt}$ , which can be formulated as:", + "bbox": [ + 76, + 402, + 468, + 523 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {M} _ {p h o, l i p} = \\operatorname {C o n v - T r a n s p o s e} (n, E _ {l i p, t x t}) \\in \\mathbb {R} ^ {T _ {y} \\times D _ {m}}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 532, + 468, + 550 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $T_{y}$ denotes the length of the desired melspectrogram, $D_{m}$ is the dimension of the initial melspectrogram. The parameters (stride and kernel size) of the transposed convolution can be set under the guidance of $n$ so that $T_{y} \\approx n \\times T_{v}$ .", + "bbox": [ + 76, + 561, + 468, + 637 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Affective-display based Prosody Adaptor", + "text_level": 1, + "bbox": [ + 76, + 646, + 426, + 662 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "One of the critical issues in the V2C task is to describe the speaker's emotions in the given video. To solve this problem, we design an affective-display based Prosody Adaptor (PA), which uses the arousal and valence extracted from facial expressions to represent the emotion. The arousal and valence are then used to predict the energy and pitch of the desired speech model.", + "bbox": [ + 76, + 670, + 468, + 775 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Valence and Arousal Feature. To accurately capture the valence and arousal information from facial expressions, we utilize an emotion face-alignment network (EmoFAN) [51] to encode the facial region into valence $\\mathbf{V}$ and arousal $\\mathbf{A}$ . $\\mathbf{V},\\mathbf{A} = \\mathrm{EmoFAN}(x_f)\\in \\mathbb{R}^{T_v\\times D_m}$ , $x_{f}\\in \\mathbb{R}^{T_{v}\\times D_{w}\\times D_{h}\\times D_{c}}$ is the face region extracted via $S^3 FD$ face detection [60]. The EmoFAN focuses on facial regions relevant to emotion estimation, which utilizes a face alignment network (FAN)", + "bbox": [ + 76, + 780, + 468, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "for facial point detection to ensure robustness by jointly predicting categorical and continuous emotions.", + "bbox": [ + 498, + 90, + 888, + 119 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Bridging Arousal with Energy. Arousal is the physiological and psychological state of being awoken or of sense organs stimulated to the point of perception. To bridge the vocal energy with arousal display, we compute an arousal context vector $A_{i}^{l}$ for frame-level energy of the desired speech from phoneme-lip representation $\\mathbf{M}_{pho,lip}$ :", + "bbox": [ + 498, + 121, + 890, + 213 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nA _ {i} ^ {l} = \\sum_ {k = 0} ^ {T _ {y} - 1} \\xi_ {i, k} M _ {p h o, l i p} ^ {k},\n$$\n", + "text_format": "latex", + "bbox": [ + 545, + 224, + 707, + 268 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\xi_ {i, k} = \\exp (\\hat {\\xi} _ {i, k}) / \\sum_ {j = 0} ^ {T _ {y} - 1} \\exp (\\hat {\\xi} _ {i, j}), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 539, + 272, + 890, + 315 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\xi} _ {i, k} = \\mathbf {w} _ {\\mathbf {a}} ^ {\\top} \\tanh (\\mathbf {W} _ {\\mathbf {a}} ^ {\\top} A _ {i} + \\mathbf {U} _ {\\mathbf {a}} ^ {\\top} M _ {p h o, l i p} ^ {k} + \\mathbf {b} _ {\\mathbf {a}})\n$$\n", + "text_format": "latex", + "bbox": [ + 539, + 318, + 849, + 338 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $i$ is frame index, $A_{i}$ is the $i$ -th row of $\\mathbf{A}$ , $\\xi_{i,k}$ is the attention weight on the $k$ -th phoneme-lip feature $M_{pho,lip}^{k}$ (the $k$ -th row of $\\mathbf{M}_{pho,lip}$ ) regarding $i$ -th arousal display; $\\mathbf{w}_{\\mathbf{a}}^{\\mathrm{T}}$ , $\\mathbf{W}_{\\mathbf{a}}^{\\mathrm{T}}$ , $\\mathbf{U}_{\\mathbf{a}}^{\\mathrm{T}}$ and $\\mathbf{b}_{\\mathbf{a}}$ are learnable parameters; $T_{y}$ is the length of the desired mel-spectrogram.", + "bbox": [ + 500, + 349, + 890, + 424 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Then, we use an energy predictor to project the arousal-related phoneme-lip embedding $A_{i}^{l}$ to the energy of speech:", + "bbox": [ + 500, + 425, + 888, + 455 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nE _ {a r o} = \\operatorname {P r e d i c t o r} \\left(\\left\\{A _ {i} ^ {l} \\right\\} _ {i = 1} ^ {T _ {y}}\\right), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 468, + 890, + 487 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $E_{aro} \\in \\mathbb{R}^{T_y}$ represents the predicted energy of speech, and the energy predictor consists of several fully-connected layers, Conv1D blocks and layer normalization.", + "bbox": [ + 498, + 500, + 890, + 546 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Bridging Valence with Pitch. In prosody linguistics, speakers can speak with a wide pitch range (this is usually associated with excitement) while, at other times, with a narrow range. Valence is the affective quality referring to the intrinsic positiveness or negativeness of an event or situation. Similar to arousal to energy, we first compute a valence context vector for the frame-level pitch of the desired speech from phoneme-lip representation $\\mathbf{M}_{pho,lip}$ :", + "bbox": [ + 498, + 551, + 890, + 674 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nV _ {i} ^ {l} = \\sum_ {k = 0} ^ {T _ {y} - 1} \\psi_ {i, k} M _ {p h o, l i p} ^ {k},\n$$\n", + "text_format": "latex", + "bbox": [ + 547, + 685, + 714, + 728 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\psi_ {i, k} = \\exp (\\hat {\\psi} _ {i, k}) / \\sum_ {j = 0} ^ {T _ {y} - 1} \\exp (\\hat {\\psi} _ {i, j}), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 539, + 732, + 890, + 775 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\psi} _ {i, k} = \\mathbf {w} _ {\\mathbf {g}} ^ {\\top} \\tanh (\\mathbf {W} _ {\\mathbf {g}} ^ {\\top} V _ {i} + \\mathbf {U} _ {\\mathbf {g}} ^ {\\top} M _ {p h o, l i p} ^ {k} + \\mathbf {b} _ {\\mathbf {g}})\n$$\n", + "text_format": "latex", + "bbox": [ + 539, + 777, + 849, + 799 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{w}_{\\mathbf{g}}^{\\mathbf{T}}, \\mathbf{W}_{\\mathbf{g}}^{\\mathbf{T}}, \\mathbf{U}_{\\mathbf{g}}^{\\mathbf{T}}$ and $\\mathbf{b}_{\\mathbf{g}}$ are learnable parameters. Then, we exploit a pitch predictor to convert the valence-related phoneme-lip embedding $V_{i}^{l}$ to the pitch of speech, which can be formulated as:", + "bbox": [ + 498, + 809, + 890, + 869 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nP _ {v a l} = \\operatorname {P r e d i c t o r} \\left(\\left\\{V _ {i} ^ {l} \\right\\} _ {i = 1} ^ {T _ {y}}\\right). \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 882, + 890, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "14690", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, we concatenate the two prosody-related features as the contextualized affect primitives:", + "bbox": [ + 76, + 90, + 468, + 121 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {M} _ {\\mathbf {p}} = \\left\\{\\left[ A _ {i} ^ {l}; V _ {i} ^ {l} \\right] \\right\\} _ {i = 1} ^ {T _ {y}}. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 197, + 128, + 468, + 148 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Scene Atmosphere Booster", + "text_level": 1, + "bbox": [ + 76, + 155, + 318, + 171 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "As a unique form of artistic expression, the scene layout and colours of the film convey an emotional atmosphere to evoke resonance with the audience [5, 56]. To reason the comprehensive emotion, we design a scene atmosphere booster to combine the global context information and generated prosody. First, we use the I3D model [6] to extract the scene representation $S$ from the video. Then, we fuse the global contextual emotion of vision with prosody information of speech through a cross-model attention mechanism by:", + "bbox": [ + 75, + 179, + 470, + 330 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {M} _ {\\mathrm {e}} = \\operatorname {S o f t m a x} \\left(\\frac {\\mathbf {M} _ {\\mathbf {p}} S ^ {\\top}}{\\sqrt {D _ {m}}}\\right) \\mathbf {M} _ {\\mathbf {p}} \\in \\mathbb {R} ^ {T _ {y} \\times D _ {w}}. \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 114, + 335, + 468, + 372 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, formulated as a maxpool and fully-connected layer, the emotional predictor projects prosody-scene context sequence $\\mathbf{M}_{\\mathrm{e}}$ to emotional embedding:", + "bbox": [ + 76, + 378, + 468, + 422 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nG _ {e m o} = \\operatorname {M a x p o o l} (\\operatorname {P r e d i c t o r} (\\mathbf {M} _ {\\mathbf {e}})). \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 148, + 431, + 468, + 450 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Audio Generation", + "text_level": 1, + "bbox": [ + 76, + 457, + 250, + 472 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We fuse the three kinds of speech attribute information by concatenation operation in the spatial dimension. Then, we use transformer-based mel-spectrogram decoder to convert the adapted hidden sequence into mel-spectrogram sequence in parallel by:", + "bbox": [ + 76, + 479, + 468, + 556 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {f} = \\text {T r a n s f o r m e r D e c o d e r} \\left(\\mathbf {M} _ {\\text {p h o}, l i p} \\oplus \\mathbf {M} _ {\\mathbf {p}} \\oplus \\mathbf {M} _ {\\mathbf {e}}\\right), \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 84, + 565, + 468, + 583 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $M_{pho,lip}$ , $M_p$ , and $M_e$ denote the hidden representation of phoneme-lip feature, prosody variances, and emotional tone, respectively. Specifically, our mel-spectrogram decoder consists of a stack of self-attention layers [52] and 1D-convolution layers as in FastSpeech [47]. We then use the mel-linear layer and postnet [48] to refine the hidden states into final dimensional mel-spectrograms by:", + "bbox": [ + 76, + 590, + 468, + 696 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\ny = \\operatorname {P o s t N e t} (\\operatorname {F C} (f)). \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 194, + 705, + 468, + 722 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, to generate the time-domain waveform $y_{w}$ from mel-spectrogram $y$ , we use HiFi-GAN [23] as our vocoder, which mainly consists of a transposed convolution network and a multi-receptive field fusion module.", + "bbox": [ + 76, + 729, + 468, + 790 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.5. Loss Functions", + "text_level": 1, + "bbox": [ + 76, + 799, + 230, + 814 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our model is trained in an end-to-end fashion via optimizing the sum of all losses:", + "bbox": [ + 76, + 821, + 468, + 853 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {p i t c h}} = \\frac {1}{T _ {y}} \\sum_ {t = 1} ^ {T _ {y} - 1} \\left(P _ {t} - \\hat {P} _ {\\text {v a l}} ^ {t}\\right) ^ {2}, \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 163, + 861, + 468, + 902 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {e n e r g y}} = \\frac {1}{T _ {y}} \\sum_ {t = 1} ^ {T _ {y} - 1} \\left(E _ {t} - \\hat {E} _ {a r o} ^ {t}\\right) ^ {2}, \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 580, + 88, + 890, + 130 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {e m o} = - \\sum_ {i = 1} ^ {C} G _ {i} \\log \\left(\\hat {G} _ {e m o} ^ {i}\\right), \\tag {16}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 136, + 890, + 176 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {m e l} = \\frac {1}{T _ {y}} \\sum_ {t = 1} ^ {T _ {y} - 1} \\left\\| \\mathbf {f} _ {t} - \\hat {\\mathbf {f}} _ {t} \\right\\|. \\tag {17}\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 181, + 890, + 224 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {S} = \\lambda_ {1} \\mathcal {L} _ {\\text {m e l}} + \\lambda_ {2} \\mathcal {L} _ {\\text {p i t c h}} + \\lambda_ {3} \\mathcal {L} _ {\\text {e n e r g y}} + \\lambda_ {4} \\mathcal {L} _ {\\text {e m o}}, \\tag {18}\n$$\n", + "text_format": "latex", + "bbox": [ + 509, + 229, + 890, + 247 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{mel}$ , $\\mathcal{L}_{pitch}$ , $\\mathcal{L}_{energy}$ and $\\mathcal{L}_{emo}$ , denote the losses of mel-spectrogram, pitch, energy and global emotion respectively. $P_{t}$ , $E_{t}$ , and $\\mathbf{f}_t$ are ground-truth pitch, energy, and mel-spectrogram on frame-level, respectively. G is the ground-truth emotional label and C denotes all categories.", + "bbox": [ + 496, + 252, + 890, + 328 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experimental Results", + "text_level": 1, + "bbox": [ + 500, + 340, + 705, + 357 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we first briefly describe the datasets used for evaluation and the evaluation metric. Then we present the implementation details of the proposed method. Last, we show the results compared to state-of-the-art methods and the ablation study.", + "bbox": [ + 496, + 364, + 890, + 441 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets", + "text_level": 1, + "bbox": [ + 500, + 450, + 601, + 464 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "V2C is a multi-speaker dataset for animation movie dubbing with identity and emotion annotations [9]. It is collected from 26 Disney cartoon movies and covers 153 diverse characters. V2C not only needs to generate voices with identity characteristics according to the reference audio but also capture emotional information based on the reference movie clips. The whole dataset has 10,217 video clips with paired audio and subtitles. The training/validation/test size are $60\\%$ , $10\\%$ , $30\\%$ .", + "bbox": [ + 496, + 476, + 890, + 611 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Chem is a single-speaker dataset composed of 6,640 short video clips collected from the YouTube, with the total video length of approximately nine hours [20]. The Chem dataset is originally used for the unconstrained single-speaker lipto-speech synthesis [42], which takes place in a chemistry lecture. For fluency and complete dubbing, each video clip has sentence-level text and audio based on the start and end timestamps. There are 6,240, 200, and 200 dubbing clips for training, validation, and testing, respectively.", + "bbox": [ + 496, + 612, + 890, + 750 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Evaluation Metrics", + "text_level": 1, + "bbox": [ + 500, + 756, + 684, + 771 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Audio-visual synchronization. To evaluate the synchronization between the generated speech and the video quantitatively, we adopt Lip Sync Error Distance (LSE-D) and Lip Sync Error Confidence (LSE-C) as our metrics, which can explicitly test for synchronization between lip motions and speech in unconstrained videos in the wild [12, 43].", + "bbox": [ + 496, + 779, + 890, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Mel Cepstral Distortion and its variants. MCD [24], MCD-DTW [4] and MCD-DTW-SL [9] are adopted, which", + "bbox": [ + 496, + 869, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "14691", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/5c6fc8ca9442a8a0fb0d3e801b7805f10414a0c52bffbb1d9e98192c2735314d.jpg", + "table_caption": [], + "table_footnote": [ + "Table 1. Results on the V2C dataset with comparisons against state-of-the-art methods. We provide the results using both objective metrics (i.e., LSE-D, LSE-C, MCD, MCD-DTW and MCD-DTW-SL) and subjective metrics (i.e., MOS-Naturalness and MOS-Similarity). \"Id. Acc.\" and \"Emo. Acc.\" are the identity and emotion accuracy of the generated speech, respectively. The method with “*” refers to a variant taking video (emotion) embedding as an additional input as in [9]. $\\uparrow (\\downarrow)$ means that the higher (lower) value is better." + ], + "table_body": "
MethodsLSE-D ↓LSE-C ↑MCD ↓MCD-DTW ↓MCD-DTW-SL ↓Id. Acc. ↑Emo. Acc. ↑MOS-N ↑MOS-S ↑
Ground Truth6.7347.81300.0000.0000.0090.6284.384.61 ± 0.154.74 ± 0.12
SV2TTS [21]13.7332.72521.0812.8749.5633.6237.192.03 ± 0.221.92 ± 0.15
SV2TTS* [21]12.6173.34919.3812.7334.5135.1842.052.07 ± 0.072.15 ± 0.09
Tacotron* [54]13.4752.93819.7918.7342.1532.4939.682.12 ± 0.172.06 ± 0.12
FastSpeech2 [46]12.2612.95820.7814.3919.4121.7246.822.79 ± 0.102.63 ± 0.09
FastSpeech2* [46]12.1132.60420.6614.5920.7923.4446.903.08 ± 0.062.89 ± 0.07
V2C-Net1 [9]11.7843.02620.6114.2319.1526.8448.413.19 ± 0.043.06 ± 0.06
Ours8.0365.60815.6612.2913.4837.7561.464.03 ± 0.083.89 ± 0.07
", + "bbox": [ + 81, + 88, + 893, + 223 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "reflect the similarity of mel-spectrograms. MCD-DTW uses the Dynamic Time Warping (DTW) [37] algorithm to find the minimum MCD between two speeches, while MCD-DTW-SL introduces the duration measure coefficient to consider the length and the quality of generated speech [9].", + "bbox": [ + 75, + 297, + 467, + 375 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Emotion and identity accuracy. To measure whether the generated speech carries proper emotion and speaker identity, we adopt an emotion accuracy (Emo. Acc.) and an identity accuracy (Id. Acc.) as our metrics as in [9].", + "bbox": [ + 75, + 375, + 467, + 436 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Subjective evaluations. To further evaluate the quality of generated speech, we conduct a human study using a subjective evaluation metric, following the settings in [9]. Specifically, we adopt the MOS-naturalness (MOS-N) and MOS-similarity (MOS-S) to assess the naturalness of the generated speech and the recognition of the desired voice.", + "bbox": [ + 75, + 436, + 467, + 527 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Implementation Details", + "text_level": 1, + "bbox": [ + 76, + 542, + 294, + 558 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For the duration aligner, we use 4 Feed-Forward Transformer (FFT) blocks and 3 FFT blocks for the phoneme encoder and lip movement encoder, respectively. We set the dimension of the phoneme feature $\\mathcal{O}$ and lip features $E_{lip}$ to 256. We use 8 attention heads for alignment between lip and phoneme. For each movie clip, Our FPS set is 25, the sampling rate $sr$ is $22050Hz$ . We use short-time fourier transform (STFT) to obtain the mel-spectrum, and the number of points of the fourier transform is 1024. We use the Conv-Transpose1D module with 2 stride and 4 kernel sizes to obtain the duration features. In Valence and Arousal Feature Encoder, the EmoFAN consists of one 2D convolution with a kernel size of $7\\times 7$ and 3 convolution blocks (ConvBlock) with a kernel size of $3\\times 3$ and Average Pooling stride of $2\\times 2$ . Similarly, we set the dimension of the valence and arousal feature to 256. For the mel-spectrograms generator, the mel-spectrogram decoder consists of 6 FFT blocks and the hidden state of mel-linear layer is of size 80.", + "bbox": [ + 75, + 566, + 467, + 838 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For training, we use Adam [22] with $\\beta_{1} = 0.9$ , $\\beta_{2} = 0.98$ , $\\epsilon = 10^{-9}$ to optimize our model. For V2C and Chem dataset, we set the learning rate schedule to 0.00001 and 0.00005, respectively. In this work, we use pretrained HiFiGAN [23]", + "bbox": [ + 75, + 839, + 467, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "as the vocoder to transform the generated mel-spectrograms into audio samples. We set the batch size to 16 on two datasets. Our model is implemented in PyTorch [39]. All the models are performed on a single NVIDIA GTX3090Ti GPU. We train the model with 600 epochs on the V2C dataset and 400 epochs on the Chem dataset.", + "bbox": [ + 496, + 297, + 890, + 388 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.4.Quantitative Evaluation", + "text_level": 1, + "bbox": [ + 498, + 404, + 720, + 420 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We compare with five related baselines of speech synthesizes. (1) SV2TTS [21] is a basic TTS model to generate speech with reference audio for multi-speakers; (2) Tacotron [54] is an end-to-end generative TTS model that synthesizes speech directly from textual characters; (3) FastSpeech2 [46] introduces the variance adaptor to convert the text to waveform by end-to-end; (4) Neural Dubber [20] synthesizes human speech for given video according to the corresponding text; (5) V2C-Net [9] is the first model for movie dubbing, which match the speaker's emotion presented in video. Note that we do not compare with Neural Dubber [20] on the V2C benchmark due to the unavailability of its code and missing implementation details.", + "bbox": [ + 496, + 430, + 890, + 626 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Results on the V2C benchmark. The results are presented in Table 1. Our method achieves the best performance on all nine metrics. Specifically, in terms of audio-visual sync, our method achieves 8.036 of LSE-D and 5.608 of LSE-C, which significantly surpasses the previous best results and is much closer to human performance. In terms of MCD, MCD-DTW, and MCD-DTW-SL, our method achieves relative $24.02\\%$ , $13.63\\%$ and $29.61\\%$ improvements, respectively. This indicates our method can achieve a better mel-spectrogram than others. The above results together show that bridging specific attributes of speech with corresponding visual counterparts can make the generated speech present better prosodies and lip motion sync. Additionally, our method outperforms the previous method by a large margin in emotion accuracy and can gain better identification accuracy. This indicates the proposed method can better capture and convey emotions, which is of great importance for the movie dubbing task. Last, the human subjec", + "bbox": [ + 496, + 628, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "14692", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/2e49237791e3487f49b5906e4027caa3c9d40490fc3ab7a11c1f2bd848575042.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodsAQ↑AV Sync ↑LSE-D ↓LSE-C ↑
Ground Truth3.93 ± 0.084.13±0.076.9267.711
FastSpeech2 [46]3.71±0.083.29±0.0911.862.805
Tacotron* [54]3.55±0.093.03±0.1011.792.231
V2C-Net [9]3.48±0.143.25±0.1111.262.907
Neural Dubber [20]3.74±0.083.91±0.077.2127.037
Ours3.84±0.113.97±0.086.9757.176
", + "bbox": [ + 81, + 88, + 467, + 193 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/8f66cce0ae19dc6226e1fab082b79f3793fe58e328778b118db2a6a6f7d35a1b.jpg", + "table_caption": [ + "Table 2. Results on the Chem dataset with comparisons against state-of-the-art methods. AQ (Audio Quality) and AV Sync (audio-visual synchronization) are subjective metrics. Note that the Chem dataset is a single-speaker non-movie dataset, and thus there is no identity accuracy and emotion accuracy." + ], + "table_footnote": [], + "table_body": "
#MethodsLSE-D ↓LSE-C ↑MCD ↓Id. Acc. ↑Emo. Acc. ↑
1w/o DA11.8353.71619.5318.7538.33
2w/o PA8.5145.27416.3410.4222.08
3w/o AB8.2615.40815.9033.3353.92
4w/o Valence9.2154.93516.3129.8135.31
5w/o Arousal8.7935.21615.7932.7446.38
6VA v.s. FF9.1605.01119.8723.6739.58
7w/o multi-head10.9483.89418.6524.8540.25
8Duplication11.4753.81418.6321.7837.92
9Full model8.0365.60815.6637.7561.46
", + "bbox": [ + 80, + 289, + 468, + 422 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Ablation study of the proposed method on the V2C benchmark dataset.", + "bbox": [ + 76, + 431, + 468, + 460 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "tive evaluation results (see MOS-N and MOS-S) also show that our method can generate speeches that are closer to realistic speech according to naturalness and similarity.", + "bbox": [ + 76, + 476, + 468, + 521 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Results on the Chem benchmark. As shown in Table 2, our model is ahead of the state-of-the-art methods in all metrics on the Chem benchmark. In terms of the audiovisual sync, our method achieves 6.975 LSE-D and 7.176 LSE-C. Furthermore, in the subjective evaluations, our method improves $10.34\\%$ on AQ and $22.15\\%$ on AV Sync. The results show that our performance is much closer to the ground truth recording, which indicates that our model synthesizes high-quality natural speech by controlling the prosody from hierarchical visual representation.", + "bbox": [ + 75, + 522, + 468, + 674 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5. Ablation Studies", + "text_level": 1, + "bbox": [ + 76, + 685, + 241, + 699 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effectiveness of Duration Aligner, Prosody Adaptor, and Atmosphere Booster. We evaluate the effectiveness of these three modules by removing them separately and retraining the model. The results are shown in Row $1\\sim 3$ of Table 3. The result shows that all the proposed modules contribute significantly to the overall performance, and each module has a different focus. Specifically, the performance on the audio-visual metrics (LSE-D, LSE-C, and MCD) drops the most when removing the Duration Aligner (DA). This reflects the DA module indeed helps the model learn a better temporal synchronization. By contrast, the perfor", + "bbox": [ + 75, + 708, + 468, + 875 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/cda78790666a031496c81aae36e5d814c561af2fac3e2dd0f4de0ea5ee9eb9a6.jpg", + "image_caption": [ + "Figure 3. Audiovisual consistency visualization on V2C dataset: Ground Truth (GT), our model, FastSpeech 2 (FS2) and V2C-Net." + ], + "image_footnote": [], + "bbox": [ + 506, + 90, + 887, + 361 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "mance on identity accuracy and emotion accuracy drop the most when removing the Prosody Adaptor (PA). This can be attributed to the predicted pitch and energy representing a speaker's identity and his/her emotion. When removing the Atmosphere Booster (AB), the performance drops compared to the full model (Row 9) but does not drop as much as when removing the other two modules. This indicates the AB module also contributes the overall performance improvement but contributes the least in the three modules.", + "bbox": [ + 496, + 417, + 890, + 554 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effectiveness of valence and arousal. In our model, we bridge arousal with energy and valence with pitch by attention mechanism. To evaluate their effectiveness, we cut off the connection and predict energy and pitch directly from the phoneme-lip representation. The results are presented in Row $4\\sim 5$ of Table 3. It shows that the performance drops significantly when removing either of them and drops more when removing valence. This indicates valence contributes more than arousal on the V2C task.", + "bbox": [ + 496, + 559, + 892, + 694 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Valence-and-Arousal v.s. Facial Expression. To compare the role of affective display and facial expressions on prosody inference, we replace the input of the APA module with original embeddings of facial features from CNN. As row “VA v.s. FF” of Table 3 shows, the model performance drops significantly. This is likely caused by facial features that are still far from the information-containing emotion, which is not enough to guide prosody generation.", + "bbox": [ + 496, + 699, + 890, + 821 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Multi-head Attention in Duration Adaptor. In our duration aligner, we exploit multi-head attention to learn the relation between the phoneme sequences and the lip motion. To verify its effectiveness, we conduct experiments using conventional dot-product attention. The results are", + "bbox": [ + 496, + 825, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_footnote", + "text": "$^{1}$ V2C republish official results on https://github.com/chenqi008/V2C.", + "bbox": [ + 94, + 886, + 460, + 898 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "14693", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4168d8e5209fd61ce3b9922e2f0d9862cd498d254be589953774697641ab5e67.jpg", + "image_caption": [ + "Figure 4. Visualization of audio on V2C dataset (top) and Chem dataset (bottom). Orange curves are $F_{0}$ contours, where $F_{0}$ is the fundamental frequency of audio. Purple curves refer to the energy (volume) of audio. The horizontal axis is the duration of the audio. The red circles highlight the mel-spectrograms at the same moment as the frame shown on the left side." + ], + "image_footnote": [], + "bbox": [ + 80, + 90, + 888, + 351 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "presented in Row 7 of Table 3. The performance drops significantly on all metrics, such as $26.59\\%$ and $44.02\\%$ decrease on LSE-D and LSE-C compared to Row 9, respectively. This indicates multi-head attention learns a much better correlation between phonemes with lip movement.", + "bbox": [ + 75, + 420, + 468, + 494 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Conv-Transpose v.s. Duplication. In our duration aligner, we propose to use Conv-Transpose to learn the expansion of the fused phoneme-lip representation to its desired length. To verify its effectiveness, we replace it with simple make $n$ duplications as in [20]. The results are shown in Row 8 of Table 3. It shows that the performance drops significantly. For example, it falls $15.94\\%$ on MCD metric. This demonstrates the superiority of using transposed convolution to learn the upsampling than simply copying.", + "bbox": [ + 75, + 500, + 470, + 636 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.6. Qualitative Results", + "text_level": 1, + "bbox": [ + 76, + 646, + 261, + 662 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Audiovisual consistency visualization. Figure 3 presents the mel-spectrograms of generated audios along with its frames. Blue and red bounding boxes denote whether the character is speaking or not, respectively. Compared with other methods, the mel-spectrogram generated by our model is closer to the ground truth, indicating better audiovisual synchronization. This can be attributed to our duration aligner, which exploits multi-head alignment between the text phoneme sequence and the lip movement sequence. By controlling the lip movements explicitly, we obtain the desired length of mel-spectrograms, which makes the speech well synchronized with the input video.", + "bbox": [ + 75, + 670, + 468, + 851 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Arousal and valence with prosody visualization. We selected two examples from the test set of the V2C dataset and Chem dataset to demonstrate the alignment between en", + "bbox": [ + 75, + 854, + 470, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "ergy and arousal as well as pitch and valence. The valence (positive or negative) and arousal (calming or exciting) of facial expressions are shown in the first column. The main pitch and energy are shown in orange and blue curves in the right column. We use the red circle to highlight the pitch and energy that correspond to the video frame shown in the left column. The result shows that our method achieves energy and pitch closer to the ground truth speech. When the chemistry teacher becomes excited and positive, our model successfully leverages the affective display to synthesize a similar pitch and energy as the ground-truth speech.", + "bbox": [ + 496, + 420, + 893, + 588 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 602, + 619, + 617 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we propose a hierarchical prosody modeling network for movie dubbing, which bridges video representations and speech attributes from three levels: lip, facial expression, and scene. By associating these visual representations with their voice counterparts, we obtain more powerful representations for dubbing. Furthermore, we design an affective-display based prosody adaptor, which effectively learns to align the valence and arousal to the pitch and energy of speeches. Our proposed model sets new state-of-the-art on both Chem and V2C-Animation benchmarks.", + "bbox": [ + 496, + 628, + 890, + 777 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement. This work was supported in part by the National Key R&D Program of China under Grant 2018AAA0102000, and in part by National Natural Science Foundation of China under Grants 62225207 and U19B2038, the Key R&D Plan Project of Zhejiang Province (No. 2023C01004), Shandong Provincial Natural Science Foundation (ZR2020MA064), Youth Innovation Promotion Association of CAS under Grant 2020108.", + "bbox": [ + 496, + 780, + 892, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "14694", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Triantafyllos Afouras, Joon Son Chung, and Andrew Zisserman. Deep lip reading: A comparison of models and an online application. In Interspeech, pages 3514-3518, 2018. 2", + "[2] Sercan Ömer Arik, Mike Chrzanowski, Adam Coates, Gregory Frederick Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Y. Ng, Jonathan Raiman, Shubho Sengupta, and Mohammad Shoeeybi. Deep voice: Real-time neural text-to-speech. In ICML, pages 195-204, 2017. 2", + "[3] Yannis M Assael, Brendan Shillingford, Shimon Whiteson, and Nando De Freitas. Lipnet: End-to-end sentence-level lipreading. arXiv preprint arXiv:1611.01599, 2016. 2", + "[4] Eric Battenberg, R. J. Skerry-Ryan, Soroosh Mariooryad, Daisy Stanton, David Kao, Matt Shannon, and Tom Bagby. Location-relative attention mechanisms for robust long-form speech synthesis. In ICASSP, pages 6194-6198, 2020. 5", + "[5] Manuel Burghardt, Michael Kao, and Christian Wolff. Beyond shot lengths - using language data and color information as additional parameters for quantitative movie analysis. In ADHO, pages 753-755, 2016. 5", + "[6] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In CVPR, pages 6299-6308, 2017. 5", + "[7] Frederic Chaume. Dubbing practices in europe: localisation beats globalisation. LANS-TTS, 6, 2007. 2", + "[8] Lele Chen, Ross K. Maddox, Zhiyao Duan, and Chenliang Xu. Hierarchical cross-modal talking face generation with dynamic pixel-wise loss. In CVPR, pages 7832-7841, 2019. 2", + "[9] Qi Chen, Mingkui Tan, Yuankai Qi, Jiaqiu Zhou, Yuanqing Li, and Qi Wu. V2C: visual voice cloning. In CVPR, pages 21210-21219, 2022. 1, 2, 3, 5, 6, 7", + "[10] Weidong Chen, Dexiang Hong, Yuankai Qi, Zhenjun Han, Shuhui Wang, Laiyun Qing, Qingming Huang, and Guorong Li. Multi-attention network for compressed video referring object segmentation. In ACM MM, pages 4416–4425, 2022. 3", + "[11] Joon Son Chung, Andrew W. Senior, Oriol Vinyals, and Andrew Zisserman. Lip reading sentences in the wild. In CVPR, pages 3444-3453, 2017. 2", + "[12] Joon Son Chung and Andrew Zisserman. Out of time: Automated lip sync in the wild. In ACCV Workshop, pages 251-263, 2016. 5", + "[13] Joon Son Chung and Andrew Zisserman. Lip reading in profile. In BMVC, 2017. 2", + "[14] Gaoxiang Cong, Liang Li, Zhenhuan Liu, Yunbin Tu, Weijun Qin, Shenyuan Zhang, Chengang Yan, Wenyu Wang, and Bin Jiang. LS-GAN: iterative language-based image manipulation via long and short term consistency reasoning. In ACM MM, pages 4496-4504, 2022. 3", + "[15] Rodrigo Schoburg Carrillo de Mira, Alexandros Haliassos, Stavros Petridis, Bjorn W. Schuller, and Maja Pantic. SVTS: scalable video-to-speech synthesis. In INTER-SPEECH, pages 1836-1840, 2022. 2" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[16] Ariel Ephrat and Shmuel Peleg. Vid2speech: Speech reconstruction from silent video. In ICASSP, pages 5095-5099, 2017. 2", + "[17] Michael Hassid, Michelle Tadmor Ramanovich, Brendan Shillingford, Miaosen Wang, Ye Jia, and Tal Remez. More than words: In-the-wild visually-driven prosody for text-to-speech. In CVPR, pages 10577–10587, 2022. 2", + "[18] Sindhu B. Hegde, K. R. Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C. V. Jawahar. Lip-to-speech synthesis for arbitrary speakers in the wild. In ACM MM, pages 6250-6258, 2022. 1, 2", + "[19] Yicong Hong, Qi Wu, Yuankai Qi, Cristian Rodriguez Opazo, and Stephen Gould. A recurrent vision-and-language BERT for navigation. CoRR, abs/2011.13922, 2020. 3", + "[20] Chenxu Hu, Qiao Tian, Tingle Li, Yuping Wang, Yuxuan Wang, and Hang Zhao. Neural dubber: Dubbing for videos according to scripts. In NeurIPS, pages 16582-16595, 2021. 2, 3, 4, 5, 6, 7, 8", + "[21] Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez-Moreno, and Yonghui Wu. Transfer learning from speaker verification to multispeaker text-to-speech synthesis. In NeurIPS, pages 4485-4495, 2018. 6", + "[22] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Yoshua Bengio and Yann LeCun, editors, ICLR, 2015. 6", + "[23] Jungil Kong, Jaehyeon Kim, and Jaekyoung Bae. Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. In NeurlPS, 2020. 5, 6", + "[24] R. Kubichek. Mel-cepstral distance measure for objective speech quality assessment. In PACRIM, pages 125–128, 1993. 5", + "[25] Yaman Kumar, Rohit Jain, Khwaja Mohd. Salik, Rajiv Ratn Shah, Yifang Yin, and Roger Zimmermann. Lipper: Synthesizing thy speech using multi-view lipreading. In AAAI, pages 2588-2595, 2019. 2", + "[26] Liang Li, Xingyu Gao, Jincan Deng, Yunbin Tu, Zheng-Jun Zha, and Qingming Huang. Long short-term relation transformer with global gating for video captioning. IEEE Trans. Image Process., pages 2726-2738, 2022. 3", + "[27] Liang Li, Chenggang Clarence Yan, Xing Chen, Chunjie Zhang, Jian Yin, Baochen Jiang, and Qingming Huang. Distributed image understanding with semantic dictionary and semantic expansion. Neurocomputing, pages 384-392, 2016. 3", + "[28] Liang Li, Xinge Zhu, Yiming Hao, Shuhui Wang, Xingyu Gao, and Qingming Huang. A hierarchical cnn-rnn approach for visual emotion classification. ACM Trans. Multim. Comput. Commun. Appl., pages 1-17, 2019. 2", + "[29] Naihan Li, Shujie Liu, Yanqing Liu, Sheng Zhao, and Ming Liu. Neural speech synthesis with transformer network. In AAAI, pages 6706-6713, 2019. 2", + "[30] Borong Liang, Yan Pan, Zhizhi Guo, Hang Zhou, Zhibin Hong, Xiaoguang Han, Junyu Han, Jingtuo Liu, Errui Ding, and Jingdong Wang. Expressive talking head generation with granular audio-visual control. In CVPR, pages 3377–3386, 2022. 2" + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "14695", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[31] Xian Liu, Qianyi Wu, Hang Zhou, Yinghao Xu, Rui Qian, Xinyi Lin, Xiaowei Zhou, Wayne Wu, Bo Dai, and Bolei Zhou. Learning hierarchical cross-modal association for cospeech gesture generation. In CVPR, pages 10452-10462, 2022. 2", + "[32] Junchen Lu, Berrak Sisman, Rui Liu, Mingyang Zhang, and Haizhou Li. Visualts: TTS with accurate lip-speech synchronization for automatic voice over. In ICASSP, pages 8032-8036, 2022. 1, 2", + "[33] Pingchuan Ma, Brais Martinez, Stavros Petridis, and Maja Pantic. Towards practical lipreading with distilled and efficient models. In ICASSP, pages 7608-7612, 2021. 3", + "[34] Pingchuan Ma, Yujiang Wang, Stavros Petridis, Jie Shen, and Maja Pantic. Training strategies for improved lip-reading. In ICASSP, pages 8472–8476, 2022. 3", + "[35] Pingchuan Ma, Yujiang Wang, Jie Shen, Stavros Petridis, and Maja Pantic. Lip-reading with densely connected temporal convolutional networks. In WACV, pages 2857–2866, 2021. 3", + "[36] Brais Martinez, Pingchuan Ma, Stavros Petridis, and Maja Pantic. Lipreading using temporal convolutional networks. In ICASSP, pages 6319-6323, 2020. 3", + "[37] Meinard Müller. Dynamic time warping. Information Retrieval for Music and Motion, pages 69-84, 2007. 6", + "[38] Foivos Paraperas Papantoniou, Panagiotis Paraskevas Filntisis, Petros Maragos, and Anastasios Roussos. Neural emotion director: Speech-preserving semantic control of facial expressions in \"in-the-wild\" videos. In CVPR, pages 18759-18768, 2022. 2", + "[39] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, pages 8024-8035, 2019. 6", + "[40] Wei Ping, Kainan Peng, and Jitong Chen. Clarinet: Parallel wave generation in end-to-end text-to-speech. In ICLR, 2019. 2", + "[41] Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan Ömer Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, and John Miller. Deep voice 3: Scaling text-to-speech with convolutional sequence learning. In ICLR, 2018. 2", + "[42] K. R. Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C. V. Jawahar. Learning individual speaking styles for accurate lip to speech synthesis. In CVPR, pages 13793–13802, 2020. 2, 5", + "[43] K. R. Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C. V. Jawahar. A lip sync expert is all you need for speech to lip generation in the wild. In ACM MM, pages 484-492, 2020. 2, 5", + "[44] Yanyuan Qiao, Qi Chen, Chaorui Deng, Ning Ding, Yuankai Qi, Mingkui Tan, Xincheng Ren, and Qi Wu. R-GAN: exploring human-like way for reasonable text-to-image synthesis via generative adversarial networks. In ACM MM, pages 2085-2093, 2021. 3" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[45] Yanyuan Qiao, Yuankai Qi, Yicong Hong, Zheng Yu, Peng Wang, and Qi Wu. Hop+: History-enhanced and order-aware pre-training for vision-and-language navigation. TPAMI, 2023. 3", + "[46] Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu. Fastspeech 2: Fast and high-quality end-to-end text to speech. In ICLR, 2021. 1, 2, 6, 7", + "[47] Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu. Fastspeech: Fast, robust and controllable text to speech. In NeurIPS, pages 3165-3174, 2019. 1, 2, 5", + "[48] Jonathan Shen, Ruoming Pang, Ron J. Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang, Yuxuan Wang, RJ-Skerrv Ryan, Rif A. Saurous, Yannis Agiomyrgiannakis, and Yonghui Wu. Natural TTS synthesis by conditioning wavenet on MEL spectrogram predictions. In ICASSP, pages 4779-4783, 2018. 1, 2, 5", + "[49] Bowen Shi, Wei-Ning Hsu, Kushal Lakhotia, and Abdelrahman Mohamed. Learning audio-visual speech representation by masked multimodal cluster prediction. In ICLR, 2022. 2", + "[50] Linsen Song, Wayne Wu, Chen Qian, Ran He, and Chen Change Loy. Everybody's talkin': Let me talk as you want. IEEE Trans. Inf. Forensics Secur., 17:585-598, 2022. 2", + "[51] Antoine Toisoul, Jean Kossaifi, Adrian Bulat, Georgios Tzimiropoulos, and Maja Pantic. Estimation of continuous valence and arousal levels from faces in naturalistic conditions. Nat. Mach. Intell., 3(1):42-50, 2021. 2, 4", + "[52] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, pages 5998-6008, 2017. 5", + "[53] Suzhen Wang, Lincheng Li, Yu Ding, and Xin Yu. One-shot talking face generation from single-speaker audio-visual correlation learning. In AAAI, pages 2531–2539, 2022. 2", + "[54] Yuxuan Wang, R. J. Skerry-Ryan, Daisy Stanton, Yonghui Wu, Ron J. Weiss, Navdeep Jaitly, Zongheng Yang, Ying Xiao, Zhifeng Chen, Samy Bengio, Quoc V. Le, Yannis Agiomyrgiannakis, Rob Clark, and Rif A. Saurous. Tacotron: Towards end-to-end speech synthesis. In *Interspeech*, pages 4006-4010, 2017. 1, 2, 6, 7", + "[55] Yongqi Wang and Zhou Zhao. Fastlts: Non-autoregressive end-to-end unconstrained lip-to-speech synthesis. In ACM MM, pages 5678-5687, 2022. 1, 2", + "[56] Jonatas Wehrmann and Rodrigo C. Barros. Movie genre classification: A multi-label approach based on convolutions through time. Appl. Soft Comput., 61:973-982, 2017. 5", + "[57] Tianyi Xie, Liucheng Liao, Cheng Bi, Benlai Tang, Xiang Yin, Jianfei Yang, Mingjie Wang, Jiali Yao, Yang Zhang, and Zejun Ma. Towards realistic visual dubbing with heterogeneous sources. In ACM MM, pages 1739-1747, 2021. 2", + "[58] Yi Yang, Brendan Shillingford, Yannis M. Assael, Miaosen Wang, Wendi Liu, Yutian Chen, Yu Zhang, Eren Sezener, Luis C. Cobo, Misha Denil, Yusuf Aytar, and Nando de Freitas. Large-scale multilingual audio visual dubbing. CoRR, abs/2011.03530, 2020. 2" + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "14696", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[59] Fei Yin, Yong Zhang, Xiaodong Cun, Mingdeng Cao, Yanbo Fan, Xuan Wang, Qingyan Bai, Baoyuan Wu, Jue Wang, and Yujiu Yang. Styleheat: One-shot high-resolution editable talking face generation via pretrained stylegan. arXiv preprint arXiv:2203.04036, 2022. 2", + "[60] Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, and Stan Z Li. S3fd: Single shot scale-invariant face detector. In CVPR, pages 192-201, 2017. 4", + "[61] Hang Zhou, Yu Liu, Ziwei Liu, Ping Luo, and Xiaogang Wang. Talking face generation by adversarially disentangled audio-visual representation. In AAAI, pages 9299–9306, 2019. 2", + "[62] Yang Zhou, Xintong Han, Eli Shechtman, Jose Echevarria, Evangelos Kalogerakis, and Dingzeyu Li. Makelttalk: speaker-aware talking-head animation. ACM Trans. Graph., 39(6):221:1-221:15, 2020. 2", + "[63] Yang Zhou, Jimei Yang, Dingzeyu Li, Jun Saito, Deepali Aneja, and Evangelos Kalogerakis. Audio-driven neural gesture reenactment with video motion graphs. In CVPR, pages 3408-3418, 2022. 2", + "[64] Wanrong Zhu, Yuankai Qi, Pradyumna Narayana, Kazoo Sone, Sugato Basu, Xin Eric Wang, Qi Wu, Miguel P. Eckstein, and William Yang Wang. Diagnosing vision-and-language navigation: What really matters. CoRR, abs/2103.16561, 2021. 3" + ], + "bbox": [ + 78, + 90, + 468, + 443 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "14697", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Learning To Dub Movies via Hierarchical Prosody Models/a85158d4-00c7-4fae-9712-36af3c2f8ea8_model.json b/2023/Learning To Dub Movies via Hierarchical Prosody Models/a85158d4-00c7-4fae-9712-36af3c2f8ea8_model.json new file mode 100644 index 0000000000000000000000000000000000000000..ab7c40d724fffcbe3fc0be1035c33f9f730a156c --- /dev/null +++ b/2023/Learning To Dub Movies via Hierarchical Prosody Models/a85158d4-00c7-4fae-9712-36af3c2f8ea8_model.json @@ -0,0 +1,2433 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.194, + 0.131, + 0.778, + 0.154 + ], + "angle": 0, + "content": "Learning to Dub Movies via Hierarchical Prosody Models" + }, + { + "type": "text", + "bbox": [ + 0.138, + 0.179, + 0.829, + 0.217 + ], + "angle": 0, + "content": "Gaoxiang Cong\\(^{1}\\) Liang Li\\(^{2,3\\dagger}\\) Yuankai Qi\\(^{4}\\) Zheng-Jun Zha\\(^{5}\\) Qi Wu\\(^{4}\\) Wenyu Wang\\(^{1}\\) Bin Jiang\\(^{1}\\) Ming-Hsuan Yang\\(^{6,7}\\) Qingming Huang\\(^{2}\\)" + }, + { + "type": "text", + "bbox": [ + 0.126, + 0.217, + 0.845, + 0.252 + ], + "angle": 0, + "content": "\\(^{1}\\)Shandong University \\(^{2}\\)Institute of Computing Technology, Chinese Academy of Sciences \\(^{3}\\)Lishui Institute of Hangzhou Dianzi University" + }, + { + "type": "text", + "bbox": [ + 0.221, + 0.252, + 0.749, + 0.269 + ], + "angle": 0, + "content": "\\(^{4}\\)Australian Institute for Machine Learning, University of Adelaide" + }, + { + "type": "text", + "bbox": [ + 0.107, + 0.269, + 0.862, + 0.288 + ], + "angle": 0, + "content": "\\(^{5}\\)University of Science and Technology of China \\(^{6}\\)University of California \\(^{7}\\)Yonsei University" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.322, + 0.314, + 0.339 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.354, + 0.474, + 0.732 + ], + "angle": 0, + "content": "Given a piece of text, a video clip and a reference audio, the movie dubbing (also known as visual voice clone, V2C) task aims to generate speeches that match the speaker's emotion presented in the video using the desired speaker voice as reference. V2C is more challenging than conventional text-to-speech tasks as it additionally requires the generated speech to exactly match the varying emotions and speaking speed presented in the video. Unlike previous works, we propose a novel movie dubbing architecture to tackle these problems via hierarchical prosody modeling, which bridges the visual information to corresponding speech prosody from three aspects: lip, face, and scene. Specifically, we align lip movement to the speech duration, and convey facial expression to speech energy and pitch via attention mechanism based on valence and arousal representations inspired by the psychology findings. Moreover, we design an emotion booster to capture the atmosphere from global video scenes. All these embeddings are used together to generate mel-spectrogram, which is then converted into speech waves by an existing vocoder. Extensive experimental results on the V2C and Chem benchmark datasets demonstrate the favourable performance of the proposed method. The code and trained models will be made available at https://github.com/GalaxyCong/HPMDubbing." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.761, + 0.21, + 0.777 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.786, + 0.47, + 0.879 + ], + "angle": 0, + "content": "Movie dubbing, also known as visual voice clone (V2C) [9], aims to convert a paragraph of text to a speech with both desired voice specified by reference audio and desired emotion and speed presented in the reference video as shown in the top panel of Figure 1. V2C is more challenging than other speech synthesis tasks in two aspects: first," + }, + { + "type": "image", + "bbox": [ + 0.507, + 0.323, + 0.889, + 0.424 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.617, + 0.425, + 0.778, + 0.438 + ], + "angle": 0, + "content": "(a) Visual Voice Cloning (V2C)" + }, + { + "type": "image", + "bbox": [ + 0.508, + 0.438, + 0.888, + 0.561 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.609, + 0.563, + 0.786, + 0.576 + ], + "angle": 0, + "content": "(b) Hierarchical Prosody Modeling" + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.588, + 0.895, + 0.645 + ], + "angle": 0, + "content": "Figure 1. (a) Illustration of the V2C tasks. (b) To generate natural speech with proper emotions, we align the phonemes with lip motion, estimate pitch and energy based on facial expression's arousal and valence, and predict global emotion from video scenes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.673, + 0.892, + 0.749 + ], + "angle": 0, + "content": "it requires synchronization between lip motion and generated speech; second, it requires proper prosodic variations of the generated speech to reflect the speaker's emotion in the video (i.e., the movie's plot). These pose significant challenges to existing voice cloning methods." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.893, + 0.902 + ], + "angle": 0, + "content": "Although significant progress has been made, existing methods do not handle the challenges in V2C well. Specifically, text-based dubbing methods [46-48, 54] construct speeches from given text conditioned on the different speaker embedding but do not consider audio-visual synchronization. On the other hand, lip-referred dubbing schemes [18, 32, 55] predict mel-spectrograms directly from a sequence of lip movements typically by encoder-decoder models. Due to high error rates in generated words, these methods can hardly guarantee high-quality results. Further" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.222, + 0.9 + ], + "angle": 0, + "content": "† Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14687" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.152 + ], + "angle": 0, + "content": "more, video-and-text based dubbing methods [17, 20, 32] focus on inferring speaker characters (e.g., age and gender). However, these visual references usually do not convey targeted emotion well as intended in V2C." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.154, + 0.471, + 0.306 + ], + "angle": 0, + "content": "An ideal dub should align well with the target character so that the audiences feel it is the character speaking instead of the dubber [7]. Thus, a professional dubber usually has a keen sense of observing the unique characteristics of the subject and acts on voice accordingly. In this work, we address these issues with a hierarchical dubbing architecture to synthesize speech. Unlike previous methods, our model connects video representations to speech counterparts at three levels: lip, face, and scene, as shown in Figure 1." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.307, + 0.471, + 0.67 + ], + "angle": 0, + "content": "In this paper, we propose a hierarchical prosody modeling for movie dubbing, which could keep the audio-visual sync and synthesis speech with proper prosody following the movie's plot. Specifically, we first design a duration alignment module that controls speech speed by learning temporal correspondence via multi-head attention over phonemes and lip motion. Second, we propose an affective-display based Prosody Adaptor (PA), which learns affective psychology computing conditioned on facial expression and is supervised by corresponding energy and pitch in the target voice. In particular, we introduce arousal and valence features extracted from facial regions as emotion representations. This is inspired by the affective computing method [51], which analyses the facial affect relying on dimensional measures, namely valence (how positive the emotional display is) and arousal (how calming or exciting the expression looks). Third, we exploit a scene-atmosphere based emotion booster, which fuses the global video representation with the above adapted hidden sequence and is supervised by the emotive state of the whole voice. The outputs of these three modules are fed into a transformer-based decoder, which converts the speech-related representations into mel-spectrogram. Finally, we output the target speech waves from the mel-spectrogram via a powerful vocoder." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.672, + 0.458, + 0.687 + ], + "angle": 0, + "content": "The contributions of this paper are summarized below:" + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.696, + 0.468, + 0.757 + ], + "angle": 0, + "content": "- We propose a novel hierarchical movie dubbing architecture to better synthesize speech with proper prosody by associating them with visual counterparts: lips, facial expressions, and surrounding scenes." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.769, + 0.468, + 0.844 + ], + "angle": 0, + "content": "- We design an affective display-based prosody adaptor to predict the energy and pitch of speech from the arousal and valence fluctuations of facial regions in videos, which provides a fine-grained alignment with speakers' emotions." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.856, + 0.468, + 0.9 + ], + "angle": 0, + "content": "- Extensive experimental results demonstrate the proposed method performs well against state-of-the-art models on two benchmark datasets." + }, + { + "type": "list", + "bbox": [ + 0.097, + 0.696, + 0.468, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.09, + 0.642, + 0.106 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.116, + 0.892, + 0.313 + ], + "angle": 0, + "content": "Text to Speech Synthesis. Over the recent years, numerous TTS models [2, 29, 40, 41, 47, 48, 54] have been proposed for generating high-quality natural speech conditioned on given text. Tacotron [54] is an end-to-end generative TTS model that synthesizes speech directly from characters. Then, Tacotron2 [29] replaces the RNN structures by introducing the attention mechanism to improve training efficiency and solve the long dependency issue. Furthermore, FastSpeech [47] and Fastspeech2 [46] exploit the Feed-Forward Transformer (FFT) to generate mel-spectrogram from phoneme sequences. Despite the impressive voice generated, these methods cannot provide the audio with desired emotion and audio-visual sync for movie dubbing." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.317, + 0.892, + 0.485 + ], + "angle": 0, + "content": "Lip to Speech Synthesis. This task aims to reconstruct speech based on the lip motions alone [3,25]. Lip2Wav [42] is a sequence-to-sequence architecture focusing on learning mappings between lip and speech for individual speakers. Recently, [15, 18, 49, 55] improve the architecture and training methods, and provide the possibility of unconstrained speech synthesis in the wild. However, lip-to-speech is incompetent for movie dubbing because the word error rate is still high [1, 3, 11, 13, 16]. In this work, we focus on reconstructing accurate speech from lip motions and generating the desired emotion and identity with proper prosody." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.488, + 0.892, + 0.656 + ], + "angle": 0, + "content": "Talking Heads. Numerous methods have been developed for audio-visual translation [58] or speaking style transfer [57] by reconstructing the visual content in video [8, 30, 31, 50, 53, 59, 61–63]. Wav2Lip [43] uses an expert lip-syncs discriminator to morph lip movements of arbitrary identities. Recently, Papantoniou et al. [38] develop a Neural Emotion Director (NED) to manipulate emotions while preserving speech-related lip movements. However, these methods cannot adapt to the movie dubbing task because they emphasize using generative models to readjust the facial regions instead of reconstructing the desired speech." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Visual Voice Cloning. Movie dubbing, also known as visual voice clone, aims to convert scripts to speech with both desired voice identity and emotion based on the reference audio and video. To control the speed of generated speech, Neural Dubber [20] exploits a text-video aligner by using scaled dot-product attention mechanism. VTTS [17] uses multi-source attention to fuse the triplets feature and outputs the mel-spectrogram via an RNN-based decoder. Since explicit emotion categories [28] do not exist in these methods, Chen et al. [9] develops a V2C model on a more challenging Densiny Animation dataset, which concentrates on emotional dubbing for movie characters. Although the V2C considers emotion labels, the adopted global video representation negatively affects the fine-grained emotional expression and makes it challenging to render correct prosody corresponding to plot developments. To solve this issue, we" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.956 + ], + "angle": 0, + "content": "14688" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.09, + 0.891, + 0.399 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.415, + 0.896, + 0.486 + ], + "angle": 0, + "content": "Figure 2. Architecture of the proposed hierarchical modular network for movie dubbing, which consists of four main components: Duration Aligner (Sec. 3.1), which learns to predict speech duration based on aligning lip movement and text phoneme; Prosody Adaptor (Sec. 3.2), which predicts energy and pitch from facial arousal and valence, respectively; Atmosphere Booster (Sec. 3.3), which learns a global emotion embedding from a video scene level; and Mel-Generator (Sec. 3.4), which generates mel-spectrograms from embeddings obtained by the aforementioned three modules. The mel-spectrograms are finally converted to audio by a widely adopted vocoder." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.502, + 0.471, + 0.534 + ], + "angle": 0, + "content": "propose a hierarchical movie dubbing architecture to better synthesize speech with proper prosody and emotion." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.549, + 0.17, + 0.566 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.576, + 0.473, + 0.834 + ], + "angle": 0, + "content": "The main architecture of the proposed model is shown in Fig. 2. First, we use a phoneme encoder [9] to convert the input text \\( Z_{text} \\) to a series of phoneme embeddings \\( \\mathcal{O} = \\{\\mathbf{o}_1,\\dots,\\mathbf{o}_L\\} \\) and use a speaker encoder \\( F_{spk} \\) [9] to capture the voice characteristics \\( \\mathcal{U} \\) from different speakers. Then, taking phonemes and lip regions as input, the duration aligner module uses a multi-head attention mechanism to learn to associate phonemes with related lip movements. Next, the affective display-based prosody adaptor module learns to predict the energy and pitch of the desired speech based on arousal and valence features extracted from facial expressions, respectively. And then, the scene atmosphere booster encodes a global representation of emotion of the entire video content. All the outputs of the above three modules are combined to generate mel-spectrograms, which are finally transformed to a waveform \\( Y_{voice} \\) using a adopted vocoder. We detail each module below." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.846, + 0.248, + 0.863 + ], + "angle": 0, + "content": "3.1. Duration Aligner" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.472, + 0.903 + ], + "angle": 0, + "content": "The duration aligner contains three steps: (1) extracting the lip features from movie; (2) aligning the phonemes of" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.501, + 0.892, + 0.533 + ], + "angle": 0, + "content": "text with the lips; (3) expanding the fused phoneme-lip representation to the desired mel-spectrogram length." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.535, + 0.893, + 0.626 + ], + "angle": 0, + "content": "Extracting lip feature. Let \\( D_w \\), \\( D_h \\) and \\( D_c \\) be the width, height and number of channels of the video frames, respectively. We first extract lip regions \\( \\mathbf{x_m} \\in \\mathbb{R}^{T_v \\times D_w \\times D_h \\times D_c} \\) from the given video using mouth region pre-processing from [20, 33-36]. Then we exploit the LipEncoder to obtain the lip movement representation:" + }, + { + "type": "equation", + "bbox": [ + 0.567, + 0.631, + 0.892, + 0.651 + ], + "angle": 0, + "content": "\\[\n\\mathbf {E} _ {\\mathrm {l i p}} = \\operatorname {L i p E n c o d e r} \\left(\\mathbf {x} _ {\\mathbf {m}}\\right) \\in \\mathbb {R} ^ {T _ {v} \\times D _ {m}}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.656, + 0.892, + 0.732 + ], + "angle": 0, + "content": "where \\( T_{v} \\) denotes the number of video frames, and \\( D_{m} \\) is the hidden dimension of the dynamic lip feature. The LipEncoder consists of several feed-forward transformer blocks that are suitable for capturing both long-term and short-term dynamics lip movement features." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.895, + 0.905 + ], + "angle": 0, + "content": "Aligning text with lips. Inspired by the success of attention mechanism for cross-modality alignment [10, 14, 19, 26, 27, 44, 45, 64], we adopt multi-head attention to learn the alignment between the text phoneme and the lip movement sequence. We use lip embedding as a query to compute the attention on text phonemes. The larger the attention, the more related between a lip embedding and a text phoneme. Due to variations of mouth shapes and pronunciations, the multi-head attention mechanism is suitable for learning their alignments from different aspects. The text-video context sequence \\(\\mathbf{E}_{\\mathrm{lip},\\mathrm{txt}} = [\\alpha_{\\mathrm{lip},\\mathrm{txt}}^{1},\\dots,\\alpha_{\\mathrm{lip},\\mathrm{txt}}^{n}]\\in\\)" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "14689" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.09, + 0.468, + 0.125 + ], + "angle": 0, + "content": "\\(\\mathbb{R}^{T_v\\times D_m}\\) is a concatenation of outputs of \\(n\\) attention heads. Concretely, the \\(k\\) -th head's output \\(\\alpha_{lip,txt}^k\\) is obtained by:" + }, + { + "type": "equation", + "bbox": [ + 0.177, + 0.134, + 0.469, + 0.166 + ], + "angle": 0, + "content": "\\[\n\\alpha_ {l i p, t x t} ^ {k} = \\operatorname {s o f t m a x} \\left(\\frac {\\mathrm {Q} ^ {\\top} \\mathrm {K}}{\\sqrt {d _ {k}}} + \\mathrm {M} _ {\\mathrm {t}}\\right) \\mathrm {V} ^ {\\top}, \\tag {2}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.136, + 0.167, + 0.413, + 0.185 + ], + "angle": 0, + "content": "\\[\n\\mathrm {Q} = \\mathrm {W} _ {j} ^ {Q} E _ {l i p} ^ {\\top}, \\mathrm {K} = \\mathrm {W} _ {j} ^ {K} \\mathcal {O} ^ {\\top}, \\mathrm {V} = \\mathrm {W} _ {j} ^ {V} \\mathcal {O} ^ {\\top},\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.194, + 0.469, + 0.271 + ], + "angle": 0, + "content": "where \\(\\mathbf{W}_j^*\\) is a learnable parameter matrix, \\(d_k\\) is the embedding dimension of \\(\\alpha_{lip,txt}^k\\), and \\(\\mathbf{M_t}\\) is a mask matrix indicating whether a token can be attended. The aligned representation \\(E_{lip,txt}\\) is later expanded to the length of the desired mel-spectrogram." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.276, + 0.469, + 0.351 + ], + "angle": 0, + "content": "Expanding to the desired length. According to the findings in [20], in an audio-visual clip, the length of a melspectrograms sequence is \\( n \\) times that of a video frame sequence because they are temporally synchronized in audio and visual modalities. The number \\( n \\) is computed as:" + }, + { + "type": "equation", + "bbox": [ + 0.18, + 0.362, + 0.469, + 0.394 + ], + "angle": 0, + "content": "\\[\nn = \\frac {T _ {m e l}}{T _ {v}} = \\frac {s r / h s}{F P S} \\in \\mathbb {N} ^ {+}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.404, + 0.469, + 0.524 + ], + "angle": 0, + "content": "where \\(FPS\\) denotes the Frames per Second of the video, \\(sr\\) denotes the sampling rate of the audio, and \\(hs\\) denotes hop size when transforming the raw waveform into melspectrograms. Phoneme-lip feature \\(E_{lip,txt}\\) is simply duplicated \\(n\\) times in [20] as the final phoneme-lip representation, which lacks flexibility. Instead, we propose to use transposed convolutions to learn the expansion of \\(E_{lip,txt}\\), which can be formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.095, + 0.534, + 0.469, + 0.551 + ], + "angle": 0, + "content": "\\[\n\\mathbf {M} _ {p h o, l i p} = \\operatorname {C o n v - T r a n s p o s e} (n, E _ {l i p, t x t}) \\in \\mathbb {R} ^ {T _ {y} \\times D _ {m}}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.562, + 0.469, + 0.638 + ], + "angle": 0, + "content": "where \\( T_{y} \\) denotes the length of the desired melspectrogram, \\( D_{m} \\) is the dimension of the initial melspectrogram. The parameters (stride and kernel size) of the transposed convolution can be set under the guidance of \\( n \\) so that \\( T_{y} \\approx n \\times T_{v} \\)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.647, + 0.427, + 0.663 + ], + "angle": 0, + "content": "3.2. Affective-display based Prosody Adaptor" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.671, + 0.469, + 0.776 + ], + "angle": 0, + "content": "One of the critical issues in the V2C task is to describe the speaker's emotions in the given video. To solve this problem, we design an affective-display based Prosody Adaptor (PA), which uses the arousal and valence extracted from facial expressions to represent the emotion. The arousal and valence are then used to predict the energy and pitch of the desired speech model." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.781, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Valence and Arousal Feature. To accurately capture the valence and arousal information from facial expressions, we utilize an emotion face-alignment network (EmoFAN) [51] to encode the facial region into valence \\(\\mathbf{V}\\) and arousal \\(\\mathbf{A}\\). \\(\\mathbf{V},\\mathbf{A} = \\mathrm{EmoFAN}(x_f)\\in \\mathbb{R}^{T_v\\times D_m}\\), \\(x_{f}\\in \\mathbb{R}^{T_{v}\\times D_{w}\\times D_{h}\\times D_{c}}\\) is the face region extracted via \\(S^3 FD\\) face detection [60]. The EmoFAN focuses on facial regions relevant to emotion estimation, which utilizes a face alignment network (FAN)" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.89, + 0.121 + ], + "angle": 0, + "content": "for facial point detection to ensure robustness by jointly predicting categorical and continuous emotions." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.122, + 0.892, + 0.214 + ], + "angle": 0, + "content": "Bridging Arousal with Energy. Arousal is the physiological and psychological state of being awoken or of sense organs stimulated to the point of perception. To bridge the vocal energy with arousal display, we compute an arousal context vector \\(A_{i}^{l}\\) for frame-level energy of the desired speech from phoneme-lip representation \\(\\mathbf{M}_{pho,lip}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.546, + 0.226, + 0.709, + 0.269 + ], + "angle": 0, + "content": "\\[\nA _ {i} ^ {l} = \\sum_ {k = 0} ^ {T _ {y} - 1} \\xi_ {i, k} M _ {p h o, l i p} ^ {k},\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.54, + 0.273, + 0.891, + 0.316 + ], + "angle": 0, + "content": "\\[\n\\xi_ {i, k} = \\exp (\\hat {\\xi} _ {i, k}) / \\sum_ {j = 0} ^ {T _ {y} - 1} \\exp (\\hat {\\xi} _ {i, j}), \\tag {5}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.54, + 0.319, + 0.85, + 0.339 + ], + "angle": 0, + "content": "\\[\n\\hat {\\xi} _ {i, k} = \\mathbf {w} _ {\\mathbf {a}} ^ {\\top} \\tanh (\\mathbf {W} _ {\\mathbf {a}} ^ {\\top} A _ {i} + \\mathbf {U} _ {\\mathbf {a}} ^ {\\top} M _ {p h o, l i p} ^ {k} + \\mathbf {b} _ {\\mathbf {a}})\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.35, + 0.891, + 0.425 + ], + "angle": 0, + "content": "where \\(i\\) is frame index, \\(A_{i}\\) is the \\(i\\)-th row of \\(\\mathbf{A}\\), \\(\\xi_{i,k}\\) is the attention weight on the \\(k\\)-th phoneme-lip feature \\(M_{pho,lip}^{k}\\) (the \\(k\\)-th row of \\(\\mathbf{M}_{pho,lip}\\)) regarding \\(i\\)-th arousal display; \\(\\mathbf{w}_{\\mathbf{a}}^{\\mathrm{T}}\\), \\(\\mathbf{W}_{\\mathbf{a}}^{\\mathrm{T}}\\), \\(\\mathbf{U}_{\\mathbf{a}}^{\\mathrm{T}}\\) and \\(\\mathbf{b}_{\\mathbf{a}}\\) are learnable parameters; \\(T_{y}\\) is the length of the desired mel-spectrogram." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.426, + 0.89, + 0.456 + ], + "angle": 0, + "content": "Then, we use an energy predictor to project the arousal-related phoneme-lip embedding \\( A_{i}^{l} \\) to the energy of speech:" + }, + { + "type": "equation", + "bbox": [ + 0.597, + 0.469, + 0.891, + 0.488 + ], + "angle": 0, + "content": "\\[\nE _ {a r o} = \\operatorname {P r e d i c t o r} \\left(\\left\\{A _ {i} ^ {l} \\right\\} _ {i = 1} ^ {T _ {y}}\\right), \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.501, + 0.891, + 0.547 + ], + "angle": 0, + "content": "where \\(E_{aro} \\in \\mathbb{R}^{T_y}\\) represents the predicted energy of speech, and the energy predictor consists of several fully-connected layers, Conv1D blocks and layer normalization." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.552, + 0.892, + 0.675 + ], + "angle": 0, + "content": "Bridging Valence with Pitch. In prosody linguistics, speakers can speak with a wide pitch range (this is usually associated with excitement) while, at other times, with a narrow range. Valence is the affective quality referring to the intrinsic positiveness or negativeness of an event or situation. Similar to arousal to energy, we first compute a valence context vector for the frame-level pitch of the desired speech from phoneme-lip representation \\(\\mathbf{M}_{pho,lip}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.548, + 0.686, + 0.715, + 0.729 + ], + "angle": 0, + "content": "\\[\nV _ {i} ^ {l} = \\sum_ {k = 0} ^ {T _ {y} - 1} \\psi_ {i, k} M _ {p h o, l i p} ^ {k},\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.54, + 0.733, + 0.891, + 0.776 + ], + "angle": 0, + "content": "\\[\n\\psi_ {i, k} = \\exp (\\hat {\\psi} _ {i, k}) / \\sum_ {j = 0} ^ {T _ {y} - 1} \\exp (\\hat {\\psi} _ {i, j}), \\tag {7}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.54, + 0.779, + 0.85, + 0.8 + ], + "angle": 0, + "content": "\\[\n\\hat {\\psi} _ {i, k} = \\mathbf {w} _ {\\mathbf {g}} ^ {\\top} \\tanh (\\mathbf {W} _ {\\mathbf {g}} ^ {\\top} V _ {i} + \\mathbf {U} _ {\\mathbf {g}} ^ {\\top} M _ {p h o, l i p} ^ {k} + \\mathbf {b} _ {\\mathbf {g}})\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.81, + 0.891, + 0.871 + ], + "angle": 0, + "content": "where \\(\\mathbf{w}_{\\mathbf{g}}^{\\mathbf{T}}, \\mathbf{W}_{\\mathbf{g}}^{\\mathbf{T}}, \\mathbf{U}_{\\mathbf{g}}^{\\mathbf{T}}\\) and \\(\\mathbf{b}_{\\mathbf{g}}\\) are learnable parameters. Then, we exploit a pitch predictor to convert the valence-related phoneme-lip embedding \\(V_{i}^{l}\\) to the pitch of speech, which can be formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.598, + 0.883, + 0.891, + 0.904 + ], + "angle": 0, + "content": "\\[\nP _ {v a l} = \\operatorname {P r e d i c t o r} \\left(\\left\\{V _ {i} ^ {l} \\right\\} _ {i = 1} ^ {T _ {y}}\\right). \\tag {8}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14690" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.078, + 0.092, + 0.47, + 0.122 + ], + "angle": 0, + "content": "Finally, we concatenate the two prosody-related features as the contextualized affect primitives:" + }, + { + "type": "equation", + "bbox": [ + 0.199, + 0.129, + 0.469, + 0.15 + ], + "angle": 0, + "content": "\\[\n\\mathbf {M} _ {\\mathbf {p}} = \\left\\{\\left[ A _ {i} ^ {l}; V _ {i} ^ {l} \\right] \\right\\} _ {i = 1} ^ {T _ {y}}. \\tag {9}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.156, + 0.32, + 0.172 + ], + "angle": 0, + "content": "3.3. Scene Atmosphere Booster" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.18, + 0.471, + 0.331 + ], + "angle": 0, + "content": "As a unique form of artistic expression, the scene layout and colours of the film convey an emotional atmosphere to evoke resonance with the audience [5, 56]. To reason the comprehensive emotion, we design a scene atmosphere booster to combine the global context information and generated prosody. First, we use the I3D model [6] to extract the scene representation \\( S \\) from the video. Then, we fuse the global contextual emotion of vision with prosody information of speech through a cross-model attention mechanism by:" + }, + { + "type": "equation", + "bbox": [ + 0.116, + 0.337, + 0.469, + 0.373 + ], + "angle": 0, + "content": "\\[\n\\mathbf {M} _ {\\mathrm {e}} = \\operatorname {S o f t m a x} \\left(\\frac {\\mathbf {M} _ {\\mathbf {p}} S ^ {\\top}}{\\sqrt {D _ {m}}}\\right) \\mathbf {M} _ {\\mathbf {p}} \\in \\mathbb {R} ^ {T _ {y} \\times D _ {w}}. \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.379, + 0.47, + 0.424 + ], + "angle": 0, + "content": "Finally, formulated as a maxpool and fully-connected layer, the emotional predictor projects prosody-scene context sequence \\(\\mathbf{M}_{\\mathrm{e}}\\) to emotional embedding:" + }, + { + "type": "equation", + "bbox": [ + 0.149, + 0.433, + 0.469, + 0.451 + ], + "angle": 0, + "content": "\\[\nG _ {e m o} = \\operatorname {M a x p o o l} (\\operatorname {P r e d i c t o r} (\\mathbf {M} _ {\\mathbf {e}})). \\tag {11}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.458, + 0.251, + 0.473 + ], + "angle": 0, + "content": "3.4. Audio Generation" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.481, + 0.469, + 0.557 + ], + "angle": 0, + "content": "We fuse the three kinds of speech attribute information by concatenation operation in the spatial dimension. Then, we use transformer-based mel-spectrogram decoder to convert the adapted hidden sequence into mel-spectrogram sequence in parallel by:" + }, + { + "type": "equation", + "bbox": [ + 0.085, + 0.566, + 0.469, + 0.584 + ], + "angle": 0, + "content": "\\[\n\\mathbf {f} = \\text {T r a n s f o r m e r D e c o d e r} \\left(\\mathbf {M} _ {\\text {p h o}, l i p} \\oplus \\mathbf {M} _ {\\mathbf {p}} \\oplus \\mathbf {M} _ {\\mathbf {e}}\\right), \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.591, + 0.469, + 0.698 + ], + "angle": 0, + "content": "where \\( M_{pho,lip} \\), \\( M_p \\), and \\( M_e \\) denote the hidden representation of phoneme-lip feature, prosody variances, and emotional tone, respectively. Specifically, our mel-spectrogram decoder consists of a stack of self-attention layers [52] and 1D-convolution layers as in FastSpeech [47]. We then use the mel-linear layer and postnet [48] to refine the hidden states into final dimensional mel-spectrograms by:" + }, + { + "type": "equation", + "bbox": [ + 0.195, + 0.706, + 0.469, + 0.723 + ], + "angle": 0, + "content": "\\[\ny = \\operatorname {P o s t N e t} (\\operatorname {F C} (f)). \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.731, + 0.469, + 0.791 + ], + "angle": 0, + "content": "Finally, to generate the time-domain waveform \\( y_{w} \\) from mel-spectrogram \\( y \\), we use HiFi-GAN [23] as our vocoder, which mainly consists of a transposed convolution network and a multi-receptive field fusion module." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.8, + 0.231, + 0.815 + ], + "angle": 0, + "content": "3.5. Loss Functions" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.823, + 0.469, + 0.854 + ], + "angle": 0, + "content": "Our model is trained in an end-to-end fashion via optimizing the sum of all losses:" + }, + { + "type": "equation", + "bbox": [ + 0.165, + 0.862, + 0.469, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {p i t c h}} = \\frac {1}{T _ {y}} \\sum_ {t = 1} ^ {T _ {y} - 1} \\left(P _ {t} - \\hat {P} _ {\\text {v a l}} ^ {t}\\right) ^ {2}, \\tag {14}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.581, + 0.089, + 0.892, + 0.131 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {e n e r g y}} = \\frac {1}{T _ {y}} \\sum_ {t = 1} ^ {T _ {y} - 1} \\left(E _ {t} - \\hat {E} _ {a r o} ^ {t}\\right) ^ {2}, \\tag {15}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.597, + 0.137, + 0.892, + 0.178 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {e m o} = - \\sum_ {i = 1} ^ {C} G _ {i} \\log \\left(\\hat {G} _ {e m o} ^ {i}\\right), \\tag {16}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.603, + 0.183, + 0.892, + 0.226 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {m e l} = \\frac {1}{T _ {y}} \\sum_ {t = 1} ^ {T _ {y} - 1} \\left\\| \\mathbf {f} _ {t} - \\hat {\\mathbf {f}} _ {t} \\right\\|. \\tag {17}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.511, + 0.231, + 0.892, + 0.248 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {S} = \\lambda_ {1} \\mathcal {L} _ {\\text {m e l}} + \\lambda_ {2} \\mathcal {L} _ {\\text {p i t c h}} + \\lambda_ {3} \\mathcal {L} _ {\\text {e n e r g y}} + \\lambda_ {4} \\mathcal {L} _ {\\text {e m o}}, \\tag {18}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.253, + 0.892, + 0.329 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{mel}\\), \\(\\mathcal{L}_{pitch}\\), \\(\\mathcal{L}_{energy}\\) and \\(\\mathcal{L}_{emo}\\), denote the losses of mel-spectrogram, pitch, energy and global emotion respectively. \\(P_{t}\\), \\(E_{t}\\), and \\(\\mathbf{f}_t\\) are ground-truth pitch, energy, and mel-spectrogram on frame-level, respectively. G is the ground-truth emotional label and C denotes all categories." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.341, + 0.706, + 0.358 + ], + "angle": 0, + "content": "4. Experimental Results" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.366, + 0.892, + 0.442 + ], + "angle": 0, + "content": "In this section, we first briefly describe the datasets used for evaluation and the evaluation metric. Then we present the implementation details of the proposed method. Last, we show the results compared to state-of-the-art methods and the ablation study." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.451, + 0.602, + 0.465 + ], + "angle": 0, + "content": "4.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.477, + 0.892, + 0.612 + ], + "angle": 0, + "content": "V2C is a multi-speaker dataset for animation movie dubbing with identity and emotion annotations [9]. It is collected from 26 Disney cartoon movies and covers 153 diverse characters. V2C not only needs to generate voices with identity characteristics according to the reference audio but also capture emotional information based on the reference movie clips. The whole dataset has 10,217 video clips with paired audio and subtitles. The training/validation/test size are \\(60\\%\\), \\(10\\%\\), \\(30\\%\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.613, + 0.892, + 0.75 + ], + "angle": 0, + "content": "Chem is a single-speaker dataset composed of 6,640 short video clips collected from the YouTube, with the total video length of approximately nine hours [20]. The Chem dataset is originally used for the unconstrained single-speaker lipto-speech synthesis [42], which takes place in a chemistry lecture. For fluency and complete dubbing, each video clip has sentence-level text and audio based on the start and end timestamps. There are 6,240, 200, and 200 dubbing clips for training, validation, and testing, respectively." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.757, + 0.685, + 0.772 + ], + "angle": 0, + "content": "4.2. Evaluation Metrics" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.892, + 0.87 + ], + "angle": 0, + "content": "Audio-visual synchronization. To evaluate the synchronization between the generated speech and the video quantitatively, we adopt Lip Sync Error Distance (LSE-D) and Lip Sync Error Confidence (LSE-C) as our metrics, which can explicitly test for synchronization between lip motions and speech in unconstrained videos in the wild [12, 43]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Mel Cepstral Distortion and its variants. MCD [24], MCD-DTW [4] and MCD-DTW-SL [9] are adopted, which" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "14691" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.082, + 0.089, + 0.894, + 0.224 + ], + "angle": 0, + "content": "
MethodsLSE-D ↓LSE-C ↑MCD ↓MCD-DTW ↓MCD-DTW-SL ↓Id. Acc. ↑Emo. Acc. ↑MOS-N ↑MOS-S ↑
Ground Truth6.7347.81300.0000.0000.0090.6284.384.61 ± 0.154.74 ± 0.12
SV2TTS [21]13.7332.72521.0812.8749.5633.6237.192.03 ± 0.221.92 ± 0.15
SV2TTS* [21]12.6173.34919.3812.7334.5135.1842.052.07 ± 0.072.15 ± 0.09
Tacotron* [54]13.4752.93819.7918.7342.1532.4939.682.12 ± 0.172.06 ± 0.12
FastSpeech2 [46]12.2612.95820.7814.3919.4121.7246.822.79 ± 0.102.63 ± 0.09
FastSpeech2* [46]12.1132.60420.6614.5920.7923.4446.903.08 ± 0.062.89 ± 0.07
V2C-Net1 [9]11.7843.02620.6114.2319.1526.8448.413.19 ± 0.043.06 ± 0.06
Ours8.0365.60815.6612.2913.4837.7561.464.03 ± 0.083.89 ± 0.07
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.076, + 0.229, + 0.895, + 0.286 + ], + "angle": 0, + "content": "Table 1. Results on the V2C dataset with comparisons against state-of-the-art methods. We provide the results using both objective metrics (i.e., LSE-D, LSE-C, MCD, MCD-DTW and MCD-DTW-SL) and subjective metrics (i.e., MOS-Naturalness and MOS-Similarity). \"Id. Acc.\" and \"Emo. Acc.\" are the identity and emotion accuracy of the generated speech, respectively. The method with “*” refers to a variant taking video (emotion) embedding as an additional input as in [9]. \\(\\uparrow (\\downarrow)\\) means that the higher (lower) value is better." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.299, + 0.468, + 0.375 + ], + "angle": 0, + "content": "reflect the similarity of mel-spectrograms. MCD-DTW uses the Dynamic Time Warping (DTW) [37] algorithm to find the minimum MCD between two speeches, while MCD-DTW-SL introduces the duration measure coefficient to consider the length and the quality of generated speech [9]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.376, + 0.468, + 0.437 + ], + "angle": 0, + "content": "Emotion and identity accuracy. To measure whether the generated speech carries proper emotion and speaker identity, we adopt an emotion accuracy (Emo. Acc.) and an identity accuracy (Id. Acc.) as our metrics as in [9]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.438, + 0.468, + 0.529 + ], + "angle": 0, + "content": "Subjective evaluations. To further evaluate the quality of generated speech, we conduct a human study using a subjective evaluation metric, following the settings in [9]. Specifically, we adopt the MOS-naturalness (MOS-N) and MOS-similarity (MOS-S) to assess the naturalness of the generated speech and the recognition of the desired voice." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.543, + 0.295, + 0.559 + ], + "angle": 0, + "content": "4.3. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.567, + 0.468, + 0.839 + ], + "angle": 0, + "content": "For the duration aligner, we use 4 Feed-Forward Transformer (FFT) blocks and 3 FFT blocks for the phoneme encoder and lip movement encoder, respectively. We set the dimension of the phoneme feature \\(\\mathcal{O}\\) and lip features \\(E_{lip}\\) to 256. We use 8 attention heads for alignment between lip and phoneme. For each movie clip, Our FPS set is 25, the sampling rate \\(sr\\) is \\(22050Hz\\). We use short-time fourier transform (STFT) to obtain the mel-spectrum, and the number of points of the fourier transform is 1024. We use the Conv-Transpose1D module with 2 stride and 4 kernel sizes to obtain the duration features. In Valence and Arousal Feature Encoder, the EmoFAN consists of one 2D convolution with a kernel size of \\(7\\times 7\\) and 3 convolution blocks (ConvBlock) with a kernel size of \\(3\\times 3\\) and Average Pooling stride of \\(2\\times 2\\). Similarly, we set the dimension of the valence and arousal feature to 256. For the mel-spectrograms generator, the mel-spectrogram decoder consists of 6 FFT blocks and the hidden state of mel-linear layer is of size 80." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.468, + 0.902 + ], + "angle": 0, + "content": "For training, we use Adam [22] with \\(\\beta_{1} = 0.9\\), \\(\\beta_{2} = 0.98\\), \\(\\epsilon = 10^{-9}\\) to optimize our model. For V2C and Chem dataset, we set the learning rate schedule to 0.00001 and 0.00005, respectively. In this work, we use pretrained HiFiGAN [23]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.299, + 0.892, + 0.39 + ], + "angle": 0, + "content": "as the vocoder to transform the generated mel-spectrograms into audio samples. We set the batch size to 16 on two datasets. Our model is implemented in PyTorch [39]. All the models are performed on a single NVIDIA GTX3090Ti GPU. We train the model with 600 epochs on the V2C dataset and 400 epochs on the Chem dataset." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.405, + 0.722, + 0.421 + ], + "angle": 0, + "content": "4.4.Quantitative Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.431, + 0.892, + 0.627 + ], + "angle": 0, + "content": "We compare with five related baselines of speech synthesizes. (1) SV2TTS [21] is a basic TTS model to generate speech with reference audio for multi-speakers; (2) Tacotron [54] is an end-to-end generative TTS model that synthesizes speech directly from textual characters; (3) FastSpeech2 [46] introduces the variance adaptor to convert the text to waveform by end-to-end; (4) Neural Dubber [20] synthesizes human speech for given video according to the corresponding text; (5) V2C-Net [9] is the first model for movie dubbing, which match the speaker's emotion presented in video. Note that we do not compare with Neural Dubber [20] on the V2C benchmark due to the unavailability of its code and missing implementation details." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.629, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Results on the V2C benchmark. The results are presented in Table 1. Our method achieves the best performance on all nine metrics. Specifically, in terms of audio-visual sync, our method achieves 8.036 of LSE-D and 5.608 of LSE-C, which significantly surpasses the previous best results and is much closer to human performance. In terms of MCD, MCD-DTW, and MCD-DTW-SL, our method achieves relative \\(24.02\\%\\), \\(13.63\\%\\) and \\(29.61\\%\\) improvements, respectively. This indicates our method can achieve a better mel-spectrogram than others. The above results together show that bridging specific attributes of speech with corresponding visual counterparts can make the generated speech present better prosodies and lip motion sync. Additionally, our method outperforms the previous method by a large margin in emotion accuracy and can gain better identification accuracy. This indicates the proposed method can better capture and convey emotions, which is of great importance for the movie dubbing task. Last, the human subjec" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14692" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.082, + 0.089, + 0.468, + 0.194 + ], + "angle": 0, + "content": "
MethodsAQ↑AV Sync ↑LSE-D ↓LSE-C ↑
Ground Truth3.93 ± 0.084.13±0.076.9267.711
FastSpeech2 [46]3.71±0.083.29±0.0911.862.805
Tacotron* [54]3.55±0.093.03±0.1011.792.231
V2C-Net [9]3.48±0.143.25±0.1111.262.907
Neural Dubber [20]3.74±0.083.91±0.077.2127.037
Ours3.84±0.113.97±0.086.9757.176
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.205, + 0.471, + 0.275 + ], + "angle": 0, + "content": "Table 2. Results on the Chem dataset with comparisons against state-of-the-art methods. AQ (Audio Quality) and AV Sync (audio-visual synchronization) are subjective metrics. Note that the Chem dataset is a single-speaker non-movie dataset, and thus there is no identity accuracy and emotion accuracy." + }, + { + "type": "table", + "bbox": [ + 0.081, + 0.29, + 0.469, + 0.423 + ], + "angle": 0, + "content": "
#MethodsLSE-D ↓LSE-C ↑MCD ↓Id. Acc. ↑Emo. Acc. ↑
1w/o DA11.8353.71619.5318.7538.33
2w/o PA8.5145.27416.3410.4222.08
3w/o AB8.2615.40815.9033.3353.92
4w/o Valence9.2154.93516.3129.8135.31
5w/o Arousal8.7935.21615.7932.7446.38
6VA v.s. FF9.1605.01119.8723.6739.58
7w/o multi-head10.9483.89418.6524.8540.25
8Duplication11.4753.81418.6321.7837.92
9Full model8.0365.60815.6637.7561.46
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.433, + 0.47, + 0.461 + ], + "angle": 0, + "content": "Table 3. Ablation study of the proposed method on the V2C benchmark dataset." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.477, + 0.469, + 0.522 + ], + "angle": 0, + "content": "tive evaluation results (see MOS-N and MOS-S) also show that our method can generate speeches that are closer to realistic speech according to naturalness and similarity." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.523, + 0.469, + 0.675 + ], + "angle": 0, + "content": "Results on the Chem benchmark. As shown in Table 2, our model is ahead of the state-of-the-art methods in all metrics on the Chem benchmark. In terms of the audiovisual sync, our method achieves 6.975 LSE-D and 7.176 LSE-C. Furthermore, in the subjective evaluations, our method improves \\(10.34\\%\\) on AQ and \\(22.15\\%\\) on AV Sync. The results show that our performance is much closer to the ground truth recording, which indicates that our model synthesizes high-quality natural speech by controlling the prosody from hierarchical visual representation." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.686, + 0.242, + 0.7 + ], + "angle": 0, + "content": "4.5. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.709, + 0.47, + 0.876 + ], + "angle": 0, + "content": "Effectiveness of Duration Aligner, Prosody Adaptor, and Atmosphere Booster. We evaluate the effectiveness of these three modules by removing them separately and retraining the model. The results are shown in Row \\(1\\sim 3\\) of Table 3. The result shows that all the proposed modules contribute significantly to the overall performance, and each module has a different focus. Specifically, the performance on the audio-visual metrics (LSE-D, LSE-C, and MCD) drops the most when removing the Duration Aligner (DA). This reflects the DA module indeed helps the model learn a better temporal synchronization. By contrast, the perfor" + }, + { + "type": "image", + "bbox": [ + 0.508, + 0.091, + 0.888, + 0.362 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.375, + 0.892, + 0.404 + ], + "angle": 0, + "content": "Figure 3. Audiovisual consistency visualization on V2C dataset: Ground Truth (GT), our model, FastSpeech 2 (FS2) and V2C-Net." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.419, + 0.892, + 0.555 + ], + "angle": 0, + "content": "mance on identity accuracy and emotion accuracy drop the most when removing the Prosody Adaptor (PA). This can be attributed to the predicted pitch and energy representing a speaker's identity and his/her emotion. When removing the Atmosphere Booster (AB), the performance drops compared to the full model (Row 9) but does not drop as much as when removing the other two modules. This indicates the AB module also contributes the overall performance improvement but contributes the least in the three modules." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.56, + 0.893, + 0.695 + ], + "angle": 0, + "content": "Effectiveness of valence and arousal. In our model, we bridge arousal with energy and valence with pitch by attention mechanism. To evaluate their effectiveness, we cut off the connection and predict energy and pitch directly from the phoneme-lip representation. The results are presented in Row \\(4\\sim 5\\) of Table 3. It shows that the performance drops significantly when removing either of them and drops more when removing valence. This indicates valence contributes more than arousal on the V2C task." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.7, + 0.892, + 0.822 + ], + "angle": 0, + "content": "Valence-and-Arousal v.s. Facial Expression. To compare the role of affective display and facial expressions on prosody inference, we replace the input of the APA module with original embeddings of facial features from CNN. As row “VA v.s. FF” of Table 3 shows, the model performance drops significantly. This is likely caused by facial features that are still far from the information-containing emotion, which is not enough to guide prosody generation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.826, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Multi-head Attention in Duration Adaptor. In our duration aligner, we exploit multi-head attention to learn the relation between the phoneme sequences and the lip motion. To verify its effectiveness, we conduct experiments using conventional dot-product attention. The results are" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.462, + 0.9 + ], + "angle": 0, + "content": "\\(^{1}\\)V2C republish official results on https://github.com/chenqi008/V2C." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14693" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.092, + 0.89, + 0.352 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.365, + 0.893, + 0.407 + ], + "angle": 0, + "content": "Figure 4. Visualization of audio on V2C dataset (top) and Chem dataset (bottom). Orange curves are \\( F_{0} \\) contours, where \\( F_{0} \\) is the fundamental frequency of audio. Purple curves refer to the energy (volume) of audio. The horizontal axis is the duration of the audio. The red circles highlight the mel-spectrograms at the same moment as the frame shown on the left side." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.421, + 0.47, + 0.496 + ], + "angle": 0, + "content": "presented in Row 7 of Table 3. The performance drops significantly on all metrics, such as \\(26.59\\%\\) and \\(44.02\\%\\) decrease on LSE-D and LSE-C compared to Row 9, respectively. This indicates multi-head attention learns a much better correlation between phonemes with lip movement." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.501, + 0.471, + 0.637 + ], + "angle": 0, + "content": "Conv-Transpose v.s. Duplication. In our duration aligner, we propose to use Conv-Transpose to learn the expansion of the fused phoneme-lip representation to its desired length. To verify its effectiveness, we replace it with simple make \\( n \\) duplications as in [20]. The results are shown in Row 8 of Table 3. It shows that the performance drops significantly. For example, it falls \\( 15.94\\% \\) on MCD metric. This demonstrates the superiority of using transposed convolution to learn the upsampling than simply copying." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.647, + 0.262, + 0.663 + ], + "angle": 0, + "content": "4.6. Qualitative Results" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.671, + 0.47, + 0.852 + ], + "angle": 0, + "content": "Audiovisual consistency visualization. Figure 3 presents the mel-spectrograms of generated audios along with its frames. Blue and red bounding boxes denote whether the character is speaking or not, respectively. Compared with other methods, the mel-spectrogram generated by our model is closer to the ground truth, indicating better audiovisual synchronization. This can be attributed to our duration aligner, which exploits multi-head alignment between the text phoneme sequence and the lip movement sequence. By controlling the lip movements explicitly, we obtain the desired length of mel-spectrograms, which makes the speech well synchronized with the input video." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Arousal and valence with prosody visualization. We selected two examples from the test set of the V2C dataset and Chem dataset to demonstrate the alignment between en" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.421, + 0.895, + 0.589 + ], + "angle": 0, + "content": "ergy and arousal as well as pitch and valence. The valence (positive or negative) and arousal (calming or exciting) of facial expressions are shown in the first column. The main pitch and energy are shown in orange and blue curves in the right column. We use the red circle to highlight the pitch and energy that correspond to the video frame shown in the left column. The result shows that our method achieves energy and pitch closer to the ground truth speech. When the chemistry teacher becomes excited and positive, our model successfully leverages the affective display to synthesize a similar pitch and energy as the ground-truth speech." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.603, + 0.62, + 0.618 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.629, + 0.892, + 0.779 + ], + "angle": 0, + "content": "In this work, we propose a hierarchical prosody modeling network for movie dubbing, which bridges video representations and speech attributes from three levels: lip, facial expression, and scene. By associating these visual representations with their voice counterparts, we obtain more powerful representations for dubbing. Furthermore, we design an affective-display based prosody adaptor, which effectively learns to align the valence and arousal to the pitch and energy of speeches. Our proposed model sets new state-of-the-art on both Chem and V2C-Animation benchmarks." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.781, + 0.893, + 0.9 + ], + "angle": 0, + "content": "Acknowledgement. This work was supported in part by the National Key R&D Program of China under Grant 2018AAA0102000, and in part by National Natural Science Foundation of China under Grants 62225207 and U19B2038, the Key R&D Plan Project of Zhejiang Province (No. 2023C01004), Shandong Provincial Natural Science Foundation (ZR2020MA064), Youth Innovation Promotion Association of CAS under Grant 2020108." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "14694" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.175, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.17 + ], + "angle": 0, + "content": "[1] Triantafyllos Afouras, Joon Son Chung, and Andrew Zisserman. Deep lip reading: A comparison of models and an online application. In Interspeech, pages 3514-3518, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.173, + 0.472, + 0.255 + ], + "angle": 0, + "content": "[2] Sercan Ömer Arik, Mike Chrzanowski, Adam Coates, Gregory Frederick Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Y. Ng, Jonathan Raiman, Shubho Sengupta, and Mohammad Shoeeybi. Deep voice: Real-time neural text-to-speech. In ICML, pages 195-204, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.258, + 0.471, + 0.299 + ], + "angle": 0, + "content": "[3] Yannis M Assael, Brendan Shillingford, Shimon Whiteson, and Nando De Freitas. Lipnet: End-to-end sentence-level lipreading. arXiv preprint arXiv:1611.01599, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.301, + 0.47, + 0.357 + ], + "angle": 0, + "content": "[4] Eric Battenberg, R. J. Skerry-Ryan, Soroosh Mariooryad, Daisy Stanton, David Kao, Matt Shannon, and Tom Bagby. Location-relative attention mechanisms for robust long-form speech synthesis. In ICASSP, pages 6194-6198, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.359, + 0.47, + 0.413 + ], + "angle": 0, + "content": "[5] Manuel Burghardt, Michael Kao, and Christian Wolff. Beyond shot lengths - using language data and color information as additional parameters for quantitative movie analysis. In ADHO, pages 753-755, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.415, + 0.47, + 0.457 + ], + "angle": 0, + "content": "[6] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In CVPR, pages 6299-6308, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.459, + 0.47, + 0.485 + ], + "angle": 0, + "content": "[7] Frederic Chaume. Dubbing practices in europe: localisation beats globalisation. LANS-TTS, 6, 2007. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.488, + 0.47, + 0.543 + ], + "angle": 0, + "content": "[8] Lele Chen, Ross K. Maddox, Zhiyao Duan, and Chenliang Xu. Hierarchical cross-modal talking face generation with dynamic pixel-wise loss. In CVPR, pages 7832-7841, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.545, + 0.47, + 0.586 + ], + "angle": 0, + "content": "[9] Qi Chen, Mingkui Tan, Yuankai Qi, Jiaqiu Zhou, Yuanqing Li, and Qi Wu. V2C: visual voice cloning. In CVPR, pages 21210-21219, 2022. 1, 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.588, + 0.47, + 0.655 + ], + "angle": 0, + "content": "[10] Weidong Chen, Dexiang Hong, Yuankai Qi, Zhenjun Han, Shuhui Wang, Laiyun Qing, Qingming Huang, and Guorong Li. Multi-attention network for compressed video referring object segmentation. In ACM MM, pages 4416–4425, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.659, + 0.469, + 0.7 + ], + "angle": 0, + "content": "[11] Joon Son Chung, Andrew W. Senior, Oriol Vinyals, and Andrew Zisserman. Lip reading sentences in the wild. In CVPR, pages 3444-3453, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.702, + 0.469, + 0.743 + ], + "angle": 0, + "content": "[12] Joon Son Chung and Andrew Zisserman. Out of time: Automated lip sync in the wild. In ACCV Workshop, pages 251-263, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.746, + 0.469, + 0.772 + ], + "angle": 0, + "content": "[13] Joon Son Chung and Andrew Zisserman. Lip reading in profile. In BMVC, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.775, + 0.47, + 0.844 + ], + "angle": 0, + "content": "[14] Gaoxiang Cong, Liang Li, Zhenhuan Liu, Yunbin Tu, Weijun Qin, Shenyuan Zhang, Chengang Yan, Wenyu Wang, and Bin Jiang. LS-GAN: iterative language-based image manipulation via long and short term consistency reasoning. In ACM MM, pages 4496-4504, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[15] Rodrigo Schoburg Carrillo de Mira, Alexandros Haliassos, Stavros Petridis, Bjorn W. Schuller, and Maja Pantic. SVTS: scalable video-to-speech synthesis. In INTER-SPEECH, pages 1836-1840, 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.133 + ], + "angle": 0, + "content": "[16] Ariel Ephrat and Shmuel Peleg. Vid2speech: Speech reconstruction from silent video. In ICASSP, pages 5095-5099, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[17] Michael Hassid, Michelle Tadmor Ramanovich, Brendan Shillingford, Miaosen Wang, Ye Jia, and Tal Remez. More than words: In-the-wild visually-driven prosody for text-to-speech. In CVPR, pages 10577–10587, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.193, + 0.892, + 0.247 + ], + "angle": 0, + "content": "[18] Sindhu B. Hegde, K. R. Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C. V. Jawahar. Lip-to-speech synthesis for arbitrary speakers in the wild. In ACM MM, pages 6250-6258, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.25, + 0.892, + 0.29 + ], + "angle": 0, + "content": "[19] Yicong Hong, Qi Wu, Yuankai Qi, Cristian Rodriguez Opazo, and Stephen Gould. A recurrent vision-and-language BERT for navigation. CoRR, abs/2011.13922, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.292, + 0.892, + 0.346 + ], + "angle": 0, + "content": "[20] Chenxu Hu, Qiao Tian, Tingle Li, Yuping Wang, Yuxuan Wang, and Hang Zhao. Neural dubber: Dubbing for videos according to scripts. In NeurIPS, pages 16582-16595, 2021. 2, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.349, + 0.892, + 0.418 + ], + "angle": 0, + "content": "[21] Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez-Moreno, and Yonghui Wu. Transfer learning from speaker verification to multispeaker text-to-speech synthesis. In NeurIPS, pages 4485-4495, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.42, + 0.892, + 0.46 + ], + "angle": 0, + "content": "[22] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Yoshua Bengio and Yann LeCun, editors, ICLR, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.462, + 0.892, + 0.503 + ], + "angle": 0, + "content": "[23] Jungil Kong, Jaehyeon Kim, and Jaekyoung Bae. Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. In NeurlPS, 2020. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.505, + 0.892, + 0.545 + ], + "angle": 0, + "content": "[24] R. Kubichek. Mel-cepstral distance measure for objective speech quality assessment. In PACRIM, pages 125–128, 1993. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.548, + 0.892, + 0.604 + ], + "angle": 0, + "content": "[25] Yaman Kumar, Rohit Jain, Khwaja Mohd. Salik, Rajiv Ratn Shah, Yifang Yin, and Roger Zimmermann. Lipper: Synthesizing thy speech using multi-view lipreading. In AAAI, pages 2588-2595, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.605, + 0.892, + 0.659 + ], + "angle": 0, + "content": "[26] Liang Li, Xingyu Gao, Jincan Deng, Yunbin Tu, Zheng-Jun Zha, and Qingming Huang. Long short-term relation transformer with global gating for video captioning. IEEE Trans. Image Process., pages 2726-2738, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.661, + 0.892, + 0.729 + ], + "angle": 0, + "content": "[27] Liang Li, Chenggang Clarence Yan, Xing Chen, Chunjie Zhang, Jian Yin, Baochen Jiang, and Qingming Huang. Distributed image understanding with semantic dictionary and semantic expansion. Neurocomputing, pages 384-392, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.732, + 0.892, + 0.788 + ], + "angle": 0, + "content": "[28] Liang Li, Xinge Zhu, Yiming Hao, Shuhui Wang, Xingyu Gao, and Qingming Huang. A hierarchical cnn-rnn approach for visual emotion classification. ACM Trans. Multim. Comput. Commun. Appl., pages 1-17, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.789, + 0.892, + 0.83 + ], + "angle": 0, + "content": "[29] Naihan Li, Shujie Liu, Yanqing Liu, Sheng Zhao, and Ming Liu. Neural speech synthesis with transformer network. In AAAI, pages 6706-6713, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.832, + 0.892, + 0.899 + ], + "angle": 0, + "content": "[30] Borong Liang, Yan Pan, Zhizhi Guo, Hang Zhou, Zhibin Hong, Xiaoguang Han, Junyu Han, Jingtuo Liu, Errui Ding, and Jingdong Wang. Expressive talking head generation with granular audio-visual control. In CVPR, pages 3377–3386, 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "14695" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.161 + ], + "angle": 0, + "content": "[31] Xian Liu, Qianyi Wu, Hang Zhou, Yinghao Xu, Rui Qian, Xinyi Lin, Xiaowei Zhou, Wayne Wu, Bo Dai, and Bolei Zhou. Learning hierarchical cross-modal association for cospeech gesture generation. In CVPR, pages 10452-10462, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.163, + 0.47, + 0.219 + ], + "angle": 0, + "content": "[32] Junchen Lu, Berrak Sisman, Rui Liu, Mingyang Zhang, and Haizhou Li. Visualts: TTS with accurate lip-speech synchronization for automatic voice over. In ICASSP, pages 8032-8036, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.221, + 0.47, + 0.262 + ], + "angle": 0, + "content": "[33] Pingchuan Ma, Brais Martinez, Stavros Petridis, and Maja Pantic. Towards practical lipreading with distilled and efficient models. In ICASSP, pages 7608-7612, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.264, + 0.469, + 0.305 + ], + "angle": 0, + "content": "[34] Pingchuan Ma, Yujiang Wang, Stavros Petridis, Jie Shen, and Maja Pantic. Training strategies for improved lip-reading. In ICASSP, pages 8472–8476, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.307, + 0.469, + 0.36 + ], + "angle": 0, + "content": "[35] Pingchuan Ma, Yujiang Wang, Jie Shen, Stavros Petridis, and Maja Pantic. Lip-reading with densely connected temporal convolutional networks. In WACV, pages 2857–2866, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.363, + 0.469, + 0.404 + ], + "angle": 0, + "content": "[36] Brais Martinez, Pingchuan Ma, Stavros Petridis, and Maja Pantic. Lipreading using temporal convolutional networks. In ICASSP, pages 6319-6323, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.407, + 0.469, + 0.434 + ], + "angle": 0, + "content": "[37] Meinard Müller. Dynamic time warping. Information Retrieval for Music and Motion, pages 69-84, 2007. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.436, + 0.469, + 0.503 + ], + "angle": 0, + "content": "[38] Foivos Paraperas Papantoniou, Panagiotis Paraskevas Filntisis, Petros Maragos, and Anastasios Roussos. Neural emotion director: Speech-preserving semantic control of facial expressions in \"in-the-wild\" videos. In CVPR, pages 18759-18768, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.506, + 0.469, + 0.616 + ], + "angle": 0, + "content": "[39] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, pages 8024-8035, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.619, + 0.469, + 0.657 + ], + "angle": 0, + "content": "[40] Wei Ping, Kainan Peng, and Jitong Chen. Clarinet: Parallel wave generation in end-to-end text-to-speech. In ICLR, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.661, + 0.469, + 0.716 + ], + "angle": 0, + "content": "[41] Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan Ömer Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, and John Miller. Deep voice 3: Scaling text-to-speech with convolutional sequence learning. In ICLR, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.718, + 0.469, + 0.772 + ], + "angle": 0, + "content": "[42] K. R. Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C. V. Jawahar. Learning individual speaking styles for accurate lip to speech synthesis. In CVPR, pages 13793–13802, 2020. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.775, + 0.469, + 0.829 + ], + "angle": 0, + "content": "[43] K. R. Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C. V. Jawahar. A lip sync expert is all you need for speech to lip generation in the wild. In ACM MM, pages 484-492, 2020. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.832, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[44] Yanyuan Qiao, Qi Chen, Chaorui Deng, Ning Ding, Yuankai Qi, Mingkui Tan, Xincheng Ren, and Qi Wu. R-GAN: exploring human-like way for reasonable text-to-image synthesis via generative adversarial networks. In ACM MM, pages 2085-2093, 2021. 3" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[45] Yanyuan Qiao, Yuankai Qi, Yicong Hong, Zheng Yu, Peng Wang, and Qi Wu. Hop+: History-enhanced and order-aware pre-training for vision-and-language navigation. TPAMI, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.15, + 0.892, + 0.191 + ], + "angle": 0, + "content": "[46] Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu. Fastspeech 2: Fast and high-quality end-to-end text to speech. In ICLR, 2021. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.193, + 0.892, + 0.247 + ], + "angle": 0, + "content": "[47] Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu. Fastspeech: Fast, robust and controllable text to speech. In NeurIPS, pages 3165-3174, 2019. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.25, + 0.892, + 0.332 + ], + "angle": 0, + "content": "[48] Jonathan Shen, Ruoming Pang, Ron J. Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang, Yuxuan Wang, RJ-Skerrv Ryan, Rif A. Saurous, Yannis Agiomyrgiannakis, and Yonghui Wu. Natural TTS synthesis by conditioning wavenet on MEL spectrogram predictions. In ICASSP, pages 4779-4783, 2018. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.334, + 0.892, + 0.375 + ], + "angle": 0, + "content": "[49] Bowen Shi, Wei-Ning Hsu, Kushal Lakhotia, and Abdelrahman Mohamed. Learning audio-visual speech representation by masked multimodal cluster prediction. In ICLR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.378, + 0.892, + 0.431 + ], + "angle": 0, + "content": "[50] Linsen Song, Wayne Wu, Chen Qian, Ran He, and Chen Change Loy. Everybody's talkin': Let me talk as you want. IEEE Trans. Inf. Forensics Secur., 17:585-598, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.434, + 0.892, + 0.489 + ], + "angle": 0, + "content": "[51] Antoine Toisoul, Jean Kossaifi, Adrian Bulat, Georgios Tzimiropoulos, and Maja Pantic. Estimation of continuous valence and arousal levels from faces in naturalistic conditions. Nat. Mach. Intell., 3(1):42-50, 2021. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.491, + 0.892, + 0.544 + ], + "angle": 0, + "content": "[52] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, pages 5998-6008, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.548, + 0.892, + 0.589 + ], + "angle": 0, + "content": "[53] Suzhen Wang, Lincheng Li, Yu Ding, and Xin Yu. One-shot talking face generation from single-speaker audio-visual correlation learning. In AAAI, pages 2531–2539, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.591, + 0.892, + 0.673 + ], + "angle": 0, + "content": "[54] Yuxuan Wang, R. J. Skerry-Ryan, Daisy Stanton, Yonghui Wu, Ron J. Weiss, Navdeep Jaitly, Zongheng Yang, Ying Xiao, Zhifeng Chen, Samy Bengio, Quoc V. Le, Yannis Agiomyrgiannakis, Rob Clark, and Rif A. Saurous. Tacotron: Towards end-to-end speech synthesis. In *Interspeech*, pages 4006-4010, 2017. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.676, + 0.892, + 0.716 + ], + "angle": 0, + "content": "[55] Yongqi Wang and Zhou Zhao. Fastlts: Non-autoregressive end-to-end unconstrained lip-to-speech synthesis. In ACM MM, pages 5678-5687, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.719, + 0.892, + 0.761 + ], + "angle": 0, + "content": "[56] Jonatas Wehrmann and Rodrigo C. Barros. Movie genre classification: A multi-label approach based on convolutions through time. Appl. Soft Comput., 61:973-982, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.762, + 0.892, + 0.829 + ], + "angle": 0, + "content": "[57] Tianyi Xie, Liucheng Liao, Cheng Bi, Benlai Tang, Xiang Yin, Jianfei Yang, Mingjie Wang, Jiali Yao, Yang Zhang, and Zejun Ma. Towards realistic visual dubbing with heterogeneous sources. In ACM MM, pages 1739-1747, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.832, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[58] Yi Yang, Brendan Shillingford, Yannis M. Assael, Miaosen Wang, Wendi Liu, Yutian Chen, Yu Zhang, Eren Sezener, Luis C. Cobo, Misha Denil, Yusuf Aytar, and Nando de Freitas. Large-scale multilingual audio visual dubbing. CoRR, abs/2011.03530, 2020. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "14696" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.161 + ], + "angle": 0, + "content": "[59] Fei Yin, Yong Zhang, Xiaodong Cun, Mingdeng Cao, Yanbo Fan, Xuan Wang, Qingyan Bai, Baoyuan Wu, Jue Wang, and Yujiu Yang. Styleheat: One-shot high-resolution editable talking face generation via pretrained stylegan. arXiv preprint arXiv:2203.04036, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.163, + 0.469, + 0.204 + ], + "angle": 0, + "content": "[60] Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, and Stan Z Li. S3fd: Single shot scale-invariant face detector. In CVPR, pages 192-201, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.207, + 0.469, + 0.26 + ], + "angle": 0, + "content": "[61] Hang Zhou, Yu Liu, Ziwei Liu, Ping Luo, and Xiaogang Wang. Talking face generation by adversarially disentangled audio-visual representation. In AAAI, pages 9299–9306, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.263, + 0.469, + 0.317 + ], + "angle": 0, + "content": "[62] Yang Zhou, Xintong Han, Eli Shechtman, Jose Echevarria, Evangelos Kalogerakis, and Dingzeyu Li. Makelttalk: speaker-aware talking-head animation. ACM Trans. Graph., 39(6):221:1-221:15, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.32, + 0.469, + 0.374 + ], + "angle": 0, + "content": "[63] Yang Zhou, Jimei Yang, Dingzeyu Li, Jun Saito, Deepali Aneja, and Evangelos Kalogerakis. Audio-driven neural gesture reenactment with video motion graphs. In CVPR, pages 3408-3418, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.376, + 0.469, + 0.444 + ], + "angle": 0, + "content": "[64] Wanrong Zhu, Yuankai Qi, Pradyumna Narayana, Kazoo Sone, Sugato Basu, Xin Eric Wang, Qi Wu, Miguel P. Eckstein, and William Yang Wang. Diagnosing vision-and-language navigation: What really matters. CoRR, abs/2103.16561, 2021. 3" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.444 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.956 + ], + "angle": 0, + "content": "14697" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Dub Movies via Hierarchical Prosody Models/a85158d4-00c7-4fae-9712-36af3c2f8ea8_origin.pdf b/2023/Learning To Dub Movies via Hierarchical Prosody Models/a85158d4-00c7-4fae-9712-36af3c2f8ea8_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..35d2ef033b11b5500858edf0eb9535cff618e4d6 --- /dev/null +++ b/2023/Learning To Dub Movies via Hierarchical Prosody Models/a85158d4-00c7-4fae-9712-36af3c2f8ea8_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:44f4910848d1837bc3aa299b057bad9e02c9fde882bef13b0475c6680df78575 +size 2386624 diff --git a/2023/Learning To Dub Movies via Hierarchical Prosody Models/full.md b/2023/Learning To Dub Movies via Hierarchical Prosody Models/full.md new file mode 100644 index 0000000000000000000000000000000000000000..51f6c127cac004a619219b8c4a81d5919534816d --- /dev/null +++ b/2023/Learning To Dub Movies via Hierarchical Prosody Models/full.md @@ -0,0 +1,374 @@ +# Learning to Dub Movies via Hierarchical Prosody Models + +Gaoxiang Cong $^{1}$ Liang Li $^{2,3\dagger}$ Yuankai Qi $^{4}$ Zheng-Jun Zha $^{5}$ Qi Wu $^{4}$ Wenyu Wang $^{1}$ Bin Jiang $^{1}$ Ming-Hsuan Yang $^{6,7}$ Qingming Huang $^{2}$ + +$^{1}$ Shandong University $^{2}$ Institute of Computing Technology, Chinese Academy of Sciences $^{3}$ Lishui Institute of Hangzhou Dianzi University + +$^{4}$ Australian Institute for Machine Learning, University of Adelaide + +$^{5}$ University of Science and Technology of China $^{6}$ University of California $^{7}$ Yonsei University + +# Abstract + +Given a piece of text, a video clip and a reference audio, the movie dubbing (also known as visual voice clone, V2C) task aims to generate speeches that match the speaker's emotion presented in the video using the desired speaker voice as reference. V2C is more challenging than conventional text-to-speech tasks as it additionally requires the generated speech to exactly match the varying emotions and speaking speed presented in the video. Unlike previous works, we propose a novel movie dubbing architecture to tackle these problems via hierarchical prosody modeling, which bridges the visual information to corresponding speech prosody from three aspects: lip, face, and scene. Specifically, we align lip movement to the speech duration, and convey facial expression to speech energy and pitch via attention mechanism based on valence and arousal representations inspired by the psychology findings. Moreover, we design an emotion booster to capture the atmosphere from global video scenes. All these embeddings are used together to generate mel-spectrogram, which is then converted into speech waves by an existing vocoder. Extensive experimental results on the V2C and Chem benchmark datasets demonstrate the favourable performance of the proposed method. The code and trained models will be made available at https://github.com/GalaxyCong/HPMDubbing. + +# 1. Introduction + +Movie dubbing, also known as visual voice clone (V2C) [9], aims to convert a paragraph of text to a speech with both desired voice specified by reference audio and desired emotion and speed presented in the reference video as shown in the top panel of Figure 1. V2C is more challenging than other speech synthesis tasks in two aspects: first, + +![](images/49237742c5ba9740a90e5b7990a4b1b1fdda9e72f5e8341acf93b0d4d54b0e6d.jpg) + +![](images/590ed0ce8c7446d507fe98c68f38e05f7b27adffb3d42c5340f53b914380413a.jpg) +(a) Visual Voice Cloning (V2C) +(b) Hierarchical Prosody Modeling +Figure 1. (a) Illustration of the V2C tasks. (b) To generate natural speech with proper emotions, we align the phonemes with lip motion, estimate pitch and energy based on facial expression's arousal and valence, and predict global emotion from video scenes. + +it requires synchronization between lip motion and generated speech; second, it requires proper prosodic variations of the generated speech to reflect the speaker's emotion in the video (i.e., the movie's plot). These pose significant challenges to existing voice cloning methods. + +Although significant progress has been made, existing methods do not handle the challenges in V2C well. Specifically, text-based dubbing methods [46-48, 54] construct speeches from given text conditioned on the different speaker embedding but do not consider audio-visual synchronization. On the other hand, lip-referred dubbing schemes [18, 32, 55] predict mel-spectrograms directly from a sequence of lip movements typically by encoder-decoder models. Due to high error rates in generated words, these methods can hardly guarantee high-quality results. Further + +more, video-and-text based dubbing methods [17, 20, 32] focus on inferring speaker characters (e.g., age and gender). However, these visual references usually do not convey targeted emotion well as intended in V2C. + +An ideal dub should align well with the target character so that the audiences feel it is the character speaking instead of the dubber [7]. Thus, a professional dubber usually has a keen sense of observing the unique characteristics of the subject and acts on voice accordingly. In this work, we address these issues with a hierarchical dubbing architecture to synthesize speech. Unlike previous methods, our model connects video representations to speech counterparts at three levels: lip, face, and scene, as shown in Figure 1. + +In this paper, we propose a hierarchical prosody modeling for movie dubbing, which could keep the audio-visual sync and synthesis speech with proper prosody following the movie's plot. Specifically, we first design a duration alignment module that controls speech speed by learning temporal correspondence via multi-head attention over phonemes and lip motion. Second, we propose an affective-display based Prosody Adaptor (PA), which learns affective psychology computing conditioned on facial expression and is supervised by corresponding energy and pitch in the target voice. In particular, we introduce arousal and valence features extracted from facial regions as emotion representations. This is inspired by the affective computing method [51], which analyses the facial affect relying on dimensional measures, namely valence (how positive the emotional display is) and arousal (how calming or exciting the expression looks). Third, we exploit a scene-atmosphere based emotion booster, which fuses the global video representation with the above adapted hidden sequence and is supervised by the emotive state of the whole voice. The outputs of these three modules are fed into a transformer-based decoder, which converts the speech-related representations into mel-spectrogram. Finally, we output the target speech waves from the mel-spectrogram via a powerful vocoder. + +The contributions of this paper are summarized below: + +- We propose a novel hierarchical movie dubbing architecture to better synthesize speech with proper prosody by associating them with visual counterparts: lips, facial expressions, and surrounding scenes. +- We design an affective display-based prosody adaptor to predict the energy and pitch of speech from the arousal and valence fluctuations of facial regions in videos, which provides a fine-grained alignment with speakers' emotions. +- Extensive experimental results demonstrate the proposed method performs well against state-of-the-art models on two benchmark datasets. + +# 2. Related Work + +Text to Speech Synthesis. Over the recent years, numerous TTS models [2, 29, 40, 41, 47, 48, 54] have been proposed for generating high-quality natural speech conditioned on given text. Tacotron [54] is an end-to-end generative TTS model that synthesizes speech directly from characters. Then, Tacotron2 [29] replaces the RNN structures by introducing the attention mechanism to improve training efficiency and solve the long dependency issue. Furthermore, FastSpeech [47] and Fastspeech2 [46] exploit the Feed-Forward Transformer (FFT) to generate mel-spectrogram from phoneme sequences. Despite the impressive voice generated, these methods cannot provide the audio with desired emotion and audio-visual sync for movie dubbing. + +Lip to Speech Synthesis. This task aims to reconstruct speech based on the lip motions alone [3,25]. Lip2Wav [42] is a sequence-to-sequence architecture focusing on learning mappings between lip and speech for individual speakers. Recently, [15, 18, 49, 55] improve the architecture and training methods, and provide the possibility of unconstrained speech synthesis in the wild. However, lip-to-speech is incompetent for movie dubbing because the word error rate is still high [1, 3, 11, 13, 16]. In this work, we focus on reconstructing accurate speech from lip motions and generating the desired emotion and identity with proper prosody. + +Talking Heads. Numerous methods have been developed for audio-visual translation [58] or speaking style transfer [57] by reconstructing the visual content in video [8, 30, 31, 50, 53, 59, 61–63]. Wav2Lip [43] uses an expert lip-syncs discriminator to morph lip movements of arbitrary identities. Recently, Papantoniou et al. [38] develop a Neural Emotion Director (NED) to manipulate emotions while preserving speech-related lip movements. However, these methods cannot adapt to the movie dubbing task because they emphasize using generative models to readjust the facial regions instead of reconstructing the desired speech. + +Visual Voice Cloning. Movie dubbing, also known as visual voice clone, aims to convert scripts to speech with both desired voice identity and emotion based on the reference audio and video. To control the speed of generated speech, Neural Dubber [20] exploits a text-video aligner by using scaled dot-product attention mechanism. VTTS [17] uses multi-source attention to fuse the triplets feature and outputs the mel-spectrogram via an RNN-based decoder. Since explicit emotion categories [28] do not exist in these methods, Chen et al. [9] develops a V2C model on a more challenging Densiny Animation dataset, which concentrates on emotional dubbing for movie characters. Although the V2C considers emotion labels, the adopted global video representation negatively affects the fine-grained emotional expression and makes it challenging to render correct prosody corresponding to plot developments. To solve this issue, we + +![](images/ddac5c75842e9b374005ec2daed22d9bc18b8fe847feef9e7e4cfc59c9d4ea4f.jpg) +Figure 2. Architecture of the proposed hierarchical modular network for movie dubbing, which consists of four main components: Duration Aligner (Sec. 3.1), which learns to predict speech duration based on aligning lip movement and text phoneme; Prosody Adaptor (Sec. 3.2), which predicts energy and pitch from facial arousal and valence, respectively; Atmosphere Booster (Sec. 3.3), which learns a global emotion embedding from a video scene level; and Mel-Generator (Sec. 3.4), which generates mel-spectrograms from embeddings obtained by the aforementioned three modules. The mel-spectrograms are finally converted to audio by a widely adopted vocoder. + +propose a hierarchical movie dubbing architecture to better synthesize speech with proper prosody and emotion. + +# 3. Method + +The main architecture of the proposed model is shown in Fig. 2. First, we use a phoneme encoder [9] to convert the input text $Z_{text}$ to a series of phoneme embeddings $\mathcal{O} = \{\mathbf{o}_1,\dots,\mathbf{o}_L\}$ and use a speaker encoder $F_{spk}$ [9] to capture the voice characteristics $\mathcal{U}$ from different speakers. Then, taking phonemes and lip regions as input, the duration aligner module uses a multi-head attention mechanism to learn to associate phonemes with related lip movements. Next, the affective display-based prosody adaptor module learns to predict the energy and pitch of the desired speech based on arousal and valence features extracted from facial expressions, respectively. And then, the scene atmosphere booster encodes a global representation of emotion of the entire video content. All the outputs of the above three modules are combined to generate mel-spectrograms, which are finally transformed to a waveform $Y_{voice}$ using a adopted vocoder. We detail each module below. + +# 3.1. Duration Aligner + +The duration aligner contains three steps: (1) extracting the lip features from movie; (2) aligning the phonemes of + +text with the lips; (3) expanding the fused phoneme-lip representation to the desired mel-spectrogram length. + +Extracting lip feature. Let $D_w$ , $D_h$ and $D_c$ be the width, height and number of channels of the video frames, respectively. We first extract lip regions $\mathbf{x_m} \in \mathbb{R}^{T_v \times D_w \times D_h \times D_c}$ from the given video using mouth region pre-processing from [20, 33-36]. Then we exploit the LipEncoder to obtain the lip movement representation: + +$$ +\mathbf {E} _ {\mathrm {l i p}} = \operatorname {L i p E n c o d e r} \left(\mathbf {x} _ {\mathbf {m}}\right) \in \mathbb {R} ^ {T _ {v} \times D _ {m}}, \tag {1} +$$ + +where $T_{v}$ denotes the number of video frames, and $D_{m}$ is the hidden dimension of the dynamic lip feature. The LipEncoder consists of several feed-forward transformer blocks that are suitable for capturing both long-term and short-term dynamics lip movement features. + +Aligning text with lips. Inspired by the success of attention mechanism for cross-modality alignment [10, 14, 19, 26, 27, 44, 45, 64], we adopt multi-head attention to learn the alignment between the text phoneme and the lip movement sequence. We use lip embedding as a query to compute the attention on text phonemes. The larger the attention, the more related between a lip embedding and a text phoneme. Due to variations of mouth shapes and pronunciations, the multi-head attention mechanism is suitable for learning their alignments from different aspects. The text-video context sequence $\mathbf{E}_{\mathrm{lip},\mathrm{txt}} = [\alpha_{\mathrm{lip},\mathrm{txt}}^{1},\dots,\alpha_{\mathrm{lip},\mathrm{txt}}^{n}]\in$ + +$\mathbb{R}^{T_v\times D_m}$ is a concatenation of outputs of $n$ attention heads. Concretely, the $k$ -th head's output $\alpha_{lip,txt}^k$ is obtained by: + +$$ +\alpha_ {l i p, t x t} ^ {k} = \operatorname {s o f t m a x} \left(\frac {\mathrm {Q} ^ {\top} \mathrm {K}}{\sqrt {d _ {k}}} + \mathrm {M} _ {\mathrm {t}}\right) \mathrm {V} ^ {\top}, \tag {2} +$$ + +$$ +\mathrm {Q} = \mathrm {W} _ {j} ^ {Q} E _ {l i p} ^ {\top}, \mathrm {K} = \mathrm {W} _ {j} ^ {K} \mathcal {O} ^ {\top}, \mathrm {V} = \mathrm {W} _ {j} ^ {V} \mathcal {O} ^ {\top}, +$$ + +where $\mathbf{W}_j^*$ is a learnable parameter matrix, $d_k$ is the embedding dimension of $\alpha_{lip,txt}^k$ , and $\mathbf{M_t}$ is a mask matrix indicating whether a token can be attended. The aligned representation $E_{lip,txt}$ is later expanded to the length of the desired mel-spectrogram. + +Expanding to the desired length. According to the findings in [20], in an audio-visual clip, the length of a melspectrograms sequence is $n$ times that of a video frame sequence because they are temporally synchronized in audio and visual modalities. The number $n$ is computed as: + +$$ +n = \frac {T _ {m e l}}{T _ {v}} = \frac {s r / h s}{F P S} \in \mathbb {N} ^ {+}, \tag {3} +$$ + +where $FPS$ denotes the Frames per Second of the video, $sr$ denotes the sampling rate of the audio, and $hs$ denotes hop size when transforming the raw waveform into melspectrograms. Phoneme-lip feature $E_{lip,txt}$ is simply duplicated $n$ times in [20] as the final phoneme-lip representation, which lacks flexibility. Instead, we propose to use transposed convolutions to learn the expansion of $E_{lip,txt}$ , which can be formulated as: + +$$ +\mathbf {M} _ {p h o, l i p} = \operatorname {C o n v - T r a n s p o s e} (n, E _ {l i p, t x t}) \in \mathbb {R} ^ {T _ {y} \times D _ {m}}, \tag {4} +$$ + +where $T_{y}$ denotes the length of the desired melspectrogram, $D_{m}$ is the dimension of the initial melspectrogram. The parameters (stride and kernel size) of the transposed convolution can be set under the guidance of $n$ so that $T_{y} \approx n \times T_{v}$ . + +# 3.2. Affective-display based Prosody Adaptor + +One of the critical issues in the V2C task is to describe the speaker's emotions in the given video. To solve this problem, we design an affective-display based Prosody Adaptor (PA), which uses the arousal and valence extracted from facial expressions to represent the emotion. The arousal and valence are then used to predict the energy and pitch of the desired speech model. + +Valence and Arousal Feature. To accurately capture the valence and arousal information from facial expressions, we utilize an emotion face-alignment network (EmoFAN) [51] to encode the facial region into valence $\mathbf{V}$ and arousal $\mathbf{A}$ . $\mathbf{V},\mathbf{A} = \mathrm{EmoFAN}(x_f)\in \mathbb{R}^{T_v\times D_m}$ , $x_{f}\in \mathbb{R}^{T_{v}\times D_{w}\times D_{h}\times D_{c}}$ is the face region extracted via $S^3 FD$ face detection [60]. The EmoFAN focuses on facial regions relevant to emotion estimation, which utilizes a face alignment network (FAN) + +for facial point detection to ensure robustness by jointly predicting categorical and continuous emotions. + +Bridging Arousal with Energy. Arousal is the physiological and psychological state of being awoken or of sense organs stimulated to the point of perception. To bridge the vocal energy with arousal display, we compute an arousal context vector $A_{i}^{l}$ for frame-level energy of the desired speech from phoneme-lip representation $\mathbf{M}_{pho,lip}$ : + +$$ +A _ {i} ^ {l} = \sum_ {k = 0} ^ {T _ {y} - 1} \xi_ {i, k} M _ {p h o, l i p} ^ {k}, +$$ + +$$ +\xi_ {i, k} = \exp (\hat {\xi} _ {i, k}) / \sum_ {j = 0} ^ {T _ {y} - 1} \exp (\hat {\xi} _ {i, j}), \tag {5} +$$ + +$$ +\hat {\xi} _ {i, k} = \mathbf {w} _ {\mathbf {a}} ^ {\top} \tanh (\mathbf {W} _ {\mathbf {a}} ^ {\top} A _ {i} + \mathbf {U} _ {\mathbf {a}} ^ {\top} M _ {p h o, l i p} ^ {k} + \mathbf {b} _ {\mathbf {a}}) +$$ + +where $i$ is frame index, $A_{i}$ is the $i$ -th row of $\mathbf{A}$ , $\xi_{i,k}$ is the attention weight on the $k$ -th phoneme-lip feature $M_{pho,lip}^{k}$ (the $k$ -th row of $\mathbf{M}_{pho,lip}$ ) regarding $i$ -th arousal display; $\mathbf{w}_{\mathbf{a}}^{\mathrm{T}}$ , $\mathbf{W}_{\mathbf{a}}^{\mathrm{T}}$ , $\mathbf{U}_{\mathbf{a}}^{\mathrm{T}}$ and $\mathbf{b}_{\mathbf{a}}$ are learnable parameters; $T_{y}$ is the length of the desired mel-spectrogram. + +Then, we use an energy predictor to project the arousal-related phoneme-lip embedding $A_{i}^{l}$ to the energy of speech: + +$$ +E _ {a r o} = \operatorname {P r e d i c t o r} \left(\left\{A _ {i} ^ {l} \right\} _ {i = 1} ^ {T _ {y}}\right), \tag {6} +$$ + +where $E_{aro} \in \mathbb{R}^{T_y}$ represents the predicted energy of speech, and the energy predictor consists of several fully-connected layers, Conv1D blocks and layer normalization. + +Bridging Valence with Pitch. In prosody linguistics, speakers can speak with a wide pitch range (this is usually associated with excitement) while, at other times, with a narrow range. Valence is the affective quality referring to the intrinsic positiveness or negativeness of an event or situation. Similar to arousal to energy, we first compute a valence context vector for the frame-level pitch of the desired speech from phoneme-lip representation $\mathbf{M}_{pho,lip}$ : + +$$ +V _ {i} ^ {l} = \sum_ {k = 0} ^ {T _ {y} - 1} \psi_ {i, k} M _ {p h o, l i p} ^ {k}, +$$ + +$$ +\psi_ {i, k} = \exp (\hat {\psi} _ {i, k}) / \sum_ {j = 0} ^ {T _ {y} - 1} \exp (\hat {\psi} _ {i, j}), \tag {7} +$$ + +$$ +\hat {\psi} _ {i, k} = \mathbf {w} _ {\mathbf {g}} ^ {\top} \tanh (\mathbf {W} _ {\mathbf {g}} ^ {\top} V _ {i} + \mathbf {U} _ {\mathbf {g}} ^ {\top} M _ {p h o, l i p} ^ {k} + \mathbf {b} _ {\mathbf {g}}) +$$ + +where $\mathbf{w}_{\mathbf{g}}^{\mathbf{T}}, \mathbf{W}_{\mathbf{g}}^{\mathbf{T}}, \mathbf{U}_{\mathbf{g}}^{\mathbf{T}}$ and $\mathbf{b}_{\mathbf{g}}$ are learnable parameters. Then, we exploit a pitch predictor to convert the valence-related phoneme-lip embedding $V_{i}^{l}$ to the pitch of speech, which can be formulated as: + +$$ +P _ {v a l} = \operatorname {P r e d i c t o r} \left(\left\{V _ {i} ^ {l} \right\} _ {i = 1} ^ {T _ {y}}\right). \tag {8} +$$ + +Finally, we concatenate the two prosody-related features as the contextualized affect primitives: + +$$ +\mathbf {M} _ {\mathbf {p}} = \left\{\left[ A _ {i} ^ {l}; V _ {i} ^ {l} \right] \right\} _ {i = 1} ^ {T _ {y}}. \tag {9} +$$ + +# 3.3. Scene Atmosphere Booster + +As a unique form of artistic expression, the scene layout and colours of the film convey an emotional atmosphere to evoke resonance with the audience [5, 56]. To reason the comprehensive emotion, we design a scene atmosphere booster to combine the global context information and generated prosody. First, we use the I3D model [6] to extract the scene representation $S$ from the video. Then, we fuse the global contextual emotion of vision with prosody information of speech through a cross-model attention mechanism by: + +$$ +\mathbf {M} _ {\mathrm {e}} = \operatorname {S o f t m a x} \left(\frac {\mathbf {M} _ {\mathbf {p}} S ^ {\top}}{\sqrt {D _ {m}}}\right) \mathbf {M} _ {\mathbf {p}} \in \mathbb {R} ^ {T _ {y} \times D _ {w}}. \tag {10} +$$ + +Finally, formulated as a maxpool and fully-connected layer, the emotional predictor projects prosody-scene context sequence $\mathbf{M}_{\mathrm{e}}$ to emotional embedding: + +$$ +G _ {e m o} = \operatorname {M a x p o o l} (\operatorname {P r e d i c t o r} (\mathbf {M} _ {\mathbf {e}})). \tag {11} +$$ + +# 3.4. Audio Generation + +We fuse the three kinds of speech attribute information by concatenation operation in the spatial dimension. Then, we use transformer-based mel-spectrogram decoder to convert the adapted hidden sequence into mel-spectrogram sequence in parallel by: + +$$ +\mathbf {f} = \text {T r a n s f o r m e r D e c o d e r} \left(\mathbf {M} _ {\text {p h o}, l i p} \oplus \mathbf {M} _ {\mathbf {p}} \oplus \mathbf {M} _ {\mathbf {e}}\right), \tag {12} +$$ + +where $M_{pho,lip}$ , $M_p$ , and $M_e$ denote the hidden representation of phoneme-lip feature, prosody variances, and emotional tone, respectively. Specifically, our mel-spectrogram decoder consists of a stack of self-attention layers [52] and 1D-convolution layers as in FastSpeech [47]. We then use the mel-linear layer and postnet [48] to refine the hidden states into final dimensional mel-spectrograms by: + +$$ +y = \operatorname {P o s t N e t} (\operatorname {F C} (f)). \tag {13} +$$ + +Finally, to generate the time-domain waveform $y_{w}$ from mel-spectrogram $y$ , we use HiFi-GAN [23] as our vocoder, which mainly consists of a transposed convolution network and a multi-receptive field fusion module. + +# 3.5. Loss Functions + +Our model is trained in an end-to-end fashion via optimizing the sum of all losses: + +$$ +\mathcal {L} _ {\text {p i t c h}} = \frac {1}{T _ {y}} \sum_ {t = 1} ^ {T _ {y} - 1} \left(P _ {t} - \hat {P} _ {\text {v a l}} ^ {t}\right) ^ {2}, \tag {14} +$$ + +$$ +\mathcal {L} _ {\text {e n e r g y}} = \frac {1}{T _ {y}} \sum_ {t = 1} ^ {T _ {y} - 1} \left(E _ {t} - \hat {E} _ {a r o} ^ {t}\right) ^ {2}, \tag {15} +$$ + +$$ +\mathcal {L} _ {e m o} = - \sum_ {i = 1} ^ {C} G _ {i} \log \left(\hat {G} _ {e m o} ^ {i}\right), \tag {16} +$$ + +$$ +\mathcal {L} _ {m e l} = \frac {1}{T _ {y}} \sum_ {t = 1} ^ {T _ {y} - 1} \left\| \mathbf {f} _ {t} - \hat {\mathbf {f}} _ {t} \right\|. \tag {17} +$$ + +$$ +\mathcal {L} _ {S} = \lambda_ {1} \mathcal {L} _ {\text {m e l}} + \lambda_ {2} \mathcal {L} _ {\text {p i t c h}} + \lambda_ {3} \mathcal {L} _ {\text {e n e r g y}} + \lambda_ {4} \mathcal {L} _ {\text {e m o}}, \tag {18} +$$ + +where $\mathcal{L}_{mel}$ , $\mathcal{L}_{pitch}$ , $\mathcal{L}_{energy}$ and $\mathcal{L}_{emo}$ , denote the losses of mel-spectrogram, pitch, energy and global emotion respectively. $P_{t}$ , $E_{t}$ , and $\mathbf{f}_t$ are ground-truth pitch, energy, and mel-spectrogram on frame-level, respectively. G is the ground-truth emotional label and C denotes all categories. + +# 4. Experimental Results + +In this section, we first briefly describe the datasets used for evaluation and the evaluation metric. Then we present the implementation details of the proposed method. Last, we show the results compared to state-of-the-art methods and the ablation study. + +# 4.1. Datasets + +V2C is a multi-speaker dataset for animation movie dubbing with identity and emotion annotations [9]. It is collected from 26 Disney cartoon movies and covers 153 diverse characters. V2C not only needs to generate voices with identity characteristics according to the reference audio but also capture emotional information based on the reference movie clips. The whole dataset has 10,217 video clips with paired audio and subtitles. The training/validation/test size are $60\%$ , $10\%$ , $30\%$ . + +Chem is a single-speaker dataset composed of 6,640 short video clips collected from the YouTube, with the total video length of approximately nine hours [20]. The Chem dataset is originally used for the unconstrained single-speaker lipto-speech synthesis [42], which takes place in a chemistry lecture. For fluency and complete dubbing, each video clip has sentence-level text and audio based on the start and end timestamps. There are 6,240, 200, and 200 dubbing clips for training, validation, and testing, respectively. + +# 4.2. Evaluation Metrics + +Audio-visual synchronization. To evaluate the synchronization between the generated speech and the video quantitatively, we adopt Lip Sync Error Distance (LSE-D) and Lip Sync Error Confidence (LSE-C) as our metrics, which can explicitly test for synchronization between lip motions and speech in unconstrained videos in the wild [12, 43]. + +Mel Cepstral Distortion and its variants. MCD [24], MCD-DTW [4] and MCD-DTW-SL [9] are adopted, which + +
MethodsLSE-D ↓LSE-C ↑MCD ↓MCD-DTW ↓MCD-DTW-SL ↓Id. Acc. ↑Emo. Acc. ↑MOS-N ↑MOS-S ↑
Ground Truth6.7347.81300.0000.0000.0090.6284.384.61 ± 0.154.74 ± 0.12
SV2TTS [21]13.7332.72521.0812.8749.5633.6237.192.03 ± 0.221.92 ± 0.15
SV2TTS* [21]12.6173.34919.3812.7334.5135.1842.052.07 ± 0.072.15 ± 0.09
Tacotron* [54]13.4752.93819.7918.7342.1532.4939.682.12 ± 0.172.06 ± 0.12
FastSpeech2 [46]12.2612.95820.7814.3919.4121.7246.822.79 ± 0.102.63 ± 0.09
FastSpeech2* [46]12.1132.60420.6614.5920.7923.4446.903.08 ± 0.062.89 ± 0.07
V2C-Net1 [9]11.7843.02620.6114.2319.1526.8448.413.19 ± 0.043.06 ± 0.06
Ours8.0365.60815.6612.2913.4837.7561.464.03 ± 0.083.89 ± 0.07
+ +Table 1. Results on the V2C dataset with comparisons against state-of-the-art methods. We provide the results using both objective metrics (i.e., LSE-D, LSE-C, MCD, MCD-DTW and MCD-DTW-SL) and subjective metrics (i.e., MOS-Naturalness and MOS-Similarity). "Id. Acc." and "Emo. Acc." are the identity and emotion accuracy of the generated speech, respectively. The method with “*” refers to a variant taking video (emotion) embedding as an additional input as in [9]. $\uparrow (\downarrow)$ means that the higher (lower) value is better. + +reflect the similarity of mel-spectrograms. MCD-DTW uses the Dynamic Time Warping (DTW) [37] algorithm to find the minimum MCD between two speeches, while MCD-DTW-SL introduces the duration measure coefficient to consider the length and the quality of generated speech [9]. + +Emotion and identity accuracy. To measure whether the generated speech carries proper emotion and speaker identity, we adopt an emotion accuracy (Emo. Acc.) and an identity accuracy (Id. Acc.) as our metrics as in [9]. + +Subjective evaluations. To further evaluate the quality of generated speech, we conduct a human study using a subjective evaluation metric, following the settings in [9]. Specifically, we adopt the MOS-naturalness (MOS-N) and MOS-similarity (MOS-S) to assess the naturalness of the generated speech and the recognition of the desired voice. + +# 4.3. Implementation Details + +For the duration aligner, we use 4 Feed-Forward Transformer (FFT) blocks and 3 FFT blocks for the phoneme encoder and lip movement encoder, respectively. We set the dimension of the phoneme feature $\mathcal{O}$ and lip features $E_{lip}$ to 256. We use 8 attention heads for alignment between lip and phoneme. For each movie clip, Our FPS set is 25, the sampling rate $sr$ is $22050Hz$ . We use short-time fourier transform (STFT) to obtain the mel-spectrum, and the number of points of the fourier transform is 1024. We use the Conv-Transpose1D module with 2 stride and 4 kernel sizes to obtain the duration features. In Valence and Arousal Feature Encoder, the EmoFAN consists of one 2D convolution with a kernel size of $7\times 7$ and 3 convolution blocks (ConvBlock) with a kernel size of $3\times 3$ and Average Pooling stride of $2\times 2$ . Similarly, we set the dimension of the valence and arousal feature to 256. For the mel-spectrograms generator, the mel-spectrogram decoder consists of 6 FFT blocks and the hidden state of mel-linear layer is of size 80. + +For training, we use Adam [22] with $\beta_{1} = 0.9$ , $\beta_{2} = 0.98$ , $\epsilon = 10^{-9}$ to optimize our model. For V2C and Chem dataset, we set the learning rate schedule to 0.00001 and 0.00005, respectively. In this work, we use pretrained HiFiGAN [23] + +as the vocoder to transform the generated mel-spectrograms into audio samples. We set the batch size to 16 on two datasets. Our model is implemented in PyTorch [39]. All the models are performed on a single NVIDIA GTX3090Ti GPU. We train the model with 600 epochs on the V2C dataset and 400 epochs on the Chem dataset. + +# 4.4.Quantitative Evaluation + +We compare with five related baselines of speech synthesizes. (1) SV2TTS [21] is a basic TTS model to generate speech with reference audio for multi-speakers; (2) Tacotron [54] is an end-to-end generative TTS model that synthesizes speech directly from textual characters; (3) FastSpeech2 [46] introduces the variance adaptor to convert the text to waveform by end-to-end; (4) Neural Dubber [20] synthesizes human speech for given video according to the corresponding text; (5) V2C-Net [9] is the first model for movie dubbing, which match the speaker's emotion presented in video. Note that we do not compare with Neural Dubber [20] on the V2C benchmark due to the unavailability of its code and missing implementation details. + +Results on the V2C benchmark. The results are presented in Table 1. Our method achieves the best performance on all nine metrics. Specifically, in terms of audio-visual sync, our method achieves 8.036 of LSE-D and 5.608 of LSE-C, which significantly surpasses the previous best results and is much closer to human performance. In terms of MCD, MCD-DTW, and MCD-DTW-SL, our method achieves relative $24.02\%$ , $13.63\%$ and $29.61\%$ improvements, respectively. This indicates our method can achieve a better mel-spectrogram than others. The above results together show that bridging specific attributes of speech with corresponding visual counterparts can make the generated speech present better prosodies and lip motion sync. Additionally, our method outperforms the previous method by a large margin in emotion accuracy and can gain better identification accuracy. This indicates the proposed method can better capture and convey emotions, which is of great importance for the movie dubbing task. Last, the human subjec + +
MethodsAQ↑AV Sync ↑LSE-D ↓LSE-C ↑
Ground Truth3.93 ± 0.084.13±0.076.9267.711
FastSpeech2 [46]3.71±0.083.29±0.0911.862.805
Tacotron* [54]3.55±0.093.03±0.1011.792.231
V2C-Net [9]3.48±0.143.25±0.1111.262.907
Neural Dubber [20]3.74±0.083.91±0.077.2127.037
Ours3.84±0.113.97±0.086.9757.176
+ +Table 2. Results on the Chem dataset with comparisons against state-of-the-art methods. AQ (Audio Quality) and AV Sync (audio-visual synchronization) are subjective metrics. Note that the Chem dataset is a single-speaker non-movie dataset, and thus there is no identity accuracy and emotion accuracy. + +
#MethodsLSE-D ↓LSE-C ↑MCD ↓Id. Acc. ↑Emo. Acc. ↑
1w/o DA11.8353.71619.5318.7538.33
2w/o PA8.5145.27416.3410.4222.08
3w/o AB8.2615.40815.9033.3353.92
4w/o Valence9.2154.93516.3129.8135.31
5w/o Arousal8.7935.21615.7932.7446.38
6VA v.s. FF9.1605.01119.8723.6739.58
7w/o multi-head10.9483.89418.6524.8540.25
8Duplication11.4753.81418.6321.7837.92
9Full model8.0365.60815.6637.7561.46
+ +Table 3. Ablation study of the proposed method on the V2C benchmark dataset. + +tive evaluation results (see MOS-N and MOS-S) also show that our method can generate speeches that are closer to realistic speech according to naturalness and similarity. + +Results on the Chem benchmark. As shown in Table 2, our model is ahead of the state-of-the-art methods in all metrics on the Chem benchmark. In terms of the audiovisual sync, our method achieves 6.975 LSE-D and 7.176 LSE-C. Furthermore, in the subjective evaluations, our method improves $10.34\%$ on AQ and $22.15\%$ on AV Sync. The results show that our performance is much closer to the ground truth recording, which indicates that our model synthesizes high-quality natural speech by controlling the prosody from hierarchical visual representation. + +# 4.5. Ablation Studies + +Effectiveness of Duration Aligner, Prosody Adaptor, and Atmosphere Booster. We evaluate the effectiveness of these three modules by removing them separately and retraining the model. The results are shown in Row $1\sim 3$ of Table 3. The result shows that all the proposed modules contribute significantly to the overall performance, and each module has a different focus. Specifically, the performance on the audio-visual metrics (LSE-D, LSE-C, and MCD) drops the most when removing the Duration Aligner (DA). This reflects the DA module indeed helps the model learn a better temporal synchronization. By contrast, the perfor + +![](images/cda78790666a031496c81aae36e5d814c561af2fac3e2dd0f4de0ea5ee9eb9a6.jpg) +Figure 3. Audiovisual consistency visualization on V2C dataset: Ground Truth (GT), our model, FastSpeech 2 (FS2) and V2C-Net. + +mance on identity accuracy and emotion accuracy drop the most when removing the Prosody Adaptor (PA). This can be attributed to the predicted pitch and energy representing a speaker's identity and his/her emotion. When removing the Atmosphere Booster (AB), the performance drops compared to the full model (Row 9) but does not drop as much as when removing the other two modules. This indicates the AB module also contributes the overall performance improvement but contributes the least in the three modules. + +Effectiveness of valence and arousal. In our model, we bridge arousal with energy and valence with pitch by attention mechanism. To evaluate their effectiveness, we cut off the connection and predict energy and pitch directly from the phoneme-lip representation. The results are presented in Row $4\sim 5$ of Table 3. It shows that the performance drops significantly when removing either of them and drops more when removing valence. This indicates valence contributes more than arousal on the V2C task. + +Valence-and-Arousal v.s. Facial Expression. To compare the role of affective display and facial expressions on prosody inference, we replace the input of the APA module with original embeddings of facial features from CNN. As row “VA v.s. FF” of Table 3 shows, the model performance drops significantly. This is likely caused by facial features that are still far from the information-containing emotion, which is not enough to guide prosody generation. + +Multi-head Attention in Duration Adaptor. In our duration aligner, we exploit multi-head attention to learn the relation between the phoneme sequences and the lip motion. To verify its effectiveness, we conduct experiments using conventional dot-product attention. The results are + +![](images/4168d8e5209fd61ce3b9922e2f0d9862cd498d254be589953774697641ab5e67.jpg) +Figure 4. Visualization of audio on V2C dataset (top) and Chem dataset (bottom). Orange curves are $F_{0}$ contours, where $F_{0}$ is the fundamental frequency of audio. Purple curves refer to the energy (volume) of audio. The horizontal axis is the duration of the audio. The red circles highlight the mel-spectrograms at the same moment as the frame shown on the left side. + +presented in Row 7 of Table 3. The performance drops significantly on all metrics, such as $26.59\%$ and $44.02\%$ decrease on LSE-D and LSE-C compared to Row 9, respectively. This indicates multi-head attention learns a much better correlation between phonemes with lip movement. + +Conv-Transpose v.s. Duplication. In our duration aligner, we propose to use Conv-Transpose to learn the expansion of the fused phoneme-lip representation to its desired length. To verify its effectiveness, we replace it with simple make $n$ duplications as in [20]. The results are shown in Row 8 of Table 3. It shows that the performance drops significantly. For example, it falls $15.94\%$ on MCD metric. This demonstrates the superiority of using transposed convolution to learn the upsampling than simply copying. + +# 4.6. Qualitative Results + +Audiovisual consistency visualization. Figure 3 presents the mel-spectrograms of generated audios along with its frames. Blue and red bounding boxes denote whether the character is speaking or not, respectively. Compared with other methods, the mel-spectrogram generated by our model is closer to the ground truth, indicating better audiovisual synchronization. This can be attributed to our duration aligner, which exploits multi-head alignment between the text phoneme sequence and the lip movement sequence. By controlling the lip movements explicitly, we obtain the desired length of mel-spectrograms, which makes the speech well synchronized with the input video. + +Arousal and valence with prosody visualization. We selected two examples from the test set of the V2C dataset and Chem dataset to demonstrate the alignment between en + +ergy and arousal as well as pitch and valence. The valence (positive or negative) and arousal (calming or exciting) of facial expressions are shown in the first column. The main pitch and energy are shown in orange and blue curves in the right column. We use the red circle to highlight the pitch and energy that correspond to the video frame shown in the left column. The result shows that our method achieves energy and pitch closer to the ground truth speech. When the chemistry teacher becomes excited and positive, our model successfully leverages the affective display to synthesize a similar pitch and energy as the ground-truth speech. + +# 5. Conclusion + +In this work, we propose a hierarchical prosody modeling network for movie dubbing, which bridges video representations and speech attributes from three levels: lip, facial expression, and scene. By associating these visual representations with their voice counterparts, we obtain more powerful representations for dubbing. Furthermore, we design an affective-display based prosody adaptor, which effectively learns to align the valence and arousal to the pitch and energy of speeches. Our proposed model sets new state-of-the-art on both Chem and V2C-Animation benchmarks. + +Acknowledgement. This work was supported in part by the National Key R&D Program of China under Grant 2018AAA0102000, and in part by National Natural Science Foundation of China under Grants 62225207 and U19B2038, the Key R&D Plan Project of Zhejiang Province (No. 2023C01004), Shandong Provincial Natural Science Foundation (ZR2020MA064), Youth Innovation Promotion Association of CAS under Grant 2020108. + +# References + +[1] Triantafyllos Afouras, Joon Son Chung, and Andrew Zisserman. Deep lip reading: A comparison of models and an online application. In Interspeech, pages 3514-3518, 2018. 2 +[2] Sercan Ömer Arik, Mike Chrzanowski, Adam Coates, Gregory Frederick Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Y. Ng, Jonathan Raiman, Shubho Sengupta, and Mohammad Shoeeybi. Deep voice: Real-time neural text-to-speech. In ICML, pages 195-204, 2017. 2 +[3] Yannis M Assael, Brendan Shillingford, Shimon Whiteson, and Nando De Freitas. Lipnet: End-to-end sentence-level lipreading. arXiv preprint arXiv:1611.01599, 2016. 2 +[4] Eric Battenberg, R. J. Skerry-Ryan, Soroosh Mariooryad, Daisy Stanton, David Kao, Matt Shannon, and Tom Bagby. Location-relative attention mechanisms for robust long-form speech synthesis. In ICASSP, pages 6194-6198, 2020. 5 +[5] Manuel Burghardt, Michael Kao, and Christian Wolff. Beyond shot lengths - using language data and color information as additional parameters for quantitative movie analysis. In ADHO, pages 753-755, 2016. 5 +[6] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In CVPR, pages 6299-6308, 2017. 5 +[7] Frederic Chaume. Dubbing practices in europe: localisation beats globalisation. LANS-TTS, 6, 2007. 2 +[8] Lele Chen, Ross K. Maddox, Zhiyao Duan, and Chenliang Xu. Hierarchical cross-modal talking face generation with dynamic pixel-wise loss. In CVPR, pages 7832-7841, 2019. 2 +[9] Qi Chen, Mingkui Tan, Yuankai Qi, Jiaqiu Zhou, Yuanqing Li, and Qi Wu. V2C: visual voice cloning. In CVPR, pages 21210-21219, 2022. 1, 2, 3, 5, 6, 7 +[10] Weidong Chen, Dexiang Hong, Yuankai Qi, Zhenjun Han, Shuhui Wang, Laiyun Qing, Qingming Huang, and Guorong Li. Multi-attention network for compressed video referring object segmentation. In ACM MM, pages 4416–4425, 2022. 3 +[11] Joon Son Chung, Andrew W. Senior, Oriol Vinyals, and Andrew Zisserman. Lip reading sentences in the wild. In CVPR, pages 3444-3453, 2017. 2 +[12] Joon Son Chung and Andrew Zisserman. Out of time: Automated lip sync in the wild. In ACCV Workshop, pages 251-263, 2016. 5 +[13] Joon Son Chung and Andrew Zisserman. Lip reading in profile. In BMVC, 2017. 2 +[14] Gaoxiang Cong, Liang Li, Zhenhuan Liu, Yunbin Tu, Weijun Qin, Shenyuan Zhang, Chengang Yan, Wenyu Wang, and Bin Jiang. LS-GAN: iterative language-based image manipulation via long and short term consistency reasoning. In ACM MM, pages 4496-4504, 2022. 3 +[15] Rodrigo Schoburg Carrillo de Mira, Alexandros Haliassos, Stavros Petridis, Bjorn W. Schuller, and Maja Pantic. SVTS: scalable video-to-speech synthesis. In INTER-SPEECH, pages 1836-1840, 2022. 2 + +[16] Ariel Ephrat and Shmuel Peleg. Vid2speech: Speech reconstruction from silent video. In ICASSP, pages 5095-5099, 2017. 2 +[17] Michael Hassid, Michelle Tadmor Ramanovich, Brendan Shillingford, Miaosen Wang, Ye Jia, and Tal Remez. More than words: In-the-wild visually-driven prosody for text-to-speech. In CVPR, pages 10577–10587, 2022. 2 +[18] Sindhu B. Hegde, K. R. Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C. V. Jawahar. Lip-to-speech synthesis for arbitrary speakers in the wild. In ACM MM, pages 6250-6258, 2022. 1, 2 +[19] Yicong Hong, Qi Wu, Yuankai Qi, Cristian Rodriguez Opazo, and Stephen Gould. A recurrent vision-and-language BERT for navigation. CoRR, abs/2011.13922, 2020. 3 +[20] Chenxu Hu, Qiao Tian, Tingle Li, Yuping Wang, Yuxuan Wang, and Hang Zhao. Neural dubber: Dubbing for videos according to scripts. In NeurIPS, pages 16582-16595, 2021. 2, 3, 4, 5, 6, 7, 8 +[21] Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez-Moreno, and Yonghui Wu. Transfer learning from speaker verification to multispeaker text-to-speech synthesis. In NeurIPS, pages 4485-4495, 2018. 6 +[22] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Yoshua Bengio and Yann LeCun, editors, ICLR, 2015. 6 +[23] Jungil Kong, Jaehyeon Kim, and Jaekyoung Bae. Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. In NeurlPS, 2020. 5, 6 +[24] R. Kubichek. Mel-cepstral distance measure for objective speech quality assessment. In PACRIM, pages 125–128, 1993. 5 +[25] Yaman Kumar, Rohit Jain, Khwaja Mohd. Salik, Rajiv Ratn Shah, Yifang Yin, and Roger Zimmermann. Lipper: Synthesizing thy speech using multi-view lipreading. In AAAI, pages 2588-2595, 2019. 2 +[26] Liang Li, Xingyu Gao, Jincan Deng, Yunbin Tu, Zheng-Jun Zha, and Qingming Huang. Long short-term relation transformer with global gating for video captioning. IEEE Trans. Image Process., pages 2726-2738, 2022. 3 +[27] Liang Li, Chenggang Clarence Yan, Xing Chen, Chunjie Zhang, Jian Yin, Baochen Jiang, and Qingming Huang. Distributed image understanding with semantic dictionary and semantic expansion. Neurocomputing, pages 384-392, 2016. 3 +[28] Liang Li, Xinge Zhu, Yiming Hao, Shuhui Wang, Xingyu Gao, and Qingming Huang. A hierarchical cnn-rnn approach for visual emotion classification. ACM Trans. Multim. Comput. Commun. Appl., pages 1-17, 2019. 2 +[29] Naihan Li, Shujie Liu, Yanqing Liu, Sheng Zhao, and Ming Liu. Neural speech synthesis with transformer network. In AAAI, pages 6706-6713, 2019. 2 +[30] Borong Liang, Yan Pan, Zhizhi Guo, Hang Zhou, Zhibin Hong, Xiaoguang Han, Junyu Han, Jingtuo Liu, Errui Ding, and Jingdong Wang. Expressive talking head generation with granular audio-visual control. In CVPR, pages 3377–3386, 2022. 2 + +[31] Xian Liu, Qianyi Wu, Hang Zhou, Yinghao Xu, Rui Qian, Xinyi Lin, Xiaowei Zhou, Wayne Wu, Bo Dai, and Bolei Zhou. Learning hierarchical cross-modal association for cospeech gesture generation. In CVPR, pages 10452-10462, 2022. 2 +[32] Junchen Lu, Berrak Sisman, Rui Liu, Mingyang Zhang, and Haizhou Li. Visualts: TTS with accurate lip-speech synchronization for automatic voice over. In ICASSP, pages 8032-8036, 2022. 1, 2 +[33] Pingchuan Ma, Brais Martinez, Stavros Petridis, and Maja Pantic. Towards practical lipreading with distilled and efficient models. In ICASSP, pages 7608-7612, 2021. 3 +[34] Pingchuan Ma, Yujiang Wang, Stavros Petridis, Jie Shen, and Maja Pantic. Training strategies for improved lip-reading. In ICASSP, pages 8472–8476, 2022. 3 +[35] Pingchuan Ma, Yujiang Wang, Jie Shen, Stavros Petridis, and Maja Pantic. Lip-reading with densely connected temporal convolutional networks. In WACV, pages 2857–2866, 2021. 3 +[36] Brais Martinez, Pingchuan Ma, Stavros Petridis, and Maja Pantic. Lipreading using temporal convolutional networks. In ICASSP, pages 6319-6323, 2020. 3 +[37] Meinard Müller. Dynamic time warping. Information Retrieval for Music and Motion, pages 69-84, 2007. 6 +[38] Foivos Paraperas Papantoniou, Panagiotis Paraskevas Filntisis, Petros Maragos, and Anastasios Roussos. Neural emotion director: Speech-preserving semantic control of facial expressions in "in-the-wild" videos. In CVPR, pages 18759-18768, 2022. 2 +[39] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, pages 8024-8035, 2019. 6 +[40] Wei Ping, Kainan Peng, and Jitong Chen. Clarinet: Parallel wave generation in end-to-end text-to-speech. In ICLR, 2019. 2 +[41] Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan Ömer Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, and John Miller. Deep voice 3: Scaling text-to-speech with convolutional sequence learning. In ICLR, 2018. 2 +[42] K. R. Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C. V. Jawahar. Learning individual speaking styles for accurate lip to speech synthesis. In CVPR, pages 13793–13802, 2020. 2, 5 +[43] K. R. Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C. V. Jawahar. A lip sync expert is all you need for speech to lip generation in the wild. In ACM MM, pages 484-492, 2020. 2, 5 +[44] Yanyuan Qiao, Qi Chen, Chaorui Deng, Ning Ding, Yuankai Qi, Mingkui Tan, Xincheng Ren, and Qi Wu. R-GAN: exploring human-like way for reasonable text-to-image synthesis via generative adversarial networks. In ACM MM, pages 2085-2093, 2021. 3 + +[45] Yanyuan Qiao, Yuankai Qi, Yicong Hong, Zheng Yu, Peng Wang, and Qi Wu. Hop+: History-enhanced and order-aware pre-training for vision-and-language navigation. TPAMI, 2023. 3 +[46] Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu. Fastspeech 2: Fast and high-quality end-to-end text to speech. In ICLR, 2021. 1, 2, 6, 7 +[47] Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu. Fastspeech: Fast, robust and controllable text to speech. In NeurIPS, pages 3165-3174, 2019. 1, 2, 5 +[48] Jonathan Shen, Ruoming Pang, Ron J. Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang, Yuxuan Wang, RJ-Skerrv Ryan, Rif A. Saurous, Yannis Agiomyrgiannakis, and Yonghui Wu. Natural TTS synthesis by conditioning wavenet on MEL spectrogram predictions. In ICASSP, pages 4779-4783, 2018. 1, 2, 5 +[49] Bowen Shi, Wei-Ning Hsu, Kushal Lakhotia, and Abdelrahman Mohamed. Learning audio-visual speech representation by masked multimodal cluster prediction. In ICLR, 2022. 2 +[50] Linsen Song, Wayne Wu, Chen Qian, Ran He, and Chen Change Loy. Everybody's talkin': Let me talk as you want. IEEE Trans. Inf. Forensics Secur., 17:585-598, 2022. 2 +[51] Antoine Toisoul, Jean Kossaifi, Adrian Bulat, Georgios Tzimiropoulos, and Maja Pantic. Estimation of continuous valence and arousal levels from faces in naturalistic conditions. Nat. Mach. Intell., 3(1):42-50, 2021. 2, 4 +[52] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, pages 5998-6008, 2017. 5 +[53] Suzhen Wang, Lincheng Li, Yu Ding, and Xin Yu. One-shot talking face generation from single-speaker audio-visual correlation learning. In AAAI, pages 2531–2539, 2022. 2 +[54] Yuxuan Wang, R. J. Skerry-Ryan, Daisy Stanton, Yonghui Wu, Ron J. Weiss, Navdeep Jaitly, Zongheng Yang, Ying Xiao, Zhifeng Chen, Samy Bengio, Quoc V. Le, Yannis Agiomyrgiannakis, Rob Clark, and Rif A. Saurous. Tacotron: Towards end-to-end speech synthesis. In *Interspeech*, pages 4006-4010, 2017. 1, 2, 6, 7 +[55] Yongqi Wang and Zhou Zhao. Fastlts: Non-autoregressive end-to-end unconstrained lip-to-speech synthesis. In ACM MM, pages 5678-5687, 2022. 1, 2 +[56] Jonatas Wehrmann and Rodrigo C. Barros. Movie genre classification: A multi-label approach based on convolutions through time. Appl. Soft Comput., 61:973-982, 2017. 5 +[57] Tianyi Xie, Liucheng Liao, Cheng Bi, Benlai Tang, Xiang Yin, Jianfei Yang, Mingjie Wang, Jiali Yao, Yang Zhang, and Zejun Ma. Towards realistic visual dubbing with heterogeneous sources. In ACM MM, pages 1739-1747, 2021. 2 +[58] Yi Yang, Brendan Shillingford, Yannis M. Assael, Miaosen Wang, Wendi Liu, Yutian Chen, Yu Zhang, Eren Sezener, Luis C. Cobo, Misha Denil, Yusuf Aytar, and Nando de Freitas. Large-scale multilingual audio visual dubbing. CoRR, abs/2011.03530, 2020. 2 + +[59] Fei Yin, Yong Zhang, Xiaodong Cun, Mingdeng Cao, Yanbo Fan, Xuan Wang, Qingyan Bai, Baoyuan Wu, Jue Wang, and Yujiu Yang. Styleheat: One-shot high-resolution editable talking face generation via pretrained stylegan. arXiv preprint arXiv:2203.04036, 2022. 2 +[60] Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, and Stan Z Li. S3fd: Single shot scale-invariant face detector. In CVPR, pages 192-201, 2017. 4 +[61] Hang Zhou, Yu Liu, Ziwei Liu, Ping Luo, and Xiaogang Wang. Talking face generation by adversarially disentangled audio-visual representation. In AAAI, pages 9299–9306, 2019. 2 +[62] Yang Zhou, Xintong Han, Eli Shechtman, Jose Echevarria, Evangelos Kalogerakis, and Dingzeyu Li. Makelttalk: speaker-aware talking-head animation. ACM Trans. Graph., 39(6):221:1-221:15, 2020. 2 +[63] Yang Zhou, Jimei Yang, Dingzeyu Li, Jun Saito, Deepali Aneja, and Evangelos Kalogerakis. Audio-driven neural gesture reenactment with video motion graphs. In CVPR, pages 3408-3418, 2022. 2 +[64] Wanrong Zhu, Yuankai Qi, Pradyumna Narayana, Kazoo Sone, Sugato Basu, Xin Eric Wang, Qi Wu, Miguel P. Eckstein, and William Yang Wang. Diagnosing vision-and-language navigation: What really matters. 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and a reference audio, the movie dubbing (also known as visual voice clone, V2C) task aims to generate speeches that match the speaker's emotion presented in the video using the desired speaker voice as reference. V2C is more challenging than conventional text-to-speech tasks as it additionally requires the generated speech to exactly match the varying emotions and speaking speed presented in the video. Unlike previous works, we propose a novel movie dubbing architecture to tackle these problems via hierarchical prosody modeling, which bridges the visual information to corresponding speech prosody from three aspects: lip, face, and scene. Specifically, we align lip movement to the speech duration, and convey facial expression to speech energy and pitch via attention mechanism based on valence and arousal representations inspired by the psychology findings. Moreover, we design an emotion booster to capture the atmosphere from global video scenes. All these embeddings are used together to generate mel-spectrogram, which is then converted into speech waves by an existing vocoder. Extensive experimental results on the V2C and Chem benchmark datasets demonstrate the favourable performance of the proposed method. The code and trained models will be made available at https://github.com/GalaxyCong/HPMDubbing." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 602, + 128, + 615 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 602, + 128, + 615 + ], + "spans": [ + { + "bbox": [ + 47, + 602, + 128, + 615 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 622, + 287, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 622, + 287, + 696 + ], + "spans": [ + { + "bbox": [ + 47, + 622, + 287, + 696 + ], + "type": "text", + "content": "Movie dubbing, also known as visual voice clone (V2C) [9], aims to convert a paragraph of text to a speech with both desired voice specified by reference audio and desired emotion and speed presented in the reference video as shown in the top panel of Figure 1. V2C is more challenging than other speech synthesis tasks in two aspects: first," + } + ] + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 310, + 255, + 544, + 335 + ], + "blocks": [ + { + "bbox": [ + 310, + 255, + 544, + 335 + ], + "lines": [ + { + "bbox": [ + 310, + 255, + 544, + 335 + ], + "spans": [ + { + "bbox": [ + 310, + 255, + 544, + 335 + ], + "type": "image", + "image_path": "49237742c5ba9740a90e5b7990a4b1b1fdda9e72f5e8341acf93b0d4d54b0e6d.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 310, + 346, + 543, + 444 + ], + "blocks": [ + { + "bbox": [ + 377, + 336, + 476, + 346 + ], + "lines": [ + { + "bbox": [ + 377, + 336, + 476, + 346 + ], + "spans": [ + { + "bbox": [ + 377, + 336, + 476, + 346 + ], + "type": "text", + "content": "(a) Visual Voice Cloning (V2C)" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 310, + 346, + 543, + 444 + ], + "lines": [ + { + "bbox": [ + 310, + 346, + 543, + 444 + ], + "spans": [ + { + "bbox": [ + 310, + 346, + 543, + 444 + ], + "type": "image", + "image_path": "590ed0ce8c7446d507fe98c68f38e05f7b27adffb3d42c5340f53b914380413a.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 372, + 445, + 481, + 456 + ], + "lines": [ + { + "bbox": [ + 372, + 445, + 481, + 456 + ], + "spans": [ + { + "bbox": [ + 372, + 445, + 481, + 456 + ], + "type": "text", + "content": "(b) Hierarchical Prosody Modeling" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 305, + 465, + 547, + 510 + ], + "lines": [ + { + "bbox": [ + 305, + 465, + 547, + 510 + ], + "spans": [ + { + "bbox": [ + 305, + 465, + 547, + 510 + ], + "type": "text", + "content": "Figure 1. (a) Illustration of the V2C tasks. (b) To generate natural speech with proper emotions, we align the phonemes with lip motion, estimate pitch and energy based on facial expression's arousal and valence, and predict global emotion from video scenes." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "spans": [ + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "type": "text", + "content": "it requires synchronization between lip motion and generated speech; second, it requires proper prosodic variations of the generated speech to reflect the speaker's emotion in the video (i.e., the movie's plot). These pose significant challenges to existing voice cloning methods." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 546, + 714 + ], + "type": "text", + "content": "Although significant progress has been made, existing methods do not handle the challenges in V2C well. Specifically, text-based dubbing methods [46-48, 54] construct speeches from given text conditioned on the different speaker embedding but do not consider audio-visual synchronization. On the other hand, lip-referred dubbing schemes [18, 32, 55] predict mel-spectrograms directly from a sequence of lip movements typically by encoder-decoder models. Due to high error rates in generated words, these methods can hardly guarantee high-quality results. Further" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "type": "text", + "content": "† Corresponding author." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14687" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "type": "text", + "content": "more, video-and-text based dubbing methods [17, 20, 32] focus on inferring speaker characters (e.g., age and gender). However, these visual references usually do not convey targeted emotion well as intended in V2C." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 121, + 288, + 242 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 121, + 288, + 242 + ], + "spans": [ + { + "bbox": [ + 46, + 121, + 288, + 242 + ], + "type": "text", + "content": "An ideal dub should align well with the target character so that the audiences feel it is the character speaking instead of the dubber [7]. Thus, a professional dubber usually has a keen sense of observing the unique characteristics of the subject and acts on voice accordingly. In this work, we address these issues with a hierarchical dubbing architecture to synthesize speech. Unlike previous methods, our model connects video representations to speech counterparts at three levels: lip, face, and scene, as shown in Figure 1." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 243, + 288, + 530 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 243, + 288, + 530 + ], + "spans": [ + { + "bbox": [ + 46, + 243, + 288, + 530 + ], + "type": "text", + "content": "In this paper, we propose a hierarchical prosody modeling for movie dubbing, which could keep the audio-visual sync and synthesis speech with proper prosody following the movie's plot. Specifically, we first design a duration alignment module that controls speech speed by learning temporal correspondence via multi-head attention over phonemes and lip motion. Second, we propose an affective-display based Prosody Adaptor (PA), which learns affective psychology computing conditioned on facial expression and is supervised by corresponding energy and pitch in the target voice. In particular, we introduce arousal and valence features extracted from facial regions as emotion representations. This is inspired by the affective computing method [51], which analyses the facial affect relying on dimensional measures, namely valence (how positive the emotional display is) and arousal (how calming or exciting the expression looks). Third, we exploit a scene-atmosphere based emotion booster, which fuses the global video representation with the above adapted hidden sequence and is supervised by the emotive state of the whole voice. The outputs of these three modules are fed into a transformer-based decoder, which converts the speech-related representations into mel-spectrogram. Finally, we output the target speech waves from the mel-spectrogram via a powerful vocoder." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 59, + 532, + 280, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 532, + 280, + 544 + ], + "spans": [ + { + "bbox": [ + 59, + 532, + 280, + 544 + ], + "type": "text", + "content": "The contributions of this paper are summarized below:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 59, + 551, + 286, + 712 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 59, + 551, + 286, + 599 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 551, + 286, + 599 + ], + "spans": [ + { + "bbox": [ + 59, + 551, + 286, + 599 + ], + "type": "text", + "content": "- We propose a novel hierarchical movie dubbing architecture to better synthesize speech with proper prosody by associating them with visual counterparts: lips, facial expressions, and surrounding scenes." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 59, + 609, + 286, + 668 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 609, + 286, + 668 + ], + "spans": [ + { + "bbox": [ + 59, + 609, + 286, + 668 + ], + "type": "text", + "content": "- We design an affective display-based prosody adaptor to predict the energy and pitch of speech from the arousal and valence fluctuations of facial regions in videos, which provides a fine-grained alignment with speakers' emotions." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 59, + 677, + 286, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 677, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 677, + 286, + 712 + ], + "type": "text", + "content": "- Extensive experimental results demonstrate the proposed method performs well against state-of-the-art models on two benchmark datasets." + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 71, + 392, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 71, + 392, + 83 + ], + "spans": [ + { + "bbox": [ + 306, + 71, + 392, + 83 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 91, + 545, + 247 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 91, + 545, + 247 + ], + "spans": [ + { + "bbox": [ + 304, + 91, + 545, + 247 + ], + "type": "text", + "content": "Text to Speech Synthesis. Over the recent years, numerous TTS models [2, 29, 40, 41, 47, 48, 54] have been proposed for generating high-quality natural speech conditioned on given text. Tacotron [54] is an end-to-end generative TTS model that synthesizes speech directly from characters. Then, Tacotron2 [29] replaces the RNN structures by introducing the attention mechanism to improve training efficiency and solve the long dependency issue. Furthermore, FastSpeech [47] and Fastspeech2 [46] exploit the Feed-Forward Transformer (FFT) to generate mel-spectrogram from phoneme sequences. Despite the impressive voice generated, these methods cannot provide the audio with desired emotion and audio-visual sync for movie dubbing." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 251, + 545, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 251, + 545, + 384 + ], + "spans": [ + { + "bbox": [ + 304, + 251, + 545, + 384 + ], + "type": "text", + "content": "Lip to Speech Synthesis. This task aims to reconstruct speech based on the lip motions alone [3,25]. Lip2Wav [42] is a sequence-to-sequence architecture focusing on learning mappings between lip and speech for individual speakers. Recently, [15, 18, 49, 55] improve the architecture and training methods, and provide the possibility of unconstrained speech synthesis in the wild. However, lip-to-speech is incompetent for movie dubbing because the word error rate is still high [1, 3, 11, 13, 16]. In this work, we focus on reconstructing accurate speech from lip motions and generating the desired emotion and identity with proper prosody." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 386, + 545, + 519 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 386, + 545, + 519 + ], + "spans": [ + { + "bbox": [ + 304, + 386, + 545, + 519 + ], + "type": "text", + "content": "Talking Heads. Numerous methods have been developed for audio-visual translation [58] or speaking style transfer [57] by reconstructing the visual content in video [8, 30, 31, 50, 53, 59, 61–63]. Wav2Lip [43] uses an expert lip-syncs discriminator to morph lip movements of arbitrary identities. Recently, Papantoniou et al. [38] develop a Neural Emotion Director (NED) to manipulate emotions while preserving speech-related lip movements. However, these methods cannot adapt to the movie dubbing task because they emphasize using generative models to readjust the facial regions instead of reconstructing the desired speech." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 545, + 713 + ], + "type": "text", + "content": "Visual Voice Cloning. Movie dubbing, also known as visual voice clone, aims to convert scripts to speech with both desired voice identity and emotion based on the reference audio and video. To control the speed of generated speech, Neural Dubber [20] exploits a text-video aligner by using scaled dot-product attention mechanism. VTTS [17] uses multi-source attention to fuse the triplets feature and outputs the mel-spectrogram via an RNN-based decoder. Since explicit emotion categories [28] do not exist in these methods, Chen et al. [9] develops a V2C model on a more challenging Densiny Animation dataset, which concentrates on emotional dubbing for movie characters. Although the V2C considers emotion labels, the adopted global video representation negatively affects the fine-grained emotional expression and makes it challenging to render correct prosody corresponding to plot developments. To solve this issue, we" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14688" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 71, + 545, + 316 + ], + "blocks": [ + { + "bbox": [ + 50, + 71, + 545, + 316 + ], + "lines": [ + { + "bbox": [ + 50, + 71, + 545, + 316 + ], + "spans": [ + { + "bbox": [ + 50, + 71, + 545, + 316 + ], + "type": "image", + "image_path": "ddac5c75842e9b374005ec2daed22d9bc18b8fe847feef9e7e4cfc59c9d4ea4f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 328, + 548, + 384 + ], + "lines": [ + { + "bbox": [ + 46, + 328, + 548, + 384 + ], + "spans": [ + { + "bbox": [ + 46, + 328, + 548, + 384 + ], + "type": "text", + "content": "Figure 2. Architecture of the proposed hierarchical modular network for movie dubbing, which consists of four main components: Duration Aligner (Sec. 3.1), which learns to predict speech duration based on aligning lip movement and text phoneme; Prosody Adaptor (Sec. 3.2), which predicts energy and pitch from facial arousal and valence, respectively; Atmosphere Booster (Sec. 3.3), which learns a global emotion embedding from a video scene level; and Mel-Generator (Sec. 3.4), which generates mel-spectrograms from embeddings obtained by the aforementioned three modules. The mel-spectrograms are finally converted to audio by a widely adopted vocoder." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 397, + 288, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 397, + 288, + 422 + ], + "spans": [ + { + "bbox": [ + 46, + 397, + 288, + 422 + ], + "type": "text", + "content": "propose a hierarchical movie dubbing architecture to better synthesize speech with proper prosody and emotion." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 434, + 104, + 448 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 434, + 104, + 448 + ], + "spans": [ + { + "bbox": [ + 47, + 434, + 104, + 448 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 456, + 289, + 660 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 456, + 289, + 660 + ], + "spans": [ + { + "bbox": [ + 46, + 456, + 289, + 660 + ], + "type": "text", + "content": "The main architecture of the proposed model is shown in Fig. 2. First, we use a phoneme encoder [9] to convert the input text " + }, + { + "bbox": [ + 46, + 456, + 289, + 660 + ], + "type": "inline_equation", + "content": "Z_{text}" + }, + { + "bbox": [ + 46, + 456, + 289, + 660 + ], + "type": "text", + "content": " to a series of phoneme embeddings " + }, + { + "bbox": [ + 46, + 456, + 289, + 660 + ], + "type": "inline_equation", + "content": "\\mathcal{O} = \\{\\mathbf{o}_1,\\dots,\\mathbf{o}_L\\}" + }, + { + "bbox": [ + 46, + 456, + 289, + 660 + ], + "type": "text", + "content": " and use a speaker encoder " + }, + { + "bbox": [ + 46, + 456, + 289, + 660 + ], + "type": "inline_equation", + "content": "F_{spk}" + }, + { + "bbox": [ + 46, + 456, + 289, + 660 + ], + "type": "text", + "content": " [9] to capture the voice characteristics " + }, + { + "bbox": [ + 46, + 456, + 289, + 660 + ], + "type": "inline_equation", + "content": "\\mathcal{U}" + }, + { + "bbox": [ + 46, + 456, + 289, + 660 + ], + "type": "text", + "content": " from different speakers. Then, taking phonemes and lip regions as input, the duration aligner module uses a multi-head attention mechanism to learn to associate phonemes with related lip movements. Next, the affective display-based prosody adaptor module learns to predict the energy and pitch of the desired speech based on arousal and valence features extracted from facial expressions, respectively. And then, the scene atmosphere booster encodes a global representation of emotion of the entire video content. All the outputs of the above three modules are combined to generate mel-spectrograms, which are finally transformed to a waveform " + }, + { + "bbox": [ + 46, + 456, + 289, + 660 + ], + "type": "inline_equation", + "content": "Y_{voice}" + }, + { + "bbox": [ + 46, + 456, + 289, + 660 + ], + "type": "text", + "content": " using a adopted vocoder. We detail each module below." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 670, + 151, + 683 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 670, + 151, + 683 + ], + "spans": [ + { + "bbox": [ + 47, + 670, + 151, + 683 + ], + "type": "text", + "content": "3.1. Duration Aligner" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "type": "text", + "content": "The duration aligner contains three steps: (1) extracting the lip features from movie; (2) aligning the phonemes of" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 396, + 545, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 396, + 545, + 422 + ], + "spans": [ + { + "bbox": [ + 304, + 396, + 545, + 422 + ], + "type": "text", + "content": "text with the lips; (3) expanding the fused phoneme-lip representation to the desired mel-spectrogram length." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 423, + 546, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 423, + 546, + 495 + ], + "spans": [ + { + "bbox": [ + 304, + 423, + 546, + 495 + ], + "type": "text", + "content": "Extracting lip feature. Let " + }, + { + "bbox": [ + 304, + 423, + 546, + 495 + ], + "type": "inline_equation", + "content": "D_w" + }, + { + "bbox": [ + 304, + 423, + 546, + 495 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 423, + 546, + 495 + ], + "type": "inline_equation", + "content": "D_h" + }, + { + "bbox": [ + 304, + 423, + 546, + 495 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 423, + 546, + 495 + ], + "type": "inline_equation", + "content": "D_c" + }, + { + "bbox": [ + 304, + 423, + 546, + 495 + ], + "type": "text", + "content": " be the width, height and number of channels of the video frames, respectively. We first extract lip regions " + }, + { + "bbox": [ + 304, + 423, + 546, + 495 + ], + "type": "inline_equation", + "content": "\\mathbf{x_m} \\in \\mathbb{R}^{T_v \\times D_w \\times D_h \\times D_c}" + }, + { + "bbox": [ + 304, + 423, + 546, + 495 + ], + "type": "text", + "content": " from the given video using mouth region pre-processing from [20, 33-36]. Then we exploit the LipEncoder to obtain the lip movement representation:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 347, + 499, + 545, + 515 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 499, + 545, + 515 + ], + "spans": [ + { + "bbox": [ + 347, + 499, + 545, + 515 + ], + "type": "interline_equation", + "content": "\\mathbf {E} _ {\\mathrm {l i p}} = \\operatorname {L i p E n c o d e r} \\left(\\mathbf {x} _ {\\mathbf {m}}\\right) \\in \\mathbb {R} ^ {T _ {v} \\times D _ {m}}, \\tag {1}", + "image_path": "0c15cbba55251348c5f01323fdcd05be64ff4cf638b8dcaeb5820c7a7533d3f9.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 519, + 545, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 519, + 545, + 579 + ], + "spans": [ + { + "bbox": [ + 304, + 519, + 545, + 579 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 519, + 545, + 579 + ], + "type": "inline_equation", + "content": "T_{v}" + }, + { + "bbox": [ + 304, + 519, + 545, + 579 + ], + "type": "text", + "content": " denotes the number of video frames, and " + }, + { + "bbox": [ + 304, + 519, + 545, + 579 + ], + "type": "inline_equation", + "content": "D_{m}" + }, + { + "bbox": [ + 304, + 519, + 545, + 579 + ], + "type": "text", + "content": " is the hidden dimension of the dynamic lip feature. The LipEncoder consists of several feed-forward transformer blocks that are suitable for capturing both long-term and short-term dynamics lip movement features." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 582, + 547, + 716 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 547, + 716 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 547, + 716 + ], + "type": "text", + "content": "Aligning text with lips. Inspired by the success of attention mechanism for cross-modality alignment [10, 14, 19, 26, 27, 44, 45, 64], we adopt multi-head attention to learn the alignment between the text phoneme and the lip movement sequence. We use lip embedding as a query to compute the attention on text phonemes. The larger the attention, the more related between a lip embedding and a text phoneme. Due to variations of mouth shapes and pronunciations, the multi-head attention mechanism is suitable for learning their alignments from different aspects. The text-video context sequence " + }, + { + "bbox": [ + 304, + 582, + 547, + 716 + ], + "type": "inline_equation", + "content": "\\mathbf{E}_{\\mathrm{lip},\\mathrm{txt}} = [\\alpha_{\\mathrm{lip},\\mathrm{txt}}^{1},\\dots,\\alpha_{\\mathrm{lip},\\mathrm{txt}}^{n}]\\in" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "14689" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 71, + 286, + 99 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 71, + 286, + 99 + ], + "spans": [ + { + "bbox": [ + 47, + 71, + 286, + 99 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{T_v\\times D_m}" + }, + { + "bbox": [ + 47, + 71, + 286, + 99 + ], + "type": "text", + "content": " is a concatenation of outputs of " + }, + { + "bbox": [ + 47, + 71, + 286, + 99 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 47, + 71, + 286, + 99 + ], + "type": "text", + "content": " attention heads. 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The aligned representation " + }, + { + "bbox": [ + 47, + 153, + 287, + 214 + ], + "type": "inline_equation", + "content": "E_{lip,txt}" + }, + { + "bbox": [ + 47, + 153, + 287, + 214 + ], + "type": "text", + "content": " is later expanded to the length of the desired mel-spectrogram." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 218, + 287, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 218, + 287, + 277 + ], + "spans": [ + { + "bbox": [ + 47, + 218, + 287, + 277 + ], + "type": "text", + "content": "Expanding to the desired length. According to the findings in [20], in an audio-visual clip, the length of a melspectrograms sequence is " + }, + { + "bbox": [ + 47, + 218, + 287, + 277 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 47, + 218, + 287, + 277 + ], + "type": "text", + "content": " times that of a video frame sequence because they are temporally synchronized in audio and visual modalities. The number " + }, + { + "bbox": [ + 47, + 218, + 287, + 277 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 47, + 218, + 287, + 277 + ], + "type": "text", + "content": " is computed as:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 110, + 286, + 287, + 312 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 110, + 286, + 287, + 312 + ], + "spans": [ + { + "bbox": [ + 110, + 286, + 287, + 312 + ], + "type": "interline_equation", + "content": "n = \\frac {T _ {m e l}}{T _ {v}} = \\frac {s r / h s}{F P S} \\in \\mathbb {N} ^ {+}, \\tag {3}", + "image_path": "12d3d27ca5e4add9d1a068347b5e7e8ed561f6fa03dafe161a26309944333e1b.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "spans": [ + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "inline_equation", + "content": "FPS" + }, + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "text", + "content": " denotes the Frames per Second of the video, " + }, + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "inline_equation", + "content": "sr" + }, + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "text", + "content": " denotes the sampling rate of the audio, and " + }, + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "inline_equation", + "content": "hs" + }, + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "text", + "content": " denotes hop size when transforming the raw waveform into melspectrograms. Phoneme-lip feature " + }, + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "inline_equation", + "content": "E_{lip,txt}" + }, + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "text", + "content": " is simply duplicated " + }, + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "text", + "content": " times in [20] as the final phoneme-lip representation, which lacks flexibility. Instead, we propose to use transposed convolutions to learn the expansion of " + }, + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "inline_equation", + "content": "E_{lip,txt}" + }, + { + "bbox": [ + 47, + 319, + 287, + 415 + ], + "type": "text", + "content": ", which can be formulated as:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 58, + 422, + 287, + 436 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 422, + 287, + 436 + ], + "spans": [ + { + "bbox": [ + 58, + 422, + 287, + 436 + ], + "type": "interline_equation", + "content": "\\mathbf {M} _ {p h o, l i p} = \\operatorname {C o n v - T r a n s p o s e} (n, E _ {l i p, t x t}) \\in \\mathbb {R} ^ {T _ {y} \\times D _ {m}}, \\tag {4}", + "image_path": "2d964fbc00b443ed8249188f920e0112f02fe6d4712860ae38466d10171a6e99.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 445, + 287, + 505 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 445, + 287, + 505 + ], + "spans": [ + { + "bbox": [ + 47, + 445, + 287, + 505 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 445, + 287, + 505 + ], + "type": "inline_equation", + "content": "T_{y}" + }, + { + "bbox": [ + 47, + 445, + 287, + 505 + ], + "type": "text", + "content": " denotes the length of the desired melspectrogram, " + }, + { + "bbox": [ + 47, + 445, + 287, + 505 + ], + "type": "inline_equation", + "content": "D_{m}" + }, + { + "bbox": [ + 47, + 445, + 287, + 505 + ], + "type": "text", + "content": " is the dimension of the initial melspectrogram. The parameters (stride and kernel size) of the transposed convolution can be set under the guidance of " + }, + { + "bbox": [ + 47, + 445, + 287, + 505 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 47, + 445, + 287, + 505 + ], + "type": "text", + "content": " so that " + }, + { + "bbox": [ + 47, + 445, + 287, + 505 + ], + "type": "inline_equation", + "content": "T_{y} \\approx n \\times T_{v}" + }, + { + "bbox": [ + 47, + 445, + 287, + 505 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 512, + 261, + 525 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 512, + 261, + 525 + ], + "spans": [ + { + "bbox": [ + 47, + 512, + 261, + 525 + ], + "type": "text", + "content": "3.2. Affective-display based Prosody Adaptor" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 531, + 287, + 614 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 531, + 287, + 614 + ], + "spans": [ + { + "bbox": [ + 47, + 531, + 287, + 614 + ], + "type": "text", + "content": "One of the critical issues in the V2C task is to describe the speaker's emotions in the given video. To solve this problem, we design an affective-display based Prosody Adaptor (PA), which uses the arousal and valence extracted from facial expressions to represent the emotion. The arousal and valence are then used to predict the energy and pitch of the desired speech model." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "text", + "content": "Valence and Arousal Feature. To accurately capture the valence and arousal information from facial expressions, we utilize an emotion face-alignment network (EmoFAN) [51] to encode the facial region into valence " + }, + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{V}" + }, + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "text", + "content": " and arousal " + }, + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{A}" + }, + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{V},\\mathbf{A} = \\mathrm{EmoFAN}(x_f)\\in \\mathbb{R}^{T_v\\times D_m}" + }, + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "inline_equation", + "content": "x_{f}\\in \\mathbb{R}^{T_{v}\\times D_{w}\\times D_{h}\\times D_{c}}" + }, + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "text", + "content": " is the face region extracted via " + }, + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "inline_equation", + "content": "S^3 FD" + }, + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "text", + "content": " face detection [60]. The EmoFAN focuses on facial regions relevant to emotion estimation, which utilizes a face alignment network (FAN)" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 72, + 544, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 544, + 95 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 544, + 95 + ], + "type": "text", + "content": "for facial point detection to ensure robustness by jointly predicting categorical and continuous emotions." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 96, + 545, + 169 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 96, + 545, + 169 + ], + "spans": [ + { + "bbox": [ + 305, + 96, + 545, + 169 + ], + "type": "text", + "content": "Bridging Arousal with Energy. Arousal is the physiological and psychological state of being awoken or of sense organs stimulated to the point of perception. To bridge the vocal energy with arousal display, we compute an arousal context vector " + }, + { + "bbox": [ + 305, + 96, + 545, + 169 + ], + "type": "inline_equation", + "content": "A_{i}^{l}" + }, + { + "bbox": [ + 305, + 96, + 545, + 169 + ], + "type": "text", + "content": " for frame-level energy of the desired speech from phoneme-lip representation " + }, + { + "bbox": [ + 305, + 96, + 545, + 169 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{pho,lip}" + }, + { + "bbox": [ + 305, + 96, + 545, + 169 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 334, + 178, + 433, + 213 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 334, + 178, + 433, + 213 + ], + "spans": [ + { + "bbox": [ + 334, + 178, + 433, + 213 + ], + "type": "interline_equation", + "content": "A _ {i} ^ {l} = \\sum_ {k = 0} ^ {T _ {y} - 1} \\xi_ {i, k} M _ {p h o, l i p} ^ {k},", + "image_path": "8753a8282e4353829139d3461f5fbb012b1cc8ee793c6efca899bdc0cad12366.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 330, + 216, + 545, + 250 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 330, + 216, + 545, + 250 + ], + "spans": [ + { + "bbox": [ + 330, + 216, + 545, + 250 + ], + "type": "interline_equation", + "content": "\\xi_ {i, k} = \\exp (\\hat {\\xi} _ {i, k}) / \\sum_ {j = 0} ^ {T _ {y} - 1} \\exp (\\hat {\\xi} _ {i, j}), \\tag {5}", + "image_path": "b9908b1aa999165dbae3b04604780dfa1b0a2762477ef495f21fd0d9c82c62c5.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 330, + 252, + 520, + 268 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 330, + 252, + 520, + 268 + ], + "spans": [ + { + "bbox": [ + 330, + 252, + 520, + 268 + ], + "type": "interline_equation", + "content": "\\hat {\\xi} _ {i, k} = \\mathbf {w} _ {\\mathbf {a}} ^ {\\top} \\tanh (\\mathbf {W} _ {\\mathbf {a}} ^ {\\top} A _ {i} + \\mathbf {U} _ {\\mathbf {a}} ^ {\\top} M _ {p h o, l i p} ^ {k} + \\mathbf {b} _ {\\mathbf {a}})", + "image_path": "7c4a3e99dbc4cf2ea88fb7b2da5e0704f2fe6f392169ce4a518f4044016f202e.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "spans": [ + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": " is frame index, " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "A_{i}" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": " is the " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": "-th row of " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "\\mathbf{A}" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "\\xi_{i,k}" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": " is the attention weight on the " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": "-th phoneme-lip feature " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "M_{pho,lip}^{k}" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": " (the " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": "-th row of " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{pho,lip}" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": ") regarding " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": "-th arousal display; " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "\\mathbf{w}_{\\mathbf{a}}^{\\mathrm{T}}" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "\\mathbf{W}_{\\mathbf{a}}^{\\mathrm{T}}" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "\\mathbf{U}_{\\mathbf{a}}^{\\mathrm{T}}" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "\\mathbf{b}_{\\mathbf{a}}" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": " are learnable parameters; " + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "inline_equation", + "content": "T_{y}" + }, + { + "bbox": [ + 306, + 277, + 545, + 336 + ], + "type": "text", + "content": " is the length of the desired mel-spectrogram." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 306, + 337, + 544, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 337, + 544, + 361 + ], + "spans": [ + { + "bbox": [ + 306, + 337, + 544, + 361 + ], + "type": "text", + "content": "Then, we use an energy predictor to project the arousal-related phoneme-lip embedding " + }, + { + "bbox": [ + 306, + 337, + 544, + 361 + ], + "type": "inline_equation", + "content": "A_{i}^{l}" + }, + { + "bbox": [ + 306, + 337, + 544, + 361 + ], + "type": "text", + "content": " to the energy of speech:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 365, + 371, + 545, + 386 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 371, + 545, + 386 + ], + "spans": [ + { + "bbox": [ + 365, + 371, + 545, + 386 + ], + "type": "interline_equation", + "content": "E _ {a r o} = \\operatorname {P r e d i c t o r} \\left(\\left\\{A _ {i} ^ {l} \\right\\} _ {i = 1} ^ {T _ {y}}\\right), \\tag {6}", + "image_path": "458ebff0ef8791aaa8ec0885c48a31729aa6cbec4d7c55f19044132aac6d9a6d.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 305, + 396, + 545, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 396, + 545, + 433 + ], + "spans": [ + { + "bbox": [ + 305, + 396, + 545, + 433 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 396, + 545, + 433 + ], + "type": "inline_equation", + "content": "E_{aro} \\in \\mathbb{R}^{T_y}" + }, + { + "bbox": [ + 305, + 396, + 545, + 433 + ], + "type": "text", + "content": " represents the predicted energy of speech, and the energy predictor consists of several fully-connected layers, Conv1D blocks and layer normalization." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 305, + 437, + 545, + 534 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 437, + 545, + 534 + ], + "spans": [ + { + "bbox": [ + 305, + 437, + 545, + 534 + ], + "type": "text", + "content": "Bridging Valence with Pitch. In prosody linguistics, speakers can speak with a wide pitch range (this is usually associated with excitement) while, at other times, with a narrow range. Valence is the affective quality referring to the intrinsic positiveness or negativeness of an event or situation. Similar to arousal to energy, we first compute a valence context vector for the frame-level pitch of the desired speech from phoneme-lip representation " + }, + { + "bbox": [ + 305, + 437, + 545, + 534 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{pho,lip}" + }, + { + "bbox": [ + 305, + 437, + 545, + 534 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 335, + 543, + 437, + 577 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 543, + 437, + 577 + ], + "spans": [ + { + "bbox": [ + 335, + 543, + 437, + 577 + ], + "type": "interline_equation", + "content": "V _ {i} ^ {l} = \\sum_ {k = 0} ^ {T _ {y} - 1} \\psi_ {i, k} M _ {p h o, l i p} ^ {k},", + "image_path": "fdbfbea41fa283c38ad6cb50a915019e5c82c08db20a93c676a3428562486737.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 330, + 580, + 545, + 614 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 330, + 580, + 545, + 614 + ], + "spans": [ + { + "bbox": [ + 330, + 580, + 545, + 614 + ], + "type": "interline_equation", + "content": "\\psi_ {i, k} = \\exp (\\hat {\\psi} _ {i, k}) / \\sum_ {j = 0} ^ {T _ {y} - 1} \\exp (\\hat {\\psi} _ {i, j}), \\tag {7}", + "image_path": "85c5266a9bd5183faf03353349341500e03d8ef660c472ff0de004cfde5af8fa.jpg" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 330, + 616, + 520, + 633 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 330, + 616, + 520, + 633 + ], + "spans": [ + { + "bbox": [ + 330, + 616, + 520, + 633 + ], + "type": "interline_equation", + "content": "\\hat {\\psi} _ {i, k} = \\mathbf {w} _ {\\mathbf {g}} ^ {\\top} \\tanh (\\mathbf {W} _ {\\mathbf {g}} ^ {\\top} V _ {i} + \\mathbf {U} _ {\\mathbf {g}} ^ {\\top} M _ {p h o, l i p} ^ {k} + \\mathbf {b} _ {\\mathbf {g}})", + "image_path": "1bdd7fed263ba0812955e8736631a80273a462ec61034c2494b42be0f240fc1a.jpg" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 305, + 641, + 545, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 641, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 305, + 641, + 545, + 689 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 641, + 545, + 689 + ], + "type": "inline_equation", + "content": "\\mathbf{w}_{\\mathbf{g}}^{\\mathbf{T}}, \\mathbf{W}_{\\mathbf{g}}^{\\mathbf{T}}, \\mathbf{U}_{\\mathbf{g}}^{\\mathbf{T}}" + }, + { + "bbox": [ + 305, + 641, + 545, + 689 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 641, + 545, + 689 + ], + "type": "inline_equation", + "content": "\\mathbf{b}_{\\mathbf{g}}" + }, + { + "bbox": [ + 305, + 641, + 545, + 689 + ], + "type": "text", + "content": " are learnable parameters. Then, we exploit a pitch predictor to convert the valence-related phoneme-lip embedding " + }, + { + "bbox": [ + 305, + 641, + 545, + 689 + ], + "type": "inline_equation", + "content": "V_{i}^{l}" + }, + { + "bbox": [ + 305, + 641, + 545, + 689 + ], + "type": "text", + "content": " to the pitch of speech, which can be formulated as:" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 365, + 699, + 545, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 699, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 365, + 699, + 545, + 715 + ], + "type": "interline_equation", + "content": "P _ {v a l} = \\operatorname {P r e d i c t o r} \\left(\\left\\{V _ {i} ^ {l} \\right\\} _ {i = 1} ^ {T _ {y}}\\right). \\tag {8}", + "image_path": "9e034680afaee8a33e47de6d93428c15eb16527da8d8daf535788b7b0df1d23b.jpg" + } + ] + } + ], + "index": 26 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14690" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "content": "Finally, we concatenate the two prosody-related features as the contextualized affect primitives:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 121, + 102, + 287, + 118 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 121, + 102, + 287, + 118 + ], + "spans": [ + { + "bbox": [ + 121, + 102, + 287, + 118 + ], + "type": "interline_equation", + "content": "\\mathbf {M} _ {\\mathbf {p}} = \\left\\{\\left[ A _ {i} ^ {l}; V _ {i} ^ {l} \\right] \\right\\} _ {i = 1} ^ {T _ {y}}. \\tag {9}", + "image_path": "ac3f897171408e2ec155b12cba893aa080803384a4798e145ff9b80ca06ba5c3.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 123, + 195, + 136 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 123, + 195, + 136 + ], + "spans": [ + { + "bbox": [ + 47, + 123, + 195, + 136 + ], + "type": "text", + "content": "3.3. Scene Atmosphere Booster" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 142, + 288, + 262 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 142, + 288, + 262 + ], + "spans": [ + { + "bbox": [ + 46, + 142, + 288, + 262 + ], + "type": "text", + "content": "As a unique form of artistic expression, the scene layout and colours of the film convey an emotional atmosphere to evoke resonance with the audience [5, 56]. To reason the comprehensive emotion, we design a scene atmosphere booster to combine the global context information and generated prosody. First, we use the I3D model [6] to extract the scene representation " + }, + { + "bbox": [ + 46, + 142, + 288, + 262 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 46, + 142, + 288, + 262 + ], + "type": "text", + "content": " from the video. Then, we fuse the global contextual emotion of vision with prosody information of speech through a cross-model attention mechanism by:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 70, + 266, + 287, + 295 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 266, + 287, + 295 + ], + "spans": [ + { + "bbox": [ + 70, + 266, + 287, + 295 + ], + "type": "interline_equation", + "content": "\\mathbf {M} _ {\\mathrm {e}} = \\operatorname {S o f t m a x} \\left(\\frac {\\mathbf {M} _ {\\mathbf {p}} S ^ {\\top}}{\\sqrt {D _ {m}}}\\right) \\mathbf {M} _ {\\mathbf {p}} \\in \\mathbb {R} ^ {T _ {y} \\times D _ {w}}. \\tag {10}", + "image_path": "781e325b6b74168fba31a7d78c668af26af42a713cd00aad070063065ef4166e.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 300, + 287, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 300, + 287, + 335 + ], + "spans": [ + { + "bbox": [ + 47, + 300, + 287, + 335 + ], + "type": "text", + "content": "Finally, formulated as a maxpool and fully-connected layer, the emotional predictor projects prosody-scene context sequence " + }, + { + "bbox": [ + 47, + 300, + 287, + 335 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{\\mathrm{e}}" + }, + { + "bbox": [ + 47, + 300, + 287, + 335 + ], + "type": "text", + "content": " to emotional embedding:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 91, + 342, + 287, + 357 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 342, + 287, + 357 + ], + "spans": [ + { + "bbox": [ + 91, + 342, + 287, + 357 + ], + "type": "interline_equation", + "content": "G _ {e m o} = \\operatorname {M a x p o o l} (\\operatorname {P r e d i c t o r} (\\mathbf {M} _ {\\mathbf {e}})). \\tag {11}", + "image_path": "083f5b206b5b59ade71eea0bb880a93dcaef5226c5254f9bdfbaff0a741bfeb5.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 362, + 153, + 374 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 362, + 153, + 374 + ], + "spans": [ + { + "bbox": [ + 47, + 362, + 153, + 374 + ], + "type": "text", + "content": "3.4. Audio Generation" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 380, + 287, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 380, + 287, + 441 + ], + "spans": [ + { + "bbox": [ + 47, + 380, + 287, + 441 + ], + "type": "text", + "content": "We fuse the three kinds of speech attribute information by concatenation operation in the spatial dimension. Then, we use transformer-based mel-spectrogram decoder to convert the adapted hidden sequence into mel-spectrogram sequence in parallel by:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 52, + 448, + 287, + 462 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 448, + 287, + 462 + ], + "spans": [ + { + "bbox": [ + 52, + 448, + 287, + 462 + ], + "type": "interline_equation", + "content": "\\mathbf {f} = \\text {T r a n s f o r m e r D e c o d e r} \\left(\\mathbf {M} _ {\\text {p h o}, l i p} \\oplus \\mathbf {M} _ {\\mathbf {p}} \\oplus \\mathbf {M} _ {\\mathbf {e}}\\right), \\tag {12}", + "image_path": "538d0de024ac49e6da58a7167bf21ac6c3fe65c93b6fdd5686ceebef6efb48fa.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 468, + 287, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 468, + 287, + 552 + ], + "spans": [ + { + "bbox": [ + 47, + 468, + 287, + 552 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 468, + 287, + 552 + ], + "type": "inline_equation", + "content": "M_{pho,lip}" + }, + { + "bbox": [ + 47, + 468, + 287, + 552 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 468, + 287, + 552 + ], + "type": "inline_equation", + "content": "M_p" + }, + { + "bbox": [ + 47, + 468, + 287, + 552 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 47, + 468, + 287, + 552 + ], + "type": "inline_equation", + "content": "M_e" + }, + { + "bbox": [ + 47, + 468, + 287, + 552 + ], + "type": "text", + "content": " denote the hidden representation of phoneme-lip feature, prosody variances, and emotional tone, respectively. Specifically, our mel-spectrogram decoder consists of a stack of self-attention layers [52] and 1D-convolution layers as in FastSpeech [47]. We then use the mel-linear layer and postnet [48] to refine the hidden states into final dimensional mel-spectrograms by:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 119, + 559, + 287, + 572 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 119, + 559, + 287, + 572 + ], + "spans": [ + { + "bbox": [ + 119, + 559, + 287, + 572 + ], + "type": "interline_equation", + "content": "y = \\operatorname {P o s t N e t} (\\operatorname {F C} (f)). \\tag {13}", + "image_path": "608a72210ac1cdbce4023493af28d4bac3773041fd6faebe723c2fdacf2af5bb.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 578, + 287, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 578, + 287, + 626 + ], + "spans": [ + { + "bbox": [ + 47, + 578, + 287, + 626 + ], + "type": "text", + "content": "Finally, to generate the time-domain waveform " + }, + { + "bbox": [ + 47, + 578, + 287, + 626 + ], + "type": "inline_equation", + "content": "y_{w}" + }, + { + "bbox": [ + 47, + 578, + 287, + 626 + ], + "type": "text", + "content": " from mel-spectrogram " + }, + { + "bbox": [ + 47, + 578, + 287, + 626 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 47, + 578, + 287, + 626 + ], + "type": "text", + "content": ", we use HiFi-GAN [23] as our vocoder, which mainly consists of a transposed convolution network and a multi-receptive field fusion module." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 47, + 633, + 141, + 645 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 633, + 141, + 645 + ], + "spans": [ + { + "bbox": [ + 47, + 633, + 141, + 645 + ], + "type": "text", + "content": "3.5. Loss Functions" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 47, + 651, + 287, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 651, + 287, + 676 + ], + "spans": [ + { + "bbox": [ + 47, + 651, + 287, + 676 + ], + "type": "text", + "content": "Our model is trained in an end-to-end fashion via optimizing the sum of all losses:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 100, + 682, + 287, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 682, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 100, + 682, + 287, + 715 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {p i t c h}} = \\frac {1}{T _ {y}} \\sum_ {t = 1} ^ {T _ {y} - 1} \\left(P _ {t} - \\hat {P} _ {\\text {v a l}} ^ {t}\\right) ^ {2}, \\tag {14}", + "image_path": "cf457fc00b26817d36621b8deb81e61a13f5b398581f1616ba884fff9cee4f5b.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 355, + 70, + 545, + 103 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 355, + 70, + 545, + 103 + ], + "spans": [ + { + "bbox": [ + 355, + 70, + 545, + 103 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {e n e r g y}} = \\frac {1}{T _ {y}} \\sum_ {t = 1} ^ {T _ {y} - 1} \\left(E _ {t} - \\hat {E} _ {a r o} ^ {t}\\right) ^ {2}, \\tag {15}", + "image_path": "a7141b67c149956e6d5a50d4e59ad5cc2407a5309e08543080acef6a47dcef8c.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 365, + 108, + 545, + 140 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 108, + 545, + 140 + ], + "spans": [ + { + "bbox": [ + 365, + 108, + 545, + 140 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {e m o} = - \\sum_ {i = 1} ^ {C} G _ {i} \\log \\left(\\hat {G} _ {e m o} ^ {i}\\right), \\tag {16}", + "image_path": "e5ac1f0dfee6a22dbd161e090393e22ebdedaf2662188cfc750d94ede1f24e27.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 369, + 144, + 545, + 178 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 144, + 545, + 178 + ], + "spans": [ + { + "bbox": [ + 369, + 144, + 545, + 178 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {m e l} = \\frac {1}{T _ {y}} \\sum_ {t = 1} ^ {T _ {y} - 1} \\left\\| \\mathbf {f} _ {t} - \\hat {\\mathbf {f}} _ {t} \\right\\|. \\tag {17}", + "image_path": "edde1d7c9ce55f86e43596b242e764c05c2f591794c7c2d1f69a6bca850ed498.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 312, + 182, + 545, + 196 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 182, + 545, + 196 + ], + "spans": [ + { + "bbox": [ + 312, + 182, + 545, + 196 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {S} = \\lambda_ {1} \\mathcal {L} _ {\\text {m e l}} + \\lambda_ {2} \\mathcal {L} _ {\\text {p i t c h}} + \\lambda_ {3} \\mathcal {L} _ {\\text {e n e r g y}} + \\lambda_ {4} \\mathcal {L} _ {\\text {e m o}}, \\tag {18}", + "image_path": "75778b8939c1a2062b2ccc36625ddc4b4370e42ba8c600333be9921d92890a1e.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "spans": [ + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{mel}" + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{pitch}" + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{energy}" + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{emo}" + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "text", + "content": ", denote the losses of mel-spectrogram, pitch, energy and global emotion respectively. " + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "inline_equation", + "content": "P_{t}" + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "inline_equation", + "content": "E_{t}" + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_t" + }, + { + "bbox": [ + 304, + 200, + 545, + 260 + ], + "type": "text", + "content": " are ground-truth pitch, energy, and mel-spectrogram on frame-level, respectively. G is the ground-truth emotional label and C denotes all categories." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 306, + 270, + 432, + 283 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 270, + 432, + 283 + ], + "spans": [ + { + "bbox": [ + 306, + 270, + 432, + 283 + ], + "type": "text", + "content": "4. Experimental Results" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 289, + 545, + 350 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 289, + 545, + 350 + ], + "spans": [ + { + "bbox": [ + 304, + 289, + 545, + 350 + ], + "type": "text", + "content": "In this section, we first briefly describe the datasets used for evaluation and the evaluation metric. Then we present the implementation details of the proposed method. Last, we show the results compared to state-of-the-art methods and the ablation study." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 306, + 357, + 368, + 368 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 357, + 368, + 368 + ], + "spans": [ + { + "bbox": [ + 306, + 357, + 368, + 368 + ], + "type": "text", + "content": "4.1. Datasets" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 377, + 545, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 377, + 545, + 484 + ], + "spans": [ + { + "bbox": [ + 304, + 377, + 545, + 484 + ], + "type": "text", + "content": "V2C is a multi-speaker dataset for animation movie dubbing with identity and emotion annotations [9]. It is collected from 26 Disney cartoon movies and covers 153 diverse characters. V2C not only needs to generate voices with identity characteristics according to the reference audio but also capture emotional information based on the reference movie clips. The whole dataset has 10,217 video clips with paired audio and subtitles. The training/validation/test size are " + }, + { + "bbox": [ + 304, + 377, + 545, + 484 + ], + "type": "inline_equation", + "content": "60\\%" + }, + { + "bbox": [ + 304, + 377, + 545, + 484 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 377, + 545, + 484 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 304, + 377, + 545, + 484 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 377, + 545, + 484 + ], + "type": "inline_equation", + "content": "30\\%" + }, + { + "bbox": [ + 304, + 377, + 545, + 484 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 304, + 485, + 545, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 485, + 545, + 594 + ], + "spans": [ + { + "bbox": [ + 304, + 485, + 545, + 594 + ], + "type": "text", + "content": "Chem is a single-speaker dataset composed of 6,640 short video clips collected from the YouTube, with the total video length of approximately nine hours [20]. The Chem dataset is originally used for the unconstrained single-speaker lipto-speech synthesis [42], which takes place in a chemistry lecture. For fluency and complete dubbing, each video clip has sentence-level text and audio based on the start and end timestamps. There are 6,240, 200, and 200 dubbing clips for training, validation, and testing, respectively." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 306, + 599, + 419, + 611 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 599, + 419, + 611 + ], + "spans": [ + { + "bbox": [ + 306, + 599, + 419, + 611 + ], + "type": "text", + "content": "4.2. Evaluation Metrics" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 304, + 617, + 545, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 545, + 689 + ], + "type": "text", + "content": "Audio-visual synchronization. To evaluate the synchronization between the generated speech and the video quantitatively, we adopt Lip Sync Error Distance (LSE-D) and Lip Sync Error Confidence (LSE-C) as our metrics, which can explicitly test for synchronization between lip motions and speech in unconstrained videos in the wild [12, 43]." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "text", + "content": "Mel Cepstral Distortion and its variants. MCD [24], MCD-DTW [4] and MCD-DTW-SL [9] are adopted, which" + } + ] + } + ], + "index": 28 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14691" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 50, + 70, + 547, + 177 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 547, + 177 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 547, + 177 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 547, + 177 + ], + "type": "table", + "html": "
MethodsLSE-D ↓LSE-C ↑MCD ↓MCD-DTW ↓MCD-DTW-SL ↓Id. Acc. ↑Emo. Acc. ↑MOS-N ↑MOS-S ↑
Ground Truth6.7347.81300.0000.0000.0090.6284.384.61 ± 0.154.74 ± 0.12
SV2TTS [21]13.7332.72521.0812.8749.5633.6237.192.03 ± 0.221.92 ± 0.15
SV2TTS* [21]12.6173.34919.3812.7334.5135.1842.052.07 ± 0.072.15 ± 0.09
Tacotron* [54]13.4752.93819.7918.7342.1532.4939.682.12 ± 0.172.06 ± 0.12
FastSpeech2 [46]12.2612.95820.7814.3919.4121.7246.822.79 ± 0.102.63 ± 0.09
FastSpeech2* [46]12.1132.60420.6614.5920.7923.4446.903.08 ± 0.062.89 ± 0.07
V2C-Net1 [9]11.7843.02620.6114.2319.1526.8448.413.19 ± 0.043.06 ± 0.06
Ours8.0365.60815.6612.2913.4837.7561.464.03 ± 0.083.89 ± 0.07
", + "image_path": "5c6fc8ca9442a8a0fb0d3e801b7805f10414a0c52bffbb1d9e98192c2735314d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 46, + 181, + 547, + 226 + ], + "lines": [ + { + "bbox": [ + 46, + 181, + 547, + 226 + ], + "spans": [ + { + "bbox": [ + 46, + 181, + 547, + 226 + ], + "type": "text", + "content": "Table 1. Results on the V2C dataset with comparisons against state-of-the-art methods. We provide the results using both objective metrics (i.e., LSE-D, LSE-C, MCD, MCD-DTW and MCD-DTW-SL) and subjective metrics (i.e., MOS-Naturalness and MOS-Similarity). \"Id. Acc.\" and \"Emo. Acc.\" are the identity and emotion accuracy of the generated speech, respectively. The method with “*” refers to a variant taking video (emotion) embedding as an additional input as in [9]. " + }, + { + "bbox": [ + 46, + 181, + 547, + 226 + ], + "type": "inline_equation", + "content": "\\uparrow (\\downarrow)" + }, + { + "bbox": [ + 46, + 181, + 547, + 226 + ], + "type": "text", + "content": " means that the higher (lower) value is better." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 236, + 286, + 297 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 236, + 286, + 297 + ], + "spans": [ + { + "bbox": [ + 46, + 236, + 286, + 297 + ], + "type": "text", + "content": "reflect the similarity of mel-spectrograms. MCD-DTW uses the Dynamic Time Warping (DTW) [37] algorithm to find the minimum MCD between two speeches, while MCD-DTW-SL introduces the duration measure coefficient to consider the length and the quality of generated speech [9]." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 297, + 286, + 346 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 297, + 286, + 346 + ], + "spans": [ + { + "bbox": [ + 46, + 297, + 286, + 346 + ], + "type": "text", + "content": "Emotion and identity accuracy. To measure whether the generated speech carries proper emotion and speaker identity, we adopt an emotion accuracy (Emo. Acc.) and an identity accuracy (Id. Acc.) as our metrics as in [9]." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 346, + 286, + 418 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 346, + 286, + 418 + ], + "spans": [ + { + "bbox": [ + 46, + 346, + 286, + 418 + ], + "type": "text", + "content": "Subjective evaluations. To further evaluate the quality of generated speech, we conduct a human study using a subjective evaluation metric, following the settings in [9]. Specifically, we adopt the MOS-naturalness (MOS-N) and MOS-similarity (MOS-S) to assess the naturalness of the generated speech and the recognition of the desired voice." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 430, + 180, + 442 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 430, + 180, + 442 + ], + "spans": [ + { + "bbox": [ + 47, + 430, + 180, + 442 + ], + "type": "text", + "content": "4.3. Implementation Details" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "spans": [ + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "text", + "content": "For the duration aligner, we use 4 Feed-Forward Transformer (FFT) blocks and 3 FFT blocks for the phoneme encoder and lip movement encoder, respectively. We set the dimension of the phoneme feature " + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "inline_equation", + "content": "\\mathcal{O}" + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "text", + "content": " and lip features " + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "inline_equation", + "content": "E_{lip}" + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "text", + "content": " to 256. We use 8 attention heads for alignment between lip and phoneme. For each movie clip, Our FPS set is 25, the sampling rate " + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "inline_equation", + "content": "sr" + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "text", + "content": " is " + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "inline_equation", + "content": "22050Hz" + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "text", + "content": ". We use short-time fourier transform (STFT) to obtain the mel-spectrum, and the number of points of the fourier transform is 1024. We use the Conv-Transpose1D module with 2 stride and 4 kernel sizes to obtain the duration features. In Valence and Arousal Feature Encoder, the EmoFAN consists of one 2D convolution with a kernel size of " + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "inline_equation", + "content": "7\\times 7" + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "text", + "content": " and 3 convolution blocks (ConvBlock) with a kernel size of " + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "inline_equation", + "content": "3\\times 3" + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "text", + "content": " and Average Pooling stride of " + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "inline_equation", + "content": "2\\times 2" + }, + { + "bbox": [ + 46, + 449, + 286, + 664 + ], + "type": "text", + "content": ". Similarly, we set the dimension of the valence and arousal feature to 256. For the mel-spectrograms generator, the mel-spectrogram decoder consists of 6 FFT blocks and the hidden state of mel-linear layer is of size 80." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 665, + 286, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 286, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 286, + 714 + ], + "type": "text", + "content": "For training, we use Adam [22] with " + }, + { + "bbox": [ + 46, + 665, + 286, + 714 + ], + "type": "inline_equation", + "content": "\\beta_{1} = 0.9" + }, + { + "bbox": [ + 46, + 665, + 286, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 665, + 286, + 714 + ], + "type": "inline_equation", + "content": "\\beta_{2} = 0.98" + }, + { + "bbox": [ + 46, + 665, + 286, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 665, + 286, + 714 + ], + "type": "inline_equation", + "content": "\\epsilon = 10^{-9}" + }, + { + "bbox": [ + 46, + 665, + 286, + 714 + ], + "type": "text", + "content": " to optimize our model. For V2C and Chem dataset, we set the learning rate schedule to 0.00001 and 0.00005, respectively. In this work, we use pretrained HiFiGAN [23]" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 236, + 545, + 308 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 236, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 304, + 236, + 545, + 308 + ], + "type": "text", + "content": "as the vocoder to transform the generated mel-spectrograms into audio samples. We set the batch size to 16 on two datasets. Our model is implemented in PyTorch [39]. All the models are performed on a single NVIDIA GTX3090Ti GPU. We train the model with 600 epochs on the V2C dataset and 400 epochs on the Chem dataset." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 320, + 441, + 333 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 320, + 441, + 333 + ], + "spans": [ + { + "bbox": [ + 305, + 320, + 441, + 333 + ], + "type": "text", + "content": "4.4.Quantitative Evaluation" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 341, + 545, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 341, + 545, + 496 + ], + "spans": [ + { + "bbox": [ + 304, + 341, + 545, + 496 + ], + "type": "text", + "content": "We compare with five related baselines of speech synthesizes. (1) SV2TTS [21] is a basic TTS model to generate speech with reference audio for multi-speakers; (2) Tacotron [54] is an end-to-end generative TTS model that synthesizes speech directly from textual characters; (3) FastSpeech2 [46] introduces the variance adaptor to convert the text to waveform by end-to-end; (4) Neural Dubber [20] synthesizes human speech for given video according to the corresponding text; (5) V2C-Net [9] is the first model for movie dubbing, which match the speaker's emotion presented in video. Note that we do not compare with Neural Dubber [20] on the V2C benchmark due to the unavailability of its code and missing implementation details." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "text", + "content": "Results on the V2C benchmark. The results are presented in Table 1. Our method achieves the best performance on all nine metrics. Specifically, in terms of audio-visual sync, our method achieves 8.036 of LSE-D and 5.608 of LSE-C, which significantly surpasses the previous best results and is much closer to human performance. In terms of MCD, MCD-DTW, and MCD-DTW-SL, our method achieves relative " + }, + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "inline_equation", + "content": "24.02\\%" + }, + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "inline_equation", + "content": "13.63\\%" + }, + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "inline_equation", + "content": "29.61\\%" + }, + { + "bbox": [ + 304, + 498, + 545, + 713 + ], + "type": "text", + "content": " improvements, respectively. This indicates our method can achieve a better mel-spectrogram than others. The above results together show that bridging specific attributes of speech with corresponding visual counterparts can make the generated speech present better prosodies and lip motion sync. Additionally, our method outperforms the previous method by a large margin in emotion accuracy and can gain better identification accuracy. This indicates the proposed method can better capture and convey emotions, which is of great importance for the movie dubbing task. Last, the human subjec" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14692" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 50, + 70, + 286, + 153 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 286, + 153 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 286, + 153 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 286, + 153 + ], + "type": "table", + "html": "
MethodsAQ↑AV Sync ↑LSE-D ↓LSE-C ↑
Ground Truth3.93 ± 0.084.13±0.076.9267.711
FastSpeech2 [46]3.71±0.083.29±0.0911.862.805
Tacotron* [54]3.55±0.093.03±0.1011.792.231
V2C-Net [9]3.48±0.143.25±0.1111.262.907
Neural Dubber [20]3.74±0.083.91±0.077.2127.037
Ours3.84±0.113.97±0.086.9757.176
", + "image_path": "2e49237791e3487f49b5906e4027caa3c9d40490fc3ab7a11c1f2bd848575042.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 49, + 229, + 287, + 335 + ], + "blocks": [ + { + "bbox": [ + 47, + 162, + 288, + 217 + ], + "lines": [ + { + "bbox": [ + 47, + 162, + 288, + 217 + ], + "spans": [ + { + "bbox": [ + 47, + 162, + 288, + 217 + ], + "type": "text", + "content": "Table 2. Results on the Chem dataset with comparisons against state-of-the-art methods. AQ (Audio Quality) and AV Sync (audio-visual synchronization) are subjective metrics. Note that the Chem dataset is a single-speaker non-movie dataset, and thus there is no identity accuracy and emotion accuracy." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 49, + 229, + 287, + 335 + ], + "lines": [ + { + "bbox": [ + 49, + 229, + 287, + 335 + ], + "spans": [ + { + "bbox": [ + 49, + 229, + 287, + 335 + ], + "type": "table", + "html": "
#MethodsLSE-D ↓LSE-C ↑MCD ↓Id. Acc. ↑Emo. Acc. ↑
1w/o DA11.8353.71619.5318.7538.33
2w/o PA8.5145.27416.3410.4222.08
3w/o AB8.2615.40815.9033.3353.92
4w/o Valence9.2154.93516.3129.8135.31
5w/o Arousal8.7935.21615.7932.7446.38
6VA v.s. FF9.1605.01119.8723.6739.58
7w/o multi-head10.9483.89418.6524.8540.25
8Duplication11.4753.81418.6321.7837.92
9Full model8.0365.60815.6637.7561.46
", + "image_path": "8f66cce0ae19dc6226e1fab082b79f3793fe58e328778b118db2a6a6f7d35a1b.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 342, + 287, + 365 + ], + "lines": [ + { + "bbox": [ + 47, + 342, + 287, + 365 + ], + "spans": [ + { + "bbox": [ + 47, + 342, + 287, + 365 + ], + "type": "text", + "content": "Table 3. Ablation study of the proposed method on the V2C benchmark dataset." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 47, + 377, + 287, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 377, + 287, + 413 + ], + "spans": [ + { + "bbox": [ + 47, + 377, + 287, + 413 + ], + "type": "text", + "content": "tive evaluation results (see MOS-N and MOS-S) also show that our method can generate speeches that are closer to realistic speech according to naturalness and similarity." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 414, + 287, + 534 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 414, + 287, + 534 + ], + "spans": [ + { + "bbox": [ + 46, + 414, + 287, + 534 + ], + "type": "text", + "content": "Results on the Chem benchmark. As shown in Table 2, our model is ahead of the state-of-the-art methods in all metrics on the Chem benchmark. In terms of the audiovisual sync, our method achieves 6.975 LSE-D and 7.176 LSE-C. Furthermore, in the subjective evaluations, our method improves " + }, + { + "bbox": [ + 46, + 414, + 287, + 534 + ], + "type": "inline_equation", + "content": "10.34\\%" + }, + { + "bbox": [ + 46, + 414, + 287, + 534 + ], + "type": "text", + "content": " on AQ and " + }, + { + "bbox": [ + 46, + 414, + 287, + 534 + ], + "type": "inline_equation", + "content": "22.15\\%" + }, + { + "bbox": [ + 46, + 414, + 287, + 534 + ], + "type": "text", + "content": " on AV Sync. The results show that our performance is much closer to the ground truth recording, which indicates that our model synthesizes high-quality natural speech by controlling the prosody from hierarchical visual representation." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 543, + 148, + 554 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 543, + 148, + 554 + ], + "spans": [ + { + "bbox": [ + 47, + 543, + 148, + 554 + ], + "type": "text", + "content": "4.5. Ablation Studies" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 561, + 287, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 561, + 287, + 693 + ], + "spans": [ + { + "bbox": [ + 46, + 561, + 287, + 693 + ], + "type": "text", + "content": "Effectiveness of Duration Aligner, Prosody Adaptor, and Atmosphere Booster. We evaluate the effectiveness of these three modules by removing them separately and retraining the model. The results are shown in Row " + }, + { + "bbox": [ + 46, + 561, + 287, + 693 + ], + "type": "inline_equation", + "content": "1\\sim 3" + }, + { + "bbox": [ + 46, + 561, + 287, + 693 + ], + "type": "text", + "content": " of Table 3. The result shows that all the proposed modules contribute significantly to the overall performance, and each module has a different focus. Specifically, the performance on the audio-visual metrics (LSE-D, LSE-C, and MCD) drops the most when removing the Duration Aligner (DA). This reflects the DA module indeed helps the model learn a better temporal synchronization. By contrast, the perfor" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 310, + 72, + 543, + 286 + ], + "blocks": [ + { + "bbox": [ + 310, + 72, + 543, + 286 + ], + "lines": [ + { + "bbox": [ + 310, + 72, + 543, + 286 + ], + "spans": [ + { + "bbox": [ + 310, + 72, + 543, + 286 + ], + "type": "image", + "image_path": "cda78790666a031496c81aae36e5d814c561af2fac3e2dd0f4de0ea5ee9eb9a6.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 306, + 297, + 545, + 319 + ], + "lines": [ + { + "bbox": [ + 306, + 297, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 306, + 297, + 545, + 319 + ], + "type": "text", + "content": "Figure 3. Audiovisual consistency visualization on V2C dataset: Ground Truth (GT), our model, FastSpeech 2 (FS2) and V2C-Net." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 331, + 545, + 439 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 331, + 545, + 439 + ], + "spans": [ + { + "bbox": [ + 304, + 331, + 545, + 439 + ], + "type": "text", + "content": "mance on identity accuracy and emotion accuracy drop the most when removing the Prosody Adaptor (PA). This can be attributed to the predicted pitch and energy representing a speaker's identity and his/her emotion. When removing the Atmosphere Booster (AB), the performance drops compared to the full model (Row 9) but does not drop as much as when removing the other two modules. This indicates the AB module also contributes the overall performance improvement but contributes the least in the three modules." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 443, + 546, + 550 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 443, + 546, + 550 + ], + "spans": [ + { + "bbox": [ + 304, + 443, + 546, + 550 + ], + "type": "text", + "content": "Effectiveness of valence and arousal. In our model, we bridge arousal with energy and valence with pitch by attention mechanism. To evaluate their effectiveness, we cut off the connection and predict energy and pitch directly from the phoneme-lip representation. The results are presented in Row " + }, + { + "bbox": [ + 304, + 443, + 546, + 550 + ], + "type": "inline_equation", + "content": "4\\sim 5" + }, + { + "bbox": [ + 304, + 443, + 546, + 550 + ], + "type": "text", + "content": " of Table 3. It shows that the performance drops significantly when removing either of them and drops more when removing valence. This indicates valence contributes more than arousal on the V2C task." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 554, + 545, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 554, + 545, + 651 + ], + "spans": [ + { + "bbox": [ + 304, + 554, + 545, + 651 + ], + "type": "text", + "content": "Valence-and-Arousal v.s. Facial Expression. To compare the role of affective display and facial expressions on prosody inference, we replace the input of the APA module with original embeddings of facial features from CNN. As row “VA v.s. FF” of Table 3 shows, the model performance drops significantly. This is likely caused by facial features that are still far from the information-containing emotion, which is not enough to guide prosody generation." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "content": "Multi-head Attention in Duration Adaptor. In our duration aligner, we exploit multi-head attention to learn the relation between the phoneme sequences and the lip motion. To verify its effectiveness, we conduct experiments using conventional dot-product attention. The results are" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 58, + 702, + 282, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 282, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 282, + 712 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 58, + 702, + 282, + 712 + ], + "type": "text", + "content": "V2C republish official results on https://github.com/chenqi008/V2C." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "14693" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 72, + 544, + 278 + ], + "blocks": [ + { + "bbox": [ + 49, + 72, + 544, + 278 + ], + "lines": [ + { + "bbox": [ + 49, + 72, + 544, + 278 + ], + "spans": [ + { + "bbox": [ + 49, + 72, + 544, + 278 + ], + "type": "image", + "image_path": "4168d8e5209fd61ce3b9922e2f0d9862cd498d254be589953774697641ab5e67.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 289, + 546, + 322 + ], + "lines": [ + { + "bbox": [ + 46, + 289, + 546, + 322 + ], + "spans": [ + { + "bbox": [ + 46, + 289, + 546, + 322 + ], + "type": "text", + "content": "Figure 4. Visualization of audio on V2C dataset (top) and Chem dataset (bottom). Orange curves are " + }, + { + "bbox": [ + 46, + 289, + 546, + 322 + ], + "type": "inline_equation", + "content": "F_{0}" + }, + { + "bbox": [ + 46, + 289, + 546, + 322 + ], + "type": "text", + "content": " contours, where " + }, + { + "bbox": [ + 46, + 289, + 546, + 322 + ], + "type": "inline_equation", + "content": "F_{0}" + }, + { + "bbox": [ + 46, + 289, + 546, + 322 + ], + "type": "text", + "content": " is the fundamental frequency of audio. Purple curves refer to the energy (volume) of audio. The horizontal axis is the duration of the audio. The red circles highlight the mel-spectrograms at the same moment as the frame shown on the left side." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 333, + 287, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 333, + 287, + 392 + ], + "spans": [ + { + "bbox": [ + 46, + 333, + 287, + 392 + ], + "type": "text", + "content": "presented in Row 7 of Table 3. The performance drops significantly on all metrics, such as " + }, + { + "bbox": [ + 46, + 333, + 287, + 392 + ], + "type": "inline_equation", + "content": "26.59\\%" + }, + { + "bbox": [ + 46, + 333, + 287, + 392 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 333, + 287, + 392 + ], + "type": "inline_equation", + "content": "44.02\\%" + }, + { + "bbox": [ + 46, + 333, + 287, + 392 + ], + "type": "text", + "content": " decrease on LSE-D and LSE-C compared to Row 9, respectively. This indicates multi-head attention learns a much better correlation between phonemes with lip movement." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 396, + 288, + 504 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 396, + 288, + 504 + ], + "spans": [ + { + "bbox": [ + 46, + 396, + 288, + 504 + ], + "type": "text", + "content": "Conv-Transpose v.s. Duplication. In our duration aligner, we propose to use Conv-Transpose to learn the expansion of the fused phoneme-lip representation to its desired length. To verify its effectiveness, we replace it with simple make " + }, + { + "bbox": [ + 46, + 396, + 288, + 504 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 46, + 396, + 288, + 504 + ], + "type": "text", + "content": " duplications as in [20]. The results are shown in Row 8 of Table 3. It shows that the performance drops significantly. For example, it falls " + }, + { + "bbox": [ + 46, + 396, + 288, + 504 + ], + "type": "inline_equation", + "content": "15.94\\%" + }, + { + "bbox": [ + 46, + 396, + 288, + 504 + ], + "type": "text", + "content": " on MCD metric. This demonstrates the superiority of using transposed convolution to learn the upsampling than simply copying." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 512, + 160, + 525 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 512, + 160, + 525 + ], + "spans": [ + { + "bbox": [ + 47, + 512, + 160, + 525 + ], + "type": "text", + "content": "4.6. Qualitative Results" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 531, + 287, + 674 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 531, + 287, + 674 + ], + "spans": [ + { + "bbox": [ + 46, + 531, + 287, + 674 + ], + "type": "text", + "content": "Audiovisual consistency visualization. Figure 3 presents the mel-spectrograms of generated audios along with its frames. Blue and red bounding boxes denote whether the character is speaking or not, respectively. Compared with other methods, the mel-spectrogram generated by our model is closer to the ground truth, indicating better audiovisual synchronization. This can be attributed to our duration aligner, which exploits multi-head alignment between the text phoneme sequence and the lip movement sequence. By controlling the lip movements explicitly, we obtain the desired length of mel-spectrograms, which makes the speech well synchronized with the input video." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 677, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 288, + 713 + ], + "type": "text", + "content": "Arousal and valence with prosody visualization. We selected two examples from the test set of the V2C dataset and Chem dataset to demonstrate the alignment between en" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 333, + 547, + 466 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 333, + 547, + 466 + ], + "spans": [ + { + "bbox": [ + 304, + 333, + 547, + 466 + ], + "type": "text", + "content": "ergy and arousal as well as pitch and valence. The valence (positive or negative) and arousal (calming or exciting) of facial expressions are shown in the first column. The main pitch and energy are shown in orange and blue curves in the right column. We use the red circle to highlight the pitch and energy that correspond to the video frame shown in the left column. The result shows that our method achieves energy and pitch closer to the ground truth speech. When the chemistry teacher becomes excited and positive, our model successfully leverages the affective display to synthesize a similar pitch and energy as the ground-truth speech." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 477, + 379, + 489 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 477, + 379, + 489 + ], + "spans": [ + { + "bbox": [ + 306, + 477, + 379, + 489 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 498, + 545, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 545, + 616 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 545, + 616 + ], + "type": "text", + "content": "In this work, we propose a hierarchical prosody modeling network for movie dubbing, which bridges video representations and speech attributes from three levels: lip, facial expression, and scene. By associating these visual representations with their voice counterparts, we obtain more powerful representations for dubbing. Furthermore, we design an affective-display based prosody adaptor, which effectively learns to align the valence and arousal to the pitch and energy of speeches. Our proposed model sets new state-of-the-art on both Chem and V2C-Animation benchmarks." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 618, + 546, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 618, + 546, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 618, + 546, + 712 + ], + "type": "text", + "content": "Acknowledgement. This work was supported in part by the National Key R&D Program of China under Grant 2018AAA0102000, and in part by National Natural Science Foundation of China under Grants 62225207 and U19B2038, the Key R&D Plan Project of Zhejiang Province (No. 2023C01004), Shandong Provincial Natural Science Foundation (ZR2020MA064), Youth Innovation Promotion Association of CAS under Grant 2020108." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "14694" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "type": "text", + "content": "[1] Triantafyllos Afouras, Joon Son Chung, and Andrew Zisserman. Deep lip reading: A comparison of models and an online application. In Interspeech, pages 3514-3518, 2018. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 137, + 288, + 201 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 137, + 288, + 201 + ], + "spans": [ + { + "bbox": [ + 53, + 137, + 288, + 201 + ], + "type": "text", + "content": "[2] Sercan Ömer Arik, Mike Chrzanowski, Adam Coates, Gregory Frederick Diamos, Andrew Gibiansky, Yongguo Kang, Xian Li, John Miller, Andrew Y. Ng, Jonathan Raiman, Shubho Sengupta, and Mohammad Shoeeybi. Deep voice: Real-time neural text-to-speech. In ICML, pages 195-204, 2017. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 204, + 288, + 236 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 204, + 288, + 236 + ], + "spans": [ + { + "bbox": [ + 53, + 204, + 288, + 236 + ], + "type": "text", + "content": "[3] Yannis M Assael, Brendan Shillingford, Shimon Whiteson, and Nando De Freitas. Lipnet: End-to-end sentence-level lipreading. arXiv preprint arXiv:1611.01599, 2016. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 238, + 287, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 238, + 287, + 282 + ], + "spans": [ + { + "bbox": [ + 53, + 238, + 287, + 282 + ], + "type": "text", + "content": "[4] Eric Battenberg, R. J. Skerry-Ryan, Soroosh Mariooryad, Daisy Stanton, David Kao, Matt Shannon, and Tom Bagby. Location-relative attention mechanisms for robust long-form speech synthesis. In ICASSP, pages 6194-6198, 2020. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 284, + 287, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 284, + 287, + 327 + ], + "spans": [ + { + "bbox": [ + 53, + 284, + 287, + 327 + ], + "type": "text", + "content": "[5] Manuel Burghardt, Michael Kao, and Christian Wolff. Beyond shot lengths - using language data and color information as additional parameters for quantitative movie analysis. In ADHO, pages 753-755, 2016. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 328, + 287, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 328, + 287, + 361 + ], + "spans": [ + { + "bbox": [ + 53, + 328, + 287, + 361 + ], + "type": "text", + "content": "[6] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In CVPR, pages 6299-6308, 2017. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 363, + 287, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 363, + 287, + 384 + ], + "spans": [ + { + "bbox": [ + 53, + 363, + 287, + 384 + ], + "type": "text", + "content": "[7] Frederic Chaume. Dubbing practices in europe: localisation beats globalisation. LANS-TTS, 6, 2007. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 386, + 287, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 386, + 287, + 430 + ], + "spans": [ + { + "bbox": [ + 53, + 386, + 287, + 430 + ], + "type": "text", + "content": "[8] Lele Chen, Ross K. Maddox, Zhiyao Duan, and Chenliang Xu. Hierarchical cross-modal talking face generation with dynamic pixel-wise loss. In CVPR, pages 7832-7841, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 431, + 287, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 431, + 287, + 464 + ], + "spans": [ + { + "bbox": [ + 53, + 431, + 287, + 464 + ], + "type": "text", + "content": "[9] Qi Chen, Mingkui Tan, Yuankai Qi, Jiaqiu Zhou, Yuanqing Li, and Qi Wu. V2C: visual voice cloning. In CVPR, pages 21210-21219, 2022. 1, 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 465, + 287, + 518 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 465, + 287, + 518 + ], + "spans": [ + { + "bbox": [ + 48, + 465, + 287, + 518 + ], + "type": "text", + "content": "[10] Weidong Chen, Dexiang Hong, Yuankai Qi, Zhenjun Han, Shuhui Wang, Laiyun Qing, Qingming Huang, and Guorong Li. Multi-attention network for compressed video referring object segmentation. In ACM MM, pages 4416–4425, 2022. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 521, + 287, + 554 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 521, + 287, + 554 + ], + "spans": [ + { + "bbox": [ + 48, + 521, + 287, + 554 + ], + "type": "text", + "content": "[11] Joon Son Chung, Andrew W. Senior, Oriol Vinyals, and Andrew Zisserman. Lip reading sentences in the wild. In CVPR, pages 3444-3453, 2017. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 555, + 287, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 555, + 287, + 588 + ], + "spans": [ + { + "bbox": [ + 48, + 555, + 287, + 588 + ], + "type": "text", + "content": "[12] Joon Son Chung and Andrew Zisserman. Out of time: Automated lip sync in the wild. In ACCV Workshop, pages 251-263, 2016. 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 590, + 287, + 611 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 590, + 287, + 611 + ], + "spans": [ + { + "bbox": [ + 48, + 590, + 287, + 611 + ], + "type": "text", + "content": "[13] Joon Son Chung and Andrew Zisserman. Lip reading in profile. In BMVC, 2017. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 613, + 287, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 613, + 287, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 613, + 287, + 668 + ], + "type": "text", + "content": "[14] Gaoxiang Cong, Liang Li, Zhenhuan Liu, Yunbin Tu, Weijun Qin, Shenyuan Zhang, Chengang Yan, Wenyu Wang, and Bin Jiang. LS-GAN: iterative language-based image manipulation via long and short term consistency reasoning. In ACM MM, pages 4496-4504, 2022. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "type": "text", + "content": "[15] Rodrigo Schoburg Carrillo de Mira, Alexandros Haliassos, Stavros Petridis, Bjorn W. Schuller, and Maja Pantic. SVTS: scalable video-to-speech synthesis. In INTER-SPEECH, pages 1836-1840, 2022. 2" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 32, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 105 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 105 + ], + "type": "text", + "content": "[16] Ariel Ephrat and Shmuel Peleg. Vid2speech: Speech reconstruction from silent video. In ICASSP, pages 5095-5099, 2017. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "type": "text", + "content": "[17] Michael Hassid, Michelle Tadmor Ramanovich, Brendan Shillingford, Miaosen Wang, Ye Jia, and Tal Remez. More than words: In-the-wild visually-driven prosody for text-to-speech. In CVPR, pages 10577–10587, 2022. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "text", + "content": "[18] Sindhu B. Hegde, K. R. Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C. V. Jawahar. Lip-to-speech synthesis for arbitrary speakers in the wild. In ACM MM, pages 6250-6258, 2022. 1, 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 198, + 545, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 198, + 545, + 229 + ], + "spans": [ + { + "bbox": [ + 307, + 198, + 545, + 229 + ], + "type": "text", + "content": "[19] Yicong Hong, Qi Wu, Yuankai Qi, Cristian Rodriguez Opazo, and Stephen Gould. A recurrent vision-and-language BERT for navigation. CoRR, abs/2011.13922, 2020. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 231, + 545, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 231, + 545, + 274 + ], + "spans": [ + { + "bbox": [ + 307, + 231, + 545, + 274 + ], + "type": "text", + "content": "[20] Chenxu Hu, Qiao Tian, Tingle Li, Yuping Wang, Yuxuan Wang, and Hang Zhao. Neural dubber: Dubbing for videos according to scripts. In NeurIPS, pages 16582-16595, 2021. 2, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 276, + 545, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 276, + 545, + 331 + ], + "spans": [ + { + "bbox": [ + 307, + 276, + 545, + 331 + ], + "type": "text", + "content": "[21] Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez-Moreno, and Yonghui Wu. Transfer learning from speaker verification to multispeaker text-to-speech synthesis. In NeurIPS, pages 4485-4495, 2018. 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 332, + 545, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 332, + 545, + 364 + ], + "spans": [ + { + "bbox": [ + 307, + 332, + 545, + 364 + ], + "type": "text", + "content": "[22] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Yoshua Bengio and Yann LeCun, editors, ICLR, 2015. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 365, + 545, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 365, + 545, + 398 + ], + "spans": [ + { + "bbox": [ + 307, + 365, + 545, + 398 + ], + "type": "text", + "content": "[23] Jungil Kong, Jaehyeon Kim, and Jaekyoung Bae. Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. In NeurlPS, 2020. 5, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 399, + 545, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 399, + 545, + 431 + ], + "spans": [ + { + "bbox": [ + 307, + 399, + 545, + 431 + ], + "type": "text", + "content": "[24] R. Kubichek. Mel-cepstral distance measure for objective speech quality assessment. In PACRIM, pages 125–128, 1993. 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 434, + 545, + 478 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 434, + 545, + 478 + ], + "spans": [ + { + "bbox": [ + 307, + 434, + 545, + 478 + ], + "type": "text", + "content": "[25] Yaman Kumar, Rohit Jain, Khwaja Mohd. Salik, Rajiv Ratn Shah, Yifang Yin, and Roger Zimmermann. Lipper: Synthesizing thy speech using multi-view lipreading. In AAAI, pages 2588-2595, 2019. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 479, + 545, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 479, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 307, + 479, + 545, + 521 + ], + "type": "text", + "content": "[26] Liang Li, Xingyu Gao, Jincan Deng, Yunbin Tu, Zheng-Jun Zha, and Qingming Huang. Long short-term relation transformer with global gating for video captioning. IEEE Trans. Image Process., pages 2726-2738, 2022. 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 523, + 545, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 523, + 545, + 577 + ], + "spans": [ + { + "bbox": [ + 307, + 523, + 545, + 577 + ], + "type": "text", + "content": "[27] Liang Li, Chenggang Clarence Yan, Xing Chen, Chunjie Zhang, Jian Yin, Baochen Jiang, and Qingming Huang. Distributed image understanding with semantic dictionary and semantic expansion. Neurocomputing, pages 384-392, 2016. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 579, + 545, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 579, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 307, + 579, + 545, + 624 + ], + "type": "text", + "content": "[28] Liang Li, Xinge Zhu, Yiming Hao, Shuhui Wang, Xingyu Gao, and Qingming Huang. A hierarchical cnn-rnn approach for visual emotion classification. ACM Trans. Multim. Comput. Commun. Appl., pages 1-17, 2019. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 307, + 624, + 545, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 624, + 545, + 657 + ], + "spans": [ + { + "bbox": [ + 307, + 624, + 545, + 657 + ], + "type": "text", + "content": "[29] Naihan Li, Shujie Liu, Yanqing Liu, Sheng Zhao, and Ming Liu. Neural speech synthesis with transformer network. In AAAI, pages 6706-6713, 2019. 2" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 307, + 658, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 658, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 307, + 658, + 545, + 712 + ], + "type": "text", + "content": "[30] Borong Liang, Yan Pan, Zhizhi Guo, Hang Zhou, Zhibin Hong, Xiaoguang Han, Junyu Han, Jingtuo Liu, Errui Ding, and Jingdong Wang. Expressive talking head generation with granular audio-visual control. In CVPR, pages 3377–3386, 2022. 2" + } + ] + } + ], + "index": 31 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "14695" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "text", + "content": "[31] Xian Liu, Qianyi Wu, Hang Zhou, Yinghao Xu, Rui Qian, Xinyi Lin, Xiaowei Zhou, Wayne Wu, Bo Dai, and Bolei Zhou. Learning hierarchical cross-modal association for cospeech gesture generation. In CVPR, pages 10452-10462, 2022. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 129, + 287, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 129, + 287, + 173 + ], + "spans": [ + { + "bbox": [ + 48, + 129, + 287, + 173 + ], + "type": "text", + "content": "[32] Junchen Lu, Berrak Sisman, Rui Liu, Mingyang Zhang, and Haizhou Li. Visualts: TTS with accurate lip-speech synchronization for automatic voice over. In ICASSP, pages 8032-8036, 2022. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 175, + 287, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 175, + 287, + 207 + ], + "spans": [ + { + "bbox": [ + 48, + 175, + 287, + 207 + ], + "type": "text", + "content": "[33] Pingchuan Ma, Brais Martinez, Stavros Petridis, and Maja Pantic. Towards practical lipreading with distilled and efficient models. In ICASSP, pages 7608-7612, 2021. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 209, + 287, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 209, + 287, + 241 + ], + "spans": [ + { + "bbox": [ + 48, + 209, + 287, + 241 + ], + "type": "text", + "content": "[34] Pingchuan Ma, Yujiang Wang, Stavros Petridis, Jie Shen, and Maja Pantic. Training strategies for improved lip-reading. In ICASSP, pages 8472–8476, 2022. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 243, + 287, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 243, + 287, + 285 + ], + "spans": [ + { + "bbox": [ + 48, + 243, + 287, + 285 + ], + "type": "text", + "content": "[35] Pingchuan Ma, Yujiang Wang, Jie Shen, Stavros Petridis, and Maja Pantic. Lip-reading with densely connected temporal convolutional networks. In WACV, pages 2857–2866, 2021. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 287, + 287, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 287, + 287, + 319 + ], + "spans": [ + { + "bbox": [ + 48, + 287, + 287, + 319 + ], + "type": "text", + "content": "[36] Brais Martinez, Pingchuan Ma, Stavros Petridis, and Maja Pantic. Lipreading using temporal convolutional networks. In ICASSP, pages 6319-6323, 2020. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 322, + 287, + 343 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 322, + 287, + 343 + ], + "spans": [ + { + "bbox": [ + 48, + 322, + 287, + 343 + ], + "type": "text", + "content": "[37] Meinard Müller. Dynamic time warping. Information Retrieval for Music and Motion, pages 69-84, 2007. 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 345, + 287, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 345, + 287, + 398 + ], + "spans": [ + { + "bbox": [ + 48, + 345, + 287, + 398 + ], + "type": "text", + "content": "[38] Foivos Paraperas Papantoniou, Panagiotis Paraskevas Filntisis, Petros Maragos, and Anastasios Roussos. Neural emotion director: Speech-preserving semantic control of facial expressions in \"in-the-wild\" videos. In CVPR, pages 18759-18768, 2022. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 400, + 287, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 400, + 287, + 487 + ], + "spans": [ + { + "bbox": [ + 48, + 400, + 287, + 487 + ], + "type": "text", + "content": "[39] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS, pages 8024-8035, 2019. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 490, + 287, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 490, + 287, + 520 + ], + "spans": [ + { + "bbox": [ + 48, + 490, + 287, + 520 + ], + "type": "text", + "content": "[40] Wei Ping, Kainan Peng, and Jitong Chen. Clarinet: Parallel wave generation in end-to-end text-to-speech. In ICLR, 2019. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 523, + 287, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 523, + 287, + 567 + ], + "spans": [ + { + "bbox": [ + 48, + 523, + 287, + 567 + ], + "type": "text", + "content": "[41] Wei Ping, Kainan Peng, Andrew Gibiansky, Sercan Ömer Arik, Ajay Kannan, Sharan Narang, Jonathan Raiman, and John Miller. Deep voice 3: Scaling text-to-speech with convolutional sequence learning. In ICLR, 2018. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 568, + 287, + 611 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 568, + 287, + 611 + ], + "spans": [ + { + "bbox": [ + 48, + 568, + 287, + 611 + ], + "type": "text", + "content": "[42] K. R. Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C. V. Jawahar. Learning individual speaking styles for accurate lip to speech synthesis. In CVPR, pages 13793–13802, 2020. 2, 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 613, + 287, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 613, + 287, + 656 + ], + "spans": [ + { + "bbox": [ + 48, + 613, + 287, + 656 + ], + "type": "text", + "content": "[43] K. R. Prajwal, Rudrabha Mukhopadhyay, Vinay P. Namboodiri, and C. V. Jawahar. A lip sync expert is all you need for speech to lip generation in the wild. In ACM MM, pages 484-492, 2020. 2, 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "type": "text", + "content": "[44] Yanyuan Qiao, Qi Chen, Chaorui Deng, Ning Ding, Yuankai Qi, Mingkui Tan, Xincheng Ren, and Qi Wu. R-GAN: exploring human-like way for reasonable text-to-image synthesis via generative adversarial networks. In ACM MM, pages 2085-2093, 2021. 3" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "text", + "content": "[45] Yanyuan Qiao, Yuankai Qi, Yicong Hong, Zheng Yu, Peng Wang, and Qi Wu. Hop+: History-enhanced and order-aware pre-training for vision-and-language navigation. TPAMI, 2023. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 118, + 545, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 151 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 151 + ], + "type": "text", + "content": "[46] Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu. Fastspeech 2: Fast and high-quality end-to-end text to speech. In ICLR, 2021. 1, 2, 6, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "text", + "content": "[47] Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu. Fastspeech: Fast, robust and controllable text to speech. In NeurIPS, pages 3165-3174, 2019. 1, 2, 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 198, + 545, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 198, + 545, + 262 + ], + "spans": [ + { + "bbox": [ + 307, + 198, + 545, + 262 + ], + "type": "text", + "content": "[48] Jonathan Shen, Ruoming Pang, Ron J. Weiss, Mike Schuster, Navdeep Jaitly, Zongheng Yang, Zhifeng Chen, Yu Zhang, Yuxuan Wang, RJ-Skerrv Ryan, Rif A. Saurous, Yannis Agiomyrgiannakis, and Yonghui Wu. Natural TTS synthesis by conditioning wavenet on MEL spectrogram predictions. In ICASSP, pages 4779-4783, 2018. 1, 2, 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 264, + 545, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 264, + 545, + 297 + ], + "spans": [ + { + "bbox": [ + 307, + 264, + 545, + 297 + ], + "type": "text", + "content": "[49] Bowen Shi, Wei-Ning Hsu, Kushal Lakhotia, and Abdelrahman Mohamed. Learning audio-visual speech representation by masked multimodal cluster prediction. In ICLR, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 299, + 545, + 341 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 299, + 545, + 341 + ], + "spans": [ + { + "bbox": [ + 308, + 299, + 545, + 341 + ], + "type": "text", + "content": "[50] Linsen Song, Wayne Wu, Chen Qian, Ran He, and Chen Change Loy. Everybody's talkin': Let me talk as you want. IEEE Trans. Inf. Forensics Secur., 17:585-598, 2022. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 343, + 545, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 343, + 545, + 387 + ], + "spans": [ + { + "bbox": [ + 308, + 343, + 545, + 387 + ], + "type": "text", + "content": "[51] Antoine Toisoul, Jean Kossaifi, Adrian Bulat, Georgios Tzimiropoulos, and Maja Pantic. Estimation of continuous valence and arousal levels from faces in naturalistic conditions. Nat. Mach. Intell., 3(1):42-50, 2021. 2, 4" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 388, + 545, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 388, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 308, + 388, + 545, + 430 + ], + "type": "text", + "content": "[52] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, pages 5998-6008, 2017. 5" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 434, + 545, + 466 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 434, + 545, + 466 + ], + "spans": [ + { + "bbox": [ + 308, + 434, + 545, + 466 + ], + "type": "text", + "content": "[53] Suzhen Wang, Lincheng Li, Yu Ding, and Xin Yu. One-shot talking face generation from single-speaker audio-visual correlation learning. In AAAI, pages 2531–2539, 2022. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 468, + 545, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 468, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 308, + 468, + 545, + 533 + ], + "type": "text", + "content": "[54] Yuxuan Wang, R. J. Skerry-Ryan, Daisy Stanton, Yonghui Wu, Ron J. Weiss, Navdeep Jaitly, Zongheng Yang, Ying Xiao, Zhifeng Chen, Samy Bengio, Quoc V. Le, Yannis Agiomyrgiannakis, Rob Clark, and Rif A. Saurous. Tacotron: Towards end-to-end speech synthesis. In *Interspeech*, pages 4006-4010, 2017. 1, 2, 6, 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 535, + 545, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 535, + 545, + 567 + ], + "spans": [ + { + "bbox": [ + 308, + 535, + 545, + 567 + ], + "type": "text", + "content": "[55] Yongqi Wang and Zhou Zhao. Fastlts: Non-autoregressive end-to-end unconstrained lip-to-speech synthesis. In ACM MM, pages 5678-5687, 2022. 1, 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 569, + 545, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 569, + 545, + 602 + ], + "spans": [ + { + "bbox": [ + 308, + 569, + 545, + 602 + ], + "type": "text", + "content": "[56] Jonatas Wehrmann and Rodrigo C. Barros. Movie genre classification: A multi-label approach based on convolutions through time. Appl. Soft Comput., 61:973-982, 2017. 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 603, + 545, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 603, + 545, + 656 + ], + "spans": [ + { + "bbox": [ + 308, + 603, + 545, + 656 + ], + "type": "text", + "content": "[57] Tianyi Xie, Liucheng Liao, Cheng Bi, Benlai Tang, Xiang Yin, Jianfei Yang, Mingjie Wang, Jiali Yao, Yang Zhang, and Zejun Ma. Towards realistic visual dubbing with heterogeneous sources. In ACM MM, pages 1739-1747, 2021. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 658, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 658, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 308, + 658, + 545, + 712 + ], + "type": "text", + "content": "[58] Yi Yang, Brendan Shillingford, Yannis M. Assael, Miaosen Wang, Wendi Liu, Yutian Chen, Yu Zhang, Eren Sezener, Luis C. Cobo, Misha Denil, Yusuf Aytar, and Nando de Freitas. Large-scale multilingual audio visual dubbing. CoRR, abs/2011.03530, 2020. 2" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "14696" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 351 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "text", + "content": "[59] Fei Yin, Yong Zhang, Xiaodong Cun, Mingdeng Cao, Yanbo Fan, Xuan Wang, Qingyan Bai, Baoyuan Wu, Jue Wang, and Yujiu Yang. Styleheat: One-shot high-resolution editable talking face generation via pretrained stylegan. arXiv preprint arXiv:2203.04036, 2022. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 129, + 287, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 129, + 287, + 161 + ], + "spans": [ + { + "bbox": [ + 49, + 129, + 287, + 161 + ], + "type": "text", + "content": "[60] Shifeng Zhang, Xiangyu Zhu, Zhen Lei, Hailin Shi, Xiaobo Wang, and Stan Z Li. S3fd: Single shot scale-invariant face detector. In CVPR, pages 192-201, 2017. 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 163, + 287, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 163, + 287, + 205 + ], + "spans": [ + { + "bbox": [ + 49, + 163, + 287, + 205 + ], + "type": "text", + "content": "[61] Hang Zhou, Yu Liu, Ziwei Liu, Ping Luo, and Xiaogang Wang. Talking face generation by adversarially disentangled audio-visual representation. In AAAI, pages 9299–9306, 2019. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 208, + 287, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 208, + 287, + 251 + ], + "spans": [ + { + "bbox": [ + 49, + 208, + 287, + 251 + ], + "type": "text", + "content": "[62] Yang Zhou, Xintong Han, Eli Shechtman, Jose Echevarria, Evangelos Kalogerakis, and Dingzeyu Li. Makelttalk: speaker-aware talking-head animation. ACM Trans. Graph., 39(6):221:1-221:15, 2020. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 253, + 287, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 253, + 287, + 296 + ], + "spans": [ + { + "bbox": [ + 49, + 253, + 287, + 296 + ], + "type": "text", + "content": "[63] Yang Zhou, Jimei Yang, Dingzeyu Li, Jun Saito, Deepali Aneja, and Evangelos Kalogerakis. Audio-driven neural gesture reenactment with video motion graphs. In CVPR, pages 3408-3418, 2022. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 297, + 287, + 351 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 297, + 287, + 351 + ], + "spans": [ + { + "bbox": [ + 49, + 297, + 287, + 351 + ], + "type": "text", + "content": "[64] Wanrong Zhu, Yuankai Qi, Pradyumna Narayana, Kazoo Sone, Sugato Basu, Xin Eric Wang, Qi Wu, Miguel P. Eckstein, and William Yang Wang. Diagnosing vision-and-language navigation: What really matters. CoRR, abs/2103.16561, 2021. 3" + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "14697" + } + ] + } + ], + "index": 7 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing/fc7c021d-c682-4b4a-b7f3-6c9ca632109a_content_list.json b/2023/Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing/fc7c021d-c682-4b4a-b7f3-6c9ca632109a_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..f51d0190c6856f64077a77f99d413891c121a815 --- /dev/null +++ b/2023/Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing/fc7c021d-c682-4b4a-b7f3-6c9ca632109a_content_list.json @@ -0,0 +1,1701 @@ +[ + { + "type": "text", + "text": "Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing", + "text_level": 1, + "bbox": [ + 264, + 130, + 707, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Shruthi Bannur,\\* Stephanie Hyland\\*, Qianchu Liu, Fernando Pérez-García, Maximilian Ilse, Daniel C. Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anja Thieme, Anton Schwaighofer, Maria Wetscherek, Matthew P. Lungren, Aditya Nori Javier Alvarez-Valle, and Ozan Oktay†", + "bbox": [ + 181, + 205, + 787, + 276 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Microsoft Health Futures", + "bbox": [ + 398, + 282, + 570, + 297 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 333, + 313, + 349 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data.", + "bbox": [ + 75, + 366, + 473, + 744 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 775, + 209, + 790 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Self-supervision from image-text pairs has enabled the development of flexible general-purpose vision-language models both in the general domain [40, 53, 77] and for specialised domains such as biomedicine and radiology", + "bbox": [ + 75, + 801, + 468, + 863 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/ba8a6ac68ac80aebfc26fbae6d9ef3cec20ad8954c2e19cb39fd0aa5961f4c6c.jpg", + "image_caption": [ + "Existing methods" + ], + "image_footnote": [], + "bbox": [ + 506, + 354, + 674, + 450 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/631b61db4483c495c4ccbff15baf2d2310487f77ec37f4eb5b43e1316e98d8d5.jpg", + "image_caption": [ + "Proposed method" + ], + "image_footnote": [], + "bbox": [ + 684, + 354, + 883, + 452 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/3109fc488c0faea5247b56d1f99ccede9ccb7c417c16fac97840ecc3c4442985.jpg", + "image_caption": [ + "(b) Spatial modelling", + "Figure 1. (a) Existing visual-language pre-training approaches [9, 32, 81] often use only a single image for contrastive learning (e.g., InfoNCE [49]). (b) In such settings, discarding the temporal connectivity of images limits the alignment of image-text pairs as shown with the affinity matrix, leading to suboptimal pre-training and missed opportunity to create additional model supervision for free. (c, d) Our approach exploits this domain knowledge by learning to incorporate a series of images and correlate them to reports, leading to pre-trained models that can generalise to a wider range of downstream tasks whilst achieving SOTA performance." + ], + "image_footnote": [], + "bbox": [ + 540, + 468, + 841, + 563 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "[9, 32, 81]. Vision-language processing (VLP) has shown that cross-modal supervision can provide a richer signal for training both image [19] and text [9] models. However, the success of VLP relies on paired samples sharing semantics, i.e., given an image and text pair, the text should describe the image with minimal extraneous detail [15, 16, 35].", + "bbox": [ + 496, + 757, + 893, + 849 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this regard, VLP in biomedicine and radiology poses a distinctive challenge, as reports routinely include comparisons to prior imaging studies [3, 47, 57]. Without knowledge of the underlying mechanisms of these comparisons, it is difficult to determine whether the results are consistent with those obtained by other methods.", + "bbox": [ + 498, + 854, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* These authors contributed equally.", + "bbox": [ + 93, + 875, + 285, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "†Corresponding author: ozan.oktay@microsoft.com", + "bbox": [ + 96, + 887, + 415, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "15016", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "edge of this prior image1, temporal information in the text modality, e.g. \"Pneumonia is improving\", could pertain to any image containing \"Pneumonia\", producing ambiguity during contrastive training (Figure 1). Despite this, the existing VLP work to date considers alignment between only single images and reports [9,32,46,81], going so far as to remove temporal content from reports in training data to prevent 'hallucinations' in downstream report generation [54]. However, temporal information can provide complementary self-supervision, solely by exploiting existing structure, and without requiring any additional data.", + "bbox": [ + 75, + 90, + 472, + 257 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this work, we neither ignore nor remove temporal information in the text modality, but explicitly account for it during pre-training. Rather than treating all image-report pairs in the dataset as independent, we exploit temporal correlations by making prior images available for comparison to a given report. To learn from this structure, we develop a temporal VLP pre-training framework named BioViL-T. A core component is its new multi-image encoder that can handle the absence of prior images and potential spatial misalignment between images across time. BioViL-T takes into account prior images where available, removing cross-modal ambiguity as illustrated in Fig. 1. Linking multiple images during pre-training proves beneficial to both image and text models: we report state-of-the-art (SOTA) performance on both temporal image classification and report generation. In the latter case, we show that prefixing the prior report substantially increases performance, again reflecting the value of prior information. We emphasise that the benefit is not restricted to temporal downstream tasks: our approach also achieves SOTA on non-temporal tasks of pneumonia detection [60] and phrase grounding [10], underscoring the value of a cleaner learning signal during VLP without needing to modify or add to the training dataset. Our contributions can be summarised as follows:", + "bbox": [ + 75, + 258, + 472, + 619 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We introduce a novel pre-training framework called BioViL-T. It leverages the temporal relationship of samples to self-supervise VLP models, making commonly used biomedical VLP models (e.g., [9, 32, 81]) more applicable to a wider range of downstream tasks without compromising performance on existing benchmarks.", + "- We develop a generic multi-image encoder that handles missing image inputs and incorporates longitudinal information without requiring explicit image registration.", + "- We achieve SOTA results in chest X-ray (CXR) report generation, temporal image classification, and phrase grounding downstream benchmarks by accounting for prior context in self-supervised training and fine-tuning.", + "- We release a new multimodal benchmark dataset, MS-CXR-T, curated by an expert radiologist. It enables" + ], + "bbox": [ + 86, + 623, + 468, + 858 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "benchmarking of CXR VLP models in terms of temporal semantics extracted from image and text data.", + "bbox": [ + 522, + 90, + 890, + 121 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related work", + "text_level": 1, + "bbox": [ + 500, + 137, + 637, + 154 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Vision-language processing Self-supervised VLP can significantly reduce the need for manual labels required for the training of image encoders [19, 53]. The availability of large-scale paired image-text datasets has thus led to rapid development of general-purpose VLP models. Objectives include contrastive and discriminative image-text matching [40, 53, 69] including local variants [32, 76], auto-regressive (AR) captioning [4, 39, 77] and multi-modal masked modelling objectives [13, 40, 61].", + "bbox": [ + 496, + 162, + 893, + 301 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Biomedical vision-language processing Paired medical image-report datasets were originally used for supervised learning via (typically) automated label extraction from clinical reports [33, 63, 70]. Using such datasets, advances in general-domain self-supervised VLP have been demonstrated to benefit biomedical imaging applications [9, 32, 81]. Work has incorporated ideas from general-domain VLP such as the original CLIP-style cross-modal contrastive objective [81], multi-modal masking with merged co-attention on image-text representations [46], and adaptations to the data of the domain. For example, a radiology report may have sparse image-specific details, prompting a local modification to the contrastive loss enabling alignment between text tokens and image patches [32]. Domain-specific pre-training of the text model is shown to benefit biomedical VLP [9], and preferential masking of medical terms during masked language modelling (MLM) was explored [75]. Here we use a local loss and domain-specific pre-training of the text model, but did not find a benefit to preferential masking. Similarly, cross-attention [22] is used rather than merged co-attention for image-guided MLM.", + "bbox": [ + 496, + 311, + 893, + 630 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Longitudinal modelling of medical images While prior images are used in unimodal supervised longitudinal analysis of medical images [37,58,68,74], temporal information has not directly been employed for self-supervision. The closest work exploits patient metadata to select positive or negative examples in unimodal contrastive learning [67,79].", + "bbox": [ + 496, + 643, + 890, + 734 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Existing models typically employ either late fusion of global image representations [58,64,68,74], which can miss fine-grained localised changes [32], or explicit spatial correspondence of features, using fixed spatial grids [48] or object detection [37]. Registering image pairs is commonly used for change detection in other contexts [17,52,59], and has been applied to medical imaging [5, 23]. For CXRs however, registration entails the ill-posed problem of aligning 2D projections of 3D geometry, which inevitably results in residual misalignment. Our approach does not rely on bounding boxes or explicit graph construction as it uses", + "bbox": [ + 496, + 734, + 893, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_footnote", + "text": "In the MIMIC-CXR v2 dataset [36], around $40\\%$ of reports explicitly reference a previous image. See Appendix B for details.", + "bbox": [ + 76, + 875, + 468, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "15017", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "self-attention of visual tokens across time to handle any spatial misalignment.", + "bbox": [ + 75, + 90, + 468, + 121 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Self-supervision across time Self-supervision has found applications on densely-sampled time series data (e.g., video) to capture temporal information [30, 55, 78, 80]. Our problem setting involves sparsely and sporadically sampled data where temporal pretext tasks are less applicable [2]. Similarly, it requires text supervision to enable both static and temporal learning, when temporal structure is present.", + "bbox": [ + 75, + 128, + 467, + 233 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. BioViL-T training framework", + "text_level": 1, + "bbox": [ + 76, + 247, + 352, + 263 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our approach comprises a multi-image encoder designed to extract spatio-temporal features from sequences of images (Section 3.1) and a text encoder incorporating optional cross-attention on image features. The models are trained jointly with image-guided MLM and cross-modal global and local contrastive objectives (Section 3.2). The resulting image and text models are later adapted for uni- or multimodal downstream tasks as described in Section 3.3. Implementation details are presented in Appendices E and F.", + "bbox": [ + 75, + 272, + 467, + 407 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For a given image and report pair $(\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}},\\mathbf{x}_{\\mathrm{txt}}^{\\mathrm{curr}})$ , the report $\\mathbf{x}_{\\mathrm{txt}}^{\\mathrm{curr}}$ describes the current image content and changes in reference to prior images. Our proposed formulation focuses on a single prior image; however, it can be generalised to multiple prior images depending on the application. Hence, we construct datasets by including the prior image whenever it exists: $(\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}},\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{prior}},\\mathbf{x}_{\\mathrm{txt}}^{\\mathrm{curr}}) \\in \\mathcal{D}_m$ or $(\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}},\\varnothing ,\\mathbf{x}_{\\mathrm{txt}}^{\\mathrm{curr}}) \\in \\mathcal{D}_s$ with the resulting dataset being a union of single and multi-image examples: $\\mathcal{D} = \\mathcal{D}_m \\cup \\mathcal{D}_s$ .", + "bbox": [ + 75, + 407, + 467, + 545 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Extracting spatio-temporal image features", + "text_level": 1, + "bbox": [ + 76, + 554, + 437, + 569 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Clinical findings are often observed across different image regions and co-occur simultaneously, which requires dense level visual reasoning across time to capture both static and temporal features. In contrast to late global fusion [64] and bounding-box based approaches [37], BioViL-T leverages local correspondences between image regions across time using transformer self-attention blocks [21]. Thus our method does not require an explicit image registration step between time points.", + "bbox": [ + 75, + 577, + 467, + 712 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We propose a hybrid CNN-Transformer encoder model due to its data efficiency and spatial flexibility of cross-attention across time points: $E_{\\mathrm{img}}$ : $\\mathbb{R}^{W\\times H}\\rightarrow \\mathbb{R}^{W^{\\prime}\\times H^{\\prime}\\times D_{\\mathrm{img}}}$ (e.g., ResNet-50 [31]) and $A_{\\mathrm{img}}$ : $\\mathbb{R}^{T\\times L\\times D_{\\mathrm{img}}}\\rightarrow \\mathbb{R}^{L\\times D_{\\mathrm{img}}}$ (e.g., transformer [21]), where $W$ , $H$ , and $T$ correspond to spatiotemporal dimensions, $L = W^{\\prime}H^{\\prime}$ is the number of visual tokens per image, and $D_{\\mathrm{img}}$ is the embedding dimension. Here $E_{\\mathrm{img}}$ serves as a stem network [51] to provide visual token features of individual images. The CNN's inductive biases [24, 51] en", + "bbox": [ + 75, + 713, + 467, + 864 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "sure data efficiency of our hybrid model, making it ideal for smaller scale biomedical datasets. $E_{\\mathrm{img}}$ is initialised with BioViL weights [9]. The main purpose of $A_{\\mathrm{img}}$ is to capture patch embedding interactions across time when a prior image $\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{prior}}$ is available and to aggregate them into a fixed-length token representation. Input visual tokens, $\\mathbf{H}_0^{\\mathrm{curr}} = \\mathbf{P}^{\\mathrm{curr}} \\coloneqq E_{\\mathrm{img}}(\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}})$ , $\\mathbf{H}_0^{\\mathrm{prior}} \\coloneqq E_{\\mathrm{img}}(\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{prior}})$ are augmented with spatio-temporal positional encodings and flattened across the spatial dimensions. They are then processed by $K$ transformer encoder [66] layers $A$ as follows:", + "bbox": [ + 500, + 90, + 890, + 242 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\left[ \\begin{array}{l} \\mathbf {H} _ {k} ^ {\\text {c u r r}} \\\\ \\mathbf {H} _ {k} ^ {\\text {p r i o r}} \\end{array} \\right] = A _ {k} \\left(\\left[ \\begin{array}{l} \\mathbf {H} _ {k - 1} ^ {\\text {c u r r}} + \\mathbf {S} + \\mathbf {1} _ {L} \\otimes \\mathbf {t} ^ {\\text {c u r r}} \\\\ \\mathbf {H} _ {k - 1} ^ {\\text {p r i o r}} + \\mathbf {S} + \\mathbf {1} _ {L} \\otimes \\mathbf {t} ^ {\\text {p r i o r}} \\end{array} \\right]\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 545, + 253, + 890, + 289 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "for $k = 1,\\dots ,K$ where $\\mathbf{S}\\in \\mathbb{R}^{L\\times D_{\\mathrm{img}}}$ denotes 2D sinusoidal positional encodings [12] and $\\mathbf{T} = [\\mathbf{t}^{\\mathrm{curr}};\\mathbf{t}^{\\mathrm{prior}}]\\in \\mathbb{R}^{2\\times D_{\\mathrm{img}}}$ is its temporal counterpart, which is learnt (Fig. 2) [4]. The layer-normalised (LN) [6] output of the final transformer encoder block $\\mathbf{P}^{\\mathrm{diff}}\\coloneqq \\mathrm{LN}(\\mathbf{H}_K^{\\mathrm{curr}})$ is an 'aggregated' representation of patch-level progression information anchored on the current image. Figure 3 shows attention roll-out [1] applied to $\\mathbf{P}^{\\mathrm{diff}}$ after pre-training, showing how the prior image contributes to the fused representation. Figure A.3 further highlights the robustness to variations in pose underlining that registration is not necessary for this encoder.", + "bbox": [ + 498, + 296, + 890, + 463 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Static-temporal feature decomposition When a prior image is available the final image representation $\\mathbf{V} := \\mathbf{P}^{\\mathrm{curr}} \\oplus \\mathbf{P}^{\\mathrm{diff}} \\in \\mathbb{R}^{W' \\times H' \\times 2D_{\\mathrm{img}}}$ is formed by concatenating two sets of features (similar to [7]): those from the current image alone ( $\\mathbf{P}^{\\mathrm{curr}}$ ) and the temporal features from current and prior images ( $\\mathbf{P}^{\\mathrm{diff}}$ ). In this way, self-attention is mainly required to cope with pose variations and patch comparisons across time in extracting temporal content, removing the need for registration or explicit spatial feature alignment. When no prior scan is available ( $\\mathbf{x} \\in \\mathcal{D}_s$ ), $A_{\\mathrm{img}}$ is not used and $\\mathbf{P}^{\\mathrm{diff}}$ is replaced by a learnable token $\\mathbf{p}^{\\mathrm{miss}} \\in \\mathbb{R}^{D_{\\mathrm{img}}}$ , replicated across the spatial dimensions. Section 4.5 later demonstrates that $A_{\\mathrm{img}}$ highlights the value of feature decomposition for tasks such as phrase grounding which require well-localised features [10].", + "bbox": [ + 498, + 473, + 890, + 698 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Hereafter, downstream tasks that require solely single image features, $\\mathbf{P}^{\\mathrm{curr}}$ , are referred to as static tasks, and the ones that benefit from additional progression information, $\\mathbf{P}^{\\mathrm{diff}}$ , as temporal tasks, e.g., report decoding.", + "bbox": [ + 498, + 699, + 890, + 760 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Text-supervision for spatio-temporal learning", + "text_level": 1, + "bbox": [ + 500, + 771, + 883, + 787 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Let $\\mathbf{w} = (w_{1},\\dots ,w_{M})$ denote a vector of $M$ tokens of a report $\\mathbf{x}_{\\mathrm{txt}}$ after tokenisation. We first obtain contextualised token features $E_{\\mathrm{txt}}(\\mathbf{w})\\in \\mathbb{R}^{M\\times D_{\\mathrm{xt}}}$ by passing a sequence of text tokens $\\mathbf{w} = (w_{1},\\dots ,w_{M})$ through a BERT encoder $E_{\\mathrm{txt}}$ [20]. The input sequence is prepended with either a [CLS] or [MLM] token associated with a downstream training objective, conditioning the output features", + "bbox": [ + 498, + 794, + 890, + 898 + ], + "page_idx": 2 + }, + { + "type": "page_footnote", + "text": "2The prior report is not included during pre-training as it may further reference an earlier study, reintroducing temporal ambiguity.", + "bbox": [ + 75, + 875, + 467, + 898 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "15018", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/a750e5216a349c2950cf5f0d66ab179ac9b31e4129d44e9b34a0a01ad2cd8b41.jpg", + "image_caption": [ + "Figure 2. The proposed self-supervised VLP training framework BioViL-T: Image representations $\\mathbf{V}$ are extracted from single and multiple input scans (whenever available) using a hybrid CNN and transformer encoder [24, 51]. This design choice is to increase the data-efficiency and enable the fusion of temporal content without requiring image registration. They are later matched with their corresponding text representations obtained with CXR-BERT [9] using local [32] and global InfoNCE [49] training objectives. As an additional model supervision, multi-modal fused representations, obtained with cross-attention, are used for image-guided masked language modelling." + ], + "image_footnote": [], + "bbox": [ + 122, + 88, + 844, + 364 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "similar to [39, 42]. During training, we do two forward passes through $E_{\\mathrm{txt}}$ : once with masking at $45\\%$ probability (for the MLM objective) and once without masking for contrastive learning, as shown in Figure 2. The text encoder is initialised with the weights of CXR-BERT[9] canonical model, trained on domain-specific vocabulary and corpora.", + "bbox": [ + 75, + 472, + 468, + 564 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Both text and image features are later projected into a joint latent space with $\\phi_{\\mathrm{txt}}: \\mathbb{R}^{D_{\\mathrm{txt}}} \\to \\mathbb{R}^D$ , and similarly $\\mathbf{v}_{w,h}^{\\mathrm{proj}} := \\phi_{\\mathrm{img}}(\\mathbf{v}_{w,h})$ where $\\phi_{\\mathrm{img}}: \\mathbb{R}^{D_{\\mathrm{img}}} \\to \\mathbb{R}^D$ , with $\\phi$ being a two-layer perceptron in our experiments.", + "bbox": [ + 75, + 564, + 468, + 625 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Contrastive objectives Let $\\mathbf{r} \\coloneqq [E_{\\mathrm{xt}}(\\mathbf{w})]_{[\\mathrm{CLS}]}$ denote the global representation of $\\mathbf{w}$ , with $\\mathbf{r}^{\\mathrm{proj}} \\coloneqq \\phi_{\\mathrm{xt}}(\\mathbf{r})$ its projected version. Given projected patch embeddings $\\mathbf{v}_{w,h}^{\\mathrm{proj}}$ , we can compute a global cosine similarity $S_C(\\bar{\\mathbf{v}}^{\\mathrm{proj}}, \\mathbf{r}^{\\mathrm{proj}})$ and a local similarity using weighted pairwise cosine similarities across text tokens and projected patch embeddings [32, 76]. These similarities are used in both global and local contrastive objectives with the InfoNCE loss [49, 53]. The local loss proves crucial both for static phrase-grounding and temporal image classification (see Table 7), highlighting the importance of localised self-supervision.", + "bbox": [ + 75, + 631, + 468, + 801 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Image-guided masked language modelling Prior work [9, 46] has shown that biomedical visual-language learning benefits from an auxiliary task such as MLM since capturing the joint distribution of tokens can stabilise and improve", + "bbox": [ + 75, + 806, + 468, + 868 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "language understanding during joint learning. Given a batch $\\mathcal{B}$ of token vectors $\\mathbf{w}$ , it is often defined as the cross-entropy for predicting the randomly sampled masked tokens, $m\\subset$ $\\{1,\\dots ,M\\}$ , $\\mathcal{L}_{\\mathrm{MLM}} = -\\frac{1}{|\\mathcal{B}|}\\sum_{\\mathbf{w}\\in \\mathcal{B}}\\log p_{\\theta}(\\mathbf{w}_m|\\mathbf{w}_{\\backslash m})$ , where $\\theta$ are the weights of the text encoder $E_{\\mathrm{txt}}$ .", + "bbox": [ + 500, + 472, + 890, + 550 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In the absence of image information, however, certain masked findings and attributes are not readily predicted, e.g., \"[MASK] is worsening\". As shown in the general domain [13], visual information can help disambiguate such masked predictions and provide additional cross-modal supervision. Thus, we use cross-attention [22,66] to the image features $\\mathbf{v}_{w,h}^{\\mathrm{proj}}$ during this task. Specifically, for our image-guided MLM objective we model $p_{\\theta}(\\mathbf{w}_m|\\mathbf{w}_{\\backslash m},\\mathbf{v}_{w,h}^{\\mathrm{proj}})$ .", + "bbox": [ + 496, + 551, + 890, + 679 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Adaptations to downstream tasks", + "text_level": 1, + "bbox": [ + 500, + 686, + 792, + 703 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "BioViL-T can be adapted to various downstream tasks. For phrase-grounding and zero-shot inference, we rely on $S_{C}(\\mathbf{r}^{\\mathrm{proj}},\\mathbf{v}_{w,h}^{\\mathrm{proj}})$ similar to [9, 32]. For multiple-text prompts, projected text embeddings are marginalised prior to $\\ell_2$ -normalisation [53]. To enable language decoding, $\\mathbf{v}_{w,h}^{\\mathrm{proj}}$ inputs are cross-attended by text queries $\\mathbf{w}$ , and causal-attention is utilised between text tokens [39,66]. Differing from [9, 32, 81], we show that report generation tasks can greatly benefit from temporal joint latent space.", + "bbox": [ + 496, + 710, + 890, + 847 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Conditioning on prior reports In contrast to existing work, we incorporate the prior report as a prompt to contextualise the report generation task: $p_{\\Phi}(\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{curr}}|\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{prior}},\\mathbf{v}_{w,h}^{\\mathrm{proj}})$", + "bbox": [ + 496, + 854, + 890, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_footnote", + "text": "3https://huggingface.co/microsoft/BiomedVLP-CXR-BERT-general", + "bbox": [ + 76, + 875, + 467, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "15019", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\Phi$ are the multi-modal encoder-decoder network's weights, and $\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{curr}}$ , $\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{prior}}$ denote text tokens for current and prior reports respectively. This is analogous to fine-tuning GPT-3 [11] with prompts and instructions [71], but conditioning on both images and the previous report. A dedicated separation token [SEP] is added into the input sequence $[\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{prior}}, [\\mathrm{SEP}], \\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{curr}}]$ .", + "bbox": [ + 75, + 90, + 472, + 198 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Curation of imaging datasets CXR datasets [36] often contain multiple image acquisitions $\\mathcal{Z} = \\{\\mathbf{x}_1^{\\mathrm{img}},\\dots ,\\mathbf{x}_Z^{\\mathrm{img}}\\}$ in a single visit due to data quality issues such as a limited field-of-view or scanning the wrong body part (Figure A.4). Unlike [9, 32, 81], we conduct curation to choose higher quality images among the potential candidates instead of performing a random selection. For this step, a separate BioViL-T is trained on 'clean' studies with single acquisitions and later used in a zero-shot setting to detect out-of-distribution samples [26, 27] arising from the re-imaging process. The candidate $\\hat{z}$ is selected as follows: $\\hat{z} = \\arg \\max_{z\\in \\mathcal{Z}}S_C(\\bar{\\mathbf{v}}_z^{\\mathrm{proj}},\\mathbf{r}^{\\mathrm{proj}})$ s.t. $|s_{\\hat{z}} - s_{\\mathcal{Z}\\backslash \\hat{z}}| > \\delta$ for a margin $\\delta$ . This approach is applied to enhance the quality of the temporal classification dataset given its limited size.", + "bbox": [ + 75, + 204, + 472, + 417 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Datasets & experiments", + "text_level": 1, + "bbox": [ + 76, + 430, + 302, + 448 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Here, we demonstrate BioViL-T's data efficiency and adaptability to a wide range of applications, and show how the model achieves SOTA performance on various downstream tasks by learning from data instances linked across time, making effective use of domain priors and the available training data. Specifically, our model is evaluated on a diverse set of downstream tasks including zero- and few-shot static and temporal image classification, report generation, phrase-grounding [10], and sentence similarity.", + "bbox": [ + 75, + 455, + 468, + 592 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "MS-CXR-T benchmark We release a new multi-modal benchmark dataset, MS-CXR-T, to evaluate chest X-ray VLP models on two distinct temporal tasks: image classification and sentence similarity. The former comprises multi-image and ground-truth label pairs ( $N = 1326$ ) across 5 findings, with classes corresponding to 3 states of disease progression for each finding: {Improving, Stable, Worsening}. The latter quantifies the temporal-semantic similarity of text embeddings extracted from pairs of sentences ( $N = 361$ ). The pairs can be either paraphrases or contradictions in terms of disease progression. The data for both tasks was manually annotated and reviewed by a board certified radiologist. Appendix C provides further details on its data distribution and annotation protocol.", + "bbox": [ + 75, + 599, + 468, + 811 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets For pre-training, we use the MIMIC-CXR v2 [28, 36] chest X-ray dataset, which contains longitudinal imaging studies with corresponding radiological reports,", + "bbox": [ + 75, + 819, + 468, + 866 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/9b80633388fde47040f844eb765027e40fa2a8c1633841b7428f1329e3150816.jpg", + "image_caption": [ + "Prior image" + ], + "image_footnote": [], + "bbox": [ + 501, + 102, + 594, + 172 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/9e3374aca330c8f6ad13e42f253762665b3926bb4b465dd5d846e41330fe39a9.jpg", + "image_caption": [ + "Current image" + ], + "image_footnote": [], + "bbox": [ + 596, + 102, + 687, + 172 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/763eaa5fbed791e21a14790fb1fceab3437bc4d5211a20cc447c66bac14b8279.jpg", + "image_caption": [ + "Prior image", + "Figure 3. Attention rollout maps [1] from the reference patch (marked with $\\star$ ) to the current and prior images. The bounding boxes, annotated by a radiologist, show the extent of consolidation. Note that the reference patch attends to its anatomical neighbourhood in the prior image despite the misalignment between prior and current images. The grid $(14 \\times 14)$ represents the patch tokens processed in the transformer encoder blocks." + ], + "image_footnote": [], + "bbox": [ + 705, + 102, + 797, + 172 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/a927b1d4f69a7347ee14bb8b27fb26fe785383e7f2bbfc866cbe0f4f59540275.jpg", + "image_caption": [ + "Current image" + ], + "image_footnote": [], + "bbox": [ + 797, + 102, + 890, + 172 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "see Fig. B.1 for the distribution of studies. We only use frontal view scans and discard samples where reports do not contain an IMPRESSION section. From this data, we gather 174.1k and 4.9k text-image pairs for training and validation respectively, with a majority of pairs including a prior image: $|\\mathcal{D}_m^{\\mathrm{train}}| = 118.8k$ , $|\\mathcal{D}_s^{\\mathrm{train}}| = 55.3k$ . The text consists of the IMPRESSION section and, for MLM additionally the FINDINGS section if available. Note that no manual labels are used during pre-training and no additional data is used for the methods that leverage the link between current and prior images. For early stopping we track the validation loss, see Appendix E for implementation details.", + "bbox": [ + 496, + 310, + 890, + 491 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Downstream evaluations are performed on a disjoint held-out test set shared across all tasks, $|\\mathcal{D}^{\\mathrm{test}}| = 2971$ . For report generation, we extend this test set with samples from healthy subjects ( $N = 815$ ) to match the prevalence of pathological studies used in prior work [14, 25, 45]. For fine-tuning on temporal image classification, we use labels from the Chest ImaGenome dataset [72] as in [37] (statistics in Table F.2). In detail, we use the following benchmark datasets: (I) MS-CXR [10] for phrase grounding, (II) the RSNA Pneumonia dataset [60, 70] to test zero-shot and fine-tuned classification, (III) MS-CXR-T for temporal sentence similarity and temporal image classification.", + "bbox": [ + 496, + 493, + 892, + 675 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Comparison approaches We compare our approach to other domain-specific SOTA pre-training frameworks [9, 32] specifically on phrase-grounding and zero-shot predictive performance. The non-temporal BioViL framework [9] is most similar to our approach and provides insight into non-temporal pre-training. We additionally compare to internal ablations such as removing the past report during report generation and masking prior images during phrase grounding. For SOTA performance comparison, various AR and nearest-neighbour (NN) based language decoding approaches are used as baselines: IFCC [45], R2Gen [14], CXR-RePaiR-2 [25], and CXR-RePaiR-Select [25].", + "bbox": [ + 496, + 688, + 890, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For the temporal classification task, we compare against a baseline exploiting the BioViL image encoder [9], and an", + "bbox": [ + 500, + 869, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_footnote", + "text": "4MS-CXR-T benchmark dataset can be accessed through PhysioNet: https://aka.ms/ms-cxr-t", + "bbox": [ + 76, + 875, + 467, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "15020", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/10c2d57e71885304f52b12cc909406fbc354f36a47976aee7ca60c934bd0d242.jpg", + "table_caption": [ + "Table 1. Results for report generation task: Predictions are evaluated in terms of lexical (BLEU-4, ROUGE) and factuality metrics (CHEXBERT, TEM). Approaches are grouped into two broad categories: nearest-neighbour (NN) and auto-regressive (AR). BioViL-T pre-training consistently yields improved decoding. Further, the consistent performance gains of using prior image and report demonstrate the importance of such domain priors. 'PI / PR' indicate usage of prior image and report, respectively." + ], + "table_footnote": [], + "table_body": "
MethodPre-trainingPI / PRBLEU-4ROUGECHEXBERTTEM
NCXR-RePaiR-2 [25]BioViLX/X2.114.328.112.5
Baseline (NN) [9]BioViLX/X3.720.028.311.1
Proposed (NN)BioViL-T✓/X4.520.529.013.0
MBaseline (AR) [9]BioViLX/X7.5 ± 0.127.9 ± 0.129.3 ± 0.313.8 ± 0.1
ProposedBioViL-T✓/X8.2 ± 0.128.7 ± 0.130.2 ± 0.716.0 ± 0.3
ProposedBioViL-T✓/✓9.2 ± 0.329.6 ± 0.131.7 ± 1.017.5 ± 0.1
", + "bbox": [ + 81, + 210, + 468, + 287 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/891c369ade000421305218a06469d7da2c696807780ecf32e385b282ed96ad08.jpg", + "table_caption": [ + "Table 2. Temporal image classification results (repeated for 4 random seeds) on the MS-CXR-T benchmark for fully-supervised and zero-/few-shot (Z&F) learning settings, in terms of macroaccuracy across the three classes for each finding. Affine registration is performed for the baseline method (denoted with suffix 'w/ reg'), to partially address the pose variations across scans." + ], + "table_footnote": [], + "table_body": "
Method (% of labels)Pre-trainConsolidationPl. effusionPneumoniaPneumothoraxEdema
Z&FBioViL-T prompt (0%)Temporal53.6 ± 1.959.7 ± 2.158.0 ± 3.934.9 ± 1.064.2 ± 1.5
BioViL-T (10%)Temporal59.7 ± 2.462.4 ± 1.460.1 ± 2.135.3 ± 2.662.6 ± 1.7
SupervisedCNN + TransformerImageNet44.0 ± 2.061.3 ± 1.645.1 ± 3.531.5 ± 3.165.5 ± 1.1
CheXRelNet [37]ImageNet4747473649
BioViL [9]Static56.1 ± 1.562.3 ± 1.159.4 ± 1.041.7 ± 2.867.5 ± 0.8
BioViL w/reg [9]Static56.0 ± 1.563.0 ± 0.960.2 ± 0.742.5 ± 2.767.5 ± 0.9
BioViL-T wout curationTemporal58.9 ± 1.765.5 ± 0.761.5 ± 2.244.4 ± 2.167.4 ± 0.8
BioViL-TTemporal61.1 ± 2.467.0 ± 0.861.9 ± 1.942.6 ± 1.668.5 ± 0.8
", + "bbox": [ + 81, + 396, + 468, + 483 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "approach that makes use of graph convolutions across regions of interest extracted from bounding boxes [37]. For BioViL, we perform affine image registration (with 4 DoF) for each pair of scans to cope with pose variations, and the encoded images are concatenated along the feature dimension and classified via a multilayer perceptron. For [37], we compare to the three-class setting. Lastly, we benchmark our final text model in isolation against domain specific SOTA models in a temporal sentence similarity task: CXR-BERT [9] and PubMedBert [29].", + "bbox": [ + 75, + 510, + 468, + 662 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metrics Due to class imbalance, we report macroaccuracy for temporal image classification. For phrase grounding, we use mean Intersection-Over-Union (mIoU) and Contrast-to-Noise-Ratio (CNR) [9]. The latter measures the discrepancies between cosine similarities inside and out of the bounding box region without requiring hard thresholds. To evaluate the quality of generated reports, we use both the standard lexical metrics, e.g., BLEU [50], ROUGE-L [41], and also domain-specific factuality metric: CheXbert [62]. To directly probe the generation of change-related information, we introduce a new metric called temporal entity matching (TEM) to compute the match score of a fixed set of temporal entities (see Appendix D).", + "bbox": [ + 75, + 669, + 470, + 864 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/1ddff95b5f92d1c38179684f3b0a25554e096dbb3e5438a4be705c33069a889c.jpg", + "table_caption": [ + "Table 3. Report generation results using the same train/test splits from [25], measured by lexical (BLEU-2) and factuality (CHEXBERT) metrics. Baseline results were also collected from [25]. Note the CHEXBERT score covers all 14 observations." + ], + "table_footnote": [], + "table_body": "
MethodDecoded sectionsBLEU-2CHEXBERT
R2gen [14]Findings & Impression21.20 ± 0.1014.80 ± 0.30
IFCC [45]Findings21.70 ± 0.1027.00 ± 0.40
CXR-RePaiR-Sel [25]Impression5.00 ± 0.1027.40 ± 0.30
BioViL-TImpression15.86 ± 0.1434.83 ± 0.73
BioViL-TFindings & Impression21.31 ± 0.1935.86 ± 0.35
", + "bbox": [ + 501, + 156, + 888, + 229 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Temporal pre-training yields data efficiency", + "text_level": 1, + "bbox": [ + 498, + 257, + 870, + 273 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Downstream tasks are enabled with minimal labels.", + "text_level": 1, + "bbox": [ + 504, + 282, + 846, + 297 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The sections 'NN' and 'Z&F' on Tables 1 and 2 report zero- and few-shot performance on tasks benefitting from temporal information: temporal image classification and report generation. Here we measure the quality of the learnt joint latent space and the extent to which BioViL-T enables efficient use of raw data. For zero-shot classification we prompt the AR fine-tuned model with prefix: \"[FINDING] is\" and compare the next-token probability of words meaning 'improving', 'stable', and 'worsening' (Appendix F.4).", + "bbox": [ + 496, + 305, + 890, + 441 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Without using any labelled data, Table 2 shows that the proposed AR-based approach already yields performance superior to prior fully-supervised work [37] on temporal image classification. With only $10\\%$ of labels, classification fine-tuning provides a further boost, indicating that BioViL-T produces a multi-image encoder readily adapted to temporal tasks. Similarly, in a zero-shot report-retrieval setting, the findings show that compared to temporally-agnostic pre-training, BioViL-T leveraging prior images improves across all metrics. Consistent with prior work [25], the retrieved reports already preserve factuality with high CheXbert scores, more-so than the other metrics which measure fine-grained specifics of phrasing. This demonstrates that the latent space captures the high-level semantics of the clinical features. Fine-grained phrasing however will be substantially improved by AR fine-tuning.", + "bbox": [ + 496, + 443, + 892, + 685 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Achieving SOTA performance with BioViL-T", + "text_level": 1, + "bbox": [ + 500, + 696, + 880, + 713 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "A wide range of downstream tasks benefit substantially from temporally-aware pre-training.", + "text_level": 1, + "bbox": [ + 503, + 723, + 885, + 753 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Through downstream adaptations and fine-tuning our model, we report SOTA performance on report generation and temporal image classification tasks. For the former, using both prior images and reports during fine-tuning substantially improves across metrics (Table 1). In particular, TEM metric results show that temporal context is key for accurately describing change in the generated report while avoiding hallucinations (see Table A.1 for examples). Comparing to published results on a comparable test split and", + "bbox": [ + 496, + 763, + 890, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_footnote", + "text": "5The average of the weighted- $F_{1}$ score across 14 pathological observations labelled by CheXbert.", + "bbox": [ + 75, + 875, + 468, + 898 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "15021", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/8078d773067eab756f7f2c4b1b21133845a78fe38da3e56ee013574216bf59b2.jpg", + "table_caption": [ + "Table 4. Image classification results on RSNA Pneumonia Detection Benchmark [60] for train and test splits of $70\\% -30\\%$ respectively." + ], + "table_footnote": [], + "table_body": "
Method% of LabelsSupervisionAcc.F1AUROC
GLoRIA [32]XZero-shot0.700.58-
BioViL [9]XZero-shot0.7320.6650.831
BioViL-TXZero-shot0.8050.7060.871
BioViL [9]1%Few-shot0.8050.7230.881
BioViL-T1%Few-shot0.8140.7300.890
", + "bbox": [ + 76, + 126, + 452, + 210 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "metrics (Sec. 4.1), we conclude that BioViL-T with fine-tuning achieves SOTA on report generation, producing reports that are lexically on par with prior work but substantially more factually accurate. Note that we do 'vanilla' AR fine-tuning to focus on the impact of the pre-trained encoders, so application-specific supervision [45] could be used in conjunction to further boost performance.", + "bbox": [ + 75, + 233, + 467, + 338 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In temporal image classification (Tab. 2), BioViL-T pretraining outperforms the non-temporal baseline (BioViL) and improves on previously-reported results [37] by up to 20 percentage points (pp). Furthermore, baseline methods that rely on image registration (BioViL w/reg), underperform compared to the proposed approach. Further analysis reveals that errors tend to be in cases with disagreement between radiologists (Appendix A.2). We also note that pretraining is critical for a hybrid CNN-transformer model on this task, likely due to the small labelled dataset. Lastly, curation of temporal training data is observed to improve the classification results by .68 pp aggregated across the findings, see Appendix A.4 for details.", + "bbox": [ + 75, + 339, + 467, + 535 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Static tasks benefit from temporal learning", + "text_level": 1, + "bbox": [ + 76, + 544, + 441, + 560 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "BioViL-T broadens the range of applicable downstream tasks whilst contributing to performance on static tasks.", + "bbox": [ + 81, + 569, + 464, + 599 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this section, we demonstrate that performance improvements afforded by BioViL-T are not restricted to temporal tasks – static tasks also benefit. Table 4 reports results on zero- and few-shot pneumonia classification from single images [60], where BioViL-T establishes a new SOTA compared to prior work [9,32].", + "bbox": [ + 75, + 606, + 467, + 696 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We see a similar trend on the MS-CXR phrase grounding benchmark (Tab. 5). This task can be solved with single images, however we show that the inclusion of the prior image (where available) does not impair the performance of BioViL-T. Feature decomposition effectively preserves localised information from the current image.", + "bbox": [ + 75, + 696, + 467, + 787 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Towards better sentence embedding quality", + "text_level": 1, + "bbox": [ + 76, + 792, + 444, + 809 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Language models acquire increased temporal sensitivity.", + "bbox": [ + 81, + 818, + 457, + 834 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We hypothesise that text encoders learn temporal semantics through supervision from longitudinal image series. To verify this, RadNLI [45] and MS-CXR-T datasets are used in a zero-shot binary classification setting. Cosine similarity", + "bbox": [ + 75, + 839, + 467, + 901 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/1e2e8266fde5f1e7abbb33cfe4e469354365225cee301e5c91aed038710adbf9.jpg", + "table_caption": [ + "Table 5. Results on MS-CXR benchmark [10] (5-runs with different seeds), \"Multi-image\" column indicates the input images used at test time." + ], + "table_footnote": [], + "table_body": "
MethodMulti-ImageAvg. CNRAvg. mIoU
BioViL [9]X1.07 ± 0.040.229 ± 0.005
+ Local loss [9,32]X1.21 ± 0.050.202 ± 0.010
BioViL-TX1.33 ± 0.040.243 ± 0.005
BioViL-T✓1.32 ± 0.040.240 ± 0.005
", + "bbox": [ + 503, + 126, + 887, + 205 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/ba62ad8b68ae9ec45490ffd3c88d07387c1514b9ff9074d52824ad7d86663498.jpg", + "table_caption": [ + "Table 6. Results on MS-CXR-T sentence similarity benchmark." + ], + "table_footnote": [], + "table_body": "
Text ModelMS-CXR-T (361 pairs)RadNLI (145 pairs)
AccuracyROC-AUCAccuracyROC-AUC
PubMedBERT [29]60.39.54281.38.727
CXR-BERT-G [9]62.60.60187.59.902
CXR-BERT-S [9]78.12.83789.66.932
BioViL-T87.77 ± 0.5.933 ± .00390.52 ± 1.0.947 ± .003
", + "bbox": [ + 503, + 244, + 890, + 328 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "of sentence pair embeddings [56] are treated as class-logits to label each pair either as paraphrase or contradiction. See Appendix F.6 for further details.", + "bbox": [ + 498, + 354, + 890, + 398 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Our text model is benchmarked against SOTA domain-specific BERT models. Table 6 shows that the proposed framework greatly increases the sensitivity of sentence embeddings to temporal content whilst better capturing the static content (RadNLI). Note that CXR-BERT-Specialised [9] is learnt through single-images starting from the same canonical model, illustrating the substantial increase in temporal and static sensitivity due to BioViL-T pre-training.", + "bbox": [ + 496, + 400, + 890, + 521 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5. Ablation experiments", + "text_level": 1, + "bbox": [ + 500, + 530, + 700, + 546 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In Table 7 we report extensive ablations across the multi-image encoder architecture, pre-training choices, and AR fine-tuning for report generation.", + "bbox": [ + 498, + 553, + 890, + 599 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Image encoder Table 7 shows that decomposition of static and progression features is essential to ensure good performance on single-image tasks, such as phrase grounding. For temporal representations, on the other hand, positional encodings (T) are essential to disambiguate the order of scans, i.e., permutation variance across time.", + "bbox": [ + 496, + 607, + 890, + 699 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Model pre-training The corresponding results are shown in the middle section of Table 7. The local contrastive loss proves crucial to ensure meaningful language supervision during pre-training, followed by the image-guided MLM objective. Lastly, use of the FINDINGS section results in only minor performance gains as the key findings are already captured in the IMPRESSION section.", + "bbox": [ + 496, + 710, + 890, + 816 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Report generation The importance of prior image and report is demonstrated by the substantial drop in the \"no prior image and report\" ablation, confirming our hypothesis that temporal context is crucial for improving report quality. While both inputs are crucial for optimal performance,", + "bbox": [ + 496, + 824, + 890, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "15022", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/faaa48f943a5f3bbf182c607a3fa978e55c7ed4aba4c8ae36a28b6a3c14f8bd3.jpg", + "table_caption": [ + "Table 7. Ablation study on image encoder, pre-training settings, and report generation (one component at a time, and repeated for 4 random seeds). Note that for temporal classification, linear probing is applied to frozen image embeddings. In report generation, the baseline method is fine-tuned with both prior image and report." + ], + "table_footnote": [], + "table_body": "
AblationAvg. CNR (mIoU)Pl. Effusion Acc.
EncoderBaseline1.33 ± 0.02 (.248)64.8 ± 0.6
- Temporal pos. encoding1.32 ± 0.02 (.242)62.9 ± 1.0
- Feature decomposition1.11 ± 0.08 (.203)64.0 ± 0.6
Pre-trainingBaseline1.33 ± 0.02 (.248)64.8 ± 0.6
- Use of findings section1.32 ± 0.01 (.246)63.8 ± 0.8
- MLM loss1.28 ± 0.02 (.238)63.2 ± 0.7
- Local contrastive loss1.18 ± 0.02 (.236)60.2 ± 0.6
AblationROUGETEM
Report gen.Baseline29.64 ± 0.0817.54 ± 0.11
- Prior image29.35 ± 0.2516.30 ± 0.40
- Prior report28.67 ± 0.1216.00 ± 0.30
-(Prior image and report)27.78 ± 0.0913.65 ± 0.48
- Separation token26.00 ± 0.4015.50 ± 1.06
", + "bbox": [ + 78, + 169, + 470, + 353 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "the prior report is more so because it summarises the image and provides a clearer signal. The prior image however cannot be dismissed entirely as it provides granular details which may not always be documented in a report. Finally, we found the separation token is crucial in differentiating between the predicted tokens for the current report and tokens from the prior report.", + "bbox": [ + 75, + 375, + 468, + 479 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.6. Which tokens require a prior image in MLM?", + "text_level": 1, + "bbox": [ + 76, + 487, + 465, + 503 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We leverage the MLM objective in an inference setting to analyse the influence of prior images in predicting masked tokens. Inspired by the $\\Delta$ image loss of [8], we define $\\Delta_{\\mathrm{img}}^{\\mathrm{prior}}$ as the change in loss by conditioning the estimation with a prior image for a given token $w$ as follows:", + "bbox": [ + 76, + 511, + 468, + 585 + ], + "page_idx": 7 + }, + { + "type": "equation", + "text": "\n$$\n\\Delta_ {\\mathrm {i m g}} ^ {\\text {p r i o r}} (w) = l (w, \\mathbf {x} _ {\\mathrm {i m g}} ^ {\\text {c u r r}}, \\emptyset) - l (w, \\mathbf {x} _ {\\mathrm {i m g}} ^ {\\text {c u r r}}, \\mathbf {x} _ {\\mathrm {i m g}} ^ {\\text {p r i o r}}) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 104, + 592, + 468, + 613 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "where $l(w, \\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}}, \\mathbf{x}_{\\mathrm{img}}^{\\mathrm{prior}})$ is the cross-entropy of predicting the masked token $w$ given visual features (MLM loss for a single token), averaged over sentences in which $w$ appears. $\\Delta_{\\mathrm{img}}^{\\mathrm{prior}}$ is a measure of how much that token benefits from access to the prior image, as well as an assessment of the contribution of the prior image to the image representation. In Figure 4 we show the distribution of $\\Delta_{\\mathrm{img}}^{\\mathrm{prior}}$ as a function of token category (e.g., Anatomy, Positional; see F.5 for annotation details). For Progression-type terms in particular, the model heavily relies on the prior image for image-guided MLM. We further observe that this effect is specific to temporal tokens; as expected, those from other semantic categories do not consistently rely on the prior image.", + "bbox": [ + 75, + 619, + 468, + 818 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 76, + 829, + 194, + 845 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we introduced BioViL-T, a vision-language pre-training framework enabling alignment between text and multiple images. BioViL-T makes use of", + "bbox": [ + 75, + 854, + 470, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/1774f3c5ed4a9edaa21125a7a8ec112dc540c51122453ce33b74887b58a753a1.jpg", + "image_caption": [ + "Figure 4. Mean token-level increase in image-guided MLM loss when prior image is discarded, grouped by token category. The prior image is excluded during inference to measure its impact on masked token predictions. Progression tokens are significantly better predicted when prior images are incorporated into image embeddings. The top five Progression tokens are 'persist', 'improving', 'remains', 'unchanged', and 'residual'." + ], + "image_footnote": [], + "bbox": [ + 501, + 87, + 893, + 284 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "a novel multi-image encoder and explicitly decomposes static-temporal features to augment the current image representation with information from prior images. This enables the grounding of temporal references in the text. To our knowledge, this is the first method capable of leveraging the temporal content commonly present in biomedical text. It addresses an important limitation in existing VLP approaches, which simply discard such context. Also, incorporating such multi-modal temporal content provides strong learning signals to the model, resulting in richer representations and improved downstream performance.", + "bbox": [ + 496, + 417, + 890, + 583 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We demonstrate the value of this paradigm through extensive experiments: BioViL-T excels on both static and temporal tasks, establishing new SOTA on report generation, temporal image classification, few/zero-shot pneumonia detection, and phrase grounding. Furthermore, we release a new multi-modal benchmark (MS-CXR-T) to measure the quality of image and text representations in terms of temporal semantics, enabling more diverse evaluation of biomedical VLP models. The corresponding model weights $^6$ and code $^7$ are publicly available.", + "bbox": [ + 496, + 585, + 892, + 736 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Further exploration and evaluation are required on diverse datasets to characterise what kinds of tasks would benefit from a temporal modelling approach, and specifically from the proposed methodology.", + "bbox": [ + 496, + 737, + 890, + 797 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements: We would like to thank Hannah Richardson, Hoifung Poon, Melanie Bernhardt, Melissa Bristow and Naoto Usuyama for their valuable feedback.", + "bbox": [ + 498, + 813, + 890, + 859 + ], + "page_idx": 7 + }, + { + "type": "page_footnote", + "text": "6Models can be found at: https://aka.ms/biovil-t-model", + "bbox": [ + 514, + 875, + 885, + 886 + ], + "page_idx": 7 + }, + { + "type": "page_footnote", + "text": "7Code can be found at: https://aka.ms/biovil-t-code", + "bbox": [ + 519, + 887, + 867, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "15023", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Samira Abnar and Willem Zuidema. Quantifying attention flow in transformers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4190-4197, Online, July 2020. Association for Computational Linguistics. 3, 5, 13, 14", + "[2] Monica N Agrawal, Hunter Lang, Michael Offin, Lior Gazit, and David Sontag. Leveraging time irreversibility with order-contrastive pre-training. In International Conference on Artificial Intelligence and Statistics, pages 2330–2353. PMLR, 2022. 3", + "[3] Uwa O. Aideyan, Kevin Berbaum, and Wilbur L. Smith. Influence of prior radiologic information on the interpretation of radiographic examinations. Academic Radiology, 2(3):205-208, 1995. 1", + "[4] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning. arXiv preprint arXiv:2204.14198, 2022. 2, 3", + "[5] B.B. Avants, C.L. Epstein, M. Grossman, and J.C. Gee. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1):26-41, 2008. Special Issue on The Third International Workshop on Biomedical Image Registration - WBIR 2006. 2", + "[6] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016.3", + "[7] Nadine Behrmann, Mohsen Fayyaz, Juergen Gall, and Mehdi Noroozi. Long short view feature decomposition via contrastive video representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9244-9253, 2021. 3", + "[8] Yonatan Bitton, Gabriel Stanovsky, Michael Elhadad, and Roy Schwartz. Data efficient masked language modeling for vision and language. In 2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021, pages 3013-3028. Association for Computational Linguistics (ACL), 2021. 8", + "[9] Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, and Ozan Oktay. Making the most of text semantics to improve biomedical vision-language processing. In European Conference on Computer Vision (ECCV), pages 1–21. Springer, 2022. 1, 2, 3, 4, 5, 6, 7, 17, 18, 19, 20", + "[10] Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, and Ozan Oktay. MS-CXR: Making the most of text semantics to improve biomedical vision-language processing (version 0.1). PhysioNet, 2022. 2, 3, 5, 7, 15", + "[11] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakan-" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "tan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020. 5, 19", + "[12] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 3", + "[13] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. UNITER: UNiversal Image-TExt Representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 2, 4", + "[14] Zhihong Chen, Yan Song, Tsung-Hui Chang, and Xiang Wan. Generating radiology reports via memory-driven transformer. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Nov. 2020. 5, 6", + "[15] Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, and Yale Song. Robust contrastive learning against noisy views. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16670-16681, 2022. 1", + "[16] Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka. Debiased contrastive learning. Advances in neural information processing systems, 33:8765-8775, 2020. 1", + "[17] R. C. Daudt, B. L. Saux, and Alexandre Boulch. Fully convolutional siamese networks for change detection. In 2018 25th IEEE International Conference on Image Processing (ICIP), pages 4063-4067, 2018. 2", + "[18] Dina Demner-Fushman, Marc D Kohli, Marc B Rosenman, Sonya E Shooshan, Laritza Rodriguez, Sameer Antani, George R Thoma, and Clement J McDonald. Preparing a collection of radiology examinations for distribution and retrieval. Journal of the American Medical Informatics Association, 23(2):304-310, 2016. 15", + "[19] Karan Desai and Justin Johnson. Virtex: Learning visual representations from textual annotations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11162-11173, 2021. 1, 2", + "[20] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 3", + "[21] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2020. 3, 13", + "[22] Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, et al. An empirical study of training end-to-end vision-and-language transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18166-18176, 2022. 2, 4", + "[23] Stanley Durrleman, Xavier Pennec, Alain Trouve, José Braga, Guido Gerig, and Nicholas Ayache. Toward a com" + ], + "bbox": [ + 503, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "15024", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "prehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision, 103:22-59, 2013. 2", + "[24] Stéphane d'Ascoli, Hugo Touvron, Matthew L Leavitt, Ari S Morcos, Giulio Biroli, and Levent Sagun. Convit: Improving vision transformers with soft convolutional inductive biases. In International Conference on Machine Learning, pages 2286-2296. PMLR, 2021. 3, 4", + "[25] Mark Endo, Rayan Krishnan, Viswesh Krishna, Andrew Y Ng, and Pranav Rajpurkar. Retrieval-based chest x-ray report generation using a pre-trained contrastive language-image model. In Machine Learning for Health, pages 209-219. PMLR, 2021. 5, 6, 18", + "[26] Sepideh Esmaeilpour, Bing Liu, Eric Robertson, and Lei Shu. Zero-shot out-of-distribution detection based on the pretrained model clip. In Proceedings of the AAAI conference on artificial intelligence, 2022. 5", + "[27] Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan. Exploring the limits of out-of-distribution detection. Advances in Neural Information Processing Systems, 34:7068-7081, 2021. 5", + "[28] Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23):e215-e220, 2000. 5", + "[29] Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare (HEALTH), 3(1):1-23, 2021. 6, 7, 20", + "[30] Tengda Han, Weidi Xie, and Andrew Zisserman. Self-supervised co-training for video representation learning. Advances in Neural Information Processing Systems, 33:5679-5690, 2020. 3", + "[31] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016. 3", + "[32] Shih-Cheng Huang, Liyue Shen, Matthew P Lungren, and Serena Yeung. GLoRIA: A multimodal global-local representation learning framework for label-efficient medical image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3942-3951, 2021. 1, 2, 4, 5, 7", + "[33] Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilicus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al. CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the AAAI conference on artificial intelligence, 33(01):590-597, 2019. 2", + "[34] Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven Truong, Tan Bui, Pierre Chambon, Yuhao Zhang, Matthew P Lungren, Andrew Y Ng, Curtis Langlotz, et al. Radgraph: Extracting clinical entities and relations from radiology re" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ports. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021. 17", + "[35] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 13", + "[36] A. Johnson, T. Pollard, S.J. Berkowitz, R. Mark, and S. Horng. MIMIC-CXR database (version 2.0.0). PhysioNet, 2019. 2, 5", + "[37] Gaurang Karwande, Amarachi B Mbakwe, Joy T Wu, Leo A Celi, Mehdi Moradi, and Ismini Lourentzou. Chexrelnet: An anatomy-aware model for tracking longitudinal relationships between chest x-rays. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2022, pages 581-591, 2022. 2, 3, 5, 6, 7", + "[38] Jiann-Shu Lee, Jing-Wein Wang, Hsing-Hsien Wu, and Ming-Zheng Yuan. A nonparametric-based rib suppression method for chest radiographs. Computers & Mathematics with Applications, 64(5):1390-1399, 2012. 20", + "[39] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 12888-12900. PMLR, 17-23 Jul 2022. 2, 4", + "[40] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems, 34:9694-9705, 2021. 1, 2", + "[41] Chin-Yew Lin. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74-81, Barcelona, Spain, July 2004. Association for Computational Linguistics. 6", + "[42] Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. Generating wikipedia by summarizing long sequences. In International Conference on Learning Representations, 2018. 4", + "[43] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018. 17, 19", + "[44] Bradley C Lowekamp, David T Chen, Luis Ibanez, and Daniel Blezek. The design of simpleitk. Frontiers in neuroinformatics, 7:45, 2013. 20", + "[45] Yasuhide Miura, Yuhao Zhang, Emily Tsai, Curtis Langlotz, and Dan Jurafsky. Improving factual completeness and consistency of image-to-text radiology report generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5288-5304, Online, June 2021. Association for Computational Linguistics. 5, 6, 7, 18" + ], + "bbox": [ + 501, + 92, + 893, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "15025", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[46] Jong Hak Moon, Hyungyung Lee, Woncheol Shin, Young-Hak Kim, and Edward Choi. Multi-modal understanding and generation for medical images and text via vision-language pre-training. IEEE Journal of Biomedical and Health Informatics, 2022. 2, 4", + "[47] American College of Radiology (ACR). ACR practice guideline for communication of diagnostic imaging findings. Practice guidelines & technical standards, 2020. 1", + "[48] Dong Yul Oh, Jihang Kim, and Kyong Joon Lee. Longitudinal change detection on chest X-rays using geometric correlation maps. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 748-756. Springer, 2019. 2", + "[49] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 1, 4", + "[50] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311-318, Philadelphia, Pennsylvania, USA, July 2002. Association for Computational Linguistics. 6", + "[51] Namuk Park and Songkuk Kim. How do vision transformers work? In International Conference on Learning Representations, 2022. 3, 4", + "[52] Daifeng Peng, Yongjun Zhang, and Haiyan Guan. End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sensing, 11(11):1382, 2019. 2", + "[53] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 4", + "[54] Vignav Ramesh, Nathan Andrew Chi, and Pranav Rajpurkar. Improving radiology report generation systems by removing hallucinated references to non-existent priors. arXiv preprint arXiv:2210.06340, 2022. 2", + "[55] Adria Recasens, Pauline Luc, Jean-Baptiste Alayrac, Luyu Wang, Florian Strub, Corentin Tallec, Mateusz Malinowski, Viorica Pătrăcean, Florent Altché, Michal Valko, et al. Broaden your views for self-supervised video learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1255–1265, 2021. 3", + "[56] Nils Reimers, Iryna Gurevych, Nils Reimers, Iryna Gurevych, Nandan Thakur, Nils Reimers, Johannes Daxenberger, Iryna Gurevych, Nils Reimers, Iryna Gurevych, et al. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, pages 671-688. Association for Computational Linguistics, 2019. 7, 20", + "[57] Liqa A Rousan, Eyhab Elobeid, Musaab Karrar, and Yousef Khader. Chest x-ray findings and temporal lung changes in patients with Covid-19 pneumonia. BMC Pulmonary Medicine, 20(1):1-9, 2020. 1" + ], + "bbox": [ + 78, + 90, + 470, + 898 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[58] Ruggiero Santeramo, Samuel Joseph Withey, and G. Montana. Longitudinal detection of radiological abnormalities with time-modulated LSTM. In MICCAI 2018 Workshop on Deep Learning in Medical Imaging Analysis, 2018. 2", + "[59] Wenzhong Shi, Min Zhang, Rui Zhang, Shanxiong Chen, and Zhao Zhan. Change detection based on artificial intelligence: State-of-the-art and challenges. Remote Sensing, 12(10):1688, 2020. 2", + "[60] George Shih, Carol C Wu, Safwan S Halabi, Marc D Kohli, Luciano M Prevedello, Tessa S Cook, Arjun Sharma, Judith K Amorosa, Veronica Arteaga, Maya Galperin-Aizenberg, et al. Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia. Radiology: Artificial Intelligence, 1(1):e180041, 2019. 2, 5, 7", + "[61] Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela. Flava: A foundational language and vision alignment model. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15617-15629, 2022. 2", + "[62] Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Ng, and Matthew Lungren. Combining automatic labelers and expert annotations for accurate radiology report labeling using BERT. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1500–1519, Online, Nov. 2020. Association for Computational Linguistics. 6", + "[63] Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Y Ng, and Matthew P Lungren. CheXbert: combining automatic labelers and expert annotations for accurate radiology report labeling using bert. arXiv preprint arXiv:2004.09167, 2020. 2, 16", + "[64] Anuroop Sriram, Matthew Muckley, Koustuv Sinha, F. Shamout, Joelle Pineau, K. Geras, L. Azour, Y. Aphinyanaphongs, N. Yakubova, and William H. Moore. COVID-19 prognosis via self-supervised representation learning and multi-image prediction. arXiv preprint arXiv:2101.04909, 2021. 2, 3", + "[65] Philippe Thévenaz and Michael Unser. Optimization of mutual information for multiresolution image registration. IEEE transactions on image processing, 9(12):2083-2099, 2000. 20", + "[66] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3, 4", + "[67] Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar, Can Liu, Andrew Y Ng, and Pranav Rajpurkar. Medaug: Contrastive learning leveraging patient metadata improves representations for chest x-ray interpretation. In Machine Learning for Healthcare Conference, pages 755-769. PMLR, 2021. 2", + "[68] Chuang Wang, Andreas Rimner, Yu chi Hu, Neelam Tyagi, Jue Jiang, Ellen Yorke, Sadegh Riyadhi, Gig S. Mageras, Joseph O. Deasy, and Pengpeng Zhang. Towards predicting the evolution of lung tumors during radiotherapy observed" + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "15026", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "on a longitudinal MR imaging study via a deep learning algorithm. Medical Physics, 2019. 2", + "[69] Wenhui Wang, Hangbo Bao, Li Dong, and Furu Wei. Vlmo: Unified vision-language pre-training with mixture-of-modality-experts. arXiv preprint arXiv:2111.02358, 2021. 2", + "[70] Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers. ChestX-Ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 2097-2106. IEEE Computer Society, 2017. 2, 5", + "[71] Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. Finetuned language models are zero-shot learners. In International Conference on Learning Representations, 2021. 5", + "[72] Joy Wu, Nkechinyere Agu, Ismini Lourentzou, Arjun Sharma, Joseph Paguio, Jasper Seth Yao, Edward Christopher Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo Anthony Celi, Tanveer Syeda-Mahmood, and Mehdi Moradi. Chest imagenome dataset (version 1.0.0). PhysioNet, 2021. 5, 15, 18", + "[73] Kan Wu, Houwen Peng, Minghao Chen, Jianlong Fu, and Hongyang Chao. Rethinking and improving relative position encoding for vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10033-10041, 2021. 19", + "[74] Yiwen Xu, Ahmed Hosny, Roman Zeleznik, Chintan Parmar, Thibaud P. Coroller, Idalid Ivy Franco, Raymond H. Mak, and Hugo J.W.L. Aerts. Deep learning predicts lung cancer treatment response from serial medical imaging. Clinical Cancer Research, 25:3266 - 3275, 2019. 2", + "[75] Bin Yan and Mingtao Pei. Clinical-BERT: Vision-language pre-training for radiograph diagnosis and reports generation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(3):2982-2990, 2022. 2", + "[76] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. Filip: Fine-grained interactive language-image pre-training. arXiv preprint arXiv:2111.07783, 2021. 2, 4", + "[77] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917, 2022. 1, 2", + "[78] Sukmin Yun, Jaehyung Kim, Dongyoon Han, Hwanjun Song, Jung-Woo Ha, and Jinwoo Shin. Time is matter: Temporal self-supervision for video transformers. In International Conference on Machine Learning, pages 25804-25816. PMLR, 2022. 3", + "[79] Dewen Zeng, John N Kheir, Peng Zeng, and Yiyu Shi. Contrastive learning with temporal correlated medical images: A case study using lung segmentation in chest x-rays. In 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), pages 1-7. IEEE, 2021. 2", + "[80] Zhaoyang Zeng, Daniel McDuff, Yale Song, et al. Contrastive learning of global and local video representations" + ], + "bbox": [ + 78, + 92, + 468, + 900 + ], + "page_idx": 11 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "tions. Advances in Neural Information Processing Systems, 34:7025-7040, 2021. 3", + "[81] Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D Manning, and Curtis P Langlotz. Contrastive learning of medical visual representations from paired images and text. arXiv preprint arXiv:2010.00747, 2020. 1, 2, 4, 5", + "[82] Yuhao Zhang, Yuhui Zhang, Peng Qi, Christopher D Manning, and Curtis P Langlotz. Biomedical and clinical english model packages for the stanza python nlp library. Journal of the American Medical Informatics Association, 28(9):1892-1899, 2021. 16, 17, 18" + ], + "bbox": [ + 503, + 92, + 890, + 246 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "15027", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/2023/Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing/fc7c021d-c682-4b4a-b7f3-6c9ca632109a_model.json b/2023/Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing/fc7c021d-c682-4b4a-b7f3-6c9ca632109a_model.json new file mode 100644 index 0000000000000000000000000000000000000000..71299bc2659e29faaa75522af94449d79a235307 --- /dev/null +++ b/2023/Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing/fc7c021d-c682-4b4a-b7f3-6c9ca632109a_model.json @@ -0,0 +1,2743 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.0, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.265, + 0.131, + 0.709, + 0.177 + ], + "angle": 0, + "content": "Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing" + }, + { + "type": "text", + "bbox": [ + 0.182, + 0.206, + 0.788, + 0.277 + ], + "angle": 0, + "content": "Shruthi Bannur,\\* Stephanie Hyland\\*, Qianchu Liu, Fernando Pérez-García, Maximilian Ilse, Daniel C. Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anja Thieme, Anton Schwaighofer, Maria Wetscherek, Matthew P. Lungren, Aditya Nori Javier Alvarez-Valle, and Ozan Oktay†" + }, + { + "type": "text", + "bbox": [ + 0.4, + 0.284, + 0.571, + 0.298 + ], + "angle": 0, + "content": "Microsoft Health Futures" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.334, + 0.314, + 0.35 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.367, + 0.474, + 0.745 + ], + "angle": 0, + "content": "Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.776, + 0.21, + 0.791 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.802, + 0.47, + 0.864 + ], + "angle": 0, + "content": "Self-supervision from image-text pairs has enabled the development of flexible general-purpose vision-language models both in the general domain [40, 53, 77] and for specialised domains such as biomedicine and radiology" + }, + { + "type": "image_caption", + "bbox": [ + 0.558, + 0.34, + 0.658, + 0.352 + ], + "angle": 0, + "content": "Existing methods" + }, + { + "type": "image", + "bbox": [ + 0.508, + 0.355, + 0.675, + 0.452 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.734, + 0.34, + 0.836, + 0.352 + ], + "angle": 0, + "content": "Proposed method" + }, + { + "type": "image", + "bbox": [ + 0.686, + 0.356, + 0.885, + 0.453 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.509, + 0.455, + 0.622, + 0.467 + ], + "angle": 0, + "content": "(b) Spatial modelling" + }, + { + "type": "image", + "bbox": [ + 0.541, + 0.469, + 0.843, + 0.564 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.578, + 0.895, + 0.718 + ], + "angle": 0, + "content": "Figure 1. (a) Existing visual-language pre-training approaches [9, 32, 81] often use only a single image for contrastive learning (e.g., InfoNCE [49]). (b) In such settings, discarding the temporal connectivity of images limits the alignment of image-text pairs as shown with the affinity matrix, leading to suboptimal pre-training and missed opportunity to create additional model supervision for free. (c, d) Our approach exploits this domain knowledge by learning to incorporate a series of images and correlate them to reports, leading to pre-trained models that can generalise to a wider range of downstream tasks whilst achieving SOTA performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.758, + 0.894, + 0.85 + ], + "angle": 0, + "content": "[9, 32, 81]. Vision-language processing (VLP) has shown that cross-modal supervision can provide a richer signal for training both image [19] and text [9] models. However, the success of VLP relies on paired samples sharing semantics, i.e., given an image and text pair, the text should describe the image with minimal extraneous detail [15, 16, 35]." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.894, + 0.903 + ], + "angle": 0, + "content": "In this regard, VLP in biomedicine and radiology poses a distinctive challenge, as reports routinely include comparisons to prior imaging studies [3, 47, 57]. Without knowledge of the underlying mechanisms of these comparisons, it is difficult to determine whether the results are consistent with those obtained by other methods." + }, + { + "type": "page_footnote", + "bbox": [ + 0.094, + 0.875, + 0.286, + 0.888 + ], + "angle": 0, + "content": "* These authors contributed equally." + }, + { + "type": "page_footnote", + "bbox": [ + 0.097, + 0.888, + 0.416, + 0.9 + ], + "angle": 0, + "content": "†Corresponding author: ozan.oktay@microsoft.com" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.875, + 0.416, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15016" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.091, + 0.473, + 0.258 + ], + "angle": 0, + "content": "edge of this prior image1, temporal information in the text modality, e.g. \"Pneumonia is improving\", could pertain to any image containing \"Pneumonia\", producing ambiguity during contrastive training (Figure 1). Despite this, the existing VLP work to date considers alignment between only single images and reports [9,32,46,81], going so far as to remove temporal content from reports in training data to prevent 'hallucinations' in downstream report generation [54]. However, temporal information can provide complementary self-supervision, solely by exploiting existing structure, and without requiring any additional data." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.26, + 0.473, + 0.621 + ], + "angle": 0, + "content": "In this work, we neither ignore nor remove temporal information in the text modality, but explicitly account for it during pre-training. Rather than treating all image-report pairs in the dataset as independent, we exploit temporal correlations by making prior images available for comparison to a given report. To learn from this structure, we develop a temporal VLP pre-training framework named BioViL-T. A core component is its new multi-image encoder that can handle the absence of prior images and potential spatial misalignment between images across time. BioViL-T takes into account prior images where available, removing cross-modal ambiguity as illustrated in Fig. 1. Linking multiple images during pre-training proves beneficial to both image and text models: we report state-of-the-art (SOTA) performance on both temporal image classification and report generation. In the latter case, we show that prefixing the prior report substantially increases performance, again reflecting the value of prior information. We emphasise that the benefit is not restricted to temporal downstream tasks: our approach also achieves SOTA on non-temporal tasks of pneumonia detection [60] and phrase grounding [10], underscoring the value of a cleaner learning signal during VLP without needing to modify or add to the training dataset. Our contributions can be summarised as follows:" + }, + { + "type": "text", + "bbox": [ + 0.087, + 0.624, + 0.47, + 0.715 + ], + "angle": 0, + "content": "- We introduce a novel pre-training framework called BioViL-T. It leverages the temporal relationship of samples to self-supervise VLP models, making commonly used biomedical VLP models (e.g., [9, 32, 81]) more applicable to a wider range of downstream tasks without compromising performance on existing benchmarks." + }, + { + "type": "text", + "bbox": [ + 0.087, + 0.717, + 0.47, + 0.763 + ], + "angle": 0, + "content": "- We develop a generic multi-image encoder that handles missing image inputs and incorporates longitudinal information without requiring explicit image registration." + }, + { + "type": "text", + "bbox": [ + 0.087, + 0.765, + 0.47, + 0.826 + ], + "angle": 0, + "content": "- We achieve SOTA results in chest X-ray (CXR) report generation, temporal image classification, and phrase grounding downstream benchmarks by accounting for prior context in self-supervised training and fine-tuning." + }, + { + "type": "text", + "bbox": [ + 0.087, + 0.828, + 0.47, + 0.859 + ], + "angle": 0, + "content": "- We release a new multimodal benchmark dataset, MS-CXR-T, curated by an expert radiologist. It enables" + }, + { + "type": "list", + "bbox": [ + 0.087, + 0.624, + 0.47, + 0.859 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.523, + 0.092, + 0.892, + 0.122 + ], + "angle": 0, + "content": "benchmarking of CXR VLP models in terms of temporal semantics extracted from image and text data." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.138, + 0.638, + 0.155 + ], + "angle": 0, + "content": "2. Related work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.164, + 0.895, + 0.302 + ], + "angle": 0, + "content": "Vision-language processing Self-supervised VLP can significantly reduce the need for manual labels required for the training of image encoders [19, 53]. The availability of large-scale paired image-text datasets has thus led to rapid development of general-purpose VLP models. Objectives include contrastive and discriminative image-text matching [40, 53, 69] including local variants [32, 76], auto-regressive (AR) captioning [4, 39, 77] and multi-modal masked modelling objectives [13, 40, 61]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.313, + 0.895, + 0.631 + ], + "angle": 0, + "content": "Biomedical vision-language processing Paired medical image-report datasets were originally used for supervised learning via (typically) automated label extraction from clinical reports [33, 63, 70]. Using such datasets, advances in general-domain self-supervised VLP have been demonstrated to benefit biomedical imaging applications [9, 32, 81]. Work has incorporated ideas from general-domain VLP such as the original CLIP-style cross-modal contrastive objective [81], multi-modal masking with merged co-attention on image-text representations [46], and adaptations to the data of the domain. For example, a radiology report may have sparse image-specific details, prompting a local modification to the contrastive loss enabling alignment between text tokens and image patches [32]. Domain-specific pre-training of the text model is shown to benefit biomedical VLP [9], and preferential masking of medical terms during masked language modelling (MLM) was explored [75]. Here we use a local loss and domain-specific pre-training of the text model, but did not find a benefit to preferential masking. Similarly, cross-attention [22] is used rather than merged co-attention for image-guided MLM." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.644, + 0.892, + 0.735 + ], + "angle": 0, + "content": "Longitudinal modelling of medical images While prior images are used in unimodal supervised longitudinal analysis of medical images [37,58,68,74], temporal information has not directly been employed for self-supervision. The closest work exploits patient metadata to select positive or negative examples in unimodal contrastive learning [67,79]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.895, + 0.902 + ], + "angle": 0, + "content": "Existing models typically employ either late fusion of global image representations [58,64,68,74], which can miss fine-grained localised changes [32], or explicit spatial correspondence of features, using fixed spatial grids [48] or object detection [37]. Registering image pairs is commonly used for change detection in other contexts [17,52,59], and has been applied to medical imaging [5, 23]. For CXRs however, registration entails the ill-posed problem of aligning 2D projections of 3D geometry, which inevitably results in residual misalignment. Our approach does not rely on bounding boxes or explicit graph construction as it uses" + }, + { + "type": "page_footnote", + "bbox": [ + 0.077, + 0.875, + 0.47, + 0.901 + ], + "angle": 0, + "content": "In the MIMIC-CXR v2 dataset [36], around \\(40\\%\\) of reports explicitly reference a previous image. See Appendix B for details." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15017" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.122 + ], + "angle": 0, + "content": "self-attention of visual tokens across time to handle any spatial misalignment." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.129, + 0.468, + 0.234 + ], + "angle": 0, + "content": "Self-supervision across time Self-supervision has found applications on densely-sampled time series data (e.g., video) to capture temporal information [30, 55, 78, 80]. Our problem setting involves sparsely and sporadically sampled data where temporal pretext tasks are less applicable [2]. Similarly, it requires text supervision to enable both static and temporal learning, when temporal structure is present." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.248, + 0.353, + 0.264 + ], + "angle": 0, + "content": "3. BioViL-T training framework" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.273, + 0.468, + 0.408 + ], + "angle": 0, + "content": "Our approach comprises a multi-image encoder designed to extract spatio-temporal features from sequences of images (Section 3.1) and a text encoder incorporating optional cross-attention on image features. The models are trained jointly with image-guided MLM and cross-modal global and local contrastive objectives (Section 3.2). The resulting image and text models are later adapted for uni- or multimodal downstream tasks as described in Section 3.3. Implementation details are presented in Appendices E and F." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.409, + 0.468, + 0.546 + ], + "angle": 0, + "content": "For a given image and report pair \\((\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}},\\mathbf{x}_{\\mathrm{txt}}^{\\mathrm{curr}})\\), the report \\(\\mathbf{x}_{\\mathrm{txt}}^{\\mathrm{curr}}\\) describes the current image content and changes in reference to prior images. Our proposed formulation focuses on a single prior image; however, it can be generalised to multiple prior images depending on the application. Hence, we construct datasets by including the prior image whenever it exists: \\((\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}},\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{prior}},\\mathbf{x}_{\\mathrm{txt}}^{\\mathrm{curr}}) \\in \\mathcal{D}_m\\) or \\((\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}},\\varnothing ,\\mathbf{x}_{\\mathrm{txt}}^{\\mathrm{curr}}) \\in \\mathcal{D}_s\\) with the resulting dataset being a union of single and multi-image examples: \\(\\mathcal{D} = \\mathcal{D}_m \\cup \\mathcal{D}_s\\)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.555, + 0.438, + 0.57 + ], + "angle": 0, + "content": "3.1. Extracting spatio-temporal image features" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.578, + 0.468, + 0.713 + ], + "angle": 0, + "content": "Clinical findings are often observed across different image regions and co-occur simultaneously, which requires dense level visual reasoning across time to capture both static and temporal features. In contrast to late global fusion [64] and bounding-box based approaches [37], BioViL-T leverages local correspondences between image regions across time using transformer self-attention blocks [21]. Thus our method does not require an explicit image registration step between time points." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.714, + 0.468, + 0.866 + ], + "angle": 0, + "content": "We propose a hybrid CNN-Transformer encoder model due to its data efficiency and spatial flexibility of cross-attention across time points: \\( E_{\\mathrm{img}} \\): \\( \\mathbb{R}^{W\\times H}\\rightarrow \\mathbb{R}^{W^{\\prime}\\times H^{\\prime}\\times D_{\\mathrm{img}}} \\) (e.g., ResNet-50 [31]) and \\( A_{\\mathrm{img}} \\): \\( \\mathbb{R}^{T\\times L\\times D_{\\mathrm{img}}}\\rightarrow \\mathbb{R}^{L\\times D_{\\mathrm{img}}} \\) (e.g., transformer [21]), where \\( W \\), \\( H \\), and \\( T \\) correspond to spatiotemporal dimensions, \\( L = W^{\\prime}H^{\\prime} \\) is the number of visual tokens per image, and \\( D_{\\mathrm{img}} \\) is the embedding dimension. Here \\( E_{\\mathrm{img}} \\) serves as a stem network [51] to provide visual token features of individual images. The CNN's inductive biases [24, 51] en" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.092, + 0.892, + 0.243 + ], + "angle": 0, + "content": "sure data efficiency of our hybrid model, making it ideal for smaller scale biomedical datasets. \\( E_{\\mathrm{img}} \\) is initialised with BioViL weights [9]. The main purpose of \\( A_{\\mathrm{img}} \\) is to capture patch embedding interactions across time when a prior image \\( \\mathbf{x}_{\\mathrm{img}}^{\\mathrm{prior}} \\) is available and to aggregate them into a fixed-length token representation. Input visual tokens, \\( \\mathbf{H}_0^{\\mathrm{curr}} = \\mathbf{P}^{\\mathrm{curr}} \\coloneqq E_{\\mathrm{img}}(\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}}) \\), \\( \\mathbf{H}_0^{\\mathrm{prior}} \\coloneqq E_{\\mathrm{img}}(\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{prior}}) \\) are augmented with spatio-temporal positional encodings and flattened across the spatial dimensions. They are then processed by \\( K \\) transformer encoder [66] layers \\( A \\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.546, + 0.254, + 0.892, + 0.29 + ], + "angle": 0, + "content": "\\[\n\\left[ \\begin{array}{l} \\mathbf {H} _ {k} ^ {\\text {c u r r}} \\\\ \\mathbf {H} _ {k} ^ {\\text {p r i o r}} \\end{array} \\right] = A _ {k} \\left(\\left[ \\begin{array}{l} \\mathbf {H} _ {k - 1} ^ {\\text {c u r r}} + \\mathbf {S} + \\mathbf {1} _ {L} \\otimes \\mathbf {t} ^ {\\text {c u r r}} \\\\ \\mathbf {H} _ {k - 1} ^ {\\text {p r i o r}} + \\mathbf {S} + \\mathbf {1} _ {L} \\otimes \\mathbf {t} ^ {\\text {p r i o r}} \\end{array} \\right]\\right), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.297, + 0.892, + 0.464 + ], + "angle": 0, + "content": "for \\(k = 1,\\dots ,K\\) where \\(\\mathbf{S}\\in \\mathbb{R}^{L\\times D_{\\mathrm{img}}}\\) denotes 2D sinusoidal positional encodings [12] and \\(\\mathbf{T} = [\\mathbf{t}^{\\mathrm{curr}};\\mathbf{t}^{\\mathrm{prior}}]\\in \\mathbb{R}^{2\\times D_{\\mathrm{img}}}\\) is its temporal counterpart, which is learnt (Fig. 2) [4]. The layer-normalised (LN) [6] output of the final transformer encoder block \\(\\mathbf{P}^{\\mathrm{diff}}\\coloneqq \\mathrm{LN}(\\mathbf{H}_K^{\\mathrm{curr}})\\) is an 'aggregated' representation of patch-level progression information anchored on the current image. Figure 3 shows attention roll-out [1] applied to \\(\\mathbf{P}^{\\mathrm{diff}}\\) after pre-training, showing how the prior image contributes to the fused representation. Figure A.3 further highlights the robustness to variations in pose underlining that registration is not necessary for this encoder." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.474, + 0.892, + 0.699 + ], + "angle": 0, + "content": "Static-temporal feature decomposition When a prior image is available the final image representation \\(\\mathbf{V} := \\mathbf{P}^{\\mathrm{curr}} \\oplus \\mathbf{P}^{\\mathrm{diff}} \\in \\mathbb{R}^{W' \\times H' \\times 2D_{\\mathrm{img}}}\\) is formed by concatenating two sets of features (similar to [7]): those from the current image alone (\\(\\mathbf{P}^{\\mathrm{curr}}\\)) and the temporal features from current and prior images (\\(\\mathbf{P}^{\\mathrm{diff}}\\)). In this way, self-attention is mainly required to cope with pose variations and patch comparisons across time in extracting temporal content, removing the need for registration or explicit spatial feature alignment. When no prior scan is available (\\(\\mathbf{x} \\in \\mathcal{D}_s\\)), \\(A_{\\mathrm{img}}\\) is not used and \\(\\mathbf{P}^{\\mathrm{diff}}\\) is replaced by a learnable token \\(\\mathbf{p}^{\\mathrm{miss}} \\in \\mathbb{R}^{D_{\\mathrm{img}}}\\), replicated across the spatial dimensions. Section 4.5 later demonstrates that \\(A_{\\mathrm{img}}\\) highlights the value of feature decomposition for tasks such as phrase grounding which require well-localised features [10]." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.7, + 0.891, + 0.761 + ], + "angle": 0, + "content": "Hereafter, downstream tasks that require solely single image features, \\(\\mathbf{P}^{\\mathrm{curr}}\\), are referred to as static tasks, and the ones that benefit from additional progression information, \\(\\mathbf{P}^{\\mathrm{diff}}\\), as temporal tasks, e.g., report decoding." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.772, + 0.885, + 0.788 + ], + "angle": 0, + "content": "3.2. Text-supervision for spatio-temporal learning" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.795, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Let \\(\\mathbf{w} = (w_{1},\\dots ,w_{M})\\) denote a vector of \\(M\\) tokens of a report \\(\\mathbf{x}_{\\mathrm{txt}}\\) after tokenisation. We first obtain contextualised token features \\(E_{\\mathrm{txt}}(\\mathbf{w})\\in \\mathbb{R}^{M\\times D_{\\mathrm{xt}}}\\) by passing a sequence of text tokens \\(\\mathbf{w} = (w_{1},\\dots ,w_{M})\\) through a BERT encoder \\(E_{\\mathrm{txt}}\\) [20]. The input sequence is prepended with either a [CLS] or [MLM] token associated with a downstream training objective, conditioning the output features" + }, + { + "type": "page_footnote", + "bbox": [ + 0.076, + 0.875, + 0.468, + 0.9 + ], + "angle": 0, + "content": "2The prior report is not included during pre-training as it may further reference an earlier study, reintroducing temporal ambiguity." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.956 + ], + "angle": 0, + "content": "15018" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.123, + 0.089, + 0.845, + 0.366 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.075, + 0.377, + 0.892, + 0.448 + ], + "angle": 0, + "content": "Figure 2. The proposed self-supervised VLP training framework BioViL-T: Image representations \\(\\mathbf{V}\\) are extracted from single and multiple input scans (whenever available) using a hybrid CNN and transformer encoder [24, 51]. This design choice is to increase the data-efficiency and enable the fusion of temporal content without requiring image registration. They are later matched with their corresponding text representations obtained with CXR-BERT [9] using local [32] and global InfoNCE [49] training objectives. As an additional model supervision, multi-modal fused representations, obtained with cross-attention, are used for image-guided masked language modelling." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.473, + 0.47, + 0.565 + ], + "angle": 0, + "content": "similar to [39, 42]. During training, we do two forward passes through \\( E_{\\mathrm{txt}} \\): once with masking at \\( 45\\% \\) probability (for the MLM objective) and once without masking for contrastive learning, as shown in Figure 2. The text encoder is initialised with the weights of CXR-BERT[9] canonical model, trained on domain-specific vocabulary and corpora." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.565, + 0.47, + 0.626 + ], + "angle": 0, + "content": "Both text and image features are later projected into a joint latent space with \\(\\phi_{\\mathrm{txt}}: \\mathbb{R}^{D_{\\mathrm{txt}}} \\to \\mathbb{R}^D\\), and similarly \\(\\mathbf{v}_{w,h}^{\\mathrm{proj}} := \\phi_{\\mathrm{img}}(\\mathbf{v}_{w,h})\\) where \\(\\phi_{\\mathrm{img}}: \\mathbb{R}^{D_{\\mathrm{img}}} \\to \\mathbb{R}^D\\), with \\(\\phi\\) being a two-layer perceptron in our experiments." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.632, + 0.47, + 0.802 + ], + "angle": 0, + "content": "Contrastive objectives Let \\(\\mathbf{r} \\coloneqq [E_{\\mathrm{xt}}(\\mathbf{w})]_{[\\mathrm{CLS}]}\\) denote the global representation of \\(\\mathbf{w}\\), with \\(\\mathbf{r}^{\\mathrm{proj}} \\coloneqq \\phi_{\\mathrm{xt}}(\\mathbf{r})\\) its projected version. Given projected patch embeddings \\(\\mathbf{v}_{w,h}^{\\mathrm{proj}}\\), we can compute a global cosine similarity \\(S_C(\\bar{\\mathbf{v}}^{\\mathrm{proj}}, \\mathbf{r}^{\\mathrm{proj}})\\) and a local similarity using weighted pairwise cosine similarities across text tokens and projected patch embeddings [32, 76]. These similarities are used in both global and local contrastive objectives with the InfoNCE loss [49, 53]. The local loss proves crucial both for static phrase-grounding and temporal image classification (see Table 7), highlighting the importance of localised self-supervision." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.807, + 0.47, + 0.869 + ], + "angle": 0, + "content": "Image-guided masked language modelling Prior work [9, 46] has shown that biomedical visual-language learning benefits from an auxiliary task such as MLM since capturing the joint distribution of tokens can stabilise and improve" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.473, + 0.892, + 0.551 + ], + "angle": 0, + "content": "language understanding during joint learning. Given a batch \\(\\mathcal{B}\\) of token vectors \\(\\mathbf{w}\\), it is often defined as the cross-entropy for predicting the randomly sampled masked tokens, \\(m\\subset\\) \\(\\{1,\\dots ,M\\}\\), \\(\\mathcal{L}_{\\mathrm{MLM}} = -\\frac{1}{|\\mathcal{B}|}\\sum_{\\mathbf{w}\\in \\mathcal{B}}\\log p_{\\theta}(\\mathbf{w}_m|\\mathbf{w}_{\\backslash m})\\), where \\(\\theta\\) are the weights of the text encoder \\(E_{\\mathrm{txt}}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.552, + 0.892, + 0.68 + ], + "angle": 0, + "content": "In the absence of image information, however, certain masked findings and attributes are not readily predicted, e.g., \"[MASK] is worsening\". As shown in the general domain [13], visual information can help disambiguate such masked predictions and provide additional cross-modal supervision. Thus, we use cross-attention [22,66] to the image features \\(\\mathbf{v}_{w,h}^{\\mathrm{proj}}\\) during this task. Specifically, for our image-guided MLM objective we model \\(p_{\\theta}(\\mathbf{w}_m|\\mathbf{w}_{\\backslash m},\\mathbf{v}_{w,h}^{\\mathrm{proj}})\\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.688, + 0.794, + 0.704 + ], + "angle": 0, + "content": "3.3. Adaptations to downstream tasks" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.711, + 0.892, + 0.848 + ], + "angle": 0, + "content": "BioViL-T can be adapted to various downstream tasks. For phrase-grounding and zero-shot inference, we rely on \\( S_{C}(\\mathbf{r}^{\\mathrm{proj}},\\mathbf{v}_{w,h}^{\\mathrm{proj}}) \\) similar to [9, 32]. For multiple-text prompts, projected text embeddings are marginalised prior to \\( \\ell_2 \\)-normalisation [53]. To enable language decoding, \\( \\mathbf{v}_{w,h}^{\\mathrm{proj}} \\) inputs are cross-attended by text queries \\( \\mathbf{w} \\), and causal-attention is utilised between text tokens [39,66]. Differing from [9, 32, 81], we show that report generation tasks can greatly benefit from temporal joint latent space." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.892, + 0.904 + ], + "angle": 0, + "content": "Conditioning on prior reports In contrast to existing work, we incorporate the prior report as a prompt to contextualise the report generation task: \\(p_{\\Phi}(\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{curr}}|\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{prior}},\\mathbf{v}_{w,h}^{\\mathrm{proj}})\\)" + }, + { + "type": "page_footnote", + "bbox": [ + 0.078, + 0.875, + 0.468, + 0.901 + ], + "angle": 0, + "content": "3https://huggingface.co/microsoft/BiomedVLP-CXR-BERT-general" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15019" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.199 + ], + "angle": 0, + "content": "where \\(\\Phi\\) are the multi-modal encoder-decoder network's weights, and \\(\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{curr}}\\), \\(\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{prior}}\\) denote text tokens for current and prior reports respectively. This is analogous to fine-tuning GPT-3 [11] with prompts and instructions [71], but conditioning on both images and the previous report. A dedicated separation token [SEP] is added into the input sequence \\([\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{prior}}, [\\mathrm{SEP}], \\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{curr}}]\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.205, + 0.473, + 0.419 + ], + "angle": 0, + "content": "Curation of imaging datasets CXR datasets [36] often contain multiple image acquisitions \\(\\mathcal{Z} = \\{\\mathbf{x}_1^{\\mathrm{img}},\\dots ,\\mathbf{x}_Z^{\\mathrm{img}}\\}\\) in a single visit due to data quality issues such as a limited field-of-view or scanning the wrong body part (Figure A.4). Unlike [9, 32, 81], we conduct curation to choose higher quality images among the potential candidates instead of performing a random selection. For this step, a separate BioViL-T is trained on 'clean' studies with single acquisitions and later used in a zero-shot setting to detect out-of-distribution samples [26, 27] arising from the re-imaging process. The candidate \\(\\hat{z}\\) is selected as follows: \\(\\hat{z} = \\arg \\max_{z\\in \\mathcal{Z}}S_C(\\bar{\\mathbf{v}}_z^{\\mathrm{proj}},\\mathbf{r}^{\\mathrm{proj}})\\) s.t. \\(|s_{\\hat{z}} - s_{\\mathcal{Z}\\backslash \\hat{z}}| > \\delta\\) for a margin \\(\\delta\\). This approach is applied to enhance the quality of the temporal classification dataset given its limited size." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.431, + 0.303, + 0.449 + ], + "angle": 0, + "content": "4. Datasets & experiments" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.456, + 0.47, + 0.593 + ], + "angle": 0, + "content": "Here, we demonstrate BioViL-T's data efficiency and adaptability to a wide range of applications, and show how the model achieves SOTA performance on various downstream tasks by learning from data instances linked across time, making effective use of domain priors and the available training data. Specifically, our model is evaluated on a diverse set of downstream tasks including zero- and few-shot static and temporal image classification, report generation, phrase-grounding [10], and sentence similarity." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.6, + 0.47, + 0.813 + ], + "angle": 0, + "content": "MS-CXR-T benchmark We release a new multi-modal benchmark dataset, MS-CXR-T, to evaluate chest X-ray VLP models on two distinct temporal tasks: image classification and sentence similarity. The former comprises multi-image and ground-truth label pairs (\\(N = 1326\\)) across 5 findings, with classes corresponding to 3 states of disease progression for each finding: {Improving, Stable, Worsening}. The latter quantifies the temporal-semantic similarity of text embeddings extracted from pairs of sentences (\\(N = 361\\)). The pairs can be either paraphrases or contradictions in terms of disease progression. The data for both tasks was manually annotated and reviewed by a board certified radiologist. Appendix C provides further details on its data distribution and annotation protocol." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.82, + 0.47, + 0.867 + ], + "angle": 0, + "content": "Datasets For pre-training, we use the MIMIC-CXR v2 [28, 36] chest X-ray dataset, which contains longitudinal imaging studies with corresponding radiological reports," + }, + { + "type": "image_caption", + "bbox": [ + 0.518, + 0.089, + 0.578, + 0.1 + ], + "angle": 0, + "content": "Prior image" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.103, + 0.596, + 0.174 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.607, + 0.089, + 0.676, + 0.1 + ], + "angle": 0, + "content": "Current image" + }, + { + "type": "image", + "bbox": [ + 0.597, + 0.103, + 0.688, + 0.174 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.724, + 0.089, + 0.781, + 0.1 + ], + "angle": 0, + "content": "Prior image" + }, + { + "type": "image", + "bbox": [ + 0.706, + 0.103, + 0.799, + 0.174 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.812, + 0.089, + 0.88, + 0.1 + ], + "angle": 0, + "content": "Current image" + }, + { + "type": "image", + "bbox": [ + 0.799, + 0.103, + 0.892, + 0.174 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.185, + 0.894, + 0.283 + ], + "angle": 0, + "content": "Figure 3. Attention rollout maps [1] from the reference patch (marked with \\(\\star\\)) to the current and prior images. The bounding boxes, annotated by a radiologist, show the extent of consolidation. Note that the reference patch attends to its anatomical neighbourhood in the prior image despite the misalignment between prior and current images. The grid \\((14 \\times 14)\\) represents the patch tokens processed in the transformer encoder blocks." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.311, + 0.892, + 0.492 + ], + "angle": 0, + "content": "see Fig. B.1 for the distribution of studies. We only use frontal view scans and discard samples where reports do not contain an IMPRESSION section. From this data, we gather 174.1k and 4.9k text-image pairs for training and validation respectively, with a majority of pairs including a prior image: \\( |\\mathcal{D}_m^{\\mathrm{train}}| = 118.8k \\), \\( |\\mathcal{D}_s^{\\mathrm{train}}| = 55.3k \\). The text consists of the IMPRESSION section and, for MLM additionally the FINDINGS section if available. Note that no manual labels are used during pre-training and no additional data is used for the methods that leverage the link between current and prior images. For early stopping we track the validation loss, see Appendix E for implementation details." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.494, + 0.893, + 0.676 + ], + "angle": 0, + "content": "Downstream evaluations are performed on a disjoint held-out test set shared across all tasks, \\( |\\mathcal{D}^{\\mathrm{test}}| = 2971 \\). For report generation, we extend this test set with samples from healthy subjects (\\( N = 815 \\)) to match the prevalence of pathological studies used in prior work [14, 25, 45]. For fine-tuning on temporal image classification, we use labels from the Chest ImaGenome dataset [72] as in [37] (statistics in Table F.2). In detail, we use the following benchmark datasets: (I) MS-CXR [10] for phrase grounding, (II) the RSNA Pneumonia dataset [60, 70] to test zero-shot and fine-tuned classification, (III) MS-CXR-T for temporal sentence similarity and temporal image classification." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.892, + 0.87 + ], + "angle": 0, + "content": "Comparison approaches We compare our approach to other domain-specific SOTA pre-training frameworks [9, 32] specifically on phrase-grounding and zero-shot predictive performance. The non-temporal BioViL framework [9] is most similar to our approach and provides insight into non-temporal pre-training. We additionally compare to internal ablations such as removing the past report during report generation and masking prior images during phrase grounding. For SOTA performance comparison, various AR and nearest-neighbour (NN) based language decoding approaches are used as baselines: IFCC [45], R2Gen [14], CXR-RePaiR-2 [25], and CXR-RePaiR-Select [25]." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "For the temporal classification task, we compare against a baseline exploiting the BioViL image encoder [9], and an" + }, + { + "type": "page_footnote", + "bbox": [ + 0.078, + 0.875, + 0.468, + 0.901 + ], + "angle": 0, + "content": "4MS-CXR-T benchmark dataset can be accessed through PhysioNet: https://aka.ms/ms-cxr-t" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15020" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.089, + 0.473, + 0.203 + ], + "angle": 0, + "content": "Table 1. Results for report generation task: Predictions are evaluated in terms of lexical (BLEU-4, ROUGE) and factuality metrics (CHEXBERT, TEM). Approaches are grouped into two broad categories: nearest-neighbour (NN) and auto-regressive (AR). BioViL-T pre-training consistently yields improved decoding. Further, the consistent performance gains of using prior image and report demonstrate the importance of such domain priors. 'PI / PR' indicate usage of prior image and report, respectively." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.212, + 0.47, + 0.288 + ], + "angle": 0, + "content": "
MethodPre-trainingPI / PRBLEU-4ROUGECHEXBERTTEM
NCXR-RePaiR-2 [25]BioViLX/X2.114.328.112.5
Baseline (NN) [9]BioViLX/X3.720.028.311.1
Proposed (NN)BioViL-T✓/X4.520.529.013.0
MBaseline (AR) [9]BioViLX/X7.5 ± 0.127.9 ± 0.129.3 ± 0.313.8 ± 0.1
ProposedBioViL-T✓/X8.2 ± 0.128.7 ± 0.130.2 ± 0.716.0 ± 0.3
ProposedBioViL-T✓/✓9.2 ± 0.329.6 ± 0.131.7 ± 1.017.5 ± 0.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.303, + 0.471, + 0.387 + ], + "angle": 0, + "content": "Table 2. Temporal image classification results (repeated for 4 random seeds) on the MS-CXR-T benchmark for fully-supervised and zero-/few-shot (Z&F) learning settings, in terms of macroaccuracy across the three classes for each finding. Affine registration is performed for the baseline method (denoted with suffix 'w/ reg'), to partially address the pose variations across scans." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.397, + 0.47, + 0.484 + ], + "angle": 0, + "content": "
Method (% of labels)Pre-trainConsolidationPl. effusionPneumoniaPneumothoraxEdema
Z&FBioViL-T prompt (0%)Temporal53.6 ± 1.959.7 ± 2.158.0 ± 3.934.9 ± 1.064.2 ± 1.5
BioViL-T (10%)Temporal59.7 ± 2.462.4 ± 1.460.1 ± 2.135.3 ± 2.662.6 ± 1.7
SupervisedCNN + TransformerImageNet44.0 ± 2.061.3 ± 1.645.1 ± 3.531.5 ± 3.165.5 ± 1.1
CheXRelNet [37]ImageNet4747473649
BioViL [9]Static56.1 ± 1.562.3 ± 1.159.4 ± 1.041.7 ± 2.867.5 ± 0.8
BioViL w/reg [9]Static56.0 ± 1.563.0 ± 0.960.2 ± 0.742.5 ± 2.767.5 ± 0.9
BioViL-T wout curationTemporal58.9 ± 1.765.5 ± 0.761.5 ± 2.244.4 ± 2.167.4 ± 0.8
BioViL-TTemporal61.1 ± 2.467.0 ± 0.861.9 ± 1.942.6 ± 1.668.5 ± 0.8
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.511, + 0.47, + 0.663 + ], + "angle": 0, + "content": "approach that makes use of graph convolutions across regions of interest extracted from bounding boxes [37]. For BioViL, we perform affine image registration (with 4 DoF) for each pair of scans to cope with pose variations, and the encoded images are concatenated along the feature dimension and classified via a multilayer perceptron. For [37], we compare to the three-class setting. Lastly, we benchmark our final text model in isolation against domain specific SOTA models in a temporal sentence similarity task: CXR-BERT [9] and PubMedBert [29]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.67, + 0.471, + 0.866 + ], + "angle": 0, + "content": "Metrics Due to class imbalance, we report macroaccuracy for temporal image classification. For phrase grounding, we use mean Intersection-Over-Union (mIoU) and Contrast-to-Noise-Ratio (CNR) [9]. The latter measures the discrepancies between cosine similarities inside and out of the bounding box region without requiring hard thresholds. To evaluate the quality of generated reports, we use both the standard lexical metrics, e.g., BLEU [50], ROUGE-L [41], and also domain-specific factuality metric: CheXbert [62]. To directly probe the generation of change-related information, we introduce a new metric called temporal entity matching (TEM) to compute the match score of a fixed set of temporal entities (see Appendix D)." + }, + { + "type": "page_footnote", + "bbox": [ + 0.076, + 0.875, + 0.47, + 0.9 + ], + "angle": 0, + "content": "5The average of the weighted- \\(F_{1}\\) score across 14 pathological observations labelled by CheXbert." + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.089, + 0.892, + 0.145 + ], + "angle": 0, + "content": "Table 3. Report generation results using the same train/test splits from [25], measured by lexical (BLEU-2) and factuality (CHEXBERT) metrics. Baseline results were also collected from [25]. Note the CHEXBERT score covers all 14 observations." + }, + { + "type": "table", + "bbox": [ + 0.503, + 0.157, + 0.89, + 0.23 + ], + "angle": 0, + "content": "
MethodDecoded sectionsBLEU-2CHEXBERT
R2gen [14]Findings & Impression21.20 ± 0.1014.80 ± 0.30
IFCC [45]Findings21.70 ± 0.1027.00 ± 0.40
CXR-RePaiR-Sel [25]Impression5.00 ± 0.1027.40 ± 0.30
BioViL-TImpression15.86 ± 0.1434.83 ± 0.73
BioViL-TFindings & Impression21.31 ± 0.1935.86 ± 0.35
" + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.258, + 0.871, + 0.275 + ], + "angle": 0, + "content": "4.1. Temporal pre-training yields data efficiency" + }, + { + "type": "title", + "bbox": [ + 0.505, + 0.284, + 0.847, + 0.298 + ], + "angle": 0, + "content": "Downstream tasks are enabled with minimal labels." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.306, + 0.892, + 0.442 + ], + "angle": 0, + "content": "The sections 'NN' and 'Z&F' on Tables 1 and 2 report zero- and few-shot performance on tasks benefitting from temporal information: temporal image classification and report generation. Here we measure the quality of the learnt joint latent space and the extent to which BioViL-T enables efficient use of raw data. For zero-shot classification we prompt the AR fine-tuned model with prefix: \"[FINDING] is\" and compare the next-token probability of words meaning 'improving', 'stable', and 'worsening' (Appendix F.4)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.444, + 0.893, + 0.686 + ], + "angle": 0, + "content": "Without using any labelled data, Table 2 shows that the proposed AR-based approach already yields performance superior to prior fully-supervised work [37] on temporal image classification. With only \\(10\\%\\) of labels, classification fine-tuning provides a further boost, indicating that BioViL-T produces a multi-image encoder readily adapted to temporal tasks. Similarly, in a zero-shot report-retrieval setting, the findings show that compared to temporally-agnostic pre-training, BioViL-T leveraging prior images improves across all metrics. Consistent with prior work [25], the retrieved reports already preserve factuality with high CheXbert scores, more-so than the other metrics which measure fine-grained specifics of phrasing. This demonstrates that the latent space captures the high-level semantics of the clinical features. Fine-grained phrasing however will be substantially improved by AR fine-tuning." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.698, + 0.882, + 0.714 + ], + "angle": 0, + "content": "4.2. Achieving SOTA performance with BioViL-T" + }, + { + "type": "title", + "bbox": [ + 0.504, + 0.724, + 0.886, + 0.755 + ], + "angle": 0, + "content": "A wide range of downstream tasks benefit substantially from temporally-aware pre-training." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Through downstream adaptations and fine-tuning our model, we report SOTA performance on report generation and temporal image classification tasks. For the former, using both prior images and reports during fine-tuning substantially improves across metrics (Table 1). In particular, TEM metric results show that temporal context is key for accurately describing change in the generated report while avoiding hallucinations (see Table A.1 for examples). Comparing to published results on a comparable test split and" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "15021" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.09, + 0.47, + 0.116 + ], + "angle": 0, + "content": "Table 4. Image classification results on RSNA Pneumonia Detection Benchmark [60] for train and test splits of \\(70\\% -30\\%\\) respectively." + }, + { + "type": "table", + "bbox": [ + 0.078, + 0.127, + 0.454, + 0.212 + ], + "angle": 0, + "content": "
Method% of LabelsSupervisionAcc.F1AUROC
GLoRIA [32]XZero-shot0.700.58-
BioViL [9]XZero-shot0.7320.6650.831
BioViL-TXZero-shot0.8050.7060.871
BioViL [9]1%Few-shot0.8050.7230.881
BioViL-T1%Few-shot0.8140.7300.890
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.234, + 0.468, + 0.339 + ], + "angle": 0, + "content": "metrics (Sec. 4.1), we conclude that BioViL-T with fine-tuning achieves SOTA on report generation, producing reports that are lexically on par with prior work but substantially more factually accurate. Note that we do 'vanilla' AR fine-tuning to focus on the impact of the pre-trained encoders, so application-specific supervision [45] could be used in conjunction to further boost performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.34, + 0.468, + 0.536 + ], + "angle": 0, + "content": "In temporal image classification (Tab. 2), BioViL-T pretraining outperforms the non-temporal baseline (BioViL) and improves on previously-reported results [37] by up to 20 percentage points (pp). Furthermore, baseline methods that rely on image registration (BioViL w/reg), underperform compared to the proposed approach. Further analysis reveals that errors tend to be in cases with disagreement between radiologists (Appendix A.2). We also note that pretraining is critical for a hybrid CNN-transformer model on this task, likely due to the small labelled dataset. Lastly, curation of temporal training data is observed to improve the classification results by .68 pp aggregated across the findings, see Appendix A.4 for details." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.545, + 0.442, + 0.561 + ], + "angle": 0, + "content": "4.3. Static tasks benefit from temporal learning" + }, + { + "type": "text", + "bbox": [ + 0.082, + 0.57, + 0.465, + 0.601 + ], + "angle": 0, + "content": "BioViL-T broadens the range of applicable downstream tasks whilst contributing to performance on static tasks." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.607, + 0.468, + 0.698 + ], + "angle": 0, + "content": "In this section, we demonstrate that performance improvements afforded by BioViL-T are not restricted to temporal tasks – static tasks also benefit. Table 4 reports results on zero- and few-shot pneumonia classification from single images [60], where BioViL-T establishes a new SOTA compared to prior work [9,32]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.698, + 0.468, + 0.789 + ], + "angle": 0, + "content": "We see a similar trend on the MS-CXR phrase grounding benchmark (Tab. 5). This task can be solved with single images, however we show that the inclusion of the prior image (where available) does not impair the performance of BioViL-T. Feature decomposition effectively preserves localised information from the current image." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.794, + 0.446, + 0.81 + ], + "angle": 0, + "content": "4.4. Towards better sentence embedding quality" + }, + { + "type": "text", + "bbox": [ + 0.082, + 0.819, + 0.458, + 0.835 + ], + "angle": 0, + "content": "Language models acquire increased temporal sensitivity." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.468, + 0.902 + ], + "angle": 0, + "content": "We hypothesise that text encoders learn temporal semantics through supervision from longitudinal image series. To verify this, RadNLI [45] and MS-CXR-T datasets are used in a zero-shot binary classification setting. Cosine similarity" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.09, + 0.892, + 0.116 + ], + "angle": 0, + "content": "Table 5. Results on MS-CXR benchmark [10] (5-runs with different seeds), \"Multi-image\" column indicates the input images used at test time." + }, + { + "type": "table", + "bbox": [ + 0.504, + 0.127, + 0.888, + 0.207 + ], + "angle": 0, + "content": "
MethodMulti-ImageAvg. CNRAvg. mIoU
BioViL [9]X1.07 ± 0.040.229 ± 0.005
+ Local loss [9,32]X1.21 ± 0.050.202 ± 0.010
BioViL-TX1.33 ± 0.040.243 ± 0.005
BioViL-T✓1.32 ± 0.040.240 ± 0.005
" + }, + { + "type": "table_caption", + "bbox": [ + 0.509, + 0.22, + 0.882, + 0.234 + ], + "angle": 0, + "content": "Table 6. Results on MS-CXR-T sentence similarity benchmark." + }, + { + "type": "table", + "bbox": [ + 0.504, + 0.245, + 0.891, + 0.329 + ], + "angle": 0, + "content": "
Text ModelMS-CXR-T (361 pairs)RadNLI (145 pairs)
AccuracyROC-AUCAccuracyROC-AUC
PubMedBERT [29]60.39.54281.38.727
CXR-BERT-G [9]62.60.60187.59.902
CXR-BERT-S [9]78.12.83789.66.932
BioViL-T87.77 ± 0.5.933 ± .00390.52 ± 1.0.947 ± .003
" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.355, + 0.891, + 0.4 + ], + "angle": 0, + "content": "of sentence pair embeddings [56] are treated as class-logits to label each pair either as paraphrase or contradiction. See Appendix F.6 for further details." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.401, + 0.892, + 0.522 + ], + "angle": 0, + "content": "Our text model is benchmarked against SOTA domain-specific BERT models. Table 6 shows that the proposed framework greatly increases the sensitivity of sentence embeddings to temporal content whilst better capturing the static content (RadNLI). Note that CXR-BERT-Specialised [9] is learnt through single-images starting from the same canonical model, illustrating the substantial increase in temporal and static sensitivity due to BioViL-T pre-training." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.531, + 0.702, + 0.547 + ], + "angle": 0, + "content": "4.5. Ablation experiments" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.554, + 0.891, + 0.601 + ], + "angle": 0, + "content": "In Table 7 we report extensive ablations across the multi-image encoder architecture, pre-training choices, and AR fine-tuning for report generation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.608, + 0.892, + 0.7 + ], + "angle": 0, + "content": "Image encoder Table 7 shows that decomposition of static and progression features is essential to ensure good performance on single-image tasks, such as phrase grounding. For temporal representations, on the other hand, positional encodings (T) are essential to disambiguate the order of scans, i.e., permutation variance across time." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.711, + 0.892, + 0.817 + ], + "angle": 0, + "content": "Model pre-training The corresponding results are shown in the middle section of Table 7. The local contrastive loss proves crucial to ensure meaningful language supervision during pre-training, followed by the image-guided MLM objective. Lastly, use of the FINDINGS section results in only minor performance gains as the key findings are already captured in the IMPRESSION section." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Report generation The importance of prior image and report is demonstrated by the substantial drop in the \"no prior image and report\" ablation, confirming our hypothesis that temporal context is crucial for improving report quality. While both inputs are crucial for optimal performance," + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15022" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.089, + 0.472, + 0.16 + ], + "angle": 0, + "content": "Table 7. Ablation study on image encoder, pre-training settings, and report generation (one component at a time, and repeated for 4 random seeds). Note that for temporal classification, linear probing is applied to frozen image embeddings. In report generation, the baseline method is fine-tuned with both prior image and report." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.17, + 0.472, + 0.354 + ], + "angle": 0, + "content": "
AblationAvg. CNR (mIoU)Pl. Effusion Acc.
EncoderBaseline1.33 ± 0.02 (.248)64.8 ± 0.6
- Temporal pos. encoding1.32 ± 0.02 (.242)62.9 ± 1.0
- Feature decomposition1.11 ± 0.08 (.203)64.0 ± 0.6
Pre-trainingBaseline1.33 ± 0.02 (.248)64.8 ± 0.6
- Use of findings section1.32 ± 0.01 (.246)63.8 ± 0.8
- MLM loss1.28 ± 0.02 (.238)63.2 ± 0.7
- Local contrastive loss1.18 ± 0.02 (.236)60.2 ± 0.6
AblationROUGETEM
Report gen.Baseline29.64 ± 0.0817.54 ± 0.11
- Prior image29.35 ± 0.2516.30 ± 0.40
- Prior report28.67 ± 0.1216.00 ± 0.30
-(Prior image and report)27.78 ± 0.0913.65 ± 0.48
- Separation token26.00 ± 0.4015.50 ± 1.06
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.375, + 0.47, + 0.48 + ], + "angle": 0, + "content": "the prior report is more so because it summarises the image and provides a clearer signal. The prior image however cannot be dismissed entirely as it provides granular details which may not always be documented in a report. Finally, we found the separation token is crucial in differentiating between the predicted tokens for the current report and tokens from the prior report." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.488, + 0.467, + 0.505 + ], + "angle": 0, + "content": "4.6. Which tokens require a prior image in MLM?" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.512, + 0.47, + 0.587 + ], + "angle": 0, + "content": "We leverage the MLM objective in an inference setting to analyse the influence of prior images in predicting masked tokens. Inspired by the \\(\\Delta\\) image loss of [8], we define \\(\\Delta_{\\mathrm{img}}^{\\mathrm{prior}}\\) as the change in loss by conditioning the estimation with a prior image for a given token \\(w\\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.106, + 0.593, + 0.47, + 0.614 + ], + "angle": 0, + "content": "\\[\n\\Delta_ {\\mathrm {i m g}} ^ {\\text {p r i o r}} (w) = l (w, \\mathbf {x} _ {\\mathrm {i m g}} ^ {\\text {c u r r}}, \\emptyset) - l (w, \\mathbf {x} _ {\\mathrm {i m g}} ^ {\\text {c u r r}}, \\mathbf {x} _ {\\mathrm {i m g}} ^ {\\text {p r i o r}}) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.621, + 0.47, + 0.819 + ], + "angle": 0, + "content": "where \\( l(w, \\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}}, \\mathbf{x}_{\\mathrm{img}}^{\\mathrm{prior}}) \\) is the cross-entropy of predicting the masked token \\( w \\) given visual features (MLM loss for a single token), averaged over sentences in which \\( w \\) appears. \\( \\Delta_{\\mathrm{img}}^{\\mathrm{prior}} \\) is a measure of how much that token benefits from access to the prior image, as well as an assessment of the contribution of the prior image to the image representation. In Figure 4 we show the distribution of \\( \\Delta_{\\mathrm{img}}^{\\mathrm{prior}} \\) as a function of token category (e.g., Anatomy, Positional; see F.5 for annotation details). For Progression-type terms in particular, the model heavily relies on the prior image for image-guided MLM. We further observe that this effect is specific to temporal tokens; as expected, those from other semantic categories do not consistently rely on the prior image." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.83, + 0.196, + 0.846 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.471, + 0.901 + ], + "angle": 0, + "content": "In this paper, we introduced BioViL-T, a vision-language pre-training framework enabling alignment between text and multiple images. BioViL-T makes use of" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.088, + 0.895, + 0.285 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.291, + 0.895, + 0.389 + ], + "angle": 0, + "content": "Figure 4. Mean token-level increase in image-guided MLM loss when prior image is discarded, grouped by token category. The prior image is excluded during inference to measure its impact on masked token predictions. Progression tokens are significantly better predicted when prior images are incorporated into image embeddings. The top five Progression tokens are 'persist', 'improving', 'remains', 'unchanged', and 'residual'." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.418, + 0.892, + 0.584 + ], + "angle": 0, + "content": "a novel multi-image encoder and explicitly decomposes static-temporal features to augment the current image representation with information from prior images. This enables the grounding of temporal references in the text. To our knowledge, this is the first method capable of leveraging the temporal content commonly present in biomedical text. It addresses an important limitation in existing VLP approaches, which simply discard such context. Also, incorporating such multi-modal temporal content provides strong learning signals to the model, resulting in richer representations and improved downstream performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.586, + 0.893, + 0.737 + ], + "angle": 0, + "content": "We demonstrate the value of this paradigm through extensive experiments: BioViL-T excels on both static and temporal tasks, establishing new SOTA on report generation, temporal image classification, few/zero-shot pneumonia detection, and phrase grounding. Furthermore, we release a new multi-modal benchmark (MS-CXR-T) to measure the quality of image and text representations in terms of temporal semantics, enabling more diverse evaluation of biomedical VLP models. The corresponding model weights\\(^6\\) and code\\(^7\\) are publicly available." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.738, + 0.892, + 0.799 + ], + "angle": 0, + "content": "Further exploration and evaluation are required on diverse datasets to characterise what kinds of tasks would benefit from a temporal modelling approach, and specifically from the proposed methodology." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.814, + 0.892, + 0.86 + ], + "angle": 0, + "content": "Acknowledgements: We would like to thank Hannah Richardson, Hoifung Poon, Melanie Bernhardt, Melissa Bristow and Naoto Usuyama for their valuable feedback." + }, + { + "type": "page_footnote", + "bbox": [ + 0.516, + 0.875, + 0.886, + 0.887 + ], + "angle": 0, + "content": "6Models can be found at: https://aka.ms/biovil-t-model" + }, + { + "type": "page_footnote", + "bbox": [ + 0.521, + 0.888, + 0.868, + 0.9 + ], + "angle": 0, + "content": "7Code can be found at: https://aka.ms/biovil-t-code" + }, + { + "type": "list", + "bbox": [ + 0.516, + 0.875, + 0.886, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "15023" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.184 + ], + "angle": 0, + "content": "[1] Samira Abnar and Willem Zuidema. Quantifying attention flow in transformers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4190-4197, Online, July 2020. Association for Computational Linguistics. 3, 5, 13, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.186, + 0.472, + 0.254 + ], + "angle": 0, + "content": "[2] Monica N Agrawal, Hunter Lang, Michael Offin, Lior Gazit, and David Sontag. Leveraging time irreversibility with order-contrastive pre-training. In International Conference on Artificial Intelligence and Statistics, pages 2330–2353. PMLR, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.256, + 0.471, + 0.311 + ], + "angle": 0, + "content": "[3] Uwa O. Aideyan, Kevin Berbaum, and Wilbur L. Smith. Influence of prior radiologic information on the interpretation of radiographic examinations. Academic Radiology, 2(3):205-208, 1995. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.313, + 0.471, + 0.38 + ], + "angle": 0, + "content": "[4] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning. arXiv preprint arXiv:2204.14198, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.382, + 0.47, + 0.465 + ], + "angle": 0, + "content": "[5] B.B. Avants, C.L. Epstein, M. Grossman, and J.C. Gee. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1):26-41, 2008. Special Issue on The Third International Workshop on Biomedical Image Registration - WBIR 2006. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.467, + 0.47, + 0.507 + ], + "angle": 0, + "content": "[6] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.509, + 0.47, + 0.579 + ], + "angle": 0, + "content": "[7] Nadine Behrmann, Mohsen Fayyaz, Juergen Gall, and Mehdi Noroozi. Long short view feature decomposition via contrastive video representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9244-9253, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.58, + 0.471, + 0.662 + ], + "angle": 0, + "content": "[8] Yonatan Bitton, Gabriel Stanovsky, Michael Elhadad, and Roy Schwartz. Data efficient masked language modeling for vision and language. In 2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021, pages 3013-3028. Association for Computational Linguistics (ACL), 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.664, + 0.471, + 0.773 + ], + "angle": 0, + "content": "[9] Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, and Ozan Oktay. Making the most of text semantics to improve biomedical vision-language processing. In European Conference on Computer Vision (ECCV), pages 1–21. Springer, 2022. 1, 2, 3, 4, 5, 6, 7, 17, 18, 19, 20" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.776, + 0.47, + 0.871 + ], + "angle": 0, + "content": "[10] Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, and Ozan Oktay. MS-CXR: Making the most of text semantics to improve biomedical vision-language processing (version 0.1). PhysioNet, 2022. 2, 3, 5, 7, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[11] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakan-" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.135 + ], + "angle": 0, + "content": "tan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020. 5, 19" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.136, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[12] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.191, + 0.892, + 0.26 + ], + "angle": 0, + "content": "[13] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. UNITER: UNiversal Image-TExt Representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.261, + 0.892, + 0.315 + ], + "angle": 0, + "content": "[14] Zhihong Chen, Yan Song, Tsung-Hui Chang, and Xiang Wan. Generating radiology reports via memory-driven transformer. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Nov. 2020. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.317, + 0.892, + 0.397 + ], + "angle": 0, + "content": "[15] Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, and Yale Song. Robust contrastive learning against noisy views. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16670-16681, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.4, + 0.892, + 0.455 + ], + "angle": 0, + "content": "[16] Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka. Debiased contrastive learning. Advances in neural information processing systems, 33:8765-8775, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.456, + 0.892, + 0.511 + ], + "angle": 0, + "content": "[17] R. C. Daudt, B. L. Saux, and Alexandre Boulch. Fully convolutional siamese networks for change detection. In 2018 25th IEEE International Conference on Image Processing (ICIP), pages 4063-4067, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.512, + 0.892, + 0.593 + ], + "angle": 0, + "content": "[18] Dina Demner-Fushman, Marc D Kohli, Marc B Rosenman, Sonya E Shooshan, Laritza Rodriguez, Sameer Antani, George R Thoma, and Clement J McDonald. Preparing a collection of radiology examinations for distribution and retrieval. Journal of the American Medical Informatics Association, 23(2):304-310, 2016. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.595, + 0.892, + 0.65 + ], + "angle": 0, + "content": "[19] Karan Desai and Justin Johnson. Virtex: Learning visual representations from textual annotations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11162-11173, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.651, + 0.892, + 0.705 + ], + "angle": 0, + "content": "[20] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.707, + 0.892, + 0.789 + ], + "angle": 0, + "content": "[21] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2020. 3, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.79, + 0.892, + 0.872 + ], + "angle": 0, + "content": "[22] Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, et al. An empirical study of training end-to-end vision-and-language transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18166-18176, 2022. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.873, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[23] Stanley Durrleman, Xavier Pennec, Alain Trouve, José Braga, Guido Gerig, and Nicholas Ayache. Toward a com" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15024" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.092, + 0.472, + 0.134 + ], + "angle": 0, + "content": "prehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision, 103:22-59, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.136, + 0.47, + 0.207 + ], + "angle": 0, + "content": "[24] Stéphane d'Ascoli, Hugo Touvron, Matthew L Leavitt, Ari S Morcos, Giulio Biroli, and Levent Sagun. Convit: Improving vision transformers with soft convolutional inductive biases. In International Conference on Machine Learning, pages 2286-2296. PMLR, 2021. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.208, + 0.471, + 0.276 + ], + "angle": 0, + "content": "[25] Mark Endo, Rayan Krishnan, Viswesh Krishna, Andrew Y Ng, and Pranav Rajpurkar. Retrieval-based chest x-ray report generation using a pre-trained contrastive language-image model. In Machine Learning for Health, pages 209-219. PMLR, 2021. 5, 6, 18" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.278, + 0.471, + 0.333 + ], + "angle": 0, + "content": "[26] Sepideh Esmaeilpour, Bing Liu, Eric Robertson, and Lei Shu. Zero-shot out-of-distribution detection based on the pretrained model clip. In Proceedings of the AAAI conference on artificial intelligence, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.335, + 0.471, + 0.39 + ], + "angle": 0, + "content": "[27] Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan. Exploring the limits of out-of-distribution detection. Advances in Neural Information Processing Systems, 34:7068-7081, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.392, + 0.471, + 0.476 + ], + "angle": 0, + "content": "[28] Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23):e215-e220, 2000. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.477, + 0.471, + 0.56 + ], + "angle": 0, + "content": "[29] Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare (HEALTH), 3(1):1-23, 2021. 6, 7, 20" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.562, + 0.471, + 0.617 + ], + "angle": 0, + "content": "[30] Tengda Han, Weidi Xie, and Andrew Zisserman. Self-supervised co-training for video representation learning. Advances in Neural Information Processing Systems, 33:5679-5690, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.619, + 0.471, + 0.675 + ], + "angle": 0, + "content": "[31] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.676, + 0.471, + 0.759 + ], + "angle": 0, + "content": "[32] Shih-Cheng Huang, Liyue Shen, Matthew P Lungren, and Serena Yeung. GLoRIA: A multimodal global-local representation learning framework for label-efficient medical image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3942-3951, 2021. 1, 2, 4, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.761, + 0.471, + 0.844 + ], + "angle": 0, + "content": "[33] Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilicus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al. CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the AAAI conference on artificial intelligence, 33(01):590-597, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[34] Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven Truong, Tan Bui, Pierre Chambon, Yuhao Zhang, Matthew P Lungren, Andrew Y Ng, Curtis Langlotz, et al. Radgraph: Extracting clinical entities and relations from radiology re" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.134 + ], + "angle": 0, + "content": "ports. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021. 17" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.894, + 0.219 + ], + "angle": 0, + "content": "[35] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.221, + 0.892, + 0.262 + ], + "angle": 0, + "content": "[36] A. Johnson, T. Pollard, S.J. Berkowitz, R. Mark, and S. Horng. MIMIC-CXR database (version 2.0.0). PhysioNet, 2019. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.264, + 0.892, + 0.347 + ], + "angle": 0, + "content": "[37] Gaurang Karwande, Amarachi B Mbakwe, Joy T Wu, Leo A Celi, Mehdi Moradi, and Ismini Lourentzou. Chexrelnet: An anatomy-aware model for tracking longitudinal relationships between chest x-rays. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2022, pages 581-591, 2022. 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.349, + 0.892, + 0.404 + ], + "angle": 0, + "content": "[38] Jiann-Shu Lee, Jing-Wein Wang, Hsing-Hsien Wu, and Ming-Zheng Yuan. A nonparametric-based rib suppression method for chest radiographs. Computers & Mathematics with Applications, 64(5):1390-1399, 2012. 20" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.406, + 0.892, + 0.517 + ], + "angle": 0, + "content": "[39] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 12888-12900. PMLR, 17-23 Jul 2022. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.519, + 0.892, + 0.588 + ], + "angle": 0, + "content": "[40] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems, 34:9694-9705, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.59, + 0.892, + 0.645 + ], + "angle": 0, + "content": "[41] Chin-Yew Lin. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74-81, Barcelona, Spain, July 2004. Association for Computational Linguistics. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.647, + 0.892, + 0.702 + ], + "angle": 0, + "content": "[42] Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. Generating wikipedia by summarizing long sequences. In International Conference on Learning Representations, 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.704, + 0.892, + 0.745 + ], + "angle": 0, + "content": "[43] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018. 17, 19" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.747, + 0.892, + 0.788 + ], + "angle": 0, + "content": "[44] Bradley C Lowekamp, David T Chen, Luis Ibanez, and Daniel Blezek. The design of simpleitk. Frontiers in neuroinformatics, 7:45, 2013. 20" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.79, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[45] Yasuhide Miura, Yuhao Zhang, Emily Tsai, Curtis Langlotz, and Dan Jurafsky. Improving factual completeness and consistency of image-to-text radiology report generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5288-5304, Online, June 2021. Association for Computational Linguistics. 5, 6, 7, 18" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15025" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.161 + ], + "angle": 0, + "content": "[46] Jong Hak Moon, Hyungyung Lee, Woncheol Shin, Young-Hak Kim, and Edward Choi. Multi-modal understanding and generation for medical images and text via vision-language pre-training. IEEE Journal of Biomedical and Health Informatics, 2022. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.164, + 0.47, + 0.207 + ], + "angle": 0, + "content": "[47] American College of Radiology (ACR). ACR practice guideline for communication of diagnostic imaging findings. Practice guidelines & technical standards, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.208, + 0.471, + 0.277 + ], + "angle": 0, + "content": "[48] Dong Yul Oh, Jihang Kim, and Kyong Joon Lee. Longitudinal change detection on chest X-rays using geometric correlation maps. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 748-756. Springer, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.278, + 0.471, + 0.32 + ], + "angle": 0, + "content": "[49] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 1, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.322, + 0.471, + 0.404 + ], + "angle": 0, + "content": "[50] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311-318, Philadelphia, Pennsylvania, USA, July 2002. Association for Computational Linguistics. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.406, + 0.471, + 0.448 + ], + "angle": 0, + "content": "[51] Namuk Park and Songkuk Kim. How do vision transformers work? In International Conference on Learning Representations, 2022. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.449, + 0.471, + 0.504 + ], + "angle": 0, + "content": "[52] Daifeng Peng, Yongjun Zhang, and Haiyan Guan. End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sensing, 11(11):1382, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.506, + 0.471, + 0.59 + ], + "angle": 0, + "content": "[53] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.591, + 0.471, + 0.646 + ], + "angle": 0, + "content": "[54] Vignav Ramesh, Nathan Andrew Chi, and Pranav Rajpurkar. Improving radiology report generation systems by removing hallucinated references to non-existent priors. arXiv preprint arXiv:2210.06340, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.649, + 0.471, + 0.732 + ], + "angle": 0, + "content": "[55] Adria Recasens, Pauline Luc, Jean-Baptiste Alayrac, Luyu Wang, Florian Strub, Corentin Tallec, Mateusz Malinowski, Viorica Pătrăcean, Florent Altché, Michal Valko, et al. Broaden your views for self-supervised video learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1255–1265, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.733, + 0.471, + 0.843 + ], + "angle": 0, + "content": "[56] Nils Reimers, Iryna Gurevych, Nils Reimers, Iryna Gurevych, Nandan Thakur, Nils Reimers, Johannes Daxenberger, Iryna Gurevych, Nils Reimers, Iryna Gurevych, et al. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, pages 671-688. Association for Computational Linguistics, 2019. 7, 20" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.845, + 0.471, + 0.9 + ], + "angle": 0, + "content": "[57] Liqa A Rousan, Eyhab Elobeid, Musaab Karrar, and Yousef Khader. Chest x-ray findings and temporal lung changes in patients with Covid-19 pneumonia. BMC Pulmonary Medicine, 20(1):1-9, 2020. 1" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.471, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.149 + ], + "angle": 0, + "content": "[58] Ruggiero Santeramo, Samuel Joseph Withey, and G. Montana. Longitudinal detection of radiological abnormalities with time-modulated LSTM. In MICCAI 2018 Workshop on Deep Learning in Medical Imaging Analysis, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.151, + 0.892, + 0.205 + ], + "angle": 0, + "content": "[59] Wenzhong Shi, Min Zhang, Rui Zhang, Shanxiong Chen, and Zhao Zhan. Change detection based on artificial intelligence: State-of-the-art and challenges. Remote Sensing, 12(10):1688, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.208, + 0.892, + 0.304 + ], + "angle": 0, + "content": "[60] George Shih, Carol C Wu, Safwan S Halabi, Marc D Kohli, Luciano M Prevedello, Tessa S Cook, Arjun Sharma, Judith K Amorosa, Veronica Arteaga, Maya Galperin-Aizenberg, et al. Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia. Radiology: Artificial Intelligence, 1(1):e180041, 2019. 2, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.305, + 0.892, + 0.389 + ], + "angle": 0, + "content": "[61] Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela. Flava: A foundational language and vision alignment model. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15617-15629, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.391, + 0.892, + 0.489 + ], + "angle": 0, + "content": "[62] Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Ng, and Matthew Lungren. Combining automatic labelers and expert annotations for accurate radiology report labeling using BERT. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1500–1519, Online, Nov. 2020. Association for Computational Linguistics. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.49, + 0.892, + 0.559 + ], + "angle": 0, + "content": "[63] Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Y Ng, and Matthew P Lungren. CheXbert: combining automatic labelers and expert annotations for accurate radiology report labeling using bert. arXiv preprint arXiv:2004.09167, 2020. 2, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.561, + 0.892, + 0.644 + ], + "angle": 0, + "content": "[64] Anuroop Sriram, Matthew Muckley, Koustuv Sinha, F. Shamout, Joelle Pineau, K. Geras, L. Azour, Y. Aphinyanaphongs, N. Yakubova, and William H. Moore. COVID-19 prognosis via self-supervised representation learning and multi-image prediction. arXiv preprint arXiv:2101.04909, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.646, + 0.892, + 0.7 + ], + "angle": 0, + "content": "[65] Philippe Thévenaz and Michael Unser. Optimization of mutual information for multiresolution image registration. IEEE transactions on image processing, 9(12):2083-2099, 2000. 20" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.703, + 0.892, + 0.759 + ], + "angle": 0, + "content": "[66] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.761, + 0.892, + 0.843 + ], + "angle": 0, + "content": "[67] Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar, Can Liu, Andrew Y Ng, and Pranav Rajpurkar. Medaug: Contrastive learning leveraging patient metadata improves representations for chest x-ray interpretation. In Machine Learning for Healthcare Conference, pages 755-769. PMLR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.845, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[68] Chuang Wang, Andreas Rimner, Yu chi Hu, Neelam Tyagi, Jue Jiang, Ellen Yorke, Sadegh Riyadhi, Gig S. Mageras, Joseph O. Deasy, and Pengpeng Zhang. Towards predicting the evolution of lung tumors during radiotherapy observed" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "15026" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.093, + 0.468, + 0.12 + ], + "angle": 0, + "content": "on a longitudinal MR imaging study via a deep learning algorithm. Medical Physics, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.47, + 0.162 + ], + "angle": 0, + "content": "[69] Wenhui Wang, Hangbo Bao, Li Dong, and Furu Wei. Vlmo: Unified vision-language pre-training with mixture-of-modality-experts. arXiv preprint arXiv:2111.02358, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.164, + 0.468, + 0.272 + ], + "angle": 0, + "content": "[70] Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers. ChestX-Ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 2097-2106. IEEE Computer Society, 2017. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.274, + 0.468, + 0.342 + ], + "angle": 0, + "content": "[71] Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. Finetuned language models are zero-shot learners. In International Conference on Learning Representations, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.344, + 0.468, + 0.426 + ], + "angle": 0, + "content": "[72] Joy Wu, Nkechinyere Agu, Ismini Lourentzou, Arjun Sharma, Joseph Paguio, Jasper Seth Yao, Edward Christopher Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo Anthony Celi, Tanveer Syeda-Mahmood, and Mehdi Moradi. Chest imagenome dataset (version 1.0.0). PhysioNet, 2021. 5, 15, 18" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.428, + 0.468, + 0.496 + ], + "angle": 0, + "content": "[73] Kan Wu, Houwen Peng, Minghao Chen, Jianlong Fu, and Hongyang Chao. Rethinking and improving relative position encoding for vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10033-10041, 2021. 19" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.498, + 0.468, + 0.564 + ], + "angle": 0, + "content": "[74] Yiwen Xu, Ahmed Hosny, Roman Zeleznik, Chintan Parmar, Thibaud P. Coroller, Idalid Ivy Franco, Raymond H. Mak, and Hugo J.W.L. Aerts. Deep learning predicts lung cancer treatment response from serial medical imaging. Clinical Cancer Research, 25:3266 - 3275, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.567, + 0.468, + 0.621 + ], + "angle": 0, + "content": "[75] Bin Yan and Mingtao Pei. Clinical-BERT: Vision-language pre-training for radiograph diagnosis and reports generation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(3):2982-2990, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.623, + 0.468, + 0.677 + ], + "angle": 0, + "content": "[76] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. Filip: Fine-grained interactive language-image pre-training. arXiv preprint arXiv:2111.07783, 2021. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.679, + 0.468, + 0.732 + ], + "angle": 0, + "content": "[77] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.734, + 0.468, + 0.802 + ], + "angle": 0, + "content": "[78] Sukmin Yun, Jaehyung Kim, Dongyoon Han, Hwanjun Song, Jung-Woo Ha, and Jinwoo Shin. Time is matter: Temporal self-supervision for video transformers. In International Conference on Machine Learning, pages 25804-25816. PMLR, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.804, + 0.468, + 0.872 + ], + "angle": 0, + "content": "[79] Dewen Zeng, John N Kheir, Peng Zeng, and Yiyu Shi. Contrastive learning with temporal correlated medical images: A case study using lung segmentation in chest x-rays. In 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), pages 1-7. IEEE, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.874, + 0.468, + 0.901 + ], + "angle": 0, + "content": "[80] Zhaoyang Zeng, Daniel McDuff, Yale Song, et al. Contrastive learning of global and local video representations" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.093, + 0.47, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "tions. Advances in Neural Information Processing Systems, 34:7025-7040, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.892, + 0.177 + ], + "angle": 0, + "content": "[81] Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D Manning, and Curtis P Langlotz. Contrastive learning of medical visual representations from paired images and text. arXiv preprint arXiv:2010.00747, 2020. 1, 2, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.179, + 0.892, + 0.247 + ], + "angle": 0, + "content": "[82] Yuhao Zhang, Yuhui Zhang, Peng Qi, Christopher D Manning, and Curtis P Langlotz. Biomedical and clinical english model packages for the stanza python nlp library. Journal of the American Medical Informatics Association, 28(9):1892-1899, 2021. 16, 17, 18" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.247 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "15027" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing/fc7c021d-c682-4b4a-b7f3-6c9ca632109a_origin.pdf b/2023/Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing/fc7c021d-c682-4b4a-b7f3-6c9ca632109a_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..11c52eec829ebc865f16a2875062f256e2dc80c3 --- /dev/null +++ b/2023/Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing/fc7c021d-c682-4b4a-b7f3-6c9ca632109a_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3c066f8e9c27b8afdc5a34f863bb8d79690e1ea9639f85e5bf0eb12c62d73164 +size 2188864 diff --git a/2023/Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing/full.md b/2023/Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing/full.md new file mode 100644 index 0000000000000000000000000000000000000000..bfee42a6c4c91a2b2be5f4c7a83cf6058f0d6c38 --- /dev/null +++ b/2023/Learning To Exploit Temporal Structure for Biomedical Vision-Language Processing/full.md @@ -0,0 +1,336 @@ +# Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing + +Shruthi Bannur,\* Stephanie Hyland\*, Qianchu Liu, Fernando Pérez-García, Maximilian Ilse, Daniel C. Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anja Thieme, Anton Schwaighofer, Maria Wetscherek, Matthew P. Lungren, Aditya Nori Javier Alvarez-Valle, and Ozan Oktay† + +Microsoft Health Futures + +# Abstract + +Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data. + +# 1. Introduction + +Self-supervision from image-text pairs has enabled the development of flexible general-purpose vision-language models both in the general domain [40, 53, 77] and for specialised domains such as biomedicine and radiology + +![](images/ba8a6ac68ac80aebfc26fbae6d9ef3cec20ad8954c2e19cb39fd0aa5961f4c6c.jpg) +Existing methods + +![](images/631b61db4483c495c4ccbff15baf2d2310487f77ec37f4eb5b43e1316e98d8d5.jpg) +Proposed method + +![](images/3109fc488c0faea5247b56d1f99ccede9ccb7c417c16fac97840ecc3c4442985.jpg) +(b) Spatial modelling +Figure 1. (a) Existing visual-language pre-training approaches [9, 32, 81] often use only a single image for contrastive learning (e.g., InfoNCE [49]). (b) In such settings, discarding the temporal connectivity of images limits the alignment of image-text pairs as shown with the affinity matrix, leading to suboptimal pre-training and missed opportunity to create additional model supervision for free. (c, d) Our approach exploits this domain knowledge by learning to incorporate a series of images and correlate them to reports, leading to pre-trained models that can generalise to a wider range of downstream tasks whilst achieving SOTA performance. + +[9, 32, 81]. Vision-language processing (VLP) has shown that cross-modal supervision can provide a richer signal for training both image [19] and text [9] models. However, the success of VLP relies on paired samples sharing semantics, i.e., given an image and text pair, the text should describe the image with minimal extraneous detail [15, 16, 35]. + +In this regard, VLP in biomedicine and radiology poses a distinctive challenge, as reports routinely include comparisons to prior imaging studies [3, 47, 57]. Without knowledge of the underlying mechanisms of these comparisons, it is difficult to determine whether the results are consistent with those obtained by other methods. + +edge of this prior image1, temporal information in the text modality, e.g. "Pneumonia is improving", could pertain to any image containing "Pneumonia", producing ambiguity during contrastive training (Figure 1). Despite this, the existing VLP work to date considers alignment between only single images and reports [9,32,46,81], going so far as to remove temporal content from reports in training data to prevent 'hallucinations' in downstream report generation [54]. However, temporal information can provide complementary self-supervision, solely by exploiting existing structure, and without requiring any additional data. + +In this work, we neither ignore nor remove temporal information in the text modality, but explicitly account for it during pre-training. Rather than treating all image-report pairs in the dataset as independent, we exploit temporal correlations by making prior images available for comparison to a given report. To learn from this structure, we develop a temporal VLP pre-training framework named BioViL-T. A core component is its new multi-image encoder that can handle the absence of prior images and potential spatial misalignment between images across time. BioViL-T takes into account prior images where available, removing cross-modal ambiguity as illustrated in Fig. 1. Linking multiple images during pre-training proves beneficial to both image and text models: we report state-of-the-art (SOTA) performance on both temporal image classification and report generation. In the latter case, we show that prefixing the prior report substantially increases performance, again reflecting the value of prior information. We emphasise that the benefit is not restricted to temporal downstream tasks: our approach also achieves SOTA on non-temporal tasks of pneumonia detection [60] and phrase grounding [10], underscoring the value of a cleaner learning signal during VLP without needing to modify or add to the training dataset. Our contributions can be summarised as follows: + +- We introduce a novel pre-training framework called BioViL-T. It leverages the temporal relationship of samples to self-supervise VLP models, making commonly used biomedical VLP models (e.g., [9, 32, 81]) more applicable to a wider range of downstream tasks without compromising performance on existing benchmarks. +- We develop a generic multi-image encoder that handles missing image inputs and incorporates longitudinal information without requiring explicit image registration. +- We achieve SOTA results in chest X-ray (CXR) report generation, temporal image classification, and phrase grounding downstream benchmarks by accounting for prior context in self-supervised training and fine-tuning. +- We release a new multimodal benchmark dataset, MS-CXR-T, curated by an expert radiologist. It enables + +benchmarking of CXR VLP models in terms of temporal semantics extracted from image and text data. + +# 2. Related work + +Vision-language processing Self-supervised VLP can significantly reduce the need for manual labels required for the training of image encoders [19, 53]. The availability of large-scale paired image-text datasets has thus led to rapid development of general-purpose VLP models. Objectives include contrastive and discriminative image-text matching [40, 53, 69] including local variants [32, 76], auto-regressive (AR) captioning [4, 39, 77] and multi-modal masked modelling objectives [13, 40, 61]. + +Biomedical vision-language processing Paired medical image-report datasets were originally used for supervised learning via (typically) automated label extraction from clinical reports [33, 63, 70]. Using such datasets, advances in general-domain self-supervised VLP have been demonstrated to benefit biomedical imaging applications [9, 32, 81]. Work has incorporated ideas from general-domain VLP such as the original CLIP-style cross-modal contrastive objective [81], multi-modal masking with merged co-attention on image-text representations [46], and adaptations to the data of the domain. For example, a radiology report may have sparse image-specific details, prompting a local modification to the contrastive loss enabling alignment between text tokens and image patches [32]. Domain-specific pre-training of the text model is shown to benefit biomedical VLP [9], and preferential masking of medical terms during masked language modelling (MLM) was explored [75]. Here we use a local loss and domain-specific pre-training of the text model, but did not find a benefit to preferential masking. Similarly, cross-attention [22] is used rather than merged co-attention for image-guided MLM. + +Longitudinal modelling of medical images While prior images are used in unimodal supervised longitudinal analysis of medical images [37,58,68,74], temporal information has not directly been employed for self-supervision. The closest work exploits patient metadata to select positive or negative examples in unimodal contrastive learning [67,79]. + +Existing models typically employ either late fusion of global image representations [58,64,68,74], which can miss fine-grained localised changes [32], or explicit spatial correspondence of features, using fixed spatial grids [48] or object detection [37]. Registering image pairs is commonly used for change detection in other contexts [17,52,59], and has been applied to medical imaging [5, 23]. For CXRs however, registration entails the ill-posed problem of aligning 2D projections of 3D geometry, which inevitably results in residual misalignment. Our approach does not rely on bounding boxes or explicit graph construction as it uses + +self-attention of visual tokens across time to handle any spatial misalignment. + +Self-supervision across time Self-supervision has found applications on densely-sampled time series data (e.g., video) to capture temporal information [30, 55, 78, 80]. Our problem setting involves sparsely and sporadically sampled data where temporal pretext tasks are less applicable [2]. Similarly, it requires text supervision to enable both static and temporal learning, when temporal structure is present. + +# 3. BioViL-T training framework + +Our approach comprises a multi-image encoder designed to extract spatio-temporal features from sequences of images (Section 3.1) and a text encoder incorporating optional cross-attention on image features. The models are trained jointly with image-guided MLM and cross-modal global and local contrastive objectives (Section 3.2). The resulting image and text models are later adapted for uni- or multimodal downstream tasks as described in Section 3.3. Implementation details are presented in Appendices E and F. + +For a given image and report pair $(\mathbf{x}_{\mathrm{img}}^{\mathrm{curr}},\mathbf{x}_{\mathrm{txt}}^{\mathrm{curr}})$ , the report $\mathbf{x}_{\mathrm{txt}}^{\mathrm{curr}}$ describes the current image content and changes in reference to prior images. Our proposed formulation focuses on a single prior image; however, it can be generalised to multiple prior images depending on the application. Hence, we construct datasets by including the prior image whenever it exists: $(\mathbf{x}_{\mathrm{img}}^{\mathrm{curr}},\mathbf{x}_{\mathrm{img}}^{\mathrm{prior}},\mathbf{x}_{\mathrm{txt}}^{\mathrm{curr}}) \in \mathcal{D}_m$ or $(\mathbf{x}_{\mathrm{img}}^{\mathrm{curr}},\varnothing ,\mathbf{x}_{\mathrm{txt}}^{\mathrm{curr}}) \in \mathcal{D}_s$ with the resulting dataset being a union of single and multi-image examples: $\mathcal{D} = \mathcal{D}_m \cup \mathcal{D}_s$ . + +# 3.1. Extracting spatio-temporal image features + +Clinical findings are often observed across different image regions and co-occur simultaneously, which requires dense level visual reasoning across time to capture both static and temporal features. In contrast to late global fusion [64] and bounding-box based approaches [37], BioViL-T leverages local correspondences between image regions across time using transformer self-attention blocks [21]. Thus our method does not require an explicit image registration step between time points. + +We propose a hybrid CNN-Transformer encoder model due to its data efficiency and spatial flexibility of cross-attention across time points: $E_{\mathrm{img}}$ : $\mathbb{R}^{W\times H}\rightarrow \mathbb{R}^{W^{\prime}\times H^{\prime}\times D_{\mathrm{img}}}$ (e.g., ResNet-50 [31]) and $A_{\mathrm{img}}$ : $\mathbb{R}^{T\times L\times D_{\mathrm{img}}}\rightarrow \mathbb{R}^{L\times D_{\mathrm{img}}}$ (e.g., transformer [21]), where $W$ , $H$ , and $T$ correspond to spatiotemporal dimensions, $L = W^{\prime}H^{\prime}$ is the number of visual tokens per image, and $D_{\mathrm{img}}$ is the embedding dimension. Here $E_{\mathrm{img}}$ serves as a stem network [51] to provide visual token features of individual images. The CNN's inductive biases [24, 51] en + +sure data efficiency of our hybrid model, making it ideal for smaller scale biomedical datasets. $E_{\mathrm{img}}$ is initialised with BioViL weights [9]. The main purpose of $A_{\mathrm{img}}$ is to capture patch embedding interactions across time when a prior image $\mathbf{x}_{\mathrm{img}}^{\mathrm{prior}}$ is available and to aggregate them into a fixed-length token representation. Input visual tokens, $\mathbf{H}_0^{\mathrm{curr}} = \mathbf{P}^{\mathrm{curr}} \coloneqq E_{\mathrm{img}}(\mathbf{x}_{\mathrm{img}}^{\mathrm{curr}})$ , $\mathbf{H}_0^{\mathrm{prior}} \coloneqq E_{\mathrm{img}}(\mathbf{x}_{\mathrm{img}}^{\mathrm{prior}})$ are augmented with spatio-temporal positional encodings and flattened across the spatial dimensions. They are then processed by $K$ transformer encoder [66] layers $A$ as follows: + +$$ +\left[ \begin{array}{l} \mathbf {H} _ {k} ^ {\text {c u r r}} \\ \mathbf {H} _ {k} ^ {\text {p r i o r}} \end{array} \right] = A _ {k} \left(\left[ \begin{array}{l} \mathbf {H} _ {k - 1} ^ {\text {c u r r}} + \mathbf {S} + \mathbf {1} _ {L} \otimes \mathbf {t} ^ {\text {c u r r}} \\ \mathbf {H} _ {k - 1} ^ {\text {p r i o r}} + \mathbf {S} + \mathbf {1} _ {L} \otimes \mathbf {t} ^ {\text {p r i o r}} \end{array} \right]\right), \tag {1} +$$ + +for $k = 1,\dots ,K$ where $\mathbf{S}\in \mathbb{R}^{L\times D_{\mathrm{img}}}$ denotes 2D sinusoidal positional encodings [12] and $\mathbf{T} = [\mathbf{t}^{\mathrm{curr}};\mathbf{t}^{\mathrm{prior}}]\in \mathbb{R}^{2\times D_{\mathrm{img}}}$ is its temporal counterpart, which is learnt (Fig. 2) [4]. The layer-normalised (LN) [6] output of the final transformer encoder block $\mathbf{P}^{\mathrm{diff}}\coloneqq \mathrm{LN}(\mathbf{H}_K^{\mathrm{curr}})$ is an 'aggregated' representation of patch-level progression information anchored on the current image. Figure 3 shows attention roll-out [1] applied to $\mathbf{P}^{\mathrm{diff}}$ after pre-training, showing how the prior image contributes to the fused representation. Figure A.3 further highlights the robustness to variations in pose underlining that registration is not necessary for this encoder. + +Static-temporal feature decomposition When a prior image is available the final image representation $\mathbf{V} := \mathbf{P}^{\mathrm{curr}} \oplus \mathbf{P}^{\mathrm{diff}} \in \mathbb{R}^{W' \times H' \times 2D_{\mathrm{img}}}$ is formed by concatenating two sets of features (similar to [7]): those from the current image alone ( $\mathbf{P}^{\mathrm{curr}}$ ) and the temporal features from current and prior images ( $\mathbf{P}^{\mathrm{diff}}$ ). In this way, self-attention is mainly required to cope with pose variations and patch comparisons across time in extracting temporal content, removing the need for registration or explicit spatial feature alignment. When no prior scan is available ( $\mathbf{x} \in \mathcal{D}_s$ ), $A_{\mathrm{img}}$ is not used and $\mathbf{P}^{\mathrm{diff}}$ is replaced by a learnable token $\mathbf{p}^{\mathrm{miss}} \in \mathbb{R}^{D_{\mathrm{img}}}$ , replicated across the spatial dimensions. Section 4.5 later demonstrates that $A_{\mathrm{img}}$ highlights the value of feature decomposition for tasks such as phrase grounding which require well-localised features [10]. + +Hereafter, downstream tasks that require solely single image features, $\mathbf{P}^{\mathrm{curr}}$ , are referred to as static tasks, and the ones that benefit from additional progression information, $\mathbf{P}^{\mathrm{diff}}$ , as temporal tasks, e.g., report decoding. + +# 3.2. Text-supervision for spatio-temporal learning + +Let $\mathbf{w} = (w_{1},\dots ,w_{M})$ denote a vector of $M$ tokens of a report $\mathbf{x}_{\mathrm{txt}}$ after tokenisation. We first obtain contextualised token features $E_{\mathrm{txt}}(\mathbf{w})\in \mathbb{R}^{M\times D_{\mathrm{xt}}}$ by passing a sequence of text tokens $\mathbf{w} = (w_{1},\dots ,w_{M})$ through a BERT encoder $E_{\mathrm{txt}}$ [20]. The input sequence is prepended with either a [CLS] or [MLM] token associated with a downstream training objective, conditioning the output features + +![](images/a750e5216a349c2950cf5f0d66ab179ac9b31e4129d44e9b34a0a01ad2cd8b41.jpg) +Figure 2. The proposed self-supervised VLP training framework BioViL-T: Image representations $\mathbf{V}$ are extracted from single and multiple input scans (whenever available) using a hybrid CNN and transformer encoder [24, 51]. This design choice is to increase the data-efficiency and enable the fusion of temporal content without requiring image registration. They are later matched with their corresponding text representations obtained with CXR-BERT [9] using local [32] and global InfoNCE [49] training objectives. As an additional model supervision, multi-modal fused representations, obtained with cross-attention, are used for image-guided masked language modelling. + +similar to [39, 42]. During training, we do two forward passes through $E_{\mathrm{txt}}$ : once with masking at $45\%$ probability (for the MLM objective) and once without masking for contrastive learning, as shown in Figure 2. The text encoder is initialised with the weights of CXR-BERT[9] canonical model, trained on domain-specific vocabulary and corpora. + +Both text and image features are later projected into a joint latent space with $\phi_{\mathrm{txt}}: \mathbb{R}^{D_{\mathrm{txt}}} \to \mathbb{R}^D$ , and similarly $\mathbf{v}_{w,h}^{\mathrm{proj}} := \phi_{\mathrm{img}}(\mathbf{v}_{w,h})$ where $\phi_{\mathrm{img}}: \mathbb{R}^{D_{\mathrm{img}}} \to \mathbb{R}^D$ , with $\phi$ being a two-layer perceptron in our experiments. + +Contrastive objectives Let $\mathbf{r} \coloneqq [E_{\mathrm{xt}}(\mathbf{w})]_{[\mathrm{CLS}]}$ denote the global representation of $\mathbf{w}$ , with $\mathbf{r}^{\mathrm{proj}} \coloneqq \phi_{\mathrm{xt}}(\mathbf{r})$ its projected version. Given projected patch embeddings $\mathbf{v}_{w,h}^{\mathrm{proj}}$ , we can compute a global cosine similarity $S_C(\bar{\mathbf{v}}^{\mathrm{proj}}, \mathbf{r}^{\mathrm{proj}})$ and a local similarity using weighted pairwise cosine similarities across text tokens and projected patch embeddings [32, 76]. These similarities are used in both global and local contrastive objectives with the InfoNCE loss [49, 53]. The local loss proves crucial both for static phrase-grounding and temporal image classification (see Table 7), highlighting the importance of localised self-supervision. + +Image-guided masked language modelling Prior work [9, 46] has shown that biomedical visual-language learning benefits from an auxiliary task such as MLM since capturing the joint distribution of tokens can stabilise and improve + +language understanding during joint learning. Given a batch $\mathcal{B}$ of token vectors $\mathbf{w}$ , it is often defined as the cross-entropy for predicting the randomly sampled masked tokens, $m\subset$ $\{1,\dots ,M\}$ , $\mathcal{L}_{\mathrm{MLM}} = -\frac{1}{|\mathcal{B}|}\sum_{\mathbf{w}\in \mathcal{B}}\log p_{\theta}(\mathbf{w}_m|\mathbf{w}_{\backslash m})$ , where $\theta$ are the weights of the text encoder $E_{\mathrm{txt}}$ . + +In the absence of image information, however, certain masked findings and attributes are not readily predicted, e.g., "[MASK] is worsening". As shown in the general domain [13], visual information can help disambiguate such masked predictions and provide additional cross-modal supervision. Thus, we use cross-attention [22,66] to the image features $\mathbf{v}_{w,h}^{\mathrm{proj}}$ during this task. Specifically, for our image-guided MLM objective we model $p_{\theta}(\mathbf{w}_m|\mathbf{w}_{\backslash m},\mathbf{v}_{w,h}^{\mathrm{proj}})$ . + +# 3.3. Adaptations to downstream tasks + +BioViL-T can be adapted to various downstream tasks. For phrase-grounding and zero-shot inference, we rely on $S_{C}(\mathbf{r}^{\mathrm{proj}},\mathbf{v}_{w,h}^{\mathrm{proj}})$ similar to [9, 32]. For multiple-text prompts, projected text embeddings are marginalised prior to $\ell_2$ -normalisation [53]. To enable language decoding, $\mathbf{v}_{w,h}^{\mathrm{proj}}$ inputs are cross-attended by text queries $\mathbf{w}$ , and causal-attention is utilised between text tokens [39,66]. Differing from [9, 32, 81], we show that report generation tasks can greatly benefit from temporal joint latent space. + +Conditioning on prior reports In contrast to existing work, we incorporate the prior report as a prompt to contextualise the report generation task: $p_{\Phi}(\mathbf{w}_{\mathrm{txt}}^{\mathrm{curr}}|\mathbf{w}_{\mathrm{txt}}^{\mathrm{prior}},\mathbf{v}_{w,h}^{\mathrm{proj}})$ + +where $\Phi$ are the multi-modal encoder-decoder network's weights, and $\mathbf{w}_{\mathrm{txt}}^{\mathrm{curr}}$ , $\mathbf{w}_{\mathrm{txt}}^{\mathrm{prior}}$ denote text tokens for current and prior reports respectively. This is analogous to fine-tuning GPT-3 [11] with prompts and instructions [71], but conditioning on both images and the previous report. A dedicated separation token [SEP] is added into the input sequence $[\mathbf{w}_{\mathrm{txt}}^{\mathrm{prior}}, [\mathrm{SEP}], \mathbf{w}_{\mathrm{txt}}^{\mathrm{curr}}]$ . + +Curation of imaging datasets CXR datasets [36] often contain multiple image acquisitions $\mathcal{Z} = \{\mathbf{x}_1^{\mathrm{img}},\dots ,\mathbf{x}_Z^{\mathrm{img}}\}$ in a single visit due to data quality issues such as a limited field-of-view or scanning the wrong body part (Figure A.4). Unlike [9, 32, 81], we conduct curation to choose higher quality images among the potential candidates instead of performing a random selection. For this step, a separate BioViL-T is trained on 'clean' studies with single acquisitions and later used in a zero-shot setting to detect out-of-distribution samples [26, 27] arising from the re-imaging process. The candidate $\hat{z}$ is selected as follows: $\hat{z} = \arg \max_{z\in \mathcal{Z}}S_C(\bar{\mathbf{v}}_z^{\mathrm{proj}},\mathbf{r}^{\mathrm{proj}})$ s.t. $|s_{\hat{z}} - s_{\mathcal{Z}\backslash \hat{z}}| > \delta$ for a margin $\delta$ . This approach is applied to enhance the quality of the temporal classification dataset given its limited size. + +# 4. Datasets & experiments + +Here, we demonstrate BioViL-T's data efficiency and adaptability to a wide range of applications, and show how the model achieves SOTA performance on various downstream tasks by learning from data instances linked across time, making effective use of domain priors and the available training data. Specifically, our model is evaluated on a diverse set of downstream tasks including zero- and few-shot static and temporal image classification, report generation, phrase-grounding [10], and sentence similarity. + +MS-CXR-T benchmark We release a new multi-modal benchmark dataset, MS-CXR-T, to evaluate chest X-ray VLP models on two distinct temporal tasks: image classification and sentence similarity. The former comprises multi-image and ground-truth label pairs ( $N = 1326$ ) across 5 findings, with classes corresponding to 3 states of disease progression for each finding: {Improving, Stable, Worsening}. The latter quantifies the temporal-semantic similarity of text embeddings extracted from pairs of sentences ( $N = 361$ ). The pairs can be either paraphrases or contradictions in terms of disease progression. The data for both tasks was manually annotated and reviewed by a board certified radiologist. Appendix C provides further details on its data distribution and annotation protocol. + +Datasets For pre-training, we use the MIMIC-CXR v2 [28, 36] chest X-ray dataset, which contains longitudinal imaging studies with corresponding radiological reports, + +![](images/9b80633388fde47040f844eb765027e40fa2a8c1633841b7428f1329e3150816.jpg) +Prior image + +![](images/9e3374aca330c8f6ad13e42f253762665b3926bb4b465dd5d846e41330fe39a9.jpg) +Current image + +![](images/763eaa5fbed791e21a14790fb1fceab3437bc4d5211a20cc447c66bac14b8279.jpg) +Prior image +Figure 3. Attention rollout maps [1] from the reference patch (marked with $\star$ ) to the current and prior images. The bounding boxes, annotated by a radiologist, show the extent of consolidation. Note that the reference patch attends to its anatomical neighbourhood in the prior image despite the misalignment between prior and current images. The grid $(14 \times 14)$ represents the patch tokens processed in the transformer encoder blocks. + +![](images/a927b1d4f69a7347ee14bb8b27fb26fe785383e7f2bbfc866cbe0f4f59540275.jpg) +Current image + +see Fig. B.1 for the distribution of studies. We only use frontal view scans and discard samples where reports do not contain an IMPRESSION section. From this data, we gather 174.1k and 4.9k text-image pairs for training and validation respectively, with a majority of pairs including a prior image: $|\mathcal{D}_m^{\mathrm{train}}| = 118.8k$ , $|\mathcal{D}_s^{\mathrm{train}}| = 55.3k$ . The text consists of the IMPRESSION section and, for MLM additionally the FINDINGS section if available. Note that no manual labels are used during pre-training and no additional data is used for the methods that leverage the link between current and prior images. For early stopping we track the validation loss, see Appendix E for implementation details. + +Downstream evaluations are performed on a disjoint held-out test set shared across all tasks, $|\mathcal{D}^{\mathrm{test}}| = 2971$ . For report generation, we extend this test set with samples from healthy subjects ( $N = 815$ ) to match the prevalence of pathological studies used in prior work [14, 25, 45]. For fine-tuning on temporal image classification, we use labels from the Chest ImaGenome dataset [72] as in [37] (statistics in Table F.2). In detail, we use the following benchmark datasets: (I) MS-CXR [10] for phrase grounding, (II) the RSNA Pneumonia dataset [60, 70] to test zero-shot and fine-tuned classification, (III) MS-CXR-T for temporal sentence similarity and temporal image classification. + +Comparison approaches We compare our approach to other domain-specific SOTA pre-training frameworks [9, 32] specifically on phrase-grounding and zero-shot predictive performance. The non-temporal BioViL framework [9] is most similar to our approach and provides insight into non-temporal pre-training. We additionally compare to internal ablations such as removing the past report during report generation and masking prior images during phrase grounding. For SOTA performance comparison, various AR and nearest-neighbour (NN) based language decoding approaches are used as baselines: IFCC [45], R2Gen [14], CXR-RePaiR-2 [25], and CXR-RePaiR-Select [25]. + +For the temporal classification task, we compare against a baseline exploiting the BioViL image encoder [9], and an + +Table 1. Results for report generation task: Predictions are evaluated in terms of lexical (BLEU-4, ROUGE) and factuality metrics (CHEXBERT, TEM). Approaches are grouped into two broad categories: nearest-neighbour (NN) and auto-regressive (AR). BioViL-T pre-training consistently yields improved decoding. Further, the consistent performance gains of using prior image and report demonstrate the importance of such domain priors. 'PI / PR' indicate usage of prior image and report, respectively. + +
MethodPre-trainingPI / PRBLEU-4ROUGECHEXBERTTEM
NCXR-RePaiR-2 [25]BioViLX/X2.114.328.112.5
Baseline (NN) [9]BioViLX/X3.720.028.311.1
Proposed (NN)BioViL-T✓/X4.520.529.013.0
MBaseline (AR) [9]BioViLX/X7.5 ± 0.127.9 ± 0.129.3 ± 0.313.8 ± 0.1
ProposedBioViL-T✓/X8.2 ± 0.128.7 ± 0.130.2 ± 0.716.0 ± 0.3
ProposedBioViL-T✓/✓9.2 ± 0.329.6 ± 0.131.7 ± 1.017.5 ± 0.1
+ +Table 2. Temporal image classification results (repeated for 4 random seeds) on the MS-CXR-T benchmark for fully-supervised and zero-/few-shot (Z&F) learning settings, in terms of macroaccuracy across the three classes for each finding. Affine registration is performed for the baseline method (denoted with suffix 'w/ reg'), to partially address the pose variations across scans. + +
Method (% of labels)Pre-trainConsolidationPl. effusionPneumoniaPneumothoraxEdema
Z&FBioViL-T prompt (0%)Temporal53.6 ± 1.959.7 ± 2.158.0 ± 3.934.9 ± 1.064.2 ± 1.5
BioViL-T (10%)Temporal59.7 ± 2.462.4 ± 1.460.1 ± 2.135.3 ± 2.662.6 ± 1.7
SupervisedCNN + TransformerImageNet44.0 ± 2.061.3 ± 1.645.1 ± 3.531.5 ± 3.165.5 ± 1.1
CheXRelNet [37]ImageNet4747473649
BioViL [9]Static56.1 ± 1.562.3 ± 1.159.4 ± 1.041.7 ± 2.867.5 ± 0.8
BioViL w/reg [9]Static56.0 ± 1.563.0 ± 0.960.2 ± 0.742.5 ± 2.767.5 ± 0.9
BioViL-T wout curationTemporal58.9 ± 1.765.5 ± 0.761.5 ± 2.244.4 ± 2.167.4 ± 0.8
BioViL-TTemporal61.1 ± 2.467.0 ± 0.861.9 ± 1.942.6 ± 1.668.5 ± 0.8
+ +approach that makes use of graph convolutions across regions of interest extracted from bounding boxes [37]. For BioViL, we perform affine image registration (with 4 DoF) for each pair of scans to cope with pose variations, and the encoded images are concatenated along the feature dimension and classified via a multilayer perceptron. For [37], we compare to the three-class setting. Lastly, we benchmark our final text model in isolation against domain specific SOTA models in a temporal sentence similarity task: CXR-BERT [9] and PubMedBert [29]. + +Metrics Due to class imbalance, we report macroaccuracy for temporal image classification. For phrase grounding, we use mean Intersection-Over-Union (mIoU) and Contrast-to-Noise-Ratio (CNR) [9]. The latter measures the discrepancies between cosine similarities inside and out of the bounding box region without requiring hard thresholds. To evaluate the quality of generated reports, we use both the standard lexical metrics, e.g., BLEU [50], ROUGE-L [41], and also domain-specific factuality metric: CheXbert [62]. To directly probe the generation of change-related information, we introduce a new metric called temporal entity matching (TEM) to compute the match score of a fixed set of temporal entities (see Appendix D). + +Table 3. Report generation results using the same train/test splits from [25], measured by lexical (BLEU-2) and factuality (CHEXBERT) metrics. Baseline results were also collected from [25]. Note the CHEXBERT score covers all 14 observations. + +
MethodDecoded sectionsBLEU-2CHEXBERT
R2gen [14]Findings & Impression21.20 ± 0.1014.80 ± 0.30
IFCC [45]Findings21.70 ± 0.1027.00 ± 0.40
CXR-RePaiR-Sel [25]Impression5.00 ± 0.1027.40 ± 0.30
BioViL-TImpression15.86 ± 0.1434.83 ± 0.73
BioViL-TFindings & Impression21.31 ± 0.1935.86 ± 0.35
+ +# 4.1. Temporal pre-training yields data efficiency + +# Downstream tasks are enabled with minimal labels. + +The sections 'NN' and 'Z&F' on Tables 1 and 2 report zero- and few-shot performance on tasks benefitting from temporal information: temporal image classification and report generation. Here we measure the quality of the learnt joint latent space and the extent to which BioViL-T enables efficient use of raw data. For zero-shot classification we prompt the AR fine-tuned model with prefix: "[FINDING] is" and compare the next-token probability of words meaning 'improving', 'stable', and 'worsening' (Appendix F.4). + +Without using any labelled data, Table 2 shows that the proposed AR-based approach already yields performance superior to prior fully-supervised work [37] on temporal image classification. With only $10\%$ of labels, classification fine-tuning provides a further boost, indicating that BioViL-T produces a multi-image encoder readily adapted to temporal tasks. Similarly, in a zero-shot report-retrieval setting, the findings show that compared to temporally-agnostic pre-training, BioViL-T leveraging prior images improves across all metrics. Consistent with prior work [25], the retrieved reports already preserve factuality with high CheXbert scores, more-so than the other metrics which measure fine-grained specifics of phrasing. This demonstrates that the latent space captures the high-level semantics of the clinical features. Fine-grained phrasing however will be substantially improved by AR fine-tuning. + +# 4.2. Achieving SOTA performance with BioViL-T + +# A wide range of downstream tasks benefit substantially from temporally-aware pre-training. + +Through downstream adaptations and fine-tuning our model, we report SOTA performance on report generation and temporal image classification tasks. For the former, using both prior images and reports during fine-tuning substantially improves across metrics (Table 1). In particular, TEM metric results show that temporal context is key for accurately describing change in the generated report while avoiding hallucinations (see Table A.1 for examples). Comparing to published results on a comparable test split and + +Table 4. Image classification results on RSNA Pneumonia Detection Benchmark [60] for train and test splits of $70\% -30\%$ respectively. + +
Method% of LabelsSupervisionAcc.F1AUROC
GLoRIA [32]XZero-shot0.700.58-
BioViL [9]XZero-shot0.7320.6650.831
BioViL-TXZero-shot0.8050.7060.871
BioViL [9]1%Few-shot0.8050.7230.881
BioViL-T1%Few-shot0.8140.7300.890
+ +metrics (Sec. 4.1), we conclude that BioViL-T with fine-tuning achieves SOTA on report generation, producing reports that are lexically on par with prior work but substantially more factually accurate. Note that we do 'vanilla' AR fine-tuning to focus on the impact of the pre-trained encoders, so application-specific supervision [45] could be used in conjunction to further boost performance. + +In temporal image classification (Tab. 2), BioViL-T pretraining outperforms the non-temporal baseline (BioViL) and improves on previously-reported results [37] by up to 20 percentage points (pp). Furthermore, baseline methods that rely on image registration (BioViL w/reg), underperform compared to the proposed approach. Further analysis reveals that errors tend to be in cases with disagreement between radiologists (Appendix A.2). We also note that pretraining is critical for a hybrid CNN-transformer model on this task, likely due to the small labelled dataset. Lastly, curation of temporal training data is observed to improve the classification results by .68 pp aggregated across the findings, see Appendix A.4 for details. + +# 4.3. Static tasks benefit from temporal learning + +BioViL-T broadens the range of applicable downstream tasks whilst contributing to performance on static tasks. + +In this section, we demonstrate that performance improvements afforded by BioViL-T are not restricted to temporal tasks – static tasks also benefit. Table 4 reports results on zero- and few-shot pneumonia classification from single images [60], where BioViL-T establishes a new SOTA compared to prior work [9,32]. + +We see a similar trend on the MS-CXR phrase grounding benchmark (Tab. 5). This task can be solved with single images, however we show that the inclusion of the prior image (where available) does not impair the performance of BioViL-T. Feature decomposition effectively preserves localised information from the current image. + +# 4.4. Towards better sentence embedding quality + +Language models acquire increased temporal sensitivity. + +We hypothesise that text encoders learn temporal semantics through supervision from longitudinal image series. To verify this, RadNLI [45] and MS-CXR-T datasets are used in a zero-shot binary classification setting. Cosine similarity + +Table 5. Results on MS-CXR benchmark [10] (5-runs with different seeds), "Multi-image" column indicates the input images used at test time. + +
MethodMulti-ImageAvg. CNRAvg. mIoU
BioViL [9]X1.07 ± 0.040.229 ± 0.005
+ Local loss [9,32]X1.21 ± 0.050.202 ± 0.010
BioViL-TX1.33 ± 0.040.243 ± 0.005
BioViL-T✓1.32 ± 0.040.240 ± 0.005
+ +Table 6. Results on MS-CXR-T sentence similarity benchmark. + +
Text ModelMS-CXR-T (361 pairs)RadNLI (145 pairs)
AccuracyROC-AUCAccuracyROC-AUC
PubMedBERT [29]60.39.54281.38.727
CXR-BERT-G [9]62.60.60187.59.902
CXR-BERT-S [9]78.12.83789.66.932
BioViL-T87.77 ± 0.5.933 ± .00390.52 ± 1.0.947 ± .003
+ +of sentence pair embeddings [56] are treated as class-logits to label each pair either as paraphrase or contradiction. See Appendix F.6 for further details. + +Our text model is benchmarked against SOTA domain-specific BERT models. Table 6 shows that the proposed framework greatly increases the sensitivity of sentence embeddings to temporal content whilst better capturing the static content (RadNLI). Note that CXR-BERT-Specialised [9] is learnt through single-images starting from the same canonical model, illustrating the substantial increase in temporal and static sensitivity due to BioViL-T pre-training. + +# 4.5. Ablation experiments + +In Table 7 we report extensive ablations across the multi-image encoder architecture, pre-training choices, and AR fine-tuning for report generation. + +Image encoder Table 7 shows that decomposition of static and progression features is essential to ensure good performance on single-image tasks, such as phrase grounding. For temporal representations, on the other hand, positional encodings (T) are essential to disambiguate the order of scans, i.e., permutation variance across time. + +Model pre-training The corresponding results are shown in the middle section of Table 7. The local contrastive loss proves crucial to ensure meaningful language supervision during pre-training, followed by the image-guided MLM objective. Lastly, use of the FINDINGS section results in only minor performance gains as the key findings are already captured in the IMPRESSION section. + +Report generation The importance of prior image and report is demonstrated by the substantial drop in the "no prior image and report" ablation, confirming our hypothesis that temporal context is crucial for improving report quality. While both inputs are crucial for optimal performance, + +Table 7. Ablation study on image encoder, pre-training settings, and report generation (one component at a time, and repeated for 4 random seeds). Note that for temporal classification, linear probing is applied to frozen image embeddings. In report generation, the baseline method is fine-tuned with both prior image and report. + +
AblationAvg. CNR (mIoU)Pl. Effusion Acc.
EncoderBaseline1.33 ± 0.02 (.248)64.8 ± 0.6
- Temporal pos. encoding1.32 ± 0.02 (.242)62.9 ± 1.0
- Feature decomposition1.11 ± 0.08 (.203)64.0 ± 0.6
Pre-trainingBaseline1.33 ± 0.02 (.248)64.8 ± 0.6
- Use of findings section1.32 ± 0.01 (.246)63.8 ± 0.8
- MLM loss1.28 ± 0.02 (.238)63.2 ± 0.7
- Local contrastive loss1.18 ± 0.02 (.236)60.2 ± 0.6
AblationROUGETEM
Report gen.Baseline29.64 ± 0.0817.54 ± 0.11
- Prior image29.35 ± 0.2516.30 ± 0.40
- Prior report28.67 ± 0.1216.00 ± 0.30
-(Prior image and report)27.78 ± 0.0913.65 ± 0.48
- Separation token26.00 ± 0.4015.50 ± 1.06
+ +the prior report is more so because it summarises the image and provides a clearer signal. The prior image however cannot be dismissed entirely as it provides granular details which may not always be documented in a report. Finally, we found the separation token is crucial in differentiating between the predicted tokens for the current report and tokens from the prior report. + +# 4.6. Which tokens require a prior image in MLM? + +We leverage the MLM objective in an inference setting to analyse the influence of prior images in predicting masked tokens. Inspired by the $\Delta$ image loss of [8], we define $\Delta_{\mathrm{img}}^{\mathrm{prior}}$ as the change in loss by conditioning the estimation with a prior image for a given token $w$ as follows: + +$$ +\Delta_ {\mathrm {i m g}} ^ {\text {p r i o r}} (w) = l (w, \mathbf {x} _ {\mathrm {i m g}} ^ {\text {c u r r}}, \emptyset) - l (w, \mathbf {x} _ {\mathrm {i m g}} ^ {\text {c u r r}}, \mathbf {x} _ {\mathrm {i m g}} ^ {\text {p r i o r}}) \tag {2} +$$ + +where $l(w, \mathbf{x}_{\mathrm{img}}^{\mathrm{curr}}, \mathbf{x}_{\mathrm{img}}^{\mathrm{prior}})$ is the cross-entropy of predicting the masked token $w$ given visual features (MLM loss for a single token), averaged over sentences in which $w$ appears. $\Delta_{\mathrm{img}}^{\mathrm{prior}}$ is a measure of how much that token benefits from access to the prior image, as well as an assessment of the contribution of the prior image to the image representation. In Figure 4 we show the distribution of $\Delta_{\mathrm{img}}^{\mathrm{prior}}$ as a function of token category (e.g., Anatomy, Positional; see F.5 for annotation details). For Progression-type terms in particular, the model heavily relies on the prior image for image-guided MLM. We further observe that this effect is specific to temporal tokens; as expected, those from other semantic categories do not consistently rely on the prior image. + +# 5. Conclusion + +In this paper, we introduced BioViL-T, a vision-language pre-training framework enabling alignment between text and multiple images. BioViL-T makes use of + +![](images/1774f3c5ed4a9edaa21125a7a8ec112dc540c51122453ce33b74887b58a753a1.jpg) +Figure 4. Mean token-level increase in image-guided MLM loss when prior image is discarded, grouped by token category. The prior image is excluded during inference to measure its impact on masked token predictions. Progression tokens are significantly better predicted when prior images are incorporated into image embeddings. The top five Progression tokens are 'persist', 'improving', 'remains', 'unchanged', and 'residual'. + +a novel multi-image encoder and explicitly decomposes static-temporal features to augment the current image representation with information from prior images. This enables the grounding of temporal references in the text. To our knowledge, this is the first method capable of leveraging the temporal content commonly present in biomedical text. It addresses an important limitation in existing VLP approaches, which simply discard such context. Also, incorporating such multi-modal temporal content provides strong learning signals to the model, resulting in richer representations and improved downstream performance. + +We demonstrate the value of this paradigm through extensive experiments: BioViL-T excels on both static and temporal tasks, establishing new SOTA on report generation, temporal image classification, few/zero-shot pneumonia detection, and phrase grounding. Furthermore, we release a new multi-modal benchmark (MS-CXR-T) to measure the quality of image and text representations in terms of temporal semantics, enabling more diverse evaluation of biomedical VLP models. The corresponding model weights $^6$ and code $^7$ are publicly available. + +Further exploration and evaluation are required on diverse datasets to characterise what kinds of tasks would benefit from a temporal modelling approach, and specifically from the proposed methodology. + +Acknowledgements: We would like to thank Hannah Richardson, Hoifung Poon, Melanie Bernhardt, Melissa Bristow and Naoto Usuyama for their valuable feedback. + +# References + +[1] Samira Abnar and Willem Zuidema. Quantifying attention flow in transformers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4190-4197, Online, July 2020. Association for Computational Linguistics. 3, 5, 13, 14 +[2] Monica N Agrawal, Hunter Lang, Michael Offin, Lior Gazit, and David Sontag. Leveraging time irreversibility with order-contrastive pre-training. In International Conference on Artificial Intelligence and Statistics, pages 2330–2353. PMLR, 2022. 3 +[3] Uwa O. Aideyan, Kevin Berbaum, and Wilbur L. Smith. Influence of prior radiologic information on the interpretation of radiographic examinations. Academic Radiology, 2(3):205-208, 1995. 1 +[4] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning. arXiv preprint arXiv:2204.14198, 2022. 2, 3 +[5] B.B. Avants, C.L. Epstein, M. Grossman, and J.C. Gee. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1):26-41, 2008. Special Issue on The Third International Workshop on Biomedical Image Registration - WBIR 2006. 2 +[6] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016.3 +[7] Nadine Behrmann, Mohsen Fayyaz, Juergen Gall, and Mehdi Noroozi. Long short view feature decomposition via contrastive video representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9244-9253, 2021. 3 +[8] Yonatan Bitton, Gabriel Stanovsky, Michael Elhadad, and Roy Schwartz. Data efficient masked language modeling for vision and language. In 2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021, pages 3013-3028. Association for Computational Linguistics (ACL), 2021. 8 +[9] Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, and Ozan Oktay. Making the most of text semantics to improve biomedical vision-language processing. In European Conference on Computer Vision (ECCV), pages 1–21. Springer, 2022. 1, 2, 3, 4, 5, 6, 7, 17, 18, 19, 20 +[10] Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, and Ozan Oktay. MS-CXR: Making the most of text semantics to improve biomedical vision-language processing (version 0.1). PhysioNet, 2022. 2, 3, 5, 7, 15 +[11] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakan- + +tan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020. 5, 19 +[12] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 3 +[13] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. UNITER: UNiversal Image-TExt Representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 2, 4 +[14] Zhihong Chen, Yan Song, Tsung-Hui Chang, and Xiang Wan. Generating radiology reports via memory-driven transformer. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Nov. 2020. 5, 6 +[15] Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, and Yale Song. Robust contrastive learning against noisy views. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16670-16681, 2022. 1 +[16] Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka. Debiased contrastive learning. Advances in neural information processing systems, 33:8765-8775, 2020. 1 +[17] R. C. Daudt, B. L. Saux, and Alexandre Boulch. Fully convolutional siamese networks for change detection. In 2018 25th IEEE International Conference on Image Processing (ICIP), pages 4063-4067, 2018. 2 +[18] Dina Demner-Fushman, Marc D Kohli, Marc B Rosenman, Sonya E Shooshan, Laritza Rodriguez, Sameer Antani, George R Thoma, and Clement J McDonald. Preparing a collection of radiology examinations for distribution and retrieval. Journal of the American Medical Informatics Association, 23(2):304-310, 2016. 15 +[19] Karan Desai and Justin Johnson. Virtex: Learning visual representations from textual annotations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11162-11173, 2021. 1, 2 +[20] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 3 +[21] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2020. 3, 13 +[22] Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, et al. An empirical study of training end-to-end vision-and-language transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18166-18176, 2022. 2, 4 +[23] Stanley Durrleman, Xavier Pennec, Alain Trouve, José Braga, Guido Gerig, and Nicholas Ayache. Toward a com + +prehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision, 103:22-59, 2013. 2 +[24] Stéphane d'Ascoli, Hugo Touvron, Matthew L Leavitt, Ari S Morcos, Giulio Biroli, and Levent Sagun. Convit: Improving vision transformers with soft convolutional inductive biases. In International Conference on Machine Learning, pages 2286-2296. PMLR, 2021. 3, 4 +[25] Mark Endo, Rayan Krishnan, Viswesh Krishna, Andrew Y Ng, and Pranav Rajpurkar. Retrieval-based chest x-ray report generation using a pre-trained contrastive language-image model. In Machine Learning for Health, pages 209-219. PMLR, 2021. 5, 6, 18 +[26] Sepideh Esmaeilpour, Bing Liu, Eric Robertson, and Lei Shu. Zero-shot out-of-distribution detection based on the pretrained model clip. In Proceedings of the AAAI conference on artificial intelligence, 2022. 5 +[27] Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan. Exploring the limits of out-of-distribution detection. Advances in Neural Information Processing Systems, 34:7068-7081, 2021. 5 +[28] Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23):e215-e220, 2000. 5 +[29] Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare (HEALTH), 3(1):1-23, 2021. 6, 7, 20 +[30] Tengda Han, Weidi Xie, and Andrew Zisserman. Self-supervised co-training for video representation learning. Advances in Neural Information Processing Systems, 33:5679-5690, 2020. 3 +[31] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016. 3 +[32] Shih-Cheng Huang, Liyue Shen, Matthew P Lungren, and Serena Yeung. GLoRIA: A multimodal global-local representation learning framework for label-efficient medical image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3942-3951, 2021. 1, 2, 4, 5, 7 +[33] Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilicus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al. CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the AAAI conference on artificial intelligence, 33(01):590-597, 2019. 2 +[34] Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven Truong, Tan Bui, Pierre Chambon, Yuhao Zhang, Matthew P Lungren, Andrew Y Ng, Curtis Langlotz, et al. Radgraph: Extracting clinical entities and relations from radiology re + +ports. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021. 17 +[35] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 13 +[36] A. Johnson, T. Pollard, S.J. Berkowitz, R. Mark, and S. Horng. MIMIC-CXR database (version 2.0.0). PhysioNet, 2019. 2, 5 +[37] Gaurang Karwande, Amarachi B Mbakwe, Joy T Wu, Leo A Celi, Mehdi Moradi, and Ismini Lourentzou. Chexrelnet: An anatomy-aware model for tracking longitudinal relationships between chest x-rays. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2022, pages 581-591, 2022. 2, 3, 5, 6, 7 +[38] Jiann-Shu Lee, Jing-Wein Wang, Hsing-Hsien Wu, and Ming-Zheng Yuan. A nonparametric-based rib suppression method for chest radiographs. Computers & Mathematics with Applications, 64(5):1390-1399, 2012. 20 +[39] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 12888-12900. PMLR, 17-23 Jul 2022. 2, 4 +[40] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems, 34:9694-9705, 2021. 1, 2 +[41] Chin-Yew Lin. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74-81, Barcelona, Spain, July 2004. Association for Computational Linguistics. 6 +[42] Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. Generating wikipedia by summarizing long sequences. In International Conference on Learning Representations, 2018. 4 +[43] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018. 17, 19 +[44] Bradley C Lowekamp, David T Chen, Luis Ibanez, and Daniel Blezek. The design of simpleitk. Frontiers in neuroinformatics, 7:45, 2013. 20 +[45] Yasuhide Miura, Yuhao Zhang, Emily Tsai, Curtis Langlotz, and Dan Jurafsky. Improving factual completeness and consistency of image-to-text radiology report generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5288-5304, Online, June 2021. Association for Computational Linguistics. 5, 6, 7, 18 + +[46] Jong Hak Moon, Hyungyung Lee, Woncheol Shin, Young-Hak Kim, and Edward Choi. Multi-modal understanding and generation for medical images and text via vision-language pre-training. IEEE Journal of Biomedical and Health Informatics, 2022. 2, 4 +[47] American College of Radiology (ACR). ACR practice guideline for communication of diagnostic imaging findings. Practice guidelines & technical standards, 2020. 1 +[48] Dong Yul Oh, Jihang Kim, and Kyong Joon Lee. Longitudinal change detection on chest X-rays using geometric correlation maps. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 748-756. Springer, 2019. 2 +[49] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 1, 4 +[50] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311-318, Philadelphia, Pennsylvania, USA, July 2002. Association for Computational Linguistics. 6 +[51] Namuk Park and Songkuk Kim. How do vision transformers work? In International Conference on Learning Representations, 2022. 3, 4 +[52] Daifeng Peng, Yongjun Zhang, and Haiyan Guan. End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sensing, 11(11):1382, 2019. 2 +[53] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 4 +[54] Vignav Ramesh, Nathan Andrew Chi, and Pranav Rajpurkar. Improving radiology report generation systems by removing hallucinated references to non-existent priors. arXiv preprint arXiv:2210.06340, 2022. 2 +[55] Adria Recasens, Pauline Luc, Jean-Baptiste Alayrac, Luyu Wang, Florian Strub, Corentin Tallec, Mateusz Malinowski, Viorica Pătrăcean, Florent Altché, Michal Valko, et al. Broaden your views for self-supervised video learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1255–1265, 2021. 3 +[56] Nils Reimers, Iryna Gurevych, Nils Reimers, Iryna Gurevych, Nandan Thakur, Nils Reimers, Johannes Daxenberger, Iryna Gurevych, Nils Reimers, Iryna Gurevych, et al. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, pages 671-688. Association for Computational Linguistics, 2019. 7, 20 +[57] Liqa A Rousan, Eyhab Elobeid, Musaab Karrar, and Yousef Khader. Chest x-ray findings and temporal lung changes in patients with Covid-19 pneumonia. BMC Pulmonary Medicine, 20(1):1-9, 2020. 1 + +[58] Ruggiero Santeramo, Samuel Joseph Withey, and G. Montana. Longitudinal detection of radiological abnormalities with time-modulated LSTM. In MICCAI 2018 Workshop on Deep Learning in Medical Imaging Analysis, 2018. 2 +[59] Wenzhong Shi, Min Zhang, Rui Zhang, Shanxiong Chen, and Zhao Zhan. Change detection based on artificial intelligence: State-of-the-art and challenges. Remote Sensing, 12(10):1688, 2020. 2 +[60] George Shih, Carol C Wu, Safwan S Halabi, Marc D Kohli, Luciano M Prevedello, Tessa S Cook, Arjun Sharma, Judith K Amorosa, Veronica Arteaga, Maya Galperin-Aizenberg, et al. Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia. Radiology: Artificial Intelligence, 1(1):e180041, 2019. 2, 5, 7 +[61] Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela. Flava: A foundational language and vision alignment model. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15617-15629, 2022. 2 +[62] Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Ng, and Matthew Lungren. Combining automatic labelers and expert annotations for accurate radiology report labeling using BERT. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1500–1519, Online, Nov. 2020. Association for Computational Linguistics. 6 +[63] Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Y Ng, and Matthew P Lungren. CheXbert: combining automatic labelers and expert annotations for accurate radiology report labeling using bert. arXiv preprint arXiv:2004.09167, 2020. 2, 16 +[64] Anuroop Sriram, Matthew Muckley, Koustuv Sinha, F. Shamout, Joelle Pineau, K. Geras, L. Azour, Y. Aphinyanaphongs, N. Yakubova, and William H. Moore. COVID-19 prognosis via self-supervised representation learning and multi-image prediction. arXiv preprint arXiv:2101.04909, 2021. 2, 3 +[65] Philippe Thévenaz and Michael Unser. Optimization of mutual information for multiresolution image registration. IEEE transactions on image processing, 9(12):2083-2099, 2000. 20 +[66] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3, 4 +[67] Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar, Can Liu, Andrew Y Ng, and Pranav Rajpurkar. Medaug: Contrastive learning leveraging patient metadata improves representations for chest x-ray interpretation. In Machine Learning for Healthcare Conference, pages 755-769. PMLR, 2021. 2 +[68] Chuang Wang, Andreas Rimner, Yu chi Hu, Neelam Tyagi, Jue Jiang, Ellen Yorke, Sadegh Riyadhi, Gig S. Mageras, Joseph O. Deasy, and Pengpeng Zhang. Towards predicting the evolution of lung tumors during radiotherapy observed + +on a longitudinal MR imaging study via a deep learning algorithm. Medical Physics, 2019. 2 +[69] Wenhui Wang, Hangbo Bao, Li Dong, and Furu Wei. Vlmo: Unified vision-language pre-training with mixture-of-modality-experts. arXiv preprint arXiv:2111.02358, 2021. 2 +[70] Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers. ChestX-Ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 2097-2106. IEEE Computer Society, 2017. 2, 5 +[71] Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. Finetuned language models are zero-shot learners. In International Conference on Learning Representations, 2021. 5 +[72] Joy Wu, Nkechinyere Agu, Ismini Lourentzou, Arjun Sharma, Joseph Paguio, Jasper Seth Yao, Edward Christopher Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo Anthony Celi, Tanveer Syeda-Mahmood, and Mehdi Moradi. Chest imagenome dataset (version 1.0.0). PhysioNet, 2021. 5, 15, 18 +[73] Kan Wu, Houwen Peng, Minghao Chen, Jianlong Fu, and Hongyang Chao. Rethinking and improving relative position encoding for vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10033-10041, 2021. 19 +[74] Yiwen Xu, Ahmed Hosny, Roman Zeleznik, Chintan Parmar, Thibaud P. Coroller, Idalid Ivy Franco, Raymond H. Mak, and Hugo J.W.L. Aerts. Deep learning predicts lung cancer treatment response from serial medical imaging. Clinical Cancer Research, 25:3266 - 3275, 2019. 2 +[75] Bin Yan and Mingtao Pei. Clinical-BERT: Vision-language pre-training for radiograph diagnosis and reports generation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(3):2982-2990, 2022. 2 +[76] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. Filip: Fine-grained interactive language-image pre-training. arXiv preprint arXiv:2111.07783, 2021. 2, 4 +[77] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917, 2022. 1, 2 +[78] Sukmin Yun, Jaehyung Kim, Dongyoon Han, Hwanjun Song, Jung-Woo Ha, and Jinwoo Shin. Time is matter: Temporal self-supervision for video transformers. In International Conference on Machine Learning, pages 25804-25816. PMLR, 2022. 3 +[79] Dewen Zeng, John N Kheir, Peng Zeng, and Yiyu Shi. Contrastive learning with temporal correlated medical images: A case study using lung segmentation in chest x-rays. In 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), pages 1-7. IEEE, 2021. 2 +[80] Zhaoyang Zeng, Daniel McDuff, Yale Song, et al. Contrastive learning of global and local video representations + +tions. Advances in Neural Information Processing Systems, 34:7025-7040, 2021. 3 +[81] Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D Manning, and Curtis P Langlotz. Contrastive learning of medical visual representations from paired images and text. arXiv preprint arXiv:2010.00747, 2020. 1, 2, 4, 5 +[82] Yuhao Zhang, Yuhui Zhang, Peng Qi, Christopher D Manning, and Curtis P Langlotz. Biomedical and clinical english model packages for the stanza python nlp library. 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Lungren, Aditya Nori Javier Alvarez-Valle, and Ozan Oktay†" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 244, + 224, + 349, + 236 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 244, + 224, + 349, + 236 + ], + "spans": [ + { + "bbox": [ + 244, + 224, + 349, + 236 + ], + "type": "text", + "content": "Microsoft Health Futures" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 143, + 264, + 192, + 277 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 264, + 192, + 277 + ], + "spans": [ + { + "bbox": [ + 143, + 264, + 192, + 277 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 290, + 290, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 290, + 290, + 590 + ], + "spans": [ + { + "bbox": [ + 46, + 290, + 290, + 590 + ], + "type": "text", + "content": "Self-supervised learning in vision-language processing (VLP) exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes commonly refer to prior images. This does not only introduce poor alignment between the modalities but also a missed opportunity to exploit rich self-supervision through existing temporal content in the data. In this work, we explicitly account for prior images and reports when available during both training and fine-tuning. Our approach, named BioViL-T, uses a CNN-Transformer hybrid multi-image encoder trained jointly with a text model. It is designed to be versatile to arising challenges such as pose variations and missing input images across time. The resulting model excels on downstream tasks both in single- and multi-image setups, achieving state-of-the-art (SOTA) performance on (I) progression classification, (II) phrase grounding, and (III) report generation, whilst offering consistent improvements on disease classification and sentence-similarity tasks. We release a novel multi-modal temporal benchmark dataset, MS-CXR-T, to quantify the quality of vision-language representations in terms of temporal semantics. Our experimental results show the advantages of incorporating prior images and reports to make most use of the data." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 614, + 128, + 626 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 614, + 128, + 626 + ], + "spans": [ + { + "bbox": [ + 47, + 614, + 128, + 626 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 635, + 287, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 635, + 287, + 684 + ], + "spans": [ + { + "bbox": [ + 46, + 635, + 287, + 684 + ], + "type": "text", + "content": "Self-supervision from image-text pairs has enabled the development of flexible general-purpose vision-language models both in the general domain [40, 53, 77] and for specialised domains such as biomedicine and radiology" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 310, + 281, + 413, + 357 + ], + "blocks": [ + { + "bbox": [ + 341, + 269, + 402, + 278 + ], + "lines": [ + { + "bbox": [ + 341, + 269, + 402, + 278 + ], + "spans": [ + { + "bbox": [ + 341, + 269, + 402, + 278 + ], + "type": "text", + "content": "Existing methods" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 310, + 281, + 413, + 357 + ], + "lines": [ + { + "bbox": [ + 310, + 281, + 413, + 357 + ], + "spans": [ + { + "bbox": [ + 310, + 281, + 413, + 357 + ], + "type": "image", + "image_path": "ba8a6ac68ac80aebfc26fbae6d9ef3cec20ad8954c2e19cb39fd0aa5961f4c6c.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 419, + 281, + 541, + 358 + ], + "blocks": [ + { + "bbox": [ + 449, + 269, + 511, + 278 + ], + "lines": [ + { + "bbox": [ + 449, + 269, + 511, + 278 + ], + "spans": [ + { + "bbox": [ + 449, + 269, + 511, + 278 + ], + "type": "text", + "content": "Proposed method" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 419, + 281, + 541, + 358 + ], + "lines": [ + { + "bbox": [ + 419, + 281, + 541, + 358 + ], + "spans": [ + { + "bbox": [ + 419, + 281, + 541, + 358 + ], + "type": "image", + "image_path": "631b61db4483c495c4ccbff15baf2d2310487f77ec37f4eb5b43e1316e98d8d5.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 331, + 371, + 515, + 446 + ], + "blocks": [ + { + "bbox": [ + 311, + 360, + 380, + 369 + ], + "lines": [ + { + "bbox": [ + 311, + 360, + 380, + 369 + ], + "spans": [ + { + "bbox": [ + 311, + 360, + 380, + 369 + ], + "type": "text", + "content": "(b) Spatial modelling" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 331, + 371, + 515, + 446 + ], + "lines": [ + { + "bbox": [ + 331, + 371, + 515, + 446 + ], + "spans": [ + { + "bbox": [ + 331, + 371, + 515, + 446 + ], + "type": "image", + "image_path": "3109fc488c0faea5247b56d1f99ccede9ccb7c417c16fac97840ecc3c4442985.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 457, + 547, + 568 + ], + "lines": [ + { + "bbox": [ + 304, + 457, + 547, + 568 + ], + "spans": [ + { + "bbox": [ + 304, + 457, + 547, + 568 + ], + "type": "text", + "content": "Figure 1. (a) Existing visual-language pre-training approaches [9, 32, 81] often use only a single image for contrastive learning (e.g., InfoNCE [49]). (b) In such settings, discarding the temporal connectivity of images limits the alignment of image-text pairs as shown with the affinity matrix, leading to suboptimal pre-training and missed opportunity to create additional model supervision for free. (c, d) Our approach exploits this domain knowledge by learning to incorporate a series of images and correlate them to reports, leading to pre-trained models that can generalise to a wider range of downstream tasks whilst achieving SOTA performance." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 600, + 547, + 673 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 600, + 547, + 673 + ], + "spans": [ + { + "bbox": [ + 304, + 600, + 547, + 673 + ], + "type": "text", + "content": "[9, 32, 81]. Vision-language processing (VLP) has shown that cross-modal supervision can provide a richer signal for training both image [19] and text [9] models. However, the success of VLP relies on paired samples sharing semantics, i.e., given an image and text pair, the text should describe the image with minimal extraneous detail [15, 16, 35]." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 677, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 547, + 715 + ], + "type": "text", + "content": "In this regard, VLP in biomedicine and radiology poses a distinctive challenge, as reports routinely include comparisons to prior imaging studies [3, 47, 57]. Without knowledge of the underlying mechanisms of these comparisons, it is difficult to determine whether the results are consistent with those obtained by other methods." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 693, + 175, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 693, + 175, + 703 + ], + "spans": [ + { + "bbox": [ + 57, + 693, + 175, + 703 + ], + "type": "text", + "content": "* These authors contributed equally." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 59, + 703, + 254, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 703, + 254, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 703, + 254, + 712 + ], + "type": "text", + "content": "†Corresponding author: ozan.oktay@microsoft.com" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "15016" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "content": "edge of this prior image1, temporal information in the text modality, e.g. \"Pneumonia is improving\", could pertain to any image containing \"Pneumonia\", producing ambiguity during contrastive training (Figure 1). Despite this, the existing VLP work to date considers alignment between only single images and reports [9,32,46,81], going so far as to remove temporal content from reports in training data to prevent 'hallucinations' in downstream report generation [54]. However, temporal information can provide complementary self-supervision, solely by exploiting existing structure, and without requiring any additional data." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 205, + 289, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 205, + 289, + 491 + ], + "spans": [ + { + "bbox": [ + 46, + 205, + 289, + 491 + ], + "type": "text", + "content": "In this work, we neither ignore nor remove temporal information in the text modality, but explicitly account for it during pre-training. Rather than treating all image-report pairs in the dataset as independent, we exploit temporal correlations by making prior images available for comparison to a given report. To learn from this structure, we develop a temporal VLP pre-training framework named BioViL-T. A core component is its new multi-image encoder that can handle the absence of prior images and potential spatial misalignment between images across time. BioViL-T takes into account prior images where available, removing cross-modal ambiguity as illustrated in Fig. 1. Linking multiple images during pre-training proves beneficial to both image and text models: we report state-of-the-art (SOTA) performance on both temporal image classification and report generation. In the latter case, we show that prefixing the prior report substantially increases performance, again reflecting the value of prior information. We emphasise that the benefit is not restricted to temporal downstream tasks: our approach also achieves SOTA on non-temporal tasks of pneumonia detection [60] and phrase grounding [10], underscoring the value of a cleaner learning signal during VLP without needing to modify or add to the training dataset. Our contributions can be summarised as follows:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 494, + 287, + 680 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 53, + 494, + 287, + 566 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 494, + 287, + 566 + ], + "spans": [ + { + "bbox": [ + 53, + 494, + 287, + 566 + ], + "type": "text", + "content": "- We introduce a novel pre-training framework called BioViL-T. It leverages the temporal relationship of samples to self-supervise VLP models, making commonly used biomedical VLP models (e.g., [9, 32, 81]) more applicable to a wider range of downstream tasks without compromising performance on existing benchmarks." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 567, + 287, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 567, + 287, + 604 + ], + "spans": [ + { + "bbox": [ + 53, + 567, + 287, + 604 + ], + "type": "text", + "content": "- We develop a generic multi-image encoder that handles missing image inputs and incorporates longitudinal information without requiring explicit image registration." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 605, + 287, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 605, + 287, + 654 + ], + "spans": [ + { + "bbox": [ + 53, + 605, + 287, + 654 + ], + "type": "text", + "content": "- We achieve SOTA results in chest X-ray (CXR) report generation, temporal image classification, and phrase grounding downstream benchmarks by accounting for prior context in self-supervised training and fine-tuning." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 655, + 287, + 680 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 655, + 287, + 680 + ], + "spans": [ + { + "bbox": [ + 53, + 655, + 287, + 680 + ], + "type": "text", + "content": "- We release a new multimodal benchmark dataset, MS-CXR-T, curated by an expert radiologist. It enables" + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 320, + 72, + 545, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 72, + 545, + 96 + ], + "spans": [ + { + "bbox": [ + 320, + 72, + 545, + 96 + ], + "type": "text", + "content": "benchmarking of CXR VLP models in terms of temporal semantics extracted from image and text data." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 109, + 390, + 122 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 109, + 390, + 122 + ], + "spans": [ + { + "bbox": [ + 306, + 109, + 390, + 122 + ], + "type": "text", + "content": "2. Related work" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 129, + 547, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 129, + 547, + 239 + ], + "spans": [ + { + "bbox": [ + 304, + 129, + 547, + 239 + ], + "type": "text", + "content": "Vision-language processing Self-supervised VLP can significantly reduce the need for manual labels required for the training of image encoders [19, 53]. The availability of large-scale paired image-text datasets has thus led to rapid development of general-purpose VLP models. Objectives include contrastive and discriminative image-text matching [40, 53, 69] including local variants [32, 76], auto-regressive (AR) captioning [4, 39, 77] and multi-modal masked modelling objectives [13, 40, 61]." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 247, + 547, + 499 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 247, + 547, + 499 + ], + "spans": [ + { + "bbox": [ + 304, + 247, + 547, + 499 + ], + "type": "text", + "content": "Biomedical vision-language processing Paired medical image-report datasets were originally used for supervised learning via (typically) automated label extraction from clinical reports [33, 63, 70]. Using such datasets, advances in general-domain self-supervised VLP have been demonstrated to benefit biomedical imaging applications [9, 32, 81]. Work has incorporated ideas from general-domain VLP such as the original CLIP-style cross-modal contrastive objective [81], multi-modal masking with merged co-attention on image-text representations [46], and adaptations to the data of the domain. For example, a radiology report may have sparse image-specific details, prompting a local modification to the contrastive loss enabling alignment between text tokens and image patches [32]. Domain-specific pre-training of the text model is shown to benefit biomedical VLP [9], and preferential masking of medical terms during masked language modelling (MLM) was explored [75]. Here we use a local loss and domain-specific pre-training of the text model, but did not find a benefit to preferential masking. Similarly, cross-attention [22] is used rather than merged co-attention for image-guided MLM." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 510, + 545, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 510, + 545, + 582 + ], + "spans": [ + { + "bbox": [ + 304, + 510, + 545, + 582 + ], + "type": "text", + "content": "Longitudinal modelling of medical images While prior images are used in unimodal supervised longitudinal analysis of medical images [37,58,68,74], temporal information has not directly been employed for self-supervision. The closest work exploits patient metadata to select positive or negative examples in unimodal contrastive learning [67,79]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 582, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 547, + 714 + ], + "type": "text", + "content": "Existing models typically employ either late fusion of global image representations [58,64,68,74], which can miss fine-grained localised changes [32], or explicit spatial correspondence of features, using fixed spatial grids [48] or object detection [37]. Registering image pairs is commonly used for change detection in other contexts [17,52,59], and has been applied to medical imaging [5, 23]. For CXRs however, registration entails the ill-posed problem of aligning 2D projections of 3D geometry, which inevitably results in residual misalignment. Our approach does not rely on bounding boxes or explicit graph construction as it uses" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 47, + 693, + 287, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 693, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 693, + 287, + 713 + ], + "type": "text", + "content": "In the MIMIC-CXR v2 dataset [36], around " + }, + { + "bbox": [ + 47, + 693, + 287, + 713 + ], + "type": "inline_equation", + "content": "40\\%" + }, + { + "bbox": [ + 47, + 693, + 287, + 713 + ], + "type": "text", + "content": " of reports explicitly reference a previous image. See Appendix B for details." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "15017" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 96 + ], + "type": "text", + "content": "self-attention of visual tokens across time to handle any spatial misalignment." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 102, + 286, + 185 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 102, + 286, + 185 + ], + "spans": [ + { + "bbox": [ + 46, + 102, + 286, + 185 + ], + "type": "text", + "content": "Self-supervision across time Self-supervision has found applications on densely-sampled time series data (e.g., video) to capture temporal information [30, 55, 78, 80]. Our problem setting involves sparsely and sporadically sampled data where temporal pretext tasks are less applicable [2]. Similarly, it requires text supervision to enable both static and temporal learning, when temporal structure is present." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 196, + 216, + 209 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 196, + 216, + 209 + ], + "spans": [ + { + "bbox": [ + 47, + 196, + 216, + 209 + ], + "type": "text", + "content": "3. BioViL-T training framework" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 216, + 286, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 216, + 286, + 323 + ], + "spans": [ + { + "bbox": [ + 46, + 216, + 286, + 323 + ], + "type": "text", + "content": "Our approach comprises a multi-image encoder designed to extract spatio-temporal features from sequences of images (Section 3.1) and a text encoder incorporating optional cross-attention on image features. The models are trained jointly with image-guided MLM and cross-modal global and local contrastive objectives (Section 3.2). The resulting image and text models are later adapted for uni- or multimodal downstream tasks as described in Section 3.3. Implementation details are presented in Appendices E and F." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 323, + 286, + 432 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 323, + 286, + 432 + ], + "spans": [ + { + "bbox": [ + 46, + 323, + 286, + 432 + ], + "type": "text", + "content": "For a given image and report pair " + }, + { + "bbox": [ + 46, + 323, + 286, + 432 + ], + "type": "inline_equation", + "content": "(\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}},\\mathbf{x}_{\\mathrm{txt}}^{\\mathrm{curr}})" + }, + { + "bbox": [ + 46, + 323, + 286, + 432 + ], + "type": "text", + "content": ", the report " + }, + { + "bbox": [ + 46, + 323, + 286, + 432 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_{\\mathrm{txt}}^{\\mathrm{curr}}" + }, + { + "bbox": [ + 46, + 323, + 286, + 432 + ], + "type": "text", + "content": " describes the current image content and changes in reference to prior images. Our proposed formulation focuses on a single prior image; however, it can be generalised to multiple prior images depending on the application. Hence, we construct datasets by including the prior image whenever it exists: " + }, + { + "bbox": [ + 46, + 323, + 286, + 432 + ], + "type": "inline_equation", + "content": "(\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}},\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{prior}},\\mathbf{x}_{\\mathrm{txt}}^{\\mathrm{curr}}) \\in \\mathcal{D}_m" + }, + { + "bbox": [ + 46, + 323, + 286, + 432 + ], + "type": "text", + "content": " or " + }, + { + "bbox": [ + 46, + 323, + 286, + 432 + ], + "type": "inline_equation", + "content": "(\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}},\\varnothing ,\\mathbf{x}_{\\mathrm{txt}}^{\\mathrm{curr}}) \\in \\mathcal{D}_s" + }, + { + "bbox": [ + 46, + 323, + 286, + 432 + ], + "type": "text", + "content": " with the resulting dataset being a union of single and multi-image examples: " + }, + { + "bbox": [ + 46, + 323, + 286, + 432 + ], + "type": "inline_equation", + "content": "\\mathcal{D} = \\mathcal{D}_m \\cup \\mathcal{D}_s" + }, + { + "bbox": [ + 46, + 323, + 286, + 432 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 439, + 268, + 451 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 439, + 268, + 451 + ], + "spans": [ + { + "bbox": [ + 47, + 439, + 268, + 451 + ], + "type": "text", + "content": "3.1. Extracting spatio-temporal image features" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 457, + 286, + 564 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 457, + 286, + 564 + ], + "spans": [ + { + "bbox": [ + 46, + 457, + 286, + 564 + ], + "type": "text", + "content": "Clinical findings are often observed across different image regions and co-occur simultaneously, which requires dense level visual reasoning across time to capture both static and temporal features. In contrast to late global fusion [64] and bounding-box based approaches [37], BioViL-T leverages local correspondences between image regions across time using transformer self-attention blocks [21]. Thus our method does not require an explicit image registration step between time points." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "spans": [ + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "text", + "content": "We propose a hybrid CNN-Transformer encoder model due to its data efficiency and spatial flexibility of cross-attention across time points: " + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{img}}" + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "text", + "content": ": " + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{W\\times H}\\rightarrow \\mathbb{R}^{W^{\\prime}\\times H^{\\prime}\\times D_{\\mathrm{img}}}" + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "text", + "content": " (e.g., ResNet-50 [31]) and " + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "inline_equation", + "content": "A_{\\mathrm{img}}" + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "text", + "content": ": " + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{T\\times L\\times D_{\\mathrm{img}}}\\rightarrow \\mathbb{R}^{L\\times D_{\\mathrm{img}}}" + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "text", + "content": " (e.g., transformer [21]), where " + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "text", + "content": " correspond to spatiotemporal dimensions, " + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "inline_equation", + "content": "L = W^{\\prime}H^{\\prime}" + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "text", + "content": " is the number of visual tokens per image, and " + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "inline_equation", + "content": "D_{\\mathrm{img}}" + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "text", + "content": " is the embedding dimension. Here " + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{img}}" + }, + { + "bbox": [ + 46, + 565, + 286, + 685 + ], + "type": "text", + "content": " serves as a stem network [51] to provide visual token features of individual images. The CNN's inductive biases [24, 51] en" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "text", + "content": "sure data efficiency of our hybrid model, making it ideal for smaller scale biomedical datasets. " + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{img}}" + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "text", + "content": " is initialised with BioViL weights [9]. The main purpose of " + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "inline_equation", + "content": "A_{\\mathrm{img}}" + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "text", + "content": " is to capture patch embedding interactions across time when a prior image " + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{prior}}" + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "text", + "content": " is available and to aggregate them into a fixed-length token representation. Input visual tokens, " + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "inline_equation", + "content": "\\mathbf{H}_0^{\\mathrm{curr}} = \\mathbf{P}^{\\mathrm{curr}} \\coloneqq E_{\\mathrm{img}}(\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}})" + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "inline_equation", + "content": "\\mathbf{H}_0^{\\mathrm{prior}} \\coloneqq E_{\\mathrm{img}}(\\mathbf{x}_{\\mathrm{img}}^{\\mathrm{prior}})" + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "text", + "content": " are augmented with spatio-temporal positional encodings and flattened across the spatial dimensions. They are then processed by " + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "text", + "content": " transformer encoder [66] layers " + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "inline_equation", + "content": "A" + }, + { + "bbox": [ + 306, + 72, + 545, + 192 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 334, + 201, + 545, + 229 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 334, + 201, + 545, + 229 + ], + "spans": [ + { + "bbox": [ + 334, + 201, + 545, + 229 + ], + "type": "interline_equation", + "content": "\\left[ \\begin{array}{l} \\mathbf {H} _ {k} ^ {\\text {c u r r}} \\\\ \\mathbf {H} _ {k} ^ {\\text {p r i o r}} \\end{array} \\right] = A _ {k} \\left(\\left[ \\begin{array}{l} \\mathbf {H} _ {k - 1} ^ {\\text {c u r r}} + \\mathbf {S} + \\mathbf {1} _ {L} \\otimes \\mathbf {t} ^ {\\text {c u r r}} \\\\ \\mathbf {H} _ {k - 1} ^ {\\text {p r i o r}} + \\mathbf {S} + \\mathbf {1} _ {L} \\otimes \\mathbf {t} ^ {\\text {p r i o r}} \\end{array} \\right]\\right), \\tag {1}", + "image_path": "6fcc4fc815540fb210fb50e6721a172369c9db1786a32e2ab26aa14bf00919c6.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 235, + 545, + 367 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 235, + 545, + 367 + ], + "spans": [ + { + "bbox": [ + 305, + 235, + 545, + 367 + ], + "type": "text", + "content": "for " + }, + { + "bbox": [ + 305, + 235, + 545, + 367 + ], + "type": "inline_equation", + "content": "k = 1,\\dots ,K" + }, + { + "bbox": [ + 305, + 235, + 545, + 367 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 305, + 235, + 545, + 367 + ], + "type": "inline_equation", + "content": "\\mathbf{S}\\in \\mathbb{R}^{L\\times D_{\\mathrm{img}}}" + }, + { + "bbox": [ + 305, + 235, + 545, + 367 + ], + "type": "text", + "content": " denotes 2D sinusoidal positional encodings [12] and " + }, + { + "bbox": [ + 305, + 235, + 545, + 367 + ], + "type": "inline_equation", + "content": "\\mathbf{T} = [\\mathbf{t}^{\\mathrm{curr}};\\mathbf{t}^{\\mathrm{prior}}]\\in \\mathbb{R}^{2\\times D_{\\mathrm{img}}}" + }, + { + "bbox": [ + 305, + 235, + 545, + 367 + ], + "type": "text", + "content": " is its temporal counterpart, which is learnt (Fig. 2) [4]. The layer-normalised (LN) [6] output of the final transformer encoder block " + }, + { + "bbox": [ + 305, + 235, + 545, + 367 + ], + "type": "inline_equation", + "content": "\\mathbf{P}^{\\mathrm{diff}}\\coloneqq \\mathrm{LN}(\\mathbf{H}_K^{\\mathrm{curr}})" + }, + { + "bbox": [ + 305, + 235, + 545, + 367 + ], + "type": "text", + "content": " is an 'aggregated' representation of patch-level progression information anchored on the current image. Figure 3 shows attention roll-out [1] applied to " + }, + { + "bbox": [ + 305, + 235, + 545, + 367 + ], + "type": "inline_equation", + "content": "\\mathbf{P}^{\\mathrm{diff}}" + }, + { + "bbox": [ + 305, + 235, + 545, + 367 + ], + "type": "text", + "content": " after pre-training, showing how the prior image contributes to the fused representation. Figure A.3 further highlights the robustness to variations in pose underlining that registration is not necessary for this encoder." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "spans": [ + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "text", + "content": "Static-temporal feature decomposition When a prior image is available the final image representation " + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "inline_equation", + "content": "\\mathbf{V} := \\mathbf{P}^{\\mathrm{curr}} \\oplus \\mathbf{P}^{\\mathrm{diff}} \\in \\mathbb{R}^{W' \\times H' \\times 2D_{\\mathrm{img}}}" + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "text", + "content": " is formed by concatenating two sets of features (similar to [7]): those from the current image alone (" + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "inline_equation", + "content": "\\mathbf{P}^{\\mathrm{curr}}" + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "text", + "content": ") and the temporal features from current and prior images (" + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "inline_equation", + "content": "\\mathbf{P}^{\\mathrm{diff}}" + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "text", + "content": "). In this way, self-attention is mainly required to cope with pose variations and patch comparisons across time in extracting temporal content, removing the need for registration or explicit spatial feature alignment. When no prior scan is available (" + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "inline_equation", + "content": "\\mathbf{x} \\in \\mathcal{D}_s" + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "text", + "content": "), " + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "inline_equation", + "content": "A_{\\mathrm{img}}" + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "text", + "content": " is not used and " + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "inline_equation", + "content": "\\mathbf{P}^{\\mathrm{diff}}" + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "text", + "content": " is replaced by a learnable token " + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "inline_equation", + "content": "\\mathbf{p}^{\\mathrm{miss}} \\in \\mathbb{R}^{D_{\\mathrm{img}}}" + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "text", + "content": ", replicated across the spatial dimensions. Section 4.5 later demonstrates that " + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "inline_equation", + "content": "A_{\\mathrm{img}}" + }, + { + "bbox": [ + 305, + 375, + 545, + 553 + ], + "type": "text", + "content": " highlights the value of feature decomposition for tasks such as phrase grounding which require well-localised features [10]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 554, + 545, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 554, + 545, + 602 + ], + "spans": [ + { + "bbox": [ + 305, + 554, + 545, + 602 + ], + "type": "text", + "content": "Hereafter, downstream tasks that require solely single image features, " + }, + { + "bbox": [ + 305, + 554, + 545, + 602 + ], + "type": "inline_equation", + "content": "\\mathbf{P}^{\\mathrm{curr}}" + }, + { + "bbox": [ + 305, + 554, + 545, + 602 + ], + "type": "text", + "content": ", are referred to as static tasks, and the ones that benefit from additional progression information, " + }, + { + "bbox": [ + 305, + 554, + 545, + 602 + ], + "type": "inline_equation", + "content": "\\mathbf{P}^{\\mathrm{diff}}" + }, + { + "bbox": [ + 305, + 554, + 545, + 602 + ], + "type": "text", + "content": ", as temporal tasks, e.g., report decoding." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 611, + 541, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 611, + 541, + 624 + ], + "spans": [ + { + "bbox": [ + 306, + 611, + 541, + 624 + ], + "type": "text", + "content": "3.2. Text-supervision for spatio-temporal learning" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "text", + "content": "Let " + }, + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "inline_equation", + "content": "\\mathbf{w} = (w_{1},\\dots ,w_{M})" + }, + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "text", + "content": " denote a vector of " + }, + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "text", + "content": " tokens of a report " + }, + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_{\\mathrm{txt}}" + }, + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "text", + "content": " after tokenisation. We first obtain contextualised token features " + }, + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{txt}}(\\mathbf{w})\\in \\mathbb{R}^{M\\times D_{\\mathrm{xt}}}" + }, + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "text", + "content": " by passing a sequence of text tokens " + }, + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "inline_equation", + "content": "\\mathbf{w} = (w_{1},\\dots ,w_{M})" + }, + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "text", + "content": " through a BERT encoder " + }, + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{txt}}" + }, + { + "bbox": [ + 305, + 629, + 545, + 712 + ], + "type": "text", + "content": " [20]. The input sequence is prepended with either a [CLS] or [MLM] token associated with a downstream training objective, conditioning the output features" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 46, + 693, + 286, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 693, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 693, + 286, + 712 + ], + "type": "text", + "content": "2The prior report is not included during pre-training as it may further reference an earlier study, reintroducing temporal ambiguity." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15018" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 75, + 70, + 517, + 289 + ], + "blocks": [ + { + "bbox": [ + 75, + 70, + 517, + 289 + ], + "lines": [ + { + "bbox": [ + 75, + 70, + 517, + 289 + ], + "spans": [ + { + "bbox": [ + 75, + 70, + 517, + 289 + ], + "type": "image", + "image_path": "a750e5216a349c2950cf5f0d66ab179ac9b31e4129d44e9b34a0a01ad2cd8b41.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 298, + 545, + 354 + ], + "lines": [ + { + "bbox": [ + 45, + 298, + 545, + 354 + ], + "spans": [ + { + "bbox": [ + 45, + 298, + 545, + 354 + ], + "type": "text", + "content": "Figure 2. The proposed self-supervised VLP training framework BioViL-T: Image representations " + }, + { + "bbox": [ + 45, + 298, + 545, + 354 + ], + "type": "inline_equation", + "content": "\\mathbf{V}" + }, + { + "bbox": [ + 45, + 298, + 545, + 354 + ], + "type": "text", + "content": " are extracted from single and multiple input scans (whenever available) using a hybrid CNN and transformer encoder [24, 51]. This design choice is to increase the data-efficiency and enable the fusion of temporal content without requiring image registration. They are later matched with their corresponding text representations obtained with CXR-BERT [9] using local [32] and global InfoNCE [49] training objectives. As an additional model supervision, multi-modal fused representations, obtained with cross-attention, are used for image-guided masked language modelling." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 374, + 287, + 447 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 374, + 287, + 447 + ], + "spans": [ + { + "bbox": [ + 46, + 374, + 287, + 447 + ], + "type": "text", + "content": "similar to [39, 42]. During training, we do two forward passes through " + }, + { + "bbox": [ + 46, + 374, + 287, + 447 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{txt}}" + }, + { + "bbox": [ + 46, + 374, + 287, + 447 + ], + "type": "text", + "content": ": once with masking at " + }, + { + "bbox": [ + 46, + 374, + 287, + 447 + ], + "type": "inline_equation", + "content": "45\\%" + }, + { + "bbox": [ + 46, + 374, + 287, + 447 + ], + "type": "text", + "content": " probability (for the MLM objective) and once without masking for contrastive learning, as shown in Figure 2. The text encoder is initialised with the weights of CXR-BERT[9] canonical model, trained on domain-specific vocabulary and corpora." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 447, + 287, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 447, + 287, + 495 + ], + "spans": [ + { + "bbox": [ + 46, + 447, + 287, + 495 + ], + "type": "text", + "content": "Both text and image features are later projected into a joint latent space with " + }, + { + "bbox": [ + 46, + 447, + 287, + 495 + ], + "type": "inline_equation", + "content": "\\phi_{\\mathrm{txt}}: \\mathbb{R}^{D_{\\mathrm{txt}}} \\to \\mathbb{R}^D" + }, + { + "bbox": [ + 46, + 447, + 287, + 495 + ], + "type": "text", + "content": ", and similarly " + }, + { + "bbox": [ + 46, + 447, + 287, + 495 + ], + "type": "inline_equation", + "content": "\\mathbf{v}_{w,h}^{\\mathrm{proj}} := \\phi_{\\mathrm{img}}(\\mathbf{v}_{w,h})" + }, + { + "bbox": [ + 46, + 447, + 287, + 495 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 46, + 447, + 287, + 495 + ], + "type": "inline_equation", + "content": "\\phi_{\\mathrm{img}}: \\mathbb{R}^{D_{\\mathrm{img}}} \\to \\mathbb{R}^D" + }, + { + "bbox": [ + 46, + 447, + 287, + 495 + ], + "type": "text", + "content": ", with " + }, + { + "bbox": [ + 46, + 447, + 287, + 495 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 46, + 447, + 287, + 495 + ], + "type": "text", + "content": " being a two-layer perceptron in our experiments." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 500, + 287, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 500, + 287, + 635 + ], + "spans": [ + { + "bbox": [ + 46, + 500, + 287, + 635 + ], + "type": "text", + "content": "Contrastive objectives Let " + }, + { + "bbox": [ + 46, + 500, + 287, + 635 + ], + "type": "inline_equation", + "content": "\\mathbf{r} \\coloneqq [E_{\\mathrm{xt}}(\\mathbf{w})]_{[\\mathrm{CLS}]}" + }, + { + "bbox": [ + 46, + 500, + 287, + 635 + ], + "type": "text", + "content": " denote the global representation of " + }, + { + "bbox": [ + 46, + 500, + 287, + 635 + ], + "type": "inline_equation", + "content": "\\mathbf{w}" + }, + { + "bbox": [ + 46, + 500, + 287, + 635 + ], + "type": "text", + "content": ", with " + }, + { + "bbox": [ + 46, + 500, + 287, + 635 + ], + "type": "inline_equation", + "content": "\\mathbf{r}^{\\mathrm{proj}} \\coloneqq \\phi_{\\mathrm{xt}}(\\mathbf{r})" + }, + { + "bbox": [ + 46, + 500, + 287, + 635 + ], + "type": "text", + "content": " its projected version. Given projected patch embeddings " + }, + { + "bbox": [ + 46, + 500, + 287, + 635 + ], + "type": "inline_equation", + "content": "\\mathbf{v}_{w,h}^{\\mathrm{proj}}" + }, + { + "bbox": [ + 46, + 500, + 287, + 635 + ], + "type": "text", + "content": ", we can compute a global cosine similarity " + }, + { + "bbox": [ + 46, + 500, + 287, + 635 + ], + "type": "inline_equation", + "content": "S_C(\\bar{\\mathbf{v}}^{\\mathrm{proj}}, \\mathbf{r}^{\\mathrm{proj}})" + }, + { + "bbox": [ + 46, + 500, + 287, + 635 + ], + "type": "text", + "content": " and a local similarity using weighted pairwise cosine similarities across text tokens and projected patch embeddings [32, 76]. These similarities are used in both global and local contrastive objectives with the InfoNCE loss [49, 53]. The local loss proves crucial both for static phrase-grounding and temporal image classification (see Table 7), highlighting the importance of localised self-supervision." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 639, + 287, + 688 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 639, + 287, + 688 + ], + "spans": [ + { + "bbox": [ + 46, + 639, + 287, + 688 + ], + "type": "text", + "content": "Image-guided masked language modelling Prior work [9, 46] has shown that biomedical visual-language learning benefits from an auxiliary task such as MLM since capturing the joint distribution of tokens can stabilise and improve" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "spans": [ + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "text", + "content": "language understanding during joint learning. Given a batch " + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "text", + "content": " of token vectors " + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "inline_equation", + "content": "\\mathbf{w}" + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "text", + "content": ", it is often defined as the cross-entropy for predicting the randomly sampled masked tokens, " + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "inline_equation", + "content": "m\\subset" + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "inline_equation", + "content": "\\{1,\\dots ,M\\}" + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{MLM}} = -\\frac{1}{|\\mathcal{B}|}\\sum_{\\mathbf{w}\\in \\mathcal{B}}\\log p_{\\theta}(\\mathbf{w}_m|\\mathbf{w}_{\\backslash m})" + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "text", + "content": " are the weights of the text encoder " + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "inline_equation", + "content": "E_{\\mathrm{txt}}" + }, + { + "bbox": [ + 306, + 374, + 545, + 436 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 437, + 545, + 538 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 437, + 545, + 538 + ], + "spans": [ + { + "bbox": [ + 304, + 437, + 545, + 538 + ], + "type": "text", + "content": "In the absence of image information, however, certain masked findings and attributes are not readily predicted, e.g., \"[MASK] is worsening\". As shown in the general domain [13], visual information can help disambiguate such masked predictions and provide additional cross-modal supervision. Thus, we use cross-attention [22,66] to the image features " + }, + { + "bbox": [ + 304, + 437, + 545, + 538 + ], + "type": "inline_equation", + "content": "\\mathbf{v}_{w,h}^{\\mathrm{proj}}" + }, + { + "bbox": [ + 304, + 437, + 545, + 538 + ], + "type": "text", + "content": " during this task. Specifically, for our image-guided MLM objective we model " + }, + { + "bbox": [ + 304, + 437, + 545, + 538 + ], + "type": "inline_equation", + "content": "p_{\\theta}(\\mathbf{w}_m|\\mathbf{w}_{\\backslash m},\\mathbf{v}_{w,h}^{\\mathrm{proj}})" + }, + { + "bbox": [ + 304, + 437, + 545, + 538 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 544, + 485, + 557 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 544, + 485, + 557 + ], + "spans": [ + { + "bbox": [ + 306, + 544, + 485, + 557 + ], + "type": "text", + "content": "3.3. Adaptations to downstream tasks" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 563, + 545, + 671 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 563, + 545, + 671 + ], + "spans": [ + { + "bbox": [ + 304, + 563, + 545, + 671 + ], + "type": "text", + "content": "BioViL-T can be adapted to various downstream tasks. For phrase-grounding and zero-shot inference, we rely on " + }, + { + "bbox": [ + 304, + 563, + 545, + 671 + ], + "type": "inline_equation", + "content": "S_{C}(\\mathbf{r}^{\\mathrm{proj}},\\mathbf{v}_{w,h}^{\\mathrm{proj}})" + }, + { + "bbox": [ + 304, + 563, + 545, + 671 + ], + "type": "text", + "content": " similar to [9, 32]. For multiple-text prompts, projected text embeddings are marginalised prior to " + }, + { + "bbox": [ + 304, + 563, + 545, + 671 + ], + "type": "inline_equation", + "content": "\\ell_2" + }, + { + "bbox": [ + 304, + 563, + 545, + 671 + ], + "type": "text", + "content": "-normalisation [53]. To enable language decoding, " + }, + { + "bbox": [ + 304, + 563, + 545, + 671 + ], + "type": "inline_equation", + "content": "\\mathbf{v}_{w,h}^{\\mathrm{proj}}" + }, + { + "bbox": [ + 304, + 563, + 545, + 671 + ], + "type": "text", + "content": " inputs are cross-attended by text queries " + }, + { + "bbox": [ + 304, + 563, + 545, + 671 + ], + "type": "inline_equation", + "content": "\\mathbf{w}" + }, + { + "bbox": [ + 304, + 563, + 545, + 671 + ], + "type": "text", + "content": ", and causal-attention is utilised between text tokens [39,66]. Differing from [9, 32, 81], we show that report generation tasks can greatly benefit from temporal joint latent space." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "text", + "content": "Conditioning on prior reports In contrast to existing work, we incorporate the prior report as a prompt to contextualise the report generation task: " + }, + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "inline_equation", + "content": "p_{\\Phi}(\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{curr}}|\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{prior}},\\mathbf{v}_{w,h}^{\\mathrm{proj}})" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 47, + 693, + 286, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 693, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 693, + 286, + 713 + ], + "type": "text", + "content": "3https://huggingface.co/microsoft/BiomedVLP-CXR-BERT-general" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15019" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 157 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 157 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 72, + 289, + 157 + ], + "type": "inline_equation", + "content": "\\Phi" + }, + { + "bbox": [ + 46, + 72, + 289, + 157 + ], + "type": "text", + "content": " are the multi-modal encoder-decoder network's weights, and " + }, + { + "bbox": [ + 46, + 72, + 289, + 157 + ], + "type": "inline_equation", + "content": "\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{curr}}" + }, + { + "bbox": [ + 46, + 72, + 289, + 157 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 72, + 289, + 157 + ], + "type": "inline_equation", + "content": "\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{prior}}" + }, + { + "bbox": [ + 46, + 72, + 289, + 157 + ], + "type": "text", + "content": " denote text tokens for current and prior reports respectively. This is analogous to fine-tuning GPT-3 [11] with prompts and instructions [71], but conditioning on both images and the previous report. A dedicated separation token [SEP] is added into the input sequence " + }, + { + "bbox": [ + 46, + 72, + 289, + 157 + ], + "type": "inline_equation", + "content": "[\\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{prior}}, [\\mathrm{SEP}], \\mathbf{w}_{\\mathrm{txt}}^{\\mathrm{curr}}]" + }, + { + "bbox": [ + 46, + 72, + 289, + 157 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 162, + 289, + 331 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 162, + 289, + 331 + ], + "spans": [ + { + "bbox": [ + 46, + 162, + 289, + 331 + ], + "type": "text", + "content": "Curation of imaging datasets CXR datasets [36] often contain multiple image acquisitions " + }, + { + "bbox": [ + 46, + 162, + 289, + 331 + ], + "type": "inline_equation", + "content": "\\mathcal{Z} = \\{\\mathbf{x}_1^{\\mathrm{img}},\\dots ,\\mathbf{x}_Z^{\\mathrm{img}}\\}" + }, + { + "bbox": [ + 46, + 162, + 289, + 331 + ], + "type": "text", + "content": " in a single visit due to data quality issues such as a limited field-of-view or scanning the wrong body part (Figure A.4). Unlike [9, 32, 81], we conduct curation to choose higher quality images among the potential candidates instead of performing a random selection. For this step, a separate BioViL-T is trained on 'clean' studies with single acquisitions and later used in a zero-shot setting to detect out-of-distribution samples [26, 27] arising from the re-imaging process. The candidate " + }, + { + "bbox": [ + 46, + 162, + 289, + 331 + ], + "type": "inline_equation", + "content": "\\hat{z}" + }, + { + "bbox": [ + 46, + 162, + 289, + 331 + ], + "type": "text", + "content": " is selected as follows: " + }, + { + "bbox": [ + 46, + 162, + 289, + 331 + ], + "type": "inline_equation", + "content": "\\hat{z} = \\arg \\max_{z\\in \\mathcal{Z}}S_C(\\bar{\\mathbf{v}}_z^{\\mathrm{proj}},\\mathbf{r}^{\\mathrm{proj}})" + }, + { + "bbox": [ + 46, + 162, + 289, + 331 + ], + "type": "text", + "content": " s.t. " + }, + { + "bbox": [ + 46, + 162, + 289, + 331 + ], + "type": "inline_equation", + "content": "|s_{\\hat{z}} - s_{\\mathcal{Z}\\backslash \\hat{z}}| > \\delta" + }, + { + "bbox": [ + 46, + 162, + 289, + 331 + ], + "type": "text", + "content": " for a margin " + }, + { + "bbox": [ + 46, + 162, + 289, + 331 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 46, + 162, + 289, + 331 + ], + "type": "text", + "content": ". This approach is applied to enhance the quality of the temporal classification dataset given its limited size." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 341, + 185, + 355 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 341, + 185, + 355 + ], + "spans": [ + { + "bbox": [ + 47, + 341, + 185, + 355 + ], + "type": "text", + "content": "4. Datasets & experiments" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 361, + 287, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 361, + 287, + 469 + ], + "spans": [ + { + "bbox": [ + 46, + 361, + 287, + 469 + ], + "type": "text", + "content": "Here, we demonstrate BioViL-T's data efficiency and adaptability to a wide range of applications, and show how the model achieves SOTA performance on various downstream tasks by learning from data instances linked across time, making effective use of domain priors and the available training data. Specifically, our model is evaluated on a diverse set of downstream tasks including zero- and few-shot static and temporal image classification, report generation, phrase-grounding [10], and sentence similarity." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 475, + 287, + 643 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 475, + 287, + 643 + ], + "spans": [ + { + "bbox": [ + 46, + 475, + 287, + 643 + ], + "type": "text", + "content": "MS-CXR-T benchmark We release a new multi-modal benchmark dataset, MS-CXR-T, to evaluate chest X-ray VLP models on two distinct temporal tasks: image classification and sentence similarity. The former comprises multi-image and ground-truth label pairs (" + }, + { + "bbox": [ + 46, + 475, + 287, + 643 + ], + "type": "inline_equation", + "content": "N = 1326" + }, + { + "bbox": [ + 46, + 475, + 287, + 643 + ], + "type": "text", + "content": ") across 5 findings, with classes corresponding to 3 states of disease progression for each finding: {Improving, Stable, Worsening}. The latter quantifies the temporal-semantic similarity of text embeddings extracted from pairs of sentences (" + }, + { + "bbox": [ + 46, + 475, + 287, + 643 + ], + "type": "inline_equation", + "content": "N = 361" + }, + { + "bbox": [ + 46, + 475, + 287, + 643 + ], + "type": "text", + "content": "). The pairs can be either paraphrases or contradictions in terms of disease progression. The data for both tasks was manually annotated and reviewed by a board certified radiologist. Appendix C provides further details on its data distribution and annotation protocol." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 649, + 287, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 649, + 287, + 686 + ], + "spans": [ + { + "bbox": [ + 46, + 649, + 287, + 686 + ], + "type": "text", + "content": "Datasets For pre-training, we use the MIMIC-CXR v2 [28, 36] chest X-ray dataset, which contains longitudinal imaging studies with corresponding radiological reports," + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 307, + 81, + 364, + 137 + ], + "blocks": [ + { + "bbox": [ + 317, + 70, + 353, + 79 + ], + "lines": [ + { + "bbox": [ + 317, + 70, + 353, + 79 + ], + "spans": [ + { + "bbox": [ + 317, + 70, + 353, + 79 + ], + "type": "text", + "content": "Prior image" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 307, + 81, + 364, + 137 + ], + "lines": [ + { + "bbox": [ + 307, + 81, + 364, + 137 + ], + "spans": [ + { + "bbox": [ + 307, + 81, + 364, + 137 + ], + "type": "image", + "image_path": "9b80633388fde47040f844eb765027e40fa2a8c1633841b7428f1329e3150816.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 365, + 81, + 421, + 137 + ], + "blocks": [ + { + "bbox": [ + 371, + 70, + 413, + 79 + ], + "lines": [ + { + "bbox": [ + 371, + 70, + 413, + 79 + ], + "spans": [ + { + "bbox": [ + 371, + 70, + 413, + 79 + ], + "type": "text", + "content": "Current image" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 365, + 81, + 421, + 137 + ], + "lines": [ + { + "bbox": [ + 365, + 81, + 421, + 137 + ], + "spans": [ + { + "bbox": [ + 365, + 81, + 421, + 137 + ], + "type": "image", + "image_path": "9e3374aca330c8f6ad13e42f253762665b3926bb4b465dd5d846e41330fe39a9.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 432, + 81, + 488, + 137 + ], + "blocks": [ + { + "bbox": [ + 443, + 70, + 477, + 79 + ], + "lines": [ + { + "bbox": [ + 443, + 70, + 477, + 79 + ], + "spans": [ + { + "bbox": [ + 443, + 70, + 477, + 79 + ], + "type": "text", + "content": "Prior image" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 432, + 81, + 488, + 137 + ], + "lines": [ + { + "bbox": [ + 432, + 81, + 488, + 137 + ], + "spans": [ + { + "bbox": [ + 432, + 81, + 488, + 137 + ], + "type": "image", + "image_path": "763eaa5fbed791e21a14790fb1fceab3437bc4d5211a20cc447c66bac14b8279.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 146, + 547, + 224 + ], + "lines": [ + { + "bbox": [ + 304, + 146, + 547, + 224 + ], + "spans": [ + { + "bbox": [ + 304, + 146, + 547, + 224 + ], + "type": "text", + "content": "Figure 3. Attention rollout maps [1] from the reference patch (marked with " + }, + { + "bbox": [ + 304, + 146, + 547, + 224 + ], + "type": "inline_equation", + "content": "\\star" + }, + { + "bbox": [ + 304, + 146, + 547, + 224 + ], + "type": "text", + "content": ") to the current and prior images. The bounding boxes, annotated by a radiologist, show the extent of consolidation. Note that the reference patch attends to its anatomical neighbourhood in the prior image despite the misalignment between prior and current images. The grid " + }, + { + "bbox": [ + 304, + 146, + 547, + 224 + ], + "type": "inline_equation", + "content": "(14 \\times 14)" + }, + { + "bbox": [ + 304, + 146, + 547, + 224 + ], + "type": "text", + "content": " represents the patch tokens processed in the transformer encoder blocks." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 488, + 81, + 545, + 137 + ], + "blocks": [ + { + "bbox": [ + 496, + 70, + 538, + 79 + ], + "lines": [ + { + "bbox": [ + 496, + 70, + 538, + 79 + ], + "spans": [ + { + "bbox": [ + 496, + 70, + 538, + 79 + ], + "type": "text", + "content": "Current image" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 488, + 81, + 545, + 137 + ], + "lines": [ + { + "bbox": [ + 488, + 81, + 545, + 137 + ], + "spans": [ + { + "bbox": [ + 488, + 81, + 545, + 137 + ], + "type": "image", + "image_path": "a927b1d4f69a7347ee14bb8b27fb26fe785383e7f2bbfc866cbe0f4f59540275.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 246, + 545, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 246, + 545, + 389 + ], + "spans": [ + { + "bbox": [ + 304, + 246, + 545, + 389 + ], + "type": "text", + "content": "see Fig. B.1 for the distribution of studies. We only use frontal view scans and discard samples where reports do not contain an IMPRESSION section. From this data, we gather 174.1k and 4.9k text-image pairs for training and validation respectively, with a majority of pairs including a prior image: " + }, + { + "bbox": [ + 304, + 246, + 545, + 389 + ], + "type": "inline_equation", + "content": "|\\mathcal{D}_m^{\\mathrm{train}}| = 118.8k" + }, + { + "bbox": [ + 304, + 246, + 545, + 389 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 246, + 545, + 389 + ], + "type": "inline_equation", + "content": "|\\mathcal{D}_s^{\\mathrm{train}}| = 55.3k" + }, + { + "bbox": [ + 304, + 246, + 545, + 389 + ], + "type": "text", + "content": ". The text consists of the IMPRESSION section and, for MLM additionally the FINDINGS section if available. Note that no manual labels are used during pre-training and no additional data is used for the methods that leverage the link between current and prior images. For early stopping we track the validation loss, see Appendix E for implementation details." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 391, + 546, + 535 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 391, + 546, + 535 + ], + "spans": [ + { + "bbox": [ + 304, + 391, + 546, + 535 + ], + "type": "text", + "content": "Downstream evaluations are performed on a disjoint held-out test set shared across all tasks, " + }, + { + "bbox": [ + 304, + 391, + 546, + 535 + ], + "type": "inline_equation", + "content": "|\\mathcal{D}^{\\mathrm{test}}| = 2971" + }, + { + "bbox": [ + 304, + 391, + 546, + 535 + ], + "type": "text", + "content": ". For report generation, we extend this test set with samples from healthy subjects (" + }, + { + "bbox": [ + 304, + 391, + 546, + 535 + ], + "type": "inline_equation", + "content": "N = 815" + }, + { + "bbox": [ + 304, + 391, + 546, + 535 + ], + "type": "text", + "content": ") to match the prevalence of pathological studies used in prior work [14, 25, 45]. For fine-tuning on temporal image classification, we use labels from the Chest ImaGenome dataset [72] as in [37] (statistics in Table F.2). In detail, we use the following benchmark datasets: (I) MS-CXR [10] for phrase grounding, (II) the RSNA Pneumonia dataset [60, 70] to test zero-shot and fine-tuned classification, (III) MS-CXR-T for temporal sentence similarity and temporal image classification." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 545, + 545, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 545, + 689 + ], + "type": "text", + "content": "Comparison approaches We compare our approach to other domain-specific SOTA pre-training frameworks [9, 32] specifically on phrase-grounding and zero-shot predictive performance. The non-temporal BioViL framework [9] is most similar to our approach and provides insight into non-temporal pre-training. We additionally compare to internal ablations such as removing the past report during report generation and masking prior images during phrase grounding. For SOTA performance comparison, various AR and nearest-neighbour (NN) based language decoding approaches are used as baselines: IFCC [45], R2Gen [14], CXR-RePaiR-2 [25], and CXR-RePaiR-Select [25]." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "type": "text", + "content": "For the temporal classification task, we compare against a baseline exploiting the BioViL image encoder [9], and an" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 47, + 693, + 286, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 693, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 693, + 286, + 713 + ], + "type": "text", + "content": "4MS-CXR-T benchmark dataset can be accessed through PhysioNet: https://aka.ms/ms-cxr-t" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15020" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 50, + 167, + 287, + 228 + ], + "blocks": [ + { + "bbox": [ + 46, + 70, + 289, + 160 + ], + "lines": [ + { + "bbox": [ + 46, + 70, + 289, + 160 + ], + "spans": [ + { + "bbox": [ + 46, + 70, + 289, + 160 + ], + "type": "text", + "content": "Table 1. Results for report generation task: Predictions are evaluated in terms of lexical (BLEU-4, ROUGE) and factuality metrics (CHEXBERT, TEM). Approaches are grouped into two broad categories: nearest-neighbour (NN) and auto-regressive (AR). BioViL-T pre-training consistently yields improved decoding. Further, the consistent performance gains of using prior image and report demonstrate the importance of such domain priors. 'PI / PR' indicate usage of prior image and report, respectively." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 167, + 287, + 228 + ], + "lines": [ + { + "bbox": [ + 50, + 167, + 287, + 228 + ], + "spans": [ + { + "bbox": [ + 50, + 167, + 287, + 228 + ], + "type": "table", + "html": "
MethodPre-trainingPI / PRBLEU-4ROUGECHEXBERTTEM
NCXR-RePaiR-2 [25]BioViLX/X2.114.328.112.5
Baseline (NN) [9]BioViLX/X3.720.028.311.1
Proposed (NN)BioViL-T✓/X4.520.529.013.0
MBaseline (AR) [9]BioViLX/X7.5 ± 0.127.9 ± 0.129.3 ± 0.313.8 ± 0.1
ProposedBioViL-T✓/X8.2 ± 0.128.7 ± 0.130.2 ± 0.716.0 ± 0.3
ProposedBioViL-T✓/✓9.2 ± 0.329.6 ± 0.131.7 ± 1.017.5 ± 0.1
", + "image_path": "10c2d57e71885304f52b12cc909406fbc354f36a47976aee7ca60c934bd0d242.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 50, + 314, + 287, + 383 + ], + "blocks": [ + { + "bbox": [ + 46, + 239, + 288, + 306 + ], + "lines": [ + { + "bbox": [ + 46, + 239, + 288, + 306 + ], + "spans": [ + { + "bbox": [ + 46, + 239, + 288, + 306 + ], + "type": "text", + "content": "Table 2. Temporal image classification results (repeated for 4 random seeds) on the MS-CXR-T benchmark for fully-supervised and zero-/few-shot (Z&F) learning settings, in terms of macroaccuracy across the three classes for each finding. Affine registration is performed for the baseline method (denoted with suffix 'w/ reg'), to partially address the pose variations across scans." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 314, + 287, + 383 + ], + "lines": [ + { + "bbox": [ + 50, + 314, + 287, + 383 + ], + "spans": [ + { + "bbox": [ + 50, + 314, + 287, + 383 + ], + "type": "table", + "html": "
Method (% of labels)Pre-trainConsolidationPl. effusionPneumoniaPneumothoraxEdema
Z&FBioViL-T prompt (0%)Temporal53.6 ± 1.959.7 ± 2.158.0 ± 3.934.9 ± 1.064.2 ± 1.5
BioViL-T (10%)Temporal59.7 ± 2.462.4 ± 1.460.1 ± 2.135.3 ± 2.662.6 ± 1.7
SupervisedCNN + TransformerImageNet44.0 ± 2.061.3 ± 1.645.1 ± 3.531.5 ± 3.165.5 ± 1.1
CheXRelNet [37]ImageNet4747473649
BioViL [9]Static56.1 ± 1.562.3 ± 1.159.4 ± 1.041.7 ± 2.867.5 ± 0.8
BioViL w/reg [9]Static56.0 ± 1.563.0 ± 0.960.2 ± 0.742.5 ± 2.767.5 ± 0.9
BioViL-T wout curationTemporal58.9 ± 1.765.5 ± 0.761.5 ± 2.244.4 ± 2.167.4 ± 0.8
BioViL-TTemporal61.1 ± 2.467.0 ± 0.861.9 ± 1.942.6 ± 1.668.5 ± 0.8
", + "image_path": "891c369ade000421305218a06469d7da2c696807780ecf32e385b282ed96ad08.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 404, + 287, + 525 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 404, + 287, + 525 + ], + "spans": [ + { + "bbox": [ + 46, + 404, + 287, + 525 + ], + "type": "text", + "content": "approach that makes use of graph convolutions across regions of interest extracted from bounding boxes [37]. For BioViL, we perform affine image registration (with 4 DoF) for each pair of scans to cope with pose variations, and the encoded images are concatenated along the feature dimension and classified via a multilayer perceptron. For [37], we compare to the three-class setting. Lastly, we benchmark our final text model in isolation against domain specific SOTA models in a temporal sentence similarity task: CXR-BERT [9] and PubMedBert [29]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 530, + 288, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 530, + 288, + 685 + ], + "spans": [ + { + "bbox": [ + 46, + 530, + 288, + 685 + ], + "type": "text", + "content": "Metrics Due to class imbalance, we report macroaccuracy for temporal image classification. For phrase grounding, we use mean Intersection-Over-Union (mIoU) and Contrast-to-Noise-Ratio (CNR) [9]. The latter measures the discrepancies between cosine similarities inside and out of the bounding box region without requiring hard thresholds. To evaluate the quality of generated reports, we use both the standard lexical metrics, e.g., BLEU [50], ROUGE-L [41], and also domain-specific factuality metric: CheXbert [62]. To directly probe the generation of change-related information, we introduce a new metric called temporal entity matching (TEM) to compute the match score of a fixed set of temporal entities (see Appendix D)." + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 307, + 124, + 544, + 182 + ], + "blocks": [ + { + "bbox": [ + 305, + 70, + 545, + 114 + ], + "lines": [ + { + "bbox": [ + 305, + 70, + 545, + 114 + ], + "spans": [ + { + "bbox": [ + 305, + 70, + 545, + 114 + ], + "type": "text", + "content": "Table 3. Report generation results using the same train/test splits from [25], measured by lexical (BLEU-2) and factuality (CHEXBERT) metrics. Baseline results were also collected from [25]. Note the CHEXBERT score covers all 14 observations." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 307, + 124, + 544, + 182 + ], + "lines": [ + { + "bbox": [ + 307, + 124, + 544, + 182 + ], + "spans": [ + { + "bbox": [ + 307, + 124, + 544, + 182 + ], + "type": "table", + "html": "
MethodDecoded sectionsBLEU-2CHEXBERT
R2gen [14]Findings & Impression21.20 ± 0.1014.80 ± 0.30
IFCC [45]Findings21.70 ± 0.1027.00 ± 0.40
CXR-RePaiR-Sel [25]Impression5.00 ± 0.1027.40 ± 0.30
BioViL-TImpression15.86 ± 0.1434.83 ± 0.73
BioViL-TFindings & Impression21.31 ± 0.1935.86 ± 0.35
", + "image_path": "1ddff95b5f92d1c38179684f3b0a25554e096dbb3e5438a4be705c33069a889c.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 204, + 533, + 217 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 204, + 533, + 217 + ], + "spans": [ + { + "bbox": [ + 305, + 204, + 533, + 217 + ], + "type": "text", + "content": "4.1. Temporal pre-training yields data efficiency" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 309, + 224, + 518, + 236 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 309, + 224, + 518, + 236 + ], + "spans": [ + { + "bbox": [ + 309, + 224, + 518, + 236 + ], + "type": "text", + "content": "Downstream tasks are enabled with minimal labels." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 242, + 545, + 350 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 242, + 545, + 350 + ], + "spans": [ + { + "bbox": [ + 304, + 242, + 545, + 350 + ], + "type": "text", + "content": "The sections 'NN' and 'Z&F' on Tables 1 and 2 report zero- and few-shot performance on tasks benefitting from temporal information: temporal image classification and report generation. Here we measure the quality of the learnt joint latent space and the extent to which BioViL-T enables efficient use of raw data. For zero-shot classification we prompt the AR fine-tuned model with prefix: \"[FINDING] is\" and compare the next-token probability of words meaning 'improving', 'stable', and 'worsening' (Appendix F.4)." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 351, + 546, + 543 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 351, + 546, + 543 + ], + "spans": [ + { + "bbox": [ + 304, + 351, + 546, + 543 + ], + "type": "text", + "content": "Without using any labelled data, Table 2 shows that the proposed AR-based approach already yields performance superior to prior fully-supervised work [37] on temporal image classification. With only " + }, + { + "bbox": [ + 304, + 351, + 546, + 543 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 304, + 351, + 546, + 543 + ], + "type": "text", + "content": " of labels, classification fine-tuning provides a further boost, indicating that BioViL-T produces a multi-image encoder readily adapted to temporal tasks. Similarly, in a zero-shot report-retrieval setting, the findings show that compared to temporally-agnostic pre-training, BioViL-T leveraging prior images improves across all metrics. Consistent with prior work [25], the retrieved reports already preserve factuality with high CheXbert scores, more-so than the other metrics which measure fine-grained specifics of phrasing. This demonstrates that the latent space captures the high-level semantics of the clinical features. Fine-grained phrasing however will be substantially improved by AR fine-tuning." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 552, + 539, + 565 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 552, + 539, + 565 + ], + "spans": [ + { + "bbox": [ + 306, + 552, + 539, + 565 + ], + "type": "text", + "content": "4.2. Achieving SOTA performance with BioViL-T" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 308, + 573, + 542, + 597 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 573, + 542, + 597 + ], + "spans": [ + { + "bbox": [ + 308, + 573, + 542, + 597 + ], + "type": "text", + "content": "A wide range of downstream tasks benefit substantially from temporally-aware pre-training." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 605, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 545, + 714 + ], + "type": "text", + "content": "Through downstream adaptations and fine-tuning our model, we report SOTA performance on report generation and temporal image classification tasks. For the former, using both prior images and reports during fine-tuning substantially improves across metrics (Table 1). In particular, TEM metric results show that temporal context is key for accurately describing change in the generated report while avoiding hallucinations (see Table A.1 for examples). Comparing to published results on a comparable test split and" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 46, + 693, + 287, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 693, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 693, + 287, + 712 + ], + "type": "text", + "content": "5The average of the weighted- " + }, + { + "bbox": [ + 46, + 693, + 287, + 712 + ], + "type": "inline_equation", + "content": "F_{1}" + }, + { + "bbox": [ + 46, + 693, + 287, + 712 + ], + "type": "text", + "content": " score across 14 pathological observations labelled by CheXbert." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15021" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 47, + 100, + 277, + 167 + ], + "blocks": [ + { + "bbox": [ + 47, + 71, + 287, + 91 + ], + "lines": [ + { + "bbox": [ + 47, + 71, + 287, + 91 + ], + "spans": [ + { + "bbox": [ + 47, + 71, + 287, + 91 + ], + "type": "text", + "content": "Table 4. Image classification results on RSNA Pneumonia Detection Benchmark [60] for train and test splits of " + }, + { + "bbox": [ + 47, + 71, + 287, + 91 + ], + "type": "inline_equation", + "content": "70\\% -30\\%" + }, + { + "bbox": [ + 47, + 71, + 287, + 91 + ], + "type": "text", + "content": " respectively." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 47, + 100, + 277, + 167 + ], + "lines": [ + { + "bbox": [ + 47, + 100, + 277, + 167 + ], + "spans": [ + { + "bbox": [ + 47, + 100, + 277, + 167 + ], + "type": "table", + "html": "
Method% of LabelsSupervisionAcc.F1AUROC
GLoRIA [32]XZero-shot0.700.58-
BioViL [9]XZero-shot0.7320.6650.831
BioViL-TXZero-shot0.8050.7060.871
BioViL [9]1%Few-shot0.8050.7230.881
BioViL-T1%Few-shot0.8140.7300.890
", + "image_path": "8078d773067eab756f7f2c4b1b21133845a78fe38da3e56ee013574216bf59b2.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 185, + 286, + 268 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 185, + 286, + 268 + ], + "spans": [ + { + "bbox": [ + 46, + 185, + 286, + 268 + ], + "type": "text", + "content": "metrics (Sec. 4.1), we conclude that BioViL-T with fine-tuning achieves SOTA on report generation, producing reports that are lexically on par with prior work but substantially more factually accurate. Note that we do 'vanilla' AR fine-tuning to focus on the impact of the pre-trained encoders, so application-specific supervision [45] could be used in conjunction to further boost performance." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 269, + 286, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 269, + 286, + 424 + ], + "spans": [ + { + "bbox": [ + 46, + 269, + 286, + 424 + ], + "type": "text", + "content": "In temporal image classification (Tab. 2), BioViL-T pretraining outperforms the non-temporal baseline (BioViL) and improves on previously-reported results [37] by up to 20 percentage points (pp). Furthermore, baseline methods that rely on image registration (BioViL w/reg), underperform compared to the proposed approach. Further analysis reveals that errors tend to be in cases with disagreement between radiologists (Appendix A.2). We also note that pretraining is critical for a hybrid CNN-transformer model on this task, likely due to the small labelled dataset. Lastly, curation of temporal training data is observed to improve the classification results by .68 pp aggregated across the findings, see Appendix A.4 for details." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 431, + 270, + 444 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 431, + 270, + 444 + ], + "spans": [ + { + "bbox": [ + 47, + 431, + 270, + 444 + ], + "type": "text", + "content": "4.3. Static tasks benefit from temporal learning" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 50, + 451, + 284, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 50, + 451, + 284, + 475 + ], + "spans": [ + { + "bbox": [ + 50, + 451, + 284, + 475 + ], + "type": "text", + "content": "BioViL-T broadens the range of applicable downstream tasks whilst contributing to performance on static tasks." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 480, + 286, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 480, + 286, + 552 + ], + "spans": [ + { + "bbox": [ + 46, + 480, + 286, + 552 + ], + "type": "text", + "content": "In this section, we demonstrate that performance improvements afforded by BioViL-T are not restricted to temporal tasks – static tasks also benefit. Table 4 reports results on zero- and few-shot pneumonia classification from single images [60], where BioViL-T establishes a new SOTA compared to prior work [9,32]." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 552, + 286, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 552, + 286, + 624 + ], + "spans": [ + { + "bbox": [ + 46, + 552, + 286, + 624 + ], + "type": "text", + "content": "We see a similar trend on the MS-CXR phrase grounding benchmark (Tab. 5). This task can be solved with single images, however we show that the inclusion of the prior image (where available) does not impair the performance of BioViL-T. Feature decomposition effectively preserves localised information from the current image." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 628, + 272, + 641 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 628, + 272, + 641 + ], + "spans": [ + { + "bbox": [ + 47, + 628, + 272, + 641 + ], + "type": "text", + "content": "4.4. Towards better sentence embedding quality" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 50, + 648, + 280, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 50, + 648, + 280, + 661 + ], + "spans": [ + { + "bbox": [ + 50, + 648, + 280, + 661 + ], + "type": "text", + "content": "Language models acquire increased temporal sensitivity." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 665, + 286, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 286, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 286, + 714 + ], + "type": "text", + "content": "We hypothesise that text encoders learn temporal semantics through supervision from longitudinal image series. To verify this, RadNLI [45] and MS-CXR-T datasets are used in a zero-shot binary classification setting. Cosine similarity" + } + ] + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 308, + 100, + 543, + 163 + ], + "blocks": [ + { + "bbox": [ + 306, + 71, + 545, + 91 + ], + "lines": [ + { + "bbox": [ + 306, + 71, + 545, + 91 + ], + "spans": [ + { + "bbox": [ + 306, + 71, + 545, + 91 + ], + "type": "text", + "content": "Table 5. Results on MS-CXR benchmark [10] (5-runs with different seeds), \"Multi-image\" column indicates the input images used at test time." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 308, + 100, + 543, + 163 + ], + "lines": [ + { + "bbox": [ + 308, + 100, + 543, + 163 + ], + "spans": [ + { + "bbox": [ + 308, + 100, + 543, + 163 + ], + "type": "table", + "html": "
MethodMulti-ImageAvg. CNRAvg. mIoU
BioViL [9]X1.07 ± 0.040.229 ± 0.005
+ Local loss [9,32]X1.21 ± 0.050.202 ± 0.010
BioViL-TX1.33 ± 0.040.243 ± 0.005
BioViL-T✓1.32 ± 0.040.240 ± 0.005
", + "image_path": "1e2e8266fde5f1e7abbb33cfe4e469354365225cee301e5c91aed038710adbf9.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "type": "table", + "bbox": [ + 308, + 194, + 545, + 260 + ], + "blocks": [ + { + "bbox": [ + 311, + 174, + 539, + 185 + ], + "lines": [ + { + "bbox": [ + 311, + 174, + 539, + 185 + ], + "spans": [ + { + "bbox": [ + 311, + 174, + 539, + 185 + ], + "type": "text", + "content": "Table 6. Results on MS-CXR-T sentence similarity benchmark." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 308, + 194, + 545, + 260 + ], + "lines": [ + { + "bbox": [ + 308, + 194, + 545, + 260 + ], + "spans": [ + { + "bbox": [ + 308, + 194, + 545, + 260 + ], + "type": "table", + "html": "
Text ModelMS-CXR-T (361 pairs)RadNLI (145 pairs)
AccuracyROC-AUCAccuracyROC-AUC
PubMedBERT [29]60.39.54281.38.727
CXR-BERT-G [9]62.60.60187.59.902
CXR-BERT-S [9]78.12.83789.66.932
BioViL-T87.77 ± 0.5.933 ± .00390.52 ± 1.0.947 ± .003
", + "image_path": "ba62ad8b68ae9ec45490ffd3c88d07387c1514b9ff9074d52824ad7d86663498.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_body" + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 281, + 545, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 281, + 545, + 316 + ], + "spans": [ + { + "bbox": [ + 305, + 281, + 545, + 316 + ], + "type": "text", + "content": "of sentence pair embeddings [56] are treated as class-logits to label each pair either as paraphrase or contradiction. See Appendix F.6 for further details." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 317, + 545, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 317, + 545, + 413 + ], + "spans": [ + { + "bbox": [ + 304, + 317, + 545, + 413 + ], + "type": "text", + "content": "Our text model is benchmarked against SOTA domain-specific BERT models. Table 6 shows that the proposed framework greatly increases the sensitivity of sentence embeddings to temporal content whilst better capturing the static content (RadNLI). Note that CXR-BERT-Specialised [9] is learnt through single-images starting from the same canonical model, illustrating the substantial increase in temporal and static sensitivity due to BioViL-T pre-training." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 420, + 429, + 433 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 420, + 429, + 433 + ], + "spans": [ + { + "bbox": [ + 306, + 420, + 429, + 433 + ], + "type": "text", + "content": "4.5. Ablation experiments" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 438, + 545, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 438, + 545, + 475 + ], + "spans": [ + { + "bbox": [ + 305, + 438, + 545, + 475 + ], + "type": "text", + "content": "In Table 7 we report extensive ablations across the multi-image encoder architecture, pre-training choices, and AR fine-tuning for report generation." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 481, + 545, + 554 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 481, + 545, + 554 + ], + "spans": [ + { + "bbox": [ + 304, + 481, + 545, + 554 + ], + "type": "text", + "content": "Image encoder Table 7 shows that decomposition of static and progression features is essential to ensure good performance on single-image tasks, such as phrase grounding. For temporal representations, on the other hand, positional encodings (T) are essential to disambiguate the order of scans, i.e., permutation variance across time." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 563, + 545, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 563, + 545, + 647 + ], + "spans": [ + { + "bbox": [ + 304, + 563, + 545, + 647 + ], + "type": "text", + "content": "Model pre-training The corresponding results are shown in the middle section of Table 7. The local contrastive loss proves crucial to ensure meaningful language supervision during pre-training, followed by the image-guided MLM objective. Lastly, use of the FINDINGS section results in only minor performance gains as the key findings are already captured in the IMPRESSION section." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 653, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 545, + 714 + ], + "type": "text", + "content": "Report generation The importance of prior image and report is demonstrated by the substantial drop in the \"no prior image and report\" ablation, confirming our hypothesis that temporal context is crucial for improving report quality. While both inputs are crucial for optimal performance," + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15022" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 48, + 134, + 288, + 280 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 288, + 126 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 288, + 126 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 288, + 126 + ], + "type": "text", + "content": "Table 7. Ablation study on image encoder, pre-training settings, and report generation (one component at a time, and repeated for 4 random seeds). Note that for temporal classification, linear probing is applied to frozen image embeddings. In report generation, the baseline method is fine-tuned with both prior image and report." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 134, + 288, + 280 + ], + "lines": [ + { + "bbox": [ + 48, + 134, + 288, + 280 + ], + "spans": [ + { + "bbox": [ + 48, + 134, + 288, + 280 + ], + "type": "table", + "html": "
AblationAvg. CNR (mIoU)Pl. Effusion Acc.
EncoderBaseline1.33 ± 0.02 (.248)64.8 ± 0.6
- Temporal pos. encoding1.32 ± 0.02 (.242)62.9 ± 1.0
- Feature decomposition1.11 ± 0.08 (.203)64.0 ± 0.6
Pre-trainingBaseline1.33 ± 0.02 (.248)64.8 ± 0.6
- Use of findings section1.32 ± 0.01 (.246)63.8 ± 0.8
- MLM loss1.28 ± 0.02 (.238)63.2 ± 0.7
- Local contrastive loss1.18 ± 0.02 (.236)60.2 ± 0.6
AblationROUGETEM
Report gen.Baseline29.64 ± 0.0817.54 ± 0.11
- Prior image29.35 ± 0.2516.30 ± 0.40
- Prior report28.67 ± 0.1216.00 ± 0.30
-(Prior image and report)27.78 ± 0.0913.65 ± 0.48
- Separation token26.00 ± 0.4015.50 ± 1.06
", + "image_path": "faaa48f943a5f3bbf182c607a3fa978e55c7ed4aba4c8ae36a28b6a3c14f8bd3.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 297, + 287, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 297, + 287, + 380 + ], + "spans": [ + { + "bbox": [ + 46, + 297, + 287, + 380 + ], + "type": "text", + "content": "the prior report is more so because it summarises the image and provides a clearer signal. The prior image however cannot be dismissed entirely as it provides granular details which may not always be documented in a report. Finally, we found the separation token is crucial in differentiating between the predicted tokens for the current report and tokens from the prior report." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 386, + 285, + 399 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 386, + 285, + 399 + ], + "spans": [ + { + "bbox": [ + 47, + 386, + 285, + 399 + ], + "type": "text", + "content": "4.6. Which tokens require a prior image in MLM?" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 405, + 287, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 405, + 287, + 464 + ], + "spans": [ + { + "bbox": [ + 47, + 405, + 287, + 464 + ], + "type": "text", + "content": "We leverage the MLM objective in an inference setting to analyse the influence of prior images in predicting masked tokens. Inspired by the " + }, + { + "bbox": [ + 47, + 405, + 287, + 464 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 47, + 405, + 287, + 464 + ], + "type": "text", + "content": " image loss of [8], we define " + }, + { + "bbox": [ + 47, + 405, + 287, + 464 + ], + "type": "inline_equation", + "content": "\\Delta_{\\mathrm{img}}^{\\mathrm{prior}}" + }, + { + "bbox": [ + 47, + 405, + 287, + 464 + ], + "type": "text", + "content": " as the change in loss by conditioning the estimation with a prior image for a given token " + }, + { + "bbox": [ + 47, + 405, + 287, + 464 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 47, + 405, + 287, + 464 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 64, + 469, + 287, + 486 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 469, + 287, + 486 + ], + "spans": [ + { + "bbox": [ + 64, + 469, + 287, + 486 + ], + "type": "interline_equation", + "content": "\\Delta_ {\\mathrm {i m g}} ^ {\\text {p r i o r}} (w) = l (w, \\mathbf {x} _ {\\mathrm {i m g}} ^ {\\text {c u r r}}, \\emptyset) - l (w, \\mathbf {x} _ {\\mathrm {i m g}} ^ {\\text {c u r r}}, \\mathbf {x} _ {\\mathrm {i m g}} ^ {\\text {p r i o r}}) \\tag {2}", + "image_path": "ecc7c3d71e1a4730c0d64e2075ac835a4cd9c469f5a50f0e1cc0eccb33cb46f3.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 491, + 287, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 491, + 287, + 648 + ], + "spans": [ + { + "bbox": [ + 46, + 491, + 287, + 648 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 491, + 287, + 648 + ], + "type": "inline_equation", + "content": "l(w, \\mathbf{x}_{\\mathrm{img}}^{\\mathrm{curr}}, \\mathbf{x}_{\\mathrm{img}}^{\\mathrm{prior}})" + }, + { + "bbox": [ + 46, + 491, + 287, + 648 + ], + "type": "text", + "content": " is the cross-entropy of predicting the masked token " + }, + { + "bbox": [ + 46, + 491, + 287, + 648 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 46, + 491, + 287, + 648 + ], + "type": "text", + "content": " given visual features (MLM loss for a single token), averaged over sentences in which " + }, + { + "bbox": [ + 46, + 491, + 287, + 648 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 46, + 491, + 287, + 648 + ], + "type": "text", + "content": " appears. " + }, + { + "bbox": [ + 46, + 491, + 287, + 648 + ], + "type": "inline_equation", + "content": "\\Delta_{\\mathrm{img}}^{\\mathrm{prior}}" + }, + { + "bbox": [ + 46, + 491, + 287, + 648 + ], + "type": "text", + "content": " is a measure of how much that token benefits from access to the prior image, as well as an assessment of the contribution of the prior image to the image representation. In Figure 4 we show the distribution of " + }, + { + "bbox": [ + 46, + 491, + 287, + 648 + ], + "type": "inline_equation", + "content": "\\Delta_{\\mathrm{img}}^{\\mathrm{prior}}" + }, + { + "bbox": [ + 46, + 491, + 287, + 648 + ], + "type": "text", + "content": " as a function of token category (e.g., Anatomy, Positional; see F.5 for annotation details). For Progression-type terms in particular, the model heavily relies on the prior image for image-guided MLM. We further observe that this effect is specific to temporal tokens; as expected, those from other semantic categories do not consistently rely on the prior image." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 657, + 119, + 670 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 657, + 119, + 670 + ], + "spans": [ + { + "bbox": [ + 47, + 657, + 119, + 670 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 677, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 288, + 713 + ], + "type": "text", + "content": "In this paper, we introduced BioViL-T, a vision-language pre-training framework enabling alignment between text and multiple images. BioViL-T makes use of" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 307, + 69, + 547, + 225 + ], + "blocks": [ + { + "bbox": [ + 307, + 69, + 547, + 225 + ], + "lines": [ + { + "bbox": [ + 307, + 69, + 547, + 225 + ], + "spans": [ + { + "bbox": [ + 307, + 69, + 547, + 225 + ], + "type": "image", + "image_path": "1774f3c5ed4a9edaa21125a7a8ec112dc540c51122453ce33b74887b58a753a1.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 230, + 547, + 308 + ], + "lines": [ + { + "bbox": [ + 304, + 230, + 547, + 308 + ], + "spans": [ + { + "bbox": [ + 304, + 230, + 547, + 308 + ], + "type": "text", + "content": "Figure 4. Mean token-level increase in image-guided MLM loss when prior image is discarded, grouped by token category. The prior image is excluded during inference to measure its impact on masked token predictions. Progression tokens are significantly better predicted when prior images are incorporated into image embeddings. The top five Progression tokens are 'persist', 'improving', 'remains', 'unchanged', and 'residual'." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 331, + 545, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 331, + 545, + 462 + ], + "spans": [ + { + "bbox": [ + 304, + 331, + 545, + 462 + ], + "type": "text", + "content": "a novel multi-image encoder and explicitly decomposes static-temporal features to augment the current image representation with information from prior images. This enables the grounding of temporal references in the text. To our knowledge, this is the first method capable of leveraging the temporal content commonly present in biomedical text. It addresses an important limitation in existing VLP approaches, which simply discard such context. Also, incorporating such multi-modal temporal content provides strong learning signals to the model, resulting in richer representations and improved downstream performance." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 464, + 546, + 583 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 464, + 546, + 583 + ], + "spans": [ + { + "bbox": [ + 304, + 464, + 546, + 583 + ], + "type": "text", + "content": "We demonstrate the value of this paradigm through extensive experiments: BioViL-T excels on both static and temporal tasks, establishing new SOTA on report generation, temporal image classification, few/zero-shot pneumonia detection, and phrase grounding. Furthermore, we release a new multi-modal benchmark (MS-CXR-T) to measure the quality of image and text representations in terms of temporal semantics, enabling more diverse evaluation of biomedical VLP models. The corresponding model weights" + }, + { + "bbox": [ + 304, + 464, + 546, + 583 + ], + "type": "inline_equation", + "content": "^6" + }, + { + "bbox": [ + 304, + 464, + 546, + 583 + ], + "type": "text", + "content": " and code" + }, + { + "bbox": [ + 304, + 464, + 546, + 583 + ], + "type": "inline_equation", + "content": "^7" + }, + { + "bbox": [ + 304, + 464, + 546, + 583 + ], + "type": "text", + "content": " are publicly available." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 584, + 545, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 584, + 545, + 632 + ], + "spans": [ + { + "bbox": [ + 304, + 584, + 545, + 632 + ], + "type": "text", + "content": "Further exploration and evaluation are required on diverse datasets to characterise what kinds of tasks would benefit from a temporal modelling approach, and specifically from the proposed methodology." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 644, + 545, + 681 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 644, + 545, + 681 + ], + "spans": [ + { + "bbox": [ + 305, + 644, + 545, + 681 + ], + "type": "text", + "content": "Acknowledgements: We would like to thank Hannah Richardson, Hoifung Poon, Melanie Bernhardt, Melissa Bristow and Naoto Usuyama for their valuable feedback." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 315, + 693, + 542, + 702 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 693, + 542, + 702 + ], + "spans": [ + { + "bbox": [ + 315, + 693, + 542, + 702 + ], + "type": "text", + "content": "6Models can be found at: https://aka.ms/biovil-t-model" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 318, + 703, + 531, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 703, + 531, + 712 + ], + "spans": [ + { + "bbox": [ + 318, + 703, + 531, + 712 + ], + "type": "text", + "content": "7Code can be found at: https://aka.ms/biovil-t-code" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "15023" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "text", + "content": "[1] Samira Abnar and Willem Zuidema. Quantifying attention flow in transformers. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4190-4197, Online, July 2020. Association for Computational Linguistics. 3, 5, 13, 14" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 147, + 288, + 201 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 147, + 288, + 201 + ], + "spans": [ + { + "bbox": [ + 53, + 147, + 288, + 201 + ], + "type": "text", + "content": "[2] Monica N Agrawal, Hunter Lang, Michael Offin, Lior Gazit, and David Sontag. Leveraging time irreversibility with order-contrastive pre-training. In International Conference on Artificial Intelligence and Statistics, pages 2330–2353. PMLR, 2022. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 202, + 288, + 246 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 202, + 288, + 246 + ], + "spans": [ + { + "bbox": [ + 53, + 202, + 288, + 246 + ], + "type": "text", + "content": "[3] Uwa O. Aideyan, Kevin Berbaum, and Wilbur L. Smith. Influence of prior radiologic information on the interpretation of radiographic examinations. Academic Radiology, 2(3):205-208, 1995. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 247, + 288, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 247, + 288, + 300 + ], + "spans": [ + { + "bbox": [ + 53, + 247, + 288, + 300 + ], + "type": "text", + "content": "[4] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, et al. Flamingo: a visual language model for few-shot learning. arXiv preprint arXiv:2204.14198, 2022. 2, 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 302, + 287, + 368 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 302, + 287, + 368 + ], + "spans": [ + { + "bbox": [ + 53, + 302, + 287, + 368 + ], + "type": "text", + "content": "[5] B.B. Avants, C.L. Epstein, M. Grossman, and J.C. Gee. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis, 12(1):26-41, 2008. Special Issue on The Third International Workshop on Biomedical Image Registration - WBIR 2006. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 369, + 287, + 401 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 369, + 287, + 401 + ], + "spans": [ + { + "bbox": [ + 53, + 369, + 287, + 401 + ], + "type": "text", + "content": "[6] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016.3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 403, + 287, + 458 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 403, + 287, + 458 + ], + "spans": [ + { + "bbox": [ + 53, + 403, + 287, + 458 + ], + "type": "text", + "content": "[7] Nadine Behrmann, Mohsen Fayyaz, Juergen Gall, and Mehdi Noroozi. Long short view feature decomposition via contrastive video representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9244-9253, 2021. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 459, + 288, + 524 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 459, + 288, + 524 + ], + "spans": [ + { + "bbox": [ + 53, + 459, + 288, + 524 + ], + "type": "text", + "content": "[8] Yonatan Bitton, Gabriel Stanovsky, Michael Elhadad, and Roy Schwartz. Data efficient masked language modeling for vision and language. In 2021 Findings of the Association for Computational Linguistics, Findings of ACL: EMNLP 2021, pages 3013-3028. Association for Computational Linguistics (ACL), 2021. 8" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 525, + 288, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 525, + 288, + 612 + ], + "spans": [ + { + "bbox": [ + 53, + 525, + 288, + 612 + ], + "type": "text", + "content": "[9] Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, and Ozan Oktay. Making the most of text semantics to improve biomedical vision-language processing. In European Conference on Computer Vision (ECCV), pages 1–21. Springer, 2022. 1, 2, 3, 4, 5, 6, 7, 17, 18, 19, 20" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 614, + 287, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 614, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 48, + 614, + 287, + 689 + ], + "type": "text", + "content": "[10] Benedikt Boecking, Naoto Usuyama, Shruthi Bannur, Daniel C. Castro, Anton Schwaighofer, Stephanie Hyland, Maria Wetscherek, Tristan Naumann, Aditya Nori, Javier Alvarez-Valle, Hoifung Poon, and Ozan Oktay. MS-CXR: Making the most of text semantics to improve biomedical vision-language processing (version 0.1). PhysioNet, 2022. 2, 3, 5, 7, 15" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "text", + "content": "[11] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakan-" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 106 + ], + "type": "text", + "content": "tan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in neural information processing systems, 33:1877-1901, 2020. 5, 19" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 308, + 107, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 107, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 308, + 107, + 545, + 150 + ], + "type": "text", + "content": "[12] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In European conference on computer vision, pages 213-229. Springer, 2020. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 308, + 151, + 545, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 151, + 545, + 205 + ], + "spans": [ + { + "bbox": [ + 308, + 151, + 545, + 205 + ], + "type": "text", + "content": "[13] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. UNITER: UNiversal Image-TExt Representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 2, 4" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 206, + 545, + 249 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 206, + 545, + 249 + ], + "spans": [ + { + "bbox": [ + 308, + 206, + 545, + 249 + ], + "type": "text", + "content": "[14] Zhihong Chen, Yan Song, Tsung-Hui Chang, and Xiang Wan. Generating radiology reports via memory-driven transformer. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, Nov. 2020. 5, 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 251, + 545, + 314 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 251, + 545, + 314 + ], + "spans": [ + { + "bbox": [ + 308, + 251, + 545, + 314 + ], + "type": "text", + "content": "[15] Ching-Yao Chuang, R Devon Hjelm, Xin Wang, Vibhav Vineet, Neel Joshi, Antonio Torralba, Stefanie Jegelka, and Yale Song. Robust contrastive learning against noisy views. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16670-16681, 2022. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 316, + 545, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 316, + 545, + 360 + ], + "spans": [ + { + "bbox": [ + 308, + 316, + 545, + 360 + ], + "type": "text", + "content": "[16] Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka. Debiased contrastive learning. Advances in neural information processing systems, 33:8765-8775, 2020. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 361, + 545, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 361, + 545, + 404 + ], + "spans": [ + { + "bbox": [ + 308, + 361, + 545, + 404 + ], + "type": "text", + "content": "[17] R. C. Daudt, B. L. Saux, and Alexandre Boulch. Fully convolutional siamese networks for change detection. In 2018 25th IEEE International Conference on Image Processing (ICIP), pages 4063-4067, 2018. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 405, + 545, + 469 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 405, + 545, + 469 + ], + "spans": [ + { + "bbox": [ + 308, + 405, + 545, + 469 + ], + "type": "text", + "content": "[18] Dina Demner-Fushman, Marc D Kohli, Marc B Rosenman, Sonya E Shooshan, Laritza Rodriguez, Sameer Antani, George R Thoma, and Clement J McDonald. Preparing a collection of radiology examinations for distribution and retrieval. Journal of the American Medical Informatics Association, 23(2):304-310, 2016. 15" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 471, + 545, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 471, + 545, + 514 + ], + "spans": [ + { + "bbox": [ + 308, + 471, + 545, + 514 + ], + "type": "text", + "content": "[19] Karan Desai and Justin Johnson. Virtex: Learning visual representations from textual annotations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11162-11173, 2021. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 515, + 545, + 558 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 515, + 545, + 558 + ], + "spans": [ + { + "bbox": [ + 308, + 515, + 545, + 558 + ], + "type": "text", + "content": "[20] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 559, + 545, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 559, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 308, + 559, + 545, + 624 + ], + "type": "text", + "content": "[21] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations, 2020. 3, 13" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 625, + 545, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 625, + 545, + 690 + ], + "spans": [ + { + "bbox": [ + 308, + 625, + 545, + 690 + ], + "type": "text", + "content": "[22] Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, et al. An empirical study of training end-to-end vision-and-language transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18166-18176, 2022. 2, 4" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "type": "text", + "content": "[23] Stanley Durrleman, Xavier Pennec, Alain Trouve, José Braga, Guido Gerig, and Nicholas Ayache. Toward a com" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "15024" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 66, + 72, + 288, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 72, + 288, + 106 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 288, + 106 + ], + "type": "text", + "content": "prehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. International Journal of Computer Vision, 103:22-59, 2013. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 107, + 287, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 107, + 287, + 163 + ], + "spans": [ + { + "bbox": [ + 48, + 107, + 287, + 163 + ], + "type": "text", + "content": "[24] Stéphane d'Ascoli, Hugo Touvron, Matthew L Leavitt, Ari S Morcos, Giulio Biroli, and Levent Sagun. Convit: Improving vision transformers with soft convolutional inductive biases. In International Conference on Machine Learning, pages 2286-2296. PMLR, 2021. 3, 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 164, + 288, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 164, + 288, + 218 + ], + "spans": [ + { + "bbox": [ + 48, + 164, + 288, + 218 + ], + "type": "text", + "content": "[25] Mark Endo, Rayan Krishnan, Viswesh Krishna, Andrew Y Ng, and Pranav Rajpurkar. Retrieval-based chest x-ray report generation using a pre-trained contrastive language-image model. In Machine Learning for Health, pages 209-219. PMLR, 2021. 5, 6, 18" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 220, + 288, + 263 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 220, + 288, + 263 + ], + "spans": [ + { + "bbox": [ + 48, + 220, + 288, + 263 + ], + "type": "text", + "content": "[26] Sepideh Esmaeilpour, Bing Liu, Eric Robertson, and Lei Shu. Zero-shot out-of-distribution detection based on the pretrained model clip. In Proceedings of the AAAI conference on artificial intelligence, 2022. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 265, + 288, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 265, + 288, + 308 + ], + "spans": [ + { + "bbox": [ + 48, + 265, + 288, + 308 + ], + "type": "text", + "content": "[27] Stanislav Fort, Jie Ren, and Balaji Lakshminarayanan. Exploring the limits of out-of-distribution detection. Advances in Neural Information Processing Systems, 34:7068-7081, 2021. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 310, + 288, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 310, + 288, + 376 + ], + "spans": [ + { + "bbox": [ + 48, + 310, + 288, + 376 + ], + "type": "text", + "content": "[28] Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23):e215-e220, 2000. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 377, + 288, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 377, + 288, + 443 + ], + "spans": [ + { + "bbox": [ + 48, + 377, + 288, + 443 + ], + "type": "text", + "content": "[29] Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. Domain-specific language model pretraining for biomedical natural language processing. ACM Transactions on Computing for Healthcare (HEALTH), 3(1):1-23, 2021. 6, 7, 20" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 445, + 288, + 488 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 445, + 288, + 488 + ], + "spans": [ + { + "bbox": [ + 48, + 445, + 288, + 488 + ], + "type": "text", + "content": "[30] Tengda Han, Weidi Xie, and Andrew Zisserman. Self-supervised co-training for video representation learning. Advances in Neural Information Processing Systems, 33:5679-5690, 2020. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 490, + 288, + 534 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 490, + 288, + 534 + ], + "spans": [ + { + "bbox": [ + 48, + 490, + 288, + 534 + ], + "type": "text", + "content": "[31] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 535, + 288, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 535, + 288, + 601 + ], + "spans": [ + { + "bbox": [ + 48, + 535, + 288, + 601 + ], + "type": "text", + "content": "[32] Shih-Cheng Huang, Liyue Shen, Matthew P Lungren, and Serena Yeung. GLoRIA: A multimodal global-local representation learning framework for label-efficient medical image recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3942-3951, 2021. 1, 2, 4, 5, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 602, + 288, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 602, + 288, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 602, + 288, + 668 + ], + "type": "text", + "content": "[33] Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilicus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al. CheXpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the AAAI conference on artificial intelligence, 33(01):590-597, 2019. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "type": "text", + "content": "[34] Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven Truong, Tan Bui, Pierre Chambon, Yuhao Zhang, Matthew P Lungren, Andrew Y Ng, Curtis Langlotz, et al. Radgraph: Extracting clinical entities and relations from radiology re" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 106 + ], + "type": "text", + "content": "ports. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1), 2021. 17" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 307, + 107, + 547, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 547, + 173 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 547, + 173 + ], + "type": "text", + "content": "[35] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 13" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 175, + 545, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 175, + 545, + 207 + ], + "spans": [ + { + "bbox": [ + 307, + 175, + 545, + 207 + ], + "type": "text", + "content": "[36] A. Johnson, T. Pollard, S.J. Berkowitz, R. Mark, and S. Horng. MIMIC-CXR database (version 2.0.0). PhysioNet, 2019. 2, 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 209, + 545, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 209, + 545, + 274 + ], + "spans": [ + { + "bbox": [ + 307, + 209, + 545, + 274 + ], + "type": "text", + "content": "[37] Gaurang Karwande, Amarachi B Mbakwe, Joy T Wu, Leo A Celi, Mehdi Moradi, and Ismini Lourentzou. Chexrelnet: An anatomy-aware model for tracking longitudinal relationships between chest x-rays. In Medical Image Computing and Computer Assisted Intervention-MICCAI 2022, pages 581-591, 2022. 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 276, + 545, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 276, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 307, + 276, + 545, + 319 + ], + "type": "text", + "content": "[38] Jiann-Shu Lee, Jing-Wein Wang, Hsing-Hsien Wu, and Ming-Zheng Yuan. A nonparametric-based rib suppression method for chest radiographs. Computers & Mathematics with Applications, 64(5):1390-1399, 2012. 20" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 321, + 545, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 321, + 545, + 409 + ], + "spans": [ + { + "bbox": [ + 307, + 321, + 545, + 409 + ], + "type": "text", + "content": "[39] Junnan Li, Dongxu Li, Caiming Xiong, and Steven Hoi. BLIP: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 12888-12900. PMLR, 17-23 Jul 2022. 2, 4" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 411, + 545, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 411, + 545, + 465 + ], + "spans": [ + { + "bbox": [ + 307, + 411, + 545, + 465 + ], + "type": "text", + "content": "[40] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. Align before fuse: Vision and language representation learning with momentum distillation. Advances in neural information processing systems, 34:9694-9705, 2021. 1, 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 467, + 545, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 467, + 545, + 510 + ], + "spans": [ + { + "bbox": [ + 307, + 467, + 545, + 510 + ], + "type": "text", + "content": "[41] Chin-Yew Lin. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out, pages 74-81, Barcelona, Spain, July 2004. Association for Computational Linguistics. 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 512, + 545, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 512, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 307, + 512, + 545, + 555 + ], + "type": "text", + "content": "[42] Peter J Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, and Noam Shazeer. Generating wikipedia by summarizing long sequences. In International Conference on Learning Representations, 2018. 4" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 557, + 545, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 557, + 545, + 590 + ], + "spans": [ + { + "bbox": [ + 307, + 557, + 545, + 590 + ], + "type": "text", + "content": "[43] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018. 17, 19" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 591, + 545, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 591, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 307, + 591, + 545, + 624 + ], + "type": "text", + "content": "[44] Bradley C Lowekamp, David T Chen, Luis Ibanez, and Daniel Blezek. The design of simpleitk. Frontiers in neuroinformatics, 7:45, 2013. 20" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 625, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 625, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 625, + 545, + 713 + ], + "type": "text", + "content": "[45] Yasuhide Miura, Yuhao Zhang, Emily Tsai, Curtis Langlotz, and Dan Jurafsky. Improving factual completeness and consistency of image-to-text radiology report generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5288-5304, Online, June 2021. Association for Computational Linguistics. 5, 6, 7, 18" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "15025" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "text", + "content": "[46] Jong Hak Moon, Hyungyung Lee, Woncheol Shin, Young-Hak Kim, and Edward Choi. Multi-modal understanding and generation for medical images and text via vision-language pre-training. IEEE Journal of Biomedical and Health Informatics, 2022. 2, 4" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 129, + 287, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 129, + 287, + 163 + ], + "spans": [ + { + "bbox": [ + 48, + 129, + 287, + 163 + ], + "type": "text", + "content": "[47] American College of Radiology (ACR). ACR practice guideline for communication of diagnostic imaging findings. Practice guidelines & technical standards, 2020. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 164, + 288, + 219 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 164, + 288, + 219 + ], + "spans": [ + { + "bbox": [ + 48, + 164, + 288, + 219 + ], + "type": "text", + "content": "[48] Dong Yul Oh, Jihang Kim, and Kyong Joon Lee. Longitudinal change detection on chest X-rays using geometric correlation maps. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 748-756. Springer, 2019. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 220, + 288, + 253 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 220, + 288, + 253 + ], + "spans": [ + { + "bbox": [ + 48, + 220, + 288, + 253 + ], + "type": "text", + "content": "[49] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 1, 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 255, + 288, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 255, + 288, + 319 + ], + "spans": [ + { + "bbox": [ + 48, + 255, + 288, + 319 + ], + "type": "text", + "content": "[50] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pages 311-318, Philadelphia, Pennsylvania, USA, July 2002. Association for Computational Linguistics. 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 321, + 288, + 354 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 321, + 288, + 354 + ], + "spans": [ + { + "bbox": [ + 48, + 321, + 288, + 354 + ], + "type": "text", + "content": "[51] Namuk Park and Songkuk Kim. How do vision transformers work? In International Conference on Learning Representations, 2022. 3, 4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 355, + 288, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 355, + 288, + 399 + ], + "spans": [ + { + "bbox": [ + 48, + 355, + 288, + 399 + ], + "type": "text", + "content": "[52] Daifeng Peng, Yongjun Zhang, and Haiyan Guan. End-to-end change detection for high resolution satellite images using improved UNet++. Remote Sensing, 11(11):1382, 2019. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 400, + 288, + 467 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 400, + 288, + 467 + ], + "spans": [ + { + "bbox": [ + 48, + 400, + 288, + 467 + ], + "type": "text", + "content": "[53] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 4" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 468, + 288, + 511 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 468, + 288, + 511 + ], + "spans": [ + { + "bbox": [ + 48, + 468, + 288, + 511 + ], + "type": "text", + "content": "[54] Vignav Ramesh, Nathan Andrew Chi, and Pranav Rajpurkar. Improving radiology report generation systems by removing hallucinated references to non-existent priors. arXiv preprint arXiv:2210.06340, 2022. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 514, + 288, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 514, + 288, + 579 + ], + "spans": [ + { + "bbox": [ + 48, + 514, + 288, + 579 + ], + "type": "text", + "content": "[55] Adria Recasens, Pauline Luc, Jean-Baptiste Alayrac, Luyu Wang, Florian Strub, Corentin Tallec, Mateusz Malinowski, Viorica Pătrăcean, Florent Altché, Michal Valko, et al. Broaden your views for self-supervised video learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1255–1265, 2021. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 580, + 288, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 580, + 288, + 667 + ], + "spans": [ + { + "bbox": [ + 48, + 580, + 288, + 667 + ], + "type": "text", + "content": "[56] Nils Reimers, Iryna Gurevych, Nils Reimers, Iryna Gurevych, Nandan Thakur, Nils Reimers, Johannes Daxenberger, Iryna Gurevych, Nils Reimers, Iryna Gurevych, et al. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, pages 671-688. Association for Computational Linguistics, 2019. 7, 20" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 669, + 288, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 669, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 669, + 288, + 712 + ], + "type": "text", + "content": "[57] Liqa A Rousan, Eyhab Elobeid, Musaab Karrar, and Yousef Khader. Chest x-ray findings and temporal lung changes in patients with Covid-19 pneumonia. BMC Pulmonary Medicine, 20(1):1-9, 2020. 1" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 24, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 118 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 118 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 118 + ], + "type": "text", + "content": "[58] Ruggiero Santeramo, Samuel Joseph Withey, and G. Montana. Longitudinal detection of radiological abnormalities with time-modulated LSTM. In MICCAI 2018 Workshop on Deep Learning in Medical Imaging Analysis, 2018. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 307, + 119, + 545, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 119, + 545, + 162 + ], + "spans": [ + { + "bbox": [ + 307, + 119, + 545, + 162 + ], + "type": "text", + "content": "[59] Wenzhong Shi, Min Zhang, Rui Zhang, Shanxiong Chen, and Zhao Zhan. Change detection based on artificial intelligence: State-of-the-art and challenges. Remote Sensing, 12(10):1688, 2020. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 308, + 164, + 545, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 164, + 545, + 240 + ], + "spans": [ + { + "bbox": [ + 308, + 164, + 545, + 240 + ], + "type": "text", + "content": "[60] George Shih, Carol C Wu, Safwan S Halabi, Marc D Kohli, Luciano M Prevedello, Tessa S Cook, Arjun Sharma, Judith K Amorosa, Veronica Arteaga, Maya Galperin-Aizenberg, et al. Augmenting the national institutes of health chest radiograph dataset with expert annotations of possible pneumonia. Radiology: Artificial Intelligence, 1(1):e180041, 2019. 2, 5, 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 241, + 545, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 241, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 308, + 241, + 545, + 308 + ], + "type": "text", + "content": "[61] Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela. Flava: A foundational language and vision alignment model. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 15617-15629, 2022. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 309, + 545, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 309, + 545, + 387 + ], + "spans": [ + { + "bbox": [ + 308, + 309, + 545, + 387 + ], + "type": "text", + "content": "[62] Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Ng, and Matthew Lungren. Combining automatic labelers and expert annotations for accurate radiology report labeling using BERT. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1500–1519, Online, Nov. 2020. Association for Computational Linguistics. 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 388, + 545, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 388, + 545, + 442 + ], + "spans": [ + { + "bbox": [ + 308, + 388, + 545, + 442 + ], + "type": "text", + "content": "[63] Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Y Ng, and Matthew P Lungren. CheXbert: combining automatic labelers and expert annotations for accurate radiology report labeling using bert. arXiv preprint arXiv:2004.09167, 2020. 2, 16" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 444, + 545, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 444, + 545, + 510 + ], + "spans": [ + { + "bbox": [ + 308, + 444, + 545, + 510 + ], + "type": "text", + "content": "[64] Anuroop Sriram, Matthew Muckley, Koustuv Sinha, F. Shamout, Joelle Pineau, K. Geras, L. Azour, Y. Aphinyanaphongs, N. Yakubova, and William H. Moore. COVID-19 prognosis via self-supervised representation learning and multi-image prediction. arXiv preprint arXiv:2101.04909, 2021. 2, 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 511, + 545, + 554 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 511, + 545, + 554 + ], + "spans": [ + { + "bbox": [ + 308, + 511, + 545, + 554 + ], + "type": "text", + "content": "[65] Philippe Thévenaz and Michael Unser. Optimization of mutual information for multiresolution image registration. IEEE transactions on image processing, 9(12):2083-2099, 2000. 20" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 556, + 545, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 556, + 545, + 601 + ], + "spans": [ + { + "bbox": [ + 308, + 556, + 545, + 601 + ], + "type": "text", + "content": "[66] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3, 4" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 602, + 545, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 602, + 545, + 667 + ], + "spans": [ + { + "bbox": [ + 308, + 602, + 545, + 667 + ], + "type": "text", + "content": "[67] Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar, Can Liu, Andrew Y Ng, and Pranav Rajpurkar. Medaug: Contrastive learning leveraging patient metadata improves representations for chest x-ray interpretation. In Machine Learning for Healthcare Conference, pages 755-769. PMLR, 2021. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 669, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 669, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 308, + 669, + 545, + 712 + ], + "type": "text", + "content": "[68] Chuang Wang, Andreas Rimner, Yu chi Hu, Neelam Tyagi, Jue Jiang, Ellen Yorke, Sadegh Riyadhi, Gig S. Mageras, Joseph O. Deasy, and Pengpeng Zhang. Towards predicting the evolution of lung tumors during radiotherapy observed" + } + ] + } + ], + "index": 23 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "15026" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 73, + 287, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 67, + 73, + 286, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 73, + 286, + 95 + ], + "spans": [ + { + "bbox": [ + 67, + 73, + 286, + 95 + ], + "type": "text", + "content": "on a longitudinal MR imaging study via a deep learning algorithm. Medical Physics, 2019. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 287, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 287, + 128 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 287, + 128 + ], + "type": "text", + "content": "[69] Wenhui Wang, Hangbo Bao, Li Dong, and Furu Wei. Vlmo: Unified vision-language pre-training with mixture-of-modality-experts. arXiv preprint arXiv:2111.02358, 2021. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 129, + 286, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 129, + 286, + 215 + ], + "spans": [ + { + "bbox": [ + 48, + 129, + 286, + 215 + ], + "type": "text", + "content": "[70] Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M Summers. ChestX-Ray8: Hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 2097-2106. IEEE Computer Society, 2017. 2, 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 217, + 286, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 217, + 286, + 270 + ], + "spans": [ + { + "bbox": [ + 48, + 217, + 286, + 270 + ], + "type": "text", + "content": "[71] Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M Dai, and Quoc V Le. Finetuned language models are zero-shot learners. In International Conference on Learning Representations, 2021. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 272, + 286, + 337 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 272, + 286, + 337 + ], + "spans": [ + { + "bbox": [ + 48, + 272, + 286, + 337 + ], + "type": "text", + "content": "[72] Joy Wu, Nkechinyere Agu, Ismini Lourentzou, Arjun Sharma, Joseph Paguio, Jasper Seth Yao, Edward Christopher Dee, William Mitchell, Satyananda Kashyap, Andrea Giovannini, Leo Anthony Celi, Tanveer Syeda-Mahmood, and Mehdi Moradi. Chest imagenome dataset (version 1.0.0). PhysioNet, 2021. 5, 15, 18" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 338, + 286, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 338, + 286, + 392 + ], + "spans": [ + { + "bbox": [ + 48, + 338, + 286, + 392 + ], + "type": "text", + "content": "[73] Kan Wu, Houwen Peng, Minghao Chen, Jianlong Fu, and Hongyang Chao. Rethinking and improving relative position encoding for vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10033-10041, 2021. 19" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 394, + 286, + 446 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 394, + 286, + 446 + ], + "spans": [ + { + "bbox": [ + 48, + 394, + 286, + 446 + ], + "type": "text", + "content": "[74] Yiwen Xu, Ahmed Hosny, Roman Zeleznik, Chintan Parmar, Thibaud P. Coroller, Idalid Ivy Franco, Raymond H. Mak, and Hugo J.W.L. Aerts. Deep learning predicts lung cancer treatment response from serial medical imaging. Clinical Cancer Research, 25:3266 - 3275, 2019. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 449, + 286, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 449, + 286, + 491 + ], + "spans": [ + { + "bbox": [ + 48, + 449, + 286, + 491 + ], + "type": "text", + "content": "[75] Bin Yan and Mingtao Pei. Clinical-BERT: Vision-language pre-training for radiograph diagnosis and reports generation. Proceedings of the AAAI Conference on Artificial Intelligence, 36(3):2982-2990, 2022. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 493, + 286, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 493, + 286, + 536 + ], + "spans": [ + { + "bbox": [ + 48, + 493, + 286, + 536 + ], + "type": "text", + "content": "[76] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. Filip: Fine-grained interactive language-image pre-training. arXiv preprint arXiv:2111.07783, 2021. 2, 4" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 537, + 286, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 537, + 286, + 579 + ], + "spans": [ + { + "bbox": [ + 48, + 537, + 286, + 579 + ], + "type": "text", + "content": "[77] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. Coca: Contrastive captioners are image-text foundation models. arXiv preprint arXiv:2205.01917, 2022. 1, 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 581, + 286, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 581, + 286, + 635 + ], + "spans": [ + { + "bbox": [ + 48, + 581, + 286, + 635 + ], + "type": "text", + "content": "[78] Sukmin Yun, Jaehyung Kim, Dongyoon Han, Hwanjun Song, Jung-Woo Ha, and Jinwoo Shin. Time is matter: Temporal self-supervision for video transformers. In International Conference on Machine Learning, pages 25804-25816. PMLR, 2022. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 636, + 286, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 636, + 286, + 690 + ], + "spans": [ + { + "bbox": [ + 48, + 636, + 286, + 690 + ], + "type": "text", + "content": "[79] Dewen Zeng, John N Kheir, Peng Zeng, and Yiyu Shi. Contrastive learning with temporal correlated medical images: A case study using lung segmentation in chest x-rays. In 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), pages 1-7. IEEE, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 692, + 286, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 692, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 692, + 286, + 713 + ], + "type": "text", + "content": "[80] Zhaoyang Zeng, Daniel McDuff, Yale Song, et al. Contrastive learning of global and local video representations" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 195 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "text", + "content": "tions. Advances in Neural Information Processing Systems, 34:7025-7040, 2021. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 308, + 96, + 545, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 545, + 140 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 545, + 140 + ], + "type": "text", + "content": "[81] Yuhao Zhang, Hang Jiang, Yasuhide Miura, Christopher D Manning, and Curtis P Langlotz. Contrastive learning of medical visual representations from paired images and text. arXiv preprint arXiv:2010.00747, 2020. 1, 2, 4, 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 141, + 545, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 141, + 545, + 195 + ], + "spans": [ + { + "bbox": [ + 308, + 141, + 545, + 195 + ], + "type": "text", + "content": "[82] Yuhao Zhang, Yuhui Zhang, Peng Qi, Christopher D Manning, and Curtis P Langlotz. Biomedical and clinical english model packages for the stanza python nlp library. Journal of the American Medical Informatics Association, 28(9):1892-1899, 2021. 16, 17, 18" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "15027" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 11 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization/5043327e-5e28-4a8f-8c57-7b23c08760b9_content_list.json b/2023/Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization/5043327e-5e28-4a8f-8c57-7b23c08760b9_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..a53e4c893d15e70aa5f164015158c62cc0f6464d --- /dev/null +++ b/2023/Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization/5043327e-5e28-4a8f-8c57-7b23c08760b9_content_list.json @@ -0,0 +1,1664 @@ +[ + { + "type": "text", + "text": "Learning to Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization", + "text_level": 1, + "bbox": [ + 166, + 128, + 803, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Haina Qin $^{1,2}$ Longfei Han $^{3}$ Weihua Xiong $^{1}$ Juan Wang $^{1}$ Wentao Ma $^{4}$", + "bbox": [ + 129, + 200, + 841, + 222 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Bing $\\mathrm{Li}^{1(\\boxtimes)}$ Weiming $\\mathrm{Hu}^{1,2}$", + "bbox": [ + 336, + 227, + 633, + 247 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences", + "bbox": [ + 217, + 247, + 763, + 281 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ School of Artificial Intelligence, University of Chinese Academy of Sciences", + "bbox": [ + 184, + 282, + 797, + 299 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{3}$ Beijing Technology and Business University $^{4}$ Zeku Technology, Shanghai", + "bbox": [ + 186, + 300, + 795, + 316 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{qinhaina2020@,bli@nlpr.,wmhu@nlpr.}ia.ac.cn longfeihan@btbu.edu.cn Wallace.xiong@gmail.com", + "bbox": [ + 86, + 320, + 890, + 335 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 369, + 313, + 386 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The hardware image signal processing (ISP) pipeline is the intermediate layer between the imaging sensor and the downstream application, processing the sensor signal into an RGB image. The ISP is less programmable and consists of a series of processing modules. Each processing module handles a subtask and contains a set of tunable hyperparameters. A large number of hyperparameters form a complex mapping with the ISP output. The industry typically relies on manual and time-consuming hyperparameter tuning by image experts, biased towards human perception. Recently, several automatic ISP hyperparameter optimization methods using downstream evaluation metrics come into sight. However, existing methods for ISP tuning treat the high-dimensional parameter space as a global space for optimization and prediction all at once without inducing the structure knowledge of ISP. To this end, we propose a sequential ISP hyperparameter prediction framework that utilizes the sequential relationship within ISP modules and the similarity among parameters to guide the model sequence process. We validate the proposed method on object detection, image segmentation, and image quality tasks.", + "bbox": [ + 75, + 402, + 472, + 720 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 752, + 209, + 768 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Hardware ISPs are low-level image processing pipelines that convert RAW images into high-quality RGB images. Typically, ISPs include a series of processing modules [5], each of which handles a subtask such as denoising, white balance, demosaicing, or sharpening. Compared to software image processing pipelines, hardware ISPs are faster, more power-efficient, and widely used in real-time products [7, 34], including cameras [28], smartphones, surveil", + "bbox": [ + 75, + 779, + 468, + 901 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "lance [21], IoT and driven-assistance systems. ISPs are highly modular and less programmable but with a set of tunable hyperparameters. The industry always relies on manual and costly hyperparameter tuning by image experts [1] to adapt the ISP to different application scenarios.", + "bbox": [ + 496, + 369, + 892, + 446 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The ISP is always designed as a sequential pipeline [5] and the configurable hyperparameters of various modules from any ISP aggregate to be a complex parameter space (with tens to hundreds of parameters), making the manual tuning process time-consuming. It is also difficult to subjectively find optimal hyperparameters settings for various downstream tasks (such as object detection and image segmentation [29, 30]). Recently, several automatic ISP hyperparameter optimization methods [17, 31] using downstream evaluation metrics come into sight. These methods tuning hyperparameters for downstream tasks include derivative-free [18] or gradient methods [12, 25, 27] based on differentiable approximation. There are also methods to demonstrate the potential of predicting specific hyperparameters for each image or scene [22]. However, existing methods treat the high-dimensional parameter space as a global black-box space for optimization and prediction all at once, while ignoring the inherent sequence of the ISP modules and the critical intra-correlation among hyperparameters.", + "bbox": [ + 496, + 446, + 892, + 734 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Inspired by the operating principles and structure knowledge of ISPs, we first propose a sequential ISP hyperparameter prediction framework (as shown in Fig. 1) that contains Sequential CNN model and Global Similarity Grouping. The sequential CNN model runs recurrently by predicting a group of parameters from several ISP modules, not all parameters, at each step. Meanwhile, the predicted parameters, along with the network's hidden layer and the input data, are in turn encoded as prior knowledge for predicting the following grouping of parameters. The Global Similarity Grouping module divides ISP parameters into multiple", + "bbox": [ + 496, + 734, + 893, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "22314", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/a92c5b6112f17fdeb90bae49d36d073956f9be0d8dc07faf76323462bfb55623.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 86, + 89, + 459, + 215 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/cd26e4ca6833577ab324316cff218c43d270ac4904a3b69ceb2dd4321008c605.jpg", + "image_caption": [ + "Figure 1. (A) Previous methods treat the hyperparameter space as a black box optimization problem and estimate all parameters at once without considering the prior knowledge of the ISP. (B) Our proposed method first decouples ISP structural knowledge and treats ISP tuning as a sequential prediction problem. It is effective to introduce sequence information and similarity relations in the high-dimensional hyperparameter space." + ], + "image_footnote": [], + "bbox": [ + 86, + 224, + 459, + 467 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "disjoint groupings. The sequence order between groupings is explored heuristically using prior knowledge of the order of ISP modules. Given the flexibility, groupings are determined based on similarity among parameters, not limited to the same module parameters. The correlation of parameter activation maps learned through the model is used as the basis for parameter groupings.", + "bbox": [ + 75, + 603, + 467, + 708 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contributions can be summarized as the following:", + "bbox": [ + 96, + 709, + 459, + 723 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a new sequential CNN structure to exploit the sequence processing knowledge within ISP. The potential sequential information among parameters is used to guide the processing of the model.", + "- We exploit the correlation among parameters by the proposed similarity grouping module. The flexible parameter groupings allow the exploration of cross-module relationships among parameters.", + "- We validate the effectiveness of our method in a variety of downstream tasks, including object detection, image segmentation, and image quality. In these applications," + ], + "bbox": [ + 94, + 734, + 467, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "our method outperforms existing methods.", + "bbox": [ + 531, + 90, + 813, + 106 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 119, + 640, + 135 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "ISPs contain several components, and each module carries a specific image-processing algorithm. These algorithms are permanently configured with tens to hundreds of handcrafted parameters, for instance, noise reduction, color space transformation, and sharpening. It is desirable to find parameter configurations for imaging experts that make ISP work well in real-world scenarios and output compressed images with good human perception. Recently, to tackle this challenging optimization problem, several automatic ISP tuning methods were proposed by optimizing the hyperparameters with downstream task feedback [3,4,35], for instance, object detection [27,29], object segmentation [17], and image quality [2,6,30,32].", + "bbox": [ + 496, + 143, + 890, + 340 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "With the high-level feedback information, recent works always leverage a differentiable mapping between the parameter space and high-level evaluation metrics. Some existing optimization methods try to mimic the whole hardware ISP pipeline as an approximate CNN proxy model and construct an end-to-end differentiable CNN framework to optimize the proxy network [8, 12, 33]. Tseng et al. [27] trained an approximate CNN proxy model to mimic hardware ISP and optimized the differentiable CNN model with Stochastic Gradient Descent. Onzon et al. [19] propose a neural network for exposure selection that is jointly end-to-end with an object detector and ISP pipeline. Pfister et al. [20] proposes to optimize sparsity regularization for denoising. These methods are based on the assumption that a single proxy network can mimic the whole ISP pipeline and cannot be used to optimize the hardware ISPs. Other methods can directly put the hardware ISP into the optimization stage and predict all hyperparameters at once. For instance, Mosleh et al. [17, 24] directly optimize hardware ISP by a novel CMA-ES strategy [9] with max-rank-based multi-objective scalarization and initial search space reduction. Kim et al. [14] utilize the objective function of multi-output regression for modeling the relation between ISP parameter and IQM score. Qin et al. [22] directly infer all ISP hyperparameters via an attention-based CNN method. However, these methods do not take sequential information into consideration. Only Nishimura et al. [18] optimize ISP components with sequential prior knowledge based on a 0-th order Nelder-Mead method. Nevertheless, it can only be used to optimize ISP modules one at a time, ignores cross-module connections while viewing the modules in isolation, and is difficult to apply to hardware ISPs.", + "bbox": [ + 496, + 340, + 890, + 824 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Therefore, with the massive number of parameters in hardware ISPs, it needs to exploit the structural knowledge to tune ISPs in high-dimensional parameter space accurately. ISP tuning tasks should introduce ISP prior information and explore implicit relationships among parameters.", + "bbox": [ + 496, + 825, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "22315", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Image Processing Pipelines", + "text_level": 1, + "bbox": [ + 76, + 89, + 328, + 107 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For better comparison, we follow the most common ISP modules, and contains the following typical stages [5]:", + "bbox": [ + 76, + 114, + 468, + 145 + ], + "page_idx": 2 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "(1) Optics and Sensor: The sensor captures the illumination through the optics, and the RAW image is the sensor output. The pixel value of RAW is linearly related to the intensity of illumination.", + "(2) Denoising: Noise is generally considered to be a random variable with zero mean. Denoising will reduce noise but also remove details by blurring the image.", + "(3) DigitalGain and White Balance: The RAW pixel values are gain-adjusted. Color correction is processed by automatically estimating illuminations or the specific white-balance matrices of common illuminations.", + "(4) Demosaicking: Convert the color filter array over pixel sensors to RGB values for each pixel by performing interpolation.", + "(5) Color Space Transform: Map the RAW pixel values to CIE XYZ color space using a 3x3 transform matrix.", + "(6) Sharpening: Detect edges in the image. Apply filtering measures to eligible edges to improve edge strength and enhance details.", + "(7) Color and Tone Correction: Applying gamma curves and adjusting image contrast by histogram operations to improve the image's global appearance.", + "(8) Compression: Image pixel values are compressed to JPEG and storage." + ], + "bbox": [ + 75, + 145, + 468, + 507 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The ISP in this work models stages (2) to (8). This ISP $f_{\\mathrm{ISP}}$ takes the RAW image $\\mathbf{I}$ as input and converts into an RGB image $\\mathbf{O}_{\\mathrm{ISP}}$ :", + "bbox": [ + 76, + 507, + 468, + 553 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {O} _ {\\mathrm {I S P}} = f _ {\\mathrm {I S P}} (\\mathbf {I}; \\mathcal {P}), \\mathcal {P} \\in \\mathbb {R} _ {[ 0, 1 ]} ^ {N}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 163, + 565, + 468, + 585 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathbf{I} \\in \\mathbb{R}^{W \\times H}$ , $\\mathbf{O}_{\\mathrm{ISP}} \\in \\mathbb{R}^{W \\times H \\times 3}$ . The ISP is controlled by $N$ continuous hyperparameters $\\mathcal{P}$ . For the discrete parameters, mapping them to continuous values within the range of values facilitates prediction [17].", + "bbox": [ + 75, + 589, + 468, + 651 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Method", + "text_level": 1, + "bbox": [ + 76, + 662, + 168, + 679 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we devise the sequence model for predicting ISP hyperparameters, as shown in Fig. 2. We first introduce the ISP hyperparameter prediction task and the proposed model framework in Sec. 4.1. The Sequential CNN model design and the Global Similarity Grouping (GSG) for hyperparameters are introduced in Sec. 4.2 and Sec. 4.3.", + "bbox": [ + 75, + 689, + 468, + 780 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.1. Formulation", + "text_level": 1, + "bbox": [ + 76, + 787, + 210, + 803 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given a RAW image $\\mathbf{I}$ and a downstream task, the proposed method needs to maximize the following objectives:", + "bbox": [ + 76, + 811, + 468, + 842 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\theta} ^ {*} = \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\sum_ {i = 1} ^ {M} \\mathcal {L} _ {\\text {t a s k}} \\left(f _ {I S P} \\left(\\mathbf {I} _ {i}; f _ {\\text {p r e d}} \\left(\\mathbf {I} _ {i}; \\boldsymbol {\\theta}\\right)\\right)\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 862, + 468, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\theta$ are the weights of the model, $M$ is the images' number, $f_{pred}$ is the proposed method, and $\\hat{\\mathcal{P}} = f_{pred}(\\mathbf{I};\\pmb {\\theta})$ is the prediction ISP parameters. Since ISP is a black-box hardware unit, we convert the optimization objectives to:", + "bbox": [ + 498, + 90, + 890, + 151 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\theta} ^ {*} = \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\sum_ {i = 1} ^ {M} \\mathcal {L} _ {2} \\left(\\mathcal {P} ^ {*}, f _ {p r e d} \\left(\\mathbf {I} _ {i}; \\boldsymbol {\\theta}\\right)\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 560, + 162, + 890, + 203 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathcal{P}^*$ is the optimal ISP parameter for image $\\mathbf{I}$ and the downstream task. Our method divides the parameter $\\mathcal{P}$ into $T$ disjoint subsets, $\\mathcal{P} = \\mathbf{P}_1 \\cup \\mathbf{P}_2 \\cup \\ldots \\cup \\mathbf{P}_T$ . There is an order between them, and $\\mathbf{P}_t$ is predicted at step $t$ . This division decomposes the loss function into ordered conditions.", + "bbox": [ + 498, + 210, + 890, + 286 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {2} \\left(\\mathcal {P} ^ {*}, \\hat {\\mathcal {P}}\\right) = \\sum_ {t = 1} ^ {T} \\mathcal {L} _ {2} \\left(\\mathbf {P} _ {t} ^ {*}, \\hat {\\mathbf {P}} _ {t}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 591, + 297, + 890, + 339 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The proposed Sequential CNN model and Global Similarity Grouping achieve the above process. Sequential CNN model uses a fully convolutional structure and runs the loop $T$ times to predict the sequential parameters groupings. Global Similarity Grouping exploits the implicit relations among parameters to achieve flexible parameter grouping.", + "bbox": [ + 498, + 344, + 890, + 435 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.2. Sequential CNN Model", + "text_level": 1, + "bbox": [ + 500, + 445, + 714, + 460 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In ISPs, one module's output is input to the next module. The effect of one set of parameters will be reflected in the image features and influence the next set of parameters. So we encode visual contextual features $\\pmb{c}_t$ as the following:", + "bbox": [ + 498, + 468, + 890, + 529 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {c} _ {t} = f _ {\\text {e n c o d e r}} (\\boldsymbol {v}, \\mathbf {P} _ {1} \\cup \\mathbf {P} _ {2} \\cup \\dots \\mathbf {P} _ {t - 1}), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 562, + 544, + 890, + 560 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\pmb{v}$ is the visual feature extracted by the feature extractor on image I. The feature extractor is a pre-trained ResNet [11] model. At step $t$ , the encoder encodes the visual features $\\pmb{v}$ and the predicted parameters sets at the time from 1 to $t - 1$ into visual context features $\\pmb{c}_t$ , after which $\\pmb{c}_t$ is used to predict of $\\mathbf{P}_t$ . Specifically, we concatenate the visual features $\\pmb{v}$ with parameters channels and convolve them. The number of parameters channels is the same as that of hyperparameter $\\mathcal{P}$ . Each channel replicates the value of the corresponding hyperparameter in the spatial dimension. Only the channels corresponding to $\\{\\mathbf{P}_1,\\dots ,\\mathbf{P}_{t - 1}\\}$ are assigned parameters' values, and the rest of the channels are assigned 0. The subsequent convolution module is designed as three parallel branching structures with 1x1, 3x3, and 5x5 convolutions. The three output features are concatenated to obtain the visual contextual features $\\pmb{c}_t$ . Concatenating feature maps with different receptive fields generate multi-scale feature representations, which help encode parameters with different effect ranges.", + "bbox": [ + 496, + 568, + 890, + 854 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "There are five decoders to complete the decoding process. Each decoder contains a 3x3 convolution followed by a ReLU unit and a span2 upsampling. Each decoder reduces", + "bbox": [ + 498, + 854, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "22316", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/e3b3ebbdf7924433adb4d1567812bdacc8e1ae4a3bf52a59cc5aa308c08a6d50.jpg", + "image_caption": [ + "Figure 2. Proposed sequence parameters prediction framework and ISP tuning process. Sequential CNN model has a fully convolutional encoder-decoder structure and predicts parameter groupings sequentially. Global Similarity Grouping module divides the parameters into ordered disjoint sub-groupings by implicit relations among parameters. Multi-step prediction results are concatenated to obtain the predicted hyperparameters, which are set as the parameters setting of the ISP." + ], + "image_footnote": [], + "bbox": [ + 89, + 85, + 883, + 415 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "the number of feature channels by half, and the last one outputs a parameter feature map for $N$ channels. We replicate the feature maps output by all decoders as hidden states $\\pmb{h}_t$ and concatenate them with the intermediate feature maps of the decoders at step $t + 1$ . The input feature maps of the decoder at step $t + 1$ are the concatenate of the feature maps generated by the previous decoder at step $t + 1$ and step $t$ . In this process, the intermediate features of the convolutional network are passed as the hidden state $\\pmb{h}_t$ and combined with the visual context $\\pmb{c}_t$ for parameter prediction.", + "bbox": [ + 75, + 508, + 468, + 657 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The visual context features $\\pmb{c}_t$ and the network hidden state $\\pmb{h}_{t - 1}$ at time $t - 1$ are input to the decoder, and the parameter feature maps for $N$ channels are generated:", + "bbox": [ + 75, + 659, + 468, + 705 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\boldsymbol {\\Phi} _ {t} = f _ {\\text {d e c o d e r}} \\left(\\boldsymbol {c} _ {t}, \\boldsymbol {h} _ {t - 1}\\right) \\\\ = f _ {\\text {d e c o d e r}} \\left(f _ {\\text {e n c o d e r}} \\left(\\boldsymbol {v}, \\mathbf {P} _ {1} \\cup \\dots \\mathbf {P} _ {t - 1}\\right), \\boldsymbol {h} _ {t - 1}\\right), \\tag {6} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 98, + 724, + 465, + 758 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\Phi_t = \\{\\pmb{p}_t^{(1)},\\pmb{p}_t^{(2)},\\dots,\\pmb{p}_t^{(N)}\\}$ is the set of feature maps of all parameters at step $t$ . And $\\pmb{p}_t^{(i)}$ is the feature map of parameter $p_i$ at time $t$ . And $\\Phi_t$ go through the GSG module and the global average pooling (GAP) layer to obtain the predicted parameter grouping $\\mathbf{P}_t$ at step $t$ :", + "bbox": [ + 75, + 767, + 468, + 848 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathbf {P} _ {t} = f _ {g a p} \\left(f _ {g s g} \\left(\\boldsymbol {\\Phi} _ {t}\\right)\\right) \\\\ = f _ {g a p} \\left(f _ {g s g} \\left(f _ {s c n n} \\left(\\boldsymbol {v}, \\boldsymbol {h} _ {t - 1}, \\mathbf {P} _ {1} \\cup \\dots \\mathbf {P} _ {t - 1}\\right)\\right)\\right), \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 98, + 868, + 465, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For convenience, $f_{scnn}$ is used to express the Sequential CNN model that contains the encoder and decoder mentioned above. Eq. 7 shows the function of the sequential model at one step. The model is run $T$ times recurrently by passing the hidden layer $h_i$ and the predicted parameter groupings $\\mathbf{P}_i$ , where $i = 1, \\dots, T$ . All the predicted groupings are concatenated to obtain the predicted parameters.", + "bbox": [ + 496, + 508, + 890, + 614 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.3. Global Similarity Grouping", + "text_level": 1, + "bbox": [ + 500, + 628, + 750, + 646 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "GSG runs once at the beginning of an iteration, and partitions hyperparameters into $T$ disjoint subsets. Multiple steps of an iteration run mask operation (before GAP) according to the results of GSG. Parameters groups number $T$ is less than or equal to the number of ISP modules $M$ . Some modules in ISP have similar roles. Grouping these similar parameters can reduce the model steps and make it more efficient for a large number of parameters and modules.", + "bbox": [ + 496, + 654, + 890, + 775 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Hyperparameters $\\mathcal{P}$ have an original division $\\mathcal{P} = \\mathbf{P}_1' \\cup \\mathbf{P}_2' \\cup \\ldots \\cup \\mathbf{P}_M'$ , where $\\mathbf{P}_i'$ is the set of parameters of module $i$ . Details are described in Sec. 3 and supplement material. Considering the flexibility, we take the primal division as prior knowledge and use the correlation between parameters to explore the sequential divisions of parameters.", + "bbox": [ + 496, + 777, + 890, + 868 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The Sequential CNN model generates feature maps of all parameters, and the feature maps are passed through the", + "bbox": [ + 498, + 869, + 890, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "22317", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "code", + "sub_type": "algorithm", + "code_caption": [ + "Algorithm 1: Sequential initial clustering centers" + ], + "code_body": "Input: Model partition graphs: $\\mathbf{P}_1^{\\prime},\\ldots ,\\mathbf{P}_M^{\\prime}$ Weighted adjacency matrix: W; Steps in the prediction model: $T\\leq M$", + "bbox": [ + 98, + 112, + 442, + 157 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Output: Sequential clustering centers: $c_{1},\\ldots ,c_{T}$", + "bbox": [ + 98, + 159, + 431, + 174 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nv o l (\\mathbf {P} _ {x} ^ {\\prime}) = \\sum_ {p ^ {(i)} \\in \\mathbf {P} _ {x} ^ {\\prime}} \\sum_ {j = 1} ^ {n} w _ {i j};\n$$\n", + "text_format": "latex", + "bbox": [ + 98, + 176, + 316, + 196 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nN c u t \\left(\\mathbf {P} _ {x} ^ {\\prime}, \\mathbf {P} _ {y} ^ {\\prime}\\right) =\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 200, + 218, + 217 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\left(\\frac {1}{v o l (\\mathbf {P} _ {x} ^ {\\prime})} + \\frac {1}{v o l (\\mathbf {P} _ {y} ^ {\\prime})}\\right) \\sum_ {p ^ {(i)} \\in \\mathbf {P} _ {x}, p ^ {(j)} \\in \\overline {{\\mathbf {P} _ {y}}}} w _ {i j};\n$$\n", + "text_format": "latex", + "bbox": [ + 107, + 217, + 388, + 238 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "for $I\\in [1,M - T]$ do", + "bbox": [ + 98, + 243, + 250, + 258 + ], + "page_idx": 4 + }, + { + "type": "code", + "sub_type": "algorithm", + "code_caption": [], + "code_body": "$i\\gets i$ s.t.max{Ncut $(\\mathbf{P}_i^{\\prime},\\mathbf{P}_{i + 1}^{\\prime})|i\\in [1,M - I]\\}$ $\\mathbf{P}_i^\\prime \\leftarrow \\mathbf{P}_i^\\prime \\cup \\mathbf{P}_{i + 1}^\\prime$ if $i\\neq M - I$ then $\\begin{array}{r}\\big{\\lfloor}\\mathbf{P}_{i + 1}^{\\prime},\\ldots ,\\mathbf{P}_{M - I}^{\\prime}\\leftarrow \\mathbf{P}_{i + 2}^{\\prime},\\ldots ,\\mathbf{P}_{M - I + 1}^{\\prime} \\end{array}$", + "bbox": [ + 99, + 260, + 442, + 340 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {C} _ {1}, \\dots , \\mathbf {C} _ {T} \\leftarrow \\mathbf {P} _ {1}, \\dots , \\mathbf {P} _ {T};\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 342, + 290, + 357 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "for $J\\in [1,T]$ do", + "bbox": [ + 99, + 362, + 214, + 377 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\left\\lfloor c _ {J} \\leftarrow p ^ {(x)} s. t. \\max \\left\\{\\sum_ {p (y) \\in \\mathbf {C} _ {J}} w _ {x y} \\right| p ^ {(x)} \\in \\mathbf {C} _ {J} \\right\\}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 377, + 442, + 396 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "GAP module to obtain the predicted parameters. The pixel values on the feature map represent the predicted parameter values in the corresponding region of the image $\\mathbf{I}$ . The feature map is a higher-dimension representation of the effect of the parameter on the image [22]. Therefore, we use the similarity between feature maps to perform the division of similar parameters. Here we directly use the cosine similarity to measure the feature map similarity as follows:", + "bbox": [ + 75, + 430, + 468, + 551 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL _ {c o s} \\left(p ^ {(i)}, p ^ {(j)}\\right) = \\frac {\\boldsymbol {p} ^ {(i)} * \\boldsymbol {p} ^ {(j)}}{\\left| \\boldsymbol {p} ^ {(i)} \\right| \\left| \\boldsymbol {p} ^ {(j)} \\right|}, \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 169, + 561, + 468, + 598 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\pmb{p}^{(i)}$ and $\\pmb{p}^{(j)}$ are the feature maps of $p^{(i)}$ and $p^{(j)}$ , respectively, and $L_{cos}(p^{(i)}, p^{(j)})$ is the similarity of $p^{(i)}$ and $p^{(j)}$ . After the decoder outputs the parameter feature maps, We can obtain the similarity values between all parameters. All parameters can be expressed in the form of a graph:", + "bbox": [ + 76, + 601, + 468, + 679 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nG = (\\mathbf {V}, \\mathbf {E}), \\mathbf {V} = \\{p ^ {(1)}, p ^ {(2)}, \\dots , p ^ {(N)} \\}. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 133, + 691, + 468, + 710 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Graph $G$ is weighted, and $\\mathbf{E}$ is the set of edges between the vertices. The weight of each edge between two vertices $p^{(i)}$ and $p^{(j)}$ is $w_{ij} = L_{\\cos}(p^{(i)}, p^{(j)}) \\geq 0$ . The weighted adjacency matrix is $\\mathbf{W} = (w_{ij})_{i,j=1,\\dots,n}$ .", + "bbox": [ + 75, + 714, + 468, + 775 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The problem of partitioning the set $\\mathcal{P}$ of parameters into $T$ disjoint subsets is transformed into partitioning graph $G$ :", + "bbox": [ + 76, + 776, + 468, + 806 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {V} = \\mathbf {P} _ {1} \\cup \\mathbf {P} _ {2} \\cup \\dots \\mathbf {P} _ {T}, \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 827, + 468, + 849 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {P} _ {i} \\cap \\mathbf {P} _ {j} = \\varnothing , 1 \\leq i < j \\leq T.\n$$\n", + "text_format": "latex", + "bbox": [ + 187, + 845, + 388, + 862 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Graph $G$ should be divided so that the parameters are dissimilar between groups and similar in the same group. That", + "bbox": [ + 76, + 869, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "means the edges between different groups have low weights and the edges within a group have high weights. The above optimization objective can be expressed as follows:", + "bbox": [ + 498, + 90, + 890, + 137 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\min \\sum_ {t = 1} ^ {T} \\frac {W \\left(\\mathbf {P} _ {t} , \\overline {{\\mathbf {P} _ {t}}}\\right)}{v o l \\left(\\mathbf {P} _ {t}\\right)} = \\sum_ {t = 1} ^ {T} \\frac {\\sum_ {p ^ {(i)} \\in \\mathbf {P} _ {t} , p ^ {(j)} \\in \\overline {{\\mathbf {P} _ {t}}}} w _ {i j}}{\\sum_ {p ^ {(i)} \\in \\mathbf {P} _ {t}} \\sum_ {j = 1} ^ {N} w _ {i j}}. \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 511, + 157, + 890, + 200 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For the above optimization objectives and sequence partitioning of the parameters, we use the prior structure information of ISP and perform spectral clustering of the parameters. Precisely, we first compute the normalized Laplacian matrix $\\mathbf{L}$ of the adjacency matrix $\\mathbf{W}$ . The first $T$ eigenvectors of $\\mathbf{L}$ are used as columns to form $\\mathbf{T} \\in \\mathbb{R}^{n \\times T}$ . After $\\mathbf{T}$ is normalized by rows, the vector $\\mathbf{y}^{(i)}$ of the parameter $p^{(i)}$ corresponds to the $i$ -th row of $\\mathbf{T}$ .", + "bbox": [ + 496, + 205, + 890, + 325 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To make cluster $\\mathbf{P}_1, \\ldots, \\mathbf{P}_T$ with sequence information, we introduce serialized initial clustering centers $c_1, \\ldots, c_T$ . The sequential partition graphs $\\mathbf{P}_1', \\ldots, \\mathbf{P}_M'$ can be obtained based on the structure information of ISP. The graphs that are adjacent and related in order are merged to obtain $T$ partitioned graphs $\\mathbf{C}_1, \\ldots, \\mathbf{C}_T$ , and $c_i$ is the vertex in $\\mathbf{C}_i$ that is most closely connected to other vertices. The specific process is described in Algorithm 1. With $c_1, \\ldots, c_T$ as the initial clustering center, the points $(\\boldsymbol{y}^{(i)})_{i=1,\\dots,N}$ are clustered using the $k$ -means algorithm to obtain the clusters $\\mathbf{P}_1, \\ldots, \\mathbf{P}_T$ . $\\mathbf{P}_t$ is the cluster with $c_t$ as the initial cluster center and is predicted at step $t$ in the sequential model.", + "bbox": [ + 496, + 325, + 890, + 507 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The final obtained $\\mathbf{P}_1, \\ldots, \\mathbf{P}_T$ for the division of parameters $\\mathcal{P}$ is sequential, and its order is determined by the original ISP structure information. The parameters are similar within each grouping based on the similarity of the feature maps corresponding to the parameters. This flexible division enables cross-module grouping of similar parameters.", + "bbox": [ + 496, + 508, + 890, + 599 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 500, + 609, + 632, + 628 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1. Settings", + "text_level": 1, + "bbox": [ + 500, + 635, + 596, + 651 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we introduce the ISPs, datasets, and downstream tasks used for evaluation.", + "bbox": [ + 496, + 659, + 890, + 686 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "(1) Object detection on MS COCO [15] using [23]. For comparison with [22] and [17], we use the same synthetic (simulated) ISP to process simulated RAW as the upstream module for the downstream task. The ISP contains 20 hyperparameters, and detailed information is described in the supplementary material.", + "(2) Image segmentation using [10] on COCO with the same experimental setup as the object detection task.", + "(3) ISP parameter prediction for image quality. A SONY IMX766 CMOS sensor is used to collect the RAW dataset. The RAW data were processed using Qualcomm Spectra 580 ISP oriented to image quality. The ISP contains 144 hyperparameters, and detailed information is described in the supplementary material." + ], + "bbox": [ + 496, + 689, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "22318", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/a37e6e9c48ebffac9b70c5a12dd95a18c6fc5a91b4529bfe740bddcefd12dba1.jpg", + "image_caption": [ + "Figure 3. Image analytic vision tasks: Object Detection (left) and Image Segmentation (right) on COCO dataset. Default ISP hyperparameters (top), expert-tuned hyperparameters (middle), and the proposed method (bottom). The proposed method achieve better performance for downstream tasks." + ], + "image_footnote": [], + "bbox": [ + 83, + 89, + 495, + 420 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/4667458006a2d8bd1d0ab6a38f0786177a661b0509675162712b151eed1bcd9c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 506, + 90, + 883, + 417 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/b1cbca96265a73b7b5e4d428fb17331df6af60f959484687a33c37ccb8a80ecd.jpg", + "table_caption": [ + "Table 1. Synthetic ISP optimization for Object Detection on COCO. We borrow the results from [17] by constructing a similar processing pipeline with the ISP used in other methods." + ], + "table_footnote": [], + "table_body": "
ISP ModelmAP 0.5mAP 0.75mAP 0.5:0.95
Default Pblockwise-tuned [18]ISP[17]15.--
20.--
Hardware-tuned [17]39.--
Default PExpert-tunedISPSec.334.122.421.4
56.840.837.7
Attention-ware [22]61.044.141.0
Sequential-tuned62.845.242.3
", + "bbox": [ + 78, + 549, + 480, + 691 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "During the training stage, the RAW images are input into the model, which sequentially predicts several groups of hyperparameters. As described in Sec. 4.1, the predicted parameters and ground-truth parameters are used to calculate the losses for training the model. We use a combination of iterative search and manual annotation to batch annotate the training set, as described in detail in the supplementary material. The pre-trained Resnet [11] for feature extraction and the rest of the model is trained using loss defined in Eq. 4 and the RMSprop optimizer. The learning rate was initially set to $10^{-4}$ and reduced to $10^{-6}$ after 200 calendar hours. The training process was performed for 400 epochs.", + "bbox": [ + 75, + 719, + 470, + 902 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/7031d5d07dda3c4efe46d96ffa548d10ecaad4c98ec69a648ad9a4c4a4533c55.jpg", + "table_caption": [ + "Table 2. Synthetic ISP optimization for Image Segmentation on COCO. We borrow the results from [17] by constructing a similar processing pipeline with the ISP used in other methods." + ], + "table_footnote": [], + "table_body": "
ISP ModelmAP 0.5mAP 0.75mAP 0.5:0.95
Default PISP12.--
Hardware-tuned [17][17]32.--
Default P22.713.212.0
Expert-tunedISP46.928.227.1
Attention-ware [22]Sec.352.333.431.5
Sequential-tuned54.235.132.6
", + "bbox": [ + 501, + 549, + 901, + 676 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In the evaluation stage, we used the test set as model input to predict the hyperparameters for each RAW image. The RAW image and predicted hyperparameters were input to the ISP to obtain RGB output as input to the downstream tasks. The upstream RGB output is used as input for the object detection and instance segmentation tasks, and the evaluation metric uses the mean accuracy (mAP) [15]. The PSNR and SSIM between the RGB output and the reference image are used as evaluation metrics for the image quality.", + "bbox": [ + 496, + 702, + 890, + 839 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Optimization for Object Detection", + "text_level": 1, + "bbox": [ + 500, + 847, + 799, + 864 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In this task, we use an existing large sRGB dataset and a synthetic ISP to evaluate our ISP hyperparameter sequence", + "bbox": [ + 500, + 869, + 892, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "22319", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/05e862bfbfb609f7d57a761ab759d979581eb059b17d267c10df18a02df6eeef.jpg", + "image_caption": [ + "Default ISP Hyperparameters Output" + ], + "image_footnote": [], + "bbox": [ + 86, + 99, + 272, + 218 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/60c6027bec7f71832d1671b69b6d40565c8004d008fbf6b321a01931111d6981.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 274, + 99, + 459, + 218 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/65b7461437e6d75603e5d4767d06df12468b7393d1137931263f9fe7dd2cccd2.jpg", + "image_caption": [ + "Predicted ISP Hyperparameters Output (proposed)" + ], + "image_footnote": [], + "bbox": [ + 86, + 233, + 272, + 352 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/77a5856a59d1c8e45a0e3745f6abd6c2d76d345543d4bd05818aa08c235ab847.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 274, + 233, + 459, + 352 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/21bc17da815045df59e0c985df6f0535af5e0094a509a08ac36eac0d8fca25a2.jpg", + "image_caption": [ + "Reference Output" + ], + "image_footnote": [], + "bbox": [ + 86, + 367, + 272, + 446 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2afa938b721c77a6b1b296669be0d5c97da993e580d1b92671295a1fb9f14c66.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 274, + 368, + 459, + 446 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2e18431b9c5d2d23712f458935a0ca1865bee0c7a209050e816a919a34502e00.jpg", + "image_caption": [ + "Figure 4. Default setting hyperparameters (top), proposed method (middle), and reference images (bottom). The proposed method achieves an image quality close to the reference image." + ], + "image_footnote": [], + "bbox": [ + 86, + 448, + 181, + 486 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/31f14cdb719fba7c0fa6234e576384759ec61d8818c0b8f049d06e10120f7486.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 181, + 449, + 271, + 486 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d6142ad5025dbf54e571e4abf3e9f568c0212b291cd52ea1e560b2c362e9fe0c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 274, + 448, + 362, + 486 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9447757bcef037e6172f4493e902ed3c1c4db18f48589480ae92c63d6410f81d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 367, + 449, + 459, + 486 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/10ad65a5fd137cd54ae5cf0f42fdd8bd5babb5bb03758cd0aa9dff1bf17db4a3.jpg", + "table_caption": [ + "Table 3. Perceptual image quality using default parameters setting, recent method [22], and the proposed method." + ], + "table_footnote": [], + "table_body": "
ISP ModelPSNRSSIM
Default ParametersHardware ISP30.730.720
Attention-ware [22]Sec. 5.140.080.965
Sequential-tuned45.290.982
", + "bbox": [ + 78, + 595, + 462, + 662 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "prediction method. The RGB images are converted to RAW as input to the ISP by simulating the processing of sRGB images with RAW data [13]. We use pre-trained [23] for object detection on the COCO dataset [15] as a downstream task. The proposed model is trained for the object detection task and evaluated using the mAP [15]. The quantitative evaluation results are shown in Table 1. Compared to the default parameter settings of the untuned ISP, our method shows a more significant improvement over current methods and better results than non-sequential prediction methods. The subjective results are shown in Fig. 3. The proposed method emphasizes the image's texture details and color features compared to the expert-tuned image. Although expert-tuned images are more consistent with hu", + "bbox": [ + 75, + 688, + 468, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "man visual preferences, the predicted parameters allow for better results in these images for the object detection tasks.", + "bbox": [ + 498, + 90, + 890, + 121 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Optimization for Image Segmentation", + "text_level": 1, + "bbox": [ + 500, + 130, + 826, + 147 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Next, we use pre-trained [10] for image segmentation task on the COCO dataset as the downstream task. The proposed method is trained and tested on the simulated RAW COCO dataset and synthetic ISP. The results in Table 2 show that the proposed method has the best final results for the instance segmentation task compared to the recent methods. It also significantly improves over the default parameters (baseline). Fig. 3 shows the default parameters, the expert tuning parameters, and examples of the proposed method. Similar to the results obtained in the object detection task, the predicted parameters improve the performance of the downstream task (image segmentation) by adjusting the texture and color of the image. The predicted parameters of our model achieve an improvement of $0.3\\mathrm{mAP}$ and $0.2\\mathrm{mAP}$ compared to the default parameters and the expert-tuned parameters, respectively, and $0.1\\mathrm{mAP}$ compared to the non-sequential prediction method.", + "bbox": [ + 496, + 154, + 890, + 411 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.4. Optimization for Image Quality", + "text_level": 1, + "bbox": [ + 500, + 421, + 779, + 436 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The image quality optimization task aims to achieve a specific image rendering preference [16]. This specific rendering preference allows the output RGB to match the human visual preference by combining the visibility of various distortions in the image (noise, color shifts, blockiness) [26] and the imaging style. In this task, we aim to make the RAW image undergoes ISP to produce an RGB as close as possible to the reference image by the hyperparameters predicted by the proposed method. We collected a dataset for the image quality optimization task. The dataset contains images from 251 scenes acquired by the IMX766 sensor. The reference images are generated through the Spectra 580 ISP with the RAW images and the expert-tuned parameters as input. We used PSNR and SSIM metrics to evaluate the consistency between the images generated by the predicted parameters and the reference images. The quantitative results are shown in Table 3, and the proposed method consists of better PSNR and SSIM results than recent methods. Our method brings more improvements based on the default parameter settings. Fig. 4 shows some examples of image quality tasks. Our method produces images with better visual quality that conform to human preferences regarding texture details, noise control, and color representation.", + "bbox": [ + 496, + 444, + 890, + 792 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.5. Ablation Study", + "text_level": 1, + "bbox": [ + 500, + 801, + 653, + 816 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Sequential Structure. Recent ISP hyperparameter tuning methods, both iterative optimization and direct prediction methods, treat the set of ISP hyperparameters as a black box space and estimate all the parameters at once. In contrast, the proposed method allows step-by-step sequential", + "bbox": [ + 496, + 825, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "22320", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/4e96ade2acd0624970da7cdceb2339c3921364841534c5d93542cd99c32961eb.jpg", + "table_caption": [ + "Table 4. Ablation study of evaluating the proposed sequential structure and similarity grouping. The first column is the results for multiple step settings of the proposed method, and the second is the results of multiple changes in Global Similarity Grouping module." + ], + "table_footnote": [], + "table_body": "
StepsSequencePrior KnowledgeSimilarity GroupPSNRSSIM
Sequence prediction (Multi step-settings)one---39.520.957
two✓✓✓40.050.962
three✓✓✓42.660.971
four✓✓✓45.290.982
five✓✓✓45.230.982
Blockwise sequence & similarity groupfour✓✓✓45.290.982
Blockwise sequence & blockwise group✓✓-43.570.969
Random sequence & similarity group✓-✓42.620.960
Random sequence & random group✓--39.300.947
", + "bbox": [ + 78, + 127, + 890, + 284 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "prediction of ISP parameters, achieving more refined predictions by modeling the sequential relationships among parameter groupings. We verify that this sequential tuning method has better results than the non-sequential method.", + "bbox": [ + 75, + 311, + 467, + 369 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The effectiveness of sequence prediction is tested on the image quality dataset and a Spectra 580 ISP with 144 parameters. The results are shown in Table 4. We tested the non-sequential and sequential methods under multiple settings, and the test metrics are the PSNR and SSIM results of the output and the reference image. The number of model running steps equals the number of parameter groupings. For the same number of hyperparameters, the fewer steps of the model, the more parameters are predicted in each step. When the step is set to one, the model predicts all hyperparameters simultaneously, equivalent to the non-sequential method. The sequential prediction results have better results than the non-sequential ones. We also validate the results of several experiments with different step counts. The method loop steps size larger to generate long-distance dependencies, which will be more challenging to learn and converge. Endlessly increasing the size of steps with the same dataset may suffer performance degradation. Experimental results show that a compromise step count setting achieves the best results for a particular experimental setting.", + "bbox": [ + 75, + 369, + 467, + 672 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Global Similarity Grouping. In sequential prediction, each step predicts a sub-grouping of the hyperparameters. Based on the block-wise structure of the ISP and the correlation between parameters, the Global Similarity Grouping module defines the order between different groups and how the parameters within the groups are divided. We validated the effectiveness of this adaptive grouping approach. The experimental setup is consistent with the ablation experiments above, and the results are shown in Table 4.", + "bbox": [ + 75, + 672, + 467, + 808 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To verify the effectiveness of the prior structural information of ISP, we changed the Global Similarity Grouping in Sec. 4.3 by removing the block-wise sequence clustering centers and directly performing spectral clustering for all parameters. The model uses random order for prediction for multiple parameter groupings clustered out. The results", + "bbox": [ + 75, + 810, + 467, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "in Table 4 show that the approach introducing the ISP prior structure information has a better result. To verify the effectiveness of cross-module grouping achieved by parameter similarity, we define the parameter grouping pattern directly with the block-wise structural information of ISP. Parameters of the same module are grouped into the same group, while the order between groups is consistent with the ISP module sequence. The results in Table 4 show the effectiveness of the similarity grouping with the introduction of parameter correlation. Similarity group compared to blockwise group is obtained from GSG by parameter similarity while predicting similar parameters at once will improve the accuracy. Finally, we remove both prior knowledge and similarity grouping by randomly defining the hyperparameter division and the order among groups. As shown in Table 4, such a setup has the worst results.", + "bbox": [ + 496, + 310, + 890, + 551 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusions", + "text_level": 1, + "bbox": [ + 500, + 570, + 625, + 585 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work proposes a novel sequential method for ISP tuning that uses module relation and parameter similarity to guide the process. It decouples the ISP structure and treats tuning as a sequential prediction problem. It groups parameters to tune ISP sequentially. Experiments show that introducing sequence information and similarity relations in the high-dimensional parameter space improves performance.", + "bbox": [ + 496, + 597, + 890, + 702 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments This work was supported by the National Key Research and Development Program of China (Grant No. 2020AAA0106800), the National Natural Science Foundation of China (No. 62192785, No. 61972071, No. U1936204, No. 62122086, No. 62036011, No. U203-3210, No. 62172413, No. 62192782 and No. 61721004), the Beijing Natural Science Foundation No. M22005, and L223003, the Major Projects of Guangdong Education Department for Foundation Research and Applied Research (Grant: 2017KZDXM081, 2018KZDXM066), Guangdong Provincial University Innovation Team Project (Project No. 2020KCXTD045). The work of Bing Li was also supported by the Youth Innovation Promotion Association, CAS.", + "bbox": [ + 496, + 704, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "22321", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 90, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] IEEE standard for camera phone image quality. IEEE Std 1858-2016 (Incorporating IEEE Std 1858-2016/Cor 1-2017), pages 1-146, 2017. 1", + "[2] Rémi Bardenet, Mátyás Brendel, Balázs Kégl, and Michele Sebag. Collaborative hyperparameter tuning. In International conference on machine learning, pages 199–207, 2013. 2", + "[3] James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyper-parameter optimization. Advances in neural information processing systems, 24, 2011. 2", + "[4] James Bergstra, Daniel Yamins, and David Cox. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International conference on machine learning, pages 115-123, 2013. 2", + "[5] Michael S Brown and SJ Kim. Understanding the in-camera image processing pipeline for computer vision. In IEEE International Conference on Computer Vision, volume 3, 2019. 1, 3", + "[6] Mark Buckler, Suren Jayasuriya, and Adrian Sampson. Reconfiguring the imaging pipeline for computer vision. In Proceedings of the IEEE International Conference on Computer Vision, pages 975-984, 2017. 2", + "[7] Yue Cao, Xiaohe Wu, Shuran Qi, Xiao Liu, Zhongqin Wu, and Wangmeng Zuo. Pseudo-isp: Learning pseudo in-camera signal processing pipeline from a color image denoiser. arXiv preprint arXiv:2103.10234, 2021. 1", + "[8] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3291-3300, 2018. 2", + "[9] Nikolaus Hansen, Sibylle D Müller, and Petros Koumoutsakos. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cmaes). Evolutionary computation, 11(1):1-18, 2003. 2", + "[10] Kaiming He, Georgia Gkioxari, Piotr Dólar, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961-2969, 2017. 5, 7", + "[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3, 6", + "[12] Andrey Ignatov, Luc Van Gool, and Radu Timofte. Replacing mobile camera isp with a single deep learning model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 536-537, 2020. 1, 2", + "[13] Seon Joo Kim, Hai Ting Lin, Zheng Lu, Sabine Süssstrunk, Stephen Lin, and Michael S Brown. A new in-camera imaging model for color computer vision and its application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(12):2289-2302, 2012. 7", + "[14] Younghoon Kim, Jungmin Lee, Sung-Su Kim, Cheoljong Yang, TaeHyung Kim, and JoonSeo Yim. Dnn-based isp" + ], + "bbox": [ + 78, + 114, + 470, + 902 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "parameter inference algorithm for automatic image quality optimization. Electronic Imaging, 2020(9):315-1, 2020. 2", + "[15] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755, 2014. 5, 6, 7", + "[16] Rafal K Mantiuk, Anna Tomaszewska, and Radosław Mantiuk. Comparison of four subjective methods for image quality assessment. In Computer graphics forum, volume 31, pages 2478-2491. Wiley Online Library, 2012. 7", + "[17] Ali Mosleh, Avinash Sharma, Emmanuel Onzon, Fahim Mannan, Nicolas Robidoux, and Felix Heide. Hardware-in-the-loop end-to-end optimization of camera image processing pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7529-7538, 2020. 1, 2, 3, 5, 6", + "[18] Jun Nishimura, Timo Gerasimow, Rao Sushma, Aleksandar Sutic, Chyuan-Tyng Wu, and Gilad Michael. Automatic isp image quality tuning using nonlinear optimization. In 2018 25th IEEE International Conference on Image Processing, pages 2471-2475. IEEE, 2018. 1, 2, 6", + "[19] Emmanuel Onzon, Fahim Mannan, and Felix Heide. Neural auto-exposure for high-dynamic range object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7710-7720, 2021. 2", + "[20] Luke Pfister and Yoram Bresler. Learning filter bank sparsifying transforms. IEEE Transactions on Signal Processing, 67(2):504-519, 2018. 2", + "[21] Buu Phan, Fahim Mannan, and Felix Heide. Adversarial imaging pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16051-16061, 2021. 1", + "[22] Haina Qin, Longfei Han, Juan Wang, Congxuan Zhang, Yanwei Li, Bing Li, and Weiming Hu. Attention-aware learning for hyperparameters prediction in image processing pipelines. In European Conference on Computer Vision, pages 271-287. Springer, 2022. 1, 2, 5, 6, 7", + "[23] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. 5, 7", + "[24] Nicolas Robidoux, Luis E Garcia Capel, Dong-eun Seo, Avinash Sharma, Federico Ariza, and Felix Heide. End-to-end high dynamic range camera pipeline optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6297-6307, 2021. 2", + "[25] Eli Schwartz, Raja Giryes, and Alex M Bronstein. Deepisp: Toward learning an end-to-end image processing pipeline. IEEE Transactions on Image Processing, 28(2):912-923, 2018. 1", + "[26] Kim-Han Thung and Paramesran Raveendran. A survey of image quality measures. In IEEE international conference for technical postgraduates, pages 1-4, 2009. 7", + "[27] Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl ST Arnaud, Derek Nowrouzezahrai, Jean-François Lalonde, and Felix Heide. Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Transactions on Graphics, 38(4):27-1, 2019. 1, 2" + ], + "bbox": [ + 503, + 92, + 890, + 902 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "22322", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[28] Peter van Beek, Chyuan-Tyng Roger Wu, Baishali Chaudhury, and Thomas R Gardos. Boosting computer vision performance by enhancing camera isp. Electronic Imaging, 2021(17):174-1, 2021. 1", + "[29] Chyuan-Tyng Wu, Leo F Isikdogan, Sushma Rao, Bhavin Nayak, Timo Gerasimow, Aleksandar Sutic, Liron Ainkedem, and Gilad Michael. Visionisp: Repurposing the image signal processor for computer vision applications. In IEEE International Conference on Image Processing, pages 4624-4628. IEEE, 2019. 1, 2", + "[30] Lucie Yahiaoui, Ciarán Hughes, Jonathan Horgan, Brian Deegan, Patrick Denny, and Senthil Yogamani. Optimization of isp parameters for object detection algorithms. Electronic Imaging, 2019(15):44-1, 2019. 1, 2", + "[31] Cheoljong Yang, Jinhyun Kim, Jungmin Lee, Younghoon Kim, Sung-Su Kim, TaeHyung Kim, and JoonSeo Yim. Effective isp tuning framework based on user preference feedback. Electronic Imaging, 2020(9):316-1, 2020. 1", + "[32] Dani Yogatama and Gideon Mann. Efficient transfer learning method for automatic hyperparameter tuning. In Artificial intelligence and statistics, pages 1077-1085. PMLR, 2014. 2", + "[33] Ke Yu, Zexian Li, Yue Peng, Chen Change Loy, and Jinwei Gu. Reconfigisp: Reconfigurable camera image processing pipeline. arXiv preprint arXiv:2109.04760, 2021. 2", + "[34] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Cycleisp: Real image restoration via improved data synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2696-2705, 2020. 1", + "[35] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8697-8710, 2018. 2" + ], + "bbox": [ + 78, + 90, + 468, + 597 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "22323", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization/5043327e-5e28-4a8f-8c57-7b23c08760b9_model.json b/2023/Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization/5043327e-5e28-4a8f-8c57-7b23c08760b9_model.json new file mode 100644 index 0000000000000000000000000000000000000000..c554011eeaa7fb5c516936fad0ebbc95768b087c --- /dev/null +++ b/2023/Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization/5043327e-5e28-4a8f-8c57-7b23c08760b9_model.json @@ -0,0 +1,2200 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.168, + 0.13, + 0.804, + 0.177 + ], + "angle": 0, + "content": "Learning to Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization" + }, + { + "type": "text", + "bbox": [ + 0.13, + 0.202, + 0.842, + 0.223 + ], + "angle": 0, + "content": "Haina Qin\\(^{1,2}\\) Longfei Han\\(^{3}\\) Weihua Xiong\\(^{1}\\) Juan Wang\\(^{1}\\) Wentao Ma\\(^{4}\\)" + }, + { + "type": "text", + "bbox": [ + 0.338, + 0.228, + 0.635, + 0.248 + ], + "angle": 0, + "content": "Bing \\(\\mathrm{Li}^{1(\\boxtimes)}\\) Weiming \\(\\mathrm{Hu}^{1,2}\\)" + }, + { + "type": "text", + "bbox": [ + 0.218, + 0.248, + 0.764, + 0.282 + ], + "angle": 0, + "content": "\\(^{1}\\)State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences" + }, + { + "type": "text", + "bbox": [ + 0.185, + 0.283, + 0.798, + 0.3 + ], + "angle": 0, + "content": "\\(^{2}\\)School of Artificial Intelligence, University of Chinese Academy of Sciences" + }, + { + "type": "text", + "bbox": [ + 0.187, + 0.301, + 0.797, + 0.318 + ], + "angle": 0, + "content": "\\(^{3}\\)Beijing Technology and Business University \\(^{4}\\)Zeku Technology, Shanghai" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.321, + 0.891, + 0.336 + ], + "angle": 0, + "content": "{qinhaina2020@,bli@nlpr.,wmhu@nlpr.}ia.ac.cn longfeihan@btbu.edu.cn Wallace.xiong@gmail.com" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.371, + 0.314, + 0.387 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.403, + 0.473, + 0.722 + ], + "angle": 0, + "content": "The hardware image signal processing (ISP) pipeline is the intermediate layer between the imaging sensor and the downstream application, processing the sensor signal into an RGB image. The ISP is less programmable and consists of a series of processing modules. Each processing module handles a subtask and contains a set of tunable hyperparameters. A large number of hyperparameters form a complex mapping with the ISP output. The industry typically relies on manual and time-consuming hyperparameter tuning by image experts, biased towards human perception. Recently, several automatic ISP hyperparameter optimization methods using downstream evaluation metrics come into sight. However, existing methods for ISP tuning treat the high-dimensional parameter space as a global space for optimization and prediction all at once without inducing the structure knowledge of ISP. To this end, we propose a sequential ISP hyperparameter prediction framework that utilizes the sequential relationship within ISP modules and the similarity among parameters to guide the model sequence process. We validate the proposed method on object detection, image segmentation, and image quality tasks." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.753, + 0.21, + 0.769 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Hardware ISPs are low-level image processing pipelines that convert RAW images into high-quality RGB images. Typically, ISPs include a series of processing modules [5], each of which handles a subtask such as denoising, white balance, demosaicing, or sharpening. Compared to software image processing pipelines, hardware ISPs are faster, more power-efficient, and widely used in real-time products [7, 34], including cameras [28], smartphones, surveil" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.371, + 0.893, + 0.447 + ], + "angle": 0, + "content": "lance [21], IoT and driven-assistance systems. ISPs are highly modular and less programmable but with a set of tunable hyperparameters. The industry always relies on manual and costly hyperparameter tuning by image experts [1] to adapt the ISP to different application scenarios." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.448, + 0.893, + 0.735 + ], + "angle": 0, + "content": "The ISP is always designed as a sequential pipeline [5] and the configurable hyperparameters of various modules from any ISP aggregate to be a complex parameter space (with tens to hundreds of parameters), making the manual tuning process time-consuming. It is also difficult to subjectively find optimal hyperparameters settings for various downstream tasks (such as object detection and image segmentation [29, 30]). Recently, several automatic ISP hyperparameter optimization methods [17, 31] using downstream evaluation metrics come into sight. These methods tuning hyperparameters for downstream tasks include derivative-free [18] or gradient methods [12, 25, 27] based on differentiable approximation. There are also methods to demonstrate the potential of predicting specific hyperparameters for each image or scene [22]. However, existing methods treat the high-dimensional parameter space as a global black-box space for optimization and prediction all at once, while ignoring the inherent sequence of the ISP modules and the critical intra-correlation among hyperparameters." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.895, + 0.902 + ], + "angle": 0, + "content": "Inspired by the operating principles and structure knowledge of ISPs, we first propose a sequential ISP hyperparameter prediction framework (as shown in Fig. 1) that contains Sequential CNN model and Global Similarity Grouping. The sequential CNN model runs recurrently by predicting a group of parameters from several ISP modules, not all parameters, at each step. Meanwhile, the predicted parameters, along with the network's hidden layer and the input data, are in turn encoded as prior knowledge for predicting the following grouping of parameters. The Global Similarity Grouping module divides ISP parameters into multiple" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22314" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.088, + 0.09, + 0.46, + 0.216 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.087, + 0.226, + 0.46, + 0.468 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.479, + 0.47, + 0.577 + ], + "angle": 0, + "content": "Figure 1. (A) Previous methods treat the hyperparameter space as a black box optimization problem and estimate all parameters at once without considering the prior knowledge of the ISP. (B) Our proposed method first decouples ISP structural knowledge and treats ISP tuning as a sequential prediction problem. It is effective to introduce sequence information and similarity relations in the high-dimensional hyperparameter space." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.604, + 0.468, + 0.709 + ], + "angle": 0, + "content": "disjoint groupings. The sequence order between groupings is explored heuristically using prior knowledge of the order of ISP modules. Given the flexibility, groupings are determined based on similarity among parameters, not limited to the same module parameters. The correlation of parameter activation maps learned through the model is used as the basis for parameter groupings." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.71, + 0.46, + 0.724 + ], + "angle": 0, + "content": "Our contributions can be summarized as the following:" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.735, + 0.468, + 0.795 + ], + "angle": 0, + "content": "- We propose a new sequential CNN structure to exploit the sequence processing knowledge within ISP. The potential sequential information among parameters is used to guide the processing of the model." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.796, + 0.468, + 0.856 + ], + "angle": 0, + "content": "- We exploit the correlation among parameters by the proposed similarity grouping module. The flexible parameter groupings allow the exploration of cross-module relationships among parameters." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.856, + 0.468, + 0.901 + ], + "angle": 0, + "content": "- We validate the effectiveness of our method in a variety of downstream tasks, including object detection, image segmentation, and image quality. In these applications," + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.735, + 0.468, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.532, + 0.092, + 0.815, + 0.107 + ], + "angle": 0, + "content": "our method outperforms existing methods." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.121, + 0.642, + 0.136 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.145, + 0.892, + 0.342 + ], + "angle": 0, + "content": "ISPs contain several components, and each module carries a specific image-processing algorithm. These algorithms are permanently configured with tens to hundreds of handcrafted parameters, for instance, noise reduction, color space transformation, and sharpening. It is desirable to find parameter configurations for imaging experts that make ISP work well in real-world scenarios and output compressed images with good human perception. Recently, to tackle this challenging optimization problem, several automatic ISP tuning methods were proposed by optimizing the hyperparameters with downstream task feedback [3,4,35], for instance, object detection [27,29], object segmentation [17], and image quality [2,6,30,32]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.342, + 0.892, + 0.825 + ], + "angle": 0, + "content": "With the high-level feedback information, recent works always leverage a differentiable mapping between the parameter space and high-level evaluation metrics. Some existing optimization methods try to mimic the whole hardware ISP pipeline as an approximate CNN proxy model and construct an end-to-end differentiable CNN framework to optimize the proxy network [8, 12, 33]. Tseng et al. [27] trained an approximate CNN proxy model to mimic hardware ISP and optimized the differentiable CNN model with Stochastic Gradient Descent. Onzon et al. [19] propose a neural network for exposure selection that is jointly end-to-end with an object detector and ISP pipeline. Pfister et al. [20] proposes to optimize sparsity regularization for denoising. These methods are based on the assumption that a single proxy network can mimic the whole ISP pipeline and cannot be used to optimize the hardware ISPs. Other methods can directly put the hardware ISP into the optimization stage and predict all hyperparameters at once. For instance, Mosleh et al. [17, 24] directly optimize hardware ISP by a novel CMA-ES strategy [9] with max-rank-based multi-objective scalarization and initial search space reduction. Kim et al. [14] utilize the objective function of multi-output regression for modeling the relation between ISP parameter and IQM score. Qin et al. [22] directly infer all ISP hyperparameters via an attention-based CNN method. However, these methods do not take sequential information into consideration. Only Nishimura et al. [18] optimize ISP components with sequential prior knowledge based on a 0-th order Nelder-Mead method. Nevertheless, it can only be used to optimize ISP modules one at a time, ignores cross-module connections while viewing the modules in isolation, and is difficult to apply to hardware ISPs." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.826, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Therefore, with the massive number of parameters in hardware ISPs, it needs to exploit the structural knowledge to tune ISPs in high-dimensional parameter space accurately. ISP tuning tasks should introduce ISP prior information and explore implicit relationships among parameters." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22315" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.078, + 0.09, + 0.33, + 0.108 + ], + "angle": 0, + "content": "3. Image Processing Pipelines" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.115, + 0.47, + 0.146 + ], + "angle": 0, + "content": "For better comparison, we follow the most common ISP modules, and contains the following typical stages [5]:" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.146, + 0.47, + 0.206 + ], + "angle": 0, + "content": "(1) Optics and Sensor: The sensor captures the illumination through the optics, and the RAW image is the sensor output. The pixel value of RAW is linearly related to the intensity of illumination." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.206, + 0.47, + 0.252 + ], + "angle": 0, + "content": "(2) Denoising: Noise is generally considered to be a random variable with zero mean. Denoising will reduce noise but also remove details by blurring the image." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.252, + 0.469, + 0.311 + ], + "angle": 0, + "content": "(3) DigitalGain and White Balance: The RAW pixel values are gain-adjusted. Color correction is processed by automatically estimating illuminations or the specific white-balance matrices of common illuminations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.312, + 0.469, + 0.357 + ], + "angle": 0, + "content": "(4) Demosaicking: Convert the color filter array over pixel sensors to RGB values for each pixel by performing interpolation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.357, + 0.469, + 0.387 + ], + "angle": 0, + "content": "(5) Color Space Transform: Map the RAW pixel values to CIE XYZ color space using a 3x3 transform matrix." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.387, + 0.469, + 0.432 + ], + "angle": 0, + "content": "(6) Sharpening: Detect edges in the image. Apply filtering measures to eligible edges to improve edge strength and enhance details." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.433, + 0.469, + 0.478 + ], + "angle": 0, + "content": "(7) Color and Tone Correction: Applying gamma curves and adjusting image contrast by histogram operations to improve the image's global appearance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.479, + 0.469, + 0.508 + ], + "angle": 0, + "content": "(8) Compression: Image pixel values are compressed to JPEG and storage." + }, + { + "type": "list", + "bbox": [ + 0.076, + 0.146, + 0.47, + 0.508 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.508, + 0.47, + 0.554 + ], + "angle": 0, + "content": "The ISP in this work models stages (2) to (8). This ISP \\( f_{\\mathrm{ISP}} \\) takes the RAW image \\( \\mathbf{I} \\) as input and converts into an RGB image \\( \\mathbf{O}_{\\mathrm{ISP}} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.165, + 0.566, + 0.469, + 0.587 + ], + "angle": 0, + "content": "\\[\n\\mathbf {O} _ {\\mathrm {I S P}} = f _ {\\mathrm {I S P}} (\\mathbf {I}; \\mathcal {P}), \\mathcal {P} \\in \\mathbb {R} _ {[ 0, 1 ]} ^ {N}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.59, + 0.469, + 0.652 + ], + "angle": 0, + "content": "where \\(\\mathbf{I} \\in \\mathbb{R}^{W \\times H}\\), \\(\\mathbf{O}_{\\mathrm{ISP}} \\in \\mathbb{R}^{W \\times H \\times 3}\\). The ISP is controlled by \\(N\\) continuous hyperparameters \\(\\mathcal{P}\\). For the discrete parameters, mapping them to continuous values within the range of values facilitates prediction [17]." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.664, + 0.169, + 0.68 + ], + "angle": 0, + "content": "4. Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.69, + 0.469, + 0.781 + ], + "angle": 0, + "content": "In this section, we devise the sequence model for predicting ISP hyperparameters, as shown in Fig. 2. We first introduce the ISP hyperparameter prediction task and the proposed model framework in Sec. 4.1. The Sequential CNN model design and the Global Similarity Grouping (GSG) for hyperparameters are introduced in Sec. 4.2 and Sec. 4.3." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.789, + 0.212, + 0.804 + ], + "angle": 0, + "content": "4.1. Formulation" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.812, + 0.469, + 0.843 + ], + "angle": 0, + "content": "Given a RAW image \\(\\mathbf{I}\\) and a downstream task, the proposed method needs to maximize the following objectives:" + }, + { + "type": "equation", + "bbox": [ + 0.101, + 0.863, + 0.469, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\theta} ^ {*} = \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\sum_ {i = 1} ^ {M} \\mathcal {L} _ {\\text {t a s k}} \\left(f _ {I S P} \\left(\\mathbf {I} _ {i}; f _ {\\text {p r e d}} \\left(\\mathbf {I} _ {i}; \\boldsymbol {\\theta}\\right)\\right)\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.152 + ], + "angle": 0, + "content": "where \\(\\theta\\) are the weights of the model, \\(M\\) is the images' number, \\(f_{pred}\\) is the proposed method, and \\(\\hat{\\mathcal{P}} = f_{pred}(\\mathbf{I};\\pmb {\\theta})\\) is the prediction ISP parameters. Since ISP is a black-box hardware unit, we convert the optimization objectives to:" + }, + { + "type": "equation", + "bbox": [ + 0.562, + 0.164, + 0.892, + 0.204 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\theta} ^ {*} = \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\sum_ {i = 1} ^ {M} \\mathcal {L} _ {2} \\left(\\mathcal {P} ^ {*}, f _ {p r e d} \\left(\\mathbf {I} _ {i}; \\boldsymbol {\\theta}\\right)\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.211, + 0.892, + 0.287 + ], + "angle": 0, + "content": "where \\(\\mathcal{P}^*\\) is the optimal ISP parameter for image \\(\\mathbf{I}\\) and the downstream task. Our method divides the parameter \\(\\mathcal{P}\\) into \\(T\\) disjoint subsets, \\(\\mathcal{P} = \\mathbf{P}_1 \\cup \\mathbf{P}_2 \\cup \\ldots \\cup \\mathbf{P}_T\\). There is an order between them, and \\(\\mathbf{P}_t\\) is predicted at step \\(t\\). This division decomposes the loss function into ordered conditions." + }, + { + "type": "equation", + "bbox": [ + 0.593, + 0.298, + 0.892, + 0.34 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {2} \\left(\\mathcal {P} ^ {*}, \\hat {\\mathcal {P}}\\right) = \\sum_ {t = 1} ^ {T} \\mathcal {L} _ {2} \\left(\\mathbf {P} _ {t} ^ {*}, \\hat {\\mathbf {P}} _ {t}\\right), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.345, + 0.892, + 0.436 + ], + "angle": 0, + "content": "The proposed Sequential CNN model and Global Similarity Grouping achieve the above process. Sequential CNN model uses a fully convolutional structure and runs the loop \\( T \\) times to predict the sequential parameters groupings. Global Similarity Grouping exploits the implicit relations among parameters to achieve flexible parameter grouping." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.446, + 0.715, + 0.462 + ], + "angle": 0, + "content": "4.2. Sequential CNN Model" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.469, + 0.892, + 0.53 + ], + "angle": 0, + "content": "In ISPs, one module's output is input to the next module. The effect of one set of parameters will be reflected in the image features and influence the next set of parameters. So we encode visual contextual features \\( \\pmb{c}_t \\) as the following:" + }, + { + "type": "equation", + "bbox": [ + 0.563, + 0.545, + 0.892, + 0.561 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {c} _ {t} = f _ {\\text {e n c o d e r}} (\\boldsymbol {v}, \\mathbf {P} _ {1} \\cup \\mathbf {P} _ {2} \\cup \\dots \\mathbf {P} _ {t - 1}), \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.569, + 0.892, + 0.855 + ], + "angle": 0, + "content": "where \\(\\pmb{v}\\) is the visual feature extracted by the feature extractor on image I. The feature extractor is a pre-trained ResNet [11] model. At step \\(t\\), the encoder encodes the visual features \\(\\pmb{v}\\) and the predicted parameters sets at the time from 1 to \\(t - 1\\) into visual context features \\(\\pmb{c}_t\\), after which \\(\\pmb{c}_t\\) is used to predict of \\(\\mathbf{P}_t\\). Specifically, we concatenate the visual features \\(\\pmb{v}\\) with parameters channels and convolve them. The number of parameters channels is the same as that of hyperparameter \\(\\mathcal{P}\\). Each channel replicates the value of the corresponding hyperparameter in the spatial dimension. Only the channels corresponding to \\(\\{\\mathbf{P}_1,\\dots ,\\mathbf{P}_{t - 1}\\}\\) are assigned parameters' values, and the rest of the channels are assigned 0. The subsequent convolution module is designed as three parallel branching structures with 1x1, 3x3, and 5x5 convolutions. The three output features are concatenated to obtain the visual contextual features \\(\\pmb{c}_t\\). Concatenating feature maps with different receptive fields generate multi-scale feature representations, which help encode parameters with different effect ranges." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "There are five decoders to complete the decoding process. Each decoder contains a 3x3 convolution followed by a ReLU unit and a span2 upsampling. Each decoder reduces" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "22316" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.091, + 0.087, + 0.885, + 0.416 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.426, + 0.895, + 0.482 + ], + "angle": 0, + "content": "Figure 2. Proposed sequence parameters prediction framework and ISP tuning process. Sequential CNN model has a fully convolutional encoder-decoder structure and predicts parameter groupings sequentially. Global Similarity Grouping module divides the parameters into ordered disjoint sub-groupings by implicit relations among parameters. Multi-step prediction results are concatenated to obtain the predicted hyperparameters, which are set as the parameters setting of the ISP." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.509, + 0.47, + 0.659 + ], + "angle": 0, + "content": "the number of feature channels by half, and the last one outputs a parameter feature map for \\(N\\) channels. We replicate the feature maps output by all decoders as hidden states \\(\\pmb{h}_t\\) and concatenate them with the intermediate feature maps of the decoders at step \\(t + 1\\). The input feature maps of the decoder at step \\(t + 1\\) are the concatenate of the feature maps generated by the previous decoder at step \\(t + 1\\) and step \\(t\\). In this process, the intermediate features of the convolutional network are passed as the hidden state \\(\\pmb{h}_t\\) and combined with the visual context \\(\\pmb{c}_t\\) for parameter prediction." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.66, + 0.47, + 0.706 + ], + "angle": 0, + "content": "The visual context features \\( \\pmb{c}_t \\) and the network hidden state \\( \\pmb{h}_{t - 1} \\) at time \\( t - 1 \\) are input to the decoder, and the parameter feature maps for \\( N \\) channels are generated:" + }, + { + "type": "equation", + "bbox": [ + 0.099, + 0.726, + 0.466, + 0.76 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\boldsymbol {\\Phi} _ {t} = f _ {\\text {d e c o d e r}} \\left(\\boldsymbol {c} _ {t}, \\boldsymbol {h} _ {t - 1}\\right) \\\\ = f _ {\\text {d e c o d e r}} \\left(f _ {\\text {e n c o d e r}} \\left(\\boldsymbol {v}, \\mathbf {P} _ {1} \\cup \\dots \\mathbf {P} _ {t - 1}\\right), \\boldsymbol {h} _ {t - 1}\\right), \\tag {6} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.768, + 0.47, + 0.849 + ], + "angle": 0, + "content": "where \\(\\Phi_t = \\{\\pmb{p}_t^{(1)},\\pmb{p}_t^{(2)},\\dots,\\pmb{p}_t^{(N)}\\}\\) is the set of feature maps of all parameters at step \\(t\\). And \\(\\pmb{p}_t^{(i)}\\) is the feature map of parameter \\(p_i\\) at time \\(t\\). And \\(\\Phi_t\\) go through the GSG module and the global average pooling (GAP) layer to obtain the predicted parameter grouping \\(\\mathbf{P}_t\\) at step \\(t\\):" + }, + { + "type": "equation", + "bbox": [ + 0.099, + 0.869, + 0.466, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathbf {P} _ {t} = f _ {g a p} \\left(f _ {g s g} \\left(\\boldsymbol {\\Phi} _ {t}\\right)\\right) \\\\ = f _ {g a p} \\left(f _ {g s g} \\left(f _ {s c n n} \\left(\\boldsymbol {v}, \\boldsymbol {h} _ {t - 1}, \\mathbf {P} _ {1} \\cup \\dots \\mathbf {P} _ {t - 1}\\right)\\right)\\right), \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.509, + 0.892, + 0.616 + ], + "angle": 0, + "content": "For convenience, \\( f_{scnn} \\) is used to express the Sequential CNN model that contains the encoder and decoder mentioned above. Eq. 7 shows the function of the sequential model at one step. The model is run \\( T \\) times recurrently by passing the hidden layer \\( h_i \\) and the predicted parameter groupings \\( \\mathbf{P}_i \\), where \\( i = 1, \\dots, T \\). All the predicted groupings are concatenated to obtain the predicted parameters." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.63, + 0.75, + 0.647 + ], + "angle": 0, + "content": "4.3. Global Similarity Grouping" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.655, + 0.892, + 0.776 + ], + "angle": 0, + "content": "GSG runs once at the beginning of an iteration, and partitions hyperparameters into \\( T \\) disjoint subsets. Multiple steps of an iteration run mask operation (before GAP) according to the results of GSG. Parameters groups number \\( T \\) is less than or equal to the number of ISP modules \\( M \\). Some modules in ISP have similar roles. Grouping these similar parameters can reduce the model steps and make it more efficient for a large number of parameters and modules." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.778, + 0.892, + 0.869 + ], + "angle": 0, + "content": "Hyperparameters \\(\\mathcal{P}\\) have an original division \\(\\mathcal{P} = \\mathbf{P}_1' \\cup \\mathbf{P}_2' \\cup \\ldots \\cup \\mathbf{P}_M'\\), where \\(\\mathbf{P}_i'\\) is the set of parameters of module \\(i\\). Details are described in Sec. 3 and supplement material. Considering the flexibility, we take the primal division as prior knowledge and use the correlation between parameters to explore the sequential divisions of parameters." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.892, + 0.902 + ], + "angle": 0, + "content": "The Sequential CNN model generates feature maps of all parameters, and the feature maps are passed through the" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22317" + } + ], + [ + { + "type": "code_caption", + "bbox": [ + 0.088, + 0.096, + 0.416, + 0.111 + ], + "angle": 0, + "content": "Algorithm 1: Sequential initial clustering centers" + }, + { + "type": "algorithm", + "bbox": [ + 0.099, + 0.113, + 0.443, + 0.158 + ], + "angle": 0, + "content": "Input: Model partition graphs: \\(\\mathbf{P}_1^{\\prime},\\ldots ,\\mathbf{P}_M^{\\prime}\\) Weighted adjacency matrix: W; Steps in the prediction model: \\(T\\leq M\\)" + }, + { + "type": "text", + "bbox": [ + 0.099, + 0.16, + 0.432, + 0.175 + ], + "angle": 0, + "content": "Output: Sequential clustering centers: \\(c_{1},\\ldots ,c_{T}\\)" + }, + { + "type": "equation", + "bbox": [ + 0.099, + 0.178, + 0.318, + 0.198 + ], + "angle": 0, + "content": "\\[\nv o l (\\mathbf {P} _ {x} ^ {\\prime}) = \\sum_ {p ^ {(i)} \\in \\mathbf {P} _ {x} ^ {\\prime}} \\sum_ {j = 1} ^ {n} w _ {i j};\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.1, + 0.201, + 0.219, + 0.218 + ], + "angle": 0, + "content": "\\[\nN c u t \\left(\\mathbf {P} _ {x} ^ {\\prime}, \\mathbf {P} _ {y} ^ {\\prime}\\right) =\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.108, + 0.218, + 0.39, + 0.239 + ], + "angle": 0, + "content": "\\[\n\\left(\\frac {1}{v o l (\\mathbf {P} _ {x} ^ {\\prime})} + \\frac {1}{v o l (\\mathbf {P} _ {y} ^ {\\prime})}\\right) \\sum_ {p ^ {(i)} \\in \\mathbf {P} _ {x}, p ^ {(j)} \\in \\overline {{\\mathbf {P} _ {y}}}} w _ {i j};\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.099, + 0.244, + 0.25, + 0.259 + ], + "angle": 0, + "content": "for \\(I\\in [1,M - T]\\) do" + }, + { + "type": "algorithm", + "bbox": [ + 0.1, + 0.261, + 0.443, + 0.342 + ], + "angle": 0, + "content": "\\(i\\gets i\\) s.t.max{Ncut \\((\\mathbf{P}_i^{\\prime},\\mathbf{P}_{i + 1}^{\\prime})|i\\in [1,M - I]\\}\\) \\(\\mathbf{P}_i^\\prime \\leftarrow \\mathbf{P}_i^\\prime \\cup \\mathbf{P}_{i + 1}^\\prime\\) if \\(i\\neq M - I\\) then \\(\\begin{array}{r}\\big{\\lfloor}\\mathbf{P}_{i + 1}^{\\prime},\\ldots ,\\mathbf{P}_{M - I}^{\\prime}\\leftarrow \\mathbf{P}_{i + 2}^{\\prime},\\ldots ,\\mathbf{P}_{M - I + 1}^{\\prime} \\end{array}\\)" + }, + { + "type": "equation", + "bbox": [ + 0.1, + 0.343, + 0.292, + 0.358 + ], + "angle": 0, + "content": "\\[\n\\mathbf {C} _ {1}, \\dots , \\mathbf {C} _ {T} \\leftarrow \\mathbf {P} _ {1}, \\dots , \\mathbf {P} _ {T};\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.363, + 0.215, + 0.378 + ], + "angle": 0, + "content": "for \\(J\\in [1,T]\\) do" + }, + { + "type": "equation", + "bbox": [ + 0.1, + 0.378, + 0.443, + 0.397 + ], + "angle": 0, + "content": "\\[\n\\left\\lfloor c _ {J} \\leftarrow p ^ {(x)} s. t. \\max \\left\\{\\sum_ {p (y) \\in \\mathbf {C} _ {J}} w _ {x y} \\right| p ^ {(x)} \\in \\mathbf {C} _ {J} \\right\\}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.431, + 0.47, + 0.553 + ], + "angle": 0, + "content": "GAP module to obtain the predicted parameters. The pixel values on the feature map represent the predicted parameter values in the corresponding region of the image \\(\\mathbf{I}\\). The feature map is a higher-dimension representation of the effect of the parameter on the image [22]. Therefore, we use the similarity between feature maps to perform the division of similar parameters. Here we directly use the cosine similarity to measure the feature map similarity as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.171, + 0.563, + 0.469, + 0.599 + ], + "angle": 0, + "content": "\\[\nL _ {c o s} \\left(p ^ {(i)}, p ^ {(j)}\\right) = \\frac {\\boldsymbol {p} ^ {(i)} * \\boldsymbol {p} ^ {(j)}}{\\left| \\boldsymbol {p} ^ {(i)} \\right| \\left| \\boldsymbol {p} ^ {(j)} \\right|}, \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.602, + 0.469, + 0.68 + ], + "angle": 0, + "content": "where \\(\\pmb{p}^{(i)}\\) and \\(\\pmb{p}^{(j)}\\) are the feature maps of \\(p^{(i)}\\) and \\(p^{(j)}\\), respectively, and \\(L_{cos}(p^{(i)}, p^{(j)})\\) is the similarity of \\(p^{(i)}\\) and \\(p^{(j)}\\). After the decoder outputs the parameter feature maps, We can obtain the similarity values between all parameters. All parameters can be expressed in the form of a graph:" + }, + { + "type": "equation", + "bbox": [ + 0.135, + 0.692, + 0.469, + 0.711 + ], + "angle": 0, + "content": "\\[\nG = (\\mathbf {V}, \\mathbf {E}), \\mathbf {V} = \\{p ^ {(1)}, p ^ {(2)}, \\dots , p ^ {(N)} \\}. \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.715, + 0.469, + 0.776 + ], + "angle": 0, + "content": "Graph \\(G\\) is weighted, and \\(\\mathbf{E}\\) is the set of edges between the vertices. The weight of each edge between two vertices \\(p^{(i)}\\) and \\(p^{(j)}\\) is \\(w_{ij} = L_{\\cos}(p^{(i)}, p^{(j)}) \\geq 0\\). The weighted adjacency matrix is \\(\\mathbf{W} = (w_{ij})_{i,j=1,\\dots,n}\\)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.777, + 0.469, + 0.807 + ], + "angle": 0, + "content": "The problem of partitioning the set \\(\\mathcal{P}\\) of parameters into \\(T\\) disjoint subsets is transformed into partitioning graph \\(G\\):" + }, + { + "type": "equation", + "bbox": [ + 0.154, + 0.828, + 0.469, + 0.851 + ], + "angle": 0, + "content": "\\[\n\\mathbf {V} = \\mathbf {P} _ {1} \\cup \\mathbf {P} _ {2} \\cup \\dots \\mathbf {P} _ {T}, \\tag {10}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.188, + 0.847, + 0.39, + 0.863 + ], + "angle": 0, + "content": "\\[\n\\mathbf {P} _ {i} \\cap \\mathbf {P} _ {j} = \\varnothing , 1 \\leq i < j \\leq T.\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Graph \\(G\\) should be divided so that the parameters are dissimilar between groups and similar in the same group. That" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.138 + ], + "angle": 0, + "content": "means the edges between different groups have low weights and the edges within a group have high weights. The above optimization objective can be expressed as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.513, + 0.158, + 0.892, + 0.201 + ], + "angle": 0, + "content": "\\[\n\\min \\sum_ {t = 1} ^ {T} \\frac {W \\left(\\mathbf {P} _ {t} , \\overline {{\\mathbf {P} _ {t}}}\\right)}{v o l \\left(\\mathbf {P} _ {t}\\right)} = \\sum_ {t = 1} ^ {T} \\frac {\\sum_ {p ^ {(i)} \\in \\mathbf {P} _ {t} , p ^ {(j)} \\in \\overline {{\\mathbf {P} _ {t}}}} w _ {i j}}{\\sum_ {p ^ {(i)} \\in \\mathbf {P} _ {t}} \\sum_ {j = 1} ^ {N} w _ {i j}}. \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.207, + 0.892, + 0.326 + ], + "angle": 0, + "content": "For the above optimization objectives and sequence partitioning of the parameters, we use the prior structure information of ISP and perform spectral clustering of the parameters. Precisely, we first compute the normalized Laplacian matrix \\(\\mathbf{L}\\) of the adjacency matrix \\(\\mathbf{W}\\). The first \\(T\\) eigenvectors of \\(\\mathbf{L}\\) are used as columns to form \\(\\mathbf{T} \\in \\mathbb{R}^{n \\times T}\\). After \\(\\mathbf{T}\\) is normalized by rows, the vector \\(\\mathbf{y}^{(i)}\\) of the parameter \\(p^{(i)}\\) corresponds to the \\(i\\)-th row of \\(\\mathbf{T}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.327, + 0.892, + 0.508 + ], + "angle": 0, + "content": "To make cluster \\(\\mathbf{P}_1, \\ldots, \\mathbf{P}_T\\) with sequence information, we introduce serialized initial clustering centers \\(c_1, \\ldots, c_T\\). The sequential partition graphs \\(\\mathbf{P}_1', \\ldots, \\mathbf{P}_M'\\) can be obtained based on the structure information of ISP. The graphs that are adjacent and related in order are merged to obtain \\(T\\) partitioned graphs \\(\\mathbf{C}_1, \\ldots, \\mathbf{C}_T\\), and \\(c_i\\) is the vertex in \\(\\mathbf{C}_i\\) that is most closely connected to other vertices. The specific process is described in Algorithm 1. With \\(c_1, \\ldots, c_T\\) as the initial clustering center, the points \\((\\boldsymbol{y}^{(i)})_{i=1,\\dots,N}\\) are clustered using the \\(k\\)-means algorithm to obtain the clusters \\(\\mathbf{P}_1, \\ldots, \\mathbf{P}_T\\). \\(\\mathbf{P}_t\\) is the cluster with \\(c_t\\) as the initial cluster center and is predicted at step \\(t\\) in the sequential model." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.509, + 0.892, + 0.6 + ], + "angle": 0, + "content": "The final obtained \\(\\mathbf{P}_1, \\ldots, \\mathbf{P}_T\\) for the division of parameters \\(\\mathcal{P}\\) is sequential, and its order is determined by the original ISP structure information. The parameters are similar within each grouping based on the similarity of the feature maps corresponding to the parameters. This flexible division enables cross-module grouping of similar parameters." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.611, + 0.633, + 0.629 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.636, + 0.598, + 0.652 + ], + "angle": 0, + "content": "5.1. Settings" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.66, + 0.892, + 0.688 + ], + "angle": 0, + "content": "In this section, we introduce the ISPs, datasets, and downstream tasks used for evaluation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.69, + 0.892, + 0.78 + ], + "angle": 0, + "content": "(1) Object detection on MS COCO [15] using [23]. For comparison with [22] and [17], we use the same synthetic (simulated) ISP to process simulated RAW as the upstream module for the downstream task. The ISP contains 20 hyperparameters, and detailed information is described in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.781, + 0.891, + 0.81 + ], + "angle": 0, + "content": "(2) Image segmentation using [10] on COCO with the same experimental setup as the object detection task." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.901 + ], + "angle": 0, + "content": "(3) ISP parameter prediction for image quality. A SONY IMX766 CMOS sensor is used to collect the RAW dataset. The RAW data were processed using Qualcomm Spectra 580 ISP oriented to image quality. The ISP contains 144 hyperparameters, and detailed information is described in the supplementary material." + }, + { + "type": "list", + "bbox": [ + 0.498, + 0.69, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22318" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.09, + 0.496, + 0.421 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.507, + 0.091, + 0.885, + 0.419 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.431, + 0.895, + 0.473 + ], + "angle": 0, + "content": "Figure 3. Image analytic vision tasks: Object Detection (left) and Image Segmentation (right) on COCO dataset. Default ISP hyperparameters (top), expert-tuned hyperparameters (middle), and the proposed method (bottom). The proposed method achieve better performance for downstream tasks." + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.497, + 0.471, + 0.54 + ], + "angle": 0, + "content": "Table 1. Synthetic ISP optimization for Object Detection on COCO. We borrow the results from [17] by constructing a similar processing pipeline with the ISP used in other methods." + }, + { + "type": "table", + "bbox": [ + 0.08, + 0.55, + 0.482, + 0.692 + ], + "angle": 0, + "content": "
ISP ModelmAP 0.5mAP 0.75mAP 0.5:0.95
Default Pblockwise-tuned [18]ISP[17]15.--
20.--
Hardware-tuned [17]39.--
Default PExpert-tunedISPSec.334.122.421.4
56.840.837.7
Attention-ware [22]61.044.141.0
Sequential-tuned62.845.242.3
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.72, + 0.471, + 0.903 + ], + "angle": 0, + "content": "During the training stage, the RAW images are input into the model, which sequentially predicts several groups of hyperparameters. As described in Sec. 4.1, the predicted parameters and ground-truth parameters are used to calculate the losses for training the model. We use a combination of iterative search and manual annotation to batch annotate the training set, as described in detail in the supplementary material. The pre-trained Resnet [11] for feature extraction and the rest of the model is trained using loss defined in Eq. 4 and the RMSprop optimizer. The learning rate was initially set to \\(10^{-4}\\) and reduced to \\(10^{-6}\\) after 200 calendar hours. The training process was performed for 400 epochs." + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.497, + 0.895, + 0.54 + ], + "angle": 0, + "content": "Table 2. Synthetic ISP optimization for Image Segmentation on COCO. We borrow the results from [17] by constructing a similar processing pipeline with the ISP used in other methods." + }, + { + "type": "table", + "bbox": [ + 0.502, + 0.55, + 0.903, + 0.678 + ], + "angle": 0, + "content": "
ISP ModelmAP 0.5mAP 0.75mAP 0.5:0.95
Default PISP12.--
Hardware-tuned [17][17]32.--
Default P22.713.212.0
Expert-tunedISP46.928.227.1
Attention-ware [22]Sec.352.333.431.5
Sequential-tuned54.235.132.6
" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.703, + 0.892, + 0.84 + ], + "angle": 0, + "content": "In the evaluation stage, we used the test set as model input to predict the hyperparameters for each RAW image. The RAW image and predicted hyperparameters were input to the ISP to obtain RGB output as input to the downstream tasks. The upstream RGB output is used as input for the object detection and instance segmentation tasks, and the evaluation metric uses the mean accuracy (mAP) [15]. The PSNR and SSIM between the RGB output and the reference image are used as evaluation metrics for the image quality." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.848, + 0.8, + 0.865 + ], + "angle": 0, + "content": "5.2. Optimization for Object Detection" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.893, + 0.903 + ], + "angle": 0, + "content": "In this task, we use an existing large sRGB dataset and a synthetic ISP to evaluate our ISP hyperparameter sequence" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22319" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.182, + 0.089, + 0.368, + 0.099 + ], + "angle": 0, + "content": "Default ISP Hyperparameters Output" + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.1, + 0.273, + 0.219 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.276, + 0.1, + 0.46, + 0.219 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.146, + 0.223, + 0.402, + 0.234 + ], + "angle": 0, + "content": "Predicted ISP Hyperparameters Output (proposed)" + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.234, + 0.273, + 0.353 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.276, + 0.234, + 0.46, + 0.353 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.228, + 0.358, + 0.321, + 0.368 + ], + "angle": 0, + "content": "Reference Output" + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.368, + 0.273, + 0.447 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.276, + 0.369, + 0.46, + 0.447 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.449, + 0.182, + 0.487 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.183, + 0.45, + 0.272, + 0.487 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.276, + 0.449, + 0.364, + 0.487 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.368, + 0.45, + 0.46, + 0.487 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.5, + 0.47, + 0.542 + ], + "angle": 0, + "content": "Figure 4. Default setting hyperparameters (top), proposed method (middle), and reference images (bottom). The proposed method achieves an image quality close to the reference image." + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.558, + 0.468, + 0.587 + ], + "angle": 0, + "content": "Table 3. Perceptual image quality using default parameters setting, recent method [22], and the proposed method." + }, + { + "type": "table", + "bbox": [ + 0.08, + 0.596, + 0.464, + 0.664 + ], + "angle": 0, + "content": "
ISP ModelPSNRSSIM
Default ParametersHardware ISP30.730.720
Attention-ware [22]Sec. 5.140.080.965
Sequential-tuned45.290.982
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.689, + 0.47, + 0.901 + ], + "angle": 0, + "content": "prediction method. The RGB images are converted to RAW as input to the ISP by simulating the processing of sRGB images with RAW data [13]. We use pre-trained [23] for object detection on the COCO dataset [15] as a downstream task. The proposed model is trained for the object detection task and evaluated using the mAP [15]. The quantitative evaluation results are shown in Table 1. Compared to the default parameter settings of the untuned ISP, our method shows a more significant improvement over current methods and better results than non-sequential prediction methods. The subjective results are shown in Fig. 3. The proposed method emphasizes the image's texture details and color features compared to the expert-tuned image. Although expert-tuned images are more consistent with hu" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.122 + ], + "angle": 0, + "content": "man visual preferences, the predicted parameters allow for better results in these images for the object detection tasks." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.131, + 0.828, + 0.148 + ], + "angle": 0, + "content": "5.3. Optimization for Image Segmentation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.155, + 0.892, + 0.412 + ], + "angle": 0, + "content": "Next, we use pre-trained [10] for image segmentation task on the COCO dataset as the downstream task. The proposed method is trained and tested on the simulated RAW COCO dataset and synthetic ISP. The results in Table 2 show that the proposed method has the best final results for the instance segmentation task compared to the recent methods. It also significantly improves over the default parameters (baseline). Fig. 3 shows the default parameters, the expert tuning parameters, and examples of the proposed method. Similar to the results obtained in the object detection task, the predicted parameters improve the performance of the downstream task (image segmentation) by adjusting the texture and color of the image. The predicted parameters of our model achieve an improvement of \\(0.3\\mathrm{mAP}\\) and \\(0.2\\mathrm{mAP}\\) compared to the default parameters and the expert-tuned parameters, respectively, and \\(0.1\\mathrm{mAP}\\) compared to the non-sequential prediction method." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.422, + 0.78, + 0.438 + ], + "angle": 0, + "content": "5.4. Optimization for Image Quality" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.445, + 0.892, + 0.793 + ], + "angle": 0, + "content": "The image quality optimization task aims to achieve a specific image rendering preference [16]. This specific rendering preference allows the output RGB to match the human visual preference by combining the visibility of various distortions in the image (noise, color shifts, blockiness) [26] and the imaging style. In this task, we aim to make the RAW image undergoes ISP to produce an RGB as close as possible to the reference image by the hyperparameters predicted by the proposed method. We collected a dataset for the image quality optimization task. The dataset contains images from 251 scenes acquired by the IMX766 sensor. The reference images are generated through the Spectra 580 ISP with the RAW images and the expert-tuned parameters as input. We used PSNR and SSIM metrics to evaluate the consistency between the images generated by the predicted parameters and the reference images. The quantitative results are shown in Table 3, and the proposed method consists of better PSNR and SSIM results than recent methods. Our method brings more improvements based on the default parameter settings. Fig. 4 shows some examples of image quality tasks. Our method produces images with better visual quality that conform to human preferences regarding texture details, noise control, and color representation." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.802, + 0.654, + 0.818 + ], + "angle": 0, + "content": "5.5. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.826, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Sequential Structure. Recent ISP hyperparameter tuning methods, both iterative optimization and direct prediction methods, treat the set of ISP hyperparameters as a black box space and estimate all the parameters at once. In contrast, the proposed method allows step-by-step sequential" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22320" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.089, + 0.892, + 0.119 + ], + "angle": 0, + "content": "Table 4. Ablation study of evaluating the proposed sequential structure and similarity grouping. The first column is the results for multiple step settings of the proposed method, and the second is the results of multiple changes in Global Similarity Grouping module." + }, + { + "type": "table", + "bbox": [ + 0.08, + 0.128, + 0.892, + 0.285 + ], + "angle": 0, + "content": "
StepsSequencePrior KnowledgeSimilarity GroupPSNRSSIM
Sequence prediction (Multi step-settings)one---39.520.957
two✓✓✓40.050.962
three✓✓✓42.660.971
four✓✓✓45.290.982
five✓✓✓45.230.982
Blockwise sequence & similarity groupfour✓✓✓45.290.982
Blockwise sequence & blockwise group✓✓-43.570.969
Random sequence & similarity group✓-✓42.620.960
Random sequence & random group✓--39.300.947
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.312, + 0.468, + 0.371 + ], + "angle": 0, + "content": "prediction of ISP parameters, achieving more refined predictions by modeling the sequential relationships among parameter groupings. We verify that this sequential tuning method has better results than the non-sequential method." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.371, + 0.468, + 0.673 + ], + "angle": 0, + "content": "The effectiveness of sequence prediction is tested on the image quality dataset and a Spectra 580 ISP with 144 parameters. The results are shown in Table 4. We tested the non-sequential and sequential methods under multiple settings, and the test metrics are the PSNR and SSIM results of the output and the reference image. The number of model running steps equals the number of parameter groupings. For the same number of hyperparameters, the fewer steps of the model, the more parameters are predicted in each step. When the step is set to one, the model predicts all hyperparameters simultaneously, equivalent to the non-sequential method. The sequential prediction results have better results than the non-sequential ones. We also validate the results of several experiments with different step counts. The method loop steps size larger to generate long-distance dependencies, which will be more challenging to learn and converge. Endlessly increasing the size of steps with the same dataset may suffer performance degradation. Experimental results show that a compromise step count setting achieves the best results for a particular experimental setting." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.674, + 0.468, + 0.809 + ], + "angle": 0, + "content": "Global Similarity Grouping. In sequential prediction, each step predicts a sub-grouping of the hyperparameters. Based on the block-wise structure of the ISP and the correlation between parameters, the Global Similarity Grouping module defines the order between different groups and how the parameters within the groups are divided. We validated the effectiveness of this adaptive grouping approach. The experimental setup is consistent with the ablation experiments above, and the results are shown in Table 4." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.468, + 0.901 + ], + "angle": 0, + "content": "To verify the effectiveness of the prior structural information of ISP, we changed the Global Similarity Grouping in Sec. 4.3 by removing the block-wise sequence clustering centers and directly performing spectral clustering for all parameters. The model uses random order for prediction for multiple parameter groupings clustered out. The results" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.311, + 0.892, + 0.553 + ], + "angle": 0, + "content": "in Table 4 show that the approach introducing the ISP prior structure information has a better result. To verify the effectiveness of cross-module grouping achieved by parameter similarity, we define the parameter grouping pattern directly with the block-wise structural information of ISP. Parameters of the same module are grouped into the same group, while the order between groups is consistent with the ISP module sequence. The results in Table 4 show the effectiveness of the similarity grouping with the introduction of parameter correlation. Similarity group compared to blockwise group is obtained from GSG by parameter similarity while predicting similar parameters at once will improve the accuracy. Finally, we remove both prior knowledge and similarity grouping by randomly defining the hyperparameter division and the order among groups. As shown in Table 4, such a setup has the worst results." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.571, + 0.627, + 0.587 + ], + "angle": 0, + "content": "6. Conclusions" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.598, + 0.892, + 0.703 + ], + "angle": 0, + "content": "This work proposes a novel sequential method for ISP tuning that uses module relation and parameter similarity to guide the process. It decouples the ISP structure and treats tuning as a sequential prediction problem. It groups parameters to tune ISP sequentially. Experiments show that introducing sequence information and similarity relations in the high-dimensional parameter space improves performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Acknowledgments This work was supported by the National Key Research and Development Program of China (Grant No. 2020AAA0106800), the National Natural Science Foundation of China (No. 62192785, No. 61972071, No. U1936204, No. 62122086, No. 62036011, No. U203-3210, No. 62172413, No. 62192782 and No. 61721004), the Beijing Natural Science Foundation No. M22005, and L223003, the Major Projects of Guangdong Education Department for Foundation Research and Applied Research (Grant: 2017KZDXM081, 2018KZDXM066), Guangdong Provincial University Innovation Team Project (Project No. 2020KCXTD045). The work of Bing Li was also supported by the Youth Innovation Promotion Association, CAS." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22321" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.091, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.156 + ], + "angle": 0, + "content": "[1] IEEE standard for camera phone image quality. IEEE Std 1858-2016 (Incorporating IEEE Std 1858-2016/Cor 1-2017), pages 1-146, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.158, + 0.472, + 0.212 + ], + "angle": 0, + "content": "[2] Rémi Bardenet, Mátyás Brendel, Balázs Kégl, and Michele Sebag. Collaborative hyperparameter tuning. In International conference on machine learning, pages 199–207, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.214, + 0.47, + 0.268 + ], + "angle": 0, + "content": "[3] James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyper-parameter optimization. Advances in neural information processing systems, 24, 2011. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.271, + 0.47, + 0.338 + ], + "angle": 0, + "content": "[4] James Bergstra, Daniel Yamins, and David Cox. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International conference on machine learning, pages 115-123, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.34, + 0.47, + 0.394 + ], + "angle": 0, + "content": "[5] Michael S Brown and SJ Kim. Understanding the in-camera image processing pipeline for computer vision. In IEEE International Conference on Computer Vision, volume 3, 2019. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.397, + 0.47, + 0.452 + ], + "angle": 0, + "content": "[6] Mark Buckler, Suren Jayasuriya, and Adrian Sampson. Reconfiguring the imaging pipeline for computer vision. In Proceedings of the IEEE International Conference on Computer Vision, pages 975-984, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.453, + 0.47, + 0.508 + ], + "angle": 0, + "content": "[7] Yue Cao, Xiaohe Wu, Shuran Qi, Xiao Liu, Zhongqin Wu, and Wangmeng Zuo. Pseudo-isp: Learning pseudo in-camera signal processing pipeline from a color image denoiser. arXiv preprint arXiv:2103.10234, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.509, + 0.47, + 0.564 + ], + "angle": 0, + "content": "[8] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3291-3300, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.565, + 0.47, + 0.62 + ], + "angle": 0, + "content": "[9] Nikolaus Hansen, Sibylle D Müller, and Petros Koumoutsakos. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cmaes). Evolutionary computation, 11(1):1-18, 2003. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.621, + 0.47, + 0.675 + ], + "angle": 0, + "content": "[10] Kaiming He, Georgia Gkioxari, Piotr Dólar, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961-2969, 2017. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.677, + 0.47, + 0.733 + ], + "angle": 0, + "content": "[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.734, + 0.47, + 0.801 + ], + "angle": 0, + "content": "[12] Andrey Ignatov, Luc Van Gool, and Radu Timofte. Replacing mobile camera isp with a single deep learning model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 536-537, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.804, + 0.47, + 0.872 + ], + "angle": 0, + "content": "[13] Seon Joo Kim, Hai Ting Lin, Zheng Lu, Sabine Süssstrunk, Stephen Lin, and Michael S Brown. A new in-camera imaging model for color computer vision and its application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(12):2289-2302, 2012. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.47, + 0.903 + ], + "angle": 0, + "content": "[14] Younghoon Kim, Jungmin Lee, Sung-Su Kim, Cheoljong Yang, TaeHyung Kim, and JoonSeo Yim. Dnn-based isp" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.472, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.121 + ], + "angle": 0, + "content": "parameter inference algorithm for automatic image quality optimization. Electronic Imaging, 2020(9):315-1, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.123, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[15] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755, 2014. 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.193, + 0.892, + 0.248 + ], + "angle": 0, + "content": "[16] Rafal K Mantiuk, Anna Tomaszewska, and Radosław Mantiuk. Comparison of four subjective methods for image quality assessment. In Computer graphics forum, volume 31, pages 2478-2491. Wiley Online Library, 2012. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.25, + 0.892, + 0.332 + ], + "angle": 0, + "content": "[17] Ali Mosleh, Avinash Sharma, Emmanuel Onzon, Fahim Mannan, Nicolas Robidoux, and Felix Heide. Hardware-in-the-loop end-to-end optimization of camera image processing pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7529-7538, 2020. 1, 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.334, + 0.892, + 0.403 + ], + "angle": 0, + "content": "[18] Jun Nishimura, Timo Gerasimow, Rao Sushma, Aleksandar Sutic, Chyuan-Tyng Wu, and Gilad Michael. Automatic isp image quality tuning using nonlinear optimization. In 2018 25th IEEE International Conference on Image Processing, pages 2471-2475. IEEE, 2018. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.405, + 0.892, + 0.461 + ], + "angle": 0, + "content": "[19] Emmanuel Onzon, Fahim Mannan, and Felix Heide. Neural auto-exposure for high-dynamic range object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7710-7720, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.462, + 0.892, + 0.503 + ], + "angle": 0, + "content": "[20] Luke Pfister and Yoram Bresler. Learning filter bank sparsifying transforms. IEEE Transactions on Signal Processing, 67(2):504-519, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.504, + 0.892, + 0.56 + ], + "angle": 0, + "content": "[21] Buu Phan, Fahim Mannan, and Felix Heide. Adversarial imaging pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16051-16061, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.561, + 0.892, + 0.631 + ], + "angle": 0, + "content": "[22] Haina Qin, Longfei Han, Juan Wang, Congxuan Zhang, Yanwei Li, Bing Li, and Weiming Hu. Attention-aware learning for hyperparameters prediction in image processing pipelines. In European Conference on Computer Vision, pages 271-287. Springer, 2022. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.632, + 0.892, + 0.66 + ], + "angle": 0, + "content": "[23] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.661, + 0.892, + 0.731 + ], + "angle": 0, + "content": "[24] Nicolas Robidoux, Luis E Garcia Capel, Dong-eun Seo, Avinash Sharma, Federico Ariza, and Felix Heide. End-to-end high dynamic range camera pipeline optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6297-6307, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.732, + 0.892, + 0.786 + ], + "angle": 0, + "content": "[25] Eli Schwartz, Raja Giryes, and Alex M Bronstein. Deepisp: Toward learning an end-to-end image processing pipeline. IEEE Transactions on Image Processing, 28(2):912-923, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.789, + 0.892, + 0.831 + ], + "angle": 0, + "content": "[26] Kim-Han Thung and Paramesran Raveendran. A survey of image quality measures. In IEEE international conference for technical postgraduates, pages 1-4, 2009. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.832, + 0.892, + 0.903 + ], + "angle": 0, + "content": "[27] Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl ST Arnaud, Derek Nowrouzezahrai, Jean-François Lalonde, and Felix Heide. Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Transactions on Graphics, 38(4):27-1, 2019. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "22322" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "[28] Peter van Beek, Chyuan-Tyng Roger Wu, Baishali Chaudhury, and Thomas R Gardos. Boosting computer vision performance by enhancing camera isp. Electronic Imaging, 2021(17):174-1, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.149, + 0.47, + 0.232 + ], + "angle": 0, + "content": "[29] Chyuan-Tyng Wu, Leo F Isikdogan, Sushma Rao, Bhavin Nayak, Timo Gerasimow, Aleksandar Sutic, Liron Ainkedem, and Gilad Michael. Visionisp: Repurposing the image signal processor for computer vision applications. In IEEE International Conference on Image Processing, pages 4624-4628. IEEE, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.234, + 0.47, + 0.289 + ], + "angle": 0, + "content": "[30] Lucie Yahiaoui, Ciarán Hughes, Jonathan Horgan, Brian Deegan, Patrick Denny, and Senthil Yogamani. Optimization of isp parameters for object detection algorithms. Electronic Imaging, 2019(15):44-1, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.291, + 0.47, + 0.346 + ], + "angle": 0, + "content": "[31] Cheoljong Yang, Jinhyun Kim, Jungmin Lee, Younghoon Kim, Sung-Su Kim, TaeHyung Kim, and JoonSeo Yim. Effective isp tuning framework based on user preference feedback. Electronic Imaging, 2020(9):316-1, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.348, + 0.47, + 0.401 + ], + "angle": 0, + "content": "[32] Dani Yogatama and Gideon Mann. Efficient transfer learning method for automatic hyperparameter tuning. In Artificial intelligence and statistics, pages 1077-1085. PMLR, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.403, + 0.47, + 0.445 + ], + "angle": 0, + "content": "[33] Ke Yu, Zexian Li, Yue Peng, Chen Change Loy, and Jinwei Gu. Reconfigisp: Reconfigurable camera image processing pipeline. arXiv preprint arXiv:2109.04760, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.447, + 0.47, + 0.528 + ], + "angle": 0, + "content": "[34] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Cycleisp: Real image restoration via improved data synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2696-2705, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.531, + 0.47, + 0.598 + ], + "angle": 0, + "content": "[35] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8697-8710, 2018. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.598 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22323" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization/5043327e-5e28-4a8f-8c57-7b23c08760b9_origin.pdf b/2023/Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization/5043327e-5e28-4a8f-8c57-7b23c08760b9_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..3c900924b65ce1cf5e6901ef9b557f9d92dc80a6 --- /dev/null +++ b/2023/Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization/5043327e-5e28-4a8f-8c57-7b23c08760b9_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:05cb5005ab3932b8b8c96e38406ccaf6f61c7168da54fb3be447249db7b0e2ce +size 1514954 diff --git a/2023/Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization/full.md b/2023/Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization/full.md new file mode 100644 index 0000000000000000000000000000000000000000..ad4eead2b7055682286e6bf68b212e5a4dbf797b --- /dev/null +++ b/2023/Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization/full.md @@ -0,0 +1,341 @@ +# Learning to Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization + +Haina Qin $^{1,2}$ Longfei Han $^{3}$ Weihua Xiong $^{1}$ Juan Wang $^{1}$ Wentao Ma $^{4}$ + +Bing $\mathrm{Li}^{1(\boxtimes)}$ Weiming $\mathrm{Hu}^{1,2}$ + +$^{1}$ State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences + +$^{2}$ School of Artificial Intelligence, University of Chinese Academy of Sciences + +$^{3}$ Beijing Technology and Business University $^{4}$ Zeku Technology, Shanghai + +{qinhaina2020@,bli@nlpr.,wmhu@nlpr.}ia.ac.cn longfeihan@btbu.edu.cn Wallace.xiong@gmail.com + +# Abstract + +The hardware image signal processing (ISP) pipeline is the intermediate layer between the imaging sensor and the downstream application, processing the sensor signal into an RGB image. The ISP is less programmable and consists of a series of processing modules. Each processing module handles a subtask and contains a set of tunable hyperparameters. A large number of hyperparameters form a complex mapping with the ISP output. The industry typically relies on manual and time-consuming hyperparameter tuning by image experts, biased towards human perception. Recently, several automatic ISP hyperparameter optimization methods using downstream evaluation metrics come into sight. However, existing methods for ISP tuning treat the high-dimensional parameter space as a global space for optimization and prediction all at once without inducing the structure knowledge of ISP. To this end, we propose a sequential ISP hyperparameter prediction framework that utilizes the sequential relationship within ISP modules and the similarity among parameters to guide the model sequence process. We validate the proposed method on object detection, image segmentation, and image quality tasks. + +# 1. Introduction + +Hardware ISPs are low-level image processing pipelines that convert RAW images into high-quality RGB images. Typically, ISPs include a series of processing modules [5], each of which handles a subtask such as denoising, white balance, demosaicing, or sharpening. Compared to software image processing pipelines, hardware ISPs are faster, more power-efficient, and widely used in real-time products [7, 34], including cameras [28], smartphones, surveil + +lance [21], IoT and driven-assistance systems. ISPs are highly modular and less programmable but with a set of tunable hyperparameters. The industry always relies on manual and costly hyperparameter tuning by image experts [1] to adapt the ISP to different application scenarios. + +The ISP is always designed as a sequential pipeline [5] and the configurable hyperparameters of various modules from any ISP aggregate to be a complex parameter space (with tens to hundreds of parameters), making the manual tuning process time-consuming. It is also difficult to subjectively find optimal hyperparameters settings for various downstream tasks (such as object detection and image segmentation [29, 30]). Recently, several automatic ISP hyperparameter optimization methods [17, 31] using downstream evaluation metrics come into sight. These methods tuning hyperparameters for downstream tasks include derivative-free [18] or gradient methods [12, 25, 27] based on differentiable approximation. There are also methods to demonstrate the potential of predicting specific hyperparameters for each image or scene [22]. However, existing methods treat the high-dimensional parameter space as a global black-box space for optimization and prediction all at once, while ignoring the inherent sequence of the ISP modules and the critical intra-correlation among hyperparameters. + +Inspired by the operating principles and structure knowledge of ISPs, we first propose a sequential ISP hyperparameter prediction framework (as shown in Fig. 1) that contains Sequential CNN model and Global Similarity Grouping. The sequential CNN model runs recurrently by predicting a group of parameters from several ISP modules, not all parameters, at each step. Meanwhile, the predicted parameters, along with the network's hidden layer and the input data, are in turn encoded as prior knowledge for predicting the following grouping of parameters. The Global Similarity Grouping module divides ISP parameters into multiple + +![](images/a92c5b6112f17fdeb90bae49d36d073956f9be0d8dc07faf76323462bfb55623.jpg) + +![](images/cd26e4ca6833577ab324316cff218c43d270ac4904a3b69ceb2dd4321008c605.jpg) +Figure 1. (A) Previous methods treat the hyperparameter space as a black box optimization problem and estimate all parameters at once without considering the prior knowledge of the ISP. (B) Our proposed method first decouples ISP structural knowledge and treats ISP tuning as a sequential prediction problem. It is effective to introduce sequence information and similarity relations in the high-dimensional hyperparameter space. + +disjoint groupings. The sequence order between groupings is explored heuristically using prior knowledge of the order of ISP modules. Given the flexibility, groupings are determined based on similarity among parameters, not limited to the same module parameters. The correlation of parameter activation maps learned through the model is used as the basis for parameter groupings. + +Our contributions can be summarized as the following: + +- We propose a new sequential CNN structure to exploit the sequence processing knowledge within ISP. The potential sequential information among parameters is used to guide the processing of the model. +- We exploit the correlation among parameters by the proposed similarity grouping module. The flexible parameter groupings allow the exploration of cross-module relationships among parameters. +- We validate the effectiveness of our method in a variety of downstream tasks, including object detection, image segmentation, and image quality. In these applications, + +our method outperforms existing methods. + +# 2. Related Work + +ISPs contain several components, and each module carries a specific image-processing algorithm. These algorithms are permanently configured with tens to hundreds of handcrafted parameters, for instance, noise reduction, color space transformation, and sharpening. It is desirable to find parameter configurations for imaging experts that make ISP work well in real-world scenarios and output compressed images with good human perception. Recently, to tackle this challenging optimization problem, several automatic ISP tuning methods were proposed by optimizing the hyperparameters with downstream task feedback [3,4,35], for instance, object detection [27,29], object segmentation [17], and image quality [2,6,30,32]. + +With the high-level feedback information, recent works always leverage a differentiable mapping between the parameter space and high-level evaluation metrics. Some existing optimization methods try to mimic the whole hardware ISP pipeline as an approximate CNN proxy model and construct an end-to-end differentiable CNN framework to optimize the proxy network [8, 12, 33]. Tseng et al. [27] trained an approximate CNN proxy model to mimic hardware ISP and optimized the differentiable CNN model with Stochastic Gradient Descent. Onzon et al. [19] propose a neural network for exposure selection that is jointly end-to-end with an object detector and ISP pipeline. Pfister et al. [20] proposes to optimize sparsity regularization for denoising. These methods are based on the assumption that a single proxy network can mimic the whole ISP pipeline and cannot be used to optimize the hardware ISPs. Other methods can directly put the hardware ISP into the optimization stage and predict all hyperparameters at once. For instance, Mosleh et al. [17, 24] directly optimize hardware ISP by a novel CMA-ES strategy [9] with max-rank-based multi-objective scalarization and initial search space reduction. Kim et al. [14] utilize the objective function of multi-output regression for modeling the relation between ISP parameter and IQM score. Qin et al. [22] directly infer all ISP hyperparameters via an attention-based CNN method. However, these methods do not take sequential information into consideration. Only Nishimura et al. [18] optimize ISP components with sequential prior knowledge based on a 0-th order Nelder-Mead method. Nevertheless, it can only be used to optimize ISP modules one at a time, ignores cross-module connections while viewing the modules in isolation, and is difficult to apply to hardware ISPs. + +Therefore, with the massive number of parameters in hardware ISPs, it needs to exploit the structural knowledge to tune ISPs in high-dimensional parameter space accurately. ISP tuning tasks should introduce ISP prior information and explore implicit relationships among parameters. + +# 3. Image Processing Pipelines + +For better comparison, we follow the most common ISP modules, and contains the following typical stages [5]: + +(1) Optics and Sensor: The sensor captures the illumination through the optics, and the RAW image is the sensor output. The pixel value of RAW is linearly related to the intensity of illumination. +(2) Denoising: Noise is generally considered to be a random variable with zero mean. Denoising will reduce noise but also remove details by blurring the image. +(3) DigitalGain and White Balance: The RAW pixel values are gain-adjusted. Color correction is processed by automatically estimating illuminations or the specific white-balance matrices of common illuminations. +(4) Demosaicking: Convert the color filter array over pixel sensors to RGB values for each pixel by performing interpolation. +(5) Color Space Transform: Map the RAW pixel values to CIE XYZ color space using a 3x3 transform matrix. +(6) Sharpening: Detect edges in the image. Apply filtering measures to eligible edges to improve edge strength and enhance details. +(7) Color and Tone Correction: Applying gamma curves and adjusting image contrast by histogram operations to improve the image's global appearance. +(8) Compression: Image pixel values are compressed to JPEG and storage. + +The ISP in this work models stages (2) to (8). This ISP $f_{\mathrm{ISP}}$ takes the RAW image $\mathbf{I}$ as input and converts into an RGB image $\mathbf{O}_{\mathrm{ISP}}$ : + +$$ +\mathbf {O} _ {\mathrm {I S P}} = f _ {\mathrm {I S P}} (\mathbf {I}; \mathcal {P}), \mathcal {P} \in \mathbb {R} _ {[ 0, 1 ]} ^ {N}, \tag {1} +$$ + +where $\mathbf{I} \in \mathbb{R}^{W \times H}$ , $\mathbf{O}_{\mathrm{ISP}} \in \mathbb{R}^{W \times H \times 3}$ . The ISP is controlled by $N$ continuous hyperparameters $\mathcal{P}$ . For the discrete parameters, mapping them to continuous values within the range of values facilitates prediction [17]. + +# 4. Method + +In this section, we devise the sequence model for predicting ISP hyperparameters, as shown in Fig. 2. We first introduce the ISP hyperparameter prediction task and the proposed model framework in Sec. 4.1. The Sequential CNN model design and the Global Similarity Grouping (GSG) for hyperparameters are introduced in Sec. 4.2 and Sec. 4.3. + +# 4.1. Formulation + +Given a RAW image $\mathbf{I}$ and a downstream task, the proposed method needs to maximize the following objectives: + +$$ +\boldsymbol {\theta} ^ {*} = \underset {\boldsymbol {\theta}} {\arg \min } \sum_ {i = 1} ^ {M} \mathcal {L} _ {\text {t a s k}} \left(f _ {I S P} \left(\mathbf {I} _ {i}; f _ {\text {p r e d}} \left(\mathbf {I} _ {i}; \boldsymbol {\theta}\right)\right)\right), \tag {2} +$$ + +where $\theta$ are the weights of the model, $M$ is the images' number, $f_{pred}$ is the proposed method, and $\hat{\mathcal{P}} = f_{pred}(\mathbf{I};\pmb {\theta})$ is the prediction ISP parameters. Since ISP is a black-box hardware unit, we convert the optimization objectives to: + +$$ +\boldsymbol {\theta} ^ {*} = \underset {\boldsymbol {\theta}} {\arg \min } \sum_ {i = 1} ^ {M} \mathcal {L} _ {2} \left(\mathcal {P} ^ {*}, f _ {p r e d} \left(\mathbf {I} _ {i}; \boldsymbol {\theta}\right)\right), \tag {3} +$$ + +where $\mathcal{P}^*$ is the optimal ISP parameter for image $\mathbf{I}$ and the downstream task. Our method divides the parameter $\mathcal{P}$ into $T$ disjoint subsets, $\mathcal{P} = \mathbf{P}_1 \cup \mathbf{P}_2 \cup \ldots \cup \mathbf{P}_T$ . There is an order between them, and $\mathbf{P}_t$ is predicted at step $t$ . This division decomposes the loss function into ordered conditions. + +$$ +\mathcal {L} _ {2} \left(\mathcal {P} ^ {*}, \hat {\mathcal {P}}\right) = \sum_ {t = 1} ^ {T} \mathcal {L} _ {2} \left(\mathbf {P} _ {t} ^ {*}, \hat {\mathbf {P}} _ {t}\right), \tag {4} +$$ + +The proposed Sequential CNN model and Global Similarity Grouping achieve the above process. Sequential CNN model uses a fully convolutional structure and runs the loop $T$ times to predict the sequential parameters groupings. Global Similarity Grouping exploits the implicit relations among parameters to achieve flexible parameter grouping. + +# 4.2. Sequential CNN Model + +In ISPs, one module's output is input to the next module. The effect of one set of parameters will be reflected in the image features and influence the next set of parameters. So we encode visual contextual features $\pmb{c}_t$ as the following: + +$$ +\boldsymbol {c} _ {t} = f _ {\text {e n c o d e r}} (\boldsymbol {v}, \mathbf {P} _ {1} \cup \mathbf {P} _ {2} \cup \dots \mathbf {P} _ {t - 1}), \tag {5} +$$ + +where $\pmb{v}$ is the visual feature extracted by the feature extractor on image I. The feature extractor is a pre-trained ResNet [11] model. At step $t$ , the encoder encodes the visual features $\pmb{v}$ and the predicted parameters sets at the time from 1 to $t - 1$ into visual context features $\pmb{c}_t$ , after which $\pmb{c}_t$ is used to predict of $\mathbf{P}_t$ . Specifically, we concatenate the visual features $\pmb{v}$ with parameters channels and convolve them. The number of parameters channels is the same as that of hyperparameter $\mathcal{P}$ . Each channel replicates the value of the corresponding hyperparameter in the spatial dimension. Only the channels corresponding to $\{\mathbf{P}_1,\dots ,\mathbf{P}_{t - 1}\}$ are assigned parameters' values, and the rest of the channels are assigned 0. The subsequent convolution module is designed as three parallel branching structures with 1x1, 3x3, and 5x5 convolutions. The three output features are concatenated to obtain the visual contextual features $\pmb{c}_t$ . Concatenating feature maps with different receptive fields generate multi-scale feature representations, which help encode parameters with different effect ranges. + +There are five decoders to complete the decoding process. Each decoder contains a 3x3 convolution followed by a ReLU unit and a span2 upsampling. Each decoder reduces + +![](images/e3b3ebbdf7924433adb4d1567812bdacc8e1ae4a3bf52a59cc5aa308c08a6d50.jpg) +Figure 2. Proposed sequence parameters prediction framework and ISP tuning process. Sequential CNN model has a fully convolutional encoder-decoder structure and predicts parameter groupings sequentially. Global Similarity Grouping module divides the parameters into ordered disjoint sub-groupings by implicit relations among parameters. Multi-step prediction results are concatenated to obtain the predicted hyperparameters, which are set as the parameters setting of the ISP. + +the number of feature channels by half, and the last one outputs a parameter feature map for $N$ channels. We replicate the feature maps output by all decoders as hidden states $\pmb{h}_t$ and concatenate them with the intermediate feature maps of the decoders at step $t + 1$ . The input feature maps of the decoder at step $t + 1$ are the concatenate of the feature maps generated by the previous decoder at step $t + 1$ and step $t$ . In this process, the intermediate features of the convolutional network are passed as the hidden state $\pmb{h}_t$ and combined with the visual context $\pmb{c}_t$ for parameter prediction. + +The visual context features $\pmb{c}_t$ and the network hidden state $\pmb{h}_{t - 1}$ at time $t - 1$ are input to the decoder, and the parameter feature maps for $N$ channels are generated: + +$$ +\begin{array}{l} \boldsymbol {\Phi} _ {t} = f _ {\text {d e c o d e r}} \left(\boldsymbol {c} _ {t}, \boldsymbol {h} _ {t - 1}\right) \\ = f _ {\text {d e c o d e r}} \left(f _ {\text {e n c o d e r}} \left(\boldsymbol {v}, \mathbf {P} _ {1} \cup \dots \mathbf {P} _ {t - 1}\right), \boldsymbol {h} _ {t - 1}\right), \tag {6} \\ \end{array} +$$ + +where $\Phi_t = \{\pmb{p}_t^{(1)},\pmb{p}_t^{(2)},\dots,\pmb{p}_t^{(N)}\}$ is the set of feature maps of all parameters at step $t$ . And $\pmb{p}_t^{(i)}$ is the feature map of parameter $p_i$ at time $t$ . And $\Phi_t$ go through the GSG module and the global average pooling (GAP) layer to obtain the predicted parameter grouping $\mathbf{P}_t$ at step $t$ : + +$$ +\begin{array}{l} \mathbf {P} _ {t} = f _ {g a p} \left(f _ {g s g} \left(\boldsymbol {\Phi} _ {t}\right)\right) \\ = f _ {g a p} \left(f _ {g s g} \left(f _ {s c n n} \left(\boldsymbol {v}, \boldsymbol {h} _ {t - 1}, \mathbf {P} _ {1} \cup \dots \mathbf {P} _ {t - 1}\right)\right)\right), \\ \end{array} +$$ + +For convenience, $f_{scnn}$ is used to express the Sequential CNN model that contains the encoder and decoder mentioned above. Eq. 7 shows the function of the sequential model at one step. The model is run $T$ times recurrently by passing the hidden layer $h_i$ and the predicted parameter groupings $\mathbf{P}_i$ , where $i = 1, \dots, T$ . All the predicted groupings are concatenated to obtain the predicted parameters. + +# 4.3. Global Similarity Grouping + +GSG runs once at the beginning of an iteration, and partitions hyperparameters into $T$ disjoint subsets. Multiple steps of an iteration run mask operation (before GAP) according to the results of GSG. Parameters groups number $T$ is less than or equal to the number of ISP modules $M$ . Some modules in ISP have similar roles. Grouping these similar parameters can reduce the model steps and make it more efficient for a large number of parameters and modules. + +Hyperparameters $\mathcal{P}$ have an original division $\mathcal{P} = \mathbf{P}_1' \cup \mathbf{P}_2' \cup \ldots \cup \mathbf{P}_M'$ , where $\mathbf{P}_i'$ is the set of parameters of module $i$ . Details are described in Sec. 3 and supplement material. Considering the flexibility, we take the primal division as prior knowledge and use the correlation between parameters to explore the sequential divisions of parameters. + +The Sequential CNN model generates feature maps of all parameters, and the feature maps are passed through the + +Algorithm 1: Sequential initial clustering centers +Input: Model partition graphs: $\mathbf{P}_1^{\prime},\ldots ,\mathbf{P}_M^{\prime}$ Weighted adjacency matrix: W; Steps in the prediction model: $T\leq M$ + +Output: Sequential clustering centers: $c_{1},\ldots ,c_{T}$ + +$$ +v o l (\mathbf {P} _ {x} ^ {\prime}) = \sum_ {p ^ {(i)} \in \mathbf {P} _ {x} ^ {\prime}} \sum_ {j = 1} ^ {n} w _ {i j}; +$$ + +$$ +N c u t \left(\mathbf {P} _ {x} ^ {\prime}, \mathbf {P} _ {y} ^ {\prime}\right) = +$$ + +$$ +\left(\frac {1}{v o l (\mathbf {P} _ {x} ^ {\prime})} + \frac {1}{v o l (\mathbf {P} _ {y} ^ {\prime})}\right) \sum_ {p ^ {(i)} \in \mathbf {P} _ {x}, p ^ {(j)} \in \overline {{\mathbf {P} _ {y}}}} w _ {i j}; +$$ + +for $I\in [1,M - T]$ do + +$i\gets i$ s.t.max{Ncut $(\mathbf{P}_i^{\prime},\mathbf{P}_{i + 1}^{\prime})|i\in [1,M - I]\}$ $\mathbf{P}_i^\prime \leftarrow \mathbf{P}_i^\prime \cup \mathbf{P}_{i + 1}^\prime$ if $i\neq M - I$ then $\begin{array}{r}\big{\lfloor}\mathbf{P}_{i + 1}^{\prime},\ldots ,\mathbf{P}_{M - I}^{\prime}\leftarrow \mathbf{P}_{i + 2}^{\prime},\ldots ,\mathbf{P}_{M - I + 1}^{\prime} \end{array}$ + +$$ +\mathbf {C} _ {1}, \dots , \mathbf {C} _ {T} \leftarrow \mathbf {P} _ {1}, \dots , \mathbf {P} _ {T}; +$$ + +for $J\in [1,T]$ do + +$$ +\left\lfloor c _ {J} \leftarrow p ^ {(x)} s. t. \max \left\{\sum_ {p (y) \in \mathbf {C} _ {J}} w _ {x y} \right| p ^ {(x)} \in \mathbf {C} _ {J} \right\} +$$ + +GAP module to obtain the predicted parameters. The pixel values on the feature map represent the predicted parameter values in the corresponding region of the image $\mathbf{I}$ . The feature map is a higher-dimension representation of the effect of the parameter on the image [22]. Therefore, we use the similarity between feature maps to perform the division of similar parameters. Here we directly use the cosine similarity to measure the feature map similarity as follows: + +$$ +L _ {c o s} \left(p ^ {(i)}, p ^ {(j)}\right) = \frac {\boldsymbol {p} ^ {(i)} * \boldsymbol {p} ^ {(j)}}{\left| \boldsymbol {p} ^ {(i)} \right| \left| \boldsymbol {p} ^ {(j)} \right|}, \tag {8} +$$ + +where $\pmb{p}^{(i)}$ and $\pmb{p}^{(j)}$ are the feature maps of $p^{(i)}$ and $p^{(j)}$ , respectively, and $L_{cos}(p^{(i)}, p^{(j)})$ is the similarity of $p^{(i)}$ and $p^{(j)}$ . After the decoder outputs the parameter feature maps, We can obtain the similarity values between all parameters. All parameters can be expressed in the form of a graph: + +$$ +G = (\mathbf {V}, \mathbf {E}), \mathbf {V} = \{p ^ {(1)}, p ^ {(2)}, \dots , p ^ {(N)} \}. \tag {9} +$$ + +Graph $G$ is weighted, and $\mathbf{E}$ is the set of edges between the vertices. The weight of each edge between two vertices $p^{(i)}$ and $p^{(j)}$ is $w_{ij} = L_{\cos}(p^{(i)}, p^{(j)}) \geq 0$ . The weighted adjacency matrix is $\mathbf{W} = (w_{ij})_{i,j=1,\dots,n}$ . + +The problem of partitioning the set $\mathcal{P}$ of parameters into $T$ disjoint subsets is transformed into partitioning graph $G$ : + +$$ +\mathbf {V} = \mathbf {P} _ {1} \cup \mathbf {P} _ {2} \cup \dots \mathbf {P} _ {T}, \tag {10} +$$ + +$$ +\mathbf {P} _ {i} \cap \mathbf {P} _ {j} = \varnothing , 1 \leq i < j \leq T. +$$ + +Graph $G$ should be divided so that the parameters are dissimilar between groups and similar in the same group. That + +means the edges between different groups have low weights and the edges within a group have high weights. The above optimization objective can be expressed as follows: + +$$ +\min \sum_ {t = 1} ^ {T} \frac {W \left(\mathbf {P} _ {t} , \overline {{\mathbf {P} _ {t}}}\right)}{v o l \left(\mathbf {P} _ {t}\right)} = \sum_ {t = 1} ^ {T} \frac {\sum_ {p ^ {(i)} \in \mathbf {P} _ {t} , p ^ {(j)} \in \overline {{\mathbf {P} _ {t}}}} w _ {i j}}{\sum_ {p ^ {(i)} \in \mathbf {P} _ {t}} \sum_ {j = 1} ^ {N} w _ {i j}}. \tag {11} +$$ + +For the above optimization objectives and sequence partitioning of the parameters, we use the prior structure information of ISP and perform spectral clustering of the parameters. Precisely, we first compute the normalized Laplacian matrix $\mathbf{L}$ of the adjacency matrix $\mathbf{W}$ . The first $T$ eigenvectors of $\mathbf{L}$ are used as columns to form $\mathbf{T} \in \mathbb{R}^{n \times T}$ . After $\mathbf{T}$ is normalized by rows, the vector $\mathbf{y}^{(i)}$ of the parameter $p^{(i)}$ corresponds to the $i$ -th row of $\mathbf{T}$ . + +To make cluster $\mathbf{P}_1, \ldots, \mathbf{P}_T$ with sequence information, we introduce serialized initial clustering centers $c_1, \ldots, c_T$ . The sequential partition graphs $\mathbf{P}_1', \ldots, \mathbf{P}_M'$ can be obtained based on the structure information of ISP. The graphs that are adjacent and related in order are merged to obtain $T$ partitioned graphs $\mathbf{C}_1, \ldots, \mathbf{C}_T$ , and $c_i$ is the vertex in $\mathbf{C}_i$ that is most closely connected to other vertices. The specific process is described in Algorithm 1. With $c_1, \ldots, c_T$ as the initial clustering center, the points $(\boldsymbol{y}^{(i)})_{i=1,\dots,N}$ are clustered using the $k$ -means algorithm to obtain the clusters $\mathbf{P}_1, \ldots, \mathbf{P}_T$ . $\mathbf{P}_t$ is the cluster with $c_t$ as the initial cluster center and is predicted at step $t$ in the sequential model. + +The final obtained $\mathbf{P}_1, \ldots, \mathbf{P}_T$ for the division of parameters $\mathcal{P}$ is sequential, and its order is determined by the original ISP structure information. The parameters are similar within each grouping based on the similarity of the feature maps corresponding to the parameters. This flexible division enables cross-module grouping of similar parameters. + +# 5. Experiments + +# 5.1. Settings + +In this section, we introduce the ISPs, datasets, and downstream tasks used for evaluation. + +(1) Object detection on MS COCO [15] using [23]. For comparison with [22] and [17], we use the same synthetic (simulated) ISP to process simulated RAW as the upstream module for the downstream task. The ISP contains 20 hyperparameters, and detailed information is described in the supplementary material. +(2) Image segmentation using [10] on COCO with the same experimental setup as the object detection task. +(3) ISP parameter prediction for image quality. A SONY IMX766 CMOS sensor is used to collect the RAW dataset. The RAW data were processed using Qualcomm Spectra 580 ISP oriented to image quality. The ISP contains 144 hyperparameters, and detailed information is described in the supplementary material. + +![](images/a37e6e9c48ebffac9b70c5a12dd95a18c6fc5a91b4529bfe740bddcefd12dba1.jpg) +Figure 3. Image analytic vision tasks: Object Detection (left) and Image Segmentation (right) on COCO dataset. Default ISP hyperparameters (top), expert-tuned hyperparameters (middle), and the proposed method (bottom). The proposed method achieve better performance for downstream tasks. + +![](images/4667458006a2d8bd1d0ab6a38f0786177a661b0509675162712b151eed1bcd9c.jpg) + +Table 1. Synthetic ISP optimization for Object Detection on COCO. We borrow the results from [17] by constructing a similar processing pipeline with the ISP used in other methods. + +
ISP ModelmAP 0.5mAP 0.75mAP 0.5:0.95
Default Pblockwise-tuned [18]ISP[17]15.--
20.--
Hardware-tuned [17]39.--
Default PExpert-tunedISPSec.334.122.421.4
56.840.837.7
Attention-ware [22]61.044.141.0
Sequential-tuned62.845.242.3
+ +During the training stage, the RAW images are input into the model, which sequentially predicts several groups of hyperparameters. As described in Sec. 4.1, the predicted parameters and ground-truth parameters are used to calculate the losses for training the model. We use a combination of iterative search and manual annotation to batch annotate the training set, as described in detail in the supplementary material. The pre-trained Resnet [11] for feature extraction and the rest of the model is trained using loss defined in Eq. 4 and the RMSprop optimizer. The learning rate was initially set to $10^{-4}$ and reduced to $10^{-6}$ after 200 calendar hours. The training process was performed for 400 epochs. + +Table 2. Synthetic ISP optimization for Image Segmentation on COCO. We borrow the results from [17] by constructing a similar processing pipeline with the ISP used in other methods. + +
ISP ModelmAP 0.5mAP 0.75mAP 0.5:0.95
Default PISP12.--
Hardware-tuned [17][17]32.--
Default P22.713.212.0
Expert-tunedISP46.928.227.1
Attention-ware [22]Sec.352.333.431.5
Sequential-tuned54.235.132.6
+ +In the evaluation stage, we used the test set as model input to predict the hyperparameters for each RAW image. The RAW image and predicted hyperparameters were input to the ISP to obtain RGB output as input to the downstream tasks. The upstream RGB output is used as input for the object detection and instance segmentation tasks, and the evaluation metric uses the mean accuracy (mAP) [15]. The PSNR and SSIM between the RGB output and the reference image are used as evaluation metrics for the image quality. + +# 5.2. Optimization for Object Detection + +In this task, we use an existing large sRGB dataset and a synthetic ISP to evaluate our ISP hyperparameter sequence + +![](images/05e862bfbfb609f7d57a761ab759d979581eb059b17d267c10df18a02df6eeef.jpg) +Default ISP Hyperparameters Output + +![](images/60c6027bec7f71832d1671b69b6d40565c8004d008fbf6b321a01931111d6981.jpg) + +![](images/65b7461437e6d75603e5d4767d06df12468b7393d1137931263f9fe7dd2cccd2.jpg) +Predicted ISP Hyperparameters Output (proposed) + +![](images/77a5856a59d1c8e45a0e3745f6abd6c2d76d345543d4bd05818aa08c235ab847.jpg) + +![](images/21bc17da815045df59e0c985df6f0535af5e0094a509a08ac36eac0d8fca25a2.jpg) +Reference Output + +![](images/2afa938b721c77a6b1b296669be0d5c97da993e580d1b92671295a1fb9f14c66.jpg) + +![](images/2e18431b9c5d2d23712f458935a0ca1865bee0c7a209050e816a919a34502e00.jpg) +Figure 4. Default setting hyperparameters (top), proposed method (middle), and reference images (bottom). The proposed method achieves an image quality close to the reference image. + +![](images/31f14cdb719fba7c0fa6234e576384759ec61d8818c0b8f049d06e10120f7486.jpg) + +![](images/d6142ad5025dbf54e571e4abf3e9f568c0212b291cd52ea1e560b2c362e9fe0c.jpg) + +![](images/9447757bcef037e6172f4493e902ed3c1c4db18f48589480ae92c63d6410f81d.jpg) + +Table 3. Perceptual image quality using default parameters setting, recent method [22], and the proposed method. + +
ISP ModelPSNRSSIM
Default ParametersHardware ISP30.730.720
Attention-ware [22]Sec. 5.140.080.965
Sequential-tuned45.290.982
+ +prediction method. The RGB images are converted to RAW as input to the ISP by simulating the processing of sRGB images with RAW data [13]. We use pre-trained [23] for object detection on the COCO dataset [15] as a downstream task. The proposed model is trained for the object detection task and evaluated using the mAP [15]. The quantitative evaluation results are shown in Table 1. Compared to the default parameter settings of the untuned ISP, our method shows a more significant improvement over current methods and better results than non-sequential prediction methods. The subjective results are shown in Fig. 3. The proposed method emphasizes the image's texture details and color features compared to the expert-tuned image. Although expert-tuned images are more consistent with hu + +man visual preferences, the predicted parameters allow for better results in these images for the object detection tasks. + +# 5.3. Optimization for Image Segmentation + +Next, we use pre-trained [10] for image segmentation task on the COCO dataset as the downstream task. The proposed method is trained and tested on the simulated RAW COCO dataset and synthetic ISP. The results in Table 2 show that the proposed method has the best final results for the instance segmentation task compared to the recent methods. It also significantly improves over the default parameters (baseline). Fig. 3 shows the default parameters, the expert tuning parameters, and examples of the proposed method. Similar to the results obtained in the object detection task, the predicted parameters improve the performance of the downstream task (image segmentation) by adjusting the texture and color of the image. The predicted parameters of our model achieve an improvement of $0.3\mathrm{mAP}$ and $0.2\mathrm{mAP}$ compared to the default parameters and the expert-tuned parameters, respectively, and $0.1\mathrm{mAP}$ compared to the non-sequential prediction method. + +# 5.4. Optimization for Image Quality + +The image quality optimization task aims to achieve a specific image rendering preference [16]. This specific rendering preference allows the output RGB to match the human visual preference by combining the visibility of various distortions in the image (noise, color shifts, blockiness) [26] and the imaging style. In this task, we aim to make the RAW image undergoes ISP to produce an RGB as close as possible to the reference image by the hyperparameters predicted by the proposed method. We collected a dataset for the image quality optimization task. The dataset contains images from 251 scenes acquired by the IMX766 sensor. The reference images are generated through the Spectra 580 ISP with the RAW images and the expert-tuned parameters as input. We used PSNR and SSIM metrics to evaluate the consistency between the images generated by the predicted parameters and the reference images. The quantitative results are shown in Table 3, and the proposed method consists of better PSNR and SSIM results than recent methods. Our method brings more improvements based on the default parameter settings. Fig. 4 shows some examples of image quality tasks. Our method produces images with better visual quality that conform to human preferences regarding texture details, noise control, and color representation. + +# 5.5. Ablation Study + +Sequential Structure. Recent ISP hyperparameter tuning methods, both iterative optimization and direct prediction methods, treat the set of ISP hyperparameters as a black box space and estimate all the parameters at once. In contrast, the proposed method allows step-by-step sequential + +Table 4. Ablation study of evaluating the proposed sequential structure and similarity grouping. The first column is the results for multiple step settings of the proposed method, and the second is the results of multiple changes in Global Similarity Grouping module. + +
StepsSequencePrior KnowledgeSimilarity GroupPSNRSSIM
Sequence prediction (Multi step-settings)one---39.520.957
two✓✓✓40.050.962
three✓✓✓42.660.971
four✓✓✓45.290.982
five✓✓✓45.230.982
Blockwise sequence & similarity groupfour✓✓✓45.290.982
Blockwise sequence & blockwise group✓✓-43.570.969
Random sequence & similarity group✓-✓42.620.960
Random sequence & random group✓--39.300.947
+ +prediction of ISP parameters, achieving more refined predictions by modeling the sequential relationships among parameter groupings. We verify that this sequential tuning method has better results than the non-sequential method. + +The effectiveness of sequence prediction is tested on the image quality dataset and a Spectra 580 ISP with 144 parameters. The results are shown in Table 4. We tested the non-sequential and sequential methods under multiple settings, and the test metrics are the PSNR and SSIM results of the output and the reference image. The number of model running steps equals the number of parameter groupings. For the same number of hyperparameters, the fewer steps of the model, the more parameters are predicted in each step. When the step is set to one, the model predicts all hyperparameters simultaneously, equivalent to the non-sequential method. The sequential prediction results have better results than the non-sequential ones. We also validate the results of several experiments with different step counts. The method loop steps size larger to generate long-distance dependencies, which will be more challenging to learn and converge. Endlessly increasing the size of steps with the same dataset may suffer performance degradation. Experimental results show that a compromise step count setting achieves the best results for a particular experimental setting. + +Global Similarity Grouping. In sequential prediction, each step predicts a sub-grouping of the hyperparameters. Based on the block-wise structure of the ISP and the correlation between parameters, the Global Similarity Grouping module defines the order between different groups and how the parameters within the groups are divided. We validated the effectiveness of this adaptive grouping approach. The experimental setup is consistent with the ablation experiments above, and the results are shown in Table 4. + +To verify the effectiveness of the prior structural information of ISP, we changed the Global Similarity Grouping in Sec. 4.3 by removing the block-wise sequence clustering centers and directly performing spectral clustering for all parameters. The model uses random order for prediction for multiple parameter groupings clustered out. The results + +in Table 4 show that the approach introducing the ISP prior structure information has a better result. To verify the effectiveness of cross-module grouping achieved by parameter similarity, we define the parameter grouping pattern directly with the block-wise structural information of ISP. Parameters of the same module are grouped into the same group, while the order between groups is consistent with the ISP module sequence. The results in Table 4 show the effectiveness of the similarity grouping with the introduction of parameter correlation. Similarity group compared to blockwise group is obtained from GSG by parameter similarity while predicting similar parameters at once will improve the accuracy. Finally, we remove both prior knowledge and similarity grouping by randomly defining the hyperparameter division and the order among groups. As shown in Table 4, such a setup has the worst results. + +# 6. Conclusions + +This work proposes a novel sequential method for ISP tuning that uses module relation and parameter similarity to guide the process. It decouples the ISP structure and treats tuning as a sequential prediction problem. It groups parameters to tune ISP sequentially. Experiments show that introducing sequence information and similarity relations in the high-dimensional parameter space improves performance. + +Acknowledgments This work was supported by the National Key Research and Development Program of China (Grant No. 2020AAA0106800), the National Natural Science Foundation of China (No. 62192785, No. 61972071, No. U1936204, No. 62122086, No. 62036011, No. U203-3210, No. 62172413, No. 62192782 and No. 61721004), the Beijing Natural Science Foundation No. M22005, and L223003, the Major Projects of Guangdong Education Department for Foundation Research and Applied Research (Grant: 2017KZDXM081, 2018KZDXM066), Guangdong Provincial University Innovation Team Project (Project No. 2020KCXTD045). The work of Bing Li was also supported by the Youth Innovation Promotion Association, CAS. + +# References + +[1] IEEE standard for camera phone image quality. IEEE Std 1858-2016 (Incorporating IEEE Std 1858-2016/Cor 1-2017), pages 1-146, 2017. 1 +[2] Rémi Bardenet, Mátyás Brendel, Balázs Kégl, and Michele Sebag. Collaborative hyperparameter tuning. In International conference on machine learning, pages 199–207, 2013. 2 +[3] James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyper-parameter optimization. Advances in neural information processing systems, 24, 2011. 2 +[4] James Bergstra, Daniel Yamins, and David Cox. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International conference on machine learning, pages 115-123, 2013. 2 +[5] Michael S Brown and SJ Kim. Understanding the in-camera image processing pipeline for computer vision. In IEEE International Conference on Computer Vision, volume 3, 2019. 1, 3 +[6] Mark Buckler, Suren Jayasuriya, and Adrian Sampson. Reconfiguring the imaging pipeline for computer vision. In Proceedings of the IEEE International Conference on Computer Vision, pages 975-984, 2017. 2 +[7] Yue Cao, Xiaohe Wu, Shuran Qi, Xiao Liu, Zhongqin Wu, and Wangmeng Zuo. Pseudo-isp: Learning pseudo in-camera signal processing pipeline from a color image denoiser. arXiv preprint arXiv:2103.10234, 2021. 1 +[8] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3291-3300, 2018. 2 +[9] Nikolaus Hansen, Sibylle D Müller, and Petros Koumoutsakos. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cmaes). Evolutionary computation, 11(1):1-18, 2003. 2 +[10] Kaiming He, Georgia Gkioxari, Piotr Dólar, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961-2969, 2017. 5, 7 +[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3, 6 +[12] Andrey Ignatov, Luc Van Gool, and Radu Timofte. Replacing mobile camera isp with a single deep learning model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 536-537, 2020. 1, 2 +[13] Seon Joo Kim, Hai Ting Lin, Zheng Lu, Sabine Süssstrunk, Stephen Lin, and Michael S Brown. A new in-camera imaging model for color computer vision and its application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(12):2289-2302, 2012. 7 +[14] Younghoon Kim, Jungmin Lee, Sung-Su Kim, Cheoljong Yang, TaeHyung Kim, and JoonSeo Yim. Dnn-based isp + +parameter inference algorithm for automatic image quality optimization. Electronic Imaging, 2020(9):315-1, 2020. 2 +[15] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755, 2014. 5, 6, 7 +[16] Rafal K Mantiuk, Anna Tomaszewska, and Radosław Mantiuk. Comparison of four subjective methods for image quality assessment. In Computer graphics forum, volume 31, pages 2478-2491. Wiley Online Library, 2012. 7 +[17] Ali Mosleh, Avinash Sharma, Emmanuel Onzon, Fahim Mannan, Nicolas Robidoux, and Felix Heide. Hardware-in-the-loop end-to-end optimization of camera image processing pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7529-7538, 2020. 1, 2, 3, 5, 6 +[18] Jun Nishimura, Timo Gerasimow, Rao Sushma, Aleksandar Sutic, Chyuan-Tyng Wu, and Gilad Michael. Automatic isp image quality tuning using nonlinear optimization. In 2018 25th IEEE International Conference on Image Processing, pages 2471-2475. IEEE, 2018. 1, 2, 6 +[19] Emmanuel Onzon, Fahim Mannan, and Felix Heide. Neural auto-exposure for high-dynamic range object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7710-7720, 2021. 2 +[20] Luke Pfister and Yoram Bresler. Learning filter bank sparsifying transforms. IEEE Transactions on Signal Processing, 67(2):504-519, 2018. 2 +[21] Buu Phan, Fahim Mannan, and Felix Heide. Adversarial imaging pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16051-16061, 2021. 1 +[22] Haina Qin, Longfei Han, Juan Wang, Congxuan Zhang, Yanwei Li, Bing Li, and Weiming Hu. Attention-aware learning for hyperparameters prediction in image processing pipelines. In European Conference on Computer Vision, pages 271-287. Springer, 2022. 1, 2, 5, 6, 7 +[23] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. 5, 7 +[24] Nicolas Robidoux, Luis E Garcia Capel, Dong-eun Seo, Avinash Sharma, Federico Ariza, and Felix Heide. End-to-end high dynamic range camera pipeline optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6297-6307, 2021. 2 +[25] Eli Schwartz, Raja Giryes, and Alex M Bronstein. Deepisp: Toward learning an end-to-end image processing pipeline. IEEE Transactions on Image Processing, 28(2):912-923, 2018. 1 +[26] Kim-Han Thung and Paramesran Raveendran. A survey of image quality measures. In IEEE international conference for technical postgraduates, pages 1-4, 2009. 7 +[27] Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl ST Arnaud, Derek Nowrouzezahrai, Jean-François Lalonde, and Felix Heide. Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Transactions on Graphics, 38(4):27-1, 2019. 1, 2 + +[28] Peter van Beek, Chyuan-Tyng Roger Wu, Baishali Chaudhury, and Thomas R Gardos. Boosting computer vision performance by enhancing camera isp. Electronic Imaging, 2021(17):174-1, 2021. 1 +[29] Chyuan-Tyng Wu, Leo F Isikdogan, Sushma Rao, Bhavin Nayak, Timo Gerasimow, Aleksandar Sutic, Liron Ainkedem, and Gilad Michael. Visionisp: Repurposing the image signal processor for computer vision applications. In IEEE International Conference on Image Processing, pages 4624-4628. IEEE, 2019. 1, 2 +[30] Lucie Yahiaoui, Ciarán Hughes, Jonathan Horgan, Brian Deegan, Patrick Denny, and Senthil Yogamani. Optimization of isp parameters for object detection algorithms. Electronic Imaging, 2019(15):44-1, 2019. 1, 2 +[31] Cheoljong Yang, Jinhyun Kim, Jungmin Lee, Younghoon Kim, Sung-Su Kim, TaeHyung Kim, and JoonSeo Yim. Effective isp tuning framework based on user preference feedback. Electronic Imaging, 2020(9):316-1, 2020. 1 +[32] Dani Yogatama and Gideon Mann. Efficient transfer learning method for automatic hyperparameter tuning. In Artificial intelligence and statistics, pages 1077-1085. PMLR, 2014. 2 +[33] Ke Yu, Zexian Li, Yue Peng, Chen Change Loy, and Jinwei Gu. Reconfigisp: Reconfigurable camera image processing pipeline. arXiv preprint arXiv:2109.04760, 2021. 2 +[34] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Cycleisp: Real image restoration via improved data synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2696-2705, 2020. 1 +[35] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. Learning transferable architectures for scalable image recognition. 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(ISP) pipeline is the intermediate layer between the imaging sensor and the downstream application, processing the sensor signal into an RGB image. The ISP is less programmable and consists of a series of processing modules. Each processing module handles a subtask and contains a set of tunable hyperparameters. A large number of hyperparameters form a complex mapping with the ISP output. The industry typically relies on manual and time-consuming hyperparameter tuning by image experts, biased towards human perception. Recently, several automatic ISP hyperparameter optimization methods using downstream evaluation metrics come into sight. However, existing methods for ISP tuning treat the high-dimensional parameter space as a global space for optimization and prediction all at once without inducing the structure knowledge of ISP. To this end, we propose a sequential ISP hyperparameter prediction framework that utilizes the sequential relationship within ISP modules and the similarity among parameters to guide the model sequence process. We validate the proposed method on object detection, image segmentation, and image quality tasks." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 596, + 128, + 609 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 596, + 128, + 609 + ], + "spans": [ + { + "bbox": [ + 47, + 596, + 128, + 609 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 287, + 714 + ], + "type": "text", + "content": "Hardware ISPs are low-level image processing pipelines that convert RAW images into high-quality RGB images. Typically, ISPs include a series of processing modules [5], each of which handles a subtask such as denoising, white balance, demosaicing, or sharpening. Compared to software image processing pipelines, hardware ISPs are faster, more power-efficient, and widely used in real-time products [7, 34], including cameras [28], smartphones, surveil" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 293, + 546, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 293, + 546, + 354 + ], + "spans": [ + { + "bbox": [ + 304, + 293, + 546, + 354 + ], + "type": "text", + "content": "lance [21], IoT and driven-assistance systems. ISPs are highly modular and less programmable but with a set of tunable hyperparameters. The industry always relies on manual and costly hyperparameter tuning by image experts [1] to adapt the ISP to different application scenarios." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 354, + 546, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 354, + 546, + 582 + ], + "spans": [ + { + "bbox": [ + 304, + 354, + 546, + 582 + ], + "type": "text", + "content": "The ISP is always designed as a sequential pipeline [5] and the configurable hyperparameters of various modules from any ISP aggregate to be a complex parameter space (with tens to hundreds of parameters), making the manual tuning process time-consuming. It is also difficult to subjectively find optimal hyperparameters settings for various downstream tasks (such as object detection and image segmentation [29, 30]). Recently, several automatic ISP hyperparameter optimization methods [17, 31] using downstream evaluation metrics come into sight. These methods tuning hyperparameters for downstream tasks include derivative-free [18] or gradient methods [12, 25, 27] based on differentiable approximation. There are also methods to demonstrate the potential of predicting specific hyperparameters for each image or scene [22]. However, existing methods treat the high-dimensional parameter space as a global black-box space for optimization and prediction all at once, while ignoring the inherent sequence of the ISP modules and the critical intra-correlation among hyperparameters." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 582, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 547, + 714 + ], + "type": "text", + "content": "Inspired by the operating principles and structure knowledge of ISPs, we first propose a sequential ISP hyperparameter prediction framework (as shown in Fig. 1) that contains Sequential CNN model and Global Similarity Grouping. The sequential CNN model runs recurrently by predicting a group of parameters from several ISP modules, not all parameters, at each step. Meanwhile, the predicted parameters, along with the network's hidden layer and the input data, are in turn encoded as prior knowledge for predicting the following grouping of parameters. The Global Similarity Grouping module divides ISP parameters into multiple" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22314" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 71, + 281, + 171 + ], + "blocks": [ + { + "bbox": [ + 53, + 71, + 281, + 171 + ], + "lines": [ + { + "bbox": [ + 53, + 71, + 281, + 171 + ], + "spans": [ + { + "bbox": [ + 53, + 71, + 281, + 171 + ], + "type": "image", + "image_path": "a92c5b6112f17fdeb90bae49d36d073956f9be0d8dc07faf76323462bfb55623.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 53, + 178, + 281, + 370 + ], + "blocks": [ + { + "bbox": [ + 53, + 178, + 281, + 370 + ], + "lines": [ + { + "bbox": [ + 53, + 178, + 281, + 370 + ], + "spans": [ + { + "bbox": [ + 53, + 178, + 281, + 370 + ], + "type": "image", + "image_path": "cd26e4ca6833577ab324316cff218c43d270ac4904a3b69ceb2dd4321008c605.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 379, + 287, + 456 + ], + "lines": [ + { + "bbox": [ + 46, + 379, + 287, + 456 + ], + "spans": [ + { + "bbox": [ + 46, + 379, + 287, + 456 + ], + "type": "text", + "content": "Figure 1. (A) Previous methods treat the hyperparameter space as a black box optimization problem and estimate all parameters at once without considering the prior knowledge of the ISP. (B) Our proposed method first decouples ISP structural knowledge and treats ISP tuning as a sequential prediction problem. It is effective to introduce sequence information and similarity relations in the high-dimensional hyperparameter space." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 478, + 286, + 561 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 478, + 286, + 561 + ], + "spans": [ + { + "bbox": [ + 46, + 478, + 286, + 561 + ], + "type": "text", + "content": "disjoint groupings. The sequence order between groupings is explored heuristically using prior knowledge of the order of ISP modules. Given the flexibility, groupings are determined based on similarity among parameters, not limited to the same module parameters. The correlation of parameter activation maps learned through the model is used as the basis for parameter groupings." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 59, + 562, + 281, + 573 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 562, + 281, + 573 + ], + "spans": [ + { + "bbox": [ + 59, + 562, + 281, + 573 + ], + "type": "text", + "content": "Our contributions can be summarized as the following:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 58, + 582, + 286, + 713 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 58, + 582, + 286, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 582, + 286, + 629 + ], + "spans": [ + { + "bbox": [ + 58, + 582, + 286, + 629 + ], + "type": "text", + "content": "- We propose a new sequential CNN structure to exploit the sequence processing knowledge within ISP. The potential sequential information among parameters is used to guide the processing of the model." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 58, + 630, + 286, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 630, + 286, + 677 + ], + "spans": [ + { + "bbox": [ + 58, + 630, + 286, + 677 + ], + "type": "text", + "content": "- We exploit the correlation among parameters by the proposed similarity grouping module. The flexible parameter groupings allow the exploration of cross-module relationships among parameters." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 58, + 677, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 677, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 58, + 677, + 286, + 713 + ], + "type": "text", + "content": "- We validate the effectiveness of our method in a variety of downstream tasks, including object detection, image segmentation, and image quality. In these applications," + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 325, + 72, + 498, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 72, + 498, + 84 + ], + "spans": [ + { + "bbox": [ + 325, + 72, + 498, + 84 + ], + "type": "text", + "content": "our method outperforms existing methods." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 95, + 392, + 107 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 95, + 392, + 107 + ], + "spans": [ + { + "bbox": [ + 306, + 95, + 392, + 107 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 114, + 545, + 270 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 114, + 545, + 270 + ], + "spans": [ + { + "bbox": [ + 304, + 114, + 545, + 270 + ], + "type": "text", + "content": "ISPs contain several components, and each module carries a specific image-processing algorithm. These algorithms are permanently configured with tens to hundreds of handcrafted parameters, for instance, noise reduction, color space transformation, and sharpening. It is desirable to find parameter configurations for imaging experts that make ISP work well in real-world scenarios and output compressed images with good human perception. Recently, to tackle this challenging optimization problem, several automatic ISP tuning methods were proposed by optimizing the hyperparameters with downstream task feedback [3,4,35], for instance, object detection [27,29], object segmentation [17], and image quality [2,6,30,32]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 270, + 545, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 270, + 545, + 653 + ], + "spans": [ + { + "bbox": [ + 304, + 270, + 545, + 653 + ], + "type": "text", + "content": "With the high-level feedback information, recent works always leverage a differentiable mapping between the parameter space and high-level evaluation metrics. Some existing optimization methods try to mimic the whole hardware ISP pipeline as an approximate CNN proxy model and construct an end-to-end differentiable CNN framework to optimize the proxy network [8, 12, 33]. Tseng et al. [27] trained an approximate CNN proxy model to mimic hardware ISP and optimized the differentiable CNN model with Stochastic Gradient Descent. Onzon et al. [19] propose a neural network for exposure selection that is jointly end-to-end with an object detector and ISP pipeline. Pfister et al. [20] proposes to optimize sparsity regularization for denoising. These methods are based on the assumption that a single proxy network can mimic the whole ISP pipeline and cannot be used to optimize the hardware ISPs. Other methods can directly put the hardware ISP into the optimization stage and predict all hyperparameters at once. For instance, Mosleh et al. [17, 24] directly optimize hardware ISP by a novel CMA-ES strategy [9] with max-rank-based multi-objective scalarization and initial search space reduction. Kim et al. [14] utilize the objective function of multi-output regression for modeling the relation between ISP parameter and IQM score. Qin et al. [22] directly infer all ISP hyperparameters via an attention-based CNN method. However, these methods do not take sequential information into consideration. Only Nishimura et al. [18] optimize ISP components with sequential prior knowledge based on a 0-th order Nelder-Mead method. Nevertheless, it can only be used to optimize ISP modules one at a time, ignores cross-module connections while viewing the modules in isolation, and is difficult to apply to hardware ISPs." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "content": "Therefore, with the massive number of parameters in hardware ISPs, it needs to exploit the structural knowledge to tune ISPs in high-dimensional parameter space accurately. ISP tuning tasks should introduce ISP prior information and explore implicit relationships among parameters." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22315" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 71, + 201, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 71, + 201, + 85 + ], + "spans": [ + { + "bbox": [ + 47, + 71, + 201, + 85 + ], + "type": "text", + "content": "3. Image Processing Pipelines" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 91, + 287, + 115 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 91, + 287, + 115 + ], + "spans": [ + { + "bbox": [ + 47, + 91, + 287, + 115 + ], + "type": "text", + "content": "For better comparison, we follow the most common ISP modules, and contains the following typical stages [5]:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 115, + 287, + 402 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 46, + 115, + 287, + 163 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 115, + 287, + 163 + ], + "spans": [ + { + "bbox": [ + 46, + 115, + 287, + 163 + ], + "type": "text", + "content": "(1) Optics and Sensor: The sensor captures the illumination through the optics, and the RAW image is the sensor output. The pixel value of RAW is linearly related to the intensity of illumination." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 163, + 287, + 199 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 163, + 287, + 199 + ], + "spans": [ + { + "bbox": [ + 46, + 163, + 287, + 199 + ], + "type": "text", + "content": "(2) Denoising: Noise is generally considered to be a random variable with zero mean. Denoising will reduce noise but also remove details by blurring the image." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 199, + 287, + 246 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 199, + 287, + 246 + ], + "spans": [ + { + "bbox": [ + 46, + 199, + 287, + 246 + ], + "type": "text", + "content": "(3) DigitalGain and White Balance: The RAW pixel values are gain-adjusted. Color correction is processed by automatically estimating illuminations or the specific white-balance matrices of common illuminations." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 247, + 287, + 282 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 247, + 287, + 282 + ], + "spans": [ + { + "bbox": [ + 46, + 247, + 287, + 282 + ], + "type": "text", + "content": "(4) Demosaicking: Convert the color filter array over pixel sensors to RGB values for each pixel by performing interpolation." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 282, + 287, + 306 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 282, + 287, + 306 + ], + "spans": [ + { + "bbox": [ + 46, + 282, + 287, + 306 + ], + "type": "text", + "content": "(5) Color Space Transform: Map the RAW pixel values to CIE XYZ color space using a 3x3 transform matrix." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 306, + 287, + 342 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 306, + 287, + 342 + ], + "spans": [ + { + "bbox": [ + 46, + 306, + 287, + 342 + ], + "type": "text", + "content": "(6) Sharpening: Detect edges in the image. Apply filtering measures to eligible edges to improve edge strength and enhance details." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 342, + 287, + 378 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 342, + 287, + 378 + ], + "spans": [ + { + "bbox": [ + 46, + 342, + 287, + 378 + ], + "type": "text", + "content": "(7) Color and Tone Correction: Applying gamma curves and adjusting image contrast by histogram operations to improve the image's global appearance." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 379, + 287, + 402 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 379, + 287, + 402 + ], + "spans": [ + { + "bbox": [ + 46, + 379, + 287, + 402 + ], + "type": "text", + "content": "(8) Compression: Image pixel values are compressed to JPEG and storage." + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 47, + 402, + 287, + 438 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 402, + 287, + 438 + ], + "spans": [ + { + "bbox": [ + 47, + 402, + 287, + 438 + ], + "type": "text", + "content": "The ISP in this work models stages (2) to (8). This ISP " + }, + { + "bbox": [ + 47, + 402, + 287, + 438 + ], + "type": "inline_equation", + "content": "f_{\\mathrm{ISP}}" + }, + { + "bbox": [ + 47, + 402, + 287, + 438 + ], + "type": "text", + "content": " takes the RAW image " + }, + { + "bbox": [ + 47, + 402, + 287, + 438 + ], + "type": "inline_equation", + "content": "\\mathbf{I}" + }, + { + "bbox": [ + 47, + 402, + 287, + 438 + ], + "type": "text", + "content": " as input and converts into an RGB image " + }, + { + "bbox": [ + 47, + 402, + 287, + 438 + ], + "type": "inline_equation", + "content": "\\mathbf{O}_{\\mathrm{ISP}}" + }, + { + "bbox": [ + 47, + 402, + 287, + 438 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 100, + 448, + 287, + 464 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 448, + 287, + 464 + ], + "spans": [ + { + "bbox": [ + 100, + 448, + 287, + 464 + ], + "type": "interline_equation", + "content": "\\mathbf {O} _ {\\mathrm {I S P}} = f _ {\\mathrm {I S P}} (\\mathbf {I}; \\mathcal {P}), \\mathcal {P} \\in \\mathbb {R} _ {[ 0, 1 ]} ^ {N}, \\tag {1}", + "image_path": "2f4e396b43299c4b4c4ff59977370b63e022382281c025090b6fdde4d7306a82.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 467, + 287, + 516 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 467, + 287, + 516 + ], + "spans": [ + { + "bbox": [ + 46, + 467, + 287, + 516 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 467, + 287, + 516 + ], + "type": "inline_equation", + "content": "\\mathbf{I} \\in \\mathbb{R}^{W \\times H}" + }, + { + "bbox": [ + 46, + 467, + 287, + 516 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 467, + 287, + 516 + ], + "type": "inline_equation", + "content": "\\mathbf{O}_{\\mathrm{ISP}} \\in \\mathbb{R}^{W \\times H \\times 3}" + }, + { + "bbox": [ + 46, + 467, + 287, + 516 + ], + "type": "text", + "content": ". The ISP is controlled by " + }, + { + "bbox": [ + 46, + 467, + 287, + 516 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 467, + 287, + 516 + ], + "type": "text", + "content": " continuous hyperparameters " + }, + { + "bbox": [ + 46, + 467, + 287, + 516 + ], + "type": "inline_equation", + "content": "\\mathcal{P}" + }, + { + "bbox": [ + 46, + 467, + 287, + 516 + ], + "type": "text", + "content": ". For the discrete parameters, mapping them to continuous values within the range of values facilitates prediction [17]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 47, + 525, + 103, + 538 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 525, + 103, + 538 + ], + "spans": [ + { + "bbox": [ + 47, + 525, + 103, + 538 + ], + "type": "text", + "content": "4. Method" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 546, + 287, + 618 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 546, + 287, + 618 + ], + "spans": [ + { + "bbox": [ + 46, + 546, + 287, + 618 + ], + "type": "text", + "content": "In this section, we devise the sequence model for predicting ISP hyperparameters, as shown in Fig. 2. We first introduce the ISP hyperparameter prediction task and the proposed model framework in Sec. 4.1. The Sequential CNN model design and the Global Similarity Grouping (GSG) for hyperparameters are introduced in Sec. 4.2 and Sec. 4.3." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 47, + 624, + 129, + 636 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 624, + 129, + 636 + ], + "spans": [ + { + "bbox": [ + 47, + 624, + 129, + 636 + ], + "type": "text", + "content": "4.1. Formulation" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 47, + 643, + 287, + 667 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 643, + 287, + 667 + ], + "spans": [ + { + "bbox": [ + 47, + 643, + 287, + 667 + ], + "type": "text", + "content": "Given a RAW image " + }, + { + "bbox": [ + 47, + 643, + 287, + 667 + ], + "type": "inline_equation", + "content": "\\mathbf{I}" + }, + { + "bbox": [ + 47, + 643, + 287, + 667 + ], + "type": "text", + "content": " and a downstream task, the proposed method needs to maximize the following objectives:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 61, + 683, + 287, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 683, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 61, + 683, + 287, + 715 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\theta} ^ {*} = \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\sum_ {i = 1} ^ {M} \\mathcal {L} _ {\\text {t a s k}} \\left(f _ {I S P} \\left(\\mathbf {I} _ {i}; f _ {\\text {p r e d}} \\left(\\mathbf {I} _ {i}; \\boldsymbol {\\theta}\\right)\\right)\\right), \\tag {2}", + "image_path": "0cd1a45f2c6712de50ce9360ceecad340355a5adfa4db9294565aad0091b392c.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " are the weights of the model, " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " is the images' number, " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "f_{pred}" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " is the proposed method, and " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "\\hat{\\mathcal{P}} = f_{pred}(\\mathbf{I};\\pmb {\\theta})" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " is the prediction ISP parameters. Since ISP is a black-box hardware unit, we convert the optimization objectives to:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 343, + 129, + 545, + 161 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 129, + 545, + 161 + ], + "spans": [ + { + "bbox": [ + 343, + 129, + 545, + 161 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\theta} ^ {*} = \\underset {\\boldsymbol {\\theta}} {\\arg \\min } \\sum_ {i = 1} ^ {M} \\mathcal {L} _ {2} \\left(\\mathcal {P} ^ {*}, f _ {p r e d} \\left(\\mathbf {I} _ {i}; \\boldsymbol {\\theta}\\right)\\right), \\tag {3}", + "image_path": "767b129d43acf0eeede77f387d3ac2ae7699f310afd646f1fa09c0df616b7e5b.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "spans": [ + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "inline_equation", + "content": "\\mathcal{P}^*" + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "text", + "content": " is the optimal ISP parameter for image " + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "inline_equation", + "content": "\\mathbf{I}" + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "text", + "content": " and the downstream task. Our method divides the parameter " + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "inline_equation", + "content": "\\mathcal{P}" + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "text", + "content": " into " + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "text", + "content": " disjoint subsets, " + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "inline_equation", + "content": "\\mathcal{P} = \\mathbf{P}_1 \\cup \\mathbf{P}_2 \\cup \\ldots \\cup \\mathbf{P}_T" + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "text", + "content": ". There is an order between them, and " + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_t" + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "text", + "content": " is predicted at step " + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 305, + 167, + 545, + 227 + ], + "type": "text", + "content": ". This division decomposes the loss function into ordered conditions." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 362, + 236, + 545, + 269 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 362, + 236, + 545, + 269 + ], + "spans": [ + { + "bbox": [ + 362, + 236, + 545, + 269 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {2} \\left(\\mathcal {P} ^ {*}, \\hat {\\mathcal {P}}\\right) = \\sum_ {t = 1} ^ {T} \\mathcal {L} _ {2} \\left(\\mathbf {P} _ {t} ^ {*}, \\hat {\\mathbf {P}} _ {t}\\right), \\tag {4}", + "image_path": "f873f6c04b282c0dee16721fe7a882cf0e307ed3cfee9c95849557541375aee4.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 305, + 273, + 545, + 345 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 273, + 545, + 345 + ], + "spans": [ + { + "bbox": [ + 305, + 273, + 545, + 345 + ], + "type": "text", + "content": "The proposed Sequential CNN model and Global Similarity Grouping achieve the above process. Sequential CNN model uses a fully convolutional structure and runs the loop " + }, + { + "bbox": [ + 305, + 273, + 545, + 345 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 305, + 273, + 545, + 345 + ], + "type": "text", + "content": " times to predict the sequential parameters groupings. Global Similarity Grouping exploits the implicit relations among parameters to achieve flexible parameter grouping." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 306, + 353, + 437, + 365 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 353, + 437, + 365 + ], + "spans": [ + { + "bbox": [ + 306, + 353, + 437, + 365 + ], + "type": "text", + "content": "4.2. Sequential CNN Model" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 305, + 371, + 545, + 419 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 371, + 545, + 419 + ], + "spans": [ + { + "bbox": [ + 305, + 371, + 545, + 419 + ], + "type": "text", + "content": "In ISPs, one module's output is input to the next module. The effect of one set of parameters will be reflected in the image features and influence the next set of parameters. So we encode visual contextual features " + }, + { + "bbox": [ + 305, + 371, + 545, + 419 + ], + "type": "inline_equation", + "content": "\\pmb{c}_t" + }, + { + "bbox": [ + 305, + 371, + 545, + 419 + ], + "type": "text", + "content": " as the following:" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 344, + 431, + 545, + 444 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 344, + 431, + 545, + 444 + ], + "spans": [ + { + "bbox": [ + 344, + 431, + 545, + 444 + ], + "type": "interline_equation", + "content": "\\boldsymbol {c} _ {t} = f _ {\\text {e n c o d e r}} (\\boldsymbol {v}, \\mathbf {P} _ {1} \\cup \\mathbf {P} _ {2} \\cup \\dots \\mathbf {P} _ {t - 1}), \\tag {5}", + "image_path": "23f2d16e58b0187c4e249a5743ac774955615dffbb8b44837bf4db9ac3306070.jpg" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "spans": [ + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "inline_equation", + "content": "\\pmb{v}" + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "text", + "content": " is the visual feature extracted by the feature extractor on image I. The feature extractor is a pre-trained ResNet [11] model. At step " + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "text", + "content": ", the encoder encodes the visual features " + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "inline_equation", + "content": "\\pmb{v}" + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "text", + "content": " and the predicted parameters sets at the time from 1 to " + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "inline_equation", + "content": "t - 1" + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "text", + "content": " into visual context features " + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "inline_equation", + "content": "\\pmb{c}_t" + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "text", + "content": ", after which " + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "inline_equation", + "content": "\\pmb{c}_t" + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "text", + "content": " is used to predict of " + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_t" + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "text", + "content": ". Specifically, we concatenate the visual features " + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "inline_equation", + "content": "\\pmb{v}" + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "text", + "content": " with parameters channels and convolve them. The number of parameters channels is the same as that of hyperparameter " + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "inline_equation", + "content": "\\mathcal{P}" + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "text", + "content": ". Each channel replicates the value of the corresponding hyperparameter in the spatial dimension. Only the channels corresponding to " + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "inline_equation", + "content": "\\{\\mathbf{P}_1,\\dots ,\\mathbf{P}_{t - 1}\\}" + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "text", + "content": " are assigned parameters' values, and the rest of the channels are assigned 0. The subsequent convolution module is designed as three parallel branching structures with 1x1, 3x3, and 5x5 convolutions. The three output features are concatenated to obtain the visual contextual features " + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "inline_equation", + "content": "\\pmb{c}_t" + }, + { + "bbox": [ + 304, + 450, + 545, + 677 + ], + "type": "text", + "content": ". Concatenating feature maps with different receptive fields generate multi-scale feature representations, which help encode parameters with different effect ranges." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": "There are five decoders to complete the decoding process. Each decoder contains a 3x3 convolution followed by a ReLU unit and a span2 upsampling. Each decoder reduces" + } + ] + } + ], + "index": 28 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "22316" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 55, + 68, + 541, + 329 + ], + "blocks": [ + { + "bbox": [ + 55, + 68, + 541, + 329 + ], + "lines": [ + { + "bbox": [ + 55, + 68, + 541, + 329 + ], + "spans": [ + { + "bbox": [ + 55, + 68, + 541, + 329 + ], + "type": "image", + "image_path": "e3b3ebbdf7924433adb4d1567812bdacc8e1ae4a3bf52a59cc5aa308c08a6d50.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 337, + 547, + 381 + ], + "lines": [ + { + "bbox": [ + 46, + 337, + 547, + 381 + ], + "spans": [ + { + "bbox": [ + 46, + 337, + 547, + 381 + ], + "type": "text", + "content": "Figure 2. Proposed sequence parameters prediction framework and ISP tuning process. Sequential CNN model has a fully convolutional encoder-decoder structure and predicts parameter groupings sequentially. Global Similarity Grouping module divides the parameters into ordered disjoint sub-groupings by implicit relations among parameters. Multi-step prediction results are concatenated to obtain the predicted hyperparameters, which are set as the parameters setting of the ISP." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "spans": [ + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "text", + "content": "the number of feature channels by half, and the last one outputs a parameter feature map for " + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "text", + "content": " channels. We replicate the feature maps output by all decoders as hidden states " + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "inline_equation", + "content": "\\pmb{h}_t" + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "text", + "content": " and concatenate them with the intermediate feature maps of the decoders at step " + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "inline_equation", + "content": "t + 1" + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "text", + "content": ". The input feature maps of the decoder at step " + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "inline_equation", + "content": "t + 1" + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "text", + "content": " are the concatenate of the feature maps generated by the previous decoder at step " + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "inline_equation", + "content": "t + 1" + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "text", + "content": " and step " + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "text", + "content": ". In this process, the intermediate features of the convolutional network are passed as the hidden state " + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "inline_equation", + "content": "\\pmb{h}_t" + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "text", + "content": " and combined with the visual context " + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "inline_equation", + "content": "\\pmb{c}_t" + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "text", + "content": " for parameter prediction." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 522, + 287, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 522, + 287, + 559 + ], + "spans": [ + { + "bbox": [ + 46, + 522, + 287, + 559 + ], + "type": "text", + "content": "The visual context features " + }, + { + "bbox": [ + 46, + 522, + 287, + 559 + ], + "type": "inline_equation", + "content": "\\pmb{c}_t" + }, + { + "bbox": [ + 46, + 522, + 287, + 559 + ], + "type": "text", + "content": " and the network hidden state " + }, + { + "bbox": [ + 46, + 522, + 287, + 559 + ], + "type": "inline_equation", + "content": "\\pmb{h}_{t - 1}" + }, + { + "bbox": [ + 46, + 522, + 287, + 559 + ], + "type": "text", + "content": " at time " + }, + { + "bbox": [ + 46, + 522, + 287, + 559 + ], + "type": "inline_equation", + "content": "t - 1" + }, + { + "bbox": [ + 46, + 522, + 287, + 559 + ], + "type": "text", + "content": " are input to the decoder, and the parameter feature maps for " + }, + { + "bbox": [ + 46, + 522, + 287, + 559 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 522, + 287, + 559 + ], + "type": "text", + "content": " channels are generated:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 60, + 574, + 285, + 601 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 574, + 285, + 601 + ], + "spans": [ + { + "bbox": [ + 60, + 574, + 285, + 601 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\boldsymbol {\\Phi} _ {t} = f _ {\\text {d e c o d e r}} \\left(\\boldsymbol {c} _ {t}, \\boldsymbol {h} _ {t - 1}\\right) \\\\ = f _ {\\text {d e c o d e r}} \\left(f _ {\\text {e n c o d e r}} \\left(\\boldsymbol {v}, \\mathbf {P} _ {1} \\cup \\dots \\mathbf {P} _ {t - 1}\\right), \\boldsymbol {h} _ {t - 1}\\right), \\tag {6} \\\\ \\end{array}", + "image_path": "81b9e1d4d6297cc68f3218e878ec9ec954259979e6e4dcbc8ceee63b162b5661.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "spans": [ + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "inline_equation", + "content": "\\Phi_t = \\{\\pmb{p}_t^{(1)},\\pmb{p}_t^{(2)},\\dots,\\pmb{p}_t^{(N)}\\}" + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "text", + "content": " is the set of feature maps of all parameters at step " + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "text", + "content": ". And " + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "inline_equation", + "content": "\\pmb{p}_t^{(i)}" + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "text", + "content": " is the feature map of parameter " + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "inline_equation", + "content": "p_i" + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "text", + "content": " at time " + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "text", + "content": ". And " + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "inline_equation", + "content": "\\Phi_t" + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "text", + "content": " go through the GSG module and the global average pooling (GAP) layer to obtain the predicted parameter grouping " + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_t" + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "text", + "content": " at step " + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 608, + 287, + 672 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 60, + 688, + 285, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 688, + 285, + 715 + ], + "spans": [ + { + "bbox": [ + 60, + 688, + 285, + 715 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathbf {P} _ {t} = f _ {g a p} \\left(f _ {g s g} \\left(\\boldsymbol {\\Phi} _ {t}\\right)\\right) \\\\ = f _ {g a p} \\left(f _ {g s g} \\left(f _ {s c n n} \\left(\\boldsymbol {v}, \\boldsymbol {h} _ {t - 1}, \\mathbf {P} _ {1} \\cup \\dots \\mathbf {P} _ {t - 1}\\right)\\right)\\right), \\\\ \\end{array}", + "image_path": "c5ae3b99c86f7800289935862ce0c07cd18422213a932cebb6ebc058946f50df.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 403, + 545, + 487 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 403, + 545, + 487 + ], + "spans": [ + { + "bbox": [ + 304, + 403, + 545, + 487 + ], + "type": "text", + "content": "For convenience, " + }, + { + "bbox": [ + 304, + 403, + 545, + 487 + ], + "type": "inline_equation", + "content": "f_{scnn}" + }, + { + "bbox": [ + 304, + 403, + 545, + 487 + ], + "type": "text", + "content": " is used to express the Sequential CNN model that contains the encoder and decoder mentioned above. Eq. 7 shows the function of the sequential model at one step. The model is run " + }, + { + "bbox": [ + 304, + 403, + 545, + 487 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 403, + 545, + 487 + ], + "type": "text", + "content": " times recurrently by passing the hidden layer " + }, + { + "bbox": [ + 304, + 403, + 545, + 487 + ], + "type": "inline_equation", + "content": "h_i" + }, + { + "bbox": [ + 304, + 403, + 545, + 487 + ], + "type": "text", + "content": " and the predicted parameter groupings " + }, + { + "bbox": [ + 304, + 403, + 545, + 487 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_i" + }, + { + "bbox": [ + 304, + 403, + 545, + 487 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 403, + 545, + 487 + ], + "type": "inline_equation", + "content": "i = 1, \\dots, T" + }, + { + "bbox": [ + 304, + 403, + 545, + 487 + ], + "type": "text", + "content": ". All the predicted groupings are concatenated to obtain the predicted parameters." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 498, + 459, + 512 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 498, + 459, + 512 + ], + "spans": [ + { + "bbox": [ + 306, + 498, + 459, + 512 + ], + "type": "text", + "content": "4.3. Global Similarity Grouping" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 518, + 545, + 614 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 518, + 545, + 614 + ], + "spans": [ + { + "bbox": [ + 304, + 518, + 545, + 614 + ], + "type": "text", + "content": "GSG runs once at the beginning of an iteration, and partitions hyperparameters into " + }, + { + "bbox": [ + 304, + 518, + 545, + 614 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 518, + 545, + 614 + ], + "type": "text", + "content": " disjoint subsets. Multiple steps of an iteration run mask operation (before GAP) according to the results of GSG. Parameters groups number " + }, + { + "bbox": [ + 304, + 518, + 545, + 614 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 518, + 545, + 614 + ], + "type": "text", + "content": " is less than or equal to the number of ISP modules " + }, + { + "bbox": [ + 304, + 518, + 545, + 614 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 518, + 545, + 614 + ], + "type": "text", + "content": ". Some modules in ISP have similar roles. Grouping these similar parameters can reduce the model steps and make it more efficient for a large number of parameters and modules." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 616, + 545, + 688 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 616, + 545, + 688 + ], + "spans": [ + { + "bbox": [ + 304, + 616, + 545, + 688 + ], + "type": "text", + "content": "Hyperparameters " + }, + { + "bbox": [ + 304, + 616, + 545, + 688 + ], + "type": "inline_equation", + "content": "\\mathcal{P}" + }, + { + "bbox": [ + 304, + 616, + 545, + 688 + ], + "type": "text", + "content": " have an original division " + }, + { + "bbox": [ + 304, + 616, + 545, + 688 + ], + "type": "inline_equation", + "content": "\\mathcal{P} = \\mathbf{P}_1' \\cup \\mathbf{P}_2' \\cup \\ldots \\cup \\mathbf{P}_M'" + }, + { + "bbox": [ + 304, + 616, + 545, + 688 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 616, + 545, + 688 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_i'" + }, + { + "bbox": [ + 304, + 616, + 545, + 688 + ], + "type": "text", + "content": " is the set of parameters of module " + }, + { + "bbox": [ + 304, + 616, + 545, + 688 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 616, + 545, + 688 + ], + "type": "text", + "content": ". Details are described in Sec. 3 and supplement material. Considering the flexibility, we take the primal division as prior knowledge and use the correlation between parameters to explore the sequential divisions of parameters." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "type": "text", + "content": "The Sequential CNN model generates feature maps of all parameters, and the feature maps are passed through the" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22317" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "code", + "bbox": [ + 60, + 89, + 271, + 125 + ], + "blocks": [ + { + "bbox": [ + 53, + 76, + 254, + 87 + ], + "lines": [ + { + "bbox": [ + 53, + 76, + 254, + 87 + ], + "spans": [ + { + "bbox": [ + 53, + 76, + 254, + 87 + ], + "type": "text", + "content": "Algorithm 1: Sequential initial clustering centers" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "code_caption" + }, + { + "bbox": [ + 60, + 89, + 271, + 125 + ], + "lines": [ + { + "bbox": [ + 60, + 89, + 271, + 125 + ], + "spans": [ + { + "bbox": [ + 60, + 89, + 271, + 125 + ], + "type": "text", + "content": "Input: Model partition graphs: " + }, + { + "bbox": [ + 60, + 89, + 271, + 125 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_1^{\\prime},\\ldots ,\\mathbf{P}_M^{\\prime}" + }, + { + "bbox": [ + 60, + 89, + 271, + 125 + ], + "type": "text", + "content": " Weighted adjacency matrix: W; Steps in the prediction model: " + }, + { + "bbox": [ + 60, + 89, + 271, + 125 + ], + "type": "inline_equation", + "content": "T\\leq M" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "code_body" + } + ], + "index": 1, + "sub_type": "algorithm" + }, + { + "bbox": [ + 60, + 126, + 264, + 138 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 126, + 264, + 138 + ], + "spans": [ + { + "bbox": [ + 60, + 126, + 264, + 138 + ], + "type": "text", + "content": "Output: Sequential clustering centers: " + }, + { + "bbox": [ + 60, + 126, + 264, + 138 + ], + "type": "inline_equation", + "content": "c_{1},\\ldots ,c_{T}" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 60, + 140, + 194, + 156 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 140, + 194, + 156 + ], + "spans": [ + { + "bbox": [ + 60, + 140, + 194, + 156 + ], + "type": "interline_equation", + "content": "v o l (\\mathbf {P} _ {x} ^ {\\prime}) = \\sum_ {p ^ {(i)} \\in \\mathbf {P} _ {x} ^ {\\prime}} \\sum_ {j = 1} ^ {n} w _ {i j};", + "image_path": "c359704b739c9a30b7aa52fd6f7dc0ab7b142b41eccbecc6e0d81735dbeed572.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 61, + 159, + 134, + 172 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 159, + 134, + 172 + ], + "spans": [ + { + "bbox": [ + 61, + 159, + 134, + 172 + ], + "type": "interline_equation", + "content": "N c u t \\left(\\mathbf {P} _ {x} ^ {\\prime}, \\mathbf {P} _ {y} ^ {\\prime}\\right) =", + "image_path": "1d02c8781f2d4f7a9bcd73e962d59fc8f15bd5e5ef81143f09b29937f6d953bd.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 66, + 172, + 238, + 189 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 172, + 238, + 189 + ], + "spans": [ + { + "bbox": [ + 66, + 172, + 238, + 189 + ], + "type": "interline_equation", + "content": "\\left(\\frac {1}{v o l (\\mathbf {P} _ {x} ^ {\\prime})} + \\frac {1}{v o l (\\mathbf {P} _ {y} ^ {\\prime})}\\right) \\sum_ {p ^ {(i)} \\in \\mathbf {P} _ {x}, p ^ {(j)} \\in \\overline {{\\mathbf {P} _ {y}}}} w _ {i j};", + "image_path": "0b2f672600655efc9b9480aa2724c6761d8644085d6be2c780845ec550336c34.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 60, + 193, + 153, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 193, + 153, + 205 + ], + "spans": [ + { + "bbox": [ + 60, + 193, + 153, + 205 + ], + "type": "text", + "content": "for " + }, + { + "bbox": [ + 60, + 193, + 153, + 205 + ], + "type": "inline_equation", + "content": "I\\in [1,M - T]" + }, + { + "bbox": [ + 60, + 193, + 153, + 205 + ], + "type": "text", + "content": " do" + } + ] + } + ], + "index": 6 + }, + { + "type": "code", + "bbox": [ + 61, + 206, + 271, + 270 + ], + "blocks": [ + { + "bbox": [ + 61, + 206, + 271, + 270 + ], + "lines": [ + { + "bbox": [ + 61, + 206, + 271, + 270 + ], + "spans": [ + { + "bbox": [ + 61, + 206, + 271, + 270 + ], + "type": "inline_equation", + "content": "i\\gets i" + }, + { + "bbox": [ + 61, + 206, + 271, + 270 + ], + "type": "text", + "content": " s.t.max{Ncut " + }, + { + "bbox": [ + 61, + 206, + 271, + 270 + ], + "type": "inline_equation", + "content": "(\\mathbf{P}_i^{\\prime},\\mathbf{P}_{i + 1}^{\\prime})|i\\in [1,M - I]\\}" + }, + { + "bbox": [ + 61, + 206, + 271, + 270 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_i^\\prime \\leftarrow \\mathbf{P}_i^\\prime \\cup \\mathbf{P}_{i + 1}^\\prime" + }, + { + "bbox": [ + 61, + 206, + 271, + 270 + ], + "type": "text", + "content": " if " + }, + { + "bbox": [ + 61, + 206, + 271, + 270 + ], + "type": "inline_equation", + "content": "i\\neq M - I" + }, + { + "bbox": [ + 61, + 206, + 271, + 270 + ], + "type": "text", + "content": " then " + }, + { + "bbox": [ + 61, + 206, + 271, + 270 + ], + "type": "inline_equation", + "content": "\\begin{array}{r}\\big{\\lfloor}\\mathbf{P}_{i + 1}^{\\prime},\\ldots ,\\mathbf{P}_{M - I}^{\\prime}\\leftarrow \\mathbf{P}_{i + 2}^{\\prime},\\ldots ,\\mathbf{P}_{M - I + 1}^{\\prime} \\end{array}" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "code_body" + } + ], + "index": 7, + "sub_type": "algorithm" + }, + { + "bbox": [ + 61, + 271, + 178, + 283 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 271, + 178, + 283 + ], + "spans": [ + { + "bbox": [ + 61, + 271, + 178, + 283 + ], + "type": "interline_equation", + "content": "\\mathbf {C} _ {1}, \\dots , \\mathbf {C} _ {T} \\leftarrow \\mathbf {P} _ {1}, \\dots , \\mathbf {P} _ {T};", + "image_path": "0b83f800c3c2beb455b5dea0838cc23859d66cdd496af612fbaf08098e603552.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 61, + 287, + 131, + 299 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 287, + 131, + 299 + ], + "spans": [ + { + "bbox": [ + 61, + 287, + 131, + 299 + ], + "type": "text", + "content": "for " + }, + { + "bbox": [ + 61, + 287, + 131, + 299 + ], + "type": "inline_equation", + "content": "J\\in [1,T]" + }, + { + "bbox": [ + 61, + 287, + 131, + 299 + ], + "type": "text", + "content": " do" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 61, + 299, + 271, + 314 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 299, + 271, + 314 + ], + "spans": [ + { + "bbox": [ + 61, + 299, + 271, + 314 + ], + "type": "interline_equation", + "content": "\\left\\lfloor c _ {J} \\leftarrow p ^ {(x)} s. t. \\max \\left\\{\\sum_ {p (y) \\in \\mathbf {C} _ {J}} w _ {x y} \\right| p ^ {(x)} \\in \\mathbf {C} _ {J} \\right\\}", + "image_path": "fd1d34aac08dccfad399f46e36c89272d756e7f0042dca134412f8e16fa725be.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 341, + 287, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 341, + 287, + 437 + ], + "spans": [ + { + "bbox": [ + 46, + 341, + 287, + 437 + ], + "type": "text", + "content": "GAP module to obtain the predicted parameters. The pixel values on the feature map represent the predicted parameter values in the corresponding region of the image " + }, + { + "bbox": [ + 46, + 341, + 287, + 437 + ], + "type": "inline_equation", + "content": "\\mathbf{I}" + }, + { + "bbox": [ + 46, + 341, + 287, + 437 + ], + "type": "text", + "content": ". The feature map is a higher-dimension representation of the effect of the parameter on the image [22]. Therefore, we use the similarity between feature maps to perform the division of similar parameters. Here we directly use the cosine similarity to measure the feature map similarity as follows:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 104, + 445, + 287, + 474 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 445, + 287, + 474 + ], + "spans": [ + { + "bbox": [ + 104, + 445, + 287, + 474 + ], + "type": "interline_equation", + "content": "L _ {c o s} \\left(p ^ {(i)}, p ^ {(j)}\\right) = \\frac {\\boldsymbol {p} ^ {(i)} * \\boldsymbol {p} ^ {(j)}}{\\left| \\boldsymbol {p} ^ {(i)} \\right| \\left| \\boldsymbol {p} ^ {(j)} \\right|}, \\tag {8}", + "image_path": "8ef476274566ed4202f12b05beea4f16e7313e81ee74acf827ac92578ea42b8f.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "spans": [ + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "inline_equation", + "content": "\\pmb{p}^{(i)}" + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "inline_equation", + "content": "\\pmb{p}^{(j)}" + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "text", + "content": " are the feature maps of " + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "inline_equation", + "content": "p^{(i)}" + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "inline_equation", + "content": "p^{(j)}" + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "text", + "content": ", respectively, and " + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "inline_equation", + "content": "L_{cos}(p^{(i)}, p^{(j)})" + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "text", + "content": " is the similarity of " + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "inline_equation", + "content": "p^{(i)}" + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "inline_equation", + "content": "p^{(j)}" + }, + { + "bbox": [ + 47, + 476, + 287, + 538 + ], + "type": "text", + "content": ". After the decoder outputs the parameter feature maps, We can obtain the similarity values between all parameters. All parameters can be expressed in the form of a graph:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 82, + 548, + 287, + 563 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 548, + 287, + 563 + ], + "spans": [ + { + "bbox": [ + 82, + 548, + 287, + 563 + ], + "type": "interline_equation", + "content": "G = (\\mathbf {V}, \\mathbf {E}), \\mathbf {V} = \\{p ^ {(1)}, p ^ {(2)}, \\dots , p ^ {(N)} \\}. \\tag {9}", + "image_path": "3817d28349eaacd4a54ddc0ca7bc2230e430777821160ea0a53f34c9cb0d3742.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "spans": [ + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "text", + "content": "Graph " + }, + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "text", + "content": " is weighted, and " + }, + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "inline_equation", + "content": "\\mathbf{E}" + }, + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "text", + "content": " is the set of edges between the vertices. The weight of each edge between two vertices " + }, + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "inline_equation", + "content": "p^{(i)}" + }, + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "inline_equation", + "content": "p^{(j)}" + }, + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "text", + "content": " is " + }, + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "inline_equation", + "content": "w_{ij} = L_{\\cos}(p^{(i)}, p^{(j)}) \\geq 0" + }, + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "text", + "content": ". The weighted adjacency matrix is " + }, + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "inline_equation", + "content": "\\mathbf{W} = (w_{ij})_{i,j=1,\\dots,n}" + }, + { + "bbox": [ + 46, + 566, + 287, + 614 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 47, + 615, + 287, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 615, + 287, + 639 + ], + "spans": [ + { + "bbox": [ + 47, + 615, + 287, + 639 + ], + "type": "text", + "content": "The problem of partitioning the set " + }, + { + "bbox": [ + 47, + 615, + 287, + 639 + ], + "type": "inline_equation", + "content": "\\mathcal{P}" + }, + { + "bbox": [ + 47, + 615, + 287, + 639 + ], + "type": "text", + "content": " of parameters into " + }, + { + "bbox": [ + 47, + 615, + 287, + 639 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 47, + 615, + 287, + 639 + ], + "type": "text", + "content": " disjoint subsets is transformed into partitioning graph " + }, + { + "bbox": [ + 47, + 615, + 287, + 639 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 47, + 615, + 287, + 639 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 94, + 655, + 287, + 673 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 655, + 287, + 673 + ], + "spans": [ + { + "bbox": [ + 94, + 655, + 287, + 673 + ], + "type": "interline_equation", + "content": "\\mathbf {V} = \\mathbf {P} _ {1} \\cup \\mathbf {P} _ {2} \\cup \\dots \\mathbf {P} _ {T}, \\tag {10}", + "image_path": "2828569cae28486ae908b018182019b3a2577e8a6b24458e22f446ddd8308c9c.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 115, + 670, + 238, + 683 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 115, + 670, + 238, + 683 + ], + "spans": [ + { + "bbox": [ + 115, + 670, + 238, + 683 + ], + "type": "interline_equation", + "content": "\\mathbf {P} _ {i} \\cap \\mathbf {P} _ {j} = \\varnothing , 1 \\leq i < j \\leq T.", + "image_path": "6d60e03f68ce5513704095088a3ec078210e6e6cbeba719a5946cff7149aeb13.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": "Graph " + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": " should be divided so that the parameters are dissimilar between groups and similar in the same group. That" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 305, + 72, + 545, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 109 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 109 + ], + "type": "text", + "content": "means the edges between different groups have low weights and the edges within a group have high weights. The above optimization objective can be expressed as follows:" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 313, + 125, + 545, + 159 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 125, + 545, + 159 + ], + "spans": [ + { + "bbox": [ + 313, + 125, + 545, + 159 + ], + "type": "interline_equation", + "content": "\\min \\sum_ {t = 1} ^ {T} \\frac {W \\left(\\mathbf {P} _ {t} , \\overline {{\\mathbf {P} _ {t}}}\\right)}{v o l \\left(\\mathbf {P} _ {t}\\right)} = \\sum_ {t = 1} ^ {T} \\frac {\\sum_ {p ^ {(i)} \\in \\mathbf {P} _ {t} , p ^ {(j)} \\in \\overline {{\\mathbf {P} _ {t}}}} w _ {i j}}{\\sum_ {p ^ {(i)} \\in \\mathbf {P} _ {t}} \\sum_ {j = 1} ^ {N} w _ {i j}}. \\tag {11}", + "image_path": "e62dadce2c85b38edcb3d045dee8471ef0bb0668cb6e1820042ea52ba9b9b695.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "spans": [ + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "text", + "content": "For the above optimization objectives and sequence partitioning of the parameters, we use the prior structure information of ISP and perform spectral clustering of the parameters. Precisely, we first compute the normalized Laplacian matrix " + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "inline_equation", + "content": "\\mathbf{L}" + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "text", + "content": " of the adjacency matrix " + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "inline_equation", + "content": "\\mathbf{W}" + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "text", + "content": ". The first " + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "text", + "content": " eigenvectors of " + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "inline_equation", + "content": "\\mathbf{L}" + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "text", + "content": " are used as columns to form " + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "inline_equation", + "content": "\\mathbf{T} \\in \\mathbb{R}^{n \\times T}" + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "text", + "content": ". After " + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "inline_equation", + "content": "\\mathbf{T}" + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "text", + "content": " is normalized by rows, the vector " + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "inline_equation", + "content": "\\mathbf{y}^{(i)}" + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "text", + "content": " of the parameter " + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "inline_equation", + "content": "p^{(i)}" + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "text", + "content": " corresponds to the " + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "text", + "content": "-th row of " + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "inline_equation", + "content": "\\mathbf{T}" + }, + { + "bbox": [ + 304, + 163, + 545, + 258 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "spans": [ + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": "To make cluster " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_1, \\ldots, \\mathbf{P}_T" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": " with sequence information, we introduce serialized initial clustering centers " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "c_1, \\ldots, c_T" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": ". The sequential partition graphs " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_1', \\ldots, \\mathbf{P}_M'" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": " can be obtained based on the structure information of ISP. The graphs that are adjacent and related in order are merged to obtain " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": " partitioned graphs " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_1, \\ldots, \\mathbf{C}_T" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "c_i" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": " is the vertex in " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "\\mathbf{C}_i" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": " that is most closely connected to other vertices. The specific process is described in Algorithm 1. With " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "c_1, \\ldots, c_T" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": " as the initial clustering center, the points " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "(\\boldsymbol{y}^{(i)})_{i=1,\\dots,N}" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": " are clustered using the " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": "-means algorithm to obtain the clusters " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_1, \\ldots, \\mathbf{P}_T" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_t" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": " is the cluster with " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "c_t" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": " as the initial cluster center and is predicted at step " + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 258, + 545, + 402 + ], + "type": "text", + "content": " in the sequential model." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 403, + 545, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 403, + 545, + 475 + ], + "spans": [ + { + "bbox": [ + 304, + 403, + 545, + 475 + ], + "type": "text", + "content": "The final obtained " + }, + { + "bbox": [ + 304, + 403, + 545, + 475 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_1, \\ldots, \\mathbf{P}_T" + }, + { + "bbox": [ + 304, + 403, + 545, + 475 + ], + "type": "text", + "content": " for the division of parameters " + }, + { + "bbox": [ + 304, + 403, + 545, + 475 + ], + "type": "inline_equation", + "content": "\\mathcal{P}" + }, + { + "bbox": [ + 304, + 403, + 545, + 475 + ], + "type": "text", + "content": " is sequential, and its order is determined by the original ISP structure information. The parameters are similar within each grouping based on the similarity of the feature maps corresponding to the parameters. This flexible division enables cross-module grouping of similar parameters." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 306, + 483, + 387, + 498 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 483, + 387, + 498 + ], + "spans": [ + { + "bbox": [ + 306, + 483, + 387, + 498 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 306, + 503, + 365, + 516 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 503, + 365, + 516 + ], + "spans": [ + { + "bbox": [ + 306, + 503, + 365, + 516 + ], + "type": "text", + "content": "5.1. Settings" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 304, + 522, + 545, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 522, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 304, + 522, + 545, + 544 + ], + "type": "text", + "content": "In this section, we introduce the ISPs, datasets, and downstream tasks used for evaluation." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 304, + 546, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 31, + "blocks": [ + { + "bbox": [ + 304, + 546, + 545, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 546, + 545, + 617 + ], + "spans": [ + { + "bbox": [ + 304, + 546, + 545, + 617 + ], + "type": "text", + "content": "(1) Object detection on MS COCO [15] using [23]. For comparison with [22] and [17], we use the same synthetic (simulated) ISP to process simulated RAW as the upstream module for the downstream task. The ISP contains 20 hyperparameters, and detailed information is described in the supplementary material." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 304, + 618, + 545, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 618, + 545, + 641 + ], + "spans": [ + { + "bbox": [ + 304, + 618, + 545, + 641 + ], + "type": "text", + "content": "(2) Image segmentation using [10] on COCO with the same experimental setup as the object detection task." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": "(3) ISP parameter prediction for image quality. A SONY IMX766 CMOS sensor is used to collect the RAW dataset. The RAW data were processed using Qualcomm Spectra 580 ISP oriented to image quality. The ISP contains 144 hyperparameters, and detailed information is described in the supplementary material." + } + ] + } + ], + "index": 30 + } + ], + "sub_type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22318" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 51, + 71, + 303, + 333 + ], + "blocks": [ + { + "bbox": [ + 51, + 71, + 303, + 333 + ], + "lines": [ + { + "bbox": [ + 51, + 71, + 303, + 333 + ], + "spans": [ + { + "bbox": [ + 51, + 71, + 303, + 333 + ], + "type": "image", + "image_path": "a37e6e9c48ebffac9b70c5a12dd95a18c6fc5a91b4529bfe740bddcefd12dba1.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 341, + 547, + 374 + ], + "lines": [ + { + "bbox": [ + 46, + 341, + 547, + 374 + ], + "spans": [ + { + "bbox": [ + 46, + 341, + 547, + 374 + ], + "type": "text", + "content": "Figure 3. Image analytic vision tasks: Object Detection (left) and Image Segmentation (right) on COCO dataset. Default ISP hyperparameters (top), expert-tuned hyperparameters (middle), and the proposed method (bottom). The proposed method achieve better performance for downstream tasks." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 310, + 72, + 541, + 331 + ], + "blocks": [ + { + "bbox": [ + 310, + 72, + 541, + 331 + ], + "lines": [ + { + "bbox": [ + 310, + 72, + 541, + 331 + ], + "spans": [ + { + "bbox": [ + 310, + 72, + 541, + 331 + ], + "type": "image", + "image_path": "4667458006a2d8bd1d0ab6a38f0786177a661b0509675162712b151eed1bcd9c.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 48, + 435, + 294, + 548 + ], + "blocks": [ + { + "bbox": [ + 47, + 393, + 288, + 427 + ], + "lines": [ + { + "bbox": [ + 47, + 393, + 288, + 427 + ], + "spans": [ + { + "bbox": [ + 47, + 393, + 288, + 427 + ], + "type": "text", + "content": "Table 1. Synthetic ISP optimization for Object Detection on COCO. We borrow the results from [17] by constructing a similar processing pipeline with the ISP used in other methods." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 435, + 294, + 548 + ], + "lines": [ + { + "bbox": [ + 48, + 435, + 294, + 548 + ], + "spans": [ + { + "bbox": [ + 48, + 435, + 294, + 548 + ], + "type": "table", + "html": "
ISP ModelmAP 0.5mAP 0.75mAP 0.5:0.95
Default Pblockwise-tuned [18]ISP[17]15.--
20.--
Hardware-tuned [17]39.--
Default PExpert-tunedISPSec.334.122.421.4
56.840.837.7
Attention-ware [22]61.044.141.0
Sequential-tuned62.845.242.3
", + "image_path": "b1cbca96265a73b7b5e4d428fb17331df6af60f959484687a33c37ccb8a80ecd.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 570, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 570, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 570, + 288, + 715 + ], + "type": "text", + "content": "During the training stage, the RAW images are input into the model, which sequentially predicts several groups of hyperparameters. As described in Sec. 4.1, the predicted parameters and ground-truth parameters are used to calculate the losses for training the model. We use a combination of iterative search and manual annotation to batch annotate the training set, as described in detail in the supplementary material. The pre-trained Resnet [11] for feature extraction and the rest of the model is trained using loss defined in Eq. 4 and the RMSprop optimizer. The learning rate was initially set to " + }, + { + "bbox": [ + 46, + 570, + 288, + 715 + ], + "type": "inline_equation", + "content": "10^{-4}" + }, + { + "bbox": [ + 46, + 570, + 288, + 715 + ], + "type": "text", + "content": " and reduced to " + }, + { + "bbox": [ + 46, + 570, + 288, + 715 + ], + "type": "inline_equation", + "content": "10^{-6}" + }, + { + "bbox": [ + 46, + 570, + 288, + 715 + ], + "type": "text", + "content": " after 200 calendar hours. The training process was performed for 400 epochs." + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 307, + 435, + 552, + 536 + ], + "blocks": [ + { + "bbox": [ + 305, + 393, + 547, + 427 + ], + "lines": [ + { + "bbox": [ + 305, + 393, + 547, + 427 + ], + "spans": [ + { + "bbox": [ + 305, + 393, + 547, + 427 + ], + "type": "text", + "content": "Table 2. Synthetic ISP optimization for Image Segmentation on COCO. We borrow the results from [17] by constructing a similar processing pipeline with the ISP used in other methods." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 307, + 435, + 552, + 536 + ], + "lines": [ + { + "bbox": [ + 307, + 435, + 552, + 536 + ], + "spans": [ + { + "bbox": [ + 307, + 435, + 552, + 536 + ], + "type": "table", + "html": "
ISP ModelmAP 0.5mAP 0.75mAP 0.5:0.95
Default PISP12.--
Hardware-tuned [17][17]32.--
Default P22.713.212.0
Expert-tunedISP46.928.227.1
Attention-ware [22]Sec.352.333.431.5
Sequential-tuned54.235.132.6
", + "image_path": "7031d5d07dda3c4efe46d96ffa548d10ecaad4c98ec69a648ad9a4c4a4533c55.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 556, + 545, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 556, + 545, + 665 + ], + "spans": [ + { + "bbox": [ + 304, + 556, + 545, + 665 + ], + "type": "text", + "content": "In the evaluation stage, we used the test set as model input to predict the hyperparameters for each RAW image. The RAW image and predicted hyperparameters were input to the ISP to obtain RGB output as input to the downstream tasks. The upstream RGB output is used as input for the object detection and instance segmentation tasks, and the evaluation metric uses the mean accuracy (mAP) [15]. 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Optimization for Object Detection" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 689, + 546, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 546, + 715 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 546, + 715 + ], + "type": "text", + "content": "In this task, we use an existing large sRGB dataset and a synthetic ISP to evaluate our ISP hyperparameter sequence" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22319" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 79, + 167, + 173 + ], + "blocks": [ + { + "bbox": [ + 111, + 70, + 225, + 78 + ], + "lines": [ + { + 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Default setting hyperparameters (top), proposed method (middle), and reference images (bottom). The proposed method achieves an image quality close to the reference image." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 111, + 356, + 166, + 385 + ], + "blocks": [ + { + "bbox": [ + 111, + 356, + 166, + 385 + ], + "lines": [ + { + "bbox": [ + 111, + 356, + 166, + 385 + ], + "spans": [ + { + "bbox": [ + 111, + 356, + 166, + 385 + ], + "type": "image", + "image_path": "31f14cdb719fba7c0fa6234e576384759ec61d8818c0b8f049d06e10120f7486.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 168, + 355, + 222, + 385 + ], + "blocks": [ + { + "bbox": [ + 168, + 355, + 222, + 385 + ], + "lines": [ + { + "bbox": [ + 168, + 355, + 222, + 385 + ], + "spans": [ + { + "bbox": [ + 168, + 355, + 222, + 385 + ], + "type": "image", + "image_path": "d6142ad5025dbf54e571e4abf3e9f568c0212b291cd52ea1e560b2c362e9fe0c.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 225, + 356, + 281, + 385 + ], + "blocks": [ + { + "bbox": [ + 225, + 356, + 281, + 385 + ], + "lines": [ + { + "bbox": [ + 225, + 356, + 281, + 385 + ], + "spans": [ + { + "bbox": [ + 225, + 356, + 281, + 385 + ], + "type": "image", + "image_path": "9447757bcef037e6172f4493e902ed3c1c4db18f48589480ae92c63d6410f81d.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "table", + "bbox": [ + 48, + 472, + 283, + 525 + ], + "blocks": [ + { + "bbox": [ + 47, + 441, + 286, + 464 + ], + "lines": [ + { + "bbox": [ + 47, + 441, + 286, + 464 + ], + "spans": [ + { + "bbox": [ + 47, + 441, + 286, + 464 + ], + "type": "text", + "content": "Table 3. Perceptual image quality using default parameters setting, recent method [22], and the proposed method." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 472, + 283, + 525 + ], + "lines": [ + { + "bbox": [ + 48, + 472, + 283, + 525 + ], + "spans": [ + { + "bbox": [ + 48, + 472, + 283, + 525 + ], + "type": "table", + "html": "
ISP ModelPSNRSSIM
Default ParametersHardware ISP30.730.720
Attention-ware [22]Sec. 5.140.080.965
Sequential-tuned45.290.982
", + "image_path": "10ad65a5fd137cd54ae5cf0f42fdd8bd5babb5bb03758cd0aa9dff1bf17db4a3.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "table_body" + } + ], + "index": 15 + }, + { + "bbox": [ + 46, + 545, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 545, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 545, + 287, + 713 + ], + "type": "text", + "content": "prediction method. The RGB images are converted to RAW as input to the ISP by simulating the processing of sRGB images with RAW data [13]. We use pre-trained [23] for object detection on the COCO dataset [15] as a downstream task. The proposed model is trained for the object detection task and evaluated using the mAP [15]. The quantitative evaluation results are shown in Table 1. Compared to the default parameter settings of the untuned ISP, our method shows a more significant improvement over current methods and better results than non-sequential prediction methods. The subjective results are shown in Fig. 3. The proposed method emphasizes the image's texture details and color features compared to the expert-tuned image. Although expert-tuned images are more consistent with hu" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "text", + "content": "man visual preferences, the predicted parameters allow for better results in these images for the object detection tasks." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 306, + 103, + 506, + 117 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 103, + 506, + 117 + ], + "spans": [ + { + "bbox": [ + 306, + 103, + 506, + 117 + ], + "type": "text", + "content": "5.3. Optimization for Image Segmentation" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 122, + 545, + 326 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 122, + 545, + 326 + ], + "spans": [ + { + "bbox": [ + 304, + 122, + 545, + 326 + ], + "type": "text", + "content": "Next, we use pre-trained [10] for image segmentation task on the COCO dataset as the downstream task. The proposed method is trained and tested on the simulated RAW COCO dataset and synthetic ISP. The results in Table 2 show that the proposed method has the best final results for the instance segmentation task compared to the recent methods. It also significantly improves over the default parameters (baseline). Fig. 3 shows the default parameters, the expert tuning parameters, and examples of the proposed method. Similar to the results obtained in the object detection task, the predicted parameters improve the performance of the downstream task (image segmentation) by adjusting the texture and color of the image. The predicted parameters of our model achieve an improvement of " + }, + { + "bbox": [ + 304, + 122, + 545, + 326 + ], + "type": "inline_equation", + "content": "0.3\\mathrm{mAP}" + }, + { + "bbox": [ + 304, + 122, + 545, + 326 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 122, + 545, + 326 + ], + "type": "inline_equation", + "content": "0.2\\mathrm{mAP}" + }, + { + "bbox": [ + 304, + 122, + 545, + 326 + ], + "type": "text", + "content": " compared to the default parameters and the expert-tuned parameters, respectively, and " + }, + { + "bbox": [ + 304, + 122, + 545, + 326 + ], + "type": "inline_equation", + "content": "0.1\\mathrm{mAP}" + }, + { + "bbox": [ + 304, + 122, + 545, + 326 + ], + "type": "text", + "content": " compared to the non-sequential prediction method." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 334, + 477, + 346 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 334, + 477, + 346 + ], + "spans": [ + { + "bbox": [ + 306, + 334, + 477, + 346 + ], + "type": "text", + "content": "5.4. Optimization for Image Quality" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 352, + 545, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 352, + 545, + 628 + ], + "spans": [ + { + "bbox": [ + 304, + 352, + 545, + 628 + ], + "type": "text", + "content": "The image quality optimization task aims to achieve a specific image rendering preference [16]. This specific rendering preference allows the output RGB to match the human visual preference by combining the visibility of various distortions in the image (noise, color shifts, blockiness) [26] and the imaging style. In this task, we aim to make the RAW image undergoes ISP to produce an RGB as close as possible to the reference image by the hyperparameters predicted by the proposed method. We collected a dataset for the image quality optimization task. The dataset contains images from 251 scenes acquired by the IMX766 sensor. The reference images are generated through the Spectra 580 ISP with the RAW images and the expert-tuned parameters as input. We used PSNR and SSIM metrics to evaluate the consistency between the images generated by the predicted parameters and the reference images. The quantitative results are shown in Table 3, and the proposed method consists of better PSNR and SSIM results than recent methods. Our method brings more improvements based on the default parameter settings. Fig. 4 shows some examples of image quality tasks. Our method produces images with better visual quality that conform to human preferences regarding texture details, noise control, and color representation." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 306, + 635, + 400, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 635, + 400, + 647 + ], + "spans": [ + { + "bbox": [ + 306, + 635, + 400, + 647 + ], + "type": "text", + "content": "5.5. Ablation Study" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "content": "Sequential Structure. Recent ISP hyperparameter tuning methods, both iterative optimization and direct prediction methods, treat the set of ISP hyperparameters as a black box space and estimate all the parameters at once. In contrast, the proposed method allows step-by-step sequential" + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22320" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 48, + 101, + 545, + 225 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 545, + 94 + ], + "type": "text", + "content": "Table 4. Ablation study of evaluating the proposed sequential structure and similarity grouping. The first column is the results for multiple step settings of the proposed method, and the second is the results of multiple changes in Global Similarity Grouping module." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 101, + 545, + 225 + ], + "lines": [ + { + "bbox": [ + 48, + 101, + 545, + 225 + ], + "spans": [ + { + "bbox": [ + 48, + 101, + 545, + 225 + ], + "type": "table", + "html": "
StepsSequencePrior KnowledgeSimilarity GroupPSNRSSIM
Sequence prediction (Multi step-settings)one---39.520.957
two✓✓✓40.050.962
three✓✓✓42.660.971
four✓✓✓45.290.982
five✓✓✓45.230.982
Blockwise sequence & similarity groupfour✓✓✓45.290.982
Blockwise sequence & blockwise group✓✓-43.570.969
Random sequence & similarity group✓-✓42.620.960
Random sequence & random group✓--39.300.947
", + "image_path": "4e96ade2acd0624970da7cdceb2339c3921364841534c5d93542cd99c32961eb.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 247, + 286, + 293 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 247, + 286, + 293 + ], + "spans": [ + { + "bbox": [ + 46, + 247, + 286, + 293 + ], + "type": "text", + "content": "prediction of ISP parameters, achieving more refined predictions by modeling the sequential relationships among parameter groupings. We verify that this sequential tuning method has better results than the non-sequential method." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 293, + 286, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 293, + 286, + 533 + ], + "spans": [ + { + "bbox": [ + 46, + 293, + 286, + 533 + ], + "type": "text", + "content": "The effectiveness of sequence prediction is tested on the image quality dataset and a Spectra 580 ISP with 144 parameters. The results are shown in Table 4. We tested the non-sequential and sequential methods under multiple settings, and the test metrics are the PSNR and SSIM results of the output and the reference image. The number of model running steps equals the number of parameter groupings. For the same number of hyperparameters, the fewer steps of the model, the more parameters are predicted in each step. When the step is set to one, the model predicts all hyperparameters simultaneously, equivalent to the non-sequential method. The sequential prediction results have better results than the non-sequential ones. We also validate the results of several experiments with different step counts. The method loop steps size larger to generate long-distance dependencies, which will be more challenging to learn and converge. Endlessly increasing the size of steps with the same dataset may suffer performance degradation. Experimental results show that a compromise step count setting achieves the best results for a particular experimental setting." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 533, + 286, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 533, + 286, + 640 + ], + "spans": [ + { + "bbox": [ + 46, + 533, + 286, + 640 + ], + "type": "text", + "content": "Global Similarity Grouping. In sequential prediction, each step predicts a sub-grouping of the hyperparameters. Based on the block-wise structure of the ISP and the correlation between parameters, the Global Similarity Grouping module defines the order between different groups and how the parameters within the groups are divided. We validated the effectiveness of this adaptive grouping approach. The experimental setup is consistent with the ablation experiments above, and the results are shown in Table 4." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 642, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 286, + 713 + ], + "type": "text", + "content": "To verify the effectiveness of the prior structural information of ISP, we changed the Global Similarity Grouping in Sec. 4.3 by removing the block-wise sequence clustering centers and directly performing spectral clustering for all parameters. The model uses random order for prediction for multiple parameter groupings clustered out. The results" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 246, + 545, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 246, + 545, + 437 + ], + "spans": [ + { + "bbox": [ + 304, + 246, + 545, + 437 + ], + "type": "text", + "content": "in Table 4 show that the approach introducing the ISP prior structure information has a better result. To verify the effectiveness of cross-module grouping achieved by parameter similarity, we define the parameter grouping pattern directly with the block-wise structural information of ISP. Parameters of the same module are grouped into the same group, while the order between groups is consistent with the ISP module sequence. The results in Table 4 show the effectiveness of the similarity grouping with the introduction of parameter correlation. Similarity group compared to blockwise group is obtained from GSG by parameter similarity while predicting similar parameters at once will improve the accuracy. Finally, we remove both prior knowledge and similarity grouping by randomly defining the hyperparameter division and the order among groups. As shown in Table 4, such a setup has the worst results." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 452, + 383, + 464 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 452, + 383, + 464 + ], + "spans": [ + { + "bbox": [ + 306, + 452, + 383, + 464 + ], + "type": "text", + "content": "6. Conclusions" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 473, + 545, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 473, + 545, + 556 + ], + "spans": [ + { + "bbox": [ + 304, + 473, + 545, + 556 + ], + "type": "text", + "content": "This work proposes a novel sequential method for ISP tuning that uses module relation and parameter similarity to guide the process. It decouples the ISP structure and treats tuning as a sequential prediction problem. It groups parameters to tune ISP sequentially. Experiments show that introducing sequence information and similarity relations in the high-dimensional parameter space improves performance." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 558, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 545, + 713 + ], + "type": "text", + "content": "Acknowledgments This work was supported by the National Key Research and Development Program of China (Grant No. 2020AAA0106800), the National Natural Science Foundation of China (No. 62192785, No. 61972071, No. U1936204, No. 62122086, No. 62036011, No. U203-3210, No. 62172413, No. 62192782 and No. 61721004), the Beijing Natural Science Foundation No. M22005, and L223003, the Major Projects of Guangdong Education Department for Foundation Research and Applied Research (Grant: 2017KZDXM081, 2018KZDXM066), Guangdong Provincial University Innovation Team Project (Project No. 2020KCXTD045). The work of Bing Li was also supported by the Youth Innovation Promotion Association, CAS." + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22321" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 715 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 123 + ], + "type": "text", + "content": "[1] IEEE standard for camera phone image quality. IEEE Std 1858-2016 (Incorporating IEEE Std 1858-2016/Cor 1-2017), pages 1-146, 2017. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 125, + 288, + 167 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 125, + 288, + 167 + ], + "spans": [ + { + "bbox": [ + 53, + 125, + 288, + 167 + ], + "type": "text", + "content": "[2] Rémi Bardenet, Mátyás Brendel, Balázs Kégl, and Michele Sebag. Collaborative hyperparameter tuning. In International conference on machine learning, pages 199–207, 2013. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 169, + 287, + 212 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 169, + 287, + 212 + ], + "spans": [ + { + "bbox": [ + 53, + 169, + 287, + 212 + ], + "type": "text", + "content": "[3] James Bergstra, Rémi Bardenet, Yoshua Bengio, and Balázs Kégl. Algorithms for hyper-parameter optimization. Advances in neural information processing systems, 24, 2011. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 214, + 287, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 214, + 287, + 267 + ], + "spans": [ + { + "bbox": [ + 53, + 214, + 287, + 267 + ], + "type": "text", + "content": "[4] James Bergstra, Daniel Yamins, and David Cox. Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures. In International conference on machine learning, pages 115-123, 2013. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 269, + 287, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 269, + 287, + 312 + ], + "spans": [ + { + "bbox": [ + 53, + 269, + 287, + 312 + ], + "type": "text", + "content": "[5] Michael S Brown and SJ Kim. Understanding the in-camera image processing pipeline for computer vision. In IEEE International Conference on Computer Vision, volume 3, 2019. 1, 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 314, + 287, + 357 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 314, + 287, + 357 + ], + "spans": [ + { + "bbox": [ + 53, + 314, + 287, + 357 + ], + "type": "text", + "content": "[6] Mark Buckler, Suren Jayasuriya, and Adrian Sampson. Reconfiguring the imaging pipeline for computer vision. In Proceedings of the IEEE International Conference on Computer Vision, pages 975-984, 2017. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 358, + 287, + 402 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 358, + 287, + 402 + ], + "spans": [ + { + "bbox": [ + 53, + 358, + 287, + 402 + ], + "type": "text", + "content": "[7] Yue Cao, Xiaohe Wu, Shuran Qi, Xiao Liu, Zhongqin Wu, and Wangmeng Zuo. Pseudo-isp: Learning pseudo in-camera signal processing pipeline from a color image denoiser. arXiv preprint arXiv:2103.10234, 2021. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 403, + 287, + 446 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 403, + 287, + 446 + ], + "spans": [ + { + "bbox": [ + 53, + 403, + 287, + 446 + ], + "type": "text", + "content": "[8] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3291-3300, 2018. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 447, + 287, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 447, + 287, + 491 + ], + "spans": [ + { + "bbox": [ + 53, + 447, + 287, + 491 + ], + "type": "text", + "content": "[9] Nikolaus Hansen, Sibylle D Müller, and Petros Koumoutsakos. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cmaes). Evolutionary computation, 11(1):1-18, 2003. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 491, + 287, + 534 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 491, + 287, + 534 + ], + "spans": [ + { + "bbox": [ + 48, + 491, + 287, + 534 + ], + "type": "text", + "content": "[10] Kaiming He, Georgia Gkioxari, Piotr Dólar, and Ross Girshick. Mask r-cnn. In Proceedings of the IEEE international conference on computer vision, pages 2961-2969, 2017. 5, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 536, + 287, + 580 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 536, + 287, + 580 + ], + "spans": [ + { + "bbox": [ + 48, + 536, + 287, + 580 + ], + "type": "text", + "content": "[11] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 3, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 581, + 287, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 581, + 287, + 634 + ], + "spans": [ + { + "bbox": [ + 48, + 581, + 287, + 634 + ], + "type": "text", + "content": "[12] Andrey Ignatov, Luc Van Gool, and Radu Timofte. Replacing mobile camera isp with a single deep learning model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 536-537, 2020. 1, 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 636, + 287, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 636, + 287, + 690 + ], + "spans": [ + { + "bbox": [ + 48, + 636, + 287, + 690 + ], + "type": "text", + "content": "[13] Seon Joo Kim, Hai Ting Lin, Zheng Lu, Sabine Süssstrunk, Stephen Lin, and Michael S Brown. A new in-camera imaging model for color computer vision and its application. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(12):2289-2302, 2012. 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 691, + 287, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 287, + 715 + ], + "type": "text", + "content": "[14] Younghoon Kim, Jungmin Lee, Sung-Su Kim, Cheoljong Yang, TaeHyung Kim, and JoonSeo Yim. Dnn-based isp" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 715 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "parameter inference algorithm for automatic image quality optimization. Electronic Imaging, 2020(9):315-1, 2020. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 97, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 97, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 308, + 97, + 545, + 150 + ], + "type": "text", + "content": "[15] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755, 2014. 5, 6, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 152, + 545, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 152, + 545, + 196 + ], + "spans": [ + { + "bbox": [ + 308, + 152, + 545, + 196 + ], + "type": "text", + "content": "[16] Rafal K Mantiuk, Anna Tomaszewska, and Radosław Mantiuk. Comparison of four subjective methods for image quality assessment. In Computer graphics forum, volume 31, pages 2478-2491. Wiley Online Library, 2012. 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 198, + 545, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 198, + 545, + 262 + ], + "spans": [ + { + "bbox": [ + 308, + 198, + 545, + 262 + ], + "type": "text", + "content": "[17] Ali Mosleh, Avinash Sharma, Emmanuel Onzon, Fahim Mannan, Nicolas Robidoux, and Felix Heide. Hardware-in-the-loop end-to-end optimization of camera image processing pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7529-7538, 2020. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 264, + 545, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 264, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 308, + 264, + 545, + 319 + ], + "type": "text", + "content": "[18] Jun Nishimura, Timo Gerasimow, Rao Sushma, Aleksandar Sutic, Chyuan-Tyng Wu, and Gilad Michael. Automatic isp image quality tuning using nonlinear optimization. In 2018 25th IEEE International Conference on Image Processing, pages 2471-2475. IEEE, 2018. 1, 2, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 320, + 545, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 320, + 545, + 365 + ], + "spans": [ + { + "bbox": [ + 308, + 320, + 545, + 365 + ], + "type": "text", + "content": "[19] Emmanuel Onzon, Fahim Mannan, and Felix Heide. Neural auto-exposure for high-dynamic range object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7710-7720, 2021. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 365, + 545, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 365, + 545, + 398 + ], + "spans": [ + { + "bbox": [ + 308, + 365, + 545, + 398 + ], + "type": "text", + "content": "[20] Luke Pfister and Yoram Bresler. Learning filter bank sparsifying transforms. IEEE Transactions on Signal Processing, 67(2):504-519, 2018. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 399, + 545, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 399, + 545, + 443 + ], + "spans": [ + { + "bbox": [ + 308, + 399, + 545, + 443 + ], + "type": "text", + "content": "[21] Buu Phan, Fahim Mannan, and Felix Heide. Adversarial imaging pipelines. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16051-16061, 2021. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 444, + 545, + 499 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 444, + 545, + 499 + ], + "spans": [ + { + "bbox": [ + 308, + 444, + 545, + 499 + ], + "type": "text", + "content": "[22] Haina Qin, Longfei Han, Juan Wang, Congxuan Zhang, Yanwei Li, Bing Li, and Weiming Hu. Attention-aware learning for hyperparameters prediction in image processing pipelines. In European Conference on Computer Vision, pages 271-287. Springer, 2022. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 500, + 545, + 522 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 500, + 545, + 522 + ], + "spans": [ + { + "bbox": [ + 308, + 500, + 545, + 522 + ], + "type": "text", + "content": "[23] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. 5, 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 523, + 545, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 523, + 545, + 578 + ], + "spans": [ + { + "bbox": [ + 308, + 523, + 545, + 578 + ], + "type": "text", + "content": "[24] Nicolas Robidoux, Luis E Garcia Capel, Dong-eun Seo, Avinash Sharma, Federico Ariza, and Felix Heide. End-to-end high dynamic range camera pipeline optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6297-6307, 2021. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 579, + 545, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 579, + 545, + 622 + ], + "spans": [ + { + "bbox": [ + 308, + 579, + 545, + 622 + ], + "type": "text", + "content": "[25] Eli Schwartz, Raja Giryes, and Alex M Bronstein. Deepisp: Toward learning an end-to-end image processing pipeline. IEEE Transactions on Image Processing, 28(2):912-923, 2018. 1" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 624, + 545, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 624, + 545, + 658 + ], + "spans": [ + { + "bbox": [ + 308, + 624, + 545, + 658 + ], + "type": "text", + "content": "[26] Kim-Han Thung and Paramesran Raveendran. A survey of image quality measures. In IEEE international conference for technical postgraduates, pages 1-4, 2009. 7" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 658, + 545, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 658, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 308, + 658, + 545, + 715 + ], + "type": "text", + "content": "[27] Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl ST Arnaud, Derek Nowrouzezahrai, Jean-François Lalonde, and Felix Heide. Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Transactions on Graphics, 38(4):27-1, 2019. 1, 2" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "22322" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 473 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "text", + "content": "[28] Peter van Beek, Chyuan-Tyng Roger Wu, Baishali Chaudhury, and Thomas R Gardos. Boosting computer vision performance by enhancing camera isp. Electronic Imaging, 2021(17):174-1, 2021. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 118, + 287, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 118, + 287, + 183 + ], + "spans": [ + { + "bbox": [ + 48, + 118, + 287, + 183 + ], + "type": "text", + "content": "[29] Chyuan-Tyng Wu, Leo F Isikdogan, Sushma Rao, Bhavin Nayak, Timo Gerasimow, Aleksandar Sutic, Liron Ainkedem, and Gilad Michael. Visionisp: Repurposing the image signal processor for computer vision applications. In IEEE International Conference on Image Processing, pages 4624-4628. IEEE, 2019. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 185, + 287, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 185, + 287, + 228 + ], + "spans": [ + { + "bbox": [ + 48, + 185, + 287, + 228 + ], + "type": "text", + "content": "[30] Lucie Yahiaoui, Ciarán Hughes, Jonathan Horgan, Brian Deegan, Patrick Denny, and Senthil Yogamani. Optimization of isp parameters for object detection algorithms. Electronic Imaging, 2019(15):44-1, 2019. 1, 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 230, + 287, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 230, + 287, + 274 + ], + "spans": [ + { + "bbox": [ + 48, + 230, + 287, + 274 + ], + "type": "text", + "content": "[31] Cheoljong Yang, Jinhyun Kim, Jungmin Lee, Younghoon Kim, Sung-Su Kim, TaeHyung Kim, and JoonSeo Yim. Effective isp tuning framework based on user preference feedback. Electronic Imaging, 2020(9):316-1, 2020. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 275, + 287, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 275, + 287, + 317 + ], + "spans": [ + { + "bbox": [ + 48, + 275, + 287, + 317 + ], + "type": "text", + "content": "[32] Dani Yogatama and Gideon Mann. Efficient transfer learning method for automatic hyperparameter tuning. In Artificial intelligence and statistics, pages 1077-1085. PMLR, 2014. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 319, + 287, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 319, + 287, + 352 + ], + "spans": [ + { + "bbox": [ + 48, + 319, + 287, + 352 + ], + "type": "text", + "content": "[33] Ke Yu, Zexian Li, Yue Peng, Chen Change Loy, and Jinwei Gu. Reconfigisp: Reconfigurable camera image processing pipeline. arXiv preprint arXiv:2109.04760, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 354, + 287, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 354, + 287, + 418 + ], + "spans": [ + { + "bbox": [ + 48, + 354, + 287, + 418 + ], + "type": "text", + "content": "[34] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Cycleisp: Real image restoration via improved data synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2696-2705, 2020. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 420, + 287, + 473 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 420, + 287, + 473 + ], + "spans": [ + { + "bbox": [ + 48, + 420, + 287, + 473 + ], + "type": "text", + "content": "[35] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8697-8710, 2018. 2" + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22323" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning To Fuse Monocular and Multi-View Cues for Multi-Frame Depth Estimation in Dynamic Scenes/62c5b343-b419-485f-9576-4b167b86494f_content_list.json b/2023/Learning To Fuse Monocular and Multi-View Cues for Multi-Frame Depth Estimation in Dynamic Scenes/62c5b343-b419-485f-9576-4b167b86494f_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..44dc25de60122a7216b3f9ae9289bb60be30148d --- /dev/null +++ b/2023/Learning To Fuse Monocular and Multi-View Cues for Multi-Frame Depth Estimation in Dynamic Scenes/62c5b343-b419-485f-9576-4b167b86494f_content_list.json @@ -0,0 +1,1537 @@ +[ + { + "type": "text", + "text": "Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes", + "text_level": 1, + "bbox": [ + 214, + 130, + 756, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Rui Li $^{1}$ , Dong Gong $^{2}$ , Wei Yin $^{3}$ , Hao Chen $^{4}$ , Yu Zhu $^{1}$ , Kaixuan Wang $^{3}$ , Xiaozhi Chen $^{3}$ , Jinqiu Sun $^{1}$ , Yanning Zhang $^{1}$", + "bbox": [ + 220, + 202, + 785, + 238 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1Northwestern Polytechnical University, 2The University of New South Wales, 3DJI, 4Zhejiang University", + "bbox": [ + 88, + 239, + 919, + 257 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "https://github.com/ruili3/dynamic-multiframe-depth", + "bbox": [ + 277, + 258, + 723, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 308, + 313, + 325 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Multi-frame depth estimation generally achieves high accuracy relying on the multi-view geometric consistency. When applied in dynamic scenes, e.g., autonomous driving, this consistency is usually violated in the dynamic areas, leading to corrupted estimations. Many multi-frame methods handle dynamic areas by identifying them with explicit masks and compensating the multi-view cues with monocular cues represented as local monocular depth or features. The improvements are limited due to the uncontrolled quality of the masks and the underutilized benefits of the fusion of the two types of cues. In this paper, we propose a novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the heuristically crafted masks. As unveiled in our analyses, the multiview cues capture more accurate geometric information in static areas, and the monocular cues capture more useful contexts in dynamic areas. To let the geometric perception learned from multi-view cues in static areas propagate to the monocular representation in dynamic areas and let monocular cues enhance the representation of multi-view cost volume, we propose a cross-cue fusion (CCF) module, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other. Experiments on real-world datasets prove the significant effectiveness and generalization ability of the proposed method.", + "bbox": [ + 76, + 340, + 473, + 734 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 762, + 209, + 777 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Depth estimation is a fundamental and challenging task for 3D scene understanding in various application scenarios, such as autonomous driving [10, 16, 27]. With the advent of convolutional neural networks (CNNs) [12, 18], depth estimation methods [2, 24-26, 40, 45, 46] are capable of predicting promising results given either single or mul", + "bbox": [ + 75, + 786, + 468, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/7cd273c8879928efaed4a1f5fcf6ae2c35565e6943f6eb442ff04e5f009752c3.jpg", + "image_caption": [ + "Figure 1. Depth estimation in dynamic scenes. Multi-frame predictions reserve high overall accuracy while degrading in dynamic areas. The monocular method better handles moving areas while suffering in static areas. Our method fuses both multi-frame and monocular depth cues for final prediction, yielding superior performance of the whole scene." + ], + "image_footnote": [], + "bbox": [ + 509, + 296, + 879, + 494 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "tiple images. The single image-based methods learn the monocular cues, e.g., the texture or object-level features, to predict the depth [2, 42, 44], while multi-frame methods [36, 37, 40] can generally obtain higher overall accuracy relying on the multi-view geometric cues. Specifically, the 3D cost volume has been proved simple and effective for depth estimation, which encodes the multi-frame matching probabilities with a set of depth hypotheses [15, 37, 40].", + "bbox": [ + 496, + 582, + 892, + 703 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Although multi-frame methods are widely used in scene reconstruction [15, 34, 40], they encounter non-negligible challenges in the dynamic scenes with dynamic areas (e.g., moving cars and pedestrians). The dynamic areas cause corrupted values in the cost volume due to the violation of multi-view consistency [9, 33] and mislead the network predictions. However, depth estimation for the dynamic areas is usually crucial in most applications [10, 16, 19]. As shown in Fig. 1, multi-frame depth estimation for the dynamic cars is more challenging than the static backgrounds.", + "bbox": [ + 496, + 704, + 893, + 854 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To handle the dynamic areas violating the multi-view consistency, a few multi-frame depth estimation methods [9, 36, 37] try to identify and exclude the dynamic areas", + "bbox": [ + 498, + 854, + 893, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "Corresponding author: Y. Zhang, D. Gong, W. Yin.", + "bbox": [ + 99, + 886, + 372, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "21539", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "through an explicit mask obtained relying on some assumptions or heuristics. Specifically, some method [37] excludes the multi-frame cost volume relying on a learned dynamic mask and compensates the excluded areas with monocular features; some methods directly adjust the dynamic object locations in input images [9] or supervise multi-frame depth [36] with predicted monocular depth. However, these methods are usually sensitive to the explicit mask's quality, and the masks are obtained from additional networks or manually crafted criteria [9, 36, 37]. Despite better performances than pure multi-frame methods, these methods exhibit limited performance improvement compared with the additionally introduced monocular cues (as shown in Tab. 4), implying underutilized benefits from the fusion of the multiview and monocular cues. Although some self-supervised monocular depth estimation methods [4,5,11,14,22,23] also address the multi-view inconsistency issues, they mainly focus on handling the unfaithful self-supervision signals.", + "bbox": [ + 75, + 90, + 472, + 363 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To tackle the above issues, we propose a novel method that fuses the respective benefits from the monocular and multi-view cues, leading to significant improvement upon each individual source in dynamic scenes. We first analyze the behaviors of monocular and multi-frame cues in dynamic scenes, that the pure monocular method can generally learn good structural relations around the dynamic areas, and the pure multi-view cue preserves more accurate geometric properties in the static areas. We then unveil the effectiveness of leveraging the benefits of both depth cues by directly fusing depth volumes (Sec. 3). Inspired by the above observations, beyond treating monocular cues as a local supplement of multi-frame methods [9, 36, 37], we propose a cross-cue fusion (CCF) module to enhance the representations of multi-view and monocular cues with the other, and fuse them together for dynamic depth estimation. We use the spatially non-local relative intra-relations encoded in cross-observation attention (CCA) weights from each source to guide the representation of the other, as shown in Fig. 4. Specifically, the intra-relations of monocular cues can help to address multi-view inconsistency in dynamic areas, while the intra-relations from multi-view cues help to enhance the geometric property of the monocular representation, as visualized in Fig. 5. Unlike [1, 9, 36, 37], the proposed method unifies the input format of both cues as volumes and conducts fusion on them, which achieves better performances (as shown in Fig. 6). The proposed fusion module is learnable and does not require any heuristic masks, leading to better generalization and flexibility.", + "bbox": [ + 75, + 367, + 472, + 806 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our main contributions are summarized as follows:", + "bbox": [ + 96, + 810, + 436, + 823 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- We analyze multi-frame and monocular depth estimations in dynamic scenes and unveil their respective advantages in static and dynamic areas. Inspired by this, we propose a novel method that fuses depth volumes from each cue to achieve significant improve", + "bbox": [ + 94, + 825, + 468, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "ment upon individual estimations in dynamic scenes.", + "bbox": [ + 529, + 90, + 880, + 106 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a cross-cue fusion (CCF) module that utilizes the cross-cue attention to encode non-local intrarelations from one depth cue to guide the representation of the other. Different from methods using local masks, the attention weights learn mask-free global geometric information according to the geometric properties of each depth cue (as shown in Fig. 5).", + "- The proposed method outperforms the state-of-the-art method in dynamic areas with a significant error reduction of $21.3\\%$ while retaining its superiority in overall performance on KITTI. It also achieves the best generalization ability on the DDAD dataset in dynamic areas than the competing methods." + ], + "bbox": [ + 517, + 108, + 893, + 306 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 315, + 640, + 330 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Learning-based multi-frame depth estimation. Learning depth estimation from multiple images has attracted much attention these years. Current methods [7, 15, 32, 39-41] typically construct a cost volume using homography warping [40] between multi-view images. Under the premise that the scene is static, the cost volume encodes the probabilities of different depth hypotheses for each pixel, which can be regularized by 3D CNNs to yield final depth prediction. Aiming at recovering the accurate structure of static scenes, many endeavors have been made in improving the quality of the cost volume [15, 28], network efficiency [15, 39, 41] as well as the temporal consistency [7, 32], etc. The effectiveness of static scene reconstruction has sparked several attempts [17, 36, 37] that extend the multi-frame depth estimation to the large-scale outdoor scenes. The cost-volume between temporal consistent images provides extra depth information than image contexts [36]. However, for the dynamic areas that violate the static scene assumption, the cost volume provides wrong depth probabilities, which mislead the network to produce wrong depth even under supervised learning [37]. In this regard, processing the dynamic areas has become one of the main challenges for multi-frame depth estimation in outdoor scenes.", + "bbox": [ + 496, + 340, + 893, + 688 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Dynamic depth estimation in outdoor scenes. Dynamic areas are ubiquitous in real-world scenarios and are important in applications such as autonomous driving. Some literature [4, 5, 14, 22, 23] seeks to handle dynamic objects in self-supervised monocular depth estimation, where the network inputs a single image and the dynamic areas mainly affect the supervisory signals. They typically identify possible moving objects by semantic [21] or instance segmentation [3-5, 22], then conduct robust learning by masking out dynamic areas during loss computation [21] or directly model the object motion [3-5, 14]. However, these methods differ from the topic discussed in this paper in that: 1) we focus on addressing multi-frame dynamic depth estimation issue, where the main challenge lies in the corrupted cost", + "bbox": [ + 496, + 688, + 893, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "21540", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/f5ef4c08322b0b795bb3d7af292d70f3b77c0976c57166a96573dc3a1ea506e1.jpg", + "image_caption": [ + "Figure 2. Multi-frame and monocular cues in the dynamic scene. a) Multi-frame cues preserve accurate geometric properties in the static area (yellow box), while the monocular cues learn good structural relations in dynamic areas (red box). b) Multi-frame and monocular cues show respective benefits in different areas. While our plain volume fusion scheme shows obvious performance improvement, the proposed cross-cue fusion demonstrates better capabilities to handle dynamic depth. c) Depth predictions that show progressive improvements from the plain volume fusion and cross-cue volume fusion." + ], + "image_footnote": [], + "bbox": [ + 102, + 65, + 848, + 279 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "volumes than the self-supervised loss; 2) our method is supervised so that the robustness of loss function is beyond the main concern of this task.", + "bbox": [ + 75, + 358, + 470, + 402 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In the context of multi-frame depth estimation, current methods leverage the depth information from the single image to improve depth in dynamic areas. Manydepth [36] proposes a self-discovered mask and supervises the potential dynamic areas with monocular depth. MonoRec [37] proposes a motion segmentation network to mask out the dynamic areas in the cost volume and use only monocular image features to infer depth. Feng et al. [9] proposes to correct the dynamic object locations (with instance mask) in the image plane using monocular depth before computing the cost volume. Despite the higher dynamic results than pure multi-frame estimations, their performances are quite comparable [1,9,36], if not worse than, their proposed monocular branch (as shown in Tab. 4). Moreover, they require pre-computed instance masks [9,37] that bring extra computation burden for network training or inference. There also exists another multi-frame method [1] which guides multi-frame cost reconstruction with a large monocular network. However, due to the reliance on monocular network predictions, it demonstrates weaker generalization capabilities than multi-frame methods (Tab. 3). In contrast, our methods do not require any pre-computed object masks and achieve obvious improvement upon both monocular and multi-frame predictions in dynamic areas while retaining good generalization abilities across datasets.", + "bbox": [ + 75, + 404, + 470, + 782 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Analyses on Multi-view & Monocular Cues", + "text_level": 1, + "bbox": [ + 76, + 797, + 464, + 815 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Focusing on the dynamic scenes, we first analyze the behaviors of the depth estimation methods relying on multiview and monocular cues, as in Sec. 3.1. Beyond the overall performance on the whole scene, we specifically study their behaviors in the dynamic area. Then we analyze how", + "bbox": [ + 75, + 825, + 468, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "the proper fusion of multi-view and monocular cues may benefit depth estimation in dynamic areas in Sec. 3.2.", + "bbox": [ + 498, + 358, + 890, + 388 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Behaviours of Multi-view and Monocular Cues", + "text_level": 1, + "bbox": [ + 498, + 401, + 890, + 416 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We implement two depth estimation methods with the two types of cues, i.e., Multi Cues and Mono Cues in Fig. 2, and train them on KITTI [10], where the U-Net [37] is used. Specifically, the model with multi-view cues (Multi Cues) takes the cost volume as the input. To analyze the performance in the dynamic areas, we use the dynamic mask from [37] computed by thresholding photometric error and depth inconsistency.", + "bbox": [ + 496, + 425, + 892, + 545 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As shown in Fig. 2 (b), multi-frame depth estimation generally achieves high overall accuracy relying on the multi-view clues. But the performance in the dynamic areas is degraded due to the violation of the multi-view consistency in the input cost volume. Fig. 2 (a) shows that the multi-frame method generates fine 3D structures in static areas (the yellow box), whereas its estimation of the moving car (the red box) is corrupted. Compared with the multi-frame method, the monocular depth estimation method achieves worse overall performance as shown in Fig. 2 (b), without the geometric cues. On the other hand, it does not suffer from multi-view inconsistency and thus performs better in dynamic areas. In Fig. 2 (a), we can observe that the two depth cues show respective benefits in different areas and different aspects of the dynamic scene.", + "bbox": [ + 496, + 547, + 893, + 773 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Potential Benefits of Fusion", + "text_level": 1, + "bbox": [ + 500, + 785, + 746, + 800 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To handle the dynamic areas violating the multi-view consistency, previous multi-frame depth estimation methods [9, 36, 37] try to use the monocular information as the input for the dynamic areas. However, the improvement is limited due to the underutilized benefits of both cues, as discussed in Sec. 1. Especially the performance in the dynamic", + "bbox": [ + 496, + 810, + 890, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "21541", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/4714c9cac1b01afdd7b5375a908efcffc5df67b46eb2e4ac6da34ad05a3159da.jpg", + "image_caption": [ + "Figure 3. Overview of the proposed method. We first extract multi-frame depth cues with cost volume and monocular depth cues using one-hot depth volume. Then, we fuse the two volumes with the proposed cross-cue fusion module (CCF) to yield an improved fused depth feature. The fused depth feature is sent to the depth module for final depth estimation." + ], + "image_footnote": [], + "bbox": [ + 112, + 74, + 851, + 258 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "areas is easily bounded by the monocular cues.", + "bbox": [ + 75, + 316, + 387, + 330 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We seek to explore better fusion schemes to use the complementary benefits of these two cues, as discussed in Sec. 3.1. Apart from using the monocular cues in the dynamic area, we also hope the geometric information learned with the multi-view cues in the static areas can be propagated to boost the monocular cues in dynamic areas, which requires a more comprehensive fusion process.", + "bbox": [ + 75, + 332, + 468, + 436 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To unify the information from both multi-frame and monocular cues, we encode both depth cues in the form of a cost volume [40] and a one-hot depth volume transferred from the estimated monocular depth map (in Sec. 4.2). In the analyses, we first consider the plain volume fusion using concatenation and convolution operations. This learnable fusion scheme brings obvious improvement in dynamic areas, implying proper fusion methods help to utilize the potential benefits in dynamic scenes. Based on the feasibility of learnable fusion, we further investigate the cross-cue volume fusion and it exhibits further improvement (in Fig. 2 (b), (c)), which indicates a better way to utilize depth cues as introduced in the next section.", + "bbox": [ + 75, + 439, + 470, + 633 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. The Proposed Method", + "text_level": 1, + "bbox": [ + 76, + 647, + 287, + 664 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We aim to learn depth $D_{t}$ of the target image $I_{t}$ from a short image sequence, with known or estimated camera parameters $K, T$ . In this paper, we define the image sequence as $\\{I_{t-1}, I_{t}, I_{t+1}\\}$ , where $\\{I_{t-1}, I_{t+1}\\}$ are adjacent images to the target frame $I_{t}$ , $K, T$ are camera intrinsic and extrinsic provided as known values. The goal is to conduct accurate estimations of the dynamic scenes that contain various challenging dynamic objects.", + "bbox": [ + 75, + 672, + 468, + 792 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Overview", + "text_level": 1, + "bbox": [ + 76, + 801, + 187, + 816 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As shown in Fig. 3, the proposed method consists of three major parts - the multi-view and monocular cues are first represented as volumes through cost volume construction and depth one-hot vector transformation, the cross-cue fusion (CCF) module then fuses both multi-frame", + "bbox": [ + 75, + 824, + 470, + 898 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "and monocular cues by leveraging attention mechanisms [13, 31, 38], i.e., extracting relative intra-relations of each cue to guide the other to yield improved geometric representations of dynamic scene structure. The depth module takes the fused representation to estimate the final depth.", + "bbox": [ + 496, + 316, + 890, + 393 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Representing Monocular and Multi-view Cues", + "text_level": 1, + "bbox": [ + 498, + 406, + 890, + 422 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Multi-view cues as cost volume. We represent the multi-view cues by computing the cost volume following the pipeline of multi-view stereo (MVS) [37, 40]. We warp the adjacent images $\\{I_{t-1}, I_{t+1}\\}$ to the target view using $K, T$ and a set of depth hypotheses $d \\in \\{d_k\\}_{k=1}^M$ uniformly sampled in the inverse depth space $[\\frac{1}{d_{\\min}}, \\frac{1}{d_{\\max}}]$ , where $M$ denotes the number of depth hypotheses and is set to 32 in our paper. We construct the multi-frame cost volume $C_{\\mathrm{multi}} \\in \\mathbb{R}^{H \\times W \\times M}$ by measuring the pixel-wise similarity between the warped images and the target image, using SSIM [35] as introduced in [37]. For each pixel $(i,j)$ of $C_{\\mathrm{multi}} \\in [0,1]^{H \\times W \\times M}$ , channel positions $k \\in \\{1,\\dots,M\\}$ with large matching scores indicates a higher possibility to include real scene depth.", + "bbox": [ + 496, + 431, + 892, + 642 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Monocular cues as depth volume. We construct the monocular cues by estimating the single-view depth map and then transform the depth into one-hot depth volume. We leverage a U-Net architecture [37] without any complex designs for single-view depth estimation, yielding monocular prediction $D_{\\mathrm{mono}} = f_{\\theta}^{\\mathrm{mono}}(I_t)$ , where $D_{\\mathrm{mono}} \\in \\mathbb{R}^{H \\times W}$ and $f_{\\theta}^{\\mathrm{mono}}$ is the monocular network. To facilitate smooth fusion between the multi-frame and monocular cues, different from methods using single-view features [34, 37] or depth values [9, 36], we transform the whole depth map $D_{\\mathrm{mono}}$ into the depth volume $C_{\\mathrm{mono}} \\in \\{0,1\\}^{H \\times W \\times M}$ by converting each absolute depth value to a one-hot vector with", + "bbox": [ + 496, + 643, + 890, + 825 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nC _ {\\text {m o n o}, (i, j)} [ k ] = \\left\\{1 \\mid d _ {\\text {m o n o}} \\in \\left(d _ {k - 1}, d _ {k} \\right] \\right\\} _ {k = 1} ^ {M}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 540, + 838, + 890, + 857 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $C_{\\mathrm{mono},(i,j)} \\in \\{0,1\\}^M$ represents the one-hot vector in $C_{\\mathrm{mono}}$ corresponding to the pixel at $(i,j)$ , $d_{\\mathrm{mono}}$ is the", + "bbox": [ + 496, + 869, + 890, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "21542", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/b25ac88cdadf3807de061b641b5ab43b4d8b8e9ad897b56cace229304d22f016.jpg", + "image_caption": [ + "Figure 4. The cross-cue fusion (CCF) module. Taking both multi-frame and monocular depth volume as input, the CCF module enhances multi-frame depth features with the relative intra-relations of monocular depth volume $(R_{\\mathrm{mono}})$ . Meanwhile, the intra-relations of the multi-frame depth volume $(R_{\\mathrm{multi}})$ also enhance the monocular module, yielding enhanced depth features for the final depth estimation." + ], + "image_footnote": [], + "bbox": [ + 86, + 85, + 460, + 309 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "pixel-wise depth value. Note that we also try some soft representations [1, 2] but observe no obvious improvement.", + "bbox": [ + 75, + 417, + 468, + 450 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. The Cross-cue Fusion Module", + "text_level": 1, + "bbox": [ + 76, + 455, + 344, + 470 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We propose a cross-cue fusion (CCF) module to fuse the multi-frame and monocular depth cues. Different from methods [9,36,37] that utilize the masked local depth cues for dynamic depth estimation, the CCF module fuses the whole depth cues using depth volumes, retaining the potential to leverage both benefits for further improvement.", + "bbox": [ + 75, + 479, + 468, + 570 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The Cross-cue fusion pipeline. Given volumes $C_{\\mathrm{multi}}, C_{\\mathrm{mono}} \\in \\mathbb{R}^{H \\times W \\times M}$ , we first process them via shallow convolution layers, yielding down-sampled monocular and multi-frame depth features $F_{\\mathrm{multi}}, F_{\\mathrm{mono}}$ in shape $\\mathbb{R}^{h \\times w \\times M}$ . We then feed $F_{\\mathrm{multi}}$ and $F_{\\mathrm{mono}}$ to the proposed cross-cue attention (CCA) to enhance each depth cue by extracting the relative intra-relations from the other", + "bbox": [ + 76, + 570, + 470, + 676 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\widetilde {F} _ {\\text {m u l t i}} = \\mathrm {C C A} _ {\\text {m u l t i}} \\left(F _ {\\text {m o n o}}, F _ {\\text {m u l t i}}\\right), \\\\ \\sim \\end{array} \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 165, + 686, + 468, + 712 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\widetilde {F} _ {\\text {m o n o}} = \\mathrm {C C A} _ {\\text {m o n o}} \\left(F _ {\\text {m u l t i}}, F _ {\\text {m o n o}}\\right),\n$$\n", + "text_format": "latex", + "bbox": [ + 163, + 710, + 383, + 727 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "the enhanced features are concatenated to yield the fused feature $\\widetilde{F}_{\\mathrm{fused}}$ . To retain detailed information from initial depth cues, we process the input depth cues via $F_{\\mathrm{cat}} = \\mathrm{Cat}(\\mathrm{Conv}(C_{\\mathrm{multi}}), \\mathrm{Conv}(C_{\\mathrm{mono}}))$ and add the residual connection. The final cross-cue features can be written as", + "bbox": [ + 76, + 731, + 468, + 806 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nF = \\gamma \\widetilde {F} _ {\\text {f u s e d}} \\uparrow + F _ {\\text {c a t}}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 200, + 813, + 468, + 832 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\gamma$ is a learned weighting factor and $\\uparrow$ denotes the up-sampling operation. The fused feature $F$ is then sent to the depth network along with the image context features to yield the final depth prediction $D_{t}\\in \\mathbb{R}^{H\\times W}$ .", + "bbox": [ + 75, + 839, + 470, + 902 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/c99e62fa290f354c703a60d29951645b95f7fe302846e76dd28eafa56f7d71e0.jpg", + "image_caption": [ + "(a) Input image" + ], + "image_footnote": [], + "bbox": [ + 508, + 82, + 633, + 186 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/8c5a7157631463911327bc44b59608a053c1591a3885a16da9480ae5c792c6e6.jpg", + "image_caption": [ + "(b) $R_{\\mathrm{mono}}$ attenu. map" + ], + "image_footnote": [], + "bbox": [ + 635, + 80, + 761, + 186 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/72b0d90319d4ce4a0d8f5380df6abbb0b046d514f92be3004be29e321726ad0b.jpg", + "image_caption": [ + "(c) $R_{\\mathrm{multi}}$ attain.map", + "Figure 5. Attention maps of the dynamic position (red dots) examples. $R_{\\mathrm{mono}}$ boosts multi-frame features by attending monocular features around the dynamic areas for better use of the monocular cues in the corresponding areas. $R_{\\mathrm{multi}}$ enhances monocular features by attending multi-frame features in the static areas which have more reliable geometric information." + ], + "image_footnote": [], + "bbox": [ + 763, + 80, + 888, + 186 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Cross-cue attention. The cross-cue attention (CCA) targets utilizing the relative intra-relation of one depth cue to improve the geometric information of another. Since the CCA modules are deployed in a parallel manner as shown in Fig. 4, we introduce $\\widetilde{F}_{\\mathrm{multi}} = \\mathrm{CCA}_{\\mathrm{multi}}(F_{\\mathrm{mono}}, F_{\\mathrm{multi}})$ in detail as an example.", + "bbox": [ + 496, + 303, + 890, + 393 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Given depth features $F_{\\mathrm{mono}}$ , $F_{\\mathrm{multi}} \\in \\mathbb{R}^{h \\times w \\times M}$ , we transform $F_{\\mathrm{mono}}$ into query feature $Q_{\\mathrm{mono}}$ and key feature $K_{\\mathrm{mono}}$ , then transform $F_{\\mathrm{multi}}$ into value feature $V_{\\mathrm{multi}}$ using convolution operation $f(\\cdot, \\theta)$", + "bbox": [ + 496, + 393, + 890, + 455 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nQ _ {\\text {m o n o}} = f \\left(F _ {\\text {m o n o}}, \\theta_ {\\text {m o n o}} ^ {Q}\\right),\n$$\n", + "text_format": "latex", + "bbox": [ + 607, + 465, + 781, + 483 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nK _ {\\text {m o n o}} = f \\left(F _ {\\text {m o n o}}, \\theta_ {\\text {m o n o}} ^ {K}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 609, + 484, + 890, + 503 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nV _ {\\text {m u l t i}} = f \\left(F _ {\\text {m u l t i}}, \\theta_ {\\text {m u l t i}} ^ {V}\\right),\n$$\n", + "text_format": "latex", + "bbox": [ + 620, + 506, + 782, + 525 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "after reshaping all features into size $(hw, M)$ , we compute the non-local relative intra-relations of monocular features by matrix multiplication $\\otimes$ followed by softmax operation", + "bbox": [ + 496, + 535, + 890, + 580 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nR _ {\\text {m o n o}} = \\operatorname {S o f t m a x} \\left(Q _ {\\text {m o n o}} \\otimes K _ {\\text {m o n o}} ^ {T}\\right), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 576, + 590, + 890, + 609 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $R_{\\mathrm{mono}} \\in \\mathbb{R}^{hw \\times hw}$ stands for the non-local intrarelations of the monocular depth cue. We then utilize $R_{\\mathrm{mono}}$ to improve the geometric representations of the multi-frame feature $V_{\\mathrm{multi}}$ by", + "bbox": [ + 496, + 619, + 890, + 680 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\widetilde {F} _ {\\text {m u l t i}} = R _ {\\text {m o n o}} \\otimes V _ {\\text {m u l t i}}, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 614, + 689, + 890, + 709 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\widetilde{F}_{\\mathrm{multi}}$ denotes the improved multi-frame representations benefited from monocular depth cues. Similar operations are done for $\\widetilde{F}_{\\mathrm{mono}} = \\mathrm{CCA}_{\\mathrm{mono}}(F_{\\mathrm{multi}}, F_{\\mathrm{mono}})$ , where $R_{\\mathrm{multi}}$ stands for the intra-relations of multi-frame cues that can be used to improve monocular depth feature $V_{\\mathrm{mono}}$ . Please refer to Fig. 4 for details.", + "bbox": [ + 496, + 718, + 890, + 809 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Effectiveness of the CCA. As shown in Fig. 5, despite using the same CCA operation, the intra-relations $R_{\\mathrm{mono}}$ and $R_{\\mathrm{multi}}$ attend different areas for improving the dynamic depth, showing their ability to capture respective benefits from monocular (better dynamic depth) and multi-frame cues (better static depth). This learned property enables", + "bbox": [ + 496, + 810, + 890, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "21543", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/e8b395ac189a501be04e761652c9edeb6d33ad16ea91d050b41c86531bdf6d2b.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
EvalMethodBack.Reso.Sup.Abs RelSq RelRMSERMSElogδ < 1.25δ < 1.252δ < 1.253
OverallManydepth [36]Res-18MRM0.0710.3433.1840.1080.9450.9910.998
DynamicDepth [9]Res-18MRM0.0680.2963.0670.1060.9450.9910.998
MonoRec [37]Res-18MRD*0.0500.2902.2660.0820.9720.9910.996
OursRes-18MRD0.0430.1512.1130.0730.9750.9960.999
MaGNet [1]Effi-B5MRD0.0570.2152.5970.0880.9670.9960.999
OursEffi-B5MRD0.0460.1552.1120.0760.9730.9960.999
MaGNet [1]Effi-B5HRD0.0430.1352.0470.0820.9810.9970.999
OursEffi-B5HRD0.0390.1031.7180.0670.9810.9970.999
DynamicManydepth [36]Res-18MRM0.2223.3907.9210.2370.6760.9020.964
DynamicDepth [9]Res-18MRM0.2082.7577.3620.2270.6820.9110.971
MonoRec [37]Res-18MRD*0.3609.08310.9630.3460.5900.8820.780
OursRes-18MRD0.1180.8354.2970.1460.8710.9750.990
MaGNet [1]Effi-B5MRD0.1411.2194.8770.1680.8300.9550.986
OursEffi-B5MRD0.1110.7684.1170.1350.8810.9800.994
MaGNet [1]Effi-B5HRD0.1401.0604.5810.2020.8340.9540.982
OursEffi-B5HRD0.1120.8304.1010.1370.8850.9780.992
", + "bbox": [ + 88, + 75, + 879, + 333 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Quantitative comparisons on KITTI [10] Odometry dataset. 'Back.' denotes the network backbone. 'Reso.' denotes the image resolutions, where 'MR' refers to the resolution of $256 \\times 512$ and 'HR' is $352 \\times 1216$ . In the 'Sup.' column, 'M' are self-supervised methods, 'D*' refers to semi-supervised methods trained with pseudo GT depth, while 'D' denotes fully-supervised methods. Color blue denotes 'lower is better', while red means 'higher is better'. The best results are in bold.", + "bbox": [ + 75, + 338, + 890, + 398 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "unbounded dynamic depth performance upon both predictions, i.e., the monocular depth can be improved by attending static depth (using $R_{\\mathrm{multi}}$ ) from multi-frame cues, and the improved monocular depth in dynamic areas will further be propagated (with $R_{\\mathrm{mono}}$ ) to multi-frame predictions.", + "bbox": [ + 75, + 406, + 468, + 483 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.4. Loss Function", + "text_level": 1, + "bbox": [ + 76, + 503, + 220, + 518 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Given the predicted depth $D$ and the ground truth depth $\\hat{D}$ , the loss can be described as", + "bbox": [ + 76, + 527, + 468, + 556 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} (D, \\hat {D}) = \\beta \\mathcal {L} _ {\\mathrm {S I}} (D, \\hat {D}) + \\mathcal {L} _ {\\mathrm {V N L}} (D, \\hat {D}), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 135, + 563, + 468, + 580 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{\\mathrm{SI}}$ denotes the scale-invariant loss [2,8], $\\mathcal{L}_{\\mathrm{VNL}}$ is the virtual normal loss [42, 43], $\\beta$ is the weighing factor which is set to 4. Since we have both monocular depth $D_{\\mathrm{mono}}$ and the final depth prediction $D_{\\mathrm{t}}$ , the final loss $\\mathcal{L}_{\\mathrm{final}}$ is", + "bbox": [ + 75, + 587, + 468, + 648 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {f i n a l}} = \\mathcal {L} \\left(D _ {\\text {m o n o}}, \\hat {D}\\right) + \\mathcal {L} \\left(D _ {t}, \\hat {D}\\right). \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 155, + 652, + 468, + 671 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 76, + 678, + 207, + 695 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In this section, we compare our method with state-of-the-art multi-frame depth estimation methods [1,9,36,37] for the dynamic scenes, including self-supervised methods [9,36], semi-supervised method [37] as well as fully supervised method [6] (Sec. 5.3). We then conduct ablation studies (Sec. 5.4) to evaluate different variants of our method. We also validate the generalization ability (Sec. 5.5) and evaluate different methods' improvements upon monocular networks for depth estimation in dynamic areas (Sec. 5.6).", + "bbox": [ + 75, + 704, + 468, + 839 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Datasets", + "text_level": 1, + "bbox": [ + 76, + 847, + 179, + 861 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "KITTI. We follow [37] to evaluate dynamic sequential data on KITTI Odometry dataset, which contains 13666", + "bbox": [ + 75, + 869, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "training and 8634 testing samples. We use the dynamic masks provided by [37] for evaluation. More than 1300 samples in the test set contain dynamic objects. We evaluate both medium-resolution (MR: $256 \\times 512$ ) and high-resolution (HR: $352 \\times 1216$ ) depth results, and the metrics are computed within the 0-80m range.", + "bbox": [ + 496, + 407, + 890, + 497 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "DDAD. We use the forward-facing cameras with 3848 testing samples to evaluate the methods' generalization ability. Since there is no pre-defined dynamic mask for DDAD, we construct it by warping adjacent images with GT depth and selecting instance masks with high photometric error. More than $70\\%$ of the test set contains dynamic objects. All methods are evaluated within the 0-80m depth range.", + "bbox": [ + 496, + 497, + 890, + 604 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Implementation Details", + "text_level": 1, + "bbox": [ + 500, + 614, + 718, + 630 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We implement our method with Pytorch [29] and train it using NVIDIA TITAN RTX GPUs. Unless specified, both the monocular network and the depth network are consistent with the depth module of [37], with ResNet-18 [18] as backbone using ImageNet [6] pre-trained parameters. We train our model for 80 epochs using the Adam [20] optimizer and learning rate of $10^{-4}$ , which further drops to $10^{-5}$ after 65 epochs. The batch size is set to 8.", + "bbox": [ + 496, + 638, + 890, + 758 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.3. Results on KITTI", + "text_level": 1, + "bbox": [ + 500, + 770, + 671, + 786 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We show both overall and dynamic performances of different methods in Table 1. Our method achieves the best performance in dynamic depth estimation. Specifically, it outperforms the SOTA fully-supervised MaGNet [1] with $21.28\\%$ reduction of Abs.Rel $(0.141\\rightarrow 0.111)$ , using the same Efficient-B5 [30] backbone. Significant improvements are also observed compared to self/semi-supervised", + "bbox": [ + 496, + 794, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "21544", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/8257fbf1b5bd1855b8342a67fe1225a98a55e151f9f6f79894cfae41263f5805.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
#CategoryVariantDynamicOverall
Abs RelAbs SqRMSE\\( RMSE_{log} \\)δ ≤ 1.25AbsRel
1Depth with single cuesPure multi-frame cues0.3827.16710.2920.350.5090.041
2Pure monocular cues0.1491.3695.2820.1780.8100.106
3Volume fusion with masksSelf-discovered mask [36]0.1300.9904.6920.1600.8370.043
4MaskNetwork [37]0.2202.8966.2990.2230.7350.040
5Volume fusion without masksStack & 3D Convs0.1541.4795.8660.1890.7770.046
6Stack & 3D U-Net [15]0.1551.4445.7620.1910.7720.040
7Concat & 2D Convs0.1381.1245.1100.1740.8150.043
8Ours CCF w./o. \\( R_{multi} \\)0.1240.9394.6100.1540.8550.043
9Ours CCF w./o. \\( R_{mono} \\)0.1230.9264.5450.1530.8610.043
10Ours CCF w./ only intra-cue self-attention0.1220.8964.5440.1520.8600.042
11Ours CCF w./o. residual connection0.1300.9614.6160.1570.8400.048
12Ours depth module w./o. \\( I_t \\)0.1260.9544.6360.1550.8440.042
13Ours CCF - full0.1180.8354.2970.1460.8710.043
", + "bbox": [ + 86, + 80, + 879, + 314 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2. Ablation experiments on KITTI. We show the results of different fusion types of multi-frame and monocular cues. 'CCF' denotes the proposed cross-cue fusion module. 'Dynamic' denotes dynamic depth errors while 'Overall' refers to the overall depth error.", + "bbox": [ + 75, + 318, + 888, + 347 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "methods [9, 36, 37]. Besides the obvious improvement in dynamic areas, our method also outperforms other methods in most metrics for overall performance. Qualitative results are shown in Fig. 6. While the dynamic areas generally lead to performance decline for other multi-frame methods, our method conducts obviously better estimations in the moving objects, while retaining the overall accuracy.", + "bbox": [ + 75, + 354, + 468, + 460 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.4. Ablation Study", + "text_level": 1, + "bbox": [ + 76, + 469, + 230, + 486 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "As shown in Sec. 3, the way to fuse the two volumes in our method influences the final dynamic depth performance. As shown in Tab. 2, we fuse the multi-frame and monocular volumes in ways that the explicit mask is needed ('Volume fusion with masks') and the explicit mask is not needed ('Volume fusion without masks'). We also evaluate the variants of our method ('Ours CCF') as individual mask-free methods. Row #1~2 shows individual depth results leveraging pure multi-frame and monocular cues.", + "bbox": [ + 75, + 493, + 468, + 628 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Volume fusion with explicit masks. We leverage the computed [36] or learned masks [37] to fuse our volumes from two depth cues. As shown in row #3~4, The dynamic mask generated by [37] does not surpass the baseline fusion method. While the self-discovered mask [36] shows certain improvements in dynamic areas, it is computed using heuristics and thus has uncontrolled dynamic mask quality, which leads to more restricted performances than ours.", + "bbox": [ + 75, + 628, + 468, + 750 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Volume fusion without masks. Besides our method that uses a mask-free fusion scheme, we also fuse the volumes without any mask using convolutions. We first stack the depth volumes in a new dimension and process the fused features with layers of 3D convolutions as well as the 3D U-Net, which is commonly used in the MVS methods [15,40]. As shown in row #5~6 of Table 2, the 3D convolutions have no obvious improvement in dynamic areas. However, as we concatenate the two depth volumes and process them with 2D convolution layers (as in Fig. 2), we observe further im", + "bbox": [ + 75, + 750, + 470, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "provements (row #7) upon the pure monocular results, but this mask-free fusion scheme still lags behind our method with obvious margins.", + "bbox": [ + 496, + 354, + 890, + 398 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Design choices of the network modules. We evaluate different variants of our method as an individual type of mask-free fusion method. We respectively remove the multiframe (row #8) and monocular (row #9) intra-relations and directly feed the other cues to the fused feature. Then, we implement another variant of our method with intra-cue self-attention in the proposed CCF (row #10). We also disable the residual connection of $F_{\\mathrm{cat}}$ (row #11) and the input target image $I_{t}$ of the depth module (row #12). Results show the effectiveness and necessity of the proposed technical designs, which achieves the best performance.", + "bbox": [ + 496, + 400, + 892, + 566 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.5. Generalization on DDAD", + "text_level": 1, + "bbox": [ + 500, + 574, + 728, + 589 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To validate the generalization ability of our method, we test the KITTI models of all supervised methods [1, 37] on the challenging DDAD dataset. As shown in Table 3, previous methods either degrade on the dynamic areas due to the violation of multi-view consistency (e.g., AbsRel 0.544 for [37]) or exhibit worse overall performance owing to the cross-domain issue of the monocular network (e.g., AbsRel 0.208 for [1]). Our method shows more promising results that it not only outperforms other methods in dynamic depth estimation but also retains the advantage of multi-frame estimation with competitive overall performance.", + "bbox": [ + 496, + 597, + 890, + 763 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.6. Improvement upon Monocular Estimation", + "text_level": 1, + "bbox": [ + 500, + 771, + 861, + 787 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Though previous methods handle dynamic areas with monocular cues, the improvement is usually constrained by monocular results. In Tab. 4, we show the final depth results in dynamic areas and compare them against the monocular results of each method. Our method achieves the largest Abs.Rel reduction of $20.8\\%$ compared to other methods which explicitly use the monocular network. Meanwhile,", + "bbox": [ + 496, + 795, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "21545", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2fe7e7f3830285cd039b3a988a558ed46f8f023c3643adc2f1b4e46f9d5fa465.jpg", + "image_caption": [ + "Figure 6. Qualitative results on KITTI dataset [10]. From left to right: depth predictions (dynamic objects are highlighted with red boxes), error maps, and the reconstructed point clouds of dynamic areas. Our method achieves the best dynamic results and reconstructs more reasonable object shapes than state-of-the-art methods." + ], + "image_footnote": [], + "bbox": [ + 91, + 71, + 880, + 436 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/f1c5b2d5e3056dc34a327c5e73f4f0572338c9b215fa09d9ffeff9bd42507242.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
EvalMethodBackboneAbs RelSq RelRMSE\\( RMSE_{log} \\)δ<1.25\\( \\delta < 1.25^2 \\)\\( \\delta < 1.25^3 \\)
OverallMonoRec [37]Res-180.1583.1027.5530.2270.8540.9310.961
MaGNet [1]Effi-B50.2082.64110.7390.3820.6200.8780.942
OursRes-180.1582.4169.8550.2990.7470.8940.947
DynamicMonoRec [37]Res-180.54416.70316.1160.4820.4600.6670.798
MaGNet [1]Effi-B50.2663.98211.7150.3980.4620.8150.917
OursRes-180.2343.61111.0070.3310.5760.8350.921
", + "bbox": [ + 117, + 486, + 849, + 603 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/6491af377f7dede6f640344ee7b8676ea85da8efb754e9d4c97280a7e5ffa9c3.jpg", + "table_caption": [ + "Table 3. Generalizations on DDAD [16] dataset. The best results are in bold and the second best results are underlined. Our method achieves a competitive overall performance with other method while achieving the best result in dynamic areas." + ], + "table_footnote": [], + "table_body": "
MethodMono. Err.Final Err.Err. Redu.
Manydepth [36]0.2120.222-4.72%
Dynamicdepth [9]0.2140.2082.83%
MaGNet [1]0.1530.1417.84%
Ours - Res.180.1490.11820.81%
Ours - Res.500.1450.11620.00%
", + "bbox": [ + 91, + 646, + 455, + 752 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 4. Error reduction upon monocular estimation in dynamic areas. We show different methods' dynamic Abs.Rel depth errors (Final Err.) and their reduction (Err. Redu.) compared to their individual monocular performances (Mono. Err.). Our method achieves the largest error reduction over others and exhibits a consistent error reduction when a better backbone is used.", + "bbox": [ + 75, + 757, + 468, + 840 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "when using a backbone with larger capacities (ResNet-50), our method achieves consistent error reduction upon the monocular network, showing its scalability and flexibility.", + "bbox": [ + 75, + 854, + 468, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 648, + 619, + 664 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We improve multi-frame dynamic depth estimation by fusing the multi-view and monocular depth cues with the proposed cross-cue fusion. Experiments show its effectiveness as well as the generalization ability. Limitation: there still exists a performance gap to fill, especially in dynamic areas, with a better fusion of the cues. Meanwhile, the challenging occlusion issues for general multi-frame methods require further exploration.", + "bbox": [ + 496, + 672, + 890, + 794 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements Y. Zhu, J. Sun, and Y. Zhang acknowledge the support from National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology. This work was partially supported by NSFC (No.U19B2037) and the Natural Science Basic Research Program of Shaanxi (No.2021JCW-03) to Y. Zhang; an ARC DECRA Fellowship DE230101591 to D. Gong.", + "bbox": [ + 496, + 795, + 892, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "21546", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Gwangbin Bae, Ignas Budvyitis, and Roberto Cipolla. Multiview depth estimation by fusing single-view depth probability with multi-view geometry. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2842-2851, 2022. 2, 3, 5, 6, 7, 8", + "[2] Shariq Farooq Bhat, Ibrahim Alhashim, and Peter Wonka. Adabins: Depth estimation using adaptive bins. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4009-4018, 2021. 1, 5, 6", + "[3] Fabian Brickwedde, Steffen Abraham, and Rudolf Mester. Mono-sf: Multi-view geometry meets single-view depth for monocular scene flow estimation of dynamic traffic scenes. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2780-2790, 2019. 2", + "[4] Zhe Cao, Abhishek Kar, Christian Hane, and Jitendra Malik. Learning independent object motion from unlabelled stereoscopic videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5594-5603, 2019. 2", + "[5] Vincent Casser, Soeren Pirk, Reza Mahjourian, and Anelia Angelova. Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 8001-8008, 2019. 2", + "[6] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 6", + "[7] Arda Duzceker, Silvano Galliani, Christoph Vogel, Pablo Speciale, Mihai Dusmanu, and Marc Pollefeys. Deepvideovs: Multi-view stereo on video with recurrent spatiotemporal fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15324-15333, 2021. 2", + "[8] David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. In Advances in neural information processing systems, pages 2366-2374, 2014. 6", + "[9] Ziyue Feng, Liang Yang, Longlong Jing, Haiyan Wang, YingLi Tian, and Bing Li. Disentangling object motion and occlusion for unsupervised multi-frame monocular depth. arXiv preprint arXiv:2203.15174, 2022. 1, 2, 3, 4, 5, 6, 7, 8", + "[10] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3354-3361. IEEE, 2012. 1, 3, 6, 8", + "[11] Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J Brostow. Digging into self-supervised monocular depth estimation. In Proceedings of the IEEE International Conference on Computer Vision, pages 3828-3838, 2019. 2", + "[12] Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton Van Den Hengel, and Qinfeng Shi. From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur. In Proceed-" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ings of the IEEE conference on computer vision and pattern recognition, pages 2319-2328, 2017. 1", + "[13] Dong Gong, Frederic Z Zhang, Javen Qinfeng Shi, and Anton Van Den Hengel. Memory-augmented dynamic neural relational inference. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11843-11852, 2021. 4", + "[14] Ariel Gordon, Hanhan Li, Rico Jonschkowski, and Anelia Angelova. Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8977-8986, 2019. 2", + "[15] Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, and Ping Tan. Cascade cost volume for high-resolution multi-view stereo and stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2495-2504, 2020. 1, 2, 7", + "[16] Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon. 3d packing for self-supervised monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2485-2494, 2020. 1, 8", + "[17] Vitor Guizilini, Rares Ambrus, Dian Chen, Sergey Zakharov, and Adrien Gaidon. Multi-frame self-supervised depth with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 160-170, 2022. 2", + "[18] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 1, 6", + "[19] Matthias Innnmann, Kihwan Kim, Jinwei Gu, Matthias Nießner, Charles Loop, Marc Stamminger, and Jan Kautz. Nrmvs: Non-rigid multi-view stereo. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2754-2763, 2020. 1", + "[20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6", + "[21] Marvin Klingner, Jan-Aike Termöhlen, Jonas Mikolajczyk, and Tim Fingscheidt. Self-supervised monocular depth estimation: Solving the dynamic object problem by semantic guidance. In European Conference on Computer Vision, pages 582-600. Springer, 2020. 2", + "[22] Seokju Lee, Sunghoon Im, Stephen Lin, and In So Kweon. Learning monocular depth in dynamic scenes via instance-aware projection consistency. arXiv preprint arXiv:2102.02629, 2021. 2", + "[23] Hanhan Li, Ariel Gordon, Hang Zhao, Vincent Casser, and Anelia Angelova. Unsupervised monocular depth learning in dynamic scenes. arXiv preprint arXiv:2010.16404, 2020. 2", + "[24] Rui Li, Xiantuo He, Yu Zhu, Xianjun Li, Jinqiu Sun, and Yanning Zhang. Enhancing self-supervised monocular depth estimation via incorporating robust constraints. In Proceedings of the 28th ACM International Conference on Multimedia, pages 3108-3117, 2020. 1" + ], + "bbox": [ + 501, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "21547", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[25] Rui Li, Danna Xue, Shaolin Su, Xiantuo He, Qing Mao, Yu Zhu, Jinqiu Sun, and Yanning Zhang. Learning depth via leveraging semantics: Self-supervised monocular depth estimation with both implicit and explicit semantic guidance. Pattern Recognition, page 109297, 2023. 1", + "[26] Rui Li, Danna Xue, Yu Zhu, Hao Wu, Jinqiu Sun, and Yanning Zhang. Self-supervised monocular depth estimation with frequency-based recurrent refinement. IEEE Transactions on Multimedia, 2022. 1", + "[27] Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, and Zeming Li. Bevdepth: Acquisition of reliable depth for multi-view 3d object detection. arXiv preprint arXiv:2206.10092, 2022. 1", + "[28] Zhenxing Mi, Chang Di, and Dan Xu. Generalized binary search network for highly-efficient multi-view stereo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12991-13000, 2022. 2", + "[29] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017. 6", + "[30] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019. 6", + "[31] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing systems, pages 5998-6008, 2017. 4", + "[32] Fangjinhua Wang, Silvano Galliani, Christoph Vogel, and Marc Pollefeys. Itermvs: Iterative probability estimation for efficient multi-view stereo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8606-8615, 2022. 2", + "[33] Tai Wang, Jiangmiao Pang, and Dahua Lin. Monocular 3d object detection with depth from motion. arXiv preprint arXiv:2207.12988, 2022. 1", + "[34] Xiaofeng Wang, Zheng Zhu, Guan Huang, Fangbo Qin, Yun Ye, Yijia He, Xu Chi, and Xingang Wang. Mvster: epipolar transformer for efficient multi-view stereo. In European Conference on Computer Vision, pages 573-591. Springer, 2022. 1, 4", + "[35] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004. 4", + "[36] Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel Brostow, and Michael Firman. The temporal opportunist: Self-supervised multi-frame monocular depth. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1164-1174, 2021. 1, 2, 3, 4, 5, 6, 7, 8", + "[37] Felix Wimbauer, Nan Yang, Lukas Von Stumberg, Niclas Zeller, and Daniel Cremers. Monorec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6112-6122, 2021. 1, 2, 3, 4, 5, 6, 7, 8" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[38] Qingsen Yan, Dong Gong, Javen Qinfeng Shi, Anton van den Hengel, Chunhua Shen, Ian Reid, and Yanning Zhang. Dual-attention-guided network for ghost-free high dynamic range imaging. International Journal of Computer Vision, pages 1–19, 2022. 4", + "[39] Zhenpei Yang, Zhile Ren, Qi Shan, and Qixing Huang. Mvs2d: Efficient multi-view stereo via attention-driven 2d convolutions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8574-8584, 2022. 2", + "[40] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. Mvsnet: Depth inference for unstructured multi-view stereo. In Proceedings of the European conference on computer vision (ECCV), pages 767-783, 2018. 1, 2, 4, 7", + "[41] Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, and Long Quan. Recurrent mvsnet for high-resolution multi-view stereo depth inference. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5525-5534, 2019. 2", + "[42] Wei Yin, Yifan Liu, and Chunhua Shen. Virtual normal: Enforcing geometric constraints for accurate and robust depth prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. 1, 6", + "[43] Wei Yin, Yifan Liu, Chunhua Shen, and Youliang Yan. Enforcing geometric constraints of virtual normal for depth prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5684-5693, 2019. 6", + "[44] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Simon Chen, Yifan Liu, and Chunhua Shen. Towards accurate reconstruction of 3d scene shape from a single monocular image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 1", + "[45] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, and Chunhua Shen. Learning to recover 3d scene shape from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 204-213, 2021. 1", + "[46] Weihao Yuan, Xiaodong Gu, Zuozhuo Dai, Siyu Zhu, and Ping Tan. New crfs: Neural window fully-connected crfs for monocular depth estimation. arXiv preprint arXiv:2203.01502, 2022. 1" + ], + "bbox": [ + 501, + 90, + 892, + 667 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "21548", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning To Fuse Monocular and Multi-View Cues for Multi-Frame Depth Estimation in Dynamic Scenes/62c5b343-b419-485f-9576-4b167b86494f_model.json b/2023/Learning To Fuse Monocular and Multi-View Cues for Multi-Frame Depth Estimation in Dynamic Scenes/62c5b343-b419-485f-9576-4b167b86494f_model.json new file mode 100644 index 0000000000000000000000000000000000000000..1a8aff862282d1d36a69b0ad8432d10a52a8fb21 --- /dev/null +++ b/2023/Learning To Fuse Monocular and Multi-View Cues for Multi-Frame Depth Estimation in Dynamic Scenes/62c5b343-b419-485f-9576-4b167b86494f_model.json @@ -0,0 +1,2068 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.0, + 0.812, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.215, + 0.131, + 0.758, + 0.177 + ], + "angle": 0, + "content": "Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes" + }, + { + "type": "text", + "bbox": [ + 0.221, + 0.203, + 0.786, + 0.239 + ], + "angle": 0, + "content": "Rui Li\\(^{1}\\), Dong Gong\\(^{2}\\), Wei Yin\\(^{3}\\), Hao Chen\\(^{4}\\), Yu Zhu\\(^{1}\\), Kaixuan Wang\\(^{3}\\), Xiaozhi Chen\\(^{3}\\), Jinqiu Sun\\(^{1}\\), Yanning Zhang\\(^{1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.24, + 0.92, + 0.258 + ], + "angle": 0, + "content": "1Northwestern Polytechnical University, 2The University of New South Wales, 3DJI, 4Zhejiang University" + }, + { + "type": "text", + "bbox": [ + 0.278, + 0.26, + 0.725, + 0.275 + ], + "angle": 0, + "content": "https://github.com/ruili3/dynamic-multiframe-depth" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.309, + 0.314, + 0.327 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.341, + 0.474, + 0.735 + ], + "angle": 0, + "content": "Multi-frame depth estimation generally achieves high accuracy relying on the multi-view geometric consistency. When applied in dynamic scenes, e.g., autonomous driving, this consistency is usually violated in the dynamic areas, leading to corrupted estimations. Many multi-frame methods handle dynamic areas by identifying them with explicit masks and compensating the multi-view cues with monocular cues represented as local monocular depth or features. The improvements are limited due to the uncontrolled quality of the masks and the underutilized benefits of the fusion of the two types of cues. In this paper, we propose a novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the heuristically crafted masks. As unveiled in our analyses, the multiview cues capture more accurate geometric information in static areas, and the monocular cues capture more useful contexts in dynamic areas. To let the geometric perception learned from multi-view cues in static areas propagate to the monocular representation in dynamic areas and let monocular cues enhance the representation of multi-view cost volume, we propose a cross-cue fusion (CCF) module, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other. Experiments on real-world datasets prove the significant effectiveness and generalization ability of the proposed method." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.763, + 0.21, + 0.779 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.787, + 0.47, + 0.879 + ], + "angle": 0, + "content": "Depth estimation is a fundamental and challenging task for 3D scene understanding in various application scenarios, such as autonomous driving [10, 16, 27]. With the advent of convolutional neural networks (CNNs) [12, 18], depth estimation methods [2, 24-26, 40, 45, 46] are capable of predicting promising results given either single or mul" + }, + { + "type": "image", + "bbox": [ + 0.511, + 0.297, + 0.88, + 0.496 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.496, + 0.893, + 0.578 + ], + "angle": 0, + "content": "Figure 1. Depth estimation in dynamic scenes. Multi-frame predictions reserve high overall accuracy while degrading in dynamic areas. The monocular method better handles moving areas while suffering in static areas. Our method fuses both multi-frame and monocular depth cues for final prediction, yielding superior performance of the whole scene." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.583, + 0.893, + 0.704 + ], + "angle": 0, + "content": "tiple images. The single image-based methods learn the monocular cues, e.g., the texture or object-level features, to predict the depth [2, 42, 44], while multi-frame methods [36, 37, 40] can generally obtain higher overall accuracy relying on the multi-view geometric cues. Specifically, the 3D cost volume has been proved simple and effective for depth estimation, which encodes the multi-frame matching probabilities with a set of depth hypotheses [15, 37, 40]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.895, + 0.856 + ], + "angle": 0, + "content": "Although multi-frame methods are widely used in scene reconstruction [15, 34, 40], they encounter non-negligible challenges in the dynamic scenes with dynamic areas (e.g., moving cars and pedestrians). The dynamic areas cause corrupted values in the cost volume due to the violation of multi-view consistency [9, 33] and mislead the network predictions. However, depth estimation for the dynamic areas is usually crucial in most applications [10, 16, 19]. As shown in Fig. 1, multi-frame depth estimation for the dynamic cars is more challenging than the static backgrounds." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.895, + 0.901 + ], + "angle": 0, + "content": "To handle the dynamic areas violating the multi-view consistency, a few multi-frame depth estimation methods [9, 36, 37] try to identify and exclude the dynamic areas" + }, + { + "type": "page_footnote", + "bbox": [ + 0.1, + 0.887, + 0.374, + 0.901 + ], + "angle": 0, + "content": "Corresponding author: Y. Zhang, D. Gong, W. Yin." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21539" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.364 + ], + "angle": 0, + "content": "through an explicit mask obtained relying on some assumptions or heuristics. Specifically, some method [37] excludes the multi-frame cost volume relying on a learned dynamic mask and compensates the excluded areas with monocular features; some methods directly adjust the dynamic object locations in input images [9] or supervise multi-frame depth [36] with predicted monocular depth. However, these methods are usually sensitive to the explicit mask's quality, and the masks are obtained from additional networks or manually crafted criteria [9, 36, 37]. Despite better performances than pure multi-frame methods, these methods exhibit limited performance improvement compared with the additionally introduced monocular cues (as shown in Tab. 4), implying underutilized benefits from the fusion of the multiview and monocular cues. Although some self-supervised monocular depth estimation methods [4,5,11,14,22,23] also address the multi-view inconsistency issues, they mainly focus on handling the unfaithful self-supervision signals." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.368, + 0.473, + 0.807 + ], + "angle": 0, + "content": "To tackle the above issues, we propose a novel method that fuses the respective benefits from the monocular and multi-view cues, leading to significant improvement upon each individual source in dynamic scenes. We first analyze the behaviors of monocular and multi-frame cues in dynamic scenes, that the pure monocular method can generally learn good structural relations around the dynamic areas, and the pure multi-view cue preserves more accurate geometric properties in the static areas. We then unveil the effectiveness of leveraging the benefits of both depth cues by directly fusing depth volumes (Sec. 3). Inspired by the above observations, beyond treating monocular cues as a local supplement of multi-frame methods [9, 36, 37], we propose a cross-cue fusion (CCF) module to enhance the representations of multi-view and monocular cues with the other, and fuse them together for dynamic depth estimation. We use the spatially non-local relative intra-relations encoded in cross-observation attention (CCA) weights from each source to guide the representation of the other, as shown in Fig. 4. Specifically, the intra-relations of monocular cues can help to address multi-view inconsistency in dynamic areas, while the intra-relations from multi-view cues help to enhance the geometric property of the monocular representation, as visualized in Fig. 5. Unlike [1, 9, 36, 37], the proposed method unifies the input format of both cues as volumes and conducts fusion on them, which achieves better performances (as shown in Fig. 6). The proposed fusion module is learnable and does not require any heuristic masks, leading to better generalization and flexibility." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.811, + 0.437, + 0.824 + ], + "angle": 0, + "content": "Our main contributions are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.826, + 0.47, + 0.902 + ], + "angle": 0, + "content": "- We analyze multi-frame and monocular depth estimations in dynamic scenes and unveil their respective advantages in static and dynamic areas. Inspired by this, we propose a novel method that fuses depth volumes from each cue to achieve significant improve" + }, + { + "type": "text", + "bbox": [ + 0.531, + 0.092, + 0.882, + 0.107 + ], + "angle": 0, + "content": "ment upon individual estimations in dynamic scenes." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.109, + 0.892, + 0.215 + ], + "angle": 0, + "content": "- We propose a cross-cue fusion (CCF) module that utilizes the cross-cue attention to encode non-local intrarelations from one depth cue to guide the representation of the other. Different from methods using local masks, the attention weights learn mask-free global geometric information according to the geometric properties of each depth cue (as shown in Fig. 5)." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.218, + 0.894, + 0.308 + ], + "angle": 0, + "content": "- The proposed method outperforms the state-of-the-art method in dynamic areas with a significant error reduction of \\(21.3\\%\\) while retaining its superiority in overall performance on KITTI. It also achieves the best generalization ability on the DDAD dataset in dynamic areas than the competing methods." + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.109, + 0.894, + 0.308 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.316, + 0.642, + 0.332 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.342, + 0.895, + 0.689 + ], + "angle": 0, + "content": "Learning-based multi-frame depth estimation. Learning depth estimation from multiple images has attracted much attention these years. Current methods [7, 15, 32, 39-41] typically construct a cost volume using homography warping [40] between multi-view images. Under the premise that the scene is static, the cost volume encodes the probabilities of different depth hypotheses for each pixel, which can be regularized by 3D CNNs to yield final depth prediction. Aiming at recovering the accurate structure of static scenes, many endeavors have been made in improving the quality of the cost volume [15, 28], network efficiency [15, 39, 41] as well as the temporal consistency [7, 32], etc. The effectiveness of static scene reconstruction has sparked several attempts [17, 36, 37] that extend the multi-frame depth estimation to the large-scale outdoor scenes. The cost-volume between temporal consistent images provides extra depth information than image contexts [36]. However, for the dynamic areas that violate the static scene assumption, the cost volume provides wrong depth probabilities, which mislead the network to produce wrong depth even under supervised learning [37]. In this regard, processing the dynamic areas has become one of the main challenges for multi-frame depth estimation in outdoor scenes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.895, + 0.902 + ], + "angle": 0, + "content": "Dynamic depth estimation in outdoor scenes. Dynamic areas are ubiquitous in real-world scenarios and are important in applications such as autonomous driving. Some literature [4, 5, 14, 22, 23] seeks to handle dynamic objects in self-supervised monocular depth estimation, where the network inputs a single image and the dynamic areas mainly affect the supervisory signals. They typically identify possible moving objects by semantic [21] or instance segmentation [3-5, 22], then conduct robust learning by masking out dynamic areas during loss computation [21] or directly model the object motion [3-5, 14]. However, these methods differ from the topic discussed in this paper in that: 1) we focus on addressing multi-frame dynamic depth estimation issue, where the main challenge lies in the corrupted cost" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21540" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.103, + 0.066, + 0.849, + 0.28 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.282, + 0.894, + 0.351 + ], + "angle": 0, + "content": "Figure 2. Multi-frame and monocular cues in the dynamic scene. a) Multi-frame cues preserve accurate geometric properties in the static area (yellow box), while the monocular cues learn good structural relations in dynamic areas (red box). b) Multi-frame and monocular cues show respective benefits in different areas. While our plain volume fusion scheme shows obvious performance improvement, the proposed cross-cue fusion demonstrates better capabilities to handle dynamic depth. c) Depth predictions that show progressive improvements from the plain volume fusion and cross-cue volume fusion." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.359, + 0.471, + 0.403 + ], + "angle": 0, + "content": "volumes than the self-supervised loss; 2) our method is supervised so that the robustness of loss function is beyond the main concern of this task." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.405, + 0.471, + 0.784 + ], + "angle": 0, + "content": "In the context of multi-frame depth estimation, current methods leverage the depth information from the single image to improve depth in dynamic areas. Manydepth [36] proposes a self-discovered mask and supervises the potential dynamic areas with monocular depth. MonoRec [37] proposes a motion segmentation network to mask out the dynamic areas in the cost volume and use only monocular image features to infer depth. Feng et al. [9] proposes to correct the dynamic object locations (with instance mask) in the image plane using monocular depth before computing the cost volume. Despite the higher dynamic results than pure multi-frame estimations, their performances are quite comparable [1,9,36], if not worse than, their proposed monocular branch (as shown in Tab. 4). Moreover, they require pre-computed instance masks [9,37] that bring extra computation burden for network training or inference. There also exists another multi-frame method [1] which guides multi-frame cost reconstruction with a large monocular network. However, due to the reliance on monocular network predictions, it demonstrates weaker generalization capabilities than multi-frame methods (Tab. 3). In contrast, our methods do not require any pre-computed object masks and achieve obvious improvement upon both monocular and multi-frame predictions in dynamic areas while retaining good generalization abilities across datasets." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.799, + 0.465, + 0.816 + ], + "angle": 0, + "content": "3. Analyses on Multi-view & Monocular Cues" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.47, + 0.903 + ], + "angle": 0, + "content": "Focusing on the dynamic scenes, we first analyze the behaviors of the depth estimation methods relying on multiview and monocular cues, as in Sec. 3.1. Beyond the overall performance on the whole scene, we specifically study their behaviors in the dynamic area. Then we analyze how" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.359, + 0.892, + 0.39 + ], + "angle": 0, + "content": "the proper fusion of multi-view and monocular cues may benefit depth estimation in dynamic areas in Sec. 3.2." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.402, + 0.892, + 0.417 + ], + "angle": 0, + "content": "3.1. Behaviours of Multi-view and Monocular Cues" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.426, + 0.893, + 0.546 + ], + "angle": 0, + "content": "We implement two depth estimation methods with the two types of cues, i.e., Multi Cues and Mono Cues in Fig. 2, and train them on KITTI [10], where the U-Net [37] is used. Specifically, the model with multi-view cues (Multi Cues) takes the cost volume as the input. To analyze the performance in the dynamic areas, we use the dynamic mask from [37] computed by thresholding photometric error and depth inconsistency." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.548, + 0.895, + 0.775 + ], + "angle": 0, + "content": "As shown in Fig. 2 (b), multi-frame depth estimation generally achieves high overall accuracy relying on the multi-view clues. But the performance in the dynamic areas is degraded due to the violation of the multi-view consistency in the input cost volume. Fig. 2 (a) shows that the multi-frame method generates fine 3D structures in static areas (the yellow box), whereas its estimation of the moving car (the red box) is corrupted. Compared with the multi-frame method, the monocular depth estimation method achieves worse overall performance as shown in Fig. 2 (b), without the geometric cues. On the other hand, it does not suffer from multi-view inconsistency and thus performs better in dynamic areas. In Fig. 2 (a), we can observe that the two depth cues show respective benefits in different areas and different aspects of the dynamic scene." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.786, + 0.747, + 0.801 + ], + "angle": 0, + "content": "3.2. Potential Benefits of Fusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.902 + ], + "angle": 0, + "content": "To handle the dynamic areas violating the multi-view consistency, previous multi-frame depth estimation methods [9, 36, 37] try to use the monocular information as the input for the dynamic areas. However, the improvement is limited due to the underutilized benefits of both cues, as discussed in Sec. 1. Especially the performance in the dynamic" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "21541" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.114, + 0.075, + 0.852, + 0.259 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.262, + 0.895, + 0.306 + ], + "angle": 0, + "content": "Figure 3. Overview of the proposed method. We first extract multi-frame depth cues with cost volume and monocular depth cues using one-hot depth volume. Then, we fuse the two volumes with the proposed cross-cue fusion module (CCF) to yield an improved fused depth feature. The fused depth feature is sent to the depth module for final depth estimation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.318, + 0.388, + 0.332 + ], + "angle": 0, + "content": "areas is easily bounded by the monocular cues." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.333, + 0.469, + 0.438 + ], + "angle": 0, + "content": "We seek to explore better fusion schemes to use the complementary benefits of these two cues, as discussed in Sec. 3.1. Apart from using the monocular cues in the dynamic area, we also hope the geometric information learned with the multi-view cues in the static areas can be propagated to boost the monocular cues in dynamic areas, which requires a more comprehensive fusion process." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.44, + 0.471, + 0.635 + ], + "angle": 0, + "content": "To unify the information from both multi-frame and monocular cues, we encode both depth cues in the form of a cost volume [40] and a one-hot depth volume transferred from the estimated monocular depth map (in Sec. 4.2). In the analyses, we first consider the plain volume fusion using concatenation and convolution operations. This learnable fusion scheme brings obvious improvement in dynamic areas, implying proper fusion methods help to utilize the potential benefits in dynamic scenes. Based on the feasibility of learnable fusion, we further investigate the cross-cue volume fusion and it exhibits further improvement (in Fig. 2 (b), (c)), which indicates a better way to utilize depth cues as introduced in the next section." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.648, + 0.288, + 0.665 + ], + "angle": 0, + "content": "4. The Proposed Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.673, + 0.47, + 0.794 + ], + "angle": 0, + "content": "We aim to learn depth \\( D_{t} \\) of the target image \\( I_{t} \\) from a short image sequence, with known or estimated camera parameters \\( K, T \\). In this paper, we define the image sequence as \\( \\{I_{t-1}, I_{t}, I_{t+1}\\} \\), where \\( \\{I_{t-1}, I_{t+1}\\} \\) are adjacent images to the target frame \\( I_{t} \\), \\( K, T \\) are camera intrinsic and extrinsic provided as known values. The goal is to conduct accurate estimations of the dynamic scenes that contain various challenging dynamic objects." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.802, + 0.188, + 0.817 + ], + "angle": 0, + "content": "4.1. Overview" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.471, + 0.9 + ], + "angle": 0, + "content": "As shown in Fig. 3, the proposed method consists of three major parts - the multi-view and monocular cues are first represented as volumes through cost volume construction and depth one-hot vector transformation, the cross-cue fusion (CCF) module then fuses both multi-frame" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.318, + 0.892, + 0.395 + ], + "angle": 0, + "content": "and monocular cues by leveraging attention mechanisms [13, 31, 38], i.e., extracting relative intra-relations of each cue to guide the other to yield improved geometric representations of dynamic scene structure. The depth module takes the fused representation to estimate the final depth." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.407, + 0.891, + 0.423 + ], + "angle": 0, + "content": "4.2. Representing Monocular and Multi-view Cues" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.432, + 0.893, + 0.643 + ], + "angle": 0, + "content": "Multi-view cues as cost volume. We represent the multi-view cues by computing the cost volume following the pipeline of multi-view stereo (MVS) [37, 40]. We warp the adjacent images \\(\\{I_{t-1}, I_{t+1}\\}\\) to the target view using \\(K, T\\) and a set of depth hypotheses \\(d \\in \\{d_k\\}_{k=1}^M\\) uniformly sampled in the inverse depth space \\([\\frac{1}{d_{\\min}}, \\frac{1}{d_{\\max}}]\\), where \\(M\\) denotes the number of depth hypotheses and is set to 32 in our paper. We construct the multi-frame cost volume \\(C_{\\mathrm{multi}} \\in \\mathbb{R}^{H \\times W \\times M}\\) by measuring the pixel-wise similarity between the warped images and the target image, using SSIM [35] as introduced in [37]. For each pixel \\((i,j)\\) of \\(C_{\\mathrm{multi}} \\in [0,1]^{H \\times W \\times M}\\), channel positions \\(k \\in \\{1,\\dots,M\\}\\) with large matching scores indicates a higher possibility to include real scene depth." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.644, + 0.892, + 0.826 + ], + "angle": 0, + "content": "Monocular cues as depth volume. We construct the monocular cues by estimating the single-view depth map and then transform the depth into one-hot depth volume. We leverage a U-Net architecture [37] without any complex designs for single-view depth estimation, yielding monocular prediction \\( D_{\\mathrm{mono}} = f_{\\theta}^{\\mathrm{mono}}(I_t) \\), where \\( D_{\\mathrm{mono}} \\in \\mathbb{R}^{H \\times W} \\) and \\( f_{\\theta}^{\\mathrm{mono}} \\) is the monocular network. To facilitate smooth fusion between the multi-frame and monocular cues, different from methods using single-view features [34, 37] or depth values [9, 36], we transform the whole depth map \\( D_{\\mathrm{mono}} \\) into the depth volume \\( C_{\\mathrm{mono}} \\in \\{0,1\\}^{H \\times W \\times M} \\) by converting each absolute depth value to a one-hot vector with" + }, + { + "type": "equation", + "bbox": [ + 0.541, + 0.839, + 0.892, + 0.858 + ], + "angle": 0, + "content": "\\[\nC _ {\\text {m o n o}, (i, j)} [ k ] = \\left\\{1 \\mid d _ {\\text {m o n o}} \\in \\left(d _ {k - 1}, d _ {k} \\right] \\right\\} _ {k = 1} ^ {M}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.87, + 0.892, + 0.902 + ], + "angle": 0, + "content": "where \\( C_{\\mathrm{mono},(i,j)} \\in \\{0,1\\}^M \\) represents the one-hot vector in \\( C_{\\mathrm{mono}} \\) corresponding to the pixel at \\( (i,j) \\), \\( d_{\\mathrm{mono}} \\) is the" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "21542" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.087, + 0.086, + 0.462, + 0.31 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.316, + 0.471, + 0.414 + ], + "angle": 0, + "content": "Figure 4. The cross-cue fusion (CCF) module. Taking both multi-frame and monocular depth volume as input, the CCF module enhances multi-frame depth features with the relative intra-relations of monocular depth volume \\((R_{\\mathrm{mono}})\\). Meanwhile, the intra-relations of the multi-frame depth volume \\((R_{\\mathrm{multi}})\\) also enhance the monocular module, yielding enhanced depth features for the final depth estimation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.419, + 0.47, + 0.451 + ], + "angle": 0, + "content": "pixel-wise depth value. Note that we also try some soft representations [1, 2] but observe no obvious improvement." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.457, + 0.345, + 0.472 + ], + "angle": 0, + "content": "4.3. The Cross-cue Fusion Module" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.48, + 0.47, + 0.571 + ], + "angle": 0, + "content": "We propose a cross-cue fusion (CCF) module to fuse the multi-frame and monocular depth cues. Different from methods [9,36,37] that utilize the masked local depth cues for dynamic depth estimation, the CCF module fuses the whole depth cues using depth volumes, retaining the potential to leverage both benefits for further improvement." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.571, + 0.471, + 0.678 + ], + "angle": 0, + "content": "The Cross-cue fusion pipeline. Given volumes \\( C_{\\mathrm{multi}}, C_{\\mathrm{mono}} \\in \\mathbb{R}^{H \\times W \\times M} \\), we first process them via shallow convolution layers, yielding down-sampled monocular and multi-frame depth features \\( F_{\\mathrm{multi}}, F_{\\mathrm{mono}} \\) in shape \\( \\mathbb{R}^{h \\times w \\times M} \\). We then feed \\( F_{\\mathrm{multi}} \\) and \\( F_{\\mathrm{mono}} \\) to the proposed cross-cue attention (CCA) to enhance each depth cue by extracting the relative intra-relations from the other" + }, + { + "type": "equation", + "bbox": [ + 0.166, + 0.688, + 0.469, + 0.713 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\widetilde {F} _ {\\text {m u l t i}} = \\mathrm {C C A} _ {\\text {m u l t i}} \\left(F _ {\\text {m o n o}}, F _ {\\text {m u l t i}}\\right), \\\\ \\sim \\end{array} \\tag {2}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.165, + 0.711, + 0.384, + 0.728 + ], + "angle": 0, + "content": "\\[\n\\widetilde {F} _ {\\text {m o n o}} = \\mathrm {C C A} _ {\\text {m o n o}} \\left(F _ {\\text {m u l t i}}, F _ {\\text {m o n o}}\\right),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.732, + 0.47, + 0.807 + ], + "angle": 0, + "content": "the enhanced features are concatenated to yield the fused feature \\(\\widetilde{F}_{\\mathrm{fused}}\\). To retain detailed information from initial depth cues, we process the input depth cues via \\(F_{\\mathrm{cat}} = \\mathrm{Cat}(\\mathrm{Conv}(C_{\\mathrm{multi}}), \\mathrm{Conv}(C_{\\mathrm{mono}}))\\) and add the residual connection. The final cross-cue features can be written as" + }, + { + "type": "equation", + "bbox": [ + 0.201, + 0.814, + 0.47, + 0.833 + ], + "angle": 0, + "content": "\\[\nF = \\gamma \\widetilde {F} _ {\\text {f u s e d}} \\uparrow + F _ {\\text {c a t}}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.903 + ], + "angle": 0, + "content": "where \\(\\gamma\\) is a learned weighting factor and \\(\\uparrow\\) denotes the up-sampling operation. The fused feature \\(F\\) is then sent to the depth network along with the image context features to yield the final depth prediction \\(D_{t}\\in \\mathbb{R}^{H\\times W}\\)." + }, + { + "type": "image", + "bbox": [ + 0.509, + 0.083, + 0.635, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.526, + 0.189, + 0.61, + 0.201 + ], + "angle": 0, + "content": "(a) Input image" + }, + { + "type": "image", + "bbox": [ + 0.636, + 0.082, + 0.762, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.64, + 0.189, + 0.752, + 0.201 + ], + "angle": 0, + "content": "(b) \\(R_{\\mathrm{mono}}\\) attenu. map" + }, + { + "type": "image", + "bbox": [ + 0.764, + 0.082, + 0.89, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.77, + 0.189, + 0.882, + 0.201 + ], + "angle": 0, + "content": "(c) \\(R_{\\mathrm{multi}}\\) attain.map" + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.207, + 0.894, + 0.291 + ], + "angle": 0, + "content": "Figure 5. Attention maps of the dynamic position (red dots) examples. \\( R_{\\mathrm{mono}} \\) boosts multi-frame features by attending monocular features around the dynamic areas for better use of the monocular cues in the corresponding areas. \\( R_{\\mathrm{multi}} \\) enhances monocular features by attending multi-frame features in the static areas which have more reliable geometric information." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.304, + 0.892, + 0.394 + ], + "angle": 0, + "content": "Cross-cue attention. The cross-cue attention (CCA) targets utilizing the relative intra-relation of one depth cue to improve the geometric information of another. Since the CCA modules are deployed in a parallel manner as shown in Fig. 4, we introduce \\(\\widetilde{F}_{\\mathrm{multi}} = \\mathrm{CCA}_{\\mathrm{multi}}(F_{\\mathrm{mono}}, F_{\\mathrm{multi}})\\) in detail as an example." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.394, + 0.892, + 0.456 + ], + "angle": 0, + "content": "Given depth features \\(F_{\\mathrm{mono}}\\), \\(F_{\\mathrm{multi}} \\in \\mathbb{R}^{h \\times w \\times M}\\), we transform \\(F_{\\mathrm{mono}}\\) into query feature \\(Q_{\\mathrm{mono}}\\) and key feature \\(K_{\\mathrm{mono}}\\), then transform \\(F_{\\mathrm{multi}}\\) into value feature \\(V_{\\mathrm{multi}}\\) using convolution operation \\(f(\\cdot, \\theta)\\)" + }, + { + "type": "equation", + "bbox": [ + 0.609, + 0.466, + 0.782, + 0.484 + ], + "angle": 0, + "content": "\\[\nQ _ {\\text {m o n o}} = f \\left(F _ {\\text {m o n o}}, \\theta_ {\\text {m o n o}} ^ {Q}\\right),\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.61, + 0.486, + 0.891, + 0.505 + ], + "angle": 0, + "content": "\\[\nK _ {\\text {m o n o}} = f \\left(F _ {\\text {m o n o}}, \\theta_ {\\text {m o n o}} ^ {K}\\right), \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.621, + 0.507, + 0.783, + 0.526 + ], + "angle": 0, + "content": "\\[\nV _ {\\text {m u l t i}} = f \\left(F _ {\\text {m u l t i}}, \\theta_ {\\text {m u l t i}} ^ {V}\\right),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.536, + 0.892, + 0.582 + ], + "angle": 0, + "content": "after reshaping all features into size \\((hw, M)\\), we compute the non-local relative intra-relations of monocular features by matrix multiplication \\(\\otimes\\) followed by softmax operation" + }, + { + "type": "equation", + "bbox": [ + 0.577, + 0.592, + 0.891, + 0.61 + ], + "angle": 0, + "content": "\\[\nR _ {\\text {m o n o}} = \\operatorname {S o f t m a x} \\left(Q _ {\\text {m o n o}} \\otimes K _ {\\text {m o n o}} ^ {T}\\right), \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.62, + 0.892, + 0.681 + ], + "angle": 0, + "content": "where \\( R_{\\mathrm{mono}} \\in \\mathbb{R}^{hw \\times hw} \\) stands for the non-local intrarelations of the monocular depth cue. We then utilize \\( R_{\\mathrm{mono}} \\) to improve the geometric representations of the multi-frame feature \\( V_{\\mathrm{multi}} \\) by" + }, + { + "type": "equation", + "bbox": [ + 0.616, + 0.69, + 0.891, + 0.71 + ], + "angle": 0, + "content": "\\[\n\\widetilde {F} _ {\\text {m u l t i}} = R _ {\\text {m o n o}} \\otimes V _ {\\text {m u l t i}}, \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.719, + 0.892, + 0.81 + ], + "angle": 0, + "content": "where \\(\\widetilde{F}_{\\mathrm{multi}}\\) denotes the improved multi-frame representations benefited from monocular depth cues. Similar operations are done for \\(\\widetilde{F}_{\\mathrm{mono}} = \\mathrm{CCA}_{\\mathrm{mono}}(F_{\\mathrm{multi}}, F_{\\mathrm{mono}})\\), where \\(R_{\\mathrm{multi}}\\) stands for the intra-relations of multi-frame cues that can be used to improve monocular depth feature \\(V_{\\mathrm{mono}}\\). Please refer to Fig. 4 for details." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Effectiveness of the CCA. As shown in Fig. 5, despite using the same CCA operation, the intra-relations \\( R_{\\mathrm{mono}} \\) and \\( R_{\\mathrm{multi}} \\) attend different areas for improving the dynamic depth, showing their ability to capture respective benefits from monocular (better dynamic depth) and multi-frame cues (better static depth). This learned property enables" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "21543" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.089, + 0.077, + 0.88, + 0.334 + ], + "angle": 0, + "content": "
EvalMethodBack.Reso.Sup.Abs RelSq RelRMSERMSElogδ < 1.25δ < 1.252δ < 1.253
OverallManydepth [36]Res-18MRM0.0710.3433.1840.1080.9450.9910.998
DynamicDepth [9]Res-18MRM0.0680.2963.0670.1060.9450.9910.998
MonoRec [37]Res-18MRD*0.0500.2902.2660.0820.9720.9910.996
OursRes-18MRD0.0430.1512.1130.0730.9750.9960.999
MaGNet [1]Effi-B5MRD0.0570.2152.5970.0880.9670.9960.999
OursEffi-B5MRD0.0460.1552.1120.0760.9730.9960.999
MaGNet [1]Effi-B5HRD0.0430.1352.0470.0820.9810.9970.999
OursEffi-B5HRD0.0390.1031.7180.0670.9810.9970.999
DynamicManydepth [36]Res-18MRM0.2223.3907.9210.2370.6760.9020.964
DynamicDepth [9]Res-18MRM0.2082.7577.3620.2270.6820.9110.971
MonoRec [37]Res-18MRD*0.3609.08310.9630.3460.5900.8820.780
OursRes-18MRD0.1180.8354.2970.1460.8710.9750.990
MaGNet [1]Effi-B5MRD0.1411.2194.8770.1680.8300.9550.986
OursEffi-B5MRD0.1110.7684.1170.1350.8810.9800.994
MaGNet [1]Effi-B5HRD0.1401.0604.5810.2020.8340.9540.982
OursEffi-B5HRD0.1120.8304.1010.1370.8850.9780.992
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.339, + 0.892, + 0.4 + ], + "angle": 0, + "content": "Table 1. Quantitative comparisons on KITTI [10] Odometry dataset. 'Back.' denotes the network backbone. 'Reso.' denotes the image resolutions, where 'MR' refers to the resolution of \\(256 \\times 512\\) and 'HR' is \\(352 \\times 1216\\). In the 'Sup.' column, 'M' are self-supervised methods, 'D*' refers to semi-supervised methods trained with pseudo GT depth, while 'D' denotes fully-supervised methods. Color blue denotes 'lower is better', while red means 'higher is better'. The best results are in bold." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.407, + 0.47, + 0.484 + ], + "angle": 0, + "content": "unbounded dynamic depth performance upon both predictions, i.e., the monocular depth can be improved by attending static depth (using \\( R_{\\mathrm{multi}} \\)) from multi-frame cues, and the improved monocular depth in dynamic areas will further be propagated (with \\( R_{\\mathrm{mono}} \\)) to multi-frame predictions." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.505, + 0.222, + 0.519 + ], + "angle": 0, + "content": "4.4. Loss Function" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.528, + 0.469, + 0.557 + ], + "angle": 0, + "content": "Given the predicted depth \\(D\\) and the ground truth depth \\(\\hat{D}\\), the loss can be described as" + }, + { + "type": "equation", + "bbox": [ + 0.136, + 0.564, + 0.469, + 0.582 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} (D, \\hat {D}) = \\beta \\mathcal {L} _ {\\mathrm {S I}} (D, \\hat {D}) + \\mathcal {L} _ {\\mathrm {V N L}} (D, \\hat {D}), \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.588, + 0.469, + 0.649 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{\\mathrm{SI}}\\) denotes the scale-invariant loss [2,8], \\(\\mathcal{L}_{\\mathrm{VNL}}\\) is the virtual normal loss [42, 43], \\(\\beta\\) is the weighing factor which is set to 4. Since we have both monocular depth \\(D_{\\mathrm{mono}}\\) and the final depth prediction \\(D_{\\mathrm{t}}\\), the final loss \\(\\mathcal{L}_{\\mathrm{final}}\\) is" + }, + { + "type": "equation", + "bbox": [ + 0.156, + 0.654, + 0.469, + 0.672 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {f i n a l}} = \\mathcal {L} \\left(D _ {\\text {m o n o}}, \\hat {D}\\right) + \\mathcal {L} \\left(D _ {t}, \\hat {D}\\right). \\tag {8}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.679, + 0.209, + 0.696 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.705, + 0.469, + 0.84 + ], + "angle": 0, + "content": "In this section, we compare our method with state-of-the-art multi-frame depth estimation methods [1,9,36,37] for the dynamic scenes, including self-supervised methods [9,36], semi-supervised method [37] as well as fully supervised method [6] (Sec. 5.3). We then conduct ablation studies (Sec. 5.4) to evaluate different variants of our method. We also validate the generalization ability (Sec. 5.5) and evaluate different methods' improvements upon monocular networks for depth estimation in dynamic areas (Sec. 5.6)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.848, + 0.18, + 0.862 + ], + "angle": 0, + "content": "5.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.469, + 0.901 + ], + "angle": 0, + "content": "KITTI. We follow [37] to evaluate dynamic sequential data on KITTI Odometry dataset, which contains 13666" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.408, + 0.892, + 0.498 + ], + "angle": 0, + "content": "training and 8634 testing samples. We use the dynamic masks provided by [37] for evaluation. More than 1300 samples in the test set contain dynamic objects. We evaluate both medium-resolution (MR: \\(256 \\times 512\\)) and high-resolution (HR: \\(352 \\times 1216\\)) depth results, and the metrics are computed within the 0-80m range." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.498, + 0.892, + 0.605 + ], + "angle": 0, + "content": "DDAD. We use the forward-facing cameras with 3848 testing samples to evaluate the methods' generalization ability. Since there is no pre-defined dynamic mask for DDAD, we construct it by warping adjacent images with GT depth and selecting instance masks with high photometric error. More than \\(70\\%\\) of the test set contains dynamic objects. All methods are evaluated within the 0-80m depth range." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.615, + 0.719, + 0.631 + ], + "angle": 0, + "content": "5.2. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.639, + 0.892, + 0.76 + ], + "angle": 0, + "content": "We implement our method with Pytorch [29] and train it using NVIDIA TITAN RTX GPUs. Unless specified, both the monocular network and the depth network are consistent with the depth module of [37], with ResNet-18 [18] as backbone using ImageNet [6] pre-trained parameters. We train our model for 80 epochs using the Adam [20] optimizer and learning rate of \\(10^{-4}\\), which further drops to \\(10^{-5}\\) after 65 epochs. The batch size is set to 8." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.771, + 0.673, + 0.787 + ], + "angle": 0, + "content": "5.3. Results on KITTI" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.795, + 0.892, + 0.901 + ], + "angle": 0, + "content": "We show both overall and dynamic performances of different methods in Table 1. Our method achieves the best performance in dynamic depth estimation. Specifically, it outperforms the SOTA fully-supervised MaGNet [1] with \\(21.28\\%\\) reduction of Abs.Rel \\((0.141\\rightarrow 0.111)\\), using the same Efficient-B5 [30] backbone. Significant improvements are also observed compared to self/semi-supervised" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "21544" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.087, + 0.082, + 0.88, + 0.315 + ], + "angle": 0, + "content": "
#CategoryVariantDynamicOverall
Abs RelAbs SqRMSE\\( RMSE_{log} \\)δ ≤ 1.25AbsRel
1Depth with single cuesPure multi-frame cues0.3827.16710.2920.350.5090.041
2Pure monocular cues0.1491.3695.2820.1780.8100.106
3Volume fusion with masksSelf-discovered mask [36]0.1300.9904.6920.1600.8370.043
4MaskNetwork [37]0.2202.8966.2990.2230.7350.040
5Volume fusion without masksStack & 3D Convs0.1541.4795.8660.1890.7770.046
6Stack & 3D U-Net [15]0.1551.4445.7620.1910.7720.040
7Concat & 2D Convs0.1381.1245.1100.1740.8150.043
8Ours CCF w./o. \\( R_{multi} \\)0.1240.9394.6100.1540.8550.043
9Ours CCF w./o. \\( R_{mono} \\)0.1230.9264.5450.1530.8610.043
10Ours CCF w./ only intra-cue self-attention0.1220.8964.5440.1520.8600.042
11Ours CCF w./o. residual connection0.1300.9614.6160.1570.8400.048
12Ours depth module w./o. \\( I_t \\)0.1260.9544.6360.1550.8440.042
13Ours CCF - full0.1180.8354.2970.1460.8710.043
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.319, + 0.89, + 0.348 + ], + "angle": 0, + "content": "Table 2. Ablation experiments on KITTI. We show the results of different fusion types of multi-frame and monocular cues. 'CCF' denotes the proposed cross-cue fusion module. 'Dynamic' denotes dynamic depth errors while 'Overall' refers to the overall depth error." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.355, + 0.47, + 0.462 + ], + "angle": 0, + "content": "methods [9, 36, 37]. Besides the obvious improvement in dynamic areas, our method also outperforms other methods in most metrics for overall performance. Qualitative results are shown in Fig. 6. While the dynamic areas generally lead to performance decline for other multi-frame methods, our method conducts obviously better estimations in the moving objects, while retaining the overall accuracy." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.47, + 0.231, + 0.487 + ], + "angle": 0, + "content": "5.4. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.494, + 0.47, + 0.629 + ], + "angle": 0, + "content": "As shown in Sec. 3, the way to fuse the two volumes in our method influences the final dynamic depth performance. As shown in Tab. 2, we fuse the multi-frame and monocular volumes in ways that the explicit mask is needed ('Volume fusion with masks') and the explicit mask is not needed ('Volume fusion without masks'). We also evaluate the variants of our method ('Ours CCF') as individual mask-free methods. Row #1~2 shows individual depth results leveraging pure multi-frame and monocular cues." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.63, + 0.47, + 0.75 + ], + "angle": 0, + "content": "Volume fusion with explicit masks. We leverage the computed [36] or learned masks [37] to fuse our volumes from two depth cues. As shown in row #3~4, The dynamic mask generated by [37] does not surpass the baseline fusion method. While the self-discovered mask [36] shows certain improvements in dynamic areas, it is computed using heuristics and thus has uncontrolled dynamic mask quality, which leads to more restricted performances than ours." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Volume fusion without masks. Besides our method that uses a mask-free fusion scheme, we also fuse the volumes without any mask using convolutions. We first stack the depth volumes in a new dimension and process the fused features with layers of 3D convolutions as well as the 3D U-Net, which is commonly used in the MVS methods [15,40]. As shown in row #5~6 of Table 2, the 3D convolutions have no obvious improvement in dynamic areas. However, as we concatenate the two depth volumes and process them with 2D convolution layers (as in Fig. 2), we observe further im" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.355, + 0.892, + 0.4 + ], + "angle": 0, + "content": "provements (row #7) upon the pure monocular results, but this mask-free fusion scheme still lags behind our method with obvious margins." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.401, + 0.893, + 0.567 + ], + "angle": 0, + "content": "Design choices of the network modules. We evaluate different variants of our method as an individual type of mask-free fusion method. We respectively remove the multiframe (row #8) and monocular (row #9) intra-relations and directly feed the other cues to the fused feature. Then, we implement another variant of our method with intra-cue self-attention in the proposed CCF (row #10). We also disable the residual connection of \\( F_{\\mathrm{cat}} \\) (row #11) and the input target image \\( I_{t} \\) of the depth module (row #12). Results show the effectiveness and necessity of the proposed technical designs, which achieves the best performance." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.575, + 0.73, + 0.59 + ], + "angle": 0, + "content": "5.5. Generalization on DDAD" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.598, + 0.892, + 0.765 + ], + "angle": 0, + "content": "To validate the generalization ability of our method, we test the KITTI models of all supervised methods [1, 37] on the challenging DDAD dataset. As shown in Table 3, previous methods either degrade on the dynamic areas due to the violation of multi-view consistency (e.g., AbsRel 0.544 for [37]) or exhibit worse overall performance owing to the cross-domain issue of the monocular network (e.g., AbsRel 0.208 for [1]). Our method shows more promising results that it not only outperforms other methods in dynamic depth estimation but also retains the advantage of multi-frame estimation with competitive overall performance." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.772, + 0.862, + 0.789 + ], + "angle": 0, + "content": "5.6. Improvement upon Monocular Estimation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Though previous methods handle dynamic areas with monocular cues, the improvement is usually constrained by monocular results. In Tab. 4, we show the final depth results in dynamic areas and compare them against the monocular results of each method. Our method achieves the largest Abs.Rel reduction of \\(20.8\\%\\) compared to other methods which explicitly use the monocular network. Meanwhile," + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "21545" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.073, + 0.882, + 0.438 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.44, + 0.895, + 0.482 + ], + "angle": 0, + "content": "Figure 6. Qualitative results on KITTI dataset [10]. From left to right: depth predictions (dynamic objects are highlighted with red boxes), error maps, and the reconstructed point clouds of dynamic areas. Our method achieves the best dynamic results and reconstructs more reasonable object shapes than state-of-the-art methods." + }, + { + "type": "table", + "bbox": [ + 0.118, + 0.487, + 0.85, + 0.604 + ], + "angle": 0, + "content": "
EvalMethodBackboneAbs RelSq RelRMSE\\( RMSE_{log} \\)δ<1.25\\( \\delta < 1.25^2 \\)\\( \\delta < 1.25^3 \\)
OverallMonoRec [37]Res-180.1583.1027.5530.2270.8540.9310.961
MaGNet [1]Effi-B50.2082.64110.7390.3820.6200.8780.942
OursRes-180.1582.4169.8550.2990.7470.8940.947
DynamicMonoRec [37]Res-180.54416.70316.1160.4820.4600.6670.798
MaGNet [1]Effi-B50.2663.98211.7150.3980.4620.8150.917
OursRes-180.2343.61111.0070.3310.5760.8350.921
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.608, + 0.893, + 0.637 + ], + "angle": 0, + "content": "Table 3. Generalizations on DDAD [16] dataset. The best results are in bold and the second best results are underlined. Our method achieves a competitive overall performance with other method while achieving the best result in dynamic areas." + }, + { + "type": "table", + "bbox": [ + 0.092, + 0.647, + 0.457, + 0.753 + ], + "angle": 0, + "content": "
MethodMono. Err.Final Err.Err. Redu.
Manydepth [36]0.2120.222-4.72%
Dynamicdepth [9]0.2140.2082.83%
MaGNet [1]0.1530.1417.84%
Ours - Res.180.1490.11820.81%
Ours - Res.500.1450.11620.00%
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.758, + 0.47, + 0.841 + ], + "angle": 0, + "content": "Table 4. Error reduction upon monocular estimation in dynamic areas. We show different methods' dynamic Abs.Rel depth errors (Final Err.) and their reduction (Err. Redu.) compared to their individual monocular performances (Mono. Err.). Our method achieves the largest error reduction over others and exhibits a consistent error reduction when a better backbone is used." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.47, + 0.903 + ], + "angle": 0, + "content": "when using a backbone with larger capacities (ResNet-50), our method achieves consistent error reduction upon the monocular network, showing its scalability and flexibility." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.649, + 0.62, + 0.665 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.674, + 0.892, + 0.795 + ], + "angle": 0, + "content": "We improve multi-frame dynamic depth estimation by fusing the multi-view and monocular depth cues with the proposed cross-cue fusion. Experiments show its effectiveness as well as the generalization ability. Limitation: there still exists a performance gap to fill, especially in dynamic areas, with a better fusion of the cues. Meanwhile, the challenging occlusion issues for general multi-frame methods require further exploration." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.893, + 0.903 + ], + "angle": 0, + "content": "Acknowledgements Y. Zhu, J. Sun, and Y. Zhang acknowledge the support from National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology. This work was partially supported by NSFC (No.U19B2037) and the Natural Science Basic Research Program of Shaanxi (No.2021JCW-03) to Y. Zhang; an ARC DECRA Fellowship DE230101591 to D. Gong." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21546" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.176, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.186 + ], + "angle": 0, + "content": "[1] Gwangbin Bae, Ignas Budvyitis, and Roberto Cipolla. Multiview depth estimation by fusing single-view depth probability with multi-view geometry. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2842-2851, 2022. 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.187, + 0.472, + 0.243 + ], + "angle": 0, + "content": "[2] Shariq Farooq Bhat, Ibrahim Alhashim, and Peter Wonka. Adabins: Depth estimation using adaptive bins. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4009-4018, 2021. 1, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.245, + 0.472, + 0.315 + ], + "angle": 0, + "content": "[3] Fabian Brickwedde, Steffen Abraham, and Rudolf Mester. Mono-sf: Multi-view geometry meets single-view depth for monocular scene flow estimation of dynamic traffic scenes. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2780-2790, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.316, + 0.472, + 0.384 + ], + "angle": 0, + "content": "[4] Zhe Cao, Abhishek Kar, Christian Hane, and Jitendra Malik. Learning independent object motion from unlabelled stereoscopic videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5594-5603, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.387, + 0.472, + 0.457 + ], + "angle": 0, + "content": "[5] Vincent Casser, Soeren Pirk, Reza Mahjourian, and Anelia Angelova. Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 8001-8008, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.459, + 0.472, + 0.515 + ], + "angle": 0, + "content": "[6] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.517, + 0.472, + 0.599 + ], + "angle": 0, + "content": "[7] Arda Duzceker, Silvano Galliani, Christoph Vogel, Pablo Speciale, Mihai Dusmanu, and Marc Pollefeys. Deepvideovs: Multi-view stereo on video with recurrent spatiotemporal fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15324-15333, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.601, + 0.472, + 0.657 + ], + "angle": 0, + "content": "[8] David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. In Advances in neural information processing systems, pages 2366-2374, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.659, + 0.472, + 0.715 + ], + "angle": 0, + "content": "[9] Ziyue Feng, Liang Yang, Longlong Jing, Haiyan Wang, YingLi Tian, and Bing Li. Disentangling object motion and occlusion for unsupervised multi-frame monocular depth. arXiv preprint arXiv:2203.15174, 2022. 1, 2, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.716, + 0.472, + 0.785 + ], + "angle": 0, + "content": "[10] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3354-3361. IEEE, 2012. 1, 3, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.788, + 0.472, + 0.844 + ], + "angle": 0, + "content": "[11] Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J Brostow. Digging into self-supervised monocular depth estimation. In Proceedings of the IEEE International Conference on Computer Vision, pages 3828-3838, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.845, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[12] Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton Van Den Hengel, and Qinfeng Shi. From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur. In Proceed-" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.116, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.121 + ], + "angle": 0, + "content": "ings of the IEEE conference on computer vision and pattern recognition, pages 2319-2328, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.894, + 0.19 + ], + "angle": 0, + "content": "[13] Dong Gong, Frederic Z Zhang, Javen Qinfeng Shi, and Anton Van Den Hengel. Memory-augmented dynamic neural relational inference. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11843-11852, 2021. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.192, + 0.894, + 0.262 + ], + "angle": 0, + "content": "[14] Ariel Gordon, Hanhan Li, Rico Jonschkowski, and Anelia Angelova. Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8977-8986, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.264, + 0.895, + 0.332 + ], + "angle": 0, + "content": "[15] Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, and Ping Tan. Cascade cost volume for high-resolution multi-view stereo and stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2495-2504, 2020. 1, 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.334, + 0.895, + 0.404 + ], + "angle": 0, + "content": "[16] Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon. 3d packing for self-supervised monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2485-2494, 2020. 1, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.405, + 0.895, + 0.473 + ], + "angle": 0, + "content": "[17] Vitor Guizilini, Rares Ambrus, Dian Chen, Sergey Zakharov, and Adrien Gaidon. Multi-frame self-supervised depth with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 160-170, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.476, + 0.894, + 0.532 + ], + "angle": 0, + "content": "[18] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.533, + 0.894, + 0.603 + ], + "angle": 0, + "content": "[19] Matthias Innnmann, Kihwan Kim, Jinwei Gu, Matthias Nießner, Charles Loop, Marc Stamminger, and Jan Kautz. Nrmvs: Non-rigid multi-view stereo. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2754-2763, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.604, + 0.894, + 0.644 + ], + "angle": 0, + "content": "[20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.646, + 0.894, + 0.716 + ], + "angle": 0, + "content": "[21] Marvin Klingner, Jan-Aike Termöhlen, Jonas Mikolajczyk, and Tim Fingscheidt. Self-supervised monocular depth estimation: Solving the dynamic object problem by semantic guidance. In European Conference on Computer Vision, pages 582-600. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.718, + 0.894, + 0.773 + ], + "angle": 0, + "content": "[22] Seokju Lee, Sunghoon Im, Stephen Lin, and In So Kweon. Learning monocular depth in dynamic scenes via instance-aware projection consistency. arXiv preprint arXiv:2102.02629, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.774, + 0.894, + 0.829 + ], + "angle": 0, + "content": "[23] Hanhan Li, Ariel Gordon, Hang Zhao, Vincent Casser, and Anelia Angelova. Unsupervised monocular depth learning in dynamic scenes. arXiv preprint arXiv:2010.16404, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.831, + 0.894, + 0.901 + ], + "angle": 0, + "content": "[24] Rui Li, Xiantuo He, Yu Zhu, Xianjun Li, Jinqiu Sun, and Yanning Zhang. Enhancing self-supervised monocular depth estimation via incorporating robust constraints. In Proceedings of the 28th ACM International Conference on Multimedia, pages 3108-3117, 2020. 1" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.895, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21547" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.161 + ], + "angle": 0, + "content": "[25] Rui Li, Danna Xue, Shaolin Su, Xiantuo He, Qing Mao, Yu Zhu, Jinqiu Sun, and Yanning Zhang. Learning depth via leveraging semantics: Self-supervised monocular depth estimation with both implicit and explicit semantic guidance. Pattern Recognition, page 109297, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.162, + 0.47, + 0.218 + ], + "angle": 0, + "content": "[26] Rui Li, Danna Xue, Yu Zhu, Hao Wu, Jinqiu Sun, and Yanning Zhang. Self-supervised monocular depth estimation with frequency-based recurrent refinement. IEEE Transactions on Multimedia, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.218, + 0.47, + 0.273 + ], + "angle": 0, + "content": "[27] Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, and Zeming Li. Bevdepth: Acquisition of reliable depth for multi-view 3d object detection. arXiv preprint arXiv:2206.10092, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.273, + 0.47, + 0.329 + ], + "angle": 0, + "content": "[28] Zhenxing Mi, Chang Di, and Dan Xu. Generalized binary search network for highly-efficient multi-view stereo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12991-13000, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.33, + 0.47, + 0.385 + ], + "angle": 0, + "content": "[29] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.386, + 0.47, + 0.44 + ], + "angle": 0, + "content": "[30] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.441, + 0.47, + 0.497 + ], + "angle": 0, + "content": "[31] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing systems, pages 5998-6008, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.497, + 0.47, + 0.566 + ], + "angle": 0, + "content": "[32] Fangjinhua Wang, Silvano Galliani, Christoph Vogel, and Marc Pollefeys. Itermvs: Iterative probability estimation for efficient multi-view stereo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8606-8615, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.567, + 0.47, + 0.608 + ], + "angle": 0, + "content": "[33] Tai Wang, Jiangmiao Pang, and Dahua Lin. Monocular 3d object detection with depth from motion. arXiv preprint arXiv:2207.12988, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.609, + 0.47, + 0.677 + ], + "angle": 0, + "content": "[34] Xiaofeng Wang, Zheng Zhu, Guan Huang, Fangbo Qin, Yun Ye, Yijia He, Xu Chi, and Xingang Wang. Mvster: epipolar transformer for efficient multi-view stereo. In European Conference on Computer Vision, pages 573-591. Springer, 2022. 1, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.678, + 0.47, + 0.734 + ], + "angle": 0, + "content": "[35] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.735, + 0.47, + 0.816 + ], + "angle": 0, + "content": "[36] Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel Brostow, and Michael Firman. The temporal opportunist: Self-supervised multi-frame monocular depth. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1164-1174, 2021. 1, 2, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.818, + 0.47, + 0.9 + ], + "angle": 0, + "content": "[37] Felix Wimbauer, Nan Yang, Lukas Von Stumberg, Niclas Zeller, and Daniel Cremers. Monorec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6112-6122, 2021. 1, 2, 3, 4, 5, 6, 7, 8" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.092, + 0.893, + 0.161 + ], + "angle": 0, + "content": "[38] Qingsen Yan, Dong Gong, Javen Qinfeng Shi, Anton van den Hengel, Chunhua Shen, Ian Reid, and Yanning Zhang. Dual-attention-guided network for ghost-free high dynamic range imaging. International Journal of Computer Vision, pages 1–19, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.162, + 0.893, + 0.232 + ], + "angle": 0, + "content": "[39] Zhenpei Yang, Zhile Ren, Qi Shan, and Qixing Huang. Mvs2d: Efficient multi-view stereo via attention-driven 2d convolutions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8574-8584, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.233, + 0.893, + 0.289 + ], + "angle": 0, + "content": "[40] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. Mvsnet: Depth inference for unstructured multi-view stereo. In Proceedings of the European conference on computer vision (ECCV), pages 767-783, 2018. 1, 2, 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.29, + 0.893, + 0.359 + ], + "angle": 0, + "content": "[41] Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, and Long Quan. Recurrent mvsnet for high-resolution multi-view stereo depth inference. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5525-5534, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.36, + 0.893, + 0.416 + ], + "angle": 0, + "content": "[42] Wei Yin, Yifan Liu, and Chunhua Shen. Virtual normal: Enforcing geometric constraints for accurate and robust depth prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.417, + 0.893, + 0.473 + ], + "angle": 0, + "content": "[43] Wei Yin, Yifan Liu, Chunhua Shen, and Youliang Yan. Enforcing geometric constraints of virtual normal for depth prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5684-5693, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.473, + 0.893, + 0.543 + ], + "angle": 0, + "content": "[44] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Simon Chen, Yifan Liu, and Chunhua Shen. Towards accurate reconstruction of 3d scene shape from a single monocular image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.544, + 0.893, + 0.613 + ], + "angle": 0, + "content": "[45] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, and Chunhua Shen. Learning to recover 3d scene shape from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 204-213, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.614, + 0.893, + 0.669 + ], + "angle": 0, + "content": "[46] Weihao Yuan, Xiaodong Gu, Zuozhuo Dai, Siyu Zhu, and Ping Tan. New crfs: Neural window fully-connected crfs for monocular depth estimation. arXiv preprint arXiv:2203.01502, 2022. 1" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.092, + 0.893, + 0.669 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "21548" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Fuse Monocular and Multi-View Cues for Multi-Frame Depth Estimation in Dynamic Scenes/62c5b343-b419-485f-9576-4b167b86494f_origin.pdf b/2023/Learning To Fuse Monocular and Multi-View Cues for Multi-Frame Depth Estimation in Dynamic Scenes/62c5b343-b419-485f-9576-4b167b86494f_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..298417e7a23288e6bbbb331be4e3aeb275093d29 --- /dev/null +++ b/2023/Learning To Fuse Monocular and Multi-View Cues for Multi-Frame Depth Estimation in Dynamic Scenes/62c5b343-b419-485f-9576-4b167b86494f_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7e8323a1e53e06b7f98334af9588bef7ddc567ae7e4305b4a8b547655394b38b +size 5265180 diff --git a/2023/Learning To Fuse Monocular and Multi-View Cues for Multi-Frame Depth Estimation in Dynamic Scenes/full.md b/2023/Learning To Fuse Monocular and Multi-View Cues for Multi-Frame Depth Estimation in Dynamic Scenes/full.md new file mode 100644 index 0000000000000000000000000000000000000000..bff5081e54f03d65acbdc675a79129436395ab0f --- /dev/null +++ b/2023/Learning To Fuse Monocular and Multi-View Cues for Multi-Frame Depth Estimation in Dynamic Scenes/full.md @@ -0,0 +1,304 @@ +# Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes + +Rui Li $^{1}$ , Dong Gong $^{2}$ , Wei Yin $^{3}$ , Hao Chen $^{4}$ , Yu Zhu $^{1}$ , Kaixuan Wang $^{3}$ , Xiaozhi Chen $^{3}$ , Jinqiu Sun $^{1}$ , Yanning Zhang $^{1}$ + +1Northwestern Polytechnical University, 2The University of New South Wales, 3DJI, 4Zhejiang University + +https://github.com/ruili3/dynamic-multiframe-depth + +# Abstract + +Multi-frame depth estimation generally achieves high accuracy relying on the multi-view geometric consistency. When applied in dynamic scenes, e.g., autonomous driving, this consistency is usually violated in the dynamic areas, leading to corrupted estimations. Many multi-frame methods handle dynamic areas by identifying them with explicit masks and compensating the multi-view cues with monocular cues represented as local monocular depth or features. The improvements are limited due to the uncontrolled quality of the masks and the underutilized benefits of the fusion of the two types of cues. In this paper, we propose a novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the heuristically crafted masks. As unveiled in our analyses, the multiview cues capture more accurate geometric information in static areas, and the monocular cues capture more useful contexts in dynamic areas. To let the geometric perception learned from multi-view cues in static areas propagate to the monocular representation in dynamic areas and let monocular cues enhance the representation of multi-view cost volume, we propose a cross-cue fusion (CCF) module, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other. Experiments on real-world datasets prove the significant effectiveness and generalization ability of the proposed method. + +# 1. Introduction + +Depth estimation is a fundamental and challenging task for 3D scene understanding in various application scenarios, such as autonomous driving [10, 16, 27]. With the advent of convolutional neural networks (CNNs) [12, 18], depth estimation methods [2, 24-26, 40, 45, 46] are capable of predicting promising results given either single or mul + +![](images/7cd273c8879928efaed4a1f5fcf6ae2c35565e6943f6eb442ff04e5f009752c3.jpg) +Figure 1. Depth estimation in dynamic scenes. Multi-frame predictions reserve high overall accuracy while degrading in dynamic areas. The monocular method better handles moving areas while suffering in static areas. Our method fuses both multi-frame and monocular depth cues for final prediction, yielding superior performance of the whole scene. + +tiple images. The single image-based methods learn the monocular cues, e.g., the texture or object-level features, to predict the depth [2, 42, 44], while multi-frame methods [36, 37, 40] can generally obtain higher overall accuracy relying on the multi-view geometric cues. Specifically, the 3D cost volume has been proved simple and effective for depth estimation, which encodes the multi-frame matching probabilities with a set of depth hypotheses [15, 37, 40]. + +Although multi-frame methods are widely used in scene reconstruction [15, 34, 40], they encounter non-negligible challenges in the dynamic scenes with dynamic areas (e.g., moving cars and pedestrians). The dynamic areas cause corrupted values in the cost volume due to the violation of multi-view consistency [9, 33] and mislead the network predictions. However, depth estimation for the dynamic areas is usually crucial in most applications [10, 16, 19]. As shown in Fig. 1, multi-frame depth estimation for the dynamic cars is more challenging than the static backgrounds. + +To handle the dynamic areas violating the multi-view consistency, a few multi-frame depth estimation methods [9, 36, 37] try to identify and exclude the dynamic areas + +through an explicit mask obtained relying on some assumptions or heuristics. Specifically, some method [37] excludes the multi-frame cost volume relying on a learned dynamic mask and compensates the excluded areas with monocular features; some methods directly adjust the dynamic object locations in input images [9] or supervise multi-frame depth [36] with predicted monocular depth. However, these methods are usually sensitive to the explicit mask's quality, and the masks are obtained from additional networks or manually crafted criteria [9, 36, 37]. Despite better performances than pure multi-frame methods, these methods exhibit limited performance improvement compared with the additionally introduced monocular cues (as shown in Tab. 4), implying underutilized benefits from the fusion of the multiview and monocular cues. Although some self-supervised monocular depth estimation methods [4,5,11,14,22,23] also address the multi-view inconsistency issues, they mainly focus on handling the unfaithful self-supervision signals. + +To tackle the above issues, we propose a novel method that fuses the respective benefits from the monocular and multi-view cues, leading to significant improvement upon each individual source in dynamic scenes. We first analyze the behaviors of monocular and multi-frame cues in dynamic scenes, that the pure monocular method can generally learn good structural relations around the dynamic areas, and the pure multi-view cue preserves more accurate geometric properties in the static areas. We then unveil the effectiveness of leveraging the benefits of both depth cues by directly fusing depth volumes (Sec. 3). Inspired by the above observations, beyond treating monocular cues as a local supplement of multi-frame methods [9, 36, 37], we propose a cross-cue fusion (CCF) module to enhance the representations of multi-view and monocular cues with the other, and fuse them together for dynamic depth estimation. We use the spatially non-local relative intra-relations encoded in cross-observation attention (CCA) weights from each source to guide the representation of the other, as shown in Fig. 4. Specifically, the intra-relations of monocular cues can help to address multi-view inconsistency in dynamic areas, while the intra-relations from multi-view cues help to enhance the geometric property of the monocular representation, as visualized in Fig. 5. Unlike [1, 9, 36, 37], the proposed method unifies the input format of both cues as volumes and conducts fusion on them, which achieves better performances (as shown in Fig. 6). The proposed fusion module is learnable and does not require any heuristic masks, leading to better generalization and flexibility. + +Our main contributions are summarized as follows: + +- We analyze multi-frame and monocular depth estimations in dynamic scenes and unveil their respective advantages in static and dynamic areas. Inspired by this, we propose a novel method that fuses depth volumes from each cue to achieve significant improve + +ment upon individual estimations in dynamic scenes. + +- We propose a cross-cue fusion (CCF) module that utilizes the cross-cue attention to encode non-local intrarelations from one depth cue to guide the representation of the other. Different from methods using local masks, the attention weights learn mask-free global geometric information according to the geometric properties of each depth cue (as shown in Fig. 5). +- The proposed method outperforms the state-of-the-art method in dynamic areas with a significant error reduction of $21.3\%$ while retaining its superiority in overall performance on KITTI. It also achieves the best generalization ability on the DDAD dataset in dynamic areas than the competing methods. + +# 2. Related Work + +Learning-based multi-frame depth estimation. Learning depth estimation from multiple images has attracted much attention these years. Current methods [7, 15, 32, 39-41] typically construct a cost volume using homography warping [40] between multi-view images. Under the premise that the scene is static, the cost volume encodes the probabilities of different depth hypotheses for each pixel, which can be regularized by 3D CNNs to yield final depth prediction. Aiming at recovering the accurate structure of static scenes, many endeavors have been made in improving the quality of the cost volume [15, 28], network efficiency [15, 39, 41] as well as the temporal consistency [7, 32], etc. The effectiveness of static scene reconstruction has sparked several attempts [17, 36, 37] that extend the multi-frame depth estimation to the large-scale outdoor scenes. The cost-volume between temporal consistent images provides extra depth information than image contexts [36]. However, for the dynamic areas that violate the static scene assumption, the cost volume provides wrong depth probabilities, which mislead the network to produce wrong depth even under supervised learning [37]. In this regard, processing the dynamic areas has become one of the main challenges for multi-frame depth estimation in outdoor scenes. + +Dynamic depth estimation in outdoor scenes. Dynamic areas are ubiquitous in real-world scenarios and are important in applications such as autonomous driving. Some literature [4, 5, 14, 22, 23] seeks to handle dynamic objects in self-supervised monocular depth estimation, where the network inputs a single image and the dynamic areas mainly affect the supervisory signals. They typically identify possible moving objects by semantic [21] or instance segmentation [3-5, 22], then conduct robust learning by masking out dynamic areas during loss computation [21] or directly model the object motion [3-5, 14]. However, these methods differ from the topic discussed in this paper in that: 1) we focus on addressing multi-frame dynamic depth estimation issue, where the main challenge lies in the corrupted cost + +![](images/f5ef4c08322b0b795bb3d7af292d70f3b77c0976c57166a96573dc3a1ea506e1.jpg) +Figure 2. Multi-frame and monocular cues in the dynamic scene. a) Multi-frame cues preserve accurate geometric properties in the static area (yellow box), while the monocular cues learn good structural relations in dynamic areas (red box). b) Multi-frame and monocular cues show respective benefits in different areas. While our plain volume fusion scheme shows obvious performance improvement, the proposed cross-cue fusion demonstrates better capabilities to handle dynamic depth. c) Depth predictions that show progressive improvements from the plain volume fusion and cross-cue volume fusion. + +volumes than the self-supervised loss; 2) our method is supervised so that the robustness of loss function is beyond the main concern of this task. + +In the context of multi-frame depth estimation, current methods leverage the depth information from the single image to improve depth in dynamic areas. Manydepth [36] proposes a self-discovered mask and supervises the potential dynamic areas with monocular depth. MonoRec [37] proposes a motion segmentation network to mask out the dynamic areas in the cost volume and use only monocular image features to infer depth. Feng et al. [9] proposes to correct the dynamic object locations (with instance mask) in the image plane using monocular depth before computing the cost volume. Despite the higher dynamic results than pure multi-frame estimations, their performances are quite comparable [1,9,36], if not worse than, their proposed monocular branch (as shown in Tab. 4). Moreover, they require pre-computed instance masks [9,37] that bring extra computation burden for network training or inference. There also exists another multi-frame method [1] which guides multi-frame cost reconstruction with a large monocular network. However, due to the reliance on monocular network predictions, it demonstrates weaker generalization capabilities than multi-frame methods (Tab. 3). In contrast, our methods do not require any pre-computed object masks and achieve obvious improvement upon both monocular and multi-frame predictions in dynamic areas while retaining good generalization abilities across datasets. + +# 3. Analyses on Multi-view & Monocular Cues + +Focusing on the dynamic scenes, we first analyze the behaviors of the depth estimation methods relying on multiview and monocular cues, as in Sec. 3.1. Beyond the overall performance on the whole scene, we specifically study their behaviors in the dynamic area. Then we analyze how + +the proper fusion of multi-view and monocular cues may benefit depth estimation in dynamic areas in Sec. 3.2. + +# 3.1. Behaviours of Multi-view and Monocular Cues + +We implement two depth estimation methods with the two types of cues, i.e., Multi Cues and Mono Cues in Fig. 2, and train them on KITTI [10], where the U-Net [37] is used. Specifically, the model with multi-view cues (Multi Cues) takes the cost volume as the input. To analyze the performance in the dynamic areas, we use the dynamic mask from [37] computed by thresholding photometric error and depth inconsistency. + +As shown in Fig. 2 (b), multi-frame depth estimation generally achieves high overall accuracy relying on the multi-view clues. But the performance in the dynamic areas is degraded due to the violation of the multi-view consistency in the input cost volume. Fig. 2 (a) shows that the multi-frame method generates fine 3D structures in static areas (the yellow box), whereas its estimation of the moving car (the red box) is corrupted. Compared with the multi-frame method, the monocular depth estimation method achieves worse overall performance as shown in Fig. 2 (b), without the geometric cues. On the other hand, it does not suffer from multi-view inconsistency and thus performs better in dynamic areas. In Fig. 2 (a), we can observe that the two depth cues show respective benefits in different areas and different aspects of the dynamic scene. + +# 3.2. Potential Benefits of Fusion + +To handle the dynamic areas violating the multi-view consistency, previous multi-frame depth estimation methods [9, 36, 37] try to use the monocular information as the input for the dynamic areas. However, the improvement is limited due to the underutilized benefits of both cues, as discussed in Sec. 1. Especially the performance in the dynamic + +![](images/4714c9cac1b01afdd7b5375a908efcffc5df67b46eb2e4ac6da34ad05a3159da.jpg) +Figure 3. Overview of the proposed method. We first extract multi-frame depth cues with cost volume and monocular depth cues using one-hot depth volume. Then, we fuse the two volumes with the proposed cross-cue fusion module (CCF) to yield an improved fused depth feature. The fused depth feature is sent to the depth module for final depth estimation. + +areas is easily bounded by the monocular cues. + +We seek to explore better fusion schemes to use the complementary benefits of these two cues, as discussed in Sec. 3.1. Apart from using the monocular cues in the dynamic area, we also hope the geometric information learned with the multi-view cues in the static areas can be propagated to boost the monocular cues in dynamic areas, which requires a more comprehensive fusion process. + +To unify the information from both multi-frame and monocular cues, we encode both depth cues in the form of a cost volume [40] and a one-hot depth volume transferred from the estimated monocular depth map (in Sec. 4.2). In the analyses, we first consider the plain volume fusion using concatenation and convolution operations. This learnable fusion scheme brings obvious improvement in dynamic areas, implying proper fusion methods help to utilize the potential benefits in dynamic scenes. Based on the feasibility of learnable fusion, we further investigate the cross-cue volume fusion and it exhibits further improvement (in Fig. 2 (b), (c)), which indicates a better way to utilize depth cues as introduced in the next section. + +# 4. The Proposed Method + +We aim to learn depth $D_{t}$ of the target image $I_{t}$ from a short image sequence, with known or estimated camera parameters $K, T$ . In this paper, we define the image sequence as $\{I_{t-1}, I_{t}, I_{t+1}\}$ , where $\{I_{t-1}, I_{t+1}\}$ are adjacent images to the target frame $I_{t}$ , $K, T$ are camera intrinsic and extrinsic provided as known values. The goal is to conduct accurate estimations of the dynamic scenes that contain various challenging dynamic objects. + +# 4.1. Overview + +As shown in Fig. 3, the proposed method consists of three major parts - the multi-view and monocular cues are first represented as volumes through cost volume construction and depth one-hot vector transformation, the cross-cue fusion (CCF) module then fuses both multi-frame + +and monocular cues by leveraging attention mechanisms [13, 31, 38], i.e., extracting relative intra-relations of each cue to guide the other to yield improved geometric representations of dynamic scene structure. The depth module takes the fused representation to estimate the final depth. + +# 4.2. Representing Monocular and Multi-view Cues + +Multi-view cues as cost volume. We represent the multi-view cues by computing the cost volume following the pipeline of multi-view stereo (MVS) [37, 40]. We warp the adjacent images $\{I_{t-1}, I_{t+1}\}$ to the target view using $K, T$ and a set of depth hypotheses $d \in \{d_k\}_{k=1}^M$ uniformly sampled in the inverse depth space $[\frac{1}{d_{\min}}, \frac{1}{d_{\max}}]$ , where $M$ denotes the number of depth hypotheses and is set to 32 in our paper. We construct the multi-frame cost volume $C_{\mathrm{multi}} \in \mathbb{R}^{H \times W \times M}$ by measuring the pixel-wise similarity between the warped images and the target image, using SSIM [35] as introduced in [37]. For each pixel $(i,j)$ of $C_{\mathrm{multi}} \in [0,1]^{H \times W \times M}$ , channel positions $k \in \{1,\dots,M\}$ with large matching scores indicates a higher possibility to include real scene depth. + +Monocular cues as depth volume. We construct the monocular cues by estimating the single-view depth map and then transform the depth into one-hot depth volume. We leverage a U-Net architecture [37] without any complex designs for single-view depth estimation, yielding monocular prediction $D_{\mathrm{mono}} = f_{\theta}^{\mathrm{mono}}(I_t)$ , where $D_{\mathrm{mono}} \in \mathbb{R}^{H \times W}$ and $f_{\theta}^{\mathrm{mono}}$ is the monocular network. To facilitate smooth fusion between the multi-frame and monocular cues, different from methods using single-view features [34, 37] or depth values [9, 36], we transform the whole depth map $D_{\mathrm{mono}}$ into the depth volume $C_{\mathrm{mono}} \in \{0,1\}^{H \times W \times M}$ by converting each absolute depth value to a one-hot vector with + +$$ +C _ {\text {m o n o}, (i, j)} [ k ] = \left\{1 \mid d _ {\text {m o n o}} \in \left(d _ {k - 1}, d _ {k} \right] \right\} _ {k = 1} ^ {M}, \tag {1} +$$ + +where $C_{\mathrm{mono},(i,j)} \in \{0,1\}^M$ represents the one-hot vector in $C_{\mathrm{mono}}$ corresponding to the pixel at $(i,j)$ , $d_{\mathrm{mono}}$ is the + +![](images/b25ac88cdadf3807de061b641b5ab43b4d8b8e9ad897b56cace229304d22f016.jpg) +Figure 4. The cross-cue fusion (CCF) module. Taking both multi-frame and monocular depth volume as input, the CCF module enhances multi-frame depth features with the relative intra-relations of monocular depth volume $(R_{\mathrm{mono}})$ . Meanwhile, the intra-relations of the multi-frame depth volume $(R_{\mathrm{multi}})$ also enhance the monocular module, yielding enhanced depth features for the final depth estimation. + +pixel-wise depth value. Note that we also try some soft representations [1, 2] but observe no obvious improvement. + +# 4.3. The Cross-cue Fusion Module + +We propose a cross-cue fusion (CCF) module to fuse the multi-frame and monocular depth cues. Different from methods [9,36,37] that utilize the masked local depth cues for dynamic depth estimation, the CCF module fuses the whole depth cues using depth volumes, retaining the potential to leverage both benefits for further improvement. + +The Cross-cue fusion pipeline. Given volumes $C_{\mathrm{multi}}, C_{\mathrm{mono}} \in \mathbb{R}^{H \times W \times M}$ , we first process them via shallow convolution layers, yielding down-sampled monocular and multi-frame depth features $F_{\mathrm{multi}}, F_{\mathrm{mono}}$ in shape $\mathbb{R}^{h \times w \times M}$ . We then feed $F_{\mathrm{multi}}$ and $F_{\mathrm{mono}}$ to the proposed cross-cue attention (CCA) to enhance each depth cue by extracting the relative intra-relations from the other + +$$ +\begin{array}{l} \widetilde {F} _ {\text {m u l t i}} = \mathrm {C C A} _ {\text {m u l t i}} \left(F _ {\text {m o n o}}, F _ {\text {m u l t i}}\right), \\ \sim \end{array} \tag {2} +$$ + +$$ +\widetilde {F} _ {\text {m o n o}} = \mathrm {C C A} _ {\text {m o n o}} \left(F _ {\text {m u l t i}}, F _ {\text {m o n o}}\right), +$$ + +the enhanced features are concatenated to yield the fused feature $\widetilde{F}_{\mathrm{fused}}$ . To retain detailed information from initial depth cues, we process the input depth cues via $F_{\mathrm{cat}} = \mathrm{Cat}(\mathrm{Conv}(C_{\mathrm{multi}}), \mathrm{Conv}(C_{\mathrm{mono}}))$ and add the residual connection. The final cross-cue features can be written as + +$$ +F = \gamma \widetilde {F} _ {\text {f u s e d}} \uparrow + F _ {\text {c a t}}, \tag {3} +$$ + +where $\gamma$ is a learned weighting factor and $\uparrow$ denotes the up-sampling operation. The fused feature $F$ is then sent to the depth network along with the image context features to yield the final depth prediction $D_{t}\in \mathbb{R}^{H\times W}$ . + +![](images/c99e62fa290f354c703a60d29951645b95f7fe302846e76dd28eafa56f7d71e0.jpg) +(a) Input image + +![](images/8c5a7157631463911327bc44b59608a053c1591a3885a16da9480ae5c792c6e6.jpg) +(b) $R_{\mathrm{mono}}$ attenu. map + +![](images/72b0d90319d4ce4a0d8f5380df6abbb0b046d514f92be3004be29e321726ad0b.jpg) +(c) $R_{\mathrm{multi}}$ attain.map +Figure 5. Attention maps of the dynamic position (red dots) examples. $R_{\mathrm{mono}}$ boosts multi-frame features by attending monocular features around the dynamic areas for better use of the monocular cues in the corresponding areas. $R_{\mathrm{multi}}$ enhances monocular features by attending multi-frame features in the static areas which have more reliable geometric information. + +Cross-cue attention. The cross-cue attention (CCA) targets utilizing the relative intra-relation of one depth cue to improve the geometric information of another. Since the CCA modules are deployed in a parallel manner as shown in Fig. 4, we introduce $\widetilde{F}_{\mathrm{multi}} = \mathrm{CCA}_{\mathrm{multi}}(F_{\mathrm{mono}}, F_{\mathrm{multi}})$ in detail as an example. + +Given depth features $F_{\mathrm{mono}}$ , $F_{\mathrm{multi}} \in \mathbb{R}^{h \times w \times M}$ , we transform $F_{\mathrm{mono}}$ into query feature $Q_{\mathrm{mono}}$ and key feature $K_{\mathrm{mono}}$ , then transform $F_{\mathrm{multi}}$ into value feature $V_{\mathrm{multi}}$ using convolution operation $f(\cdot, \theta)$ + +$$ +Q _ {\text {m o n o}} = f \left(F _ {\text {m o n o}}, \theta_ {\text {m o n o}} ^ {Q}\right), +$$ + +$$ +K _ {\text {m o n o}} = f \left(F _ {\text {m o n o}}, \theta_ {\text {m o n o}} ^ {K}\right), \tag {4} +$$ + +$$ +V _ {\text {m u l t i}} = f \left(F _ {\text {m u l t i}}, \theta_ {\text {m u l t i}} ^ {V}\right), +$$ + +after reshaping all features into size $(hw, M)$ , we compute the non-local relative intra-relations of monocular features by matrix multiplication $\otimes$ followed by softmax operation + +$$ +R _ {\text {m o n o}} = \operatorname {S o f t m a x} \left(Q _ {\text {m o n o}} \otimes K _ {\text {m o n o}} ^ {T}\right), \tag {5} +$$ + +where $R_{\mathrm{mono}} \in \mathbb{R}^{hw \times hw}$ stands for the non-local intrarelations of the monocular depth cue. We then utilize $R_{\mathrm{mono}}$ to improve the geometric representations of the multi-frame feature $V_{\mathrm{multi}}$ by + +$$ +\widetilde {F} _ {\text {m u l t i}} = R _ {\text {m o n o}} \otimes V _ {\text {m u l t i}}, \tag {6} +$$ + +where $\widetilde{F}_{\mathrm{multi}}$ denotes the improved multi-frame representations benefited from monocular depth cues. Similar operations are done for $\widetilde{F}_{\mathrm{mono}} = \mathrm{CCA}_{\mathrm{mono}}(F_{\mathrm{multi}}, F_{\mathrm{mono}})$ , where $R_{\mathrm{multi}}$ stands for the intra-relations of multi-frame cues that can be used to improve monocular depth feature $V_{\mathrm{mono}}$ . Please refer to Fig. 4 for details. + +Effectiveness of the CCA. As shown in Fig. 5, despite using the same CCA operation, the intra-relations $R_{\mathrm{mono}}$ and $R_{\mathrm{multi}}$ attend different areas for improving the dynamic depth, showing their ability to capture respective benefits from monocular (better dynamic depth) and multi-frame cues (better static depth). This learned property enables + +
EvalMethodBack.Reso.Sup.Abs RelSq RelRMSERMSElogδ < 1.25δ < 1.252δ < 1.253
OverallManydepth [36]Res-18MRM0.0710.3433.1840.1080.9450.9910.998
DynamicDepth [9]Res-18MRM0.0680.2963.0670.1060.9450.9910.998
MonoRec [37]Res-18MRD*0.0500.2902.2660.0820.9720.9910.996
OursRes-18MRD0.0430.1512.1130.0730.9750.9960.999
MaGNet [1]Effi-B5MRD0.0570.2152.5970.0880.9670.9960.999
OursEffi-B5MRD0.0460.1552.1120.0760.9730.9960.999
MaGNet [1]Effi-B5HRD0.0430.1352.0470.0820.9810.9970.999
OursEffi-B5HRD0.0390.1031.7180.0670.9810.9970.999
DynamicManydepth [36]Res-18MRM0.2223.3907.9210.2370.6760.9020.964
DynamicDepth [9]Res-18MRM0.2082.7577.3620.2270.6820.9110.971
MonoRec [37]Res-18MRD*0.3609.08310.9630.3460.5900.8820.780
OursRes-18MRD0.1180.8354.2970.1460.8710.9750.990
MaGNet [1]Effi-B5MRD0.1411.2194.8770.1680.8300.9550.986
OursEffi-B5MRD0.1110.7684.1170.1350.8810.9800.994
MaGNet [1]Effi-B5HRD0.1401.0604.5810.2020.8340.9540.982
OursEffi-B5HRD0.1120.8304.1010.1370.8850.9780.992
+ +Table 1. Quantitative comparisons on KITTI [10] Odometry dataset. 'Back.' denotes the network backbone. 'Reso.' denotes the image resolutions, where 'MR' refers to the resolution of $256 \times 512$ and 'HR' is $352 \times 1216$ . In the 'Sup.' column, 'M' are self-supervised methods, 'D*' refers to semi-supervised methods trained with pseudo GT depth, while 'D' denotes fully-supervised methods. Color blue denotes 'lower is better', while red means 'higher is better'. The best results are in bold. + +unbounded dynamic depth performance upon both predictions, i.e., the monocular depth can be improved by attending static depth (using $R_{\mathrm{multi}}$ ) from multi-frame cues, and the improved monocular depth in dynamic areas will further be propagated (with $R_{\mathrm{mono}}$ ) to multi-frame predictions. + +# 4.4. Loss Function + +Given the predicted depth $D$ and the ground truth depth $\hat{D}$ , the loss can be described as + +$$ +\mathcal {L} (D, \hat {D}) = \beta \mathcal {L} _ {\mathrm {S I}} (D, \hat {D}) + \mathcal {L} _ {\mathrm {V N L}} (D, \hat {D}), \tag {7} +$$ + +where $\mathcal{L}_{\mathrm{SI}}$ denotes the scale-invariant loss [2,8], $\mathcal{L}_{\mathrm{VNL}}$ is the virtual normal loss [42, 43], $\beta$ is the weighing factor which is set to 4. Since we have both monocular depth $D_{\mathrm{mono}}$ and the final depth prediction $D_{\mathrm{t}}$ , the final loss $\mathcal{L}_{\mathrm{final}}$ is + +$$ +\mathcal {L} _ {\text {f i n a l}} = \mathcal {L} \left(D _ {\text {m o n o}}, \hat {D}\right) + \mathcal {L} \left(D _ {t}, \hat {D}\right). \tag {8} +$$ + +# 5. Experiments + +In this section, we compare our method with state-of-the-art multi-frame depth estimation methods [1,9,36,37] for the dynamic scenes, including self-supervised methods [9,36], semi-supervised method [37] as well as fully supervised method [6] (Sec. 5.3). We then conduct ablation studies (Sec. 5.4) to evaluate different variants of our method. We also validate the generalization ability (Sec. 5.5) and evaluate different methods' improvements upon monocular networks for depth estimation in dynamic areas (Sec. 5.6). + +# 5.1. Datasets + +KITTI. We follow [37] to evaluate dynamic sequential data on KITTI Odometry dataset, which contains 13666 + +training and 8634 testing samples. We use the dynamic masks provided by [37] for evaluation. More than 1300 samples in the test set contain dynamic objects. We evaluate both medium-resolution (MR: $256 \times 512$ ) and high-resolution (HR: $352 \times 1216$ ) depth results, and the metrics are computed within the 0-80m range. + +DDAD. We use the forward-facing cameras with 3848 testing samples to evaluate the methods' generalization ability. Since there is no pre-defined dynamic mask for DDAD, we construct it by warping adjacent images with GT depth and selecting instance masks with high photometric error. More than $70\%$ of the test set contains dynamic objects. All methods are evaluated within the 0-80m depth range. + +# 5.2. Implementation Details + +We implement our method with Pytorch [29] and train it using NVIDIA TITAN RTX GPUs. Unless specified, both the monocular network and the depth network are consistent with the depth module of [37], with ResNet-18 [18] as backbone using ImageNet [6] pre-trained parameters. We train our model for 80 epochs using the Adam [20] optimizer and learning rate of $10^{-4}$ , which further drops to $10^{-5}$ after 65 epochs. The batch size is set to 8. + +# 5.3. Results on KITTI + +We show both overall and dynamic performances of different methods in Table 1. Our method achieves the best performance in dynamic depth estimation. Specifically, it outperforms the SOTA fully-supervised MaGNet [1] with $21.28\%$ reduction of Abs.Rel $(0.141\rightarrow 0.111)$ , using the same Efficient-B5 [30] backbone. Significant improvements are also observed compared to self/semi-supervised + +
#CategoryVariantDynamicOverall
Abs RelAbs SqRMSE\( RMSE_{log} \)δ ≤ 1.25AbsRel
1Depth with single cuesPure multi-frame cues0.3827.16710.2920.350.5090.041
2Pure monocular cues0.1491.3695.2820.1780.8100.106
3Volume fusion with masksSelf-discovered mask [36]0.1300.9904.6920.1600.8370.043
4MaskNetwork [37]0.2202.8966.2990.2230.7350.040
5Volume fusion without masksStack & 3D Convs0.1541.4795.8660.1890.7770.046
6Stack & 3D U-Net [15]0.1551.4445.7620.1910.7720.040
7Concat & 2D Convs0.1381.1245.1100.1740.8150.043
8Ours CCF w./o. \( R_{multi} \)0.1240.9394.6100.1540.8550.043
9Ours CCF w./o. \( R_{mono} \)0.1230.9264.5450.1530.8610.043
10Ours CCF w./ only intra-cue self-attention0.1220.8964.5440.1520.8600.042
11Ours CCF w./o. residual connection0.1300.9614.6160.1570.8400.048
12Ours depth module w./o. \( I_t \)0.1260.9544.6360.1550.8440.042
13Ours CCF - full0.1180.8354.2970.1460.8710.043
+ +Table 2. Ablation experiments on KITTI. We show the results of different fusion types of multi-frame and monocular cues. 'CCF' denotes the proposed cross-cue fusion module. 'Dynamic' denotes dynamic depth errors while 'Overall' refers to the overall depth error. + +methods [9, 36, 37]. Besides the obvious improvement in dynamic areas, our method also outperforms other methods in most metrics for overall performance. Qualitative results are shown in Fig. 6. While the dynamic areas generally lead to performance decline for other multi-frame methods, our method conducts obviously better estimations in the moving objects, while retaining the overall accuracy. + +# 5.4. Ablation Study + +As shown in Sec. 3, the way to fuse the two volumes in our method influences the final dynamic depth performance. As shown in Tab. 2, we fuse the multi-frame and monocular volumes in ways that the explicit mask is needed ('Volume fusion with masks') and the explicit mask is not needed ('Volume fusion without masks'). We also evaluate the variants of our method ('Ours CCF') as individual mask-free methods. Row #1~2 shows individual depth results leveraging pure multi-frame and monocular cues. + +Volume fusion with explicit masks. We leverage the computed [36] or learned masks [37] to fuse our volumes from two depth cues. As shown in row #3~4, The dynamic mask generated by [37] does not surpass the baseline fusion method. While the self-discovered mask [36] shows certain improvements in dynamic areas, it is computed using heuristics and thus has uncontrolled dynamic mask quality, which leads to more restricted performances than ours. + +Volume fusion without masks. Besides our method that uses a mask-free fusion scheme, we also fuse the volumes without any mask using convolutions. We first stack the depth volumes in a new dimension and process the fused features with layers of 3D convolutions as well as the 3D U-Net, which is commonly used in the MVS methods [15,40]. As shown in row #5~6 of Table 2, the 3D convolutions have no obvious improvement in dynamic areas. However, as we concatenate the two depth volumes and process them with 2D convolution layers (as in Fig. 2), we observe further im + +provements (row #7) upon the pure monocular results, but this mask-free fusion scheme still lags behind our method with obvious margins. + +Design choices of the network modules. We evaluate different variants of our method as an individual type of mask-free fusion method. We respectively remove the multiframe (row #8) and monocular (row #9) intra-relations and directly feed the other cues to the fused feature. Then, we implement another variant of our method with intra-cue self-attention in the proposed CCF (row #10). We also disable the residual connection of $F_{\mathrm{cat}}$ (row #11) and the input target image $I_{t}$ of the depth module (row #12). Results show the effectiveness and necessity of the proposed technical designs, which achieves the best performance. + +# 5.5. Generalization on DDAD + +To validate the generalization ability of our method, we test the KITTI models of all supervised methods [1, 37] on the challenging DDAD dataset. As shown in Table 3, previous methods either degrade on the dynamic areas due to the violation of multi-view consistency (e.g., AbsRel 0.544 for [37]) or exhibit worse overall performance owing to the cross-domain issue of the monocular network (e.g., AbsRel 0.208 for [1]). Our method shows more promising results that it not only outperforms other methods in dynamic depth estimation but also retains the advantage of multi-frame estimation with competitive overall performance. + +# 5.6. Improvement upon Monocular Estimation + +Though previous methods handle dynamic areas with monocular cues, the improvement is usually constrained by monocular results. In Tab. 4, we show the final depth results in dynamic areas and compare them against the monocular results of each method. Our method achieves the largest Abs.Rel reduction of $20.8\%$ compared to other methods which explicitly use the monocular network. Meanwhile, + +![](images/2fe7e7f3830285cd039b3a988a558ed46f8f023c3643adc2f1b4e46f9d5fa465.jpg) +Figure 6. Qualitative results on KITTI dataset [10]. From left to right: depth predictions (dynamic objects are highlighted with red boxes), error maps, and the reconstructed point clouds of dynamic areas. Our method achieves the best dynamic results and reconstructs more reasonable object shapes than state-of-the-art methods. + +
EvalMethodBackboneAbs RelSq RelRMSE\( RMSE_{log} \)δ<1.25\( \delta < 1.25^2 \)\( \delta < 1.25^3 \)
OverallMonoRec [37]Res-180.1583.1027.5530.2270.8540.9310.961
MaGNet [1]Effi-B50.2082.64110.7390.3820.6200.8780.942
OursRes-180.1582.4169.8550.2990.7470.8940.947
DynamicMonoRec [37]Res-180.54416.70316.1160.4820.4600.6670.798
MaGNet [1]Effi-B50.2663.98211.7150.3980.4620.8150.917
OursRes-180.2343.61111.0070.3310.5760.8350.921
+ +Table 3. Generalizations on DDAD [16] dataset. The best results are in bold and the second best results are underlined. Our method achieves a competitive overall performance with other method while achieving the best result in dynamic areas. + +
MethodMono. Err.Final Err.Err. Redu.
Manydepth [36]0.2120.222-4.72%
Dynamicdepth [9]0.2140.2082.83%
MaGNet [1]0.1530.1417.84%
Ours - Res.180.1490.11820.81%
Ours - Res.500.1450.11620.00%
+ +Table 4. Error reduction upon monocular estimation in dynamic areas. We show different methods' dynamic Abs.Rel depth errors (Final Err.) and their reduction (Err. Redu.) compared to their individual monocular performances (Mono. Err.). Our method achieves the largest error reduction over others and exhibits a consistent error reduction when a better backbone is used. + +when using a backbone with larger capacities (ResNet-50), our method achieves consistent error reduction upon the monocular network, showing its scalability and flexibility. + +# 6. Conclusion + +We improve multi-frame dynamic depth estimation by fusing the multi-view and monocular depth cues with the proposed cross-cue fusion. Experiments show its effectiveness as well as the generalization ability. Limitation: there still exists a performance gap to fill, especially in dynamic areas, with a better fusion of the cues. Meanwhile, the challenging occlusion issues for general multi-frame methods require further exploration. + +Acknowledgements Y. Zhu, J. Sun, and Y. Zhang acknowledge the support from National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology. This work was partially supported by NSFC (No.U19B2037) and the Natural Science Basic Research Program of Shaanxi (No.2021JCW-03) to Y. Zhang; an ARC DECRA Fellowship DE230101591 to D. Gong. + +# References + +[1] Gwangbin Bae, Ignas Budvyitis, and Roberto Cipolla. Multiview depth estimation by fusing single-view depth probability with multi-view geometry. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2842-2851, 2022. 2, 3, 5, 6, 7, 8 +[2] Shariq Farooq Bhat, Ibrahim Alhashim, and Peter Wonka. Adabins: Depth estimation using adaptive bins. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4009-4018, 2021. 1, 5, 6 +[3] Fabian Brickwedde, Steffen Abraham, and Rudolf Mester. Mono-sf: Multi-view geometry meets single-view depth for monocular scene flow estimation of dynamic traffic scenes. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2780-2790, 2019. 2 +[4] Zhe Cao, Abhishek Kar, Christian Hane, and Jitendra Malik. Learning independent object motion from unlabelled stereoscopic videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5594-5603, 2019. 2 +[5] Vincent Casser, Soeren Pirk, Reza Mahjourian, and Anelia Angelova. Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 8001-8008, 2019. 2 +[6] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 6 +[7] Arda Duzceker, Silvano Galliani, Christoph Vogel, Pablo Speciale, Mihai Dusmanu, and Marc Pollefeys. Deepvideovs: Multi-view stereo on video with recurrent spatiotemporal fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15324-15333, 2021. 2 +[8] David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. In Advances in neural information processing systems, pages 2366-2374, 2014. 6 +[9] Ziyue Feng, Liang Yang, Longlong Jing, Haiyan Wang, YingLi Tian, and Bing Li. Disentangling object motion and occlusion for unsupervised multi-frame monocular depth. arXiv preprint arXiv:2203.15174, 2022. 1, 2, 3, 4, 5, 6, 7, 8 +[10] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3354-3361. IEEE, 2012. 1, 3, 6, 8 +[11] Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J Brostow. Digging into self-supervised monocular depth estimation. In Proceedings of the IEEE International Conference on Computer Vision, pages 3828-3838, 2019. 2 +[12] Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton Van Den Hengel, and Qinfeng Shi. From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur. In Proceed- + +ings of the IEEE conference on computer vision and pattern recognition, pages 2319-2328, 2017. 1 +[13] Dong Gong, Frederic Z Zhang, Javen Qinfeng Shi, and Anton Van Den Hengel. Memory-augmented dynamic neural relational inference. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11843-11852, 2021. 4 +[14] Ariel Gordon, Hanhan Li, Rico Jonschkowski, and Anelia Angelova. Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8977-8986, 2019. 2 +[15] Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, and Ping Tan. Cascade cost volume for high-resolution multi-view stereo and stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2495-2504, 2020. 1, 2, 7 +[16] Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon. 3d packing for self-supervised monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2485-2494, 2020. 1, 8 +[17] Vitor Guizilini, Rares Ambrus, Dian Chen, Sergey Zakharov, and Adrien Gaidon. Multi-frame self-supervised depth with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 160-170, 2022. 2 +[18] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 1, 6 +[19] Matthias Innnmann, Kihwan Kim, Jinwei Gu, Matthias Nießner, Charles Loop, Marc Stamminger, and Jan Kautz. Nrmvs: Non-rigid multi-view stereo. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2754-2763, 2020. 1 +[20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6 +[21] Marvin Klingner, Jan-Aike Termöhlen, Jonas Mikolajczyk, and Tim Fingscheidt. Self-supervised monocular depth estimation: Solving the dynamic object problem by semantic guidance. In European Conference on Computer Vision, pages 582-600. Springer, 2020. 2 +[22] Seokju Lee, Sunghoon Im, Stephen Lin, and In So Kweon. Learning monocular depth in dynamic scenes via instance-aware projection consistency. arXiv preprint arXiv:2102.02629, 2021. 2 +[23] Hanhan Li, Ariel Gordon, Hang Zhao, Vincent Casser, and Anelia Angelova. Unsupervised monocular depth learning in dynamic scenes. arXiv preprint arXiv:2010.16404, 2020. 2 +[24] Rui Li, Xiantuo He, Yu Zhu, Xianjun Li, Jinqiu Sun, and Yanning Zhang. Enhancing self-supervised monocular depth estimation via incorporating robust constraints. In Proceedings of the 28th ACM International Conference on Multimedia, pages 3108-3117, 2020. 1 + +[25] Rui Li, Danna Xue, Shaolin Su, Xiantuo He, Qing Mao, Yu Zhu, Jinqiu Sun, and Yanning Zhang. Learning depth via leveraging semantics: Self-supervised monocular depth estimation with both implicit and explicit semantic guidance. Pattern Recognition, page 109297, 2023. 1 +[26] Rui Li, Danna Xue, Yu Zhu, Hao Wu, Jinqiu Sun, and Yanning Zhang. Self-supervised monocular depth estimation with frequency-based recurrent refinement. IEEE Transactions on Multimedia, 2022. 1 +[27] Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, and Zeming Li. Bevdepth: Acquisition of reliable depth for multi-view 3d object detection. arXiv preprint arXiv:2206.10092, 2022. 1 +[28] Zhenxing Mi, Chang Di, and Dan Xu. Generalized binary search network for highly-efficient multi-view stereo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12991-13000, 2022. 2 +[29] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017. 6 +[30] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019. 6 +[31] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing systems, pages 5998-6008, 2017. 4 +[32] Fangjinhua Wang, Silvano Galliani, Christoph Vogel, and Marc Pollefeys. Itermvs: Iterative probability estimation for efficient multi-view stereo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8606-8615, 2022. 2 +[33] Tai Wang, Jiangmiao Pang, and Dahua Lin. Monocular 3d object detection with depth from motion. arXiv preprint arXiv:2207.12988, 2022. 1 +[34] Xiaofeng Wang, Zheng Zhu, Guan Huang, Fangbo Qin, Yun Ye, Yijia He, Xu Chi, and Xingang Wang. Mvster: epipolar transformer for efficient multi-view stereo. In European Conference on Computer Vision, pages 573-591. Springer, 2022. 1, 4 +[35] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004. 4 +[36] Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel Brostow, and Michael Firman. The temporal opportunist: Self-supervised multi-frame monocular depth. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1164-1174, 2021. 1, 2, 3, 4, 5, 6, 7, 8 +[37] Felix Wimbauer, Nan Yang, Lukas Von Stumberg, Niclas Zeller, and Daniel Cremers. Monorec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6112-6122, 2021. 1, 2, 3, 4, 5, 6, 7, 8 + +[38] Qingsen Yan, Dong Gong, Javen Qinfeng Shi, Anton van den Hengel, Chunhua Shen, Ian Reid, and Yanning Zhang. Dual-attention-guided network for ghost-free high dynamic range imaging. International Journal of Computer Vision, pages 1–19, 2022. 4 +[39] Zhenpei Yang, Zhile Ren, Qi Shan, and Qixing Huang. Mvs2d: Efficient multi-view stereo via attention-driven 2d convolutions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8574-8584, 2022. 2 +[40] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. Mvsnet: Depth inference for unstructured multi-view stereo. In Proceedings of the European conference on computer vision (ECCV), pages 767-783, 2018. 1, 2, 4, 7 +[41] Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, and Long Quan. Recurrent mvsnet for high-resolution multi-view stereo depth inference. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5525-5534, 2019. 2 +[42] Wei Yin, Yifan Liu, and Chunhua Shen. Virtual normal: Enforcing geometric constraints for accurate and robust depth prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. 1, 6 +[43] Wei Yin, Yifan Liu, Chunhua Shen, and Youliang Yan. Enforcing geometric constraints of virtual normal for depth prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5684-5693, 2019. 6 +[44] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Simon Chen, Yifan Liu, and Chunhua Shen. Towards accurate reconstruction of 3d scene shape from a single monocular image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 1 +[45] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, and Chunhua Shen. Learning to recover 3d scene shape from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 204-213, 2021. 1 +[46] Weihao Yuan, Xiaodong Gu, Zuozhuo Dai, Siyu Zhu, and Ping Tan. 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When applied in dynamic scenes, e.g., autonomous driving, this consistency is usually violated in the dynamic areas, leading to corrupted estimations. Many multi-frame methods handle dynamic areas by identifying them with explicit masks and compensating the multi-view cues with monocular cues represented as local monocular depth or features. The improvements are limited due to the uncontrolled quality of the masks and the underutilized benefits of the fusion of the two types of cues. In this paper, we propose a novel method to learn to fuse the multi-view and monocular cues encoded as volumes without needing the heuristically crafted masks. As unveiled in our analyses, the multiview cues capture more accurate geometric information in static areas, and the monocular cues capture more useful contexts in dynamic areas. To let the geometric perception learned from multi-view cues in static areas propagate to the monocular representation in dynamic areas and let monocular cues enhance the representation of multi-view cost volume, we propose a cross-cue fusion (CCF) module, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other. Experiments on real-world datasets prove the significant effectiveness and generalization ability of the proposed method." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 604, + 128, + 616 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 604, + 128, + 616 + ], + "spans": [ + { + "bbox": [ + 47, + 604, + 128, + 616 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 623, + 287, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 623, + 287, + 696 + ], + "spans": [ + { + "bbox": [ + 46, + 623, + 287, + 696 + ], + "type": "text", + "content": "Depth estimation is a fundamental and challenging task for 3D scene understanding in various application scenarios, such as autonomous driving [10, 16, 27]. With the advent of convolutional neural networks (CNNs) [12, 18], depth estimation methods [2, 24-26, 40, 45, 46] are capable of predicting promising results given either single or mul" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 312, + 235, + 538, + 392 + ], + "blocks": [ + { + "bbox": [ + 312, + 235, + 538, + 392 + ], + "lines": [ + { + "bbox": [ + 312, + 235, + 538, + 392 + ], + "spans": [ + { + "bbox": [ + 312, + 235, + 538, + 392 + ], + "type": "image", + "image_path": "7cd273c8879928efaed4a1f5fcf6ae2c35565e6943f6eb442ff04e5f009752c3.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 392, + 546, + 457 + ], + "lines": [ + { + "bbox": [ + 305, + 392, + 546, + 457 + ], + "spans": [ + { + "bbox": [ + 305, + 392, + 546, + 457 + ], + "type": "text", + "content": "Figure 1. Depth estimation in dynamic scenes. Multi-frame predictions reserve high overall accuracy while degrading in dynamic areas. The monocular method better handles moving areas while suffering in static areas. Our method fuses both multi-frame and monocular depth cues for final prediction, yielding superior performance of the whole scene." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 461, + 546, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 461, + 546, + 557 + ], + "spans": [ + { + "bbox": [ + 304, + 461, + 546, + 557 + ], + "type": "text", + "content": "tiple images. The single image-based methods learn the monocular cues, e.g., the texture or object-level features, to predict the depth [2, 42, 44], while multi-frame methods [36, 37, 40] can generally obtain higher overall accuracy relying on the multi-view geometric cues. Specifically, the 3D cost volume has been proved simple and effective for depth estimation, which encodes the multi-frame matching probabilities with a set of depth hypotheses [15, 37, 40]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 558, + 547, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 547, + 677 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 547, + 677 + ], + "type": "text", + "content": "Although multi-frame methods are widely used in scene reconstruction [15, 34, 40], they encounter non-negligible challenges in the dynamic scenes with dynamic areas (e.g., moving cars and pedestrians). The dynamic areas cause corrupted values in the cost volume due to the violation of multi-view consistency [9, 33] and mislead the network predictions. However, depth estimation for the dynamic areas is usually crucial in most applications [10, 16, 19]. As shown in Fig. 1, multi-frame depth estimation for the dynamic cars is more challenging than the static backgrounds." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 677, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 547, + 713 + ], + "type": "text", + "content": "To handle the dynamic areas violating the multi-view consistency, a few multi-frame depth estimation methods [9, 36, 37] try to identify and exclude the dynamic areas" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 702, + 228, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 702, + 228, + 713 + ], + "spans": [ + { + "bbox": [ + 61, + 702, + 228, + 713 + ], + "type": "text", + "content": "Corresponding author: Y. Zhang, D. Gong, W. Yin." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21539" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 288 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 288 + ], + "type": "text", + "content": "through an explicit mask obtained relying on some assumptions or heuristics. Specifically, some method [37] excludes the multi-frame cost volume relying on a learned dynamic mask and compensates the excluded areas with monocular features; some methods directly adjust the dynamic object locations in input images [9] or supervise multi-frame depth [36] with predicted monocular depth. However, these methods are usually sensitive to the explicit mask's quality, and the masks are obtained from additional networks or manually crafted criteria [9, 36, 37]. Despite better performances than pure multi-frame methods, these methods exhibit limited performance improvement compared with the additionally introduced monocular cues (as shown in Tab. 4), implying underutilized benefits from the fusion of the multiview and monocular cues. Although some self-supervised monocular depth estimation methods [4,5,11,14,22,23] also address the multi-view inconsistency issues, they mainly focus on handling the unfaithful self-supervision signals." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 291, + 289, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 291, + 289, + 639 + ], + "spans": [ + { + "bbox": [ + 46, + 291, + 289, + 639 + ], + "type": "text", + "content": "To tackle the above issues, we propose a novel method that fuses the respective benefits from the monocular and multi-view cues, leading to significant improvement upon each individual source in dynamic scenes. We first analyze the behaviors of monocular and multi-frame cues in dynamic scenes, that the pure monocular method can generally learn good structural relations around the dynamic areas, and the pure multi-view cue preserves more accurate geometric properties in the static areas. We then unveil the effectiveness of leveraging the benefits of both depth cues by directly fusing depth volumes (Sec. 3). Inspired by the above observations, beyond treating monocular cues as a local supplement of multi-frame methods [9, 36, 37], we propose a cross-cue fusion (CCF) module to enhance the representations of multi-view and monocular cues with the other, and fuse them together for dynamic depth estimation. We use the spatially non-local relative intra-relations encoded in cross-observation attention (CCA) weights from each source to guide the representation of the other, as shown in Fig. 4. Specifically, the intra-relations of monocular cues can help to address multi-view inconsistency in dynamic areas, while the intra-relations from multi-view cues help to enhance the geometric property of the monocular representation, as visualized in Fig. 5. Unlike [1, 9, 36, 37], the proposed method unifies the input format of both cues as volumes and conducts fusion on them, which achieves better performances (as shown in Fig. 6). The proposed fusion module is learnable and does not require any heuristic masks, leading to better generalization and flexibility." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 59, + 642, + 267, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 642, + 267, + 652 + ], + "spans": [ + { + "bbox": [ + 59, + 642, + 267, + 652 + ], + "type": "text", + "content": "Our main contributions are summarized as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 58, + 654, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 654, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 58, + 654, + 287, + 714 + ], + "type": "text", + "content": "- We analyze multi-frame and monocular depth estimations in dynamic scenes and unveil their respective advantages in static and dynamic areas. Inspired by this, we propose a novel method that fuses depth volumes from each cue to achieve significant improve" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 324, + 72, + 539, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 72, + 539, + 84 + ], + "spans": [ + { + "bbox": [ + 324, + 72, + 539, + 84 + ], + "type": "text", + "content": "ment upon individual estimations in dynamic scenes." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 317, + 86, + 547, + 243 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 317, + 86, + 545, + 170 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 86, + 545, + 170 + ], + "spans": [ + { + "bbox": [ + 317, + 86, + 545, + 170 + ], + "type": "text", + "content": "- We propose a cross-cue fusion (CCF) module that utilizes the cross-cue attention to encode non-local intrarelations from one depth cue to guide the representation of the other. Different from methods using local masks, the attention weights learn mask-free global geometric information according to the geometric properties of each depth cue (as shown in Fig. 5)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 317, + 172, + 547, + 243 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 172, + 547, + 243 + ], + "spans": [ + { + "bbox": [ + 317, + 172, + 547, + 243 + ], + "type": "text", + "content": "- The proposed method outperforms the state-of-the-art method in dynamic areas with a significant error reduction of " + }, + { + "bbox": [ + 317, + 172, + 547, + 243 + ], + "type": "inline_equation", + "content": "21.3\\%" + }, + { + "bbox": [ + 317, + 172, + 547, + 243 + ], + "type": "text", + "content": " while retaining its superiority in overall performance on KITTI. It also achieves the best generalization ability on the DDAD dataset in dynamic areas than the competing methods." + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 250, + 392, + 262 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 250, + 392, + 262 + ], + "spans": [ + { + "bbox": [ + 306, + 250, + 392, + 262 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 270, + 547, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 270, + 547, + 545 + ], + "spans": [ + { + "bbox": [ + 304, + 270, + 547, + 545 + ], + "type": "text", + "content": "Learning-based multi-frame depth estimation. Learning depth estimation from multiple images has attracted much attention these years. Current methods [7, 15, 32, 39-41] typically construct a cost volume using homography warping [40] between multi-view images. Under the premise that the scene is static, the cost volume encodes the probabilities of different depth hypotheses for each pixel, which can be regularized by 3D CNNs to yield final depth prediction. Aiming at recovering the accurate structure of static scenes, many endeavors have been made in improving the quality of the cost volume [15, 28], network efficiency [15, 39, 41] as well as the temporal consistency [7, 32], etc. The effectiveness of static scene reconstruction has sparked several attempts [17, 36, 37] that extend the multi-frame depth estimation to the large-scale outdoor scenes. The cost-volume between temporal consistent images provides extra depth information than image contexts [36]. However, for the dynamic areas that violate the static scene assumption, the cost volume provides wrong depth probabilities, which mislead the network to produce wrong depth even under supervised learning [37]. In this regard, processing the dynamic areas has become one of the main challenges for multi-frame depth estimation in outdoor scenes." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 545, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 547, + 714 + ], + "type": "text", + "content": "Dynamic depth estimation in outdoor scenes. Dynamic areas are ubiquitous in real-world scenarios and are important in applications such as autonomous driving. Some literature [4, 5, 14, 22, 23] seeks to handle dynamic objects in self-supervised monocular depth estimation, where the network inputs a single image and the dynamic areas mainly affect the supervisory signals. They typically identify possible moving objects by semantic [21] or instance segmentation [3-5, 22], then conduct robust learning by masking out dynamic areas during loss computation [21] or directly model the object motion [3-5, 14]. However, these methods differ from the topic discussed in this paper in that: 1) we focus on addressing multi-frame dynamic depth estimation issue, where the main challenge lies in the corrupted cost" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21540" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 52, + 519, + 221 + ], + "blocks": [ + { + "bbox": [ + 63, + 52, + 519, + 221 + ], + "lines": [ + { + "bbox": [ + 63, + 52, + 519, + 221 + ], + "spans": [ + { + "bbox": [ + 63, + 52, + 519, + 221 + ], + "type": "image", + "image_path": "f5ef4c08322b0b795bb3d7af292d70f3b77c0976c57166a96573dc3a1ea506e1.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 223, + 547, + 277 + ], + "lines": [ + { + "bbox": [ + 46, + 223, + 547, + 277 + ], + "spans": [ + { + "bbox": [ + 46, + 223, + 547, + 277 + ], + "type": "text", + "content": "Figure 2. Multi-frame and monocular cues in the dynamic scene. a) Multi-frame cues preserve accurate geometric properties in the static area (yellow box), while the monocular cues learn good structural relations in dynamic areas (red box). b) Multi-frame and monocular cues show respective benefits in different areas. While our plain volume fusion scheme shows obvious performance improvement, the proposed cross-cue fusion demonstrates better capabilities to handle dynamic depth. c) Depth predictions that show progressive improvements from the plain volume fusion and cross-cue volume fusion." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 284, + 288, + 319 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 284, + 288, + 319 + ], + "spans": [ + { + "bbox": [ + 46, + 284, + 288, + 319 + ], + "type": "text", + "content": "volumes than the self-supervised loss; 2) our method is supervised so that the robustness of loss function is beyond the main concern of this task." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 320, + 288, + 620 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 320, + 288, + 620 + ], + "spans": [ + { + "bbox": [ + 46, + 320, + 288, + 620 + ], + "type": "text", + "content": "In the context of multi-frame depth estimation, current methods leverage the depth information from the single image to improve depth in dynamic areas. Manydepth [36] proposes a self-discovered mask and supervises the potential dynamic areas with monocular depth. MonoRec [37] proposes a motion segmentation network to mask out the dynamic areas in the cost volume and use only monocular image features to infer depth. Feng et al. [9] proposes to correct the dynamic object locations (with instance mask) in the image plane using monocular depth before computing the cost volume. Despite the higher dynamic results than pure multi-frame estimations, their performances are quite comparable [1,9,36], if not worse than, their proposed monocular branch (as shown in Tab. 4). Moreover, they require pre-computed instance masks [9,37] that bring extra computation burden for network training or inference. There also exists another multi-frame method [1] which guides multi-frame cost reconstruction with a large monocular network. However, due to the reliance on monocular network predictions, it demonstrates weaker generalization capabilities than multi-frame methods (Tab. 3). In contrast, our methods do not require any pre-computed object masks and achieve obvious improvement upon both monocular and multi-frame predictions in dynamic areas while retaining good generalization abilities across datasets." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 632, + 284, + 646 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 632, + 284, + 646 + ], + "spans": [ + { + "bbox": [ + 47, + 632, + 284, + 646 + ], + "type": "text", + "content": "3. Analyses on Multi-view & Monocular Cues" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 654, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 287, + 715 + ], + "type": "text", + "content": "Focusing on the dynamic scenes, we first analyze the behaviors of the depth estimation methods relying on multiview and monocular cues, as in Sec. 3.1. Beyond the overall performance on the whole scene, we specifically study their behaviors in the dynamic area. Then we analyze how" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 284, + 545, + 308 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 284, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 305, + 284, + 545, + 308 + ], + "type": "text", + "content": "the proper fusion of multi-view and monocular cues may benefit depth estimation in dynamic areas in Sec. 3.2." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 318, + 545, + 330 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 318, + 545, + 330 + ], + "spans": [ + { + "bbox": [ + 305, + 318, + 545, + 330 + ], + "type": "text", + "content": "3.1. Behaviours of Multi-view and Monocular Cues" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 337, + 546, + 432 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 337, + 546, + 432 + ], + "spans": [ + { + "bbox": [ + 304, + 337, + 546, + 432 + ], + "type": "text", + "content": "We implement two depth estimation methods with the two types of cues, i.e., Multi Cues and Mono Cues in Fig. 2, and train them on KITTI [10], where the U-Net [37] is used. Specifically, the model with multi-view cues (Multi Cues) takes the cost volume as the input. To analyze the performance in the dynamic areas, we use the dynamic mask from [37] computed by thresholding photometric error and depth inconsistency." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 434, + 547, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 434, + 547, + 613 + ], + "spans": [ + { + "bbox": [ + 304, + 434, + 547, + 613 + ], + "type": "text", + "content": "As shown in Fig. 2 (b), multi-frame depth estimation generally achieves high overall accuracy relying on the multi-view clues. But the performance in the dynamic areas is degraded due to the violation of the multi-view consistency in the input cost volume. Fig. 2 (a) shows that the multi-frame method generates fine 3D structures in static areas (the yellow box), whereas its estimation of the moving car (the red box) is corrupted. Compared with the multi-frame method, the monocular depth estimation method achieves worse overall performance as shown in Fig. 2 (b), without the geometric cues. On the other hand, it does not suffer from multi-view inconsistency and thus performs better in dynamic areas. In Fig. 2 (a), we can observe that the two depth cues show respective benefits in different areas and different aspects of the dynamic scene." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 622, + 457, + 634 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 622, + 457, + 634 + ], + "spans": [ + { + "bbox": [ + 306, + 622, + 457, + 634 + ], + "type": "text", + "content": "3.2. Potential Benefits of Fusion" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 642, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 714 + ], + "type": "text", + "content": "To handle the dynamic areas violating the multi-view consistency, previous multi-frame depth estimation methods [9, 36, 37] try to use the monocular information as the input for the dynamic areas. However, the improvement is limited due to the underutilized benefits of both cues, as discussed in Sec. 1. Especially the performance in the dynamic" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21541" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 69, + 59, + 521, + 205 + ], + "blocks": [ + { + "bbox": [ + 69, + 59, + 521, + 205 + ], + "lines": [ + { + "bbox": [ + 69, + 59, + 521, + 205 + ], + "spans": [ + { + "bbox": [ + 69, + 59, + 521, + 205 + ], + "type": "image", + "image_path": "4714c9cac1b01afdd7b5375a908efcffc5df67b46eb2e4ac6da34ad05a3159da.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 207, + 547, + 242 + ], + "lines": [ + { + "bbox": [ + 46, + 207, + 547, + 242 + ], + "spans": [ + { + "bbox": [ + 46, + 207, + 547, + 242 + ], + "type": "text", + "content": "Figure 3. Overview of the proposed method. We first extract multi-frame depth cues with cost volume and monocular depth cues using one-hot depth volume. Then, we fuse the two volumes with the proposed cross-cue fusion module (CCF) to yield an improved fused depth feature. The fused depth feature is sent to the depth module for final depth estimation." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 251, + 237, + 262 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 251, + 237, + 262 + ], + "spans": [ + { + "bbox": [ + 46, + 251, + 237, + 262 + ], + "type": "text", + "content": "areas is easily bounded by the monocular cues." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 263, + 287, + 346 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 263, + 287, + 346 + ], + "spans": [ + { + "bbox": [ + 46, + 263, + 287, + 346 + ], + "type": "text", + "content": "We seek to explore better fusion schemes to use the complementary benefits of these two cues, as discussed in Sec. 3.1. Apart from using the monocular cues in the dynamic area, we also hope the geometric information learned with the multi-view cues in the static areas can be propagated to boost the monocular cues in dynamic areas, which requires a more comprehensive fusion process." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 348, + 288, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 348, + 288, + 502 + ], + "spans": [ + { + "bbox": [ + 46, + 348, + 288, + 502 + ], + "type": "text", + "content": "To unify the information from both multi-frame and monocular cues, we encode both depth cues in the form of a cost volume [40] and a one-hot depth volume transferred from the estimated monocular depth map (in Sec. 4.2). In the analyses, we first consider the plain volume fusion using concatenation and convolution operations. This learnable fusion scheme brings obvious improvement in dynamic areas, implying proper fusion methods help to utilize the potential benefits in dynamic scenes. Based on the feasibility of learnable fusion, we further investigate the cross-cue volume fusion and it exhibits further improvement (in Fig. 2 (b), (c)), which indicates a better way to utilize depth cues as introduced in the next section." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 513, + 176, + 526 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 513, + 176, + 526 + ], + "spans": [ + { + "bbox": [ + 47, + 513, + 176, + 526 + ], + "type": "text", + "content": "4. The Proposed Method" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "spans": [ + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "text", + "content": "We aim to learn depth " + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "inline_equation", + "content": "D_{t}" + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "text", + "content": " of the target image " + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "text", + "content": " from a short image sequence, with known or estimated camera parameters " + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "inline_equation", + "content": "K, T" + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "text", + "content": ". In this paper, we define the image sequence as " + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "inline_equation", + "content": "\\{I_{t-1}, I_{t}, I_{t+1}\\}" + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "inline_equation", + "content": "\\{I_{t-1}, I_{t+1}\\}" + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "text", + "content": " are adjacent images to the target frame " + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "inline_equation", + "content": "K, T" + }, + { + "bbox": [ + 46, + 533, + 287, + 628 + ], + "type": "text", + "content": " are camera intrinsic and extrinsic provided as known values. The goal is to conduct accurate estimations of the dynamic scenes that contain various challenging dynamic objects." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 635, + 115, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 635, + 115, + 647 + ], + "spans": [ + { + "bbox": [ + 47, + 635, + 115, + 647 + ], + "type": "text", + "content": "4.1. Overview" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 653, + 288, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 288, + 712 + ], + "type": "text", + "content": "As shown in Fig. 3, the proposed method consists of three major parts - the multi-view and monocular cues are first represented as volumes through cost volume construction and depth one-hot vector transformation, the cross-cue fusion (CCF) module then fuses both multi-frame" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 251, + 545, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 251, + 545, + 312 + ], + "spans": [ + { + "bbox": [ + 304, + 251, + 545, + 312 + ], + "type": "text", + "content": "and monocular cues by leveraging attention mechanisms [13, 31, 38], i.e., extracting relative intra-relations of each cue to guide the other to yield improved geometric representations of dynamic scene structure. The depth module takes the fused representation to estimate the final depth." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 322, + 545, + 335 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 322, + 545, + 335 + ], + "spans": [ + { + "bbox": [ + 305, + 322, + 545, + 335 + ], + "type": "text", + "content": "4.2. Representing Monocular and Multi-view Cues" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "spans": [ + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "text", + "content": "Multi-view cues as cost volume. We represent the multi-view cues by computing the cost volume following the pipeline of multi-view stereo (MVS) [37, 40]. We warp the adjacent images " + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "inline_equation", + "content": "\\{I_{t-1}, I_{t+1}\\}" + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "text", + "content": " to the target view using " + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "inline_equation", + "content": "K, T" + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "text", + "content": " and a set of depth hypotheses " + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "inline_equation", + "content": "d \\in \\{d_k\\}_{k=1}^M" + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "text", + "content": " uniformly sampled in the inverse depth space " + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "inline_equation", + "content": "[\\frac{1}{d_{\\min}}, \\frac{1}{d_{\\max}}]" + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "text", + "content": " denotes the number of depth hypotheses and is set to 32 in our paper. We construct the multi-frame cost volume " + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{multi}} \\in \\mathbb{R}^{H \\times W \\times M}" + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "text", + "content": " by measuring the pixel-wise similarity between the warped images and the target image, using SSIM [35] as introduced in [37]. For each pixel " + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "inline_equation", + "content": "(i,j)" + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{multi}} \\in [0,1]^{H \\times W \\times M}" + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "text", + "content": ", channel positions " + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "inline_equation", + "content": "k \\in \\{1,\\dots,M\\}" + }, + { + "bbox": [ + 304, + 342, + 546, + 509 + ], + "type": "text", + "content": " with large matching scores indicates a higher possibility to include real scene depth." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 510, + 545, + 654 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 510, + 545, + 654 + ], + "spans": [ + { + "bbox": [ + 304, + 510, + 545, + 654 + ], + "type": "text", + "content": "Monocular cues as depth volume. We construct the monocular cues by estimating the single-view depth map and then transform the depth into one-hot depth volume. We leverage a U-Net architecture [37] without any complex designs for single-view depth estimation, yielding monocular prediction " + }, + { + "bbox": [ + 304, + 510, + 545, + 654 + ], + "type": "inline_equation", + "content": "D_{\\mathrm{mono}} = f_{\\theta}^{\\mathrm{mono}}(I_t)" + }, + { + "bbox": [ + 304, + 510, + 545, + 654 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 510, + 545, + 654 + ], + "type": "inline_equation", + "content": "D_{\\mathrm{mono}} \\in \\mathbb{R}^{H \\times W}" + }, + { + "bbox": [ + 304, + 510, + 545, + 654 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 510, + 545, + 654 + ], + "type": "inline_equation", + "content": "f_{\\theta}^{\\mathrm{mono}}" + }, + { + "bbox": [ + 304, + 510, + 545, + 654 + ], + "type": "text", + "content": " is the monocular network. To facilitate smooth fusion between the multi-frame and monocular cues, different from methods using single-view features [34, 37] or depth values [9, 36], we transform the whole depth map " + }, + { + "bbox": [ + 304, + 510, + 545, + 654 + ], + "type": "inline_equation", + "content": "D_{\\mathrm{mono}}" + }, + { + "bbox": [ + 304, + 510, + 545, + 654 + ], + "type": "text", + "content": " into the depth volume " + }, + { + "bbox": [ + 304, + 510, + 545, + 654 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{mono}} \\in \\{0,1\\}^{H \\times W \\times M}" + }, + { + "bbox": [ + 304, + 510, + 545, + 654 + ], + "type": "text", + "content": " by converting each absolute depth value to a one-hot vector with" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 331, + 664, + 545, + 679 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 331, + 664, + 545, + 679 + ], + "spans": [ + { + "bbox": [ + 331, + 664, + 545, + 679 + ], + "type": "interline_equation", + "content": "C _ {\\text {m o n o}, (i, j)} [ k ] = \\left\\{1 \\mid d _ {\\text {m o n o}} \\in \\left(d _ {k - 1}, d _ {k} \\right] \\right\\} _ {k = 1} ^ {M}, \\tag {1}", + "image_path": "79e95cc14bba523946502b7f1bdeca2e30acdcffe846ccd2e12c1ec7204cce91.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{mono},(i,j)} \\in \\{0,1\\}^M" + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "text", + "content": " represents the one-hot vector in " + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{mono}}" + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "text", + "content": " corresponding to the pixel at " + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "inline_equation", + "content": "(i,j)" + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{mono}}" + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "text", + "content": " is the" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21542" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 68, + 282, + 245 + ], + "blocks": [ + { + "bbox": [ + 53, + 68, + 282, + 245 + ], + "lines": [ + { + "bbox": [ + 53, + 68, + 282, + 245 + ], + "spans": [ + { + "bbox": [ + 53, + 68, + 282, + 245 + ], + "type": "image", + "image_path": "b25ac88cdadf3807de061b641b5ab43b4d8b8e9ad897b56cace229304d22f016.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 250, + 288, + 327 + ], + "lines": [ + { + "bbox": [ + 46, + 250, + 288, + 327 + ], + "spans": [ + { + "bbox": [ + 46, + 250, + 288, + 327 + ], + "type": "text", + "content": "Figure 4. The cross-cue fusion (CCF) module. Taking both multi-frame and monocular depth volume as input, the CCF module enhances multi-frame depth features with the relative intra-relations of monocular depth volume " + }, + { + "bbox": [ + 46, + 250, + 288, + 327 + ], + "type": "inline_equation", + "content": "(R_{\\mathrm{mono}})" + }, + { + "bbox": [ + 46, + 250, + 288, + 327 + ], + "type": "text", + "content": ". Meanwhile, the intra-relations of the multi-frame depth volume " + }, + { + "bbox": [ + 46, + 250, + 288, + 327 + ], + "type": "inline_equation", + "content": "(R_{\\mathrm{multi}})" + }, + { + "bbox": [ + 46, + 250, + 288, + 327 + ], + "type": "text", + "content": " also enhance the monocular module, yielding enhanced depth features for the final depth estimation." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 331, + 287, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 331, + 287, + 357 + ], + "spans": [ + { + "bbox": [ + 46, + 331, + 287, + 357 + ], + "type": "text", + "content": "pixel-wise depth value. Note that we also try some soft representations [1, 2] but observe no obvious improvement." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 361, + 211, + 373 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 361, + 211, + 373 + ], + "spans": [ + { + "bbox": [ + 47, + 361, + 211, + 373 + ], + "type": "text", + "content": "4.3. The Cross-cue Fusion Module" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 380, + 287, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 380, + 287, + 452 + ], + "spans": [ + { + "bbox": [ + 46, + 380, + 287, + 452 + ], + "type": "text", + "content": "We propose a cross-cue fusion (CCF) module to fuse the multi-frame and monocular depth cues. Different from methods [9,36,37] that utilize the masked local depth cues for dynamic depth estimation, the CCF module fuses the whole depth cues using depth volumes, retaining the potential to leverage both benefits for further improvement." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 452, + 288, + 536 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 452, + 288, + 536 + ], + "spans": [ + { + "bbox": [ + 47, + 452, + 288, + 536 + ], + "type": "text", + "content": "The Cross-cue fusion pipeline. Given volumes " + }, + { + "bbox": [ + 47, + 452, + 288, + 536 + ], + "type": "inline_equation", + "content": "C_{\\mathrm{multi}}, C_{\\mathrm{mono}} \\in \\mathbb{R}^{H \\times W \\times M}" + }, + { + "bbox": [ + 47, + 452, + 288, + 536 + ], + "type": "text", + "content": ", we first process them via shallow convolution layers, yielding down-sampled monocular and multi-frame depth features " + }, + { + "bbox": [ + 47, + 452, + 288, + 536 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{multi}}, F_{\\mathrm{mono}}" + }, + { + "bbox": [ + 47, + 452, + 288, + 536 + ], + "type": "text", + "content": " in shape " + }, + { + "bbox": [ + 47, + 452, + 288, + 536 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{h \\times w \\times M}" + }, + { + "bbox": [ + 47, + 452, + 288, + 536 + ], + "type": "text", + "content": ". We then feed " + }, + { + "bbox": [ + 47, + 452, + 288, + 536 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{multi}}" + }, + { + "bbox": [ + 47, + 452, + 288, + 536 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 452, + 288, + 536 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{mono}}" + }, + { + "bbox": [ + 47, + 452, + 288, + 536 + ], + "type": "text", + "content": " to the proposed cross-cue attention (CCA) to enhance each depth cue by extracting the relative intra-relations from the other" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 101, + 544, + 287, + 564 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 101, + 544, + 287, + 564 + ], + "spans": [ + { + "bbox": [ + 101, + 544, + 287, + 564 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\widetilde {F} _ {\\text {m u l t i}} = \\mathrm {C C A} _ {\\text {m u l t i}} \\left(F _ {\\text {m o n o}}, F _ {\\text {m u l t i}}\\right), \\\\ \\sim \\end{array} \\tag {2}", + "image_path": "12b8829b41518fad3ffc6b2b5bf0308e2af9903f2b956e2ea8d603e4a73887fa.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 100, + 563, + 235, + 576 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 563, + 235, + 576 + ], + "spans": [ + { + "bbox": [ + 100, + 563, + 235, + 576 + ], + "type": "interline_equation", + "content": "\\widetilde {F} _ {\\text {m o n o}} = \\mathrm {C C A} _ {\\text {m o n o}} \\left(F _ {\\text {m u l t i}}, F _ {\\text {m o n o}}\\right),", + "image_path": "679182025294988d240edb1c81947e244b33b5bc0b482ead8f2853e7f8a97d1b.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 579, + 287, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 579, + 287, + 639 + ], + "spans": [ + { + "bbox": [ + 47, + 579, + 287, + 639 + ], + "type": "text", + "content": "the enhanced features are concatenated to yield the fused feature " + }, + { + "bbox": [ + 47, + 579, + 287, + 639 + ], + "type": "inline_equation", + "content": "\\widetilde{F}_{\\mathrm{fused}}" + }, + { + "bbox": [ + 47, + 579, + 287, + 639 + ], + "type": "text", + "content": ". To retain detailed information from initial depth cues, we process the input depth cues via " + }, + { + "bbox": [ + 47, + 579, + 287, + 639 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{cat}} = \\mathrm{Cat}(\\mathrm{Conv}(C_{\\mathrm{multi}}), \\mathrm{Conv}(C_{\\mathrm{mono}}))" + }, + { + "bbox": [ + 47, + 579, + 287, + 639 + ], + "type": "text", + "content": " and add the residual connection. The final cross-cue features can be written as" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 123, + 644, + 287, + 659 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 123, + 644, + 287, + 659 + ], + "spans": [ + { + "bbox": [ + 123, + 644, + 287, + 659 + ], + "type": "interline_equation", + "content": "F = \\gamma \\widetilde {F} _ {\\text {f u s e d}} \\uparrow + F _ {\\text {c a t}}, \\tag {3}", + "image_path": "db6d48e5d33d42681a4a308224f7ee94a0f4dc32b1aa414e22cd2b86b0e50cd5.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "content": " is a learned weighting factor and " + }, + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\uparrow" + }, + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "content": " denotes the up-sampling operation. 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Attention maps of the dynamic position (red dots) examples. " + }, + { + "bbox": [ + 304, + 163, + 547, + 230 + ], + "type": "inline_equation", + "content": "R_{\\mathrm{mono}}" + }, + { + "bbox": [ + 304, + 163, + 547, + 230 + ], + "type": "text", + "content": " boosts multi-frame features by attending monocular features around the dynamic areas for better use of the monocular cues in the corresponding areas. " + }, + { + "bbox": [ + 304, + 163, + 547, + 230 + ], + "type": "inline_equation", + "content": "R_{\\mathrm{multi}}" + }, + { + "bbox": [ + 304, + 163, + 547, + 230 + ], + "type": "text", + "content": " enhances monocular features by attending multi-frame features in the static areas which have more reliable geometric information." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 240, + 545, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 240, + 545, + 312 + ], + "spans": [ + { + "bbox": [ + 304, + 240, + 545, + 312 + ], + "type": "text", + "content": "Cross-cue attention. The cross-cue attention (CCA) targets utilizing the relative intra-relation of one depth cue to improve the geometric information of another. Since the CCA modules are deployed in a parallel manner as shown in Fig. 4, we introduce " + }, + { + "bbox": [ + 304, + 240, + 545, + 312 + ], + "type": "inline_equation", + "content": "\\widetilde{F}_{\\mathrm{multi}} = \\mathrm{CCA}_{\\mathrm{multi}}(F_{\\mathrm{mono}}, F_{\\mathrm{multi}})" + }, + { + "bbox": [ + 304, + 240, + 545, + 312 + ], + "type": "text", + "content": " in detail as an example." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "spans": [ + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "text", + "content": "Given depth features " + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{mono}}" + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{multi}} \\in \\mathbb{R}^{h \\times w \\times M}" + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "text", + "content": ", we transform " + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{mono}}" + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "text", + "content": " into query feature " + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "inline_equation", + "content": "Q_{\\mathrm{mono}}" + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "text", + "content": " and key feature " + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "inline_equation", + "content": "K_{\\mathrm{mono}}" + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "text", + "content": ", then transform " + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{multi}}" + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "text", + "content": " into value feature " + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "inline_equation", + "content": "V_{\\mathrm{multi}}" + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "text", + "content": " using convolution operation " + }, + { + "bbox": [ + 304, + 312, + 545, + 361 + ], + "type": "inline_equation", + "content": "f(\\cdot, \\theta)" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 372, + 369, + 478, + 383 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 372, + 369, + 478, + 383 + ], + "spans": [ + { + "bbox": [ + 372, + 369, + 478, + 383 + ], + "type": "interline_equation", + "content": "Q _ {\\text {m o n o}} = f \\left(F _ {\\text {m o n o}}, \\theta_ {\\text {m o n o}} ^ {Q}\\right),", + "image_path": "544b41ded1040be4eb11f614e42abc2de526f7b11e87e11041e76a9ab842956d.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 373, + 384, + 545, + 399 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 373, + 384, + 545, + 399 + ], + "spans": [ + { + "bbox": [ + 373, + 384, + 545, + 399 + ], + "type": "interline_equation", + "content": "K _ {\\text {m o n o}} = f \\left(F _ {\\text {m o n o}}, \\theta_ {\\text {m o n o}} ^ {K}\\right), \\tag {4}", + "image_path": "71484e09f5506d1415a8b3c7158e81e6111e4921fb03b06dc2b2639e001d0b60.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 380, + 401, + 479, + 416 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 401, + 479, + 416 + ], + "spans": [ + { + "bbox": [ + 380, + 401, + 479, + 416 + ], + "type": "interline_equation", + "content": "V _ {\\text {m u l t i}} = f \\left(F _ {\\text {m u l t i}}, \\theta_ {\\text {m u l t i}} ^ {V}\\right),", + "image_path": "cebffcd5cf3ae5b5d8b1b7209987f8d32efc7cc8da3d758444257d4cca3d2644.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 424, + 545, + 460 + ], + "type": "text", 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"interline_equation", + "content": "R _ {\\text {m o n o}} = \\operatorname {S o f t m a x} \\left(Q _ {\\text {m o n o}} \\otimes K _ {\\text {m o n o}} ^ {T}\\right), \\tag {5}", + "image_path": "6371048ca14736478e8cc640d82badd9a322357ef1daf0151c27b5728f4a00eb.jpg" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 304, + 491, + 545, + 539 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 491, + 545, + 539 + ], + "spans": [ + { + "bbox": [ + 304, + 491, + 545, + 539 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 491, + 545, + 539 + ], + "type": "inline_equation", + "content": "R_{\\mathrm{mono}} \\in \\mathbb{R}^{hw \\times hw}" + }, + { + "bbox": [ + 304, + 491, + 545, + 539 + ], + "type": "text", + "content": " stands for the non-local intrarelations of the monocular depth cue. We then utilize " + }, + { + "bbox": [ + 304, + 491, + 545, + 539 + ], + "type": "inline_equation", + "content": "R_{\\mathrm{mono}}" + }, + { + "bbox": [ + 304, + 491, + 545, + 539 + ], + "type": "text", + "content": " to improve the geometric representations of the multi-frame feature " + }, + { + "bbox": [ + 304, + 491, + 545, + 539 + ], + "type": "inline_equation", + "content": "V_{\\mathrm{multi}}" + }, + { + "bbox": [ + 304, + 491, + 545, + 539 + ], + "type": "text", + "content": " by" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 376, + 546, + 545, + 562 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 376, + 546, + 545, + 562 + ], + "spans": [ + { + "bbox": [ + 376, + 546, + 545, + 562 + ], + "type": "interline_equation", + "content": "\\widetilde {F} _ {\\text {m u l t i}} = R _ {\\text {m o n o}} \\otimes V _ {\\text {m u l t i}}, \\tag {6}", + "image_path": "adc435a890b317d5a23f6c4de410e5621d25f19e7043aa7d8a66e924b2cb2280.jpg" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 304, + 569, + 545, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 569, + 545, + 641 + ], + "spans": [ + { + "bbox": [ + 304, + 569, + 545, + 641 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 569, + 545, + 641 + ], + "type": "inline_equation", + "content": "\\widetilde{F}_{\\mathrm{multi}}" + }, + { + "bbox": [ + 304, + 569, + 545, + 641 + ], + "type": "text", + "content": " denotes the improved multi-frame representations benefited from monocular depth cues. Similar operations are done for " + }, + { + "bbox": [ + 304, + 569, + 545, + 641 + ], + "type": "inline_equation", + "content": "\\widetilde{F}_{\\mathrm{mono}} = \\mathrm{CCA}_{\\mathrm{mono}}(F_{\\mathrm{multi}}, F_{\\mathrm{mono}})" + }, + { + "bbox": [ + 304, + 569, + 545, + 641 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 569, + 545, + 641 + ], + "type": "inline_equation", + "content": "R_{\\mathrm{multi}}" + }, + { + "bbox": [ + 304, + 569, + 545, + 641 + ], + "type": "text", + "content": " stands for the intra-relations of multi-frame cues that can be used to improve monocular depth feature " + }, + { + "bbox": [ + 304, + 569, + 545, + 641 + ], + "type": "inline_equation", + "content": "V_{\\mathrm{mono}}" + }, + { + "bbox": [ + 304, + 569, + 545, + 641 + ], + "type": "text", + "content": ". Please refer to Fig. 4 for details." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 304, + 642, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 714 + ], + "type": "text", + "content": "Effectiveness of the CCA. As shown in Fig. 5, despite using the same CCA operation, the intra-relations " + }, + { + "bbox": [ + 304, + 642, + 545, + 714 + ], + "type": "inline_equation", + "content": "R_{\\mathrm{mono}}" + }, + { + "bbox": [ + 304, + 642, + 545, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 642, + 545, + 714 + ], + "type": "inline_equation", + "content": "R_{\\mathrm{multi}}" + }, + { + "bbox": [ + 304, + 642, + 545, + 714 + ], + "type": "text", + "content": " attend different areas for improving the dynamic depth, showing their ability to capture respective benefits from monocular (better dynamic depth) and multi-frame cues (better static depth). 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EvalMethodBack.Reso.Sup.Abs RelSq RelRMSERMSElogδ < 1.25δ < 1.252δ < 1.253
OverallManydepth [36]Res-18MRM0.0710.3433.1840.1080.9450.9910.998
DynamicDepth [9]Res-18MRM0.0680.2963.0670.1060.9450.9910.998
MonoRec [37]Res-18MRD*0.0500.2902.2660.0820.9720.9910.996
OursRes-18MRD0.0430.1512.1130.0730.9750.9960.999
MaGNet [1]Effi-B5MRD0.0570.2152.5970.0880.9670.9960.999
OursEffi-B5MRD0.0460.1552.1120.0760.9730.9960.999
MaGNet [1]Effi-B5HRD0.0430.1352.0470.0820.9810.9970.999
OursEffi-B5HRD0.0390.1031.7180.0670.9810.9970.999
DynamicManydepth [36]Res-18MRM0.2223.3907.9210.2370.6760.9020.964
DynamicDepth [9]Res-18MRM0.2082.7577.3620.2270.6820.9110.971
MonoRec [37]Res-18MRD*0.3609.08310.9630.3460.5900.8820.780
OursRes-18MRD0.1180.8354.2970.1460.8710.9750.990
MaGNet [1]Effi-B5MRD0.1411.2194.8770.1680.8300.9550.986
OursEffi-B5MRD0.1110.7684.1170.1350.8810.9800.994
MaGNet [1]Effi-B5HRD0.1401.0604.5810.2020.8340.9540.982
OursEffi-B5HRD0.1120.8304.1010.1370.8850.9780.992
", + "image_path": "e8b395ac189a501be04e761652c9edeb6d33ad16ea91d050b41c86531bdf6d2b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 268, + 545, + 316 + ], + "lines": [ + { + "bbox": [ + 46, + 268, + 545, + 316 + ], + "spans": [ + { + "bbox": [ + 46, + 268, + 545, + 316 + ], + "type": "text", + "content": "Table 1. Quantitative comparisons on KITTI [10] Odometry dataset. 'Back.' denotes the network backbone. 'Reso.' denotes the image resolutions, where 'MR' refers to the resolution of " + }, + { + "bbox": [ + 46, + 268, + 545, + 316 + ], + "type": "inline_equation", + "content": "256 \\times 512" + }, + { + "bbox": [ + 46, + 268, + 545, + 316 + ], + "type": "text", + "content": " and 'HR' is " + }, + { + "bbox": [ + 46, + 268, + 545, + 316 + ], + "type": "inline_equation", + "content": "352 \\times 1216" + }, + { + "bbox": [ + 46, + 268, + 545, + 316 + ], + "type": "text", + "content": ". In the 'Sup.' column, 'M' are self-supervised methods, 'D*' refers to semi-supervised methods trained with pseudo GT depth, while 'D' denotes fully-supervised methods. Color blue denotes 'lower is better', while red means 'higher is better'. The best results are in bold." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 322, + 287, + 383 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 322, + 287, + 383 + ], + "spans": [ + { + "bbox": [ + 46, + 322, + 287, + 383 + ], + "type": "text", + "content": "unbounded dynamic depth performance upon both predictions, i.e., the monocular depth can be improved by attending static depth (using " + }, + { + "bbox": [ + 46, + 322, + 287, + 383 + ], + "type": "inline_equation", + "content": "R_{\\mathrm{multi}}" + }, + { + "bbox": [ + 46, + 322, + 287, + 383 + ], + "type": "text", + "content": ") from multi-frame cues, and the improved monocular depth in dynamic areas will further be propagated (with " + }, + { + "bbox": [ + 46, + 322, + 287, + 383 + ], + "type": "inline_equation", + "content": "R_{\\mathrm{mono}}" + }, + { + "bbox": [ + 46, + 322, + 287, + 383 + ], + "type": "text", + "content": ") to multi-frame predictions." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 399, + 135, + 411 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 399, + 135, + 411 + ], + "spans": [ + { + "bbox": [ + 47, + 399, + 135, + 411 + ], + "type": "text", + "content": "4.4. Loss Function" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 418, + 287, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 418, + 287, + 441 + ], + "spans": [ + { + "bbox": [ + 47, + 418, + 287, + 441 + ], + "type": "text", + "content": "Given the predicted depth " + }, + { + "bbox": [ + 47, + 418, + 287, + 441 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 47, + 418, + 287, + 441 + ], + "type": "text", + "content": " and the ground truth depth " + }, + { + "bbox": [ + 47, + 418, + 287, + 441 + ], + "type": "inline_equation", + "content": "\\hat{D}" + }, + { + "bbox": [ + 47, + 418, + 287, + 441 + ], + "type": "text", + "content": ", the loss can be described as" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 83, + 446, + 287, + 460 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 446, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 83, + 446, + 287, + 460 + ], + "type": "interline_equation", + "content": "\\mathcal {L} (D, \\hat {D}) = \\beta \\mathcal {L} _ {\\mathrm {S I}} (D, \\hat {D}) + \\mathcal {L} _ {\\mathrm {V N L}} (D, \\hat {D}), \\tag {7}", + "image_path": "fe58ed3e1091bbc60524c93022e39aa81b467726ff67f1259912e3b9f96c1c92.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "spans": [ + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{SI}}" + }, + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "text", + "content": " denotes the scale-invariant loss [2,8], " + }, + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{VNL}}" + }, + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "text", + "content": " is the virtual normal loss [42, 43], " + }, + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "text", + "content": " is the weighing factor which is set to 4. Since we have both monocular depth " + }, + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "inline_equation", + "content": "D_{\\mathrm{mono}}" + }, + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "text", + "content": " and the final depth prediction " + }, + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "inline_equation", + "content": "D_{\\mathrm{t}}" + }, + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "text", + "content": ", the final loss " + }, + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{final}}" + }, + { + "bbox": [ + 46, + 465, + 287, + 514 + ], + "type": "text", + "content": " is" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 95, + 517, + 287, + 532 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 95, + 517, + 287, + 532 + ], + "spans": [ + { + "bbox": [ + 95, + 517, + 287, + 532 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {f i n a l}} = \\mathcal {L} \\left(D _ {\\text {m o n o}}, \\hat {D}\\right) + \\mathcal {L} \\left(D _ {t}, \\hat {D}\\right). \\tag {8}", + "image_path": "183862a79d8997a0022a8ee761a8462d8bae6baf9321df4b601c94ce7e499e43.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 537, + 127, + 551 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 537, + 127, + 551 + ], + "spans": [ + { + "bbox": [ + 47, + 537, + 127, + 551 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 558, + 287, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 558, + 287, + 665 + ], + "spans": [ + { + "bbox": [ + 46, + 558, + 287, + 665 + ], + "type": "text", + "content": "In this section, we compare our method with state-of-the-art multi-frame depth estimation methods [1,9,36,37] for the dynamic scenes, including self-supervised methods [9,36], semi-supervised method [37] as well as fully supervised method [6] (Sec. 5.3). We then conduct ablation studies (Sec. 5.4) to evaluate different variants of our method. We also validate the generalization ability (Sec. 5.5) and evaluate different methods' improvements upon monocular networks for depth estimation in dynamic areas (Sec. 5.6)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 671, + 110, + 682 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 671, + 110, + 682 + ], + "spans": [ + { + "bbox": [ + 47, + 671, + 110, + 682 + ], + "type": "text", + "content": "5.1. Datasets" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "text", + "content": "KITTI. We follow [37] to evaluate dynamic sequential data on KITTI Odometry dataset, which contains 13666" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 323, + 545, + 394 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 323, + 545, + 394 + ], + "spans": [ + { + "bbox": [ + 304, + 323, + 545, + 394 + ], + "type": "text", + "content": "training and 8634 testing samples. We use the dynamic masks provided by [37] for evaluation. More than 1300 samples in the test set contain dynamic objects. We evaluate both medium-resolution (MR: " + }, + { + "bbox": [ + 304, + 323, + 545, + 394 + ], + "type": "inline_equation", + "content": "256 \\times 512" + }, + { + "bbox": [ + 304, + 323, + 545, + 394 + ], + "type": "text", + "content": ") and high-resolution (HR: " + }, + { + "bbox": [ + 304, + 323, + 545, + 394 + ], + "type": "inline_equation", + "content": "352 \\times 1216" + }, + { + "bbox": [ + 304, + 323, + 545, + 394 + ], + "type": "text", + "content": ") depth results, and the metrics are computed within the 0-80m range." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 394, + 545, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 394, + 545, + 479 + ], + "spans": [ + { + "bbox": [ + 304, + 394, + 545, + 479 + ], + "type": "text", + "content": "DDAD. We use the forward-facing cameras with 3848 testing samples to evaluate the methods' generalization ability. Since there is no pre-defined dynamic mask for DDAD, we construct it by warping adjacent images with GT depth and selecting instance masks with high photometric error. More than " + }, + { + "bbox": [ + 304, + 394, + 545, + 479 + ], + "type": "inline_equation", + "content": "70\\%" + }, + { + "bbox": [ + 304, + 394, + 545, + 479 + ], + "type": "text", + "content": " of the test set contains dynamic objects. All methods are evaluated within the 0-80m depth range." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 487, + 440, + 499 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 487, + 440, + 499 + ], + "spans": [ + { + "bbox": [ + 306, + 487, + 440, + 499 + ], + "type": "text", + "content": "5.2. Implementation Details" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 506, + 545, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 506, + 545, + 601 + ], + "spans": [ + { + "bbox": [ + 304, + 506, + 545, + 601 + ], + "type": "text", + "content": "We implement our method with Pytorch [29] and train it using NVIDIA TITAN RTX GPUs. Unless specified, both the monocular network and the depth network are consistent with the depth module of [37], with ResNet-18 [18] as backbone using ImageNet [6] pre-trained parameters. We train our model for 80 epochs using the Adam [20] optimizer and learning rate of " + }, + { + "bbox": [ + 304, + 506, + 545, + 601 + ], + "type": "inline_equation", + "content": "10^{-4}" + }, + { + "bbox": [ + 304, + 506, + 545, + 601 + ], + "type": "text", + "content": ", which further drops to " + }, + { + "bbox": [ + 304, + 506, + 545, + 601 + ], + "type": "inline_equation", + "content": "10^{-5}" + }, + { + "bbox": [ + 304, + 506, + 545, + 601 + ], + "type": "text", + "content": " after 65 epochs. The batch size is set to 8." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 610, + 411, + 623 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 610, + 411, + 623 + ], + "spans": [ + { + "bbox": [ + 306, + 610, + 411, + 623 + ], + "type": "text", + "content": "5.3. Results on KITTI" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "content": "We show both overall and dynamic performances of different methods in Table 1. Our method achieves the best performance in dynamic depth estimation. Specifically, it outperforms the SOTA fully-supervised MaGNet [1] with " + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "inline_equation", + "content": "21.28\\%" + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "content": " reduction of Abs.Rel " + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "inline_equation", + "content": "(0.141\\rightarrow 0.111)" + }, + { + "bbox": [ + 304, + 629, + 545, + 713 + ], + "type": "text", + "content": ", using the same Efficient-B5 [30] backbone. Significant improvements are also observed compared to self/semi-supervised" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "21544" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 53, + 64, + 538, + 249 + ], + "blocks": [ + { + "bbox": [ + 53, + 64, + 538, + 249 + ], + "lines": [ + { + "bbox": [ + 53, + 64, + 538, + 249 + ], + "spans": [ + { + "bbox": [ + 53, + 64, + 538, + 249 + ], + "type": "table", + "html": "
#CategoryVariantDynamicOverall
Abs RelAbs SqRMSE\\( RMSE_{log} \\)δ ≤ 1.25AbsRel
1Depth with single cuesPure multi-frame cues0.3827.16710.2920.350.5090.041
2Pure monocular cues0.1491.3695.2820.1780.8100.106
3Volume fusion with masksSelf-discovered mask [36]0.1300.9904.6920.1600.8370.043
4MaskNetwork [37]0.2202.8966.2990.2230.7350.040
5Volume fusion without masksStack & 3D Convs0.1541.4795.8660.1890.7770.046
6Stack & 3D U-Net [15]0.1551.4445.7620.1910.7720.040
7Concat & 2D Convs0.1381.1245.1100.1740.8150.043
8Ours CCF w./o. \\( R_{multi} \\)0.1240.9394.6100.1540.8550.043
9Ours CCF w./o. \\( R_{mono} \\)0.1230.9264.5450.1530.8610.043
10Ours CCF w./ only intra-cue self-attention0.1220.8964.5440.1520.8600.042
11Ours CCF w./o. residual connection0.1300.9614.6160.1570.8400.048
12Ours depth module w./o. \\( I_t \\)0.1260.9544.6360.1550.8440.042
13Ours CCF - full0.1180.8354.2970.1460.8710.043
", + "image_path": "8257fbf1b5bd1855b8342a67fe1225a98a55e151f9f6f79894cfae41263f5805.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 252, + 544, + 275 + ], + "lines": [ + { + "bbox": [ + 46, + 252, + 544, + 275 + ], + "spans": [ + { + "bbox": [ + 46, + 252, + 544, + 275 + ], + "type": "text", + "content": "Table 2. Ablation experiments on KITTI. We show the results of different fusion types of multi-frame and monocular cues. 'CCF' denotes the proposed cross-cue fusion module. 'Dynamic' denotes dynamic depth errors while 'Overall' refers to the overall depth error." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 281, + 287, + 365 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 281, + 287, + 365 + ], + "spans": [ + { + "bbox": [ + 46, + 281, + 287, + 365 + ], + "type": "text", + "content": "methods [9, 36, 37]. Besides the obvious improvement in dynamic areas, our method also outperforms other methods in most metrics for overall performance. Qualitative results are shown in Fig. 6. While the dynamic areas generally lead to performance decline for other multi-frame methods, our method conducts obviously better estimations in the moving objects, while retaining the overall accuracy." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 372, + 141, + 385 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 372, + 141, + 385 + ], + "spans": [ + { + "bbox": [ + 47, + 372, + 141, + 385 + ], + "type": "text", + "content": "5.4. Ablation Study" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 391, + 287, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 391, + 287, + 498 + ], + "spans": [ + { + "bbox": [ + 46, + 391, + 287, + 498 + ], + "type": "text", + "content": "As shown in Sec. 3, the way to fuse the two volumes in our method influences the final dynamic depth performance. As shown in Tab. 2, we fuse the multi-frame and monocular volumes in ways that the explicit mask is needed ('Volume fusion with masks') and the explicit mask is not needed ('Volume fusion without masks'). We also evaluate the variants of our method ('Ours CCF') as individual mask-free methods. Row #1~2 shows individual depth results leveraging pure multi-frame and monocular cues." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 498, + 287, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 498, + 287, + 594 + ], + "spans": [ + { + "bbox": [ + 46, + 498, + 287, + 594 + ], + "type": "text", + "content": "Volume fusion with explicit masks. We leverage the computed [36] or learned masks [37] to fuse our volumes from two depth cues. As shown in row #3~4, The dynamic mask generated by [37] does not surpass the baseline fusion method. While the self-discovered mask [36] shows certain improvements in dynamic areas, it is computed using heuristics and thus has uncontrolled dynamic mask quality, which leads to more restricted performances than ours." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 594, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 288, + 714 + ], + "type": "text", + "content": "Volume fusion without masks. Besides our method that uses a mask-free fusion scheme, we also fuse the volumes without any mask using convolutions. We first stack the depth volumes in a new dimension and process the fused features with layers of 3D convolutions as well as the 3D U-Net, which is commonly used in the MVS methods [15,40]. As shown in row #5~6 of Table 2, the 3D convolutions have no obvious improvement in dynamic areas. However, as we concatenate the two depth volumes and process them with 2D convolution layers (as in Fig. 2), we observe further im" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 281, + 545, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 281, + 545, + 316 + ], + "spans": [ + { + "bbox": [ + 304, + 281, + 545, + 316 + ], + "type": "text", + "content": "provements (row #7) upon the pure monocular results, but this mask-free fusion scheme still lags behind our method with obvious margins." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 317, + 546, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 317, + 546, + 449 + ], + "spans": [ + { + "bbox": [ + 304, + 317, + 546, + 449 + ], + "type": "text", + "content": "Design choices of the network modules. We evaluate different variants of our method as an individual type of mask-free fusion method. We respectively remove the multiframe (row #8) and monocular (row #9) intra-relations and directly feed the other cues to the fused feature. Then, we implement another variant of our method with intra-cue self-attention in the proposed CCF (row #10). We also disable the residual connection of " + }, + { + "bbox": [ + 304, + 317, + 546, + 449 + ], + "type": "inline_equation", + "content": "F_{\\mathrm{cat}}" + }, + { + "bbox": [ + 304, + 317, + 546, + 449 + ], + "type": "text", + "content": " (row #11) and the input target image " + }, + { + "bbox": [ + 304, + 317, + 546, + 449 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 304, + 317, + 546, + 449 + ], + "type": "text", + "content": " of the depth module (row #12). Results show the effectiveness and necessity of the proposed technical designs, which achieves the best performance." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 455, + 446, + 467 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 455, + 446, + 467 + ], + "spans": [ + { + "bbox": [ + 306, + 455, + 446, + 467 + ], + "type": "text", + "content": "5.5. Generalization on DDAD" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 473, + 545, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 473, + 545, + 605 + ], + "spans": [ + { + "bbox": [ + 304, + 473, + 545, + 605 + ], + "type": "text", + "content": "To validate the generalization ability of our method, we test the KITTI models of all supervised methods [1, 37] on the challenging DDAD dataset. As shown in Table 3, previous methods either degrade on the dynamic areas due to the violation of multi-view consistency (e.g., AbsRel 0.544 for [37]) or exhibit worse overall performance owing to the cross-domain issue of the monocular network (e.g., AbsRel 0.208 for [1]). Our method shows more promising results that it not only outperforms other methods in dynamic depth estimation but also retains the advantage of multi-frame estimation with competitive overall performance." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 611, + 527, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 611, + 527, + 624 + ], + "spans": [ + { + "bbox": [ + 306, + 611, + 527, + 624 + ], + "type": "text", + "content": "5.6. Improvement upon Monocular Estimation" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": "Though previous methods handle dynamic areas with monocular cues, the improvement is usually constrained by monocular results. In Tab. 4, we show the final depth results in dynamic areas and compare them against the monocular results of each method. Our method achieves the largest Abs.Rel reduction of " + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "inline_equation", + "content": "20.8\\%" + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": " compared to other methods which explicitly use the monocular network. Meanwhile," + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21545" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 57, + 539, + 346 + ], + "blocks": [ + { + "bbox": [ + 56, + 57, + 539, + 346 + ], + "lines": [ + { + "bbox": [ + 56, + 57, + 539, + 346 + ], + "spans": [ + { + "bbox": [ + 56, + 57, + 539, + 346 + ], + "type": "image", + "image_path": "2fe7e7f3830285cd039b3a988a558ed46f8f023c3643adc2f1b4e46f9d5fa465.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 348, + 547, + 381 + ], + "lines": [ + { + "bbox": [ + 46, + 348, + 547, + 381 + ], + "spans": [ + { + "bbox": [ + 46, + 348, + 547, + 381 + ], + "type": "text", + "content": "Figure 6. Qualitative results on KITTI dataset [10]. From left to right: depth predictions (dynamic objects are highlighted with red boxes), error maps, and the reconstructed point clouds of dynamic areas. Our method achieves the best dynamic results and reconstructs more reasonable object shapes than state-of-the-art methods." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 72, + 385, + 520, + 478 + ], + "blocks": [ + { + "bbox": [ + 72, + 385, + 520, + 478 + ], + "lines": [ + { + "bbox": [ + 72, + 385, + 520, + 478 + ], + "spans": [ + { + "bbox": [ + 72, + 385, + 520, + 478 + ], + "type": "table", + "html": "
EvalMethodBackboneAbs RelSq RelRMSE\\( RMSE_{log} \\)δ<1.25\\( \\delta < 1.25^2 \\)\\( \\delta < 1.25^3 \\)
OverallMonoRec [37]Res-180.1583.1027.5530.2270.8540.9310.961
MaGNet [1]Effi-B50.2082.64110.7390.3820.6200.8780.942
OursRes-180.1582.4169.8550.2990.7470.8940.947
DynamicMonoRec [37]Res-180.54416.70316.1160.4820.4600.6670.798
MaGNet [1]Effi-B50.2663.98211.7150.3980.4620.8150.917
OursRes-180.2343.61111.0070.3310.5760.8350.921
", + "image_path": "f1c5b2d5e3056dc34a327c5e73f4f0572338c9b215fa09d9ffeff9bd42507242.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 56, + 512, + 279, + 596 + ], + "blocks": [ + { + "bbox": [ + 46, + 481, + 546, + 504 + ], + "lines": [ + { + "bbox": [ + 46, + 481, + 546, + 504 + ], + "spans": [ + { + "bbox": [ + 46, + 481, + 546, + 504 + ], + "type": "text", + "content": "Table 3. Generalizations on DDAD [16] dataset. The best results are in bold and the second best results are underlined. Our method achieves a competitive overall performance with other method while achieving the best result in dynamic areas." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 512, + 279, + 596 + ], + "lines": [ + { + "bbox": [ + 56, + 512, + 279, + 596 + ], + "spans": [ + { + "bbox": [ + 56, + 512, + 279, + 596 + ], + "type": "table", + "html": "
MethodMono. Err.Final Err.Err. Redu.
Manydepth [36]0.2120.222-4.72%
Dynamicdepth [9]0.2140.2082.83%
MaGNet [1]0.1530.1417.84%
Ours - Res.180.1490.11820.81%
Ours - Res.500.1450.11620.00%
", + "image_path": "6491af377f7dede6f640344ee7b8676ea85da8efb754e9d4c97280a7e5ffa9c3.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 600, + 287, + 666 + ], + "lines": [ + { + "bbox": [ + 46, + 600, + 287, + 666 + ], + "spans": [ + { + "bbox": [ + 46, + 600, + 287, + 666 + ], + "type": "text", + "content": "Table 4. Error reduction upon monocular estimation in dynamic areas. We show different methods' dynamic Abs.Rel depth errors (Final Err.) and their reduction (Err. Redu.) compared to their individual monocular performances (Mono. Err.). Our method achieves the largest error reduction over others and exhibits a consistent error reduction when a better backbone is used." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 677, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 287, + 715 + ], + "type": "text", + "content": "when using a backbone with larger capacities (ResNet-50), our method achieves consistent error reduction upon the monocular network, showing its scalability and flexibility." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 514, + 379, + 526 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 514, + 379, + 526 + ], + "spans": [ + { + "bbox": [ + 306, + 514, + 379, + 526 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 533, + 545, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 533, + 545, + 629 + ], + "spans": [ + { + "bbox": [ + 304, + 533, + 545, + 629 + ], + "type": "text", + "content": "We improve multi-frame dynamic depth estimation by fusing the multi-view and monocular depth cues with the proposed cross-cue fusion. Experiments show its effectiveness as well as the generalization ability. Limitation: there still exists a performance gap to fill, especially in dynamic areas, with a better fusion of the cues. Meanwhile, the challenging occlusion issues for general multi-frame methods require further exploration." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 630, + 546, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 546, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 546, + 715 + ], + "type": "text", + "content": "Acknowledgements Y. Zhu, J. Sun, and Y. Zhang acknowledge the support from National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology. This work was partially supported by NSFC (No.U19B2037) and the Natural Science Basic Research Program of Shaanxi (No.2021JCW-03) to Y. Zhang; an ARC DECRA Fellowship DE230101591 to D. Gong." + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21546" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 147 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 147 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 147 + ], + "type": "text", + "content": "[1] Gwangbin Bae, Ignas Budvyitis, and Roberto Cipolla. Multiview depth estimation by fusing single-view depth probability with multi-view geometry. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2842-2851, 2022. 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 148, + 288, + 192 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 148, + 288, + 192 + ], + "spans": [ + { + "bbox": [ + 53, + 148, + 288, + 192 + ], + "type": "text", + "content": "[2] Shariq Farooq Bhat, Ibrahim Alhashim, and Peter Wonka. Adabins: Depth estimation using adaptive bins. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4009-4018, 2021. 1, 5, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 194, + 288, + 249 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 194, + 288, + 249 + ], + "spans": [ + { + "bbox": [ + 53, + 194, + 288, + 249 + ], + "type": "text", + "content": "[3] Fabian Brickwedde, Steffen Abraham, and Rudolf Mester. Mono-sf: Multi-view geometry meets single-view depth for monocular scene flow estimation of dynamic traffic scenes. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2780-2790, 2019. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 250, + 288, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 250, + 288, + 304 + ], + "spans": [ + { + "bbox": [ + 53, + 250, + 288, + 304 + ], + "type": "text", + "content": "[4] Zhe Cao, Abhishek Kar, Christian Hane, and Jitendra Malik. Learning independent object motion from unlabelled stereoscopic videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5594-5603, 2019. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 306, + 288, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 306, + 288, + 361 + ], + "spans": [ + { + "bbox": [ + 53, + 306, + 288, + 361 + ], + "type": "text", + "content": "[5] Vincent Casser, Soeren Pirk, Reza Mahjourian, and Anelia Angelova. Depth prediction without the sensors: Leveraging structure for unsupervised learning from monocular videos. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 8001-8008, 2019. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 363, + 288, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 363, + 288, + 407 + ], + "spans": [ + { + "bbox": [ + 53, + 363, + 288, + 407 + ], + "type": "text", + "content": "[6] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 409, + 288, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 409, + 288, + 474 + ], + "spans": [ + { + "bbox": [ + 53, + 409, + 288, + 474 + ], + "type": "text", + "content": "[7] Arda Duzceker, Silvano Galliani, Christoph Vogel, Pablo Speciale, Mihai Dusmanu, and Marc Pollefeys. Deepvideovs: Multi-view stereo on video with recurrent spatiotemporal fusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15324-15333, 2021. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 475, + 288, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 475, + 288, + 520 + ], + "spans": [ + { + "bbox": [ + 53, + 475, + 288, + 520 + ], + "type": "text", + "content": "[8] David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. In Advances in neural information processing systems, pages 2366-2374, 2014. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 521, + 288, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 521, + 288, + 566 + ], + "spans": [ + { + "bbox": [ + 53, + 521, + 288, + 566 + ], + "type": "text", + "content": "[9] Ziyue Feng, Liang Yang, Longlong Jing, Haiyan Wang, YingLi Tian, and Bing Li. Disentangling object motion and occlusion for unsupervised multi-frame monocular depth. arXiv preprint arXiv:2203.15174, 2022. 1, 2, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 567, + 288, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 567, + 288, + 621 + ], + "spans": [ + { + "bbox": [ + 48, + 567, + 288, + 621 + ], + "type": "text", + "content": "[10] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3354-3361. IEEE, 2012. 1, 3, 6, 8" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 624, + 288, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 288, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 288, + 668 + ], + "type": "text", + "content": "[11] Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J Brostow. Digging into self-supervised monocular depth estimation. In Proceedings of the IEEE International Conference on Computer Vision, pages 3828-3838, 2019. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 669, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 669, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 669, + 288, + 713 + ], + "type": "text", + "content": "[12] Dong Gong, Jie Yang, Lingqiao Liu, Yanning Zhang, Ian Reid, Chunhua Shen, Anton Van Den Hengel, and Qinfeng Shi. From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur. In Proceed-" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "ings of the IEEE conference on computer vision and pattern recognition, pages 2319-2328, 2017. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 308, + 96, + 547, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 547, + 150 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 547, + 150 + ], + "type": "text", + "content": "[13] Dong Gong, Frederic Z Zhang, Javen Qinfeng Shi, and Anton Van Den Hengel. Memory-augmented dynamic neural relational inference. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11843-11852, 2021. 4" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 152, + 547, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 547, + 207 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 547, + 207 + ], + "type": "text", + "content": "[14] Ariel Gordon, Hanhan Li, Rico Jonschkowski, and Anelia Angelova. Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8977-8986, 2019. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 209, + 547, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 209, + 547, + 262 + ], + "spans": [ + { + "bbox": [ + 308, + 209, + 547, + 262 + ], + "type": "text", + "content": "[15] Xiaodong Gu, Zhiwen Fan, Siyu Zhu, Zuozhuo Dai, Feitong Tan, and Ping Tan. Cascade cost volume for high-resolution multi-view stereo and stereo matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2495-2504, 2020. 1, 2, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 264, + 547, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 264, + 547, + 319 + ], + "spans": [ + { + "bbox": [ + 308, + 264, + 547, + 319 + ], + "type": "text", + "content": "[16] Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon. 3d packing for self-supervised monocular depth estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2485-2494, 2020. 1, 8" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 320, + 547, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 320, + 547, + 374 + ], + "spans": [ + { + "bbox": [ + 308, + 320, + 547, + 374 + ], + "type": "text", + "content": "[17] Vitor Guizilini, Rares Ambrus, Dian Chen, Sergey Zakharov, and Adrien Gaidon. Multi-frame self-supervised depth with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 160-170, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 376, + 547, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 376, + 547, + 421 + ], + "spans": [ + { + "bbox": [ + 308, + 376, + 547, + 421 + ], + "type": "text", + "content": "[18] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 1, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 422, + 547, + 477 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 422, + 547, + 477 + ], + "spans": [ + { + "bbox": [ + 308, + 422, + 547, + 477 + ], + "type": "text", + "content": "[19] Matthias Innnmann, Kihwan Kim, Jinwei Gu, Matthias Nießner, Charles Loop, Marc Stamminger, and Jan Kautz. Nrmvs: Non-rigid multi-view stereo. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2754-2763, 2020. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 478, + 547, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 478, + 547, + 510 + ], + "spans": [ + { + "bbox": [ + 308, + 478, + 547, + 510 + ], + "type": "text", + "content": "[20] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 511, + 547, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 511, + 547, + 567 + ], + "spans": [ + { + "bbox": [ + 308, + 511, + 547, + 567 + ], + "type": "text", + "content": "[21] Marvin Klingner, Jan-Aike Termöhlen, Jonas Mikolajczyk, and Tim Fingscheidt. Self-supervised monocular depth estimation: Solving the dynamic object problem by semantic guidance. In European Conference on Computer Vision, pages 582-600. Springer, 2020. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 568, + 547, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 568, + 547, + 612 + ], + "spans": [ + { + "bbox": [ + 308, + 568, + 547, + 612 + ], + "type": "text", + "content": "[22] Seokju Lee, Sunghoon Im, Stephen Lin, and In So Kweon. Learning monocular depth in dynamic scenes via instance-aware projection consistency. arXiv preprint arXiv:2102.02629, 2021. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 613, + 547, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 613, + 547, + 656 + ], + "spans": [ + { + "bbox": [ + 308, + 613, + 547, + 656 + ], + "type": "text", + "content": "[23] Hanhan Li, Ariel Gordon, Hang Zhao, Vincent Casser, and Anelia Angelova. Unsupervised monocular depth learning in dynamic scenes. arXiv preprint arXiv:2010.16404, 2020. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 658, + 547, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 658, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 658, + 547, + 713 + ], + "type": "text", + "content": "[24] Rui Li, Xiantuo He, Yu Zhu, Xianjun Li, Jinqiu Sun, and Yanning Zhang. Enhancing self-supervised monocular depth estimation via incorporating robust constraints. In Proceedings of the 28th ACM International Conference on Multimedia, pages 3108-3117, 2020. 1" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21547" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "text", + "content": "[25] Rui Li, Danna Xue, Shaolin Su, Xiantuo He, Qing Mao, Yu Zhu, Jinqiu Sun, and Yanning Zhang. Learning depth via leveraging semantics: Self-supervised monocular depth estimation with both implicit and explicit semantic guidance. Pattern Recognition, page 109297, 2023. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 128, + 287, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 128, + 287, + 172 + ], + "spans": [ + { + "bbox": [ + 48, + 128, + 287, + 172 + ], + "type": "text", + "content": "[26] Rui Li, Danna Xue, Yu Zhu, Hao Wu, Jinqiu Sun, and Yanning Zhang. Self-supervised monocular depth estimation with frequency-based recurrent refinement. IEEE Transactions on Multimedia, 2022. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 172, + 287, + 216 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 172, + 287, + 216 + ], + "spans": [ + { + "bbox": [ + 48, + 172, + 287, + 216 + ], + "type": "text", + "content": "[27] Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, and Zeming Li. Bevdepth: Acquisition of reliable depth for multi-view 3d object detection. arXiv preprint arXiv:2206.10092, 2022. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 216, + 287, + 260 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 216, + 287, + 260 + ], + "spans": [ + { + "bbox": [ + 48, + 216, + 287, + 260 + ], + "type": "text", + "content": "[28] Zhenxing Mi, Chang Di, and Dan Xu. Generalized binary search network for highly-efficient multi-view stereo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12991-13000, 2022. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 261, + 287, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 261, + 287, + 304 + ], + "spans": [ + { + "bbox": [ + 48, + 261, + 287, + 304 + ], + "type": "text", + "content": "[29] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in pytorch. 2017. 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 305, + 287, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 305, + 287, + 348 + ], + "spans": [ + { + "bbox": [ + 48, + 305, + 287, + 348 + ], + "type": "text", + "content": "[30] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019. 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 349, + 287, + 393 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 349, + 287, + 393 + ], + "spans": [ + { + "bbox": [ + 48, + 349, + 287, + 393 + ], + "type": "text", + "content": "[31] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing systems, pages 5998-6008, 2017. 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 393, + 287, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 393, + 287, + 448 + ], + "spans": [ + { + "bbox": [ + 48, + 393, + 287, + 448 + ], + "type": "text", + "content": "[32] Fangjinhua Wang, Silvano Galliani, Christoph Vogel, and Marc Pollefeys. Itermvs: Iterative probability estimation for efficient multi-view stereo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8606-8615, 2022. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 449, + 287, + 481 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 449, + 287, + 481 + ], + "spans": [ + { + "bbox": [ + 48, + 449, + 287, + 481 + ], + "type": "text", + "content": "[33] Tai Wang, Jiangmiao Pang, and Dahua Lin. Monocular 3d object detection with depth from motion. arXiv preprint arXiv:2207.12988, 2022. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 482, + 287, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 482, + 287, + 536 + ], + "spans": [ + { + "bbox": [ + 48, + 482, + 287, + 536 + ], + "type": "text", + "content": "[34] Xiaofeng Wang, Zheng Zhu, Guan Huang, Fangbo Qin, Yun Ye, Yijia He, Xu Chi, and Xingang Wang. Mvster: epipolar transformer for efficient multi-view stereo. In European Conference on Computer Vision, pages 573-591. Springer, 2022. 1, 4" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 536, + 287, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 536, + 287, + 581 + ], + "spans": [ + { + "bbox": [ + 48, + 536, + 287, + 581 + ], + "type": "text", + "content": "[35] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004. 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 582, + 287, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 582, + 287, + 646 + ], + "spans": [ + { + "bbox": [ + 48, + 582, + 287, + 646 + ], + "type": "text", + "content": "[36] Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel Brostow, and Michael Firman. The temporal opportunist: Self-supervised multi-frame monocular depth. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1164-1174, 2021. 1, 2, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 647, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 287, + 712 + ], + "type": "text", + "content": "[37] Felix Wimbauer, Nan Yang, Lukas Von Stumberg, Niclas Zeller, and Daniel Cremers. Monorec: Semi-supervised dense reconstruction in dynamic environments from a single moving camera. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6112-6122, 2021. 1, 2, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 72, + 546, + 529 + ], + "type": "list", + "angle": 0, + "index": 23, + "blocks": [ + { + "bbox": [ + 307, + 72, + 546, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 72, + 546, + 127 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 546, + 127 + ], + "type": "text", + "content": "[38] Qingsen Yan, Dong Gong, Javen Qinfeng Shi, Anton van den Hengel, Chunhua Shen, Ian Reid, and Yanning Zhang. Dual-attention-guided network for ghost-free high dynamic range imaging. International Journal of Computer Vision, pages 1–19, 2022. 4" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 128, + 546, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 128, + 546, + 183 + ], + "spans": [ + { + "bbox": [ + 307, + 128, + 546, + 183 + ], + "type": "text", + "content": "[39] Zhenpei Yang, Zhile Ren, Qi Shan, and Qixing Huang. Mvs2d: Efficient multi-view stereo via attention-driven 2d convolutions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8574-8584, 2022. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 184, + 546, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 184, + 546, + 228 + ], + "spans": [ + { + "bbox": [ + 307, + 184, + 546, + 228 + ], + "type": "text", + "content": "[40] Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. Mvsnet: Depth inference for unstructured multi-view stereo. In Proceedings of the European conference on computer vision (ECCV), pages 767-783, 2018. 1, 2, 4, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 229, + 546, + 284 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 229, + 546, + 284 + ], + "spans": [ + { + "bbox": [ + 307, + 229, + 546, + 284 + ], + "type": "text", + "content": "[41] Yao Yao, Zixin Luo, Shiwei Li, Tianwei Shen, Tian Fang, and Long Quan. Recurrent mvsnet for high-resolution multi-view stereo depth inference. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5525-5534, 2019. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 285, + 546, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 285, + 546, + 329 + ], + "spans": [ + { + "bbox": [ + 307, + 285, + 546, + 329 + ], + "type": "text", + "content": "[42] Wei Yin, Yifan Liu, and Chunhua Shen. Virtual normal: Enforcing geometric constraints for accurate and robust depth prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. 1, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 330, + 546, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 330, + 546, + 374 + ], + "spans": [ + { + "bbox": [ + 307, + 330, + 546, + 374 + ], + "type": "text", + "content": "[43] Wei Yin, Yifan Liu, Chunhua Shen, and Youliang Yan. Enforcing geometric constraints of virtual normal for depth prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 5684-5693, 2019. 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 374, + 546, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 374, + 546, + 430 + ], + "spans": [ + { + "bbox": [ + 307, + 374, + 546, + 430 + ], + "type": "text", + "content": "[44] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Simon Chen, Yifan Liu, and Chunhua Shen. Towards accurate reconstruction of 3d scene shape from a single monocular image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 430, + 546, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 430, + 546, + 485 + ], + "spans": [ + { + "bbox": [ + 307, + 430, + 546, + 485 + ], + "type": "text", + "content": "[45] Wei Yin, Jianming Zhang, Oliver Wang, Simon Niklaus, Long Mai, Simon Chen, and Chunhua Shen. Learning to recover 3d scene shape from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 204-213, 2021. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 486, + 546, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 486, + 546, + 529 + ], + "spans": [ + { + "bbox": [ + 307, + 486, + 546, + 529 + ], + "type": "text", + "content": "[46] Weihao Yuan, Xiaodong Gu, Zuozhuo Dai, Siyu Zhu, and Ping Tan. New crfs: Neural window fully-connected crfs for monocular depth estimation. arXiv preprint arXiv:2203.01502, 2022. 1" + } + ] + } + ], + "index": 22 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "21548" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning To Generate Image Embeddings With User-Level Differential Privacy/35eb4863-9483-4c5e-b3f4-41696055c435_content_list.json b/2023/Learning To Generate Image Embeddings With User-Level Differential Privacy/35eb4863-9483-4c5e-b3f4-41696055c435_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..ee53f38604316f113222927f30ab29b749af25c3 --- /dev/null +++ b/2023/Learning To Generate Image Embeddings With User-Level Differential Privacy/35eb4863-9483-4c5e-b3f4-41696055c435_content_list.json @@ -0,0 +1,1459 @@ +[ + { + "type": "text", + "text": "Learning to Generate Image Embeddings with User-level Differential Privacy", + "text_level": 1, + "bbox": [ + 94, + 130, + 875, + 154 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Zheng Xu*", + "bbox": [ + 129, + 180, + 220, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Sean Augenstein", + "bbox": [ + 156, + 199, + 292, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Maxwell Collins*", + "bbox": [ + 256, + 181, + 397, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Ting Liu", + "bbox": [ + 339, + 199, + 411, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yuxiao Wang", + "bbox": [ + 442, + 181, + 550, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Florian Schroff", + "bbox": [ + 457, + 199, + 581, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Liviu Panait", + "bbox": [ + 591, + 181, + 692, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Sewoong Oh", + "bbox": [ + 733, + 181, + 838, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "H. Brendan McMahan", + "bbox": [ + 627, + 199, + 807, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Google Research", + "bbox": [ + 410, + 222, + 550, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 265, + 313, + 282 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class space. To achieve user-level DP for large image-to-embedding feature extractors, we propose DP-FedEmb, a variant of federated learning algorithms with per-user sensitivity control and noise addition, to train from user-partitioned data centralized in the datacenter. DP-FedEmb combines virtual clients, partial aggregation, private local fine-tuning, and public pretraining to achieve strong privacy utility trade-offs. We apply DP-FedEmb to train image embedding models for faces, landmarks and natural species, and demonstrate its superior utility under same privacy budget on benchmark datasets DigiFace, EMNIST, GLD and iNaturalist. We further illustrate it is possible to achieve strong user-level DP guarantees of $\\epsilon < 2$ while controlling the utility drop within $5\\%$ , when millions of users can participate in training.", + "bbox": [ + 75, + 297, + 473, + 599 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 630, + 209, + 645 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Representation learning, by training deep neural networks as feature extractors to generate compact embedding vectors from images, is a fundamental component in computer vision. Metric learning, a kind of representation learning using supervised data, has been widely applied to image recognition, clustering, and retrieval [61, 75, 77]. Machine learning models have the capacity to memorize training data [10, 11], leading to privacy risks when the models are deployed. Privacy risk can also be audited by membership inference attacks [9, 63], i.e. detecting whether certain data was used to train a model and potentially exposing users' usage behaviors. Defending against such risks is a critical responsibility when training on privacy-sensitive data.", + "bbox": [ + 75, + 656, + 468, + 864 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Differential Privacy (DP) [23] is an extensively used quantifiable measurement of privacy risk, now generally accepted as a standard notion of privacy in both industry and government [5, 18, 50, 70]. Applied to machine learning, DP requires a training procedure with explicit randomness, and guarantees that the distribution over output models is quantifiably similar given a certain scope of change to the training dataset. A DP guarantee with respect to the change of a single arbitrary training example is known as example-level DP, which provides plausible deniability (in the binary hypothesis testing sense of [38]) that any single example (e.g., image) occurred in the training dataset. If we instead consider how the distribution of output models changes if the data (including even the number of examples) from any single user change arbitrarily, we have user-level DP [22]. This ensures model training is quantifiably insensitive to all of the data from any one user, and hence it is impossible to tell if a user has participated in training with high confidence. This guarantee can be exponentially stronger than example-level DP if one user may contribute many examples to training.", + "bbox": [ + 500, + 266, + 893, + 583 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recently, DP-SGD [1] (essentially, SGD with the additional steps of clipping each individual gradient to have a maximum norm, and adding correspondingly calibrated noise) has been used to achieve example-level DP for relatively large models in language modeling and image classification tasks [4, 16, 42, 45, 81], often utilizing techniques like large batch training and pretraining on public data. DP-SGD can be modified to guarantee user-level DP, which is often combined with federated learning algorithms and called DP-FedAvg [49]. User-level DP has only been studied for small on-device models that have less than 10 million parameters [36, 49, 57].", + "bbox": [ + 498, + 590, + 893, + 772 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We consider user-level DP for relatively large models in representation learning with supervised data. In our setting, similar to federated learning (FL), the data are user-partitioned; but in contrast to decentralized FL, we are primarily motivated by centralized data that benefit from access to richer computation resources and the ability to form virtual clients at random. Throughout this work, we use user as the basic unit of data partitioning and the granular-", + "bbox": [ + 498, + 779, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*The first two authors contributed equally. Correspondence to Zheng Xu xuzheng@google.com", + "bbox": [ + 76, + 875, + 470, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "7969", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/a94fb1d2cc42efdc91b99a8c9bcbef77a57e3da3b08aec6cf3cc3263dd0ceeb0.jpg", + "image_caption": [ + "Figure 1. DP-FedEmb combines virtual clients, local fine-tuning, partial aggregation, and public pretraining to achieve strong privacy utility trade-offs. Colors indicate different users and the simplified case of a single class per user, coloring the softmax head accordingly." + ], + "image_footnote": [], + "bbox": [ + 143, + 89, + 831, + 324 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "ity for privacy; a user owns their (image) data, and class, identity and label are used interchangeably for the supervised information.", + "bbox": [ + 75, + 376, + 470, + 421 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Though typically each user only contributes images for a small number of classes, the combined class space from the union of all users can be very large, which proves challenging for existing DP algorithms. When the model size is fixed independent of the number of users, and at a relatively small scale with a few million parameters, previous methods can only achieve strong user-level DP when millions of users are available [36, 49, 57]. In contrast, when considering learning embedding models for applications like facial images, the number of classes (and hence, the total model size) can grow linearly with the number of users, and so simply scaling up to larger datasets with more users no longer ensures that good privacy-utility trade-offs can be achieved. For example, in a standard multi-class training paradigm with 128-dimensional embedding vectors, with one million users we expect the final dense layer for prediction alone to have over 128 million trainable parameters. Further, the fact that most users will only have examples from a small number of classes implies that gradients are approximately sparse, whereas DP-SGD requires the addition of dense noise to the full gradient, leading to a poor signal-to-noise ratio in the updates. Hence, existing methods can easily fail on the problems we consider.", + "bbox": [ + 75, + 426, + 472, + 773 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We propose DP-FedEmb to train embedding models with user-level differential privacy; Fig. 1 provides a high-level overview. DP-FedEmb combines public pretraining, virtual clients, local fine-tuning, and partial aggregation to achieve strong privacy-utility trade-offs. The key to the approach is partitioning the model into a backbone network that generates embeddings, and a classification softmax head specific to the classes in the training data. In", + "bbox": [ + 75, + 779, + 470, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "each training round, users are grouped into virtual clients and initialized from the global backbone. A local randomly-initialized softmax head layer is added for the limited number of classes on the virtual client, and the complete local model is fine-tuned in order to produce an update to the backbone. The local head parameters are not included in the private aggregation and hence require no noise addition. This is in contrast to existing methods like DP-FedAvg/DP-SGD, which would add noise to all parameters including the softmax head. The backbone updates are clipped to a maximum L2 norm, aggregated across virtual clients, and combined with appropriate DP noise. At this point, the noised update is the output of a DP mechanism and satisfies the corresponding DP guarantee. This update is then applied to the global backbone, which inherits the DP guarantee, and passed to the next round of training. DP-FedEmb significantly improves the scalability of DP training for embedding models, as only the parameters of the backbone network are privatized and released, and the size of this portion of the model does not grow with the number of users. Pretraining the backbone network on public data to learn general visual representations before applying DP-FedEmb for more privacy sensitive tasks further improves performance.", + "bbox": [ + 496, + 376, + 893, + 724 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We demonstrate the superior performance of DP-FedEmb for embedding models by experiments on datasets with moderate size of users and classes (DigiFace of 98.96K or 9047 users/identities, Google Landmarks Dataset of 1262 users and 2028 classes, and iNaturalist of 9275 users and 1203 classes). We also show relatively strong privacy guarantees of single digit $\\epsilon$ can be achieved while maintaining strong utility if millions users can participate in training. To our knowledge, this is the first report of training a commonly used large vision model, ResNet-50, with non-negligible noise for user-level DP.", + "bbox": [ + 496, + 734, + 895, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "7970", + "bbox": [ + 482, + 944, + 516, + 957 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Learning Embedding Models", + "text_level": 1, + "bbox": [ + 76, + 89, + 346, + 107 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.1. Problem formulation and centralized training", + "text_level": 1, + "bbox": [ + 76, + 114, + 464, + 130 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We learn a backbone network parameterized by $\\theta$ , that outputs an embedding vector $z = f(\\theta, x)$ for an input image $x$ . The backbone network, $\\theta$ , is trained on paired examples of image $x$ and class $y$ . The training dataset is naturally partitioned by $M$ users, i.e., $\\mathcal{D} = \\bigcup_{i=1}^{M} \\mathcal{D}_i$ .", + "bbox": [ + 75, + 137, + 467, + 213 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We adopt the popular multi-class training framework for embedding models, where a proxy weight vector $w_{j}$ is learnt for each class $j$ . We use $\\omega$ to denote the union of proxy weight vectors $\\{w_{j}\\}$ , which is called the head of the network. Given a training image-class pair $(x,y)$ , logits are computed by taking the inner product between the embedding vector $f(\\theta ,x)$ and the proxy weight vectors in the network head $\\omega$ . This is effectively passing $f(\\theta ,x)$ through a dense network layer parameterized by $\\omega$ without the bias terms. With a supervised training loss $\\ell$ , such as the cross entropy loss, the following objective is optimized", + "bbox": [ + 75, + 213, + 467, + 378 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\theta , \\omega} \\sum_ {(x, y) \\in \\mathcal {D}} \\ell (\\langle \\omega , f (\\theta , x) \\rangle , y), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 158, + 385, + 468, + 419 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where we overload the inner product notation, i.e., $\\langle \\omega ,f(\\theta ,x)\\rangle$ , to denote a set of inner products with each element in $\\omega$ . Typically, variants of the gradient descent method are used to solve the optimization in (1). In each iteration, the average gradient is computed on a sampled minibatch of data $B\\subset \\mathcal{D}$ , and is then used to update the model parameters $\\theta$ and $\\omega$ . Furthermore, when the output space, i.e., the number of classes, is very large, sampled softmax is often applied [35], where only a subset of proxy weights sampled from $\\omega$ are used in each training iteration.", + "bbox": [ + 75, + 424, + 467, + 575 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. User-level DP and DP-FedAvg", + "text_level": 1, + "bbox": [ + 76, + 583, + 344, + 599 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To achieve user-level DP, we control the sensitivity of each user and add corresponding noise for anonymization. To effectively control the sensitivity, it is important to understand and account for the contributions of each user in the model updates; hence it is convenient to consider the data at a granularity of users instead of individual samples. Grouping together each user's data, the objective (1) can be rewritten as", + "bbox": [ + 75, + 606, + 467, + 724 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\theta , \\omega} \\sum_ {i = 1} ^ {M} \\sum_ {(x, y) \\in \\mathcal {D} _ {i}} \\ell (\\langle \\omega , f (\\theta , x) \\rangle , y). \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 143, + 729, + 467, + 773 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The above objective of two level sum is often found in federated learning [73], which can be optimized by the (generalized) FedAvg algorithm [48, 58]. In generalized FedAvg, each round $t$ starts with the server broadcasting $\\theta^{(t)},\\omega^{(t)}$ to a subset of clients. Each client $i$ will then update the local model parameters by CLIENTOPT with private data $\\mathcal{D}_i$ , and send back the updates for model parameters $\\Delta_i(\\theta^{(t)}),\\Delta_i(\\omega^{(t)})$ . The model deltas from sampled", + "bbox": [ + 75, + 779, + 467, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "clients are then aggregated and used by SERVEROPT to get $\\theta^{(t + 1)},\\omega^{(t + 1)}$ for the next round.", + "bbox": [ + 498, + 90, + 890, + 119 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The generalized FedAvg algorithm can be extended for user-level DP by clipping the model deltas and adding noise proportional to the sensitivity [27,49]. We can use either independent Gaussian noise [49], or correlated noise that can achieve comparable privacy-utility trade-off without relying on the assumption of sampling [36]. The two variants are effectively applying DP-SGD [1] or DP-FTRL [36] as SERVEROPT in the generalized FedAvg framework. Unlike the cross-device FL setting where sampling is extremely hard, it is possible to control user sampling in the datacenter and use DP-SGD. But DP-FTRL provides the possibility of handling the online setting where the user data are streamed instead of collected, and can be accounted for zCDP [7] reported by US census bureau [70]. A complete description of DP-FedAvg for training a backbone network to generate image embeddings is in Alg. 2 in Appendix B", + "bbox": [ + 496, + 122, + 890, + 364 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In addition to the flexibility of generalized FedAvg for user-level DP, there are a few side effects of FedAvg that make it particularly effective for differentially private training. The model deltas are computed based on data for each user before clipping in DP, which can potentially reduce the bias introduced by clipping. As [16] suggested that averaging gradients from augmented data before clipping can improve training for example-level DP, model deltas from user data for user-level DP can be considered a natural extension to improve the bias-variance trade-off. The communication efficiency of FedAvg that leads to infrequent aggregation and model release is also desirable for DP training. The local model updates by private data on clients introduce no additional privacy cost, and only communication rounds between clients and server have to be accounted for DP. Though the theoretical advantages of FedAvg are only proved under certain assumptions [74, 78], FedAvg with local updates can achieve communication efficiency and fast convergence in various practical applications [73].", + "bbox": [ + 496, + 364, + 890, + 652 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.3. Proposed DP-FedEmb method", + "text_level": 1, + "bbox": [ + 500, + 662, + 769, + 680 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "While generalized DP-FedAvg can be applied to train a backbone network $\\theta$ to generate embedding $f(\\theta, x)$ from image $x$ , there are challenges that significantly affect the efficiency and feasibility of the method. We propose DP-FedEmb with a few key features: virtual clients, partial aggregation, local fine-tuning, public pretraining, and parameter freezing. Details of DP-FedEmb are provided in Alg. 1.", + "bbox": [ + 496, + 686, + 890, + 792 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Virtual clients. Data heterogeneity is one of the key problems in federated optimization [73]. When we train embedding models in the multi-class framework, the class space can be very large and each user may only observe a limited number of classes. In the extreme case, when training embedding models on facial images [61, 66], each user may only have images for their own identity. This signif", + "bbox": [ + 496, + 795, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "7971", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 2 + }, + { + "type": "code", + "sub_type": "algorithm", + "code_caption": [ + "Algorithm 1: DP-FedEmb: learning embedding model $\\theta$ with user-level DP" + ], + "code_body": "Input:SERVEROPT with learning rate $\\alpha$ .. CLIENTOPT with learning rate $\\beta_{1}$ and $\\beta_{2}$ clip norm $\\gamma$ and noise multiplier $\\sigma$ (optional) pretrained model $\\theta^{(0)}$ \nfor round $t = 0,1,\\ldots ,T - 1$ do \nSample a subset of users $\\mathcal{U}^{(t)}$ \nPartition users $\\mathcal{U}^{(t)}$ to virtual clients $S^{(t)}$ \nfor each virtual client $V\\in S^{(t)}$ in parallel do Initialize backbone $\\theta_V^{(t,0)} = \\theta^{\\mathrm{(t)}}$ \nRandomly initialize head $\\omega_{V}^{(t,0)}$ \nfor $k = 0,\\dots ,K - 1$ do Sample minibatch $B\\subset \\bigcup_{i\\in V}\\mathcal{D}_i$ Compute gradients $\\nabla_{\\theta_V}\\ell_B,\\nabla_{\\omega_V}\\ell_B$ where $\\ell_B = \\mathbb{E}_{(x,y)\\in B}\\ell (\\langle \\omega_V,f(\\theta_V,x)\\rangle ,y)$ Update $\\theta_V^{(t,k + 1)}$ by CLIENTOPT, $\\theta_V^{(t,k)}$ $\\nabla_{\\theta_V}\\ell_B,\\beta_1$ Update $\\omega_{V}^{(t,k + 1)}$ by CLIENTOPT, $\\omega_{V}^{(t,k)}$ $\\nabla_{\\omega_V}\\ell_B,\\beta_2$ \nend \nCompute clipped model update \n $\\Delta_V^{(t)} = \\mathrm{Clip}(\\theta_V^{(t,K)} - \\theta_V^{(t,0)},\\gamma)$ \nend \nAggregate model updates \n $\\Delta^{(t)} = \\mathrm{AddNoise}\\bigl (\\sum_{V\\in S^{(t)}}\\Delta_{V}^{(t)},\\sigma \\gamma \\bigr) / |S^{(t)}|$ \nUpdate global backbone parameters \n $\\theta^{(t + 1)} = \\mathrm{SERVEROPT}(\\theta^{(t)},\\Delta^{(t)},\\alpha)$", + "bbox": [ + 78, + 125, + 441, + 580 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "icently limits the advantage of local updates due to client drift [39], and even with specialized regularization like [82], FedSGD [48] with frequent aggregation and model release has to be used instead of FedAvg. It is challenging to use some specialized techniques for handling data heterogeneity [73] in DP training. Instead, we propose a simple yet effective approach: randomly groups the data of sampled users into virtual clients.", + "bbox": [ + 75, + 613, + 468, + 733 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Unlike the cross-device FL setting where the on-device data of users cannot be directly communicated, virtual clients are feasible for user data in the datacenter. It is important to guarantee that a user will not be included in two virtual clients in a single round for user-level DP, analogous to microbatches for DP-SGD and example-level DP [1,51]. When the grouping of users for virtual clients is fixed in advance across rounds, the granularity of the DP definition can slightly change: the adjacent dataset for DP is based on virtual clients (a group of users) instead of a single user, which has conceptually stronger privacy guarantees. However,", + "bbox": [ + 75, + 734, + 470, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "when virtual clients are randomly regrouped across rounds as in Alg. 1, we can only show user-level DP as discussed in Appendix C. Virtual clients also control the interpolation between federated training and centralized training: when all users are grouped into a single virtual client, federated training is equivalent to centralized training, which removes heterogeneity but is challenging for DP mechanism. Virtual clients are used for both baseline DP-FedAvg and the proposed DP-FedEmb method.", + "bbox": [ + 496, + 90, + 890, + 226 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Partial aggregation and local fine-tuning. Another challenge is the number of parameters in DP training. A common backbone $\\theta$ of ResNet-50 for 128 dimensional embedding vectors has 23.77 million parameters. However, the parameter size of head $\\omega$ can linearly grow with the number of classes. Taking FaceNet [61, 66] as an example again, $\\omega$ can easily grow to 1280 million for 10 million identities in real-world applications. Sampled softmax [35, 72] can be applied to improve training efficiency. However, as both backbone $\\theta$ and head $\\omega$ are shared among users and need to be privatized by adding noise during training, the combined parameter size of $(\\theta, \\omega)$ will significantly affect the privacy utility trade-off, which cannot be mitigated by sampled softmax.", + "bbox": [ + 496, + 229, + 892, + 439 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In DP-FedEmb, inspired by federated reconstruction [65] and DP personalization [34], we only aggregate and privatize the backbone network $\\theta$ , which is used in inference and has fixed parameter size that does not grow with classes. A local head $\\omega_{V}$ is randomly initialized and updated on each virtual client $V$ . A fine-tuning approach is adopted for local updates, where different learning rates $\\beta_{1}, \\beta_{2}$ are used for the backbone $\\theta_{V}$ and head $\\omega_{V}$ , respectively. When combined with virtual clients, the partial aggregation and local fine-tuning approach can be interpreted in various ways: each virtual client is performing transfer learning given a shared backbone network for representation learning; the data of each class are their own positive samples as well as negative samples for other classes on the same virtual client; the size of local head $\\omega_{V}$ is also significantly smaller than $\\omega$ for all classes, which is effectively a user-based sampling for softmax.", + "bbox": [ + 496, + 441, + 892, + 698 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Public pretraining. The parameter size of the backbone to be privatized can still be large after applying partial aggregation and local fine-tuning with virtual clients, e.g., 23.77 million for ResNet-50. Inspired by recent research on applying DP-SGD for example-level DP on large language modeling [45, 81] and image classification [16, 42], we use a model pretrained on public images to initialize the DP training of the backbone network. There is a relatively clear distinction between the public and private domains for our task: we use public images collected from open webpages for pretraining, and then privately train on users' data collected in a datacenter.", + "bbox": [ + 496, + 700, + 890, + 881 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Parameter freezing. Neural networks are known to be", + "bbox": [ + 519, + 885, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "7972", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "overparameterized, and not all weights are equally important [26, 84]. Freezing some parameters to be non-trainable has been shown to be effective when the privacy budget is small [64], especially when combined with public pretraining for large models [16, 81]. For backbone convolutional neural networks with normalization layers, we experiment with training parameters with all normalization layers, and some of the convolutional kernels. However, freezing is found to be less efficient in our setting that performs representation learning, instead of classification, for a moderate size model in the high-utility-moderate-noise regime.", + "bbox": [ + 75, + 90, + 472, + 257 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "DP mechanism and hyperparameters. Similar to generalized DP-FedAvg, we perform clipping for model deltas and add noise for aggregated updates. The clip norm $\\gamma$ is estimated by adaptive clipping [3] in the parameter tuning stage. For DP-FedEmb, we perform extensive studies on several configurations in Sec. 3. Differentially private hyperparameter tuning [55] is a topic out of the scope of this paper, and automating hyperparameter tuning is an important future work. Either independent Gaussian noise like DP-SGD [49] or tree-based correlated noise like DP-FTRL [36] can be added. Under the same noise multiplier, DP-FedEmb will achieve the same privacy bound as DP-FedAvg with virtual clients, while utility can be improved for training a backbone network with a large head.", + "bbox": [ + 75, + 257, + 472, + 470 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3. Experiments", + "text_level": 1, + "bbox": [ + 76, + 482, + 209, + 500 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We conduct experiments to train image-to-embedding backbone networks with user-level DP. We use the DigiFace dataset [6] of synthetic faces based on ethical and responsible development considerations, and verified that the conclusions on DigiFace are very similar to conclusions generated from experiments on natural facial images. We randomly split the DigiFace dataset of $110K$ identities and $1.22M$ into subsets of $98.96K$ identities with $1.10M$ images for training, 5443 identities with $58.24K$ images for validation, and 5598 identities with $60.82K$ images for testing. We extensively use a smaller training set, DigiFace10K, which contains the 9047 training identities of 72 images sampled from the DigiFace training set. We also run experiments on public datasets of natural images: EMNIST, Google Landmarks Dataset (GLD) and iNaturalist (iNat) dataset. These datasets are summarized in Tab. 3.", + "bbox": [ + 75, + 507, + 472, + 750 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In our setting, each user holds only the images of their own identities, i.e., user-level DP is also identity-level DP. We use ResNet-50 [31] and MobileNetV2 [32, 60, 73] as backbone networks, replace batch normalization [33] with group normalization [80], and use a multi-class framework with a large softmax head to train the backbone. The dimension of the embeddings are 128 for all experiments.", + "bbox": [ + 75, + 750, + 472, + 854 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We evaluate the performance of the backbone network based on predicting identity matches from the distance between two image embeddings. By varying a threshold on", + "bbox": [ + 75, + 854, + 472, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "the pairwise similarity, a recall versus false accept rate (FAR) curve on the test data can be generated for a trained model. A scalar value of recall@FAR=1e-3 is often reported. The privacy guarantees are computed by either using Renyi differential privacy (RDP) [53] and converting to $(\\epsilon, \\delta)$ -DP by [8], or DP-FTRL accounting without restart [36]. More discussion on privacy accounting can be found in Appendix C. We aim for single-digit $\\epsilon$ when $\\delta$ is small, and sometimes relax to $\\epsilon \\sim 20$ as we use the stronger substitute-one DP definition [56].", + "bbox": [ + 496, + 90, + 893, + 242 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We compare the proposed DP-FedEmb with non-private oracle performance of centralized training, and baseline methods DP-FedAvg. We tune the learning rate with learning rate scheduling for standard centralized training. The centralized baseline is provided as an oracle for non-private training performance. We exclude tricks like data augmentation for either centralized or federated training as the goal is not achieving state-of-the-art performance. Virtual clients are used to improve DP-FedAvg performance, and the same tuning strategy is applied for DP-FedEmb and DP-FedAvg. In most of the experiments, unless otherwise specified, we fix the hyperparameters in the federated setting and only tune the learning rates; the backbone networks are pretrained on classifying the 1000 classes of ImageNet [59]; both CLIENTOPT and SERVEROPT are SGD optimizers with momentum 0.9; and more details are provided in Sec. 3.3. Code is released at https://github.com/google-research/federated/tree/master/dpvisual_embedding.", + "bbox": [ + 496, + 243, + 893, + 531 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.1. Privacy-utility-computation trade-off", + "text_level": 1, + "bbox": [ + 500, + 542, + 823, + 559 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We study the privacy-utility-computation trade-offs of training ResNet-50 on DigiFace10K in Fig. 2 and Fig. 6. Figure 2a shows the privacy-utility trade-off. Without adding noise, the federated algorithms (DP-)FedAvg and (DP-)FedEmb can achieve even better results than the non-private centralized baseline, which is consistent with recent empirical and theoretical justifications that FedAvg is more accurate when learning representations [14, 15]. When the same noise multiplier is used, i.e., under same privacy budget, DP-FedEmb outperforms DP-FedAvg; and the margin increases when increasing the noise. We can observe the advantage of DP-FedEmb over DP-FedAvg even if we only have a small number of identities in DigiFace: the size of the head is $9047 \\times 128 \\sim 1.16M$ , only $4.6\\%$ of the backbone ResNet-50 with $23.77M$ parameters. For large scale data with 10 million identities, the size of the head can grow to $1280M$ , which is much larger than the backbone networks, and DP-FedAvg can easily fail in such settings.", + "bbox": [ + 496, + 566, + 893, + 839 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "It can be difficult to achieve a strong formal differential privacy bound without significantly hurting utility for Digi-Face10K, which only has a small number of total users. We consider the practical setting of more available users, and", + "bbox": [ + 496, + 839, + 893, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "7973", + "bbox": [ + 482, + 944, + 514, + 957 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/9d4c36498095a8bf410a0a03836ab6b359100a0246c1ab0fd843ea7ed5283e15.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 89, + 93, + 274, + 200 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/66252260106b2b602574679b4f12a44fe9feb23c6f18bce9b65c26e60129d3d5.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 282, + 92, + 455, + 200 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/7357beb9cb84f09069ff83abc9b274e19d64e47cdcdc446cbb85f332dae4fb05.jpg", + "image_caption": [ + "(c)" + ], + "image_footnote": [], + "bbox": [ + 464, + 90, + 665, + 200 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/3fe94c771904601ad7c16478d2c11f4ba518084717bccf999dc3d092a36b9aa5.jpg", + "image_caption": [ + "(d)" + ], + "image_footnote": [], + "bbox": [ + 674, + 90, + 874, + 200 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/4fc031a2a1c3a1809c5a4f61d3300d60f12ecd3cf21648e1952a3f9f5f569496.jpg", + "image_caption": [ + "(a)", + "Figure 3. DP-FedEmb and DP-FedAvg on DigiFace10K under the same privacy budget with noise multiplier 0.08, and additional oracle baseline of centralized training w/o DP: (a) recall@FAR=1e-3 on validation dataset during training; dashed lines are approximation by sampling a subset of validation users; (b) ROC curve of trained model on test set with log scale x-axis." + ], + "image_footnote": [], + "bbox": [ + 81, + 285, + 267, + 402 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/6d1bf0227dc60546d95158fd64dd177fb4c96a29f6624fd8211e9d42f4b37a03.jpg", + "image_caption": [ + "Figure 2. (a) Recall@FAR=1e-3 on DigiFace validation set under different noise multiplier; (b) zoom in the high utility regime in (a); (c) and (d) privacy-computation trade-off by extrapolating based on 3M and 10M total users, respectively. \"r400\" and \"r800\" represent the result at 400 or 800 training rounds; target $71.77\\%$ is $95\\%$ of centralized non-private recall@FAR=1e-3 at $75.55\\%$ .", + "(b)" + ], + "image_footnote": [], + "bbox": [ + 274, + 282, + 460, + 401 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "study the privacy-computation trade-off in Figs. 2c, 2d, 6b and 6c based on extrapolation. A key hypothesis following [36,49] is used: utility (Recall@FAR) is non-decreasing when simultaneously increasing the number of clients per round and noise multiplier. The hypothesis is based on the fact that the signal-to-noise ratio is non-decreasing when linearly increasing the number of clients per round and noise multiplier, and has been verified in practice [50, 57].", + "bbox": [ + 75, + 535, + 468, + 656 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We first choose the noise multiplier that is within $5\\%$ of recall@FAR=1e-3 compared to centralized non-private training in Figs. 2a and 6a, based on simulation that samples 64 virtual clients that each have 32 users per round: recall@FAR=1e-3 is 72.16 for running DP-FedAvg with 0.01 noise multiplier for 400 rounds, 72.37 for running DP-FedAvg with 0.015 noise multiplier for 800 rounds, 71.3 for running DP-FedEmb with 0.015 noise multiplier for 400 rounds, 72.6 for running DP-FedEmb with 0.02 noise multiplier for 800 rounds, and 71.85 for running DP-FTRL-FedEmb with 0.26 noise multiplier for 800 rounds. Then we linearly increase the number of users sampled and use the increased noise multiplier in RDP accounting to compute privacy bound $\\epsilon$ given $\\delta = 10^{-7}$ to generate Figs. 2c, 2d and 6b, and compute zCDP for Fig. 6c. Comparing curves of r400 and r800, training longer with larger noise is more", + "bbox": [ + 75, + 659, + 470, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "effective than training shorter with smaller noise. Figure 2c suggests $\\sim 98K$ users per round is enough for DP-FedEmb to achieve single-digit $\\epsilon$ if $3M$ users are available, while $\\sim 57K$ users per round are needed if $10M$ users are available in Fig. 2d. $98K$ users is $48\\times$ the number of users per round in our current simulation, which can be achieved by training with $8\\times$ computing resources for $6\\times$ longer. In Fig. 6b, there is a crossover point when using DP-FTRL versus DP-SGD for DP-FedEmb, and DP-FTRL is more effective for relatively large privacy $\\epsilon$ . Figure 6c shows that DP-FTRL-FedEmb can achieve zCDP smaller than 2.6, as used by US Census Bureau [70], when $8\\times$ users per round and $10M$ total users are available.", + "bbox": [ + 496, + 282, + 893, + 479 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3.2. Model evaluation", + "text_level": 1, + "bbox": [ + 500, + 492, + 669, + 507 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In Tab. 1, we summarize the quantitative results from the privacy-utility-computation trade-off analysis in Sec. 3.1. Each experiment runs three times to compute the mean and standard deviation. For similar recall@FAR=1e-3 on the DigiFace validation set, DP-FedEmb achieves stronger privacy guarantee than baseline DP-FedAvg, and the advantage of DP-FedEmb is expected to be more pronounced if a head for larger $10M$ identities is used for training. When $10M$ users are available and $64\\times$ users per round in training, privacy $\\epsilon = 3.90$ of single digit and zCDP= 1.28 smaller than 2.6 can be achieved when recall@FAR=1e-3 is within a $5\\%$ drop compared with non-private centralized training. In addition to validation performance, the private models also perform well on the left-out test dataset. Figure 3 presents the training curves and ROC curves for comparing DP-FedEmb and DP-FedAvg under the same privacy budget. DP-FedEmb outperforms DP-FedAvg in all training rounds, and trains a stronger private model with better recall at different false accept rates.", + "bbox": [ + 496, + 516, + 893, + 803 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3.3. Ablation study", + "text_level": 1, + "bbox": [ + 500, + 815, + 648, + 830 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We primarily use MobileNetV2 for ablation studies on DigiFace10K for two reasons: MobileNetV2 is smaller and faster for training in experiments; to test the generalization of DP-FedEmb and avoid overfitting on ResNet-50.", + "bbox": [ + 496, + 839, + 893, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "7974", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/93e49dd0ff86e7de1ca0d17e171ba275ec25cdd69533a8f34e19219dadcb7278.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
AlgorithmHyperparametersPrivacy (10M users)Recall@FAR=1e-3
NoiseSerLRRDP-εzCDPValidationTest
Centralized00.05∞∞75.55 ± 0.0575.53 ± 0.12
DP-FedAvg0.015 × 640.55.62-72.57 ± 0.1272.37 ± 0.09
DP-FedEmb0.02 × 640.23.90-72.63 ± 0.0572.37 ± 0.09
DP-FTRL-FedEmb0.26 × 640.29.671.2872.2 ± 0.2971.87 ± 0.26
", + "bbox": [ + 161, + 88, + 807, + 183 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 1. Quantitative results of privacy and utility on the DigiFace10K dataset. The client learning rate and clip norm for federated algorithms are 0.002 and 0.6, respectively; $\\delta = 10^{-7}$ for privacy guarantees. Centralized training has a standard learning rate scheduling, while tricks like data augmentation are excluded for all methods. The privacy guarantees are extrapolated based on $10M$ users and $131K$ users per round. A strong privacy guarantee can be achieved within a $5\\%$ drop on recall@FAR=1e-3.", + "bbox": [ + 75, + 195, + 890, + 251 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5d1b1a354c601ab088f4c46f073d0819d972f4483cfab68539dbc6dc1f69fdd6.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 83, + 277, + 282, + 393 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c444cddf3ae25623b0982b1a3c67ca3faae21e981aa4e6f8eb200116b50c97d7.jpg", + "image_caption": [ + "(b)", + "Figure 4. Recall@FAR=1e-3 on DigiFace validation set when training a MobileNet (a) with DP-FedEmb w/ pretraining, and baselines of DP-FedEmb w/o pretraining and DP-FedAvg; (b) with different pretrained models." + ], + "image_footnote": [], + "bbox": [ + 292, + 280, + 464, + 393 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Parameter freezing and public pretraining. For similar parameter size, MobileNetV2 outperforms ResNet-50 with frozen parameters, and Fig. 4a shows the privacy-utility trade-off. Recall@FAR=1e-3 of DP-FedEmb-r800 on MobileNetV2 only drops from $68.86\\%$ to $67.56\\%$ when 0.02 noise is added, while ResNet-50 drops from $79.09\\%$ to $72.6\\%$ . However, ResNet-50 still outperforms MobileNetV2 by a large margin in the high utility regime. DP-FedEmb consistently outperforms DP-FedAvg when noises are added. In Fig. 4b, though the private fine-tuning utility is not linearly increasing with pretraining accuracy, there seems to be a general positive correlation: better pretrained models can lead to better private models except for one outlier where a inferior pretrained model causes difficulty in training. More discussion on parameter freezing and public pretraining is provided in Appendix D.4.", + "bbox": [ + 75, + 492, + 468, + 733 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Federated settings. In the above experiments, we fix important hyperparameters for the federated setting: users per virtual client is 32, virtual clients per round is 64, examples per client is capped at 2048, the head learning rate (LR) scale $\\beta_{2} / \\beta_{1}$ is 100, the buffer size for data shuffling on clients is 2048, and the batch size for local SGD is 32. In Fig. 9, instead of tuning these hyperparameters in advance, we conduct a study on these hyperparameters to understand DP-FedEmb. Among these hyperparameters, Figs. 9a and 9d suggest virtual clients and head LR scale are particularly important for DP-FedEmb to be on par with", + "bbox": [ + 75, + 734, + 468, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "non-private centralized training, which is an important contribution of this work. Figures 9a to 9c suggest users per virtual client, clients per round, and examples per client only need to be large enough under privacy consideration and computation resources for the best practice. The head LR scale has a large tuning range between 50 and 500 in Fig. 9d. Recall@FAR=1e-3 is not sensitive to shuffle buffer size in Fig. 9e. The model utility can be potentially improved if we further tune the client batch size as suggested by Fig. 9f. We fixed the learning rate and other hyperparameters while varying one of the hyperparameters in the ablation study. How to automate tuning, especially tuning with differential privacy guarantees, is an important future work.", + "bbox": [ + 496, + 277, + 890, + 474 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Learning rate (LR). For experiments in Secs. 3.1 and 3.2, server and client learning rates are first tuned for non-private federated training with adaptive clipping of quantile 0.5 [3]. Then server learning rate is tuned when adding noise for private tuning, while estimated clip norm and client learning rate are fixed. The tuning range of learning rates are $\\{1,2,5\\} * 10^{-n}$ . Figures 5a and 5b suggest the optimal learning rates are similar for training 400 rounds or 800 rounds in non-private training. We then fix the client learning rate to be 0.002 and use the estimated clip norm 0.6 for MobileNetV2 in Fig. 5c, and observe the fixed clip results are very similar to adaptive clipping results. The best server learning rate for private training with noise can be smaller than non-private training, where the difference is even more notable for larger model ResNet-50 in Fig. 5d.", + "bbox": [ + 496, + 476, + 890, + 702 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Variants of DP-FedEmb. We use local fine-tuning with different learning rates $\\beta_{1},\\beta_{2}$ to update backbone and head parameters. An alternative is to reconstruct the head first before fine-tuning the backbone [41, 65]. We empirically find that head reconstruction can only achieve similar performance as the proposed fine-tuning when there are same or more number of updates on the backbone network, and hence use local fine-tuning for efficiency. It is also possible to use binary or triplet loss within a virtual clients. In our preliminary results, they achieve inferior results compared to DP-FedEmb that uses multi-class cross-entropy loss. We leave other improvement like arcface loss [17] as future work.", + "bbox": [ + 496, + 703, + 890, + 898 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "7975", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8f920cbc4074b48c99de2b3372c873bf3bcfa56a90d9220d493e12d971ce779a.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 86, + 95, + 256, + 223 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d4922e5c36cc404885995f7a289ab6cf798977a6b0fddd40939eb313ac098d10.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 266, + 95, + 464, + 223 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d2c3d84b5f203ffeccf65c163c40c27da5da71b1b19abb440a53ae7092ec88c1.jpg", + "image_caption": [ + "(c)", + "Figure 5. Study for learning rates. (a) and (b) recall@FAR=1e-3 after training MobileNetV2 using DP-FedEmb with adaptive clipping [3] and 0 noise for 400 rounds and 800 rounds, respectively; (c) and (d) varying server learning rate with fixed client learning rate and clip norm for MobileNetV2 and ResNet-50." + ], + "image_footnote": [], + "bbox": [ + 472, + 95, + 668, + 222 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d517d3bd690a3f3654e3f3e3704f18c5462e2ee53d2693ae688e057f737c8c17.jpg", + "image_caption": [ + "(d)" + ], + "image_footnote": [], + "bbox": [ + 676, + 92, + 879, + 222 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/0942012e3c208a6ba58d49f058c686e58c9a7dac8e73d40bff6af0b9e894321a.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetAlgorithmRecall@FAR=1e-3/0.1
ApproxAllPair
DigiFaceDP-FedEmb-19.76 ± 0.43
DP-FedAvg-13.38 ± 0.27
EMNISTDP-FedEmb10.64 ± 0.510.47 ± 0.5
DP-FedAvg9.78 ± 0.449.67 ± 0.41
GLDDP-FedEmb26.18 ± 0.4427.07 ± 0.04
DP-FedAvg24.48 ± 1.026.27 ± 0.17
iNatDP-FedEmb40.49 ± 1.0840.93 ± 0.94
DP-FedAvg29.57 ± 0.7829.6 ± 0.65
", + "bbox": [ + 78, + 301, + 478, + 460 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 2. The utility under same privacy budget for DigiFace [6], EMNIST [67], GLD [68] and iNat [69] datasets.", + "bbox": [ + 76, + 469, + 468, + 498 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "3.4. Additional results", + "text_level": 1, + "bbox": [ + 76, + 526, + 250, + 542 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We run additional experiments of ResNet-50 on the larger DigiFace of $98.96K$ users. Because a lot of users in DigiFace have only 5 images, we set each virtual client to contain 64 users, and use 3072 samples per virtual client. We sample 128 virtual clients per round, and a relatively large noise 1.39 in RDP accounting can achieve $\\epsilon = 24.86$ , $\\delta = 10^{-5}$ without extrapolation. The utility measured by recall@FAR=1e-3 is shown in Tab. 2. Though the utilities of both methods are significantly degraded by the large noise, DP-FedEmb is much better than the DP-FedAvg baseline because the noise is only added to backbone of $\\sim 2.4M$ parameters instead of backbone plus head of $\\sim 15M$ parameters. The full table including EM-NIST [67] results for reproducibility and hyperparameter choices is provided in Tab. 4 in Appendix D.", + "bbox": [ + 75, + 551, + 468, + 777 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In Tab. 2, we also conduct experiments with MobileNetV2 on Google Landmark Dataset (GLD) [32, 68, 77] and iNaturalist (iNat) dataset [32, 47, 69] to demonstrate the generalization of DP-FedEmb. We use a public model pretrained on ImageNet, and report extra approximate recall@FAR by computing pairwise similarity for minibatches, which is easy to reproduce and consistent with the all pair recall@FAR. We fix the hyperparameters", + "bbox": [ + 75, + 779, + 470, + 901 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "for the federated settings, and compare the performance of DP-FedEmb and DP-FedAvg under the same privacy budget (noise multiplier). Since each user already has multiple classes in GLD, we use a smaller number of users, 8, in each virtual client. We also use a smaller number of virtual clients per round, 32, for fast experiments and strong sampling effect. Recall@FAR=1e-3 of DP-FedEmb and DP-FedAvg with small noise multiplier 0.02 on GLD outperforms centralized training. For iNat, we use an even smaller four users per virtual client and train for only 400 rounds, and use a relatively large noise multiplier 0.5 to get a single-digit $\\epsilon = 16.06$ DP guarantee for 9275 users. We report recall@FAR=0.1 instead of recall@FAR=1e-3 for the challenging iNat task. In all experiments, DP-FedEmb consistently outperforms DP-FedAvg.", + "bbox": [ + 496, + 305, + 893, + 532 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 566, + 617, + 580 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This paper presented DP-FedEmb for training embedding models with user-level differential privacy. We show how practical utility with strong privacy guarantees can be achieved in the data center, thanks to key algorithm design choices around the construction of virtual clients and in the selection of what information is shared among users. Our experiments validate this improves the privacy utility trade-off upon vanilla DP-FedAvg for supervised representation learning. Though strong formal DP bounds at practical levels of utility could only be achieved when millions of users participate in training, DP-FedEmb is designed to be exceptionally scalable when model size and class space increases with number of users. DP-FedEmb can also be applied to decentralized FL when each real client contains multiple classes, possibly reducing the necessity of virtual clients. Finally, DP is a worst-case guarantee that can be improved by both algorithmic design and advanced accounting methods; the non-negligible noise we added for the small scale datasets in experiments are ready to be empirically audited for privacy.", + "bbox": [ + 496, + 598, + 893, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "7976", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pages 308-318, 2016. 1, 3, 4, 14", + "[2] Kareem Amin, Alex Kulesza, Andres Munoz, and Sergei Vassilvtiskii. Bounding user contributions: A bias-variance trade-off in differential privacy. In International Conference on Machine Learning, pages 263-271. PMLR, 2019. 13", + "[3] Galen Andrew, Om Thakkar, H Brendan McMahan, and Swaroop Ramaswamy. Differentially private learning with adaptive clipping. Conference on Neural Information Processing Systems (NeurIPS), 2021. 5, 7, 8, 13, 17", + "[4] Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, and Pasin Manurangsi. Large-scale differentially private bert. arXiv preprint arXiv:2108.01624, 2021. 1", + "[5] Apple Privacy Team. Learning with privacy at scale. Available at https://machinelearning.apple.com/2017/12/06/learning-with-privacy-at-scale.html, 2017.1", + "[6] Gwangbin Bae, Martin de La Gorce, Tadas Baltrusaitis, Charlie Hewitt, Dong Chen, Julien Valentin, Roberto Cipolla, and Jingjing Shen. Digiface-1m: 1 million digital face images for face recognition. In 2023 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2023. 5, 8, 17", + "[7] Mark Bun and Thomas Steinke. Concentrated differential privacy: Simplifications, extensions, and lower bounds. In Theory of Cryptography Conference, pages 635-658. Springer, 2016. 3, 13", + "[8] Clément L Canonne, Gautam Kamath, and Thomas Steinke. The discrete gaussian for differential privacy. Advances in Neural Information Processing Systems, 33:15676-15688, 2020. 5", + "[9] Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer. Membership inference attacks from first principles. In 2022 IEEE Symposium on Security and Privacy (SP), pages 1897-1914. IEEE, 2022. 1", + "[10] Nicholas Carlini, Chang Liu, Ülfar Erlingsson, Jernej Kos, and Dawn Song. The secret sharer: Evaluating and testing unintended memorization in neural networks. In 28th USENIX Security Symposium (USENIX Security 19), pages 267-284, 2019. 1", + "[11] Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21), pages 2633-2650, 2021. 1", + "[12] Yannis Cattan, Christopher A Choquette-Choo, Nicolas Papernot, and Abhradeep Thakurta. Fine-tuning with differential privacy necessitates an additional hyperparameter search. arXiv preprint arXiv:2210.02156, 2022. 16", + "[13] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning" + ], + "bbox": [ + 78, + 114, + 470, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 13", + "[14] Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. Fedavg with fine tuning: Local updates lead to representation learning. arXiv e-prints, pages arXiv-2205, 2022. 5", + "[15] Liam Collins, Aryan Mokhtari, Sewoong Oh, and Sanjay Shakkottai. Maml and anil provably learn representations. arXiv preprint arXiv:2202.03483, 2022. 5", + "[16] Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle. Unlocking high-accuracy differentially private image classification through scale. arXiv preprint arXiv:2204.13650, 2022. 1, 3, 4, 5", + "[17] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4690-4699, 2019. 7, 13", + "[18] Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin. Collecting telemetry data privately. Advances in Neural Information Processing Systems, 30, 2017. 1", + "[19] Xin Dong, Sai Qian Zhang, Ang Li, and HT Kung. Spherefed: Hyperspherical federated learning. arXiv preprint arXiv:2207.09413, 2022. 13", + "[20] Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, and Pasin Manurangsi. Connect the dots: Tighter discrete approximations of privacy loss distributions. arXiv preprint arXiv:2207.04380, 2022. 13, 14", + "[21] DP Team. Google's differential privacy libraries., 2020. https://github.com/google/differential-privacy.14", + "[22] Cynthia Dwork. Differential privacy in new settings. In Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms, pages 174-183. SIAM, 2010. 1, 13", + "[23] Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference, pages 265-284. Springer, 2006. 1, 13", + "[24] Cynthia Dwork, Aaron Roth, et al. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3-4):211-407, 2014. 14", + "[25] Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, and Lijie Ren. Smoothly bounding user contributions in differential privacy. Advances in Neural Information Processing Systems, 33:13999-14010, 2020. 13", + "[26] Jonathan Frankle, David J. Schwab, and Ari S. Morcos. Training batchnorm and only batchnorm: On the expressive power of random features in CNNs, 2021. 5, 16", + "[27] Robin C Geyer, Tassilo Klein, and Moin Nabi. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557, 2017. 3, 13", + "[28] Badih Ghazi, Ravi Kumar, and Pasin Manurangsi. User-level private learning via correlated sampling. arXiv preprint arXiv:2110.11208, 2021. 13", + "[29] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch," + ], + "bbox": [ + 503, + 92, + 890, + 901 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "7977", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020. 13", + "[30] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000-16009, 2022. 13", + "[31] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5", + "[32] Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. Federated visual classification with real-world data distribution. In European Conference on Computer Vision, pages 76-92. Springer, 2020. 5, 8", + "[33] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pages 448-456. PMLR, 2015. 5", + "[34] Prateek Jain, John Rush, Adam Smith, Shuang Song, and Abhradeep Guha Thakurta. Differentially private model personalization. Advances in Neural Information Processing Systems, 34:29723-29735, 2021. 4, 13", + "[35] Sébastien Jean, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. On using very large target vocabulary for neural machine translation. arXiv preprint arXiv:1412.2007, 2014. 3, 4", + "[36] Peter Kairouz, Brendan Mcmahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu. Practical and private (deep) learning without sampling or shuffling. In International Conference on Machine Learning (ICML), pages 5213-5225, 2021. 1, 2, 3, 5, 6, 13, 14, 15, 16", + "[37] Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977, 2019. 13", + "[38] Peter Kairouz, Sewoong Oh, and Pramod Viswanath. The composition theorem for differential privacy. In International conference on machine learning, pages 1376-1385. PMLR, 2015. 1", + "[39] Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning, pages 5132-5143. PMLR, 2020. 4", + "[40] Antti Koskela, Joonas Jälkö, Lukas Prediger, and Antti Honkela. Tight approximate differential privacy for discrete-valued mechanisms using fft, 2020. 13, 14", + "[41] Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang. Fine-tuning can distort pretrained features and underperform out-of-distribution. arXiv preprint arXiv:2202.10054, 2022.7", + "[42] Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, and Abhradeep Thakurta. Toward" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "training at imagenet scale with differential privacy. arXiv preprint arXiv:2201.12328, 2022. 1, 4", + "[43] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998. 18", + "[44] Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, and Ananda Theertha Suresh. Learning with user-level privacy. Advances in Neural Information Processing Systems, 34:12466-12479, 2021. 13", + "[45] Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto. Large language models can be strong differentially private learners. arXiv preprint arXiv:2110.05679, 2021. 1, 4", + "[46] Yuhan Liu, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Michael Riley. Learning discrete distributions: user vs item-level privacy. Advances in Neural Information Processing Systems, 33:20965-20976, 2020. 13", + "[47] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV), December 2015. 8", + "[48] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In AISTATS, pages 1273-1282. PMLR, 2017. 3, 4", + "[49] Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. Learning differentially private recurrent language models. In International Conference on Learning Representations (ICLR), 2018. 1, 2, 3, 5, 6, 13, 14", + "[50] Brendan McMahan and Abhradeep Thakurta. Federated learning with formal differential privacy guarantees, 2022. 1, 6", + "[51] H Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, and Peter Kairouz. A general approach to adding differential privacy to iterative training procedures. arXiv preprint arXiv:1812.06210, 2018. 4, 13, 14", + "[52] Qiang Meng, Feng Zhou, Hainan Ren, Tianshu Feng, Guochao Liu, and Yuanqing Lin. Improving federated learning face recognition via privacy-agnostic clusters. arXiv preprint arXiv:2201.12467, 2022. 13", + "[53] Ilya Mironov. Rényi differential privacy. In 2017 IEEE 30th computer security foundations symposium (CSF), pages 263-275. IEEE, 2017. 5, 13", + "[54] Kevin Musgrave, Serge Belongie, and Ser-Nam Lim. A metric learning reality check. In European Conference on Computer Vision, pages 681-699. Springer, 2020. 13", + "[55] Nicolas Papernot and Thomas Steinke. Hyperparameter tuning with renyi differential privacy. arXiv preprint arXiv:2110.03620, 2021. 5", + "[56] Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Thakurta. How to dp-fy ml: A practical guide to machine learning with differential privacy. arXiv preprint arXiv:2303.00654, 2023. 5, 15" + ], + "bbox": [ + 501, + 92, + 893, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "7978", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[57] Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H Brendan McMahan, and Françoise Beaufays. Training production language models without memorizing user data. arXiv preprint arXiv:2009.10031, 2020. 1, 2, 6, 13", + "[58] Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečny, Sanjiv Kumar, and Hugh Brendan McMahan. Adaptive federated optimization. In International Conference on Learning Representations, 2021. 3", + "[59] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252, 2015. 5", + "[60] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhoginov, and Liang-Chieh Chen. *Mobilenetv2: Inverted residuals and linear bottlenecks*. In *Proceedings of the IEEE conference on computer vision and pattern recognition*, pages 4510–4520, 2018. 5", + "[61] Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 815-823, 2015. 1, 3, 4, 13", + "[62] Zebang Shen, Jiayuan Ye, Anmin Kang, Hamed Hassani, and Reza Shokri. Share your representation only: Guaranteed improvement of the privacy-utility tradeoff in federated learning. In The Eleventh International Conference on Learning Representations, 2023. 13", + "[63] Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP), pages 3-18. IEEE, 2017. 1", + "[64] Hakim Sidahmed, Zheng Xu, Ankush Garg, Yuan Cao, and Mingqing Chen. Efficient and private federated learning with partially trainable networks. arXiv preprint arXiv:2110.03450, 2021. 5, 16", + "[65] Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, and Sushant Prakash. Federated reconstruction: Partially local federated learning. Advances in Neural Information Processing Systems (NeurIPS), 2021. 4, 7, 13", + "[66] Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, and Lior Wolf. Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1701-1708, 2014. 3, 4, 13", + "[67] TFF Authors. TensorFlow Federated EMNIST dataset, 2022. https://www.tensorflow.org/federated/api_docs/python/tff/simulation/datasets/ emnist.8,15,17", + "[68] TFF Authors. TensorFlow Federated Google Landmark v2 dataset, 2022. https://www.tensorflow.org/federated/api_docs/python/tff/simulation/datasets/gldv2.8,15,17", + "[69] TFF Authors. TensorFlow Federated iNaturalist dataset, 2022. https://www.tensorflow.com" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "org / federated / api __docs / python / tff / simulation/datasets/inaturalist. 8, 15, 17", + "[70] US Census Bureau. Disclosure avoidance for the 2020 census: An introduction, 2021. 1, 3, 6", + "[71] Salil Vadhan. The complexity of differential privacy. In *Tutorialis on the Foundations of Cryptography*, pages 347–450. Springer, 2017. 14", + "[72] Sagar M Waghmare, Hang Qi, Huizhong Chen, Mikhail Sirotenko, and Tomer Meron. Efficient image representation learning with federated sampled softmax. arXiv preprint arXiv:2203.04888, 2022. 4, 13", + "[73] Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, et al. A field guide to federated optimization. arXiv:2107.06917, 2021. 3, 4, 5, 13, 14", + "[74] Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, and Tong Zhang. On the unreasonable effectiveness of federated averaging with heterogeneous data. arXiv preprint arXiv:2206.04723, 2022. 3", + "[75] Kilian Q Weinberger and Lawrence K Saul. Distance metric learning for large margin nearest neighbor classification. Journal of machine learning research, 10(2), 2009. 1", + "[76] Yandong Wen, Weiyang Liu, Adrian Weller, Bhiksha Raj, and Rita Singh. Sphereface2: Binary classification is all you need for deep face recognition. arXiv preprint arXiv:2108.01513, 2021. 13", + "[77] Tobias Weyand, Andre Araujo, Bingyi Cao, and Jack Sim. Google landmarks dataset v2-a large-scale benchmark for instance-level recognition and retrieval. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2575-2584, 2020. 1, 8, 13", + "[78] Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins, Brendan McMahan, Ohad Shamir, and Nathan Srebro. Is local sgd better than minibatch sgd? In International Conference on Machine Learning, pages 10334-10343. PMLR, 2020. 3", + "[79] Lin Wu, Chunhua Shen, and Anton van den Hengel. Personnet: Person re-identification with deep convolutional neural networks. arXiv preprint arXiv:1601.07255, 2016. 13", + "[80] Yuxin Wu and Kaiming He. Group normalization. In Proceedings of the European conference on computer vision (ECCV), pages 3-19, 2018. 5", + "[81] Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al. Differentially private fine-tuning of language models. arXiv preprint arXiv:2110.06500, 2021. 1, 4, 5", + "[82] Felix Yu, Ankit Singh Rawat, Aditya Menon, and Sanjiv Kumar. Federated learning with only positive labels. In International Conference on Machine Learning, pages 10946-10956. PMLR, 2020. 4", + "[83] Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny. Barlow twins: Self-supervised learning via redundancy reduction. In International Conference on Machine Learning, pages 12310-12320. PMLR, 2021. 13" + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "7979", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 10 + }, + { + "type": "ref_text", + "text": "[84] Chiyuan Zhang, Samy Bengio, and Yoram Singer. Are all layers created equal?, 2019. 5", + "bbox": [ + 78, + 90, + 470, + 119 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "7980", + "bbox": [ + 482, + 945, + 516, + 955 + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/2023/Learning To Generate Image Embeddings With User-Level Differential Privacy/35eb4863-9483-4c5e-b3f4-41696055c435_model.json b/2023/Learning To Generate Image Embeddings With User-Level Differential Privacy/35eb4863-9483-4c5e-b3f4-41696055c435_model.json new file mode 100644 index 0000000000000000000000000000000000000000..3092fddb043338758ef52e4c248f86dfed23d794 --- /dev/null +++ b/2023/Learning To Generate Image Embeddings With User-Level Differential Privacy/35eb4863-9483-4c5e-b3f4-41696055c435_model.json @@ -0,0 +1,2457 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.096, + 0.131, + 0.877, + 0.155 + ], + "angle": 0, + "content": "Learning to Generate Image Embeddings with User-level Differential Privacy" + }, + { + "type": "text", + "bbox": [ + 0.13, + 0.181, + 0.221, + 0.199 + ], + "angle": 0, + "content": "Zheng Xu*" + }, + { + "type": "text", + "bbox": [ + 0.157, + 0.2, + 0.294, + 0.217 + ], + "angle": 0, + "content": "Sean Augenstein" + }, + { + "type": "text", + "bbox": [ + 0.257, + 0.182, + 0.398, + 0.198 + ], + "angle": 0, + "content": "Maxwell Collins*" + }, + { + "type": "text", + "bbox": [ + 0.34, + 0.2, + 0.412, + 0.217 + ], + "angle": 0, + "content": "Ting Liu" + }, + { + "type": "text", + "bbox": [ + 0.443, + 0.182, + 0.551, + 0.199 + ], + "angle": 0, + "content": "Yuxiao Wang" + }, + { + "type": "text", + "bbox": [ + 0.459, + 0.2, + 0.583, + 0.216 + ], + "angle": 0, + "content": "Florian Schroff" + }, + { + "type": "text", + "bbox": [ + 0.593, + 0.182, + 0.693, + 0.199 + ], + "angle": 0, + "content": "Liviu Panait" + }, + { + "type": "text", + "bbox": [ + 0.734, + 0.182, + 0.839, + 0.199 + ], + "angle": 0, + "content": "Sewoong Oh" + }, + { + "type": "text", + "bbox": [ + 0.629, + 0.2, + 0.808, + 0.216 + ], + "angle": 0, + "content": "H. Brendan McMahan" + }, + { + "type": "text", + "bbox": [ + 0.411, + 0.223, + 0.551, + 0.24 + ], + "angle": 0, + "content": "Google Research" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.266, + 0.314, + 0.283 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.298, + 0.474, + 0.601 + ], + "angle": 0, + "content": "Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class space. To achieve user-level DP for large image-to-embedding feature extractors, we propose DP-FedEmb, a variant of federated learning algorithms with per-user sensitivity control and noise addition, to train from user-partitioned data centralized in the datacenter. DP-FedEmb combines virtual clients, partial aggregation, private local fine-tuning, and public pretraining to achieve strong privacy utility trade-offs. We apply DP-FedEmb to train image embedding models for faces, landmarks and natural species, and demonstrate its superior utility under same privacy budget on benchmark datasets DigiFace, EMNIST, GLD and iNaturalist. We further illustrate it is possible to achieve strong user-level DP guarantees of \\(\\epsilon < 2\\) while controlling the utility drop within \\(5\\%\\), when millions of users can participate in training." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.631, + 0.21, + 0.646 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.657, + 0.47, + 0.866 + ], + "angle": 0, + "content": "Representation learning, by training deep neural networks as feature extractors to generate compact embedding vectors from images, is a fundamental component in computer vision. Metric learning, a kind of representation learning using supervised data, has been widely applied to image recognition, clustering, and retrieval [61, 75, 77]. Machine learning models have the capacity to memorize training data [10, 11], leading to privacy risks when the models are deployed. Privacy risk can also be audited by membership inference attacks [9, 63], i.e. detecting whether certain data was used to train a model and potentially exposing users' usage behaviors. Defending against such risks is a critical responsibility when training on privacy-sensitive data." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.267, + 0.895, + 0.584 + ], + "angle": 0, + "content": "Differential Privacy (DP) [23] is an extensively used quantifiable measurement of privacy risk, now generally accepted as a standard notion of privacy in both industry and government [5, 18, 50, 70]. Applied to machine learning, DP requires a training procedure with explicit randomness, and guarantees that the distribution over output models is quantifiably similar given a certain scope of change to the training dataset. A DP guarantee with respect to the change of a single arbitrary training example is known as example-level DP, which provides plausible deniability (in the binary hypothesis testing sense of [38]) that any single example (e.g., image) occurred in the training dataset. If we instead consider how the distribution of output models changes if the data (including even the number of examples) from any single user change arbitrarily, we have user-level DP [22]. This ensures model training is quantifiably insensitive to all of the data from any one user, and hence it is impossible to tell if a user has participated in training with high confidence. This guarantee can be exponentially stronger than example-level DP if one user may contribute many examples to training." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.592, + 0.895, + 0.773 + ], + "angle": 0, + "content": "Recently, DP-SGD [1] (essentially, SGD with the additional steps of clipping each individual gradient to have a maximum norm, and adding correspondingly calibrated noise) has been used to achieve example-level DP for relatively large models in language modeling and image classification tasks [4, 16, 42, 45, 81], often utilizing techniques like large batch training and pretraining on public data. DP-SGD can be modified to guarantee user-level DP, which is often combined with federated learning algorithms and called DP-FedAvg [49]. User-level DP has only been studied for small on-device models that have less than 10 million parameters [36, 49, 57]." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.78, + 0.895, + 0.903 + ], + "angle": 0, + "content": "We consider user-level DP for relatively large models in representation learning with supervised data. In our setting, similar to federated learning (FL), the data are user-partitioned; but in contrast to decentralized FL, we are primarily motivated by centralized data that benefit from access to richer computation resources and the ability to form virtual clients at random. Throughout this work, we use user as the basic unit of data partitioning and the granular-" + }, + { + "type": "page_footnote", + "bbox": [ + 0.077, + 0.875, + 0.471, + 0.901 + ], + "angle": 0, + "content": "*The first two authors contributed equally. Correspondence to Zheng Xu xuzheng@google.com" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "7969" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.144, + 0.09, + 0.833, + 0.325 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.332, + 0.895, + 0.364 + ], + "angle": 0, + "content": "Figure 1. DP-FedEmb combines virtual clients, local fine-tuning, partial aggregation, and public pretraining to achieve strong privacy utility trade-offs. Colors indicate different users and the simplified case of a single class per user, coloring the softmax head accordingly." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.377, + 0.471, + 0.422 + ], + "angle": 0, + "content": "ity for privacy; a user owns their (image) data, and class, identity and label are used interchangeably for the supervised information." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.428, + 0.473, + 0.775 + ], + "angle": 0, + "content": "Though typically each user only contributes images for a small number of classes, the combined class space from the union of all users can be very large, which proves challenging for existing DP algorithms. When the model size is fixed independent of the number of users, and at a relatively small scale with a few million parameters, previous methods can only achieve strong user-level DP when millions of users are available [36, 49, 57]. In contrast, when considering learning embedding models for applications like facial images, the number of classes (and hence, the total model size) can grow linearly with the number of users, and so simply scaling up to larger datasets with more users no longer ensures that good privacy-utility trade-offs can be achieved. For example, in a standard multi-class training paradigm with 128-dimensional embedding vectors, with one million users we expect the final dense layer for prediction alone to have over 128 million trainable parameters. Further, the fact that most users will only have examples from a small number of classes implies that gradients are approximately sparse, whereas DP-SGD requires the addition of dense noise to the full gradient, leading to a poor signal-to-noise ratio in the updates. Hence, existing methods can easily fail on the problems we consider." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.471, + 0.903 + ], + "angle": 0, + "content": "We propose DP-FedEmb to train embedding models with user-level differential privacy; Fig. 1 provides a high-level overview. DP-FedEmb combines public pretraining, virtual clients, local fine-tuning, and partial aggregation to achieve strong privacy-utility trade-offs. The key to the approach is partitioning the model into a backbone network that generates embeddings, and a classification softmax head specific to the classes in the training data. In" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.377, + 0.895, + 0.726 + ], + "angle": 0, + "content": "each training round, users are grouped into virtual clients and initialized from the global backbone. A local randomly-initialized softmax head layer is added for the limited number of classes on the virtual client, and the complete local model is fine-tuned in order to produce an update to the backbone. The local head parameters are not included in the private aggregation and hence require no noise addition. This is in contrast to existing methods like DP-FedAvg/DP-SGD, which would add noise to all parameters including the softmax head. The backbone updates are clipped to a maximum L2 norm, aggregated across virtual clients, and combined with appropriate DP noise. At this point, the noised update is the output of a DP mechanism and satisfies the corresponding DP guarantee. This update is then applied to the global backbone, which inherits the DP guarantee, and passed to the next round of training. DP-FedEmb significantly improves the scalability of DP training for embedding models, as only the parameters of the backbone network are privatized and released, and the size of this portion of the model does not grow with the number of users. Pretraining the backbone network on public data to learn general visual representations before applying DP-FedEmb for more privacy sensitive tasks further improves performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.897, + 0.903 + ], + "angle": 0, + "content": "We demonstrate the superior performance of DP-FedEmb for embedding models by experiments on datasets with moderate size of users and classes (DigiFace of 98.96K or 9047 users/identities, Google Landmarks Dataset of 1262 users and 2028 classes, and iNaturalist of 9275 users and 1203 classes). We also show relatively strong privacy guarantees of single digit \\(\\epsilon\\) can be achieved while maintaining strong utility if millions users can participate in training. To our knowledge, this is the first report of training a commonly used large vision model, ResNet-50, with non-negligible noise for user-level DP." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.958 + ], + "angle": 0, + "content": "7970" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.077, + 0.09, + 0.348, + 0.108 + ], + "angle": 0, + "content": "2. Learning Embedding Models" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.115, + 0.465, + 0.131 + ], + "angle": 0, + "content": "2.1. Problem formulation and centralized training" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.138, + 0.468, + 0.214 + ], + "angle": 0, + "content": "We learn a backbone network parameterized by \\(\\theta\\), that outputs an embedding vector \\(z = f(\\theta, x)\\) for an input image \\(x\\). The backbone network, \\(\\theta\\), is trained on paired examples of image \\(x\\) and class \\(y\\). The training dataset is naturally partitioned by \\(M\\) users, i.e., \\(\\mathcal{D} = \\bigcup_{i=1}^{M} \\mathcal{D}_i\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.214, + 0.468, + 0.38 + ], + "angle": 0, + "content": "We adopt the popular multi-class training framework for embedding models, where a proxy weight vector \\( w_{j} \\) is learnt for each class \\( j \\). We use \\( \\omega \\) to denote the union of proxy weight vectors \\( \\{w_{j}\\} \\), which is called the head of the network. Given a training image-class pair \\( (x,y) \\), logits are computed by taking the inner product between the embedding vector \\( f(\\theta ,x) \\) and the proxy weight vectors in the network head \\( \\omega \\). This is effectively passing \\( f(\\theta ,x) \\) through a dense network layer parameterized by \\( \\omega \\) without the bias terms. With a supervised training loss \\( \\ell \\), such as the cross entropy loss, the following objective is optimized" + }, + { + "type": "equation", + "bbox": [ + 0.16, + 0.386, + 0.469, + 0.42 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\theta , \\omega} \\sum_ {(x, y) \\in \\mathcal {D}} \\ell (\\langle \\omega , f (\\theta , x) \\rangle , y), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.425, + 0.468, + 0.576 + ], + "angle": 0, + "content": "where we overload the inner product notation, i.e., \\(\\langle \\omega ,f(\\theta ,x)\\rangle\\), to denote a set of inner products with each element in \\(\\omega\\). Typically, variants of the gradient descent method are used to solve the optimization in (1). In each iteration, the average gradient is computed on a sampled minibatch of data \\(B\\subset \\mathcal{D}\\), and is then used to update the model parameters \\(\\theta\\) and \\(\\omega\\). Furthermore, when the output space, i.e., the number of classes, is very large, sampled softmax is often applied [35], where only a subset of proxy weights sampled from \\(\\omega\\) are used in each training iteration." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.584, + 0.345, + 0.6 + ], + "angle": 0, + "content": "2.2. User-level DP and DP-FedAvg" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.607, + 0.468, + 0.726 + ], + "angle": 0, + "content": "To achieve user-level DP, we control the sensitivity of each user and add corresponding noise for anonymization. To effectively control the sensitivity, it is important to understand and account for the contributions of each user in the model updates; hence it is convenient to consider the data at a granularity of users instead of individual samples. Grouping together each user's data, the objective (1) can be rewritten as" + }, + { + "type": "equation", + "bbox": [ + 0.145, + 0.731, + 0.468, + 0.775 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\theta , \\omega} \\sum_ {i = 1} ^ {M} \\sum_ {(x, y) \\in \\mathcal {D} _ {i}} \\ell (\\langle \\omega , f (\\theta , x) \\rangle , y). \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.468, + 0.901 + ], + "angle": 0, + "content": "The above objective of two level sum is often found in federated learning [73], which can be optimized by the (generalized) FedAvg algorithm [48, 58]. In generalized FedAvg, each round \\(t\\) starts with the server broadcasting \\(\\theta^{(t)},\\omega^{(t)}\\) to a subset of clients. Each client \\(i\\) will then update the local model parameters by CLIENTOPT with private data \\(\\mathcal{D}_i\\), and send back the updates for model parameters \\(\\Delta_i(\\theta^{(t)}),\\Delta_i(\\omega^{(t)})\\). The model deltas from sampled" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.121 + ], + "angle": 0, + "content": "clients are then aggregated and used by SERVEROPT to get \\(\\theta^{(t + 1)},\\omega^{(t + 1)}\\) for the next round." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.123, + 0.892, + 0.365 + ], + "angle": 0, + "content": "The generalized FedAvg algorithm can be extended for user-level DP by clipping the model deltas and adding noise proportional to the sensitivity [27,49]. We can use either independent Gaussian noise [49], or correlated noise that can achieve comparable privacy-utility trade-off without relying on the assumption of sampling [36]. The two variants are effectively applying DP-SGD [1] or DP-FTRL [36] as SERVEROPT in the generalized FedAvg framework. Unlike the cross-device FL setting where sampling is extremely hard, it is possible to control user sampling in the datacenter and use DP-SGD. But DP-FTRL provides the possibility of handling the online setting where the user data are streamed instead of collected, and can be accounted for zCDP [7] reported by US census bureau [70]. A complete description of DP-FedAvg for training a backbone network to generate image embeddings is in Alg. 2 in Appendix B" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.365, + 0.892, + 0.653 + ], + "angle": 0, + "content": "In addition to the flexibility of generalized FedAvg for user-level DP, there are a few side effects of FedAvg that make it particularly effective for differentially private training. The model deltas are computed based on data for each user before clipping in DP, which can potentially reduce the bias introduced by clipping. As [16] suggested that averaging gradients from augmented data before clipping can improve training for example-level DP, model deltas from user data for user-level DP can be considered a natural extension to improve the bias-variance trade-off. The communication efficiency of FedAvg that leads to infrequent aggregation and model release is also desirable for DP training. The local model updates by private data on clients introduce no additional privacy cost, and only communication rounds between clients and server have to be accounted for DP. Though the theoretical advantages of FedAvg are only proved under certain assumptions [74, 78], FedAvg with local updates can achieve communication efficiency and fast convergence in various practical applications [73]." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.664, + 0.771, + 0.681 + ], + "angle": 0, + "content": "2.3. Proposed DP-FedEmb method" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.688, + 0.892, + 0.794 + ], + "angle": 0, + "content": "While generalized DP-FedAvg can be applied to train a backbone network \\(\\theta\\) to generate embedding \\(f(\\theta, x)\\) from image \\(x\\), there are challenges that significantly affect the efficiency and feasibility of the method. We propose DP-FedEmb with a few key features: virtual clients, partial aggregation, local fine-tuning, public pretraining, and parameter freezing. Details of DP-FedEmb are provided in Alg. 1." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Virtual clients. Data heterogeneity is one of the key problems in federated optimization [73]. When we train embedding models in the multi-class framework, the class space can be very large and each user may only observe a limited number of classes. In the extreme case, when training embedding models on facial images [61, 66], each user may only have images for their own identity. This signif" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "7971" + } + ], + [ + { + "type": "code_caption", + "bbox": [ + 0.08, + 0.094, + 0.427, + 0.124 + ], + "angle": 0, + "content": "Algorithm 1: DP-FedEmb: learning embedding model \\(\\theta\\) with user-level DP" + }, + { + "type": "algorithm", + "bbox": [ + 0.08, + 0.125, + 0.442, + 0.581 + ], + "angle": 0, + "content": "Input:SERVEROPT with learning rate \\(\\alpha\\) .. CLIENTOPT with learning rate \\(\\beta_{1}\\) and \\(\\beta_{2}\\) clip norm \\(\\gamma\\) and noise multiplier \\(\\sigma\\) (optional) pretrained model \\(\\theta^{(0)}\\) \nfor round \\(t = 0,1,\\ldots ,T - 1\\) do \nSample a subset of users \\(\\mathcal{U}^{(t)}\\) \nPartition users \\(\\mathcal{U}^{(t)}\\) to virtual clients \\(S^{(t)}\\) \nfor each virtual client \\(V\\in S^{(t)}\\) in parallel do Initialize backbone \\(\\theta_V^{(t,0)} = \\theta^{\\mathrm{(t)}}\\) \nRandomly initialize head \\(\\omega_{V}^{(t,0)}\\) \nfor \\(k = 0,\\dots ,K - 1\\) do Sample minibatch \\(B\\subset \\bigcup_{i\\in V}\\mathcal{D}_i\\) Compute gradients \\(\\nabla_{\\theta_V}\\ell_B,\\nabla_{\\omega_V}\\ell_B\\) where \\(\\ell_B = \\mathbb{E}_{(x,y)\\in B}\\ell (\\langle \\omega_V,f(\\theta_V,x)\\rangle ,y)\\) Update \\(\\theta_V^{(t,k + 1)}\\) by CLIENTOPT, \\(\\theta_V^{(t,k)}\\) \\(\\nabla_{\\theta_V}\\ell_B,\\beta_1\\) Update \\(\\omega_{V}^{(t,k + 1)}\\) by CLIENTOPT, \\(\\omega_{V}^{(t,k)}\\) \\(\\nabla_{\\omega_V}\\ell_B,\\beta_2\\) \nend \nCompute clipped model update \n\\(\\Delta_V^{(t)} = \\mathrm{Clip}(\\theta_V^{(t,K)} - \\theta_V^{(t,0)},\\gamma)\\) \nend \nAggregate model updates \n\\(\\Delta^{(t)} = \\mathrm{AddNoise}\\bigl (\\sum_{V\\in S^{(t)}}\\Delta_{V}^{(t)},\\sigma \\gamma \\bigr) / |S^{(t)}|\\) \nUpdate global backbone parameters \n\\(\\theta^{(t + 1)} = \\mathrm{SERVEROPT}(\\theta^{(t)},\\Delta^{(t)},\\alpha)\\)" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.614, + 0.47, + 0.734 + ], + "angle": 0, + "content": "icently limits the advantage of local updates due to client drift [39], and even with specialized regularization like [82], FedSGD [48] with frequent aggregation and model release has to be used instead of FedAvg. It is challenging to use some specialized techniques for handling data heterogeneity [73] in DP training. Instead, we propose a simple yet effective approach: randomly groups the data of sampled users into virtual clients." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Unlike the cross-device FL setting where the on-device data of users cannot be directly communicated, virtual clients are feasible for user data in the datacenter. It is important to guarantee that a user will not be included in two virtual clients in a single round for user-level DP, analogous to microbatches for DP-SGD and example-level DP [1,51]. When the grouping of users for virtual clients is fixed in advance across rounds, the granularity of the DP definition can slightly change: the adjacent dataset for DP is based on virtual clients (a group of users) instead of a single user, which has conceptually stronger privacy guarantees. However," + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.227 + ], + "angle": 0, + "content": "when virtual clients are randomly regrouped across rounds as in Alg. 1, we can only show user-level DP as discussed in Appendix C. Virtual clients also control the interpolation between federated training and centralized training: when all users are grouped into a single virtual client, federated training is equivalent to centralized training, which removes heterogeneity but is challenging for DP mechanism. Virtual clients are used for both baseline DP-FedAvg and the proposed DP-FedEmb method." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.23, + 0.893, + 0.44 + ], + "angle": 0, + "content": "Partial aggregation and local fine-tuning. Another challenge is the number of parameters in DP training. A common backbone \\(\\theta\\) of ResNet-50 for 128 dimensional embedding vectors has 23.77 million parameters. However, the parameter size of head \\(\\omega\\) can linearly grow with the number of classes. Taking FaceNet [61, 66] as an example again, \\(\\omega\\) can easily grow to 1280 million for 10 million identities in real-world applications. Sampled softmax [35, 72] can be applied to improve training efficiency. However, as both backbone \\(\\theta\\) and head \\(\\omega\\) are shared among users and need to be privatized by adding noise during training, the combined parameter size of \\((\\theta, \\omega)\\) will significantly affect the privacy utility trade-off, which cannot be mitigated by sampled softmax." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.443, + 0.893, + 0.699 + ], + "angle": 0, + "content": "In DP-FedEmb, inspired by federated reconstruction [65] and DP personalization [34], we only aggregate and privatize the backbone network \\(\\theta\\), which is used in inference and has fixed parameter size that does not grow with classes. A local head \\(\\omega_{V}\\) is randomly initialized and updated on each virtual client \\(V\\). A fine-tuning approach is adopted for local updates, where different learning rates \\(\\beta_{1}, \\beta_{2}\\) are used for the backbone \\(\\theta_{V}\\) and head \\(\\omega_{V}\\), respectively. When combined with virtual clients, the partial aggregation and local fine-tuning approach can be interpreted in various ways: each virtual client is performing transfer learning given a shared backbone network for representation learning; the data of each class are their own positive samples as well as negative samples for other classes on the same virtual client; the size of local head \\(\\omega_{V}\\) is also significantly smaller than \\(\\omega\\) for all classes, which is effectively a user-based sampling for softmax." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.702, + 0.892, + 0.882 + ], + "angle": 0, + "content": "Public pretraining. The parameter size of the backbone to be privatized can still be large after applying partial aggregation and local fine-tuning with virtual clients, e.g., 23.77 million for ResNet-50. Inspired by recent research on applying DP-SGD for example-level DP on large language modeling [45, 81] and image classification [16, 42], we use a model pretrained on public images to initialize the DP training of the backbone network. There is a relatively clear distinction between the public and private domains for our task: we use public images collected from open webpages for pretraining, and then privately train on users' data collected in a datacenter." + }, + { + "type": "text", + "bbox": [ + 0.52, + 0.886, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Parameter freezing. Neural networks are known to be" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7972" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.258 + ], + "angle": 0, + "content": "overparameterized, and not all weights are equally important [26, 84]. Freezing some parameters to be non-trainable has been shown to be effective when the privacy budget is small [64], especially when combined with public pretraining for large models [16, 81]. For backbone convolutional neural networks with normalization layers, we experiment with training parameters with all normalization layers, and some of the convolutional kernels. However, freezing is found to be less efficient in our setting that performs representation learning, instead of classification, for a moderate size model in the high-utility-moderate-noise regime." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.258, + 0.473, + 0.471 + ], + "angle": 0, + "content": "DP mechanism and hyperparameters. Similar to generalized DP-FedAvg, we perform clipping for model deltas and add noise for aggregated updates. The clip norm \\(\\gamma\\) is estimated by adaptive clipping [3] in the parameter tuning stage. For DP-FedEmb, we perform extensive studies on several configurations in Sec. 3. Differentially private hyperparameter tuning [55] is a topic out of the scope of this paper, and automating hyperparameter tuning is an important future work. Either independent Gaussian noise like DP-SGD [49] or tree-based correlated noise like DP-FTRL [36] can be added. Under the same noise multiplier, DP-FedEmb will achieve the same privacy bound as DP-FedAvg with virtual clients, while utility can be improved for training a backbone network with a large head." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.483, + 0.21, + 0.5 + ], + "angle": 0, + "content": "3. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.508, + 0.473, + 0.75 + ], + "angle": 0, + "content": "We conduct experiments to train image-to-embedding backbone networks with user-level DP. We use the DigiFace dataset [6] of synthetic faces based on ethical and responsible development considerations, and verified that the conclusions on DigiFace are very similar to conclusions generated from experiments on natural facial images. We randomly split the DigiFace dataset of \\(110K\\) identities and \\(1.22M\\) into subsets of \\(98.96K\\) identities with \\(1.10M\\) images for training, 5443 identities with \\(58.24K\\) images for validation, and 5598 identities with \\(60.82K\\) images for testing. We extensively use a smaller training set, DigiFace10K, which contains the 9047 training identities of 72 images sampled from the DigiFace training set. We also run experiments on public datasets of natural images: EMNIST, Google Landmarks Dataset (GLD) and iNaturalist (iNat) dataset. These datasets are summarized in Tab. 3." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.473, + 0.856 + ], + "angle": 0, + "content": "In our setting, each user holds only the images of their own identities, i.e., user-level DP is also identity-level DP. We use ResNet-50 [31] and MobileNetV2 [32, 60, 73] as backbone networks, replace batch normalization [33] with group normalization [80], and use a multi-class framework with a large softmax head to train the backbone. The dimension of the embeddings are 128 for all experiments." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.473, + 0.903 + ], + "angle": 0, + "content": "We evaluate the performance of the backbone network based on predicting identity matches from the distance between two image embeddings. By varying a threshold on" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.243 + ], + "angle": 0, + "content": "the pairwise similarity, a recall versus false accept rate (FAR) curve on the test data can be generated for a trained model. A scalar value of recall@FAR=1e-3 is often reported. The privacy guarantees are computed by either using Renyi differential privacy (RDP) [53] and converting to \\((\\epsilon, \\delta)\\)-DP by [8], or DP-FTRL accounting without restart [36]. More discussion on privacy accounting can be found in Appendix C. We aim for single-digit \\(\\epsilon\\) when \\(\\delta\\) is small, and sometimes relax to \\(\\epsilon \\sim 20\\) as we use the stronger substitute-one DP definition [56]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.244, + 0.895, + 0.532 + ], + "angle": 0, + "content": "We compare the proposed DP-FedEmb with non-private oracle performance of centralized training, and baseline methods DP-FedAvg. We tune the learning rate with learning rate scheduling for standard centralized training. The centralized baseline is provided as an oracle for non-private training performance. We exclude tricks like data augmentation for either centralized or federated training as the goal is not achieving state-of-the-art performance. Virtual clients are used to improve DP-FedAvg performance, and the same tuning strategy is applied for DP-FedEmb and DP-FedAvg. In most of the experiments, unless otherwise specified, we fix the hyperparameters in the federated setting and only tune the learning rates; the backbone networks are pretrained on classifying the 1000 classes of ImageNet [59]; both CLIENTOPT and SERVEROPT are SGD optimizers with momentum 0.9; and more details are provided in Sec. 3.3. Code is released at https://github.com/google-research/federated/tree/master/dpvisual_embedding." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.543, + 0.825, + 0.56 + ], + "angle": 0, + "content": "3.1. Privacy-utility-computation trade-off" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.568, + 0.895, + 0.84 + ], + "angle": 0, + "content": "We study the privacy-utility-computation trade-offs of training ResNet-50 on DigiFace10K in Fig. 2 and Fig. 6. Figure 2a shows the privacy-utility trade-off. Without adding noise, the federated algorithms (DP-)FedAvg and (DP-)FedEmb can achieve even better results than the non-private centralized baseline, which is consistent with recent empirical and theoretical justifications that FedAvg is more accurate when learning representations [14, 15]. When the same noise multiplier is used, i.e., under same privacy budget, DP-FedEmb outperforms DP-FedAvg; and the margin increases when increasing the noise. We can observe the advantage of DP-FedEmb over DP-FedAvg even if we only have a small number of identities in DigiFace: the size of the head is \\(9047 \\times 128 \\sim 1.16M\\), only \\(4.6\\%\\) of the backbone ResNet-50 with \\(23.77M\\) parameters. For large scale data with 10 million identities, the size of the head can grow to \\(1280M\\), which is much larger than the backbone networks, and DP-FedAvg can easily fail in such settings." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.895, + 0.903 + ], + "angle": 0, + "content": "It can be difficult to achieve a strong formal differential privacy bound without significantly hurting utility for Digi-Face10K, which only has a small number of total users. We consider the practical setting of more available users, and" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.958 + ], + "angle": 0, + "content": "7973" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.091, + 0.094, + 0.276, + 0.201 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.179, + 0.204, + 0.194, + 0.214 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.284, + 0.093, + 0.457, + 0.201 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.365, + 0.204, + 0.381, + 0.214 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.465, + 0.092, + 0.666, + 0.201 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.559, + 0.204, + 0.574, + 0.214 + ], + "angle": 0, + "content": "(c)" + }, + { + "type": "image", + "bbox": [ + 0.676, + 0.092, + 0.875, + 0.201 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.769, + 0.204, + 0.784, + 0.214 + ], + "angle": 0, + "content": "(d)" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.226, + 0.895, + 0.268 + ], + "angle": 0, + "content": "Figure 2. (a) Recall@FAR=1e-3 on DigiFace validation set under different noise multiplier; (b) zoom in the high utility regime in (a); (c) and (d) privacy-computation trade-off by extrapolating based on 3M and 10M total users, respectively. \"r400\" and \"r800\" represent the result at 400 or 800 training rounds; target \\(71.77\\%\\) is \\(95\\%\\) of centralized non-private recall@FAR=1e-3 at \\(75.55\\%\\)." + }, + { + "type": "image", + "bbox": [ + 0.082, + 0.286, + 0.268, + 0.403 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.167, + 0.406, + 0.183, + 0.416 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.276, + 0.284, + 0.462, + 0.402 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.361, + 0.405, + 0.378, + 0.416 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.428, + 0.471, + 0.512 + ], + "angle": 0, + "content": "Figure 3. DP-FedEmb and DP-FedAvg on DigiFace10K under the same privacy budget with noise multiplier 0.08, and additional oracle baseline of centralized training w/o DP: (a) recall@FAR=1e-3 on validation dataset during training; dashed lines are approximation by sampling a subset of validation users; (b) ROC curve of trained model on test set with log scale x-axis." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.536, + 0.47, + 0.657 + ], + "angle": 0, + "content": "study the privacy-computation trade-off in Figs. 2c, 2d, 6b and 6c based on extrapolation. A key hypothesis following [36,49] is used: utility (Recall@FAR) is non-decreasing when simultaneously increasing the number of clients per round and noise multiplier. The hypothesis is based on the fact that the signal-to-noise ratio is non-decreasing when linearly increasing the number of clients per round and noise multiplier, and has been verified in practice [50, 57]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.66, + 0.471, + 0.901 + ], + "angle": 0, + "content": "We first choose the noise multiplier that is within \\(5\\%\\) of recall@FAR=1e-3 compared to centralized non-private training in Figs. 2a and 6a, based on simulation that samples 64 virtual clients that each have 32 users per round: recall@FAR=1e-3 is 72.16 for running DP-FedAvg with 0.01 noise multiplier for 400 rounds, 72.37 for running DP-FedAvg with 0.015 noise multiplier for 800 rounds, 71.3 for running DP-FedEmb with 0.015 noise multiplier for 400 rounds, 72.6 for running DP-FedEmb with 0.02 noise multiplier for 800 rounds, and 71.85 for running DP-FTRL-FedEmb with 0.26 noise multiplier for 800 rounds. Then we linearly increase the number of users sampled and use the increased noise multiplier in RDP accounting to compute privacy bound \\(\\epsilon\\) given \\(\\delta = 10^{-7}\\) to generate Figs. 2c, 2d and 6b, and compute zCDP for Fig. 6c. Comparing curves of r400 and r800, training longer with larger noise is more" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.284, + 0.895, + 0.48 + ], + "angle": 0, + "content": "effective than training shorter with smaller noise. Figure 2c suggests \\(\\sim 98K\\) users per round is enough for DP-FedEmb to achieve single-digit \\(\\epsilon\\) if \\(3M\\) users are available, while \\(\\sim 57K\\) users per round are needed if \\(10M\\) users are available in Fig. 2d. \\(98K\\) users is \\(48\\times\\) the number of users per round in our current simulation, which can be achieved by training with \\(8\\times\\) computing resources for \\(6\\times\\) longer. In Fig. 6b, there is a crossover point when using DP-FTRL versus DP-SGD for DP-FedEmb, and DP-FTRL is more effective for relatively large privacy \\(\\epsilon\\). Figure 6c shows that DP-FTRL-FedEmb can achieve zCDP smaller than 2.6, as used by US Census Bureau [70], when \\(8\\times\\) users per round and \\(10M\\) total users are available." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.493, + 0.671, + 0.508 + ], + "angle": 0, + "content": "3.2. Model evaluation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.517, + 0.895, + 0.804 + ], + "angle": 0, + "content": "In Tab. 1, we summarize the quantitative results from the privacy-utility-computation trade-off analysis in Sec. 3.1. Each experiment runs three times to compute the mean and standard deviation. For similar recall@FAR=1e-3 on the DigiFace validation set, DP-FedEmb achieves stronger privacy guarantee than baseline DP-FedAvg, and the advantage of DP-FedEmb is expected to be more pronounced if a head for larger \\(10M\\) identities is used for training. When \\(10M\\) users are available and \\(64\\times\\) users per round in training, privacy \\(\\epsilon = 3.90\\) of single digit and zCDP= 1.28 smaller than 2.6 can be achieved when recall@FAR=1e-3 is within a \\(5\\%\\) drop compared with non-private centralized training. In addition to validation performance, the private models also perform well on the left-out test dataset. Figure 3 presents the training curves and ROC curves for comparing DP-FedEmb and DP-FedAvg under the same privacy budget. DP-FedEmb outperforms DP-FedAvg in all training rounds, and trains a stronger private model with better recall at different false accept rates." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.816, + 0.65, + 0.832 + ], + "angle": 0, + "content": "3.3. Ablation study" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.894, + 0.901 + ], + "angle": 0, + "content": "We primarily use MobileNetV2 for ablation studies on DigiFace10K for two reasons: MobileNetV2 is smaller and faster for training in experiments; to test the generalization of DP-FedEmb and avoid overfitting on ResNet-50." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7974" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.162, + 0.089, + 0.808, + 0.184 + ], + "angle": 0, + "content": "
AlgorithmHyperparametersPrivacy (10M users)Recall@FAR=1e-3
NoiseSerLRRDP-εzCDPValidationTest
Centralized00.05∞∞75.55 ± 0.0575.53 ± 0.12
DP-FedAvg0.015 × 640.55.62-72.57 ± 0.1272.37 ± 0.09
DP-FedEmb0.02 × 640.23.90-72.63 ± 0.0572.37 ± 0.09
DP-FTRL-FedEmb0.26 × 640.29.671.2872.2 ± 0.2971.87 ± 0.26
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.196, + 0.892, + 0.252 + ], + "angle": 0, + "content": "Table 1. Quantitative results of privacy and utility on the DigiFace10K dataset. The client learning rate and clip norm for federated algorithms are 0.002 and 0.6, respectively; \\(\\delta = 10^{-7}\\) for privacy guarantees. Centralized training has a standard learning rate scheduling, while tricks like data augmentation are excluded for all methods. The privacy guarantees are extrapolated based on \\(10M\\) users and \\(131K\\) users per round. A strong privacy guarantee can be achieved within a \\(5\\%\\) drop on recall@FAR=1e-3." + }, + { + "type": "image", + "bbox": [ + 0.084, + 0.279, + 0.283, + 0.394 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.178, + 0.397, + 0.192, + 0.407 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.293, + 0.281, + 0.465, + 0.395 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.371, + 0.397, + 0.387, + 0.408 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.419, + 0.47, + 0.475 + ], + "angle": 0, + "content": "Figure 4. Recall@FAR=1e-3 on DigiFace validation set when training a MobileNet (a) with DP-FedEmb w/ pretraining, and baselines of DP-FedEmb w/o pretraining and DP-FedAvg; (b) with different pretrained models." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.493, + 0.469, + 0.734 + ], + "angle": 0, + "content": "Parameter freezing and public pretraining. For similar parameter size, MobileNetV2 outperforms ResNet-50 with frozen parameters, and Fig. 4a shows the privacy-utility trade-off. Recall@FAR=1e-3 of DP-FedEmb-r800 on MobileNetV2 only drops from \\(68.86\\%\\) to \\(67.56\\%\\) when 0.02 noise is added, while ResNet-50 drops from \\(79.09\\%\\) to \\(72.6\\%\\). However, ResNet-50 still outperforms MobileNetV2 by a large margin in the high utility regime. DP-FedEmb consistently outperforms DP-FedAvg when noises are added. In Fig. 4b, though the private fine-tuning utility is not linearly increasing with pretraining accuracy, there seems to be a general positive correlation: better pretrained models can lead to better private models except for one outlier where a inferior pretrained model causes difficulty in training. More discussion on parameter freezing and public pretraining is provided in Appendix D.4." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Federated settings. In the above experiments, we fix important hyperparameters for the federated setting: users per virtual client is 32, virtual clients per round is 64, examples per client is capped at 2048, the head learning rate (LR) scale \\(\\beta_{2} / \\beta_{1}\\) is 100, the buffer size for data shuffling on clients is 2048, and the batch size for local SGD is 32. In Fig. 9, instead of tuning these hyperparameters in advance, we conduct a study on these hyperparameters to understand DP-FedEmb. Among these hyperparameters, Figs. 9a and 9d suggest virtual clients and head LR scale are particularly important for DP-FedEmb to be on par with" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.279, + 0.892, + 0.475 + ], + "angle": 0, + "content": "non-private centralized training, which is an important contribution of this work. Figures 9a to 9c suggest users per virtual client, clients per round, and examples per client only need to be large enough under privacy consideration and computation resources for the best practice. The head LR scale has a large tuning range between 50 and 500 in Fig. 9d. Recall@FAR=1e-3 is not sensitive to shuffle buffer size in Fig. 9e. The model utility can be potentially improved if we further tune the client batch size as suggested by Fig. 9f. We fixed the learning rate and other hyperparameters while varying one of the hyperparameters in the ablation study. How to automate tuning, especially tuning with differential privacy guarantees, is an important future work." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.477, + 0.892, + 0.703 + ], + "angle": 0, + "content": "Learning rate (LR). For experiments in Secs. 3.1 and 3.2, server and client learning rates are first tuned for non-private federated training with adaptive clipping of quantile 0.5 [3]. Then server learning rate is tuned when adding noise for private tuning, while estimated clip norm and client learning rate are fixed. The tuning range of learning rates are \\(\\{1,2,5\\} * 10^{-n}\\). Figures 5a and 5b suggest the optimal learning rates are similar for training 400 rounds or 800 rounds in non-private training. We then fix the client learning rate to be 0.002 and use the estimated clip norm 0.6 for MobileNetV2 in Fig. 5c, and observe the fixed clip results are very similar to adaptive clipping results. The best server learning rate for private training with noise can be smaller than non-private training, where the difference is even more notable for larger model ResNet-50 in Fig. 5d." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.704, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Variants of DP-FedEmb. We use local fine-tuning with different learning rates \\(\\beta_{1},\\beta_{2}\\) to update backbone and head parameters. An alternative is to reconstruct the head first before fine-tuning the backbone [41, 65]. We empirically find that head reconstruction can only achieve similar performance as the proposed fine-tuning when there are same or more number of updates on the backbone network, and hence use local fine-tuning for efficiency. It is also possible to use binary or triplet loss within a virtual clients. In our preliminary results, they achieve inferior results compared to DP-FedEmb that uses multi-class cross-entropy loss. We leave other improvement like arcface loss [17] as future work." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7975" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.088, + 0.096, + 0.258, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.167, + 0.226, + 0.182, + 0.236 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.267, + 0.097, + 0.465, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.359, + 0.226, + 0.374, + 0.236 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.473, + 0.096, + 0.669, + 0.223 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.564, + 0.226, + 0.578, + 0.236 + ], + "angle": 0, + "content": "(c)" + }, + { + "type": "image", + "bbox": [ + 0.677, + 0.093, + 0.88, + 0.223 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.771, + 0.226, + 0.787, + 0.236 + ], + "angle": 0, + "content": "(d)" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.248, + 0.895, + 0.29 + ], + "angle": 0, + "content": "Figure 5. Study for learning rates. (a) and (b) recall@FAR=1e-3 after training MobileNetV2 using DP-FedEmb with adaptive clipping [3] and 0 noise for 400 rounds and 800 rounds, respectively; (c) and (d) varying server learning rate with fixed client learning rate and clip norm for MobileNetV2 and ResNet-50." + }, + { + "type": "table", + "bbox": [ + 0.08, + 0.303, + 0.48, + 0.461 + ], + "angle": 0, + "content": "
DatasetAlgorithmRecall@FAR=1e-3/0.1
ApproxAllPair
DigiFaceDP-FedEmb-19.76 ± 0.43
DP-FedAvg-13.38 ± 0.27
EMNISTDP-FedEmb10.64 ± 0.510.47 ± 0.5
DP-FedAvg9.78 ± 0.449.67 ± 0.41
GLDDP-FedEmb26.18 ± 0.4427.07 ± 0.04
DP-FedAvg24.48 ± 1.026.27 ± 0.17
iNatDP-FedEmb40.49 ± 1.0840.93 ± 0.94
DP-FedAvg29.57 ± 0.7829.6 ± 0.65
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.47, + 0.47, + 0.499 + ], + "angle": 0, + "content": "Table 2. The utility under same privacy budget for DigiFace [6], EMNIST [67], GLD [68] and iNat [69] datasets." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.527, + 0.251, + 0.543 + ], + "angle": 0, + "content": "3.4. Additional results" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.552, + 0.47, + 0.779 + ], + "angle": 0, + "content": "We run additional experiments of ResNet-50 on the larger DigiFace of \\(98.96K\\) users. Because a lot of users in DigiFace have only 5 images, we set each virtual client to contain 64 users, and use 3072 samples per virtual client. We sample 128 virtual clients per round, and a relatively large noise 1.39 in RDP accounting can achieve \\(\\epsilon = 24.86\\), \\(\\delta = 10^{-5}\\) without extrapolation. The utility measured by recall@FAR=1e-3 is shown in Tab. 2. Though the utilities of both methods are significantly degraded by the large noise, DP-FedEmb is much better than the DP-FedAvg baseline because the noise is only added to backbone of \\(\\sim 2.4M\\) parameters instead of backbone plus head of \\(\\sim 15M\\) parameters. The full table including EM-NIST [67] results for reproducibility and hyperparameter choices is provided in Tab. 4 in Appendix D." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.471, + 0.902 + ], + "angle": 0, + "content": "In Tab. 2, we also conduct experiments with MobileNetV2 on Google Landmark Dataset (GLD) [32, 68, 77] and iNaturalist (iNat) dataset [32, 47, 69] to demonstrate the generalization of DP-FedEmb. We use a public model pretrained on ImageNet, and report extra approximate recall@FAR by computing pairwise similarity for minibatches, which is easy to reproduce and consistent with the all pair recall@FAR. We fix the hyperparameters" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.306, + 0.895, + 0.533 + ], + "angle": 0, + "content": "for the federated settings, and compare the performance of DP-FedEmb and DP-FedAvg under the same privacy budget (noise multiplier). Since each user already has multiple classes in GLD, we use a smaller number of users, 8, in each virtual client. We also use a smaller number of virtual clients per round, 32, for fast experiments and strong sampling effect. Recall@FAR=1e-3 of DP-FedEmb and DP-FedAvg with small noise multiplier 0.02 on GLD outperforms centralized training. For iNat, we use an even smaller four users per virtual client and train for only 400 rounds, and use a relatively large noise multiplier 0.5 to get a single-digit \\(\\epsilon = 16.06\\) DP guarantee for 9275 users. We report recall@FAR=0.1 instead of recall@FAR=1e-3 for the challenging iNat task. In all experiments, DP-FedEmb consistently outperforms DP-FedAvg." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.567, + 0.619, + 0.582 + ], + "angle": 0, + "content": "4. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.599, + 0.895, + 0.903 + ], + "angle": 0, + "content": "This paper presented DP-FedEmb for training embedding models with user-level differential privacy. We show how practical utility with strong privacy guarantees can be achieved in the data center, thanks to key algorithm design choices around the construction of virtual clients and in the selection of what information is shared among users. Our experiments validate this improves the privacy utility trade-off upon vanilla DP-FedAvg for supervised representation learning. Though strong formal DP bounds at practical levels of utility could only be achieved when millions of users participate in training, DP-FedEmb is designed to be exceptionally scalable when model size and class space increases with number of users. DP-FedEmb can also be applied to decentralized FL when each real client contains multiple classes, possibly reducing the necessity of virtual clients. Finally, DP is a worst-case guarantee that can be improved by both algorithmic design and advanced accounting methods; the non-negligible noise we added for the small scale datasets in experiments are ready to be empirically audited for privacy." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7976" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.186 + ], + "angle": 0, + "content": "[1] Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pages 308-318, 2016. 1, 3, 4, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.187, + 0.472, + 0.243 + ], + "angle": 0, + "content": "[2] Kareem Amin, Alex Kulesza, Andres Munoz, and Sergei Vassilvtiskii. Bounding user contributions: A bias-variance trade-off in differential privacy. In International Conference on Machine Learning, pages 263-271. PMLR, 2019. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.245, + 0.471, + 0.3 + ], + "angle": 0, + "content": "[3] Galen Andrew, Om Thakkar, H Brendan McMahan, and Swaroop Ramaswamy. Differentially private learning with adaptive clipping. Conference on Neural Information Processing Systems (NeurIPS), 2021. 5, 7, 8, 13, 17" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.302, + 0.471, + 0.344 + ], + "angle": 0, + "content": "[4] Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, and Pasin Manurangsi. Large-scale differentially private bert. arXiv preprint arXiv:2108.01624, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.346, + 0.469, + 0.401 + ], + "angle": 0, + "content": "[5] Apple Privacy Team. Learning with privacy at scale. Available at https://machinelearning.apple.com/2017/12/06/learning-with-privacy-at-scale.html, 2017.1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.403, + 0.47, + 0.485 + ], + "angle": 0, + "content": "[6] Gwangbin Bae, Martin de La Gorce, Tadas Baltrusaitis, Charlie Hewitt, Dong Chen, Julien Valentin, Roberto Cipolla, and Jingjing Shen. Digiface-1m: 1 million digital face images for face recognition. In 2023 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2023. 5, 8, 17" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.488, + 0.47, + 0.543 + ], + "angle": 0, + "content": "[7] Mark Bun and Thomas Steinke. Concentrated differential privacy: Simplifications, extensions, and lower bounds. In Theory of Cryptography Conference, pages 635-658. Springer, 2016. 3, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.546, + 0.469, + 0.599 + ], + "angle": 0, + "content": "[8] Clément L Canonne, Gautam Kamath, and Thomas Steinke. The discrete gaussian for differential privacy. Advances in Neural Information Processing Systems, 33:15676-15688, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.603, + 0.47, + 0.657 + ], + "angle": 0, + "content": "[9] Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer. Membership inference attacks from first principles. In 2022 IEEE Symposium on Security and Privacy (SP), pages 1897-1914. IEEE, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.66, + 0.469, + 0.728 + ], + "angle": 0, + "content": "[10] Nicholas Carlini, Chang Liu, Ülfar Erlingsson, Jernej Kos, and Dawn Song. The secret sharer: Evaluating and testing unintended memorization in neural networks. In 28th USENIX Security Symposium (USENIX Security 19), pages 267-284, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.731, + 0.469, + 0.813 + ], + "angle": 0, + "content": "[11] Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21), pages 2633-2650, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.816, + 0.469, + 0.871 + ], + "angle": 0, + "content": "[12] Yannis Cattan, Christopher A Choquette-Choo, Nicolas Papernot, and Abhradeep Thakurta. Fine-tuning with differential privacy necessitates an additional hyperparameter search. arXiv preprint arXiv:2210.02156, 2022. 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.469, + 0.902 + ], + "angle": 0, + "content": "[13] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.472, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.121 + ], + "angle": 0, + "content": "of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.123, + 0.892, + 0.178 + ], + "angle": 0, + "content": "[14] Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. Fedavg with fine tuning: Local updates lead to representation learning. arXiv e-prints, pages arXiv-2205, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.18, + 0.892, + 0.222 + ], + "angle": 0, + "content": "[15] Liam Collins, Aryan Mokhtari, Sewoong Oh, and Sanjay Shakkottai. Maml and anil provably learn representations. arXiv preprint arXiv:2202.03483, 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.224, + 0.892, + 0.279 + ], + "angle": 0, + "content": "[16] Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle. Unlocking high-accuracy differentially private image classification through scale. arXiv preprint arXiv:2204.13650, 2022. 1, 3, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.281, + 0.892, + 0.349 + ], + "angle": 0, + "content": "[17] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4690-4699, 2019. 7, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.352, + 0.892, + 0.394 + ], + "angle": 0, + "content": "[18] Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin. Collecting telemetry data privately. Advances in Neural Information Processing Systems, 30, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.396, + 0.892, + 0.437 + ], + "angle": 0, + "content": "[19] Xin Dong, Sai Qian Zhang, Ang Li, and HT Kung. Spherefed: Hyperspherical federated learning. arXiv preprint arXiv:2207.09413, 2022. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.439, + 0.892, + 0.495 + ], + "angle": 0, + "content": "[20] Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, and Pasin Manurangsi. Connect the dots: Tighter discrete approximations of privacy loss distributions. arXiv preprint arXiv:2207.04380, 2022. 13, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.497, + 0.892, + 0.539 + ], + "angle": 0, + "content": "[21] DP Team. Google's differential privacy libraries., 2020. https://github.com/google/differential-privacy.14" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.541, + 0.892, + 0.582 + ], + "angle": 0, + "content": "[22] Cynthia Dwork. Differential privacy in new settings. In Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms, pages 174-183. SIAM, 2010. 1, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.584, + 0.892, + 0.64 + ], + "angle": 0, + "content": "[23] Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference, pages 265-284. Springer, 2006. 1, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.642, + 0.892, + 0.683 + ], + "angle": 0, + "content": "[24] Cynthia Dwork, Aaron Roth, et al. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3-4):211-407, 2014. 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.686, + 0.892, + 0.74 + ], + "angle": 0, + "content": "[25] Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, and Lijie Ren. Smoothly bounding user contributions in differential privacy. Advances in Neural Information Processing Systems, 33:13999-14010, 2020. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.743, + 0.892, + 0.784 + ], + "angle": 0, + "content": "[26] Jonathan Frankle, David J. Schwab, and Ari S. Morcos. Training batchnorm and only batchnorm: On the expressive power of random features in CNNs, 2021. 5, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.786, + 0.892, + 0.828 + ], + "angle": 0, + "content": "[27] Robin C Geyer, Tassilo Klein, and Moin Nabi. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557, 2017. 3, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.83, + 0.892, + 0.871 + ], + "angle": 0, + "content": "[28] Badih Ghazi, Ravi Kumar, and Pasin Manurangsi. User-level private learning via correlated sampling. arXiv preprint arXiv:2110.11208, 2021. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.873, + 0.892, + 0.902 + ], + "angle": 0, + "content": "[29] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch," + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7977" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.092, + 0.47, + 0.148 + ], + "angle": 0, + "content": "Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.15, + 0.472, + 0.218 + ], + "angle": 0, + "content": "[30] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000-16009, 2022. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.22, + 0.471, + 0.276 + ], + "angle": 0, + "content": "[31] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.278, + 0.47, + 0.332 + ], + "angle": 0, + "content": "[32] Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. Federated visual classification with real-world data distribution. In European Conference on Computer Vision, pages 76-92. Springer, 2020. 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.334, + 0.47, + 0.389 + ], + "angle": 0, + "content": "[33] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pages 448-456. PMLR, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.391, + 0.47, + 0.446 + ], + "angle": 0, + "content": "[34] Prateek Jain, John Rush, Adam Smith, Shuang Song, and Abhradeep Guha Thakurta. Differentially private model personalization. Advances in Neural Information Processing Systems, 34:29723-29735, 2021. 4, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.448, + 0.47, + 0.502 + ], + "angle": 0, + "content": "[35] Sébastien Jean, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. On using very large target vocabulary for neural machine translation. arXiv preprint arXiv:1412.2007, 2014. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.504, + 0.47, + 0.573 + ], + "angle": 0, + "content": "[36] Peter Kairouz, Brendan Mcmahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu. Practical and private (deep) learning without sampling or shuffling. In International Conference on Machine Learning (ICML), pages 5213-5225, 2021. 1, 2, 3, 5, 6, 13, 14, 15, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.576, + 0.47, + 0.645 + ], + "angle": 0, + "content": "[37] Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977, 2019. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.647, + 0.47, + 0.7 + ], + "angle": 0, + "content": "[38] Peter Kairouz, Sewoong Oh, and Pramod Viswanath. The composition theorem for differential privacy. In International conference on machine learning, pages 1376-1385. PMLR, 2015. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.703, + 0.47, + 0.771 + ], + "angle": 0, + "content": "[39] Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning, pages 5132-5143. PMLR, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.774, + 0.47, + 0.815 + ], + "angle": 0, + "content": "[40] Antti Koskela, Joonas Jälkö, Lukas Prediger, and Antti Honkela. Tight approximate differential privacy for discrete-valued mechanisms using fft, 2020. 13, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.817, + 0.47, + 0.871 + ], + "angle": 0, + "content": "[41] Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang. Fine-tuning can distort pretrained features and underperform out-of-distribution. arXiv preprint arXiv:2202.10054, 2022.7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[42] Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, and Abhradeep Thakurta. Toward" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.121 + ], + "angle": 0, + "content": "training at imagenet scale with differential privacy. arXiv preprint arXiv:2201.12328, 2022. 1, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.123, + 0.892, + 0.176 + ], + "angle": 0, + "content": "[43] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998. 18" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.179, + 0.892, + 0.234 + ], + "angle": 0, + "content": "[44] Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, and Ananda Theertha Suresh. Learning with user-level privacy. Advances in Neural Information Processing Systems, 34:12466-12479, 2021. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.236, + 0.892, + 0.29 + ], + "angle": 0, + "content": "[45] Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto. Large language models can be strong differentially private learners. arXiv preprint arXiv:2110.05679, 2021. 1, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.293, + 0.892, + 0.347 + ], + "angle": 0, + "content": "[46] Yuhan Liu, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Michael Riley. Learning discrete distributions: user vs item-level privacy. Advances in Neural Information Processing Systems, 33:20965-20976, 2020. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.349, + 0.894, + 0.403 + ], + "angle": 0, + "content": "[47] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV), December 2015. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.406, + 0.892, + 0.461 + ], + "angle": 0, + "content": "[48] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In AISTATS, pages 1273-1282. PMLR, 2017. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.463, + 0.892, + 0.517 + ], + "angle": 0, + "content": "[49] Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. Learning differentially private recurrent language models. In International Conference on Learning Representations (ICLR), 2018. 1, 2, 3, 5, 6, 13, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.519, + 0.892, + 0.559 + ], + "angle": 0, + "content": "[50] Brendan McMahan and Abhradeep Thakurta. Federated learning with formal differential privacy guarantees, 2022. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.563, + 0.892, + 0.63 + ], + "angle": 0, + "content": "[51] H Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, and Peter Kairouz. A general approach to adding differential privacy to iterative training procedures. arXiv preprint arXiv:1812.06210, 2018. 4, 13, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.633, + 0.892, + 0.688 + ], + "angle": 0, + "content": "[52] Qiang Meng, Feng Zhou, Hainan Ren, Tianshu Feng, Guochao Liu, and Yuanqing Lin. Improving federated learning face recognition via privacy-agnostic clusters. arXiv preprint arXiv:2201.12467, 2022. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.69, + 0.892, + 0.731 + ], + "angle": 0, + "content": "[53] Ilya Mironov. Rényi differential privacy. In 2017 IEEE 30th computer security foundations symposium (CSF), pages 263-275. IEEE, 2017. 5, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.733, + 0.892, + 0.774 + ], + "angle": 0, + "content": "[54] Kevin Musgrave, Serge Belongie, and Ser-Nam Lim. A metric learning reality check. In European Conference on Computer Vision, pages 681-699. Springer, 2020. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.776, + 0.892, + 0.815 + ], + "angle": 0, + "content": "[55] Nicolas Papernot and Thomas Steinke. Hyperparameter tuning with renyi differential privacy. arXiv preprint arXiv:2110.03620, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.818, + 0.892, + 0.899 + ], + "angle": 0, + "content": "[56] Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Thakurta. How to dp-fy ml: A practical guide to machine learning with differential privacy. arXiv preprint arXiv:2303.00654, 2023. 5, 15" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7978" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.16 + ], + "angle": 0, + "content": "[57] Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H Brendan McMahan, and Françoise Beaufays. Training production language models without memorizing user data. arXiv preprint arXiv:2009.10031, 2020. 1, 2, 6, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.163, + 0.47, + 0.232 + ], + "angle": 0, + "content": "[58] Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečny, Sanjiv Kumar, and Hugh Brendan McMahan. Adaptive federated optimization. In International Conference on Learning Representations, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.234, + 0.471, + 0.303 + ], + "angle": 0, + "content": "[59] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.305, + 0.47, + 0.374 + ], + "angle": 0, + "content": "[60] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhoginov, and Liang-Chieh Chen. *Mobilenetv2: Inverted residuals and linear bottlenecks*. In *Proceedings of the IEEE conference on computer vision and pattern recognition*, pages 4510–4520, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.376, + 0.469, + 0.444 + ], + "angle": 0, + "content": "[61] Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 815-823, 2015. 1, 3, 4, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.446, + 0.469, + 0.515 + ], + "angle": 0, + "content": "[62] Zebang Shen, Jiayuan Ye, Anmin Kang, Hamed Hassani, and Reza Shokri. Share your representation only: Guaranteed improvement of the privacy-utility tradeoff in federated learning. In The Eleventh International Conference on Learning Representations, 2023. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.518, + 0.469, + 0.573 + ], + "angle": 0, + "content": "[63] Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP), pages 3-18. IEEE, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.575, + 0.469, + 0.629 + ], + "angle": 0, + "content": "[64] Hakim Sidahmed, Zheng Xu, Ankush Garg, Yuan Cao, and Mingqing Chen. Efficient and private federated learning with partially trainable networks. arXiv preprint arXiv:2110.03450, 2021. 5, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.632, + 0.469, + 0.687 + ], + "angle": 0, + "content": "[65] Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, and Sushant Prakash. Federated reconstruction: Partially local federated learning. Advances in Neural Information Processing Systems (NeurIPS), 2021. 4, 7, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.689, + 0.469, + 0.757 + ], + "angle": 0, + "content": "[66] Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, and Lior Wolf. Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1701-1708, 2014. 3, 4, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.759, + 0.469, + 0.814 + ], + "angle": 0, + "content": "[67] TFF Authors. TensorFlow Federated EMNIST dataset, 2022. https://www.tensorflow.org/federated/api_docs/python/tff/simulation/datasets/ emnist.8,15,17" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.816, + 0.469, + 0.871 + ], + "angle": 0, + "content": "[68] TFF Authors. TensorFlow Federated Google Landmark v2 dataset, 2022. https://www.tensorflow.org/federated/api_docs/python/tff/simulation/datasets/gldv2.8,15,17" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[69] TFF Authors. TensorFlow Federated iNaturalist dataset, 2022. https://www.tensorflow.com" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.471, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.133 + ], + "angle": 0, + "content": "org / federated / api __docs / python / tff / simulation/datasets/inaturalist. 8, 15, 17" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.892, + 0.162 + ], + "angle": 0, + "content": "[70] US Census Bureau. Disclosure avoidance for the 2020 census: An introduction, 2021. 1, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.165, + 0.892, + 0.207 + ], + "angle": 0, + "content": "[71] Salil Vadhan. The complexity of differential privacy. In *Tutorialis on the Foundations of Cryptography*, pages 347–450. Springer, 2017. 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.208, + 0.892, + 0.262 + ], + "angle": 0, + "content": "[72] Sagar M Waghmare, Hang Qi, Huizhong Chen, Mikhail Sirotenko, and Tomer Meron. Efficient image representation learning with federated sampled softmax. arXiv preprint arXiv:2203.04888, 2022. 4, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.264, + 0.892, + 0.332 + ], + "angle": 0, + "content": "[73] Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, et al. A field guide to federated optimization. arXiv:2107.06917, 2021. 3, 4, 5, 13, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.334, + 0.892, + 0.389 + ], + "angle": 0, + "content": "[74] Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, and Tong Zhang. On the unreasonable effectiveness of federated averaging with heterogeneous data. arXiv preprint arXiv:2206.04723, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.391, + 0.892, + 0.433 + ], + "angle": 0, + "content": "[75] Kilian Q Weinberger and Lawrence K Saul. Distance metric learning for large margin nearest neighbor classification. Journal of machine learning research, 10(2), 2009. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.435, + 0.892, + 0.489 + ], + "angle": 0, + "content": "[76] Yandong Wen, Weiyang Liu, Adrian Weller, Bhiksha Raj, and Rita Singh. Sphereface2: Binary classification is all you need for deep face recognition. arXiv preprint arXiv:2108.01513, 2021. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.491, + 0.892, + 0.56 + ], + "angle": 0, + "content": "[77] Tobias Weyand, Andre Araujo, Bingyi Cao, and Jack Sim. Google landmarks dataset v2-a large-scale benchmark for instance-level recognition and retrieval. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2575-2584, 2020. 1, 8, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.562, + 0.892, + 0.63 + ], + "angle": 0, + "content": "[78] Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins, Brendan McMahan, Ohad Shamir, and Nathan Srebro. Is local sgd better than minibatch sgd? In International Conference on Machine Learning, pages 10334-10343. PMLR, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.632, + 0.892, + 0.674 + ], + "angle": 0, + "content": "[79] Lin Wu, Chunhua Shen, and Anton van den Hengel. Personnet: Person re-identification with deep convolutional neural networks. arXiv preprint arXiv:1601.07255, 2016. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.676, + 0.892, + 0.717 + ], + "angle": 0, + "content": "[80] Yuxin Wu and Kaiming He. Group normalization. In Proceedings of the European conference on computer vision (ECCV), pages 3-19, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.719, + 0.892, + 0.786 + ], + "angle": 0, + "content": "[81] Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al. Differentially private fine-tuning of language models. arXiv preprint arXiv:2110.06500, 2021. 1, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.789, + 0.892, + 0.843 + ], + "angle": 0, + "content": "[82] Felix Yu, Ankit Singh Rawat, Aditya Menon, and Sanjiv Kumar. Federated learning with only positive labels. In International Conference on Machine Learning, pages 10946-10956. PMLR, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.846, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[83] Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny. Barlow twins: Self-supervised learning via redundancy reduction. In International Conference on Machine Learning, pages 12310-12320. PMLR, 2021. 13" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7979" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.092, + 0.472, + 0.121 + ], + "angle": 0, + "content": "[84] Chiyuan Zhang, Samy Bengio, and Yoram Singer. Are all layers created equal?, 2019. 5" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.517, + 0.957 + ], + "angle": 0, + "content": "7980" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Generate Image Embeddings With User-Level Differential Privacy/35eb4863-9483-4c5e-b3f4-41696055c435_origin.pdf b/2023/Learning To Generate Image Embeddings With User-Level Differential Privacy/35eb4863-9483-4c5e-b3f4-41696055c435_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..593880e3db90f7a16f49c0963ffda93133e73628 --- /dev/null +++ b/2023/Learning To Generate Image Embeddings With User-Level Differential Privacy/35eb4863-9483-4c5e-b3f4-41696055c435_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2820b199b252010e4534270104fe336902d4a18b822c345a97f324234cdf71e3 +size 4351557 diff --git a/2023/Learning To Generate Image Embeddings With User-Level Differential Privacy/full.md b/2023/Learning To Generate Image Embeddings With User-Level Differential Privacy/full.md new file mode 100644 index 0000000000000000000000000000000000000000..b10776f4478421581dbdde35db44b406ea1c1826 --- /dev/null +++ b/2023/Learning To Generate Image Embeddings With User-Level Differential Privacy/full.md @@ -0,0 +1,318 @@ +# Learning to Generate Image Embeddings with User-level Differential Privacy + +Zheng Xu* + +Sean Augenstein + +Maxwell Collins* + +Ting Liu + +Yuxiao Wang + +Florian Schroff + +Liviu Panait + +Sewoong Oh + +H. Brendan McMahan + +Google Research + +# Abstract + +Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class space. To achieve user-level DP for large image-to-embedding feature extractors, we propose DP-FedEmb, a variant of federated learning algorithms with per-user sensitivity control and noise addition, to train from user-partitioned data centralized in the datacenter. DP-FedEmb combines virtual clients, partial aggregation, private local fine-tuning, and public pretraining to achieve strong privacy utility trade-offs. We apply DP-FedEmb to train image embedding models for faces, landmarks and natural species, and demonstrate its superior utility under same privacy budget on benchmark datasets DigiFace, EMNIST, GLD and iNaturalist. We further illustrate it is possible to achieve strong user-level DP guarantees of $\epsilon < 2$ while controlling the utility drop within $5\%$ , when millions of users can participate in training. + +# 1. Introduction + +Representation learning, by training deep neural networks as feature extractors to generate compact embedding vectors from images, is a fundamental component in computer vision. Metric learning, a kind of representation learning using supervised data, has been widely applied to image recognition, clustering, and retrieval [61, 75, 77]. Machine learning models have the capacity to memorize training data [10, 11], leading to privacy risks when the models are deployed. Privacy risk can also be audited by membership inference attacks [9, 63], i.e. detecting whether certain data was used to train a model and potentially exposing users' usage behaviors. Defending against such risks is a critical responsibility when training on privacy-sensitive data. + +Differential Privacy (DP) [23] is an extensively used quantifiable measurement of privacy risk, now generally accepted as a standard notion of privacy in both industry and government [5, 18, 50, 70]. Applied to machine learning, DP requires a training procedure with explicit randomness, and guarantees that the distribution over output models is quantifiably similar given a certain scope of change to the training dataset. A DP guarantee with respect to the change of a single arbitrary training example is known as example-level DP, which provides plausible deniability (in the binary hypothesis testing sense of [38]) that any single example (e.g., image) occurred in the training dataset. If we instead consider how the distribution of output models changes if the data (including even the number of examples) from any single user change arbitrarily, we have user-level DP [22]. This ensures model training is quantifiably insensitive to all of the data from any one user, and hence it is impossible to tell if a user has participated in training with high confidence. This guarantee can be exponentially stronger than example-level DP if one user may contribute many examples to training. + +Recently, DP-SGD [1] (essentially, SGD with the additional steps of clipping each individual gradient to have a maximum norm, and adding correspondingly calibrated noise) has been used to achieve example-level DP for relatively large models in language modeling and image classification tasks [4, 16, 42, 45, 81], often utilizing techniques like large batch training and pretraining on public data. DP-SGD can be modified to guarantee user-level DP, which is often combined with federated learning algorithms and called DP-FedAvg [49]. User-level DP has only been studied for small on-device models that have less than 10 million parameters [36, 49, 57]. + +We consider user-level DP for relatively large models in representation learning with supervised data. In our setting, similar to federated learning (FL), the data are user-partitioned; but in contrast to decentralized FL, we are primarily motivated by centralized data that benefit from access to richer computation resources and the ability to form virtual clients at random. Throughout this work, we use user as the basic unit of data partitioning and the granular- + +![](images/a94fb1d2cc42efdc91b99a8c9bcbef77a57e3da3b08aec6cf3cc3263dd0ceeb0.jpg) +Figure 1. DP-FedEmb combines virtual clients, local fine-tuning, partial aggregation, and public pretraining to achieve strong privacy utility trade-offs. Colors indicate different users and the simplified case of a single class per user, coloring the softmax head accordingly. + +ity for privacy; a user owns their (image) data, and class, identity and label are used interchangeably for the supervised information. + +Though typically each user only contributes images for a small number of classes, the combined class space from the union of all users can be very large, which proves challenging for existing DP algorithms. When the model size is fixed independent of the number of users, and at a relatively small scale with a few million parameters, previous methods can only achieve strong user-level DP when millions of users are available [36, 49, 57]. In contrast, when considering learning embedding models for applications like facial images, the number of classes (and hence, the total model size) can grow linearly with the number of users, and so simply scaling up to larger datasets with more users no longer ensures that good privacy-utility trade-offs can be achieved. For example, in a standard multi-class training paradigm with 128-dimensional embedding vectors, with one million users we expect the final dense layer for prediction alone to have over 128 million trainable parameters. Further, the fact that most users will only have examples from a small number of classes implies that gradients are approximately sparse, whereas DP-SGD requires the addition of dense noise to the full gradient, leading to a poor signal-to-noise ratio in the updates. Hence, existing methods can easily fail on the problems we consider. + +We propose DP-FedEmb to train embedding models with user-level differential privacy; Fig. 1 provides a high-level overview. DP-FedEmb combines public pretraining, virtual clients, local fine-tuning, and partial aggregation to achieve strong privacy-utility trade-offs. The key to the approach is partitioning the model into a backbone network that generates embeddings, and a classification softmax head specific to the classes in the training data. In + +each training round, users are grouped into virtual clients and initialized from the global backbone. A local randomly-initialized softmax head layer is added for the limited number of classes on the virtual client, and the complete local model is fine-tuned in order to produce an update to the backbone. The local head parameters are not included in the private aggregation and hence require no noise addition. This is in contrast to existing methods like DP-FedAvg/DP-SGD, which would add noise to all parameters including the softmax head. The backbone updates are clipped to a maximum L2 norm, aggregated across virtual clients, and combined with appropriate DP noise. At this point, the noised update is the output of a DP mechanism and satisfies the corresponding DP guarantee. This update is then applied to the global backbone, which inherits the DP guarantee, and passed to the next round of training. DP-FedEmb significantly improves the scalability of DP training for embedding models, as only the parameters of the backbone network are privatized and released, and the size of this portion of the model does not grow with the number of users. Pretraining the backbone network on public data to learn general visual representations before applying DP-FedEmb for more privacy sensitive tasks further improves performance. + +We demonstrate the superior performance of DP-FedEmb for embedding models by experiments on datasets with moderate size of users and classes (DigiFace of 98.96K or 9047 users/identities, Google Landmarks Dataset of 1262 users and 2028 classes, and iNaturalist of 9275 users and 1203 classes). We also show relatively strong privacy guarantees of single digit $\epsilon$ can be achieved while maintaining strong utility if millions users can participate in training. To our knowledge, this is the first report of training a commonly used large vision model, ResNet-50, with non-negligible noise for user-level DP. + +# 2. Learning Embedding Models + +# 2.1. Problem formulation and centralized training + +We learn a backbone network parameterized by $\theta$ , that outputs an embedding vector $z = f(\theta, x)$ for an input image $x$ . The backbone network, $\theta$ , is trained on paired examples of image $x$ and class $y$ . The training dataset is naturally partitioned by $M$ users, i.e., $\mathcal{D} = \bigcup_{i=1}^{M} \mathcal{D}_i$ . + +We adopt the popular multi-class training framework for embedding models, where a proxy weight vector $w_{j}$ is learnt for each class $j$ . We use $\omega$ to denote the union of proxy weight vectors $\{w_{j}\}$ , which is called the head of the network. Given a training image-class pair $(x,y)$ , logits are computed by taking the inner product between the embedding vector $f(\theta ,x)$ and the proxy weight vectors in the network head $\omega$ . This is effectively passing $f(\theta ,x)$ through a dense network layer parameterized by $\omega$ without the bias terms. With a supervised training loss $\ell$ , such as the cross entropy loss, the following objective is optimized + +$$ +\min _ {\theta , \omega} \sum_ {(x, y) \in \mathcal {D}} \ell (\langle \omega , f (\theta , x) \rangle , y), \tag {1} +$$ + +where we overload the inner product notation, i.e., $\langle \omega ,f(\theta ,x)\rangle$ , to denote a set of inner products with each element in $\omega$ . Typically, variants of the gradient descent method are used to solve the optimization in (1). In each iteration, the average gradient is computed on a sampled minibatch of data $B\subset \mathcal{D}$ , and is then used to update the model parameters $\theta$ and $\omega$ . Furthermore, when the output space, i.e., the number of classes, is very large, sampled softmax is often applied [35], where only a subset of proxy weights sampled from $\omega$ are used in each training iteration. + +# 2.2. User-level DP and DP-FedAvg + +To achieve user-level DP, we control the sensitivity of each user and add corresponding noise for anonymization. To effectively control the sensitivity, it is important to understand and account for the contributions of each user in the model updates; hence it is convenient to consider the data at a granularity of users instead of individual samples. Grouping together each user's data, the objective (1) can be rewritten as + +$$ +\min _ {\theta , \omega} \sum_ {i = 1} ^ {M} \sum_ {(x, y) \in \mathcal {D} _ {i}} \ell (\langle \omega , f (\theta , x) \rangle , y). \tag {2} +$$ + +The above objective of two level sum is often found in federated learning [73], which can be optimized by the (generalized) FedAvg algorithm [48, 58]. In generalized FedAvg, each round $t$ starts with the server broadcasting $\theta^{(t)},\omega^{(t)}$ to a subset of clients. Each client $i$ will then update the local model parameters by CLIENTOPT with private data $\mathcal{D}_i$ , and send back the updates for model parameters $\Delta_i(\theta^{(t)}),\Delta_i(\omega^{(t)})$ . The model deltas from sampled + +clients are then aggregated and used by SERVEROPT to get $\theta^{(t + 1)},\omega^{(t + 1)}$ for the next round. + +The generalized FedAvg algorithm can be extended for user-level DP by clipping the model deltas and adding noise proportional to the sensitivity [27,49]. We can use either independent Gaussian noise [49], or correlated noise that can achieve comparable privacy-utility trade-off without relying on the assumption of sampling [36]. The two variants are effectively applying DP-SGD [1] or DP-FTRL [36] as SERVEROPT in the generalized FedAvg framework. Unlike the cross-device FL setting where sampling is extremely hard, it is possible to control user sampling in the datacenter and use DP-SGD. But DP-FTRL provides the possibility of handling the online setting where the user data are streamed instead of collected, and can be accounted for zCDP [7] reported by US census bureau [70]. A complete description of DP-FedAvg for training a backbone network to generate image embeddings is in Alg. 2 in Appendix B + +In addition to the flexibility of generalized FedAvg for user-level DP, there are a few side effects of FedAvg that make it particularly effective for differentially private training. The model deltas are computed based on data for each user before clipping in DP, which can potentially reduce the bias introduced by clipping. As [16] suggested that averaging gradients from augmented data before clipping can improve training for example-level DP, model deltas from user data for user-level DP can be considered a natural extension to improve the bias-variance trade-off. The communication efficiency of FedAvg that leads to infrequent aggregation and model release is also desirable for DP training. The local model updates by private data on clients introduce no additional privacy cost, and only communication rounds between clients and server have to be accounted for DP. Though the theoretical advantages of FedAvg are only proved under certain assumptions [74, 78], FedAvg with local updates can achieve communication efficiency and fast convergence in various practical applications [73]. + +# 2.3. Proposed DP-FedEmb method + +While generalized DP-FedAvg can be applied to train a backbone network $\theta$ to generate embedding $f(\theta, x)$ from image $x$ , there are challenges that significantly affect the efficiency and feasibility of the method. We propose DP-FedEmb with a few key features: virtual clients, partial aggregation, local fine-tuning, public pretraining, and parameter freezing. Details of DP-FedEmb are provided in Alg. 1. + +Virtual clients. Data heterogeneity is one of the key problems in federated optimization [73]. When we train embedding models in the multi-class framework, the class space can be very large and each user may only observe a limited number of classes. In the extreme case, when training embedding models on facial images [61, 66], each user may only have images for their own identity. This signif + +Algorithm 1: DP-FedEmb: learning embedding model $\theta$ with user-level DP +Input:SERVEROPT with learning rate $\alpha$ .. CLIENTOPT with learning rate $\beta_{1}$ and $\beta_{2}$ clip norm $\gamma$ and noise multiplier $\sigma$ (optional) pretrained model $\theta^{(0)}$ +for round $t = 0,1,\ldots ,T - 1$ do +Sample a subset of users $\mathcal{U}^{(t)}$ +Partition users $\mathcal{U}^{(t)}$ to virtual clients $S^{(t)}$ +for each virtual client $V\in S^{(t)}$ in parallel do Initialize backbone $\theta_V^{(t,0)} = \theta^{\mathrm{(t)}}$ +Randomly initialize head $\omega_{V}^{(t,0)}$ +for $k = 0,\dots ,K - 1$ do Sample minibatch $B\subset \bigcup_{i\in V}\mathcal{D}_i$ Compute gradients $\nabla_{\theta_V}\ell_B,\nabla_{\omega_V}\ell_B$ where $\ell_B = \mathbb{E}_{(x,y)\in B}\ell (\langle \omega_V,f(\theta_V,x)\rangle ,y)$ Update $\theta_V^{(t,k + 1)}$ by CLIENTOPT, $\theta_V^{(t,k)}$ $\nabla_{\theta_V}\ell_B,\beta_1$ Update $\omega_{V}^{(t,k + 1)}$ by CLIENTOPT, $\omega_{V}^{(t,k)}$ $\nabla_{\omega_V}\ell_B,\beta_2$ +end +Compute clipped model update + $\Delta_V^{(t)} = \mathrm{Clip}(\theta_V^{(t,K)} - \theta_V^{(t,0)},\gamma)$ +end +Aggregate model updates + $\Delta^{(t)} = \mathrm{AddNoise}\bigl (\sum_{V\in S^{(t)}}\Delta_{V}^{(t)},\sigma \gamma \bigr) / |S^{(t)}|$ +Update global backbone parameters + $\theta^{(t + 1)} = \mathrm{SERVEROPT}(\theta^{(t)},\Delta^{(t)},\alpha)$ + +icently limits the advantage of local updates due to client drift [39], and even with specialized regularization like [82], FedSGD [48] with frequent aggregation and model release has to be used instead of FedAvg. It is challenging to use some specialized techniques for handling data heterogeneity [73] in DP training. Instead, we propose a simple yet effective approach: randomly groups the data of sampled users into virtual clients. + +Unlike the cross-device FL setting where the on-device data of users cannot be directly communicated, virtual clients are feasible for user data in the datacenter. It is important to guarantee that a user will not be included in two virtual clients in a single round for user-level DP, analogous to microbatches for DP-SGD and example-level DP [1,51]. When the grouping of users for virtual clients is fixed in advance across rounds, the granularity of the DP definition can slightly change: the adjacent dataset for DP is based on virtual clients (a group of users) instead of a single user, which has conceptually stronger privacy guarantees. However, + +when virtual clients are randomly regrouped across rounds as in Alg. 1, we can only show user-level DP as discussed in Appendix C. Virtual clients also control the interpolation between federated training and centralized training: when all users are grouped into a single virtual client, federated training is equivalent to centralized training, which removes heterogeneity but is challenging for DP mechanism. Virtual clients are used for both baseline DP-FedAvg and the proposed DP-FedEmb method. + +Partial aggregation and local fine-tuning. Another challenge is the number of parameters in DP training. A common backbone $\theta$ of ResNet-50 for 128 dimensional embedding vectors has 23.77 million parameters. However, the parameter size of head $\omega$ can linearly grow with the number of classes. Taking FaceNet [61, 66] as an example again, $\omega$ can easily grow to 1280 million for 10 million identities in real-world applications. Sampled softmax [35, 72] can be applied to improve training efficiency. However, as both backbone $\theta$ and head $\omega$ are shared among users and need to be privatized by adding noise during training, the combined parameter size of $(\theta, \omega)$ will significantly affect the privacy utility trade-off, which cannot be mitigated by sampled softmax. + +In DP-FedEmb, inspired by federated reconstruction [65] and DP personalization [34], we only aggregate and privatize the backbone network $\theta$ , which is used in inference and has fixed parameter size that does not grow with classes. A local head $\omega_{V}$ is randomly initialized and updated on each virtual client $V$ . A fine-tuning approach is adopted for local updates, where different learning rates $\beta_{1}, \beta_{2}$ are used for the backbone $\theta_{V}$ and head $\omega_{V}$ , respectively. When combined with virtual clients, the partial aggregation and local fine-tuning approach can be interpreted in various ways: each virtual client is performing transfer learning given a shared backbone network for representation learning; the data of each class are their own positive samples as well as negative samples for other classes on the same virtual client; the size of local head $\omega_{V}$ is also significantly smaller than $\omega$ for all classes, which is effectively a user-based sampling for softmax. + +Public pretraining. The parameter size of the backbone to be privatized can still be large after applying partial aggregation and local fine-tuning with virtual clients, e.g., 23.77 million for ResNet-50. Inspired by recent research on applying DP-SGD for example-level DP on large language modeling [45, 81] and image classification [16, 42], we use a model pretrained on public images to initialize the DP training of the backbone network. There is a relatively clear distinction between the public and private domains for our task: we use public images collected from open webpages for pretraining, and then privately train on users' data collected in a datacenter. + +Parameter freezing. Neural networks are known to be + +overparameterized, and not all weights are equally important [26, 84]. Freezing some parameters to be non-trainable has been shown to be effective when the privacy budget is small [64], especially when combined with public pretraining for large models [16, 81]. For backbone convolutional neural networks with normalization layers, we experiment with training parameters with all normalization layers, and some of the convolutional kernels. However, freezing is found to be less efficient in our setting that performs representation learning, instead of classification, for a moderate size model in the high-utility-moderate-noise regime. + +DP mechanism and hyperparameters. Similar to generalized DP-FedAvg, we perform clipping for model deltas and add noise for aggregated updates. The clip norm $\gamma$ is estimated by adaptive clipping [3] in the parameter tuning stage. For DP-FedEmb, we perform extensive studies on several configurations in Sec. 3. Differentially private hyperparameter tuning [55] is a topic out of the scope of this paper, and automating hyperparameter tuning is an important future work. Either independent Gaussian noise like DP-SGD [49] or tree-based correlated noise like DP-FTRL [36] can be added. Under the same noise multiplier, DP-FedEmb will achieve the same privacy bound as DP-FedAvg with virtual clients, while utility can be improved for training a backbone network with a large head. + +# 3. Experiments + +We conduct experiments to train image-to-embedding backbone networks with user-level DP. We use the DigiFace dataset [6] of synthetic faces based on ethical and responsible development considerations, and verified that the conclusions on DigiFace are very similar to conclusions generated from experiments on natural facial images. We randomly split the DigiFace dataset of $110K$ identities and $1.22M$ into subsets of $98.96K$ identities with $1.10M$ images for training, 5443 identities with $58.24K$ images for validation, and 5598 identities with $60.82K$ images for testing. We extensively use a smaller training set, DigiFace10K, which contains the 9047 training identities of 72 images sampled from the DigiFace training set. We also run experiments on public datasets of natural images: EMNIST, Google Landmarks Dataset (GLD) and iNaturalist (iNat) dataset. These datasets are summarized in Tab. 3. + +In our setting, each user holds only the images of their own identities, i.e., user-level DP is also identity-level DP. We use ResNet-50 [31] and MobileNetV2 [32, 60, 73] as backbone networks, replace batch normalization [33] with group normalization [80], and use a multi-class framework with a large softmax head to train the backbone. The dimension of the embeddings are 128 for all experiments. + +We evaluate the performance of the backbone network based on predicting identity matches from the distance between two image embeddings. By varying a threshold on + +the pairwise similarity, a recall versus false accept rate (FAR) curve on the test data can be generated for a trained model. A scalar value of recall@FAR=1e-3 is often reported. The privacy guarantees are computed by either using Renyi differential privacy (RDP) [53] and converting to $(\epsilon, \delta)$ -DP by [8], or DP-FTRL accounting without restart [36]. More discussion on privacy accounting can be found in Appendix C. We aim for single-digit $\epsilon$ when $\delta$ is small, and sometimes relax to $\epsilon \sim 20$ as we use the stronger substitute-one DP definition [56]. + +We compare the proposed DP-FedEmb with non-private oracle performance of centralized training, and baseline methods DP-FedAvg. We tune the learning rate with learning rate scheduling for standard centralized training. The centralized baseline is provided as an oracle for non-private training performance. We exclude tricks like data augmentation for either centralized or federated training as the goal is not achieving state-of-the-art performance. Virtual clients are used to improve DP-FedAvg performance, and the same tuning strategy is applied for DP-FedEmb and DP-FedAvg. In most of the experiments, unless otherwise specified, we fix the hyperparameters in the federated setting and only tune the learning rates; the backbone networks are pretrained on classifying the 1000 classes of ImageNet [59]; both CLIENTOPT and SERVEROPT are SGD optimizers with momentum 0.9; and more details are provided in Sec. 3.3. Code is released at https://github.com/google-research/federated/tree/master/dpvisual_embedding. + +# 3.1. Privacy-utility-computation trade-off + +We study the privacy-utility-computation trade-offs of training ResNet-50 on DigiFace10K in Fig. 2 and Fig. 6. Figure 2a shows the privacy-utility trade-off. Without adding noise, the federated algorithms (DP-)FedAvg and (DP-)FedEmb can achieve even better results than the non-private centralized baseline, which is consistent with recent empirical and theoretical justifications that FedAvg is more accurate when learning representations [14, 15]. When the same noise multiplier is used, i.e., under same privacy budget, DP-FedEmb outperforms DP-FedAvg; and the margin increases when increasing the noise. We can observe the advantage of DP-FedEmb over DP-FedAvg even if we only have a small number of identities in DigiFace: the size of the head is $9047 \times 128 \sim 1.16M$ , only $4.6\%$ of the backbone ResNet-50 with $23.77M$ parameters. For large scale data with 10 million identities, the size of the head can grow to $1280M$ , which is much larger than the backbone networks, and DP-FedAvg can easily fail in such settings. + +It can be difficult to achieve a strong formal differential privacy bound without significantly hurting utility for Digi-Face10K, which only has a small number of total users. We consider the practical setting of more available users, and + +![](images/9d4c36498095a8bf410a0a03836ab6b359100a0246c1ab0fd843ea7ed5283e15.jpg) +(a) + +![](images/66252260106b2b602574679b4f12a44fe9feb23c6f18bce9b65c26e60129d3d5.jpg) +(b) + +![](images/7357beb9cb84f09069ff83abc9b274e19d64e47cdcdc446cbb85f332dae4fb05.jpg) +(c) + +![](images/3fe94c771904601ad7c16478d2c11f4ba518084717bccf999dc3d092a36b9aa5.jpg) +(d) + +![](images/4fc031a2a1c3a1809c5a4f61d3300d60f12ecd3cf21648e1952a3f9f5f569496.jpg) +(a) +Figure 3. DP-FedEmb and DP-FedAvg on DigiFace10K under the same privacy budget with noise multiplier 0.08, and additional oracle baseline of centralized training w/o DP: (a) recall@FAR=1e-3 on validation dataset during training; dashed lines are approximation by sampling a subset of validation users; (b) ROC curve of trained model on test set with log scale x-axis. + +![](images/6d1bf0227dc60546d95158fd64dd177fb4c96a29f6624fd8211e9d42f4b37a03.jpg) +Figure 2. (a) Recall@FAR=1e-3 on DigiFace validation set under different noise multiplier; (b) zoom in the high utility regime in (a); (c) and (d) privacy-computation trade-off by extrapolating based on 3M and 10M total users, respectively. "r400" and "r800" represent the result at 400 or 800 training rounds; target $71.77\%$ is $95\%$ of centralized non-private recall@FAR=1e-3 at $75.55\%$ . +(b) + +study the privacy-computation trade-off in Figs. 2c, 2d, 6b and 6c based on extrapolation. A key hypothesis following [36,49] is used: utility (Recall@FAR) is non-decreasing when simultaneously increasing the number of clients per round and noise multiplier. The hypothesis is based on the fact that the signal-to-noise ratio is non-decreasing when linearly increasing the number of clients per round and noise multiplier, and has been verified in practice [50, 57]. + +We first choose the noise multiplier that is within $5\%$ of recall@FAR=1e-3 compared to centralized non-private training in Figs. 2a and 6a, based on simulation that samples 64 virtual clients that each have 32 users per round: recall@FAR=1e-3 is 72.16 for running DP-FedAvg with 0.01 noise multiplier for 400 rounds, 72.37 for running DP-FedAvg with 0.015 noise multiplier for 800 rounds, 71.3 for running DP-FedEmb with 0.015 noise multiplier for 400 rounds, 72.6 for running DP-FedEmb with 0.02 noise multiplier for 800 rounds, and 71.85 for running DP-FTRL-FedEmb with 0.26 noise multiplier for 800 rounds. Then we linearly increase the number of users sampled and use the increased noise multiplier in RDP accounting to compute privacy bound $\epsilon$ given $\delta = 10^{-7}$ to generate Figs. 2c, 2d and 6b, and compute zCDP for Fig. 6c. Comparing curves of r400 and r800, training longer with larger noise is more + +effective than training shorter with smaller noise. Figure 2c suggests $\sim 98K$ users per round is enough for DP-FedEmb to achieve single-digit $\epsilon$ if $3M$ users are available, while $\sim 57K$ users per round are needed if $10M$ users are available in Fig. 2d. $98K$ users is $48\times$ the number of users per round in our current simulation, which can be achieved by training with $8\times$ computing resources for $6\times$ longer. In Fig. 6b, there is a crossover point when using DP-FTRL versus DP-SGD for DP-FedEmb, and DP-FTRL is more effective for relatively large privacy $\epsilon$ . Figure 6c shows that DP-FTRL-FedEmb can achieve zCDP smaller than 2.6, as used by US Census Bureau [70], when $8\times$ users per round and $10M$ total users are available. + +# 3.2. Model evaluation + +In Tab. 1, we summarize the quantitative results from the privacy-utility-computation trade-off analysis in Sec. 3.1. Each experiment runs three times to compute the mean and standard deviation. For similar recall@FAR=1e-3 on the DigiFace validation set, DP-FedEmb achieves stronger privacy guarantee than baseline DP-FedAvg, and the advantage of DP-FedEmb is expected to be more pronounced if a head for larger $10M$ identities is used for training. When $10M$ users are available and $64\times$ users per round in training, privacy $\epsilon = 3.90$ of single digit and zCDP= 1.28 smaller than 2.6 can be achieved when recall@FAR=1e-3 is within a $5\%$ drop compared with non-private centralized training. In addition to validation performance, the private models also perform well on the left-out test dataset. Figure 3 presents the training curves and ROC curves for comparing DP-FedEmb and DP-FedAvg under the same privacy budget. DP-FedEmb outperforms DP-FedAvg in all training rounds, and trains a stronger private model with better recall at different false accept rates. + +# 3.3. Ablation study + +We primarily use MobileNetV2 for ablation studies on DigiFace10K for two reasons: MobileNetV2 is smaller and faster for training in experiments; to test the generalization of DP-FedEmb and avoid overfitting on ResNet-50. + +
AlgorithmHyperparametersPrivacy (10M users)Recall@FAR=1e-3
NoiseSerLRRDP-εzCDPValidationTest
Centralized00.05∞∞75.55 ± 0.0575.53 ± 0.12
DP-FedAvg0.015 × 640.55.62-72.57 ± 0.1272.37 ± 0.09
DP-FedEmb0.02 × 640.23.90-72.63 ± 0.0572.37 ± 0.09
DP-FTRL-FedEmb0.26 × 640.29.671.2872.2 ± 0.2971.87 ± 0.26
+ +Table 1. Quantitative results of privacy and utility on the DigiFace10K dataset. The client learning rate and clip norm for federated algorithms are 0.002 and 0.6, respectively; $\delta = 10^{-7}$ for privacy guarantees. Centralized training has a standard learning rate scheduling, while tricks like data augmentation are excluded for all methods. The privacy guarantees are extrapolated based on $10M$ users and $131K$ users per round. A strong privacy guarantee can be achieved within a $5\%$ drop on recall@FAR=1e-3. + +![](images/5d1b1a354c601ab088f4c46f073d0819d972f4483cfab68539dbc6dc1f69fdd6.jpg) +(a) + +![](images/c444cddf3ae25623b0982b1a3c67ca3faae21e981aa4e6f8eb200116b50c97d7.jpg) +(b) +Figure 4. Recall@FAR=1e-3 on DigiFace validation set when training a MobileNet (a) with DP-FedEmb w/ pretraining, and baselines of DP-FedEmb w/o pretraining and DP-FedAvg; (b) with different pretrained models. + +Parameter freezing and public pretraining. For similar parameter size, MobileNetV2 outperforms ResNet-50 with frozen parameters, and Fig. 4a shows the privacy-utility trade-off. Recall@FAR=1e-3 of DP-FedEmb-r800 on MobileNetV2 only drops from $68.86\%$ to $67.56\%$ when 0.02 noise is added, while ResNet-50 drops from $79.09\%$ to $72.6\%$ . However, ResNet-50 still outperforms MobileNetV2 by a large margin in the high utility regime. DP-FedEmb consistently outperforms DP-FedAvg when noises are added. In Fig. 4b, though the private fine-tuning utility is not linearly increasing with pretraining accuracy, there seems to be a general positive correlation: better pretrained models can lead to better private models except for one outlier where a inferior pretrained model causes difficulty in training. More discussion on parameter freezing and public pretraining is provided in Appendix D.4. + +Federated settings. In the above experiments, we fix important hyperparameters for the federated setting: users per virtual client is 32, virtual clients per round is 64, examples per client is capped at 2048, the head learning rate (LR) scale $\beta_{2} / \beta_{1}$ is 100, the buffer size for data shuffling on clients is 2048, and the batch size for local SGD is 32. In Fig. 9, instead of tuning these hyperparameters in advance, we conduct a study on these hyperparameters to understand DP-FedEmb. Among these hyperparameters, Figs. 9a and 9d suggest virtual clients and head LR scale are particularly important for DP-FedEmb to be on par with + +non-private centralized training, which is an important contribution of this work. Figures 9a to 9c suggest users per virtual client, clients per round, and examples per client only need to be large enough under privacy consideration and computation resources for the best practice. The head LR scale has a large tuning range between 50 and 500 in Fig. 9d. Recall@FAR=1e-3 is not sensitive to shuffle buffer size in Fig. 9e. The model utility can be potentially improved if we further tune the client batch size as suggested by Fig. 9f. We fixed the learning rate and other hyperparameters while varying one of the hyperparameters in the ablation study. How to automate tuning, especially tuning with differential privacy guarantees, is an important future work. + +Learning rate (LR). For experiments in Secs. 3.1 and 3.2, server and client learning rates are first tuned for non-private federated training with adaptive clipping of quantile 0.5 [3]. Then server learning rate is tuned when adding noise for private tuning, while estimated clip norm and client learning rate are fixed. The tuning range of learning rates are $\{1,2,5\} * 10^{-n}$ . Figures 5a and 5b suggest the optimal learning rates are similar for training 400 rounds or 800 rounds in non-private training. We then fix the client learning rate to be 0.002 and use the estimated clip norm 0.6 for MobileNetV2 in Fig. 5c, and observe the fixed clip results are very similar to adaptive clipping results. The best server learning rate for private training with noise can be smaller than non-private training, where the difference is even more notable for larger model ResNet-50 in Fig. 5d. + +Variants of DP-FedEmb. We use local fine-tuning with different learning rates $\beta_{1},\beta_{2}$ to update backbone and head parameters. An alternative is to reconstruct the head first before fine-tuning the backbone [41, 65]. We empirically find that head reconstruction can only achieve similar performance as the proposed fine-tuning when there are same or more number of updates on the backbone network, and hence use local fine-tuning for efficiency. It is also possible to use binary or triplet loss within a virtual clients. In our preliminary results, they achieve inferior results compared to DP-FedEmb that uses multi-class cross-entropy loss. We leave other improvement like arcface loss [17] as future work. + +![](images/8f920cbc4074b48c99de2b3372c873bf3bcfa56a90d9220d493e12d971ce779a.jpg) +(a) + +![](images/d4922e5c36cc404885995f7a289ab6cf798977a6b0fddd40939eb313ac098d10.jpg) +(b) + +![](images/d2c3d84b5f203ffeccf65c163c40c27da5da71b1b19abb440a53ae7092ec88c1.jpg) +(c) +Figure 5. Study for learning rates. (a) and (b) recall@FAR=1e-3 after training MobileNetV2 using DP-FedEmb with adaptive clipping [3] and 0 noise for 400 rounds and 800 rounds, respectively; (c) and (d) varying server learning rate with fixed client learning rate and clip norm for MobileNetV2 and ResNet-50. + +![](images/d517d3bd690a3f3654e3f3e3704f18c5462e2ee53d2693ae688e057f737c8c17.jpg) +(d) + +
DatasetAlgorithmRecall@FAR=1e-3/0.1
ApproxAllPair
DigiFaceDP-FedEmb-19.76 ± 0.43
DP-FedAvg-13.38 ± 0.27
EMNISTDP-FedEmb10.64 ± 0.510.47 ± 0.5
DP-FedAvg9.78 ± 0.449.67 ± 0.41
GLDDP-FedEmb26.18 ± 0.4427.07 ± 0.04
DP-FedAvg24.48 ± 1.026.27 ± 0.17
iNatDP-FedEmb40.49 ± 1.0840.93 ± 0.94
DP-FedAvg29.57 ± 0.7829.6 ± 0.65
+ +Table 2. The utility under same privacy budget for DigiFace [6], EMNIST [67], GLD [68] and iNat [69] datasets. + +# 3.4. Additional results + +We run additional experiments of ResNet-50 on the larger DigiFace of $98.96K$ users. Because a lot of users in DigiFace have only 5 images, we set each virtual client to contain 64 users, and use 3072 samples per virtual client. We sample 128 virtual clients per round, and a relatively large noise 1.39 in RDP accounting can achieve $\epsilon = 24.86$ , $\delta = 10^{-5}$ without extrapolation. The utility measured by recall@FAR=1e-3 is shown in Tab. 2. Though the utilities of both methods are significantly degraded by the large noise, DP-FedEmb is much better than the DP-FedAvg baseline because the noise is only added to backbone of $\sim 2.4M$ parameters instead of backbone plus head of $\sim 15M$ parameters. The full table including EM-NIST [67] results for reproducibility and hyperparameter choices is provided in Tab. 4 in Appendix D. + +In Tab. 2, we also conduct experiments with MobileNetV2 on Google Landmark Dataset (GLD) [32, 68, 77] and iNaturalist (iNat) dataset [32, 47, 69] to demonstrate the generalization of DP-FedEmb. We use a public model pretrained on ImageNet, and report extra approximate recall@FAR by computing pairwise similarity for minibatches, which is easy to reproduce and consistent with the all pair recall@FAR. We fix the hyperparameters + +for the federated settings, and compare the performance of DP-FedEmb and DP-FedAvg under the same privacy budget (noise multiplier). Since each user already has multiple classes in GLD, we use a smaller number of users, 8, in each virtual client. We also use a smaller number of virtual clients per round, 32, for fast experiments and strong sampling effect. Recall@FAR=1e-3 of DP-FedEmb and DP-FedAvg with small noise multiplier 0.02 on GLD outperforms centralized training. For iNat, we use an even smaller four users per virtual client and train for only 400 rounds, and use a relatively large noise multiplier 0.5 to get a single-digit $\epsilon = 16.06$ DP guarantee for 9275 users. We report recall@FAR=0.1 instead of recall@FAR=1e-3 for the challenging iNat task. In all experiments, DP-FedEmb consistently outperforms DP-FedAvg. + +# 4. Conclusion + +This paper presented DP-FedEmb for training embedding models with user-level differential privacy. We show how practical utility with strong privacy guarantees can be achieved in the data center, thanks to key algorithm design choices around the construction of virtual clients and in the selection of what information is shared among users. Our experiments validate this improves the privacy utility trade-off upon vanilla DP-FedAvg for supervised representation learning. Though strong formal DP bounds at practical levels of utility could only be achieved when millions of users participate in training, DP-FedEmb is designed to be exceptionally scalable when model size and class space increases with number of users. DP-FedEmb can also be applied to decentralized FL when each real client contains multiple classes, possibly reducing the necessity of virtual clients. Finally, DP is a worst-case guarantee that can be improved by both algorithmic design and advanced accounting methods; the non-negligible noise we added for the small scale datasets in experiments are ready to be empirically audited for privacy. + +# References + +[1] Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pages 308-318, 2016. 1, 3, 4, 14 +[2] Kareem Amin, Alex Kulesza, Andres Munoz, and Sergei Vassilvtiskii. Bounding user contributions: A bias-variance trade-off in differential privacy. In International Conference on Machine Learning, pages 263-271. PMLR, 2019. 13 +[3] Galen Andrew, Om Thakkar, H Brendan McMahan, and Swaroop Ramaswamy. Differentially private learning with adaptive clipping. Conference on Neural Information Processing Systems (NeurIPS), 2021. 5, 7, 8, 13, 17 +[4] Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, and Pasin Manurangsi. Large-scale differentially private bert. arXiv preprint arXiv:2108.01624, 2021. 1 +[5] Apple Privacy Team. Learning with privacy at scale. Available at https://machinelearning.apple.com/2017/12/06/learning-with-privacy-at-scale.html, 2017.1 +[6] Gwangbin Bae, Martin de La Gorce, Tadas Baltrusaitis, Charlie Hewitt, Dong Chen, Julien Valentin, Roberto Cipolla, and Jingjing Shen. Digiface-1m: 1 million digital face images for face recognition. In 2023 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2023. 5, 8, 17 +[7] Mark Bun and Thomas Steinke. Concentrated differential privacy: Simplifications, extensions, and lower bounds. In Theory of Cryptography Conference, pages 635-658. Springer, 2016. 3, 13 +[8] Clément L Canonne, Gautam Kamath, and Thomas Steinke. The discrete gaussian for differential privacy. Advances in Neural Information Processing Systems, 33:15676-15688, 2020. 5 +[9] Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer. Membership inference attacks from first principles. In 2022 IEEE Symposium on Security and Privacy (SP), pages 1897-1914. IEEE, 2022. 1 +[10] Nicholas Carlini, Chang Liu, Ülfar Erlingsson, Jernej Kos, and Dawn Song. The secret sharer: Evaluating and testing unintended memorization in neural networks. In 28th USENIX Security Symposium (USENIX Security 19), pages 267-284, 2019. 1 +[11] Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21), pages 2633-2650, 2021. 1 +[12] Yannis Cattan, Christopher A Choquette-Choo, Nicolas Papernot, and Abhradeep Thakurta. Fine-tuning with differential privacy necessitates an additional hyperparameter search. arXiv preprint arXiv:2210.02156, 2022. 16 +[13] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning + +of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 13 +[14] Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. Fedavg with fine tuning: Local updates lead to representation learning. arXiv e-prints, pages arXiv-2205, 2022. 5 +[15] Liam Collins, Aryan Mokhtari, Sewoong Oh, and Sanjay Shakkottai. Maml and anil provably learn representations. arXiv preprint arXiv:2202.03483, 2022. 5 +[16] Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle. Unlocking high-accuracy differentially private image classification through scale. arXiv preprint arXiv:2204.13650, 2022. 1, 3, 4, 5 +[17] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4690-4699, 2019. 7, 13 +[18] Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin. Collecting telemetry data privately. Advances in Neural Information Processing Systems, 30, 2017. 1 +[19] Xin Dong, Sai Qian Zhang, Ang Li, and HT Kung. Spherefed: Hyperspherical federated learning. arXiv preprint arXiv:2207.09413, 2022. 13 +[20] Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, and Pasin Manurangsi. Connect the dots: Tighter discrete approximations of privacy loss distributions. arXiv preprint arXiv:2207.04380, 2022. 13, 14 +[21] DP Team. Google's differential privacy libraries., 2020. https://github.com/google/differential-privacy.14 +[22] Cynthia Dwork. Differential privacy in new settings. In Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms, pages 174-183. SIAM, 2010. 1, 13 +[23] Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference, pages 265-284. Springer, 2006. 1, 13 +[24] Cynthia Dwork, Aaron Roth, et al. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3-4):211-407, 2014. 14 +[25] Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, and Lijie Ren. Smoothly bounding user contributions in differential privacy. Advances in Neural Information Processing Systems, 33:13999-14010, 2020. 13 +[26] Jonathan Frankle, David J. Schwab, and Ari S. Morcos. Training batchnorm and only batchnorm: On the expressive power of random features in CNNs, 2021. 5, 16 +[27] Robin C Geyer, Tassilo Klein, and Moin Nabi. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557, 2017. 3, 13 +[28] Badih Ghazi, Ravi Kumar, and Pasin Manurangsi. User-level private learning via correlated sampling. arXiv preprint arXiv:2110.11208, 2021. 13 +[29] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, + +Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020. 13 +[30] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000-16009, 2022. 13 +[31] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5 +[32] Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. Federated visual classification with real-world data distribution. In European Conference on Computer Vision, pages 76-92. Springer, 2020. 5, 8 +[33] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pages 448-456. PMLR, 2015. 5 +[34] Prateek Jain, John Rush, Adam Smith, Shuang Song, and Abhradeep Guha Thakurta. Differentially private model personalization. Advances in Neural Information Processing Systems, 34:29723-29735, 2021. 4, 13 +[35] Sébastien Jean, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. On using very large target vocabulary for neural machine translation. arXiv preprint arXiv:1412.2007, 2014. 3, 4 +[36] Peter Kairouz, Brendan Mcmahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu. Practical and private (deep) learning without sampling or shuffling. In International Conference on Machine Learning (ICML), pages 5213-5225, 2021. 1, 2, 3, 5, 6, 13, 14, 15, 16 +[37] Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977, 2019. 13 +[38] Peter Kairouz, Sewoong Oh, and Pramod Viswanath. The composition theorem for differential privacy. In International conference on machine learning, pages 1376-1385. PMLR, 2015. 1 +[39] Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning, pages 5132-5143. PMLR, 2020. 4 +[40] Antti Koskela, Joonas Jälkö, Lukas Prediger, and Antti Honkela. Tight approximate differential privacy for discrete-valued mechanisms using fft, 2020. 13, 14 +[41] Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang. Fine-tuning can distort pretrained features and underperform out-of-distribution. arXiv preprint arXiv:2202.10054, 2022.7 +[42] Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, and Abhradeep Thakurta. Toward + +training at imagenet scale with differential privacy. arXiv preprint arXiv:2201.12328, 2022. 1, 4 +[43] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998. 18 +[44] Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, and Ananda Theertha Suresh. Learning with user-level privacy. Advances in Neural Information Processing Systems, 34:12466-12479, 2021. 13 +[45] Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto. Large language models can be strong differentially private learners. arXiv preprint arXiv:2110.05679, 2021. 1, 4 +[46] Yuhan Liu, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Michael Riley. Learning discrete distributions: user vs item-level privacy. Advances in Neural Information Processing Systems, 33:20965-20976, 2020. 13 +[47] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV), December 2015. 8 +[48] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In AISTATS, pages 1273-1282. PMLR, 2017. 3, 4 +[49] Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. Learning differentially private recurrent language models. In International Conference on Learning Representations (ICLR), 2018. 1, 2, 3, 5, 6, 13, 14 +[50] Brendan McMahan and Abhradeep Thakurta. Federated learning with formal differential privacy guarantees, 2022. 1, 6 +[51] H Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, and Peter Kairouz. A general approach to adding differential privacy to iterative training procedures. arXiv preprint arXiv:1812.06210, 2018. 4, 13, 14 +[52] Qiang Meng, Feng Zhou, Hainan Ren, Tianshu Feng, Guochao Liu, and Yuanqing Lin. Improving federated learning face recognition via privacy-agnostic clusters. arXiv preprint arXiv:2201.12467, 2022. 13 +[53] Ilya Mironov. Rényi differential privacy. In 2017 IEEE 30th computer security foundations symposium (CSF), pages 263-275. IEEE, 2017. 5, 13 +[54] Kevin Musgrave, Serge Belongie, and Ser-Nam Lim. A metric learning reality check. In European Conference on Computer Vision, pages 681-699. Springer, 2020. 13 +[55] Nicolas Papernot and Thomas Steinke. Hyperparameter tuning with renyi differential privacy. arXiv preprint arXiv:2110.03620, 2021. 5 +[56] Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Thakurta. How to dp-fy ml: A practical guide to machine learning with differential privacy. arXiv preprint arXiv:2303.00654, 2023. 5, 15 + +[57] Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H Brendan McMahan, and Françoise Beaufays. Training production language models without memorizing user data. arXiv preprint arXiv:2009.10031, 2020. 1, 2, 6, 13 +[58] Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečny, Sanjiv Kumar, and Hugh Brendan McMahan. Adaptive federated optimization. In International Conference on Learning Representations, 2021. 3 +[59] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252, 2015. 5 +[60] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhoginov, and Liang-Chieh Chen. *Mobilenetv2: Inverted residuals and linear bottlenecks*. In *Proceedings of the IEEE conference on computer vision and pattern recognition*, pages 4510–4520, 2018. 5 +[61] Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 815-823, 2015. 1, 3, 4, 13 +[62] Zebang Shen, Jiayuan Ye, Anmin Kang, Hamed Hassani, and Reza Shokri. Share your representation only: Guaranteed improvement of the privacy-utility tradeoff in federated learning. In The Eleventh International Conference on Learning Representations, 2023. 13 +[63] Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP), pages 3-18. IEEE, 2017. 1 +[64] Hakim Sidahmed, Zheng Xu, Ankush Garg, Yuan Cao, and Mingqing Chen. Efficient and private federated learning with partially trainable networks. arXiv preprint arXiv:2110.03450, 2021. 5, 16 +[65] Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, and Sushant Prakash. Federated reconstruction: Partially local federated learning. Advances in Neural Information Processing Systems (NeurIPS), 2021. 4, 7, 13 +[66] Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, and Lior Wolf. Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1701-1708, 2014. 3, 4, 13 +[67] TFF Authors. TensorFlow Federated EMNIST dataset, 2022. https://www.tensorflow.org/federated/api_docs/python/tff/simulation/datasets/ emnist.8,15,17 +[68] TFF Authors. TensorFlow Federated Google Landmark v2 dataset, 2022. https://www.tensorflow.org/federated/api_docs/python/tff/simulation/datasets/gldv2.8,15,17 +[69] TFF Authors. TensorFlow Federated iNaturalist dataset, 2022. https://www.tensorflow.com + +org / federated / api __docs / python / tff / simulation/datasets/inaturalist. 8, 15, 17 +[70] US Census Bureau. Disclosure avoidance for the 2020 census: An introduction, 2021. 1, 3, 6 +[71] Salil Vadhan. The complexity of differential privacy. In *Tutorialis on the Foundations of Cryptography*, pages 347–450. Springer, 2017. 14 +[72] Sagar M Waghmare, Hang Qi, Huizhong Chen, Mikhail Sirotenko, and Tomer Meron. Efficient image representation learning with federated sampled softmax. arXiv preprint arXiv:2203.04888, 2022. 4, 13 +[73] Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, et al. A field guide to federated optimization. arXiv:2107.06917, 2021. 3, 4, 5, 13, 14 +[74] Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, and Tong Zhang. On the unreasonable effectiveness of federated averaging with heterogeneous data. arXiv preprint arXiv:2206.04723, 2022. 3 +[75] Kilian Q Weinberger and Lawrence K Saul. Distance metric learning for large margin nearest neighbor classification. Journal of machine learning research, 10(2), 2009. 1 +[76] Yandong Wen, Weiyang Liu, Adrian Weller, Bhiksha Raj, and Rita Singh. Sphereface2: Binary classification is all you need for deep face recognition. arXiv preprint arXiv:2108.01513, 2021. 13 +[77] Tobias Weyand, Andre Araujo, Bingyi Cao, and Jack Sim. Google landmarks dataset v2-a large-scale benchmark for instance-level recognition and retrieval. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2575-2584, 2020. 1, 8, 13 +[78] Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins, Brendan McMahan, Ohad Shamir, and Nathan Srebro. Is local sgd better than minibatch sgd? In International Conference on Machine Learning, pages 10334-10343. PMLR, 2020. 3 +[79] Lin Wu, Chunhua Shen, and Anton van den Hengel. Personnet: Person re-identification with deep convolutional neural networks. arXiv preprint arXiv:1601.07255, 2016. 13 +[80] Yuxin Wu and Kaiming He. Group normalization. In Proceedings of the European conference on computer vision (ECCV), pages 3-19, 2018. 5 +[81] Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al. Differentially private fine-tuning of language models. arXiv preprint arXiv:2110.06500, 2021. 1, 4, 5 +[82] Felix Yu, Ankit Singh Rawat, Aditya Menon, and Sanjiv Kumar. Federated learning with only positive labels. In International Conference on Machine Learning, pages 10946-10956. PMLR, 2020. 4 +[83] Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny. Barlow twins: Self-supervised learning via redundancy reduction. In International Conference on Machine Learning, pages 12310-12320. PMLR, 2021. 13 + +[84] Chiyuan Zhang, Samy Bengio, and Yoram Singer. 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However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class space. To achieve user-level DP for large image-to-embedding feature extractors, we propose DP-FedEmb, a variant of federated learning algorithms with per-user sensitivity control and noise addition, to train from user-partitioned data centralized in the datacenter. DP-FedEmb combines virtual clients, partial aggregation, private local fine-tuning, and public pretraining to achieve strong privacy utility trade-offs. We apply DP-FedEmb to train image embedding models for faces, landmarks and natural species, and demonstrate its superior utility under same privacy budget on benchmark datasets DigiFace, EMNIST, GLD and iNaturalist. We further illustrate it is possible to achieve strong user-level DP guarantees of " + }, + { + "bbox": [ + 46, + 236, + 290, + 475 + ], + "type": "inline_equation", + "content": "\\epsilon < 2" + }, + { + "bbox": [ + 46, + 236, + 290, + 475 + ], + "type": "text", + "content": " while controlling the utility drop within " + }, + { + "bbox": [ + 46, + 236, + 290, + 475 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 46, + 236, + 290, + 475 + ], + "type": "text", + "content": ", when millions of users can participate in training." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 47, + 499, + 128, + 511 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 499, + 128, + 511 + ], + "spans": [ + { + "bbox": [ + 47, + 499, + 128, + 511 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 46, + 520, + 287, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 520, + 287, + 685 + ], + "spans": [ + { + "bbox": [ + 46, + 520, + 287, + 685 + ], + "type": "text", + "content": "Representation learning, by training deep neural networks as feature extractors to generate compact embedding vectors from images, is a fundamental component in computer vision. Metric learning, a kind of representation learning using supervised data, has been widely applied to image recognition, clustering, and retrieval [61, 75, 77]. Machine learning models have the capacity to memorize training data [10, 11], leading to privacy risks when the models are deployed. Privacy risk can also be audited by membership inference attacks [9, 63], i.e. detecting whether certain data was used to train a model and potentially exposing users' usage behaviors. Defending against such risks is a critical responsibility when training on privacy-sensitive data." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 211, + 547, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 211, + 547, + 462 + ], + "spans": [ + { + "bbox": [ + 306, + 211, + 547, + 462 + ], + "type": "text", + "content": "Differential Privacy (DP) [23] is an extensively used quantifiable measurement of privacy risk, now generally accepted as a standard notion of privacy in both industry and government [5, 18, 50, 70]. Applied to machine learning, DP requires a training procedure with explicit randomness, and guarantees that the distribution over output models is quantifiably similar given a certain scope of change to the training dataset. A DP guarantee with respect to the change of a single arbitrary training example is known as example-level DP, which provides plausible deniability (in the binary hypothesis testing sense of [38]) that any single example (e.g., image) occurred in the training dataset. If we instead consider how the distribution of output models changes if the data (including even the number of examples) from any single user change arbitrarily, we have user-level DP [22]. This ensures model training is quantifiably insensitive to all of the data from any one user, and hence it is impossible to tell if a user has participated in training with high confidence. This guarantee can be exponentially stronger than example-level DP if one user may contribute many examples to training." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 468, + 547, + 612 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 468, + 547, + 612 + ], + "spans": [ + { + "bbox": [ + 305, + 468, + 547, + 612 + ], + "type": "text", + "content": "Recently, DP-SGD [1] (essentially, SGD with the additional steps of clipping each individual gradient to have a maximum norm, and adding correspondingly calibrated noise) has been used to achieve example-level DP for relatively large models in language modeling and image classification tasks [4, 16, 42, 45, 81], often utilizing techniques like large batch training and pretraining on public data. DP-SGD can be modified to guarantee user-level DP, which is often combined with federated learning algorithms and called DP-FedAvg [49]. User-level DP has only been studied for small on-device models that have less than 10 million parameters [36, 49, 57]." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 305, + 617, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 617, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 305, + 617, + 547, + 715 + ], + "type": "text", + "content": "We consider user-level DP for relatively large models in representation learning with supervised data. In our setting, similar to federated learning (FL), the data are user-partitioned; but in contrast to decentralized FL, we are primarily motivated by centralized data that benefit from access to richer computation resources and the ability to form virtual clients at random. Throughout this work, we use user as the basic unit of data partitioning and the granular-" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 693, + 288, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 693, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 693, + 288, + 713 + ], + "type": "text", + "content": "*The first two authors contributed equally. Correspondence to Zheng Xu xuzheng@google.com" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "7969" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 88, + 71, + 509, + 257 + ], + "blocks": [ + { + "bbox": [ + 88, + 71, + 509, + 257 + ], + "lines": [ + { + "bbox": [ + 88, + 71, + 509, + 257 + ], + "spans": [ + { + "bbox": [ + 88, + 71, + 509, + 257 + ], + "type": "image", + "image_path": "a94fb1d2cc42efdc91b99a8c9bcbef77a57e3da3b08aec6cf3cc3263dd0ceeb0.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 262, + 547, + 288 + ], + "lines": [ + { + "bbox": [ + 46, + 262, + 547, + 288 + ], + "spans": [ + { + "bbox": [ + 46, + 262, + 547, + 288 + ], + "type": "text", + "content": "Figure 1. DP-FedEmb combines virtual clients, local fine-tuning, partial aggregation, and public pretraining to achieve strong privacy utility trade-offs. Colors indicate different users and the simplified case of a single class per user, coloring the softmax head accordingly." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 298, + 288, + 334 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 298, + 288, + 334 + ], + "spans": [ + { + "bbox": [ + 46, + 298, + 288, + 334 + ], + "type": "text", + "content": "ity for privacy; a user owns their (image) data, and class, identity and label are used interchangeably for the supervised information." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 338, + 289, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 338, + 289, + 613 + ], + "spans": [ + { + "bbox": [ + 46, + 338, + 289, + 613 + ], + "type": "text", + "content": "Though typically each user only contributes images for a small number of classes, the combined class space from the union of all users can be very large, which proves challenging for existing DP algorithms. When the model size is fixed independent of the number of users, and at a relatively small scale with a few million parameters, previous methods can only achieve strong user-level DP when millions of users are available [36, 49, 57]. In contrast, when considering learning embedding models for applications like facial images, the number of classes (and hence, the total model size) can grow linearly with the number of users, and so simply scaling up to larger datasets with more users no longer ensures that good privacy-utility trade-offs can be achieved. For example, in a standard multi-class training paradigm with 128-dimensional embedding vectors, with one million users we expect the final dense layer for prediction alone to have over 128 million trainable parameters. Further, the fact that most users will only have examples from a small number of classes implies that gradients are approximately sparse, whereas DP-SGD requires the addition of dense noise to the full gradient, leading to a poor signal-to-noise ratio in the updates. Hence, existing methods can easily fail on the problems we consider." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "type": "text", + "content": "We propose DP-FedEmb to train embedding models with user-level differential privacy; Fig. 1 provides a high-level overview. DP-FedEmb combines public pretraining, virtual clients, local fine-tuning, and partial aggregation to achieve strong privacy-utility trade-offs. The key to the approach is partitioning the model into a backbone network that generates embeddings, and a classification softmax head specific to the classes in the training data. In" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 298, + 547, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 298, + 547, + 574 + ], + "spans": [ + { + "bbox": [ + 304, + 298, + 547, + 574 + ], + "type": "text", + "content": "each training round, users are grouped into virtual clients and initialized from the global backbone. A local randomly-initialized softmax head layer is added for the limited number of classes on the virtual client, and the complete local model is fine-tuned in order to produce an update to the backbone. The local head parameters are not included in the private aggregation and hence require no noise addition. This is in contrast to existing methods like DP-FedAvg/DP-SGD, which would add noise to all parameters including the softmax head. The backbone updates are clipped to a maximum L2 norm, aggregated across virtual clients, and combined with appropriate DP noise. At this point, the noised update is the output of a DP mechanism and satisfies the corresponding DP guarantee. This update is then applied to the global backbone, which inherits the DP guarantee, and passed to the next round of training. DP-FedEmb significantly improves the scalability of DP training for embedding models, as only the parameters of the backbone network are privatized and released, and the size of this portion of the model does not grow with the number of users. Pretraining the backbone network on public data to learn general visual representations before applying DP-FedEmb for more privacy sensitive tasks further improves performance." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "content": "We demonstrate the superior performance of DP-FedEmb for embedding models by experiments on datasets with moderate size of users and classes (DigiFace of 98.96K or 9047 users/identities, Google Landmarks Dataset of 1262 users and 2028 classes, and iNaturalist of 9275 users and 1203 classes). We also show relatively strong privacy guarantees of single digit " + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "content": " can be achieved while maintaining strong utility if millions users can participate in training. To our knowledge, this is the first report of training a commonly used large vision model, ResNet-50, with non-negligible noise for user-level DP." + } + ] + } + ], + "index": 6 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 758 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 758 + ], + "type": "text", + "content": "7970" + } + ] + } + ], + "index": 7 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 71, + 212, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 71, + 212, + 85 + ], + "spans": [ + { + "bbox": [ + 47, + 71, + 212, + 85 + ], + "type": "text", + "content": "2. Learning Embedding Models" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 91, + 284, + 103 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 91, + 284, + 103 + ], + "spans": [ + { + "bbox": [ + 47, + 91, + 284, + 103 + ], + "type": "text", + "content": "2.1. Problem formulation and centralized training" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "spans": [ + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "text", + "content": "We learn a backbone network parameterized by " + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "text", + "content": ", that outputs an embedding vector " + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "inline_equation", + "content": "z = f(\\theta, x)" + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "text", + "content": " for an input image " + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "text", + "content": ". The backbone network, " + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "text", + "content": ", is trained on paired examples of image " + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "text", + "content": " and class " + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "text", + "content": ". The training dataset is naturally partitioned by " + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "text", + "content": " users, i.e., " + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "inline_equation", + "content": "\\mathcal{D} = \\bigcup_{i=1}^{M} \\mathcal{D}_i" + }, + { + "bbox": [ + 46, + 109, + 286, + 169 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "spans": [ + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "text", + "content": "We adopt the popular multi-class training framework for embedding models, where a proxy weight vector " + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "inline_equation", + "content": "w_{j}" + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "text", + "content": " is learnt for each class " + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "text", + "content": ". We use " + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "text", + "content": " to denote the union of proxy weight vectors " + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "inline_equation", + "content": "\\{w_{j}\\}" + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "text", + "content": ", which is called the head of the network. Given a training image-class pair " + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "inline_equation", + "content": "(x,y)" + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "text", + "content": ", logits are computed by taking the inner product between the embedding vector " + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "inline_equation", + "content": "f(\\theta ,x)" + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "text", + "content": " and the proxy weight vectors in the network head " + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "text", + "content": ". This is effectively passing " + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "inline_equation", + "content": "f(\\theta ,x)" + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "text", + "content": " through a dense network layer parameterized by " + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "text", + "content": " without the bias terms. With a supervised training loss " + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "inline_equation", + "content": "\\ell" + }, + { + "bbox": [ + 46, + 169, + 286, + 300 + ], + "type": "text", + "content": ", such as the cross entropy loss, the following objective is optimized" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 97, + 305, + 287, + 332 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 305, + 287, + 332 + ], + "spans": [ + { + "bbox": [ + 97, + 305, + 287, + 332 + ], + "type": "interline_equation", + "content": "\\min _ {\\theta , \\omega} \\sum_ {(x, y) \\in \\mathcal {D}} \\ell (\\langle \\omega , f (\\theta , x) \\rangle , y), \\tag {1}", + "image_path": "f7ef9bcbf9780b1f53e163717b95505f8f6d1723d921b6e02175bbe01d6241bb.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "spans": [ + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "text", + "content": "where we overload the inner product notation, i.e., " + }, + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "inline_equation", + "content": "\\langle \\omega ,f(\\theta ,x)\\rangle" + }, + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "text", + "content": ", to denote a set of inner products with each element in " + }, + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "text", + "content": ". Typically, variants of the gradient descent method are used to solve the optimization in (1). In each iteration, the average gradient is computed on a sampled minibatch of data " + }, + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "inline_equation", + "content": "B\\subset \\mathcal{D}" + }, + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "text", + "content": ", and is then used to update the model parameters " + }, + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "text", + "content": ". Furthermore, when the output space, i.e., the number of classes, is very large, sampled softmax is often applied [35], where only a subset of proxy weights sampled from " + }, + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 46, + 336, + 286, + 456 + ], + "type": "text", + "content": " are used in each training iteration." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 462, + 211, + 475 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 462, + 211, + 475 + ], + "spans": [ + { + "bbox": [ + 47, + 462, + 211, + 475 + ], + "type": "text", + "content": "2.2. User-level DP and DP-FedAvg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 480, + 286, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 480, + 286, + 574 + ], + "spans": [ + { + "bbox": [ + 46, + 480, + 286, + 574 + ], + "type": "text", + "content": "To achieve user-level DP, we control the sensitivity of each user and add corresponding noise for anonymization. To effectively control the sensitivity, it is important to understand and account for the contributions of each user in the model updates; hence it is convenient to consider the data at a granularity of users instead of individual samples. Grouping together each user's data, the objective (1) can be rewritten as" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 88, + 578, + 286, + 613 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 88, + 578, + 286, + 613 + ], + "spans": [ + { + "bbox": [ + 88, + 578, + 286, + 613 + ], + "type": "interline_equation", + "content": "\\min _ {\\theta , \\omega} \\sum_ {i = 1} ^ {M} \\sum_ {(x, y) \\in \\mathcal {D} _ {i}} \\ell (\\langle \\omega , f (\\theta , x) \\rangle , y). \\tag {2}", + "image_path": "af1327539fdd3803e53b28e050502e764e5911ec27fcf270af73a93b7a51cf2e.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 617, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 286, + 713 + ], + "type": "text", + "content": "The above objective of two level sum is often found in federated learning [73], which can be optimized by the (generalized) FedAvg algorithm [48, 58]. In generalized FedAvg, each round " + }, + { + "bbox": [ + 46, + 617, + 286, + 713 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 617, + 286, + 713 + ], + "type": "text", + "content": " starts with the server broadcasting " + }, + { + "bbox": [ + 46, + 617, + 286, + 713 + ], + "type": "inline_equation", + "content": "\\theta^{(t)},\\omega^{(t)}" + }, + { + "bbox": [ + 46, + 617, + 286, + 713 + ], + "type": "text", + "content": " to a subset of clients. Each client " + }, + { + "bbox": [ + 46, + 617, + 286, + 713 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 617, + 286, + 713 + ], + "type": "text", + "content": " will then update the local model parameters by CLIENTOPT with private data " + }, + { + "bbox": [ + 46, + 617, + 286, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_i" + }, + { + "bbox": [ + 46, + 617, + 286, + 713 + ], + "type": "text", + "content": ", and send back the updates for model parameters " + }, + { + "bbox": [ + 46, + 617, + 286, + 713 + ], + "type": "inline_equation", + "content": "\\Delta_i(\\theta^{(t)}),\\Delta_i(\\omega^{(t)})" + }, + { + "bbox": [ + 46, + 617, + 286, + 713 + ], + "type": "text", + "content": ". The model deltas from sampled" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 72, + 545, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 95 + ], + "type": "text", + "content": "clients are then aggregated and used by SERVEROPT to get " + }, + { + "bbox": [ + 305, + 72, + 545, + 95 + ], + "type": "inline_equation", + "content": "\\theta^{(t + 1)},\\omega^{(t + 1)}" + }, + { + "bbox": [ + 305, + 72, + 545, + 95 + ], + "type": "text", + "content": " for the next round." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 97, + 545, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 97, + 545, + 289 + ], + "spans": [ + { + "bbox": [ + 304, + 97, + 545, + 289 + ], + "type": "text", + "content": "The generalized FedAvg algorithm can be extended for user-level DP by clipping the model deltas and adding noise proportional to the sensitivity [27,49]. We can use either independent Gaussian noise [49], or correlated noise that can achieve comparable privacy-utility trade-off without relying on the assumption of sampling [36]. The two variants are effectively applying DP-SGD [1] or DP-FTRL [36] as SERVEROPT in the generalized FedAvg framework. Unlike the cross-device FL setting where sampling is extremely hard, it is possible to control user sampling in the datacenter and use DP-SGD. But DP-FTRL provides the possibility of handling the online setting where the user data are streamed instead of collected, and can be accounted for zCDP [7] reported by US census bureau [70]. A complete description of DP-FedAvg for training a backbone network to generate image embeddings is in Alg. 2 in Appendix B" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 289, + 545, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 289, + 545, + 517 + ], + "spans": [ + { + "bbox": [ + 304, + 289, + 545, + 517 + ], + "type": "text", + "content": "In addition to the flexibility of generalized FedAvg for user-level DP, there are a few side effects of FedAvg that make it particularly effective for differentially private training. The model deltas are computed based on data for each user before clipping in DP, which can potentially reduce the bias introduced by clipping. As [16] suggested that averaging gradients from augmented data before clipping can improve training for example-level DP, model deltas from user data for user-level DP can be considered a natural extension to improve the bias-variance trade-off. The communication efficiency of FedAvg that leads to infrequent aggregation and model release is also desirable for DP training. The local model updates by private data on clients introduce no additional privacy cost, and only communication rounds between clients and server have to be accounted for DP. Though the theoretical advantages of FedAvg are only proved under certain assumptions [74, 78], FedAvg with local updates can achieve communication efficiency and fast convergence in various practical applications [73]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 525, + 471, + 539 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 525, + 471, + 539 + ], + "spans": [ + { + "bbox": [ + 306, + 525, + 471, + 539 + ], + "type": "text", + "content": "2.3. Proposed DP-FedEmb method" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "spans": [ + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "text", + "content": "While generalized DP-FedAvg can be applied to train a backbone network " + }, + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "text", + "content": " to generate embedding " + }, + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "inline_equation", + "content": "f(\\theta, x)" + }, + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "text", + "content": " from image " + }, + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 304, + 544, + 545, + 628 + ], + "type": "text", + "content": ", there are challenges that significantly affect the efficiency and feasibility of the method. We propose DP-FedEmb with a few key features: virtual clients, partial aggregation, local fine-tuning, public pretraining, and parameter freezing. Details of DP-FedEmb are provided in Alg. 1." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": "Virtual clients. Data heterogeneity is one of the key problems in federated optimization [73]. When we train embedding models in the multi-class framework, the class space can be very large and each user may only observe a limited number of classes. In the extreme case, when training embedding models on facial images [61, 66], each user may only have images for their own identity. This signif" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "7971" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "code", + "bbox": [ + 48, + 99, + 270, + 460 + ], + "blocks": [ + { + "bbox": [ + 48, + 74, + 261, + 98 + ], + "lines": [ + { + "bbox": [ + 48, + 74, + 261, + 98 + ], + "spans": [ + { + "bbox": [ + 48, + 74, + 261, + 98 + ], + "type": "text", + "content": "Algorithm 1: DP-FedEmb: learning embedding model " + }, + { + "bbox": [ + 48, + 74, + 261, + 98 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 48, + 74, + 261, + 98 + ], + "type": "text", + "content": " with user-level DP" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "code_caption" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "lines": [ + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "spans": [ + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": "Input:SERVEROPT with learning rate " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " .. CLIENTOPT with learning rate " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\beta_{1}" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\beta_{2}" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " clip norm " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " and noise multiplier " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " (optional) pretrained model " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\theta^{(0)}" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " \nfor round " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "t = 0,1,\\ldots ,T - 1" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " do \nSample a subset of users " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\mathcal{U}^{(t)}" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " \nPartition users " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\mathcal{U}^{(t)}" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " to virtual clients " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "S^{(t)}" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " \nfor each virtual client " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "V\\in S^{(t)}" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " in parallel do Initialize backbone " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\theta_V^{(t,0)} = \\theta^{\\mathrm{(t)}}" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " \nRandomly initialize head " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\omega_{V}^{(t,0)}" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " \nfor " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "k = 0,\\dots ,K - 1" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " do Sample minibatch " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "B\\subset \\bigcup_{i\\in V}\\mathcal{D}_i" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " Compute gradients " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\nabla_{\\theta_V}\\ell_B,\\nabla_{\\omega_V}\\ell_B" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\ell_B = \\mathbb{E}_{(x,y)\\in B}\\ell (\\langle \\omega_V,f(\\theta_V,x)\\rangle ,y)" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " Update " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\theta_V^{(t,k + 1)}" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " by CLIENTOPT, " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\theta_V^{(t,k)}" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\nabla_{\\theta_V}\\ell_B,\\beta_1" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " Update " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\omega_{V}^{(t,k + 1)}" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " by CLIENTOPT, " + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\omega_{V}^{(t,k)}" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\nabla_{\\omega_V}\\ell_B,\\beta_2" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " \nend \nCompute clipped model update \n" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\Delta_V^{(t)} = \\mathrm{Clip}(\\theta_V^{(t,K)} - \\theta_V^{(t,0)},\\gamma)" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " \nend \nAggregate model updates \n" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\Delta^{(t)} = \\mathrm{AddNoise}\\bigl (\\sum_{V\\in S^{(t)}}\\Delta_{V}^{(t)},\\sigma \\gamma \\bigr) / |S^{(t)}|" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "text", + "content": " \nUpdate global backbone parameters \n" + }, + { + "bbox": [ + 48, + 99, + 270, + 460 + ], + "type": "inline_equation", + "content": "\\theta^{(t + 1)} = \\mathrm{SERVEROPT}(\\theta^{(t)},\\Delta^{(t)},\\alpha)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "code_body" + } + ], + "index": 1, + "sub_type": "algorithm" + }, + { + "bbox": [ + 46, + 486, + 287, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 486, + 287, + 581 + ], + "spans": [ + { + "bbox": [ + 46, + 486, + 287, + 581 + ], + "type": "text", + "content": "icently limits the advantage of local updates due to client drift [39], and even with specialized regularization like [82], FedSGD [48] with frequent aggregation and model release has to be used instead of FedAvg. It is challenging to use some specialized techniques for handling data heterogeneity [73] in DP training. Instead, we propose a simple yet effective approach: randomly groups the data of sampled users into virtual clients." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "content": "Unlike the cross-device FL setting where the on-device data of users cannot be directly communicated, virtual clients are feasible for user data in the datacenter. It is important to guarantee that a user will not be included in two virtual clients in a single round for user-level DP, analogous to microbatches for DP-SGD and example-level DP [1,51]. When the grouping of users for virtual clients is fixed in advance across rounds, the granularity of the DP definition can slightly change: the adjacent dataset for DP is based on virtual clients (a group of users) instead of a single user, which has conceptually stronger privacy guarantees. However," + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 179 + ], + "type": "text", + "content": "when virtual clients are randomly regrouped across rounds as in Alg. 1, we can only show user-level DP as discussed in Appendix C. Virtual clients also control the interpolation between federated training and centralized training: when all users are grouped into a single virtual client, federated training is equivalent to centralized training, which removes heterogeneity but is challenging for DP mechanism. Virtual clients are used for both baseline DP-FedAvg and the proposed DP-FedEmb method." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "spans": [ + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "text", + "content": "Partial aggregation and local fine-tuning. Another challenge is the number of parameters in DP training. A common backbone " + }, + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "text", + "content": " of ResNet-50 for 128 dimensional embedding vectors has 23.77 million parameters. However, the parameter size of head " + }, + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "text", + "content": " can linearly grow with the number of classes. Taking FaceNet [61, 66] as an example again, " + }, + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "text", + "content": " can easily grow to 1280 million for 10 million identities in real-world applications. Sampled softmax [35, 72] can be applied to improve training efficiency. However, as both backbone " + }, + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "text", + "content": " and head " + }, + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "text", + "content": " are shared among users and need to be privatized by adding noise during training, the combined parameter size of " + }, + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "inline_equation", + "content": "(\\theta, \\omega)" + }, + { + "bbox": [ + 304, + 182, + 546, + 348 + ], + "type": "text", + "content": " will significantly affect the privacy utility trade-off, which cannot be mitigated by sampled softmax." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "spans": [ + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "text", + "content": "In DP-FedEmb, inspired by federated reconstruction [65] and DP personalization [34], we only aggregate and privatize the backbone network " + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "text", + "content": ", which is used in inference and has fixed parameter size that does not grow with classes. A local head " + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "inline_equation", + "content": "\\omega_{V}" + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "text", + "content": " is randomly initialized and updated on each virtual client " + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "text", + "content": ". A fine-tuning approach is adopted for local updates, where different learning rates " + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "inline_equation", + "content": "\\beta_{1}, \\beta_{2}" + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "text", + "content": " are used for the backbone " + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "inline_equation", + "content": "\\theta_{V}" + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "text", + "content": " and head " + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "inline_equation", + "content": "\\omega_{V}" + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "text", + "content": ", respectively. When combined with virtual clients, the partial aggregation and local fine-tuning approach can be interpreted in various ways: each virtual client is performing transfer learning given a shared backbone network for representation learning; the data of each class are their own positive samples as well as negative samples for other classes on the same virtual client; the size of local head " + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "inline_equation", + "content": "\\omega_{V}" + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "text", + "content": " is also significantly smaller than " + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 304, + 350, + 546, + 553 + ], + "type": "text", + "content": " for all classes, which is effectively a user-based sampling for softmax." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 555, + 545, + 698 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 555, + 545, + 698 + ], + "spans": [ + { + "bbox": [ + 304, + 555, + 545, + 698 + ], + "type": "text", + "content": "Public pretraining. The parameter size of the backbone to be privatized can still be large after applying partial aggregation and local fine-tuning with virtual clients, e.g., 23.77 million for ResNet-50. Inspired by recent research on applying DP-SGD for example-level DP on large language modeling [45, 81] and image classification [16, 42], we use a model pretrained on public images to initialize the DP training of the backbone network. There is a relatively clear distinction between the public and private domains for our task: we use public images collected from open webpages for pretraining, and then privately train on users' data collected in a datacenter." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 318, + 701, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 701, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 318, + 701, + 545, + 713 + ], + "type": "text", + "content": "Parameter freezing. Neural networks are known to be" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7972" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "content": "overparameterized, and not all weights are equally important [26, 84]. Freezing some parameters to be non-trainable has been shown to be effective when the privacy budget is small [64], especially when combined with public pretraining for large models [16, 81]. For backbone convolutional neural networks with normalization layers, we experiment with training parameters with all normalization layers, and some of the convolutional kernels. However, freezing is found to be less efficient in our setting that performs representation learning, instead of classification, for a moderate size model in the high-utility-moderate-noise regime." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 204, + 289, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 204, + 289, + 373 + ], + "spans": [ + { + "bbox": [ + 46, + 204, + 289, + 373 + ], + "type": "text", + "content": "DP mechanism and hyperparameters. Similar to generalized DP-FedAvg, we perform clipping for model deltas and add noise for aggregated updates. The clip norm " + }, + { + "bbox": [ + 46, + 204, + 289, + 373 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 46, + 204, + 289, + 373 + ], + "type": "text", + "content": " is estimated by adaptive clipping [3] in the parameter tuning stage. For DP-FedEmb, we perform extensive studies on several configurations in Sec. 3. Differentially private hyperparameter tuning [55] is a topic out of the scope of this paper, and automating hyperparameter tuning is an important future work. Either independent Gaussian noise like DP-SGD [49] or tree-based correlated noise like DP-FTRL [36] can be added. Under the same noise multiplier, DP-FedEmb will achieve the same privacy bound as DP-FedAvg with virtual clients, while utility can be improved for training a backbone network with a large head." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 382, + 128, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 382, + 128, + 396 + ], + "spans": [ + { + "bbox": [ + 47, + 382, + 128, + 396 + ], + "type": "text", + "content": "3. Experiments" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "spans": [ + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "text", + "content": "We conduct experiments to train image-to-embedding backbone networks with user-level DP. We use the DigiFace dataset [6] of synthetic faces based on ethical and responsible development considerations, and verified that the conclusions on DigiFace are very similar to conclusions generated from experiments on natural facial images. We randomly split the DigiFace dataset of " + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "inline_equation", + "content": "110K" + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "text", + "content": " identities and " + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "inline_equation", + "content": "1.22M" + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "text", + "content": " into subsets of " + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "inline_equation", + "content": "98.96K" + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "text", + "content": " identities with " + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "inline_equation", + "content": "1.10M" + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "text", + "content": " images for training, 5443 identities with " + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "inline_equation", + "content": "58.24K" + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "text", + "content": " images for validation, and 5598 identities with " + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "inline_equation", + "content": "60.82K" + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "text", + "content": " images for testing. We extensively use a smaller training set, DigiFace10K, which contains the 9047 training identities of 72 images sampled from the DigiFace training set. We also run experiments on public datasets of natural images: EMNIST, Google Landmarks Dataset (GLD) and iNaturalist (iNat) dataset. These datasets are summarized in Tab. 3." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 594, + 289, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 289, + 677 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 289, + 677 + ], + "type": "text", + "content": "In our setting, each user holds only the images of their own identities, i.e., user-level DP is also identity-level DP. We use ResNet-50 [31] and MobileNetV2 [32, 60, 73] as backbone networks, replace batch normalization [33] with group normalization [80], and use a multi-class framework with a large softmax head to train the backbone. The dimension of the embeddings are 128 for all experiments." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 677, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 289, + 715 + ], + "type": "text", + "content": "We evaluate the performance of the backbone network based on predicting identity matches from the distance between two image embeddings. By varying a threshold on" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "type": "text", + "content": "the pairwise similarity, a recall versus false accept rate (FAR) curve on the test data can be generated for a trained model. A scalar value of recall@FAR=1e-3 is often reported. The privacy guarantees are computed by either using Renyi differential privacy (RDP) [53] and converting to " + }, + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "type": "inline_equation", + "content": "(\\epsilon, \\delta)" + }, + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "type": "text", + "content": "-DP by [8], or DP-FTRL accounting without restart [36]. More discussion on privacy accounting can be found in Appendix C. We aim for single-digit " + }, + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "type": "text", + "content": " when " + }, + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "type": "text", + "content": " is small, and sometimes relax to " + }, + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "type": "inline_equation", + "content": "\\epsilon \\sim 20" + }, + { + "bbox": [ + 304, + 72, + 547, + 192 + ], + "type": "text", + "content": " as we use the stronger substitute-one DP definition [56]." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 193, + 547, + 421 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 193, + 547, + 421 + ], + "spans": [ + { + "bbox": [ + 304, + 193, + 547, + 421 + ], + "type": "text", + "content": "We compare the proposed DP-FedEmb with non-private oracle performance of centralized training, and baseline methods DP-FedAvg. We tune the learning rate with learning rate scheduling for standard centralized training. The centralized baseline is provided as an oracle for non-private training performance. We exclude tricks like data augmentation for either centralized or federated training as the goal is not achieving state-of-the-art performance. Virtual clients are used to improve DP-FedAvg performance, and the same tuning strategy is applied for DP-FedEmb and DP-FedAvg. In most of the experiments, unless otherwise specified, we fix the hyperparameters in the federated setting and only tune the learning rates; the backbone networks are pretrained on classifying the 1000 classes of ImageNet [59]; both CLIENTOPT and SERVEROPT are SGD optimizers with momentum 0.9; and more details are provided in Sec. 3.3. Code is released at https://github.com/google-research/federated/tree/master/dpvisual_embedding." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 430, + 504, + 443 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 430, + 504, + 443 + ], + "spans": [ + { + "bbox": [ + 306, + 430, + 504, + 443 + ], + "type": "text", + "content": "3.1. Privacy-utility-computation trade-off" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 449, + 547, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 449, + 547, + 665 + ], + "spans": [ + { + "bbox": [ + 304, + 449, + 547, + 665 + ], + "type": "text", + "content": "We study the privacy-utility-computation trade-offs of training ResNet-50 on DigiFace10K in Fig. 2 and Fig. 6. Figure 2a shows the privacy-utility trade-off. Without adding noise, the federated algorithms (DP-)FedAvg and (DP-)FedEmb can achieve even better results than the non-private centralized baseline, which is consistent with recent empirical and theoretical justifications that FedAvg is more accurate when learning representations [14, 15]. When the same noise multiplier is used, i.e., under same privacy budget, DP-FedEmb outperforms DP-FedAvg; and the margin increases when increasing the noise. We can observe the advantage of DP-FedEmb over DP-FedAvg even if we only have a small number of identities in DigiFace: the size of the head is " + }, + { + "bbox": [ + 304, + 449, + 547, + 665 + ], + "type": "inline_equation", + "content": "9047 \\times 128 \\sim 1.16M" + }, + { + "bbox": [ + 304, + 449, + 547, + 665 + ], + "type": "text", + "content": ", only " + }, + { + "bbox": [ + 304, + 449, + 547, + 665 + ], + "type": "inline_equation", + "content": "4.6\\%" + }, + { + "bbox": [ + 304, + 449, + 547, + 665 + ], + "type": "text", + "content": " of the backbone ResNet-50 with " + }, + { + "bbox": [ + 304, + 449, + 547, + 665 + ], + "type": "inline_equation", + "content": "23.77M" + }, + { + "bbox": [ + 304, + 449, + 547, + 665 + ], + "type": "text", + "content": " parameters. For large scale data with 10 million identities, the size of the head can grow to " + }, + { + "bbox": [ + 304, + 449, + 547, + 665 + ], + "type": "inline_equation", + "content": "1280M" + }, + { + "bbox": [ + 304, + 449, + 547, + 665 + ], + "type": "text", + "content": ", which is much larger than the backbone networks, and DP-FedAvg can easily fail in such settings." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "content": "It can be difficult to achieve a strong formal differential privacy bound without significantly hurting utility for Digi-Face10K, which only has a small number of total users. We consider the practical setting of more available users, and" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "type": "text", + "content": "7973" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 55, + 74, + 168, + 159 + ], + "blocks": [ + { + "bbox": [ + 55, + 74, + 168, + 159 + ], + "lines": [ + { + "bbox": [ + 55, + 74, + 168, + 159 + ], + "spans": [ + { + "bbox": [ + 55, + 74, + 168, + 159 + ], + "type": "image", + "image_path": "9d4c36498095a8bf410a0a03836ab6b359100a0246c1ab0fd843ea7ed5283e15.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 109, + 161, + 118, + 169 + ], + "lines": [ + { + "bbox": [ + 109, + 161, + 118, + 169 + ], + "spans": [ + { + "bbox": [ + 109, + 161, + 118, + 169 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 173, + 73, + 279, + 159 + ], + "blocks": [ + { + "bbox": [ + 173, + 73, + 279, + 159 + ], + "lines": [ + { + "bbox": [ + 173, + 73, + 279, + 159 + ], + "spans": [ + { + "bbox": [ + 173, + 73, + 279, + 159 + ], + "type": "image", + "image_path": "66252260106b2b602574679b4f12a44fe9feb23c6f18bce9b65c26e60129d3d5.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 223, + 161, + 233, + 169 + ], + "lines": [ + { + "bbox": [ + 223, + 161, + 233, + 169 + ], + "spans": [ + { + "bbox": [ + 223, + 161, + 233, + 169 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 284, + 72, + 407, + 159 + ], + "blocks": [ + { + "bbox": [ + 284, + 72, + 407, + 159 + ], + "lines": [ + { + "bbox": [ + 284, + 72, + 407, + 159 + ], + "spans": [ + { + "bbox": [ + 284, + 72, + 407, + 159 + ], + "type": "image", + "image_path": "7357beb9cb84f09069ff83abc9b274e19d64e47cdcdc446cbb85f332dae4fb05.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 342, + 161, + 351, + 169 + ], + "lines": [ + { + "bbox": [ + 342, + 161, + 351, + 169 + ], + "spans": [ + { + "bbox": [ + 342, + 161, + 351, + 169 + ], + "type": "text", + "content": "(c)" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 413, + 72, + 535, + 159 + ], + "blocks": [ + { + "bbox": [ + 413, + 72, + 535, + 159 + ], + "lines": [ + { + "bbox": [ + 413, + 72, + 535, + 159 + ], + "spans": [ + { + "bbox": [ + 413, + 72, + 535, + 159 + ], + "type": "image", + "image_path": "3fe94c771904601ad7c16478d2c11f4ba518084717bccf999dc3d092a36b9aa5.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 470, + 161, + 479, + 169 + ], + "lines": [ + { + "bbox": [ + 470, + 161, + 479, + 169 + ], + "spans": [ + { + "bbox": [ + 470, + 161, + 479, + 169 + ], + "type": "text", + "content": "(d)" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 50, + 226, + 164, + 319 + ], + "blocks": [ + { + "bbox": [ + 50, + 226, + 164, + 319 + ], + "lines": [ + { + "bbox": [ + 50, + 226, + 164, + 319 + ], + "spans": [ + { + "bbox": [ + 50, + 226, + 164, + 319 + ], + "type": "image", + "image_path": "4fc031a2a1c3a1809c5a4f61d3300d60f12ecd3cf21648e1952a3f9f5f569496.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 102, + 321, + 111, + 329 + ], + "lines": [ + { + "bbox": [ + 102, + 321, + 111, + 329 + ], + "spans": [ + { + "bbox": [ + 102, + 321, + 111, + 329 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 46, + 338, + 288, + 405 + ], + "lines": [ + { + "bbox": [ + 46, + 338, + 288, + 405 + ], + "spans": [ + { + "bbox": [ + 46, + 338, + 288, + 405 + ], + "type": "text", + "content": "Figure 3. DP-FedEmb and DP-FedAvg on DigiFace10K under the same privacy budget with noise multiplier 0.08, and additional oracle baseline of centralized training w/o DP: (a) recall@FAR=1e-3 on validation dataset during training; dashed lines are approximation by sampling a subset of validation users; (b) ROC curve of trained model on test set with log scale x-axis." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 168, + 224, + 282, + 318 + ], + "blocks": [ + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "lines": [ + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "spans": [ + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "text", + "content": "Figure 2. (a) Recall@FAR=1e-3 on DigiFace validation set under different noise multiplier; (b) zoom in the high utility regime in (a); (c) and (d) privacy-computation trade-off by extrapolating based on 3M and 10M total users, respectively. \"r400\" and \"r800\" represent the result at 400 or 800 training rounds; target " + }, + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "inline_equation", + "content": "71.77\\%" + }, + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "text", + "content": " is " + }, + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "inline_equation", + "content": "95\\%" + }, + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "text", + "content": " of centralized non-private recall@FAR=1e-3 at " + }, + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "inline_equation", + "content": "75.55\\%" + }, + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 168, + 224, + 282, + 318 + ], + "lines": [ + { + "bbox": [ + 168, + 224, + 282, + 318 + ], + "spans": [ + { + "bbox": [ + 168, + 224, + 282, + 318 + ], + "type": "image", + "image_path": "6d1bf0227dc60546d95158fd64dd177fb4c96a29f6624fd8211e9d42f4b37a03.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 220, + 320, + 231, + 329 + ], + "lines": [ + { + "bbox": [ + 220, + 320, + 231, + 329 + ], + "spans": [ + { + "bbox": [ + 220, + 320, + 231, + 329 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 424, + 287, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 424, + 287, + 520 + ], + "spans": [ + { + "bbox": [ + 46, + 424, + 287, + 520 + ], + "type": "text", + "content": "study the privacy-computation trade-off in Figs. 2c, 2d, 6b and 6c based on extrapolation. A key hypothesis following [36,49] is used: utility (Recall@FAR) is non-decreasing when simultaneously increasing the number of clients per round and noise multiplier. The hypothesis is based on the fact that the signal-to-noise ratio is non-decreasing when linearly increasing the number of clients per round and noise multiplier, and has been verified in practice [50, 57]." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "text", + "content": "We first choose the noise multiplier that is within " + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "text", + "content": " of recall@FAR=1e-3 compared to centralized non-private training in Figs. 2a and 6a, based on simulation that samples 64 virtual clients that each have 32 users per round: recall@FAR=1e-3 is 72.16 for running DP-FedAvg with 0.01 noise multiplier for 400 rounds, 72.37 for running DP-FedAvg with 0.015 noise multiplier for 800 rounds, 71.3 for running DP-FedEmb with 0.015 noise multiplier for 400 rounds, 72.6 for running DP-FedEmb with 0.02 noise multiplier for 800 rounds, and 71.85 for running DP-FTRL-FedEmb with 0.26 noise multiplier for 800 rounds. Then we linearly increase the number of users sampled and use the increased noise multiplier in RDP accounting to compute privacy bound " + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "text", + "content": " given " + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "inline_equation", + "content": "\\delta = 10^{-7}" + }, + { + "bbox": [ + 46, + 522, + 288, + 713 + ], + "type": "text", + "content": " to generate Figs. 2c, 2d and 6b, and compute zCDP for Fig. 6c. Comparing curves of r400 and r800, training longer with larger noise is more" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "spans": [ + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "content": "effective than training shorter with smaller noise. Figure 2c suggests " + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "inline_equation", + "content": "\\sim 98K" + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "content": " users per round is enough for DP-FedEmb to achieve single-digit " + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "content": " if " + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "inline_equation", + "content": "3M" + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "content": " users are available, while " + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "inline_equation", + "content": "\\sim 57K" + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "content": " users per round are needed if " + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "inline_equation", + "content": "10M" + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "content": " users are available in Fig. 2d. " + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "inline_equation", + "content": "98K" + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "content": " users is " + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "inline_equation", + "content": "48\\times" + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "content": " the number of users per round in our current simulation, which can be achieved by training with " + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "inline_equation", + "content": "8\\times" + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "content": " computing resources for " + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "inline_equation", + "content": "6\\times" + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "content": " longer. In Fig. 6b, there is a crossover point when using DP-FTRL versus DP-SGD for DP-FedEmb, and DP-FTRL is more effective for relatively large privacy " + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "content": ". Figure 6c shows that DP-FTRL-FedEmb can achieve zCDP smaller than 2.6, as used by US Census Bureau [70], when " + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "inline_equation", + "content": "8\\times" + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "content": " users per round and " + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "inline_equation", + "content": "10M" + }, + { + "bbox": [ + 304, + 224, + 547, + 380 + ], + "type": "text", + "content": " total users are available." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 390, + 410, + 402 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 390, + 410, + 402 + ], + "spans": [ + { + "bbox": [ + 306, + 390, + 410, + 402 + ], + "type": "text", + "content": "3.2. Model evaluation" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 409, + 547, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 409, + 547, + 636 + ], + "spans": [ + { + "bbox": [ + 304, + 409, + 547, + 636 + ], + "type": "text", + "content": "In Tab. 1, we summarize the quantitative results from the privacy-utility-computation trade-off analysis in Sec. 3.1. Each experiment runs three times to compute the mean and standard deviation. For similar recall@FAR=1e-3 on the DigiFace validation set, DP-FedEmb achieves stronger privacy guarantee than baseline DP-FedAvg, and the advantage of DP-FedEmb is expected to be more pronounced if a head for larger " + }, + { + "bbox": [ + 304, + 409, + 547, + 636 + ], + "type": "inline_equation", + "content": "10M" + }, + { + "bbox": [ + 304, + 409, + 547, + 636 + ], + "type": "text", + "content": " identities is used for training. When " + }, + { + "bbox": [ + 304, + 409, + 547, + 636 + ], + "type": "inline_equation", + "content": "10M" + }, + { + "bbox": [ + 304, + 409, + 547, + 636 + ], + "type": "text", + "content": " users are available and " + }, + { + "bbox": [ + 304, + 409, + 547, + 636 + ], + "type": "inline_equation", + "content": "64\\times" + }, + { + "bbox": [ + 304, + 409, + 547, + 636 + ], + "type": "text", + "content": " users per round in training, privacy " + }, + { + "bbox": [ + 304, + 409, + 547, + 636 + ], + "type": "inline_equation", + "content": "\\epsilon = 3.90" + }, + { + "bbox": [ + 304, + 409, + 547, + 636 + ], + "type": "text", + "content": " of single digit and zCDP= 1.28 smaller than 2.6 can be achieved when recall@FAR=1e-3 is within a " + }, + { + "bbox": [ + 304, + 409, + 547, + 636 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 304, + 409, + 547, + 636 + ], + "type": "text", + "content": " drop compared with non-private centralized training. In addition to validation performance, the private models also perform well on the left-out test dataset. Figure 3 presents the training curves and ROC curves for comparing DP-FedEmb and DP-FedAvg under the same privacy budget. DP-FedEmb outperforms DP-FedAvg in all training rounds, and trains a stronger private model with better recall at different false accept rates." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 646, + 397, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 646, + 397, + 658 + ], + "spans": [ + { + "bbox": [ + 306, + 646, + 397, + 658 + ], + "type": "text", + "content": "3.3. Ablation study" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 665, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 547, + 713 + ], + "type": "text", + "content": "We primarily use MobileNetV2 for ablation studies on DigiFace10K for two reasons: MobileNetV2 is smaller and faster for training in experiments; to test the generalization of DP-FedEmb and avoid overfitting on ResNet-50." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7974" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 99, + 70, + 494, + 145 + ], + "blocks": [ + { + "bbox": [ + 99, + 70, + 494, + 145 + ], + "lines": [ + { + "bbox": [ + 99, + 70, + 494, + 145 + ], + "spans": [ + { + "bbox": [ + 99, + 70, + 494, + 145 + ], + "type": "table", + "html": "
AlgorithmHyperparametersPrivacy (10M users)Recall@FAR=1e-3
NoiseSerLRRDP-εzCDPValidationTest
Centralized00.05∞∞75.55 ± 0.0575.53 ± 0.12
DP-FedAvg0.015 × 640.55.62-72.57 ± 0.1272.37 ± 0.09
DP-FedEmb0.02 × 640.23.90-72.63 ± 0.0572.37 ± 0.09
DP-FTRL-FedEmb0.26 × 640.29.671.2872.2 ± 0.2971.87 ± 0.26
", + "image_path": "93e49dd0ff86e7de1ca0d17e171ba275ec25cdd69533a8f34e19219dadcb7278.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 155, + 545, + 199 + ], + "lines": [ + { + "bbox": [ + 46, + 155, + 545, + 199 + ], + "spans": [ + { + "bbox": [ + 46, + 155, + 545, + 199 + ], + "type": "text", + "content": "Table 1. Quantitative results of privacy and utility on the DigiFace10K dataset. The client learning rate and clip norm for federated algorithms are 0.002 and 0.6, respectively; " + }, + { + "bbox": [ + 46, + 155, + 545, + 199 + ], + "type": "inline_equation", + "content": "\\delta = 10^{-7}" + }, + { + "bbox": [ + 46, + 155, + 545, + 199 + ], + "type": "text", + "content": " for privacy guarantees. Centralized training has a standard learning rate scheduling, while tricks like data augmentation are excluded for all methods. The privacy guarantees are extrapolated based on " + }, + { + "bbox": [ + 46, + 155, + 545, + 199 + ], + "type": "inline_equation", + "content": "10M" + }, + { + "bbox": [ + 46, + 155, + 545, + 199 + ], + "type": "text", + "content": " users and " + }, + { + "bbox": [ + 46, + 155, + 545, + 199 + ], + "type": "inline_equation", + "content": "131K" + }, + { + "bbox": [ + 46, + 155, + 545, + 199 + ], + "type": "text", + "content": " users per round. A strong privacy guarantee can be achieved within a " + }, + { + "bbox": [ + 46, + 155, + 545, + 199 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 46, + 155, + 545, + 199 + ], + "type": "text", + "content": " drop on recall@FAR=1e-3." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 51, + 220, + 173, + 312 + ], + "blocks": [ + { + "bbox": [ + 51, + 220, + 173, + 312 + ], + "lines": [ + { + "bbox": [ + 51, + 220, + 173, + 312 + ], + "spans": [ + { + "bbox": [ + 51, + 220, + 173, + 312 + ], + "type": "image", + "image_path": "5d1b1a354c601ab088f4c46f073d0819d972f4483cfab68539dbc6dc1f69fdd6.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 108, + 314, + 117, + 322 + ], + "lines": [ + { + "bbox": [ + 108, + 314, + 117, + 322 + ], + "spans": [ + { + "bbox": [ + 108, + 314, + 117, + 322 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 179, + 222, + 284, + 312 + ], + "blocks": [ + { + "bbox": [ + 179, + 222, + 284, + 312 + ], + "lines": [ + { + "bbox": [ + 179, + 222, + 284, + 312 + ], + "spans": [ + { + "bbox": [ + 179, + 222, + 284, + 312 + ], + "type": "image", + "image_path": "c444cddf3ae25623b0982b1a3c67ca3faae21e981aa4e6f8eb200116b50c97d7.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 227, + 314, + 236, + 323 + ], + "lines": [ + { + "bbox": [ + 227, + 314, + 236, + 323 + ], + "spans": [ + { + "bbox": [ + 227, + 314, + 236, + 323 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 46, + 331, + 287, + 376 + ], + "lines": [ + { + "bbox": [ + 46, + 331, + 287, + 376 + ], + "spans": [ + { + "bbox": [ + 46, + 331, + 287, + 376 + ], + "type": "text", + "content": "Figure 4. Recall@FAR=1e-3 on DigiFace validation set when training a MobileNet (a) with DP-FedEmb w/ pretraining, and baselines of DP-FedEmb w/o pretraining and DP-FedAvg; (b) with different pretrained models." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 390, + 287, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 390, + 287, + 581 + ], + "spans": [ + { + "bbox": [ + 46, + 390, + 287, + 581 + ], + "type": "text", + "content": "Parameter freezing and public pretraining. For similar parameter size, MobileNetV2 outperforms ResNet-50 with frozen parameters, and Fig. 4a shows the privacy-utility trade-off. Recall@FAR=1e-3 of DP-FedEmb-r800 on MobileNetV2 only drops from " + }, + { + "bbox": [ + 46, + 390, + 287, + 581 + ], + "type": "inline_equation", + "content": "68.86\\%" + }, + { + "bbox": [ + 46, + 390, + 287, + 581 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 46, + 390, + 287, + 581 + ], + "type": "inline_equation", + "content": "67.56\\%" + }, + { + "bbox": [ + 46, + 390, + 287, + 581 + ], + "type": "text", + "content": " when 0.02 noise is added, while ResNet-50 drops from " + }, + { + "bbox": [ + 46, + 390, + 287, + 581 + ], + "type": "inline_equation", + "content": "79.09\\%" + }, + { + "bbox": [ + 46, + 390, + 287, + 581 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 46, + 390, + 287, + 581 + ], + "type": "inline_equation", + "content": "72.6\\%" + }, + { + "bbox": [ + 46, + 390, + 287, + 581 + ], + "type": "text", + "content": ". However, ResNet-50 still outperforms MobileNetV2 by a large margin in the high utility regime. DP-FedEmb consistently outperforms DP-FedAvg when noises are added. In Fig. 4b, though the private fine-tuning utility is not linearly increasing with pretraining accuracy, there seems to be a general positive correlation: better pretrained models can lead to better private models except for one outlier where a inferior pretrained model causes difficulty in training. More discussion on parameter freezing and public pretraining is provided in Appendix D.4." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 582, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 287, + 713 + ], + "type": "text", + "content": "Federated settings. In the above experiments, we fix important hyperparameters for the federated setting: users per virtual client is 32, virtual clients per round is 64, examples per client is capped at 2048, the head learning rate (LR) scale " + }, + { + "bbox": [ + 46, + 582, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\beta_{2} / \\beta_{1}" + }, + { + "bbox": [ + 46, + 582, + 287, + 713 + ], + "type": "text", + "content": " is 100, the buffer size for data shuffling on clients is 2048, and the batch size for local SGD is 32. In Fig. 9, instead of tuning these hyperparameters in advance, we conduct a study on these hyperparameters to understand DP-FedEmb. Among these hyperparameters, Figs. 9a and 9d suggest virtual clients and head LR scale are particularly important for DP-FedEmb to be on par with" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 220, + 545, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 220, + 545, + 376 + ], + "spans": [ + { + "bbox": [ + 304, + 220, + 545, + 376 + ], + "type": "text", + "content": "non-private centralized training, which is an important contribution of this work. Figures 9a to 9c suggest users per virtual client, clients per round, and examples per client only need to be large enough under privacy consideration and computation resources for the best practice. The head LR scale has a large tuning range between 50 and 500 in Fig. 9d. Recall@FAR=1e-3 is not sensitive to shuffle buffer size in Fig. 9e. The model utility can be potentially improved if we further tune the client batch size as suggested by Fig. 9f. We fixed the learning rate and other hyperparameters while varying one of the hyperparameters in the ablation study. How to automate tuning, especially tuning with differential privacy guarantees, is an important future work." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 377, + 545, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 377, + 545, + 556 + ], + "spans": [ + { + "bbox": [ + 304, + 377, + 545, + 556 + ], + "type": "text", + "content": "Learning rate (LR). For experiments in Secs. 3.1 and 3.2, server and client learning rates are first tuned for non-private federated training with adaptive clipping of quantile 0.5 [3]. Then server learning rate is tuned when adding noise for private tuning, while estimated clip norm and client learning rate are fixed. The tuning range of learning rates are " + }, + { + "bbox": [ + 304, + 377, + 545, + 556 + ], + "type": "inline_equation", + "content": "\\{1,2,5\\} * 10^{-n}" + }, + { + "bbox": [ + 304, + 377, + 545, + 556 + ], + "type": "text", + "content": ". Figures 5a and 5b suggest the optimal learning rates are similar for training 400 rounds or 800 rounds in non-private training. We then fix the client learning rate to be 0.002 and use the estimated clip norm 0.6 for MobileNetV2 in Fig. 5c, and observe the fixed clip results are very similar to adaptive clipping results. The best server learning rate for private training with noise can be smaller than non-private training, where the difference is even more notable for larger model ResNet-50 in Fig. 5d." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 557, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 557, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 557, + 545, + 712 + ], + "type": "text", + "content": "Variants of DP-FedEmb. We use local fine-tuning with different learning rates " + }, + { + "bbox": [ + 304, + 557, + 545, + 712 + ], + "type": "inline_equation", + "content": "\\beta_{1},\\beta_{2}" + }, + { + "bbox": [ + 304, + 557, + 545, + 712 + ], + "type": "text", + "content": " to update backbone and head parameters. An alternative is to reconstruct the head first before fine-tuning the backbone [41, 65]. We empirically find that head reconstruction can only achieve similar performance as the proposed fine-tuning when there are same or more number of updates on the backbone network, and hence use local fine-tuning for efficiency. It is also possible to use binary or triplet loss within a virtual clients. In our preliminary results, they achieve inferior results compared to DP-FedEmb that uses multi-class cross-entropy loss. We leave other improvement like arcface loss [17] as future work." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7975" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 76, + 157, + 177 + ], + "blocks": [ + { + "bbox": [ + 53, + 76, + 157, + 177 + ], + "lines": [ + { + "bbox": [ + 53, + 76, + 157, + 177 + ], + "spans": [ + { + "bbox": [ + 53, + 76, + 157, + 177 + ], + "type": "image", + "image_path": "8f920cbc4074b48c99de2b3372c873bf3bcfa56a90d9220d493e12d971ce779a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 102, + 178, + 111, + 186 + ], + "lines": [ + { + "bbox": [ + 102, + 178, + 111, + 186 + ], + "spans": [ + { + "bbox": [ + 102, + 178, + 111, + 186 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 163, + 76, + 284, + 177 + ], + "blocks": [ + { + "bbox": [ + 163, + 76, + 284, + 177 + ], + "lines": [ + { + "bbox": [ + 163, + 76, + 284, + 177 + ], + "spans": [ + { + "bbox": [ + 163, + 76, + 284, + 177 + ], + "type": "image", + "image_path": "d4922e5c36cc404885995f7a289ab6cf798977a6b0fddd40939eb313ac098d10.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 219, + 178, + 228, + 186 + ], + "lines": [ + { + "bbox": [ + 219, + 178, + 228, + 186 + ], + "spans": [ + { + "bbox": [ + 219, + 178, + 228, + 186 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 289, + 76, + 409, + 176 + ], + "blocks": [ + { + "bbox": [ + 289, + 76, + 409, + 176 + ], + "lines": [ + { + "bbox": [ + 289, + 76, + 409, + 176 + ], + "spans": [ + { + "bbox": [ + 289, + 76, + 409, + 176 + ], + "type": "image", + "image_path": "d2c3d84b5f203ffeccf65c163c40c27da5da71b1b19abb440a53ae7092ec88c1.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 345, + 178, + 353, + 186 + ], + "lines": [ + { + "bbox": [ + 345, + 178, + 353, + 186 + ], + "spans": [ + { + "bbox": [ + 345, + 178, + 353, + 186 + ], + "type": "text", + "content": "(c)" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 46, + 196, + 547, + 229 + ], + "lines": [ + { + "bbox": [ + 46, + 196, + 547, + 229 + ], + "spans": [ + { + "bbox": [ + 46, + 196, + 547, + 229 + ], + "type": "text", + "content": "Figure 5. Study for learning rates. (a) and (b) recall@FAR=1e-3 after training MobileNetV2 using DP-FedEmb with adaptive clipping [3] and 0 noise for 400 rounds and 800 rounds, respectively; (c) and (d) varying server learning rate with fixed client learning rate and clip norm for MobileNetV2 and ResNet-50." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 414, + 73, + 538, + 176 + ], + "blocks": [ + { + "bbox": [ + 414, + 73, + 538, + 176 + ], + "lines": [ + { + "bbox": [ + 414, + 73, + 538, + 176 + ], + "spans": [ + { + "bbox": [ + 414, + 73, + 538, + 176 + ], + "type": "image", + "image_path": "d517d3bd690a3f3654e3f3e3704f18c5462e2ee53d2693ae688e057f737c8c17.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 471, + 178, + 481, + 186 + ], + "lines": [ + { + "bbox": [ + 471, + 178, + 481, + 186 + ], + "spans": [ + { + "bbox": [ + 471, + 178, + 481, + 186 + ], + "type": "text", + "content": "(d)" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 48, + 239, + 293, + 365 + ], + "blocks": [ + { + "bbox": [ + 48, + 239, + 293, + 365 + ], + "lines": [ + { + "bbox": [ + 48, + 239, + 293, + 365 + ], + "spans": [ + { + "bbox": [ + 48, + 239, + 293, + 365 + ], + "type": "table", + "html": "
DatasetAlgorithmRecall@FAR=1e-3/0.1
ApproxAllPair
DigiFaceDP-FedEmb-19.76 ± 0.43
DP-FedAvg-13.38 ± 0.27
EMNISTDP-FedEmb10.64 ± 0.510.47 ± 0.5
DP-FedAvg9.78 ± 0.449.67 ± 0.41
GLDDP-FedEmb26.18 ± 0.4427.07 ± 0.04
DP-FedAvg24.48 ± 1.026.27 ± 0.17
iNatDP-FedEmb40.49 ± 1.0840.93 ± 0.94
DP-FedAvg29.57 ± 0.7829.6 ± 0.65
", + "image_path": "0942012e3c208a6ba58d49f058c686e58c9a7dac8e73d40bff6af0b9e894321a.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 372, + 287, + 395 + ], + "lines": [ + { + "bbox": [ + 47, + 372, + 287, + 395 + ], + "spans": [ + { + "bbox": [ + 47, + 372, + 287, + 395 + ], + "type": "text", + "content": "Table 2. The utility under same privacy budget for DigiFace [6], EMNIST [67], GLD [68] and iNat [69] datasets." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 47, + 417, + 153, + 430 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 417, + 153, + 430 + ], + "spans": [ + { + "bbox": [ + 47, + 417, + 153, + 430 + ], + "type": "text", + "content": "3.4. Additional results" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "spans": [ + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "text", + "content": "We run additional experiments of ResNet-50 on the larger DigiFace of " + }, + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "inline_equation", + "content": "98.96K" + }, + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "text", + "content": " users. Because a lot of users in DigiFace have only 5 images, we set each virtual client to contain 64 users, and use 3072 samples per virtual client. We sample 128 virtual clients per round, and a relatively large noise 1.39 in RDP accounting can achieve " + }, + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "inline_equation", + "content": "\\epsilon = 24.86" + }, + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "inline_equation", + "content": "\\delta = 10^{-5}" + }, + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "text", + "content": " without extrapolation. The utility measured by recall@FAR=1e-3 is shown in Tab. 2. Though the utilities of both methods are significantly degraded by the large noise, DP-FedEmb is much better than the DP-FedAvg baseline because the noise is only added to backbone of " + }, + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "inline_equation", + "content": "\\sim 2.4M" + }, + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "text", + "content": " parameters instead of backbone plus head of " + }, + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "inline_equation", + "content": "\\sim 15M" + }, + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "text", + "content": " parameters. The full table including EM-NIST [67] results for reproducibility and hyperparameter choices is provided in Tab. 4 in Appendix D." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 617, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 288, + 714 + ], + "type": "text", + "content": "In Tab. 2, we also conduct experiments with MobileNetV2 on Google Landmark Dataset (GLD) [32, 68, 77] and iNaturalist (iNat) dataset [32, 47, 69] to demonstrate the generalization of DP-FedEmb. We use a public model pretrained on ImageNet, and report extra approximate recall@FAR by computing pairwise similarity for minibatches, which is easy to reproduce and consistent with the all pair recall@FAR. We fix the hyperparameters" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 242, + 547, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 242, + 547, + 422 + ], + "spans": [ + { + "bbox": [ + 304, + 242, + 547, + 422 + ], + "type": "text", + "content": "for the federated settings, and compare the performance of DP-FedEmb and DP-FedAvg under the same privacy budget (noise multiplier). Since each user already has multiple classes in GLD, we use a smaller number of users, 8, in each virtual client. We also use a smaller number of virtual clients per round, 32, for fast experiments and strong sampling effect. Recall@FAR=1e-3 of DP-FedEmb and DP-FedAvg with small noise multiplier 0.02 on GLD outperforms centralized training. For iNat, we use an even smaller four users per virtual client and train for only 400 rounds, and use a relatively large noise multiplier 0.5 to get a single-digit " + }, + { + "bbox": [ + 304, + 242, + 547, + 422 + ], + "type": "inline_equation", + "content": "\\epsilon = 16.06" + }, + { + "bbox": [ + 304, + 242, + 547, + 422 + ], + "type": "text", + "content": " DP guarantee for 9275 users. We report recall@FAR=0.1 instead of recall@FAR=1e-3 for the challenging iNat task. In all experiments, DP-FedEmb consistently outperforms DP-FedAvg." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 449, + 378, + 460 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 449, + 378, + 460 + ], + "spans": [ + { + "bbox": [ + 306, + 449, + 378, + 460 + ], + "type": "text", + "content": "4. Conclusion" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 474, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 474, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 474, + 547, + 715 + ], + "type": "text", + "content": "This paper presented DP-FedEmb for training embedding models with user-level differential privacy. We show how practical utility with strong privacy guarantees can be achieved in the data center, thanks to key algorithm design choices around the construction of virtual clients and in the selection of what information is shared among users. Our experiments validate this improves the privacy utility trade-off upon vanilla DP-FedAvg for supervised representation learning. Though strong formal DP bounds at practical levels of utility could only be achieved when millions of users participate in training, DP-FedEmb is designed to be exceptionally scalable when model size and class space increases with number of users. DP-FedEmb can also be applied to decentralized FL when each real client contains multiple classes, possibly reducing the necessity of virtual clients. Finally, DP is a worst-case guarantee that can be improved by both algorithmic design and advanced accounting methods; the non-negligible noise we added for the small scale datasets in experiments are ready to be empirically audited for privacy." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7976" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 147 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 147 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 147 + ], + "type": "text", + "content": "[1] Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, pages 308-318, 2016. 1, 3, 4, 14" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 148, + 288, + 192 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 148, + 288, + 192 + ], + "spans": [ + { + "bbox": [ + 53, + 148, + 288, + 192 + ], + "type": "text", + "content": "[2] Kareem Amin, Alex Kulesza, Andres Munoz, and Sergei Vassilvtiskii. Bounding user contributions: A bias-variance trade-off in differential privacy. In International Conference on Machine Learning, pages 263-271. PMLR, 2019. 13" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 194, + 288, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 194, + 288, + 237 + ], + "spans": [ + { + "bbox": [ + 53, + 194, + 288, + 237 + ], + "type": "text", + "content": "[3] Galen Andrew, Om Thakkar, H Brendan McMahan, and Swaroop Ramaswamy. Differentially private learning with adaptive clipping. Conference on Neural Information Processing Systems (NeurIPS), 2021. 5, 7, 8, 13, 17" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 239, + 288, + 272 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 239, + 288, + 272 + ], + "spans": [ + { + "bbox": [ + 53, + 239, + 288, + 272 + ], + "type": "text", + "content": "[4] Rohan Anil, Badih Ghazi, Vineet Gupta, Ravi Kumar, and Pasin Manurangsi. Large-scale differentially private bert. arXiv preprint arXiv:2108.01624, 2021. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 274, + 287, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 274, + 287, + 317 + ], + "spans": [ + { + "bbox": [ + 53, + 274, + 287, + 317 + ], + "type": "text", + "content": "[5] Apple Privacy Team. Learning with privacy at scale. Available at https://machinelearning.apple.com/2017/12/06/learning-with-privacy-at-scale.html, 2017.1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 319, + 287, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 319, + 287, + 384 + ], + "spans": [ + { + "bbox": [ + 53, + 319, + 287, + 384 + ], + "type": "text", + "content": "[6] Gwangbin Bae, Martin de La Gorce, Tadas Baltrusaitis, Charlie Hewitt, Dong Chen, Julien Valentin, Roberto Cipolla, and Jingjing Shen. Digiface-1m: 1 million digital face images for face recognition. In 2023 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2023. 5, 8, 17" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 386, + 287, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 386, + 287, + 430 + ], + "spans": [ + { + "bbox": [ + 53, + 386, + 287, + 430 + ], + "type": "text", + "content": "[7] Mark Bun and Thomas Steinke. Concentrated differential privacy: Simplifications, extensions, and lower bounds. In Theory of Cryptography Conference, pages 635-658. Springer, 2016. 3, 13" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 432, + 287, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 432, + 287, + 474 + ], + "spans": [ + { + "bbox": [ + 53, + 432, + 287, + 474 + ], + "type": "text", + "content": "[8] Clément L Canonne, Gautam Kamath, and Thomas Steinke. The discrete gaussian for differential privacy. Advances in Neural Information Processing Systems, 33:15676-15688, 2020. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 477, + 287, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 477, + 287, + 520 + ], + "spans": [ + { + "bbox": [ + 53, + 477, + 287, + 520 + ], + "type": "text", + "content": "[9] Nicholas Carlini, Steve Chien, Milad Nasr, Shuang Song, Andreas Terzis, and Florian Tramer. Membership inference attacks from first principles. In 2022 IEEE Symposium on Security and Privacy (SP), pages 1897-1914. IEEE, 2022. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 522, + 287, + 576 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 522, + 287, + 576 + ], + "spans": [ + { + "bbox": [ + 48, + 522, + 287, + 576 + ], + "type": "text", + "content": "[10] Nicholas Carlini, Chang Liu, Ülfar Erlingsson, Jernej Kos, and Dawn Song. The secret sharer: Evaluating and testing unintended memorization in neural networks. In 28th USENIX Security Symposium (USENIX Security 19), pages 267-284, 2019. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 578, + 287, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 578, + 287, + 643 + ], + "spans": [ + { + "bbox": [ + 48, + 578, + 287, + 643 + ], + "type": "text", + "content": "[11] Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al. Extracting training data from large language models. In 30th USENIX Security Symposium (USENIX Security 21), pages 2633-2650, 2021. 1" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 646, + 287, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 646, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 48, + 646, + 287, + 689 + ], + "type": "text", + "content": "[12] Yannis Cattan, Christopher A Choquette-Choo, Nicolas Papernot, and Abhradeep Thakurta. Fine-tuning with differential privacy necessitates an additional hyperparameter search. arXiv preprint arXiv:2210.02156, 2022. 16" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 691, + 287, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 287, + 714 + ], + "type": "text", + "content": "[13] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 714 + ], + "type": "list", + "angle": 0, + "index": 32, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 13" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 97, + 545, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 97, + 545, + 140 + ], + "spans": [ + { + "bbox": [ + 308, + 97, + 545, + 140 + ], + "type": "text", + "content": "[14] Liam Collins, Hamed Hassani, Aryan Mokhtari, and Sanjay Shakkottai. Fedavg with fine tuning: Local updates lead to representation learning. arXiv e-prints, pages arXiv-2205, 2022. 5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 142, + 545, + 175 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 142, + 545, + 175 + ], + "spans": [ + { + "bbox": [ + 308, + 142, + 545, + 175 + ], + "type": "text", + "content": "[15] Liam Collins, Aryan Mokhtari, Sewoong Oh, and Sanjay Shakkottai. Maml and anil provably learn representations. arXiv preprint arXiv:2202.03483, 2022. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 177, + 545, + 220 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 177, + 545, + 220 + ], + "spans": [ + { + "bbox": [ + 308, + 177, + 545, + 220 + ], + "type": "text", + "content": "[16] Soham De, Leonard Berrada, Jamie Hayes, Samuel L Smith, and Borja Balle. Unlocking high-accuracy differentially private image classification through scale. arXiv preprint arXiv:2204.13650, 2022. 1, 3, 4, 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 222, + 545, + 276 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 222, + 545, + 276 + ], + "spans": [ + { + "bbox": [ + 308, + 222, + 545, + 276 + ], + "type": "text", + "content": "[17] Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. Arcface: Additive angular margin loss for deep face recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4690-4699, 2019. 7, 13" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 278, + 545, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 278, + 545, + 312 + ], + "spans": [ + { + "bbox": [ + 308, + 278, + 545, + 312 + ], + "type": "text", + "content": "[18] Bolin Ding, Janardhan Kulkarni, and Sergey Yekhanin. Collecting telemetry data privately. Advances in Neural Information Processing Systems, 30, 2017. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 313, + 545, + 346 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 313, + 545, + 346 + ], + "spans": [ + { + "bbox": [ + 308, + 313, + 545, + 346 + ], + "type": "text", + "content": "[19] Xin Dong, Sai Qian Zhang, Ang Li, and HT Kung. Spherefed: Hyperspherical federated learning. arXiv preprint arXiv:2207.09413, 2022. 13" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 347, + 545, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 347, + 545, + 392 + ], + "spans": [ + { + "bbox": [ + 308, + 347, + 545, + 392 + ], + "type": "text", + "content": "[20] Vadym Doroshenko, Badih Ghazi, Pritish Kamath, Ravi Kumar, and Pasin Manurangsi. Connect the dots: Tighter discrete approximations of privacy loss distributions. arXiv preprint arXiv:2207.04380, 2022. 13, 14" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 393, + 545, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 393, + 545, + 426 + ], + "spans": [ + { + "bbox": [ + 308, + 393, + 545, + 426 + ], + "type": "text", + "content": "[21] DP Team. Google's differential privacy libraries., 2020. https://github.com/google/differential-privacy.14" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 428, + 545, + 460 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 428, + 545, + 460 + ], + "spans": [ + { + "bbox": [ + 308, + 428, + 545, + 460 + ], + "type": "text", + "content": "[22] Cynthia Dwork. Differential privacy in new settings. In Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms, pages 174-183. SIAM, 2010. 1, 13" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 462, + 545, + 506 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 462, + 545, + 506 + ], + "spans": [ + { + "bbox": [ + 308, + 462, + 545, + 506 + ], + "type": "text", + "content": "[23] Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Theory of cryptography conference, pages 265-284. Springer, 2006. 1, 13" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 508, + 545, + 540 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 508, + 545, + 540 + ], + "spans": [ + { + "bbox": [ + 308, + 508, + 545, + 540 + ], + "type": "text", + "content": "[24] Cynthia Dwork, Aaron Roth, et al. The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science, 9(3-4):211-407, 2014. 14" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 543, + 545, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 543, + 545, + 586 + ], + "spans": [ + { + "bbox": [ + 308, + 543, + 545, + 586 + ], + "type": "text", + "content": "[25] Alessandro Epasto, Mohammad Mahdian, Jieming Mao, Vahab Mirrokni, and Lijie Ren. Smoothly bounding user contributions in differential privacy. Advances in Neural Information Processing Systems, 33:13999-14010, 2020. 13" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 588, + 545, + 620 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 588, + 545, + 620 + ], + "spans": [ + { + "bbox": [ + 308, + 588, + 545, + 620 + ], + "type": "text", + "content": "[26] Jonathan Frankle, David J. Schwab, and Ari S. Morcos. Training batchnorm and only batchnorm: On the expressive power of random features in CNNs, 2021. 5, 16" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 622, + 545, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 622, + 545, + 655 + ], + "spans": [ + { + "bbox": [ + 308, + 622, + 545, + 655 + ], + "type": "text", + "content": "[27] Robin C Geyer, Tassilo Klein, and Moin Nabi. Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557, 2017. 3, 13" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 657, + 545, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 657, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 308, + 657, + 545, + 689 + ], + "type": "text", + "content": "[28] Badih Ghazi, Ravi Kumar, and Pasin Manurangsi. User-level private learning via correlated sampling. arXiv preprint arXiv:2110.11208, 2021. 13" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 691, + 545, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 691, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 308, + 691, + 545, + 714 + ], + "type": "text", + "content": "[29] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch," + } + ] + } + ], + "index": 31 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7977" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 66, + 72, + 287, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 72, + 287, + 117 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 287, + 117 + ], + "type": "text", + "content": "Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020. 13" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 118, + 288, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 118, + 288, + 172 + ], + "spans": [ + { + "bbox": [ + 48, + 118, + 288, + 172 + ], + "type": "text", + "content": "[30] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000-16009, 2022. 13" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 174, + 288, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 174, + 288, + 218 + ], + "spans": [ + { + "bbox": [ + 48, + 174, + 288, + 218 + ], + "type": "text", + "content": "[31] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 220, + 287, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 220, + 287, + 262 + ], + "spans": [ + { + "bbox": [ + 48, + 220, + 287, + 262 + ], + "type": "text", + "content": "[32] Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. Federated visual classification with real-world data distribution. In European Conference on Computer Vision, pages 76-92. Springer, 2020. 5, 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 264, + 287, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 264, + 287, + 308 + ], + "spans": [ + { + "bbox": [ + 48, + 264, + 287, + 308 + ], + "type": "text", + "content": "[33] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning, pages 448-456. PMLR, 2015. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 309, + 287, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 309, + 287, + 353 + ], + "spans": [ + { + "bbox": [ + 48, + 309, + 287, + 353 + ], + "type": "text", + "content": "[34] Prateek Jain, John Rush, Adam Smith, Shuang Song, and Abhradeep Guha Thakurta. Differentially private model personalization. Advances in Neural Information Processing Systems, 34:29723-29735, 2021. 4, 13" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 354, + 287, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 354, + 287, + 397 + ], + "spans": [ + { + "bbox": [ + 48, + 354, + 287, + 397 + ], + "type": "text", + "content": "[35] Sébastien Jean, Kyunghyun Cho, Roland Memisevic, and Yoshua Bengio. On using very large target vocabulary for neural machine translation. arXiv preprint arXiv:1412.2007, 2014. 3, 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 399, + 287, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 399, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 48, + 399, + 287, + 453 + ], + "type": "text", + "content": "[36] Peter Kairouz, Brendan Mcmahan, Shuang Song, Om Thakkar, Abhradeep Thakurta, and Zheng Xu. Practical and private (deep) learning without sampling or shuffling. In International Conference on Machine Learning (ICML), pages 5213-5225, 2021. 1, 2, 3, 5, 6, 13, 14, 15, 16" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 456, + 287, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 456, + 287, + 510 + ], + "spans": [ + { + "bbox": [ + 48, + 456, + 287, + 510 + ], + "type": "text", + "content": "[37] Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977, 2019. 13" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 512, + 287, + 554 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 512, + 287, + 554 + ], + "spans": [ + { + "bbox": [ + 48, + 512, + 287, + 554 + ], + "type": "text", + "content": "[38] Peter Kairouz, Sewoong Oh, and Pramod Viswanath. The composition theorem for differential privacy. In International conference on machine learning, pages 1376-1385. PMLR, 2015. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 556, + 287, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 556, + 287, + 610 + ], + "spans": [ + { + "bbox": [ + 48, + 556, + 287, + 610 + ], + "type": "text", + "content": "[39] Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. Scaffold: Stochastic controlled averaging for federated learning. In International Conference on Machine Learning, pages 5132-5143. PMLR, 2020. 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 613, + 287, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 613, + 287, + 645 + ], + "spans": [ + { + "bbox": [ + 48, + 613, + 287, + 645 + ], + "type": "text", + "content": "[40] Antti Koskela, Joonas Jälkö, Lukas Prediger, and Antti Honkela. Tight approximate differential privacy for discrete-valued mechanisms using fft, 2020. 13, 14" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 647, + 287, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 287, + 689 + ], + "type": "text", + "content": "[41] Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang. Fine-tuning can distort pretrained features and underperform out-of-distribution. arXiv preprint arXiv:2202.10054, 2022.7" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "text", + "content": "[42] Alexey Kurakin, Steve Chien, Shuang Song, Roxana Geambasu, Andreas Terzis, and Abhradeep Thakurta. Toward" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 712 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "training at imagenet scale with differential privacy. arXiv preprint arXiv:2201.12328, 2022. 1, 4" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 97, + 545, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 97, + 545, + 139 + ], + "spans": [ + { + "bbox": [ + 307, + 97, + 545, + 139 + ], + "type": "text", + "content": "[43] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998. 18" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 141, + 545, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 141, + 545, + 185 + ], + "spans": [ + { + "bbox": [ + 307, + 141, + 545, + 185 + ], + "type": "text", + "content": "[44] Daniel Levy, Ziteng Sun, Kareem Amin, Satyen Kale, Alex Kulesza, Mehryar Mohri, and Ananda Theertha Suresh. Learning with user-level privacy. Advances in Neural Information Processing Systems, 34:12466-12479, 2021. 13" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 186, + 545, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 545, + 229 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 545, + 229 + ], + "type": "text", + "content": "[45] Xuechen Li, Florian Tramer, Percy Liang, and Tatsunori Hashimoto. Large language models can be strong differentially private learners. arXiv preprint arXiv:2110.05679, 2021. 1, 4" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 232, + 545, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 232, + 545, + 274 + ], + "spans": [ + { + "bbox": [ + 308, + 232, + 545, + 274 + ], + "type": "text", + "content": "[46] Yuhan Liu, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Michael Riley. Learning discrete distributions: user vs item-level privacy. Advances in Neural Information Processing Systems, 33:20965-20976, 2020. 13" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 276, + 547, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 276, + 547, + 319 + ], + "spans": [ + { + "bbox": [ + 308, + 276, + 547, + 319 + ], + "type": "text", + "content": "[47] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV), December 2015. 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 321, + 545, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 321, + 545, + 365 + ], + "spans": [ + { + "bbox": [ + 308, + 321, + 545, + 365 + ], + "type": "text", + "content": "[48] Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. In AISTATS, pages 1273-1282. PMLR, 2017. 3, 4" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 366, + 545, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 366, + 545, + 409 + ], + "spans": [ + { + "bbox": [ + 308, + 366, + 545, + 409 + ], + "type": "text", + "content": "[49] Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang. Learning differentially private recurrent language models. In International Conference on Learning Representations (ICLR), 2018. 1, 2, 3, 5, 6, 13, 14" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 411, + 545, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 411, + 545, + 442 + ], + "spans": [ + { + "bbox": [ + 308, + 411, + 545, + 442 + ], + "type": "text", + "content": "[50] Brendan McMahan and Abhradeep Thakurta. Federated learning with formal differential privacy guarantees, 2022. 1, 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 445, + 545, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 445, + 545, + 498 + ], + "spans": [ + { + "bbox": [ + 308, + 445, + 545, + 498 + ], + "type": "text", + "content": "[51] H Brendan McMahan, Galen Andrew, Ulfar Erlingsson, Steve Chien, Ilya Mironov, Nicolas Papernot, and Peter Kairouz. A general approach to adding differential privacy to iterative training procedures. arXiv preprint arXiv:1812.06210, 2018. 4, 13, 14" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 501, + 545, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 501, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 308, + 501, + 545, + 544 + ], + "type": "text", + "content": "[52] Qiang Meng, Feng Zhou, Hainan Ren, Tianshu Feng, Guochao Liu, and Yuanqing Lin. Improving federated learning face recognition via privacy-agnostic clusters. arXiv preprint arXiv:2201.12467, 2022. 13" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 546, + 545, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 546, + 545, + 578 + ], + "spans": [ + { + "bbox": [ + 308, + 546, + 545, + 578 + ], + "type": "text", + "content": "[53] Ilya Mironov. Rényi differential privacy. In 2017 IEEE 30th computer security foundations symposium (CSF), pages 263-275. IEEE, 2017. 5, 13" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 580, + 545, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 580, + 545, + 613 + ], + "spans": [ + { + "bbox": [ + 308, + 580, + 545, + 613 + ], + "type": "text", + "content": "[54] Kevin Musgrave, Serge Belongie, and Ser-Nam Lim. A metric learning reality check. In European Conference on Computer Vision, pages 681-699. Springer, 2020. 13" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 614, + 545, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 614, + 545, + 645 + ], + "spans": [ + { + "bbox": [ + 308, + 614, + 545, + 645 + ], + "type": "text", + "content": "[55] Nicolas Papernot and Thomas Steinke. Hyperparameter tuning with renyi differential privacy. arXiv preprint arXiv:2110.03620, 2021. 5" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 647, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 647, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 308, + 647, + 545, + 712 + ], + "type": "text", + "content": "[56] Natalia Ponomareva, Hussein Hazimeh, Alex Kurakin, Zheng Xu, Carson Denison, H Brendan McMahan, Sergei Vassilvitskii, Steve Chien, and Abhradeep Thakurta. How to dp-fy ml: A practical guide to machine learning with differential privacy. arXiv preprint arXiv:2303.00654, 2023. 5, 15" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "7978" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 126 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 126 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 126 + ], + "type": "text", + "content": "[57] Swaroop Ramaswamy, Om Thakkar, Rajiv Mathews, Galen Andrew, H Brendan McMahan, and Françoise Beaufays. Training production language models without memorizing user data. arXiv preprint arXiv:2009.10031, 2020. 1, 2, 6, 13" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 129, + 287, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 129, + 287, + 183 + ], + "spans": [ + { + "bbox": [ + 48, + 129, + 287, + 183 + ], + "type": "text", + "content": "[58] Sashank J. Reddi, Zachary Charles, Manzil Zaheer, Zachary Garrett, Keith Rush, Jakub Konečny, Sanjiv Kumar, and Hugh Brendan McMahan. Adaptive federated optimization. In International Conference on Learning Representations, 2021. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 185, + 288, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 185, + 288, + 239 + ], + "spans": [ + { + "bbox": [ + 48, + 185, + 288, + 239 + ], + "type": "text", + "content": "[59] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252, 2015. 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 241, + 287, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 241, + 287, + 296 + ], + "spans": [ + { + "bbox": [ + 48, + 241, + 287, + 296 + ], + "type": "text", + "content": "[60] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhoginov, and Liang-Chieh Chen. *Mobilenetv2: Inverted residuals and linear bottlenecks*. In *Proceedings of the IEEE conference on computer vision and pattern recognition*, pages 4510–4520, 2018. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 297, + 287, + 351 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 297, + 287, + 351 + ], + "spans": [ + { + "bbox": [ + 48, + 297, + 287, + 351 + ], + "type": "text", + "content": "[61] Florian Schroff, Dmitry Kalenichenko, and James Philbin. Facenet: A unified embedding for face recognition and clustering. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 815-823, 2015. 1, 3, 4, 13" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 353, + 287, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 353, + 287, + 407 + ], + "spans": [ + { + "bbox": [ + 48, + 353, + 287, + 407 + ], + "type": "text", + "content": "[62] Zebang Shen, Jiayuan Ye, Anmin Kang, Hamed Hassani, and Reza Shokri. Share your representation only: Guaranteed improvement of the privacy-utility tradeoff in federated learning. In The Eleventh International Conference on Learning Representations, 2023. 13" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 410, + 287, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 410, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 48, + 410, + 287, + 453 + ], + "type": "text", + "content": "[63] Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. Membership inference attacks against machine learning models. In 2017 IEEE symposium on security and privacy (SP), pages 3-18. IEEE, 2017. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 455, + 287, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 455, + 287, + 498 + ], + "spans": [ + { + "bbox": [ + 48, + 455, + 287, + 498 + ], + "type": "text", + "content": "[64] Hakim Sidahmed, Zheng Xu, Ankush Garg, Yuan Cao, and Mingqing Chen. Efficient and private federated learning with partially trainable networks. arXiv preprint arXiv:2110.03450, 2021. 5, 16" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 500, + 287, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 500, + 287, + 544 + ], + "spans": [ + { + "bbox": [ + 48, + 500, + 287, + 544 + ], + "type": "text", + "content": "[65] Karan Singhal, Hakim Sidahmed, Zachary Garrett, Shanshan Wu, Keith Rush, and Sushant Prakash. Federated reconstruction: Partially local federated learning. Advances in Neural Information Processing Systems (NeurIPS), 2021. 4, 7, 13" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 545, + 287, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 545, + 287, + 599 + ], + "spans": [ + { + "bbox": [ + 48, + 545, + 287, + 599 + ], + "type": "text", + "content": "[66] Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, and Lior Wolf. Deepface: Closing the gap to human-level performance in face verification. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1701-1708, 2014. 3, 4, 13" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 601, + 287, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 601, + 287, + 644 + ], + "spans": [ + { + "bbox": [ + 48, + 601, + 287, + 644 + ], + "type": "text", + "content": "[67] TFF Authors. TensorFlow Federated EMNIST dataset, 2022. https://www.tensorflow.org/federated/api_docs/python/tff/simulation/datasets/ emnist.8,15,17" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 646, + 287, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 646, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 48, + 646, + 287, + 689 + ], + "type": "text", + "content": "[68] TFF Authors. TensorFlow Federated Google Landmark v2 dataset, 2022. https://www.tensorflow.org/federated/api_docs/python/tff/simulation/datasets/gldv2.8,15,17" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "text", + "content": "[69] TFF Authors. TensorFlow Federated iNaturalist dataset, 2022. https://www.tensorflow.com" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 105 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 105 + ], + "type": "text", + "content": "org / federated / api __docs / python / tff / simulation/datasets/inaturalist. 8, 15, 17" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 107, + 545, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 545, + 128 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 545, + 128 + ], + "type": "text", + "content": "[70] US Census Bureau. Disclosure avoidance for the 2020 census: An introduction, 2021. 1, 3, 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 130, + 545, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 130, + 545, + 163 + ], + "spans": [ + { + "bbox": [ + 307, + 130, + 545, + 163 + ], + "type": "text", + "content": "[71] Salil Vadhan. The complexity of differential privacy. In *Tutorialis on the Foundations of Cryptography*, pages 347–450. Springer, 2017. 14" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 164, + 545, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 164, + 545, + 207 + ], + "spans": [ + { + "bbox": [ + 307, + 164, + 545, + 207 + ], + "type": "text", + "content": "[72] Sagar M Waghmare, Hang Qi, Huizhong Chen, Mikhail Sirotenko, and Tomer Meron. Efficient image representation learning with federated sampled softmax. arXiv preprint arXiv:2203.04888, 2022. 4, 13" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 209, + 545, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 209, + 545, + 262 + ], + "spans": [ + { + "bbox": [ + 308, + 209, + 545, + 262 + ], + "type": "text", + "content": "[73] Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H Brendan McMahan, Blaise Aguera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, et al. A field guide to federated optimization. arXiv:2107.06917, 2021. 3, 4, 5, 13, 14" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 264, + 545, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 264, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 308, + 264, + 545, + 308 + ], + "type": "text", + "content": "[74] Jianyu Wang, Rudrajit Das, Gauri Joshi, Satyen Kale, Zheng Xu, and Tong Zhang. On the unreasonable effectiveness of federated averaging with heterogeneous data. arXiv preprint arXiv:2206.04723, 2022. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 309, + 545, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 309, + 545, + 342 + ], + "spans": [ + { + "bbox": [ + 308, + 309, + 545, + 342 + ], + "type": "text", + "content": "[75] Kilian Q Weinberger and Lawrence K Saul. Distance metric learning for large margin nearest neighbor classification. Journal of machine learning research, 10(2), 2009. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 344, + 545, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 344, + 545, + 387 + ], + "spans": [ + { + "bbox": [ + 308, + 344, + 545, + 387 + ], + "type": "text", + "content": "[76] Yandong Wen, Weiyang Liu, Adrian Weller, Bhiksha Raj, and Rita Singh. Sphereface2: Binary classification is all you need for deep face recognition. arXiv preprint arXiv:2108.01513, 2021. 13" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 388, + 545, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 388, + 545, + 443 + ], + "spans": [ + { + "bbox": [ + 308, + 388, + 545, + 443 + ], + "type": "text", + "content": "[77] Tobias Weyand, Andre Araujo, Bingyi Cao, and Jack Sim. Google landmarks dataset v2-a large-scale benchmark for instance-level recognition and retrieval. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2575-2584, 2020. 1, 8, 13" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 445, + 545, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 445, + 545, + 498 + ], + "spans": [ + { + "bbox": [ + 308, + 445, + 545, + 498 + ], + "type": "text", + "content": "[78] Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins, Brendan McMahan, Ohad Shamir, and Nathan Srebro. Is local sgd better than minibatch sgd? In International Conference on Machine Learning, pages 10334-10343. PMLR, 2020. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 500, + 545, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 500, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 308, + 500, + 545, + 533 + ], + "type": "text", + "content": "[79] Lin Wu, Chunhua Shen, and Anton van den Hengel. Personnet: Person re-identification with deep convolutional neural networks. arXiv preprint arXiv:1601.07255, 2016. 13" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 535, + 545, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 535, + 545, + 567 + ], + "spans": [ + { + "bbox": [ + 308, + 535, + 545, + 567 + ], + "type": "text", + "content": "[80] Yuxin Wu and Kaiming He. Group normalization. In Proceedings of the European conference on computer vision (ECCV), pages 3-19, 2018. 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 569, + 545, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 569, + 545, + 622 + ], + "spans": [ + { + "bbox": [ + 308, + 569, + 545, + 622 + ], + "type": "text", + "content": "[81] Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al. Differentially private fine-tuning of language models. arXiv preprint arXiv:2110.06500, 2021. 1, 4, 5" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 624, + 545, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 624, + 545, + 667 + ], + "spans": [ + { + "bbox": [ + 308, + 624, + 545, + 667 + ], + "type": "text", + "content": "[82] Felix Yu, Ankit Singh Rawat, Aditya Menon, and Sanjiv Kumar. Federated learning with only positive labels. In International Conference on Machine Learning, pages 10946-10956. PMLR, 2020. 4" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "text", + "content": "[83] Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny. Barlow twins: Self-supervised learning via redundancy reduction. In International Conference on Machine Learning, pages 12310-12320. PMLR, 2021. 13" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7979" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 288, + 95 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 288, + 95 + ], + "type": "text", + "content": "[84] Chiyuan Zhang, Samy Bengio, and Yoram Singer. 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However, two knotty obstacles limit the practicability of current SGG methods in real-world scenarios: 1) training SGG models requires time-consuming ground-truth annotations, and 2) the closed-set object categories make the SGG models limited in their ability to recognize novel objects outside of training corpora. To address these issues, we novelly exploit a powerful pre-trained visual-semantic space (VSS) to trigger language-supervised and open-vocabulary SGG in a simple yet effective manner. Specifically, cheap scene graph supervision data can be easily obtained by parsing image language descriptions into semantic graphs. Next, the noun phrases on such semantic graphs are directly grounded over image regions through region-word alignment in the pre-trained VSS. In this way, we enable open-vocabulary object detection by performing object category name grounding with a text prompt in this VSS. On the basis of visually-grounded objects, the relation representations are naturally built for relation recognition, pursuing open-vocabulary SGG. We validate our proposed approach with extensive experiments on the Visual Genome benchmark across various SGG scenarios (i.e., supervised / language-supervised, closed-set / open-vocabulary). Consistent superior performances are achieved compared with existing methods, demonstrating the potential of exploiting pre-trained VSS for SGG in more practical scenarios.", + "bbox": [ + 76, + 340, + 472, + 763 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 792, + 209, + 806 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Scene graph [10] is a structured representation for describing image semantics. It abstracts visual objects as graph nodes and represents their relations as labeled graph edges. The task of scene graph generation (SGG) [6, 14,", + "bbox": [ + 76, + 816, + 468, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d96fbdcc4d88f1581bf3a10d302f429444dad4b147a7bf92cb3db3ec2e8b10e0.jpg", + "image_caption": [ + "Figure 1. An illustration of exploiting a pre-trained visual-semantic space (VSS) to trigger language-supervised and open-vocabulary scene graph generation (SGG). (a) We acquire weak scene graph supervision by semantically parsing the image language description and grounding noun phrases on image regions via VSS. (b) At SGG inference time, thanks to the open-vocabulary generalization naturally rooted in VSS, the novel object name (e.g., player) in the text prompt input can be well aligned to one image region, which is regarded as its detection." + ], + "image_footnote": [], + "bbox": [ + 517, + 311, + 874, + 494 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "20, 26, 40, 47, 48, 50, 51, 57, 60, 63, 64] plays an important role for fine-grained visual understanding, which has shown promising results in facilitating various downstream applications, such as image-text retrieval [24, 38, 49], image captioning [2, 22, 32, 35, 52, 54, 55, 66], cross-media knowledge graph construction [18, 45] and robot planning [1].", + "bbox": [ + 496, + 628, + 893, + 719 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Though great effort has been made, SGG of the current stage still faces two knotty obstacles that limit its practicability in real-world scenarios. 1) Training SGG models requires massive ground-truth scene graphs that are expensive for manual annotation. Annotators have to draw bounding boxes for all objects in an image and connect possible interacted object pairs, and assign object/relation labels. Since assigned labels might be ambiguous, further verification and canonicalization processing are usually required [14]. Finally, a scene graph in the form of a set of $\\langle \\text{subject}, \\text{predicate}, \\text{object} \\rangle$ triplets with subject and object bounding boxes is constructed. Such annotating pro", + "bbox": [ + 496, + 719, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation.", + "bbox": [ + 236, + 1, + 807, + 18 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "Except for this watermark, it is identical to the accepted version;", + "bbox": [ + 323, + 16, + 722, + 30 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 292, + 31, + 753, + 45 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 94, + 887, + 220, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "2915", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "cess is time-consuming and tedious, costing much human labor and patience. 2) Almost all existing SGG methods [20,21,26,47,48,50,51,60] involve a pre-defined closed set of object categories, making them limited in recognizing novel objects outside of training corpora. However, real-world scenes contain a border set of visual concepts than any pre-defined category pool. It is very likely to encounter unseen/novel categories. When this happens, current SGG models either classify novel objects to a known category or fail to detect them like background regions. Accordingly, the prediction of their interactions/relations with other objects is negatively affected or just neglected. This may lead to problems. For example, a real-world robot may take inappropriate actions using such closed-set SGG models [1,42].", + "bbox": [ + 75, + 90, + 472, + 303 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recently, there is a trend of leveraging free-form language supervision for benefiting visual recognition tasks via large-scale language-image pre-training [7, 15, 17, 36, 53, 59, 67]. These methods (e.g., CLIP [36]) perform pre-training on massive easily-obtained image-text pairs to learn a visual-semantic space (VSS), and have demonstrated great zero-shot transferability. Especially, the recent grounded language-image pre-training (GLIP) [17] has learned an object-level and semantic-rich VSS. Based on the learned VSS, it has established new state-of-the-art performances in phrase grounding and zero-shot object detection. This indicates such pre-trained VSS has powerful multi-modal alignment ability (i.e., image regions and text phrases that have similar semantics get close embeddings) and open-vocabulary generalization ability (i.e., covering virtually any concepts in the pre-training image-text corpus). This inspires our thought of addressing the aforementioned obstacles in SGG using the pre-trained VSS. On the one hand, taking advantage of its multi-modal alignment ability, we can cheaply acquire scene graph supervision from an image description (e.g., retrieving image regions aligned with noun phrases and re-arranging the description into a scene-graph-like form). On the other hand, by leveraging its open-vocabulary generalization ability, it is promising to enable novel category prediction in SGG.", + "bbox": [ + 75, + 306, + 472, + 685 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this work, we investigate the opportunity of fully exploiting the VSS learned by language-image pre-training to trigger language-supervised and open-vocabulary SGG. Specifically, we obtain weak scene graph supervision by semantically parsing an image language description into a semantic graph, then grounding its noun phrases over image regions through region-word alignment in the pre-trained VSS (Figure 1 (a)). Moreover, we propose a novel SGG model, namely Visual-Semantic Space for Scene graph generation $\\mathbf{V}\\mathbf{S}^{3})$ . It takes a raw image and a text prompt containing object category names as inputs, and projects them into the shared VSS as embeddings. Next, $\\mathrm{VS}^3$ performs object detection by aligning the embeddings of category names and image regions. Based on high-confidence de", + "bbox": [ + 75, + 689, + 472, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "tected objects, VS³ builds relation representations for object pairs with a devised relation embedding module that fully mines relation patterns from visual and spatial perspectives. Finally, a relation prediction module takes relation representations to infer relation labels. The predicted scene graph is composed by combining object detections and inferred relation labels. During training, visually-grounded semantic graphs parsed from image descriptions could be used as weak scene graph supervision, achieving language-supervised SGG. At SGG inference time, when using a text prompt input containing novel categories, VS³ manages to detect novel objects thanks to the open-vocabulary generalization ability naturally rooted in VSS, hence allowing for open-vocabulary SGG (Figure 1 (b)).", + "bbox": [ + 496, + 90, + 893, + 301 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In summary, we have made the following contributions: (1) the exploitation of a pre-trained VSS provides an elegant solution for addressing obstacles to triggering both language-supervised and open-vocabulary SGG, making a solid step toward real-world usage of SGG. (2) The proposed VS3 model is a new and versatile framework, which effectively transfers language-image pre-training knowledge for benefiting SGG. (3) We fully validate the effectiveness of our approach through extensive experiments on the Visual Genome benchmark, and have set new state-of-the-art performances spanning across all settings (i.e., supervised / language-supervised, closed-set / open-vocabulary).", + "bbox": [ + 496, + 301, + 893, + 484 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 497, + 640, + 513 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Fully supervised SGG. The concept of scene graph as a structured image representation is first introduced in [10]. Next, the Visual Genome benchmark [14] is manually annotated with large-scale scene graphs on images. Such annotated dataset triggers a series of innovations [6,20,26,40,47, 48,50,51,57,60] for the fully supervised SGG task. Typically, an object detector (e.g., Faster-RCNN [37]) is trained to retrieve image regions as scene graph nodes. Then, relation representations of object pairs are constructed from visual, spatial and language perspectives, and are used for relation classification to label scene graph edges. To achieve desirable SGG, researchers have devised message-passing mechanisms [20,26,48,50,60] to exploit contextual information, derived contrastive loss functions [62] or incorporated external knowledge [6,40,57]. However, all these methods rely on training with expensive scene graph annotations. Our proposed VS $^3$ model is compatible with fully supervised SGG, but we seek to make SGG training cheaper, which is more practical in real-world applications.", + "bbox": [ + 496, + 523, + 893, + 810 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Language-supervised SGG. This task aims to train SGG models using language descriptions. It has recently attracted increasing attention [21,41,56,58,65], which is also referred as weakly supervised SGG in [21,41,58]. Particularly, based on a graph alignment algorithm, VSPNet [58] first proposes to supervise SGG training with scene graphs", + "bbox": [ + 496, + 810, + 893, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "2916", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "that have no object locations. Subsequent works [21,41,65] extract entities and relations from image captions to compose such unlocalized scene graph, which is achieved via an off-the-shelf language parser [31, 39]. They next follow a common paradigm: first grounding text entities on image regions, and then leveraging grounded scene graphs as pseudo labels to train standard SGG models. To acquire entity groundings, Shi et al [41] devise an efficient graph matching module optimized via contrastive learning; Zhong et al [65] simply match text entity names with predicted object labels from a pre-trained object detector using semantic rules such as WordNet [33] synsets matching. More recently, Li et al [21] integrate interaction-aware knowledge distilled from pre-trained language-image models [16] for enhancing grounding reliability. Instead, we propose to obtain groundings through region-word alignment in a pretrained VSS, which is much simple yet more effective to collect scene graph supervision from language.", + "bbox": [ + 75, + 90, + 472, + 363 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Language-image pre-training. This has been shown effective for boosting various vision-language downstream tasks [11, 16, 23, 29, 30, 34, 43], e.g., image-text retrieval, image captioning. Also, recent studies present remarkable results on transferring pre-trained language-image knowledge to solve vision recognition problems, such as zero-shot image classification [9,36], open-vocabulary object detection [7, 17, 53, 59, 67] and zero-shot semantic segmentation [15]. For example, CLIP [36] and ALIGN [9] learn separate encoders to embed image and text into a shared space by pre-training on massive image-text pairs using a contrastive loss. They have demonstrated remarkable generalization ability on zero-shot image classification after pretraining. Distinct from CLIP and ALIGN that learn image-level representations, GLIP [17] focuses on learning object-level visual representations through region-word alignment. It has attained strong zero-shot and few-shot transferability to various object-level recognition tasks such as object detection and phrase grounding. Most recently, He et al [8] investigate a visual-relation pre-training and prompt-based fine-tuning method for open-vocabulary SGG. However, its image encoder relies on a pre-trained region proposal extractor, which is a bottleneck for achieving open-vocabulary SGG under the SGDET protocol. Unlike [8], our proposed VS3 directly encodes an image into region tokens, avoiding the bottleneck of region proposals. More importantly, our approach addresses obstacles to achieving both language-supervised and open-vocabulary SGG using a unified framework, while He et al [8] only focus on the latter.", + "bbox": [ + 75, + 364, + 472, + 803 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Approach", + "text_level": 1, + "bbox": [ + 76, + 819, + 187, + 835 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Notation & Overview", + "text_level": 1, + "bbox": [ + 76, + 845, + 277, + 859 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The task of scene graph generation (SGG) aims to map an image into an abstract graph $SG = \\{O, R\\}$ , where graph", + "bbox": [ + 76, + 869, + 468, + 902 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/81365590105a84ab7998a9dacfe46de8c72bb2de6f75bb3263515c341b3e425a.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
SG\\(\\mathcal{C}_{o}^{target}\\)Not containing novel object classesContaining novel object classes
Manually annotatedfully supervised & closed-setfully supervised & open-vocabulary
Automatically parsed from image descriptionslanguage-supervised & closed-setlanguage-supervised & open-vocabulary
", + "bbox": [ + 504, + 87, + 890, + 161 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Table 1. Definitions of different SGG settings, according to scene graph supervision $SG$ and the object set at inference $\\mathcal{C}_o^{target}$ .", + "bbox": [ + 498, + 164, + 890, + 191 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "nodes $O = \\{o_1,\\dots,o_N\\}$ correspond to image objects, and graph edges $R = \\{r_1,\\dots,r_M\\}$ represents their relations. Each object $o_i = \\{\\mathbf{b}_i,l_i\\} \\in O$ contains the bounding box coordinates $\\mathbf{b}_i\\in \\mathbb{R}^4$ and its class label information $l_{i}\\in \\mathcal{C}_{o}$ where $\\mathcal{C}_o$ denotes the set of object categories. Each relation $r_m\\in R$ is a $\\langle$ subject,predicate,object $\\rangle$ triplet, and we represent it as $r_m = r_{i\\rightarrow j} = \\{o_i,p_{ij},o_j\\}$ , in which $p_{ij}$ is the predicate/relation label belonging to category set $\\mathcal{C}_r$ .", + "bbox": [ + 496, + 196, + 890, + 318 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Most existing SGG methods require expensive manually annotated scene graphs $SG$ as supervision. And they involve a closed set of object categories $\\mathcal{C}_o$ in both training and inference. These issues limit SGG for practical usage. In this work, we propose to fully exploit a pre-trained VSS to push SGG towards language-supervised and open-vocabulary scenarios. We illustrate the definitions of different SGG settings in Table 1. Concretely, from the perspective of scene graph supervision $SG$ , SGG is categorized into fully supervised and language-supervised, using $SG$ from manual annotation and language respectively. From the other perspective of object categories $\\mathcal{C}_o^{target}$ at inference, it is referred to as open-vocabulary or closed-set according to whether or not $\\mathcal{C}_o^{target}$ contains novel objects.", + "bbox": [ + 496, + 319, + 892, + 531 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Next, in Section 3.2, we present a new SGG model named VS3, which is versatile to handle with all SGG settings in Table 1. In Section 3.3, we devise a scheme to obtain scene graph supervision from language descriptions, allowing for language-supervised SGG. Finally, Section 3.4 details the strategy of transferring the proposed VS3 to pursue open-vocabulary SGG.", + "bbox": [ + 496, + 531, + 893, + 638 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. The Proposed VS3 Model", + "text_level": 1, + "bbox": [ + 500, + 648, + 732, + 666 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We propose the VS3 model for tackling the SGG task by extending the GLIP [17] framework with relation recognition modules, as shown in Figure 2.", + "bbox": [ + 496, + 672, + 890, + 718 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Preliminary. GLIP unifies object detection and phrase grounding into one framework. It has an image encoder $\\mathrm{Enc}_I$ (e.g., Swin Transformer backbone [28]) and a text encoder $\\mathrm{Enc}_L$ (e.g., BERT [12]). $\\mathrm{Enc}_I$ extracts region/box features $\\bar{O} \\in \\mathbb{R}^{\\bar{N} \\times d}$ from an input image, where $\\bar{N}$ is the number of regions and $d$ is the feature dimension. $\\mathrm{Enc}_L$ encodes a text input into contextualized word-token embeddings $\\bar{P} \\in \\mathbb{R}^{\\bar{T} \\times d}$ , where $\\bar{T}$ is the text length. Further, GLIP uses a cross-modal fusion module to achieve feature communication between $\\bar{O}$ and $\\bar{P}$ , resulting in enriched region embeddings $\\tilde{O} \\in \\mathbb{R}^{\\bar{N} \\times d}$ and word embeddings $\\tilde{P} \\in \\mathbb{R}^{\\bar{T} \\times d}$ . Finally, the region-word alignment", + "bbox": [ + 496, + 719, + 893, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "2917", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/c4653213c8d505a41d7c4292da75a5b33f71d82284a36c0a2bf967849c07f73f.jpg", + "image_caption": [ + "Figure 2. An overview of the proposed Visual-Semantic Space for Scene graph generation $(\\mathrm{VS}^3)$ model. It inherits the image encoder, the text encoder and the cross-modal fusion module from GLIP [17], so as to project image regions and text prompt words in a pre-trained visual-semantic space (VSS). Object regions are detected by aligning the embeddings of category names and image regions in VSS. Next, high-confidence detected results are retained to compose subject-object pairs. After that, the relation embedding module constructs their relation representations by extracting visual and spatial features, on which relation prediction is performed. At test time, thanks to the open-vocabulary generalization ability of VSS, $\\mathrm{VS}^3$ manages to detect novel objects by switching to a text prompt containing novel classes." + ], + "image_footnote": [], + "bbox": [ + 102, + 95, + 867, + 292 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "scores $\\hat{S}_{\\text {ground }} = \\tilde{O}\\tilde{P}^{\\top} \\in \\mathbb{R}^{\\bar{N} \\times \\bar{T}}$ and the predicted locations $\\vec{B} = \\text { box\\_predictor }(\\vec{O}) \\in \\mathbb{R}^{\\bar{N} \\times 4}$ are supervised by ground-truth text grounding data. After large-scale pretraining, $\\mathrm{Enc}_I$ and $\\mathrm{Enc}_L$ embed the input image and text into a joint VSS, which aligns multi-modal embeddings and covers open-vocabulary concepts. VS3 inherits Enc $I$ , Enc $L$ and the cross-module fusion module from GLIP.", + "bbox": [ + 75, + 388, + 468, + 496 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Text prompt. Considering that object detection has been reformulated as phrase grounding, VS3 also requires a text prompt input except for the image input. Following GLIP's design, we set the text prompt for object detection in the form of \"name(c1). name(c2). ... name(c|cO)\". where $c_i \\in \\mathcal{C}_o$ and $\\text{name}(c_i)$ gets the category name of $c_i$ (e.g., person). Hence, an object is detected according to the alignment score between a region embedding $\\tilde{\\mathbf{o}}_i \\in \\tilde{\\mathcal{O}}$ (the $i$ th row) and the category name embeddings $\\tilde{P}$ .", + "bbox": [ + 75, + 498, + 468, + 633 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Relation embedding module. To further enable VS3 with relation recognition ability, we devise the relation embedding module to build relation representations. Based on the region/box features $\\tilde{O}$ after cross-modal fusion, we first sample a subset of regions $\\tilde{O}' \\in \\mathbb{R}^{N' \\times d}$ that are most likely to be valid objects. This is achieved by matching predicted bounding boxes $\\hat{B}$ with ground-truth objects during training, and by retaining top- $N'$ regions with the highest confidence scores after non-maximum suppression (NMS) at inference. Next, we construct relation representations for all possible subject-object pairs. Given an object pair $(\\tilde{\\mathbf{o}}_i, \\tilde{\\mathbf{o}}_j)$ and their normalized bounding boxes $(\\mathbf{b}_i, \\mathbf{b}_j)$ , the pairwise relation representation is represented as $\\mathbf{pair}_{i \\rightarrow j} = cat[\\mathbf{pair}_{i \\rightarrow j}^{\\text{visual}}, \\mathbf{pair}_{i \\rightarrow j}^{\\text{spatial}}]$ . This is the concatenation of features mined from the visual and spatial perspectives. The visual feature is computed by", + "bbox": [ + 75, + 633, + 470, + 878 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {p a i r} _ {i \\rightarrow j} ^ {\\text {v i s u a l}} = \\boldsymbol {f} _ {\\text {d i f f}} (\\tilde {\\mathbf {o}} _ {i} - \\tilde {\\mathbf {o}} _ {j}) + \\boldsymbol {f} _ {\\text {s u m}} (\\tilde {\\mathbf {o}} _ {i} + \\tilde {\\mathbf {o}} _ {j}), \\qquad (1)\n$$\n", + "text_format": "latex", + "bbox": [ + 122, + 883, + 468, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\pmb{f}_{diff}$ and $\\pmb{f}_{sum}$ are two mapping functions implemented as 2-layer MLPs (multi-layer perceptron). By defining the normalized center coordinates of two involved objects as $(ct_i^x, ct_i^y)$ and $(ct_j^x, ct_j^y)$ , the spatial feature is measured as", + "bbox": [ + 496, + 390, + 890, + 465 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {p a i r} _ {i \\rightarrow j} ^ {\\text {s p a t i a l}} = \\operatorname {c a t} \\left[ \\mathbf {b} _ {i}, \\mathbf {b} _ {j}, d x, d y, \\operatorname {d i s}, \\theta , A _ {i}, A _ {j}, I, U \\right], \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 519, + 473, + 890, + 492 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $dx = ct_i^x - ct_j^x, dy = ct_i^y - ct_j^y,$ $\\sqrt{dx^2 + dy^2}, \\theta = \\arctan \\left(\\frac{dy}{dx}\\right). A_i, A_j, I, U$ denote the areas of the subject, the object, their intersection, and union boxes, respectively.", + "bbox": [ + 498, + 500, + 890, + 563 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Relation prediction. Conditioned on the relation representation $\\mathbf{pair}_{i\\rightarrow j}$ of each object pair, we predict a relateness score $\\hat{z}_{i\\rightarrow j} = f_{relateness}(\\mathbf{pair}_{i\\rightarrow j})\\in [0,1]$ and a semantic label probability $\\hat{\\pmb{y}}_{i\\rightarrow j} = \\pmb{f}_{semantic}(\\pmb{pair}_{i\\rightarrow j})\\in$ $[0,1]^{|\\mathcal{C}_r|}$ . The relateness $\\hat{z}_{i\\rightarrow j}$ represents the probability that relations exist between the object pair. $f_{relateness}$ is implemented with an MLP coupled with Sigmoid activation. $\\pmb{f}_{semantic}$ is implemented with another MLP using Softmax activation. The total loss for relation recognition $L_{rel\\_rcg}$ is measured as", + "bbox": [ + 498, + 564, + 890, + 715 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nL _ {\\text {r e l a t e n e s s}} = F L \\left(\\hat {z} _ {i \\rightarrow j}, z _ {i \\rightarrow j}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 573, + 729, + 890, + 744 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nL _ {\\text {s e m a n t i c}} = C E \\left(\\hat {\\boldsymbol {y}} _ {i \\rightarrow j}, \\boldsymbol {y} _ {i \\rightarrow j}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 573, + 748, + 890, + 763 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nL _ {r e l. r c g} = L _ {r e l a t e n e s s} + L _ {s e m a n t i c}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 573, + 768, + 890, + 782 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $FL$ and $CE$ denote focal loss [25] and cross-entropy loss functions. $z_{i\\rightarrow j}$ and $\\pmb{y}_{i\\rightarrow j}$ represent the ground-truth relateness label and predicate category label respectively.", + "bbox": [ + 496, + 794, + 890, + 840 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Training & inference. During training, we initialize parameters from pre-trained GLIP models for inherited modules in VS3. To ease training difficulty, we freeze the image encoder and text encoder, and only fine-tune the", + "bbox": [ + 496, + 840, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "2918", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "cross-modal fusion module and devised modules for relation recognition. This also avoids the degeneration of the pre-trained VSS. At inference, by retaining high-confidence detected objects and further predicting their relations, we generate an image scene graph representation.", + "bbox": [ + 75, + 90, + 470, + 167 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Obtaining Language Scene Graph Supervision", + "text_level": 1, + "bbox": [ + 76, + 176, + 468, + 194 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Ground-truth scene graphs are time-consuming to annotate. Alternatively, we can parse semantic graphs from image language descriptions, and obtain noun phrase groundings through region-word alignment in the pre-trained VSS (implemented with an off-the-shelf GLIP). This is a much cheaper way to obtain weak scene graph supervision.", + "bbox": [ + 75, + 200, + 468, + 291 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Semantic graph parsing. Concretely, for each image language description, we parse it into a semantic graph $SG^{text} = \\{O^{text}, R^{text}\\}$ using the Standard Scene Graph Parser based on [39]. The parser not only extracts noun phrases as entities/objects ( $O^{text}$ ), but also extracts the words describing their relations ( $R^{text}$ ). For example, the sentence \"a woman is playing the piano in the room.\" is parsed to the $SG^{text}$ , of which $O^{text} = \\{woman, piano, room\\}$ and $R^{text} = \\{\\langle 0, playing, 1\\rangle, \\langle 0, in, 2\\rangle\\}$ (numbers denote object indices). Considering that parsed object/relation words are free-form, we map them to our concerning categories (e.g., VG150 object/relation categories in experiments) by rules such as direct string matching and WordNet [33] synsets matching following [65].", + "bbox": [ + 75, + 291, + 468, + 518 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Semantic graph grounding. Note that each element of $O^{text}$ only contains a text label name so far, and its bounding box information is still missing. To obtain bounding boxes, we construct a text prompt using triplets in $SG^{text}$ , e.g., \"woman playing piano. woman in room.\" Then, we feed such text prompt together with the raw image into a pre-trained GLIP, in order to acquire grounding boxes of $O^{text}$ . Specifically, for each element in $O^{text}$ , we select the image region that has the highest alignment score with its category name as its grounding box. Since there might be multiple objects in $O^{text}$ that actually refer to the same object, we perform a post-processing NMS to merge boxes with the same label and high IoU (intersection over union) scores ( $\\geq 0.9$ ). Finally, with box information, the visually-grounded $SG^{text}$ is ready to be used as weak supervision for training SGG models, e.g., the proposed VS3.", + "bbox": [ + 75, + 518, + 468, + 761 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Transferring to Open-vocabulary SGG", + "text_level": 1, + "bbox": [ + 76, + 770, + 410, + 787 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Open-vocabulary SGG [8] aims to train SGG models that can recognize objects of novel categories and their involved relations. Formally, we train the SGG model with scene graphs containing objects in the base category set $\\mathcal{C}_o^{base}$ . At inference, the object category set is $\\mathcal{C}_o^{target}$ , which contains novel categories in $\\mathcal{C}_o^{novel} = \\mathcal{C}_o^{target} \\backslash \\mathcal{C}_o^{base} \\neq \\emptyset$ .", + "bbox": [ + 75, + 794, + 468, + 885 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Back to our proposed VS3, an open-vocabulary VSS is", + "bbox": [ + 96, + 885, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "maintained by freezing the image and text encoders. Taking this advantage, we devise a scheme to adapt VS3 for open-vocabulary SGG. Concretely, during training, we set the text prompt as “name(c1). name(c2). ... name(cObase)”. where $c_{i} \\in \\mathcal{C}_{o}^{base}$ . And only relation triplets involving base object categories are kept for training. At inference, the text prompt is switched to be “name(c1). name(c2). ... name(cOtarget)”. where $c_{i} \\in \\mathcal{C}_{o}^{target}$ . In this way, a novel object class (e.g., lady) may have an embedding close to a base category (e.g., woman) embedding. This makes the novel class also able to find well-aligned image regions. Note that relation representations are constructed from visual and spatial cues, which are usually class-agnostic. Hence, the following relation recognition in VS3 will not be affected when encountering novel objects.", + "bbox": [ + 496, + 90, + 890, + 320 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 498, + 335, + 633, + 353 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets and Experimental Settings", + "text_level": 1, + "bbox": [ + 498, + 361, + 808, + 378 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. To evaluate the SGG task, we adopt the widely-used VG150 version [50] of the Visual Genome (VG) dataset [14]. VG150 retains the most frequent 150 categories and 50 relation/predicate categories in VG. It contains $\\sim 108\\mathrm{K}$ images, of which $70\\%$ images are used for training (including 5K for validation), and the remaining $30\\%$ images are used for testing. The annotated scene graph of each image has 11.5 objects and 6.2 relation triplets on average. In addition, images of VG are densely annotated with region descriptions, about 50 descriptions for each image. We refer to these descriptions as VG caption, which provides a text source for evaluating the language-supervised SGG setting. Moreover, we consider the challenging setting of using image-text pairs in COCO caption [4] for training SGG models. This dataset contains 123K images in total. Each image has 5 human-annotated captions. We keep $\\sim 106\\mathrm{k}$ images by filtering out those images that also exist in the VG150 test split.", + "bbox": [ + 496, + 386, + 890, + 657 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation protocols and metrics. We mainly adopt the SGDET [47,50] protocol, which generates a scene graph from the input image without any given box information. We report the performance on Recall@K (K=20/50/100) following previous works [21, 41, 47, 50, 56, 65], which measures the fraction of correctly predicted relation triplets in top $K$ predictions. A triplet prediction is considered as correct when its subject, object, predicate labels and both the subject and object regions match with (same label or IoU>0.5) a ground-truth triplet. Note that we obtain triplet predictions using graph constraint, which limits each subject-object pair to have only the most confident predicate. All recall metrics across different SGG settings in experiments are computed over VG150 test images. Considering that the adopted GLIP pre-trained VSS has seen part of images in the original VG150 test split ( $\\sim$ 26k) during", + "bbox": [ + 496, + 659, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "2919", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "pre-training, we exclude these images and get a new split of $\\sim 15\\mathrm{k}$ test images. We have validated that such VG150 test split is sufficiently large for computing stable metrics as the original, by comparing computed metrics of several SGG models (in codebase [46]) on these two splits ( $< 0.15$ points variation, see supplementary materials).", + "bbox": [ + 75, + 90, + 470, + 181 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation details. We initialize VS3 from pretrained GLIP [17] models, i.e., the GLIP-T and the larger GLIP-L trained with more data. Both construct a VSS of dimension $d = 256$ . We retain the top 36 object detections per image for pairwise relation recognition. The whole framework is fine-tuned on 8 Nvidia 2080Ti GPUs with AdamW optimizer. During fine-tuning, we freeze the parameters of the image and text encoder; and set the learning rate for the cross-modal fusion module as 1e-5 and 10x larger learning rates for the relation embedding and prediction modules. The maximum fine-tuning epoch number is 10, with learning rates dropping by 10x after 6 epochs.", + "bbox": [ + 75, + 181, + 470, + 364 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Fully Supervised SGG", + "text_level": 1, + "bbox": [ + 76, + 376, + 284, + 391 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Setup. We first evaluate our proposed VS3 under the conventional fully supervised SGG setting. This setting trains SGG models using manually annotated scene graphs, consisting of object labels coupled with bounding boxes, and relation labels. We adopt VG150 for training and evaluation following previous methods [3, 19, 26, 27, 48, 50, 60, 61, 65]. All these methods involve a closed set of object categories. Specifically, the text prompt input of VS3 is constructed from VG150 object category names, i.e., \"airplane. animal. ... zebra\". We train VS3 by fine-tuning over two GLIP variants: GLIP-T with the Swin-T [28] backbone, GLIP-L with the Swin-L [28] backbone.", + "bbox": [ + 75, + 398, + 468, + 580 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparison with state-of-the-arts. The results are summarized in Table 2. Our proposed VS3 model using the Swin-T backbone already achieves competitive recall metrics. When upgrading to the larger Swin-L variant, the performance improvements become significant (1.8 to 3.4 points improvement than the previous best results). Note that previous methods [26, 50, 60] build their models upon an off-the-shelf object detector, and they usually design heavy message-passing modules to incorporate context information. Instead, VS3 devises a light-weighted relation recognition head (including the relation embedding and prediction modules) over a pre-trained VSS. The superior performances clearly suggest the merits of transferring language-image pre-trained models for boosting SGG.", + "bbox": [ + 75, + 583, + 468, + 792 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Ablation on relation representation. Next, we carry out ablation studies on relation representation construction in the relation embedding module. As shown in Table 2, by removing visual and spatial feature components, the relation triplet recalls drop accordingly. Also notice that the removal of visual features which is built from subject and object region embeddings leads to relatively larger perfor", + "bbox": [ + 75, + 795, + 468, + 901 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/ba2e641767da4fa1e7cb8085631ac7d38074fc58775feaf9e0bba357857355a5.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
SGG modelDetectorBackboneR@20R@50R@100
FCSGG [27]-HRNetW4816.121.325.1
SGTR [19]DETRR-101-24.628.4
IMP [50]Faster-RCNNVGG-1614.620.724.5
KERN [3]Faster-RCNNVGG-16-27.129.8
MOTIFS [60]Faster-RCNNVGG-1621.427.230.3
RelDN [62]Faster-RCNNVGG-1621.128.332.7
VTransE [61]Faster-RCNNRX-10123.029.734.3
MOTIFS [60]Faster-RCNNRX-10125.132.136.9
VCTREE [48]Faster-RCNNRX-10124.731.536.2
SGNLS [65]Faster-RCNNRX-10124.631.836.3
HL-Net [26]Faster-RCNNRX-10126.033.738.1
VS3-Swin-T26.134.539.2
w/o visual-Swin-T23.131.636.7
w/o spatial-Swin-T24.332.837.8
VS3-Swin-L27.836.641.5
", + "bbox": [ + 506, + 88, + 890, + 277 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Experimental results of fully supervised SGG. w/o visual and w/o spatial indicate removing spatial and visual features in the relation embedding module for relation representation. All metrics are computed under the SGDET protocol on VG150 test images.", + "bbox": [ + 498, + 280, + 893, + 337 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "mance drops than spatial. This suggests the region embeddings in the pre-trained VSS provide strong cues for relation recognition. Overall, these observations validate the effectiveness of our design to mine relation patterns.", + "bbox": [ + 498, + 351, + 890, + 412 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Language-supervised SGG", + "text_level": 1, + "bbox": [ + 500, + 422, + 741, + 439 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Setup. Language-supervised SGG [21, 41, 56, 65] explores to train SGG models with language descriptions of images. Concretely, we parse each image description into a semantic graph, in the form of a set of $\\langle$ subject, predicate, object $\\rangle$ triplets. Note that parsed object/relation phrases from language descriptions are freeform, we map them to VG150 categories by semantic rules following [65], such as WordNet [33] synsets matching. This makes the learned SGG model compatible for evaluating on VG150. Next, the parsed semantic graph is grounded to image regions using grounding methods, i.e., the pretrained GLIP-L [17] in our approach. Finally, the visually-grounded semantic graphs are used as weak supervision to train our proposed VS $^3$ like the fully supervised setting.", + "bbox": [ + 496, + 446, + 890, + 656 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Particularly, we have trained VS3 with text triplets parsed from three different sources of text following [21, 65]. 1) The unlocalized graph setting uses ground-truth triplet annotations in VG. 2) The VG caption setting uses triplets that are automatically parsed from natural image descriptions in VG. 3) The COCO caption setting leverages triplets parsed from captions in COCO. This setting is the most challenging since COCO captions are image-level descriptions. Such captions are different from the region-level descriptions in VG, which focus on describing object interactions. Also, note that the number of annotated captions for each COCO image (average 5) is much less than the number for each VG image (average $\\sim 50$ ).", + "bbox": [ + 496, + 657, + 890, + 853 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparison with state-of-the-arts. The experimental results compared with previous methods are presented in Table 3. All evaluation metrics are com", + "bbox": [ + 498, + 854, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "2920", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/9bceb9c8380890e1713655e7c83596d95fac3854093da4605484c520753504c1.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
SGG modelGroundingR@20R@50R@100
Unlocalized graphVSPNet [58]--4.705.40
LSWS [56]--7.308.73
MOTIFS [60]WSGM [41]4.125.596.45
MOTIFS [60]SGNLS [65]7.239.2810.71
MOTIFS [60]Li et.al [21]9.0911.3912.89
Uniter† [5]SGNLS [65]7.8110.0311.50
Uniter† [5]Li et.al [21]9.5711.8013.15
VS3(Swin-T)GLIP-L [17]18.0223.8928.19
VS3(Swin-T+FreqBias)GLIP-L [17]20.0626.7231.75
VS3(Swin-L+FreqBias)GLIP-L [17]22.1829.8134.96
VG captionLSWS [56]--3.854.04
MOTIFS [60]SGNLS [65]6.318.059.21
MOTIFS [60]Li et.al [21]8.2510.5011.98
Uniter† [5]SGNLS [65]-9.2010.30
Uniter† [5]Li et.al [21]8.9010.9312.14
VS3(Swin-T)GLIP-L [17]11.7816.2519.7
VS3(Swin-L)GLIP-L [17]13.0117.3820.54
COCO captionLSWS [56]--3.283.69
MOTIFS [60]Li et.al [21]5.026.407.33
Uniter† [5]SGNLS [65]-5.806.70
Uniter† [5]Li et.al [21]5.426.747.62
VS3(Swin-T)GLIP-L [17]5.597.308.62
VS3(Swin-L)GLIP-L [17]6.048.159.90
", + "bbox": [ + 96, + 88, + 450, + 369 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "puted on the VG150 test set under the SGDET protocol. Specifically, under the unlocalized scene graphs setting, $\\mathrm{VS}^3$ with the Swin-T backbone ( $\\mathrm{VS}^{3}_{(Swin-T)}$ ) obtains substantial improvements on recall metrics over existing best results ( $R@20/50/100$ from 9.57/11.80/13.15 to 18.02/23.89/28.19). Since relation frequency statistics are available in this setting, we use them as frequency biases [60] in predicate classification, leading to further performance gains ( $\\mathrm{VS}^{3}_{(Swin-T+FreqBias)}$ ). When using the stronger Swin-L backbone ( $\\mathrm{VS}^{3}_{(Swin-L+FreqBias)}$ ), we attain the highest performances ( $R@20/50/100 = 22.18/29.81/34.96$ ), which even outperform many fully supervised methods (see Table 2). The VG caption setting provides weaker scene graph supervision via language parsing. We observe that our approach also outperforms previous state-of-the-art methods significantly. As for the most challenging COCO caption setting, it suffers from the additional domain shift problem since it trains on COCO but evaluates on VG150. As expected, the performances are lower than in the two aforementioned settings. But when comparing with previous works using the same text source, our approach still manages to achieve better performances. Overall, our approach consistently surpasses previous methods for language-supervised SGG. This demonstrates the benefits brought by pre-trained language-image models in terms of both grounding box acquirement and task transferring to tackle SGG.", + "bbox": [ + 73, + 446, + 470, + 852 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablation on scene graph parsing strategy. We also conduct ablation studies on different scene graph parsing strategies for obtaining language SGG supervision. The re", + "bbox": [ + 75, + 854, + 468, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/1919eb229a5a79d05f13f8ce1d690250ab325794f7346c260ef1c88393ac2a6b.jpg", + "table_caption": [ + "Table 3. Comparison with state-of-the-art language-supervised SGG methods, using weak scene graph supervision from three different text sources: unlocalized scene graphs, VG caption and COCO caption. All metrics are computed under the SGDET protocol on VG150 images. ( $\\dagger$ indicates adapted for SGG.)" + ], + "table_footnote": [], + "table_body": "
SG fromSG parserR@20/50/100
Single captionSimple5.07 / 6.25 / 7.36
All captionsSimple5.42 / 6.82 / 7.93
All captionsAdvanced5.59 / 7.30 / 8.62
", + "bbox": [ + 560, + 88, + 828, + 147 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Ablation on scene graph parsing strategies for language-supervised SGG. Results are obtained with VS $^3$ (Swin-T) trained on scene graph supervision parsed from COCO captions.", + "bbox": [ + 498, + 150, + 890, + 191 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "sults are shown in Table 4. Note that each image in COCO is annotated with 5 captions, and these captions are usually complimentary in describing image content. At first, we compare the performances between training with triplets from a single caption and all captions. We see the recalls achieve relative $10\\%$ performance boosts by replacing triplets from a single caption with all captions. This suggests the completeness of extracted scene graphs from image descriptions is a non-negligible factor for training a high-quality SGG model.", + "bbox": [ + 496, + 196, + 890, + 348 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Moreover, we compare two language parsers for extracting $\\langle$ subject, predicate, object $\\rangle$ triplets: the simple SG parser [31], and the advanced SG parser [39]. Both parsers apply pre-defined rules to extract object and relation concepts from the semantic graphs of image language descriptions. Compared with the simple SG parser, the advanced SG parser covers additional features for dealing with complex quantificational modifiers (e.g., a lot of), resolving pronouns (e.g., it) and handling plural nouns (e.g., three men). The performance boosts of the advanced SG parser over the simple one (recalls from 5.42/6.82/7.93 to 5.59/7.30/8.62), indicates that the quality of semantic parsing is also important for language-supervised SGG.", + "bbox": [ + 496, + 349, + 892, + 547 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Open-vocabulary SGG", + "text_level": 1, + "bbox": [ + 500, + 558, + 712, + 574 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Setup. Following [8], we train the proposed VS3 with the same $70\\%$ object categories of VG150 as base categories. With the aid of the pre-trained VSS, we hope VS3 can generalize to recognize the remaining $30\\%$ novel objects and their involved relations at inference. Concretely, we compute evaluation metrics over two object category sets: $70\\%$ base + $30\\%$ novel objects (dubbed as open-vocabulary SGG (Ov-SGG) evaluation), and $30\\%$ novel objects (dubbed as zero-shot SGG (ZsO-SGG) evaluation).", + "bbox": [ + 496, + 582, + 890, + 717 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In addition, we adopt the PREDCLS and SGDET evaluation protocols [50]. PREDCLS assumes object information given, yet SGDET generates scene graphs from the raw image using predicted objects. Since VS3 detects objects in a one-stage manner, we implement PREDCLS by selecting image regions that best match the ground-truth objects in post-processing, then performing relation recognition. We neglect the SGCLS protocol that assumes bounding box information given. This is because given bounding boxes can be directly used as region proposals in two-stage detectors, while the adopted one-stage manner in VS3 has no region proposal counterpart.", + "bbox": [ + 496, + 719, + 892, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "2921", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/5f77b02586a42a8e825df61af8bcd6802c835b044ab8ed8b9a8b2b5ddd35a82b.jpg", + "table_caption": [], + "table_footnote": [ + "Table 5. Evaluation results (R@50/100) of fully supervised open-vocabulary SGG. Ov-SGG evaluates on $70\\%$ base categories + $30\\%$ novel categories in VG150, while ZsO-SGG only evaluates on $30\\%$ novel categories." + ], + "table_body": "
MethodOv-SGG (70%+30%)ZsO-SGG (30%)
PREDCLSSGDETPREDCLSSGDET
IMP [50]40.02 / 43.40-37.01 / 39.46-
MOTIFS [60]41.14 / 44.70-39.53 / 41.14-
VCTREE [48]42.56 / 45.84-41.27 / 42.52-
TDE [47]38.29 / 40.38-34.15 / 36.37-
GCA [13]43.48 / 46.26-42.56 / 43.18-
EBM [44]44.09 / 46.95-43.27 / 44.03-
SVRP [8]47.62 / 49.94-45.75 / 48.39-
VS3(Swin-T)50.10 / 52.0515.07 / 18.7346.91 / 49.1310.08 / 13.65
VS3(Swin-L)55.88 / 58.1823.13 / 28.4954.44 / 57.3521.51 / 27.62
", + "bbox": [ + 84, + 88, + 460, + 209 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Fully supervised results. We first conduct experiments using manually annotated scene graphs. The results are presented in Table 5. For both Ov-SGG and ZsO-SGG, VS3 achieves substantial performance improvements under PREDCLS. When upgrading to the stronger backbone Swin-L, more significant improvements are obtained. More importantly, we report performances for the challenging and more practical SGDET, which are neglected by all previous methods since their used object detector cannot handle open-vocabulary detection [8]. The SGDET performances $(R@50/100 = 10.08/13.65)$ of ZsO-SGG using VS3 are even higher than SGCLS metrics of SVRP $(R@50/100 = 9.30/11.32$ in [8]). This reveals the superiority of our approach to recognizing novel objects thanks to the open-vocabulary generalization ability of the pre-trained VSS.", + "bbox": [ + 75, + 276, + 468, + 503 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Language-supervised results. Next, we evaluate the most challenging setting, i.e., open-vocabulary SGG using language supervision. To our knowledge, we are the first to propose such a new and practical SGG setting, and present the benchmark performances in Table 5. Not surprisingly, the recalls obtained via language-supervised training (i.e., $SG$ from VG caption or COCO caption) are lower than supervised results (i.e., $SG$ from annotated). When comparing $VS^{3}_{(Swin-T)}$ and $VS^{3}_{(Swin-L)}$ that is transferred from a stronger pre-trained model, the latter gets substantially higher Ov-SGG and ZsO-SGG performances. More importantly, we observe the performance gap between Ov-SGG and ZsO-SGG get closer in $VS^{3}_{(Swin-L)}$ , e.g., the $R@50$ gap under the VG caption setting becomes $12.98 - 10.71 = 2.27$ from $7.61 - 4.06 = 3.55$ . This is due to the better generalization ability for recognizing novel classes. Moreover, the superior performances obtained by VG caption over COCO caption, indicate that using dense region-level descriptions and avoiding domain shift will help improve language-supervised open-vocabulary SGG in practice.", + "bbox": [ + 75, + 505, + 470, + 808 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Qualitative analysis. We further showcase qualitative results of open-vocabulary SGG in Figure 3. The results demonstrate that our approach manages to detect novel objects and their relations with other objects. We also find that, compared with the fully supervised setting, the language-supervised results bias to predict simple relations such as", + "bbox": [ + 75, + 810, + 468, + 901 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/88ba72315d3ad3324aa7e9af6121bad5ca097ce3ad832f1f4f9dbeddafe85110.jpg", + "table_caption": [], + "table_footnote": [ + "Table 6. Evaluation results (R@50/100) of open-vocabulary SGG using three different scene graph supervisions: manual annotation, VG caption and COCO caption (language-supervised). Ov-SGG evaluates on $70\\%$ base categories $+30\\%$ novel categories in VG150, while ZsO-SGG only evaluates on $30\\%$ novel categories." + ], + "table_body": "
MethodSG supervisionOv-SGG (70%+30%)ZsO-SGG (30%)
VS3(Swin-T)Manual annotation15.07 / 18.7310.08 / 13.65
VG caption7.61 / 9.604.06 / 5.58
COCO caption4.39 / 5.633.65 / 4.73
VS3(Swin-L)Manual annotation23.13 / 28.4921.51 / 27.62
VG caption12.98 / 16.2910.71 / 13.70
COCO caption6.76 / 8.456.26 / 7.89
", + "bbox": [ + 514, + 88, + 877, + 170 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/103f5cb601e283c17694db8df1eb46c148f8e2e52471ccffe8d3a7be65cd87ef.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 513, + 252, + 620, + 316 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/ac19ba4940bbd6447deca22fd9a6dc3fc939522be06be2c2af1c1253e63f39fc.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 622, + 252, + 743, + 315 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/ea3bde8512e62820b8d19df96fe2812104bc9febce43dfe50c7b19d40687cfea.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 750, + 252, + 870, + 315 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/3293ae606c9d9f0665b34a5af8f5140f747ef1e0db6c71d3bf69fcd45b0cc687.jpg", + "image_caption": [ + "Input images" + ], + "image_footnote": [], + "bbox": [ + 513, + 319, + 620, + 377 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/713aec097612011545dd1b8a77aaedffdcaa283e5f39c59dbaa72d8a81798f8a.jpg", + "image_caption": [ + "Fully supervised results" + ], + "image_footnote": [], + "bbox": [ + 622, + 318, + 743, + 378 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d6b4304c07418ddfc9bea6c3602c8183117de0c6ea9ecbe52d3c52990aedf2b5.jpg", + "image_caption": [ + "Language-supervised results", + "Figure 3. Qualitative results of open-vocabulary SGG, particularly from fully supervised and language-supervised (VG caption) settings. Note that dotted nodes denote novel objects. For clarity, we only show triplets among the top 20 predictions that depict relations of highlighted image regions (i.e., boxes on input images)." + ], + "image_footnote": [], + "bbox": [ + 750, + 318, + 870, + 378 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "'on', 'of'. Presumably, it's because scene graph supervision parsed from language is more likely to extract such simple words as relation predicates.", + "bbox": [ + 498, + 476, + 890, + 522 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 540, + 617, + 556 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we have proposed a novel approach to exploit a powerful pre-trained VSS for triggering language-supervised and open-vocabulary SGG. Particularly, we obtain cheap scene graph supervision by semantically parsing image language descriptions into semantic graphs and grounding the noun phrases through region-word alignment in the VSS. In addition, we devise the VS3 model, which performs object detection as category name grounding in the VSS and naturally builds relation representations for relation recognition. Thanks to the open-vocabulary generalization ability of the VSS, VS3 manages to detect novel objects and their relations with other objects, achieving open-vocabulary SGG. We validate our approach on the Visual Genome benchmark across supervised, language-supervised and open-vocabulary SGG settings, and have set new state-of-the-art performances. This demonstrates the merits of transferring pre-training knowledge to push SGG toward more practical scenarios.", + "bbox": [ + 496, + 566, + 890, + 838 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgment. This work was supported in part by the Shenzhen Science and Technology Program under Grant JCYJ20220818103006012 and ZDSYS20211021111415025.", + "bbox": [ + 496, + 840, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "2922", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Saeid Amiri, Kishan Chandan, and Shiqi Zhang. Reasoning with scene graphs for robot planning under partial observability. IEEE Robotics and Automation Letters, 7(2):5560-5567, 2022. 1, 2", + "[2] Shizhe Chen, Qin Jin, Peng Wang, and Qi Wu. Say as you wish: Fine-grained control of image caption generation with abstract scene graphs. In CVPR, 2020. 1", + "[3] Tianshui Chen, Weihao Yu, Riquan Chen, and Liang Lin. Knowledge-embedded routing network for scene graph generation. In CVPR, 2019. 6", + "[4] Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, and C Lawrence Zitnick. Microsoft coco captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325, 2015. 5", + "[5] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, 2020. 7", + "[6] Jieuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai, and Mingyang Ling. Scene graph generation with external knowledge and image reconstruction. In CVPR, 2019. 1, 2", + "[7] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. In ICLR, 2021. 2, 3", + "[8] Tao He, Lianli Gao, Jingkuan Song, and Yuan-Fang Li. Towards open-vocabulary scene graph generation with prompt-based finetuning. In ECCV, 2022. 3, 5, 7, 8", + "[9] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICLR, 2021. 3", + "[10] Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David Shamma, Michael Bernstein, and Li Fei-Fei. Image retrieval using scene graphs. In CVPR, 2015. 1, 2", + "[11] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In ICCV, 2021. 3", + "[12] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, 2019. 3", + "[13] Boris Knyazev, Harm de Vries, Catalina Cangea, Graham W Taylor, Aaron Courville, and Eugene Belilovsky. Generative compositional augmentations for scene graph prediction. In ICCV, 2021. 8", + "[14] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJCV, 2017. 1, 2, 5", + "[15] Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and Rene Ranftl. Language-driven semantic segmentation. In ICLR, 2021. 2, 3", + "[16] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi." + ], + "bbox": [ + 78, + 114, + 468, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Align before fuse: Vision and language representation learning with momentum distillation. NeurIPS, 2021. 3", + "[17] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, et al. Grounded language-image pre-training. In CVPR, 2022. 2, 3, 4, 6, 7", + "[18] Manling Li, Alireza Zareian, Qi Zeng, Spencer Whitehead, Di Lu, Heng Ji, and Shih-Fu Chang. Cross-media structured common space for multimedia event extraction. arXiv preprint arXiv:2005.02472, 2020. 1", + "[19] Rongjie Li, Songyang Zhang, and Xuming He. Sgtr: End-to-end scene graph generation with transformer. In CVPR, 2022. 6", + "[20] Rongjie Li, Songyang Zhang, Bo Wan, and Xuming He. Bipartite graph network with adaptive message passing for unbiased scene graph generation. In CVPR, 2021. 1, 2", + "[21] Xingchen Li, Long Chen, Wenbo Ma, Yi Yang, and Jun Xiao. Integrating object-aware and interaction-aware knowledge for weakly supervised scene graph generation. In ACM MM, 2022. 2, 3, 5, 6, 7", + "[22] Yehao Li, Yingwei Pan, Jingwen Chen, Ting Yao, and Tao Mei. X-modaler: A versatile and high-performance codebase for cross-modal analytics. In ACM MM, 2021. 1", + "[23] Yehao Li, Yingwei Pan, Ting Yao, Jingwen Chen, and Tao Mei. Scheduled sampling in vision-language pretraining with decoupled encoder-decoder network. In AAAI, 2021. 3", + "[24] Yongzhi Li, Duo Zhang, and Yadong Mu. Visual-semantic matching by exploring high-order attention and distraction. In CVPR, 2020. 1", + "[25] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dólár. Focal loss for dense object detection. In ICCV, 2017. 4", + "[26] Xin Lin, Changxing Ding, Yibing Zhan, Zijian Li, and Dacheng Tao. Hl-net: Heterophily learning network for scene graph generation. In CVPR, 2022. 1, 2, 6", + "[27] Hengyue Liu, Ning Yan, Masood Mortazavi, and Bir Bhanu. Fully convolutional scene graph generation. In CVPR, 2021. 6", + "[28] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, 2021. 3, 6", + "[29] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS, 2019. 3", + "[30] Jianjie Luo, Yehao Li, Yingwei Pan, Ting Yao, Hongyang Chao, and Tao Mei. Coco-bert: Improving video-language pre-training with contrastive cross-modal matching and denoising. In ACM MM, 2021. 3", + "[31] Jiayuan Mao. Scenegraphparser, 2019. https://github.com/vacancy/SceneGraphParser (Access date: 2022-8-11). 3, 7", + "[32] Victor Milewski, Marie-Francine Moens, and Iacer Calixto. Are scene graphs good enough to improve image captioning? arXiv preprint arXiv:2009.12313, 2020. 1", + "[33] George A Miller. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39-41, 1995. 3, 5, 6" + ], + "bbox": [ + 503, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "2923", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[34] Yingwei Pan, Yehao Li, Jianjie Luo, Jun Xu, Ting Yao, and Tao Mei. Auto-captions on gif: A large-scale video-sentence dataset for vision-language pre-training. In ACM Multimedia, 2022. 3", + "[35] Yingwei Pan, Ting Yao, Yehao Li, and Tao Mei. X-linear attention networks for image captioning. In CVPR, 2020. 1", + "[36] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICLR, 2021. 2, 3", + "[37] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE TPAMI, 2016. 2", + "[38] Brigit Schroeder and Subarna Tripathi. Structured query-based image retrieval using scene graphs. In CVPR Workshops, 2020. 1", + "[39] Sebastian Schuster, Ranjay Krishna, Angel Chang, Li Fei-Fei, and Christopher D Manning. Generating semantically precise scene graphs from textual descriptions for improved image retrieval. In Proceedings of the fourth workshop on vision and language, 2015. 3, 5, 7", + "[40] Sahand Sharifzadeh, Sina Moayed Baharlou, and Volker Tresp. Classification by attention: Scene graph classification with prior knowledge. arXiv preprint arXiv:2011.10084, 2020. 1, 2", + "[41] Jing Shi, Yiwu Zhong, Ning Xu, Yin Li, and Chenliang Xu. A simple baseline for weakly-supervised scene graph generation. In ICCV, 2021. 2, 3, 5, 6, 7", + "[42] Motoharu Sonogashira, Masaaki Iiyama, and Yasutomo Kawanishi. Towards open-set scene graph generation with unknown objects. IEEE Access, 10:11574-11583, 2022. 2", + "[43] Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Vl-bert: Pre-training of generic visual-linguistic representations. In ICLR, 2019. 3", + "[44] Mohammed Suhail, Abhay Mittal, Behjat Siddiquie, Chris Broaddus, Jayan Eledath, Gerard Medioni, and Leonid Sigal. Energy-based learning for scene graph generation. In CVPR, 2021. 8", + "[45] Rui Sun, Xuezhi Cao, Yan Zhao, Junchen Wan, Kun Zhou, Fuzheng Zhang, Zhongyuan Wang, and Kai Zheng. Multimodal knowledge graphs for recommender systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020. 1", + "[46] Kaihua Tang. A scene graph generation codebase in python, 2020. https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch.6", + "[47] Kaihua Tang, Yulei Niu, Jianqiang Huang, Jiaxin Shi, and Hanwang Zhang. Unbiased scene graph generation from biased training. In CVPR, 2020. 1, 2, 5, 8", + "[48] Kaihua Tang, Hanwang Zhang, Baoyuan Wu, Wenhan Luo, and Wei Liu. Learning to compose dynamic tree structures for visual contexts. In CVPR, 2019. 1, 2, 6, 8", + "[49] Sijin Wang, Ruiping Wang, Ziwei Yao, Shiguang Shan, and Xilin Chen. Cross-modal scene graph matching for relationship-aware image-text retrieval. In WACV, 2020. 1" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[50] Danfei Xu, Yuke Zhu, Christopher B Choy, and Li Fei-Fei. Scene graph generation by iterative message passing. In CVPR, 2017. 1, 2, 5, 6, 7, 8", + "[51] Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, and Devi Parikh. Graph r-cnn for scene graph generation. In ECCV, 2018. 1, 2", + "[52] Xu Yang, Kaihua Tang, Hanwang Zhang, and Jianfei Cai. Auto-encoding scene graphs for image captioning. In CVPR, 2019. 1", + "[53] Lewei Yao, Jianhua Han, Youpeng Wen, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, Chunjing Xu, and Hang Xu. Detclip: Dictionary-enriched visual-concept paralleled pre-training for open-world detection. arXiv preprint arXiv:2209.09407, 2022. 2, 3", + "[54] Ting Yao, Yingwei Pan, Yehao Li, and Tao Mei. Exploring visual relationship for image captioning. In ECCV, 2018. 1", + "[55] Ting Yao, Yingwei Pan, Yehao Li, and Tao Mei. Hierarchy parsing for image captioning. In ICCV, 2019. 1", + "[56] Keren Ye and Adriana Kovashka. Linguistic structures as weak supervision for visual scene graph generation. In CVPR, 2021. 2, 5, 6, 7", + "[57] Alireza Zareian, Svebor Karaman, and Shih-Fu Chang. Bridging knowledge graphs to generate scene graphs. In ECCV, 2020. 1, 2", + "[58] Alireza Zareian, Svebor Karaman, and Shih-Fu Chang. Weakly supervised visual semantic parsing. In CVPR, 2020. 2, 7", + "[59] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In CVPR, 2021. 2, 3", + "[60] Rowan Zellers, Mark Yatskar, Sam Thomson, and Yejin Choi. Neural motifs: Scene graph parsing with global context. In CVPR, 2018. 1, 2, 6, 7, 8", + "[61] Hanwang Zhang, Zawlin Kyaw, Shih-Fu Chang, and TatSeng Chua. Visual translation embedding network for visual relation detection. In CVPR, 2017. 6", + "[62] Ji Zhang, Kevin J Shih, Ahmed Elgammal, Andrew Tao, and Bryan Catanzaro. Graphical contrastive losses for scene graph parsing. In CVPR, 2019. 2, 6", + "[63] Yong Zhang, Yingwei Pan, Ting Yao, Rui Huang, Tao Mei, and Chang-Wen Chen. Exploring structure-aware transformer over interaction proposals for human-object interaction detection. In CVPR, 2022. 1", + "[64] Yong Zhang, Yingwei Pan, Ting Yao, Rui Huang, Tao Mei, and Chang-Wen Chen. Boosting scene graph generation with visual relation saliency. ACM TOMM, 2023. 1", + "[65] Yiwu Zhong, Jing Shi, Jianwei Yang, Chenliang Xu, and Yin Li. Learning to generate scene graph from natural language supervision. In ICCV, 2021. 2, 3, 5, 6, 7", + "[66] Yiwu Zhong, Liwei Wang, Jianshu Chen, Dong Yu, and Yin Li. Comprehensive image captioning via scene graph decomposition. In ECCV, 2020. 1", + "[67] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al. Regionclip: Region-based language-image pretraining. In CVPR, 2022. 2, 3" + ], + "bbox": [ + 503, + 92, + 890, + 888 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "2924", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning To Generate Language-Supervised and Open-Vocabulary Scene Graph Using Pre-Trained Visual-Semantic Space/b669970d-e1b1-4a7a-a8da-18909a4dbc7b_model.json b/2023/Learning To Generate Language-Supervised and Open-Vocabulary Scene Graph Using Pre-Trained Visual-Semantic Space/b669970d-e1b1-4a7a-a8da-18909a4dbc7b_model.json new file mode 100644 index 0000000000000000000000000000000000000000..960d9af6481d463fa136399c3bdee59f5b3df9ec --- /dev/null +++ b/2023/Learning To Generate Language-Supervised and Open-Vocabulary Scene Graph Using Pre-Trained Visual-Semantic Space/b669970d-e1b1-4a7a-a8da-18909a4dbc7b_model.json @@ -0,0 +1,2167 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.002, + 0.808, + 0.02 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation." + }, + { + "type": "header", + "bbox": [ + 0.325, + 0.017, + 0.723, + 0.031 + ], + "angle": 0, + "content": "Except for this watermark, it is identical to the accepted version;" + }, + { + "type": "header", + "bbox": [ + 0.294, + 0.032, + 0.754, + 0.046 + ], + "angle": 0, + "content": "the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.085, + 0.131, + 0.885, + 0.177 + ], + "angle": 0, + "content": "Learning to Generate Language-supervised and Open-vocabulary Scene Graph using Pre-trained Visual-Semantic Space" + }, + { + "type": "text", + "bbox": [ + 0.138, + 0.203, + 0.832, + 0.223 + ], + "angle": 0, + "content": "Yong Zhang†, Yingwei Pan‡, Ting Yao‡, Rui Huang†*, Tao Mei‡, and Chang-Wen Chen§" + }, + { + "type": "text", + "bbox": [ + 0.138, + 0.224, + 0.832, + 0.24 + ], + "angle": 0, + "content": "† The Chinese University of Hong Kong, Shenzhen ‡ HiDream.ai Inc. § The Hong Kong Polytechnic University" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.242, + 0.867, + 0.258 + ], + "angle": 0, + "content": "yongzhang@link.cuhk.edu.cn, {panyw.ustc, tingyao.ustc}@gmail.com, ruihuang@cuhk.edu.cn," + }, + { + "type": "text", + "bbox": [ + 0.291, + 0.26, + 0.675, + 0.275 + ], + "angle": 0, + "content": "tmei@hidream.ai, changwen.chen@polyu.edu.hk" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.309, + 0.314, + 0.327 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.342, + 0.473, + 0.765 + ], + "angle": 0, + "content": "Scene graph generation (SGG) aims to abstract an image into a graph structure, by representing objects as graph nodes and their relations as labeled edges. However, two knotty obstacles limit the practicability of current SGG methods in real-world scenarios: 1) training SGG models requires time-consuming ground-truth annotations, and 2) the closed-set object categories make the SGG models limited in their ability to recognize novel objects outside of training corpora. To address these issues, we novelly exploit a powerful pre-trained visual-semantic space (VSS) to trigger language-supervised and open-vocabulary SGG in a simple yet effective manner. Specifically, cheap scene graph supervision data can be easily obtained by parsing image language descriptions into semantic graphs. Next, the noun phrases on such semantic graphs are directly grounded over image regions through region-word alignment in the pre-trained VSS. In this way, we enable open-vocabulary object detection by performing object category name grounding with a text prompt in this VSS. On the basis of visually-grounded objects, the relation representations are naturally built for relation recognition, pursuing open-vocabulary SGG. We validate our proposed approach with extensive experiments on the Visual Genome benchmark across various SGG scenarios (i.e., supervised / language-supervised, closed-set / open-vocabulary). Consistent superior performances are achieved compared with existing methods, demonstrating the potential of exploiting pre-trained VSS for SGG in more practical scenarios." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.793, + 0.21, + 0.808 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.818, + 0.47, + 0.88 + ], + "angle": 0, + "content": "Scene graph [10] is a structured representation for describing image semantics. It abstracts visual objects as graph nodes and represents their relations as labeled graph edges. The task of scene graph generation (SGG) [6, 14," + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.312, + 0.875, + 0.496 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.5, + 0.895, + 0.626 + ], + "angle": 0, + "content": "Figure 1. An illustration of exploiting a pre-trained visual-semantic space (VSS) to trigger language-supervised and open-vocabulary scene graph generation (SGG). (a) We acquire weak scene graph supervision by semantically parsing the image language description and grounding noun phrases on image regions via VSS. (b) At SGG inference time, thanks to the open-vocabulary generalization naturally rooted in VSS, the novel object name (e.g., player) in the text prompt input can be well aligned to one image region, which is regarded as its detection." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.629, + 0.894, + 0.72 + ], + "angle": 0, + "content": "20, 26, 40, 47, 48, 50, 51, 57, 60, 63, 64] plays an important role for fine-grained visual understanding, which has shown promising results in facilitating various downstream applications, such as image-text retrieval [24, 38, 49], image captioning [2, 22, 32, 35, 52, 54, 55, 66], cross-media knowledge graph construction [18, 45] and robot planning [1]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Though great effort has been made, SGG of the current stage still faces two knotty obstacles that limit its practicability in real-world scenarios. 1) Training SGG models requires massive ground-truth scene graphs that are expensive for manual annotation. Annotators have to draw bounding boxes for all objects in an image and connect possible interacted object pairs, and assign object/relation labels. Since assigned labels might be ambiguous, further verification and canonicalization processing are usually required [14]. Finally, a scene graph in the form of a set of \\(\\langle \\text{subject}, \\text{predicate}, \\text{object} \\rangle\\) triplets with subject and object bounding boxes is constructed. Such annotating pro" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.888, + 0.221, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2915" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.304 + ], + "angle": 0, + "content": "cess is time-consuming and tedious, costing much human labor and patience. 2) Almost all existing SGG methods [20,21,26,47,48,50,51,60] involve a pre-defined closed set of object categories, making them limited in recognizing novel objects outside of training corpora. However, real-world scenes contain a border set of visual concepts than any pre-defined category pool. It is very likely to encounter unseen/novel categories. When this happens, current SGG models either classify novel objects to a known category or fail to detect them like background regions. Accordingly, the prediction of their interactions/relations with other objects is negatively affected or just neglected. This may lead to problems. For example, a real-world robot may take inappropriate actions using such closed-set SGG models [1,42]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.308, + 0.473, + 0.686 + ], + "angle": 0, + "content": "Recently, there is a trend of leveraging free-form language supervision for benefiting visual recognition tasks via large-scale language-image pre-training [7, 15, 17, 36, 53, 59, 67]. These methods (e.g., CLIP [36]) perform pre-training on massive easily-obtained image-text pairs to learn a visual-semantic space (VSS), and have demonstrated great zero-shot transferability. Especially, the recent grounded language-image pre-training (GLIP) [17] has learned an object-level and semantic-rich VSS. Based on the learned VSS, it has established new state-of-the-art performances in phrase grounding and zero-shot object detection. This indicates such pre-trained VSS has powerful multi-modal alignment ability (i.e., image regions and text phrases that have similar semantics get close embeddings) and open-vocabulary generalization ability (i.e., covering virtually any concepts in the pre-training image-text corpus). This inspires our thought of addressing the aforementioned obstacles in SGG using the pre-trained VSS. On the one hand, taking advantage of its multi-modal alignment ability, we can cheaply acquire scene graph supervision from an image description (e.g., retrieving image regions aligned with noun phrases and re-arranging the description into a scene-graph-like form). On the other hand, by leveraging its open-vocabulary generalization ability, it is promising to enable novel category prediction in SGG." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.69, + 0.473, + 0.902 + ], + "angle": 0, + "content": "In this work, we investigate the opportunity of fully exploiting the VSS learned by language-image pre-training to trigger language-supervised and open-vocabulary SGG. Specifically, we obtain weak scene graph supervision by semantically parsing an image language description into a semantic graph, then grounding its noun phrases over image regions through region-word alignment in the pre-trained VSS (Figure 1 (a)). Moreover, we propose a novel SGG model, namely Visual-Semantic Space for Scene graph generation \\(\\mathbf{V}\\mathbf{S}^{3})\\). It takes a raw image and a text prompt containing object category names as inputs, and projects them into the shared VSS as embeddings. Next, \\(\\mathrm{VS}^3\\) performs object detection by aligning the embeddings of category names and image regions. Based on high-confidence de" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.303 + ], + "angle": 0, + "content": "tected objects, VS³ builds relation representations for object pairs with a devised relation embedding module that fully mines relation patterns from visual and spatial perspectives. Finally, a relation prediction module takes relation representations to infer relation labels. The predicted scene graph is composed by combining object detections and inferred relation labels. During training, visually-grounded semantic graphs parsed from image descriptions could be used as weak scene graph supervision, achieving language-supervised SGG. At SGG inference time, when using a text prompt input containing novel categories, VS³ manages to detect novel objects thanks to the open-vocabulary generalization ability naturally rooted in VSS, hence allowing for open-vocabulary SGG (Figure 1 (b))." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.303, + 0.895, + 0.486 + ], + "angle": 0, + "content": "In summary, we have made the following contributions: (1) the exploitation of a pre-trained VSS provides an elegant solution for addressing obstacles to triggering both language-supervised and open-vocabulary SGG, making a solid step toward real-world usage of SGG. (2) The proposed VS3 model is a new and versatile framework, which effectively transfers language-image pre-training knowledge for benefiting SGG. (3) We fully validate the effectiveness of our approach through extensive experiments on the Visual Genome benchmark, and have set new state-of-the-art performances spanning across all settings (i.e., supervised / language-supervised, closed-set / open-vocabulary)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.498, + 0.642, + 0.514 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.524, + 0.895, + 0.811 + ], + "angle": 0, + "content": "Fully supervised SGG. The concept of scene graph as a structured image representation is first introduced in [10]. Next, the Visual Genome benchmark [14] is manually annotated with large-scale scene graphs on images. Such annotated dataset triggers a series of innovations [6,20,26,40,47, 48,50,51,57,60] for the fully supervised SGG task. Typically, an object detector (e.g., Faster-RCNN [37]) is trained to retrieve image regions as scene graph nodes. Then, relation representations of object pairs are constructed from visual, spatial and language perspectives, and are used for relation classification to label scene graph edges. To achieve desirable SGG, researchers have devised message-passing mechanisms [20,26,48,50,60] to exploit contextual information, derived contrastive loss functions [62] or incorporated external knowledge [6,40,57]. However, all these methods rely on training with expensive scene graph annotations. Our proposed VS\\(^3\\) model is compatible with fully supervised SGG, but we seek to make SGG training cheaper, which is more practical in real-world applications." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Language-supervised SGG. This task aims to train SGG models using language descriptions. It has recently attracted increasing attention [21,41,56,58,65], which is also referred as weakly supervised SGG in [21,41,58]. Particularly, based on a graph alignment algorithm, VSPNet [58] first proposes to supervise SGG training with scene graphs" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "2916" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.091, + 0.473, + 0.364 + ], + "angle": 0, + "content": "that have no object locations. Subsequent works [21,41,65] extract entities and relations from image captions to compose such unlocalized scene graph, which is achieved via an off-the-shelf language parser [31, 39]. They next follow a common paradigm: first grounding text entities on image regions, and then leveraging grounded scene graphs as pseudo labels to train standard SGG models. To acquire entity groundings, Shi et al [41] devise an efficient graph matching module optimized via contrastive learning; Zhong et al [65] simply match text entity names with predicted object labels from a pre-trained object detector using semantic rules such as WordNet [33] synsets matching. More recently, Li et al [21] integrate interaction-aware knowledge distilled from pre-trained language-image models [16] for enhancing grounding reliability. Instead, we propose to obtain groundings through region-word alignment in a pretrained VSS, which is much simple yet more effective to collect scene graph supervision from language." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.365, + 0.473, + 0.804 + ], + "angle": 0, + "content": "Language-image pre-training. This has been shown effective for boosting various vision-language downstream tasks [11, 16, 23, 29, 30, 34, 43], e.g., image-text retrieval, image captioning. Also, recent studies present remarkable results on transferring pre-trained language-image knowledge to solve vision recognition problems, such as zero-shot image classification [9,36], open-vocabulary object detection [7, 17, 53, 59, 67] and zero-shot semantic segmentation [15]. For example, CLIP [36] and ALIGN [9] learn separate encoders to embed image and text into a shared space by pre-training on massive image-text pairs using a contrastive loss. They have demonstrated remarkable generalization ability on zero-shot image classification after pretraining. Distinct from CLIP and ALIGN that learn image-level representations, GLIP [17] focuses on learning object-level visual representations through region-word alignment. It has attained strong zero-shot and few-shot transferability to various object-level recognition tasks such as object detection and phrase grounding. Most recently, He et al [8] investigate a visual-relation pre-training and prompt-based fine-tuning method for open-vocabulary SGG. However, its image encoder relies on a pre-trained region proposal extractor, which is a bottleneck for achieving open-vocabulary SGG under the SGDET protocol. Unlike [8], our proposed VS3 directly encodes an image into region tokens, avoiding the bottleneck of region proposals. More importantly, our approach addresses obstacles to achieving both language-supervised and open-vocabulary SGG using a unified framework, while He et al [8] only focus on the latter." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.82, + 0.188, + 0.837 + ], + "angle": 0, + "content": "3. Approach" + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.846, + 0.279, + 0.861 + ], + "angle": 0, + "content": "3.1. Notation & Overview" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.47, + 0.903 + ], + "angle": 0, + "content": "The task of scene graph generation (SGG) aims to map an image into an abstract graph \\( SG = \\{O, R\\} \\), where graph" + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.088, + 0.892, + 0.162 + ], + "angle": 0, + "content": "
SG\\(\\mathcal{C}_{o}^{target}\\)Not containing novel object classesContaining novel object classes
Manually annotatedfully supervised & closed-setfully supervised & open-vocabulary
Automatically parsed from image descriptionslanguage-supervised & closed-setlanguage-supervised & open-vocabulary
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.165, + 0.892, + 0.193 + ], + "angle": 0, + "content": "Table 1. Definitions of different SGG settings, according to scene graph supervision \\( SG \\) and the object set at inference \\( \\mathcal{C}_o^{target} \\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.198, + 0.892, + 0.319 + ], + "angle": 0, + "content": "nodes \\(O = \\{o_1,\\dots,o_N\\}\\) correspond to image objects, and graph edges \\(R = \\{r_1,\\dots,r_M\\}\\) represents their relations. Each object \\(o_i = \\{\\mathbf{b}_i,l_i\\} \\in O\\) contains the bounding box coordinates \\(\\mathbf{b}_i\\in \\mathbb{R}^4\\) and its class label information \\(l_{i}\\in \\mathcal{C}_{o}\\) where \\(\\mathcal{C}_o\\) denotes the set of object categories. Each relation \\(r_m\\in R\\) is a \\(\\langle\\) subject,predicate,object \\(\\rangle\\) triplet, and we represent it as \\(r_m = r_{i\\rightarrow j} = \\{o_i,p_{ij},o_j\\}\\), in which \\(p_{ij}\\) is the predicate/relation label belonging to category set \\(\\mathcal{C}_r\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.32, + 0.893, + 0.532 + ], + "angle": 0, + "content": "Most existing SGG methods require expensive manually annotated scene graphs \\( SG \\) as supervision. And they involve a closed set of object categories \\( \\mathcal{C}_o \\) in both training and inference. These issues limit SGG for practical usage. In this work, we propose to fully exploit a pre-trained VSS to push SGG towards language-supervised and open-vocabulary scenarios. We illustrate the definitions of different SGG settings in Table 1. Concretely, from the perspective of scene graph supervision \\( SG \\), SGG is categorized into fully supervised and language-supervised, using \\( SG \\) from manual annotation and language respectively. From the other perspective of object categories \\( \\mathcal{C}_o^{target} \\) at inference, it is referred to as open-vocabulary or closed-set according to whether or not \\( \\mathcal{C}_o^{target} \\) contains novel objects." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.532, + 0.894, + 0.639 + ], + "angle": 0, + "content": "Next, in Section 3.2, we present a new SGG model named VS3, which is versatile to handle with all SGG settings in Table 1. In Section 3.3, we devise a scheme to obtain scene graph supervision from language descriptions, allowing for language-supervised SGG. Finally, Section 3.4 details the strategy of transferring the proposed VS3 to pursue open-vocabulary SGG." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.65, + 0.733, + 0.667 + ], + "angle": 0, + "content": "3.2. The Proposed VS3 Model" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.673, + 0.892, + 0.719 + ], + "angle": 0, + "content": "We propose the VS3 model for tackling the SGG task by extending the GLIP [17] framework with relation recognition modules, as shown in Figure 2." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.895, + 0.902 + ], + "angle": 0, + "content": "Preliminary. GLIP unifies object detection and phrase grounding into one framework. It has an image encoder \\(\\mathrm{Enc}_I\\) (e.g., Swin Transformer backbone [28]) and a text encoder \\(\\mathrm{Enc}_L\\) (e.g., BERT [12]). \\(\\mathrm{Enc}_I\\) extracts region/box features \\(\\bar{O} \\in \\mathbb{R}^{\\bar{N} \\times d}\\) from an input image, where \\(\\bar{N}\\) is the number of regions and \\(d\\) is the feature dimension. \\(\\mathrm{Enc}_L\\) encodes a text input into contextualized word-token embeddings \\(\\bar{P} \\in \\mathbb{R}^{\\bar{T} \\times d}\\), where \\(\\bar{T}\\) is the text length. Further, GLIP uses a cross-modal fusion module to achieve feature communication between \\(\\bar{O}\\) and \\(\\bar{P}\\), resulting in enriched region embeddings \\(\\tilde{O} \\in \\mathbb{R}^{\\bar{N} \\times d}\\) and word embeddings \\(\\tilde{P} \\in \\mathbb{R}^{\\bar{T} \\times d}\\). Finally, the region-word alignment" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2917" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.104, + 0.096, + 0.868, + 0.293 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.299, + 0.892, + 0.385 + ], + "angle": 0, + "content": "Figure 2. An overview of the proposed Visual-Semantic Space for Scene graph generation \\((\\mathrm{VS}^3)\\) model. It inherits the image encoder, the text encoder and the cross-modal fusion module from GLIP [17], so as to project image regions and text prompt words in a pre-trained visual-semantic space (VSS). Object regions are detected by aligning the embeddings of category names and image regions in VSS. Next, high-confidence detected results are retained to compose subject-object pairs. After that, the relation embedding module constructs their relation representations by extracting visual and spatial features, on which relation prediction is performed. At test time, thanks to the open-vocabulary generalization ability of VSS, \\(\\mathrm{VS}^3\\) manages to detect novel objects by switching to a text prompt containing novel classes." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.389, + 0.47, + 0.497 + ], + "angle": 0, + "content": "scores \\(\\hat{S}_{\\text {ground }} = \\tilde{O}\\tilde{P}^{\\top} \\in \\mathbb{R}^{\\bar{N} \\times \\bar{T}}\\) and the predicted locations \\(\\vec{B} = \\text { box\\_predictor }(\\vec{O}) \\in \\mathbb{R}^{\\bar{N} \\times 4}\\) are supervised by ground-truth text grounding data. After large-scale pretraining, \\(\\mathrm{Enc}_I\\) and \\(\\mathrm{Enc}_L\\) embed the input image and text into a joint VSS, which aligns multi-modal embeddings and covers open-vocabulary concepts. VS3 inherits Enc \\(I\\), Enc \\(L\\) and the cross-module fusion module from GLIP." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.499, + 0.47, + 0.634 + ], + "angle": 0, + "content": "Text prompt. Considering that object detection has been reformulated as phrase grounding, VS3 also requires a text prompt input except for the image input. Following GLIP's design, we set the text prompt for object detection in the form of \"name(c1). name(c2). ... name(c|cO)\". where \\( c_i \\in \\mathcal{C}_o \\) and \\( \\text{name}(c_i) \\) gets the category name of \\( c_i \\) (e.g., person). Hence, an object is detected according to the alignment score between a region embedding \\( \\tilde{\\mathbf{o}}_i \\in \\tilde{\\mathcal{O}} \\) (the \\( i \\)th row) and the category name embeddings \\( \\tilde{P} \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.634, + 0.471, + 0.879 + ], + "angle": 0, + "content": "Relation embedding module. To further enable VS3 with relation recognition ability, we devise the relation embedding module to build relation representations. Based on the region/box features \\(\\tilde{O}\\) after cross-modal fusion, we first sample a subset of regions \\(\\tilde{O}' \\in \\mathbb{R}^{N' \\times d}\\) that are most likely to be valid objects. This is achieved by matching predicted bounding boxes \\(\\hat{B}\\) with ground-truth objects during training, and by retaining top-\\(N'\\) regions with the highest confidence scores after non-maximum suppression (NMS) at inference. Next, we construct relation representations for all possible subject-object pairs. Given an object pair \\((\\tilde{\\mathbf{o}}_i, \\tilde{\\mathbf{o}}_j)\\) and their normalized bounding boxes \\((\\mathbf{b}_i, \\mathbf{b}_j)\\), the pairwise relation representation is represented as \\(\\mathbf{pair}_{i \\rightarrow j} = cat[\\mathbf{pair}_{i \\rightarrow j}^{\\text{visual}}, \\mathbf{pair}_{i \\rightarrow j}^{\\text{spatial}}]\\). This is the concatenation of features mined from the visual and spatial perspectives. The visual feature is computed by" + }, + { + "type": "equation", + "bbox": [ + 0.124, + 0.884, + 0.47, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\mathbf {p a i r} _ {i \\rightarrow j} ^ {\\text {v i s u a l}} = \\boldsymbol {f} _ {\\text {d i f f}} (\\tilde {\\mathbf {o}} _ {i} - \\tilde {\\mathbf {o}} _ {j}) + \\boldsymbol {f} _ {\\text {s u m}} (\\tilde {\\mathbf {o}} _ {i} + \\tilde {\\mathbf {o}} _ {j}), \\qquad (1)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.391, + 0.892, + 0.466 + ], + "angle": 0, + "content": "where \\( \\pmb{f}_{diff} \\) and \\( \\pmb{f}_{sum} \\) are two mapping functions implemented as 2-layer MLPs (multi-layer perceptron). By defining the normalized center coordinates of two involved objects as \\( (ct_i^x, ct_i^y) \\) and \\( (ct_j^x, ct_j^y) \\), the spatial feature is measured as" + }, + { + "type": "equation", + "bbox": [ + 0.521, + 0.474, + 0.892, + 0.493 + ], + "angle": 0, + "content": "\\[\n\\mathbf {p a i r} _ {i \\rightarrow j} ^ {\\text {s p a t i a l}} = \\operatorname {c a t} \\left[ \\mathbf {b} _ {i}, \\mathbf {b} _ {j}, d x, d y, \\operatorname {d i s}, \\theta , A _ {i}, A _ {j}, I, U \\right], \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.5, + 0.892, + 0.564 + ], + "angle": 0, + "content": "where \\( dx = ct_i^x - ct_j^x, dy = ct_i^y - ct_j^y, \\) \\( \\sqrt{dx^2 + dy^2}, \\theta = \\arctan \\left(\\frac{dy}{dx}\\right). A_i, A_j, I, U \\) denote the areas of the subject, the object, their intersection, and union boxes, respectively." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.565, + 0.892, + 0.717 + ], + "angle": 0, + "content": "Relation prediction. Conditioned on the relation representation \\(\\mathbf{pair}_{i\\rightarrow j}\\) of each object pair, we predict a relateness score \\(\\hat{z}_{i\\rightarrow j} = f_{relateness}(\\mathbf{pair}_{i\\rightarrow j})\\in [0,1]\\) and a semantic label probability \\(\\hat{\\pmb{y}}_{i\\rightarrow j} = \\pmb{f}_{semantic}(\\pmb{pair}_{i\\rightarrow j})\\in\\) \\([0,1]^{|\\mathcal{C}_r|}\\). The relateness \\(\\hat{z}_{i\\rightarrow j}\\) represents the probability that relations exist between the object pair. \\(f_{relateness}\\) is implemented with an MLP coupled with Sigmoid activation. \\(\\pmb{f}_{semantic}\\) is implemented with another MLP using Softmax activation. The total loss for relation recognition \\(L_{rel\\_rcg}\\) is measured as" + }, + { + "type": "equation", + "bbox": [ + 0.574, + 0.73, + 0.891, + 0.746 + ], + "angle": 0, + "content": "\\[\nL _ {\\text {r e l a t e n e s s}} = F L \\left(\\hat {z} _ {i \\rightarrow j}, z _ {i \\rightarrow j}\\right), \\tag {3}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.575, + 0.749, + 0.891, + 0.765 + ], + "angle": 0, + "content": "\\[\nL _ {\\text {s e m a n t i c}} = C E \\left(\\hat {\\boldsymbol {y}} _ {i \\rightarrow j}, \\boldsymbol {y} _ {i \\rightarrow j}\\right), \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.575, + 0.769, + 0.891, + 0.784 + ], + "angle": 0, + "content": "\\[\nL _ {r e l. r c g} = L _ {r e l a t e n e s s} + L _ {s e m a n t i c}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.795, + 0.891, + 0.841 + ], + "angle": 0, + "content": "where \\(FL\\) and \\(CE\\) denote focal loss [25] and cross-entropy loss functions. \\(z_{i\\rightarrow j}\\) and \\(\\pmb{y}_{i\\rightarrow j}\\) represent the ground-truth relateness label and predicate category label respectively." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.841, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Training & inference. During training, we initialize parameters from pre-trained GLIP models for inherited modules in VS3. To ease training difficulty, we freeze the image encoder and text encoder, and only fine-tune the" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2918" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.471, + 0.168 + ], + "angle": 0, + "content": "cross-modal fusion module and devised modules for relation recognition. This also avoids the degeneration of the pre-trained VSS. At inference, by retaining high-confidence detected objects and further predicting their relations, we generate an image scene graph representation." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.177, + 0.47, + 0.195 + ], + "angle": 0, + "content": "3.3. Obtaining Language Scene Graph Supervision" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.201, + 0.469, + 0.292 + ], + "angle": 0, + "content": "Ground-truth scene graphs are time-consuming to annotate. Alternatively, we can parse semantic graphs from image language descriptions, and obtain noun phrase groundings through region-word alignment in the pre-trained VSS (implemented with an off-the-shelf GLIP). This is a much cheaper way to obtain weak scene graph supervision." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.292, + 0.469, + 0.519 + ], + "angle": 0, + "content": "Semantic graph parsing. Concretely, for each image language description, we parse it into a semantic graph \\( SG^{text} = \\{O^{text}, R^{text}\\} \\) using the Standard Scene Graph Parser based on [39]. The parser not only extracts noun phrases as entities/objects (\\( O^{text} \\)), but also extracts the words describing their relations (\\( R^{text} \\)). For example, the sentence \"a woman is playing the piano in the room.\" is parsed to the \\( SG^{text} \\), of which \\( O^{text} = \\{woman, piano, room\\} \\) and \\( R^{text} = \\{\\langle 0, playing, 1\\rangle, \\langle 0, in, 2\\rangle\\} \\) (numbers denote object indices). Considering that parsed object/relation words are free-form, we map them to our concerning categories (e.g., VG150 object/relation categories in experiments) by rules such as direct string matching and WordNet [33] synsets matching following [65]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.52, + 0.47, + 0.762 + ], + "angle": 0, + "content": "Semantic graph grounding. Note that each element of \\( O^{text} \\) only contains a text label name so far, and its bounding box information is still missing. To obtain bounding boxes, we construct a text prompt using triplets in \\( SG^{text} \\), e.g., \"woman playing piano. woman in room.\" Then, we feed such text prompt together with the raw image into a pre-trained GLIP, in order to acquire grounding boxes of \\( O^{text} \\). Specifically, for each element in \\( O^{text} \\), we select the image region that has the highest alignment score with its category name as its grounding box. Since there might be multiple objects in \\( O^{text} \\) that actually refer to the same object, we perform a post-processing NMS to merge boxes with the same label and high IoU (intersection over union) scores (\\( \\geq 0.9 \\)). Finally, with box information, the visually-grounded \\( SG^{text} \\) is ready to be used as weak supervision for training SGG models, e.g., the proposed VS3." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.771, + 0.411, + 0.788 + ], + "angle": 0, + "content": "3.4. Transferring to Open-vocabulary SGG" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.795, + 0.469, + 0.886 + ], + "angle": 0, + "content": "Open-vocabulary SGG [8] aims to train SGG models that can recognize objects of novel categories and their involved relations. Formally, we train the SGG model with scene graphs containing objects in the base category set \\(\\mathcal{C}_o^{base}\\). At inference, the object category set is \\(\\mathcal{C}_o^{target}\\), which contains novel categories in \\(\\mathcal{C}_o^{novel} = \\mathcal{C}_o^{target} \\backslash \\mathcal{C}_o^{base} \\neq \\emptyset\\)." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.886, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Back to our proposed VS3, an open-vocabulary VSS is" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.321 + ], + "angle": 0, + "content": "maintained by freezing the image and text encoders. Taking this advantage, we devise a scheme to adapt VS3 for open-vocabulary SGG. Concretely, during training, we set the text prompt as “name(c1). name(c2). ... name(cObase)”. where \\( c_{i} \\in \\mathcal{C}_{o}^{base} \\). And only relation triplets involving base object categories are kept for training. At inference, the text prompt is switched to be “name(c1). name(c2). ... name(cOtarget)”. where \\( c_{i} \\in \\mathcal{C}_{o}^{target} \\). In this way, a novel object class (e.g., lady) may have an embedding close to a base category (e.g., woman) embedding. This makes the novel class also able to find well-aligned image regions. Note that relation representations are constructed from visual and spatial cues, which are usually class-agnostic. Hence, the following relation recognition in VS3 will not be affected when encountering novel objects." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.337, + 0.634, + 0.354 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.362, + 0.809, + 0.379 + ], + "angle": 0, + "content": "4.1. Datasets and Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.387, + 0.892, + 0.658 + ], + "angle": 0, + "content": "Datasets. To evaluate the SGG task, we adopt the widely-used VG150 version [50] of the Visual Genome (VG) dataset [14]. VG150 retains the most frequent 150 categories and 50 relation/predicate categories in VG. It contains \\(\\sim 108\\mathrm{K}\\) images, of which \\(70\\%\\) images are used for training (including 5K for validation), and the remaining \\(30\\%\\) images are used for testing. The annotated scene graph of each image has 11.5 objects and 6.2 relation triplets on average. In addition, images of VG are densely annotated with region descriptions, about 50 descriptions for each image. We refer to these descriptions as VG caption, which provides a text source for evaluating the language-supervised SGG setting. Moreover, we consider the challenging setting of using image-text pairs in COCO caption [4] for training SGG models. This dataset contains 123K images in total. Each image has 5 human-annotated captions. We keep \\(\\sim 106\\mathrm{k}\\) images by filtering out those images that also exist in the VG150 test split." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.66, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Evaluation protocols and metrics. We mainly adopt the SGDET [47,50] protocol, which generates a scene graph from the input image without any given box information. We report the performance on Recall@K (K=20/50/100) following previous works [21, 41, 47, 50, 56, 65], which measures the fraction of correctly predicted relation triplets in top \\( K \\) predictions. A triplet prediction is considered as correct when its subject, object, predicate labels and both the subject and object regions match with (same label or IoU>0.5) a ground-truth triplet. Note that we obtain triplet predictions using graph constraint, which limits each subject-object pair to have only the most confident predicate. All recall metrics across different SGG settings in experiments are computed over VG150 test images. Considering that the adopted GLIP pre-trained VSS has seen part of images in the original VG150 test split (\\(\\sim\\)26k) during" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2919" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.471, + 0.182 + ], + "angle": 0, + "content": "pre-training, we exclude these images and get a new split of \\(\\sim 15\\mathrm{k}\\) test images. We have validated that such VG150 test split is sufficiently large for computing stable metrics as the original, by comparing computed metrics of several SGG models (in codebase [46]) on these two splits (\\(< 0.15\\) points variation, see supplementary materials)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.183, + 0.471, + 0.365 + ], + "angle": 0, + "content": "Implementation details. We initialize VS3 from pretrained GLIP [17] models, i.e., the GLIP-T and the larger GLIP-L trained with more data. Both construct a VSS of dimension \\( d = 256 \\). We retain the top 36 object detections per image for pairwise relation recognition. The whole framework is fine-tuned on 8 Nvidia 2080Ti GPUs with AdamW optimizer. During fine-tuning, we freeze the parameters of the image and text encoder; and set the learning rate for the cross-modal fusion module as 1e-5 and 10x larger learning rates for the relation embedding and prediction modules. The maximum fine-tuning epoch number is 10, with learning rates dropping by 10x after 6 epochs." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.377, + 0.285, + 0.392 + ], + "angle": 0, + "content": "4.2. Fully Supervised SGG" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.4, + 0.47, + 0.581 + ], + "angle": 0, + "content": "Setup. We first evaluate our proposed VS3 under the conventional fully supervised SGG setting. This setting trains SGG models using manually annotated scene graphs, consisting of object labels coupled with bounding boxes, and relation labels. We adopt VG150 for training and evaluation following previous methods [3, 19, 26, 27, 48, 50, 60, 61, 65]. All these methods involve a closed set of object categories. Specifically, the text prompt input of VS3 is constructed from VG150 object category names, i.e., \"airplane. animal. ... zebra\". We train VS3 by fine-tuning over two GLIP variants: GLIP-T with the Swin-T [28] backbone, GLIP-L with the Swin-L [28] backbone." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.584, + 0.47, + 0.794 + ], + "angle": 0, + "content": "Comparison with state-of-the-arts. The results are summarized in Table 2. Our proposed VS3 model using the Swin-T backbone already achieves competitive recall metrics. When upgrading to the larger Swin-L variant, the performance improvements become significant (1.8 to 3.4 points improvement than the previous best results). Note that previous methods [26, 50, 60] build their models upon an off-the-shelf object detector, and they usually design heavy message-passing modules to incorporate context information. Instead, VS3 devises a light-weighted relation recognition head (including the relation embedding and prediction modules) over a pre-trained VSS. The superior performances clearly suggest the merits of transferring language-image pre-trained models for boosting SGG." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.796, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Ablation on relation representation. Next, we carry out ablation studies on relation representation construction in the relation embedding module. As shown in Table 2, by removing visual and spatial feature components, the relation triplet recalls drop accordingly. Also notice that the removal of visual features which is built from subject and object region embeddings leads to relatively larger perfor" + }, + { + "type": "table", + "bbox": [ + 0.507, + 0.089, + 0.892, + 0.279 + ], + "angle": 0, + "content": "
SGG modelDetectorBackboneR@20R@50R@100
FCSGG [27]-HRNetW4816.121.325.1
SGTR [19]DETRR-101-24.628.4
IMP [50]Faster-RCNNVGG-1614.620.724.5
KERN [3]Faster-RCNNVGG-16-27.129.8
MOTIFS [60]Faster-RCNNVGG-1621.427.230.3
RelDN [62]Faster-RCNNVGG-1621.128.332.7
VTransE [61]Faster-RCNNRX-10123.029.734.3
MOTIFS [60]Faster-RCNNRX-10125.132.136.9
VCTREE [48]Faster-RCNNRX-10124.731.536.2
SGNLS [65]Faster-RCNNRX-10124.631.836.3
HL-Net [26]Faster-RCNNRX-10126.033.738.1
VS3-Swin-T26.134.539.2
w/o visual-Swin-T23.131.636.7
w/o spatial-Swin-T24.332.837.8
VS3-Swin-L27.836.641.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.281, + 0.894, + 0.338 + ], + "angle": 0, + "content": "Table 2. Experimental results of fully supervised SGG. w/o visual and w/o spatial indicate removing spatial and visual features in the relation embedding module for relation representation. All metrics are computed under the SGDET protocol on VG150 test images." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.352, + 0.892, + 0.413 + ], + "angle": 0, + "content": "mance drops than spatial. This suggests the region embeddings in the pre-trained VSS provide strong cues for relation recognition. Overall, these observations validate the effectiveness of our design to mine relation patterns." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.424, + 0.742, + 0.44 + ], + "angle": 0, + "content": "4.3. Language-supervised SGG" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.447, + 0.892, + 0.657 + ], + "angle": 0, + "content": "Setup. Language-supervised SGG [21, 41, 56, 65] explores to train SGG models with language descriptions of images. Concretely, we parse each image description into a semantic graph, in the form of a set of \\(\\langle\\) subject, predicate, object \\(\\rangle\\) triplets. Note that parsed object/relation phrases from language descriptions are freeform, we map them to VG150 categories by semantic rules following [65], such as WordNet [33] synsets matching. This makes the learned SGG model compatible for evaluating on VG150. Next, the parsed semantic graph is grounded to image regions using grounding methods, i.e., the pretrained GLIP-L [17] in our approach. Finally, the visually-grounded semantic graphs are used as weak supervision to train our proposed VS\\(^3\\) like the fully supervised setting." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.892, + 0.854 + ], + "angle": 0, + "content": "Particularly, we have trained VS3 with text triplets parsed from three different sources of text following [21, 65]. 1) The unlocalized graph setting uses ground-truth triplet annotations in VG. 2) The VG caption setting uses triplets that are automatically parsed from natural image descriptions in VG. 3) The COCO caption setting leverages triplets parsed from captions in COCO. This setting is the most challenging since COCO captions are image-level descriptions. Such captions are different from the region-level descriptions in VG, which focus on describing object interactions. Also, note that the number of annotated captions for each COCO image (average 5) is much less than the number for each VG image (average \\(\\sim 50\\))." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Comparison with state-of-the-arts. The experimental results compared with previous methods are presented in Table 3. All evaluation metrics are com" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "2920" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.097, + 0.089, + 0.451, + 0.371 + ], + "angle": 0, + "content": "
SGG modelGroundingR@20R@50R@100
Unlocalized graphVSPNet [58]--4.705.40
LSWS [56]--7.308.73
MOTIFS [60]WSGM [41]4.125.596.45
MOTIFS [60]SGNLS [65]7.239.2810.71
MOTIFS [60]Li et.al [21]9.0911.3912.89
Uniter† [5]SGNLS [65]7.8110.0311.50
Uniter† [5]Li et.al [21]9.5711.8013.15
VS3(Swin-T)GLIP-L [17]18.0223.8928.19
VS3(Swin-T+FreqBias)GLIP-L [17]20.0626.7231.75
VS3(Swin-L+FreqBias)GLIP-L [17]22.1829.8134.96
VG captionLSWS [56]--3.854.04
MOTIFS [60]SGNLS [65]6.318.059.21
MOTIFS [60]Li et.al [21]8.2510.5011.98
Uniter† [5]SGNLS [65]-9.2010.30
Uniter† [5]Li et.al [21]8.9010.9312.14
VS3(Swin-T)GLIP-L [17]11.7816.2519.7
VS3(Swin-L)GLIP-L [17]13.0117.3820.54
COCO captionLSWS [56]--3.283.69
MOTIFS [60]Li et.al [21]5.026.407.33
Uniter† [5]SGNLS [65]-5.806.70
Uniter† [5]Li et.al [21]5.426.747.62
VS3(Swin-T)GLIP-L [17]5.597.308.62
VS3(Swin-L)GLIP-L [17]6.048.159.90
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.374, + 0.47, + 0.445 + ], + "angle": 0, + "content": "Table 3. Comparison with state-of-the-art language-supervised SGG methods, using weak scene graph supervision from three different text sources: unlocalized scene graphs, VG caption and COCO caption. All metrics are computed under the SGDET protocol on VG150 images. (\\(\\dagger\\) indicates adapted for SGG.)" + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.447, + 0.471, + 0.853 + ], + "angle": 0, + "content": "puted on the VG150 test set under the SGDET protocol. Specifically, under the unlocalized scene graphs setting, \\(\\mathrm{VS}^3\\) with the Swin-T backbone (\\(\\mathrm{VS}^{3}_{(Swin-T)}\\)) obtains substantial improvements on recall metrics over existing best results (\\(R@20/50/100\\) from 9.57/11.80/13.15 to 18.02/23.89/28.19). Since relation frequency statistics are available in this setting, we use them as frequency biases [60] in predicate classification, leading to further performance gains (\\(\\mathrm{VS}^{3}_{(Swin-T+FreqBias)}\\)). When using the stronger Swin-L backbone (\\(\\mathrm{VS}^{3}_{(Swin-L+FreqBias)}\\)), we attain the highest performances (\\(R@20/50/100 = 22.18/29.81/34.96\\)), which even outperform many fully supervised methods (see Table 2). The VG caption setting provides weaker scene graph supervision via language parsing. We observe that our approach also outperforms previous state-of-the-art methods significantly. As for the most challenging COCO caption setting, it suffers from the additional domain shift problem since it trains on COCO but evaluates on VG150. As expected, the performances are lower than in the two aforementioned settings. But when comparing with previous works using the same text source, our approach still manages to achieve better performances. Overall, our approach consistently surpasses previous methods for language-supervised SGG. This demonstrates the benefits brought by pre-trained language-image models in terms of both grounding box acquirement and task transferring to tackle SGG." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.47, + 0.903 + ], + "angle": 0, + "content": "Ablation on scene graph parsing strategy. We also conduct ablation studies on different scene graph parsing strategies for obtaining language SGG supervision. The re" + }, + { + "type": "table", + "bbox": [ + 0.562, + 0.089, + 0.83, + 0.148 + ], + "angle": 0, + "content": "
SG fromSG parserR@20/50/100
Single captionSimple5.07 / 6.25 / 7.36
All captionsSimple5.42 / 6.82 / 7.93
All captionsAdvanced5.59 / 7.30 / 8.62
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.151, + 0.892, + 0.193 + ], + "angle": 0, + "content": "Table 4. Ablation on scene graph parsing strategies for language-supervised SGG. Results are obtained with VS\\(^3\\) (Swin-T) trained on scene graph supervision parsed from COCO captions." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.198, + 0.892, + 0.349 + ], + "angle": 0, + "content": "sults are shown in Table 4. Note that each image in COCO is annotated with 5 captions, and these captions are usually complimentary in describing image content. At first, we compare the performances between training with triplets from a single caption and all captions. We see the recalls achieve relative \\(10\\%\\) performance boosts by replacing triplets from a single caption with all captions. This suggests the completeness of extracted scene graphs from image descriptions is a non-negligible factor for training a high-quality SGG model." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.351, + 0.893, + 0.548 + ], + "angle": 0, + "content": "Moreover, we compare two language parsers for extracting \\(\\langle\\) subject, predicate, object \\(\\rangle\\) triplets: the simple SG parser [31], and the advanced SG parser [39]. Both parsers apply pre-defined rules to extract object and relation concepts from the semantic graphs of image language descriptions. Compared with the simple SG parser, the advanced SG parser covers additional features for dealing with complex quantificational modifiers (e.g., a lot of), resolving pronouns (e.g., it) and handling plural nouns (e.g., three men). The performance boosts of the advanced SG parser over the simple one (recalls from 5.42/6.82/7.93 to 5.59/7.30/8.62), indicates that the quality of semantic parsing is also important for language-supervised SGG." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.559, + 0.714, + 0.575 + ], + "angle": 0, + "content": "4.4. Open-vocabulary SGG" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.583, + 0.892, + 0.718 + ], + "angle": 0, + "content": "Setup. Following [8], we train the proposed VS3 with the same \\(70\\%\\) object categories of VG150 as base categories. With the aid of the pre-trained VSS, we hope VS3 can generalize to recognize the remaining \\(30\\%\\) novel objects and their involved relations at inference. Concretely, we compute evaluation metrics over two object category sets: \\(70\\%\\) base + \\(30\\%\\) novel objects (dubbed as open-vocabulary SGG (Ov-SGG) evaluation), and \\(30\\%\\) novel objects (dubbed as zero-shot SGG (ZsO-SGG) evaluation)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.893, + 0.902 + ], + "angle": 0, + "content": "In addition, we adopt the PREDCLS and SGDET evaluation protocols [50]. PREDCLS assumes object information given, yet SGDET generates scene graphs from the raw image using predicted objects. Since VS3 detects objects in a one-stage manner, we implement PREDCLS by selecting image regions that best match the ground-truth objects in post-processing, then performing relation recognition. We neglect the SGCLS protocol that assumes bounding box information given. This is because given bounding boxes can be directly used as region proposals in two-stage detectors, while the adopted one-stage manner in VS3 has no region proposal counterpart." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "2921" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.086, + 0.089, + 0.462, + 0.21 + ], + "angle": 0, + "content": "
MethodOv-SGG (70%+30%)ZsO-SGG (30%)
PREDCLSSGDETPREDCLSSGDET
IMP [50]40.02 / 43.40-37.01 / 39.46-
MOTIFS [60]41.14 / 44.70-39.53 / 41.14-
VCTREE [48]42.56 / 45.84-41.27 / 42.52-
TDE [47]38.29 / 40.38-34.15 / 36.37-
GCA [13]43.48 / 46.26-42.56 / 43.18-
EBM [44]44.09 / 46.95-43.27 / 44.03-
SVRP [8]47.62 / 49.94-45.75 / 48.39-
VS3(Swin-T)50.10 / 52.0515.07 / 18.7346.91 / 49.1310.08 / 13.65
VS3(Swin-L)55.88 / 58.1823.13 / 28.4954.44 / 57.3521.51 / 27.62
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.077, + 0.212, + 0.47, + 0.268 + ], + "angle": 0, + "content": "Table 5. Evaluation results (R@50/100) of fully supervised open-vocabulary SGG. Ov-SGG evaluates on \\(70\\%\\) base categories + \\(30\\%\\) novel categories in VG150, while ZsO-SGG only evaluates on \\(30\\%\\) novel categories." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.277, + 0.47, + 0.504 + ], + "angle": 0, + "content": "Fully supervised results. We first conduct experiments using manually annotated scene graphs. The results are presented in Table 5. For both Ov-SGG and ZsO-SGG, VS3 achieves substantial performance improvements under PREDCLS. When upgrading to the stronger backbone Swin-L, more significant improvements are obtained. More importantly, we report performances for the challenging and more practical SGDET, which are neglected by all previous methods since their used object detector cannot handle open-vocabulary detection [8]. The SGDET performances \\((R@50/100 = 10.08/13.65)\\) of ZsO-SGG using VS3 are even higher than SGCLS metrics of SVRP \\((R@50/100 = 9.30/11.32\\) in [8]). This reveals the superiority of our approach to recognizing novel objects thanks to the open-vocabulary generalization ability of the pre-trained VSS." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.506, + 0.471, + 0.809 + ], + "angle": 0, + "content": "Language-supervised results. Next, we evaluate the most challenging setting, i.e., open-vocabulary SGG using language supervision. To our knowledge, we are the first to propose such a new and practical SGG setting, and present the benchmark performances in Table 5. Not surprisingly, the recalls obtained via language-supervised training (i.e., \\( SG \\) from VG caption or COCO caption) are lower than supervised results (i.e., \\( SG \\) from annotated). When comparing \\( VS^{3}_{(Swin-T)} \\) and \\( VS^{3}_{(Swin-L)} \\) that is transferred from a stronger pre-trained model, the latter gets substantially higher Ov-SGG and ZsO-SGG performances. More importantly, we observe the performance gap between Ov-SGG and ZsO-SGG get closer in \\( VS^{3}_{(Swin-L)} \\), e.g., the \\( R@50 \\) gap under the VG caption setting becomes \\( 12.98 - 10.71 = 2.27 \\) from \\( 7.61 - 4.06 = 3.55 \\). This is due to the better generalization ability for recognizing novel classes. Moreover, the superior performances obtained by VG caption over COCO caption, indicate that using dense region-level descriptions and avoiding domain shift will help improve language-supervised open-vocabulary SGG in practice." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Qualitative analysis. We further showcase qualitative results of open-vocabulary SGG in Figure 3. The results demonstrate that our approach manages to detect novel objects and their relations with other objects. We also find that, compared with the fully supervised setting, the language-supervised results bias to predict simple relations such as" + }, + { + "type": "table", + "bbox": [ + 0.516, + 0.089, + 0.878, + 0.171 + ], + "angle": 0, + "content": "
MethodSG supervisionOv-SGG (70%+30%)ZsO-SGG (30%)
VS3(Swin-T)Manual annotation15.07 / 18.7310.08 / 13.65
VG caption7.61 / 9.604.06 / 5.58
COCO caption4.39 / 5.633.65 / 4.73
VS3(Swin-L)Manual annotation23.13 / 28.4921.51 / 27.62
VG caption12.98 / 16.2910.71 / 13.70
COCO caption6.76 / 8.456.26 / 7.89
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.499, + 0.173, + 0.892, + 0.243 + ], + "angle": 0, + "content": "Table 6. Evaluation results (R@50/100) of open-vocabulary SGG using three different scene graph supervisions: manual annotation, VG caption and COCO caption (language-supervised). Ov-SGG evaluates on \\(70\\%\\) base categories \\(+30\\%\\) novel categories in VG150, while ZsO-SGG only evaluates on \\(30\\%\\) novel categories." + }, + { + "type": "image", + "bbox": [ + 0.514, + 0.253, + 0.621, + 0.317 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.624, + 0.253, + 0.745, + 0.316 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.75, + 0.253, + 0.871, + 0.316 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.514, + 0.32, + 0.622, + 0.378 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.624, + 0.319, + 0.744, + 0.38 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.75, + 0.319, + 0.871, + 0.379 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.542, + 0.384, + 0.593, + 0.393 + ], + "angle": 0, + "content": "Input images" + }, + { + "type": "image_caption", + "bbox": [ + 0.64, + 0.383, + 0.728, + 0.392 + ], + "angle": 0, + "content": "Fully supervised results" + }, + { + "type": "image_caption", + "bbox": [ + 0.756, + 0.383, + 0.863, + 0.392 + ], + "angle": 0, + "content": "Language-supervised results" + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.4, + 0.892, + 0.471 + ], + "angle": 0, + "content": "Figure 3. Qualitative results of open-vocabulary SGG, particularly from fully supervised and language-supervised (VG caption) settings. Note that dotted nodes denote novel objects. For clarity, we only show triplets among the top 20 predictions that depict relations of highlighted image regions (i.e., boxes on input images)." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.477, + 0.892, + 0.523 + ], + "angle": 0, + "content": "'on', 'of'. Presumably, it's because scene graph supervision parsed from language is more likely to extract such simple words as relation predicates." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.541, + 0.619, + 0.557 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.567, + 0.892, + 0.839 + ], + "angle": 0, + "content": "In this work, we have proposed a novel approach to exploit a powerful pre-trained VSS for triggering language-supervised and open-vocabulary SGG. Particularly, we obtain cheap scene graph supervision by semantically parsing image language descriptions into semantic graphs and grounding the noun phrases through region-word alignment in the VSS. In addition, we devise the VS3 model, which performs object detection as category name grounding in the VSS and naturally builds relation representations for relation recognition. Thanks to the open-vocabulary generalization ability of the VSS, VS3 manages to detect novel objects and their relations with other objects, achieving open-vocabulary SGG. We validate our approach on the Visual Genome benchmark across supervised, language-supervised and open-vocabulary SGG settings, and have set new state-of-the-art performances. This demonstrates the merits of transferring pre-training knowledge to push SGG toward more practical scenarios." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.841, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Acknowledgment. This work was supported in part by the Shenzhen Science and Technology Program under Grant JCYJ20220818103006012 and ZDSYS20211021111415025." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2922" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.17 + ], + "angle": 0, + "content": "[1] Saeid Amiri, Kishan Chandan, and Shiqi Zhang. Reasoning with scene graphs for robot planning under partial observability. IEEE Robotics and Automation Letters, 7(2):5560-5567, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.173, + 0.47, + 0.214 + ], + "angle": 0, + "content": "[2] Shizhe Chen, Qin Jin, Peng Wang, and Qi Wu. Say as you wish: Fine-grained control of image caption generation with abstract scene graphs. In CVPR, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.216, + 0.469, + 0.257 + ], + "angle": 0, + "content": "[3] Tianshui Chen, Weihao Yu, Riquan Chen, and Liang Lin. Knowledge-embedded routing network for scene graph generation. In CVPR, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.259, + 0.469, + 0.314 + ], + "angle": 0, + "content": "[4] Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, and C Lawrence Zitnick. Microsoft coco captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.316, + 0.469, + 0.37 + ], + "angle": 0, + "content": "[5] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.373, + 0.469, + 0.414 + ], + "angle": 0, + "content": "[6] Jieuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai, and Mingyang Ling. Scene graph generation with external knowledge and image reconstruction. In CVPR, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.416, + 0.469, + 0.457 + ], + "angle": 0, + "content": "[7] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. In ICLR, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.46, + 0.469, + 0.5 + ], + "angle": 0, + "content": "[8] Tao He, Lianli Gao, Jingkuan Song, and Yuan-Fang Li. Towards open-vocabulary scene graph generation with prompt-based finetuning. In ECCV, 2022. 3, 5, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.502, + 0.469, + 0.558 + ], + "angle": 0, + "content": "[9] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICLR, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.56, + 0.469, + 0.6 + ], + "angle": 0, + "content": "[10] Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David Shamma, Michael Bernstein, and Li Fei-Fei. Image retrieval using scene graphs. In CVPR, 2015. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.602, + 0.469, + 0.657 + ], + "angle": 0, + "content": "[11] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In ICCV, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.66, + 0.47, + 0.713 + ], + "angle": 0, + "content": "[12] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.716, + 0.469, + 0.77 + ], + "angle": 0, + "content": "[13] Boris Knyazev, Harm de Vries, Catalina Cangea, Graham W Taylor, Aaron Courville, and Eugene Belilovsky. Generative compositional augmentations for scene graph prediction. In ICCV, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.773, + 0.469, + 0.828 + ], + "angle": 0, + "content": "[14] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJCV, 2017. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.831, + 0.469, + 0.871 + ], + "angle": 0, + "content": "[15] Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and Rene Ranftl. Language-driven semantic segmentation. In ICLR, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[16] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi." + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.47, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "Align before fuse: Vision and language representation learning with momentum distillation. NeurIPS, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.892, + 0.163 + ], + "angle": 0, + "content": "[17] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, et al. Grounded language-image pre-training. In CVPR, 2022. 2, 3, 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.165, + 0.892, + 0.219 + ], + "angle": 0, + "content": "[18] Manling Li, Alireza Zareian, Qi Zeng, Spencer Whitehead, Di Lu, Heng Ji, and Shih-Fu Chang. Cross-media structured common space for multimedia event extraction. arXiv preprint arXiv:2005.02472, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.221, + 0.892, + 0.26 + ], + "angle": 0, + "content": "[19] Rongjie Li, Songyang Zhang, and Xuming He. Sgtr: End-to-end scene graph generation with transformer. In CVPR, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.263, + 0.892, + 0.304 + ], + "angle": 0, + "content": "[20] Rongjie Li, Songyang Zhang, Bo Wan, and Xuming He. Bipartite graph network with adaptive message passing for unbiased scene graph generation. In CVPR, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.307, + 0.892, + 0.36 + ], + "angle": 0, + "content": "[21] Xingchen Li, Long Chen, Wenbo Ma, Yi Yang, and Jun Xiao. Integrating object-aware and interaction-aware knowledge for weakly supervised scene graph generation. In ACM MM, 2022. 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.363, + 0.892, + 0.403 + ], + "angle": 0, + "content": "[22] Yehao Li, Yingwei Pan, Jingwen Chen, Ting Yao, and Tao Mei. X-modaler: A versatile and high-performance codebase for cross-modal analytics. In ACM MM, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.406, + 0.892, + 0.459 + ], + "angle": 0, + "content": "[23] Yehao Li, Yingwei Pan, Ting Yao, Jingwen Chen, and Tao Mei. Scheduled sampling in vision-language pretraining with decoupled encoder-decoder network. In AAAI, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.462, + 0.892, + 0.502 + ], + "angle": 0, + "content": "[24] Yongzhi Li, Duo Zhang, and Yadong Mu. Visual-semantic matching by exploring high-order attention and distraction. In CVPR, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.504, + 0.892, + 0.544 + ], + "angle": 0, + "content": "[25] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dólár. Focal loss for dense object detection. In ICCV, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.547, + 0.892, + 0.588 + ], + "angle": 0, + "content": "[26] Xin Lin, Changxing Ding, Yibing Zhan, Zijian Li, and Dacheng Tao. Hl-net: Heterophily learning network for scene graph generation. In CVPR, 2022. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.59, + 0.892, + 0.629 + ], + "angle": 0, + "content": "[27] Hengyue Liu, Ning Yan, Masood Mortazavi, and Bir Bhanu. Fully convolutional scene graph generation. In CVPR, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.633, + 0.892, + 0.687 + ], + "angle": 0, + "content": "[28] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, 2021. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.689, + 0.892, + 0.73 + ], + "angle": 0, + "content": "[29] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.732, + 0.892, + 0.786 + ], + "angle": 0, + "content": "[30] Jianjie Luo, Yehao Li, Yingwei Pan, Ting Yao, Hongyang Chao, and Tao Mei. Coco-bert: Improving video-language pre-training with contrastive cross-modal matching and denoising. In ACM MM, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.789, + 0.892, + 0.828 + ], + "angle": 0, + "content": "[31] Jiayuan Mao. Scenegraphparser, 2019. https://github.com/vacancy/SceneGraphParser (Access date: 2022-8-11). 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.831, + 0.892, + 0.871 + ], + "angle": 0, + "content": "[32] Victor Milewski, Marie-Francine Moens, and Iacer Calixto. Are scene graphs good enough to improve image captioning? arXiv preprint arXiv:2009.12313, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.873, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[33] George A Miller. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39-41, 1995. 3, 5, 6" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "2923" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "[34] Yingwei Pan, Yehao Li, Jianjie Luo, Jun Xu, Ting Yao, and Tao Mei. Auto-captions on gif: A large-scale video-sentence dataset for vision-language pre-training. In ACM Multimedia, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.15, + 0.47, + 0.178 + ], + "angle": 0, + "content": "[35] Yingwei Pan, Ting Yao, Yehao Li, and Tao Mei. X-linear attention networks for image captioning. In CVPR, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.181, + 0.469, + 0.248 + ], + "angle": 0, + "content": "[36] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICLR, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.252, + 0.469, + 0.293 + ], + "angle": 0, + "content": "[37] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE TPAMI, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.296, + 0.469, + 0.336 + ], + "angle": 0, + "content": "[38] Brigit Schroeder and Subarna Tripathi. Structured query-based image retrieval using scene graphs. In CVPR Workshops, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.339, + 0.469, + 0.408 + ], + "angle": 0, + "content": "[39] Sebastian Schuster, Ranjay Krishna, Angel Chang, Li Fei-Fei, and Christopher D Manning. Generating semantically precise scene graphs from textual descriptions for improved image retrieval. In Proceedings of the fourth workshop on vision and language, 2015. 3, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.411, + 0.469, + 0.464 + ], + "angle": 0, + "content": "[40] Sahand Sharifzadeh, Sina Moayed Baharlou, and Volker Tresp. Classification by attention: Scene graph classification with prior knowledge. arXiv preprint arXiv:2011.10084, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.468, + 0.469, + 0.509 + ], + "angle": 0, + "content": "[41] Jing Shi, Yiwu Zhong, Ning Xu, Yin Li, and Chenliang Xu. A simple baseline for weakly-supervised scene graph generation. In ICCV, 2021. 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.512, + 0.469, + 0.552 + ], + "angle": 0, + "content": "[42] Motoharu Sonogashira, Masaaki Iiyama, and Yasutomo Kawanishi. Towards open-set scene graph generation with unknown objects. IEEE Access, 10:11574-11583, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.556, + 0.469, + 0.597 + ], + "angle": 0, + "content": "[43] Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Vl-bert: Pre-training of generic visual-linguistic representations. In ICLR, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.6, + 0.469, + 0.653 + ], + "angle": 0, + "content": "[44] Mohammed Suhail, Abhay Mittal, Behjat Siddiquie, Chris Broaddus, Jayan Eledath, Gerard Medioni, and Leonid Sigal. Energy-based learning for scene graph generation. In CVPR, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.657, + 0.469, + 0.726 + ], + "angle": 0, + "content": "[45] Rui Sun, Xuezhi Cao, Yan Zhao, Junchen Wan, Kun Zhou, Fuzheng Zhang, Zhongyuan Wang, and Kai Zheng. Multimodal knowledge graphs for recommender systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.729, + 0.469, + 0.769 + ], + "angle": 0, + "content": "[46] Kaihua Tang. A scene graph generation codebase in python, 2020. https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch.6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.773, + 0.469, + 0.813 + ], + "angle": 0, + "content": "[47] Kaihua Tang, Yulei Niu, Jianqiang Huang, Jiaxin Shi, and Hanwang Zhang. Unbiased scene graph generation from biased training. In CVPR, 2020. 1, 2, 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.816, + 0.469, + 0.856 + ], + "angle": 0, + "content": "[48] Kaihua Tang, Hanwang Zhang, Baoyuan Wu, Wenhan Luo, and Wei Liu. Learning to compose dynamic tree structures for visual contexts. In CVPR, 2019. 1, 2, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.86, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[49] Sijin Wang, Ruiping Wang, Ziwei Yao, Shiguang Shan, and Xilin Chen. Cross-modal scene graph matching for relationship-aware image-text retrieval. In WACV, 2020. 1" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.133 + ], + "angle": 0, + "content": "[50] Danfei Xu, Yuke Zhu, Christopher B Choy, and Li Fei-Fei. Scene graph generation by iterative message passing. In CVPR, 2017. 1, 2, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.136, + 0.892, + 0.175 + ], + "angle": 0, + "content": "[51] Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, and Devi Parikh. Graph r-cnn for scene graph generation. In ECCV, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.179, + 0.892, + 0.218 + ], + "angle": 0, + "content": "[52] Xu Yang, Kaihua Tang, Hanwang Zhang, and Jianfei Cai. Auto-encoding scene graphs for image captioning. In CVPR, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.222, + 0.892, + 0.289 + ], + "angle": 0, + "content": "[53] Lewei Yao, Jianhua Han, Youpeng Wen, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, Chunjing Xu, and Hang Xu. Detclip: Dictionary-enriched visual-concept paralleled pre-training for open-world detection. arXiv preprint arXiv:2209.09407, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.292, + 0.892, + 0.319 + ], + "angle": 0, + "content": "[54] Ting Yao, Yingwei Pan, Yehao Li, and Tao Mei. Exploring visual relationship for image captioning. In ECCV, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.321, + 0.892, + 0.348 + ], + "angle": 0, + "content": "[55] Ting Yao, Yingwei Pan, Yehao Li, and Tao Mei. Hierarchy parsing for image captioning. In ICCV, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.35, + 0.892, + 0.389 + ], + "angle": 0, + "content": "[56] Keren Ye and Adriana Kovashka. Linguistic structures as weak supervision for visual scene graph generation. In CVPR, 2021. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.392, + 0.892, + 0.432 + ], + "angle": 0, + "content": "[57] Alireza Zareian, Svebor Karaman, and Shih-Fu Chang. Bridging knowledge graphs to generate scene graphs. In ECCV, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.435, + 0.892, + 0.474 + ], + "angle": 0, + "content": "[58] Alireza Zareian, Svebor Karaman, and Shih-Fu Chang. Weakly supervised visual semantic parsing. In CVPR, 2020. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.478, + 0.892, + 0.517 + ], + "angle": 0, + "content": "[59] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In CVPR, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.521, + 0.892, + 0.56 + ], + "angle": 0, + "content": "[60] Rowan Zellers, Mark Yatskar, Sam Thomson, and Yejin Choi. Neural motifs: Scene graph parsing with global context. In CVPR, 2018. 1, 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.564, + 0.892, + 0.604 + ], + "angle": 0, + "content": "[61] Hanwang Zhang, Zawlin Kyaw, Shih-Fu Chang, and TatSeng Chua. Visual translation embedding network for visual relation detection. In CVPR, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.607, + 0.892, + 0.647 + ], + "angle": 0, + "content": "[62] Ji Zhang, Kevin J Shih, Ahmed Elgammal, Andrew Tao, and Bryan Catanzaro. Graphical contrastive losses for scene graph parsing. In CVPR, 2019. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.65, + 0.892, + 0.702 + ], + "angle": 0, + "content": "[63] Yong Zhang, Yingwei Pan, Ting Yao, Rui Huang, Tao Mei, and Chang-Wen Chen. Exploring structure-aware transformer over interaction proposals for human-object interaction detection. In CVPR, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.706, + 0.892, + 0.746 + ], + "angle": 0, + "content": "[64] Yong Zhang, Yingwei Pan, Ting Yao, Rui Huang, Tao Mei, and Chang-Wen Chen. Boosting scene graph generation with visual relation saliency. ACM TOMM, 2023. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.749, + 0.892, + 0.789 + ], + "angle": 0, + "content": "[65] Yiwu Zhong, Jing Shi, Jianwei Yang, Chenliang Xu, and Yin Li. Learning to generate scene graph from natural language supervision. In ICCV, 2021. 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.792, + 0.892, + 0.831 + ], + "angle": 0, + "content": "[66] Yiwu Zhong, Liwei Wang, Jianshu Chen, Dong Yu, and Yin Li. Comprehensive image captioning via scene graph decomposition. In ECCV, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.835, + 0.892, + 0.889 + ], + "angle": 0, + "content": "[67] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al. Regionclip: Region-based language-image pretraining. 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However, two knotty obstacles limit the practicability of current SGG methods in real-world scenarios: 1) training SGG models requires time-consuming ground-truth annotations, and 2) the closed-set object categories make the SGG models limited in their ability to recognize novel objects outside of training corpora. To address these issues, we novelly exploit a powerful pre-trained visual-semantic space (VSS) to trigger language-supervised and open-vocabulary SGG in a simple yet effective manner. Specifically, cheap scene graph supervision data can be easily obtained by parsing image language descriptions into semantic graphs. Next, the noun phrases on such semantic graphs are directly grounded over image regions through region-word alignment in the pre-trained VSS. In this way, we enable open-vocabulary object detection by performing object category name grounding with a text prompt in this VSS. On the basis of visually-grounded objects, the relation representations are naturally built for relation recognition, pursuing open-vocabulary SGG. We validate our proposed approach with extensive experiments on the Visual Genome benchmark across various SGG scenarios (i.e., supervised / language-supervised, closed-set / open-vocabulary). Consistent superior performances are achieved compared with existing methods, demonstrating the potential of exploiting pre-trained VSS for SGG in more practical scenarios. + +# 1. Introduction + +Scene graph [10] is a structured representation for describing image semantics. It abstracts visual objects as graph nodes and represents their relations as labeled graph edges. The task of scene graph generation (SGG) [6, 14, + +![](images/d96fbdcc4d88f1581bf3a10d302f429444dad4b147a7bf92cb3db3ec2e8b10e0.jpg) +Figure 1. An illustration of exploiting a pre-trained visual-semantic space (VSS) to trigger language-supervised and open-vocabulary scene graph generation (SGG). (a) We acquire weak scene graph supervision by semantically parsing the image language description and grounding noun phrases on image regions via VSS. (b) At SGG inference time, thanks to the open-vocabulary generalization naturally rooted in VSS, the novel object name (e.g., player) in the text prompt input can be well aligned to one image region, which is regarded as its detection. + +20, 26, 40, 47, 48, 50, 51, 57, 60, 63, 64] plays an important role for fine-grained visual understanding, which has shown promising results in facilitating various downstream applications, such as image-text retrieval [24, 38, 49], image captioning [2, 22, 32, 35, 52, 54, 55, 66], cross-media knowledge graph construction [18, 45] and robot planning [1]. + +Though great effort has been made, SGG of the current stage still faces two knotty obstacles that limit its practicability in real-world scenarios. 1) Training SGG models requires massive ground-truth scene graphs that are expensive for manual annotation. Annotators have to draw bounding boxes for all objects in an image and connect possible interacted object pairs, and assign object/relation labels. Since assigned labels might be ambiguous, further verification and canonicalization processing are usually required [14]. Finally, a scene graph in the form of a set of $\langle \text{subject}, \text{predicate}, \text{object} \rangle$ triplets with subject and object bounding boxes is constructed. Such annotating pro + +cess is time-consuming and tedious, costing much human labor and patience. 2) Almost all existing SGG methods [20,21,26,47,48,50,51,60] involve a pre-defined closed set of object categories, making them limited in recognizing novel objects outside of training corpora. However, real-world scenes contain a border set of visual concepts than any pre-defined category pool. It is very likely to encounter unseen/novel categories. When this happens, current SGG models either classify novel objects to a known category or fail to detect them like background regions. Accordingly, the prediction of their interactions/relations with other objects is negatively affected or just neglected. This may lead to problems. For example, a real-world robot may take inappropriate actions using such closed-set SGG models [1,42]. + +Recently, there is a trend of leveraging free-form language supervision for benefiting visual recognition tasks via large-scale language-image pre-training [7, 15, 17, 36, 53, 59, 67]. These methods (e.g., CLIP [36]) perform pre-training on massive easily-obtained image-text pairs to learn a visual-semantic space (VSS), and have demonstrated great zero-shot transferability. Especially, the recent grounded language-image pre-training (GLIP) [17] has learned an object-level and semantic-rich VSS. Based on the learned VSS, it has established new state-of-the-art performances in phrase grounding and zero-shot object detection. This indicates such pre-trained VSS has powerful multi-modal alignment ability (i.e., image regions and text phrases that have similar semantics get close embeddings) and open-vocabulary generalization ability (i.e., covering virtually any concepts in the pre-training image-text corpus). This inspires our thought of addressing the aforementioned obstacles in SGG using the pre-trained VSS. On the one hand, taking advantage of its multi-modal alignment ability, we can cheaply acquire scene graph supervision from an image description (e.g., retrieving image regions aligned with noun phrases and re-arranging the description into a scene-graph-like form). On the other hand, by leveraging its open-vocabulary generalization ability, it is promising to enable novel category prediction in SGG. + +In this work, we investigate the opportunity of fully exploiting the VSS learned by language-image pre-training to trigger language-supervised and open-vocabulary SGG. Specifically, we obtain weak scene graph supervision by semantically parsing an image language description into a semantic graph, then grounding its noun phrases over image regions through region-word alignment in the pre-trained VSS (Figure 1 (a)). Moreover, we propose a novel SGG model, namely Visual-Semantic Space for Scene graph generation $\mathbf{V}\mathbf{S}^{3})$ . It takes a raw image and a text prompt containing object category names as inputs, and projects them into the shared VSS as embeddings. Next, $\mathrm{VS}^3$ performs object detection by aligning the embeddings of category names and image regions. Based on high-confidence de + +tected objects, VS³ builds relation representations for object pairs with a devised relation embedding module that fully mines relation patterns from visual and spatial perspectives. Finally, a relation prediction module takes relation representations to infer relation labels. The predicted scene graph is composed by combining object detections and inferred relation labels. During training, visually-grounded semantic graphs parsed from image descriptions could be used as weak scene graph supervision, achieving language-supervised SGG. At SGG inference time, when using a text prompt input containing novel categories, VS³ manages to detect novel objects thanks to the open-vocabulary generalization ability naturally rooted in VSS, hence allowing for open-vocabulary SGG (Figure 1 (b)). + +In summary, we have made the following contributions: (1) the exploitation of a pre-trained VSS provides an elegant solution for addressing obstacles to triggering both language-supervised and open-vocabulary SGG, making a solid step toward real-world usage of SGG. (2) The proposed VS3 model is a new and versatile framework, which effectively transfers language-image pre-training knowledge for benefiting SGG. (3) We fully validate the effectiveness of our approach through extensive experiments on the Visual Genome benchmark, and have set new state-of-the-art performances spanning across all settings (i.e., supervised / language-supervised, closed-set / open-vocabulary). + +# 2. Related Work + +Fully supervised SGG. The concept of scene graph as a structured image representation is first introduced in [10]. Next, the Visual Genome benchmark [14] is manually annotated with large-scale scene graphs on images. Such annotated dataset triggers a series of innovations [6,20,26,40,47, 48,50,51,57,60] for the fully supervised SGG task. Typically, an object detector (e.g., Faster-RCNN [37]) is trained to retrieve image regions as scene graph nodes. Then, relation representations of object pairs are constructed from visual, spatial and language perspectives, and are used for relation classification to label scene graph edges. To achieve desirable SGG, researchers have devised message-passing mechanisms [20,26,48,50,60] to exploit contextual information, derived contrastive loss functions [62] or incorporated external knowledge [6,40,57]. However, all these methods rely on training with expensive scene graph annotations. Our proposed VS $^3$ model is compatible with fully supervised SGG, but we seek to make SGG training cheaper, which is more practical in real-world applications. + +Language-supervised SGG. This task aims to train SGG models using language descriptions. It has recently attracted increasing attention [21,41,56,58,65], which is also referred as weakly supervised SGG in [21,41,58]. Particularly, based on a graph alignment algorithm, VSPNet [58] first proposes to supervise SGG training with scene graphs + +that have no object locations. Subsequent works [21,41,65] extract entities and relations from image captions to compose such unlocalized scene graph, which is achieved via an off-the-shelf language parser [31, 39]. They next follow a common paradigm: first grounding text entities on image regions, and then leveraging grounded scene graphs as pseudo labels to train standard SGG models. To acquire entity groundings, Shi et al [41] devise an efficient graph matching module optimized via contrastive learning; Zhong et al [65] simply match text entity names with predicted object labels from a pre-trained object detector using semantic rules such as WordNet [33] synsets matching. More recently, Li et al [21] integrate interaction-aware knowledge distilled from pre-trained language-image models [16] for enhancing grounding reliability. Instead, we propose to obtain groundings through region-word alignment in a pretrained VSS, which is much simple yet more effective to collect scene graph supervision from language. + +Language-image pre-training. This has been shown effective for boosting various vision-language downstream tasks [11, 16, 23, 29, 30, 34, 43], e.g., image-text retrieval, image captioning. Also, recent studies present remarkable results on transferring pre-trained language-image knowledge to solve vision recognition problems, such as zero-shot image classification [9,36], open-vocabulary object detection [7, 17, 53, 59, 67] and zero-shot semantic segmentation [15]. For example, CLIP [36] and ALIGN [9] learn separate encoders to embed image and text into a shared space by pre-training on massive image-text pairs using a contrastive loss. They have demonstrated remarkable generalization ability on zero-shot image classification after pretraining. Distinct from CLIP and ALIGN that learn image-level representations, GLIP [17] focuses on learning object-level visual representations through region-word alignment. It has attained strong zero-shot and few-shot transferability to various object-level recognition tasks such as object detection and phrase grounding. Most recently, He et al [8] investigate a visual-relation pre-training and prompt-based fine-tuning method for open-vocabulary SGG. However, its image encoder relies on a pre-trained region proposal extractor, which is a bottleneck for achieving open-vocabulary SGG under the SGDET protocol. Unlike [8], our proposed VS3 directly encodes an image into region tokens, avoiding the bottleneck of region proposals. More importantly, our approach addresses obstacles to achieving both language-supervised and open-vocabulary SGG using a unified framework, while He et al [8] only focus on the latter. + +# 3. Approach + +# 3.1. Notation & Overview + +The task of scene graph generation (SGG) aims to map an image into an abstract graph $SG = \{O, R\}$ , where graph + +
SG\(\mathcal{C}_{o}^{target}\)Not containing novel object classesContaining novel object classes
Manually annotatedfully supervised & closed-setfully supervised & open-vocabulary
Automatically parsed from image descriptionslanguage-supervised & closed-setlanguage-supervised & open-vocabulary
+ +Table 1. Definitions of different SGG settings, according to scene graph supervision $SG$ and the object set at inference $\mathcal{C}_o^{target}$ . + +nodes $O = \{o_1,\dots,o_N\}$ correspond to image objects, and graph edges $R = \{r_1,\dots,r_M\}$ represents their relations. Each object $o_i = \{\mathbf{b}_i,l_i\} \in O$ contains the bounding box coordinates $\mathbf{b}_i\in \mathbb{R}^4$ and its class label information $l_{i}\in \mathcal{C}_{o}$ where $\mathcal{C}_o$ denotes the set of object categories. Each relation $r_m\in R$ is a $\langle$ subject,predicate,object $\rangle$ triplet, and we represent it as $r_m = r_{i\rightarrow j} = \{o_i,p_{ij},o_j\}$ , in which $p_{ij}$ is the predicate/relation label belonging to category set $\mathcal{C}_r$ . + +Most existing SGG methods require expensive manually annotated scene graphs $SG$ as supervision. And they involve a closed set of object categories $\mathcal{C}_o$ in both training and inference. These issues limit SGG for practical usage. In this work, we propose to fully exploit a pre-trained VSS to push SGG towards language-supervised and open-vocabulary scenarios. We illustrate the definitions of different SGG settings in Table 1. Concretely, from the perspective of scene graph supervision $SG$ , SGG is categorized into fully supervised and language-supervised, using $SG$ from manual annotation and language respectively. From the other perspective of object categories $\mathcal{C}_o^{target}$ at inference, it is referred to as open-vocabulary or closed-set according to whether or not $\mathcal{C}_o^{target}$ contains novel objects. + +Next, in Section 3.2, we present a new SGG model named VS3, which is versatile to handle with all SGG settings in Table 1. In Section 3.3, we devise a scheme to obtain scene graph supervision from language descriptions, allowing for language-supervised SGG. Finally, Section 3.4 details the strategy of transferring the proposed VS3 to pursue open-vocabulary SGG. + +# 3.2. The Proposed VS3 Model + +We propose the VS3 model for tackling the SGG task by extending the GLIP [17] framework with relation recognition modules, as shown in Figure 2. + +Preliminary. GLIP unifies object detection and phrase grounding into one framework. It has an image encoder $\mathrm{Enc}_I$ (e.g., Swin Transformer backbone [28]) and a text encoder $\mathrm{Enc}_L$ (e.g., BERT [12]). $\mathrm{Enc}_I$ extracts region/box features $\bar{O} \in \mathbb{R}^{\bar{N} \times d}$ from an input image, where $\bar{N}$ is the number of regions and $d$ is the feature dimension. $\mathrm{Enc}_L$ encodes a text input into contextualized word-token embeddings $\bar{P} \in \mathbb{R}^{\bar{T} \times d}$ , where $\bar{T}$ is the text length. Further, GLIP uses a cross-modal fusion module to achieve feature communication between $\bar{O}$ and $\bar{P}$ , resulting in enriched region embeddings $\tilde{O} \in \mathbb{R}^{\bar{N} \times d}$ and word embeddings $\tilde{P} \in \mathbb{R}^{\bar{T} \times d}$ . Finally, the region-word alignment + +![](images/c4653213c8d505a41d7c4292da75a5b33f71d82284a36c0a2bf967849c07f73f.jpg) +Figure 2. An overview of the proposed Visual-Semantic Space for Scene graph generation $(\mathrm{VS}^3)$ model. It inherits the image encoder, the text encoder and the cross-modal fusion module from GLIP [17], so as to project image regions and text prompt words in a pre-trained visual-semantic space (VSS). Object regions are detected by aligning the embeddings of category names and image regions in VSS. Next, high-confidence detected results are retained to compose subject-object pairs. After that, the relation embedding module constructs their relation representations by extracting visual and spatial features, on which relation prediction is performed. At test time, thanks to the open-vocabulary generalization ability of VSS, $\mathrm{VS}^3$ manages to detect novel objects by switching to a text prompt containing novel classes. + +scores $\hat{S}_{\text {ground }} = \tilde{O}\tilde{P}^{\top} \in \mathbb{R}^{\bar{N} \times \bar{T}}$ and the predicted locations $\vec{B} = \text { box\_predictor }(\vec{O}) \in \mathbb{R}^{\bar{N} \times 4}$ are supervised by ground-truth text grounding data. After large-scale pretraining, $\mathrm{Enc}_I$ and $\mathrm{Enc}_L$ embed the input image and text into a joint VSS, which aligns multi-modal embeddings and covers open-vocabulary concepts. VS3 inherits Enc $I$ , Enc $L$ and the cross-module fusion module from GLIP. + +Text prompt. Considering that object detection has been reformulated as phrase grounding, VS3 also requires a text prompt input except for the image input. Following GLIP's design, we set the text prompt for object detection in the form of "name(c1). name(c2). ... name(c|cO)". where $c_i \in \mathcal{C}_o$ and $\text{name}(c_i)$ gets the category name of $c_i$ (e.g., person). Hence, an object is detected according to the alignment score between a region embedding $\tilde{\mathbf{o}}_i \in \tilde{\mathcal{O}}$ (the $i$ th row) and the category name embeddings $\tilde{P}$ . + +Relation embedding module. To further enable VS3 with relation recognition ability, we devise the relation embedding module to build relation representations. Based on the region/box features $\tilde{O}$ after cross-modal fusion, we first sample a subset of regions $\tilde{O}' \in \mathbb{R}^{N' \times d}$ that are most likely to be valid objects. This is achieved by matching predicted bounding boxes $\hat{B}$ with ground-truth objects during training, and by retaining top- $N'$ regions with the highest confidence scores after non-maximum suppression (NMS) at inference. Next, we construct relation representations for all possible subject-object pairs. Given an object pair $(\tilde{\mathbf{o}}_i, \tilde{\mathbf{o}}_j)$ and their normalized bounding boxes $(\mathbf{b}_i, \mathbf{b}_j)$ , the pairwise relation representation is represented as $\mathbf{pair}_{i \rightarrow j} = cat[\mathbf{pair}_{i \rightarrow j}^{\text{visual}}, \mathbf{pair}_{i \rightarrow j}^{\text{spatial}}]$ . This is the concatenation of features mined from the visual and spatial perspectives. The visual feature is computed by + +$$ +\mathbf {p a i r} _ {i \rightarrow j} ^ {\text {v i s u a l}} = \boldsymbol {f} _ {\text {d i f f}} (\tilde {\mathbf {o}} _ {i} - \tilde {\mathbf {o}} _ {j}) + \boldsymbol {f} _ {\text {s u m}} (\tilde {\mathbf {o}} _ {i} + \tilde {\mathbf {o}} _ {j}), \qquad (1) +$$ + +where $\pmb{f}_{diff}$ and $\pmb{f}_{sum}$ are two mapping functions implemented as 2-layer MLPs (multi-layer perceptron). By defining the normalized center coordinates of two involved objects as $(ct_i^x, ct_i^y)$ and $(ct_j^x, ct_j^y)$ , the spatial feature is measured as + +$$ +\mathbf {p a i r} _ {i \rightarrow j} ^ {\text {s p a t i a l}} = \operatorname {c a t} \left[ \mathbf {b} _ {i}, \mathbf {b} _ {j}, d x, d y, \operatorname {d i s}, \theta , A _ {i}, A _ {j}, I, U \right], \tag {2} +$$ + +where $dx = ct_i^x - ct_j^x, dy = ct_i^y - ct_j^y,$ $\sqrt{dx^2 + dy^2}, \theta = \arctan \left(\frac{dy}{dx}\right). A_i, A_j, I, U$ denote the areas of the subject, the object, their intersection, and union boxes, respectively. + +Relation prediction. Conditioned on the relation representation $\mathbf{pair}_{i\rightarrow j}$ of each object pair, we predict a relateness score $\hat{z}_{i\rightarrow j} = f_{relateness}(\mathbf{pair}_{i\rightarrow j})\in [0,1]$ and a semantic label probability $\hat{\pmb{y}}_{i\rightarrow j} = \pmb{f}_{semantic}(\pmb{pair}_{i\rightarrow j})\in$ $[0,1]^{|\mathcal{C}_r|}$ . The relateness $\hat{z}_{i\rightarrow j}$ represents the probability that relations exist between the object pair. $f_{relateness}$ is implemented with an MLP coupled with Sigmoid activation. $\pmb{f}_{semantic}$ is implemented with another MLP using Softmax activation. The total loss for relation recognition $L_{rel\_rcg}$ is measured as + +$$ +L _ {\text {r e l a t e n e s s}} = F L \left(\hat {z} _ {i \rightarrow j}, z _ {i \rightarrow j}\right), \tag {3} +$$ + +$$ +L _ {\text {s e m a n t i c}} = C E \left(\hat {\boldsymbol {y}} _ {i \rightarrow j}, \boldsymbol {y} _ {i \rightarrow j}\right), \tag {4} +$$ + +$$ +L _ {r e l. r c g} = L _ {r e l a t e n e s s} + L _ {s e m a n t i c}, \tag {5} +$$ + +where $FL$ and $CE$ denote focal loss [25] and cross-entropy loss functions. $z_{i\rightarrow j}$ and $\pmb{y}_{i\rightarrow j}$ represent the ground-truth relateness label and predicate category label respectively. + +Training & inference. During training, we initialize parameters from pre-trained GLIP models for inherited modules in VS3. To ease training difficulty, we freeze the image encoder and text encoder, and only fine-tune the + +cross-modal fusion module and devised modules for relation recognition. This also avoids the degeneration of the pre-trained VSS. At inference, by retaining high-confidence detected objects and further predicting their relations, we generate an image scene graph representation. + +# 3.3. Obtaining Language Scene Graph Supervision + +Ground-truth scene graphs are time-consuming to annotate. Alternatively, we can parse semantic graphs from image language descriptions, and obtain noun phrase groundings through region-word alignment in the pre-trained VSS (implemented with an off-the-shelf GLIP). This is a much cheaper way to obtain weak scene graph supervision. + +Semantic graph parsing. Concretely, for each image language description, we parse it into a semantic graph $SG^{text} = \{O^{text}, R^{text}\}$ using the Standard Scene Graph Parser based on [39]. The parser not only extracts noun phrases as entities/objects ( $O^{text}$ ), but also extracts the words describing their relations ( $R^{text}$ ). For example, the sentence "a woman is playing the piano in the room." is parsed to the $SG^{text}$ , of which $O^{text} = \{woman, piano, room\}$ and $R^{text} = \{\langle 0, playing, 1\rangle, \langle 0, in, 2\rangle\}$ (numbers denote object indices). Considering that parsed object/relation words are free-form, we map them to our concerning categories (e.g., VG150 object/relation categories in experiments) by rules such as direct string matching and WordNet [33] synsets matching following [65]. + +Semantic graph grounding. Note that each element of $O^{text}$ only contains a text label name so far, and its bounding box information is still missing. To obtain bounding boxes, we construct a text prompt using triplets in $SG^{text}$ , e.g., "woman playing piano. woman in room." Then, we feed such text prompt together with the raw image into a pre-trained GLIP, in order to acquire grounding boxes of $O^{text}$ . Specifically, for each element in $O^{text}$ , we select the image region that has the highest alignment score with its category name as its grounding box. Since there might be multiple objects in $O^{text}$ that actually refer to the same object, we perform a post-processing NMS to merge boxes with the same label and high IoU (intersection over union) scores ( $\geq 0.9$ ). Finally, with box information, the visually-grounded $SG^{text}$ is ready to be used as weak supervision for training SGG models, e.g., the proposed VS3. + +# 3.4. Transferring to Open-vocabulary SGG + +Open-vocabulary SGG [8] aims to train SGG models that can recognize objects of novel categories and their involved relations. Formally, we train the SGG model with scene graphs containing objects in the base category set $\mathcal{C}_o^{base}$ . At inference, the object category set is $\mathcal{C}_o^{target}$ , which contains novel categories in $\mathcal{C}_o^{novel} = \mathcal{C}_o^{target} \backslash \mathcal{C}_o^{base} \neq \emptyset$ . + +Back to our proposed VS3, an open-vocabulary VSS is + +maintained by freezing the image and text encoders. Taking this advantage, we devise a scheme to adapt VS3 for open-vocabulary SGG. Concretely, during training, we set the text prompt as “name(c1). name(c2). ... name(cObase)”. where $c_{i} \in \mathcal{C}_{o}^{base}$ . And only relation triplets involving base object categories are kept for training. At inference, the text prompt is switched to be “name(c1). name(c2). ... name(cOtarget)”. where $c_{i} \in \mathcal{C}_{o}^{target}$ . In this way, a novel object class (e.g., lady) may have an embedding close to a base category (e.g., woman) embedding. This makes the novel class also able to find well-aligned image regions. Note that relation representations are constructed from visual and spatial cues, which are usually class-agnostic. Hence, the following relation recognition in VS3 will not be affected when encountering novel objects. + +# 4. Experiments + +# 4.1. Datasets and Experimental Settings + +Datasets. To evaluate the SGG task, we adopt the widely-used VG150 version [50] of the Visual Genome (VG) dataset [14]. VG150 retains the most frequent 150 categories and 50 relation/predicate categories in VG. It contains $\sim 108\mathrm{K}$ images, of which $70\%$ images are used for training (including 5K for validation), and the remaining $30\%$ images are used for testing. The annotated scene graph of each image has 11.5 objects and 6.2 relation triplets on average. In addition, images of VG are densely annotated with region descriptions, about 50 descriptions for each image. We refer to these descriptions as VG caption, which provides a text source for evaluating the language-supervised SGG setting. Moreover, we consider the challenging setting of using image-text pairs in COCO caption [4] for training SGG models. This dataset contains 123K images in total. Each image has 5 human-annotated captions. We keep $\sim 106\mathrm{k}$ images by filtering out those images that also exist in the VG150 test split. + +Evaluation protocols and metrics. We mainly adopt the SGDET [47,50] protocol, which generates a scene graph from the input image without any given box information. We report the performance on Recall@K (K=20/50/100) following previous works [21, 41, 47, 50, 56, 65], which measures the fraction of correctly predicted relation triplets in top $K$ predictions. A triplet prediction is considered as correct when its subject, object, predicate labels and both the subject and object regions match with (same label or IoU>0.5) a ground-truth triplet. Note that we obtain triplet predictions using graph constraint, which limits each subject-object pair to have only the most confident predicate. All recall metrics across different SGG settings in experiments are computed over VG150 test images. Considering that the adopted GLIP pre-trained VSS has seen part of images in the original VG150 test split ( $\sim$ 26k) during + +pre-training, we exclude these images and get a new split of $\sim 15\mathrm{k}$ test images. We have validated that such VG150 test split is sufficiently large for computing stable metrics as the original, by comparing computed metrics of several SGG models (in codebase [46]) on these two splits ( $< 0.15$ points variation, see supplementary materials). + +Implementation details. We initialize VS3 from pretrained GLIP [17] models, i.e., the GLIP-T and the larger GLIP-L trained with more data. Both construct a VSS of dimension $d = 256$ . We retain the top 36 object detections per image for pairwise relation recognition. The whole framework is fine-tuned on 8 Nvidia 2080Ti GPUs with AdamW optimizer. During fine-tuning, we freeze the parameters of the image and text encoder; and set the learning rate for the cross-modal fusion module as 1e-5 and 10x larger learning rates for the relation embedding and prediction modules. The maximum fine-tuning epoch number is 10, with learning rates dropping by 10x after 6 epochs. + +# 4.2. Fully Supervised SGG + +Setup. We first evaluate our proposed VS3 under the conventional fully supervised SGG setting. This setting trains SGG models using manually annotated scene graphs, consisting of object labels coupled with bounding boxes, and relation labels. We adopt VG150 for training and evaluation following previous methods [3, 19, 26, 27, 48, 50, 60, 61, 65]. All these methods involve a closed set of object categories. Specifically, the text prompt input of VS3 is constructed from VG150 object category names, i.e., "airplane. animal. ... zebra". We train VS3 by fine-tuning over two GLIP variants: GLIP-T with the Swin-T [28] backbone, GLIP-L with the Swin-L [28] backbone. + +Comparison with state-of-the-arts. The results are summarized in Table 2. Our proposed VS3 model using the Swin-T backbone already achieves competitive recall metrics. When upgrading to the larger Swin-L variant, the performance improvements become significant (1.8 to 3.4 points improvement than the previous best results). Note that previous methods [26, 50, 60] build their models upon an off-the-shelf object detector, and they usually design heavy message-passing modules to incorporate context information. Instead, VS3 devises a light-weighted relation recognition head (including the relation embedding and prediction modules) over a pre-trained VSS. The superior performances clearly suggest the merits of transferring language-image pre-trained models for boosting SGG. + +Ablation on relation representation. Next, we carry out ablation studies on relation representation construction in the relation embedding module. As shown in Table 2, by removing visual and spatial feature components, the relation triplet recalls drop accordingly. Also notice that the removal of visual features which is built from subject and object region embeddings leads to relatively larger perfor + +
SGG modelDetectorBackboneR@20R@50R@100
FCSGG [27]-HRNetW4816.121.325.1
SGTR [19]DETRR-101-24.628.4
IMP [50]Faster-RCNNVGG-1614.620.724.5
KERN [3]Faster-RCNNVGG-16-27.129.8
MOTIFS [60]Faster-RCNNVGG-1621.427.230.3
RelDN [62]Faster-RCNNVGG-1621.128.332.7
VTransE [61]Faster-RCNNRX-10123.029.734.3
MOTIFS [60]Faster-RCNNRX-10125.132.136.9
VCTREE [48]Faster-RCNNRX-10124.731.536.2
SGNLS [65]Faster-RCNNRX-10124.631.836.3
HL-Net [26]Faster-RCNNRX-10126.033.738.1
VS3-Swin-T26.134.539.2
w/o visual-Swin-T23.131.636.7
w/o spatial-Swin-T24.332.837.8
VS3-Swin-L27.836.641.5
+ +Table 2. Experimental results of fully supervised SGG. w/o visual and w/o spatial indicate removing spatial and visual features in the relation embedding module for relation representation. All metrics are computed under the SGDET protocol on VG150 test images. + +mance drops than spatial. This suggests the region embeddings in the pre-trained VSS provide strong cues for relation recognition. Overall, these observations validate the effectiveness of our design to mine relation patterns. + +# 4.3. Language-supervised SGG + +Setup. Language-supervised SGG [21, 41, 56, 65] explores to train SGG models with language descriptions of images. Concretely, we parse each image description into a semantic graph, in the form of a set of $\langle$ subject, predicate, object $\rangle$ triplets. Note that parsed object/relation phrases from language descriptions are freeform, we map them to VG150 categories by semantic rules following [65], such as WordNet [33] synsets matching. This makes the learned SGG model compatible for evaluating on VG150. Next, the parsed semantic graph is grounded to image regions using grounding methods, i.e., the pretrained GLIP-L [17] in our approach. Finally, the visually-grounded semantic graphs are used as weak supervision to train our proposed VS $^3$ like the fully supervised setting. + +Particularly, we have trained VS3 with text triplets parsed from three different sources of text following [21, 65]. 1) The unlocalized graph setting uses ground-truth triplet annotations in VG. 2) The VG caption setting uses triplets that are automatically parsed from natural image descriptions in VG. 3) The COCO caption setting leverages triplets parsed from captions in COCO. This setting is the most challenging since COCO captions are image-level descriptions. Such captions are different from the region-level descriptions in VG, which focus on describing object interactions. Also, note that the number of annotated captions for each COCO image (average 5) is much less than the number for each VG image (average $\sim 50$ ). + +Comparison with state-of-the-arts. The experimental results compared with previous methods are presented in Table 3. All evaluation metrics are com + +
SGG modelGroundingR@20R@50R@100
Unlocalized graphVSPNet [58]--4.705.40
LSWS [56]--7.308.73
MOTIFS [60]WSGM [41]4.125.596.45
MOTIFS [60]SGNLS [65]7.239.2810.71
MOTIFS [60]Li et.al [21]9.0911.3912.89
Uniter† [5]SGNLS [65]7.8110.0311.50
Uniter† [5]Li et.al [21]9.5711.8013.15
VS3(Swin-T)GLIP-L [17]18.0223.8928.19
VS3(Swin-T+FreqBias)GLIP-L [17]20.0626.7231.75
VS3(Swin-L+FreqBias)GLIP-L [17]22.1829.8134.96
VG captionLSWS [56]--3.854.04
MOTIFS [60]SGNLS [65]6.318.059.21
MOTIFS [60]Li et.al [21]8.2510.5011.98
Uniter† [5]SGNLS [65]-9.2010.30
Uniter† [5]Li et.al [21]8.9010.9312.14
VS3(Swin-T)GLIP-L [17]11.7816.2519.7
VS3(Swin-L)GLIP-L [17]13.0117.3820.54
COCO captionLSWS [56]--3.283.69
MOTIFS [60]Li et.al [21]5.026.407.33
Uniter† [5]SGNLS [65]-5.806.70
Uniter† [5]Li et.al [21]5.426.747.62
VS3(Swin-T)GLIP-L [17]5.597.308.62
VS3(Swin-L)GLIP-L [17]6.048.159.90
+ +puted on the VG150 test set under the SGDET protocol. Specifically, under the unlocalized scene graphs setting, $\mathrm{VS}^3$ with the Swin-T backbone ( $\mathrm{VS}^{3}_{(Swin-T)}$ ) obtains substantial improvements on recall metrics over existing best results ( $R@20/50/100$ from 9.57/11.80/13.15 to 18.02/23.89/28.19). Since relation frequency statistics are available in this setting, we use them as frequency biases [60] in predicate classification, leading to further performance gains ( $\mathrm{VS}^{3}_{(Swin-T+FreqBias)}$ ). When using the stronger Swin-L backbone ( $\mathrm{VS}^{3}_{(Swin-L+FreqBias)}$ ), we attain the highest performances ( $R@20/50/100 = 22.18/29.81/34.96$ ), which even outperform many fully supervised methods (see Table 2). The VG caption setting provides weaker scene graph supervision via language parsing. We observe that our approach also outperforms previous state-of-the-art methods significantly. As for the most challenging COCO caption setting, it suffers from the additional domain shift problem since it trains on COCO but evaluates on VG150. As expected, the performances are lower than in the two aforementioned settings. But when comparing with previous works using the same text source, our approach still manages to achieve better performances. Overall, our approach consistently surpasses previous methods for language-supervised SGG. This demonstrates the benefits brought by pre-trained language-image models in terms of both grounding box acquirement and task transferring to tackle SGG. + +Ablation on scene graph parsing strategy. We also conduct ablation studies on different scene graph parsing strategies for obtaining language SGG supervision. The re + +Table 3. Comparison with state-of-the-art language-supervised SGG methods, using weak scene graph supervision from three different text sources: unlocalized scene graphs, VG caption and COCO caption. All metrics are computed under the SGDET protocol on VG150 images. ( $\dagger$ indicates adapted for SGG.) + +
SG fromSG parserR@20/50/100
Single captionSimple5.07 / 6.25 / 7.36
All captionsSimple5.42 / 6.82 / 7.93
All captionsAdvanced5.59 / 7.30 / 8.62
+ +Table 4. Ablation on scene graph parsing strategies for language-supervised SGG. Results are obtained with VS $^3$ (Swin-T) trained on scene graph supervision parsed from COCO captions. + +sults are shown in Table 4. Note that each image in COCO is annotated with 5 captions, and these captions are usually complimentary in describing image content. At first, we compare the performances between training with triplets from a single caption and all captions. We see the recalls achieve relative $10\%$ performance boosts by replacing triplets from a single caption with all captions. This suggests the completeness of extracted scene graphs from image descriptions is a non-negligible factor for training a high-quality SGG model. + +Moreover, we compare two language parsers for extracting $\langle$ subject, predicate, object $\rangle$ triplets: the simple SG parser [31], and the advanced SG parser [39]. Both parsers apply pre-defined rules to extract object and relation concepts from the semantic graphs of image language descriptions. Compared with the simple SG parser, the advanced SG parser covers additional features for dealing with complex quantificational modifiers (e.g., a lot of), resolving pronouns (e.g., it) and handling plural nouns (e.g., three men). The performance boosts of the advanced SG parser over the simple one (recalls from 5.42/6.82/7.93 to 5.59/7.30/8.62), indicates that the quality of semantic parsing is also important for language-supervised SGG. + +# 4.4. Open-vocabulary SGG + +Setup. Following [8], we train the proposed VS3 with the same $70\%$ object categories of VG150 as base categories. With the aid of the pre-trained VSS, we hope VS3 can generalize to recognize the remaining $30\%$ novel objects and their involved relations at inference. Concretely, we compute evaluation metrics over two object category sets: $70\%$ base + $30\%$ novel objects (dubbed as open-vocabulary SGG (Ov-SGG) evaluation), and $30\%$ novel objects (dubbed as zero-shot SGG (ZsO-SGG) evaluation). + +In addition, we adopt the PREDCLS and SGDET evaluation protocols [50]. PREDCLS assumes object information given, yet SGDET generates scene graphs from the raw image using predicted objects. Since VS3 detects objects in a one-stage manner, we implement PREDCLS by selecting image regions that best match the ground-truth objects in post-processing, then performing relation recognition. We neglect the SGCLS protocol that assumes bounding box information given. This is because given bounding boxes can be directly used as region proposals in two-stage detectors, while the adopted one-stage manner in VS3 has no region proposal counterpart. + +
MethodOv-SGG (70%+30%)ZsO-SGG (30%)
PREDCLSSGDETPREDCLSSGDET
IMP [50]40.02 / 43.40-37.01 / 39.46-
MOTIFS [60]41.14 / 44.70-39.53 / 41.14-
VCTREE [48]42.56 / 45.84-41.27 / 42.52-
TDE [47]38.29 / 40.38-34.15 / 36.37-
GCA [13]43.48 / 46.26-42.56 / 43.18-
EBM [44]44.09 / 46.95-43.27 / 44.03-
SVRP [8]47.62 / 49.94-45.75 / 48.39-
VS3(Swin-T)50.10 / 52.0515.07 / 18.7346.91 / 49.1310.08 / 13.65
VS3(Swin-L)55.88 / 58.1823.13 / 28.4954.44 / 57.3521.51 / 27.62
+ +Table 5. Evaluation results (R@50/100) of fully supervised open-vocabulary SGG. Ov-SGG evaluates on $70\%$ base categories + $30\%$ novel categories in VG150, while ZsO-SGG only evaluates on $30\%$ novel categories. + +Fully supervised results. We first conduct experiments using manually annotated scene graphs. The results are presented in Table 5. For both Ov-SGG and ZsO-SGG, VS3 achieves substantial performance improvements under PREDCLS. When upgrading to the stronger backbone Swin-L, more significant improvements are obtained. More importantly, we report performances for the challenging and more practical SGDET, which are neglected by all previous methods since their used object detector cannot handle open-vocabulary detection [8]. The SGDET performances $(R@50/100 = 10.08/13.65)$ of ZsO-SGG using VS3 are even higher than SGCLS metrics of SVRP $(R@50/100 = 9.30/11.32$ in [8]). This reveals the superiority of our approach to recognizing novel objects thanks to the open-vocabulary generalization ability of the pre-trained VSS. + +Language-supervised results. Next, we evaluate the most challenging setting, i.e., open-vocabulary SGG using language supervision. To our knowledge, we are the first to propose such a new and practical SGG setting, and present the benchmark performances in Table 5. Not surprisingly, the recalls obtained via language-supervised training (i.e., $SG$ from VG caption or COCO caption) are lower than supervised results (i.e., $SG$ from annotated). When comparing $VS^{3}_{(Swin-T)}$ and $VS^{3}_{(Swin-L)}$ that is transferred from a stronger pre-trained model, the latter gets substantially higher Ov-SGG and ZsO-SGG performances. More importantly, we observe the performance gap between Ov-SGG and ZsO-SGG get closer in $VS^{3}_{(Swin-L)}$ , e.g., the $R@50$ gap under the VG caption setting becomes $12.98 - 10.71 = 2.27$ from $7.61 - 4.06 = 3.55$ . This is due to the better generalization ability for recognizing novel classes. Moreover, the superior performances obtained by VG caption over COCO caption, indicate that using dense region-level descriptions and avoiding domain shift will help improve language-supervised open-vocabulary SGG in practice. + +Qualitative analysis. We further showcase qualitative results of open-vocabulary SGG in Figure 3. The results demonstrate that our approach manages to detect novel objects and their relations with other objects. We also find that, compared with the fully supervised setting, the language-supervised results bias to predict simple relations such as + +
MethodSG supervisionOv-SGG (70%+30%)ZsO-SGG (30%)
VS3(Swin-T)Manual annotation15.07 / 18.7310.08 / 13.65
VG caption7.61 / 9.604.06 / 5.58
COCO caption4.39 / 5.633.65 / 4.73
VS3(Swin-L)Manual annotation23.13 / 28.4921.51 / 27.62
VG caption12.98 / 16.2910.71 / 13.70
COCO caption6.76 / 8.456.26 / 7.89
+ +Table 6. Evaluation results (R@50/100) of open-vocabulary SGG using three different scene graph supervisions: manual annotation, VG caption and COCO caption (language-supervised). Ov-SGG evaluates on $70\%$ base categories $+30\%$ novel categories in VG150, while ZsO-SGG only evaluates on $30\%$ novel categories. + +![](images/103f5cb601e283c17694db8df1eb46c148f8e2e52471ccffe8d3a7be65cd87ef.jpg) + +![](images/ac19ba4940bbd6447deca22fd9a6dc3fc939522be06be2c2af1c1253e63f39fc.jpg) + +![](images/ea3bde8512e62820b8d19df96fe2812104bc9febce43dfe50c7b19d40687cfea.jpg) + +![](images/3293ae606c9d9f0665b34a5af8f5140f747ef1e0db6c71d3bf69fcd45b0cc687.jpg) +Input images + +![](images/713aec097612011545dd1b8a77aaedffdcaa283e5f39c59dbaa72d8a81798f8a.jpg) +Fully supervised results + +![](images/d6b4304c07418ddfc9bea6c3602c8183117de0c6ea9ecbe52d3c52990aedf2b5.jpg) +Language-supervised results +Figure 3. Qualitative results of open-vocabulary SGG, particularly from fully supervised and language-supervised (VG caption) settings. Note that dotted nodes denote novel objects. For clarity, we only show triplets among the top 20 predictions that depict relations of highlighted image regions (i.e., boxes on input images). + +'on', 'of'. Presumably, it's because scene graph supervision parsed from language is more likely to extract such simple words as relation predicates. + +# 5. Conclusion + +In this work, we have proposed a novel approach to exploit a powerful pre-trained VSS for triggering language-supervised and open-vocabulary SGG. Particularly, we obtain cheap scene graph supervision by semantically parsing image language descriptions into semantic graphs and grounding the noun phrases through region-word alignment in the VSS. In addition, we devise the VS3 model, which performs object detection as category name grounding in the VSS and naturally builds relation representations for relation recognition. Thanks to the open-vocabulary generalization ability of the VSS, VS3 manages to detect novel objects and their relations with other objects, achieving open-vocabulary SGG. We validate our approach on the Visual Genome benchmark across supervised, language-supervised and open-vocabulary SGG settings, and have set new state-of-the-art performances. This demonstrates the merits of transferring pre-training knowledge to push SGG toward more practical scenarios. + +Acknowledgment. This work was supported in part by the Shenzhen Science and Technology Program under Grant JCYJ20220818103006012 and ZDSYS20211021111415025. + +# References + +[1] Saeid Amiri, Kishan Chandan, and Shiqi Zhang. Reasoning with scene graphs for robot planning under partial observability. IEEE Robotics and Automation Letters, 7(2):5560-5567, 2022. 1, 2 +[2] Shizhe Chen, Qin Jin, Peng Wang, and Qi Wu. Say as you wish: Fine-grained control of image caption generation with abstract scene graphs. In CVPR, 2020. 1 +[3] Tianshui Chen, Weihao Yu, Riquan Chen, and Liang Lin. Knowledge-embedded routing network for scene graph generation. In CVPR, 2019. 6 +[4] Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, and C Lawrence Zitnick. Microsoft coco captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325, 2015. 5 +[5] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, 2020. 7 +[6] Jieuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai, and Mingyang Ling. Scene graph generation with external knowledge and image reconstruction. In CVPR, 2019. 1, 2 +[7] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. In ICLR, 2021. 2, 3 +[8] Tao He, Lianli Gao, Jingkuan Song, and Yuan-Fang Li. Towards open-vocabulary scene graph generation with prompt-based finetuning. In ECCV, 2022. 3, 5, 7, 8 +[9] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICLR, 2021. 3 +[10] Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David Shamma, Michael Bernstein, and Li Fei-Fei. Image retrieval using scene graphs. In CVPR, 2015. 1, 2 +[11] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In ICCV, 2021. 3 +[12] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, 2019. 3 +[13] Boris Knyazev, Harm de Vries, Catalina Cangea, Graham W Taylor, Aaron Courville, and Eugene Belilovsky. Generative compositional augmentations for scene graph prediction. In ICCV, 2021. 8 +[14] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJCV, 2017. 1, 2, 5 +[15] Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and Rene Ranftl. Language-driven semantic segmentation. In ICLR, 2021. 2, 3 +[16] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi. + +Align before fuse: Vision and language representation learning with momentum distillation. NeurIPS, 2021. 3 +[17] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, et al. Grounded language-image pre-training. In CVPR, 2022. 2, 3, 4, 6, 7 +[18] Manling Li, Alireza Zareian, Qi Zeng, Spencer Whitehead, Di Lu, Heng Ji, and Shih-Fu Chang. Cross-media structured common space for multimedia event extraction. arXiv preprint arXiv:2005.02472, 2020. 1 +[19] Rongjie Li, Songyang Zhang, and Xuming He. Sgtr: End-to-end scene graph generation with transformer. In CVPR, 2022. 6 +[20] Rongjie Li, Songyang Zhang, Bo Wan, and Xuming He. Bipartite graph network with adaptive message passing for unbiased scene graph generation. In CVPR, 2021. 1, 2 +[21] Xingchen Li, Long Chen, Wenbo Ma, Yi Yang, and Jun Xiao. Integrating object-aware and interaction-aware knowledge for weakly supervised scene graph generation. In ACM MM, 2022. 2, 3, 5, 6, 7 +[22] Yehao Li, Yingwei Pan, Jingwen Chen, Ting Yao, and Tao Mei. X-modaler: A versatile and high-performance codebase for cross-modal analytics. In ACM MM, 2021. 1 +[23] Yehao Li, Yingwei Pan, Ting Yao, Jingwen Chen, and Tao Mei. Scheduled sampling in vision-language pretraining with decoupled encoder-decoder network. In AAAI, 2021. 3 +[24] Yongzhi Li, Duo Zhang, and Yadong Mu. Visual-semantic matching by exploring high-order attention and distraction. In CVPR, 2020. 1 +[25] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dólár. Focal loss for dense object detection. In ICCV, 2017. 4 +[26] Xin Lin, Changxing Ding, Yibing Zhan, Zijian Li, and Dacheng Tao. Hl-net: Heterophily learning network for scene graph generation. In CVPR, 2022. 1, 2, 6 +[27] Hengyue Liu, Ning Yan, Masood Mortazavi, and Bir Bhanu. Fully convolutional scene graph generation. In CVPR, 2021. 6 +[28] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, 2021. 3, 6 +[29] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS, 2019. 3 +[30] Jianjie Luo, Yehao Li, Yingwei Pan, Ting Yao, Hongyang Chao, and Tao Mei. Coco-bert: Improving video-language pre-training with contrastive cross-modal matching and denoising. In ACM MM, 2021. 3 +[31] Jiayuan Mao. Scenegraphparser, 2019. https://github.com/vacancy/SceneGraphParser (Access date: 2022-8-11). 3, 7 +[32] Victor Milewski, Marie-Francine Moens, and Iacer Calixto. Are scene graphs good enough to improve image captioning? arXiv preprint arXiv:2009.12313, 2020. 1 +[33] George A Miller. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39-41, 1995. 3, 5, 6 + +[34] Yingwei Pan, Yehao Li, Jianjie Luo, Jun Xu, Ting Yao, and Tao Mei. Auto-captions on gif: A large-scale video-sentence dataset for vision-language pre-training. In ACM Multimedia, 2022. 3 +[35] Yingwei Pan, Ting Yao, Yehao Li, and Tao Mei. X-linear attention networks for image captioning. In CVPR, 2020. 1 +[36] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICLR, 2021. 2, 3 +[37] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE TPAMI, 2016. 2 +[38] Brigit Schroeder and Subarna Tripathi. Structured query-based image retrieval using scene graphs. In CVPR Workshops, 2020. 1 +[39] Sebastian Schuster, Ranjay Krishna, Angel Chang, Li Fei-Fei, and Christopher D Manning. Generating semantically precise scene graphs from textual descriptions for improved image retrieval. In Proceedings of the fourth workshop on vision and language, 2015. 3, 5, 7 +[40] Sahand Sharifzadeh, Sina Moayed Baharlou, and Volker Tresp. Classification by attention: Scene graph classification with prior knowledge. arXiv preprint arXiv:2011.10084, 2020. 1, 2 +[41] Jing Shi, Yiwu Zhong, Ning Xu, Yin Li, and Chenliang Xu. A simple baseline for weakly-supervised scene graph generation. In ICCV, 2021. 2, 3, 5, 6, 7 +[42] Motoharu Sonogashira, Masaaki Iiyama, and Yasutomo Kawanishi. Towards open-set scene graph generation with unknown objects. IEEE Access, 10:11574-11583, 2022. 2 +[43] Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Vl-bert: Pre-training of generic visual-linguistic representations. In ICLR, 2019. 3 +[44] Mohammed Suhail, Abhay Mittal, Behjat Siddiquie, Chris Broaddus, Jayan Eledath, Gerard Medioni, and Leonid Sigal. Energy-based learning for scene graph generation. In CVPR, 2021. 8 +[45] Rui Sun, Xuezhi Cao, Yan Zhao, Junchen Wan, Kun Zhou, Fuzheng Zhang, Zhongyuan Wang, and Kai Zheng. Multimodal knowledge graphs for recommender systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020. 1 +[46] Kaihua Tang. A scene graph generation codebase in python, 2020. https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch.6 +[47] Kaihua Tang, Yulei Niu, Jianqiang Huang, Jiaxin Shi, and Hanwang Zhang. Unbiased scene graph generation from biased training. In CVPR, 2020. 1, 2, 5, 8 +[48] Kaihua Tang, Hanwang Zhang, Baoyuan Wu, Wenhan Luo, and Wei Liu. Learning to compose dynamic tree structures for visual contexts. In CVPR, 2019. 1, 2, 6, 8 +[49] Sijin Wang, Ruiping Wang, Ziwei Yao, Shiguang Shan, and Xilin Chen. Cross-modal scene graph matching for relationship-aware image-text retrieval. In WACV, 2020. 1 + +[50] Danfei Xu, Yuke Zhu, Christopher B Choy, and Li Fei-Fei. Scene graph generation by iterative message passing. In CVPR, 2017. 1, 2, 5, 6, 7, 8 +[51] Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, and Devi Parikh. Graph r-cnn for scene graph generation. In ECCV, 2018. 1, 2 +[52] Xu Yang, Kaihua Tang, Hanwang Zhang, and Jianfei Cai. Auto-encoding scene graphs for image captioning. In CVPR, 2019. 1 +[53] Lewei Yao, Jianhua Han, Youpeng Wen, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, Chunjing Xu, and Hang Xu. Detclip: Dictionary-enriched visual-concept paralleled pre-training for open-world detection. arXiv preprint arXiv:2209.09407, 2022. 2, 3 +[54] Ting Yao, Yingwei Pan, Yehao Li, and Tao Mei. Exploring visual relationship for image captioning. In ECCV, 2018. 1 +[55] Ting Yao, Yingwei Pan, Yehao Li, and Tao Mei. Hierarchy parsing for image captioning. In ICCV, 2019. 1 +[56] Keren Ye and Adriana Kovashka. Linguistic structures as weak supervision for visual scene graph generation. In CVPR, 2021. 2, 5, 6, 7 +[57] Alireza Zareian, Svebor Karaman, and Shih-Fu Chang. Bridging knowledge graphs to generate scene graphs. In ECCV, 2020. 1, 2 +[58] Alireza Zareian, Svebor Karaman, and Shih-Fu Chang. Weakly supervised visual semantic parsing. In CVPR, 2020. 2, 7 +[59] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In CVPR, 2021. 2, 3 +[60] Rowan Zellers, Mark Yatskar, Sam Thomson, and Yejin Choi. Neural motifs: Scene graph parsing with global context. In CVPR, 2018. 1, 2, 6, 7, 8 +[61] Hanwang Zhang, Zawlin Kyaw, Shih-Fu Chang, and TatSeng Chua. Visual translation embedding network for visual relation detection. In CVPR, 2017. 6 +[62] Ji Zhang, Kevin J Shih, Ahmed Elgammal, Andrew Tao, and Bryan Catanzaro. Graphical contrastive losses for scene graph parsing. In CVPR, 2019. 2, 6 +[63] Yong Zhang, Yingwei Pan, Ting Yao, Rui Huang, Tao Mei, and Chang-Wen Chen. Exploring structure-aware transformer over interaction proposals for human-object interaction detection. In CVPR, 2022. 1 +[64] Yong Zhang, Yingwei Pan, Ting Yao, Rui Huang, Tao Mei, and Chang-Wen Chen. Boosting scene graph generation with visual relation saliency. ACM TOMM, 2023. 1 +[65] Yiwu Zhong, Jing Shi, Jianwei Yang, Chenliang Xu, and Yin Li. Learning to generate scene graph from natural language supervision. In ICCV, 2021. 2, 3, 5, 6, 7 +[66] Yiwu Zhong, Liwei Wang, Jianshu Chen, Dong Yu, and Yin Li. Comprehensive image captioning via scene graph decomposition. In ECCV, 2020. 1 +[67] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al. Regionclip: Region-based language-image pretraining. 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However, two knotty obstacles limit the practicability of current SGG methods in real-world scenarios: 1) training SGG models requires time-consuming ground-truth annotations, and 2) the closed-set object categories make the SGG models limited in their ability to recognize novel objects outside of training corpora. To address these issues, we novelly exploit a powerful pre-trained visual-semantic space (VSS) to trigger language-supervised and open-vocabulary SGG in a simple yet effective manner. Specifically, cheap scene graph supervision data can be easily obtained by parsing image language descriptions into semantic graphs. Next, the noun phrases on such semantic graphs are directly grounded over image regions through region-word alignment in the pre-trained VSS. In this way, we enable open-vocabulary object detection by performing object category name grounding with a text prompt in this VSS. On the basis of visually-grounded objects, the relation representations are naturally built for relation recognition, pursuing open-vocabulary SGG. We validate our proposed approach with extensive experiments on the Visual Genome benchmark across various SGG scenarios (i.e., supervised / language-supervised, closed-set / open-vocabulary). Consistent superior performances are achieved compared with existing methods, demonstrating the potential of exploiting pre-trained VSS for SGG in more practical scenarios." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 628, + 128, + 639 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 628, + 128, + 639 + ], + "spans": [ + { + "bbox": [ + 47, + 628, + 128, + 639 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 647, + 287, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 647, + 287, + 696 + ], + "spans": [ + { + "bbox": [ + 47, + 647, + 287, + 696 + ], + "type": "text", + "content": "Scene graph [10] is a structured representation for describing image semantics. It abstracts visual objects as graph nodes and represents their relations as labeled graph edges. The task of scene graph generation (SGG) [6, 14," + } + ] + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 317, + 247, + 535, + 392 + ], + "blocks": [ + { + "bbox": [ + 317, + 247, + 535, + 392 + ], + "lines": [ + { + "bbox": [ + 317, + 247, + 535, + 392 + ], + "spans": [ + { + "bbox": [ + 317, + 247, + 535, + 392 + ], + "type": "image", + "image_path": "d96fbdcc4d88f1581bf3a10d302f429444dad4b147a7bf92cb3db3ec2e8b10e0.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 396, + 547, + 495 + ], + "lines": [ + { + "bbox": [ + 304, + 396, + 547, + 495 + ], + "spans": [ + { + "bbox": [ + 304, + 396, + 547, + 495 + ], + "type": "text", + "content": "Figure 1. An illustration of exploiting a pre-trained visual-semantic space (VSS) to trigger language-supervised and open-vocabulary scene graph generation (SGG). (a) We acquire weak scene graph supervision by semantically parsing the image language description and grounding noun phrases on image regions via VSS. (b) At SGG inference time, thanks to the open-vocabulary generalization naturally rooted in VSS, the novel object name (e.g., player) in the text prompt input can be well aligned to one image region, which is regarded as its detection." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 498, + 547, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 547, + 570 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 547, + 570 + ], + "type": "text", + "content": "20, 26, 40, 47, 48, 50, 51, 57, 60, 63, 64] plays an important role for fine-grained visual understanding, which has shown promising results in facilitating various downstream applications, such as image-text retrieval [24, 38, 49], image captioning [2, 22, 32, 35, 52, 54, 55, 66], cross-media knowledge graph construction [18, 45] and robot planning [1]." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 570, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 547, + 715 + ], + "type": "text", + "content": "Though great effort has been made, SGG of the current stage still faces two knotty obstacles that limit its practicability in real-world scenarios. 1) Training SGG models requires massive ground-truth scene graphs that are expensive for manual annotation. Annotators have to draw bounding boxes for all objects in an image and connect possible interacted object pairs, and assign object/relation labels. Since assigned labels might be ambiguous, further verification and canonicalization processing are usually required [14]. Finally, a scene graph in the form of a set of " + }, + { + "bbox": [ + 304, + 570, + 547, + 715 + ], + "type": "inline_equation", + "content": "\\langle \\text{subject}, \\text{predicate}, \\text{object} \\rangle" + }, + { + "bbox": [ + 304, + 570, + 547, + 715 + ], + "type": "text", + "content": " triplets with subject and object bounding boxes is constructed. Such annotating pro" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 1, + 494, + 15 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 1, + 494, + 15 + ], + "spans": [ + { + "bbox": [ + 145, + 1, + 494, + 15 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 198, + 13, + 442, + 24 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 198, + 13, + 442, + 24 + ], + "spans": [ + { + "bbox": [ + 198, + 13, + 442, + 24 + ], + "type": "text", + "content": "Except for this watermark, it is identical to the accepted version;" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 179, + 25, + 461, + 36 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 179, + 25, + 461, + 36 + ], + "spans": [ + { + "bbox": [ + 179, + 25, + 461, + 36 + ], + "type": "text", + "content": "the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 58, + 703, + 135, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 703, + 135, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 703, + 135, + 712 + ], + "type": "text", + "content": "*Corresponding author" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2915" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "type": "text", + "content": "cess is time-consuming and tedious, costing much human labor and patience. 2) Almost all existing SGG methods [20,21,26,47,48,50,51,60] involve a pre-defined closed set of object categories, making them limited in recognizing novel objects outside of training corpora. However, real-world scenes contain a border set of visual concepts than any pre-defined category pool. It is very likely to encounter unseen/novel categories. When this happens, current SGG models either classify novel objects to a known category or fail to detect them like background regions. Accordingly, the prediction of their interactions/relations with other objects is negatively affected or just neglected. This may lead to problems. For example, a real-world robot may take inappropriate actions using such closed-set SGG models [1,42]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 243, + 289, + 543 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 243, + 289, + 543 + ], + "spans": [ + { + "bbox": [ + 46, + 243, + 289, + 543 + ], + "type": "text", + "content": "Recently, there is a trend of leveraging free-form language supervision for benefiting visual recognition tasks via large-scale language-image pre-training [7, 15, 17, 36, 53, 59, 67]. These methods (e.g., CLIP [36]) perform pre-training on massive easily-obtained image-text pairs to learn a visual-semantic space (VSS), and have demonstrated great zero-shot transferability. Especially, the recent grounded language-image pre-training (GLIP) [17] has learned an object-level and semantic-rich VSS. Based on the learned VSS, it has established new state-of-the-art performances in phrase grounding and zero-shot object detection. This indicates such pre-trained VSS has powerful multi-modal alignment ability (i.e., image regions and text phrases that have similar semantics get close embeddings) and open-vocabulary generalization ability (i.e., covering virtually any concepts in the pre-training image-text corpus). This inspires our thought of addressing the aforementioned obstacles in SGG using the pre-trained VSS. On the one hand, taking advantage of its multi-modal alignment ability, we can cheaply acquire scene graph supervision from an image description (e.g., retrieving image regions aligned with noun phrases and re-arranging the description into a scene-graph-like form). On the other hand, by leveraging its open-vocabulary generalization ability, it is promising to enable novel category prediction in SGG." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 546, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 546, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 546, + 289, + 714 + ], + "type": "text", + "content": "In this work, we investigate the opportunity of fully exploiting the VSS learned by language-image pre-training to trigger language-supervised and open-vocabulary SGG. Specifically, we obtain weak scene graph supervision by semantically parsing an image language description into a semantic graph, then grounding its noun phrases over image regions through region-word alignment in the pre-trained VSS (Figure 1 (a)). Moreover, we propose a novel SGG model, namely Visual-Semantic Space for Scene graph generation " + }, + { + "bbox": [ + 46, + 546, + 289, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{V}\\mathbf{S}^{3})" + }, + { + "bbox": [ + 46, + 546, + 289, + 714 + ], + "type": "text", + "content": ". It takes a raw image and a text prompt containing object category names as inputs, and projects them into the shared VSS as embeddings. Next, " + }, + { + "bbox": [ + 46, + 546, + 289, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{VS}^3" + }, + { + "bbox": [ + 46, + 546, + 289, + 714 + ], + "type": "text", + "content": " performs object detection by aligning the embeddings of category names and image regions. Based on high-confidence de" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 304, + 72, + 547, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 239 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 239 + ], + "type": "text", + "content": "tected objects, VS³ builds relation representations for object pairs with a devised relation embedding module that fully mines relation patterns from visual and spatial perspectives. Finally, a relation prediction module takes relation representations to infer relation labels. The predicted scene graph is composed by combining object detections and inferred relation labels. During training, visually-grounded semantic graphs parsed from image descriptions could be used as weak scene graph supervision, achieving language-supervised SGG. At SGG inference time, when using a text prompt input containing novel categories, VS³ manages to detect novel objects thanks to the open-vocabulary generalization ability naturally rooted in VSS, hence allowing for open-vocabulary SGG (Figure 1 (b))." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 239, + 547, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 239, + 547, + 384 + ], + "spans": [ + { + "bbox": [ + 304, + 239, + 547, + 384 + ], + "type": "text", + "content": "In summary, we have made the following contributions: (1) the exploitation of a pre-trained VSS provides an elegant solution for addressing obstacles to triggering both language-supervised and open-vocabulary SGG, making a solid step toward real-world usage of SGG. (2) The proposed VS3 model is a new and versatile framework, which effectively transfers language-image pre-training knowledge for benefiting SGG. (3) We fully validate the effectiveness of our approach through extensive experiments on the Visual Genome benchmark, and have set new state-of-the-art performances spanning across all settings (i.e., supervised / language-supervised, closed-set / open-vocabulary)." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 306, + 394, + 392, + 407 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 394, + 392, + 407 + ], + "spans": [ + { + "bbox": [ + 306, + 394, + 392, + 407 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 415, + 547, + 642 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 415, + 547, + 642 + ], + "spans": [ + { + "bbox": [ + 304, + 415, + 547, + 642 + ], + "type": "text", + "content": "Fully supervised SGG. The concept of scene graph as a structured image representation is first introduced in [10]. Next, the Visual Genome benchmark [14] is manually annotated with large-scale scene graphs on images. Such annotated dataset triggers a series of innovations [6,20,26,40,47, 48,50,51,57,60] for the fully supervised SGG task. Typically, an object detector (e.g., Faster-RCNN [37]) is trained to retrieve image regions as scene graph nodes. Then, relation representations of object pairs are constructed from visual, spatial and language perspectives, and are used for relation classification to label scene graph edges. To achieve desirable SGG, researchers have devised message-passing mechanisms [20,26,48,50,60] to exploit contextual information, derived contrastive loss functions [62] or incorporated external knowledge [6,40,57]. However, all these methods rely on training with expensive scene graph annotations. Our proposed VS" + }, + { + "bbox": [ + 304, + 415, + 547, + 642 + ], + "type": "inline_equation", + "content": "^3" + }, + { + "bbox": [ + 304, + 415, + 547, + 642 + ], + "type": "text", + "content": " model is compatible with fully supervised SGG, but we seek to make SGG training cheaper, which is more practical in real-world applications." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 642, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 547, + 715 + ], + "type": "text", + "content": "Language-supervised SGG. This task aims to train SGG models using language descriptions. It has recently attracted increasing attention [21,41,56,58,65], which is also referred as weakly supervised SGG in [21,41,58]. Particularly, based on a graph alignment algorithm, VSPNet [58] first proposes to supervise SGG training with scene graphs" + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "2916" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 288 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 288 + ], + "type": "text", + "content": "that have no object locations. Subsequent works [21,41,65] extract entities and relations from image captions to compose such unlocalized scene graph, which is achieved via an off-the-shelf language parser [31, 39]. They next follow a common paradigm: first grounding text entities on image regions, and then leveraging grounded scene graphs as pseudo labels to train standard SGG models. To acquire entity groundings, Shi et al [41] devise an efficient graph matching module optimized via contrastive learning; Zhong et al [65] simply match text entity names with predicted object labels from a pre-trained object detector using semantic rules such as WordNet [33] synsets matching. More recently, Li et al [21] integrate interaction-aware knowledge distilled from pre-trained language-image models [16] for enhancing grounding reliability. Instead, we propose to obtain groundings through region-word alignment in a pretrained VSS, which is much simple yet more effective to collect scene graph supervision from language." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 289, + 289, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 289, + 289, + 636 + ], + "spans": [ + { + "bbox": [ + 46, + 289, + 289, + 636 + ], + "type": "text", + "content": "Language-image pre-training. This has been shown effective for boosting various vision-language downstream tasks [11, 16, 23, 29, 30, 34, 43], e.g., image-text retrieval, image captioning. Also, recent studies present remarkable results on transferring pre-trained language-image knowledge to solve vision recognition problems, such as zero-shot image classification [9,36], open-vocabulary object detection [7, 17, 53, 59, 67] and zero-shot semantic segmentation [15]. For example, CLIP [36] and ALIGN [9] learn separate encoders to embed image and text into a shared space by pre-training on massive image-text pairs using a contrastive loss. They have demonstrated remarkable generalization ability on zero-shot image classification after pretraining. Distinct from CLIP and ALIGN that learn image-level representations, GLIP [17] focuses on learning object-level visual representations through region-word alignment. It has attained strong zero-shot and few-shot transferability to various object-level recognition tasks such as object detection and phrase grounding. Most recently, He et al [8] investigate a visual-relation pre-training and prompt-based fine-tuning method for open-vocabulary SGG. However, its image encoder relies on a pre-trained region proposal extractor, which is a bottleneck for achieving open-vocabulary SGG under the SGDET protocol. Unlike [8], our proposed VS3 directly encodes an image into region tokens, avoiding the bottleneck of region proposals. More importantly, our approach addresses obstacles to achieving both language-supervised and open-vocabulary SGG using a unified framework, while He et al [8] only focus on the latter." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 649, + 115, + 662 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 649, + 115, + 662 + ], + "spans": [ + { + "bbox": [ + 47, + 649, + 115, + 662 + ], + "type": "text", + "content": "3. Approach" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 670, + 170, + 681 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 670, + 170, + 681 + ], + "spans": [ + { + "bbox": [ + 47, + 670, + 170, + 681 + ], + "type": "text", + "content": "3.1. Notation & Overview" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 689, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 715 + ], + "type": "text", + "content": "The task of scene graph generation (SGG) aims to map an image into an abstract graph " + }, + { + "bbox": [ + 47, + 689, + 287, + 715 + ], + "type": "inline_equation", + "content": "SG = \\{O, R\\}" + }, + { + "bbox": [ + 47, + 689, + 287, + 715 + ], + "type": "text", + "content": ", where graph" + } + ] + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 309, + 69, + 545, + 128 + ], + "blocks": [ + { + "bbox": [ + 309, + 69, + 545, + 128 + ], + "lines": [ + { + "bbox": [ + 309, + 69, + 545, + 128 + ], + "spans": [ + { + "bbox": [ + 309, + 69, + 545, + 128 + ], + "type": "table", + "html": "
SG\\(\\mathcal{C}_{o}^{target}\\)Not containing novel object classesContaining novel object classes
Manually annotatedfully supervised & closed-setfully supervised & open-vocabulary
Automatically parsed from image descriptionslanguage-supervised & closed-setlanguage-supervised & open-vocabulary
", + "image_path": "81365590105a84ab7998a9dacfe46de8c72bb2de6f75bb3263515c341b3e425a.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 130, + 545, + 152 + ], + "lines": [ + { + "bbox": [ + 305, + 130, + 545, + 152 + ], + "spans": [ + { + "bbox": [ + 305, + 130, + 545, + 152 + ], + "type": "text", + "content": "Table 1. Definitions of different SGG settings, according to scene graph supervision " + }, + { + "bbox": [ + 305, + 130, + 545, + 152 + ], + "type": "inline_equation", + "content": "SG" + }, + { + "bbox": [ + 305, + 130, + 545, + 152 + ], + "type": "text", + "content": " and the object set at inference " + }, + { + "bbox": [ + 305, + 130, + 545, + 152 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_o^{target}" + }, + { + "bbox": [ + 305, + 130, + 545, + 152 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "spans": [ + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "content": "nodes " + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "inline_equation", + "content": "O = \\{o_1,\\dots,o_N\\}" + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "content": " correspond to image objects, and graph edges " + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "inline_equation", + "content": "R = \\{r_1,\\dots,r_M\\}" + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "content": " represents their relations. Each object " + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "inline_equation", + "content": "o_i = \\{\\mathbf{b}_i,l_i\\} \\in O" + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "content": " contains the bounding box coordinates " + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "inline_equation", + "content": "\\mathbf{b}_i\\in \\mathbb{R}^4" + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "content": " and its class label information " + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "inline_equation", + "content": "l_{i}\\in \\mathcal{C}_{o}" + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_o" + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "content": " denotes the set of object categories. Each relation " + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "inline_equation", + "content": "r_m\\in R" + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "content": " is a " + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "inline_equation", + "content": "\\langle" + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "content": " subject,predicate,object " + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "inline_equation", + "content": "\\rangle" + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "content": " triplet, and we represent it as " + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "inline_equation", + "content": "r_m = r_{i\\rightarrow j} = \\{o_i,p_{ij},o_j\\}" + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "content": ", in which " + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "inline_equation", + "content": "p_{ij}" + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "content": " is the predicate/relation label belonging to category set " + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_r" + }, + { + "bbox": [ + 304, + 156, + 545, + 252 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "spans": [ + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "text", + "content": "Most existing SGG methods require expensive manually annotated scene graphs " + }, + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "inline_equation", + "content": "SG" + }, + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "text", + "content": " as supervision. And they involve a closed set of object categories " + }, + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_o" + }, + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "text", + "content": " in both training and inference. These issues limit SGG for practical usage. In this work, we propose to fully exploit a pre-trained VSS to push SGG towards language-supervised and open-vocabulary scenarios. We illustrate the definitions of different SGG settings in Table 1. Concretely, from the perspective of scene graph supervision " + }, + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "inline_equation", + "content": "SG" + }, + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "text", + "content": ", SGG is categorized into fully supervised and language-supervised, using " + }, + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "inline_equation", + "content": "SG" + }, + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "text", + "content": " from manual annotation and language respectively. From the other perspective of object categories " + }, + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_o^{target}" + }, + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "text", + "content": " at inference, it is referred to as open-vocabulary or closed-set according to whether or not " + }, + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_o^{target}" + }, + { + "bbox": [ + 304, + 253, + 546, + 421 + ], + "type": "text", + "content": " contains novel objects." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 421, + 547, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 421, + 547, + 506 + ], + "spans": [ + { + "bbox": [ + 304, + 421, + 547, + 506 + ], + "type": "text", + "content": "Next, in Section 3.2, we present a new SGG model named VS3, which is versatile to handle with all SGG settings in Table 1. In Section 3.3, we devise a scheme to obtain scene graph supervision from language descriptions, allowing for language-supervised SGG. Finally, Section 3.4 details the strategy of transferring the proposed VS3 to pursue open-vocabulary SGG." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 514, + 448, + 528 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 514, + 448, + 528 + ], + "spans": [ + { + "bbox": [ + 306, + 514, + 448, + 528 + ], + "type": "text", + "content": "3.2. The Proposed VS3 Model" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 533, + 545, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 533, + 545, + 569 + ], + "spans": [ + { + "bbox": [ + 304, + 533, + 545, + 569 + ], + "type": "text", + "content": "We propose the VS3 model for tackling the SGG task by extending the GLIP [17] framework with relation recognition modules, as shown in Figure 2." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": "Preliminary. GLIP unifies object detection and phrase grounding into one framework. It has an image encoder " + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{Enc}_I" + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": " (e.g., Swin Transformer backbone [28]) and a text encoder " + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{Enc}_L" + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": " (e.g., BERT [12]). " + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{Enc}_I" + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": " extracts region/box features " + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\bar{O} \\in \\mathbb{R}^{\\bar{N} \\times d}" + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": " from an input image, where " + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\bar{N}" + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": " is the number of regions and " + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": " is the feature dimension. " + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{Enc}_L" + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": " encodes a text input into contextualized word-token embeddings " + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\bar{P} \\in \\mathbb{R}^{\\bar{T} \\times d}" + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\bar{T}" + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": " is the text length. Further, GLIP uses a cross-modal fusion module to achieve feature communication between " + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\bar{O}" + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\bar{P}" + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": ", resulting in enriched region embeddings " + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\tilde{O} \\in \\mathbb{R}^{\\bar{N} \\times d}" + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": " and word embeddings " + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\tilde{P} \\in \\mathbb{R}^{\\bar{T} \\times d}" + }, + { + "bbox": [ + 304, + 570, + 547, + 714 + ], + "type": "text", + "content": ". Finally, the region-word alignment" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2917" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 76, + 531, + 232 + ], + "blocks": [ + { + "bbox": [ + 63, + 76, + 531, + 232 + ], + "lines": [ + { + "bbox": [ + 63, + 76, + 531, + 232 + ], + "spans": [ + { + "bbox": [ + 63, + 76, + 531, + 232 + ], + "type": "image", + "image_path": "c4653213c8d505a41d7c4292da75a5b33f71d82284a36c0a2bf967849c07f73f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 236, + 545, + 304 + ], + "lines": [ + { + "bbox": [ + 46, + 236, + 545, + 304 + ], + "spans": [ + { + "bbox": [ + 46, + 236, + 545, + 304 + ], + "type": "text", + "content": "Figure 2. An overview of the proposed Visual-Semantic Space for Scene graph generation " + }, + { + "bbox": [ + 46, + 236, + 545, + 304 + ], + "type": "inline_equation", + "content": "(\\mathrm{VS}^3)" + }, + { + "bbox": [ + 46, + 236, + 545, + 304 + ], + "type": "text", + "content": " model. It inherits the image encoder, the text encoder and the cross-modal fusion module from GLIP [17], so as to project image regions and text prompt words in a pre-trained visual-semantic space (VSS). Object regions are detected by aligning the embeddings of category names and image regions in VSS. Next, high-confidence detected results are retained to compose subject-object pairs. After that, the relation embedding module constructs their relation representations by extracting visual and spatial features, on which relation prediction is performed. At test time, thanks to the open-vocabulary generalization ability of VSS, " + }, + { + "bbox": [ + 46, + 236, + 545, + 304 + ], + "type": "inline_equation", + "content": "\\mathrm{VS}^3" + }, + { + "bbox": [ + 46, + 236, + 545, + 304 + ], + "type": "text", + "content": " manages to detect novel objects by switching to a text prompt containing novel classes." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "spans": [ + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "text", + "content": "scores " + }, + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "inline_equation", + "content": "\\hat{S}_{\\text {ground }} = \\tilde{O}\\tilde{P}^{\\top} \\in \\mathbb{R}^{\\bar{N} \\times \\bar{T}}" + }, + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "text", + "content": " and the predicted locations " + }, + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "inline_equation", + "content": "\\vec{B} = \\text { box\\_predictor }(\\vec{O}) \\in \\mathbb{R}^{\\bar{N} \\times 4}" + }, + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "text", + "content": " are supervised by ground-truth text grounding data. After large-scale pretraining, " + }, + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "inline_equation", + "content": "\\mathrm{Enc}_I" + }, + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "inline_equation", + "content": "\\mathrm{Enc}_L" + }, + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "text", + "content": " embed the input image and text into a joint VSS, which aligns multi-modal embeddings and covers open-vocabulary concepts. VS3 inherits Enc " + }, + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "text", + "content": ", Enc " + }, + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 46, + 308, + 287, + 393 + ], + "type": "text", + "content": " and the cross-module fusion module from GLIP." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "spans": [ + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "text", + "content": "Text prompt. Considering that object detection has been reformulated as phrase grounding, VS3 also requires a text prompt input except for the image input. Following GLIP's design, we set the text prompt for object detection in the form of \"name(c1). name(c2). ... name(c|cO)\". where " + }, + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "inline_equation", + "content": "c_i \\in \\mathcal{C}_o" + }, + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "inline_equation", + "content": "\\text{name}(c_i)" + }, + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "text", + "content": " gets the category name of " + }, + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "inline_equation", + "content": "c_i" + }, + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "text", + "content": " (e.g., person). Hence, an object is detected according to the alignment score between a region embedding " + }, + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathbf{o}}_i \\in \\tilde{\\mathcal{O}}" + }, + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "text", + "content": " (the " + }, + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "text", + "content": "th row) and the category name embeddings " + }, + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "inline_equation", + "content": "\\tilde{P}" + }, + { + "bbox": [ + 46, + 395, + 287, + 502 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "spans": [ + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "text", + "content": "Relation embedding module. To further enable VS3 with relation recognition ability, we devise the relation embedding module to build relation representations. Based on the region/box features " + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "inline_equation", + "content": "\\tilde{O}" + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "text", + "content": " after cross-modal fusion, we first sample a subset of regions " + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "inline_equation", + "content": "\\tilde{O}' \\in \\mathbb{R}^{N' \\times d}" + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "text", + "content": " that are most likely to be valid objects. This is achieved by matching predicted bounding boxes " + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "inline_equation", + "content": "\\hat{B}" + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "text", + "content": " with ground-truth objects during training, and by retaining top-" + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "inline_equation", + "content": "N'" + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "text", + "content": " regions with the highest confidence scores after non-maximum suppression (NMS) at inference. Next, we construct relation representations for all possible subject-object pairs. Given an object pair " + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "inline_equation", + "content": "(\\tilde{\\mathbf{o}}_i, \\tilde{\\mathbf{o}}_j)" + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "text", + "content": " and their normalized bounding boxes " + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "inline_equation", + "content": "(\\mathbf{b}_i, \\mathbf{b}_j)" + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "text", + "content": ", the pairwise relation representation is represented as " + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "inline_equation", + "content": "\\mathbf{pair}_{i \\rightarrow j} = cat[\\mathbf{pair}_{i \\rightarrow j}^{\\text{visual}}, \\mathbf{pair}_{i \\rightarrow j}^{\\text{spatial}}]" + }, + { + "bbox": [ + 46, + 502, + 288, + 696 + ], + "type": "text", + "content": ". This is the concatenation of features mined from the visual and spatial perspectives. The visual feature is computed by" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 75, + 700, + 287, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 700, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 75, + 700, + 287, + 715 + ], + "type": "interline_equation", + "content": "\\mathbf {p a i r} _ {i \\rightarrow j} ^ {\\text {v i s u a l}} = \\boldsymbol {f} _ {\\text {d i f f}} (\\tilde {\\mathbf {o}} _ {i} - \\tilde {\\mathbf {o}} _ {j}) + \\boldsymbol {f} _ {\\text {s u m}} (\\tilde {\\mathbf {o}} _ {i} + \\tilde {\\mathbf {o}} _ {j}), \\qquad (1)", + "image_path": "99743295660a494e1b32c4fd30ad80e8b0d4f563dc62eb0315c9016d00a85f7e.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 309, + 545, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 309, + 545, + 369 + ], + "spans": [ + { + "bbox": [ + 304, + 309, + 545, + 369 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 309, + 545, + 369 + ], + "type": "inline_equation", + "content": "\\pmb{f}_{diff}" + }, + { + "bbox": [ + 304, + 309, + 545, + 369 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 309, + 545, + 369 + ], + "type": "inline_equation", + "content": "\\pmb{f}_{sum}" + }, + { + "bbox": [ + 304, + 309, + 545, + 369 + ], + "type": "text", + "content": " are two mapping functions implemented as 2-layer MLPs (multi-layer perceptron). By defining the normalized center coordinates of two involved objects as " + }, + { + "bbox": [ + 304, + 309, + 545, + 369 + ], + "type": "inline_equation", + "content": "(ct_i^x, ct_i^y)" + }, + { + "bbox": [ + 304, + 309, + 545, + 369 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 309, + 545, + 369 + ], + "type": "inline_equation", + "content": "(ct_j^x, ct_j^y)" + }, + { + "bbox": [ + 304, + 309, + 545, + 369 + ], + "type": "text", + "content": ", the spatial feature is measured as" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 318, + 375, + 545, + 390 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 375, + 545, + 390 + ], + "spans": [ + { + "bbox": [ + 318, + 375, + 545, + 390 + ], + "type": "interline_equation", + "content": "\\mathbf {p a i r} _ {i \\rightarrow j} ^ {\\text {s p a t i a l}} = \\operatorname {c a t} \\left[ \\mathbf {b} _ {i}, \\mathbf {b} _ {j}, d x, d y, \\operatorname {d i s}, \\theta , A _ {i}, A _ {j}, I, U \\right], \\tag {2}", + "image_path": "b19c0aec31a1f4945e6916f058ecab28dec52d0f7fea4b9aa314c8c8b64f7fcc.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 396, + 545, + 446 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 396, + 545, + 446 + ], + "spans": [ + { + "bbox": [ + 305, + 396, + 545, + 446 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 396, + 545, + 446 + ], + "type": "inline_equation", + "content": "dx = ct_i^x - ct_j^x, dy = ct_i^y - ct_j^y," + }, + { + "bbox": [ + 305, + 396, + 545, + 446 + ], + "type": "inline_equation", + "content": "\\sqrt{dx^2 + dy^2}, \\theta = \\arctan \\left(\\frac{dy}{dx}\\right). A_i, A_j, I, U" + }, + { + "bbox": [ + 305, + 396, + 545, + 446 + ], + "type": "text", + "content": " denote the areas of the subject, the object, their intersection, and union boxes, respectively." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "spans": [ + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "text", + "content": "Relation prediction. Conditioned on the relation representation " + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\mathbf{pair}_{i\\rightarrow j}" + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "text", + "content": " of each object pair, we predict a relateness score " + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\hat{z}_{i\\rightarrow j} = f_{relateness}(\\mathbf{pair}_{i\\rightarrow j})\\in [0,1]" + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "text", + "content": " and a semantic label probability " + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\hat{\\pmb{y}}_{i\\rightarrow j} = \\pmb{f}_{semantic}(\\pmb{pair}_{i\\rightarrow j})\\in" + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "inline_equation", + "content": "[0,1]^{|\\mathcal{C}_r|}" + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "text", + "content": ". The relateness " + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\hat{z}_{i\\rightarrow j}" + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "text", + "content": " represents the probability that relations exist between the object pair. " + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "inline_equation", + "content": "f_{relateness}" + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "text", + "content": " is implemented with an MLP coupled with Sigmoid activation. " + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\pmb{f}_{semantic}" + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "text", + "content": " is implemented with another MLP using Softmax activation. The total loss for relation recognition " + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "inline_equation", + "content": "L_{rel\\_rcg}" + }, + { + "bbox": [ + 305, + 447, + 545, + 567 + ], + "type": "text", + "content": " is measured as" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 351, + 578, + 545, + 590 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 351, + 578, + 545, + 590 + ], + "spans": [ + { + "bbox": [ + 351, + 578, + 545, + 590 + ], + "type": "interline_equation", + "content": "L _ {\\text {r e l a t e n e s s}} = F L \\left(\\hat {z} _ {i \\rightarrow j}, z _ {i \\rightarrow j}\\right), \\tag {3}", + "image_path": "9a7203bcb32ab7024ac4cb34da1c6fac8a95c4899b70ffbabe800ef879bf34fb.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 351, + 593, + 545, + 605 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 351, + 593, + 545, + 605 + ], + "spans": [ + { + "bbox": [ + 351, + 593, + 545, + 605 + ], + "type": "interline_equation", + "content": "L _ {\\text {s e m a n t i c}} = C E \\left(\\hat {\\boldsymbol {y}} _ {i \\rightarrow j}, \\boldsymbol {y} _ {i \\rightarrow j}\\right), \\tag {4}", + "image_path": "0e59b8e8a5d3c79bded6f21d85955d598560ef8e25c48da7c450b8d72bbf6114.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 351, + 609, + 545, + 620 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 351, + 609, + 545, + 620 + ], + "spans": [ + { + "bbox": [ + 351, + 609, + 545, + 620 + ], + "type": "interline_equation", + "content": "L _ {r e l. r c g} = L _ {r e l a t e n e s s} + L _ {s e m a n t i c}, \\tag {5}", + "image_path": "38d1ab003982acd869a782ba0bc2d685a2f4122fe7abd9bf7253db7ef7a03700.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "spans": [ + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "inline_equation", + "content": "FL" + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "inline_equation", + "content": "CE" + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "text", + "content": " denote focal loss [25] and cross-entropy loss functions. " + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "inline_equation", + "content": "z_{i\\rightarrow j}" + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "inline_equation", + "content": "\\pmb{y}_{i\\rightarrow j}" + }, + { + "bbox": [ + 304, + 629, + 545, + 666 + ], + "type": "text", + "content": " represent the ground-truth relateness label and predicate category label respectively." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "type": "text", + "content": "Training & inference. During training, we initialize parameters from pre-trained GLIP models for inherited modules in VS3. To ease training difficulty, we freeze the image encoder and text encoder, and only fine-tune the" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2918" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 288, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 288, + 133 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 288, + 133 + ], + "type": "text", + "content": "cross-modal fusion module and devised modules for relation recognition. This also avoids the degeneration of the pre-trained VSS. At inference, by retaining high-confidence detected objects and further predicting their relations, we generate an image scene graph representation." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 140, + 287, + 154 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 140, + 287, + 154 + ], + "spans": [ + { + "bbox": [ + 47, + 140, + 287, + 154 + ], + "type": "text", + "content": "3.3. Obtaining Language Scene Graph Supervision" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 159, + 287, + 231 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 159, + 287, + 231 + ], + "spans": [ + { + "bbox": [ + 46, + 159, + 287, + 231 + ], + "type": "text", + "content": "Ground-truth scene graphs are time-consuming to annotate. Alternatively, we can parse semantic graphs from image language descriptions, and obtain noun phrase groundings through region-word alignment in the pre-trained VSS (implemented with an off-the-shelf GLIP). This is a much cheaper way to obtain weak scene graph supervision." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "spans": [ + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "text", + "content": "Semantic graph parsing. Concretely, for each image language description, we parse it into a semantic graph " + }, + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "inline_equation", + "content": "SG^{text} = \\{O^{text}, R^{text}\\}" + }, + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "text", + "content": " using the Standard Scene Graph Parser based on [39]. The parser not only extracts noun phrases as entities/objects (" + }, + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "inline_equation", + "content": "O^{text}" + }, + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "text", + "content": "), but also extracts the words describing their relations (" + }, + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "inline_equation", + "content": "R^{text}" + }, + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "text", + "content": "). For example, the sentence \"a woman is playing the piano in the room.\" is parsed to the " + }, + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "inline_equation", + "content": "SG^{text}" + }, + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "text", + "content": ", of which " + }, + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "inline_equation", + "content": "O^{text} = \\{woman, piano, room\\}" + }, + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "inline_equation", + "content": "R^{text} = \\{\\langle 0, playing, 1\\rangle, \\langle 0, in, 2\\rangle\\}" + }, + { + "bbox": [ + 46, + 231, + 287, + 411 + ], + "type": "text", + "content": " (numbers denote object indices). Considering that parsed object/relation words are free-form, we map them to our concerning categories (e.g., VG150 object/relation categories in experiments) by rules such as direct string matching and WordNet [33] synsets matching following [65]." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "spans": [ + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "text", + "content": "Semantic graph grounding. Note that each element of " + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "inline_equation", + "content": "O^{text}" + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "text", + "content": " only contains a text label name so far, and its bounding box information is still missing. To obtain bounding boxes, we construct a text prompt using triplets in " + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "inline_equation", + "content": "SG^{text}" + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "text", + "content": ", e.g., \"woman playing piano. woman in room.\" Then, we feed such text prompt together with the raw image into a pre-trained GLIP, in order to acquire grounding boxes of " + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "inline_equation", + "content": "O^{text}" + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "text", + "content": ". Specifically, for each element in " + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "inline_equation", + "content": "O^{text}" + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "text", + "content": ", we select the image region that has the highest alignment score with its category name as its grounding box. Since there might be multiple objects in " + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "inline_equation", + "content": "O^{text}" + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "text", + "content": " that actually refer to the same object, we perform a post-processing NMS to merge boxes with the same label and high IoU (intersection over union) scores (" + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "inline_equation", + "content": "\\geq 0.9" + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "text", + "content": "). Finally, with box information, the visually-grounded " + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "inline_equation", + "content": "SG^{text}" + }, + { + "bbox": [ + 46, + 411, + 287, + 603 + ], + "type": "text", + "content": " is ready to be used as weak supervision for training SGG models, e.g., the proposed VS3." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 610, + 251, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 610, + 251, + 624 + ], + "spans": [ + { + "bbox": [ + 47, + 610, + 251, + 624 + ], + "type": "text", + "content": "3.4. Transferring to Open-vocabulary SGG" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 629, + 287, + 701 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 629, + 287, + 701 + ], + "spans": [ + { + "bbox": [ + 46, + 629, + 287, + 701 + ], + "type": "text", + "content": "Open-vocabulary SGG [8] aims to train SGG models that can recognize objects of novel categories and their involved relations. Formally, we train the SGG model with scene graphs containing objects in the base category set " + }, + { + "bbox": [ + 46, + 629, + 287, + 701 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_o^{base}" + }, + { + "bbox": [ + 46, + 629, + 287, + 701 + ], + "type": "text", + "content": ". At inference, the object category set is " + }, + { + "bbox": [ + 46, + 629, + 287, + 701 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_o^{target}" + }, + { + "bbox": [ + 46, + 629, + 287, + 701 + ], + "type": "text", + "content": ", which contains novel categories in " + }, + { + "bbox": [ + 46, + 629, + 287, + 701 + ], + "type": "inline_equation", + "content": "\\mathcal{C}_o^{novel} = \\mathcal{C}_o^{target} \\backslash \\mathcal{C}_o^{base} \\neq \\emptyset" + }, + { + "bbox": [ + 46, + 629, + 287, + 701 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 59, + 701, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 701, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 59, + 701, + 287, + 713 + ], + "type": "text", + "content": "Back to our proposed VS3, an open-vocabulary VSS is" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 72, + 545, + 254 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 254 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 254 + ], + "type": "text", + "content": "maintained by freezing the image and text encoders. Taking this advantage, we devise a scheme to adapt VS3 for open-vocabulary SGG. Concretely, during training, we set the text prompt as “name(c1). name(c2). ... name(cObase)”. where " + }, + { + "bbox": [ + 304, + 72, + 545, + 254 + ], + "type": "inline_equation", + "content": "c_{i} \\in \\mathcal{C}_{o}^{base}" + }, + { + "bbox": [ + 304, + 72, + 545, + 254 + ], + "type": "text", + "content": ". And only relation triplets involving base object categories are kept for training. At inference, the text prompt is switched to be “name(c1). name(c2). ... name(cOtarget)”. where " + }, + { + "bbox": [ + 304, + 72, + 545, + 254 + ], + "type": "inline_equation", + "content": "c_{i} \\in \\mathcal{C}_{o}^{target}" + }, + { + "bbox": [ + 304, + 72, + 545, + 254 + ], + "type": "text", + "content": ". In this way, a novel object class (e.g., lady) may have an embedding close to a base category (e.g., woman) embedding. This makes the novel class also able to find well-aligned image regions. Note that relation representations are constructed from visual and spatial cues, which are usually class-agnostic. Hence, the following relation recognition in VS3 will not be affected when encountering novel objects." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 266, + 388, + 280 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 266, + 388, + 280 + ], + "spans": [ + { + "bbox": [ + 305, + 266, + 388, + 280 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 286, + 495, + 300 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 286, + 495, + 300 + ], + "spans": [ + { + "bbox": [ + 305, + 286, + 495, + 300 + ], + "type": "text", + "content": "4.1. Datasets and Experimental Settings" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 306, + 545, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 306, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 304, + 306, + 545, + 521 + ], + "type": "text", + "content": "Datasets. To evaluate the SGG task, we adopt the widely-used VG150 version [50] of the Visual Genome (VG) dataset [14]. VG150 retains the most frequent 150 categories and 50 relation/predicate categories in VG. It contains " + }, + { + "bbox": [ + 304, + 306, + 545, + 521 + ], + "type": "inline_equation", + "content": "\\sim 108\\mathrm{K}" + }, + { + "bbox": [ + 304, + 306, + 545, + 521 + ], + "type": "text", + "content": " images, of which " + }, + { + "bbox": [ + 304, + 306, + 545, + 521 + ], + "type": "inline_equation", + "content": "70\\%" + }, + { + "bbox": [ + 304, + 306, + 545, + 521 + ], + "type": "text", + "content": " images are used for training (including 5K for validation), and the remaining " + }, + { + "bbox": [ + 304, + 306, + 545, + 521 + ], + "type": "inline_equation", + "content": "30\\%" + }, + { + "bbox": [ + 304, + 306, + 545, + 521 + ], + "type": "text", + "content": " images are used for testing. The annotated scene graph of each image has 11.5 objects and 6.2 relation triplets on average. In addition, images of VG are densely annotated with region descriptions, about 50 descriptions for each image. We refer to these descriptions as VG caption, which provides a text source for evaluating the language-supervised SGG setting. Moreover, we consider the challenging setting of using image-text pairs in COCO caption [4] for training SGG models. This dataset contains 123K images in total. Each image has 5 human-annotated captions. We keep " + }, + { + "bbox": [ + 304, + 306, + 545, + 521 + ], + "type": "inline_equation", + "content": "\\sim 106\\mathrm{k}" + }, + { + "bbox": [ + 304, + 306, + 545, + 521 + ], + "type": "text", + "content": " images by filtering out those images that also exist in the VG150 test split." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 522, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 522, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 522, + 545, + 713 + ], + "type": "text", + "content": "Evaluation protocols and metrics. We mainly adopt the SGDET [47,50] protocol, which generates a scene graph from the input image without any given box information. We report the performance on Recall@K (K=20/50/100) following previous works [21, 41, 47, 50, 56, 65], which measures the fraction of correctly predicted relation triplets in top " + }, + { + "bbox": [ + 304, + 522, + 545, + 713 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 522, + 545, + 713 + ], + "type": "text", + "content": " predictions. A triplet prediction is considered as correct when its subject, object, predicate labels and both the subject and object regions match with (same label or IoU>0.5) a ground-truth triplet. Note that we obtain triplet predictions using graph constraint, which limits each subject-object pair to have only the most confident predicate. All recall metrics across different SGG settings in experiments are computed over VG150 test images. Considering that the adopted GLIP pre-trained VSS has seen part of images in the original VG150 test split (" + }, + { + "bbox": [ + 304, + 522, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\sim" + }, + { + "bbox": [ + 304, + 522, + 545, + 713 + ], + "type": "text", + "content": "26k) during" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2919" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 288, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 288, + 144 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 288, + 144 + ], + "type": "text", + "content": "pre-training, we exclude these images and get a new split of " + }, + { + "bbox": [ + 46, + 72, + 288, + 144 + ], + "type": "inline_equation", + "content": "\\sim 15\\mathrm{k}" + }, + { + "bbox": [ + 46, + 72, + 288, + 144 + ], + "type": "text", + "content": " test images. We have validated that such VG150 test split is sufficiently large for computing stable metrics as the original, by comparing computed metrics of several SGG models (in codebase [46]) on these two splits (" + }, + { + "bbox": [ + 46, + 72, + 288, + 144 + ], + "type": "inline_equation", + "content": "< 0.15" + }, + { + "bbox": [ + 46, + 72, + 288, + 144 + ], + "type": "text", + "content": " points variation, see supplementary materials)." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 144, + 288, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 144, + 288, + 289 + ], + "spans": [ + { + "bbox": [ + 46, + 144, + 288, + 289 + ], + "type": "text", + "content": "Implementation details. We initialize VS3 from pretrained GLIP [17] models, i.e., the GLIP-T and the larger GLIP-L trained with more data. Both construct a VSS of dimension " + }, + { + "bbox": [ + 46, + 144, + 288, + 289 + ], + "type": "inline_equation", + "content": "d = 256" + }, + { + "bbox": [ + 46, + 144, + 288, + 289 + ], + "type": "text", + "content": ". We retain the top 36 object detections per image for pairwise relation recognition. The whole framework is fine-tuned on 8 Nvidia 2080Ti GPUs with AdamW optimizer. During fine-tuning, we freeze the parameters of the image and text encoder; and set the learning rate for the cross-modal fusion module as 1e-5 and 10x larger learning rates for the relation embedding and prediction modules. The maximum fine-tuning epoch number is 10, with learning rates dropping by 10x after 6 epochs." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 298, + 174, + 310 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 298, + 174, + 310 + ], + "spans": [ + { + "bbox": [ + 47, + 298, + 174, + 310 + ], + "type": "text", + "content": "4.2. Fully Supervised SGG" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 316, + 287, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 316, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 46, + 316, + 287, + 460 + ], + "type": "text", + "content": "Setup. We first evaluate our proposed VS3 under the conventional fully supervised SGG setting. This setting trains SGG models using manually annotated scene graphs, consisting of object labels coupled with bounding boxes, and relation labels. We adopt VG150 for training and evaluation following previous methods [3, 19, 26, 27, 48, 50, 60, 61, 65]. All these methods involve a closed set of object categories. Specifically, the text prompt input of VS3 is constructed from VG150 object category names, i.e., \"airplane. animal. ... zebra\". We train VS3 by fine-tuning over two GLIP variants: GLIP-T with the Swin-T [28] backbone, GLIP-L with the Swin-L [28] backbone." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 462, + 287, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 462, + 287, + 628 + ], + "spans": [ + { + "bbox": [ + 46, + 462, + 287, + 628 + ], + "type": "text", + "content": "Comparison with state-of-the-arts. The results are summarized in Table 2. Our proposed VS3 model using the Swin-T backbone already achieves competitive recall metrics. When upgrading to the larger Swin-L variant, the performance improvements become significant (1.8 to 3.4 points improvement than the previous best results). Note that previous methods [26, 50, 60] build their models upon an off-the-shelf object detector, and they usually design heavy message-passing modules to incorporate context information. Instead, VS3 devises a light-weighted relation recognition head (including the relation embedding and prediction modules) over a pre-trained VSS. The superior performances clearly suggest the merits of transferring language-image pre-trained models for boosting SGG." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 630, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 630, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 630, + 287, + 714 + ], + "type": "text", + "content": "Ablation on relation representation. Next, we carry out ablation studies on relation representation construction in the relation embedding module. As shown in Table 2, by removing visual and spatial feature components, the relation triplet recalls drop accordingly. Also notice that the removal of visual features which is built from subject and object region embeddings leads to relatively larger perfor" + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 310, + 70, + 545, + 220 + ], + "blocks": [ + { + "bbox": [ + 310, + 70, + 545, + 220 + ], + "lines": [ + { + "bbox": [ + 310, + 70, + 545, + 220 + ], + "spans": [ + { + "bbox": [ + 310, + 70, + 545, + 220 + ], + "type": "table", + "html": "
SGG modelDetectorBackboneR@20R@50R@100
FCSGG [27]-HRNetW4816.121.325.1
SGTR [19]DETRR-101-24.628.4
IMP [50]Faster-RCNNVGG-1614.620.724.5
KERN [3]Faster-RCNNVGG-16-27.129.8
MOTIFS [60]Faster-RCNNVGG-1621.427.230.3
RelDN [62]Faster-RCNNVGG-1621.128.332.7
VTransE [61]Faster-RCNNRX-10123.029.734.3
MOTIFS [60]Faster-RCNNRX-10125.132.136.9
VCTREE [48]Faster-RCNNRX-10124.731.536.2
SGNLS [65]Faster-RCNNRX-10124.631.836.3
HL-Net [26]Faster-RCNNRX-10126.033.738.1
VS3-Swin-T26.134.539.2
w/o visual-Swin-T23.131.636.7
w/o spatial-Swin-T24.332.837.8
VS3-Swin-L27.836.641.5
", + "image_path": "ba2e641767da4fa1e7cb8085631ac7d38074fc58775feaf9e0bba357857355a5.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 222, + 547, + 267 + ], + "lines": [ + { + "bbox": [ + 305, + 222, + 547, + 267 + ], + "spans": [ + { + "bbox": [ + 305, + 222, + 547, + 267 + ], + "type": "text", + "content": "Table 2. Experimental results of fully supervised SGG. w/o visual and w/o spatial indicate removing spatial and visual features in the relation embedding module for relation representation. All metrics are computed under the SGDET protocol on VG150 test images." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 305, + 278, + 545, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 278, + 545, + 327 + ], + "spans": [ + { + "bbox": [ + 305, + 278, + 545, + 327 + ], + "type": "text", + "content": "mance drops than spatial. This suggests the region embeddings in the pre-trained VSS provide strong cues for relation recognition. Overall, these observations validate the effectiveness of our design to mine relation patterns." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 335, + 454, + 348 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 335, + 454, + 348 + ], + "spans": [ + { + "bbox": [ + 306, + 335, + 454, + 348 + ], + "type": "text", + "content": "4.3. Language-supervised SGG" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 354, + 545, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 354, + 545, + 520 + ], + "spans": [ + { + "bbox": [ + 304, + 354, + 545, + 520 + ], + "type": "text", + "content": "Setup. Language-supervised SGG [21, 41, 56, 65] explores to train SGG models with language descriptions of images. Concretely, we parse each image description into a semantic graph, in the form of a set of " + }, + { + "bbox": [ + 304, + 354, + 545, + 520 + ], + "type": "inline_equation", + "content": "\\langle" + }, + { + "bbox": [ + 304, + 354, + 545, + 520 + ], + "type": "text", + "content": " subject, predicate, object " + }, + { + "bbox": [ + 304, + 354, + 545, + 520 + ], + "type": "inline_equation", + "content": "\\rangle" + }, + { + "bbox": [ + 304, + 354, + 545, + 520 + ], + "type": "text", + "content": " triplets. Note that parsed object/relation phrases from language descriptions are freeform, we map them to VG150 categories by semantic rules following [65], such as WordNet [33] synsets matching. This makes the learned SGG model compatible for evaluating on VG150. Next, the parsed semantic graph is grounded to image regions using grounding methods, i.e., the pretrained GLIP-L [17] in our approach. Finally, the visually-grounded semantic graphs are used as weak supervision to train our proposed VS" + }, + { + "bbox": [ + 304, + 354, + 545, + 520 + ], + "type": "inline_equation", + "content": "^3" + }, + { + "bbox": [ + 304, + 354, + 545, + 520 + ], + "type": "text", + "content": " like the fully supervised setting." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 521, + 545, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 545, + 676 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 545, + 676 + ], + "type": "text", + "content": "Particularly, we have trained VS3 with text triplets parsed from three different sources of text following [21, 65]. 1) The unlocalized graph setting uses ground-truth triplet annotations in VG. 2) The VG caption setting uses triplets that are automatically parsed from natural image descriptions in VG. 3) The COCO caption setting leverages triplets parsed from captions in COCO. This setting is the most challenging since COCO captions are image-level descriptions. Such captions are different from the region-level descriptions in VG, which focus on describing object interactions. Also, note that the number of annotated captions for each COCO image (average 5) is much less than the number for each VG image (average " + }, + { + "bbox": [ + 304, + 521, + 545, + 676 + ], + "type": "inline_equation", + "content": "\\sim 50" + }, + { + "bbox": [ + 304, + 521, + 545, + 676 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": "Comparison with state-of-the-arts. The experimental results compared with previous methods are presented in Table 3. All evaluation metrics are com" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2920" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 59, + 70, + 276, + 293 + ], + "blocks": [ + { + "bbox": [ + 59, + 70, + 276, + 293 + ], + "lines": [ + { + "bbox": [ + 59, + 70, + 276, + 293 + ], + "spans": [ + { + "bbox": [ + 59, + 70, + 276, + 293 + ], + "type": "table", + "html": "
SGG modelGroundingR@20R@50R@100
Unlocalized graphVSPNet [58]--4.705.40
LSWS [56]--7.308.73
MOTIFS [60]WSGM [41]4.125.596.45
MOTIFS [60]SGNLS [65]7.239.2810.71
MOTIFS [60]Li et.al [21]9.0911.3912.89
Uniter† [5]SGNLS [65]7.8110.0311.50
Uniter† [5]Li et.al [21]9.5711.8013.15
VS3(Swin-T)GLIP-L [17]18.0223.8928.19
VS3(Swin-T+FreqBias)GLIP-L [17]20.0626.7231.75
VS3(Swin-L+FreqBias)GLIP-L [17]22.1829.8134.96
VG captionLSWS [56]--3.854.04
MOTIFS [60]SGNLS [65]6.318.059.21
MOTIFS [60]Li et.al [21]8.2510.5011.98
Uniter† [5]SGNLS [65]-9.2010.30
Uniter† [5]Li et.al [21]8.9010.9312.14
VS3(Swin-T)GLIP-L [17]11.7816.2519.7
VS3(Swin-L)GLIP-L [17]13.0117.3820.54
COCO captionLSWS [56]--3.283.69
MOTIFS [60]Li et.al [21]5.026.407.33
Uniter† [5]SGNLS [65]-5.806.70
Uniter† [5]Li et.al [21]5.426.747.62
VS3(Swin-T)GLIP-L [17]5.597.308.62
VS3(Swin-L)GLIP-L [17]6.048.159.90
", + "image_path": "9bceb9c8380890e1713655e7c83596d95fac3854093da4605484c520753504c1.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "spans": [ + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "text", + "content": "puted on the VG150 test set under the SGDET protocol. Specifically, under the unlocalized scene graphs setting, " + }, + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "inline_equation", + "content": "\\mathrm{VS}^3" + }, + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "text", + "content": " with the Swin-T backbone (" + }, + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "inline_equation", + "content": "\\mathrm{VS}^{3}_{(Swin-T)}" + }, + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "text", + "content": ") obtains substantial improvements on recall metrics over existing best results (" + }, + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "inline_equation", + "content": "R@20/50/100" + }, + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "text", + "content": " from 9.57/11.80/13.15 to 18.02/23.89/28.19). Since relation frequency statistics are available in this setting, we use them as frequency biases [60] in predicate classification, leading to further performance gains (" + }, + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "inline_equation", + "content": "\\mathrm{VS}^{3}_{(Swin-T+FreqBias)}" + }, + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "text", + "content": "). When using the stronger Swin-L backbone (" + }, + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "inline_equation", + "content": "\\mathrm{VS}^{3}_{(Swin-L+FreqBias)}" + }, + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "text", + "content": "), we attain the highest performances (" + }, + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "inline_equation", + "content": "R@20/50/100 = 22.18/29.81/34.96" + }, + { + "bbox": [ + 45, + 354, + 288, + 675 + ], + "type": "text", + "content": "), which even outperform many fully supervised methods (see Table 2). The VG caption setting provides weaker scene graph supervision via language parsing. We observe that our approach also outperforms previous state-of-the-art methods significantly. As for the most challenging COCO caption setting, it suffers from the additional domain shift problem since it trains on COCO but evaluates on VG150. As expected, the performances are lower than in the two aforementioned settings. But when comparing with previous works using the same text source, our approach still manages to achieve better performances. Overall, our approach consistently surpasses previous methods for language-supervised SGG. This demonstrates the benefits brought by pre-trained language-image models in terms of both grounding box acquirement and task transferring to tackle SGG." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 677, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 287, + 715 + ], + "type": "text", + "content": "Ablation on scene graph parsing strategy. We also conduct ablation studies on different scene graph parsing strategies for obtaining language SGG supervision. The re" + } + ] + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 343, + 70, + 507, + 117 + ], + "blocks": [ + { + "bbox": [ + 46, + 296, + 287, + 352 + ], + "lines": [ + { + "bbox": [ + 46, + 296, + 287, + 352 + ], + "spans": [ + { + "bbox": [ + 46, + 296, + 287, + 352 + ], + "type": "text", + "content": "Table 3. Comparison with state-of-the-art language-supervised SGG methods, using weak scene graph supervision from three different text sources: unlocalized scene graphs, VG caption and COCO caption. All metrics are computed under the SGDET protocol on VG150 images. (" + }, + { + "bbox": [ + 46, + 296, + 287, + 352 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 46, + 296, + 287, + 352 + ], + "type": "text", + "content": " indicates adapted for SGG.)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 343, + 70, + 507, + 117 + ], + "lines": [ + { + "bbox": [ + 343, + 70, + 507, + 117 + ], + "spans": [ + { + "bbox": [ + 343, + 70, + 507, + 117 + ], + "type": "table", + "html": "
SG fromSG parserR@20/50/100
Single captionSimple5.07 / 6.25 / 7.36
All captionsSimple5.42 / 6.82 / 7.93
All captionsAdvanced5.59 / 7.30 / 8.62
", + "image_path": "1919eb229a5a79d05f13f8ce1d690250ab325794f7346c260ef1c88393ac2a6b.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 305, + 119, + 545, + 152 + ], + "lines": [ + { + "bbox": [ + 305, + 119, + 545, + 152 + ], + "spans": [ + { + "bbox": [ + 305, + 119, + 545, + 152 + ], + "type": "text", + "content": "Table 4. Ablation on scene graph parsing strategies for language-supervised SGG. Results are obtained with VS" + }, + { + "bbox": [ + 305, + 119, + 545, + 152 + ], + "type": "inline_equation", + "content": "^3" + }, + { + "bbox": [ + 305, + 119, + 545, + 152 + ], + "type": "text", + "content": " (Swin-T) trained on scene graph supervision parsed from COCO captions." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "spans": [ + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "text", + "content": "sults are shown in Table 4. Note that each image in COCO is annotated with 5 captions, and these captions are usually complimentary in describing image content. At first, we compare the performances between training with triplets from a single caption and all captions. We see the recalls achieve relative " + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "text", + "content": " performance boosts by replacing triplets from a single caption with all captions. This suggests the completeness of extracted scene graphs from image descriptions is a non-negligible factor for training a high-quality SGG model." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 277, + 546, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 277, + 546, + 434 + ], + "spans": [ + { + "bbox": [ + 304, + 277, + 546, + 434 + ], + "type": "text", + "content": "Moreover, we compare two language parsers for extracting " + }, + { + "bbox": [ + 304, + 277, + 546, + 434 + ], + "type": "inline_equation", + "content": "\\langle" + }, + { + "bbox": [ + 304, + 277, + 546, + 434 + ], + "type": "text", + "content": " subject, predicate, object " + }, + { + "bbox": [ + 304, + 277, + 546, + 434 + ], + "type": "inline_equation", + "content": "\\rangle" + }, + { + "bbox": [ + 304, + 277, + 546, + 434 + ], + "type": "text", + "content": " triplets: the simple SG parser [31], and the advanced SG parser [39]. Both parsers apply pre-defined rules to extract object and relation concepts from the semantic graphs of image language descriptions. Compared with the simple SG parser, the advanced SG parser covers additional features for dealing with complex quantificational modifiers (e.g., a lot of), resolving pronouns (e.g., it) and handling plural nouns (e.g., three men). The performance boosts of the advanced SG parser over the simple one (recalls from 5.42/6.82/7.93 to 5.59/7.30/8.62), indicates that the quality of semantic parsing is also important for language-supervised SGG." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 442, + 436, + 455 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 442, + 436, + 455 + ], + "spans": [ + { + "bbox": [ + 306, + 442, + 436, + 455 + ], + "type": "text", + "content": "4.4. Open-vocabulary SGG" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 461, + 545, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 461, + 545, + 568 + ], + "spans": [ + { + "bbox": [ + 304, + 461, + 545, + 568 + ], + "type": "text", + "content": "Setup. Following [8], we train the proposed VS3 with the same " + }, + { + "bbox": [ + 304, + 461, + 545, + 568 + ], + "type": "inline_equation", + "content": "70\\%" + }, + { + "bbox": [ + 304, + 461, + 545, + 568 + ], + "type": "text", + "content": " object categories of VG150 as base categories. With the aid of the pre-trained VSS, we hope VS3 can generalize to recognize the remaining " + }, + { + "bbox": [ + 304, + 461, + 545, + 568 + ], + "type": "inline_equation", + "content": "30\\%" + }, + { + "bbox": [ + 304, + 461, + 545, + 568 + ], + "type": "text", + "content": " novel objects and their involved relations at inference. Concretely, we compute evaluation metrics over two object category sets: " + }, + { + "bbox": [ + 304, + 461, + 545, + 568 + ], + "type": "inline_equation", + "content": "70\\%" + }, + { + "bbox": [ + 304, + 461, + 545, + 568 + ], + "type": "text", + "content": " base + " + }, + { + "bbox": [ + 304, + 461, + 545, + 568 + ], + "type": "inline_equation", + "content": "30\\%" + }, + { + "bbox": [ + 304, + 461, + 545, + 568 + ], + "type": "text", + "content": " novel objects (dubbed as open-vocabulary SGG (Ov-SGG) evaluation), and " + }, + { + "bbox": [ + 304, + 461, + 545, + 568 + ], + "type": "inline_equation", + "content": "30\\%" + }, + { + "bbox": [ + 304, + 461, + 545, + 568 + ], + "type": "text", + "content": " novel objects (dubbed as zero-shot SGG (ZsO-SGG) evaluation)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 570, + 546, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 546, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 546, + 714 + ], + "type": "text", + "content": "In addition, we adopt the PREDCLS and SGDET evaluation protocols [50]. PREDCLS assumes object information given, yet SGDET generates scene graphs from the raw image using predicted objects. Since VS3 detects objects in a one-stage manner, we implement PREDCLS by selecting image regions that best match the ground-truth objects in post-processing, then performing relation recognition. We neglect the SGCLS protocol that assumes bounding box information given. This is because given bounding boxes can be directly used as region proposals in two-stage detectors, while the adopted one-stage manner in VS3 has no region proposal counterpart." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "2921" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 52, + 70, + 282, + 166 + ], + "blocks": [ + { + "bbox": [ + 52, + 70, + 282, + 166 + ], + "lines": [ + { + "bbox": [ + 52, + 70, + 282, + 166 + ], + "spans": [ + { + "bbox": [ + 52, + 70, + 282, + 166 + ], + "type": "table", + "html": "
MethodOv-SGG (70%+30%)ZsO-SGG (30%)
PREDCLSSGDETPREDCLSSGDET
IMP [50]40.02 / 43.40-37.01 / 39.46-
MOTIFS [60]41.14 / 44.70-39.53 / 41.14-
VCTREE [48]42.56 / 45.84-41.27 / 42.52-
TDE [47]38.29 / 40.38-34.15 / 36.37-
GCA [13]43.48 / 46.26-42.56 / 43.18-
EBM [44]44.09 / 46.95-43.27 / 44.03-
SVRP [8]47.62 / 49.94-45.75 / 48.39-
VS3(Swin-T)50.10 / 52.0515.07 / 18.7346.91 / 49.1310.08 / 13.65
VS3(Swin-L)55.88 / 58.1823.13 / 28.4954.44 / 57.3521.51 / 27.62
", + "image_path": "5f77b02586a42a8e825df61af8bcd6802c835b044ab8ed8b9a8b2b5ddd35a82b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 47, + 167, + 287, + 212 + ], + "lines": [ + { + "bbox": [ + 47, + 167, + 287, + 212 + ], + "spans": [ + { + "bbox": [ + 47, + 167, + 287, + 212 + ], + "type": "text", + "content": "Table 5. Evaluation results (R@50/100) of fully supervised open-vocabulary SGG. Ov-SGG evaluates on " + }, + { + "bbox": [ + 47, + 167, + 287, + 212 + ], + "type": "inline_equation", + "content": "70\\%" + }, + { + "bbox": [ + 47, + 167, + 287, + 212 + ], + "type": "text", + "content": " base categories + " + }, + { + "bbox": [ + 47, + 167, + 287, + 212 + ], + "type": "inline_equation", + "content": "30\\%" + }, + { + "bbox": [ + 47, + 167, + 287, + 212 + ], + "type": "text", + "content": " novel categories in VG150, while ZsO-SGG only evaluates on " + }, + { + "bbox": [ + 47, + 167, + 287, + 212 + ], + "type": "inline_equation", + "content": "30\\%" + }, + { + "bbox": [ + 47, + 167, + 287, + 212 + ], + "type": "text", + "content": " novel categories." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 219, + 287, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 219, + 287, + 399 + ], + "spans": [ + { + "bbox": [ + 46, + 219, + 287, + 399 + ], + "type": "text", + "content": "Fully supervised results. We first conduct experiments using manually annotated scene graphs. The results are presented in Table 5. For both Ov-SGG and ZsO-SGG, VS3 achieves substantial performance improvements under PREDCLS. When upgrading to the stronger backbone Swin-L, more significant improvements are obtained. More importantly, we report performances for the challenging and more practical SGDET, which are neglected by all previous methods since their used object detector cannot handle open-vocabulary detection [8]. The SGDET performances " + }, + { + "bbox": [ + 46, + 219, + 287, + 399 + ], + "type": "inline_equation", + "content": "(R@50/100 = 10.08/13.65)" + }, + { + "bbox": [ + 46, + 219, + 287, + 399 + ], + "type": "text", + "content": " of ZsO-SGG using VS3 are even higher than SGCLS metrics of SVRP " + }, + { + "bbox": [ + 46, + 219, + 287, + 399 + ], + "type": "inline_equation", + "content": "(R@50/100 = 9.30/11.32" + }, + { + "bbox": [ + 46, + 219, + 287, + 399 + ], + "type": "text", + "content": " in [8]). This reveals the superiority of our approach to recognizing novel objects thanks to the open-vocabulary generalization ability of the pre-trained VSS." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "spans": [ + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "text", + "content": "Language-supervised results. Next, we evaluate the most challenging setting, i.e., open-vocabulary SGG using language supervision. To our knowledge, we are the first to propose such a new and practical SGG setting, and present the benchmark performances in Table 5. Not surprisingly, the recalls obtained via language-supervised training (i.e., " + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "inline_equation", + "content": "SG" + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "text", + "content": " from VG caption or COCO caption) are lower than supervised results (i.e., " + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "inline_equation", + "content": "SG" + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "text", + "content": " from annotated). When comparing " + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "inline_equation", + "content": "VS^{3}_{(Swin-T)}" + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "inline_equation", + "content": "VS^{3}_{(Swin-L)}" + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "text", + "content": " that is transferred from a stronger pre-trained model, the latter gets substantially higher Ov-SGG and ZsO-SGG performances. More importantly, we observe the performance gap between Ov-SGG and ZsO-SGG get closer in " + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "inline_equation", + "content": "VS^{3}_{(Swin-L)}" + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "text", + "content": ", e.g., the " + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "inline_equation", + "content": "R@50" + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "text", + "content": " gap under the VG caption setting becomes " + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "inline_equation", + "content": "12.98 - 10.71 = 2.27" + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "inline_equation", + "content": "7.61 - 4.06 = 3.55" + }, + { + "bbox": [ + 46, + 400, + 288, + 640 + ], + "type": "text", + "content": ". This is due to the better generalization ability for recognizing novel classes. Moreover, the superior performances obtained by VG caption over COCO caption, indicate that using dense region-level descriptions and avoiding domain shift will help improve language-supervised open-vocabulary SGG in practice." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 642, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 287, + 714 + ], + "type": "text", + "content": "Qualitative analysis. We further showcase qualitative results of open-vocabulary SGG in Figure 3. The results demonstrate that our approach manages to detect novel objects and their relations with other objects. We also find that, compared with the fully supervised setting, the language-supervised results bias to predict simple relations such as" + } + ] + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 315, + 70, + 537, + 135 + ], + "blocks": [ + { + "bbox": [ + 315, + 70, + 537, + 135 + ], + "lines": [ + { + "bbox": [ + 315, + 70, + 537, + 135 + ], + "spans": [ + { + "bbox": [ + 315, + 70, + 537, + 135 + ], + "type": "table", + "html": "
MethodSG supervisionOv-SGG (70%+30%)ZsO-SGG (30%)
VS3(Swin-T)Manual annotation15.07 / 18.7310.08 / 13.65
VG caption7.61 / 9.604.06 / 5.58
COCO caption4.39 / 5.633.65 / 4.73
VS3(Swin-L)Manual annotation23.13 / 28.4921.51 / 27.62
VG caption12.98 / 16.2910.71 / 13.70
COCO caption6.76 / 8.456.26 / 7.89
", + "image_path": "88ba72315d3ad3324aa7e9af6121bad5ca097ce3ad832f1f4f9dbeddafe85110.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 305, + 137, + 545, + 192 + ], + "lines": [ + { + "bbox": [ + 305, + 137, + 545, + 192 + ], + "spans": [ + { + "bbox": [ + 305, + 137, + 545, + 192 + ], + "type": "text", + "content": "Table 6. Evaluation results (R@50/100) of open-vocabulary SGG using three different scene graph supervisions: manual annotation, VG caption and COCO caption (language-supervised). Ov-SGG evaluates on " + }, + { + "bbox": [ + 305, + 137, + 545, + 192 + ], + "type": "inline_equation", + "content": "70\\%" + }, + { + "bbox": [ + 305, + 137, + 545, + 192 + ], + "type": "text", + "content": " base categories " + }, + { + "bbox": [ + 305, + 137, + 545, + 192 + ], + "type": "inline_equation", + "content": "+30\\%" + }, + { + "bbox": [ + 305, + 137, + 545, + 192 + ], + "type": "text", + "content": " novel categories in VG150, while ZsO-SGG only evaluates on " + }, + { + "bbox": [ + 305, + 137, + 545, + 192 + ], + "type": "inline_equation", + "content": "30\\%" + }, + { + "bbox": [ + 305, + 137, + 545, + 192 + ], + "type": "text", + "content": " novel categories." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 314, + 200, + 380, + 251 + ], + "blocks": [ + { + "bbox": [ + 314, + 200, + 380, + 251 + ], + "lines": [ + { + "bbox": [ + 314, + 200, + 380, + 251 + ], + "spans": [ + { + "bbox": [ + 314, + 200, + 380, + 251 + ], + "type": "image", + "image_path": "103f5cb601e283c17694db8df1eb46c148f8e2e52471ccffe8d3a7be65cd87ef.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 381, + 200, + 455, + 250 + ], + "blocks": [ + { + "bbox": [ + 381, + 200, + 455, + 250 + ], + "lines": [ + { + "bbox": [ + 381, + 200, + 455, + 250 + ], + "spans": [ + { + "bbox": [ + 381, + 200, + 455, + 250 + ], + "type": "image", + "image_path": "ac19ba4940bbd6447deca22fd9a6dc3fc939522be06be2c2af1c1253e63f39fc.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 459, + 200, + 533, + 250 + ], + "blocks": [ + { + "bbox": [ + 459, + 200, + 533, + 250 + ], + "lines": [ + { + "bbox": [ + 459, + 200, + 533, + 250 + ], + "spans": [ + { + "bbox": [ + 459, + 200, + 533, + 250 + ], + "type": "image", + "image_path": "ea3bde8512e62820b8d19df96fe2812104bc9febce43dfe50c7b19d40687cfea.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 314, + 253, + 380, + 299 + ], + "blocks": [ + { + "bbox": [ + 314, + 253, + 380, + 299 + ], + "lines": [ + { + "bbox": [ + 314, + 253, + 380, + 299 + ], + "spans": [ + { + "bbox": [ + 314, + 253, + 380, + 299 + ], + "type": "image", + "image_path": "3293ae606c9d9f0665b34a5af8f5140f747ef1e0db6c71d3bf69fcd45b0cc687.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 331, + 304, + 362, + 311 + ], + "lines": [ + { + "bbox": [ + 331, + 304, + 362, + 311 + ], + "spans": [ + { + "bbox": [ + 331, + 304, + 362, + 311 + ], + "type": "text", + "content": "Input images" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 381, + 252, + 455, + 300 + ], + "blocks": [ + { + "bbox": [ + 381, + 252, + 455, + 300 + ], + "lines": [ + { + "bbox": [ + 381, + 252, + 455, + 300 + ], + "spans": [ + { + "bbox": [ + 381, + 252, + 455, + 300 + ], + "type": "image", + "image_path": "713aec097612011545dd1b8a77aaedffdcaa283e5f39c59dbaa72d8a81798f8a.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 391, + 303, + 445, + 310 + ], + "lines": [ + { + "bbox": [ + 391, + 303, + 445, + 310 + ], + "spans": [ + { + "bbox": [ + 391, + 303, + 445, + 310 + ], + "type": "text", + "content": "Fully supervised results" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 459, + 252, + 533, + 300 + ], + "blocks": [ + { + "bbox": [ + 459, + 252, + 533, + 300 + ], + "lines": [ + { + "bbox": [ + 459, + 252, + 533, + 300 + ], + "spans": [ + { + "bbox": [ + 459, + 252, + 533, + 300 + ], + "type": "image", + "image_path": "d6b4304c07418ddfc9bea6c3602c8183117de0c6ea9ecbe52d3c52990aedf2b5.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 462, + 303, + 528, + 310 + ], + "lines": [ + { + "bbox": [ + 462, + 303, + 528, + 310 + ], + "spans": [ + { + "bbox": [ + 462, + 303, + 528, + 310 + ], + "type": "text", + "content": "Language-supervised results" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 305, + 316, + 545, + 373 + ], + "lines": [ + { + "bbox": [ + 305, + 316, + 545, + 373 + ], + "spans": [ + { + "bbox": [ + 305, + 316, + 545, + 373 + ], + "type": "text", + "content": "Figure 3. Qualitative results of open-vocabulary SGG, particularly from fully supervised and language-supervised (VG caption) settings. Note that dotted nodes denote novel objects. For clarity, we only show triplets among the top 20 predictions that depict relations of highlighted image regions (i.e., boxes on input images)." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 377, + 545, + 414 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 377, + 545, + 414 + ], + "spans": [ + { + "bbox": [ + 305, + 377, + 545, + 414 + ], + "type": "text", + "content": "'on', 'of'. Presumably, it's because scene graph supervision parsed from language is more likely to extract such simple words as relation predicates." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 306, + 428, + 378, + 441 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 428, + 378, + 441 + ], + "spans": [ + { + "bbox": [ + 306, + 428, + 378, + 441 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 449, + 545, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 449, + 545, + 664 + ], + "spans": [ + { + "bbox": [ + 304, + 449, + 545, + 664 + ], + "type": "text", + "content": "In this work, we have proposed a novel approach to exploit a powerful pre-trained VSS for triggering language-supervised and open-vocabulary SGG. Particularly, we obtain cheap scene graph supervision by semantically parsing image language descriptions into semantic graphs and grounding the noun phrases through region-word alignment in the VSS. In addition, we devise the VS3 model, which performs object detection as category name grounding in the VSS and naturally builds relation representations for relation recognition. Thanks to the open-vocabulary generalization ability of the VSS, VS3 manages to detect novel objects and their relations with other objects, achieving open-vocabulary SGG. We validate our approach on the Visual Genome benchmark across supervised, language-supervised and open-vocabulary SGG settings, and have set new state-of-the-art performances. This demonstrates the merits of transferring pre-training knowledge to push SGG toward more practical scenarios." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 666, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 666, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 666, + 545, + 712 + ], + "type": "text", + "content": "Acknowledgment. This work was supported in part by the Shenzhen Science and Technology Program under Grant JCYJ20220818103006012 and ZDSYS20211021111415025." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2922" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 287, + 713 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "type": "text", + "content": "[1] Saeid Amiri, Kishan Chandan, and Shiqi Zhang. Reasoning with scene graphs for robot planning under partial observability. IEEE Robotics and Automation Letters, 7(2):5560-5567, 2022. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 137, + 287, + 169 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 137, + 287, + 169 + ], + "spans": [ + { + "bbox": [ + 53, + 137, + 287, + 169 + ], + "type": "text", + "content": "[2] Shizhe Chen, Qin Jin, Peng Wang, and Qi Wu. Say as you wish: Fine-grained control of image caption generation with abstract scene graphs. In CVPR, 2020. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 171, + 287, + 203 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 171, + 287, + 203 + ], + "spans": [ + { + "bbox": [ + 53, + 171, + 287, + 203 + ], + "type": "text", + "content": "[3] Tianshui Chen, Weihao Yu, Riquan Chen, and Liang Lin. Knowledge-embedded routing network for scene graph generation. In CVPR, 2019. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 205, + 287, + 248 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 205, + 287, + 248 + ], + "spans": [ + { + "bbox": [ + 53, + 205, + 287, + 248 + ], + "type": "text", + "content": "[4] Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam, Saurabh Gupta, Piotr Dollar, and C Lawrence Zitnick. Microsoft coco captions: Data collection and evaluation server. arXiv preprint arXiv:1504.00325, 2015. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 250, + 287, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 250, + 287, + 293 + ], + "spans": [ + { + "bbox": [ + 53, + 250, + 287, + 293 + ], + "type": "text", + "content": "[5] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, 2020. 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 295, + 287, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 295, + 287, + 327 + ], + "spans": [ + { + "bbox": [ + 53, + 295, + 287, + 327 + ], + "type": "text", + "content": "[6] Jieuxiang Gu, Handong Zhao, Zhe Lin, Sheng Li, Jianfei Cai, and Mingyang Ling. Scene graph generation with external knowledge and image reconstruction. In CVPR, 2019. 1, 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 329, + 287, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 329, + 287, + 361 + ], + "spans": [ + { + "bbox": [ + 53, + 329, + 287, + 361 + ], + "type": "text", + "content": "[7] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. In ICLR, 2021. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 364, + 287, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 364, + 287, + 396 + ], + "spans": [ + { + "bbox": [ + 53, + 364, + 287, + 396 + ], + "type": "text", + "content": "[8] Tao He, Lianli Gao, Jingkuan Song, and Yuan-Fang Li. Towards open-vocabulary scene graph generation with prompt-based finetuning. In ECCV, 2022. 3, 5, 7, 8" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 397, + 287, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 397, + 287, + 441 + ], + "spans": [ + { + "bbox": [ + 53, + 397, + 287, + 441 + ], + "type": "text", + "content": "[9] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICLR, 2021. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 443, + 287, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 443, + 287, + 475 + ], + "spans": [ + { + "bbox": [ + 48, + 443, + 287, + 475 + ], + "type": "text", + "content": "[10] Justin Johnson, Ranjay Krishna, Michael Stark, Li-Jia Li, David Shamma, Michael Bernstein, and Li Fei-Fei. Image retrieval using scene graphs. In CVPR, 2015. 1, 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 476, + 287, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 476, + 287, + 520 + ], + "spans": [ + { + "bbox": [ + 48, + 476, + 287, + 520 + ], + "type": "text", + "content": "[11] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In ICCV, 2021. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 522, + 287, + 564 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 522, + 287, + 564 + ], + "spans": [ + { + "bbox": [ + 48, + 522, + 287, + 564 + ], + "type": "text", + "content": "[12] Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT, 2019. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 567, + 287, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 567, + 287, + 609 + ], + "spans": [ + { + "bbox": [ + 48, + 567, + 287, + 609 + ], + "type": "text", + "content": "[13] Boris Knyazev, Harm de Vries, Catalina Cangea, Graham W Taylor, Aaron Courville, and Eugene Belilovsky. Generative compositional augmentations for scene graph prediction. In ICCV, 2021. 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 612, + 287, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 612, + 287, + 655 + ], + "spans": [ + { + "bbox": [ + 48, + 612, + 287, + 655 + ], + "type": "text", + "content": "[14] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. IJCV, 2017. 1, 2, 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 658, + 287, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 287, + 689 + ], + "type": "text", + "content": "[15] Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and Rene Ranftl. Language-driven semantic segmentation. In ICLR, 2021. 2, 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "text", + "content": "[16] Junnan Li, Ramprasaath Selvaraju, Akhilesh Gotmare, Shafiq Joty, Caiming Xiong, and Steven Chu Hong Hoi." + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 36, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "text", + "content": "Align before fuse: Vision and language representation learning with momentum distillation. NeurIPS, 2021. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 96, + 545, + 129 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 545, + 129 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 545, + 129 + ], + "type": "text", + "content": "[17] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, et al. Grounded language-image pre-training. In CVPR, 2022. 2, 3, 4, 6, 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 130, + 545, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 130, + 545, + 173 + ], + "spans": [ + { + "bbox": [ + 308, + 130, + 545, + 173 + ], + "type": "text", + "content": "[18] Manling Li, Alireza Zareian, Qi Zeng, Spencer Whitehead, Di Lu, Heng Ji, and Shih-Fu Chang. Cross-media structured common space for multimedia event extraction. arXiv preprint arXiv:2005.02472, 2020. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 175, + 545, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 175, + 545, + 205 + ], + "spans": [ + { + "bbox": [ + 308, + 175, + 545, + 205 + ], + "type": "text", + "content": "[19] Rongjie Li, Songyang Zhang, and Xuming He. Sgtr: End-to-end scene graph generation with transformer. In CVPR, 2022. 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 208, + 545, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 208, + 545, + 240 + ], + "spans": [ + { + "bbox": [ + 308, + 208, + 545, + 240 + ], + "type": "text", + "content": "[20] Rongjie Li, Songyang Zhang, Bo Wan, and Xuming He. Bipartite graph network with adaptive message passing for unbiased scene graph generation. In CVPR, 2021. 1, 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 243, + 545, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 243, + 545, + 285 + ], + "spans": [ + { + "bbox": [ + 308, + 243, + 545, + 285 + ], + "type": "text", + "content": "[21] Xingchen Li, Long Chen, Wenbo Ma, Yi Yang, and Jun Xiao. Integrating object-aware and interaction-aware knowledge for weakly supervised scene graph generation. In ACM MM, 2022. 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 287, + 545, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 287, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 308, + 287, + 545, + 319 + ], + "type": "text", + "content": "[22] Yehao Li, Yingwei Pan, Jingwen Chen, Ting Yao, and Tao Mei. X-modaler: A versatile and high-performance codebase for cross-modal analytics. In ACM MM, 2021. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 321, + 545, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 321, + 545, + 363 + ], + "spans": [ + { + "bbox": [ + 308, + 321, + 545, + 363 + ], + "type": "text", + "content": "[23] Yehao Li, Yingwei Pan, Ting Yao, Jingwen Chen, and Tao Mei. Scheduled sampling in vision-language pretraining with decoupled encoder-decoder network. In AAAI, 2021. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 365, + 545, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 365, + 545, + 397 + ], + "spans": [ + { + "bbox": [ + 308, + 365, + 545, + 397 + ], + "type": "text", + "content": "[24] Yongzhi Li, Duo Zhang, and Yadong Mu. Visual-semantic matching by exploring high-order attention and distraction. In CVPR, 2020. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 399, + 545, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 399, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 308, + 399, + 545, + 430 + ], + "type": "text", + "content": "[25] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dólár. Focal loss for dense object detection. In ICCV, 2017. 4" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 433, + 545, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 433, + 545, + 465 + ], + "spans": [ + { + "bbox": [ + 308, + 433, + 545, + 465 + ], + "type": "text", + "content": "[26] Xin Lin, Changxing Ding, Yibing Zhan, Zijian Li, and Dacheng Tao. Hl-net: Heterophily learning network for scene graph generation. In CVPR, 2022. 1, 2, 6" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 467, + 545, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 467, + 545, + 498 + ], + "spans": [ + { + "bbox": [ + 308, + 467, + 545, + 498 + ], + "type": "text", + "content": "[27] Hengyue Liu, Ning Yan, Masood Mortazavi, and Bir Bhanu. Fully convolutional scene graph generation. In CVPR, 2021. 6" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 501, + 545, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 501, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 308, + 501, + 545, + 544 + ], + "type": "text", + "content": "[28] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, 2021. 3, 6" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 545, + 545, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 545, + 545, + 578 + ], + "spans": [ + { + "bbox": [ + 308, + 545, + 545, + 578 + ], + "type": "text", + "content": "[29] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS, 2019. 3" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 308, + 579, + 545, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 579, + 545, + 622 + ], + "spans": [ + { + "bbox": [ + 308, + 579, + 545, + 622 + ], + "type": "text", + "content": "[30] Jianjie Luo, Yehao Li, Yingwei Pan, Ting Yao, Hongyang Chao, and Tao Mei. Coco-bert: Improving video-language pre-training with contrastive cross-modal matching and denoising. In ACM MM, 2021. 3" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 308, + 624, + 545, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 624, + 545, + 655 + ], + "spans": [ + { + "bbox": [ + 308, + 624, + 545, + 655 + ], + "type": "text", + "content": "[31] Jiayuan Mao. Scenegraphparser, 2019. https://github.com/vacancy/SceneGraphParser (Access date: 2022-8-11). 3, 7" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 308, + 658, + 545, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 658, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 308, + 658, + 545, + 689 + ], + "type": "text", + "content": "[32] Victor Milewski, Marie-Francine Moens, and Iacer Calixto. Are scene graphs good enough to improve image captioning? arXiv preprint arXiv:2009.12313, 2020. 1" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "type": "text", + "content": "[33] George A Miller. Wordnet: a lexical database for english. Communications of the ACM, 38(11):39-41, 1995. 3, 5, 6" + } + ] + } + ], + "index": 35 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "2923" + } + ] + } + ], + "index": 37 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "text", + "content": "[34] Yingwei Pan, Yehao Li, Jianjie Luo, Jun Xu, Ting Yao, and Tao Mei. Auto-captions on gif: A large-scale video-sentence dataset for vision-language pre-training. In ACM Multimedia, 2022. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 118, + 287, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 118, + 287, + 140 + ], + "spans": [ + { + "bbox": [ + 49, + 118, + 287, + 140 + ], + "type": "text", + "content": "[35] Yingwei Pan, Ting Yao, Yehao Li, and Tao Mei. X-linear attention networks for image captioning. In CVPR, 2020. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 143, + 287, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 143, + 287, + 196 + ], + "spans": [ + { + "bbox": [ + 49, + 143, + 287, + 196 + ], + "type": "text", + "content": "[36] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In ICLR, 2021. 2, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 199, + 287, + 232 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 199, + 287, + 232 + ], + "spans": [ + { + "bbox": [ + 49, + 199, + 287, + 232 + ], + "type": "text", + "content": "[37] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE TPAMI, 2016. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 234, + 287, + 266 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 234, + 287, + 266 + ], + "spans": [ + { + "bbox": [ + 49, + 234, + 287, + 266 + ], + "type": "text", + "content": "[38] Brigit Schroeder and Subarna Tripathi. Structured query-based image retrieval using scene graphs. In CVPR Workshops, 2020. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 268, + 287, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 268, + 287, + 323 + ], + "spans": [ + { + "bbox": [ + 49, + 268, + 287, + 323 + ], + "type": "text", + "content": "[39] Sebastian Schuster, Ranjay Krishna, Angel Chang, Li Fei-Fei, and Christopher D Manning. Generating semantically precise scene graphs from textual descriptions for improved image retrieval. In Proceedings of the fourth workshop on vision and language, 2015. 3, 5, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 325, + 287, + 367 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 325, + 287, + 367 + ], + "spans": [ + { + "bbox": [ + 49, + 325, + 287, + 367 + ], + "type": "text", + "content": "[40] Sahand Sharifzadeh, Sina Moayed Baharlou, and Volker Tresp. Classification by attention: Scene graph classification with prior knowledge. arXiv preprint arXiv:2011.10084, 2020. 1, 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 49, + 370, + 287, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 370, + 287, + 403 + ], + "spans": [ + { + "bbox": [ + 49, + 370, + 287, + 403 + ], + "type": "text", + "content": "[41] Jing Shi, Yiwu Zhong, Ning Xu, Yin Li, and Chenliang Xu. A simple baseline for weakly-supervised scene graph generation. In ICCV, 2021. 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 49, + 405, + 287, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 405, + 287, + 437 + ], + "spans": [ + { + "bbox": [ + 49, + 405, + 287, + 437 + ], + "type": "text", + "content": "[42] Motoharu Sonogashira, Masaaki Iiyama, and Yasutomo Kawanishi. Towards open-set scene graph generation with unknown objects. IEEE Access, 10:11574-11583, 2022. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 49, + 440, + 287, + 472 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 440, + 287, + 472 + ], + "spans": [ + { + "bbox": [ + 49, + 440, + 287, + 472 + ], + "type": "text", + "content": "[43] Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Vl-bert: Pre-training of generic visual-linguistic representations. In ICLR, 2019. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 49, + 475, + 287, + 517 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 475, + 287, + 517 + ], + "spans": [ + { + "bbox": [ + 49, + 475, + 287, + 517 + ], + "type": "text", + "content": "[44] Mohammed Suhail, Abhay Mittal, Behjat Siddiquie, Chris Broaddus, Jayan Eledath, Gerard Medioni, and Leonid Sigal. Energy-based learning for scene graph generation. In CVPR, 2021. 8" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 49, + 520, + 287, + 574 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 520, + 287, + 574 + ], + "spans": [ + { + "bbox": [ + 49, + 520, + 287, + 574 + ], + "type": "text", + "content": "[45] Rui Sun, Xuezhi Cao, Yan Zhao, Junchen Wan, Kun Zhou, Fuzheng Zhang, Zhongyuan Wang, and Kai Zheng. Multimodal knowledge graphs for recommender systems. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 2020. 1" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 49, + 577, + 287, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 577, + 287, + 609 + ], + "spans": [ + { + "bbox": [ + 49, + 577, + 287, + 609 + ], + "type": "text", + "content": "[46] Kaihua Tang. A scene graph generation codebase in python, 2020. https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch.6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 49, + 612, + 287, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 612, + 287, + 643 + ], + "spans": [ + { + "bbox": [ + 49, + 612, + 287, + 643 + ], + "type": "text", + "content": "[47] Kaihua Tang, Yulei Niu, Jianqiang Huang, Jiaxin Shi, and Hanwang Zhang. Unbiased scene graph generation from biased training. In CVPR, 2020. 1, 2, 5, 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 49, + 646, + 287, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 646, + 287, + 677 + ], + "spans": [ + { + "bbox": [ + 49, + 646, + 287, + 677 + ], + "type": "text", + "content": "[48] Kaihua Tang, Hanwang Zhang, Baoyuan Wu, Wenhan Luo, and Wei Liu. Learning to compose dynamic tree structures for visual contexts. In CVPR, 2019. 1, 2, 6, 8" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 49, + 681, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 681, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 49, + 681, + 287, + 712 + ], + "type": "text", + "content": "[49] Sijin Wang, Ruiping Wang, Ziwei Yao, Shiguang Shan, and Xilin Chen. Cross-modal scene graph matching for relationship-aware image-text retrieval. In WACV, 2020. 1" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 704 + ], + "type": "list", + "angle": 0, + "index": 35, + "blocks": [ + { + "bbox": [ + 308, + 73, + 545, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 73, + 545, + 105 + ], + "spans": [ + { + "bbox": [ + 308, + 73, + 545, + 105 + ], + "type": "text", + "content": "[50] Danfei Xu, Yuke Zhu, Christopher B Choy, and Li Fei-Fei. Scene graph generation by iterative message passing. In CVPR, 2017. 1, 2, 5, 6, 7, 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 107, + 545, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 107, + 545, + 138 + ], + "spans": [ + { + "bbox": [ + 308, + 107, + 545, + 138 + ], + "type": "text", + "content": "[51] Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, and Devi Parikh. Graph r-cnn for scene graph generation. In ECCV, 2018. 1, 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 141, + 545, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 141, + 545, + 172 + ], + "spans": [ + { + "bbox": [ + 308, + 141, + 545, + 172 + ], + "type": "text", + "content": "[52] Xu Yang, Kaihua Tang, Hanwang Zhang, and Jianfei Cai. Auto-encoding scene graphs for image captioning. In CVPR, 2019. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 175, + 545, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 175, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 308, + 175, + 545, + 228 + ], + "type": "text", + "content": "[53] Lewei Yao, Jianhua Han, Youpeng Wen, Xiaodan Liang, Dan Xu, Wei Zhang, Zhenguo Li, Chunjing Xu, and Hang Xu. Detclip: Dictionary-enriched visual-concept paralleled pre-training for open-world detection. arXiv preprint arXiv:2209.09407, 2022. 2, 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 231, + 545, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 231, + 545, + 252 + ], + "spans": [ + { + "bbox": [ + 308, + 231, + 545, + 252 + ], + "type": "text", + "content": "[54] Ting Yao, Yingwei Pan, Yehao Li, and Tao Mei. Exploring visual relationship for image captioning. In ECCV, 2018. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 254, + 545, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 254, + 545, + 275 + ], + "spans": [ + { + "bbox": [ + 308, + 254, + 545, + 275 + ], + "type": "text", + "content": "[55] Ting Yao, Yingwei Pan, Yehao Li, and Tao Mei. Hierarchy parsing for image captioning. In ICCV, 2019. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 277, + 545, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 277, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 308, + 277, + 545, + 308 + ], + "type": "text", + "content": "[56] Keren Ye and Adriana Kovashka. Linguistic structures as weak supervision for visual scene graph generation. In CVPR, 2021. 2, 5, 6, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 310, + 545, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 310, + 545, + 342 + ], + "spans": [ + { + "bbox": [ + 308, + 310, + 545, + 342 + ], + "type": "text", + "content": "[57] Alireza Zareian, Svebor Karaman, and Shih-Fu Chang. Bridging knowledge graphs to generate scene graphs. In ECCV, 2020. 1, 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 344, + 545, + 375 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 344, + 545, + 375 + ], + "spans": [ + { + "bbox": [ + 308, + 344, + 545, + 375 + ], + "type": "text", + "content": "[58] Alireza Zareian, Svebor Karaman, and Shih-Fu Chang. Weakly supervised visual semantic parsing. In CVPR, 2020. 2, 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 378, + 545, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 378, + 545, + 409 + ], + "spans": [ + { + "bbox": [ + 308, + 378, + 545, + 409 + ], + "type": "text", + "content": "[59] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In CVPR, 2021. 2, 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 412, + 545, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 412, + 545, + 443 + ], + "spans": [ + { + "bbox": [ + 308, + 412, + 545, + 443 + ], + "type": "text", + "content": "[60] Rowan Zellers, Mark Yatskar, Sam Thomson, and Yejin Choi. Neural motifs: Scene graph parsing with global context. In CVPR, 2018. 1, 2, 6, 7, 8" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 446, + 545, + 478 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 446, + 545, + 478 + ], + "spans": [ + { + "bbox": [ + 308, + 446, + 545, + 478 + ], + "type": "text", + "content": "[61] Hanwang Zhang, Zawlin Kyaw, Shih-Fu Chang, and TatSeng Chua. Visual translation embedding network for visual relation detection. In CVPR, 2017. 6" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 480, + 545, + 512 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 480, + 545, + 512 + ], + "spans": [ + { + "bbox": [ + 308, + 480, + 545, + 512 + ], + "type": "text", + "content": "[62] Ji Zhang, Kevin J Shih, Ahmed Elgammal, Andrew Tao, and Bryan Catanzaro. Graphical contrastive losses for scene graph parsing. In CVPR, 2019. 2, 6" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 514, + 545, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 514, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 308, + 514, + 545, + 555 + ], + "type": "text", + "content": "[63] Yong Zhang, Yingwei Pan, Ting Yao, Rui Huang, Tao Mei, and Chang-Wen Chen. Exploring structure-aware transformer over interaction proposals for human-object interaction detection. In CVPR, 2022. 1" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 559, + 545, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 559, + 545, + 590 + ], + "spans": [ + { + "bbox": [ + 308, + 559, + 545, + 590 + ], + "type": "text", + "content": "[64] Yong Zhang, Yingwei Pan, Ting Yao, Rui Huang, Tao Mei, and Chang-Wen Chen. Boosting scene graph generation with visual relation saliency. ACM TOMM, 2023. 1" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 308, + 593, + 545, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 593, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 308, + 593, + 545, + 624 + ], + "type": "text", + "content": "[65] Yiwu Zhong, Jing Shi, Jianwei Yang, Chenliang Xu, and Yin Li. Learning to generate scene graph from natural language supervision. In ICCV, 2021. 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 308, + 627, + 545, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 627, + 545, + 658 + ], + "spans": [ + { + "bbox": [ + 308, + 627, + 545, + 658 + ], + "type": "text", + "content": "[66] Yiwu Zhong, Liwei Wang, Jianshu Chen, Dong Yu, and Yin Li. Comprehensive image captioning via scene graph decomposition. In ECCV, 2020. 1" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 308, + 661, + 545, + 704 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 661, + 545, + 704 + ], + "spans": [ + { + "bbox": [ + 308, + 661, + 545, + 704 + ], + "type": "text", + "content": "[67] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, et al. Regionclip: Region-based language-image pretraining. 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Existing open-world segmentation methods have shown impressive advances by employing contrastive learning (CL) to learn diverse visual concepts and transferring the learned image-level understanding to the segmentation task. However, these CL-based methods suffer from a train-test discrepancy, since it only considers image-text alignment during training, whereas segmentation requires region-text alignment during testing. In this paper, we proposed a novel Text-grounded Contrastive Learning (TCL) framework that enables a model to directly learn region-text alignment. Our method generates a segmentation mask for a given text, extracts text-grounded image embedding from the masked region, and aligns it with text embedding via TCL. By learning region-text alignment directly, our framework encourages a model to directly improve the quality of generated segmentation masks. In addition, for a rigorous and fair comparison, we present a unified evaluation protocol with widely used 8 semantic segmentation datasets. TCL achieves state-of-the-art zero-shot segmentation performances with large margins in all datasets. Code is available at https://github.com/kakaobrain/tcl.", + "bbox": [ + 75, + 335, + 473, + 698 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 729, + 209, + 744 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Open-world semantic segmentation aims to identify the arbitrary semantic concepts in the open world1. Conventional semantic segmentation aims to learn segmentation capability for the small number of pre-defined target categories, whereas open-world semantic segmentation addresses unrestricted arbitrary categories or free-form texts. Such segmentation capability over unlimited targets drasti-", + "bbox": [ + 75, + 755, + 468, + 863 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/80971d0e0724e7660fa796a783f6ccd8781fcbaf109513e3a821d0cdc3e85947.jpg", + "image_caption": [ + "Figure 1. Open-world segmentation performance comparison. The proposed method remarkably outperforms existing methods in all 8 segmentation benchmark datasets." + ], + "image_footnote": [], + "bbox": [ + 504, + 303, + 890, + 579 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "cally extends the application scope of the open-world segmentation models.", + "bbox": [ + 498, + 656, + 890, + 686 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The first challenge for open-world segmentation is how to learn arbitrary concepts, beyond pre-defined categories. Inspired by the success of CLIP [23], previous approaches [11, 17-19, 28, 30, 33] tackle this challenge by exploiting massive web-crawled image-text paired data; since the texts in web-crawled data contain a global semantic description for the paired images, the large-scale image-text pairs can provide rich knowledge for arbitrary semantic categories. However, there still remains another challenge in how to achieve precise localization of arbitrary concepts without dense annotations. There are several approaches that simply address this issue using dense annotation (segmentation masks) in addition to image-text pairs [11, 17, 18]. The dense annotation helps to improve segmentation perfor", + "bbox": [ + 496, + 688, + 892, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "This setting is often called both open-world and open-vocabulary. In this paper, we mainly refer to this setting as open-world for clarity.", + "bbox": [ + 75, + 875, + 468, + 901 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "11165", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/7fbcfb4a59ec97b008d0fa225d53467eaec0e55d1704e91d293edceec65e077c.jpg", + "image_caption": [ + "Figure 2. A conceptual comparison between the previous approach and ours. Open-world segmentation is typically achieved through region-text alignment, which involves matching region features and text embeddings. However, previous methods learn image-text alignment during training, thus suffering from the alignment-level discrepancy between training and testing. In contrast, our method facilitates end-to-end learning of region-text alignment with only image-text pairs." + ], + "image_footnote": [], + "bbox": [ + 80, + 90, + 467, + 294 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "mance in a fixed benchmark dataset, but the requirements of expensive dense annotation still limit the applicable domains and scalability of the method.", + "bbox": [ + 75, + 431, + 468, + 477 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, therefore, we focus on open-world semantic segmentation from only image-text pairs without any dense annotation. For this setting, the existing methods [19, 28, 30, 33] learn an image-text alignment capability during training and heavily rely on the transferability of the image-text alignment to perform region-text alignment at inference. More specifically, MaskCLIP [33] leverages CLIP models pre-trained to learn image-text alignment. To perform region-text alignment using CLIP, MaskCLIP applies a simple heuristic modification to the CLIP image encoder. GroupViT [30] and ViL-Seg [19] propose to cluster region-level visual features into distinct groups and generate segmentation masks by matching the groups and texts. Note that they match the text embeddings and clustered region features in test time, but in training time, the text embeddings are aligned with global image embeddings. While the existing methods have shown impressive results even through the training with image-text alignment, they still suffer from the alignment-level discrepancy between training and testing phases as depicted in Fig. 2.", + "bbox": [ + 75, + 478, + 468, + 779 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To address this train-test discrepancy, we propose the Text-grounded Contrastive Learning (TCL) framework, which allows a model to learn region-text alignment directly from the image-text pairs without any dense annotations. Our key idea is to incorporate a text grounding procedure within contrastive learning as illustrated in Fig. 2, where TCL generates a segmentation mask indicating text-grounded regions, computes grounded region embeddings", + "bbox": [ + 75, + 780, + 468, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "using the mask, and applies contrastive learning between text and grounded region. By re-formulating the contrastive loss to be directly affected by the segmentation quality, TCL enables end-to-end training of the grounder and directly improves the quality of region-text level alignment. We also present a unified evaluation protocol using widely used 8 semantic segmentation datasets and compare existing methods in the same setting. As a result, TCL achieves state-of-the-art zero-shot segmentation performance with large margins in all datasets, as shown in Fig. 1.", + "bbox": [ + 496, + 90, + 890, + 242 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our main contributions are summarized as follows:", + "bbox": [ + 517, + 243, + 857, + 257 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We introduce a novel framework for open-world segmentation, named Text-grounded Contrastive Learning (TCL), which enables learning region-text alignment directly without train-test discrepancy, thus learning to generate more precise segmentation masks through only image-text pairs.", + "- We present a unified evaluation protocol and re-evaluate recent open-world segmentation models for a fair and direct comparison.", + "- We achieve the new state-of-the-art zero-shot segmentation performance on 8 segmentation datasets with large margins compared to existing methods." + ], + "bbox": [ + 517, + 258, + 890, + 439 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 500, + 455, + 648, + 472 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Open-world Semantic Segmentation", + "text_level": 1, + "bbox": [ + 500, + 483, + 812, + 500 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Open-world scenario aims to recognize arbitrary concepts in the open world. It is also called open-vocabulary because the target vocabulary is open rather than closed. Contrastive Language-Image Pre-training (CLIP) [23] ushered in the era of open-world image recognition using largescale image-text pairs [2, 5, 26, 27]. CLIP learns the alignment between an image and a text in training time, then transfer it to the zero-shot classification by aligning image and texts indicating target classes at inference time. The advent of CLIP enables open-world settings in various fields such as object detection [12, 31], image captioning [13], or semantic segmentation [30, 33].", + "bbox": [ + 496, + 507, + 890, + 688 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Open-world semantic segmentation with image-text pairs is addressed in two different settings. The first is a semi-supervised setting, which uses dense annotation (i.e., segmentation masks) in addition to image-text pairs [11, 17, 18]. Semi-supervised approaches learn segmentation capability using dense annotation and expand the target vocabulary using image-text supervision. LSeg [17] expands target class vocabulary using image-label datasets and CLIP text encoder [23]. OpenSeg [11] and OVSeg [18] first train a mask generator using dense annotation and expand target vocabulary using image-text datasets. The use of dense annotation makes the model learn region-level alignment instead of image-level alignment, leading to high-quality segmentation masks. However, it still relies on", + "bbox": [ + 496, + 689, + 892, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "11166", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "costly dense annotation, and applicable domains are limited to the domains where dense annotation is available.", + "bbox": [ + 75, + 90, + 468, + 119 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The target of this paper is an unsupervised setting, which aims to learn segmentation from only image-text pairs without any dense annotation [19, 28, 30, 33]. Since the massive image-text pairs are easily obtained by web crawling without human annotators, applicable domains of unsupervised methods become almost unlimited. In order to achieve segmentation capability using only image-text pairs, we need to learn region-text alignment instead of image-text alignment and train a text-grounded mask generator. However, the absence of dense annotation makes this approach challenging. Existing open-world semantic segmentation studies have taken a strategy to bypass this issue. Instead of learning region-level alignment directly, they transfer image-level alignment to region-level by heuristic modification [28, 33] or clustering [19, 30]. MaskCLIP [33] proposes to obtain a dense image embedding from CLIP image encoder through heuristic modification of the last attention layer. Even though it has several limitations, such as low output resolution or noisy segmentation results, they show it is a simple yet effective way to obtain an initial segmentation map for refinement. ReCo [28] proposes an advanced refinement method based on MaskCLIP, by retrieval and co-segmentation. Clustering-based methods [19, 30] learn representations using CL with image-text pairs. They compute region-level image embedding by clustering sub-region embeddings. These approaches also have shown impressive results but have several limitations: (i) the learning objective is still image-level alignment due to lack of the region annotation, (ii) the number of clusters is pre-defined independent of the given image, and (iii) clustering sub-region image embeddings is independent of the query text. In summary, existing methods indirectly address region-level alignment problems by learning image-level alignment. To tackle this problem, we propose a novel region-level alignment objective, named Text-grounded Contrastive Learning (TCL).", + "bbox": [ + 75, + 122, + 472, + 667 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Region-level Contrastive Learning", + "text_level": 1, + "bbox": [ + 76, + 679, + 377, + 696 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Learning region-level alignment instead of image-level alignment is a fundamental target objective in dense tasks, such as segmentation or object detection. There are approaches to learn region-level alignment using dense annotation in the semi-supervised setting. They first train mask or region proposal networks using dense annotation and learn alignment between the proposals and texts [11, 15, 31]. For example, OpenSeg [11] trains a class-agnostic mask generator using dense annotation. In the object detection field, RegionCLIP [31] employs an off-the-shelf region proposal network and learns region-level alignment. In contrast to the existing region-level methods, the proposed method learns region-level alignment without any dense annotation.", + "bbox": [ + 75, + 703, + 470, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methods", + "text_level": 1, + "bbox": [ + 500, + 89, + 599, + 106 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Overview", + "text_level": 1, + "bbox": [ + 500, + 114, + 609, + 128 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Open-world semantic segmentation is a task that aims to learn a model capable of zero-shot segmentation for arbitrary visual concepts, not restricted to pre-defined ones. Our main goal is to develop an open-world segmentation algorithm using only image-text pairs. However, achieving this objective is challenging because there is no explicit supervision (i.e., pixel-level dense annotations) for text-described region segmentation. Existing methods learn models parametrized by $\\theta$ to maximize the mutual information between paired images and texts [22, 23] as follows:", + "bbox": [ + 496, + 138, + 890, + 289 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\underset {\\theta} {\\arg \\max } I _ {\\theta} \\left(\\mathbf {x} ^ {V}; \\mathbf {x} ^ {T}\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 622, + 301, + 890, + 328 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $(\\mathbf{x}^V, \\mathbf{x}^T)$ is a random image and text pair. This objective encourages the model to learn the alignment between images and texts, however, at test time, the learned model generates segmentation masks for arbitrary concepts by computing region-text alignments. Such alignment-level discrepancy between train and test time can lead the model to a sub-optimal solution as shown in Fig. 2. With this in consideration, to bridge the gap between the objective of conventional contrastive learning (CL) and the requirement of the zero-shot segmentation, we propose Text-grounded Contrastive Learning (TCL) which incorporates a text grounding process within CL to enable learning region-text alignment directly. As a text grounding module, we introduce a groundeder to generate segmentation masks for the given texts. In a nutshell, TCL learns a model to maximize mutual information between text-grounded regions and texts as follows:", + "bbox": [ + 496, + 335, + 892, + 592 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\underset {\\theta} {\\arg \\max } I _ {\\theta} \\left(\\mathbf {m} \\cdot \\mathbf {x} ^ {V}; \\mathbf {x} ^ {T}\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 609, + 603, + 890, + 630 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathbf{m}$ is a text-grounded mask of random variable indicating the text-described region. Compared to contrastive learning that implicitly learns a grounding capability, TCL has a clear advantage of explicitly learning the grounding capability through the end-to-end trainable grounder.", + "bbox": [ + 496, + 640, + 890, + 715 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In the rest of this section, we first explain the text-grounded mask generation procedure by the grounder. Then, we describe how we define losses using the generated mask to train our open-world grounder with text-grounded contrastive learning. Lastly, we explain how our model performs zero-shot inference for arbitrary concepts.", + "bbox": [ + 496, + 715, + 890, + 806 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Grounder", + "text_level": 1, + "bbox": [ + 500, + 816, + 612, + 830 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Fig. 3 illustrates our overall training pipeline. For an input batch of paired texts $\\mathbf{X}^T$ and images $\\mathbf{X}^V$ , TCL first performs a grounding process to identify text-grounded regions for a text via a grounder. The grounder consists of", + "bbox": [ + 496, + 839, + 893, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "11167", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/55183c3c1e4ed7cf0bd5c194f490ea35eee154d524c2c6de47f8888fdf0acebe.jpg", + "image_caption": [ + "Figure 3. Overall training pipeline of TCL. The proposed TCL framework first obtains text-grounded masks using a grounder and then learns the grounder using text-grounded contrastive learning. By incorporating the text grounding process with contrastive learning, our framework can directly learn region-text alignment that is required for precise segmentation. $\\mathrm{CLIP_T}$ and $\\mathrm{CLIP_I}$ indicate CLIP text and image encoders, respectively. CLIP encoders are frozen and we train the grounding decoder only. After training, the TCL block is discarded, and only the Grounder block is used to generate the text-grounded segmentation mask for inference." + ], + "image_footnote": [], + "bbox": [ + 101, + 90, + 869, + 311 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "three components: (i) image encoder $E_{v}$ is in charge of providing a single (L2-normalized) global feature as well as dense patch-level features, (ii) text encoder $E_{t}$ provides a (L2-normalized) text embedding feature, and (iii) grounding decoder $D_{g}$ converts dense features from image encoder into finer pixel-level embeddings for alignment with text. In practice, we adopt a pre-trained CLIP model [23] to initialize two encoders and freeze them to preserve and exploit the rich knowledge of CLIP learned during largescale pre-training. The text-grounded masks are computed by the position-wise dot product between text embedding and dense pixel-level embedding. The overall process of grounder is summarized as follows:", + "bbox": [ + 75, + 407, + 472, + 603 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {T} = E _ {t} \\left(\\mathbf {X} ^ {T}\\right) \\quad \\text {a n d} \\quad \\mathbf {V} ^ {g}, \\mathbf {V} ^ {d} = E _ {v} \\left(\\mathbf {X} ^ {V}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 120, + 614, + 468, + 633 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {V} ^ {s} = D _ {g} \\left(\\mathbf {V} ^ {d}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 122, + 636, + 468, + 654 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {M} _ {i, j} = \\sigma \\left(w \\cdot \\mathbf {t} _ {j} ^ {\\top} \\mathbf {V} _ {i} ^ {s} + b\\right), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 122, + 656, + 468, + 676 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{T} \\in \\mathbb{R}^{B \\times C}$ , $\\mathbf{V}^g \\in \\mathbb{R}^{B \\times C}$ , and $\\mathbf{V}^d \\in \\mathbb{R}^{B \\times L \\times C}$ are normalized text embeddings, normalized global image embeddings, and dense image features from CLIP encoders, $\\mathbf{V}^s \\in \\mathbb{R}^{B \\times C \\times H \\times W}$ is normalized pixel-level dense embeddings by the grounding decoder, and $\\mathbf{M} \\in \\mathbb{R}^{B \\times B \\times H \\times W}$ is text-grounded masks between images and texts in the batch. $B$ , $C$ , and $L$ indicate a batch size, the embedding dimension size, and the number of patches, respectively. $\\sigma$ is a sigmoid function, and $w$ , $b$ are learnable scalar projection.", + "bbox": [ + 76, + 686, + 468, + 824 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The generated text-grounded masks are used to extract text-grounded image embedding. By replacing the global image embedding with text-grounded image embedding in the contrastive learning framework, TCL enables the model to learn region-text alignment in an end-to-end manner. In", + "bbox": [ + 75, + 825, + 470, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "the following section, we describe how the generated mask $\\mathbf{M}$ is used for text-grounded contrastive learning.", + "bbox": [ + 498, + 407, + 890, + 439 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Text-grounded Contrastive Learning", + "text_level": 1, + "bbox": [ + 500, + 455, + 818, + 470 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Recall that the main idea of TCL is to use text-grounded images instead of whole images, unlike conventional CL. For this purpose, we define TCL losses in three different levels—image-level, feature-level, and area-level—using the generated masks M for all pairs of images and texts in a batch; the detailed pseudo code to compute TCL losses is given in Appendix A. We also employ smooth regularization to further improve the quality of generated masks.", + "bbox": [ + 496, + 481, + 890, + 603 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Image-level TCL loss. One intuitive way to compute the text-grounded image embedding is to encode only the regions of the image that contains the semantics of the paired text, using the image encoder. To make the whole process end-to-end trainable, we compute a differentiable masked image by multiplying the given image $\\mathbf{X}_i^V$ and a binarized mask $\\mathbf{M}_{i,i}^b$ obtained from the generated mask $\\mathbf{M}_{i,i}$ using Gumbel-Max [14]. The masked image is then fed into the image encoder, $\\tilde{\\mathbf{v}}_i^g,\\tilde{\\mathbf{v}}_i^d = E_v\\left(\\mathbf{M}_{i,i}^b\\cdot \\mathbf{X}_i^V\\right)$ , to obtain text-grounded image embedding $\\tilde{\\mathbf{v}}_i^g$ . We compute the cosine similarity matrix between text-grounded image embeddings and text embeddings in a batch by $S_{i,j}^{m} = \\tilde{\\mathbf{v}}_i^{g^\\top}\\mathbf{t}_j$ . Finally, we use the symmetric version of InfoNCE [22, 23] to define the image-level TCL loss $\\mathcal{L}_{\\mathrm{TCL}_v}$ to make the representations of positive image-text pairs similar to each other while the representations of negative pairs dissimilar based on the similarity matrix:", + "bbox": [ + 496, + 608, + 892, + 867 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {T C L} _ {v}} = \\operatorname {I n f o N C E} \\left(\\mathbf {S} ^ {m}\\right), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 609, + 885, + 890, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "11168", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\operatorname {I n f o N C E} \\left(\\mathbf {S}\\right) = - \\frac {1}{2 B} \\sum_ {i} ^ {B} \\log \\frac {\\exp \\left(S _ {i , i} / \\tau\\right)}{\\sum_ {j} ^ {B} \\exp \\left(S _ {i , j} / \\tau\\right)} \\\\ - \\frac {1}{2 B} \\sum_ {i} ^ {B} \\log \\frac {\\exp \\left(S _ {i , i} / \\tau\\right)}{\\sum_ {j} ^ {B} \\exp \\left(S _ {j , i} / \\tau\\right)}, \\tag {7} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 109, + 468, + 196 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\tau$ is a learnable temperature.", + "bbox": [ + 76, + 200, + 312, + 215 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Feature-level TCL loss. The image-level TCL loss drives a model to generate segmentation masks for the paired texts (i.e., texts of positive pairs). However, we observe that this loss alone is insufficient to prevent the model from generating masks for regions not described in the text, particularly for salient regions. This raises the need to suppress negative masks obtained from unrelated texts (i.e., texts of negative pairs), but computing the image-level TCL loss for negative masks is infeasible due to the high computational cost of encoding text-grounded images. To overcome this challenge, we introduce the feature-level TCL loss, which enables the effective computation of features of the negative masks. Specifically, for pixel-level dense embeddings $\\mathbf{V}_i^s\\in \\mathbb{R}^{C\\times H\\times W}$ from grounding decoder and a text embedding vector $\\mathbf{t}_j$ , we compute feature-level text-grounded image embedding $\\mathbf{v}_{i,j}^{f}\\in \\mathbb{R}^{C}$ by:", + "bbox": [ + 76, + 220, + 472, + 465 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {v} _ {i, j} ^ {f} = \\frac {\\sum_ {h , w} M _ {i , j , h , w} \\cdot \\mathbf {v} _ {i , : , h , w} ^ {s}}{\\sum_ {h , w} M _ {i , j , h , w}}. \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 163, + 472, + 468, + 508 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Note that this feature-level embedding is computed using negative masks $\\mathbf{M}_{i,j (i \\neq j)}$ , different from the image-level TCL loss. We then compute the cosine similarity $S_{i,j}^{f} = \\mathbf{v}_{i,j}^{f}^{\\top} \\mathbf{t}_{j}$ between all pairs of text embeddings and feature-level text-grounded image embeddings in the batch. The feature-level TCL loss is defined as follows:", + "bbox": [ + 76, + 513, + 468, + 613 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {T C L} _ {f}} = \\operatorname {I n f o N C E} \\left(\\mathbf {S} ^ {f}\\right). \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 187, + 619, + 468, + 640 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Area TCL loss. The image-level and feature-level TCL losses focus on generating a mask to capture the text-described region in the image. However, the model can collapse into a trivial solution with only these losses—generating a mask for the entire image instead of the desired region. To prevent this collapse, we introduce an additional objective to our TCL framework, named area TCL loss, which incorporates priors on the mask area to ensure capturing only the text-described region. To be specific, for the positive masks (masks from positive pairs) $\\mathbf{M}^{+}$ and the negative masks (masks from negative pair) $\\mathbf{M}^{-}$ , we denote the area of positive and negative masks by $\\overline{\\mathbf{M}^{+}}$ and $\\overline{\\mathbf{M}^{-}}$ , respectively. The area TCL loss is defined by L1-distance between the area priors and the expected area of each mask:", + "bbox": [ + 76, + 648, + 472, + 861 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {a r e a}} = \\left\\| p ^ {+} - \\mathbb {E} \\left[ \\overline {{\\mathbf {M} ^ {+}}} \\right] \\right\\| _ {1} + \\left\\| p ^ {-} - \\mathbb {E} \\left[ \\overline {{\\mathbf {M} ^ {-}}} \\right] \\right\\| _ {1}, \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 877, + 468, + 905 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $p^+$ and $p^-$ are positive and negative area priors. For the negative area prior $p^-$ , intuitively, we can expect the area of the negative masks to be 0.0. We set the positive area prior $p^+$ to 0.4, which is the average text-described region area measured by MaskCLIP [33] in the CC3M dataset [27].", + "bbox": [ + 496, + 90, + 890, + 167 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Smooth regularization. In the image-text dataset, a text usually describes the salient object or concept in the paired image. We observe that the regions described by the text are generally smooth rather than noisy. We employ total variation (TV) regularization loss [24] to incorporate this smoothness observation in the objective. The TV loss is applied to both mask and pixel-level dense embedding:", + "bbox": [ + 496, + 171, + 893, + 277 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {T V}} = \\left\\| \\mathbf {M} \\right\\| _ {\\mathrm {T V}} + \\left\\| \\mathbf {V} ^ {s} \\right\\| _ {\\mathrm {T V}}, \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 607, + 290, + 890, + 308 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\| \\cdot \\|_{\\mathrm{TV}}$ is the anisotropic TV norm.", + "bbox": [ + 500, + 319, + 777, + 335 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Final loss. Our final loss function is defined by:", + "bbox": [ + 500, + 339, + 816, + 354 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\underbrace {\\lambda_ {\\mathrm {T C L}} \\mathcal {L} _ {\\mathrm {T C L}} + \\lambda_ {\\text {a r e a}} \\mathcal {L} _ {\\text {a r e a}}} _ {\\text {T C L l o s s e s}} + \\underbrace {\\lambda_ {\\mathrm {t v}} \\mathcal {L} _ {\\mathrm {t v}}} _ {\\text {r e g u l a r i z a t i o n}}, \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 560, + 368, + 890, + 404 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{\\mathrm{TCL}} = \\mathcal{L}_{\\mathrm{TCL}_v} + \\mathcal{L}_{\\mathrm{TCL}_f}$ , and $\\lambda_{\\mathrm{TCL}}$ , $\\lambda_{\\mathrm{area}}$ , $\\lambda_{\\mathrm{tv}}$ are hyperparameters to balance three losses.", + "bbox": [ + 496, + 415, + 890, + 445 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Inference Pipeline", + "text_level": 1, + "bbox": [ + 500, + 455, + 674, + 472 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The zero-shot inference pipeline is similar to CLIP [23], except for performing pixel-level classification instead of image-level classification. Specifically, for text embeddings $\\mathbf{T} \\in \\mathbb{R}^{N \\times C}$ and a pixel-level dense embedding $\\mathbf{v}^s \\in \\mathbb{R}^{C \\times H \\times W}$ , text-grounded mask $\\mathbf{M} \\in \\mathbb{R}^{N \\times H \\times W}$ is computed by Eq. (5), where $N$ is the number of target classes. The final segmentation map $\\mathcal{M}$ is computed by:", + "bbox": [ + 496, + 479, + 890, + 585 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {M} _ {h, w} = \\underset {n} {\\arg \\max } M _ {n, h, w}. \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 604, + 599, + 890, + 622 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Prompt templates such as \"a photo of a {label}.\" are used to generate text embeddings as in CLIP [23].", + "bbox": [ + 496, + 635, + 890, + 666 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 681, + 633, + 698 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experiment Settings", + "text_level": 1, + "bbox": [ + 500, + 705, + 692, + 722 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Unified evaluation protocol. In open-world semantic segmentation, a standard evaluation protocol is not yet established. Previous studies conduct an evaluation using their own protocols such as different data processing strategies on different datasets [19, 28, 30, 33]; surprisingly, even for the same dataset, the target classes are sometimes different across studies. For a fair comparison, we present a unified evaluation protocol following the open-world scenario where prior access to the target data before evaluation is not allowed. Under this scenario, the proposed protocol prohibits dataset-specific hyperparameters or tricks, e.g., class", + "bbox": [ + 496, + 734, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "11169", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/8fcd393858a543e0fe583e82994dfa5b5ac3209938824a3c51af4d3e8d027a45.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Methodswith background classwithout background classAvg.
VOCContextObjectVOC20Context59StuffCityADE
GroupViT (YFCC)49.519.024.374.120.812.66.98.727.0
GroupViT (RedCaps)50.418.727.579.723.415.311.19.229.4
MaskCLIP†29.321.115.553.723.314.721.610.823.7
MaskCLIP38.823.620.674.926.416.412.69.827.9
ReCo25.119.915.757.722.314.821.111.223.5
TCL (Ours)55.030.431.683.233.922.424.017.137.2
(+4.6)(+6.8)(+4.1)(+3.5)(+7.5)(+6.0)(+2.4)(+5.9)(+7.8)
", + "bbox": [ + 171, + 88, + 795, + 234 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Zero-shot segmentation performance comparison on 8 semantic segmentation datasets. mIoU metric is used in every experiment. We highlight the best and second-best results. MaskCLIP† indicates their baseline method without additional refinement techniques. The YFCC and RedCaps of GroupViT indicate their training datasets in addition to CC12M. Each dataset abbreviation stands for VOC: PASCAL VOC, Context: PASCAL Context, Object: COCO-Object, Stuff: COCO-Stuff, City: Cityscapes, ADE: ADE20K.", + "bbox": [ + 75, + 243, + 893, + 299 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "name expansion or rephrasing, leading to performance overestimation. For example, we observe that TCL can get significant performance gains by expanding the target class of \"person\" to its sub-concepts (e.g., man, woman, worker, rider, etc.), but the kinds of class name-based tricks are not allowed in our unified evaluation protocol because the expansion depends on the target class names. With this consideration, we evaluate models using unified class names from the default version of MMSegmentation [8] without class name-based tricks. Dense CRF [16] is not used identically due to its expensive computational cost. All other evaluation settings follow GroupViT [30], where the input image is resized to have a shorter side of 448. We employ mean intersection-over-union (mIoU) as a performance metric, which is a standard metric in semantic segmentation. While we aim to provide a fair comparison, defining fair conditions can be subjective. Thus, we provide further results and discussion on this topic in Appendix C, especially regarding dataset scale and refinement methods.", + "bbox": [ + 75, + 314, + 472, + 602 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Benchmark datasets and comparison methods. We provide an extensive evaluation on widely used 8 benchmarks, categorized into two groups: (i) with background class (PASCAL VOC [10], PASCAL Context [21], and COCO-Object [3]), and (ii) without background class (PASCAL VOC20 [10], PASCAL Context59 [21], COCO-Stuff [3], Cityscapes [9], and ADE20K [32]). Note that open-world segmentation methods rely on the textual description of class names, which may require additional considerations for the background class, such as probability thresholding instead of using the \"background\" description as is. The datasets with background class evaluate this aspect. We compare TCL with all existing open-sourced methods, including GroupViT [30], MaskCLIP [33], and ReCo [28] under the unified protocol. We also include their variants in comparison baselines for an extensive comparison. Additional details and comparisons are given in Appendix E.", + "bbox": [ + 75, + 607, + 470, + 864 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation details. For the grounder, we use the CLIP ViT-B/16 model where the size of input images is", + "bbox": [ + 75, + 869, + 470, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "$224 \\times 224$ and the patch size is $16 \\times 16$ . Following MaskCLIP [33], we modify the last attention layer of the CLIP image encoder to acquire the dense embedding representing local semantics. The grounding decoder consists of four gated convolution blocks with two upsampling interpolations, and we use pixel-adaptive mask refinement (PAMR) [1] for mask refinement. Further details on the model architecture are provided in Appendix B. We use CC 3M and 12M datasets [5, 27] for training. The loss weights of $\\lambda_{\\mathrm{TCL}} = 0.1$ , $\\lambda_{\\mathrm{area}} = 0.4$ , $\\lambda_{\\mathrm{tv}} = 1.0$ are used. We train the model with a batch size of 1024 and a learning rate of $7.5 \\times 10^{-5}$ for total 50,000 iterations with 15,000 warmup steps and cosine schedule. AdamW optimizer [20] is used with a weight decay of 0.05.", + "bbox": [ + 496, + 314, + 893, + 526 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Zero-shot Transfer to Semantic Segmentation", + "text_level": 1, + "bbox": [ + 498, + 536, + 885, + 551 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparison of existing methods. We extensively compare existing open-world semantic segmentation methods in Table 1 using the proposed unified protocol, including two checkpoints of GroupViT [30] and two variants of MaskCLIP [33]. Between the existing methods, GroupViT achieves the best average performance, particularly on object-oriented datasets such as VOC, VOC20, and COCO-Object. However, its performance tends to decrease when the target dataset is dominated by stuff classes. On the other hand, MaskCLIP performs the best on stuff-oriented datasets such as Context, Context59, and COCO-Stuff, benefiting from the large-scale pre-trained CLIP model. We conjecture it benefits from leveraging a large-scale pretrained CLIP model. The refinement techniques proposed in MaskCLIP [33] improve the average performance but significantly degrade it on Cityscapes $(21.6\\rightarrow 12.6)$ , suggesting the limitation of the heuristic refinement methods. The significant performance degradation of MaskCLIP and ReCo between VOC20 and VOC may imply the need for consideration for background class.", + "bbox": [ + 496, + 564, + 890, + 866 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "TCL remarkably outperforms existing methods. Although the performances of existing methods vary depend", + "bbox": [ + 498, + 869, + 893, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "11170", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/e851e1f3745a5d88987708d4bf0a861217716dea89a9c52f6f5c896d68a2ec89.jpg", + "image_caption": [ + "Figure 4. Visualization of the generated text-grounded masks. \"w/o $D_g$ \" rows show the generated text-grounded masks without the grounding decoder ( $D_g$ ), i.e., CLIP dense features $\\mathbf{V}^d$ are used instead of pixel-level dense embeddings $\\mathbf{V}^s$ . \"w/ $D_g$ \" rows show the results with the grounding decoder. Each image is compared using both positive (blue) and negative (red) prompts. The results show that the grounded accurately and finely captures the text-described region with less noise via the grounding decoder." + ], + "image_footnote": [], + "bbox": [ + 81, + 88, + 467, + 305 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ing on the characteristics of the evaluation datasets, TCL outperforms all the other methods by large margins across all datasets as shown in Table 1. These results demonstrate that our TCL framework successfully addresses the alignment-level train-test discrepancy that exists in the previous methods by learning the region-level alignment. In addition, region-level alignment learning of TCL allows our model to learn the capability to distinguish the background region in a data-driven manner, thus, our method can address the background class without any heuristic post-processing that the previous methods typically rely on.", + "bbox": [ + 75, + 444, + 468, + 612 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Qualitative Results", + "text_level": 1, + "bbox": [ + 76, + 625, + 261, + 640 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Visualization of the generated text-grounded masks. Fig. 4 illustrates the impact of the learned grounding decoder. Since we follow MaskCLIP [33] modification, the results in \"w/o $D_g$ \" rows can be regarded as the initial results of MaskCLIP before refinement. Despite the vast pretraining scale and remarkable zero-shot classification performance of CLIP [23], its grounding capability is limited because the learning objective targets image-level alignment (See \"w/o $D_g$ \" rows). In contrast, the grounding decoder ( $D_g$ ) learns the region-level alignment by TCL, resulting in more precise, finer, and less noisy generated masks (See \"w/ $D_g$ \" rows).", + "bbox": [ + 75, + 652, + 467, + 835 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Qualitative comparison. We qualitatively compare the proposed method in Fig. 5. On the PASCAL VOC dataset (Fig. 5a), we observe various types of errors in each comparison method. The grouping procedure of GroupViT [30]", + "bbox": [ + 75, + 839, + 468, + 902 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "makes the segmentation results less noisy, but it also causes an incorrect segmentation of a large group. ReCo [28] struggles with the segmentation of background regions due to the lack of consideration about the background class. MaskCLIP [33] does not take this into account as well, but its refinement methods make the results less noisy. In addition, we present examples in the wild to show open-world segmentation capability in Fig. 5b. We collect test samples containing visual concepts not included in conventional segmentation datasets (e.g., moon, sunset) or free-form texts (e.g., \"two women and one man with a smiling snowman\"). GroupViT tends to focus on the main object of the image and regard the other objects as background, which is consistent with its good performance in object-oriented datasets. Interestingly, in this qualitative comparison in the wild, we observe ReCo consistently outperforms MaskCLIP contrary to the quantitative results. We conjecture that this is because the refinement approach of ReCo is data-driven, while the refinement approach of MaskCLIP depends on heuristic post-processing, which may not guarantee general improvement. Compared to the baselines, TCL consistently generates more precise segmentation masks. These results demonstrate that our proposed method, which learns region-level alignment, improves the segmentation quality both in the evaluation dataset and in web images in the wild. Additional qualitative results are provided in Appendix H.", + "bbox": [ + 496, + 90, + 890, + 484 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Additional analysis on failure cases and model behavior are provided in Appendices F and G, respectively.", + "bbox": [ + 498, + 489, + 890, + 520 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation Studies", + "text_level": 1, + "bbox": [ + 500, + 532, + 663, + 547 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We investigate the impact of individual components of the proposed framework by ablation studies on the training split of the PASCAL VOC20 dataset. We use a short learning schedule with a batch size of 512 for total 40,000 iterations including 10,000 warmup steps.", + "bbox": [ + 496, + 556, + 890, + 633 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Baseline to TCL. Table 2a presents cumulative ablation studies on the grounding decoder and the TCL losses. Our initial model before training based on MaskCLIP [33] is referred to as the baseline (A), which modifies the last attention layer of the CLIP image encoder. When we add only the grounding decoder to the baseline without TCL loss (B), there is no improvement in performance. This suggests that training the decoder with the same CL loss as the pre-training (CLIP) does not enhance the localization capabilities. As shown in (C), the proposed framework becomes complete with TCL loss.", + "bbox": [ + 496, + 638, + 890, + 803 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Impact of individual TCL losses. The influence of each component of the proposed TCL loss and its effect on the segmentation performance compared to the conventional CL loss are shown in Table 2b. Smooth regularization is used for all experiments in this table. The CL loss (D) is computed by applying attention pooling [23] to the dense", + "bbox": [ + 496, + 809, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "11171", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/dccaf6c356c76e50b0cb033f0deef5aa49b9f1c9ee53b8db3245ae22e0226e38.jpg", + "image_caption": [ + "(a) Examples in PASCAL VOC." + ], + "image_footnote": [], + "bbox": [ + 84, + 88, + 570, + 362 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/874a81f53d3d325fde6b6457bbf701a5a44a29ae9953c312fe6c9c5e0adc7e59.jpg", + "image_caption": [ + "(b) Examples in the wild.", + "Figure 5. (a) The comparison shows the error types of each method in the VOC dataset. GroupViT tends to make an error on a large group rather than noisy results. ReCo suffers from segmentation of the background region. MaskCLIP tends to fail at capturing the target area precisely. (b) shows results on the wild web images and free-form texts. Texts used as target classes are shown at the bottom of the images." + ], + "image_footnote": [], + "bbox": [ + 571, + 89, + 880, + 362 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "image embedding $\\mathbf{V}^s$ . When comparing (D) and (C), the proposed TCL loss remarkably improves the segmentation performance $(61.1 \\rightarrow 77.4)$ . Image-level or feature-level TCL loss (E, F) solely improves the performance significantly, and using both losses together provides further performance gain. Using CL in addition to TCL (G) does not improve performance, and it is essential to use area TCL loss in TCL framework to prevent model collapse (H), as described in Sec. 3.3. The difference between (B) and (D) is the use of smooth regularization.", + "bbox": [ + 75, + 446, + 472, + 598 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Hyperparameters. Tables 2c to 2e show the performance changes according to the variation of the loss weight hyperparameters (HPs). The first rows show the importance of each loss ( $\\lambda = 0.0$ cases). The absence of area TCL loss causes a significant performance drop (Table 2d), as mentioned above. Smooth regularization also significantly contributes to the final performance (Table 2e), supporting our assumption that the text-described region is smooth rather than noisy. Note that the sensitivity on HPs is about loss balancing, not about the target dataset. As an open-world segmentation method, TCL does not require any tuning with the target dataset, including model fine-tuning and inference HPs tuning. Once a TCL model is trained, we evaluate the model for every benchmark without any fine-tuning.", + "bbox": [ + 75, + 603, + 470, + 815 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 76, + 829, + 194, + 844 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We propose a novel framework for open-world semantic segmentation with only image-text pairs, addressing the alignment-level discrepancy between training (image-text)", + "bbox": [ + 76, + 854, + 470, + 901 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/fc19186d6099531dc744cc06ceb49121f12d1d36c5d617634e08c6693971a932.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodVOC20
A Baseline53.2
B + Decoder52.3
C + TCL77.4
", + "bbox": [ + 522, + 444, + 661, + 506 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/bf5d6dc000335d7c08e40b72ad77b42327a6cea792d82ef25bd6c59b97b059b1.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
TCLvTCLfLareaCLVOC20
D✓61.1
E✓✓74.6
F✓✓76.0
C✓✓✓77.4
G✓✓✓✓75.6
H✓✓67.1
", + "bbox": [ + 676, + 445, + 888, + 549 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/957527c411a4b6b314e95b7f70475d03757fad8a49cb1cd9b7138359b5caa2e4.jpg", + "table_caption": [ + "(b) TCL losses.", + "(a) Baseline to T" + ], + "table_footnote": [], + "table_body": "
λTCLVOC20
0.0-
0.0176.8
0.177.4
1.068.2
", + "bbox": [ + 524, + 573, + 624, + 648 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/98ab78fe141f6da5ca98088c65b441e42cc0d55fba539b73f351811a711af12c.jpg", + "table_caption": [ + "(c) $\\mathcal{L}_{\\mathrm{TCL}}$" + ], + "table_footnote": [], + "table_body": "
λareaVOC20
0.067.1
0.0469.5
0.477.4
4.076.7
", + "bbox": [ + 653, + 573, + 743, + 648 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/bbbfab5f92942a2393b568adc287a065e55bbba4e6efb8ca59efc069218d4e78.jpg", + "table_caption": [ + "(d) $\\mathcal{L}_{\\mathrm{area}}$" + ], + "table_footnote": [], + "table_body": "
λtvVOC20
0.073.8
0.175.2
1.077.4
10.070.7
", + "bbox": [ + 774, + 573, + 870, + 648 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "(e) $\\mathcal{L}_{\\mathrm{tv}}$", + "bbox": [ + 799, + 651, + 836, + 664 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 2. Ablation studies on TCL losses and hyperparameters. Refinement techniques are not applied to reveal the effect of each loss function clearly. Default settings are marked in gray.", + "bbox": [ + 500, + 674, + 890, + 717 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "and testing (region-text) in existing methods. In the proposed framework, we incorporate the grounding process within contrastive learning, thus allowing explicitly learning alignment between text and text-grounded regions (i.e., segmentation mask). We also present a unified evaluation protocol for a fair comparison of existing methods, where TCL achieves state-of-the-art zero-shot segmentation performance on all 8 benchmarks, remarkably surpassing previous methods. We hope that this study encourages a new research direction of explicitly learning region-text alignment for open-world semantic segmentation.", + "bbox": [ + 496, + 733, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "11172", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Nikita Araslanov and Stefan Roth. Single-stage semantic segmentation from image labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. 6, 12", + "[2] Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, and Saehoon Kim. Coyo-700m: Image-text pair dataset, 2022. 2, 13", + "[3] Holger Caesar, Jasper Uijlings, and Vittorio Ferrari. Cocosstuff: Thing and stuff classes in context. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018. 6, 14", + "[4] Junbum Cha, Kyungjae Lee, Sungrae Park, and Sanghyuk Chun. Domain generalization by mutual-information regularization with pre-trained models. European Conference on Computer Vision (ECCV), 2022. 11", + "[5] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. 2, 6", + "[6] Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. Advances in Neural Information Processing Systems, 34:17864-17875, 2021. 15", + "[7] François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017. 13", + "[8] MMSegmentation Contributors. MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https://github.com/open-mmlab/mmsegmentation, 2020.6", + "[9] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 6", + "[10] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010. 6", + "[11] Golnaz Ghiasi, Xiuye Gu, Yin Cui, and Tsung-Yi Lin. Scaling open-vocabulary image segmentation with image-level labels. In European Conference on Computer Vision, pages 540-557. Springer, 2022. 1, 2, 3", + "[12] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. *ICLR*, 2022. 2", + "[13] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. EMNLP, 2021. 2", + "[14] Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. In International Conference on Learning Representations, 2017. 4" + ], + "bbox": [ + 78, + 114, + 472, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[15] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790, 2021. 3", + "[16] Philipp Krahenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. Advances in neural information processing systems, 24, 2011. 6", + "[17] Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and Rene Ranftl. Language-driven semantic segmentation. In International Conference on Learning Representations, 2021. 1, 2", + "[18] Feng Liang, Bichen Wu, Xiaoliang Dai, Kunpeng Li, Yinan Zhao, Hang Zhang, Peizhao Zhang, Peter Vajda, and Diana Marculescu. Open-vocabulary semantic segmentation with mask-adapted clip. arXiv preprint arXiv:2210.04150, 2022. 1, 2", + "[19] Quande Liu, Youpeng Wen, Jianhua Han, Chunjing Xu, Hang Xu, and Xiaodan Liang. Open-world semantic segmentation via contrasting and clustering vision-language embedding. European Conference on Computer Vision (ECCV), 2022. 1, 2, 3, 5, 14", + "[20] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018. 6", + "[21] Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic segmentation in the wild. CVPR, 2014. 6", + "[22] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 3, 4", + "[23] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 3, 4, 5, 7, 11, 12, 13", + "[24] Leonid I Rudin, Stanley Osher, and Emad Fatemi. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4):259-268, 1992. 5", + "[25] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252, 2015. 13", + "[26] Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. arXiv preprint arXiv:2111.02114, 2021. 2", + "[27] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of ACL, 2018. 2, 5, 6", + "[28] Gyungin Shin, Weidi Xie, and Samuel Albanie. Reco: Retrieve and co-segment for zero-shot transfer. Advances in" + ], + "bbox": [ + 501, + 92, + 890, + 901 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "11173", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Neural Information Processing Systems, 2022. 1, 2, 3, 5, 6, 7, 13, 15, 16", + "[29] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34, 2021. 15", + "[30] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1, 2, 3, 5, 6, 7, 14, 15, 16", + "[31] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, and Jianfeng Gao. Regionclip: Region-based language-image pretraining. CVPR, 2022. 2, 3", + "[32] Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127(3):302-321, 2019. 6, 14", + "[33] Chong Zhou, Chen Change Loy, and Bo Dai. Extract free dense labels from clip. European Conference on Computer Vision (ECCV), 2022. 1, 2, 3, 5, 6, 7, 11, 14, 15, 16" + ], + "bbox": [ + 78, + 90, + 468, + 443 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "11174", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/c9bc34a3-cd10-43e4-9d6d-81c2b8ccce5c_model.json b/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/c9bc34a3-cd10-43e4-9d6d-81c2b8ccce5c_model.json new file mode 100644 index 0000000000000000000000000000000000000000..3f0791fae9fb94a9c8fa7077fbf801d442db1e6b --- /dev/null +++ b/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/c9bc34a3-cd10-43e4-9d6d-81c2b8ccce5c_model.json @@ -0,0 +1,1903 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.0, + 0.812, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.158, + 0.131, + 0.817, + 0.177 + ], + "angle": 0, + "content": "Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs" + }, + { + "type": "text", + "bbox": [ + 0.242, + 0.204, + 0.346, + 0.221 + ], + "angle": 0, + "content": "Junbum Cha" + }, + { + "type": "text", + "bbox": [ + 0.406, + 0.204, + 0.532, + 0.222 + ], + "angle": 0, + "content": "Jonghwan Mun" + }, + { + "type": "text", + "bbox": [ + 0.593, + 0.204, + 0.723, + 0.222 + ], + "angle": 0, + "content": "Byungseok Roh" + }, + { + "type": "text", + "bbox": [ + 0.433, + 0.233, + 0.537, + 0.248 + ], + "angle": 0, + "content": "Kakao Brain" + }, + { + "type": "text", + "bbox": [ + 0.266, + 0.254, + 0.7, + 0.269 + ], + "angle": 0, + "content": "{junbum.cha, jason.mun, peter. roh}@kakaobrain.com" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.303, + 0.314, + 0.319 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.336, + 0.474, + 0.699 + ], + "angle": 0, + "content": "We tackle open-world semantic segmentation, which aims at learning to segment arbitrary visual concepts in images, by using only image-text pairs without dense annotations. Existing open-world segmentation methods have shown impressive advances by employing contrastive learning (CL) to learn diverse visual concepts and transferring the learned image-level understanding to the segmentation task. However, these CL-based methods suffer from a train-test discrepancy, since it only considers image-text alignment during training, whereas segmentation requires region-text alignment during testing. In this paper, we proposed a novel Text-grounded Contrastive Learning (TCL) framework that enables a model to directly learn region-text alignment. Our method generates a segmentation mask for a given text, extracts text-grounded image embedding from the masked region, and aligns it with text embedding via TCL. By learning region-text alignment directly, our framework encourages a model to directly improve the quality of generated segmentation masks. In addition, for a rigorous and fair comparison, we present a unified evaluation protocol with widely used 8 semantic segmentation datasets. TCL achieves state-of-the-art zero-shot segmentation performances with large margins in all datasets. Code is available at https://github.com/kakaobrain/tcl." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.73, + 0.21, + 0.745 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.756, + 0.47, + 0.864 + ], + "angle": 0, + "content": "Open-world semantic segmentation aims to identify the arbitrary semantic concepts in the open world1. Conventional semantic segmentation aims to learn segmentation capability for the small number of pre-defined target categories, whereas open-world semantic segmentation addresses unrestricted arbitrary categories or free-form texts. Such segmentation capability over unlimited targets drasti-" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.304, + 0.891, + 0.58 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.59, + 0.892, + 0.634 + ], + "angle": 0, + "content": "Figure 1. Open-world segmentation performance comparison. The proposed method remarkably outperforms existing methods in all 8 segmentation benchmark datasets." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.657, + 0.891, + 0.687 + ], + "angle": 0, + "content": "cally extends the application scope of the open-world segmentation models." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.893, + 0.903 + ], + "angle": 0, + "content": "The first challenge for open-world segmentation is how to learn arbitrary concepts, beyond pre-defined categories. Inspired by the success of CLIP [23], previous approaches [11, 17-19, 28, 30, 33] tackle this challenge by exploiting massive web-crawled image-text paired data; since the texts in web-crawled data contain a global semantic description for the paired images, the large-scale image-text pairs can provide rich knowledge for arbitrary semantic categories. However, there still remains another challenge in how to achieve precise localization of arbitrary concepts without dense annotations. There are several approaches that simply address this issue using dense annotation (segmentation masks) in addition to image-text pairs [11, 17, 18]. The dense annotation helps to improve segmentation perfor" + }, + { + "type": "page_footnote", + "bbox": [ + 0.076, + 0.875, + 0.47, + 0.902 + ], + "angle": 0, + "content": "This setting is often called both open-world and open-vocabulary. In this paper, we mainly refer to this setting as open-world for clarity." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11165" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.092, + 0.468, + 0.295 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.308, + 0.47, + 0.419 + ], + "angle": 0, + "content": "Figure 2. A conceptual comparison between the previous approach and ours. Open-world segmentation is typically achieved through region-text alignment, which involves matching region features and text embeddings. However, previous methods learn image-text alignment during training, thus suffering from the alignment-level discrepancy between training and testing. In contrast, our method facilitates end-to-end learning of region-text alignment with only image-text pairs." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.432, + 0.469, + 0.478 + ], + "angle": 0, + "content": "mance in a fixed benchmark dataset, but the requirements of expensive dense annotation still limit the applicable domains and scalability of the method." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.479, + 0.469, + 0.78 + ], + "angle": 0, + "content": "In this paper, therefore, we focus on open-world semantic segmentation from only image-text pairs without any dense annotation. For this setting, the existing methods [19, 28, 30, 33] learn an image-text alignment capability during training and heavily rely on the transferability of the image-text alignment to perform region-text alignment at inference. More specifically, MaskCLIP [33] leverages CLIP models pre-trained to learn image-text alignment. To perform region-text alignment using CLIP, MaskCLIP applies a simple heuristic modification to the CLIP image encoder. GroupViT [30] and ViL-Seg [19] propose to cluster region-level visual features into distinct groups and generate segmentation masks by matching the groups and texts. Note that they match the text embeddings and clustered region features in test time, but in training time, the text embeddings are aligned with global image embeddings. While the existing methods have shown impressive results even through the training with image-text alignment, they still suffer from the alignment-level discrepancy between training and testing phases as depicted in Fig. 2." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.781, + 0.469, + 0.902 + ], + "angle": 0, + "content": "To address this train-test discrepancy, we propose the Text-grounded Contrastive Learning (TCL) framework, which allows a model to learn region-text alignment directly from the image-text pairs without any dense annotations. Our key idea is to incorporate a text grounding procedure within contrastive learning as illustrated in Fig. 2, where TCL generates a segmentation mask indicating text-grounded regions, computes grounded region embeddings" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.243 + ], + "angle": 0, + "content": "using the mask, and applies contrastive learning between text and grounded region. By re-formulating the contrastive loss to be directly affected by the segmentation quality, TCL enables end-to-end training of the grounder and directly improves the quality of region-text level alignment. We also present a unified evaluation protocol using widely used 8 semantic segmentation datasets and compare existing methods in the same setting. As a result, TCL achieves state-of-the-art zero-shot segmentation performance with large margins in all datasets, as shown in Fig. 1." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.244, + 0.859, + 0.258 + ], + "angle": 0, + "content": "Our main contributions are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.26, + 0.892, + 0.35 + ], + "angle": 0, + "content": "- We introduce a novel framework for open-world segmentation, named Text-grounded Contrastive Learning (TCL), which enables learning region-text alignment directly without train-test discrepancy, thus learning to generate more precise segmentation masks through only image-text pairs." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.351, + 0.892, + 0.395 + ], + "angle": 0, + "content": "- We present a unified evaluation protocol and re-evaluate recent open-world segmentation models for a fair and direct comparison." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.396, + 0.892, + 0.44 + ], + "angle": 0, + "content": "- We achieve the new state-of-the-art zero-shot segmentation performance on 8 segmentation datasets with large margins compared to existing methods." + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.26, + 0.892, + 0.44 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.457, + 0.65, + 0.473 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.484, + 0.813, + 0.5 + ], + "angle": 0, + "content": "2.1. Open-world Semantic Segmentation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.508, + 0.892, + 0.689 + ], + "angle": 0, + "content": "Open-world scenario aims to recognize arbitrary concepts in the open world. It is also called open-vocabulary because the target vocabulary is open rather than closed. Contrastive Language-Image Pre-training (CLIP) [23] ushered in the era of open-world image recognition using largescale image-text pairs [2, 5, 26, 27]. CLIP learns the alignment between an image and a text in training time, then transfer it to the zero-shot classification by aligning image and texts indicating target classes at inference time. The advent of CLIP enables open-world settings in various fields such as object detection [12, 31], image captioning [13], or semantic segmentation [30, 33]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.69, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Open-world semantic segmentation with image-text pairs is addressed in two different settings. The first is a semi-supervised setting, which uses dense annotation (i.e., segmentation masks) in addition to image-text pairs [11, 17, 18]. Semi-supervised approaches learn segmentation capability using dense annotation and expand the target vocabulary using image-text supervision. LSeg [17] expands target class vocabulary using image-label datasets and CLIP text encoder [23]. OpenSeg [11] and OVSeg [18] first train a mask generator using dense annotation and expand target vocabulary using image-text datasets. The use of dense annotation makes the model learn region-level alignment instead of image-level alignment, leading to high-quality segmentation masks. However, it still relies on" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11166" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.121 + ], + "angle": 0, + "content": "costly dense annotation, and applicable domains are limited to the domains where dense annotation is available." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.123, + 0.473, + 0.668 + ], + "angle": 0, + "content": "The target of this paper is an unsupervised setting, which aims to learn segmentation from only image-text pairs without any dense annotation [19, 28, 30, 33]. Since the massive image-text pairs are easily obtained by web crawling without human annotators, applicable domains of unsupervised methods become almost unlimited. In order to achieve segmentation capability using only image-text pairs, we need to learn region-text alignment instead of image-text alignment and train a text-grounded mask generator. However, the absence of dense annotation makes this approach challenging. Existing open-world semantic segmentation studies have taken a strategy to bypass this issue. Instead of learning region-level alignment directly, they transfer image-level alignment to region-level by heuristic modification [28, 33] or clustering [19, 30]. MaskCLIP [33] proposes to obtain a dense image embedding from CLIP image encoder through heuristic modification of the last attention layer. Even though it has several limitations, such as low output resolution or noisy segmentation results, they show it is a simple yet effective way to obtain an initial segmentation map for refinement. ReCo [28] proposes an advanced refinement method based on MaskCLIP, by retrieval and co-segmentation. Clustering-based methods [19, 30] learn representations using CL with image-text pairs. They compute region-level image embedding by clustering sub-region embeddings. These approaches also have shown impressive results but have several limitations: (i) the learning objective is still image-level alignment due to lack of the region annotation, (ii) the number of clusters is pre-defined independent of the given image, and (iii) clustering sub-region image embeddings is independent of the query text. In summary, existing methods indirectly address region-level alignment problems by learning image-level alignment. To tackle this problem, we propose a novel region-level alignment objective, named Text-grounded Contrastive Learning (TCL)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.68, + 0.379, + 0.697 + ], + "angle": 0, + "content": "2.2. Region-level Contrastive Learning" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.704, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Learning region-level alignment instead of image-level alignment is a fundamental target objective in dense tasks, such as segmentation or object detection. There are approaches to learn region-level alignment using dense annotation in the semi-supervised setting. They first train mask or region proposal networks using dense annotation and learn alignment between the proposals and texts [11, 15, 31]. For example, OpenSeg [11] trains a class-agnostic mask generator using dense annotation. In the object detection field, RegionCLIP [31] employs an off-the-shelf region proposal network and learns region-level alignment. In contrast to the existing region-level methods, the proposed method learns region-level alignment without any dense annotation." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.09, + 0.6, + 0.107 + ], + "angle": 0, + "content": "3. Methods" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.115, + 0.611, + 0.13 + ], + "angle": 0, + "content": "3.1. Overview" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.139, + 0.892, + 0.29 + ], + "angle": 0, + "content": "Open-world semantic segmentation is a task that aims to learn a model capable of zero-shot segmentation for arbitrary visual concepts, not restricted to pre-defined ones. Our main goal is to develop an open-world segmentation algorithm using only image-text pairs. However, achieving this objective is challenging because there is no explicit supervision (i.e., pixel-level dense annotations) for text-described region segmentation. Existing methods learn models parametrized by \\(\\theta\\) to maximize the mutual information between paired images and texts [22, 23] as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.623, + 0.303, + 0.892, + 0.329 + ], + "angle": 0, + "content": "\\[\n\\underset {\\theta} {\\arg \\max } I _ {\\theta} \\left(\\mathbf {x} ^ {V}; \\mathbf {x} ^ {T}\\right), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.336, + 0.893, + 0.593 + ], + "angle": 0, + "content": "where \\((\\mathbf{x}^V, \\mathbf{x}^T)\\) is a random image and text pair. This objective encourages the model to learn the alignment between images and texts, however, at test time, the learned model generates segmentation masks for arbitrary concepts by computing region-text alignments. Such alignment-level discrepancy between train and test time can lead the model to a sub-optimal solution as shown in Fig. 2. With this in consideration, to bridge the gap between the objective of conventional contrastive learning (CL) and the requirement of the zero-shot segmentation, we propose Text-grounded Contrastive Learning (TCL) which incorporates a text grounding process within CL to enable learning region-text alignment directly. As a text grounding module, we introduce a groundeder to generate segmentation masks for the given texts. In a nutshell, TCL learns a model to maximize mutual information between text-grounded regions and texts as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.61, + 0.604, + 0.892, + 0.631 + ], + "angle": 0, + "content": "\\[\n\\underset {\\theta} {\\arg \\max } I _ {\\theta} \\left(\\mathbf {m} \\cdot \\mathbf {x} ^ {V}; \\mathbf {x} ^ {T}\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.641, + 0.892, + 0.717 + ], + "angle": 0, + "content": "where \\(\\mathbf{m}\\) is a text-grounded mask of random variable indicating the text-described region. Compared to contrastive learning that implicitly learns a grounding capability, TCL has a clear advantage of explicitly learning the grounding capability through the end-to-end trainable grounder." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.717, + 0.892, + 0.808 + ], + "angle": 0, + "content": "In the rest of this section, we first explain the text-grounded mask generation procedure by the grounder. Then, we describe how we define losses using the generated mask to train our open-world grounder with text-grounded contrastive learning. Lastly, we explain how our model performs zero-shot inference for arbitrary concepts." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.817, + 0.614, + 0.832 + ], + "angle": 0, + "content": "3.2. Grounder" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.894, + 0.902 + ], + "angle": 0, + "content": "Fig. 3 illustrates our overall training pipeline. For an input batch of paired texts \\(\\mathbf{X}^T\\) and images \\(\\mathbf{X}^V\\), TCL first performs a grounding process to identify text-grounded regions for a text via a grounder. The grounder consists of" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11167" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.102, + 0.091, + 0.87, + 0.312 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.075, + 0.322, + 0.895, + 0.396 + ], + "angle": 0, + "content": "Figure 3. Overall training pipeline of TCL. The proposed TCL framework first obtains text-grounded masks using a grounder and then learns the grounder using text-grounded contrastive learning. By incorporating the text grounding process with contrastive learning, our framework can directly learn region-text alignment that is required for precise segmentation. \\(\\mathrm{CLIP_T}\\) and \\(\\mathrm{CLIP_I}\\) indicate CLIP text and image encoders, respectively. CLIP encoders are frozen and we train the grounding decoder only. After training, the TCL block is discarded, and only the Grounder block is used to generate the text-grounded segmentation mask for inference." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.408, + 0.473, + 0.604 + ], + "angle": 0, + "content": "three components: (i) image encoder \\( E_{v} \\) is in charge of providing a single (L2-normalized) global feature as well as dense patch-level features, (ii) text encoder \\( E_{t} \\) provides a (L2-normalized) text embedding feature, and (iii) grounding decoder \\( D_{g} \\) converts dense features from image encoder into finer pixel-level embeddings for alignment with text. In practice, we adopt a pre-trained CLIP model [23] to initialize two encoders and freeze them to preserve and exploit the rich knowledge of CLIP learned during largescale pre-training. The text-grounded masks are computed by the position-wise dot product between text embedding and dense pixel-level embedding. The overall process of grounder is summarized as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.121, + 0.615, + 0.47, + 0.635 + ], + "angle": 0, + "content": "\\[\n\\mathbf {T} = E _ {t} \\left(\\mathbf {X} ^ {T}\\right) \\quad \\text {a n d} \\quad \\mathbf {V} ^ {g}, \\mathbf {V} ^ {d} = E _ {v} \\left(\\mathbf {X} ^ {V}\\right), \\tag {3}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.124, + 0.637, + 0.47, + 0.655 + ], + "angle": 0, + "content": "\\[\n\\mathbf {V} ^ {s} = D _ {g} \\left(\\mathbf {V} ^ {d}\\right), \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.124, + 0.657, + 0.469, + 0.677 + ], + "angle": 0, + "content": "\\[\n\\mathbf {M} _ {i, j} = \\sigma \\left(w \\cdot \\mathbf {t} _ {j} ^ {\\top} \\mathbf {V} _ {i} ^ {s} + b\\right), \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.688, + 0.47, + 0.825 + ], + "angle": 0, + "content": "where \\(\\mathbf{T} \\in \\mathbb{R}^{B \\times C}\\), \\(\\mathbf{V}^g \\in \\mathbb{R}^{B \\times C}\\), and \\(\\mathbf{V}^d \\in \\mathbb{R}^{B \\times L \\times C}\\) are normalized text embeddings, normalized global image embeddings, and dense image features from CLIP encoders, \\(\\mathbf{V}^s \\in \\mathbb{R}^{B \\times C \\times H \\times W}\\) is normalized pixel-level dense embeddings by the grounding decoder, and \\(\\mathbf{M} \\in \\mathbb{R}^{B \\times B \\times H \\times W}\\) is text-grounded masks between images and texts in the batch. \\(B\\), \\(C\\), and \\(L\\) indicate a batch size, the embedding dimension size, and the number of patches, respectively. \\(\\sigma\\) is a sigmoid function, and \\(w\\), \\(b\\) are learnable scalar projection." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.471, + 0.902 + ], + "angle": 0, + "content": "The generated text-grounded masks are used to extract text-grounded image embedding. By replacing the global image embedding with text-grounded image embedding in the contrastive learning framework, TCL enables the model to learn region-text alignment in an end-to-end manner. In" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.409, + 0.892, + 0.44 + ], + "angle": 0, + "content": "the following section, we describe how the generated mask \\(\\mathbf{M}\\) is used for text-grounded contrastive learning." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.456, + 0.82, + 0.472 + ], + "angle": 0, + "content": "3.3. Text-grounded Contrastive Learning" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.482, + 0.892, + 0.604 + ], + "angle": 0, + "content": "Recall that the main idea of TCL is to use text-grounded images instead of whole images, unlike conventional CL. For this purpose, we define TCL losses in three different levels—image-level, feature-level, and area-level—using the generated masks M for all pairs of images and texts in a batch; the detailed pseudo code to compute TCL losses is given in Appendix A. We also employ smooth regularization to further improve the quality of generated masks." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.609, + 0.893, + 0.868 + ], + "angle": 0, + "content": "Image-level TCL loss. One intuitive way to compute the text-grounded image embedding is to encode only the regions of the image that contains the semantics of the paired text, using the image encoder. To make the whole process end-to-end trainable, we compute a differentiable masked image by multiplying the given image \\(\\mathbf{X}_i^V\\) and a binarized mask \\(\\mathbf{M}_{i,i}^b\\) obtained from the generated mask \\(\\mathbf{M}_{i,i}\\) using Gumbel-Max [14]. The masked image is then fed into the image encoder, \\(\\tilde{\\mathbf{v}}_i^g,\\tilde{\\mathbf{v}}_i^d = E_v\\left(\\mathbf{M}_{i,i}^b\\cdot \\mathbf{X}_i^V\\right)\\), to obtain text-grounded image embedding \\(\\tilde{\\mathbf{v}}_i^g\\). We compute the cosine similarity matrix between text-grounded image embeddings and text embeddings in a batch by \\(S_{i,j}^{m} = \\tilde{\\mathbf{v}}_i^{g^\\top}\\mathbf{t}_j\\). Finally, we use the symmetric version of InfoNCE [22, 23] to define the image-level TCL loss \\(\\mathcal{L}_{\\mathrm{TCL}_v}\\) to make the representations of positive image-text pairs similar to each other while the representations of negative pairs dissimilar based on the similarity matrix:" + }, + { + "type": "equation", + "bbox": [ + 0.61, + 0.886, + 0.892, + 0.902 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {T C L} _ {v}} = \\operatorname {I n f o N C E} \\left(\\mathbf {S} ^ {m}\\right), \\tag {6}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11168" + } + ], + [ + { + "type": "equation", + "bbox": [ + 0.101, + 0.11, + 0.47, + 0.197 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\operatorname {I n f o N C E} \\left(\\mathbf {S}\\right) = - \\frac {1}{2 B} \\sum_ {i} ^ {B} \\log \\frac {\\exp \\left(S _ {i , i} / \\tau\\right)}{\\sum_ {j} ^ {B} \\exp \\left(S _ {i , j} / \\tau\\right)} \\\\ - \\frac {1}{2 B} \\sum_ {i} ^ {B} \\log \\frac {\\exp \\left(S _ {i , i} / \\tau\\right)}{\\sum_ {j} ^ {B} \\exp \\left(S _ {j , i} / \\tau\\right)}, \\tag {7} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.202, + 0.313, + 0.217 + ], + "angle": 0, + "content": "where \\(\\tau\\) is a learnable temperature." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.221, + 0.473, + 0.466 + ], + "angle": 0, + "content": "Feature-level TCL loss. The image-level TCL loss drives a model to generate segmentation masks for the paired texts (i.e., texts of positive pairs). However, we observe that this loss alone is insufficient to prevent the model from generating masks for regions not described in the text, particularly for salient regions. This raises the need to suppress negative masks obtained from unrelated texts (i.e., texts of negative pairs), but computing the image-level TCL loss for negative masks is infeasible due to the high computational cost of encoding text-grounded images. To overcome this challenge, we introduce the feature-level TCL loss, which enables the effective computation of features of the negative masks. Specifically, for pixel-level dense embeddings \\(\\mathbf{V}_i^s\\in \\mathbb{R}^{C\\times H\\times W}\\) from grounding decoder and a text embedding vector \\(\\mathbf{t}_j\\), we compute feature-level text-grounded image embedding \\(\\mathbf{v}_{i,j}^{f}\\in \\mathbb{R}^{C}\\) by:" + }, + { + "type": "equation", + "bbox": [ + 0.164, + 0.473, + 0.47, + 0.51 + ], + "angle": 0, + "content": "\\[\n\\mathbf {v} _ {i, j} ^ {f} = \\frac {\\sum_ {h , w} M _ {i , j , h , w} \\cdot \\mathbf {v} _ {i , : , h , w} ^ {s}}{\\sum_ {h , w} M _ {i , j , h , w}}. \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.515, + 0.47, + 0.614 + ], + "angle": 0, + "content": "Note that this feature-level embedding is computed using negative masks \\(\\mathbf{M}_{i,j (i \\neq j)}\\), different from the image-level TCL loss. We then compute the cosine similarity \\(S_{i,j}^{f} = \\mathbf{v}_{i,j}^{f}^{\\top} \\mathbf{t}_{j}\\) between all pairs of text embeddings and feature-level text-grounded image embeddings in the batch. The feature-level TCL loss is defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.188, + 0.621, + 0.469, + 0.641 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {T C L} _ {f}} = \\operatorname {I n f o N C E} \\left(\\mathbf {S} ^ {f}\\right). \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.65, + 0.473, + 0.862 + ], + "angle": 0, + "content": "Area TCL loss. The image-level and feature-level TCL losses focus on generating a mask to capture the text-described region in the image. However, the model can collapse into a trivial solution with only these losses—generating a mask for the entire image instead of the desired region. To prevent this collapse, we introduce an additional objective to our TCL framework, named area TCL loss, which incorporates priors on the mask area to ensure capturing only the text-described region. To be specific, for the positive masks (masks from positive pairs) \\(\\mathbf{M}^{+}\\) and the negative masks (masks from negative pair) \\(\\mathbf{M}^{-}\\), we denote the area of positive and negative masks by \\(\\overline{\\mathbf{M}^{+}}\\) and \\(\\overline{\\mathbf{M}^{-}}\\), respectively. The area TCL loss is defined by L1-distance between the area priors and the expected area of each mask:" + }, + { + "type": "equation", + "bbox": [ + 0.095, + 0.878, + 0.47, + 0.906 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {a r e a}} = \\left\\| p ^ {+} - \\mathbb {E} \\left[ \\overline {{\\mathbf {M} ^ {+}}} \\right] \\right\\| _ {1} + \\left\\| p ^ {-} - \\mathbb {E} \\left[ \\overline {{\\mathbf {M} ^ {-}}} \\right] \\right\\| _ {1}, \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.168 + ], + "angle": 0, + "content": "where \\(p^+\\) and \\(p^-\\) are positive and negative area priors. For the negative area prior \\(p^-\\), intuitively, we can expect the area of the negative masks to be 0.0. We set the positive area prior \\(p^+\\) to 0.4, which is the average text-described region area measured by MaskCLIP [33] in the CC3M dataset [27]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.172, + 0.894, + 0.278 + ], + "angle": 0, + "content": "Smooth regularization. In the image-text dataset, a text usually describes the salient object or concept in the paired image. We observe that the regions described by the text are generally smooth rather than noisy. We employ total variation (TV) regularization loss [24] to incorporate this smoothness observation in the objective. The TV loss is applied to both mask and pixel-level dense embedding:" + }, + { + "type": "equation", + "bbox": [ + 0.608, + 0.291, + 0.892, + 0.309 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {T V}} = \\left\\| \\mathbf {M} \\right\\| _ {\\mathrm {T V}} + \\left\\| \\mathbf {V} ^ {s} \\right\\| _ {\\mathrm {T V}}, \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.32, + 0.779, + 0.336 + ], + "angle": 0, + "content": "where \\(\\| \\cdot \\|_{\\mathrm{TV}}\\) is the anisotropic TV norm." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.34, + 0.818, + 0.356 + ], + "angle": 0, + "content": "Final loss. Our final loss function is defined by:" + }, + { + "type": "equation", + "bbox": [ + 0.561, + 0.369, + 0.892, + 0.405 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\underbrace {\\lambda_ {\\mathrm {T C L}} \\mathcal {L} _ {\\mathrm {T C L}} + \\lambda_ {\\text {a r e a}} \\mathcal {L} _ {\\text {a r e a}}} _ {\\text {T C L l o s s e s}} + \\underbrace {\\lambda_ {\\mathrm {t v}} \\mathcal {L} _ {\\mathrm {t v}}} _ {\\text {r e g u l a r i z a t i o n}}, \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.416, + 0.892, + 0.446 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{\\mathrm{TCL}} = \\mathcal{L}_{\\mathrm{TCL}_v} + \\mathcal{L}_{\\mathrm{TCL}_f}\\), and \\(\\lambda_{\\mathrm{TCL}}\\), \\(\\lambda_{\\mathrm{area}}\\), \\(\\lambda_{\\mathrm{tv}}\\) are hyperparameters to balance three losses." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.457, + 0.676, + 0.473 + ], + "angle": 0, + "content": "3.4. Inference Pipeline" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.481, + 0.892, + 0.587 + ], + "angle": 0, + "content": "The zero-shot inference pipeline is similar to CLIP [23], except for performing pixel-level classification instead of image-level classification. Specifically, for text embeddings \\(\\mathbf{T} \\in \\mathbb{R}^{N \\times C}\\) and a pixel-level dense embedding \\(\\mathbf{v}^s \\in \\mathbb{R}^{C \\times H \\times W}\\), text-grounded mask \\(\\mathbf{M} \\in \\mathbb{R}^{N \\times H \\times W}\\) is computed by Eq. (5), where \\(N\\) is the number of target classes. The final segmentation map \\(\\mathcal{M}\\) is computed by:" + }, + { + "type": "equation", + "bbox": [ + 0.605, + 0.6, + 0.891, + 0.623 + ], + "angle": 0, + "content": "\\[\n\\mathcal {M} _ {h, w} = \\underset {n} {\\arg \\max } M _ {n, h, w}. \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.636, + 0.892, + 0.667 + ], + "angle": 0, + "content": "Prompt templates such as \"a photo of a {label}.\" are used to generate text embeddings as in CLIP [23]." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.682, + 0.634, + 0.699 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.707, + 0.694, + 0.723 + ], + "angle": 0, + "content": "4.1. Experiment Settings" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Unified evaluation protocol. In open-world semantic segmentation, a standard evaluation protocol is not yet established. Previous studies conduct an evaluation using their own protocols such as different data processing strategies on different datasets [19, 28, 30, 33]; surprisingly, even for the same dataset, the target classes are sometimes different across studies. For a fair comparison, we present a unified evaluation protocol following the open-world scenario where prior access to the target data before evaluation is not allowed. Under this scenario, the proposed protocol prohibits dataset-specific hyperparameters or tricks, e.g., class" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11169" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.173, + 0.089, + 0.796, + 0.235 + ], + "angle": 0, + "content": "
Methodswith background classwithout background classAvg.
VOCContextObjectVOC20Context59StuffCityADE
GroupViT (YFCC)49.519.024.374.120.812.66.98.727.0
GroupViT (RedCaps)50.418.727.579.723.415.311.19.229.4
MaskCLIP†29.321.115.553.723.314.721.610.823.7
MaskCLIP38.823.620.674.926.416.412.69.827.9
ReCo25.119.915.757.722.314.821.111.223.5
TCL (Ours)55.030.431.683.233.922.424.017.137.2
(+4.6)(+6.8)(+4.1)(+3.5)(+7.5)(+6.0)(+2.4)(+5.9)(+7.8)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.244, + 0.895, + 0.3 + ], + "angle": 0, + "content": "Table 1. Zero-shot segmentation performance comparison on 8 semantic segmentation datasets. mIoU metric is used in every experiment. We highlight the best and second-best results. MaskCLIP† indicates their baseline method without additional refinement techniques. The YFCC and RedCaps of GroupViT indicate their training datasets in addition to CC12M. Each dataset abbreviation stands for VOC: PASCAL VOC, Context: PASCAL Context, Object: COCO-Object, Stuff: COCO-Stuff, City: Cityscapes, ADE: ADE20K." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.315, + 0.473, + 0.603 + ], + "angle": 0, + "content": "name expansion or rephrasing, leading to performance overestimation. For example, we observe that TCL can get significant performance gains by expanding the target class of \"person\" to its sub-concepts (e.g., man, woman, worker, rider, etc.), but the kinds of class name-based tricks are not allowed in our unified evaluation protocol because the expansion depends on the target class names. With this consideration, we evaluate models using unified class names from the default version of MMSegmentation [8] without class name-based tricks. Dense CRF [16] is not used identically due to its expensive computational cost. All other evaluation settings follow GroupViT [30], where the input image is resized to have a shorter side of 448. We employ mean intersection-over-union (mIoU) as a performance metric, which is a standard metric in semantic segmentation. While we aim to provide a fair comparison, defining fair conditions can be subjective. Thus, we provide further results and discussion on this topic in Appendix C, especially regarding dataset scale and refinement methods." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.608, + 0.472, + 0.866 + ], + "angle": 0, + "content": "Benchmark datasets and comparison methods. We provide an extensive evaluation on widely used 8 benchmarks, categorized into two groups: (i) with background class (PASCAL VOC [10], PASCAL Context [21], and COCO-Object [3]), and (ii) without background class (PASCAL VOC20 [10], PASCAL Context59 [21], COCO-Stuff [3], Cityscapes [9], and ADE20K [32]). Note that open-world segmentation methods rely on the textual description of class names, which may require additional considerations for the background class, such as probability thresholding instead of using the \"background\" description as is. The datasets with background class evaluate this aspect. We compare TCL with all existing open-sourced methods, including GroupViT [30], MaskCLIP [33], and ReCo [28] under the unified protocol. We also include their variants in comparison baselines for an extensive comparison. Additional details and comparisons are given in Appendix E." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.472, + 0.903 + ], + "angle": 0, + "content": "Implementation details. For the grounder, we use the CLIP ViT-B/16 model where the size of input images is" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.315, + 0.895, + 0.527 + ], + "angle": 0, + "content": "\\(224 \\times 224\\) and the patch size is \\(16 \\times 16\\). Following MaskCLIP [33], we modify the last attention layer of the CLIP image encoder to acquire the dense embedding representing local semantics. The grounding decoder consists of four gated convolution blocks with two upsampling interpolations, and we use pixel-adaptive mask refinement (PAMR) [1] for mask refinement. Further details on the model architecture are provided in Appendix B. We use CC 3M and 12M datasets [5, 27] for training. The loss weights of \\(\\lambda_{\\mathrm{TCL}} = 0.1\\), \\(\\lambda_{\\mathrm{area}} = 0.4\\), \\(\\lambda_{\\mathrm{tv}} = 1.0\\) are used. We train the model with a batch size of 1024 and a learning rate of \\(7.5 \\times 10^{-5}\\) for total 50,000 iterations with 15,000 warmup steps and cosine schedule. AdamW optimizer [20] is used with a weight decay of 0.05." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.537, + 0.886, + 0.553 + ], + "angle": 0, + "content": "4.2. Zero-shot Transfer to Semantic Segmentation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.565, + 0.892, + 0.867 + ], + "angle": 0, + "content": "Comparison of existing methods. We extensively compare existing open-world semantic segmentation methods in Table 1 using the proposed unified protocol, including two checkpoints of GroupViT [30] and two variants of MaskCLIP [33]. Between the existing methods, GroupViT achieves the best average performance, particularly on object-oriented datasets such as VOC, VOC20, and COCO-Object. However, its performance tends to decrease when the target dataset is dominated by stuff classes. On the other hand, MaskCLIP performs the best on stuff-oriented datasets such as Context, Context59, and COCO-Stuff, benefiting from the large-scale pre-trained CLIP model. We conjecture it benefits from leveraging a large-scale pretrained CLIP model. The refinement techniques proposed in MaskCLIP [33] improve the average performance but significantly degrade it on Cityscapes \\((21.6\\rightarrow 12.6)\\), suggesting the limitation of the heuristic refinement methods. The significant performance degradation of MaskCLIP and ReCo between VOC20 and VOC may imply the need for consideration for background class." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.895, + 0.903 + ], + "angle": 0, + "content": "TCL remarkably outperforms existing methods. Although the performances of existing methods vary depend" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "11170" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.089, + 0.468, + 0.306 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.316, + 0.472, + 0.429 + ], + "angle": 0, + "content": "Figure 4. Visualization of the generated text-grounded masks. \"w/o \\( D_g \\)\" rows show the generated text-grounded masks without the grounding decoder (\\( D_g \\)), i.e., CLIP dense features \\( \\mathbf{V}^d \\) are used instead of pixel-level dense embeddings \\( \\mathbf{V}^s \\). \"w/ \\( D_g \\)\" rows show the results with the grounding decoder. Each image is compared using both positive (blue) and negative (red) prompts. The results show that the grounded accurately and finely captures the text-described region with less noise via the grounding decoder." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.445, + 0.47, + 0.613 + ], + "angle": 0, + "content": "ing on the characteristics of the evaluation datasets, TCL outperforms all the other methods by large margins across all datasets as shown in Table 1. These results demonstrate that our TCL framework successfully addresses the alignment-level train-test discrepancy that exists in the previous methods by learning the region-level alignment. In addition, region-level alignment learning of TCL allows our model to learn the capability to distinguish the background region in a data-driven manner, thus, our method can address the background class without any heuristic post-processing that the previous methods typically rely on." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.625, + 0.262, + 0.641 + ], + "angle": 0, + "content": "4.3. Qualitative Results" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.654, + 0.468, + 0.836 + ], + "angle": 0, + "content": "Visualization of the generated text-grounded masks. Fig. 4 illustrates the impact of the learned grounding decoder. Since we follow MaskCLIP [33] modification, the results in \"w/o \\( D_g \\)\" rows can be regarded as the initial results of MaskCLIP before refinement. Despite the vast pretraining scale and remarkable zero-shot classification performance of CLIP [23], its grounding capability is limited because the learning objective targets image-level alignment (See \"w/o \\( D_g \\)\" rows). In contrast, the grounding decoder (\\( D_g \\)) learns the region-level alignment by TCL, resulting in more precise, finer, and less noisy generated masks (See \"w/ \\( D_g \\)\" rows)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.47, + 0.903 + ], + "angle": 0, + "content": "Qualitative comparison. We qualitatively compare the proposed method in Fig. 5. On the PASCAL VOC dataset (Fig. 5a), we observe various types of errors in each comparison method. The grouping procedure of GroupViT [30]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.485 + ], + "angle": 0, + "content": "makes the segmentation results less noisy, but it also causes an incorrect segmentation of a large group. ReCo [28] struggles with the segmentation of background regions due to the lack of consideration about the background class. MaskCLIP [33] does not take this into account as well, but its refinement methods make the results less noisy. In addition, we present examples in the wild to show open-world segmentation capability in Fig. 5b. We collect test samples containing visual concepts not included in conventional segmentation datasets (e.g., moon, sunset) or free-form texts (e.g., \"two women and one man with a smiling snowman\"). GroupViT tends to focus on the main object of the image and regard the other objects as background, which is consistent with its good performance in object-oriented datasets. Interestingly, in this qualitative comparison in the wild, we observe ReCo consistently outperforms MaskCLIP contrary to the quantitative results. We conjecture that this is because the refinement approach of ReCo is data-driven, while the refinement approach of MaskCLIP depends on heuristic post-processing, which may not guarantee general improvement. Compared to the baselines, TCL consistently generates more precise segmentation masks. These results demonstrate that our proposed method, which learns region-level alignment, improves the segmentation quality both in the evaluation dataset and in web images in the wild. Additional qualitative results are provided in Appendix H." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.49, + 0.892, + 0.521 + ], + "angle": 0, + "content": "Additional analysis on failure cases and model behavior are provided in Appendices F and G, respectively." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.533, + 0.665, + 0.548 + ], + "angle": 0, + "content": "4.4. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.558, + 0.892, + 0.634 + ], + "angle": 0, + "content": "We investigate the impact of individual components of the proposed framework by ablation studies on the training split of the PASCAL VOC20 dataset. We use a short learning schedule with a batch size of 512 for total 40,000 iterations including 10,000 warmup steps." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.639, + 0.892, + 0.804 + ], + "angle": 0, + "content": "Baseline to TCL. Table 2a presents cumulative ablation studies on the grounding decoder and the TCL losses. Our initial model before training based on MaskCLIP [33] is referred to as the baseline (A), which modifies the last attention layer of the CLIP image encoder. When we add only the grounding decoder to the baseline without TCL loss (B), there is no improvement in performance. This suggests that training the decoder with the same CL loss as the pre-training (CLIP) does not enhance the localization capabilities. As shown in (C), the proposed framework becomes complete with TCL loss." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Impact of individual TCL losses. The influence of each component of the proposed TCL loss and its effect on the segmentation performance compared to the conventional CL loss are shown in Table 2b. Smooth regularization is used for all experiments in this table. The CL loss (D) is computed by applying attention pooling [23] to the dense" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "11171" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.086, + 0.089, + 0.571, + 0.363 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.228, + 0.365, + 0.427, + 0.377 + ], + "angle": 0, + "content": "(a) Examples in PASCAL VOC." + }, + { + "type": "image", + "bbox": [ + 0.573, + 0.09, + 0.882, + 0.363 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.651, + 0.365, + 0.806, + 0.377 + ], + "angle": 0, + "content": "(b) Examples in the wild." + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.386, + 0.895, + 0.43 + ], + "angle": 0, + "content": "Figure 5. (a) The comparison shows the error types of each method in the VOC dataset. GroupViT tends to make an error on a large group rather than noisy results. ReCo suffers from segmentation of the background region. MaskCLIP tends to fail at capturing the target area precisely. (b) shows results on the wild web images and free-form texts. Texts used as target classes are shown at the bottom of the images." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.447, + 0.473, + 0.599 + ], + "angle": 0, + "content": "image embedding \\(\\mathbf{V}^s\\). When comparing (D) and (C), the proposed TCL loss remarkably improves the segmentation performance \\((61.1 \\rightarrow 77.4)\\). Image-level or feature-level TCL loss (E, F) solely improves the performance significantly, and using both losses together provides further performance gain. Using CL in addition to TCL (G) does not improve performance, and it is essential to use area TCL loss in TCL framework to prevent model collapse (H), as described in Sec. 3.3. The difference between (B) and (D) is the use of smooth regularization." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.604, + 0.472, + 0.816 + ], + "angle": 0, + "content": "Hyperparameters. Tables 2c to 2e show the performance changes according to the variation of the loss weight hyperparameters (HPs). The first rows show the importance of each loss (\\(\\lambda = 0.0\\) cases). The absence of area TCL loss causes a significant performance drop (Table 2d), as mentioned above. Smooth regularization also significantly contributes to the final performance (Table 2e), supporting our assumption that the text-described region is smooth rather than noisy. Note that the sensitivity on HPs is about loss balancing, not about the target dataset. As an open-world segmentation method, TCL does not require any tuning with the target dataset, including model fine-tuning and inference HPs tuning. Once a TCL model is trained, we evaluate the model for every benchmark without any fine-tuning." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.83, + 0.196, + 0.845 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.856, + 0.471, + 0.902 + ], + "angle": 0, + "content": "We propose a novel framework for open-world semantic segmentation with only image-text pairs, addressing the alignment-level discrepancy between training (image-text)" + }, + { + "type": "table", + "bbox": [ + 0.523, + 0.445, + 0.662, + 0.507 + ], + "angle": 0, + "content": "
MethodVOC20
A Baseline53.2
B + Decoder52.3
C + TCL77.4
" + }, + { + "type": "table", + "bbox": [ + 0.678, + 0.446, + 0.889, + 0.55 + ], + "angle": 0, + "content": "
TCLvTCLfLareaCLVOC20
D✓61.1
E✓✓74.6
F✓✓76.0
C✓✓✓77.4
G✓✓✓✓75.6
H✓✓67.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.734, + 0.552, + 0.826, + 0.565 + ], + "angle": 0, + "content": "(b) TCL losses." + }, + { + "type": "table_caption", + "bbox": [ + 0.525, + 0.551, + 0.625, + 0.564 + ], + "angle": 0, + "content": "(a) Baseline to T" + }, + { + "type": "table", + "bbox": [ + 0.526, + 0.574, + 0.625, + 0.65 + ], + "angle": 0, + "content": "
λTCLVOC20
0.0-
0.0176.8
0.177.4
1.068.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.547, + 0.653, + 0.595, + 0.664 + ], + "angle": 0, + "content": "(c) \\(\\mathcal{L}_{\\mathrm{TCL}}\\)" + }, + { + "type": "table", + "bbox": [ + 0.654, + 0.574, + 0.744, + 0.65 + ], + "angle": 0, + "content": "
λareaVOC20
0.067.1
0.0469.5
0.477.4
4.076.7
" + }, + { + "type": "table_caption", + "bbox": [ + 0.671, + 0.652, + 0.719, + 0.664 + ], + "angle": 0, + "content": "(d) \\(\\mathcal{L}_{\\mathrm{area}}\\)" + }, + { + "type": "table", + "bbox": [ + 0.775, + 0.574, + 0.871, + 0.65 + ], + "angle": 0, + "content": "
λtvVOC20
0.073.8
0.175.2
1.077.4
10.070.7
" + }, + { + "type": "table_caption", + "bbox": [ + 0.8, + 0.652, + 0.837, + 0.665 + ], + "angle": 0, + "content": "(e) \\(\\mathcal{L}_{\\mathrm{tv}}\\)" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.675, + 0.892, + 0.718 + ], + "angle": 0, + "content": "Table 2. Ablation studies on TCL losses and hyperparameters. Refinement techniques are not applied to reveal the effect of each loss function clearly. Default settings are marked in gray." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.734, + 0.892, + 0.901 + ], + "angle": 0, + "content": "and testing (region-text) in existing methods. In the proposed framework, we incorporate the grounding process within contrastive learning, thus allowing explicitly learning alignment between text and text-grounded regions (i.e., segmentation mask). We also present a unified evaluation protocol for a fair comparison of existing methods, where TCL achieves state-of-the-art zero-shot segmentation performance on all 8 benchmarks, remarkably surpassing previous methods. We hope that this study encourages a new research direction of explicitly learning region-text alignment for open-world semantic segmentation." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11172" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.175, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.473, + 0.171 + ], + "angle": 0, + "content": "[1] Nikita Araslanov and Stefan Roth. Single-stage semantic segmentation from image labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. 6, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.173, + 0.472, + 0.214 + ], + "angle": 0, + "content": "[2] Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, and Saehoon Kim. Coyo-700m: Image-text pair dataset, 2022. 2, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.216, + 0.472, + 0.271 + ], + "angle": 0, + "content": "[3] Holger Caesar, Jasper Uijlings, and Vittorio Ferrari. Cocosstuff: Thing and stuff classes in context. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018. 6, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.273, + 0.472, + 0.328 + ], + "angle": 0, + "content": "[4] Junbum Cha, Kyungjae Lee, Sungrae Park, and Sanghyuk Chun. Domain generalization by mutual-information regularization with pre-trained models. European Conference on Computer Vision (ECCV), 2022. 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.33, + 0.472, + 0.4 + ], + "angle": 0, + "content": "[5] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.402, + 0.472, + 0.457 + ], + "angle": 0, + "content": "[6] Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. Advances in Neural Information Processing Systems, 34:17864-17875, 2021. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.459, + 0.472, + 0.514 + ], + "angle": 0, + "content": "[7] François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.516, + 0.472, + 0.572 + ], + "angle": 0, + "content": "[8] MMSegmentation Contributors. MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https://github.com/open-mmlab/mmsegmentation, 2020.6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.574, + 0.472, + 0.657 + ], + "angle": 0, + "content": "[9] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.659, + 0.472, + 0.713 + ], + "angle": 0, + "content": "[10] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.716, + 0.472, + 0.77 + ], + "angle": 0, + "content": "[11] Golnaz Ghiasi, Xiuye Gu, Yin Cui, and Tsung-Yi Lin. Scaling open-vocabulary image segmentation with image-level labels. In European Conference on Computer Vision, pages 540-557. Springer, 2022. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.772, + 0.472, + 0.814 + ], + "angle": 0, + "content": "[12] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. *ICLR*, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.816, + 0.472, + 0.858 + ], + "angle": 0, + "content": "[13] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. EMNLP, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.86, + 0.472, + 0.902 + ], + "angle": 0, + "content": "[14] Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. In International Conference on Learning Representations, 2017. 4" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.473, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.162 + ], + "angle": 0, + "content": "[15] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.163, + 0.892, + 0.217 + ], + "angle": 0, + "content": "[16] Philipp Krahenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. Advances in neural information processing systems, 24, 2011. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.219, + 0.892, + 0.273 + ], + "angle": 0, + "content": "[17] Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and Rene Ranftl. Language-driven semantic segmentation. In International Conference on Learning Representations, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.274, + 0.892, + 0.342 + ], + "angle": 0, + "content": "[18] Feng Liang, Bichen Wu, Xiaoliang Dai, Kunpeng Li, Yinan Zhao, Hang Zhang, Peizhao Zhang, Peter Vajda, and Diana Marculescu. Open-vocabulary semantic segmentation with mask-adapted clip. arXiv preprint arXiv:2210.04150, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.343, + 0.892, + 0.412 + ], + "angle": 0, + "content": "[19] Quande Liu, Youpeng Wen, Jianhua Han, Chunjing Xu, Hang Xu, and Xiaodan Liang. Open-world semantic segmentation via contrasting and clustering vision-language embedding. European Conference on Computer Vision (ECCV), 2022. 1, 2, 3, 5, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.413, + 0.892, + 0.455 + ], + "angle": 0, + "content": "[20] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.456, + 0.892, + 0.511 + ], + "angle": 0, + "content": "[21] Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic segmentation in the wild. CVPR, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.511, + 0.892, + 0.553 + ], + "angle": 0, + "content": "[22] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.554, + 0.892, + 0.636 + ], + "angle": 0, + "content": "[23] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 3, 4, 5, 7, 11, 12, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.637, + 0.892, + 0.678 + ], + "angle": 0, + "content": "[24] Leonid I Rudin, Stanley Osher, and Emad Fatemi. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4):259-268, 1992. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.679, + 0.892, + 0.748 + ], + "angle": 0, + "content": "[25] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252, 2015. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.748, + 0.892, + 0.816 + ], + "angle": 0, + "content": "[26] Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. arXiv preprint arXiv:2111.02114, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.818, + 0.892, + 0.872 + ], + "angle": 0, + "content": "[27] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of ACL, 2018. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.873, + 0.892, + 0.902 + ], + "angle": 0, + "content": "[28] Gyungin Shin, Weidi Xie, and Samuel Albanie. Reco: Retrieve and co-segment for zero-shot transfer. Advances in" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11173" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.092, + 0.468, + 0.12 + ], + "angle": 0, + "content": "Neural Information Processing Systems, 2022. 1, 2, 3, 5, 6, 7, 13, 15, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.469, + 0.189 + ], + "angle": 0, + "content": "[29] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34, 2021. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.192, + 0.469, + 0.274 + ], + "angle": 0, + "content": "[30] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1, 2, 3, 5, 6, 7, 14, 15, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.277, + 0.469, + 0.343 + ], + "angle": 0, + "content": "[31] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, and Jianfeng Gao. Regionclip: Region-based language-image pretraining. CVPR, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.346, + 0.469, + 0.401 + ], + "angle": 0, + "content": "[32] Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127(3):302-321, 2019. 6, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.403, + 0.469, + 0.444 + ], + "angle": 0, + "content": "[33] Chong Zhou, Chen Change Loy, and Bo Dai. Extract free dense labels from clip. European Conference on Computer Vision (ECCV), 2022. 1, 2, 3, 5, 6, 7, 11, 14, 15, 16" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.469, + 0.444 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.956 + ], + "angle": 0, + "content": "11174" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/c9bc34a3-cd10-43e4-9d6d-81c2b8ccce5c_origin.pdf b/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/c9bc34a3-cd10-43e4-9d6d-81c2b8ccce5c_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..fb745b371353359847c08bd0c0e29636dc9d3bf4 --- /dev/null +++ b/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/c9bc34a3-cd10-43e4-9d6d-81c2b8ccce5c_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2e78eb50f3d01e3e437981181ba034c35aa8df75a31d774e6f272aaac6f6b235 +size 1574727 diff --git a/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/full.md b/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/full.md new file mode 100644 index 0000000000000000000000000000000000000000..bc381612e5b654670007afba8cb32646c05c46c4 --- /dev/null +++ b/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/full.md @@ -0,0 +1,292 @@ +# Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs + +Junbum Cha + +Jonghwan Mun + +Byungseok Roh + +Kakao Brain + +{junbum.cha, jason.mun, peter. roh}@kakaobrain.com + +# Abstract + +We tackle open-world semantic segmentation, which aims at learning to segment arbitrary visual concepts in images, by using only image-text pairs without dense annotations. Existing open-world segmentation methods have shown impressive advances by employing contrastive learning (CL) to learn diverse visual concepts and transferring the learned image-level understanding to the segmentation task. However, these CL-based methods suffer from a train-test discrepancy, since it only considers image-text alignment during training, whereas segmentation requires region-text alignment during testing. In this paper, we proposed a novel Text-grounded Contrastive Learning (TCL) framework that enables a model to directly learn region-text alignment. Our method generates a segmentation mask for a given text, extracts text-grounded image embedding from the masked region, and aligns it with text embedding via TCL. By learning region-text alignment directly, our framework encourages a model to directly improve the quality of generated segmentation masks. In addition, for a rigorous and fair comparison, we present a unified evaluation protocol with widely used 8 semantic segmentation datasets. TCL achieves state-of-the-art zero-shot segmentation performances with large margins in all datasets. Code is available at https://github.com/kakaobrain/tcl. + +# 1. Introduction + +Open-world semantic segmentation aims to identify the arbitrary semantic concepts in the open world1. Conventional semantic segmentation aims to learn segmentation capability for the small number of pre-defined target categories, whereas open-world semantic segmentation addresses unrestricted arbitrary categories or free-form texts. Such segmentation capability over unlimited targets drasti- + +![](images/80971d0e0724e7660fa796a783f6ccd8781fcbaf109513e3a821d0cdc3e85947.jpg) +Figure 1. Open-world segmentation performance comparison. The proposed method remarkably outperforms existing methods in all 8 segmentation benchmark datasets. + +cally extends the application scope of the open-world segmentation models. + +The first challenge for open-world segmentation is how to learn arbitrary concepts, beyond pre-defined categories. Inspired by the success of CLIP [23], previous approaches [11, 17-19, 28, 30, 33] tackle this challenge by exploiting massive web-crawled image-text paired data; since the texts in web-crawled data contain a global semantic description for the paired images, the large-scale image-text pairs can provide rich knowledge for arbitrary semantic categories. However, there still remains another challenge in how to achieve precise localization of arbitrary concepts without dense annotations. There are several approaches that simply address this issue using dense annotation (segmentation masks) in addition to image-text pairs [11, 17, 18]. The dense annotation helps to improve segmentation perfor + +![](images/7fbcfb4a59ec97b008d0fa225d53467eaec0e55d1704e91d293edceec65e077c.jpg) +Figure 2. A conceptual comparison between the previous approach and ours. Open-world segmentation is typically achieved through region-text alignment, which involves matching region features and text embeddings. However, previous methods learn image-text alignment during training, thus suffering from the alignment-level discrepancy between training and testing. In contrast, our method facilitates end-to-end learning of region-text alignment with only image-text pairs. + +mance in a fixed benchmark dataset, but the requirements of expensive dense annotation still limit the applicable domains and scalability of the method. + +In this paper, therefore, we focus on open-world semantic segmentation from only image-text pairs without any dense annotation. For this setting, the existing methods [19, 28, 30, 33] learn an image-text alignment capability during training and heavily rely on the transferability of the image-text alignment to perform region-text alignment at inference. More specifically, MaskCLIP [33] leverages CLIP models pre-trained to learn image-text alignment. To perform region-text alignment using CLIP, MaskCLIP applies a simple heuristic modification to the CLIP image encoder. GroupViT [30] and ViL-Seg [19] propose to cluster region-level visual features into distinct groups and generate segmentation masks by matching the groups and texts. Note that they match the text embeddings and clustered region features in test time, but in training time, the text embeddings are aligned with global image embeddings. While the existing methods have shown impressive results even through the training with image-text alignment, they still suffer from the alignment-level discrepancy between training and testing phases as depicted in Fig. 2. + +To address this train-test discrepancy, we propose the Text-grounded Contrastive Learning (TCL) framework, which allows a model to learn region-text alignment directly from the image-text pairs without any dense annotations. Our key idea is to incorporate a text grounding procedure within contrastive learning as illustrated in Fig. 2, where TCL generates a segmentation mask indicating text-grounded regions, computes grounded region embeddings + +using the mask, and applies contrastive learning between text and grounded region. By re-formulating the contrastive loss to be directly affected by the segmentation quality, TCL enables end-to-end training of the grounder and directly improves the quality of region-text level alignment. We also present a unified evaluation protocol using widely used 8 semantic segmentation datasets and compare existing methods in the same setting. As a result, TCL achieves state-of-the-art zero-shot segmentation performance with large margins in all datasets, as shown in Fig. 1. + +Our main contributions are summarized as follows: + +- We introduce a novel framework for open-world segmentation, named Text-grounded Contrastive Learning (TCL), which enables learning region-text alignment directly without train-test discrepancy, thus learning to generate more precise segmentation masks through only image-text pairs. +- We present a unified evaluation protocol and re-evaluate recent open-world segmentation models for a fair and direct comparison. +- We achieve the new state-of-the-art zero-shot segmentation performance on 8 segmentation datasets with large margins compared to existing methods. + +# 2. Related Works + +# 2.1. Open-world Semantic Segmentation + +Open-world scenario aims to recognize arbitrary concepts in the open world. It is also called open-vocabulary because the target vocabulary is open rather than closed. Contrastive Language-Image Pre-training (CLIP) [23] ushered in the era of open-world image recognition using largescale image-text pairs [2, 5, 26, 27]. CLIP learns the alignment between an image and a text in training time, then transfer it to the zero-shot classification by aligning image and texts indicating target classes at inference time. The advent of CLIP enables open-world settings in various fields such as object detection [12, 31], image captioning [13], or semantic segmentation [30, 33]. + +Open-world semantic segmentation with image-text pairs is addressed in two different settings. The first is a semi-supervised setting, which uses dense annotation (i.e., segmentation masks) in addition to image-text pairs [11, 17, 18]. Semi-supervised approaches learn segmentation capability using dense annotation and expand the target vocabulary using image-text supervision. LSeg [17] expands target class vocabulary using image-label datasets and CLIP text encoder [23]. OpenSeg [11] and OVSeg [18] first train a mask generator using dense annotation and expand target vocabulary using image-text datasets. The use of dense annotation makes the model learn region-level alignment instead of image-level alignment, leading to high-quality segmentation masks. However, it still relies on + +costly dense annotation, and applicable domains are limited to the domains where dense annotation is available. + +The target of this paper is an unsupervised setting, which aims to learn segmentation from only image-text pairs without any dense annotation [19, 28, 30, 33]. Since the massive image-text pairs are easily obtained by web crawling without human annotators, applicable domains of unsupervised methods become almost unlimited. In order to achieve segmentation capability using only image-text pairs, we need to learn region-text alignment instead of image-text alignment and train a text-grounded mask generator. However, the absence of dense annotation makes this approach challenging. Existing open-world semantic segmentation studies have taken a strategy to bypass this issue. Instead of learning region-level alignment directly, they transfer image-level alignment to region-level by heuristic modification [28, 33] or clustering [19, 30]. MaskCLIP [33] proposes to obtain a dense image embedding from CLIP image encoder through heuristic modification of the last attention layer. Even though it has several limitations, such as low output resolution or noisy segmentation results, they show it is a simple yet effective way to obtain an initial segmentation map for refinement. ReCo [28] proposes an advanced refinement method based on MaskCLIP, by retrieval and co-segmentation. Clustering-based methods [19, 30] learn representations using CL with image-text pairs. They compute region-level image embedding by clustering sub-region embeddings. These approaches also have shown impressive results but have several limitations: (i) the learning objective is still image-level alignment due to lack of the region annotation, (ii) the number of clusters is pre-defined independent of the given image, and (iii) clustering sub-region image embeddings is independent of the query text. In summary, existing methods indirectly address region-level alignment problems by learning image-level alignment. To tackle this problem, we propose a novel region-level alignment objective, named Text-grounded Contrastive Learning (TCL). + +# 2.2. Region-level Contrastive Learning + +Learning region-level alignment instead of image-level alignment is a fundamental target objective in dense tasks, such as segmentation or object detection. There are approaches to learn region-level alignment using dense annotation in the semi-supervised setting. They first train mask or region proposal networks using dense annotation and learn alignment between the proposals and texts [11, 15, 31]. For example, OpenSeg [11] trains a class-agnostic mask generator using dense annotation. In the object detection field, RegionCLIP [31] employs an off-the-shelf region proposal network and learns region-level alignment. In contrast to the existing region-level methods, the proposed method learns region-level alignment without any dense annotation. + +# 3. Methods + +# 3.1. Overview + +Open-world semantic segmentation is a task that aims to learn a model capable of zero-shot segmentation for arbitrary visual concepts, not restricted to pre-defined ones. Our main goal is to develop an open-world segmentation algorithm using only image-text pairs. However, achieving this objective is challenging because there is no explicit supervision (i.e., pixel-level dense annotations) for text-described region segmentation. Existing methods learn models parametrized by $\theta$ to maximize the mutual information between paired images and texts [22, 23] as follows: + +$$ +\underset {\theta} {\arg \max } I _ {\theta} \left(\mathbf {x} ^ {V}; \mathbf {x} ^ {T}\right), \tag {1} +$$ + +where $(\mathbf{x}^V, \mathbf{x}^T)$ is a random image and text pair. This objective encourages the model to learn the alignment between images and texts, however, at test time, the learned model generates segmentation masks for arbitrary concepts by computing region-text alignments. Such alignment-level discrepancy between train and test time can lead the model to a sub-optimal solution as shown in Fig. 2. With this in consideration, to bridge the gap between the objective of conventional contrastive learning (CL) and the requirement of the zero-shot segmentation, we propose Text-grounded Contrastive Learning (TCL) which incorporates a text grounding process within CL to enable learning region-text alignment directly. As a text grounding module, we introduce a groundeder to generate segmentation masks for the given texts. In a nutshell, TCL learns a model to maximize mutual information between text-grounded regions and texts as follows: + +$$ +\underset {\theta} {\arg \max } I _ {\theta} \left(\mathbf {m} \cdot \mathbf {x} ^ {V}; \mathbf {x} ^ {T}\right), \tag {2} +$$ + +where $\mathbf{m}$ is a text-grounded mask of random variable indicating the text-described region. Compared to contrastive learning that implicitly learns a grounding capability, TCL has a clear advantage of explicitly learning the grounding capability through the end-to-end trainable grounder. + +In the rest of this section, we first explain the text-grounded mask generation procedure by the grounder. Then, we describe how we define losses using the generated mask to train our open-world grounder with text-grounded contrastive learning. Lastly, we explain how our model performs zero-shot inference for arbitrary concepts. + +# 3.2. Grounder + +Fig. 3 illustrates our overall training pipeline. For an input batch of paired texts $\mathbf{X}^T$ and images $\mathbf{X}^V$ , TCL first performs a grounding process to identify text-grounded regions for a text via a grounder. The grounder consists of + +![](images/55183c3c1e4ed7cf0bd5c194f490ea35eee154d524c2c6de47f8888fdf0acebe.jpg) +Figure 3. Overall training pipeline of TCL. The proposed TCL framework first obtains text-grounded masks using a grounder and then learns the grounder using text-grounded contrastive learning. By incorporating the text grounding process with contrastive learning, our framework can directly learn region-text alignment that is required for precise segmentation. $\mathrm{CLIP_T}$ and $\mathrm{CLIP_I}$ indicate CLIP text and image encoders, respectively. CLIP encoders are frozen and we train the grounding decoder only. After training, the TCL block is discarded, and only the Grounder block is used to generate the text-grounded segmentation mask for inference. + +three components: (i) image encoder $E_{v}$ is in charge of providing a single (L2-normalized) global feature as well as dense patch-level features, (ii) text encoder $E_{t}$ provides a (L2-normalized) text embedding feature, and (iii) grounding decoder $D_{g}$ converts dense features from image encoder into finer pixel-level embeddings for alignment with text. In practice, we adopt a pre-trained CLIP model [23] to initialize two encoders and freeze them to preserve and exploit the rich knowledge of CLIP learned during largescale pre-training. The text-grounded masks are computed by the position-wise dot product between text embedding and dense pixel-level embedding. The overall process of grounder is summarized as follows: + +$$ +\mathbf {T} = E _ {t} \left(\mathbf {X} ^ {T}\right) \quad \text {a n d} \quad \mathbf {V} ^ {g}, \mathbf {V} ^ {d} = E _ {v} \left(\mathbf {X} ^ {V}\right), \tag {3} +$$ + +$$ +\mathbf {V} ^ {s} = D _ {g} \left(\mathbf {V} ^ {d}\right), \tag {4} +$$ + +$$ +\mathbf {M} _ {i, j} = \sigma \left(w \cdot \mathbf {t} _ {j} ^ {\top} \mathbf {V} _ {i} ^ {s} + b\right), \tag {5} +$$ + +where $\mathbf{T} \in \mathbb{R}^{B \times C}$ , $\mathbf{V}^g \in \mathbb{R}^{B \times C}$ , and $\mathbf{V}^d \in \mathbb{R}^{B \times L \times C}$ are normalized text embeddings, normalized global image embeddings, and dense image features from CLIP encoders, $\mathbf{V}^s \in \mathbb{R}^{B \times C \times H \times W}$ is normalized pixel-level dense embeddings by the grounding decoder, and $\mathbf{M} \in \mathbb{R}^{B \times B \times H \times W}$ is text-grounded masks between images and texts in the batch. $B$ , $C$ , and $L$ indicate a batch size, the embedding dimension size, and the number of patches, respectively. $\sigma$ is a sigmoid function, and $w$ , $b$ are learnable scalar projection. + +The generated text-grounded masks are used to extract text-grounded image embedding. By replacing the global image embedding with text-grounded image embedding in the contrastive learning framework, TCL enables the model to learn region-text alignment in an end-to-end manner. In + +the following section, we describe how the generated mask $\mathbf{M}$ is used for text-grounded contrastive learning. + +# 3.3. Text-grounded Contrastive Learning + +Recall that the main idea of TCL is to use text-grounded images instead of whole images, unlike conventional CL. For this purpose, we define TCL losses in three different levels—image-level, feature-level, and area-level—using the generated masks M for all pairs of images and texts in a batch; the detailed pseudo code to compute TCL losses is given in Appendix A. We also employ smooth regularization to further improve the quality of generated masks. + +Image-level TCL loss. One intuitive way to compute the text-grounded image embedding is to encode only the regions of the image that contains the semantics of the paired text, using the image encoder. To make the whole process end-to-end trainable, we compute a differentiable masked image by multiplying the given image $\mathbf{X}_i^V$ and a binarized mask $\mathbf{M}_{i,i}^b$ obtained from the generated mask $\mathbf{M}_{i,i}$ using Gumbel-Max [14]. The masked image is then fed into the image encoder, $\tilde{\mathbf{v}}_i^g,\tilde{\mathbf{v}}_i^d = E_v\left(\mathbf{M}_{i,i}^b\cdot \mathbf{X}_i^V\right)$ , to obtain text-grounded image embedding $\tilde{\mathbf{v}}_i^g$ . We compute the cosine similarity matrix between text-grounded image embeddings and text embeddings in a batch by $S_{i,j}^{m} = \tilde{\mathbf{v}}_i^{g^\top}\mathbf{t}_j$ . Finally, we use the symmetric version of InfoNCE [22, 23] to define the image-level TCL loss $\mathcal{L}_{\mathrm{TCL}_v}$ to make the representations of positive image-text pairs similar to each other while the representations of negative pairs dissimilar based on the similarity matrix: + +$$ +\mathcal {L} _ {\mathrm {T C L} _ {v}} = \operatorname {I n f o N C E} \left(\mathbf {S} ^ {m}\right), \tag {6} +$$ + +$$ +\begin{array}{l} \operatorname {I n f o N C E} \left(\mathbf {S}\right) = - \frac {1}{2 B} \sum_ {i} ^ {B} \log \frac {\exp \left(S _ {i , i} / \tau\right)}{\sum_ {j} ^ {B} \exp \left(S _ {i , j} / \tau\right)} \\ - \frac {1}{2 B} \sum_ {i} ^ {B} \log \frac {\exp \left(S _ {i , i} / \tau\right)}{\sum_ {j} ^ {B} \exp \left(S _ {j , i} / \tau\right)}, \tag {7} \\ \end{array} +$$ + +where $\tau$ is a learnable temperature. + +Feature-level TCL loss. The image-level TCL loss drives a model to generate segmentation masks for the paired texts (i.e., texts of positive pairs). However, we observe that this loss alone is insufficient to prevent the model from generating masks for regions not described in the text, particularly for salient regions. This raises the need to suppress negative masks obtained from unrelated texts (i.e., texts of negative pairs), but computing the image-level TCL loss for negative masks is infeasible due to the high computational cost of encoding text-grounded images. To overcome this challenge, we introduce the feature-level TCL loss, which enables the effective computation of features of the negative masks. Specifically, for pixel-level dense embeddings $\mathbf{V}_i^s\in \mathbb{R}^{C\times H\times W}$ from grounding decoder and a text embedding vector $\mathbf{t}_j$ , we compute feature-level text-grounded image embedding $\mathbf{v}_{i,j}^{f}\in \mathbb{R}^{C}$ by: + +$$ +\mathbf {v} _ {i, j} ^ {f} = \frac {\sum_ {h , w} M _ {i , j , h , w} \cdot \mathbf {v} _ {i , : , h , w} ^ {s}}{\sum_ {h , w} M _ {i , j , h , w}}. \tag {8} +$$ + +Note that this feature-level embedding is computed using negative masks $\mathbf{M}_{i,j (i \neq j)}$ , different from the image-level TCL loss. We then compute the cosine similarity $S_{i,j}^{f} = \mathbf{v}_{i,j}^{f}^{\top} \mathbf{t}_{j}$ between all pairs of text embeddings and feature-level text-grounded image embeddings in the batch. The feature-level TCL loss is defined as follows: + +$$ +\mathcal {L} _ {\mathrm {T C L} _ {f}} = \operatorname {I n f o N C E} \left(\mathbf {S} ^ {f}\right). \tag {9} +$$ + +Area TCL loss. The image-level and feature-level TCL losses focus on generating a mask to capture the text-described region in the image. However, the model can collapse into a trivial solution with only these losses—generating a mask for the entire image instead of the desired region. To prevent this collapse, we introduce an additional objective to our TCL framework, named area TCL loss, which incorporates priors on the mask area to ensure capturing only the text-described region. To be specific, for the positive masks (masks from positive pairs) $\mathbf{M}^{+}$ and the negative masks (masks from negative pair) $\mathbf{M}^{-}$ , we denote the area of positive and negative masks by $\overline{\mathbf{M}^{+}}$ and $\overline{\mathbf{M}^{-}}$ , respectively. The area TCL loss is defined by L1-distance between the area priors and the expected area of each mask: + +$$ +\mathcal {L} _ {\text {a r e a}} = \left\| p ^ {+} - \mathbb {E} \left[ \overline {{\mathbf {M} ^ {+}}} \right] \right\| _ {1} + \left\| p ^ {-} - \mathbb {E} \left[ \overline {{\mathbf {M} ^ {-}}} \right] \right\| _ {1}, \tag {10} +$$ + +where $p^+$ and $p^-$ are positive and negative area priors. For the negative area prior $p^-$ , intuitively, we can expect the area of the negative masks to be 0.0. We set the positive area prior $p^+$ to 0.4, which is the average text-described region area measured by MaskCLIP [33] in the CC3M dataset [27]. + +Smooth regularization. In the image-text dataset, a text usually describes the salient object or concept in the paired image. We observe that the regions described by the text are generally smooth rather than noisy. We employ total variation (TV) regularization loss [24] to incorporate this smoothness observation in the objective. The TV loss is applied to both mask and pixel-level dense embedding: + +$$ +\mathcal {L} _ {\mathrm {T V}} = \left\| \mathbf {M} \right\| _ {\mathrm {T V}} + \left\| \mathbf {V} ^ {s} \right\| _ {\mathrm {T V}}, \tag {11} +$$ + +where $\| \cdot \|_{\mathrm{TV}}$ is the anisotropic TV norm. + +Final loss. Our final loss function is defined by: + +$$ +\mathcal {L} = \underbrace {\lambda_ {\mathrm {T C L}} \mathcal {L} _ {\mathrm {T C L}} + \lambda_ {\text {a r e a}} \mathcal {L} _ {\text {a r e a}}} _ {\text {T C L l o s s e s}} + \underbrace {\lambda_ {\mathrm {t v}} \mathcal {L} _ {\mathrm {t v}}} _ {\text {r e g u l a r i z a t i o n}}, \tag {12} +$$ + +where $\mathcal{L}_{\mathrm{TCL}} = \mathcal{L}_{\mathrm{TCL}_v} + \mathcal{L}_{\mathrm{TCL}_f}$ , and $\lambda_{\mathrm{TCL}}$ , $\lambda_{\mathrm{area}}$ , $\lambda_{\mathrm{tv}}$ are hyperparameters to balance three losses. + +# 3.4. Inference Pipeline + +The zero-shot inference pipeline is similar to CLIP [23], except for performing pixel-level classification instead of image-level classification. Specifically, for text embeddings $\mathbf{T} \in \mathbb{R}^{N \times C}$ and a pixel-level dense embedding $\mathbf{v}^s \in \mathbb{R}^{C \times H \times W}$ , text-grounded mask $\mathbf{M} \in \mathbb{R}^{N \times H \times W}$ is computed by Eq. (5), where $N$ is the number of target classes. The final segmentation map $\mathcal{M}$ is computed by: + +$$ +\mathcal {M} _ {h, w} = \underset {n} {\arg \max } M _ {n, h, w}. \tag {13} +$$ + +Prompt templates such as "a photo of a {label}." are used to generate text embeddings as in CLIP [23]. + +# 4. Experiments + +# 4.1. Experiment Settings + +Unified evaluation protocol. In open-world semantic segmentation, a standard evaluation protocol is not yet established. Previous studies conduct an evaluation using their own protocols such as different data processing strategies on different datasets [19, 28, 30, 33]; surprisingly, even for the same dataset, the target classes are sometimes different across studies. For a fair comparison, we present a unified evaluation protocol following the open-world scenario where prior access to the target data before evaluation is not allowed. Under this scenario, the proposed protocol prohibits dataset-specific hyperparameters or tricks, e.g., class + +
Methodswith background classwithout background classAvg.
VOCContextObjectVOC20Context59StuffCityADE
GroupViT (YFCC)49.519.024.374.120.812.66.98.727.0
GroupViT (RedCaps)50.418.727.579.723.415.311.19.229.4
MaskCLIP†29.321.115.553.723.314.721.610.823.7
MaskCLIP38.823.620.674.926.416.412.69.827.9
ReCo25.119.915.757.722.314.821.111.223.5
TCL (Ours)55.030.431.683.233.922.424.017.137.2
(+4.6)(+6.8)(+4.1)(+3.5)(+7.5)(+6.0)(+2.4)(+5.9)(+7.8)
+ +Table 1. Zero-shot segmentation performance comparison on 8 semantic segmentation datasets. mIoU metric is used in every experiment. We highlight the best and second-best results. MaskCLIP† indicates their baseline method without additional refinement techniques. The YFCC and RedCaps of GroupViT indicate their training datasets in addition to CC12M. Each dataset abbreviation stands for VOC: PASCAL VOC, Context: PASCAL Context, Object: COCO-Object, Stuff: COCO-Stuff, City: Cityscapes, ADE: ADE20K. + +name expansion or rephrasing, leading to performance overestimation. For example, we observe that TCL can get significant performance gains by expanding the target class of "person" to its sub-concepts (e.g., man, woman, worker, rider, etc.), but the kinds of class name-based tricks are not allowed in our unified evaluation protocol because the expansion depends on the target class names. With this consideration, we evaluate models using unified class names from the default version of MMSegmentation [8] without class name-based tricks. Dense CRF [16] is not used identically due to its expensive computational cost. All other evaluation settings follow GroupViT [30], where the input image is resized to have a shorter side of 448. We employ mean intersection-over-union (mIoU) as a performance metric, which is a standard metric in semantic segmentation. While we aim to provide a fair comparison, defining fair conditions can be subjective. Thus, we provide further results and discussion on this topic in Appendix C, especially regarding dataset scale and refinement methods. + +Benchmark datasets and comparison methods. We provide an extensive evaluation on widely used 8 benchmarks, categorized into two groups: (i) with background class (PASCAL VOC [10], PASCAL Context [21], and COCO-Object [3]), and (ii) without background class (PASCAL VOC20 [10], PASCAL Context59 [21], COCO-Stuff [3], Cityscapes [9], and ADE20K [32]). Note that open-world segmentation methods rely on the textual description of class names, which may require additional considerations for the background class, such as probability thresholding instead of using the "background" description as is. The datasets with background class evaluate this aspect. We compare TCL with all existing open-sourced methods, including GroupViT [30], MaskCLIP [33], and ReCo [28] under the unified protocol. We also include their variants in comparison baselines for an extensive comparison. Additional details and comparisons are given in Appendix E. + +Implementation details. For the grounder, we use the CLIP ViT-B/16 model where the size of input images is + +$224 \times 224$ and the patch size is $16 \times 16$ . Following MaskCLIP [33], we modify the last attention layer of the CLIP image encoder to acquire the dense embedding representing local semantics. The grounding decoder consists of four gated convolution blocks with two upsampling interpolations, and we use pixel-adaptive mask refinement (PAMR) [1] for mask refinement. Further details on the model architecture are provided in Appendix B. We use CC 3M and 12M datasets [5, 27] for training. The loss weights of $\lambda_{\mathrm{TCL}} = 0.1$ , $\lambda_{\mathrm{area}} = 0.4$ , $\lambda_{\mathrm{tv}} = 1.0$ are used. We train the model with a batch size of 1024 and a learning rate of $7.5 \times 10^{-5}$ for total 50,000 iterations with 15,000 warmup steps and cosine schedule. AdamW optimizer [20] is used with a weight decay of 0.05. + +# 4.2. Zero-shot Transfer to Semantic Segmentation + +Comparison of existing methods. We extensively compare existing open-world semantic segmentation methods in Table 1 using the proposed unified protocol, including two checkpoints of GroupViT [30] and two variants of MaskCLIP [33]. Between the existing methods, GroupViT achieves the best average performance, particularly on object-oriented datasets such as VOC, VOC20, and COCO-Object. However, its performance tends to decrease when the target dataset is dominated by stuff classes. On the other hand, MaskCLIP performs the best on stuff-oriented datasets such as Context, Context59, and COCO-Stuff, benefiting from the large-scale pre-trained CLIP model. We conjecture it benefits from leveraging a large-scale pretrained CLIP model. The refinement techniques proposed in MaskCLIP [33] improve the average performance but significantly degrade it on Cityscapes $(21.6\rightarrow 12.6)$ , suggesting the limitation of the heuristic refinement methods. The significant performance degradation of MaskCLIP and ReCo between VOC20 and VOC may imply the need for consideration for background class. + +TCL remarkably outperforms existing methods. Although the performances of existing methods vary depend + +![](images/e851e1f3745a5d88987708d4bf0a861217716dea89a9c52f6f5c896d68a2ec89.jpg) +Figure 4. Visualization of the generated text-grounded masks. "w/o $D_g$ " rows show the generated text-grounded masks without the grounding decoder ( $D_g$ ), i.e., CLIP dense features $\mathbf{V}^d$ are used instead of pixel-level dense embeddings $\mathbf{V}^s$ . "w/ $D_g$ " rows show the results with the grounding decoder. Each image is compared using both positive (blue) and negative (red) prompts. The results show that the grounded accurately and finely captures the text-described region with less noise via the grounding decoder. + +ing on the characteristics of the evaluation datasets, TCL outperforms all the other methods by large margins across all datasets as shown in Table 1. These results demonstrate that our TCL framework successfully addresses the alignment-level train-test discrepancy that exists in the previous methods by learning the region-level alignment. In addition, region-level alignment learning of TCL allows our model to learn the capability to distinguish the background region in a data-driven manner, thus, our method can address the background class without any heuristic post-processing that the previous methods typically rely on. + +# 4.3. Qualitative Results + +Visualization of the generated text-grounded masks. Fig. 4 illustrates the impact of the learned grounding decoder. Since we follow MaskCLIP [33] modification, the results in "w/o $D_g$ " rows can be regarded as the initial results of MaskCLIP before refinement. Despite the vast pretraining scale and remarkable zero-shot classification performance of CLIP [23], its grounding capability is limited because the learning objective targets image-level alignment (See "w/o $D_g$ " rows). In contrast, the grounding decoder ( $D_g$ ) learns the region-level alignment by TCL, resulting in more precise, finer, and less noisy generated masks (See "w/ $D_g$ " rows). + +Qualitative comparison. We qualitatively compare the proposed method in Fig. 5. On the PASCAL VOC dataset (Fig. 5a), we observe various types of errors in each comparison method. The grouping procedure of GroupViT [30] + +makes the segmentation results less noisy, but it also causes an incorrect segmentation of a large group. ReCo [28] struggles with the segmentation of background regions due to the lack of consideration about the background class. MaskCLIP [33] does not take this into account as well, but its refinement methods make the results less noisy. In addition, we present examples in the wild to show open-world segmentation capability in Fig. 5b. We collect test samples containing visual concepts not included in conventional segmentation datasets (e.g., moon, sunset) or free-form texts (e.g., "two women and one man with a smiling snowman"). GroupViT tends to focus on the main object of the image and regard the other objects as background, which is consistent with its good performance in object-oriented datasets. Interestingly, in this qualitative comparison in the wild, we observe ReCo consistently outperforms MaskCLIP contrary to the quantitative results. We conjecture that this is because the refinement approach of ReCo is data-driven, while the refinement approach of MaskCLIP depends on heuristic post-processing, which may not guarantee general improvement. Compared to the baselines, TCL consistently generates more precise segmentation masks. These results demonstrate that our proposed method, which learns region-level alignment, improves the segmentation quality both in the evaluation dataset and in web images in the wild. Additional qualitative results are provided in Appendix H. + +Additional analysis on failure cases and model behavior are provided in Appendices F and G, respectively. + +# 4.4. Ablation Studies + +We investigate the impact of individual components of the proposed framework by ablation studies on the training split of the PASCAL VOC20 dataset. We use a short learning schedule with a batch size of 512 for total 40,000 iterations including 10,000 warmup steps. + +Baseline to TCL. Table 2a presents cumulative ablation studies on the grounding decoder and the TCL losses. Our initial model before training based on MaskCLIP [33] is referred to as the baseline (A), which modifies the last attention layer of the CLIP image encoder. When we add only the grounding decoder to the baseline without TCL loss (B), there is no improvement in performance. This suggests that training the decoder with the same CL loss as the pre-training (CLIP) does not enhance the localization capabilities. As shown in (C), the proposed framework becomes complete with TCL loss. + +Impact of individual TCL losses. The influence of each component of the proposed TCL loss and its effect on the segmentation performance compared to the conventional CL loss are shown in Table 2b. Smooth regularization is used for all experiments in this table. The CL loss (D) is computed by applying attention pooling [23] to the dense + +![](images/dccaf6c356c76e50b0cb033f0deef5aa49b9f1c9ee53b8db3245ae22e0226e38.jpg) +(a) Examples in PASCAL VOC. + +![](images/874a81f53d3d325fde6b6457bbf701a5a44a29ae9953c312fe6c9c5e0adc7e59.jpg) +(b) Examples in the wild. +Figure 5. (a) The comparison shows the error types of each method in the VOC dataset. GroupViT tends to make an error on a large group rather than noisy results. ReCo suffers from segmentation of the background region. MaskCLIP tends to fail at capturing the target area precisely. (b) shows results on the wild web images and free-form texts. Texts used as target classes are shown at the bottom of the images. + +image embedding $\mathbf{V}^s$ . When comparing (D) and (C), the proposed TCL loss remarkably improves the segmentation performance $(61.1 \rightarrow 77.4)$ . Image-level or feature-level TCL loss (E, F) solely improves the performance significantly, and using both losses together provides further performance gain. Using CL in addition to TCL (G) does not improve performance, and it is essential to use area TCL loss in TCL framework to prevent model collapse (H), as described in Sec. 3.3. The difference between (B) and (D) is the use of smooth regularization. + +Hyperparameters. Tables 2c to 2e show the performance changes according to the variation of the loss weight hyperparameters (HPs). The first rows show the importance of each loss ( $\lambda = 0.0$ cases). The absence of area TCL loss causes a significant performance drop (Table 2d), as mentioned above. Smooth regularization also significantly contributes to the final performance (Table 2e), supporting our assumption that the text-described region is smooth rather than noisy. Note that the sensitivity on HPs is about loss balancing, not about the target dataset. As an open-world segmentation method, TCL does not require any tuning with the target dataset, including model fine-tuning and inference HPs tuning. Once a TCL model is trained, we evaluate the model for every benchmark without any fine-tuning. + +# 5. Conclusion + +We propose a novel framework for open-world semantic segmentation with only image-text pairs, addressing the alignment-level discrepancy between training (image-text) + +
MethodVOC20
A Baseline53.2
B + Decoder52.3
C + TCL77.4
+ +
TCLvTCLfLareaCLVOC20
D✓61.1
E✓✓74.6
F✓✓76.0
C✓✓✓77.4
G✓✓✓✓75.6
H✓✓67.1
+ +(b) TCL losses. +(a) Baseline to T + +
λTCLVOC20
0.0-
0.0176.8
0.177.4
1.068.2
+ +(c) $\mathcal{L}_{\mathrm{TCL}}$ + +
λareaVOC20
0.067.1
0.0469.5
0.477.4
4.076.7
+ +(d) $\mathcal{L}_{\mathrm{area}}$ + +
λtvVOC20
0.073.8
0.175.2
1.077.4
10.070.7
+ +(e) $\mathcal{L}_{\mathrm{tv}}$ + +Table 2. Ablation studies on TCL losses and hyperparameters. Refinement techniques are not applied to reveal the effect of each loss function clearly. Default settings are marked in gray. + +and testing (region-text) in existing methods. In the proposed framework, we incorporate the grounding process within contrastive learning, thus allowing explicitly learning alignment between text and text-grounded regions (i.e., segmentation mask). We also present a unified evaluation protocol for a fair comparison of existing methods, where TCL achieves state-of-the-art zero-shot segmentation performance on all 8 benchmarks, remarkably surpassing previous methods. We hope that this study encourages a new research direction of explicitly learning region-text alignment for open-world semantic segmentation. + +# References + +[1] Nikita Araslanov and Stefan Roth. Single-stage semantic segmentation from image labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. 6, 12 +[2] Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, and Saehoon Kim. Coyo-700m: Image-text pair dataset, 2022. 2, 13 +[3] Holger Caesar, Jasper Uijlings, and Vittorio Ferrari. Cocosstuff: Thing and stuff classes in context. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018. 6, 14 +[4] Junbum Cha, Kyungjae Lee, Sungrae Park, and Sanghyuk Chun. Domain generalization by mutual-information regularization with pre-trained models. European Conference on Computer Vision (ECCV), 2022. 11 +[5] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. 2, 6 +[6] Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. Advances in Neural Information Processing Systems, 34:17864-17875, 2021. 15 +[7] François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017. 13 +[8] MMSegmentation Contributors. MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https://github.com/open-mmlab/mmsegmentation, 2020.6 +[9] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 6 +[10] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010. 6 +[11] Golnaz Ghiasi, Xiuye Gu, Yin Cui, and Tsung-Yi Lin. Scaling open-vocabulary image segmentation with image-level labels. In European Conference on Computer Vision, pages 540-557. Springer, 2022. 1, 2, 3 +[12] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. *ICLR*, 2022. 2 +[13] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. EMNLP, 2021. 2 +[14] Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. In International Conference on Learning Representations, 2017. 4 + +[15] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790, 2021. 3 +[16] Philipp Krahenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. Advances in neural information processing systems, 24, 2011. 6 +[17] Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and Rene Ranftl. Language-driven semantic segmentation. In International Conference on Learning Representations, 2021. 1, 2 +[18] Feng Liang, Bichen Wu, Xiaoliang Dai, Kunpeng Li, Yinan Zhao, Hang Zhang, Peizhao Zhang, Peter Vajda, and Diana Marculescu. Open-vocabulary semantic segmentation with mask-adapted clip. arXiv preprint arXiv:2210.04150, 2022. 1, 2 +[19] Quande Liu, Youpeng Wen, Jianhua Han, Chunjing Xu, Hang Xu, and Xiaodan Liang. Open-world semantic segmentation via contrasting and clustering vision-language embedding. European Conference on Computer Vision (ECCV), 2022. 1, 2, 3, 5, 14 +[20] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018. 6 +[21] Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic segmentation in the wild. CVPR, 2014. 6 +[22] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 3, 4 +[23] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 3, 4, 5, 7, 11, 12, 13 +[24] Leonid I Rudin, Stanley Osher, and Emad Fatemi. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4):259-268, 1992. 5 +[25] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252, 2015. 13 +[26] Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. arXiv preprint arXiv:2111.02114, 2021. 2 +[27] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of ACL, 2018. 2, 5, 6 +[28] Gyungin Shin, Weidi Xie, and Samuel Albanie. Reco: Retrieve and co-segment for zero-shot transfer. Advances in + +Neural Information Processing Systems, 2022. 1, 2, 3, 5, 6, 7, 13, 15, 16 +[29] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34, 2021. 15 +[30] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1, 2, 3, 5, 6, 7, 14, 15, 16 +[31] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, and Jianfeng Gao. Regionclip: Region-based language-image pretraining. CVPR, 2022. 2, 3 +[32] Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127(3):302-321, 2019. 6, 14 +[33] Chong Zhou, Chen Change Loy, and Bo Dai. Extract free dense labels from clip. European Conference on Computer Vision (ECCV), 2022. 1, 2, 3, 5, 6, 7, 11, 14, 15, 16 \ No newline at end of file diff --git a/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/images.zip b/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..b7526b5da87579a7d1518d7ed8a82fbaef1515de --- /dev/null +++ b/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b79cdd6a83df99e2e0ba1b74b4a2b527213150aa497cc4edcb4d987047c75fd7 +size 493211 diff --git a/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/layout.json b/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..e35e87c6fc160aaef9de09e120475fbf02cc31a2 --- /dev/null +++ b/2023/Learning To Generate Text-Grounded Mask for Open-World Semantic Segmentation From Only Image-Text Pairs/layout.json @@ -0,0 +1,7306 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 96, + 103, + 500, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 103, + 500, + 140 + ], + "spans": [ + { + "bbox": [ + 96, + 103, + 500, + 140 + ], + "type": "text", + "content": "Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 148, + 161, + 211, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 148, + 161, + 211, + 175 + ], + "spans": [ + { + "bbox": [ + 148, + 161, + 211, + 175 + ], + "type": "text", + "content": "Junbum Cha" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 248, + 161, + 325, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 248, + 161, + 325, + 175 + ], + "spans": [ + { + "bbox": [ + 248, + 161, + 325, + 175 + ], + "type": "text", + "content": "Jonghwan Mun" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 362, + 161, + 442, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 362, + 161, + 442, + 175 + ], + "spans": [ + { + "bbox": [ + 362, + 161, + 442, + 175 + ], + "type": "text", + "content": "Byungseok Roh" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 264, + 184, + 328, + 196 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 264, + 184, + 328, + 196 + ], + "spans": [ + { + "bbox": [ + 264, + 184, + 328, + 196 + ], + "type": "text", + "content": "Kakao Brain" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 162, + 201, + 428, + 213 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 162, + 201, + 428, + 213 + ], + "spans": [ + { + "bbox": [ + 162, + 201, + 428, + 213 + ], + "type": "text", + "content": "{junbum.cha, jason.mun, peter. roh}@kakaobrain.com" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 143, + 239, + 192, + 252 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 239, + 192, + 252 + ], + "spans": [ + { + "bbox": [ + 143, + 239, + 192, + 252 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 266, + 290, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 266, + 290, + 553 + ], + "spans": [ + { + "bbox": [ + 46, + 266, + 290, + 553 + ], + "type": "text", + "content": "We tackle open-world semantic segmentation, which aims at learning to segment arbitrary visual concepts in images, by using only image-text pairs without dense annotations. Existing open-world segmentation methods have shown impressive advances by employing contrastive learning (CL) to learn diverse visual concepts and transferring the learned image-level understanding to the segmentation task. However, these CL-based methods suffer from a train-test discrepancy, since it only considers image-text alignment during training, whereas segmentation requires region-text alignment during testing. In this paper, we proposed a novel Text-grounded Contrastive Learning (TCL) framework that enables a model to directly learn region-text alignment. Our method generates a segmentation mask for a given text, extracts text-grounded image embedding from the masked region, and aligns it with text embedding via TCL. By learning region-text alignment directly, our framework encourages a model to directly improve the quality of generated segmentation masks. In addition, for a rigorous and fair comparison, we present a unified evaluation protocol with widely used 8 semantic segmentation datasets. TCL achieves state-of-the-art zero-shot segmentation performances with large margins in all datasets. Code is available at https://github.com/kakaobrain/tcl." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 578, + 128, + 590 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 578, + 128, + 590 + ], + "spans": [ + { + "bbox": [ + 47, + 578, + 128, + 590 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 598, + 287, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 598, + 287, + 684 + ], + "spans": [ + { + "bbox": [ + 46, + 598, + 287, + 684 + ], + "type": "text", + "content": "Open-world semantic segmentation aims to identify the arbitrary semantic concepts in the open world1. Conventional semantic segmentation aims to learn segmentation capability for the small number of pre-defined target categories, whereas open-world semantic segmentation addresses unrestricted arbitrary categories or free-form texts. Such segmentation capability over unlimited targets drasti-" + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 309, + 240, + 545, + 459 + ], + "blocks": [ + { + "bbox": [ + 309, + 240, + 545, + 459 + ], + "lines": [ + { + "bbox": [ + 309, + 240, + 545, + 459 + ], + "spans": [ + { + "bbox": [ + 309, + 240, + 545, + 459 + ], + "type": "image", + "image_path": "80971d0e0724e7660fa796a783f6ccd8781fcbaf109513e3a821d0cdc3e85947.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 467, + 545, + 502 + ], + "lines": [ + { + "bbox": [ + 305, + 467, + 545, + 502 + ], + "spans": [ + { + "bbox": [ + 305, + 467, + 545, + 502 + ], + "type": "text", + "content": "Figure 1. Open-world segmentation performance comparison. The proposed method remarkably outperforms existing methods in all 8 segmentation benchmark datasets." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 520, + 545, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 520, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 305, + 520, + 545, + 544 + ], + "type": "text", + "content": "cally extends the application scope of the open-world segmentation models." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 545, + 546, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 546, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 546, + 715 + ], + "type": "text", + "content": "The first challenge for open-world segmentation is how to learn arbitrary concepts, beyond pre-defined categories. Inspired by the success of CLIP [23], previous approaches [11, 17-19, 28, 30, 33] tackle this challenge by exploiting massive web-crawled image-text paired data; since the texts in web-crawled data contain a global semantic description for the paired images, the large-scale image-text pairs can provide rich knowledge for arbitrary semantic categories. However, there still remains another challenge in how to achieve precise localization of arbitrary concepts without dense annotations. There are several approaches that simply address this issue using dense annotation (segmentation masks) in addition to image-text pairs [11, 17, 18]. The dense annotation helps to improve segmentation perfor" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 693, + 287, + 714 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 693, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 693, + 287, + 714 + ], + "type": "text", + "content": "This setting is often called both open-world and open-vocabulary. In this paper, we mainly refer to this setting as open-world for clarity." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11165" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 72, + 286, + 233 + ], + "blocks": [ + { + "bbox": [ + 49, + 72, + 286, + 233 + ], + "lines": [ + { + "bbox": [ + 49, + 72, + 286, + 233 + ], + "spans": [ + { + "bbox": [ + 49, + 72, + 286, + 233 + ], + "type": "image", + "image_path": "7fbcfb4a59ec97b008d0fa225d53467eaec0e55d1704e91d293edceec65e077c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 243, + 287, + 331 + ], + "lines": [ + { + "bbox": [ + 46, + 243, + 287, + 331 + ], + "spans": [ + { + "bbox": [ + 46, + 243, + 287, + 331 + ], + "type": "text", + "content": "Figure 2. A conceptual comparison between the previous approach and ours. Open-world segmentation is typically achieved through region-text alignment, which involves matching region features and text embeddings. However, previous methods learn image-text alignment during training, thus suffering from the alignment-level discrepancy between training and testing. In contrast, our method facilitates end-to-end learning of region-text alignment with only image-text pairs." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 342, + 287, + 378 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 342, + 287, + 378 + ], + "spans": [ + { + "bbox": [ + 46, + 342, + 287, + 378 + ], + "type": "text", + "content": "mance in a fixed benchmark dataset, but the requirements of expensive dense annotation still limit the applicable domains and scalability of the method." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 379, + 287, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 379, + 287, + 617 + ], + "spans": [ + { + "bbox": [ + 46, + 379, + 287, + 617 + ], + "type": "text", + "content": "In this paper, therefore, we focus on open-world semantic segmentation from only image-text pairs without any dense annotation. For this setting, the existing methods [19, 28, 30, 33] learn an image-text alignment capability during training and heavily rely on the transferability of the image-text alignment to perform region-text alignment at inference. More specifically, MaskCLIP [33] leverages CLIP models pre-trained to learn image-text alignment. To perform region-text alignment using CLIP, MaskCLIP applies a simple heuristic modification to the CLIP image encoder. GroupViT [30] and ViL-Seg [19] propose to cluster region-level visual features into distinct groups and generate segmentation masks by matching the groups and texts. Note that they match the text embeddings and clustered region features in test time, but in training time, the text embeddings are aligned with global image embeddings. While the existing methods have shown impressive results even through the training with image-text alignment, they still suffer from the alignment-level discrepancy between training and testing phases as depicted in Fig. 2." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "text", + "content": "To address this train-test discrepancy, we propose the Text-grounded Contrastive Learning (TCL) framework, which allows a model to learn region-text alignment directly from the image-text pairs without any dense annotations. Our key idea is to incorporate a text grounding procedure within contrastive learning as illustrated in Fig. 2, where TCL generates a segmentation mask indicating text-grounded regions, computes grounded region embeddings" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 72, + 545, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 192 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 192 + ], + "type": "text", + "content": "using the mask, and applies contrastive learning between text and grounded region. By re-formulating the contrastive loss to be directly affected by the segmentation quality, TCL enables end-to-end training of the grounder and directly improves the quality of region-text level alignment. We also present a unified evaluation protocol using widely used 8 semantic segmentation datasets and compare existing methods in the same setting. As a result, TCL achieves state-of-the-art zero-shot segmentation performance with large margins in all datasets, as shown in Fig. 1." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 317, + 193, + 525, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 193, + 525, + 204 + ], + "spans": [ + { + "bbox": [ + 317, + 193, + 525, + 204 + ], + "type": "text", + "content": "Our main contributions are summarized as follows:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 317, + 205, + 545, + 348 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 317, + 205, + 545, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 205, + 545, + 277 + ], + "spans": [ + { + "bbox": [ + 317, + 205, + 545, + 277 + ], + "type": "text", + "content": "- We introduce a novel framework for open-world segmentation, named Text-grounded Contrastive Learning (TCL), which enables learning region-text alignment directly without train-test discrepancy, thus learning to generate more precise segmentation masks through only image-text pairs." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 317, + 277, + 545, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 277, + 545, + 312 + ], + "spans": [ + { + "bbox": [ + 317, + 277, + 545, + 312 + ], + "type": "text", + "content": "- We present a unified evaluation protocol and re-evaluate recent open-world segmentation models for a fair and direct comparison." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 317, + 313, + 545, + 348 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 313, + 545, + 348 + ], + "spans": [ + { + "bbox": [ + 317, + 313, + 545, + 348 + ], + "type": "text", + "content": "- We achieve the new state-of-the-art zero-shot segmentation performance on 8 segmentation datasets with large margins compared to existing methods." + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 361, + 397, + 374 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 361, + 397, + 374 + ], + "spans": [ + { + "bbox": [ + 306, + 361, + 397, + 374 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 383, + 497, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 383, + 497, + 396 + ], + "spans": [ + { + "bbox": [ + 306, + 383, + 497, + 396 + ], + "type": "text", + "content": "2.1. Open-world Semantic Segmentation" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 402, + 545, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 402, + 545, + 545 + ], + "spans": [ + { + "bbox": [ + 304, + 402, + 545, + 545 + ], + "type": "text", + "content": "Open-world scenario aims to recognize arbitrary concepts in the open world. It is also called open-vocabulary because the target vocabulary is open rather than closed. Contrastive Language-Image Pre-training (CLIP) [23] ushered in the era of open-world image recognition using largescale image-text pairs [2, 5, 26, 27]. CLIP learns the alignment between an image and a text in training time, then transfer it to the zero-shot classification by aligning image and texts indicating target classes at inference time. The advent of CLIP enables open-world settings in various fields such as object detection [12, 31], image captioning [13], or semantic segmentation [30, 33]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 546, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 546, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 546, + 546, + 713 + ], + "type": "text", + "content": "Open-world semantic segmentation with image-text pairs is addressed in two different settings. The first is a semi-supervised setting, which uses dense annotation (i.e., segmentation masks) in addition to image-text pairs [11, 17, 18]. Semi-supervised approaches learn segmentation capability using dense annotation and expand the target vocabulary using image-text supervision. LSeg [17] expands target class vocabulary using image-label datasets and CLIP text encoder [23]. OpenSeg [11] and OVSeg [18] first train a mask generator using dense annotation and expand target vocabulary using image-text datasets. The use of dense annotation makes the model learn region-level alignment instead of image-level alignment, leading to high-quality segmentation masks. However, it still relies on" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11166" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 95 + ], + "type": "text", + "content": "costly dense annotation, and applicable domains are limited to the domains where dense annotation is available." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 97, + 289, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 97, + 289, + 529 + ], + "spans": [ + { + "bbox": [ + 46, + 97, + 289, + 529 + ], + "type": "text", + "content": "The target of this paper is an unsupervised setting, which aims to learn segmentation from only image-text pairs without any dense annotation [19, 28, 30, 33]. Since the massive image-text pairs are easily obtained by web crawling without human annotators, applicable domains of unsupervised methods become almost unlimited. In order to achieve segmentation capability using only image-text pairs, we need to learn region-text alignment instead of image-text alignment and train a text-grounded mask generator. However, the absence of dense annotation makes this approach challenging. Existing open-world semantic segmentation studies have taken a strategy to bypass this issue. Instead of learning region-level alignment directly, they transfer image-level alignment to region-level by heuristic modification [28, 33] or clustering [19, 30]. MaskCLIP [33] proposes to obtain a dense image embedding from CLIP image encoder through heuristic modification of the last attention layer. Even though it has several limitations, such as low output resolution or noisy segmentation results, they show it is a simple yet effective way to obtain an initial segmentation map for refinement. ReCo [28] proposes an advanced refinement method based on MaskCLIP, by retrieval and co-segmentation. Clustering-based methods [19, 30] learn representations using CL with image-text pairs. They compute region-level image embedding by clustering sub-region embeddings. These approaches also have shown impressive results but have several limitations: (i) the learning objective is still image-level alignment due to lack of the region annotation, (ii) the number of clusters is pre-defined independent of the given image, and (iii) clustering sub-region image embeddings is independent of the query text. In summary, existing methods indirectly address region-level alignment problems by learning image-level alignment. To tackle this problem, we propose a novel region-level alignment objective, named Text-grounded Contrastive Learning (TCL)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 538, + 231, + 552 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 538, + 231, + 552 + ], + "spans": [ + { + "bbox": [ + 47, + 538, + 231, + 552 + ], + "type": "text", + "content": "2.2. Region-level Contrastive Learning" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 557, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 557, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 557, + 288, + 713 + ], + "type": "text", + "content": "Learning region-level alignment instead of image-level alignment is a fundamental target objective in dense tasks, such as segmentation or object detection. There are approaches to learn region-level alignment using dense annotation in the semi-supervised setting. They first train mask or region proposal networks using dense annotation and learn alignment between the proposals and texts [11, 15, 31]. For example, OpenSeg [11] trains a class-agnostic mask generator using dense annotation. In the object detection field, RegionCLIP [31] employs an off-the-shelf region proposal network and learns region-level alignment. In contrast to the existing region-level methods, the proposed method learns region-level alignment without any dense annotation." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 306, + 71, + 367, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 71, + 367, + 84 + ], + "spans": [ + { + "bbox": [ + 306, + 71, + 367, + 84 + ], + "type": "text", + "content": "3. Methods" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 306, + 91, + 373, + 102 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 91, + 373, + 102 + ], + "spans": [ + { + "bbox": [ + 306, + 91, + 373, + 102 + ], + "type": "text", + "content": "3.1. Overview" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 110, + 545, + 229 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 110, + 545, + 229 + ], + "spans": [ + { + "bbox": [ + 304, + 110, + 545, + 229 + ], + "type": "text", + "content": "Open-world semantic segmentation is a task that aims to learn a model capable of zero-shot segmentation for arbitrary visual concepts, not restricted to pre-defined ones. Our main goal is to develop an open-world segmentation algorithm using only image-text pairs. However, achieving this objective is challenging because there is no explicit supervision (i.e., pixel-level dense annotations) for text-described region segmentation. Existing methods learn models parametrized by " + }, + { + "bbox": [ + 304, + 110, + 545, + 229 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 304, + 110, + 545, + 229 + ], + "type": "text", + "content": " to maximize the mutual information between paired images and texts [22, 23] as follows:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 381, + 239, + 545, + 260 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 381, + 239, + 545, + 260 + ], + "spans": [ + { + "bbox": [ + 381, + 239, + 545, + 260 + ], + "type": "interline_equation", + "content": "\\underset {\\theta} {\\arg \\max } I _ {\\theta} \\left(\\mathbf {x} ^ {V}; \\mathbf {x} ^ {T}\\right), \\tag {1}", + "image_path": "ceda9c038862a940d653965ca41005df2f8ccaff6e7c035ad1858cc016cce1f4.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 266, + 546, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 266, + 546, + 469 + ], + "spans": [ + { + "bbox": [ + 304, + 266, + 546, + 469 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 266, + 546, + 469 + ], + "type": "inline_equation", + "content": "(\\mathbf{x}^V, \\mathbf{x}^T)" + }, + { + "bbox": [ + 304, + 266, + 546, + 469 + ], + "type": "text", + "content": " is a random image and text pair. This objective encourages the model to learn the alignment between images and texts, however, at test time, the learned model generates segmentation masks for arbitrary concepts by computing region-text alignments. Such alignment-level discrepancy between train and test time can lead the model to a sub-optimal solution as shown in Fig. 2. With this in consideration, to bridge the gap between the objective of conventional contrastive learning (CL) and the requirement of the zero-shot segmentation, we propose Text-grounded Contrastive Learning (TCL) which incorporates a text grounding process within CL to enable learning region-text alignment directly. As a text grounding module, we introduce a groundeder to generate segmentation masks for the given texts. In a nutshell, TCL learns a model to maximize mutual information between text-grounded regions and texts as follows:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 373, + 478, + 545, + 499 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 373, + 478, + 545, + 499 + ], + "spans": [ + { + "bbox": [ + 373, + 478, + 545, + 499 + ], + "type": "interline_equation", + "content": "\\underset {\\theta} {\\arg \\max } I _ {\\theta} \\left(\\mathbf {m} \\cdot \\mathbf {x} ^ {V}; \\mathbf {x} ^ {T}\\right), \\tag {2}", + "image_path": "4d9a3f9f58d661af75508971cefb37a3bf200dcc1224c1d2bfe8fa612fdcb8d6.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 507, + 545, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 507, + 545, + 567 + ], + "spans": [ + { + "bbox": [ + 304, + 507, + 545, + 567 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 507, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\mathbf{m}" + }, + { + "bbox": [ + 304, + 507, + 545, + 567 + ], + "type": "text", + "content": " is a text-grounded mask of random variable indicating the text-described region. Compared to contrastive learning that implicitly learns a grounding capability, TCL has a clear advantage of explicitly learning the grounding capability through the end-to-end trainable grounder." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 567, + 545, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 567, + 545, + 639 + ], + "spans": [ + { + "bbox": [ + 304, + 567, + 545, + 639 + ], + "type": "text", + "content": "In the rest of this section, we first explain the text-grounded mask generation procedure by the grounder. Then, we describe how we define losses using the generated mask to train our open-world grounder with text-grounded contrastive learning. Lastly, we explain how our model performs zero-shot inference for arbitrary concepts." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 647, + 375, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 647, + 375, + 658 + ], + "spans": [ + { + "bbox": [ + 306, + 647, + 375, + 658 + ], + "type": "text", + "content": "3.2. Grounder" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "text", + "content": "Fig. 3 illustrates our overall training pipeline. For an input batch of paired texts " + }, + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{X}^T" + }, + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "text", + "content": " and images " + }, + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{X}^V" + }, + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "text", + "content": ", TCL first performs a grounding process to identify text-grounded regions for a text via a grounder. The grounder consists of" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "11167" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 62, + 72, + 532, + 247 + ], + "blocks": [ + { + "bbox": [ + 62, + 72, + 532, + 247 + ], + "lines": [ + { + "bbox": [ + 62, + 72, + 532, + 247 + ], + "spans": [ + { + "bbox": [ + 62, + 72, + 532, + 247 + ], + "type": "image", + "image_path": "55183c3c1e4ed7cf0bd5c194f490ea35eee154d524c2c6de47f8888fdf0acebe.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 255, + 547, + 313 + ], + "lines": [ + { + "bbox": [ + 45, + 255, + 547, + 313 + ], + "spans": [ + { + "bbox": [ + 45, + 255, + 547, + 313 + ], + "type": "text", + "content": "Figure 3. Overall training pipeline of TCL. The proposed TCL framework first obtains text-grounded masks using a grounder and then learns the grounder using text-grounded contrastive learning. By incorporating the text grounding process with contrastive learning, our framework can directly learn region-text alignment that is required for precise segmentation. " + }, + { + "bbox": [ + 45, + 255, + 547, + 313 + ], + "type": "inline_equation", + "content": "\\mathrm{CLIP_T}" + }, + { + "bbox": [ + 45, + 255, + 547, + 313 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 45, + 255, + 547, + 313 + ], + "type": "inline_equation", + "content": "\\mathrm{CLIP_I}" + }, + { + "bbox": [ + 45, + 255, + 547, + 313 + ], + "type": "text", + "content": " indicate CLIP text and image encoders, respectively. CLIP encoders are frozen and we train the grounding decoder only. After training, the TCL block is discarded, and only the Grounder block is used to generate the text-grounded segmentation mask for inference." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 323, + 289, + 478 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 323, + 289, + 478 + ], + "spans": [ + { + "bbox": [ + 46, + 323, + 289, + 478 + ], + "type": "text", + "content": "three components: (i) image encoder " + }, + { + "bbox": [ + 46, + 323, + 289, + 478 + ], + "type": "inline_equation", + "content": "E_{v}" + }, + { + "bbox": [ + 46, + 323, + 289, + 478 + ], + "type": "text", + "content": " is in charge of providing a single (L2-normalized) global feature as well as dense patch-level features, (ii) text encoder " + }, + { + "bbox": [ + 46, + 323, + 289, + 478 + ], + "type": "inline_equation", + "content": "E_{t}" + }, + { + "bbox": [ + 46, + 323, + 289, + 478 + ], + "type": "text", + "content": " provides a (L2-normalized) text embedding feature, and (iii) grounding decoder " + }, + { + "bbox": [ + 46, + 323, + 289, + 478 + ], + "type": "inline_equation", + "content": "D_{g}" + }, + { + "bbox": [ + 46, + 323, + 289, + 478 + ], + "type": "text", + "content": " converts dense features from image encoder into finer pixel-level embeddings for alignment with text. In practice, we adopt a pre-trained CLIP model [23] to initialize two encoders and freeze them to preserve and exploit the rich knowledge of CLIP learned during largescale pre-training. The text-grounded masks are computed by the position-wise dot product between text embedding and dense pixel-level embedding. The overall process of grounder is summarized as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 74, + 487, + 287, + 502 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 487, + 287, + 502 + ], + "spans": [ + { + "bbox": [ + 74, + 487, + 287, + 502 + ], + "type": "interline_equation", + "content": "\\mathbf {T} = E _ {t} \\left(\\mathbf {X} ^ {T}\\right) \\quad \\text {a n d} \\quad \\mathbf {V} ^ {g}, \\mathbf {V} ^ {d} = E _ {v} \\left(\\mathbf {X} ^ {V}\\right), \\tag {3}", + "image_path": "4a3f0f92e90e98350183a5e288ecb54c163414581531f80e736b3b9994b3ed9e.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 75, + 504, + 287, + 518 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 504, + 287, + 518 + ], + "spans": [ + { + "bbox": [ + 75, + 504, + 287, + 518 + ], + "type": "interline_equation", + "content": "\\mathbf {V} ^ {s} = D _ {g} \\left(\\mathbf {V} ^ {d}\\right), \\tag {4}", + "image_path": "b05939309e0606af1215498d54c01a0b321be592b82075f09df6286cc7a5daaa.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 75, + 520, + 287, + 536 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 520, + 287, + 536 + ], + "spans": [ + { + "bbox": [ + 75, + 520, + 287, + 536 + ], + "type": "interline_equation", + "content": "\\mathbf {M} _ {i, j} = \\sigma \\left(w \\cdot \\mathbf {t} _ {j} ^ {\\top} \\mathbf {V} _ {i} ^ {s} + b\\right), \\tag {5}", + "image_path": "558b12a6366f581707d68c598e631ca6435a6e84e55743ce0d4597795e5edf7a.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "spans": [ + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "inline_equation", + "content": "\\mathbf{T} \\in \\mathbb{R}^{B \\times C}" + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "inline_equation", + "content": "\\mathbf{V}^g \\in \\mathbb{R}^{B \\times C}" + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "inline_equation", + "content": "\\mathbf{V}^d \\in \\mathbb{R}^{B \\times L \\times C}" + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "text", + "content": " are normalized text embeddings, normalized global image embeddings, and dense image features from CLIP encoders, " + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "inline_equation", + "content": "\\mathbf{V}^s \\in \\mathbb{R}^{B \\times C \\times H \\times W}" + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "text", + "content": " is normalized pixel-level dense embeddings by the grounding decoder, and " + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "inline_equation", + "content": "\\mathbf{M} \\in \\mathbb{R}^{B \\times B \\times H \\times W}" + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "text", + "content": " is text-grounded masks between images and texts in the batch. " + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "text", + "content": " indicate a batch size, the embedding dimension size, and the number of patches, respectively. " + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "text", + "content": " is a sigmoid function, and " + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 47, + 544, + 287, + 653 + ], + "type": "text", + "content": " are learnable scalar projection." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 654, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 288, + 714 + ], + "type": "text", + "content": "The generated text-grounded masks are used to extract text-grounded image embedding. By replacing the global image embedding with text-grounded image embedding in the contrastive learning framework, TCL enables the model to learn region-text alignment in an end-to-end manner. In" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 323, + 545, + 348 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 323, + 545, + 348 + ], + "spans": [ + { + "bbox": [ + 305, + 323, + 545, + 348 + ], + "type": "text", + "content": "the following section, we describe how the generated mask " + }, + { + "bbox": [ + 305, + 323, + 545, + 348 + ], + "type": "inline_equation", + "content": "\\mathbf{M}" + }, + { + "bbox": [ + 305, + 323, + 545, + 348 + ], + "type": "text", + "content": " is used for text-grounded contrastive learning." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 361, + 501, + 373 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 361, + 501, + 373 + ], + "spans": [ + { + "bbox": [ + 306, + 361, + 501, + 373 + ], + "type": "text", + "content": "3.3. Text-grounded Contrastive Learning" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 381, + 545, + 478 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 381, + 545, + 478 + ], + "spans": [ + { + "bbox": [ + 304, + 381, + 545, + 478 + ], + "type": "text", + "content": "Recall that the main idea of TCL is to use text-grounded images instead of whole images, unlike conventional CL. For this purpose, we define TCL losses in three different levels—image-level, feature-level, and area-level—using the generated masks M for all pairs of images and texts in a batch; the detailed pseudo code to compute TCL losses is given in Appendix A. We also employ smooth regularization to further improve the quality of generated masks." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "spans": [ + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "text", + "content": "Image-level TCL loss. One intuitive way to compute the text-grounded image embedding is to encode only the regions of the image that contains the semantics of the paired text, using the image encoder. To make the whole process end-to-end trainable, we compute a differentiable masked image by multiplying the given image " + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_i^V" + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "text", + "content": " and a binarized mask " + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{i,i}^b" + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "text", + "content": " obtained from the generated mask " + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{i,i}" + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "text", + "content": " using Gumbel-Max [14]. The masked image is then fed into the image encoder, " + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathbf{v}}_i^g,\\tilde{\\mathbf{v}}_i^d = E_v\\left(\\mathbf{M}_{i,i}^b\\cdot \\mathbf{X}_i^V\\right)" + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "text", + "content": ", to obtain text-grounded image embedding " + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "inline_equation", + "content": "\\tilde{\\mathbf{v}}_i^g" + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "text", + "content": ". We compute the cosine similarity matrix between text-grounded image embeddings and text embeddings in a batch by " + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "inline_equation", + "content": "S_{i,j}^{m} = \\tilde{\\mathbf{v}}_i^{g^\\top}\\mathbf{t}_j" + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "text", + "content": ". Finally, we use the symmetric version of InfoNCE [22, 23] to define the image-level TCL loss " + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{TCL}_v}" + }, + { + "bbox": [ + 304, + 482, + 546, + 687 + ], + "type": "text", + "content": " to make the representations of positive image-text pairs similar to each other while the representations of negative pairs dissimilar based on the similarity matrix:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 373, + 701, + 545, + 714 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 373, + 701, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 373, + 701, + 545, + 714 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {T C L} _ {v}} = \\operatorname {I n f o N C E} \\left(\\mathbf {S} ^ {m}\\right), \\tag {6}", + "image_path": "edde1698d541f5bb37c48f98fb2e605f82a7ac938a278375effe32d610467d59.jpg" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11168" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 87, + 287, + 156 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 87, + 287, + 156 + ], + "spans": [ + { + "bbox": [ + 61, + 87, + 287, + 156 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\operatorname {I n f o N C E} \\left(\\mathbf {S}\\right) = - \\frac {1}{2 B} \\sum_ {i} ^ {B} \\log \\frac {\\exp \\left(S _ {i , i} / \\tau\\right)}{\\sum_ {j} ^ {B} \\exp \\left(S _ {i , j} / \\tau\\right)} \\\\ - \\frac {1}{2 B} \\sum_ {i} ^ {B} \\log \\frac {\\exp \\left(S _ {i , i} / \\tau\\right)}{\\sum_ {j} ^ {B} \\exp \\left(S _ {j , i} / \\tau\\right)}, \\tag {7} \\\\ \\end{array}", + "image_path": "8c34b4612cb2b5d3c03cb208fdff0b2a969f3e1c696e5f16d757cb04838dbfb9.jpg" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 159, + 191, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 159, + 191, + 171 + ], + "spans": [ + { + "bbox": [ + 47, + 159, + 191, + 171 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 159, + 191, + 171 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 47, + 159, + 191, + 171 + ], + "type": "text", + "content": " is a learnable temperature." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 175, + 289, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 175, + 289, + 369 + ], + "spans": [ + { + "bbox": [ + 47, + 175, + 289, + 369 + ], + "type": "text", + "content": "Feature-level TCL loss. The image-level TCL loss drives a model to generate segmentation masks for the paired texts (i.e., texts of positive pairs). However, we observe that this loss alone is insufficient to prevent the model from generating masks for regions not described in the text, particularly for salient regions. This raises the need to suppress negative masks obtained from unrelated texts (i.e., texts of negative pairs), but computing the image-level TCL loss for negative masks is infeasible due to the high computational cost of encoding text-grounded images. To overcome this challenge, we introduce the feature-level TCL loss, which enables the effective computation of features of the negative masks. Specifically, for pixel-level dense embeddings " + }, + { + "bbox": [ + 47, + 175, + 289, + 369 + ], + "type": "inline_equation", + "content": "\\mathbf{V}_i^s\\in \\mathbb{R}^{C\\times H\\times W}" + }, + { + "bbox": [ + 47, + 175, + 289, + 369 + ], + "type": "text", + "content": " from grounding decoder and a text embedding vector " + }, + { + "bbox": [ + 47, + 175, + 289, + 369 + ], + "type": "inline_equation", + "content": "\\mathbf{t}_j" + }, + { + "bbox": [ + 47, + 175, + 289, + 369 + ], + "type": "text", + "content": ", we compute feature-level text-grounded image embedding " + }, + { + "bbox": [ + 47, + 175, + 289, + 369 + ], + "type": "inline_equation", + "content": "\\mathbf{v}_{i,j}^{f}\\in \\mathbb{R}^{C}" + }, + { + "bbox": [ + 47, + 175, + 289, + 369 + ], + "type": "text", + "content": " by:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 100, + 374, + 287, + 403 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 374, + 287, + 403 + ], + "spans": [ + { + "bbox": [ + 100, + 374, + 287, + 403 + ], + "type": "interline_equation", + "content": "\\mathbf {v} _ {i, j} ^ {f} = \\frac {\\sum_ {h , w} M _ {i , j , h , w} \\cdot \\mathbf {v} _ {i , : , h , w} ^ {s}}{\\sum_ {h , w} M _ {i , j , h , w}}. \\tag {8}", + "image_path": "3fc9ac0c4b0bad7616c3e6b295179efab7e89c6d181d3255f29b7b5427d607a7.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 407, + 287, + 486 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 407, + 287, + 486 + ], + "spans": [ + { + "bbox": [ + 47, + 407, + 287, + 486 + ], + "type": "text", + "content": "Note that this feature-level embedding is computed using negative masks " + }, + { + "bbox": [ + 47, + 407, + 287, + 486 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{i,j (i \\neq j)}" + }, + { + "bbox": [ + 47, + 407, + 287, + 486 + ], + "type": "text", + "content": ", different from the image-level TCL loss. We then compute the cosine similarity " + }, + { + "bbox": [ + 47, + 407, + 287, + 486 + ], + "type": "inline_equation", + "content": "S_{i,j}^{f} = \\mathbf{v}_{i,j}^{f}^{\\top} \\mathbf{t}_{j}" + }, + { + "bbox": [ + 47, + 407, + 287, + 486 + ], + "type": "text", + "content": " between all pairs of text embeddings and feature-level text-grounded image embeddings in the batch. The feature-level TCL loss is defined as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 115, + 491, + 287, + 507 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 115, + 491, + 287, + 507 + ], + "spans": [ + { + "bbox": [ + 115, + 491, + 287, + 507 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {T C L} _ {f}} = \\operatorname {I n f o N C E} \\left(\\mathbf {S} ^ {f}\\right). \\tag {9}", + "image_path": "b37366a36f36cd79152fe43c4304d4a5ef7c84b639c018f59080a377f2255e82.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 514, + 289, + 682 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 514, + 289, + 682 + ], + "spans": [ + { + "bbox": [ + 47, + 514, + 289, + 682 + ], + "type": "text", + "content": "Area TCL loss. The image-level and feature-level TCL losses focus on generating a mask to capture the text-described region in the image. However, the model can collapse into a trivial solution with only these losses—generating a mask for the entire image instead of the desired region. To prevent this collapse, we introduce an additional objective to our TCL framework, named area TCL loss, which incorporates priors on the mask area to ensure capturing only the text-described region. To be specific, for the positive masks (masks from positive pairs) " + }, + { + "bbox": [ + 47, + 514, + 289, + 682 + ], + "type": "inline_equation", + "content": "\\mathbf{M}^{+}" + }, + { + "bbox": [ + 47, + 514, + 289, + 682 + ], + "type": "text", + "content": " and the negative masks (masks from negative pair) " + }, + { + "bbox": [ + 47, + 514, + 289, + 682 + ], + "type": "inline_equation", + "content": "\\mathbf{M}^{-}" + }, + { + "bbox": [ + 47, + 514, + 289, + 682 + ], + "type": "text", + "content": ", we denote the area of positive and negative masks by " + }, + { + "bbox": [ + 47, + 514, + 289, + 682 + ], + "type": "inline_equation", + "content": "\\overline{\\mathbf{M}^{+}}" + }, + { + "bbox": [ + 47, + 514, + 289, + 682 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 514, + 289, + 682 + ], + "type": "inline_equation", + "content": "\\overline{\\mathbf{M}^{-}}" + }, + { + "bbox": [ + 47, + 514, + 289, + 682 + ], + "type": "text", + "content": ", respectively. The area TCL loss is defined by L1-distance between the area priors and the expected area of each mask:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 58, + 695, + 287, + 717 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 695, + 287, + 717 + ], + "spans": [ + { + "bbox": [ + 58, + 695, + 287, + 717 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {a r e a}} = \\left\\| p ^ {+} - \\mathbb {E} \\left[ \\overline {{\\mathbf {M} ^ {+}}} \\right] \\right\\| _ {1} + \\left\\| p ^ {-} - \\mathbb {E} \\left[ \\overline {{\\mathbf {M} ^ {-}}} \\right] \\right\\| _ {1}, \\tag {10}", + "image_path": "53fc3555912175bc68bbc0db11723bad1ff86476e8766c45649229b886b156df.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "inline_equation", + "content": "p^+" + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "inline_equation", + "content": "p^-" + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "content": " are positive and negative area priors. For the negative area prior " + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "inline_equation", + "content": "p^-" + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "content": ", intuitively, we can expect the area of the negative masks to be 0.0. We set the positive area prior " + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "inline_equation", + "content": "p^+" + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "content": " to 0.4, which is the average text-described region area measured by MaskCLIP [33] in the CC3M dataset [27]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 136, + 547, + 220 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 136, + 547, + 220 + ], + "spans": [ + { + "bbox": [ + 304, + 136, + 547, + 220 + ], + "type": "text", + "content": "Smooth regularization. In the image-text dataset, a text usually describes the salient object or concept in the paired image. We observe that the regions described by the text are generally smooth rather than noisy. We employ total variation (TV) regularization loss [24] to incorporate this smoothness observation in the objective. The TV loss is applied to both mask and pixel-level dense embedding:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 372, + 230, + 545, + 244 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 372, + 230, + 545, + 244 + ], + "spans": [ + { + "bbox": [ + 372, + 230, + 545, + 244 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {T V}} = \\left\\| \\mathbf {M} \\right\\| _ {\\mathrm {T V}} + \\left\\| \\mathbf {V} ^ {s} \\right\\| _ {\\mathrm {T V}}, \\tag {11}", + "image_path": "5a50bd29768715b2c5a8c7bdd470ea7e88a8c07208c1f91f04fc2f7c14e3a793.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 253, + 476, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 253, + 476, + 266 + ], + "spans": [ + { + "bbox": [ + 306, + 253, + 476, + 266 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 306, + 253, + 476, + 266 + ], + "type": "inline_equation", + "content": "\\| \\cdot \\|_{\\mathrm{TV}}" + }, + { + "bbox": [ + 306, + 253, + 476, + 266 + ], + "type": "text", + "content": " is the anisotropic TV norm." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 269, + 500, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 269, + 500, + 281 + ], + "spans": [ + { + "bbox": [ + 306, + 269, + 500, + 281 + ], + "type": "text", + "content": "Final loss. Our final loss function is defined by:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 343, + 292, + 545, + 320 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 292, + 545, + 320 + ], + "spans": [ + { + "bbox": [ + 343, + 292, + 545, + 320 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\underbrace {\\lambda_ {\\mathrm {T C L}} \\mathcal {L} _ {\\mathrm {T C L}} + \\lambda_ {\\text {a r e a}} \\mathcal {L} _ {\\text {a r e a}}} _ {\\text {T C L l o s s e s}} + \\underbrace {\\lambda_ {\\mathrm {t v}} \\mathcal {L} _ {\\mathrm {t v}}} _ {\\text {r e g u l a r i z a t i o n}}, \\tag {12}", + "image_path": "6b70d349279d75e2c1ff59ebd0853b75b39f7c15e3a1a907d1f94bc218053e32.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 329, + 545, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 329, + 545, + 353 + ], + "spans": [ + { + "bbox": [ + 304, + 329, + 545, + 353 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 329, + 545, + 353 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{TCL}} = \\mathcal{L}_{\\mathrm{TCL}_v} + \\mathcal{L}_{\\mathrm{TCL}_f}" + }, + { + "bbox": [ + 304, + 329, + 545, + 353 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 329, + 545, + 353 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{TCL}}" + }, + { + "bbox": [ + 304, + 329, + 545, + 353 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 329, + 545, + 353 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{area}}" + }, + { + "bbox": [ + 304, + 329, + 545, + 353 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 329, + 545, + 353 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{tv}}" + }, + { + "bbox": [ + 304, + 329, + 545, + 353 + ], + "type": "text", + "content": " are hyperparameters to balance three losses." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 361, + 413, + 374 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 361, + 413, + 374 + ], + "spans": [ + { + "bbox": [ + 306, + 361, + 413, + 374 + ], + "type": "text", + "content": "3.4. Inference Pipeline" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 380, + 545, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 380, + 545, + 464 + ], + "spans": [ + { + "bbox": [ + 304, + 380, + 545, + 464 + ], + "type": "text", + "content": "The zero-shot inference pipeline is similar to CLIP [23], except for performing pixel-level classification instead of image-level classification. Specifically, for text embeddings " + }, + { + "bbox": [ + 304, + 380, + 545, + 464 + ], + "type": "inline_equation", + "content": "\\mathbf{T} \\in \\mathbb{R}^{N \\times C}" + }, + { + "bbox": [ + 304, + 380, + 545, + 464 + ], + "type": "text", + "content": " and a pixel-level dense embedding " + }, + { + "bbox": [ + 304, + 380, + 545, + 464 + ], + "type": "inline_equation", + "content": "\\mathbf{v}^s \\in \\mathbb{R}^{C \\times H \\times W}" + }, + { + "bbox": [ + 304, + 380, + 545, + 464 + ], + "type": "text", + "content": ", text-grounded mask " + }, + { + "bbox": [ + 304, + 380, + 545, + 464 + ], + "type": "inline_equation", + "content": "\\mathbf{M} \\in \\mathbb{R}^{N \\times H \\times W}" + }, + { + "bbox": [ + 304, + 380, + 545, + 464 + ], + "type": "text", + "content": " is computed by Eq. (5), where " + }, + { + "bbox": [ + 304, + 380, + 545, + 464 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 304, + 380, + 545, + 464 + ], + "type": "text", + "content": " is the number of target classes. The final segmentation map " + }, + { + "bbox": [ + 304, + 380, + 545, + 464 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 304, + 380, + 545, + 464 + ], + "type": "text", + "content": " is computed by:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 370, + 475, + 545, + 493 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 370, + 475, + 545, + 493 + ], + "spans": [ + { + "bbox": [ + 370, + 475, + 545, + 493 + ], + "type": "interline_equation", + "content": "\\mathcal {M} _ {h, w} = \\underset {n} {\\arg \\max } M _ {n, h, w}. \\tag {13}", + "image_path": "11807621e156656a5cee0ea63981ec2317ac96e662e64110e2b2a172124d90d1.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 503, + 545, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 503, + 545, + 528 + ], + "spans": [ + { + "bbox": [ + 304, + 503, + 545, + 528 + ], + "type": "text", + "content": "Prompt templates such as \"a photo of a {label}.\" are used to generate text embeddings as in CLIP [23]." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 540, + 388, + 553 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 540, + 388, + 553 + ], + "spans": [ + { + "bbox": [ + 306, + 540, + 388, + 553 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 559, + 424, + 572 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 559, + 424, + 572 + ], + "spans": [ + { + "bbox": [ + 306, + 559, + 424, + 572 + ], + "type": "text", + "content": "4.1. Experiment Settings" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "content": "Unified evaluation protocol. In open-world semantic segmentation, a standard evaluation protocol is not yet established. Previous studies conduct an evaluation using their own protocols such as different data processing strategies on different datasets [19, 28, 30, 33]; surprisingly, even for the same dataset, the target classes are sometimes different across studies. For a fair comparison, we present a unified evaluation protocol following the open-world scenario where prior access to the target data before evaluation is not allowed. Under this scenario, the proposed protocol prohibits dataset-specific hyperparameters or tricks, e.g., class" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11169" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 105, + 70, + 487, + 186 + ], + "blocks": [ + { + "bbox": [ + 105, + 70, + 487, + 186 + ], + "lines": [ + { + "bbox": [ + 105, + 70, + 487, + 186 + ], + "spans": [ + { + "bbox": [ + 105, + 70, + 487, + 186 + ], + "type": "table", + "html": "
Methodswith background classwithout background classAvg.
VOCContextObjectVOC20Context59StuffCityADE
GroupViT (YFCC)49.519.024.374.120.812.66.98.727.0
GroupViT (RedCaps)50.418.727.579.723.415.311.19.229.4
MaskCLIP†29.321.115.553.723.314.721.610.823.7
MaskCLIP38.823.620.674.926.416.412.69.827.9
ReCo25.119.915.757.722.314.821.111.223.5
TCL (Ours)55.030.431.683.233.922.424.017.137.2
(+4.6)(+6.8)(+4.1)(+3.5)(+7.5)(+6.0)(+2.4)(+5.9)(+7.8)
", + "image_path": "8fcd393858a543e0fe583e82994dfa5b5ac3209938824a3c51af4d3e8d027a45.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 193, + 547, + 237 + ], + "lines": [ + { + "bbox": [ + 46, + 193, + 547, + 237 + ], + "spans": [ + { + "bbox": [ + 46, + 193, + 547, + 237 + ], + "type": "text", + "content": "Table 1. Zero-shot segmentation performance comparison on 8 semantic segmentation datasets. mIoU metric is used in every experiment. We highlight the best and second-best results. MaskCLIP† indicates their baseline method without additional refinement techniques. The YFCC and RedCaps of GroupViT indicate their training datasets in addition to CC12M. Each dataset abbreviation stands for VOC: PASCAL VOC, Context: PASCAL Context, Object: COCO-Object, Stuff: COCO-Stuff, City: Cityscapes, ADE: ADE20K." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 249, + 289, + 477 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 249, + 289, + 477 + ], + "spans": [ + { + "bbox": [ + 46, + 249, + 289, + 477 + ], + "type": "text", + "content": "name expansion or rephrasing, leading to performance overestimation. For example, we observe that TCL can get significant performance gains by expanding the target class of \"person\" to its sub-concepts (e.g., man, woman, worker, rider, etc.), but the kinds of class name-based tricks are not allowed in our unified evaluation protocol because the expansion depends on the target class names. With this consideration, we evaluate models using unified class names from the default version of MMSegmentation [8] without class name-based tricks. Dense CRF [16] is not used identically due to its expensive computational cost. All other evaluation settings follow GroupViT [30], where the input image is resized to have a shorter side of 448. We employ mean intersection-over-union (mIoU) as a performance metric, which is a standard metric in semantic segmentation. While we aim to provide a fair comparison, defining fair conditions can be subjective. Thus, we provide further results and discussion on this topic in Appendix C, especially regarding dataset scale and refinement methods." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 481, + 288, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 481, + 288, + 685 + ], + "spans": [ + { + "bbox": [ + 46, + 481, + 288, + 685 + ], + "type": "text", + "content": "Benchmark datasets and comparison methods. We provide an extensive evaluation on widely used 8 benchmarks, categorized into two groups: (i) with background class (PASCAL VOC [10], PASCAL Context [21], and COCO-Object [3]), and (ii) without background class (PASCAL VOC20 [10], PASCAL Context59 [21], COCO-Stuff [3], Cityscapes [9], and ADE20K [32]). Note that open-world segmentation methods rely on the textual description of class names, which may require additional considerations for the background class, such as probability thresholding instead of using the \"background\" description as is. The datasets with background class evaluate this aspect. We compare TCL with all existing open-sourced methods, including GroupViT [30], MaskCLIP [33], and ReCo [28] under the unified protocol. We also include their variants in comparison baselines for an extensive comparison. Additional details and comparisons are given in Appendix E." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 288, + 715 + ], + "type": "text", + "content": "Implementation details. For the grounder, we use the CLIP ViT-B/16 model where the size of input images is" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "spans": [ + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "type": "inline_equation", + "content": "224 \\times 224" + }, + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "type": "text", + "content": " and the patch size is " + }, + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "type": "inline_equation", + "content": "16 \\times 16" + }, + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "type": "text", + "content": ". Following MaskCLIP [33], we modify the last attention layer of the CLIP image encoder to acquire the dense embedding representing local semantics. The grounding decoder consists of four gated convolution blocks with two upsampling interpolations, and we use pixel-adaptive mask refinement (PAMR) [1] for mask refinement. Further details on the model architecture are provided in Appendix B. We use CC 3M and 12M datasets [5, 27] for training. The loss weights of " + }, + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{TCL}} = 0.1" + }, + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{area}} = 0.4" + }, + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{tv}} = 1.0" + }, + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "type": "text", + "content": " are used. We train the model with a batch size of 1024 and a learning rate of " + }, + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "type": "inline_equation", + "content": "7.5 \\times 10^{-5}" + }, + { + "bbox": [ + 304, + 249, + 547, + 417 + ], + "type": "text", + "content": " for total 50,000 iterations with 15,000 warmup steps and cosine schedule. AdamW optimizer [20] is used with a weight decay of 0.05." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 425, + 542, + 437 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 425, + 542, + 437 + ], + "spans": [ + { + "bbox": [ + 305, + 425, + 542, + 437 + ], + "type": "text", + "content": "4.2. Zero-shot Transfer to Semantic Segmentation" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 447, + 545, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 447, + 545, + 686 + ], + "spans": [ + { + "bbox": [ + 304, + 447, + 545, + 686 + ], + "type": "text", + "content": "Comparison of existing methods. We extensively compare existing open-world semantic segmentation methods in Table 1 using the proposed unified protocol, including two checkpoints of GroupViT [30] and two variants of MaskCLIP [33]. Between the existing methods, GroupViT achieves the best average performance, particularly on object-oriented datasets such as VOC, VOC20, and COCO-Object. However, its performance tends to decrease when the target dataset is dominated by stuff classes. On the other hand, MaskCLIP performs the best on stuff-oriented datasets such as Context, Context59, and COCO-Stuff, benefiting from the large-scale pre-trained CLIP model. We conjecture it benefits from leveraging a large-scale pretrained CLIP model. The refinement techniques proposed in MaskCLIP [33] improve the average performance but significantly degrade it on Cityscapes " + }, + { + "bbox": [ + 304, + 447, + 545, + 686 + ], + "type": "inline_equation", + "content": "(21.6\\rightarrow 12.6)" + }, + { + "bbox": [ + 304, + 447, + 545, + 686 + ], + "type": "text", + "content": ", suggesting the limitation of the heuristic refinement methods. The significant performance degradation of MaskCLIP and ReCo between VOC20 and VOC may imply the need for consideration for background class." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 689, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 547, + 715 + ], + "type": "text", + "content": "TCL remarkably outperforms existing methods. Although the performances of existing methods vary depend" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "11170" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 70, + 286, + 242 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 286, + 242 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 286, + 242 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 286, + 242 + ], + "type": "image", + "image_path": "e851e1f3745a5d88987708d4bf0a861217716dea89a9c52f6f5c896d68a2ec89.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 250, + 288, + 339 + ], + "lines": [ + { + "bbox": [ + 46, + 250, + 288, + 339 + ], + "spans": [ + { + "bbox": [ + 46, + 250, + 288, + 339 + ], + "type": "text", + "content": "Figure 4. Visualization of the generated text-grounded masks. \"w/o " + }, + { + "bbox": [ + 46, + 250, + 288, + 339 + ], + "type": "inline_equation", + "content": "D_g" + }, + { + "bbox": [ + 46, + 250, + 288, + 339 + ], + "type": "text", + "content": "\" rows show the generated text-grounded masks without the grounding decoder (" + }, + { + "bbox": [ + 46, + 250, + 288, + 339 + ], + "type": "inline_equation", + "content": "D_g" + }, + { + "bbox": [ + 46, + 250, + 288, + 339 + ], + "type": "text", + "content": "), i.e., CLIP dense features " + }, + { + "bbox": [ + 46, + 250, + 288, + 339 + ], + "type": "inline_equation", + "content": "\\mathbf{V}^d" + }, + { + "bbox": [ + 46, + 250, + 288, + 339 + ], + "type": "text", + "content": " are used instead of pixel-level dense embeddings " + }, + { + "bbox": [ + 46, + 250, + 288, + 339 + ], + "type": "inline_equation", + "content": "\\mathbf{V}^s" + }, + { + "bbox": [ + 46, + 250, + 288, + 339 + ], + "type": "text", + "content": ". \"w/ " + }, + { + "bbox": [ + 46, + 250, + 288, + 339 + ], + "type": "inline_equation", + "content": "D_g" + }, + { + "bbox": [ + 46, + 250, + 288, + 339 + ], + "type": "text", + "content": "\" rows show the results with the grounding decoder. Each image is compared using both positive (blue) and negative (red) prompts. The results show that the grounded accurately and finely captures the text-described region with less noise via the grounding decoder." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 352, + 287, + 485 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 352, + 287, + 485 + ], + "spans": [ + { + "bbox": [ + 46, + 352, + 287, + 485 + ], + "type": "text", + "content": "ing on the characteristics of the evaluation datasets, TCL outperforms all the other methods by large margins across all datasets as shown in Table 1. These results demonstrate that our TCL framework successfully addresses the alignment-level train-test discrepancy that exists in the previous methods by learning the region-level alignment. In addition, region-level alignment learning of TCL allows our model to learn the capability to distinguish the background region in a data-driven manner, thus, our method can address the background class without any heuristic post-processing that the previous methods typically rely on." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 495, + 160, + 507 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 495, + 160, + 507 + ], + "spans": [ + { + "bbox": [ + 47, + 495, + 160, + 507 + ], + "type": "text", + "content": "4.3. Qualitative Results" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 517, + 286, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 517, + 286, + 662 + ], + "spans": [ + { + "bbox": [ + 46, + 517, + 286, + 662 + ], + "type": "text", + "content": "Visualization of the generated text-grounded masks. Fig. 4 illustrates the impact of the learned grounding decoder. Since we follow MaskCLIP [33] modification, the results in \"w/o " + }, + { + "bbox": [ + 46, + 517, + 286, + 662 + ], + "type": "inline_equation", + "content": "D_g" + }, + { + "bbox": [ + 46, + 517, + 286, + 662 + ], + "type": "text", + "content": "\" rows can be regarded as the initial results of MaskCLIP before refinement. Despite the vast pretraining scale and remarkable zero-shot classification performance of CLIP [23], its grounding capability is limited because the learning objective targets image-level alignment (See \"w/o " + }, + { + "bbox": [ + 46, + 517, + 286, + 662 + ], + "type": "inline_equation", + "content": "D_g" + }, + { + "bbox": [ + 46, + 517, + 286, + 662 + ], + "type": "text", + "content": "\" rows). In contrast, the grounding decoder (" + }, + { + "bbox": [ + 46, + 517, + 286, + 662 + ], + "type": "inline_equation", + "content": "D_g" + }, + { + "bbox": [ + 46, + 517, + 286, + 662 + ], + "type": "text", + "content": ") learns the region-level alignment by TCL, resulting in more precise, finer, and less noisy generated masks (See \"w/ " + }, + { + "bbox": [ + 46, + 517, + 286, + 662 + ], + "type": "inline_equation", + "content": "D_g" + }, + { + "bbox": [ + 46, + 517, + 286, + 662 + ], + "type": "text", + "content": "\" rows)." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 665, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 715 + ], + "type": "text", + "content": "Qualitative comparison. We qualitatively compare the proposed method in Fig. 5. On the PASCAL VOC dataset (Fig. 5a), we observe various types of errors in each comparison method. The grouping procedure of GroupViT [30]" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 72, + 545, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 384 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 384 + ], + "type": "text", + "content": "makes the segmentation results less noisy, but it also causes an incorrect segmentation of a large group. ReCo [28] struggles with the segmentation of background regions due to the lack of consideration about the background class. MaskCLIP [33] does not take this into account as well, but its refinement methods make the results less noisy. In addition, we present examples in the wild to show open-world segmentation capability in Fig. 5b. We collect test samples containing visual concepts not included in conventional segmentation datasets (e.g., moon, sunset) or free-form texts (e.g., \"two women and one man with a smiling snowman\"). GroupViT tends to focus on the main object of the image and regard the other objects as background, which is consistent with its good performance in object-oriented datasets. Interestingly, in this qualitative comparison in the wild, we observe ReCo consistently outperforms MaskCLIP contrary to the quantitative results. We conjecture that this is because the refinement approach of ReCo is data-driven, while the refinement approach of MaskCLIP depends on heuristic post-processing, which may not guarantee general improvement. Compared to the baselines, TCL consistently generates more precise segmentation masks. These results demonstrate that our proposed method, which learns region-level alignment, improves the segmentation quality both in the evaluation dataset and in web images in the wild. Additional qualitative results are provided in Appendix H." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 388, + 545, + 412 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 388, + 545, + 412 + ], + "spans": [ + { + "bbox": [ + 305, + 388, + 545, + 412 + ], + "type": "text", + "content": "Additional analysis on failure cases and model behavior are provided in Appendices F and G, respectively." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 422, + 406, + 434 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 422, + 406, + 434 + ], + "spans": [ + { + "bbox": [ + 306, + 422, + 406, + 434 + ], + "type": "text", + "content": "4.4. Ablation Studies" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 441, + 545, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 441, + 545, + 502 + ], + "spans": [ + { + "bbox": [ + 304, + 441, + 545, + 502 + ], + "type": "text", + "content": "We investigate the impact of individual components of the proposed framework by ablation studies on the training split of the PASCAL VOC20 dataset. We use a short learning schedule with a batch size of 512 for total 40,000 iterations including 10,000 warmup steps." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 506, + 545, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 506, + 545, + 636 + ], + "spans": [ + { + "bbox": [ + 304, + 506, + 545, + 636 + ], + "type": "text", + "content": "Baseline to TCL. Table 2a presents cumulative ablation studies on the grounding decoder and the TCL losses. Our initial model before training based on MaskCLIP [33] is referred to as the baseline (A), which modifies the last attention layer of the CLIP image encoder. When we add only the grounding decoder to the baseline without TCL loss (B), there is no improvement in performance. This suggests that training the decoder with the same CL loss as the pre-training (CLIP) does not enhance the localization capabilities. As shown in (C), the proposed framework becomes complete with TCL loss." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "type": "text", + "content": "Impact of individual TCL losses. The influence of each component of the proposed TCL loss and its effect on the segmentation performance compared to the conventional CL loss are shown in Table 2b. Smooth regularization is used for all experiments in this table. The CL loss (D) is computed by applying attention pooling [23] to the dense" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11171" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 52, + 70, + 349, + 287 + ], + "blocks": [ + { + "bbox": [ + 52, + 70, + 349, + 287 + ], + "lines": [ + { + "bbox": [ + 52, + 70, + 349, + 287 + ], + "spans": [ + { + "bbox": [ + 52, + 70, + 349, + 287 + ], + "type": "image", + "image_path": "dccaf6c356c76e50b0cb033f0deef5aa49b9f1c9ee53b8db3245ae22e0226e38.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 139, + 289, + 261, + 298 + ], + "lines": [ + { + "bbox": [ + 139, + 289, + 261, + 298 + ], + "spans": [ + { + "bbox": [ + 139, + 289, + 261, + 298 + ], + "type": "text", + "content": "(a) Examples in PASCAL VOC." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 350, + 71, + 539, + 287 + ], + "blocks": [ + { + "bbox": [ + 350, + 71, + 539, + 287 + ], + "lines": [ + { + "bbox": [ + 350, + 71, + 539, + 287 + ], + "spans": [ + { + "bbox": [ + 350, + 71, + 539, + 287 + ], + "type": "image", + "image_path": "874a81f53d3d325fde6b6457bbf701a5a44a29ae9953c312fe6c9c5e0adc7e59.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 398, + 289, + 493, + 298 + ], + "lines": [ + { + "bbox": [ + 398, + 289, + 493, + 298 + ], + "spans": [ + { + "bbox": [ + 398, + 289, + 493, + 298 + ], + "type": "text", + "content": "(b) Examples in the wild." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "lines": [ + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "spans": [ + { + "bbox": [ + 46, + 305, + 547, + 340 + ], + "type": "text", + "content": "Figure 5. (a) The comparison shows the error types of each method in the VOC dataset. GroupViT tends to make an error on a large group rather than noisy results. ReCo suffers from segmentation of the background region. MaskCLIP tends to fail at capturing the target area precisely. (b) shows results on the wild web images and free-form texts. Texts used as target classes are shown at the bottom of the images." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 354, + 289, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 354, + 289, + 474 + ], + "spans": [ + { + "bbox": [ + 46, + 354, + 289, + 474 + ], + "type": "text", + "content": "image embedding " + }, + { + "bbox": [ + 46, + 354, + 289, + 474 + ], + "type": "inline_equation", + "content": "\\mathbf{V}^s" + }, + { + "bbox": [ + 46, + 354, + 289, + 474 + ], + "type": "text", + "content": ". When comparing (D) and (C), the proposed TCL loss remarkably improves the segmentation performance " + }, + { + "bbox": [ + 46, + 354, + 289, + 474 + ], + "type": "inline_equation", + "content": "(61.1 \\rightarrow 77.4)" + }, + { + "bbox": [ + 46, + 354, + 289, + 474 + ], + "type": "text", + "content": ". Image-level or feature-level TCL loss (E, F) solely improves the performance significantly, and using both losses together provides further performance gain. Using CL in addition to TCL (G) does not improve performance, and it is essential to use area TCL loss in TCL framework to prevent model collapse (H), as described in Sec. 3.3. The difference between (B) and (D) is the use of smooth regularization." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 478, + 288, + 646 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 478, + 288, + 646 + ], + "spans": [ + { + "bbox": [ + 46, + 478, + 288, + 646 + ], + "type": "text", + "content": "Hyperparameters. Tables 2c to 2e show the performance changes according to the variation of the loss weight hyperparameters (HPs). The first rows show the importance of each loss (" + }, + { + "bbox": [ + 46, + 478, + 288, + 646 + ], + "type": "inline_equation", + "content": "\\lambda = 0.0" + }, + { + "bbox": [ + 46, + 478, + 288, + 646 + ], + "type": "text", + "content": " cases). The absence of area TCL loss causes a significant performance drop (Table 2d), as mentioned above. Smooth regularization also significantly contributes to the final performance (Table 2e), supporting our assumption that the text-described region is smooth rather than noisy. Note that the sensitivity on HPs is about loss balancing, not about the target dataset. As an open-world segmentation method, TCL does not require any tuning with the target dataset, including model fine-tuning and inference HPs tuning. Once a TCL model is trained, we evaluate the model for every benchmark without any fine-tuning." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 657, + 119, + 669 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 657, + 119, + 669 + ], + "spans": [ + { + "bbox": [ + 47, + 657, + 119, + 669 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 677, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 677, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 677, + 288, + 714 + ], + "type": "text", + "content": "We propose a novel framework for open-world semantic segmentation with only image-text pairs, addressing the alignment-level discrepancy between training (image-text)" + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 320, + 352, + 405, + 401 + ], + "blocks": [ + { + "bbox": [ + 320, + 352, + 405, + 401 + ], + "lines": [ + { + "bbox": [ + 320, + 352, + 405, + 401 + ], + "spans": [ + { + "bbox": [ + 320, + 352, + 405, + 401 + ], + "type": "table", + "html": "
MethodVOC20
A Baseline53.2
B + Decoder52.3
C + TCL77.4
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TCLvTCLfLareaCLVOC20
D✓61.1
E✓✓74.6
F✓✓76.0
C✓✓✓77.4
G✓✓✓✓75.6
H✓✓67.1
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λTCLVOC20
0.0-
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λareaVOC20
0.067.1
0.0469.5
0.477.4
4.076.7
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λtvVOC20
0.073.8
0.175.2
1.077.4
10.070.7
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Default settings are marked in gray." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 581, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 581, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 581, + 545, + 713 + ], + "type": "text", + "content": "and testing (region-text) in existing methods. In the proposed framework, we incorporate the grounding process within contrastive learning, thus allowing explicitly learning alignment between text and text-grounded regions (i.e., segmentation mask). We also present a unified evaluation protocol for a fair comparison of existing methods, where TCL achieves state-of-the-art zero-shot segmentation performance on all 8 benchmarks, remarkably surpassing previous methods. We hope that this study encourages a new research direction of explicitly learning region-text alignment for open-world semantic segmentation." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "11172" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 289, + 714 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 53, + 91, + 289, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 289, + 135 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 289, + 135 + ], + "type": "text", + "content": "[1] Nikita Araslanov and Stefan Roth. Single-stage semantic segmentation from image labels. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020. 6, 12" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 137, + 288, + 169 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 137, + 288, + 169 + ], + "spans": [ + { + "bbox": [ + 53, + 137, + 288, + 169 + ], + "type": "text", + "content": "[2] Minwoo Byeon, Beomhee Park, Haecheon Kim, Sungjun Lee, Woonhyuk Baek, and Saehoon Kim. Coyo-700m: Image-text pair dataset, 2022. 2, 13" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 171, + 288, + 214 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 171, + 288, + 214 + ], + "spans": [ + { + "bbox": [ + 53, + 171, + 288, + 214 + ], + "type": "text", + "content": "[3] Holger Caesar, Jasper Uijlings, and Vittorio Ferrari. Cocosstuff: Thing and stuff classes in context. In Proceedings of the IEEE conference on computer vision and pattern recognition, 2018. 6, 14" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 216, + 288, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 216, + 288, + 259 + ], + "spans": [ + { + "bbox": [ + 53, + 216, + 288, + 259 + ], + "type": "text", + "content": "[4] Junbum Cha, Kyungjae Lee, Sungrae Park, and Sanghyuk Chun. Domain generalization by mutual-information regularization with pre-trained models. European Conference on Computer Vision (ECCV), 2022. 11" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 261, + 288, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 261, + 288, + 316 + ], + "spans": [ + { + "bbox": [ + 53, + 261, + 288, + 316 + ], + "type": "text", + "content": "[5] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021. 2, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 318, + 288, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 318, + 288, + 361 + ], + "spans": [ + { + "bbox": [ + 53, + 318, + 288, + 361 + ], + "type": "text", + "content": "[6] Bowen Cheng, Alex Schwing, and Alexander Kirillov. Per-pixel classification is not all you need for semantic segmentation. Advances in Neural Information Processing Systems, 34:17864-17875, 2021. 15" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 363, + 288, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 363, + 288, + 407 + ], + "spans": [ + { + "bbox": [ + 53, + 363, + 288, + 407 + ], + "type": "text", + "content": "[7] François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017. 13" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 408, + 288, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 408, + 288, + 453 + ], + "spans": [ + { + "bbox": [ + 53, + 408, + 288, + 453 + ], + "type": "text", + "content": "[8] MMSegmentation Contributors. MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https://github.com/open-mmlab/mmsegmentation, 2020.6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 454, + 288, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 454, + 288, + 520 + ], + "spans": [ + { + "bbox": [ + 53, + 454, + 288, + 520 + ], + "type": "text", + "content": "[9] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 521, + 288, + 564 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 521, + 288, + 564 + ], + "spans": [ + { + "bbox": [ + 48, + 521, + 288, + 564 + ], + "type": "text", + "content": "[10] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 567, + 288, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 567, + 288, + 609 + ], + "spans": [ + { + "bbox": [ + 48, + 567, + 288, + 609 + ], + "type": "text", + "content": "[11] Golnaz Ghiasi, Xiuye Gu, Yin Cui, and Tsung-Yi Lin. Scaling open-vocabulary image segmentation with image-level labels. In European Conference on Computer Vision, pages 540-557. Springer, 2022. 1, 2, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 611, + 288, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 611, + 288, + 644 + ], + "spans": [ + { + "bbox": [ + 48, + 611, + 288, + 644 + ], + "type": "text", + "content": "[12] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Open-vocabulary object detection via vision and language knowledge distillation. *ICLR*, 2022. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 646, + 288, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 646, + 288, + 679 + ], + "spans": [ + { + "bbox": [ + 48, + 646, + 288, + 679 + ], + "type": "text", + "content": "[13] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. EMNLP, 2021. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 681, + 288, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 681, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 48, + 681, + 288, + 714 + ], + "type": "text", + "content": "[14] Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. In International Conference on Learning Representations, 2017. 4" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 714 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "type": "text", + "content": "[15] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790, 2021. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 129, + 545, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 545, + 171 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 545, + 171 + ], + "type": "text", + "content": "[16] Philipp Krahenbuhl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. Advances in neural information processing systems, 24, 2011. 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 173, + 545, + 216 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 173, + 545, + 216 + ], + "spans": [ + { + "bbox": [ + 307, + 173, + 545, + 216 + ], + "type": "text", + "content": "[17] Boyi Li, Kilian Q Weinberger, Serge Belongie, Vladlen Koltun, and Rene Ranftl. Language-driven semantic segmentation. In International Conference on Learning Representations, 2021. 1, 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 217, + 545, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 217, + 545, + 270 + ], + "spans": [ + { + "bbox": [ + 307, + 217, + 545, + 270 + ], + "type": "text", + "content": "[18] Feng Liang, Bichen Wu, Xiaoliang Dai, Kunpeng Li, Yinan Zhao, Hang Zhang, Peizhao Zhang, Peter Vajda, and Diana Marculescu. Open-vocabulary semantic segmentation with mask-adapted clip. arXiv preprint arXiv:2210.04150, 2022. 1, 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 271, + 545, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 271, + 545, + 326 + ], + "spans": [ + { + "bbox": [ + 307, + 271, + 545, + 326 + ], + "type": "text", + "content": "[19] Quande Liu, Youpeng Wen, Jianhua Han, Chunjing Xu, Hang Xu, and Xiaodan Liang. Open-world semantic segmentation via contrasting and clustering vision-language embedding. European Conference on Computer Vision (ECCV), 2022. 1, 2, 3, 5, 14" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 327, + 545, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 327, + 545, + 360 + ], + "spans": [ + { + "bbox": [ + 307, + 327, + 545, + 360 + ], + "type": "text", + "content": "[20] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2018. 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 361, + 545, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 361, + 545, + 404 + ], + "spans": [ + { + "bbox": [ + 307, + 361, + 545, + 404 + ], + "type": "text", + "content": "[21] Roozbeh Mottaghi, Xianjie Chen, Xiaobai Liu, Nam-Gyu Cho, Seong-Whan Lee, Sanja Fidler, Raquel Urtasun, and Alan Yuille. The role of context for object detection and semantic segmentation in the wild. CVPR, 2014. 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 404, + 545, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 404, + 545, + 437 + ], + "spans": [ + { + "bbox": [ + 307, + 404, + 545, + 437 + ], + "type": "text", + "content": "[22] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 3, 4" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 438, + 545, + 503 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 438, + 545, + 503 + ], + "spans": [ + { + "bbox": [ + 307, + 438, + 545, + 503 + ], + "type": "text", + "content": "[23] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 3, 4, 5, 7, 11, 12, 13" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 504, + 545, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 504, + 545, + 536 + ], + "spans": [ + { + "bbox": [ + 307, + 504, + 545, + 536 + ], + "type": "text", + "content": "[24] Leonid I Rudin, Stanley Osher, and Emad Fatemi. Nonlinear total variation based noise removal algorithms. Physica D: nonlinear phenomena, 60(1-4):259-268, 1992. 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 537, + 545, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 537, + 545, + 592 + ], + "spans": [ + { + "bbox": [ + 307, + 537, + 545, + 592 + ], + "type": "text", + "content": "[25] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252, 2015. 13" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 592, + 545, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 592, + 545, + 646 + ], + "spans": [ + { + "bbox": [ + 307, + 592, + 545, + 646 + ], + "type": "text", + "content": "[26] Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs. arXiv preprint arXiv:2111.02114, 2021. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 647, + 545, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 647, + 545, + 690 + ], + "spans": [ + { + "bbox": [ + 307, + 647, + 545, + 690 + ], + "type": "text", + "content": "[27] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of ACL, 2018. 2, 5, 6" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 691, + 545, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 691, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 307, + 691, + 545, + 714 + ], + "type": "text", + "content": "[28] Gyungin Shin, Weidi Xie, and Samuel Albanie. Reco: Retrieve and co-segment for zero-shot transfer. Advances in" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11173" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 351 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 67, + 72, + 286, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 72, + 286, + 95 + ], + "spans": [ + { + "bbox": [ + 67, + 72, + 286, + 95 + ], + "type": "text", + "content": "Neural Information Processing Systems, 2022. 1, 2, 3, 5, 6, 7, 13, 15, 16" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 287, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 287, + 149 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 287, + 149 + ], + "type": "text", + "content": "[29] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34, 2021. 15" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 152, + 287, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 152, + 287, + 217 + ], + "spans": [ + { + "bbox": [ + 48, + 152, + 287, + 217 + ], + "type": "text", + "content": "[30] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022. 1, 2, 3, 5, 6, 7, 14, 15, 16" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 219, + 287, + 271 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 219, + 287, + 271 + ], + "spans": [ + { + "bbox": [ + 48, + 219, + 287, + 271 + ], + "type": "text", + "content": "[31] Yiwu Zhong, Jianwei Yang, Pengchuan Zhang, Chunyuan Li, Noel Codella, Liunian Harold Li, Luowei Zhou, Xiyang Dai, Lu Yuan, Yin Li, and Jianfeng Gao. Regionclip: Region-based language-image pretraining. CVPR, 2022. 2, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 274, + 287, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 274, + 287, + 317 + ], + "spans": [ + { + "bbox": [ + 48, + 274, + 287, + 317 + ], + "type": "text", + "content": "[32] Bolei Zhou, Hang Zhao, Xavier Puig, Tete Xiao, Sanja Fidler, Adela Barriuso, and Antonio Torralba. Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127(3):302-321, 2019. 6, 14" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 319, + 287, + 351 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 319, + 287, + 351 + ], + "spans": [ + { + "bbox": [ + 48, + 319, + 287, + 351 + ], + "type": "text", + "content": "[33] Chong Zhou, Chen Change Loy, and Bo Dai. Extract free dense labels from clip. 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Most existing works measure the training loss with point-to-point distance, which may introduce extra defects as predefined matching rules may deviate from the real shape differences. Although some learning-based works have been proposed to overcome the weaknesses of manually-defined rules, they still measure the shape differences in 3D Euclidean space, which may limit their ability to capture defects in reconstructed shapes. In this work, we propose a learning-based Contrastive Adversarial Loss (CALoss) to measure the point cloud reconstruction loss dynamically in a non-linear representation space by combining the contrastive constraint with the adversarial strategy. Specifically, we use the contrastive constraint to help CALoss learn a representation space with shape similarity, while we introduce the adversarial strategy to help CALoss mine differences between reconstructed results and ground truths. According to experiments on reconstruction-related tasks, CALoss can help task networks improve reconstruction performances and learn more representative representations.", + "bbox": [ + 76, + 340, + 473, + 690 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 715, + 209, + 732 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Point clouds, as the common description for 3D shapes, have been broadly used in many areas such as 3D detection [17, 18] and surface reconstruction [9, 13, 19]. For the point cloud reconstruction-related tasks [5, 7, 11, 16], networks need to predict point clouds as similar as possible to the ground truths. Reconstruction losses that can differentiably calculate the shape differences between reconstructed results and ground truths are required to train the task networks. Existing works often use the matching-based", + "bbox": [ + 75, + 742, + 470, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d242d8c35b5f7c13ad896495a164603fc1834f0217e28e9d2df66306d2541d13.jpg", + "image_caption": [ + "Figure 1. $S_{g}$ and $S_{o}$ denote ground truths and point clouds generated by the task network. $S_{p}$ is a positive sample with similar shapes as $S_{g}$ acquired by perturbation [2]. Matching-based losses measure distances between points matched by different predefined rules. PCLoss [6] learns to extract descriptors in 3D Euclidean space by linearly weighting coordinates according to their distances to predicted center points, while our method dynamically measures the shape differences with distances between learned global representations in the constructed representation space. $L_{p}$ and $L_{r}$ denote representation distances between $S_{g}$ , $S_{p}$ and $S_{g}$ , $S_{o}$ , respectively. $L_{r}^{adv}$ is an adversarial loss to maximize representation distances between $S_{g}$ , $S_{o}$ , $L_{r}^{adv}$ and $L_{p}$ are used to optimize CALoss, while $L_{r}$ is adopted to train the task network." + ], + "image_footnote": [], + "bbox": [ + 511, + 306, + 897, + 477 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "reconstruction losses Chamfer Distance (CD) and Earth Mover's Distance (EMD) to constrain shape differences. CD matches points firstly with their nearest neighbors in another point cloud and then calculates the shape difference as average point-to-point distance, while EMD calculates the average point-to-point distance under an optimization-based global matching. We can find that CD and EMD actually measure the distances between matched points instead of the distances between shapes. As the predefined matching rules are static and unlearnable, training results of CD and EMD may fall into inappropriate local minimums where the reconstruction losses are small but the shapes are obviously different. Learning-based losses in", + "bbox": [ + 496, + 704, + 895, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* means the corresponding author", + "bbox": [ + 101, + 887, + 282, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "12208", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "PFNet [8], PUGAN [10], and CRN [21] introduce extra supervision from discriminators trained with the adversarial strategy to find the detailed differences. Their reconstruction performances are improved by introducing adversarial losses, while they still need CD/EMD to evaluate the basic shape distances and cannot fully get rid of the influence from predefined matching rules. PCLoss [6] presents a reconstruction loss measured by the distances between extracted intermediate descriptors in 3D Euclidean space without any manually-defined matching rule, which is updated together with the task network in a generative-adversarial process to search shape differences during training. However, the descriptor extraction in Euclidean space actually limits the searching of shape differences, while the training efficiency is also restricted as the descriptors are constructed with dense connections between multiple predicted center points and all points. In summary, existing reconstruction losses mainly rely on distances in 3D Euclidean space to measure the shape differences.", + "bbox": [ + 76, + 90, + 472, + 377 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this work, we propose a novel framework named Contrastive Adversarial Loss (CALoss) learning to measure the point cloud reconstruction loss dynamically in a high dimensional representation space constructed by a series of fully differentiable structures. The differences between our work and existing works are presented in Fig. 1. CALoss is composed of $L_{p}$ , $L_{r}$ , and $L_{r}^{adv}$ acquired from distances between global representations. $L_{r}^{adv}$ and $L_{p}$ are used to optimize CALoss, where $L_{r}$ is used to train the task network.", + "bbox": [ + 76, + 378, + 472, + 513 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We introduce $L_{p}$ as the contrastive constraint to help CALoss construct a representation space with the shape similarity that similar shapes should have close representations. In this way, by adding adversarial loss on representations, $L_{r}^{adv}$ can guide CALoss to search for the shape differences between ground truths $S_{g}$ and reconstructed results $S_{o}$ . By updating dynamically according to the reconstructed results in each iteration, CALoss can continuously find existing defects in reconstructed shapes and prevent the task network from falling into unexpected local minimums. As the measurement for shape differences is implemented in non-linear representation space, CALoss has more extensive searching space. Besides, the representations adopted to measure shape differences are aggregated with the global pooling operation without any requirement of dense connections as PCLoss [6], which can improve the training efficiency.", + "bbox": [ + 76, + 513, + 472, + 755 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contribution in this work can be summarized as", + "bbox": [ + 96, + 756, + 439, + 770 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a novel Contrastive Adversarial Loss (CALoss) learning to measure the point cloud reconstruction loss with distances between high-dimensional global representations.", + "- By combining the contrastive constraint and adversarial training strategy, CALoss can construct a representation space where similar shapes have close represen" + ], + "bbox": [ + 93, + 784, + 470, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "tations and learn to search for shape differences in this space dynamically during training.", + "bbox": [ + 529, + 90, + 890, + 121 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- Experiments on point cloud reconstruction, unsupervised classification, and point cloud completion confirm that CALoss can help the task network improve reconstruction performances and learn more representative representations with higher training efficiency.", + "bbox": [ + 516, + 130, + 893, + 205 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 217, + 640, + 232 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Point Cloud Reconstruction-related Tasks", + "text_level": 1, + "bbox": [ + 500, + 241, + 856, + 257 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Base on the basic point cloud reconstruction framework to predict similar output point clouds as inputs, e.g. autoencoder, many related tasks have been developed such as the unsupervised classification and point cloud completion. The unsupervised classification task raised by [1, 25] trains auto-encoders to learn representations of point clouds. The representations are then adopted to train a Support Vector Machine (SVM) with provided labels for further classification. The classification accuracy of SVM can reflect the distinctiveness of learned representations. Many researchers have improved the classification performances by modifying the network structures [12, 22, 28], while some researchers [16] introduce extra supervision to enhance the learning effect. Point cloud completion predicts completed point clouds as identical as possible to the ground truths from partial input point clouds. Early works [11, 27] often use typical auto-encoders to abstract long global features from partial inputs and predict completed results, while recent work [7, 21] add more diverse network structures to improve the completion performances. Reconstruction losses CD or EMD to capture the shape differences are always adopted in these works. In this condition, we adopt three tasks including basic point cloud reconstruction, point cloud unsupervised classification, and completion to evaluate the performances of CALoss.", + "bbox": [ + 496, + 265, + 893, + 642 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Losses to Evaluate Shape Differences", + "text_level": 1, + "bbox": [ + 500, + 650, + 820, + 666 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The Chamfer Distance (CD) [27] and Earth Mover's Distance (EMD) [3] are two basic and broadly used reconstruction losses to constrain the shape differences, which calculate the distance between point clouds with different matching strategies. With the matching by nearest neighbors, CD concentrates on differences between contours, while it often constructs non-uniform surfaces as discussed in [3, 23]. EMD aims to find an one-to-one optimal mapping $\\phi$ from one point set to another by optimizing the minimum matching distances between the point sets. Though EMD can create more uniform shapes, it takes large time cost due to the optimized matching and can only be applied to reconstructed output with the same number of input, where the quality of optimization also limits the performances. Since the development of GAN [4], researchers", + "bbox": [ + 496, + 672, + 893, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "12209", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/420f49686b377e7ed5a01f3f3cafd165bcdf40238dc56e5c0828b3deb5b58281.jpg", + "image_caption": [ + "Figure 2. The pipeline of CALoss. $S_{g}$ and $S_{o}$ denote ground truths and point clouds generated by the task network. $S_{p}$ is acquired from $S_{g}$ with small perturbations. $f(\\cdot)$ is a group of 1D-convolutions to transform point clouds $S_{g}, S_{p}$ , and $S_{o}$ with $N_{g}, N_{p}$ and $N_{o}$ points into $D$ -dim features $F_{g}, F_{p}$ , and $F_{o}$ . $g(\\cdot)$ denotes our proposed Adaptive Pooling to aggregate $F_{g}, F_{p}$ , and $F_{o}$ into global representations $C_{g}, C_{p}$ , and $C_{o}$ with multiple weight distributions, where $h(\\cdot)$ is Pooling Controller predicting parameters $\\delta$ to control the widths of weight distributions in $g(\\cdot)$ according to $F_{g}, C_{g}, C_{p}$ , and $C_{o}$ will be used to calculate the required losses $L_{p}, L_{r}^{adv}$ , and $L_{r}$ to train CALoss and the task network. We introduce adversarial loss to dynamically search for the shape defects in $S_{o}$ , while maximizing representation distances in a mini-batch like [2] may not work because it lacks dynamic feedback from $S_{o}$ and cannot capture detailed shape differences." + ], + "image_footnote": [], + "bbox": [ + 81, + 88, + 553, + 329 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/dea06987ccdb82769c5e261f9be0bf4fb0be5a12dbc2e1ffd6dc037d410c50fc.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 563, + 88, + 898, + 329 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "have introduced different learning-based discriminators in reconstruction losses as extra supervisions to better capture the shape differences and improve reconstruction performances [8, 10, 21]. However, these works still need CD or EMD as basic shape constraints. Some works [14,23] further modify the matching rules to improve the constraining performances. All these works measure shape differences based on the point-to-point distance calculated with predefined matching rules. DPDist [20] estimates the shape distances with a pre-trained network without matching. But it is mainly designed for registration and has inferior performances on point cloud reconstruction. PCLoss [6] presents a fully learning-based reconstruction loss to overcome the limitation of predefined matching rules, which learns to aggregate descriptors from point clouds and measure reconstruction losses with distances between extracted descriptors. It learns to search shape differences by an adversarial training together with the task network.", + "bbox": [ + 75, + 464, + 472, + 736 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "However, PCLoss constructs descriptors in 3D Euclidean space with linear weights calculated by distances between multiple predicted center points and all existing points, which may limit both its searching space for shape differences and training efficiency. In this work, we propose CALoss to measure the reconstruction losses in a high dimensional representation space. By the concise designation with only non-linear transformation and the global pooling operation, CALoss can achieve both better performances and higher training efficiency than existing learning-based", + "bbox": [ + 75, + 750, + 472, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "reconstruction losses.", + "bbox": [ + 500, + 465, + 643, + 479 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 500, + 491, + 635, + 508 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The pipeline of CALoss is presented as Fig. 2. The reconstructed result $S_{o}$ and ground truth $S_{g}$ from the task is fed into CALoss to evaluate the shape differences. A positive sample $S_{p}$ is constructed by small perturbations. $S_{g}$ , $S_{p}$ , and $S_{o}$ are transformed into features $F_{g}, F_{p}$ and $F_{o}$ by 1-D convolutions $f(\\cdot)$ . Features $F_{g}, F_{p}$ , and $F_{o}$ are finally aggregated into global representations $C_{g}, C_{p}$ , and $C_{o}$ with Adaptive Pooling $g(\\cdot)$ to calculate losses $L_{p}, L_{r}^{adv}$ , and $L_{r}$ . $L_{p}$ and $L_{r}$ can be calculated by:", + "bbox": [ + 498, + 516, + 895, + 654 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nL _ {p} = \\left\\| C _ {g}, C _ {p} \\right\\| _ {1}, L _ {r} = \\left\\| C _ {g}, C _ {o} \\right\\| _ {1}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 576, + 660, + 890, + 679 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where the adversarial loss $L_{r}^{adv}$ is defined as:", + "bbox": [ + 498, + 684, + 800, + 699 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nL _ {r} ^ {\\text {a d v}} = - \\log \\left(L _ {r} + \\sigma_ {r}\\right). \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 612, + 705, + 890, + 724 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "$\\sigma_r$ is a tiny value to avoid errors when $L_r \\to 0$ . These losses are used to optimize CALoss and the task network together.", + "bbox": [ + 498, + 731, + 893, + 762 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Perturbation Operation", + "text_level": 1, + "bbox": [ + 500, + 768, + 720, + 786 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this work, we perturb ground truths $S_{g}$ with tiny Gaussian noises, which can be defined as", + "bbox": [ + 498, + 792, + 893, + 821 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nS _ {p} = S _ {g} + \\mathcal {N} _ {\\sigma}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 640, + 830, + 890, + 848 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathcal{N}_{\\sigma} = \\mathrm{randn}(\\sigma)$ . The noise width is controlled by a constant $\\sigma$ . This operation creates perturbed point clouds with similar but different shapes as ground truths.", + "bbox": [ + 498, + 854, + 893, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "12210", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/e59718cca2634c11698f32a97c30b70577dcfb5d4b2de206f9d4b4021ace8b0e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
GTCDEMDPUDCRNDPFDDCDPCLossOurs
", + "bbox": [ + 83, + 89, + 895, + 340 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Figure 3. The qualitative comparisons with different losses based on AE [1]. Our method can help the task network create more complete shapes and clearer details.", + "bbox": [ + 75, + 345, + 893, + 377 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Adaptive Pooling and Pooling Controller", + "text_level": 1, + "bbox": [ + 76, + 404, + 426, + 422 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Adaptive Pooling is an important operation to aggregate features based on all points into a global representation. Unlike max pooling or average pooling, Adaptive Pooling is dynamically changed and controlled by Pooling Controller during the training process. The structure of Pooling Controller includes a simple network structure to predict parameters $\\delta$ for Adaptive Pooling as shown in Fig. 2. If we defined $Con(\\cdot)$ as concatenation, $maxpool(\\cdot)$ , $avgpool(\\cdot)$ and $MLP(\\cdot)$ as max pooling, average pooling and Multi Layer Perceptrons (MLPs), the Pooling Controller can be described as:", + "bbox": [ + 75, + 429, + 473, + 594 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\delta = h \\left(F _ {g}\\right) = M L P \\left(\\operatorname {C o n} \\left(\\operatorname {m a x p o o l} \\left(F _ {g}\\right), \\operatorname {a v g p o o l} \\left(F _ {g}\\right)\\right)\\right). \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 80, + 607, + 467, + 637 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "It takes both max pooled and average pooled features to acquire more extensive information about $F_{g}$ . In this condition, let us take $F_{g}$ as an example, then the global representation $C_{g}$ can be defined as:", + "bbox": [ + 75, + 638, + 472, + 700 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nC _ {g} = g \\left(F _ {g}, \\delta\\right) = \\sum_ {i = 1} ^ {\\left| F _ {g} \\right|} \\frac {e ^ {- \\left\\| F _ {g} ^ {i} - m a x p o o l \\left(F _ {g}\\right) \\right\\| / \\delta}}{\\sum_ {i = 1} ^ {\\left| F _ {g} \\right|} e ^ {- \\left\\| F _ {g} ^ {i} - m a x p o o l \\left(F _ {g}\\right) \\right\\| / \\delta}} \\cdot F _ {g}. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 83, + 712, + 468, + 767 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$C_p$ and $C_o$ can be acquired by the same equations:", + "bbox": [ + 76, + 768, + 413, + 785 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nC _ {p} = g \\left(F _ {p}, \\delta\\right), C _ {o} = g \\left(F _ {o}, \\delta\\right). \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 168, + 795, + 468, + 813 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We can see that the features are aggregated into global representations with multiple weight distributions around the max pooled features, where $\\delta$ actually controls the widths of weight distributions for $F_{g}, F_{p}$ , and $F_{o}$ . So, we share the same $\\delta$ for $F_{g}, F_{p}$ , and $F_{o}$ to keep that they are aggregated", + "bbox": [ + 75, + 825, + 472, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Algorithm 1 Training Process", + "text_level": 1, + "bbox": [ + 500, + 404, + 702, + 420 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Input: Input $S_{i}$ , ground truths $S_{g}$ , the number of iterations iter, the task network TaskNet(·)", + "bbox": [ + 514, + 422, + 893, + 455 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "for $n = 1$ to iter do", + "bbox": [ + 516, + 455, + 656, + 468 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Calculate output of the task network:", + "bbox": [ + 532, + 470, + 777, + 484 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nS _ {o} ^ {n} = \\operatorname {T a s k N e t} \\left(S _ {i} ^ {n}\\right).\n$$\n", + "text_format": "latex", + "bbox": [ + 532, + 484, + 681, + 500 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Let $\\theta_C$ and $\\theta_T$ be the parameters of CALoss and the task network, respectively.", + "bbox": [ + 532, + 501, + 890, + 530 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Fix the parameter of task network and update CALoss by descending gradient:", + "bbox": [ + 532, + 531, + 890, + 559 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\nabla_ {\\theta_ {C}} L _ {C} \\left(S _ {o} ^ {n}, S _ {q} ^ {n}\\right).\n$$\n", + "text_format": "latex", + "bbox": [ + 532, + 560, + 651, + 575 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Fix CALoss and update the task network by descending gradient:", + "bbox": [ + 532, + 575, + 893, + 604 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\nabla_ {\\theta_ {T}} L _ {T} (S _ {o} ^ {n}, S _ {g} ^ {n}).\n$$\n", + "text_format": "latex", + "bbox": [ + 532, + 604, + 651, + 622 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "end for", + "bbox": [ + 517, + 622, + 571, + 633 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "by distributions with same widths. With such an Adaptive Pooling operation in Eq. 5 and Eq. 6, each item in $F_{g}$ , $F_{p}$ , and $F_{o}$ can acquire various gradients during the back propagation process, instead of gradients all the same in average pooling or only constraining max items in max pooling.", + "bbox": [ + 496, + 664, + 893, + 742 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Contrastive Adversarial Training", + "text_level": 1, + "bbox": [ + 500, + 751, + 792, + 768 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As presented in Fig. 2, losses $L_{p}$ , $L_{r}^{adv}$ , and $L_{r}$ are calculated by ground truths and reconstructed results from the task. The training losses for CALoss and the task network can be defined as:", + "bbox": [ + 496, + 773, + 893, + 835 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} L _ {C} = L _ {r} ^ {a d v} + \\frac {\\epsilon}{| \\mathcal {N} _ {\\sigma} |} \\cdot L _ {p} + \\epsilon_ {w} \\cdot | \\delta | ^ {2} \\\\ = - \\log \\left(L _ {r} + \\sigma_ {r}\\right) + \\frac {\\epsilon}{\\left| \\mathcal {N} _ {\\sigma} \\right|} \\cdot L _ {p} + \\epsilon_ {w} \\cdot | \\delta | ^ {2}, \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 527, + 844, + 841, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "12211", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/201e7adb770a68af828fb8e7f49055545979ed846d0ad747a1db67a102e6f6a4.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetShapeNetModelNet40
NetworksAEFoldingLAELFoldingAEFoldingLAELFolding
MetricsMCDHDMCDHDMCDHDMCDHDMCDHDMCDHDMCDHDMCDHD
CD [3]0.321.870.404.130.311.020.281.200.756.080.837.350.441.690.392.16
EMD [3]0.252.23--0.232.480.212.490.616.18--0.333.820.323.88
PUD [10]0.321.880.363.830.321.020.271.110.735.850.777.290.451.710.381.97
PFD [8]0.321.870.414.140.310.990.260.970.746.280.887.550.441.690.351.69
CRND [21]0.311.860.343.170.311.000.260.990.715.660.767.240.441.690.351.76
DCD [23]0.281.750.918.410.130.890.181.200.686.021.2211.860.171.370.241.81
PCLoss [6]0.231.660.332.570.130.650.140.760.595.300.756.650.171.050.191.37
Ours0.211.530.302.570.120.760.120.790.585.230.726.320.161.180.161.24
", + "bbox": [ + 81, + 89, + 893, + 286 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 1. Comparison with reconstruction losses on ShapeNet (SP) and ModelNet40 (MN40). Bold marks the best results.", + "bbox": [ + 124, + 289, + 841, + 304 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "and", + "bbox": [ + 76, + 321, + 106, + 335 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL _ {T} = L _ {r}, \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 236, + 339, + 468, + 356 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\epsilon$ and $\\epsilon_w$ are two hyper-parameters to adjust the weights. The whole training process for CALoss and the task network can be described as Alg. 1. Parameters of CALoss and the task network are updated by turns in each iteration. CALoss is updated by $L_r^{adv}$ and $L_p$ . $L_p$ is used to constrain that similar shapes $S_p$ , $S_g$ have close representations, where $L_r^{adv}$ can promote CALoss to find the shape differences between $S_g$ and $S_o$ . We give a dynamic weight for $L_p$ controlled by $1 / |\\mathcal{N}_{\\sigma}|$ , which means more noised $S_p$ are allowed to have relatively further representations. Besides, we add a L2 regularization for $\\delta$ to prevent the weights for $F_g$ , $F_p$ , and $F_o$ from over-smoothness. According to Eq. 5, too large $\\delta$ will result in roughly the same weighting for each item in $F_g$ , $F_p$ , or $F_o$ , which is harmful for delivering variable gradients. The task network is optimized by $L_r$ to reduce the differences found by CALoss between reconstructed results $S_o$ and ground truth $S_g$ .", + "bbox": [ + 75, + 364, + 473, + 623 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 638, + 209, + 655 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets and Implementation Details", + "text_level": 1, + "bbox": [ + 76, + 662, + 397, + 680 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this work, three point cloud datasets: ShapeNet [26], ModelNet10 (MN10), and ModelNet40 (MN40) [24] are adopted. We use the ShapeNet part dataset [1, 25] containing 12288 models in the train split and 2874 models in the test split. ModelNet10 and ModelNet40 are subsets of ModelNet, which contain 10 categories and 40 categories of CAD models, respectively. Each model consists of 2048 points randomly sampled from the surfaces of original mesh models. We conduct comparisons with other losses on three tasks, including point cloud reconstruction, unsupervised classification, and point cloud completion.", + "bbox": [ + 75, + 686, + 470, + 854 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For the reconstruction task, we train networks with different reconstruction losses on the train split of ShapeNet part dataset and evaluate performances on both the test split", + "bbox": [ + 75, + 854, + 472, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "of ShapeNet and MN40 to provide a robust and exhaustive evaluation. For the unsupervised classification task, we compare the performances of different losses on multiple auto-encoders constructed by [1, 15, 22, 25] following PCLoss [6]. As for GLRNet [16], we follow its setting and retrain it with the original adopted CD and CALoss to observe the differences. For the point cloud completion task, we introduce 3 popular works PCN [27], CRN [21], and RFNet [7] to compare the completion performances before and after replacing the adopted reconstruction losses with CALoss. PCN and CRN are trained on the dataset provided by CRN with 2048 points to compare the completion performances on sparse point clouds, while RFNet is trained on the corresponding dataset with 16384 points to see the completion performances on dense point clouds. All data are normalized to $-1\\sim 1$ for the fairness of comparison.", + "bbox": [ + 496, + 321, + 893, + 564 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Metrics. To provide a clear and accurate evaluation of the performance, we adopt Multi-scale Chamfer Distance (MCD) and Hausdorff Distance (HD) introduced in [6] as metrics for reconstruction quality assessment in this work.", + "bbox": [ + 496, + 564, + 893, + 625 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Comparisons on Point Cloud Reconstruction", + "text_level": 1, + "bbox": [ + 498, + 633, + 879, + 651 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we conduct comparisons with different reconstruction losses based on a few commonly-used networks AE [1], Folding [25] and local feature-based LAE and LFolding following PCLoss [6]. We retrain the networks with different reconstruction losses and evaluate the reconstruction errors of trained networks on the test split of ShapeNet and ModelNet40. As introduced in Sec. 2.2, CD and EMD are widely-used matching-based reconstruction losses, while PUD [10], PFD [8], CRND [21] are constraints introducing extra discriminators to improve the reconstruction performances. DCD [23] is a recent variant of CD achieved by modifying the matching rule. In this work, we choose the better-performed LNSA proposed in PCLoss [6] as PCLoss for comparison in following sections.", + "bbox": [ + 496, + 657, + 893, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The quantitative results are presented in Table 1. We can see that CALoss can achieve the best performances in", + "bbox": [ + 498, + 869, + 893, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "12212", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "most conditions. To intuitively present the differences in reconstructed results, we also conduct a qualitative comparison in Fig. 3. We can see that CD may create quite nonuniform results with missing local details, while EMD may produce distorted shapes. Though PUD, CRND, and PFD can improve the integrity of shapes, they still produce similar shapes as CD contained within. PCLoss improves both the uniformity and integrity of reconstructed shapes. But it gets relatively rough details as shown in Fig. 3. As shown by circled regions in the last two rows of Fig. 3, the results of PCLoss create rough contours on chair legs and lamp stands, while our method produces clearer details on these regions. It confirms the effectiveness of CALoss.", + "bbox": [ + 75, + 90, + 472, + 287 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Comparisons on Unsupervised Classification", + "text_level": 1, + "bbox": [ + 76, + 296, + 455, + 313 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In this section, we evaluate the performances of CALoss on point cloud unsupervised classification based on multiple auto-encoders constructed by [1, 15, 22, 25] with 128-dim bottleneck following PCLoss [6], and GLRNet [16]. The experimental settings are kept the same as [1, 16, 25].", + "bbox": [ + 75, + 320, + 470, + 396 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The auto-encoders are trained on ShapeNet and applied on ModelNet10 and ModelNet40 to extract point cloud representations, where the representations extracted from the training splits and corresponding labels are adopted to train Supported Vector Machines (SVMs). Then the distinguishability of extracted representations can be evaluated by the classification accuracy of these SVMs. We conduct comparisons on these networks by replacing the adopted reconstruction losses to train the auto-encoders with CALoss and observe the changing in classification accuracy. From the results in Table 2, we can see that most networks can achieve improvements by replacing the reconstruction losses with CALoss, which confirms that CALoss can help the task networks learn more representative representations.", + "bbox": [ + 75, + 396, + 473, + 608 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.4. Comparisons on Point Cloud Completion", + "text_level": 1, + "bbox": [ + 76, + 617, + 429, + 633 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Point Cloud Completion predicts completed results as similar as possible to ground truths from partial inputs, which is usually trained with reconstruction losses between completed results and ground truths. To further verify the performances of CALoss, we apply it to a few popular point cloud completion works, including PCN [27], CRN [21], and RFNet [7]. As these works may have multilevel constraints, we conduct comparisons by replacing the reconstruction losses of the last level with CALoss and retraining the networks. The results are presented in Table 3. The completion performances have improvements in most conditions by introducing CALoss, which further confirms that CALoss is effective for different task networks.", + "bbox": [ + 75, + 641, + 472, + 835 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.5. Analysis about the Training Process", + "text_level": 1, + "bbox": [ + 76, + 847, + 388, + 864 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Visualization during training. We visualize a model generated by the task network AE [1] during training to ob-", + "bbox": [ + 76, + 869, + 472, + 901 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/11027cc61cbe04c5e8f5ef9279e5c524bf1849f60dcf86eb08974f2cd367e33d.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
TaskNetDatasetMethods
CDEMDPCLossOurs
AEMN1090.6089.4991.4891.48
MN4085.9285.4786.3686.81
FoldingMN1091.03-91.7092.26
MN4085.22-85.3586.24
AEMN1090.3890.1592.0493.47
(PN++)MN4088.0388.0787.5488.15
FoldingMN1091.48-91.4892.59
(PN++)MN4087.01-86.7387.13
AEMN1091.3791.2692.3792.81
(DGCNN)MN4087.5087.5488.1187.46
FoldingMN1091.26-91.8192.70
(DGCNN)MN4086.85-87.5087.74
GLRNetMN1093.58--95.24
MN4091.07--91.31
", + "bbox": [ + 516, + 88, + 879, + 385 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/daacaedfc920c0e73d149783a53e1163848e6cff958f5cced72ddab59c5aea38.jpg", + "table_caption": [ + "Table 2. Comparison on unsupervised classification." + ], + "table_footnote": [], + "table_body": "
NetworkPCNCRNRFNetRFNet*
MetroMCD HDMCD HDMCD HDMCD HD
w/o CALoss0.31 2.670.29 2.420.21 2.920.29 3.82
w/ CALoss0.31 2.550.29 2.440.20 2.630.27 3.28
", + "bbox": [ + 504, + 422, + 890, + 507 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. Comparisons on point cloud completion. RFNet and RFNet* denote results evaluated on known and novel categories on ShapeNet following RFNet [7].", + "bbox": [ + 498, + 511, + 890, + 554 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "serve the convergence of different losses. The results are presented in Fig. 4. We can see that CD and EMD have unchanged results with obvious defects after 200 iterations, which means they actually converge to inappropriate local minimums. The reconstructed results trained with CALoss converge to a simple shape after 100 iterations, which may be the effect of contrastive constraint to help the task network find a shape similar to ground truths. From $200 \\sim 600$ iterations, the trained results of CALoss gradually remove differences and approach the ground truth, which confirms the adversarial loss can continuously help find the defects and promote the task network to get better performances. Although PCLoss also removes defects during iterations, it produces rougher results, which may come from the limitation of 3D Euclidean space-based descriptors.", + "bbox": [ + 496, + 566, + 893, + 792 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Training curves. In this section, we visualize the reconstruction errors based on the AE network [1] and ShapeNet dataset [24] through the training iterations to observe the convergences of difference loss functions. We can see that our method has relatively inferior performances at the beginning of iterations, where it is learning to search shape differences. But it will converge steadily to low errors after", + "bbox": [ + 496, + 795, + 893, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "12213", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/f039f88fb7398b51bc49f4ec2a05c7be67c93953d42e6f3ab13467449b0325ac.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Iteration100200300400500600GT
CD
EMD
PCLoss
Ours
", + "bbox": [ + 86, + 88, + 879, + 369 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d2269baa65091235c713afed55ca3977fba2deee5eaaff374a544fd14e84fc06.jpg", + "image_caption": [ + "Figure 5. The reconstruction error curves through the iterations." + ], + "image_footnote": [], + "bbox": [ + 81, + 446, + 267, + 604 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/788f701e3ac709f51ef2b05296078e7f41c314b27a00653e1c7e61623438c796.jpg", + "image_caption": [ + "Figure 6. Qualitative comparisons between our method dynamically updated and pre-trained without updating." + ], + "image_footnote": [], + "bbox": [ + 267, + 446, + 441, + 604 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "enough iterations. We can also see that CALoss converge faster than PCLoss, which reveals the priority of our representation space-based measurement.", + "bbox": [ + 75, + 637, + 470, + 681 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.6. Training Efficiency Comparison", + "text_level": 1, + "bbox": [ + 76, + 690, + 359, + 707 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this section, we evaluate the training efficiencies of different reconstruction losses on the AE network [1], which are measured by the time consumed for the training of a single batch. The results are presented in Table 4. Though our method performs slightly slower than CD, it has the highest training efficiency in learning-based losses.", + "bbox": [ + 75, + 713, + 470, + 805 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/cde34be993333f05214fab3b11192ac3c5586d21c0e4f47fe2bdd1db3f0cb586.jpg", + "table_caption": [ + "Figure 4. The visualization of training processes with different reconstruction losses. CD/EMD fall into local minimums and acquire unchanged results after 200 iterations, while PCLoss produces relatively rough shapes. Our method can gradually remove the shape defects and create more accurate results." + ], + "table_footnote": [], + "table_body": "
Non-learningLearning-based
MethodsCDEMDPUDPFDCRNDPCLossOurs
Time(ms)232167745975739
", + "bbox": [ + 81, + 808, + 473, + 876 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/0e0e2af9c5ed452eaf01125d12014cc6b26cbf419314c7ec8f4658ec9208a292.jpg", + "table_caption": [ + "Table 4. Training efficiency comparison conducted on an NVIDIA 2080ti with a 2.9GHz i5-9400 CPU." + ], + "table_footnote": [], + "table_body": "
GT
Pretrained
Ours
", + "bbox": [ + 506, + 446, + 893, + 589 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.7. Necessity of the Dynamic Updating", + "text_level": 1, + "bbox": [ + 500, + 641, + 805, + 659 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "CALoss is dynamically optimized together with the task network as claimed in Sec. 3. To show the necessity of the dynamic updating, we make an attempt to train the task network AE [1] with a pre-trained CALoss directly without further updating. The results are demonstrated in Fig. 6 and Table 5. We can see that the task network trained with pre-trained CALoss can only reconstruct quite rough shapes, which confirms that training with CALoss is actually a continuous procedure to search shape differences between ground truths and the task network outputs.", + "bbox": [ + 496, + 666, + 893, + 816 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.8. Ablation Study", + "text_level": 1, + "bbox": [ + 500, + 830, + 653, + 847 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablation study for components. We conduct an ablation study for the components adopted in CALoss as mentioned in Eq. 7. The results are presented in Table 6. $L_{p}$ and", + "bbox": [ + 498, + 854, + 893, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "12214", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/68cf3222a88d587ae5133774a1735088d0b704a05b480d977debffaf5b21f060.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Pre-trainedOurs
DatasetSPMN40SPMN40
MCD0.751.040.210.58
HD14.4814.151.535.23
", + "bbox": [ + 106, + 88, + 442, + 171 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "$L_{r}^{adv}$ are the basic contrastive and adversarial constraints, respectively, while $|\\delta|^2$ is the regularization constraint for $\\delta$ . $1 / |\\mathcal{N}_{\\sigma}|$ is the dynamic coefficient for the weight of $L_{p}$ .", + "bbox": [ + 76, + 219, + 470, + 265 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "As finding shape difference is an essential constraint to prevent CALoss from acquiring all-zero output under the supervision of only $L_{p}$ , we remove the $L_{r}^{adv}$ by replacing it with the negative implementation of metric-learning method [2, 16] by maximizing the representation distances between models in the same mini-batch.", + "bbox": [ + 75, + 266, + 470, + 354 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We can see that $L_{p}$ and $L_{r}^{adv}$ both have very significant influences on the final performance, which means they are cores of CALoss. Replacing $L_{r}^{adv}$ with metric-learning method has weaker results. It confirms that maximizing representations between shapes within a mini-batch is not enough to learn the shape differences because it lacks of dynamic feedback from reconstructed outputs and cannot accurately find the shape differences. The regularization $|\\sigma|^{2}$ also has obvious influence, which means it is important to control the widths of weight distributions to aggregate representations in $g(\\cdot)$ as shown in Fig. 2 and Eq. 5.", + "bbox": [ + 75, + 357, + 473, + 525 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/81ce13dadccc7f346a98d37ffc780636d38663aa8f5330488fadaa4bed5424d3.jpg", + "table_caption": [ + "Table 5. Quantitative comparisons between our method dynamically updated and pre-trained without updating." + ], + "table_footnote": [], + "table_body": "
LpPerturbLadv|δ|21/|Nσ|MCDHD
Lp/Ladv✓✓✓3.557.57
1.8312.76
Others✓✓✓0.778.13
✓✓✓✓0.221.63
✓✓✓✓✓0.211.53
", + "bbox": [ + 86, + 537, + 460, + 652 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation study for the Pooling Controller. In Pooling Controller, we introduce both max pooled and average pooled features to acquire more extensive information about the overall distribution in $F_{g}$ as presented in Fig. 2 and Eq. 4. We conduct a simple ablation study for this operation in Table 7. We can see that the designation of concatenation can indeed reduce the reconstruction errors.", + "bbox": [ + 75, + 718, + 470, + 823 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation study the Adaptive Pooling operation. Here, We present a simple discussion about the proposed Adaptive Pooling operation. From Table 8, we can see that both max and average pooling have quite inferior performances, which prove the necessity of Adaptive Pooling operation.", + "bbox": [ + 75, + 824, + 470, + 902 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/11e6f4fd0b63469a9581c9188a7fcd9cbb7eadd337a96381d6766421104c1d76.jpg", + "table_caption": [ + "Table 6. Ablation for components. Perturb denotes the perturbation, while $L_{p}$ , $L_{r}^{adv}$ and $|\\delta|^{2}$ are components included in Eq. 7. $1 / |\\mathcal{N}_{\\sigma}|$ is the dynamic coefficient for the weight of $L_{p}$ in Eq. 7." + ], + "table_footnote": [], + "table_body": "
MetricsMaxAvgOurs
MCD0.220.230.21
HD1.581.551.53
", + "bbox": [ + 558, + 88, + 834, + 150 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/d08c3ed5a6ce1e3b49b4b560dd32d58fa6de8f2e3b1f9003ddbe7e3c53ae3973.jpg", + "table_caption": [ + "Table 7. Comparisons between different implementations of Pooling Controller on ShapeNet and AE [1]. Max and Avg denote introducing only max and average pooling, respectively." + ], + "table_footnote": [], + "table_body": "
MetricsMaxAvgOurs
MCD0.340.610.21
HD1.726.791.53
", + "bbox": [ + 558, + 203, + 834, + 263 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 8. Comparisons between pooling operations on ShapeNet and AE [1]. Max and Avg denote max and average pooling.", + "bbox": [ + 496, + 268, + 890, + 297 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In CALoss, the pooling operation is used to aggregate features from all points into a global representation. To train the task network, the global representation needs to provide variable gradients for each point feature to distinguish them. However, average pooling can only propagate same and indistinguishable gradients for each points. Although max pooling can provide different gradients for point features, it provides a hard 0-1 distribution where only max items are constrained. In this condition, we design such an Adaptive Pooling to get a variable weight for each point feature according to the distance to the max pooled feature, which can be regarded as a \"soft max pooling\". All point features can be constrained with distinguishable gradients, which is controlled by widths of weight distributions predicted with Pooling Controller $h(\\cdot)$ as shown in Fig. 2 and Eq. 5.", + "bbox": [ + 496, + 306, + 893, + 534 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 547, + 619, + 564 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we propose a novel learning-based framework named CALoss to train the point cloud reconstruction-related task networks by measuring the differences between reconstructed shapes and ground truths with distances in a representation space. With the measurement of reconstruction loss in the learned non-linear representation space, CALoss has more extensive searching space for shape differences and better performances than the Euclidean space-based methods. According to the experiments, CALoss can achieve improvements above existing reconstruction losses based on predefined matching rules on multiple tasks including point cloud reconstruction, unsupervised classification and completion, which confirms it can help the task network achieve better reconstruction performances and extract more representative representations.", + "bbox": [ + 496, + 573, + 893, + 800 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 500, + 814, + 624, + 830 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We thank all authors, reviewers and the chair for excellent contributions. This work is supported by the Key Research and Development Project of Zhejiang Province under Grant 2021C01035.", + "bbox": [ + 496, + 839, + 893, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "12215", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. Learning representations and generative models for 3d point clouds. In International conference on machine learning, pages 40-49. PMLR, 2018. 2, 4, 5, 6, 7, 8", + "[2] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 1, 3, 8", + "[3] Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 605-613, 2017. 2, 5", + "[4] Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. arXiv preprint arXiv:1406.2661, 2014. 2", + "[5] Tianxin Huang and Yong Liu. 3d point cloud geometry compression on deep learning. In Proceedings of the 27th ACM International Conference on Multimedia, pages 890-898, 2019. 1", + "[6] Tianxin Huang, Xuemeng Yang, Jiangning Zhang, Jinhao Cui, Hao Zou, Jun Chen, Xiangrui Zhao, and Yong Liu. Learning to train a point cloud reconstruction network without matching. In European Conference on Computer Vision, pages 179-194. Springer, 2022. 1, 2, 3, 5, 6", + "[7] Tianxin Huang, Hao Zou, Jinhao Cui, Xuemeng Yang, Mengmeng Wang, Xiangrui Zhao, Jiangning Zhang, Yi Yuan, Yifan Xu, and Yong Liu. Rfnet: Recurrent forward network for dense point cloud completion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12508-12517, 2021. 1, 2, 5, 6", + "[8] Zitian Huang, Yikuan Yu, Jiawen Xu, Feng Ni, and Xinyi Le. Pf-net: Point fractal network for 3d point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7662-7670, 2020. 2, 3, 5", + "[9] Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, Thomas Funkhouser, et al. Local implicit grid representations for 3d scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6001–6010, 2020. 1", + "[10] Ruihui Li, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, and Pheng-Ann Heng. Pu-gan: a point cloud upsampling adversarial network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7203-7212, 2019. 2, 3, 5", + "[11] Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, and ShiMin Hu. Morphing and sampling network for dense point cloud completion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11596-11603, 2020. 1, 2", + "[12] Yongcheng Liu, Bin Fan, Shiming Xiang, and Chunhong Pan. Relation-shape convolutional neural network for point cloud analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8895-8904, 2019. 2" + ], + "bbox": [ + 76, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4460-4470, 2019. 1", + "[14] Trung Nguyen, Quang-Hieu Pham, Tam Le, Tung Pham, Nhat Ho, and Binh-Son Hua. Point-set distances for learning representations of 3d point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10478-10487, 2021. 3", + "[15] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in neural information processing systems, pages 5099-5108, 2017. 5, 6", + "[16] Yongming Rao, Jiwen Lu, and Jie Zhou. Global-local bidirectional reasoning for unsupervised representation learning of 3d point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5376-5385, 2020. 1, 2, 5, 6, 8", + "[17] N Dinesh Reddy, Minh Vo, and Srinivasa G Narasimhan. Carfusion: Combining point tracking and part detection for dynamic 3d reconstruction of vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1906-1915, 2018. 1", + "[18] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointcnn: 3d object proposal generation and detection from point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 770-779, 2019. 1", + "[19] Jia-Heng Tang, Weikai Chen, Jie Yang, Bo Wang, Songrun Liu, Bo Yang, and Lin Gao. Octfield: Hierarchical implicit functions for 3d modeling. arXiv preprint arXiv:2111.01067, 2021. 1", + "[20] Dahlia Urbach, Yizhak Ben-Shabat, and Michael Lindenbaum. Dpdist: Comparing point clouds using deep point cloud distance. In European Conference on Computer Vision, pages 545-560. Springer, 2020. 3", + "[21] Xiaogang Wang, Marcelo H Ang Jr, and Gim Hee Lee. Cascaded refinement network for point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 790-799, 2020. 2, 3, 5, 6", + "[22] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (tog), 38(5):1-12, 2019. 2, 5, 6", + "[23] Tong Wu, Liang Pan, Junzhe Zhang, Tai Wang, Ziwei Liu, and Dahua Lin. Density-aware chamfer distance as a comprehensive metric for point cloud completion. arXiv preprint arXiv:2111.12702, 2021. 2, 3, 5", + "[24] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1912-1920, 2015. 5, 6", + "[25] Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian. Foldingnet: Point cloud auto-encoder via deep grid deformation." + ], + "bbox": [ + 501, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "12216", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 206-215, 2018. 2, 5, 6", + "[26] Li Yi, Vladimir G Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas. A scalable active framework for region annotation in 3d shape collections. ACM Transactions on Graphics (ToG), 35(6):1-12, 2016. 5", + "[27] Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert. Pcn: Point completion network. In 2018 International Conference on 3D Vision (3DV), pages 728-737. IEEE, 2018. 2, 5, 6", + "[28] Yongheng Zhao, Tolga Birdal, Haowen Deng, and Federico Tombari. 3d point capsule networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1009-1018, 2019. 2" + ], + "bbox": [ + 78, + 90, + 470, + 304 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "12217", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space/0855f478-27d1-47de-b23b-7a901bf12cf2_model.json b/2023/Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space/0855f478-27d1-47de-b23b-7a901bf12cf2_model.json new file mode 100644 index 0000000000000000000000000000000000000000..807d2fb05927656d437bf817fc06608f262dd16a --- /dev/null +++ b/2023/Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space/0855f478-27d1-47de-b23b-7a901bf12cf2_model.json @@ -0,0 +1,1914 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.0, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.17, + 0.131, + 0.803, + 0.178 + ], + "angle": 0, + "content": "Learning to Measure the Point Cloud Reconstruction Loss in a Representation Space" + }, + { + "type": "text", + "bbox": [ + 0.167, + 0.203, + 0.803, + 0.277 + ], + "angle": 0, + "content": "Tianxin Huang1 Zhonggan Ding2 Jiangning Zhang2 Ying Tai2 Zhenyu Zhang2 Mingang Chen3 Chengjie Wang2 Yong Liu1* \n1APRIL Lab, Zhejiang University 2Tencent YouTu Lab \n3Shanghai Development Center of Computer Software Technology" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.309, + 0.314, + 0.327 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.342, + 0.474, + 0.691 + ], + "angle": 0, + "content": "For point cloud reconstruction-related tasks, the reconstruction losses to evaluate the shape differences between reconstructed results and the ground truths are typically used to train the task networks. Most existing works measure the training loss with point-to-point distance, which may introduce extra defects as predefined matching rules may deviate from the real shape differences. Although some learning-based works have been proposed to overcome the weaknesses of manually-defined rules, they still measure the shape differences in 3D Euclidean space, which may limit their ability to capture defects in reconstructed shapes. In this work, we propose a learning-based Contrastive Adversarial Loss (CALoss) to measure the point cloud reconstruction loss dynamically in a non-linear representation space by combining the contrastive constraint with the adversarial strategy. Specifically, we use the contrastive constraint to help CALoss learn a representation space with shape similarity, while we introduce the adversarial strategy to help CALoss mine differences between reconstructed results and ground truths. According to experiments on reconstruction-related tasks, CALoss can help task networks improve reconstruction performances and learn more representative representations." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.717, + 0.21, + 0.733 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.743, + 0.471, + 0.879 + ], + "angle": 0, + "content": "Point clouds, as the common description for 3D shapes, have been broadly used in many areas such as 3D detection [17, 18] and surface reconstruction [9, 13, 19]. For the point cloud reconstruction-related tasks [5, 7, 11, 16], networks need to predict point clouds as similar as possible to the ground truths. Reconstruction losses that can differentiably calculate the shape differences between reconstructed results and ground truths are required to train the task networks. Existing works often use the matching-based" + }, + { + "type": "image", + "bbox": [ + 0.513, + 0.308, + 0.898, + 0.478 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.493, + 0.896, + 0.674 + ], + "angle": 0, + "content": "Figure 1. \\( S_{g} \\) and \\( S_{o} \\) denote ground truths and point clouds generated by the task network. \\( S_{p} \\) is a positive sample with similar shapes as \\( S_{g} \\) acquired by perturbation [2]. Matching-based losses measure distances between points matched by different predefined rules. PCLoss [6] learns to extract descriptors in 3D Euclidean space by linearly weighting coordinates according to their distances to predicted center points, while our method dynamically measures the shape differences with distances between learned global representations in the constructed representation space. \\( L_{p} \\) and \\( L_{r} \\) denote representation distances between \\( S_{g} \\), \\( S_{p} \\) and \\( S_{g} \\), \\( S_{o} \\), respectively. \\( L_{r}^{adv} \\) is an adversarial loss to maximize representation distances between \\( S_{g} \\), \\( S_{o} \\), \\( L_{r}^{adv} \\) and \\( L_{p} \\) are used to optimize CALoss, while \\( L_{r} \\) is adopted to train the task network." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.896, + 0.903 + ], + "angle": 0, + "content": "reconstruction losses Chamfer Distance (CD) and Earth Mover's Distance (EMD) to constrain shape differences. CD matches points firstly with their nearest neighbors in another point cloud and then calculates the shape difference as average point-to-point distance, while EMD calculates the average point-to-point distance under an optimization-based global matching. We can find that CD and EMD actually measure the distances between matched points instead of the distances between shapes. As the predefined matching rules are static and unlearnable, training results of CD and EMD may fall into inappropriate local minimums where the reconstruction losses are small but the shapes are obviously different. Learning-based losses in" + }, + { + "type": "page_footnote", + "bbox": [ + 0.102, + 0.888, + 0.283, + 0.901 + ], + "angle": 0, + "content": "* means the corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12208" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.091, + 0.473, + 0.378 + ], + "angle": 0, + "content": "PFNet [8], PUGAN [10], and CRN [21] introduce extra supervision from discriminators trained with the adversarial strategy to find the detailed differences. Their reconstruction performances are improved by introducing adversarial losses, while they still need CD/EMD to evaluate the basic shape distances and cannot fully get rid of the influence from predefined matching rules. PCLoss [6] presents a reconstruction loss measured by the distances between extracted intermediate descriptors in 3D Euclidean space without any manually-defined matching rule, which is updated together with the task network in a generative-adversarial process to search shape differences during training. However, the descriptor extraction in Euclidean space actually limits the searching of shape differences, while the training efficiency is also restricted as the descriptors are constructed with dense connections between multiple predicted center points and all points. In summary, existing reconstruction losses mainly rely on distances in 3D Euclidean space to measure the shape differences." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.379, + 0.473, + 0.514 + ], + "angle": 0, + "content": "In this work, we propose a novel framework named Contrastive Adversarial Loss (CALoss) learning to measure the point cloud reconstruction loss dynamically in a high dimensional representation space constructed by a series of fully differentiable structures. The differences between our work and existing works are presented in Fig. 1. CALoss is composed of \\( L_{p} \\), \\( L_{r} \\), and \\( L_{r}^{adv} \\) acquired from distances between global representations. \\( L_{r}^{adv} \\) and \\( L_{p} \\) are used to optimize CALoss, where \\( L_{r} \\) is used to train the task network." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.515, + 0.473, + 0.756 + ], + "angle": 0, + "content": "We introduce \\( L_{p} \\) as the contrastive constraint to help CALoss construct a representation space with the shape similarity that similar shapes should have close representations. In this way, by adding adversarial loss on representations, \\( L_{r}^{adv} \\) can guide CALoss to search for the shape differences between ground truths \\( S_{g} \\) and reconstructed results \\( S_{o} \\). By updating dynamically according to the reconstructed results in each iteration, CALoss can continuously find existing defects in reconstructed shapes and prevent the task network from falling into unexpected local minimums. As the measurement for shape differences is implemented in non-linear representation space, CALoss has more extensive searching space. Besides, the representations adopted to measure shape differences are aggregated with the global pooling operation without any requirement of dense connections as PCLoss [6], which can improve the training efficiency." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.757, + 0.441, + 0.771 + ], + "angle": 0, + "content": "Our contribution in this work can be summarized as" + }, + { + "type": "text", + "bbox": [ + 0.094, + 0.785, + 0.47, + 0.844 + ], + "angle": 0, + "content": "- We propose a novel Contrastive Adversarial Loss (CALoss) learning to measure the point cloud reconstruction loss with distances between high-dimensional global representations." + }, + { + "type": "text", + "bbox": [ + 0.094, + 0.856, + 0.471, + 0.901 + ], + "angle": 0, + "content": "- By combining the contrastive constraint and adversarial training strategy, CALoss can construct a representation space where similar shapes have close represen" + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.785, + 0.471, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.531, + 0.092, + 0.892, + 0.122 + ], + "angle": 0, + "content": "tations and learn to search for shape differences in this space dynamically during training." + }, + { + "type": "text", + "bbox": [ + 0.517, + 0.131, + 0.895, + 0.207 + ], + "angle": 0, + "content": "- Experiments on point cloud reconstruction, unsupervised classification, and point cloud completion confirm that CALoss can help the task network improve reconstruction performances and learn more representative representations with higher training efficiency." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.218, + 0.642, + 0.233 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.242, + 0.857, + 0.258 + ], + "angle": 0, + "content": "2.1. Point Cloud Reconstruction-related Tasks" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.266, + 0.895, + 0.643 + ], + "angle": 0, + "content": "Base on the basic point cloud reconstruction framework to predict similar output point clouds as inputs, e.g. autoencoder, many related tasks have been developed such as the unsupervised classification and point cloud completion. The unsupervised classification task raised by [1, 25] trains auto-encoders to learn representations of point clouds. The representations are then adopted to train a Support Vector Machine (SVM) with provided labels for further classification. The classification accuracy of SVM can reflect the distinctiveness of learned representations. Many researchers have improved the classification performances by modifying the network structures [12, 22, 28], while some researchers [16] introduce extra supervision to enhance the learning effect. Point cloud completion predicts completed point clouds as identical as possible to the ground truths from partial input point clouds. Early works [11, 27] often use typical auto-encoders to abstract long global features from partial inputs and predict completed results, while recent work [7, 21] add more diverse network structures to improve the completion performances. Reconstruction losses CD or EMD to capture the shape differences are always adopted in these works. In this condition, we adopt three tasks including basic point cloud reconstruction, point cloud unsupervised classification, and completion to evaluate the performances of CALoss." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.651, + 0.821, + 0.667 + ], + "angle": 0, + "content": "2.2. Losses to Evaluate Shape Differences" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.674, + 0.895, + 0.901 + ], + "angle": 0, + "content": "The Chamfer Distance (CD) [27] and Earth Mover's Distance (EMD) [3] are two basic and broadly used reconstruction losses to constrain the shape differences, which calculate the distance between point clouds with different matching strategies. With the matching by nearest neighbors, CD concentrates on differences between contours, while it often constructs non-uniform surfaces as discussed in [3, 23]. EMD aims to find an one-to-one optimal mapping \\(\\phi\\) from one point set to another by optimizing the minimum matching distances between the point sets. Though EMD can create more uniform shapes, it takes large time cost due to the optimized matching and can only be applied to reconstructed output with the same number of input, where the quality of optimization also limits the performances. Since the development of GAN [4], researchers" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "12209" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.089, + 0.554, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.564, + 0.089, + 0.9, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.337, + 0.897, + 0.437 + ], + "angle": 0, + "content": "Figure 2. The pipeline of CALoss. \\( S_{g} \\) and \\( S_{o} \\) denote ground truths and point clouds generated by the task network. \\( S_{p} \\) is acquired from \\( S_{g} \\) with small perturbations. \\( f(\\cdot) \\) is a group of 1D-convolutions to transform point clouds \\( S_{g}, S_{p} \\), and \\( S_{o} \\) with \\( N_{g}, N_{p} \\) and \\( N_{o} \\) points into \\( D \\)-dim features \\( F_{g}, F_{p} \\), and \\( F_{o} \\). \\( g(\\cdot) \\) denotes our proposed Adaptive Pooling to aggregate \\( F_{g}, F_{p} \\), and \\( F_{o} \\) into global representations \\( C_{g}, C_{p} \\), and \\( C_{o} \\) with multiple weight distributions, where \\( h(\\cdot) \\) is Pooling Controller predicting parameters \\( \\delta \\) to control the widths of weight distributions in \\( g(\\cdot) \\) according to \\( F_{g}, C_{g}, C_{p} \\), and \\( C_{o} \\) will be used to calculate the required losses \\( L_{p}, L_{r}^{adv} \\), and \\( L_{r} \\) to train CALoss and the task network. We introduce adversarial loss to dynamically search for the shape defects in \\( S_{o} \\), while maximizing representation distances in a mini-batch like [2] may not work because it lacks dynamic feedback from \\( S_{o} \\) and cannot capture detailed shape differences." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.465, + 0.473, + 0.737 + ], + "angle": 0, + "content": "have introduced different learning-based discriminators in reconstruction losses as extra supervisions to better capture the shape differences and improve reconstruction performances [8, 10, 21]. However, these works still need CD or EMD as basic shape constraints. Some works [14,23] further modify the matching rules to improve the constraining performances. All these works measure shape differences based on the point-to-point distance calculated with predefined matching rules. DPDist [20] estimates the shape distances with a pre-trained network without matching. But it is mainly designed for registration and has inferior performances on point cloud reconstruction. PCLoss [6] presents a fully learning-based reconstruction loss to overcome the limitation of predefined matching rules, which learns to aggregate descriptors from point clouds and measure reconstruction losses with distances between extracted descriptors. It learns to search shape differences by an adversarial training together with the task network." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.473, + 0.903 + ], + "angle": 0, + "content": "However, PCLoss constructs descriptors in 3D Euclidean space with linear weights calculated by distances between multiple predicted center points and all existing points, which may limit both its searching space for shape differences and training efficiency. In this work, we propose CALoss to measure the reconstruction losses in a high dimensional representation space. By the concise designation with only non-linear transformation and the global pooling operation, CALoss can achieve both better performances and higher training efficiency than existing learning-based" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.466, + 0.645, + 0.48 + ], + "angle": 0, + "content": "reconstruction losses." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.492, + 0.636, + 0.51 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.517, + 0.896, + 0.655 + ], + "angle": 0, + "content": "The pipeline of CALoss is presented as Fig. 2. The reconstructed result \\( S_{o} \\) and ground truth \\( S_{g} \\) from the task is fed into CALoss to evaluate the shape differences. A positive sample \\( S_{p} \\) is constructed by small perturbations. \\( S_{g} \\), \\( S_{p} \\), and \\( S_{o} \\) are transformed into features \\( F_{g}, F_{p} \\) and \\( F_{o} \\) by 1-D convolutions \\( f(\\cdot) \\). Features \\( F_{g}, F_{p} \\), and \\( F_{o} \\) are finally aggregated into global representations \\( C_{g}, C_{p} \\), and \\( C_{o} \\) with Adaptive Pooling \\( g(\\cdot) \\) to calculate losses \\( L_{p}, L_{r}^{adv} \\), and \\( L_{r} \\). \\( L_{p} \\) and \\( L_{r} \\) can be calculated by:" + }, + { + "type": "equation", + "bbox": [ + 0.577, + 0.661, + 0.892, + 0.68 + ], + "angle": 0, + "content": "\\[\nL _ {p} = \\left\\| C _ {g}, C _ {p} \\right\\| _ {1}, L _ {r} = \\left\\| C _ {g}, C _ {o} \\right\\| _ {1}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.685, + 0.801, + 0.7 + ], + "angle": 0, + "content": "where the adversarial loss \\( L_{r}^{adv} \\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.614, + 0.707, + 0.892, + 0.726 + ], + "angle": 0, + "content": "\\[\nL _ {r} ^ {\\text {a d v}} = - \\log \\left(L _ {r} + \\sigma_ {r}\\right). \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.732, + 0.895, + 0.763 + ], + "angle": 0, + "content": "\\(\\sigma_r\\) is a tiny value to avoid errors when \\(L_r \\to 0\\). These losses are used to optimize CALoss and the task network together." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.77, + 0.721, + 0.787 + ], + "angle": 0, + "content": "3.1. Perturbation Operation" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.794, + 0.895, + 0.823 + ], + "angle": 0, + "content": "In this work, we perturb ground truths \\( S_{g} \\) with tiny Gaussian noises, which can be defined as" + }, + { + "type": "equation", + "bbox": [ + 0.641, + 0.832, + 0.892, + 0.849 + ], + "angle": 0, + "content": "\\[\nS _ {p} = S _ {g} + \\mathcal {N} _ {\\sigma}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.855, + 0.895, + 0.901 + ], + "angle": 0, + "content": "where \\(\\mathcal{N}_{\\sigma} = \\mathrm{randn}(\\sigma)\\). The noise width is controlled by a constant \\(\\sigma\\). This operation creates perturbed point clouds with similar but different shapes as ground truths." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "12210" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.084, + 0.09, + 0.896, + 0.342 + ], + "angle": 0, + "content": "
GTCDEMDPUDCRNDPFDDCDPCLossOurs
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.347, + 0.895, + 0.378 + ], + "angle": 0, + "content": "Figure 3. The qualitative comparisons with different losses based on AE [1]. Our method can help the task network create more complete shapes and clearer details." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.405, + 0.427, + 0.423 + ], + "angle": 0, + "content": "3.2. Adaptive Pooling and Pooling Controller" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.43, + 0.474, + 0.595 + ], + "angle": 0, + "content": "Adaptive Pooling is an important operation to aggregate features based on all points into a global representation. Unlike max pooling or average pooling, Adaptive Pooling is dynamically changed and controlled by Pooling Controller during the training process. The structure of Pooling Controller includes a simple network structure to predict parameters \\(\\delta\\) for Adaptive Pooling as shown in Fig. 2. If we defined \\(Con(\\cdot)\\) as concatenation, \\(maxpool(\\cdot)\\), \\(avgpool(\\cdot)\\) and \\(MLP(\\cdot)\\) as max pooling, average pooling and Multi Layer Perceptrons (MLPs), the Pooling Controller can be described as:" + }, + { + "type": "equation", + "bbox": [ + 0.081, + 0.608, + 0.468, + 0.638 + ], + "angle": 0, + "content": "\\[\n\\delta = h \\left(F _ {g}\\right) = M L P \\left(\\operatorname {C o n} \\left(\\operatorname {m a x p o o l} \\left(F _ {g}\\right), \\operatorname {a v g p o o l} \\left(F _ {g}\\right)\\right)\\right). \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.64, + 0.473, + 0.701 + ], + "angle": 0, + "content": "It takes both max pooled and average pooled features to acquire more extensive information about \\( F_{g} \\). In this condition, let us take \\( F_{g} \\) as an example, then the global representation \\( C_{g} \\) can be defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.084, + 0.713, + 0.47, + 0.768 + ], + "angle": 0, + "content": "\\[\nC _ {g} = g \\left(F _ {g}, \\delta\\right) = \\sum_ {i = 1} ^ {\\left| F _ {g} \\right|} \\frac {e ^ {- \\left\\| F _ {g} ^ {i} - m a x p o o l \\left(F _ {g}\\right) \\right\\| / \\delta}}{\\sum_ {i = 1} ^ {\\left| F _ {g} \\right|} e ^ {- \\left\\| F _ {g} ^ {i} - m a x p o o l \\left(F _ {g}\\right) \\right\\| / \\delta}} \\cdot F _ {g}. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.769, + 0.414, + 0.786 + ], + "angle": 0, + "content": "\\(C_p\\) and \\(C_o\\) can be acquired by the same equations:" + }, + { + "type": "equation", + "bbox": [ + 0.169, + 0.796, + 0.47, + 0.814 + ], + "angle": 0, + "content": "\\[\nC _ {p} = g \\left(F _ {p}, \\delta\\right), C _ {o} = g \\left(F _ {o}, \\delta\\right). \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.473, + 0.904 + ], + "angle": 0, + "content": "We can see that the features are aggregated into global representations with multiple weight distributions around the max pooled features, where \\(\\delta\\) actually controls the widths of weight distributions for \\(F_{g}, F_{p}\\), and \\(F_{o}\\). So, we share the same \\(\\delta\\) for \\(F_{g}, F_{p}\\), and \\(F_{o}\\) to keep that they are aggregated" + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.405, + 0.704, + 0.421 + ], + "angle": 0, + "content": "Algorithm 1 Training Process" + }, + { + "type": "text", + "bbox": [ + 0.516, + 0.424, + 0.895, + 0.456 + ], + "angle": 0, + "content": "Input: Input \\( S_{i} \\), ground truths \\( S_{g} \\), the number of iterations iter, the task network TaskNet(·)" + }, + { + "type": "text", + "bbox": [ + 0.517, + 0.457, + 0.658, + 0.469 + ], + "angle": 0, + "content": "for \\(n = 1\\) to iter do" + }, + { + "type": "text", + "bbox": [ + 0.533, + 0.471, + 0.779, + 0.485 + ], + "angle": 0, + "content": "Calculate output of the task network:" + }, + { + "type": "equation", + "bbox": [ + 0.534, + 0.486, + 0.682, + 0.501 + ], + "angle": 0, + "content": "\\[\nS _ {o} ^ {n} = \\operatorname {T a s k N e t} \\left(S _ {i} ^ {n}\\right).\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.533, + 0.502, + 0.891, + 0.531 + ], + "angle": 0, + "content": "Let \\(\\theta_C\\) and \\(\\theta_T\\) be the parameters of CALoss and the task network, respectively." + }, + { + "type": "text", + "bbox": [ + 0.533, + 0.532, + 0.891, + 0.56 + ], + "angle": 0, + "content": "Fix the parameter of task network and update CALoss by descending gradient:" + }, + { + "type": "equation", + "bbox": [ + 0.534, + 0.561, + 0.653, + 0.577 + ], + "angle": 0, + "content": "\\[\n\\nabla_ {\\theta_ {C}} L _ {C} \\left(S _ {o} ^ {n}, S _ {q} ^ {n}\\right).\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.533, + 0.577, + 0.894, + 0.605 + ], + "angle": 0, + "content": "Fix CALoss and update the task network by descending gradient:" + }, + { + "type": "equation", + "bbox": [ + 0.534, + 0.606, + 0.652, + 0.623 + ], + "angle": 0, + "content": "\\[\n\\nabla_ {\\theta_ {T}} L _ {T} (S _ {o} ^ {n}, S _ {g} ^ {n}).\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.623, + 0.573, + 0.635 + ], + "angle": 0, + "content": "end for" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.665, + 0.895, + 0.743 + ], + "angle": 0, + "content": "by distributions with same widths. With such an Adaptive Pooling operation in Eq. 5 and Eq. 6, each item in \\( F_{g} \\), \\( F_{p} \\), and \\( F_{o} \\) can acquire various gradients during the back propagation process, instead of gradients all the same in average pooling or only constraining max items in max pooling." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.752, + 0.794, + 0.769 + ], + "angle": 0, + "content": "3.3. Contrastive Adversarial Training" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.775, + 0.895, + 0.836 + ], + "angle": 0, + "content": "As presented in Fig. 2, losses \\( L_{p} \\), \\( L_{r}^{adv} \\), and \\( L_{r} \\) are calculated by ground truths and reconstructed results from the task. The training losses for CALoss and the task network can be defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.528, + 0.845, + 0.843, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} L _ {C} = L _ {r} ^ {a d v} + \\frac {\\epsilon}{| \\mathcal {N} _ {\\sigma} |} \\cdot L _ {p} + \\epsilon_ {w} \\cdot | \\delta | ^ {2} \\\\ = - \\log \\left(L _ {r} + \\sigma_ {r}\\right) + \\frac {\\epsilon}{\\left| \\mathcal {N} _ {\\sigma} \\right|} \\cdot L _ {p} + \\epsilon_ {w} \\cdot | \\delta | ^ {2}, \\\\ \\end{array}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "12211" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.082, + 0.09, + 0.894, + 0.287 + ], + "angle": 0, + "content": "
DatasetShapeNetModelNet40
NetworksAEFoldingLAELFoldingAEFoldingLAELFolding
MetricsMCDHDMCDHDMCDHDMCDHDMCDHDMCDHDMCDHDMCDHD
CD [3]0.321.870.404.130.311.020.281.200.756.080.837.350.441.690.392.16
EMD [3]0.252.23--0.232.480.212.490.616.18--0.333.820.323.88
PUD [10]0.321.880.363.830.321.020.271.110.735.850.777.290.451.710.381.97
PFD [8]0.321.870.414.140.310.990.260.970.746.280.887.550.441.690.351.69
CRND [21]0.311.860.343.170.311.000.260.990.715.660.767.240.441.690.351.76
DCD [23]0.281.750.918.410.130.890.181.200.686.021.2211.860.171.370.241.81
PCLoss [6]0.231.660.332.570.130.650.140.760.595.300.756.650.171.050.191.37
Ours0.211.530.302.570.120.760.120.790.585.230.726.320.161.180.161.24
" + }, + { + "type": "table_caption", + "bbox": [ + 0.125, + 0.29, + 0.843, + 0.305 + ], + "angle": 0, + "content": "Table 1. Comparison with reconstruction losses on ShapeNet (SP) and ModelNet40 (MN40). Bold marks the best results." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.323, + 0.107, + 0.336 + ], + "angle": 0, + "content": "and" + }, + { + "type": "equation", + "bbox": [ + 0.238, + 0.34, + 0.47, + 0.357 + ], + "angle": 0, + "content": "\\[\nL _ {T} = L _ {r}, \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.366, + 0.474, + 0.624 + ], + "angle": 0, + "content": "where \\(\\epsilon\\) and \\(\\epsilon_w\\) are two hyper-parameters to adjust the weights. The whole training process for CALoss and the task network can be described as Alg. 1. Parameters of CALoss and the task network are updated by turns in each iteration. CALoss is updated by \\(L_r^{adv}\\) and \\(L_p\\). \\(L_p\\) is used to constrain that similar shapes \\(S_p\\), \\(S_g\\) have close representations, where \\(L_r^{adv}\\) can promote CALoss to find the shape differences between \\(S_g\\) and \\(S_o\\). We give a dynamic weight for \\(L_p\\) controlled by \\(1 / |\\mathcal{N}_{\\sigma}|\\), which means more noised \\(S_p\\) are allowed to have relatively further representations. Besides, we add a L2 regularization for \\(\\delta\\) to prevent the weights for \\(F_g\\), \\(F_p\\), and \\(F_o\\) from over-smoothness. According to Eq. 5, too large \\(\\delta\\) will result in roughly the same weighting for each item in \\(F_g\\), \\(F_p\\), or \\(F_o\\), which is harmful for delivering variable gradients. The task network is optimized by \\(L_r\\) to reduce the differences found by CALoss between reconstructed results \\(S_o\\) and ground truth \\(S_g\\)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.639, + 0.21, + 0.656 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.664, + 0.398, + 0.681 + ], + "angle": 0, + "content": "4.1. Datasets and Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.688, + 0.471, + 0.855 + ], + "angle": 0, + "content": "In this work, three point cloud datasets: ShapeNet [26], ModelNet10 (MN10), and ModelNet40 (MN40) [24] are adopted. We use the ShapeNet part dataset [1, 25] containing 12288 models in the train split and 2874 models in the test split. ModelNet10 and ModelNet40 are subsets of ModelNet, which contain 10 categories and 40 categories of CAD models, respectively. Each model consists of 2048 points randomly sampled from the surfaces of original mesh models. We conduct comparisons with other losses on three tasks, including point cloud reconstruction, unsupervised classification, and point cloud completion." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.473, + 0.903 + ], + "angle": 0, + "content": "For the reconstruction task, we train networks with different reconstruction losses on the train split of ShapeNet part dataset and evaluate performances on both the test split" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.322, + 0.895, + 0.565 + ], + "angle": 0, + "content": "of ShapeNet and MN40 to provide a robust and exhaustive evaluation. For the unsupervised classification task, we compare the performances of different losses on multiple auto-encoders constructed by [1, 15, 22, 25] following PCLoss [6]. As for GLRNet [16], we follow its setting and retrain it with the original adopted CD and CALoss to observe the differences. For the point cloud completion task, we introduce 3 popular works PCN [27], CRN [21], and RFNet [7] to compare the completion performances before and after replacing the adopted reconstruction losses with CALoss. PCN and CRN are trained on the dataset provided by CRN with 2048 points to compare the completion performances on sparse point clouds, while RFNet is trained on the corresponding dataset with 16384 points to see the completion performances on dense point clouds. All data are normalized to \\(-1\\sim 1\\) for the fairness of comparison." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.565, + 0.895, + 0.626 + ], + "angle": 0, + "content": "Metrics. To provide a clear and accurate evaluation of the performance, we adopt Multi-scale Chamfer Distance (MCD) and Hausdorff Distance (HD) introduced in [6] as metrics for reconstruction quality assessment in this work." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.635, + 0.88, + 0.652 + ], + "angle": 0, + "content": "4.2. Comparisons on Point Cloud Reconstruction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.895, + 0.87 + ], + "angle": 0, + "content": "In this section, we conduct comparisons with different reconstruction losses based on a few commonly-used networks AE [1], Folding [25] and local feature-based LAE and LFolding following PCLoss [6]. We retrain the networks with different reconstruction losses and evaluate the reconstruction errors of trained networks on the test split of ShapeNet and ModelNet40. As introduced in Sec. 2.2, CD and EMD are widely-used matching-based reconstruction losses, while PUD [10], PFD [8], CRND [21] are constraints introducing extra discriminators to improve the reconstruction performances. DCD [23] is a recent variant of CD achieved by modifying the matching rule. In this work, we choose the better-performed LNSA proposed in PCLoss [6] as PCLoss for comparison in following sections." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.894, + 0.901 + ], + "angle": 0, + "content": "The quantitative results are presented in Table 1. We can see that CALoss can achieve the best performances in" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "12212" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.288 + ], + "angle": 0, + "content": "most conditions. To intuitively present the differences in reconstructed results, we also conduct a qualitative comparison in Fig. 3. We can see that CD may create quite nonuniform results with missing local details, while EMD may produce distorted shapes. Though PUD, CRND, and PFD can improve the integrity of shapes, they still produce similar shapes as CD contained within. PCLoss improves both the uniformity and integrity of reconstructed shapes. But it gets relatively rough details as shown in Fig. 3. As shown by circled regions in the last two rows of Fig. 3, the results of PCLoss create rough contours on chair legs and lamp stands, while our method produces clearer details on these regions. It confirms the effectiveness of CALoss." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.297, + 0.456, + 0.314 + ], + "angle": 0, + "content": "4.3. Comparisons on Unsupervised Classification" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.321, + 0.471, + 0.397 + ], + "angle": 0, + "content": "In this section, we evaluate the performances of CALoss on point cloud unsupervised classification based on multiple auto-encoders constructed by [1, 15, 22, 25] with 128-dim bottleneck following PCLoss [6], and GLRNet [16]. The experimental settings are kept the same as [1, 16, 25]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.397, + 0.474, + 0.609 + ], + "angle": 0, + "content": "The auto-encoders are trained on ShapeNet and applied on ModelNet10 and ModelNet40 to extract point cloud representations, where the representations extracted from the training splits and corresponding labels are adopted to train Supported Vector Machines (SVMs). Then the distinguishability of extracted representations can be evaluated by the classification accuracy of these SVMs. We conduct comparisons on these networks by replacing the adopted reconstruction losses to train the auto-encoders with CALoss and observe the changing in classification accuracy. From the results in Table 2, we can see that most networks can achieve improvements by replacing the reconstruction losses with CALoss, which confirms that CALoss can help the task networks learn more representative representations." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.618, + 0.431, + 0.635 + ], + "angle": 0, + "content": "4.4. Comparisons on Point Cloud Completion" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.642, + 0.473, + 0.837 + ], + "angle": 0, + "content": "Point Cloud Completion predicts completed results as similar as possible to ground truths from partial inputs, which is usually trained with reconstruction losses between completed results and ground truths. To further verify the performances of CALoss, we apply it to a few popular point cloud completion works, including PCN [27], CRN [21], and RFNet [7]. As these works may have multilevel constraints, we conduct comparisons by replacing the reconstruction losses of the last level with CALoss and retraining the networks. The results are presented in Table 3. The completion performances have improvements in most conditions by introducing CALoss, which further confirms that CALoss is effective for different task networks." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.848, + 0.39, + 0.865 + ], + "angle": 0, + "content": "4.5. Analysis about the Training Process" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.473, + 0.902 + ], + "angle": 0, + "content": "Visualization during training. We visualize a model generated by the task network AE [1] during training to ob-" + }, + { + "type": "table", + "bbox": [ + 0.517, + 0.089, + 0.88, + 0.386 + ], + "angle": 0, + "content": "
TaskNetDatasetMethods
CDEMDPCLossOurs
AEMN1090.6089.4991.4891.48
MN4085.9285.4786.3686.81
FoldingMN1091.03-91.7092.26
MN4085.22-85.3586.24
AEMN1090.3890.1592.0493.47
(PN++)MN4088.0388.0787.5488.15
FoldingMN1091.48-91.4892.59
(PN++)MN4087.01-86.7387.13
AEMN1091.3791.2692.3792.81
(DGCNN)MN4087.5087.5488.1187.46
FoldingMN1091.26-91.8192.70
(DGCNN)MN4086.85-87.5087.74
GLRNetMN1093.58--95.24
MN4091.07--91.31
" + }, + { + "type": "table_caption", + "bbox": [ + 0.54, + 0.39, + 0.851, + 0.404 + ], + "angle": 0, + "content": "Table 2. Comparison on unsupervised classification." + }, + { + "type": "table", + "bbox": [ + 0.506, + 0.424, + 0.892, + 0.508 + ], + "angle": 0, + "content": "
NetworkPCNCRNRFNetRFNet*
MetroMCD HDMCD HDMCD HDMCD HD
w/o CALoss0.31 2.670.29 2.420.21 2.920.29 3.82
w/ CALoss0.31 2.550.29 2.440.20 2.630.27 3.28
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.512, + 0.892, + 0.555 + ], + "angle": 0, + "content": "Table 3. Comparisons on point cloud completion. RFNet and RFNet* denote results evaluated on known and novel categories on ShapeNet following RFNet [7]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.567, + 0.895, + 0.793 + ], + "angle": 0, + "content": "serve the convergence of different losses. The results are presented in Fig. 4. We can see that CD and EMD have unchanged results with obvious defects after 200 iterations, which means they actually converge to inappropriate local minimums. The reconstructed results trained with CALoss converge to a simple shape after 100 iterations, which may be the effect of contrastive constraint to help the task network find a shape similar to ground truths. From \\(200 \\sim 600\\) iterations, the trained results of CALoss gradually remove differences and approach the ground truth, which confirms the adversarial loss can continuously help find the defects and promote the task network to get better performances. Although PCLoss also removes defects during iterations, it produces rougher results, which may come from the limitation of 3D Euclidean space-based descriptors." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Training curves. In this section, we visualize the reconstruction errors based on the AE network [1] and ShapeNet dataset [24] through the training iterations to observe the convergences of difference loss functions. We can see that our method has relatively inferior performances at the beginning of iterations, where it is learning to search shape differences. But it will converge steadily to low errors after" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12213" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.087, + 0.089, + 0.88, + 0.37 + ], + "angle": 0, + "content": "
Iteration100200300400500600GT
CD
EMD
PCLoss
Ours
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.377, + 0.895, + 0.419 + ], + "angle": 0, + "content": "Figure 4. The visualization of training processes with different reconstruction losses. CD/EMD fall into local minimums and acquire unchanged results after 200 iterations, while PCLoss produces relatively rough shapes. Our method can gradually remove the shape defects and create more accurate results." + }, + { + "type": "image", + "bbox": [ + 0.083, + 0.447, + 0.268, + 0.606 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.268, + 0.447, + 0.442, + 0.605 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.083, + 0.614, + 0.462, + 0.628 + ], + "angle": 0, + "content": "Figure 5. The reconstruction error curves through the iterations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.638, + 0.471, + 0.682 + ], + "angle": 0, + "content": "enough iterations. We can also see that CALoss converge faster than PCLoss, which reveals the priority of our representation space-based measurement." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.691, + 0.36, + 0.708 + ], + "angle": 0, + "content": "4.6. Training Efficiency Comparison" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.714, + 0.471, + 0.806 + ], + "angle": 0, + "content": "In this section, we evaluate the training efficiencies of different reconstruction losses on the AE network [1], which are measured by the time consumed for the training of a single batch. The results are presented in Table 4. Though our method performs slightly slower than CD, it has the highest training efficiency in learning-based losses." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.809, + 0.474, + 0.877 + ], + "angle": 0, + "content": "
Non-learningLearning-based
MethodsCDEMDPUDPFDCRNDPCLossOurs
Time(ms)232167745975739
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.882, + 0.469, + 0.909 + ], + "angle": 0, + "content": "Table 4. Training efficiency comparison conducted on an NVIDIA 2080ti with a 2.9GHz i5-9400 CPU." + }, + { + "type": "table", + "bbox": [ + 0.508, + 0.448, + 0.895, + 0.59 + ], + "angle": 0, + "content": "
GT
Pretrained
Ours
" + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.6, + 0.895, + 0.629 + ], + "angle": 0, + "content": "Figure 6. Qualitative comparisons between our method dynamically updated and pre-trained without updating." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.642, + 0.807, + 0.66 + ], + "angle": 0, + "content": "4.7. Necessity of the Dynamic Updating" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.667, + 0.895, + 0.818 + ], + "angle": 0, + "content": "CALoss is dynamically optimized together with the task network as claimed in Sec. 3. To show the necessity of the dynamic updating, we make an attempt to train the task network AE [1] with a pre-trained CALoss directly without further updating. The results are demonstrated in Fig. 6 and Table 5. We can see that the task network trained with pre-trained CALoss can only reconstruct quite rough shapes, which confirms that training with CALoss is actually a continuous procedure to search shape differences between ground truths and the task network outputs." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.831, + 0.655, + 0.848 + ], + "angle": 0, + "content": "4.8. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Ablation study for components. We conduct an ablation study for the components adopted in CALoss as mentioned in Eq. 7. The results are presented in Table 6. \\( L_{p} \\) and" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "12214" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.107, + 0.089, + 0.444, + 0.172 + ], + "angle": 0, + "content": "
Pre-trainedOurs
DatasetSPMN40SPMN40
MCD0.751.040.210.58
HD14.4814.151.535.23
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.182, + 0.471, + 0.211 + ], + "angle": 0, + "content": "Table 5. Quantitative comparisons between our method dynamically updated and pre-trained without updating." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.22, + 0.472, + 0.266 + ], + "angle": 0, + "content": "\\(L_{r}^{adv}\\) are the basic contrastive and adversarial constraints, respectively, while \\(|\\delta|^2\\) is the regularization constraint for \\(\\delta\\). \\(1 / |\\mathcal{N}_{\\sigma}|\\) is the dynamic coefficient for the weight of \\(L_{p}\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.267, + 0.472, + 0.356 + ], + "angle": 0, + "content": "As finding shape difference is an essential constraint to prevent CALoss from acquiring all-zero output under the supervision of only \\( L_{p} \\), we remove the \\( L_{r}^{adv} \\) by replacing it with the negative implementation of metric-learning method [2, 16] by maximizing the representation distances between models in the same mini-batch." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.358, + 0.474, + 0.526 + ], + "angle": 0, + "content": "We can see that \\( L_{p} \\) and \\( L_{r}^{adv} \\) both have very significant influences on the final performance, which means they are cores of CALoss. Replacing \\( L_{r}^{adv} \\) with metric-learning method has weaker results. It confirms that maximizing representations between shapes within a mini-batch is not enough to learn the shape differences because it lacks of dynamic feedback from reconstructed outputs and cannot accurately find the shape differences. The regularization \\( |\\sigma|^{2} \\) also has obvious influence, which means it is important to control the widths of weight distributions to aggregate representations in \\( g(\\cdot) \\) as shown in Fig. 2 and Eq. 5." + }, + { + "type": "table", + "bbox": [ + 0.088, + 0.539, + 0.462, + 0.654 + ], + "angle": 0, + "content": "
LpPerturbLadv|δ|21/|Nσ|MCDHD
Lp/Ladv✓✓✓3.557.57
1.8312.76
Others✓✓✓0.778.13
✓✓✓✓0.221.63
✓✓✓✓✓0.211.53
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.667, + 0.473, + 0.71 + ], + "angle": 0, + "content": "Table 6. Ablation for components. Perturb denotes the perturbation, while \\( L_{p} \\), \\( L_{r}^{adv} \\) and \\( |\\delta|^{2} \\) are components included in Eq. 7. \\( 1 / |\\mathcal{N}_{\\sigma}| \\) is the dynamic coefficient for the weight of \\( L_{p} \\) in Eq. 7." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.719, + 0.472, + 0.824 + ], + "angle": 0, + "content": "Ablation study for the Pooling Controller. In Pooling Controller, we introduce both max pooled and average pooled features to acquire more extensive information about the overall distribution in \\( F_{g} \\) as presented in Fig. 2 and Eq. 4. We conduct a simple ablation study for this operation in Table 7. We can see that the designation of concatenation can indeed reduce the reconstruction errors." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.472, + 0.903 + ], + "angle": 0, + "content": "Ablation study the Adaptive Pooling operation. Here, We present a simple discussion about the proposed Adaptive Pooling operation. From Table 8, we can see that both max and average pooling have quite inferior performances, which prove the necessity of Adaptive Pooling operation." + }, + { + "type": "table", + "bbox": [ + 0.56, + 0.089, + 0.835, + 0.151 + ], + "angle": 0, + "content": "
MetricsMaxAvgOurs
MCD0.220.230.21
HD1.581.551.53
" + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.155, + 0.895, + 0.199 + ], + "angle": 0, + "content": "Table 7. Comparisons between different implementations of Pooling Controller on ShapeNet and AE [1]. Max and Avg denote introducing only max and average pooling, respectively." + }, + { + "type": "table", + "bbox": [ + 0.559, + 0.204, + 0.835, + 0.265 + ], + "angle": 0, + "content": "
MetricsMaxAvgOurs
MCD0.340.610.21
HD1.726.791.53
" + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.27, + 0.892, + 0.299 + ], + "angle": 0, + "content": "Table 8. Comparisons between pooling operations on ShapeNet and AE [1]. Max and Avg denote max and average pooling." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.308, + 0.895, + 0.535 + ], + "angle": 0, + "content": "In CALoss, the pooling operation is used to aggregate features from all points into a global representation. To train the task network, the global representation needs to provide variable gradients for each point feature to distinguish them. However, average pooling can only propagate same and indistinguishable gradients for each points. Although max pooling can provide different gradients for point features, it provides a hard 0-1 distribution where only max items are constrained. In this condition, we design such an Adaptive Pooling to get a variable weight for each point feature according to the distance to the max pooled feature, which can be regarded as a \"soft max pooling\". All point features can be constrained with distinguishable gradients, which is controlled by widths of weight distributions predicted with Pooling Controller \\( h(\\cdot) \\) as shown in Fig. 2 and Eq. 5." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.548, + 0.62, + 0.565 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.574, + 0.895, + 0.801 + ], + "angle": 0, + "content": "In this work, we propose a novel learning-based framework named CALoss to train the point cloud reconstruction-related task networks by measuring the differences between reconstructed shapes and ground truths with distances in a representation space. With the measurement of reconstruction loss in the learned non-linear representation space, CALoss has more extensive searching space for shape differences and better performances than the Euclidean space-based methods. According to the experiments, CALoss can achieve improvements above existing reconstruction losses based on predefined matching rules on multiple tasks including point cloud reconstruction, unsupervised classification and completion, which confirms it can help the task network achieve better reconstruction performances and extract more representative representations." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.815, + 0.625, + 0.832 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.895, + 0.9 + ], + "angle": 0, + "content": "We thank all authors, reviewers and the chair for excellent contributions. This work is supported by the Key Research and Development Project of Zhejiang Province under Grant 2021C01035." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12215" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.176, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.171 + ], + "angle": 0, + "content": "[1] Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. Learning representations and generative models for 3d point clouds. In International conference on machine learning, pages 40-49. PMLR, 2018. 2, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.171, + 0.472, + 0.227 + ], + "angle": 0, + "content": "[2] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 1, 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.228, + 0.471, + 0.284 + ], + "angle": 0, + "content": "[3] Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 605-613, 2017. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.285, + 0.471, + 0.34 + ], + "angle": 0, + "content": "[4] Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. arXiv preprint arXiv:1406.2661, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.341, + 0.472, + 0.395 + ], + "angle": 0, + "content": "[5] Tianxin Huang and Yong Liu. 3d point cloud geometry compression on deep learning. In Proceedings of the 27th ACM International Conference on Multimedia, pages 890-898, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.397, + 0.472, + 0.467 + ], + "angle": 0, + "content": "[6] Tianxin Huang, Xuemeng Yang, Jiangning Zhang, Jinhao Cui, Hao Zou, Jun Chen, Xiangrui Zhao, and Yong Liu. Learning to train a point cloud reconstruction network without matching. In European Conference on Computer Vision, pages 179-194. Springer, 2022. 1, 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.468, + 0.472, + 0.55 + ], + "angle": 0, + "content": "[7] Tianxin Huang, Hao Zou, Jinhao Cui, Xuemeng Yang, Mengmeng Wang, Xiangrui Zhao, Jiangning Zhang, Yi Yuan, Yifan Xu, and Yong Liu. Rfnet: Recurrent forward network for dense point cloud completion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12508-12517, 2021. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.551, + 0.472, + 0.62 + ], + "angle": 0, + "content": "[8] Zitian Huang, Yikuan Yu, Jiawen Xu, Feng Ni, and Xinyi Le. Pf-net: Point fractal network for 3d point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7662-7670, 2020. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.621, + 0.472, + 0.691 + ], + "angle": 0, + "content": "[9] Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, Thomas Funkhouser, et al. Local implicit grid representations for 3d scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6001–6010, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.078, + 0.692, + 0.472, + 0.76 + ], + "angle": 0, + "content": "[10] Ruihui Li, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, and Pheng-Ann Heng. Pu-gan: a point cloud upsampling adversarial network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7203-7212, 2019. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.078, + 0.761, + 0.472, + 0.831 + ], + "angle": 0, + "content": "[11] Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, and ShiMin Hu. Morphing and sampling network for dense point cloud completion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11596-11603, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.078, + 0.832, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[12] Yongcheng Liu, Bin Fan, Shiming Xiang, and Chunhong Pan. Relation-shape convolutional neural network for point cloud analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8895-8904, 2019. 2" + }, + { + "type": "list", + "bbox": [ + 0.078, + 0.115, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.895, + 0.163 + ], + "angle": 0, + "content": "[13] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4460-4470, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.164, + 0.895, + 0.233 + ], + "angle": 0, + "content": "[14] Trung Nguyen, Quang-Hieu Pham, Tam Le, Tung Pham, Nhat Ho, and Binh-Son Hua. Point-set distances for learning representations of 3d point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10478-10487, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.234, + 0.895, + 0.29 + ], + "angle": 0, + "content": "[15] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in neural information processing systems, pages 5099-5108, 2017. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.291, + 0.895, + 0.36 + ], + "angle": 0, + "content": "[16] Yongming Rao, Jiwen Lu, and Jie Zhou. Global-local bidirectional reasoning for unsupervised representation learning of 3d point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5376-5385, 2020. 1, 2, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.361, + 0.895, + 0.432 + ], + "angle": 0, + "content": "[17] N Dinesh Reddy, Minh Vo, and Srinivasa G Narasimhan. Carfusion: Combining point tracking and part detection for dynamic 3d reconstruction of vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1906-1915, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.433, + 0.895, + 0.502 + ], + "angle": 0, + "content": "[18] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointcnn: 3d object proposal generation and detection from point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 770-779, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.503, + 0.895, + 0.558 + ], + "angle": 0, + "content": "[19] Jia-Heng Tang, Weikai Chen, Jie Yang, Bo Wang, Songrun Liu, Bo Yang, and Lin Gao. Octfield: Hierarchical implicit functions for 3d modeling. arXiv preprint arXiv:2111.01067, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.56, + 0.895, + 0.617 + ], + "angle": 0, + "content": "[20] Dahlia Urbach, Yizhak Ben-Shabat, and Michael Lindenbaum. Dpdist: Comparing point clouds using deep point cloud distance. In European Conference on Computer Vision, pages 545-560. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.618, + 0.895, + 0.686 + ], + "angle": 0, + "content": "[21] Xiaogang Wang, Marcelo H Ang Jr, and Gim Hee Lee. Cascaded refinement network for point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 790-799, 2020. 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.688, + 0.895, + 0.744 + ], + "angle": 0, + "content": "[22] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (tog), 38(5):1-12, 2019. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.745, + 0.895, + 0.801 + ], + "angle": 0, + "content": "[23] Tong Wu, Liang Pan, Junzhe Zhang, Tai Wang, Ziwei Liu, and Dahua Lin. Density-aware chamfer distance as a comprehensive metric for point cloud completion. arXiv preprint arXiv:2111.12702, 2021. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.802, + 0.895, + 0.872 + ], + "angle": 0, + "content": "[24] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1912-1920, 2015. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.873, + 0.895, + 0.901 + ], + "angle": 0, + "content": "[25] Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian. Foldingnet: Point cloud auto-encoder via deep grid deformation." + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.895, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "12216" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.092, + 0.469, + 0.12 + ], + "angle": 0, + "content": "In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 206-215, 2018. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.472, + 0.19 + ], + "angle": 0, + "content": "[26] Li Yi, Vladimir G Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas. A scalable active framework for region annotation in 3d shape collections. ACM Transactions on Graphics (ToG), 35(6):1-12, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.192, + 0.472, + 0.247 + ], + "angle": 0, + "content": "[27] Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert. Pcn: Point completion network. In 2018 International Conference on 3D Vision (3DV), pages 728-737. IEEE, 2018. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.249, + 0.472, + 0.305 + ], + "angle": 0, + "content": "[28] Yongheng Zhao, Tolga Birdal, Haowen Deng, and Federico Tombari. 3d point capsule networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1009-1018, 2019. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.305 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12217" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space/0855f478-27d1-47de-b23b-7a901bf12cf2_origin.pdf b/2023/Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space/0855f478-27d1-47de-b23b-7a901bf12cf2_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..85121e1378eb736ace901ed4e8a4c32c171a94e6 --- /dev/null +++ b/2023/Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space/0855f478-27d1-47de-b23b-7a901bf12cf2_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4fe5dbe749106ed0fc2a50fffba02f9bda51291b3653429dbc97697cf5f60c25 +size 928313 diff --git a/2023/Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space/full.md b/2023/Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space/full.md new file mode 100644 index 0000000000000000000000000000000000000000..ef831472436fb4f3d30a8d1adf3f43a9d846986f --- /dev/null +++ b/2023/Learning To Measure the Point Cloud Reconstruction Loss in a Representation Space/full.md @@ -0,0 +1,302 @@ +# Learning to Measure the Point Cloud Reconstruction Loss in a Representation Space + +Tianxin Huang1 Zhonggan Ding2 Jiangning Zhang2 Ying Tai2 Zhenyu Zhang2 Mingang Chen3 Chengjie Wang2 Yong Liu1* +1APRIL Lab, Zhejiang University 2Tencent YouTu Lab +3Shanghai Development Center of Computer Software Technology + +# Abstract + +For point cloud reconstruction-related tasks, the reconstruction losses to evaluate the shape differences between reconstructed results and the ground truths are typically used to train the task networks. Most existing works measure the training loss with point-to-point distance, which may introduce extra defects as predefined matching rules may deviate from the real shape differences. Although some learning-based works have been proposed to overcome the weaknesses of manually-defined rules, they still measure the shape differences in 3D Euclidean space, which may limit their ability to capture defects in reconstructed shapes. In this work, we propose a learning-based Contrastive Adversarial Loss (CALoss) to measure the point cloud reconstruction loss dynamically in a non-linear representation space by combining the contrastive constraint with the adversarial strategy. Specifically, we use the contrastive constraint to help CALoss learn a representation space with shape similarity, while we introduce the adversarial strategy to help CALoss mine differences between reconstructed results and ground truths. According to experiments on reconstruction-related tasks, CALoss can help task networks improve reconstruction performances and learn more representative representations. + +# 1. Introduction + +Point clouds, as the common description for 3D shapes, have been broadly used in many areas such as 3D detection [17, 18] and surface reconstruction [9, 13, 19]. For the point cloud reconstruction-related tasks [5, 7, 11, 16], networks need to predict point clouds as similar as possible to the ground truths. Reconstruction losses that can differentiably calculate the shape differences between reconstructed results and ground truths are required to train the task networks. Existing works often use the matching-based + +![](images/d242d8c35b5f7c13ad896495a164603fc1834f0217e28e9d2df66306d2541d13.jpg) +Figure 1. $S_{g}$ and $S_{o}$ denote ground truths and point clouds generated by the task network. $S_{p}$ is a positive sample with similar shapes as $S_{g}$ acquired by perturbation [2]. Matching-based losses measure distances between points matched by different predefined rules. PCLoss [6] learns to extract descriptors in 3D Euclidean space by linearly weighting coordinates according to their distances to predicted center points, while our method dynamically measures the shape differences with distances between learned global representations in the constructed representation space. $L_{p}$ and $L_{r}$ denote representation distances between $S_{g}$ , $S_{p}$ and $S_{g}$ , $S_{o}$ , respectively. $L_{r}^{adv}$ is an adversarial loss to maximize representation distances between $S_{g}$ , $S_{o}$ , $L_{r}^{adv}$ and $L_{p}$ are used to optimize CALoss, while $L_{r}$ is adopted to train the task network. + +reconstruction losses Chamfer Distance (CD) and Earth Mover's Distance (EMD) to constrain shape differences. CD matches points firstly with their nearest neighbors in another point cloud and then calculates the shape difference as average point-to-point distance, while EMD calculates the average point-to-point distance under an optimization-based global matching. We can find that CD and EMD actually measure the distances between matched points instead of the distances between shapes. As the predefined matching rules are static and unlearnable, training results of CD and EMD may fall into inappropriate local minimums where the reconstruction losses are small but the shapes are obviously different. Learning-based losses in + +PFNet [8], PUGAN [10], and CRN [21] introduce extra supervision from discriminators trained with the adversarial strategy to find the detailed differences. Their reconstruction performances are improved by introducing adversarial losses, while they still need CD/EMD to evaluate the basic shape distances and cannot fully get rid of the influence from predefined matching rules. PCLoss [6] presents a reconstruction loss measured by the distances between extracted intermediate descriptors in 3D Euclidean space without any manually-defined matching rule, which is updated together with the task network in a generative-adversarial process to search shape differences during training. However, the descriptor extraction in Euclidean space actually limits the searching of shape differences, while the training efficiency is also restricted as the descriptors are constructed with dense connections between multiple predicted center points and all points. In summary, existing reconstruction losses mainly rely on distances in 3D Euclidean space to measure the shape differences. + +In this work, we propose a novel framework named Contrastive Adversarial Loss (CALoss) learning to measure the point cloud reconstruction loss dynamically in a high dimensional representation space constructed by a series of fully differentiable structures. The differences between our work and existing works are presented in Fig. 1. CALoss is composed of $L_{p}$ , $L_{r}$ , and $L_{r}^{adv}$ acquired from distances between global representations. $L_{r}^{adv}$ and $L_{p}$ are used to optimize CALoss, where $L_{r}$ is used to train the task network. + +We introduce $L_{p}$ as the contrastive constraint to help CALoss construct a representation space with the shape similarity that similar shapes should have close representations. In this way, by adding adversarial loss on representations, $L_{r}^{adv}$ can guide CALoss to search for the shape differences between ground truths $S_{g}$ and reconstructed results $S_{o}$ . By updating dynamically according to the reconstructed results in each iteration, CALoss can continuously find existing defects in reconstructed shapes and prevent the task network from falling into unexpected local minimums. As the measurement for shape differences is implemented in non-linear representation space, CALoss has more extensive searching space. Besides, the representations adopted to measure shape differences are aggregated with the global pooling operation without any requirement of dense connections as PCLoss [6], which can improve the training efficiency. + +Our contribution in this work can be summarized as + +- We propose a novel Contrastive Adversarial Loss (CALoss) learning to measure the point cloud reconstruction loss with distances between high-dimensional global representations. +- By combining the contrastive constraint and adversarial training strategy, CALoss can construct a representation space where similar shapes have close represen + +tations and learn to search for shape differences in this space dynamically during training. + +- Experiments on point cloud reconstruction, unsupervised classification, and point cloud completion confirm that CALoss can help the task network improve reconstruction performances and learn more representative representations with higher training efficiency. + +# 2. Related Work + +# 2.1. Point Cloud Reconstruction-related Tasks + +Base on the basic point cloud reconstruction framework to predict similar output point clouds as inputs, e.g. autoencoder, many related tasks have been developed such as the unsupervised classification and point cloud completion. The unsupervised classification task raised by [1, 25] trains auto-encoders to learn representations of point clouds. The representations are then adopted to train a Support Vector Machine (SVM) with provided labels for further classification. The classification accuracy of SVM can reflect the distinctiveness of learned representations. Many researchers have improved the classification performances by modifying the network structures [12, 22, 28], while some researchers [16] introduce extra supervision to enhance the learning effect. Point cloud completion predicts completed point clouds as identical as possible to the ground truths from partial input point clouds. Early works [11, 27] often use typical auto-encoders to abstract long global features from partial inputs and predict completed results, while recent work [7, 21] add more diverse network structures to improve the completion performances. Reconstruction losses CD or EMD to capture the shape differences are always adopted in these works. In this condition, we adopt three tasks including basic point cloud reconstruction, point cloud unsupervised classification, and completion to evaluate the performances of CALoss. + +# 2.2. Losses to Evaluate Shape Differences + +The Chamfer Distance (CD) [27] and Earth Mover's Distance (EMD) [3] are two basic and broadly used reconstruction losses to constrain the shape differences, which calculate the distance between point clouds with different matching strategies. With the matching by nearest neighbors, CD concentrates on differences between contours, while it often constructs non-uniform surfaces as discussed in [3, 23]. EMD aims to find an one-to-one optimal mapping $\phi$ from one point set to another by optimizing the minimum matching distances between the point sets. Though EMD can create more uniform shapes, it takes large time cost due to the optimized matching and can only be applied to reconstructed output with the same number of input, where the quality of optimization also limits the performances. Since the development of GAN [4], researchers + +![](images/420f49686b377e7ed5a01f3f3cafd165bcdf40238dc56e5c0828b3deb5b58281.jpg) +Figure 2. The pipeline of CALoss. $S_{g}$ and $S_{o}$ denote ground truths and point clouds generated by the task network. $S_{p}$ is acquired from $S_{g}$ with small perturbations. $f(\cdot)$ is a group of 1D-convolutions to transform point clouds $S_{g}, S_{p}$ , and $S_{o}$ with $N_{g}, N_{p}$ and $N_{o}$ points into $D$ -dim features $F_{g}, F_{p}$ , and $F_{o}$ . $g(\cdot)$ denotes our proposed Adaptive Pooling to aggregate $F_{g}, F_{p}$ , and $F_{o}$ into global representations $C_{g}, C_{p}$ , and $C_{o}$ with multiple weight distributions, where $h(\cdot)$ is Pooling Controller predicting parameters $\delta$ to control the widths of weight distributions in $g(\cdot)$ according to $F_{g}, C_{g}, C_{p}$ , and $C_{o}$ will be used to calculate the required losses $L_{p}, L_{r}^{adv}$ , and $L_{r}$ to train CALoss and the task network. We introduce adversarial loss to dynamically search for the shape defects in $S_{o}$ , while maximizing representation distances in a mini-batch like [2] may not work because it lacks dynamic feedback from $S_{o}$ and cannot capture detailed shape differences. + +![](images/dea06987ccdb82769c5e261f9be0bf4fb0be5a12dbc2e1ffd6dc037d410c50fc.jpg) + +have introduced different learning-based discriminators in reconstruction losses as extra supervisions to better capture the shape differences and improve reconstruction performances [8, 10, 21]. However, these works still need CD or EMD as basic shape constraints. Some works [14,23] further modify the matching rules to improve the constraining performances. All these works measure shape differences based on the point-to-point distance calculated with predefined matching rules. DPDist [20] estimates the shape distances with a pre-trained network without matching. But it is mainly designed for registration and has inferior performances on point cloud reconstruction. PCLoss [6] presents a fully learning-based reconstruction loss to overcome the limitation of predefined matching rules, which learns to aggregate descriptors from point clouds and measure reconstruction losses with distances between extracted descriptors. It learns to search shape differences by an adversarial training together with the task network. + +However, PCLoss constructs descriptors in 3D Euclidean space with linear weights calculated by distances between multiple predicted center points and all existing points, which may limit both its searching space for shape differences and training efficiency. In this work, we propose CALoss to measure the reconstruction losses in a high dimensional representation space. By the concise designation with only non-linear transformation and the global pooling operation, CALoss can achieve both better performances and higher training efficiency than existing learning-based + +reconstruction losses. + +# 3. Methodology + +The pipeline of CALoss is presented as Fig. 2. The reconstructed result $S_{o}$ and ground truth $S_{g}$ from the task is fed into CALoss to evaluate the shape differences. A positive sample $S_{p}$ is constructed by small perturbations. $S_{g}$ , $S_{p}$ , and $S_{o}$ are transformed into features $F_{g}, F_{p}$ and $F_{o}$ by 1-D convolutions $f(\cdot)$ . Features $F_{g}, F_{p}$ , and $F_{o}$ are finally aggregated into global representations $C_{g}, C_{p}$ , and $C_{o}$ with Adaptive Pooling $g(\cdot)$ to calculate losses $L_{p}, L_{r}^{adv}$ , and $L_{r}$ . $L_{p}$ and $L_{r}$ can be calculated by: + +$$ +L _ {p} = \left\| C _ {g}, C _ {p} \right\| _ {1}, L _ {r} = \left\| C _ {g}, C _ {o} \right\| _ {1}, \tag {1} +$$ + +where the adversarial loss $L_{r}^{adv}$ is defined as: + +$$ +L _ {r} ^ {\text {a d v}} = - \log \left(L _ {r} + \sigma_ {r}\right). \tag {2} +$$ + +$\sigma_r$ is a tiny value to avoid errors when $L_r \to 0$ . These losses are used to optimize CALoss and the task network together. + +# 3.1. Perturbation Operation + +In this work, we perturb ground truths $S_{g}$ with tiny Gaussian noises, which can be defined as + +$$ +S _ {p} = S _ {g} + \mathcal {N} _ {\sigma}, \tag {3} +$$ + +where $\mathcal{N}_{\sigma} = \mathrm{randn}(\sigma)$ . The noise width is controlled by a constant $\sigma$ . This operation creates perturbed point clouds with similar but different shapes as ground truths. + +
GTCDEMDPUDCRNDPFDDCDPCLossOurs
+ +Figure 3. The qualitative comparisons with different losses based on AE [1]. Our method can help the task network create more complete shapes and clearer details. + +# 3.2. Adaptive Pooling and Pooling Controller + +Adaptive Pooling is an important operation to aggregate features based on all points into a global representation. Unlike max pooling or average pooling, Adaptive Pooling is dynamically changed and controlled by Pooling Controller during the training process. The structure of Pooling Controller includes a simple network structure to predict parameters $\delta$ for Adaptive Pooling as shown in Fig. 2. If we defined $Con(\cdot)$ as concatenation, $maxpool(\cdot)$ , $avgpool(\cdot)$ and $MLP(\cdot)$ as max pooling, average pooling and Multi Layer Perceptrons (MLPs), the Pooling Controller can be described as: + +$$ +\delta = h \left(F _ {g}\right) = M L P \left(\operatorname {C o n} \left(\operatorname {m a x p o o l} \left(F _ {g}\right), \operatorname {a v g p o o l} \left(F _ {g}\right)\right)\right). \tag {4} +$$ + +It takes both max pooled and average pooled features to acquire more extensive information about $F_{g}$ . In this condition, let us take $F_{g}$ as an example, then the global representation $C_{g}$ can be defined as: + +$$ +C _ {g} = g \left(F _ {g}, \delta\right) = \sum_ {i = 1} ^ {\left| F _ {g} \right|} \frac {e ^ {- \left\| F _ {g} ^ {i} - m a x p o o l \left(F _ {g}\right) \right\| / \delta}}{\sum_ {i = 1} ^ {\left| F _ {g} \right|} e ^ {- \left\| F _ {g} ^ {i} - m a x p o o l \left(F _ {g}\right) \right\| / \delta}} \cdot F _ {g}. \tag {5} +$$ + +$C_p$ and $C_o$ can be acquired by the same equations: + +$$ +C _ {p} = g \left(F _ {p}, \delta\right), C _ {o} = g \left(F _ {o}, \delta\right). \tag {6} +$$ + +We can see that the features are aggregated into global representations with multiple weight distributions around the max pooled features, where $\delta$ actually controls the widths of weight distributions for $F_{g}, F_{p}$ , and $F_{o}$ . So, we share the same $\delta$ for $F_{g}, F_{p}$ , and $F_{o}$ to keep that they are aggregated + +# Algorithm 1 Training Process + +Input: Input $S_{i}$ , ground truths $S_{g}$ , the number of iterations iter, the task network TaskNet(·) + +for $n = 1$ to iter do + +Calculate output of the task network: + +$$ +S _ {o} ^ {n} = \operatorname {T a s k N e t} \left(S _ {i} ^ {n}\right). +$$ + +Let $\theta_C$ and $\theta_T$ be the parameters of CALoss and the task network, respectively. + +Fix the parameter of task network and update CALoss by descending gradient: + +$$ +\nabla_ {\theta_ {C}} L _ {C} \left(S _ {o} ^ {n}, S _ {q} ^ {n}\right). +$$ + +Fix CALoss and update the task network by descending gradient: + +$$ +\nabla_ {\theta_ {T}} L _ {T} (S _ {o} ^ {n}, S _ {g} ^ {n}). +$$ + +end for + +by distributions with same widths. With such an Adaptive Pooling operation in Eq. 5 and Eq. 6, each item in $F_{g}$ , $F_{p}$ , and $F_{o}$ can acquire various gradients during the back propagation process, instead of gradients all the same in average pooling or only constraining max items in max pooling. + +# 3.3. Contrastive Adversarial Training + +As presented in Fig. 2, losses $L_{p}$ , $L_{r}^{adv}$ , and $L_{r}$ are calculated by ground truths and reconstructed results from the task. The training losses for CALoss and the task network can be defined as: + +$$ +\begin{array}{l} L _ {C} = L _ {r} ^ {a d v} + \frac {\epsilon}{| \mathcal {N} _ {\sigma} |} \cdot L _ {p} + \epsilon_ {w} \cdot | \delta | ^ {2} \\ = - \log \left(L _ {r} + \sigma_ {r}\right) + \frac {\epsilon}{\left| \mathcal {N} _ {\sigma} \right|} \cdot L _ {p} + \epsilon_ {w} \cdot | \delta | ^ {2}, \\ \end{array} +$$ + +
DatasetShapeNetModelNet40
NetworksAEFoldingLAELFoldingAEFoldingLAELFolding
MetricsMCDHDMCDHDMCDHDMCDHDMCDHDMCDHDMCDHDMCDHD
CD [3]0.321.870.404.130.311.020.281.200.756.080.837.350.441.690.392.16
EMD [3]0.252.23--0.232.480.212.490.616.18--0.333.820.323.88
PUD [10]0.321.880.363.830.321.020.271.110.735.850.777.290.451.710.381.97
PFD [8]0.321.870.414.140.310.990.260.970.746.280.887.550.441.690.351.69
CRND [21]0.311.860.343.170.311.000.260.990.715.660.767.240.441.690.351.76
DCD [23]0.281.750.918.410.130.890.181.200.686.021.2211.860.171.370.241.81
PCLoss [6]0.231.660.332.570.130.650.140.760.595.300.756.650.171.050.191.37
Ours0.211.530.302.570.120.760.120.790.585.230.726.320.161.180.161.24
+ +Table 1. Comparison with reconstruction losses on ShapeNet (SP) and ModelNet40 (MN40). Bold marks the best results. + +and + +$$ +L _ {T} = L _ {r}, \tag {8} +$$ + +where $\epsilon$ and $\epsilon_w$ are two hyper-parameters to adjust the weights. The whole training process for CALoss and the task network can be described as Alg. 1. Parameters of CALoss and the task network are updated by turns in each iteration. CALoss is updated by $L_r^{adv}$ and $L_p$ . $L_p$ is used to constrain that similar shapes $S_p$ , $S_g$ have close representations, where $L_r^{adv}$ can promote CALoss to find the shape differences between $S_g$ and $S_o$ . We give a dynamic weight for $L_p$ controlled by $1 / |\mathcal{N}_{\sigma}|$ , which means more noised $S_p$ are allowed to have relatively further representations. Besides, we add a L2 regularization for $\delta$ to prevent the weights for $F_g$ , $F_p$ , and $F_o$ from over-smoothness. According to Eq. 5, too large $\delta$ will result in roughly the same weighting for each item in $F_g$ , $F_p$ , or $F_o$ , which is harmful for delivering variable gradients. The task network is optimized by $L_r$ to reduce the differences found by CALoss between reconstructed results $S_o$ and ground truth $S_g$ . + +# 4. Experiments + +# 4.1. Datasets and Implementation Details + +In this work, three point cloud datasets: ShapeNet [26], ModelNet10 (MN10), and ModelNet40 (MN40) [24] are adopted. We use the ShapeNet part dataset [1, 25] containing 12288 models in the train split and 2874 models in the test split. ModelNet10 and ModelNet40 are subsets of ModelNet, which contain 10 categories and 40 categories of CAD models, respectively. Each model consists of 2048 points randomly sampled from the surfaces of original mesh models. We conduct comparisons with other losses on three tasks, including point cloud reconstruction, unsupervised classification, and point cloud completion. + +For the reconstruction task, we train networks with different reconstruction losses on the train split of ShapeNet part dataset and evaluate performances on both the test split + +of ShapeNet and MN40 to provide a robust and exhaustive evaluation. For the unsupervised classification task, we compare the performances of different losses on multiple auto-encoders constructed by [1, 15, 22, 25] following PCLoss [6]. As for GLRNet [16], we follow its setting and retrain it with the original adopted CD and CALoss to observe the differences. For the point cloud completion task, we introduce 3 popular works PCN [27], CRN [21], and RFNet [7] to compare the completion performances before and after replacing the adopted reconstruction losses with CALoss. PCN and CRN are trained on the dataset provided by CRN with 2048 points to compare the completion performances on sparse point clouds, while RFNet is trained on the corresponding dataset with 16384 points to see the completion performances on dense point clouds. All data are normalized to $-1\sim 1$ for the fairness of comparison. + +Metrics. To provide a clear and accurate evaluation of the performance, we adopt Multi-scale Chamfer Distance (MCD) and Hausdorff Distance (HD) introduced in [6] as metrics for reconstruction quality assessment in this work. + +# 4.2. Comparisons on Point Cloud Reconstruction + +In this section, we conduct comparisons with different reconstruction losses based on a few commonly-used networks AE [1], Folding [25] and local feature-based LAE and LFolding following PCLoss [6]. We retrain the networks with different reconstruction losses and evaluate the reconstruction errors of trained networks on the test split of ShapeNet and ModelNet40. As introduced in Sec. 2.2, CD and EMD are widely-used matching-based reconstruction losses, while PUD [10], PFD [8], CRND [21] are constraints introducing extra discriminators to improve the reconstruction performances. DCD [23] is a recent variant of CD achieved by modifying the matching rule. In this work, we choose the better-performed LNSA proposed in PCLoss [6] as PCLoss for comparison in following sections. + +The quantitative results are presented in Table 1. We can see that CALoss can achieve the best performances in + +most conditions. To intuitively present the differences in reconstructed results, we also conduct a qualitative comparison in Fig. 3. We can see that CD may create quite nonuniform results with missing local details, while EMD may produce distorted shapes. Though PUD, CRND, and PFD can improve the integrity of shapes, they still produce similar shapes as CD contained within. PCLoss improves both the uniformity and integrity of reconstructed shapes. But it gets relatively rough details as shown in Fig. 3. As shown by circled regions in the last two rows of Fig. 3, the results of PCLoss create rough contours on chair legs and lamp stands, while our method produces clearer details on these regions. It confirms the effectiveness of CALoss. + +# 4.3. Comparisons on Unsupervised Classification + +In this section, we evaluate the performances of CALoss on point cloud unsupervised classification based on multiple auto-encoders constructed by [1, 15, 22, 25] with 128-dim bottleneck following PCLoss [6], and GLRNet [16]. The experimental settings are kept the same as [1, 16, 25]. + +The auto-encoders are trained on ShapeNet and applied on ModelNet10 and ModelNet40 to extract point cloud representations, where the representations extracted from the training splits and corresponding labels are adopted to train Supported Vector Machines (SVMs). Then the distinguishability of extracted representations can be evaluated by the classification accuracy of these SVMs. We conduct comparisons on these networks by replacing the adopted reconstruction losses to train the auto-encoders with CALoss and observe the changing in classification accuracy. From the results in Table 2, we can see that most networks can achieve improvements by replacing the reconstruction losses with CALoss, which confirms that CALoss can help the task networks learn more representative representations. + +# 4.4. Comparisons on Point Cloud Completion + +Point Cloud Completion predicts completed results as similar as possible to ground truths from partial inputs, which is usually trained with reconstruction losses between completed results and ground truths. To further verify the performances of CALoss, we apply it to a few popular point cloud completion works, including PCN [27], CRN [21], and RFNet [7]. As these works may have multilevel constraints, we conduct comparisons by replacing the reconstruction losses of the last level with CALoss and retraining the networks. The results are presented in Table 3. The completion performances have improvements in most conditions by introducing CALoss, which further confirms that CALoss is effective for different task networks. + +# 4.5. Analysis about the Training Process + +Visualization during training. We visualize a model generated by the task network AE [1] during training to ob- + +
TaskNetDatasetMethods
CDEMDPCLossOurs
AEMN1090.6089.4991.4891.48
MN4085.9285.4786.3686.81
FoldingMN1091.03-91.7092.26
MN4085.22-85.3586.24
AEMN1090.3890.1592.0493.47
(PN++)MN4088.0388.0787.5488.15
FoldingMN1091.48-91.4892.59
(PN++)MN4087.01-86.7387.13
AEMN1091.3791.2692.3792.81
(DGCNN)MN4087.5087.5488.1187.46
FoldingMN1091.26-91.8192.70
(DGCNN)MN4086.85-87.5087.74
GLRNetMN1093.58--95.24
MN4091.07--91.31
+ +Table 2. Comparison on unsupervised classification. + +
NetworkPCNCRNRFNetRFNet*
MetroMCD HDMCD HDMCD HDMCD HD
w/o CALoss0.31 2.670.29 2.420.21 2.920.29 3.82
w/ CALoss0.31 2.550.29 2.440.20 2.630.27 3.28
+ +Table 3. Comparisons on point cloud completion. RFNet and RFNet* denote results evaluated on known and novel categories on ShapeNet following RFNet [7]. + +serve the convergence of different losses. The results are presented in Fig. 4. We can see that CD and EMD have unchanged results with obvious defects after 200 iterations, which means they actually converge to inappropriate local minimums. The reconstructed results trained with CALoss converge to a simple shape after 100 iterations, which may be the effect of contrastive constraint to help the task network find a shape similar to ground truths. From $200 \sim 600$ iterations, the trained results of CALoss gradually remove differences and approach the ground truth, which confirms the adversarial loss can continuously help find the defects and promote the task network to get better performances. Although PCLoss also removes defects during iterations, it produces rougher results, which may come from the limitation of 3D Euclidean space-based descriptors. + +Training curves. In this section, we visualize the reconstruction errors based on the AE network [1] and ShapeNet dataset [24] through the training iterations to observe the convergences of difference loss functions. We can see that our method has relatively inferior performances at the beginning of iterations, where it is learning to search shape differences. But it will converge steadily to low errors after + +
Iteration100200300400500600GT
CD
EMD
PCLoss
Ours
+ +![](images/d2269baa65091235c713afed55ca3977fba2deee5eaaff374a544fd14e84fc06.jpg) +Figure 5. The reconstruction error curves through the iterations. + +![](images/788f701e3ac709f51ef2b05296078e7f41c314b27a00653e1c7e61623438c796.jpg) +Figure 6. Qualitative comparisons between our method dynamically updated and pre-trained without updating. + +enough iterations. We can also see that CALoss converge faster than PCLoss, which reveals the priority of our representation space-based measurement. + +# 4.6. Training Efficiency Comparison + +In this section, we evaluate the training efficiencies of different reconstruction losses on the AE network [1], which are measured by the time consumed for the training of a single batch. The results are presented in Table 4. Though our method performs slightly slower than CD, it has the highest training efficiency in learning-based losses. + +Figure 4. The visualization of training processes with different reconstruction losses. CD/EMD fall into local minimums and acquire unchanged results after 200 iterations, while PCLoss produces relatively rough shapes. Our method can gradually remove the shape defects and create more accurate results. + +
Non-learningLearning-based
MethodsCDEMDPUDPFDCRNDPCLossOurs
Time(ms)232167745975739
+ +Table 4. Training efficiency comparison conducted on an NVIDIA 2080ti with a 2.9GHz i5-9400 CPU. + +
GT
Pretrained
Ours
+ +# 4.7. Necessity of the Dynamic Updating + +CALoss is dynamically optimized together with the task network as claimed in Sec. 3. To show the necessity of the dynamic updating, we make an attempt to train the task network AE [1] with a pre-trained CALoss directly without further updating. The results are demonstrated in Fig. 6 and Table 5. We can see that the task network trained with pre-trained CALoss can only reconstruct quite rough shapes, which confirms that training with CALoss is actually a continuous procedure to search shape differences between ground truths and the task network outputs. + +# 4.8. Ablation Study + +Ablation study for components. We conduct an ablation study for the components adopted in CALoss as mentioned in Eq. 7. The results are presented in Table 6. $L_{p}$ and + +
Pre-trainedOurs
DatasetSPMN40SPMN40
MCD0.751.040.210.58
HD14.4814.151.535.23
+ +$L_{r}^{adv}$ are the basic contrastive and adversarial constraints, respectively, while $|\delta|^2$ is the regularization constraint for $\delta$ . $1 / |\mathcal{N}_{\sigma}|$ is the dynamic coefficient for the weight of $L_{p}$ . + +As finding shape difference is an essential constraint to prevent CALoss from acquiring all-zero output under the supervision of only $L_{p}$ , we remove the $L_{r}^{adv}$ by replacing it with the negative implementation of metric-learning method [2, 16] by maximizing the representation distances between models in the same mini-batch. + +We can see that $L_{p}$ and $L_{r}^{adv}$ both have very significant influences on the final performance, which means they are cores of CALoss. Replacing $L_{r}^{adv}$ with metric-learning method has weaker results. It confirms that maximizing representations between shapes within a mini-batch is not enough to learn the shape differences because it lacks of dynamic feedback from reconstructed outputs and cannot accurately find the shape differences. The regularization $|\sigma|^{2}$ also has obvious influence, which means it is important to control the widths of weight distributions to aggregate representations in $g(\cdot)$ as shown in Fig. 2 and Eq. 5. + +Table 5. Quantitative comparisons between our method dynamically updated and pre-trained without updating. + +
LpPerturbLadv|δ|21/|Nσ|MCDHD
Lp/Ladv✓✓✓3.557.57
1.8312.76
Others✓✓✓0.778.13
✓✓✓✓0.221.63
✓✓✓✓✓0.211.53
+ +Ablation study for the Pooling Controller. In Pooling Controller, we introduce both max pooled and average pooled features to acquire more extensive information about the overall distribution in $F_{g}$ as presented in Fig. 2 and Eq. 4. We conduct a simple ablation study for this operation in Table 7. We can see that the designation of concatenation can indeed reduce the reconstruction errors. + +Ablation study the Adaptive Pooling operation. Here, We present a simple discussion about the proposed Adaptive Pooling operation. From Table 8, we can see that both max and average pooling have quite inferior performances, which prove the necessity of Adaptive Pooling operation. + +Table 6. Ablation for components. Perturb denotes the perturbation, while $L_{p}$ , $L_{r}^{adv}$ and $|\delta|^{2}$ are components included in Eq. 7. $1 / |\mathcal{N}_{\sigma}|$ is the dynamic coefficient for the weight of $L_{p}$ in Eq. 7. + +
MetricsMaxAvgOurs
MCD0.220.230.21
HD1.581.551.53
+ +Table 7. Comparisons between different implementations of Pooling Controller on ShapeNet and AE [1]. Max and Avg denote introducing only max and average pooling, respectively. + +
MetricsMaxAvgOurs
MCD0.340.610.21
HD1.726.791.53
+ +Table 8. Comparisons between pooling operations on ShapeNet and AE [1]. Max and Avg denote max and average pooling. + +In CALoss, the pooling operation is used to aggregate features from all points into a global representation. To train the task network, the global representation needs to provide variable gradients for each point feature to distinguish them. However, average pooling can only propagate same and indistinguishable gradients for each points. Although max pooling can provide different gradients for point features, it provides a hard 0-1 distribution where only max items are constrained. In this condition, we design such an Adaptive Pooling to get a variable weight for each point feature according to the distance to the max pooled feature, which can be regarded as a "soft max pooling". All point features can be constrained with distinguishable gradients, which is controlled by widths of weight distributions predicted with Pooling Controller $h(\cdot)$ as shown in Fig. 2 and Eq. 5. + +# 5. Conclusion + +In this work, we propose a novel learning-based framework named CALoss to train the point cloud reconstruction-related task networks by measuring the differences between reconstructed shapes and ground truths with distances in a representation space. With the measurement of reconstruction loss in the learned non-linear representation space, CALoss has more extensive searching space for shape differences and better performances than the Euclidean space-based methods. According to the experiments, CALoss can achieve improvements above existing reconstruction losses based on predefined matching rules on multiple tasks including point cloud reconstruction, unsupervised classification and completion, which confirms it can help the task network achieve better reconstruction performances and extract more representative representations. + +# Acknowledgements + +We thank all authors, reviewers and the chair for excellent contributions. This work is supported by the Key Research and Development Project of Zhejiang Province under Grant 2021C01035. + +# References + +[1] Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. Learning representations and generative models for 3d point clouds. In International conference on machine learning, pages 40-49. PMLR, 2018. 2, 4, 5, 6, 7, 8 +[2] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 1, 3, 8 +[3] Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 605-613, 2017. 2, 5 +[4] Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. arXiv preprint arXiv:1406.2661, 2014. 2 +[5] Tianxin Huang and Yong Liu. 3d point cloud geometry compression on deep learning. In Proceedings of the 27th ACM International Conference on Multimedia, pages 890-898, 2019. 1 +[6] Tianxin Huang, Xuemeng Yang, Jiangning Zhang, Jinhao Cui, Hao Zou, Jun Chen, Xiangrui Zhao, and Yong Liu. Learning to train a point cloud reconstruction network without matching. In European Conference on Computer Vision, pages 179-194. Springer, 2022. 1, 2, 3, 5, 6 +[7] Tianxin Huang, Hao Zou, Jinhao Cui, Xuemeng Yang, Mengmeng Wang, Xiangrui Zhao, Jiangning Zhang, Yi Yuan, Yifan Xu, and Yong Liu. Rfnet: Recurrent forward network for dense point cloud completion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12508-12517, 2021. 1, 2, 5, 6 +[8] Zitian Huang, Yikuan Yu, Jiawen Xu, Feng Ni, and Xinyi Le. Pf-net: Point fractal network for 3d point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7662-7670, 2020. 2, 3, 5 +[9] Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, Thomas Funkhouser, et al. Local implicit grid representations for 3d scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6001–6010, 2020. 1 +[10] Ruihui Li, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, and Pheng-Ann Heng. Pu-gan: a point cloud upsampling adversarial network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7203-7212, 2019. 2, 3, 5 +[11] Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, and ShiMin Hu. Morphing and sampling network for dense point cloud completion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11596-11603, 2020. 1, 2 +[12] Yongcheng Liu, Bin Fan, Shiming Xiang, and Chunhong Pan. Relation-shape convolutional neural network for point cloud analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8895-8904, 2019. 2 + +[13] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4460-4470, 2019. 1 +[14] Trung Nguyen, Quang-Hieu Pham, Tam Le, Tung Pham, Nhat Ho, and Binh-Son Hua. Point-set distances for learning representations of 3d point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10478-10487, 2021. 3 +[15] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in neural information processing systems, pages 5099-5108, 2017. 5, 6 +[16] Yongming Rao, Jiwen Lu, and Jie Zhou. Global-local bidirectional reasoning for unsupervised representation learning of 3d point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5376-5385, 2020. 1, 2, 5, 6, 8 +[17] N Dinesh Reddy, Minh Vo, and Srinivasa G Narasimhan. Carfusion: Combining point tracking and part detection for dynamic 3d reconstruction of vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1906-1915, 2018. 1 +[18] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointcnn: 3d object proposal generation and detection from point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 770-779, 2019. 1 +[19] Jia-Heng Tang, Weikai Chen, Jie Yang, Bo Wang, Songrun Liu, Bo Yang, and Lin Gao. Octfield: Hierarchical implicit functions for 3d modeling. arXiv preprint arXiv:2111.01067, 2021. 1 +[20] Dahlia Urbach, Yizhak Ben-Shabat, and Michael Lindenbaum. Dpdist: Comparing point clouds using deep point cloud distance. In European Conference on Computer Vision, pages 545-560. Springer, 2020. 3 +[21] Xiaogang Wang, Marcelo H Ang Jr, and Gim Hee Lee. Cascaded refinement network for point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 790-799, 2020. 2, 3, 5, 6 +[22] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (tog), 38(5):1-12, 2019. 2, 5, 6 +[23] Tong Wu, Liang Pan, Junzhe Zhang, Tai Wang, Ziwei Liu, and Dahua Lin. Density-aware chamfer distance as a comprehensive metric for point cloud completion. arXiv preprint arXiv:2111.12702, 2021. 2, 3, 5 +[24] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1912-1920, 2015. 5, 6 +[25] Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian. Foldingnet: Point cloud auto-encoder via deep grid deformation. + +In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 206-215, 2018. 2, 5, 6 +[26] Li Yi, Vladimir G Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas. A scalable active framework for region annotation in 3d shape collections. ACM Transactions on Graphics (ToG), 35(6):1-12, 2016. 5 +[27] Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert. Pcn: Point completion network. In 2018 International Conference on 3D Vision (3DV), pages 728-737. IEEE, 2018. 2, 5, 6 +[28] Yongheng Zhao, Tolga Birdal, Haowen Deng, and Federico Tombari. 3d point capsule networks. 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Matching-based losses measure distances between points matched by different predefined rules. PCLoss [6] learns to extract descriptors in 3D Euclidean space by linearly weighting coordinates according to their distances to predicted center points, while our method dynamically measures the shape differences with distances between learned global representations in the constructed representation space. " + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "inline_equation", + "content": "L_{p}" + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "inline_equation", + "content": "L_{r}" + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "text", + "content": " denote representation distances between " + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "inline_equation", + "content": "S_{g}" + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "inline_equation", + "content": "S_{p}" + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "inline_equation", + "content": "S_{g}" + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "inline_equation", + "content": "S_{o}" + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "text", + "content": ", respectively. " + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "inline_equation", + "content": "L_{r}^{adv}" + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "text", + "content": " is an adversarial loss to maximize representation distances between " + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "inline_equation", + "content": "S_{g}" + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "inline_equation", + "content": "S_{o}" + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "inline_equation", + "content": "L_{r}^{adv}" + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "inline_equation", + "content": "L_{p}" + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "text", + "content": " are used to optimize CALoss, while " + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "inline_equation", + "content": "L_{r}" + }, + { + "bbox": [ + 304, + 390, + 548, + 533 + ], + "type": "text", + "content": " is adopted to train the task network." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 558, + 548, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 548, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 548, + 715 + ], + "type": "text", + "content": "reconstruction losses Chamfer Distance (CD) and Earth Mover's Distance (EMD) to constrain shape differences. CD matches points firstly with their nearest neighbors in another point cloud and then calculates the shape difference as average point-to-point distance, while EMD calculates the average point-to-point distance under an optimization-based global matching. We can find that CD and EMD actually measure the distances between matched points instead of the distances between shapes. As the predefined matching rules are static and unlearnable, training results of CD and EMD may fall into inappropriate local minimums where the reconstruction losses are small but the shapes are obviously different. Learning-based losses in" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 62, + 703, + 173, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 703, + 173, + 713 + ], + "spans": [ + { + "bbox": [ + 62, + 703, + 173, + 713 + ], + "type": "text", + "content": "* means the corresponding author" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12208" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 289, + 299 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 289, + 299 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 289, + 299 + ], + "type": "text", + "content": "PFNet [8], PUGAN [10], and CRN [21] introduce extra supervision from discriminators trained with the adversarial strategy to find the detailed differences. Their reconstruction performances are improved by introducing adversarial losses, while they still need CD/EMD to evaluate the basic shape distances and cannot fully get rid of the influence from predefined matching rules. PCLoss [6] presents a reconstruction loss measured by the distances between extracted intermediate descriptors in 3D Euclidean space without any manually-defined matching rule, which is updated together with the task network in a generative-adversarial process to search shape differences during training. However, the descriptor extraction in Euclidean space actually limits the searching of shape differences, while the training efficiency is also restricted as the descriptors are constructed with dense connections between multiple predicted center points and all points. In summary, existing reconstruction losses mainly rely on distances in 3D Euclidean space to measure the shape differences." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "spans": [ + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "text", + "content": "In this work, we propose a novel framework named Contrastive Adversarial Loss (CALoss) learning to measure the point cloud reconstruction loss dynamically in a high dimensional representation space constructed by a series of fully differentiable structures. The differences between our work and existing works are presented in Fig. 1. CALoss is composed of " + }, + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "inline_equation", + "content": "L_{p}" + }, + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "inline_equation", + "content": "L_{r}" + }, + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "inline_equation", + "content": "L_{r}^{adv}" + }, + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "text", + "content": " acquired from distances between global representations. " + }, + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "inline_equation", + "content": "L_{r}^{adv}" + }, + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "inline_equation", + "content": "L_{p}" + }, + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "text", + "content": " are used to optimize CALoss, where " + }, + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "inline_equation", + "content": "L_{r}" + }, + { + "bbox": [ + 47, + 300, + 289, + 407 + ], + "type": "text", + "content": " is used to train the task network." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 407, + 289, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 407, + 289, + 598 + ], + "spans": [ + { + "bbox": [ + 47, + 407, + 289, + 598 + ], + "type": "text", + "content": "We introduce " + }, + { + "bbox": [ + 47, + 407, + 289, + 598 + ], + "type": "inline_equation", + "content": "L_{p}" + }, + { + "bbox": [ + 47, + 407, + 289, + 598 + ], + "type": "text", + "content": " as the contrastive constraint to help CALoss construct a representation space with the shape similarity that similar shapes should have close representations. In this way, by adding adversarial loss on representations, " + }, + { + "bbox": [ + 47, + 407, + 289, + 598 + ], + "type": "inline_equation", + "content": "L_{r}^{adv}" + }, + { + "bbox": [ + 47, + 407, + 289, + 598 + ], + "type": "text", + "content": " can guide CALoss to search for the shape differences between ground truths " + }, + { + "bbox": [ + 47, + 407, + 289, + 598 + ], + "type": "inline_equation", + "content": "S_{g}" + }, + { + "bbox": [ + 47, + 407, + 289, + 598 + ], + "type": "text", + "content": " and reconstructed results " + }, + { + "bbox": [ + 47, + 407, + 289, + 598 + ], + "type": "inline_equation", + "content": "S_{o}" + }, + { + "bbox": [ + 47, + 407, + 289, + 598 + ], + "type": "text", + "content": ". By updating dynamically according to the reconstructed results in each iteration, CALoss can continuously find existing defects in reconstructed shapes and prevent the task network from falling into unexpected local minimums. As the measurement for shape differences is implemented in non-linear representation space, CALoss has more extensive searching space. Besides, the representations adopted to measure shape differences are aggregated with the global pooling operation without any requirement of dense connections as PCLoss [6], which can improve the training efficiency." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 59, + 599, + 269, + 610 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 599, + 269, + 610 + ], + "spans": [ + { + "bbox": [ + 59, + 599, + 269, + 610 + ], + "type": "text", + "content": "Our contribution in this work can be summarized as" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 57, + 621, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 57, + 621, + 287, + 668 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 621, + 287, + 668 + ], + "spans": [ + { + "bbox": [ + 57, + 621, + 287, + 668 + ], + "type": "text", + "content": "- We propose a novel Contrastive Adversarial Loss (CALoss) learning to measure the point cloud reconstruction loss with distances between high-dimensional global representations." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 57, + 677, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 677, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 57, + 677, + 288, + 713 + ], + "type": "text", + "content": "- By combining the contrastive constraint and adversarial training strategy, CALoss can construct a representation space where similar shapes have close represen" + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 324, + 72, + 545, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 324, + 72, + 545, + 96 + ], + "spans": [ + { + "bbox": [ + 324, + 72, + 545, + 96 + ], + "type": "text", + "content": "tations and learn to search for shape differences in this space dynamically during training." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 316, + 103, + 547, + 163 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 103, + 547, + 163 + ], + "spans": [ + { + "bbox": [ + 316, + 103, + 547, + 163 + ], + "type": "text", + "content": "- Experiments on point cloud reconstruction, unsupervised classification, and point cloud completion confirm that CALoss can help the task network improve reconstruction performances and learn more representative representations with higher training efficiency." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 172, + 392, + 184 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 172, + 392, + 184 + ], + "spans": [ + { + "bbox": [ + 306, + 172, + 392, + 184 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 191, + 524, + 204 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 191, + 524, + 204 + ], + "spans": [ + { + "bbox": [ + 306, + 191, + 524, + 204 + ], + "type": "text", + "content": "2.1. Point Cloud Reconstruction-related Tasks" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 210, + 547, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 210, + 547, + 509 + ], + "spans": [ + { + "bbox": [ + 304, + 210, + 547, + 509 + ], + "type": "text", + "content": "Base on the basic point cloud reconstruction framework to predict similar output point clouds as inputs, e.g. autoencoder, many related tasks have been developed such as the unsupervised classification and point cloud completion. The unsupervised classification task raised by [1, 25] trains auto-encoders to learn representations of point clouds. The representations are then adopted to train a Support Vector Machine (SVM) with provided labels for further classification. The classification accuracy of SVM can reflect the distinctiveness of learned representations. Many researchers have improved the classification performances by modifying the network structures [12, 22, 28], while some researchers [16] introduce extra supervision to enhance the learning effect. Point cloud completion predicts completed point clouds as identical as possible to the ground truths from partial input point clouds. Early works [11, 27] often use typical auto-encoders to abstract long global features from partial inputs and predict completed results, while recent work [7, 21] add more diverse network structures to improve the completion performances. Reconstruction losses CD or EMD to capture the shape differences are always adopted in these works. In this condition, we adopt three tasks including basic point cloud reconstruction, point cloud unsupervised classification, and completion to evaluate the performances of CALoss." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 515, + 502, + 528 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 515, + 502, + 528 + ], + "spans": [ + { + "bbox": [ + 306, + 515, + 502, + 528 + ], + "type": "text", + "content": "2.2. Losses to Evaluate Shape Differences" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "text", + "content": "The Chamfer Distance (CD) [27] and Earth Mover's Distance (EMD) [3] are two basic and broadly used reconstruction losses to constrain the shape differences, which calculate the distance between point clouds with different matching strategies. With the matching by nearest neighbors, CD concentrates on differences between contours, while it often constructs non-uniform surfaces as discussed in [3, 23]. EMD aims to find an one-to-one optimal mapping " + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 304, + 533, + 547, + 713 + ], + "type": "text", + "content": " from one point set to another by optimizing the minimum matching distances between the point sets. Though EMD can create more uniform shapes, it takes large time cost due to the optimized matching and can only be applied to reconstructed output with the same number of input, where the quality of optimization also limits the performances. Since the development of GAN [4], researchers" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "12209" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 70, + 339, + 261 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 339, + 261 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 339, + 261 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 339, + 261 + ], + "type": "image", + "image_path": "420f49686b377e7ed5a01f3f3cafd165bcdf40238dc56e5c0828b3deb5b58281.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 266, + 548, + 346 + ], + "lines": [ + { + "bbox": [ + 46, + 266, + 548, + 346 + ], + "spans": [ + { + "bbox": [ + 46, + 266, + 548, + 346 + ], + "type": "text", + "content": "Figure 2. The pipeline of CALoss. " + }, + { + "bbox": [ + 46, + 266, + 548, + 346 + ], + "type": "inline_equation", + "content": "S_{g}" + }, + { + "bbox": [ + 46, + 266, + 548, + 346 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 266, + 548, + 346 + ], + "type": "inline_equation", + "content": "S_{o}" + }, + { + "bbox": [ + 46, + 266, + 548, + 346 + ], + "type": "text", + "content": " denote ground truths and point clouds generated by the task network. " + }, + { + "bbox": [ + 46, + 266, + 548, + 346 + ], + "type": "inline_equation", + "content": "S_{p}" + }, + { + "bbox": [ + 46, + 266, + 548, + 346 + ], + "type": "text", + "content": " is acquired from " + }, + { + "bbox": [ + 46, + 266, + 548, + 346 + ], + "type": "inline_equation", + "content": "S_{g}" + }, + { + "bbox": [ + 46, + 266, + 548, + 346 + ], + "type": "text", + "content": " with small perturbations. 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However, these works still need CD or EMD as basic shape constraints. Some works [14,23] further modify the matching rules to improve the constraining performances. All these works measure shape differences based on the point-to-point distance calculated with predefined matching rules. DPDist [20] estimates the shape distances with a pre-trained network without matching. But it is mainly designed for registration and has inferior performances on point cloud reconstruction. PCLoss [6] presents a fully learning-based reconstruction loss to overcome the limitation of predefined matching rules, which learns to aggregate descriptors from point clouds and measure reconstruction losses with distances between extracted descriptors. 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If we defined " + }, + { + "bbox": [ + 46, + 340, + 290, + 471 + ], + "type": "inline_equation", + "content": "Con(\\cdot)" + }, + { + "bbox": [ + 46, + 340, + 290, + 471 + ], + "type": "text", + "content": " as concatenation, " + }, + { + "bbox": [ + 46, + 340, + 290, + 471 + ], + "type": "inline_equation", + "content": "maxpool(\\cdot)" + }, + { + "bbox": [ + 46, + 340, + 290, + 471 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 340, + 290, + 471 + ], + "type": "inline_equation", + "content": "avgpool(\\cdot)" + }, + { + "bbox": [ + 46, + 340, + 290, + 471 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 340, + 290, + 471 + ], + "type": "inline_equation", + "content": "MLP(\\cdot)" + }, + { + "bbox": [ + 46, + 340, + 290, + 471 + ], + "type": "text", + "content": " as max pooling, average pooling and Multi Layer Perceptrons (MLPs), the Pooling Controller can be described as:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 481, + 286, + 505 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 481, + 286, + 505 + ], + "spans": [ + { + "bbox": [ + 49, + 481, + 286, + 505 + ], + "type": "interline_equation", + "content": "\\delta = h \\left(F _ {g}\\right) = M L P \\left(\\operatorname {C o n} \\left(\\operatorname {m a x p o o l} \\left(F _ {g}\\right), \\operatorname {a v g p o o l} \\left(F _ {g}\\right)\\right)\\right). \\tag {4}", + "image_path": "cd1769ec7301870bbbd5c65133827b591734eedf78e673b79e7ecf0970a770f6.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 506, + 289, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 506, + 289, + 555 + ], + "spans": [ + { + "bbox": [ + 46, + 506, + 289, + 555 + ], + "type": "text", + "content": "It takes both max pooled and average pooled features to acquire more extensive information about " + }, + { + "bbox": [ + 46, + 506, + 289, + 555 + ], + "type": "inline_equation", + "content": "F_{g}" + }, + { + "bbox": [ + 46, + 506, + 289, + 555 + ], + "type": "text", + "content": ". In this condition, let us take " + }, + { + "bbox": [ + 46, + 506, + 289, + 555 + ], + "type": "inline_equation", + "content": "F_{g}" + }, + { + "bbox": [ + 46, + 506, + 289, + 555 + ], + "type": "text", + "content": " as an example, then the global representation " + }, + { + "bbox": [ + 46, + 506, + 289, + 555 + ], + "type": "inline_equation", + "content": "C_{g}" + }, + { + "bbox": [ + 46, + 506, + 289, + 555 + ], + "type": "text", + "content": " can be defined as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 51, + 564, + 287, + 608 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 564, + 287, + 608 + ], + "spans": [ + { + "bbox": [ + 51, + 564, + 287, + 608 + ], + "type": "interline_equation", + "content": "C _ {g} = g \\left(F _ {g}, \\delta\\right) = \\sum_ {i = 1} ^ {\\left| F _ {g} \\right|} \\frac {e ^ {- \\left\\| F _ {g} ^ {i} - m a x p o o l \\left(F _ {g}\\right) \\right\\| / \\delta}}{\\sum_ {i = 1} ^ {\\left| F _ {g} \\right|} e ^ {- \\left\\| F _ {g} ^ {i} - m a x p o o l \\left(F _ {g}\\right) \\right\\| / \\delta}} \\cdot F _ {g}. \\tag {5}", + "image_path": "2541b5c44249cf94aa79045b62e8f6b304fc8005bb8b6dc20684a4da659667fd.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 609, + 253, + 622 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 609, + 253, + 622 + ], + "spans": [ + { + "bbox": [ + 47, + 609, + 253, + 622 + ], + "type": "inline_equation", + "content": "C_p" + }, + { + "bbox": [ + 47, + 609, + 253, + 622 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 609, + 253, + 622 + ], + "type": "inline_equation", + "content": "C_o" + }, + { + "bbox": [ + 47, + 609, + 253, + 622 + ], + "type": "text", + "content": " can be acquired by the same equations:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 103, + 630, + 287, + 644 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 103, + 630, + 287, + 644 + ], + "spans": [ + { + "bbox": [ + 103, + 630, + 287, + 644 + ], + "type": "interline_equation", + "content": "C _ {p} = g \\left(F _ {p}, \\delta\\right), C _ {o} = g \\left(F _ {o}, \\delta\\right). \\tag {6}", + "image_path": "953fd53cb35169a7d5ce0b22f9aea81ec679bd097699f95ae644b2e8a1436e63.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 654, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 289, + 715 + ], + "type": "text", + "content": "We can see that the features are aggregated into global representations with multiple weight distributions around the max pooled features, where " + }, + { + "bbox": [ + 46, + 654, + 289, + 715 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 46, + 654, + 289, + 715 + ], + "type": "text", + "content": " actually controls the widths of weight distributions for " + }, + { + "bbox": [ + 46, + 654, + 289, + 715 + ], + "type": "inline_equation", + "content": "F_{g}, F_{p}" + }, + { + "bbox": [ + 46, + 654, + 289, + 715 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 654, + 289, + 715 + ], + "type": "inline_equation", + "content": "F_{o}" + }, + { + "bbox": [ + 46, + 654, + 289, + 715 + ], + "type": "text", + "content": ". 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Contrastive Adversarial Training" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 613, + 547, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 613, + 547, + 662 + ], + "spans": [ + { + "bbox": [ + 304, + 613, + 547, + 662 + ], + "type": "text", + "content": "As presented in Fig. 2, losses " + }, + { + "bbox": [ + 304, + 613, + 547, + 662 + ], + "type": "inline_equation", + "content": "L_{p}" + }, + { + "bbox": [ + 304, + 613, + 547, + 662 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 613, + 547, + 662 + ], + "type": "inline_equation", + "content": "L_{r}^{adv}" + }, + { + "bbox": [ + 304, + 613, + 547, + 662 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 613, + 547, + 662 + ], + "type": "inline_equation", + "content": "L_{r}" + }, + { + "bbox": [ + 304, + 613, + 547, + 662 + ], + "type": "text", + "content": " are calculated by ground truths and reconstructed results from the task. 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DatasetShapeNetModelNet40
NetworksAEFoldingLAELFoldingAEFoldingLAELFolding
MetricsMCDHDMCDHDMCDHDMCDHDMCDHDMCDHDMCDHDMCDHD
CD [3]0.321.870.404.130.311.020.281.200.756.080.837.350.441.690.392.16
EMD [3]0.252.23--0.232.480.212.490.616.18--0.333.820.323.88
PUD [10]0.321.880.363.830.321.020.271.110.735.850.777.290.451.710.381.97
PFD [8]0.321.870.414.140.310.990.260.970.746.280.887.550.441.690.351.69
CRND [21]0.311.860.343.170.311.000.260.990.715.660.767.240.441.690.351.76
DCD [23]0.281.750.918.410.130.890.181.200.686.021.2211.860.171.370.241.81
PCLoss [6]0.231.660.332.570.130.650.140.760.595.300.756.650.171.050.191.37
Ours0.211.530.302.570.120.760.120.790.585.230.726.320.161.180.161.24
", + "image_path": "201e7adb770a68af828fb8e7f49055545979ed846d0ad747a1db67a102e6f6a4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 76, + 229, + 515, + 241 + ], + "lines": [ + { + "bbox": [ + 76, + 229, + 515, + 241 + ], + "spans": [ + { + "bbox": [ + 76, + 229, + 515, + 241 + ], + "type": "text", + "content": "Table 1. Comparison with reconstruction losses on ShapeNet (SP) and ModelNet40 (MN40). Bold marks the best results." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 47, + 255, + 65, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 255, + 65, + 266 + ], + "spans": [ + { + "bbox": [ + 47, + 255, + 65, + 266 + ], + "type": "text", + "content": "and" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 145, + 269, + 287, + 282 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 269, + 287, + 282 + ], + "spans": [ + { + "bbox": [ + 145, + 269, + 287, + 282 + ], + "type": "interline_equation", + "content": "L _ {T} = L _ {r}, \\tag {8}", + "image_path": "0751616f8052f9f3a7bd8632bc12bd6422a2e234fb1293be78ca1d907c811f75.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "spans": [ + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "\\epsilon_w" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": " are two hyper-parameters to adjust the weights. The whole training process for CALoss and the task network can be described as Alg. 1. Parameters of CALoss and the task network are updated by turns in each iteration. CALoss is updated by " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "L_r^{adv}" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "L_p" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": ". 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We give a dynamic weight for " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "L_p" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": " controlled by " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "1 / |\\mathcal{N}_{\\sigma}|" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": ", which means more noised " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "S_p" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": " are allowed to have relatively further representations. Besides, we add a L2 regularization for " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": " to prevent the weights for " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "F_g" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "F_p" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "F_o" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": " from over-smoothness. According to Eq. 5, too large " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": " will result in roughly the same weighting for each item in " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "F_g" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "F_p" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": ", or " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "F_o" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": ", which is harmful for delivering variable gradients. The task network is optimized by " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "L_r" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": " to reduce the differences found by CALoss between reconstructed results " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "S_o" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": " and ground truth " + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "inline_equation", + "content": "S_g" + }, + { + "bbox": [ + 46, + 289, + 290, + 494 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 506, + 128, + 519 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 506, + 128, + 519 + ], + "spans": [ + { + "bbox": [ + 47, + 506, + 128, + 519 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 525, + 243, + 539 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 525, + 243, + 539 + ], + "spans": [ + { + "bbox": [ + 47, + 525, + 243, + 539 + ], + "type": "text", + "content": "4.1. Datasets and Implementation Details" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 544, + 288, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 544, + 288, + 677 + ], + "spans": [ + { + "bbox": [ + 46, + 544, + 288, + 677 + ], + "type": "text", + "content": "In this work, three point cloud datasets: ShapeNet [26], ModelNet10 (MN10), and ModelNet40 (MN40) [24] are adopted. We use the ShapeNet part dataset [1, 25] containing 12288 models in the train split and 2874 models in the test split. ModelNet10 and ModelNet40 are subsets of ModelNet, which contain 10 categories and 40 categories of CAD models, respectively. Each model consists of 2048 points randomly sampled from the surfaces of original mesh models. We conduct comparisons with other losses on three tasks, including point cloud reconstruction, unsupervised classification, and point cloud completion." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 677, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 289, + 715 + ], + "type": "text", + "content": "For the reconstruction task, we train networks with different reconstruction losses on the train split of ShapeNet part dataset and evaluate performances on both the test split" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 255, + 547, + 447 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 255, + 547, + 447 + ], + "spans": [ + { + "bbox": [ + 304, + 255, + 547, + 447 + ], + "type": "text", + "content": "of ShapeNet and MN40 to provide a robust and exhaustive evaluation. For the unsupervised classification task, we compare the performances of different losses on multiple auto-encoders constructed by [1, 15, 22, 25] following PCLoss [6]. As for GLRNet [16], we follow its setting and retrain it with the original adopted CD and CALoss to observe the differences. For the point cloud completion task, we introduce 3 popular works PCN [27], CRN [21], and RFNet [7] to compare the completion performances before and after replacing the adopted reconstruction losses with CALoss. PCN and CRN are trained on the dataset provided by CRN with 2048 points to compare the completion performances on sparse point clouds, while RFNet is trained on the corresponding dataset with 16384 points to see the completion performances on dense point clouds. All data are normalized to " + }, + { + "bbox": [ + 304, + 255, + 547, + 447 + ], + "type": "inline_equation", + "content": "-1\\sim 1" + }, + { + "bbox": [ + 304, + 255, + 547, + 447 + ], + "type": "text", + "content": " for the fairness of comparison." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 447, + 547, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 447, + 547, + 495 + ], + "spans": [ + { + "bbox": [ + 304, + 447, + 547, + 495 + ], + "type": "text", + "content": "Metrics. To provide a clear and accurate evaluation of the performance, we adopt Multi-scale Chamfer Distance (MCD) and Hausdorff Distance (HD) introduced in [6] as metrics for reconstruction quality assessment in this work." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 502, + 538, + 516 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 502, + 538, + 516 + ], + "spans": [ + { + "bbox": [ + 305, + 502, + 538, + 516 + ], + "type": "text", + "content": "4.2. Comparisons on Point Cloud Reconstruction" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 521, + 547, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 547, + 689 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 547, + 689 + ], + "type": "text", + "content": "In this section, we conduct comparisons with different reconstruction losses based on a few commonly-used networks AE [1], Folding [25] and local feature-based LAE and LFolding following PCLoss [6]. We retrain the networks with different reconstruction losses and evaluate the reconstruction errors of trained networks on the test split of ShapeNet and ModelNet40. As introduced in Sec. 2.2, CD and EMD are widely-used matching-based reconstruction losses, while PUD [10], PFD [8], CRND [21] are constraints introducing extra discriminators to improve the reconstruction performances. DCD [23] is a recent variant of CD achieved by modifying the matching rule. In this work, we choose the better-performed LNSA proposed in PCLoss [6] as PCLoss for comparison in following sections." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 689, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 547, + 713 + ], + "type": "text", + "content": "The quantitative results are presented in Table 1. We can see that CALoss can achieve the best performances in" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "12212" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "content": "most conditions. To intuitively present the differences in reconstructed results, we also conduct a qualitative comparison in Fig. 3. We can see that CD may create quite nonuniform results with missing local details, while EMD may produce distorted shapes. Though PUD, CRND, and PFD can improve the integrity of shapes, they still produce similar shapes as CD contained within. PCLoss improves both the uniformity and integrity of reconstructed shapes. But it gets relatively rough details as shown in Fig. 3. As shown by circled regions in the last two rows of Fig. 3, the results of PCLoss create rough contours on chair legs and lamp stands, while our method produces clearer details on these regions. It confirms the effectiveness of CALoss." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 235, + 279, + 248 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 235, + 279, + 248 + ], + "spans": [ + { + "bbox": [ + 47, + 235, + 279, + 248 + ], + "type": "text", + "content": "4.3. Comparisons on Unsupervised Classification" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 254, + 288, + 314 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 254, + 288, + 314 + ], + "spans": [ + { + "bbox": [ + 46, + 254, + 288, + 314 + ], + "type": "text", + "content": "In this section, we evaluate the performances of CALoss on point cloud unsupervised classification based on multiple auto-encoders constructed by [1, 15, 22, 25] with 128-dim bottleneck following PCLoss [6], and GLRNet [16]. The experimental settings are kept the same as [1, 16, 25]." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 314, + 290, + 482 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 314, + 290, + 482 + ], + "spans": [ + { + "bbox": [ + 46, + 314, + 290, + 482 + ], + "type": "text", + "content": "The auto-encoders are trained on ShapeNet and applied on ModelNet10 and ModelNet40 to extract point cloud representations, where the representations extracted from the training splits and corresponding labels are adopted to train Supported Vector Machines (SVMs). Then the distinguishability of extracted representations can be evaluated by the classification accuracy of these SVMs. We conduct comparisons on these networks by replacing the adopted reconstruction losses to train the auto-encoders with CALoss and observe the changing in classification accuracy. From the results in Table 2, we can see that most networks can achieve improvements by replacing the reconstruction losses with CALoss, which confirms that CALoss can help the task networks learn more representative representations." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 489, + 263, + 502 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 489, + 263, + 502 + ], + "spans": [ + { + "bbox": [ + 47, + 489, + 263, + 502 + ], + "type": "text", + "content": "4.4. Comparisons on Point Cloud Completion" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 508, + 289, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 508, + 289, + 662 + ], + "spans": [ + { + "bbox": [ + 46, + 508, + 289, + 662 + ], + "type": "text", + "content": "Point Cloud Completion predicts completed results as similar as possible to ground truths from partial inputs, which is usually trained with reconstruction losses between completed results and ground truths. To further verify the performances of CALoss, we apply it to a few popular point cloud completion works, including PCN [27], CRN [21], and RFNet [7]. As these works may have multilevel constraints, we conduct comparisons by replacing the reconstruction losses of the last level with CALoss and retraining the networks. The results are presented in Table 3. The completion performances have improvements in most conditions by introducing CALoss, which further confirms that CALoss is effective for different task networks." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 671, + 238, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 671, + 238, + 685 + ], + "spans": [ + { + "bbox": [ + 47, + 671, + 238, + 685 + ], + "type": "text", + "content": "4.5. Analysis about the Training Process" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 689, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 289, + 714 + ], + "type": "text", + "content": "Visualization during training. We visualize a model generated by the task network AE [1] during training to ob-" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 316, + 70, + 538, + 305 + ], + "blocks": [ + { + "bbox": [ + 316, + 70, + 538, + 305 + ], + "lines": [ + { + "bbox": [ + 316, + 70, + 538, + 305 + ], + "spans": [ + { + "bbox": [ + 316, + 70, + 538, + 305 + ], + "type": "table", + "html": "
TaskNetDatasetMethods
CDEMDPCLossOurs
AEMN1090.6089.4991.4891.48
MN4085.9285.4786.3686.81
FoldingMN1091.03-91.7092.26
MN4085.22-85.3586.24
AEMN1090.3890.1592.0493.47
(PN++)MN4088.0388.0787.5488.15
FoldingMN1091.48-91.4892.59
(PN++)MN4087.01-86.7387.13
AEMN1091.3791.2692.3792.81
(DGCNN)MN4087.5087.5488.1187.46
FoldingMN1091.26-91.8192.70
(DGCNN)MN4086.85-87.5087.74
GLRNetMN1093.58--95.24
MN4091.07--91.31
", + "image_path": "11027cc61cbe04c5e8f5ef9279e5c524bf1849f60dcf86eb08974f2cd367e33d.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 309, + 335, + 545, + 402 + ], + "blocks": [ + { + "bbox": [ + 330, + 308, + 520, + 319 + ], + "lines": [ + { + "bbox": [ + 330, + 308, + 520, + 319 + ], + "spans": [ + { + "bbox": [ + 330, + 308, + 520, + 319 + ], + "type": "text", + "content": "Table 2. Comparison on unsupervised classification." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 335, + 545, + 402 + ], + "lines": [ + { + "bbox": [ + 309, + 335, + 545, + 402 + ], + "spans": [ + { + "bbox": [ + 309, + 335, + 545, + 402 + ], + "type": "table", + "html": "
NetworkPCNCRNRFNetRFNet*
MetroMCD HDMCD HDMCD HDMCD HD
w/o CALoss0.31 2.670.29 2.420.21 2.920.29 3.82
w/ CALoss0.31 2.550.29 2.440.20 2.630.27 3.28
", + "image_path": "daacaedfc920c0e73d149783a53e1163848e6cff958f5cced72ddab59c5aea38.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 405, + 545, + 439 + ], + "lines": [ + { + "bbox": [ + 305, + 405, + 545, + 439 + ], + "spans": [ + { + "bbox": [ + 305, + 405, + 545, + 439 + ], + "type": "text", + "content": "Table 3. Comparisons on point cloud completion. RFNet and RFNet* denote results evaluated on known and novel categories on ShapeNet following RFNet [7]." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 449, + 547, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 449, + 547, + 628 + ], + "spans": [ + { + "bbox": [ + 304, + 449, + 547, + 628 + ], + "type": "text", + "content": "serve the convergence of different losses. The results are presented in Fig. 4. We can see that CD and EMD have unchanged results with obvious defects after 200 iterations, which means they actually converge to inappropriate local minimums. The reconstructed results trained with CALoss converge to a simple shape after 100 iterations, which may be the effect of contrastive constraint to help the task network find a shape similar to ground truths. From " + }, + { + "bbox": [ + 304, + 449, + 547, + 628 + ], + "type": "inline_equation", + "content": "200 \\sim 600" + }, + { + "bbox": [ + 304, + 449, + 547, + 628 + ], + "type": "text", + "content": " iterations, the trained results of CALoss gradually remove differences and approach the ground truth, which confirms the adversarial loss can continuously help find the defects and promote the task network to get better performances. Although PCLoss also removes defects during iterations, it produces rougher results, which may come from the limitation of 3D Euclidean space-based descriptors." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "type": "text", + "content": "Training curves. In this section, we visualize the reconstruction errors based on the AE network [1] and ShapeNet dataset [24] through the training iterations to observe the convergences of difference loss functions. We can see that our method has relatively inferior performances at the beginning of iterations, where it is learning to search shape differences. But it will converge steadily to low errors after" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12213" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 53, + 70, + 538, + 293 + ], + "blocks": [ + { + "bbox": [ + 53, + 70, + 538, + 293 + ], + "lines": [ + { + "bbox": [ + 53, + 70, + 538, + 293 + ], + "spans": [ + { + "bbox": [ + 53, + 70, + 538, + 293 + ], + "type": "table", + "html": "
Iteration100200300400500600GT
CD
EMD
PCLoss
Ours
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Non-learningLearning-based
MethodsCDEMDPUDPFDCRNDPCLossOurs
Time(ms)232167745975739
", + "image_path": "cde34be993333f05214fab3b11192ac3c5586d21c0e4f47fe2bdd1db3f0cb586.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 310, + 354, + 547, + 467 + ], + "blocks": [ + { + "bbox": [ + 47, + 698, + 287, + 719 + ], + "lines": [ + { + "bbox": [ + 47, + 698, + 287, + 719 + ], + "spans": [ + { + "bbox": [ + 47, + 698, + 287, + 719 + ], + "type": "text", + "content": "Table 4. Training efficiency comparison conducted on an NVIDIA 2080ti with a 2.9GHz i5-9400 CPU." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 310, + 354, + 547, + 467 + ], + "lines": [ + { + "bbox": [ + 310, + 354, + 547, + 467 + ], + "spans": [ + { + "bbox": [ + 310, + 354, + 547, + 467 + ], + "type": "table", + "html": "
GT
Pretrained
Ours
", + "image_path": "0e0e2af9c5ed452eaf01125d12014cc6b26cbf419314c7ec8f4658ec9208a292.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 508, + 493, + 522 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 508, + 493, + 522 + ], + "spans": [ + { + "bbox": [ + 306, + 508, + 493, + 522 + ], + "type": "text", + "content": "4.7. Necessity of the Dynamic Updating" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 528, + 547, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 528, + 547, + 647 + ], + "spans": [ + { + "bbox": [ + 304, + 528, + 547, + 647 + ], + "type": "text", + "content": "CALoss is dynamically optimized together with the task network as claimed in Sec. 3. To show the necessity of the dynamic updating, we make an attempt to train the task network AE [1] with a pre-trained CALoss directly without further updating. The results are demonstrated in Fig. 6 and Table 5. We can see that the task network trained with pre-trained CALoss can only reconstruct quite rough shapes, which confirms that training with CALoss is actually a continuous procedure to search shape differences between ground truths and the task network outputs." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 658, + 400, + 671 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 658, + 400, + 671 + ], + "spans": [ + { + "bbox": [ + 306, + 658, + 400, + 671 + ], + "type": "text", + "content": "4.8. Ablation Study" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 677, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 547, + 715 + ], + "type": "text", + "content": "Ablation study for components. We conduct an ablation study for the components adopted in CALoss as mentioned in Eq. 7. The results are presented in Table 6. " + }, + { + "bbox": [ + 305, + 677, + 547, + 715 + ], + "type": "inline_equation", + "content": "L_{p}" + }, + { + "bbox": [ + 305, + 677, + 547, + 715 + ], + "type": "text", + "content": " and" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "12214" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 65, + 70, + 271, + 136 + ], + "blocks": [ + { + "bbox": [ + 65, + 70, + 271, + 136 + ], + "lines": [ + { + "bbox": [ + 65, + 70, + 271, + 136 + ], + "spans": [ + { + "bbox": [ + 65, + 70, + 271, + 136 + ], + "type": "table", + "html": "
Pre-trainedOurs
DatasetSPMN40SPMN40
MCD0.751.040.210.58
HD14.4814.151.535.23
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LpPerturbLadv|δ|21/|Nσ|MCDHD
Lp/Ladv✓✓✓3.557.57
1.8312.76
Others✓✓✓0.778.13
✓✓✓✓0.221.63
✓✓✓✓✓0.211.53
", + "image_path": "81ce13dadccc7f346a98d37ffc780636d38663aa8f5330488fadaa4bed5424d3.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 569, + 288, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 569, + 288, + 652 + ], + "spans": [ + { + "bbox": [ + 46, + 569, + 288, + 652 + ], + "type": "text", + "content": "Ablation study for the Pooling Controller. In Pooling Controller, we introduce both max pooled and average pooled features to acquire more extensive information about the overall distribution in " + }, + { + "bbox": [ + 46, + 569, + 288, + 652 + ], + "type": "inline_equation", + "content": "F_{g}" + }, + { + "bbox": [ + 46, + 569, + 288, + 652 + ], + "type": "text", + "content": " as presented in Fig. 2 and Eq. 4. We conduct a simple ablation study for this operation in Table 7. We can see that the designation of concatenation can indeed reduce the reconstruction errors." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "type": "text", + "content": "Ablation study the Adaptive Pooling operation. Here, We present a simple discussion about the proposed Adaptive Pooling operation. From Table 8, we can see that both max and average pooling have quite inferior performances, which prove the necessity of Adaptive Pooling operation." + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 342, + 70, + 511, + 119 + ], + "blocks": [ + { + "bbox": [ + 46, + 528, + 289, + 562 + ], + "lines": [ + { + "bbox": [ + 46, + 528, + 289, + 562 + ], + "spans": [ + { + "bbox": [ + 46, + 528, + 289, + 562 + ], + "type": "text", + "content": "Table 6. Ablation for components. Perturb denotes the perturbation, while " + }, + { + "bbox": [ + 46, + 528, + 289, + 562 + ], + "type": "inline_equation", + "content": "L_{p}" + }, + { + "bbox": [ + 46, + 528, + 289, + 562 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 528, + 289, + 562 + ], + "type": "inline_equation", + "content": "L_{r}^{adv}" + }, + { + "bbox": [ + 46, + 528, + 289, + 562 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 528, + 289, + 562 + ], + "type": "inline_equation", + "content": "|\\delta|^{2}" + }, + { + "bbox": [ + 46, + 528, + 289, + 562 + ], + "type": "text", + "content": " are components included in Eq. 7. " + }, + { + "bbox": [ + 46, + 528, + 289, + 562 + ], + "type": "inline_equation", + "content": "1 / |\\mathcal{N}_{\\sigma}|" + }, + { + "bbox": [ + 46, + 528, + 289, + 562 + ], + "type": "text", + "content": " is the dynamic coefficient for the weight of " + }, + { + "bbox": [ + 46, + 528, + 289, + 562 + ], + "type": "inline_equation", + "content": "L_{p}" + }, + { + "bbox": [ + 46, + 528, + 289, + 562 + ], + "type": "text", + "content": " in Eq. 7." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 342, + 70, + 511, + 119 + ], + "lines": [ + { + "bbox": [ + 342, + 70, + 511, + 119 + ], + "spans": [ + { + "bbox": [ + 342, + 70, + 511, + 119 + ], + "type": "table", + "html": "
MetricsMaxAvgOurs
MCD0.220.230.21
HD1.581.551.53
", + "image_path": "11e6f4fd0b63469a9581c9188a7fcd9cbb7eadd337a96381d6766421104c1d76.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 342, + 161, + 511, + 209 + ], + "blocks": [ + { + "bbox": [ + 304, + 122, + 547, + 157 + ], + "lines": [ + { + "bbox": [ + 304, + 122, + 547, + 157 + ], + "spans": [ + { + "bbox": [ + 304, + 122, + 547, + 157 + ], + "type": "text", + "content": "Table 7. Comparisons between different implementations of Pooling Controller on ShapeNet and AE [1]. Max and Avg denote introducing only max and average pooling, respectively." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 342, + 161, + 511, + 209 + ], + "lines": [ + { + "bbox": [ + 342, + 161, + 511, + 209 + ], + "spans": [ + { + "bbox": [ + 342, + 161, + 511, + 209 + ], + "type": "table", + "html": "
MetricsMaxAvgOurs
MCD0.340.610.21
HD1.726.791.53
", + "image_path": "d08c3ed5a6ce1e3b49b4b560dd32d58fa6de8f2e3b1f9003ddbe7e3c53ae3973.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 213, + 545, + 236 + ], + "lines": [ + { + "bbox": [ + 304, + 213, + 545, + 236 + ], + "spans": [ + { + "bbox": [ + 304, + 213, + 545, + 236 + ], + "type": "text", + "content": "Table 8. Comparisons between pooling operations on ShapeNet and AE [1]. Max and Avg denote max and average pooling." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 243, + 547, + 423 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 243, + 547, + 423 + ], + "spans": [ + { + "bbox": [ + 304, + 243, + 547, + 423 + ], + "type": "text", + "content": "In CALoss, the pooling operation is used to aggregate features from all points into a global representation. To train the task network, the global representation needs to provide variable gradients for each point feature to distinguish them. However, average pooling can only propagate same and indistinguishable gradients for each points. Although max pooling can provide different gradients for point features, it provides a hard 0-1 distribution where only max items are constrained. In this condition, we design such an Adaptive Pooling to get a variable weight for each point feature according to the distance to the max pooled feature, which can be regarded as a \"soft max pooling\". All point features can be constrained with distinguishable gradients, which is controlled by widths of weight distributions predicted with Pooling Controller " + }, + { + "bbox": [ + 304, + 243, + 547, + 423 + ], + "type": "inline_equation", + "content": "h(\\cdot)" + }, + { + "bbox": [ + 304, + 243, + 547, + 423 + ], + "type": "text", + "content": " as shown in Fig. 2 and Eq. 5." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 434, + 379, + 447 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 434, + 379, + 447 + ], + "spans": [ + { + "bbox": [ + 306, + 434, + 379, + 447 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 454, + 547, + 634 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 454, + 547, + 634 + ], + "spans": [ + { + "bbox": [ + 304, + 454, + 547, + 634 + ], + "type": "text", + "content": "In this work, we propose a novel learning-based framework named CALoss to train the point cloud reconstruction-related task networks by measuring the differences between reconstructed shapes and ground truths with distances in a representation space. With the measurement of reconstruction loss in the learned non-linear representation space, CALoss has more extensive searching space for shape differences and better performances than the Euclidean space-based methods. According to the experiments, CALoss can achieve improvements above existing reconstruction losses based on predefined matching rules on multiple tasks including point cloud reconstruction, unsupervised classification and completion, which confirms it can help the task network achieve better reconstruction performances and extract more representative representations." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 645, + 382, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 645, + 382, + 658 + ], + "spans": [ + { + "bbox": [ + 306, + 645, + 382, + 658 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 665, + 547, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 547, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 547, + 712 + ], + "type": "text", + "content": "We thank all authors, reviewers and the chair for excellent contributions. This work is supported by the Key Research and Development Project of Zhejiang Province under Grant 2021C01035." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12215" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "text", + "content": "[1] Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. Learning representations and generative models for 3d point clouds. In International conference on machine learning, pages 40-49. PMLR, 2018. 2, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 135, + 288, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 135, + 288, + 179 + ], + "spans": [ + { + "bbox": [ + 53, + 135, + 288, + 179 + ], + "type": "text", + "content": "[2] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 1, 3, 8" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 180, + 288, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 180, + 288, + 224 + ], + "spans": [ + { + "bbox": [ + 53, + 180, + 288, + 224 + ], + "type": "text", + "content": "[3] Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 605-613, 2017. 2, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 225, + 288, + 269 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 225, + 288, + 269 + ], + "spans": [ + { + "bbox": [ + 53, + 225, + 288, + 269 + ], + "type": "text", + "content": "[4] Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. arXiv preprint arXiv:1406.2661, 2014. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 270, + 288, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 270, + 288, + 312 + ], + "spans": [ + { + "bbox": [ + 53, + 270, + 288, + 312 + ], + "type": "text", + "content": "[5] Tianxin Huang and Yong Liu. 3d point cloud geometry compression on deep learning. In Proceedings of the 27th ACM International Conference on Multimedia, pages 890-898, 2019. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 314, + 288, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 314, + 288, + 369 + ], + "spans": [ + { + "bbox": [ + 53, + 314, + 288, + 369 + ], + "type": "text", + "content": "[6] Tianxin Huang, Xuemeng Yang, Jiangning Zhang, Jinhao Cui, Hao Zou, Jun Chen, Xiangrui Zhao, and Yong Liu. Learning to train a point cloud reconstruction network without matching. In European Conference on Computer Vision, pages 179-194. Springer, 2022. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 370, + 288, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 370, + 288, + 435 + ], + "spans": [ + { + "bbox": [ + 53, + 370, + 288, + 435 + ], + "type": "text", + "content": "[7] Tianxin Huang, Hao Zou, Jinhao Cui, Xuemeng Yang, Mengmeng Wang, Xiangrui Zhao, Jiangning Zhang, Yi Yuan, Yifan Xu, and Yong Liu. Rfnet: Recurrent forward network for dense point cloud completion. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12508-12517, 2021. 1, 2, 5, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 436, + 288, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 436, + 288, + 491 + ], + "spans": [ + { + "bbox": [ + 53, + 436, + 288, + 491 + ], + "type": "text", + "content": "[8] Zitian Huang, Yikuan Yu, Jiawen Xu, Feng Ni, and Xinyi Le. Pf-net: Point fractal network for 3d point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7662-7670, 2020. 2, 3, 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 491, + 288, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 491, + 288, + 547 + ], + "spans": [ + { + "bbox": [ + 53, + 491, + 288, + 547 + ], + "type": "text", + "content": "[9] Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, Thomas Funkhouser, et al. Local implicit grid representations for 3d scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6001–6010, 2020. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 548, + 288, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 548, + 288, + 601 + ], + "spans": [ + { + "bbox": [ + 47, + 548, + 288, + 601 + ], + "type": "text", + "content": "[10] Ruihui Li, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, and Pheng-Ann Heng. Pu-gan: a point cloud upsampling adversarial network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7203-7212, 2019. 2, 3, 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 602, + 288, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 602, + 288, + 658 + ], + "spans": [ + { + "bbox": [ + 47, + 602, + 288, + 658 + ], + "type": "text", + "content": "[11] Minghua Liu, Lu Sheng, Sheng Yang, Jing Shao, and ShiMin Hu. Morphing and sampling network for dense point cloud completion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 11596-11603, 2020. 1, 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 658, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 658, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 658, + 288, + 713 + ], + "type": "text", + "content": "[12] Yongcheng Liu, Bin Fan, Shiming Xiang, and Chunhong Pan. Relation-shape convolutional neural network for point cloud analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8895-8904, 2019. 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 307, + 73, + 547, + 129 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 547, + 129 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 547, + 129 + ], + "type": "text", + "content": "[13] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4460-4470, 2019. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 129, + 547, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 547, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 547, + 184 + ], + "type": "text", + "content": "[14] Trung Nguyen, Quang-Hieu Pham, Tam Le, Tung Pham, Nhat Ho, and Binh-Son Hua. Point-set distances for learning representations of 3d point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10478-10487, 2021. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 185, + 547, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 185, + 547, + 229 + ], + "spans": [ + { + "bbox": [ + 307, + 185, + 547, + 229 + ], + "type": "text", + "content": "[15] Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. In Advances in neural information processing systems, pages 5099-5108, 2017. 5, 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 230, + 547, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 230, + 547, + 285 + ], + "spans": [ + { + "bbox": [ + 307, + 230, + 547, + 285 + ], + "type": "text", + "content": "[16] Yongming Rao, Jiwen Lu, and Jie Zhou. Global-local bidirectional reasoning for unsupervised representation learning of 3d point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5376-5385, 2020. 1, 2, 5, 6, 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 285, + 547, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 285, + 547, + 342 + ], + "spans": [ + { + "bbox": [ + 307, + 285, + 547, + 342 + ], + "type": "text", + "content": "[17] N Dinesh Reddy, Minh Vo, and Srinivasa G Narasimhan. Carfusion: Combining point tracking and part detection for dynamic 3d reconstruction of vehicles. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1906-1915, 2018. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 342, + 547, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 342, + 547, + 397 + ], + "spans": [ + { + "bbox": [ + 307, + 342, + 547, + 397 + ], + "type": "text", + "content": "[18] Shaoshuai Shi, Xiaogang Wang, and Hongsheng Li. Pointcnn: 3d object proposal generation and detection from point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 770-779, 2019. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 398, + 547, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 398, + 547, + 441 + ], + "spans": [ + { + "bbox": [ + 307, + 398, + 547, + 441 + ], + "type": "text", + "content": "[19] Jia-Heng Tang, Weikai Chen, Jie Yang, Bo Wang, Songrun Liu, Bo Yang, and Lin Gao. Octfield: Hierarchical implicit functions for 3d modeling. arXiv preprint arXiv:2111.01067, 2021. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 443, + 547, + 488 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 443, + 547, + 488 + ], + "spans": [ + { + "bbox": [ + 307, + 443, + 547, + 488 + ], + "type": "text", + "content": "[20] Dahlia Urbach, Yizhak Ben-Shabat, and Michael Lindenbaum. Dpdist: Comparing point clouds using deep point cloud distance. In European Conference on Computer Vision, pages 545-560. Springer, 2020. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 489, + 547, + 543 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 489, + 547, + 543 + ], + "spans": [ + { + "bbox": [ + 307, + 489, + 547, + 543 + ], + "type": "text", + "content": "[21] Xiaogang Wang, Marcelo H Ang Jr, and Gim Hee Lee. Cascaded refinement network for point cloud completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 790-799, 2020. 2, 3, 5, 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 544, + 547, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 544, + 547, + 589 + ], + "spans": [ + { + "bbox": [ + 307, + 544, + 547, + 589 + ], + "type": "text", + "content": "[22] Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (tog), 38(5):1-12, 2019. 2, 5, 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 590, + 547, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 590, + 547, + 634 + ], + "spans": [ + { + "bbox": [ + 307, + 590, + 547, + 634 + ], + "type": "text", + "content": "[23] Tong Wu, Liang Pan, Junzhe Zhang, Tai Wang, Ziwei Liu, and Dahua Lin. Density-aware chamfer distance as a comprehensive metric for point cloud completion. arXiv preprint arXiv:2111.12702, 2021. 2, 3, 5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 635, + 547, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 635, + 547, + 690 + ], + "spans": [ + { + "bbox": [ + 307, + 635, + 547, + 690 + ], + "type": "text", + "content": "[24] Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaou Tang, and Jianxiong Xiao. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1912-1920, 2015. 5, 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 691, + 547, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 691, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 691, + 547, + 713 + ], + "type": "text", + "content": "[25] Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian. Foldingnet: Point cloud auto-encoder via deep grid deformation." + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "12216" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 241 + ], + "type": "list", + "angle": 0, + "index": 4, + "blocks": [ + { + "bbox": [ + 66, + 72, + 287, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 72, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 287, + 95 + ], + "type": "text", + "content": "In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 206-215, 2018. 2, 5, 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 288, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 288, + 150 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 288, + 150 + ], + "type": "text", + "content": "[26] Li Yi, Vladimir G Kim, Duygu Ceylan, I-Chao Shen, Mengyan Yan, Hao Su, Cewu Lu, Qixing Huang, Alla Sheffer, and Leonidas Guibas. A scalable active framework for region annotation in 3d shape collections. ACM Transactions on Graphics (ToG), 35(6):1-12, 2016. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 152, + 288, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 152, + 288, + 195 + ], + "spans": [ + { + "bbox": [ + 48, + 152, + 288, + 195 + ], + "type": "text", + "content": "[27] Wentao Yuan, Tejas Khot, David Held, Christoph Mertz, and Martial Hebert. Pcn: Point completion network. In 2018 International Conference on 3D Vision (3DV), pages 728-737. IEEE, 2018. 2, 5, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 197, + 288, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 197, + 288, + 241 + ], + "spans": [ + { + "bbox": [ + 48, + 197, + 288, + 241 + ], + "type": "text", + "content": "[28] Yongheng Zhao, Tolga Birdal, Haowen Deng, and Federico Tombari. 3d point capsule networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1009-1018, 2019. 2" + } + ] + } + ], + "index": 3 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "12217" + } + ] + } + ], + "index": 5 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning To Name Classes for Vision and Language Models/784488d3-1965-4a7e-8402-437c5a3d1158_content_list.json b/2023/Learning To Name Classes for Vision and Language Models/784488d3-1965-4a7e-8402-437c5a3d1158_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..44d206e097151a00cd8d7fc65a690fa8daad1e69 --- /dev/null +++ b/2023/Learning To Name Classes for Vision and Language Models/784488d3-1965-4a7e-8402-437c5a3d1158_content_list.json @@ -0,0 +1,1293 @@ +[ + { + "type": "text", + "text": "Learning to Name Classes for Vision and Language Models", + "text_level": 1, + "bbox": [ + 199, + 130, + 772, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Sarah Parisot Yongxin Yang Steven McDonagh Huawei Noah's Ark Lab", + "bbox": [ + 267, + 181, + 702, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{sarah.parisot，yongxin.yang，steven.mcdonagh}@huawei.com", + "bbox": [ + 267, + 219, + 697, + 233 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 236, + 268, + 310, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing these problems, we propose to leverage available data to learn, for each class, an optimal word embedding as a function of the visual content. By learning new word embeddings on an otherwise frozen model, we are able to retain zero-shot capabilities for new classes, easily adapt models to new datasets, and adjust potentially erroneous, non-descriptive or ambiguous class names. We show that our solution can easily be integrated in image classification and object detection pipelines, yields significant performance gains in multiple scenarios and provides insights into model biases and labelling errors.", + "bbox": [ + 75, + 301, + 473, + 542 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 571, + 202, + 588 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The introduction of large-scale vision and language pretraining techniques has led to significant breakthroughs in object recognition, notably for tasks where data efficiency forms a crucial component (e.g. zero-shot scenarios). The key idea is to learn a mapping between image and text spaces such that a latent image representation will be close to sentences that describe the image content, in representation space [33, 36]. Learning from image-specific descriptions, rather than a fixed and common class label, with limited semantic meaning, yields models with very strong representational power [36]. Such multi-modal approaches address one key shortcoming of visual-only recognition methods; they provide an ability to seamlessly expand the model's output space without the requirement of additional training (i.e. open-set recognition). In contrast, visual-only classification and object detection methods have historically relied on training a linear classification layer in order to identify objects of interest, resulting in the undesirable consequence that any introduction of new classes (with or without available data) is very challenging. A plethora of complex mechanisms have been proposed, towards addressing this problem [12, 19]. Vision", + "bbox": [ + 75, + 598, + 472, + 902 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9ccf6870a3ea9ec1dbf63a0f2eb3b2d5348ae4947a3630c55b61750ea972cb20.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 511, + 257, + 681, + 400 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/2ec0b5750c50d9d61bc0cabfaf16ffe9714336a046846a88a34aed1a1185a2b7.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 700, + 257, + 879, + 400 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f81e2a67916517456184ce7a571a630b59d408557481efcfc10451ae54df5b34.jpg", + "image_caption": [ + "(a) Ambiguous class names", + "(b) Technical class names", + "Figure 1. Vision and language recognition models are highly sensitive to class prompts. Ambiguous or highly technical class names can lead to poor classification performance. Rather than adjusting class names directly, current solutions rely on prompt context to reduce ambiguity, e.g.: 'a photo of a bow, a type of weapon'." + ], + "image_footnote": [], + "bbox": [ + 699, + 405, + 893, + 531 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "and language multi-modal approaches alternatively perform classification by computing a similarity between image features and a set of textual features, corresponding to text descriptions from the classes of interest. As the text component constitutes an input, such models naturally enable a route to identify new categories, simply by modifying the queried text, yielding very strong zero-shot and open-set performance [22, 36].", + "bbox": [ + 496, + 657, + 890, + 763 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We draw attention to the fact that such performant multi-modal models suffer from two remaining key limitations. Firstly, performance is highly sensitive to class-prompt textual content; both in terms of $(i)$ the selected class name and $(ii)$ the surrounding sentence context. Class name sensitivity can be illustrated by considering class homonyms; words that have the same spelling but different meanings and origins (Figure 1a). These lead to ambiguous queries that negatively effect performance and mitigation necessitates time consuming prompt", + "bbox": [ + 496, + 763, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "23477", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "engineering. A further example of class name sensitivity can be demonstrated by classes exhibiting highly technical characterisations (Figure 1b). Poor performance is typically observed for datasets exhibiting such complex naming strategies [36]. We hypothesise that lack of clear semantic meaning leave it difficult for multi-modal models to obtain valuable embeddings.", + "bbox": [ + 75, + 90, + 470, + 181 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Secondly; model adaptation to new data, that becomes available, is not straightforward – especially when datasets are small. A natural solution is to use the existing representation layers and learn a new classification layer (i.e. linear probing). However this strategy must sacrifice the flexibility of text input, for potential subsequent adaptations [36]. Alternatives consider replacing text queries with image queries [33], or learning optimal prompt contexts from the new data [6,50,51]. The former can struggle to leverage larger amounts of available data successfully as image queries are averaged over all available class specific data. The latter alternatively updates a fixed set of parameters, which can lead to forgetting issues under sequential adaptation. Furthermore, the approach fails to learn a new class representation or mapping from the new data, as the focus lies solely with the prompt context. Finally; while context learning is highly successful in simple classification tasks, it becomes more challenging for object detection purposes and requires more complex training mechanisms [6]. We conjecture that this may be attributed to the fact that context then depends on the spatial region of the image, in addition to object type (i.e. foreground or background).", + "bbox": [ + 75, + 184, + 473, + 487 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "These considerations lead us to propose a complementary solution to prompt learning, which can be successfully leveraged for both classification and detection problems, without any task-specific tailoring or requirements. We build upon ideas from the field of text to image generation, and propose to leverage the concept of textual inversion [10] to adapt models to new data and thus address suboptimal, and potentially wrong, labels (e.g. incorrect class names, mislabelled data). The crux of our idea is to supersede manual word engineering, describing categories of interest, by leveraging available data to learn optimal word embeddings, as a function of the visual content. This simple solution affords several advantages: 1) in contrast to linear probing, open-set capabilities are maintained; learned class representations remain in text space, 2) our approach learns class specific parameters, therefore preventing forgetting under sequential training, 3) direct applicability to any classification or object detection technique that utilises textual input, 4) interpretability properties, with regards to learned class names.", + "bbox": [ + 75, + 489, + 470, + 762 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Experimentally, we realise our ideas in conjunction with two contemporary models; for image classification [36] and object detection [33]. Given a pre-trained model and (potentially previously unseen, additional) training dataset, we introduce a new set of word embeddings in the model text branch, each corresponding to a class of interest. Following context tuning methods [50, 51], embeddings are learned using standard losses on an otherwise frozen model. Comprehensive experiments across thirteen classification and detection datasets help to evidence that", + "bbox": [ + 75, + 763, + 470, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "learning optimal class names can significantly enhance model performance, maintain open-vocabulary properties, successfully enable continual model adaptation, and improve performance for rare and longtail classes. Last but not least, we demonstrate the method's potential for interpretability, notably with regards to mislabelling and inadequately chosen original class names.", + "bbox": [ + 496, + 90, + 890, + 181 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our main contributions can be summarised as:", + "bbox": [ + 517, + 183, + 810, + 196 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- A simple proposition for data-efficient adaptation of large vision-language models: optimisation of class names from visual content that retains attractive open-vocabulary properties.", + "- Performance improvements complementary to those of prompt tuning; consistent experimental gains across 13 classification and detection datasets. Strong sequential adaptation, open-vocabulary and long tail performance.", + "- Model interpretability insights: we visualise class name changes and related semantic meanings. We highlight how model biases and labelling errors can be identified, and how vision-language models suffer from long-tail issues." + ], + "bbox": [ + 517, + 210, + 890, + 419 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related work", + "text_level": 1, + "bbox": [ + 500, + 436, + 630, + 450 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Vision and language models. Early vision and language multi-modal alignment work tended to enable learning by encoding distinct modalities individually and independently. Model branch outputs would then be connected by losses [41]. Cross-modality alignment has been performed using techniques including metric learning [8], multi-label classification [20] and transformers [48]. A contemporary alternative approach pertains to so called \"vision-language\" models [9, 18, 29, 36] that recently gain popularity and instead are designed to jointly learn modality encoders; sharing information between modalities to the benefit of the global model. A representative approach is CLIP [36], which trains two neural network-based encoders on a very large image-caption dataset using a contrastive cross-entropy loss, to match pairs of images and texts, and shows impressive zero-shot image recognition performance. This seminal work has been extensively investigated in recent literature, and several improvements have been proposed [9, 18, 46].", + "bbox": [ + 496, + 460, + 893, + 718 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Open vocabulary object detection was originally explored in a two-step manner [45]: firstly learning a mapping between the two modalities using a large-scale dataset, then leveraging the obtained encoders and mapping layers to learn an object detector that can perform classification by computing a distance to class names' text embeddings. More recent methods fully integrate vision-language mappings into the object detection training process, providing more flexible and adaptable multi-modal detection solutions [2, 13, 22, 26, 31, 33]. Alternative methods generate a multi-modal joint representation via a fusion block [22, 31], while contemporary solutions adopt a CLIP-like parallel training strategy [2, 13, 33], facilitating querying of large numbers of", + "bbox": [ + 496, + 719, + 895, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "23478", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "classes. Similarly to the classification setting, multi-modal detection models achieve impressive zero-shot performance.", + "bbox": [ + 75, + 90, + 468, + 119 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Prompting methods. Prompt learning aims to exploit the rich knowledge contained in large pre-trained language models (e.g. GPT [37]), to predict new downstream tasks. The idea prescribes that we can reformulate these new tasks to \"look more like\" those solved during the original pre-training stage, through the help of an appropriate textual prompt, such that the model can be used to predict the desired output without any additional training [11,30,37,39]. The disadvantage with such methods, is the necessity for prompt engineering; searching for the most suitable prompt to allow the language model to solve the desired task. Recent work has looked to learn optimal prompts $cf$ . manually designing them. Work on continuous prompt learning methods [24, 28, 49] can be considered closely related to our work, where the main idea is to convert the prompt into a set of continuous vectors that can be optimised, with respect to an objective function, in end-to-end manner.", + "bbox": [ + 75, + 123, + 470, + 364 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The benefits of the prompting strategy have recently started to be explored in computer vision [21,38,44,47,51]. CoOp [51] can be considered the first work to bring continuous prompt learning to the vision domain, towards offering efficient solutions for adaptation of pre-trained vision-language models to downstream vision tasks. CoOp was recently extended by [50] where they propose to learn an additional light-weight neural network to generate, for each image, an input-conditional token that yields an image-specific dynamic prompt, outperforming CoOp's static prompts. This comes at the price of efficiency however, as the proposed extension is substantially both more memory and computationally expensive. We note that [50, 51] facilitate model adaptation to new domains in a highly parameter efficient manner, yet may still struggle in settings where new classes (and their names) widely differ from that which the model has initially learned. A more complex context prompting strategy has been recently proposed, specifically for detection tasks [6]; integrating a background specific loss function, as well as a context dependent scheme in the prompt learning process.", + "bbox": [ + 75, + 367, + 470, + 654 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Crucially, for all of these methods, classification is still performed with respect to a handcrafted class name, making accuracy highly dependent on the quality of chosen names. In certain settings, finding an accurate semantic description of a class can be challenging, and a poor choice can impair the model's performance. To the best of our knowledge, this limitation has not been addressed yet in classification and object detection settings.", + "bbox": [ + 75, + 656, + 470, + 762 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Textual Inversion. Textual inversion was recently introduced in [10] for text to image generative diffusion models, as a means to condition image generation towards incorporating specific objects or visual components. Given a set of images, all displaying the same object in diverse settings, a specific word embedding is learned so as to ensure the model is able to reconstruct images, sampled from this small set. Simple prompt contexts are used (e.g. 'a photo of') to ensure a simple image of the object is generated. After training, the model displays impressive ability", + "bbox": [ + 75, + 763, + 470, + 901 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/53bca57f66d7e4cb9ef2bb82c16a8005f9c3b25dd7e7c75488520a2614d9a3c8.jpg", + "image_caption": [ + "Figure 2. Overview of our method. Classes of interest are mapped to a set of learnable word embeddings, and integrated within the input sentence representation. These embeddings can be learned for classification and object detection tasks using standard losses, and used in conjunction with pre-trained word embeddings in open-vocabulary settings." + ], + "image_footnote": [], + "bbox": [ + 501, + 87, + 893, + 404 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "to integrate specific objects in generated images, via inputting learned words at specific locations in the input sentence. In the solution proposed by [10], each word is learned individually.", + "bbox": [ + 496, + 510, + 890, + 556 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 500, + 569, + 629, + 588 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We consider a vision and language object recognition model pre-trained on a large-scale dataset. Following the multi-modal architecture, the model takes as input an image-text pair $\\mathbf{x} = \\{I,T\\}$ , where the text provides a list of $N$ candidate classes $t_i$ to recognise; $T = [t_1,t_2,\\dots,t_N]$ . We assume the $i$ -th class specific text $t_i$ to be in the format: $t_i = [\\text{prompt prefix}] + [\\text{CLASS}] + [\\text{prompt suffix}]$ , where prompt prefixes and suffixes are shared across all classes. To obtain text feature representations, a tokenizer converts each sentence into a series of tokens such that each token maps to a pre-trained word embedding $\\mathbf{e_k}$ . Embeddings are then concatenated and used as input to a text transformer. Our final text representation is the transformer output for the End Of Sequence (EOS) token $f(T) = f_i(t_i)$ , where $i \\in \\{1,\\dots,N\\}$ .", + "bbox": [ + 496, + 595, + 893, + 808 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Baseline multi-modal models", + "text_level": 1, + "bbox": [ + 500, + 816, + 758, + 830 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Multi-modal Image classification. In the context of image classification [36], our objective is to assign a specific category to the image as a whole. Image inputs are processed in parallel to the text input through an image encoder $g(\\cdot)$ , yielding repre", + "bbox": [ + 496, + 839, + 893, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "23479", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "sentation $g(I)$ . The classification task is then simply performed by evaluating the cosine similarity between image and text embeddings, and selecting the class with the highest similarity.", + "bbox": [ + 75, + 90, + 470, + 136 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Multi-modal Object detection. There are two main contemporary strategies for vision and language based object detection. The first (simpler) strategy is similar to multi-modal classification, wherein image and text information are only combined latterly at the bounding box classification stage [2, 13, 33]. In this setting, the image input is processed through an object detector entirely disjointly from the text modality. The detector outputs a set of coordinates and a class embedding for each bounding box candidate. We then compute class embedding similarity with text representations $f(T)$ following the multi-modal classification approach to assign a classification label to each bounding box.", + "bbox": [ + 75, + 137, + 468, + 316 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The second class of strategies propose to fuse image and text inputs; either when generating text and image embeddings [27] or via a third encoder $h(g(I), f(T))$ that outputs bounding box coordinates and class embeddings [13, 22, 31]. For this latter fusion strategy, classification is performed over sentence tokens rather than across sentences, with an aim of associating bounding boxes with a keyword. This technique is typically more computationally expensive than the first method class as it requires computation of new image features, for each queried class.", + "bbox": [ + 75, + 319, + 468, + 455 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Learning optimal class names", + "text_level": 1, + "bbox": [ + 76, + 467, + 341, + 483 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We conjecture that the performance of pre-trained recognition models is intrinsically linked to the suitability of the class names, chosen to represent certain visual categories. Selecting class names that do not reflect an object's visual appearance can substantially alter recognition performance. We thus propose to remove the model's sensitivity to hand crafted class names, by learning class specific word embeddings from image content. An overview of the method is provided in Figure 2.", + "bbox": [ + 75, + 491, + 468, + 611 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We consider the matrix of pre-trained word embeddings $E \\in \\mathbb{R}^{V \\times F}$ , where $V$ is the vocabulary size, and $F$ the feature dimension. We propose to extend this matrix with a set of new, learnable word embeddings $E^l \\in \\mathbb{R}^{N \\times F}$ , where each row of $E^l$ corresponds to a class specific learnable embedding and $N$ pertains to the total number of dataset classes. In this setting, we replace the class name in each text query with a class specific placeholder $t_i = [\\text{prompt prefix}] + [\\text{pl}^i] + [\\text{prompt suffix}]$ . While the tokenizer maps context prompts to matrix $E$ , our placeholder maps to its corresponding class embedding $\\mathbf{e}_i^l$ . This new class query is used as input to the text transformer encoder, and the remainder of the recognition model is unchanged. We note that it is possible to map one category to a series of $m$ word embeddings, using queries of the type $t_i = [\\text{prompt prefix}] + [\\text{pl}_1^i] + \\dots + [\\text{pl}_m^i] + [\\text{prompt suffix}]$ .", + "bbox": [ + 75, + 612, + 470, + 840 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Training. During training, we freeze the entire pre-trained model (including pre-trained word embeddings) and learn our new set of word embeddings $E^{l}$ with the standard losses used to train the base model (e.g. cross entropy loss for classification,", + "bbox": [ + 75, + 840, + 470, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "and bounding box regression losses for detection). We identify three key scenarios where our approach can be beneficial: 1) adapting the pre-trained model on a new dataset with a new set of classes; 2) adjusting suboptimal class names on the same dataset used to train the model; 3) continual adaptation of a pre-trained model, where new classes are available after a first training stage. Inference. At inference time, we follow the process described in Section 3.1. Categories whose names were learned are mapped to their new embeddings $e^l \\in E^l$ . For unseen object categories (i.e. not in our new dataset) we use their original embeddings, $e \\in E$ , from the pre-trained model. As a result, our solution maintains the ability to perform open-vocabulary recognition.", + "bbox": [ + 496, + 90, + 893, + 272 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 498, + 285, + 625, + 301 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We evaluate the impact of learning class names for the tasks of image classification and object detection. We consider four settings where our strategy can be beneficial:", + "bbox": [ + 496, + 310, + 890, + 354 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Task 1: Model adaptation to new datasets: considering a new dataset of interest, we adapt our pre-trained model to this new set of potentially unseen classes by learning optimal class names from the new image content.", + "bbox": [ + 496, + 354, + 890, + 415 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Task 2: Post-training class name adjustment: the model is first fine-tuned on our target dataset, then class names are optimised in a subsequent learning stage. This setting is more valuable for object detection where fine-tuning can have important impact on bounding box localisation.", + "bbox": [ + 496, + 416, + 890, + 491 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Task 3: Open-vocabulary recognition: open-vocabulary is one of the key advantages of vision-language models, and maintaining strong performance is crucial. Evaluation of the compatibility between our learned word embeddings, and standard pre-trained word embeddings can provide a measure of strength on this axis. We adapt the model to only a subset of classes from our new dataset and measure overall performance on all classes, such that inference combines learned and text based word embeddings.", + "bbox": [ + 496, + 491, + 890, + 626 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Task 4: Continual model adaptation: to measure the model ability for sequential learning of new word embeddings, we first adapt the model to a subset of classes, then learn the remaining class names in a second, separate, training stage.", + "bbox": [ + 496, + 627, + 890, + 686 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We highlight that we do not seek state of the art performance, as this is intrinsically linked to the pre-trained model's base performance, pre-training data, and architecture. Rather, our objective is to demonstrate the potential of learning class names to improve model performance, while maintaining zero-shot properties. We therefore perform experiments on models with simple multi-modal mechanisms, use a single word embedding per class, and a standard prompt: (prompt prefix = 'a photo of a'; prompt suffix = '.'), in all experimental settings unless specified otherwise.", + "bbox": [ + 496, + 688, + 890, + 839 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Image classification: CLIP model", + "text_level": 1, + "bbox": [ + 498, + 847, + 794, + 863 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To evaluate our method on classification tasks, we employ CLIP [36] as our base pre-trained model with a vision", + "bbox": [ + 500, + 869, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "23480", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/2c32572b584f3818dee9ff42148f12aafdcf9d1ddc4535737dfb27eeb6b16cdf.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Class group| CLIP*CoOpOursOurs*
Base classes69.482.682.782.8
New classes74.264.173.174.2
All classes65.265.766.568.7
", + "bbox": [ + 127, + 88, + 419, + 156 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "transformer ViT-B/16 [5] fulfilling the image encoder $g(\\cdot)$ role. Here, we seek to evaluate method performance in previously described Tasks 1, 3 and 4; i.e. model adaptation, open-vocabulary performance, and continual adaptation.", + "bbox": [ + 75, + 250, + 468, + 310 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. We follow the setting proposed in [50, 51]. We adapt the CLIP model to a selection of eleven diverse datasets, focusing on different types of recognition tasks: standard image classification (ImageNet [4], Caltech 101 [7]), fine-grained classification (Stanford cars [23], Oxford Pets [35], Flowers 102 [34], Food 101 [1] and FGVC Aircraft [32]), scene recognition (SUN 397 [43]), action recognition (UCF 101 [42]), texture classification (DTD [3]) and satellite image recognition (Eurosat [17]). Training details. We adopt a set of parameters similar to [51]. For each dataset, we learn class names with a batch size of 32, over 200 epochs (50 for ImageNet), SGD optimiser with a learning rate of $2e^{-4}$ and cosine scheduling, with one warm up epoch. We initialise our learnable word embeddings using the average embedding of all word tokens associated with hand-crafted class names. For each dataset, 16 training images per category are sampled, and we report mean statistics over three random seeds. Baselines. The method we directly compare to is the zero-shot CLIP model, as we aim to evaluate the benefits of learning class names. We additionally report classification accuracy for the CoOp model [51], which learns optimal prompt context with fixed handcrafted class names. We retrain CoOp in our settings of interest and use four context words, initialised with 'a photo of a', as in [50] (this differs from the 16 words used in [51]). This allows to compare advantages and disadvantages of learning class names over prompt context, and evaluate any complementary nature between the strategies. We report the best possible CLIP performance with engineered prompts, and report our results with a standard prompt, as well as using the same engineered prompts as CLIP. We note that four of the datasets (Stanford cars, ImageNet, Caltech 101 and Sun 397) do not have engineered prompt, and we provide all dataset-specific prompts in the supplementary materials.", + "bbox": [ + 75, + 311, + 470, + 792 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Model adaptation (Task 1) and Open vocabulary (Task 3). We adopt the base-to-new generalisation setting proposed in [50]. For each dataset, we equally split classes into a set of 'base' and 'new' classes. We use base classes for training and evaluate performance on new classes in a zero-shot manner. Additionally, to measure open-vocabulary performance, we report performance on 'all' classes, i.e. a mixture of learned and handcrafted class", + "bbox": [ + 75, + 795, + 470, + 900 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/43d3eba2a129c75b432f4764fce334f0058fb0de47f2623e695f11f0d59bd65f.jpg", + "table_caption": [ + "Table 1. CLIP model: Base to new classification accuracy. Results on Base classes show method ability to adapt to new data (Task 1). Results on New and All classes show open vocabulary ability (Task 3). * Manually engineered prompt templates." + ], + "table_footnote": [], + "table_body": "
Class groupCLIP*CoOpOursOurs*
New classes74.084.485.985.9
All classes65.271.775.776.3
", + "bbox": [ + 549, + 89, + 843, + 143 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 2. CLIP model: Classification accuracy for continual model adaptation (Task 4) experiments. * manually engineered prompt templates.", + "bbox": [ + 500, + 154, + 893, + 183 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "names. Results are reported in Table 1. Results on base classes highlight our ability to perform model adaptation (Task 1) by learning class names only. For this task, we observe that our strategy can achieve equivalent gains as context tuning on base classes (82.6% CoOP) vs. 82.8% (Ours) average accuracy). However, our approach guarantees that zero-shot CLIP performance is maintained on new classes, as our learned parameters are class specific. Our open-vocabulary experiments (Task 3) show that we are able to maintain the CLIP model's ability to expand the output set, and to introduce new classes from text alone (CLIP and Ours 74.2%, new classes). Experiments on all classes confirm that we are able to use a mixture of learned and new classes successfully, outperforming CoOp (65.7% CoOp, 68.7% Ours, all classes). We further note sensitivity to context prompts, as substantial gains are obtained using engineered prompts (+2.2% Ours, all classes). This is to be expected, as new class performance can be strongly impacted by prompts, and highlights the importance of prompt context tuning in this setting.", + "bbox": [ + 496, + 210, + 893, + 484 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Sequential adaptation (Task 4). We extend the generalisation setting to evaluate our ability to perform sequential adaptation; i.e. after training on base classes, we subsequently train on new classes with no access to base classes. We measure performance on new classes, as well as all classes after this second training stage. For CoOp [51] experiments, we fine-tune prompt parameters, and adjust learning rate to $2e^{-4}$ which provided best empirical performance in this setting. Results are reported in Table 2. As in the previous setting, performance on new classes is highly similar when performing context or class name learning $(84.4\\% \\text{CoOp}, 85.9\\% \\text{Ours, new classes})$ , achieving strong gains over zero-shot CLIP $(10 + \\%)$ . On all classes, we can see that we achieve significant gains $(+4\\%, \\text{Ours vs. CoOp})$ , which can be attributed to the fact that class names are (all) learned independently. Last but not least, we note a much higher robustness to change in prompt context when all classes are learned; we achieve very similar results when using a standard or engineered template in this setting $(+0.6\\% \\text{vs.} +2.2\\%$ in Table 1, all classes).", + "bbox": [ + 496, + 484, + 893, + 757 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Object detection: OWL-vit model", + "text_level": 1, + "bbox": [ + 500, + 770, + 795, + 786 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For object detection experiments, we evaluate all 4 Tasks. We select OWL-vit [33] as our base multi-modal detection model due to noted strong zero-shot performance and clear separation between vision and language branches. This allows us to evaluate the impact of class learning more directly, in contrast to methods using complex text and image fusion mechanisms [26]. OWL-vit leverages a pre-trained multi-modal classification", + "bbox": [ + 496, + 795, + 893, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "23481", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/8c1b77f314fda43be6e6db88479937a2563638808b26548884cca6c3a4a0a377.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Methodbackbonepre-training (image)pre-training (detection)APAP/fAP/cAP/r
Zero-shot models
MDETR [22]DETRN/AMDETR data6.49.83.61.9
XDETR [2]DETRN/AMDETR data, LocNar16.418.815.29.6
GLIP-T [26]SWIN-TCap4MO365,GOLDG26.031.021.420.8
GLIP-L [26]SWIN-LCap24MO365,FourODs37.341.543.328.2
Zero-shot OWL-vit base model
OWL-vit zero-shot [33]VIT B/16CLIPO365, VG25.327.025.017.6
Prompt learning all [51]VIT B/16CLIPO365, VG24.425.924.017.8
Ours base (Task 3)VIT B/16CLIPO365, VG25.726.825.919.0
Ours sequential (Task 4)VIT B/16CLIPO365, VG26.026.625.923.1
Ours all (Task 1)VIT B/16CLIPO365, VG26.627.027.519.8
OWL-vit base model fine-tuned on LVIS base classes
OWL-vit [33]VIT B/16CLIPO365, VG28.834.424.717.7
Prompt learning all [51]VIT B/16CLIPO365, VG31.334.729.222.4
Ours base (Task 3)VIT B/16CLIPO365, VG33.335.233.920.3
Ours rare (Task 3)VIT B/16CLIPO365, VG30.434.525.434.5
Ours sequential (Task 4)VIT B/16CLIPO365, VG34.135.033.830.6
Ours all (Task 2)VIT B/16CLIPO365, VG34.535.634.428.5
OWL-vit base model fine-tuned on all LVIS classes
OWL-vit [33]VIT B/16CLIPO365, VG34.538.533.219.1
", + "bbox": [ + 178, + 87, + 794, + 393 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. Average precision results on LVIS mini-validation set for all (AP), frequent (AP/f), common (AP/c) and rare (AP/r) classes. Our method uses only $10\\%$ of the LVIS training dataset. State of the art methods (in grey) use different training datasets and architectures of different capacities. They are added for context.{base, rare, all} describes which class group is used for training. Sequential means base and rare classes are trained sequentially. Task 1: model adaptation to new data, Task 2: class name learning after fine-tuning, Task 3: Open vocabulary ability (mixture of seen and unseen classes), Task 4: continual adaptation (learning class groups in a sequential manner).", + "bbox": [ + 75, + 402, + 893, + 476 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "model (e.g. CLIP) with a vision transformer backbone. To adapt this model to the object detection task, the pooling layer that combines vision transformer outputs is removed, and two new modules are learned: a class prediction module and a box prediction module that output class embeddings and bounding-box coordinates, respectively, for all transformer outputs. The OWL-vit model is fine-tuned from the CLIP weights using a mixture of standard object detection datasets. Additionally, a set of engineered CLIP prompt templates are used as class prompt contexts.", + "bbox": [ + 75, + 500, + 470, + 637 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets. We evaluate the impact of learning class names on two object detection datasets. Firstly, we consider LVIS [14], a large scale natural image object detection dataset comprising over $100k$ training images and 1203 classes, characterised by a long tail distribution with 'frequent' $(x > 100)$ , 'common' $(100 \\geq x > 10)$ , and 'rare' $(10 \\geq x \\geq 1)$ class groups, for $x$ images per class. Following prior work [2]; we use pre-defined category names, but remove the text in the parentheses, e.g., \"flip-flop (sandal)\" is replaced by \"flip-flop\". We note that this leads to a few instances of classes sharing the same name (e.g. \"bow\").", + "bbox": [ + 75, + 638, + 470, + 792 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Secondly, we run experiments on CODA 2.0 [25], a self-driving dataset focusing on detection of rare, corner case instances. It comprises validation and test sets containing $5k$ images each, and 43 classes, of which only 29 appear in the validation set. In addition to the base OWL-vit model, we consider a model fine-tuned on the SODA dataset [15] (fully labelled instance), closer to our test domain. Designed for semi-supervised", + "bbox": [ + 75, + 795, + 470, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "training of self-driving models, SODA comprises six classes that overlap with CODA classes. Following [25], we refer to the{pedestrian, cyclist, car, bus, tram, truck, tricycle} classes as common classes, and remaining classes as corner-cases.", + "bbox": [ + 496, + 500, + 890, + 561 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Training details. We fine-tune the pre-trained OWL-vit model on LVIS, SODA datasets for 10 epochs, using 7 GPUs (batch size 2/GPU), with learning rates $5e^{-6}$ (main model) and $2e^{-7}$ (text branch) using cosine scheduling and an Adam optimiser. Weight decay is unused and gradient clipping is set to 0.1. For LVIS fine-tuning, we train two models: one on all classes, and one on base (frequent + common) classes only, thus following the evaluation set-up of [33]. For CODA 2.0 experiments, models are fine-tuned on the SODA and LVIS datasets, to simulate the setting where corner cases are discovered after training.", + "bbox": [ + 496, + 564, + 893, + 714 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For our class learning process, we freeze the entire model aside from new class embeddings. Embeddings are learned for 20 epochs, using a learning rate of $1e^{-2}$ (cosine schedule), 7 GPU (batch size 10/GPU). They are initialised with the average token embedding of the corresponding class name. For the LVIS dataset, we learn class names with a balanced subsampling of $10\\%$ of the training set.", + "bbox": [ + 496, + 715, + 893, + 821 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "LVIS experiments. We provide average precision results for all 4 Tasks on the LVIS mini-validation set [22] in Table 3. For context, we report methods competitive with OWL-vit, but highlight the key point of comparison is with respect to the base model that exhibits pre-defined class names. We also provide comparison", + "bbox": [ + 496, + 824, + 893, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "23482", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/0ead5ec322780f31a8e0ab7ba87d03489b0974b8c05558947cdc69ab6838661e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodAP-ComAP-AAR-AAR-CorSum
RetinaNet0.250.380.480.251.36
Faster RCNN0.280.370.430.171.27
Cascade RCNN0.300.420.480.211.41
Zero-shot OWL-vit base model
OWL-vit zero-shot0.100.200.440.451.19
Ours (Task 3)0.170.310.470.501.45
OWL-vit model fine-tuned on SODA
OWL-vit0.210.350.500.441.44
Ours (Task 3)0.230.370.510.471.58
OWL-vit model fine-tuned on SODA + LVIS
OWL-vit0.210.350.520.511.59
Ours (Task 3)0.230.390.530.531.67
", + "bbox": [ + 101, + 88, + 450, + 282 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Detection results on the CODA test set. AP-Com (AP-Common): average precision (AP) on common classes, AP-A (AP-agnostic): AP in the class agnostic setting. AR-A and AR-Cor: average recall for class agnostic and corner case detection respectively. Task 3: Open vocabulary ability (mixture of seen and unseen classes)", + "bbox": [ + 75, + 294, + 470, + 364 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "to prompt learning, using four learnable context words initialised as 'a photo of a', and identical training parameters to ours.", + "bbox": [ + 75, + 395, + 468, + 425 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "When learning names for all classes, we achieve consistent performance gains for all class groups in both zero-shot (Task 1: adapting to a new dataset, +1.3 AP) and fine-tuned settings (Task 2: post training class adjustment, +5.7 AP). Larger gains are observed for the fine-tuned model, which can be attributed to the improved bounding box regression component, facilitating the classification task. Gains are more substantial for rare classes (+2.2 AP (zero-shot), +10.8 AP (fine-tuned)), which can be attributed to the removal of name ambiguity and the fact that these names are less common in pre-training datasets, leading to potentially poorer mapping with image content (e.g. 'arctic' shoe class). In contrast, frequent classes remain mostly stable (+0 AP (zero-shot), +1.2 (fine-tuned)), highlighting the quality of image to name mapping for this group. We provide more evidence of this in Section 4.3.", + "bbox": [ + 75, + 428, + 470, + 654 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Open vocabulary experiments (Task 3), where class names are only learned on base classes, show modest gains in the zero-shot setting (+0.5 AP), but large improvements on a fine-tuned model (+4.5 AP), notably highlighting the importance of the localisation task. Due to the multi-label nature of LVIS, base and rare classes are not competing, facilitating the open vocabulary task. Learning base class names additionally reduces ambiguity with rare class names, improving rare class performance.", + "bbox": [ + 75, + 656, + 468, + 777 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Sequential learning experiments (Task 4; training on base then rare classes) achieve comparable performance to directly training on all classes (35.0 AP (seq.) vs. 35.6 AP (all classes)), further highlighting how well suited our approach is for sequential adaptation. LVIS is a highly imbalanced dataset. While our $10\\%$ subsampling strategy reduces class imbalance, it is not entirely eliminated. This is evidenced by the improved rare class performance in the sequential setting (+3.3", + "bbox": [ + 75, + 780, + 470, + 902 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/93baac3c12a0e14e91ec9112fc4bf401060d54172b136aad2d42726d4598e381.jpg", + "image_caption": [ + "Figure 3. Correlation between class rarity (log scale) and embedding similarity between learned and pre-defined class names. LVIS dataset." + ], + "image_footnote": [], + "bbox": [ + 591, + 90, + 803, + 253 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "(zero-shot), +2.1 (fine-tuned)). Fine-tuning the entire OWL-vit model on all classes (100% of the data) further evidences the impact of this imbalance and our model's potential to address long tail issues. By reducing data requirements (only 10% achieves equivalent overall performance; 34.5 AP), we are able to train on a balanced data subset. In both fine-tuned and zero-shot settings, learning class names on 10% of the data achieves better or equivalent rare class performance (AP/r) than the fine-tuned model (-0.1 to +15.4 AP/r gains).", + "bbox": [ + 496, + 323, + 890, + 459 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Finally, prompt tuning methods struggle to achieve good Task 1 performance in the zero-shot setting (-0.9 AP), but obtain better gains after fine-tuning (+2.5 AP). We note that prompt tuning performance is overall poorer than our strategy (-2.2 zero shot, -3.2 fine-tuned vs. ours all), which we hypothesise is partially linked to OWL-vit pre-training using fixed templates, and the diversity of object detection contexts.", + "bbox": [ + 496, + 460, + 892, + 566 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "CODA experiments, Task 3. Experimental results on the CODA 2.0 dataset are reported in Table 4. Following the setup proposed in [25], we report average precision (AP) on common classes (AP-Com), class agnostic AP on all classes (AP-A), class agnostic average recall on all classes (AR-A) and on corner classes (AR-Cor). We compare to state of the art self-driving models, trained in a semi-supervised setting on the SODA dataset [25]. The zero-shot OWL-vit model is not trained on any self-driving data, therefore achieves an overall poorer common class performance (0.10 AP-Com). However, the open-vocabulary setting allows this model to achieve very strong corner classes detection (0.45 AR-Cor). Our method largely improves performance for all metrics, with an overall performance (1.45 Sum) exceeding that of self-driving models (1.41 Sum), including our OWL-vit model fine-tuned on SODA (1.44 Sum). Fine-tuning OWL-vit on SODA, and a mixture of SODA and LVIS training data further improves results (1.44 and 1.59 Sum, respectively), and our strategy consistently increases all metrics (1.58 and 1.67 Sum, respectively). Common class performance remains below the reference models (-0.07 AP-Com), which we conjecture can be attributed to our relatively small self driving training data (only $10k$ images).", + "bbox": [ + 496, + 568, + 893, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "23483", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/90fed5bd5f60a91b58e34c60e7ebd919f8c3fd7114e540dba2538d18f94d3c96.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetOriginal nameClosest to new name (embedding similarity)Example image
Apperance based Correction
LVISarcticboot (0.46), ski boot (0.30)
CODAtricyclecart (0.62), rickshaw (0.67)
CODAmiscwaterwheel (0.47), wheel (0.45)
British to American English Correction
LVISclothes hamper wall socket garbage postbox trouserslaundry basket (0.52), shopping basket (0.39) outlet (0.62), power outlet (0.60) trash can (0.47), trashcan (0.45) mailbox (0.47), telephone booth (0.46) clothes (0.42), pants (0.41)
Object type first
LVISbell pepper bridal gown horse buggy horse carriage celery cymbalvegetable (0.55), pepper (0.51), bell pepper (0.51) gown (0.57), dress (0.57), bridal gown (0.55) horse-drawn vehicle (0.66), horse buggy (0.64) horse-drawn vehicle (0.68), horse carriage (0.65) green vegetables (0.50), celery (0.47) musical instrument (0.55), cymbal (0.46)
", + "bbox": [ + 99, + 88, + 450, + 380 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Interpretability results: changes to original class names.", + "bbox": [ + 91, + 390, + 452, + 405 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.3. Interpretability", + "text_level": 1, + "bbox": [ + 76, + 434, + 232, + 450 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "One key advantage of the proposed approach is a beneficial effect on model interpretability; the method allows evaluation of the manner in which class names are modified, based on image content. Here, we aim to evaluate how object classes were renamed in our object detection experiments. This experiment can usefully inform us about model biases, sources of classification errors, potential mislabelling errors and the suitability of handcrafted names. For this purpose, we build a reference vocabulary of 3031 object names, using the following dataset class names (filtered for duplicates): LVIS (1203 classes), CODA (43 classes), Objects365 [40] (365 classes), ImageNet (1000 classes), and the THINGS [16] database (1854 concepts).", + "bbox": [ + 75, + 459, + 468, + 640 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Individual word embeddings may not have very valuable meanings in terms of interpretability, especially considering that certain class names comprise multiple tokens. As a result, we compare learned representations to our reference vocabulary at the output of the text transformer encoder, using the shared prompt template context: 'a photo of a'. More formally, for each word $\\mathbf{e}^l$ , in our list of learned embeddings, we compute the sentence representation $f(t(\\mathbf{e}^l))$ , where $t(\\mathbf{e}^l)$ is the sentence constructed using our prompt prefix and word embedding $\\mathbf{e}^l$ . We repeat the procedure for each instance in our vocabulary, using the model's pre-trained word embeddings $\\mathbf{e}^v$ , and obtain $f(t(\\mathbf{e}^v))$ . We then simply compute the cosine distance between all pairs of learned and reference word representations.", + "bbox": [ + 75, + 642, + 470, + 838 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We carry out this process for models trained on the LVIS dataset (base model fine-tuned on base classes), and on the CODA 2.0 dataset (zero-shot model). In Table 5, we show examples of classes that were renamed towards a semantic meaning", + "bbox": [ + 75, + 839, + 470, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "closer to image content, and highlight some labelling errors. \"Arctic\" and \"misc\" are evidence of non representative class names. The \"tricycle\" class appears to have been mislabelled, with labelled vehicles looking closer to \"rickshaws\" and \"carts\". We additionally report two interesting trends: 1) the model tends to adjust word embeddings from British English representations to American English, where the latter is expected to be the dominant language, on which large scale CLIP models were trained; 2) fine-grained category representations tend to be closer to supercategory word types (e.g. vegetable, musical instrument), suggesting that the model also leverages cross-class similarities towards the learning of representations for rare classes. The first observation notably highlights our method's potential for cross-lingual model adaptation: given a dataset in one language, and a model trained in another language, we can learn optimal class names adapted to the pre-trained language model without any additional language translation efforts.", + "bbox": [ + 496, + 90, + 890, + 347 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In Figure 3 we highlight the relationship connecting class rarity and embedding similarity between learned and original class names, on the LVIS dataset. As previously conjectured in Section 4.2, there is a clear correlation between these variables, highlighting how multi-modal models also suffer from common long tail related issues (i.e. poorer semantic mapping between rare words and their corresponding visual appearance). Additional results are available in our supplementary materials.", + "bbox": [ + 496, + 347, + 892, + 469 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 482, + 612, + 498 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work focuses on the concept of class name and resulting impact on performance of vision-language recognition models. Complementary to prompt tuning, which seeks to optimise the context of text queries, we propose to automatically learn optimal class names from visual content. Our solution can easily be integrated in classification and object detection models, achieving significant gains, with respect to the base model, in all settings. We additionally demonstrate promising interpretability properties by showing how class semantic meanings were adjusted, providing data and model analysis properties.", + "bbox": [ + 496, + 508, + 890, + 657 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Learning class names can be viewed as an alternative to linear probing. The former approach, in contrast to probing, maintains key properties inherent to vision-language models (e.g. open-vocabulary). One observed limitation of our approach, in common with linear probing, is relatively weaker performance, in comparison with prompt tuning and image queries, under very low data regimes (i.e. 1-2 shots). Exploring a combination of learned text and image queries makes for a promising future avenue. While learning class names reduces dependency on prompt context, we still observe improvements using engineering prompts, in particular in open-vocabulary settings. As such, our method has the potential to further benefit from combination with prompt tuning techniques [50, 51], where prompts can provide key domain adaptation properties. Last but not least, learning class names has the potential to benefit class agnostic detection, via clustering and learning cluster semantics.", + "bbox": [ + 496, + 659, + 892, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "23484", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 169, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101-mining discriminative components with random forests. In European conference on computer vision, pages 446-461. Springer, 2014. 5", + "[2] Zhaowei Cai, Gukyeong Kwon, Avinash Ravichandran, Erhan Bas, Zhuowen Tu, Rahul Bhotika, and Stefano Soatto. X-detr: A versatile architecture for instance-wise vision-language tasks. arXiv preprint arXiv:2204.05626, 2022. 2, 4, 6", + "[3] Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describing textures in the wild. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 3606-3613, 2014. 5", + "[4] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 5", + "[5] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 5", + "[6] Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, and Guoqi Li. Learning to prompt for open-vocabulary object detection with vision-language model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14084-14093, 2022. 2, 3", + "[7] Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In 2004 conference on computer vision and pattern recognition workshop, pages 178-178. IEEE, 2004. 5", + "[8] Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, and Tomas Mikolov. Devise: A deep visual-semantic embedding model. Advances in neural information processing systems, 26, 2013. 2", + "[9] Andreas Fürst, Elisabeth Rumetshofer, Viet Tran, Hubert Ramsauer, Fei Tang, Johannes Lehner, David Kreil, Michael Kopp, Günter Klambauer, Angela Bitto-Nemling, et al. Cloob: Modern hopfield networks with infoloob outperform clip. arXiv preprint arXiv:2110.11316, 2021. 2", + "[10] Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618, 2022. 2, 3", + "[11] Tianyu Gao, Adam Fisch, and Danqi Chen. Making pre-trained language models better few-shot learners. arXiv preprint arXiv:2012.15723, 2020. 3", + "[12] Chuanxing Geng, Sheng-jun Huang, and Songcan Chen. Recent advances in open set recognition: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(10):3614-3631, 2020. 1", + "[13] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Zero-shot detection via vision and language knowledge distillation. CoRR, abs/2104.13921, 2021. 2, 4", + "[14] Agrim Gupta, Piotr Dollar, and Ross Girshick. LVIS: A dataset for large vocabulary instance segmentation. In Proceedings of the" + ], + "bbox": [ + 78, + 114, + 470, + 902 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "IEEE Conference on Computer Vision and Pattern Recognition, 2019. 6", + "[15] Jianhua Han, Xiwen Liang, Hang Xu, Kai Chen, Lanqing Hong, Jiageng Mao, Chaoqiang Ye, Wei Zhang, Zhenguo Li, Xiaodan Liang, et al. Soda10m: A large-scale 2d self/semi-supervised object detection dataset for autonomous driving. arXiv preprint arXiv:2106.11118, 2021. 6", + "[16] Martin N. Hebart, Adam H. Dickter, Alexis Kidder, Wan Y. Kwok, Anna Corriveau, Caitlin Van Wicklin, and Chris I. Baker. Things: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PLOS ONE, 14:1-24, 10 2019. 8", + "[17] Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pages 204-207, 2018. 5", + "[18] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 2", + "[19] KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubramanian. Towards open world object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5830-5840, 2021. 1", + "[20] Armand Joulin, Laurens van der Maaten, Allan Jabri, and Nicolas Vasilache. Learning visual features from large weakly supervised data. In European Conference on Computer Vision, pages 67-84. Springer, 2016. 2", + "[21] Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, and Weidi Xie. Prompting visual-language models for efficient video understanding. In European Conference on Computer Vision, pages 105–124. Springer, 2022. 3", + "[22] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790, 2021. 1, 2, 4, 6", + "[23] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In Proceedings of the IEEE international conference on computer vision workshops, pages 554–561, 2013. 5", + "[24] Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691, 2021. 3", + "[25] Kaican Li, Kai Chen, Haoyu Wang, Lanqing Hong, Chaoqiang Ye, Jianhua Han, Yukuai Chen, Wei Zhang, Chunjing Xu, Dit-Yan Yeung, et al. Coda: A real-world road corner case dataset for object detection in autonomous driving. arXiv preprint arXiv:2203.07724, 2022. 6, 7", + "[26] Liunian Harold Li*, Pengchuan Zhang*, Haotian Zhang*, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, and Jianfeng Gao. Grounded language-image pre-training. In CVPR, 2022. 2, 5, 6", + "[27] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image" + ], + "bbox": [ + 503, + 92, + 893, + 901 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "23485", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10965-10975, 2022. 4", + "[28] Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190, 2021.3", + "[29] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208, 2021. 2", + "[30] Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586, 2021. 3", + "[31] Muhammad Maaz, Hanoona Bangalath Rasheed, Salman Hameed Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, and Ming-Hsuan Yang. Multi-modal transformers excel at class-agnostic object detection. arXiv, 2021. 2, 4", + "[32] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. 06 2013. 5", + "[33] Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, et al. Simple open-vocabulary object detection with vision transformers. arXiv preprint arXiv:2205.06230, 2022.1,2,4,5,6", + "[34] Maria-Elena Nilsback and Andrew Zisserman. Automated flower classification over a large number of classes. In 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pages 722-729. IEEE, 2008. 5", + "[35] Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar. Cats and dogs. In 2012 IEEE conference on computer vision and pattern recognition, pages 3498-3505. IEEE, 2012. 5", + "[36] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 3, 4", + "[37] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 3", + "[38] Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18082-18091, 2022. 3", + "[39] Timo Schick and Hinrich Schütze. It's not just size that matters: Small language models are also few-shot learners. arXiv preprint arXiv:2009.07118, 2020. 3", + "[40] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 8429-8438, 2019. 8", + "[41] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 2" + ], + "bbox": [ + 78, + 92, + 470, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[42] Khurram Soomro, Amir Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. CoRR, 12 2012. 5", + "[43] Jianxiong Xiao, James Hays, Krista A. Ehinger, Aude Oliva, and Antonio Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 3485-3492, 2010. 5", + "[44] Yuan Yao, Ao Zhang, Zhengyan Zhang, Zhiyuan Liu, Tat-Seng Chua, and Maosong Sun. Cpt: Colorful prompt tuning for pre-trained vision-language models. arXiv preprint arXiv:2109.11797, 2021. 3", + "[45] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14393-14402, 2021. 2", + "[46] Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer. Lit: Zero-shot transfer with locked-image text tuning. CoRR, abs/2111.07991, 2021. 2", + "[47] Renrui Zhang, Ziyu Guo, Wei Zhang, Kunchang Li, Xupeng Miao, Bin Cui, Yu Qiao, Peng Gao, and Hongsheng Li. Pointclip: Point cloud understanding by clip. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8552-8562, 2022. 3", + "[48] Chen Zheng, Quan Guo, and Parisa Kordjamshidi. Cross-modality relevance for reasoning on language and vision. arXiv preprint arXiv:2005.06035, 2020. 2", + "[49] Zexuan Zhong, Dan Friedman, and Danqi Chen. Factual probing is [mask]: Learning vs. learning to recall. arXiv preprint arXiv:2104.05240, 2021. 3", + "[50] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Conditional prompt learning for vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16816-16825, 2022. 2, 3, 5, 8", + "[51] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9):2337-2348, 2022. 2, 3, 5, 6, 8" + ], + "bbox": [ + 501, + 92, + 893, + 628 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "23486", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning To Name Classes for Vision and Language Models/784488d3-1965-4a7e-8402-437c5a3d1158_model.json b/2023/Learning To Name Classes for Vision and Language Models/784488d3-1965-4a7e-8402-437c5a3d1158_model.json new file mode 100644 index 0000000000000000000000000000000000000000..a1d354e3c441d5fe2800866664ff0bc709b6b294 --- /dev/null +++ b/2023/Learning To Name Classes for Vision and Language Models/784488d3-1965-4a7e-8402-437c5a3d1158_model.json @@ -0,0 +1,1881 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.2, + 0.131, + 0.773, + 0.154 + ], + "angle": 0, + "content": "Learning to Name Classes for Vision and Language Models" + }, + { + "type": "text", + "bbox": [ + 0.268, + 0.182, + 0.703, + 0.216 + ], + "angle": 0, + "content": "Sarah Parisot Yongxin Yang Steven McDonagh Huawei Noah's Ark Lab" + }, + { + "type": "text", + "bbox": [ + 0.268, + 0.22, + 0.698, + 0.234 + ], + "angle": 0, + "content": "{sarah.parisot，yongxin.yang，steven.mcdonagh}@huawei.com" + }, + { + "type": "title", + "bbox": [ + 0.237, + 0.269, + 0.312, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.302, + 0.474, + 0.543 + ], + "angle": 0, + "content": "Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing these problems, we propose to leverage available data to learn, for each class, an optimal word embedding as a function of the visual content. By learning new word embeddings on an otherwise frozen model, we are able to retain zero-shot capabilities for new classes, easily adapt models to new datasets, and adjust potentially erroneous, non-descriptive or ambiguous class names. We show that our solution can easily be integrated in image classification and object detection pipelines, yields significant performance gains in multiple scenarios and provides insights into model biases and labelling errors." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.573, + 0.204, + 0.589 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.599, + 0.473, + 0.903 + ], + "angle": 0, + "content": "The introduction of large-scale vision and language pretraining techniques has led to significant breakthroughs in object recognition, notably for tasks where data efficiency forms a crucial component (e.g. zero-shot scenarios). The key idea is to learn a mapping between image and text spaces such that a latent image representation will be close to sentences that describe the image content, in representation space [33, 36]. Learning from image-specific descriptions, rather than a fixed and common class label, with limited semantic meaning, yields models with very strong representational power [36]. Such multi-modal approaches address one key shortcoming of visual-only recognition methods; they provide an ability to seamlessly expand the model's output space without the requirement of additional training (i.e. open-set recognition). In contrast, visual-only classification and object detection methods have historically relied on training a linear classification layer in order to identify objects of interest, resulting in the undesirable consequence that any introduction of new classes (with or without available data) is very challenging. A plethora of complex mechanisms have been proposed, towards addressing this problem [12, 19]. Vision" + }, + { + "type": "image", + "bbox": [ + 0.513, + 0.258, + 0.682, + 0.401 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.627, + 0.406, + 0.766, + 0.418 + ], + "angle": 0, + "content": "(a) Ambiguous class names" + }, + { + "type": "image", + "bbox": [ + 0.701, + 0.258, + 0.88, + 0.401 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.7, + 0.406, + 0.895, + 0.532 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.637, + 0.534, + 0.766, + 0.546 + ], + "angle": 0, + "content": "(b) Technical class names" + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.559, + 0.895, + 0.63 + ], + "angle": 0, + "content": "Figure 1. Vision and language recognition models are highly sensitive to class prompts. Ambiguous or highly technical class names can lead to poor classification performance. Rather than adjusting class names directly, current solutions rely on prompt context to reduce ambiguity, e.g.: 'a photo of a bow, a type of weapon'." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.658, + 0.892, + 0.764 + ], + "angle": 0, + "content": "and language multi-modal approaches alternatively perform classification by computing a similarity between image features and a set of textual features, corresponding to text descriptions from the classes of interest. As the text component constitutes an input, such models naturally enable a route to identify new categories, simply by modifying the queried text, yielding very strong zero-shot and open-set performance [22, 36]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.895, + 0.903 + ], + "angle": 0, + "content": "We draw attention to the fact that such performant multi-modal models suffer from two remaining key limitations. Firstly, performance is highly sensitive to class-prompt textual content; both in terms of \\((i)\\) the selected class name and \\((ii)\\) the surrounding sentence context. Class name sensitivity can be illustrated by considering class homonyms; words that have the same spelling but different meanings and origins (Figure 1a). These lead to ambiguous queries that negatively effect performance and mitigation necessitates time consuming prompt" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "23477" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.471, + 0.182 + ], + "angle": 0, + "content": "engineering. A further example of class name sensitivity can be demonstrated by classes exhibiting highly technical characterisations (Figure 1b). Poor performance is typically observed for datasets exhibiting such complex naming strategies [36]. We hypothesise that lack of clear semantic meaning leave it difficult for multi-modal models to obtain valuable embeddings." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.185, + 0.474, + 0.488 + ], + "angle": 0, + "content": "Secondly; model adaptation to new data, that becomes available, is not straightforward – especially when datasets are small. A natural solution is to use the existing representation layers and learn a new classification layer (i.e. linear probing). However this strategy must sacrifice the flexibility of text input, for potential subsequent adaptations [36]. Alternatives consider replacing text queries with image queries [33], or learning optimal prompt contexts from the new data [6,50,51]. The former can struggle to leverage larger amounts of available data successfully as image queries are averaged over all available class specific data. The latter alternatively updates a fixed set of parameters, which can lead to forgetting issues under sequential adaptation. Furthermore, the approach fails to learn a new class representation or mapping from the new data, as the focus lies solely with the prompt context. Finally; while context learning is highly successful in simple classification tasks, it becomes more challenging for object detection purposes and requires more complex training mechanisms [6]. We conjecture that this may be attributed to the fact that context then depends on the spatial region of the image, in addition to object type (i.e. foreground or background)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.49, + 0.471, + 0.763 + ], + "angle": 0, + "content": "These considerations lead us to propose a complementary solution to prompt learning, which can be successfully leveraged for both classification and detection problems, without any task-specific tailoring or requirements. We build upon ideas from the field of text to image generation, and propose to leverage the concept of textual inversion [10] to adapt models to new data and thus address suboptimal, and potentially wrong, labels (e.g. incorrect class names, mislabelled data). The crux of our idea is to supersede manual word engineering, describing categories of interest, by leveraging available data to learn optimal word embeddings, as a function of the visual content. This simple solution affords several advantages: 1) in contrast to linear probing, open-set capabilities are maintained; learned class representations remain in text space, 2) our approach learns class specific parameters, therefore preventing forgetting under sequential training, 3) direct applicability to any classification or object detection technique that utilises textual input, 4) interpretability properties, with regards to learned class names." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.765, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Experimentally, we realise our ideas in conjunction with two contemporary models; for image classification [36] and object detection [33]. Given a pre-trained model and (potentially previously unseen, additional) training dataset, we introduce a new set of word embeddings in the model text branch, each corresponding to a class of interest. Following context tuning methods [50, 51], embeddings are learned using standard losses on an otherwise frozen model. Comprehensive experiments across thirteen classification and detection datasets help to evidence that" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.182 + ], + "angle": 0, + "content": "learning optimal class names can significantly enhance model performance, maintain open-vocabulary properties, successfully enable continual model adaptation, and improve performance for rare and longtail classes. Last but not least, we demonstrate the method's potential for interpretability, notably with regards to mislabelling and inadequately chosen original class names." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.184, + 0.812, + 0.197 + ], + "angle": 0, + "content": "Our main contributions can be summarised as:" + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.211, + 0.892, + 0.272 + ], + "angle": 0, + "content": "- A simple proposition for data-efficient adaptation of large vision-language models: optimisation of class names from visual content that retains attractive open-vocabulary properties." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.285, + 0.892, + 0.346 + ], + "angle": 0, + "content": "- Performance improvements complementary to those of prompt tuning; consistent experimental gains across 13 classification and detection datasets. Strong sequential adaptation, open-vocabulary and long tail performance." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.359, + 0.892, + 0.42 + ], + "angle": 0, + "content": "- Model interpretability insights: we visualise class name changes and related semantic meanings. We highlight how model biases and labelling errors can be identified, and how vision-language models suffer from long-tail issues." + }, + { + "type": "list", + "bbox": [ + 0.519, + 0.211, + 0.892, + 0.42 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.437, + 0.631, + 0.451 + ], + "angle": 0, + "content": "2. Related work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.462, + 0.895, + 0.719 + ], + "angle": 0, + "content": "Vision and language models. Early vision and language multi-modal alignment work tended to enable learning by encoding distinct modalities individually and independently. Model branch outputs would then be connected by losses [41]. Cross-modality alignment has been performed using techniques including metric learning [8], multi-label classification [20] and transformers [48]. A contemporary alternative approach pertains to so called \"vision-language\" models [9, 18, 29, 36] that recently gain popularity and instead are designed to jointly learn modality encoders; sharing information between modalities to the benefit of the global model. A representative approach is CLIP [36], which trains two neural network-based encoders on a very large image-caption dataset using a contrastive cross-entropy loss, to match pairs of images and texts, and shows impressive zero-shot image recognition performance. This seminal work has been extensively investigated in recent literature, and several improvements have been proposed [9, 18, 46]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.897, + 0.902 + ], + "angle": 0, + "content": "Open vocabulary object detection was originally explored in a two-step manner [45]: firstly learning a mapping between the two modalities using a large-scale dataset, then leveraging the obtained encoders and mapping layers to learn an object detector that can perform classification by computing a distance to class names' text embeddings. More recent methods fully integrate vision-language mappings into the object detection training process, providing more flexible and adaptable multi-modal detection solutions [2, 13, 22, 26, 31, 33]. Alternative methods generate a multi-modal joint representation via a fusion block [22, 31], while contemporary solutions adopt a CLIP-like parallel training strategy [2, 13, 33], facilitating querying of large numbers of" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "23478" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.121 + ], + "angle": 0, + "content": "classes. Similarly to the classification setting, multi-modal detection models achieve impressive zero-shot performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.124, + 0.471, + 0.365 + ], + "angle": 0, + "content": "Prompting methods. Prompt learning aims to exploit the rich knowledge contained in large pre-trained language models (e.g. GPT [37]), to predict new downstream tasks. The idea prescribes that we can reformulate these new tasks to \"look more like\" those solved during the original pre-training stage, through the help of an appropriate textual prompt, such that the model can be used to predict the desired output without any additional training [11,30,37,39]. The disadvantage with such methods, is the necessity for prompt engineering; searching for the most suitable prompt to allow the language model to solve the desired task. Recent work has looked to learn optimal prompts \\( cf \\). manually designing them. Work on continuous prompt learning methods [24, 28, 49] can be considered closely related to our work, where the main idea is to convert the prompt into a set of continuous vectors that can be optimised, with respect to an objective function, in end-to-end manner." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.368, + 0.471, + 0.655 + ], + "angle": 0, + "content": "The benefits of the prompting strategy have recently started to be explored in computer vision [21,38,44,47,51]. CoOp [51] can be considered the first work to bring continuous prompt learning to the vision domain, towards offering efficient solutions for adaptation of pre-trained vision-language models to downstream vision tasks. CoOp was recently extended by [50] where they propose to learn an additional light-weight neural network to generate, for each image, an input-conditional token that yields an image-specific dynamic prompt, outperforming CoOp's static prompts. This comes at the price of efficiency however, as the proposed extension is substantially both more memory and computationally expensive. We note that [50, 51] facilitate model adaptation to new domains in a highly parameter efficient manner, yet may still struggle in settings where new classes (and their names) widely differ from that which the model has initially learned. A more complex context prompting strategy has been recently proposed, specifically for detection tasks [6]; integrating a background specific loss function, as well as a context dependent scheme in the prompt learning process." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.657, + 0.472, + 0.763 + ], + "angle": 0, + "content": "Crucially, for all of these methods, classification is still performed with respect to a handcrafted class name, making accuracy highly dependent on the quality of chosen names. In certain settings, finding an accurate semantic description of a class can be challenging, and a poor choice can impair the model's performance. To the best of our knowledge, this limitation has not been addressed yet in classification and object detection settings." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.765, + 0.472, + 0.902 + ], + "angle": 0, + "content": "Textual Inversion. Textual inversion was recently introduced in [10] for text to image generative diffusion models, as a means to condition image generation towards incorporating specific objects or visual components. Given a set of images, all displaying the same object in diverse settings, a specific word embedding is learned so as to ensure the model is able to reconstruct images, sampled from this small set. Simple prompt contexts are used (e.g. 'a photo of') to ensure a simple image of the object is generated. After training, the model displays impressive ability" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.088, + 0.895, + 0.405 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.414, + 0.895, + 0.485 + ], + "angle": 0, + "content": "Figure 2. Overview of our method. Classes of interest are mapped to a set of learnable word embeddings, and integrated within the input sentence representation. These embeddings can be learned for classification and object detection tasks using standard losses, and used in conjunction with pre-trained word embeddings in open-vocabulary settings." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.511, + 0.892, + 0.557 + ], + "angle": 0, + "content": "to integrate specific objects in generated images, via inputting learned words at specific locations in the input sentence. In the solution proposed by [10], each word is learned individually." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.57, + 0.63, + 0.589 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.596, + 0.895, + 0.809 + ], + "angle": 0, + "content": "We consider a vision and language object recognition model pre-trained on a large-scale dataset. Following the multi-modal architecture, the model takes as input an image-text pair \\(\\mathbf{x} = \\{I,T\\}\\), where the text provides a list of \\(N\\) candidate classes \\(t_i\\) to recognise; \\(T = [t_1,t_2,\\dots,t_N]\\). We assume the \\(i\\)-th class specific text \\(t_i\\) to be in the format: \\(t_i = [\\text{prompt prefix}] + [\\text{CLASS}] + [\\text{prompt suffix}]\\), where prompt prefixes and suffixes are shared across all classes. To obtain text feature representations, a tokenizer converts each sentence into a series of tokens such that each token maps to a pre-trained word embedding \\(\\mathbf{e_k}\\). Embeddings are then concatenated and used as input to a text transformer. Our final text representation is the transformer output for the End Of Sequence (EOS) token \\(f(T) = f_i(t_i)\\), where \\(i \\in \\{1,\\dots,N\\}\\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.817, + 0.759, + 0.832 + ], + "angle": 0, + "content": "3.1. Baseline multi-modal models" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Multi-modal Image classification. In the context of image classification [36], our objective is to assign a specific category to the image as a whole. Image inputs are processed in parallel to the text input through an image encoder \\( g(\\cdot) \\), yielding repre" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "23479" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.471, + 0.137 + ], + "angle": 0, + "content": "sentation \\( g(I) \\). The classification task is then simply performed by evaluating the cosine similarity between image and text embeddings, and selecting the class with the highest similarity." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.138, + 0.47, + 0.318 + ], + "angle": 0, + "content": "Multi-modal Object detection. There are two main contemporary strategies for vision and language based object detection. The first (simpler) strategy is similar to multi-modal classification, wherein image and text information are only combined latterly at the bounding box classification stage [2, 13, 33]. In this setting, the image input is processed through an object detector entirely disjointly from the text modality. The detector outputs a set of coordinates and a class embedding for each bounding box candidate. We then compute class embedding similarity with text representations \\( f(T) \\) following the multi-modal classification approach to assign a classification label to each bounding box." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.32, + 0.47, + 0.456 + ], + "angle": 0, + "content": "The second class of strategies propose to fuse image and text inputs; either when generating text and image embeddings [27] or via a third encoder \\( h(g(I), f(T)) \\) that outputs bounding box coordinates and class embeddings [13, 22, 31]. For this latter fusion strategy, classification is performed over sentence tokens rather than across sentences, with an aim of associating bounding boxes with a keyword. This technique is typically more computationally expensive than the first method class as it requires computation of new image features, for each queried class." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.468, + 0.342, + 0.484 + ], + "angle": 0, + "content": "3.2. Learning optimal class names" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.492, + 0.47, + 0.612 + ], + "angle": 0, + "content": "We conjecture that the performance of pre-trained recognition models is intrinsically linked to the suitability of the class names, chosen to represent certain visual categories. Selecting class names that do not reflect an object's visual appearance can substantially alter recognition performance. We thus propose to remove the model's sensitivity to hand crafted class names, by learning class specific word embeddings from image content. An overview of the method is provided in Figure 2." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.613, + 0.471, + 0.841 + ], + "angle": 0, + "content": "We consider the matrix of pre-trained word embeddings \\( E \\in \\mathbb{R}^{V \\times F} \\), where \\( V \\) is the vocabulary size, and \\( F \\) the feature dimension. We propose to extend this matrix with a set of new, learnable word embeddings \\( E^l \\in \\mathbb{R}^{N \\times F} \\), where each row of \\( E^l \\) corresponds to a class specific learnable embedding and \\( N \\) pertains to the total number of dataset classes. In this setting, we replace the class name in each text query with a class specific placeholder \\( t_i = [\\text{prompt prefix}] + [\\text{pl}^i] + [\\text{prompt suffix}] \\). While the tokenizer maps context prompts to matrix \\( E \\), our placeholder maps to its corresponding class embedding \\( \\mathbf{e}_i^l \\). This new class query is used as input to the text transformer encoder, and the remainder of the recognition model is unchanged. We note that it is possible to map one category to a series of \\( m \\) word embeddings, using queries of the type \\( t_i = [\\text{prompt prefix}] + [\\text{pl}_1^i] + \\dots + [\\text{pl}_m^i] + [\\text{prompt suffix}] \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.841, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Training. During training, we freeze the entire pre-trained model (including pre-trained word embeddings) and learn our new set of word embeddings \\( E^{l} \\) with the standard losses used to train the base model (e.g. cross entropy loss for classification," + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.894, + 0.273 + ], + "angle": 0, + "content": "and bounding box regression losses for detection). We identify three key scenarios where our approach can be beneficial: 1) adapting the pre-trained model on a new dataset with a new set of classes; 2) adjusting suboptimal class names on the same dataset used to train the model; 3) continual adaptation of a pre-trained model, where new classes are available after a first training stage. Inference. At inference time, we follow the process described in Section 3.1. Categories whose names were learned are mapped to their new embeddings \\( e^l \\in E^l \\). For unseen object categories (i.e. not in our new dataset) we use their original embeddings, \\( e \\in E \\), from the pre-trained model. As a result, our solution maintains the ability to perform open-vocabulary recognition." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.286, + 0.627, + 0.303 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.311, + 0.892, + 0.356 + ], + "angle": 0, + "content": "We evaluate the impact of learning class names for the tasks of image classification and object detection. We consider four settings where our strategy can be beneficial:" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.356, + 0.892, + 0.416 + ], + "angle": 0, + "content": "Task 1: Model adaptation to new datasets: considering a new dataset of interest, we adapt our pre-trained model to this new set of potentially unseen classes by learning optimal class names from the new image content." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.417, + 0.892, + 0.492 + ], + "angle": 0, + "content": "Task 2: Post-training class name adjustment: the model is first fine-tuned on our target dataset, then class names are optimised in a subsequent learning stage. This setting is more valuable for object detection where fine-tuning can have important impact on bounding box localisation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.492, + 0.892, + 0.627 + ], + "angle": 0, + "content": "Task 3: Open-vocabulary recognition: open-vocabulary is one of the key advantages of vision-language models, and maintaining strong performance is crucial. Evaluation of the compatibility between our learned word embeddings, and standard pre-trained word embeddings can provide a measure of strength on this axis. We adapt the model to only a subset of classes from our new dataset and measure overall performance on all classes, such that inference combines learned and text based word embeddings." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.628, + 0.892, + 0.688 + ], + "angle": 0, + "content": "Task 4: Continual model adaptation: to measure the model ability for sequential learning of new word embeddings, we first adapt the model to a subset of classes, then learn the remaining class names in a second, separate, training stage." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.892, + 0.84 + ], + "angle": 0, + "content": "We highlight that we do not seek state of the art performance, as this is intrinsically linked to the pre-trained model's base performance, pre-training data, and architecture. Rather, our objective is to demonstrate the potential of learning class names to improve model performance, while maintaining zero-shot properties. We therefore perform experiments on models with simple multi-modal mechanisms, use a single word embedding per class, and a standard prompt: (prompt prefix = 'a photo of a'; prompt suffix = '.'), in all experimental settings unless specified otherwise." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.848, + 0.795, + 0.864 + ], + "angle": 0, + "content": "4.1. Image classification: CLIP model" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "To evaluate our method on classification tasks, we employ CLIP [36] as our base pre-trained model with a vision" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "23480" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.128, + 0.089, + 0.421, + 0.157 + ], + "angle": 0, + "content": "
Class group| CLIP*CoOpOursOurs*
Base classes69.482.682.782.8
New classes74.264.173.174.2
All classes65.265.766.568.7
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.168, + 0.472, + 0.225 + ], + "angle": 0, + "content": "Table 1. CLIP model: Base to new classification accuracy. Results on Base classes show method ability to adapt to new data (Task 1). Results on New and All classes show open vocabulary ability (Task 3). * Manually engineered prompt templates." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.251, + 0.47, + 0.311 + ], + "angle": 0, + "content": "transformer ViT-B/16 [5] fulfilling the image encoder \\( g(\\cdot) \\) role. Here, we seek to evaluate method performance in previously described Tasks 1, 3 and 4; i.e. model adaptation, open-vocabulary performance, and continual adaptation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.312, + 0.472, + 0.794 + ], + "angle": 0, + "content": "Datasets. We follow the setting proposed in [50, 51]. We adapt the CLIP model to a selection of eleven diverse datasets, focusing on different types of recognition tasks: standard image classification (ImageNet [4], Caltech 101 [7]), fine-grained classification (Stanford cars [23], Oxford Pets [35], Flowers 102 [34], Food 101 [1] and FGVC Aircraft [32]), scene recognition (SUN 397 [43]), action recognition (UCF 101 [42]), texture classification (DTD [3]) and satellite image recognition (Eurosat [17]). Training details. We adopt a set of parameters similar to [51]. For each dataset, we learn class names with a batch size of 32, over 200 epochs (50 for ImageNet), SGD optimiser with a learning rate of \\(2e^{-4}\\) and cosine scheduling, with one warm up epoch. We initialise our learnable word embeddings using the average embedding of all word tokens associated with hand-crafted class names. For each dataset, 16 training images per category are sampled, and we report mean statistics over three random seeds. Baselines. The method we directly compare to is the zero-shot CLIP model, as we aim to evaluate the benefits of learning class names. We additionally report classification accuracy for the CoOp model [51], which learns optimal prompt context with fixed handcrafted class names. We retrain CoOp in our settings of interest and use four context words, initialised with 'a photo of a', as in [50] (this differs from the 16 words used in [51]). This allows to compare advantages and disadvantages of learning class names over prompt context, and evaluate any complementary nature between the strategies. We report the best possible CLIP performance with engineered prompts, and report our results with a standard prompt, as well as using the same engineered prompts as CLIP. We note that four of the datasets (Stanford cars, ImageNet, Caltech 101 and Sun 397) do not have engineered prompt, and we provide all dataset-specific prompts in the supplementary materials." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.796, + 0.472, + 0.901 + ], + "angle": 0, + "content": "Model adaptation (Task 1) and Open vocabulary (Task 3). We adopt the base-to-new generalisation setting proposed in [50]. For each dataset, we equally split classes into a set of 'base' and 'new' classes. We use base classes for training and evaluate performance on new classes in a zero-shot manner. Additionally, to measure open-vocabulary performance, we report performance on 'all' classes, i.e. a mixture of learned and handcrafted class" + }, + { + "type": "table", + "bbox": [ + 0.55, + 0.09, + 0.844, + 0.145 + ], + "angle": 0, + "content": "
Class groupCLIP*CoOpOursOurs*
New classes74.084.485.985.9
All classes65.271.775.776.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.155, + 0.894, + 0.184 + ], + "angle": 0, + "content": "Table 2. CLIP model: Classification accuracy for continual model adaptation (Task 4) experiments. * manually engineered prompt templates." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.212, + 0.895, + 0.485 + ], + "angle": 0, + "content": "names. Results are reported in Table 1. Results on base classes highlight our ability to perform model adaptation (Task 1) by learning class names only. For this task, we observe that our strategy can achieve equivalent gains as context tuning on base classes (82.6% CoOP) vs. 82.8% (Ours) average accuracy). However, our approach guarantees that zero-shot CLIP performance is maintained on new classes, as our learned parameters are class specific. Our open-vocabulary experiments (Task 3) show that we are able to maintain the CLIP model's ability to expand the output set, and to introduce new classes from text alone (CLIP and Ours 74.2%, new classes). Experiments on all classes confirm that we are able to use a mixture of learned and new classes successfully, outperforming CoOp (65.7% CoOp, 68.7% Ours, all classes). We further note sensitivity to context prompts, as substantial gains are obtained using engineered prompts (+2.2% Ours, all classes). This is to be expected, as new class performance can be strongly impacted by prompts, and highlights the importance of prompt context tuning in this setting." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.486, + 0.895, + 0.758 + ], + "angle": 0, + "content": "Sequential adaptation (Task 4). We extend the generalisation setting to evaluate our ability to perform sequential adaptation; i.e. after training on base classes, we subsequently train on new classes with no access to base classes. We measure performance on new classes, as well as all classes after this second training stage. For CoOp [51] experiments, we fine-tune prompt parameters, and adjust learning rate to \\(2e^{-4}\\) which provided best empirical performance in this setting. Results are reported in Table 2. As in the previous setting, performance on new classes is highly similar when performing context or class name learning \\((84.4\\% \\text{CoOp}, 85.9\\% \\text{Ours, new classes})\\), achieving strong gains over zero-shot CLIP \\((10 + \\%)\\). On all classes, we can see that we achieve significant gains \\((+4\\%, \\text{Ours vs. CoOp})\\), which can be attributed to the fact that class names are (all) learned independently. Last but not least, we note a much higher robustness to change in prompt context when all classes are learned; we achieve very similar results when using a standard or engineered template in this setting \\((+0.6\\% \\text{vs.} +2.2\\%\\) in Table 1, all classes)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.771, + 0.796, + 0.787 + ], + "angle": 0, + "content": "4.2. Object detection: OWL-vit model" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.895, + 0.901 + ], + "angle": 0, + "content": "For object detection experiments, we evaluate all 4 Tasks. We select OWL-vit [33] as our base multi-modal detection model due to noted strong zero-shot performance and clear separation between vision and language branches. This allows us to evaluate the impact of class learning more directly, in contrast to methods using complex text and image fusion mechanisms [26]. OWL-vit leverages a pre-trained multi-modal classification" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "23481" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.179, + 0.088, + 0.795, + 0.395 + ], + "angle": 0, + "content": "
Methodbackbonepre-training (image)pre-training (detection)APAP/fAP/cAP/r
Zero-shot models
MDETR [22]DETRN/AMDETR data6.49.83.61.9
XDETR [2]DETRN/AMDETR data, LocNar16.418.815.29.6
GLIP-T [26]SWIN-TCap4MO365,GOLDG26.031.021.420.8
GLIP-L [26]SWIN-LCap24MO365,FourODs37.341.543.328.2
Zero-shot OWL-vit base model
OWL-vit zero-shot [33]VIT B/16CLIPO365, VG25.327.025.017.6
Prompt learning all [51]VIT B/16CLIPO365, VG24.425.924.017.8
Ours base (Task 3)VIT B/16CLIPO365, VG25.726.825.919.0
Ours sequential (Task 4)VIT B/16CLIPO365, VG26.026.625.923.1
Ours all (Task 1)VIT B/16CLIPO365, VG26.627.027.519.8
OWL-vit base model fine-tuned on LVIS base classes
OWL-vit [33]VIT B/16CLIPO365, VG28.834.424.717.7
Prompt learning all [51]VIT B/16CLIPO365, VG31.334.729.222.4
Ours base (Task 3)VIT B/16CLIPO365, VG33.335.233.920.3
Ours rare (Task 3)VIT B/16CLIPO365, VG30.434.525.434.5
Ours sequential (Task 4)VIT B/16CLIPO365, VG34.135.033.830.6
Ours all (Task 2)VIT B/16CLIPO365, VG34.535.634.428.5
OWL-vit base model fine-tuned on all LVIS classes
OWL-vit [33]VIT B/16CLIPO365, VG34.538.533.219.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.404, + 0.895, + 0.477 + ], + "angle": 0, + "content": "Table 3. Average precision results on LVIS mini-validation set for all (AP), frequent (AP/f), common (AP/c) and rare (AP/r) classes. Our method uses only \\(10\\%\\) of the LVIS training dataset. State of the art methods (in grey) use different training datasets and architectures of different capacities. They are added for context.{base, rare, all} describes which class group is used for training. Sequential means base and rare classes are trained sequentially. Task 1: model adaptation to new data, Task 2: class name learning after fine-tuning, Task 3: Open vocabulary ability (mixture of seen and unseen classes), Task 4: continual adaptation (learning class groups in a sequential manner)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.501, + 0.472, + 0.638 + ], + "angle": 0, + "content": "model (e.g. CLIP) with a vision transformer backbone. To adapt this model to the object detection task, the pooling layer that combines vision transformer outputs is removed, and two new modules are learned: a class prediction module and a box prediction module that output class embeddings and bounding-box coordinates, respectively, for all transformer outputs. The OWL-vit model is fine-tuned from the CLIP weights using a mixture of standard object detection datasets. Additionally, a set of engineered CLIP prompt templates are used as class prompt contexts." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.64, + 0.472, + 0.793 + ], + "angle": 0, + "content": "Datasets. We evaluate the impact of learning class names on two object detection datasets. Firstly, we consider LVIS [14], a large scale natural image object detection dataset comprising over \\(100k\\) training images and 1203 classes, characterised by a long tail distribution with 'frequent' \\((x > 100)\\), 'common' \\((100 \\geq x > 10)\\), and 'rare' \\((10 \\geq x \\geq 1)\\) class groups, for \\(x\\) images per class. Following prior work [2]; we use pre-defined category names, but remove the text in the parentheses, e.g., \"flip-flop (sandal)\" is replaced by \"flip-flop\". We note that this leads to a few instances of classes sharing the same name (e.g. \"bow\")." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.796, + 0.472, + 0.903 + ], + "angle": 0, + "content": "Secondly, we run experiments on CODA 2.0 [25], a self-driving dataset focusing on detection of rare, corner case instances. It comprises validation and test sets containing \\(5k\\) images each, and 43 classes, of which only 29 appear in the validation set. In addition to the base OWL-vit model, we consider a model fine-tuned on the SODA dataset [15] (fully labelled instance), closer to our test domain. Designed for semi-supervised" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.501, + 0.892, + 0.562 + ], + "angle": 0, + "content": "training of self-driving models, SODA comprises six classes that overlap with CODA classes. Following [25], we refer to the{pedestrian, cyclist, car, bus, tram, truck, tricycle} classes as common classes, and remaining classes as corner-cases." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.565, + 0.895, + 0.715 + ], + "angle": 0, + "content": "Training details. We fine-tune the pre-trained OWL-vit model on LVIS, SODA datasets for 10 epochs, using 7 GPUs (batch size 2/GPU), with learning rates \\(5e^{-6}\\) (main model) and \\(2e^{-7}\\) (text branch) using cosine scheduling and an Adam optimiser. Weight decay is unused and gradient clipping is set to 0.1. For LVIS fine-tuning, we train two models: one on all classes, and one on base (frequent + common) classes only, thus following the evaluation set-up of [33]. For CODA 2.0 experiments, models are fine-tuned on the SODA and LVIS datasets, to simulate the setting where corner cases are discovered after training." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.716, + 0.895, + 0.822 + ], + "angle": 0, + "content": "For our class learning process, we freeze the entire model aside from new class embeddings. Embeddings are learned for 20 epochs, using a learning rate of \\(1e^{-2}\\) (cosine schedule), 7 GPU (batch size 10/GPU). They are initialised with the average token embedding of the corresponding class name. For the LVIS dataset, we learn class names with a balanced subsampling of \\(10\\%\\) of the training set." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.895, + 0.903 + ], + "angle": 0, + "content": "LVIS experiments. We provide average precision results for all 4 Tasks on the LVIS mini-validation set [22] in Table 3. For context, we report methods competitive with OWL-vit, but highlight the key point of comparison is with respect to the base model that exhibits pre-defined class names. We also provide comparison" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "23482" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.102, + 0.089, + 0.451, + 0.284 + ], + "angle": 0, + "content": "
MethodAP-ComAP-AAR-AAR-CorSum
RetinaNet0.250.380.480.251.36
Faster RCNN0.280.370.430.171.27
Cascade RCNN0.300.420.480.211.41
Zero-shot OWL-vit base model
OWL-vit zero-shot0.100.200.440.451.19
Ours (Task 3)0.170.310.470.501.45
OWL-vit model fine-tuned on SODA
OWL-vit0.210.350.500.441.44
Ours (Task 3)0.230.370.510.471.58
OWL-vit model fine-tuned on SODA + LVIS
OWL-vit0.210.350.520.511.59
Ours (Task 3)0.230.390.530.531.67
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.295, + 0.472, + 0.366 + ], + "angle": 0, + "content": "Table 4. Detection results on the CODA test set. AP-Com (AP-Common): average precision (AP) on common classes, AP-A (AP-agnostic): AP in the class agnostic setting. AR-A and AR-Cor: average recall for class agnostic and corner case detection respectively. Task 3: Open vocabulary ability (mixture of seen and unseen classes)" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.396, + 0.47, + 0.426 + ], + "angle": 0, + "content": "to prompt learning, using four learnable context words initialised as 'a photo of a', and identical training parameters to ours." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.429, + 0.471, + 0.655 + ], + "angle": 0, + "content": "When learning names for all classes, we achieve consistent performance gains for all class groups in both zero-shot (Task 1: adapting to a new dataset, +1.3 AP) and fine-tuned settings (Task 2: post training class adjustment, +5.7 AP). Larger gains are observed for the fine-tuned model, which can be attributed to the improved bounding box regression component, facilitating the classification task. Gains are more substantial for rare classes (+2.2 AP (zero-shot), +10.8 AP (fine-tuned)), which can be attributed to the removal of name ambiguity and the fact that these names are less common in pre-training datasets, leading to potentially poorer mapping with image content (e.g. 'arctic' shoe class). In contrast, frequent classes remain mostly stable (+0 AP (zero-shot), +1.2 (fine-tuned)), highlighting the quality of image to name mapping for this group. We provide more evidence of this in Section 4.3." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.657, + 0.47, + 0.779 + ], + "angle": 0, + "content": "Open vocabulary experiments (Task 3), where class names are only learned on base classes, show modest gains in the zero-shot setting (+0.5 AP), but large improvements on a fine-tuned model (+4.5 AP), notably highlighting the importance of the localisation task. Due to the multi-label nature of LVIS, base and rare classes are not competing, facilitating the open vocabulary task. Learning base class names additionally reduces ambiguity with rare class names, improving rare class performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.781, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Sequential learning experiments (Task 4; training on base then rare classes) achieve comparable performance to directly training on all classes (35.0 AP (seq.) vs. 35.6 AP (all classes)), further highlighting how well suited our approach is for sequential adaptation. LVIS is a highly imbalanced dataset. While our \\(10\\%\\) subsampling strategy reduces class imbalance, it is not entirely eliminated. This is evidenced by the improved rare class performance in the sequential setting (+3.3" + }, + { + "type": "image", + "bbox": [ + 0.592, + 0.091, + 0.805, + 0.255 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.267, + 0.892, + 0.295 + ], + "angle": 0, + "content": "Figure 3. Correlation between class rarity (log scale) and embedding similarity between learned and pre-defined class names. LVIS dataset." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.324, + 0.892, + 0.46 + ], + "angle": 0, + "content": "(zero-shot), +2.1 (fine-tuned)). Fine-tuning the entire OWL-vit model on all classes (100% of the data) further evidences the impact of this imbalance and our model's potential to address long tail issues. By reducing data requirements (only 10% achieves equivalent overall performance; 34.5 AP), we are able to train on a balanced data subset. In both fine-tuned and zero-shot settings, learning class names on 10% of the data achieves better or equivalent rare class performance (AP/r) than the fine-tuned model (-0.1 to +15.4 AP/r gains)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.462, + 0.893, + 0.568 + ], + "angle": 0, + "content": "Finally, prompt tuning methods struggle to achieve good Task 1 performance in the zero-shot setting (-0.9 AP), but obtain better gains after fine-tuning (+2.5 AP). We note that prompt tuning performance is overall poorer than our strategy (-2.2 zero shot, -3.2 fine-tuned vs. ours all), which we hypothesise is partially linked to OWL-vit pre-training using fixed templates, and the diversity of object detection contexts." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.569, + 0.895, + 0.903 + ], + "angle": 0, + "content": "CODA experiments, Task 3. Experimental results on the CODA 2.0 dataset are reported in Table 4. Following the setup proposed in [25], we report average precision (AP) on common classes (AP-Com), class agnostic AP on all classes (AP-A), class agnostic average recall on all classes (AR-A) and on corner classes (AR-Cor). We compare to state of the art self-driving models, trained in a semi-supervised setting on the SODA dataset [25]. The zero-shot OWL-vit model is not trained on any self-driving data, therefore achieves an overall poorer common class performance (0.10 AP-Com). However, the open-vocabulary setting allows this model to achieve very strong corner classes detection (0.45 AR-Cor). Our method largely improves performance for all metrics, with an overall performance (1.45 Sum) exceeding that of self-driving models (1.41 Sum), including our OWL-vit model fine-tuned on SODA (1.44 Sum). Fine-tuning OWL-vit on SODA, and a mixture of SODA and LVIS training data further improves results (1.44 and 1.59 Sum, respectively), and our strategy consistently increases all metrics (1.58 and 1.67 Sum, respectively). Common class performance remains below the reference models (-0.07 AP-Com), which we conjecture can be attributed to our relatively small self driving training data (only \\(10k\\) images)." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23483" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.1, + 0.089, + 0.451, + 0.381 + ], + "angle": 0, + "content": "
DatasetOriginal nameClosest to new name (embedding similarity)Example image
Apperance based Correction
LVISarcticboot (0.46), ski boot (0.30)
CODAtricyclecart (0.62), rickshaw (0.67)
CODAmiscwaterwheel (0.47), wheel (0.45)
British to American English Correction
LVISclothes hamper wall socket garbage postbox trouserslaundry basket (0.52), shopping basket (0.39) outlet (0.62), power outlet (0.60) trash can (0.47), trashcan (0.45) mailbox (0.47), telephone booth (0.46) clothes (0.42), pants (0.41)
Object type first
LVISbell pepper bridal gown horse buggy horse carriage celery cymbalvegetable (0.55), pepper (0.51), bell pepper (0.51) gown (0.57), dress (0.57), bridal gown (0.55) horse-drawn vehicle (0.66), horse buggy (0.64) horse-drawn vehicle (0.68), horse carriage (0.65) green vegetables (0.50), celery (0.47) musical instrument (0.55), cymbal (0.46)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.092, + 0.391, + 0.454, + 0.406 + ], + "angle": 0, + "content": "Table 5. Interpretability results: changes to original class names." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.435, + 0.233, + 0.451 + ], + "angle": 0, + "content": "4.3. Interpretability" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.46, + 0.47, + 0.641 + ], + "angle": 0, + "content": "One key advantage of the proposed approach is a beneficial effect on model interpretability; the method allows evaluation of the manner in which class names are modified, based on image content. Here, we aim to evaluate how object classes were renamed in our object detection experiments. This experiment can usefully inform us about model biases, sources of classification errors, potential mislabelling errors and the suitability of handcrafted names. For this purpose, we build a reference vocabulary of 3031 object names, using the following dataset class names (filtered for duplicates): LVIS (1203 classes), CODA (43 classes), Objects365 [40] (365 classes), ImageNet (1000 classes), and the THINGS [16] database (1854 concepts)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.643, + 0.471, + 0.839 + ], + "angle": 0, + "content": "Individual word embeddings may not have very valuable meanings in terms of interpretability, especially considering that certain class names comprise multiple tokens. As a result, we compare learned representations to our reference vocabulary at the output of the text transformer encoder, using the shared prompt template context: 'a photo of a'. More formally, for each word \\(\\mathbf{e}^l\\), in our list of learned embeddings, we compute the sentence representation \\(f(t(\\mathbf{e}^l))\\), where \\(t(\\mathbf{e}^l)\\) is the sentence constructed using our prompt prefix and word embedding \\(\\mathbf{e}^l\\). We repeat the procedure for each instance in our vocabulary, using the model's pre-trained word embeddings \\(\\mathbf{e}^v\\), and obtain \\(f(t(\\mathbf{e}^v))\\). We then simply compute the cosine distance between all pairs of learned and reference word representations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.903 + ], + "angle": 0, + "content": "We carry out this process for models trained on the LVIS dataset (base model fine-tuned on base classes), and on the CODA 2.0 dataset (zero-shot model). In Table 5, we show examples of classes that were renamed towards a semantic meaning" + }, + { + "type": "text", + "bbox": [ + 0.497, + 0.092, + 0.892, + 0.348 + ], + "angle": 0, + "content": "closer to image content, and highlight some labelling errors. \"Arctic\" and \"misc\" are evidence of non representative class names. The \"tricycle\" class appears to have been mislabelled, with labelled vehicles looking closer to \"rickshaws\" and \"carts\". We additionally report two interesting trends: 1) the model tends to adjust word embeddings from British English representations to American English, where the latter is expected to be the dominant language, on which large scale CLIP models were trained; 2) fine-grained category representations tend to be closer to supercategory word types (e.g. vegetable, musical instrument), suggesting that the model also leverages cross-class similarities towards the learning of representations for rare classes. The first observation notably highlights our method's potential for cross-lingual model adaptation: given a dataset in one language, and a model trained in another language, we can learn optimal class names adapted to the pre-trained language model without any additional language translation efforts." + }, + { + "type": "text", + "bbox": [ + 0.497, + 0.348, + 0.893, + 0.47 + ], + "angle": 0, + "content": "In Figure 3 we highlight the relationship connecting class rarity and embedding similarity between learned and original class names, on the LVIS dataset. As previously conjectured in Section 4.2, there is a clear correlation between these variables, highlighting how multi-modal models also suffer from common long tail related issues (i.e. poorer semantic mapping between rare words and their corresponding visual appearance). Additional results are available in our supplementary materials." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.483, + 0.613, + 0.499 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.497, + 0.509, + 0.892, + 0.659 + ], + "angle": 0, + "content": "This work focuses on the concept of class name and resulting impact on performance of vision-language recognition models. Complementary to prompt tuning, which seeks to optimise the context of text queries, we propose to automatically learn optimal class names from visual content. Our solution can easily be integrated in classification and object detection models, achieving significant gains, with respect to the base model, in all settings. We additionally demonstrate promising interpretability properties by showing how class semantic meanings were adjusted, providing data and model analysis properties." + }, + { + "type": "text", + "bbox": [ + 0.497, + 0.66, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Learning class names can be viewed as an alternative to linear probing. The former approach, in contrast to probing, maintains key properties inherent to vision-language models (e.g. open-vocabulary). One observed limitation of our approach, in common with linear probing, is relatively weaker performance, in comparison with prompt tuning and image queries, under very low data regimes (i.e. 1-2 shots). Exploring a combination of learned text and image queries makes for a promising future avenue. While learning class names reduces dependency on prompt context, we still observe improvements using engineering prompts, in particular in open-vocabulary settings. As such, our method has the potential to further benefit from combination with prompt tuning techniques [50, 51], where prompts can provide key domain adaptation properties. Last but not least, learning class names has the potential to benefit class agnostic detection, via clustering and learning cluster semantics." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23484" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.17, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.171 + ], + "angle": 0, + "content": "[1] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101-mining discriminative components with random forests. In European conference on computer vision, pages 446-461. Springer, 2014. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.172, + 0.472, + 0.227 + ], + "angle": 0, + "content": "[2] Zhaowei Cai, Gukyeong Kwon, Avinash Ravichandran, Erhan Bas, Zhuowen Tu, Rahul Bhotika, and Stefano Soatto. X-detr: A versatile architecture for instance-wise vision-language tasks. arXiv preprint arXiv:2204.05626, 2022. 2, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.228, + 0.471, + 0.284 + ], + "angle": 0, + "content": "[3] Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describing textures in the wild. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 3606-3613, 2014. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.285, + 0.471, + 0.34 + ], + "angle": 0, + "content": "[4] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.341, + 0.471, + 0.41 + ], + "angle": 0, + "content": "[5] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.411, + 0.472, + 0.48 + ], + "angle": 0, + "content": "[6] Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, and Guoqi Li. Learning to prompt for open-vocabulary object detection with vision-language model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14084-14093, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.481, + 0.471, + 0.55 + ], + "angle": 0, + "content": "[7] Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In 2004 conference on computer vision and pattern recognition workshop, pages 178-178. IEEE, 2004. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.55, + 0.471, + 0.606 + ], + "angle": 0, + "content": "[8] Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, and Tomas Mikolov. Devise: A deep visual-semantic embedding model. Advances in neural information processing systems, 26, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.607, + 0.471, + 0.675 + ], + "angle": 0, + "content": "[9] Andreas Fürst, Elisabeth Rumetshofer, Viet Tran, Hubert Ramsauer, Fei Tang, Johannes Lehner, David Kreil, Michael Kopp, Günter Klambauer, Angela Bitto-Nemling, et al. Cloob: Modern hopfield networks with infoloob outperform clip. arXiv preprint arXiv:2110.11316, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.676, + 0.47, + 0.732 + ], + "angle": 0, + "content": "[10] Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.733, + 0.47, + 0.773 + ], + "angle": 0, + "content": "[11] Tianyu Gao, Adam Fisch, and Danqi Chen. Making pre-trained language models better few-shot learners. arXiv preprint arXiv:2012.15723, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.775, + 0.471, + 0.829 + ], + "angle": 0, + "content": "[12] Chuanxing Geng, Sheng-jun Huang, and Songcan Chen. Recent advances in open set recognition: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(10):3614-3631, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.831, + 0.471, + 0.872 + ], + "angle": 0, + "content": "[13] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Zero-shot detection via vision and language knowledge distillation. CoRR, abs/2104.13921, 2021. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.471, + 0.903 + ], + "angle": 0, + "content": "[14] Agrim Gupta, Piotr Dollar, and Ross Girshick. LVIS: A dataset for large vocabulary instance segmentation. In Proceedings of the" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.115, + 0.472, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.894, + 0.12 + ], + "angle": 0, + "content": "IEEE Conference on Computer Vision and Pattern Recognition, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.894, + 0.19 + ], + "angle": 0, + "content": "[15] Jianhua Han, Xiwen Liang, Hang Xu, Kai Chen, Lanqing Hong, Jiageng Mao, Chaoqiang Ye, Wei Zhang, Zhenguo Li, Xiaodan Liang, et al. Soda10m: A large-scale 2d self/semi-supervised object detection dataset for autonomous driving. arXiv preprint arXiv:2106.11118, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.192, + 0.894, + 0.248 + ], + "angle": 0, + "content": "[16] Martin N. Hebart, Adam H. Dickter, Alexis Kidder, Wan Y. Kwok, Anna Corriveau, Caitlin Van Wicklin, and Chris I. Baker. Things: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PLOS ONE, 14:1-24, 10 2019. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.25, + 0.893, + 0.319 + ], + "angle": 0, + "content": "[17] Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pages 204-207, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.321, + 0.893, + 0.39 + ], + "angle": 0, + "content": "[18] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.391, + 0.893, + 0.447 + ], + "angle": 0, + "content": "[19] KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubramanian. Towards open world object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5830-5840, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.448, + 0.894, + 0.504 + ], + "angle": 0, + "content": "[20] Armand Joulin, Laurens van der Maaten, Allan Jabri, and Nicolas Vasilache. Learning visual features from large weakly supervised data. In European Conference on Computer Vision, pages 67-84. Springer, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.505, + 0.893, + 0.56 + ], + "angle": 0, + "content": "[21] Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, and Weidi Xie. Prompting visual-language models for efficient video understanding. In European Conference on Computer Vision, pages 105–124. Springer, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.561, + 0.893, + 0.631 + ], + "angle": 0, + "content": "[22] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790, 2021. 1, 2, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.632, + 0.894, + 0.688 + ], + "angle": 0, + "content": "[23] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In Proceedings of the IEEE international conference on computer vision workshops, pages 554–561, 2013. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.689, + 0.893, + 0.73 + ], + "angle": 0, + "content": "[24] Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.732, + 0.893, + 0.801 + ], + "angle": 0, + "content": "[25] Kaican Li, Kai Chen, Haoyu Wang, Lanqing Hong, Chaoqiang Ye, Jianhua Han, Yukuai Chen, Wei Zhang, Chunjing Xu, Dit-Yan Yeung, et al. Coda: A real-world road corner case dataset for object detection in autonomous driving. arXiv preprint arXiv:2203.07724, 2022. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.802, + 0.894, + 0.858 + ], + "angle": 0, + "content": "[26] Liunian Harold Li*, Pengchuan Zhang*, Haotian Zhang*, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, and Jianfeng Gao. Grounded language-image pre-training. In CVPR, 2022. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.86, + 0.893, + 0.902 + ], + "angle": 0, + "content": "[27] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.894, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23485" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.093, + 0.471, + 0.133 + ], + "angle": 0, + "content": "pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10965-10975, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.134, + 0.472, + 0.175 + ], + "angle": 0, + "content": "[28] Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190, 2021.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.177, + 0.471, + 0.232 + ], + "angle": 0, + "content": "[29] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.233, + 0.471, + 0.288 + ], + "angle": 0, + "content": "[30] Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.289, + 0.471, + 0.343 + ], + "angle": 0, + "content": "[31] Muhammad Maaz, Hanoona Bangalath Rasheed, Salman Hameed Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, and Ming-Hsuan Yang. Multi-modal transformers excel at class-agnostic object detection. arXiv, 2021. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.344, + 0.471, + 0.384 + ], + "angle": 0, + "content": "[32] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. 06 2013. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.385, + 0.471, + 0.455 + ], + "angle": 0, + "content": "[33] Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, et al. Simple open-vocabulary object detection with vision transformers. arXiv preprint arXiv:2205.06230, 2022.1,2,4,5,6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.455, + 0.471, + 0.511 + ], + "angle": 0, + "content": "[34] Maria-Elena Nilsback and Andrew Zisserman. Automated flower classification over a large number of classes. In 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pages 722-729. IEEE, 2008. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.511, + 0.471, + 0.553 + ], + "angle": 0, + "content": "[35] Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar. Cats and dogs. In 2012 IEEE conference on computer vision and pattern recognition, pages 3498-3505. IEEE, 2012. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.553, + 0.471, + 0.635 + ], + "angle": 0, + "content": "[36] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.636, + 0.471, + 0.678 + ], + "angle": 0, + "content": "[37] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.679, + 0.471, + 0.748 + ], + "angle": 0, + "content": "[38] Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18082-18091, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.748, + 0.471, + 0.789 + ], + "angle": 0, + "content": "[39] Timo Schick and Hinrich Schütze. It's not just size that matters: Small language models are also few-shot learners. arXiv preprint arXiv:2009.07118, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.79, + 0.471, + 0.858 + ], + "angle": 0, + "content": "[40] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 8429-8438, 2019. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.471, + 0.9 + ], + "angle": 0, + "content": "[41] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.093, + 0.472, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.134 + ], + "angle": 0, + "content": "[42] Khurram Soomro, Amir Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. CoRR, 12 2012. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.894, + 0.204 + ], + "angle": 0, + "content": "[43] Jianxiong Xiao, James Hays, Krista A. Ehinger, Aude Oliva, and Antonio Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 3485-3492, 2010. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.206, + 0.894, + 0.261 + ], + "angle": 0, + "content": "[44] Yuan Yao, Ao Zhang, Zhengyan Zhang, Zhiyuan Liu, Tat-Seng Chua, and Maosong Sun. Cpt: Colorful prompt tuning for pre-trained vision-language models. arXiv preprint arXiv:2109.11797, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.262, + 0.894, + 0.319 + ], + "angle": 0, + "content": "[45] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14393-14402, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.32, + 0.894, + 0.374 + ], + "angle": 0, + "content": "[46] Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer. Lit: Zero-shot transfer with locked-image text tuning. CoRR, abs/2111.07991, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.375, + 0.894, + 0.445 + ], + "angle": 0, + "content": "[47] Renrui Zhang, Ziyu Guo, Wei Zhang, Kunchang Li, Xupeng Miao, Bin Cui, Yu Qiao, Peng Gao, and Hongsheng Li. Pointclip: Point cloud understanding by clip. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8552-8562, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.446, + 0.894, + 0.489 + ], + "angle": 0, + "content": "[48] Chen Zheng, Quan Guo, and Parisa Kordjamshidi. Cross-modality relevance for reasoning on language and vision. arXiv preprint arXiv:2005.06035, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.489, + 0.894, + 0.53 + ], + "angle": 0, + "content": "[49] Zexuan Zhong, Dan Friedman, and Danqi Chen. Factual probing is [mask]: Learning vs. learning to recall. arXiv preprint arXiv:2104.05240, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.531, + 0.894, + 0.587 + ], + "angle": 0, + "content": "[50] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Conditional prompt learning for vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16816-16825, 2022. 2, 3, 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.588, + 0.894, + 0.63 + ], + "angle": 0, + "content": "[51] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. 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Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing these problems, we propose to leverage available data to learn, for each class, an optimal word embedding as a function of the visual content. By learning new word embeddings on an otherwise frozen model, we are able to retain zero-shot capabilities for new classes, easily adapt models to new datasets, and adjust potentially erroneous, non-descriptive or ambiguous class names. We show that our solution can easily be integrated in image classification and object detection pipelines, yields significant performance gains in multiple scenarios and provides insights into model biases and labelling errors. + +# 1. Introduction + +The introduction of large-scale vision and language pretraining techniques has led to significant breakthroughs in object recognition, notably for tasks where data efficiency forms a crucial component (e.g. zero-shot scenarios). The key idea is to learn a mapping between image and text spaces such that a latent image representation will be close to sentences that describe the image content, in representation space [33, 36]. Learning from image-specific descriptions, rather than a fixed and common class label, with limited semantic meaning, yields models with very strong representational power [36]. Such multi-modal approaches address one key shortcoming of visual-only recognition methods; they provide an ability to seamlessly expand the model's output space without the requirement of additional training (i.e. open-set recognition). In contrast, visual-only classification and object detection methods have historically relied on training a linear classification layer in order to identify objects of interest, resulting in the undesirable consequence that any introduction of new classes (with or without available data) is very challenging. A plethora of complex mechanisms have been proposed, towards addressing this problem [12, 19]. Vision + +![](images/9ccf6870a3ea9ec1dbf63a0f2eb3b2d5348ae4947a3630c55b61750ea972cb20.jpg) + +![](images/2ec0b5750c50d9d61bc0cabfaf16ffe9714336a046846a88a34aed1a1185a2b7.jpg) + +![](images/f81e2a67916517456184ce7a571a630b59d408557481efcfc10451ae54df5b34.jpg) +(a) Ambiguous class names +(b) Technical class names +Figure 1. Vision and language recognition models are highly sensitive to class prompts. Ambiguous or highly technical class names can lead to poor classification performance. Rather than adjusting class names directly, current solutions rely on prompt context to reduce ambiguity, e.g.: 'a photo of a bow, a type of weapon'. + +and language multi-modal approaches alternatively perform classification by computing a similarity between image features and a set of textual features, corresponding to text descriptions from the classes of interest. As the text component constitutes an input, such models naturally enable a route to identify new categories, simply by modifying the queried text, yielding very strong zero-shot and open-set performance [22, 36]. + +We draw attention to the fact that such performant multi-modal models suffer from two remaining key limitations. Firstly, performance is highly sensitive to class-prompt textual content; both in terms of $(i)$ the selected class name and $(ii)$ the surrounding sentence context. Class name sensitivity can be illustrated by considering class homonyms; words that have the same spelling but different meanings and origins (Figure 1a). These lead to ambiguous queries that negatively effect performance and mitigation necessitates time consuming prompt + +engineering. A further example of class name sensitivity can be demonstrated by classes exhibiting highly technical characterisations (Figure 1b). Poor performance is typically observed for datasets exhibiting such complex naming strategies [36]. We hypothesise that lack of clear semantic meaning leave it difficult for multi-modal models to obtain valuable embeddings. + +Secondly; model adaptation to new data, that becomes available, is not straightforward – especially when datasets are small. A natural solution is to use the existing representation layers and learn a new classification layer (i.e. linear probing). However this strategy must sacrifice the flexibility of text input, for potential subsequent adaptations [36]. Alternatives consider replacing text queries with image queries [33], or learning optimal prompt contexts from the new data [6,50,51]. The former can struggle to leverage larger amounts of available data successfully as image queries are averaged over all available class specific data. The latter alternatively updates a fixed set of parameters, which can lead to forgetting issues under sequential adaptation. Furthermore, the approach fails to learn a new class representation or mapping from the new data, as the focus lies solely with the prompt context. Finally; while context learning is highly successful in simple classification tasks, it becomes more challenging for object detection purposes and requires more complex training mechanisms [6]. We conjecture that this may be attributed to the fact that context then depends on the spatial region of the image, in addition to object type (i.e. foreground or background). + +These considerations lead us to propose a complementary solution to prompt learning, which can be successfully leveraged for both classification and detection problems, without any task-specific tailoring or requirements. We build upon ideas from the field of text to image generation, and propose to leverage the concept of textual inversion [10] to adapt models to new data and thus address suboptimal, and potentially wrong, labels (e.g. incorrect class names, mislabelled data). The crux of our idea is to supersede manual word engineering, describing categories of interest, by leveraging available data to learn optimal word embeddings, as a function of the visual content. This simple solution affords several advantages: 1) in contrast to linear probing, open-set capabilities are maintained; learned class representations remain in text space, 2) our approach learns class specific parameters, therefore preventing forgetting under sequential training, 3) direct applicability to any classification or object detection technique that utilises textual input, 4) interpretability properties, with regards to learned class names. + +Experimentally, we realise our ideas in conjunction with two contemporary models; for image classification [36] and object detection [33]. Given a pre-trained model and (potentially previously unseen, additional) training dataset, we introduce a new set of word embeddings in the model text branch, each corresponding to a class of interest. Following context tuning methods [50, 51], embeddings are learned using standard losses on an otherwise frozen model. Comprehensive experiments across thirteen classification and detection datasets help to evidence that + +learning optimal class names can significantly enhance model performance, maintain open-vocabulary properties, successfully enable continual model adaptation, and improve performance for rare and longtail classes. Last but not least, we demonstrate the method's potential for interpretability, notably with regards to mislabelling and inadequately chosen original class names. + +Our main contributions can be summarised as: + +- A simple proposition for data-efficient adaptation of large vision-language models: optimisation of class names from visual content that retains attractive open-vocabulary properties. +- Performance improvements complementary to those of prompt tuning; consistent experimental gains across 13 classification and detection datasets. Strong sequential adaptation, open-vocabulary and long tail performance. +- Model interpretability insights: we visualise class name changes and related semantic meanings. We highlight how model biases and labelling errors can be identified, and how vision-language models suffer from long-tail issues. + +# 2. Related work + +Vision and language models. Early vision and language multi-modal alignment work tended to enable learning by encoding distinct modalities individually and independently. Model branch outputs would then be connected by losses [41]. Cross-modality alignment has been performed using techniques including metric learning [8], multi-label classification [20] and transformers [48]. A contemporary alternative approach pertains to so called "vision-language" models [9, 18, 29, 36] that recently gain popularity and instead are designed to jointly learn modality encoders; sharing information between modalities to the benefit of the global model. A representative approach is CLIP [36], which trains two neural network-based encoders on a very large image-caption dataset using a contrastive cross-entropy loss, to match pairs of images and texts, and shows impressive zero-shot image recognition performance. This seminal work has been extensively investigated in recent literature, and several improvements have been proposed [9, 18, 46]. + +Open vocabulary object detection was originally explored in a two-step manner [45]: firstly learning a mapping between the two modalities using a large-scale dataset, then leveraging the obtained encoders and mapping layers to learn an object detector that can perform classification by computing a distance to class names' text embeddings. More recent methods fully integrate vision-language mappings into the object detection training process, providing more flexible and adaptable multi-modal detection solutions [2, 13, 22, 26, 31, 33]. Alternative methods generate a multi-modal joint representation via a fusion block [22, 31], while contemporary solutions adopt a CLIP-like parallel training strategy [2, 13, 33], facilitating querying of large numbers of + +classes. Similarly to the classification setting, multi-modal detection models achieve impressive zero-shot performance. + +Prompting methods. Prompt learning aims to exploit the rich knowledge contained in large pre-trained language models (e.g. GPT [37]), to predict new downstream tasks. The idea prescribes that we can reformulate these new tasks to "look more like" those solved during the original pre-training stage, through the help of an appropriate textual prompt, such that the model can be used to predict the desired output without any additional training [11,30,37,39]. The disadvantage with such methods, is the necessity for prompt engineering; searching for the most suitable prompt to allow the language model to solve the desired task. Recent work has looked to learn optimal prompts $cf$ . manually designing them. Work on continuous prompt learning methods [24, 28, 49] can be considered closely related to our work, where the main idea is to convert the prompt into a set of continuous vectors that can be optimised, with respect to an objective function, in end-to-end manner. + +The benefits of the prompting strategy have recently started to be explored in computer vision [21,38,44,47,51]. CoOp [51] can be considered the first work to bring continuous prompt learning to the vision domain, towards offering efficient solutions for adaptation of pre-trained vision-language models to downstream vision tasks. CoOp was recently extended by [50] where they propose to learn an additional light-weight neural network to generate, for each image, an input-conditional token that yields an image-specific dynamic prompt, outperforming CoOp's static prompts. This comes at the price of efficiency however, as the proposed extension is substantially both more memory and computationally expensive. We note that [50, 51] facilitate model adaptation to new domains in a highly parameter efficient manner, yet may still struggle in settings where new classes (and their names) widely differ from that which the model has initially learned. A more complex context prompting strategy has been recently proposed, specifically for detection tasks [6]; integrating a background specific loss function, as well as a context dependent scheme in the prompt learning process. + +Crucially, for all of these methods, classification is still performed with respect to a handcrafted class name, making accuracy highly dependent on the quality of chosen names. In certain settings, finding an accurate semantic description of a class can be challenging, and a poor choice can impair the model's performance. To the best of our knowledge, this limitation has not been addressed yet in classification and object detection settings. + +Textual Inversion. Textual inversion was recently introduced in [10] for text to image generative diffusion models, as a means to condition image generation towards incorporating specific objects or visual components. Given a set of images, all displaying the same object in diverse settings, a specific word embedding is learned so as to ensure the model is able to reconstruct images, sampled from this small set. Simple prompt contexts are used (e.g. 'a photo of') to ensure a simple image of the object is generated. After training, the model displays impressive ability + +![](images/53bca57f66d7e4cb9ef2bb82c16a8005f9c3b25dd7e7c75488520a2614d9a3c8.jpg) +Figure 2. Overview of our method. Classes of interest are mapped to a set of learnable word embeddings, and integrated within the input sentence representation. These embeddings can be learned for classification and object detection tasks using standard losses, and used in conjunction with pre-trained word embeddings in open-vocabulary settings. + +to integrate specific objects in generated images, via inputting learned words at specific locations in the input sentence. In the solution proposed by [10], each word is learned individually. + +# 3. Methodology + +We consider a vision and language object recognition model pre-trained on a large-scale dataset. Following the multi-modal architecture, the model takes as input an image-text pair $\mathbf{x} = \{I,T\}$ , where the text provides a list of $N$ candidate classes $t_i$ to recognise; $T = [t_1,t_2,\dots,t_N]$ . We assume the $i$ -th class specific text $t_i$ to be in the format: $t_i = [\text{prompt prefix}] + [\text{CLASS}] + [\text{prompt suffix}]$ , where prompt prefixes and suffixes are shared across all classes. To obtain text feature representations, a tokenizer converts each sentence into a series of tokens such that each token maps to a pre-trained word embedding $\mathbf{e_k}$ . Embeddings are then concatenated and used as input to a text transformer. Our final text representation is the transformer output for the End Of Sequence (EOS) token $f(T) = f_i(t_i)$ , where $i \in \{1,\dots,N\}$ . + +# 3.1. Baseline multi-modal models + +Multi-modal Image classification. In the context of image classification [36], our objective is to assign a specific category to the image as a whole. Image inputs are processed in parallel to the text input through an image encoder $g(\cdot)$ , yielding repre + +sentation $g(I)$ . The classification task is then simply performed by evaluating the cosine similarity between image and text embeddings, and selecting the class with the highest similarity. + +Multi-modal Object detection. There are two main contemporary strategies for vision and language based object detection. The first (simpler) strategy is similar to multi-modal classification, wherein image and text information are only combined latterly at the bounding box classification stage [2, 13, 33]. In this setting, the image input is processed through an object detector entirely disjointly from the text modality. The detector outputs a set of coordinates and a class embedding for each bounding box candidate. We then compute class embedding similarity with text representations $f(T)$ following the multi-modal classification approach to assign a classification label to each bounding box. + +The second class of strategies propose to fuse image and text inputs; either when generating text and image embeddings [27] or via a third encoder $h(g(I), f(T))$ that outputs bounding box coordinates and class embeddings [13, 22, 31]. For this latter fusion strategy, classification is performed over sentence tokens rather than across sentences, with an aim of associating bounding boxes with a keyword. This technique is typically more computationally expensive than the first method class as it requires computation of new image features, for each queried class. + +# 3.2. Learning optimal class names + +We conjecture that the performance of pre-trained recognition models is intrinsically linked to the suitability of the class names, chosen to represent certain visual categories. Selecting class names that do not reflect an object's visual appearance can substantially alter recognition performance. We thus propose to remove the model's sensitivity to hand crafted class names, by learning class specific word embeddings from image content. An overview of the method is provided in Figure 2. + +We consider the matrix of pre-trained word embeddings $E \in \mathbb{R}^{V \times F}$ , where $V$ is the vocabulary size, and $F$ the feature dimension. We propose to extend this matrix with a set of new, learnable word embeddings $E^l \in \mathbb{R}^{N \times F}$ , where each row of $E^l$ corresponds to a class specific learnable embedding and $N$ pertains to the total number of dataset classes. In this setting, we replace the class name in each text query with a class specific placeholder $t_i = [\text{prompt prefix}] + [\text{pl}^i] + [\text{prompt suffix}]$ . While the tokenizer maps context prompts to matrix $E$ , our placeholder maps to its corresponding class embedding $\mathbf{e}_i^l$ . This new class query is used as input to the text transformer encoder, and the remainder of the recognition model is unchanged. We note that it is possible to map one category to a series of $m$ word embeddings, using queries of the type $t_i = [\text{prompt prefix}] + [\text{pl}_1^i] + \dots + [\text{pl}_m^i] + [\text{prompt suffix}]$ . + +Training. During training, we freeze the entire pre-trained model (including pre-trained word embeddings) and learn our new set of word embeddings $E^{l}$ with the standard losses used to train the base model (e.g. cross entropy loss for classification, + +and bounding box regression losses for detection). We identify three key scenarios where our approach can be beneficial: 1) adapting the pre-trained model on a new dataset with a new set of classes; 2) adjusting suboptimal class names on the same dataset used to train the model; 3) continual adaptation of a pre-trained model, where new classes are available after a first training stage. Inference. At inference time, we follow the process described in Section 3.1. Categories whose names were learned are mapped to their new embeddings $e^l \in E^l$ . For unseen object categories (i.e. not in our new dataset) we use their original embeddings, $e \in E$ , from the pre-trained model. As a result, our solution maintains the ability to perform open-vocabulary recognition. + +# 4. Experiments + +We evaluate the impact of learning class names for the tasks of image classification and object detection. We consider four settings where our strategy can be beneficial: + +Task 1: Model adaptation to new datasets: considering a new dataset of interest, we adapt our pre-trained model to this new set of potentially unseen classes by learning optimal class names from the new image content. + +Task 2: Post-training class name adjustment: the model is first fine-tuned on our target dataset, then class names are optimised in a subsequent learning stage. This setting is more valuable for object detection where fine-tuning can have important impact on bounding box localisation. + +Task 3: Open-vocabulary recognition: open-vocabulary is one of the key advantages of vision-language models, and maintaining strong performance is crucial. Evaluation of the compatibility between our learned word embeddings, and standard pre-trained word embeddings can provide a measure of strength on this axis. We adapt the model to only a subset of classes from our new dataset and measure overall performance on all classes, such that inference combines learned and text based word embeddings. + +Task 4: Continual model adaptation: to measure the model ability for sequential learning of new word embeddings, we first adapt the model to a subset of classes, then learn the remaining class names in a second, separate, training stage. + +We highlight that we do not seek state of the art performance, as this is intrinsically linked to the pre-trained model's base performance, pre-training data, and architecture. Rather, our objective is to demonstrate the potential of learning class names to improve model performance, while maintaining zero-shot properties. We therefore perform experiments on models with simple multi-modal mechanisms, use a single word embedding per class, and a standard prompt: (prompt prefix = 'a photo of a'; prompt suffix = '.'), in all experimental settings unless specified otherwise. + +# 4.1. Image classification: CLIP model + +To evaluate our method on classification tasks, we employ CLIP [36] as our base pre-trained model with a vision + +
Class group| CLIP*CoOpOursOurs*
Base classes69.482.682.782.8
New classes74.264.173.174.2
All classes65.265.766.568.7
+ +transformer ViT-B/16 [5] fulfilling the image encoder $g(\cdot)$ role. Here, we seek to evaluate method performance in previously described Tasks 1, 3 and 4; i.e. model adaptation, open-vocabulary performance, and continual adaptation. + +Datasets. We follow the setting proposed in [50, 51]. We adapt the CLIP model to a selection of eleven diverse datasets, focusing on different types of recognition tasks: standard image classification (ImageNet [4], Caltech 101 [7]), fine-grained classification (Stanford cars [23], Oxford Pets [35], Flowers 102 [34], Food 101 [1] and FGVC Aircraft [32]), scene recognition (SUN 397 [43]), action recognition (UCF 101 [42]), texture classification (DTD [3]) and satellite image recognition (Eurosat [17]). Training details. We adopt a set of parameters similar to [51]. For each dataset, we learn class names with a batch size of 32, over 200 epochs (50 for ImageNet), SGD optimiser with a learning rate of $2e^{-4}$ and cosine scheduling, with one warm up epoch. We initialise our learnable word embeddings using the average embedding of all word tokens associated with hand-crafted class names. For each dataset, 16 training images per category are sampled, and we report mean statistics over three random seeds. Baselines. The method we directly compare to is the zero-shot CLIP model, as we aim to evaluate the benefits of learning class names. We additionally report classification accuracy for the CoOp model [51], which learns optimal prompt context with fixed handcrafted class names. We retrain CoOp in our settings of interest and use four context words, initialised with 'a photo of a', as in [50] (this differs from the 16 words used in [51]). This allows to compare advantages and disadvantages of learning class names over prompt context, and evaluate any complementary nature between the strategies. We report the best possible CLIP performance with engineered prompts, and report our results with a standard prompt, as well as using the same engineered prompts as CLIP. We note that four of the datasets (Stanford cars, ImageNet, Caltech 101 and Sun 397) do not have engineered prompt, and we provide all dataset-specific prompts in the supplementary materials. + +Model adaptation (Task 1) and Open vocabulary (Task 3). We adopt the base-to-new generalisation setting proposed in [50]. For each dataset, we equally split classes into a set of 'base' and 'new' classes. We use base classes for training and evaluate performance on new classes in a zero-shot manner. Additionally, to measure open-vocabulary performance, we report performance on 'all' classes, i.e. a mixture of learned and handcrafted class + +Table 1. CLIP model: Base to new classification accuracy. Results on Base classes show method ability to adapt to new data (Task 1). Results on New and All classes show open vocabulary ability (Task 3). * Manually engineered prompt templates. + +
Class groupCLIP*CoOpOursOurs*
New classes74.084.485.985.9
All classes65.271.775.776.3
+ +Table 2. CLIP model: Classification accuracy for continual model adaptation (Task 4) experiments. * manually engineered prompt templates. + +names. Results are reported in Table 1. Results on base classes highlight our ability to perform model adaptation (Task 1) by learning class names only. For this task, we observe that our strategy can achieve equivalent gains as context tuning on base classes (82.6% CoOP) vs. 82.8% (Ours) average accuracy). However, our approach guarantees that zero-shot CLIP performance is maintained on new classes, as our learned parameters are class specific. Our open-vocabulary experiments (Task 3) show that we are able to maintain the CLIP model's ability to expand the output set, and to introduce new classes from text alone (CLIP and Ours 74.2%, new classes). Experiments on all classes confirm that we are able to use a mixture of learned and new classes successfully, outperforming CoOp (65.7% CoOp, 68.7% Ours, all classes). We further note sensitivity to context prompts, as substantial gains are obtained using engineered prompts (+2.2% Ours, all classes). This is to be expected, as new class performance can be strongly impacted by prompts, and highlights the importance of prompt context tuning in this setting. + +Sequential adaptation (Task 4). We extend the generalisation setting to evaluate our ability to perform sequential adaptation; i.e. after training on base classes, we subsequently train on new classes with no access to base classes. We measure performance on new classes, as well as all classes after this second training stage. For CoOp [51] experiments, we fine-tune prompt parameters, and adjust learning rate to $2e^{-4}$ which provided best empirical performance in this setting. Results are reported in Table 2. As in the previous setting, performance on new classes is highly similar when performing context or class name learning $(84.4\% \text{CoOp}, 85.9\% \text{Ours, new classes})$ , achieving strong gains over zero-shot CLIP $(10 + \%)$ . On all classes, we can see that we achieve significant gains $(+4\%, \text{Ours vs. CoOp})$ , which can be attributed to the fact that class names are (all) learned independently. Last but not least, we note a much higher robustness to change in prompt context when all classes are learned; we achieve very similar results when using a standard or engineered template in this setting $(+0.6\% \text{vs.} +2.2\%$ in Table 1, all classes). + +# 4.2. Object detection: OWL-vit model + +For object detection experiments, we evaluate all 4 Tasks. We select OWL-vit [33] as our base multi-modal detection model due to noted strong zero-shot performance and clear separation between vision and language branches. This allows us to evaluate the impact of class learning more directly, in contrast to methods using complex text and image fusion mechanisms [26]. OWL-vit leverages a pre-trained multi-modal classification + +
Methodbackbonepre-training (image)pre-training (detection)APAP/fAP/cAP/r
Zero-shot models
MDETR [22]DETRN/AMDETR data6.49.83.61.9
XDETR [2]DETRN/AMDETR data, LocNar16.418.815.29.6
GLIP-T [26]SWIN-TCap4MO365,GOLDG26.031.021.420.8
GLIP-L [26]SWIN-LCap24MO365,FourODs37.341.543.328.2
Zero-shot OWL-vit base model
OWL-vit zero-shot [33]VIT B/16CLIPO365, VG25.327.025.017.6
Prompt learning all [51]VIT B/16CLIPO365, VG24.425.924.017.8
Ours base (Task 3)VIT B/16CLIPO365, VG25.726.825.919.0
Ours sequential (Task 4)VIT B/16CLIPO365, VG26.026.625.923.1
Ours all (Task 1)VIT B/16CLIPO365, VG26.627.027.519.8
OWL-vit base model fine-tuned on LVIS base classes
OWL-vit [33]VIT B/16CLIPO365, VG28.834.424.717.7
Prompt learning all [51]VIT B/16CLIPO365, VG31.334.729.222.4
Ours base (Task 3)VIT B/16CLIPO365, VG33.335.233.920.3
Ours rare (Task 3)VIT B/16CLIPO365, VG30.434.525.434.5
Ours sequential (Task 4)VIT B/16CLIPO365, VG34.135.033.830.6
Ours all (Task 2)VIT B/16CLIPO365, VG34.535.634.428.5
OWL-vit base model fine-tuned on all LVIS classes
OWL-vit [33]VIT B/16CLIPO365, VG34.538.533.219.1
+ +Table 3. Average precision results on LVIS mini-validation set for all (AP), frequent (AP/f), common (AP/c) and rare (AP/r) classes. Our method uses only $10\%$ of the LVIS training dataset. State of the art methods (in grey) use different training datasets and architectures of different capacities. They are added for context.{base, rare, all} describes which class group is used for training. Sequential means base and rare classes are trained sequentially. Task 1: model adaptation to new data, Task 2: class name learning after fine-tuning, Task 3: Open vocabulary ability (mixture of seen and unseen classes), Task 4: continual adaptation (learning class groups in a sequential manner). + +model (e.g. CLIP) with a vision transformer backbone. To adapt this model to the object detection task, the pooling layer that combines vision transformer outputs is removed, and two new modules are learned: a class prediction module and a box prediction module that output class embeddings and bounding-box coordinates, respectively, for all transformer outputs. The OWL-vit model is fine-tuned from the CLIP weights using a mixture of standard object detection datasets. Additionally, a set of engineered CLIP prompt templates are used as class prompt contexts. + +Datasets. We evaluate the impact of learning class names on two object detection datasets. Firstly, we consider LVIS [14], a large scale natural image object detection dataset comprising over $100k$ training images and 1203 classes, characterised by a long tail distribution with 'frequent' $(x > 100)$ , 'common' $(100 \geq x > 10)$ , and 'rare' $(10 \geq x \geq 1)$ class groups, for $x$ images per class. Following prior work [2]; we use pre-defined category names, but remove the text in the parentheses, e.g., "flip-flop (sandal)" is replaced by "flip-flop". We note that this leads to a few instances of classes sharing the same name (e.g. "bow"). + +Secondly, we run experiments on CODA 2.0 [25], a self-driving dataset focusing on detection of rare, corner case instances. It comprises validation and test sets containing $5k$ images each, and 43 classes, of which only 29 appear in the validation set. In addition to the base OWL-vit model, we consider a model fine-tuned on the SODA dataset [15] (fully labelled instance), closer to our test domain. Designed for semi-supervised + +training of self-driving models, SODA comprises six classes that overlap with CODA classes. Following [25], we refer to the{pedestrian, cyclist, car, bus, tram, truck, tricycle} classes as common classes, and remaining classes as corner-cases. + +Training details. We fine-tune the pre-trained OWL-vit model on LVIS, SODA datasets for 10 epochs, using 7 GPUs (batch size 2/GPU), with learning rates $5e^{-6}$ (main model) and $2e^{-7}$ (text branch) using cosine scheduling and an Adam optimiser. Weight decay is unused and gradient clipping is set to 0.1. For LVIS fine-tuning, we train two models: one on all classes, and one on base (frequent + common) classes only, thus following the evaluation set-up of [33]. For CODA 2.0 experiments, models are fine-tuned on the SODA and LVIS datasets, to simulate the setting where corner cases are discovered after training. + +For our class learning process, we freeze the entire model aside from new class embeddings. Embeddings are learned for 20 epochs, using a learning rate of $1e^{-2}$ (cosine schedule), 7 GPU (batch size 10/GPU). They are initialised with the average token embedding of the corresponding class name. For the LVIS dataset, we learn class names with a balanced subsampling of $10\%$ of the training set. + +LVIS experiments. We provide average precision results for all 4 Tasks on the LVIS mini-validation set [22] in Table 3. For context, we report methods competitive with OWL-vit, but highlight the key point of comparison is with respect to the base model that exhibits pre-defined class names. We also provide comparison + +
MethodAP-ComAP-AAR-AAR-CorSum
RetinaNet0.250.380.480.251.36
Faster RCNN0.280.370.430.171.27
Cascade RCNN0.300.420.480.211.41
Zero-shot OWL-vit base model
OWL-vit zero-shot0.100.200.440.451.19
Ours (Task 3)0.170.310.470.501.45
OWL-vit model fine-tuned on SODA
OWL-vit0.210.350.500.441.44
Ours (Task 3)0.230.370.510.471.58
OWL-vit model fine-tuned on SODA + LVIS
OWL-vit0.210.350.520.511.59
Ours (Task 3)0.230.390.530.531.67
+ +Table 4. Detection results on the CODA test set. AP-Com (AP-Common): average precision (AP) on common classes, AP-A (AP-agnostic): AP in the class agnostic setting. AR-A and AR-Cor: average recall for class agnostic and corner case detection respectively. Task 3: Open vocabulary ability (mixture of seen and unseen classes) + +to prompt learning, using four learnable context words initialised as 'a photo of a', and identical training parameters to ours. + +When learning names for all classes, we achieve consistent performance gains for all class groups in both zero-shot (Task 1: adapting to a new dataset, +1.3 AP) and fine-tuned settings (Task 2: post training class adjustment, +5.7 AP). Larger gains are observed for the fine-tuned model, which can be attributed to the improved bounding box regression component, facilitating the classification task. Gains are more substantial for rare classes (+2.2 AP (zero-shot), +10.8 AP (fine-tuned)), which can be attributed to the removal of name ambiguity and the fact that these names are less common in pre-training datasets, leading to potentially poorer mapping with image content (e.g. 'arctic' shoe class). In contrast, frequent classes remain mostly stable (+0 AP (zero-shot), +1.2 (fine-tuned)), highlighting the quality of image to name mapping for this group. We provide more evidence of this in Section 4.3. + +Open vocabulary experiments (Task 3), where class names are only learned on base classes, show modest gains in the zero-shot setting (+0.5 AP), but large improvements on a fine-tuned model (+4.5 AP), notably highlighting the importance of the localisation task. Due to the multi-label nature of LVIS, base and rare classes are not competing, facilitating the open vocabulary task. Learning base class names additionally reduces ambiguity with rare class names, improving rare class performance. + +Sequential learning experiments (Task 4; training on base then rare classes) achieve comparable performance to directly training on all classes (35.0 AP (seq.) vs. 35.6 AP (all classes)), further highlighting how well suited our approach is for sequential adaptation. LVIS is a highly imbalanced dataset. While our $10\%$ subsampling strategy reduces class imbalance, it is not entirely eliminated. This is evidenced by the improved rare class performance in the sequential setting (+3.3 + +![](images/93baac3c12a0e14e91ec9112fc4bf401060d54172b136aad2d42726d4598e381.jpg) +Figure 3. Correlation between class rarity (log scale) and embedding similarity between learned and pre-defined class names. LVIS dataset. + +(zero-shot), +2.1 (fine-tuned)). Fine-tuning the entire OWL-vit model on all classes (100% of the data) further evidences the impact of this imbalance and our model's potential to address long tail issues. By reducing data requirements (only 10% achieves equivalent overall performance; 34.5 AP), we are able to train on a balanced data subset. In both fine-tuned and zero-shot settings, learning class names on 10% of the data achieves better or equivalent rare class performance (AP/r) than the fine-tuned model (-0.1 to +15.4 AP/r gains). + +Finally, prompt tuning methods struggle to achieve good Task 1 performance in the zero-shot setting (-0.9 AP), but obtain better gains after fine-tuning (+2.5 AP). We note that prompt tuning performance is overall poorer than our strategy (-2.2 zero shot, -3.2 fine-tuned vs. ours all), which we hypothesise is partially linked to OWL-vit pre-training using fixed templates, and the diversity of object detection contexts. + +CODA experiments, Task 3. Experimental results on the CODA 2.0 dataset are reported in Table 4. Following the setup proposed in [25], we report average precision (AP) on common classes (AP-Com), class agnostic AP on all classes (AP-A), class agnostic average recall on all classes (AR-A) and on corner classes (AR-Cor). We compare to state of the art self-driving models, trained in a semi-supervised setting on the SODA dataset [25]. The zero-shot OWL-vit model is not trained on any self-driving data, therefore achieves an overall poorer common class performance (0.10 AP-Com). However, the open-vocabulary setting allows this model to achieve very strong corner classes detection (0.45 AR-Cor). Our method largely improves performance for all metrics, with an overall performance (1.45 Sum) exceeding that of self-driving models (1.41 Sum), including our OWL-vit model fine-tuned on SODA (1.44 Sum). Fine-tuning OWL-vit on SODA, and a mixture of SODA and LVIS training data further improves results (1.44 and 1.59 Sum, respectively), and our strategy consistently increases all metrics (1.58 and 1.67 Sum, respectively). Common class performance remains below the reference models (-0.07 AP-Com), which we conjecture can be attributed to our relatively small self driving training data (only $10k$ images). + +
DatasetOriginal nameClosest to new name (embedding similarity)Example image
Apperance based Correction
LVISarcticboot (0.46), ski boot (0.30)
CODAtricyclecart (0.62), rickshaw (0.67)
CODAmiscwaterwheel (0.47), wheel (0.45)
British to American English Correction
LVISclothes hamper wall socket garbage postbox trouserslaundry basket (0.52), shopping basket (0.39) outlet (0.62), power outlet (0.60) trash can (0.47), trashcan (0.45) mailbox (0.47), telephone booth (0.46) clothes (0.42), pants (0.41)
Object type first
LVISbell pepper bridal gown horse buggy horse carriage celery cymbalvegetable (0.55), pepper (0.51), bell pepper (0.51) gown (0.57), dress (0.57), bridal gown (0.55) horse-drawn vehicle (0.66), horse buggy (0.64) horse-drawn vehicle (0.68), horse carriage (0.65) green vegetables (0.50), celery (0.47) musical instrument (0.55), cymbal (0.46)
+ +Table 5. Interpretability results: changes to original class names. + +# 4.3. Interpretability + +One key advantage of the proposed approach is a beneficial effect on model interpretability; the method allows evaluation of the manner in which class names are modified, based on image content. Here, we aim to evaluate how object classes were renamed in our object detection experiments. This experiment can usefully inform us about model biases, sources of classification errors, potential mislabelling errors and the suitability of handcrafted names. For this purpose, we build a reference vocabulary of 3031 object names, using the following dataset class names (filtered for duplicates): LVIS (1203 classes), CODA (43 classes), Objects365 [40] (365 classes), ImageNet (1000 classes), and the THINGS [16] database (1854 concepts). + +Individual word embeddings may not have very valuable meanings in terms of interpretability, especially considering that certain class names comprise multiple tokens. As a result, we compare learned representations to our reference vocabulary at the output of the text transformer encoder, using the shared prompt template context: 'a photo of a'. More formally, for each word $\mathbf{e}^l$ , in our list of learned embeddings, we compute the sentence representation $f(t(\mathbf{e}^l))$ , where $t(\mathbf{e}^l)$ is the sentence constructed using our prompt prefix and word embedding $\mathbf{e}^l$ . We repeat the procedure for each instance in our vocabulary, using the model's pre-trained word embeddings $\mathbf{e}^v$ , and obtain $f(t(\mathbf{e}^v))$ . We then simply compute the cosine distance between all pairs of learned and reference word representations. + +We carry out this process for models trained on the LVIS dataset (base model fine-tuned on base classes), and on the CODA 2.0 dataset (zero-shot model). In Table 5, we show examples of classes that were renamed towards a semantic meaning + +closer to image content, and highlight some labelling errors. "Arctic" and "misc" are evidence of non representative class names. The "tricycle" class appears to have been mislabelled, with labelled vehicles looking closer to "rickshaws" and "carts". We additionally report two interesting trends: 1) the model tends to adjust word embeddings from British English representations to American English, where the latter is expected to be the dominant language, on which large scale CLIP models were trained; 2) fine-grained category representations tend to be closer to supercategory word types (e.g. vegetable, musical instrument), suggesting that the model also leverages cross-class similarities towards the learning of representations for rare classes. The first observation notably highlights our method's potential for cross-lingual model adaptation: given a dataset in one language, and a model trained in another language, we can learn optimal class names adapted to the pre-trained language model without any additional language translation efforts. + +In Figure 3 we highlight the relationship connecting class rarity and embedding similarity between learned and original class names, on the LVIS dataset. As previously conjectured in Section 4.2, there is a clear correlation between these variables, highlighting how multi-modal models also suffer from common long tail related issues (i.e. poorer semantic mapping between rare words and their corresponding visual appearance). Additional results are available in our supplementary materials. + +# 5. Conclusion + +This work focuses on the concept of class name and resulting impact on performance of vision-language recognition models. Complementary to prompt tuning, which seeks to optimise the context of text queries, we propose to automatically learn optimal class names from visual content. Our solution can easily be integrated in classification and object detection models, achieving significant gains, with respect to the base model, in all settings. We additionally demonstrate promising interpretability properties by showing how class semantic meanings were adjusted, providing data and model analysis properties. + +Learning class names can be viewed as an alternative to linear probing. The former approach, in contrast to probing, maintains key properties inherent to vision-language models (e.g. open-vocabulary). One observed limitation of our approach, in common with linear probing, is relatively weaker performance, in comparison with prompt tuning and image queries, under very low data regimes (i.e. 1-2 shots). Exploring a combination of learned text and image queries makes for a promising future avenue. While learning class names reduces dependency on prompt context, we still observe improvements using engineering prompts, in particular in open-vocabulary settings. As such, our method has the potential to further benefit from combination with prompt tuning techniques [50, 51], where prompts can provide key domain adaptation properties. Last but not least, learning class names has the potential to benefit class agnostic detection, via clustering and learning cluster semantics. + +# References + +[1] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101-mining discriminative components with random forests. In European conference on computer vision, pages 446-461. Springer, 2014. 5 +[2] Zhaowei Cai, Gukyeong Kwon, Avinash Ravichandran, Erhan Bas, Zhuowen Tu, Rahul Bhotika, and Stefano Soatto. X-detr: A versatile architecture for instance-wise vision-language tasks. arXiv preprint arXiv:2204.05626, 2022. 2, 4, 6 +[3] Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describing textures in the wild. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 3606-3613, 2014. 5 +[4] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 5 +[5] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 5 +[6] Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, and Guoqi Li. Learning to prompt for open-vocabulary object detection with vision-language model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14084-14093, 2022. 2, 3 +[7] Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In 2004 conference on computer vision and pattern recognition workshop, pages 178-178. IEEE, 2004. 5 +[8] Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, and Tomas Mikolov. Devise: A deep visual-semantic embedding model. Advances in neural information processing systems, 26, 2013. 2 +[9] Andreas Fürst, Elisabeth Rumetshofer, Viet Tran, Hubert Ramsauer, Fei Tang, Johannes Lehner, David Kreil, Michael Kopp, Günter Klambauer, Angela Bitto-Nemling, et al. Cloob: Modern hopfield networks with infoloob outperform clip. arXiv preprint arXiv:2110.11316, 2021. 2 +[10] Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618, 2022. 2, 3 +[11] Tianyu Gao, Adam Fisch, and Danqi Chen. Making pre-trained language models better few-shot learners. arXiv preprint arXiv:2012.15723, 2020. 3 +[12] Chuanxing Geng, Sheng-jun Huang, and Songcan Chen. Recent advances in open set recognition: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(10):3614-3631, 2020. 1 +[13] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Zero-shot detection via vision and language knowledge distillation. CoRR, abs/2104.13921, 2021. 2, 4 +[14] Agrim Gupta, Piotr Dollar, and Ross Girshick. LVIS: A dataset for large vocabulary instance segmentation. In Proceedings of the + +IEEE Conference on Computer Vision and Pattern Recognition, 2019. 6 +[15] Jianhua Han, Xiwen Liang, Hang Xu, Kai Chen, Lanqing Hong, Jiageng Mao, Chaoqiang Ye, Wei Zhang, Zhenguo Li, Xiaodan Liang, et al. Soda10m: A large-scale 2d self/semi-supervised object detection dataset for autonomous driving. arXiv preprint arXiv:2106.11118, 2021. 6 +[16] Martin N. Hebart, Adam H. Dickter, Alexis Kidder, Wan Y. Kwok, Anna Corriveau, Caitlin Van Wicklin, and Chris I. Baker. Things: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PLOS ONE, 14:1-24, 10 2019. 8 +[17] Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pages 204-207, 2018. 5 +[18] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 2 +[19] KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubramanian. Towards open world object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5830-5840, 2021. 1 +[20] Armand Joulin, Laurens van der Maaten, Allan Jabri, and Nicolas Vasilache. Learning visual features from large weakly supervised data. In European Conference on Computer Vision, pages 67-84. Springer, 2016. 2 +[21] Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, and Weidi Xie. Prompting visual-language models for efficient video understanding. In European Conference on Computer Vision, pages 105–124. Springer, 2022. 3 +[22] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790, 2021. 1, 2, 4, 6 +[23] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In Proceedings of the IEEE international conference on computer vision workshops, pages 554–561, 2013. 5 +[24] Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691, 2021. 3 +[25] Kaican Li, Kai Chen, Haoyu Wang, Lanqing Hong, Chaoqiang Ye, Jianhua Han, Yukuai Chen, Wei Zhang, Chunjing Xu, Dit-Yan Yeung, et al. Coda: A real-world road corner case dataset for object detection in autonomous driving. arXiv preprint arXiv:2203.07724, 2022. 6, 7 +[26] Liunian Harold Li*, Pengchuan Zhang*, Haotian Zhang*, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, and Jianfeng Gao. Grounded language-image pre-training. In CVPR, 2022. 2, 5, 6 +[27] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image + +pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10965-10975, 2022. 4 +[28] Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190, 2021.3 +[29] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208, 2021. 2 +[30] Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586, 2021. 3 +[31] Muhammad Maaz, Hanoona Bangalath Rasheed, Salman Hameed Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, and Ming-Hsuan Yang. Multi-modal transformers excel at class-agnostic object detection. arXiv, 2021. 2, 4 +[32] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. 06 2013. 5 +[33] Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, et al. Simple open-vocabulary object detection with vision transformers. arXiv preprint arXiv:2205.06230, 2022.1,2,4,5,6 +[34] Maria-Elena Nilsback and Andrew Zisserman. Automated flower classification over a large number of classes. In 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pages 722-729. IEEE, 2008. 5 +[35] Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar. Cats and dogs. In 2012 IEEE conference on computer vision and pattern recognition, pages 3498-3505. IEEE, 2012. 5 +[36] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 3, 4 +[37] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 3 +[38] Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18082-18091, 2022. 3 +[39] Timo Schick and Hinrich Schütze. It's not just size that matters: Small language models are also few-shot learners. arXiv preprint arXiv:2009.07118, 2020. 3 +[40] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 8429-8438, 2019. 8 +[41] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 2 + +[42] Khurram Soomro, Amir Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. CoRR, 12 2012. 5 +[43] Jianxiong Xiao, James Hays, Krista A. Ehinger, Aude Oliva, and Antonio Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 3485-3492, 2010. 5 +[44] Yuan Yao, Ao Zhang, Zhengyan Zhang, Zhiyuan Liu, Tat-Seng Chua, and Maosong Sun. Cpt: Colorful prompt tuning for pre-trained vision-language models. arXiv preprint arXiv:2109.11797, 2021. 3 +[45] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14393-14402, 2021. 2 +[46] Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer. Lit: Zero-shot transfer with locked-image text tuning. CoRR, abs/2111.07991, 2021. 2 +[47] Renrui Zhang, Ziyu Guo, Wei Zhang, Kunchang Li, Xupeng Miao, Bin Cui, Yu Qiao, Peng Gao, and Hongsheng Li. Pointclip: Point cloud understanding by clip. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8552-8562, 2022. 3 +[48] Chen Zheng, Quan Guo, and Parisa Kordjamshidi. Cross-modality relevance for reasoning on language and vision. arXiv preprint arXiv:2005.06035, 2020. 2 +[49] Zexuan Zhong, Dan Friedman, and Danqi Chen. Factual probing is [mask]: Learning vs. learning to recall. arXiv preprint arXiv:2104.05240, 2021. 3 +[50] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Conditional prompt learning for vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16816-16825, 2022. 2, 3, 5, 8 +[51] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9):2337-2348, 2022. 2, 3, 5, 6, 8 \ No newline at end of file diff --git a/2023/Learning To Name Classes for Vision and Language Models/images.zip b/2023/Learning To Name Classes for Vision and Language Models/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..3d574d72cb839497b492c9040525f121533b0db5 --- /dev/null +++ b/2023/Learning To Name Classes for Vision and Language Models/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f9fdd4aa88790fe6b4dcd195cbdb143a2313dd89e94d1f8947eda3e580120596 +size 362110 diff --git a/2023/Learning To Name Classes for Vision and Language Models/layout.json b/2023/Learning To Name Classes for Vision and Language Models/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..4998dee3b48f7bd6302b0801d3f2f1cf38f81e5a --- /dev/null +++ b/2023/Learning To Name Classes for Vision and Language Models/layout.json @@ -0,0 +1,6863 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 122, + 103, + 473, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 122, + 103, + 473, + 121 + ], + "spans": [ + { + "bbox": [ + 122, + 103, + 473, + 121 + ], + "type": "text", + "content": "Learning to Name Classes for Vision and Language Models" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 164, + 144, + 430, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 164, + 144, + 430, + 171 + ], + "spans": [ + { + "bbox": [ + 164, + 144, + 430, + 171 + ], + "type": "text", + "content": "Sarah Parisot Yongxin Yang Steven McDonagh Huawei Noah's Ark Lab" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 164, + 174, + 427, + 185 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 164, + 174, + 427, + 185 + ], + "spans": [ + { + "bbox": [ + 164, + 174, + 427, + 185 + ], + "type": "text", + "content": "{sarah.parisot，yongxin.yang，steven.mcdonagh}@huawei.com" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 145, + 213, + 190, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 213, + 190, + 225 + ], + "spans": [ + { + "bbox": [ + 145, + 213, + 190, + 225 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 239, + 290, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 239, + 290, + 430 + ], + "spans": [ + { + "bbox": [ + 46, + 239, + 290, + 430 + ], + "type": "text", + "content": "Large scale vision and language models can achieve impressive zero-shot recognition performance by mapping class specific text queries to image content. Two distinct challenges that remain however, are high sensitivity to the choice of handcrafted class names that define queries, and the difficulty of adaptation to new, smaller datasets. Towards addressing these problems, we propose to leverage available data to learn, for each class, an optimal word embedding as a function of the visual content. By learning new word embeddings on an otherwise frozen model, we are able to retain zero-shot capabilities for new classes, easily adapt models to new datasets, and adjust potentially erroneous, non-descriptive or ambiguous class names. We show that our solution can easily be integrated in image classification and object detection pipelines, yields significant performance gains in multiple scenarios and provides insights into model biases and labelling errors." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 453, + 124, + 466 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 453, + 124, + 466 + ], + "spans": [ + { + "bbox": [ + 47, + 453, + 124, + 466 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 474, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 474, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 474, + 289, + 715 + ], + "type": "text", + "content": "The introduction of large-scale vision and language pretraining techniques has led to significant breakthroughs in object recognition, notably for tasks where data efficiency forms a crucial component (e.g. zero-shot scenarios). The key idea is to learn a mapping between image and text spaces such that a latent image representation will be close to sentences that describe the image content, in representation space [33, 36]. Learning from image-specific descriptions, rather than a fixed and common class label, with limited semantic meaning, yields models with very strong representational power [36]. Such multi-modal approaches address one key shortcoming of visual-only recognition methods; they provide an ability to seamlessly expand the model's output space without the requirement of additional training (i.e. open-set recognition). In contrast, visual-only classification and object detection methods have historically relied on training a linear classification layer in order to identify objects of interest, resulting in the undesirable consequence that any introduction of new classes (with or without available data) is very challenging. A plethora of complex mechanisms have been proposed, towards addressing this problem [12, 19]. Vision" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 313, + 204, + 417, + 317 + ], + "blocks": [ + { + "bbox": [ + 313, + 204, + 417, + 317 + ], + "lines": [ + { + "bbox": [ + 313, + 204, + 417, + 317 + ], + "spans": [ + { + "bbox": [ + 313, + 204, + 417, + 317 + ], + "type": "image", + "image_path": "9ccf6870a3ea9ec1dbf63a0f2eb3b2d5348ae4947a3630c55b61750ea972cb20.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 429, + 204, + 538, + 317 + ], + "blocks": [ + { + "bbox": [ + 429, + 204, + 538, + 317 + ], + "lines": [ + { + "bbox": [ + 429, + 204, + 538, + 317 + ], + "spans": [ + { + "bbox": [ + 429, + 204, + 538, + 317 + ], + "type": "image", + "image_path": "2ec0b5750c50d9d61bc0cabfaf16ffe9714336a046846a88a34aed1a1185a2b7.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 428, + 321, + 547, + 421 + ], + "blocks": [ + { + "bbox": [ + 383, + 321, + 468, + 331 + ], + "lines": [ + { + "bbox": [ + 383, + 321, + 468, + 331 + ], + "spans": [ + { + "bbox": [ + 383, + 321, + 468, + 331 + ], + "type": "text", + "content": "(a) Ambiguous class names" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 428, + 321, + 547, + 421 + ], + "lines": [ + { + "bbox": [ + 428, + 321, + 547, + 421 + ], + "spans": [ + { + "bbox": [ + 428, + 321, + 547, + 421 + ], + "type": "image", + "image_path": "f81e2a67916517456184ce7a571a630b59d408557481efcfc10451ae54df5b34.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 389, + 422, + 468, + 432 + ], + "lines": [ + { + "bbox": [ + 389, + 422, + 468, + 432 + ], + "spans": [ + { + "bbox": [ + 389, + 422, + 468, + 432 + ], + "type": "text", + "content": "(b) Technical class names" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 305, + 442, + 547, + 498 + ], + "lines": [ + { + "bbox": [ + 305, + 442, + 547, + 498 + ], + "spans": [ + { + "bbox": [ + 305, + 442, + 547, + 498 + ], + "type": "text", + "content": "Figure 1. Vision and language recognition models are highly sensitive to class prompts. Ambiguous or highly technical class names can lead to poor classification performance. Rather than adjusting class names directly, current solutions rely on prompt context to reduce ambiguity, e.g.: 'a photo of a bow, a type of weapon'." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 521, + 545, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 545, + 605 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 545, + 605 + ], + "type": "text", + "content": "and language multi-modal approaches alternatively perform classification by computing a similarity between image features and a set of textual features, corresponding to text descriptions from the classes of interest. As the text component constitutes an input, such models naturally enable a route to identify new categories, simply by modifying the queried text, yielding very strong zero-shot and open-set performance [22, 36]." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "type": "text", + "content": "We draw attention to the fact that such performant multi-modal models suffer from two remaining key limitations. Firstly, performance is highly sensitive to class-prompt textual content; both in terms of " + }, + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "type": "inline_equation", + "content": "(i)" + }, + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "type": "text", + "content": " the selected class name and " + }, + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "type": "inline_equation", + "content": "(ii)" + }, + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "type": "text", + "content": " the surrounding sentence context. Class name sensitivity can be illustrated by considering class homonyms; words that have the same spelling but different meanings and origins (Figure 1a). These lead to ambiguous queries that negatively effect performance and mitigation necessitates time consuming prompt" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23477" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 288, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 288, + 144 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 288, + 144 + ], + "type": "text", + "content": "engineering. A further example of class name sensitivity can be demonstrated by classes exhibiting highly technical characterisations (Figure 1b). Poor performance is typically observed for datasets exhibiting such complex naming strategies [36]. We hypothesise that lack of clear semantic meaning leave it difficult for multi-modal models to obtain valuable embeddings." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 146, + 290, + 386 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 146, + 290, + 386 + ], + "spans": [ + { + "bbox": [ + 46, + 146, + 290, + 386 + ], + "type": "text", + "content": "Secondly; model adaptation to new data, that becomes available, is not straightforward – especially when datasets are small. A natural solution is to use the existing representation layers and learn a new classification layer (i.e. linear probing). However this strategy must sacrifice the flexibility of text input, for potential subsequent adaptations [36]. Alternatives consider replacing text queries with image queries [33], or learning optimal prompt contexts from the new data [6,50,51]. The former can struggle to leverage larger amounts of available data successfully as image queries are averaged over all available class specific data. The latter alternatively updates a fixed set of parameters, which can lead to forgetting issues under sequential adaptation. Furthermore, the approach fails to learn a new class representation or mapping from the new data, as the focus lies solely with the prompt context. Finally; while context learning is highly successful in simple classification tasks, it becomes more challenging for object detection purposes and requires more complex training mechanisms [6]. We conjecture that this may be attributed to the fact that context then depends on the spatial region of the image, in addition to object type (i.e. foreground or background)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 388, + 288, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 388, + 288, + 604 + ], + "spans": [ + { + "bbox": [ + 46, + 388, + 288, + 604 + ], + "type": "text", + "content": "These considerations lead us to propose a complementary solution to prompt learning, which can be successfully leveraged for both classification and detection problems, without any task-specific tailoring or requirements. We build upon ideas from the field of text to image generation, and propose to leverage the concept of textual inversion [10] to adapt models to new data and thus address suboptimal, and potentially wrong, labels (e.g. incorrect class names, mislabelled data). The crux of our idea is to supersede manual word engineering, describing categories of interest, by leveraging available data to learn optimal word embeddings, as a function of the visual content. This simple solution affords several advantages: 1) in contrast to linear probing, open-set capabilities are maintained; learned class representations remain in text space, 2) our approach learns class specific parameters, therefore preventing forgetting under sequential training, 3) direct applicability to any classification or object detection technique that utilises textual input, 4) interpretability properties, with regards to learned class names." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 605, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 288, + 713 + ], + "type": "text", + "content": "Experimentally, we realise our ideas in conjunction with two contemporary models; for image classification [36] and object detection [33]. Given a pre-trained model and (potentially previously unseen, additional) training dataset, we introduce a new set of word embeddings in the model text branch, each corresponding to a class of interest. Following context tuning methods [50, 51], embeddings are learned using standard losses on an otherwise frozen model. Comprehensive experiments across thirteen classification and detection datasets help to evidence that" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "content": "learning optimal class names can significantly enhance model performance, maintain open-vocabulary properties, successfully enable continual model adaptation, and improve performance for rare and longtail classes. Last but not least, we demonstrate the method's potential for interpretability, notably with regards to mislabelling and inadequately chosen original class names." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 317, + 145, + 496, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 145, + 496, + 156 + ], + "spans": [ + { + "bbox": [ + 317, + 145, + 496, + 156 + ], + "type": "text", + "content": "Our main contributions can be summarised as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 317, + 167, + 545, + 332 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 317, + 167, + 545, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 167, + 545, + 215 + ], + "spans": [ + { + "bbox": [ + 317, + 167, + 545, + 215 + ], + "type": "text", + "content": "- A simple proposition for data-efficient adaptation of large vision-language models: optimisation of class names from visual content that retains attractive open-vocabulary properties." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 317, + 225, + 545, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 225, + 545, + 274 + ], + "spans": [ + { + "bbox": [ + 317, + 225, + 545, + 274 + ], + "type": "text", + "content": "- Performance improvements complementary to those of prompt tuning; consistent experimental gains across 13 classification and detection datasets. Strong sequential adaptation, open-vocabulary and long tail performance." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 317, + 284, + 545, + 332 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 284, + 545, + 332 + ], + "spans": [ + { + "bbox": [ + 317, + 284, + 545, + 332 + ], + "type": "text", + "content": "- Model interpretability insights: we visualise class name changes and related semantic meanings. We highlight how model biases and labelling errors can be identified, and how vision-language models suffer from long-tail issues." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 346, + 386, + 357 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 346, + 386, + 357 + ], + "spans": [ + { + "bbox": [ + 306, + 346, + 386, + 357 + ], + "type": "text", + "content": "2. Related work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 365, + 547, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 365, + 547, + 569 + ], + "spans": [ + { + "bbox": [ + 304, + 365, + 547, + 569 + ], + "type": "text", + "content": "Vision and language models. Early vision and language multi-modal alignment work tended to enable learning by encoding distinct modalities individually and independently. Model branch outputs would then be connected by losses [41]. Cross-modality alignment has been performed using techniques including metric learning [8], multi-label classification [20] and transformers [48]. A contemporary alternative approach pertains to so called \"vision-language\" models [9, 18, 29, 36] that recently gain popularity and instead are designed to jointly learn modality encoders; sharing information between modalities to the benefit of the global model. A representative approach is CLIP [36], which trains two neural network-based encoders on a very large image-caption dataset using a contrastive cross-entropy loss, to match pairs of images and texts, and shows impressive zero-shot image recognition performance. This seminal work has been extensively investigated in recent literature, and several improvements have been proposed [9, 18, 46]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 570, + 548, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 548, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 548, + 714 + ], + "type": "text", + "content": "Open vocabulary object detection was originally explored in a two-step manner [45]: firstly learning a mapping between the two modalities using a large-scale dataset, then leveraging the obtained encoders and mapping layers to learn an object detector that can perform classification by computing a distance to class names' text embeddings. More recent methods fully integrate vision-language mappings into the object detection training process, providing more flexible and adaptable multi-modal detection solutions [2, 13, 22, 26, 31, 33]. Alternative methods generate a multi-modal joint representation via a fusion block [22, 31], while contemporary solutions adopt a CLIP-like parallel training strategy [2, 13, 33], facilitating querying of large numbers of" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "23478" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 95 + ], + "type": "text", + "content": "classes. Similarly to the classification setting, multi-modal detection models achieve impressive zero-shot performance." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 98, + 288, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 98, + 288, + 289 + ], + "spans": [ + { + "bbox": [ + 46, + 98, + 288, + 289 + ], + "type": "text", + "content": "Prompting methods. Prompt learning aims to exploit the rich knowledge contained in large pre-trained language models (e.g. GPT [37]), to predict new downstream tasks. The idea prescribes that we can reformulate these new tasks to \"look more like\" those solved during the original pre-training stage, through the help of an appropriate textual prompt, such that the model can be used to predict the desired output without any additional training [11,30,37,39]. The disadvantage with such methods, is the necessity for prompt engineering; searching for the most suitable prompt to allow the language model to solve the desired task. Recent work has looked to learn optimal prompts " + }, + { + "bbox": [ + 46, + 98, + 288, + 289 + ], + "type": "inline_equation", + "content": "cf" + }, + { + "bbox": [ + 46, + 98, + 288, + 289 + ], + "type": "text", + "content": ". manually designing them. Work on continuous prompt learning methods [24, 28, 49] can be considered closely related to our work, where the main idea is to convert the prompt into a set of continuous vectors that can be optimised, with respect to an objective function, in end-to-end manner." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 291, + 288, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 291, + 288, + 518 + ], + "spans": [ + { + "bbox": [ + 46, + 291, + 288, + 518 + ], + "type": "text", + "content": "The benefits of the prompting strategy have recently started to be explored in computer vision [21,38,44,47,51]. CoOp [51] can be considered the first work to bring continuous prompt learning to the vision domain, towards offering efficient solutions for adaptation of pre-trained vision-language models to downstream vision tasks. CoOp was recently extended by [50] where they propose to learn an additional light-weight neural network to generate, for each image, an input-conditional token that yields an image-specific dynamic prompt, outperforming CoOp's static prompts. This comes at the price of efficiency however, as the proposed extension is substantially both more memory and computationally expensive. We note that [50, 51] facilitate model adaptation to new domains in a highly parameter efficient manner, yet may still struggle in settings where new classes (and their names) widely differ from that which the model has initially learned. A more complex context prompting strategy has been recently proposed, specifically for detection tasks [6]; integrating a background specific loss function, as well as a context dependent scheme in the prompt learning process." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 520, + 288, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 520, + 288, + 604 + ], + "spans": [ + { + "bbox": [ + 46, + 520, + 288, + 604 + ], + "type": "text", + "content": "Crucially, for all of these methods, classification is still performed with respect to a handcrafted class name, making accuracy highly dependent on the quality of chosen names. In certain settings, finding an accurate semantic description of a class can be challenging, and a poor choice can impair the model's performance. To the best of our knowledge, this limitation has not been addressed yet in classification and object detection settings." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 605, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 288, + 714 + ], + "type": "text", + "content": "Textual Inversion. Textual inversion was recently introduced in [10] for text to image generative diffusion models, as a means to condition image generation towards incorporating specific objects or visual components. Given a set of images, all displaying the same object in diverse settings, a specific word embedding is learned so as to ensure the model is able to reconstruct images, sampled from this small set. Simple prompt contexts are used (e.g. 'a photo of') to ensure a simple image of the object is generated. After training, the model displays impressive ability" + } + ] + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 307, + 69, + 547, + 320 + ], + "blocks": [ + { + "bbox": [ + 307, + 69, + 547, + 320 + ], + "lines": [ + { + "bbox": [ + 307, + 69, + 547, + 320 + ], + "spans": [ + { + "bbox": [ + 307, + 69, + 547, + 320 + ], + "type": "image", + "image_path": "53bca57f66d7e4cb9ef2bb82c16a8005f9c3b25dd7e7c75488520a2614d9a3c8.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 327, + 547, + 384 + ], + "lines": [ + { + "bbox": [ + 304, + 327, + 547, + 384 + ], + "spans": [ + { + "bbox": [ + 304, + 327, + 547, + 384 + ], + "type": "text", + "content": "Figure 2. Overview of our method. Classes of interest are mapped to a set of learnable word embeddings, and integrated within the input sentence representation. These embeddings can be learned for classification and object detection tasks using standard losses, and used in conjunction with pre-trained word embeddings in open-vocabulary settings." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 404, + 545, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 404, + 545, + 441 + ], + "spans": [ + { + "bbox": [ + 304, + 404, + 545, + 441 + ], + "type": "text", + "content": "to integrate specific objects in generated images, via inputting learned words at specific locations in the input sentence. In the solution proposed by [10], each word is learned individually." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 451, + 385, + 466 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 451, + 385, + 466 + ], + "spans": [ + { + "bbox": [ + 306, + 451, + 385, + 466 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "spans": [ + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "text", + "content": "We consider a vision and language object recognition model pre-trained on a large-scale dataset. Following the multi-modal architecture, the model takes as input an image-text pair " + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "inline_equation", + "content": "\\mathbf{x} = \\{I,T\\}" + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "text", + "content": ", where the text provides a list of " + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "text", + "content": " candidate classes " + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "inline_equation", + "content": "t_i" + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "text", + "content": " to recognise; " + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "inline_equation", + "content": "T = [t_1,t_2,\\dots,t_N]" + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "text", + "content": ". We assume the " + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "text", + "content": "-th class specific text " + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "inline_equation", + "content": "t_i" + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "text", + "content": " to be in the format: " + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "inline_equation", + "content": "t_i = [\\text{prompt prefix}] + [\\text{CLASS}] + [\\text{prompt suffix}]" + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "text", + "content": ", where prompt prefixes and suffixes are shared across all classes. To obtain text feature representations, a tokenizer converts each sentence into a series of tokens such that each token maps to a pre-trained word embedding " + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "inline_equation", + "content": "\\mathbf{e_k}" + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "text", + "content": ". Embeddings are then concatenated and used as input to a text transformer. Our final text representation is the transformer output for the End Of Sequence (EOS) token " + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "inline_equation", + "content": "f(T) = f_i(t_i)" + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "inline_equation", + "content": "i \\in \\{1,\\dots,N\\}" + }, + { + "bbox": [ + 304, + 472, + 547, + 640 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 647, + 464, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 647, + 464, + 658 + ], + "spans": [ + { + "bbox": [ + 306, + 647, + 464, + 658 + ], + "type": "text", + "content": "3.1. Baseline multi-modal models" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "content": "Multi-modal Image classification. In the context of image classification [36], our objective is to assign a specific category to the image as a whole. Image inputs are processed in parallel to the text input through an image encoder " + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "inline_equation", + "content": "g(\\cdot)" + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "content": ", yielding repre" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23479" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 288, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 288, + 108 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 288, + 108 + ], + "type": "text", + "content": "sentation " + }, + { + "bbox": [ + 46, + 72, + 288, + 108 + ], + "type": "inline_equation", + "content": "g(I)" + }, + { + "bbox": [ + 46, + 72, + 288, + 108 + ], + "type": "text", + "content": ". The classification task is then simply performed by evaluating the cosine similarity between image and text embeddings, and selecting the class with the highest similarity." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 109, + 287, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 109, + 287, + 251 + ], + "spans": [ + { + "bbox": [ + 46, + 109, + 287, + 251 + ], + "type": "text", + "content": "Multi-modal Object detection. There are two main contemporary strategies for vision and language based object detection. The first (simpler) strategy is similar to multi-modal classification, wherein image and text information are only combined latterly at the bounding box classification stage [2, 13, 33]. In this setting, the image input is processed through an object detector entirely disjointly from the text modality. The detector outputs a set of coordinates and a class embedding for each bounding box candidate. We then compute class embedding similarity with text representations " + }, + { + "bbox": [ + 46, + 109, + 287, + 251 + ], + "type": "inline_equation", + "content": "f(T)" + }, + { + "bbox": [ + 46, + 109, + 287, + 251 + ], + "type": "text", + "content": " following the multi-modal classification approach to assign a classification label to each bounding box." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 253, + 287, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 253, + 287, + 361 + ], + "spans": [ + { + "bbox": [ + 46, + 253, + 287, + 361 + ], + "type": "text", + "content": "The second class of strategies propose to fuse image and text inputs; either when generating text and image embeddings [27] or via a third encoder " + }, + { + "bbox": [ + 46, + 253, + 287, + 361 + ], + "type": "inline_equation", + "content": "h(g(I), f(T))" + }, + { + "bbox": [ + 46, + 253, + 287, + 361 + ], + "type": "text", + "content": " that outputs bounding box coordinates and class embeddings [13, 22, 31]. For this latter fusion strategy, classification is performed over sentence tokens rather than across sentences, with an aim of associating bounding boxes with a keyword. This technique is typically more computationally expensive than the first method class as it requires computation of new image features, for each queried class." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 370, + 209, + 383 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 370, + 209, + 383 + ], + "spans": [ + { + "bbox": [ + 47, + 370, + 209, + 383 + ], + "type": "text", + "content": "3.2. Learning optimal class names" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "spans": [ + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "content": "We conjecture that the performance of pre-trained recognition models is intrinsically linked to the suitability of the class names, chosen to represent certain visual categories. Selecting class names that do not reflect an object's visual appearance can substantially alter recognition performance. We thus propose to remove the model's sensitivity to hand crafted class names, by learning class specific word embeddings from image content. An overview of the method is provided in Figure 2." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "spans": [ + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "text", + "content": "We consider the matrix of pre-trained word embeddings " + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "inline_equation", + "content": "E \\in \\mathbb{R}^{V \\times F}" + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "text", + "content": " is the vocabulary size, and " + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "inline_equation", + "content": "F" + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "text", + "content": " the feature dimension. We propose to extend this matrix with a set of new, learnable word embeddings " + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "inline_equation", + "content": "E^l \\in \\mathbb{R}^{N \\times F}" + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "text", + "content": ", where each row of " + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "inline_equation", + "content": "E^l" + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "text", + "content": " corresponds to a class specific learnable embedding and " + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "text", + "content": " pertains to the total number of dataset classes. In this setting, we replace the class name in each text query with a class specific placeholder " + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "inline_equation", + "content": "t_i = [\\text{prompt prefix}] + [\\text{pl}^i] + [\\text{prompt suffix}]" + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "text", + "content": ". While the tokenizer maps context prompts to matrix " + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "inline_equation", + "content": "E" + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "text", + "content": ", our placeholder maps to its corresponding class embedding " + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "inline_equation", + "content": "\\mathbf{e}_i^l" + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "text", + "content": ". This new class query is used as input to the text transformer encoder, and the remainder of the recognition model is unchanged. We note that it is possible to map one category to a series of " + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "text", + "content": " word embeddings, using queries of the type " + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "inline_equation", + "content": "t_i = [\\text{prompt prefix}] + [\\text{pl}_1^i] + \\dots + [\\text{pl}_m^i] + [\\text{prompt suffix}]" + }, + { + "bbox": [ + 46, + 485, + 288, + 666 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 666, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 666, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 666, + 288, + 714 + ], + "type": "text", + "content": "Training. During training, we freeze the entire pre-trained model (including pre-trained word embeddings) and learn our new set of word embeddings " + }, + { + "bbox": [ + 46, + 666, + 288, + 714 + ], + "type": "inline_equation", + "content": "E^{l}" + }, + { + "bbox": [ + 46, + 666, + 288, + 714 + ], + "type": "text", + "content": " with the standard losses used to train the base model (e.g. cross entropy loss for classification," + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 72, + 547, + 216 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 216 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 216 + ], + "type": "text", + "content": "and bounding box regression losses for detection). We identify three key scenarios where our approach can be beneficial: 1) adapting the pre-trained model on a new dataset with a new set of classes; 2) adjusting suboptimal class names on the same dataset used to train the model; 3) continual adaptation of a pre-trained model, where new classes are available after a first training stage. Inference. At inference time, we follow the process described in Section 3.1. Categories whose names were learned are mapped to their new embeddings " + }, + { + "bbox": [ + 304, + 72, + 547, + 216 + ], + "type": "inline_equation", + "content": "e^l \\in E^l" + }, + { + "bbox": [ + 304, + 72, + 547, + 216 + ], + "type": "text", + "content": ". For unseen object categories (i.e. not in our new dataset) we use their original embeddings, " + }, + { + "bbox": [ + 304, + 72, + 547, + 216 + ], + "type": "inline_equation", + "content": "e \\in E" + }, + { + "bbox": [ + 304, + 72, + 547, + 216 + ], + "type": "text", + "content": ", from the pre-trained model. As a result, our solution maintains the ability to perform open-vocabulary recognition." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 226, + 383, + 239 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 226, + 383, + 239 + ], + "spans": [ + { + "bbox": [ + 305, + 226, + 383, + 239 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 246, + 545, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 246, + 545, + 281 + ], + "spans": [ + { + "bbox": [ + 304, + 246, + 545, + 281 + ], + "type": "text", + "content": "We evaluate the impact of learning class names for the tasks of image classification and object detection. We consider four settings where our strategy can be beneficial:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 281, + 545, + 329 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 281, + 545, + 329 + ], + "spans": [ + { + "bbox": [ + 304, + 281, + 545, + 329 + ], + "type": "text", + "content": "Task 1: Model adaptation to new datasets: considering a new dataset of interest, we adapt our pre-trained model to this new set of potentially unseen classes by learning optimal class names from the new image content." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 330, + 545, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 330, + 545, + 389 + ], + "spans": [ + { + "bbox": [ + 304, + 330, + 545, + 389 + ], + "type": "text", + "content": "Task 2: Post-training class name adjustment: the model is first fine-tuned on our target dataset, then class names are optimised in a subsequent learning stage. This setting is more valuable for object detection where fine-tuning can have important impact on bounding box localisation." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 389, + 545, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 389, + 545, + 496 + ], + "spans": [ + { + "bbox": [ + 304, + 389, + 545, + 496 + ], + "type": "text", + "content": "Task 3: Open-vocabulary recognition: open-vocabulary is one of the key advantages of vision-language models, and maintaining strong performance is crucial. Evaluation of the compatibility between our learned word embeddings, and standard pre-trained word embeddings can provide a measure of strength on this axis. We adapt the model to only a subset of classes from our new dataset and measure overall performance on all classes, such that inference combines learned and text based word embeddings." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 497, + 545, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 497, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 304, + 497, + 545, + 544 + ], + "type": "text", + "content": "Task 4: Continual model adaptation: to measure the model ability for sequential learning of new word embeddings, we first adapt the model to a subset of classes, then learn the remaining class names in a second, separate, training stage." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 545, + 545, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 545, + 665 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 545, + 665 + ], + "type": "text", + "content": "We highlight that we do not seek state of the art performance, as this is intrinsically linked to the pre-trained model's base performance, pre-training data, and architecture. Rather, our objective is to demonstrate the potential of learning class names to improve model performance, while maintaining zero-shot properties. We therefore perform experiments on models with simple multi-modal mechanisms, use a single word embedding per class, and a standard prompt: (prompt prefix = 'a photo of a'; prompt suffix = '.'), in all experimental settings unless specified otherwise." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 671, + 486, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 671, + 486, + 684 + ], + "spans": [ + { + "bbox": [ + 305, + 671, + 486, + 684 + ], + "type": "text", + "content": "4.1. Image classification: CLIP model" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "type": "text", + "content": "To evaluate our method on classification tasks, we employ CLIP [36] as our base pre-trained model with a vision" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "23480" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 78, + 70, + 257, + 124 + ], + "blocks": [ + { + "bbox": [ + 78, + 70, + 257, + 124 + ], + "lines": [ + { + "bbox": [ + 78, + 70, + 257, + 124 + ], + "spans": [ + { + "bbox": [ + 78, + 70, + 257, + 124 + ], + "type": "table", + "html": "
Class group| CLIP*CoOpOursOurs*
Base classes69.482.682.782.8
New classes74.264.173.174.2
All classes65.265.766.568.7
", + "image_path": "2c32572b584f3818dee9ff42148f12aafdcf9d1ddc4535737dfb27eeb6b16cdf.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 198, + 287, + 246 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 198, + 287, + 246 + ], + "spans": [ + { + "bbox": [ + 46, + 198, + 287, + 246 + ], + "type": "text", + "content": "transformer ViT-B/16 [5] fulfilling the image encoder " + }, + { + "bbox": [ + 46, + 198, + 287, + 246 + ], + "type": "inline_equation", + "content": "g(\\cdot)" + }, + { + "bbox": [ + 46, + 198, + 287, + 246 + ], + "type": "text", + "content": " role. Here, we seek to evaluate method performance in previously described Tasks 1, 3 and 4; i.e. model adaptation, open-vocabulary performance, and continual adaptation." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 247, + 288, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 247, + 288, + 628 + ], + "spans": [ + { + "bbox": [ + 46, + 247, + 288, + 628 + ], + "type": "text", + "content": "Datasets. We follow the setting proposed in [50, 51]. We adapt the CLIP model to a selection of eleven diverse datasets, focusing on different types of recognition tasks: standard image classification (ImageNet [4], Caltech 101 [7]), fine-grained classification (Stanford cars [23], Oxford Pets [35], Flowers 102 [34], Food 101 [1] and FGVC Aircraft [32]), scene recognition (SUN 397 [43]), action recognition (UCF 101 [42]), texture classification (DTD [3]) and satellite image recognition (Eurosat [17]). Training details. We adopt a set of parameters similar to [51]. For each dataset, we learn class names with a batch size of 32, over 200 epochs (50 for ImageNet), SGD optimiser with a learning rate of " + }, + { + "bbox": [ + 46, + 247, + 288, + 628 + ], + "type": "inline_equation", + "content": "2e^{-4}" + }, + { + "bbox": [ + 46, + 247, + 288, + 628 + ], + "type": "text", + "content": " and cosine scheduling, with one warm up epoch. We initialise our learnable word embeddings using the average embedding of all word tokens associated with hand-crafted class names. For each dataset, 16 training images per category are sampled, and we report mean statistics over three random seeds. Baselines. The method we directly compare to is the zero-shot CLIP model, as we aim to evaluate the benefits of learning class names. We additionally report classification accuracy for the CoOp model [51], which learns optimal prompt context with fixed handcrafted class names. We retrain CoOp in our settings of interest and use four context words, initialised with 'a photo of a', as in [50] (this differs from the 16 words used in [51]). This allows to compare advantages and disadvantages of learning class names over prompt context, and evaluate any complementary nature between the strategies. We report the best possible CLIP performance with engineered prompts, and report our results with a standard prompt, as well as using the same engineered prompts as CLIP. We note that four of the datasets (Stanford cars, ImageNet, Caltech 101 and Sun 397) do not have engineered prompt, and we provide all dataset-specific prompts in the supplementary materials." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 630, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 630, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 630, + 288, + 713 + ], + "type": "text", + "content": "Model adaptation (Task 1) and Open vocabulary (Task 3). We adopt the base-to-new generalisation setting proposed in [50]. For each dataset, we equally split classes into a set of 'base' and 'new' classes. We use base classes for training and evaluate performance on new classes in a zero-shot manner. Additionally, to measure open-vocabulary performance, we report performance on 'all' classes, i.e. a mixture of learned and handcrafted class" + } + ] + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 336, + 71, + 516, + 114 + ], + "blocks": [ + { + "bbox": [ + 47, + 133, + 288, + 178 + ], + "lines": [ + { + "bbox": [ + 47, + 133, + 288, + 178 + ], + "spans": [ + { + "bbox": [ + 47, + 133, + 288, + 178 + ], + "type": "text", + "content": "Table 1. CLIP model: Base to new classification accuracy. Results on Base classes show method ability to adapt to new data (Task 1). Results on New and All classes show open vocabulary ability (Task 3). * Manually engineered prompt templates." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 336, + 71, + 516, + 114 + ], + "lines": [ + { + "bbox": [ + 336, + 71, + 516, + 114 + ], + "spans": [ + { + "bbox": [ + 336, + 71, + 516, + 114 + ], + "type": "table", + "html": "
Class groupCLIP*CoOpOursOurs*
New classes74.084.485.985.9
All classes65.271.775.776.3
", + "image_path": "43d3eba2a129c75b432f4764fce334f0058fb0de47f2623e695f11f0d59bd65f.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 306, + 122, + 547, + 145 + ], + "lines": [ + { + "bbox": [ + 306, + 122, + 547, + 145 + ], + "spans": [ + { + "bbox": [ + 306, + 122, + 547, + 145 + ], + "type": "text", + "content": "Table 2. CLIP model: Classification accuracy for continual model adaptation (Task 4) experiments. * manually engineered prompt templates." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 167, + 547, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 167, + 547, + 384 + ], + "spans": [ + { + "bbox": [ + 304, + 167, + 547, + 384 + ], + "type": "text", + "content": "names. Results are reported in Table 1. Results on base classes highlight our ability to perform model adaptation (Task 1) by learning class names only. For this task, we observe that our strategy can achieve equivalent gains as context tuning on base classes (82.6% CoOP) vs. 82.8% (Ours) average accuracy). However, our approach guarantees that zero-shot CLIP performance is maintained on new classes, as our learned parameters are class specific. Our open-vocabulary experiments (Task 3) show that we are able to maintain the CLIP model's ability to expand the output set, and to introduce new classes from text alone (CLIP and Ours 74.2%, new classes). Experiments on all classes confirm that we are able to use a mixture of learned and new classes successfully, outperforming CoOp (65.7% CoOp, 68.7% Ours, all classes). We further note sensitivity to context prompts, as substantial gains are obtained using engineered prompts (+2.2% Ours, all classes). This is to be expected, as new class performance can be strongly impacted by prompts, and highlights the importance of prompt context tuning in this setting." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 384, + 547, + 600 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 384, + 547, + 600 + ], + "spans": [ + { + "bbox": [ + 304, + 384, + 547, + 600 + ], + "type": "text", + "content": "Sequential adaptation (Task 4). We extend the generalisation setting to evaluate our ability to perform sequential adaptation; i.e. after training on base classes, we subsequently train on new classes with no access to base classes. We measure performance on new classes, as well as all classes after this second training stage. For CoOp [51] experiments, we fine-tune prompt parameters, and adjust learning rate to " + }, + { + "bbox": [ + 304, + 384, + 547, + 600 + ], + "type": "inline_equation", + "content": "2e^{-4}" + }, + { + "bbox": [ + 304, + 384, + 547, + 600 + ], + "type": "text", + "content": " which provided best empirical performance in this setting. Results are reported in Table 2. As in the previous setting, performance on new classes is highly similar when performing context or class name learning " + }, + { + "bbox": [ + 304, + 384, + 547, + 600 + ], + "type": "inline_equation", + "content": "(84.4\\% \\text{CoOp}, 85.9\\% \\text{Ours, new classes})" + }, + { + "bbox": [ + 304, + 384, + 547, + 600 + ], + "type": "text", + "content": ", achieving strong gains over zero-shot CLIP " + }, + { + "bbox": [ + 304, + 384, + 547, + 600 + ], + "type": "inline_equation", + "content": "(10 + \\%)" + }, + { + "bbox": [ + 304, + 384, + 547, + 600 + ], + "type": "text", + "content": ". On all classes, we can see that we achieve significant gains " + }, + { + "bbox": [ + 304, + 384, + 547, + 600 + ], + "type": "inline_equation", + "content": "(+4\\%, \\text{Ours vs. CoOp})" + }, + { + "bbox": [ + 304, + 384, + 547, + 600 + ], + "type": "text", + "content": ", which can be attributed to the fact that class names are (all) learned independently. Last but not least, we note a much higher robustness to change in prompt context when all classes are learned; we achieve very similar results when using a standard or engineered template in this setting " + }, + { + "bbox": [ + 304, + 384, + 547, + 600 + ], + "type": "inline_equation", + "content": "(+0.6\\% \\text{vs.} +2.2\\%" + }, + { + "bbox": [ + 304, + 384, + 547, + 600 + ], + "type": "text", + "content": " in Table 1, all classes)." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 610, + 487, + 623 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 610, + 487, + 623 + ], + "spans": [ + { + "bbox": [ + 306, + 610, + 487, + 623 + ], + "type": "text", + "content": "4.2. Object detection: OWL-vit model" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "type": "text", + "content": "For object detection experiments, we evaluate all 4 Tasks. We select OWL-vit [33] as our base multi-modal detection model due to noted strong zero-shot performance and clear separation between vision and language branches. This allows us to evaluate the impact of class learning more directly, in contrast to methods using complex text and image fusion mechanisms [26]. OWL-vit leverages a pre-trained multi-modal classification" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "23481" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 109, + 69, + 486, + 312 + ], + "blocks": [ + { + "bbox": [ + 109, + 69, + 486, + 312 + ], + "lines": [ + { + "bbox": [ + 109, + 69, + 486, + 312 + ], + "spans": [ + { + "bbox": [ + 109, + 69, + 486, + 312 + ], + "type": "table", + "html": "
Methodbackbonepre-training (image)pre-training (detection)APAP/fAP/cAP/r
Zero-shot models
MDETR [22]DETRN/AMDETR data6.49.83.61.9
XDETR [2]DETRN/AMDETR data, LocNar16.418.815.29.6
GLIP-T [26]SWIN-TCap4MO365,GOLDG26.031.021.420.8
GLIP-L [26]SWIN-LCap24MO365,FourODs37.341.543.328.2
Zero-shot OWL-vit base model
OWL-vit zero-shot [33]VIT B/16CLIPO365, VG25.327.025.017.6
Prompt learning all [51]VIT B/16CLIPO365, VG24.425.924.017.8
Ours base (Task 3)VIT B/16CLIPO365, VG25.726.825.919.0
Ours sequential (Task 4)VIT B/16CLIPO365, VG26.026.625.923.1
Ours all (Task 1)VIT B/16CLIPO365, VG26.627.027.519.8
OWL-vit base model fine-tuned on LVIS base classes
OWL-vit [33]VIT B/16CLIPO365, VG28.834.424.717.7
Prompt learning all [51]VIT B/16CLIPO365, VG31.334.729.222.4
Ours base (Task 3)VIT B/16CLIPO365, VG33.335.233.920.3
Ours rare (Task 3)VIT B/16CLIPO365, VG30.434.525.434.5
Ours sequential (Task 4)VIT B/16CLIPO365, VG34.135.033.830.6
Ours all (Task 2)VIT B/16CLIPO365, VG34.535.634.428.5
OWL-vit base model fine-tuned on all LVIS classes
OWL-vit [33]VIT B/16CLIPO365, VG34.538.533.219.1
", + "image_path": "8c1b77f314fda43be6e6db88479937a2563638808b26548884cca6c3a4a0a377.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 319, + 547, + 377 + ], + "lines": [ + { + "bbox": [ + 46, + 319, + 547, + 377 + ], + "spans": [ + { + "bbox": [ + 46, + 319, + 547, + 377 + ], + "type": "text", + "content": "Table 3. Average precision results on LVIS mini-validation set for all (AP), frequent (AP/f), common (AP/c) and rare (AP/r) classes. Our method uses only " + }, + { + "bbox": [ + 46, + 319, + 547, + 377 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 319, + 547, + 377 + ], + "type": "text", + "content": " of the LVIS training dataset. State of the art methods (in grey) use different training datasets and architectures of different capacities. They are added for context.{base, rare, all} describes which class group is used for training. Sequential means base and rare classes are trained sequentially. Task 1: model adaptation to new data, Task 2: class name learning after fine-tuning, Task 3: Open vocabulary ability (mixture of seen and unseen classes), Task 4: continual adaptation (learning class groups in a sequential manner)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 396, + 288, + 505 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 396, + 288, + 505 + ], + "spans": [ + { + "bbox": [ + 46, + 396, + 288, + 505 + ], + "type": "text", + "content": "model (e.g. CLIP) with a vision transformer backbone. To adapt this model to the object detection task, the pooling layer that combines vision transformer outputs is removed, and two new modules are learned: a class prediction module and a box prediction module that output class embeddings and bounding-box coordinates, respectively, for all transformer outputs. The OWL-vit model is fine-tuned from the CLIP weights using a mixture of standard object detection datasets. Additionally, a set of engineered CLIP prompt templates are used as class prompt contexts." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 506, + 288, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 506, + 288, + 628 + ], + "spans": [ + { + "bbox": [ + 46, + 506, + 288, + 628 + ], + "type": "text", + "content": "Datasets. We evaluate the impact of learning class names on two object detection datasets. Firstly, we consider LVIS [14], a large scale natural image object detection dataset comprising over " + }, + { + "bbox": [ + 46, + 506, + 288, + 628 + ], + "type": "inline_equation", + "content": "100k" + }, + { + "bbox": [ + 46, + 506, + 288, + 628 + ], + "type": "text", + "content": " training images and 1203 classes, characterised by a long tail distribution with 'frequent' " + }, + { + "bbox": [ + 46, + 506, + 288, + 628 + ], + "type": "inline_equation", + "content": "(x > 100)" + }, + { + "bbox": [ + 46, + 506, + 288, + 628 + ], + "type": "text", + "content": ", 'common' " + }, + { + "bbox": [ + 46, + 506, + 288, + 628 + ], + "type": "inline_equation", + "content": "(100 \\geq x > 10)" + }, + { + "bbox": [ + 46, + 506, + 288, + 628 + ], + "type": "text", + "content": ", and 'rare' " + }, + { + "bbox": [ + 46, + 506, + 288, + 628 + ], + "type": "inline_equation", + "content": "(10 \\geq x \\geq 1)" + }, + { + "bbox": [ + 46, + 506, + 288, + 628 + ], + "type": "text", + "content": " class groups, for " + }, + { + "bbox": [ + 46, + 506, + 288, + 628 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 46, + 506, + 288, + 628 + ], + "type": "text", + "content": " images per class. Following prior work [2]; we use pre-defined category names, but remove the text in the parentheses, e.g., \"flip-flop (sandal)\" is replaced by \"flip-flop\". We note that this leads to a few instances of classes sharing the same name (e.g. \"bow\")." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "type": "text", + "content": "Secondly, we run experiments on CODA 2.0 [25], a self-driving dataset focusing on detection of rare, corner case instances. It comprises validation and test sets containing " + }, + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "type": "inline_equation", + "content": "5k" + }, + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "type": "text", + "content": " images each, and 43 classes, of which only 29 appear in the validation set. In addition to the base OWL-vit model, we consider a model fine-tuned on the SODA dataset [15] (fully labelled instance), closer to our test domain. Designed for semi-supervised" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 396, + 545, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 396, + 545, + 445 + ], + "spans": [ + { + "bbox": [ + 304, + 396, + 545, + 445 + ], + "type": "text", + "content": "training of self-driving models, SODA comprises six classes that overlap with CODA classes. Following [25], we refer to the{pedestrian, cyclist, car, bus, tram, truck, tricycle} classes as common classes, and remaining classes as corner-cases." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 447, + 547, + 566 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 447, + 547, + 566 + ], + "spans": [ + { + "bbox": [ + 304, + 447, + 547, + 566 + ], + "type": "text", + "content": "Training details. We fine-tune the pre-trained OWL-vit model on LVIS, SODA datasets for 10 epochs, using 7 GPUs (batch size 2/GPU), with learning rates " + }, + { + "bbox": [ + 304, + 447, + 547, + 566 + ], + "type": "inline_equation", + "content": "5e^{-6}" + }, + { + "bbox": [ + 304, + 447, + 547, + 566 + ], + "type": "text", + "content": " (main model) and " + }, + { + "bbox": [ + 304, + 447, + 547, + 566 + ], + "type": "inline_equation", + "content": "2e^{-7}" + }, + { + "bbox": [ + 304, + 447, + 547, + 566 + ], + "type": "text", + "content": " (text branch) using cosine scheduling and an Adam optimiser. Weight decay is unused and gradient clipping is set to 0.1. For LVIS fine-tuning, we train two models: one on all classes, and one on base (frequent + common) classes only, thus following the evaluation set-up of [33]. For CODA 2.0 experiments, models are fine-tuned on the SODA and LVIS datasets, to simulate the setting where corner cases are discovered after training." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 567, + 547, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 567, + 547, + 651 + ], + "spans": [ + { + "bbox": [ + 304, + 567, + 547, + 651 + ], + "type": "text", + "content": "For our class learning process, we freeze the entire model aside from new class embeddings. Embeddings are learned for 20 epochs, using a learning rate of " + }, + { + "bbox": [ + 304, + 567, + 547, + 651 + ], + "type": "inline_equation", + "content": "1e^{-2}" + }, + { + "bbox": [ + 304, + 567, + 547, + 651 + ], + "type": "text", + "content": " (cosine schedule), 7 GPU (batch size 10/GPU). They are initialised with the average token embedding of the corresponding class name. For the LVIS dataset, we learn class names with a balanced subsampling of " + }, + { + "bbox": [ + 304, + 567, + 547, + 651 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 304, + 567, + 547, + 651 + ], + "type": "text", + "content": " of the training set." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "content": "LVIS experiments. We provide average precision results for all 4 Tasks on the LVIS mini-validation set [22] in Table 3. For context, we report methods competitive with OWL-vit, but highlight the key point of comparison is with respect to the base model that exhibits pre-defined class names. We also provide comparison" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23482" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 62, + 70, + 276, + 224 + ], + "blocks": [ + { + "bbox": [ + 62, + 70, + 276, + 224 + ], + "lines": [ + { + "bbox": [ + 62, + 70, + 276, + 224 + ], + "spans": [ + { + "bbox": [ + 62, + 70, + 276, + 224 + ], + "type": "table", + "html": "
MethodAP-ComAP-AAR-AAR-CorSum
RetinaNet0.250.380.480.251.36
Faster RCNN0.280.370.430.171.27
Cascade RCNN0.300.420.480.211.41
Zero-shot OWL-vit base model
OWL-vit zero-shot0.100.200.440.451.19
Ours (Task 3)0.170.310.470.501.45
OWL-vit model fine-tuned on SODA
OWL-vit0.210.350.500.441.44
Ours (Task 3)0.230.370.510.471.58
OWL-vit model fine-tuned on SODA + LVIS
OWL-vit0.210.350.520.511.59
Ours (Task 3)0.230.390.530.531.67
", + "image_path": "0ead5ec322780f31a8e0ab7ba87d03489b0974b8c05558947cdc69ab6838661e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 233, + 288, + 289 + ], + "lines": [ + { + "bbox": [ + 46, + 233, + 288, + 289 + ], + "spans": [ + { + "bbox": [ + 46, + 233, + 288, + 289 + ], + "type": "text", + "content": "Table 4. Detection results on the CODA test set. AP-Com (AP-Common): average precision (AP) on common classes, AP-A (AP-agnostic): AP in the class agnostic setting. AR-A and AR-Cor: average recall for class agnostic and corner case detection respectively. Task 3: Open vocabulary ability (mixture of seen and unseen classes)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 313, + 287, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 313, + 287, + 337 + ], + "spans": [ + { + "bbox": [ + 46, + 313, + 287, + 337 + ], + "type": "text", + "content": "to prompt learning, using four learnable context words initialised as 'a photo of a', and identical training parameters to ours." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 339, + 288, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 339, + 288, + 518 + ], + "spans": [ + { + "bbox": [ + 46, + 339, + 288, + 518 + ], + "type": "text", + "content": "When learning names for all classes, we achieve consistent performance gains for all class groups in both zero-shot (Task 1: adapting to a new dataset, +1.3 AP) and fine-tuned settings (Task 2: post training class adjustment, +5.7 AP). Larger gains are observed for the fine-tuned model, which can be attributed to the improved bounding box regression component, facilitating the classification task. Gains are more substantial for rare classes (+2.2 AP (zero-shot), +10.8 AP (fine-tuned)), which can be attributed to the removal of name ambiguity and the fact that these names are less common in pre-training datasets, leading to potentially poorer mapping with image content (e.g. 'arctic' shoe class). In contrast, frequent classes remain mostly stable (+0 AP (zero-shot), +1.2 (fine-tuned)), highlighting the quality of image to name mapping for this group. We provide more evidence of this in Section 4.3." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 520, + 287, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 520, + 287, + 616 + ], + "spans": [ + { + "bbox": [ + 46, + 520, + 287, + 616 + ], + "type": "text", + "content": "Open vocabulary experiments (Task 3), where class names are only learned on base classes, show modest gains in the zero-shot setting (+0.5 AP), but large improvements on a fine-tuned model (+4.5 AP), notably highlighting the importance of the localisation task. Due to the multi-label nature of LVIS, base and rare classes are not competing, facilitating the open vocabulary task. Learning base class names additionally reduces ambiguity with rare class names, improving rare class performance." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 618, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 618, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 618, + 288, + 715 + ], + "type": "text", + "content": "Sequential learning experiments (Task 4; training on base then rare classes) achieve comparable performance to directly training on all classes (35.0 AP (seq.) vs. 35.6 AP (all classes)), further highlighting how well suited our approach is for sequential adaptation. LVIS is a highly imbalanced dataset. While our " + }, + { + "bbox": [ + 46, + 618, + 288, + 715 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 618, + 288, + 715 + ], + "type": "text", + "content": " subsampling strategy reduces class imbalance, it is not entirely eliminated. This is evidenced by the improved rare class performance in the sequential setting (+3.3" + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 362, + 72, + 492, + 201 + ], + "blocks": [ + { + "bbox": [ + 362, + 72, + 492, + 201 + ], + "lines": [ + { + "bbox": [ + 362, + 72, + 492, + 201 + ], + "spans": [ + { + "bbox": [ + 362, + 72, + 492, + 201 + ], + "type": "image", + "image_path": "93baac3c12a0e14e91ec9112fc4bf401060d54172b136aad2d42726d4598e381.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 211, + 545, + 233 + ], + "lines": [ + { + "bbox": [ + 305, + 211, + 545, + 233 + ], + "spans": [ + { + "bbox": [ + 305, + 211, + 545, + 233 + ], + "type": "text", + "content": "Figure 3. Correlation between class rarity (log scale) and embedding similarity between learned and pre-defined class names. LVIS dataset." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 256, + 545, + 364 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 256, + 545, + 364 + ], + "spans": [ + { + "bbox": [ + 304, + 256, + 545, + 364 + ], + "type": "text", + "content": "(zero-shot), +2.1 (fine-tuned)). Fine-tuning the entire OWL-vit model on all classes (100% of the data) further evidences the impact of this imbalance and our model's potential to address long tail issues. By reducing data requirements (only 10% achieves equivalent overall performance; 34.5 AP), we are able to train on a balanced data subset. In both fine-tuned and zero-shot settings, learning class names on 10% of the data achieves better or equivalent rare class performance (AP/r) than the fine-tuned model (-0.1 to +15.4 AP/r gains)." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 365, + 546, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 365, + 546, + 449 + ], + "spans": [ + { + "bbox": [ + 304, + 365, + 546, + 449 + ], + "type": "text", + "content": "Finally, prompt tuning methods struggle to achieve good Task 1 performance in the zero-shot setting (-0.9 AP), but obtain better gains after fine-tuning (+2.5 AP). We note that prompt tuning performance is overall poorer than our strategy (-2.2 zero shot, -3.2 fine-tuned vs. ours all), which we hypothesise is partially linked to OWL-vit pre-training using fixed templates, and the diversity of object detection contexts." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 450, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 450, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 450, + 547, + 715 + ], + "type": "text", + "content": "CODA experiments, Task 3. Experimental results on the CODA 2.0 dataset are reported in Table 4. Following the setup proposed in [25], we report average precision (AP) on common classes (AP-Com), class agnostic AP on all classes (AP-A), class agnostic average recall on all classes (AR-A) and on corner classes (AR-Cor). We compare to state of the art self-driving models, trained in a semi-supervised setting on the SODA dataset [25]. The zero-shot OWL-vit model is not trained on any self-driving data, therefore achieves an overall poorer common class performance (0.10 AP-Com). However, the open-vocabulary setting allows this model to achieve very strong corner classes detection (0.45 AR-Cor). Our method largely improves performance for all metrics, with an overall performance (1.45 Sum) exceeding that of self-driving models (1.41 Sum), including our OWL-vit model fine-tuned on SODA (1.44 Sum). Fine-tuning OWL-vit on SODA, and a mixture of SODA and LVIS training data further improves results (1.44 and 1.59 Sum, respectively), and our strategy consistently increases all metrics (1.58 and 1.67 Sum, respectively). Common class performance remains below the reference models (-0.07 AP-Com), which we conjecture can be attributed to our relatively small self driving training data (only " + }, + { + "bbox": [ + 304, + 450, + 547, + 715 + ], + "type": "inline_equation", + "content": "10k" + }, + { + "bbox": [ + 304, + 450, + 547, + 715 + ], + "type": "text", + "content": " images)." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23483" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 61, + 70, + 276, + 301 + ], + "blocks": [ + { + "bbox": [ + 61, + 70, + 276, + 301 + ], + "lines": [ + { + "bbox": [ + 61, + 70, + 276, + 301 + ], + "spans": [ + { + "bbox": [ + 61, + 70, + 276, + 301 + ], + "type": "table", + "html": "
DatasetOriginal nameClosest to new name (embedding similarity)Example image
Apperance based Correction
LVISarcticboot (0.46), ski boot (0.30)
CODAtricyclecart (0.62), rickshaw (0.67)
CODAmiscwaterwheel (0.47), wheel (0.45)
British to American English Correction
LVISclothes hamper wall socket garbage postbox trouserslaundry basket (0.52), shopping basket (0.39) outlet (0.62), power outlet (0.60) trash can (0.47), trashcan (0.45) mailbox (0.47), telephone booth (0.46) clothes (0.42), pants (0.41)
Object type first
LVISbell pepper bridal gown horse buggy horse carriage celery cymbalvegetable (0.55), pepper (0.51), bell pepper (0.51) gown (0.57), dress (0.57), bridal gown (0.55) horse-drawn vehicle (0.66), horse buggy (0.64) horse-drawn vehicle (0.68), horse carriage (0.65) green vegetables (0.50), celery (0.47) musical instrument (0.55), cymbal (0.46)
", + "image_path": "90fed5bd5f60a91b58e34c60e7ebd919f8c3fd7114e540dba2538d18f94d3c96.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 56, + 309, + 277, + 321 + ], + "lines": [ + { + "bbox": [ + 56, + 309, + 277, + 321 + ], + "spans": [ + { + "bbox": [ + 56, + 309, + 277, + 321 + ], + "type": "text", + "content": "Table 5. Interpretability results: changes to original class names." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 47, + 344, + 142, + 357 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 344, + 142, + 357 + ], + "spans": [ + { + "bbox": [ + 47, + 344, + 142, + 357 + ], + "type": "text", + "content": "4.3. Interpretability" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 364, + 287, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 364, + 287, + 507 + ], + "spans": [ + { + "bbox": [ + 46, + 364, + 287, + 507 + ], + "type": "text", + "content": "One key advantage of the proposed approach is a beneficial effect on model interpretability; the method allows evaluation of the manner in which class names are modified, based on image content. Here, we aim to evaluate how object classes were renamed in our object detection experiments. This experiment can usefully inform us about model biases, sources of classification errors, potential mislabelling errors and the suitability of handcrafted names. For this purpose, we build a reference vocabulary of 3031 object names, using the following dataset class names (filtered for duplicates): LVIS (1203 classes), CODA (43 classes), Objects365 [40] (365 classes), ImageNet (1000 classes), and the THINGS [16] database (1854 concepts)." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "spans": [ + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "text", + "content": "Individual word embeddings may not have very valuable meanings in terms of interpretability, especially considering that certain class names comprise multiple tokens. As a result, we compare learned representations to our reference vocabulary at the output of the text transformer encoder, using the shared prompt template context: 'a photo of a'. More formally, for each word " + }, + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "inline_equation", + "content": "\\mathbf{e}^l" + }, + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "text", + "content": ", in our list of learned embeddings, we compute the sentence representation " + }, + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "inline_equation", + "content": "f(t(\\mathbf{e}^l))" + }, + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "inline_equation", + "content": "t(\\mathbf{e}^l)" + }, + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "text", + "content": " is the sentence constructed using our prompt prefix and word embedding " + }, + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "inline_equation", + "content": "\\mathbf{e}^l" + }, + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "text", + "content": ". We repeat the procedure for each instance in our vocabulary, using the model's pre-trained word embeddings " + }, + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "inline_equation", + "content": "\\mathbf{e}^v" + }, + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "text", + "content": ", and obtain " + }, + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "inline_equation", + "content": "f(t(\\mathbf{e}^v))" + }, + { + "bbox": [ + 46, + 509, + 288, + 664 + ], + "type": "text", + "content": ". We then simply compute the cosine distance between all pairs of learned and reference word representations." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "content": "We carry out this process for models trained on the LVIS dataset (base model fine-tuned on base classes), and on the CODA 2.0 dataset (zero-shot model). In Table 5, we show examples of classes that were renamed towards a semantic meaning" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 72, + 545, + 275 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 275 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 275 + ], + "type": "text", + "content": "closer to image content, and highlight some labelling errors. \"Arctic\" and \"misc\" are evidence of non representative class names. The \"tricycle\" class appears to have been mislabelled, with labelled vehicles looking closer to \"rickshaws\" and \"carts\". We additionally report two interesting trends: 1) the model tends to adjust word embeddings from British English representations to American English, where the latter is expected to be the dominant language, on which large scale CLIP models were trained; 2) fine-grained category representations tend to be closer to supercategory word types (e.g. vegetable, musical instrument), suggesting that the model also leverages cross-class similarities towards the learning of representations for rare classes. The first observation notably highlights our method's potential for cross-lingual model adaptation: given a dataset in one language, and a model trained in another language, we can learn optimal class names adapted to the pre-trained language model without any additional language translation efforts." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 275, + 546, + 372 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 275, + 546, + 372 + ], + "spans": [ + { + "bbox": [ + 304, + 275, + 546, + 372 + ], + "type": "text", + "content": "In Figure 3 we highlight the relationship connecting class rarity and embedding similarity between learned and original class names, on the LVIS dataset. As previously conjectured in Section 4.2, there is a clear correlation between these variables, highlighting how multi-modal models also suffer from common long tail related issues (i.e. poorer semantic mapping between rare words and their corresponding visual appearance). Additional results are available in our supplementary materials." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 382, + 375, + 395 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 382, + 375, + 395 + ], + "spans": [ + { + "bbox": [ + 306, + 382, + 375, + 395 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 403, + 545, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 403, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 304, + 403, + 545, + 521 + ], + "type": "text", + "content": "This work focuses on the concept of class name and resulting impact on performance of vision-language recognition models. Complementary to prompt tuning, which seeks to optimise the context of text queries, we propose to automatically learn optimal class names from visual content. Our solution can easily be integrated in classification and object detection models, achieving significant gains, with respect to the base model, in all settings. We additionally demonstrate promising interpretability properties by showing how class semantic meanings were adjusted, providing data and model analysis properties." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 522, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 522, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 522, + 546, + 713 + ], + "type": "text", + "content": "Learning class names can be viewed as an alternative to linear probing. The former approach, in contrast to probing, maintains key properties inherent to vision-language models (e.g. open-vocabulary). One observed limitation of our approach, in common with linear probing, is relatively weaker performance, in comparison with prompt tuning and image queries, under very low data regimes (i.e. 1-2 shots). Exploring a combination of learned text and image queries makes for a promising future avenue. While learning class names reduces dependency on prompt context, we still observe improvements using engineering prompts, in particular in open-vocabulary settings. As such, our method has the potential to further benefit from combination with prompt tuning techniques [50, 51], where prompts can provide key domain adaptation properties. Last but not least, learning class names has the potential to benefit class agnostic detection, via clustering and learning cluster semantics." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23484" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 104, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 104, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 104, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 715 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "text", + "content": "[1] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101-mining discriminative components with random forests. In European conference on computer vision, pages 446-461. Springer, 2014. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 136, + 288, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 136, + 288, + 179 + ], + "spans": [ + { + "bbox": [ + 53, + 136, + 288, + 179 + ], + "type": "text", + "content": "[2] Zhaowei Cai, Gukyeong Kwon, Avinash Ravichandran, Erhan Bas, Zhuowen Tu, Rahul Bhotika, and Stefano Soatto. X-detr: A versatile architecture for instance-wise vision-language tasks. arXiv preprint arXiv:2204.05626, 2022. 2, 4, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 180, + 288, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 180, + 288, + 224 + ], + "spans": [ + { + "bbox": [ + 53, + 180, + 288, + 224 + ], + "type": "text", + "content": "[3] Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi. Describing textures in the wild. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 3606-3613, 2014. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 225, + 288, + 269 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 225, + 288, + 269 + ], + "spans": [ + { + "bbox": [ + 53, + 225, + 288, + 269 + ], + "type": "text", + "content": "[4] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248-255. IEEE, 2009. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 270, + 288, + 324 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 270, + 288, + 324 + ], + "spans": [ + { + "bbox": [ + 53, + 270, + 288, + 324 + ], + "type": "text", + "content": "[5] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 325, + 288, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 325, + 288, + 380 + ], + "spans": [ + { + "bbox": [ + 53, + 325, + 288, + 380 + ], + "type": "text", + "content": "[6] Yu Du, Fangyun Wei, Zihe Zhang, Miaojing Shi, Yue Gao, and Guoqi Li. Learning to prompt for open-vocabulary object detection with vision-language model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14084-14093, 2022. 2, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 380, + 288, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 380, + 288, + 435 + ], + "spans": [ + { + "bbox": [ + 53, + 380, + 288, + 435 + ], + "type": "text", + "content": "[7] Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. In 2004 conference on computer vision and pattern recognition workshop, pages 178-178. IEEE, 2004. 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 435, + 288, + 479 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 435, + 288, + 479 + ], + "spans": [ + { + "bbox": [ + 53, + 435, + 288, + 479 + ], + "type": "text", + "content": "[8] Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, and Tomas Mikolov. Devise: A deep visual-semantic embedding model. Advances in neural information processing systems, 26, 2013. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 480, + 288, + 534 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 480, + 288, + 534 + ], + "spans": [ + { + "bbox": [ + 53, + 480, + 288, + 534 + ], + "type": "text", + "content": "[9] Andreas Fürst, Elisabeth Rumetshofer, Viet Tran, Hubert Ramsauer, Fei Tang, Johannes Lehner, David Kreil, Michael Kopp, Günter Klambauer, Angela Bitto-Nemling, et al. Cloob: Modern hopfield networks with infoloob outperform clip. arXiv preprint arXiv:2110.11316, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 535, + 287, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 535, + 287, + 579 + ], + "spans": [ + { + "bbox": [ + 48, + 535, + 287, + 579 + ], + "type": "text", + "content": "[10] Rinon Gal, Yuval Alaluf, Yuval Atzmon, Or Patashnik, Amit H Bermano, Gal Chechik, and Daniel Cohen-Or. An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618, 2022. 2, 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 580, + 287, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 580, + 287, + 612 + ], + "spans": [ + { + "bbox": [ + 48, + 580, + 287, + 612 + ], + "type": "text", + "content": "[11] Tianyu Gao, Adam Fisch, and Danqi Chen. Making pre-trained language models better few-shot learners. arXiv preprint arXiv:2012.15723, 2020. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 613, + 288, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 613, + 288, + 656 + ], + "spans": [ + { + "bbox": [ + 48, + 613, + 288, + 656 + ], + "type": "text", + "content": "[12] Chuanxing Geng, Sheng-jun Huang, and Songcan Chen. Recent advances in open set recognition: A survey. IEEE transactions on pattern analysis and machine intelligence, 43(10):3614-3631, 2020. 1" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 658, + 288, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 288, + 690 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 288, + 690 + ], + "type": "text", + "content": "[13] Xiuye Gu, Tsung-Yi Lin, Weicheng Kuo, and Yin Cui. Zero-shot detection via vision and language knowledge distillation. CoRR, abs/2104.13921, 2021. 2, 4" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 691, + 288, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 288, + 715 + ], + "type": "text", + "content": "[14] Agrim Gupta, Piotr Dollar, and Ross Girshick. LVIS: A dataset for large vocabulary instance segmentation. In Proceedings of the" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 547, + 714 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 326, + 73, + 547, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 547, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 547, + 95 + ], + "type": "text", + "content": "IEEE Conference on Computer Vision and Pattern Recognition, 2019. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 96, + 547, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 547, + 150 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 547, + 150 + ], + "type": "text", + "content": "[15] Jianhua Han, Xiwen Liang, Hang Xu, Kai Chen, Lanqing Hong, Jiageng Mao, Chaoqiang Ye, Wei Zhang, Zhenguo Li, Xiaodan Liang, et al. Soda10m: A large-scale 2d self/semi-supervised object detection dataset for autonomous driving. arXiv preprint arXiv:2106.11118, 2021. 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 152, + 547, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 152, + 547, + 196 + ], + "spans": [ + { + "bbox": [ + 308, + 152, + 547, + 196 + ], + "type": "text", + "content": "[16] Martin N. Hebart, Adam H. Dickter, Alexis Kidder, Wan Y. Kwok, Anna Corriveau, Caitlin Van Wicklin, and Chris I. Baker. Things: A database of 1,854 object concepts and more than 26,000 naturalistic object images. PLOS ONE, 14:1-24, 10 2019. 8" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 198, + 546, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 198, + 546, + 252 + ], + "spans": [ + { + "bbox": [ + 308, + 198, + 546, + 252 + ], + "type": "text", + "content": "[17] Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. Introducing eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pages 204-207, 2018. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 254, + 546, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 254, + 546, + 308 + ], + "spans": [ + { + "bbox": [ + 308, + 254, + 546, + 308 + ], + "type": "text", + "content": "[18] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 309, + 546, + 354 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 309, + 546, + 354 + ], + "spans": [ + { + "bbox": [ + 308, + 309, + 546, + 354 + ], + "type": "text", + "content": "[19] KJ Joseph, Salman Khan, Fahad Shahbaz Khan, and Vineeth N Balasubramanian. Towards open world object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5830-5840, 2021. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 354, + 547, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 354, + 547, + 399 + ], + "spans": [ + { + "bbox": [ + 308, + 354, + 547, + 399 + ], + "type": "text", + "content": "[20] Armand Joulin, Laurens van der Maaten, Allan Jabri, and Nicolas Vasilache. Learning visual features from large weakly supervised data. In European Conference on Computer Vision, pages 67-84. Springer, 2016. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 399, + 546, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 399, + 546, + 443 + ], + "spans": [ + { + "bbox": [ + 308, + 399, + 546, + 443 + ], + "type": "text", + "content": "[21] Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, and Weidi Xie. Prompting visual-language models for efficient video understanding. In European Conference on Computer Vision, pages 105–124. Springer, 2022. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 444, + 546, + 499 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 444, + 546, + 499 + ], + "spans": [ + { + "bbox": [ + 308, + 444, + 546, + 499 + ], + "type": "text", + "content": "[22] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790, 2021. 1, 2, 4, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 500, + 547, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 500, + 547, + 544 + ], + "spans": [ + { + "bbox": [ + 308, + 500, + 547, + 544 + ], + "type": "text", + "content": "[23] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In Proceedings of the IEEE international conference on computer vision workshops, pages 554–561, 2013. 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 545, + 546, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 545, + 546, + 578 + ], + "spans": [ + { + "bbox": [ + 308, + 545, + 546, + 578 + ], + "type": "text", + "content": "[24] Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691, 2021. 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 579, + 546, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 579, + 546, + 634 + ], + "spans": [ + { + "bbox": [ + 308, + 579, + 546, + 634 + ], + "type": "text", + "content": "[25] Kaican Li, Kai Chen, Haoyu Wang, Lanqing Hong, Chaoqiang Ye, Jianhua Han, Yukuai Chen, Wei Zhang, Chunjing Xu, Dit-Yan Yeung, et al. Coda: A real-world road corner case dataset for object detection in autonomous driving. arXiv preprint arXiv:2203.07724, 2022. 6, 7" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 635, + 547, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 635, + 547, + 679 + ], + "spans": [ + { + "bbox": [ + 308, + 635, + 547, + 679 + ], + "type": "text", + "content": "[26] Liunian Harold Li*, Pengchuan Zhang*, Haotian Zhang*, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, Kai-Wei Chang, and Jianfeng Gao. Grounded language-image pre-training. In CVPR, 2022. 2, 5, 6" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 681, + 546, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 681, + 546, + 714 + ], + "spans": [ + { + "bbox": [ + 308, + 681, + 546, + 714 + ], + "type": "text", + "content": "[27] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "23485" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 73, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 66, + 73, + 288, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 73, + 288, + 105 + ], + "spans": [ + { + "bbox": [ + 66, + 73, + 288, + 105 + ], + "type": "text", + "content": "pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10965-10975, 2022. 4" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 106, + 288, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 106, + 288, + 138 + ], + "spans": [ + { + "bbox": [ + 48, + 106, + 288, + 138 + ], + "type": "text", + "content": "[28] Xiang Lisa Li and Percy Liang. Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190, 2021.3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 140, + 288, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 140, + 288, + 183 + ], + "spans": [ + { + "bbox": [ + 48, + 140, + 288, + 183 + ], + "type": "text", + "content": "[29] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208, 2021. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 184, + 288, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 184, + 288, + 228 + ], + "spans": [ + { + "bbox": [ + 48, + 184, + 288, + 228 + ], + "type": "text", + "content": "[30] Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. arXiv preprint arXiv:2107.13586, 2021. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 228, + 288, + 271 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 228, + 288, + 271 + ], + "spans": [ + { + "bbox": [ + 48, + 228, + 288, + 271 + ], + "type": "text", + "content": "[31] Muhammad Maaz, Hanoona Bangalath Rasheed, Salman Hameed Khan, Fahad Shahbaz Khan, Rao Muhammad Anwer, and Ming-Hsuan Yang. Multi-modal transformers excel at class-agnostic object detection. arXiv, 2021. 2, 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 272, + 288, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 272, + 288, + 304 + ], + "spans": [ + { + "bbox": [ + 48, + 272, + 288, + 304 + ], + "type": "text", + "content": "[32] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. 06 2013. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 304, + 288, + 360 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 304, + 288, + 360 + ], + "spans": [ + { + "bbox": [ + 48, + 304, + 288, + 360 + ], + "type": "text", + "content": "[33] Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, et al. Simple open-vocabulary object detection with vision transformers. arXiv preprint arXiv:2205.06230, 2022.1,2,4,5,6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 360, + 288, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 360, + 288, + 404 + ], + "spans": [ + { + "bbox": [ + 48, + 360, + 288, + 404 + ], + "type": "text", + "content": "[34] Maria-Elena Nilsback and Andrew Zisserman. Automated flower classification over a large number of classes. In 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing, pages 722-729. IEEE, 2008. 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 404, + 288, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 404, + 288, + 437 + ], + "spans": [ + { + "bbox": [ + 48, + 404, + 288, + 437 + ], + "type": "text", + "content": "[35] Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar. Cats and dogs. In 2012 IEEE conference on computer vision and pattern recognition, pages 3498-3505. IEEE, 2012. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 437, + 288, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 437, + 288, + 502 + ], + "spans": [ + { + "bbox": [ + 48, + 437, + 288, + 502 + ], + "type": "text", + "content": "[36] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 2, 3, 4" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 503, + 288, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 503, + 288, + 536 + ], + "spans": [ + { + "bbox": [ + 48, + 503, + 288, + 536 + ], + "type": "text", + "content": "[37] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 537, + 288, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 537, + 288, + 592 + ], + "spans": [ + { + "bbox": [ + 48, + 537, + 288, + 592 + ], + "type": "text", + "content": "[38] Yongming Rao, Wenliang Zhao, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu. Denseclip: Language-guided dense prediction with context-aware prompting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18082-18091, 2022. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 592, + 288, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 592, + 288, + 624 + ], + "spans": [ + { + "bbox": [ + 48, + 592, + 288, + 624 + ], + "type": "text", + "content": "[39] Timo Schick and Hinrich Schütze. It's not just size that matters: Small language models are also few-shot learners. arXiv preprint arXiv:2009.07118, 2020. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 625, + 288, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 625, + 288, + 679 + ], + "spans": [ + { + "bbox": [ + 48, + 625, + 288, + 679 + ], + "type": "text", + "content": "[40] Shuai Shao, Zeming Li, Tianyuan Zhang, Chao Peng, Gang Yu, Xiangyu Zhang, Jing Li, and Jian Sun. Objects365: A large-scale, high-quality dataset for object detection. In 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 8429-8438, 2019. 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 680, + 288, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 288, + 712 + ], + "type": "text", + "content": "[41] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014. 2" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 498 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 307, + 73, + 547, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 547, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 547, + 106 + ], + "type": "text", + "content": "[42] Khurram Soomro, Amir Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. CoRR, 12 2012. 5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 107, + 547, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 547, + 161 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 547, + 161 + ], + "type": "text", + "content": "[43] Jianxiong Xiao, James Hays, Krista A. Ehinger, Aude Oliva, and Antonio Torralba. Sun database: Large-scale scene recognition from abbey to zoo. In 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 3485-3492, 2010. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 163, + 547, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 163, + 547, + 206 + ], + "spans": [ + { + "bbox": [ + 307, + 163, + 547, + 206 + ], + "type": "text", + "content": "[44] Yuan Yao, Ao Zhang, Zhengyan Zhang, Zhiyuan Liu, Tat-Seng Chua, and Maosong Sun. Cpt: Colorful prompt tuning for pre-trained vision-language models. arXiv preprint arXiv:2109.11797, 2021. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 207, + 547, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 207, + 547, + 252 + ], + "spans": [ + { + "bbox": [ + 307, + 207, + 547, + 252 + ], + "type": "text", + "content": "[45] Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, and Shih-Fu Chang. Open-vocabulary object detection using captions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14393-14402, 2021. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 253, + 547, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 253, + 547, + 296 + ], + "spans": [ + { + "bbox": [ + 307, + 253, + 547, + 296 + ], + "type": "text", + "content": "[46] Xiaohua Zhai, Xiao Wang, Basil Mustafa, Andreas Steiner, Daniel Keysers, Alexander Kolesnikov, and Lucas Beyer. Lit: Zero-shot transfer with locked-image text tuning. CoRR, abs/2111.07991, 2021. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 297, + 547, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 297, + 547, + 352 + ], + "spans": [ + { + "bbox": [ + 307, + 297, + 547, + 352 + ], + "type": "text", + "content": "[47] Renrui Zhang, Ziyu Guo, Wei Zhang, Kunchang Li, Xupeng Miao, Bin Cui, Yu Qiao, Peng Gao, and Hongsheng Li. Pointclip: Point cloud understanding by clip. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8552-8562, 2022. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 353, + 547, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 353, + 547, + 387 + ], + "spans": [ + { + "bbox": [ + 307, + 353, + 547, + 387 + ], + "type": "text", + "content": "[48] Chen Zheng, Quan Guo, and Parisa Kordjamshidi. Cross-modality relevance for reasoning on language and vision. arXiv preprint arXiv:2005.06035, 2020. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 387, + 547, + 419 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 387, + 547, + 419 + ], + "spans": [ + { + "bbox": [ + 307, + 387, + 547, + 419 + ], + "type": "text", + "content": "[49] Zexuan Zhong, Dan Friedman, and Danqi Chen. Factual probing is [mask]: Learning vs. learning to recall. arXiv preprint arXiv:2104.05240, 2021. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 420, + 547, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 420, + 547, + 464 + ], + "spans": [ + { + "bbox": [ + 307, + 420, + 547, + 464 + ], + "type": "text", + "content": "[50] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Conditional prompt learning for vision-language models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16816-16825, 2022. 2, 3, 5, 8" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 465, + 547, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 465, + 547, + 498 + ], + "spans": [ + { + "bbox": [ + 307, + 465, + 547, + 498 + ], + "type": "text", + "content": "[51] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9):2337-2348, 2022. 2, 3, 5, 6, 8" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "23486" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/9e708598-d8b4-40c1-87d1-c7aee00317d3_content_list.json b/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/9e708598-d8b4-40c1-87d1-c7aee00317d3_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..8ce84c4ef895d0a705a7f593736c70330a9961d8 --- /dev/null +++ b/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/9e708598-d8b4-40c1-87d1-c7aee00317d3_content_list.json @@ -0,0 +1,1961 @@ +[ + { + "type": "text", + "text": "Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data", + "text_level": 1, + "bbox": [ + 138, + 130, + 831, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Nilesh Kulkarni, Linyi Jin, Justin Johnson, David F. Fouhey \nUniversity of Michigan", + "bbox": [ + 246, + 180, + 722, + 217 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 251, + 313, + 266 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We introduce a method that can learn to predict scene-level implicit functions for 3D reconstruction from posed RGBD data. At test time, our system maps a previously unseen RGB image to a 3D reconstruction of a scene via implicit functions. While implicit functions for 3D reconstruction have often been tied to meshes, we show that we can train one using only a set of posed RGBD images. This setting may help 3D reconstruction unlock the sea of accelerometer+RGBD data that is coming with new phones. Our system, D2-DRDF, can match and sometimes outperform current methods that use mesh supervision and shows better robustness to sparse data.", + "bbox": [ + 75, + 282, + 473, + 465 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 494, + 209, + 510 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Consider the image in Figure 1. From this ordinary RGB image, we can understand the complete 3D geometry of the scene, including the floor and walls behind the chairs. Our goal is to enable computers to recover this geometry from a single RGB image. To this end, we present a method that does so while learning only on posed RGBD images.", + "bbox": [ + 75, + 521, + 468, + 611 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The task of reconstructing the full 3D of a scene from a single previously unseen RGB image has long been known to be challenging. Early work on full 3D relied on voxels [6, 17] or meshes [19], but these representations fail on scenes due to topology and memory challenges. Implicit functions (or learning to map each point in $\\mathbb{R}^3$ to a value like the distance to the nearest surface) have shown substantial promise at overcoming these challenges. When conditioned on an image, these have led to several successful methods.", + "bbox": [ + 75, + 612, + 468, + 747 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Unfortunately, the implicit function status quo mainly ties implicit function reconstruction methods to mesh supervision. This symbiosis has emerged since meshes give excellent direct supervision. However, methods are limited to training with an image-aligned mesh that is usually watertight (and often artist-created) [35, 38, 44] and occasionally non-watertight but professionally-scanned [27, 61].", + "bbox": [ + 75, + 750, + 468, + 854 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We present a method, Depth to DRDF (D2-DRDF), that breaks the implicit-function/mesh connection and can train an effective single RGB image implicit 3D reconstruction", + "bbox": [ + 75, + 854, + 468, + 900 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9972482f06efcb8da865e3c47bc664583d66b3eda8446f41321720a2ddec2d5c.jpg", + "image_caption": [ + "Inference: Single RGB Image to 3D" + ], + "image_footnote": [], + "bbox": [ + 504, + 263, + 622, + 349 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/c6e9b749af9806e2e64547e15241b3cfdafb972dc5b029dfa4c02766402192e6.jpg", + "image_caption": [ + "Figure 1. We propose a method, D2-DRDF, that reconstructs full 3D from a single RGB image. During inference (left), our method uses implicit functions to reconstruct the complete 3D including visible and occluded surfaces (visualized as surface normals $\\square$ ) such as the occluded wall and empty floor. For training, our method uses only RGBD images and poses, unlike most prior works that need an explicit and often watertight 3D mesh." + ], + "image_footnote": [], + "bbox": [ + 504, + 349, + 625, + 431 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9e8c7a9425bbd1ed098c420a18029ed3fc4a0963c12954ce962cd50b64781218.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 633, + 263, + 759, + 347 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e8238ac8f3c60b99e4bf280208ce0fa2fdf1449a9231b377344eaddfc13059ed.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 633, + 348, + 756, + 431 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/aa54fd3fd925dac8072d7aefd755222d6ac610ce269de6ace8fdab2ded5cf8f3.jpg", + "image_caption": [ + "Training Data" + ], + "image_footnote": [], + "bbox": [ + 761, + 265, + 888, + 348 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/c6405c2a2f2928f3b60bec0b79754a75b1d7e5d56cc36c8cf95275494e3f17c6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 761, + 348, + 888, + 431 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "system using a set of RGBD images with known pose. We envision that being able to entirely skip meshing will enable the use of vast quantities of lower-quality data from consumers (e.g., from increasingly common consumer phones with LiDAR scanners and accelerometers) as well as robots. In addition to permitting training, the bypassing of meshing may enable adaptation in a new environment on raw data without needing an expert to ensure mesh acquisition.", + "bbox": [ + 496, + 551, + 892, + 672 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Our key insight is that we can use segments of observed free space in depth maps in other views to constrain distance functions. We show this using the Directed Ray Distance Function (DRDF) [27] that has recently shown good performance in 3D reconstruction using implicit functions and has the benefit of not needing watertight meshes for training. Given an input reference view, the DRDF breaks the problem of predicting the 3D surface into a set of independent 1D distance functions, each along a ray through a pixel in the reference view and accounting for only surfaces on the ray. Rather than use an ordinary unsigned distance function, the DRDF signs the distance using the location of the camera's pinhole and the intersections along the ray. While [27] showed their method could be trained on non-watertight meshes, their method is still dependent", + "bbox": [ + 496, + 674, + 893, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "17256", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "on meshes. In our paper, we show that the DRDF can be cleanly supervised using auxiliary views of RGBD observations and their poses. We derive constraints on the DRDF that power loss functions for training our system. While the losses on any one image are insufficient to produce a reconstruction, they provide a powerful signal when accumulated across thousands of training views.", + "bbox": [ + 75, + 90, + 470, + 196 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We evaluate our method on realistic indoor scene datasets and compare it with methods that train with full mesh supervision. Our method is competitive and sometimes even better compared to the best-performing mesh-supervised method [28] with full, professional captures. As we degrade scan completeness, our method largely maintains its performance while mesh-based methods perform substantially worse. We conclude by showing that fine-tuning of our method on a handful of images enables a simple, effective method for fusing and completing scenes from a handful of RGBD images.", + "bbox": [ + 75, + 198, + 470, + 363 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 76, + 376, + 218, + 392 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our approach infers a complete 3D scene from a single RGB image using implicit functions that are supervised via posed RGBD scans. This touches on several long-term goals of 3D computer vision that we discuss below.", + "bbox": [ + 75, + 402, + 468, + 462 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Reconstructing Scenes from a Single Image. At test time, our system produces a full 3D reconstruction from a single RGB image, including occluded regions. This means that our desired output goes beyond 2.5D properties such as depth [4, 12], surface normals [12, 14, 55], or other intrinsic-image properties [23, 47]. Works on attempting to reconstruct complete 3D usually have focused on reconstructing objects from image by using voxels [6, 17] or meshes [18, 19], point clouds [13, 31] or CAD models [22]. These are usually trained on synthetic datasets like ShapeNet [2] or scene datasets [16, 43, 50] and do not generalize well to realistic scenes. Another line of works tries to learn holistic structures [37, 59] or planar surfaces [24, 32, 58] from realistic scanned mesh data [1, 8] Creating realistic mesh based data for scenes requires postprocessing using Poisson surface reconstruction [9, 25] which leads to deviation from raw captures. Manually aligning them is expensive hence 3D object-aligned datasets like [49, 52] are scarce and limited in diversity. Our method avoids these limitations by directly operating on the raw captured RGBD data.", + "bbox": [ + 75, + 463, + 470, + 779 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Reconstructing Scenes from Posed Scans. There has been considerable work on using multiview RGBD data at inference time to produce 3D reconstructions, starting with analytic techniques [7, 9, 21, 29] and now using learning [20, 51, 54]. We use some similar tools to this community: for instance, the ray distances we use are known in this community as a projected distance functions [7]. However, their works solve a fundamentally different problem", + "bbox": [ + 75, + 780, + 468, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "by using posed RGBD data at test time: their goal is to produce a reconstruction from a set of posed RGBD images from one particular scene; our goal is to use a large dataset of posed RGBD data to train a neural network that can map a new single RGB image to a 3D reconstruction.", + "bbox": [ + 496, + 90, + 890, + 167 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our goal of learning to predict reconstructions from a single previously unseen image also distinguishes our work from NeRF [36] and other similar radiance field approaches [3]. Their goal is to learn a radiance field for a particular scene from a set of posed scans. There are methods that try to predict this radiance field from a single image [57]; we compare with a model using similar losses and find that an objective specialized for 3D works better.", + "bbox": [ + 496, + 167, + 890, + 287 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Implicit Functions for 3D Reconstruction. We perform reconstruction with implicit functions. Implicit functions have used for shape and scene modeling as level sets [34], signed distance functions [38], occupancy function [35, 40, 44], distance functions [5, 27, 48] and other modifications like [56]. Our work has two key distinctions. First, many approaches [5, 48, 56] fit an implicit function to one shape (i.e., there is no generalization to new shapes). In contrast, our work produces reconstructions from previously unseen RGB images. Second, the methods that predict an implicit function from a new image [27, 61] assume access to a mesh at training time. On other hand, our work assumes access only to posed RGBD data for training.", + "bbox": [ + 496, + 287, + 890, + 482 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The most similar work to ours is [27] that also aims to learn to reconstruct scenes from a single image by training on realistic data. While [27] shows how to reduce supervision requirements by enabling the use of non-watertight meshes, their method is still limited to mesh supervision. We show how to use posed RGBD data for supervision instead of using an image-aligned mesh. This substantially reduces the requirements for collecting training data.", + "bbox": [ + 496, + 484, + 890, + 604 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Pixel-Aligned 3D Reconstruction & DRDF", + "text_level": 1, + "bbox": [ + 498, + 618, + 879, + 635 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We propose a method to predict full 3D scene structure from a previously unseen RGB image. At inference time, our method receives a single image with no depth information. We refer to this view as the reference view. As output, the method produces an estimate of a distance function at each point in a pre-defined set of 3D points. Given this volume of predicted distances, one can decode the predicted distance into a set of surfaces: e.g., if the predicted distance were the unsigned distance to the nearest surface, one could declare all points with sufficiently small predicted distance to be surfaces. At training time, our network is given supervision for predicting its distance function. Previously, supervision been done via a mesh that provides oracle distance calculations. In §4, we show how to derive supervision from posed RGBD images at auxiliary views instead.", + "bbox": [ + 496, + 643, + 890, + 869 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The approaches in the paper follow pixelNerf [57] for predicting a distance at a 3D point $\\mathbf{x}$ . The network accepts", + "bbox": [ + 498, + 869, + 890, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "17257", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/cab7f3ed4744a5aaaa785430b3e0d5da55ed6e40e041b1157ef33398d891a61a.jpg", + "image_caption": [ + "Figure 2. Model and DRDF overview. (Left) Given a single input RGB image $I$ and query point $\\mathbf{x}$ on a ray $\\vec{\\mathbf{r}}$ in a pre-defined grid, all models, like [57] extract image features at the projection $\\pi(\\mathbf{x})$ of $\\mathbf{x}$ with a backbone and then predict the distance using a MLP on the backbone features and a positional encoding of $\\mathbf{x}$ . (Right) Given a ray $\\vec{\\mathbf{r}}$ through the scene, ray distance functions are defined as the distance to intersections of the ray with the scene. This permits analyzing the 3D scene as a set of 1D distances per ray. The Directed Ray Distance Function is further signed to be positive before and negative after an intersection." + ], + "image_footnote": [], + "bbox": [ + 86, + 88, + 449, + 224 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "an image $I$ and 3D point $\\mathbf{x}$ and makes a prediction $f_{\\theta}(\\mathbf{x}, I)$ . As shown in Fig. 2, the network extracts a convolutional feature from a backbone $\\mathbf{B}$ at the projection of $\\mathbf{x}$ , denoted $\\pi(\\mathbf{x})$ . The image feature is concatenated to a positional encoding of $\\mathbf{x}$ and put into a MLP to produce the final prediction $f_{\\theta}(\\mathbf{x}, I)$ . By repeating this inference for a set of 3D points, the network can infer the full 3D of the scene.", + "bbox": [ + 75, + 383, + 468, + 489 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Training. In a conventional setup, the network $f_{\\theta}(\\mathbf{x},I)$ is trained by obtaining samples from a mesh and directly training the network via regression. Given a mesh that is coaligned with an image, one can obtain an $n$ tuples consisting of an image $I_{i}$ , 3D point $\\mathbf{x}_i$ and ground-truth distance $d_{i}$ . Then the network is trained by minimizing the empirical risk $\\frac{1}{n}\\sum_{i = 1}^{n}\\mathcal{L}(f_{\\theta}(\\mathbf{x}_i,I_i),d_i)$ for some loss $\\mathcal{L}$ like L1.", + "bbox": [ + 75, + 489, + 468, + 598 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "What Distance Function Should We Predict? There are multiple distance functions in the literature, and the above formulation enables predicting any of them. To explain distinctions, assume we are given a point $\\mathbf{x}$ at which we want to evaluate a distance and a scene $S \\subseteq \\mathbb{R}^3$ that we want to evaluate the distance to.", + "bbox": [ + 75, + 597, + 468, + 686 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In a standard distance function, one defines a distance from a point $\\mathbf{x}$ to the entire scene $S$ . For instance, the Unsigned Distance Function (UDF) is defined as $\\min_{\\mathbf{x}' \\in S} ||\\mathbf{x} - \\mathbf{x}'||$ . In a ray-based distance function [7, 27], the distance is defined from $\\mathbf{x}$ only to the points in the scene that lie on the ray $\\vec{\\mathbf{r}}$ from the reference view pinhole towards $\\mathbf{x}$ . In other words, the ray distance function is defined as the distance to the intersections of the ray with the scene. For instance, the Unsigned Ray Distance Function (URDF) is defined as $\\min_{\\mathbf{x}' \\in S, \\mathbf{x}' \\text{ on } \\vec{\\mathbf{r}}} ||\\mathbf{x} - \\mathbf{x}'||$ . This definition means that when predicting the distance at a point $\\mathbf{x}$ , all of the points that define the ray distance function project to the same pixel, $\\pi(\\mathbf{x})$ , which in turn means that neural networks do not need as large receptive fields [27].", + "bbox": [ + 75, + 689, + 470, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Since a ray-based distance function is defined only by scene points that intersect a ray, we can reduce the 3D problem to a 1D problem, as illustrated in Fig. 2. Rather than represent each 3D point as a 3D point, we represent it as the distance along the ray $\\vec{\\mathbf{r}}$ : the vector $\\mathbf{x}$ is represented via a scalar $z$ such that $\\mathbf{x} = z\\vec{\\mathbf{r}}$ . Then, if the $i^{th}$ intersection along the ray is $s_i\\vec{\\mathbf{r}}$ for $s_i \\in \\mathbb{R}$ , the URDF is defined as the distance to the nearest intersection along that ray, or $d_{\\mathrm{UR}}(z) = \\min_{i=1,\\dots,k} |z - s_i|$ .", + "bbox": [ + 496, + 90, + 890, + 227 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Directed Ray Distance Function. The Directed Ray Distance Function (DRDF) is a ray distance function that incorporates a sign into the URDF. In particular, the DRDF is defined as $d_{\\mathrm{DR}}(z) = \\operatorname{dir}(z)d_{\\mathrm{UR}}(z)$ , where the predicate $\\operatorname{dir}(z)$ is positive if the nearest intersection is ahead of $z$ along the ray from the pinhole and negative otherwise. This is pictured in Fig. 2. The presence of both positive and negative components leads to better behavior under uncertainty [27]. Critical Properties of DRDFs. Two critical properties that we use to define supervision are that at $z$ , the distance to the nearest intersection is $|d_{\\mathrm{DR}}(z)|$ and the nearest intersection on the ray is at $z + d_{\\mathrm{DR}}(z)$ .", + "bbox": [ + 496, + 227, + 892, + 409 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Depth-Supervised DRDF Reconstruction", + "text_level": 1, + "bbox": [ + 498, + 422, + 856, + 439 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Having introduced the setup in §3, we now explain how to train a network to predict an image-conditioned DRDF without access to an underlying mesh but instead access to posed RGBD images. Concretely, given a point $z\\vec{\\mathbf{r}}$ , we aim to define a loss function $\\mathcal{L}$ that evaluates a network's prediction of the DRDF $f_{\\theta}((z\\vec{\\mathbf{r}}),I)$ .", + "bbox": [ + 496, + 446, + 890, + 537 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our key insight is that we can sometimes see this point $z\\vec{\\mathbf{r}}$ in other views and these observations of $z\\vec{\\mathbf{r}}$ provide a constraints on what the value of the DRDF can be. Consider, for instance, the point 3m out from reference view camera pinhole along the red ray in Fig. 3. If we project this point into the auxiliary view, we know that its distance to the camera is closer to auxiliary view's pinhole than the depthmap observed at the auxiliary view. Moreover, we can see that a segment of the ray is visible, starting at the point $s\\vec{\\mathbf{r}}$ and ending at the point $e\\vec{\\mathbf{r}}$ with the ending point $e\\vec{\\mathbf{r}}$ closer to $z\\vec{\\mathbf{r}}$ than $s\\vec{\\mathbf{r}}$ is.", + "bbox": [ + 496, + 537, + 892, + 703 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We can derive supervision on $d_{\\mathrm{DR}}(z)$ from the segment of visible free space observable in the auxiliary view by using the fact that the nearest intersection at $z$ is $|d_{\\mathrm{DR}}(z)|$ units away. Since $e$ is nearer than $s$ , and there are no intersections in the free space between $s$ and $e$ , we know that there cannot be an intersection within $(e - z)$ of $z$ . This gives an inequality that $|d_{\\mathrm{DR}}(z)| \\geq (e - z)$ : otherwise there would be an intersection in the visible free space. If the ends of the segment are actual intersections where the ray visibly intersects an object, we obtain an equality constraint on $d_{\\mathrm{DR}}(z)$ since we actually see the nearest intersection. In other cases, the ray simply disoccludes or is occluded, and we can only have an inequality. We denote intersection start/end events as $\\mathcal{I}$ .", + "bbox": [ + 496, + 704, + 893, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "17258", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/42c17679cbf9f97ccf8ad087dbe7f3a0a9e58596c79e9aa6d7ef42a0b96fb39a.jpg", + "image_caption": [ + "Reference" + ], + "image_footnote": [], + "bbox": [ + 88, + 109, + 207, + 185 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/e8e9a9367ab7c2ceaf0bbfd5f4b71bcd2cf9bd86d013e41b5a51a7cf241ec4ed.jpg", + "image_caption": [ + "Auxiliary View + Zoomed" + ], + "image_footnote": [], + "bbox": [ + 207, + 109, + 330, + 184 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/9537e6a13690426262a9b83e7186eafe37778326690bd42f73ca09b95742dd55.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 331, + 111, + 452, + 185 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/e67b18dcfef93ea2e6be37e25786b1012540dfb14c4cd415513442eeaa79f6b5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 96, + 186, + 199, + 255 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/04a74f8bcbc027a2d5fd840e947a9ebc76fa612aa3b51b602f2408267d8aa966.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 207, + 186, + 330, + 258 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/c46cbe256f550127d8a93792d16ec36df5816cb263fbea8c8ad51303821d871b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 331, + 186, + 452, + 258 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/459e5a4e8508c89e1b9e318de1c21ae9d0f0c4caea637f8c35a3ed5f4b713c10.jpg", + "image_caption": [ + "Figure 3. A red ray originates in the reference camera. Given a point $\\mathbf{x}$ that is $z$ units along the ray $\\vec{\\mathbf{r}}$ , we can test if $z\\vec{\\mathbf{r}}$ is visible in an auxiliary view by comparing its projected depth with the RGBD image. The point $z$ is visible, along with a segment of the ray from $s$ to $e$ units. Since the distance to the nearest intersection is $|d_{\\mathrm{DR}}(z)|$ and we know there are no intersections within $(e - z)$ units of $z$ , we can constrain that $|d_{\\mathrm{DR}}(z)| \\geq (e - z)$ ." + ], + "image_footnote": [], + "bbox": [ + 122, + 263, + 419, + 286 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "and occlusion start/end events as $\\mathcal{O}$ and distinguish them by checking if the projected z-value of the ray in auxiliary view is sufficiently close to the recorded auxiliary view's depthmap value.", + "bbox": [ + 75, + 395, + 468, + 455 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Losses", + "text_level": 1, + "bbox": [ + 76, + 467, + 163, + 481 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We now convert the concept of using freespace to constrain the DRDF into concrete losses. Recall that our goal is to evaluate a prediction $y = f_{\\theta}(z\\vec{\\mathbf{r}},I)$ from our network and that the point at $z$ along $\\vec{\\mathbf{r}}$ is in some segment of visible freespace from $s$ to $e$ along $\\vec{\\mathbf{r}}$ . Our goal is to penalize the prediction $y$ . Since there are two start/end event classes $(1,0)$ , there are four segment types: $11,10,01$ , and $00$ . We show several of these segments in Figure 5.", + "bbox": [ + 75, + 491, + 468, + 611 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To assist the loss definitions, we define variables $l_{s} = s - z$ , and $l_{e} = e - z$ which we plot in Fig. 4 as a function of $z$ . $l_{s}$ is the value of $d_{\\mathrm{DR}}$ if the closest intersection is at $s$ , and $l_{e}$ is the value of $d_{\\mathrm{DR}}$ if the closest intersection is at $e$ . The values $l_{s}, l_{e}$ define equalities for intersections and inequalities for occlusions.", + "bbox": [ + 75, + 612, + 468, + 702 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$\\mathcal{L}_{\\mathcal{II}}$ : II segment. Given a segment bounded by two intersections, the nearest intersections are known exactly as $l_{s}$ or $l_{e}$ depending on whether $z < \\frac{s + e}{2}$ or not. We penalize the $\\ell_1$ error between the prediction $y$ and the known DRDF, or $\\mathcal{L}_{\\mathcal{II}}(y) = |y - l_s|$ if $z < \\frac{s + e}{2}$ and $|y - l_e|$ otherwise. This penalty is zero only when $y$ is equal to the known DRDF. $\\mathcal{L}_{\\mathcal{OO}}$ : OO segment. Given a segment bounded by two occlusion events, the exact DRDF is not known, but the visible free space rules out potential values. Since $|d_{\\mathrm{DR}}(z)|$ is the distance to the nearest intersection, $d_{\\mathrm{DR}}(z)$ cannot lie in $[l_s,l_e]$ since such a value would imply an intersection in free space between $s$ and $e$ . We penalize incursions into $[l_s,l_e]$ with a $\\ell_1$ penalty: if we denote halfway be", + "bbox": [ + 75, + 703, + 468, + 901 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/ae9a371f96585ca101e40c16c92787742c99dd71c964cce5dc90dcaced684ce1.jpg", + "image_caption": [ + "Figure 4. Loss functions for a starting $s$ and ending $e$ events spaced a unit apart. Intersection events (blue) define the DRDF precisely, and we penalize the network from deviating. Occlusion events (purple) provide bounds that we penalize the network for violating. We plot $l_{s}$ as a line from the start event and $l_{e}$ as a line from the end event. Loss: 0 1.7" + ], + "image_footnote": [], + "bbox": [ + 500, + 87, + 617, + 210 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/9c28f6c999a70ab482978fae2a158782515bc61fc275dc05a0259af811b42ab0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 622, + 88, + 709, + 210 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/0c1666bcc0bec8fc35c2c2d8f9558df12d24aaab427375aed3ffe2a4ad10d72f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 714, + 88, + 802, + 210 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/6c01a2522fc92aa086a1c86dc879de1bb9ee4a6a18edc8e9b3101bb4fa111b2c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 807, + 88, + 893, + 210 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "tween $l_s$ and $l_e$ as $h$ , then this can be done as $\\mathcal{L}_{\\mathcal{O}\\mathcal{O}}(y) = \\max (0, l_e - h - |y - h|)$ . The resulting penalty is zero if $y \\leq l_s$ or if $y \\geq l_e$ .", + "bbox": [ + 496, + 319, + 893, + 364 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$\\mathcal{L}_{\\mathcal{IO}}$ : IO segment. When the segment is bounded by an intersection event followed by an occlusion event, the situation is more complex and we define $\\mathcal{L}_{\\mathcal{IO}}$ piecewise. In the first half of the segment $(z < \\frac{s + e}{2})$ , $d_{\\mathrm{DR}}$ is exactly known, and so we can use an $\\ell_1$ penalty like the II case, so $\\mathcal{L}_{\\mathcal{IO}}(y) = |y - l_s|$ . In the second half, there are two options. If the nearest intersection is $s$ , then $d_{\\mathrm{DR}}(z) = l_s$ . Otherwise, the nearest intersection is unknown but after $e$ and so $d_{\\mathrm{DR}}(z) > l_e$ must hold (since $d_{\\mathrm{DR}}(z) \\leq l_e$ would imply the existence of an intersection before $e$ ). We take the minimum of errors for the two cases: $\\ell_1$ distance to $l_s$ and a $\\ell_1$ penalty function $\\max(0, l_e - y)$ , resulting in $\\mathcal{L}_{\\mathcal{IO}}(y) = \\min(\\max(0, l_e - y), |y - l_s|)$ . This part of the penalty is zero if either $y = l_s$ or if $y > l_e$ .", + "bbox": [ + 496, + 366, + 892, + 578 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$\\mathcal{L}_{\\mathcal{O}\\mathcal{I}}$ : $\\mathcal{O}\\mathcal{I}$ segment. The $\\mathcal{L}_{\\mathcal{O}\\mathcal{I}}$ loss is defined symmetrically to $\\mathcal{L}_{\\mathcal{I}\\mathcal{O}}$ , simply by exchanging the role of $s$ and $e$ . In addition to occluded regions, this loss occurs in the reference view up to the depthmap, where a disocclusion into the reference camera's view is followed by an intersection.", + "bbox": [ + 496, + 579, + 890, + 654 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To assist the network, we add two auxiliary losses that are true statistically: $\\mathcal{L}_{\\mathrm{sep}}$ represents a prior that surfaces tend to be separated by distances and $\\mathcal{L}_{\\mathrm{ent}}$ captures a property of the DRDF that is true in the limit if our observations are randomly chosen.", + "bbox": [ + 496, + 656, + 890, + 732 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$\\mathcal{L}_{\\mathrm{sep}}$ : Minimum Separation Loss. Since the cameras never see the insides of objects, there is no incentive to predict inside objects. This prevents the generation of zero crossings, e.g., after the first intersection. To assist the network, we add a loss $\\mathcal{L}_{\\mathrm{sep}}$ that assumes that surfaces are separated by a minimum distance unless there is evidence otherwise. We continue the DRDF's known value for $t = 0.2m$ before and after each intersection event to make a continued value $c$ . We then penalize $\\mathcal{L}_{\\mathrm{sep}}(y) = |y - c|$ , so long as there is no conflicting free space evidence from another view.", + "bbox": [ + 496, + 733, + 890, + 883 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$\\mathcal{L}_{\\mathrm{ent}}$ : Sign Entropy Loss. The occlusion-based constraints", + "bbox": [ + 500, + 885, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "17259", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/2b718943b488dc9b61dfec7c7b4d9cce0e58c9f69381eb850fe0d83c00ac8b0f.jpg", + "image_caption": [ + "Scene Point Cloud, Cameras, and Ray" + ], + "image_footnote": [], + "bbox": [ + 86, + 88, + 380, + 244 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/8bbca5f1c4edf9bdf6eca6041cfdc188e141814d98154bb1a2b03805e6e217b7.jpg", + "image_caption": [ + "Reference (R)", + "Figure 5. A red ray originates from the reference camera and intersects the table before entering the island. This ray is seen by three other auxiliary views (a, b, c). For each view we show just one of the many segments in that view for readability, showing intersections as blue s and occlusions as purple o. The segments in views (a) and (b) are of the form OI, where the O comes from the ray entering the frustum in (a) and disoccluding in (b). In view (c), we show an OO segment. We discuss the penalties for these segments in §4.1" + ], + "image_footnote": [], + "bbox": [ + 385, + 88, + 509, + 243 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/4bfa762410c8dc01fa2aeb6abf88607ad58bd8d1d14e3f34d900a63992a87d84.jpg", + "image_caption": [ + "View (a)" + ], + "image_footnote": [], + "bbox": [ + 511, + 88, + 637, + 243 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/490d6ecc63550dfd559c1a4f81b0d9445ff302b9897301e6565c7951d416454c.jpg", + "image_caption": [ + "View (b)" + ], + "image_footnote": [], + "bbox": [ + 638, + 89, + 754, + 243 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/974c9c2c06b04e55dd4db62622f682fc51b8c043c45a005cd4a8d7adc5271428.jpg", + "image_caption": [ + "View (c)" + ], + "image_footnote": [], + "bbox": [ + 756, + 89, + 885, + 243 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "can be satisfied by making $y$ positive or negative. In theory, after the first intersection, $d_{\\mathrm{DR}}$ is positive half of the time. Learning to produce the signs uniformly with gradient descent is difficult due to the large loss between the acceptable solutions. To encourage a uniform distribution of signs, we would like to maximize the entropy of the distribution of the signs of the predictions (achieved when the distribution is uniform). Since sign is non-differentiable, we optimize a differentiable surrogate. Suppose $Y$ is the set of predictions at points that are occluded in the reference view and $H$ is the binary entropy $H(p) = p \\log(p) + (1 - p) \\log(1 - p)$ . Then our loss is $\\mathcal{L}_{\\mathrm{ent}} = H(\\sum_{y \\in Y} \\sigma(y / \\tau) / |Y|)$ where $\\sigma$ functions like a soft-sign. As seen in the supplement, $\\mathcal{L}_{\\mathrm{ent}}$ can be minimized by distributing $Y$ symmetric about 0. Using $\\mathcal{L}_{\\mathrm{ent}}$ improves the prediction and Scene F1 by 2 points.", + "bbox": [ + 75, + 329, + 473, + 556 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Implementation Details", + "text_level": 1, + "bbox": [ + 76, + 570, + 294, + 585 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "View Selection. For every candidate auxiliary view we compute the fraction of visible points in this view that are occluded in the reference view. Auxiliary views with large number of such points provide supervision for key occluded regions in the reference view. We sample up to 20 auxiliary views per reference image from this set of views.", + "bbox": [ + 75, + 595, + 468, + 686 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Sampling Strategy. Given a set of fixed auxiliary views and a reference view, we sample over 200 rays per input image with 512 points per ray. We re-balance this set by sampling $20K$ points that are visible and $20K$ points that are occluded in reference view. We rebalance as most points are from the region between the camera and the first hit.", + "bbox": [ + 75, + 686, + 468, + 777 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Combining segments from different views. We merge information from multiple posed RGBD images to produce a concise merged set of non-overlapping segments along the ray. This prevents double-counting losses (e.g., if a region of the ray is seen by multiple auxiliary views). We safely merge segments that provide the same information: e.g., if one depthmap provides an $\\mathcal{O}\\mathcal{O}$ segment that is contained within another depthmap's $\\mathcal{I}\\mathcal{I}$ segment, then the $\\mathcal{O}\\mathcal{O}$", + "bbox": [ + 75, + 780, + 470, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "segment can be safely dropped since $\\mathcal{L}_{\\mathcal{O}\\mathcal{O}}(y) \\leq \\mathcal{L}_{\\mathcal{I}\\mathcal{I}}(y)$ . When segments disagree (e.g., due to inaccurate poses), we keep the segment with more auxiliary views in agreement. This approach handles merging $\\mathcal{L}_{\\mathrm{sep}}$ : $\\mathcal{L}_{\\mathrm{sep}}$ is seen by no auxiliary views, so any visible Freespace overrules it.", + "bbox": [ + 496, + 329, + 890, + 405 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Network Architecture. We follow [27] to facilitate fair comparison. Additionally, we clamp the outputs of our network to be $\\in [-1, 1]$ by applying a tanh activation and adjust the loss to account for this clipping.", + "bbox": [ + 496, + 405, + 890, + 465 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training. We follow a two-stage training procedure. We first train with only the reference view followed by adding auxiliary views. In the first stage, we train for 100 epochs minimizing $\\mathcal{L}_{\\mathcal{O}I}$ and $\\mathcal{L}_{\\mathrm{sep}}$ . We then train for 100 epochs with auxiliary losses, minimizing a sum of the segment losses $\\mathcal{L}_{II}, \\mathcal{L}_{IO}, \\mathcal{L}_{OI}, \\mathcal{L}_{OO}$ , and $\\mathcal{L}_{\\mathrm{sep}}$ as well as $\\lambda \\mathcal{L}_{\\mathrm{ent}}$ with $\\lambda$ set to 0.1 to balance loss scales. We minimize the loss with AdamW [26, 33] as the optimizer with learning rate warmup for $0.5\\%$ of the iterations followed by cosine learning rate decay with maximum value $3 \\times 10^{-4}$ . Our models are implemented using PyTorch [39] and visualizations in this paper use PyTorch3D [30, 42].", + "bbox": [ + 496, + 467, + 892, + 648 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 500, + 662, + 633, + 680 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We evaluate our method to address three experimental questions. First, we examine how training with RGBD data compares with mesh supervision. Next, we test how RGBD and mesh supervision compare when one has less complete scans. Finally, we show that our method can quickly adapt to multiple posed RGBD inputs.", + "bbox": [ + 496, + 688, + 890, + 779 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Metrics. Throughout, we follow [27] and use two metrics that evaluate predictions against a ground-truth mesh. Following [45, 53], these metrics are based on: Accuracy/Acc (the fraction of predicted points that are within $t$ to a ground-truth point), Completeness/Cmp (the fraction of ground-truth points that are within $t$ to a predicted point), as well as F1 (the harmonic mean of Accuracy and Completeness). $t$ for both Acc and Cmp is $0.5\\mathrm{m}$ . In", + "bbox": [ + 496, + 780, + 892, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "17260", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/c0acbeb0fbc0b04b5b98368146e6abac627ecc157489a229d7f1d78f345fcb2b.jpg", + "image_caption": [ + "Image" + ], + "image_footnote": [], + "bbox": [ + 80, + 102, + 194, + 191 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/715f13721eb80a9112b70c7dc99973128d8fc1420c37acac7897fffed9a20d7f.jpg", + "image_caption": [ + "Density Field" + ], + "image_footnote": [], + "bbox": [ + 202, + 102, + 339, + 193 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/efc0709af70894ed6ec2e1486b13226dff17925faee14fd8f1923040aa716824.jpg", + "image_caption": [ + "Mesh DRDF" + ], + "image_footnote": [], + "bbox": [ + 344, + 102, + 480, + 193 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/56185d28c64f828cd6ecc084f3557baee812963b3fac2cd20f4692383cbd7f3d.jpg", + "image_caption": [ + "Figure 6. Comparison with baselines. 3D outputs generated by all methods trained on Matterport3D. We color the first intersection with image colors and occluded intersections with computed surface normals. We highlight regions of interest in the reconstructions in selected crops. D2-DRDF achieves results on par with Mesh DRDF while the density fields baselines fails to model the occluded parts faithfully. In row 1, our method recovers the back of sofa, and a hidden room behind the hallway in row 2. Surface Normal Map" + ], + "image_footnote": [], + "bbox": [ + 78, + 194, + 194, + 282 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/0891b9eb3c0b2c16d61322068f51eabdfcd7f581b667c305258808b24b0bb340.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 202, + 194, + 339, + 282 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/95cf1c50dad81a075ed5c968087dc7d912089df9d7577d0f56fa7fae470414db.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 344, + 194, + 480, + 282 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/c796136539e8353f6c722a1cc2e45874be4dbcd3bab2a34e9b4885f7fe913ada.jpg", + "image_caption": [ + "Selected Crops" + ], + "image_footnote": [], + "bbox": [ + 486, + 101, + 606, + 191 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/51ae88fd12ca658c28617ee63b1253205c0451f3a8188e0b0cf231d02eda2b5f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 488, + 193, + 606, + 282 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/8d3759adcc3f01946231652dac7c79d2205d5c28aec44b144e44dfa39e8d9867.jpg", + "image_caption": [ + "D2-DRDF" + ], + "image_footnote": [], + "bbox": [ + 614, + 101, + 750, + 194 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/2cebeeb524fb8401e121969c8e0f201ca8c77199a7a209a0afe26b3ebeaee655.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 614, + 194, + 750, + 282 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/0e41028b6603658502f2f38e39e38b8664321eb8c528e6ab041a269f07fa23a4.jpg", + "image_caption": [ + "Ground Truth" + ], + "image_footnote": [], + "bbox": [ + 756, + 101, + 890, + 194 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/316eb4f2e613656d87da1e411014d01b82be6e02cfb252e3ed2aa260c07f9fc5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 754, + 194, + 890, + 282 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "(Scene Acc/Cmp/F1), we evaluate the predicted mesh of the full scene against the ground-truth, using 10K samples per mesh. In (Ray Occ. Acc/Cmp/F1), We evaluate the performance on occluded points, evaluating per-ray and then averaging. We define occluded points for both the ground truth and prediction as any surface past the first intersection. Ray Occ. is a challenging metric as mistakes in one ray cannot be accounted for in another ray.", + "bbox": [ + 75, + 361, + 468, + 481 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets. We use Matterport3D [1] as our primary dataset following an identical setup to [28]. We choose Matterport3D because it has substantial occluded geometry (unlike ScanNet [8]) and was captured by a real scanner (unlike 3DFront [15], which is synthetic). We note that we use the raw images captured by the scanner rather than re-renders. We follow the split from [27], which splits train/val/test by house into 60/15/15 houses. After filtering and selecting images, there are 15K/1K/1K input images for each split.", + "bbox": [ + 75, + 483, + 468, + 619 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Mesh Prediction Results", + "text_level": 1, + "bbox": [ + 76, + 632, + 302, + 647 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Baselines. Our primary point of comparison is (1) Mesh DRDF [27], which learns to predict the DRDF from direct mesh supervision. For context, we also report the baselines from [27] and summarize them: (2) LDI [46] predicts a set of 4 layered depthmaps, where the first represents the depth and the next three represent occluded intersections; (3) UDF [5] predicts an unsigned scene distance function; (4) URDF [5] predicts an unsigned ray distance function; (5) ORF predicts an occupancy function, or whether the surface is within a fixed distance. We note that for each of these approaches, there are a number of variants (e.g., of finding intersections in a URDF along a ray). We report the highest performance reported by [27], who document extensive and detailed tuning of these baselines.", + "bbox": [ + 75, + 657, + 468, + 867 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Our final baseline, (6) Density Field [57] tests the value of predicting a DRDF compared to a density. Like our", + "bbox": [ + 75, + 869, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/308ded876e3ae3ba7a8f84ad8eab6ecbef30e4a10c804d3c1d4b94c8009fc8dc.jpg", + "image_caption": [ + "Image" + ], + "image_footnote": [], + "bbox": [ + 503, + 375, + 617, + 463 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/93f39d828ae170698ee6505c22503ae7f9317cb27f30b8a2749fa4b7482deee2.jpg", + "image_caption": [ + "GT" + ], + "image_footnote": [], + "bbox": [ + 620, + 375, + 753, + 463 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/654b1416b2959abf9ba81f5536e295e4714400f636ffc847fb4da46a0b72f3df.jpg", + "image_caption": [ + "D2-DRDF" + ], + "image_footnote": [], + "bbox": [ + 754, + 375, + 890, + 463 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/c9e9ed97af8c2dcf248c206a96913f0eaa932a460dc726cdda948c7c8cc227e2.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 503, + 465, + 617, + 553 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/d1762434e3a794537783b294f2282e7d204e5bb0b025a7b2dacc1100d206f547.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 619, + 465, + 753, + 553 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/fcbcb0a1dbe3bc6ec8ac2ebcef6466dc51ceeea5adc6ae5ae3a4bbe6931fb015.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 754, + 465, + 890, + 553 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/facab6af4c7ac9c2f6042b89cd0d6491df0efaa7c97bddf3f767236ce537e710.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 503, + 555, + 617, + 643 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/371b16cf78b109f479da4dc89af30c8e1059c6100a54f6fa6d1e9cc19fb6bb18.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 619, + 555, + 753, + 643 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/d06da2c7ca7644f482fa0148c109bd25a524609632d26bcad4fd9d34411f4423.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 754, + 555, + 890, + 643 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/e37c81497c795c353d46715bc5e440932d4c0a3845f7e21123402e980ef81ece.jpg", + "image_caption": [ + "Figure 7. Novels Views Comparison between D2-DRDF and Ground Truth(GT) from novel views. Rows 1, 2 are from unseen images on Matterport3D [1] and 3,4 from Omnidata [11]. Our method trained with only RGBD data recovers occluded empty floors. Kitchen cabinets (row 1) and sides of kitchen island(row 3)." + ], + "image_footnote": [], + "bbox": [ + 503, + 646, + 617, + 734 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/95048076d256158667aa5e1e60b6dd076dfd95d542f35b10dcbbeeaba0cd6831.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 619, + 646, + 753, + 734 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/5db495ee1874ec1f265cf2506f745d53ec4de5bc7900a993fb97f5101f053bde.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 754, + 646, + 890, + 734 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "method, Density Field only uses posed RGBD data for supervision and does not depend on mesh supervision. We adapt pixelNerf [57] to our setting, modifying the implementation from [41] to permit training from a single reference view as input and multiple auxiliary views for su", + "bbox": [ + 496, + 824, + 892, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "17261", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/6e238fb128e5aab3a9b1ba72c670db7283ee14b1d56098a8f081c2b64ce6e1be.jpg", + "table_caption": [ + "Table 1. Matterport3D [1] Acc/Comp/F1Score. We separate methods that use ground-truth mesh from ones using posed RGBD by a horizontal line. D2-DRDF is comparable to the best Mesh supervised method DRDF, and is better than all other mesh based methods on Scene and Ray F1 scores." + ], + "table_footnote": [], + "table_body": "
MethodSceneRay
AccCmpF1AccCmpF1
MeshLDI [46]66.272.467.413.942.819.3
UDF [5]58.776.064.715.523.016.6
ORF73.469.469.626.220.521.6
URDF [5]74.567.168.724.920.620.7
DRDF [27]75.472.071.928.430.027.3
D2-DRDF73.773.572.128.222.625.1
Density Field [57]45.880.257.524.814.017.9
", + "bbox": [ + 98, + 162, + 446, + 321 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "pervision. In addition to the standard color loss, we supervised the network to match the ground-truth auxiliary depth RGBD depth as in [10]. At inference time, we integrate along the ray to decode a set of intersections as in [10].", + "bbox": [ + 75, + 334, + 468, + 393 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Qualitative Results. We show qualitative results from our method and the baselines in Fig 6. Our methods outputs are comparable to the outputs of the mesh based DRDF that is trained with much stronger supervision. The density fields approach struggles to model the occluded hits and has a lots of floating blobs in the empty space. We show additional results from D2-DRDF in Fig. 7 where it recovers occlude kitchen cabinets, empty floor space behind kitchen island, sides of kitchen island, and hollow bed.", + "bbox": [ + 75, + 395, + 470, + 530 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Quantitative Results. We report results in Table 1. Without access to ground-truth meshes, our approach slightly outperforms Mesh-based DRDF on Scene F1 and approaches its performance on Ray Occ. Our approach matches it in accuracy (i.e., the fraction of predicted points that are correct) but does worse on completeness. We hypothesize that this is due to mesh-based techniques obtaining supervision in adjacent rooms. Nonetheless, our approach outperforms all other baselines besides mesh-supervised DRDF by a large margin (25.1 vs 21.6 F1). D2-DRDF has substantially higher performance compared to the Density Field baseline. This shows that density fields representation has difficulties in learning from a supervision of less views with large occlusions.", + "bbox": [ + 75, + 532, + 470, + 744 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.2. Training on Incomplete Data", + "text_level": 1, + "bbox": [ + 76, + 755, + 334, + 771 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "One advantage to using posed RGBD data compared to meshes is that it opens the door to learning from more data that is of lower quality. One can use lots of lower-quality scene captures that would produce poor meshes due to substantial amounts of incomplete data. In the case of direct mesh supervision methods, having an incomplete mesh may lead to incorrect signals for a mesh-based system: for instance, the network may learn to predict that an intersec", + "bbox": [ + 75, + 779, + 468, + 902 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5d9644b47f3e19ab2f6aec9ebe7fa333efc398758c5672c0630a747ffe38c7eb.jpg", + "image_caption": [ + "Figure 8. Mesh Degradation We show examples of how drop in images creates holes, reduces mesh area available for supervision. In (c), after retaining $\\approx \\frac{1}{4}$ of image data, we only lose $\\approx \\frac{1}{2}$ the mesh area. Im.: Image Coverage, M: Mesh Coverage, A: Area" + ], + "image_footnote": [], + "bbox": [ + 506, + 87, + 880, + 262 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "tion does not exist simply because it was not scanned. In contrast, for D2-DRDF, missing data simply increases the fraction of points without supervision. We now test this hypothesis by reducing the number of views in datasets.", + "bbox": [ + 496, + 334, + 892, + 393 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Optimistic Degradation Setup (ODS). We simulate the degradation of dataset collection by subsampling views. To avoid conflating errors in training with suboptimal meshing with incomplete data, we optimistically degrade the meshes to provide an upper bound on supervision. We assume that the mesh with fewer views is identical to the mesh from all views, minus triangles with no vertices in any view. We show ODS mesh examples in Fig. 8.", + "bbox": [ + 496, + 396, + 892, + 517 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We degrade meshes by selecting $1/2^i$ views per dataset for an increasing $i$ . While reducing the views linearly impacts the sample count for RGBD training setup, it has a non-linear impact on mesh completeness since a triangle is removed only if all of the views seeing it are removed (which is unlikely until most views are removed). For any given image retention $(\\mathrm{Im.})\\%$ mesh coverage $(\\mathbf{M})\\%$ degrades less giving an edge to mesh based methods.", + "bbox": [ + 496, + 518, + 893, + 638 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Usually, when dealing with limited data, we use a method called Screened Poisson Reconstruction(SPR) [25]. However, SPR does not perform well when there is not enough data available. To avoid conflating errors caused by poor quality inadequate meshing, we establish an upper limit on the performance of methods that rely on direct supervision. Our ODS strategy is much better than using SPR, but it cannot be used in real-world scenarios. We employ Open3D's[60]'s SPR with hyper-parameters similar to [1] to reconstruct meshes.", + "bbox": [ + 496, + 641, + 893, + 791 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Datasets. We evaluate on Matterport3D [1] and apply our method as is without any modifications on OmniData [11] which has a substantially different image view distribution, more rooms and more floors compared to Matterport3D.", + "bbox": [ + 496, + 792, + 893, + 853 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Quantitative Results. We compare models trained on different amount of data available for supervision by using metrics defined in §5.1. In Tab. 2 we compare against the", + "bbox": [ + 496, + 854, + 893, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "17262", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2. Robustness to Sparse Data Performance on partial data on Matterport3D[1]. We compare (SPR and ODS) trained DRDF [27] which uses mesh supervision and (Depth) Depth-based DRDF (ours), which uses posed RGBD supervision. In each row, we degrade the training data and report test performance of the trained model. At $100\\%$ data there is no M degradation for ODS or SPR. Our approach is more robust to drop in Im.: at $50\\%$ view sparsity (Im.), models using ODS or SPR suffer substantial performance drops. Scene F1 drop by 16.3 for SPR; 3.5 for ODS; 2.1 for Depth (ours).", + "bbox": [ + 75, + 88, + 472, + 227 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/74ebeed38db615e82ef284c6cc79aa42520e1817833dd28dd59bd4e077759de4.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Im. %ODS M %Scene F1Ray Occ F1
SPRODSDepthSPRODSDepth
10010071.971.972.127.327.325.1
505655.668.470.021.423.624.4
254356.866.870.021.521.224.9
", + "bbox": [ + 83, + 232, + 460, + 323 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/cd17c3fdf9618a18e546143fd0ba08df37b4bc804f1b8cc9eddabe9201927041.jpg", + "table_caption": [ + "Table 3. Robustness to Sparse Data Performance of ODS (mesh) and Depth (ours) based DRDF on partial Omnidata [11] following the same setup as Table 2. RGBD-based training is substantially more robust to partial data." + ], + "table_footnote": [], + "table_body": "
ODS\nIm. % M %Scene F1Ray Occ F1
ODSDepthODSDepth
258677.272.840.332.1
12.58375.3 (-1.9)70.9 (-1.9)37.1. (-3.2)29.3 (-2.8)
6.37873.4 (-3.8)71.8 (-1.0)32.6 (-7.7)28.1 (-3.0)
36969.8 (-7.4)70.4 (-2.4)20.3 (-20.0)26.7 (-5.4)
", + "bbox": [ + 78, + 388, + 468, + 487 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Mesh-DRDF trained ODS & SPR meshes. For any given view sampling level, the supervised M % area is high, resulting in stronger supervision for methods trained with ODS than RGBD. However, on Matterport3D, our method outperforms DRDF on all metrics at $25\\%$ Im. and outperforms all other baseline methods in Scene F1 at $100\\%$ Im..", + "bbox": [ + 75, + 497, + 468, + 588 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In Tab. 3 we show robustness trends on OmniData. At $25\\%$ Im./ $86\\%$ M completion, mesh-based does better. We hypothesize this gain is due to better handling of estimates beyond the room. However, as Im. reduces mesh degrades, resulting steep fall for ODS DRDF: with $3\\%$ Im./ $69\\%$ M, MeshDRDF's Ray Occ F1 drops by 20 points; ours is reduced by just 5.4. Moreover for SPR DRDF, at low Im. values, meshing performs dismally and the poor meshing performance translates into poor reconstruction performance: at $6\\%$ Im., training DRDF on SPR meshes produces a Ray F1 of just $5.7\\%$ and a scene F1 of $40.9\\%$ .", + "bbox": [ + 75, + 589, + 470, + 755 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.3. Adapting With Multiple Inputs", + "text_level": 1, + "bbox": [ + 76, + 768, + 354, + 787 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Since D2-DRDF can directly train on posed RGBD images, this enables test-time adaptation given a few auxiliary posed RGBD images. We start with the pre-trained model from §5.1 and then fine-tune for 500 iterations.", + "bbox": [ + 75, + 792, + 468, + 853 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Dataset and Metrics. We generate 300 quadruplets of scenes consisting of a reference view as well as three auxiliary RGBD images with poses. These three auxiliary views", + "bbox": [ + 75, + 854, + 470, + 901 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/06b86ead86807f88867973b75fdd60e17e46b22efb7021e26ae408c91cfd783d.jpg", + "image_caption": [ + "Figure 9. Adaptation to a few images We optimize our pretrained model on the three auxiliary views (yellow) and a reference view (orange). The optimization lets the model fix reconstructions in occluded regions (e.g. bedside wall) as seen D2-DRDF + Adapt." + ], + "image_footnote": [], + "bbox": [ + 513, + 89, + 880, + 263 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/2175b4a33e078ce274a8c964fc3de8b689d4162d179c1cb383a0a85f8feca883.jpg", + "table_caption": [ + "Table 4. Scene, Ray F1 Score for Adaptation Using D2-DRDF for adaptation produces better reconstructions that outperform the baselines by a large margin on Ray Occ F1 scores (by 8.9 points)." + ], + "table_footnote": [], + "table_body": "
MethodSceneRay
AccCmpF1AccCmpF1
D2-DRDF (Full)79.276.476.036.233.634.9
D2-DRDF (Depth)78.470.572.533.021.426.0
D2-DRDF (Scratch)66.470.366.023.727.425.4
Density Field [57]77.374.974.612.89.911.2
", + "bbox": [ + 514, + 382, + 874, + 488 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "are randomly sampled from views that overlap with occluded parts of the reference view (see supp.). We evaluate inferred 3D using the metrics as §5.1.", + "bbox": [ + 498, + 491, + 890, + 535 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Baselines. The baselines from §5.1 cannot operate in these settings, since they require meshes for training. Therefore we compare against a number of depth-map-based methods as well as ablations to give context to our results: (1) Density Field fine-tunes the density field baseline model from §5.1; (2) Depth-Pretrained fine-tunes D2-DRDF starting with the model from the first stage of training; (3) Scratch Training fine-tunes D2-DRDF from scratch.", + "bbox": [ + 498, + 537, + 890, + 656 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Results. Our loss and penalty formulations lend themselves well to test-time adaptation as shown in Fig. 9. After adaptation, D2-DRDF can resolve uncertainties like the corner of the bed and wall. Table 4 shows D2-DRDF does the best.", + "bbox": [ + 498, + 657, + 890, + 718 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 729, + 609, + 744 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We presented a method for learning to predict 3D from a single image using implicit functions while requiring only posed RGBD supervision. We believe our method can unlock new avenues with posed RGBD data becoming available from both consumers as well as robotic agents.", + "bbox": [ + 498, + 753, + 890, + 830 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments. We thank Richard H., Sarah J., Dandan S., Shengyi Q., Karan D., Mohammed B., Chris R. for feedback. Toyota Research Institute (\"TRI\") provided funds to assist the authors with their research but this article solely reflects the opinions and conclusions of its authors and not TRI or any Toyota entity.", + "bbox": [ + 498, + 830, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "17263", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3d: Learning from rgb-d data in indoor environments. 3DV, 2017. 2, 6, 7, 8", + "[2] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 2", + "[3] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. ECCV, 2022. 2", + "[4] Weifeng Chen, Zhao Fu, Dawei Yang, and Jia Deng. Single-image depth perception in the wild. NeurIPS, 2016. 2", + "[5] Julian Chibane, Aymen Mir, and Gerard Pons-Moll. Neural unsigned distance fields for implicit function learning. In NeurIPS, 2020. 2, 6, 7", + "[6] Christopher B Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese. 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. In ECCV, 2016. 1, 2", + "[7] Brian Curless and Marc Levoy. A volumetric method for building complex models from range images. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 303-312, 1996. 2, 3", + "[8] Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In CVPR, 2017. 2, 6", + "[9] Angela Dai, Matthias Nießner, Michael Zollhöfer, Shahram Izadi, and Christian Theobalt. Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface reintegration. ACM Transactions on Graphics (ToG), 36(4):1, 2017. 2", + "[10] Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan. Depth-supervised nerf: Fewer views and faster training for free. In CVPR, 2022. 7", + "[11] Ainaz Eftekhar, Alexander Sax, Jitendra Malik, and Amir Zamir. Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans. In ICCV, 2021. 6, 7, 8", + "[12] David Eigen and Rob Fergus. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. ICCV, 2015. 2", + "[13] Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In CVPR, 2017. 2", + "[14] David F Fouhey, Abhinav Gupta, and Martial Hebert. Data-driven 3d primitives for single image understanding. In ICCV, 2013. 2", + "[15] Huan Fu, Bowen Cai, Lin Gao, Lingxiao Zhang, Cao Li, Qixun Zeng, Chengyue Sun, Yiyun Fei, Yu Zheng, Ying Li, Yi Liu, Peng Liu, Lin Ma, Le Weng, Xiaohang Hu, Xin Ma, Qian Qian, Rongfei Jia, Binqiang Zhao, and Hao Zhang. 3d-front: 3d furnished rooms with layouts and semantics. ICCV, 2021. 6", + "[16] Huan Fu, Bowen Cai, Lin Gao, Ling-Xiao Zhang, Jiaming Wang, Cao Li, Qixun Zeng, Chengyue Sun, Rongfei Jia, Binqiang Zhao, et al. 3d-front: 3d furnished rooms with layouts" + ], + "bbox": [ + 78, + 114, + 470, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "and semantics. In ICCV, 2021. 2", + "[17] Rohit Girdhar, David F Fouhey, Mikel Rodriguez, and Abhinav Gupta. Learning a predictable and generative vector representation for objects. In ECCV. Springer, 2016. 1, 2", + "[18] Georgia Gkioxari, Jitendra Malik, and Justin Johnson. Mesh r-cnn. In ICCV, 2019. 2", + "[19] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. A papier-mâché approach to learning 3d surface generation. In CVPR, 2018. 1, 2", + "[20] Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, and Jia-Bin Huang. Deepmvs: Learning multi-view stereopsis. In CVPR, 2018. 2", + "[21] Shahram Izadi, David Kim, Otmar Hilliges, David Molyneaux, Richard Newcombe, Pushmeet Kohli, Jamie Shotton, Steve Hodges, Dustin Freeman, Andrew Davison, et al. Kinectfusion: real-time 3d reconstruction and interaction using a moving depth camera. In Proceedings of the 24th annual ACM symposium on User interface software and technology, pages 559-568, 2011. 2", + "[22] Hamid Izadinia, Qi Shan, and Steven M Seitz. Im2cad. In CVPR, 2017. 2", + "[23] Michael Janner, Jiajun Wu, Tejas D Kulkarni, Ilker Yildirim, and Josh Tenenbaum. Self-supervised intrinsic image decomposition. NeurIPS, 2017. 2", + "[24] Ziyu Jiang, Buyu Liu, Samuel Schulter, Zhangyang Wang, and Manmohan Chandraker. Peek-a-boo: Occlusion reasoning in indoor scenes with plane representations. In CVPR, 2020. 2", + "[25] Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Proceedings of the fourth Eurographics symposium on Geometry processing, 2006. 2, 7", + "[26] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. *ICLR*, 2015. 5", + "[27] Nilesh Kulkarni, Justin Johnson, and David F. Fouhey. Directed ray distance function for 3d scene reconstruction. In ECCV, 2022. 1, 2, 3, 5, 6, 7, 8", + "[28] Nilesh Kulkarni, Ishan Misra, Shubham Tulsiani, and Abhinav Gupta. 3d-relnet: Joint object and relational network for 3d prediction. In ICCV, 2019. 2, 6", + "[29] Kiriakos N Kutulakos and Steven M Seitz. A theory of shape by space carving. *IJCV*, 2000. 2", + "[30] Christoph Lassner and Michael Zollhöfer. Pulsar: Efficient sphere-based neural rendering. arXiv:2004.07484, 2020. 5", + "[31] Chen-Hsuan Lin, Chen Kong, and Simon Lucey. Learning efficient point cloud generation for dense 3d object reconstruction. In AAAI, volume 32, 2018. 2", + "[32] Chen Liu, Kihwan Kim, Jinwei Gu, Yasutaka Furukawa, and Jan Kautz. Planercnn: 3d plane detection and reconstruction from a single image. In CVPR, 2019. 2", + "[33] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. *ICLR*, 2019. 5", + "[34] Ravi Malladi, James A Sethian, and Baba C Vemuri. Shape modeling with front propagation: A level set approach. TPAMI, 1995. 2", + "[35] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In CVPR, 2019. 1, 2" + ], + "bbox": [ + 503, + 92, + 890, + 897 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "17264", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[36] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. arXiv preprint arXiv:2003.08934, 2020. 2", + "[37] Yinyu Nie, Xiaoguang Han, Shihui Guo, Yujuan Zheng, Jian Chang, and Jian Jun Zhang. Total3dunderstanding: Joint layout, object pose and mesh reconstruction for indoor scenes from a single image. In CVPR, 2020. 2", + "[38] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, 2019. 1, 2", + "[39] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS. 2019. 5", + "[40] Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In ECCV, 2020. 2", + "[41] Shengyi Qian, Alexander Kirillov, Nikhila Ravi, Devendra Singh Chaplot, Justin Johnson, David F Fouhey, and Georgia Gkioxari. Recognizing scenes from novel viewpoints. arXiv preprint arXiv:2112.01520, 2021. 6", + "[42] Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari. Accelerating 3d deep learning with pytorch3d. arXiv:2007.08501, 2020. 5", + "[43] Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, and Joshua M. Susskind. Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding. In ICCV, 2021. 2", + "[44] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In ICCV, 2019. 1, 2", + "[45] Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In CVPR, 2006. 5", + "[46] Jonathan Shade, Steven Gortler, Li-wei He, and Richard Szeliski. Layered depth images. In Proceedings of the 25th annual conference on Computer graphics and interactive techniques, pages 231–242, 1998. 6, 7", + "[47] Jian Shi, Yue Dong, Hao Su, and Stella X Yu. Learning non-lambertian object intrinsics across shapenet categories. In CVPR, 2017. 2", + "[48] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. NeurIPS, 2020. 2", + "[49] Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao. Sun rgb-d: A rgb-d scene understanding benchmark suite. In CVPR, 2015. 2", + "[50] Shuran Song, Fisher Yu, Andy Zeng, Angel X Chang, Manolis Savva, and Thomas Funkhouser. Semantic scene comple" + ], + "bbox": [ + 78, + 90, + 468, + 892 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "tion from a single depth image. CVPR, 2017. 2", + "[51] Jiaming Sun, Yiming Xie, Linghao Chen, Xiaowei Zhou, and Hujun Bao. Neuralrecon: Real-time coherent 3d reconstruction from monocular video. In CVPR, 2021. 2", + "[52] Xingyuan Sun, Jiajun Wu, Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Tianfan Xue, Joshua B Tenenbaum, and William T Freeman. Pix3d: Dataset and methods for single-image 3d shape modeling. In CVPR, 2018. 2", + "[53] Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox. What do single-view 3d reconstruction networks learn? In CVPR, 2019. 5", + "[54] Kaixuan Wang and Shaojie Shen. Mvdepthnet: Real-time multiview depth estimation neural network. In 3DV, 2018. 2", + "[55] X. Wang, David F. Fouhey, and A. Gupta. Designing deep networks for surface normal estimation. In CVPR, 2015. 2", + "[56] Jianglong Ye, Yuntao Chen, Naiyan Wang, and Xiaolong Wang. Gifs: Neural implicit function for general shape representation. In CVPR, 2022. 2", + "[57] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelNeRF: Neural radiance fields from one or few images. In CVPR, 2021. 2, 3, 6, 7, 8", + "[58] Zehao Yu, Jia Zheng, Dongze Lian, Zihan Zhou, and Shenghua Gao. Single-image piece-wise planar 3d reconstruction via associative embedding. In CVPR, 2019. 2", + "[59] Cheng Zhang, Zhaopeng Cui, Yinda Zhang, Bing Zeng, Marc Pollefeys, and Shuaicheng Liu. Holistic 3d scene understanding from a single image with implicit representation. In CVPR, 2021. 2", + "[60] Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. Open3D: A modern library for 3D data processing. arXiv:1801.09847, 2018. 7", + "[61] Shizhan Zhu, Sayna Ebrahimi, Angjoo Kanazawa, and Trevor Darrell. Differentiable gradient sampling for learning implicit 3d scene reconstructions from a single image. In ICLE, 2022. 1, 2" + ], + "bbox": [ + 503, + 92, + 890, + 575 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "17265", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/9e708598-d8b4-40c1-87d1-c7aee00317d3_model.json b/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/9e708598-d8b4-40c1-87d1-c7aee00317d3_model.json new file mode 100644 index 0000000000000000000000000000000000000000..e10364d11f758166ae02822c949acbdf660dc44c --- /dev/null +++ b/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/9e708598-d8b4-40c1-87d1-c7aee00317d3_model.json @@ -0,0 +1,2750 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.139, + 0.131, + 0.833, + 0.154 + ], + "angle": 0, + "content": "Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data" + }, + { + "type": "text", + "bbox": [ + 0.248, + 0.181, + 0.723, + 0.218 + ], + "angle": 0, + "content": "Nilesh Kulkarni, Linyi Jin, Justin Johnson, David F. Fouhey \nUniversity of Michigan" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.252, + 0.314, + 0.267 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.284, + 0.474, + 0.466 + ], + "angle": 0, + "content": "We introduce a method that can learn to predict scene-level implicit functions for 3D reconstruction from posed RGBD data. At test time, our system maps a previously unseen RGB image to a 3D reconstruction of a scene via implicit functions. While implicit functions for 3D reconstruction have often been tied to meshes, we show that we can train one using only a set of posed RGBD images. This setting may help 3D reconstruction unlock the sea of accelerometer+RGBD data that is coming with new phones. Our system, D2-DRDF, can match and sometimes outperform current methods that use mesh supervision and shows better robustness to sparse data." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.496, + 0.21, + 0.511 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.522, + 0.47, + 0.612 + ], + "angle": 0, + "content": "Consider the image in Figure 1. From this ordinary RGB image, we can understand the complete 3D geometry of the scene, including the floor and walls behind the chairs. Our goal is to enable computers to recover this geometry from a single RGB image. To this end, we present a method that does so while learning only on posed RGBD images." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.613, + 0.47, + 0.748 + ], + "angle": 0, + "content": "The task of reconstructing the full 3D of a scene from a single previously unseen RGB image has long been known to be challenging. Early work on full 3D relied on voxels [6, 17] or meshes [19], but these representations fail on scenes due to topology and memory challenges. Implicit functions (or learning to map each point in \\(\\mathbb{R}^3\\) to a value like the distance to the nearest surface) have shown substantial promise at overcoming these challenges. When conditioned on an image, these have led to several successful methods." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.47, + 0.856 + ], + "angle": 0, + "content": "Unfortunately, the implicit function status quo mainly ties implicit function reconstruction methods to mesh supervision. This symbiosis has emerged since meshes give excellent direct supervision. However, methods are limited to training with an image-aligned mesh that is usually watertight (and often artist-created) [35, 38, 44] and occasionally non-watertight but professionally-scanned [27, 61]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.47, + 0.901 + ], + "angle": 0, + "content": "We present a method, Depth to DRDF (D2-DRDF), that breaks the implicit-function/mesh connection and can train an effective single RGB image implicit 3D reconstruction" + }, + { + "type": "image_caption", + "bbox": [ + 0.548, + 0.255, + 0.708, + 0.266 + ], + "angle": 0, + "content": "Inference: Single RGB Image to 3D" + }, + { + "type": "image", + "bbox": [ + 0.506, + 0.265, + 0.624, + 0.351 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.506, + 0.351, + 0.627, + 0.433 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.634, + 0.265, + 0.761, + 0.348 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.634, + 0.349, + 0.758, + 0.433 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.794, + 0.256, + 0.858, + 0.266 + ], + "angle": 0, + "content": "Training Data" + }, + { + "type": "image", + "bbox": [ + 0.763, + 0.266, + 0.89, + 0.349 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.763, + 0.349, + 0.889, + 0.433 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.446, + 0.893, + 0.543 + ], + "angle": 0, + "content": "Figure 1. We propose a method, D2-DRDF, that reconstructs full 3D from a single RGB image. During inference (left), our method uses implicit functions to reconstruct the complete 3D including visible and occluded surfaces (visualized as surface normals \\(\\square\\)) such as the occluded wall and empty floor. For training, our method uses only RGBD images and poses, unlike most prior works that need an explicit and often watertight 3D mesh." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.553, + 0.893, + 0.674 + ], + "angle": 0, + "content": "system using a set of RGBD images with known pose. We envision that being able to entirely skip meshing will enable the use of vast quantities of lower-quality data from consumers (e.g., from increasingly common consumer phones with LiDAR scanners and accelerometers) as well as robots. In addition to permitting training, the bypassing of meshing may enable adaptation in a new environment on raw data without needing an expert to ensure mesh acquisition." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.675, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Our key insight is that we can use segments of observed free space in depth maps in other views to constrain distance functions. We show this using the Directed Ray Distance Function (DRDF) [27] that has recently shown good performance in 3D reconstruction using implicit functions and has the benefit of not needing watertight meshes for training. Given an input reference view, the DRDF breaks the problem of predicting the 3D surface into a set of independent 1D distance functions, each along a ray through a pixel in the reference view and accounting for only surfaces on the ray. Rather than use an ordinary unsigned distance function, the DRDF signs the distance using the location of the camera's pinhole and the intersections along the ray. While [27] showed their method could be trained on non-watertight meshes, their method is still dependent" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "17256" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.471, + 0.198 + ], + "angle": 0, + "content": "on meshes. In our paper, we show that the DRDF can be cleanly supervised using auxiliary views of RGBD observations and their poses. We derive constraints on the DRDF that power loss functions for training our system. While the losses on any one image are insufficient to produce a reconstruction, they provide a powerful signal when accumulated across thousands of training views." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.199, + 0.471, + 0.364 + ], + "angle": 0, + "content": "We evaluate our method on realistic indoor scene datasets and compare it with methods that train with full mesh supervision. Our method is competitive and sometimes even better compared to the best-performing mesh-supervised method [28] with full, professional captures. As we degrade scan completeness, our method largely maintains its performance while mesh-based methods perform substantially worse. We conclude by showing that fine-tuning of our method on a handful of images enables a simple, effective method for fusing and completing scenes from a handful of RGBD images." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.377, + 0.22, + 0.393 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.403, + 0.47, + 0.463 + ], + "angle": 0, + "content": "Our approach infers a complete 3D scene from a single RGB image using implicit functions that are supervised via posed RGBD scans. This touches on several long-term goals of 3D computer vision that we discuss below." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.464, + 0.471, + 0.78 + ], + "angle": 0, + "content": "Reconstructing Scenes from a Single Image. At test time, our system produces a full 3D reconstruction from a single RGB image, including occluded regions. This means that our desired output goes beyond 2.5D properties such as depth [4, 12], surface normals [12, 14, 55], or other intrinsic-image properties [23, 47]. Works on attempting to reconstruct complete 3D usually have focused on reconstructing objects from image by using voxels [6, 17] or meshes [18, 19], point clouds [13, 31] or CAD models [22]. These are usually trained on synthetic datasets like ShapeNet [2] or scene datasets [16, 43, 50] and do not generalize well to realistic scenes. Another line of works tries to learn holistic structures [37, 59] or planar surfaces [24, 32, 58] from realistic scanned mesh data [1, 8] Creating realistic mesh based data for scenes requires postprocessing using Poisson surface reconstruction [9, 25] which leads to deviation from raw captures. Manually aligning them is expensive hence 3D object-aligned datasets like [49, 52] are scarce and limited in diversity. Our method avoids these limitations by directly operating on the raw captured RGBD data." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.781, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Reconstructing Scenes from Posed Scans. There has been considerable work on using multiview RGBD data at inference time to produce 3D reconstructions, starting with analytic techniques [7, 9, 21, 29] and now using learning [20, 51, 54]. We use some similar tools to this community: for instance, the ray distances we use are known in this community as a projected distance functions [7]. However, their works solve a fundamentally different problem" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.168 + ], + "angle": 0, + "content": "by using posed RGBD data at test time: their goal is to produce a reconstruction from a set of posed RGBD images from one particular scene; our goal is to use a large dataset of posed RGBD data to train a neural network that can map a new single RGB image to a 3D reconstruction." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.168, + 0.892, + 0.288 + ], + "angle": 0, + "content": "Our goal of learning to predict reconstructions from a single previously unseen image also distinguishes our work from NeRF [36] and other similar radiance field approaches [3]. Their goal is to learn a radiance field for a particular scene from a set of posed scans. There are methods that try to predict this radiance field from a single image [57]; we compare with a model using similar losses and find that an objective specialized for 3D works better." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.289, + 0.892, + 0.483 + ], + "angle": 0, + "content": "Implicit Functions for 3D Reconstruction. We perform reconstruction with implicit functions. Implicit functions have used for shape and scene modeling as level sets [34], signed distance functions [38], occupancy function [35, 40, 44], distance functions [5, 27, 48] and other modifications like [56]. Our work has two key distinctions. First, many approaches [5, 48, 56] fit an implicit function to one shape (i.e., there is no generalization to new shapes). In contrast, our work produces reconstructions from previously unseen RGB images. Second, the methods that predict an implicit function from a new image [27, 61] assume access to a mesh at training time. On other hand, our work assumes access only to posed RGBD data for training." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.485, + 0.892, + 0.606 + ], + "angle": 0, + "content": "The most similar work to ours is [27] that also aims to learn to reconstruct scenes from a single image by training on realistic data. While [27] shows how to reduce supervision requirements by enabling the use of non-watertight meshes, their method is still limited to mesh supervision. We show how to use posed RGBD data for supervision instead of using an image-aligned mesh. This substantially reduces the requirements for collecting training data." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.619, + 0.88, + 0.636 + ], + "angle": 0, + "content": "3. Pixel-Aligned 3D Reconstruction & DRDF" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.645, + 0.892, + 0.87 + ], + "angle": 0, + "content": "We propose a method to predict full 3D scene structure from a previously unseen RGB image. At inference time, our method receives a single image with no depth information. We refer to this view as the reference view. As output, the method produces an estimate of a distance function at each point in a pre-defined set of 3D points. Given this volume of predicted distances, one can decode the predicted distance into a set of surfaces: e.g., if the predicted distance were the unsigned distance to the nearest surface, one could declare all points with sufficiently small predicted distance to be surfaces. At training time, our network is given supervision for predicting its distance function. Previously, supervision been done via a mesh that provides oracle distance calculations. In §4, we show how to derive supervision from posed RGBD images at auxiliary views instead." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.892, + 0.902 + ], + "angle": 0, + "content": "The approaches in the paper follow pixelNerf [57] for predicting a distance at a 3D point \\(\\mathbf{x}\\). The network accepts" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "17257" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.088, + 0.089, + 0.45, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.235, + 0.473, + 0.376 + ], + "angle": 0, + "content": "Figure 2. Model and DRDF overview. (Left) Given a single input RGB image \\(I\\) and query point \\(\\mathbf{x}\\) on a ray \\(\\vec{\\mathbf{r}}\\) in a pre-defined grid, all models, like [57] extract image features at the projection \\(\\pi(\\mathbf{x})\\) of \\(\\mathbf{x}\\) with a backbone and then predict the distance using a MLP on the backbone features and a positional encoding of \\(\\mathbf{x}\\). (Right) Given a ray \\(\\vec{\\mathbf{r}}\\) through the scene, ray distance functions are defined as the distance to intersections of the ray with the scene. This permits analyzing the 3D scene as a set of 1D distances per ray. The Directed Ray Distance Function is further signed to be positive before and negative after an intersection." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.385, + 0.47, + 0.491 + ], + "angle": 0, + "content": "an image \\(I\\) and 3D point \\(\\mathbf{x}\\) and makes a prediction \\(f_{\\theta}(\\mathbf{x}, I)\\). As shown in Fig. 2, the network extracts a convolutional feature from a backbone \\(\\mathbf{B}\\) at the projection of \\(\\mathbf{x}\\), denoted \\(\\pi(\\mathbf{x})\\). The image feature is concatenated to a positional encoding of \\(\\mathbf{x}\\) and put into a MLP to produce the final prediction \\(f_{\\theta}(\\mathbf{x}, I)\\). By repeating this inference for a set of 3D points, the network can infer the full 3D of the scene." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.491, + 0.47, + 0.599 + ], + "angle": 0, + "content": "Training. In a conventional setup, the network \\( f_{\\theta}(\\mathbf{x},I) \\) is trained by obtaining samples from a mesh and directly training the network via regression. Given a mesh that is coaligned with an image, one can obtain an \\( n \\) tuples consisting of an image \\( I_{i} \\), 3D point \\( \\mathbf{x}_i \\) and ground-truth distance \\( d_{i} \\). Then the network is trained by minimizing the empirical risk \\( \\frac{1}{n}\\sum_{i = 1}^{n}\\mathcal{L}(f_{\\theta}(\\mathbf{x}_i,I_i),d_i) \\) for some loss \\( \\mathcal{L} \\) like L1." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.598, + 0.47, + 0.688 + ], + "angle": 0, + "content": "What Distance Function Should We Predict? There are multiple distance functions in the literature, and the above formulation enables predicting any of them. To explain distinctions, assume we are given a point \\(\\mathbf{x}\\) at which we want to evaluate a distance and a scene \\(S \\subseteq \\mathbb{R}^3\\) that we want to evaluate the distance to." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.69, + 0.471, + 0.902 + ], + "angle": 0, + "content": "In a standard distance function, one defines a distance from a point \\(\\mathbf{x}\\) to the entire scene \\(S\\). For instance, the Unsigned Distance Function (UDF) is defined as \\(\\min_{\\mathbf{x}' \\in S} ||\\mathbf{x} - \\mathbf{x}'||\\). In a ray-based distance function [7, 27], the distance is defined from \\(\\mathbf{x}\\) only to the points in the scene that lie on the ray \\(\\vec{\\mathbf{r}}\\) from the reference view pinhole towards \\(\\mathbf{x}\\). In other words, the ray distance function is defined as the distance to the intersections of the ray with the scene. For instance, the Unsigned Ray Distance Function (URDF) is defined as \\(\\min_{\\mathbf{x}' \\in S, \\mathbf{x}' \\text{ on } \\vec{\\mathbf{r}}} ||\\mathbf{x} - \\mathbf{x}'||\\). This definition means that when predicting the distance at a point \\(\\mathbf{x}\\), all of the points that define the ray distance function project to the same pixel, \\(\\pi(\\mathbf{x})\\), which in turn means that neural networks do not need as large receptive fields [27]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.228 + ], + "angle": 0, + "content": "Since a ray-based distance function is defined only by scene points that intersect a ray, we can reduce the 3D problem to a 1D problem, as illustrated in Fig. 2. Rather than represent each 3D point as a 3D point, we represent it as the distance along the ray \\(\\vec{\\mathbf{r}}\\): the vector \\(\\mathbf{x}\\) is represented via a scalar \\(z\\) such that \\(\\mathbf{x} = z\\vec{\\mathbf{r}}\\). Then, if the \\(i^{th}\\) intersection along the ray is \\(s_i\\vec{\\mathbf{r}}\\) for \\(s_i \\in \\mathbb{R}\\), the URDF is defined as the distance to the nearest intersection along that ray, or \\(d_{\\mathrm{UR}}(z) = \\min_{i=1,\\dots,k} |z - s_i|\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.228, + 0.893, + 0.41 + ], + "angle": 0, + "content": "Directed Ray Distance Function. The Directed Ray Distance Function (DRDF) is a ray distance function that incorporates a sign into the URDF. In particular, the DRDF is defined as \\( d_{\\mathrm{DR}}(z) = \\operatorname{dir}(z)d_{\\mathrm{UR}}(z) \\), where the predicate \\( \\operatorname{dir}(z) \\) is positive if the nearest intersection is ahead of \\( z \\) along the ray from the pinhole and negative otherwise. This is pictured in Fig. 2. The presence of both positive and negative components leads to better behavior under uncertainty [27]. Critical Properties of DRDFs. Two critical properties that we use to define supervision are that at \\( z \\), the distance to the nearest intersection is \\( |d_{\\mathrm{DR}}(z)| \\) and the nearest intersection on the ray is at \\( z + d_{\\mathrm{DR}}(z) \\)." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.423, + 0.857, + 0.44 + ], + "angle": 0, + "content": "4. Depth-Supervised DRDF Reconstruction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.448, + 0.892, + 0.538 + ], + "angle": 0, + "content": "Having introduced the setup in §3, we now explain how to train a network to predict an image-conditioned DRDF without access to an underlying mesh but instead access to posed RGBD images. Concretely, given a point \\( z\\vec{\\mathbf{r}} \\), we aim to define a loss function \\( \\mathcal{L} \\) that evaluates a network's prediction of the DRDF \\( f_{\\theta}((z\\vec{\\mathbf{r}}),I) \\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.539, + 0.893, + 0.704 + ], + "angle": 0, + "content": "Our key insight is that we can sometimes see this point \\( z\\vec{\\mathbf{r}} \\) in other views and these observations of \\( z\\vec{\\mathbf{r}} \\) provide a constraints on what the value of the DRDF can be. Consider, for instance, the point 3m out from reference view camera pinhole along the red ray in Fig. 3. If we project this point into the auxiliary view, we know that its distance to the camera is closer to auxiliary view's pinhole than the depthmap observed at the auxiliary view. Moreover, we can see that a segment of the ray is visible, starting at the point \\( s\\vec{\\mathbf{r}} \\) and ending at the point \\( e\\vec{\\mathbf{r}} \\) with the ending point \\( e\\vec{\\mathbf{r}} \\) closer to \\( z\\vec{\\mathbf{r}} \\) than \\( s\\vec{\\mathbf{r}} \\) is." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.895, + 0.901 + ], + "angle": 0, + "content": "We can derive supervision on \\( d_{\\mathrm{DR}}(z) \\) from the segment of visible free space observable in the auxiliary view by using the fact that the nearest intersection at \\( z \\) is \\( |d_{\\mathrm{DR}}(z)| \\) units away. Since \\( e \\) is nearer than \\( s \\), and there are no intersections in the free space between \\( s \\) and \\( e \\), we know that there cannot be an intersection within \\( (e - z) \\) of \\( z \\). This gives an inequality that \\( |d_{\\mathrm{DR}}(z)| \\geq (e - z) \\): otherwise there would be an intersection in the visible free space. If the ends of the segment are actual intersections where the ray visibly intersects an object, we obtain an equality constraint on \\( d_{\\mathrm{DR}}(z) \\) since we actually see the nearest intersection. In other cases, the ray simply disoccludes or is occluded, and we can only have an inequality. We denote intersection start/end events as \\( \\mathcal{I} \\)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17258" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.11, + 0.094, + 0.187, + 0.108 + ], + "angle": 0, + "content": "Reference" + }, + { + "type": "image", + "bbox": [ + 0.089, + 0.111, + 0.208, + 0.186 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.234, + 0.094, + 0.429, + 0.109 + ], + "angle": 0, + "content": "Auxiliary View + Zoomed" + }, + { + "type": "image", + "bbox": [ + 0.209, + 0.111, + 0.331, + 0.185 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.332, + 0.112, + 0.453, + 0.186 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.098, + 0.188, + 0.2, + 0.256 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.209, + 0.187, + 0.331, + 0.26 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.332, + 0.187, + 0.453, + 0.26 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.124, + 0.265, + 0.421, + 0.287 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.286, + 0.47, + 0.384 + ], + "angle": 0, + "content": "Figure 3. A red ray originates in the reference camera. Given a point \\(\\mathbf{x}\\) that is \\(z\\) units along the ray \\(\\vec{\\mathbf{r}}\\), we can test if \\(z\\vec{\\mathbf{r}}\\) is visible in an auxiliary view by comparing its projected depth with the RGBD image. The point \\(z\\) is visible, along with a segment of the ray from \\(s\\) to \\(e\\) units. Since the distance to the nearest intersection is \\(|d_{\\mathrm{DR}}(z)|\\) and we know there are no intersections within \\((e - z)\\) units of \\(z\\), we can constrain that \\(|d_{\\mathrm{DR}}(z)| \\geq (e - z)\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.396, + 0.47, + 0.457 + ], + "angle": 0, + "content": "and occlusion start/end events as \\(\\mathcal{O}\\) and distinguish them by checking if the projected z-value of the ray in auxiliary view is sufficiently close to the recorded auxiliary view's depthmap value." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.468, + 0.165, + 0.482 + ], + "angle": 0, + "content": "4.1. Losses" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.492, + 0.469, + 0.612 + ], + "angle": 0, + "content": "We now convert the concept of using freespace to constrain the DRDF into concrete losses. Recall that our goal is to evaluate a prediction \\( y = f_{\\theta}(z\\vec{\\mathbf{r}},I) \\) from our network and that the point at \\( z \\) along \\( \\vec{\\mathbf{r}} \\) is in some segment of visible freespace from \\( s \\) to \\( e \\) along \\( \\vec{\\mathbf{r}} \\). Our goal is to penalize the prediction \\( y \\). Since there are two start/end event classes \\( (1,0) \\), there are four segment types: \\( 11,10,01 \\), and \\( 00 \\). We show several of these segments in Figure 5." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.613, + 0.469, + 0.703 + ], + "angle": 0, + "content": "To assist the loss definitions, we define variables \\( l_{s} = s - z \\), and \\( l_{e} = e - z \\) which we plot in Fig. 4 as a function of \\( z \\). \\( l_{s} \\) is the value of \\( d_{\\mathrm{DR}} \\) if the closest intersection is at \\( s \\), and \\( l_{e} \\) is the value of \\( d_{\\mathrm{DR}} \\) if the closest intersection is at \\( e \\). The values \\( l_{s}, l_{e} \\) define equalities for intersections and inequalities for occlusions." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.704, + 0.47, + 0.902 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{\\mathcal{II}}\\) : II segment. Given a segment bounded by two intersections, the nearest intersections are known exactly as \\(l_{s}\\) or \\(l_{e}\\) depending on whether \\(z < \\frac{s + e}{2}\\) or not. We penalize the \\(\\ell_1\\) error between the prediction \\(y\\) and the known DRDF, or \\(\\mathcal{L}_{\\mathcal{II}}(y) = |y - l_s|\\) if \\(z < \\frac{s + e}{2}\\) and \\(|y - l_e|\\) otherwise. This penalty is zero only when \\(y\\) is equal to the known DRDF. \\(\\mathcal{L}_{\\mathcal{OO}}\\) : OO segment. Given a segment bounded by two occlusion events, the exact DRDF is not known, but the visible free space rules out potential values. Since \\(|d_{\\mathrm{DR}}(z)|\\) is the distance to the nearest intersection, \\(d_{\\mathrm{DR}}(z)\\) cannot lie in \\([l_s,l_e]\\) since such a value would imply an intersection in free space between \\(s\\) and \\(e\\). We penalize incursions into \\([l_s,l_e]\\) with a \\(\\ell_1\\) penalty: if we denote halfway be" + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.088, + 0.619, + 0.212 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.624, + 0.089, + 0.71, + 0.212 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.715, + 0.089, + 0.803, + 0.212 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.808, + 0.089, + 0.894, + 0.212 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.222, + 0.894, + 0.305 + ], + "angle": 0, + "content": "Figure 4. Loss functions for a starting \\( s \\) and ending \\( e \\) events spaced a unit apart. Intersection events (blue) define the DRDF precisely, and we penalize the network from deviating. Occlusion events (purple) provide bounds that we penalize the network for violating. We plot \\( l_{s} \\) as a line from the start event and \\( l_{e} \\) as a line from the end event. Loss: 0 1.7" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.32, + 0.894, + 0.366 + ], + "angle": 0, + "content": "tween \\( l_s \\) and \\( l_e \\) as \\( h \\), then this can be done as \\( \\mathcal{L}_{\\mathcal{O}\\mathcal{O}}(y) = \\max (0, l_e - h - |y - h|) \\). The resulting penalty is zero if \\( y \\leq l_s \\) or if \\( y \\geq l_e \\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.367, + 0.893, + 0.579 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{\\mathcal{IO}}\\) : IO segment. When the segment is bounded by an intersection event followed by an occlusion event, the situation is more complex and we define \\(\\mathcal{L}_{\\mathcal{IO}}\\) piecewise. In the first half of the segment \\((z < \\frac{s + e}{2})\\), \\(d_{\\mathrm{DR}}\\) is exactly known, and so we can use an \\(\\ell_1\\) penalty like the II case, so \\(\\mathcal{L}_{\\mathcal{IO}}(y) = |y - l_s|\\). In the second half, there are two options. If the nearest intersection is \\(s\\), then \\(d_{\\mathrm{DR}}(z) = l_s\\). Otherwise, the nearest intersection is unknown but after \\(e\\) and so \\(d_{\\mathrm{DR}}(z) > l_e\\) must hold (since \\(d_{\\mathrm{DR}}(z) \\leq l_e\\) would imply the existence of an intersection before \\(e\\)). We take the minimum of errors for the two cases: \\(\\ell_1\\) distance to \\(l_s\\) and a \\(\\ell_1\\) penalty function \\(\\max(0, l_e - y)\\), resulting in \\(\\mathcal{L}_{\\mathcal{IO}}(y) = \\min(\\max(0, l_e - y), |y - l_s|)\\). This part of the penalty is zero if either \\(y = l_s\\) or if \\(y > l_e\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.58, + 0.892, + 0.655 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{\\mathcal{O}\\mathcal{I}}\\): \\(\\mathcal{O}\\mathcal{I}\\) segment. The \\(\\mathcal{L}_{\\mathcal{O}\\mathcal{I}}\\) loss is defined symmetrically to \\(\\mathcal{L}_{\\mathcal{I}\\mathcal{O}}\\), simply by exchanging the role of \\(s\\) and \\(e\\). In addition to occluded regions, this loss occurs in the reference view up to the depthmap, where a disocclusion into the reference camera's view is followed by an intersection." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.657, + 0.892, + 0.733 + ], + "angle": 0, + "content": "To assist the network, we add two auxiliary losses that are true statistically: \\(\\mathcal{L}_{\\mathrm{sep}}\\) represents a prior that surfaces tend to be separated by distances and \\(\\mathcal{L}_{\\mathrm{ent}}\\) captures a property of the DRDF that is true in the limit if our observations are randomly chosen." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.734, + 0.892, + 0.884 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{\\mathrm{sep}}\\): Minimum Separation Loss. Since the cameras never see the insides of objects, there is no incentive to predict inside objects. This prevents the generation of zero crossings, e.g., after the first intersection. To assist the network, we add a loss \\(\\mathcal{L}_{\\mathrm{sep}}\\) that assumes that surfaces are separated by a minimum distance unless there is evidence otherwise. We continue the DRDF's known value for \\(t = 0.2m\\) before and after each intersection event to make a continued value \\(c\\). We then penalize \\(\\mathcal{L}_{\\mathrm{sep}}(y) = |y - c|\\), so long as there is no conflicting free space evidence from another view." + }, + { + "type": "text", + "bbox": [ + 0.501, + 0.886, + 0.892, + 0.901 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{\\mathrm{ent}}\\): Sign Entropy Loss. The occlusion-based constraints" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17259" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.088, + 0.089, + 0.382, + 0.245 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.115, + 0.247, + 0.357, + 0.26 + ], + "angle": 0, + "content": "Scene Point Cloud, Cameras, and Ray" + }, + { + "type": "image", + "bbox": [ + 0.387, + 0.089, + 0.511, + 0.244 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.396, + 0.245, + 0.504, + 0.26 + ], + "angle": 0, + "content": "Reference (R)" + }, + { + "type": "image", + "bbox": [ + 0.513, + 0.089, + 0.638, + 0.244 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.543, + 0.245, + 0.611, + 0.26 + ], + "angle": 0, + "content": "View (a)" + }, + { + "type": "image", + "bbox": [ + 0.639, + 0.09, + 0.755, + 0.244 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.667, + 0.245, + 0.735, + 0.26 + ], + "angle": 0, + "content": "View (b)" + }, + { + "type": "image", + "bbox": [ + 0.758, + 0.09, + 0.886, + 0.244 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.791, + 0.245, + 0.858, + 0.26 + ], + "angle": 0, + "content": "View (c)" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.262, + 0.894, + 0.319 + ], + "angle": 0, + "content": "Figure 5. A red ray originates from the reference camera and intersects the table before entering the island. This ray is seen by three other auxiliary views (a, b, c). For each view we show just one of the many segments in that view for readability, showing intersections as blue s and occlusions as purple o. The segments in views (a) and (b) are of the form OI, where the O comes from the ray entering the frustum in (a) and disoccluding in (b). In view (c), we show an OO segment. We discuss the penalties for these segments in §4.1" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.33, + 0.474, + 0.558 + ], + "angle": 0, + "content": "can be satisfied by making \\(y\\) positive or negative. In theory, after the first intersection, \\(d_{\\mathrm{DR}}\\) is positive half of the time. Learning to produce the signs uniformly with gradient descent is difficult due to the large loss between the acceptable solutions. To encourage a uniform distribution of signs, we would like to maximize the entropy of the distribution of the signs of the predictions (achieved when the distribution is uniform). Since sign is non-differentiable, we optimize a differentiable surrogate. Suppose \\(Y\\) is the set of predictions at points that are occluded in the reference view and \\(H\\) is the binary entropy \\(H(p) = p \\log(p) + (1 - p) \\log(1 - p)\\). Then our loss is \\(\\mathcal{L}_{\\mathrm{ent}} = H(\\sum_{y \\in Y} \\sigma(y / \\tau) / |Y|)\\) where \\(\\sigma\\) functions like a soft-sign. As seen in the supplement, \\(\\mathcal{L}_{\\mathrm{ent}}\\) can be minimized by distributing \\(Y\\) symmetric about 0. Using \\(\\mathcal{L}_{\\mathrm{ent}}\\) improves the prediction and Scene F1 by 2 points." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.571, + 0.295, + 0.587 + ], + "angle": 0, + "content": "4.2. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.596, + 0.47, + 0.687 + ], + "angle": 0, + "content": "View Selection. For every candidate auxiliary view we compute the fraction of visible points in this view that are occluded in the reference view. Auxiliary views with large number of such points provide supervision for key occluded regions in the reference view. We sample up to 20 auxiliary views per reference image from this set of views." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.688, + 0.47, + 0.779 + ], + "angle": 0, + "content": "Sampling Strategy. Given a set of fixed auxiliary views and a reference view, we sample over 200 rays per input image with 512 points per ray. We re-balance this set by sampling \\(20K\\) points that are visible and \\(20K\\) points that are occluded in reference view. We rebalance as most points are from the region between the camera and the first hit." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.781, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Combining segments from different views. We merge information from multiple posed RGBD images to produce a concise merged set of non-overlapping segments along the ray. This prevents double-counting losses (e.g., if a region of the ray is seen by multiple auxiliary views). We safely merge segments that provide the same information: e.g., if one depthmap provides an \\(\\mathcal{O}\\mathcal{O}\\) segment that is contained within another depthmap's \\(\\mathcal{I}\\mathcal{I}\\) segment, then the \\(\\mathcal{O}\\mathcal{O}\\)" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.33, + 0.892, + 0.406 + ], + "angle": 0, + "content": "segment can be safely dropped since \\(\\mathcal{L}_{\\mathcal{O}\\mathcal{O}}(y) \\leq \\mathcal{L}_{\\mathcal{I}\\mathcal{I}}(y)\\). When segments disagree (e.g., due to inaccurate poses), we keep the segment with more auxiliary views in agreement. This approach handles merging \\(\\mathcal{L}_{\\mathrm{sep}}\\): \\(\\mathcal{L}_{\\mathrm{sep}}\\) is seen by no auxiliary views, so any visible Freespace overrules it." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.406, + 0.892, + 0.467 + ], + "angle": 0, + "content": "Network Architecture. We follow [27] to facilitate fair comparison. Additionally, we clamp the outputs of our network to be \\(\\in [-1, 1]\\) by applying a tanh activation and adjust the loss to account for this clipping." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.468, + 0.893, + 0.649 + ], + "angle": 0, + "content": "Training. We follow a two-stage training procedure. We first train with only the reference view followed by adding auxiliary views. In the first stage, we train for 100 epochs minimizing \\(\\mathcal{L}_{\\mathcal{O}I}\\) and \\(\\mathcal{L}_{\\mathrm{sep}}\\). We then train for 100 epochs with auxiliary losses, minimizing a sum of the segment losses \\(\\mathcal{L}_{II}, \\mathcal{L}_{IO}, \\mathcal{L}_{OI}, \\mathcal{L}_{OO}\\), and \\(\\mathcal{L}_{\\mathrm{sep}}\\) as well as \\(\\lambda \\mathcal{L}_{\\mathrm{ent}}\\) with \\(\\lambda\\) set to 0.1 to balance loss scales. We minimize the loss with AdamW [26, 33] as the optimizer with learning rate warmup for \\(0.5\\%\\) of the iterations followed by cosine learning rate decay with maximum value \\(3 \\times 10^{-4}\\). Our models are implemented using PyTorch [39] and visualizations in this paper use PyTorch3D [30, 42]." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.664, + 0.634, + 0.681 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.892, + 0.78 + ], + "angle": 0, + "content": "We evaluate our method to address three experimental questions. First, we examine how training with RGBD data compares with mesh supervision. Next, we test how RGBD and mesh supervision compare when one has less complete scans. Finally, we show that our method can quickly adapt to multiple posed RGBD inputs." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.781, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Metrics. Throughout, we follow [27] and use two metrics that evaluate predictions against a ground-truth mesh. Following [45, 53], these metrics are based on: Accuracy/Acc (the fraction of predicted points that are within \\( t \\) to a ground-truth point), Completeness/Cmp (the fraction of ground-truth points that are within \\( t \\) to a predicted point), as well as F1 (the harmonic mean of Accuracy and Completeness). \\( t \\) for both Acc and Cmp is \\( 0.5\\mathrm{m} \\). In" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "17260" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.117, + 0.089, + 0.158, + 0.101 + ], + "angle": 0, + "content": "Image" + }, + { + "type": "image", + "bbox": [ + 0.081, + 0.103, + 0.195, + 0.192 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.233, + 0.089, + 0.312, + 0.102 + ], + "angle": 0, + "content": "Density Field" + }, + { + "type": "image", + "bbox": [ + 0.204, + 0.103, + 0.34, + 0.194 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.375, + 0.089, + 0.45, + 0.101 + ], + "angle": 0, + "content": "Mesh DRDF" + }, + { + "type": "image", + "bbox": [ + 0.345, + 0.103, + 0.481, + 0.194 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.08, + 0.195, + 0.195, + 0.284 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.203, + 0.195, + 0.34, + 0.284 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.345, + 0.195, + 0.481, + 0.284 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.501, + 0.089, + 0.588, + 0.101 + ], + "angle": 0, + "content": "Selected Crops" + }, + { + "type": "image", + "bbox": [ + 0.488, + 0.102, + 0.607, + 0.192 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.489, + 0.194, + 0.607, + 0.284 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.652, + 0.089, + 0.713, + 0.101 + ], + "angle": 0, + "content": "D2-DRDF" + }, + { + "type": "image", + "bbox": [ + 0.615, + 0.102, + 0.751, + 0.195 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.615, + 0.195, + 0.75, + 0.284 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.783, + 0.089, + 0.864, + 0.101 + ], + "angle": 0, + "content": "Ground Truth" + }, + { + "type": "image", + "bbox": [ + 0.757, + 0.102, + 0.892, + 0.195 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.755, + 0.195, + 0.891, + 0.284 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.297, + 0.893, + 0.354 + ], + "angle": 0, + "content": "Figure 6. Comparison with baselines. 3D outputs generated by all methods trained on Matterport3D. We color the first intersection with image colors and occluded intersections with computed surface normals. We highlight regions of interest in the reconstructions in selected crops. D2-DRDF achieves results on par with Mesh DRDF while the density fields baselines fails to model the occluded parts faithfully. In row 1, our method recovers the back of sofa, and a hidden room behind the hallway in row 2. Surface Normal Map" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.362, + 0.47, + 0.482 + ], + "angle": 0, + "content": "(Scene Acc/Cmp/F1), we evaluate the predicted mesh of the full scene against the ground-truth, using 10K samples per mesh. In (Ray Occ. Acc/Cmp/F1), We evaluate the performance on occluded points, evaluating per-ray and then averaging. We define occluded points for both the ground truth and prediction as any surface past the first intersection. Ray Occ. is a challenging metric as mistakes in one ray cannot be accounted for in another ray." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.484, + 0.47, + 0.62 + ], + "angle": 0, + "content": "Datasets. We use Matterport3D [1] as our primary dataset following an identical setup to [28]. We choose Matterport3D because it has substantial occluded geometry (unlike ScanNet [8]) and was captured by a real scanner (unlike 3DFront [15], which is synthetic). We note that we use the raw images captured by the scanner rather than re-renders. We follow the split from [27], which splits train/val/test by house into 60/15/15 houses. After filtering and selecting images, there are 15K/1K/1K input images for each split." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.633, + 0.303, + 0.648 + ], + "angle": 0, + "content": "5.1. Mesh Prediction Results" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.658, + 0.47, + 0.868 + ], + "angle": 0, + "content": "Baselines. Our primary point of comparison is (1) Mesh DRDF [27], which learns to predict the DRDF from direct mesh supervision. For context, we also report the baselines from [27] and summarize them: (2) LDI [46] predicts a set of 4 layered depthmaps, where the first represents the depth and the next three represent occluded intersections; (3) UDF [5] predicts an unsigned scene distance function; (4) URDF [5] predicts an unsigned ray distance function; (5) ORF predicts an occupancy function, or whether the surface is within a fixed distance. We note that for each of these approaches, there are a number of variants (e.g., of finding intersections in a URDF along a ray). We report the highest performance reported by [27], who document extensive and detailed tuning of these baselines." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Our final baseline, (6) Density Field [57] tests the value of predicting a DRDF compared to a density. Like our" + }, + { + "type": "image_caption", + "bbox": [ + 0.538, + 0.36, + 0.585, + 0.374 + ], + "angle": 0, + "content": "Image" + }, + { + "type": "image_caption", + "bbox": [ + 0.674, + 0.36, + 0.7, + 0.373 + ], + "angle": 0, + "content": "GT" + }, + { + "type": "image_caption", + "bbox": [ + 0.785, + 0.36, + 0.861, + 0.373 + ], + "angle": 0, + "content": "D2-DRDF" + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.375, + 0.619, + 0.464 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.621, + 0.375, + 0.754, + 0.464 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.756, + 0.375, + 0.891, + 0.464 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.466, + 0.618, + 0.554 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.62, + 0.466, + 0.754, + 0.554 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.756, + 0.466, + 0.891, + 0.554 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.556, + 0.618, + 0.645 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.62, + 0.556, + 0.754, + 0.645 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.756, + 0.556, + 0.891, + 0.645 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.647, + 0.618, + 0.736 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.62, + 0.647, + 0.754, + 0.736 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.756, + 0.647, + 0.891, + 0.736 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.747, + 0.893, + 0.818 + ], + "angle": 0, + "content": "Figure 7. Novels Views Comparison between D2-DRDF and Ground Truth(GT) from novel views. Rows 1, 2 are from unseen images on Matterport3D [1] and 3,4 from Omnidata [11]. Our method trained with only RGBD data recovers occluded empty floors. Kitchen cabinets (row 1) and sides of kitchen island(row 3)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.893, + 0.901 + ], + "angle": 0, + "content": "method, Density Field only uses posed RGBD data for supervision and does not depend on mesh supervision. We adapt pixelNerf [57] to our setting, modifying the implementation from [41] to permit training from a single reference view as input and multiple auxiliary views for su" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "17261" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.089, + 0.473, + 0.16 + ], + "angle": 0, + "content": "Table 1. Matterport3D [1] Acc/Comp/F1Score. We separate methods that use ground-truth mesh from ones using posed RGBD by a horizontal line. D2-DRDF is comparable to the best Mesh supervised method DRDF, and is better than all other mesh based methods on Scene and Ray F1 scores." + }, + { + "type": "table", + "bbox": [ + 0.099, + 0.164, + 0.447, + 0.322 + ], + "angle": 0, + "content": "
MethodSceneRay
AccCmpF1AccCmpF1
MeshLDI [46]66.272.467.413.942.819.3
UDF [5]58.776.064.715.523.016.6
ORF73.469.469.626.220.521.6
URDF [5]74.567.168.724.920.620.7
DRDF [27]75.472.071.928.430.027.3
D2-DRDF73.773.572.128.222.625.1
Density Field [57]45.880.257.524.814.017.9
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.335, + 0.47, + 0.395 + ], + "angle": 0, + "content": "pervision. In addition to the standard color loss, we supervised the network to match the ground-truth auxiliary depth RGBD depth as in [10]. At inference time, we integrate along the ray to decode a set of intersections as in [10]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.396, + 0.471, + 0.531 + ], + "angle": 0, + "content": "Qualitative Results. We show qualitative results from our method and the baselines in Fig 6. Our methods outputs are comparable to the outputs of the mesh based DRDF that is trained with much stronger supervision. The density fields approach struggles to model the occluded hits and has a lots of floating blobs in the empty space. We show additional results from D2-DRDF in Fig. 7 where it recovers occlude kitchen cabinets, empty floor space behind kitchen island, sides of kitchen island, and hollow bed." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.533, + 0.471, + 0.745 + ], + "angle": 0, + "content": "Quantitative Results. We report results in Table 1. Without access to ground-truth meshes, our approach slightly outperforms Mesh-based DRDF on Scene F1 and approaches its performance on Ray Occ. Our approach matches it in accuracy (i.e., the fraction of predicted points that are correct) but does worse on completeness. We hypothesize that this is due to mesh-based techniques obtaining supervision in adjacent rooms. Nonetheless, our approach outperforms all other baselines besides mesh-supervised DRDF by a large margin (25.1 vs 21.6 F1). D2-DRDF has substantially higher performance compared to the Density Field baseline. This shows that density fields representation has difficulties in learning from a supervision of less views with large occlusions." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.756, + 0.336, + 0.772 + ], + "angle": 0, + "content": "5.2. Training on Incomplete Data" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.47, + 0.903 + ], + "angle": 0, + "content": "One advantage to using posed RGBD data compared to meshes is that it opens the door to learning from more data that is of lower quality. One can use lots of lower-quality scene captures that would produce poor meshes due to substantial amounts of incomplete data. In the case of direct mesh supervision methods, having an incomplete mesh may lead to incorrect signals for a mesh-based system: for instance, the network may learn to predict that an intersec" + }, + { + "type": "image", + "bbox": [ + 0.508, + 0.088, + 0.882, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.271, + 0.893, + 0.327 + ], + "angle": 0, + "content": "Figure 8. Mesh Degradation We show examples of how drop in images creates holes, reduces mesh area available for supervision. In (c), after retaining \\(\\approx \\frac{1}{4}\\) of image data, we only lose \\(\\approx \\frac{1}{2}\\) the mesh area. Im.: Image Coverage, M: Mesh Coverage, A: Area" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.335, + 0.893, + 0.395 + ], + "angle": 0, + "content": "tion does not exist simply because it was not scanned. In contrast, for D2-DRDF, missing data simply increases the fraction of points without supervision. We now test this hypothesis by reducing the number of views in datasets." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.397, + 0.893, + 0.518 + ], + "angle": 0, + "content": "Optimistic Degradation Setup (ODS). We simulate the degradation of dataset collection by subsampling views. To avoid conflating errors in training with suboptimal meshing with incomplete data, we optimistically degrade the meshes to provide an upper bound on supervision. We assume that the mesh with fewer views is identical to the mesh from all views, minus triangles with no vertices in any view. We show ODS mesh examples in Fig. 8." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.519, + 0.894, + 0.64 + ], + "angle": 0, + "content": "We degrade meshes by selecting \\(1/2^i\\) views per dataset for an increasing \\(i\\). While reducing the views linearly impacts the sample count for RGBD training setup, it has a non-linear impact on mesh completeness since a triangle is removed only if all of the views seeing it are removed (which is unlikely until most views are removed). For any given image retention \\((\\mathrm{Im.})\\%\\) mesh coverage \\((\\mathbf{M})\\%\\) degrades less giving an edge to mesh based methods." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.642, + 0.895, + 0.792 + ], + "angle": 0, + "content": "Usually, when dealing with limited data, we use a method called Screened Poisson Reconstruction(SPR) [25]. However, SPR does not perform well when there is not enough data available. To avoid conflating errors caused by poor quality inadequate meshing, we establish an upper limit on the performance of methods that rely on direct supervision. Our ODS strategy is much better than using SPR, but it cannot be used in real-world scenarios. We employ Open3D's[60]'s SPR with hyper-parameters similar to [1] to reconstruct meshes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.794, + 0.894, + 0.854 + ], + "angle": 0, + "content": "Datasets. We evaluate on Matterport3D [1] and apply our method as is without any modifications on OmniData [11] which has a substantially different image view distribution, more rooms and more floors compared to Matterport3D." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.894, + 0.901 + ], + "angle": 0, + "content": "Quantitative Results. We compare models trained on different amount of data available for supervision by using metrics defined in §5.1. In Tab. 2 we compare against the" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "17262" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.089, + 0.473, + 0.228 + ], + "angle": 0, + "content": "Table 2. Robustness to Sparse Data Performance on partial data on Matterport3D[1]. We compare (SPR and ODS) trained DRDF [27] which uses mesh supervision and (Depth) Depth-based DRDF (ours), which uses posed RGBD supervision. In each row, we degrade the training data and report test performance of the trained model. At \\(100\\%\\) data there is no M degradation for ODS or SPR. Our approach is more robust to drop in Im.: at \\(50\\%\\) view sparsity (Im.), models using ODS or SPR suffer substantial performance drops. Scene F1 drop by 16.3 for SPR; 3.5 for ODS; 2.1 for Depth (ours)." + }, + { + "type": "table", + "bbox": [ + 0.084, + 0.233, + 0.462, + 0.324 + ], + "angle": 0, + "content": "
Im. %ODS M %Scene F1Ray Occ F1
SPRODSDepthSPRODSDepth
10010071.971.972.127.327.325.1
505655.668.470.021.423.624.4
254356.866.870.021.521.224.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.329, + 0.472, + 0.386 + ], + "angle": 0, + "content": "Table 3. Robustness to Sparse Data Performance of ODS (mesh) and Depth (ours) based DRDF on partial Omnidata [11] following the same setup as Table 2. RGBD-based training is substantially more robust to partial data." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.39, + 0.47, + 0.488 + ], + "angle": 0, + "content": "
ODS\nIm. % M %Scene F1Ray Occ F1
ODSDepthODSDepth
258677.272.840.332.1
12.58375.3 (-1.9)70.9 (-1.9)37.1. (-3.2)29.3 (-2.8)
6.37873.4 (-3.8)71.8 (-1.0)32.6 (-7.7)28.1 (-3.0)
36969.8 (-7.4)70.4 (-2.4)20.3 (-20.0)26.7 (-5.4)
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.498, + 0.47, + 0.589 + ], + "angle": 0, + "content": "Mesh-DRDF trained ODS & SPR meshes. For any given view sampling level, the supervised M % area is high, resulting in stronger supervision for methods trained with ODS than RGBD. However, on Matterport3D, our method outperforms DRDF on all metrics at \\(25\\%\\) Im. and outperforms all other baseline methods in Scene F1 at \\(100\\%\\) Im.." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.59, + 0.471, + 0.756 + ], + "angle": 0, + "content": "In Tab. 3 we show robustness trends on OmniData. At \\(25\\%\\) Im./ \\(86\\%\\) M completion, mesh-based does better. We hypothesize this gain is due to better handling of estimates beyond the room. However, as Im. reduces mesh degrades, resulting steep fall for ODS DRDF: with \\(3\\%\\) Im./ \\(69\\%\\) M, MeshDRDF's Ray Occ F1 drops by 20 points; ours is reduced by just 5.4. Moreover for SPR DRDF, at low Im. values, meshing performs dismally and the poor meshing performance translates into poor reconstruction performance: at \\(6\\%\\) Im., training DRDF on SPR meshes produces a Ray F1 of just \\(5.7\\%\\) and a scene F1 of \\(40.9\\%\\)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.77, + 0.355, + 0.788 + ], + "angle": 0, + "content": "5.3. Adapting With Multiple Inputs" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.794, + 0.47, + 0.854 + ], + "angle": 0, + "content": "Since D2-DRDF can directly train on posed RGBD images, this enables test-time adaptation given a few auxiliary posed RGBD images. We start with the pre-trained model from §5.1 and then fine-tune for 500 iterations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Dataset and Metrics. We generate 300 quadruplets of scenes consisting of a reference view as well as three auxiliary RGBD images with poses. These three auxiliary views" + }, + { + "type": "image", + "bbox": [ + 0.514, + 0.09, + 0.882, + 0.265 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.275, + 0.892, + 0.331 + ], + "angle": 0, + "content": "Figure 9. Adaptation to a few images We optimize our pretrained model on the three auxiliary views (yellow) and a reference view (orange). The optimization lets the model fix reconstructions in occluded regions (e.g. bedside wall) as seen D2-DRDF + Adapt." + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.336, + 0.892, + 0.379 + ], + "angle": 0, + "content": "Table 4. Scene, Ray F1 Score for Adaptation Using D2-DRDF for adaptation produces better reconstructions that outperform the baselines by a large margin on Ray Occ F1 scores (by 8.9 points)." + }, + { + "type": "table", + "bbox": [ + 0.515, + 0.383, + 0.875, + 0.489 + ], + "angle": 0, + "content": "
MethodSceneRay
AccCmpF1AccCmpF1
D2-DRDF (Full)79.276.476.036.233.634.9
D2-DRDF (Depth)78.470.572.533.021.426.0
D2-DRDF (Scratch)66.470.366.023.727.425.4
Density Field [57]77.374.974.612.89.911.2
" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.492, + 0.891, + 0.536 + ], + "angle": 0, + "content": "are randomly sampled from views that overlap with occluded parts of the reference view (see supp.). We evaluate inferred 3D using the metrics as §5.1." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.538, + 0.892, + 0.657 + ], + "angle": 0, + "content": "Baselines. The baselines from §5.1 cannot operate in these settings, since they require meshes for training. Therefore we compare against a number of depth-map-based methods as well as ablations to give context to our results: (1) Density Field fine-tunes the density field baseline model from §5.1; (2) Depth-Pretrained fine-tunes D2-DRDF starting with the model from the first stage of training; (3) Scratch Training fine-tunes D2-DRDF from scratch." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.659, + 0.892, + 0.719 + ], + "angle": 0, + "content": "Results. Our loss and penalty formulations lend themselves well to test-time adaptation as shown in Fig. 9. After adaptation, D2-DRDF can resolve uncertainties like the corner of the bed and wall. Table 4 shows D2-DRDF does the best." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.731, + 0.611, + 0.746 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.755, + 0.892, + 0.831 + ], + "angle": 0, + "content": "We presented a method for learning to predict 3D from a single image using implicit functions while requiring only posed RGBD supervision. We believe our method can unlock new avenues with posed RGBD data becoming available from both consumers as well as robotic agents." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.831, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Acknowledgments. We thank Richard H., Sarah J., Dandan S., Shengyi Q., Karan D., Mohammed B., Chris R. for feedback. Toyota Research Institute (\"TRI\") provided funds to assist the authors with their research but this article solely reflects the opinions and conclusions of its authors and not TRI or any Toyota entity." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17263" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.176, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.17 + ], + "angle": 0, + "content": "[1] Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3d: Learning from rgb-d data in indoor environments. 3DV, 2017. 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.17, + 0.472, + 0.238 + ], + "angle": 0, + "content": "[2] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.238, + 0.471, + 0.264 + ], + "angle": 0, + "content": "[3] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. ECCV, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.264, + 0.471, + 0.291 + ], + "angle": 0, + "content": "[4] Weifeng Chen, Zhao Fu, Dawei Yang, and Jia Deng. Single-image depth perception in the wild. NeurIPS, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.291, + 0.471, + 0.33 + ], + "angle": 0, + "content": "[5] Julian Chibane, Aymen Mir, and Gerard Pons-Moll. Neural unsigned distance fields for implicit function learning. In NeurIPS, 2020. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.33, + 0.471, + 0.385 + ], + "angle": 0, + "content": "[6] Christopher B Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese. 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. In ECCV, 2016. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.384, + 0.471, + 0.439 + ], + "angle": 0, + "content": "[7] Brian Curless and Marc Levoy. A volumetric method for building complex models from range images. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 303-312, 1996. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.439, + 0.471, + 0.493 + ], + "angle": 0, + "content": "[8] Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In CVPR, 2017. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.493, + 0.471, + 0.56 + ], + "angle": 0, + "content": "[9] Angela Dai, Matthias Nießner, Michael Zollhöfer, Shahram Izadi, and Christian Theobalt. Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface reintegration. ACM Transactions on Graphics (ToG), 36(4):1, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.561, + 0.471, + 0.602 + ], + "angle": 0, + "content": "[10] Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan. Depth-supervised nerf: Fewer views and faster training for free. In CVPR, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.602, + 0.471, + 0.655 + ], + "angle": 0, + "content": "[11] Ainaz Eftekhar, Alexander Sax, Jitendra Malik, and Amir Zamir. Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans. In ICCV, 2021. 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.655, + 0.471, + 0.696 + ], + "angle": 0, + "content": "[12] David Eigen and Rob Fergus. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. ICCV, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.696, + 0.471, + 0.736 + ], + "angle": 0, + "content": "[13] Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In CVPR, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.736, + 0.471, + 0.775 + ], + "angle": 0, + "content": "[14] David F Fouhey, Abhinav Gupta, and Martial Hebert. Data-driven 3d primitives for single image understanding. In ICCV, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.776, + 0.471, + 0.857 + ], + "angle": 0, + "content": "[15] Huan Fu, Bowen Cai, Lin Gao, Lingxiao Zhang, Cao Li, Qixun Zeng, Chengyue Sun, Yiyun Fei, Yu Zheng, Ying Li, Yi Liu, Peng Liu, Lin Ma, Le Weng, Xiaohang Hu, Xin Ma, Qian Qian, Rongfei Jia, Binqiang Zhao, and Hao Zhang. 3d-front: 3d furnished rooms with layouts and semantics. ICCV, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.858, + 0.471, + 0.9 + ], + "angle": 0, + "content": "[16] Huan Fu, Bowen Cai, Lin Gao, Ling-Xiao Zhang, Jiaming Wang, Cao Li, Qixun Zeng, Chengyue Sun, Rongfei Jia, Binqiang Zhao, et al. 3d-front: 3d furnished rooms with layouts" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.115, + 0.472, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.733, + 0.106 + ], + "angle": 0, + "content": "and semantics. In ICCV, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.106, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[17] Rohit Girdhar, David F Fouhey, Mikel Rodriguez, and Abhinav Gupta. Learning a predictable and generative vector representation for objects. In ECCV. Springer, 2016. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.147, + 0.892, + 0.172 + ], + "angle": 0, + "content": "[18] Georgia Gkioxari, Jitendra Malik, and Justin Johnson. Mesh r-cnn. In ICCV, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.172, + 0.892, + 0.226 + ], + "angle": 0, + "content": "[19] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. A papier-mâché approach to learning 3d surface generation. In CVPR, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.226, + 0.892, + 0.267 + ], + "angle": 0, + "content": "[20] Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, and Jia-Bin Huang. Deepmvs: Learning multi-view stereopsis. In CVPR, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.267, + 0.892, + 0.363 + ], + "angle": 0, + "content": "[21] Shahram Izadi, David Kim, Otmar Hilliges, David Molyneaux, Richard Newcombe, Pushmeet Kohli, Jamie Shotton, Steve Hodges, Dustin Freeman, Andrew Davison, et al. Kinectfusion: real-time 3d reconstruction and interaction using a moving depth camera. In Proceedings of the 24th annual ACM symposium on User interface software and technology, pages 559-568, 2011. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.363, + 0.892, + 0.388 + ], + "angle": 0, + "content": "[22] Hamid Izadinia, Qi Shan, and Steven M Seitz. Im2cad. In CVPR, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.388, + 0.892, + 0.43 + ], + "angle": 0, + "content": "[23] Michael Janner, Jiajun Wu, Tejas D Kulkarni, Ilker Yildirim, and Josh Tenenbaum. Self-supervised intrinsic image decomposition. NeurIPS, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.43, + 0.892, + 0.483 + ], + "angle": 0, + "content": "[24] Ziyu Jiang, Buyu Liu, Samuel Schulter, Zhangyang Wang, and Manmohan Chandraker. Peek-a-boo: Occlusion reasoning in indoor scenes with plane representations. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.483, + 0.892, + 0.536 + ], + "angle": 0, + "content": "[25] Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Proceedings of the fourth Eurographics symposium on Geometry processing, 2006. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.536, + 0.892, + 0.564 + ], + "angle": 0, + "content": "[26] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. *ICLR*, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.564, + 0.892, + 0.605 + ], + "angle": 0, + "content": "[27] Nilesh Kulkarni, Justin Johnson, and David F. Fouhey. Directed ray distance function for 3d scene reconstruction. In ECCV, 2022. 1, 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.604, + 0.892, + 0.644 + ], + "angle": 0, + "content": "[28] Nilesh Kulkarni, Ishan Misra, Shubham Tulsiani, and Abhinav Gupta. 3d-relnet: Joint object and relational network for 3d prediction. In ICCV, 2019. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.644, + 0.892, + 0.671 + ], + "angle": 0, + "content": "[29] Kiriakos N Kutulakos and Steven M Seitz. A theory of shape by space carving. *IJCV*, 2000. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.671, + 0.892, + 0.697 + ], + "angle": 0, + "content": "[30] Christoph Lassner and Michael Zollhöfer. Pulsar: Efficient sphere-based neural rendering. arXiv:2004.07484, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.697, + 0.892, + 0.737 + ], + "angle": 0, + "content": "[31] Chen-Hsuan Lin, Chen Kong, and Simon Lucey. Learning efficient point cloud generation for dense 3d object reconstruction. In AAAI, volume 32, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.737, + 0.892, + 0.777 + ], + "angle": 0, + "content": "[32] Chen Liu, Kihwan Kim, Jinwei Gu, Yasutaka Furukawa, and Jan Kautz. Planercnn: 3d plane detection and reconstruction from a single image. In CVPR, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.777, + 0.892, + 0.804 + ], + "angle": 0, + "content": "[33] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. *ICLR*, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.804, + 0.892, + 0.844 + ], + "angle": 0, + "content": "[34] Ravi Malladi, James A Sethian, and Baba C Vemuri. Shape modeling with front propagation: A level set approach. TPAMI, 1995. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.844, + 0.892, + 0.898 + ], + "angle": 0, + "content": "[35] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In CVPR, 2019. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.898 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "17264" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "[36] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. arXiv preprint arXiv:2003.08934, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.147, + 0.47, + 0.202 + ], + "angle": 0, + "content": "[37] Yinyu Nie, Xiaoguang Han, Shihui Guo, Yujuan Zheng, Jian Chang, and Jian Jun Zhang. Total3dunderstanding: Joint layout, object pose and mesh reconstruction for indoor scenes from a single image. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.202, + 0.47, + 0.256 + ], + "angle": 0, + "content": "[38] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.256, + 0.47, + 0.365 + ], + "angle": 0, + "content": "[39] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS. 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.365, + 0.469, + 0.405 + ], + "angle": 0, + "content": "[40] Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In ECCV, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.405, + 0.469, + 0.46 + ], + "angle": 0, + "content": "[41] Shengyi Qian, Alexander Kirillov, Nikhila Ravi, Devendra Singh Chaplot, Justin Johnson, David F Fouhey, and Georgia Gkioxari. Recognizing scenes from novel viewpoints. arXiv preprint arXiv:2112.01520, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.46, + 0.469, + 0.514 + ], + "angle": 0, + "content": "[42] Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari. Accelerating 3d deep learning with pytorch3d. arXiv:2007.08501, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.514, + 0.469, + 0.58 + ], + "angle": 0, + "content": "[43] Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, and Joshua M. Susskind. Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding. In ICCV, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.58, + 0.469, + 0.636 + ], + "angle": 0, + "content": "[44] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In ICCV, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.636, + 0.469, + 0.689 + ], + "angle": 0, + "content": "[45] Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In CVPR, 2006. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.689, + 0.469, + 0.744 + ], + "angle": 0, + "content": "[46] Jonathan Shade, Steven Gortler, Li-wei He, and Richard Szeliski. Layered depth images. In Proceedings of the 25th annual conference on Computer graphics and interactive techniques, pages 231–242, 1998. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.744, + 0.469, + 0.784 + ], + "angle": 0, + "content": "[47] Jian Shi, Yue Dong, Hao Su, and Stella X Yu. Learning non-lambertian object intrinsics across shapenet categories. In CVPR, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.784, + 0.469, + 0.825 + ], + "angle": 0, + "content": "[48] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. NeurIPS, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.825, + 0.469, + 0.864 + ], + "angle": 0, + "content": "[49] Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao. Sun rgb-d: A rgb-d scene understanding benchmark suite. In CVPR, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.864, + 0.469, + 0.893 + ], + "angle": 0, + "content": "[50] Shuran Song, Fisher Yu, Andy Zeng, Angel X Chang, Manolis Savva, and Thomas Funkhouser. Semantic scene comple" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.893 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.816, + 0.107 + ], + "angle": 0, + "content": "tion from a single depth image. CVPR, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.107, + 0.892, + 0.146 + ], + "angle": 0, + "content": "[51] Jiaming Sun, Yiming Xie, Linghao Chen, Xiaowei Zhou, and Hujun Bao. Neuralrecon: Real-time coherent 3d reconstruction from monocular video. In CVPR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.146, + 0.892, + 0.201 + ], + "angle": 0, + "content": "[52] Xingyuan Sun, Jiajun Wu, Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Tianfan Xue, Joshua B Tenenbaum, and William T Freeman. Pix3d: Dataset and methods for single-image 3d shape modeling. In CVPR, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.201, + 0.892, + 0.253 + ], + "angle": 0, + "content": "[53] Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox. What do single-view 3d reconstruction networks learn? In CVPR, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.254, + 0.892, + 0.281 + ], + "angle": 0, + "content": "[54] Kaixuan Wang and Shaojie Shen. Mvdepthnet: Real-time multiview depth estimation neural network. In 3DV, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.281, + 0.892, + 0.307 + ], + "angle": 0, + "content": "[55] X. Wang, David F. Fouhey, and A. Gupta. Designing deep networks for surface normal estimation. In CVPR, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.307, + 0.892, + 0.348 + ], + "angle": 0, + "content": "[56] Jianglong Ye, Yuntao Chen, Naiyan Wang, and Xiaolong Wang. Gifs: Neural implicit function for general shape representation. In CVPR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.348, + 0.892, + 0.387 + ], + "angle": 0, + "content": "[57] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelNeRF: Neural radiance fields from one or few images. In CVPR, 2021. 2, 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.387, + 0.892, + 0.429 + ], + "angle": 0, + "content": "[58] Zehao Yu, Jia Zheng, Dongze Lian, Zihan Zhou, and Shenghua Gao. Single-image piece-wise planar 3d reconstruction via associative embedding. In CVPR, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.429, + 0.892, + 0.482 + ], + "angle": 0, + "content": "[59] Cheng Zhang, Zhaopeng Cui, Yinda Zhang, Bing Zeng, Marc Pollefeys, and Shuaicheng Liu. Holistic 3d scene understanding from a single image with implicit representation. In CVPR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.482, + 0.892, + 0.522 + ], + "angle": 0, + "content": "[60] Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. Open3D: A modern library for 3D data processing. arXiv:1801.09847, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.522, + 0.892, + 0.576 + ], + "angle": 0, + "content": "[61] Shizhan Zhu, Sayna Ebrahimi, Angjoo Kanazawa, and Trevor Darrell. Differentiable gradient sampling for learning implicit 3d scene reconstructions from a single image. In ICLE, 2022. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.576 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17265" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/9e708598-d8b4-40c1-87d1-c7aee00317d3_origin.pdf b/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/9e708598-d8b4-40c1-87d1-c7aee00317d3_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b6d7d2fcaec4f7feeb94df1766b27a643d99a986 --- /dev/null +++ b/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/9e708598-d8b4-40c1-87d1-c7aee00317d3_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ee06aac3abf2ef86cd42201d7bcad7a3b0f88d73e1956cd7c42242f50262e27c +size 9871267 diff --git a/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/full.md b/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/full.md new file mode 100644 index 0000000000000000000000000000000000000000..1ac6f7309efadfed8368dbf03f34992b5700a671 --- /dev/null +++ b/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/full.md @@ -0,0 +1,379 @@ +# Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data + +Nilesh Kulkarni, Linyi Jin, Justin Johnson, David F. Fouhey +University of Michigan + +# Abstract + +We introduce a method that can learn to predict scene-level implicit functions for 3D reconstruction from posed RGBD data. At test time, our system maps a previously unseen RGB image to a 3D reconstruction of a scene via implicit functions. While implicit functions for 3D reconstruction have often been tied to meshes, we show that we can train one using only a set of posed RGBD images. This setting may help 3D reconstruction unlock the sea of accelerometer+RGBD data that is coming with new phones. Our system, D2-DRDF, can match and sometimes outperform current methods that use mesh supervision and shows better robustness to sparse data. + +# 1. Introduction + +Consider the image in Figure 1. From this ordinary RGB image, we can understand the complete 3D geometry of the scene, including the floor and walls behind the chairs. Our goal is to enable computers to recover this geometry from a single RGB image. To this end, we present a method that does so while learning only on posed RGBD images. + +The task of reconstructing the full 3D of a scene from a single previously unseen RGB image has long been known to be challenging. Early work on full 3D relied on voxels [6, 17] or meshes [19], but these representations fail on scenes due to topology and memory challenges. Implicit functions (or learning to map each point in $\mathbb{R}^3$ to a value like the distance to the nearest surface) have shown substantial promise at overcoming these challenges. When conditioned on an image, these have led to several successful methods. + +Unfortunately, the implicit function status quo mainly ties implicit function reconstruction methods to mesh supervision. This symbiosis has emerged since meshes give excellent direct supervision. However, methods are limited to training with an image-aligned mesh that is usually watertight (and often artist-created) [35, 38, 44] and occasionally non-watertight but professionally-scanned [27, 61]. + +We present a method, Depth to DRDF (D2-DRDF), that breaks the implicit-function/mesh connection and can train an effective single RGB image implicit 3D reconstruction + +![](images/9972482f06efcb8da865e3c47bc664583d66b3eda8446f41321720a2ddec2d5c.jpg) +Inference: Single RGB Image to 3D + +![](images/c6e9b749af9806e2e64547e15241b3cfdafb972dc5b029dfa4c02766402192e6.jpg) +Figure 1. We propose a method, D2-DRDF, that reconstructs full 3D from a single RGB image. During inference (left), our method uses implicit functions to reconstruct the complete 3D including visible and occluded surfaces (visualized as surface normals $\square$ ) such as the occluded wall and empty floor. For training, our method uses only RGBD images and poses, unlike most prior works that need an explicit and often watertight 3D mesh. + +![](images/9e8c7a9425bbd1ed098c420a18029ed3fc4a0963c12954ce962cd50b64781218.jpg) + +![](images/e8238ac8f3c60b99e4bf280208ce0fa2fdf1449a9231b377344eaddfc13059ed.jpg) + +![](images/aa54fd3fd925dac8072d7aefd755222d6ac610ce269de6ace8fdab2ded5cf8f3.jpg) +Training Data + +![](images/c6405c2a2f2928f3b60bec0b79754a75b1d7e5d56cc36c8cf95275494e3f17c6.jpg) + +system using a set of RGBD images with known pose. We envision that being able to entirely skip meshing will enable the use of vast quantities of lower-quality data from consumers (e.g., from increasingly common consumer phones with LiDAR scanners and accelerometers) as well as robots. In addition to permitting training, the bypassing of meshing may enable adaptation in a new environment on raw data without needing an expert to ensure mesh acquisition. + +Our key insight is that we can use segments of observed free space in depth maps in other views to constrain distance functions. We show this using the Directed Ray Distance Function (DRDF) [27] that has recently shown good performance in 3D reconstruction using implicit functions and has the benefit of not needing watertight meshes for training. Given an input reference view, the DRDF breaks the problem of predicting the 3D surface into a set of independent 1D distance functions, each along a ray through a pixel in the reference view and accounting for only surfaces on the ray. Rather than use an ordinary unsigned distance function, the DRDF signs the distance using the location of the camera's pinhole and the intersections along the ray. While [27] showed their method could be trained on non-watertight meshes, their method is still dependent + +on meshes. In our paper, we show that the DRDF can be cleanly supervised using auxiliary views of RGBD observations and their poses. We derive constraints on the DRDF that power loss functions for training our system. While the losses on any one image are insufficient to produce a reconstruction, they provide a powerful signal when accumulated across thousands of training views. + +We evaluate our method on realistic indoor scene datasets and compare it with methods that train with full mesh supervision. Our method is competitive and sometimes even better compared to the best-performing mesh-supervised method [28] with full, professional captures. As we degrade scan completeness, our method largely maintains its performance while mesh-based methods perform substantially worse. We conclude by showing that fine-tuning of our method on a handful of images enables a simple, effective method for fusing and completing scenes from a handful of RGBD images. + +# 2. Related Work + +Our approach infers a complete 3D scene from a single RGB image using implicit functions that are supervised via posed RGBD scans. This touches on several long-term goals of 3D computer vision that we discuss below. + +Reconstructing Scenes from a Single Image. At test time, our system produces a full 3D reconstruction from a single RGB image, including occluded regions. This means that our desired output goes beyond 2.5D properties such as depth [4, 12], surface normals [12, 14, 55], or other intrinsic-image properties [23, 47]. Works on attempting to reconstruct complete 3D usually have focused on reconstructing objects from image by using voxels [6, 17] or meshes [18, 19], point clouds [13, 31] or CAD models [22]. These are usually trained on synthetic datasets like ShapeNet [2] or scene datasets [16, 43, 50] and do not generalize well to realistic scenes. Another line of works tries to learn holistic structures [37, 59] or planar surfaces [24, 32, 58] from realistic scanned mesh data [1, 8] Creating realistic mesh based data for scenes requires postprocessing using Poisson surface reconstruction [9, 25] which leads to deviation from raw captures. Manually aligning them is expensive hence 3D object-aligned datasets like [49, 52] are scarce and limited in diversity. Our method avoids these limitations by directly operating on the raw captured RGBD data. + +Reconstructing Scenes from Posed Scans. There has been considerable work on using multiview RGBD data at inference time to produce 3D reconstructions, starting with analytic techniques [7, 9, 21, 29] and now using learning [20, 51, 54]. We use some similar tools to this community: for instance, the ray distances we use are known in this community as a projected distance functions [7]. However, their works solve a fundamentally different problem + +by using posed RGBD data at test time: their goal is to produce a reconstruction from a set of posed RGBD images from one particular scene; our goal is to use a large dataset of posed RGBD data to train a neural network that can map a new single RGB image to a 3D reconstruction. + +Our goal of learning to predict reconstructions from a single previously unseen image also distinguishes our work from NeRF [36] and other similar radiance field approaches [3]. Their goal is to learn a radiance field for a particular scene from a set of posed scans. There are methods that try to predict this radiance field from a single image [57]; we compare with a model using similar losses and find that an objective specialized for 3D works better. + +Implicit Functions for 3D Reconstruction. We perform reconstruction with implicit functions. Implicit functions have used for shape and scene modeling as level sets [34], signed distance functions [38], occupancy function [35, 40, 44], distance functions [5, 27, 48] and other modifications like [56]. Our work has two key distinctions. First, many approaches [5, 48, 56] fit an implicit function to one shape (i.e., there is no generalization to new shapes). In contrast, our work produces reconstructions from previously unseen RGB images. Second, the methods that predict an implicit function from a new image [27, 61] assume access to a mesh at training time. On other hand, our work assumes access only to posed RGBD data for training. + +The most similar work to ours is [27] that also aims to learn to reconstruct scenes from a single image by training on realistic data. While [27] shows how to reduce supervision requirements by enabling the use of non-watertight meshes, their method is still limited to mesh supervision. We show how to use posed RGBD data for supervision instead of using an image-aligned mesh. This substantially reduces the requirements for collecting training data. + +# 3. Pixel-Aligned 3D Reconstruction & DRDF + +We propose a method to predict full 3D scene structure from a previously unseen RGB image. At inference time, our method receives a single image with no depth information. We refer to this view as the reference view. As output, the method produces an estimate of a distance function at each point in a pre-defined set of 3D points. Given this volume of predicted distances, one can decode the predicted distance into a set of surfaces: e.g., if the predicted distance were the unsigned distance to the nearest surface, one could declare all points with sufficiently small predicted distance to be surfaces. At training time, our network is given supervision for predicting its distance function. Previously, supervision been done via a mesh that provides oracle distance calculations. In §4, we show how to derive supervision from posed RGBD images at auxiliary views instead. + +The approaches in the paper follow pixelNerf [57] for predicting a distance at a 3D point $\mathbf{x}$ . The network accepts + +![](images/cab7f3ed4744a5aaaa785430b3e0d5da55ed6e40e041b1157ef33398d891a61a.jpg) +Figure 2. Model and DRDF overview. (Left) Given a single input RGB image $I$ and query point $\mathbf{x}$ on a ray $\vec{\mathbf{r}}$ in a pre-defined grid, all models, like [57] extract image features at the projection $\pi(\mathbf{x})$ of $\mathbf{x}$ with a backbone and then predict the distance using a MLP on the backbone features and a positional encoding of $\mathbf{x}$ . (Right) Given a ray $\vec{\mathbf{r}}$ through the scene, ray distance functions are defined as the distance to intersections of the ray with the scene. This permits analyzing the 3D scene as a set of 1D distances per ray. The Directed Ray Distance Function is further signed to be positive before and negative after an intersection. + +an image $I$ and 3D point $\mathbf{x}$ and makes a prediction $f_{\theta}(\mathbf{x}, I)$ . As shown in Fig. 2, the network extracts a convolutional feature from a backbone $\mathbf{B}$ at the projection of $\mathbf{x}$ , denoted $\pi(\mathbf{x})$ . The image feature is concatenated to a positional encoding of $\mathbf{x}$ and put into a MLP to produce the final prediction $f_{\theta}(\mathbf{x}, I)$ . By repeating this inference for a set of 3D points, the network can infer the full 3D of the scene. + +Training. In a conventional setup, the network $f_{\theta}(\mathbf{x},I)$ is trained by obtaining samples from a mesh and directly training the network via regression. Given a mesh that is coaligned with an image, one can obtain an $n$ tuples consisting of an image $I_{i}$ , 3D point $\mathbf{x}_i$ and ground-truth distance $d_{i}$ . Then the network is trained by minimizing the empirical risk $\frac{1}{n}\sum_{i = 1}^{n}\mathcal{L}(f_{\theta}(\mathbf{x}_i,I_i),d_i)$ for some loss $\mathcal{L}$ like L1. + +What Distance Function Should We Predict? There are multiple distance functions in the literature, and the above formulation enables predicting any of them. To explain distinctions, assume we are given a point $\mathbf{x}$ at which we want to evaluate a distance and a scene $S \subseteq \mathbb{R}^3$ that we want to evaluate the distance to. + +In a standard distance function, one defines a distance from a point $\mathbf{x}$ to the entire scene $S$ . For instance, the Unsigned Distance Function (UDF) is defined as $\min_{\mathbf{x}' \in S} ||\mathbf{x} - \mathbf{x}'||$ . In a ray-based distance function [7, 27], the distance is defined from $\mathbf{x}$ only to the points in the scene that lie on the ray $\vec{\mathbf{r}}$ from the reference view pinhole towards $\mathbf{x}$ . In other words, the ray distance function is defined as the distance to the intersections of the ray with the scene. For instance, the Unsigned Ray Distance Function (URDF) is defined as $\min_{\mathbf{x}' \in S, \mathbf{x}' \text{ on } \vec{\mathbf{r}}} ||\mathbf{x} - \mathbf{x}'||$ . This definition means that when predicting the distance at a point $\mathbf{x}$ , all of the points that define the ray distance function project to the same pixel, $\pi(\mathbf{x})$ , which in turn means that neural networks do not need as large receptive fields [27]. + +Since a ray-based distance function is defined only by scene points that intersect a ray, we can reduce the 3D problem to a 1D problem, as illustrated in Fig. 2. Rather than represent each 3D point as a 3D point, we represent it as the distance along the ray $\vec{\mathbf{r}}$ : the vector $\mathbf{x}$ is represented via a scalar $z$ such that $\mathbf{x} = z\vec{\mathbf{r}}$ . Then, if the $i^{th}$ intersection along the ray is $s_i\vec{\mathbf{r}}$ for $s_i \in \mathbb{R}$ , the URDF is defined as the distance to the nearest intersection along that ray, or $d_{\mathrm{UR}}(z) = \min_{i=1,\dots,k} |z - s_i|$ . + +Directed Ray Distance Function. The Directed Ray Distance Function (DRDF) is a ray distance function that incorporates a sign into the URDF. In particular, the DRDF is defined as $d_{\mathrm{DR}}(z) = \operatorname{dir}(z)d_{\mathrm{UR}}(z)$ , where the predicate $\operatorname{dir}(z)$ is positive if the nearest intersection is ahead of $z$ along the ray from the pinhole and negative otherwise. This is pictured in Fig. 2. The presence of both positive and negative components leads to better behavior under uncertainty [27]. Critical Properties of DRDFs. Two critical properties that we use to define supervision are that at $z$ , the distance to the nearest intersection is $|d_{\mathrm{DR}}(z)|$ and the nearest intersection on the ray is at $z + d_{\mathrm{DR}}(z)$ . + +# 4. Depth-Supervised DRDF Reconstruction + +Having introduced the setup in §3, we now explain how to train a network to predict an image-conditioned DRDF without access to an underlying mesh but instead access to posed RGBD images. Concretely, given a point $z\vec{\mathbf{r}}$ , we aim to define a loss function $\mathcal{L}$ that evaluates a network's prediction of the DRDF $f_{\theta}((z\vec{\mathbf{r}}),I)$ . + +Our key insight is that we can sometimes see this point $z\vec{\mathbf{r}}$ in other views and these observations of $z\vec{\mathbf{r}}$ provide a constraints on what the value of the DRDF can be. Consider, for instance, the point 3m out from reference view camera pinhole along the red ray in Fig. 3. If we project this point into the auxiliary view, we know that its distance to the camera is closer to auxiliary view's pinhole than the depthmap observed at the auxiliary view. Moreover, we can see that a segment of the ray is visible, starting at the point $s\vec{\mathbf{r}}$ and ending at the point $e\vec{\mathbf{r}}$ with the ending point $e\vec{\mathbf{r}}$ closer to $z\vec{\mathbf{r}}$ than $s\vec{\mathbf{r}}$ is. + +We can derive supervision on $d_{\mathrm{DR}}(z)$ from the segment of visible free space observable in the auxiliary view by using the fact that the nearest intersection at $z$ is $|d_{\mathrm{DR}}(z)|$ units away. Since $e$ is nearer than $s$ , and there are no intersections in the free space between $s$ and $e$ , we know that there cannot be an intersection within $(e - z)$ of $z$ . This gives an inequality that $|d_{\mathrm{DR}}(z)| \geq (e - z)$ : otherwise there would be an intersection in the visible free space. If the ends of the segment are actual intersections where the ray visibly intersects an object, we obtain an equality constraint on $d_{\mathrm{DR}}(z)$ since we actually see the nearest intersection. In other cases, the ray simply disoccludes or is occluded, and we can only have an inequality. We denote intersection start/end events as $\mathcal{I}$ . + +![](images/42c17679cbf9f97ccf8ad087dbe7f3a0a9e58596c79e9aa6d7ef42a0b96fb39a.jpg) +Reference + +![](images/e8e9a9367ab7c2ceaf0bbfd5f4b71bcd2cf9bd86d013e41b5a51a7cf241ec4ed.jpg) +Auxiliary View + Zoomed + +![](images/9537e6a13690426262a9b83e7186eafe37778326690bd42f73ca09b95742dd55.jpg) + +![](images/e67b18dcfef93ea2e6be37e25786b1012540dfb14c4cd415513442eeaa79f6b5.jpg) + +![](images/04a74f8bcbc027a2d5fd840e947a9ebc76fa612aa3b51b602f2408267d8aa966.jpg) + +![](images/c46cbe256f550127d8a93792d16ec36df5816cb263fbea8c8ad51303821d871b.jpg) + +![](images/459e5a4e8508c89e1b9e318de1c21ae9d0f0c4caea637f8c35a3ed5f4b713c10.jpg) +Figure 3. A red ray originates in the reference camera. Given a point $\mathbf{x}$ that is $z$ units along the ray $\vec{\mathbf{r}}$ , we can test if $z\vec{\mathbf{r}}$ is visible in an auxiliary view by comparing its projected depth with the RGBD image. The point $z$ is visible, along with a segment of the ray from $s$ to $e$ units. Since the distance to the nearest intersection is $|d_{\mathrm{DR}}(z)|$ and we know there are no intersections within $(e - z)$ units of $z$ , we can constrain that $|d_{\mathrm{DR}}(z)| \geq (e - z)$ . + +and occlusion start/end events as $\mathcal{O}$ and distinguish them by checking if the projected z-value of the ray in auxiliary view is sufficiently close to the recorded auxiliary view's depthmap value. + +# 4.1. Losses + +We now convert the concept of using freespace to constrain the DRDF into concrete losses. Recall that our goal is to evaluate a prediction $y = f_{\theta}(z\vec{\mathbf{r}},I)$ from our network and that the point at $z$ along $\vec{\mathbf{r}}$ is in some segment of visible freespace from $s$ to $e$ along $\vec{\mathbf{r}}$ . Our goal is to penalize the prediction $y$ . Since there are two start/end event classes $(1,0)$ , there are four segment types: $11,10,01$ , and $00$ . We show several of these segments in Figure 5. + +To assist the loss definitions, we define variables $l_{s} = s - z$ , and $l_{e} = e - z$ which we plot in Fig. 4 as a function of $z$ . $l_{s}$ is the value of $d_{\mathrm{DR}}$ if the closest intersection is at $s$ , and $l_{e}$ is the value of $d_{\mathrm{DR}}$ if the closest intersection is at $e$ . The values $l_{s}, l_{e}$ define equalities for intersections and inequalities for occlusions. + +$\mathcal{L}_{\mathcal{II}}$ : II segment. Given a segment bounded by two intersections, the nearest intersections are known exactly as $l_{s}$ or $l_{e}$ depending on whether $z < \frac{s + e}{2}$ or not. We penalize the $\ell_1$ error between the prediction $y$ and the known DRDF, or $\mathcal{L}_{\mathcal{II}}(y) = |y - l_s|$ if $z < \frac{s + e}{2}$ and $|y - l_e|$ otherwise. This penalty is zero only when $y$ is equal to the known DRDF. $\mathcal{L}_{\mathcal{OO}}$ : OO segment. Given a segment bounded by two occlusion events, the exact DRDF is not known, but the visible free space rules out potential values. Since $|d_{\mathrm{DR}}(z)|$ is the distance to the nearest intersection, $d_{\mathrm{DR}}(z)$ cannot lie in $[l_s,l_e]$ since such a value would imply an intersection in free space between $s$ and $e$ . We penalize incursions into $[l_s,l_e]$ with a $\ell_1$ penalty: if we denote halfway be + +![](images/ae9a371f96585ca101e40c16c92787742c99dd71c964cce5dc90dcaced684ce1.jpg) +Figure 4. Loss functions for a starting $s$ and ending $e$ events spaced a unit apart. Intersection events (blue) define the DRDF precisely, and we penalize the network from deviating. Occlusion events (purple) provide bounds that we penalize the network for violating. We plot $l_{s}$ as a line from the start event and $l_{e}$ as a line from the end event. Loss: 0 1.7 + +![](images/9c28f6c999a70ab482978fae2a158782515bc61fc275dc05a0259af811b42ab0.jpg) + +![](images/0c1666bcc0bec8fc35c2c2d8f9558df12d24aaab427375aed3ffe2a4ad10d72f.jpg) + +![](images/6c01a2522fc92aa086a1c86dc879de1bb9ee4a6a18edc8e9b3101bb4fa111b2c.jpg) + +tween $l_s$ and $l_e$ as $h$ , then this can be done as $\mathcal{L}_{\mathcal{O}\mathcal{O}}(y) = \max (0, l_e - h - |y - h|)$ . The resulting penalty is zero if $y \leq l_s$ or if $y \geq l_e$ . + +$\mathcal{L}_{\mathcal{IO}}$ : IO segment. When the segment is bounded by an intersection event followed by an occlusion event, the situation is more complex and we define $\mathcal{L}_{\mathcal{IO}}$ piecewise. In the first half of the segment $(z < \frac{s + e}{2})$ , $d_{\mathrm{DR}}$ is exactly known, and so we can use an $\ell_1$ penalty like the II case, so $\mathcal{L}_{\mathcal{IO}}(y) = |y - l_s|$ . In the second half, there are two options. If the nearest intersection is $s$ , then $d_{\mathrm{DR}}(z) = l_s$ . Otherwise, the nearest intersection is unknown but after $e$ and so $d_{\mathrm{DR}}(z) > l_e$ must hold (since $d_{\mathrm{DR}}(z) \leq l_e$ would imply the existence of an intersection before $e$ ). We take the minimum of errors for the two cases: $\ell_1$ distance to $l_s$ and a $\ell_1$ penalty function $\max(0, l_e - y)$ , resulting in $\mathcal{L}_{\mathcal{IO}}(y) = \min(\max(0, l_e - y), |y - l_s|)$ . This part of the penalty is zero if either $y = l_s$ or if $y > l_e$ . + +$\mathcal{L}_{\mathcal{O}\mathcal{I}}$ : $\mathcal{O}\mathcal{I}$ segment. The $\mathcal{L}_{\mathcal{O}\mathcal{I}}$ loss is defined symmetrically to $\mathcal{L}_{\mathcal{I}\mathcal{O}}$ , simply by exchanging the role of $s$ and $e$ . In addition to occluded regions, this loss occurs in the reference view up to the depthmap, where a disocclusion into the reference camera's view is followed by an intersection. + +To assist the network, we add two auxiliary losses that are true statistically: $\mathcal{L}_{\mathrm{sep}}$ represents a prior that surfaces tend to be separated by distances and $\mathcal{L}_{\mathrm{ent}}$ captures a property of the DRDF that is true in the limit if our observations are randomly chosen. + +$\mathcal{L}_{\mathrm{sep}}$ : Minimum Separation Loss. Since the cameras never see the insides of objects, there is no incentive to predict inside objects. This prevents the generation of zero crossings, e.g., after the first intersection. To assist the network, we add a loss $\mathcal{L}_{\mathrm{sep}}$ that assumes that surfaces are separated by a minimum distance unless there is evidence otherwise. We continue the DRDF's known value for $t = 0.2m$ before and after each intersection event to make a continued value $c$ . We then penalize $\mathcal{L}_{\mathrm{sep}}(y) = |y - c|$ , so long as there is no conflicting free space evidence from another view. + +$\mathcal{L}_{\mathrm{ent}}$ : Sign Entropy Loss. The occlusion-based constraints + +![](images/2b718943b488dc9b61dfec7c7b4d9cce0e58c9f69381eb850fe0d83c00ac8b0f.jpg) +Scene Point Cloud, Cameras, and Ray + +![](images/8bbca5f1c4edf9bdf6eca6041cfdc188e141814d98154bb1a2b03805e6e217b7.jpg) +Reference (R) +Figure 5. A red ray originates from the reference camera and intersects the table before entering the island. This ray is seen by three other auxiliary views (a, b, c). For each view we show just one of the many segments in that view for readability, showing intersections as blue s and occlusions as purple o. The segments in views (a) and (b) are of the form OI, where the O comes from the ray entering the frustum in (a) and disoccluding in (b). In view (c), we show an OO segment. We discuss the penalties for these segments in §4.1 + +![](images/4bfa762410c8dc01fa2aeb6abf88607ad58bd8d1d14e3f34d900a63992a87d84.jpg) +View (a) + +![](images/490d6ecc63550dfd559c1a4f81b0d9445ff302b9897301e6565c7951d416454c.jpg) +View (b) + +![](images/974c9c2c06b04e55dd4db62622f682fc51b8c043c45a005cd4a8d7adc5271428.jpg) +View (c) + +can be satisfied by making $y$ positive or negative. In theory, after the first intersection, $d_{\mathrm{DR}}$ is positive half of the time. Learning to produce the signs uniformly with gradient descent is difficult due to the large loss between the acceptable solutions. To encourage a uniform distribution of signs, we would like to maximize the entropy of the distribution of the signs of the predictions (achieved when the distribution is uniform). Since sign is non-differentiable, we optimize a differentiable surrogate. Suppose $Y$ is the set of predictions at points that are occluded in the reference view and $H$ is the binary entropy $H(p) = p \log(p) + (1 - p) \log(1 - p)$ . Then our loss is $\mathcal{L}_{\mathrm{ent}} = H(\sum_{y \in Y} \sigma(y / \tau) / |Y|)$ where $\sigma$ functions like a soft-sign. As seen in the supplement, $\mathcal{L}_{\mathrm{ent}}$ can be minimized by distributing $Y$ symmetric about 0. Using $\mathcal{L}_{\mathrm{ent}}$ improves the prediction and Scene F1 by 2 points. + +# 4.2. Implementation Details + +View Selection. For every candidate auxiliary view we compute the fraction of visible points in this view that are occluded in the reference view. Auxiliary views with large number of such points provide supervision for key occluded regions in the reference view. We sample up to 20 auxiliary views per reference image from this set of views. + +Sampling Strategy. Given a set of fixed auxiliary views and a reference view, we sample over 200 rays per input image with 512 points per ray. We re-balance this set by sampling $20K$ points that are visible and $20K$ points that are occluded in reference view. We rebalance as most points are from the region between the camera and the first hit. + +Combining segments from different views. We merge information from multiple posed RGBD images to produce a concise merged set of non-overlapping segments along the ray. This prevents double-counting losses (e.g., if a region of the ray is seen by multiple auxiliary views). We safely merge segments that provide the same information: e.g., if one depthmap provides an $\mathcal{O}\mathcal{O}$ segment that is contained within another depthmap's $\mathcal{I}\mathcal{I}$ segment, then the $\mathcal{O}\mathcal{O}$ + +segment can be safely dropped since $\mathcal{L}_{\mathcal{O}\mathcal{O}}(y) \leq \mathcal{L}_{\mathcal{I}\mathcal{I}}(y)$ . When segments disagree (e.g., due to inaccurate poses), we keep the segment with more auxiliary views in agreement. This approach handles merging $\mathcal{L}_{\mathrm{sep}}$ : $\mathcal{L}_{\mathrm{sep}}$ is seen by no auxiliary views, so any visible Freespace overrules it. + +Network Architecture. We follow [27] to facilitate fair comparison. Additionally, we clamp the outputs of our network to be $\in [-1, 1]$ by applying a tanh activation and adjust the loss to account for this clipping. + +Training. We follow a two-stage training procedure. We first train with only the reference view followed by adding auxiliary views. In the first stage, we train for 100 epochs minimizing $\mathcal{L}_{\mathcal{O}I}$ and $\mathcal{L}_{\mathrm{sep}}$ . We then train for 100 epochs with auxiliary losses, minimizing a sum of the segment losses $\mathcal{L}_{II}, \mathcal{L}_{IO}, \mathcal{L}_{OI}, \mathcal{L}_{OO}$ , and $\mathcal{L}_{\mathrm{sep}}$ as well as $\lambda \mathcal{L}_{\mathrm{ent}}$ with $\lambda$ set to 0.1 to balance loss scales. We minimize the loss with AdamW [26, 33] as the optimizer with learning rate warmup for $0.5\%$ of the iterations followed by cosine learning rate decay with maximum value $3 \times 10^{-4}$ . Our models are implemented using PyTorch [39] and visualizations in this paper use PyTorch3D [30, 42]. + +# 5. Experiments + +We evaluate our method to address three experimental questions. First, we examine how training with RGBD data compares with mesh supervision. Next, we test how RGBD and mesh supervision compare when one has less complete scans. Finally, we show that our method can quickly adapt to multiple posed RGBD inputs. + +Metrics. Throughout, we follow [27] and use two metrics that evaluate predictions against a ground-truth mesh. Following [45, 53], these metrics are based on: Accuracy/Acc (the fraction of predicted points that are within $t$ to a ground-truth point), Completeness/Cmp (the fraction of ground-truth points that are within $t$ to a predicted point), as well as F1 (the harmonic mean of Accuracy and Completeness). $t$ for both Acc and Cmp is $0.5\mathrm{m}$ . In + +![](images/c0acbeb0fbc0b04b5b98368146e6abac627ecc157489a229d7f1d78f345fcb2b.jpg) +Image + +![](images/715f13721eb80a9112b70c7dc99973128d8fc1420c37acac7897fffed9a20d7f.jpg) +Density Field + +![](images/efc0709af70894ed6ec2e1486b13226dff17925faee14fd8f1923040aa716824.jpg) +Mesh DRDF + +![](images/56185d28c64f828cd6ecc084f3557baee812963b3fac2cd20f4692383cbd7f3d.jpg) +Figure 6. Comparison with baselines. 3D outputs generated by all methods trained on Matterport3D. We color the first intersection with image colors and occluded intersections with computed surface normals. We highlight regions of interest in the reconstructions in selected crops. D2-DRDF achieves results on par with Mesh DRDF while the density fields baselines fails to model the occluded parts faithfully. In row 1, our method recovers the back of sofa, and a hidden room behind the hallway in row 2. Surface Normal Map + +![](images/0891b9eb3c0b2c16d61322068f51eabdfcd7f581b667c305258808b24b0bb340.jpg) + +![](images/95cf1c50dad81a075ed5c968087dc7d912089df9d7577d0f56fa7fae470414db.jpg) + +![](images/c796136539e8353f6c722a1cc2e45874be4dbcd3bab2a34e9b4885f7fe913ada.jpg) +Selected Crops + +![](images/51ae88fd12ca658c28617ee63b1253205c0451f3a8188e0b0cf231d02eda2b5f.jpg) + +![](images/8d3759adcc3f01946231652dac7c79d2205d5c28aec44b144e44dfa39e8d9867.jpg) +D2-DRDF + +![](images/2cebeeb524fb8401e121969c8e0f201ca8c77199a7a209a0afe26b3ebeaee655.jpg) + +![](images/0e41028b6603658502f2f38e39e38b8664321eb8c528e6ab041a269f07fa23a4.jpg) +Ground Truth + +![](images/316eb4f2e613656d87da1e411014d01b82be6e02cfb252e3ed2aa260c07f9fc5.jpg) + +(Scene Acc/Cmp/F1), we evaluate the predicted mesh of the full scene against the ground-truth, using 10K samples per mesh. In (Ray Occ. Acc/Cmp/F1), We evaluate the performance on occluded points, evaluating per-ray and then averaging. We define occluded points for both the ground truth and prediction as any surface past the first intersection. Ray Occ. is a challenging metric as mistakes in one ray cannot be accounted for in another ray. + +Datasets. We use Matterport3D [1] as our primary dataset following an identical setup to [28]. We choose Matterport3D because it has substantial occluded geometry (unlike ScanNet [8]) and was captured by a real scanner (unlike 3DFront [15], which is synthetic). We note that we use the raw images captured by the scanner rather than re-renders. We follow the split from [27], which splits train/val/test by house into 60/15/15 houses. After filtering and selecting images, there are 15K/1K/1K input images for each split. + +# 5.1. Mesh Prediction Results + +Baselines. Our primary point of comparison is (1) Mesh DRDF [27], which learns to predict the DRDF from direct mesh supervision. For context, we also report the baselines from [27] and summarize them: (2) LDI [46] predicts a set of 4 layered depthmaps, where the first represents the depth and the next three represent occluded intersections; (3) UDF [5] predicts an unsigned scene distance function; (4) URDF [5] predicts an unsigned ray distance function; (5) ORF predicts an occupancy function, or whether the surface is within a fixed distance. We note that for each of these approaches, there are a number of variants (e.g., of finding intersections in a URDF along a ray). We report the highest performance reported by [27], who document extensive and detailed tuning of these baselines. + +Our final baseline, (6) Density Field [57] tests the value of predicting a DRDF compared to a density. Like our + +![](images/308ded876e3ae3ba7a8f84ad8eab6ecbef30e4a10c804d3c1d4b94c8009fc8dc.jpg) +Image + +![](images/93f39d828ae170698ee6505c22503ae7f9317cb27f30b8a2749fa4b7482deee2.jpg) +GT + +![](images/654b1416b2959abf9ba81f5536e295e4714400f636ffc847fb4da46a0b72f3df.jpg) +D2-DRDF + +![](images/c9e9ed97af8c2dcf248c206a96913f0eaa932a460dc726cdda948c7c8cc227e2.jpg) + +![](images/d1762434e3a794537783b294f2282e7d204e5bb0b025a7b2dacc1100d206f547.jpg) + +![](images/fcbcb0a1dbe3bc6ec8ac2ebcef6466dc51ceeea5adc6ae5ae3a4bbe6931fb015.jpg) + +![](images/facab6af4c7ac9c2f6042b89cd0d6491df0efaa7c97bddf3f767236ce537e710.jpg) + +![](images/371b16cf78b109f479da4dc89af30c8e1059c6100a54f6fa6d1e9cc19fb6bb18.jpg) + +![](images/d06da2c7ca7644f482fa0148c109bd25a524609632d26bcad4fd9d34411f4423.jpg) + +![](images/e37c81497c795c353d46715bc5e440932d4c0a3845f7e21123402e980ef81ece.jpg) +Figure 7. Novels Views Comparison between D2-DRDF and Ground Truth(GT) from novel views. Rows 1, 2 are from unseen images on Matterport3D [1] and 3,4 from Omnidata [11]. Our method trained with only RGBD data recovers occluded empty floors. Kitchen cabinets (row 1) and sides of kitchen island(row 3). + +![](images/95048076d256158667aa5e1e60b6dd076dfd95d542f35b10dcbbeeaba0cd6831.jpg) + +![](images/5db495ee1874ec1f265cf2506f745d53ec4de5bc7900a993fb97f5101f053bde.jpg) + +method, Density Field only uses posed RGBD data for supervision and does not depend on mesh supervision. We adapt pixelNerf [57] to our setting, modifying the implementation from [41] to permit training from a single reference view as input and multiple auxiliary views for su + +Table 1. Matterport3D [1] Acc/Comp/F1Score. We separate methods that use ground-truth mesh from ones using posed RGBD by a horizontal line. D2-DRDF is comparable to the best Mesh supervised method DRDF, and is better than all other mesh based methods on Scene and Ray F1 scores. + +
MethodSceneRay
AccCmpF1AccCmpF1
MeshLDI [46]66.272.467.413.942.819.3
UDF [5]58.776.064.715.523.016.6
ORF73.469.469.626.220.521.6
URDF [5]74.567.168.724.920.620.7
DRDF [27]75.472.071.928.430.027.3
D2-DRDF73.773.572.128.222.625.1
Density Field [57]45.880.257.524.814.017.9
+ +pervision. In addition to the standard color loss, we supervised the network to match the ground-truth auxiliary depth RGBD depth as in [10]. At inference time, we integrate along the ray to decode a set of intersections as in [10]. + +Qualitative Results. We show qualitative results from our method and the baselines in Fig 6. Our methods outputs are comparable to the outputs of the mesh based DRDF that is trained with much stronger supervision. The density fields approach struggles to model the occluded hits and has a lots of floating blobs in the empty space. We show additional results from D2-DRDF in Fig. 7 where it recovers occlude kitchen cabinets, empty floor space behind kitchen island, sides of kitchen island, and hollow bed. + +Quantitative Results. We report results in Table 1. Without access to ground-truth meshes, our approach slightly outperforms Mesh-based DRDF on Scene F1 and approaches its performance on Ray Occ. Our approach matches it in accuracy (i.e., the fraction of predicted points that are correct) but does worse on completeness. We hypothesize that this is due to mesh-based techniques obtaining supervision in adjacent rooms. Nonetheless, our approach outperforms all other baselines besides mesh-supervised DRDF by a large margin (25.1 vs 21.6 F1). D2-DRDF has substantially higher performance compared to the Density Field baseline. This shows that density fields representation has difficulties in learning from a supervision of less views with large occlusions. + +# 5.2. Training on Incomplete Data + +One advantage to using posed RGBD data compared to meshes is that it opens the door to learning from more data that is of lower quality. One can use lots of lower-quality scene captures that would produce poor meshes due to substantial amounts of incomplete data. In the case of direct mesh supervision methods, having an incomplete mesh may lead to incorrect signals for a mesh-based system: for instance, the network may learn to predict that an intersec + +![](images/5d9644b47f3e19ab2f6aec9ebe7fa333efc398758c5672c0630a747ffe38c7eb.jpg) +Figure 8. Mesh Degradation We show examples of how drop in images creates holes, reduces mesh area available for supervision. In (c), after retaining $\approx \frac{1}{4}$ of image data, we only lose $\approx \frac{1}{2}$ the mesh area. Im.: Image Coverage, M: Mesh Coverage, A: Area + +tion does not exist simply because it was not scanned. In contrast, for D2-DRDF, missing data simply increases the fraction of points without supervision. We now test this hypothesis by reducing the number of views in datasets. + +Optimistic Degradation Setup (ODS). We simulate the degradation of dataset collection by subsampling views. To avoid conflating errors in training with suboptimal meshing with incomplete data, we optimistically degrade the meshes to provide an upper bound on supervision. We assume that the mesh with fewer views is identical to the mesh from all views, minus triangles with no vertices in any view. We show ODS mesh examples in Fig. 8. + +We degrade meshes by selecting $1/2^i$ views per dataset for an increasing $i$ . While reducing the views linearly impacts the sample count for RGBD training setup, it has a non-linear impact on mesh completeness since a triangle is removed only if all of the views seeing it are removed (which is unlikely until most views are removed). For any given image retention $(\mathrm{Im.})\%$ mesh coverage $(\mathbf{M})\%$ degrades less giving an edge to mesh based methods. + +Usually, when dealing with limited data, we use a method called Screened Poisson Reconstruction(SPR) [25]. However, SPR does not perform well when there is not enough data available. To avoid conflating errors caused by poor quality inadequate meshing, we establish an upper limit on the performance of methods that rely on direct supervision. Our ODS strategy is much better than using SPR, but it cannot be used in real-world scenarios. We employ Open3D's[60]'s SPR with hyper-parameters similar to [1] to reconstruct meshes. + +Datasets. We evaluate on Matterport3D [1] and apply our method as is without any modifications on OmniData [11] which has a substantially different image view distribution, more rooms and more floors compared to Matterport3D. + +Quantitative Results. We compare models trained on different amount of data available for supervision by using metrics defined in §5.1. In Tab. 2 we compare against the + +Table 2. Robustness to Sparse Data Performance on partial data on Matterport3D[1]. We compare (SPR and ODS) trained DRDF [27] which uses mesh supervision and (Depth) Depth-based DRDF (ours), which uses posed RGBD supervision. In each row, we degrade the training data and report test performance of the trained model. At $100\%$ data there is no M degradation for ODS or SPR. Our approach is more robust to drop in Im.: at $50\%$ view sparsity (Im.), models using ODS or SPR suffer substantial performance drops. Scene F1 drop by 16.3 for SPR; 3.5 for ODS; 2.1 for Depth (ours). + +
Im. %ODS M %Scene F1Ray Occ F1
SPRODSDepthSPRODSDepth
10010071.971.972.127.327.325.1
505655.668.470.021.423.624.4
254356.866.870.021.521.224.9
+ +Table 3. Robustness to Sparse Data Performance of ODS (mesh) and Depth (ours) based DRDF on partial Omnidata [11] following the same setup as Table 2. RGBD-based training is substantially more robust to partial data. + +
ODS +Im. % M %Scene F1Ray Occ F1
ODSDepthODSDepth
258677.272.840.332.1
12.58375.3 (-1.9)70.9 (-1.9)37.1. (-3.2)29.3 (-2.8)
6.37873.4 (-3.8)71.8 (-1.0)32.6 (-7.7)28.1 (-3.0)
36969.8 (-7.4)70.4 (-2.4)20.3 (-20.0)26.7 (-5.4)
+ +Mesh-DRDF trained ODS & SPR meshes. For any given view sampling level, the supervised M % area is high, resulting in stronger supervision for methods trained with ODS than RGBD. However, on Matterport3D, our method outperforms DRDF on all metrics at $25\%$ Im. and outperforms all other baseline methods in Scene F1 at $100\%$ Im.. + +In Tab. 3 we show robustness trends on OmniData. At $25\%$ Im./ $86\%$ M completion, mesh-based does better. We hypothesize this gain is due to better handling of estimates beyond the room. However, as Im. reduces mesh degrades, resulting steep fall for ODS DRDF: with $3\%$ Im./ $69\%$ M, MeshDRDF's Ray Occ F1 drops by 20 points; ours is reduced by just 5.4. Moreover for SPR DRDF, at low Im. values, meshing performs dismally and the poor meshing performance translates into poor reconstruction performance: at $6\%$ Im., training DRDF on SPR meshes produces a Ray F1 of just $5.7\%$ and a scene F1 of $40.9\%$ . + +# 5.3. Adapting With Multiple Inputs + +Since D2-DRDF can directly train on posed RGBD images, this enables test-time adaptation given a few auxiliary posed RGBD images. We start with the pre-trained model from §5.1 and then fine-tune for 500 iterations. + +Dataset and Metrics. We generate 300 quadruplets of scenes consisting of a reference view as well as three auxiliary RGBD images with poses. These three auxiliary views + +![](images/06b86ead86807f88867973b75fdd60e17e46b22efb7021e26ae408c91cfd783d.jpg) +Figure 9. Adaptation to a few images We optimize our pretrained model on the three auxiliary views (yellow) and a reference view (orange). The optimization lets the model fix reconstructions in occluded regions (e.g. bedside wall) as seen D2-DRDF + Adapt. + +Table 4. Scene, Ray F1 Score for Adaptation Using D2-DRDF for adaptation produces better reconstructions that outperform the baselines by a large margin on Ray Occ F1 scores (by 8.9 points). + +
MethodSceneRay
AccCmpF1AccCmpF1
D2-DRDF (Full)79.276.476.036.233.634.9
D2-DRDF (Depth)78.470.572.533.021.426.0
D2-DRDF (Scratch)66.470.366.023.727.425.4
Density Field [57]77.374.974.612.89.911.2
+ +are randomly sampled from views that overlap with occluded parts of the reference view (see supp.). We evaluate inferred 3D using the metrics as §5.1. + +Baselines. The baselines from §5.1 cannot operate in these settings, since they require meshes for training. Therefore we compare against a number of depth-map-based methods as well as ablations to give context to our results: (1) Density Field fine-tunes the density field baseline model from §5.1; (2) Depth-Pretrained fine-tunes D2-DRDF starting with the model from the first stage of training; (3) Scratch Training fine-tunes D2-DRDF from scratch. + +Results. Our loss and penalty formulations lend themselves well to test-time adaptation as shown in Fig. 9. After adaptation, D2-DRDF can resolve uncertainties like the corner of the bed and wall. Table 4 shows D2-DRDF does the best. + +# 6. Conclusion + +We presented a method for learning to predict 3D from a single image using implicit functions while requiring only posed RGBD supervision. We believe our method can unlock new avenues with posed RGBD data becoming available from both consumers as well as robotic agents. + +Acknowledgments. We thank Richard H., Sarah J., Dandan S., Shengyi Q., Karan D., Mohammed B., Chris R. for feedback. Toyota Research Institute ("TRI") provided funds to assist the authors with their research but this article solely reflects the opinions and conclusions of its authors and not TRI or any Toyota entity. + +# References + +[1] Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3d: Learning from rgb-d data in indoor environments. 3DV, 2017. 2, 6, 7, 8 +[2] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 2 +[3] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. ECCV, 2022. 2 +[4] Weifeng Chen, Zhao Fu, Dawei Yang, and Jia Deng. Single-image depth perception in the wild. NeurIPS, 2016. 2 +[5] Julian Chibane, Aymen Mir, and Gerard Pons-Moll. Neural unsigned distance fields for implicit function learning. In NeurIPS, 2020. 2, 6, 7 +[6] Christopher B Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese. 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. In ECCV, 2016. 1, 2 +[7] Brian Curless and Marc Levoy. A volumetric method for building complex models from range images. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 303-312, 1996. 2, 3 +[8] Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In CVPR, 2017. 2, 6 +[9] Angela Dai, Matthias Nießner, Michael Zollhöfer, Shahram Izadi, and Christian Theobalt. Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface reintegration. ACM Transactions on Graphics (ToG), 36(4):1, 2017. 2 +[10] Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan. Depth-supervised nerf: Fewer views and faster training for free. In CVPR, 2022. 7 +[11] Ainaz Eftekhar, Alexander Sax, Jitendra Malik, and Amir Zamir. Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans. In ICCV, 2021. 6, 7, 8 +[12] David Eigen and Rob Fergus. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. ICCV, 2015. 2 +[13] Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In CVPR, 2017. 2 +[14] David F Fouhey, Abhinav Gupta, and Martial Hebert. Data-driven 3d primitives for single image understanding. In ICCV, 2013. 2 +[15] Huan Fu, Bowen Cai, Lin Gao, Lingxiao Zhang, Cao Li, Qixun Zeng, Chengyue Sun, Yiyun Fei, Yu Zheng, Ying Li, Yi Liu, Peng Liu, Lin Ma, Le Weng, Xiaohang Hu, Xin Ma, Qian Qian, Rongfei Jia, Binqiang Zhao, and Hao Zhang. 3d-front: 3d furnished rooms with layouts and semantics. ICCV, 2021. 6 +[16] Huan Fu, Bowen Cai, Lin Gao, Ling-Xiao Zhang, Jiaming Wang, Cao Li, Qixun Zeng, Chengyue Sun, Rongfei Jia, Binqiang Zhao, et al. 3d-front: 3d furnished rooms with layouts + +and semantics. In ICCV, 2021. 2 +[17] Rohit Girdhar, David F Fouhey, Mikel Rodriguez, and Abhinav Gupta. Learning a predictable and generative vector representation for objects. In ECCV. Springer, 2016. 1, 2 +[18] Georgia Gkioxari, Jitendra Malik, and Justin Johnson. Mesh r-cnn. In ICCV, 2019. 2 +[19] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. A papier-mâché approach to learning 3d surface generation. In CVPR, 2018. 1, 2 +[20] Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, and Jia-Bin Huang. Deepmvs: Learning multi-view stereopsis. In CVPR, 2018. 2 +[21] Shahram Izadi, David Kim, Otmar Hilliges, David Molyneaux, Richard Newcombe, Pushmeet Kohli, Jamie Shotton, Steve Hodges, Dustin Freeman, Andrew Davison, et al. Kinectfusion: real-time 3d reconstruction and interaction using a moving depth camera. In Proceedings of the 24th annual ACM symposium on User interface software and technology, pages 559-568, 2011. 2 +[22] Hamid Izadinia, Qi Shan, and Steven M Seitz. Im2cad. In CVPR, 2017. 2 +[23] Michael Janner, Jiajun Wu, Tejas D Kulkarni, Ilker Yildirim, and Josh Tenenbaum. Self-supervised intrinsic image decomposition. NeurIPS, 2017. 2 +[24] Ziyu Jiang, Buyu Liu, Samuel Schulter, Zhangyang Wang, and Manmohan Chandraker. Peek-a-boo: Occlusion reasoning in indoor scenes with plane representations. In CVPR, 2020. 2 +[25] Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Proceedings of the fourth Eurographics symposium on Geometry processing, 2006. 2, 7 +[26] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. *ICLR*, 2015. 5 +[27] Nilesh Kulkarni, Justin Johnson, and David F. Fouhey. Directed ray distance function for 3d scene reconstruction. In ECCV, 2022. 1, 2, 3, 5, 6, 7, 8 +[28] Nilesh Kulkarni, Ishan Misra, Shubham Tulsiani, and Abhinav Gupta. 3d-relnet: Joint object and relational network for 3d prediction. In ICCV, 2019. 2, 6 +[29] Kiriakos N Kutulakos and Steven M Seitz. A theory of shape by space carving. *IJCV*, 2000. 2 +[30] Christoph Lassner and Michael Zollhöfer. Pulsar: Efficient sphere-based neural rendering. arXiv:2004.07484, 2020. 5 +[31] Chen-Hsuan Lin, Chen Kong, and Simon Lucey. Learning efficient point cloud generation for dense 3d object reconstruction. In AAAI, volume 32, 2018. 2 +[32] Chen Liu, Kihwan Kim, Jinwei Gu, Yasutaka Furukawa, and Jan Kautz. Planercnn: 3d plane detection and reconstruction from a single image. In CVPR, 2019. 2 +[33] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. *ICLR*, 2019. 5 +[34] Ravi Malladi, James A Sethian, and Baba C Vemuri. Shape modeling with front propagation: A level set approach. TPAMI, 1995. 2 +[35] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In CVPR, 2019. 1, 2 + +[36] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. arXiv preprint arXiv:2003.08934, 2020. 2 +[37] Yinyu Nie, Xiaoguang Han, Shihui Guo, Yujuan Zheng, Jian Chang, and Jian Jun Zhang. Total3dunderstanding: Joint layout, object pose and mesh reconstruction for indoor scenes from a single image. In CVPR, 2020. 2 +[38] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, 2019. 1, 2 +[39] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS. 2019. 5 +[40] Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In ECCV, 2020. 2 +[41] Shengyi Qian, Alexander Kirillov, Nikhila Ravi, Devendra Singh Chaplot, Justin Johnson, David F Fouhey, and Georgia Gkioxari. Recognizing scenes from novel viewpoints. arXiv preprint arXiv:2112.01520, 2021. 6 +[42] Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari. Accelerating 3d deep learning with pytorch3d. arXiv:2007.08501, 2020. 5 +[43] Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, and Joshua M. Susskind. Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding. In ICCV, 2021. 2 +[44] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In ICCV, 2019. 1, 2 +[45] Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In CVPR, 2006. 5 +[46] Jonathan Shade, Steven Gortler, Li-wei He, and Richard Szeliski. Layered depth images. In Proceedings of the 25th annual conference on Computer graphics and interactive techniques, pages 231–242, 1998. 6, 7 +[47] Jian Shi, Yue Dong, Hao Su, and Stella X Yu. Learning non-lambertian object intrinsics across shapenet categories. In CVPR, 2017. 2 +[48] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. NeurIPS, 2020. 2 +[49] Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao. Sun rgb-d: A rgb-d scene understanding benchmark suite. In CVPR, 2015. 2 +[50] Shuran Song, Fisher Yu, Andy Zeng, Angel X Chang, Manolis Savva, and Thomas Funkhouser. Semantic scene comple + +tion from a single depth image. CVPR, 2017. 2 +[51] Jiaming Sun, Yiming Xie, Linghao Chen, Xiaowei Zhou, and Hujun Bao. Neuralrecon: Real-time coherent 3d reconstruction from monocular video. In CVPR, 2021. 2 +[52] Xingyuan Sun, Jiajun Wu, Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Tianfan Xue, Joshua B Tenenbaum, and William T Freeman. Pix3d: Dataset and methods for single-image 3d shape modeling. In CVPR, 2018. 2 +[53] Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox. What do single-view 3d reconstruction networks learn? In CVPR, 2019. 5 +[54] Kaixuan Wang and Shaojie Shen. Mvdepthnet: Real-time multiview depth estimation neural network. In 3DV, 2018. 2 +[55] X. Wang, David F. Fouhey, and A. Gupta. Designing deep networks for surface normal estimation. In CVPR, 2015. 2 +[56] Jianglong Ye, Yuntao Chen, Naiyan Wang, and Xiaolong Wang. Gifs: Neural implicit function for general shape representation. In CVPR, 2022. 2 +[57] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelNeRF: Neural radiance fields from one or few images. In CVPR, 2021. 2, 3, 6, 7, 8 +[58] Zehao Yu, Jia Zheng, Dongze Lian, Zihan Zhou, and Shenghua Gao. Single-image piece-wise planar 3d reconstruction via associative embedding. In CVPR, 2019. 2 +[59] Cheng Zhang, Zhaopeng Cui, Yinda Zhang, Bing Zeng, Marc Pollefeys, and Shuaicheng Liu. Holistic 3d scene understanding from a single image with implicit representation. In CVPR, 2021. 2 +[60] Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. Open3D: A modern library for 3D data processing. arXiv:1801.09847, 2018. 7 +[61] Shizhan Zhu, Sayna Ebrahimi, Angjoo Kanazawa, and Trevor Darrell. Differentiable gradient sampling for learning implicit 3d scene reconstructions from a single image. In ICLE, 2022. 1, 2 \ No newline at end of file diff --git a/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/images.zip b/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..915bc2999eb854500760468186b84e62cca68af0 --- /dev/null +++ b/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cde3642cc7f24871ce386c78d7a670b6e6079b2caa6852d41254d302a4e6d68a +size 694501 diff --git a/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/layout.json b/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..91d77f68022fae54dae9cc4f6fbaf8084d97cb23 --- /dev/null +++ b/2023/Learning To Predict Scene-Level Implicit 3D From Posed RGBD Data/layout.json @@ -0,0 +1,13064 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 85, + 103, + 509, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 103, + 509, + 121 + ], + "spans": [ + { + "bbox": [ + 85, + 103, + 509, + 121 + ], + "type": "text", + "content": "Learning to Predict Scene-Level Implicit 3D from Posed RGBD Data" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 151, + 143, + 442, + 172 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 151, + 143, + 442, + 172 + ], + "spans": [ + { + "bbox": [ + 151, + 143, + 442, + 172 + ], + "type": "text", + "content": "Nilesh Kulkarni, Linyi Jin, Justin Johnson, David F. Fouhey \nUniversity of Michigan" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 143, + 199, + 192, + 211 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 199, + 192, + 211 + ], + "spans": [ + { + "bbox": [ + 143, + 199, + 192, + 211 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 224, + 290, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 224, + 290, + 369 + ], + "spans": [ + { + "bbox": [ + 46, + 224, + 290, + 369 + ], + "type": "text", + "content": "We introduce a method that can learn to predict scene-level implicit functions for 3D reconstruction from posed RGBD data. At test time, our system maps a previously unseen RGB image to a 3D reconstruction of a scene via implicit functions. While implicit functions for 3D reconstruction have often been tied to meshes, we show that we can train one using only a set of posed RGBD images. This setting may help 3D reconstruction unlock the sea of accelerometer+RGBD data that is coming with new phones. Our system, D2-DRDF, can match and sometimes outperform current methods that use mesh supervision and shows better robustness to sparse data." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 392, + 128, + 404 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 392, + 128, + 404 + ], + "spans": [ + { + "bbox": [ + 47, + 392, + 128, + 404 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 413, + 287, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 413, + 287, + 484 + ], + "spans": [ + { + "bbox": [ + 46, + 413, + 287, + 484 + ], + "type": "text", + "content": "Consider the image in Figure 1. From this ordinary RGB image, we can understand the complete 3D geometry of the scene, including the floor and walls behind the chairs. Our goal is to enable computers to recover this geometry from a single RGB image. To this end, we present a method that does so while learning only on posed RGBD images." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 485, + 287, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 485, + 287, + 592 + ], + "spans": [ + { + "bbox": [ + 46, + 485, + 287, + 592 + ], + "type": "text", + "content": "The task of reconstructing the full 3D of a scene from a single previously unseen RGB image has long been known to be challenging. Early work on full 3D relied on voxels [6, 17] or meshes [19], but these representations fail on scenes due to topology and memory challenges. 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We envision that being able to entirely skip meshing will enable the use of vast quantities of lower-quality data from consumers (e.g., from increasingly common consumer phones with LiDAR scanners and accelerometers) as well as robots. In addition to permitting training, the bypassing of meshing may enable adaptation in a new environment on raw data without needing an expert to ensure mesh acquisition." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 534, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 534, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 534, + 547, + 713 + ], + "type": "text", + "content": "Our key insight is that we can use segments of observed free space in depth maps in other views to constrain distance functions. We show this using the Directed Ray Distance Function (DRDF) [27] that has recently shown good performance in 3D reconstruction using implicit functions and has the benefit of not needing watertight meshes for training. Given an input reference view, the DRDF breaks the problem of predicting the 3D surface into a set of independent 1D distance functions, each along a ray through a pixel in the reference view and accounting for only surfaces on the ray. Rather than use an ordinary unsigned distance function, the DRDF signs the distance using the location of the camera's pinhole and the intersections along the ray. While [27] showed their method could be trained on non-watertight meshes, their method is still dependent" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "17256" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 288, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 288, + 156 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 288, + 156 + ], + "type": "text", + "content": "on meshes. In our paper, we show that the DRDF can be cleanly supervised using auxiliary views of RGBD observations and their poses. We derive constraints on the DRDF that power loss functions for training our system. While the losses on any one image are insufficient to produce a reconstruction, they provide a powerful signal when accumulated across thousands of training views." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 157, + 288, + 288 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 157, + 288, + 288 + ], + "spans": [ + { + "bbox": [ + 46, + 157, + 288, + 288 + ], + "type": "text", + "content": "We evaluate our method on realistic indoor scene datasets and compare it with methods that train with full mesh supervision. Our method is competitive and sometimes even better compared to the best-performing mesh-supervised method [28] with full, professional captures. As we degrade scan completeness, our method largely maintains its performance while mesh-based methods perform substantially worse. We conclude by showing that fine-tuning of our method on a handful of images enables a simple, effective method for fusing and completing scenes from a handful of RGBD images." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 298, + 134, + 311 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 298, + 134, + 311 + ], + "spans": [ + { + "bbox": [ + 47, + 298, + 134, + 311 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 319, + 287, + 366 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 319, + 287, + 366 + ], + "spans": [ + { + "bbox": [ + 46, + 319, + 287, + 366 + ], + "type": "text", + "content": "Our approach infers a complete 3D scene from a single RGB image using implicit functions that are supervised via posed RGBD scans. This touches on several long-term goals of 3D computer vision that we discuss below." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 367, + 288, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 367, + 288, + 617 + ], + "spans": [ + { + "bbox": [ + 46, + 367, + 288, + 617 + ], + "type": "text", + "content": "Reconstructing Scenes from a Single Image. At test time, our system produces a full 3D reconstruction from a single RGB image, including occluded regions. This means that our desired output goes beyond 2.5D properties such as depth [4, 12], surface normals [12, 14, 55], or other intrinsic-image properties [23, 47]. Works on attempting to reconstruct complete 3D usually have focused on reconstructing objects from image by using voxels [6, 17] or meshes [18, 19], point clouds [13, 31] or CAD models [22]. These are usually trained on synthetic datasets like ShapeNet [2] or scene datasets [16, 43, 50] and do not generalize well to realistic scenes. Another line of works tries to learn holistic structures [37, 59] or planar surfaces [24, 32, 58] from realistic scanned mesh data [1, 8] Creating realistic mesh based data for scenes requires postprocessing using Poisson surface reconstruction [9, 25] which leads to deviation from raw captures. Manually aligning them is expensive hence 3D object-aligned datasets like [49, 52] are scarce and limited in diversity. Our method avoids these limitations by directly operating on the raw captured RGBD data." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "text", + "content": "Reconstructing Scenes from Posed Scans. There has been considerable work on using multiview RGBD data at inference time to produce 3D reconstructions, starting with analytic techniques [7, 9, 21, 29] and now using learning [20, 51, 54]. We use some similar tools to this community: for instance, the ray distances we use are known in this community as a projected distance functions [7]. However, their works solve a fundamentally different problem" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "content": "by using posed RGBD data at test time: their goal is to produce a reconstruction from a set of posed RGBD images from one particular scene; our goal is to use a large dataset of posed RGBD data to train a neural network that can map a new single RGB image to a 3D reconstruction." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 133, + 545, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 133, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 304, + 133, + 545, + 228 + ], + "type": "text", + "content": "Our goal of learning to predict reconstructions from a single previously unseen image also distinguishes our work from NeRF [36] and other similar radiance field approaches [3]. Their goal is to learn a radiance field for a particular scene from a set of posed scans. There are methods that try to predict this radiance field from a single image [57]; we compare with a model using similar losses and find that an objective specialized for 3D works better." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 228, + 545, + 382 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 228, + 545, + 382 + ], + "spans": [ + { + "bbox": [ + 304, + 228, + 545, + 382 + ], + "type": "text", + "content": "Implicit Functions for 3D Reconstruction. We perform reconstruction with implicit functions. Implicit functions have used for shape and scene modeling as level sets [34], signed distance functions [38], occupancy function [35, 40, 44], distance functions [5, 27, 48] and other modifications like [56]. Our work has two key distinctions. First, many approaches [5, 48, 56] fit an implicit function to one shape (i.e., there is no generalization to new shapes). In contrast, our work produces reconstructions from previously unseen RGB images. Second, the methods that predict an implicit function from a new image [27, 61] assume access to a mesh at training time. On other hand, our work assumes access only to posed RGBD data for training." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 384, + 545, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 384, + 545, + 479 + ], + "spans": [ + { + "bbox": [ + 304, + 384, + 545, + 479 + ], + "type": "text", + "content": "The most similar work to ours is [27] that also aims to learn to reconstruct scenes from a single image by training on realistic data. While [27] shows how to reduce supervision requirements by enabling the use of non-watertight meshes, their method is still limited to mesh supervision. We show how to use posed RGBD data for supervision instead of using an image-aligned mesh. This substantially reduces the requirements for collecting training data." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 490, + 538, + 503 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 490, + 538, + 503 + ], + "spans": [ + { + "bbox": [ + 305, + 490, + 538, + 503 + ], + "type": "text", + "content": "3. Pixel-Aligned 3D Reconstruction & DRDF" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 510, + 545, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 510, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 304, + 510, + 545, + 689 + ], + "type": "text", + "content": "We propose a method to predict full 3D scene structure from a previously unseen RGB image. At inference time, our method receives a single image with no depth information. We refer to this view as the reference view. As output, the method produces an estimate of a distance function at each point in a pre-defined set of 3D points. Given this volume of predicted distances, one can decode the predicted distance into a set of surfaces: e.g., if the predicted distance were the unsigned distance to the nearest surface, one could declare all points with sufficiently small predicted distance to be surfaces. At training time, our network is given supervision for predicting its distance function. Previously, supervision been done via a mesh that provides oracle distance calculations. In §4, we show how to derive supervision from posed RGBD images at auxiliary views instead." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "type": "text", + "content": "The approaches in the paper follow pixelNerf [57] for predicting a distance at a 3D point " + }, + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 305, + 689, + 545, + 714 + ], + "type": "text", + "content": ". The network accepts" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "17257" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 70, + 275, + 178 + ], + "blocks": [ + { + "bbox": [ + 53, + 70, + 275, + 178 + ], + "lines": [ + { + "bbox": [ + 53, + 70, + 275, + 178 + ], + "spans": [ + { + "bbox": [ + 53, + 70, + 275, + 178 + ], + "type": "image", + "image_path": "cab7f3ed4744a5aaaa785430b3e0d5da55ed6e40e041b1157ef33398d891a61a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "lines": [ + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "spans": [ + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "text", + "content": "Figure 2. Model and DRDF overview. (Left) Given a single input RGB image " + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "text", + "content": " and query point " + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "text", + "content": " on a ray " + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "inline_equation", + "content": "\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "text", + "content": " in a pre-defined grid, all models, like [57] extract image features at the projection " + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "inline_equation", + "content": "\\pi(\\mathbf{x})" + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "text", + "content": " with a backbone and then predict the distance using a MLP on the backbone features and a positional encoding of " + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "text", + "content": ". (Right) Given a ray " + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "inline_equation", + "content": "\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 46, + 186, + 289, + 297 + ], + "type": "text", + "content": " through the scene, ray distance functions are defined as the distance to intersections of the ray with the scene. This permits analyzing the 3D scene as a set of 1D distances per ray. The Directed Ray Distance Function is further signed to be positive before and negative after an intersection." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "spans": [ + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "text", + "content": "an image " + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "text", + "content": " and 3D point " + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "text", + "content": " and makes a prediction " + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "inline_equation", + "content": "f_{\\theta}(\\mathbf{x}, I)" + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "text", + "content": ". As shown in Fig. 2, the network extracts a convolutional feature from a backbone " + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "inline_equation", + "content": "\\mathbf{B}" + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "text", + "content": " at the projection of " + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "text", + "content": ", denoted " + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "inline_equation", + "content": "\\pi(\\mathbf{x})" + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "text", + "content": ". The image feature is concatenated to a positional encoding of " + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "text", + "content": " and put into a MLP to produce the final prediction " + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "inline_equation", + "content": "f_{\\theta}(\\mathbf{x}, I)" + }, + { + "bbox": [ + 46, + 304, + 287, + 388 + ], + "type": "text", + "content": ". By repeating this inference for a set of 3D points, the network can infer the full 3D of the scene." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "spans": [ + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "text", + "content": "Training. In a conventional setup, the network " + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "inline_equation", + "content": "f_{\\theta}(\\mathbf{x},I)" + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "text", + "content": " is trained by obtaining samples from a mesh and directly training the network via regression. Given a mesh that is coaligned with an image, one can obtain an " + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "text", + "content": " tuples consisting of an image " + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "inline_equation", + "content": "I_{i}" + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "text", + "content": ", 3D point " + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_i" + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "text", + "content": " and ground-truth distance " + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "inline_equation", + "content": "d_{i}" + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "text", + "content": ". Then the network is trained by minimizing the empirical risk " + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "inline_equation", + "content": "\\frac{1}{n}\\sum_{i = 1}^{n}\\mathcal{L}(f_{\\theta}(\\mathbf{x}_i,I_i),d_i)" + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "text", + "content": " for some loss " + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 46, + 388, + 287, + 474 + ], + "type": "text", + "content": " like L1." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 473, + 287, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 473, + 287, + 544 + ], + "spans": [ + { + "bbox": [ + 46, + 473, + 287, + 544 + ], + "type": "text", + "content": "What Distance Function Should We Predict? There are multiple distance functions in the literature, and the above formulation enables predicting any of them. To explain distinctions, assume we are given a point " + }, + { + "bbox": [ + 46, + 473, + 287, + 544 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 473, + 287, + 544 + ], + "type": "text", + "content": " at which we want to evaluate a distance and a scene " + }, + { + "bbox": [ + 46, + 473, + 287, + 544 + ], + "type": "inline_equation", + "content": "S \\subseteq \\mathbb{R}^3" + }, + { + "bbox": [ + 46, + 473, + 287, + 544 + ], + "type": "text", + "content": " that we want to evaluate the distance to." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "text", + "content": "In a standard distance function, one defines a distance from a point " + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "text", + "content": " to the entire scene " + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "text", + "content": ". For instance, the Unsigned Distance Function (UDF) is defined as " + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\min_{\\mathbf{x}' \\in S} ||\\mathbf{x} - \\mathbf{x}'||" + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "text", + "content": ". In a ray-based distance function [7, 27], the distance is defined from " + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "text", + "content": " only to the points in the scene that lie on the ray " + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "text", + "content": " from the reference view pinhole towards " + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "text", + "content": ". In other words, the ray distance function is defined as the distance to the intersections of the ray with the scene. For instance, the Unsigned Ray Distance Function (URDF) is defined as " + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\min_{\\mathbf{x}' \\in S, \\mathbf{x}' \\text{ on } \\vec{\\mathbf{r}}} ||\\mathbf{x} - \\mathbf{x}'||" + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "text", + "content": ". This definition means that when predicting the distance at a point " + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "text", + "content": ", all of the points that define the ray distance function project to the same pixel, " + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\pi(\\mathbf{x})" + }, + { + "bbox": [ + 46, + 546, + 288, + 714 + ], + "type": "text", + "content": ", which in turn means that neural networks do not need as large receptive fields [27]." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "text", + "content": "Since a ray-based distance function is defined only by scene points that intersect a ray, we can reduce the 3D problem to a 1D problem, as illustrated in Fig. 2. Rather than represent each 3D point as a 3D point, we represent it as the distance along the ray " + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "inline_equation", + "content": "\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "text", + "content": ": the vector " + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "text", + "content": " is represented via a scalar " + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "text", + "content": " such that " + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "inline_equation", + "content": "\\mathbf{x} = z\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "text", + "content": ". Then, if the " + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "inline_equation", + "content": "i^{th}" + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "text", + "content": " intersection along the ray is " + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "inline_equation", + "content": "s_i\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "inline_equation", + "content": "s_i \\in \\mathbb{R}" + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "text", + "content": ", the URDF is defined as the distance to the nearest intersection along that ray, or " + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{UR}}(z) = \\min_{i=1,\\dots,k} |z - s_i|" + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "spans": [ + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "text", + "content": "Directed Ray Distance Function. The Directed Ray Distance Function (DRDF) is a ray distance function that incorporates a sign into the URDF. In particular, the DRDF is defined as " + }, + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{DR}}(z) = \\operatorname{dir}(z)d_{\\mathrm{UR}}(z)" + }, + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "text", + "content": ", where the predicate " + }, + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "inline_equation", + "content": "\\operatorname{dir}(z)" + }, + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "text", + "content": " is positive if the nearest intersection is ahead of " + }, + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "text", + "content": " along the ray from the pinhole and negative otherwise. This is pictured in Fig. 2. The presence of both positive and negative components leads to better behavior under uncertainty [27]. Critical Properties of DRDFs. Two critical properties that we use to define supervision are that at " + }, + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "text", + "content": ", the distance to the nearest intersection is " + }, + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "inline_equation", + "content": "|d_{\\mathrm{DR}}(z)|" + }, + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "text", + "content": " and the nearest intersection on the ray is at " + }, + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "inline_equation", + "content": "z + d_{\\mathrm{DR}}(z)" + }, + { + "bbox": [ + 304, + 180, + 546, + 324 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 335, + 524, + 348 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 335, + 524, + 348 + ], + "spans": [ + { + "bbox": [ + 305, + 335, + 524, + 348 + ], + "type": "text", + "content": "4. Depth-Supervised DRDF Reconstruction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 354, + 545, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 354, + 545, + 426 + ], + "spans": [ + { + "bbox": [ + 304, + 354, + 545, + 426 + ], + "type": "text", + "content": "Having introduced the setup in §3, we now explain how to train a network to predict an image-conditioned DRDF without access to an underlying mesh but instead access to posed RGBD images. Concretely, given a point " + }, + { + "bbox": [ + 304, + 354, + 545, + 426 + ], + "type": "inline_equation", + "content": "z\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 304, + 354, + 545, + 426 + ], + "type": "text", + "content": ", we aim to define a loss function " + }, + { + "bbox": [ + 304, + 354, + 545, + 426 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 304, + 354, + 545, + 426 + ], + "type": "text", + "content": " that evaluates a network's prediction of the DRDF " + }, + { + "bbox": [ + 304, + 354, + 545, + 426 + ], + "type": "inline_equation", + "content": "f_{\\theta}((z\\vec{\\mathbf{r}}),I)" + }, + { + "bbox": [ + 304, + 354, + 545, + 426 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "spans": [ + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "text", + "content": "Our key insight is that we can sometimes see this point " + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "inline_equation", + "content": "z\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "text", + "content": " in other views and these observations of " + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "inline_equation", + "content": "z\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "text", + "content": " provide a constraints on what the value of the DRDF can be. Consider, for instance, the point 3m out from reference view camera pinhole along the red ray in Fig. 3. If we project this point into the auxiliary view, we know that its distance to the camera is closer to auxiliary view's pinhole than the depthmap observed at the auxiliary view. Moreover, we can see that a segment of the ray is visible, starting at the point " + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "inline_equation", + "content": "s\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "text", + "content": " and ending at the point " + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "inline_equation", + "content": "e\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "text", + "content": " with the ending point " + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "inline_equation", + "content": "e\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "text", + "content": " closer to " + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "inline_equation", + "content": "z\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "text", + "content": " than " + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "inline_equation", + "content": "s\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 304, + 426, + 546, + 557 + ], + "type": "text", + "content": " is." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "content": "We can derive supervision on " + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{DR}}(z)" + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "content": " from the segment of visible free space observable in the auxiliary view by using the fact that the nearest intersection at " + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "content": " is " + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "inline_equation", + "content": "|d_{\\mathrm{DR}}(z)|" + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "content": " units away. Since " + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "inline_equation", + "content": "e" + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "content": " is nearer than " + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "content": ", and there are no intersections in the free space between " + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "inline_equation", + "content": "e" + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "content": ", we know that there cannot be an intersection within " + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "inline_equation", + "content": "(e - z)" + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "content": ". This gives an inequality that " + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "inline_equation", + "content": "|d_{\\mathrm{DR}}(z)| \\geq (e - z)" + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "content": ": otherwise there would be an intersection in the visible free space. If the ends of the segment are actual intersections where the ray visibly intersects an object, we obtain an equality constraint on " + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{DR}}(z)" + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "content": " since we actually see the nearest intersection. In other cases, the ray simply disoccludes or is occluded, and we can only have an inequality. We denote intersection start/end events as " + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{I}" + }, + { + "bbox": [ + 304, + 558, + 547, + 713 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17258" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 54, + 87, + 127, + 147 + ], + "blocks": [ + { + "bbox": [ + 67, + 74, + 114, + 85 + ], + "lines": [ + { + "bbox": [ + 67, + 74, + 114, + 85 + ], + "spans": [ + { + "bbox": [ + 67, + 74, + 114, + 85 + ], + "type": "text", + "content": "Reference" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 54, + 87, + 127, + 147 + ], + "lines": [ + { + "bbox": [ + 54, + 87, + 127, + 147 + ], + "spans": [ + { + "bbox": [ + 54, + 87, + 127, + 147 + ], + "type": "image", + "image_path": "42c17679cbf9f97ccf8ad087dbe7f3a0a9e58596c79e9aa6d7ef42a0b96fb39a.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 127, + 87, + 202, + 146 + ], + "blocks": [ + { + "bbox": [ + 143, + 74, + 262, + 86 + ], + "lines": [ + { + "bbox": [ + 143, + 74, + 262, + 86 + ], + "spans": [ + { + "bbox": [ + 143, + 74, + 262, + 86 + ], + "type": "text", + "content": "Auxiliary View + Zoomed" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 127, + 87, + 202, + 146 + ], + "lines": [ + { + "bbox": [ + 127, + 87, + 202, + 146 + ], + "spans": [ + { + "bbox": [ + 127, + 87, + 202, + 146 + ], + "type": "image", + "image_path": "e8e9a9367ab7c2ceaf0bbfd5f4b71bcd2cf9bd86d013e41b5a51a7cf241ec4ed.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 203, + 88, + 277, + 147 + ], + "blocks": [ + { + "bbox": [ + 203, + 88, + 277, + 147 + ], + "lines": [ + { + "bbox": [ + 203, + 88, + 277, + 147 + ], + "spans": [ + { + "bbox": [ + 203, + 88, + 277, + 147 + ], + "type": "image", + "image_path": "9537e6a13690426262a9b83e7186eafe37778326690bd42f73ca09b95742dd55.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 59, + 148, + 122, + 202 + ], + "blocks": [ + { + "bbox": [ + 59, + 148, + 122, + 202 + ], + "lines": [ + { + "bbox": [ + 59, + 148, + 122, + 202 + ], + "spans": [ + { + "bbox": [ + 59, + 148, + 122, + 202 + ], + "type": "image", + "image_path": "e67b18dcfef93ea2e6be37e25786b1012540dfb14c4cd415513442eeaa79f6b5.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 127, + 148, + 202, + 205 + ], + "blocks": [ + { + "bbox": [ + 127, + 148, + 202, + 205 + ], + "lines": [ + { + "bbox": [ + 127, + 148, + 202, + 205 + ], + "spans": [ + { + "bbox": [ + 127, + 148, + 202, + 205 + ], + "type": "image", + "image_path": "04a74f8bcbc027a2d5fd840e947a9ebc76fa612aa3b51b602f2408267d8aa966.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 203, + 148, + 277, + 205 + ], + "blocks": [ + { + "bbox": [ + 203, + 148, + 277, + 205 + ], + "lines": [ + { + "bbox": [ + 203, + 148, + 277, + 205 + ], + "spans": [ + { + "bbox": [ + 203, + 148, + 277, + 205 + ], + "type": "image", + "image_path": "c46cbe256f550127d8a93792d16ec36df5816cb263fbea8c8ad51303821d871b.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 75, + 209, + 257, + 227 + ], + "blocks": [ + { + "bbox": [ + 75, + 209, + 257, + 227 + ], + "lines": [ + { + "bbox": [ + 75, + 209, + 257, + 227 + ], + "spans": [ + { + "bbox": [ + 75, + 209, + 257, + 227 + ], + "type": "image", + "image_path": "459e5a4e8508c89e1b9e318de1c21ae9d0f0c4caea637f8c35a3ed5f4b713c10.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "lines": [ + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "spans": [ + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "text", + "content": "Figure 3. A red ray originates in the reference camera. Given a point " + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "text", + "content": " that is " + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "text", + "content": " units along the ray " + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "inline_equation", + "content": "\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "text", + "content": ", we can test if " + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "inline_equation", + "content": "z\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "text", + "content": " is visible in an auxiliary view by comparing its projected depth with the RGBD image. The point " + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "text", + "content": " is visible, along with a segment of the ray from " + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "inline_equation", + "content": "e" + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "text", + "content": " units. Since the distance to the nearest intersection is " + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "inline_equation", + "content": "|d_{\\mathrm{DR}}(z)|" + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "text", + "content": " and we know there are no intersections within " + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "inline_equation", + "content": "(e - z)" + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "text", + "content": " units of " + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "text", + "content": ", we can constrain that " + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "inline_equation", + "content": "|d_{\\mathrm{DR}}(z)| \\geq (e - z)" + }, + { + "bbox": [ + 46, + 226, + 287, + 304 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 313, + 287, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 313, + 287, + 361 + ], + "spans": [ + { + "bbox": [ + 46, + 313, + 287, + 361 + ], + "type": "text", + "content": "and occlusion start/end events as " + }, + { + "bbox": [ + 46, + 313, + 287, + 361 + ], + "type": "inline_equation", + "content": "\\mathcal{O}" + }, + { + "bbox": [ + 46, + 313, + 287, + 361 + ], + "type": "text", + "content": " and distinguish them by checking if the projected z-value of the ray in auxiliary view is sufficiently close to the recorded auxiliary view's depthmap value." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 370, + 100, + 381 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 370, + 100, + 381 + ], + "spans": [ + { + "bbox": [ + 47, + 370, + 100, + 381 + ], + "type": "text", + "content": "4.1. Losses" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "spans": [ + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "content": "We now convert the concept of using freespace to constrain the DRDF into concrete losses. Recall that our goal is to evaluate a prediction " + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "inline_equation", + "content": "y = f_{\\theta}(z\\vec{\\mathbf{r}},I)" + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "content": " from our network and that the point at " + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "content": " along " + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "inline_equation", + "content": "\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "content": " is in some segment of visible freespace from " + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "inline_equation", + "content": "e" + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "content": " along " + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "inline_equation", + "content": "\\vec{\\mathbf{r}}" + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "content": ". Our goal is to penalize the prediction " + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "content": ". Since there are two start/end event classes " + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "inline_equation", + "content": "(1,0)" + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "content": ", there are four segment types: " + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "inline_equation", + "content": "11,10,01" + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "inline_equation", + "content": "00" + }, + { + "bbox": [ + 46, + 389, + 287, + 484 + ], + "type": "text", + "content": ". We show several of these segments in Figure 5." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "spans": [ + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "text", + "content": "To assist the loss definitions, we define variables " + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "inline_equation", + "content": "l_{s} = s - z" + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "inline_equation", + "content": "l_{e} = e - z" + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "text", + "content": " which we plot in Fig. 4 as a function of " + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "inline_equation", + "content": "l_{s}" + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "text", + "content": " is the value of " + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{DR}}" + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "text", + "content": " if the closest intersection is at " + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "inline_equation", + "content": "l_{e}" + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "text", + "content": " is the value of " + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{DR}}" + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "text", + "content": " if the closest intersection is at " + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "inline_equation", + "content": "e" + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "text", + "content": ". The values " + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "inline_equation", + "content": "l_{s}, l_{e}" + }, + { + "bbox": [ + 46, + 485, + 287, + 556 + ], + "type": "text", + "content": " define equalities for intersections and inequalities for occlusions." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{II}}" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " : II segment. Given a segment bounded by two intersections, the nearest intersections are known exactly as " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "l_{s}" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " or " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "l_{e}" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " depending on whether " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "z < \\frac{s + e}{2}" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " or not. We penalize the " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " error between the prediction " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " and the known DRDF, or " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{II}}(y) = |y - l_s|" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " if " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "z < \\frac{s + e}{2}" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "|y - l_e|" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " otherwise. This penalty is zero only when " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " is equal to the known DRDF. " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{OO}}" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " : OO segment. Given a segment bounded by two occlusion events, the exact DRDF is not known, but the visible free space rules out potential values. Since " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "|d_{\\mathrm{DR}}(z)|" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " is the distance to the nearest intersection, " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{DR}}(z)" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " cannot lie in " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "[l_s,l_e]" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " since such a value would imply an intersection in free space between " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "e" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": ". We penalize incursions into " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "[l_s,l_e]" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " with a " + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 46, + 557, + 287, + 714 + ], + "type": "text", + "content": " penalty: if we denote halfway be" + } + ] + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 306, + 69, + 378, + 167 + ], + "blocks": [ + { + "bbox": [ + 306, + 69, + 378, + 167 + ], + "lines": [ + { + "bbox": [ + 306, + 69, + 378, + 167 + ], + "spans": [ + { + "bbox": [ + 306, + 69, + 378, + 167 + ], + "type": "image", + "image_path": "ae9a371f96585ca101e40c16c92787742c99dd71c964cce5dc90dcaced684ce1.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 175, + 547, + 241 + ], + "lines": [ + { + "bbox": [ + 304, + 175, + 547, + 241 + ], + "spans": [ + { + "bbox": [ + 304, + 175, + 547, + 241 + ], + "type": "text", + "content": "Figure 4. Loss functions for a starting " + }, + { + "bbox": [ + 304, + 175, + 547, + 241 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 175, + 547, + 241 + ], + "type": "text", + "content": " and ending " + }, + { + "bbox": [ + 304, + 175, + 547, + 241 + ], + "type": "inline_equation", + "content": "e" + }, + { + "bbox": [ + 304, + 175, + 547, + 241 + ], + "type": "text", + "content": " events spaced a unit apart. Intersection events (blue) define the DRDF precisely, and we penalize the network from deviating. Occlusion events (purple) provide bounds that we penalize the network for violating. We plot " + }, + { + "bbox": [ + 304, + 175, + 547, + 241 + ], + "type": "inline_equation", + "content": "l_{s}" + }, + { + "bbox": [ + 304, + 175, + 547, + 241 + ], + "type": "text", + "content": " as a line from the start event and " + }, + { + "bbox": [ + 304, + 175, + 547, + 241 + ], + "type": "inline_equation", + "content": "l_{e}" + }, + { + "bbox": [ + 304, + 175, + 547, + 241 + ], + "type": "text", + "content": " as a line from the end event. Loss: 0 1.7" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 381, + 70, + 434, + 167 + ], + "blocks": [ + { + "bbox": [ + 381, + 70, + 434, + 167 + ], + "lines": [ + { + "bbox": [ + 381, + 70, + 434, + 167 + ], + "spans": [ + { + "bbox": [ + 381, + 70, + 434, + 167 + ], + "type": "image", + "image_path": "9c28f6c999a70ab482978fae2a158782515bc61fc275dc05a0259af811b42ab0.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 437, + 70, + 491, + 167 + ], + "blocks": [ + { + "bbox": [ + 437, + 70, + 491, + 167 + ], + "lines": [ + { + "bbox": [ + 437, + 70, + 491, + 167 + ], + "spans": [ + { + "bbox": [ + 437, + 70, + 491, + 167 + ], + "type": "image", + "image_path": "0c1666bcc0bec8fc35c2c2d8f9558df12d24aaab427375aed3ffe2a4ad10d72f.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 494, + 70, + 547, + 167 + ], + "blocks": [ + { + "bbox": [ + 494, + 70, + 547, + 167 + ], + "lines": [ + { + "bbox": [ + 494, + 70, + 547, + 167 + ], + "spans": [ + { + "bbox": [ + 494, + 70, + 547, + 167 + ], + "type": "image", + "image_path": "6c01a2522fc92aa086a1c86dc879de1bb9ee4a6a18edc8e9b3101bb4fa111b2c.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "spans": [ + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "text", + "content": "tween " + }, + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "inline_equation", + "content": "l_s" + }, + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "inline_equation", + "content": "l_e" + }, + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "text", + "content": " as " + }, + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "text", + "content": ", then this can be done as " + }, + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{O}\\mathcal{O}}(y) = \\max (0, l_e - h - |y - h|)" + }, + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "text", + "content": ". The resulting penalty is zero if " + }, + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "inline_equation", + "content": "y \\leq l_s" + }, + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "text", + "content": " or if " + }, + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "inline_equation", + "content": "y \\geq l_e" + }, + { + "bbox": [ + 304, + 253, + 547, + 289 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "spans": [ + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{IO}}" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": " : IO segment. When the segment is bounded by an intersection event followed by an occlusion event, the situation is more complex and we define " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{IO}}" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": " piecewise. In the first half of the segment " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "(z < \\frac{s + e}{2})" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{DR}}" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": " is exactly known, and so we can use an " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": " penalty like the II case, so " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{IO}}(y) = |y - l_s|" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": ". In the second half, there are two options. If the nearest intersection is " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": ", then " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{DR}}(z) = l_s" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": ". Otherwise, the nearest intersection is unknown but after " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "e" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": " and so " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{DR}}(z) > l_e" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": " must hold (since " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{DR}}(z) \\leq l_e" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": " would imply the existence of an intersection before " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "e" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": "). We take the minimum of errors for the two cases: " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": " distance to " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "l_s" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": " and a " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": " penalty function " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "\\max(0, l_e - y)" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": ", resulting in " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{IO}}(y) = \\min(\\max(0, l_e - y), |y - l_s|)" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": ". This part of the penalty is zero if either " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "y = l_s" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": " or if " + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "inline_equation", + "content": "y > l_e" + }, + { + "bbox": [ + 304, + 290, + 546, + 458 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "spans": [ + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{O}\\mathcal{I}}" + }, + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "type": "text", + "content": ": " + }, + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "type": "inline_equation", + "content": "\\mathcal{O}\\mathcal{I}" + }, + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "type": "text", + "content": " segment. The " + }, + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{O}\\mathcal{I}}" + }, + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "type": "text", + "content": " loss is defined symmetrically to " + }, + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{I}\\mathcal{O}}" + }, + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "type": "text", + "content": ", simply by exchanging the role of " + }, + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "type": "inline_equation", + "content": "e" + }, + { + "bbox": [ + 304, + 459, + 545, + 518 + ], + "type": "text", + "content": ". In addition to occluded regions, this loss occurs in the reference view up to the depthmap, where a disocclusion into the reference camera's view is followed by an intersection." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 520, + 545, + 580 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 520, + 545, + 580 + ], + "spans": [ + { + "bbox": [ + 304, + 520, + 545, + 580 + ], + "type": "text", + "content": "To assist the network, we add two auxiliary losses that are true statistically: " + }, + { + "bbox": [ + 304, + 520, + 545, + 580 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{sep}}" + }, + { + "bbox": [ + 304, + 520, + 545, + 580 + ], + "type": "text", + "content": " represents a prior that surfaces tend to be separated by distances and " + }, + { + "bbox": [ + 304, + 520, + 545, + 580 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{ent}}" + }, + { + "bbox": [ + 304, + 520, + 545, + 580 + ], + "type": "text", + "content": " captures a property of the DRDF that is true in the limit if our observations are randomly chosen." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 581, + 545, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 581, + 545, + 700 + ], + "spans": [ + { + "bbox": [ + 304, + 581, + 545, + 700 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{sep}}" + }, + { + "bbox": [ + 304, + 581, + 545, + 700 + ], + "type": "text", + "content": ": Minimum Separation Loss. Since the cameras never see the insides of objects, there is no incentive to predict inside objects. This prevents the generation of zero crossings, e.g., after the first intersection. To assist the network, we add a loss " + }, + { + "bbox": [ + 304, + 581, + 545, + 700 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{sep}}" + }, + { + "bbox": [ + 304, + 581, + 545, + 700 + ], + "type": "text", + "content": " that assumes that surfaces are separated by a minimum distance unless there is evidence otherwise. We continue the DRDF's known value for " + }, + { + "bbox": [ + 304, + 581, + 545, + 700 + ], + "type": "inline_equation", + "content": "t = 0.2m" + }, + { + "bbox": [ + 304, + 581, + 545, + 700 + ], + "type": "text", + "content": " before and after each intersection event to make a continued value " + }, + { + "bbox": [ + 304, + 581, + 545, + 700 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 304, + 581, + 545, + 700 + ], + "type": "text", + "content": ". We then penalize " + }, + { + "bbox": [ + 304, + 581, + 545, + 700 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{sep}}(y) = |y - c|" + }, + { + "bbox": [ + 304, + 581, + 545, + 700 + ], + "type": "text", + "content": ", so long as there is no conflicting free space evidence from another view." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 306, + 701, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 701, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 701, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{ent}}" + }, + { + "bbox": [ + 306, + 701, + 545, + 713 + ], + "type": "text", + "content": ": Sign Entropy Loss. The occlusion-based constraints" + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17259" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 70, + 233, + 194 + ], + "blocks": [ + { + "bbox": [ + 53, + 70, + 233, + 194 + ], + "lines": [ + { + "bbox": [ + 53, + 70, + 233, + 194 + ], + "spans": [ + { + "bbox": [ + 53, + 70, + 233, + 194 + ], + "type": "image", + "image_path": "2b718943b488dc9b61dfec7c7b4d9cce0e58c9f69381eb850fe0d83c00ac8b0f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 70, + 195, + 218, + 205 + ], + "lines": [ + { + "bbox": [ + 70, + 195, + 218, + 205 + ], + "spans": [ + { + "bbox": [ + 70, + 195, + 218, + 205 + ], + "type": "text", + "content": "Scene Point Cloud, Cameras, and Ray" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 236, + 70, + 312, + 193 + ], + "blocks": [ + { + "bbox": [ + 236, + 70, + 312, + 193 + ], + "lines": [ + { + "bbox": [ + 236, + 70, + 312, + 193 + ], + "spans": [ + { + "bbox": [ + 236, + 70, + 312, + 193 + ], + "type": "image", + "image_path": "8bbca5f1c4edf9bdf6eca6041cfdc188e141814d98154bb1a2b03805e6e217b7.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 242, + 194, + 308, + 205 + ], + "lines": [ + { + "bbox": [ + 242, + 194, + 308, + 205 + ], + "spans": [ + { + "bbox": [ + 242, + 194, + 308, + 205 + ], + "type": "text", + "content": "Reference (R)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 46, + 207, + 547, + 252 + ], + "lines": [ + { + "bbox": [ + 46, + 207, + 547, + 252 + ], + "spans": [ + { + "bbox": [ + 46, + 207, + 547, + 252 + ], + "type": "text", + "content": "Figure 5. A red ray originates from the reference camera and intersects the table before entering the island. This ray is seen by three other auxiliary views (a, b, c). For each view we show just one of the many segments in that view for readability, showing intersections as blue s and occlusions as purple o. The segments in views (a) and (b) are of the form OI, where the O comes from the ray entering the frustum in (a) and disoccluding in (b). In view (c), we show an OO segment. We discuss the penalties for these segments in §4.1" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 313, + 70, + 390, + 193 + ], + "blocks": [ + { + "bbox": [ + 313, + 70, + 390, + 193 + ], + "lines": [ + { + "bbox": [ + 313, + 70, + 390, + 193 + ], + "spans": [ + { + "bbox": [ + 313, + 70, + 390, + 193 + ], + "type": "image", + "image_path": "4bfa762410c8dc01fa2aeb6abf88607ad58bd8d1d14e3f34d900a63992a87d84.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 332, + 194, + 373, + 205 + ], + "lines": [ + { + "bbox": [ + 332, + 194, + 373, + 205 + ], + "spans": [ + { + "bbox": [ + 332, + 194, + 373, + 205 + ], + "type": "text", + "content": "View (a)" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 391, + 71, + 462, + 193 + ], + "blocks": [ + { + "bbox": [ + 391, + 71, + 462, + 193 + ], + "lines": [ + { + "bbox": [ + 391, + 71, + 462, + 193 + ], + "spans": [ + { + "bbox": [ + 391, + 71, + 462, + 193 + ], + "type": "image", + "image_path": "490d6ecc63550dfd559c1a4f81b0d9445ff302b9897301e6565c7951d416454c.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 408, + 194, + 449, + 205 + ], + "lines": [ + { + "bbox": [ + 408, + 194, + 449, + 205 + ], + "spans": [ + { + "bbox": [ + 408, + 194, + 449, + 205 + ], + "type": "text", + "content": "View (b)" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 463, + 71, + 542, + 193 + ], + "blocks": [ + { + "bbox": [ + 463, + 71, + 542, + 193 + ], + "lines": [ + { + "bbox": [ + 463, + 71, + 542, + 193 + ], + "spans": [ + { + "bbox": [ + 463, + 71, + 542, + 193 + ], + "type": "image", + "image_path": "974c9c2c06b04e55dd4db62622f682fc51b8c043c45a005cd4a8d7adc5271428.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 484, + 194, + 525, + 205 + ], + "lines": [ + { + "bbox": [ + 484, + 194, + 525, + 205 + ], + "spans": [ + { + "bbox": [ + 484, + 194, + 525, + 205 + ], + "type": "text", + "content": "View (c)" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "spans": [ + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "text", + "content": "can be satisfied by making " + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "text", + "content": " positive or negative. In theory, after the first intersection, " + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "inline_equation", + "content": "d_{\\mathrm{DR}}" + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "text", + "content": " is positive half of the time. Learning to produce the signs uniformly with gradient descent is difficult due to the large loss between the acceptable solutions. To encourage a uniform distribution of signs, we would like to maximize the entropy of the distribution of the signs of the predictions (achieved when the distribution is uniform). Since sign is non-differentiable, we optimize a differentiable surrogate. Suppose " + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "inline_equation", + "content": "Y" + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "text", + "content": " is the set of predictions at points that are occluded in the reference view and " + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "text", + "content": " is the binary entropy " + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "inline_equation", + "content": "H(p) = p \\log(p) + (1 - p) \\log(1 - p)" + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "text", + "content": ". Then our loss is " + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{ent}} = H(\\sum_{y \\in Y} \\sigma(y / \\tau) / |Y|)" + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "text", + "content": " functions like a soft-sign. As seen in the supplement, " + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{ent}}" + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "text", + "content": " can be minimized by distributing " + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "inline_equation", + "content": "Y" + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "text", + "content": " symmetric about 0. Using " + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{ent}}" + }, + { + "bbox": [ + 46, + 261, + 290, + 441 + ], + "type": "text", + "content": " improves the prediction and Scene F1 by 2 points." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 452, + 180, + 464 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 452, + 180, + 464 + ], + "spans": [ + { + "bbox": [ + 47, + 452, + 180, + 464 + ], + "type": "text", + "content": "4.2. Implementation Details" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 472, + 287, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 472, + 287, + 544 + ], + "spans": [ + { + "bbox": [ + 46, + 472, + 287, + 544 + ], + "type": "text", + "content": "View Selection. For every candidate auxiliary view we compute the fraction of visible points in this view that are occluded in the reference view. Auxiliary views with large number of such points provide supervision for key occluded regions in the reference view. We sample up to 20 auxiliary views per reference image from this set of views." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 46, + 544, + 287, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 544, + 287, + 616 + ], + "spans": [ + { + "bbox": [ + 46, + 544, + 287, + 616 + ], + "type": "text", + "content": "Sampling Strategy. Given a set of fixed auxiliary views and a reference view, we sample over 200 rays per input image with 512 points per ray. We re-balance this set by sampling " + }, + { + "bbox": [ + 46, + 544, + 287, + 616 + ], + "type": "inline_equation", + "content": "20K" + }, + { + "bbox": [ + 46, + 544, + 287, + 616 + ], + "type": "text", + "content": " points that are visible and " + }, + { + "bbox": [ + 46, + 544, + 287, + 616 + ], + "type": "inline_equation", + "content": "20K" + }, + { + "bbox": [ + 46, + 544, + 287, + 616 + ], + "type": "text", + "content": " points that are occluded in reference view. We rebalance as most points are from the region between the camera and the first hit." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "text", + "content": "Combining segments from different views. We merge information from multiple posed RGBD images to produce a concise merged set of non-overlapping segments along the ray. This prevents double-counting losses (e.g., if a region of the ray is seen by multiple auxiliary views). We safely merge segments that provide the same information: e.g., if one depthmap provides an " + }, + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{O}\\mathcal{O}" + }, + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "text", + "content": " segment that is contained within another depthmap's " + }, + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{I}\\mathcal{I}" + }, + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "text", + "content": " segment, then the " + }, + { + "bbox": [ + 46, + 618, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{O}\\mathcal{O}" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 261, + 545, + 321 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 261, + 545, + 321 + ], + "spans": [ + { + "bbox": [ + 304, + 261, + 545, + 321 + ], + "type": "text", + "content": "segment can be safely dropped since " + }, + { + "bbox": [ + 304, + 261, + 545, + 321 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{O}\\mathcal{O}}(y) \\leq \\mathcal{L}_{\\mathcal{I}\\mathcal{I}}(y)" + }, + { + "bbox": [ + 304, + 261, + 545, + 321 + ], + "type": "text", + "content": ". When segments disagree (e.g., due to inaccurate poses), we keep the segment with more auxiliary views in agreement. This approach handles merging " + }, + { + "bbox": [ + 304, + 261, + 545, + 321 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{sep}}" + }, + { + "bbox": [ + 304, + 261, + 545, + 321 + ], + "type": "text", + "content": ": " + }, + { + "bbox": [ + 304, + 261, + 545, + 321 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{sep}}" + }, + { + "bbox": [ + 304, + 261, + 545, + 321 + ], + "type": "text", + "content": " is seen by no auxiliary views, so any visible Freespace overrules it." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 321, + 545, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 321, + 545, + 369 + ], + "spans": [ + { + "bbox": [ + 304, + 321, + 545, + 369 + ], + "type": "text", + "content": "Network Architecture. We follow [27] to facilitate fair comparison. Additionally, we clamp the outputs of our network to be " + }, + { + "bbox": [ + 304, + 321, + 545, + 369 + ], + "type": "inline_equation", + "content": "\\in [-1, 1]" + }, + { + "bbox": [ + 304, + 321, + 545, + 369 + ], + "type": "text", + "content": " by applying a tanh activation and adjust the loss to account for this clipping." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "spans": [ + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "text", + "content": "Training. We follow a two-stage training procedure. We first train with only the reference view followed by adding auxiliary views. In the first stage, we train for 100 epochs minimizing " + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{O}I}" + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{sep}}" + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "text", + "content": ". We then train for 100 epochs with auxiliary losses, minimizing a sum of the segment losses " + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{II}, \\mathcal{L}_{IO}, \\mathcal{L}_{OI}, \\mathcal{L}_{OO}" + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{sep}}" + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "text", + "content": " as well as " + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "inline_equation", + "content": "\\lambda \\mathcal{L}_{\\mathrm{ent}}" + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "text", + "content": " set to 0.1 to balance loss scales. We minimize the loss with AdamW [26, 33] as the optimizer with learning rate warmup for " + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "inline_equation", + "content": "0.5\\%" + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "text", + "content": " of the iterations followed by cosine learning rate decay with maximum value " + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "inline_equation", + "content": "3 \\times 10^{-4}" + }, + { + "bbox": [ + 304, + 370, + 546, + 514 + ], + "type": "text", + "content": ". Our models are implemented using PyTorch [39] and visualizations in this paper use PyTorch3D [30, 42]." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 525, + 388, + 539 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 525, + 388, + 539 + ], + "spans": [ + { + "bbox": [ + 306, + 525, + 388, + 539 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 545, + 545, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 545, + 617 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 545, + 617 + ], + "type": "text", + "content": "We evaluate our method to address three experimental questions. First, we examine how training with RGBD data compares with mesh supervision. Next, we test how RGBD and mesh supervision compare when one has less complete scans. Finally, we show that our method can quickly adapt to multiple posed RGBD inputs." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "text", + "content": "Metrics. Throughout, we follow [27] and use two metrics that evaluate predictions against a ground-truth mesh. Following [45, 53], these metrics are based on: Accuracy/Acc (the fraction of predicted points that are within " + }, + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "text", + "content": " to a ground-truth point), Completeness/Cmp (the fraction of ground-truth points that are within " + }, + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "text", + "content": " to a predicted point), as well as F1 (the harmonic mean of Accuracy and Completeness). " + }, + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "text", + "content": " for both Acc and Cmp is " + }, + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "inline_equation", + "content": "0.5\\mathrm{m}" + }, + { + "bbox": [ + 304, + 618, + 546, + 713 + ], + "type": "text", + "content": ". 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Comparison with baselines. 3D outputs generated by all methods trained on Matterport3D. We color the first intersection with image colors and occluded intersections with computed surface normals. We highlight regions of interest in the reconstructions in selected crops. D2-DRDF achieves results on par with Mesh DRDF while the density fields baselines fails to model the occluded parts faithfully. In row 1, our method recovers the back of sofa, and a hidden room behind the hallway in row 2. 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In (Ray Occ. Acc/Cmp/F1), We evaluate the performance on occluded points, evaluating per-ray and then averaging. We define occluded points for both the ground truth and prediction as any surface past the first intersection. Ray Occ. is a challenging metric as mistakes in one ray cannot be accounted for in another ray." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 46, + 383, + 287, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 383, + 287, + 491 + ], + "spans": [ + { + "bbox": [ + 46, + 383, + 287, + 491 + ], + "type": "text", + "content": "Datasets. We use Matterport3D [1] as our primary dataset following an identical setup to [28]. We choose Matterport3D because it has substantial occluded geometry (unlike ScanNet [8]) and was captured by a real scanner (unlike 3DFront [15], which is synthetic). We note that we use the raw images captured by the scanner rather than re-renders. We follow the split from [27], which splits train/val/test by house into 60/15/15 houses. After filtering and selecting images, there are 15K/1K/1K input images for each split." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 47, + 501, + 185, + 513 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 501, + 185, + 513 + ], + "spans": [ + { + "bbox": [ + 47, + 501, + 185, + 513 + ], + "type": "text", + "content": "5.1. Mesh Prediction Results" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 46, + 521, + 287, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 521, + 287, + 687 + ], + "spans": [ + { + "bbox": [ + 46, + 521, + 287, + 687 + ], + "type": "text", + "content": "Baselines. Our primary point of comparison is (1) Mesh DRDF [27], which learns to predict the DRDF from direct mesh supervision. For context, we also report the baselines from [27] and summarize them: (2) LDI [46] predicts a set of 4 layered depthmaps, where the first represents the depth and the next three represent occluded intersections; (3) UDF [5] predicts an unsigned scene distance function; (4) URDF [5] predicts an unsigned ray distance function; (5) ORF predicts an occupancy function, or whether the surface is within a fixed distance. We note that for each of these approaches, there are a number of variants (e.g., of finding intersections in a URDF along a ray). We report the highest performance reported by [27], who document extensive and detailed tuning of these baselines." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "text", + "content": "Our final baseline, (6) Density Field [57] tests the value of predicting a DRDF compared to a density. 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Novels Views Comparison between D2-DRDF and Ground Truth(GT) from novel views. Rows 1, 2 are from unseen images on Matterport3D [1] and 3,4 from Omnidata [11]. Our method trained with only RGBD data recovers occluded empty floors. Kitchen cabinets (row 1) and sides of kitchen island(row 3)." + } + ] + } + ], + "index": 39, + "angle": 0, + "type": "image_caption" + } + ], + "index": 36 + }, + { + "type": "image", + "bbox": [ + 379, + 512, + 461, + 582 + ], + "blocks": [ + { + "bbox": [ + 379, + 512, + 461, + 582 + ], + "lines": [ + { + "bbox": [ + 379, + 512, + 461, + 582 + ], + "spans": [ + { + "bbox": [ + 379, + 512, + 461, + 582 + ], + "type": "image", + "image_path": "95048076d256158667aa5e1e60b6dd076dfd95d542f35b10dcbbeeaba0cd6831.jpg" + } + ] + } + ], + "index": 37, + "angle": 0, + "type": "image_body" + } + ], + "index": 37 + }, + { + "type": "image", + "bbox": [ + 462, + 512, + 545, + 582 + ], + "blocks": [ + { + "bbox": [ + 462, + 512, + 545, + 582 + ], + "lines": [ + { + "bbox": [ + 462, + 512, + 545, + 582 + ], + "spans": [ + { + "bbox": [ + 462, + 512, + 545, + 582 + ], + "type": "image", + "image_path": "5db495ee1874ec1f265cf2506f745d53ec4de5bc7900a993fb97f5101f053bde.jpg" + } + ] + } + ], + "index": 38, + "angle": 0, + "type": "image_body" + } + ], + "index": 38 + }, + { + "bbox": [ + 304, + 653, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 546, + 713 + ], + "type": "text", + "content": "method, Density Field only uses posed RGBD data for supervision and does not depend on mesh supervision. We adapt pixelNerf [57] to our setting, modifying the implementation from [41] to permit training from a single reference view as input and multiple auxiliary views for su" + } + ] + } + ], + "index": 40 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17261" + } + ] + } + ], + "index": 41 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 60, + 129, + 273, + 255 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 289, + 126 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 289, + 126 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 289, + 126 + ], + "type": "text", + "content": "Table 1. Matterport3D [1] Acc/Comp/F1Score. We separate methods that use ground-truth mesh from ones using posed RGBD by a horizontal line. D2-DRDF is comparable to the best Mesh supervised method DRDF, and is better than all other mesh based methods on Scene and Ray F1 scores." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 60, + 129, + 273, + 255 + ], + "lines": [ + { + "bbox": [ + 60, + 129, + 273, + 255 + ], + "spans": [ + { + "bbox": [ + 60, + 129, + 273, + 255 + ], + "type": "table", + "html": "
MethodSceneRay
AccCmpF1AccCmpF1
MeshLDI [46]66.272.467.413.942.819.3
UDF [5]58.776.064.715.523.016.6
ORF73.469.469.626.220.521.6
URDF [5]74.567.168.724.920.620.7
DRDF [27]75.472.071.928.430.027.3
D2-DRDF73.773.572.128.222.625.1
Density Field [57]45.880.257.524.814.017.9
", + "image_path": "6e238fb128e5aab3a9b1ba72c670db7283ee14b1d56098a8f081c2b64ce6e1be.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 265, + 287, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 265, + 287, + 312 + ], + "spans": [ + { + "bbox": [ + 46, + 265, + 287, + 312 + ], + "type": "text", + "content": "pervision. In addition to the standard color loss, we supervised the network to match the ground-truth auxiliary depth RGBD depth as in [10]. At inference time, we integrate along the ray to decode a set of intersections as in [10]." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 313, + 288, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 313, + 288, + 420 + ], + "spans": [ + { + "bbox": [ + 46, + 313, + 288, + 420 + ], + "type": "text", + "content": "Qualitative Results. We show qualitative results from our method and the baselines in Fig 6. Our methods outputs are comparable to the outputs of the mesh based DRDF that is trained with much stronger supervision. The density fields approach struggles to model the occluded hits and has a lots of floating blobs in the empty space. We show additional results from D2-DRDF in Fig. 7 where it recovers occlude kitchen cabinets, empty floor space behind kitchen island, sides of kitchen island, and hollow bed." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 422, + 288, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 422, + 288, + 590 + ], + "spans": [ + { + "bbox": [ + 46, + 422, + 288, + 590 + ], + "type": "text", + "content": "Quantitative Results. We report results in Table 1. Without access to ground-truth meshes, our approach slightly outperforms Mesh-based DRDF on Scene F1 and approaches its performance on Ray Occ. Our approach matches it in accuracy (i.e., the fraction of predicted points that are correct) but does worse on completeness. We hypothesize that this is due to mesh-based techniques obtaining supervision in adjacent rooms. Nonetheless, our approach outperforms all other baselines besides mesh-supervised DRDF by a large margin (25.1 vs 21.6 F1). D2-DRDF has substantially higher performance compared to the Density Field baseline. This shows that density fields representation has difficulties in learning from a supervision of less views with large occlusions." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 598, + 205, + 611 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 598, + 205, + 611 + ], + "spans": [ + { + "bbox": [ + 47, + 598, + 205, + 611 + ], + "type": "text", + "content": "5.2. Training on Incomplete Data" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 617, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 287, + 715 + ], + "type": "text", + "content": "One advantage to using posed RGBD data compared to meshes is that it opens the door to learning from more data that is of lower quality. One can use lots of lower-quality scene captures that would produce poor meshes due to substantial amounts of incomplete data. In the case of direct mesh supervision methods, having an incomplete mesh may lead to incorrect signals for a mesh-based system: for instance, the network may learn to predict that an intersec" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 310, + 69, + 539, + 208 + ], + "blocks": [ + { + "bbox": [ + 310, + 69, + 539, + 208 + ], + "lines": [ + { + "bbox": [ + 310, + 69, + 539, + 208 + ], + "spans": [ + { + "bbox": [ + 310, + 69, + 539, + 208 + ], + "type": "image", + "image_path": "5d9644b47f3e19ab2f6aec9ebe7fa333efc398758c5672c0630a747ffe38c7eb.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 214, + 546, + 258 + ], + "lines": [ + { + "bbox": [ + 305, + 214, + 546, + 258 + ], + "spans": [ + { + "bbox": [ + 305, + 214, + 546, + 258 + ], + "type": "text", + "content": "Figure 8. Mesh Degradation We show examples of how drop in images creates holes, reduces mesh area available for supervision. In (c), after retaining " + }, + { + "bbox": [ + 305, + 214, + 546, + 258 + ], + "type": "inline_equation", + "content": "\\approx \\frac{1}{4}" + }, + { + "bbox": [ + 305, + 214, + 546, + 258 + ], + "type": "text", + "content": " of image data, we only lose " + }, + { + "bbox": [ + 305, + 214, + 546, + 258 + ], + "type": "inline_equation", + "content": "\\approx \\frac{1}{2}" + }, + { + "bbox": [ + 305, + 214, + 546, + 258 + ], + "type": "text", + "content": " the mesh area. Im.: Image Coverage, M: Mesh Coverage, A: Area" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 265, + 546, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 265, + 546, + 312 + ], + "spans": [ + { + "bbox": [ + 304, + 265, + 546, + 312 + ], + "type": "text", + "content": "tion does not exist simply because it was not scanned. In contrast, for D2-DRDF, missing data simply increases the fraction of points without supervision. We now test this hypothesis by reducing the number of views in datasets." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 314, + 546, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 314, + 546, + 410 + ], + "spans": [ + { + "bbox": [ + 304, + 314, + 546, + 410 + ], + "type": "text", + "content": "Optimistic Degradation Setup (ODS). We simulate the degradation of dataset collection by subsampling views. To avoid conflating errors in training with suboptimal meshing with incomplete data, we optimistically degrade the meshes to provide an upper bound on supervision. We assume that the mesh with fewer views is identical to the mesh from all views, minus triangles with no vertices in any view. We show ODS mesh examples in Fig. 8." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 411, + 547, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 411, + 547, + 506 + ], + "spans": [ + { + "bbox": [ + 304, + 411, + 547, + 506 + ], + "type": "text", + "content": "We degrade meshes by selecting " + }, + { + "bbox": [ + 304, + 411, + 547, + 506 + ], + "type": "inline_equation", + "content": "1/2^i" + }, + { + "bbox": [ + 304, + 411, + 547, + 506 + ], + "type": "text", + "content": " views per dataset for an increasing " + }, + { + "bbox": [ + 304, + 411, + 547, + 506 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 411, + 547, + 506 + ], + "type": "text", + "content": ". While reducing the views linearly impacts the sample count for RGBD training setup, it has a non-linear impact on mesh completeness since a triangle is removed only if all of the views seeing it are removed (which is unlikely until most views are removed). For any given image retention " + }, + { + "bbox": [ + 304, + 411, + 547, + 506 + ], + "type": "inline_equation", + "content": "(\\mathrm{Im.})\\%" + }, + { + "bbox": [ + 304, + 411, + 547, + 506 + ], + "type": "text", + "content": " mesh coverage " + }, + { + "bbox": [ + 304, + 411, + 547, + 506 + ], + "type": "inline_equation", + "content": "(\\mathbf{M})\\%" + }, + { + "bbox": [ + 304, + 411, + 547, + 506 + ], + "type": "text", + "content": " degrades less giving an edge to mesh based methods." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 508, + 547, + 627 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 508, + 547, + 627 + ], + "spans": [ + { + "bbox": [ + 304, + 508, + 547, + 627 + ], + "type": "text", + "content": "Usually, when dealing with limited data, we use a method called Screened Poisson Reconstruction(SPR) [25]. However, SPR does not perform well when there is not enough data available. To avoid conflating errors caused by poor quality inadequate meshing, we establish an upper limit on the performance of methods that rely on direct supervision. Our ODS strategy is much better than using SPR, but it cannot be used in real-world scenarios. We employ Open3D's[60]'s SPR with hyper-parameters similar to [1] to reconstruct meshes." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 628, + 547, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 628, + 547, + 676 + ], + "spans": [ + { + "bbox": [ + 304, + 628, + 547, + 676 + ], + "type": "text", + "content": "Datasets. We evaluate on Matterport3D [1] and apply our method as is without any modifications on OmniData [11] which has a substantially different image view distribution, more rooms and more floors compared to Matterport3D." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "type": "text", + "content": "Quantitative Results. We compare models trained on different amount of data available for supervision by using metrics defined in §5.1. In Tab. 2 we compare against the" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "17262" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 70, + 289, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 70, + 289, + 180 + ], + "spans": [ + { + "bbox": [ + 46, + 70, + 289, + 180 + ], + "type": "text", + "content": "Table 2. Robustness to Sparse Data Performance on partial data on Matterport3D[1]. We compare (SPR and ODS) trained DRDF [27] which uses mesh supervision and (Depth) Depth-based DRDF (ours), which uses posed RGBD supervision. In each row, we degrade the training data and report test performance of the trained model. At " + }, + { + "bbox": [ + 46, + 70, + 289, + 180 + ], + "type": "inline_equation", + "content": "100\\%" + }, + { + "bbox": [ + 46, + 70, + 289, + 180 + ], + "type": "text", + "content": " data there is no M degradation for ODS or SPR. Our approach is more robust to drop in Im.: at " + }, + { + "bbox": [ + 46, + 70, + 289, + 180 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 46, + 70, + 289, + 180 + ], + "type": "text", + "content": " view sparsity (Im.), models using ODS or SPR suffer substantial performance drops. Scene F1 drop by 16.3 for SPR; 3.5 for ODS; 2.1 for Depth (ours)." + } + ] + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 51, + 184, + 282, + 256 + ], + "blocks": [ + { + "bbox": [ + 51, + 184, + 282, + 256 + ], + "lines": [ + { + "bbox": [ + 51, + 184, + 282, + 256 + ], + "spans": [ + { + "bbox": [ + 51, + 184, + 282, + 256 + ], + "type": "table", + "html": "
Im. %ODS M %Scene F1Ray Occ F1
SPRODSDepthSPRODSDepth
10010071.971.972.127.327.325.1
505655.668.470.021.423.624.4
254356.866.870.021.521.224.9
", + "image_path": "74ebeed38db615e82ef284c6cc79aa42520e1817833dd28dd59bd4e077759de4.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 48, + 308, + 287, + 386 + ], + "blocks": [ + { + "bbox": [ + 46, + 260, + 288, + 305 + ], + "lines": [ + { + "bbox": [ + 46, + 260, + 288, + 305 + ], + "spans": [ + { + "bbox": [ + 46, + 260, + 288, + 305 + ], + "type": "text", + "content": "Table 3. Robustness to Sparse Data Performance of ODS (mesh) and Depth (ours) based DRDF on partial Omnidata [11] following the same setup as Table 2. RGBD-based training is substantially more robust to partial data." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 308, + 287, + 386 + ], + "lines": [ + { + "bbox": [ + 48, + 308, + 287, + 386 + ], + "spans": [ + { + "bbox": [ + 48, + 308, + 287, + 386 + ], + "type": "table", + "html": "
ODS\nIm. % M %Scene F1Ray Occ F1
ODSDepthODSDepth
258677.272.840.332.1
12.58375.3 (-1.9)70.9 (-1.9)37.1. (-3.2)29.3 (-2.8)
6.37873.4 (-3.8)71.8 (-1.0)32.6 (-7.7)28.1 (-3.0)
36969.8 (-7.4)70.4 (-2.4)20.3 (-20.0)26.7 (-5.4)
", + "image_path": "cd17c3fdf9618a18e546143fd0ba08df37b4bc804f1b8cc9eddabe9201927041.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 394, + 287, + 466 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 394, + 287, + 466 + ], + "spans": [ + { + "bbox": [ + 46, + 394, + 287, + 466 + ], + "type": "text", + "content": "Mesh-DRDF trained ODS & SPR meshes. For any given view sampling level, the supervised M % area is high, resulting in stronger supervision for methods trained with ODS than RGBD. However, on Matterport3D, our method outperforms DRDF on all metrics at " + }, + { + "bbox": [ + 46, + 394, + 287, + 466 + ], + "type": "inline_equation", + "content": "25\\%" + }, + { + "bbox": [ + 46, + 394, + 287, + 466 + ], + "type": "text", + "content": " Im. and outperforms all other baseline methods in Scene F1 at " + }, + { + "bbox": [ + 46, + 394, + 287, + 466 + ], + "type": "inline_equation", + "content": "100\\%" + }, + { + "bbox": [ + 46, + 394, + 287, + 466 + ], + "type": "text", + "content": " Im.." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "spans": [ + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "text", + "content": "In Tab. 3 we show robustness trends on OmniData. At " + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "inline_equation", + "content": "25\\%" + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "text", + "content": " Im./ " + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "inline_equation", + "content": "86\\%" + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "text", + "content": " M completion, mesh-based does better. We hypothesize this gain is due to better handling of estimates beyond the room. However, as Im. reduces mesh degrades, resulting steep fall for ODS DRDF: with " + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "inline_equation", + "content": "3\\%" + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "text", + "content": " Im./ " + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "inline_equation", + "content": "69\\%" + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "text", + "content": " M, MeshDRDF's Ray Occ F1 drops by 20 points; ours is reduced by just 5.4. Moreover for SPR DRDF, at low Im. values, meshing performs dismally and the poor meshing performance translates into poor reconstruction performance: at " + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "inline_equation", + "content": "6\\%" + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "text", + "content": " Im., training DRDF on SPR meshes produces a Ray F1 of just " + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "inline_equation", + "content": "5.7\\%" + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "text", + "content": " and a scene F1 of " + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "inline_equation", + "content": "40.9\\%" + }, + { + "bbox": [ + 46, + 467, + 288, + 598 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 609, + 217, + 624 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 609, + 217, + 624 + ], + "spans": [ + { + "bbox": [ + 47, + 609, + 217, + 624 + ], + "type": "text", + "content": "5.3. Adapting With Multiple Inputs" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 628, + 287, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 628, + 287, + 676 + ], + "spans": [ + { + "bbox": [ + 46, + 628, + 287, + 676 + ], + "type": "text", + "content": "Since D2-DRDF can directly train on posed RGBD images, this enables test-time adaptation given a few auxiliary posed RGBD images. We start with the pre-trained model from §5.1 and then fine-tune for 500 iterations." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "content": "Dataset and Metrics. We generate 300 quadruplets of scenes consisting of a reference view as well as three auxiliary RGBD images with poses. These three auxiliary views" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 314, + 71, + 539, + 209 + ], + "blocks": [ + { + "bbox": [ + 314, + 71, + 539, + 209 + ], + "lines": [ + { + "bbox": [ + 314, + 71, + 539, + 209 + ], + "spans": [ + { + "bbox": [ + 314, + 71, + 539, + 209 + ], + "type": "image", + "image_path": "06b86ead86807f88867973b75fdd60e17e46b22efb7021e26ae408c91cfd783d.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 217, + 545, + 262 + ], + "lines": [ + { + "bbox": [ + 305, + 217, + 545, + 262 + ], + "spans": [ + { + "bbox": [ + 305, + 217, + 545, + 262 + ], + "type": "text", + "content": "Figure 9. Adaptation to a few images We optimize our pretrained model on the three auxiliary views (yellow) and a reference view (orange). The optimization lets the model fix reconstructions in occluded regions (e.g. bedside wall) as seen D2-DRDF + Adapt." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 315, + 303, + 535, + 387 + ], + "blocks": [ + { + "bbox": [ + 305, + 266, + 545, + 300 + ], + "lines": [ + { + "bbox": [ + 305, + 266, + 545, + 300 + ], + "spans": [ + { + "bbox": [ + 305, + 266, + 545, + 300 + ], + "type": "text", + "content": "Table 4. Scene, Ray F1 Score for Adaptation Using D2-DRDF for adaptation produces better reconstructions that outperform the baselines by a large margin on Ray Occ F1 scores (by 8.9 points)." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 315, + 303, + 535, + 387 + ], + "lines": [ + { + "bbox": [ + 315, + 303, + 535, + 387 + ], + "spans": [ + { + "bbox": [ + 315, + 303, + 535, + 387 + ], + "type": "table", + "html": "
MethodSceneRay
AccCmpF1AccCmpF1
D2-DRDF (Full)79.276.476.036.233.634.9
D2-DRDF (Depth)78.470.572.533.021.426.0
D2-DRDF (Scratch)66.470.366.023.727.425.4
Density Field [57]77.374.974.612.89.911.2
", + "image_path": "2175b4a33e078ce274a8c964fc3de8b689d4162d179c1cb383a0a85f8feca883.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 389, + 545, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 389, + 545, + 424 + ], + "spans": [ + { + "bbox": [ + 305, + 389, + 545, + 424 + ], + "type": "text", + "content": "are randomly sampled from views that overlap with occluded parts of the reference view (see supp.). We evaluate inferred 3D using the metrics as §5.1." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 426, + 545, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 426, + 545, + 520 + ], + "spans": [ + { + "bbox": [ + 305, + 426, + 545, + 520 + ], + "type": "text", + "content": "Baselines. The baselines from §5.1 cannot operate in these settings, since they require meshes for training. Therefore we compare against a number of depth-map-based methods as well as ablations to give context to our results: (1) Density Field fine-tunes the density field baseline model from §5.1; (2) Depth-Pretrained fine-tunes D2-DRDF starting with the model from the first stage of training; (3) Scratch Training fine-tunes D2-DRDF from scratch." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 521, + 545, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 521, + 545, + 569 + ], + "spans": [ + { + "bbox": [ + 305, + 521, + 545, + 569 + ], + "type": "text", + "content": "Results. Our loss and penalty formulations lend themselves well to test-time adaptation as shown in Fig. 9. After adaptation, D2-DRDF can resolve uncertainties like the corner of the bed and wall. Table 4 shows D2-DRDF does the best." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 578, + 373, + 590 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 578, + 373, + 590 + ], + "spans": [ + { + "bbox": [ + 306, + 578, + 373, + 590 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 597, + 545, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 597, + 545, + 658 + ], + "spans": [ + { + "bbox": [ + 305, + 597, + 545, + 658 + ], + "type": "text", + "content": "We presented a method for learning to predict 3D from a single image using implicit functions while requiring only posed RGBD supervision. We believe our method can unlock new avenues with posed RGBD data becoming available from both consumers as well as robotic agents." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 658, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 658, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 658, + 545, + 713 + ], + "type": "text", + "content": "Acknowledgments. We thank Richard H., Sarah J., Dandan S., Shengyi Q., Karan D., Mohammed B., Chris R. for feedback. Toyota Research Institute (\"TRI\") provided funds to assist the authors with their research but this article solely reflects the opinions and conclusions of its authors and not TRI or any Toyota entity." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17263" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "type": "text", + "content": "[1] Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang. Matterport3d: Learning from rgb-d data in indoor environments. 3DV, 2017. 2, 6, 7, 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 134, + 288, + 188 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 134, + 288, + 188 + ], + "spans": [ + { + "bbox": [ + 53, + 134, + 288, + 188 + ], + "type": "text", + "content": "[2] Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 188, + 288, + 209 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 188, + 288, + 209 + ], + "spans": [ + { + "bbox": [ + 53, + 188, + 288, + 209 + ], + "type": "text", + "content": "[3] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. ECCV, 2022. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 209, + 288, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 209, + 288, + 230 + ], + "spans": [ + { + "bbox": [ + 53, + 209, + 288, + 230 + ], + "type": "text", + "content": "[4] Weifeng Chen, Zhao Fu, Dawei Yang, and Jia Deng. Single-image depth perception in the wild. NeurIPS, 2016. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 230, + 288, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 230, + 288, + 261 + ], + "spans": [ + { + "bbox": [ + 53, + 230, + 288, + 261 + ], + "type": "text", + "content": "[5] Julian Chibane, Aymen Mir, and Gerard Pons-Moll. Neural unsigned distance fields for implicit function learning. In NeurIPS, 2020. 2, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 261, + 288, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 261, + 288, + 304 + ], + "spans": [ + { + "bbox": [ + 53, + 261, + 288, + 304 + ], + "type": "text", + "content": "[6] Christopher B Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese. 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. In ECCV, 2016. 1, 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 304, + 288, + 347 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 304, + 288, + 347 + ], + "spans": [ + { + "bbox": [ + 53, + 304, + 288, + 347 + ], + "type": "text", + "content": "[7] Brian Curless and Marc Levoy. A volumetric method for building complex models from range images. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 303-312, 1996. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 347, + 288, + 390 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 347, + 288, + 390 + ], + "spans": [ + { + "bbox": [ + 53, + 347, + 288, + 390 + ], + "type": "text", + "content": "[8] Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In CVPR, 2017. 2, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 390, + 288, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 390, + 288, + 443 + ], + "spans": [ + { + "bbox": [ + 53, + 390, + 288, + 443 + ], + "type": "text", + "content": "[9] Angela Dai, Matthias Nießner, Michael Zollhöfer, Shahram Izadi, and Christian Theobalt. Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface reintegration. ACM Transactions on Graphics (ToG), 36(4):1, 2017. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 444, + 288, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 444, + 288, + 476 + ], + "spans": [ + { + "bbox": [ + 48, + 444, + 288, + 476 + ], + "type": "text", + "content": "[10] Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan. Depth-supervised nerf: Fewer views and faster training for free. In CVPR, 2022. 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 476, + 288, + 518 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 476, + 288, + 518 + ], + "spans": [ + { + "bbox": [ + 48, + 476, + 288, + 518 + ], + "type": "text", + "content": "[11] Ainaz Eftekhar, Alexander Sax, Jitendra Malik, and Amir Zamir. Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans. In ICCV, 2021. 6, 7, 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 518, + 288, + 551 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 518, + 288, + 551 + ], + "spans": [ + { + "bbox": [ + 48, + 518, + 288, + 551 + ], + "type": "text", + "content": "[12] David Eigen and Rob Fergus. Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. ICCV, 2015. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 551, + 288, + 582 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 551, + 288, + 582 + ], + "spans": [ + { + "bbox": [ + 48, + 551, + 288, + 582 + ], + "type": "text", + "content": "[13] Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In CVPR, 2017. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 582, + 288, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 582, + 288, + 613 + ], + "spans": [ + { + "bbox": [ + 48, + 582, + 288, + 613 + ], + "type": "text", + "content": "[14] David F Fouhey, Abhinav Gupta, and Martial Hebert. Data-driven 3d primitives for single image understanding. In ICCV, 2013. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 614, + 288, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 614, + 288, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 614, + 288, + 678 + ], + "type": "text", + "content": "[15] Huan Fu, Bowen Cai, Lin Gao, Lingxiao Zhang, Cao Li, Qixun Zeng, Chengyue Sun, Yiyun Fei, Yu Zheng, Ying Li, Yi Liu, Peng Liu, Lin Ma, Le Weng, Xiaohang Hu, Xin Ma, Qian Qian, Rongfei Jia, Binqiang Zhao, and Hao Zhang. 3d-front: 3d furnished rooms with layouts and semantics. ICCV, 2021. 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 679, + 288, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 679, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 679, + 288, + 712 + ], + "type": "text", + "content": "[16] Huan Fu, Bowen Cai, Lin Gao, Ling-Xiao Zhang, Jiaming Wang, Cao Li, Qixun Zeng, Chengyue Sun, Rongfei Jia, Binqiang Zhao, et al. 3d-front: 3d furnished rooms with layouts" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 711 + ], + "type": "list", + "angle": 0, + "index": 38, + "blocks": [ + { + "bbox": [ + 327, + 73, + 448, + 83 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 448, + 83 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 448, + 83 + ], + "type": "text", + "content": "and semantics. In ICCV, 2021. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 83, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 83, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 308, + 83, + 545, + 116 + ], + "type": "text", + "content": "[17] Rohit Girdhar, David F Fouhey, Mikel Rodriguez, and Abhinav Gupta. Learning a predictable and generative vector representation for objects. In ECCV. Springer, 2016. 1, 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 116, + 545, + 136 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 116, + 545, + 136 + ], + "spans": [ + { + "bbox": [ + 308, + 116, + 545, + 136 + ], + "type": "text", + "content": "[18] Georgia Gkioxari, Jitendra Malik, and Justin Johnson. Mesh r-cnn. In ICCV, 2019. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 136, + 545, + 178 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 136, + 545, + 178 + ], + "spans": [ + { + "bbox": [ + 308, + 136, + 545, + 178 + ], + "type": "text", + "content": "[19] Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. A papier-mâché approach to learning 3d surface generation. In CVPR, 2018. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 178, + 545, + 211 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 178, + 545, + 211 + ], + "spans": [ + { + "bbox": [ + 308, + 178, + 545, + 211 + ], + "type": "text", + "content": "[20] Po-Han Huang, Kevin Matzen, Johannes Kopf, Narendra Ahuja, and Jia-Bin Huang. Deepmvs: Learning multi-view stereopsis. In CVPR, 2018. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 211, + 545, + 287 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 211, + 545, + 287 + ], + "spans": [ + { + "bbox": [ + 308, + 211, + 545, + 287 + ], + "type": "text", + "content": "[21] Shahram Izadi, David Kim, Otmar Hilliges, David Molyneaux, Richard Newcombe, Pushmeet Kohli, Jamie Shotton, Steve Hodges, Dustin Freeman, Andrew Davison, et al. Kinectfusion: real-time 3d reconstruction and interaction using a moving depth camera. In Proceedings of the 24th annual ACM symposium on User interface software and technology, pages 559-568, 2011. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 287, + 545, + 307 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 287, + 545, + 307 + ], + "spans": [ + { + "bbox": [ + 308, + 287, + 545, + 307 + ], + "type": "text", + "content": "[22] Hamid Izadinia, Qi Shan, and Steven M Seitz. Im2cad. In CVPR, 2017. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 307, + 545, + 340 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 307, + 545, + 340 + ], + "spans": [ + { + "bbox": [ + 308, + 307, + 545, + 340 + ], + "type": "text", + "content": "[23] Michael Janner, Jiajun Wu, Tejas D Kulkarni, Ilker Yildirim, and Josh Tenenbaum. Self-supervised intrinsic image decomposition. NeurIPS, 2017. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 340, + 545, + 382 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 340, + 545, + 382 + ], + "spans": [ + { + "bbox": [ + 308, + 340, + 545, + 382 + ], + "type": "text", + "content": "[24] Ziyu Jiang, Buyu Liu, Samuel Schulter, Zhangyang Wang, and Manmohan Chandraker. Peek-a-boo: Occlusion reasoning in indoor scenes with plane representations. In CVPR, 2020. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 382, + 545, + 424 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 382, + 545, + 424 + ], + "spans": [ + { + "bbox": [ + 308, + 382, + 545, + 424 + ], + "type": "text", + "content": "[25] Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Proceedings of the fourth Eurographics symposium on Geometry processing, 2006. 2, 7" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 424, + 545, + 446 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 424, + 545, + 446 + ], + "spans": [ + { + "bbox": [ + 308, + 424, + 545, + 446 + ], + "type": "text", + "content": "[26] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. *ICLR*, 2015. 5" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 446, + 545, + 479 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 446, + 545, + 479 + ], + "spans": [ + { + "bbox": [ + 308, + 446, + 545, + 479 + ], + "type": "text", + "content": "[27] Nilesh Kulkarni, Justin Johnson, and David F. Fouhey. Directed ray distance function for 3d scene reconstruction. In ECCV, 2022. 1, 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 478, + 545, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 478, + 545, + 510 + ], + "spans": [ + { + "bbox": [ + 308, + 478, + 545, + 510 + ], + "type": "text", + "content": "[28] Nilesh Kulkarni, Ishan Misra, Shubham Tulsiani, and Abhinav Gupta. 3d-relnet: Joint object and relational network for 3d prediction. In ICCV, 2019. 2, 6" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 510, + 545, + 531 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 510, + 545, + 531 + ], + "spans": [ + { + "bbox": [ + 308, + 510, + 545, + 531 + ], + "type": "text", + "content": "[29] Kiriakos N Kutulakos and Steven M Seitz. A theory of shape by space carving. *IJCV*, 2000. 2" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 308, + 531, + 545, + 552 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 531, + 545, + 552 + ], + "spans": [ + { + "bbox": [ + 308, + 531, + 545, + 552 + ], + "type": "text", + "content": "[30] Christoph Lassner and Michael Zollhöfer. Pulsar: Efficient sphere-based neural rendering. arXiv:2004.07484, 2020. 5" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 308, + 552, + 545, + 583 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 552, + 545, + 583 + ], + "spans": [ + { + "bbox": [ + 308, + 552, + 545, + 583 + ], + "type": "text", + "content": "[31] Chen-Hsuan Lin, Chen Kong, and Simon Lucey. Learning efficient point cloud generation for dense 3d object reconstruction. In AAAI, volume 32, 2018. 2" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 308, + 583, + 545, + 615 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 583, + 545, + 615 + ], + "spans": [ + { + "bbox": [ + 308, + 583, + 545, + 615 + ], + "type": "text", + "content": "[32] Chen Liu, Kihwan Kim, Jinwei Gu, Yasutaka Furukawa, and Jan Kautz. Planercnn: 3d plane detection and reconstruction from a single image. In CVPR, 2019. 2" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 308, + 615, + 545, + 636 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 615, + 545, + 636 + ], + "spans": [ + { + "bbox": [ + 308, + 615, + 545, + 636 + ], + "type": "text", + "content": "[33] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. *ICLR*, 2019. 5" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 308, + 636, + 545, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 636, + 545, + 668 + ], + "spans": [ + { + "bbox": [ + 308, + 636, + 545, + 668 + ], + "type": "text", + "content": "[34] Ravi Malladi, James A Sethian, and Baba C Vemuri. Shape modeling with front propagation: A level set approach. TPAMI, 1995. 2" + } + ] + } + ], + "index": 36 + }, + { + "bbox": [ + 308, + 668, + 545, + 711 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 668, + 545, + 711 + ], + "spans": [ + { + "bbox": [ + 308, + 668, + 545, + 711 + ], + "type": "text", + "content": "[35] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In CVPR, 2019. 1, 2" + } + ] + } + ], + "index": 37 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "17264" + } + ] + } + ], + "index": 39 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 707 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "text", + "content": "[36] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. arXiv preprint arXiv:2003.08934, 2020. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 116, + 287, + 159 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 116, + 287, + 159 + ], + "spans": [ + { + "bbox": [ + 48, + 116, + 287, + 159 + ], + "type": "text", + "content": "[37] Yinyu Nie, Xiaoguang Han, Shihui Guo, Yujuan Zheng, Jian Chang, and Jian Jun Zhang. Total3dunderstanding: Joint layout, object pose and mesh reconstruction for indoor scenes from a single image. In CVPR, 2020. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 159, + 287, + 202 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 159, + 287, + 202 + ], + "spans": [ + { + "bbox": [ + 48, + 159, + 287, + 202 + ], + "type": "text", + "content": "[38] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, 2019. 1, 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 202, + 287, + 289 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 202, + 287, + 289 + ], + "spans": [ + { + "bbox": [ + 48, + 202, + 287, + 289 + ], + "type": "text", + "content": "[39] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In NeurIPS. 2019. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 289, + 287, + 320 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 289, + 287, + 320 + ], + "spans": [ + { + "bbox": [ + 48, + 289, + 287, + 320 + ], + "type": "text", + "content": "[40] Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In ECCV, 2020. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 320, + 287, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 320, + 287, + 364 + ], + "spans": [ + { + "bbox": [ + 48, + 320, + 287, + 364 + ], + "type": "text", + "content": "[41] Shengyi Qian, Alexander Kirillov, Nikhila Ravi, Devendra Singh Chaplot, Justin Johnson, David F Fouhey, and Georgia Gkioxari. Recognizing scenes from novel viewpoints. arXiv preprint arXiv:2112.01520, 2021. 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 364, + 287, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 364, + 287, + 407 + ], + "spans": [ + { + "bbox": [ + 48, + 364, + 287, + 407 + ], + "type": "text", + "content": "[42] Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari. Accelerating 3d deep learning with pytorch3d. arXiv:2007.08501, 2020. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 407, + 287, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 407, + 287, + 459 + ], + "spans": [ + { + "bbox": [ + 48, + 407, + 287, + 459 + ], + "type": "text", + "content": "[43] Mike Roberts, Jason Ramapuram, Anurag Ranjan, Atulit Kumar, Miguel Angel Bautista, Nathan Paczan, Russ Webb, and Joshua M. Susskind. Hypersim: A photorealistic synthetic dataset for holistic indoor scene understanding. In ICCV, 2021. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 459, + 287, + 503 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 459, + 287, + 503 + ], + "spans": [ + { + "bbox": [ + 48, + 459, + 287, + 503 + ], + "type": "text", + "content": "[44] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In ICCV, 2019. 1, 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 503, + 287, + 545 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 503, + 287, + 545 + ], + "spans": [ + { + "bbox": [ + 48, + 503, + 287, + 545 + ], + "type": "text", + "content": "[45] Steven M Seitz, Brian Curless, James Diebel, Daniel Scharstein, and Richard Szeliski. A comparison and evaluation of multi-view stereo reconstruction algorithms. In CVPR, 2006. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 545, + 287, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 545, + 287, + 589 + ], + "spans": [ + { + "bbox": [ + 48, + 545, + 287, + 589 + ], + "type": "text", + "content": "[46] Jonathan Shade, Steven Gortler, Li-wei He, and Richard Szeliski. Layered depth images. In Proceedings of the 25th annual conference on Computer graphics and interactive techniques, pages 231–242, 1998. 6, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 589, + 287, + 620 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 589, + 287, + 620 + ], + "spans": [ + { + "bbox": [ + 48, + 589, + 287, + 620 + ], + "type": "text", + "content": "[47] Jian Shi, Yue Dong, Hao Su, and Stella X Yu. Learning non-lambertian object intrinsics across shapenet categories. In CVPR, 2017. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 620, + 287, + 653 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 620, + 287, + 653 + ], + "spans": [ + { + "bbox": [ + 48, + 620, + 287, + 653 + ], + "type": "text", + "content": "[48] Vincent Sitzmann, Julien Martel, Alexander Bergman, David Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. NeurIPS, 2020. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 653, + 287, + 684 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 653, + 287, + 684 + ], + "spans": [ + { + "bbox": [ + 48, + 653, + 287, + 684 + ], + "type": "text", + "content": "[49] Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao. Sun rgb-d: A rgb-d scene understanding benchmark suite. In CVPR, 2015. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 684, + 287, + 707 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 684, + 287, + 707 + ], + "spans": [ + { + "bbox": [ + 48, + 684, + 287, + 707 + ], + "type": "text", + "content": "[50] Shuran Song, Fisher Yu, Andy Zeng, Angel X Chang, Manolis Savva, and Thomas Funkhouser. Semantic scene comple" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 456 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 327, + 73, + 499, + 84 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 499, + 84 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 499, + 84 + ], + "type": "text", + "content": "tion from a single depth image. CVPR, 2017. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 84, + 545, + 115 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 84, + 545, + 115 + ], + "spans": [ + { + "bbox": [ + 308, + 84, + 545, + 115 + ], + "type": "text", + "content": "[51] Jiaming Sun, Yiming Xie, Linghao Chen, Xiaowei Zhou, and Hujun Bao. Neuralrecon: Real-time coherent 3d reconstruction from monocular video. In CVPR, 2021. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 115, + 545, + 159 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 115, + 545, + 159 + ], + "spans": [ + { + "bbox": [ + 308, + 115, + 545, + 159 + ], + "type": "text", + "content": "[52] Xingyuan Sun, Jiajun Wu, Xiuming Zhang, Zhoutong Zhang, Chengkai Zhang, Tianfan Xue, Joshua B Tenenbaum, and William T Freeman. Pix3d: Dataset and methods for single-image 3d shape modeling. In CVPR, 2018. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 159, + 545, + 200 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 159, + 545, + 200 + ], + "spans": [ + { + "bbox": [ + 308, + 159, + 545, + 200 + ], + "type": "text", + "content": "[53] Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox. What do single-view 3d reconstruction networks learn? In CVPR, 2019. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 201, + 545, + 222 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 201, + 545, + 222 + ], + "spans": [ + { + "bbox": [ + 308, + 201, + 545, + 222 + ], + "type": "text", + "content": "[54] Kaixuan Wang and Shaojie Shen. Mvdepthnet: Real-time multiview depth estimation neural network. In 3DV, 2018. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 222, + 545, + 243 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 222, + 545, + 243 + ], + "spans": [ + { + "bbox": [ + 308, + 222, + 545, + 243 + ], + "type": "text", + "content": "[55] X. Wang, David F. Fouhey, and A. Gupta. Designing deep networks for surface normal estimation. In CVPR, 2015. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 243, + 545, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 243, + 545, + 275 + ], + "spans": [ + { + "bbox": [ + 308, + 243, + 545, + 275 + ], + "type": "text", + "content": "[56] Jianglong Ye, Yuntao Chen, Naiyan Wang, and Xiaolong Wang. Gifs: Neural implicit function for general shape representation. In CVPR, 2022. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 275, + 545, + 306 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 275, + 545, + 306 + ], + "spans": [ + { + "bbox": [ + 308, + 275, + 545, + 306 + ], + "type": "text", + "content": "[57] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelNeRF: Neural radiance fields from one or few images. In CVPR, 2021. 2, 3, 6, 7, 8" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 306, + 545, + 339 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 306, + 545, + 339 + ], + "spans": [ + { + "bbox": [ + 308, + 306, + 545, + 339 + ], + "type": "text", + "content": "[58] Zehao Yu, Jia Zheng, Dongze Lian, Zihan Zhou, and Shenghua Gao. Single-image piece-wise planar 3d reconstruction via associative embedding. In CVPR, 2019. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 339, + 545, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 339, + 545, + 381 + ], + "spans": [ + { + "bbox": [ + 308, + 339, + 545, + 381 + ], + "type": "text", + "content": "[59] Cheng Zhang, Zhaopeng Cui, Yinda Zhang, Bing Zeng, Marc Pollefeys, and Shuaicheng Liu. Holistic 3d scene understanding from a single image with implicit representation. In CVPR, 2021. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 381, + 545, + 413 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 381, + 545, + 413 + ], + "spans": [ + { + "bbox": [ + 308, + 381, + 545, + 413 + ], + "type": "text", + "content": "[60] Qian-Yi Zhou, Jaesik Park, and Vladlen Koltun. Open3D: A modern library for 3D data processing. arXiv:1801.09847, 2018. 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 413, + 545, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 413, + 545, + 456 + ], + "spans": [ + { + "bbox": [ + 308, + 413, + 545, + 456 + ], + "type": "text", + "content": "[61] Shizhan Zhu, Sayna Ebrahimi, Angjoo Kanazawa, and Trevor Darrell. Differentiable gradient sampling for learning implicit 3d scene reconstructions from a single image. 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Novel view synthesis from a single wide-baseline stereo image pair. In a single forward pass, our method maps a wide-baseline stereo image pair to features that enable fast rendering of novel views, trained using only posed multi-view images of static scenes without ground-truth or proxy geometry. We outperform all prior art on novel view synthesis from sparse observations, taking a significant step towards matching the quality of overfitting on single scenes in this challenging setting." + ], + "image_footnote": [], + "bbox": [ + 91, + 271, + 354, + 479 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/26bb07107e070e3a684ec9e7b549fb464dba325b26eca36ded14b192da492f68.jpg", + "image_caption": [ + "Output: Synthesized Novel Views" + ], + "image_footnote": [], + "bbox": [ + 359, + 271, + 880, + 479 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 551, + 313, + 566 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We introduce a method for novel view synthesis given only a single wide-baseline stereo image pair. In this challenging regime, 3D scene points are regularly observed only once, requiring prior-based reconstruction of scene geometry and appearance. We find that existing approaches to novel view synthesis from sparse observations fail due to recovering incorrect 3D geometry and due to the high cost of differentiable rendering that precludes their scaling to large-scale training. We take a step towards resolving these shortcomings by formulating a multi-view transformer encoder, proposing an efficient, image-space epipolar line sampling scheme to assemble image features for a target ray, and a lightweight cross-attention-based renderer. Our contributions enable training of our method on a large-scale real-world dataset of indoor and outdoor scenes. We demonstrate that our method learns powerful multi-view geometry priors while reducing the rendering time. We conduct extensive comparisons on held-out test scenes across two real-world datasets, signif", + "bbox": [ + 75, + 583, + 472, + 854 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "icantly outperforming prior work on novel view synthesis from sparse image observations and achieving multi-view-consistent novel view synthesis.", + "bbox": [ + 500, + 553, + 893, + 598 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 500, + 631, + 630, + 645 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The goal of novel view synthesis is to render images of a scene from unseen camera viewpoints given a set of image observations. In recent years, the emergence of differentiable rendering [26, 28, 44, 45, 50] has led to a leap in quality and applicability of these approaches, enabling near photorealistic results for most real-world 3D scenes. However, methods that approach photorealism require hundreds or even thousands of images carefully exploring every part of the scene, where special care must be taken by the user to densely image all 3D points in the scene from multiple angles.", + "bbox": [ + 496, + 657, + 893, + 808 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In contrast, we are interested in the regime of novel view synthesis from a sparse set of context views. Specifically, this paper explores whether it is possible to synthesize novel view images using an extremely sparse set of observations. In the most challenging case, this problem reduces to using input images such that every 3D point in the scene is only ob", + "bbox": [ + 496, + 810, + 895, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Equal Advising", + "bbox": [ + 76, + 862, + 174, + 875 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "Project website: https://yilundu.github.io/widebaseline/", + "bbox": [ + 78, + 876, + 468, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "4970", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "served from a single camera perspective. Towards this goal, we propose a system that uses only a single wide-baseline stereo image pair of the scene as input. This stereo image pair regularly has little overlap, such that many 3D points are indeed only observed in one of the images, see Fig. 1. Image observations themselves are thus insufficient information to compute 3D geometry and appearance via multi-view stereo, and we must instead learn prior-based 3D reconstruction. Nevertheless, reasoning about multi-view consistency is critical, as prior-based reconstructions must agree across images to ensure multi-view-consistent reconstruction.", + "bbox": [ + 75, + 90, + 472, + 257 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "This is a novel problem setting: While some existing methods demonstrate novel view synthesis from very sparse observations [45, 51, 58], they are limited to object-level scenes. In contrast, we are interested in large real-world scenes that are composed of multiple objects with complex geometry and occlusions. Previous approaches for novel view synthesis of scenes focus on small baseline renderings using $3 - 10$ images as input [7, 8, 18, 25, 47, 53, 58]. In this setting, most 3D points in the scene are observed in multiple input images, and multi-view feature correspondences can be used to regress 3D geometry and appearance. Thus, these methods in practice learn to amortize multi-view stereo. In our setting, we use a wide-baseline stereo image pair as input, where it is not sufficient to rely on multi-view feature correspondences due to many points only being observed in a single view. We show that in this challenging setting, existing approaches do not faithfully recover the 3D geometry of the scene. In addition, most existing methods rely on costly volume rendering for novel view synthesis, where the number of samples per ray required for high-quality rendering makes it difficult to train on complex real-world scenes.", + "bbox": [ + 75, + 260, + 472, + 578 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we propose a new method that addresses these limitations, and provides the first solution for high-quality novel view synthesis of a scene from a wide-baseline stereo image pair. To better reason about the 3D scene, we introduce a multi-view vision transformer that computes pixel-aligned features for each input image. In contrast to a monocular image encoder commonly used in previous approaches [51, 53, 58], the multi-view transformer uses the camera pose information as input to better reason about the scene geometry. We reduce the memory and computational costs for computing image features by combining this vision transformer at lower resolutions with a CNN at higher resolutions. A multi-view feature matching step further refines the geometry encoded in these feature maps for any 3D point that can be observed in both images.", + "bbox": [ + 75, + 580, + 472, + 808 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We also introduce an efficient differentiable renderer that enables large-scale training. Existing approaches that use volume rendering sample points along camera rays in 3D and project these points onto the image planes to compute the corresponding features using bilinear interpolation. Since perspective projection is a non-linear operation, uniformly", + "bbox": [ + 75, + 810, + 472, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "sampled 3D points are not uniformly distributed in 2D, leading to some pixels in the feature maps being sampled multiple times, and other pixels not being sampled at all. Thus, this sampling strategy does not use the information in the pixel-aligned feature maps optimally. We instead take an image-centric sampling approach where we first compute the epipolar lines of a target pixel in the input images, and sample points uniformly on these lines in 2D. This exploits the fact that the number of pixels along the epipolar lines is the maximum effective number of samples. In addition, we use lightweight cross-attention layers that directly aggregate the sampled features and compute the pixel color. In contrast to volume rendering where we need to sample very close to a surface in order to render its color, thus requiring a large number of samples, our learned renderer does not share this limitation and can compute the pixel color even with sparse samples. Our lightweight rendering and feature backbone components enable us to train on large-scale real-world datasets. We demonstrate through extensive experiments on two datasets that our method achieves state-of-the-art results, significantly outperforming existing approaches for novel view synthesis from sparse inputs.", + "bbox": [ + 496, + 90, + 895, + 425 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 436, + 640, + 452 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Image-based rendering. Image-based rendering (IBR) methods generate images from novel camera viewpoints by blending information from a set of input images. We provide a brief overview of some methods. Please refer to the review by Shum and Kang [41] for details. Some IBR approaches directly model the plenoptic function without using information about the scene geometry [20, 31]. Other approaches use a proxy scene geometry computed using multi-view stereo to guide the blending of information from the input images [3, 9, 16, 23]. While rendering without computing an explicit 3D geometry leads to higher-quality results, it requires a large number of input images. In contrast, methods that rely on 3D geometry can work with sparse image inputs. However, multi-view stereo from a sparse set of input views often leads to inaccurate geometry, especially for scenes with complex geometry, limiting the quality of rendered images. Methods have been proposed for higher-quality geometry computation [5, 15], optical flow-based refinement [4, 10, 11], and improved blending [14, 35, 38]. In contrast to these image-based rendering methods, we rely on priors learned from data that enable novel-view synthesis from just a wide-baseline stereo image. We do not create any explicit proxy geometry of the scene and are thus unaffected by inaccurate multi-view stereo.", + "bbox": [ + 496, + 463, + 895, + 828 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Single-Scene Volumetric Approaches. Recent progress in neural rendering [50] and neural fields [28, 42, 56] has led to a drastic jump in the quality of novel-view synthesis from several input images of a scene. Here, a 3D scene represent", + "bbox": [ + 496, + 840, + 895, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "4971", + "bbox": [ + 482, + 944, + 514, + 957 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "tation is optimized via differentiable rendering to fit a set of image observations. Early approaches leveraged voxel grids and learned renderers [26, 32, 44]. More recent approaches rely on neural fields [2, 27, 28, 56] to parameterize the 3D scene and volumetric rendering [26, 28, 49] for image synthesis. This leads to photorealistic view synthesis but requires hundreds of input images that densely sample the 3D scene. Hand-crafted and learned priors may reduce the number of required images to the order of three to ten [33], but 3D points still need to be observed from at least two perspectives. A major challenge of these approaches is the cost of accurate differentiable rendering, regularly requiring hundreds of samples per ray. Recent approaches have achieved impressive speed-ups in 3D reconstruction leveraging high-performance data structures and sparsity [6,13,24,29]. While promising, reconstruction can still take a few minutes per scene, and sparse data structures such as octrees and hash tables cannot easily be used with learned priors.", + "bbox": [ + 75, + 90, + 472, + 362 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our approach tackles a different setting than these methods, using only a single wide-baseline stereo image as input, where 3D points are regularly only observed in a single view. Our approach does not require any per-scene optimization at test time. Instead, it reconstructs the scene in a single forward pass. Note that while our method does not achieve the quality of per-scene optimization methods that use hundreds of input images, it demonstrates a significant step up in novel view synthesis from very sparse image observations.", + "bbox": [ + 75, + 363, + 472, + 500 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Prior-based 3D Reconstruction and View Synthesis. Instead of overfitting to a single scene, differentiable rendering can also be used to supervise prior-based inference methods. Some methods generalize image-based rendering techniques by computing feature maps on top of a proxy geometry [1, 19, 38, 55]. Volume rendering using multi-plane images has been used for small baseline novel view synthesis [46, 52, 60, 61]. Early neural fields-based approaches [34, 45] were conditioned on a single global latent code and rendered via sphere tracing. In contrast to a global latent code, several approaches use a feature backbone to compute pixel-aligned features that can be transformed using MLPs [21, 51, 58] or transformers layers [37, 53] to a radiance field. Ideas from multi-view stereo such as the construction of plane-swept cost volumes [7, 18, 25], or multi-view feature matching [8] have been used for higher-quality results.", + "bbox": [ + 75, + 507, + 472, + 750 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Alternatively to these radiance field-based approaches, some methods use a light field rendering formulation where an oriented camera ray can directly be transformed to the pixel color as a function of the features computed from the input images [43, 48]. Scene Representation Transformers [39] use transformers with global attention to compute a set-latent representation that can be decoded to pixel colors when queried with a target camera ray. However, global attention layers on high-resolution input images are very compute and memory intensive. Developed concurrently", + "bbox": [ + 75, + 750, + 472, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "with our work, Suhail et al. [47] proposed to use a transformer to only compute features for image patches along the epipolar rays of the pixel being rendered. This is still very expensive due to global attention layer computations over multiple image patches for every rendered pixel. In addition, this method ignores the context information of the scene, since all computation is performed only for patches that lie on the epipolar lines.", + "bbox": [ + 496, + 90, + 893, + 210 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "All existing prior-based reconstruction methods either only support object-level scenes or very small baseline renderings, or rely on multiple image observations where most 3D points are observed in multiple input images. This is different from our setting where we only use a wide-baseline stereo image pair of scenes as input.", + "bbox": [ + 496, + 212, + 893, + 303 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 498, + 315, + 591, + 330 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our goal is to render novel views of a 3D scene given a wide-baseline stereo image pair $\\mathcal{I}_1$ and $\\mathcal{I}_2$ . We assume known camera intrinsic $\\mathbf{K}_i \\in \\mathbb{R}^{3 \\times 3}$ and extrinsic $\\mathbf{E}_i \\in \\mathbb{R}^{4 \\times 3}$ expressed relative to context camera 1. We use a multiview encoder to compute pixel-aligned features, and a cross-attention-based renderer to transform the features into novel view renderings, see Figure 2 for an overview.", + "bbox": [ + 496, + 340, + 893, + 446 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Multiview Feature Encoding", + "text_level": 1, + "bbox": [ + 498, + 454, + 756, + 470 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "An essential part of novel view synthesis given context images is an accurate reconstruction of scene geometry. Our method implicitly reconstructs 3D geometry and appearance of the scene in the form of pixel-aligned feature maps for each stereo image. In prior work, pixel-aligned features are obtained by separately encoding each image via a vision transformer or CNN [21, 58]. However, in our early experiments, we found this led to artifacts in renderings observing boundary regions between context images. We hypothesize that separate encoding of images leads to inconsistent geometry reconstruction across context images. We thus introduce our multi-view encoder, which obtains pixel-aligned features by jointly processing the images and the relative pose between them. Encoding the pose information has also been shown to act as an effective inductive bias for 3D tasks [57].", + "bbox": [ + 496, + 478, + 893, + 703 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We now describe this architecture in detail, which extends the dense vision transformer proposed by Ranftl et al. [36]. Please see Figure 2 for an overview. From each stereo image, we first independently extract convolutional features via a ResNet50 CNN. We then flatten both images, obtaining $2 \\times 16 \\times 16$ features in total. To each feature, we add (1) a learned per-pixel positional embedding encoding its pixel coordinate and (2) a camera pose embedding, obtained via a linear transform of the relative camera pose between context images 1 and 2. These tokens are processed by a vision transformer, which critically performs self-attention across all tokens across both images. In-between self-attention layers, per-image features are re-assembled into a spatial grid,", + "bbox": [ + 496, + 704, + 895, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "4972", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/a1a5fd49499271751542088f95be908850a03fa70f46a3f16c4456667709215d.jpg", + "image_caption": [ + "(a) Multi-View ViT Encoder" + ], + "image_footnote": [], + "bbox": [ + 76, + 103, + 359, + 308 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/a72db9d24243f83ad08a03e084f0a6c9a221f14e6bff8dee7b0ff309878d7934.jpg", + "image_caption": [ + "(b) Image-Centric Epipolar Line Sampling", + "(c) Neural Rendering" + ], + "image_footnote": [], + "bbox": [ + 346, + 103, + 635, + 309 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/fde0ba6b988906a17251616c1a4a0c60c6835f3a250aafe29eb65c7b3c205a02.jpg", + "image_caption": [ + "Figure 2. Method Overview. (a) Given context images from different viewpoints, a multi-view encoder extracts pixel-aligned features, leveraging attention across the images and their corresponding camera pose embeddings. (b) Given a target ray, in each context view, we sample primary features along the epipolar line equidistant in pixel space. We then project the corresponding 3D points onto the other views and sample corresponding secondary epipolar line features, where out-of-bounds features are set to zero. (c) We render the target ray by performing cross-attention over the set of all primary and secondary epipolar line features from all views." + ], + "image_footnote": [], + "bbox": [ + 635, + 104, + 893, + 309 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "up-sampled, and processed by a fusion CNN [36] to yield per-image spatial feature map. Directly using these spatial feature maps for novel view synthesis leads to blurry reconstructions, due to the loss of high-frequency texture information. We thus concatenate these features with high-resolution image features obtained from a shallow CNN.", + "bbox": [ + 75, + 402, + 470, + 494 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Epipolar Line Sampling and Feature Matching", + "text_level": 1, + "bbox": [ + 76, + 501, + 470, + 518 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We aim to render an image of the scene encoded in the two pixel-aligned feature maps from a novel camera viewpoint. A common way to achieve this is volume rendering, where we cast a camera ray, compute density and color values at many depths along the ray, and integrate them to compute the color of the pixel. Sampling locations are determined in 3D. Coarse samples are either uniformly spaced in euclidean space or spaced with uniform disparity, and fine samples are distributed closer to the surface as computed by the coarse samples [2, 28, 30]. However, in our regime of generalizable novel view synthesis with pixel-aligned feature maps, this sampling scheme is suboptimal. In this case, sampling along the ray should be determined by the resolution of the context images: the number of pixels along the epipolar line is the maximum effective number of samples available for any method. More samples would not provide any extra information. We propose a sampling strategy to exploit this and demonstrate its effectiveness in an ablation study.", + "bbox": [ + 73, + 525, + 470, + 796 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Consider a pixel coordinate $\\mathbf{u}_t = (u, v)$ in the target image $\\mathcal{I}_t$ , with assumed known intrinsic $\\mathbf{K}_t$ and extrinsic $\\mathbf{T}_t = \\left[ \\begin{array}{cc} \\mathbf{R}_t & t \\\\ \\mathbf{0} & 1 \\end{array} \\right]$ camera parameters relative to the context camera $\\mathcal{I}_1$ . Its epipolar lines $\\mathbf{l}_{\\{1,2\\}}$ , in context cameras 1 and 2 are given as:", + "bbox": [ + 75, + 797, + 470, + 873 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {l} _ {i} = \\mathbf {F} _ {i} [ u, v, 1 ] ^ {T} = \\mathbf {K} _ {i} ^ {- T} ([ \\mathbf {t} _ {t} ] _ {\\times} \\mathbf {R} _ {t}) \\mathbf {K} _ {t} ^ {- 1} [ u, v, 1 ] ^ {T} (1)\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 877, + 470, + 898 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "via the fundamental matrix $\\mathbf{F}_i$ . We now uniformly sample $N$ pixel coordinates along the line segment of the epipolar line within the image boundaries. To enable the renderer to reason about whether to use a certain pixel-aligned feature or not, a critical piece of information is the depth in the context coordinate frame at which we are sampling this feature. This depth value can be computed via triangulation, using a closed-form expression. Please refer to the supplemental document for details. We now obtain $N$ tuples $\\{(d,\\mathbf{f})_k\\}_{k=1}^N$ of depth $d$ and image feature $\\mathbf{f}$ per context image for a total of $2N$ samples which we call primary samples.", + "bbox": [ + 496, + 402, + 893, + 571 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We further propose a feature matching module to refine the geometry encoded in the primary epipolar line samples via correspondence matching. Consider a primary epipolar line sample obtained from context image $i$ , a tuple $(d, \\mathbf{f})$ corresponding to a pixel coordinate $\\mathbf{u}_t$ . We propose to augment this sample by a corresponding feature in the other context image. Specifically, we first solve for the corresponding 3D point, and then project this 3D point onto the other context image to retrieve a corresponding feature $\\hat{\\mathbf{f}}$ , which we refer to as a secondary feature. The secondary features are set to zero if the projected point is out of the image bounds. Intuitively, primary and secondary features together allow a final stage of geometry refinement for 3D points that are observed in both images: if the features agree, this sample likely encodes a surface. If the projected point on the other image lies outside the image boundary, we simply set the secondary features to zeros. We obtain the input to the renderer as the final set of features by concatenating each primary epipolar line feature with its corresponding secondary feature in the other context view, yielding a set $\\{(d, \\mathbf{f}, \\hat{\\mathbf{f}})_k\\}_{k=1}^{2N}$ . In practice, we sample $N = 64$ points on the epipolar lines", + "bbox": [ + 496, + 583, + 895, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "4973", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "for both images, leading to a total of $2N = 128$ tuples.", + "bbox": [ + 76, + 90, + 439, + 107 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Differentiable Rendering via Cross-Attention", + "text_level": 1, + "bbox": [ + 76, + 114, + 460, + 131 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To render the target ray, it remains to map the set of epipolar line samples $\\{(d,\\mathbf{f},\\hat{\\mathbf{f}})_k\\}_{k = 1}^{2N}$ to a color value. As this operation has to be executed once per ray, a key consideration in the design of this function is computational cost. We propose to perform rendering via a lightweight cross-attention decoder.", + "bbox": [ + 75, + 138, + 468, + 227 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For each point on the epipolar line, we embed the target ray origin $\\mathbf{o}_t$ , target ray direction $\\mathbf{r}_t$ , depth with respect to the target ray origin $d_t$ , and context camera ray direction $\\mathbf{r}_c$ for the epipolar point into a ray query token $\\mathbf{q}$ via a shallow MLP as $\\Phi([\\mathbf{o}_t, \\mathbf{r}_t, \\mathbf{r}_c, d_t])$ . The $2N$ ray feature values are independently transformed into key and value tokens using a 2-layer MLP. Our renderer now performs two rounds of cross-attention over this set of features to obtain a final feature embedding, which is then decoded into color via a small MLP.", + "bbox": [ + 75, + 228, + 470, + 378 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The expectation of the Softmax distribution over the sampled features gives a rough idea of the scene depth as $e = \\sum_{k} d_{k} \\alpha_{k}$ , where $d_{k}$ denotes the depth of the $k$ -th epipolar ray sample along the target ray and $\\alpha_{k}$ is the corresponding Softmax weight as computed by the cross-attention operator. Note that $e$ is not the actual depth but a measure of which epipolar samples the renderer uses to compute the pixel color. Unlike volume rendering, where we need to sample very close to a surface to render its color, our light field-based renderer can reason about the surface without exactly sampling on it. The learned cross-attention layers can use the target camera ray information, along with a sparse set of epipolar samples, to compute the pixel color. Thus, our method does not require explicit computation of accurate scene depth for rendering.", + "bbox": [ + 75, + 378, + 470, + 606 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Training and Losses", + "text_level": 1, + "bbox": [ + 76, + 614, + 267, + 630 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We now have a rendered image from a novel camera viewpoint. Our loss function consists of two terms:", + "bbox": [ + 76, + 637, + 468, + 667 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {\\mathrm {i m g}} + \\lambda_ {\\mathrm {r e g}} \\mathcal {L} _ {\\mathrm {r e g}}. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 199, + 676, + 468, + 695 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The first term evaluates the difference between the rendered image from a novel camera viewpoint, $R$ and the ground truth, $G$ as:", + "bbox": [ + 76, + 703, + 468, + 748 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {i m g}} = \\left\\| R - G \\right\\| _ {1} + \\lambda_ {\\mathrm {L P I P S}} \\mathcal {L} _ {\\mathrm {L P I P S}} (R, G), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 132, + 758, + 468, + 776 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{\\mathrm{LPIPS}}$ is the LPIPS perceptual loss [59]. In practice, we render square patches with a length of 32 pixels and evaluate these image losses at the patch level.", + "bbox": [ + 75, + 784, + 468, + 829 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We also use a regularization loss on the cross-attention weights of the renderer for better multi-view consistency:", + "bbox": [ + 76, + 830, + 468, + 859 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {r e g}} = \\sum_ {(u, v)} \\sum_ {\\left(u ^ {\\prime} v ^ {\\prime}\\right) \\in \\mathcal {N} (u, v)} \\left(\\left(e (u, v) - e \\left(u ^ {\\prime}, v ^ {\\prime}\\right)\\right) ^ {2} . \\right. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 109, + 869, + 468, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Here, $e(u,v)$ denotes the expected value of the depth of the epipolar samples at pixel $(u,v)$ , and $\\mathcal{N}()$ defines the neighborhood around a pixel.", + "bbox": [ + 496, + 90, + 890, + 136 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For better generalization, we further perform several geometrically-consistent data augmentations during the training procedure. We center crop and scale the input and target images, which leads to transformation in the intrinsic of the camera. We also flip the images which leads to transformation of the extrinsics.", + "bbox": [ + 496, + 137, + 893, + 227 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 498, + 242, + 633, + 260 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We quantitatively and qualitatively show that our approach can effectively render novel views from wide-baseline stereo pairs. We describe our underlying experimental setup in Section 4.1. Next, we evaluate our approach on challenging indoor scenes with substantial occlusions in Section 4.2. We further evaluate on outdoor scenes in Section 4.3. We analyze and ablate the underlying components in Section 4.4. Finally, we illustrate how our approach can render novel views of unposed images of scenes captured in the wild in Section 4.5.", + "bbox": [ + 496, + 268, + 893, + 419 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 498, + 431, + 689, + 446 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. We train and evaluate our approach on RealEstate10k [61], a large dataset of indoor and outdoor scenes, and ACID [22], a large dataset of outdoor scenes. We use 67477 scenes for training and 7289 scenes for testing for RealEstate10k, and 11075 scenes for training and 1972 scenes for testing for ACID, following default splits. We train our method on images at $256 \\times 256$ resolution and evaluate methods on their ability to reconstruct intermediate views in test scenes (details in the supplement).", + "bbox": [ + 496, + 455, + 893, + 590 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Baselines. We compare to several existing approaches for novel view synthesis from sparse image observations. We compare to pixelNeRF [58] and IBRNet [53] that use pixel-aligned features, which are decoded into 3D volumes rendered using volumetric rendering. We also compare to Generalizable Patch-based Rendering (GPNR) [47], which uses a vision transformer-based backbone to compute epipolar features, and a light field-based renderer to compute pixel colors. These baselines cover a wide range of design choices used in existing methods, such as pixel-aligned feature maps computed using CNNs [53, 58] and transformers [47], volumetric rendering by decoding features using MLPs [58] and transformers [53], and light field-based rendering [47]. We use publicly available codebases for all baselines and train them on the same datasets we use for fair evaluations. Please refer to the supplemental for comparisons to more baselines.", + "bbox": [ + 496, + 602, + 893, + 844 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation Metrics. We use LPIPS [59], PSNR, SSIM [54], and MSE metrics to compare the image quality of rendered images with the ground truth.", + "bbox": [ + 496, + 854, + 893, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "4974", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/6371e3f4bca11f700b52ee3f6d20be9065952e43fb7e46de40e667a17b55e988.jpg", + "image_caption": [ + "Figure 3. Comparative Rendering Results on RealEstate10k. Our approach can render novel views of indoor scenes with substantial occlusions with high fidelity using a wide-baseline input image pair, outperforming all baselines. Note that many points of the 3D scene are only observed in a single image in such inputs. Our method can correctly reason about the 3D structures from such sparse views." + ], + "image_footnote": [], + "bbox": [ + 91, + 93, + 880, + 369 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/49a25949360998c0f07f49215acf57c74d3c35008fb265098897928e34cc4598.jpg", + "image_caption": [ + "Input Images" + ], + "image_footnote": [], + "bbox": [ + 91, + 470, + 235, + 527 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/21cb3a05ce7146e045124fe8a6f77ea945682722349153419edfa65b08c3867f.jpg", + "image_caption": [ + "Rendered Novel Views" + ], + "image_footnote": [], + "bbox": [ + 241, + 470, + 455, + 527 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/7c1a38aac5c8d14d34b55b5ff15339dea9581db951fa18d568c5ccbd1b6ffd8c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 91, + 535, + 235, + 585 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/b9b70f2b0abd4c780345dfb182420516a4b46aca7db015474419608d430c39bf.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 241, + 535, + 455, + 585 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/e4c10e1a348f79a1bf502158ea250a4790884c613af7d18af35e5c701e8d164d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 91, + 588, + 235, + 645 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/ef5d548a5ee09b3d083fd6989fe50f0aa8f0c376e81ce3a951d9c3fa74c9e2cc.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 240, + 588, + 455, + 645 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/12c79c3ef8ed8b195a039d298ebd00ff61f91169473370553cd90ab826edf0cf.jpg", + "image_caption": [ + "Figure 4. Novel view renderings of our approach given a large baseline stereo pair. Our approach can synthesize intermediate views that are substantially different from input images, even with very limited overlap between images." + ], + "image_footnote": [], + "bbox": [ + 91, + 647, + 235, + 703 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/d194d9e77f70e031c15abb6268450361e8caa5b4a4333ae9e358e3ad38d517a3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 240, + 647, + 455, + 703 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Indoor Scene Neural Rendering", + "text_level": 1, + "bbox": [ + 76, + 787, + 357, + 804 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We first evaluate the ability of our approach and baselines to render novel views in complex indoor environments with substantial occlusions between objects.", + "bbox": [ + 75, + 811, + 468, + 858 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Qualitative Results. In Figure 3, we provide qualitative results of novel view renderings of our approach, compared", + "bbox": [ + 75, + 869, + 468, + 902 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/ef05ad5046aaebca66ce6d021f6cf6e6b6bd37ca4f1b6ba29e184a43bf5dadc4.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodLPIPS ↓SSIM ↑PSNR ↑MSE ↓
pixelNeRF [58]0.5910.46013.910.0440
IBRNet [53]0.5320.48415.990.0280
GPNR [47]0.4590.74818.550.0165
Ours0.2620.83921.380.0110
", + "bbox": [ + 511, + 455, + 879, + 537 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/eadb5e292cde490ce809d4ae642b3b02f8c400a909821846f8826c35dcb343fc.jpg", + "table_caption": [ + "Table 1. Novel view rendering performance on RealEstate10K. Our method outperforms all baselines on all metrics." + ], + "table_footnote": [], + "table_body": "
MethodLPIPS ↓SSIM ↑PSNR ↑MSE ↓
pixelNeRF [58]0.6280.46416.480.0275
IBRNet [53]0.3850.51319.240.0167
GPNR [47]0.5580.71917.570.0218
Ours0.3640.78123.630.0074
", + "bbox": [ + 511, + 599, + 877, + 681 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Novel view rendering performance on ACID. Our method outperforms all baselines on all metrics.", + "bbox": [ + 500, + 686, + 890, + 715 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "to each of our baselines. We provide additional novel view results of our method in Figure 4. Compared to the baselines, our approach reconstructs the 3D structure of the scene better, and also captures more high-frequency details.", + "bbox": [ + 498, + 737, + 893, + 797 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Quantitative Results. We quantitatively evaluate our approach and baselines in Table 1. We find that our approach substantially outperforms each compared baseline in terms of all of our metrics.", + "bbox": [ + 498, + 840, + 893, + 898 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "4975", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/42f14e2deba0b5e61167025577668655bcb477141e05160d58022eed9373b72a.jpg", + "image_caption": [ + "Input Images" + ], + "image_footnote": [], + "bbox": [ + 119, + 103, + 320, + 263 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/abddbbb816f119ea8bd7e1149a20aef19c0c06a8163f238040c7c9ff298b711a.jpg", + "image_caption": [ + "PixelNeRF" + ], + "image_footnote": [], + "bbox": [ + 326, + 103, + 428, + 263 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/01cd0ef4ed995a7c1071a479cbb0096b6041ea83b023b166cfba1c9315434a03.jpg", + "image_caption": [ + "IBRNet", + "Figure 5. Comparative Results on ACID. Our approach is able to render novels views with higher quality than all baselines." + ], + "image_footnote": [], + "bbox": [ + 431, + 103, + 532, + 263 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9fa88af3cb717bd1266cec53b31d29cbbf696ae52aba0f6918d6bfa4f7fdb350.jpg", + "image_caption": [ + "GPNR" + ], + "image_footnote": [], + "bbox": [ + 535, + 103, + 637, + 263 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/6cb6c68fbf8e48040560716476eff22a51045703b5675dc26f0e08152047c3c1.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 640, + 103, + 741, + 263 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/133bb792f2ca8c67017a0894c47318c4ebb45acbbd83740667f036a74b4ed448.jpg", + "image_caption": [ + "Target" + ], + "image_footnote": [], + "bbox": [ + 745, + 103, + 846, + 263 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Outdoor Scene Neural Rendering", + "text_level": 1, + "bbox": [ + 76, + 305, + 370, + 321 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We further evaluate on outdoor scenes with potentially unbounded depth.", + "bbox": [ + 76, + 329, + 468, + 359 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Qualitative Results. We illustrate qualitative results in Figure 5. In comparison to the baselines, our approach is able to more accurately reconstruct the geometry, and is able to synthesize multi-view consistent renderings from two large baseline views.", + "bbox": [ + 75, + 369, + 468, + 445 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Quantitative Results. Similar to indoor scenes, our approach also outperforms all baselines in terms of all metrics on outdoor scenes, see Table 2.", + "bbox": [ + 75, + 455, + 470, + 501 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablations and Analysis", + "text_level": 1, + "bbox": [ + 76, + 511, + 290, + 527 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We next analyze and ablate individual components of our approach. We use the RealEstate10k dataset for these experiments.", + "bbox": [ + 75, + 535, + 468, + 579 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablations. We evaluate the importance of different components of our method in Table 3. The \"Base Model\" corresponds to a vanilla architecture that does not include some of our proposed contributions. It samples points uniformly in 3D, instead of our proposed 2D epipolar line sampling. It uses a monocular encoder instead of our proposed multiview encoder, and does not use correspondence matching across views for refining the geometry. It also does not use the regularization loss for multi-view consistency or any data augmentation during training. We find that all components of our approach are essential for high-quality performance. The results in Table 3 show that sampling in 3D sub-optimally uses the information in the feature maps, that our multi-view encoder and cross-image correspondence matching can compute features that better encode the 3D scene structure compared to monocular encoders, and that data augmentation helps with generalization. While we found that the incorporation of the regularization loss led to a slight decrease in PSNR, we found that it improved multi-view consistency in the rendered video results, and also improved both LPIPS and SSIM perceptual metrics.", + "bbox": [ + 75, + 590, + 472, + 909 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/a72a9309cad5533f30273651d6ef8d699e3fe4e28be0acd37b7bca6c5e1c0091.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ModelsLPIPS↓SSIM↑PSNR↑MSE↓
Base Model0.4520.73518.110.0201
+ 2D Sampling0.4280.76219.020.0159
+ Cross Correspondence0.4150.76619.520.0142
+ Multiview Encoder0.3610.79420.430.0132
+ Regularization Loss0.3580.80819.840.0139
+ Data Aug0.2620.83921.380.0110
", + "bbox": [ + 501, + 303, + 888, + 405 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Ablations. All components of our proposed method are essential for high-quality novel view synthesis.", + "bbox": [ + 498, + 410, + 890, + 439 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/488b91f085d526d6298d15870de45d7520469f89499d6c01d4c1fa5ee2e40296.jpg", + "image_caption": [ + "Figure 6. FPS vs PSNR. Our approach strikes the best trade-off between rendering quality and rendering speed. We can further reduce the number of Epipolar samples (\"Ours (Faster)\"), which makes our method faster than all baselines, while still significantly outperforming them in terms of rendering quality." + ], + "image_footnote": [], + "bbox": [ + 537, + 446, + 848, + 621 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Speed. Next, in Figure 6, we study the relationship between rendering quality and rendering speed for all approaches. Our lightweight approach achieves the best tradeoff, significantly outperforming all methods in terms of rendering quality, while being at-par with the most efficient baseline. By reducing the number of sampled epipolar points from 64 to 48 samples per image, we can further speed up our approach, outperforming all baselines both in terms of rendering speed and image quality.", + "bbox": [ + 496, + 705, + 893, + 843 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Epipolar Attention. Finally, we visualize the underlying epipolar attention weights learned by our approach in Figure 7. The expected value of the depths of the epipolar sam", + "bbox": [ + 496, + 854, + 893, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "4976", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4c27e839dfd7bf75c2caa2601fbe1f21cefac1b7d9fc8eef4ec0b731fb85e6e7.jpg", + "image_caption": [ + "Figure 7. Visualization of Epipolar Attention Weights. The expected value of the depths of the epipolar samples under the attention weights can be seen as a depth proxy. As our renderer is not a volume renderer, these attention weights need not exactly correspond to the actual depth for correct renderings." + ], + "image_footnote": [], + "bbox": [ + 107, + 88, + 439, + 276 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "plies can be seen as a proxy depth and corresponds roughly to the underlying geometry of the scene. This enables us to analyze the learned computation of our renderer.", + "bbox": [ + 75, + 361, + 468, + 407 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.5. Novel View Synthesis from Unposed Images", + "text_level": 1, + "bbox": [ + 76, + 415, + 449, + 431 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Our method uses a wide-baseline stereo image as input with known relative pose between them. We show that our method can perform novel view synthesis even without the knowledge of this relative pose information. In this case, we utilize SuperGlue [40] to compute reliable pixel correspondences between the input images. Since we do not know the camera intrinsics for in-the-wild images, we use the average intrinsics of the RealEstate10k dataset and compute the Essential matrix from the correspondences using RANSAC [12]. We then compute the pose information from the essential matrix [17] and use it as input for our method. Note that the recovered translation is only defined up to a scale. Figure 8 demonstrates results on some in-the-wild scenes using images from the internet. Even in this unposed setting, our method can reason about the geometry of the scene by aggregating information across the sparse input views. This is an extremely challenging setting, and existing approaches for novel view synthesis from sparse views do not demonstrate any results on unposed images.", + "bbox": [ + 75, + 439, + 472, + 727 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Discussion", + "text_level": 1, + "bbox": [ + 76, + 739, + 191, + 753 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "While we have presented the first approach for novel view synthesis of scenes from very sparse input views, our approach still has several limitations. Our rendering results are not at the same quality as those obtained by methods that optimize on single scenes using more images. Learning priors that enable novel view synthesis from sparse views is a significantly more challenging problem compared to using a large number of input images, where 3D points are regularly observed in many images. Our approach takes a step towards", + "bbox": [ + 75, + 763, + 468, + 902 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4a1ef1bdf4da55787f197b8a2eef23d7a95a7c2c9949e04f3997aa9e73cd8871.jpg", + "image_caption": [ + "Input Images", + "Figure 8. Novel View Synthesis from Unposed Images. Our approach can also render novel views using two unposed images captured in the wild. Note that parts of the scene only visible in one of the images can be correctly rendered from novel viewpoints." + ], + "image_footnote": [], + "bbox": [ + 509, + 101, + 656, + 277 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c9111acf8bf164ac992915c251c5db57dd0dd77f076ecc45d15459e14efd5608.jpg", + "image_caption": [ + "Rendered Novel Views" + ], + "image_footnote": [], + "bbox": [ + 663, + 101, + 882, + 277 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "photorealistic renderings of scenes using only sparse views. As our approach relies on learned priors, it does not generalize well to new scenes with very different appearances compared to the training scenes. However, our efficient approach lends itself to large-scale training on diverse datasets, in turn enabling reconstruction of diverse scenes. Finally, while our method, in theory, can be extended to take more than two input views, we have only experimented with two views as a first step towards very sparse multi-view neural rendering.", + "bbox": [ + 496, + 363, + 893, + 513 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 544, + 617, + 559 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We introduce a method for implicit 3D reconstruction and novel view synthesis from a single, wide-baseline stereo pair, trained using only self-supervision from posed color images. By leveraging a multi-view encoder, an image-space epipolar line feature sampling scheme, and a cross-attention based renderer, our method surpasses the quality of prior art on datasets of challenging scenes. Our method further strikes a compelling trade-off between rendering speed and quality, rendering novel views significantly faster than most prior methods. Meanwhile, leveraging epipolar line geometry strikes a compelling trade-off between structured and generalist learning paradigms, enabling us to train our method on real-world datasets such as RealEstate10k. We believe that this work will inspire the community towards further exploring the regime of extreme few-shot and generalizable novel view synthesis.", + "bbox": [ + 496, + 574, + 893, + 816 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements. This work was supported by the National Science Foundation under Grant No. 2211259, and by the Singapore DSTA under DST00OECI20300823 (New Representations for Vision). Yilun Du is supported by a NSF Graduate Research Fellowship.", + "bbox": [ + 496, + 825, + 893, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "4977", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graphics. In European Conference on Computer Vision, pages 696-712. Springer, 2020. 3", + "[2] Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields, 2021. 3, 4", + "[3] Chris Buehler, Michael Bosse, Leonard McMillan, Steven Gortler, and Michael Cohen. Unstructured lumigraph rendering. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 425-432, 2001. 2", + "[4] Dan Casas, Christian Richardt, John Collomosse, Christian Theobalt, and Adrian Hilton. 4d model flow: Precomputed appearance alignment for real-time 4d video interpolation. In Computer Graphics Forum, volume 34, pages 173-182. Wiley Online Library, 2015. 2", + "[5] Gaurav Chaurasia, Sylvain Duchene, Olga Sorkine-Hornung, and George Drettakis. Depth synthesis and local warps for plausible image-based navigation. ACM Transactions on Graphics (TOG), 32(3):1-12, 2013. 2", + "[6] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. arXiv preprint arXiv:2203.09517, 2022. 3", + "[7] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14124-14133, 2021. 2, 3", + "[8] Julian Chibane, Aayush Bansal, Verica Lazova, and Gerard Pons-Moll. Stereo radiance fields (srf): Learning view synthesis from sparse views of novel scenes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, jun 2021. 2, 3", + "[9] Paul E Debevec, Camillo J Taylor, and Jitendra Malik. Modeling and rendering architecture from photographs: A hybrid geometry-and image-based approach. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 11-20, 1996. 2", + "[10] Ruofei Du, Ming Chuang, Wayne Chang, Hugues Hoppe, and Amitabh Varshney. Montage4d: Interactive seamless fusion of multiview video textures. 2018. 2", + "[11] Martin Eisemann, Bert De Decker, Marcus Magnor, Philippe Bekaert, Edilson De Aguiar, Naveed Ahmed, Christian Theobalt, and Anita Sellent. Floating textures. In Computer graphics forum, volume 27, pages 409-418. Wiley Online Library, 2008. 2", + "[12] Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 8", + "[13] Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In Proceedings of" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5501-5510, 2022. 3", + "[14] Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Brostow. Deep blending for free-viewpoint image-based rendering. ACM Transactions on Graphics (TOG), 37(6):1-15, 2018. 2", + "[15] Peter Hedman, Tobias Ritschel, George Drettakis, and Gabriel Brostow. Scalable inside-out image-based rendering. ACM Transactions on Graphics (TOG), 35(6):1-11, 2016. 2", + "[16] Benno Heigl, Reinhard Koch, Marc Pollefeys, Joachim Denzler, and L Van Gool. Plenoptic modeling and rendering from image sequences taken by a hand-held camera. In Mustererkennung 1999, pages 94-101. Springer, 1999. 2", + "[17] Berthold KP Horn. Recovering baseline and orientation from essential matrix. J. Opt. Soc. Am, 110, 1990. 8", + "[18] Mohammad Mahdi Johari, Yann Lepoittevin, and François Fleuret. Geonerf: Generalizing nerf with geometry priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18365-18375, 2022. 2, 3", + "[19] Georgios Kopanas, Julien Philip, Thomas Leimkuhler, and George Drettakis. Point-based neural rendering with perview optimization. In Computer Graphics Forum, volume 40, pages 29-43. Wiley Online Library, 2021. 3", + "[20] Marc Levoy and Pat Hanrahan. Light field rendering. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 31-42, 1996. 2", + "[21] Kai-En Lin, Lin Yen-Chen, Wei-Sheng Lai, Tsung-Yi Lin, Yi-Chang Shih, and Ravi Ramamoorthi. Vision transformer for nef-based view synthesis from a single input image. arXiv preprint arXiv:2207.05736, 2022. 3", + "[22] Andrew Liu, Richard Tucker, Varun Jampani, Ameesh Makadia, Noah Snavely, and Angjoo Kanazawa. Infinite nature: Perpetual view generation of natural scenes from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2021. 5", + "[23] Feng Liu, Michael Gleicher, Hailin Jin, and Aseem Agarwala. Content-preserving warps for 3d video stabilization. ACM Transactions on Graphics (ToG), 28(3):1-9, 2009. 2", + "[24] Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. NeurIPS, 2020. 3", + "[25] Yuan Liu, Sida Peng, Lingjie Liu, Qianqian Wang, Peng Wang, Christian Theobalt, Xiaowei Zhou, and Wenping Wang. Neural rays for occlusion-aware image-based rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7824-7833, 2022. 2, 3", + "[26] Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural volumes: Learning dynamic renderable volumes from images. ACM Trans. Graph., 2019. 1, 3", + "[27] Ricardo Martin-Brualla, Noha Radwan, Mehdi SM Sajjadi, Jonathan T Barron, Alexey Dosovitskiy, and Daniel Duckworth. Nerf in the wild: Neural radiance fields for unconstrained photo collections. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7210-7219, 2021. 3" + ], + "bbox": [ + 503, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "4978", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[28] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. In Proceedings of the European Conference on Computer Vision (ECCV), pages 405-421, 2020. 1, 2, 3, 4", + "[29] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-102:15, July 2022. 3", + "[30] Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Joerg H. Mueller, Chakravarty R. Alla Chaitanya, Anton S. Kaplanyan, and Markus Steinberger. DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks. Computer Graphics Forum, 2021. 4", + "[31] Ren Ng, Marc Levoy, Mathieu Brédif, Gene Duval, Mark Horowitz, and Pat Hanrahan. Light field photography with a hand-held plenoptic camera. PhD thesis, Stanford University, 2005. 2", + "[32] Thu H Nguyen-Phuoc, Chuan Li, Stephen Balaban, and Yongliang Yang. Rendernet: A deep convolutional network for differentiable rendering from 3d shapes. Advances in neural information processing systems, 31, 2018. 3", + "[33] Michael Niemeyer, Jonathan T Barron, Ben Mildenhall, Mehdi SM Sajjadi, Andreas Geiger, and Noha Radwan. Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5480-5490, 2022. 3", + "[34] Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3504-3515, 2020. 3", + "[35] Eric Penner and Li Zhang. Soft 3d reconstruction for view synthesis. ACM Transactions on Graphics (TOG), 36(6):1-11, 2017. 2", + "[36] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12179-12188, 2021. 3, 4", + "[37] Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10901-10911, 2021. 3", + "[38] Gernot Riegler and Vladlen Koltun. Free view synthesis. In European Conference on Computer Vision, 2020. 2, 3", + "[39] Mehdi SM Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, et al. Scene representation transformer: Geometry-free novel view synthesis through set-latent scene representations. arXiv preprint arXiv:2111.13152, 2021. 3", + "[40] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In Proceedings of the" + ], + "bbox": [ + 78, + 90, + 470, + 901 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "IEEE/CVF conference on computer vision and pattern recognition, pages 4938-4947, 2020. 8", + "[41] Harry Shum and Sing Bing Kang. Review of image-based rendering techniques. In Visual Communications and Image Processing 2000, volume 4067, pages 2-13. SPIE, 2000. 2", + "[42] Vincent Sitzmann, Julien N.P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. In Proc. NeurIPS, 2020. 2", + "[43] Vincent Sitzmann, Semon Rezchikov, Bill Freeman, Josh Tenenbaum, and Fredo Durand. Light field networks: Neural scene representations with single-evaluation rendering. In Advances in Neural Information Processing Systems (NeurIPS), 2021. 3", + "[44] Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhöfer. Deepvoxels: Learning persistent 3d feature embeddings. In Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 2019. 1, 3", + "[45] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3d-structure-aware neural scene representations. Advances in Neural Information Processing Systems, 32, 2019. 1, 2, 3", + "[46] Pratul P Srinivasan, Richard Tucker, Jonathan T Barron, Ravi Ramamoorthi, Ren Ng, and Noah Snavely. Pushing the boundaries of view extrapolation with multiplane images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 175-184, 2019. 3", + "[47] Mohammed Suhail, Carlos Esteves, Leonid Sigal, and Ameesh Makadia. Generalizable patch-based neural rendering. In European Conference on Computer Vision. Springer, 2022. 2, 3, 5, 6", + "[48] Mohammed Suhail, Carlos Esteves, Leonid Sigal, and Ameesh Makadia. Light field neural rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8269-8279, 2022. 3", + "[49] Andrea Tagliasacchi and Ben Mildenhall. Volume rendering digest (for nef). arXiv preprint arXiv:2209.02417, 2022. 3", + "[50] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, W Yifan, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, et al. Advances in neural rendering. In Computer Graphics Forum, pages 703-735. Wiley Online Library, 2022. 1, 2", + "[51] Alex Trevithick and Bo Yang. Grf: Learning a general radiance field for 3d representation and rendering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15182-15192, 2021. 2, 3", + "[52] Richard Tucker and Noah Snavely. Single-view view synthesis with multiplane images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 551-560, 2020. 3", + "[53] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo MartinBrualla, Noah Snavely, and Thomas Funkhouser. Ibrnet: Learning multi-view image-based rendering. In CVPR, 2021. 2, 3, 5, 6", + "[54] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: From error visibility to" + ], + "bbox": [ + 501, + 92, + 893, + 901 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "4979", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "structural similarity. IEEE Transactions on Image Processing, 2004. 5", + "[55] Olivia Wiles, Georgia Gkioxari, Richard Szeliski, and Justin Johnson. Synsin: End-to-end view synthesis from a single image. In Proc. CVPR, pages 7467-7477, 2020. 3", + "[56] Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. Neural fields in visual computing and beyond. Computer Graphics Forum, 2022. 2, 3", + "[57] Wang Yifan, Carl Doersch, Relja Arandjelović, João Carreira, and Andrew Zisserman. Input-level inductive biases for 3d reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6176-6186, 2022. 3", + "[58] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelNeRF: Neural radiance fields from one or few images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 2, 3, 5, 6", + "[59] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep networks as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 5", + "[60] Yunzhi Zhang and Jiajun Wu. Video extrapolation in space and time. arXiv e-prints, pages arXiv-2205, 2022. 3", + "[61] Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. ACM Trans. Graph. (Proc. SIGGRAPH), 37, 2018. 3, 5" + ], + "bbox": [ + 78, + 92, + 470, + 516 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "4980", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Learning To Render Novel Views From Wide-Baseline Stereo Pairs/81b4fe5b-6c46-4bd1-8b70-87cb209929c4_model.json b/2023/Learning To Render Novel Views From Wide-Baseline Stereo Pairs/81b4fe5b-6c46-4bd1-8b70-87cb209929c4_model.json new file mode 100644 index 0000000000000000000000000000000000000000..d119eaa511924aa4c9f0fe01fb89767d2803353e --- /dev/null +++ b/2023/Learning To Render Novel Views From Wide-Baseline Stereo Pairs/81b4fe5b-6c46-4bd1-8b70-87cb209929c4_model.json @@ -0,0 +1,2466 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.155, + 0.131, + 0.816, + 0.152 + ], + "angle": 0, + "content": "Learning to Render Novel Views from Wide-Baseline Stereo Pairs" + }, + { + "type": "text", + "bbox": [ + 0.195, + 0.181, + 0.273, + 0.198 + ], + "angle": 0, + "content": "Yilun Du" + }, + { + "type": "text", + "bbox": [ + 0.312, + 0.181, + 0.44, + 0.198 + ], + "angle": 0, + "content": "Cameron Smith" + }, + { + "type": "text", + "bbox": [ + 0.48, + 0.181, + 0.597, + 0.199 + ], + "angle": 0, + "content": "Ayush Tewari†" + }, + { + "type": "text", + "bbox": [ + 0.632, + 0.181, + 0.78, + 0.198 + ], + "angle": 0, + "content": "Vincent Sitzmann†" + }, + { + "type": "text", + "bbox": [ + 0.435, + 0.2, + 0.536, + 0.216 + ], + "angle": 0, + "content": "MIT CSAIL" + }, + { + "type": "text", + "bbox": [ + 0.285, + 0.22, + 0.681, + 0.234 + ], + "angle": 0, + "content": "{yilundu, camsmith, ayusht, sitzmann}@mit.edu" + }, + { + "type": "image_caption", + "bbox": [ + 0.128, + 0.259, + 0.319, + 0.27 + ], + "angle": 0, + "content": "Input: Wide-Baseline Stereo Image Pair" + }, + { + "type": "image", + "bbox": [ + 0.092, + 0.272, + 0.355, + 0.481 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.54, + 0.259, + 0.701, + 0.27 + ], + "angle": 0, + "content": "Output: Synthesized Novel Views" + }, + { + "type": "image", + "bbox": [ + 0.36, + 0.272, + 0.882, + 0.481 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.484, + 0.894, + 0.541 + ], + "angle": 0, + "content": "Figure 1. Novel view synthesis from a single wide-baseline stereo image pair. In a single forward pass, our method maps a wide-baseline stereo image pair to features that enable fast rendering of novel views, trained using only posed multi-view images of static scenes without ground-truth or proxy geometry. We outperform all prior art on novel view synthesis from sparse observations, taking a significant step towards matching the quality of overfitting on single scenes in this challenging setting." + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.552, + 0.314, + 0.568 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.584, + 0.473, + 0.856 + ], + "angle": 0, + "content": "We introduce a method for novel view synthesis given only a single wide-baseline stereo image pair. In this challenging regime, 3D scene points are regularly observed only once, requiring prior-based reconstruction of scene geometry and appearance. We find that existing approaches to novel view synthesis from sparse observations fail due to recovering incorrect 3D geometry and due to the high cost of differentiable rendering that precludes their scaling to large-scale training. We take a step towards resolving these shortcomings by formulating a multi-view transformer encoder, proposing an efficient, image-space epipolar line sampling scheme to assemble image features for a target ray, and a lightweight cross-attention-based renderer. Our contributions enable training of our method on a large-scale real-world dataset of indoor and outdoor scenes. We demonstrate that our method learns powerful multi-view geometry priors while reducing the rendering time. We conduct extensive comparisons on held-out test scenes across two real-world datasets, signif" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.554, + 0.894, + 0.599 + ], + "angle": 0, + "content": "icantly outperforming prior work on novel view synthesis from sparse image observations and achieving multi-view-consistent novel view synthesis." + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.632, + 0.631, + 0.646 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.658, + 0.895, + 0.809 + ], + "angle": 0, + "content": "The goal of novel view synthesis is to render images of a scene from unseen camera viewpoints given a set of image observations. In recent years, the emergence of differentiable rendering [26, 28, 44, 45, 50] has led to a leap in quality and applicability of these approaches, enabling near photorealistic results for most real-world 3D scenes. However, methods that approach photorealism require hundreds or even thousands of images carefully exploring every part of the scene, where special care must be taken by the user to densely image all 3D points in the scene from multiple angles." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.896, + 0.902 + ], + "angle": 0, + "content": "In contrast, we are interested in the regime of novel view synthesis from a sparse set of context views. Specifically, this paper explores whether it is possible to synthesize novel view images using an extremely sparse set of observations. In the most challenging case, this problem reduces to using input images such that every 3D point in the scene is only ob" + }, + { + "type": "page_footnote", + "bbox": [ + 0.078, + 0.863, + 0.175, + 0.876 + ], + "angle": 0, + "content": "† Equal Advising" + }, + { + "type": "page_footnote", + "bbox": [ + 0.08, + 0.877, + 0.47, + 0.899 + ], + "angle": 0, + "content": "Project website: https://yilundu.github.io/widebaseline/" + }, + { + "type": "list", + "bbox": [ + 0.078, + 0.863, + 0.47, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "4970" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.258 + ], + "angle": 0, + "content": "served from a single camera perspective. Towards this goal, we propose a system that uses only a single wide-baseline stereo image pair of the scene as input. This stereo image pair regularly has little overlap, such that many 3D points are indeed only observed in one of the images, see Fig. 1. Image observations themselves are thus insufficient information to compute 3D geometry and appearance via multi-view stereo, and we must instead learn prior-based 3D reconstruction. Nevertheless, reasoning about multi-view consistency is critical, as prior-based reconstructions must agree across images to ensure multi-view-consistent reconstruction." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.261, + 0.473, + 0.579 + ], + "angle": 0, + "content": "This is a novel problem setting: While some existing methods demonstrate novel view synthesis from very sparse observations [45, 51, 58], they are limited to object-level scenes. In contrast, we are interested in large real-world scenes that are composed of multiple objects with complex geometry and occlusions. Previous approaches for novel view synthesis of scenes focus on small baseline renderings using \\(3 - 10\\) images as input [7, 8, 18, 25, 47, 53, 58]. In this setting, most 3D points in the scene are observed in multiple input images, and multi-view feature correspondences can be used to regress 3D geometry and appearance. Thus, these methods in practice learn to amortize multi-view stereo. In our setting, we use a wide-baseline stereo image pair as input, where it is not sufficient to rely on multi-view feature correspondences due to many points only being observed in a single view. We show that in this challenging setting, existing approaches do not faithfully recover the 3D geometry of the scene. In addition, most existing methods rely on costly volume rendering for novel view synthesis, where the number of samples per ray required for high-quality rendering makes it difficult to train on complex real-world scenes." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.582, + 0.473, + 0.809 + ], + "angle": 0, + "content": "In this paper, we propose a new method that addresses these limitations, and provides the first solution for high-quality novel view synthesis of a scene from a wide-baseline stereo image pair. To better reason about the 3D scene, we introduce a multi-view vision transformer that computes pixel-aligned features for each input image. In contrast to a monocular image encoder commonly used in previous approaches [51, 53, 58], the multi-view transformer uses the camera pose information as input to better reason about the scene geometry. We reduce the memory and computational costs for computing image features by combining this vision transformer at lower resolutions with a CNN at higher resolutions. A multi-view feature matching step further refines the geometry encoded in these feature maps for any 3D point that can be observed in both images." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.473, + 0.904 + ], + "angle": 0, + "content": "We also introduce an efficient differentiable renderer that enables large-scale training. Existing approaches that use volume rendering sample points along camera rays in 3D and project these points onto the image planes to compute the corresponding features using bilinear interpolation. Since perspective projection is a non-linear operation, uniformly" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.896, + 0.426 + ], + "angle": 0, + "content": "sampled 3D points are not uniformly distributed in 2D, leading to some pixels in the feature maps being sampled multiple times, and other pixels not being sampled at all. Thus, this sampling strategy does not use the information in the pixel-aligned feature maps optimally. We instead take an image-centric sampling approach where we first compute the epipolar lines of a target pixel in the input images, and sample points uniformly on these lines in 2D. This exploits the fact that the number of pixels along the epipolar lines is the maximum effective number of samples. In addition, we use lightweight cross-attention layers that directly aggregate the sampled features and compute the pixel color. In contrast to volume rendering where we need to sample very close to a surface in order to render its color, thus requiring a large number of samples, our learned renderer does not share this limitation and can compute the pixel color even with sparse samples. Our lightweight rendering and feature backbone components enable us to train on large-scale real-world datasets. We demonstrate through extensive experiments on two datasets that our method achieves state-of-the-art results, significantly outperforming existing approaches for novel view synthesis from sparse inputs." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.437, + 0.642, + 0.453 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.464, + 0.897, + 0.829 + ], + "angle": 0, + "content": "Image-based rendering. Image-based rendering (IBR) methods generate images from novel camera viewpoints by blending information from a set of input images. We provide a brief overview of some methods. Please refer to the review by Shum and Kang [41] for details. Some IBR approaches directly model the plenoptic function without using information about the scene geometry [20, 31]. Other approaches use a proxy scene geometry computed using multi-view stereo to guide the blending of information from the input images [3, 9, 16, 23]. While rendering without computing an explicit 3D geometry leads to higher-quality results, it requires a large number of input images. In contrast, methods that rely on 3D geometry can work with sparse image inputs. However, multi-view stereo from a sparse set of input views often leads to inaccurate geometry, especially for scenes with complex geometry, limiting the quality of rendered images. Methods have been proposed for higher-quality geometry computation [5, 15], optical flow-based refinement [4, 10, 11], and improved blending [14, 35, 38]. In contrast to these image-based rendering methods, we rely on priors learned from data that enable novel-view synthesis from just a wide-baseline stereo image. We do not create any explicit proxy geometry of the scene and are thus unaffected by inaccurate multi-view stereo." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.841, + 0.897, + 0.903 + ], + "angle": 0, + "content": "Single-Scene Volumetric Approaches. Recent progress in neural rendering [50] and neural fields [28, 42, 56] has led to a drastic jump in the quality of novel-view synthesis from several input images of a scene. Here, a 3D scene represent" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.515, + 0.958 + ], + "angle": 0, + "content": "4971" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.091, + 0.473, + 0.363 + ], + "angle": 0, + "content": "tation is optimized via differentiable rendering to fit a set of image observations. Early approaches leveraged voxel grids and learned renderers [26, 32, 44]. More recent approaches rely on neural fields [2, 27, 28, 56] to parameterize the 3D scene and volumetric rendering [26, 28, 49] for image synthesis. This leads to photorealistic view synthesis but requires hundreds of input images that densely sample the 3D scene. Hand-crafted and learned priors may reduce the number of required images to the order of three to ten [33], but 3D points still need to be observed from at least two perspectives. A major challenge of these approaches is the cost of accurate differentiable rendering, regularly requiring hundreds of samples per ray. Recent approaches have achieved impressive speed-ups in 3D reconstruction leveraging high-performance data structures and sparsity [6,13,24,29]. While promising, reconstruction can still take a few minutes per scene, and sparse data structures such as octrees and hash tables cannot easily be used with learned priors." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.364, + 0.473, + 0.501 + ], + "angle": 0, + "content": "Our approach tackles a different setting than these methods, using only a single wide-baseline stereo image as input, where 3D points are regularly only observed in a single view. Our approach does not require any per-scene optimization at test time. Instead, it reconstructs the scene in a single forward pass. Note that while our method does not achieve the quality of per-scene optimization methods that use hundreds of input images, it demonstrates a significant step up in novel view synthesis from very sparse image observations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.508, + 0.473, + 0.75 + ], + "angle": 0, + "content": "Prior-based 3D Reconstruction and View Synthesis. Instead of overfitting to a single scene, differentiable rendering can also be used to supervise prior-based inference methods. Some methods generalize image-based rendering techniques by computing feature maps on top of a proxy geometry [1, 19, 38, 55]. Volume rendering using multi-plane images has been used for small baseline novel view synthesis [46, 52, 60, 61]. Early neural fields-based approaches [34, 45] were conditioned on a single global latent code and rendered via sphere tracing. In contrast to a global latent code, several approaches use a feature backbone to compute pixel-aligned features that can be transformed using MLPs [21, 51, 58] or transformers layers [37, 53] to a radiance field. Ideas from multi-view stereo such as the construction of plane-swept cost volumes [7, 18, 25], or multi-view feature matching [8] have been used for higher-quality results." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.751, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Alternatively to these radiance field-based approaches, some methods use a light field rendering formulation where an oriented camera ray can directly be transformed to the pixel color as a function of the features computed from the input images [43, 48]. Scene Representation Transformers [39] use transformers with global attention to compute a set-latent representation that can be decoded to pixel colors when queried with a target camera ray. However, global attention layers on high-resolution input images are very compute and memory intensive. Developed concurrently" + }, + { + "type": "text", + "bbox": [ + 0.497, + 0.092, + 0.895, + 0.212 + ], + "angle": 0, + "content": "with our work, Suhail et al. [47] proposed to use a transformer to only compute features for image patches along the epipolar rays of the pixel being rendered. This is still very expensive due to global attention layer computations over multiple image patches for every rendered pixel. In addition, this method ignores the context information of the scene, since all computation is performed only for patches that lie on the epipolar lines." + }, + { + "type": "text", + "bbox": [ + 0.497, + 0.213, + 0.895, + 0.304 + ], + "angle": 0, + "content": "All existing prior-based reconstruction methods either only support object-level scenes or very small baseline renderings, or rely on multiple image observations where most 3D points are observed in multiple input images. This is different from our setting where we only use a wide-baseline stereo image pair of scenes as input." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.316, + 0.593, + 0.332 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.497, + 0.341, + 0.895, + 0.448 + ], + "angle": 0, + "content": "Our goal is to render novel views of a 3D scene given a wide-baseline stereo image pair \\(\\mathcal{I}_1\\) and \\(\\mathcal{I}_2\\). We assume known camera intrinsic \\(\\mathbf{K}_i \\in \\mathbb{R}^{3 \\times 3}\\) and extrinsic \\(\\mathbf{E}_i \\in \\mathbb{R}^{4 \\times 3}\\) expressed relative to context camera 1. We use a multiview encoder to compute pixel-aligned features, and a cross-attention-based renderer to transform the features into novel view renderings, see Figure 2 for an overview." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.455, + 0.757, + 0.472 + ], + "angle": 0, + "content": "3.1. Multiview Feature Encoding" + }, + { + "type": "text", + "bbox": [ + 0.497, + 0.479, + 0.895, + 0.704 + ], + "angle": 0, + "content": "An essential part of novel view synthesis given context images is an accurate reconstruction of scene geometry. Our method implicitly reconstructs 3D geometry and appearance of the scene in the form of pixel-aligned feature maps for each stereo image. In prior work, pixel-aligned features are obtained by separately encoding each image via a vision transformer or CNN [21, 58]. However, in our early experiments, we found this led to artifacts in renderings observing boundary regions between context images. We hypothesize that separate encoding of images leads to inconsistent geometry reconstruction across context images. We thus introduce our multi-view encoder, which obtains pixel-aligned features by jointly processing the images and the relative pose between them. Encoding the pose information has also been shown to act as an effective inductive bias for 3D tasks [57]." + }, + { + "type": "text", + "bbox": [ + 0.497, + 0.705, + 0.897, + 0.903 + ], + "angle": 0, + "content": "We now describe this architecture in detail, which extends the dense vision transformer proposed by Ranftl et al. [36]. Please see Figure 2 for an overview. From each stereo image, we first independently extract convolutional features via a ResNet50 CNN. We then flatten both images, obtaining \\(2 \\times 16 \\times 16\\) features in total. To each feature, we add (1) a learned per-pixel positional embedding encoding its pixel coordinate and (2) a camera pose embedding, obtained via a linear transform of the relative camera pose between context images 1 and 2. These tokens are processed by a vision transformer, which critically performs self-attention across all tokens across both images. In-between self-attention layers, per-image features are re-assembled into a spatial grid," + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "4972" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.078, + 0.088, + 0.269, + 0.102 + ], + "angle": 0, + "content": "(a) Multi-View ViT Encoder" + }, + { + "type": "image", + "bbox": [ + 0.078, + 0.104, + 0.36, + 0.309 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.348, + 0.089, + 0.625, + 0.103 + ], + "angle": 0, + "content": "(b) Image-Centric Epipolar Line Sampling" + }, + { + "type": "image", + "bbox": [ + 0.348, + 0.104, + 0.636, + 0.31 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.638, + 0.089, + 0.779, + 0.103 + ], + "angle": 0, + "content": "(c) Neural Rendering" + }, + { + "type": "image", + "bbox": [ + 0.636, + 0.106, + 0.895, + 0.31 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.075, + 0.321, + 0.895, + 0.391 + ], + "angle": 0, + "content": "Figure 2. Method Overview. (a) Given context images from different viewpoints, a multi-view encoder extracts pixel-aligned features, leveraging attention across the images and their corresponding camera pose embeddings. (b) Given a target ray, in each context view, we sample primary features along the epipolar line equidistant in pixel space. We then project the corresponding 3D points onto the other views and sample corresponding secondary epipolar line features, where out-of-bounds features are set to zero. (c) We render the target ray by performing cross-attention over the set of all primary and secondary epipolar line features from all views." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.404, + 0.471, + 0.495 + ], + "angle": 0, + "content": "up-sampled, and processed by a fusion CNN [36] to yield per-image spatial feature map. Directly using these spatial feature maps for novel view synthesis leads to blurry reconstructions, due to the loss of high-frequency texture information. We thus concatenate these features with high-resolution image features obtained from a shallow CNN." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.502, + 0.471, + 0.52 + ], + "angle": 0, + "content": "3.2. Epipolar Line Sampling and Feature Matching" + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.526, + 0.471, + 0.797 + ], + "angle": 0, + "content": "We aim to render an image of the scene encoded in the two pixel-aligned feature maps from a novel camera viewpoint. A common way to achieve this is volume rendering, where we cast a camera ray, compute density and color values at many depths along the ray, and integrate them to compute the color of the pixel. Sampling locations are determined in 3D. Coarse samples are either uniformly spaced in euclidean space or spaced with uniform disparity, and fine samples are distributed closer to the surface as computed by the coarse samples [2, 28, 30]. However, in our regime of generalizable novel view synthesis with pixel-aligned feature maps, this sampling scheme is suboptimal. In this case, sampling along the ray should be determined by the resolution of the context images: the number of pixels along the epipolar line is the maximum effective number of samples available for any method. More samples would not provide any extra information. We propose a sampling strategy to exploit this and demonstrate its effectiveness in an ablation study." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.798, + 0.471, + 0.874 + ], + "angle": 0, + "content": "Consider a pixel coordinate \\(\\mathbf{u}_t = (u, v)\\) in the target image \\(\\mathcal{I}_t\\), with assumed known intrinsic \\(\\mathbf{K}_t\\) and extrinsic \\(\\mathbf{T}_t = \\left[ \\begin{array}{cc} \\mathbf{R}_t & t \\\\ \\mathbf{0} & 1 \\end{array} \\right]\\) camera parameters relative to the context camera \\(\\mathcal{I}_1\\). Its epipolar lines \\(\\mathbf{l}_{\\{1,2\\}}\\), in context cameras 1 and 2 are given as:" + }, + { + "type": "equation", + "bbox": [ + 0.096, + 0.878, + 0.471, + 0.899 + ], + "angle": 0, + "content": "\\[\n\\mathbf {l} _ {i} = \\mathbf {F} _ {i} [ u, v, 1 ] ^ {T} = \\mathbf {K} _ {i} ^ {- T} ([ \\mathbf {t} _ {t} ] _ {\\times} \\mathbf {R} _ {t}) \\mathbf {K} _ {t} ^ {- 1} [ u, v, 1 ] ^ {T} (1)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.404, + 0.895, + 0.572 + ], + "angle": 0, + "content": "via the fundamental matrix \\(\\mathbf{F}_i\\). We now uniformly sample \\(N\\) pixel coordinates along the line segment of the epipolar line within the image boundaries. To enable the renderer to reason about whether to use a certain pixel-aligned feature or not, a critical piece of information is the depth in the context coordinate frame at which we are sampling this feature. This depth value can be computed via triangulation, using a closed-form expression. Please refer to the supplemental document for details. We now obtain \\(N\\) tuples \\(\\{(d,\\mathbf{f})_k\\}_{k=1}^N\\) of depth \\(d\\) and image feature \\(\\mathbf{f}\\) per context image for a total of \\(2N\\) samples which we call primary samples." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.584, + 0.897, + 0.903 + ], + "angle": 0, + "content": "We further propose a feature matching module to refine the geometry encoded in the primary epipolar line samples via correspondence matching. Consider a primary epipolar line sample obtained from context image \\(i\\), a tuple \\((d, \\mathbf{f})\\) corresponding to a pixel coordinate \\(\\mathbf{u}_t\\). We propose to augment this sample by a corresponding feature in the other context image. Specifically, we first solve for the corresponding 3D point, and then project this 3D point onto the other context image to retrieve a corresponding feature \\(\\hat{\\mathbf{f}}\\), which we refer to as a secondary feature. The secondary features are set to zero if the projected point is out of the image bounds. Intuitively, primary and secondary features together allow a final stage of geometry refinement for 3D points that are observed in both images: if the features agree, this sample likely encodes a surface. If the projected point on the other image lies outside the image boundary, we simply set the secondary features to zeros. We obtain the input to the renderer as the final set of features by concatenating each primary epipolar line feature with its corresponding secondary feature in the other context view, yielding a set \\(\\{(d, \\mathbf{f}, \\hat{\\mathbf{f}})_k\\}_{k=1}^{2N}\\). In practice, we sample \\(N = 64\\) points on the epipolar lines" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "4973" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.441, + 0.108 + ], + "angle": 0, + "content": "for both images, leading to a total of \\(2N = 128\\) tuples." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.115, + 0.461, + 0.132 + ], + "angle": 0, + "content": "3.3. Differentiable Rendering via Cross-Attention" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.139, + 0.47, + 0.228 + ], + "angle": 0, + "content": "To render the target ray, it remains to map the set of epipolar line samples \\(\\{(d,\\mathbf{f},\\hat{\\mathbf{f}})_k\\}_{k = 1}^{2N}\\) to a color value. As this operation has to be executed once per ray, a key consideration in the design of this function is computational cost. We propose to perform rendering via a lightweight cross-attention decoder." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.229, + 0.471, + 0.379 + ], + "angle": 0, + "content": "For each point on the epipolar line, we embed the target ray origin \\(\\mathbf{o}_t\\), target ray direction \\(\\mathbf{r}_t\\), depth with respect to the target ray origin \\(d_t\\), and context camera ray direction \\(\\mathbf{r}_c\\) for the epipolar point into a ray query token \\(\\mathbf{q}\\) via a shallow MLP as \\(\\Phi([\\mathbf{o}_t, \\mathbf{r}_t, \\mathbf{r}_c, d_t])\\). The \\(2N\\) ray feature values are independently transformed into key and value tokens using a 2-layer MLP. Our renderer now performs two rounds of cross-attention over this set of features to obtain a final feature embedding, which is then decoded into color via a small MLP." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.38, + 0.472, + 0.607 + ], + "angle": 0, + "content": "The expectation of the Softmax distribution over the sampled features gives a rough idea of the scene depth as \\( e = \\sum_{k} d_{k} \\alpha_{k} \\), where \\( d_{k} \\) denotes the depth of the \\( k \\)-th epipolar ray sample along the target ray and \\( \\alpha_{k} \\) is the corresponding Softmax weight as computed by the cross-attention operator. Note that \\( e \\) is not the actual depth but a measure of which epipolar samples the renderer uses to compute the pixel color. Unlike volume rendering, where we need to sample very close to a surface to render its color, our light field-based renderer can reason about the surface without exactly sampling on it. The learned cross-attention layers can use the target camera ray information, along with a sparse set of epipolar samples, to compute the pixel color. Thus, our method does not require explicit computation of accurate scene depth for rendering." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.615, + 0.268, + 0.631 + ], + "angle": 0, + "content": "3.4. Training and Losses" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.638, + 0.47, + 0.669 + ], + "angle": 0, + "content": "We now have a rendered image from a novel camera viewpoint. Our loss function consists of two terms:" + }, + { + "type": "equation", + "bbox": [ + 0.2, + 0.678, + 0.47, + 0.696 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathcal {L} _ {\\mathrm {i m g}} + \\lambda_ {\\mathrm {r e g}} \\mathcal {L} _ {\\mathrm {r e g}}. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.704, + 0.47, + 0.749 + ], + "angle": 0, + "content": "The first term evaluates the difference between the rendered image from a novel camera viewpoint, \\( R \\) and the ground truth, \\( G \\) as:" + }, + { + "type": "equation", + "bbox": [ + 0.133, + 0.759, + 0.47, + 0.777 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {i m g}} = \\left\\| R - G \\right\\| _ {1} + \\lambda_ {\\mathrm {L P I P S}} \\mathcal {L} _ {\\mathrm {L P I P S}} (R, G), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.785, + 0.47, + 0.83 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{\\mathrm{LPIPS}}\\) is the LPIPS perceptual loss [59]. In practice, we render square patches with a length of 32 pixels and evaluate these image losses at the patch level." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.831, + 0.469, + 0.861 + ], + "angle": 0, + "content": "We also use a regularization loss on the cross-attention weights of the renderer for better multi-view consistency:" + }, + { + "type": "equation", + "bbox": [ + 0.111, + 0.87, + 0.47, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {r e g}} = \\sum_ {(u, v)} \\sum_ {\\left(u ^ {\\prime} v ^ {\\prime}\\right) \\in \\mathcal {N} (u, v)} \\left(\\left(e (u, v) - e \\left(u ^ {\\prime}, v ^ {\\prime}\\right)\\right) ^ {2} . \\right. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.137 + ], + "angle": 0, + "content": "Here, \\( e(u,v) \\) denotes the expected value of the depth of the epipolar samples at pixel \\( (u,v) \\), and \\( \\mathcal{N}() \\) defines the neighborhood around a pixel." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.138, + 0.894, + 0.228 + ], + "angle": 0, + "content": "For better generalization, we further perform several geometrically-consistent data augmentations during the training procedure. We center crop and scale the input and target images, which leads to transformation in the intrinsic of the camera. We also flip the images which leads to transformation of the extrinsics." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.243, + 0.634, + 0.261 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.269, + 0.895, + 0.42 + ], + "angle": 0, + "content": "We quantitatively and qualitatively show that our approach can effectively render novel views from wide-baseline stereo pairs. We describe our underlying experimental setup in Section 4.1. Next, we evaluate our approach on challenging indoor scenes with substantial occlusions in Section 4.2. We further evaluate on outdoor scenes in Section 4.3. We analyze and ablate the underlying components in Section 4.4. Finally, we illustrate how our approach can render novel views of unposed images of scenes captured in the wild in Section 4.5." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.432, + 0.691, + 0.448 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.456, + 0.894, + 0.591 + ], + "angle": 0, + "content": "Datasets. We train and evaluate our approach on RealEstate10k [61], a large dataset of indoor and outdoor scenes, and ACID [22], a large dataset of outdoor scenes. We use 67477 scenes for training and 7289 scenes for testing for RealEstate10k, and 11075 scenes for training and 1972 scenes for testing for ACID, following default splits. We train our method on images at \\(256 \\times 256\\) resolution and evaluate methods on their ability to reconstruct intermediate views in test scenes (details in the supplement)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.603, + 0.895, + 0.845 + ], + "angle": 0, + "content": "Baselines. We compare to several existing approaches for novel view synthesis from sparse image observations. We compare to pixelNeRF [58] and IBRNet [53] that use pixel-aligned features, which are decoded into 3D volumes rendered using volumetric rendering. We also compare to Generalizable Patch-based Rendering (GPNR) [47], which uses a vision transformer-based backbone to compute epipolar features, and a light field-based renderer to compute pixel colors. These baselines cover a wide range of design choices used in existing methods, such as pixel-aligned feature maps computed using CNNs [53, 58] and transformers [47], volumetric rendering by decoding features using MLPs [58] and transformers [53], and light field-based rendering [47]. We use publicly available codebases for all baselines and train them on the same datasets we use for fair evaluations. Please refer to the supplemental for comparisons to more baselines." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.894, + 0.901 + ], + "angle": 0, + "content": "Evaluation Metrics. We use LPIPS [59], PSNR, SSIM [54], and MSE metrics to compare the image quality of rendered images with the ground truth." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "4974" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.093, + 0.094, + 0.881, + 0.371 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.39, + 0.893, + 0.434 + ], + "angle": 0, + "content": "Figure 3. Comparative Rendering Results on RealEstate10k. Our approach can render novel views of indoor scenes with substantial occlusions with high fidelity using a wide-baseline input image pair, outperforming all baselines. Note that many points of the 3D scene are only observed in a single image in such inputs. Our method can correctly reason about the 3D structures from such sparse views." + }, + { + "type": "image_caption", + "bbox": [ + 0.129, + 0.458, + 0.198, + 0.47 + ], + "angle": 0, + "content": "Input Images" + }, + { + "type": "image", + "bbox": [ + 0.092, + 0.471, + 0.236, + 0.529 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.288, + 0.458, + 0.409, + 0.469 + ], + "angle": 0, + "content": "Rendered Novel Views" + }, + { + "type": "image", + "bbox": [ + 0.242, + 0.471, + 0.457, + 0.529 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.092, + 0.536, + 0.236, + 0.587 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.242, + 0.536, + 0.456, + 0.586 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.092, + 0.589, + 0.236, + 0.646 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.241, + 0.589, + 0.456, + 0.646 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.092, + 0.648, + 0.236, + 0.704 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.241, + 0.648, + 0.456, + 0.704 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.718, + 0.47, + 0.775 + ], + "angle": 0, + "content": "Figure 4. Novel view renderings of our approach given a large baseline stereo pair. Our approach can synthesize intermediate views that are substantially different from input images, even with very limited overlap between images." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.789, + 0.358, + 0.805 + ], + "angle": 0, + "content": "4.2. Indoor Scene Neural Rendering" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.813, + 0.469, + 0.859 + ], + "angle": 0, + "content": "We first evaluate the ability of our approach and baselines to render novel views in complex indoor environments with substantial occlusions between objects." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.47, + 0.903 + ], + "angle": 0, + "content": "Qualitative Results. In Figure 3, we provide qualitative results of novel view renderings of our approach, compared" + }, + { + "type": "table", + "bbox": [ + 0.512, + 0.456, + 0.88, + 0.539 + ], + "angle": 0, + "content": "
MethodLPIPS ↓SSIM ↑PSNR ↑MSE ↓
pixelNeRF [58]0.5910.46013.910.0440
IBRNet [53]0.5320.48415.990.0280
GPNR [47]0.4590.74818.550.0165
Ours0.2620.83921.380.0110
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.55, + 0.894, + 0.578 + ], + "angle": 0, + "content": "Table 1. Novel view rendering performance on RealEstate10K. Our method outperforms all baselines on all metrics." + }, + { + "type": "table", + "bbox": [ + 0.512, + 0.6, + 0.879, + 0.683 + ], + "angle": 0, + "content": "
MethodLPIPS ↓SSIM ↑PSNR ↑MSE ↓
pixelNeRF [58]0.6280.46416.480.0275
IBRNet [53]0.3850.51319.240.0167
GPNR [47]0.5580.71917.570.0218
Ours0.3640.78123.630.0074
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.687, + 0.892, + 0.716 + ], + "angle": 0, + "content": "Table 2. Novel view rendering performance on ACID. Our method outperforms all baselines on all metrics." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.738, + 0.894, + 0.799 + ], + "angle": 0, + "content": "to each of our baselines. We provide additional novel view results of our method in Figure 4. Compared to the baselines, our approach reconstructs the 3D structure of the scene better, and also captures more high-frequency details." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.841, + 0.894, + 0.9 + ], + "angle": 0, + "content": "Quantitative Results. We quantitatively evaluate our approach and baselines in Table 1. We find that our approach substantially outperforms each compared baseline in terms of all of our metrics." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "4975" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.184, + 0.091, + 0.253, + 0.102 + ], + "angle": 0, + "content": "Input Images" + }, + { + "type": "image", + "bbox": [ + 0.12, + 0.104, + 0.321, + 0.265 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.351, + 0.091, + 0.407, + 0.102 + ], + "angle": 0, + "content": "PixelNeRF" + }, + { + "type": "image", + "bbox": [ + 0.328, + 0.104, + 0.429, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.464, + 0.091, + 0.503, + 0.102 + ], + "angle": 0, + "content": "IBRNet" + }, + { + "type": "image", + "bbox": [ + 0.432, + 0.104, + 0.534, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.571, + 0.091, + 0.604, + 0.102 + ], + "angle": 0, + "content": "GPNR" + }, + { + "type": "image", + "bbox": [ + 0.537, + 0.104, + 0.638, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.679, + 0.091, + 0.705, + 0.102 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image", + "bbox": [ + 0.642, + 0.104, + 0.743, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.78, + 0.091, + 0.812, + 0.102 + ], + "angle": 0, + "content": "Target" + }, + { + "type": "image", + "bbox": [ + 0.746, + 0.104, + 0.848, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.114, + 0.272, + 0.854, + 0.287 + ], + "angle": 0, + "content": "Figure 5. Comparative Results on ACID. Our approach is able to render novels views with higher quality than all baselines." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.306, + 0.371, + 0.323 + ], + "angle": 0, + "content": "4.3. Outdoor Scene Neural Rendering" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.33, + 0.47, + 0.36 + ], + "angle": 0, + "content": "We further evaluate on outdoor scenes with potentially unbounded depth." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.37, + 0.47, + 0.446 + ], + "angle": 0, + "content": "Qualitative Results. We illustrate qualitative results in Figure 5. In comparison to the baselines, our approach is able to more accurately reconstruct the geometry, and is able to synthesize multi-view consistent renderings from two large baseline views." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.457, + 0.471, + 0.502 + ], + "angle": 0, + "content": "Quantitative Results. Similar to indoor scenes, our approach also outperforms all baselines in terms of all metrics on outdoor scenes, see Table 2." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.512, + 0.291, + 0.529 + ], + "angle": 0, + "content": "4.4. Ablations and Analysis" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.536, + 0.47, + 0.58 + ], + "angle": 0, + "content": "We next analyze and ablate individual components of our approach. We use the RealEstate10k dataset for these experiments." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.591, + 0.473, + 0.91 + ], + "angle": 0, + "content": "Ablations. We evaluate the importance of different components of our method in Table 3. The \"Base Model\" corresponds to a vanilla architecture that does not include some of our proposed contributions. It samples points uniformly in 3D, instead of our proposed 2D epipolar line sampling. It uses a monocular encoder instead of our proposed multiview encoder, and does not use correspondence matching across views for refining the geometry. It also does not use the regularization loss for multi-view consistency or any data augmentation during training. We find that all components of our approach are essential for high-quality performance. The results in Table 3 show that sampling in 3D sub-optimally uses the information in the feature maps, that our multi-view encoder and cross-image correspondence matching can compute features that better encode the 3D scene structure compared to monocular encoders, and that data augmentation helps with generalization. While we found that the incorporation of the regularization loss led to a slight decrease in PSNR, we found that it improved multi-view consistency in the rendered video results, and also improved both LPIPS and SSIM perceptual metrics." + }, + { + "type": "table", + "bbox": [ + 0.502, + 0.304, + 0.89, + 0.406 + ], + "angle": 0, + "content": "
ModelsLPIPS↓SSIM↑PSNR↑MSE↓
Base Model0.4520.73518.110.0201
+ 2D Sampling0.4280.76219.020.0159
+ Cross Correspondence0.4150.76619.520.0142
+ Multiview Encoder0.3610.79420.430.0132
+ Regularization Loss0.3580.80819.840.0139
+ Data Aug0.2620.83921.380.0110
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.411, + 0.892, + 0.44 + ], + "angle": 0, + "content": "Table 3. Ablations. All components of our proposed method are essential for high-quality novel view synthesis." + }, + { + "type": "image", + "bbox": [ + 0.539, + 0.447, + 0.849, + 0.622 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.629, + 0.894, + 0.7 + ], + "angle": 0, + "content": "Figure 6. FPS vs PSNR. Our approach strikes the best trade-off between rendering quality and rendering speed. We can further reduce the number of Epipolar samples (\"Ours (Faster)\"), which makes our method faster than all baselines, while still significantly outperforming them in terms of rendering quality." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.707, + 0.895, + 0.844 + ], + "angle": 0, + "content": "Speed. Next, in Figure 6, we study the relationship between rendering quality and rendering speed for all approaches. Our lightweight approach achieves the best tradeoff, significantly outperforming all methods in terms of rendering quality, while being at-par with the most efficient baseline. By reducing the number of sampled epipolar points from 64 to 48 samples per image, we can further speed up our approach, outperforming all baselines both in terms of rendering speed and image quality." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Epipolar Attention. Finally, we visualize the underlying epipolar attention weights learned by our approach in Figure 7. The expected value of the depths of the epipolar sam" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "4976" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.109, + 0.089, + 0.441, + 0.277 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.285, + 0.472, + 0.356 + ], + "angle": 0, + "content": "Figure 7. Visualization of Epipolar Attention Weights. The expected value of the depths of the epipolar samples under the attention weights can be seen as a depth proxy. As our renderer is not a volume renderer, these attention weights need not exactly correspond to the actual depth for correct renderings." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.362, + 0.47, + 0.408 + ], + "angle": 0, + "content": "plies can be seen as a proxy depth and corresponds roughly to the underlying geometry of the scene. This enables us to analyze the learned computation of our renderer." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.416, + 0.45, + 0.433 + ], + "angle": 0, + "content": "4.5. Novel View Synthesis from Unposed Images" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.44, + 0.473, + 0.728 + ], + "angle": 0, + "content": "Our method uses a wide-baseline stereo image as input with known relative pose between them. We show that our method can perform novel view synthesis even without the knowledge of this relative pose information. In this case, we utilize SuperGlue [40] to compute reliable pixel correspondences between the input images. Since we do not know the camera intrinsics for in-the-wild images, we use the average intrinsics of the RealEstate10k dataset and compute the Essential matrix from the correspondences using RANSAC [12]. We then compute the pose information from the essential matrix [17] and use it as input for our method. Note that the recovered translation is only defined up to a scale. Figure 8 demonstrates results on some in-the-wild scenes using images from the internet. Even in this unposed setting, our method can reason about the geometry of the scene by aggregating information across the sparse input views. This is an extremely challenging setting, and existing approaches for novel view synthesis from sparse views do not demonstrate any results on unposed images." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.74, + 0.192, + 0.755 + ], + "angle": 0, + "content": "5. Discussion" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.765, + 0.47, + 0.903 + ], + "angle": 0, + "content": "While we have presented the first approach for novel view synthesis of scenes from very sparse input views, our approach still has several limitations. Our rendering results are not at the same quality as those obtained by methods that optimize on single scenes using more images. Learning priors that enable novel view synthesis from sparse views is a significantly more challenging problem compared to using a large number of input images, where 3D points are regularly observed in many images. Our approach takes a step towards" + }, + { + "type": "image_caption", + "bbox": [ + 0.549, + 0.089, + 0.619, + 0.101 + ], + "angle": 0, + "content": "Input Images" + }, + { + "type": "image_caption", + "bbox": [ + 0.714, + 0.089, + 0.835, + 0.1 + ], + "angle": 0, + "content": "Rendered Novel Views" + }, + { + "type": "image", + "bbox": [ + 0.511, + 0.102, + 0.657, + 0.278 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.665, + 0.102, + 0.883, + 0.278 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.29, + 0.895, + 0.348 + ], + "angle": 0, + "content": "Figure 8. Novel View Synthesis from Unposed Images. Our approach can also render novel views using two unposed images captured in the wild. Note that parts of the scene only visible in one of the images can be correctly rendered from novel viewpoints." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.364, + 0.895, + 0.515 + ], + "angle": 0, + "content": "photorealistic renderings of scenes using only sparse views. As our approach relies on learned priors, it does not generalize well to new scenes with very different appearances compared to the training scenes. However, our efficient approach lends itself to large-scale training on diverse datasets, in turn enabling reconstruction of diverse scenes. Finally, while our method, in theory, can be extended to take more than two input views, we have only experimented with two views as a first step towards very sparse multi-view neural rendering." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.545, + 0.619, + 0.56 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.575, + 0.895, + 0.817 + ], + "angle": 0, + "content": "We introduce a method for implicit 3D reconstruction and novel view synthesis from a single, wide-baseline stereo pair, trained using only self-supervision from posed color images. By leveraging a multi-view encoder, an image-space epipolar line feature sampling scheme, and a cross-attention based renderer, our method surpasses the quality of prior art on datasets of challenging scenes. Our method further strikes a compelling trade-off between rendering speed and quality, rendering novel views significantly faster than most prior methods. Meanwhile, leveraging epipolar line geometry strikes a compelling trade-off between structured and generalist learning paradigms, enabling us to train our method on real-world datasets such as RealEstate10k. We believe that this work will inspire the community towards further exploring the regime of extreme few-shot and generalizable novel view synthesis." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.826, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Acknowledgements. This work was supported by the National Science Foundation under Grant No. 2211259, and by the Singapore DSTA under DST00OECI20300823 (New Representations for Vision). Yilun Du is supported by a NSF Graduate Research Fellowship." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "4977" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.105 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.472, + 0.171 + ], + "angle": 0, + "content": "[1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graphics. In European Conference on Computer Vision, pages 696-712. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.173, + 0.472, + 0.227 + ], + "angle": 0, + "content": "[2] Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields, 2021. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.23, + 0.472, + 0.299 + ], + "angle": 0, + "content": "[3] Chris Buehler, Michael Bosse, Leonard McMillan, Steven Gortler, and Michael Cohen. Unstructured lumigraph rendering. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 425-432, 2001. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.302, + 0.472, + 0.371 + ], + "angle": 0, + "content": "[4] Dan Casas, Christian Richardt, John Collomosse, Christian Theobalt, and Adrian Hilton. 4d model flow: Precomputed appearance alignment for real-time 4d video interpolation. In Computer Graphics Forum, volume 34, pages 173-182. Wiley Online Library, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.373, + 0.472, + 0.428 + ], + "angle": 0, + "content": "[5] Gaurav Chaurasia, Sylvain Duchene, Olga Sorkine-Hornung, and George Drettakis. Depth synthesis and local warps for plausible image-based navigation. ACM Transactions on Graphics (TOG), 32(3):1-12, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.43, + 0.472, + 0.471 + ], + "angle": 0, + "content": "[6] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. arXiv preprint arXiv:2203.09517, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.474, + 0.472, + 0.543 + ], + "angle": 0, + "content": "[7] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14124-14133, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.545, + 0.472, + 0.613 + ], + "angle": 0, + "content": "[8] Julian Chibane, Aayush Bansal, Verica Lazova, and Gerard Pons-Moll. Stereo radiance fields (srf): Learning view synthesis from sparse views of novel scenes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, jun 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.616, + 0.472, + 0.685 + ], + "angle": 0, + "content": "[9] Paul E Debevec, Camillo J Taylor, and Jitendra Malik. Modeling and rendering architecture from photographs: A hybrid geometry-and image-based approach. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 11-20, 1996. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.687, + 0.472, + 0.728 + ], + "angle": 0, + "content": "[10] Ruofei Du, Ming Chuang, Wayne Chang, Hugues Hoppe, and Amitabh Varshney. Montage4d: Interactive seamless fusion of multiview video textures. 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.731, + 0.472, + 0.8 + ], + "angle": 0, + "content": "[11] Martin Eisemann, Bert De Decker, Marcus Magnor, Philippe Bekaert, Edilson De Aguiar, Naveed Ahmed, Christian Theobalt, and Anita Sellent. Floating textures. In Computer graphics forum, volume 27, pages 409-418. Wiley Online Library, 2008. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.802, + 0.472, + 0.857 + ], + "angle": 0, + "content": "[12] Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[13] Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In Proceedings of" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5501-5510, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.121, + 0.894, + 0.176 + ], + "angle": 0, + "content": "[14] Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Brostow. Deep blending for free-viewpoint image-based rendering. ACM Transactions on Graphics (TOG), 37(6):1-15, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.177, + 0.892, + 0.218 + ], + "angle": 0, + "content": "[15] Peter Hedman, Tobias Ritschel, George Drettakis, and Gabriel Brostow. Scalable inside-out image-based rendering. ACM Transactions on Graphics (TOG), 35(6):1-11, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.219, + 0.894, + 0.274 + ], + "angle": 0, + "content": "[16] Benno Heigl, Reinhard Koch, Marc Pollefeys, Joachim Denzler, and L Van Gool. Plenoptic modeling and rendering from image sequences taken by a hand-held camera. In Mustererkennung 1999, pages 94-101. Springer, 1999. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.275, + 0.892, + 0.302 + ], + "angle": 0, + "content": "[17] Berthold KP Horn. Recovering baseline and orientation from essential matrix. J. Opt. Soc. Am, 110, 1990. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.303, + 0.894, + 0.369 + ], + "angle": 0, + "content": "[18] Mohammad Mahdi Johari, Yann Lepoittevin, and François Fleuret. Geonerf: Generalizing nerf with geometry priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18365-18375, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.372, + 0.894, + 0.428 + ], + "angle": 0, + "content": "[19] Georgios Kopanas, Julien Philip, Thomas Leimkuhler, and George Drettakis. Point-based neural rendering with perview optimization. In Computer Graphics Forum, volume 40, pages 29-43. Wiley Online Library, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.429, + 0.894, + 0.469 + ], + "angle": 0, + "content": "[20] Marc Levoy and Pat Hanrahan. Light field rendering. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 31-42, 1996. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.47, + 0.894, + 0.525 + ], + "angle": 0, + "content": "[21] Kai-En Lin, Lin Yen-Chen, Wei-Sheng Lai, Tsung-Yi Lin, Yi-Chang Shih, and Ravi Ramamoorthi. Vision transformer for nef-based view synthesis from a single input image. arXiv preprint arXiv:2207.05736, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.526, + 0.894, + 0.594 + ], + "angle": 0, + "content": "[22] Andrew Liu, Richard Tucker, Varun Jampani, Ameesh Makadia, Noah Snavely, and Angjoo Kanazawa. Infinite nature: Perpetual view generation of natural scenes from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.595, + 0.894, + 0.637 + ], + "angle": 0, + "content": "[23] Feng Liu, Michael Gleicher, Hailin Jin, and Aseem Agarwala. Content-preserving warps for 3d video stabilization. ACM Transactions on Graphics (ToG), 28(3):1-9, 2009. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.638, + 0.894, + 0.677 + ], + "angle": 0, + "content": "[24] Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. NeurIPS, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.679, + 0.894, + 0.761 + ], + "angle": 0, + "content": "[25] Yuan Liu, Sida Peng, Lingjie Liu, Qianqian Wang, Peng Wang, Christian Theobalt, Xiaowei Zhou, and Wenping Wang. Neural rays for occlusion-aware image-based rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7824-7833, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.762, + 0.894, + 0.817 + ], + "angle": 0, + "content": "[26] Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural volumes: Learning dynamic renderable volumes from images. ACM Trans. Graph., 2019. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.818, + 0.894, + 0.901 + ], + "angle": 0, + "content": "[27] Ricardo Martin-Brualla, Noha Radwan, Mehdi SM Sajjadi, Jonathan T Barron, Alexey Dosovitskiy, and Daniel Duckworth. Nerf in the wild: Neural radiance fields for unconstrained photo collections. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7210-7219, 2021. 3" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "4978" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.161 + ], + "angle": 0, + "content": "[28] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. In Proceedings of the European Conference on Computer Vision (ECCV), pages 405-421, 2020. 1, 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.162, + 0.472, + 0.218 + ], + "angle": 0, + "content": "[29] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-102:15, July 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.218, + 0.472, + 0.3 + ], + "angle": 0, + "content": "[30] Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Joerg H. Mueller, Chakravarty R. Alla Chaitanya, Anton S. Kaplanyan, and Markus Steinberger. DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks. Computer Graphics Forum, 2021. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.301, + 0.472, + 0.357 + ], + "angle": 0, + "content": "[31] Ren Ng, Marc Levoy, Mathieu Brédif, Gene Duval, Mark Horowitz, and Pat Hanrahan. Light field photography with a hand-held plenoptic camera. PhD thesis, Stanford University, 2005. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.357, + 0.472, + 0.413 + ], + "angle": 0, + "content": "[32] Thu H Nguyen-Phuoc, Chuan Li, Stephen Balaban, and Yongliang Yang. Rendernet: A deep convolutional network for differentiable rendering from 3d shapes. Advances in neural information processing systems, 31, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.413, + 0.472, + 0.496 + ], + "angle": 0, + "content": "[33] Michael Niemeyer, Jonathan T Barron, Ben Mildenhall, Mehdi SM Sajjadi, Andreas Geiger, and Noha Radwan. Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5480-5490, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.497, + 0.472, + 0.566 + ], + "angle": 0, + "content": "[34] Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3504-3515, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.567, + 0.472, + 0.607 + ], + "angle": 0, + "content": "[35] Eric Penner and Li Zhang. Soft 3d reconstruction for view synthesis. ACM Transactions on Graphics (TOG), 36(6):1-11, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.608, + 0.472, + 0.664 + ], + "angle": 0, + "content": "[36] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12179-12188, 2021. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.664, + 0.472, + 0.748 + ], + "angle": 0, + "content": "[37] Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10901-10911, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.748, + 0.472, + 0.775 + ], + "angle": 0, + "content": "[38] Gernot Riegler and Vladlen Koltun. Free view synthesis. In European Conference on Computer Vision, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.776, + 0.472, + 0.858 + ], + "angle": 0, + "content": "[39] Mehdi SM Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, et al. Scene representation transformer: Geometry-free novel view synthesis through set-latent scene representations. arXiv preprint arXiv:2111.13152, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.472, + 0.902 + ], + "angle": 0, + "content": "[40] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In Proceedings of the" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.894, + 0.12 + ], + "angle": 0, + "content": "IEEE/CVF conference on computer vision and pattern recognition, pages 4938-4947, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.121, + 0.893, + 0.162 + ], + "angle": 0, + "content": "[41] Harry Shum and Sing Bing Kang. Review of image-based rendering techniques. In Visual Communications and Image Processing 2000, volume 4067, pages 2-13. SPIE, 2000. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.163, + 0.894, + 0.218 + ], + "angle": 0, + "content": "[42] Vincent Sitzmann, Julien N.P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. In Proc. NeurIPS, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.218, + 0.894, + 0.287 + ], + "angle": 0, + "content": "[43] Vincent Sitzmann, Semon Rezchikov, Bill Freeman, Josh Tenenbaum, and Fredo Durand. Light field networks: Neural scene representations with single-evaluation rendering. In Advances in Neural Information Processing Systems (NeurIPS), 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.288, + 0.894, + 0.356 + ], + "angle": 0, + "content": "[44] Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhöfer. Deepvoxels: Learning persistent 3d feature embeddings. In Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 2019. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.357, + 0.894, + 0.413 + ], + "angle": 0, + "content": "[45] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3d-structure-aware neural scene representations. Advances in Neural Information Processing Systems, 32, 2019. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.413, + 0.894, + 0.483 + ], + "angle": 0, + "content": "[46] Pratul P Srinivasan, Richard Tucker, Jonathan T Barron, Ravi Ramamoorthi, Ren Ng, and Noah Snavely. Pushing the boundaries of view extrapolation with multiplane images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 175-184, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.483, + 0.894, + 0.537 + ], + "angle": 0, + "content": "[47] Mohammed Suhail, Carlos Esteves, Leonid Sigal, and Ameesh Makadia. Generalizable patch-based neural rendering. In European Conference on Computer Vision. Springer, 2022. 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.538, + 0.894, + 0.594 + ], + "angle": 0, + "content": "[48] Mohammed Suhail, Carlos Esteves, Leonid Sigal, and Ameesh Makadia. Light field neural rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8269-8279, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.595, + 0.893, + 0.622 + ], + "angle": 0, + "content": "[49] Andrea Tagliasacchi and Ben Mildenhall. Volume rendering digest (for nef). arXiv preprint arXiv:2209.02417, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.622, + 0.893, + 0.692 + ], + "angle": 0, + "content": "[50] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, W Yifan, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, et al. Advances in neural rendering. In Computer Graphics Forum, pages 703-735. Wiley Online Library, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.692, + 0.893, + 0.748 + ], + "angle": 0, + "content": "[51] Alex Trevithick and Bo Yang. Grf: Learning a general radiance field for 3d representation and rendering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15182-15192, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.748, + 0.893, + 0.803 + ], + "angle": 0, + "content": "[52] Richard Tucker and Noah Snavely. Single-view view synthesis with multiplane images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 551-560, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.804, + 0.893, + 0.871 + ], + "angle": 0, + "content": "[53] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo MartinBrualla, Noah Snavely, and Thomas Funkhouser. Ibrnet: Learning multi-view image-based rendering. In CVPR, 2021. 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.872, + 0.893, + 0.902 + ], + "angle": 0, + "content": "[54] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: From error visibility to" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "4979" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.093, + 0.47, + 0.12 + ], + "angle": 0, + "content": "structural similarity. IEEE Transactions on Image Processing, 2004. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.47, + 0.163 + ], + "angle": 0, + "content": "[55] Olivia Wiles, Georgia Gkioxari, Richard Szeliski, and Justin Johnson. Synsin: End-to-end view synthesis from a single image. In Proc. CVPR, pages 7467-7477, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.165, + 0.472, + 0.233 + ], + "angle": 0, + "content": "[56] Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. Neural fields in visual computing and beyond. Computer Graphics Forum, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.235, + 0.472, + 0.303 + ], + "angle": 0, + "content": "[57] Wang Yifan, Carl Doersch, Relja Arandjelović, João Carreira, and Andrew Zisserman. Input-level inductive biases for 3d reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6176-6186, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.305, + 0.472, + 0.361 + ], + "angle": 0, + "content": "[58] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelNeRF: Neural radiance fields from one or few images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.362, + 0.472, + 0.432 + ], + "angle": 0, + "content": "[59] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep networks as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.433, + 0.472, + 0.46 + ], + "angle": 0, + "content": "[60] Yunzhi Zhang and Jiajun Wu. Video extrapolation in space and time. arXiv e-prints, pages arXiv-2205, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.462, + 0.472, + 0.517 + ], + "angle": 0, + "content": "[61] Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. ACM Trans. Graph. (Proc. SIGGRAPH), 37, 2018. 3, 5" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.093, + 0.472, + 0.517 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.956 + ], + "angle": 0, + "content": "4980" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Render Novel Views From Wide-Baseline Stereo Pairs/81b4fe5b-6c46-4bd1-8b70-87cb209929c4_origin.pdf b/2023/Learning To Render Novel Views From Wide-Baseline Stereo Pairs/81b4fe5b-6c46-4bd1-8b70-87cb209929c4_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..613f9459133bb1fcad0ee4ef751f89d553ebad4f --- /dev/null +++ b/2023/Learning To Render Novel Views From Wide-Baseline Stereo Pairs/81b4fe5b-6c46-4bd1-8b70-87cb209929c4_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e936124ca9608a8b77a39ca5ced67a5764b7d6c7ed5bb020cf6a12353a11565e +size 9729305 diff --git a/2023/Learning To Render Novel Views From Wide-Baseline Stereo Pairs/full.md b/2023/Learning To Render Novel Views From Wide-Baseline Stereo Pairs/full.md new file mode 100644 index 0000000000000000000000000000000000000000..53e5311dc9574f1edfe4dfe08d2cc4f6e86af23f --- /dev/null +++ b/2023/Learning To Render Novel Views From Wide-Baseline Stereo Pairs/full.md @@ -0,0 +1,327 @@ +# Learning to Render Novel Views from Wide-Baseline Stereo Pairs + +Yilun Du + +Cameron Smith + +Ayush Tewari† + +Vincent Sitzmann† + +MIT CSAIL + +{yilundu, camsmith, ayusht, sitzmann}@mit.edu + +![](images/eb963a50d145581080a2bff2b59d5e19383a7dcb1e8d0cfc8c84d71a6ee6a3ce.jpg) +Input: Wide-Baseline Stereo Image Pair +Figure 1. Novel view synthesis from a single wide-baseline stereo image pair. In a single forward pass, our method maps a wide-baseline stereo image pair to features that enable fast rendering of novel views, trained using only posed multi-view images of static scenes without ground-truth or proxy geometry. We outperform all prior art on novel view synthesis from sparse observations, taking a significant step towards matching the quality of overfitting on single scenes in this challenging setting. + +![](images/26bb07107e070e3a684ec9e7b549fb464dba325b26eca36ded14b192da492f68.jpg) +Output: Synthesized Novel Views + +# Abstract + +We introduce a method for novel view synthesis given only a single wide-baseline stereo image pair. In this challenging regime, 3D scene points are regularly observed only once, requiring prior-based reconstruction of scene geometry and appearance. We find that existing approaches to novel view synthesis from sparse observations fail due to recovering incorrect 3D geometry and due to the high cost of differentiable rendering that precludes their scaling to large-scale training. We take a step towards resolving these shortcomings by formulating a multi-view transformer encoder, proposing an efficient, image-space epipolar line sampling scheme to assemble image features for a target ray, and a lightweight cross-attention-based renderer. Our contributions enable training of our method on a large-scale real-world dataset of indoor and outdoor scenes. We demonstrate that our method learns powerful multi-view geometry priors while reducing the rendering time. We conduct extensive comparisons on held-out test scenes across two real-world datasets, signif + +icantly outperforming prior work on novel view synthesis from sparse image observations and achieving multi-view-consistent novel view synthesis. + +# 1. Introduction + +The goal of novel view synthesis is to render images of a scene from unseen camera viewpoints given a set of image observations. In recent years, the emergence of differentiable rendering [26, 28, 44, 45, 50] has led to a leap in quality and applicability of these approaches, enabling near photorealistic results for most real-world 3D scenes. However, methods that approach photorealism require hundreds or even thousands of images carefully exploring every part of the scene, where special care must be taken by the user to densely image all 3D points in the scene from multiple angles. + +In contrast, we are interested in the regime of novel view synthesis from a sparse set of context views. Specifically, this paper explores whether it is possible to synthesize novel view images using an extremely sparse set of observations. In the most challenging case, this problem reduces to using input images such that every 3D point in the scene is only ob + +served from a single camera perspective. Towards this goal, we propose a system that uses only a single wide-baseline stereo image pair of the scene as input. This stereo image pair regularly has little overlap, such that many 3D points are indeed only observed in one of the images, see Fig. 1. Image observations themselves are thus insufficient information to compute 3D geometry and appearance via multi-view stereo, and we must instead learn prior-based 3D reconstruction. Nevertheless, reasoning about multi-view consistency is critical, as prior-based reconstructions must agree across images to ensure multi-view-consistent reconstruction. + +This is a novel problem setting: While some existing methods demonstrate novel view synthesis from very sparse observations [45, 51, 58], they are limited to object-level scenes. In contrast, we are interested in large real-world scenes that are composed of multiple objects with complex geometry and occlusions. Previous approaches for novel view synthesis of scenes focus on small baseline renderings using $3 - 10$ images as input [7, 8, 18, 25, 47, 53, 58]. In this setting, most 3D points in the scene are observed in multiple input images, and multi-view feature correspondences can be used to regress 3D geometry and appearance. Thus, these methods in practice learn to amortize multi-view stereo. In our setting, we use a wide-baseline stereo image pair as input, where it is not sufficient to rely on multi-view feature correspondences due to many points only being observed in a single view. We show that in this challenging setting, existing approaches do not faithfully recover the 3D geometry of the scene. In addition, most existing methods rely on costly volume rendering for novel view synthesis, where the number of samples per ray required for high-quality rendering makes it difficult to train on complex real-world scenes. + +In this paper, we propose a new method that addresses these limitations, and provides the first solution for high-quality novel view synthesis of a scene from a wide-baseline stereo image pair. To better reason about the 3D scene, we introduce a multi-view vision transformer that computes pixel-aligned features for each input image. In contrast to a monocular image encoder commonly used in previous approaches [51, 53, 58], the multi-view transformer uses the camera pose information as input to better reason about the scene geometry. We reduce the memory and computational costs for computing image features by combining this vision transformer at lower resolutions with a CNN at higher resolutions. A multi-view feature matching step further refines the geometry encoded in these feature maps for any 3D point that can be observed in both images. + +We also introduce an efficient differentiable renderer that enables large-scale training. Existing approaches that use volume rendering sample points along camera rays in 3D and project these points onto the image planes to compute the corresponding features using bilinear interpolation. Since perspective projection is a non-linear operation, uniformly + +sampled 3D points are not uniformly distributed in 2D, leading to some pixels in the feature maps being sampled multiple times, and other pixels not being sampled at all. Thus, this sampling strategy does not use the information in the pixel-aligned feature maps optimally. We instead take an image-centric sampling approach where we first compute the epipolar lines of a target pixel in the input images, and sample points uniformly on these lines in 2D. This exploits the fact that the number of pixels along the epipolar lines is the maximum effective number of samples. In addition, we use lightweight cross-attention layers that directly aggregate the sampled features and compute the pixel color. In contrast to volume rendering where we need to sample very close to a surface in order to render its color, thus requiring a large number of samples, our learned renderer does not share this limitation and can compute the pixel color even with sparse samples. Our lightweight rendering and feature backbone components enable us to train on large-scale real-world datasets. We demonstrate through extensive experiments on two datasets that our method achieves state-of-the-art results, significantly outperforming existing approaches for novel view synthesis from sparse inputs. + +# 2. Related Work + +Image-based rendering. Image-based rendering (IBR) methods generate images from novel camera viewpoints by blending information from a set of input images. We provide a brief overview of some methods. Please refer to the review by Shum and Kang [41] for details. Some IBR approaches directly model the plenoptic function without using information about the scene geometry [20, 31]. Other approaches use a proxy scene geometry computed using multi-view stereo to guide the blending of information from the input images [3, 9, 16, 23]. While rendering without computing an explicit 3D geometry leads to higher-quality results, it requires a large number of input images. In contrast, methods that rely on 3D geometry can work with sparse image inputs. However, multi-view stereo from a sparse set of input views often leads to inaccurate geometry, especially for scenes with complex geometry, limiting the quality of rendered images. Methods have been proposed for higher-quality geometry computation [5, 15], optical flow-based refinement [4, 10, 11], and improved blending [14, 35, 38]. In contrast to these image-based rendering methods, we rely on priors learned from data that enable novel-view synthesis from just a wide-baseline stereo image. We do not create any explicit proxy geometry of the scene and are thus unaffected by inaccurate multi-view stereo. + +Single-Scene Volumetric Approaches. Recent progress in neural rendering [50] and neural fields [28, 42, 56] has led to a drastic jump in the quality of novel-view synthesis from several input images of a scene. Here, a 3D scene represent + +tation is optimized via differentiable rendering to fit a set of image observations. Early approaches leveraged voxel grids and learned renderers [26, 32, 44]. More recent approaches rely on neural fields [2, 27, 28, 56] to parameterize the 3D scene and volumetric rendering [26, 28, 49] for image synthesis. This leads to photorealistic view synthesis but requires hundreds of input images that densely sample the 3D scene. Hand-crafted and learned priors may reduce the number of required images to the order of three to ten [33], but 3D points still need to be observed from at least two perspectives. A major challenge of these approaches is the cost of accurate differentiable rendering, regularly requiring hundreds of samples per ray. Recent approaches have achieved impressive speed-ups in 3D reconstruction leveraging high-performance data structures and sparsity [6,13,24,29]. While promising, reconstruction can still take a few minutes per scene, and sparse data structures such as octrees and hash tables cannot easily be used with learned priors. + +Our approach tackles a different setting than these methods, using only a single wide-baseline stereo image as input, where 3D points are regularly only observed in a single view. Our approach does not require any per-scene optimization at test time. Instead, it reconstructs the scene in a single forward pass. Note that while our method does not achieve the quality of per-scene optimization methods that use hundreds of input images, it demonstrates a significant step up in novel view synthesis from very sparse image observations. + +Prior-based 3D Reconstruction and View Synthesis. Instead of overfitting to a single scene, differentiable rendering can also be used to supervise prior-based inference methods. Some methods generalize image-based rendering techniques by computing feature maps on top of a proxy geometry [1, 19, 38, 55]. Volume rendering using multi-plane images has been used for small baseline novel view synthesis [46, 52, 60, 61]. Early neural fields-based approaches [34, 45] were conditioned on a single global latent code and rendered via sphere tracing. In contrast to a global latent code, several approaches use a feature backbone to compute pixel-aligned features that can be transformed using MLPs [21, 51, 58] or transformers layers [37, 53] to a radiance field. Ideas from multi-view stereo such as the construction of plane-swept cost volumes [7, 18, 25], or multi-view feature matching [8] have been used for higher-quality results. + +Alternatively to these radiance field-based approaches, some methods use a light field rendering formulation where an oriented camera ray can directly be transformed to the pixel color as a function of the features computed from the input images [43, 48]. Scene Representation Transformers [39] use transformers with global attention to compute a set-latent representation that can be decoded to pixel colors when queried with a target camera ray. However, global attention layers on high-resolution input images are very compute and memory intensive. Developed concurrently + +with our work, Suhail et al. [47] proposed to use a transformer to only compute features for image patches along the epipolar rays of the pixel being rendered. This is still very expensive due to global attention layer computations over multiple image patches for every rendered pixel. In addition, this method ignores the context information of the scene, since all computation is performed only for patches that lie on the epipolar lines. + +All existing prior-based reconstruction methods either only support object-level scenes or very small baseline renderings, or rely on multiple image observations where most 3D points are observed in multiple input images. This is different from our setting where we only use a wide-baseline stereo image pair of scenes as input. + +# 3. Method + +Our goal is to render novel views of a 3D scene given a wide-baseline stereo image pair $\mathcal{I}_1$ and $\mathcal{I}_2$ . We assume known camera intrinsic $\mathbf{K}_i \in \mathbb{R}^{3 \times 3}$ and extrinsic $\mathbf{E}_i \in \mathbb{R}^{4 \times 3}$ expressed relative to context camera 1. We use a multiview encoder to compute pixel-aligned features, and a cross-attention-based renderer to transform the features into novel view renderings, see Figure 2 for an overview. + +# 3.1. Multiview Feature Encoding + +An essential part of novel view synthesis given context images is an accurate reconstruction of scene geometry. Our method implicitly reconstructs 3D geometry and appearance of the scene in the form of pixel-aligned feature maps for each stereo image. In prior work, pixel-aligned features are obtained by separately encoding each image via a vision transformer or CNN [21, 58]. However, in our early experiments, we found this led to artifacts in renderings observing boundary regions between context images. We hypothesize that separate encoding of images leads to inconsistent geometry reconstruction across context images. We thus introduce our multi-view encoder, which obtains pixel-aligned features by jointly processing the images and the relative pose between them. Encoding the pose information has also been shown to act as an effective inductive bias for 3D tasks [57]. + +We now describe this architecture in detail, which extends the dense vision transformer proposed by Ranftl et al. [36]. Please see Figure 2 for an overview. From each stereo image, we first independently extract convolutional features via a ResNet50 CNN. We then flatten both images, obtaining $2 \times 16 \times 16$ features in total. To each feature, we add (1) a learned per-pixel positional embedding encoding its pixel coordinate and (2) a camera pose embedding, obtained via a linear transform of the relative camera pose between context images 1 and 2. These tokens are processed by a vision transformer, which critically performs self-attention across all tokens across both images. In-between self-attention layers, per-image features are re-assembled into a spatial grid, + +![](images/a1a5fd49499271751542088f95be908850a03fa70f46a3f16c4456667709215d.jpg) +(a) Multi-View ViT Encoder + +![](images/a72db9d24243f83ad08a03e084f0a6c9a221f14e6bff8dee7b0ff309878d7934.jpg) +(b) Image-Centric Epipolar Line Sampling +(c) Neural Rendering + +![](images/fde0ba6b988906a17251616c1a4a0c60c6835f3a250aafe29eb65c7b3c205a02.jpg) +Figure 2. Method Overview. (a) Given context images from different viewpoints, a multi-view encoder extracts pixel-aligned features, leveraging attention across the images and their corresponding camera pose embeddings. (b) Given a target ray, in each context view, we sample primary features along the epipolar line equidistant in pixel space. We then project the corresponding 3D points onto the other views and sample corresponding secondary epipolar line features, where out-of-bounds features are set to zero. (c) We render the target ray by performing cross-attention over the set of all primary and secondary epipolar line features from all views. + +up-sampled, and processed by a fusion CNN [36] to yield per-image spatial feature map. Directly using these spatial feature maps for novel view synthesis leads to blurry reconstructions, due to the loss of high-frequency texture information. We thus concatenate these features with high-resolution image features obtained from a shallow CNN. + +# 3.2. Epipolar Line Sampling and Feature Matching + +We aim to render an image of the scene encoded in the two pixel-aligned feature maps from a novel camera viewpoint. A common way to achieve this is volume rendering, where we cast a camera ray, compute density and color values at many depths along the ray, and integrate them to compute the color of the pixel. Sampling locations are determined in 3D. Coarse samples are either uniformly spaced in euclidean space or spaced with uniform disparity, and fine samples are distributed closer to the surface as computed by the coarse samples [2, 28, 30]. However, in our regime of generalizable novel view synthesis with pixel-aligned feature maps, this sampling scheme is suboptimal. In this case, sampling along the ray should be determined by the resolution of the context images: the number of pixels along the epipolar line is the maximum effective number of samples available for any method. More samples would not provide any extra information. We propose a sampling strategy to exploit this and demonstrate its effectiveness in an ablation study. + +Consider a pixel coordinate $\mathbf{u}_t = (u, v)$ in the target image $\mathcal{I}_t$ , with assumed known intrinsic $\mathbf{K}_t$ and extrinsic $\mathbf{T}_t = \left[ \begin{array}{cc} \mathbf{R}_t & t \\ \mathbf{0} & 1 \end{array} \right]$ camera parameters relative to the context camera $\mathcal{I}_1$ . Its epipolar lines $\mathbf{l}_{\{1,2\}}$ , in context cameras 1 and 2 are given as: + +$$ +\mathbf {l} _ {i} = \mathbf {F} _ {i} [ u, v, 1 ] ^ {T} = \mathbf {K} _ {i} ^ {- T} ([ \mathbf {t} _ {t} ] _ {\times} \mathbf {R} _ {t}) \mathbf {K} _ {t} ^ {- 1} [ u, v, 1 ] ^ {T} (1) +$$ + +via the fundamental matrix $\mathbf{F}_i$ . We now uniformly sample $N$ pixel coordinates along the line segment of the epipolar line within the image boundaries. To enable the renderer to reason about whether to use a certain pixel-aligned feature or not, a critical piece of information is the depth in the context coordinate frame at which we are sampling this feature. This depth value can be computed via triangulation, using a closed-form expression. Please refer to the supplemental document for details. We now obtain $N$ tuples $\{(d,\mathbf{f})_k\}_{k=1}^N$ of depth $d$ and image feature $\mathbf{f}$ per context image for a total of $2N$ samples which we call primary samples. + +We further propose a feature matching module to refine the geometry encoded in the primary epipolar line samples via correspondence matching. Consider a primary epipolar line sample obtained from context image $i$ , a tuple $(d, \mathbf{f})$ corresponding to a pixel coordinate $\mathbf{u}_t$ . We propose to augment this sample by a corresponding feature in the other context image. Specifically, we first solve for the corresponding 3D point, and then project this 3D point onto the other context image to retrieve a corresponding feature $\hat{\mathbf{f}}$ , which we refer to as a secondary feature. The secondary features are set to zero if the projected point is out of the image bounds. Intuitively, primary and secondary features together allow a final stage of geometry refinement for 3D points that are observed in both images: if the features agree, this sample likely encodes a surface. If the projected point on the other image lies outside the image boundary, we simply set the secondary features to zeros. We obtain the input to the renderer as the final set of features by concatenating each primary epipolar line feature with its corresponding secondary feature in the other context view, yielding a set $\{(d, \mathbf{f}, \hat{\mathbf{f}})_k\}_{k=1}^{2N}$ . In practice, we sample $N = 64$ points on the epipolar lines + +for both images, leading to a total of $2N = 128$ tuples. + +# 3.3. Differentiable Rendering via Cross-Attention + +To render the target ray, it remains to map the set of epipolar line samples $\{(d,\mathbf{f},\hat{\mathbf{f}})_k\}_{k = 1}^{2N}$ to a color value. As this operation has to be executed once per ray, a key consideration in the design of this function is computational cost. We propose to perform rendering via a lightweight cross-attention decoder. + +For each point on the epipolar line, we embed the target ray origin $\mathbf{o}_t$ , target ray direction $\mathbf{r}_t$ , depth with respect to the target ray origin $d_t$ , and context camera ray direction $\mathbf{r}_c$ for the epipolar point into a ray query token $\mathbf{q}$ via a shallow MLP as $\Phi([\mathbf{o}_t, \mathbf{r}_t, \mathbf{r}_c, d_t])$ . The $2N$ ray feature values are independently transformed into key and value tokens using a 2-layer MLP. Our renderer now performs two rounds of cross-attention over this set of features to obtain a final feature embedding, which is then decoded into color via a small MLP. + +The expectation of the Softmax distribution over the sampled features gives a rough idea of the scene depth as $e = \sum_{k} d_{k} \alpha_{k}$ , where $d_{k}$ denotes the depth of the $k$ -th epipolar ray sample along the target ray and $\alpha_{k}$ is the corresponding Softmax weight as computed by the cross-attention operator. Note that $e$ is not the actual depth but a measure of which epipolar samples the renderer uses to compute the pixel color. Unlike volume rendering, where we need to sample very close to a surface to render its color, our light field-based renderer can reason about the surface without exactly sampling on it. The learned cross-attention layers can use the target camera ray information, along with a sparse set of epipolar samples, to compute the pixel color. Thus, our method does not require explicit computation of accurate scene depth for rendering. + +# 3.4. Training and Losses + +We now have a rendered image from a novel camera viewpoint. Our loss function consists of two terms: + +$$ +\mathcal {L} = \mathcal {L} _ {\mathrm {i m g}} + \lambda_ {\mathrm {r e g}} \mathcal {L} _ {\mathrm {r e g}}. \tag {2} +$$ + +The first term evaluates the difference between the rendered image from a novel camera viewpoint, $R$ and the ground truth, $G$ as: + +$$ +\mathcal {L} _ {\mathrm {i m g}} = \left\| R - G \right\| _ {1} + \lambda_ {\mathrm {L P I P S}} \mathcal {L} _ {\mathrm {L P I P S}} (R, G), \tag {3} +$$ + +where $\mathcal{L}_{\mathrm{LPIPS}}$ is the LPIPS perceptual loss [59]. In practice, we render square patches with a length of 32 pixels and evaluate these image losses at the patch level. + +We also use a regularization loss on the cross-attention weights of the renderer for better multi-view consistency: + +$$ +\mathcal {L} _ {\text {r e g}} = \sum_ {(u, v)} \sum_ {\left(u ^ {\prime} v ^ {\prime}\right) \in \mathcal {N} (u, v)} \left(\left(e (u, v) - e \left(u ^ {\prime}, v ^ {\prime}\right)\right) ^ {2} . \right. \tag {4} +$$ + +Here, $e(u,v)$ denotes the expected value of the depth of the epipolar samples at pixel $(u,v)$ , and $\mathcal{N}()$ defines the neighborhood around a pixel. + +For better generalization, we further perform several geometrically-consistent data augmentations during the training procedure. We center crop and scale the input and target images, which leads to transformation in the intrinsic of the camera. We also flip the images which leads to transformation of the extrinsics. + +# 4. Experiments + +We quantitatively and qualitatively show that our approach can effectively render novel views from wide-baseline stereo pairs. We describe our underlying experimental setup in Section 4.1. Next, we evaluate our approach on challenging indoor scenes with substantial occlusions in Section 4.2. We further evaluate on outdoor scenes in Section 4.3. We analyze and ablate the underlying components in Section 4.4. Finally, we illustrate how our approach can render novel views of unposed images of scenes captured in the wild in Section 4.5. + +# 4.1. Experimental Setup + +Datasets. We train and evaluate our approach on RealEstate10k [61], a large dataset of indoor and outdoor scenes, and ACID [22], a large dataset of outdoor scenes. We use 67477 scenes for training and 7289 scenes for testing for RealEstate10k, and 11075 scenes for training and 1972 scenes for testing for ACID, following default splits. We train our method on images at $256 \times 256$ resolution and evaluate methods on their ability to reconstruct intermediate views in test scenes (details in the supplement). + +Baselines. We compare to several existing approaches for novel view synthesis from sparse image observations. We compare to pixelNeRF [58] and IBRNet [53] that use pixel-aligned features, which are decoded into 3D volumes rendered using volumetric rendering. We also compare to Generalizable Patch-based Rendering (GPNR) [47], which uses a vision transformer-based backbone to compute epipolar features, and a light field-based renderer to compute pixel colors. These baselines cover a wide range of design choices used in existing methods, such as pixel-aligned feature maps computed using CNNs [53, 58] and transformers [47], volumetric rendering by decoding features using MLPs [58] and transformers [53], and light field-based rendering [47]. We use publicly available codebases for all baselines and train them on the same datasets we use for fair evaluations. Please refer to the supplemental for comparisons to more baselines. + +Evaluation Metrics. We use LPIPS [59], PSNR, SSIM [54], and MSE metrics to compare the image quality of rendered images with the ground truth. + +![](images/6371e3f4bca11f700b52ee3f6d20be9065952e43fb7e46de40e667a17b55e988.jpg) +Figure 3. Comparative Rendering Results on RealEstate10k. Our approach can render novel views of indoor scenes with substantial occlusions with high fidelity using a wide-baseline input image pair, outperforming all baselines. Note that many points of the 3D scene are only observed in a single image in such inputs. Our method can correctly reason about the 3D structures from such sparse views. + +![](images/49a25949360998c0f07f49215acf57c74d3c35008fb265098897928e34cc4598.jpg) +Input Images + +![](images/21cb3a05ce7146e045124fe8a6f77ea945682722349153419edfa65b08c3867f.jpg) +Rendered Novel Views + +![](images/7c1a38aac5c8d14d34b55b5ff15339dea9581db951fa18d568c5ccbd1b6ffd8c.jpg) + +![](images/b9b70f2b0abd4c780345dfb182420516a4b46aca7db015474419608d430c39bf.jpg) + +![](images/e4c10e1a348f79a1bf502158ea250a4790884c613af7d18af35e5c701e8d164d.jpg) + +![](images/ef5d548a5ee09b3d083fd6989fe50f0aa8f0c376e81ce3a951d9c3fa74c9e2cc.jpg) + +![](images/12c79c3ef8ed8b195a039d298ebd00ff61f91169473370553cd90ab826edf0cf.jpg) +Figure 4. Novel view renderings of our approach given a large baseline stereo pair. Our approach can synthesize intermediate views that are substantially different from input images, even with very limited overlap between images. + +![](images/d194d9e77f70e031c15abb6268450361e8caa5b4a4333ae9e358e3ad38d517a3.jpg) + +# 4.2. Indoor Scene Neural Rendering + +We first evaluate the ability of our approach and baselines to render novel views in complex indoor environments with substantial occlusions between objects. + +Qualitative Results. In Figure 3, we provide qualitative results of novel view renderings of our approach, compared + +
MethodLPIPS ↓SSIM ↑PSNR ↑MSE ↓
pixelNeRF [58]0.5910.46013.910.0440
IBRNet [53]0.5320.48415.990.0280
GPNR [47]0.4590.74818.550.0165
Ours0.2620.83921.380.0110
+ +Table 1. Novel view rendering performance on RealEstate10K. Our method outperforms all baselines on all metrics. + +
MethodLPIPS ↓SSIM ↑PSNR ↑MSE ↓
pixelNeRF [58]0.6280.46416.480.0275
IBRNet [53]0.3850.51319.240.0167
GPNR [47]0.5580.71917.570.0218
Ours0.3640.78123.630.0074
+ +Table 2. Novel view rendering performance on ACID. Our method outperforms all baselines on all metrics. + +to each of our baselines. We provide additional novel view results of our method in Figure 4. Compared to the baselines, our approach reconstructs the 3D structure of the scene better, and also captures more high-frequency details. + +Quantitative Results. We quantitatively evaluate our approach and baselines in Table 1. We find that our approach substantially outperforms each compared baseline in terms of all of our metrics. + +![](images/42f14e2deba0b5e61167025577668655bcb477141e05160d58022eed9373b72a.jpg) +Input Images + +![](images/abddbbb816f119ea8bd7e1149a20aef19c0c06a8163f238040c7c9ff298b711a.jpg) +PixelNeRF + +![](images/01cd0ef4ed995a7c1071a479cbb0096b6041ea83b023b166cfba1c9315434a03.jpg) +IBRNet +Figure 5. Comparative Results on ACID. Our approach is able to render novels views with higher quality than all baselines. + +![](images/9fa88af3cb717bd1266cec53b31d29cbbf696ae52aba0f6918d6bfa4f7fdb350.jpg) +GPNR + +![](images/6cb6c68fbf8e48040560716476eff22a51045703b5675dc26f0e08152047c3c1.jpg) +Ours + +![](images/133bb792f2ca8c67017a0894c47318c4ebb45acbbd83740667f036a74b4ed448.jpg) +Target + +# 4.3. Outdoor Scene Neural Rendering + +We further evaluate on outdoor scenes with potentially unbounded depth. + +Qualitative Results. We illustrate qualitative results in Figure 5. In comparison to the baselines, our approach is able to more accurately reconstruct the geometry, and is able to synthesize multi-view consistent renderings from two large baseline views. + +Quantitative Results. Similar to indoor scenes, our approach also outperforms all baselines in terms of all metrics on outdoor scenes, see Table 2. + +# 4.4. Ablations and Analysis + +We next analyze and ablate individual components of our approach. We use the RealEstate10k dataset for these experiments. + +Ablations. We evaluate the importance of different components of our method in Table 3. The "Base Model" corresponds to a vanilla architecture that does not include some of our proposed contributions. It samples points uniformly in 3D, instead of our proposed 2D epipolar line sampling. It uses a monocular encoder instead of our proposed multiview encoder, and does not use correspondence matching across views for refining the geometry. It also does not use the regularization loss for multi-view consistency or any data augmentation during training. We find that all components of our approach are essential for high-quality performance. The results in Table 3 show that sampling in 3D sub-optimally uses the information in the feature maps, that our multi-view encoder and cross-image correspondence matching can compute features that better encode the 3D scene structure compared to monocular encoders, and that data augmentation helps with generalization. While we found that the incorporation of the regularization loss led to a slight decrease in PSNR, we found that it improved multi-view consistency in the rendered video results, and also improved both LPIPS and SSIM perceptual metrics. + +
ModelsLPIPS↓SSIM↑PSNR↑MSE↓
Base Model0.4520.73518.110.0201
+ 2D Sampling0.4280.76219.020.0159
+ Cross Correspondence0.4150.76619.520.0142
+ Multiview Encoder0.3610.79420.430.0132
+ Regularization Loss0.3580.80819.840.0139
+ Data Aug0.2620.83921.380.0110
+ +Table 3. Ablations. All components of our proposed method are essential for high-quality novel view synthesis. + +![](images/488b91f085d526d6298d15870de45d7520469f89499d6c01d4c1fa5ee2e40296.jpg) +Figure 6. FPS vs PSNR. Our approach strikes the best trade-off between rendering quality and rendering speed. We can further reduce the number of Epipolar samples ("Ours (Faster)"), which makes our method faster than all baselines, while still significantly outperforming them in terms of rendering quality. + +Speed. Next, in Figure 6, we study the relationship between rendering quality and rendering speed for all approaches. Our lightweight approach achieves the best tradeoff, significantly outperforming all methods in terms of rendering quality, while being at-par with the most efficient baseline. By reducing the number of sampled epipolar points from 64 to 48 samples per image, we can further speed up our approach, outperforming all baselines both in terms of rendering speed and image quality. + +Epipolar Attention. Finally, we visualize the underlying epipolar attention weights learned by our approach in Figure 7. The expected value of the depths of the epipolar sam + +![](images/4c27e839dfd7bf75c2caa2601fbe1f21cefac1b7d9fc8eef4ec0b731fb85e6e7.jpg) +Figure 7. Visualization of Epipolar Attention Weights. The expected value of the depths of the epipolar samples under the attention weights can be seen as a depth proxy. As our renderer is not a volume renderer, these attention weights need not exactly correspond to the actual depth for correct renderings. + +plies can be seen as a proxy depth and corresponds roughly to the underlying geometry of the scene. This enables us to analyze the learned computation of our renderer. + +# 4.5. Novel View Synthesis from Unposed Images + +Our method uses a wide-baseline stereo image as input with known relative pose between them. We show that our method can perform novel view synthesis even without the knowledge of this relative pose information. In this case, we utilize SuperGlue [40] to compute reliable pixel correspondences between the input images. Since we do not know the camera intrinsics for in-the-wild images, we use the average intrinsics of the RealEstate10k dataset and compute the Essential matrix from the correspondences using RANSAC [12]. We then compute the pose information from the essential matrix [17] and use it as input for our method. Note that the recovered translation is only defined up to a scale. Figure 8 demonstrates results on some in-the-wild scenes using images from the internet. Even in this unposed setting, our method can reason about the geometry of the scene by aggregating information across the sparse input views. This is an extremely challenging setting, and existing approaches for novel view synthesis from sparse views do not demonstrate any results on unposed images. + +# 5. Discussion + +While we have presented the first approach for novel view synthesis of scenes from very sparse input views, our approach still has several limitations. Our rendering results are not at the same quality as those obtained by methods that optimize on single scenes using more images. Learning priors that enable novel view synthesis from sparse views is a significantly more challenging problem compared to using a large number of input images, where 3D points are regularly observed in many images. Our approach takes a step towards + +![](images/4a1ef1bdf4da55787f197b8a2eef23d7a95a7c2c9949e04f3997aa9e73cd8871.jpg) +Input Images +Figure 8. Novel View Synthesis from Unposed Images. Our approach can also render novel views using two unposed images captured in the wild. Note that parts of the scene only visible in one of the images can be correctly rendered from novel viewpoints. + +![](images/c9111acf8bf164ac992915c251c5db57dd0dd77f076ecc45d15459e14efd5608.jpg) +Rendered Novel Views + +photorealistic renderings of scenes using only sparse views. As our approach relies on learned priors, it does not generalize well to new scenes with very different appearances compared to the training scenes. However, our efficient approach lends itself to large-scale training on diverse datasets, in turn enabling reconstruction of diverse scenes. Finally, while our method, in theory, can be extended to take more than two input views, we have only experimented with two views as a first step towards very sparse multi-view neural rendering. + +# 6. Conclusion + +We introduce a method for implicit 3D reconstruction and novel view synthesis from a single, wide-baseline stereo pair, trained using only self-supervision from posed color images. By leveraging a multi-view encoder, an image-space epipolar line feature sampling scheme, and a cross-attention based renderer, our method surpasses the quality of prior art on datasets of challenging scenes. Our method further strikes a compelling trade-off between rendering speed and quality, rendering novel views significantly faster than most prior methods. Meanwhile, leveraging epipolar line geometry strikes a compelling trade-off between structured and generalist learning paradigms, enabling us to train our method on real-world datasets such as RealEstate10k. We believe that this work will inspire the community towards further exploring the regime of extreme few-shot and generalizable novel view synthesis. + +Acknowledgements. This work was supported by the National Science Foundation under Grant No. 2211259, and by the Singapore DSTA under DST00OECI20300823 (New Representations for Vision). Yilun Du is supported by a NSF Graduate Research Fellowship. + +# References + +[1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graphics. In European Conference on Computer Vision, pages 696-712. Springer, 2020. 3 +[2] Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields, 2021. 3, 4 +[3] Chris Buehler, Michael Bosse, Leonard McMillan, Steven Gortler, and Michael Cohen. Unstructured lumigraph rendering. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 425-432, 2001. 2 +[4] Dan Casas, Christian Richardt, John Collomosse, Christian Theobalt, and Adrian Hilton. 4d model flow: Precomputed appearance alignment for real-time 4d video interpolation. In Computer Graphics Forum, volume 34, pages 173-182. Wiley Online Library, 2015. 2 +[5] Gaurav Chaurasia, Sylvain Duchene, Olga Sorkine-Hornung, and George Drettakis. Depth synthesis and local warps for plausible image-based navigation. ACM Transactions on Graphics (TOG), 32(3):1-12, 2013. 2 +[6] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. arXiv preprint arXiv:2203.09517, 2022. 3 +[7] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14124-14133, 2021. 2, 3 +[8] Julian Chibane, Aayush Bansal, Verica Lazova, and Gerard Pons-Moll. Stereo radiance fields (srf): Learning view synthesis from sparse views of novel scenes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, jun 2021. 2, 3 +[9] Paul E Debevec, Camillo J Taylor, and Jitendra Malik. Modeling and rendering architecture from photographs: A hybrid geometry-and image-based approach. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 11-20, 1996. 2 +[10] Ruofei Du, Ming Chuang, Wayne Chang, Hugues Hoppe, and Amitabh Varshney. Montage4d: Interactive seamless fusion of multiview video textures. 2018. 2 +[11] Martin Eisemann, Bert De Decker, Marcus Magnor, Philippe Bekaert, Edilson De Aguiar, Naveed Ahmed, Christian Theobalt, and Anita Sellent. Floating textures. In Computer graphics forum, volume 27, pages 409-418. Wiley Online Library, 2008. 2 +[12] Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 8 +[13] Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In Proceedings of + +the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5501-5510, 2022. 3 +[14] Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Brostow. Deep blending for free-viewpoint image-based rendering. ACM Transactions on Graphics (TOG), 37(6):1-15, 2018. 2 +[15] Peter Hedman, Tobias Ritschel, George Drettakis, and Gabriel Brostow. Scalable inside-out image-based rendering. ACM Transactions on Graphics (TOG), 35(6):1-11, 2016. 2 +[16] Benno Heigl, Reinhard Koch, Marc Pollefeys, Joachim Denzler, and L Van Gool. Plenoptic modeling and rendering from image sequences taken by a hand-held camera. In Mustererkennung 1999, pages 94-101. Springer, 1999. 2 +[17] Berthold KP Horn. Recovering baseline and orientation from essential matrix. J. Opt. Soc. Am, 110, 1990. 8 +[18] Mohammad Mahdi Johari, Yann Lepoittevin, and François Fleuret. Geonerf: Generalizing nerf with geometry priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18365-18375, 2022. 2, 3 +[19] Georgios Kopanas, Julien Philip, Thomas Leimkuhler, and George Drettakis. Point-based neural rendering with perview optimization. In Computer Graphics Forum, volume 40, pages 29-43. Wiley Online Library, 2021. 3 +[20] Marc Levoy and Pat Hanrahan. Light field rendering. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 31-42, 1996. 2 +[21] Kai-En Lin, Lin Yen-Chen, Wei-Sheng Lai, Tsung-Yi Lin, Yi-Chang Shih, and Ravi Ramamoorthi. Vision transformer for nef-based view synthesis from a single input image. arXiv preprint arXiv:2207.05736, 2022. 3 +[22] Andrew Liu, Richard Tucker, Varun Jampani, Ameesh Makadia, Noah Snavely, and Angjoo Kanazawa. Infinite nature: Perpetual view generation of natural scenes from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2021. 5 +[23] Feng Liu, Michael Gleicher, Hailin Jin, and Aseem Agarwala. Content-preserving warps for 3d video stabilization. ACM Transactions on Graphics (ToG), 28(3):1-9, 2009. 2 +[24] Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. NeurIPS, 2020. 3 +[25] Yuan Liu, Sida Peng, Lingjie Liu, Qianqian Wang, Peng Wang, Christian Theobalt, Xiaowei Zhou, and Wenping Wang. Neural rays for occlusion-aware image-based rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7824-7833, 2022. 2, 3 +[26] Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural volumes: Learning dynamic renderable volumes from images. ACM Trans. Graph., 2019. 1, 3 +[27] Ricardo Martin-Brualla, Noha Radwan, Mehdi SM Sajjadi, Jonathan T Barron, Alexey Dosovitskiy, and Daniel Duckworth. Nerf in the wild: Neural radiance fields for unconstrained photo collections. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7210-7219, 2021. 3 + +[28] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. In Proceedings of the European Conference on Computer Vision (ECCV), pages 405-421, 2020. 1, 2, 3, 4 +[29] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-102:15, July 2022. 3 +[30] Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Joerg H. Mueller, Chakravarty R. Alla Chaitanya, Anton S. Kaplanyan, and Markus Steinberger. DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks. Computer Graphics Forum, 2021. 4 +[31] Ren Ng, Marc Levoy, Mathieu Brédif, Gene Duval, Mark Horowitz, and Pat Hanrahan. Light field photography with a hand-held plenoptic camera. PhD thesis, Stanford University, 2005. 2 +[32] Thu H Nguyen-Phuoc, Chuan Li, Stephen Balaban, and Yongliang Yang. Rendernet: A deep convolutional network for differentiable rendering from 3d shapes. Advances in neural information processing systems, 31, 2018. 3 +[33] Michael Niemeyer, Jonathan T Barron, Ben Mildenhall, Mehdi SM Sajjadi, Andreas Geiger, and Noha Radwan. Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5480-5490, 2022. 3 +[34] Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3504-3515, 2020. 3 +[35] Eric Penner and Li Zhang. Soft 3d reconstruction for view synthesis. ACM Transactions on Graphics (TOG), 36(6):1-11, 2017. 2 +[36] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12179-12188, 2021. 3, 4 +[37] Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10901-10911, 2021. 3 +[38] Gernot Riegler and Vladlen Koltun. Free view synthesis. In European Conference on Computer Vision, 2020. 2, 3 +[39] Mehdi SM Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, et al. Scene representation transformer: Geometry-free novel view synthesis through set-latent scene representations. arXiv preprint arXiv:2111.13152, 2021. 3 +[40] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In Proceedings of the + +IEEE/CVF conference on computer vision and pattern recognition, pages 4938-4947, 2020. 8 +[41] Harry Shum and Sing Bing Kang. Review of image-based rendering techniques. In Visual Communications and Image Processing 2000, volume 4067, pages 2-13. SPIE, 2000. 2 +[42] Vincent Sitzmann, Julien N.P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. In Proc. NeurIPS, 2020. 2 +[43] Vincent Sitzmann, Semon Rezchikov, Bill Freeman, Josh Tenenbaum, and Fredo Durand. Light field networks: Neural scene representations with single-evaluation rendering. In Advances in Neural Information Processing Systems (NeurIPS), 2021. 3 +[44] Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhöfer. Deepvoxels: Learning persistent 3d feature embeddings. In Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 2019. 1, 3 +[45] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3d-structure-aware neural scene representations. Advances in Neural Information Processing Systems, 32, 2019. 1, 2, 3 +[46] Pratul P Srinivasan, Richard Tucker, Jonathan T Barron, Ravi Ramamoorthi, Ren Ng, and Noah Snavely. Pushing the boundaries of view extrapolation with multiplane images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 175-184, 2019. 3 +[47] Mohammed Suhail, Carlos Esteves, Leonid Sigal, and Ameesh Makadia. Generalizable patch-based neural rendering. In European Conference on Computer Vision. Springer, 2022. 2, 3, 5, 6 +[48] Mohammed Suhail, Carlos Esteves, Leonid Sigal, and Ameesh Makadia. Light field neural rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8269-8279, 2022. 3 +[49] Andrea Tagliasacchi and Ben Mildenhall. Volume rendering digest (for nef). arXiv preprint arXiv:2209.02417, 2022. 3 +[50] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, W Yifan, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, et al. Advances in neural rendering. In Computer Graphics Forum, pages 703-735. Wiley Online Library, 2022. 1, 2 +[51] Alex Trevithick and Bo Yang. Grf: Learning a general radiance field for 3d representation and rendering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15182-15192, 2021. 2, 3 +[52] Richard Tucker and Noah Snavely. Single-view view synthesis with multiplane images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 551-560, 2020. 3 +[53] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo MartinBrualla, Noah Snavely, and Thomas Funkhouser. Ibrnet: Learning multi-view image-based rendering. In CVPR, 2021. 2, 3, 5, 6 +[54] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: From error visibility to + +structural similarity. IEEE Transactions on Image Processing, 2004. 5 +[55] Olivia Wiles, Georgia Gkioxari, Richard Szeliski, and Justin Johnson. Synsin: End-to-end view synthesis from a single image. In Proc. CVPR, pages 7467-7477, 2020. 3 +[56] Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. Neural fields in visual computing and beyond. Computer Graphics Forum, 2022. 2, 3 +[57] Wang Yifan, Carl Doersch, Relja Arandjelović, João Carreira, and Andrew Zisserman. Input-level inductive biases for 3d reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6176-6186, 2022. 3 +[58] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelNeRF: Neural radiance fields from one or few images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 2, 3, 5, 6 +[59] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep networks as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 5 +[60] Yunzhi Zhang and Jiajun Wu. Video extrapolation in space and time. arXiv e-prints, pages arXiv-2205, 2022. 3 +[61] Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. ACM Trans. Graph. (Proc. 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Novel view synthesis from a single wide-baseline stereo image pair. In a single forward pass, our method maps a wide-baseline stereo image pair to features that enable fast rendering of novel views, trained using only posed multi-view images of static scenes without ground-truth or proxy geometry. 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In this challenging regime, 3D scene points are regularly observed only once, requiring prior-based reconstruction of scene geometry and appearance. We find that existing approaches to novel view synthesis from sparse observations fail due to recovering incorrect 3D geometry and due to the high cost of differentiable rendering that precludes their scaling to large-scale training. We take a step towards resolving these shortcomings by formulating a multi-view transformer encoder, proposing an efficient, image-space epipolar line sampling scheme to assemble image features for a target ray, and a lightweight cross-attention-based renderer. Our contributions enable training of our method on a large-scale real-world dataset of indoor and outdoor scenes. We demonstrate that our method learns powerful multi-view geometry priors while reducing the rendering time. We conduct extensive comparisons on held-out test scenes across two real-world datasets, signif" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 438, + 547, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 438, + 547, + 474 + ], + "spans": [ + { + "bbox": [ + 306, + 438, + 547, + 474 + ], + "type": "text", + "content": "icantly outperforming prior work on novel view synthesis from sparse image observations and achieving multi-view-consistent novel view synthesis." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 500, + 386, + 511 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 500, + 386, + 511 + ], + "spans": [ + { + "bbox": [ + 306, + 500, + 386, + 511 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 521, + 547, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 547, + 640 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 547, + 640 + ], + "type": "text", + "content": "The goal of novel view synthesis is to render images of a scene from unseen camera viewpoints given a set of image observations. In recent years, the emergence of differentiable rendering [26, 28, 44, 45, 50] has led to a leap in quality and applicability of these approaches, enabling near photorealistic results for most real-world 3D scenes. However, methods that approach photorealism require hundreds or even thousands of images carefully exploring every part of the scene, where special care must be taken by the user to densely image all 3D points in the scene from multiple angles." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 642, + 548, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 548, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 548, + 714 + ], + "type": "text", + "content": "In contrast, we are interested in the regime of novel view synthesis from a sparse set of context views. Specifically, this paper explores whether it is possible to synthesize novel view images using an extremely sparse set of observations. In the most challenging case, this problem reduces to using input images such that every 3D point in the scene is only ob" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 683, + 107, + 693 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 683, + 107, + 693 + ], + "spans": [ + { + "bbox": [ + 47, + 683, + 107, + 693 + ], + "type": "text", + "content": "† Equal Advising" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 48, + 694, + 287, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 694, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 694, + 287, + 712 + ], + "type": "text", + "content": "Project website: https://yilundu.github.io/widebaseline/" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "4970" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "content": "served from a single camera perspective. Towards this goal, we propose a system that uses only a single wide-baseline stereo image pair of the scene as input. This stereo image pair regularly has little overlap, such that many 3D points are indeed only observed in one of the images, see Fig. 1. Image observations themselves are thus insufficient information to compute 3D geometry and appearance via multi-view stereo, and we must instead learn prior-based 3D reconstruction. Nevertheless, reasoning about multi-view consistency is critical, as prior-based reconstructions must agree across images to ensure multi-view-consistent reconstruction." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 206, + 289, + 458 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 206, + 289, + 458 + ], + "spans": [ + { + "bbox": [ + 46, + 206, + 289, + 458 + ], + "type": "text", + "content": "This is a novel problem setting: While some existing methods demonstrate novel view synthesis from very sparse observations [45, 51, 58], they are limited to object-level scenes. In contrast, we are interested in large real-world scenes that are composed of multiple objects with complex geometry and occlusions. Previous approaches for novel view synthesis of scenes focus on small baseline renderings using " + }, + { + "bbox": [ + 46, + 206, + 289, + 458 + ], + "type": "inline_equation", + "content": "3 - 10" + }, + { + "bbox": [ + 46, + 206, + 289, + 458 + ], + "type": "text", + "content": " images as input [7, 8, 18, 25, 47, 53, 58]. In this setting, most 3D points in the scene are observed in multiple input images, and multi-view feature correspondences can be used to regress 3D geometry and appearance. Thus, these methods in practice learn to amortize multi-view stereo. In our setting, we use a wide-baseline stereo image pair as input, where it is not sufficient to rely on multi-view feature correspondences due to many points only being observed in a single view. We show that in this challenging setting, existing approaches do not faithfully recover the 3D geometry of the scene. In addition, most existing methods rely on costly volume rendering for novel view synthesis, where the number of samples per ray required for high-quality rendering makes it difficult to train on complex real-world scenes." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 460, + 289, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 460, + 289, + 640 + ], + "spans": [ + { + "bbox": [ + 46, + 460, + 289, + 640 + ], + "type": "text", + "content": "In this paper, we propose a new method that addresses these limitations, and provides the first solution for high-quality novel view synthesis of a scene from a wide-baseline stereo image pair. To better reason about the 3D scene, we introduce a multi-view vision transformer that computes pixel-aligned features for each input image. In contrast to a monocular image encoder commonly used in previous approaches [51, 53, 58], the multi-view transformer uses the camera pose information as input to better reason about the scene geometry. We reduce the memory and computational costs for computing image features by combining this vision transformer at lower resolutions with a CNN at higher resolutions. A multi-view feature matching step further refines the geometry encoded in these feature maps for any 3D point that can be observed in both images." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 642, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 289, + 715 + ], + "type": "text", + "content": "We also introduce an efficient differentiable renderer that enables large-scale training. Existing approaches that use volume rendering sample points along camera rays in 3D and project these points onto the image planes to compute the corresponding features using bilinear interpolation. Since perspective projection is a non-linear operation, uniformly" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 72, + 548, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 548, + 337 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 548, + 337 + ], + "type": "text", + "content": "sampled 3D points are not uniformly distributed in 2D, leading to some pixels in the feature maps being sampled multiple times, and other pixels not being sampled at all. Thus, this sampling strategy does not use the information in the pixel-aligned feature maps optimally. We instead take an image-centric sampling approach where we first compute the epipolar lines of a target pixel in the input images, and sample points uniformly on these lines in 2D. This exploits the fact that the number of pixels along the epipolar lines is the maximum effective number of samples. In addition, we use lightweight cross-attention layers that directly aggregate the sampled features and compute the pixel color. In contrast to volume rendering where we need to sample very close to a surface in order to render its color, thus requiring a large number of samples, our learned renderer does not share this limitation and can compute the pixel color even with sparse samples. Our lightweight rendering and feature backbone components enable us to train on large-scale real-world datasets. We demonstrate through extensive experiments on two datasets that our method achieves state-of-the-art results, significantly outperforming existing approaches for novel view synthesis from sparse inputs." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 306, + 346, + 392, + 358 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 346, + 392, + 358 + ], + "spans": [ + { + "bbox": [ + 306, + 346, + 392, + 358 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 367, + 548, + 656 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 367, + 548, + 656 + ], + "spans": [ + { + "bbox": [ + 304, + 367, + 548, + 656 + ], + "type": "text", + "content": "Image-based rendering. Image-based rendering (IBR) methods generate images from novel camera viewpoints by blending information from a set of input images. We provide a brief overview of some methods. Please refer to the review by Shum and Kang [41] for details. Some IBR approaches directly model the plenoptic function without using information about the scene geometry [20, 31]. Other approaches use a proxy scene geometry computed using multi-view stereo to guide the blending of information from the input images [3, 9, 16, 23]. While rendering without computing an explicit 3D geometry leads to higher-quality results, it requires a large number of input images. In contrast, methods that rely on 3D geometry can work with sparse image inputs. However, multi-view stereo from a sparse set of input views often leads to inaccurate geometry, especially for scenes with complex geometry, limiting the quality of rendered images. Methods have been proposed for higher-quality geometry computation [5, 15], optical flow-based refinement [4, 10, 11], and improved blending [14, 35, 38]. In contrast to these image-based rendering methods, we rely on priors learned from data that enable novel-view synthesis from just a wide-baseline stereo image. We do not create any explicit proxy geometry of the scene and are thus unaffected by inaccurate multi-view stereo." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 666, + 548, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 666, + 548, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 666, + 548, + 715 + ], + "type": "text", + "content": "Single-Scene Volumetric Approaches. Recent progress in neural rendering [50] and neural fields [28, 42, 56] has led to a drastic jump in the quality of novel-view synthesis from several input images of a scene. Here, a 3D scene represent" + } + ] + } + ], + "index": 7 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "type": "text", + "content": "4971" + } + ] + } + ], + "index": 8 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 287 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 287 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 287 + ], + "type": "text", + "content": "tation is optimized via differentiable rendering to fit a set of image observations. Early approaches leveraged voxel grids and learned renderers [26, 32, 44]. More recent approaches rely on neural fields [2, 27, 28, 56] to parameterize the 3D scene and volumetric rendering [26, 28, 49] for image synthesis. This leads to photorealistic view synthesis but requires hundreds of input images that densely sample the 3D scene. Hand-crafted and learned priors may reduce the number of required images to the order of three to ten [33], but 3D points still need to be observed from at least two perspectives. A major challenge of these approaches is the cost of accurate differentiable rendering, regularly requiring hundreds of samples per ray. Recent approaches have achieved impressive speed-ups in 3D reconstruction leveraging high-performance data structures and sparsity [6,13,24,29]. While promising, reconstruction can still take a few minutes per scene, and sparse data structures such as octrees and hash tables cannot easily be used with learned priors." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 288, + 289, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 288, + 289, + 396 + ], + "spans": [ + { + "bbox": [ + 46, + 288, + 289, + 396 + ], + "type": "text", + "content": "Our approach tackles a different setting than these methods, using only a single wide-baseline stereo image as input, where 3D points are regularly only observed in a single view. Our approach does not require any per-scene optimization at test time. Instead, it reconstructs the scene in a single forward pass. Note that while our method does not achieve the quality of per-scene optimization methods that use hundreds of input images, it demonstrates a significant step up in novel view synthesis from very sparse image observations." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "spans": [ + { + "bbox": [ + 46, + 402, + 289, + 594 + ], + "type": "text", + "content": "Prior-based 3D Reconstruction and View Synthesis. Instead of overfitting to a single scene, differentiable rendering can also be used to supervise prior-based inference methods. Some methods generalize image-based rendering techniques by computing feature maps on top of a proxy geometry [1, 19, 38, 55]. Volume rendering using multi-plane images has been used for small baseline novel view synthesis [46, 52, 60, 61]. Early neural fields-based approaches [34, 45] were conditioned on a single global latent code and rendered via sphere tracing. In contrast to a global latent code, several approaches use a feature backbone to compute pixel-aligned features that can be transformed using MLPs [21, 51, 58] or transformers layers [37, 53] to a radiance field. Ideas from multi-view stereo such as the construction of plane-swept cost volumes [7, 18, 25], or multi-view feature matching [8] have been used for higher-quality results." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "content": "Alternatively to these radiance field-based approaches, some methods use a light field rendering formulation where an oriented camera ray can directly be transformed to the pixel color as a function of the features computed from the input images [43, 48]. Scene Representation Transformers [39] use transformers with global attention to compute a set-latent representation that can be decoded to pixel colors when queried with a target camera ray. However, global attention layers on high-resolution input images are very compute and memory intensive. Developed concurrently" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 72, + 547, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 167 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 167 + ], + "type": "text", + "content": "with our work, Suhail et al. [47] proposed to use a transformer to only compute features for image patches along the epipolar rays of the pixel being rendered. This is still very expensive due to global attention layer computations over multiple image patches for every rendered pixel. In addition, this method ignores the context information of the scene, since all computation is performed only for patches that lie on the epipolar lines." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 168, + 547, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 168, + 547, + 240 + ], + "spans": [ + { + "bbox": [ + 304, + 168, + 547, + 240 + ], + "type": "text", + "content": "All existing prior-based reconstruction methods either only support object-level scenes or very small baseline renderings, or rely on multiple image observations where most 3D points are observed in multiple input images. This is different from our setting where we only use a wide-baseline stereo image pair of scenes as input." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 250, + 362, + 262 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 250, + 362, + 262 + ], + "spans": [ + { + "bbox": [ + 305, + 250, + 362, + 262 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 270, + 547, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 270, + 547, + 354 + ], + "spans": [ + { + "bbox": [ + 304, + 270, + 547, + 354 + ], + "type": "text", + "content": "Our goal is to render novel views of a 3D scene given a wide-baseline stereo image pair " + }, + { + "bbox": [ + 304, + 270, + 547, + 354 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_1" + }, + { + "bbox": [ + 304, + 270, + 547, + 354 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 270, + 547, + 354 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_2" + }, + { + "bbox": [ + 304, + 270, + 547, + 354 + ], + "type": "text", + "content": ". We assume known camera intrinsic " + }, + { + "bbox": [ + 304, + 270, + 547, + 354 + ], + "type": "inline_equation", + "content": "\\mathbf{K}_i \\in \\mathbb{R}^{3 \\times 3}" + }, + { + "bbox": [ + 304, + 270, + 547, + 354 + ], + "type": "text", + "content": " and extrinsic " + }, + { + "bbox": [ + 304, + 270, + 547, + 354 + ], + "type": "inline_equation", + "content": "\\mathbf{E}_i \\in \\mathbb{R}^{4 \\times 3}" + }, + { + "bbox": [ + 304, + 270, + 547, + 354 + ], + "type": "text", + "content": " expressed relative to context camera 1. We use a multiview encoder to compute pixel-aligned features, and a cross-attention-based renderer to transform the features into novel view renderings, see Figure 2 for an overview." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 360, + 463, + 373 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 360, + 463, + 373 + ], + "spans": [ + { + "bbox": [ + 305, + 360, + 463, + 373 + ], + "type": "text", + "content": "3.1. Multiview Feature Encoding" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 379, + 547, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 379, + 547, + 557 + ], + "spans": [ + { + "bbox": [ + 304, + 379, + 547, + 557 + ], + "type": "text", + "content": "An essential part of novel view synthesis given context images is an accurate reconstruction of scene geometry. Our method implicitly reconstructs 3D geometry and appearance of the scene in the form of pixel-aligned feature maps for each stereo image. In prior work, pixel-aligned features are obtained by separately encoding each image via a vision transformer or CNN [21, 58]. However, in our early experiments, we found this led to artifacts in renderings observing boundary regions between context images. We hypothesize that separate encoding of images leads to inconsistent geometry reconstruction across context images. We thus introduce our multi-view encoder, which obtains pixel-aligned features by jointly processing the images and the relative pose between them. Encoding the pose information has also been shown to act as an effective inductive bias for 3D tasks [57]." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 558, + 548, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 548, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 548, + 715 + ], + "type": "text", + "content": "We now describe this architecture in detail, which extends the dense vision transformer proposed by Ranftl et al. [36]. Please see Figure 2 for an overview. From each stereo image, we first independently extract convolutional features via a ResNet50 CNN. We then flatten both images, obtaining " + }, + { + "bbox": [ + 304, + 558, + 548, + 715 + ], + "type": "inline_equation", + "content": "2 \\times 16 \\times 16" + }, + { + "bbox": [ + 304, + 558, + 548, + 715 + ], + "type": "text", + "content": " features in total. To each feature, we add (1) a learned per-pixel positional embedding encoding its pixel coordinate and (2) a camera pose embedding, obtained via a linear transform of the relative camera pose between context images 1 and 2. These tokens are processed by a vision transformer, which critically performs self-attention across all tokens across both images. In-between self-attention layers, per-image features are re-assembled into a spatial grid," + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "4972" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 82, + 220, + 244 + ], + "blocks": [ + { + "bbox": [ + 47, + 69, + 164, + 80 + ], + "lines": [ + { + "bbox": [ + 47, + 69, + 164, + 80 + ], + "spans": [ + { + "bbox": [ + 47, + 69, + 164, + 80 + ], + "type": "text", + "content": "(a) Multi-View ViT Encoder" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 47, + 82, + 220, + 244 + ], + "lines": [ + { + "bbox": [ + 47, + 82, + 220, + 244 + ], + "spans": [ + { + "bbox": [ + 47, + 82, + 220, + 244 + ], + "type": "image", + "image_path": "a1a5fd49499271751542088f95be908850a03fa70f46a3f16c4456667709215d.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 212, + 82, + 389, + 245 + ], + "blocks": [ + { + "bbox": [ + 212, + 70, + 382, + 81 + ], + "lines": [ + { + "bbox": [ + 212, + 70, + 382, + 81 + ], + "spans": [ + { + "bbox": [ + 212, + 70, + 382, + 81 + ], + "type": "text", + "content": "(b) Image-Centric Epipolar Line Sampling" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 212, + 82, + 389, + 245 + ], + "lines": [ + { + "bbox": [ + 212, + 82, + 389, + 245 + ], + "spans": [ + { + "bbox": [ + 212, + 82, + 389, + 245 + ], + "type": "image", + "image_path": "a72db9d24243f83ad08a03e084f0a6c9a221f14e6bff8dee7b0ff309878d7934.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 390, + 70, + 476, + 81 + ], + "lines": [ + { + "bbox": [ + 390, + 70, + 476, + 81 + ], + "spans": [ + { + "bbox": [ + 390, + 70, + 476, + 81 + ], + "type": "text", + "content": "(c) Neural Rendering" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 389, + 83, + 547, + 245 + ], + "blocks": [ + { + "bbox": [ + 389, + 83, + 547, + 245 + ], + "lines": [ + { + "bbox": [ + 389, + 83, + 547, + 245 + ], + "spans": [ + { + "bbox": [ + 389, + 83, + 547, + 245 + ], + "type": "image", + "image_path": "fde0ba6b988906a17251616c1a4a0c60c6835f3a250aafe29eb65c7b3c205a02.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 254, + 547, + 309 + ], + "lines": [ + { + "bbox": [ + 45, + 254, + 547, + 309 + ], + "spans": [ + { + "bbox": [ + 45, + 254, + 547, + 309 + ], + "type": "text", + "content": "Figure 2. Method Overview. (a) Given context images from different viewpoints, a multi-view encoder extracts pixel-aligned features, leveraging attention across the images and their corresponding camera pose embeddings. (b) Given a target ray, in each context view, we sample primary features along the epipolar line equidistant in pixel space. We then project the corresponding 3D points onto the other views and sample corresponding secondary epipolar line features, where out-of-bounds features are set to zero. (c) We render the target ray by performing cross-attention over the set of all primary and secondary epipolar line features from all views." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 319, + 288, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 319, + 288, + 392 + ], + "spans": [ + { + "bbox": [ + 46, + 319, + 288, + 392 + ], + "type": "text", + "content": "up-sampled, and processed by a fusion CNN [36] to yield per-image spatial feature map. Directly using these spatial feature maps for novel view synthesis leads to blurry reconstructions, due to the loss of high-frequency texture information. We thus concatenate these features with high-resolution image features obtained from a shallow CNN." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 397, + 288, + 411 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 397, + 288, + 411 + ], + "spans": [ + { + "bbox": [ + 47, + 397, + 288, + 411 + ], + "type": "text", + "content": "3.2. Epipolar Line Sampling and Feature Matching" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 45, + 416, + 288, + 631 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 416, + 288, + 631 + ], + "spans": [ + { + "bbox": [ + 45, + 416, + 288, + 631 + ], + "type": "text", + "content": "We aim to render an image of the scene encoded in the two pixel-aligned feature maps from a novel camera viewpoint. A common way to achieve this is volume rendering, where we cast a camera ray, compute density and color values at many depths along the ray, and integrate them to compute the color of the pixel. Sampling locations are determined in 3D. Coarse samples are either uniformly spaced in euclidean space or spaced with uniform disparity, and fine samples are distributed closer to the surface as computed by the coarse samples [2, 28, 30]. However, in our regime of generalizable novel view synthesis with pixel-aligned feature maps, this sampling scheme is suboptimal. In this case, sampling along the ray should be determined by the resolution of the context images: the number of pixels along the epipolar line is the maximum effective number of samples available for any method. More samples would not provide any extra information. We propose a sampling strategy to exploit this and demonstrate its effectiveness in an ablation study." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "spans": [ + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "text", + "content": "Consider a pixel coordinate " + }, + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "inline_equation", + "content": "\\mathbf{u}_t = (u, v)" + }, + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "text", + "content": " in the target image " + }, + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_t" + }, + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "text", + "content": ", with assumed known intrinsic " + }, + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "inline_equation", + "content": "\\mathbf{K}_t" + }, + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "text", + "content": " and extrinsic " + }, + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "inline_equation", + "content": "\\mathbf{T}_t = \\left[ \\begin{array}{cc} \\mathbf{R}_t & t \\\\ \\mathbf{0} & 1 \\end{array} \\right]" + }, + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "text", + "content": " camera parameters relative to the context camera " + }, + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "inline_equation", + "content": "\\mathcal{I}_1" + }, + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "text", + "content": ". Its epipolar lines " + }, + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "inline_equation", + "content": "\\mathbf{l}_{\\{1,2\\}}" + }, + { + "bbox": [ + 46, + 632, + 288, + 692 + ], + "type": "text", + "content": ", in context cameras 1 and 2 are given as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 58, + 695, + 288, + 712 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 695, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 695, + 288, + 712 + ], + "type": "interline_equation", + "content": "\\mathbf {l} _ {i} = \\mathbf {F} _ {i} [ u, v, 1 ] ^ {T} = \\mathbf {K} _ {i} ^ {- T} ([ \\mathbf {t} _ {t} ] _ {\\times} \\mathbf {R} _ {t}) \\mathbf {K} _ {t} ^ {- 1} [ u, v, 1 ] ^ {T} (1)", + "image_path": "c826522c6f49346fdabd00c4403f09aca9533821128cb6b364fd0a43395c8235.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "spans": [ + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "text", + "content": "via the fundamental matrix " + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "inline_equation", + "content": "\\mathbf{F}_i" + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "text", + "content": ". We now uniformly sample " + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "text", + "content": " pixel coordinates along the line segment of the epipolar line within the image boundaries. To enable the renderer to reason about whether to use a certain pixel-aligned feature or not, a critical piece of information is the depth in the context coordinate frame at which we are sampling this feature. This depth value can be computed via triangulation, using a closed-form expression. Please refer to the supplemental document for details. We now obtain " + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "text", + "content": " tuples " + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "inline_equation", + "content": "\\{(d,\\mathbf{f})_k\\}_{k=1}^N" + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "text", + "content": " of depth " + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "text", + "content": " and image feature " + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "inline_equation", + "content": "\\mathbf{f}" + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "text", + "content": " per context image for a total of " + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "inline_equation", + "content": "2N" + }, + { + "bbox": [ + 304, + 319, + 547, + 453 + ], + "type": "text", + "content": " samples which we call primary samples." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "text", + "content": "We further propose a feature matching module to refine the geometry encoded in the primary epipolar line samples via correspondence matching. Consider a primary epipolar line sample obtained from context image " + }, + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "text", + "content": ", a tuple " + }, + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "inline_equation", + "content": "(d, \\mathbf{f})" + }, + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "text", + "content": " corresponding to a pixel coordinate " + }, + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{u}_t" + }, + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "text", + "content": ". We propose to augment this sample by a corresponding feature in the other context image. Specifically, we first solve for the corresponding 3D point, and then project this 3D point onto the other context image to retrieve a corresponding feature " + }, + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{f}}" + }, + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "text", + "content": ", which we refer to as a secondary feature. The secondary features are set to zero if the projected point is out of the image bounds. Intuitively, primary and secondary features together allow a final stage of geometry refinement for 3D points that are observed in both images: if the features agree, this sample likely encodes a surface. If the projected point on the other image lies outside the image boundary, we simply set the secondary features to zeros. We obtain the input to the renderer as the final set of features by concatenating each primary epipolar line feature with its corresponding secondary feature in the other context view, yielding a set " + }, + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "inline_equation", + "content": "\\{(d, \\mathbf{f}, \\hat{\\mathbf{f}})_k\\}_{k=1}^{2N}" + }, + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "text", + "content": ". In practice, we sample " + }, + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "inline_equation", + "content": "N = 64" + }, + { + "bbox": [ + 304, + 462, + 548, + 715 + ], + "type": "text", + "content": " points on the epipolar lines" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "4973" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 269, + 85 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 269, + 85 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 269, + 85 + ], + "type": "text", + "content": "for both images, leading to a total of " + }, + { + "bbox": [ + 47, + 72, + 269, + 85 + ], + "type": "inline_equation", + "content": "2N = 128" + }, + { + "bbox": [ + 47, + 72, + 269, + 85 + ], + "type": "text", + "content": " tuples." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 91, + 282, + 104 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 91, + 282, + 104 + ], + "spans": [ + { + "bbox": [ + 47, + 91, + 282, + 104 + ], + "type": "text", + "content": "3.3. Differentiable Rendering via Cross-Attention" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 110, + 287, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 110, + 287, + 180 + ], + "spans": [ + { + "bbox": [ + 46, + 110, + 287, + 180 + ], + "type": "text", + "content": "To render the target ray, it remains to map the set of epipolar line samples " + }, + { + "bbox": [ + 46, + 110, + 287, + 180 + ], + "type": "inline_equation", + "content": "\\{(d,\\mathbf{f},\\hat{\\mathbf{f}})_k\\}_{k = 1}^{2N}" + }, + { + "bbox": [ + 46, + 110, + 287, + 180 + ], + "type": "text", + "content": " to a color value. As this operation has to be executed once per ray, a key consideration in the design of this function is computational cost. We propose to perform rendering via a lightweight cross-attention decoder." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "spans": [ + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "text", + "content": "For each point on the epipolar line, we embed the target ray origin " + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "inline_equation", + "content": "\\mathbf{o}_t" + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "text", + "content": ", target ray direction " + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "inline_equation", + "content": "\\mathbf{r}_t" + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "text", + "content": ", depth with respect to the target ray origin " + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "inline_equation", + "content": "d_t" + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "text", + "content": ", and context camera ray direction " + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "inline_equation", + "content": "\\mathbf{r}_c" + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "text", + "content": " for the epipolar point into a ray query token " + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "inline_equation", + "content": "\\mathbf{q}" + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "text", + "content": " via a shallow MLP as " + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "inline_equation", + "content": "\\Phi([\\mathbf{o}_t, \\mathbf{r}_t, \\mathbf{r}_c, d_t])" + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "text", + "content": ". The " + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "inline_equation", + "content": "2N" + }, + { + "bbox": [ + 46, + 181, + 288, + 300 + ], + "type": "text", + "content": " ray feature values are independently transformed into key and value tokens using a 2-layer MLP. Our renderer now performs two rounds of cross-attention over this set of features to obtain a final feature embedding, which is then decoded into color via a small MLP." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 300, + 288, + 480 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 300, + 288, + 480 + ], + "spans": [ + { + "bbox": [ + 46, + 300, + 288, + 480 + ], + "type": "text", + "content": "The expectation of the Softmax distribution over the sampled features gives a rough idea of the scene depth as " + }, + { + "bbox": [ + 46, + 300, + 288, + 480 + ], + "type": "inline_equation", + "content": "e = \\sum_{k} d_{k} \\alpha_{k}" + }, + { + "bbox": [ + 46, + 300, + 288, + 480 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 300, + 288, + 480 + ], + "type": "inline_equation", + "content": "d_{k}" + }, + { + "bbox": [ + 46, + 300, + 288, + 480 + ], + "type": "text", + "content": " denotes the depth of the " + }, + { + "bbox": [ + 46, + 300, + 288, + 480 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 300, + 288, + 480 + ], + "type": "text", + "content": "-th epipolar ray sample along the target ray and " + }, + { + "bbox": [ + 46, + 300, + 288, + 480 + ], + "type": "inline_equation", + "content": "\\alpha_{k}" + }, + { + "bbox": [ + 46, + 300, + 288, + 480 + ], + "type": "text", + "content": " is the corresponding Softmax weight as computed by the cross-attention operator. Note that " + }, + { + "bbox": [ + 46, + 300, + 288, + 480 + ], + "type": "inline_equation", + "content": "e" + }, + { + "bbox": [ + 46, + 300, + 288, + 480 + ], + "type": "text", + "content": " is not the actual depth but a measure of which epipolar samples the renderer uses to compute the pixel color. Unlike volume rendering, where we need to sample very close to a surface to render its color, our light field-based renderer can reason about the surface without exactly sampling on it. The learned cross-attention layers can use the target camera ray information, along with a sparse set of epipolar samples, to compute the pixel color. Thus, our method does not require explicit computation of accurate scene depth for rendering." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 487, + 164, + 499 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 487, + 164, + 499 + ], + "spans": [ + { + "bbox": [ + 47, + 487, + 164, + 499 + ], + "type": "text", + "content": "3.4. Training and Losses" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 505, + 287, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 505, + 287, + 529 + ], + "spans": [ + { + "bbox": [ + 47, + 505, + 287, + 529 + ], + "type": "text", + "content": "We now have a rendered image from a novel camera viewpoint. Our loss function consists of two terms:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 122, + 536, + 287, + 551 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 122, + 536, + 287, + 551 + ], + "spans": [ + { + "bbox": [ + 122, + 536, + 287, + 551 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathcal {L} _ {\\mathrm {i m g}} + \\lambda_ {\\mathrm {r e g}} \\mathcal {L} _ {\\mathrm {r e g}}. \\tag {2}", + "image_path": "a6b9abd1588b82d8cb308140a99a5388d64c133a0edd9b028f432fbbc8f3f95b.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 557, + 287, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 557, + 287, + 593 + ], + "spans": [ + { + "bbox": [ + 47, + 557, + 287, + 593 + ], + "type": "text", + "content": "The first term evaluates the difference between the rendered image from a novel camera viewpoint, " + }, + { + "bbox": [ + 47, + 557, + 287, + 593 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 47, + 557, + 287, + 593 + ], + "type": "text", + "content": " and the ground truth, " + }, + { + "bbox": [ + 47, + 557, + 287, + 593 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 47, + 557, + 287, + 593 + ], + "type": "text", + "content": " as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 81, + 601, + 287, + 615 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 601, + 287, + 615 + ], + "spans": [ + { + "bbox": [ + 81, + 601, + 287, + 615 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {i m g}} = \\left\\| R - G \\right\\| _ {1} + \\lambda_ {\\mathrm {L P I P S}} \\mathcal {L} _ {\\mathrm {L P I P S}} (R, G), \\tag {3}", + "image_path": "698be2e100e030206c8485ae72969978cd6199dab00f40182fdfa7b89853d844.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 621, + 287, + 657 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 621, + 287, + 657 + ], + "spans": [ + { + "bbox": [ + 46, + 621, + 287, + 657 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 621, + 287, + 657 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{LPIPS}}" + }, + { + "bbox": [ + 46, + 621, + 287, + 657 + ], + "type": "text", + "content": " is the LPIPS perceptual loss [59]. In practice, we render square patches with a length of 32 pixels and evaluate these image losses at the patch level." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 658, + 287, + 681 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 658, + 287, + 681 + ], + "spans": [ + { + "bbox": [ + 47, + 658, + 287, + 681 + ], + "type": "text", + "content": "We also use a regularization loss on the cross-attention weights of the renderer for better multi-view consistency:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 67, + 689, + 287, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 689, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 67, + 689, + 287, + 715 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {r e g}} = \\sum_ {(u, v)} \\sum_ {\\left(u ^ {\\prime} v ^ {\\prime}\\right) \\in \\mathcal {N} (u, v)} \\left(\\left(e (u, v) - e \\left(u ^ {\\prime}, v ^ {\\prime}\\right)\\right) ^ {2} . \\right. \\tag {4}", + "image_path": "bd6bb520e1ad8e4306f7d91e5bb37f49acf81e1a602e5b4083cb530d1e1a300d.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 72, + 545, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 108 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 108 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 304, + 72, + 545, + 108 + ], + "type": "inline_equation", + "content": "e(u,v)" + }, + { + "bbox": [ + 304, + 72, + 545, + 108 + ], + "type": "text", + "content": " denotes the expected value of the depth of the epipolar samples at pixel " + }, + { + "bbox": [ + 304, + 72, + 545, + 108 + ], + "type": "inline_equation", + "content": "(u,v)" + }, + { + "bbox": [ + 304, + 72, + 545, + 108 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 72, + 545, + 108 + ], + "type": "inline_equation", + "content": "\\mathcal{N}()" + }, + { + "bbox": [ + 304, + 72, + 545, + 108 + ], + "type": "text", + "content": " defines the neighborhood around a pixel." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 109, + 547, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 109, + 547, + 180 + ], + "spans": [ + { + "bbox": [ + 304, + 109, + 547, + 180 + ], + "type": "text", + "content": "For better generalization, we further perform several geometrically-consistent data augmentations during the training procedure. We center crop and scale the input and target images, which leads to transformation in the intrinsic of the camera. We also flip the images which leads to transformation of the extrinsics." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 192, + 388, + 206 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 192, + 388, + 206 + ], + "spans": [ + { + "bbox": [ + 305, + 192, + 388, + 206 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 213, + 547, + 332 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 213, + 547, + 332 + ], + "spans": [ + { + "bbox": [ + 304, + 213, + 547, + 332 + ], + "type": "text", + "content": "We quantitatively and qualitatively show that our approach can effectively render novel views from wide-baseline stereo pairs. We describe our underlying experimental setup in Section 4.1. Next, we evaluate our approach on challenging indoor scenes with substantial occlusions in Section 4.2. We further evaluate on outdoor scenes in Section 4.3. We analyze and ablate the underlying components in Section 4.4. Finally, we illustrate how our approach can render novel views of unposed images of scenes captured in the wild in Section 4.5." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 342, + 422, + 354 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 342, + 422, + 354 + ], + "spans": [ + { + "bbox": [ + 305, + 342, + 422, + 354 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 361, + 547, + 468 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 361, + 547, + 468 + ], + "spans": [ + { + "bbox": [ + 304, + 361, + 547, + 468 + ], + "type": "text", + "content": "Datasets. We train and evaluate our approach on RealEstate10k [61], a large dataset of indoor and outdoor scenes, and ACID [22], a large dataset of outdoor scenes. We use 67477 scenes for training and 7289 scenes for testing for RealEstate10k, and 11075 scenes for training and 1972 scenes for testing for ACID, following default splits. We train our method on images at " + }, + { + "bbox": [ + 304, + 361, + 547, + 468 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 304, + 361, + 547, + 468 + ], + "type": "text", + "content": " resolution and evaluate methods on their ability to reconstruct intermediate views in test scenes (details in the supplement)." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 477, + 547, + 669 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 477, + 547, + 669 + ], + "spans": [ + { + "bbox": [ + 304, + 477, + 547, + 669 + ], + "type": "text", + "content": "Baselines. We compare to several existing approaches for novel view synthesis from sparse image observations. We compare to pixelNeRF [58] and IBRNet [53] that use pixel-aligned features, which are decoded into 3D volumes rendered using volumetric rendering. We also compare to Generalizable Patch-based Rendering (GPNR) [47], which uses a vision transformer-based backbone to compute epipolar features, and a light field-based renderer to compute pixel colors. These baselines cover a wide range of design choices used in existing methods, such as pixel-aligned feature maps computed using CNNs [53, 58] and transformers [47], volumetric rendering by decoding features using MLPs [58] and transformers [53], and light field-based rendering [47]. We use publicly available codebases for all baselines and train them on the same datasets we use for fair evaluations. Please refer to the supplemental for comparisons to more baselines." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "type": "text", + "content": "Evaluation Metrics. We use LPIPS [59], PSNR, SSIM [54], and MSE metrics to compare the image quality of rendered images with the ground truth." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "4974" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 74, + 539, + 293 + ], + "blocks": [ + { + "bbox": [ + 56, + 74, + 539, + 293 + ], + "lines": [ + { + "bbox": [ + 56, + 74, + 539, + 293 + ], + "spans": [ + { + "bbox": [ + 56, + 74, + 539, + 293 + ], + "type": "image", + "image_path": "6371e3f4bca11f700b52ee3f6d20be9065952e43fb7e46de40e667a17b55e988.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 308, + 546, + 343 + ], + "lines": [ + { + "bbox": [ + 46, + 308, + 546, + 343 + ], + "spans": [ + { + "bbox": [ + 46, + 308, + 546, + 343 + ], + "type": "text", + "content": "Figure 3. Comparative Rendering Results on RealEstate10k. Our approach can render novel views of indoor scenes with substantial occlusions with high fidelity using a wide-baseline input image pair, outperforming all baselines. Note that many points of the 3D scene are only observed in a single image in such inputs. 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Novel view renderings of our approach given a large baseline stereo pair. Our approach can synthesize intermediate views that are substantially different from input images, even with very limited overlap between images." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 147, + 513, + 279, + 557 + ], + "blocks": [ + { + "bbox": [ + 147, + 513, + 279, + 557 + ], + "lines": [ + { + "bbox": [ + 147, + 513, + 279, + 557 + ], + "spans": [ + { + "bbox": [ + 147, + 513, + 279, + 557 + ], + "type": "image", + "image_path": "d194d9e77f70e031c15abb6268450361e8caa5b4a4333ae9e358e3ad38d517a3.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 624, + 219, + 637 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 624, + 219, + 637 + ], + "spans": [ + { + "bbox": [ + 47, + 624, + 219, + 637 + ], + "type": "text", + "content": "4.2. Indoor Scene Neural Rendering" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 46, + 643, + 287, + 680 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 643, + 287, + 680 + ], + "spans": [ + { + "bbox": [ + 46, + 643, + 287, + 680 + ], + "type": "text", + "content": "We first evaluate the ability of our approach and baselines to render novel views in complex indoor environments with substantial occlusions between objects." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 689, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 287, + 715 + ], + "type": "text", + "content": "Qualitative Results. In Figure 3, we provide qualitative results of novel view renderings of our approach, compared" + } + ] + } + ], + "index": 15 + }, + { + "type": "table", + "bbox": [ + 313, + 361, + 538, + 426 + ], + "blocks": [ + { + "bbox": [ + 313, + 361, + 538, + 426 + ], + "lines": [ + { + "bbox": [ + 313, + 361, + 538, + 426 + ], + "spans": [ + { + "bbox": [ + 313, + 361, + 538, + 426 + ], + "type": "table", + "html": "
MethodLPIPS ↓SSIM ↑PSNR ↑MSE ↓
pixelNeRF [58]0.5910.46013.910.0440
IBRNet [53]0.5320.48415.990.0280
GPNR [47]0.4590.74818.550.0165
Ours0.2620.83921.380.0110
", + "image_path": "ef05ad5046aaebca66ce6d021f6cf6e6b6bd37ca4f1b6ba29e184a43bf5dadc4.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "table_body" + } + ], + "index": 16 + }, + { + "type": "table", + "bbox": [ + 313, + 475, + 537, + 540 + ], + "blocks": [ + { + "bbox": [ + 306, + 435, + 547, + 457 + ], + "lines": [ + { + "bbox": [ + 306, + 435, + 547, + 457 + ], + "spans": [ + { + "bbox": [ + 306, + 435, + 547, + 457 + ], + "type": "text", + "content": "Table 1. Novel view rendering performance on RealEstate10K. Our method outperforms all baselines on all metrics." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 313, + 475, + 537, + 540 + ], + "lines": [ + { + "bbox": [ + 313, + 475, + 537, + 540 + ], + "spans": [ + { + "bbox": [ + 313, + 475, + 537, + 540 + ], + "type": "table", + "html": "
MethodLPIPS ↓SSIM ↑PSNR ↑MSE ↓
pixelNeRF [58]0.6280.46416.480.0275
IBRNet [53]0.3850.51319.240.0167
GPNR [47]0.5580.71917.570.0218
Ours0.3640.78123.630.0074
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Compared to the baselines, our approach reconstructs the 3D structure of the scene better, and also captures more high-frequency details." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 305, + 666, + 547, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 666, + 547, + 712 + ], + "spans": [ + { + "bbox": [ + 305, + 666, + 547, + 712 + ], + "type": "text", + "content": "Quantitative Results. We quantitatively evaluate our approach and baselines in Table 1. 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Comparative Results on ACID. Our approach is able to render novels views with higher quality than all baselines." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 328, + 82, + 390, + 209 + ], + "blocks": [ + { + "bbox": [ + 349, + 72, + 369, + 80 + ], + "lines": [ + { + "bbox": [ + 349, + 72, + 369, + 80 + ], + "spans": [ + { + "bbox": [ + 349, + 72, + 369, + 80 + ], + "type": "text", + "content": "GPNR" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 328, + 82, + 390, + 209 + ], + "lines": [ + { + "bbox": [ + 328, + 82, + 390, + 209 + ], + "spans": [ + { + "bbox": [ + 328, + 82, + 390, + 209 + ], + "type": "image", + "image_path": "9fa88af3cb717bd1266cec53b31d29cbbf696ae52aba0f6918d6bfa4f7fdb350.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 392, + 82, + 454, + 209 + ], + "blocks": [ + { + "bbox": [ + 415, + 72, + 431, + 80 + ], + "lines": [ + { + "bbox": [ + 415, + 72, + 431, + 80 + ], + "spans": [ + { + "bbox": [ + 415, + 72, + 431, + 80 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 392, + 82, + 454, + 209 + ], + "lines": [ + { + "bbox": [ + 392, + 82, + 454, + 209 + ], + "spans": [ + { + "bbox": [ + 392, + 82, + 454, + 209 + ], + "type": "image", + "image_path": "6cb6c68fbf8e48040560716476eff22a51045703b5675dc26f0e08152047c3c1.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 456, + 82, + 518, + 209 + ], + "blocks": [ + { + "bbox": [ + 477, + 72, + 496, + 80 + ], + "lines": [ + { + "bbox": [ + 477, + 72, + 496, + 80 + ], + "spans": [ + { + "bbox": [ + 477, + 72, + 496, + 80 + ], + "type": "text", + "content": "Target" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 456, + 82, + 518, + 209 + ], + "lines": [ + { + "bbox": [ + 456, + 82, + 518, + 209 + ], + "spans": [ + { + "bbox": [ + 456, + 82, + 518, + 209 + ], + "type": "image", + "image_path": "133bb792f2ca8c67017a0894c47318c4ebb45acbbd83740667f036a74b4ed448.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 242, + 227, + 255 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 242, + 227, + 255 + ], + "spans": [ + { + "bbox": [ + 47, + 242, + 227, + 255 + ], + "type": "text", + "content": "4.3. Outdoor Scene Neural Rendering" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 47, + 261, + 287, + 285 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 261, + 287, + 285 + ], + "spans": [ + { + "bbox": [ + 47, + 261, + 287, + 285 + ], + "type": "text", + "content": "We further evaluate on outdoor scenes with potentially unbounded depth." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 293, + 287, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 293, + 287, + 353 + ], + "spans": [ + { + "bbox": [ + 46, + 293, + 287, + 353 + ], + "type": "text", + "content": "Qualitative Results. We illustrate qualitative results in Figure 5. In comparison to the baselines, our approach is able to more accurately reconstruct the geometry, and is able to synthesize multi-view consistent renderings from two large baseline views." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 46, + 361, + 288, + 397 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 361, + 288, + 397 + ], + "spans": [ + { + "bbox": [ + 46, + 361, + 288, + 397 + ], + "type": "text", + "content": "Quantitative Results. Similar to indoor scenes, our approach also outperforms all baselines in terms of all metrics on outdoor scenes, see Table 2." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 47, + 405, + 178, + 418 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 405, + 178, + 418 + ], + "spans": [ + { + "bbox": [ + 47, + 405, + 178, + 418 + ], + "type": "text", + "content": "4.4. Ablations and Analysis" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 46, + 424, + 287, + 459 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 424, + 287, + 459 + ], + "spans": [ + { + "bbox": [ + 46, + 424, + 287, + 459 + ], + "type": "text", + "content": "We next analyze and ablate individual components of our approach. We use the RealEstate10k dataset for these experiments." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 46, + 468, + 289, + 720 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 468, + 289, + 720 + ], + "spans": [ + { + "bbox": [ + 46, + 468, + 289, + 720 + ], + "type": "text", + "content": "Ablations. We evaluate the importance of different components of our method in Table 3. The \"Base Model\" corresponds to a vanilla architecture that does not include some of our proposed contributions. It samples points uniformly in 3D, instead of our proposed 2D epipolar line sampling. It uses a monocular encoder instead of our proposed multiview encoder, and does not use correspondence matching across views for refining the geometry. It also does not use the regularization loss for multi-view consistency or any data augmentation during training. We find that all components of our approach are essential for high-quality performance. The results in Table 3 show that sampling in 3D sub-optimally uses the information in the feature maps, that our multi-view encoder and cross-image correspondence matching can compute features that better encode the 3D scene structure compared to monocular encoders, and that data augmentation helps with generalization. While we found that the incorporation of the regularization loss led to a slight decrease in PSNR, we found that it improved multi-view consistency in the rendered video results, and also improved both LPIPS and SSIM perceptual metrics." + } + ] + } + ], + "index": 19 + }, + { + "type": "table", + "bbox": [ + 307, + 240, + 544, + 321 + ], + "blocks": [ + { + "bbox": [ + 307, + 240, + 544, + 321 + ], + "lines": [ + { + "bbox": [ + 307, + 240, + 544, + 321 + ], + "spans": [ + { + "bbox": [ + 307, + 240, + 544, + 321 + ], + "type": "table", + "html": "
ModelsLPIPS↓SSIM↑PSNR↑MSE↓
Base Model0.4520.73518.110.0201
+ 2D Sampling0.4280.76219.020.0159
+ Cross Correspondence0.4150.76619.520.0142
+ Multiview Encoder0.3610.79420.430.0132
+ Regularization Loss0.3580.80819.840.0139
+ Data Aug0.2620.83921.380.0110
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All components of our proposed method are essential for high-quality novel view synthesis." + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 329, + 354, + 519, + 492 + ], + "blocks": [ + { + "bbox": [ + 329, + 354, + 519, + 492 + ], + "lines": [ + { + "bbox": [ + 329, + 354, + 519, + 492 + ], + "spans": [ + { + "bbox": [ + 329, + 354, + 519, + 492 + ], + "type": "image", + "image_path": "488b91f085d526d6298d15870de45d7520469f89499d6c01d4c1fa5ee2e40296.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 498, + 547, + 554 + ], + "lines": [ + { + "bbox": [ + 304, + 498, + 547, + 554 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 547, + 554 + ], + "type": "text", + "content": "Figure 6. FPS vs PSNR. Our approach strikes the best trade-off between rendering quality and rendering speed. We can further reduce the number of Epipolar samples (\"Ours (Faster)\"), which makes our method faster than all baselines, while still significantly outperforming them in terms of rendering quality." + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_caption" + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 559, + 547, + 668 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 559, + 547, + 668 + ], + "spans": [ + { + "bbox": [ + 304, + 559, + 547, + 668 + ], + "type": "text", + "content": "Speed. Next, in Figure 6, we study the relationship between rendering quality and rendering speed for all approaches. Our lightweight approach achieves the best tradeoff, significantly outperforming all methods in terms of rendering quality, while being at-par with the most efficient baseline. By reducing the number of sampled epipolar points from 64 to 48 samples per image, we can further speed up our approach, outperforming all baselines both in terms of rendering speed and image quality." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "type": "text", + "content": "Epipolar Attention. Finally, we visualize the underlying epipolar attention weights learned by our approach in Figure 7. The expected value of the depths of the epipolar sam" + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "4976" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 66, + 70, + 269, + 219 + ], + "blocks": [ + { + "bbox": [ + 66, + 70, + 269, + 219 + ], + "lines": [ + { + "bbox": [ + 66, + 70, + 269, + 219 + ], + "spans": [ + { + "bbox": [ + 66, + 70, + 269, + 219 + ], + "type": "image", + "image_path": "4c27e839dfd7bf75c2caa2601fbe1f21cefac1b7d9fc8eef4ec0b731fb85e6e7.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 225, + 288, + 281 + ], + "lines": [ + { + "bbox": [ + 46, + 225, + 288, + 281 + ], + "spans": [ + { + "bbox": [ + 46, + 225, + 288, + 281 + ], + "type": "text", + "content": "Figure 7. Visualization of Epipolar Attention Weights. The expected value of the depths of the epipolar samples under the attention weights can be seen as a depth proxy. As our renderer is not a volume renderer, these attention weights need not exactly correspond to the actual depth for correct renderings." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 286, + 287, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 286, + 287, + 323 + ], + "spans": [ + { + "bbox": [ + 46, + 286, + 287, + 323 + ], + "type": "text", + "content": "plies can be seen as a proxy depth and corresponds roughly to the underlying geometry of the scene. This enables us to analyze the learned computation of our renderer." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 329, + 275, + 342 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 329, + 275, + 342 + ], + "spans": [ + { + "bbox": [ + 47, + 329, + 275, + 342 + ], + "type": "text", + "content": "4.5. Novel View Synthesis from Unposed Images" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 348, + 289, + 576 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 348, + 289, + 576 + ], + "spans": [ + { + "bbox": [ + 46, + 348, + 289, + 576 + ], + "type": "text", + "content": "Our method uses a wide-baseline stereo image as input with known relative pose between them. We show that our method can perform novel view synthesis even without the knowledge of this relative pose information. In this case, we utilize SuperGlue [40] to compute reliable pixel correspondences between the input images. Since we do not know the camera intrinsics for in-the-wild images, we use the average intrinsics of the RealEstate10k dataset and compute the Essential matrix from the correspondences using RANSAC [12]. We then compute the pose information from the essential matrix [17] and use it as input for our method. Note that the recovered translation is only defined up to a scale. Figure 8 demonstrates results on some in-the-wild scenes using images from the internet. Even in this unposed setting, our method can reason about the geometry of the scene by aggregating information across the sparse input views. This is an extremely challenging setting, and existing approaches for novel view synthesis from sparse views do not demonstrate any results on unposed images." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 586, + 117, + 597 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 586, + 117, + 597 + ], + "spans": [ + { + "bbox": [ + 47, + 586, + 117, + 597 + ], + "type": "text", + "content": "5. Discussion" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 605, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 287, + 715 + ], + "type": "text", + "content": "While we have presented the first approach for novel view synthesis of scenes from very sparse input views, our approach still has several limitations. Our rendering results are not at the same quality as those obtained by methods that optimize on single scenes using more images. Learning priors that enable novel view synthesis from sparse views is a significantly more challenging problem compared to using a large number of input images, where 3D points are regularly observed in many images. Our approach takes a step towards" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 312, + 80, + 402, + 220 + ], + "blocks": [ + { + "bbox": [ + 335, + 70, + 378, + 79 + ], + "lines": [ + { + "bbox": [ + 335, + 70, + 378, + 79 + ], + "spans": [ + { + "bbox": [ + 335, + 70, + 378, + 79 + ], + "type": "text", + "content": "Input Images" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 312, + 80, + 402, + 220 + ], + "lines": [ + { + "bbox": [ + 312, + 80, + 402, + 220 + ], + "spans": [ + { + "bbox": [ + 312, + 80, + 402, + 220 + ], + "type": "image", + "image_path": "4a1ef1bdf4da55787f197b8a2eef23d7a95a7c2c9949e04f3997aa9e73cd8871.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 229, + 547, + 275 + ], + "lines": [ + { + "bbox": [ + 305, + 229, + 547, + 275 + ], + "spans": [ + { + "bbox": [ + 305, + 229, + 547, + 275 + ], + "type": "text", + "content": "Figure 8. Novel View Synthesis from Unposed Images. Our approach can also render novel views using two unposed images captured in the wild. Note that parts of the scene only visible in one of the images can be correctly rendered from novel viewpoints." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 406, + 80, + 540, + 220 + ], + "blocks": [ + { + "bbox": [ + 436, + 70, + 511, + 79 + ], + "lines": [ + { + "bbox": [ + 436, + 70, + 511, + 79 + ], + "spans": [ + { + "bbox": [ + 436, + 70, + 511, + 79 + ], + "type": "text", + "content": "Rendered Novel Views" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 406, + 80, + 540, + 220 + ], + "lines": [ + { + "bbox": [ + 406, + 80, + 540, + 220 + ], + "spans": [ + { + "bbox": [ + 406, + 80, + 540, + 220 + ], + "type": "image", + "image_path": "c9111acf8bf164ac992915c251c5db57dd0dd77f076ecc45d15459e14efd5608.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 288, + 547, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 288, + 547, + 407 + ], + "spans": [ + { + "bbox": [ + 304, + 288, + 547, + 407 + ], + "type": "text", + "content": "photorealistic renderings of scenes using only sparse views. As our approach relies on learned priors, it does not generalize well to new scenes with very different appearances compared to the training scenes. However, our efficient approach lends itself to large-scale training on diverse datasets, in turn enabling reconstruction of diverse scenes. Finally, while our method, in theory, can be extended to take more than two input views, we have only experimented with two views as a first step towards very sparse multi-view neural rendering." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 431, + 378, + 443 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 431, + 378, + 443 + ], + "spans": [ + { + "bbox": [ + 306, + 431, + 378, + 443 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 455, + 547, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 455, + 547, + 647 + ], + "spans": [ + { + "bbox": [ + 304, + 455, + 547, + 647 + ], + "type": "text", + "content": "We introduce a method for implicit 3D reconstruction and novel view synthesis from a single, wide-baseline stereo pair, trained using only self-supervision from posed color images. By leveraging a multi-view encoder, an image-space epipolar line feature sampling scheme, and a cross-attention based renderer, our method surpasses the quality of prior art on datasets of challenging scenes. Our method further strikes a compelling trade-off between rendering speed and quality, rendering novel views significantly faster than most prior methods. Meanwhile, leveraging epipolar line geometry strikes a compelling trade-off between structured and generalist learning paradigms, enabling us to train our method on real-world datasets such as RealEstate10k. We believe that this work will inspire the community towards further exploring the regime of extreme few-shot and generalizable novel view synthesis." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 654, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 654, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 654, + 547, + 715 + ], + "type": "text", + "content": "Acknowledgements. This work was supported by the National Science Foundation under Grant No. 2211259, and by the Singapore DSTA under DST00OECI20300823 (New Representations for Vision). Yilun Du is supported by a NSF Graduate Research Fellowship." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "4977" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 91, + 288, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 288, + 135 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 288, + 135 + ], + "type": "text", + "content": "[1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graphics. In European Conference on Computer Vision, pages 696-712. Springer, 2020. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 137, + 288, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 137, + 288, + 179 + ], + "spans": [ + { + "bbox": [ + 53, + 137, + 288, + 179 + ], + "type": "text", + "content": "[2] Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields, 2021. 3, 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 182, + 288, + 236 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 182, + 288, + 236 + ], + "spans": [ + { + "bbox": [ + 53, + 182, + 288, + 236 + ], + "type": "text", + "content": "[3] Chris Buehler, Michael Bosse, Leonard McMillan, Steven Gortler, and Michael Cohen. Unstructured lumigraph rendering. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 425-432, 2001. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 239, + 288, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 239, + 288, + 293 + ], + "spans": [ + { + "bbox": [ + 53, + 239, + 288, + 293 + ], + "type": "text", + "content": "[4] Dan Casas, Christian Richardt, John Collomosse, Christian Theobalt, and Adrian Hilton. 4d model flow: Precomputed appearance alignment for real-time 4d video interpolation. In Computer Graphics Forum, volume 34, pages 173-182. Wiley Online Library, 2015. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 295, + 288, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 295, + 288, + 338 + ], + "spans": [ + { + "bbox": [ + 53, + 295, + 288, + 338 + ], + "type": "text", + "content": "[5] Gaurav Chaurasia, Sylvain Duchene, Olga Sorkine-Hornung, and George Drettakis. Depth synthesis and local warps for plausible image-based navigation. ACM Transactions on Graphics (TOG), 32(3):1-12, 2013. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 340, + 288, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 340, + 288, + 373 + ], + "spans": [ + { + "bbox": [ + 53, + 340, + 288, + 373 + ], + "type": "text", + "content": "[6] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. arXiv preprint arXiv:2203.09517, 2022. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 375, + 288, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 375, + 288, + 430 + ], + "spans": [ + { + "bbox": [ + 53, + 375, + 288, + 430 + ], + "type": "text", + "content": "[7] Anpei Chen, Zexiang Xu, Fuqiang Zhao, Xiaoshuai Zhang, Fanbo Xiang, Jingyi Yu, and Hao Su. Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14124-14133, 2021. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 431, + 288, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 431, + 288, + 485 + ], + "spans": [ + { + "bbox": [ + 53, + 431, + 288, + 485 + ], + "type": "text", + "content": "[8] Julian Chibane, Aayush Bansal, Verica Lazova, and Gerard Pons-Moll. Stereo radiance fields (srf): Learning view synthesis from sparse views of novel scenes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, jun 2021. 2, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 487, + 288, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 487, + 288, + 542 + ], + "spans": [ + { + "bbox": [ + 53, + 487, + 288, + 542 + ], + "type": "text", + "content": "[9] Paul E Debevec, Camillo J Taylor, and Jitendra Malik. Modeling and rendering architecture from photographs: A hybrid geometry-and image-based approach. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 11-20, 1996. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 544, + 288, + 576 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 544, + 288, + 576 + ], + "spans": [ + { + "bbox": [ + 48, + 544, + 288, + 576 + ], + "type": "text", + "content": "[10] Ruofei Du, Ming Chuang, Wayne Chang, Hugues Hoppe, and Amitabh Varshney. Montage4d: Interactive seamless fusion of multiview video textures. 2018. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 578, + 288, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 578, + 288, + 633 + ], + "spans": [ + { + "bbox": [ + 48, + 578, + 288, + 633 + ], + "type": "text", + "content": "[11] Martin Eisemann, Bert De Decker, Marcus Magnor, Philippe Bekaert, Edilson De Aguiar, Naveed Ahmed, Christian Theobalt, and Anita Sellent. Floating textures. In Computer graphics forum, volume 27, pages 409-418. Wiley Online Library, 2008. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 635, + 288, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 635, + 288, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 635, + 288, + 678 + ], + "type": "text", + "content": "[12] Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 8" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 680, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 288, + 713 + ], + "type": "text", + "content": "[13] Sara Fridovich-Keil, Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. Plenoxels: Radiance fields without neural networks. In Proceedings of" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5501-5510, 2022. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 95, + 547, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 95, + 547, + 139 + ], + "spans": [ + { + "bbox": [ + 308, + 95, + 547, + 139 + ], + "type": "text", + "content": "[14] Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Brostow. Deep blending for free-viewpoint image-based rendering. ACM Transactions on Graphics (TOG), 37(6):1-15, 2018. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 140, + 545, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 140, + 545, + 172 + ], + "spans": [ + { + "bbox": [ + 308, + 140, + 545, + 172 + ], + "type": "text", + "content": "[15] Peter Hedman, Tobias Ritschel, George Drettakis, and Gabriel Brostow. Scalable inside-out image-based rendering. ACM Transactions on Graphics (TOG), 35(6):1-11, 2016. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 173, + 547, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 173, + 547, + 217 + ], + "spans": [ + { + "bbox": [ + 308, + 173, + 547, + 217 + ], + "type": "text", + "content": "[16] Benno Heigl, Reinhard Koch, Marc Pollefeys, Joachim Denzler, and L Van Gool. Plenoptic modeling and rendering from image sequences taken by a hand-held camera. In Mustererkennung 1999, pages 94-101. Springer, 1999. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 217, + 545, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 217, + 545, + 239 + ], + "spans": [ + { + "bbox": [ + 308, + 217, + 545, + 239 + ], + "type": "text", + "content": "[17] Berthold KP Horn. Recovering baseline and orientation from essential matrix. J. Opt. Soc. Am, 110, 1990. 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 239, + 547, + 292 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 239, + 547, + 292 + ], + "spans": [ + { + "bbox": [ + 308, + 239, + 547, + 292 + ], + "type": "text", + "content": "[18] Mohammad Mahdi Johari, Yann Lepoittevin, and François Fleuret. Geonerf: Generalizing nerf with geometry priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18365-18375, 2022. 2, 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 294, + 547, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 294, + 547, + 338 + ], + "spans": [ + { + "bbox": [ + 308, + 294, + 547, + 338 + ], + "type": "text", + "content": "[19] Georgios Kopanas, Julien Philip, Thomas Leimkuhler, and George Drettakis. Point-based neural rendering with perview optimization. In Computer Graphics Forum, volume 40, pages 29-43. Wiley Online Library, 2021. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 339, + 547, + 371 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 339, + 547, + 371 + ], + "spans": [ + { + "bbox": [ + 308, + 339, + 547, + 371 + ], + "type": "text", + "content": "[20] Marc Levoy and Pat Hanrahan. Light field rendering. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, pages 31-42, 1996. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 372, + 547, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 372, + 547, + 415 + ], + "spans": [ + { + "bbox": [ + 308, + 372, + 547, + 415 + ], + "type": "text", + "content": "[21] Kai-En Lin, Lin Yen-Chen, Wei-Sheng Lai, Tsung-Yi Lin, Yi-Chang Shih, and Ravi Ramamoorthi. Vision transformer for nef-based view synthesis from a single input image. arXiv preprint arXiv:2207.05736, 2022. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 416, + 547, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 416, + 547, + 470 + ], + "spans": [ + { + "bbox": [ + 308, + 416, + 547, + 470 + ], + "type": "text", + "content": "[22] Andrew Liu, Richard Tucker, Varun Jampani, Ameesh Makadia, Noah Snavely, and Angjoo Kanazawa. Infinite nature: Perpetual view generation of natural scenes from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2021. 5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 471, + 547, + 504 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 471, + 547, + 504 + ], + "spans": [ + { + "bbox": [ + 308, + 471, + 547, + 504 + ], + "type": "text", + "content": "[23] Feng Liu, Michael Gleicher, Hailin Jin, and Aseem Agarwala. Content-preserving warps for 3d video stabilization. ACM Transactions on Graphics (ToG), 28(3):1-9, 2009. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 505, + 547, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 505, + 547, + 536 + ], + "spans": [ + { + "bbox": [ + 308, + 505, + 547, + 536 + ], + "type": "text", + "content": "[24] Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. NeurIPS, 2020. 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 537, + 547, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 537, + 547, + 602 + ], + "spans": [ + { + "bbox": [ + 308, + 537, + 547, + 602 + ], + "type": "text", + "content": "[25] Yuan Liu, Sida Peng, Lingjie Liu, Qianqian Wang, Peng Wang, Christian Theobalt, Xiaowei Zhou, and Wenping Wang. Neural rays for occlusion-aware image-based rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7824-7833, 2022. 2, 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 603, + 547, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 603, + 547, + 647 + ], + "spans": [ + { + "bbox": [ + 308, + 603, + 547, + 647 + ], + "type": "text", + "content": "[26] Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh. Neural volumes: Learning dynamic renderable volumes from images. ACM Trans. Graph., 2019. 1, 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 647, + 547, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 647, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 647, + 547, + 713 + ], + "type": "text", + "content": "[27] Ricardo Martin-Brualla, Noha Radwan, Mehdi SM Sajjadi, Jonathan T Barron, Alexey Dosovitskiy, and Daniel Duckworth. Nerf in the wild: Neural radiance fields for unconstrained photo collections. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7210-7219, 2021. 3" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "4978" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 288, + 127 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 288, + 127 + ], + "type": "text", + "content": "[28] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. In Proceedings of the European Conference on Computer Vision (ECCV), pages 405-421, 2020. 1, 2, 3, 4" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 128, + 288, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 128, + 288, + 172 + ], + "spans": [ + { + "bbox": [ + 48, + 128, + 288, + 172 + ], + "type": "text", + "content": "[29] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph., 41(4):102:1-102:15, July 2022. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 172, + 288, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 172, + 288, + 237 + ], + "spans": [ + { + "bbox": [ + 48, + 172, + 288, + 237 + ], + "type": "text", + "content": "[30] Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Joerg H. Mueller, Chakravarty R. Alla Chaitanya, Anton S. Kaplanyan, and Markus Steinberger. DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks. Computer Graphics Forum, 2021. 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 238, + 288, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 238, + 288, + 282 + ], + "spans": [ + { + "bbox": [ + 48, + 238, + 288, + 282 + ], + "type": "text", + "content": "[31] Ren Ng, Marc Levoy, Mathieu Brédif, Gene Duval, Mark Horowitz, and Pat Hanrahan. Light field photography with a hand-held plenoptic camera. PhD thesis, Stanford University, 2005. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 282, + 288, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 282, + 288, + 327 + ], + "spans": [ + { + "bbox": [ + 48, + 282, + 288, + 327 + ], + "type": "text", + "content": "[32] Thu H Nguyen-Phuoc, Chuan Li, Stephen Balaban, and Yongliang Yang. Rendernet: A deep convolutional network for differentiable rendering from 3d shapes. Advances in neural information processing systems, 31, 2018. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 327, + 288, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 327, + 288, + 392 + ], + "spans": [ + { + "bbox": [ + 48, + 327, + 288, + 392 + ], + "type": "text", + "content": "[33] Michael Niemeyer, Jonathan T Barron, Ben Mildenhall, Mehdi SM Sajjadi, Andreas Geiger, and Noha Radwan. Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5480-5490, 2022. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 393, + 288, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 393, + 288, + 448 + ], + "spans": [ + { + "bbox": [ + 48, + 393, + 288, + 448 + ], + "type": "text", + "content": "[34] Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3504-3515, 2020. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 449, + 288, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 449, + 288, + 480 + ], + "spans": [ + { + "bbox": [ + 48, + 449, + 288, + 480 + ], + "type": "text", + "content": "[35] Eric Penner and Li Zhang. Soft 3d reconstruction for view synthesis. ACM Transactions on Graphics (TOG), 36(6):1-11, 2017. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 481, + 288, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 481, + 288, + 525 + ], + "spans": [ + { + "bbox": [ + 48, + 481, + 288, + 525 + ], + "type": "text", + "content": "[36] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12179-12188, 2021. 3, 4" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 525, + 288, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 525, + 288, + 592 + ], + "spans": [ + { + "bbox": [ + 48, + 525, + 288, + 592 + ], + "type": "text", + "content": "[37] Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, and David Novotny. Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10901-10911, 2021. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 592, + 288, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 592, + 288, + 613 + ], + "spans": [ + { + "bbox": [ + 48, + 592, + 288, + 613 + ], + "type": "text", + "content": "[38] Gernot Riegler and Vladlen Koltun. Free view synthesis. In European Conference on Computer Vision, 2020. 2, 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 614, + 288, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 614, + 288, + 679 + ], + "spans": [ + { + "bbox": [ + 48, + 614, + 288, + 679 + ], + "type": "text", + "content": "[39] Mehdi SM Sajjadi, Henning Meyer, Etienne Pot, Urs Bergmann, Klaus Greff, Noha Radwan, Suhani Vora, Mario Lucic, Daniel Duckworth, Alexey Dosovitskiy, et al. Scene representation transformer: Geometry-free novel view synthesis through set-latent scene representations. arXiv preprint arXiv:2111.13152, 2021. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 680, + 288, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 288, + 714 + ], + "type": "text", + "content": "[40] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In Proceedings of the" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 714 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 326, + 73, + 547, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 547, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 547, + 95 + ], + "type": "text", + "content": "IEEE/CVF conference on computer vision and pattern recognition, pages 4938-4947, 2020. 8" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 95, + 546, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 95, + 546, + 128 + ], + "spans": [ + { + "bbox": [ + 307, + 95, + 546, + 128 + ], + "type": "text", + "content": "[41] Harry Shum and Sing Bing Kang. Review of image-based rendering techniques. In Visual Communications and Image Processing 2000, volume 4067, pages 2-13. SPIE, 2000. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 129, + 547, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 547, + 172 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 547, + 172 + ], + "type": "text", + "content": "[42] Vincent Sitzmann, Julien N.P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein. Implicit neural representations with periodic activation functions. In Proc. NeurIPS, 2020. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 172, + 547, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 172, + 547, + 227 + ], + "spans": [ + { + "bbox": [ + 307, + 172, + 547, + 227 + ], + "type": "text", + "content": "[43] Vincent Sitzmann, Semon Rezchikov, Bill Freeman, Josh Tenenbaum, and Fredo Durand. Light field networks: Neural scene representations with single-evaluation rendering. In Advances in Neural Information Processing Systems (NeurIPS), 2021. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 228, + 547, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 228, + 547, + 281 + ], + "spans": [ + { + "bbox": [ + 307, + 228, + 547, + 281 + ], + "type": "text", + "content": "[44] Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhöfer. Deepvoxels: Learning persistent 3d feature embeddings. In Proc. Computer Vision and Pattern Recognition (CVPR), IEEE, 2019. 1, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 282, + 547, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 282, + 547, + 327 + ], + "spans": [ + { + "bbox": [ + 307, + 282, + 547, + 327 + ], + "type": "text", + "content": "[45] Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. Scene representation networks: Continuous 3d-structure-aware neural scene representations. Advances in Neural Information Processing Systems, 32, 2019. 1, 2, 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 327, + 547, + 382 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 327, + 547, + 382 + ], + "spans": [ + { + "bbox": [ + 307, + 327, + 547, + 382 + ], + "type": "text", + "content": "[46] Pratul P Srinivasan, Richard Tucker, Jonathan T Barron, Ravi Ramamoorthi, Ren Ng, and Noah Snavely. Pushing the boundaries of view extrapolation with multiplane images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 175-184, 2019. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 382, + 547, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 382, + 547, + 425 + ], + "spans": [ + { + "bbox": [ + 307, + 382, + 547, + 425 + ], + "type": "text", + "content": "[47] Mohammed Suhail, Carlos Esteves, Leonid Sigal, and Ameesh Makadia. Generalizable patch-based neural rendering. In European Conference on Computer Vision. Springer, 2022. 2, 3, 5, 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 426, + 547, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 426, + 547, + 470 + ], + "spans": [ + { + "bbox": [ + 307, + 426, + 547, + 470 + ], + "type": "text", + "content": "[48] Mohammed Suhail, Carlos Esteves, Leonid Sigal, and Ameesh Makadia. Light field neural rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8269-8279, 2022. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 471, + 546, + 492 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 471, + 546, + 492 + ], + "spans": [ + { + "bbox": [ + 307, + 471, + 546, + 492 + ], + "type": "text", + "content": "[49] Andrea Tagliasacchi and Ben Mildenhall. Volume rendering digest (for nef). arXiv preprint arXiv:2209.02417, 2022. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 492, + 546, + 548 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 492, + 546, + 548 + ], + "spans": [ + { + "bbox": [ + 307, + 492, + 546, + 548 + ], + "type": "text", + "content": "[50] Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, W Yifan, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, et al. Advances in neural rendering. In Computer Graphics Forum, pages 703-735. Wiley Online Library, 2022. 1, 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 548, + 546, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 548, + 546, + 592 + ], + "spans": [ + { + "bbox": [ + 307, + 548, + 546, + 592 + ], + "type": "text", + "content": "[51] Alex Trevithick and Bo Yang. Grf: Learning a general radiance field for 3d representation and rendering. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15182-15192, 2021. 2, 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 592, + 546, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 592, + 546, + 635 + ], + "spans": [ + { + "bbox": [ + 307, + 592, + 546, + 635 + ], + "type": "text", + "content": "[52] Richard Tucker and Noah Snavely. Single-view view synthesis with multiplane images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 551-560, 2020. 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 636, + 546, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 636, + 546, + 689 + ], + "spans": [ + { + "bbox": [ + 307, + 636, + 546, + 689 + ], + "type": "text", + "content": "[53] Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo MartinBrualla, Noah Snavely, and Thomas Funkhouser. Ibrnet: Learning multi-view image-based rendering. In CVPR, 2021. 2, 3, 5, 6" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 690, + 546, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 690, + 546, + 714 + ], + "spans": [ + { + "bbox": [ + 307, + 690, + 546, + 714 + ], + "type": "text", + "content": "[54] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: From error visibility to" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "4979" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 73, + 288, + 409 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 67, + 73, + 287, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 73, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 67, + 73, + 287, + 95 + ], + "type": "text", + "content": "structural similarity. IEEE Transactions on Image Processing, 2004. 5" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 287, + 129 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 287, + 129 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 287, + 129 + ], + "type": "text", + "content": "[55] Olivia Wiles, Georgia Gkioxari, Richard Szeliski, and Justin Johnson. Synsin: End-to-end view synthesis from a single image. In Proc. CVPR, pages 7467-7477, 2020. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 130, + 288, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 130, + 288, + 184 + ], + "spans": [ + { + "bbox": [ + 48, + 130, + 288, + 184 + ], + "type": "text", + "content": "[56] Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. Neural fields in visual computing and beyond. Computer Graphics Forum, 2022. 2, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 186, + 288, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 186, + 288, + 239 + ], + "spans": [ + { + "bbox": [ + 48, + 186, + 288, + 239 + ], + "type": "text", + "content": "[57] Wang Yifan, Carl Doersch, Relja Arandjelović, João Carreira, and Andrew Zisserman. Input-level inductive biases for 3d reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6176-6186, 2022. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 241, + 288, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 241, + 288, + 285 + ], + "spans": [ + { + "bbox": [ + 48, + 241, + 288, + 285 + ], + "type": "text", + "content": "[58] Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. pixelNeRF: Neural radiance fields from one or few images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 2, 3, 5, 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 286, + 288, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 286, + 288, + 342 + ], + "spans": [ + { + "bbox": [ + 48, + 286, + 288, + 342 + ], + "type": "text", + "content": "[59] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep networks as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 342, + 288, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 342, + 288, + 364 + ], + "spans": [ + { + "bbox": [ + 48, + 342, + 288, + 364 + ], + "type": "text", + "content": "[60] Yunzhi Zhang and Jiajun Wu. Video extrapolation in space and time. arXiv e-prints, pages arXiv-2205, 2022. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 365, + 288, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 365, + 288, + 409 + ], + "spans": [ + { + "bbox": [ + 48, + 365, + 288, + 409 + ], + "type": "text", + "content": "[61] Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. Stereo magnification: Learning view synthesis using multiplane images. ACM Trans. Graph. (Proc. SIGGRAPH), 37, 2018. 3, 5" + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "4980" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/b93957f9-1257-4220-880f-a2d772770ee6_content_list.json b/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/b93957f9-1257-4220-880f-a2d772770ee6_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..14080f4d08607443bd6cb8024a4e57aec167accc --- /dev/null +++ b/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/b93957f9-1257-4220-880f-a2d772770ee6_content_list.json @@ -0,0 +1,1991 @@ +[ + { + "type": "text", + "text": "Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation", + "text_level": 1, + "bbox": [ + 135, + 119, + 831, + 162 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Gaurav Patel† Konda Reddy Mopuri‡ Qiang Qiu† \n†Purdue University ‡Indian Institute of Technology Hyderabad \n{gpatel10, qqiu}@purdue.edu, krmpuri@ai.iith.ac.in", + "bbox": [ + 228, + 190, + 738, + 244 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 256, + 313, + 272 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Data-free Knowledge Distillation (DFKD) has gained popularity recently, with the fundamental idea of carrying out knowledge transfer from a Teacher neural network to a Student neural network in the absence of training data. However, in the Adversarial DFKD framework, the student network's accuracy, suffers due to the non-stationary distribution of the pseudo-samples under multiple generator updates. To this end, at every generator update, we aim to maintain the student's performance on previously encountered examples while acquiring knowledge from samples of the current distribution. Thus, we propose a meta-learning inspired framework by treating the task of Knowledge-Acquisition (learning from newly generated samples) and KnowledgeRetention (retaining knowledge on previously met samples) as meta-train and meta-test, respectively. Hence, we dub our method as Learning to Retain while Acquiring. Moreover, we identify an implicit aligning factor between the KnowledgeRetention and Knowledge-Acquisition tasks indicating that the proposed student update strategy enforces a common gradient direction for both tasks, alleviating interference between the two objectives. Finally, we support our hypothesis by exhibiting extensive evaluation and comparison of our method with prior arts on multiple datasets.", + "bbox": [ + 75, + 287, + 473, + 619 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 645, + 209, + 660 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The primary goal of Data-Free Knowledge Distillation (DFKD) is to acquire a trustworthy alternative dataset for knowledge distillation. The quality of knowledge transfer depends on the caliber of these alternative samples. Noise optimization [11,22,30] and generative reconstruction [4,10,20] are the two primary ways to replace the original training data used in the distillation process with synthetic or pseudo samples. Adversarial DFKD methods [10, 19, 20] investigate an adversarial exploration framework to seek pseudo-samples. In such a paradigm, the Generator $(\\mathcal{G})$ is used to create pseudo-samples as surrogates to perform knowledge distillation/transfer, and the Teacher-Student $(\\mathcal{T} - \\mathcal{S})$ setup acts as a joint discriminator to penalize and update generator parameters $(\\theta_{\\mathcal{G}})$ (Figure 1). In the adversarial framework, the generator explores the input space to find suitable pseudo-samples as the distillation progresses. Consequently, the distribution", + "bbox": [ + 75, + 670, + 470, + 901 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/38262ac8e5b86430a7c092645f1c0a63db0989533a50a7629ef2627bec970671.jpg", + "image_caption": [ + "Figure 1. The top-section represents the learning evolution of a generic Adversarial Data-Free Knowledge Distillation framework; the color-intensity variation signifies the change in the distribution of the pseudo-samples, the student network, and the generator network over the learning epochs. Under the variation in the distribution of the pseudo-samples, the bottom-section shows the learning curves for cases when the student accuracy degrades (shown in Red), which is undesirable, and when the student accuracy is maintained, if not improved, as proposed (shown in Green)." + ], + "image_footnote": [], + "bbox": [ + 503, + 255, + 890, + 503 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "of the generated samples consistently keeps changing during the process due to the generator updates [25]. From the student network's perspective, at each iteration, the pseudo samples seem to be generated from different generator parameters $(\\theta_{\\mathcal{G}})$ . Hence, the convergence of the student network gets hampered due to successive distributional alterations over time [29], as depicted by the red curve in Figure 1. This observation hints that updating the student network, solely using the samples generated from the current generator parameters is not adequate to generalize the student. Moreover, the student forgets the knowledge acquired previously and decelerates the knowledge distillation. Therefore, the generator, apart from exploring the input space, seeks to compensate for the loss of knowledge in future iterations. Additionally, in a practical setting, during the distillation process, high variation in the student network's classification accuracy is undesirable, especially when the validation data is not available, since that prevents the user from tracking", + "bbox": [ + 496, + 641, + 895, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "7786", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/beb466f4525609e470e156f2b8f2735ee19dd55fc3189f366ad605e8d9153005.jpg", + "image_caption": [ + "Figure 2. Student update strategies: (a) Typical student update by optimizing the Knowledge-Acquisition loss $(\\mathcal{L}_{Acq})$ with the batch pseudo samples $(\\hat{x})$ , produced by the generator $(\\mathcal{G})$ [5, 10, 20]. (b) Student update with simultaneous optimization of the Knowledge-Acquisition loss $(\\mathcal{L}_{Acq})$ and Knowledge-Retention loss $(\\mathcal{L}_{Ret})$ on the batch of pseduo samples $(\\hat{x})$ and memory samples $(\\hat{x}_m)$ obtained from the generator $(\\mathcal{G})$ and the memory $(\\mathcal{M})$ , respectively [2, 3]. (c) The proposed student update strategy, which treats $\\mathcal{L}_{Acq}$ as meta-train and $\\mathcal{L}_{Ret}$ as meta-test, and implicitly imposes the alignment between them." + ], + "image_footnote": [], + "bbox": [ + 76, + 88, + 893, + 208 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "the student's accuracy over time, and selecting the distilled model parameters with the highest accuracy.", + "bbox": [ + 75, + 279, + 468, + 309 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To circumvent the above-discussed problems, existing methods maintain a memory buffer to rehearse the examples from previously encountered distributions while learning with current examples. Binci et al. [3] introduce Replay based methods to explicitly retrain/replay on a limited subset of previously encountered samples while training on the current examples. Then, carrying forward, they use Generative-Replay [27] to transfer the learned examples to an auxiliary generative model (VAE), and sample from the it (VAE decoder) in subsequent iterations [2]. Nonetheless, the performance of these methods is upper bounded by joint training on previous and current examples [7]. Although, recent works have focused on modeling the memory, we seek to work towards effectively utilizing the samples from memory.", + "bbox": [ + 73, + 310, + 470, + 525 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we aim to update the student network parameters $(\\theta_S)$ such that its performance does not degrade on the samples previously produced by the generator network $(\\mathcal{G})$ , aspiring towards Learning to Retain while Acquiring. Thus, we propose a meta-learning inspired strategy to achieve this goal. We treat the task of Knowledge-Acquisition (learning from newly generated samples) and Knowledge-Retention (learning from previously encountered samples from memory) as meta-train and meta-test, respectively. Hence, in the proposed approach, the student network acquires new information while maintaining performance on previously encountered samples. By doing so, the proposed strategy (Figure 2c) implicitly aligns Knowledge-Acquisition and Knowledge-Retention, as opposed to simply combining them [2,3] without any coordination or alignment (Figure 2b), which leaves them to potentially interfere with one another.", + "bbox": [ + 73, + 526, + 470, + 768 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Additionally, analyzing the proposed meta-objective, we discover that (in Section 3.4) the latent alignment factor as the dot product between the gradients of the Knowledge-Acquisition and Knowledge-Retention objectives, suggesting that the meta-objective enforces a common gradient direction for both tasks, encouraging the alignment between the task-specific gradients. Thus, the proposed method simultaneously minimizes the loss and matches the gradients corresponding to the individual tasks (Knowledge-Acquisition", + "bbox": [ + 75, + 771, + 472, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "and Knowledge-Retention), enforcing the optimization paths to be same for both tasks.", + "bbox": [ + 496, + 280, + 890, + 306 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Moreover, the proposed student update strategy is scalable to different deep architectures as the gradient alignment is implicit, and memory-agnostic, making no assumptions about the replay scheme employed. Nonetheless, recent works on gradient alignment, have shown great empirical advantages in Zero-Shot Learning [24], Distributed/Federated Learning [6] and Domain Generalization [26]. Our method extends the advantages of gradient alignment to memory-based Adversarial DFKD, thus strengthening the empirical findings in these works.", + "bbox": [ + 496, + 309, + 893, + 452 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Finally, to demonstrate the advantages of the proposed student update strategy, we evaluate and compare against current non-memory [4,5, 10] and memory-based [2, 3] Adversarial DFKD methods, and observe substantial improvement in the student learning evolution.", + "bbox": [ + 496, + 453, + 893, + 523 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In summary, the key points of this work are as follows:", + "bbox": [ + 517, + 523, + 880, + 539 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a novel meta-learning inspired student update strategy in the Adversarial DFKD setting, that aims to maintain the student's performance on previously encountered examples (Knowledge-Retention) while acquiring knowledge from samples of the current distribution (Knowledge-Acquisition).", + "- We theoretically identify (in Section 3.4) that the proposed student update strategy enforces an implicit gradient alignment between the Knowledge-Acquisition and Knowledge-Retention tasks.", + "- A major practical advantage of the proposed method is its applicability in the absence of a validation set, discussed in Section 4.2.", + "- Finally, we evaluate our method and compare against various Adversarial DFKD methods, on multiple student architectures and replay schemes (Memory Buffer and Generative Replay)." + ], + "bbox": [ + 517, + 549, + 893, + 792 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 816, + 640, + 832 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Adversarial Data-Free Knowledge Distillation: In the Adversarial Data-Free Knowledge Distillation paradigm, A generative model is trained to synthesize pseudo-samples that serve as queries for the Teacher $(\\mathcal{T})$ and the Student", + "bbox": [ + 496, + 842, + 893, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "7787", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/2dc7e81d7b305672f383d35478c9ef5696ec89121c3c1b637761e99f955a2683.jpg", + "image_caption": [ + "Generator Update" + ], + "image_footnote": [], + "bbox": [ + 76, + 114, + 444, + 224 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/3bf527a2402fbe246160b89cd3f67bd67adba3218e1d97bc8fc85820e7a790b9.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 477, + 88, + 883, + 224 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/9023c7772776c820c66710189d71ebfeb1191862aca3111212c0f3f95865fc5b.jpg", + "image_caption": [ + "Figure 3. An illustration of the proposed DFKD framework. The framework consists of the Generator $(\\mathcal{G})$ , Teacher $(\\mathcal{T})$ , Student $(\\mathcal{S})$ and the Memory $(\\mathcal{M})$ . $\\mathcal{G}$ and $\\mathcal{S}$ are updated alternatively, similar to the GAN [14] framework with the generator loss $(\\mathcal{L}_{\\mathcal{G}})$ optimizing $\\mathcal{G}$ , and the Knowledge-Acquisition loss $(\\mathcal{L}_{Acq})$ and the Knowledge-Retention loss $(\\mathcal{L}_{Ret})$ optimizing the student $(\\mathcal{S})$ . We use $\\mathcal{M}$ in a generalized way to denote the employed replay scheme (Memory Buffer or Generative Replay in our case)." + ], + "image_footnote": [], + "bbox": [ + 80, + 243, + 107, + 263 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Generator", + "bbox": [ + 109, + 244, + 145, + 252 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Network", + "bbox": [ + 107, + 253, + 145, + 260 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/aad4c598f64f69b87552a1623468de6d3e8d8d39fcb4679496e1b6fe591587c8.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 153, + 243, + 178, + 262 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Teacher", + "bbox": [ + 179, + 244, + 210, + 252 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Network", + "bbox": [ + 179, + 253, + 210, + 260 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": 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ZSKT [20] attempts data-free knowledge transfer by first training a generator in an adversarial fashion to look for samples on which the student and teacher do not match well. To improve the model discrepancy measure, it adopts the Kullback-Leibler (KL) divergence, and introduces attention transfer [33] to aid knowledge transfer. Moreover, DFAD [10] recommends Mean Absolute Error (MAE) as a model discrepancy function to prevent decayed gradients on converged samples. Furthermore, the adversarial framework was extended by Choi et al. [5] in the context of model quantization, by proposing adversarial data-free quantization (DFQ), and introducing additional regularization terms that match the mean and standard deviation of the generated pseudo-samples with the teacher model's batchnorm statistics, and imposes batch categorical entropy maximization, such that sample from each class appear equally in the generated batch. Fang et al. recently introduced FastD-FKD [9], an effective method with a meta generator to speed up the DFKD process, delivering a 100-fold increase in the knowledge transfer rate.", + "bbox": [ + 75, + 335, + 472, + 622 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Handling Distribution Shift in Adversarial DFKD: To counter the distribution mismatch and the catastrophic forgetting phenomenon in the adversarial framework [25], Binici et al. [3] suggested maintaining a dynamic collection of synthetic samples throughout training iterations to prevent catastrophic forgetting in DFKD. Moreover, in their latest work [2], they introduce generative pseudo-replay [27] in which an auxiliary generative model simultaneously learns the distribution of the samples produced by the generator $(\\mathcal{G})$ . Throughout the training process, examples are generated from the auxiliary generator to replay during training. Nonetheless, these works have focused on modeling the memory buffer. A related line of research maintains an exponentially moving average (EMA) of the generator model $\\mathcal{G}$ [8] to replace the typical Memory-Buffer and Generative Replay. Nonetheless, our work focuses on the effective utilization of the samples obtained from the memory.", + "bbox": [ + 75, + 623, + 472, + 867 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 500, + 335, + 633, + 352 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In Section 3.1, we first provide a brief overview of Adversarial DFKD. Then, in Section 3.2, we discuss the data-free knowledge distillation objective. In Sections 3.3 and 3.4, we elaborate on the proposed student update strategy. Lastly, in Section 3.5, we discuss the adopted generator update strategy used for the baselines and the proposed framework.", + "bbox": [ + 496, + 359, + 893, + 446 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Adversarial Data-Free Knowledge Distillation", + "text_level": 1, + "bbox": [ + 498, + 453, + 888, + 469 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In the Adversarial DFKD framework, a generator $(\\mathcal{G})$ is used to create pseudo-samples as surrogates to perform knowledge distillation/transfer, and the teacher-student $(\\mathcal{T}-S)$ setup acts as a joint discriminator to penalize and update generator parameters $(\\theta_{\\mathcal{G}})$ in an adversarial manner. After updating $\\theta_{\\mathcal{G}}$ , random samples are generated and used to minimize the $\\mathcal{T}-S$ discrepancy by updating the student parameters $(\\theta_{\\mathcal{S}})$ . The generator and the student are optimized alternatively up until a fixed number of pre-defined iterations. In essence, the goal of DFKD is to craft a lightweight student model $(S)$ by harnessing valuable knowledge from the well-trained Teacher model $(\\mathcal{T})$ in the absence of training data. A general overview of Adversarial DFKD framework is illustrated in Figure 1.", + "bbox": [ + 496, + 474, + 893, + 678 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Goal of Data Free Knowledge Distillation", + "text_level": 1, + "bbox": [ + 500, + 684, + 852, + 700 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "A student model $(\\mathcal{S})$ is trained to match the teacher's $(\\mathcal{T})$ predictions on its unavailable target domain $(\\mathcal{D}_{\\mathcal{T}})$ as part of the distillation process. Let, $p_{\\mathcal{S}}(x) = \\mathrm{Softmax}(S_{\\theta_{\\mathcal{S}}}(\\boldsymbol{x}))$ and $p_{\\mathcal{T}}(x) = \\mathrm{Softmax}(\\mathcal{T}_{\\theta_{\\mathcal{T}}}(\\boldsymbol{x})), \\forall \\boldsymbol{x} \\in \\mathcal{D}_{\\mathcal{T}}$ , denote the predicted Student and Teacher probability mass across the classes, respectively. We seek to find the parameters $\\theta_{\\mathcal{S}}$ of the student model that will reduce the probability of errors $(P)$ between the predictions $p_{\\mathcal{S}}(x)$ and $p_{\\mathcal{T}}(x)$ :", + "bbox": [ + 496, + 705, + 893, + 823 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\theta_ {\\mathcal {S}}} P _ {x \\sim \\mathcal {D} _ {\\mathcal {T}}} \\left(\\underset {i} {\\arg \\max } p _ {\\mathcal {S}} ^ {i} (x) \\neq \\underset {i} {\\arg \\max } p _ {\\mathcal {T}} ^ {i} (x)\\right),\n$$\n", + "text_format": "latex", + "bbox": [ + 524, + 829, + 864, + 863 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where the superscript $i$ denotes the $i^{th}$ probability score of the predicted masses, $p_{\\mathcal{S}}(x)$ and $p_{\\mathcal{T}}(x)$ .", + "bbox": [ + 498, + 869, + 893, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "7788", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/f21f0eb3dc8f5cdfa052f7fd3a8f2e8cef9f021e8a953b191e32ab9df9126dde.jpg", + "image_caption": [ + "Figure 4. Learning evolution of the proposed method compared to the prior arts (MB-DFKD [3] and PRE-DFKD [2]) that employ replay. The plots visualize the Accuracy $(\\%)$ evolution (top-row) and the Cumulative Mean Accuracy $(\\%)$ evolution (bottom-row) of the ResNet-18 [15] student network on SVHN [23], CIFAR10 [17], CIFAR100 [17], and Tiny-ImageNet [18] datasets. The proposed method is plotted Blue." + ], + "image_footnote": [], + "bbox": [ + 76, + 85, + 287, + 186 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/c5e13a2b1e0e4997a70b03ddd62de41c365be6b25724072d90291df3613b5e70.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 292, + 85, + 486, + 186 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/d8ca0364c0906518bbd95feb7bff7813f9b3b4e9c82ac3fc87a8189a8edbdf3c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 493, + 85, + 689, + 186 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/db2b41357ee158e738a9b2e3f083eb598837c9bb8dd76458880511464e284d99.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 696, + 85, + 893, + 186 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "However, DFKD suggests minimizing the student's error on a pseudo dataset $\\mathcal{D}_{\\mathcal{P}}$ since the teacher's original training data distribution $\\mathcal{D}_{\\mathcal{T}}$ is not available. Typically, a loss function, say $\\mathcal{L}_{KD}$ , that gauges disagreement between the teacher and the student is minimized $\\forall \\hat{x} \\in \\mathcal{D}_{\\mathcal{P}}$ by optimizing the student parameters $(\\theta_{S})$ :", + "bbox": [ + 75, + 237, + 472, + 325 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\underset {\\theta_ {S}} {\\min } \\mathbb {E} _ {\\hat {x} \\sim \\mathcal {D} _ {\\mathcal {P}}} \\left[ \\mathcal {L} _ {K D} \\left(\\mathcal {T} _ {\\theta_ {\\mathcal {T}}} (\\hat {x}), \\mathcal {S} _ {\\theta_ {\\mathcal {S}}} (\\hat {x})\\right) \\right]. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 148, + 334, + 470, + 357 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We use the Mean Absolute Error (MAE) to define loss measure $(\\mathcal{L}_{KD})$ as suggested in [10]. Suppose, given the predicted logits (pre-softmax predictions) $t(\\hat{x}) = \\mathcal{T}_{\\theta_{\\mathcal{T}}}(\\hat{x})$ from the teacher model and the predicted logits $s(\\hat{x}) = S_{\\theta_S}(\\hat{x})$ from the student model, we define $\\mathcal{L}_{KD}$ as:", + "bbox": [ + 75, + 366, + 472, + 441 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {K D} (t (\\hat {x}), s (\\hat {x})) = \\| t (\\hat {x}) - s (\\hat {x}) \\| _ {1}. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 150, + 449, + 470, + 467 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Learning to Retain while Acquiring", + "text_level": 1, + "bbox": [ + 76, + 474, + 387, + 492 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our novelty resides in the student update strategy, as described earlier. Knowledge-Retention and Knowledge-Acquisition relate to two independent goals and can therefore be considered as two discrete tasks. In fact, by aligning these two goals, we empirically observe that, they can cooperate to retain the knowledge on previously encountered examples while acquiring knowledge from newly generated samples. The proposed method utilizes a meta-learning-inspired optimization strategy to effectively replay the samples from memory, say $\\mathcal{M}$ .", + "bbox": [ + 75, + 497, + 470, + 641 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In the typical Adversarial DFKD setup, the student update objective with the generated pseudo samples $(\\hat{x})$ , i.e., the Knowledge-Acquisition task $(\\mathcal{L}_{Acq})$ (Figure 2a), is formulated as:", + "bbox": [ + 75, + 641, + 472, + 698 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\theta_ {S}} \\mathcal {L} _ {A c q} (\\theta_ {S}) = \\min _ {\\theta_ {S}} \\mathbb {E} _ {\\hat {x}} [ \\mathcal {L} (\\mathcal {T} _ {\\theta_ {T}} (\\hat {x}), \\mathcal {S} _ {\\theta_ {S}} (\\hat {x})) ], \\quad (3)\n$$\n", + "text_format": "latex", + "bbox": [ + 117, + 709, + 470, + 733 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathcal{L}$ is the MAE (2) between the teacher and the student logits, and $\\hat{x} = \\mathcal{G}(z), z \\sim \\mathcal{N}(0, I)$ , denotes the batch of randomly generated samples.", + "bbox": [ + 75, + 741, + 468, + 785 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Moreover, to alleviate the distribution drift during knowledge distillation in the adversarial setting, previous works have maintained a memory buffer, to store $\\hat{x}$ and replay at later iterations to help the student recall the knowledge [3]. At a fixed frequency, batches of samples from current iterations are updated in the memory [3]. Also, recent works [2] have explored using the Generative Replay [27] strategy to simultaneously store the samples, as they are encountered, in", + "bbox": [ + 75, + 785, + 472, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "a generative model (VAE [16]), and later replay by generating samples from the VAE decoder. Hence, the optimization objective for the Knowledge-Retention $(\\mathcal{L}_{Ret})$ can be defined as follows:", + "bbox": [ + 496, + 237, + 893, + 294 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{r l} \\underset {\\theta_ {S}} {\\min } \\mathcal {L} _ {R e t} (\\theta_ {S}) & = \\underset {\\theta_ {S}} {\\min } \\mathbb {E} _ {\\hat {x} _ {m}} [ \\mathcal {L} (\\mathcal {T} _ {\\theta_ {\\tau}} (\\hat {x} _ {m}), \\mathcal {S} _ {\\theta_ {S}} (\\hat {x} _ {m})) ], \\\\ & \\quad \\hat {x} _ {m} \\sim \\mathcal {M}, \\end{array} \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 514, + 306, + 890, + 349 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where, with a slight abuse of notation, $\\mathcal{M}$ denotes the Memory Buffer or Generative Replay or any other replay scheme. Thus, the overall optimization objective to update $\\theta_{\\mathcal{S}}$ while considering both the new samples (generated from the latest $\\theta_{\\mathcal{G}}$ ) and the old samples (sampled from $\\mathcal{M}$ ) (Figure 2b) is defined as:", + "bbox": [ + 496, + 359, + 893, + 446 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\theta_ {S}} \\mathcal {L} _ {A c q} \\left(\\theta_ {S}\\right) + \\mathcal {L} _ {R e t} \\left(\\theta_ {S}\\right). \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 601, + 459, + 892, + 482 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "However, the objective in (5) attempts to simultaneously optimize $\\mathcal{L}_{Ret}$ and $\\mathcal{L}_{Acq}$ but does not seek to align the objectives, which leaves them to potentially interfere with one another. Say, we denote the gradients of Knowledge Acquisition and Knowledge Retention tasks with $\\nabla \\mathcal{L}_{Acq}(\\theta)$ and $\\nabla \\mathcal{L}_{Ret}(\\theta)$ , respectively. If $\\nabla \\mathcal{L}_{Acq}(\\theta)$ and $\\nabla \\mathcal{L}_{Ret}(\\theta)$ point in a similar direction or are said to be aligned, i.e., $\\nabla \\mathcal{L}_{Acq}(\\theta). \\nabla \\mathcal{L}_{Ret}(\\theta) > 0$ , then taking a gradient step along $\\nabla \\mathcal{L}_{Acq}(\\theta)$ or $\\nabla \\mathcal{L}_{Ret}(\\theta)$ improves the model's performance on both tasks. This is however not the case when $\\nabla \\mathcal{L}_{Acq}(\\theta)$ and $\\nabla \\mathcal{L}_{Ret}(\\theta)$ point in different directions, i.e., $\\nabla \\mathcal{L}_{Acq}(\\theta). \\nabla \\mathcal{L}_{Ret}(\\theta) \\leq 0$ , and the weight parameters obtained, may not be optimal for both the tasks simultaneously. Hence, the intended effect of having the gradient directions aligned is to obtain student parameters $(\\theta_S)$ that have optimal performance on both $\\mathcal{L}_{Acq}$ and $\\mathcal{L}_{Ret}$ .", + "bbox": [ + 496, + 493, + 893, + 724 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The proposed meta-learning inspired approach, seeks to align the two tasks, $\\mathcal{L}_{Acq}$ and $\\mathcal{L}_{Ret}$ . We take cues from Model-Agnostic Meta-learning (MAML) [12], and adapt it to Adversarial DFKD.", + "bbox": [ + 496, + 724, + 890, + 780 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Typically, MAML-like methods [12, 13] are framed as a bi-level optimization [28] problem, where the objective is defined as:", + "bbox": [ + 496, + 781, + 890, + 823 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\omega} \\mathcal {L} _ {\\text {o u t e r}} (\\arg \\min _ {\\omega} \\mathcal {L} _ {\\text {i n n e r}} (\\omega , \\mathcal {D} _ {\\text {t r a i n}}), \\mathcal {D} _ {\\text {t e s t}}), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 553, + 835, + 892, + 859 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\omega$ indicates the initial parameters, $\\mathcal{L}_{\\mathrm{inner}}$ denotes the task specific inner loss (meta-train) and $\\mathcal{L}_{\\mathrm{outer}}$ denotes the", + "bbox": [ + 496, + 869, + 890, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "7789", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "outer loss (meta-test), and $\\mathcal{D}_{\\mathrm{train}}$ and $\\mathcal{D}_{\\mathrm{test}}$ denote the inner loop and the outer loop training example sets.", + "bbox": [ + 75, + 90, + 468, + 119 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Likewise, at each iteration of the student parameter $(\\theta_S)$ update, we pose Knowledge-Acquisition and KnowledgeRetention as meta-train and meta-test, respectively. I.e., we perform a single gradient descent step using the current samples $(\\hat{x})$ produced from the generator network $(\\mathcal{G})$ , on the parameters $\\theta_{S}$ and obtain $\\theta_{S}^{\\prime}$ as $\\theta_S^\\prime = \\theta_S - \\alpha \\nabla \\mathcal{L}_{Acq}(\\theta_S)$ where $\\nabla \\mathcal{L}_{Acq}(\\theta_S)$ denotes gradient of $\\mathcal{L}_{Acq}$ at $\\theta_S$ . Then we optimize on the batch of samples $(\\hat{x}_m)$ obtained from the memory $(\\mathcal{M})$ , with the parameters $\\theta_S^\\prime$ , and then make a final update on $\\theta_S$ . Thus, formally, the overall meta-optimization objective, with the task of Knowledge Acquisition serving as meta-train and the task of Knowledge Retention as meta-test (Figure 2c), can be defined as follows:", + "bbox": [ + 75, + 119, + 472, + 308 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\theta_ {S}} \\mathcal {L} _ {A c q} (\\theta_ {S}) + \\mathcal {L} _ {R e t} (\\theta_ {S} ^ {\\prime}) = \\min _ {\\theta_ {S}} \\mathcal {L} _ {A c q} (\\theta_ {S}) +\n$$\n", + "text_format": "latex", + "bbox": [ + 83, + 319, + 395, + 343 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {R e t} \\big (\\theta_ {\\mathcal {S}} - \\alpha \\nabla \\mathcal {L} _ {A c q} (\\theta_ {\\mathcal {S}}) \\big). (7)\n$$\n", + "text_format": "latex", + "bbox": [ + 263, + 345, + 468, + 364 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. How does the Proposed Student Update Strategy Promote Alignment?", + "text_level": 1, + "bbox": [ + 76, + 375, + 468, + 405 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this subsection, we analyze the proposed objective (7) to understand how it results in the desired alignment between the Knowledge-Acquisition and Knowledge-Retention tasks. We assume that meta-train and meta-test tasks provide us with losses $\\mathcal{L}_{Acq}$ and $\\mathcal{L}_{Ret}$ ; in our case, $\\mathcal{L}_{Acq}$ and $\\mathcal{L}_{Ret}$ are the same function computed on the batches $\\hat{x}$ and $\\hat{x}_m$ , respectively. We utilize Taylor's expansion to elaborate the gradient of $\\mathcal{L}_{Ret}$ at a point $\\theta$ displaced by $\\phi_{\\theta}$ , as described in Lemma 1.", + "bbox": [ + 75, + 412, + 468, + 541 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Lemma 1. If $\\mathcal{L}_{Ret}$ has Lipschitz, Hessian, i.e., $\\| \\nabla^2\\mathcal{L}_{Ret}(\\theta_1) - \\nabla^2\\mathcal{L}_{Ret}(\\theta_2)\\|\\leq \\rho \\| \\theta_1 - \\theta_2\\|$ for some $\\rho >0$ , then:", + "bbox": [ + 76, + 542, + 470, + 585 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\nabla \\mathcal {L} _ {R e t} (\\theta + \\phi_ {\\theta}) = \\nabla \\mathcal {L} _ {R e t} (\\theta) + \\nabla^ {2} \\mathcal {L} _ {R e t} (\\theta) \\phi_ {\\theta} \\\\ + \\mathcal {O} (\\| \\phi_ {\\theta} \\| ^ {2}). \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 116, + 597, + 426, + 636 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For instance, when $\\phi_{\\theta} = -\\alpha \\nabla \\mathcal{L}_{Acq}(\\theta)$ , we have,", + "bbox": [ + 75, + 647, + 403, + 664 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\nabla \\mathcal {L} _ {R e t} (\\theta - \\alpha \\nabla \\mathcal {L} _ {A c q} (\\theta)) = \\nabla \\mathcal {L} _ {R e t} (\\theta) \\\\ - \\alpha \\nabla^ {2} \\mathcal {L} _ {R e t} (\\theta) \\nabla \\mathcal {L} _ {A c q} (\\theta) \\\\ + \\mathcal {O} (\\alpha^ {2}). \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 91, + 675, + 452, + 732 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Theorem 1. If $\\theta' = \\theta - \\alpha \\nabla \\mathcal{L}_{Acq}(\\theta)$ , denotes a single gradient descent step on $\\theta$ with the objective $\\mathcal{L}_{Acq}(\\theta)$ , where $\\alpha$ is a scalar, and $\\nabla \\mathcal{L}_{Acq}(\\theta)$ denotes the gradient of the objective at $\\theta$ , then:", + "bbox": [ + 75, + 747, + 468, + 805 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\frac {\\partial \\mathcal {L} _ {R e t} \\left(\\theta^ {\\prime}\\right)}{\\partial \\theta} = \\nabla \\mathcal {L} _ {R e t} (\\theta) - \\alpha \\nabla^ {2} \\mathcal {L} _ {R e t} (\\theta). \\nabla \\mathcal {L} _ {A c q} (\\theta) \\\\ - \\alpha \\nabla^ {2} \\mathcal {L} _ {A c q} (\\theta). \\nabla \\mathcal {L} _ {R e t} (\\theta) + \\mathcal {O} (\\alpha^ {2}). \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 101, + 815, + 444, + 866 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Proof. Please refer to the Supplemental Material.", + "bbox": [ + 76, + 885, + 403, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "While optimizing the objective defined in (7) using stochastic gradient descent, we would need to compute the gradient of the $\\mathcal{L}_{Ret}(\\theta_S^{\\prime})$ w.r.t $\\theta_{S}$ . Therefore, utilizing Theorem 1 we express $\\frac{\\partial\\mathcal{L}_{Ret}(\\theta_S^{\\prime})}{\\partial\\theta_S}$ as:", + "bbox": [ + 498, + 90, + 893, + 157 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\frac {\\partial \\mathcal {L} _ {R e t} \\left(\\theta_ {\\mathcal {S}} ^ {\\prime}\\right)}{\\partial \\theta_ {\\mathcal {S}}} = \\nabla \\mathcal {L} _ {R e t} \\left(\\theta_ {\\mathcal {S}}\\right) \\\\ - \\alpha \\nabla^ {2} \\mathcal {L} _ {R e t} (\\theta_ {\\mathcal {S}}). \\nabla \\mathcal {L} _ {A c q} (\\theta_ {\\mathcal {S}}) \\\\ - \\alpha \\nabla^ {2} \\mathcal {L} _ {A c q} (\\theta_ {\\mathcal {S}}). \\nabla \\mathcal {L} _ {R e t} (\\theta_ {\\mathcal {S}}) + \\mathcal {O} (\\alpha^ {2}), \\tag {8} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 514, + 169, + 892, + 242 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "using the product rule $\\nabla a.b + \\nabla b.a = \\nabla (a.b)$ , we get:", + "bbox": [ + 498, + 256, + 861, + 272 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\frac {\\partial \\mathcal {L} _ {R e t} \\left(\\theta_ {\\mathcal {S}} ^ {\\prime}\\right)}{\\partial \\theta_ {\\mathcal {S}}} = \\nabla \\mathcal {L} _ {R e t} \\left(\\theta_ {\\mathcal {S}}\\right) \\\\ - \\alpha \\nabla \\underbrace {(\\nabla \\mathcal {L} _ {\\text {R e t}} (\\theta_ {\\mathcal {S}}) . \\nabla \\mathcal {L} _ {\\text {A c q}} (\\theta_ {\\mathcal {S}}))} _ {\\text {G r a d i e n t A l i g n m e n t}} + \\mathcal {O} \\left(\\alpha^ {2}\\right). \\tag {9} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 514, + 285, + 892, + 371 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "From the analysis above, we observe that the gradient of $\\mathcal{L}_{Ret}(\\theta_S')$ at $\\theta_S$ (in (9)) produces the gradient of the gradient-product. This indicates, when optimizing $\\mathcal{L}_{Ret}(\\theta_S')$ (in (7)), the gradient of $\\mathcal{L}_{Ret}(\\theta_S')$ at $\\theta_S$ , has a negatively scaled gradient of the gradient-product term, i.e., $\\nabla (\\nabla \\mathcal{L}_{Ret}(\\theta_S). \\nabla \\mathcal{L}_{Acq}(\\theta_S))$ (derived in (9)), suggesting that the overall-gradients minimize $\\mathcal{L}_{Ret}(\\theta_S)$ and maximize $\\nabla \\mathcal{L}_{Ret}(\\theta_S). \\nabla \\mathcal{L}_{Acq}(\\theta_S)$ . Hence, optimizing (7) enforces the updates on $\\mathcal{L}_{Ret}(\\theta_S)$ and $\\mathcal{L}_{Acq}(\\theta_S)$ to seek a common direction, by maximizing the gradient-product. Moreover, we empirically observe this phenomenon in Section A of the Supplemental Material.", + "bbox": [ + 498, + 386, + 893, + 559 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.5. Generator Update in Adversarial Exploration-based DFKD", + "text_level": 1, + "bbox": [ + 500, + 573, + 890, + 602 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In the absence of the training dataset $(\\mathcal{D}_{\\mathcal{T}})$ , the generator $(\\mathcal{G})$ is utilized to obtain pseudo-samples $(\\hat{x})$ and perform knowledge-distillation, i.e., $\\hat{x} = \\mathcal{G}(z)$ , $z \\sim \\mathcal{N}(0, I)$ . The generator is learned to maximize the disagreement between Teacher network $(\\mathcal{T}_{\\theta_{\\mathcal{T}}})$ and the Student network $(S_{\\theta_S})$ . Additionally, for the generated data $\\hat{x}$ to mimic similar responses from the teacher as the real data, we include a prior loss $\\mathcal{L}_{\\mathcal{P}}$ [4] to be minimized alongside maximizing the discrepancy $(D)$ . Hence, we update the generator parameters $(\\theta_{\\mathcal{G}})$ by maximizing the following objective:", + "bbox": [ + 498, + 612, + 893, + 756 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\max _ {\\theta_ {\\mathcal {G}}} \\mathbb {E} _ {z \\sim \\mathcal {N} (0, I)} [ D (\\mathcal {T} _ {\\theta_ {\\mathcal {T}}} (\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)), \\mathcal {S} _ {\\theta_ {\\mathcal {S}}} (\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z))) \\\\ \\left. - \\mathcal {L} _ {\\mathcal {P}} \\left(\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)\\right) \\right]. \\tag {10} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 770, + 890, + 814 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Typically the disagreement function $(D)$ for the generator is identical to the teacher-student disagreement term [5, 10]. Instead, for teacher-student discrepancy maximization we use the Jensen-Shannon (JS) $(\\mathcal{L}_{JS})$ divergence. Our motivation to use JS divergence is based on empirical study by Binici et", + "bbox": [ + 498, + 828, + 893, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "7790", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 1. Distillation results of the Adversarial DFKD methods on four image classification benchmark datasets, SVHN [23], CIFAR10 [17], CIFAR100 [17], Tiny-ImageNet [18]. Primarily we compare against the replay-based methods, present at the bottom panel of each dataset. The best numbers from our method on the Bank-based replay are highlighted in Navy, and for the one with Generative replay are highlighted in Maroon. The first five columns represent the $\\mu[S_{Acc}]$ and the $\\sigma^2[S_{Acc}]$ at different epoch percentiles (described in Section 4.1). The last column represents the maximum test accuracy ( $\\mathrm{Acc}_{\\max}$ ) attained by the student network in the entire training period. Note: For SVHN and Tiny-ImageNet we were unable to reproduce the results of DFQ [5] in our setting. DFKD* denotes the baseline method without any replay.", + "bbox": [ + 73, + 78, + 890, + 170 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/c904284e96e62ac8471e8f4278afaced668abbe598451f2caf94c45510210de7.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Method>0thPercentile>20thPercentile>40thPercentile>60thPercentile>80thPercentileAccmax(%)
μ↑σ2↓μ↑σ2↓μ↑σ2↓μ↑σ2↓μ↑σ2↓
SVHN [23]
TAcc=97.45%, SAcc=97.26%, (Emax)=200
DAFL [4]85.15241.7090.498.7191.753.7392.880.8493.590.2094.23
DFAD [10]92.5293.4294.100.0794.130.0794.140.0894.110.0794.68
DFKD* [3]91.848.5992.536.7693.434.3994.611.7295.720.0495.97
MB-DFKD [3]89.5121.1389.2824.3188.6530.6387.1438.6781.6715.2494.11
PRE-DFKD [2]94.082.1194.531.1194.970.6695.420.2695.850.0496.10
Ours-1 (Memory Bank)92.787.4493.692.2194.251.5494.940.6295.590.0595.88
Ours-2 (Generative Replay)94.481.6494.790.7195.090.5195.470.2795.910.0396.15
CIFAR10 [17]
TAcc=95.72%, SAcc=95.23%, (Emax)=200
DAFL [4]62.78629.1372.51296.4480.57110.1887.0522.4090.762.0492.23
DFAD [10]85.88152.3989.9111.9591.465.6692.660.0792.850.0493.21
DFQ [5]81.2598.8684.4137.4087.0819.5989.726.1591.870.6792.90
DFKD* [3]83.57107.7786.9418.4288.6711.8390.674.0992.320.3893.02
MB-DFKD [3]84.2995.7487.2516.4788.8111.0090.713.7992.310.3593.03
PRE-DFKD [2]87.1045.6988.8810.8690.246.2691.751.8492.920.1493.41
Ours-1 (Memory Bank)85.5391.3388.5812.6689.968.2691.662.8693.090.3293.73
Ours-2 (Generative Replay)88.0760.8690.525.2091.443.1892.451.4593.480.1894.02
CIFAR100 [17]
TAcc=77.94%, SAcc=77.10%, (Emax)=400
DAFL [4]52.48437.8861.6582.9665.8832.2369.2311.9372.231.2973.78
DFAD [10]59.62192.6465.3228.8467.712.7268.690.3369.070.2469.73
DFQ [5]68.2055.9170.459.7871.775.8873.192.2274.480.4775.39
DFKD* [3]69.3674.6772.358.3473.594.5474.861.6375.940.1876.51
MB-DFKD [3]66.05207.2971.1614.6772.975.6174.401.5675.480.1876.14
PRE-DFKD [2]70.2386.6373.396.7774.592.8875.601.0376.460.1276.93
Ours-1 (Memory Bank)69.8775.6772.968.7274.304.0375.491.3876.500.2077.11
Ours-2 (Generative Replay)71.4960.1774.164.6175.122.2676.010.7476.750.0977.21
Tiny-ImageNet [18]
TAcc=60.83%, SAcc=57.88%, (Emax)=500
DAFL [4]21.04106.0424.9552.5628.1028.2231.1911.4434.111.3335.46
DFAD [10]14.6022.3216.487.4817.761.4318.440.2618.840.1019.60
DFKD* [3]34.5586.2038.1723.8840.2813.2842.405.3244.380.7945.61
MB-DFKD [3]34.16122.9238.7732.4241.1818.4043.638.4846.171.7347.96
PRE-DFKD [2]38.8980.1242.2721.6644.2512.6146.315.4748.331.3949.94
Ours-1 (Memory Bank)36.3494.6240.0326.2042.2813.7644.435.8246.520.9547.96
Ours-2 (Generative Replay)39.0974.9542.3021.2344.1913.5546.306.2148.481.1249.88
", + "bbox": [ + 114, + 178, + 852, + 667 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "al. [3]. Hence, $D$ is defined as:", + "bbox": [ + 75, + 681, + 282, + 695 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nD (a, b) = \\mathcal {L} _ {J S} (p (a), p (b)),\n$$\n", + "text_format": "latex", + "bbox": [ + 186, + 714, + 372, + 731 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "$\\mathcal{L}_{JS}(p(a),p(b)) = \\frac{1}{2} (\\mathcal{L}_{KL}(p(a),m) + \\mathcal{L}_{KL}(p(b),m)),$ and", + "bbox": [ + 76, + 734, + 472, + 762 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nm = \\frac {1}{2} (p (a) + p (b)). \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 200, + 763, + 468, + 792 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Here $\\mathcal{L}_{KL}$ stands for the Kullback-Leibler divergence and $p(a)$ and $p(b)$ denote the probability vectors obtained after the Softmax applied to the arguments $a$ and $b$ , respectively.", + "bbox": [ + 75, + 809, + 468, + 854 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Moreover, the prior loss $\\mathcal{L}_{\\mathcal{P}}$ [3] is defined as the combination of three loss functions $(\\mathcal{L}_{OH},\\mathcal{L}_A,$ and $\\mathcal{L}_{EM})$ that capture different characteristics from the teacher model and", + "bbox": [ + 75, + 856, + 470, + 898 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "impose them on the pseudo samples $\\hat{x}$ , and is defined as:", + "bbox": [ + 500, + 681, + 872, + 696 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathcal {P}} = \\mathcal {L} _ {O H} + \\gamma \\mathcal {L} _ {A} + \\delta \\mathcal {L} _ {E M}, \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 594, + 718, + 890, + 734 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where, $\\gamma$ and $\\delta$ denote the weighing scalar coefficients. $\\mathcal{L}_{OH}$ is the one-hot loss that forces the generated samples to have strong (one-hot vector like) predictions when input to the teacher. It is defined as the cross-entropy between the teacher's softmax output $p_{\\mathcal{T}}(\\hat{x}_n) =$ Softmax $(\\mathcal{T}_{\\theta_{\\mathcal{T}}}(\\hat{x}_n)),\\hat{x}_n\\in \\hat{x},$ and its one-hot vector version $e_c\\in \\{0,1\\} ^C$ , where $C$ denotes the total number of classes and $e_c$ denotes the $c$ -th canonical one-hot-vector, and $c = \\arg \\max_{i}(p_{\\mathcal{T}}^{i}(\\hat{x}_{n}))$ , the superscript $i$ denotes the $i^{th}$ probability score of the predicted mass vector $p_{\\mathcal{T}}(\\hat{x}_n)$", + "bbox": [ + 496, + 756, + 893, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "7791", + "bbox": [ + 482, + 945, + 513, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/e74ff2b99751b2493465e668142b2c160f70f867c469eb407aff79f22b152a47.jpg", + "image_caption": [ + "Figure 5. Student learning curves depicting the learning evolution of Wide-ResNet (WRN) [32]. The WRN-16-1 (top-row), and WRN-16-2 (bottom-row) networks are distilled by a WRN-40-2 teacher network pre-trained on CIFAR10 ( $\\mathcal{T}_{Acc} = 94.87\\%$ ). Each column represents the learning curves with the Buffer-based (with different memory buffer sizes) and Generative replay schemes. The proposed method is in Blue." + ], + "image_footnote": [], + "bbox": [ + 80, + 85, + 893, + 234 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Hence, $\\mathcal{L}_{OH}$ is defined as:", + "bbox": [ + 76, + 287, + 256, + 301 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {O H} = - \\frac {1}{N} \\sum_ {n = 1} ^ {N} e _ {c} ^ {\\top} \\log \\left(p _ {\\mathcal {T}} \\left(\\hat {x} _ {n}\\right)\\right). \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 156, + 311, + 470, + 353 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "- $\\mathcal{L}_A$ is the activation loss motivated by the notion that meaningful inputs result in higher-valued activation maps in a well-trained network [4] and is defined as:", + "bbox": [ + 75, + 363, + 470, + 406 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {A} = - \\frac {1}{N L} \\sum_ {n = 1} ^ {N} \\sum_ {l = 1} ^ {L} \\| \\mathcal {A} _ {\\mathcal {T}} ^ {l} (\\hat {x} _ {n}) \\| _ {1}, \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 156, + 417, + 470, + 459 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "where $\\mathcal{A}_{\\mathcal{T}}^{l}(\\hat{x}_{n})$ denotes the activation of the $l$ -th layer in the Teacher network $(\\mathcal{T}_{\\theta \\tau})$ for the input $n^{th}$ input $\\hat{x}_n\\in \\hat{x}$ .", + "bbox": [ + 75, + 470, + 468, + 500 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "- $\\mathcal{L}_{EM}$ loss is the entropy-maximization term imposed on the generator to output an equal number of pseudo-samples from each category [5]. In other words, the intra-batch entropy is maximized, resulting in a similar number of samples for each category i.e. if $\\bar{p}_{\\mathcal{T}} = \\frac{1}{N}\\sum_{n=1}^{N}p_{\\mathcal{T}}(\\hat{x}_n)$ , then the loss $\\mathcal{L}_{EM}$ is defined as:", + "bbox": [ + 75, + 500, + 468, + 588 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {E M} = \\bar {p} _ {\\mathcal {T}} ^ {\\top} \\log (\\bar {p} _ {\\mathcal {T}}). \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 200, + 598, + 470, + 616 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In sum, the Generator loss objective $(\\mathcal{L}_{\\mathcal{G}})$ is defined as:", + "bbox": [ + 76, + 627, + 441, + 642 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathcal {G}} \\left(\\theta_ {\\mathcal {G}}\\right) = - D \\left(\\mathcal {T} _ {\\theta_ {\\mathcal {T}}} \\left(\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)\\right), \\mathcal {S} _ {\\theta_ {\\mathcal {S}}} \\left(\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)\\right)\\right) + \\mathcal {L} _ {\\mathcal {P}} \\left(\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)\\right). \\tag {16}\n$$\n", + "text_format": "latex", + "bbox": [ + 76, + 652, + 468, + 681 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 709, + 209, + 726 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.1. Experimental Settings", + "text_level": 1, + "bbox": [ + 76, + 734, + 284, + 750 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Datasets: We evaluate the proposed method on SVHN [23], CIFAR10 [17], CIFAR100 [17] and Tiny-ImageNet [18] datasets.", + "bbox": [ + 75, + 755, + 468, + 799 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Teacher model: For CIFAR10 and CIFAR100 datasets, we used the pre-trained teacher models made available by the authors of [11] and [3], respectively. For SVHN and TinyImageNet we trained the teacher network from scratch. We provide the training details of the teacher models in Section B.1 of the Supplemental Material.", + "bbox": [ + 75, + 799, + 470, + 885 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Definition of an Epoch in DFKD: In Adversarial DFKD,", + "bbox": [ + 76, + 885, + 470, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "the notion of an epoch is obscure. In a typical deep neural network-based classification training, an epoch is defined as one complete pass over the available training data. However, DFKD has no access to the training data; instead, the pseudo samples generated on-the-fly are used to distill knowledge to the student network. Therefore, prior works [3,5,10] defined an epoch in terms of a fixed number of training iterations $(\\mathcal{I})$ where each iteration consists of a set number of generator update steps $(g)$ and student update steps $(s)$ . Hence, to be consistent across the baselines and prior arts, we use the same number of training iterations, generator update steps, and student updates steps to define an epoch. For all the methods, we set $\\mathcal{I} = 72$ , $g = 1$ , and $s = 10$ and use a batch size of 512 of the sampled noise $(z)$ to generate the pseudo samples and optimize the parameters $\\theta_{\\mathcal{G}}$ and $\\theta_{\\mathcal{S}}$ .", + "bbox": [ + 496, + 287, + 893, + 502 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Training Details: The training details are provided in Section B of the Supplemental Material.", + "bbox": [ + 496, + 502, + 893, + 531 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Evaluation: We evaluate the methods by comparing the mean and variance of the student network's test accuracy $(S_{Acc})$ , denoting them as $\\mu[S_{Acc}]$ , and $\\sigma^2[S_{Acc}]$ , respectively, across the epochs, motivated by Binci et al. [2]. Specifically, we compare the different sections of the student's learning evolution by partitioning them into different epoch percentiles. For example, computing the $\\mu[S_{Acc}]$ and $\\sigma^2[S_{Acc}]$ for epochs greater than the $n^{th}$ percentile conveys the mean and variance across all the epochs greater than the $\\frac{n}{100}^{th}$ of the total number of training epochs.", + "bbox": [ + 496, + 531, + 893, + 676 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.2. Results and Observations", + "text_level": 1, + "bbox": [ + 500, + 689, + 732, + 704 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Baseline and State-of-the-art Comparisons In Table 1, we analyze our method on classification task and compare it with prior Adversarial DFKD methods [4,5,10] and closely related memory-based Adversarial DFKD methods [2,3]. For a fair comparison across the methods, we re-implemented all the methods and used the same generator architecture to generate the pseudo samples. For each of the methods we report the $\\mu[S_{Acc}]$ and $\\sigma^2[S_{Acc}]$ at different epoch percentiles and the maximum accuracy ( $\\mathrm{Acc}_{\\max}$ ) attained by the student. We observe that, compared to MB-DFKD (Memory Bank) [3], Ours-1 (Memory Bank) demonstrates consistent improvement across all the datasets. Similarly, compared to PRE-DFKD (Generative Replay) [2], utilizing the same VAE", + "bbox": [ + 496, + 712, + 893, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "7792", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/8523c8d95fa877d5413d6362cba29d353a2b4f53ae73d9c684f9516cbd28028e.jpg", + "table_caption": [], + "table_footnote": [ + "Table 2. Classification accuracy (in %) of the student trained using various DFKD methods on CIFAR100 with ResNet-34 [15] as the teacher and ResNet-18 [15] as the student. $\\mathcal{T}_{Acc}$ and $S_{Acc}$ denote the Teacher network's and the Student network's accuracy, respectively, and $\\Delta \\mathcal{T}_{Acc} - \\mathcal{S}_{Acc}$ denotes the difference between the Teacher and Student accuracies. Also, $^{\\mathrm{a}},^{\\mathrm{b}},^{\\mathrm{c}}$ and $^{\\mathrm{d}}$ denote results produced by [34], [11], [3], and our implementation, respectively." + ], + "table_body": "
ZSKDa[22]ADIb[30]CIMb[11]DeGANc[1]EATSKDc[21]KEGNETa[31]ZSKTb[20]DDADd[34]DAFLd[4]DFAdd[10]DFQd[5]MB-DFKDd[3]PRE-DFKDd[2]Ours-IOurs-2
TAcc(%)77.5078.0578.0577.9477.9477.5078.0577.5077.9477.9477.9477.9477.9477.9477.94
SAcc(%)70.2161.3277.0465.2567.1873.9167.7475.0473.7969.7375.3976.1476.9377.1177.21
ΔTAcc-SAcc7.2916.731.0112.6910.763.5910.312.464.158.212.551.801.010.830.73
", + "bbox": [ + 78, + 89, + 895, + 127 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "decoder architecture as the generative replay, we observe a similar trend for Ours-2 (Generative Replay).", + "bbox": [ + 75, + 191, + 468, + 222 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Moreover, in Figure 4, we visualize the learning curves of the student networks trained on multiple datasets. We plot the test accuracy of the student at the end of each training epoch. The proposed method exhibits significant improvement in the learning evolution and the peak accuracies achieved, suggesting that the proposed approach can retain the knowledge from previously encountered samples as the learning progresses. However, on Tiny-ImageNet, with Generative Replay, we did not observe substantial improvements; we conjecture that this may be due to the complexity of the dataset, and the inability to capture crucial samples as replay for the complex dataset, for a large number of epochs. Also, with Generative Replay we sometimes faced difficulty in training a VAE on a stream of synthetic samples (especially for complex dataset like Tiny-ImageNet) as it suffers due to the distribution drift of its own.", + "bbox": [ + 75, + 223, + 470, + 452 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Additionally, we emphasize that we do not strive toward achieving state-of-the-art student classification accuracy (requiring extensive hyper-parameter tuning) in the DFKD setting, but verify the viability of our hypothesis of retaining the previously acquired knowledge while learning on new samples. Nonetheless, we observe that our method improves upon the student classification accuracy on CIFAR100 [17] compared to the contemporary works and the current state-of-the-art [2] with the ResNet-34 [15] $(\\mathcal{T})$ and ResNet-18 [15] $(S)$ setup, as shown in Table 2. Additionally, since previous works use the same teacher network with different test accuracies, we also report the teacher accuracies of the respective methods used to distill the knowledge to the student. Nonetheless, we also compute the Teacher-Student accuracy difference $(\\Delta_{\\mathcal{T}_{Acc} - \\mathcal{S}_{Acc}})$ to assess the distance of the student from its teacher in terms of classification accuracy.", + "bbox": [ + 75, + 455, + 470, + 684 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Interpreting the Result: Because of the proposed student update strategy, we observe a global monotonicity in the student's learning evolution which the existing approaches with naive replay [2, 3] claim, but do not warrant (described in Section 3.3). The global monotonicity in the learning evolution encompasses crucial advantages. For example, when the validation data is unavailable, the developer cannot assess the student's accuracy and identify the best parameters for the student. In such a scenario, the final accuracy is dependent on the random termination epoch set by the developer. In other words, the ideal DFKD approach should sustain high accuracy via monotonically enhancing it during the course of distillation. Therefore, $\\mu[S_{Acc}]$ and $\\sigma^2[S_{Acc}]$ contribute as crucial metrics to assess the distillation method as opposed to the maximum accuracy ( $\\mathrm{Acc}_{\\max}$ ), since the $\\mathrm{Acc}_{\\max}$ value can", + "bbox": [ + 75, + 684, + 470, + 901 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "be attained at any point of time prior to the termination, and can be misleading. The improvements in the monotonicity and the $\\mu [S_{Acc}]$ and $\\sigma^2 [S_{Acc}]$ values of proposed method are evident from Table 1, Figure 4 and Figure 5.", + "bbox": [ + 496, + 191, + 890, + 250 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Architecture Scalability: The proposed student update strategy is generic and is scalable across different neural network architecture families since the method is not constrained to a particular choice of architecture. From the ResNet-18 [15], WRN-16-1 [32] and WRN-16-2 [32] student learning curves (in Figure 4 and Figure 5), we observe our method's advantage on both the network architectures. Moreover, for large student network architectures (Deeper or Wider) that include complex layers, the proposed method efficiently handles the intricacies with regard to computing the Hessian and the Hessian-product, which becomes highly essential for cumbersome models.", + "bbox": [ + 496, + 250, + 893, + 421 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Improvement across Replay Schemes: Furthermore, the proposed method is agnostic to the memory scheme employed for replay, as demonstrated by the experiments (in Table 1, Figure 4 and Figure 5) using a Memory Buffer and Generative-Replay, thus, rendering our method generalized to the choice of replay. In Table 1 and Figure 5, we can observe that the proposed method enhances the student's performance on both the replay schemes (Memory Bank and Generative Replay) used in the prior arts. Moreover, we experiment with different memory buffer sizes on WRN-16-1 [32] and WRN-16-2 [32] distillation (in Figure 5) and observe consistent and substantial improvements across different memory sizes. Here, the memory size is defined as the maximum number of pseudo-example batches that the bank can contain and each batch consists of randomly sampled 64 examples from $\\hat{x}$ .", + "bbox": [ + 496, + 422, + 893, + 652 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 672, + 617, + 686 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we proposed a meta-learning inspired student update strategy for the Adversarial DFKD setting, that treats Knowledge-Acquisition and Knowledge-Retention as meta-train and meta-test, respectively. The proposed strategy substantially improves the learning evolution of the student network by implicitly aligning the Knowledge-Retention and the Knowledge-Acquisition tasks. The intended effect of having the gradient directions aligned is to obtain student parameters $(\\theta_{S})$ that have optimal performance on both $\\mathcal{L}_{Acq}$ and $\\mathcal{L}_{Ret}$ . The conducted experiments on multiple datasets, network architectures, and replay schemes demonstrate the effectiveness, scalability and generalizability of the proposed strategy.", + "bbox": [ + 496, + 699, + 893, + 885 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement: Work partially supported by NIH.", + "bbox": [ + 500, + 885, + 861, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "7793", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Sravanti Addepalli, Gaurav Kumar Nayak, Anirban Chakraborty, and Venkatesh Babu Radhakrishnan. Degan: Data-enriching gan for retrieving representative samples from a trained classifier. In AAAI, 2020. 8", + "[2] Kuluhan Binici, Shivam Aggarwal, Nam Trung Pham, Karianto Leman, and Tulika Mitra. Robust and resource-efficient data-free knowledge distillation by generative pseudo replay. In AAAI, 2022. 2, 3, 4, 6, 7, 8", + "[3] Kuluhan Binici, Nam Trung Pham, Tulika Mitra, and Kario \nLeman. Preventing catastrophic forgetting and distribution \nmismatch in knowledge distillation via synthetic data. In \nWACV, 2022. 2, 3, 4, 6, 7, 8", + "[4] Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, and Qi Tian. Dafi: Data-free learning of student networks. In ICCV, 2019. 1, 2, 5, 6, 7, 8", + "[5] Yoojin Choi, Jihwan Choi, Mostafa El-Khamy, and Jungwon Lee. Data-free network quantization with adversarial knowledge distillation. In CVPR Workshops, 2020. 2, 3, 5, 6, 7, 8", + "[6] Yatin Dandi, Luis Barba, and Martin Jaggi. Implicit gradient alignment in distributed and federated learning. In AAAI, 2022. 2", + "[7] Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars. A continual learning survey: Defying forgetting in classification tasks. IEEE TPAMI, 44(7):3366-3385, 2021. 2", + "[8] Kien Do, Hung Le, Dung Nguyen, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, and Svetha Venkatesh. Momentum adversarial distillation: Handling large distribution shifts in data-free knowledge distillation. In NeurIPS, 2022. 3", + "[9] Gongfan Fang, Kanya Mo, Xinchao Wang, Jie Song, Shitao Bei, Haofei Zhang, and Mingli Song. Up to 100x faster data-free knowledge distillation. In AAAI, 2022. 3", + "[10] Gongfan Fang, Jie Song, Chengchao Shen, Xinchao Wang, Da Chen, and Mingli Song. Data-free adversarial distillation. In CVPR, 2020. 1, 2, 3, 4, 5, 6, 7, 8", + "[11] Gongfan Fang, Jie Song, Xinchao Wang, Chen Shen, Xingen Wang, and Mingli Song. Contrastive model inversion for data-free knowledge distillation. In IJCAI, 2021. 1, 7, 8", + "[12] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, 2017. 4", + "[13] Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil. Bilevel programming for hyperparameter optimization and meta-learning. In ICML, 2018. 4", + "[14] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. Generative adversarial nets. In NeurIPS, 2014. 3", + "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016. 4, 8", + "[16] Diederik P Kingma, Max Welling, et al. An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4):307-392, 2019. 4" + ], + "bbox": [ + 78, + 114, + 470, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[17] Alex Krizhevsky et al. Learning multiple layers of features from tiny images. 2009. 4, 6, 7, 8", + "[18] Ya Le and Xuan S. Yang. Tiny imagenet visual recognition challenge. 2015. 4, 6, 7", + "[19] Yang Liu, Wei Zhang, and Jun Wang. Zero-shot adversarial quantization. In CVPR, 2021. 1", + "[20] Paul Micaelli and Amos J Storkey. Zero-shot knowledge transfer via adversarial belief matching. In NeurIPS, 2019. 1, 2, 3, 8", + "[21] Gaurav Kumar Nayak, Konda Reddy Mopuri, and Anirban Chakraborty. Effectiveness of arbitrary transfer sets for data-free knowledge distillation. In WACV, 2021. 8", + "[22] Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, and Anirban Chakraborty. Zero-shot knowledge distillation in deep networks. In ICML, 2019. 1, 8", + "[23] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng. Reading digits in natural images with unsupervised feature learning. In NeurIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011. 4, 6, 7", + "[24] Mert Bulent Sariyildiz and Ramazan Gokberk Cinbis. Gradient matching generative networks for zero-shot learning. In CVPR, 2019. 2", + "[25] Ari Seff, Alex Beatson, Daniel Suo, and Han Liu. Continual learning in generative adversarial nets. arXiv preprint arXiv:1705.08395, 2017. 1, 3", + "[26] Yuge Shi, Jeffrey Seely, Philip Torr, Siddharth N, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve. Gradient matching for domain generalization. In ICLR, 2022. 2", + "[27] Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim. Continual learning with deep generative replay. In NeurIPS, 2017. 2, 3, 4", + "[28] Ankur Sinha, Pekka Malo, and Kalyanmoy Deb. A review on bilevel optimization: From classical to evolutionary approaches and applications. IEEE TEC, 22(2):276-295, 2018. 4", + "[29] Hoang Thanh-Tung and Truyen Tran. Catastrophic forgetting and mode collapse in gans. In IJCNN, 2020. 1", + "[30] Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz. Dreaming to distill: Data-free knowledge transfer via deepinversion. In CVPR, 2020. 1, 8", + "[31] Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang. Knowledge extraction with no observable data. In NeurIPS, 2019. 8", + "[32] Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. In BMVC, 2016. 7, 8", + "[33] Sergey Zagoruyko and Nikos Komodakis. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. In ICLR, 2017. 3", + "[34] Haoran Zhao, Xin Sun, Junyu Dong, Milos Manic, Huiyu Zhou, and Hui Yu. Dual discriminator adversarial distillation for data-free model compression. International Journal of Machine Learning and Cybernetics, 2022. 8" + ], + "bbox": [ + 501, + 92, + 890, + 837 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "7794", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 8 + } +] \ No newline at end of file diff --git a/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/b93957f9-1257-4220-880f-a2d772770ee6_model.json b/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/b93957f9-1257-4220-880f-a2d772770ee6_model.json new file mode 100644 index 0000000000000000000000000000000000000000..1f9c4a8b03e4e47afc9d613a27bf9cbe72c9cfad --- /dev/null +++ b/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/b93957f9-1257-4220-880f-a2d772770ee6_model.json @@ -0,0 +1,2363 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.137, + 0.121, + 0.833, + 0.164 + ], + "angle": 0, + "content": "Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation" + }, + { + "type": "text", + "bbox": [ + 0.23, + 0.191, + 0.74, + 0.245 + ], + "angle": 0, + "content": "Gaurav Patel† Konda Reddy Mopuri‡ Qiang Qiu† \n†Purdue University ‡Indian Institute of Technology Hyderabad \n{gpatel10, qqiu}@purdue.edu, krmpuri@ai.iith.ac.in" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.257, + 0.314, + 0.273 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.288, + 0.474, + 0.62 + ], + "angle": 0, + "content": "Data-free Knowledge Distillation (DFKD) has gained popularity recently, with the fundamental idea of carrying out knowledge transfer from a Teacher neural network to a Student neural network in the absence of training data. However, in the Adversarial DFKD framework, the student network's accuracy, suffers due to the non-stationary distribution of the pseudo-samples under multiple generator updates. To this end, at every generator update, we aim to maintain the student's performance on previously encountered examples while acquiring knowledge from samples of the current distribution. Thus, we propose a meta-learning inspired framework by treating the task of Knowledge-Acquisition (learning from newly generated samples) and KnowledgeRetention (retaining knowledge on previously met samples) as meta-train and meta-test, respectively. Hence, we dub our method as Learning to Retain while Acquiring. Moreover, we identify an implicit aligning factor between the KnowledgeRetention and Knowledge-Acquisition tasks indicating that the proposed student update strategy enforces a common gradient direction for both tasks, alleviating interference between the two objectives. Finally, we support our hypothesis by exhibiting extensive evaluation and comparison of our method with prior arts on multiple datasets." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.646, + 0.21, + 0.661 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.671, + 0.472, + 0.902 + ], + "angle": 0, + "content": "The primary goal of Data-Free Knowledge Distillation (DFKD) is to acquire a trustworthy alternative dataset for knowledge distillation. The quality of knowledge transfer depends on the caliber of these alternative samples. Noise optimization [11,22,30] and generative reconstruction [4,10,20] are the two primary ways to replace the original training data used in the distillation process with synthetic or pseudo samples. Adversarial DFKD methods [10, 19, 20] investigate an adversarial exploration framework to seek pseudo-samples. In such a paradigm, the Generator \\((\\mathcal{G})\\) is used to create pseudo-samples as surrogates to perform knowledge distillation/transfer, and the Teacher-Student \\((\\mathcal{T} - \\mathcal{S})\\) setup acts as a joint discriminator to penalize and update generator parameters \\((\\theta_{\\mathcal{G}})\\) (Figure 1). In the adversarial framework, the generator explores the input space to find suitable pseudo-samples as the distillation progresses. Consequently, the distribution" + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.256, + 0.891, + 0.504 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.512, + 0.896, + 0.632 + ], + "angle": 0, + "content": "Figure 1. The top-section represents the learning evolution of a generic Adversarial Data-Free Knowledge Distillation framework; the color-intensity variation signifies the change in the distribution of the pseudo-samples, the student network, and the generator network over the learning epochs. Under the variation in the distribution of the pseudo-samples, the bottom-section shows the learning curves for cases when the student accuracy degrades (shown in Red), which is undesirable, and when the student accuracy is maintained, if not improved, as proposed (shown in Green)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.642, + 0.897, + 0.903 + ], + "angle": 0, + "content": "of the generated samples consistently keeps changing during the process due to the generator updates [25]. From the student network's perspective, at each iteration, the pseudo samples seem to be generated from different generator parameters \\((\\theta_{\\mathcal{G}})\\). Hence, the convergence of the student network gets hampered due to successive distributional alterations over time [29], as depicted by the red curve in Figure 1. This observation hints that updating the student network, solely using the samples generated from the current generator parameters is not adequate to generalize the student. Moreover, the student forgets the knowledge acquired previously and decelerates the knowledge distillation. Therefore, the generator, apart from exploring the input space, seeks to compensate for the loss of knowledge in future iterations. Additionally, in a practical setting, during the distillation process, high variation in the student network's classification accuracy is undesirable, especially when the validation data is not available, since that prevents the user from tracking" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7786" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.078, + 0.089, + 0.895, + 0.209 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.209, + 0.894, + 0.276 + ], + "angle": 0, + "content": "Figure 2. Student update strategies: (a) Typical student update by optimizing the Knowledge-Acquisition loss \\((\\mathcal{L}_{Acq})\\) with the batch pseudo samples \\((\\hat{x})\\), produced by the generator \\((\\mathcal{G})\\) [5, 10, 20]. (b) Student update with simultaneous optimization of the Knowledge-Acquisition loss \\((\\mathcal{L}_{Acq})\\) and Knowledge-Retention loss \\((\\mathcal{L}_{Ret})\\) on the batch of pseduo samples \\((\\hat{x})\\) and memory samples \\((\\hat{x}_m)\\) obtained from the generator \\((\\mathcal{G})\\) and the memory \\((\\mathcal{M})\\), respectively [2, 3]. (c) The proposed student update strategy, which treats \\(\\mathcal{L}_{Acq}\\) as meta-train and \\(\\mathcal{L}_{Ret}\\) as meta-test, and implicitly imposes the alignment between them." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.28, + 0.47, + 0.31 + ], + "angle": 0, + "content": "the student's accuracy over time, and selecting the distilled model parameters with the highest accuracy." + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.311, + 0.471, + 0.526 + ], + "angle": 0, + "content": "To circumvent the above-discussed problems, existing methods maintain a memory buffer to rehearse the examples from previously encountered distributions while learning with current examples. Binci et al. [3] introduce Replay based methods to explicitly retrain/replay on a limited subset of previously encountered samples while training on the current examples. Then, carrying forward, they use Generative-Replay [27] to transfer the learned examples to an auxiliary generative model (VAE), and sample from the it (VAE decoder) in subsequent iterations [2]. Nonetheless, the performance of these methods is upper bounded by joint training on previous and current examples [7]. Although, recent works have focused on modeling the memory, we seek to work towards effectively utilizing the samples from memory." + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.527, + 0.471, + 0.77 + ], + "angle": 0, + "content": "In this paper, we aim to update the student network parameters \\((\\theta_S)\\) such that its performance does not degrade on the samples previously produced by the generator network \\((\\mathcal{G})\\), aspiring towards Learning to Retain while Acquiring. Thus, we propose a meta-learning inspired strategy to achieve this goal. We treat the task of Knowledge-Acquisition (learning from newly generated samples) and Knowledge-Retention (learning from previously encountered samples from memory) as meta-train and meta-test, respectively. Hence, in the proposed approach, the student network acquires new information while maintaining performance on previously encountered samples. By doing so, the proposed strategy (Figure 2c) implicitly aligns Knowledge-Acquisition and Knowledge-Retention, as opposed to simply combining them [2,3] without any coordination or alignment (Figure 2b), which leaves them to potentially interfere with one another." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.772, + 0.473, + 0.902 + ], + "angle": 0, + "content": "Additionally, analyzing the proposed meta-objective, we discover that (in Section 3.4) the latent alignment factor as the dot product between the gradients of the Knowledge-Acquisition and Knowledge-Retention objectives, suggesting that the meta-objective enforces a common gradient direction for both tasks, encouraging the alignment between the task-specific gradients. Thus, the proposed method simultaneously minimizes the loss and matches the gradients corresponding to the individual tasks (Knowledge-Acquisition" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.281, + 0.892, + 0.308 + ], + "angle": 0, + "content": "and Knowledge-Retention), enforcing the optimization paths to be same for both tasks." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.31, + 0.895, + 0.453 + ], + "angle": 0, + "content": "Moreover, the proposed student update strategy is scalable to different deep architectures as the gradient alignment is implicit, and memory-agnostic, making no assumptions about the replay scheme employed. Nonetheless, recent works on gradient alignment, have shown great empirical advantages in Zero-Shot Learning [24], Distributed/Federated Learning [6] and Domain Generalization [26]. Our method extends the advantages of gradient alignment to memory-based Adversarial DFKD, thus strengthening the empirical findings in these works." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.454, + 0.895, + 0.525 + ], + "angle": 0, + "content": "Finally, to demonstrate the advantages of the proposed student update strategy, we evaluate and compare against current non-memory [4,5, 10] and memory-based [2, 3] Adversarial DFKD methods, and observe substantial improvement in the student learning evolution." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.525, + 0.882, + 0.54 + ], + "angle": 0, + "content": "In summary, the key points of this work are as follows:" + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.55, + 0.894, + 0.636 + ], + "angle": 0, + "content": "- We propose a novel meta-learning inspired student update strategy in the Adversarial DFKD setting, that aims to maintain the student's performance on previously encountered examples (Knowledge-Retention) while acquiring knowledge from samples of the current distribution (Knowledge-Acquisition)." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.637, + 0.894, + 0.693 + ], + "angle": 0, + "content": "- We theoretically identify (in Section 3.4) that the proposed student update strategy enforces an implicit gradient alignment between the Knowledge-Acquisition and Knowledge-Retention tasks." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.694, + 0.894, + 0.735 + ], + "angle": 0, + "content": "- A major practical advantage of the proposed method is its applicability in the absence of a validation set, discussed in Section 4.2." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.737, + 0.894, + 0.794 + ], + "angle": 0, + "content": "- Finally, we evaluate our method and compare against various Adversarial DFKD methods, on multiple student architectures and replay schemes (Memory Buffer and Generative Replay)." + }, + { + "type": "list", + "bbox": [ + 0.519, + 0.55, + 0.894, + 0.794 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.818, + 0.642, + 0.833 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.843, + 0.894, + 0.901 + ], + "angle": 0, + "content": "Adversarial Data-Free Knowledge Distillation: In the Adversarial Data-Free Knowledge Distillation paradigm, A generative model is trained to synthesize pseudo-samples that serve as queries for the Teacher \\((\\mathcal{T})\\) and the Student" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7787" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.084, + 0.098, + 0.194, + 0.111 + ], + "angle": 0, + "content": "Generator Update" + }, + { + "type": "image", + "bbox": [ + 0.078, + 0.116, + 0.445, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.478, + 0.089, + 0.884, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.081, + 0.244, + 0.108, + 0.264 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.246, + 0.146, + 0.253 + ], + "angle": 0, + "content": "Generator" + }, + { + "type": "text", + "bbox": [ + 0.109, + 0.254, + 0.146, + 0.261 + ], + "angle": 0, + "content": "Network" + }, + { + "type": "image", + "bbox": [ + 0.155, + 0.244, + 0.179, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.18, + 0.246, + 0.211, + 0.253 + ], + "angle": 0, + "content": "Teacher" + }, + { + "type": "text", + "bbox": [ + 0.18, + 0.254, + 0.211, + 0.261 + ], + "angle": 0, + "content": "Network" + }, + { + "type": "image", + "bbox": [ + 0.229, + 0.244, + 0.252, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.253, + 0.246, + 0.283, + 0.253 + ], + "angle": 0, + "content": "Student" + }, + { + "type": "text", + "bbox": [ + 0.254, + 0.254, + 0.283, + 0.261 + ], + "angle": 0, + "content": "Network" + }, + { + "type": "image", + "bbox": [ + 0.3, + 0.244, + 0.323, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.324, + 0.246, + 0.362, + 0.253 + ], + "angle": 0, + "content": "Memory" + }, + { + "type": "text", + "bbox": [ + 0.324, + 0.254, + 0.362, + 0.261 + ], + "angle": 0, + "content": "Generator" + }, + { + "type": "image", + "bbox": [ + 0.371, + 0.245, + 0.393, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.395, + 0.246, + 0.427, + 0.253 + ], + "angle": 0, + "content": "Memory" + }, + { + "type": "text", + "bbox": [ + 0.395, + 0.254, + 0.426, + 0.261 + ], + "angle": 0, + "content": "Bank" + }, + { + "type": "image", + "bbox": [ + 0.443, + 0.244, + 0.466, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.467, + 0.246, + 0.498, + 0.253 + ], + "angle": 0, + "content": "generated" + }, + { + "type": "text", + "bbox": [ + 0.467, + 0.254, + 0.496, + 0.261 + ], + "angle": 0, + "content": "seudo Sa" + }, + { + "type": "image", + "bbox": [ + 0.498, + 0.244, + 0.521, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.523, + 0.246, + 0.548, + 0.253 + ], + "angle": 0, + "content": "#" + }, + { + "type": "text", + "bbox": [ + 0.524, + 0.254, + 0.548, + 0.261 + ], + "angle": 0, + "content": "#" + }, + { + "type": "image", + "bbox": [ + 0.55, + 0.244, + 0.573, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.575, + 0.246, + 0.604, + 0.253 + ], + "angle": 0, + "content": "#" + }, + { + "type": "text", + "bbox": [ + 0.605, + 0.246, + 0.629, + 0.253 + ], + "angle": 0, + "content": "#" + }, + { + "type": "text", + "bbox": [ + 0.631, + 0.246, + 0.653, + 0.253 + ], + "angle": 0, + "content": "Forwa" + }, + { + "type": "text", + "bbox": [ + 0.631, + 0.254, + 0.679, + 0.261 + ], + "angle": 0, + "content": "Propagation" + }, + { + "type": "image", + "bbox": [ + 0.683, + 0.245, + 0.696, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.708, + 0.246, + 0.731, + 0.253 + ], + "angle": 0, + "content": "Meta" + }, + { + "type": "text", + "bbox": [ + 0.709, + 0.254, + 0.738, + 0.261 + ], + "angle": 0, + "content": "Update" + }, + { + "type": "image", + "bbox": [ + 0.765, + 0.244, + 0.788, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.79, + 0.246, + 0.814, + 0.253 + ], + "angle": 0, + "content": "Studen" + }, + { + "type": "text", + "bbox": [ + 0.79, + 0.254, + 0.816, + 0.261 + ], + "angle": 0, + "content": "Update" + }, + { + "type": "image", + "bbox": [ + 0.822, + 0.244, + 0.845, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.848, + 0.246, + 0.889, + 0.253 + ], + "angle": 0, + "content": "Generator" + }, + { + "type": "text", + "bbox": [ + 0.849, + 0.254, + 0.877, + 0.261 + ], + "angle": 0, + "content": "Update" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.278, + 0.894, + 0.332 + ], + "angle": 0, + "content": "Figure 3. An illustration of the proposed DFKD framework. The framework consists of the Generator \\((\\mathcal{G})\\), Teacher \\((\\mathcal{T})\\), Student \\((\\mathcal{S})\\) and the Memory \\((\\mathcal{M})\\). \\(\\mathcal{G}\\) and \\(\\mathcal{S}\\) are updated alternatively, similar to the GAN [14] framework with the generator loss \\((\\mathcal{L}_{\\mathcal{G}})\\) optimizing \\(\\mathcal{G}\\), and the Knowledge-Acquisition loss \\((\\mathcal{L}_{Acq})\\) and the Knowledge-Retention loss \\((\\mathcal{L}_{Ret})\\) optimizing the student \\((\\mathcal{S})\\). We use \\(\\mathcal{M}\\) in a generalized way to denote the employed replay scheme (Memory Buffer or Generative Replay in our case)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.336, + 0.473, + 0.623 + ], + "angle": 0, + "content": "(S) [5, 10, 20]. ZSKT [20] attempts data-free knowledge transfer by first training a generator in an adversarial fashion to look for samples on which the student and teacher do not match well. To improve the model discrepancy measure, it adopts the Kullback-Leibler (KL) divergence, and introduces attention transfer [33] to aid knowledge transfer. Moreover, DFAD [10] recommends Mean Absolute Error (MAE) as a model discrepancy function to prevent decayed gradients on converged samples. Furthermore, the adversarial framework was extended by Choi et al. [5] in the context of model quantization, by proposing adversarial data-free quantization (DFQ), and introducing additional regularization terms that match the mean and standard deviation of the generated pseudo-samples with the teacher model's batchnorm statistics, and imposes batch categorical entropy maximization, such that sample from each class appear equally in the generated batch. Fang et al. recently introduced FastD-FKD [9], an effective method with a meta generator to speed up the DFKD process, delivering a 100-fold increase in the knowledge transfer rate." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.624, + 0.473, + 0.868 + ], + "angle": 0, + "content": "Handling Distribution Shift in Adversarial DFKD: To counter the distribution mismatch and the catastrophic forgetting phenomenon in the adversarial framework [25], Binici et al. [3] suggested maintaining a dynamic collection of synthetic samples throughout training iterations to prevent catastrophic forgetting in DFKD. Moreover, in their latest work [2], they introduce generative pseudo-replay [27] in which an auxiliary generative model simultaneously learns the distribution of the samples produced by the generator \\((\\mathcal{G})\\). Throughout the training process, examples are generated from the auxiliary generator to replay during training. Nonetheless, these works have focused on modeling the memory buffer. A related line of research maintains an exponentially moving average (EMA) of the generator model \\(\\mathcal{G}\\) [8] to replace the typical Memory-Buffer and Generative Replay. Nonetheless, our work focuses on the effective utilization of the samples obtained from the memory." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.336, + 0.635, + 0.353 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.36, + 0.895, + 0.448 + ], + "angle": 0, + "content": "In Section 3.1, we first provide a brief overview of Adversarial DFKD. Then, in Section 3.2, we discuss the data-free knowledge distillation objective. In Sections 3.3 and 3.4, we elaborate on the proposed student update strategy. Lastly, in Section 3.5, we discuss the adopted generator update strategy used for the baselines and the proposed framework." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.454, + 0.889, + 0.47 + ], + "angle": 0, + "content": "3.1. Adversarial Data-Free Knowledge Distillation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.476, + 0.895, + 0.679 + ], + "angle": 0, + "content": "In the Adversarial DFKD framework, a generator \\((\\mathcal{G})\\) is used to create pseudo-samples as surrogates to perform knowledge distillation/transfer, and the teacher-student \\((\\mathcal{T}-S)\\) setup acts as a joint discriminator to penalize and update generator parameters \\((\\theta_{\\mathcal{G}})\\) in an adversarial manner. After updating \\(\\theta_{\\mathcal{G}}\\), random samples are generated and used to minimize the \\(\\mathcal{T}-S\\) discrepancy by updating the student parameters \\((\\theta_{\\mathcal{S}})\\). The generator and the student are optimized alternatively up until a fixed number of pre-defined iterations. In essence, the goal of DFKD is to craft a lightweight student model \\((S)\\) by harnessing valuable knowledge from the well-trained Teacher model \\((\\mathcal{T})\\) in the absence of training data. A general overview of Adversarial DFKD framework is illustrated in Figure 1." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.685, + 0.854, + 0.701 + ], + "angle": 0, + "content": "3.2. Goal of Data Free Knowledge Distillation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.707, + 0.894, + 0.824 + ], + "angle": 0, + "content": "A student model \\((\\mathcal{S})\\) is trained to match the teacher's \\((\\mathcal{T})\\) predictions on its unavailable target domain \\((\\mathcal{D}_{\\mathcal{T}})\\) as part of the distillation process. Let, \\(p_{\\mathcal{S}}(x) = \\mathrm{Softmax}(S_{\\theta_{\\mathcal{S}}}(\\boldsymbol{x}))\\) and \\(p_{\\mathcal{T}}(x) = \\mathrm{Softmax}(\\mathcal{T}_{\\theta_{\\mathcal{T}}}(\\boldsymbol{x})), \\forall \\boldsymbol{x} \\in \\mathcal{D}_{\\mathcal{T}}\\), denote the predicted Student and Teacher probability mass across the classes, respectively. We seek to find the parameters \\(\\theta_{\\mathcal{S}}\\) of the student model that will reduce the probability of errors \\((P)\\) between the predictions \\(p_{\\mathcal{S}}(x)\\) and \\(p_{\\mathcal{T}}(x)\\):" + }, + { + "type": "equation", + "bbox": [ + 0.526, + 0.83, + 0.866, + 0.864 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\theta_ {\\mathcal {S}}} P _ {x \\sim \\mathcal {D} _ {\\mathcal {T}}} \\left(\\underset {i} {\\arg \\max } p _ {\\mathcal {S}} ^ {i} (x) \\neq \\underset {i} {\\arg \\max } p _ {\\mathcal {T}} ^ {i} (x)\\right),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.894, + 0.902 + ], + "angle": 0, + "content": "where the superscript \\(i\\) denotes the \\(i^{th}\\) probability score of the predicted masses, \\(p_{\\mathcal{S}}(x)\\) and \\(p_{\\mathcal{T}}(x)\\)." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7788" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.078, + 0.087, + 0.289, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.293, + 0.087, + 0.488, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.494, + 0.087, + 0.691, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.697, + 0.087, + 0.895, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.192, + 0.895, + 0.234 + ], + "angle": 0, + "content": "Figure 4. Learning evolution of the proposed method compared to the prior arts (MB-DFKD [3] and PRE-DFKD [2]) that employ replay. The plots visualize the Accuracy \\((\\%)\\) evolution (top-row) and the Cumulative Mean Accuracy \\((\\%)\\) evolution (bottom-row) of the ResNet-18 [15] student network on SVHN [23], CIFAR10 [17], CIFAR100 [17], and Tiny-ImageNet [18] datasets. The proposed method is plotted Blue." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.238, + 0.473, + 0.326 + ], + "angle": 0, + "content": "However, DFKD suggests minimizing the student's error on a pseudo dataset \\(\\mathcal{D}_{\\mathcal{P}}\\) since the teacher's original training data distribution \\(\\mathcal{D}_{\\mathcal{T}}\\) is not available. Typically, a loss function, say \\(\\mathcal{L}_{KD}\\), that gauges disagreement between the teacher and the student is minimized \\(\\forall \\hat{x} \\in \\mathcal{D}_{\\mathcal{P}}\\) by optimizing the student parameters \\((\\theta_{S})\\):" + }, + { + "type": "equation", + "bbox": [ + 0.149, + 0.335, + 0.471, + 0.358 + ], + "angle": 0, + "content": "\\[\n\\underset {\\theta_ {S}} {\\min } \\mathbb {E} _ {\\hat {x} \\sim \\mathcal {D} _ {\\mathcal {P}}} \\left[ \\mathcal {L} _ {K D} \\left(\\mathcal {T} _ {\\theta_ {\\mathcal {T}}} (\\hat {x}), \\mathcal {S} _ {\\theta_ {\\mathcal {S}}} (\\hat {x})\\right) \\right]. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.367, + 0.473, + 0.442 + ], + "angle": 0, + "content": "We use the Mean Absolute Error (MAE) to define loss measure \\((\\mathcal{L}_{KD})\\) as suggested in [10]. Suppose, given the predicted logits (pre-softmax predictions) \\(t(\\hat{x}) = \\mathcal{T}_{\\theta_{\\mathcal{T}}}(\\hat{x})\\) from the teacher model and the predicted logits \\(s(\\hat{x}) = S_{\\theta_S}(\\hat{x})\\) from the student model, we define \\(\\mathcal{L}_{KD}\\) as:" + }, + { + "type": "equation", + "bbox": [ + 0.151, + 0.45, + 0.471, + 0.468 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {K D} (t (\\hat {x}), s (\\hat {x})) = \\| t (\\hat {x}) - s (\\hat {x}) \\| _ {1}. \\tag {2}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.476, + 0.388, + 0.493 + ], + "angle": 0, + "content": "3.3. Learning to Retain while Acquiring" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.498, + 0.472, + 0.642 + ], + "angle": 0, + "content": "Our novelty resides in the student update strategy, as described earlier. Knowledge-Retention and Knowledge-Acquisition relate to two independent goals and can therefore be considered as two discrete tasks. In fact, by aligning these two goals, we empirically observe that, they can cooperate to retain the knowledge on previously encountered examples while acquiring knowledge from newly generated samples. The proposed method utilizes a meta-learning-inspired optimization strategy to effectively replay the samples from memory, say \\(\\mathcal{M}\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.642, + 0.473, + 0.699 + ], + "angle": 0, + "content": "In the typical Adversarial DFKD setup, the student update objective with the generated pseudo samples \\((\\hat{x})\\), i.e., the Knowledge-Acquisition task \\((\\mathcal{L}_{Acq})\\) (Figure 2a), is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.118, + 0.71, + 0.471, + 0.734 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\theta_ {S}} \\mathcal {L} _ {A c q} (\\theta_ {S}) = \\min _ {\\theta_ {S}} \\mathbb {E} _ {\\hat {x}} [ \\mathcal {L} (\\mathcal {T} _ {\\theta_ {T}} (\\hat {x}), \\mathcal {S} _ {\\theta_ {S}} (\\hat {x})) ], \\quad (3)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.742, + 0.47, + 0.786 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}\\) is the MAE (2) between the teacher and the student logits, and \\(\\hat{x} = \\mathcal{G}(z), z \\sim \\mathcal{N}(0, I)\\), denotes the batch of randomly generated samples." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.786, + 0.473, + 0.902 + ], + "angle": 0, + "content": "Moreover, to alleviate the distribution drift during knowledge distillation in the adversarial setting, previous works have maintained a memory buffer, to store \\(\\hat{x}\\) and replay at later iterations to help the student recall the knowledge [3]. At a fixed frequency, batches of samples from current iterations are updated in the memory [3]. Also, recent works [2] have explored using the Generative Replay [27] strategy to simultaneously store the samples, as they are encountered, in" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.238, + 0.895, + 0.295 + ], + "angle": 0, + "content": "a generative model (VAE [16]), and later replay by generating samples from the VAE decoder. Hence, the optimization objective for the Knowledge-Retention \\((\\mathcal{L}_{Ret})\\) can be defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.515, + 0.307, + 0.892, + 0.35 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{r l} \\underset {\\theta_ {S}} {\\min } \\mathcal {L} _ {R e t} (\\theta_ {S}) & = \\underset {\\theta_ {S}} {\\min } \\mathbb {E} _ {\\hat {x} _ {m}} [ \\mathcal {L} (\\mathcal {T} _ {\\theta_ {\\tau}} (\\hat {x} _ {m}), \\mathcal {S} _ {\\theta_ {S}} (\\hat {x} _ {m})) ], \\\\ & \\quad \\hat {x} _ {m} \\sim \\mathcal {M}, \\end{array} \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.361, + 0.895, + 0.448 + ], + "angle": 0, + "content": "where, with a slight abuse of notation, \\(\\mathcal{M}\\) denotes the Memory Buffer or Generative Replay or any other replay scheme. Thus, the overall optimization objective to update \\(\\theta_{\\mathcal{S}}\\) while considering both the new samples (generated from the latest \\(\\theta_{\\mathcal{G}}\\)) and the old samples (sampled from \\(\\mathcal{M}\\)) (Figure 2b) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.602, + 0.46, + 0.893, + 0.483 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\theta_ {S}} \\mathcal {L} _ {A c q} \\left(\\theta_ {S}\\right) + \\mathcal {L} _ {R e t} \\left(\\theta_ {S}\\right). \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.494, + 0.895, + 0.725 + ], + "angle": 0, + "content": "However, the objective in (5) attempts to simultaneously optimize \\(\\mathcal{L}_{Ret}\\) and \\(\\mathcal{L}_{Acq}\\) but does not seek to align the objectives, which leaves them to potentially interfere with one another. Say, we denote the gradients of Knowledge Acquisition and Knowledge Retention tasks with \\(\\nabla \\mathcal{L}_{Acq}(\\theta)\\) and \\(\\nabla \\mathcal{L}_{Ret}(\\theta)\\), respectively. If \\(\\nabla \\mathcal{L}_{Acq}(\\theta)\\) and \\(\\nabla \\mathcal{L}_{Ret}(\\theta)\\) point in a similar direction or are said to be aligned, i.e., \\(\\nabla \\mathcal{L}_{Acq}(\\theta). \\nabla \\mathcal{L}_{Ret}(\\theta) > 0\\), then taking a gradient step along \\(\\nabla \\mathcal{L}_{Acq}(\\theta)\\) or \\(\\nabla \\mathcal{L}_{Ret}(\\theta)\\) improves the model's performance on both tasks. This is however not the case when \\(\\nabla \\mathcal{L}_{Acq}(\\theta)\\) and \\(\\nabla \\mathcal{L}_{Ret}(\\theta)\\) point in different directions, i.e., \\(\\nabla \\mathcal{L}_{Acq}(\\theta). \\nabla \\mathcal{L}_{Ret}(\\theta) \\leq 0\\), and the weight parameters obtained, may not be optimal for both the tasks simultaneously. Hence, the intended effect of having the gradient directions aligned is to obtain student parameters \\((\\theta_S)\\) that have optimal performance on both \\(\\mathcal{L}_{Acq}\\) and \\(\\mathcal{L}_{Ret}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.725, + 0.892, + 0.781 + ], + "angle": 0, + "content": "The proposed meta-learning inspired approach, seeks to align the two tasks, \\(\\mathcal{L}_{Acq}\\) and \\(\\mathcal{L}_{Ret}\\). We take cues from Model-Agnostic Meta-learning (MAML) [12], and adapt it to Adversarial DFKD." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.782, + 0.892, + 0.824 + ], + "angle": 0, + "content": "Typically, MAML-like methods [12, 13] are framed as a bi-level optimization [28] problem, where the objective is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.555, + 0.837, + 0.893, + 0.861 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\omega} \\mathcal {L} _ {\\text {o u t e r}} (\\arg \\min _ {\\omega} \\mathcal {L} _ {\\text {i n n e r}} (\\omega , \\mathcal {D} _ {\\text {t r a i n}}), \\mathcal {D} _ {\\text {t e s t}}), \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.871, + 0.892, + 0.902 + ], + "angle": 0, + "content": "where \\(\\omega\\) indicates the initial parameters, \\(\\mathcal{L}_{\\mathrm{inner}}\\) denotes the task specific inner loss (meta-train) and \\(\\mathcal{L}_{\\mathrm{outer}}\\) denotes the" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7789" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.12 + ], + "angle": 0, + "content": "outer loss (meta-test), and \\(\\mathcal{D}_{\\mathrm{train}}\\) and \\(\\mathcal{D}_{\\mathrm{test}}\\) denote the inner loop and the outer loop training example sets." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.121, + 0.473, + 0.309 + ], + "angle": 0, + "content": "Likewise, at each iteration of the student parameter \\((\\theta_S)\\) update, we pose Knowledge-Acquisition and KnowledgeRetention as meta-train and meta-test, respectively. I.e., we perform a single gradient descent step using the current samples \\((\\hat{x})\\) produced from the generator network \\((\\mathcal{G})\\), on the parameters \\(\\theta_{S}\\) and obtain \\(\\theta_{S}^{\\prime}\\) as \\(\\theta_S^\\prime = \\theta_S - \\alpha \\nabla \\mathcal{L}_{Acq}(\\theta_S)\\) where \\(\\nabla \\mathcal{L}_{Acq}(\\theta_S)\\) denotes gradient of \\(\\mathcal{L}_{Acq}\\) at \\(\\theta_S\\). Then we optimize on the batch of samples \\((\\hat{x}_m)\\) obtained from the memory \\((\\mathcal{M})\\), with the parameters \\(\\theta_S^\\prime\\), and then make a final update on \\(\\theta_S\\). Thus, formally, the overall meta-optimization objective, with the task of Knowledge Acquisition serving as meta-train and the task of Knowledge Retention as meta-test (Figure 2c), can be defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.084, + 0.32, + 0.397, + 0.344 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\theta_ {S}} \\mathcal {L} _ {A c q} (\\theta_ {S}) + \\mathcal {L} _ {R e t} (\\theta_ {S} ^ {\\prime}) = \\min _ {\\theta_ {S}} \\mathcal {L} _ {A c q} (\\theta_ {S}) +\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.264, + 0.346, + 0.47, + 0.365 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {R e t} \\big (\\theta_ {\\mathcal {S}} - \\alpha \\nabla \\mathcal {L} _ {A c q} (\\theta_ {\\mathcal {S}}) \\big). (7)\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.376, + 0.469, + 0.406 + ], + "angle": 0, + "content": "3.4. How does the Proposed Student Update Strategy Promote Alignment?" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.414, + 0.47, + 0.542 + ], + "angle": 0, + "content": "In this subsection, we analyze the proposed objective (7) to understand how it results in the desired alignment between the Knowledge-Acquisition and Knowledge-Retention tasks. We assume that meta-train and meta-test tasks provide us with losses \\(\\mathcal{L}_{Acq}\\) and \\(\\mathcal{L}_{Ret}\\); in our case, \\(\\mathcal{L}_{Acq}\\) and \\(\\mathcal{L}_{Ret}\\) are the same function computed on the batches \\(\\hat{x}\\) and \\(\\hat{x}_m\\), respectively. We utilize Taylor's expansion to elaborate the gradient of \\(\\mathcal{L}_{Ret}\\) at a point \\(\\theta\\) displaced by \\(\\phi_{\\theta}\\), as described in Lemma 1." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.543, + 0.472, + 0.587 + ], + "angle": 0, + "content": "Lemma 1. If \\(\\mathcal{L}_{Ret}\\) has Lipschitz, Hessian, i.e., \\(\\| \\nabla^2\\mathcal{L}_{Ret}(\\theta_1) - \\nabla^2\\mathcal{L}_{Ret}(\\theta_2)\\|\\leq \\rho \\| \\theta_1 - \\theta_2\\|\\) for some \\(\\rho >0\\) , then:" + }, + { + "type": "equation", + "bbox": [ + 0.117, + 0.598, + 0.428, + 0.637 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\nabla \\mathcal {L} _ {R e t} (\\theta + \\phi_ {\\theta}) = \\nabla \\mathcal {L} _ {R e t} (\\theta) + \\nabla^ {2} \\mathcal {L} _ {R e t} (\\theta) \\phi_ {\\theta} \\\\ + \\mathcal {O} (\\| \\phi_ {\\theta} \\| ^ {2}). \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.648, + 0.405, + 0.665 + ], + "angle": 0, + "content": "For instance, when \\(\\phi_{\\theta} = -\\alpha \\nabla \\mathcal{L}_{Acq}(\\theta)\\), we have," + }, + { + "type": "equation", + "bbox": [ + 0.092, + 0.676, + 0.453, + 0.733 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\nabla \\mathcal {L} _ {R e t} (\\theta - \\alpha \\nabla \\mathcal {L} _ {A c q} (\\theta)) = \\nabla \\mathcal {L} _ {R e t} (\\theta) \\\\ - \\alpha \\nabla^ {2} \\mathcal {L} _ {R e t} (\\theta) \\nabla \\mathcal {L} _ {A c q} (\\theta) \\\\ + \\mathcal {O} (\\alpha^ {2}). \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.748, + 0.47, + 0.806 + ], + "angle": 0, + "content": "Theorem 1. If \\(\\theta' = \\theta - \\alpha \\nabla \\mathcal{L}_{Acq}(\\theta)\\), denotes a single gradient descent step on \\(\\theta\\) with the objective \\(\\mathcal{L}_{Acq}(\\theta)\\), where \\(\\alpha\\) is a scalar, and \\(\\nabla \\mathcal{L}_{Acq}(\\theta)\\) denotes the gradient of the objective at \\(\\theta\\), then:" + }, + { + "type": "equation", + "bbox": [ + 0.102, + 0.816, + 0.445, + 0.867 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\frac {\\partial \\mathcal {L} _ {R e t} \\left(\\theta^ {\\prime}\\right)}{\\partial \\theta} = \\nabla \\mathcal {L} _ {R e t} (\\theta) - \\alpha \\nabla^ {2} \\mathcal {L} _ {R e t} (\\theta). \\nabla \\mathcal {L} _ {A c q} (\\theta) \\\\ - \\alpha \\nabla^ {2} \\mathcal {L} _ {A c q} (\\theta). \\nabla \\mathcal {L} _ {R e t} (\\theta) + \\mathcal {O} (\\alpha^ {2}). \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.886, + 0.405, + 0.901 + ], + "angle": 0, + "content": "Proof. Please refer to the Supplemental Material." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.894, + 0.158 + ], + "angle": 0, + "content": "While optimizing the objective defined in (7) using stochastic gradient descent, we would need to compute the gradient of the \\(\\mathcal{L}_{Ret}(\\theta_S^{\\prime})\\) w.r.t \\(\\theta_{S}\\). Therefore, utilizing Theorem 1 we express \\(\\frac{\\partial\\mathcal{L}_{Ret}(\\theta_S^{\\prime})}{\\partial\\theta_S}\\) as:" + }, + { + "type": "equation", + "bbox": [ + 0.515, + 0.17, + 0.893, + 0.243 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\frac {\\partial \\mathcal {L} _ {R e t} \\left(\\theta_ {\\mathcal {S}} ^ {\\prime}\\right)}{\\partial \\theta_ {\\mathcal {S}}} = \\nabla \\mathcal {L} _ {R e t} \\left(\\theta_ {\\mathcal {S}}\\right) \\\\ - \\alpha \\nabla^ {2} \\mathcal {L} _ {R e t} (\\theta_ {\\mathcal {S}}). \\nabla \\mathcal {L} _ {A c q} (\\theta_ {\\mathcal {S}}) \\\\ - \\alpha \\nabla^ {2} \\mathcal {L} _ {A c q} (\\theta_ {\\mathcal {S}}). \\nabla \\mathcal {L} _ {R e t} (\\theta_ {\\mathcal {S}}) + \\mathcal {O} (\\alpha^ {2}), \\tag {8} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.257, + 0.862, + 0.273 + ], + "angle": 0, + "content": "using the product rule \\(\\nabla a.b + \\nabla b.a = \\nabla (a.b)\\), we get:" + }, + { + "type": "equation", + "bbox": [ + 0.515, + 0.286, + 0.893, + 0.372 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\frac {\\partial \\mathcal {L} _ {R e t} \\left(\\theta_ {\\mathcal {S}} ^ {\\prime}\\right)}{\\partial \\theta_ {\\mathcal {S}}} = \\nabla \\mathcal {L} _ {R e t} \\left(\\theta_ {\\mathcal {S}}\\right) \\\\ - \\alpha \\nabla \\underbrace {(\\nabla \\mathcal {L} _ {\\text {R e t}} (\\theta_ {\\mathcal {S}}) . \\nabla \\mathcal {L} _ {\\text {A c q}} (\\theta_ {\\mathcal {S}}))} _ {\\text {G r a d i e n t A l i g n m e n t}} + \\mathcal {O} \\left(\\alpha^ {2}\\right). \\tag {9} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.387, + 0.894, + 0.56 + ], + "angle": 0, + "content": "From the analysis above, we observe that the gradient of \\(\\mathcal{L}_{Ret}(\\theta_S')\\) at \\(\\theta_S\\) (in (9)) produces the gradient of the gradient-product. This indicates, when optimizing \\(\\mathcal{L}_{Ret}(\\theta_S')\\) (in (7)), the gradient of \\(\\mathcal{L}_{Ret}(\\theta_S')\\) at \\(\\theta_S\\), has a negatively scaled gradient of the gradient-product term, i.e., \\(\\nabla (\\nabla \\mathcal{L}_{Ret}(\\theta_S). \\nabla \\mathcal{L}_{Acq}(\\theta_S))\\) (derived in (9)), suggesting that the overall-gradients minimize \\(\\mathcal{L}_{Ret}(\\theta_S)\\) and maximize \\(\\nabla \\mathcal{L}_{Ret}(\\theta_S). \\nabla \\mathcal{L}_{Acq}(\\theta_S)\\). Hence, optimizing (7) enforces the updates on \\(\\mathcal{L}_{Ret}(\\theta_S)\\) and \\(\\mathcal{L}_{Acq}(\\theta_S)\\) to seek a common direction, by maximizing the gradient-product. Moreover, we empirically observe this phenomenon in Section A of the Supplemental Material." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.574, + 0.892, + 0.603 + ], + "angle": 0, + "content": "3.5. Generator Update in Adversarial Exploration-based DFKD" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.613, + 0.894, + 0.757 + ], + "angle": 0, + "content": "In the absence of the training dataset \\((\\mathcal{D}_{\\mathcal{T}})\\), the generator \\((\\mathcal{G})\\) is utilized to obtain pseudo-samples \\((\\hat{x})\\) and perform knowledge-distillation, i.e., \\(\\hat{x} = \\mathcal{G}(z)\\), \\(z \\sim \\mathcal{N}(0, I)\\). The generator is learned to maximize the disagreement between Teacher network \\((\\mathcal{T}_{\\theta_{\\mathcal{T}}})\\) and the Student network \\((S_{\\theta_S})\\). Additionally, for the generated data \\(\\hat{x}\\) to mimic similar responses from the teacher as the real data, we include a prior loss \\(\\mathcal{L}_{\\mathcal{P}}\\) [4] to be minimized alongside maximizing the discrepancy \\((D)\\). Hence, we update the generator parameters \\((\\theta_{\\mathcal{G}})\\) by maximizing the following objective:" + }, + { + "type": "equation", + "bbox": [ + 0.518, + 0.771, + 0.892, + 0.815 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\max _ {\\theta_ {\\mathcal {G}}} \\mathbb {E} _ {z \\sim \\mathcal {N} (0, I)} [ D (\\mathcal {T} _ {\\theta_ {\\mathcal {T}}} (\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)), \\mathcal {S} _ {\\theta_ {\\mathcal {S}}} (\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z))) \\\\ \\left. - \\mathcal {L} _ {\\mathcal {P}} \\left(\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)\\right) \\right]. \\tag {10} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.829, + 0.894, + 0.901 + ], + "angle": 0, + "content": "Typically the disagreement function \\((D)\\) for the generator is identical to the teacher-student disagreement term [5, 10]. Instead, for teacher-student discrepancy maximization we use the Jensen-Shannon (JS) \\((\\mathcal{L}_{JS})\\) divergence. Our motivation to use JS divergence is based on empirical study by Binici et" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7790" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.075, + 0.079, + 0.892, + 0.171 + ], + "angle": 0, + "content": "Table 1. Distillation results of the Adversarial DFKD methods on four image classification benchmark datasets, SVHN [23], CIFAR10 [17], CIFAR100 [17], Tiny-ImageNet [18]. Primarily we compare against the replay-based methods, present at the bottom panel of each dataset. The best numbers from our method on the Bank-based replay are highlighted in Navy, and for the one with Generative replay are highlighted in Maroon. The first five columns represent the \\(\\mu[S_{Acc}]\\) and the \\(\\sigma^2[S_{Acc}]\\) at different epoch percentiles (described in Section 4.1). The last column represents the maximum test accuracy (\\(\\mathrm{Acc}_{\\max}\\)) attained by the student network in the entire training period. Note: For SVHN and Tiny-ImageNet we were unable to reproduce the results of DFQ [5] in our setting. DFKD* denotes the baseline method without any replay." + }, + { + "type": "table", + "bbox": [ + 0.116, + 0.179, + 0.854, + 0.668 + ], + "angle": 0, + "content": "
Method>0thPercentile>20thPercentile>40thPercentile>60thPercentile>80thPercentileAccmax(%)
μ↑σ2↓μ↑σ2↓μ↑σ2↓μ↑σ2↓μ↑σ2↓
SVHN [23]
TAcc=97.45%, SAcc=97.26%, (Emax)=200
DAFL [4]85.15241.7090.498.7191.753.7392.880.8493.590.2094.23
DFAD [10]92.5293.4294.100.0794.130.0794.140.0894.110.0794.68
DFKD* [3]91.848.5992.536.7693.434.3994.611.7295.720.0495.97
MB-DFKD [3]89.5121.1389.2824.3188.6530.6387.1438.6781.6715.2494.11
PRE-DFKD [2]94.082.1194.531.1194.970.6695.420.2695.850.0496.10
Ours-1 (Memory Bank)92.787.4493.692.2194.251.5494.940.6295.590.0595.88
Ours-2 (Generative Replay)94.481.6494.790.7195.090.5195.470.2795.910.0396.15
CIFAR10 [17]
TAcc=95.72%, SAcc=95.23%, (Emax)=200
DAFL [4]62.78629.1372.51296.4480.57110.1887.0522.4090.762.0492.23
DFAD [10]85.88152.3989.9111.9591.465.6692.660.0792.850.0493.21
DFQ [5]81.2598.8684.4137.4087.0819.5989.726.1591.870.6792.90
DFKD* [3]83.57107.7786.9418.4288.6711.8390.674.0992.320.3893.02
MB-DFKD [3]84.2995.7487.2516.4788.8111.0090.713.7992.310.3593.03
PRE-DFKD [2]87.1045.6988.8810.8690.246.2691.751.8492.920.1493.41
Ours-1 (Memory Bank)85.5391.3388.5812.6689.968.2691.662.8693.090.3293.73
Ours-2 (Generative Replay)88.0760.8690.525.2091.443.1892.451.4593.480.1894.02
CIFAR100 [17]
TAcc=77.94%, SAcc=77.10%, (Emax)=400
DAFL [4]52.48437.8861.6582.9665.8832.2369.2311.9372.231.2973.78
DFAD [10]59.62192.6465.3228.8467.712.7268.690.3369.070.2469.73
DFQ [5]68.2055.9170.459.7871.775.8873.192.2274.480.4775.39
DFKD* [3]69.3674.6772.358.3473.594.5474.861.6375.940.1876.51
MB-DFKD [3]66.05207.2971.1614.6772.975.6174.401.5675.480.1876.14
PRE-DFKD [2]70.2386.6373.396.7774.592.8875.601.0376.460.1276.93
Ours-1 (Memory Bank)69.8775.6772.968.7274.304.0375.491.3876.500.2077.11
Ours-2 (Generative Replay)71.4960.1774.164.6175.122.2676.010.7476.750.0977.21
Tiny-ImageNet [18]
TAcc=60.83%, SAcc=57.88%, (Emax)=500
DAFL [4]21.04106.0424.9552.5628.1028.2231.1911.4434.111.3335.46
DFAD [10]14.6022.3216.487.4817.761.4318.440.2618.840.1019.60
DFKD* [3]34.5586.2038.1723.8840.2813.2842.405.3244.380.7945.61
MB-DFKD [3]34.16122.9238.7732.4241.1818.4043.638.4846.171.7347.96
PRE-DFKD [2]38.8980.1242.2721.6644.2512.6146.315.4748.331.3949.94
Ours-1 (Memory Bank)36.3494.6240.0326.2042.2813.7644.435.8246.520.9547.96
Ours-2 (Generative Replay)39.0974.9542.3021.2344.1913.5546.306.2148.481.1249.88
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.682, + 0.283, + 0.696 + ], + "angle": 0, + "content": "al. [3]. Hence, \\(D\\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.187, + 0.715, + 0.373, + 0.732 + ], + "angle": 0, + "content": "\\[\nD (a, b) = \\mathcal {L} _ {J S} (p (a), p (b)),\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.735, + 0.473, + 0.763 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{JS}(p(a),p(b)) = \\frac{1}{2} (\\mathcal{L}_{KL}(p(a),m) + \\mathcal{L}_{KL}(p(b),m)),\\) and" + }, + { + "type": "equation", + "bbox": [ + 0.201, + 0.765, + 0.47, + 0.794 + ], + "angle": 0, + "content": "\\[\nm = \\frac {1}{2} (p (a) + p (b)). \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.47, + 0.856 + ], + "angle": 0, + "content": "Here \\(\\mathcal{L}_{KL}\\) stands for the Kullback-Leibler divergence and \\(p(a)\\) and \\(p(b)\\) denote the probability vectors obtained after the Softmax applied to the arguments \\(a\\) and \\(b\\), respectively." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.857, + 0.471, + 0.9 + ], + "angle": 0, + "content": "Moreover, the prior loss \\(\\mathcal{L}_{\\mathcal{P}}\\) [3] is defined as the combination of three loss functions \\((\\mathcal{L}_{OH},\\mathcal{L}_A,\\) and \\(\\mathcal{L}_{EM})\\) that capture different characteristics from the teacher model and" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.682, + 0.874, + 0.697 + ], + "angle": 0, + "content": "impose them on the pseudo samples \\(\\hat{x}\\), and is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.596, + 0.719, + 0.892, + 0.735 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathcal {P}} = \\mathcal {L} _ {O H} + \\gamma \\mathcal {L} _ {A} + \\delta \\mathcal {L} _ {E M}, \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.757, + 0.894, + 0.902 + ], + "angle": 0, + "content": "where, \\(\\gamma\\) and \\(\\delta\\) denote the weighing scalar coefficients. \\(\\mathcal{L}_{OH}\\) is the one-hot loss that forces the generated samples to have strong (one-hot vector like) predictions when input to the teacher. It is defined as the cross-entropy between the teacher's softmax output \\(p_{\\mathcal{T}}(\\hat{x}_n) =\\) Softmax \\((\\mathcal{T}_{\\theta_{\\mathcal{T}}}(\\hat{x}_n)),\\hat{x}_n\\in \\hat{x},\\) and its one-hot vector version \\(e_c\\in \\{0,1\\} ^C\\) , where \\(C\\) denotes the total number of classes and \\(e_c\\) denotes the \\(c\\) -th canonical one-hot-vector, and \\(c = \\arg \\max_{i}(p_{\\mathcal{T}}^{i}(\\hat{x}_{n}))\\) , the superscript \\(i\\) denotes the \\(i^{th}\\) probability score of the predicted mass vector \\(p_{\\mathcal{T}}(\\hat{x}_n)\\)" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.514, + 0.957 + ], + "angle": 0, + "content": "7791" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.087, + 0.895, + 0.235 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.242, + 0.895, + 0.283 + ], + "angle": 0, + "content": "Figure 5. Student learning curves depicting the learning evolution of Wide-ResNet (WRN) [32]. The WRN-16-1 (top-row), and WRN-16-2 (bottom-row) networks are distilled by a WRN-40-2 teacher network pre-trained on CIFAR10 (\\( \\mathcal{T}_{Acc} = 94.87\\% \\)). Each column represents the learning curves with the Buffer-based (with different memory buffer sizes) and Generative replay schemes. The proposed method is in Blue." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.288, + 0.258, + 0.303 + ], + "angle": 0, + "content": "Hence, \\(\\mathcal{L}_{OH}\\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.157, + 0.313, + 0.471, + 0.354 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {O H} = - \\frac {1}{N} \\sum_ {n = 1} ^ {N} e _ {c} ^ {\\top} \\log \\left(p _ {\\mathcal {T}} \\left(\\hat {x} _ {n}\\right)\\right). \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.364, + 0.472, + 0.407 + ], + "angle": 0, + "content": "- \\(\\mathcal{L}_A\\) is the activation loss motivated by the notion that meaningful inputs result in higher-valued activation maps in a well-trained network [4] and is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.157, + 0.418, + 0.471, + 0.46 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {A} = - \\frac {1}{N L} \\sum_ {n = 1} ^ {N} \\sum_ {l = 1} ^ {L} \\| \\mathcal {A} _ {\\mathcal {T}} ^ {l} (\\hat {x} _ {n}) \\| _ {1}, \\tag {14}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.471, + 0.469, + 0.5 + ], + "angle": 0, + "content": "where \\(\\mathcal{A}_{\\mathcal{T}}^{l}(\\hat{x}_{n})\\) denotes the activation of the \\(l\\)-th layer in the Teacher network \\((\\mathcal{T}_{\\theta \\tau})\\) for the input \\(n^{th}\\) input \\(\\hat{x}_n\\in \\hat{x}\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.501, + 0.47, + 0.589 + ], + "angle": 0, + "content": "- \\(\\mathcal{L}_{EM}\\) loss is the entropy-maximization term imposed on the generator to output an equal number of pseudo-samples from each category [5]. In other words, the intra-batch entropy is maximized, resulting in a similar number of samples for each category i.e. if \\(\\bar{p}_{\\mathcal{T}} = \\frac{1}{N}\\sum_{n=1}^{N}p_{\\mathcal{T}}(\\hat{x}_n)\\), then the loss \\(\\mathcal{L}_{EM}\\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.202, + 0.599, + 0.471, + 0.617 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {E M} = \\bar {p} _ {\\mathcal {T}} ^ {\\top} \\log (\\bar {p} _ {\\mathcal {T}}). \\tag {15}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.628, + 0.442, + 0.643 + ], + "angle": 0, + "content": "In sum, the Generator loss objective \\((\\mathcal{L}_{\\mathcal{G}})\\) is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.078, + 0.653, + 0.47, + 0.683 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathcal {G}} \\left(\\theta_ {\\mathcal {G}}\\right) = - D \\left(\\mathcal {T} _ {\\theta_ {\\mathcal {T}}} \\left(\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)\\right), \\mathcal {S} _ {\\theta_ {\\mathcal {S}}} \\left(\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)\\right)\\right) + \\mathcal {L} _ {\\mathcal {P}} \\left(\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)\\right). \\tag {16}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.71, + 0.21, + 0.727 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.735, + 0.285, + 0.751 + ], + "angle": 0, + "content": "4.1. Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.756, + 0.47, + 0.8 + ], + "angle": 0, + "content": "Datasets: We evaluate the proposed method on SVHN [23], CIFAR10 [17], CIFAR100 [17] and Tiny-ImageNet [18] datasets." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.8, + 0.471, + 0.886 + ], + "angle": 0, + "content": "Teacher model: For CIFAR10 and CIFAR100 datasets, we used the pre-trained teacher models made available by the authors of [11] and [3], respectively. For SVHN and TinyImageNet we trained the teacher network from scratch. We provide the training details of the teacher models in Section B.1 of the Supplemental Material." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.886, + 0.472, + 0.901 + ], + "angle": 0, + "content": "Definition of an Epoch in DFKD: In Adversarial DFKD," + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.288, + 0.895, + 0.503 + ], + "angle": 0, + "content": "the notion of an epoch is obscure. In a typical deep neural network-based classification training, an epoch is defined as one complete pass over the available training data. However, DFKD has no access to the training data; instead, the pseudo samples generated on-the-fly are used to distill knowledge to the student network. Therefore, prior works [3,5,10] defined an epoch in terms of a fixed number of training iterations \\((\\mathcal{I})\\) where each iteration consists of a set number of generator update steps \\((g)\\) and student update steps \\((s)\\). Hence, to be consistent across the baselines and prior arts, we use the same number of training iterations, generator update steps, and student updates steps to define an epoch. For all the methods, we set \\(\\mathcal{I} = 72\\), \\(g = 1\\), and \\(s = 10\\) and use a batch size of 512 of the sampled noise \\((z)\\) to generate the pseudo samples and optimize the parameters \\(\\theta_{\\mathcal{G}}\\) and \\(\\theta_{\\mathcal{S}}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.503, + 0.895, + 0.532 + ], + "angle": 0, + "content": "Training Details: The training details are provided in Section B of the Supplemental Material." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.532, + 0.895, + 0.678 + ], + "angle": 0, + "content": "Evaluation: We evaluate the methods by comparing the mean and variance of the student network's test accuracy \\((S_{Acc})\\), denoting them as \\(\\mu[S_{Acc}]\\), and \\(\\sigma^2[S_{Acc}]\\), respectively, across the epochs, motivated by Binci et al. [2]. Specifically, we compare the different sections of the student's learning evolution by partitioning them into different epoch percentiles. For example, computing the \\(\\mu[S_{Acc}]\\) and \\(\\sigma^2[S_{Acc}]\\) for epochs greater than the \\(n^{th}\\) percentile conveys the mean and variance across all the epochs greater than the \\(\\frac{n}{100}^{th}\\) of the total number of training epochs." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.69, + 0.733, + 0.705 + ], + "angle": 0, + "content": "4.2. Results and Observations" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.713, + 0.894, + 0.901 + ], + "angle": 0, + "content": "Baseline and State-of-the-art Comparisons In Table 1, we analyze our method on classification task and compare it with prior Adversarial DFKD methods [4,5,10] and closely related memory-based Adversarial DFKD methods [2,3]. For a fair comparison across the methods, we re-implemented all the methods and used the same generator architecture to generate the pseudo samples. For each of the methods we report the \\(\\mu[S_{Acc}]\\) and \\(\\sigma^2[S_{Acc}]\\) at different epoch percentiles and the maximum accuracy (\\(\\mathrm{Acc}_{\\max}\\)) attained by the student. We observe that, compared to MB-DFKD (Memory Bank) [3], Ours-1 (Memory Bank) demonstrates consistent improvement across all the datasets. Similarly, compared to PRE-DFKD (Generative Replay) [2], utilizing the same VAE" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7792" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.079, + 0.09, + 0.897, + 0.128 + ], + "angle": 0, + "content": "
ZSKDa[22]ADIb[30]CIMb[11]DeGANc[1]EATSKDc[21]KEGNETa[31]ZSKTb[20]DDADd[34]DAFLd[4]DFAdd[10]DFQd[5]MB-DFKDd[3]PRE-DFKDd[2]Ours-IOurs-2
TAcc(%)77.5078.0578.0577.9477.9477.5078.0577.5077.9477.9477.9477.9477.9477.9477.94
SAcc(%)70.2161.3277.0465.2567.1873.9167.7475.0473.7969.7375.3976.1476.9377.1177.21
ΔTAcc-SAcc7.2916.731.0112.6910.763.5910.312.464.158.212.551.801.010.830.73
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.076, + 0.134, + 0.895, + 0.189 + ], + "angle": 0, + "content": "Table 2. Classification accuracy (in %) of the student trained using various DFKD methods on CIFAR100 with ResNet-34 [15] as the teacher and ResNet-18 [15] as the student. \\( \\mathcal{T}_{Acc} \\) and \\( S_{Acc} \\) denote the Teacher network's and the Student network's accuracy, respectively, and \\( \\Delta \\mathcal{T}_{Acc} - \\mathcal{S}_{Acc} \\) denotes the difference between the Teacher and Student accuracies. Also, \\( ^{\\mathrm{a}},^{\\mathrm{b}},^{\\mathrm{c}} \\) and \\( ^{\\mathrm{d}} \\) denote results produced by [34], [11], [3], and our implementation, respectively." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.193, + 0.47, + 0.223 + ], + "angle": 0, + "content": "decoder architecture as the generative replay, we observe a similar trend for Ours-2 (Generative Replay)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.224, + 0.471, + 0.453 + ], + "angle": 0, + "content": "Moreover, in Figure 4, we visualize the learning curves of the student networks trained on multiple datasets. We plot the test accuracy of the student at the end of each training epoch. The proposed method exhibits significant improvement in the learning evolution and the peak accuracies achieved, suggesting that the proposed approach can retain the knowledge from previously encountered samples as the learning progresses. However, on Tiny-ImageNet, with Generative Replay, we did not observe substantial improvements; we conjecture that this may be due to the complexity of the dataset, and the inability to capture crucial samples as replay for the complex dataset, for a large number of epochs. Also, with Generative Replay we sometimes faced difficulty in training a VAE on a stream of synthetic samples (especially for complex dataset like Tiny-ImageNet) as it suffers due to the distribution drift of its own." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.456, + 0.471, + 0.685 + ], + "angle": 0, + "content": "Additionally, we emphasize that we do not strive toward achieving state-of-the-art student classification accuracy (requiring extensive hyper-parameter tuning) in the DFKD setting, but verify the viability of our hypothesis of retaining the previously acquired knowledge while learning on new samples. Nonetheless, we observe that our method improves upon the student classification accuracy on CIFAR100 [17] compared to the contemporary works and the current state-of-the-art [2] with the ResNet-34 [15] \\((\\mathcal{T})\\) and ResNet-18 [15] \\((S)\\) setup, as shown in Table 2. Additionally, since previous works use the same teacher network with different test accuracies, we also report the teacher accuracies of the respective methods used to distill the knowledge to the student. Nonetheless, we also compute the Teacher-Student accuracy difference \\((\\Delta_{\\mathcal{T}_{Acc} - \\mathcal{S}_{Acc}})\\) to assess the distance of the student from its teacher in terms of classification accuracy." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.685, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Interpreting the Result: Because of the proposed student update strategy, we observe a global monotonicity in the student's learning evolution which the existing approaches with naive replay [2, 3] claim, but do not warrant (described in Section 3.3). The global monotonicity in the learning evolution encompasses crucial advantages. For example, when the validation data is unavailable, the developer cannot assess the student's accuracy and identify the best parameters for the student. In such a scenario, the final accuracy is dependent on the random termination epoch set by the developer. In other words, the ideal DFKD approach should sustain high accuracy via monotonically enhancing it during the course of distillation. Therefore, \\(\\mu[S_{Acc}]\\) and \\(\\sigma^2[S_{Acc}]\\) contribute as crucial metrics to assess the distillation method as opposed to the maximum accuracy (\\(\\mathrm{Acc}_{\\max}\\)), since the \\(\\mathrm{Acc}_{\\max}\\) value can" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.193, + 0.892, + 0.251 + ], + "angle": 0, + "content": "be attained at any point of time prior to the termination, and can be misleading. The improvements in the monotonicity and the \\(\\mu [S_{Acc}]\\) and \\(\\sigma^2 [S_{Acc}]\\) values of proposed method are evident from Table 1, Figure 4 and Figure 5." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.251, + 0.895, + 0.422 + ], + "angle": 0, + "content": "Architecture Scalability: The proposed student update strategy is generic and is scalable across different neural network architecture families since the method is not constrained to a particular choice of architecture. From the ResNet-18 [15], WRN-16-1 [32] and WRN-16-2 [32] student learning curves (in Figure 4 and Figure 5), we observe our method's advantage on both the network architectures. Moreover, for large student network architectures (Deeper or Wider) that include complex layers, the proposed method efficiently handles the intricacies with regard to computing the Hessian and the Hessian-product, which becomes highly essential for cumbersome models." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.424, + 0.895, + 0.653 + ], + "angle": 0, + "content": "Improvement across Replay Schemes: Furthermore, the proposed method is agnostic to the memory scheme employed for replay, as demonstrated by the experiments (in Table 1, Figure 4 and Figure 5) using a Memory Buffer and Generative-Replay, thus, rendering our method generalized to the choice of replay. In Table 1 and Figure 5, we can observe that the proposed method enhances the student's performance on both the replay schemes (Memory Bank and Generative Replay) used in the prior arts. Moreover, we experiment with different memory buffer sizes on WRN-16-1 [32] and WRN-16-2 [32] distillation (in Figure 5) and observe consistent and substantial improvements across different memory sizes. Here, the memory size is defined as the maximum number of pseudo-example batches that the bank can contain and each batch consists of randomly sampled 64 examples from \\(\\hat{x}\\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.673, + 0.619, + 0.688 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.7, + 0.895, + 0.886 + ], + "angle": 0, + "content": "In this paper, we proposed a meta-learning inspired student update strategy for the Adversarial DFKD setting, that treats Knowledge-Acquisition and Knowledge-Retention as meta-train and meta-test, respectively. The proposed strategy substantially improves the learning evolution of the student network by implicitly aligning the Knowledge-Retention and the Knowledge-Acquisition tasks. The intended effect of having the gradient directions aligned is to obtain student parameters \\((\\theta_{S})\\) that have optimal performance on both \\(\\mathcal{L}_{Acq}\\) and \\(\\mathcal{L}_{Ret}\\). The conducted experiments on multiple datasets, network architectures, and replay schemes demonstrate the effectiveness, scalability and generalizability of the proposed strategy." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.886, + 0.862, + 0.901 + ], + "angle": 0, + "content": "Acknowledgement: Work partially supported by NIH." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7793" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.167 + ], + "angle": 0, + "content": "[1] Sravanti Addepalli, Gaurav Kumar Nayak, Anirban Chakraborty, and Venkatesh Babu Radhakrishnan. Degan: Data-enriching gan for retrieving representative samples from a trained classifier. In AAAI, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.168, + 0.472, + 0.221 + ], + "angle": 0, + "content": "[2] Kuluhan Binici, Shivam Aggarwal, Nam Trung Pham, Karianto Leman, and Tulika Mitra. Robust and resource-efficient data-free knowledge distillation by generative pseudo replay. In AAAI, 2022. 2, 3, 4, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.222, + 0.471, + 0.274 + ], + "angle": 0, + "content": "[3] Kuluhan Binici, Nam Trung Pham, Tulika Mitra, and Kario \nLeman. Preventing catastrophic forgetting and distribution \nmismatch in knowledge distillation via synthetic data. In \nWACV, 2022. 2, 3, 4, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.275, + 0.471, + 0.327 + ], + "angle": 0, + "content": "[4] Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, and Qi Tian. Dafi: Data-free learning of student networks. In ICCV, 2019. 1, 2, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.328, + 0.471, + 0.379 + ], + "angle": 0, + "content": "[5] Yoojin Choi, Jihwan Choi, Mostafa El-Khamy, and Jungwon Lee. Data-free network quantization with adversarial knowledge distillation. In CVPR Workshops, 2020. 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.381, + 0.471, + 0.419 + ], + "angle": 0, + "content": "[6] Yatin Dandi, Luis Barba, and Martin Jaggi. Implicit gradient alignment in distributed and federated learning. In AAAI, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.421, + 0.471, + 0.485 + ], + "angle": 0, + "content": "[7] Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars. A continual learning survey: Defying forgetting in classification tasks. IEEE TPAMI, 44(7):3366-3385, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.488, + 0.471, + 0.552 + ], + "angle": 0, + "content": "[8] Kien Do, Hung Le, Dung Nguyen, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, and Svetha Venkatesh. Momentum adversarial distillation: Handling large distribution shifts in data-free knowledge distillation. In NeurIPS, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.554, + 0.471, + 0.594 + ], + "angle": 0, + "content": "[9] Gongfan Fang, Kanya Mo, Xinchao Wang, Jie Song, Shitao Bei, Haofei Zhang, and Mingli Song. Up to 100x faster data-free knowledge distillation. In AAAI, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.595, + 0.471, + 0.633 + ], + "angle": 0, + "content": "[10] Gongfan Fang, Jie Song, Chengchao Shen, Xinchao Wang, Da Chen, and Mingli Song. Data-free adversarial distillation. In CVPR, 2020. 1, 2, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.634, + 0.471, + 0.673 + ], + "angle": 0, + "content": "[11] Gongfan Fang, Jie Song, Xinchao Wang, Chen Shen, Xingen Wang, and Mingli Song. Contrastive model inversion for data-free knowledge distillation. In IJCAI, 2021. 1, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.674, + 0.471, + 0.713 + ], + "angle": 0, + "content": "[12] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.715, + 0.471, + 0.765 + ], + "angle": 0, + "content": "[13] Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil. Bilevel programming for hyperparameter optimization and meta-learning. In ICML, 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.767, + 0.471, + 0.819 + ], + "angle": 0, + "content": "[14] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. Generative adversarial nets. In NeurIPS, 2014. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.821, + 0.471, + 0.859 + ], + "angle": 0, + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016. 4, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.861, + 0.471, + 0.9 + ], + "angle": 0, + "content": "[16] Diederik P Kingma, Max Welling, et al. An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4):307-392, 2019. 4" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.472, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "[17] Alex Krizhevsky et al. Learning multiple layers of features from tiny images. 2009. 4, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.121, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[18] Ya Le and Xuan S. Yang. Tiny imagenet visual recognition challenge. 2015. 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.149, + 0.892, + 0.174 + ], + "angle": 0, + "content": "[19] Yang Liu, Wei Zhang, and Jun Wang. Zero-shot adversarial quantization. In CVPR, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.176, + 0.892, + 0.214 + ], + "angle": 0, + "content": "[20] Paul Micaelli and Amos J Storkey. Zero-shot knowledge transfer via adversarial belief matching. In NeurIPS, 2019. 1, 2, 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.216, + 0.892, + 0.256 + ], + "angle": 0, + "content": "[21] Gaurav Kumar Nayak, Konda Reddy Mopuri, and Anirban Chakraborty. Effectiveness of arbitrary transfer sets for data-free knowledge distillation. In WACV, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.257, + 0.892, + 0.308 + ], + "angle": 0, + "content": "[22] Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, and Anirban Chakraborty. Zero-shot knowledge distillation in deep networks. In ICML, 2019. 1, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.311, + 0.892, + 0.376 + ], + "angle": 0, + "content": "[23] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng. Reading digits in natural images with unsupervised feature learning. In NeurIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011. 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.378, + 0.892, + 0.417 + ], + "angle": 0, + "content": "[24] Mert Bulent Sariyildiz and Ramazan Gokberk Cinbis. Gradient matching generative networks for zero-shot learning. In CVPR, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.419, + 0.892, + 0.457 + ], + "angle": 0, + "content": "[25] Ari Seff, Alex Beatson, Daniel Suo, and Han Liu. Continual learning in generative adversarial nets. arXiv preprint arXiv:1705.08395, 2017. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.459, + 0.892, + 0.499 + ], + "angle": 0, + "content": "[26] Yuge Shi, Jeffrey Seely, Philip Torr, Siddharth N, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve. Gradient matching for domain generalization. In ICLR, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.5, + 0.892, + 0.539 + ], + "angle": 0, + "content": "[27] Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim. Continual learning with deep generative replay. In NeurIPS, 2017. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.541, + 0.892, + 0.591 + ], + "angle": 0, + "content": "[28] Ankur Sinha, Pekka Malo, and Kalyanmoy Deb. A review on bilevel optimization: From classical to evolutionary approaches and applications. IEEE TEC, 22(2):276-295, 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.595, + 0.892, + 0.621 + ], + "angle": 0, + "content": "[29] Hoang Thanh-Tung and Truyen Tran. Catastrophic forgetting and mode collapse in gans. In IJCNN, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.622, + 0.892, + 0.674 + ], + "angle": 0, + "content": "[30] Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz. Dreaming to distill: Data-free knowledge transfer via deepinversion. In CVPR, 2020. 1, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.676, + 0.892, + 0.715 + ], + "angle": 0, + "content": "[31] Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang. Knowledge extraction with no observable data. In NeurIPS, 2019. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.716, + 0.892, + 0.743 + ], + "angle": 0, + "content": "[32] Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. In BMVC, 2016. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.744, + 0.892, + 0.784 + ], + "angle": 0, + "content": "[33] Sergey Zagoruyko and Nikos Komodakis. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. In ICLR, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.785, + 0.892, + 0.838 + ], + "angle": 0, + "content": "[34] Haoran Zhao, Xin Sun, Junyu Dong, Milos Manic, Huiyu Zhou, and Hui Yu. Dual discriminator adversarial distillation for data-free model compression. International Journal of Machine Learning and Cybernetics, 2022. 8" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.838 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "7794" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/b93957f9-1257-4220-880f-a2d772770ee6_origin.pdf b/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/b93957f9-1257-4220-880f-a2d772770ee6_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..18f2bdc1b55963932b449f3e2f6b2492fc19f059 --- /dev/null +++ b/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/b93957f9-1257-4220-880f-a2d772770ee6_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c79653243728fe47fce96ac58196d871e51c13b867009f13d7e2b4f9726158fe +size 899169 diff --git a/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/full.md b/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..96ece33ed86d756d35feff00dcc87a43b9dd3d4b --- /dev/null +++ b/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/full.md @@ -0,0 +1,405 @@ +# Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation + +Gaurav Patel† Konda Reddy Mopuri‡ Qiang Qiu† +†Purdue University ‡Indian Institute of Technology Hyderabad +{gpatel10, qqiu}@purdue.edu, krmpuri@ai.iith.ac.in + +# Abstract + +Data-free Knowledge Distillation (DFKD) has gained popularity recently, with the fundamental idea of carrying out knowledge transfer from a Teacher neural network to a Student neural network in the absence of training data. However, in the Adversarial DFKD framework, the student network's accuracy, suffers due to the non-stationary distribution of the pseudo-samples under multiple generator updates. To this end, at every generator update, we aim to maintain the student's performance on previously encountered examples while acquiring knowledge from samples of the current distribution. Thus, we propose a meta-learning inspired framework by treating the task of Knowledge-Acquisition (learning from newly generated samples) and KnowledgeRetention (retaining knowledge on previously met samples) as meta-train and meta-test, respectively. Hence, we dub our method as Learning to Retain while Acquiring. Moreover, we identify an implicit aligning factor between the KnowledgeRetention and Knowledge-Acquisition tasks indicating that the proposed student update strategy enforces a common gradient direction for both tasks, alleviating interference between the two objectives. Finally, we support our hypothesis by exhibiting extensive evaluation and comparison of our method with prior arts on multiple datasets. + +# 1. Introduction + +The primary goal of Data-Free Knowledge Distillation (DFKD) is to acquire a trustworthy alternative dataset for knowledge distillation. The quality of knowledge transfer depends on the caliber of these alternative samples. Noise optimization [11,22,30] and generative reconstruction [4,10,20] are the two primary ways to replace the original training data used in the distillation process with synthetic or pseudo samples. Adversarial DFKD methods [10, 19, 20] investigate an adversarial exploration framework to seek pseudo-samples. In such a paradigm, the Generator $(\mathcal{G})$ is used to create pseudo-samples as surrogates to perform knowledge distillation/transfer, and the Teacher-Student $(\mathcal{T} - \mathcal{S})$ setup acts as a joint discriminator to penalize and update generator parameters $(\theta_{\mathcal{G}})$ (Figure 1). In the adversarial framework, the generator explores the input space to find suitable pseudo-samples as the distillation progresses. Consequently, the distribution + +![](images/38262ac8e5b86430a7c092645f1c0a63db0989533a50a7629ef2627bec970671.jpg) +Figure 1. The top-section represents the learning evolution of a generic Adversarial Data-Free Knowledge Distillation framework; the color-intensity variation signifies the change in the distribution of the pseudo-samples, the student network, and the generator network over the learning epochs. Under the variation in the distribution of the pseudo-samples, the bottom-section shows the learning curves for cases when the student accuracy degrades (shown in Red), which is undesirable, and when the student accuracy is maintained, if not improved, as proposed (shown in Green). + +of the generated samples consistently keeps changing during the process due to the generator updates [25]. From the student network's perspective, at each iteration, the pseudo samples seem to be generated from different generator parameters $(\theta_{\mathcal{G}})$ . Hence, the convergence of the student network gets hampered due to successive distributional alterations over time [29], as depicted by the red curve in Figure 1. This observation hints that updating the student network, solely using the samples generated from the current generator parameters is not adequate to generalize the student. Moreover, the student forgets the knowledge acquired previously and decelerates the knowledge distillation. Therefore, the generator, apart from exploring the input space, seeks to compensate for the loss of knowledge in future iterations. Additionally, in a practical setting, during the distillation process, high variation in the student network's classification accuracy is undesirable, especially when the validation data is not available, since that prevents the user from tracking + +![](images/beb466f4525609e470e156f2b8f2735ee19dd55fc3189f366ad605e8d9153005.jpg) +Figure 2. Student update strategies: (a) Typical student update by optimizing the Knowledge-Acquisition loss $(\mathcal{L}_{Acq})$ with the batch pseudo samples $(\hat{x})$ , produced by the generator $(\mathcal{G})$ [5, 10, 20]. (b) Student update with simultaneous optimization of the Knowledge-Acquisition loss $(\mathcal{L}_{Acq})$ and Knowledge-Retention loss $(\mathcal{L}_{Ret})$ on the batch of pseduo samples $(\hat{x})$ and memory samples $(\hat{x}_m)$ obtained from the generator $(\mathcal{G})$ and the memory $(\mathcal{M})$ , respectively [2, 3]. (c) The proposed student update strategy, which treats $\mathcal{L}_{Acq}$ as meta-train and $\mathcal{L}_{Ret}$ as meta-test, and implicitly imposes the alignment between them. + +the student's accuracy over time, and selecting the distilled model parameters with the highest accuracy. + +To circumvent the above-discussed problems, existing methods maintain a memory buffer to rehearse the examples from previously encountered distributions while learning with current examples. Binci et al. [3] introduce Replay based methods to explicitly retrain/replay on a limited subset of previously encountered samples while training on the current examples. Then, carrying forward, they use Generative-Replay [27] to transfer the learned examples to an auxiliary generative model (VAE), and sample from the it (VAE decoder) in subsequent iterations [2]. Nonetheless, the performance of these methods is upper bounded by joint training on previous and current examples [7]. Although, recent works have focused on modeling the memory, we seek to work towards effectively utilizing the samples from memory. + +In this paper, we aim to update the student network parameters $(\theta_S)$ such that its performance does not degrade on the samples previously produced by the generator network $(\mathcal{G})$ , aspiring towards Learning to Retain while Acquiring. Thus, we propose a meta-learning inspired strategy to achieve this goal. We treat the task of Knowledge-Acquisition (learning from newly generated samples) and Knowledge-Retention (learning from previously encountered samples from memory) as meta-train and meta-test, respectively. Hence, in the proposed approach, the student network acquires new information while maintaining performance on previously encountered samples. By doing so, the proposed strategy (Figure 2c) implicitly aligns Knowledge-Acquisition and Knowledge-Retention, as opposed to simply combining them [2,3] without any coordination or alignment (Figure 2b), which leaves them to potentially interfere with one another. + +Additionally, analyzing the proposed meta-objective, we discover that (in Section 3.4) the latent alignment factor as the dot product between the gradients of the Knowledge-Acquisition and Knowledge-Retention objectives, suggesting that the meta-objective enforces a common gradient direction for both tasks, encouraging the alignment between the task-specific gradients. Thus, the proposed method simultaneously minimizes the loss and matches the gradients corresponding to the individual tasks (Knowledge-Acquisition + +and Knowledge-Retention), enforcing the optimization paths to be same for both tasks. + +Moreover, the proposed student update strategy is scalable to different deep architectures as the gradient alignment is implicit, and memory-agnostic, making no assumptions about the replay scheme employed. Nonetheless, recent works on gradient alignment, have shown great empirical advantages in Zero-Shot Learning [24], Distributed/Federated Learning [6] and Domain Generalization [26]. Our method extends the advantages of gradient alignment to memory-based Adversarial DFKD, thus strengthening the empirical findings in these works. + +Finally, to demonstrate the advantages of the proposed student update strategy, we evaluate and compare against current non-memory [4,5, 10] and memory-based [2, 3] Adversarial DFKD methods, and observe substantial improvement in the student learning evolution. + +In summary, the key points of this work are as follows: + +- We propose a novel meta-learning inspired student update strategy in the Adversarial DFKD setting, that aims to maintain the student's performance on previously encountered examples (Knowledge-Retention) while acquiring knowledge from samples of the current distribution (Knowledge-Acquisition). +- We theoretically identify (in Section 3.4) that the proposed student update strategy enforces an implicit gradient alignment between the Knowledge-Acquisition and Knowledge-Retention tasks. +- A major practical advantage of the proposed method is its applicability in the absence of a validation set, discussed in Section 4.2. +- Finally, we evaluate our method and compare against various Adversarial DFKD methods, on multiple student architectures and replay schemes (Memory Buffer and Generative Replay). + +# 2. Related Work + +Adversarial Data-Free Knowledge Distillation: In the Adversarial Data-Free Knowledge Distillation paradigm, A generative model is trained to synthesize pseudo-samples that serve as queries for the Teacher $(\mathcal{T})$ and the Student + +![](images/2dc7e81d7b305672f383d35478c9ef5696ec89121c3c1b637761e99f955a2683.jpg) +Generator Update + +![](images/3bf527a2402fbe246160b89cd3f67bd67adba3218e1d97bc8fc85820e7a790b9.jpg) + +![](images/9023c7772776c820c66710189d71ebfeb1191862aca3111212c0f3f95865fc5b.jpg) +Figure 3. An illustration of the proposed DFKD framework. The framework consists of the Generator $(\mathcal{G})$ , Teacher $(\mathcal{T})$ , Student $(\mathcal{S})$ and the Memory $(\mathcal{M})$ . $\mathcal{G}$ and $\mathcal{S}$ are updated alternatively, similar to the GAN [14] framework with the generator loss $(\mathcal{L}_{\mathcal{G}})$ optimizing $\mathcal{G}$ , and the Knowledge-Acquisition loss $(\mathcal{L}_{Acq})$ and the Knowledge-Retention loss $(\mathcal{L}_{Ret})$ optimizing the student $(\mathcal{S})$ . We use $\mathcal{M}$ in a generalized way to denote the employed replay scheme (Memory Buffer or Generative Replay in our case). + +Generator + +Network + +![](images/aad4c598f64f69b87552a1623468de6d3e8d8d39fcb4679496e1b6fe591587c8.jpg) + +Teacher + +Network + +![](images/2c9d83952f20bf4736761a9d8fa5fd51d4063ba7a656849e24058cf9235c58a0.jpg) + +Student + +Network + +![](images/4f16b6fb60d1570ec9c3efefc10272fde108bf6cafbc25f4786610a2704d6390.jpg) + +Memory + +Generator + +![](images/56937c5bc3026d04d61f1cbcedf062d36bda2cf1bc0b6c618059b0ec52c6bf5b.jpg) + +Memory + +Bank + +![](images/67be914a0a9d45eea80e6c13c1feca423c6742dc6bb342f873eb9dd3a33e256f.jpg) + +generated + +seudo Sa + +![](images/95729dd9f6c95525bd805085736fb3b8208d00e3bb62a87dae7c75e0279038b4.jpg) + +# + +# + +![](images/ca7e345e04fdc0a4a4e642c54a8f51f5b6e510cb53b54ccc52fb42d0c6d5d2d2.jpg) + +# + +# + +Forwa + +Propagation + +![](images/d96d705826a87c2aa74e0ffa7aefb7381445dfadfe047ed4b192171dd2af21ba.jpg) + +Meta + +Update + +![](images/855610d1c63ddbd9ed2dd31d6e438d2e0015ef3af704e8df1f753277e1b6fff3.jpg) + +Studen + +Update + +![](images/783eb393fe69bee144b7292a4dfcbadd31d71f7376d8071f8fcd7f4282d7a4a9.jpg) + +Generator + +Update + +(S) [5, 10, 20]. ZSKT [20] attempts data-free knowledge transfer by first training a generator in an adversarial fashion to look for samples on which the student and teacher do not match well. To improve the model discrepancy measure, it adopts the Kullback-Leibler (KL) divergence, and introduces attention transfer [33] to aid knowledge transfer. Moreover, DFAD [10] recommends Mean Absolute Error (MAE) as a model discrepancy function to prevent decayed gradients on converged samples. Furthermore, the adversarial framework was extended by Choi et al. [5] in the context of model quantization, by proposing adversarial data-free quantization (DFQ), and introducing additional regularization terms that match the mean and standard deviation of the generated pseudo-samples with the teacher model's batchnorm statistics, and imposes batch categorical entropy maximization, such that sample from each class appear equally in the generated batch. Fang et al. recently introduced FastD-FKD [9], an effective method with a meta generator to speed up the DFKD process, delivering a 100-fold increase in the knowledge transfer rate. + +Handling Distribution Shift in Adversarial DFKD: To counter the distribution mismatch and the catastrophic forgetting phenomenon in the adversarial framework [25], Binici et al. [3] suggested maintaining a dynamic collection of synthetic samples throughout training iterations to prevent catastrophic forgetting in DFKD. Moreover, in their latest work [2], they introduce generative pseudo-replay [27] in which an auxiliary generative model simultaneously learns the distribution of the samples produced by the generator $(\mathcal{G})$ . Throughout the training process, examples are generated from the auxiliary generator to replay during training. Nonetheless, these works have focused on modeling the memory buffer. A related line of research maintains an exponentially moving average (EMA) of the generator model $\mathcal{G}$ [8] to replace the typical Memory-Buffer and Generative Replay. Nonetheless, our work focuses on the effective utilization of the samples obtained from the memory. + +# 3. Methodology + +In Section 3.1, we first provide a brief overview of Adversarial DFKD. Then, in Section 3.2, we discuss the data-free knowledge distillation objective. In Sections 3.3 and 3.4, we elaborate on the proposed student update strategy. Lastly, in Section 3.5, we discuss the adopted generator update strategy used for the baselines and the proposed framework. + +# 3.1. Adversarial Data-Free Knowledge Distillation + +In the Adversarial DFKD framework, a generator $(\mathcal{G})$ is used to create pseudo-samples as surrogates to perform knowledge distillation/transfer, and the teacher-student $(\mathcal{T}-S)$ setup acts as a joint discriminator to penalize and update generator parameters $(\theta_{\mathcal{G}})$ in an adversarial manner. After updating $\theta_{\mathcal{G}}$ , random samples are generated and used to minimize the $\mathcal{T}-S$ discrepancy by updating the student parameters $(\theta_{\mathcal{S}})$ . The generator and the student are optimized alternatively up until a fixed number of pre-defined iterations. In essence, the goal of DFKD is to craft a lightweight student model $(S)$ by harnessing valuable knowledge from the well-trained Teacher model $(\mathcal{T})$ in the absence of training data. A general overview of Adversarial DFKD framework is illustrated in Figure 1. + +# 3.2. Goal of Data Free Knowledge Distillation + +A student model $(\mathcal{S})$ is trained to match the teacher's $(\mathcal{T})$ predictions on its unavailable target domain $(\mathcal{D}_{\mathcal{T}})$ as part of the distillation process. Let, $p_{\mathcal{S}}(x) = \mathrm{Softmax}(S_{\theta_{\mathcal{S}}}(\boldsymbol{x}))$ and $p_{\mathcal{T}}(x) = \mathrm{Softmax}(\mathcal{T}_{\theta_{\mathcal{T}}}(\boldsymbol{x})), \forall \boldsymbol{x} \in \mathcal{D}_{\mathcal{T}}$ , denote the predicted Student and Teacher probability mass across the classes, respectively. We seek to find the parameters $\theta_{\mathcal{S}}$ of the student model that will reduce the probability of errors $(P)$ between the predictions $p_{\mathcal{S}}(x)$ and $p_{\mathcal{T}}(x)$ : + +$$ +\min _ {\theta_ {\mathcal {S}}} P _ {x \sim \mathcal {D} _ {\mathcal {T}}} \left(\underset {i} {\arg \max } p _ {\mathcal {S}} ^ {i} (x) \neq \underset {i} {\arg \max } p _ {\mathcal {T}} ^ {i} (x)\right), +$$ + +where the superscript $i$ denotes the $i^{th}$ probability score of the predicted masses, $p_{\mathcal{S}}(x)$ and $p_{\mathcal{T}}(x)$ . + +![](images/f21f0eb3dc8f5cdfa052f7fd3a8f2e8cef9f021e8a953b191e32ab9df9126dde.jpg) +Figure 4. Learning evolution of the proposed method compared to the prior arts (MB-DFKD [3] and PRE-DFKD [2]) that employ replay. The plots visualize the Accuracy $(\%)$ evolution (top-row) and the Cumulative Mean Accuracy $(\%)$ evolution (bottom-row) of the ResNet-18 [15] student network on SVHN [23], CIFAR10 [17], CIFAR100 [17], and Tiny-ImageNet [18] datasets. The proposed method is plotted Blue. + +![](images/c5e13a2b1e0e4997a70b03ddd62de41c365be6b25724072d90291df3613b5e70.jpg) + +![](images/d8ca0364c0906518bbd95feb7bff7813f9b3b4e9c82ac3fc87a8189a8edbdf3c.jpg) + +![](images/db2b41357ee158e738a9b2e3f083eb598837c9bb8dd76458880511464e284d99.jpg) + +However, DFKD suggests minimizing the student's error on a pseudo dataset $\mathcal{D}_{\mathcal{P}}$ since the teacher's original training data distribution $\mathcal{D}_{\mathcal{T}}$ is not available. Typically, a loss function, say $\mathcal{L}_{KD}$ , that gauges disagreement between the teacher and the student is minimized $\forall \hat{x} \in \mathcal{D}_{\mathcal{P}}$ by optimizing the student parameters $(\theta_{S})$ : + +$$ +\underset {\theta_ {S}} {\min } \mathbb {E} _ {\hat {x} \sim \mathcal {D} _ {\mathcal {P}}} \left[ \mathcal {L} _ {K D} \left(\mathcal {T} _ {\theta_ {\mathcal {T}}} (\hat {x}), \mathcal {S} _ {\theta_ {\mathcal {S}}} (\hat {x})\right) \right]. \tag {1} +$$ + +We use the Mean Absolute Error (MAE) to define loss measure $(\mathcal{L}_{KD})$ as suggested in [10]. Suppose, given the predicted logits (pre-softmax predictions) $t(\hat{x}) = \mathcal{T}_{\theta_{\mathcal{T}}}(\hat{x})$ from the teacher model and the predicted logits $s(\hat{x}) = S_{\theta_S}(\hat{x})$ from the student model, we define $\mathcal{L}_{KD}$ as: + +$$ +\mathcal {L} _ {K D} (t (\hat {x}), s (\hat {x})) = \| t (\hat {x}) - s (\hat {x}) \| _ {1}. \tag {2} +$$ + +# 3.3. Learning to Retain while Acquiring + +Our novelty resides in the student update strategy, as described earlier. Knowledge-Retention and Knowledge-Acquisition relate to two independent goals and can therefore be considered as two discrete tasks. In fact, by aligning these two goals, we empirically observe that, they can cooperate to retain the knowledge on previously encountered examples while acquiring knowledge from newly generated samples. The proposed method utilizes a meta-learning-inspired optimization strategy to effectively replay the samples from memory, say $\mathcal{M}$ . + +In the typical Adversarial DFKD setup, the student update objective with the generated pseudo samples $(\hat{x})$ , i.e., the Knowledge-Acquisition task $(\mathcal{L}_{Acq})$ (Figure 2a), is formulated as: + +$$ +\min _ {\theta_ {S}} \mathcal {L} _ {A c q} (\theta_ {S}) = \min _ {\theta_ {S}} \mathbb {E} _ {\hat {x}} [ \mathcal {L} (\mathcal {T} _ {\theta_ {T}} (\hat {x}), \mathcal {S} _ {\theta_ {S}} (\hat {x})) ], \quad (3) +$$ + +where $\mathcal{L}$ is the MAE (2) between the teacher and the student logits, and $\hat{x} = \mathcal{G}(z), z \sim \mathcal{N}(0, I)$ , denotes the batch of randomly generated samples. + +Moreover, to alleviate the distribution drift during knowledge distillation in the adversarial setting, previous works have maintained a memory buffer, to store $\hat{x}$ and replay at later iterations to help the student recall the knowledge [3]. At a fixed frequency, batches of samples from current iterations are updated in the memory [3]. Also, recent works [2] have explored using the Generative Replay [27] strategy to simultaneously store the samples, as they are encountered, in + +a generative model (VAE [16]), and later replay by generating samples from the VAE decoder. Hence, the optimization objective for the Knowledge-Retention $(\mathcal{L}_{Ret})$ can be defined as follows: + +$$ +\begin{array}{r l} \underset {\theta_ {S}} {\min } \mathcal {L} _ {R e t} (\theta_ {S}) & = \underset {\theta_ {S}} {\min } \mathbb {E} _ {\hat {x} _ {m}} [ \mathcal {L} (\mathcal {T} _ {\theta_ {\tau}} (\hat {x} _ {m}), \mathcal {S} _ {\theta_ {S}} (\hat {x} _ {m})) ], \\ & \quad \hat {x} _ {m} \sim \mathcal {M}, \end{array} \tag {4} +$$ + +where, with a slight abuse of notation, $\mathcal{M}$ denotes the Memory Buffer or Generative Replay or any other replay scheme. Thus, the overall optimization objective to update $\theta_{\mathcal{S}}$ while considering both the new samples (generated from the latest $\theta_{\mathcal{G}}$ ) and the old samples (sampled from $\mathcal{M}$ ) (Figure 2b) is defined as: + +$$ +\min _ {\theta_ {S}} \mathcal {L} _ {A c q} \left(\theta_ {S}\right) + \mathcal {L} _ {R e t} \left(\theta_ {S}\right). \tag {5} +$$ + +However, the objective in (5) attempts to simultaneously optimize $\mathcal{L}_{Ret}$ and $\mathcal{L}_{Acq}$ but does not seek to align the objectives, which leaves them to potentially interfere with one another. Say, we denote the gradients of Knowledge Acquisition and Knowledge Retention tasks with $\nabla \mathcal{L}_{Acq}(\theta)$ and $\nabla \mathcal{L}_{Ret}(\theta)$ , respectively. If $\nabla \mathcal{L}_{Acq}(\theta)$ and $\nabla \mathcal{L}_{Ret}(\theta)$ point in a similar direction or are said to be aligned, i.e., $\nabla \mathcal{L}_{Acq}(\theta). \nabla \mathcal{L}_{Ret}(\theta) > 0$ , then taking a gradient step along $\nabla \mathcal{L}_{Acq}(\theta)$ or $\nabla \mathcal{L}_{Ret}(\theta)$ improves the model's performance on both tasks. This is however not the case when $\nabla \mathcal{L}_{Acq}(\theta)$ and $\nabla \mathcal{L}_{Ret}(\theta)$ point in different directions, i.e., $\nabla \mathcal{L}_{Acq}(\theta). \nabla \mathcal{L}_{Ret}(\theta) \leq 0$ , and the weight parameters obtained, may not be optimal for both the tasks simultaneously. Hence, the intended effect of having the gradient directions aligned is to obtain student parameters $(\theta_S)$ that have optimal performance on both $\mathcal{L}_{Acq}$ and $\mathcal{L}_{Ret}$ . + +The proposed meta-learning inspired approach, seeks to align the two tasks, $\mathcal{L}_{Acq}$ and $\mathcal{L}_{Ret}$ . We take cues from Model-Agnostic Meta-learning (MAML) [12], and adapt it to Adversarial DFKD. + +Typically, MAML-like methods [12, 13] are framed as a bi-level optimization [28] problem, where the objective is defined as: + +$$ +\min _ {\omega} \mathcal {L} _ {\text {o u t e r}} (\arg \min _ {\omega} \mathcal {L} _ {\text {i n n e r}} (\omega , \mathcal {D} _ {\text {t r a i n}}), \mathcal {D} _ {\text {t e s t}}), \tag {6} +$$ + +where $\omega$ indicates the initial parameters, $\mathcal{L}_{\mathrm{inner}}$ denotes the task specific inner loss (meta-train) and $\mathcal{L}_{\mathrm{outer}}$ denotes the + +outer loss (meta-test), and $\mathcal{D}_{\mathrm{train}}$ and $\mathcal{D}_{\mathrm{test}}$ denote the inner loop and the outer loop training example sets. + +Likewise, at each iteration of the student parameter $(\theta_S)$ update, we pose Knowledge-Acquisition and KnowledgeRetention as meta-train and meta-test, respectively. I.e., we perform a single gradient descent step using the current samples $(\hat{x})$ produced from the generator network $(\mathcal{G})$ , on the parameters $\theta_{S}$ and obtain $\theta_{S}^{\prime}$ as $\theta_S^\prime = \theta_S - \alpha \nabla \mathcal{L}_{Acq}(\theta_S)$ where $\nabla \mathcal{L}_{Acq}(\theta_S)$ denotes gradient of $\mathcal{L}_{Acq}$ at $\theta_S$ . Then we optimize on the batch of samples $(\hat{x}_m)$ obtained from the memory $(\mathcal{M})$ , with the parameters $\theta_S^\prime$ , and then make a final update on $\theta_S$ . Thus, formally, the overall meta-optimization objective, with the task of Knowledge Acquisition serving as meta-train and the task of Knowledge Retention as meta-test (Figure 2c), can be defined as follows: + +$$ +\min _ {\theta_ {S}} \mathcal {L} _ {A c q} (\theta_ {S}) + \mathcal {L} _ {R e t} (\theta_ {S} ^ {\prime}) = \min _ {\theta_ {S}} \mathcal {L} _ {A c q} (\theta_ {S}) + +$$ + +$$ +\mathcal {L} _ {R e t} \big (\theta_ {\mathcal {S}} - \alpha \nabla \mathcal {L} _ {A c q} (\theta_ {\mathcal {S}}) \big). (7) +$$ + +# 3.4. How does the Proposed Student Update Strategy Promote Alignment? + +In this subsection, we analyze the proposed objective (7) to understand how it results in the desired alignment between the Knowledge-Acquisition and Knowledge-Retention tasks. We assume that meta-train and meta-test tasks provide us with losses $\mathcal{L}_{Acq}$ and $\mathcal{L}_{Ret}$ ; in our case, $\mathcal{L}_{Acq}$ and $\mathcal{L}_{Ret}$ are the same function computed on the batches $\hat{x}$ and $\hat{x}_m$ , respectively. We utilize Taylor's expansion to elaborate the gradient of $\mathcal{L}_{Ret}$ at a point $\theta$ displaced by $\phi_{\theta}$ , as described in Lemma 1. + +Lemma 1. If $\mathcal{L}_{Ret}$ has Lipschitz, Hessian, i.e., $\| \nabla^2\mathcal{L}_{Ret}(\theta_1) - \nabla^2\mathcal{L}_{Ret}(\theta_2)\|\leq \rho \| \theta_1 - \theta_2\|$ for some $\rho >0$ , then: + +$$ +\begin{array}{l} \nabla \mathcal {L} _ {R e t} (\theta + \phi_ {\theta}) = \nabla \mathcal {L} _ {R e t} (\theta) + \nabla^ {2} \mathcal {L} _ {R e t} (\theta) \phi_ {\theta} \\ + \mathcal {O} (\| \phi_ {\theta} \| ^ {2}). \\ \end{array} +$$ + +For instance, when $\phi_{\theta} = -\alpha \nabla \mathcal{L}_{Acq}(\theta)$ , we have, + +$$ +\begin{array}{l} \nabla \mathcal {L} _ {R e t} (\theta - \alpha \nabla \mathcal {L} _ {A c q} (\theta)) = \nabla \mathcal {L} _ {R e t} (\theta) \\ - \alpha \nabla^ {2} \mathcal {L} _ {R e t} (\theta) \nabla \mathcal {L} _ {A c q} (\theta) \\ + \mathcal {O} (\alpha^ {2}). \\ \end{array} +$$ + +Theorem 1. If $\theta' = \theta - \alpha \nabla \mathcal{L}_{Acq}(\theta)$ , denotes a single gradient descent step on $\theta$ with the objective $\mathcal{L}_{Acq}(\theta)$ , where $\alpha$ is a scalar, and $\nabla \mathcal{L}_{Acq}(\theta)$ denotes the gradient of the objective at $\theta$ , then: + +$$ +\begin{array}{l} \frac {\partial \mathcal {L} _ {R e t} \left(\theta^ {\prime}\right)}{\partial \theta} = \nabla \mathcal {L} _ {R e t} (\theta) - \alpha \nabla^ {2} \mathcal {L} _ {R e t} (\theta). \nabla \mathcal {L} _ {A c q} (\theta) \\ - \alpha \nabla^ {2} \mathcal {L} _ {A c q} (\theta). \nabla \mathcal {L} _ {R e t} (\theta) + \mathcal {O} (\alpha^ {2}). \\ \end{array} +$$ + +Proof. Please refer to the Supplemental Material. + +While optimizing the objective defined in (7) using stochastic gradient descent, we would need to compute the gradient of the $\mathcal{L}_{Ret}(\theta_S^{\prime})$ w.r.t $\theta_{S}$ . Therefore, utilizing Theorem 1 we express $\frac{\partial\mathcal{L}_{Ret}(\theta_S^{\prime})}{\partial\theta_S}$ as: + +$$ +\begin{array}{l} \frac {\partial \mathcal {L} _ {R e t} \left(\theta_ {\mathcal {S}} ^ {\prime}\right)}{\partial \theta_ {\mathcal {S}}} = \nabla \mathcal {L} _ {R e t} \left(\theta_ {\mathcal {S}}\right) \\ - \alpha \nabla^ {2} \mathcal {L} _ {R e t} (\theta_ {\mathcal {S}}). \nabla \mathcal {L} _ {A c q} (\theta_ {\mathcal {S}}) \\ - \alpha \nabla^ {2} \mathcal {L} _ {A c q} (\theta_ {\mathcal {S}}). \nabla \mathcal {L} _ {R e t} (\theta_ {\mathcal {S}}) + \mathcal {O} (\alpha^ {2}), \tag {8} \\ \end{array} +$$ + +using the product rule $\nabla a.b + \nabla b.a = \nabla (a.b)$ , we get: + +$$ +\begin{array}{l} \frac {\partial \mathcal {L} _ {R e t} \left(\theta_ {\mathcal {S}} ^ {\prime}\right)}{\partial \theta_ {\mathcal {S}}} = \nabla \mathcal {L} _ {R e t} \left(\theta_ {\mathcal {S}}\right) \\ - \alpha \nabla \underbrace {(\nabla \mathcal {L} _ {\text {R e t}} (\theta_ {\mathcal {S}}) . \nabla \mathcal {L} _ {\text {A c q}} (\theta_ {\mathcal {S}}))} _ {\text {G r a d i e n t A l i g n m e n t}} + \mathcal {O} \left(\alpha^ {2}\right). \tag {9} \\ \end{array} +$$ + +From the analysis above, we observe that the gradient of $\mathcal{L}_{Ret}(\theta_S')$ at $\theta_S$ (in (9)) produces the gradient of the gradient-product. This indicates, when optimizing $\mathcal{L}_{Ret}(\theta_S')$ (in (7)), the gradient of $\mathcal{L}_{Ret}(\theta_S')$ at $\theta_S$ , has a negatively scaled gradient of the gradient-product term, i.e., $\nabla (\nabla \mathcal{L}_{Ret}(\theta_S). \nabla \mathcal{L}_{Acq}(\theta_S))$ (derived in (9)), suggesting that the overall-gradients minimize $\mathcal{L}_{Ret}(\theta_S)$ and maximize $\nabla \mathcal{L}_{Ret}(\theta_S). \nabla \mathcal{L}_{Acq}(\theta_S)$ . Hence, optimizing (7) enforces the updates on $\mathcal{L}_{Ret}(\theta_S)$ and $\mathcal{L}_{Acq}(\theta_S)$ to seek a common direction, by maximizing the gradient-product. Moreover, we empirically observe this phenomenon in Section A of the Supplemental Material. + +# 3.5. Generator Update in Adversarial Exploration-based DFKD + +In the absence of the training dataset $(\mathcal{D}_{\mathcal{T}})$ , the generator $(\mathcal{G})$ is utilized to obtain pseudo-samples $(\hat{x})$ and perform knowledge-distillation, i.e., $\hat{x} = \mathcal{G}(z)$ , $z \sim \mathcal{N}(0, I)$ . The generator is learned to maximize the disagreement between Teacher network $(\mathcal{T}_{\theta_{\mathcal{T}}})$ and the Student network $(S_{\theta_S})$ . Additionally, for the generated data $\hat{x}$ to mimic similar responses from the teacher as the real data, we include a prior loss $\mathcal{L}_{\mathcal{P}}$ [4] to be minimized alongside maximizing the discrepancy $(D)$ . Hence, we update the generator parameters $(\theta_{\mathcal{G}})$ by maximizing the following objective: + +$$ +\begin{array}{l} \max _ {\theta_ {\mathcal {G}}} \mathbb {E} _ {z \sim \mathcal {N} (0, I)} [ D (\mathcal {T} _ {\theta_ {\mathcal {T}}} (\mathcal {G} _ {\theta_ {\mathcal {G}}} (z)), \mathcal {S} _ {\theta_ {\mathcal {S}}} (\mathcal {G} _ {\theta_ {\mathcal {G}}} (z))) \\ \left. - \mathcal {L} _ {\mathcal {P}} \left(\mathcal {G} _ {\theta_ {\mathcal {G}}} (z)\right) \right]. \tag {10} \\ \end{array} +$$ + +Typically the disagreement function $(D)$ for the generator is identical to the teacher-student disagreement term [5, 10]. Instead, for teacher-student discrepancy maximization we use the Jensen-Shannon (JS) $(\mathcal{L}_{JS})$ divergence. Our motivation to use JS divergence is based on empirical study by Binici et + +Table 1. Distillation results of the Adversarial DFKD methods on four image classification benchmark datasets, SVHN [23], CIFAR10 [17], CIFAR100 [17], Tiny-ImageNet [18]. Primarily we compare against the replay-based methods, present at the bottom panel of each dataset. The best numbers from our method on the Bank-based replay are highlighted in Navy, and for the one with Generative replay are highlighted in Maroon. The first five columns represent the $\mu[S_{Acc}]$ and the $\sigma^2[S_{Acc}]$ at different epoch percentiles (described in Section 4.1). The last column represents the maximum test accuracy ( $\mathrm{Acc}_{\max}$ ) attained by the student network in the entire training period. Note: For SVHN and Tiny-ImageNet we were unable to reproduce the results of DFQ [5] in our setting. DFKD* denotes the baseline method without any replay. + +
Method>0thPercentile>20thPercentile>40thPercentile>60thPercentile>80thPercentileAccmax(%)
μ↑σ2↓μ↑σ2↓μ↑σ2↓μ↑σ2↓μ↑σ2↓
SVHN [23]
TAcc=97.45%, SAcc=97.26%, (Emax)=200
DAFL [4]85.15241.7090.498.7191.753.7392.880.8493.590.2094.23
DFAD [10]92.5293.4294.100.0794.130.0794.140.0894.110.0794.68
DFKD* [3]91.848.5992.536.7693.434.3994.611.7295.720.0495.97
MB-DFKD [3]89.5121.1389.2824.3188.6530.6387.1438.6781.6715.2494.11
PRE-DFKD [2]94.082.1194.531.1194.970.6695.420.2695.850.0496.10
Ours-1 (Memory Bank)92.787.4493.692.2194.251.5494.940.6295.590.0595.88
Ours-2 (Generative Replay)94.481.6494.790.7195.090.5195.470.2795.910.0396.15
CIFAR10 [17]
TAcc=95.72%, SAcc=95.23%, (Emax)=200
DAFL [4]62.78629.1372.51296.4480.57110.1887.0522.4090.762.0492.23
DFAD [10]85.88152.3989.9111.9591.465.6692.660.0792.850.0493.21
DFQ [5]81.2598.8684.4137.4087.0819.5989.726.1591.870.6792.90
DFKD* [3]83.57107.7786.9418.4288.6711.8390.674.0992.320.3893.02
MB-DFKD [3]84.2995.7487.2516.4788.8111.0090.713.7992.310.3593.03
PRE-DFKD [2]87.1045.6988.8810.8690.246.2691.751.8492.920.1493.41
Ours-1 (Memory Bank)85.5391.3388.5812.6689.968.2691.662.8693.090.3293.73
Ours-2 (Generative Replay)88.0760.8690.525.2091.443.1892.451.4593.480.1894.02
CIFAR100 [17]
TAcc=77.94%, SAcc=77.10%, (Emax)=400
DAFL [4]52.48437.8861.6582.9665.8832.2369.2311.9372.231.2973.78
DFAD [10]59.62192.6465.3228.8467.712.7268.690.3369.070.2469.73
DFQ [5]68.2055.9170.459.7871.775.8873.192.2274.480.4775.39
DFKD* [3]69.3674.6772.358.3473.594.5474.861.6375.940.1876.51
MB-DFKD [3]66.05207.2971.1614.6772.975.6174.401.5675.480.1876.14
PRE-DFKD [2]70.2386.6373.396.7774.592.8875.601.0376.460.1276.93
Ours-1 (Memory Bank)69.8775.6772.968.7274.304.0375.491.3876.500.2077.11
Ours-2 (Generative Replay)71.4960.1774.164.6175.122.2676.010.7476.750.0977.21
Tiny-ImageNet [18]
TAcc=60.83%, SAcc=57.88%, (Emax)=500
DAFL [4]21.04106.0424.9552.5628.1028.2231.1911.4434.111.3335.46
DFAD [10]14.6022.3216.487.4817.761.4318.440.2618.840.1019.60
DFKD* [3]34.5586.2038.1723.8840.2813.2842.405.3244.380.7945.61
MB-DFKD [3]34.16122.9238.7732.4241.1818.4043.638.4846.171.7347.96
PRE-DFKD [2]38.8980.1242.2721.6644.2512.6146.315.4748.331.3949.94
Ours-1 (Memory Bank)36.3494.6240.0326.2042.2813.7644.435.8246.520.9547.96
Ours-2 (Generative Replay)39.0974.9542.3021.2344.1913.5546.306.2148.481.1249.88
+ +al. [3]. Hence, $D$ is defined as: + +$$ +D (a, b) = \mathcal {L} _ {J S} (p (a), p (b)), +$$ + +$\mathcal{L}_{JS}(p(a),p(b)) = \frac{1}{2} (\mathcal{L}_{KL}(p(a),m) + \mathcal{L}_{KL}(p(b),m)),$ and + +$$ +m = \frac {1}{2} (p (a) + p (b)). \tag {11} +$$ + +Here $\mathcal{L}_{KL}$ stands for the Kullback-Leibler divergence and $p(a)$ and $p(b)$ denote the probability vectors obtained after the Softmax applied to the arguments $a$ and $b$ , respectively. + +Moreover, the prior loss $\mathcal{L}_{\mathcal{P}}$ [3] is defined as the combination of three loss functions $(\mathcal{L}_{OH},\mathcal{L}_A,$ and $\mathcal{L}_{EM})$ that capture different characteristics from the teacher model and + +impose them on the pseudo samples $\hat{x}$ , and is defined as: + +$$ +\mathcal {L} _ {\mathcal {P}} = \mathcal {L} _ {O H} + \gamma \mathcal {L} _ {A} + \delta \mathcal {L} _ {E M}, \tag {12} +$$ + +where, $\gamma$ and $\delta$ denote the weighing scalar coefficients. $\mathcal{L}_{OH}$ is the one-hot loss that forces the generated samples to have strong (one-hot vector like) predictions when input to the teacher. It is defined as the cross-entropy between the teacher's softmax output $p_{\mathcal{T}}(\hat{x}_n) =$ Softmax $(\mathcal{T}_{\theta_{\mathcal{T}}}(\hat{x}_n)),\hat{x}_n\in \hat{x},$ and its one-hot vector version $e_c\in \{0,1\} ^C$ , where $C$ denotes the total number of classes and $e_c$ denotes the $c$ -th canonical one-hot-vector, and $c = \arg \max_{i}(p_{\mathcal{T}}^{i}(\hat{x}_{n}))$ , the superscript $i$ denotes the $i^{th}$ probability score of the predicted mass vector $p_{\mathcal{T}}(\hat{x}_n)$ + +![](images/e74ff2b99751b2493465e668142b2c160f70f867c469eb407aff79f22b152a47.jpg) +Figure 5. Student learning curves depicting the learning evolution of Wide-ResNet (WRN) [32]. The WRN-16-1 (top-row), and WRN-16-2 (bottom-row) networks are distilled by a WRN-40-2 teacher network pre-trained on CIFAR10 ( $\mathcal{T}_{Acc} = 94.87\%$ ). Each column represents the learning curves with the Buffer-based (with different memory buffer sizes) and Generative replay schemes. The proposed method is in Blue. + +Hence, $\mathcal{L}_{OH}$ is defined as: + +$$ +\mathcal {L} _ {O H} = - \frac {1}{N} \sum_ {n = 1} ^ {N} e _ {c} ^ {\top} \log \left(p _ {\mathcal {T}} \left(\hat {x} _ {n}\right)\right). \tag {13} +$$ + +- $\mathcal{L}_A$ is the activation loss motivated by the notion that meaningful inputs result in higher-valued activation maps in a well-trained network [4] and is defined as: + +$$ +\mathcal {L} _ {A} = - \frac {1}{N L} \sum_ {n = 1} ^ {N} \sum_ {l = 1} ^ {L} \| \mathcal {A} _ {\mathcal {T}} ^ {l} (\hat {x} _ {n}) \| _ {1}, \tag {14} +$$ + +where $\mathcal{A}_{\mathcal{T}}^{l}(\hat{x}_{n})$ denotes the activation of the $l$ -th layer in the Teacher network $(\mathcal{T}_{\theta \tau})$ for the input $n^{th}$ input $\hat{x}_n\in \hat{x}$ . + +- $\mathcal{L}_{EM}$ loss is the entropy-maximization term imposed on the generator to output an equal number of pseudo-samples from each category [5]. In other words, the intra-batch entropy is maximized, resulting in a similar number of samples for each category i.e. if $\bar{p}_{\mathcal{T}} = \frac{1}{N}\sum_{n=1}^{N}p_{\mathcal{T}}(\hat{x}_n)$ , then the loss $\mathcal{L}_{EM}$ is defined as: + +$$ +\mathcal {L} _ {E M} = \bar {p} _ {\mathcal {T}} ^ {\top} \log (\bar {p} _ {\mathcal {T}}). \tag {15} +$$ + +In sum, the Generator loss objective $(\mathcal{L}_{\mathcal{G}})$ is defined as: + +$$ +\mathcal {L} _ {\mathcal {G}} \left(\theta_ {\mathcal {G}}\right) = - D \left(\mathcal {T} _ {\theta_ {\mathcal {T}}} \left(\mathcal {G} _ {\theta_ {\mathcal {G}}} (z)\right), \mathcal {S} _ {\theta_ {\mathcal {S}}} \left(\mathcal {G} _ {\theta_ {\mathcal {G}}} (z)\right)\right) + \mathcal {L} _ {\mathcal {P}} \left(\mathcal {G} _ {\theta_ {\mathcal {G}}} (z)\right). \tag {16} +$$ + +# 4. Experiments + +# 4.1. Experimental Settings + +Datasets: We evaluate the proposed method on SVHN [23], CIFAR10 [17], CIFAR100 [17] and Tiny-ImageNet [18] datasets. + +Teacher model: For CIFAR10 and CIFAR100 datasets, we used the pre-trained teacher models made available by the authors of [11] and [3], respectively. For SVHN and TinyImageNet we trained the teacher network from scratch. We provide the training details of the teacher models in Section B.1 of the Supplemental Material. + +Definition of an Epoch in DFKD: In Adversarial DFKD, + +the notion of an epoch is obscure. In a typical deep neural network-based classification training, an epoch is defined as one complete pass over the available training data. However, DFKD has no access to the training data; instead, the pseudo samples generated on-the-fly are used to distill knowledge to the student network. Therefore, prior works [3,5,10] defined an epoch in terms of a fixed number of training iterations $(\mathcal{I})$ where each iteration consists of a set number of generator update steps $(g)$ and student update steps $(s)$ . Hence, to be consistent across the baselines and prior arts, we use the same number of training iterations, generator update steps, and student updates steps to define an epoch. For all the methods, we set $\mathcal{I} = 72$ , $g = 1$ , and $s = 10$ and use a batch size of 512 of the sampled noise $(z)$ to generate the pseudo samples and optimize the parameters $\theta_{\mathcal{G}}$ and $\theta_{\mathcal{S}}$ . + +Training Details: The training details are provided in Section B of the Supplemental Material. + +Evaluation: We evaluate the methods by comparing the mean and variance of the student network's test accuracy $(S_{Acc})$ , denoting them as $\mu[S_{Acc}]$ , and $\sigma^2[S_{Acc}]$ , respectively, across the epochs, motivated by Binci et al. [2]. Specifically, we compare the different sections of the student's learning evolution by partitioning them into different epoch percentiles. For example, computing the $\mu[S_{Acc}]$ and $\sigma^2[S_{Acc}]$ for epochs greater than the $n^{th}$ percentile conveys the mean and variance across all the epochs greater than the $\frac{n}{100}^{th}$ of the total number of training epochs. + +# 4.2. Results and Observations + +Baseline and State-of-the-art Comparisons In Table 1, we analyze our method on classification task and compare it with prior Adversarial DFKD methods [4,5,10] and closely related memory-based Adversarial DFKD methods [2,3]. For a fair comparison across the methods, we re-implemented all the methods and used the same generator architecture to generate the pseudo samples. For each of the methods we report the $\mu[S_{Acc}]$ and $\sigma^2[S_{Acc}]$ at different epoch percentiles and the maximum accuracy ( $\mathrm{Acc}_{\max}$ ) attained by the student. We observe that, compared to MB-DFKD (Memory Bank) [3], Ours-1 (Memory Bank) demonstrates consistent improvement across all the datasets. Similarly, compared to PRE-DFKD (Generative Replay) [2], utilizing the same VAE + +
ZSKDa[22]ADIb[30]CIMb[11]DeGANc[1]EATSKDc[21]KEGNETa[31]ZSKTb[20]DDADd[34]DAFLd[4]DFAdd[10]DFQd[5]MB-DFKDd[3]PRE-DFKDd[2]Ours-IOurs-2
TAcc(%)77.5078.0578.0577.9477.9477.5078.0577.5077.9477.9477.9477.9477.9477.9477.94
SAcc(%)70.2161.3277.0465.2567.1873.9167.7475.0473.7969.7375.3976.1476.9377.1177.21
ΔTAcc-SAcc7.2916.731.0112.6910.763.5910.312.464.158.212.551.801.010.830.73
+ +Table 2. Classification accuracy (in %) of the student trained using various DFKD methods on CIFAR100 with ResNet-34 [15] as the teacher and ResNet-18 [15] as the student. $\mathcal{T}_{Acc}$ and $S_{Acc}$ denote the Teacher network's and the Student network's accuracy, respectively, and $\Delta \mathcal{T}_{Acc} - \mathcal{S}_{Acc}$ denotes the difference between the Teacher and Student accuracies. Also, $^{\mathrm{a}},^{\mathrm{b}},^{\mathrm{c}}$ and $^{\mathrm{d}}$ denote results produced by [34], [11], [3], and our implementation, respectively. + +decoder architecture as the generative replay, we observe a similar trend for Ours-2 (Generative Replay). + +Moreover, in Figure 4, we visualize the learning curves of the student networks trained on multiple datasets. We plot the test accuracy of the student at the end of each training epoch. The proposed method exhibits significant improvement in the learning evolution and the peak accuracies achieved, suggesting that the proposed approach can retain the knowledge from previously encountered samples as the learning progresses. However, on Tiny-ImageNet, with Generative Replay, we did not observe substantial improvements; we conjecture that this may be due to the complexity of the dataset, and the inability to capture crucial samples as replay for the complex dataset, for a large number of epochs. Also, with Generative Replay we sometimes faced difficulty in training a VAE on a stream of synthetic samples (especially for complex dataset like Tiny-ImageNet) as it suffers due to the distribution drift of its own. + +Additionally, we emphasize that we do not strive toward achieving state-of-the-art student classification accuracy (requiring extensive hyper-parameter tuning) in the DFKD setting, but verify the viability of our hypothesis of retaining the previously acquired knowledge while learning on new samples. Nonetheless, we observe that our method improves upon the student classification accuracy on CIFAR100 [17] compared to the contemporary works and the current state-of-the-art [2] with the ResNet-34 [15] $(\mathcal{T})$ and ResNet-18 [15] $(S)$ setup, as shown in Table 2. Additionally, since previous works use the same teacher network with different test accuracies, we also report the teacher accuracies of the respective methods used to distill the knowledge to the student. Nonetheless, we also compute the Teacher-Student accuracy difference $(\Delta_{\mathcal{T}_{Acc} - \mathcal{S}_{Acc}})$ to assess the distance of the student from its teacher in terms of classification accuracy. + +Interpreting the Result: Because of the proposed student update strategy, we observe a global monotonicity in the student's learning evolution which the existing approaches with naive replay [2, 3] claim, but do not warrant (described in Section 3.3). The global monotonicity in the learning evolution encompasses crucial advantages. For example, when the validation data is unavailable, the developer cannot assess the student's accuracy and identify the best parameters for the student. In such a scenario, the final accuracy is dependent on the random termination epoch set by the developer. In other words, the ideal DFKD approach should sustain high accuracy via monotonically enhancing it during the course of distillation. Therefore, $\mu[S_{Acc}]$ and $\sigma^2[S_{Acc}]$ contribute as crucial metrics to assess the distillation method as opposed to the maximum accuracy ( $\mathrm{Acc}_{\max}$ ), since the $\mathrm{Acc}_{\max}$ value can + +be attained at any point of time prior to the termination, and can be misleading. The improvements in the monotonicity and the $\mu [S_{Acc}]$ and $\sigma^2 [S_{Acc}]$ values of proposed method are evident from Table 1, Figure 4 and Figure 5. + +Architecture Scalability: The proposed student update strategy is generic and is scalable across different neural network architecture families since the method is not constrained to a particular choice of architecture. From the ResNet-18 [15], WRN-16-1 [32] and WRN-16-2 [32] student learning curves (in Figure 4 and Figure 5), we observe our method's advantage on both the network architectures. Moreover, for large student network architectures (Deeper or Wider) that include complex layers, the proposed method efficiently handles the intricacies with regard to computing the Hessian and the Hessian-product, which becomes highly essential for cumbersome models. + +Improvement across Replay Schemes: Furthermore, the proposed method is agnostic to the memory scheme employed for replay, as demonstrated by the experiments (in Table 1, Figure 4 and Figure 5) using a Memory Buffer and Generative-Replay, thus, rendering our method generalized to the choice of replay. In Table 1 and Figure 5, we can observe that the proposed method enhances the student's performance on both the replay schemes (Memory Bank and Generative Replay) used in the prior arts. Moreover, we experiment with different memory buffer sizes on WRN-16-1 [32] and WRN-16-2 [32] distillation (in Figure 5) and observe consistent and substantial improvements across different memory sizes. Here, the memory size is defined as the maximum number of pseudo-example batches that the bank can contain and each batch consists of randomly sampled 64 examples from $\hat{x}$ . + +# 5. Conclusion + +In this paper, we proposed a meta-learning inspired student update strategy for the Adversarial DFKD setting, that treats Knowledge-Acquisition and Knowledge-Retention as meta-train and meta-test, respectively. The proposed strategy substantially improves the learning evolution of the student network by implicitly aligning the Knowledge-Retention and the Knowledge-Acquisition tasks. The intended effect of having the gradient directions aligned is to obtain student parameters $(\theta_{S})$ that have optimal performance on both $\mathcal{L}_{Acq}$ and $\mathcal{L}_{Ret}$ . The conducted experiments on multiple datasets, network architectures, and replay schemes demonstrate the effectiveness, scalability and generalizability of the proposed strategy. + +Acknowledgement: Work partially supported by NIH. + +# References + +[1] Sravanti Addepalli, Gaurav Kumar Nayak, Anirban Chakraborty, and Venkatesh Babu Radhakrishnan. Degan: Data-enriching gan for retrieving representative samples from a trained classifier. In AAAI, 2020. 8 +[2] Kuluhan Binici, Shivam Aggarwal, Nam Trung Pham, Karianto Leman, and Tulika Mitra. Robust and resource-efficient data-free knowledge distillation by generative pseudo replay. In AAAI, 2022. 2, 3, 4, 6, 7, 8 +[3] Kuluhan Binici, Nam Trung Pham, Tulika Mitra, and Kario +Leman. Preventing catastrophic forgetting and distribution +mismatch in knowledge distillation via synthetic data. In +WACV, 2022. 2, 3, 4, 6, 7, 8 +[4] Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, and Qi Tian. Dafi: Data-free learning of student networks. In ICCV, 2019. 1, 2, 5, 6, 7, 8 +[5] Yoojin Choi, Jihwan Choi, Mostafa El-Khamy, and Jungwon Lee. Data-free network quantization with adversarial knowledge distillation. In CVPR Workshops, 2020. 2, 3, 5, 6, 7, 8 +[6] Yatin Dandi, Luis Barba, and Martin Jaggi. Implicit gradient alignment in distributed and federated learning. In AAAI, 2022. 2 +[7] Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars. A continual learning survey: Defying forgetting in classification tasks. IEEE TPAMI, 44(7):3366-3385, 2021. 2 +[8] Kien Do, Hung Le, Dung Nguyen, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, and Svetha Venkatesh. Momentum adversarial distillation: Handling large distribution shifts in data-free knowledge distillation. In NeurIPS, 2022. 3 +[9] Gongfan Fang, Kanya Mo, Xinchao Wang, Jie Song, Shitao Bei, Haofei Zhang, and Mingli Song. Up to 100x faster data-free knowledge distillation. In AAAI, 2022. 3 +[10] Gongfan Fang, Jie Song, Chengchao Shen, Xinchao Wang, Da Chen, and Mingli Song. Data-free adversarial distillation. In CVPR, 2020. 1, 2, 3, 4, 5, 6, 7, 8 +[11] Gongfan Fang, Jie Song, Xinchao Wang, Chen Shen, Xingen Wang, and Mingli Song. Contrastive model inversion for data-free knowledge distillation. In IJCAI, 2021. 1, 7, 8 +[12] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, 2017. 4 +[13] Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil. Bilevel programming for hyperparameter optimization and meta-learning. In ICML, 2018. 4 +[14] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. Generative adversarial nets. In NeurIPS, 2014. 3 +[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016. 4, 8 +[16] Diederik P Kingma, Max Welling, et al. An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4):307-392, 2019. 4 + +[17] Alex Krizhevsky et al. Learning multiple layers of features from tiny images. 2009. 4, 6, 7, 8 +[18] Ya Le and Xuan S. Yang. Tiny imagenet visual recognition challenge. 2015. 4, 6, 7 +[19] Yang Liu, Wei Zhang, and Jun Wang. Zero-shot adversarial quantization. In CVPR, 2021. 1 +[20] Paul Micaelli and Amos J Storkey. Zero-shot knowledge transfer via adversarial belief matching. In NeurIPS, 2019. 1, 2, 3, 8 +[21] Gaurav Kumar Nayak, Konda Reddy Mopuri, and Anirban Chakraborty. Effectiveness of arbitrary transfer sets for data-free knowledge distillation. In WACV, 2021. 8 +[22] Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, and Anirban Chakraborty. Zero-shot knowledge distillation in deep networks. In ICML, 2019. 1, 8 +[23] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng. Reading digits in natural images with unsupervised feature learning. In NeurIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011. 4, 6, 7 +[24] Mert Bulent Sariyildiz and Ramazan Gokberk Cinbis. Gradient matching generative networks for zero-shot learning. In CVPR, 2019. 2 +[25] Ari Seff, Alex Beatson, Daniel Suo, and Han Liu. Continual learning in generative adversarial nets. arXiv preprint arXiv:1705.08395, 2017. 1, 3 +[26] Yuge Shi, Jeffrey Seely, Philip Torr, Siddharth N, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve. Gradient matching for domain generalization. In ICLR, 2022. 2 +[27] Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim. Continual learning with deep generative replay. In NeurIPS, 2017. 2, 3, 4 +[28] Ankur Sinha, Pekka Malo, and Kalyanmoy Deb. A review on bilevel optimization: From classical to evolutionary approaches and applications. IEEE TEC, 22(2):276-295, 2018. 4 +[29] Hoang Thanh-Tung and Truyen Tran. Catastrophic forgetting and mode collapse in gans. In IJCNN, 2020. 1 +[30] Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz. Dreaming to distill: Data-free knowledge transfer via deepinversion. In CVPR, 2020. 1, 8 +[31] Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang. Knowledge extraction with no observable data. In NeurIPS, 2019. 8 +[32] Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. In BMVC, 2016. 7, 8 +[33] Sergey Zagoruyko and Nikos Komodakis. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. In ICLR, 2017. 3 +[34] Haoran Zhao, Xin Sun, Junyu Dong, Milos Manic, Huiyu Zhou, and Hui Yu. Dual discriminator adversarial distillation for data-free model compression. International Journal of Machine Learning and Cybernetics, 2022. 8 \ No newline at end of file diff --git a/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/images.zip b/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..3ae233a571fcec384c8168dbfd4bf461673b837d --- /dev/null +++ b/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bbe648abf28c72132ae74eb5414b37cb18d115d2ddcfa684b1d7bc250aea4a7a +size 720730 diff --git a/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/layout.json b/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..a5779d09bdd20d74de6b1423c75b9d4d50605680 --- /dev/null +++ b/2023/Learning To Retain While Acquiring_ Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation/layout.json @@ -0,0 +1,11985 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 83, + 95, + 509, + 129 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 95, + 509, + 129 + ], + "spans": [ + { + "bbox": [ + 83, + 95, + 509, + 129 + ], + "type": "text", + "content": "Learning to Retain while Acquiring: Combating Distribution-Shift in Adversarial Data-Free Knowledge Distillation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 140, + 151, + 452, + 194 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 140, + 151, + 452, + 194 + ], + "spans": [ + { + "bbox": [ + 140, + 151, + 452, + 194 + ], + "type": "text", + "content": "Gaurav Patel† Konda Reddy Mopuri‡ Qiang Qiu† \n†Purdue University ‡Indian Institute of Technology Hyderabad \n{gpatel10, qqiu}@purdue.edu, krmpuri@ai.iith.ac.in" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 143, + 203, + 192, + 216 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 203, + 192, + 216 + ], + "spans": [ + { + "bbox": [ + 143, + 203, + 192, + 216 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 228, + 290, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 228, + 290, + 491 + ], + "spans": [ + { + "bbox": [ + 46, + 228, + 290, + 491 + ], + "type": "text", + "content": "Data-free Knowledge Distillation (DFKD) has gained popularity recently, with the fundamental idea of carrying out knowledge transfer from a Teacher neural network to a Student neural network in the absence of training data. However, in the Adversarial DFKD framework, the student network's accuracy, suffers due to the non-stationary distribution of the pseudo-samples under multiple generator updates. To this end, at every generator update, we aim to maintain the student's performance on previously encountered examples while acquiring knowledge from samples of the current distribution. Thus, we propose a meta-learning inspired framework by treating the task of Knowledge-Acquisition (learning from newly generated samples) and KnowledgeRetention (retaining knowledge on previously met samples) as meta-train and meta-test, respectively. Hence, we dub our method as Learning to Retain while Acquiring. Moreover, we identify an implicit aligning factor between the KnowledgeRetention and Knowledge-Acquisition tasks indicating that the proposed student update strategy enforces a common gradient direction for both tasks, alleviating interference between the two objectives. Finally, we support our hypothesis by exhibiting extensive evaluation and comparison of our method with prior arts on multiple datasets." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 511, + 128, + 523 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 511, + 128, + 523 + ], + "spans": [ + { + "bbox": [ + 47, + 511, + 128, + 523 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 531, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 531, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 531, + 288, + 714 + ], + "type": "text", + "content": "The primary goal of Data-Free Knowledge Distillation (DFKD) is to acquire a trustworthy alternative dataset for knowledge distillation. The quality of knowledge transfer depends on the caliber of these alternative samples. Noise optimization [11,22,30] and generative reconstruction [4,10,20] are the two primary ways to replace the original training data used in the distillation process with synthetic or pseudo samples. Adversarial DFKD methods [10, 19, 20] investigate an adversarial exploration framework to seek pseudo-samples. In such a paradigm, the Generator " + }, + { + "bbox": [ + 46, + 531, + 288, + 714 + ], + "type": "inline_equation", + "content": "(\\mathcal{G})" + }, + { + "bbox": [ + 46, + 531, + 288, + 714 + ], + "type": "text", + "content": " is used to create pseudo-samples as surrogates to perform knowledge distillation/transfer, and the Teacher-Student " + }, + { + "bbox": [ + 46, + 531, + 288, + 714 + ], + "type": "inline_equation", + "content": "(\\mathcal{T} - \\mathcal{S})" + }, + { + "bbox": [ + 46, + 531, + 288, + 714 + ], + "type": "text", + "content": " setup acts as a joint discriminator to penalize and update generator parameters " + }, + { + "bbox": [ + 46, + 531, + 288, + 714 + ], + "type": "inline_equation", + "content": "(\\theta_{\\mathcal{G}})" + }, + { + "bbox": [ + 46, + 531, + 288, + 714 + ], + "type": "text", + "content": " (Figure 1). In the adversarial framework, the generator explores the input space to find suitable pseudo-samples as the distillation progresses. Consequently, the distribution" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 308, + 202, + 545, + 399 + ], + "blocks": [ + { + "bbox": [ + 308, + 202, + 545, + 399 + ], + "lines": [ + { + "bbox": [ + 308, + 202, + 545, + 399 + ], + "spans": [ + { + "bbox": [ + 308, + 202, + 545, + 399 + ], + "type": "image", + "image_path": "38262ac8e5b86430a7c092645f1c0a63db0989533a50a7629ef2627bec970671.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 405, + 548, + 500 + ], + "lines": [ + { + "bbox": [ + 304, + 405, + 548, + 500 + ], + "spans": [ + { + "bbox": [ + 304, + 405, + 548, + 500 + ], + "type": "text", + "content": "Figure 1. The top-section represents the learning evolution of a generic Adversarial Data-Free Knowledge Distillation framework; the color-intensity variation signifies the change in the distribution of the pseudo-samples, the student network, and the generator network over the learning epochs. Under the variation in the distribution of the pseudo-samples, the bottom-section shows the learning curves for cases when the student accuracy degrades (shown in Red), which is undesirable, and when the student accuracy is maintained, if not improved, as proposed (shown in Green)." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 508, + 548, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 508, + 548, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 508, + 548, + 715 + ], + "type": "text", + "content": "of the generated samples consistently keeps changing during the process due to the generator updates [25]. From the student network's perspective, at each iteration, the pseudo samples seem to be generated from different generator parameters " + }, + { + "bbox": [ + 304, + 508, + 548, + 715 + ], + "type": "inline_equation", + "content": "(\\theta_{\\mathcal{G}})" + }, + { + "bbox": [ + 304, + 508, + 548, + 715 + ], + "type": "text", + "content": ". Hence, the convergence of the student network gets hampered due to successive distributional alterations over time [29], as depicted by the red curve in Figure 1. This observation hints that updating the student network, solely using the samples generated from the current generator parameters is not adequate to generalize the student. Moreover, the student forgets the knowledge acquired previously and decelerates the knowledge distillation. Therefore, the generator, apart from exploring the input space, seeks to compensate for the loss of knowledge in future iterations. Additionally, in a practical setting, during the distillation process, high variation in the student network's classification accuracy is undesirable, especially when the validation data is not available, since that prevents the user from tracking" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "7786" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 70, + 547, + 165 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 547, + 165 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 547, + 165 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 547, + 165 + ], + "type": "image", + "image_path": "beb466f4525609e470e156f2b8f2735ee19dd55fc3189f366ad605e8d9153005.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "lines": [ + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "spans": [ + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "text", + "content": "Figure 2. Student update strategies: (a) Typical student update by optimizing the Knowledge-Acquisition loss " + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{Acq})" + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "text", + "content": " with the batch pseudo samples " + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "inline_equation", + "content": "(\\hat{x})" + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "text", + "content": ", produced by the generator " + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "inline_equation", + "content": "(\\mathcal{G})" + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "text", + "content": " [5, 10, 20]. (b) Student update with simultaneous optimization of the Knowledge-Acquisition loss " + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{Acq})" + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "text", + "content": " and Knowledge-Retention loss " + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{Ret})" + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "text", + "content": " on the batch of pseduo samples " + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "inline_equation", + "content": "(\\hat{x})" + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "text", + "content": " and memory samples " + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "inline_equation", + "content": "(\\hat{x}_m)" + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "text", + "content": " obtained from the generator " + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "inline_equation", + "content": "(\\mathcal{G})" + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "text", + "content": " and the memory " + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "inline_equation", + "content": "(\\mathcal{M})" + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "text", + "content": ", respectively [2, 3]. (c) The proposed student update strategy, which treats " + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Acq}" + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "text", + "content": " as meta-train and " + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}" + }, + { + "bbox": [ + 46, + 165, + 547, + 218 + ], + "type": "text", + "content": " as meta-test, and implicitly imposes the alignment between them." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 221, + 287, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 221, + 287, + 245 + ], + "spans": [ + { + "bbox": [ + 46, + 221, + 287, + 245 + ], + "type": "text", + "content": "the student's accuracy over time, and selecting the distilled model parameters with the highest accuracy." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 45, + 246, + 288, + 416 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 246, + 288, + 416 + ], + "spans": [ + { + "bbox": [ + 45, + 246, + 288, + 416 + ], + "type": "text", + "content": "To circumvent the above-discussed problems, existing methods maintain a memory buffer to rehearse the examples from previously encountered distributions while learning with current examples. Binci et al. [3] introduce Replay based methods to explicitly retrain/replay on a limited subset of previously encountered samples while training on the current examples. Then, carrying forward, they use Generative-Replay [27] to transfer the learned examples to an auxiliary generative model (VAE), and sample from the it (VAE decoder) in subsequent iterations [2]. Nonetheless, the performance of these methods is upper bounded by joint training on previous and current examples [7]. Although, recent works have focused on modeling the memory, we seek to work towards effectively utilizing the samples from memory." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 45, + 417, + 288, + 609 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 417, + 288, + 609 + ], + "spans": [ + { + "bbox": [ + 45, + 417, + 288, + 609 + ], + "type": "text", + "content": "In this paper, we aim to update the student network parameters " + }, + { + "bbox": [ + 45, + 417, + 288, + 609 + ], + "type": "inline_equation", + "content": "(\\theta_S)" + }, + { + "bbox": [ + 45, + 417, + 288, + 609 + ], + "type": "text", + "content": " such that its performance does not degrade on the samples previously produced by the generator network " + }, + { + "bbox": [ + 45, + 417, + 288, + 609 + ], + "type": "inline_equation", + "content": "(\\mathcal{G})" + }, + { + "bbox": [ + 45, + 417, + 288, + 609 + ], + "type": "text", + "content": ", aspiring towards Learning to Retain while Acquiring. Thus, we propose a meta-learning inspired strategy to achieve this goal. We treat the task of Knowledge-Acquisition (learning from newly generated samples) and Knowledge-Retention (learning from previously encountered samples from memory) as meta-train and meta-test, respectively. Hence, in the proposed approach, the student network acquires new information while maintaining performance on previously encountered samples. By doing so, the proposed strategy (Figure 2c) implicitly aligns Knowledge-Acquisition and Knowledge-Retention, as opposed to simply combining them [2,3] without any coordination or alignment (Figure 2b), which leaves them to potentially interfere with one another." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 611, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 611, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 611, + 289, + 714 + ], + "type": "text", + "content": "Additionally, analyzing the proposed meta-objective, we discover that (in Section 3.4) the latent alignment factor as the dot product between the gradients of the Knowledge-Acquisition and Knowledge-Retention objectives, suggesting that the meta-objective enforces a common gradient direction for both tasks, encouraging the alignment between the task-specific gradients. Thus, the proposed method simultaneously minimizes the loss and matches the gradients corresponding to the individual tasks (Knowledge-Acquisition" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 222, + 545, + 243 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 222, + 545, + 243 + ], + "spans": [ + { + "bbox": [ + 304, + 222, + 545, + 243 + ], + "type": "text", + "content": "and Knowledge-Retention), enforcing the optimization paths to be same for both tasks." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 245, + 547, + 358 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 245, + 547, + 358 + ], + "spans": [ + { + "bbox": [ + 304, + 245, + 547, + 358 + ], + "type": "text", + "content": "Moreover, the proposed student update strategy is scalable to different deep architectures as the gradient alignment is implicit, and memory-agnostic, making no assumptions about the replay scheme employed. Nonetheless, recent works on gradient alignment, have shown great empirical advantages in Zero-Shot Learning [24], Distributed/Federated Learning [6] and Domain Generalization [26]. Our method extends the advantages of gradient alignment to memory-based Adversarial DFKD, thus strengthening the empirical findings in these works." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 359, + 547, + 415 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 359, + 547, + 415 + ], + "spans": [ + { + "bbox": [ + 304, + 359, + 547, + 415 + ], + "type": "text", + "content": "Finally, to demonstrate the advantages of the proposed student update strategy, we evaluate and compare against current non-memory [4,5, 10] and memory-based [2, 3] Adversarial DFKD methods, and observe substantial improvement in the student learning evolution." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 317, + 415, + 539, + 427 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 415, + 539, + 427 + ], + "spans": [ + { + "bbox": [ + 317, + 415, + 539, + 427 + ], + "type": "text", + "content": "In summary, the key points of this work are as follows:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 317, + 435, + 547, + 628 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 317, + 435, + 547, + 503 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 435, + 547, + 503 + ], + "spans": [ + { + "bbox": [ + 317, + 435, + 547, + 503 + ], + "type": "text", + "content": "- We propose a novel meta-learning inspired student update strategy in the Adversarial DFKD setting, that aims to maintain the student's performance on previously encountered examples (Knowledge-Retention) while acquiring knowledge from samples of the current distribution (Knowledge-Acquisition)." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 317, + 504, + 547, + 548 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 504, + 547, + 548 + ], + "spans": [ + { + "bbox": [ + 317, + 504, + 547, + 548 + ], + "type": "text", + "content": "- We theoretically identify (in Section 3.4) that the proposed student update strategy enforces an implicit gradient alignment between the Knowledge-Acquisition and Knowledge-Retention tasks." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 317, + 549, + 547, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 549, + 547, + 582 + ], + "spans": [ + { + "bbox": [ + 317, + 549, + 547, + 582 + ], + "type": "text", + "content": "- A major practical advantage of the proposed method is its applicability in the absence of a validation set, discussed in Section 4.2." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 317, + 583, + 547, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 583, + 547, + 628 + ], + "spans": [ + { + "bbox": [ + 317, + 583, + 547, + 628 + ], + "type": "text", + "content": "- Finally, we evaluate our method and compare against various Adversarial DFKD methods, on multiple student architectures and replay schemes (Memory Buffer and Generative Replay)." + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 647, + 392, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 647, + 392, + 659 + ], + "spans": [ + { + "bbox": [ + 306, + 647, + 392, + 659 + ], + "type": "text", + "content": "2. 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ZSKT [20] attempts data-free knowledge transfer by first training a generator in an adversarial fashion to look for samples on which the student and teacher do not match well. To improve the model discrepancy measure, it adopts the Kullback-Leibler (KL) divergence, and introduces attention transfer [33] to aid knowledge transfer. Moreover, DFAD [10] recommends Mean Absolute Error (MAE) as a model discrepancy function to prevent decayed gradients on converged samples. Furthermore, the adversarial framework was extended by Choi et al. [5] in the context of model quantization, by proposing adversarial data-free quantization (DFQ), and introducing additional regularization terms that match the mean and standard deviation of the generated pseudo-samples with the teacher model's batchnorm statistics, and imposes batch categorical entropy maximization, such that sample from each class appear equally in the generated batch. Fang et al. recently introduced FastD-FKD [9], an effective method with a meta generator to speed up the DFKD process, delivering a 100-fold increase in the knowledge transfer rate." + } + ] + } + ], + "index": 39 + }, + { + "bbox": [ + 46, + 494, + 289, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 494, + 289, + 687 + ], + "spans": [ + { + "bbox": [ + 46, + 494, + 289, + 687 + ], + "type": "text", + "content": "Handling Distribution Shift in Adversarial DFKD: To counter the distribution mismatch and the catastrophic forgetting phenomenon in the adversarial framework [25], Binici et al. [3] suggested maintaining a dynamic collection of synthetic samples throughout training iterations to prevent catastrophic forgetting in DFKD. Moreover, in their latest work [2], they introduce generative pseudo-replay [27] in which an auxiliary generative model simultaneously learns the distribution of the samples produced by the generator " + }, + { + "bbox": [ + 46, + 494, + 289, + 687 + ], + "type": "inline_equation", + "content": "(\\mathcal{G})" + }, + { + "bbox": [ + 46, + 494, + 289, + 687 + ], + "type": "text", + "content": ". Throughout the training process, examples are generated from the auxiliary generator to replay during training. Nonetheless, these works have focused on modeling the memory buffer. A related line of research maintains an exponentially moving average (EMA) of the generator model " + }, + { + "bbox": [ + 46, + 494, + 289, + 687 + ], + "type": "inline_equation", + "content": "\\mathcal{G}" + }, + { + "bbox": [ + 46, + 494, + 289, + 687 + ], + "type": "text", + "content": " [8] to replace the typical Memory-Buffer and Generative Replay. Nonetheless, our work focuses on the effective utilization of the samples obtained from the memory." + } + ] + } + ], + "index": 40 + }, + { + "bbox": [ + 306, + 266, + 388, + 279 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 266, + 388, + 279 + ], + "spans": [ + { + "bbox": [ + 306, + 266, + 388, + 279 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 41 + }, + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "spans": [ + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "type": "text", + "content": "In Section 3.1, we first provide a brief overview of Adversarial DFKD. Then, in Section 3.2, we discuss the data-free knowledge distillation objective. In Sections 3.3 and 3.4, we elaborate on the proposed student update strategy. Lastly, in Section 3.5, we discuss the adopted generator update strategy used for the baselines and the proposed framework." + } + ] + } + ], + "index": 42 + }, + { + "bbox": [ + 305, + 359, + 544, + 372 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 359, + 544, + 372 + ], + "spans": [ + { + "bbox": [ + 305, + 359, + 544, + 372 + ], + "type": "text", + "content": "3.1. Adversarial Data-Free Knowledge Distillation" + } + ] + } + ], + "index": 43 + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "spans": [ + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "text", + "content": "In the Adversarial DFKD framework, a generator " + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "inline_equation", + "content": "(\\mathcal{G})" + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "text", + "content": " is used to create pseudo-samples as surrogates to perform knowledge distillation/transfer, and the teacher-student " + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "inline_equation", + "content": "(\\mathcal{T}-S)" + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "text", + "content": " setup acts as a joint discriminator to penalize and update generator parameters " + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "inline_equation", + "content": "(\\theta_{\\mathcal{G}})" + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "text", + "content": " in an adversarial manner. After updating " + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "inline_equation", + "content": "\\theta_{\\mathcal{G}}" + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "text", + "content": ", random samples are generated and used to minimize the " + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "inline_equation", + "content": "\\mathcal{T}-S" + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "text", + "content": " discrepancy by updating the student parameters " + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "inline_equation", + "content": "(\\theta_{\\mathcal{S}})" + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "text", + "content": ". The generator and the student are optimized alternatively up until a fixed number of pre-defined iterations. In essence, the goal of DFKD is to craft a lightweight student model " + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "inline_equation", + "content": "(S)" + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "text", + "content": " by harnessing valuable knowledge from the well-trained Teacher model " + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "inline_equation", + "content": "(\\mathcal{T})" + }, + { + "bbox": [ + 304, + 376, + 547, + 537 + ], + "type": "text", + "content": " in the absence of training data. A general overview of Adversarial DFKD framework is illustrated in Figure 1." + } + ] + } + ], + "index": 44 + }, + { + "bbox": [ + 306, + 542, + 522, + 555 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 542, + 522, + 555 + ], + "spans": [ + { + "bbox": [ + 306, + 542, + 522, + 555 + ], + "type": "text", + "content": "3.2. Goal of Data Free Knowledge Distillation" + } + ] + } + ], + "index": 45 + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "spans": [ + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "text", + "content": "A student model " + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "inline_equation", + "content": "(\\mathcal{S})" + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "text", + "content": " is trained to match the teacher's " + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "inline_equation", + "content": "(\\mathcal{T})" + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "text", + "content": " predictions on its unavailable target domain " + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "inline_equation", + "content": "(\\mathcal{D}_{\\mathcal{T}})" + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "text", + "content": " as part of the distillation process. Let, " + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{S}}(x) = \\mathrm{Softmax}(S_{\\theta_{\\mathcal{S}}}(\\boldsymbol{x}))" + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{T}}(x) = \\mathrm{Softmax}(\\mathcal{T}_{\\theta_{\\mathcal{T}}}(\\boldsymbol{x})), \\forall \\boldsymbol{x} \\in \\mathcal{D}_{\\mathcal{T}}" + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "text", + "content": ", denote the predicted Student and Teacher probability mass across the classes, respectively. We seek to find the parameters " + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "inline_equation", + "content": "\\theta_{\\mathcal{S}}" + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "text", + "content": " of the student model that will reduce the probability of errors " + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "inline_equation", + "content": "(P)" + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "text", + "content": " between the predictions " + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{S}}(x)" + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{T}}(x)" + }, + { + "bbox": [ + 304, + 559, + 547, + 652 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 46 + }, + { + "bbox": [ + 321, + 657, + 529, + 684 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 657, + 529, + 684 + ], + "spans": [ + { + "bbox": [ + 321, + 657, + 529, + 684 + ], + "type": "interline_equation", + "content": "\\min _ {\\theta_ {\\mathcal {S}}} P _ {x \\sim \\mathcal {D} _ {\\mathcal {T}}} \\left(\\underset {i} {\\arg \\max } p _ {\\mathcal {S}} ^ {i} (x) \\neq \\underset {i} {\\arg \\max } p _ {\\mathcal {T}} ^ {i} (x)\\right),", + "image_path": "3286f4ec7d2c1c46a66fec1fac94cfb7780cebe4e7f7063c074c524d10fdcd9f.jpg" + } + ] + } + ], + "index": 47 + }, + { + "bbox": [ + 305, + 689, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 547, + 714 + ], + "type": "text", + "content": "where the superscript " + }, + { + "bbox": [ + 305, + 689, + 547, + 714 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 305, + 689, + 547, + 714 + ], + "type": "text", + "content": " denotes the " + }, + { + "bbox": [ + 305, + 689, + 547, + 714 + ], + "type": "inline_equation", + "content": "i^{th}" + }, + { + "bbox": [ + 305, + 689, + 547, + 714 + ], + "type": "text", + "content": " probability score of the predicted masses, " + }, + { + "bbox": [ + 305, + 689, + 547, + 714 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{S}}(x)" + }, + { + "bbox": [ + 305, + 689, + 547, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 689, + 547, + 714 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{T}}(x)" + }, + { + "bbox": [ + 305, + 689, + 547, + 714 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 48 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7788" + } + ] + } + ], + "index": 49 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 68, + 176, + 148 + ], + "blocks": [ + { + "bbox": [ + 47, + 68, + 176, + 148 + ], + "lines": [ + { + "bbox": [ + 47, + 68, + 176, + 148 + ], + "spans": [ + { + "bbox": [ + 47, + 68, + 176, + 148 + ], + "type": "image", + "image_path": "f21f0eb3dc8f5cdfa052f7fd3a8f2e8cef9f021e8a953b191e32ab9df9126dde.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 152, + 547, + 185 + ], + "lines": [ + { + "bbox": [ + 46, + 152, + 547, + 185 + ], + "spans": [ + { + "bbox": [ + 46, + 152, + 547, + 185 + ], + "type": "text", + "content": "Figure 4. Learning evolution of the proposed method compared to the prior arts (MB-DFKD [3] and PRE-DFKD [2]) that employ replay. The plots visualize the Accuracy " + }, + { + "bbox": [ + 46, + 152, + 547, + 185 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 46, + 152, + 547, + 185 + ], + "type": "text", + "content": " evolution (top-row) and the Cumulative Mean Accuracy " + }, + { + "bbox": [ + 46, + 152, + 547, + 185 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 46, + 152, + 547, + 185 + ], + "type": "text", + "content": " evolution (bottom-row) of the ResNet-18 [15] student network on SVHN [23], CIFAR10 [17], CIFAR100 [17], and Tiny-ImageNet [18] datasets. The proposed method is plotted Blue." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 179, + 68, + 298, + 148 + ], + "blocks": [ + { + "bbox": [ + 179, + 68, + 298, + 148 + ], + "lines": [ + { + "bbox": [ + 179, + 68, + 298, + 148 + ], + "spans": [ + { + "bbox": [ + 179, + 68, + 298, + 148 + ], + "type": "image", + "image_path": "c5e13a2b1e0e4997a70b03ddd62de41c365be6b25724072d90291df3613b5e70.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 302, + 68, + 422, + 148 + ], + "blocks": [ + { + "bbox": [ + 302, + 68, + 422, + 148 + ], + "lines": [ + { + "bbox": [ + 302, + 68, + 422, + 148 + ], + "spans": [ + { + "bbox": [ + 302, + 68, + 422, + 148 + ], + "type": "image", + "image_path": "d8ca0364c0906518bbd95feb7bff7813f9b3b4e9c82ac3fc87a8189a8edbdf3c.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 426, + 68, + 547, + 148 + ], + "blocks": [ + { + "bbox": [ + 426, + 68, + 547, + 148 + ], + "lines": [ + { + "bbox": [ + 426, + 68, + 547, + 148 + ], + "spans": [ + { + "bbox": [ + 426, + 68, + 547, + 148 + ], + "type": "image", + "image_path": "db2b41357ee158e738a9b2e3f083eb598837c9bb8dd76458880511464e284d99.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 188, + 289, + 258 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 188, + 289, + 258 + ], + "spans": [ + { + "bbox": [ + 46, + 188, + 289, + 258 + ], + "type": "text", + "content": "However, DFKD suggests minimizing the student's error on a pseudo dataset " + }, + { + "bbox": [ + 46, + 188, + 289, + 258 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\mathcal{P}}" + }, + { + "bbox": [ + 46, + 188, + 289, + 258 + ], + "type": "text", + "content": " since the teacher's original training data distribution " + }, + { + "bbox": [ + 46, + 188, + 289, + 258 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\mathcal{T}}" + }, + { + "bbox": [ + 46, + 188, + 289, + 258 + ], + "type": "text", + "content": " is not available. Typically, a loss function, say " + }, + { + "bbox": [ + 46, + 188, + 289, + 258 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{KD}" + }, + { + "bbox": [ + 46, + 188, + 289, + 258 + ], + "type": "text", + "content": ", that gauges disagreement between the teacher and the student is minimized " + }, + { + "bbox": [ + 46, + 188, + 289, + 258 + ], + "type": "inline_equation", + "content": "\\forall \\hat{x} \\in \\mathcal{D}_{\\mathcal{P}}" + }, + { + "bbox": [ + 46, + 188, + 289, + 258 + ], + "type": "text", + "content": " by optimizing the student parameters " + }, + { + "bbox": [ + 46, + 188, + 289, + 258 + ], + "type": "inline_equation", + "content": "(\\theta_{S})" + }, + { + "bbox": [ + 46, + 188, + 289, + 258 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 91, + 265, + 288, + 283 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 91, + 265, + 288, + 283 + ], + "spans": [ + { + "bbox": [ + 91, + 265, + 288, + 283 + ], + "type": "interline_equation", + "content": "\\underset {\\theta_ {S}} {\\min } \\mathbb {E} _ {\\hat {x} \\sim \\mathcal {D} _ {\\mathcal {P}}} \\left[ \\mathcal {L} _ {K D} \\left(\\mathcal {T} _ {\\theta_ {\\mathcal {T}}} (\\hat {x}), \\mathcal {S} _ {\\theta_ {\\mathcal {S}}} (\\hat {x})\\right) \\right]. \\tag {1}", + "image_path": "0ff9ad94332ab4c60c5c102f3152452037fad09f121a6801402322a6e852df6e.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 290, + 289, + 350 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 290, + 289, + 350 + ], + "spans": [ + { + "bbox": [ + 46, + 290, + 289, + 350 + ], + "type": "text", + "content": "We use the Mean Absolute Error (MAE) to define loss measure " + }, + { + "bbox": [ + 46, + 290, + 289, + 350 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{KD})" + }, + { + "bbox": [ + 46, + 290, + 289, + 350 + ], + "type": "text", + "content": " as suggested in [10]. Suppose, given the predicted logits (pre-softmax predictions) " + }, + { + "bbox": [ + 46, + 290, + 289, + 350 + ], + "type": "inline_equation", + "content": "t(\\hat{x}) = \\mathcal{T}_{\\theta_{\\mathcal{T}}}(\\hat{x})" + }, + { + "bbox": [ + 46, + 290, + 289, + 350 + ], + "type": "text", + "content": " from the teacher model and the predicted logits " + }, + { + "bbox": [ + 46, + 290, + 289, + 350 + ], + "type": "inline_equation", + "content": "s(\\hat{x}) = S_{\\theta_S}(\\hat{x})" + }, + { + "bbox": [ + 46, + 290, + 289, + 350 + ], + "type": "text", + "content": " from the student model, we define " + }, + { + "bbox": [ + 46, + 290, + 289, + 350 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{KD}" + }, + { + "bbox": [ + 46, + 290, + 289, + 350 + ], + "type": "text", + "content": " as:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 92, + 356, + 288, + 370 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 92, + 356, + 288, + 370 + ], + "spans": [ + { + "bbox": [ + 92, + 356, + 288, + 370 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {K D} (t (\\hat {x}), s (\\hat {x})) = \\| t (\\hat {x}) - s (\\hat {x}) \\| _ {1}. \\tag {2}", + "image_path": "f2d789367ecd588d00ebe6ae1248ba36be2635e013b880600696fb8ac9a75033.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 376, + 237, + 390 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 376, + 237, + 390 + ], + "spans": [ + { + "bbox": [ + 47, + 376, + 237, + 390 + ], + "type": "text", + "content": "3.3. Learning to Retain while Acquiring" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 394, + 288, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 394, + 288, + 508 + ], + "spans": [ + { + "bbox": [ + 46, + 394, + 288, + 508 + ], + "type": "text", + "content": "Our novelty resides in the student update strategy, as described earlier. Knowledge-Retention and Knowledge-Acquisition relate to two independent goals and can therefore be considered as two discrete tasks. In fact, by aligning these two goals, we empirically observe that, they can cooperate to retain the knowledge on previously encountered examples while acquiring knowledge from newly generated samples. The proposed method utilizes a meta-learning-inspired optimization strategy to effectively replay the samples from memory, say " + }, + { + "bbox": [ + 46, + 394, + 288, + 508 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 46, + 394, + 288, + 508 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 508, + 289, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 508, + 289, + 553 + ], + "spans": [ + { + "bbox": [ + 46, + 508, + 289, + 553 + ], + "type": "text", + "content": "In the typical Adversarial DFKD setup, the student update objective with the generated pseudo samples " + }, + { + "bbox": [ + 46, + 508, + 289, + 553 + ], + "type": "inline_equation", + "content": "(\\hat{x})" + }, + { + "bbox": [ + 46, + 508, + 289, + 553 + ], + "type": "text", + "content": ", i.e., the Knowledge-Acquisition task " + }, + { + "bbox": [ + 46, + 508, + 289, + 553 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{Acq})" + }, + { + "bbox": [ + 46, + 508, + 289, + 553 + ], + "type": "text", + "content": " (Figure 2a), is formulated as:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 72, + 562, + 288, + 581 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 562, + 288, + 581 + ], + "spans": [ + { + "bbox": [ + 72, + 562, + 288, + 581 + ], + "type": "interline_equation", + "content": "\\min _ {\\theta_ {S}} \\mathcal {L} _ {A c q} (\\theta_ {S}) = \\min _ {\\theta_ {S}} \\mathbb {E} _ {\\hat {x}} [ \\mathcal {L} (\\mathcal {T} _ {\\theta_ {T}} (\\hat {x}), \\mathcal {S} _ {\\theta_ {S}} (\\hat {x})) ], \\quad (3)", + "image_path": "09b73c4c464efd078620e9c07548ecb767805f4672110251604c825b3d64bbbd.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 587, + 287, + 622 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 587, + 287, + 622 + ], + "spans": [ + { + "bbox": [ + 46, + 587, + 287, + 622 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 587, + 287, + 622 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 46, + 587, + 287, + 622 + ], + "type": "text", + "content": " is the MAE (2) between the teacher and the student logits, and " + }, + { + "bbox": [ + 46, + 587, + 287, + 622 + ], + "type": "inline_equation", + "content": "\\hat{x} = \\mathcal{G}(z), z \\sim \\mathcal{N}(0, I)" + }, + { + "bbox": [ + 46, + 587, + 287, + 622 + ], + "type": "text", + "content": ", denotes the batch of randomly generated samples." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 46, + 622, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 622, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 622, + 289, + 714 + ], + "type": "text", + "content": "Moreover, to alleviate the distribution drift during knowledge distillation in the adversarial setting, previous works have maintained a memory buffer, to store " + }, + { + "bbox": [ + 46, + 622, + 289, + 714 + ], + "type": "inline_equation", + "content": "\\hat{x}" + }, + { + "bbox": [ + 46, + 622, + 289, + 714 + ], + "type": "text", + "content": " and replay at later iterations to help the student recall the knowledge [3]. At a fixed frequency, batches of samples from current iterations are updated in the memory [3]. Also, recent works [2] have explored using the Generative Replay [27] strategy to simultaneously store the samples, as they are encountered, in" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 188, + 547, + 233 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 188, + 547, + 233 + ], + "spans": [ + { + "bbox": [ + 304, + 188, + 547, + 233 + ], + "type": "text", + "content": "a generative model (VAE [16]), and later replay by generating samples from the VAE decoder. Hence, the optimization objective for the Knowledge-Retention " + }, + { + "bbox": [ + 304, + 188, + 547, + 233 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{Ret})" + }, + { + "bbox": [ + 304, + 188, + 547, + 233 + ], + "type": "text", + "content": " can be defined as follows:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 315, + 243, + 545, + 277 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 243, + 545, + 277 + ], + "spans": [ + { + "bbox": [ + 315, + 243, + 545, + 277 + ], + "type": "interline_equation", + "content": "\\begin{array}{r l} \\underset {\\theta_ {S}} {\\min } \\mathcal {L} _ {R e t} (\\theta_ {S}) & = \\underset {\\theta_ {S}} {\\min } \\mathbb {E} _ {\\hat {x} _ {m}} [ \\mathcal {L} (\\mathcal {T} _ {\\theta_ {\\tau}} (\\hat {x} _ {m}), \\mathcal {S} _ {\\theta_ {S}} (\\hat {x} _ {m})) ], \\\\ & \\quad \\hat {x} _ {m} \\sim \\mathcal {M}, \\end{array} \\tag {4}", + "image_path": "78579aed8ca5b34cd8fe5d85de1f89a32bf407d90413a6c26fac42a8ad1d6523.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "spans": [ + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "type": "text", + "content": "where, with a slight abuse of notation, " + }, + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "type": "text", + "content": " denotes the Memory Buffer or Generative Replay or any other replay scheme. Thus, the overall optimization objective to update " + }, + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "type": "inline_equation", + "content": "\\theta_{\\mathcal{S}}" + }, + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "type": "text", + "content": " while considering both the new samples (generated from the latest " + }, + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "type": "inline_equation", + "content": "\\theta_{\\mathcal{G}}" + }, + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "type": "text", + "content": ") and the old samples (sampled from " + }, + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 304, + 285, + 547, + 354 + ], + "type": "text", + "content": ") (Figure 2b) is defined as:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 368, + 364, + 546, + 382 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 368, + 364, + 546, + 382 + ], + "spans": [ + { + "bbox": [ + 368, + 364, + 546, + 382 + ], + "type": "interline_equation", + "content": "\\min _ {\\theta_ {S}} \\mathcal {L} _ {A c q} \\left(\\theta_ {S}\\right) + \\mathcal {L} _ {R e t} \\left(\\theta_ {S}\\right). \\tag {5}", + "image_path": "8d4ee832d1e7cfee9a4c3ba723ff5d2517f9f4b83fb882df9850e987e03816a2.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "spans": [ + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": "However, the objective in (5) attempts to simultaneously optimize " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Acq}" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": " but does not seek to align the objectives, which leaves them to potentially interfere with one another. Say, we denote the gradients of Knowledge Acquisition and Knowledge Retention tasks with " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\nabla \\mathcal{L}_{Acq}(\\theta)" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\nabla \\mathcal{L}_{Ret}(\\theta)" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": ", respectively. If " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\nabla \\mathcal{L}_{Acq}(\\theta)" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\nabla \\mathcal{L}_{Ret}(\\theta)" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": " point in a similar direction or are said to be aligned, i.e., " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\nabla \\mathcal{L}_{Acq}(\\theta). \\nabla \\mathcal{L}_{Ret}(\\theta) > 0" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": ", then taking a gradient step along " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\nabla \\mathcal{L}_{Acq}(\\theta)" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": " or " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\nabla \\mathcal{L}_{Ret}(\\theta)" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": " improves the model's performance on both tasks. This is however not the case when " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\nabla \\mathcal{L}_{Acq}(\\theta)" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\nabla \\mathcal{L}_{Ret}(\\theta)" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": " point in different directions, i.e., " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\nabla \\mathcal{L}_{Acq}(\\theta). \\nabla \\mathcal{L}_{Ret}(\\theta) \\leq 0" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": ", and the weight parameters obtained, may not be optimal for both the tasks simultaneously. Hence, the intended effect of having the gradient directions aligned is to obtain student parameters " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "(\\theta_S)" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": " that have optimal performance on both " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Acq}" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}" + }, + { + "bbox": [ + 304, + 391, + 547, + 574 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 574, + 545, + 618 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 574, + 545, + 618 + ], + "spans": [ + { + "bbox": [ + 304, + 574, + 545, + 618 + ], + "type": "text", + "content": "The proposed meta-learning inspired approach, seeks to align the two tasks, " + }, + { + "bbox": [ + 304, + 574, + 545, + 618 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Acq}" + }, + { + "bbox": [ + 304, + 574, + 545, + 618 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 574, + 545, + 618 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}" + }, + { + "bbox": [ + 304, + 574, + 545, + 618 + ], + "type": "text", + "content": ". We take cues from Model-Agnostic Meta-learning (MAML) [12], and adapt it to Adversarial DFKD." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 619, + 545, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 619, + 545, + 652 + ], + "spans": [ + { + "bbox": [ + 304, + 619, + 545, + 652 + ], + "type": "text", + "content": "Typically, MAML-like methods [12, 13] are framed as a bi-level optimization [28] problem, where the objective is defined as:" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 339, + 662, + 546, + 681 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 339, + 662, + 546, + 681 + ], + "spans": [ + { + "bbox": [ + 339, + 662, + 546, + 681 + ], + "type": "interline_equation", + "content": "\\min _ {\\omega} \\mathcal {L} _ {\\text {o u t e r}} (\\arg \\min _ {\\omega} \\mathcal {L} _ {\\text {i n n e r}} (\\omega , \\mathcal {D} _ {\\text {t r a i n}}), \\mathcal {D} _ {\\text {t e s t}}), \\tag {6}", + "image_path": "aa588627a2deee6ab41e5844913c5d1b9643831fd88c35add6126bb9a36ed63e.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "text", + "content": " indicates the initial parameters, " + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{inner}}" + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "text", + "content": " denotes the task specific inner loss (meta-train) and " + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{outer}}" + }, + { + "bbox": [ + 304, + 689, + 545, + 714 + ], + "type": "text", + "content": " denotes the" + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7789" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 95 + ], + "type": "text", + "content": "outer loss (meta-test), and " + }, + { + "bbox": [ + 46, + 72, + 287, + 95 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\mathrm{train}}" + }, + { + "bbox": [ + 46, + 72, + 287, + 95 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 72, + 287, + 95 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{\\mathrm{test}}" + }, + { + "bbox": [ + 46, + 72, + 287, + 95 + ], + "type": "text", + "content": " denote the inner loop and the outer loop training example sets." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "spans": [ + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": "Likewise, at each iteration of the student parameter " + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "inline_equation", + "content": "(\\theta_S)" + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": " update, we pose Knowledge-Acquisition and KnowledgeRetention as meta-train and meta-test, respectively. I.e., we perform a single gradient descent step using the current samples " + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "inline_equation", + "content": "(\\hat{x})" + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": " produced from the generator network " + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "inline_equation", + "content": "(\\mathcal{G})" + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": ", on the parameters " + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "inline_equation", + "content": "\\theta_{S}" + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": " and obtain " + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "inline_equation", + "content": "\\theta_{S}^{\\prime}" + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": " as " + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "inline_equation", + "content": "\\theta_S^\\prime = \\theta_S - \\alpha \\nabla \\mathcal{L}_{Acq}(\\theta_S)" + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "inline_equation", + "content": "\\nabla \\mathcal{L}_{Acq}(\\theta_S)" + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": " denotes gradient of " + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Acq}" + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": " at " + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "inline_equation", + "content": "\\theta_S" + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": ". Then we optimize on the batch of samples " + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "inline_equation", + "content": "(\\hat{x}_m)" + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": " obtained from the memory " + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "inline_equation", + "content": "(\\mathcal{M})" + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": ", with the parameters " + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "inline_equation", + "content": "\\theta_S^\\prime" + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": ", and then make a final update on " + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "inline_equation", + "content": "\\theta_S" + }, + { + "bbox": [ + 46, + 95, + 289, + 244 + ], + "type": "text", + "content": ". Thus, formally, the overall meta-optimization objective, with the task of Knowledge Acquisition serving as meta-train and the task of Knowledge Retention as meta-test (Figure 2c), can be defined as follows:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 51, + 253, + 242, + 272 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 253, + 242, + 272 + ], + "spans": [ + { + "bbox": [ + 51, + 253, + 242, + 272 + ], + "type": "interline_equation", + "content": "\\min _ {\\theta_ {S}} \\mathcal {L} _ {A c q} (\\theta_ {S}) + \\mathcal {L} _ {R e t} (\\theta_ {S} ^ {\\prime}) = \\min _ {\\theta_ {S}} \\mathcal {L} _ {A c q} (\\theta_ {S}) +", + "image_path": "21e5213acd12d822de6180ef0542cc8535e7532bc96545698390549288da2fe0.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 161, + 274, + 287, + 289 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 161, + 274, + 287, + 289 + ], + "spans": [ + { + "bbox": [ + 161, + 274, + 287, + 289 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {R e t} \\big (\\theta_ {\\mathcal {S}} - \\alpha \\nabla \\mathcal {L} _ {A c q} (\\theta_ {\\mathcal {S}}) \\big). (7)", + "image_path": "c50c370063bf5abc403ca5f0da28d98c725c9c09b30957da4fc5a9bacddb2794.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 297, + 287, + 321 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 297, + 287, + 321 + ], + "spans": [ + { + "bbox": [ + 47, + 297, + 287, + 321 + ], + "type": "text", + "content": "3.4. How does the Proposed Student Update Strategy Promote Alignment?" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "spans": [ + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "text", + "content": "In this subsection, we analyze the proposed objective (7) to understand how it results in the desired alignment between the Knowledge-Acquisition and Knowledge-Retention tasks. We assume that meta-train and meta-test tasks provide us with losses " + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Acq}" + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}" + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "text", + "content": "; in our case, " + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Acq}" + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}" + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "text", + "content": " are the same function computed on the batches " + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "inline_equation", + "content": "\\hat{x}" + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "inline_equation", + "content": "\\hat{x}_m" + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "text", + "content": ", respectively. We utilize Taylor's expansion to elaborate the gradient of " + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}" + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "text", + "content": " at a point " + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "text", + "content": " displaced by " + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "inline_equation", + "content": "\\phi_{\\theta}" + }, + { + "bbox": [ + 46, + 327, + 287, + 429 + ], + "type": "text", + "content": ", as described in Lemma 1." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 430, + 288, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 430, + 288, + 464 + ], + "spans": [ + { + "bbox": [ + 47, + 430, + 288, + 464 + ], + "type": "text", + "content": "Lemma 1. If " + }, + { + "bbox": [ + 47, + 430, + 288, + 464 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}" + }, + { + "bbox": [ + 47, + 430, + 288, + 464 + ], + "type": "text", + "content": " has Lipschitz, Hessian, i.e., " + }, + { + "bbox": [ + 47, + 430, + 288, + 464 + ], + "type": "inline_equation", + "content": "\\| \\nabla^2\\mathcal{L}_{Ret}(\\theta_1) - \\nabla^2\\mathcal{L}_{Ret}(\\theta_2)\\|\\leq \\rho \\| \\theta_1 - \\theta_2\\|" + }, + { + "bbox": [ + 47, + 430, + 288, + 464 + ], + "type": "text", + "content": " for some " + }, + { + "bbox": [ + 47, + 430, + 288, + 464 + ], + "type": "inline_equation", + "content": "\\rho >0" + }, + { + "bbox": [ + 47, + 430, + 288, + 464 + ], + "type": "text", + "content": " , then:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 71, + 473, + 261, + 504 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 473, + 261, + 504 + ], + "spans": [ + { + "bbox": [ + 71, + 473, + 261, + 504 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\nabla \\mathcal {L} _ {R e t} (\\theta + \\phi_ {\\theta}) = \\nabla \\mathcal {L} _ {R e t} (\\theta) + \\nabla^ {2} \\mathcal {L} _ {R e t} (\\theta) \\phi_ {\\theta} \\\\ + \\mathcal {O} (\\| \\phi_ {\\theta} \\| ^ {2}). \\\\ \\end{array}", + "image_path": "e6adf76a3f17763bd59d3a6878b081ada70d711dea1ee7f98dcb75d858425e44.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 513, + 247, + 526 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 513, + 247, + 526 + ], + "spans": [ + { + "bbox": [ + 46, + 513, + 247, + 526 + ], + "type": "text", + "content": "For instance, when " + }, + { + "bbox": [ + 46, + 513, + 247, + 526 + ], + "type": "inline_equation", + "content": "\\phi_{\\theta} = -\\alpha \\nabla \\mathcal{L}_{Acq}(\\theta)" + }, + { + "bbox": [ + 46, + 513, + 247, + 526 + ], + "type": "text", + "content": ", we have," + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 535, + 277, + 580 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 535, + 277, + 580 + ], + "spans": [ + { + "bbox": [ + 56, + 535, + 277, + 580 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\nabla \\mathcal {L} _ {R e t} (\\theta - \\alpha \\nabla \\mathcal {L} _ {A c q} (\\theta)) = \\nabla \\mathcal {L} _ {R e t} (\\theta) \\\\ - \\alpha \\nabla^ {2} \\mathcal {L} _ {R e t} (\\theta) \\nabla \\mathcal {L} _ {A c q} (\\theta) \\\\ + \\mathcal {O} (\\alpha^ {2}). \\\\ \\end{array}", + "image_path": "21a8e101cfd20a99ed8072b8df4e47aeff3ca56544cd141d16402ca3857ab851.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "spans": [ + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "text", + "content": "Theorem 1. If " + }, + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "inline_equation", + "content": "\\theta' = \\theta - \\alpha \\nabla \\mathcal{L}_{Acq}(\\theta)" + }, + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "text", + "content": ", denotes a single gradient descent step on " + }, + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "text", + "content": " with the objective " + }, + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Acq}(\\theta)" + }, + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "text", + "content": " is a scalar, and " + }, + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "inline_equation", + "content": "\\nabla \\mathcal{L}_{Acq}(\\theta)" + }, + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "text", + "content": " denotes the gradient of the objective at " + }, + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 46, + 592, + 287, + 638 + ], + "type": "text", + "content": ", then:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 646, + 272, + 686 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 646, + 272, + 686 + ], + "spans": [ + { + "bbox": [ + 62, + 646, + 272, + 686 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\frac {\\partial \\mathcal {L} _ {R e t} \\left(\\theta^ {\\prime}\\right)}{\\partial \\theta} = \\nabla \\mathcal {L} _ {R e t} (\\theta) - \\alpha \\nabla^ {2} \\mathcal {L} _ {R e t} (\\theta). \\nabla \\mathcal {L} _ {A c q} (\\theta) \\\\ - \\alpha \\nabla^ {2} \\mathcal {L} _ {A c q} (\\theta). \\nabla \\mathcal {L} _ {R e t} (\\theta) + \\mathcal {O} (\\alpha^ {2}). \\\\ \\end{array}", + "image_path": "0adba68301923b250d97954c931ec3b7298bd8cd84a8e15b13814c8a8197fc4a.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 701, + 247, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 701, + 247, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 701, + 247, + 713 + ], + "type": "text", + "content": "Proof. Please refer to the Supplemental Material." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 72, + 547, + 125 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 547, + 125 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 547, + 125 + ], + "type": "text", + "content": "While optimizing the objective defined in (7) using stochastic gradient descent, we would need to compute the gradient of the " + }, + { + "bbox": [ + 305, + 72, + 547, + 125 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}(\\theta_S^{\\prime})" + }, + { + "bbox": [ + 305, + 72, + 547, + 125 + ], + "type": "text", + "content": " w.r.t " + }, + { + "bbox": [ + 305, + 72, + 547, + 125 + ], + "type": "inline_equation", + "content": "\\theta_{S}" + }, + { + "bbox": [ + 305, + 72, + 547, + 125 + ], + "type": "text", + "content": ". Therefore, utilizing Theorem 1 we express " + }, + { + "bbox": [ + 305, + 72, + 547, + 125 + ], + "type": "inline_equation", + "content": "\\frac{\\partial\\mathcal{L}_{Ret}(\\theta_S^{\\prime})}{\\partial\\theta_S}" + }, + { + "bbox": [ + 305, + 72, + 547, + 125 + ], + "type": "text", + "content": " as:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 315, + 134, + 546, + 192 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 134, + 546, + 192 + ], + "spans": [ + { + "bbox": [ + 315, + 134, + 546, + 192 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\frac {\\partial \\mathcal {L} _ {R e t} \\left(\\theta_ {\\mathcal {S}} ^ {\\prime}\\right)}{\\partial \\theta_ {\\mathcal {S}}} = \\nabla \\mathcal {L} _ {R e t} \\left(\\theta_ {\\mathcal {S}}\\right) \\\\ - \\alpha \\nabla^ {2} \\mathcal {L} _ {R e t} (\\theta_ {\\mathcal {S}}). \\nabla \\mathcal {L} _ {A c q} (\\theta_ {\\mathcal {S}}) \\\\ - \\alpha \\nabla^ {2} \\mathcal {L} _ {A c q} (\\theta_ {\\mathcal {S}}). \\nabla \\mathcal {L} _ {R e t} (\\theta_ {\\mathcal {S}}) + \\mathcal {O} (\\alpha^ {2}), \\tag {8} \\\\ \\end{array}", + "image_path": "2851c1b634d0cd0266c4d251064f4ceb8aaf3418caf805b5553cd97ec08929b6.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 203, + 527, + 216 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 203, + 527, + 216 + ], + "spans": [ + { + "bbox": [ + 305, + 203, + 527, + 216 + ], + "type": "text", + "content": "using the product rule " + }, + { + "bbox": [ + 305, + 203, + 527, + 216 + ], + "type": "inline_equation", + "content": "\\nabla a.b + \\nabla b.a = \\nabla (a.b)" + }, + { + "bbox": [ + 305, + 203, + 527, + 216 + ], + "type": "text", + "content": ", we get:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 315, + 226, + 546, + 294 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 226, + 546, + 294 + ], + "spans": [ + { + "bbox": [ + 315, + 226, + 546, + 294 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\frac {\\partial \\mathcal {L} _ {R e t} \\left(\\theta_ {\\mathcal {S}} ^ {\\prime}\\right)}{\\partial \\theta_ {\\mathcal {S}}} = \\nabla \\mathcal {L} _ {R e t} \\left(\\theta_ {\\mathcal {S}}\\right) \\\\ - \\alpha \\nabla \\underbrace {(\\nabla \\mathcal {L} _ {\\text {R e t}} (\\theta_ {\\mathcal {S}}) . \\nabla \\mathcal {L} _ {\\text {A c q}} (\\theta_ {\\mathcal {S}}))} _ {\\text {G r a d i e n t A l i g n m e n t}} + \\mathcal {O} \\left(\\alpha^ {2}\\right). \\tag {9} \\\\ \\end{array}", + "image_path": "c7dfab34ba11d4f8152aae12626313ea90a0b9787a32635b81057f0e1b8a1c40.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "spans": [ + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "text", + "content": "From the analysis above, we observe that the gradient of " + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}(\\theta_S')" + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "text", + "content": " at " + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "inline_equation", + "content": "\\theta_S" + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "text", + "content": " (in (9)) produces the gradient of the gradient-product. This indicates, when optimizing " + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}(\\theta_S')" + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "text", + "content": " (in (7)), the gradient of " + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}(\\theta_S')" + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "text", + "content": " at " + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "inline_equation", + "content": "\\theta_S" + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "text", + "content": ", has a negatively scaled gradient of the gradient-product term, i.e., " + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "inline_equation", + "content": "\\nabla (\\nabla \\mathcal{L}_{Ret}(\\theta_S). \\nabla \\mathcal{L}_{Acq}(\\theta_S))" + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "text", + "content": " (derived in (9)), suggesting that the overall-gradients minimize " + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}(\\theta_S)" + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "text", + "content": " and maximize " + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "inline_equation", + "content": "\\nabla \\mathcal{L}_{Ret}(\\theta_S). \\nabla \\mathcal{L}_{Acq}(\\theta_S)" + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "text", + "content": ". Hence, optimizing (7) enforces the updates on " + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}(\\theta_S)" + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Acq}(\\theta_S)" + }, + { + "bbox": [ + 305, + 306, + 547, + 443 + ], + "type": "text", + "content": " to seek a common direction, by maximizing the gradient-product. Moreover, we empirically observe this phenomenon in Section A of the Supplemental Material." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 306, + 454, + 545, + 477 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 454, + 545, + 477 + ], + "spans": [ + { + "bbox": [ + 306, + 454, + 545, + 477 + ], + "type": "text", + "content": "3.5. Generator Update in Adversarial Exploration-based DFKD" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "spans": [ + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "text", + "content": "In the absence of the training dataset " + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "inline_equation", + "content": "(\\mathcal{D}_{\\mathcal{T}})" + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "text", + "content": ", the generator " + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "inline_equation", + "content": "(\\mathcal{G})" + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "text", + "content": " is utilized to obtain pseudo-samples " + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "inline_equation", + "content": "(\\hat{x})" + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "text", + "content": " and perform knowledge-distillation, i.e., " + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "inline_equation", + "content": "\\hat{x} = \\mathcal{G}(z)" + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "inline_equation", + "content": "z \\sim \\mathcal{N}(0, I)" + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "text", + "content": ". The generator is learned to maximize the disagreement between Teacher network " + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "inline_equation", + "content": "(\\mathcal{T}_{\\theta_{\\mathcal{T}}})" + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "text", + "content": " and the Student network " + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "inline_equation", + "content": "(S_{\\theta_S})" + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "text", + "content": ". Additionally, for the generated data " + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "inline_equation", + "content": "\\hat{x}" + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "text", + "content": " to mimic similar responses from the teacher as the real data, we include a prior loss " + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{P}}" + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "text", + "content": " [4] to be minimized alongside maximizing the discrepancy " + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "inline_equation", + "content": "(D)" + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "text", + "content": ". Hence, we update the generator parameters " + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "inline_equation", + "content": "(\\theta_{\\mathcal{G}})" + }, + { + "bbox": [ + 305, + 485, + 547, + 599 + ], + "type": "text", + "content": " by maximizing the following objective:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 317, + 610, + 545, + 645 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 610, + 545, + 645 + ], + "spans": [ + { + "bbox": [ + 317, + 610, + 545, + 645 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\max _ {\\theta_ {\\mathcal {G}}} \\mathbb {E} _ {z \\sim \\mathcal {N} (0, I)} [ D (\\mathcal {T} _ {\\theta_ {\\mathcal {T}}} (\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)), \\mathcal {S} _ {\\theta_ {\\mathcal {S}}} (\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z))) \\\\ \\left. - \\mathcal {L} _ {\\mathcal {P}} \\left(\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)\\right) \\right]. \\tag {10} \\\\ \\end{array}", + "image_path": "7fe2e5a055a387e6c91fd1faa22ca44149d75be1b1d36c0a1769a901ea02d494.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 305, + 656, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 656, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 656, + 547, + 713 + ], + "type": "text", + "content": "Typically the disagreement function " + }, + { + "bbox": [ + 305, + 656, + 547, + 713 + ], + "type": "inline_equation", + "content": "(D)" + }, + { + "bbox": [ + 305, + 656, + 547, + 713 + ], + "type": "text", + "content": " for the generator is identical to the teacher-student disagreement term [5, 10]. Instead, for teacher-student discrepancy maximization we use the Jensen-Shannon (JS) " + }, + { + "bbox": [ + 305, + 656, + 547, + 713 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{JS})" + }, + { + "bbox": [ + 305, + 656, + 547, + 713 + ], + "type": "text", + "content": " divergence. Our motivation to use JS divergence is based on empirical study by Binici et" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7790" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 45, + 62, + 545, + 135 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 62, + 545, + 135 + ], + "spans": [ + { + "bbox": [ + 45, + 62, + 545, + 135 + ], + "type": "text", + "content": "Table 1. Distillation results of the Adversarial DFKD methods on four image classification benchmark datasets, SVHN [23], CIFAR10 [17], CIFAR100 [17], Tiny-ImageNet [18]. Primarily we compare against the replay-based methods, present at the bottom panel of each dataset. The best numbers from our method on the Bank-based replay are highlighted in Navy, and for the one with Generative replay are highlighted in Maroon. The first five columns represent the " + }, + { + "bbox": [ + 45, + 62, + 545, + 135 + ], + "type": "inline_equation", + "content": "\\mu[S_{Acc}]" + }, + { + "bbox": [ + 45, + 62, + 545, + 135 + ], + "type": "text", + "content": " and the " + }, + { + "bbox": [ + 45, + 62, + 545, + 135 + ], + "type": "inline_equation", + "content": "\\sigma^2[S_{Acc}]" + }, + { + "bbox": [ + 45, + 62, + 545, + 135 + ], + "type": "text", + "content": " at different epoch percentiles (described in Section 4.1). The last column represents the maximum test accuracy (" + }, + { + "bbox": [ + 45, + 62, + 545, + 135 + ], + "type": "inline_equation", + "content": "\\mathrm{Acc}_{\\max}" + }, + { + "bbox": [ + 45, + 62, + 545, + 135 + ], + "type": "text", + "content": ") attained by the student network in the entire training period. Note: For SVHN and Tiny-ImageNet we were unable to reproduce the results of DFQ [5] in our setting. DFKD* denotes the baseline method without any replay." + } + ] + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 70, + 141, + 522, + 529 + ], + "blocks": [ + { + "bbox": [ + 70, + 141, + 522, + 529 + ], + "lines": [ + { + "bbox": [ + 70, + 141, + 522, + 529 + ], + "spans": [ + { + "bbox": [ + 70, + 141, + 522, + 529 + ], + "type": "table", + "html": "
Method>0thPercentile>20thPercentile>40thPercentile>60thPercentile>80thPercentileAccmax(%)
μ↑σ2↓μ↑σ2↓μ↑σ2↓μ↑σ2↓μ↑σ2↓
SVHN [23]
TAcc=97.45%, SAcc=97.26%, (Emax)=200
DAFL [4]85.15241.7090.498.7191.753.7392.880.8493.590.2094.23
DFAD [10]92.5293.4294.100.0794.130.0794.140.0894.110.0794.68
DFKD* [3]91.848.5992.536.7693.434.3994.611.7295.720.0495.97
MB-DFKD [3]89.5121.1389.2824.3188.6530.6387.1438.6781.6715.2494.11
PRE-DFKD [2]94.082.1194.531.1194.970.6695.420.2695.850.0496.10
Ours-1 (Memory Bank)92.787.4493.692.2194.251.5494.940.6295.590.0595.88
Ours-2 (Generative Replay)94.481.6494.790.7195.090.5195.470.2795.910.0396.15
CIFAR10 [17]
TAcc=95.72%, SAcc=95.23%, (Emax)=200
DAFL [4]62.78629.1372.51296.4480.57110.1887.0522.4090.762.0492.23
DFAD [10]85.88152.3989.9111.9591.465.6692.660.0792.850.0493.21
DFQ [5]81.2598.8684.4137.4087.0819.5989.726.1591.870.6792.90
DFKD* [3]83.57107.7786.9418.4288.6711.8390.674.0992.320.3893.02
MB-DFKD [3]84.2995.7487.2516.4788.8111.0090.713.7992.310.3593.03
PRE-DFKD [2]87.1045.6988.8810.8690.246.2691.751.8492.920.1493.41
Ours-1 (Memory Bank)85.5391.3388.5812.6689.968.2691.662.8693.090.3293.73
Ours-2 (Generative Replay)88.0760.8690.525.2091.443.1892.451.4593.480.1894.02
CIFAR100 [17]
TAcc=77.94%, SAcc=77.10%, (Emax)=400
DAFL [4]52.48437.8861.6582.9665.8832.2369.2311.9372.231.2973.78
DFAD [10]59.62192.6465.3228.8467.712.7268.690.3369.070.2469.73
DFQ [5]68.2055.9170.459.7871.775.8873.192.2274.480.4775.39
DFKD* [3]69.3674.6772.358.3473.594.5474.861.6375.940.1876.51
MB-DFKD [3]66.05207.2971.1614.6772.975.6174.401.5675.480.1876.14
PRE-DFKD [2]70.2386.6373.396.7774.592.8875.601.0376.460.1276.93
Ours-1 (Memory Bank)69.8775.6772.968.7274.304.0375.491.3876.500.2077.11
Ours-2 (Generative Replay)71.4960.1774.164.6175.122.2676.010.7476.750.0977.21
Tiny-ImageNet [18]
TAcc=60.83%, SAcc=57.88%, (Emax)=500
DAFL [4]21.04106.0424.9552.5628.1028.2231.1911.4434.111.3335.46
DFAD [10]14.6022.3216.487.4817.761.4318.440.2618.840.1019.60
DFKD* [3]34.5586.2038.1723.8840.2813.2842.405.3244.380.7945.61
MB-DFKD [3]34.16122.9238.7732.4241.1818.4043.638.4846.171.7347.96
PRE-DFKD [2]38.8980.1242.2721.6644.2512.6146.315.4748.331.3949.94
Ours-1 (Memory Bank)36.3494.6240.0326.2042.2813.7644.435.8246.520.9547.96
Ours-2 (Generative Replay)39.0974.9542.3021.2344.1913.5546.306.2148.481.1249.88
", + "image_path": "c904284e96e62ac8471e8f4278afaced668abbe598451f2caf94c45510210de7.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 540, + 173, + 551 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 540, + 173, + 551 + ], + "spans": [ + { + "bbox": [ + 46, + 540, + 173, + 551 + ], + "type": "text", + "content": "al. [3]. Hence, " + }, + { + "bbox": [ + 46, + 540, + 173, + 551 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 46, + 540, + 173, + 551 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 114, + 566, + 228, + 579 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 114, + 566, + 228, + 579 + ], + "spans": [ + { + "bbox": [ + 114, + 566, + 228, + 579 + ], + "type": "interline_equation", + "content": "D (a, b) = \\mathcal {L} _ {J S} (p (a), p (b)),", + "image_path": "15031e2bcc40c0a2d9c0ca63a6df39f6a9b02084a7b536d958561089e8828613.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 582, + 289, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 582, + 289, + 604 + ], + "spans": [ + { + "bbox": [ + 47, + 582, + 289, + 604 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{JS}(p(a),p(b)) = \\frac{1}{2} (\\mathcal{L}_{KL}(p(a),m) + \\mathcal{L}_{KL}(p(b),m))," + }, + { + "bbox": [ + 47, + 582, + 289, + 604 + ], + "type": "text", + "content": " and" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 123, + 605, + 287, + 628 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 123, + 605, + 287, + 628 + ], + "spans": [ + { + "bbox": [ + 123, + 605, + 287, + 628 + ], + "type": "interline_equation", + "content": "m = \\frac {1}{2} (p (a) + p (b)). \\tag {11}", + "image_path": "ef42d801e697bf0ea5ffea8a01f534548b5378774bdd897b0be658c04481b6cf.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 641, + 287, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 287, + 677 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 287, + 677 + ], + "type": "text", + "content": "Here " + }, + { + "bbox": [ + 46, + 641, + 287, + 677 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{KL}" + }, + { + "bbox": [ + 46, + 641, + 287, + 677 + ], + "type": "text", + "content": " stands for the Kullback-Leibler divergence and " + }, + { + "bbox": [ + 46, + 641, + 287, + 677 + ], + "type": "inline_equation", + "content": "p(a)" + }, + { + "bbox": [ + 46, + 641, + 287, + 677 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 641, + 287, + 677 + ], + "type": "inline_equation", + "content": "p(b)" + }, + { + "bbox": [ + 46, + 641, + 287, + 677 + ], + "type": "text", + "content": " denote the probability vectors obtained after the Softmax applied to the arguments " + }, + { + "bbox": [ + 46, + 641, + 287, + 677 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 46, + 641, + 287, + 677 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 641, + 287, + 677 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 46, + 641, + 287, + 677 + ], + "type": "text", + "content": ", respectively." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 678, + 288, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 678, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 678, + 288, + 712 + ], + "type": "text", + "content": "Moreover, the prior loss " + }, + { + "bbox": [ + 46, + 678, + 288, + 712 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathcal{P}}" + }, + { + "bbox": [ + 46, + 678, + 288, + 712 + ], + "type": "text", + "content": " [3] is defined as the combination of three loss functions " + }, + { + "bbox": [ + 46, + 678, + 288, + 712 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{OH},\\mathcal{L}_A," + }, + { + "bbox": [ + 46, + 678, + 288, + 712 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 678, + 288, + 712 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{EM})" + }, + { + "bbox": [ + 46, + 678, + 288, + 712 + ], + "type": "text", + "content": " that capture different characteristics from the teacher model and" + } + ] + } + ], + 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"116787acfab1f4a653cca22f5b1d64be14d1b816ef7d035db6888502a6de7fe3.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 599, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 599, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 599, + 547, + 714 + ], + "type": "text", + "content": "where, " + }, + { + "bbox": [ + 304, + 599, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 304, + 599, + 547, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 599, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 304, + 599, + 547, + 714 + ], + "type": "text", + "content": " denote the weighing scalar coefficients. " + }, + { + "bbox": [ + 304, + 599, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{OH}" + }, + { + "bbox": [ + 304, + 599, + 547, + 714 + ], + "type": "text", + "content": " is the one-hot loss that forces the generated samples to have strong (one-hot vector like) predictions when input to the teacher. 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Student learning curves depicting the learning evolution of Wide-ResNet (WRN) [32]. The WRN-16-1 (top-row), and WRN-16-2 (bottom-row) networks are distilled by a WRN-40-2 teacher network pre-trained on CIFAR10 (" + }, + { + "bbox": [ + 46, + 191, + 547, + 224 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{Acc} = 94.87\\%" + }, + { + "bbox": [ + 46, + 191, + 547, + 224 + ], + "type": "text", + "content": "). Each column represents the learning curves with the Buffer-based (with different memory buffer sizes) and Generative replay schemes. The proposed method is in Blue." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 228, + 157, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 228, + 157, + 239 + ], + "spans": [ + { + "bbox": [ + 47, + 228, + 157, + 239 + ], + "type": "text", + "content": "Hence, " + }, + { + "bbox": [ + 47, + 228, + 157, + 239 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{OH}" + }, + { + "bbox": [ + 47, + 228, + 157, + 239 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 96, + 247, + 288, + 280 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 247, + 288, + 280 + ], + "spans": [ + { + "bbox": [ + 96, + 247, + 288, + 280 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {O H} = - \\frac {1}{N} \\sum_ {n = 1} ^ {N} e _ {c} ^ {\\top} \\log \\left(p _ {\\mathcal {T}} \\left(\\hat {x} _ {n}\\right)\\right). \\tag {13}", + "image_path": "46e8c52ffabaaf993fdba4a3349c09a79390e59b60e3808ceca8616359ea4057.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 288, + 288, + 322 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 288, + 288, + 322 + ], + "spans": [ + { + "bbox": [ + 46, + 288, + 288, + 322 + ], + "type": "text", + "content": "- " + }, + { + "bbox": [ + 46, + 288, + 288, + 322 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_A" + }, + { + "bbox": [ + 46, + 288, + 288, + 322 + ], + "type": "text", + "content": " is the activation loss motivated by the notion that meaningful inputs result in higher-valued activation maps in a well-trained network [4] and is defined as:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 96, + 331, + 288, + 364 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 331, + 288, + 364 + ], + "spans": [ + { + "bbox": [ + 96, + 331, + 288, + 364 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {A} = - \\frac {1}{N L} \\sum_ {n = 1} ^ {N} \\sum_ {l = 1} ^ {L} \\| \\mathcal {A} _ {\\mathcal {T}} ^ {l} (\\hat {x} _ {n}) \\| _ {1}, \\tag {14}", + "image_path": "829e864994561ad54d7aec429d6b7497988f778d4e6807cc6c741ecfe391e9ce.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 373, + 287, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 373, + 287, + 396 + ], + "spans": [ + { + "bbox": [ + 46, + 373, + 287, + 396 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 373, + 287, + 396 + ], + "type": "inline_equation", + "content": "\\mathcal{A}_{\\mathcal{T}}^{l}(\\hat{x}_{n})" + }, + { + "bbox": [ + 46, + 373, + 287, + 396 + ], + "type": "text", + "content": " denotes the activation of the " + }, + { + "bbox": [ + 46, + 373, + 287, + 396 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 46, + 373, + 287, + 396 + ], + "type": "text", + "content": "-th layer in the Teacher network " + }, + { + "bbox": [ + 46, + 373, + 287, + 396 + ], + "type": "inline_equation", + "content": "(\\mathcal{T}_{\\theta \\tau})" + }, + { + "bbox": [ + 46, + 373, + 287, + 396 + ], + "type": "text", + "content": " for the input " + }, + { + "bbox": [ + 46, + 373, + 287, + 396 + ], + "type": "inline_equation", + "content": "n^{th}" + }, + { + "bbox": [ + 46, + 373, + 287, + 396 + ], + "type": "text", + "content": " input " + }, + { + "bbox": [ + 46, + 373, + 287, + 396 + ], + "type": "inline_equation", + "content": "\\hat{x}_n\\in \\hat{x}" + }, + { + "bbox": [ + 46, + 373, + 287, + 396 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 396, + 287, + 466 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 396, + 287, + 466 + ], + "spans": [ + { + "bbox": [ + 46, + 396, + 287, + 466 + ], + "type": "text", + "content": "- " + }, + { + "bbox": [ + 46, + 396, + 287, + 466 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{EM}" + }, + { + "bbox": [ + 46, + 396, + 287, + 466 + ], + "type": "text", + "content": " loss is the entropy-maximization term imposed on the generator to output an equal number of pseudo-samples from each category [5]. In other words, the intra-batch entropy is maximized, resulting in a similar number of samples for each category i.e. if " + }, + { + "bbox": [ + 46, + 396, + 287, + 466 + ], + "type": "inline_equation", + "content": "\\bar{p}_{\\mathcal{T}} = \\frac{1}{N}\\sum_{n=1}^{N}p_{\\mathcal{T}}(\\hat{x}_n)" + }, + { + "bbox": [ + 46, + 396, + 287, + 466 + ], + "type": "text", + "content": ", then the loss " + }, + { + "bbox": [ + 46, + 396, + 287, + 466 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{EM}" + }, + { + "bbox": [ + 46, + 396, + 287, + 466 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 123, + 474, + 288, + 488 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 123, + 474, + 288, + 488 + ], + "spans": [ + { + "bbox": [ + 123, + 474, + 288, + 488 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {E M} = \\bar {p} _ {\\mathcal {T}} ^ {\\top} \\log (\\bar {p} _ {\\mathcal {T}}). \\tag {15}", + "image_path": "0a51bdcf69d8759445754f167ba934b61763d1c61d5972037c11c94f0a38f856.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 497, + 270, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 497, + 270, + 509 + ], + "spans": [ + { + "bbox": [ + 47, + 497, + 270, + 509 + ], + "type": "text", + "content": "In sum, the Generator loss objective " + }, + { + "bbox": [ + 47, + 497, + 270, + 509 + ], + "type": "inline_equation", + "content": "(\\mathcal{L}_{\\mathcal{G}})" + }, + { + "bbox": [ + 47, + 497, + 270, + 509 + ], + "type": "text", + "content": " is defined as:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 517, + 287, + 540 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 517, + 287, + 540 + ], + "spans": [ + { + "bbox": [ + 47, + 517, + 287, + 540 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathcal {G}} \\left(\\theta_ {\\mathcal {G}}\\right) = - D \\left(\\mathcal {T} _ {\\theta_ {\\mathcal {T}}} \\left(\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)\\right), \\mathcal {S} _ {\\theta_ {\\mathcal {S}}} \\left(\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)\\right)\\right) + \\mathcal {L} _ {\\mathcal {P}} \\left(\\mathcal {G} _ {\\theta_ {\\mathcal {G}}} (z)\\right). \\tag {16}", + "image_path": "1587adfdab90794b16f7fc763f04a7853970fed8dc2cf57e96c246ce8b242769.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 562, + 128, + 575 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 562, + 128, + 575 + ], + "spans": [ + { + "bbox": [ + 47, + 562, + 128, + 575 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 582, + 174, + 594 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 582, + 174, + 594 + ], + "spans": [ + { + "bbox": [ + 47, + 582, + 174, + 594 + ], + "type": "text", + "content": "4.1. Experimental Settings" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 598, + 287, + 633 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 598, + 287, + 633 + ], + "spans": [ + { + "bbox": [ + 46, + 598, + 287, + 633 + ], + "type": "text", + "content": "Datasets: We evaluate the proposed method on SVHN [23], CIFAR10 [17], CIFAR100 [17] and Tiny-ImageNet [18] datasets." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 46, + 633, + 288, + 701 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 633, + 288, + 701 + ], + "spans": [ + { + "bbox": [ + 46, + 633, + 288, + 701 + ], + "type": "text", + "content": "Teacher model: For CIFAR10 and CIFAR100 datasets, we used the pre-trained teacher models made available by the authors of [11] and [3], respectively. For SVHN and TinyImageNet we trained the teacher network from scratch. We provide the training details of the teacher models in Section B.1 of the Supplemental Material." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 47, + 701, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 701, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 701, + 288, + 713 + ], + "type": "text", + "content": "Definition of an Epoch in DFKD: In Adversarial DFKD," + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "spans": [ + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "text", + "content": "the notion of an epoch is obscure. In a typical deep neural network-based classification training, an epoch is defined as one complete pass over the available training data. However, DFKD has no access to the training data; instead, the pseudo samples generated on-the-fly are used to distill knowledge to the student network. Therefore, prior works [3,5,10] defined an epoch in terms of a fixed number of training iterations " + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "inline_equation", + "content": "(\\mathcal{I})" + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "text", + "content": " where each iteration consists of a set number of generator update steps " + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "inline_equation", + "content": "(g)" + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "text", + "content": " and student update steps " + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "inline_equation", + "content": "(s)" + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "text", + "content": ". Hence, to be consistent across the baselines and prior arts, we use the same number of training iterations, generator update steps, and student updates steps to define an epoch. For all the methods, we set " + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "inline_equation", + "content": "\\mathcal{I} = 72" + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "inline_equation", + "content": "g = 1" + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "inline_equation", + "content": "s = 10" + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "text", + "content": " and use a batch size of 512 of the sampled noise " + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "inline_equation", + "content": "(z)" + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "text", + "content": " to generate the pseudo samples and optimize the parameters " + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "inline_equation", + "content": "\\theta_{\\mathcal{G}}" + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "inline_equation", + "content": "\\theta_{\\mathcal{S}}" + }, + { + "bbox": [ + 304, + 228, + 547, + 398 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 398, + 547, + 421 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 398, + 547, + 421 + ], + "spans": [ + { + "bbox": [ + 304, + 398, + 547, + 421 + ], + "type": "text", + "content": "Training Details: The training details are provided in Section B of the Supplemental Material." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "spans": [ + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "text", + "content": "Evaluation: We evaluate the methods by comparing the mean and variance of the student network's test accuracy " + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "inline_equation", + "content": "(S_{Acc})" + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "text", + "content": ", denoting them as " + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "inline_equation", + "content": "\\mu[S_{Acc}]" + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "inline_equation", + "content": "\\sigma^2[S_{Acc}]" + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "text", + "content": ", respectively, across the epochs, motivated by Binci et al. [2]. Specifically, we compare the different sections of the student's learning evolution by partitioning them into different epoch percentiles. For example, computing the " + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "inline_equation", + "content": "\\mu[S_{Acc}]" + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "inline_equation", + "content": "\\sigma^2[S_{Acc}]" + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "text", + "content": " for epochs greater than the " + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "inline_equation", + "content": "n^{th}" + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "text", + "content": " percentile conveys the mean and variance across all the epochs greater than the " + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "inline_equation", + "content": "\\frac{n}{100}^{th}" + }, + { + "bbox": [ + 304, + 421, + 547, + 536 + ], + "type": "text", + "content": " of the total number of training epochs." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 546, + 448, + 558 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 546, + 448, + 558 + ], + "spans": [ + { + "bbox": [ + 306, + 546, + 448, + 558 + ], + "type": "text", + "content": "4.2. Results and Observations" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 564, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 564, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 564, + 547, + 713 + ], + "type": "text", + "content": "Baseline and State-of-the-art Comparisons In Table 1, we analyze our method on classification task and compare it with prior Adversarial DFKD methods [4,5,10] and closely related memory-based Adversarial DFKD methods [2,3]. For a fair comparison across the methods, we re-implemented all the methods and used the same generator architecture to generate the pseudo samples. For each of the methods we report the " + }, + { + "bbox": [ + 304, + 564, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\mu[S_{Acc}]" + }, + { + "bbox": [ + 304, + 564, + 547, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 564, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\sigma^2[S_{Acc}]" + }, + { + "bbox": [ + 304, + 564, + 547, + 713 + ], + "type": "text", + "content": " at different epoch percentiles and the maximum accuracy (" + }, + { + "bbox": [ + 304, + 564, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{Acc}_{\\max}" + }, + { + "bbox": [ + 304, + 564, + 547, + 713 + ], + "type": "text", + "content": ") attained by the student. We observe that, compared to MB-DFKD (Memory Bank) [3], Ours-1 (Memory Bank) demonstrates consistent improvement across all the datasets. Similarly, compared to PRE-DFKD (Generative Replay) [2], utilizing the same VAE" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "7792" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 48, + 71, + 548, + 101 + ], + "blocks": [ + { + "bbox": [ + 48, + 71, + 548, + 101 + ], + "lines": [ + { + "bbox": [ + 48, + 71, + 548, + 101 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 548, + 101 + ], + "type": "table", + "html": "
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", + "image_path": "8523c8d95fa877d5413d6362cba29d353a2b4f53ae73d9c684f9516cbd28028e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 46, + 106, + 547, + 149 + ], + "lines": [ + { + "bbox": [ + 46, + 106, + 547, + 149 + ], + "spans": [ + { + "bbox": [ + 46, + 106, + 547, + 149 + ], + "type": "text", + "content": "Table 2. Classification accuracy (in %) of the student trained using various DFKD methods on CIFAR100 with ResNet-34 [15] as the teacher and ResNet-18 [15] as the student. " + }, + { + "bbox": [ + 46, + 106, + 547, + 149 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{Acc}" + }, + { + "bbox": [ + 46, + 106, + 547, + 149 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 106, + 547, + 149 + ], + "type": "inline_equation", + "content": "S_{Acc}" + }, + { + "bbox": [ + 46, + 106, + 547, + 149 + ], + "type": "text", + "content": " denote the Teacher network's and the Student network's accuracy, respectively, and " + }, + { + "bbox": [ + 46, + 106, + 547, + 149 + ], + "type": "inline_equation", + "content": "\\Delta \\mathcal{T}_{Acc} - \\mathcal{S}_{Acc}" + }, + { + "bbox": [ + 46, + 106, + 547, + 149 + ], + "type": "text", + "content": " denotes the difference between the Teacher and Student accuracies. Also, " + }, + { + "bbox": [ + 46, + 106, + 547, + 149 + ], + "type": "inline_equation", + "content": "^{\\mathrm{a}},^{\\mathrm{b}},^{\\mathrm{c}}" + }, + { + "bbox": [ + 46, + 106, + 547, + 149 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 106, + 547, + 149 + ], + "type": "inline_equation", + "content": "^{\\mathrm{d}}" + }, + { + "bbox": [ + 46, + 106, + 547, + 149 + ], + "type": "text", + "content": " denote results produced by [34], [11], [3], and our implementation, respectively." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 152, + 287, + 176 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 152, + 287, + 176 + ], + "spans": [ + { + "bbox": [ + 46, + 152, + 287, + 176 + ], + "type": "text", + "content": "decoder architecture as the generative replay, we observe a similar trend for Ours-2 (Generative Replay)." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 177, + 288, + 358 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 177, + 288, + 358 + ], + "spans": [ + { + "bbox": [ + 46, + 177, + 288, + 358 + ], + "type": "text", + "content": "Moreover, in Figure 4, we visualize the learning curves of the student networks trained on multiple datasets. We plot the test accuracy of the student at the end of each training epoch. The proposed method exhibits significant improvement in the learning evolution and the peak accuracies achieved, suggesting that the proposed approach can retain the knowledge from previously encountered samples as the learning progresses. However, on Tiny-ImageNet, with Generative Replay, we did not observe substantial improvements; we conjecture that this may be due to the complexity of the dataset, and the inability to capture crucial samples as replay for the complex dataset, for a large number of epochs. Also, with Generative Replay we sometimes faced difficulty in training a VAE on a stream of synthetic samples (especially for complex dataset like Tiny-ImageNet) as it suffers due to the distribution drift of its own." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 361, + 288, + 542 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 361, + 288, + 542 + ], + "spans": [ + { + "bbox": [ + 46, + 361, + 288, + 542 + ], + "type": "text", + "content": "Additionally, we emphasize that we do not strive toward achieving state-of-the-art student classification accuracy (requiring extensive hyper-parameter tuning) in the DFKD setting, but verify the viability of our hypothesis of retaining the previously acquired knowledge while learning on new samples. Nonetheless, we observe that our method improves upon the student classification accuracy on CIFAR100 [17] compared to the contemporary works and the current state-of-the-art [2] with the ResNet-34 [15] " + }, + { + "bbox": [ + 46, + 361, + 288, + 542 + ], + "type": "inline_equation", + "content": "(\\mathcal{T})" + }, + { + "bbox": [ + 46, + 361, + 288, + 542 + ], + "type": "text", + "content": " and ResNet-18 [15] " + }, + { + "bbox": [ + 46, + 361, + 288, + 542 + ], + "type": "inline_equation", + "content": "(S)" + }, + { + "bbox": [ + 46, + 361, + 288, + 542 + ], + "type": "text", + "content": " setup, as shown in Table 2. Additionally, since previous works use the same teacher network with different test accuracies, we also report the teacher accuracies of the respective methods used to distill the knowledge to the student. Nonetheless, we also compute the Teacher-Student accuracy difference " + }, + { + "bbox": [ + 46, + 361, + 288, + 542 + ], + "type": "inline_equation", + "content": "(\\Delta_{\\mathcal{T}_{Acc} - \\mathcal{S}_{Acc}})" + }, + { + "bbox": [ + 46, + 361, + 288, + 542 + ], + "type": "text", + "content": " to assess the distance of the student from its teacher in terms of classification accuracy." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 542, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 542, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 542, + 288, + 714 + ], + "type": "text", + "content": "Interpreting the Result: Because of the proposed student update strategy, we observe a global monotonicity in the student's learning evolution which the existing approaches with naive replay [2, 3] claim, but do not warrant (described in Section 3.3). The global monotonicity in the learning evolution encompasses crucial advantages. For example, when the validation data is unavailable, the developer cannot assess the student's accuracy and identify the best parameters for the student. In such a scenario, the final accuracy is dependent on the random termination epoch set by the developer. In other words, the ideal DFKD approach should sustain high accuracy via monotonically enhancing it during the course of distillation. Therefore, " + }, + { + "bbox": [ + 46, + 542, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mu[S_{Acc}]" + }, + { + "bbox": [ + 46, + 542, + 288, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 542, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\sigma^2[S_{Acc}]" + }, + { + "bbox": [ + 46, + 542, + 288, + 714 + ], + "type": "text", + "content": " contribute as crucial metrics to assess the distillation method as opposed to the maximum accuracy (" + }, + { + "bbox": [ + 46, + 542, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{Acc}_{\\max}" + }, + { + "bbox": [ + 46, + 542, + 288, + 714 + ], + "type": "text", + "content": "), since the " + }, + { + "bbox": [ + 46, + 542, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\mathrm{Acc}_{\\max}" + }, + { + "bbox": [ + 46, + 542, + 288, + 714 + ], + "type": "text", + "content": " value can" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 152, + 545, + 198 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 152, + 545, + 198 + ], + "spans": [ + { + "bbox": [ + 304, + 152, + 545, + 198 + ], + "type": "text", + "content": "be attained at any point of time prior to the termination, and can be misleading. The improvements in the monotonicity and the " + }, + { + "bbox": [ + 304, + 152, + 545, + 198 + ], + "type": "inline_equation", + "content": "\\mu [S_{Acc}]" + }, + { + "bbox": [ + 304, + 152, + 545, + 198 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 152, + 545, + 198 + ], + "type": "inline_equation", + "content": "\\sigma^2 [S_{Acc}]" + }, + { + "bbox": [ + 304, + 152, + 545, + 198 + ], + "type": "text", + "content": " values of proposed method are evident from Table 1, Figure 4 and Figure 5." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 198, + 547, + 334 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 198, + 547, + 334 + ], + "spans": [ + { + "bbox": [ + 304, + 198, + 547, + 334 + ], + "type": "text", + "content": "Architecture Scalability: The proposed student update strategy is generic and is scalable across different neural network architecture families since the method is not constrained to a particular choice of architecture. From the ResNet-18 [15], WRN-16-1 [32] and WRN-16-2 [32] student learning curves (in Figure 4 and Figure 5), we observe our method's advantage on both the network architectures. Moreover, for large student network architectures (Deeper or Wider) that include complex layers, the proposed method efficiently handles the intricacies with regard to computing the Hessian and the Hessian-product, which becomes highly essential for cumbersome models." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 335, + 547, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 335, + 547, + 517 + ], + "spans": [ + { + "bbox": [ + 304, + 335, + 547, + 517 + ], + "type": "text", + "content": "Improvement across Replay Schemes: Furthermore, the proposed method is agnostic to the memory scheme employed for replay, as demonstrated by the experiments (in Table 1, Figure 4 and Figure 5) using a Memory Buffer and Generative-Replay, thus, rendering our method generalized to the choice of replay. In Table 1 and Figure 5, we can observe that the proposed method enhances the student's performance on both the replay schemes (Memory Bank and Generative Replay) used in the prior arts. Moreover, we experiment with different memory buffer sizes on WRN-16-1 [32] and WRN-16-2 [32] distillation (in Figure 5) and observe consistent and substantial improvements across different memory sizes. Here, the memory size is defined as the maximum number of pseudo-example batches that the bank can contain and each batch consists of randomly sampled 64 examples from " + }, + { + "bbox": [ + 304, + 335, + 547, + 517 + ], + "type": "inline_equation", + "content": "\\hat{x}" + }, + { + "bbox": [ + 304, + 335, + 547, + 517 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 533, + 378, + 544 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 533, + 378, + 544 + ], + "spans": [ + { + "bbox": [ + 306, + 533, + 378, + 544 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 554, + 547, + 701 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 554, + 547, + 701 + ], + "spans": [ + { + "bbox": [ + 304, + 554, + 547, + 701 + ], + "type": "text", + "content": "In this paper, we proposed a meta-learning inspired student update strategy for the Adversarial DFKD setting, that treats Knowledge-Acquisition and Knowledge-Retention as meta-train and meta-test, respectively. The proposed strategy substantially improves the learning evolution of the student network by implicitly aligning the Knowledge-Retention and the Knowledge-Acquisition tasks. The intended effect of having the gradient directions aligned is to obtain student parameters " + }, + { + "bbox": [ + 304, + 554, + 547, + 701 + ], + "type": "inline_equation", + "content": "(\\theta_{S})" + }, + { + "bbox": [ + 304, + 554, + 547, + 701 + ], + "type": "text", + "content": " that have optimal performance on both " + }, + { + "bbox": [ + 304, + 554, + 547, + 701 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Acq}" + }, + { + "bbox": [ + 304, + 554, + 547, + 701 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 554, + 547, + 701 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{Ret}" + }, + { + "bbox": [ + 304, + 554, + 547, + 701 + ], + "type": "text", + "content": ". The conducted experiments on multiple datasets, network architectures, and replay schemes demonstrate the effectiveness, scalability and generalizability of the proposed strategy." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 701, + 527, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 701, + 527, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 701, + 527, + 713 + ], + "type": "text", + "content": "Acknowledgement: Work partially supported by NIH." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "7793" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 132 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 132 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 132 + ], + "type": "text", + "content": "[1] Sravanti Addepalli, Gaurav Kumar Nayak, Anirban Chakraborty, and Venkatesh Babu Radhakrishnan. Degan: Data-enriching gan for retrieving representative samples from a trained classifier. In AAAI, 2020. 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 133, + 288, + 175 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 133, + 288, + 175 + ], + "spans": [ + { + "bbox": [ + 53, + 133, + 288, + 175 + ], + "type": "text", + "content": "[2] Kuluhan Binici, Shivam Aggarwal, Nam Trung Pham, Karianto Leman, and Tulika Mitra. Robust and resource-efficient data-free knowledge distillation by generative pseudo replay. In AAAI, 2022. 2, 3, 4, 6, 7, 8" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 175, + 288, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 175, + 288, + 217 + ], + "spans": [ + { + "bbox": [ + 53, + 175, + 288, + 217 + ], + "type": "text", + "content": "[3] Kuluhan Binici, Nam Trung Pham, Tulika Mitra, and Kario \nLeman. Preventing catastrophic forgetting and distribution \nmismatch in knowledge distillation via synthetic data. In \nWACV, 2022. 2, 3, 4, 6, 7, 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 217, + 288, + 258 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 217, + 288, + 258 + ], + "spans": [ + { + "bbox": [ + 53, + 217, + 288, + 258 + ], + "type": "text", + "content": "[4] Hanting Chen, Yunhe Wang, Chang Xu, Zhaohui Yang, Chuanjian Liu, Boxin Shi, Chunjing Xu, Chao Xu, and Qi Tian. Dafi: Data-free learning of student networks. In ICCV, 2019. 1, 2, 5, 6, 7, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 259, + 288, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 259, + 288, + 300 + ], + "spans": [ + { + "bbox": [ + 53, + 259, + 288, + 300 + ], + "type": "text", + "content": "[5] Yoojin Choi, Jihwan Choi, Mostafa El-Khamy, and Jungwon Lee. Data-free network quantization with adversarial knowledge distillation. In CVPR Workshops, 2020. 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 301, + 288, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 301, + 288, + 331 + ], + "spans": [ + { + "bbox": [ + 53, + 301, + 288, + 331 + ], + "type": "text", + "content": "[6] Yatin Dandi, Luis Barba, and Martin Jaggi. Implicit gradient alignment in distributed and federated learning. In AAAI, 2022. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 333, + 288, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 333, + 288, + 384 + ], + "spans": [ + { + "bbox": [ + 53, + 333, + 288, + 384 + ], + "type": "text", + "content": "[7] Matthias De Lange, Rahaf Aljundi, Marc Masana, Sarah Parisot, Xu Jia, Aleš Leonardis, Gregory Slabaugh, and Tinne Tuytelaars. A continual learning survey: Defying forgetting in classification tasks. IEEE TPAMI, 44(7):3366-3385, 2021. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 386, + 288, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 386, + 288, + 437 + ], + "spans": [ + { + "bbox": [ + 53, + 386, + 288, + 437 + ], + "type": "text", + "content": "[8] Kien Do, Hung Le, Dung Nguyen, Dang Nguyen, Haripriya Harikumar, Truyen Tran, Santu Rana, and Svetha Venkatesh. Momentum adversarial distillation: Handling large distribution shifts in data-free knowledge distillation. In NeurIPS, 2022. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 438, + 288, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 438, + 288, + 470 + ], + "spans": [ + { + "bbox": [ + 53, + 438, + 288, + 470 + ], + "type": "text", + "content": "[9] Gongfan Fang, Kanya Mo, Xinchao Wang, Jie Song, Shitao Bei, Haofei Zhang, and Mingli Song. Up to 100x faster data-free knowledge distillation. In AAAI, 2022. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 471, + 288, + 501 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 471, + 288, + 501 + ], + "spans": [ + { + "bbox": [ + 48, + 471, + 288, + 501 + ], + "type": "text", + "content": "[10] Gongfan Fang, Jie Song, Chengchao Shen, Xinchao Wang, Da Chen, and Mingli Song. Data-free adversarial distillation. In CVPR, 2020. 1, 2, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 502, + 288, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 502, + 288, + 533 + ], + "spans": [ + { + "bbox": [ + 48, + 502, + 288, + 533 + ], + "type": "text", + "content": "[11] Gongfan Fang, Jie Song, Xinchao Wang, Chen Shen, Xingen Wang, and Mingli Song. Contrastive model inversion for data-free knowledge distillation. In IJCAI, 2021. 1, 7, 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 533, + 288, + 564 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 533, + 288, + 564 + ], + "spans": [ + { + "bbox": [ + 48, + 533, + 288, + 564 + ], + "type": "text", + "content": "[12] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, 2017. 4" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 566, + 288, + 605 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 566, + 288, + 605 + ], + "spans": [ + { + "bbox": [ + 48, + 566, + 288, + 605 + ], + "type": "text", + "content": "[13] Luca Franceschi, Paolo Frasconi, Saverio Salzo, Riccardo Grazzi, and Massimiliano Pontil. Bilevel programming for hyperparameter optimization and meta-learning. In ICML, 2018. 4" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 607, + 288, + 648 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 607, + 288, + 648 + ], + "spans": [ + { + "bbox": [ + 48, + 607, + 288, + 648 + ], + "type": "text", + "content": "[14] Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. Generative adversarial nets. In NeurIPS, 2014. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 650, + 288, + 680 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 650, + 288, + 680 + ], + "spans": [ + { + "bbox": [ + 48, + 650, + 288, + 680 + ], + "type": "text", + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016. 4, 8" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 681, + 288, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 681, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 681, + 288, + 712 + ], + "type": "text", + "content": "[16] Diederik P Kingma, Max Welling, et al. An introduction to variational autoencoders. Foundations and Trends® in Machine Learning, 12(4):307-392, 2019. 4" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 663 + ], + "type": "list", + "angle": 0, + "index": 36, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "type": "text", + "content": "[17] Alex Krizhevsky et al. Learning multiple layers of features from tiny images. 2009. 4, 6, 7, 8" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 95, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 95, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 95, + 545, + 116 + ], + "type": "text", + "content": "[18] Ya Le and Xuan S. Yang. Tiny imagenet visual recognition challenge. 2015. 4, 6, 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 118, + 545, + 137 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 118, + 545, + 137 + ], + "spans": [ + { + "bbox": [ + 308, + 118, + 545, + 137 + ], + "type": "text", + "content": "[19] Yang Liu, Wei Zhang, and Jun Wang. Zero-shot adversarial quantization. In CVPR, 2021. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 139, + 545, + 169 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 139, + 545, + 169 + ], + "spans": [ + { + "bbox": [ + 308, + 139, + 545, + 169 + ], + "type": "text", + "content": "[20] Paul Micaelli and Amos J Storkey. Zero-shot knowledge transfer via adversarial belief matching. In NeurIPS, 2019. 1, 2, 3, 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 171, + 545, + 202 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 171, + 545, + 202 + ], + "spans": [ + { + "bbox": [ + 308, + 171, + 545, + 202 + ], + "type": "text", + "content": "[21] Gaurav Kumar Nayak, Konda Reddy Mopuri, and Anirban Chakraborty. Effectiveness of arbitrary transfer sets for data-free knowledge distillation. In WACV, 2021. 8" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 203, + 545, + 243 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 203, + 545, + 243 + ], + "spans": [ + { + "bbox": [ + 308, + 203, + 545, + 243 + ], + "type": "text", + "content": "[22] Gaurav Kumar Nayak, Konda Reddy Mopuri, Vaisakh Shaj, Venkatesh Babu Radhakrishnan, and Anirban Chakraborty. Zero-shot knowledge distillation in deep networks. In ICML, 2019. 1, 8" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 246, + 545, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 246, + 545, + 297 + ], + "spans": [ + { + "bbox": [ + 308, + 246, + 545, + 297 + ], + "type": "text", + "content": "[23] Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng. Reading digits in natural images with unsupervised feature learning. In NeurIPS Workshop on Deep Learning and Unsupervised Feature Learning, 2011. 4, 6, 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 299, + 545, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 299, + 545, + 330 + ], + "spans": [ + { + "bbox": [ + 308, + 299, + 545, + 330 + ], + "type": "text", + "content": "[24] Mert Bulent Sariyildiz and Ramazan Gokberk Cinbis. Gradient matching generative networks for zero-shot learning. In CVPR, 2019. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 331, + 545, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 331, + 545, + 361 + ], + "spans": [ + { + "bbox": [ + 308, + 331, + 545, + 361 + ], + "type": "text", + "content": "[25] Ari Seff, Alex Beatson, Daniel Suo, and Han Liu. Continual learning in generative adversarial nets. arXiv preprint arXiv:1705.08395, 2017. 1, 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 363, + 545, + 395 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 363, + 545, + 395 + ], + "spans": [ + { + "bbox": [ + 308, + 363, + 545, + 395 + ], + "type": "text", + "content": "[26] Yuge Shi, Jeffrey Seely, Philip Torr, Siddharth N, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve. Gradient matching for domain generalization. In ICLR, 2022. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 396, + 545, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 396, + 545, + 426 + ], + "spans": [ + { + "bbox": [ + 308, + 396, + 545, + 426 + ], + "type": "text", + "content": "[27] Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim. Continual learning with deep generative replay. In NeurIPS, 2017. 2, 3, 4" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 428, + 545, + 468 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 428, + 545, + 468 + ], + "spans": [ + { + "bbox": [ + 308, + 428, + 545, + 468 + ], + "type": "text", + "content": "[28] Ankur Sinha, Pekka Malo, and Kalyanmoy Deb. A review on bilevel optimization: From classical to evolutionary approaches and applications. IEEE TEC, 22(2):276-295, 2018. 4" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 471, + 545, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 471, + 545, + 491 + ], + "spans": [ + { + "bbox": [ + 308, + 471, + 545, + 491 + ], + "type": "text", + "content": "[29] Hoang Thanh-Tung and Truyen Tran. Catastrophic forgetting and mode collapse in gans. In IJCNN, 2020. 1" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 492, + 545, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 492, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 308, + 492, + 545, + 533 + ], + "type": "text", + "content": "[30] Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz. Dreaming to distill: Data-free knowledge transfer via deepinversion. In CVPR, 2020. 1, 8" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 308, + 535, + 545, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 535, + 545, + 566 + ], + "spans": [ + { + "bbox": [ + 308, + 535, + 545, + 566 + ], + "type": "text", + "content": "[31] Jaemin Yoo, Minyong Cho, Taebum Kim, and U Kang. Knowledge extraction with no observable data. In NeurIPS, 2019. 8" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 308, + 567, + 545, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 567, + 545, + 588 + ], + "spans": [ + { + "bbox": [ + 308, + 567, + 545, + 588 + ], + "type": "text", + "content": "[32] Sergey Zagoruyko and Nikos Komodakis. Wide residual networks. In BMVC, 2016. 7, 8" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 308, + 589, + 545, + 620 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 589, + 545, + 620 + ], + "spans": [ + { + "bbox": [ + 308, + 589, + 545, + 620 + ], + "type": "text", + "content": "[33] Sergey Zagoruyko and Nikos Komodakis. Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer. In ICLR, 2017. 3" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 308, + 621, + 545, + 663 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 621, + 545, + 663 + ], + "spans": [ + { + "bbox": [ + 308, + 621, + 545, + 663 + ], + "type": "text", + "content": "[34] Haoran Zhao, Xin Sun, Junyu Dong, Milos Manic, Huiyu Zhou, and Hui Yu. Dual discriminator adversarial distillation for data-free model compression. International Journal of Machine Learning and Cybernetics, 2022. 8" + } + ] + } + ], + "index": 35 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "7794" + } + ] + } + ], + "index": 37 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning To Segment Every Referring Object Point by Point/3793df0f-b6b3-448f-85d5-cdd2cf084f15_content_list.json b/2023/Learning To Segment Every Referring Object Point by Point/3793df0f-b6b3-448f-85d5-cdd2cf084f15_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..414d2a285524ea202ee3d8f757a9ae43e4cbe8ac --- /dev/null +++ b/2023/Learning To Segment Every Referring Object Point by Point/3793df0f-b6b3-448f-85d5-cdd2cf084f15_content_list.json @@ -0,0 +1,1642 @@ +[ + { + "type": "text", + "text": "Learning to Segment Every Referring Object Point by Point", + "text_level": 1, + "bbox": [ + 181, + 130, + 787, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Mengxue $\\mathbf{Q}\\mathbf{u}^{1,2*}$ Yu Wu $^{3}$ Yunchao Wei $^{1,2}$ Wu Liu $^{4}$ Xiaodan Liang $^{5,6}$ Yao Zhao $^{1,2\\dagger}$", + "bbox": [ + 111, + 178, + 862, + 199 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Institute of Information Science, Beijing Jiaotong University $^{2}$ Beijing Key Laboratory of Advanced Information Science and Network Technology $^{3}$ Wuhan University $^{4}$ JD Explore Academy $^{5}$ Sun Yat-sen University $^{6}$ MBZUAI", + "bbox": [ + 147, + 214, + 823, + 268 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "qumengxue@bjtu.edu.cn, wuyucs@whu.edu.cn, yunchao.wei@bjtu.edu.cn", + "bbox": [ + 192, + 272, + 769, + 286 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 321, + 313, + 338 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Referring Expression Segmentation (RES) can facilitate pixel-level semantic alignment between vision and language. Most of the existing RES approaches require massive pixel-level annotations, which are expensive and exhaustive. In this paper, we propose a new partially supervised training paradigm for RES, i.e., training using abundant referring bounding boxes and only a few (e.g., $1\\%$ ) pixel-level referring masks. To maximize the transferability from the REC model, we construct our model based on the point-based sequence prediction model. We propose the co-content teacher-forcing to make the model explicitly associate the point coordinates (scale values) with the referred spatial features, which alleviates the exposure bias caused by the limited segmentation masks. To make the most of referring bounding box annotations, we further propose the resampling pseudo points strategy to select more accurate pseudo-points as supervision. Extensive experiments show that our model achieves $52.06\\%$ in terms of accuracy (versus $58.93\\%$ in fully supervised setting) on RefCOCO+@testA, when only using $1\\%$ of the mask annotations. Code is available at https://github.com/qumengxue/Partial-RES.git.", + "bbox": [ + 75, + 354, + 473, + 686 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 715, + 209, + 732 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Referring Expression Segmentation (RES) aims to generate a segmentation mask for the object referred to by the language expression in the image. It allows pixel-level semantic alignment between language and vision, which is meaningful to many multi-modal tasks and can be applied to various practical applications, e.g., video/image editing with sentences. Benefiting from the development of deep learning techniques, significant progress [31, 9, 12, 2, 19,", + "bbox": [ + 75, + 742, + 468, + 863 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/0c738b0943557bd70738132336cc993faea3caf37d193e08b1f2cdade5282bd3.jpg", + "image_caption": [ + "Figure 1. Comparison of MDETR and SeqTR. On the left is the streamlined framework of the two, and on the right is their IoU performance on RefCOCOg@val. Best viewed in color." + ], + "image_footnote": [], + "bbox": [ + 504, + 321, + 888, + 502 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "20, 27, 54, 9] has been made in this field and achieved remarkable performance.", + "bbox": [ + 498, + 579, + 890, + 609 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The success of current methods partially attributes to the large-scale training dataset with accurate pixel-level masks. However, labeling masks for a huge amount of images is often costly in terms of both human effort and finance, and thus hardly be scaled up. Therefore, it is meaningful to explore a new training paradigm for RES where extensive pixel-level annotations are not necessary. It is well known that bounding box annotations are much cheaper and easier to be collected compared with pixel-level masks. Thus, we try to study this question: is it possible to learn an accurate RES model using abundant box annotations and only a few mask annotations?", + "bbox": [ + 496, + 611, + 892, + 792 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To answer this question, for the first time, we explore a new partially supervised training paradigm for RES (Partial-RES). Intuitively, a naive pipeline for this partially supervised setting is to first train a Referring Expression Comprehension (REC) model and then transfer it to the RES task by fine-tuning on a limited number of data with mask annotations. However, due to the difference between", + "bbox": [ + 496, + 795, + 893, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Work done during an internship at JD Explore Academy.", + "bbox": [ + 94, + 875, + 401, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "Yao Zhao is the corresponding author.", + "bbox": [ + 96, + 887, + 303, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "3021", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "the REC and RES tasks, prevalent models (e.g., Mask R-CNN [14], MDETR [24]) have to simultaneously optimize two different heads, with one for detection prediction and the other for segmentation prediction. Such kind of structures would lead to a severe issue during the fine-tuning stage, i.e., a randomly initialized mask head can not get well optimized given only a few mask annotations (hundreds of images). As shown in Figure 1, it is not easy to optimize MDETR with only $1\\%$ mask-annotated data even transferred from a well-trained REC model.", + "bbox": [ + 75, + 90, + 470, + 241 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recently, a contour-based method named SeqTR has been proposed for unifying REC and RES. The idea is to use a sequence model (usually a Transformer decoder) to sequentially generate contour points of the referred object. The predictions are two points (top-left and bottom-right corner points of the bounding box) in the REC task while dozens of points (contour) are in the RES task. As shown in Fig. 1, both boxes and masks are converted to point sequences and optimized using the same simple cross-entropy loss in SeqTR, which ensures the consistency between REC and RES. It incorporates two optimization heads into a unified one, making the knowledge learned with four points (i.e., detection) be naturally transferred to predict multiple ones (i.e., segmentation). Inspired by SeqTR, we have the conjecture that sequence prediction might be a better solution for partially supervised training.", + "bbox": [ + 75, + 244, + 468, + 487 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we investigate methods for achieving better partially supervised training for RES sequence prediction models. Sequence-to-sequence prediction models are typically trained with Teacher-Forcing (TF) [39], wherein the model utilizes the ground truth token as input to predict the next token during the training stage. However, the model can only predict the next state by taking its previous output as input in inference, which is known as the exposure bias [34]. This issue is even more severe in the partially supervised setting due to the very limited ground truth data. Consequently, we introduce the Co-Content Teacher-Forcing (CCTF), which combines the ground truth point coordinates together with the spatial visual feature of the pointed row or column. In contrast to the previous sequence model SeqTR, our CCTF explicitly associates the point coordinates (scale values) with the referred spatial region, providing a more natural approach for visual grounding.", + "bbox": [ + 75, + 489, + 468, + 747 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Furthermore, we estimate the referring region via our proposed Point-Modulated Cross-Attention, to ensure the decoder attends to those region content while generating the point contour sequences. To fully utilize the data without mask annotation in Partial-RES, we retrain the model with the generated pseudo labels, and we present a Resampling Pseudo Points (RPP) Strategy. Unlike most pseudo-label works that directly use the network's predictions as labels, we select the appropriate pseudo masks with Dice coefficient and then resample the predicted points in a uniform", + "bbox": [ + 75, + 750, + 470, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "way to regularize the contour sequence labels.", + "bbox": [ + 498, + 90, + 805, + 106 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "With extensive experiments, our method displays significant improvement compared to SeqTR [54] baselines on all three benchmarks, i.e., at an average of $3.5\\%$ , $2.4\\%$ , and $3.0\\%$ on $1\\%$ , $5\\%$ and $10\\%$ mask-labeled data. With $10\\%$ mask annotated data, our method achieves $97\\%$ of the fully supervised performance on RefCOCO + @val. We are also able to achieve $88\\%$ of the fully supervised performance only with $1\\%$ mask-labeled data on RefCOCO + @testA.", + "bbox": [ + 496, + 107, + 890, + 227 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 498, + 243, + 640, + 258 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. REC and RES", + "text_level": 1, + "bbox": [ + 500, + 268, + 648, + 284 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Referring Expression Comprehension (REC) aims at grounding the object referred to by a sentence, which achieves instance-level vision language alignment. Compared to REC, Referring Expression Segmentation (RES) grounds language expression at the fine-grained pixel-level. REC and RES are separate but closely related fundamental multi-modal tasks.", + "bbox": [ + 496, + 292, + 890, + 398 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "For REC, previous methods can be roughly divided into two-stage and one-stage. Two-stage methods [16, 18, 29, 40, 41, 43, 49, 52, 55] typically utilize an object detector to generate region proposals in the first stage, and are trained to maximize the similarity between region and text with binary cross-entropy loss or similarity loss. One-stage methods [5, 28, 38, 45, 46] avoid being constrained by the quality of the proposal by directly fusing visual and linguistic features rather than matching region-language pairs.", + "bbox": [ + 496, + 398, + 890, + 536 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "For RES, typical solutions are to incorporate as much linguistic information as possible in various ways on pixel-level visual features [31, 9, 12, 2, 19, 20]. In previous methods, researchers propose various attention mechanisms [9, 23, 20] to better merge vision and language. Recently, since the superior ability of modeling vision and language of Transformer-based model, more high-performance methods [27, 54, 9, 36] are proposed to solve RES tasks.", + "bbox": [ + 496, + 537, + 890, + 656 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "For REC and RES, multi-task methods [32, 27] are proposed to better use the correlation between two tasks, which can facilitate learning of both tasks. However, all the prior work require different task-specific branch and loss function, whose generalization ability is limited. Recently, a simple and universal network term SeqTR [54] is presented, which regards both REC and RES as a point prediction problem. SeqTR greatly reduces the difficulty and complexity of both architecture design and optimization.", + "bbox": [ + 496, + 657, + 890, + 794 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In some work, REC is treated as a pretext task for other multi-modal tasks [24, 51], e.g., Visual Question and Answering. RES has a more fine-grained visual language alignment capability than REC, but the capability of RES to be used in multi-modal pre-training has not yet been developed because of the lack of a large-scale RES dataset. It is well known that annotating binary masks for a multi-", + "bbox": [ + 496, + 795, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "3022", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/1c39c4b36917a7c61a8d8086ad3b865a916301e7cae677b26042e1b5c49f153c.jpg", + "image_caption": [ + "(2) Transformer Encoder and Decoder", + "(1) Visual Language Fusion", + "(3) Sequence Construction", + "(a) Architecture" + ], + "image_footnote": [], + "bbox": [ + 106, + 109, + 566, + 292 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/c6c152cc553746f34be331225a82288f36e19574793e278da8d3b1e103bc6e92.jpg", + "image_caption": [ + "(b) Training Steps", + "Figure 2. (a) Overall architecture of our method. It consists of three main modules: (1) Visual-Language Fusion; (2) Transformer Encoder and Decoder; (3) Sequence Construction. \"CCTF\": Co-Content Teacher-Forcing; \"PMCA\": Point-Modulated Cross-Attention. (b) The suggested training steps of our method in the Partially Supervised RES task. Best viewed in color." + ], + "image_footnote": [], + "bbox": [ + 588, + 97, + 864, + 314 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "modal dataset is expensive while bounding box is easy. In this paper, we make use of the bounding box annotations and the consistent task expression of REC and RES in SeqTR, attempting to train RES task with less mask-labeled data based on SeqTR.", + "bbox": [ + 75, + 419, + 470, + 494 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Partially Supervised Instance Segmentation", + "text_level": 1, + "bbox": [ + 76, + 507, + 447, + 523 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Partially Supervised Instance Segmentation (PSIS) was first raised in [17], where object categories are divided into base and novel splits. Both of them have bounding box annotation while only base have mask annotation. The training target of this task is to generate segmentation for novel categories. PSIS has been studied for a period of time in the field of Instance Segmentation [1, 11, 26, 35, 53]. Some works address this problem by learning class-agnostic mask segmentation models, i.e., separate foreground and background only. They capture class-agnostic cues, e.g., shape [26] and appearance commonalities [11]. For better use of the training data from novel categories, [42] propose ContrastMask to learn a mask segmentation model on both base and novel categories under a unified pixel-level contrastive learning framework. In this paper, we suggest the partially supervised RES, drawing on the partially supervised instance segmentation. We train the RES model with bounding box annotation of all train data, and a small fraction of mask annotation.", + "bbox": [ + 75, + 531, + 468, + 818 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.3. Sequence Prediction in Visual Tasks", + "text_level": 1, + "bbox": [ + 76, + 830, + 390, + 847 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Visual tasks differ greatly in their output format and associated content, making it difficult to handle them with an identical structure. Unlike previous approaches requiring", + "bbox": [ + 75, + 854, + 468, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "prior knowledge in Object Detection, Pix2Seq [4] first cast object detection as a language modeling task conditioned on the observed pixel inputs. The class and the bounding box are expressed as sequences of discrete tokens, and train the detection model to generate the coordinate and class of each tone by one. Built on Pix2Seq, [6] proposes an object-centric vision framework Obj2Seq that takes objects as the basic unit, and human pose estimation is also been converted to a sequence-generated form. In addition, another similar work SeqTR [54] is proposed to unify REC and RES, regarding both REC and RES as a point prediction problem. Inspired by the above-mentioned work, we consider whether this sequence prediction framework can be used in scenarios with few labels to take advantage of its good format uniformly property.", + "bbox": [ + 496, + 419, + 890, + 647 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 500, + 659, + 589, + 674 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we first briefly revisit the sequence prediction model SeqTR in Sec. 3.1. Then we illustrate the architecture of our method in Sec. 3.2. In Sec. 3.3, we elaborate on our proposed Co-Content Teacher-Forcing, and the Point-Modulated Cross Attention in Sec. 3.4 and the Resampling Pseudo Point Strategy in Sec. 3.5.", + "bbox": [ + 496, + 686, + 890, + 777 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Revisit SeqTR", + "text_level": 1, + "bbox": [ + 500, + 786, + 645, + 801 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "SeqTR [54] is a sequence prediction model for Visual Grounding tasks, which first reformulate visual grounding as a point prediction problem. The bounding box and the segmentation mask are serialized into a sequence of discrete coordinate tokens $\\{x_i,y_i\\}_{i = 1}^N$ , where N is 4 vertices of the bounding box on REC or the number of contour", + "bbox": [ + 496, + 809, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "3023", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "points of the segmentation mask on RES. With a task token [TASK], SeqTR can predict the target coordinate tokens in an auto-regressive manner during inference, and end the coordinate sequence with [EOS] token. In addition, different from those loss functions adopted in DETR-like multi-task frameworks (e.g., GIoU loss, set-based matching loss, focal loss, and dice loss), SeqTR only uses a simple cross-entropy loss for REC and RES, which requires no further prior knowledge or expertise. SeqTR greatly reduces the difficulty and complexity of both architecture design and optimization for visual grounding tasks. Meanwhile, it nicely aligns REC and RES, and naturally converts them into one type of expression form. Following SeqTR, our model is also optimized with a simple cross-entropy loss.", + "bbox": [ + 75, + 90, + 472, + 303 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Architecture", + "text_level": 1, + "bbox": [ + 76, + 311, + 210, + 325 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We build our architecture on the latest RES sequence prediction method SeqTR [54], which consists of three main modules: (1) Visual-Language Fusion; (2) Transformer Encoder and Decoder; (3) Sequence Construction; The overall architecture is illustrated in Fig. 2.", + "bbox": [ + 75, + 333, + 468, + 409 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Visual-Language Fusion. We adopt the convolutional backbone DarkNet-53 [37] to obtain the visual representation for an input image $\\mathbf{I}$ . Following SeqTR, we downsample the multi-scale visual feature from the finest to coarsest spatial resolution and flatten it to $F_{v} \\in R^{(H \\times W) \\times C}$ . For referring expressions, we encode the text with a one-layer bidirectional GRU [7] as in SeqTR. Then we concatenate both unidirectional hidden states $h_t = \\left[ \\begin{array}{c} \\vec{h}_t \\\\ h_t, h_t \\end{array} \\right]$ at each step $t$ to get language feature $F_l = \\{h_t\\}_{t=1}^T$ . Before V-L fusion, we adopt max pooling $mp(\\cdot)$ along the channel dimension of $F_i$ . Then visual feature $F_v$ and language feature $F_l$ will be fused to $F_m$ by Hadamard product:", + "bbox": [ + 75, + 409, + 468, + 609 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nF _ {m} = \\delta \\left(F _ {v}\\right) \\odot \\delta \\left(m p \\left(F _ {l}\\right)\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 179, + 618, + 468, + 636 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "here the activation function $\\delta (\\cdot)$ is set to $\\tanh (\\cdot)$ if no special instructions.", + "bbox": [ + 75, + 643, + 468, + 672 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Transformer Encoder and Decoder. We use the Transformer with 6 layers encoder and 3 layers decoder to learn the multi-modal feature and decode out the vertices or the contour points. The hidden dimension of transformer is set to 256. The fused multi-modal feature $F_{m}$ is added sine and learned positional encoding [39] before entering Transformer Encoder. The original Cross-Attention between Transformer Encoder and Decoder will be replaced by Point-Modulated Cross-Attention (PMCA), which is introduced in Sec. 3.4.", + "bbox": [ + 75, + 674, + 468, + 823 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Sequence Construction. Following SeqTR [54], we construct the sequence of floating points $\\{\\tilde{x}_i,\\tilde{y}_i\\}_{i = 1}^N$ with the top-left and bottom-right corner points of the bounding box $(\\mathrm{N} = 2)$ . For the binary mask on RES, the sequence is uniformly sampled N points clockwise on top of the mask on", + "bbox": [ + 75, + 824, + 468, + 901 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/04e57b9dd0bfdf478e8f3e558315fb70e789870632345c3b8030e48bf29b2d19.jpg", + "image_caption": [ + "Figure 3. Sketch of CCTF. We extract multi-modal features from the reshaped Transformer Encoder output according to the target coordinates and fuse them by element-wise addition, e.g., $x_{n}$ corresponds to the $x_{n}W / M$ line of row of the feature map, and $y_{n}$ corresponds to the $y_{n}H / M$ line of column." + ], + "image_footnote": [], + "bbox": [ + 526, + 90, + 857, + 272 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "RES. Then they are quantized into integer bins by", + "bbox": [ + 500, + 378, + 830, + 395 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nx _ {i} = \\operatorname {r o u n d} \\left(\\frac {M \\tilde {x} _ {i}}{w}\\right), y _ {i} = \\operatorname {r o u n d} \\left(\\frac {M \\tilde {y} _ {i}}{h}\\right) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 558, + 400, + 890, + 431 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where each coordinate is normalized by image width $w$ and height $h$ , and $M$ is the number of quantization bins.", + "bbox": [ + 496, + 436, + 890, + 465 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The sequence will be embedded and then combined with the output multi-modal feature of the Transformer Encoder to conduct Co-Content Teacher-Forcing (CCTF) training. Details are described in Sec. 3.3. Moreover, we will resample pseudo points and reconstruct the sequence for model retraining. Details can be found in Sec. 3.5.", + "bbox": [ + 496, + 467, + 890, + 556 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Co-Content Teacher-Forcing", + "text_level": 1, + "bbox": [ + 500, + 566, + 756, + 582 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The Co-Content Teacher-Forcing is proposed to alleviate the over-reliance on the sampled ground truth points during training, which leads to exposure bias of the inconsistency of Training and Inference.", + "bbox": [ + 496, + 589, + 890, + 648 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In the normal Teacher-Forcing training, the sequence $\\{x_i,y_i\\}_{i = 1}^N$ will be embedded to a feature sequence $\\{m_i,n_i\\}_{i = 1}^N$ :", + "bbox": [ + 496, + 648, + 890, + 699 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\left\\{m _ {i}, n _ {i} \\right\\} _ {i = 1} ^ {N} = d i c \\left(\\left\\{x _ {i}, y _ {i} \\right\\} _ {i = 1} ^ {N}\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 586, + 705, + 890, + 726 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $dic(\\cdot)$ is a learnable coordinate embedding dictionary. The coordinate sequence $\\{x_i, y_i\\}_{i=1}^N$ can be viewed as the index of the dictionary. However, in the Co-Content Teacher-Forcing, the feature sequence will be combined with the encoder output multi-modal feature. As shown in Fig. 3, we reshape the output multi-modal feature $F_m \\in R^{H \\times W \\times C'}$ of Transformer Encoder to $F_m' \\in R^{(H \\times W) \\times C'}$ for better correspondence with coordinate points. Then, for each coordinate $x_i$ or $y_i$ , we extract the feature of the corresponding row or column from the feature map $F_m'$ and average them with $E\\{\\cdot\\}$ :", + "bbox": [ + 496, + 732, + 892, + 901 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "3024", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nf _ {x _ {i}} = E _ {k = 1} ^ {H} \\left\\{F _ {m} ^ {\\prime} \\left(\\frac {W}{M} x _ {i}, k\\right) \\right\\}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 173, + 102, + 468, + 138 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nf _ {y _ {i}} = E _ {k = 1} ^ {W} \\{F _ {m} ^ {\\prime} (k, \\frac {H}{M} y _ {i}) \\},\n$$\n", + "text_format": "latex", + "bbox": [ + 174, + 133, + 369, + 162 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$M$ is the number of quantization bins. $\\left\\{\\frac{W}{M} x_i,\\frac{H}{M} y_i\\right\\}_{i = 1}^N$ is the normalized index. Then the sequential feature $\\{m_i,n_i\\}_{i = 1}^N$ will be combined with $\\{f_{x_i},f_{y_i}\\}_{i = 1}^N$ by simple element-wise addition, the final sequence input to Decoder is represented as", + "bbox": [ + 76, + 171, + 467, + 250 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nS = \\left\\{m _ {i}, n _ {i} \\right\\} _ {i = 1} ^ {N} + \\left\\{f _ {x _ {i}}, f _ {y _ {i}} \\right\\} _ {i = 1} ^ {N} \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 158, + 261, + 468, + 282 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "With CCTF training, our model explicitly associate the point coordinates (scale values) with the referred spatial region, which is more natural for visual grounding.", + "bbox": [ + 76, + 292, + 468, + 339 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Point-Modulated Cross Attention", + "text_level": 1, + "bbox": [ + 76, + 345, + 369, + 361 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We adopt the SMCA proposed in [13] to replace the original Cross-Attention, which can constrain the cross-attention responses via estimating the Gaussian-like referring region prior. In the sequence prediction model, we generate the attention map for each co-content coordinates embedding, leading to a matched cross-attention region learned when the supervised data is reduced. To distinguish we denote it as Point-Modulated Cross-Attention (PMCA).", + "bbox": [ + 76, + 369, + 468, + 491 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For each co-content coordinate embedding $S = \\{f_j\\}_{j=1}^{2N} = \\{f_{x_i}, f_{y_i}\\}_{i=1}^N$ , we use a 3-layer MLP followed by a sigmoid activation function to generate a prior Gaussian-like attention map. The center point coordinates $c_w, c_h$ and the scales $s_w, s_h$ of the prior Gaussian map is denoted as", + "bbox": [ + 76, + 492, + 468, + 580 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\ns _ {w}, s _ {h}, c _ {w}, c _ {h} = \\operatorname {s i g m o i d} (M L P (f _ {j})), \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 142, + 595, + 468, + 612 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "and the Gaussian map is represented as", + "bbox": [ + 76, + 625, + 336, + 640 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nG (u, v) = \\exp \\left(- \\frac {\\left(u - c _ {w}\\right) ^ {2}}{\\sigma s _ {w} ^ {2}} - \\frac {\\left(v - c _ {h}\\right) ^ {2}}{\\sigma s _ {h} ^ {2}}\\right) \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 122, + 651, + 468, + 686 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $u\\in R^W,v\\in R^H$ , they are the spatial indices of $G$ and $\\sigma$ is a hyper-parameter to modulate the bandwidth of the Gaussian map.", + "bbox": [ + 76, + 698, + 468, + 744 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.5. Resampling Pseudo Point Strategy", + "text_level": 1, + "bbox": [ + 76, + 755, + 375, + 771 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To fully utilize the data without mask annotation in Partial-RES, we retrain the model with the generated pseudo label, and we propose a Resampling pseudo point strategy. To remain the consistency of the pseudo points with the original sampled contour points of the ground truth mask, we will resample the pseudo points. As shown in Fig. 4, we first connect the points predicted by the network to a binary mask. Then we compute the dice coefficient $D$", + "bbox": [ + 76, + 780, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/8ba1a256ac6fac9e60d66e3bfb6483d36ca05cf399dc1e569addfa0c67839ac4.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 545, + 90, + 702, + 183 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/8650993f31e2beba399ab0343cab2511470a82df2e360194ec2d8013f4f96ad1.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 705, + 98, + 852, + 183 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/5c402b65ae078d8df5d6a84497082ce300bef8eca240bb019c0015b3d0d3a03d.jpg", + "image_caption": [ + "(c)" + ], + "image_footnote": [], + "bbox": [ + 557, + 198, + 702, + 282 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/ad04e0543dd1d95fadac81626c5eea299edf74f08e07df338f30a5e7383fb212.jpg", + "image_caption": [ + "(d)", + "Figure 4. Sketch of Resampling Pseudo Point (RPP) Strategy. (a) Screening the appropriate pseudo mask with the dice coefficient between the predicted binary mask and the ground truth bounding box; (b) The generated points by the RES model; (c) The uniformly resampled pseudo points from pseudo mask; (d) The uniformly sampled target points from ground truth mask." + ], + "image_footnote": [], + "bbox": [ + 707, + 198, + 852, + 282 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "of the projection of this mask and the ground truth bounding box.", + "bbox": [ + 498, + 407, + 890, + 436 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nD (l, b) = d i c e \\left(l _ {y}, b _ {y})\\right) + d i c e \\left(l _ {x}, b _ {x}\\right) \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 436, + 890, + 454 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $l_x, l_y$ are the projection of the mask on x-axis, y-axis, $b_x, b_y$ are of the ground truth bounding box.", + "bbox": [ + 498, + 460, + 888, + 491 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We set a threshold $T$ for $D(l, b)$ to pick out those pseudo masks that are more accurate to avoid introducing too much noise by pseudo labels. For the selected samples, the predicted points are spaced may be inhomogeneous as shown in Fig. 4(b). So we resample the pseudo points with a uniform sampling scheme. It is considered that the contour points of previously labeled data are sampled uniformly and the pseudo point should be kept consistent with it.", + "bbox": [ + 498, + 491, + 890, + 611 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We propose a feasible training process for the Partially Supervised RES task as shown in Fig. 2 (b):", + "bbox": [ + 498, + 612, + 888, + 642 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Step1. REC Training. Since in the Partial-RES task, all the bounding box labels are seen in the model training, we first train the model on fully supervised REC to get a better representation relatively of multi-model features from the encoder output. The feature will be used to enhance TF learning in the next step of RES training. In practice, we load the REC model trained in SeqTR as a pretrained one.", + "bbox": [ + 498, + 642, + 890, + 747 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Step2. Initial RES Training. In this step, we train the model with only $1\\%$ , $5\\%$ , and $10\\%$ labeled mask data on the RES task. The model is equipped with the new method proposed in this paper, including CCTF and PMCA.", + "bbox": [ + 498, + 748, + 890, + 808 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Step3. Retraining with Pseudo Points. After obtaining the RES model from Step2, we can utilize it to generate more pseudo contour points. While these points may contain a lot of noise or may not match the way labeled data is sampled, so we re-selected and sampled out the appropriate pseudo points with the proposed RPP strategy.", + "bbox": [ + 496, + 808, + 890, + 898 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "3025", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/2e4dbfad845615e7ca2056516536bea92f5dd9a95de8785d313aa276c49eeb07.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Label RatioMethodVenueVisual EncoderRefCOCORefCOCO+RefCOCOg
valtestAtestBvaltestAtestBval-gval-utest-u
100%Fully-supervised:
CMSA [47]CVPR'19RN10158.3260.6155.0943.7647.6037.8939.98--
STEP [3]ICCV'19RN10160.0463.4657.9748.1952.3340.4146.40--
CMPC [21]CVPR'20DN5361.3664.5359.6449.5653.4443.2349.05--
LSCM [22]ECCV'20DLA3461.4764.9959.5549.3453.1243.5048.05--
MCN [32]CVPR'20DN5362.4464.2059.7150.6254.9944.69-49.2249.40
BUSNet [44]CVPR'21RN10163.2766.4161.3951.7656.8744.1350.56--
LTS[21] [23]CVPR'21DN5365.4367.7663.0854.2158.3248.02-54.4054.25
VLT [9]ICCV'21DN5665.6568.2962.7355.5059.2049.3649.7652.9956.65
ResTR [25]CVPR'22ViT-B67.2269.3064.4555.7860.4448.2754.48--
SeqTR [54]ECCV'22DN5367.2669.7964.1254.1458.9348.19-55.6755.64
10%Partially-supervised
Baseline-DN5357.9260.7455.0650.8454.8744.54-49.5049.30
Our Method-DN5363.9965.8661.5452.5655.7946.09-52.4352.40
5%Baseline-DN5358.8561.7256.3649.7854.2243.34-48.3348.27
Our Method-DN5362.2364.6760.4251.3054.6844.85-51.2850.55
1%Baseline-DN5354.6757.4852.6244.9148.8139.44-42.5742.46
Our Method-DN5357.4159.6055.8447.6852.0641.54-46.8046.12
", + "bbox": [ + 76, + 88, + 903, + 380 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Comparisons with state-of-the-art fully-supervised methods on RefCOCO [50], RefCOCO+ [50], and RefCOCOg [33] in terms of IoU scores. We defined mask label ratio as $1\\%$ , $5\\%$ , and $10\\%$ to compare our method with the baseline in partially-supervised RES. \"RN101\": ResNet-101 [15], \"DN53\": DarkNet-53 [37], \"DN56\": DarkNet-56 [37], \"DLA-34\" [48], \"ViT-B\" [10].", + "bbox": [ + 75, + 390, + 892, + 434 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 458, + 209, + 474 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Datasets", + "text_level": 1, + "bbox": [ + 76, + 482, + 179, + 497 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "RefCOCO/RefCOCO+ are proposed in [50]. There are 19,994 images in RefCOCO with 142,209 refer expressions for 50,000 objects. Similarly, 19,992 images are included in RefCOCO+ which contains 141,564 expressions for 49,856 objects.", + "bbox": [ + 75, + 505, + 468, + 580 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In these datasets, each image contains two or more objects from the same category. In RefCOCO+ dataset, positional words are not allowed in the referring expression, which is a pure dataset with appearance-based referring expression, whereas RefCOCO imposes no restriction on the phrase. In addition to the training set and validation set, the test set for RefCOCO/RefCOCO+ is divided into a testA set (containing several people in an image) and a testB set (containing multiple instances of other objects in an image).", + "bbox": [ + 75, + 580, + 468, + 715 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "RefCOCOg [33] contains 26,711 images with 85,474 referring expressions for 54,822 objects, and each image usually contains $2 \\sim 4$ objects of the same category. The length of referring expressions in this dataset is almost twice as long as those in RefCOCO and RefCOCO+. This dataset is directly divided into \"train\", \"val\" and \"test\".", + "bbox": [ + 75, + 715, + 468, + 808 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Experimental Settings", + "text_level": 1, + "bbox": [ + 76, + 816, + 284, + 834 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Each instance to be segmented is equipped with an expression sentence in RES datasets, the sentence can be seen as a kind of open category. Therefore, different from the manner of dividing data [17], we simulate the partially su", + "bbox": [ + 75, + 839, + 468, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "pervised training scenario on RES by randomly sampling the entire train data proportionally without considering the category.", + "bbox": [ + 496, + 459, + 890, + 503 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation Details. Following SeqTR [54], all parameters in the network are optimized with AdamW [30], and the batch size is 128. We train the model with $120 \\sim 150$ epochs for full convergence, and we set the learning rate as $5\\mathrm{e - }4@10\\%$ , $3\\mathrm{e - }4@5\\%$ , and $1\\mathrm{e - }4@1\\%$ in step2 training, while $5\\mathrm{e - }4$ in step3 training with pseudo points. We use DarkNet-53 [37] as the visual encoder. Following standard practices [8, 32, 23, 9], images are resized to $640 \\times 640$ , and the length of language expression is trimmed at 15 for ResCOCO/RefCOCO+ and 20 for RefCOCOg. The training epochs and the learning rate used vary slightly for different settings of mask-labeled data ratio and different training stages. For the number of sampled contour points, we use the same as in SeqTR, i.e., 18 points for RefCOCO/RefCOCO+, and 12 for RefCOCOg.", + "bbox": [ + 496, + 506, + 892, + 733 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation Metrics. Following the proposal setting in the previous work, we use mask Intersection-over-Union (IoU) and Precision with thresholds $(\\mathrm{Pr}@\\mathrm{X})$ . The mask IoU demonstrates the mask quality, which emphasizes the model's overall performance and reveals both targeting and segmenting abilities. The $\\mathrm{Pr}@\\mathrm{X}$ metric computes the ratio of successfully predicted samples using different IoU thresholds. Low threshold precision like $\\mathrm{Pr}@\\mathrm{0.5}$ reflects the identification performance of the method, and high threshold precision like $\\mathrm{Pr}@\\mathrm{0.9}$ reveals the ability to generate high-quality masks.", + "bbox": [ + 496, + 734, + 893, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "3026", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/5eb4d4d3daaaebd62ea88ad89e88a9cf2c80abc4d7459cecdca34cb9e7a3948b.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
RatioCCTFPMCAIoUPr@0.5Pr@0.6Pr@0.7Pr@0.8Pr@0.9
1%✓42.5747.5432.8917.615.070.36
✓✓44.20 (+1.63)50.68 (+3.14)37.00 (+4.11)21.21 (+3.60)7.89 (+2.82)0.66 (+0.30)
44.58 (+0.38)50.98 (+0.30)37.32 (+0.32)22.42 (+1.21)8.21 (+0.32)0.76 (+0.10)
100%✓55.9567.7362.2049.0229.557.07
✓✓56.03 (+0.08)67.79 (+0.06)62.32 (+0.12)49.88 (+0.86)30.63 (+1.08)8.04 (+0.97)
56.89 (+0.86)68.43 (+0.64)62.96 (+0.64)50.72 (+0.84)31.21 (+0.58)8.05 (+0.01)
", + "bbox": [ + 93, + 88, + 877, + 203 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/298ec9d1374be25254ada36a661f2bea36073da425a2560ed42cca46ac011344.jpg", + "table_caption": [ + "Table 2. Validity verification of our proposed CCTF and PMCA. We conduct experiments on $1\\%$ and $100\\%$ supervised data with mask labels (100% means fully-supervised training)." + ], + "table_footnote": [], + "table_body": "
MethodIoUPr@0.5Pr@0.6Pr@0.7Pr@0.8Pr@0.9
Baseline44.5850.9837.3222.428.210.76
w/o RPP44.2751.2838.2424.2810.240.96
w/ RPP46.8056.2943.1927.4010.340.98
", + "bbox": [ + 78, + 266, + 455, + 333 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Baseline and oracle We suggest the baseline in the following way: we initialize our model with the parameters of SeqTR trained on the REC task, and refine it on the RES task with $1\\%$ , $5\\%$ , and $10\\%$ mask annotation data. Our comparison with the baseline is discussed in Sec. 4.3. The \"oracle\" model is SeqTR trained on REC and refined on RES with all mask annotation data. This fully supervised model is a performance upper bound for our partially supervised RES.", + "bbox": [ + 75, + 397, + 470, + 518 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Comparison with other Method", + "text_level": 1, + "bbox": [ + 76, + 527, + 356, + 542 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We compare our method with other fully supervised state-of-the-art methods on three common benchmarks of Referring Expression Segmentation, i.e., RefCOCO, RefCOCO+, and RefCOCOg. In addition, we also compare with our suggested baseline in partially-supervised training. Results are reported in Table 1. Our method displays significant improvement over the baseline method on all three datasets, at an average of $3.5\\%$ , $2.4\\%$ , and $3.0\\%$ on $1\\%$ , $5\\%$ , and $10\\%$ mask labeled data. With $10\\%$ mask annotated data, we can achieve $97\\%$ of the fully supervised performance on RefCOCO+@val. We are also able to achieve $88\\%$ fully supervised performance on $1\\%$ mask-labeled data on RefCOCO+@testA. Our methods can even surpass some fully-supervised methods, e.g., MCN, [32], BUSNet [44].", + "bbox": [ + 75, + 550, + 468, + 763 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation Studies", + "text_level": 1, + "bbox": [ + 76, + 771, + 240, + 786 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effectiveness of CCTF and PMCA. To verify the effectiveness of Co-Content Teacher Forcing (CCTF) and Point-Modulated Cross-Attention (PMCA), we show the results of our RES model with $1\\%$ partially supervised training data and $100\\%$ fully supervised training data on Ref-COCOg@val in Table 2.", + "bbox": [ + 75, + 794, + 468, + 883 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "When trained with $100\\%$ mask label of the data, PMCA", + "bbox": [ + 96, + 885, + 468, + 898 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/b9d6cc66d774af3e442e3c4f01e94301f02632be5c8a2444fe0eb4719a2cd9df.jpg", + "table_caption": [ + "Table 3. Comparison of our method with re-sampling pseudo points and without RPP in step3 training." + ], + "table_footnote": [], + "table_body": "
MethodIoUPr@0.5Pr@0.6Pr@0.7Pr@0.8Pr@0.9
Baseline44.5850.9837.3222.428.210.76
T@1.0044.5351.3638.1422.407.290.58
T@0.0544.3550.6838.5224.189.700.78
T@0.1046.8056.2943.1927.4010.340.98
", + "bbox": [ + 506, + 266, + 888, + 334 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Performance with different Threshold $T$ of dice coefficient on RefCOCOg@val. $T@1.00$ means that all the predicted pseudo masks are used for training in step3 without selection of the more accurate one with dice coefficient.", + "bbox": [ + 498, + 344, + 893, + 401 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "embodies more performance enhancements than CCTF, which proves the effectiveness of PMCA. CCTF contributes little to performance improvement. While with $1\\%$ supervised mask labeled training data, the improvement contribution of CCTF far exceeds that of PMCA, $80\\%$ performance improvement comes from CCTF (1.63% in 2.01%). This demonstrates that the exposure bias of the inconsistency of the training and inference process may be more serious in the case of partially supervised data, which is alleviated by our proposed CCTF somewhat.", + "bbox": [ + 496, + 429, + 890, + 580 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablation of Resampling Pseudo Points To better demonstrate the effectiveness of re-sampling pseudo points (RPP), we compare the results with those obtained without resampling. In the case of re-sampling, we uniformly resample the pseudo points from the generated pseudo mask and consider them as the target sequence. In contrast, for no re-sampling, we use the points predicted by the model directly as the target sequence. As illustrated in Table 3, the reported performance is superior when the RPP strategy is employed. This is primarily attributed to the fact that after re-sampling, the unlabeled data is pseudo-labeled uniformly, which mitigates the potential noise introduced by pseudo labels.", + "bbox": [ + 496, + 582, + 892, + 777 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Performance with different Threshold $T$ of dice coefficient. We establish a threshold for the dice coefficient to select more suitable pseudo points that will serve as prediction targets for unlabeled mask annotation data in step3 mentioned in Sec. 3.5. To demonstrate the effectiveness of our pseudo points selection strategy and the impact of different threshold, we compare the results with different thresholds $T$ of the dice coefficient on RefCOCOg@val. As presented", + "bbox": [ + 496, + 779, + 893, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "3027", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f12e438bfa4270bfc8015366d3e04fa2fef441ff4355ebf9efc7a5aa581f88ae.jpg", + "image_caption": [ + "Figure 5. Qualitative examples of our method at $1\\%$ , $5\\%$ , $10\\%$ mask label in Partially Supervised RES. We identify the image and its corresponding referring expression in the first column." + ], + "image_footnote": [], + "bbox": [ + 151, + 88, + 821, + 366 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0e3f8cdbd34dfcdcae552e73cd1a6d31adb72c8cb4f5ad9c1d540e7882e5aa6e.jpg", + "image_caption": [ + "Figure 6. The mIoU with different mask-labeled data ratio." + ], + "image_footnote": [], + "bbox": [ + 96, + 429, + 444, + 574 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "in Table 4, without any constraint ( $T$ is set to 1.00), we observe that the side effect of pseudo points is greater than the main effect due to the noise contained in pseudo points. Therefore, we determine to select out those pseudo masks with dice coefficient $< T$ for re-sampling pseudo points. Meanwhile, our ablation studies declare that the threshold of 0.10 is the optimal choice for our selection strategy.", + "bbox": [ + 75, + 617, + 468, + 723 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Experiment of increasing the proportion of mask-labeled data We conduct an additional experiment to investigate the proportion of mask-labeled data in training that can reach SotA performance. As depicted in Fig. 6, comparing the mIoU with the fully supervised SotA method (55.67) on RefCOCOg@val, our method attains remarkably similar results (55.59) by using $50\\%$ mask-labeled data.", + "bbox": [ + 75, + 724, + 468, + 830 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.5. Qualitative Results", + "text_level": 1, + "bbox": [ + 76, + 845, + 259, + 861 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We evaluated the segmentation performance of our method at various ratios, i.e., $1\\%$ , $5\\%$ , and $10\\%$ of Partial-", + "bbox": [ + 76, + 869, + 468, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "RES data in Fig. 5. As the mask-labeled data increases, the contours fit more accurately. By comparing these results, we observe that with a low percentage of mask-labeled data, our segmentation results are already similar to those of the GT points.", + "bbox": [ + 498, + 429, + 892, + 505 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 532, + 617, + 549 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we investigate the partially supervised learning paradigm for RES, where only a few segmentation masks are available so the model has to learn transfer knowledge from REC. Unlike existing RES/REC works that have different decoders for the two tasks, we leverage the contour-based sequence prediction model to maximize the transfer ability. We then propose CCTF to explicitly associate the point coordinates (scale values) with the referred spatial regions, which alleviates the exposure bias brought by the limited segmentation masks. Besides, we present RPP strategy to select appropriate pseudo labels and resample pseudo points during the retraining stage. We conduct extensive experiments to demonstrate the effectiveness of our model in the Partial-RES setting. One limitation of this work is that the smoothness and edge accuracy of the point-contour mask prediction may not be as precise as the pixel-level mask predictions.", + "bbox": [ + 496, + 563, + 892, + 820 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements. This work was supported by the National Key RD Program of China (No.2018AAA0102100), the National Natural Science Foundation of China (Nos. U1936212, 62120106009), Sponsored by Beijing Nova Program (NO. 20220484063).", + "bbox": [ + 496, + 825, + 892, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "3028", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] David Biertimpel, Sindi Shkodrani, Anil S Baslamisli, and Nóra Baka. Prior to segment: Foreground cues for weakly annotated classes in partially supervised instance segmentation. In ICCV, pages 2824-2833, 2021.", + "[2] Ding-Jie Chen, Songhao Jia, Yi-Chen Lo, Hwann-Tzong Chen, and Tyng-Luh Liu. See-through-text grouping for referring image segmentation. In ICCV, pages 7454-7463, 2019.", + "[3] Ding-Jie Chen, Songhao Jia, Yi-Chen Lo, Hwann-Tzong Chen, and Tyng-Luh Liu. See-through-text grouping for referring image segmentation. In ICCV, pages 7454-7463, 2019.", + "[4] Ting Chen, Saurabh Saxena, Lala Li, David J Fleet, and Geoffrey Hinton. Pix2seq: A language modeling framework for object detection. In ICLR.", + "[5] Xinpeng Chen, Lin Ma, Jingyuan Chen, Zequn Jie, Wei Liu, and Jiebo Luo. Real-time Referring Expression Comprehension by Single-stage Grounding Network. arXiv preprint arXiv:1812.03426, 2018.", + "[6] Zhiyang Chen, Yousong Zhu, Zhaowen Li, Fan Yang, Wei Li, Haixin Wang, Chaoyang Zhao, Liwei Wu, Rui Zhao, Jinqiao Wang, et al. Obj2seq: Formatting objects as sequences with class prompt for visual tasks. In NeurIPS.", + "[7] Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014, 2014.", + "[8] Jiajun Deng, Zhengyuan Yang, Tianlang Chen, Wengang Zhou, and Houqiang Li. TransVG: End-to-End Visual Grounding with Transformers. ICCV, 2021.", + "[9] Henghui Ding, Chang Liu, Suchen Wang, and Xudong Jiang. Vision-language transformer and query generation for referring segmentation. In ICCV, pages 16321-16330, 2021.", + "[10] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR.", + "[11] Qi Fan, Lei Ke, Wenjie Pei, Chi-Keung Tang, and Yu-Wing Tai. Commonality-parsing network across shape and appearance for partially supervised instance segmentation. In ECCV, pages 379–396. Springer, 2020.", + "[12] Guang Feng, Zhiwei Hu, Lihe Zhang, and Huchuan Lu. Encoder fusion network with co-attention embedding for referring image segmentation. In CVPR, pages 15506–15515, 2021.", + "[13] Peng Gao, Minghang Zheng, Xiaogang Wang, Jifeng Dai, and Hongsheng Li. Fast convergence of detr with spatially modulated co-attention. In ICCV, pages 3621–3630, 2021.", + "[14] Kaiming He, Georgia Gkioxari, Piotr Dólar, and Ross Girshick. Mask r-cnn. In ICCV, pages 2961-2969, 2017.", + "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep Residual Learning for Image Recognition. In CVPR, 2016.", + "[16] Richang Hong, Daqing Liu, Xiaoyu Mo, Xiangnan He, and Hanwang Zhang. Learning to Compose and Reason with" + ], + "bbox": [ + 78, + 114, + 468, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Language Tree Structures for Visual Grounding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.", + "[17] Ronghang Hu, Piotr Dólar, Kaiming He, Trevor Darrell, and Ross Girshick. Learning to segment every thing. In CVPR, pages 4233-4241, 2018.", + "[18] Ronghang Hu, Marcus Rohrbach, Jacob Andreas, Trevor Darrell, and Kate Saenko. Modeling Relationships in Referential Expressions with Compositional Modular Networks. In CVPR, 2017.", + "[19] Zhiwei Hu, Guang Feng, Jiayu Sun, Lihe Zhang, and Huchuan Lu. Bi-directional relationship inferring network for referring image segmentation. In CVPR, pages 4424-4433, 2020.", + "[20] Shaofei Huang, Tianrui Hui, Si Liu, Guanbin Li, Yunchao Wei, Jizhong Han, Luoqi Liu, and Bo Li. Referring image segmentation via cross-modal progressive comprehension. In CVPR, pages 10488-10497, 2020.", + "[21] Shaofei Huang, Tianrui Hui, Si Liu, Guanbin Li, Yunchao Wei, Jizhong Han, Luoqi Liu, and Bo Li. Referring image segmentation via cross-modal progressive comprehension. In CVPR, pages 10488-10497, 2020.", + "[22] Tianrui Hui, Si Liu, Shaofei Huang, Guanbin Li, Sansi Yu, Faxi Zhang, and Jizhong Han. Linguistic structure guided context modeling for referring image segmentation. In ECCV, pages 59-75. Springer, 2020.", + "[23] Ya Jing, Tao Kong, Wei Wang, Liang Wang, Lei Li, and Tie-niu Tan. Locate then segment: A strong pipeline for referring image segmentation. In CVPR, pages 9858-9867, 2021.", + "[24] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. MDETR-Modulated Detection for End-to-End Multi-Modal Understanding. In ICCV, 2021.", + "[25] Namyup Kim, Dongwon Kim, Cuiling Lan, Wenjun Zeng, and Suha Kwak. Restr: Convolution-free referring image segmentation using transformers. In CVPR, pages 18145-18154, 2022.", + "[26] Weicheng Kuo, Anelia Angelova, Jitendra Malik, and Tsung-Yi Lin. Shapemask: Learning to segment novel objects by refining shape priors. In ICCV, pages 9207-9216, 2019.", + "[27] Muchen Li and Leonid Sigal. Referring transformer: A one-step approach to multi-task visual grounding. NeurIPS, 34:19652-19664, 2021.", + "[28] Yue Liao, Si Liu, Guanbin Li, Fei Wang, Yanjie Chen, Chen Qian, and Bo Li. A Real-time Cross-modality Correlation Filtering Method for Referring Expression Comprehension. In CVPR, 2020.", + "[29] Daqing Liu, Hanwang Zhang, Feng Wu, and Zheng-Jun Zha. Learning to Assemble Neural Module Tree Networks for Visual Grounding. In ICCV, 2019.", + "[30] Ilya Loshchilov and Frank Hutter. Fixing Weight Decay Regularization in Adam. 2018.", + "[31] Gen Luo, Yiyi Zhou, Rongrong Ji, Xiaoshuai Sun, Jinsong Su, Chia-Wen Lin, and Qi Tian. Cascade grouped attention network for referring expression segmentation. In ACM MM, pages 1274-1282, 2020." + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "3029", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[32] Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Liujuan Cao, Chenglin Wu, Cheng Deng, and Rongrong Ji. Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation. In CVPR, 2020.", + "[33] Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy. Generation and Comprehension of Unambiguous Object Descriptions. In CVPR, 2016.", + "[34] Tsvetomila Mihaylova and André FT Martins. Scheduled sampling for transformers. ACL 2019, page 351, 2019.", + "[35] Pedro O O Pinheiro, Ronan Collobert, and Piotr Dólar. Learning to segment object candidates. NeurIPS, 28, 2015.", + "[36] Mengxue Qu, Yu Wu, Wu Liu, Qiqi Gong, Xiaodan Liang, Olga Russakovsky, Yao Zhao, and Yunchao Wei. Siri: A simple selective retraining mechanism for transformer-based visual grounding. In ECCV, pages 546-562. Springer, 2022.", + "[37] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018.", + "[38] Arka Sadhu, Kan Chen, and Ram Nevatia. Zero-Shot Grounding of Objects from Natural Language Queries. In ICCV, 2019.", + "[39] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is All You Need. In NeuruIPS, 2017.", + "[40] Liwei Wang, Yin Li, Jing Huang, and Svetlana Lazebnik. Learning Two-Branch Neural Networks for Image-Text Matching Tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018.", + "[41] Peng Wang, Qi Wu, Jiewei Cao, Chunhua Shen, Lianli Gao, and Anton van den Hengel. Neighbourhood Watch: Referring Expression Comprehension via Language-Guided Graph Attention Aetworks. In CVPR, 2019.", + "[42] Xuehui Wang, Kai Zhao, Ruixin Zhang, Shouhong Ding, Yan Wang, and Wei Shen. Contrastmask: Contrastive learning to segment every thing. In CVPR, pages 11604-11613, 2022.", + "[43] Sibei Yang, Guanbin Li, and Yizhou Yu. Dynamic Graph Attention for Referring Expression Comprehension. In ICCV, 2019.", + "[44] Sibei Yang, Meng Xia, Guanbin Li, Hong-Yu Zhou, and Yizhou Yu. Bottom-up shift and reasoning for referring image segmentation. In CVPR, pages 11266-11275, 2021.", + "[45] Zhengyuan Yang, Tianlang Chen, Liwei Wang, and Jiebo Luo. Improving One-Stage Visual Grounding by Recursive Sub-Query Construction. In ECCV, 2020.", + "[46] Zhengyuan Yang, Boqing Gong, Liwei Wang, Wenbing Huang, Dong Yu, and Jiebo Luo. A Fast and Accurate One-Stage Approach to Visual Grounding. In ICCV, 2019.", + "[47] Linwei Ye, Mrigank Rochan, Zhi Liu, and Yang Wang. Cross-modal self-attention network for referring image segmentation. In CVPR, pages 10502–10511, 2019.", + "[48] Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell. Deep layer aggregation. In CVPR, pages 2403-2412, 2018.", + "[49] Licheng Yu, Zhe Lin, Xiaohui Shen, Jimei Yang, Xin Lu, Mohit Bansal, and Tamara L Berg. MAttNet: Modular Attention Network for Referring Expression Comprehension. In CVPR, 2018." + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[50] Licheng Yu, Patrick Poirson, Shan Yang, Alexander C Berg, and Tamara L Berg. Modeling Context in Referring Expressions. In ECCV, 2016.", + "[51] Xunlin Zhan, Yangxin Wu, Xiao Dong, Yunchao Wei, Minlong Lu, Yichi Zhang, Hang Xu, and Xiaodan Liang. Product1m: Towards weakly supervised instance-level product retrieval via cross-modal pretraining. In ICCV, pages 11782-11791, 2021.", + "[52] Hanwang Zhang, Yulei Niu, and Shih-Fu Chang. Grounding Referring Expressions in Images by Variational Context. In CVPR, 2018.", + "[53] Yanzhao Zhou, Xin Wang, Jianbin Jiao, Trevor Darrell, and Fisher Yu. Learning saliency propagation for semi-supervised instance segmentation. In CVPR, pages 10307-10316, 2020.", + "[54] Chaoyang Zhu, Yiyi Zhou, Yunhang Shen, Gen Luo, Xingjia Pan, Mingbao Lin, Chao Chen, Liujuan Cao, Xiaoshuai Sun, and Rongrong Ji. Seqtr: A simple yet universal network for visual grounding. In ECCV, pages 598-615. Springer, 2022.", + "[55] Bohan Zhuang, Qi Wu, Chunhua Shen, Ian Reid, and Anton Van Den Hengel. Parallel Attention: A Unified Framework for Visual Object Discovery Through Dialogs and Queries. In CVPR, 2018." + ], + "bbox": [ + 501, + 90, + 890, + 415 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "3030", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning To Segment Every Referring Object Point by Point/3793df0f-b6b3-448f-85d5-cdd2cf084f15_model.json b/2023/Learning To Segment Every Referring Object Point by Point/3793df0f-b6b3-448f-85d5-cdd2cf084f15_model.json new file mode 100644 index 0000000000000000000000000000000000000000..6b9abf7c070c34e4b36470ae01a92b062f113c55 --- /dev/null +++ b/2023/Learning To Segment Every Referring Object Point by Point/3793df0f-b6b3-448f-85d5-cdd2cf084f15_model.json @@ -0,0 +1,2321 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.182, + 0.131, + 0.788, + 0.154 + ], + "angle": 0, + "content": "Learning to Segment Every Referring Object Point by Point" + }, + { + "type": "text", + "bbox": [ + 0.112, + 0.179, + 0.863, + 0.2 + ], + "angle": 0, + "content": "Mengxue \\(\\mathbf{Q}\\mathbf{u}^{1,2*}\\) Yu Wu \\(^{3}\\) Yunchao Wei\\(^{1,2}\\) Wu Liu \\(^{4}\\) Xiaodan Liang \\(^{5,6}\\) Yao Zhao\\(^{1,2\\dagger}\\)" + }, + { + "type": "text", + "bbox": [ + 0.148, + 0.215, + 0.825, + 0.27 + ], + "angle": 0, + "content": "\\(^{1}\\)Institute of Information Science, Beijing Jiaotong University \\(^{2}\\)Beijing Key Laboratory of Advanced Information Science and Network Technology \\(^{3}\\)Wuhan University \\(^{4}\\)JD Explore Academy \\(^{5}\\)Sun Yat-sen University \\(^{6}\\)MBZUAI" + }, + { + "type": "text", + "bbox": [ + 0.194, + 0.273, + 0.771, + 0.287 + ], + "angle": 0, + "content": "qumengxue@bjtu.edu.cn, wuyucs@whu.edu.cn, yunchao.wei@bjtu.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.322, + 0.314, + 0.339 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.355, + 0.474, + 0.687 + ], + "angle": 0, + "content": "Referring Expression Segmentation (RES) can facilitate pixel-level semantic alignment between vision and language. Most of the existing RES approaches require massive pixel-level annotations, which are expensive and exhaustive. In this paper, we propose a new partially supervised training paradigm for RES, i.e., training using abundant referring bounding boxes and only a few (e.g., \\(1\\%\\)) pixel-level referring masks. To maximize the transferability from the REC model, we construct our model based on the point-based sequence prediction model. We propose the co-content teacher-forcing to make the model explicitly associate the point coordinates (scale values) with the referred spatial features, which alleviates the exposure bias caused by the limited segmentation masks. To make the most of referring bounding box annotations, we further propose the resampling pseudo points strategy to select more accurate pseudo-points as supervision. Extensive experiments show that our model achieves \\(52.06\\%\\) in terms of accuracy (versus \\(58.93\\%\\) in fully supervised setting) on RefCOCO+@testA, when only using \\(1\\%\\) of the mask annotations. Code is available at https://github.com/qumengxue/Partial-RES.git." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.717, + 0.21, + 0.733 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.743, + 0.47, + 0.864 + ], + "angle": 0, + "content": "Referring Expression Segmentation (RES) aims to generate a segmentation mask for the object referred to by the language expression in the image. It allows pixel-level semantic alignment between language and vision, which is meaningful to many multi-modal tasks and can be applied to various practical applications, e.g., video/image editing with sentences. Benefiting from the development of deep learning techniques, significant progress [31, 9, 12, 2, 19," + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.322, + 0.889, + 0.503 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.521, + 0.893, + 0.564 + ], + "angle": 0, + "content": "Figure 1. Comparison of MDETR and SeqTR. On the left is the streamlined framework of the two, and on the right is their IoU performance on RefCOCOg@val. Best viewed in color." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.58, + 0.892, + 0.61 + ], + "angle": 0, + "content": "20, 27, 54, 9] has been made in this field and achieved remarkable performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.612, + 0.893, + 0.793 + ], + "angle": 0, + "content": "The success of current methods partially attributes to the large-scale training dataset with accurate pixel-level masks. However, labeling masks for a huge amount of images is often costly in terms of both human effort and finance, and thus hardly be scaled up. Therefore, it is meaningful to explore a new training paradigm for RES where extensive pixel-level annotations are not necessary. It is well known that bounding box annotations are much cheaper and easier to be collected compared with pixel-level masks. Thus, we try to study this question: is it possible to learn an accurate RES model using abundant box annotations and only a few mask annotations?" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.895, + 0.901 + ], + "angle": 0, + "content": "To answer this question, for the first time, we explore a new partially supervised training paradigm for RES (Partial-RES). Intuitively, a naive pipeline for this partially supervised setting is to first train a Referring Expression Comprehension (REC) model and then transfer it to the RES task by fine-tuning on a limited number of data with mask annotations. However, due to the difference between" + }, + { + "type": "page_footnote", + "bbox": [ + 0.095, + 0.875, + 0.402, + 0.888 + ], + "angle": 0, + "content": "*Work done during an internship at JD Explore Academy." + }, + { + "type": "page_footnote", + "bbox": [ + 0.097, + 0.888, + 0.304, + 0.9 + ], + "angle": 0, + "content": "Yao Zhao is the corresponding author." + }, + { + "type": "list", + "bbox": [ + 0.095, + 0.875, + 0.402, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "3021" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.471, + 0.242 + ], + "angle": 0, + "content": "the REC and RES tasks, prevalent models (e.g., Mask R-CNN [14], MDETR [24]) have to simultaneously optimize two different heads, with one for detection prediction and the other for segmentation prediction. Such kind of structures would lead to a severe issue during the fine-tuning stage, i.e., a randomly initialized mask head can not get well optimized given only a few mask annotations (hundreds of images). As shown in Figure 1, it is not easy to optimize MDETR with only \\(1\\%\\) mask-annotated data even transferred from a well-trained REC model." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.246, + 0.47, + 0.488 + ], + "angle": 0, + "content": "Recently, a contour-based method named SeqTR has been proposed for unifying REC and RES. The idea is to use a sequence model (usually a Transformer decoder) to sequentially generate contour points of the referred object. The predictions are two points (top-left and bottom-right corner points of the bounding box) in the REC task while dozens of points (contour) are in the RES task. As shown in Fig. 1, both boxes and masks are converted to point sequences and optimized using the same simple cross-entropy loss in SeqTR, which ensures the consistency between REC and RES. It incorporates two optimization heads into a unified one, making the knowledge learned with four points (i.e., detection) be naturally transferred to predict multiple ones (i.e., segmentation). Inspired by SeqTR, we have the conjecture that sequence prediction might be a better solution for partially supervised training." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.49, + 0.47, + 0.748 + ], + "angle": 0, + "content": "In this paper, we investigate methods for achieving better partially supervised training for RES sequence prediction models. Sequence-to-sequence prediction models are typically trained with Teacher-Forcing (TF) [39], wherein the model utilizes the ground truth token as input to predict the next token during the training stage. However, the model can only predict the next state by taking its previous output as input in inference, which is known as the exposure bias [34]. This issue is even more severe in the partially supervised setting due to the very limited ground truth data. Consequently, we introduce the Co-Content Teacher-Forcing (CCTF), which combines the ground truth point coordinates together with the spatial visual feature of the pointed row or column. In contrast to the previous sequence model SeqTR, our CCTF explicitly associates the point coordinates (scale values) with the referred spatial region, providing a more natural approach for visual grounding." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Furthermore, we estimate the referring region via our proposed Point-Modulated Cross-Attention, to ensure the decoder attends to those region content while generating the point contour sequences. To fully utilize the data without mask annotation in Partial-RES, we retrain the model with the generated pseudo labels, and we present a Resampling Pseudo Points (RPP) Strategy. Unlike most pseudo-label works that directly use the network's predictions as labels, we select the appropriate pseudo masks with Dice coefficient and then resample the predicted points in a uniform" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.807, + 0.107 + ], + "angle": 0, + "content": "way to regularize the contour sequence labels." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.108, + 0.892, + 0.228 + ], + "angle": 0, + "content": "With extensive experiments, our method displays significant improvement compared to SeqTR [54] baselines on all three benchmarks, i.e., at an average of \\(3.5\\%\\), \\(2.4\\%\\), and \\(3.0\\%\\) on \\(1\\%\\), \\(5\\%\\) and \\(10\\%\\) mask-labeled data. With \\(10\\%\\) mask annotated data, our method achieves \\(97\\%\\) of the fully supervised performance on RefCOCO + @val. We are also able to achieve \\(88\\%\\) of the fully supervised performance only with \\(1\\%\\) mask-labeled data on RefCOCO + @testA." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.244, + 0.642, + 0.26 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.269, + 0.649, + 0.285 + ], + "angle": 0, + "content": "2.1. REC and RES" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.294, + 0.892, + 0.399 + ], + "angle": 0, + "content": "Referring Expression Comprehension (REC) aims at grounding the object referred to by a sentence, which achieves instance-level vision language alignment. Compared to REC, Referring Expression Segmentation (RES) grounds language expression at the fine-grained pixel-level. REC and RES are separate but closely related fundamental multi-modal tasks." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.4, + 0.892, + 0.537 + ], + "angle": 0, + "content": "For REC, previous methods can be roughly divided into two-stage and one-stage. Two-stage methods [16, 18, 29, 40, 41, 43, 49, 52, 55] typically utilize an object detector to generate region proposals in the first stage, and are trained to maximize the similarity between region and text with binary cross-entropy loss or similarity loss. One-stage methods [5, 28, 38, 45, 46] avoid being constrained by the quality of the proposal by directly fusing visual and linguistic features rather than matching region-language pairs." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.538, + 0.892, + 0.657 + ], + "angle": 0, + "content": "For RES, typical solutions are to incorporate as much linguistic information as possible in various ways on pixel-level visual features [31, 9, 12, 2, 19, 20]. In previous methods, researchers propose various attention mechanisms [9, 23, 20] to better merge vision and language. Recently, since the superior ability of modeling vision and language of Transformer-based model, more high-performance methods [27, 54, 9, 36] are proposed to solve RES tasks." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.892, + 0.795 + ], + "angle": 0, + "content": "For REC and RES, multi-task methods [32, 27] are proposed to better use the correlation between two tasks, which can facilitate learning of both tasks. However, all the prior work require different task-specific branch and loss function, whose generalization ability is limited. Recently, a simple and universal network term SeqTR [54] is presented, which regards both REC and RES as a point prediction problem. SeqTR greatly reduces the difficulty and complexity of both architecture design and optimization." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.892, + 0.901 + ], + "angle": 0, + "content": "In some work, REC is treated as a pretext task for other multi-modal tasks [24, 51], e.g., Visual Question and Answering. RES has a more fine-grained visual language alignment capability than REC, but the capability of RES to be used in multi-modal pre-training has not yet been developed because of the lack of a large-scale RES dataset. It is well known that annotating binary masks for a multi-" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3022" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.106, + 0.097, + 0.321, + 0.11 + ], + "angle": 0, + "content": "(2) Transformer Encoder and Decoder" + }, + { + "type": "image", + "bbox": [ + 0.107, + 0.111, + 0.568, + 0.293 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.113, + 0.298, + 0.271, + 0.311 + ], + "angle": 0, + "content": "(1) Visual Language Fusion" + }, + { + "type": "image_caption", + "bbox": [ + 0.35, + 0.298, + 0.498, + 0.311 + ], + "angle": 0, + "content": "(3) Sequence Construction" + }, + { + "type": "image_caption", + "bbox": [ + 0.269, + 0.321, + 0.364, + 0.333 + ], + "angle": 0, + "content": "(a) Architecture" + }, + { + "type": "image", + "bbox": [ + 0.589, + 0.098, + 0.865, + 0.315 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.679, + 0.32, + 0.786, + 0.334 + ], + "angle": 0, + "content": "(b) Training Steps" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.35, + 0.895, + 0.394 + ], + "angle": 0, + "content": "Figure 2. (a) Overall architecture of our method. It consists of three main modules: (1) Visual-Language Fusion; (2) Transformer Encoder and Decoder; (3) Sequence Construction. \"CCTF\": Co-Content Teacher-Forcing; \"PMCA\": Point-Modulated Cross-Attention. (b) The suggested training steps of our method in the Partially Supervised RES task. Best viewed in color." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.42, + 0.471, + 0.496 + ], + "angle": 0, + "content": "modal dataset is expensive while bounding box is easy. In this paper, we make use of the bounding box annotations and the consistent task expression of REC and RES in SeqTR, attempting to train RES task with less mask-labeled data based on SeqTR." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.508, + 0.449, + 0.525 + ], + "angle": 0, + "content": "2.2. Partially Supervised Instance Segmentation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.532, + 0.47, + 0.819 + ], + "angle": 0, + "content": "Partially Supervised Instance Segmentation (PSIS) was first raised in [17], where object categories are divided into base and novel splits. Both of them have bounding box annotation while only base have mask annotation. The training target of this task is to generate segmentation for novel categories. PSIS has been studied for a period of time in the field of Instance Segmentation [1, 11, 26, 35, 53]. Some works address this problem by learning class-agnostic mask segmentation models, i.e., separate foreground and background only. They capture class-agnostic cues, e.g., shape [26] and appearance commonalities [11]. For better use of the training data from novel categories, [42] propose ContrastMask to learn a mask segmentation model on both base and novel categories under a unified pixel-level contrastive learning framework. In this paper, we suggest the partially supervised RES, drawing on the partially supervised instance segmentation. We train the RES model with bounding box annotation of all train data, and a small fraction of mask annotation." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.832, + 0.391, + 0.848 + ], + "angle": 0, + "content": "2.3. Sequence Prediction in Visual Tasks" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.469, + 0.903 + ], + "angle": 0, + "content": "Visual tasks differ greatly in their output format and associated content, making it difficult to handle them with an identical structure. Unlike previous approaches requiring" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.42, + 0.892, + 0.648 + ], + "angle": 0, + "content": "prior knowledge in Object Detection, Pix2Seq [4] first cast object detection as a language modeling task conditioned on the observed pixel inputs. The class and the bounding box are expressed as sequences of discrete tokens, and train the detection model to generate the coordinate and class of each tone by one. Built on Pix2Seq, [6] proposes an object-centric vision framework Obj2Seq that takes objects as the basic unit, and human pose estimation is also been converted to a sequence-generated form. In addition, another similar work SeqTR [54] is proposed to unify REC and RES, regarding both REC and RES as a point prediction problem. Inspired by the above-mentioned work, we consider whether this sequence prediction framework can be used in scenarios with few labels to take advantage of its good format uniformly property." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.66, + 0.591, + 0.675 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.687, + 0.892, + 0.779 + ], + "angle": 0, + "content": "In this section, we first briefly revisit the sequence prediction model SeqTR in Sec. 3.1. Then we illustrate the architecture of our method in Sec. 3.2. In Sec. 3.3, we elaborate on our proposed Co-Content Teacher-Forcing, and the Point-Modulated Cross Attention in Sec. 3.4 and the Resampling Pseudo Point Strategy in Sec. 3.5." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.787, + 0.647, + 0.803 + ], + "angle": 0, + "content": "3.1. Revisit SeqTR" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.892, + 0.901 + ], + "angle": 0, + "content": "SeqTR [54] is a sequence prediction model for Visual Grounding tasks, which first reformulate visual grounding as a point prediction problem. The bounding box and the segmentation mask are serialized into a sequence of discrete coordinate tokens \\(\\{x_i,y_i\\}_{i = 1}^N\\) , where N is 4 vertices of the bounding box on REC or the number of contour" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3023" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.304 + ], + "angle": 0, + "content": "points of the segmentation mask on RES. With a task token [TASK], SeqTR can predict the target coordinate tokens in an auto-regressive manner during inference, and end the coordinate sequence with [EOS] token. In addition, different from those loss functions adopted in DETR-like multi-task frameworks (e.g., GIoU loss, set-based matching loss, focal loss, and dice loss), SeqTR only uses a simple cross-entropy loss for REC and RES, which requires no further prior knowledge or expertise. SeqTR greatly reduces the difficulty and complexity of both architecture design and optimization for visual grounding tasks. Meanwhile, it nicely aligns REC and RES, and naturally converts them into one type of expression form. Following SeqTR, our model is also optimized with a simple cross-entropy loss." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.312, + 0.212, + 0.326 + ], + "angle": 0, + "content": "3.2. Architecture" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.334, + 0.47, + 0.41 + ], + "angle": 0, + "content": "We build our architecture on the latest RES sequence prediction method SeqTR [54], which consists of three main modules: (1) Visual-Language Fusion; (2) Transformer Encoder and Decoder; (3) Sequence Construction; The overall architecture is illustrated in Fig. 2." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.41, + 0.47, + 0.61 + ], + "angle": 0, + "content": "Visual-Language Fusion. We adopt the convolutional backbone DarkNet-53 [37] to obtain the visual representation for an input image \\(\\mathbf{I}\\). Following SeqTR, we downsample the multi-scale visual feature from the finest to coarsest spatial resolution and flatten it to \\(F_{v} \\in R^{(H \\times W) \\times C}\\). For referring expressions, we encode the text with a one-layer bidirectional GRU [7] as in SeqTR. Then we concatenate both unidirectional hidden states \\(h_t = \\left[ \\begin{array}{c} \\vec{h}_t \\\\ h_t, h_t \\end{array} \\right]\\) at each step \\(t\\) to get language feature \\(F_l = \\{h_t\\}_{t=1}^T\\). Before V-L fusion, we adopt max pooling \\(mp(\\cdot)\\) along the channel dimension of \\(F_i\\). Then visual feature \\(F_v\\) and language feature \\(F_l\\) will be fused to \\(F_m\\) by Hadamard product:" + }, + { + "type": "equation", + "bbox": [ + 0.181, + 0.619, + 0.469, + 0.637 + ], + "angle": 0, + "content": "\\[\nF _ {m} = \\delta \\left(F _ {v}\\right) \\odot \\delta \\left(m p \\left(F _ {l}\\right)\\right), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.644, + 0.469, + 0.673 + ], + "angle": 0, + "content": "here the activation function \\(\\delta (\\cdot)\\) is set to \\(\\tanh (\\cdot)\\) if no special instructions." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.675, + 0.469, + 0.824 + ], + "angle": 0, + "content": "Transformer Encoder and Decoder. We use the Transformer with 6 layers encoder and 3 layers decoder to learn the multi-modal feature and decode out the vertices or the contour points. The hidden dimension of transformer is set to 256. The fused multi-modal feature \\( F_{m} \\) is added sine and learned positional encoding [39] before entering Transformer Encoder. The original Cross-Attention between Transformer Encoder and Decoder will be replaced by Point-Modulated Cross-Attention (PMCA), which is introduced in Sec. 3.4." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.469, + 0.902 + ], + "angle": 0, + "content": "Sequence Construction. Following SeqTR [54], we construct the sequence of floating points \\(\\{\\tilde{x}_i,\\tilde{y}_i\\}_{i = 1}^N\\) with the top-left and bottom-right corner points of the bounding box \\((\\mathrm{N} = 2)\\). For the binary mask on RES, the sequence is uniformly sampled N points clockwise on top of the mask on" + }, + { + "type": "image", + "bbox": [ + 0.527, + 0.091, + 0.858, + 0.273 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.286, + 0.892, + 0.356 + ], + "angle": 0, + "content": "Figure 3. Sketch of CCTF. We extract multi-modal features from the reshaped Transformer Encoder output according to the target coordinates and fuse them by element-wise addition, e.g., \\( x_{n} \\) corresponds to the \\( x_{n}W / M \\) line of row of the feature map, and \\( y_{n} \\) corresponds to the \\( y_{n}H / M \\) line of column." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.38, + 0.831, + 0.396 + ], + "angle": 0, + "content": "RES. Then they are quantized into integer bins by" + }, + { + "type": "equation", + "bbox": [ + 0.559, + 0.401, + 0.891, + 0.433 + ], + "angle": 0, + "content": "\\[\nx _ {i} = \\operatorname {r o u n d} \\left(\\frac {M \\tilde {x} _ {i}}{w}\\right), y _ {i} = \\operatorname {r o u n d} \\left(\\frac {M \\tilde {y} _ {i}}{h}\\right) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.438, + 0.892, + 0.467 + ], + "angle": 0, + "content": "where each coordinate is normalized by image width \\( w \\) and height \\( h \\), and \\( M \\) is the number of quantization bins." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.468, + 0.892, + 0.558 + ], + "angle": 0, + "content": "The sequence will be embedded and then combined with the output multi-modal feature of the Transformer Encoder to conduct Co-Content Teacher-Forcing (CCTF) training. Details are described in Sec. 3.3. Moreover, we will resample pseudo points and reconstruct the sequence for model retraining. Details can be found in Sec. 3.5." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.567, + 0.757, + 0.583 + ], + "angle": 0, + "content": "3.3. Co-Content Teacher-Forcing" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.59, + 0.892, + 0.65 + ], + "angle": 0, + "content": "The Co-Content Teacher-Forcing is proposed to alleviate the over-reliance on the sampled ground truth points during training, which leads to exposure bias of the inconsistency of Training and Inference." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.65, + 0.892, + 0.7 + ], + "angle": 0, + "content": "In the normal Teacher-Forcing training, the sequence \\(\\{x_i,y_i\\}_{i = 1}^N\\) will be embedded to a feature sequence \\(\\{m_i,n_i\\}_{i = 1}^N\\):" + }, + { + "type": "equation", + "bbox": [ + 0.587, + 0.706, + 0.891, + 0.727 + ], + "angle": 0, + "content": "\\[\n\\left\\{m _ {i}, n _ {i} \\right\\} _ {i = 1} ^ {N} = d i c \\left(\\left\\{x _ {i}, y _ {i} \\right\\} _ {i = 1} ^ {N}\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.733, + 0.893, + 0.902 + ], + "angle": 0, + "content": "where \\( dic(\\cdot) \\) is a learnable coordinate embedding dictionary. The coordinate sequence \\( \\{x_i, y_i\\}_{i=1}^N \\) can be viewed as the index of the dictionary. However, in the Co-Content Teacher-Forcing, the feature sequence will be combined with the encoder output multi-modal feature. As shown in Fig. 3, we reshape the output multi-modal feature \\( F_m \\in R^{H \\times W \\times C'} \\) of Transformer Encoder to \\( F_m' \\in R^{(H \\times W) \\times C'} \\) for better correspondence with coordinate points. Then, for each coordinate \\( x_i \\) or \\( y_i \\), we extract the feature of the corresponding row or column from the feature map \\( F_m' \\) and average them with \\( E\\{\\cdot\\} \\):" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3024" + } + ], + [ + { + "type": "equation", + "bbox": [ + 0.174, + 0.103, + 0.469, + 0.14 + ], + "angle": 0, + "content": "\\[\nf _ {x _ {i}} = E _ {k = 1} ^ {H} \\left\\{F _ {m} ^ {\\prime} \\left(\\frac {W}{M} x _ {i}, k\\right) \\right\\}, \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.175, + 0.135, + 0.37, + 0.164 + ], + "angle": 0, + "content": "\\[\nf _ {y _ {i}} = E _ {k = 1} ^ {W} \\{F _ {m} ^ {\\prime} (k, \\frac {H}{M} y _ {i}) \\},\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.172, + 0.468, + 0.251 + ], + "angle": 0, + "content": "\\(M\\) is the number of quantization bins. \\(\\left\\{\\frac{W}{M} x_i,\\frac{H}{M} y_i\\right\\}_{i = 1}^N\\) is the normalized index. Then the sequential feature \\(\\{m_i,n_i\\}_{i = 1}^N\\) will be combined with \\(\\{f_{x_i},f_{y_i}\\}_{i = 1}^N\\) by simple element-wise addition, the final sequence input to Decoder is represented as" + }, + { + "type": "equation", + "bbox": [ + 0.16, + 0.262, + 0.469, + 0.283 + ], + "angle": 0, + "content": "\\[\nS = \\left\\{m _ {i}, n _ {i} \\right\\} _ {i = 1} ^ {N} + \\left\\{f _ {x _ {i}}, f _ {y _ {i}} \\right\\} _ {i = 1} ^ {N} \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.294, + 0.469, + 0.34 + ], + "angle": 0, + "content": "With CCTF training, our model explicitly associate the point coordinates (scale values) with the referred spatial region, which is more natural for visual grounding." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.347, + 0.37, + 0.362 + ], + "angle": 0, + "content": "3.4. Point-Modulated Cross Attention" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.371, + 0.469, + 0.492 + ], + "angle": 0, + "content": "We adopt the SMCA proposed in [13] to replace the original Cross-Attention, which can constrain the cross-attention responses via estimating the Gaussian-like referring region prior. In the sequence prediction model, we generate the attention map for each co-content coordinates embedding, leading to a matched cross-attention region learned when the supervised data is reduced. To distinguish we denote it as Point-Modulated Cross-Attention (PMCA)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.493, + 0.469, + 0.581 + ], + "angle": 0, + "content": "For each co-content coordinate embedding \\( S = \\{f_j\\}_{j=1}^{2N} = \\{f_{x_i}, f_{y_i}\\}_{i=1}^N \\), we use a 3-layer MLP followed by a sigmoid activation function to generate a prior Gaussian-like attention map. The center point coordinates \\( c_w, c_h \\) and the scales \\( s_w, s_h \\) of the prior Gaussian map is denoted as" + }, + { + "type": "equation", + "bbox": [ + 0.143, + 0.597, + 0.469, + 0.613 + ], + "angle": 0, + "content": "\\[\ns _ {w}, s _ {h}, c _ {w}, c _ {h} = \\operatorname {s i g m o i d} (M L P (f _ {j})), \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.626, + 0.338, + 0.641 + ], + "angle": 0, + "content": "and the Gaussian map is represented as" + }, + { + "type": "equation", + "bbox": [ + 0.123, + 0.652, + 0.469, + 0.687 + ], + "angle": 0, + "content": "\\[\nG (u, v) = \\exp \\left(- \\frac {\\left(u - c _ {w}\\right) ^ {2}}{\\sigma s _ {w} ^ {2}} - \\frac {\\left(v - c _ {h}\\right) ^ {2}}{\\sigma s _ {h} ^ {2}}\\right) \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.699, + 0.469, + 0.745 + ], + "angle": 0, + "content": "where \\(u\\in R^W,v\\in R^H\\) , they are the spatial indices of \\(G\\) and \\(\\sigma\\) is a hyper-parameter to modulate the bandwidth of the Gaussian map." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.756, + 0.377, + 0.772 + ], + "angle": 0, + "content": "3.5. Resampling Pseudo Point Strategy" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.781, + 0.469, + 0.901 + ], + "angle": 0, + "content": "To fully utilize the data without mask annotation in Partial-RES, we retrain the model with the generated pseudo label, and we propose a Resampling pseudo point strategy. To remain the consistency of the pseudo points with the original sampled contour points of the ground truth mask, we will resample the pseudo points. As shown in Fig. 4, we first connect the points predicted by the network to a binary mask. Then we compute the dice coefficient \\( D \\)" + }, + { + "type": "image", + "bbox": [ + 0.546, + 0.092, + 0.703, + 0.184 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.62, + 0.185, + 0.641, + 0.197 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.706, + 0.099, + 0.853, + 0.184 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.771, + 0.185, + 0.793, + 0.197 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.558, + 0.199, + 0.703, + 0.283 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.624, + 0.285, + 0.643, + 0.297 + ], + "angle": 0, + "content": "(c)" + }, + { + "type": "image", + "bbox": [ + 0.708, + 0.199, + 0.853, + 0.283 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.775, + 0.284, + 0.796, + 0.297 + ], + "angle": 0, + "content": "(d)" + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.313, + 0.892, + 0.397 + ], + "angle": 0, + "content": "Figure 4. Sketch of Resampling Pseudo Point (RPP) Strategy. (a) Screening the appropriate pseudo mask with the dice coefficient between the predicted binary mask and the ground truth bounding box; (b) The generated points by the RES model; (c) The uniformly resampled pseudo points from pseudo mask; (d) The uniformly sampled target points from ground truth mask." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.409, + 0.891, + 0.437 + ], + "angle": 0, + "content": "of the projection of this mask and the ground truth bounding box." + }, + { + "type": "equation", + "bbox": [ + 0.567, + 0.438, + 0.891, + 0.455 + ], + "angle": 0, + "content": "\\[\nD (l, b) = d i c e \\left(l _ {y}, b _ {y})\\right) + d i c e \\left(l _ {x}, b _ {x}\\right) \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.462, + 0.89, + 0.492 + ], + "angle": 0, + "content": "where \\(l_x, l_y\\) are the projection of the mask on x-axis, y-axis, \\(b_x, b_y\\) are of the ground truth bounding box." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.492, + 0.891, + 0.612 + ], + "angle": 0, + "content": "We set a threshold \\( T \\) for \\( D(l, b) \\) to pick out those pseudo masks that are more accurate to avoid introducing too much noise by pseudo labels. For the selected samples, the predicted points are spaced may be inhomogeneous as shown in Fig. 4(b). So we resample the pseudo points with a uniform sampling scheme. It is considered that the contour points of previously labeled data are sampled uniformly and the pseudo point should be kept consistent with it." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.613, + 0.89, + 0.643 + ], + "angle": 0, + "content": "We propose a feasible training process for the Partially Supervised RES task as shown in Fig. 2 (b):" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.643, + 0.892, + 0.748 + ], + "angle": 0, + "content": "Step1. REC Training. Since in the Partial-RES task, all the bounding box labels are seen in the model training, we first train the model on fully supervised REC to get a better representation relatively of multi-model features from the encoder output. The feature will be used to enhance TF learning in the next step of RES training. In practice, we load the REC model trained in SeqTR as a pretrained one." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.749, + 0.891, + 0.809 + ], + "angle": 0, + "content": "Step2. Initial RES Training. In this step, we train the model with only \\(1\\%\\), \\(5\\%\\), and \\(10\\%\\) labeled mask data on the RES task. The model is equipped with the new method proposed in this paper, including CCTF and PMCA." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.809, + 0.892, + 0.899 + ], + "angle": 0, + "content": "Step3. Retraining with Pseudo Points. After obtaining the RES model from Step2, we can utilize it to generate more pseudo contour points. While these points may contain a lot of noise or may not match the way labeled data is sampled, so we re-selected and sampled out the appropriate pseudo points with the proposed RPP strategy." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3025" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.078, + 0.089, + 0.904, + 0.381 + ], + "angle": 0, + "content": "
Label RatioMethodVenueVisual EncoderRefCOCORefCOCO+RefCOCOg
valtestAtestBvaltestAtestBval-gval-utest-u
100%Fully-supervised:
CMSA [47]CVPR'19RN10158.3260.6155.0943.7647.6037.8939.98--
STEP [3]ICCV'19RN10160.0463.4657.9748.1952.3340.4146.40--
CMPC [21]CVPR'20DN5361.3664.5359.6449.5653.4443.2349.05--
LSCM [22]ECCV'20DLA3461.4764.9959.5549.3453.1243.5048.05--
MCN [32]CVPR'20DN5362.4464.2059.7150.6254.9944.69-49.2249.40
BUSNet [44]CVPR'21RN10163.2766.4161.3951.7656.8744.1350.56--
LTS[21] [23]CVPR'21DN5365.4367.7663.0854.2158.3248.02-54.4054.25
VLT [9]ICCV'21DN5665.6568.2962.7355.5059.2049.3649.7652.9956.65
ResTR [25]CVPR'22ViT-B67.2269.3064.4555.7860.4448.2754.48--
SeqTR [54]ECCV'22DN5367.2669.7964.1254.1458.9348.19-55.6755.64
10%Partially-supervised
Baseline-DN5357.9260.7455.0650.8454.8744.54-49.5049.30
Our Method-DN5363.9965.8661.5452.5655.7946.09-52.4352.40
5%Baseline-DN5358.8561.7256.3649.7854.2243.34-48.3348.27
Our Method-DN5362.2364.6760.4251.3054.6844.85-51.2850.55
1%Baseline-DN5354.6757.4852.6244.9148.8139.44-42.5742.46
Our Method-DN5357.4159.6055.8447.6852.0641.54-46.8046.12
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.391, + 0.893, + 0.435 + ], + "angle": 0, + "content": "Table 1. Comparisons with state-of-the-art fully-supervised methods on RefCOCO [50], RefCOCO+ [50], and RefCOCOg [33] in terms of IoU scores. We defined mask label ratio as \\(1\\%\\), \\(5\\%\\), and \\(10\\%\\) to compare our method with the baseline in partially-supervised RES. \"RN101\": ResNet-101 [15], \"DN53\": DarkNet-53 [37], \"DN56\": DarkNet-56 [37], \"DLA-34\" [48], \"ViT-B\" [10]." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.459, + 0.21, + 0.475 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.483, + 0.181, + 0.498 + ], + "angle": 0, + "content": "4.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.506, + 0.47, + 0.581 + ], + "angle": 0, + "content": "RefCOCO/RefCOCO+ are proposed in [50]. There are 19,994 images in RefCOCO with 142,209 refer expressions for 50,000 objects. Similarly, 19,992 images are included in RefCOCO+ which contains 141,564 expressions for 49,856 objects." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.582, + 0.47, + 0.717 + ], + "angle": 0, + "content": "In these datasets, each image contains two or more objects from the same category. In RefCOCO+ dataset, positional words are not allowed in the referring expression, which is a pure dataset with appearance-based referring expression, whereas RefCOCO imposes no restriction on the phrase. In addition to the training set and validation set, the test set for RefCOCO/RefCOCO+ is divided into a testA set (containing several people in an image) and a testB set (containing multiple instances of other objects in an image)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.717, + 0.47, + 0.809 + ], + "angle": 0, + "content": "RefCOCOg [33] contains 26,711 images with 85,474 referring expressions for 54,822 objects, and each image usually contains \\(2 \\sim 4\\) objects of the same category. The length of referring expressions in this dataset is almost twice as long as those in RefCOCO and RefCOCO+. This dataset is directly divided into \"train\", \"val\" and \"test\"." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.818, + 0.285, + 0.835 + ], + "angle": 0, + "content": "4.2. Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.47, + 0.903 + ], + "angle": 0, + "content": "Each instance to be segmented is equipped with an expression sentence in RES datasets, the sentence can be seen as a kind of open category. Therefore, different from the manner of dividing data [17], we simulate the partially su" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.46, + 0.892, + 0.505 + ], + "angle": 0, + "content": "pervised training scenario on RES by randomly sampling the entire train data proportionally without considering the category." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.507, + 0.893, + 0.734 + ], + "angle": 0, + "content": "Implementation Details. Following SeqTR [54], all parameters in the network are optimized with AdamW [30], and the batch size is 128. We train the model with \\(120 \\sim 150\\) epochs for full convergence, and we set the learning rate as \\(5\\mathrm{e - }4@10\\%\\), \\(3\\mathrm{e - }4@5\\%\\), and \\(1\\mathrm{e - }4@1\\%\\) in step2 training, while \\(5\\mathrm{e - }4\\) in step3 training with pseudo points. We use DarkNet-53 [37] as the visual encoder. Following standard practices [8, 32, 23, 9], images are resized to \\(640 \\times 640\\), and the length of language expression is trimmed at 15 for ResCOCO/RefCOCO+ and 20 for RefCOCOg. The training epochs and the learning rate used vary slightly for different settings of mask-labeled data ratio and different training stages. For the number of sampled contour points, we use the same as in SeqTR, i.e., 18 points for RefCOCO/RefCOCO+, and 12 for RefCOCOg." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.894, + 0.903 + ], + "angle": 0, + "content": "Evaluation Metrics. Following the proposal setting in the previous work, we use mask Intersection-over-Union (IoU) and Precision with thresholds \\((\\mathrm{Pr}@\\mathrm{X})\\). The mask IoU demonstrates the mask quality, which emphasizes the model's overall performance and reveals both targeting and segmenting abilities. The \\(\\mathrm{Pr}@\\mathrm{X}\\) metric computes the ratio of successfully predicted samples using different IoU thresholds. Low threshold precision like \\(\\mathrm{Pr}@\\mathrm{0.5}\\) reflects the identification performance of the method, and high threshold precision like \\(\\mathrm{Pr}@\\mathrm{0.9}\\) reveals the ability to generate high-quality masks." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "3026" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.094, + 0.089, + 0.878, + 0.204 + ], + "angle": 0, + "content": "
RatioCCTFPMCAIoUPr@0.5Pr@0.6Pr@0.7Pr@0.8Pr@0.9
1%✓42.5747.5432.8917.615.070.36
✓✓44.20 (+1.63)50.68 (+3.14)37.00 (+4.11)21.21 (+3.60)7.89 (+2.82)0.66 (+0.30)
44.58 (+0.38)50.98 (+0.30)37.32 (+0.32)22.42 (+1.21)8.21 (+0.32)0.76 (+0.10)
100%✓55.9567.7362.2049.0229.557.07
✓✓56.03 (+0.08)67.79 (+0.06)62.32 (+0.12)49.88 (+0.86)30.63 (+1.08)8.04 (+0.97)
56.89 (+0.86)68.43 (+0.64)62.96 (+0.64)50.72 (+0.84)31.21 (+0.58)8.05 (+0.01)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.214, + 0.895, + 0.245 + ], + "angle": 0, + "content": "Table 2. Validity verification of our proposed CCTF and PMCA. We conduct experiments on \\(1\\%\\) and \\(100\\%\\) supervised data with mask labels (100% means fully-supervised training)." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.267, + 0.457, + 0.334 + ], + "angle": 0, + "content": "
MethodIoUPr@0.5Pr@0.6Pr@0.7Pr@0.8Pr@0.9
Baseline44.5850.9837.3222.428.210.76
w/o RPP44.2751.2838.2424.2810.240.96
w/ RPP46.8056.2943.1927.4010.340.98
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.343, + 0.47, + 0.373 + ], + "angle": 0, + "content": "Table 3. Comparison of our method with re-sampling pseudo points and without RPP in step3 training." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.398, + 0.471, + 0.52 + ], + "angle": 0, + "content": "Baseline and oracle We suggest the baseline in the following way: we initialize our model with the parameters of SeqTR trained on the REC task, and refine it on the RES task with \\(1\\%\\), \\(5\\%\\), and \\(10\\%\\) mask annotation data. Our comparison with the baseline is discussed in Sec. 4.3. The \"oracle\" model is SeqTR trained on REC and refined on RES with all mask annotation data. This fully supervised model is a performance upper bound for our partially supervised RES." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.528, + 0.357, + 0.543 + ], + "angle": 0, + "content": "4.3. Comparison with other Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.551, + 0.47, + 0.765 + ], + "angle": 0, + "content": "We compare our method with other fully supervised state-of-the-art methods on three common benchmarks of Referring Expression Segmentation, i.e., RefCOCO, RefCOCO+, and RefCOCOg. In addition, we also compare with our suggested baseline in partially-supervised training. Results are reported in Table 1. Our method displays significant improvement over the baseline method on all three datasets, at an average of \\(3.5\\%\\), \\(2.4\\%\\), and \\(3.0\\%\\) on \\(1\\%\\), \\(5\\%\\), and \\(10\\%\\) mask labeled data. With \\(10\\%\\) mask annotated data, we can achieve \\(97\\%\\) of the fully supervised performance on RefCOCO+@val. We are also able to achieve \\(88\\%\\) fully supervised performance on \\(1\\%\\) mask-labeled data on RefCOCO+@testA. Our methods can even surpass some fully-supervised methods, e.g., MCN, [32], BUSNet [44]." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.772, + 0.241, + 0.787 + ], + "angle": 0, + "content": "4.4. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.795, + 0.469, + 0.884 + ], + "angle": 0, + "content": "Effectiveness of CCTF and PMCA. To verify the effectiveness of Co-Content Teacher Forcing (CCTF) and Point-Modulated Cross-Attention (PMCA), we show the results of our RES model with \\(1\\%\\) partially supervised training data and \\(100\\%\\) fully supervised training data on Ref-COCOg@val in Table 2." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.886, + 0.469, + 0.9 + ], + "angle": 0, + "content": "When trained with \\(100\\%\\) mask label of the data, PMCA" + }, + { + "type": "table", + "bbox": [ + 0.507, + 0.267, + 0.89, + 0.335 + ], + "angle": 0, + "content": "
MethodIoUPr@0.5Pr@0.6Pr@0.7Pr@0.8Pr@0.9
Baseline44.5850.9837.3222.428.210.76
T@1.0044.5351.3638.1422.407.290.58
T@0.0544.3550.6838.5224.189.700.78
T@0.1046.8056.2943.1927.4010.340.98
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.345, + 0.894, + 0.402 + ], + "angle": 0, + "content": "Table 4. Performance with different Threshold \\( T \\) of dice coefficient on RefCOCOg@val. \\( T@1.00 \\) means that all the predicted pseudo masks are used for training in step3 without selection of the more accurate one with dice coefficient." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.43, + 0.892, + 0.581 + ], + "angle": 0, + "content": "embodies more performance enhancements than CCTF, which proves the effectiveness of PMCA. CCTF contributes little to performance improvement. While with \\(1\\%\\) supervised mask labeled training data, the improvement contribution of CCTF far exceeds that of PMCA, \\(80\\%\\) performance improvement comes from CCTF (1.63% in 2.01%). This demonstrates that the exposure bias of the inconsistency of the training and inference process may be more serious in the case of partially supervised data, which is alleviated by our proposed CCTF somewhat." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.583, + 0.893, + 0.779 + ], + "angle": 0, + "content": "Ablation of Resampling Pseudo Points To better demonstrate the effectiveness of re-sampling pseudo points (RPP), we compare the results with those obtained without resampling. In the case of re-sampling, we uniformly resample the pseudo points from the generated pseudo mask and consider them as the target sequence. In contrast, for no re-sampling, we use the points predicted by the model directly as the target sequence. As illustrated in Table 3, the reported performance is superior when the RPP strategy is employed. This is primarily attributed to the fact that after re-sampling, the unlabeled data is pseudo-labeled uniformly, which mitigates the potential noise introduced by pseudo labels." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.894, + 0.901 + ], + "angle": 0, + "content": "Performance with different Threshold \\(T\\) of dice coefficient. We establish a threshold for the dice coefficient to select more suitable pseudo points that will serve as prediction targets for unlabeled mask annotation data in step3 mentioned in Sec. 3.5. To demonstrate the effectiveness of our pseudo points selection strategy and the impact of different threshold, we compare the results with different thresholds \\(T\\) of the dice coefficient on RefCOCOg@val. As presented" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3027" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.152, + 0.089, + 0.823, + 0.367 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.376, + 0.893, + 0.404 + ], + "angle": 0, + "content": "Figure 5. Qualitative examples of our method at \\(1\\%\\), \\(5\\%\\), \\(10\\%\\) mask label in Partially Supervised RES. We identify the image and its corresponding referring expression in the first column." + }, + { + "type": "image", + "bbox": [ + 0.098, + 0.43, + 0.446, + 0.575 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.098, + 0.575, + 0.448, + 0.587 + ], + "angle": 0, + "content": "Figure 6. The mIoU with different mask-labeled data ratio." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.618, + 0.47, + 0.724 + ], + "angle": 0, + "content": "in Table 4, without any constraint (\\(T\\) is set to 1.00), we observe that the side effect of pseudo points is greater than the main effect due to the noise contained in pseudo points. Therefore, we determine to select out those pseudo masks with dice coefficient \\(< T\\) for re-sampling pseudo points. Meanwhile, our ablation studies declare that the threshold of 0.10 is the optimal choice for our selection strategy." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.726, + 0.47, + 0.831 + ], + "angle": 0, + "content": "Experiment of increasing the proportion of mask-labeled data We conduct an additional experiment to investigate the proportion of mask-labeled data in training that can reach SotA performance. As depicted in Fig. 6, comparing the mIoU with the fully supervised SotA method (55.67) on RefCOCOg@val, our method attains remarkably similar results (55.59) by using \\(50\\%\\) mask-labeled data." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.846, + 0.26, + 0.862 + ], + "angle": 0, + "content": "4.5. Qualitative Results" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.47, + 0.901 + ], + "angle": 0, + "content": "We evaluated the segmentation performance of our method at various ratios, i.e., \\(1\\%\\), \\(5\\%\\), and \\(10\\%\\) of Partial-" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.43, + 0.893, + 0.506 + ], + "angle": 0, + "content": "RES data in Fig. 5. As the mask-labeled data increases, the contours fit more accurately. By comparing these results, we observe that with a low percentage of mask-labeled data, our segmentation results are already similar to those of the GT points." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.534, + 0.619, + 0.55 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.564, + 0.893, + 0.821 + ], + "angle": 0, + "content": "In this paper, we investigate the partially supervised learning paradigm for RES, where only a few segmentation masks are available so the model has to learn transfer knowledge from REC. Unlike existing RES/REC works that have different decoders for the two tasks, we leverage the contour-based sequence prediction model to maximize the transfer ability. We then propose CCTF to explicitly associate the point coordinates (scale values) with the referred spatial regions, which alleviates the exposure bias brought by the limited segmentation masks. Besides, we present RPP strategy to select appropriate pseudo labels and resample pseudo points during the retraining stage. We conduct extensive experiments to demonstrate the effectiveness of our model in the Partial-RES setting. One limitation of this work is that the smoothness and edge accuracy of the point-contour mask prediction may not be as precise as the pixel-level mask predictions." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.826, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Acknowledgements. This work was supported by the National Key RD Program of China (No.2018AAA0102100), the National Natural Science Foundation of China (Nos. U1936212, 62120106009), Sponsored by Beijing Nova Program (NO. 20220484063)." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "3028" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.105 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.17 + ], + "angle": 0, + "content": "[1] David Biertimpel, Sindi Shkodrani, Anil S Baslamisli, and Nóra Baka. Prior to segment: Foreground cues for weakly annotated classes in partially supervised instance segmentation. In ICCV, pages 2824-2833, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.172, + 0.47, + 0.225 + ], + "angle": 0, + "content": "[2] Ding-Jie Chen, Songhao Jia, Yi-Chen Lo, Hwann-Tzong Chen, and Tyng-Luh Liu. See-through-text grouping for referring image segmentation. In ICCV, pages 7454-7463, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.228, + 0.47, + 0.281 + ], + "angle": 0, + "content": "[3] Ding-Jie Chen, Songhao Jia, Yi-Chen Lo, Hwann-Tzong Chen, and Tyng-Luh Liu. See-through-text grouping for referring image segmentation. In ICCV, pages 7454-7463, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.284, + 0.47, + 0.324 + ], + "angle": 0, + "content": "[4] Ting Chen, Saurabh Saxena, Lala Li, David J Fleet, and Geoffrey Hinton. Pix2seq: A language modeling framework for object detection. In ICLR." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.326, + 0.47, + 0.38 + ], + "angle": 0, + "content": "[5] Xinpeng Chen, Lin Ma, Jingyuan Chen, Zequn Jie, Wei Liu, and Jiebo Luo. Real-time Referring Expression Comprehension by Single-stage Grounding Network. arXiv preprint arXiv:1812.03426, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.382, + 0.47, + 0.436 + ], + "angle": 0, + "content": "[6] Zhiyang Chen, Yousong Zhu, Zhaowen Li, Fan Yang, Wei Li, Haixin Wang, Chaoyang Zhao, Liwei Wu, Rui Zhao, Jinqiao Wang, et al. Obj2seq: Formatting objects as sequences with class prompt for visual tasks. In NeurIPS." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.439, + 0.47, + 0.492 + ], + "angle": 0, + "content": "[7] Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014, 2014." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.494, + 0.47, + 0.535 + ], + "angle": 0, + "content": "[8] Jiajun Deng, Zhengyuan Yang, Tianlang Chen, Wengang Zhou, and Houqiang Li. TransVG: End-to-End Visual Grounding with Transformers. ICCV, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.537, + 0.47, + 0.577 + ], + "angle": 0, + "content": "[9] Henghui Ding, Chang Liu, Suchen Wang, and Xudong Jiang. Vision-language transformer and query generation for referring segmentation. In ICCV, pages 16321-16330, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.579, + 0.47, + 0.647 + ], + "angle": 0, + "content": "[10] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.649, + 0.47, + 0.703 + ], + "angle": 0, + "content": "[11] Qi Fan, Lei Ke, Wenjie Pei, Chi-Keung Tang, and Yu-Wing Tai. Commonality-parsing network across shape and appearance for partially supervised instance segmentation. In ECCV, pages 379–396. Springer, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.705, + 0.47, + 0.758 + ], + "angle": 0, + "content": "[12] Guang Feng, Zhiwei Hu, Lihe Zhang, and Huchuan Lu. Encoder fusion network with co-attention embedding for referring image segmentation. In CVPR, pages 15506–15515, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.761, + 0.468, + 0.802 + ], + "angle": 0, + "content": "[13] Peng Gao, Minghang Zheng, Xiaogang Wang, Jifeng Dai, and Hongsheng Li. Fast convergence of detr with spatially modulated co-attention. In ICCV, pages 3621–3630, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.804, + 0.468, + 0.83 + ], + "angle": 0, + "content": "[14] Kaiming He, Georgia Gkioxari, Piotr Dólar, and Ross Girshick. Mask r-cnn. In ICCV, pages 2961-2969, 2017." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.832, + 0.468, + 0.871 + ], + "angle": 0, + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep Residual Learning for Image Recognition. In CVPR, 2016." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.468, + 0.901 + ], + "angle": 0, + "content": "[16] Richang Hong, Daqing Liu, Xiaoyu Mo, Xiangnan He, and Hanwang Zhang. Learning to Compose and Reason with" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.47, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.133 + ], + "angle": 0, + "content": "Language Tree Structures for Visual Grounding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.136, + 0.892, + 0.177 + ], + "angle": 0, + "content": "[17] Ronghang Hu, Piotr Dólar, Kaiming He, Trevor Darrell, and Ross Girshick. Learning to segment every thing. In CVPR, pages 4233-4241, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.179, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[18] Ronghang Hu, Marcus Rohrbach, Jacob Andreas, Trevor Darrell, and Kate Saenko. Modeling Relationships in Referential Expressions with Compositional Modular Networks. In CVPR, 2017." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.235, + 0.892, + 0.289 + ], + "angle": 0, + "content": "[19] Zhiwei Hu, Guang Feng, Jiayu Sun, Lihe Zhang, and Huchuan Lu. Bi-directional relationship inferring network for referring image segmentation. In CVPR, pages 4424-4433, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.291, + 0.892, + 0.347 + ], + "angle": 0, + "content": "[20] Shaofei Huang, Tianrui Hui, Si Liu, Guanbin Li, Yunchao Wei, Jizhong Han, Luoqi Liu, and Bo Li. Referring image segmentation via cross-modal progressive comprehension. In CVPR, pages 10488-10497, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.349, + 0.892, + 0.403 + ], + "angle": 0, + "content": "[21] Shaofei Huang, Tianrui Hui, Si Liu, Guanbin Li, Yunchao Wei, Jizhong Han, Luoqi Liu, and Bo Li. Referring image segmentation via cross-modal progressive comprehension. In CVPR, pages 10488-10497, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.405, + 0.892, + 0.46 + ], + "angle": 0, + "content": "[22] Tianrui Hui, Si Liu, Shaofei Huang, Guanbin Li, Sansi Yu, Faxi Zhang, and Jizhong Han. Linguistic structure guided context modeling for referring image segmentation. In ECCV, pages 59-75. Springer, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.462, + 0.892, + 0.503 + ], + "angle": 0, + "content": "[23] Ya Jing, Tao Kong, Wei Wang, Liang Wang, Lei Li, and Tie-niu Tan. Locate then segment: A strong pipeline for referring image segmentation. In CVPR, pages 9858-9867, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.505, + 0.892, + 0.559 + ], + "angle": 0, + "content": "[24] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. MDETR-Modulated Detection for End-to-End Multi-Modal Understanding. In ICCV, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.562, + 0.892, + 0.615 + ], + "angle": 0, + "content": "[25] Namyup Kim, Dongwon Kim, Cuiling Lan, Wenjun Zeng, and Suha Kwak. Restr: Convolution-free referring image segmentation using transformers. In CVPR, pages 18145-18154, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.618, + 0.892, + 0.671 + ], + "angle": 0, + "content": "[26] Weicheng Kuo, Anelia Angelova, Jitendra Malik, and Tsung-Yi Lin. Shapemask: Learning to segment novel objects by refining shape priors. In ICCV, pages 9207-9216, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.675, + 0.892, + 0.715 + ], + "angle": 0, + "content": "[27] Muchen Li and Leonid Sigal. Referring transformer: A one-step approach to multi-task visual grounding. NeurIPS, 34:19652-19664, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.717, + 0.892, + 0.771 + ], + "angle": 0, + "content": "[28] Yue Liao, Si Liu, Guanbin Li, Fei Wang, Yanjie Chen, Chen Qian, and Bo Li. A Real-time Cross-modality Correlation Filtering Method for Referring Expression Comprehension. In CVPR, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.774, + 0.892, + 0.814 + ], + "angle": 0, + "content": "[29] Daqing Liu, Hanwang Zhang, Feng Wu, and Zheng-Jun Zha. Learning to Assemble Neural Module Tree Networks for Visual Grounding. In ICCV, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.817, + 0.892, + 0.843 + ], + "angle": 0, + "content": "[30] Ilya Loshchilov and Frank Hutter. Fixing Weight Decay Regularization in Adam. 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.846, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[31] Gen Luo, Yiyi Zhou, Rongrong Ji, Xiaoshuai Sun, Jinsong Su, Chia-Wen Lin, and Qi Tian. Cascade grouped attention network for referring expression segmentation. In ACM MM, pages 1274-1282, 2020." + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "3029" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "[32] Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Liujuan Cao, Chenglin Wu, Cheng Deng, and Rongrong Ji. Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation. In CVPR, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.149, + 0.468, + 0.202 + ], + "angle": 0, + "content": "[33] Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy. Generation and Comprehension of Unambiguous Object Descriptions. In CVPR, 2016." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.204, + 0.468, + 0.232 + ], + "angle": 0, + "content": "[34] Tsvetomila Mihaylova and André FT Martins. Scheduled sampling for transformers. ACL 2019, page 351, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.233, + 0.468, + 0.26 + ], + "angle": 0, + "content": "[35] Pedro O O Pinheiro, Ronan Collobert, and Piotr Dólar. Learning to segment object candidates. NeurIPS, 28, 2015." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.261, + 0.468, + 0.315 + ], + "angle": 0, + "content": "[36] Mengxue Qu, Yu Wu, Wu Liu, Qiqi Gong, Xiaodan Liang, Olga Russakovsky, Yao Zhao, and Yunchao Wei. Siri: A simple selective retraining mechanism for transformer-based visual grounding. In ECCV, pages 546-562. Springer, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.316, + 0.468, + 0.343 + ], + "angle": 0, + "content": "[37] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.344, + 0.468, + 0.384 + ], + "angle": 0, + "content": "[38] Arka Sadhu, Kan Chen, and Ram Nevatia. Zero-Shot Grounding of Objects from Natural Language Queries. In ICCV, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.385, + 0.468, + 0.427 + ], + "angle": 0, + "content": "[39] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is All You Need. In NeuruIPS, 2017." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.428, + 0.468, + 0.482 + ], + "angle": 0, + "content": "[40] Liwei Wang, Yin Li, Jing Huang, and Svetlana Lazebnik. Learning Two-Branch Neural Networks for Image-Text Matching Tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.484, + 0.468, + 0.538 + ], + "angle": 0, + "content": "[41] Peng Wang, Qi Wu, Jiewei Cao, Chunhua Shen, Lianli Gao, and Anton van den Hengel. Neighbourhood Watch: Referring Expression Comprehension via Language-Guided Graph Attention Aetworks. In CVPR, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.539, + 0.468, + 0.592 + ], + "angle": 0, + "content": "[42] Xuehui Wang, Kai Zhao, Ruixin Zhang, Shouhong Ding, Yan Wang, and Wei Shen. Contrastmask: Contrastive learning to segment every thing. In CVPR, pages 11604-11613, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.595, + 0.468, + 0.635 + ], + "angle": 0, + "content": "[43] Sibei Yang, Guanbin Li, and Yizhou Yu. Dynamic Graph Attention for Referring Expression Comprehension. In ICCV, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.637, + 0.468, + 0.678 + ], + "angle": 0, + "content": "[44] Sibei Yang, Meng Xia, Guanbin Li, Hong-Yu Zhou, and Yizhou Yu. Bottom-up shift and reasoning for referring image segmentation. In CVPR, pages 11266-11275, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.679, + 0.468, + 0.718 + ], + "angle": 0, + "content": "[45] Zhengyuan Yang, Tianlang Chen, Liwei Wang, and Jiebo Luo. Improving One-Stage Visual Grounding by Recursive Sub-Query Construction. In ECCV, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.72, + 0.468, + 0.761 + ], + "angle": 0, + "content": "[46] Zhengyuan Yang, Boqing Gong, Liwei Wang, Wenbing Huang, Dong Yu, and Jiebo Luo. A Fast and Accurate One-Stage Approach to Visual Grounding. In ICCV, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.762, + 0.468, + 0.803 + ], + "angle": 0, + "content": "[47] Linwei Ye, Mrigank Rochan, Zhi Liu, and Yang Wang. Cross-modal self-attention network for referring image segmentation. In CVPR, pages 10502–10511, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.805, + 0.468, + 0.844 + ], + "angle": 0, + "content": "[48] Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell. Deep layer aggregation. In CVPR, pages 2403-2412, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.468, + 0.9 + ], + "angle": 0, + "content": "[49] Licheng Yu, Zhe Lin, Xiaohui Shen, Jimei Yang, Xin Lu, Mohit Bansal, and Tamara L Berg. MAttNet: Modular Attention Network for Referring Expression Comprehension. In CVPR, 2018." + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.092, + 0.892, + 0.134 + ], + "angle": 0, + "content": "[50] Licheng Yu, Patrick Poirson, Shan Yang, Alexander C Berg, and Tamara L Berg. Modeling Context in Referring Expressions. In ECCV, 2016." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.892, + 0.204 + ], + "angle": 0, + "content": "[51] Xunlin Zhan, Yangxin Wu, Xiao Dong, Yunchao Wei, Minlong Lu, Yichi Zhang, Hang Xu, and Xiaodan Liang. Product1m: Towards weakly supervised instance-level product retrieval via cross-modal pretraining. In ICCV, pages 11782-11791, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.206, + 0.892, + 0.247 + ], + "angle": 0, + "content": "[52] Hanwang Zhang, Yulei Niu, and Shih-Fu Chang. Grounding Referring Expressions in Images by Variational Context. In CVPR, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.249, + 0.892, + 0.303 + ], + "angle": 0, + "content": "[53] Yanzhao Zhou, Xin Wang, Jianbin Jiao, Trevor Darrell, and Fisher Yu. Learning saliency propagation for semi-supervised instance segmentation. In CVPR, pages 10307-10316, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.305, + 0.892, + 0.361 + ], + "angle": 0, + "content": "[54] Chaoyang Zhu, Yiyi Zhou, Yunhang Shen, Gen Luo, Xingjia Pan, Mingbao Lin, Chao Chen, Liujuan Cao, Xiaoshuai Sun, and Rongrong Ji. Seqtr: A simple yet universal network for visual grounding. In ECCV, pages 598-615. Springer, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.362, + 0.892, + 0.416 + ], + "angle": 0, + "content": "[55] Bohan Zhuang, Qi Wu, Chunhua Shen, Ian Reid, and Anton Van Den Hengel. Parallel Attention: A Unified Framework for Visual Object Discovery Through Dialogs and Queries. In CVPR, 2018." + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.092, + 0.892, + 0.416 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.956 + ], + "angle": 0, + "content": "3030" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Segment Every Referring Object Point by Point/3793df0f-b6b3-448f-85d5-cdd2cf084f15_origin.pdf b/2023/Learning To Segment Every Referring Object Point by Point/3793df0f-b6b3-448f-85d5-cdd2cf084f15_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..2414d09e9b29fdef97bfe80c41d85012b7c38867 --- /dev/null +++ b/2023/Learning To Segment Every Referring Object Point by Point/3793df0f-b6b3-448f-85d5-cdd2cf084f15_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:70966b27f5a3f2807a977d65ceb4165277da7376865f9c41895e7de35c6c7af0 +size 2244386 diff --git a/2023/Learning To Segment Every Referring Object Point by Point/full.md b/2023/Learning To Segment Every Referring Object Point by Point/full.md new file mode 100644 index 0000000000000000000000000000000000000000..a31587a30557e2316278770caef2be08b8695c63 --- /dev/null +++ b/2023/Learning To Segment Every Referring Object Point by Point/full.md @@ -0,0 +1,330 @@ +# Learning to Segment Every Referring Object Point by Point + +Mengxue $\mathbf{Q}\mathbf{u}^{1,2*}$ Yu Wu $^{3}$ Yunchao Wei $^{1,2}$ Wu Liu $^{4}$ Xiaodan Liang $^{5,6}$ Yao Zhao $^{1,2\dagger}$ + +$^{1}$ Institute of Information Science, Beijing Jiaotong University $^{2}$ Beijing Key Laboratory of Advanced Information Science and Network Technology $^{3}$ Wuhan University $^{4}$ JD Explore Academy $^{5}$ Sun Yat-sen University $^{6}$ MBZUAI + +qumengxue@bjtu.edu.cn, wuyucs@whu.edu.cn, yunchao.wei@bjtu.edu.cn + +# Abstract + +Referring Expression Segmentation (RES) can facilitate pixel-level semantic alignment between vision and language. Most of the existing RES approaches require massive pixel-level annotations, which are expensive and exhaustive. In this paper, we propose a new partially supervised training paradigm for RES, i.e., training using abundant referring bounding boxes and only a few (e.g., $1\%$ ) pixel-level referring masks. To maximize the transferability from the REC model, we construct our model based on the point-based sequence prediction model. We propose the co-content teacher-forcing to make the model explicitly associate the point coordinates (scale values) with the referred spatial features, which alleviates the exposure bias caused by the limited segmentation masks. To make the most of referring bounding box annotations, we further propose the resampling pseudo points strategy to select more accurate pseudo-points as supervision. Extensive experiments show that our model achieves $52.06\%$ in terms of accuracy (versus $58.93\%$ in fully supervised setting) on RefCOCO+@testA, when only using $1\%$ of the mask annotations. Code is available at https://github.com/qumengxue/Partial-RES.git. + +# 1. Introduction + +Referring Expression Segmentation (RES) aims to generate a segmentation mask for the object referred to by the language expression in the image. It allows pixel-level semantic alignment between language and vision, which is meaningful to many multi-modal tasks and can be applied to various practical applications, e.g., video/image editing with sentences. Benefiting from the development of deep learning techniques, significant progress [31, 9, 12, 2, 19, + +![](images/0c738b0943557bd70738132336cc993faea3caf37d193e08b1f2cdade5282bd3.jpg) +Figure 1. Comparison of MDETR and SeqTR. On the left is the streamlined framework of the two, and on the right is their IoU performance on RefCOCOg@val. Best viewed in color. + +20, 27, 54, 9] has been made in this field and achieved remarkable performance. + +The success of current methods partially attributes to the large-scale training dataset with accurate pixel-level masks. However, labeling masks for a huge amount of images is often costly in terms of both human effort and finance, and thus hardly be scaled up. Therefore, it is meaningful to explore a new training paradigm for RES where extensive pixel-level annotations are not necessary. It is well known that bounding box annotations are much cheaper and easier to be collected compared with pixel-level masks. Thus, we try to study this question: is it possible to learn an accurate RES model using abundant box annotations and only a few mask annotations? + +To answer this question, for the first time, we explore a new partially supervised training paradigm for RES (Partial-RES). Intuitively, a naive pipeline for this partially supervised setting is to first train a Referring Expression Comprehension (REC) model and then transfer it to the RES task by fine-tuning on a limited number of data with mask annotations. However, due to the difference between + +the REC and RES tasks, prevalent models (e.g., Mask R-CNN [14], MDETR [24]) have to simultaneously optimize two different heads, with one for detection prediction and the other for segmentation prediction. Such kind of structures would lead to a severe issue during the fine-tuning stage, i.e., a randomly initialized mask head can not get well optimized given only a few mask annotations (hundreds of images). As shown in Figure 1, it is not easy to optimize MDETR with only $1\%$ mask-annotated data even transferred from a well-trained REC model. + +Recently, a contour-based method named SeqTR has been proposed for unifying REC and RES. The idea is to use a sequence model (usually a Transformer decoder) to sequentially generate contour points of the referred object. The predictions are two points (top-left and bottom-right corner points of the bounding box) in the REC task while dozens of points (contour) are in the RES task. As shown in Fig. 1, both boxes and masks are converted to point sequences and optimized using the same simple cross-entropy loss in SeqTR, which ensures the consistency between REC and RES. It incorporates two optimization heads into a unified one, making the knowledge learned with four points (i.e., detection) be naturally transferred to predict multiple ones (i.e., segmentation). Inspired by SeqTR, we have the conjecture that sequence prediction might be a better solution for partially supervised training. + +In this paper, we investigate methods for achieving better partially supervised training for RES sequence prediction models. Sequence-to-sequence prediction models are typically trained with Teacher-Forcing (TF) [39], wherein the model utilizes the ground truth token as input to predict the next token during the training stage. However, the model can only predict the next state by taking its previous output as input in inference, which is known as the exposure bias [34]. This issue is even more severe in the partially supervised setting due to the very limited ground truth data. Consequently, we introduce the Co-Content Teacher-Forcing (CCTF), which combines the ground truth point coordinates together with the spatial visual feature of the pointed row or column. In contrast to the previous sequence model SeqTR, our CCTF explicitly associates the point coordinates (scale values) with the referred spatial region, providing a more natural approach for visual grounding. + +Furthermore, we estimate the referring region via our proposed Point-Modulated Cross-Attention, to ensure the decoder attends to those region content while generating the point contour sequences. To fully utilize the data without mask annotation in Partial-RES, we retrain the model with the generated pseudo labels, and we present a Resampling Pseudo Points (RPP) Strategy. Unlike most pseudo-label works that directly use the network's predictions as labels, we select the appropriate pseudo masks with Dice coefficient and then resample the predicted points in a uniform + +way to regularize the contour sequence labels. + +With extensive experiments, our method displays significant improvement compared to SeqTR [54] baselines on all three benchmarks, i.e., at an average of $3.5\%$ , $2.4\%$ , and $3.0\%$ on $1\%$ , $5\%$ and $10\%$ mask-labeled data. With $10\%$ mask annotated data, our method achieves $97\%$ of the fully supervised performance on RefCOCO + @val. We are also able to achieve $88\%$ of the fully supervised performance only with $1\%$ mask-labeled data on RefCOCO + @testA. + +# 2. Related Work + +# 2.1. REC and RES + +Referring Expression Comprehension (REC) aims at grounding the object referred to by a sentence, which achieves instance-level vision language alignment. Compared to REC, Referring Expression Segmentation (RES) grounds language expression at the fine-grained pixel-level. REC and RES are separate but closely related fundamental multi-modal tasks. + +For REC, previous methods can be roughly divided into two-stage and one-stage. Two-stage methods [16, 18, 29, 40, 41, 43, 49, 52, 55] typically utilize an object detector to generate region proposals in the first stage, and are trained to maximize the similarity between region and text with binary cross-entropy loss or similarity loss. One-stage methods [5, 28, 38, 45, 46] avoid being constrained by the quality of the proposal by directly fusing visual and linguistic features rather than matching region-language pairs. + +For RES, typical solutions are to incorporate as much linguistic information as possible in various ways on pixel-level visual features [31, 9, 12, 2, 19, 20]. In previous methods, researchers propose various attention mechanisms [9, 23, 20] to better merge vision and language. Recently, since the superior ability of modeling vision and language of Transformer-based model, more high-performance methods [27, 54, 9, 36] are proposed to solve RES tasks. + +For REC and RES, multi-task methods [32, 27] are proposed to better use the correlation between two tasks, which can facilitate learning of both tasks. However, all the prior work require different task-specific branch and loss function, whose generalization ability is limited. Recently, a simple and universal network term SeqTR [54] is presented, which regards both REC and RES as a point prediction problem. SeqTR greatly reduces the difficulty and complexity of both architecture design and optimization. + +In some work, REC is treated as a pretext task for other multi-modal tasks [24, 51], e.g., Visual Question and Answering. RES has a more fine-grained visual language alignment capability than REC, but the capability of RES to be used in multi-modal pre-training has not yet been developed because of the lack of a large-scale RES dataset. It is well known that annotating binary masks for a multi- + +![](images/1c39c4b36917a7c61a8d8086ad3b865a916301e7cae677b26042e1b5c49f153c.jpg) +(2) Transformer Encoder and Decoder +(1) Visual Language Fusion +(3) Sequence Construction +(a) Architecture + +![](images/c6c152cc553746f34be331225a82288f36e19574793e278da8d3b1e103bc6e92.jpg) +(b) Training Steps +Figure 2. (a) Overall architecture of our method. It consists of three main modules: (1) Visual-Language Fusion; (2) Transformer Encoder and Decoder; (3) Sequence Construction. "CCTF": Co-Content Teacher-Forcing; "PMCA": Point-Modulated Cross-Attention. (b) The suggested training steps of our method in the Partially Supervised RES task. Best viewed in color. + +modal dataset is expensive while bounding box is easy. In this paper, we make use of the bounding box annotations and the consistent task expression of REC and RES in SeqTR, attempting to train RES task with less mask-labeled data based on SeqTR. + +# 2.2. Partially Supervised Instance Segmentation + +Partially Supervised Instance Segmentation (PSIS) was first raised in [17], where object categories are divided into base and novel splits. Both of them have bounding box annotation while only base have mask annotation. The training target of this task is to generate segmentation for novel categories. PSIS has been studied for a period of time in the field of Instance Segmentation [1, 11, 26, 35, 53]. Some works address this problem by learning class-agnostic mask segmentation models, i.e., separate foreground and background only. They capture class-agnostic cues, e.g., shape [26] and appearance commonalities [11]. For better use of the training data from novel categories, [42] propose ContrastMask to learn a mask segmentation model on both base and novel categories under a unified pixel-level contrastive learning framework. In this paper, we suggest the partially supervised RES, drawing on the partially supervised instance segmentation. We train the RES model with bounding box annotation of all train data, and a small fraction of mask annotation. + +# 2.3. Sequence Prediction in Visual Tasks + +Visual tasks differ greatly in their output format and associated content, making it difficult to handle them with an identical structure. Unlike previous approaches requiring + +prior knowledge in Object Detection, Pix2Seq [4] first cast object detection as a language modeling task conditioned on the observed pixel inputs. The class and the bounding box are expressed as sequences of discrete tokens, and train the detection model to generate the coordinate and class of each tone by one. Built on Pix2Seq, [6] proposes an object-centric vision framework Obj2Seq that takes objects as the basic unit, and human pose estimation is also been converted to a sequence-generated form. In addition, another similar work SeqTR [54] is proposed to unify REC and RES, regarding both REC and RES as a point prediction problem. Inspired by the above-mentioned work, we consider whether this sequence prediction framework can be used in scenarios with few labels to take advantage of its good format uniformly property. + +# 3. Method + +In this section, we first briefly revisit the sequence prediction model SeqTR in Sec. 3.1. Then we illustrate the architecture of our method in Sec. 3.2. In Sec. 3.3, we elaborate on our proposed Co-Content Teacher-Forcing, and the Point-Modulated Cross Attention in Sec. 3.4 and the Resampling Pseudo Point Strategy in Sec. 3.5. + +# 3.1. Revisit SeqTR + +SeqTR [54] is a sequence prediction model for Visual Grounding tasks, which first reformulate visual grounding as a point prediction problem. The bounding box and the segmentation mask are serialized into a sequence of discrete coordinate tokens $\{x_i,y_i\}_{i = 1}^N$ , where N is 4 vertices of the bounding box on REC or the number of contour + +points of the segmentation mask on RES. With a task token [TASK], SeqTR can predict the target coordinate tokens in an auto-regressive manner during inference, and end the coordinate sequence with [EOS] token. In addition, different from those loss functions adopted in DETR-like multi-task frameworks (e.g., GIoU loss, set-based matching loss, focal loss, and dice loss), SeqTR only uses a simple cross-entropy loss for REC and RES, which requires no further prior knowledge or expertise. SeqTR greatly reduces the difficulty and complexity of both architecture design and optimization for visual grounding tasks. Meanwhile, it nicely aligns REC and RES, and naturally converts them into one type of expression form. Following SeqTR, our model is also optimized with a simple cross-entropy loss. + +# 3.2. Architecture + +We build our architecture on the latest RES sequence prediction method SeqTR [54], which consists of three main modules: (1) Visual-Language Fusion; (2) Transformer Encoder and Decoder; (3) Sequence Construction; The overall architecture is illustrated in Fig. 2. + +Visual-Language Fusion. We adopt the convolutional backbone DarkNet-53 [37] to obtain the visual representation for an input image $\mathbf{I}$ . Following SeqTR, we downsample the multi-scale visual feature from the finest to coarsest spatial resolution and flatten it to $F_{v} \in R^{(H \times W) \times C}$ . For referring expressions, we encode the text with a one-layer bidirectional GRU [7] as in SeqTR. Then we concatenate both unidirectional hidden states $h_t = \left[ \begin{array}{c} \vec{h}_t \\ h_t, h_t \end{array} \right]$ at each step $t$ to get language feature $F_l = \{h_t\}_{t=1}^T$ . Before V-L fusion, we adopt max pooling $mp(\cdot)$ along the channel dimension of $F_i$ . Then visual feature $F_v$ and language feature $F_l$ will be fused to $F_m$ by Hadamard product: + +$$ +F _ {m} = \delta \left(F _ {v}\right) \odot \delta \left(m p \left(F _ {l}\right)\right), \tag {1} +$$ + +here the activation function $\delta (\cdot)$ is set to $\tanh (\cdot)$ if no special instructions. + +Transformer Encoder and Decoder. We use the Transformer with 6 layers encoder and 3 layers decoder to learn the multi-modal feature and decode out the vertices or the contour points. The hidden dimension of transformer is set to 256. The fused multi-modal feature $F_{m}$ is added sine and learned positional encoding [39] before entering Transformer Encoder. The original Cross-Attention between Transformer Encoder and Decoder will be replaced by Point-Modulated Cross-Attention (PMCA), which is introduced in Sec. 3.4. + +Sequence Construction. Following SeqTR [54], we construct the sequence of floating points $\{\tilde{x}_i,\tilde{y}_i\}_{i = 1}^N$ with the top-left and bottom-right corner points of the bounding box $(\mathrm{N} = 2)$ . For the binary mask on RES, the sequence is uniformly sampled N points clockwise on top of the mask on + +![](images/04e57b9dd0bfdf478e8f3e558315fb70e789870632345c3b8030e48bf29b2d19.jpg) +Figure 3. Sketch of CCTF. We extract multi-modal features from the reshaped Transformer Encoder output according to the target coordinates and fuse them by element-wise addition, e.g., $x_{n}$ corresponds to the $x_{n}W / M$ line of row of the feature map, and $y_{n}$ corresponds to the $y_{n}H / M$ line of column. + +RES. Then they are quantized into integer bins by + +$$ +x _ {i} = \operatorname {r o u n d} \left(\frac {M \tilde {x} _ {i}}{w}\right), y _ {i} = \operatorname {r o u n d} \left(\frac {M \tilde {y} _ {i}}{h}\right) \tag {2} +$$ + +where each coordinate is normalized by image width $w$ and height $h$ , and $M$ is the number of quantization bins. + +The sequence will be embedded and then combined with the output multi-modal feature of the Transformer Encoder to conduct Co-Content Teacher-Forcing (CCTF) training. Details are described in Sec. 3.3. Moreover, we will resample pseudo points and reconstruct the sequence for model retraining. Details can be found in Sec. 3.5. + +# 3.3. Co-Content Teacher-Forcing + +The Co-Content Teacher-Forcing is proposed to alleviate the over-reliance on the sampled ground truth points during training, which leads to exposure bias of the inconsistency of Training and Inference. + +In the normal Teacher-Forcing training, the sequence $\{x_i,y_i\}_{i = 1}^N$ will be embedded to a feature sequence $\{m_i,n_i\}_{i = 1}^N$ : + +$$ +\left\{m _ {i}, n _ {i} \right\} _ {i = 1} ^ {N} = d i c \left(\left\{x _ {i}, y _ {i} \right\} _ {i = 1} ^ {N}\right), \tag {3} +$$ + +where $dic(\cdot)$ is a learnable coordinate embedding dictionary. The coordinate sequence $\{x_i, y_i\}_{i=1}^N$ can be viewed as the index of the dictionary. However, in the Co-Content Teacher-Forcing, the feature sequence will be combined with the encoder output multi-modal feature. As shown in Fig. 3, we reshape the output multi-modal feature $F_m \in R^{H \times W \times C'}$ of Transformer Encoder to $F_m' \in R^{(H \times W) \times C'}$ for better correspondence with coordinate points. Then, for each coordinate $x_i$ or $y_i$ , we extract the feature of the corresponding row or column from the feature map $F_m'$ and average them with $E\{\cdot\}$ : + +$$ +f _ {x _ {i}} = E _ {k = 1} ^ {H} \left\{F _ {m} ^ {\prime} \left(\frac {W}{M} x _ {i}, k\right) \right\}, \tag {4} +$$ + +$$ +f _ {y _ {i}} = E _ {k = 1} ^ {W} \{F _ {m} ^ {\prime} (k, \frac {H}{M} y _ {i}) \}, +$$ + +$M$ is the number of quantization bins. $\left\{\frac{W}{M} x_i,\frac{H}{M} y_i\right\}_{i = 1}^N$ is the normalized index. Then the sequential feature $\{m_i,n_i\}_{i = 1}^N$ will be combined with $\{f_{x_i},f_{y_i}\}_{i = 1}^N$ by simple element-wise addition, the final sequence input to Decoder is represented as + +$$ +S = \left\{m _ {i}, n _ {i} \right\} _ {i = 1} ^ {N} + \left\{f _ {x _ {i}}, f _ {y _ {i}} \right\} _ {i = 1} ^ {N} \tag {5} +$$ + +With CCTF training, our model explicitly associate the point coordinates (scale values) with the referred spatial region, which is more natural for visual grounding. + +# 3.4. Point-Modulated Cross Attention + +We adopt the SMCA proposed in [13] to replace the original Cross-Attention, which can constrain the cross-attention responses via estimating the Gaussian-like referring region prior. In the sequence prediction model, we generate the attention map for each co-content coordinates embedding, leading to a matched cross-attention region learned when the supervised data is reduced. To distinguish we denote it as Point-Modulated Cross-Attention (PMCA). + +For each co-content coordinate embedding $S = \{f_j\}_{j=1}^{2N} = \{f_{x_i}, f_{y_i}\}_{i=1}^N$ , we use a 3-layer MLP followed by a sigmoid activation function to generate a prior Gaussian-like attention map. The center point coordinates $c_w, c_h$ and the scales $s_w, s_h$ of the prior Gaussian map is denoted as + +$$ +s _ {w}, s _ {h}, c _ {w}, c _ {h} = \operatorname {s i g m o i d} (M L P (f _ {j})), \tag {6} +$$ + +and the Gaussian map is represented as + +$$ +G (u, v) = \exp \left(- \frac {\left(u - c _ {w}\right) ^ {2}}{\sigma s _ {w} ^ {2}} - \frac {\left(v - c _ {h}\right) ^ {2}}{\sigma s _ {h} ^ {2}}\right) \tag {7} +$$ + +where $u\in R^W,v\in R^H$ , they are the spatial indices of $G$ and $\sigma$ is a hyper-parameter to modulate the bandwidth of the Gaussian map. + +# 3.5. Resampling Pseudo Point Strategy + +To fully utilize the data without mask annotation in Partial-RES, we retrain the model with the generated pseudo label, and we propose a Resampling pseudo point strategy. To remain the consistency of the pseudo points with the original sampled contour points of the ground truth mask, we will resample the pseudo points. As shown in Fig. 4, we first connect the points predicted by the network to a binary mask. Then we compute the dice coefficient $D$ + +![](images/8ba1a256ac6fac9e60d66e3bfb6483d36ca05cf399dc1e569addfa0c67839ac4.jpg) +(a) + +![](images/8650993f31e2beba399ab0343cab2511470a82df2e360194ec2d8013f4f96ad1.jpg) +(b) + +![](images/5c402b65ae078d8df5d6a84497082ce300bef8eca240bb019c0015b3d0d3a03d.jpg) +(c) + +![](images/ad04e0543dd1d95fadac81626c5eea299edf74f08e07df338f30a5e7383fb212.jpg) +(d) +Figure 4. Sketch of Resampling Pseudo Point (RPP) Strategy. (a) Screening the appropriate pseudo mask with the dice coefficient between the predicted binary mask and the ground truth bounding box; (b) The generated points by the RES model; (c) The uniformly resampled pseudo points from pseudo mask; (d) The uniformly sampled target points from ground truth mask. + +of the projection of this mask and the ground truth bounding box. + +$$ +D (l, b) = d i c e \left(l _ {y}, b _ {y})\right) + d i c e \left(l _ {x}, b _ {x}\right) \tag {8} +$$ + +where $l_x, l_y$ are the projection of the mask on x-axis, y-axis, $b_x, b_y$ are of the ground truth bounding box. + +We set a threshold $T$ for $D(l, b)$ to pick out those pseudo masks that are more accurate to avoid introducing too much noise by pseudo labels. For the selected samples, the predicted points are spaced may be inhomogeneous as shown in Fig. 4(b). So we resample the pseudo points with a uniform sampling scheme. It is considered that the contour points of previously labeled data are sampled uniformly and the pseudo point should be kept consistent with it. + +We propose a feasible training process for the Partially Supervised RES task as shown in Fig. 2 (b): + +Step1. REC Training. Since in the Partial-RES task, all the bounding box labels are seen in the model training, we first train the model on fully supervised REC to get a better representation relatively of multi-model features from the encoder output. The feature will be used to enhance TF learning in the next step of RES training. In practice, we load the REC model trained in SeqTR as a pretrained one. + +Step2. Initial RES Training. In this step, we train the model with only $1\%$ , $5\%$ , and $10\%$ labeled mask data on the RES task. The model is equipped with the new method proposed in this paper, including CCTF and PMCA. + +Step3. Retraining with Pseudo Points. After obtaining the RES model from Step2, we can utilize it to generate more pseudo contour points. While these points may contain a lot of noise or may not match the way labeled data is sampled, so we re-selected and sampled out the appropriate pseudo points with the proposed RPP strategy. + +
Label RatioMethodVenueVisual EncoderRefCOCORefCOCO+RefCOCOg
valtestAtestBvaltestAtestBval-gval-utest-u
100%Fully-supervised:
CMSA [47]CVPR'19RN10158.3260.6155.0943.7647.6037.8939.98--
STEP [3]ICCV'19RN10160.0463.4657.9748.1952.3340.4146.40--
CMPC [21]CVPR'20DN5361.3664.5359.6449.5653.4443.2349.05--
LSCM [22]ECCV'20DLA3461.4764.9959.5549.3453.1243.5048.05--
MCN [32]CVPR'20DN5362.4464.2059.7150.6254.9944.69-49.2249.40
BUSNet [44]CVPR'21RN10163.2766.4161.3951.7656.8744.1350.56--
LTS[21] [23]CVPR'21DN5365.4367.7663.0854.2158.3248.02-54.4054.25
VLT [9]ICCV'21DN5665.6568.2962.7355.5059.2049.3649.7652.9956.65
ResTR [25]CVPR'22ViT-B67.2269.3064.4555.7860.4448.2754.48--
SeqTR [54]ECCV'22DN5367.2669.7964.1254.1458.9348.19-55.6755.64
10%Partially-supervised
Baseline-DN5357.9260.7455.0650.8454.8744.54-49.5049.30
Our Method-DN5363.9965.8661.5452.5655.7946.09-52.4352.40
5%Baseline-DN5358.8561.7256.3649.7854.2243.34-48.3348.27
Our Method-DN5362.2364.6760.4251.3054.6844.85-51.2850.55
1%Baseline-DN5354.6757.4852.6244.9148.8139.44-42.5742.46
Our Method-DN5357.4159.6055.8447.6852.0641.54-46.8046.12
+ +Table 1. Comparisons with state-of-the-art fully-supervised methods on RefCOCO [50], RefCOCO+ [50], and RefCOCOg [33] in terms of IoU scores. We defined mask label ratio as $1\%$ , $5\%$ , and $10\%$ to compare our method with the baseline in partially-supervised RES. "RN101": ResNet-101 [15], "DN53": DarkNet-53 [37], "DN56": DarkNet-56 [37], "DLA-34" [48], "ViT-B" [10]. + +# 4. Experiments + +# 4.1. Datasets + +RefCOCO/RefCOCO+ are proposed in [50]. There are 19,994 images in RefCOCO with 142,209 refer expressions for 50,000 objects. Similarly, 19,992 images are included in RefCOCO+ which contains 141,564 expressions for 49,856 objects. + +In these datasets, each image contains two or more objects from the same category. In RefCOCO+ dataset, positional words are not allowed in the referring expression, which is a pure dataset with appearance-based referring expression, whereas RefCOCO imposes no restriction on the phrase. In addition to the training set and validation set, the test set for RefCOCO/RefCOCO+ is divided into a testA set (containing several people in an image) and a testB set (containing multiple instances of other objects in an image). + +RefCOCOg [33] contains 26,711 images with 85,474 referring expressions for 54,822 objects, and each image usually contains $2 \sim 4$ objects of the same category. The length of referring expressions in this dataset is almost twice as long as those in RefCOCO and RefCOCO+. This dataset is directly divided into "train", "val" and "test". + +# 4.2. Experimental Settings + +Each instance to be segmented is equipped with an expression sentence in RES datasets, the sentence can be seen as a kind of open category. Therefore, different from the manner of dividing data [17], we simulate the partially su + +pervised training scenario on RES by randomly sampling the entire train data proportionally without considering the category. + +Implementation Details. Following SeqTR [54], all parameters in the network are optimized with AdamW [30], and the batch size is 128. We train the model with $120 \sim 150$ epochs for full convergence, and we set the learning rate as $5\mathrm{e - }4@10\%$ , $3\mathrm{e - }4@5\%$ , and $1\mathrm{e - }4@1\%$ in step2 training, while $5\mathrm{e - }4$ in step3 training with pseudo points. We use DarkNet-53 [37] as the visual encoder. Following standard practices [8, 32, 23, 9], images are resized to $640 \times 640$ , and the length of language expression is trimmed at 15 for ResCOCO/RefCOCO+ and 20 for RefCOCOg. The training epochs and the learning rate used vary slightly for different settings of mask-labeled data ratio and different training stages. For the number of sampled contour points, we use the same as in SeqTR, i.e., 18 points for RefCOCO/RefCOCO+, and 12 for RefCOCOg. + +Evaluation Metrics. Following the proposal setting in the previous work, we use mask Intersection-over-Union (IoU) and Precision with thresholds $(\mathrm{Pr}@\mathrm{X})$ . The mask IoU demonstrates the mask quality, which emphasizes the model's overall performance and reveals both targeting and segmenting abilities. The $\mathrm{Pr}@\mathrm{X}$ metric computes the ratio of successfully predicted samples using different IoU thresholds. Low threshold precision like $\mathrm{Pr}@\mathrm{0.5}$ reflects the identification performance of the method, and high threshold precision like $\mathrm{Pr}@\mathrm{0.9}$ reveals the ability to generate high-quality masks. + +
RatioCCTFPMCAIoUPr@0.5Pr@0.6Pr@0.7Pr@0.8Pr@0.9
1%✓42.5747.5432.8917.615.070.36
✓✓44.20 (+1.63)50.68 (+3.14)37.00 (+4.11)21.21 (+3.60)7.89 (+2.82)0.66 (+0.30)
44.58 (+0.38)50.98 (+0.30)37.32 (+0.32)22.42 (+1.21)8.21 (+0.32)0.76 (+0.10)
100%✓55.9567.7362.2049.0229.557.07
✓✓56.03 (+0.08)67.79 (+0.06)62.32 (+0.12)49.88 (+0.86)30.63 (+1.08)8.04 (+0.97)
56.89 (+0.86)68.43 (+0.64)62.96 (+0.64)50.72 (+0.84)31.21 (+0.58)8.05 (+0.01)
+ +Table 2. Validity verification of our proposed CCTF and PMCA. We conduct experiments on $1\%$ and $100\%$ supervised data with mask labels (100% means fully-supervised training). + +
MethodIoUPr@0.5Pr@0.6Pr@0.7Pr@0.8Pr@0.9
Baseline44.5850.9837.3222.428.210.76
w/o RPP44.2751.2838.2424.2810.240.96
w/ RPP46.8056.2943.1927.4010.340.98
+ +Baseline and oracle We suggest the baseline in the following way: we initialize our model with the parameters of SeqTR trained on the REC task, and refine it on the RES task with $1\%$ , $5\%$ , and $10\%$ mask annotation data. Our comparison with the baseline is discussed in Sec. 4.3. The "oracle" model is SeqTR trained on REC and refined on RES with all mask annotation data. This fully supervised model is a performance upper bound for our partially supervised RES. + +# 4.3. Comparison with other Method + +We compare our method with other fully supervised state-of-the-art methods on three common benchmarks of Referring Expression Segmentation, i.e., RefCOCO, RefCOCO+, and RefCOCOg. In addition, we also compare with our suggested baseline in partially-supervised training. Results are reported in Table 1. Our method displays significant improvement over the baseline method on all three datasets, at an average of $3.5\%$ , $2.4\%$ , and $3.0\%$ on $1\%$ , $5\%$ , and $10\%$ mask labeled data. With $10\%$ mask annotated data, we can achieve $97\%$ of the fully supervised performance on RefCOCO+@val. We are also able to achieve $88\%$ fully supervised performance on $1\%$ mask-labeled data on RefCOCO+@testA. Our methods can even surpass some fully-supervised methods, e.g., MCN, [32], BUSNet [44]. + +# 4.4. Ablation Studies + +Effectiveness of CCTF and PMCA. To verify the effectiveness of Co-Content Teacher Forcing (CCTF) and Point-Modulated Cross-Attention (PMCA), we show the results of our RES model with $1\%$ partially supervised training data and $100\%$ fully supervised training data on Ref-COCOg@val in Table 2. + +When trained with $100\%$ mask label of the data, PMCA + +Table 3. Comparison of our method with re-sampling pseudo points and without RPP in step3 training. + +
MethodIoUPr@0.5Pr@0.6Pr@0.7Pr@0.8Pr@0.9
Baseline44.5850.9837.3222.428.210.76
T@1.0044.5351.3638.1422.407.290.58
T@0.0544.3550.6838.5224.189.700.78
T@0.1046.8056.2943.1927.4010.340.98
+ +Table 4. Performance with different Threshold $T$ of dice coefficient on RefCOCOg@val. $T@1.00$ means that all the predicted pseudo masks are used for training in step3 without selection of the more accurate one with dice coefficient. + +embodies more performance enhancements than CCTF, which proves the effectiveness of PMCA. CCTF contributes little to performance improvement. While with $1\%$ supervised mask labeled training data, the improvement contribution of CCTF far exceeds that of PMCA, $80\%$ performance improvement comes from CCTF (1.63% in 2.01%). This demonstrates that the exposure bias of the inconsistency of the training and inference process may be more serious in the case of partially supervised data, which is alleviated by our proposed CCTF somewhat. + +Ablation of Resampling Pseudo Points To better demonstrate the effectiveness of re-sampling pseudo points (RPP), we compare the results with those obtained without resampling. In the case of re-sampling, we uniformly resample the pseudo points from the generated pseudo mask and consider them as the target sequence. In contrast, for no re-sampling, we use the points predicted by the model directly as the target sequence. As illustrated in Table 3, the reported performance is superior when the RPP strategy is employed. This is primarily attributed to the fact that after re-sampling, the unlabeled data is pseudo-labeled uniformly, which mitigates the potential noise introduced by pseudo labels. + +Performance with different Threshold $T$ of dice coefficient. We establish a threshold for the dice coefficient to select more suitable pseudo points that will serve as prediction targets for unlabeled mask annotation data in step3 mentioned in Sec. 3.5. To demonstrate the effectiveness of our pseudo points selection strategy and the impact of different threshold, we compare the results with different thresholds $T$ of the dice coefficient on RefCOCOg@val. As presented + +![](images/f12e438bfa4270bfc8015366d3e04fa2fef441ff4355ebf9efc7a5aa581f88ae.jpg) +Figure 5. Qualitative examples of our method at $1\%$ , $5\%$ , $10\%$ mask label in Partially Supervised RES. We identify the image and its corresponding referring expression in the first column. + +![](images/0e3f8cdbd34dfcdcae552e73cd1a6d31adb72c8cb4f5ad9c1d540e7882e5aa6e.jpg) +Figure 6. The mIoU with different mask-labeled data ratio. + +in Table 4, without any constraint ( $T$ is set to 1.00), we observe that the side effect of pseudo points is greater than the main effect due to the noise contained in pseudo points. Therefore, we determine to select out those pseudo masks with dice coefficient $< T$ for re-sampling pseudo points. Meanwhile, our ablation studies declare that the threshold of 0.10 is the optimal choice for our selection strategy. + +Experiment of increasing the proportion of mask-labeled data We conduct an additional experiment to investigate the proportion of mask-labeled data in training that can reach SotA performance. As depicted in Fig. 6, comparing the mIoU with the fully supervised SotA method (55.67) on RefCOCOg@val, our method attains remarkably similar results (55.59) by using $50\%$ mask-labeled data. + +# 4.5. Qualitative Results + +We evaluated the segmentation performance of our method at various ratios, i.e., $1\%$ , $5\%$ , and $10\%$ of Partial- + +RES data in Fig. 5. As the mask-labeled data increases, the contours fit more accurately. By comparing these results, we observe that with a low percentage of mask-labeled data, our segmentation results are already similar to those of the GT points. + +# 5. Conclusion + +In this paper, we investigate the partially supervised learning paradigm for RES, where only a few segmentation masks are available so the model has to learn transfer knowledge from REC. Unlike existing RES/REC works that have different decoders for the two tasks, we leverage the contour-based sequence prediction model to maximize the transfer ability. We then propose CCTF to explicitly associate the point coordinates (scale values) with the referred spatial regions, which alleviates the exposure bias brought by the limited segmentation masks. Besides, we present RPP strategy to select appropriate pseudo labels and resample pseudo points during the retraining stage. We conduct extensive experiments to demonstrate the effectiveness of our model in the Partial-RES setting. One limitation of this work is that the smoothness and edge accuracy of the point-contour mask prediction may not be as precise as the pixel-level mask predictions. + +Acknowledgements. This work was supported by the National Key RD Program of China (No.2018AAA0102100), the National Natural Science Foundation of China (Nos. U1936212, 62120106009), Sponsored by Beijing Nova Program (NO. 20220484063). + +# References + +[1] David Biertimpel, Sindi Shkodrani, Anil S Baslamisli, and Nóra Baka. Prior to segment: Foreground cues for weakly annotated classes in partially supervised instance segmentation. In ICCV, pages 2824-2833, 2021. +[2] Ding-Jie Chen, Songhao Jia, Yi-Chen Lo, Hwann-Tzong Chen, and Tyng-Luh Liu. See-through-text grouping for referring image segmentation. In ICCV, pages 7454-7463, 2019. +[3] Ding-Jie Chen, Songhao Jia, Yi-Chen Lo, Hwann-Tzong Chen, and Tyng-Luh Liu. See-through-text grouping for referring image segmentation. In ICCV, pages 7454-7463, 2019. +[4] Ting Chen, Saurabh Saxena, Lala Li, David J Fleet, and Geoffrey Hinton. Pix2seq: A language modeling framework for object detection. In ICLR. +[5] Xinpeng Chen, Lin Ma, Jingyuan Chen, Zequn Jie, Wei Liu, and Jiebo Luo. Real-time Referring Expression Comprehension by Single-stage Grounding Network. arXiv preprint arXiv:1812.03426, 2018. +[6] Zhiyang Chen, Yousong Zhu, Zhaowen Li, Fan Yang, Wei Li, Haixin Wang, Chaoyang Zhao, Liwei Wu, Rui Zhao, Jinqiao Wang, et al. Obj2seq: Formatting objects as sequences with class prompt for visual tasks. In NeurIPS. +[7] Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014, 2014. +[8] Jiajun Deng, Zhengyuan Yang, Tianlang Chen, Wengang Zhou, and Houqiang Li. TransVG: End-to-End Visual Grounding with Transformers. ICCV, 2021. +[9] Henghui Ding, Chang Liu, Suchen Wang, and Xudong Jiang. Vision-language transformer and query generation for referring segmentation. In ICCV, pages 16321-16330, 2021. +[10] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR. +[11] Qi Fan, Lei Ke, Wenjie Pei, Chi-Keung Tang, and Yu-Wing Tai. Commonality-parsing network across shape and appearance for partially supervised instance segmentation. In ECCV, pages 379–396. Springer, 2020. +[12] Guang Feng, Zhiwei Hu, Lihe Zhang, and Huchuan Lu. Encoder fusion network with co-attention embedding for referring image segmentation. In CVPR, pages 15506–15515, 2021. +[13] Peng Gao, Minghang Zheng, Xiaogang Wang, Jifeng Dai, and Hongsheng Li. Fast convergence of detr with spatially modulated co-attention. In ICCV, pages 3621–3630, 2021. +[14] Kaiming He, Georgia Gkioxari, Piotr Dólar, and Ross Girshick. Mask r-cnn. In ICCV, pages 2961-2969, 2017. +[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep Residual Learning for Image Recognition. In CVPR, 2016. +[16] Richang Hong, Daqing Liu, Xiaoyu Mo, Xiangnan He, and Hanwang Zhang. Learning to Compose and Reason with + +Language Tree Structures for Visual Grounding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019. +[17] Ronghang Hu, Piotr Dólar, Kaiming He, Trevor Darrell, and Ross Girshick. Learning to segment every thing. In CVPR, pages 4233-4241, 2018. +[18] Ronghang Hu, Marcus Rohrbach, Jacob Andreas, Trevor Darrell, and Kate Saenko. Modeling Relationships in Referential Expressions with Compositional Modular Networks. In CVPR, 2017. +[19] Zhiwei Hu, Guang Feng, Jiayu Sun, Lihe Zhang, and Huchuan Lu. Bi-directional relationship inferring network for referring image segmentation. In CVPR, pages 4424-4433, 2020. +[20] Shaofei Huang, Tianrui Hui, Si Liu, Guanbin Li, Yunchao Wei, Jizhong Han, Luoqi Liu, and Bo Li. Referring image segmentation via cross-modal progressive comprehension. In CVPR, pages 10488-10497, 2020. +[21] Shaofei Huang, Tianrui Hui, Si Liu, Guanbin Li, Yunchao Wei, Jizhong Han, Luoqi Liu, and Bo Li. Referring image segmentation via cross-modal progressive comprehension. In CVPR, pages 10488-10497, 2020. +[22] Tianrui Hui, Si Liu, Shaofei Huang, Guanbin Li, Sansi Yu, Faxi Zhang, and Jizhong Han. Linguistic structure guided context modeling for referring image segmentation. In ECCV, pages 59-75. Springer, 2020. +[23] Ya Jing, Tao Kong, Wei Wang, Liang Wang, Lei Li, and Tie-niu Tan. Locate then segment: A strong pipeline for referring image segmentation. In CVPR, pages 9858-9867, 2021. +[24] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. MDETR-Modulated Detection for End-to-End Multi-Modal Understanding. In ICCV, 2021. +[25] Namyup Kim, Dongwon Kim, Cuiling Lan, Wenjun Zeng, and Suha Kwak. Restr: Convolution-free referring image segmentation using transformers. In CVPR, pages 18145-18154, 2022. +[26] Weicheng Kuo, Anelia Angelova, Jitendra Malik, and Tsung-Yi Lin. Shapemask: Learning to segment novel objects by refining shape priors. In ICCV, pages 9207-9216, 2019. +[27] Muchen Li and Leonid Sigal. Referring transformer: A one-step approach to multi-task visual grounding. NeurIPS, 34:19652-19664, 2021. +[28] Yue Liao, Si Liu, Guanbin Li, Fei Wang, Yanjie Chen, Chen Qian, and Bo Li. A Real-time Cross-modality Correlation Filtering Method for Referring Expression Comprehension. In CVPR, 2020. +[29] Daqing Liu, Hanwang Zhang, Feng Wu, and Zheng-Jun Zha. Learning to Assemble Neural Module Tree Networks for Visual Grounding. In ICCV, 2019. +[30] Ilya Loshchilov and Frank Hutter. Fixing Weight Decay Regularization in Adam. 2018. +[31] Gen Luo, Yiyi Zhou, Rongrong Ji, Xiaoshuai Sun, Jinsong Su, Chia-Wen Lin, and Qi Tian. Cascade grouped attention network for referring expression segmentation. In ACM MM, pages 1274-1282, 2020. + +[32] Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Liujuan Cao, Chenglin Wu, Cheng Deng, and Rongrong Ji. Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation. In CVPR, 2020. +[33] Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy. Generation and Comprehension of Unambiguous Object Descriptions. In CVPR, 2016. +[34] Tsvetomila Mihaylova and André FT Martins. Scheduled sampling for transformers. ACL 2019, page 351, 2019. +[35] Pedro O O Pinheiro, Ronan Collobert, and Piotr Dólar. Learning to segment object candidates. NeurIPS, 28, 2015. +[36] Mengxue Qu, Yu Wu, Wu Liu, Qiqi Gong, Xiaodan Liang, Olga Russakovsky, Yao Zhao, and Yunchao Wei. Siri: A simple selective retraining mechanism for transformer-based visual grounding. In ECCV, pages 546-562. Springer, 2022. +[37] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018. +[38] Arka Sadhu, Kan Chen, and Ram Nevatia. Zero-Shot Grounding of Objects from Natural Language Queries. In ICCV, 2019. +[39] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is All You Need. In NeuruIPS, 2017. +[40] Liwei Wang, Yin Li, Jing Huang, and Svetlana Lazebnik. Learning Two-Branch Neural Networks for Image-Text Matching Tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018. +[41] Peng Wang, Qi Wu, Jiewei Cao, Chunhua Shen, Lianli Gao, and Anton van den Hengel. Neighbourhood Watch: Referring Expression Comprehension via Language-Guided Graph Attention Aetworks. In CVPR, 2019. +[42] Xuehui Wang, Kai Zhao, Ruixin Zhang, Shouhong Ding, Yan Wang, and Wei Shen. Contrastmask: Contrastive learning to segment every thing. In CVPR, pages 11604-11613, 2022. +[43] Sibei Yang, Guanbin Li, and Yizhou Yu. Dynamic Graph Attention for Referring Expression Comprehension. In ICCV, 2019. +[44] Sibei Yang, Meng Xia, Guanbin Li, Hong-Yu Zhou, and Yizhou Yu. Bottom-up shift and reasoning for referring image segmentation. In CVPR, pages 11266-11275, 2021. +[45] Zhengyuan Yang, Tianlang Chen, Liwei Wang, and Jiebo Luo. Improving One-Stage Visual Grounding by Recursive Sub-Query Construction. In ECCV, 2020. +[46] Zhengyuan Yang, Boqing Gong, Liwei Wang, Wenbing Huang, Dong Yu, and Jiebo Luo. A Fast and Accurate One-Stage Approach to Visual Grounding. In ICCV, 2019. +[47] Linwei Ye, Mrigank Rochan, Zhi Liu, and Yang Wang. Cross-modal self-attention network for referring image segmentation. In CVPR, pages 10502–10511, 2019. +[48] Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell. Deep layer aggregation. In CVPR, pages 2403-2412, 2018. +[49] Licheng Yu, Zhe Lin, Xiaohui Shen, Jimei Yang, Xin Lu, Mohit Bansal, and Tamara L Berg. MAttNet: Modular Attention Network for Referring Expression Comprehension. In CVPR, 2018. + +[50] Licheng Yu, Patrick Poirson, Shan Yang, Alexander C Berg, and Tamara L Berg. Modeling Context in Referring Expressions. In ECCV, 2016. +[51] Xunlin Zhan, Yangxin Wu, Xiao Dong, Yunchao Wei, Minlong Lu, Yichi Zhang, Hang Xu, and Xiaodan Liang. Product1m: Towards weakly supervised instance-level product retrieval via cross-modal pretraining. In ICCV, pages 11782-11791, 2021. +[52] Hanwang Zhang, Yulei Niu, and Shih-Fu Chang. Grounding Referring Expressions in Images by Variational Context. In CVPR, 2018. +[53] Yanzhao Zhou, Xin Wang, Jianbin Jiao, Trevor Darrell, and Fisher Yu. Learning saliency propagation for semi-supervised instance segmentation. In CVPR, pages 10307-10316, 2020. +[54] Chaoyang Zhu, Yiyi Zhou, Yunhang Shen, Gen Luo, Xingjia Pan, Mingbao Lin, Chao Chen, Liujuan Cao, Xiaoshuai Sun, and Rongrong Ji. Seqtr: A simple yet universal network for visual grounding. In ECCV, pages 598-615. Springer, 2022. +[55] Bohan Zhuang, Qi Wu, Chunhua Shen, Ian Reid, and Anton Van Den Hengel. Parallel Attention: A Unified Framework for Visual Object Discovery Through Dialogs and Queries. 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Most of the existing RES approaches require massive pixel-level annotations, which are expensive and exhaustive. In this paper, we propose a new partially supervised training paradigm for RES, i.e., training using abundant referring bounding boxes and only a few (e.g., " + }, + { + "bbox": [ + 46, + 281, + 290, + 544 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 46, + 281, + 290, + 544 + ], + "type": "text", + "content": ") pixel-level referring masks. To maximize the transferability from the REC model, we construct our model based on the point-based sequence prediction model. We propose the co-content teacher-forcing to make the model explicitly associate the point coordinates (scale values) with the referred spatial features, which alleviates the exposure bias caused by the limited segmentation masks. To make the most of referring bounding box annotations, we further propose the resampling pseudo points strategy to select more accurate pseudo-points as supervision. Extensive experiments show that our model achieves " + }, + { + "bbox": [ + 46, + 281, + 290, + 544 + ], + "type": "inline_equation", + "content": "52.06\\%" + }, + { + "bbox": [ + 46, + 281, + 290, + 544 + ], + "type": "text", + "content": " in terms of accuracy (versus " + }, + { + "bbox": [ + 46, + 281, + 290, + 544 + ], + "type": "inline_equation", + "content": "58.93\\%" + }, + { + "bbox": [ + 46, + 281, + 290, + 544 + ], + "type": "text", + "content": " in fully supervised setting) on RefCOCO+@testA, when only using " + }, + { + "bbox": [ + 46, + 281, + 290, + 544 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 46, + 281, + 290, + 544 + ], + "type": "text", + "content": " of the mask annotations. Code is available at https://github.com/qumengxue/Partial-RES.git." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 567, + 128, + 580 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 567, + 128, + 580 + ], + "spans": [ + { + "bbox": [ + 47, + 567, + 128, + 580 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 588, + 287, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 588, + 287, + 684 + ], + "spans": [ + { + "bbox": [ + 46, + 588, + 287, + 684 + ], + "type": "text", + "content": "Referring Expression Segmentation (RES) aims to generate a segmentation mask for the object referred to by the language expression in the image. It allows pixel-level semantic alignment between language and vision, which is meaningful to many multi-modal tasks and can be applied to various practical applications, e.g., video/image editing with sentences. Benefiting from the development of deep learning techniques, significant progress [31, 9, 12, 2, 19," + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 309, + 255, + 544, + 398 + ], + "blocks": [ + { + "bbox": [ + 309, + 255, + 544, + 398 + ], + "lines": [ + { + "bbox": [ + 309, + 255, + 544, + 398 + ], + "spans": [ + { + "bbox": [ + 309, + 255, + 544, + 398 + ], + "type": "image", + "image_path": "0c738b0943557bd70738132336cc993faea3caf37d193e08b1f2cdade5282bd3.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 412, + 546, + 446 + ], + "lines": [ + { + "bbox": [ + 305, + 412, + 546, + 446 + ], + "spans": [ + { + "bbox": [ + 305, + 412, + 546, + 446 + ], + "type": "text", + "content": "Figure 1. Comparison of MDETR and SeqTR. On the left is the streamlined framework of the two, and on the right is their IoU performance on RefCOCOg@val. Best viewed in color." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 459, + 545, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 459, + 545, + 483 + ], + "spans": [ + { + "bbox": [ + 305, + 459, + 545, + 483 + ], + "type": "text", + "content": "20, 27, 54, 9] has been made in this field and achieved remarkable performance." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 484, + 546, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 484, + 546, + 628 + ], + "spans": [ + { + "bbox": [ + 304, + 484, + 546, + 628 + ], + "type": "text", + "content": "The success of current methods partially attributes to the large-scale training dataset with accurate pixel-level masks. However, labeling masks for a huge amount of images is often costly in terms of both human effort and finance, and thus hardly be scaled up. Therefore, it is meaningful to explore a new training paradigm for RES where extensive pixel-level annotations are not necessary. It is well known that bounding box annotations are much cheaper and easier to be collected compared with pixel-level masks. Thus, we try to study this question: is it possible to learn an accurate RES model using abundant box annotations and only a few mask annotations?" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "type": "text", + "content": "To answer this question, for the first time, we explore a new partially supervised training paradigm for RES (Partial-RES). Intuitively, a naive pipeline for this partially supervised setting is to first train a Referring Expression Comprehension (REC) model and then transfer it to the RES task by fine-tuning on a limited number of data with mask annotations. However, due to the difference between" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 693, + 246, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 693, + 246, + 703 + ], + "spans": [ + { + "bbox": [ + 58, + 693, + 246, + 703 + ], + "type": "text", + "content": "*Work done during an internship at JD Explore Academy." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 59, + 703, + 186, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 703, + 186, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 703, + 186, + 712 + ], + "type": "text", + "content": "Yao Zhao is the corresponding author." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "3021" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 288, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 288, + 191 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 288, + 191 + ], + "type": "text", + "content": "the REC and RES tasks, prevalent models (e.g., Mask R-CNN [14], MDETR [24]) have to simultaneously optimize two different heads, with one for detection prediction and the other for segmentation prediction. Such kind of structures would lead to a severe issue during the fine-tuning stage, i.e., a randomly initialized mask head can not get well optimized given only a few mask annotations (hundreds of images). As shown in Figure 1, it is not easy to optimize MDETR with only " + }, + { + "bbox": [ + 46, + 72, + 288, + 191 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 46, + 72, + 288, + 191 + ], + "type": "text", + "content": " mask-annotated data even transferred from a well-trained REC model." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 194, + 287, + 386 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 194, + 287, + 386 + ], + "spans": [ + { + "bbox": [ + 46, + 194, + 287, + 386 + ], + "type": "text", + "content": "Recently, a contour-based method named SeqTR has been proposed for unifying REC and RES. The idea is to use a sequence model (usually a Transformer decoder) to sequentially generate contour points of the referred object. The predictions are two points (top-left and bottom-right corner points of the bounding box) in the REC task while dozens of points (contour) are in the RES task. As shown in Fig. 1, both boxes and masks are converted to point sequences and optimized using the same simple cross-entropy loss in SeqTR, which ensures the consistency between REC and RES. It incorporates two optimization heads into a unified one, making the knowledge learned with four points (i.e., detection) be naturally transferred to predict multiple ones (i.e., segmentation). Inspired by SeqTR, we have the conjecture that sequence prediction might be a better solution for partially supervised training." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 388, + 287, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 388, + 287, + 592 + ], + "spans": [ + { + "bbox": [ + 46, + 388, + 287, + 592 + ], + "type": "text", + "content": "In this paper, we investigate methods for achieving better partially supervised training for RES sequence prediction models. Sequence-to-sequence prediction models are typically trained with Teacher-Forcing (TF) [39], wherein the model utilizes the ground truth token as input to predict the next token during the training stage. However, the model can only predict the next state by taking its previous output as input in inference, which is known as the exposure bias [34]. This issue is even more severe in the partially supervised setting due to the very limited ground truth data. Consequently, we introduce the Co-Content Teacher-Forcing (CCTF), which combines the ground truth point coordinates together with the spatial visual feature of the pointed row or column. In contrast to the previous sequence model SeqTR, our CCTF explicitly associates the point coordinates (scale values) with the referred spatial region, providing a more natural approach for visual grounding." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": "Furthermore, we estimate the referring region via our proposed Point-Modulated Cross-Attention, to ensure the decoder attends to those region content while generating the point contour sequences. To fully utilize the data without mask annotation in Partial-RES, we retrain the model with the generated pseudo labels, and we present a Resampling Pseudo Points (RPP) Strategy. Unlike most pseudo-label works that directly use the network's predictions as labels, we select the appropriate pseudo masks with Dice coefficient and then resample the predicted points in a uniform" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 305, + 72, + 493, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 493, + 84 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 493, + 84 + ], + "type": "text", + "content": "way to regularize the contour sequence labels." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "spans": [ + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "text", + "content": "With extensive experiments, our method displays significant improvement compared to SeqTR [54] baselines on all three benchmarks, i.e., at an average of " + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "inline_equation", + "content": "3.5\\%" + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "inline_equation", + "content": "2.4\\%" + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "inline_equation", + "content": "3.0\\%" + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "text", + "content": " on " + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "text", + "content": " mask-labeled data. With " + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "text", + "content": " mask annotated data, our method achieves " + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "inline_equation", + "content": "97\\%" + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "text", + "content": " of the fully supervised performance on RefCOCO + @val. We are also able to achieve " + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "inline_equation", + "content": "88\\%" + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "text", + "content": " of the fully supervised performance only with " + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 304, + 85, + 545, + 180 + ], + "type": "text", + "content": " mask-labeled data on RefCOCO + @testA." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 193, + 392, + 205 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 193, + 392, + 205 + ], + "spans": [ + { + "bbox": [ + 305, + 193, + 392, + 205 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 213, + 397, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 213, + 397, + 225 + ], + "spans": [ + { + "bbox": [ + 306, + 213, + 397, + 225 + ], + "type": "text", + "content": "2.1. REC and RES" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 232, + 545, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 232, + 545, + 316 + ], + "spans": [ + { + "bbox": [ + 304, + 232, + 545, + 316 + ], + "type": "text", + "content": "Referring Expression Comprehension (REC) aims at grounding the object referred to by a sentence, which achieves instance-level vision language alignment. Compared to REC, Referring Expression Segmentation (RES) grounds language expression at the fine-grained pixel-level. REC and RES are separate but closely related fundamental multi-modal tasks." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 316, + 545, + 425 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 316, + 545, + 425 + ], + "spans": [ + { + "bbox": [ + 304, + 316, + 545, + 425 + ], + "type": "text", + "content": "For REC, previous methods can be roughly divided into two-stage and one-stage. Two-stage methods [16, 18, 29, 40, 41, 43, 49, 52, 55] typically utilize an object detector to generate region proposals in the first stage, and are trained to maximize the similarity between region and text with binary cross-entropy loss or similarity loss. One-stage methods [5, 28, 38, 45, 46] avoid being constrained by the quality of the proposal by directly fusing visual and linguistic features rather than matching region-language pairs." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 426, + 545, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 426, + 545, + 520 + ], + "spans": [ + { + "bbox": [ + 304, + 426, + 545, + 520 + ], + "type": "text", + "content": "For RES, typical solutions are to incorporate as much linguistic information as possible in various ways on pixel-level visual features [31, 9, 12, 2, 19, 20]. In previous methods, researchers propose various attention mechanisms [9, 23, 20] to better merge vision and language. Recently, since the superior ability of modeling vision and language of Transformer-based model, more high-performance methods [27, 54, 9, 36] are proposed to solve RES tasks." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 521, + 545, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 545, + 629 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 545, + 629 + ], + "type": "text", + "content": "For REC and RES, multi-task methods [32, 27] are proposed to better use the correlation between two tasks, which can facilitate learning of both tasks. However, all the prior work require different task-specific branch and loss function, whose generalization ability is limited. Recently, a simple and universal network term SeqTR [54] is presented, which regards both REC and RES as a point prediction problem. SeqTR greatly reduces the difficulty and complexity of both architecture design and optimization." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": "In some work, REC is treated as a pretext task for other multi-modal tasks [24, 51], e.g., Visual Question and Answering. RES has a more fine-grained visual language alignment capability than REC, but the capability of RES to be used in multi-modal pre-training has not yet been developed because of the lack of a large-scale RES dataset. It is well known that annotating binary masks for a multi-" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3022" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 87, + 347, + 232 + ], + "blocks": [ + { + "bbox": [ + 64, + 76, + 196, + 87 + ], + "lines": [ + { + "bbox": [ + 64, + 76, + 196, + 87 + ], + "spans": [ + { + "bbox": [ + 64, + 76, + 196, + 87 + ], + "type": "text", + "content": "(2) Transformer Encoder and Decoder" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 65, + 87, + 347, + 232 + ], + "lines": [ + { + "bbox": [ + 65, + 87, + 347, + 232 + ], + "spans": [ + { + "bbox": [ + 65, + 87, + 347, + 232 + ], + "type": "image", + "image_path": "1c39c4b36917a7c61a8d8086ad3b865a916301e7cae677b26042e1b5c49f153c.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 69, + 236, + 165, + 246 + ], + "lines": [ + { + "bbox": [ + 69, + 236, + 165, + 246 + ], + "spans": [ + { + "bbox": [ + 69, + 236, + 165, + 246 + ], + "type": "text", + "content": "(1) Visual Language Fusion" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 214, + 236, + 304, + 246 + ], + "lines": [ + { + "bbox": [ + 214, + 236, + 304, + 246 + ], + "spans": [ + { + "bbox": [ + 214, + 236, + 304, + 246 + ], + "type": "text", + "content": "(3) Sequence Construction" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 164, + 254, + 222, + 263 + ], + "lines": [ + { + "bbox": [ + 164, + 254, + 222, + 263 + ], + "spans": [ + { + "bbox": [ + 164, + 254, + 222, + 263 + ], + "type": "text", + "content": "(a) Architecture" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 360, + 77, + 529, + 249 + ], + "blocks": [ + { + "bbox": [ + 360, + 77, + 529, + 249 + ], + "lines": [ + { + "bbox": [ + 360, + 77, + 529, + 249 + ], + "spans": [ + { + "bbox": [ + 360, + 77, + 529, + 249 + ], + "type": "image", + "image_path": "c6c152cc553746f34be331225a82288f36e19574793e278da8d3b1e103bc6e92.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 415, + 253, + 481, + 264 + ], + "lines": [ + { + "bbox": [ + 415, + 253, + 481, + 264 + ], + "spans": [ + { + "bbox": [ + 415, + 253, + 481, + 264 + ], + "type": "text", + "content": "(b) Training Steps" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 46, + 277, + 547, + 312 + ], + "lines": [ + { + "bbox": [ + 46, + 277, + 547, + 312 + ], + "spans": [ + { + "bbox": [ + 46, + 277, + 547, + 312 + ], + "type": "text", + "content": "Figure 2. (a) Overall architecture of our method. It consists of three main modules: (1) Visual-Language Fusion; (2) Transformer Encoder and Decoder; (3) Sequence Construction. \"CCTF\": Co-Content Teacher-Forcing; \"PMCA\": Point-Modulated Cross-Attention. (b) The suggested training steps of our method in the Partially Supervised RES task. Best viewed in color." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 332, + 288, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 332, + 288, + 392 + ], + "spans": [ + { + "bbox": [ + 46, + 332, + 288, + 392 + ], + "type": "text", + "content": "modal dataset is expensive while bounding box is easy. In this paper, we make use of the bounding box annotations and the consistent task expression of REC and RES in SeqTR, attempting to train RES task with less mask-labeled data based on SeqTR." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 402, + 274, + 415 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 402, + 274, + 415 + ], + "spans": [ + { + "bbox": [ + 47, + 402, + 274, + 415 + ], + "type": "text", + "content": "2.2. Partially Supervised Instance Segmentation" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 421, + 287, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 421, + 287, + 648 + ], + "spans": [ + { + "bbox": [ + 46, + 421, + 287, + 648 + ], + "type": "text", + "content": "Partially Supervised Instance Segmentation (PSIS) was first raised in [17], where object categories are divided into base and novel splits. Both of them have bounding box annotation while only base have mask annotation. The training target of this task is to generate segmentation for novel categories. PSIS has been studied for a period of time in the field of Instance Segmentation [1, 11, 26, 35, 53]. Some works address this problem by learning class-agnostic mask segmentation models, i.e., separate foreground and background only. They capture class-agnostic cues, e.g., shape [26] and appearance commonalities [11]. For better use of the training data from novel categories, [42] propose ContrastMask to learn a mask segmentation model on both base and novel categories under a unified pixel-level contrastive learning framework. In this paper, we suggest the partially supervised RES, drawing on the partially supervised instance segmentation. We train the RES model with bounding box annotation of all train data, and a small fraction of mask annotation." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 658, + 239, + 671 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 658, + 239, + 671 + ], + "spans": [ + { + "bbox": [ + 47, + 658, + 239, + 671 + ], + "type": "text", + "content": "2.3. Sequence Prediction in Visual Tasks" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 677, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 287, + 715 + ], + "type": "text", + "content": "Visual tasks differ greatly in their output format and associated content, making it difficult to handle them with an identical structure. Unlike previous approaches requiring" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 332, + 545, + 513 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 332, + 545, + 513 + ], + "spans": [ + { + "bbox": [ + 304, + 332, + 545, + 513 + ], + "type": "text", + "content": "prior knowledge in Object Detection, Pix2Seq [4] first cast object detection as a language modeling task conditioned on the observed pixel inputs. The class and the bounding box are expressed as sequences of discrete tokens, and train the detection model to generate the coordinate and class of each tone by one. Built on Pix2Seq, [6] proposes an object-centric vision framework Obj2Seq that takes objects as the basic unit, and human pose estimation is also been converted to a sequence-generated form. In addition, another similar work SeqTR [54] is proposed to unify REC and RES, regarding both REC and RES as a point prediction problem. Inspired by the above-mentioned work, we consider whether this sequence prediction framework can be used in scenarios with few labels to take advantage of its good format uniformly property." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 522, + 361, + 534 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 522, + 361, + 534 + ], + "spans": [ + { + "bbox": [ + 306, + 522, + 361, + 534 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 544, + 545, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 544, + 545, + 616 + ], + "spans": [ + { + "bbox": [ + 304, + 544, + 545, + 616 + ], + "type": "text", + "content": "In this section, we first briefly revisit the sequence prediction model SeqTR in Sec. 3.1. Then we illustrate the architecture of our method in Sec. 3.2. In Sec. 3.3, we elaborate on our proposed Co-Content Teacher-Forcing, and the Point-Modulated Cross Attention in Sec. 3.4 and the Resampling Pseudo Point Strategy in Sec. 3.5." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 623, + 395, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 623, + 395, + 635 + ], + "spans": [ + { + "bbox": [ + 306, + 623, + 395, + 635 + ], + "type": "text", + "content": "3.1. Revisit SeqTR" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "type": "text", + "content": "SeqTR [54] is a sequence prediction model for Visual Grounding tasks, which first reformulate visual grounding as a point prediction problem. The bounding box and the segmentation mask are serialized into a sequence of discrete coordinate tokens " + }, + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\{x_i,y_i\\}_{i = 1}^N" + }, + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "type": "text", + "content": " , where N is 4 vertices of the bounding box on REC or the number of contour" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3023" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 240 + ], + "type": "text", + "content": "points of the segmentation mask on RES. With a task token [TASK], SeqTR can predict the target coordinate tokens in an auto-regressive manner during inference, and end the coordinate sequence with [EOS] token. In addition, different from those loss functions adopted in DETR-like multi-task frameworks (e.g., GIoU loss, set-based matching loss, focal loss, and dice loss), SeqTR only uses a simple cross-entropy loss for REC and RES, which requires no further prior knowledge or expertise. SeqTR greatly reduces the difficulty and complexity of both architecture design and optimization for visual grounding tasks. Meanwhile, it nicely aligns REC and RES, and naturally converts them into one type of expression form. Following SeqTR, our model is also optimized with a simple cross-entropy loss." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 247, + 129, + 258 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 247, + 129, + 258 + ], + "spans": [ + { + "bbox": [ + 47, + 247, + 129, + 258 + ], + "type": "text", + "content": "3.2. Architecture" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 264, + 287, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 264, + 287, + 324 + ], + "spans": [ + { + "bbox": [ + 46, + 264, + 287, + 324 + ], + "type": "text", + "content": "We build our architecture on the latest RES sequence prediction method SeqTR [54], which consists of three main modules: (1) Visual-Language Fusion; (2) Transformer Encoder and Decoder; (3) Sequence Construction; The overall architecture is illustrated in Fig. 2." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "spans": [ + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "text", + "content": "Visual-Language Fusion. We adopt the convolutional backbone DarkNet-53 [37] to obtain the visual representation for an input image " + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "inline_equation", + "content": "\\mathbf{I}" + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "text", + "content": ". Following SeqTR, we downsample the multi-scale visual feature from the finest to coarsest spatial resolution and flatten it to " + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "inline_equation", + "content": "F_{v} \\in R^{(H \\times W) \\times C}" + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "text", + "content": ". For referring expressions, we encode the text with a one-layer bidirectional GRU [7] as in SeqTR. Then we concatenate both unidirectional hidden states " + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "inline_equation", + "content": "h_t = \\left[ \\begin{array}{c} \\vec{h}_t \\\\ h_t, h_t \\end{array} \\right]" + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "text", + "content": " at each step " + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "text", + "content": " to get language feature " + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "inline_equation", + "content": "F_l = \\{h_t\\}_{t=1}^T" + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "text", + "content": ". Before V-L fusion, we adopt max pooling " + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "inline_equation", + "content": "mp(\\cdot)" + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "text", + "content": " along the channel dimension of " + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "inline_equation", + "content": "F_i" + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "text", + "content": ". Then visual feature " + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "inline_equation", + "content": "F_v" + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "text", + "content": " and language feature " + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "inline_equation", + "content": "F_l" + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "text", + "content": " will be fused to " + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "inline_equation", + "content": "F_m" + }, + { + "bbox": [ + 46, + 324, + 287, + 483 + ], + "type": "text", + "content": " by Hadamard product:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 110, + 490, + 287, + 504 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 110, + 490, + 287, + 504 + ], + "spans": [ + { + "bbox": [ + 110, + 490, + 287, + 504 + ], + "type": "interline_equation", + "content": "F _ {m} = \\delta \\left(F _ {v}\\right) \\odot \\delta \\left(m p \\left(F _ {l}\\right)\\right), \\tag {1}", + "image_path": "5e61c280b7d399151a5e2be2bcd139e78eeb85c787a9dc7effa44ecb84fbd83b.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 510, + 287, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 510, + 287, + 533 + ], + "spans": [ + { + "bbox": [ + 46, + 510, + 287, + 533 + ], + "type": "text", + "content": "here the activation function " + }, + { + "bbox": [ + 46, + 510, + 287, + 533 + ], + "type": "inline_equation", + "content": "\\delta (\\cdot)" + }, + { + "bbox": [ + 46, + 510, + 287, + 533 + ], + "type": "text", + "content": " is set to " + }, + { + "bbox": [ + 46, + 510, + 287, + 533 + ], + "type": "inline_equation", + "content": "\\tanh (\\cdot)" + }, + { + "bbox": [ + 46, + 510, + 287, + 533 + ], + "type": "text", + "content": " if no special instructions." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 534, + 287, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 534, + 287, + 652 + ], + "spans": [ + { + "bbox": [ + 46, + 534, + 287, + 652 + ], + "type": "text", + "content": "Transformer Encoder and Decoder. We use the Transformer with 6 layers encoder and 3 layers decoder to learn the multi-modal feature and decode out the vertices or the contour points. The hidden dimension of transformer is set to 256. The fused multi-modal feature " + }, + { + "bbox": [ + 46, + 534, + 287, + 652 + ], + "type": "inline_equation", + "content": "F_{m}" + }, + { + "bbox": [ + 46, + 534, + 287, + 652 + ], + "type": "text", + "content": " is added sine and learned positional encoding [39] before entering Transformer Encoder. The original Cross-Attention between Transformer Encoder and Decoder will be replaced by Point-Modulated Cross-Attention (PMCA), which is introduced in Sec. 3.4." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 653, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 287, + 714 + ], + "type": "text", + "content": "Sequence Construction. Following SeqTR [54], we construct the sequence of floating points " + }, + { + "bbox": [ + 46, + 653, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\{\\tilde{x}_i,\\tilde{y}_i\\}_{i = 1}^N" + }, + { + "bbox": [ + 46, + 653, + 287, + 714 + ], + "type": "text", + "content": " with the top-left and bottom-right corner points of the bounding box " + }, + { + "bbox": [ + 46, + 653, + 287, + 714 + ], + "type": "inline_equation", + "content": "(\\mathrm{N} = 2)" + }, + { + "bbox": [ + 46, + 653, + 287, + 714 + ], + "type": "text", + "content": ". For the binary mask on RES, the sequence is uniformly sampled N points clockwise on top of the mask on" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 322, + 72, + 525, + 216 + ], + "blocks": [ + { + "bbox": [ + 322, + 72, + 525, + 216 + ], + "lines": [ + { + "bbox": [ + 322, + 72, + 525, + 216 + ], + "spans": [ + { + "bbox": [ + 322, + 72, + 525, + 216 + ], + "type": "image", + "image_path": "04e57b9dd0bfdf478e8f3e558315fb70e789870632345c3b8030e48bf29b2d19.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 226, + 545, + 281 + ], + "lines": [ + { + "bbox": [ + 304, + 226, + 545, + 281 + ], + "spans": [ + { + "bbox": [ + 304, + 226, + 545, + 281 + ], + "type": "text", + "content": "Figure 3. Sketch of CCTF. We extract multi-modal features from the reshaped Transformer Encoder output according to the target coordinates and fuse them by element-wise addition, e.g., " + }, + { + "bbox": [ + 304, + 226, + 545, + 281 + ], + "type": "inline_equation", + "content": "x_{n}" + }, + { + "bbox": [ + 304, + 226, + 545, + 281 + ], + "type": "text", + "content": " corresponds to the " + }, + { + "bbox": [ + 304, + 226, + 545, + 281 + ], + "type": "inline_equation", + "content": "x_{n}W / M" + }, + { + "bbox": [ + 304, + 226, + 545, + 281 + ], + "type": "text", + "content": " line of row of the feature map, and " + }, + { + "bbox": [ + 304, + 226, + 545, + 281 + ], + "type": "inline_equation", + "content": "y_{n}" + }, + { + "bbox": [ + 304, + 226, + 545, + 281 + ], + "type": "text", + "content": " corresponds to the " + }, + { + "bbox": [ + 304, + 226, + 545, + 281 + ], + "type": "inline_equation", + "content": "y_{n}H / M" + }, + { + "bbox": [ + 304, + 226, + 545, + 281 + ], + "type": "text", + "content": " line of column." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 300, + 508, + 313 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 300, + 508, + 313 + ], + "spans": [ + { + "bbox": [ + 306, + 300, + 508, + 313 + ], + "type": "text", + "content": "RES. Then they are quantized into integer bins by" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 342, + 317, + 545, + 342 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 342, + 317, + 545, + 342 + ], + "spans": [ + { + "bbox": [ + 342, + 317, + 545, + 342 + ], + "type": "interline_equation", + "content": "x _ {i} = \\operatorname {r o u n d} \\left(\\frac {M \\tilde {x} _ {i}}{w}\\right), y _ {i} = \\operatorname {r o u n d} \\left(\\frac {M \\tilde {y} _ {i}}{h}\\right) \\tag {2}", + "image_path": "69a260f04cc62b15e11071972ae200eaaca2f43b53c159771899651e330fc26e.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 346, + 545, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 346, + 545, + 369 + ], + "spans": [ + { + "bbox": [ + 304, + 346, + 545, + 369 + ], + "type": "text", + "content": "where each coordinate is normalized by image width " + }, + { + "bbox": [ + 304, + 346, + 545, + 369 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 304, + 346, + 545, + 369 + ], + "type": "text", + "content": " and height " + }, + { + "bbox": [ + 304, + 346, + 545, + 369 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 304, + 346, + 545, + 369 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 346, + 545, + 369 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 346, + 545, + 369 + ], + "type": "text", + "content": " is the number of quantization bins." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 370, + 545, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 370, + 545, + 441 + ], + "spans": [ + { + "bbox": [ + 304, + 370, + 545, + 441 + ], + "type": "text", + "content": "The sequence will be embedded and then combined with the output multi-modal feature of the Transformer Encoder to conduct Co-Content Teacher-Forcing (CCTF) training. Details are described in Sec. 3.3. Moreover, we will resample pseudo points and reconstruct the sequence for model retraining. Details can be found in Sec. 3.5." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 449, + 463, + 461 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 449, + 463, + 461 + ], + "spans": [ + { + "bbox": [ + 306, + 449, + 463, + 461 + ], + "type": "text", + "content": "3.3. Co-Content Teacher-Forcing" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 467, + 545, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 467, + 545, + 514 + ], + "spans": [ + { + "bbox": [ + 304, + 467, + 545, + 514 + ], + "type": "text", + "content": "The Co-Content Teacher-Forcing is proposed to alleviate the over-reliance on the sampled ground truth points during training, which leads to exposure bias of the inconsistency of Training and Inference." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 514, + 545, + 554 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 514, + 545, + 554 + ], + "spans": [ + { + "bbox": [ + 304, + 514, + 545, + 554 + ], + "type": "text", + "content": "In the normal Teacher-Forcing training, the sequence " + }, + { + "bbox": [ + 304, + 514, + 545, + 554 + ], + "type": "inline_equation", + "content": "\\{x_i,y_i\\}_{i = 1}^N" + }, + { + "bbox": [ + 304, + 514, + 545, + 554 + ], + "type": "text", + "content": " will be embedded to a feature sequence " + }, + { + "bbox": [ + 304, + 514, + 545, + 554 + ], + "type": "inline_equation", + "content": "\\{m_i,n_i\\}_{i = 1}^N" + }, + { + "bbox": [ + 304, + 514, + 545, + 554 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 359, + 559, + 545, + 575 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 359, + 559, + 545, + 575 + ], + "spans": [ + { + "bbox": [ + 359, + 559, + 545, + 575 + ], + "type": "interline_equation", + "content": "\\left\\{m _ {i}, n _ {i} \\right\\} _ {i = 1} ^ {N} = d i c \\left(\\left\\{x _ {i}, y _ {i} \\right\\} _ {i = 1} ^ {N}\\right), \\tag {3}", + "image_path": "56dd75783a42d5cd7f5de0cefaf01b1b1b2ca055eb63a32c7802c4d106a18fbc.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "inline_equation", + "content": "dic(\\cdot)" + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "text", + "content": " is a learnable coordinate embedding dictionary. The coordinate sequence " + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "inline_equation", + "content": "\\{x_i, y_i\\}_{i=1}^N" + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "text", + "content": " can be viewed as the index of the dictionary. However, in the Co-Content Teacher-Forcing, the feature sequence will be combined with the encoder output multi-modal feature. As shown in Fig. 3, we reshape the output multi-modal feature " + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "inline_equation", + "content": "F_m \\in R^{H \\times W \\times C'}" + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "text", + "content": " of Transformer Encoder to " + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "inline_equation", + "content": "F_m' \\in R^{(H \\times W) \\times C'}" + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "text", + "content": " for better correspondence with coordinate points. Then, for each coordinate " + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "inline_equation", + "content": "x_i" + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "text", + "content": " or " + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "inline_equation", + "content": "y_i" + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "text", + "content": ", we extract the feature of the corresponding row or column from the feature map " + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "inline_equation", + "content": "F_m'" + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "text", + "content": " and average them with " + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "inline_equation", + "content": "E\\{\\cdot\\}" + }, + { + "bbox": [ + 304, + 580, + 546, + 714 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3024" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 106, + 81, + 287, + 110 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 106, + 81, + 287, + 110 + ], + "spans": [ + { + "bbox": [ + 106, + 81, + 287, + 110 + ], + "type": "interline_equation", + "content": "f _ {x _ {i}} = E _ {k = 1} ^ {H} \\left\\{F _ {m} ^ {\\prime} \\left(\\frac {W}{M} x _ {i}, k\\right) \\right\\}, \\tag {4}", + "image_path": "e8b5c4fdaedf20ecac253450fa5ffd27cc6108470065c66a758cdf181f4b7fd4.jpg" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 107, + 106, + 226, + 129 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 107, + 106, + 226, + 129 + ], + "spans": [ + { + "bbox": [ + 107, + 106, + 226, + 129 + ], + "type": "interline_equation", + "content": "f _ {y _ {i}} = E _ {k = 1} ^ {W} \\{F _ {m} ^ {\\prime} (k, \\frac {H}{M} y _ {i}) \\},", + "image_path": "9442ec190919e0aff55dac2e3c8a2e625fb9a8306e73dc144eb6a23eaf16c6ee.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 136, + 286, + 198 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 136, + 286, + 198 + ], + "spans": [ + { + "bbox": [ + 47, + 136, + 286, + 198 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 47, + 136, + 286, + 198 + ], + "type": "text", + "content": " is the number of quantization bins. " + }, + { + "bbox": [ + 47, + 136, + 286, + 198 + ], + "type": "inline_equation", + "content": "\\left\\{\\frac{W}{M} x_i,\\frac{H}{M} y_i\\right\\}_{i = 1}^N" + }, + { + "bbox": [ + 47, + 136, + 286, + 198 + ], + "type": "text", + "content": " is the normalized index. Then the sequential feature " + }, + { + "bbox": [ + 47, + 136, + 286, + 198 + ], + "type": "inline_equation", + "content": "\\{m_i,n_i\\}_{i = 1}^N" + }, + { + "bbox": [ + 47, + 136, + 286, + 198 + ], + "type": "text", + "content": " will be combined with " + }, + { + "bbox": [ + 47, + 136, + 286, + 198 + ], + "type": "inline_equation", + "content": "\\{f_{x_i},f_{y_i}\\}_{i = 1}^N" + }, + { + "bbox": [ + 47, + 136, + 286, + 198 + ], + "type": "text", + "content": " by simple element-wise addition, the final sequence input to Decoder is represented as" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 97, + 207, + 287, + 224 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 97, + 207, + 287, + 224 + ], + "spans": [ + { + "bbox": [ + 97, + 207, + 287, + 224 + ], + "type": "interline_equation", + "content": "S = \\left\\{m _ {i}, n _ {i} \\right\\} _ {i = 1} ^ {N} + \\left\\{f _ {x _ {i}}, f _ {y _ {i}} \\right\\} _ {i = 1} ^ {N} \\tag {5}", + "image_path": "83e44f0072729c941f8674110cd5be4a359a0c1059ce84e3b9da914711f0a3c6.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 232, + 287, + 269 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 232, + 287, + 269 + ], + "spans": [ + { + "bbox": [ + 47, + 232, + 287, + 269 + ], + "type": "text", + "content": "With CCTF training, our model explicitly associate the point coordinates (scale values) with the referred spatial region, which is more natural for visual grounding." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 274, + 226, + 286 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 274, + 226, + 286 + ], + "spans": [ + { + "bbox": [ + 47, + 274, + 226, + 286 + ], + "type": "text", + "content": "3.4. Point-Modulated Cross Attention" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 293, + 287, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 293, + 287, + 389 + ], + "spans": [ + { + "bbox": [ + 47, + 293, + 287, + 389 + ], + "type": "text", + "content": "We adopt the SMCA proposed in [13] to replace the original Cross-Attention, which can constrain the cross-attention responses via estimating the Gaussian-like referring region prior. In the sequence prediction model, we generate the attention map for each co-content coordinates embedding, leading to a matched cross-attention region learned when the supervised data is reduced. To distinguish we denote it as Point-Modulated Cross-Attention (PMCA)." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 390, + 287, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 390, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 47, + 390, + 287, + 460 + ], + "type": "text", + "content": "For each co-content coordinate embedding " + }, + { + "bbox": [ + 47, + 390, + 287, + 460 + ], + "type": "inline_equation", + "content": "S = \\{f_j\\}_{j=1}^{2N} = \\{f_{x_i}, f_{y_i}\\}_{i=1}^N" + }, + { + "bbox": [ + 47, + 390, + 287, + 460 + ], + "type": "text", + "content": ", we use a 3-layer MLP followed by a sigmoid activation function to generate a prior Gaussian-like attention map. The center point coordinates " + }, + { + "bbox": [ + 47, + 390, + 287, + 460 + ], + "type": "inline_equation", + "content": "c_w, c_h" + }, + { + "bbox": [ + 47, + 390, + 287, + 460 + ], + "type": "text", + "content": " and the scales " + }, + { + "bbox": [ + 47, + 390, + 287, + 460 + ], + "type": "inline_equation", + "content": "s_w, s_h" + }, + { + "bbox": [ + 47, + 390, + 287, + 460 + ], + "type": "text", + "content": " of the prior Gaussian map is denoted as" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 87, + 472, + 287, + 485 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 472, + 287, + 485 + ], + "spans": [ + { + "bbox": [ + 87, + 472, + 287, + 485 + ], + "type": "interline_equation", + "content": "s _ {w}, s _ {h}, c _ {w}, c _ {h} = \\operatorname {s i g m o i d} (M L P (f _ {j})), \\tag {6}", + "image_path": "0e96f1fdced144d3097cb1154b7e2b87e8455c54c0301d700b7a86b781a34e96.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 495, + 206, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 495, + 206, + 507 + ], + "spans": [ + { + "bbox": [ + 47, + 495, + 206, + 507 + ], + "type": "text", + "content": "and the Gaussian map is represented as" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 75, + 516, + 287, + 544 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 516, + 287, + 544 + ], + "spans": [ + { + "bbox": [ + 75, + 516, + 287, + 544 + ], + "type": "interline_equation", + "content": "G (u, v) = \\exp \\left(- \\frac {\\left(u - c _ {w}\\right) ^ {2}}{\\sigma s _ {w} ^ {2}} - \\frac {\\left(v - c _ {h}\\right) ^ {2}}{\\sigma s _ {h} ^ {2}}\\right) \\tag {7}", + "image_path": "cccd0ba2817049d3ed942675583b75431e375d2d78523e2f5e7bc1bb139db3c2.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 553, + 287, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 553, + 287, + 590 + ], + "spans": [ + { + "bbox": [ + 47, + 553, + 287, + 590 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 553, + 287, + 590 + ], + "type": "inline_equation", + "content": "u\\in R^W,v\\in R^H" + }, + { + "bbox": [ + 47, + 553, + 287, + 590 + ], + "type": "text", + "content": " , they are the spatial indices of " + }, + { + "bbox": [ + 47, + 553, + 287, + 590 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 47, + 553, + 287, + 590 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 553, + 287, + 590 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 47, + 553, + 287, + 590 + ], + "type": "text", + "content": " is a hyper-parameter to modulate the bandwidth of the Gaussian map." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 598, + 230, + 611 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 598, + 230, + 611 + ], + "spans": [ + { + "bbox": [ + 47, + 598, + 230, + 611 + ], + "type": "text", + "content": "3.5. Resampling Pseudo Point Strategy" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "text", + "content": "To fully utilize the data without mask annotation in Partial-RES, we retrain the model with the generated pseudo label, and we propose a Resampling pseudo point strategy. To remain the consistency of the pseudo points with the original sampled contour points of the ground truth mask, we will resample the pseudo points. As shown in Fig. 4, we first connect the points predicted by the network to a binary mask. Then we compute the dice coefficient " + }, + { + "bbox": [ + 47, + 618, + 287, + 713 + ], + "type": "inline_equation", + "content": "D" + } + ] + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 334, + 72, + 430, + 145 + ], + "blocks": [ + { + "bbox": [ + 334, + 72, + 430, + 145 + ], + "lines": [ + { + "bbox": [ + 334, + 72, + 430, + 145 + ], + "spans": [ + { + "bbox": [ + 334, + 72, + 430, + 145 + ], + "type": "image", + "image_path": "8ba1a256ac6fac9e60d66e3bfb6483d36ca05cf399dc1e569addfa0c67839ac4.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 379, + 146, + 392, + 156 + ], + "lines": [ + { + "bbox": [ + 379, + 146, + 392, + 156 + ], + "spans": [ + { + "bbox": [ + 379, + 146, + 392, + 156 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 432, + 78, + 522, + 145 + ], + "blocks": [ + { + "bbox": [ + 432, + 78, + 522, + 145 + ], + "lines": [ + { + "bbox": [ + 432, + 78, + 522, + 145 + ], + "spans": [ + { + "bbox": [ + 432, + 78, + 522, + 145 + ], + "type": "image", + "image_path": "8650993f31e2beba399ab0343cab2511470a82df2e360194ec2d8013f4f96ad1.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 471, + 146, + 485, + 156 + ], + "lines": [ + { + "bbox": [ + 471, + 146, + 485, + 156 + ], + "spans": [ + { + "bbox": [ + 471, + 146, + 485, + 156 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 341, + 157, + 430, + 224 + ], + "blocks": [ + { + "bbox": [ + 341, + 157, + 430, + 224 + ], + "lines": [ + { + "bbox": [ + 341, + 157, + 430, + 224 + ], + "spans": [ + { + "bbox": [ + 341, + 157, + 430, + 224 + ], + "type": "image", + "image_path": "5c402b65ae078d8df5d6a84497082ce300bef8eca240bb019c0015b3d0d3a03d.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 381, + 225, + 393, + 235 + ], + "lines": [ + { + "bbox": [ + 381, + 225, + 393, + 235 + ], + "spans": [ + { + "bbox": [ + 381, + 225, + 393, + 235 + ], + "type": "text", + "content": "(c)" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 433, + 157, + 522, + 224 + ], + "blocks": [ + { + "bbox": [ + 433, + 157, + 522, + 224 + ], + "lines": [ + { + "bbox": [ + 433, + 157, + 522, + 224 + ], + "spans": [ + { + "bbox": [ + 433, + 157, + 522, + 224 + ], + "type": "image", + "image_path": "ad04e0543dd1d95fadac81626c5eea299edf74f08e07df338f30a5e7383fb212.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 474, + 224, + 487, + 235 + ], + "lines": [ + { + "bbox": [ + 474, + 224, + 487, + 235 + ], + "spans": [ + { + "bbox": [ + 474, + 224, + 487, + 235 + ], + "type": "text", + "content": "(d)" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 305, + 247, + 545, + 314 + ], + "lines": [ + { + "bbox": [ + 305, + 247, + 545, + 314 + ], + "spans": [ + { + "bbox": [ + 305, + 247, + 545, + 314 + ], + "type": "text", + "content": "Figure 4. Sketch of Resampling Pseudo Point (RPP) Strategy. (a) Screening the appropriate pseudo mask with the dice coefficient between the predicted binary mask and the ground truth bounding box; (b) The generated points by the RES model; (c) The uniformly resampled pseudo points from pseudo mask; (d) The uniformly sampled target points from ground truth mask." + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_caption" + } + ], + "index": 20 + }, + { + "bbox": [ + 305, + 323, + 545, + 346 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 323, + 545, + 346 + ], + "spans": [ + { + "bbox": [ + 305, + 323, + 545, + 346 + ], + "type": "text", + "content": "of the projection of this mask and the ground truth bounding box." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 347, + 346, + 545, + 360 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 346, + 545, + 360 + ], + "spans": [ + { + "bbox": [ + 347, + 346, + 545, + 360 + ], + "type": "interline_equation", + "content": "D (l, b) = d i c e \\left(l _ {y}, b _ {y})\\right) + d i c e \\left(l _ {x}, b _ {x}\\right) \\tag {8}", + "image_path": "b74efd900c7c2228ce0e4a0ef7b22582c58f8fb31cfdb53412ca4ee51aff1f63.jpg" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 305, + 365, + 544, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 365, + 544, + 389 + ], + "spans": [ + { + "bbox": [ + 305, + 365, + 544, + 389 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 365, + 544, + 389 + ], + "type": "inline_equation", + "content": "l_x, l_y" + }, + { + "bbox": [ + 305, + 365, + 544, + 389 + ], + "type": "text", + "content": " are the projection of the mask on x-axis, y-axis, " + }, + { + "bbox": [ + 305, + 365, + 544, + 389 + ], + "type": "inline_equation", + "content": "b_x, b_y" + }, + { + "bbox": [ + 305, + 365, + 544, + 389 + ], + "type": "text", + "content": " are of the ground truth bounding box." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 305, + 389, + 545, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 389, + 545, + 484 + ], + "spans": [ + { + "bbox": [ + 305, + 389, + 545, + 484 + ], + "type": "text", + "content": "We set a threshold " + }, + { + "bbox": [ + 305, + 389, + 545, + 484 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 305, + 389, + 545, + 484 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 305, + 389, + 545, + 484 + ], + "type": "inline_equation", + "content": "D(l, b)" + }, + { + "bbox": [ + 305, + 389, + 545, + 484 + ], + "type": "text", + "content": " to pick out those pseudo masks that are more accurate to avoid introducing too much noise by pseudo labels. For the selected samples, the predicted points are spaced may be inhomogeneous as shown in Fig. 4(b). So we resample the pseudo points with a uniform sampling scheme. It is considered that the contour points of previously labeled data are sampled uniformly and the pseudo point should be kept consistent with it." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 305, + 485, + 544, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 485, + 544, + 509 + ], + "spans": [ + { + "bbox": [ + 305, + 485, + 544, + 509 + ], + "type": "text", + "content": "We propose a feasible training process for the Partially Supervised RES task as shown in Fig. 2 (b):" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 305, + 509, + 545, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 509, + 545, + 592 + ], + "spans": [ + { + "bbox": [ + 305, + 509, + 545, + 592 + ], + "type": "text", + "content": "Step1. REC Training. Since in the Partial-RES task, all the bounding box labels are seen in the model training, we first train the model on fully supervised REC to get a better representation relatively of multi-model features from the encoder output. The feature will be used to enhance TF learning in the next step of RES training. In practice, we load the REC model trained in SeqTR as a pretrained one." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 305, + 593, + 545, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 593, + 545, + 640 + ], + "spans": [ + { + "bbox": [ + 305, + 593, + 545, + 640 + ], + "type": "text", + "content": "Step2. Initial RES Training. In this step, we train the model with only " + }, + { + "bbox": [ + 305, + 593, + 545, + 640 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 305, + 593, + 545, + 640 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 305, + 593, + 545, + 640 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 305, + 593, + 545, + 640 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 305, + 593, + 545, + 640 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 305, + 593, + 545, + 640 + ], + "type": "text", + "content": " labeled mask data on the RES task. The model is equipped with the new method proposed in this paper, including CCTF and PMCA." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 304, + 640, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 640, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 640, + 545, + 712 + ], + "type": "text", + "content": "Step3. Retraining with Pseudo Points. After obtaining the RES model from Step2, we can utilize it to generate more pseudo contour points. While these points may contain a lot of noise or may not match the way labeled data is sampled, so we re-selected and sampled out the appropriate pseudo points with the proposed RPP strategy." + } + ] + } + ], + "index": 30 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3025" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 47, + 70, + 553, + 301 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 553, + 301 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 553, + 301 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 553, + 301 + ], + "type": "table", + "html": "
Label RatioMethodVenueVisual EncoderRefCOCORefCOCO+RefCOCOg
valtestAtestBvaltestAtestBval-gval-utest-u
100%Fully-supervised:
CMSA [47]CVPR'19RN10158.3260.6155.0943.7647.6037.8939.98--
STEP [3]ICCV'19RN10160.0463.4657.9748.1952.3340.4146.40--
CMPC [21]CVPR'20DN5361.3664.5359.6449.5653.4443.2349.05--
LSCM [22]ECCV'20DLA3461.4764.9959.5549.3453.1243.5048.05--
MCN [32]CVPR'20DN5362.4464.2059.7150.6254.9944.69-49.2249.40
BUSNet [44]CVPR'21RN10163.2766.4161.3951.7656.8744.1350.56--
LTS[21] [23]CVPR'21DN5365.4367.7663.0854.2158.3248.02-54.4054.25
VLT [9]ICCV'21DN5665.6568.2962.7355.5059.2049.3649.7652.9956.65
ResTR [25]CVPR'22ViT-B67.2269.3064.4555.7860.4448.2754.48--
SeqTR [54]ECCV'22DN5367.2669.7964.1254.1458.9348.19-55.6755.64
10%Partially-supervised
Baseline-DN5357.9260.7455.0650.8454.8744.54-49.5049.30
Our Method-DN5363.9965.8661.5452.5655.7946.09-52.4352.40
5%Baseline-DN5358.8561.7256.3649.7854.2243.34-48.3348.27
Our Method-DN5362.2364.6760.4251.3054.6844.85-51.2850.55
1%Baseline-DN5354.6757.4852.6244.9148.8139.44-42.5742.46
Our Method-DN5357.4159.6055.8447.6852.0641.54-46.8046.12
", + "image_path": "2e4dbfad845615e7ca2056516536bea92f5dd9a95de8785d313aa276c49eeb07.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 309, + 546, + 344 + ], + "lines": [ + { + "bbox": [ + 46, + 309, + 546, + 344 + ], + "spans": [ + { + "bbox": [ + 46, + 309, + 546, + 344 + ], + "type": "text", + "content": "Table 1. Comparisons with state-of-the-art fully-supervised methods on RefCOCO [50], RefCOCO+ [50], and RefCOCOg [33] in terms of IoU scores. We defined mask label ratio as " + }, + { + "bbox": [ + 46, + 309, + 546, + 344 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 46, + 309, + 546, + 344 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 309, + 546, + 344 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 46, + 309, + 546, + 344 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 309, + 546, + 344 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 309, + 546, + 344 + ], + "type": "text", + "content": " to compare our method with the baseline in partially-supervised RES. \"RN101\": ResNet-101 [15], \"DN53\": DarkNet-53 [37], \"DN56\": DarkNet-56 [37], \"DLA-34\" [48], \"ViT-B\" [10]." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 47, + 363, + 128, + 376 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 363, + 128, + 376 + ], + "spans": [ + { + "bbox": [ + 47, + 363, + 128, + 376 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 382, + 110, + 394 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 382, + 110, + 394 + ], + "spans": [ + { + "bbox": [ + 47, + 382, + 110, + 394 + ], + "type": "text", + "content": "4.1. Datasets" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 400, + 287, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 400, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 46, + 400, + 287, + 460 + ], + "type": "text", + "content": "RefCOCO/RefCOCO+ are proposed in [50]. There are 19,994 images in RefCOCO with 142,209 refer expressions for 50,000 objects. Similarly, 19,992 images are included in RefCOCO+ which contains 141,564 expressions for 49,856 objects." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 460, + 287, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 460, + 287, + 567 + ], + "spans": [ + { + "bbox": [ + 46, + 460, + 287, + 567 + ], + "type": "text", + "content": "In these datasets, each image contains two or more objects from the same category. In RefCOCO+ dataset, positional words are not allowed in the referring expression, which is a pure dataset with appearance-based referring expression, whereas RefCOCO imposes no restriction on the phrase. In addition to the training set and validation set, the test set for RefCOCO/RefCOCO+ is divided into a testA set (containing several people in an image) and a testB set (containing multiple instances of other objects in an image)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 567, + 287, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 567, + 287, + 640 + ], + "spans": [ + { + "bbox": [ + 46, + 567, + 287, + 640 + ], + "type": "text", + "content": "RefCOCOg [33] contains 26,711 images with 85,474 referring expressions for 54,822 objects, and each image usually contains " + }, + { + "bbox": [ + 46, + 567, + 287, + 640 + ], + "type": "inline_equation", + "content": "2 \\sim 4" + }, + { + "bbox": [ + 46, + 567, + 287, + 640 + ], + "type": "text", + "content": " objects of the same category. The length of referring expressions in this dataset is almost twice as long as those in RefCOCO and RefCOCO+. This dataset is directly divided into \"train\", \"val\" and \"test\"." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 647, + 174, + 661 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 647, + 174, + 661 + ], + "spans": [ + { + "bbox": [ + 47, + 647, + 174, + 661 + ], + "type": "text", + "content": "4.2. Experimental Settings" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 665, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 715 + ], + "type": "text", + "content": "Each instance to be segmented is equipped with an expression sentence in RES datasets, the sentence can be seen as a kind of open category. Therefore, different from the manner of dividing data [17], we simulate the partially su" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 364, + 545, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 364, + 545, + 399 + ], + "spans": [ + { + "bbox": [ + 304, + 364, + 545, + 399 + ], + "type": "text", + "content": "pervised training scenario on RES by randomly sampling the entire train data proportionally without considering the category." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "spans": [ + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "text", + "content": "Implementation Details. Following SeqTR [54], all parameters in the network are optimized with AdamW [30], and the batch size is 128. We train the model with " + }, + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "inline_equation", + "content": "120 \\sim 150" + }, + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "text", + "content": " epochs for full convergence, and we set the learning rate as " + }, + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "inline_equation", + "content": "5\\mathrm{e - }4@10\\%" + }, + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "inline_equation", + "content": "3\\mathrm{e - }4@5\\%" + }, + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "inline_equation", + "content": "1\\mathrm{e - }4@1\\%" + }, + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "text", + "content": " in step2 training, while " + }, + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "inline_equation", + "content": "5\\mathrm{e - }4" + }, + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "text", + "content": " in step3 training with pseudo points. We use DarkNet-53 [37] as the visual encoder. Following standard practices [8, 32, 23, 9], images are resized to " + }, + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "inline_equation", + "content": "640 \\times 640" + }, + { + "bbox": [ + 304, + 401, + 546, + 581 + ], + "type": "text", + "content": ", and the length of language expression is trimmed at 15 for ResCOCO/RefCOCO+ and 20 for RefCOCOg. The training epochs and the learning rate used vary slightly for different settings of mask-labeled data ratio and different training stages. For the number of sampled contour points, we use the same as in SeqTR, i.e., 18 points for RefCOCO/RefCOCO+, and 12 for RefCOCOg." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "content": "Evaluation Metrics. Following the proposal setting in the previous work, we use mask Intersection-over-Union (IoU) and Precision with thresholds " + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "inline_equation", + "content": "(\\mathrm{Pr}@\\mathrm{X})" + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "content": ". The mask IoU demonstrates the mask quality, which emphasizes the model's overall performance and reveals both targeting and segmenting abilities. The " + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "inline_equation", + "content": "\\mathrm{Pr}@\\mathrm{X}" + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "content": " metric computes the ratio of successfully predicted samples using different IoU thresholds. Low threshold precision like " + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "inline_equation", + "content": "\\mathrm{Pr}@\\mathrm{0.5}" + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "content": " reflects the identification performance of the method, and high threshold precision like " + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "inline_equation", + "content": "\\mathrm{Pr}@\\mathrm{0.9}" + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "content": " reveals the ability to generate high-quality masks." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "3026" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 57, + 70, + 537, + 161 + ], + "blocks": [ + { + "bbox": [ + 57, + 70, + 537, + 161 + ], + "lines": [ + { + "bbox": [ + 57, + 70, + 537, + 161 + ], + "spans": [ + { + "bbox": [ + 57, + 70, + 537, + 161 + ], + "type": "table", + "html": "
RatioCCTFPMCAIoUPr@0.5Pr@0.6Pr@0.7Pr@0.8Pr@0.9
1%✓42.5747.5432.8917.615.070.36
✓✓44.20 (+1.63)50.68 (+3.14)37.00 (+4.11)21.21 (+3.60)7.89 (+2.82)0.66 (+0.30)
44.58 (+0.38)50.98 (+0.30)37.32 (+0.32)22.42 (+1.21)8.21 (+0.32)0.76 (+0.10)
100%✓55.9567.7362.2049.0229.557.07
✓✓56.03 (+0.08)67.79 (+0.06)62.32 (+0.12)49.88 (+0.86)30.63 (+1.08)8.04 (+0.97)
56.89 (+0.86)68.43 (+0.64)62.96 (+0.64)50.72 (+0.84)31.21 (+0.58)8.05 (+0.01)
", + "image_path": "5eb4d4d3daaaebd62ea88ad89e88a9cf2c80abc4d7459cecdca34cb9e7a3948b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 48, + 211, + 279, + 264 + ], + "blocks": [ + { + "bbox": [ + 46, + 169, + 547, + 194 + ], + "lines": [ + { + "bbox": [ + 46, + 169, + 547, + 194 + ], + "spans": [ + { + "bbox": [ + 46, + 169, + 547, + 194 + ], + "type": "text", + "content": "Table 2. Validity verification of our proposed CCTF and PMCA. We conduct experiments on " + }, + { + "bbox": [ + 46, + 169, + 547, + 194 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 46, + 169, + 547, + 194 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 169, + 547, + 194 + ], + "type": "inline_equation", + "content": "100\\%" + }, + { + "bbox": [ + 46, + 169, + 547, + 194 + ], + "type": "text", + "content": " supervised data with mask labels (100% means fully-supervised training)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 211, + 279, + 264 + ], + "lines": [ + { + "bbox": [ + 48, + 211, + 279, + 264 + ], + "spans": [ + { + "bbox": [ + 48, + 211, + 279, + 264 + ], + "type": "table", + "html": "
MethodIoUPr@0.5Pr@0.6Pr@0.7Pr@0.8Pr@0.9
Baseline44.5850.9837.3222.428.210.76
w/o RPP44.2751.2838.2424.2810.240.96
w/ RPP46.8056.2943.1927.4010.340.98
", + "image_path": "298ec9d1374be25254ada36a661f2bea36073da425a2560ed42cca46ac011344.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 315, + 288, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 315, + 288, + 411 + ], + "spans": [ + { + "bbox": [ + 46, + 315, + 288, + 411 + ], + "type": "text", + "content": "Baseline and oracle We suggest the baseline in the following way: we initialize our model with the parameters of SeqTR trained on the REC task, and refine it on the RES task with " + }, + { + "bbox": [ + 46, + 315, + 288, + 411 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 46, + 315, + 288, + 411 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 315, + 288, + 411 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 46, + 315, + 288, + 411 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 315, + 288, + 411 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 315, + 288, + 411 + ], + "type": "text", + "content": " mask annotation data. Our comparison with the baseline is discussed in Sec. 4.3. The \"oracle\" model is SeqTR trained on REC and refined on RES with all mask annotation data. This fully supervised model is a performance upper bound for our partially supervised RES." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 418, + 218, + 430 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 418, + 218, + 430 + ], + "spans": [ + { + "bbox": [ + 47, + 418, + 218, + 430 + ], + "type": "text", + "content": "4.3. Comparison with other Method" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "spans": [ + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "text", + "content": "We compare our method with other fully supervised state-of-the-art methods on three common benchmarks of Referring Expression Segmentation, i.e., RefCOCO, RefCOCO+, and RefCOCOg. In addition, we also compare with our suggested baseline in partially-supervised training. Results are reported in Table 1. Our method displays significant improvement over the baseline method on all three datasets, at an average of " + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "inline_equation", + "content": "3.5\\%" + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "inline_equation", + "content": "2.4\\%" + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "inline_equation", + "content": "3.0\\%" + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "text", + "content": " on " + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "text", + "content": " mask labeled data. With " + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "text", + "content": " mask annotated data, we can achieve " + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "inline_equation", + "content": "97\\%" + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "text", + "content": " of the fully supervised performance on RefCOCO+@val. We are also able to achieve " + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "inline_equation", + "content": "88\\%" + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "text", + "content": " fully supervised performance on " + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 46, + 436, + 287, + 605 + ], + "type": "text", + "content": " mask-labeled data on RefCOCO+@testA. Our methods can even surpass some fully-supervised methods, e.g., MCN, [32], BUSNet [44]." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 611, + 147, + 623 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 611, + 147, + 623 + ], + "spans": [ + { + "bbox": [ + 47, + 611, + 147, + 623 + ], + "type": "text", + "content": "4.4. Ablation Studies" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 629, + 287, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 629, + 287, + 700 + ], + "spans": [ + { + "bbox": [ + 46, + 629, + 287, + 700 + ], + "type": "text", + "content": "Effectiveness of CCTF and PMCA. To verify the effectiveness of Co-Content Teacher Forcing (CCTF) and Point-Modulated Cross-Attention (PMCA), we show the results of our RES model with " + }, + { + "bbox": [ + 46, + 629, + 287, + 700 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 46, + 629, + 287, + 700 + ], + "type": "text", + "content": " partially supervised training data and " + }, + { + "bbox": [ + 46, + 629, + 287, + 700 + ], + "type": "inline_equation", + "content": "100\\%" + }, + { + "bbox": [ + 46, + 629, + 287, + 700 + ], + "type": "text", + "content": " fully supervised training data on Ref-COCOg@val in Table 2." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 59, + 701, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 701, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 701, + 287, + 712 + ], + "type": "text", + "content": "When trained with " + }, + { + "bbox": [ + 59, + 701, + 287, + 712 + ], + "type": "inline_equation", + "content": "100\\%" + }, + { + "bbox": [ + 59, + 701, + 287, + 712 + ], + "type": "text", + "content": " mask label of the data, PMCA" + } + ] + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 310, + 211, + 544, + 265 + ], + "blocks": [ + { + "bbox": [ + 46, + 271, + 287, + 295 + ], + "lines": [ + { + "bbox": [ + 46, + 271, + 287, + 295 + ], + "spans": [ + { + "bbox": [ + 46, + 271, + 287, + 295 + ], + "type": "text", + "content": "Table 3. Comparison of our method with re-sampling pseudo points and without RPP in step3 training." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 310, + 211, + 544, + 265 + ], + "lines": [ + { + "bbox": [ + 310, + 211, + 544, + 265 + ], + "spans": [ + { + "bbox": [ + 310, + 211, + 544, + 265 + ], + "type": "table", + "html": "
MethodIoUPr@0.5Pr@0.6Pr@0.7Pr@0.8Pr@0.9
Baseline44.5850.9837.3222.428.210.76
T@1.0044.5351.3638.1422.407.290.58
T@0.0544.3550.6838.5224.189.700.78
T@0.1046.8056.2943.1927.4010.340.98
", + "image_path": "b9d6cc66d774af3e442e3c4f01e94301f02632be5c8a2444fe0eb4719a2cd9df.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 273, + 547, + 318 + ], + "lines": [ + { + "bbox": [ + 305, + 273, + 547, + 318 + ], + "spans": [ + { + "bbox": [ + 305, + 273, + 547, + 318 + ], + "type": "text", + "content": "Table 4. Performance with different Threshold " + }, + { + "bbox": [ + 305, + 273, + 547, + 318 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 305, + 273, + 547, + 318 + ], + "type": "text", + "content": " of dice coefficient on RefCOCOg@val. " + }, + { + "bbox": [ + 305, + 273, + 547, + 318 + ], + "type": "inline_equation", + "content": "T@1.00" + }, + { + "bbox": [ + 305, + 273, + 547, + 318 + ], + "type": "text", + "content": " means that all the predicted pseudo masks are used for training in step3 without selection of the more accurate one with dice coefficient." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 340, + 545, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 340, + 545, + 460 + ], + "spans": [ + { + "bbox": [ + 304, + 340, + 545, + 460 + ], + "type": "text", + "content": "embodies more performance enhancements than CCTF, which proves the effectiveness of PMCA. CCTF contributes little to performance improvement. While with " + }, + { + "bbox": [ + 304, + 340, + 545, + 460 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 304, + 340, + 545, + 460 + ], + "type": "text", + "content": " supervised mask labeled training data, the improvement contribution of CCTF far exceeds that of PMCA, " + }, + { + "bbox": [ + 304, + 340, + 545, + 460 + ], + "type": "inline_equation", + "content": "80\\%" + }, + { + "bbox": [ + 304, + 340, + 545, + 460 + ], + "type": "text", + "content": " performance improvement comes from CCTF (1.63% in 2.01%). This demonstrates that the exposure bias of the inconsistency of the training and inference process may be more serious in the case of partially supervised data, which is alleviated by our proposed CCTF somewhat." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 461, + 546, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 461, + 546, + 616 + ], + "spans": [ + { + "bbox": [ + 304, + 461, + 546, + 616 + ], + "type": "text", + "content": "Ablation of Resampling Pseudo Points To better demonstrate the effectiveness of re-sampling pseudo points (RPP), we compare the results with those obtained without resampling. In the case of re-sampling, we uniformly resample the pseudo points from the generated pseudo mask and consider them as the target sequence. In contrast, for no re-sampling, we use the points predicted by the model directly as the target sequence. As illustrated in Table 3, the reported performance is superior when the RPP strategy is employed. This is primarily attributed to the fact that after re-sampling, the unlabeled data is pseudo-labeled uniformly, which mitigates the potential noise introduced by pseudo labels." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 617, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 547, + 713 + ], + "type": "text", + "content": "Performance with different Threshold " + }, + { + "bbox": [ + 304, + 617, + 547, + 713 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 617, + 547, + 713 + ], + "type": "text", + "content": " of dice coefficient. We establish a threshold for the dice coefficient to select more suitable pseudo points that will serve as prediction targets for unlabeled mask annotation data in step3 mentioned in Sec. 3.5. To demonstrate the effectiveness of our pseudo points selection strategy and the impact of different threshold, we compare the results with different thresholds " + }, + { + "bbox": [ + 304, + 617, + 547, + 713 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 617, + 547, + 713 + ], + "type": "text", + "content": " of the dice coefficient on RefCOCOg@val. As presented" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3027" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 93, + 70, + 503, + 290 + ], + "blocks": [ + { + "bbox": [ + 93, + 70, + 503, + 290 + ], + "lines": [ + { + "bbox": [ + 93, + 70, + 503, + 290 + ], + "spans": [ + { + "bbox": [ + 93, + 70, + 503, + 290 + ], + "type": "image", + "image_path": "f12e438bfa4270bfc8015366d3e04fa2fef441ff4355ebf9efc7a5aa581f88ae.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 297, + 546, + 319 + ], + "lines": [ + { + "bbox": [ + 46, + 297, + 546, + 319 + ], + "spans": [ + { + "bbox": [ + 46, + 297, + 546, + 319 + ], + "type": "text", + "content": "Figure 5. Qualitative examples of our method at " + }, + { + "bbox": [ + 46, + 297, + 546, + 319 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 46, + 297, + 546, + 319 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 297, + 546, + 319 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 46, + 297, + 546, + 319 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 297, + 546, + 319 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 297, + 546, + 319 + ], + "type": "text", + "content": " mask label in Partially Supervised RES. We identify the image and its corresponding referring expression in the first column." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 59, + 340, + 272, + 455 + ], + "blocks": [ + { + "bbox": [ + 59, + 340, + 272, + 455 + ], + "lines": [ + { + "bbox": [ + 59, + 340, + 272, + 455 + ], + "spans": [ + { + "bbox": [ + 59, + 340, + 272, + 455 + ], + "type": "image", + "image_path": "0e3f8cdbd34dfcdcae552e73cd1a6d31adb72c8cb4f5ad9c1d540e7882e5aa6e.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 59, + 455, + 274, + 464 + ], + "lines": [ + { + "bbox": [ + 59, + 455, + 274, + 464 + ], + "spans": [ + { + "bbox": [ + 59, + 455, + 274, + 464 + ], + "type": "text", + "content": "Figure 6. The mIoU with different mask-labeled data ratio." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 489, + 287, + 573 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 489, + 287, + 573 + ], + "spans": [ + { + "bbox": [ + 46, + 489, + 287, + 573 + ], + "type": "text", + "content": "in Table 4, without any constraint (" + }, + { + "bbox": [ + 46, + 489, + 287, + 573 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 46, + 489, + 287, + 573 + ], + "type": "text", + "content": " is set to 1.00), we observe that the side effect of pseudo points is greater than the main effect due to the noise contained in pseudo points. Therefore, we determine to select out those pseudo masks with dice coefficient " + }, + { + "bbox": [ + 46, + 489, + 287, + 573 + ], + "type": "inline_equation", + "content": "< T" + }, + { + "bbox": [ + 46, + 489, + 287, + 573 + ], + "type": "text", + "content": " for re-sampling pseudo points. Meanwhile, our ablation studies declare that the threshold of 0.10 is the optimal choice for our selection strategy." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 574, + 287, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 574, + 287, + 658 + ], + "spans": [ + { + "bbox": [ + 46, + 574, + 287, + 658 + ], + "type": "text", + "content": "Experiment of increasing the proportion of mask-labeled data We conduct an additional experiment to investigate the proportion of mask-labeled data in training that can reach SotA performance. As depicted in Fig. 6, comparing the mIoU with the fully supervised SotA method (55.67) on RefCOCOg@val, our method attains remarkably similar results (55.59) by using " + }, + { + "bbox": [ + 46, + 574, + 287, + 658 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 46, + 574, + 287, + 658 + ], + "type": "text", + "content": " mask-labeled data." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 670, + 159, + 682 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 670, + 159, + 682 + ], + "spans": [ + { + "bbox": [ + 47, + 670, + 159, + 682 + ], + "type": "text", + "content": "4.5. Qualitative Results" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": "We evaluated the segmentation performance of our method at various ratios, i.e., " + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": " of Partial-" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 340, + 546, + 400 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 340, + 546, + 400 + ], + "spans": [ + { + "bbox": [ + 305, + 340, + 546, + 400 + ], + "type": "text", + "content": "RES data in Fig. 5. As the mask-labeled data increases, the contours fit more accurately. By comparing these results, we observe that with a low percentage of mask-labeled data, our segmentation results are already similar to those of the GT points." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 422, + 378, + 435 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 422, + 378, + 435 + ], + "spans": [ + { + "bbox": [ + 306, + 422, + 378, + 435 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 446, + 546, + 650 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 446, + 546, + 650 + ], + "spans": [ + { + "bbox": [ + 304, + 446, + 546, + 650 + ], + "type": "text", + "content": "In this paper, we investigate the partially supervised learning paradigm for RES, where only a few segmentation masks are available so the model has to learn transfer knowledge from REC. Unlike existing RES/REC works that have different decoders for the two tasks, we leverage the contour-based sequence prediction model to maximize the transfer ability. We then propose CCTF to explicitly associate the point coordinates (scale values) with the referred spatial regions, which alleviates the exposure bias brought by the limited segmentation masks. Besides, we present RPP strategy to select appropriate pseudo labels and resample pseudo points during the retraining stage. We conduct extensive experiments to demonstrate the effectiveness of our model in the Partial-RES setting. One limitation of this work is that the smoothness and edge accuracy of the point-contour mask prediction may not be as precise as the pixel-level mask predictions." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 654, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 654, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 654, + 546, + 713 + ], + "type": "text", + "content": "Acknowledgements. This work was supported by the National Key RD Program of China (No.2018AAA0102100), the National Natural Science Foundation of China (Nos. U1936212, 62120106009), Sponsored by Beijing Nova Program (NO. 20220484063)." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3028" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 287, + 713 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "type": "text", + "content": "[1] David Biertimpel, Sindi Shkodrani, Anil S Baslamisli, and Nóra Baka. Prior to segment: Foreground cues for weakly annotated classes in partially supervised instance segmentation. In ICCV, pages 2824-2833, 2021." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 136, + 287, + 178 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 136, + 287, + 178 + ], + "spans": [ + { + "bbox": [ + 53, + 136, + 287, + 178 + ], + "type": "text", + "content": "[2] Ding-Jie Chen, Songhao Jia, Yi-Chen Lo, Hwann-Tzong Chen, and Tyng-Luh Liu. See-through-text grouping for referring image segmentation. In ICCV, pages 7454-7463, 2019." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 180, + 287, + 222 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 180, + 287, + 222 + ], + "spans": [ + { + "bbox": [ + 53, + 180, + 287, + 222 + ], + "type": "text", + "content": "[3] Ding-Jie Chen, Songhao Jia, Yi-Chen Lo, Hwann-Tzong Chen, and Tyng-Luh Liu. See-through-text grouping for referring image segmentation. In ICCV, pages 7454-7463, 2019." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 224, + 287, + 256 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 224, + 287, + 256 + ], + "spans": [ + { + "bbox": [ + 53, + 224, + 287, + 256 + ], + "type": "text", + "content": "[4] Ting Chen, Saurabh Saxena, Lala Li, David J Fleet, and Geoffrey Hinton. Pix2seq: A language modeling framework for object detection. In ICLR." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 258, + 287, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 258, + 287, + 300 + ], + "spans": [ + { + "bbox": [ + 53, + 258, + 287, + 300 + ], + "type": "text", + "content": "[5] Xinpeng Chen, Lin Ma, Jingyuan Chen, Zequn Jie, Wei Liu, and Jiebo Luo. Real-time Referring Expression Comprehension by Single-stage Grounding Network. arXiv preprint arXiv:1812.03426, 2018." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 302, + 287, + 345 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 302, + 287, + 345 + ], + "spans": [ + { + "bbox": [ + 53, + 302, + 287, + 345 + ], + "type": "text", + "content": "[6] Zhiyang Chen, Yousong Zhu, Zhaowen Li, Fan Yang, Wei Li, Haixin Wang, Chaoyang Zhao, Liwei Wu, Rui Zhao, Jinqiao Wang, et al. Obj2seq: Formatting objects as sequences with class prompt for visual tasks. In NeurIPS." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 347, + 287, + 389 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 347, + 287, + 389 + ], + "spans": [ + { + "bbox": [ + 53, + 347, + 287, + 389 + ], + "type": "text", + "content": "[7] Junyoung Chung, Caglar Gulcehre, Kyunghyun Cho, and Yoshua Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. In NIPS 2014 Workshop on Deep Learning, December 2014, 2014." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 391, + 287, + 423 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 391, + 287, + 423 + ], + "spans": [ + { + "bbox": [ + 53, + 391, + 287, + 423 + ], + "type": "text", + "content": "[8] Jiajun Deng, Zhengyuan Yang, Tianlang Chen, Wengang Zhou, and Houqiang Li. TransVG: End-to-End Visual Grounding with Transformers. ICCV, 2021." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 425, + 287, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 425, + 287, + 456 + ], + "spans": [ + { + "bbox": [ + 53, + 425, + 287, + 456 + ], + "type": "text", + "content": "[9] Henghui Ding, Chang Liu, Suchen Wang, and Xudong Jiang. Vision-language transformer and query generation for referring segmentation. In ICCV, pages 16321-16330, 2021." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 458, + 287, + 512 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 458, + 287, + 512 + ], + "spans": [ + { + "bbox": [ + 48, + 458, + 287, + 512 + ], + "type": "text", + "content": "[10] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 514, + 287, + 556 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 514, + 287, + 556 + ], + "spans": [ + { + "bbox": [ + 48, + 514, + 287, + 556 + ], + "type": "text", + "content": "[11] Qi Fan, Lei Ke, Wenjie Pei, Chi-Keung Tang, and Yu-Wing Tai. Commonality-parsing network across shape and appearance for partially supervised instance segmentation. In ECCV, pages 379–396. Springer, 2020." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 558, + 287, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 558, + 287, + 600 + ], + "spans": [ + { + "bbox": [ + 48, + 558, + 287, + 600 + ], + "type": "text", + "content": "[12] Guang Feng, Zhiwei Hu, Lihe Zhang, and Huchuan Lu. Encoder fusion network with co-attention embedding for referring image segmentation. In CVPR, pages 15506–15515, 2021." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 602, + 286, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 602, + 286, + 635 + ], + "spans": [ + { + "bbox": [ + 48, + 602, + 286, + 635 + ], + "type": "text", + "content": "[13] Peng Gao, Minghang Zheng, Xiaogang Wang, Jifeng Dai, and Hongsheng Li. Fast convergence of detr with spatially modulated co-attention. In ICCV, pages 3621–3630, 2021." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 636, + 286, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 636, + 286, + 657 + ], + "spans": [ + { + "bbox": [ + 48, + 636, + 286, + 657 + ], + "type": "text", + "content": "[14] Kaiming He, Georgia Gkioxari, Piotr Dólar, and Ross Girshick. Mask r-cnn. In ICCV, pages 2961-2969, 2017." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 658, + 286, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 286, + 689 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 286, + 689 + ], + "type": "text", + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep Residual Learning for Image Recognition. In CVPR, 2016." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 691, + 286, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 286, + 713 + ], + "type": "text", + "content": "[16] Richang Hong, Daqing Liu, Xiaoyu Mo, Xiangnan He, and Hanwang Zhang. Learning to Compose and Reason with" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 34, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 105 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 105 + ], + "type": "text", + "content": "Language Tree Structures for Visual Grounding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 107, + 545, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 107, + 545, + 140 + ], + "spans": [ + { + "bbox": [ + 308, + 107, + 545, + 140 + ], + "type": "text", + "content": "[17] Ronghang Hu, Piotr Dólar, Kaiming He, Trevor Darrell, and Ross Girshick. Learning to segment every thing. In CVPR, pages 4233-4241, 2018." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 141, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 141, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 141, + 545, + 184 + ], + "type": "text", + "content": "[18] Ronghang Hu, Marcus Rohrbach, Jacob Andreas, Trevor Darrell, and Kate Saenko. Modeling Relationships in Referential Expressions with Compositional Modular Networks. In CVPR, 2017." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 186, + 545, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 186, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 308, + 186, + 545, + 228 + ], + "type": "text", + "content": "[19] Zhiwei Hu, Guang Feng, Jiayu Sun, Lihe Zhang, and Huchuan Lu. Bi-directional relationship inferring network for referring image segmentation. In CVPR, pages 4424-4433, 2020." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 230, + 545, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 230, + 545, + 274 + ], + "spans": [ + { + "bbox": [ + 308, + 230, + 545, + 274 + ], + "type": "text", + "content": "[20] Shaofei Huang, Tianrui Hui, Si Liu, Guanbin Li, Yunchao Wei, Jizhong Han, Luoqi Liu, and Bo Li. Referring image segmentation via cross-modal progressive comprehension. In CVPR, pages 10488-10497, 2020." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 276, + 545, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 276, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 308, + 276, + 545, + 319 + ], + "type": "text", + "content": "[21] Shaofei Huang, Tianrui Hui, Si Liu, Guanbin Li, Yunchao Wei, Jizhong Han, Luoqi Liu, and Bo Li. Referring image segmentation via cross-modal progressive comprehension. In CVPR, pages 10488-10497, 2020." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 320, + 545, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 320, + 545, + 364 + ], + "spans": [ + { + "bbox": [ + 308, + 320, + 545, + 364 + ], + "type": "text", + "content": "[22] Tianrui Hui, Si Liu, Shaofei Huang, Guanbin Li, Sansi Yu, Faxi Zhang, and Jizhong Han. Linguistic structure guided context modeling for referring image segmentation. In ECCV, pages 59-75. Springer, 2020." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 365, + 545, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 365, + 545, + 398 + ], + "spans": [ + { + "bbox": [ + 308, + 365, + 545, + 398 + ], + "type": "text", + "content": "[23] Ya Jing, Tao Kong, Wei Wang, Liang Wang, Lei Li, and Tie-niu Tan. Locate then segment: A strong pipeline for referring image segmentation. In CVPR, pages 9858-9867, 2021." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 399, + 545, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 399, + 545, + 442 + ], + "spans": [ + { + "bbox": [ + 308, + 399, + 545, + 442 + ], + "type": "text", + "content": "[24] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. MDETR-Modulated Detection for End-to-End Multi-Modal Understanding. In ICCV, 2021." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 445, + 545, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 445, + 545, + 487 + ], + "spans": [ + { + "bbox": [ + 308, + 445, + 545, + 487 + ], + "type": "text", + "content": "[25] Namyup Kim, Dongwon Kim, Cuiling Lan, Wenjun Zeng, and Suha Kwak. Restr: Convolution-free referring image segmentation using transformers. In CVPR, pages 18145-18154, 2022." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 489, + 545, + 531 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 489, + 545, + 531 + ], + "spans": [ + { + "bbox": [ + 308, + 489, + 545, + 531 + ], + "type": "text", + "content": "[26] Weicheng Kuo, Anelia Angelova, Jitendra Malik, and Tsung-Yi Lin. Shapemask: Learning to segment novel objects by refining shape priors. In ICCV, pages 9207-9216, 2019." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 534, + 545, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 534, + 545, + 566 + ], + "spans": [ + { + "bbox": [ + 308, + 534, + 545, + 566 + ], + "type": "text", + "content": "[27] Muchen Li and Leonid Sigal. Referring transformer: A one-step approach to multi-task visual grounding. NeurIPS, 34:19652-19664, 2021." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 567, + 545, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 567, + 545, + 610 + ], + "spans": [ + { + "bbox": [ + 308, + 567, + 545, + 610 + ], + "type": "text", + "content": "[28] Yue Liao, Si Liu, Guanbin Li, Fei Wang, Yanjie Chen, Chen Qian, and Bo Li. A Real-time Cross-modality Correlation Filtering Method for Referring Expression Comprehension. In CVPR, 2020." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 613, + 545, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 613, + 545, + 644 + ], + "spans": [ + { + "bbox": [ + 308, + 613, + 545, + 644 + ], + "type": "text", + "content": "[29] Daqing Liu, Hanwang Zhang, Feng Wu, and Zheng-Jun Zha. Learning to Assemble Neural Module Tree Networks for Visual Grounding. In ICCV, 2019." + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 308, + 647, + 545, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 647, + 545, + 667 + ], + "spans": [ + { + "bbox": [ + 308, + 647, + 545, + 667 + ], + "type": "text", + "content": "[30] Ilya Loshchilov and Frank Hutter. Fixing Weight Decay Regularization in Adam. 2018." + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "text", + "content": "[31] Gen Luo, Yiyi Zhou, Rongrong Ji, Xiaoshuai Sun, Jinsong Su, Chia-Wen Lin, and Qi Tian. Cascade grouped attention network for referring expression segmentation. In ACM MM, pages 1274-1282, 2020." + } + ] + } + ], + "index": 33 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "3029" + } + ] + } + ], + "index": 35 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "text", + "content": "[32] Gen Luo, Yiyi Zhou, Xiaoshuai Sun, Liujuan Cao, Chenglin Wu, Cheng Deng, and Rongrong Ji. Multi-task Collaborative Network for Joint Referring Expression Comprehension and Segmentation. In CVPR, 2020." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 118, + 286, + 159 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 118, + 286, + 159 + ], + "spans": [ + { + "bbox": [ + 48, + 118, + 286, + 159 + ], + "type": "text", + "content": "[33] Junhua Mao, Jonathan Huang, Alexander Toshev, Oana Camburu, Alan L Yuille, and Kevin Murphy. Generation and Comprehension of Unambiguous Object Descriptions. In CVPR, 2016." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 161, + 286, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 161, + 286, + 183 + ], + "spans": [ + { + "bbox": [ + 48, + 161, + 286, + 183 + ], + "type": "text", + "content": "[34] Tsvetomila Mihaylova and André FT Martins. Scheduled sampling for transformers. ACL 2019, page 351, 2019." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 184, + 286, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 184, + 286, + 205 + ], + "spans": [ + { + "bbox": [ + 48, + 184, + 286, + 205 + ], + "type": "text", + "content": "[35] Pedro O O Pinheiro, Ronan Collobert, and Piotr Dólar. Learning to segment object candidates. NeurIPS, 28, 2015." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 206, + 286, + 249 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 206, + 286, + 249 + ], + "spans": [ + { + "bbox": [ + 48, + 206, + 286, + 249 + ], + "type": "text", + "content": "[36] Mengxue Qu, Yu Wu, Wu Liu, Qiqi Gong, Xiaodan Liang, Olga Russakovsky, Yao Zhao, and Yunchao Wei. Siri: A simple selective retraining mechanism for transformer-based visual grounding. In ECCV, pages 546-562. Springer, 2022." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 250, + 286, + 271 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 250, + 286, + 271 + ], + "spans": [ + { + "bbox": [ + 48, + 250, + 286, + 271 + ], + "type": "text", + "content": "[37] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767, 2018." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 272, + 286, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 272, + 286, + 304 + ], + "spans": [ + { + "bbox": [ + 48, + 272, + 286, + 304 + ], + "type": "text", + "content": "[38] Arka Sadhu, Kan Chen, and Ram Nevatia. Zero-Shot Grounding of Objects from Natural Language Queries. In ICCV, 2019." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 304, + 286, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 304, + 286, + 338 + ], + "spans": [ + { + "bbox": [ + 48, + 304, + 286, + 338 + ], + "type": "text", + "content": "[39] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is All You Need. In NeuruIPS, 2017." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 338, + 286, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 338, + 286, + 381 + ], + "spans": [ + { + "bbox": [ + 48, + 338, + 286, + 381 + ], + "type": "text", + "content": "[40] Liwei Wang, Yin Li, Jing Huang, and Svetlana Lazebnik. Learning Two-Branch Neural Networks for Image-Text Matching Tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 383, + 286, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 383, + 286, + 426 + ], + "spans": [ + { + "bbox": [ + 48, + 383, + 286, + 426 + ], + "type": "text", + "content": "[41] Peng Wang, Qi Wu, Jiewei Cao, Chunhua Shen, Lianli Gao, and Anton van den Hengel. Neighbourhood Watch: Referring Expression Comprehension via Language-Guided Graph Attention Aetworks. In CVPR, 2019." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 426, + 286, + 468 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 426, + 286, + 468 + ], + "spans": [ + { + "bbox": [ + 48, + 426, + 286, + 468 + ], + "type": "text", + "content": "[42] Xuehui Wang, Kai Zhao, Ruixin Zhang, Shouhong Ding, Yan Wang, and Wei Shen. Contrastmask: Contrastive learning to segment every thing. In CVPR, pages 11604-11613, 2022." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 471, + 286, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 471, + 286, + 502 + ], + "spans": [ + { + "bbox": [ + 48, + 471, + 286, + 502 + ], + "type": "text", + "content": "[43] Sibei Yang, Guanbin Li, and Yizhou Yu. Dynamic Graph Attention for Referring Expression Comprehension. In ICCV, 2019." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 504, + 286, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 504, + 286, + 536 + ], + "spans": [ + { + "bbox": [ + 48, + 504, + 286, + 536 + ], + "type": "text", + "content": "[44] Sibei Yang, Meng Xia, Guanbin Li, Hong-Yu Zhou, and Yizhou Yu. Bottom-up shift and reasoning for referring image segmentation. In CVPR, pages 11266-11275, 2021." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 537, + 286, + 568 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 537, + 286, + 568 + ], + "spans": [ + { + "bbox": [ + 48, + 537, + 286, + 568 + ], + "type": "text", + "content": "[45] Zhengyuan Yang, Tianlang Chen, Liwei Wang, and Jiebo Luo. Improving One-Stage Visual Grounding by Recursive Sub-Query Construction. In ECCV, 2020." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 570, + 286, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 570, + 286, + 602 + ], + "spans": [ + { + "bbox": [ + 48, + 570, + 286, + 602 + ], + "type": "text", + "content": "[46] Zhengyuan Yang, Boqing Gong, Liwei Wang, Wenbing Huang, Dong Yu, and Jiebo Luo. A Fast and Accurate One-Stage Approach to Visual Grounding. In ICCV, 2019." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 603, + 286, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 603, + 286, + 635 + ], + "spans": [ + { + "bbox": [ + 48, + 603, + 286, + 635 + ], + "type": "text", + "content": "[47] Linwei Ye, Mrigank Rochan, Zhi Liu, and Yang Wang. Cross-modal self-attention network for referring image segmentation. In CVPR, pages 10502–10511, 2019." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 637, + 286, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 637, + 286, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 637, + 286, + 668 + ], + "type": "text", + "content": "[48] Fisher Yu, Dequan Wang, Evan Shelhamer, and Trevor Darrell. Deep layer aggregation. In CVPR, pages 2403-2412, 2018." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 48, + 670, + 286, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 286, + 712 + ], + "type": "text", + "content": "[49] Licheng Yu, Zhe Lin, Xiaohui Shen, Jimei Yang, Xin Lu, Mohit Bansal, and Tamara L Berg. MAttNet: Modular Attention Network for Referring Expression Comprehension. In CVPR, 2018." + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 72, + 545, + 329 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 307, + 72, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 72, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 545, + 106 + ], + "type": "text", + "content": "[50] Licheng Yu, Patrick Poirson, Shan Yang, Alexander C Berg, and Tamara L Berg. Modeling Context in Referring Expressions. In ECCV, 2016." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 107, + 545, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 545, + 161 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 545, + 161 + ], + "type": "text", + "content": "[51] Xunlin Zhan, Yangxin Wu, Xiao Dong, Yunchao Wei, Minlong Lu, Yichi Zhang, Hang Xu, and Xiaodan Liang. Product1m: Towards weakly supervised instance-level product retrieval via cross-modal pretraining. In ICCV, pages 11782-11791, 2021." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 163, + 545, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 163, + 545, + 195 + ], + "spans": [ + { + "bbox": [ + 307, + 163, + 545, + 195 + ], + "type": "text", + "content": "[52] Hanwang Zhang, Yulei Niu, and Shih-Fu Chang. Grounding Referring Expressions in Images by Variational Context. In CVPR, 2018." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 197, + 545, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 197, + 545, + 239 + ], + "spans": [ + { + "bbox": [ + 307, + 197, + 545, + 239 + ], + "type": "text", + "content": "[53] Yanzhao Zhou, Xin Wang, Jianbin Jiao, Trevor Darrell, and Fisher Yu. Learning saliency propagation for semi-supervised instance segmentation. In CVPR, pages 10307-10316, 2020." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 241, + 545, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 241, + 545, + 285 + ], + "spans": [ + { + "bbox": [ + 307, + 241, + 545, + 285 + ], + "type": "text", + "content": "[54] Chaoyang Zhu, Yiyi Zhou, Yunhang Shen, Gen Luo, Xingjia Pan, Mingbao Lin, Chao Chen, Liujuan Cao, Xiaoshuai Sun, and Rongrong Ji. Seqtr: A simple yet universal network for visual grounding. In ECCV, pages 598-615. Springer, 2022." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 286, + 545, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 286, + 545, + 329 + ], + "spans": [ + { + "bbox": [ + 307, + 286, + 545, + 329 + ], + "type": "text", + "content": "[55] Bohan Zhuang, Qi Wu, Chunhua Shen, Ian Reid, and Anton Van Den Hengel. Parallel Attention: A Unified Framework for Visual Object Discovery Through Dialogs and Queries. In CVPR, 2018." + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "3030" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning To Zoom and Unzoom/87db51f5-d1a6-44d6-b2f5-c2462a285975_content_list.json b/2023/Learning To Zoom and Unzoom/87db51f5-d1a6-44d6-b2f5-c2462a285975_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..3b82e3b97d1d2f17ddff778cc82c9a527271a955 --- /dev/null +++ b/2023/Learning To Zoom and Unzoom/87db51f5-d1a6-44d6-b2f5-c2462a285975_content_list.json @@ -0,0 +1,1587 @@ +[ + { + "type": "text", + "text": "Learning to Zoom and Unzoom", + "text_level": 1, + "bbox": [ + 323, + 130, + 645, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Chittesh Thavamani $^{1}$ Mengtian Li $^{†1}$ Francesco Ferroni $^{‡2}$ Deva Ramanan $^{1}$ $^{1}$ Carnegie Mellon University $^{2}$ Argo AI", + "bbox": [ + 143, + 179, + 823, + 217 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "tchittesh@gmail.org mengtial@alumni.cmu.edu ffferroni@nvidia.com deva@cs.cmu.edu", + "bbox": [ + 101, + 219, + 862, + 233 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 268, + 313, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Many perception systems in mobile computing, autonomous navigation, and AR/VR face strict compute constraints that are particularly challenging for high-resolution input images. Previous works propose nonuniform downsamplers that \"learn to zoom\" on salient image regions, reducing compute while retaining task-relevant image information. However, for tasks with spatial labels (such as 2D/3D object detection and semantic segmentation), such distortions may harm performance. In this work (LZU), we \"learn to zoom\" in on the input image, compute spatial features, and then \"unzoom\" to revert any deformations. To enable efficient and differentiable unzooming, we approximate the zooming warp with a piecewise bilinear mapping that is invertible. LZU can be applied to any task with 2D spatial input and any model with 2D spatial features, and we demonstrate this versatility by evaluating on a variety of tasks and datasets: object detection on Argoverse-HD, semantic segmentation on Cityscapes, and monocular 3D object detection on nuScenes. Interestingly, we observe boosts in performance even when high-resolution sensor data is unavailable, implying that LZU can be used to \"learn to up-sample\" as well. Code and additional visuals are available at https://tchittesh.github.io/lzu/.", + "bbox": [ + 75, + 300, + 473, + 648 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 675, + 209, + 690 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In many applications, the performance of perception systems is bottlenecked by strict inference-time constraints. This can be due to limited compute (as in mobile computing), a need for strong real-time performance (as in autonomous vehicles), or both (as in augmented/virtual reality). These constraints are particularly crippling for settings with high-resolution sensor data. Even with optimizations like model compression [4] and quantization [23], it is common practice to downsample inputs during inference.", + "bbox": [ + 75, + 700, + 470, + 835 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "However, running inference at a lower resolution undeniably destroys information. While some information loss is", + "bbox": [ + 76, + 835, + 470, + 867 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/0d4321ec9f2a11b55f36bb967f50e5af3fac3dba4d66c2f23fb72028dba75558.jpg", + "image_caption": [ + "Figure 1. LZU is characterized by \"zooming\" the input image, computing spatial features, then \"unzooming\" to revert spatial deformations. LZU can be applied to any task and model that makes use of internal 2D features to process 2D inputs. We show visual examples of output tasks including 2D detection, semantic segmentation, and 3D detection from RGB images." + ], + "image_footnote": [], + "bbox": [ + 501, + 267, + 893, + 431 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "unavoidable, the usual solution of uniform downsampling assumes that each pixel is equally informative towards the task at hand. To rectify this assumption, Recasens et al. [20] propose Learning to Zoom (LZ), a nonuniform downsampler that samples more densely at salient (task-relevant) image regions. They demonstrate superior performance relative to uniform downsampling on human gaze estimation and fine-grained image classification. However, this formulation warps the input image and thus requires labels to be invariant to such deformations.", + "bbox": [ + 496, + 551, + 893, + 703 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Adapting LZ downsampling to tasks with spatial labels is trickier, but has been accomplished in followup works for semantic segmentation (LDS [11]) and 2D object detection (FOVEA [22]). LDS [11] does not unzoom during learning, and so defines losses in the warped space. This necessitates additional regularization that may not apply to non-pixel-dense tasks like detection. FOVEA [22] does unzoom bounding boxes for 2D detection, but uses a special purpose solution that avoids computing an inverse, making it inapplicable to pixel-dense tasks like semantic segmentation. Despite these otherwise elegant solutions, there doesn't seem to be a general task-agnostic solution for intelligent downsampling.", + "bbox": [ + 496, + 704, + 895, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "†Now at Waymo.", + "bbox": [ + 94, + 875, + 189, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "$\\ddagger$ Now at Nvidia.", + "bbox": [ + 96, + 887, + 184, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "5086", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Our primary contribution is a general framework in which we zoom in on an input image, process the zoomed image, and then unzoom the output back with an inverse warp. Learning to Zoom and Unzoom (LZU) can be applied to any network that uses 2D spatial features to process 2D spatial inputs (Figure 1) with no adjustments to the network or loss. To unzoom, we approximate the zooming warp with a piecewise bilinear mapping. This allows efficient and differentiable computation of the forward and inverse warps.", + "bbox": [ + 75, + 90, + 472, + 226 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To demonstrate the generality of LZU, we demonstrate performance a variety of tasks: object detection with RetinaNet [17] on Argoverse-HD [14], semantic segmentation with PSPNet [29] on Cityscapes [7], and monocular 3D detection with FCOS3D [26] on nuScenes [2]. In our experiments, to maintain favorable accuracy-latency tradeoffs, we use cheap sources of saliency (as in [22]) when determining where to zoom. On each task, LZU increases performance over uniform downsampling and prior works with minimal additional latency.", + "bbox": [ + 75, + 227, + 472, + 378 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Interestingly, for both 2D and 3D object detection, we also see performance boosts even when processing low resolution input data. While prior works focus on performance improvements via intelligent downsampling [20, 22], our results show that LZU can also improve performance by intelligently upsampling (suggesting that current networks struggle to remain scale invariant for small objects, a well-known observation in the detection community [18]).", + "bbox": [ + 75, + 378, + 472, + 500 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 76, + 511, + 218, + 526 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We split related work into two sections. The first discusses the broad class of methods aiming to improve efficiency by paying \"attention\" to specific image regions. The second delves into works like LZU that accomplish this by differentiably resampling the input image.", + "bbox": [ + 75, + 536, + 470, + 613 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Spatial Attentional Processing", + "text_level": 1, + "bbox": [ + 76, + 619, + 344, + 636 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "By design, convolutional neural networks pay equal \"attention\" (perform the same computations) to all regions of the image. In many cases, this is suboptimal, and much work has gone into developing attentional methods that resolve this inefficiency.", + "bbox": [ + 75, + 643, + 470, + 718 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "One such method is Dynamic Convolutions [24], which uses sparse convolutions to selectively compute outputs at only the salient regions. Similarly, gated convolutions are used in [12,28]. Notably, these methods implement \"hard\" attention in that the saliency is binary, and non-salient regions are ignored completely.", + "bbox": [ + 75, + 719, + 470, + 810 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Deformable Convolutions [8, 30] provides a softer implementation of spatial attention by learning per pixel offsets when applying convolutions, allowing each output pixel to attend adaptively to pixels in the input image. SegBlocks [25] also provides a softer attention mechanism by splitting the image into blocks and training a lightweight reinforcement", + "bbox": [ + 75, + 810, + 470, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "learning policy to determine whether each block should be processed at a high or low resolution. This is akin to our method, which also has variable resolution, albeit in a more continuous manner. Our method is also generalizable to tasks in which it's infeasible to \"stitch\" together outputs from different blocks of the image (e.g. in detection where an object can span multiple blocks).", + "bbox": [ + 496, + 90, + 890, + 196 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Spatial Attention via Differentiable Image Resampling", + "text_level": 1, + "bbox": [ + 498, + 215, + 890, + 247 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Spatial Transformer Networks [10] introduces a differentiable method to resample an image. They originally propose this to invert changes in appearance due to viewpoint, thereby enforcing better pose invariance.", + "bbox": [ + 496, + 257, + 893, + 316 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Learning to Zoom (LZ) [20] later adapts this resampling operation to \"zoom\" on salient image regions, acting as a spatial attention mechanism. Their key contribution is a transformation parameterized by a saliency map such that regions with higher saliency are more densely sampled. However, this deforms the image, requiring the task to have non-spatial labels.", + "bbox": [ + 496, + 320, + 893, + 425 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Followup works [11, 19, 22] adapt LZ downsampling to detection and semantic segmentation. For object detection, FOVEA [22] exploits the fact that image resampling is implemented via an inverse mapping to map predicted bounding boxes back into the original image space. This allows all processing to be done in the downsampled space and the final bounding box regression loss to be computed in the original space. However, when there are intermediate losses, as is the case with two-stage detectors containing region proposal networks (RPNs) [21], this requires more complex modifications to the usual delta loss formulation, due to the irreversibility of the inverse mapping. For semantic segmentation, Jin et al. [11] apply LZ downsampling to both the input image and the ground truth and computes the loss in the downsampled space. This is elegant and model-agnostic but leads to misalignment between the training objective and the desired evaluation metric. In the extreme case, the model learns degenerate warps that sample \"easy\" parts of the image to reduce the training loss. To address this, they introduce additional regularization on the down sampler. Independently, [19] handcraft an energy minimization formulation to sample more densely at semantic boundaries.", + "bbox": [ + 496, + 429, + 895, + 762 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In terms of warping and unwarping, the closest approach to ours is Dense Transformer Networks [13], which also inverts deformations introduced by nonuniform resampling. However, their warping formulation is not saliency-based, which makes it hard to work with spatial or temporal priors and also makes it time-consuming to produce the warping parameters. Additionally, they only show results for semantic segmentation, whereas we show that our formulation generalizes across spatial vision tasks.", + "bbox": [ + 496, + 763, + 893, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "5087", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/d3c2ba3fa9c27c6deae0c6b935c6558ad4b7eef275a4a7f725280bcea1f88a2c.jpg", + "image_caption": [ + "Figure 2. Illustration of $\\mathcal{T}_{\\mathrm{LZ}}$ [20]. Suppose we have a saliency map $\\mathbf{S} \\in \\mathbb{R}^{h \\times w}$ (visualized in the background) and want a warped image of size $H' \\times W'$ . (1) We start with a uniform grid of sample locations $\\operatorname{Grid}(h, w)$ . (2) Grid points are \"attracted\" to nearby areas with high saliency. (3) Applying this \"force\" yields $\\mathcal{T}_{\\mathrm{LZ}}[\\operatorname{Grid}(h, w)]$ . (4) Bilinear upsampling yields $\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}[\\operatorname{Grid}(H', W')]$ ." + ], + "image_footnote": [], + "bbox": [ + 78, + 88, + 176, + 162 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/2663d6d1da4c033e121bd2a7b894434a5c43309751354e9e9c7bfa8144be11c2.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 178, + 88, + 272, + 162 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/aef3d25fe9bd980e6c54f28bf8238ce7776a059e8291a9ad32ed76f3288700df.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 274, + 88, + 370, + 162 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/60db0fbde99c6927564346eff99f54e20d7c3d2c46ff3b4ccc3f66975bbda0e8.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 372, + 88, + 468, + 162 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/1401171ef7546974ed6522b2810fdd0e6cb6000c5d23a19a0235c6d79c742c82.jpg", + "image_caption": [ + "$\\mathcal{T}_{\\mathrm{LZ}}[\\mathrm{Grid}(h,w)]$" + ], + "image_footnote": [], + "bbox": [ + 78, + 289, + 174, + 362 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/94c219a72a40cb71ab5abe6e3ba9102ee409c5c7dfa0eb69057b6f912c4d3283.jpg", + "image_caption": [ + "$\\mathcal{T}_{\\mathrm{LZ,ac}}[\\mathrm{Grid}(h,w)]$" + ], + "image_footnote": [], + "bbox": [ + 176, + 289, + 272, + 363 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/da8bdb39fa4e1383368d946beed461d3bd5fdbae4a01b00f2dd6c59ba5e04bcc.jpg", + "image_caption": [ + "$\\mathcal{T}_{\\mathrm{LZ,sep}}[\\mathrm{Grid}(h,w)]$", + "Figure 3. Examples of the anti-cropping (ac) and separable (sep) variants of $\\mathcal{T}_{\\mathrm{LZ}}$ from [22]." + ], + "image_footnote": [], + "bbox": [ + 274, + 289, + 370, + 363 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/f268d02466bb9a9abe4ddbb9e1201fac731fdfd21933136da19d2b78616b511c.jpg", + "image_caption": [ + "$\\mathcal{T}_{\\mathrm{LZ,sep,ac}}[\\mathrm{Grid}(h,w)]$" + ], + "image_footnote": [], + "bbox": [ + 372, + 289, + 468, + 363 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Background", + "text_level": 1, + "bbox": [ + 76, + 438, + 205, + 455 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Since our method is a generalization of previous works [11, 20, 22], we include this section as a condensed explanation of prerequisite formulations critical to understanding LZU.", + "bbox": [ + 76, + 465, + 468, + 525 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Image Resampling", + "text_level": 1, + "bbox": [ + 76, + 537, + 256, + 554 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Suppose we want to resample an input image $\\mathbf{I}(\\mathbf{x})$ to produce an output image $\\mathbf{I}'(\\mathbf{x})$ , both indexed by spatial coordinates $\\mathbf{x} \\in [0,1]^2$ . Resampling is typically implemented via an inverse map $\\mathcal{T} : [0,1]^2 \\to [0,1]^2$ from output to input coordinates [1]. For each output coordinate, the inverse map computes the source location from which to \"steal\" the pixel value, i.e. $\\mathbf{I}'(\\mathbf{x}) = \\mathbf{I}(\\mathcal{T}(\\mathbf{x}))$ . In practice, we are often given a discretized input image $\\mathbf{I} \\in \\mathbb{R}^{H \\times W \\times C}$ and are interested in computing a discretized output $\\mathbf{I}' \\in \\mathbb{R}^{H' \\times W' \\times C}$ . To do so, we compute $\\mathbf{I}'(\\mathbf{x})$ at grid points $\\mathbf{x} \\in \\mathrm{Grid}(H',W')$ , where $\\mathrm{Grid}(H,W) := \\mathrm{Grid}(H) \\times \\mathrm{Grid}(W)$ and $\\mathrm{Grid}(D) := \\{\\frac{d-1}{D-1} : d \\in [D]\\}$ . However, $\\mathcal{T}(\\mathbf{x})$ may return non-integer pixel locations at which the exact value of $\\mathbf{I}$ is unknown. In such cases, we use bilinear interpolation to compute $\\mathbf{I}(\\mathcal{T}(\\mathbf{x}))$ . As proven in [10], such image resampling is differentiable with respect to $\\mathcal{T}$ and $\\mathbf{I}$ .", + "bbox": [ + 76, + 561, + 470, + 803 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Saliency-Guided Downsampling", + "text_level": 1, + "bbox": [ + 76, + 816, + 359, + 832 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "When using nonuniform downsampling for information retention, it is useful to parameterize $\\mathcal{T}$ with a saliency map $\\mathbf{S}(\\mathbf{x})$ representing the desired sample rate at each spatial location $\\mathbf{x} \\in [0,1]^2$ [20]. Recasens et al. [20] go on", + "bbox": [ + 76, + 839, + 468, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "to approximate this behavior by having each sample coordinate $\\mathcal{T}(\\mathbf{x})$ be \"attracted\" to nearby areas $\\mathbf{x}'$ with high saliency $\\mathbf{S}(\\mathbf{x}')$ downweighted according to a distance kernel $k(\\mathbf{x},\\mathbf{x}')$ , as illustrated in Figure 2. Concretely, $\\mathcal{T}_{\\mathrm{LZ}}(\\mathbf{x}) = (\\mathcal{T}_{\\mathrm{LZ},x}(\\mathbf{x}),\\mathcal{T}_{\\mathrm{LZ},y}(\\mathbf{x}))$ , where", + "bbox": [ + 496, + 90, + 893, + 167 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {T} _ {\\mathrm {L Z}, x} (\\mathbf {x}) = \\frac {\\int_ {\\mathbf {x} ^ {\\prime}} \\mathbf {S} \\left(\\mathbf {x} ^ {\\prime}\\right) k \\left(\\mathbf {x} , \\mathbf {x} ^ {\\prime}\\right) \\mathbf {x} _ {x} ^ {\\prime} d \\mathbf {x} ^ {\\prime}}{\\int_ {\\mathbf {x} ^ {\\prime}} \\mathbf {S} \\left(\\mathbf {x} ^ {\\prime}\\right) k \\left(\\mathbf {x} , \\mathbf {x} ^ {\\prime}\\right) d \\mathbf {x} ^ {\\prime}}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 568, + 178, + 892, + 215 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {T} _ {\\mathrm {L Z}, y} (\\mathbf {x}) = \\frac {\\int_ {\\mathbf {x} ^ {\\prime}} \\mathbf {S} \\left(\\mathbf {x} ^ {\\prime}\\right) k \\left(\\mathbf {x} , \\mathbf {x} ^ {\\prime}\\right) \\mathbf {x} _ {y} ^ {\\prime} d \\mathbf {x} ^ {\\prime}}{\\int_ {\\mathbf {x} ^ {\\prime}} \\mathbf {S} \\left(\\mathbf {x} ^ {\\prime}\\right) k \\left(\\mathbf {x} , \\mathbf {x} ^ {\\prime}\\right) d \\mathbf {x} ^ {\\prime}}. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 568, + 228, + 892, + 265 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "[22] proposes anti-cropping and separable variants of this down sampler. The anti-cropping variant $\\mathcal{T}_{\\mathrm{LZ,ac}}$ prevents the resampling operation from cropping the image. The separable variant marginalizes the saliency map $\\mathbf{S}(\\mathbf{x})$ into two 1D saliency maps $\\mathbf{S}_x(x)$ and $\\mathbf{S}_y(y)$ , and replaces the kernel $k(\\mathbf{x},\\mathbf{x}')$ with a two 1D kernels $k_{x}$ and $k_{y}$ (although generally $k_{x} = k_{y}$ ). Then, $\\mathcal{T}_{\\mathrm{LZ,sep}}(\\mathbf{x}) = (\\mathcal{T}_{\\mathrm{LZ,sep,x}}(\\mathbf{x}_x),\\mathcal{T}_{\\mathrm{LZ,sep,y}}(\\mathbf{x}_y))$ where", + "bbox": [ + 496, + 270, + 893, + 393 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {T} _ {\\mathrm {L Z}, \\operatorname {s e p}, \\mathrm {x}} (x) = \\frac {\\int_ {x ^ {\\prime}} \\mathbf {S} _ {x} \\left(x ^ {\\prime}\\right) k _ {x} \\left(x , x ^ {\\prime}\\right) x ^ {\\prime} d x ^ {\\prime}}{\\int_ {x ^ {\\prime}} \\mathbf {S} _ {x} \\left(x ^ {\\prime}\\right) k _ {x} \\left(x , x ^ {\\prime}\\right) d x ^ {\\prime}}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 553, + 402, + 892, + 440 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {T} _ {\\mathrm {L Z}, \\operatorname {s e p}, \\mathrm {y}} (y) = \\frac {\\int_ {y ^ {\\prime}} \\mathbf {S} _ {y} \\left(y ^ {\\prime}\\right) k _ {y} \\left(y , y ^ {\\prime}\\right) y ^ {\\prime} d y ^ {\\prime}}{\\int_ {y ^ {\\prime}} \\mathbf {S} _ {y} \\left(y ^ {\\prime}\\right) k _ {y} \\left(y , y ^ {\\prime}\\right) d y ^ {\\prime}}. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 557, + 453, + 892, + 492 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "This preserves axis-alignment of rectangles, which is crucial to object detection where bounding boxes are specified via corners. We refer to the above method and all variants as LZ downsamplers, after the pioneering work \"Learning to Zoom\" [20]. Examples of each variant are shown in Figure 3.", + "bbox": [ + 496, + 498, + 892, + 574 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Method", + "text_level": 1, + "bbox": [ + 500, + 588, + 589, + 603 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We begin by discussing our general technique for warp inversion. Then, we discuss the LZU framework and how we apply warp inversion to efficiently \"unzoom\".", + "bbox": [ + 496, + 614, + 890, + 659 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.1. Efficient, Differentiable Warp Inversion", + "text_level": 1, + "bbox": [ + 500, + 669, + 841, + 686 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Suppose we have a continuous map $\\mathcal{T}:[0,1]^2\\to [0,1]^2$ . Our primary technical innovation is an efficient and differentiable approximation of $\\mathcal{T}^{-1}$ , even in cases where $\\mathcal{T}$ has no closed-form inverse.", + "bbox": [ + 496, + 691, + 893, + 752 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Since $\\mathcal{T}$ is potentially difficult to invert, we first approximate it as $\\widetilde{\\mathcal{T}}$ , a piecewise tiling of simpler invertible transforms (illustrated in Figure 4). Formally,", + "bbox": [ + 496, + 753, + 893, + 799 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\widetilde {\\mathcal {T}} = \\bigcup_ { \\begin{array}{c} i \\in [ h - 1 ] \\\\ j \\in [ w - 1 ] \\end{array} } \\widetilde {\\mathcal {T}} _ {i j}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 635, + 810, + 892, + 856 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where the $ij$ -th tile $\\widetilde{\\mathcal{T}}_{ij}$ is any bijective map from the rectangle formed by corners $R_{ij} = \\left\\{\\frac{i - 1}{h - 1}, \\frac{i}{h - 1}\\right\\} \\times \\left\\{\\frac{j - 1}{w - 1}, \\frac{j}{w - 1}\\right\\}$ .", + "bbox": [ + 496, + 869, + 893, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "5088", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/0f32c9995683986f6bed0f1aa3c535863f48d0d66131cbfef006fcf1661c8f9d.jpg", + "image_caption": [ + "Figure 4. Given a warp $\\mathcal{T}$ , we construct an approximation $\\widetilde{\\mathcal{T}}$ designed for efficient inversion. As illustrated, $\\widetilde{\\mathcal{T}}$ is a piecewise tiling of simpler invertible maps. This allows us to approximate the inverse $\\widetilde{\\mathcal{T}}^{-1}$ , even when $\\mathcal{T}^{-1}$ lacks a closed form." + ], + "image_footnote": [], + "bbox": [ + 81, + 90, + 269, + 239 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/331b65f09bdf77c2416e5735c13fa508c452a4050bbd7baeff5eb329e6522d9c.jpg", + "image_caption": [ + "Figure 5. Inverting each axis of a separable warp. LZU first evaluates the forward warp $\\mathcal{T}_{\\mathrm{LZ,sep,x}}$ (solid blue arrows) at a uniform grid of target locations (blue points). The resulting source locations are shown as red points. LZU then approximates the warp in between these samples via a linear transform; this piecewise linear map is $\\widetilde{\\mathcal{T}}_{\\mathrm{LZ,sep,x}}$ (dotted blue arrows). To evaluate the inverse $\\widetilde{\\mathcal{T}}_{\\mathrm{LZ,sep,x}}^{-1}$ (dotted green arrows), we must determine for each green point which red points it falls between and invert the corresponding linear transform. An example is shown in the top-right." + ], + "image_footnote": [], + "bbox": [ + 281, + 90, + 467, + 239 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "to quadrilateral $\\mathcal{T}[R_{ij}]$ . For our purposes, we choose bilinear maps as our tile function, although homographies could work just as well. Then, so long as $\\widetilde{\\mathcal{T}}$ is injective (if each of the tiles $\\widetilde{\\mathcal{T}}_{ij}$ is nondegenerate and no two tiles overlap), we are guaranteed a well-defined left inverse $\\widetilde{\\mathcal{T}}^{-1}:[0,1]^2\\rightarrow [0,1]^2$ given by", + "bbox": [ + 75, + 335, + 472, + 429 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\widetilde {\\mathcal {T}} ^ {- 1} (\\mathbf {x}) = \\left\\{ \\begin{array}{l l} \\widetilde {\\mathcal {T}} _ {i j} ^ {- 1} (\\mathbf {x}) & \\text {i f} \\mathbf {x} \\in \\operatorname {R a n g e} \\left(\\widetilde {\\mathcal {T}} _ {i j}\\right) \\\\ 0 & \\text {e l s e} \\end{array} . \\right. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 125, + 438, + 468, + 479 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Equation 6 is efficient to compute, since determining if $\\mathbf{x} \\in \\mathrm{Range}(\\widetilde{\\mathcal{T}}_{ij})$ simply involves checking if $\\mathbf{x}$ is in the quadrilateral $\\mathcal{T}[R_{ij}]$ and computing the inverse $\\widetilde{\\mathcal{T}}_{ij}^{-1}$ of a bilinear map amounts to solving a quadratic equation [27]. This efficiency is crucial to maintaining favorable accuracy-latency tradeoffs. $\\widetilde{\\mathcal{T}}^{-1}$ is guaranteed to be differentiable with respect to $\\mathcal{T}$ , since for each $\\mathbf{x} \\in \\widetilde{\\mathcal{T}}[R(i,j)]$ , the inverse bilinear map can be written as a quadratic function of the four corners of tile $ij$ (see Appendix A.1 for exact expression). This allows gradients to flow back into $\\mathcal{T}$ , letting us learn the parameters of the warp.", + "bbox": [ + 75, + 489, + 470, + 656 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In the case of LZ warps, $\\mathcal{T}_{\\mathrm{LZ}}$ has no closed form inverse to the best of our knowledge. Because $\\mathcal{T}_{\\mathrm{LZ}}[\\mathrm{Grid}(h,w)]$ has no foldovers [20], $\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}$ must be injective, implying its inverse $\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}^{-1}$ is well-defined.", + "bbox": [ + 76, + 657, + 468, + 719 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "When applying an LZ warp, saliency can be learned (with trainable parameters) or unlearned (with fixed parameters), and fixed (invariant across frames) or adaptive (changes every frame). Adaptive saliency maps require efficient warp inversion since a different warp must be applied on every input. Learned saliency maps require differentiability. We note that fixed unlearned saliency maps do not technically require efficiency or differentiability, and most of our current results show that such saliency maps are already quite effective, outperforming prior work. We posit that LZU would shine even more in the learned adaptive setting, where it could make use of temporal priming for top-down saliency.", + "bbox": [ + 75, + 719, + 472, + 901 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/532dc8fd38427997352242a5fce768150e2b8f24a0c102d8d54a7bb501326aff.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 504, + 90, + 888, + 132 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/f5a411681f9ae66c53ebecb48f023d0ea5afbcc583f9ddb536153b752d196717.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 503, + 137, + 691, + 189 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/101ce787fa5bfac70cd8eff06aadbb3e420000b961467c8537fe6035289d75f0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 694, + 138, + 887, + 189 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Learning to Zoom and Unzoom", + "text_level": 1, + "bbox": [ + 498, + 353, + 777, + 369 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In the Learning to Zoom and Unzoom (LZU) framework, we use existing LZ downsamplers (see Section 3.2) to \"zoom\" in on the input image, compute spatial features, and then use our warp inversion formulation to \"unzoom\" and revert any deformations in the feature map, as shown in Figure 1. This framework is applicable to all tasks with 2D spatial input and all models with some intermediate 2D spatial representation.", + "bbox": [ + 496, + 376, + 893, + 496 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Notice that a poorly approximated inverse warp $\\widetilde{\\mathcal{T}}^{-1}$ would lead to misaligned features and a drop in performance. As a result, we use the approximate forward warp $\\widetilde{\\mathcal{T}}$ instead of the true forward warp $\\mathcal{T}$ , so that the composition of forward and inverse warps is actually the identity function. See Appendix A.3 for a discussion of the associated tradeoff.", + "bbox": [ + 496, + 496, + 893, + 588 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To maintain favorable accuracy-latency tradeoffs, we make several optimizations to our forward and inverse warps. As done in previous works [11, 20, 22], for the forward warp or \"zoom,\" instead of computing $\\mathcal{T}_{\\mathrm{LZ}}[\\mathrm{Grid}(H',W')]$ , we compute $\\mathcal{T}_{\\mathrm{LZ}}[\\mathrm{Grid}(h,w)]$ for smaller $h\\ll H^{\\prime}$ and $w\\ll W^{\\prime}$ and bilinearly upsample this to get $\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}[\\mathrm{Grid}(H',W')]$ . This also reduces the complexity of computing the inverse, by reducing the number of cases in our piecewise bilinear map from $H^{\\prime}\\cdot W^{\\prime}$ to $h\\cdot w$ .", + "bbox": [ + 496, + 588, + 893, + 724 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We explore efficient implementations of both separable and nonseparable warp inversion, but we find experimentally that nonseparable warps perform no better than separable warps for a strictly higher latency cost, so we use separable warps for our experiments. Details for efficiently inverting nonseparable warps are given in Appendix A.2. For separable warps $\\mathcal{T}_{\\mathrm{LZ,sep}}$ , we invert each axis separately and take the Cartesian Product:", + "bbox": [ + 496, + 724, + 893, + 845 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\widetilde {\\mathcal {T}} _ {\\mathrm {L Z}, \\operatorname {s e p}} ^ {- 1} [ \\operatorname {G r i d} \\left(H ^ {\\prime}, W ^ {\\prime}\\right) ] = \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 503, + 853, + 893, + 875 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\widetilde {\\mathcal {T}} _ {\\mathrm {L Z , s e p , x}} ^ {- 1} [ \\operatorname {G r i d} (H ^ {\\prime}) ] \\times \\widetilde {\\mathcal {T}} _ {\\mathrm {L Z , s e p , y}} ^ {- 1} [ \\operatorname {G r i d} (W ^ {\\prime}) ].\n$$\n", + "text_format": "latex", + "bbox": [ + 589, + 876, + 885, + 898 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "5089", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This further reduces our problem from inverting a piecewise bilinear map with $h \\cdot w$ pieces to inverting two piecewise linear maps with $h$ and $w$ pieces each. Figure 5 visualizes how to invert each axis.", + "bbox": [ + 75, + 90, + 468, + 150 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "When unwarping after feature pyramid networks (FPNs) [16], we may have to evaluate the inverse $\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}^{-1}$ at multiple resolutions $\\operatorname{Grid}(H', W')$ , $\\operatorname{Grid}(H'/2, W'/2)$ , etc. In practice, we evaluate $\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}^{-1}[\\operatorname{Grid}(H', W')]$ and then approximate the inverse at lower resolutions via bilinear downsampling. This is surprisingly effective (see Appendix A.3) and leads to no observable loss in performance.", + "bbox": [ + 75, + 151, + 470, + 258 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, as introduced in [22], we can also use a fixed warp to exploit dataset-wide spatial priors, such as how objects are concentrated around the horizon in many autonomous driving datasets. This allows us to cache forward and inverse warps, greatly reducing additional latency.", + "bbox": [ + 75, + 258, + 470, + 335 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 76, + 345, + 209, + 363 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "First, we compare LZU to naive uniform downsampling and previous works on the tasks of 2D object detection and semantic segmentation. We include ablations to evaluate the effectiveness of training techniques and explore the upsampling regime. Then, we evaluate LZU on monocular 3D object detection, a task which no previous works have applied \"zooming\" to. We perform all timing experiments with a batch size of 1 on a single RTX 2080 Ti GPU. Figure 6 contains qualitative results and analysis across all tasks. Full implementation details and hyperparameters are given in Appendix A.6.", + "bbox": [ + 75, + 371, + 472, + 537 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1.2D Object Detection", + "text_level": 1, + "bbox": [ + 76, + 544, + 269, + 561 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For 2D object detection, we evaluate LZU using RetinaNet [17] (with a ResNet-50 backbone [9] and FPN [16]) on Argoverse-HD [14], an object detection dataset for autonomous driving with high resolution $1920 \\times 1200$ videos. For our baselines, we compare to uniform downsampling and FOVEA [22], a previous work that applies LZ down-sampling to detection by unwarping bounding boxes. We keep the same hyperparameters and setup as in FOVEA [22]. Experiments are run at $0.25\\mathrm{x}$ , $0.5\\mathrm{x}$ , $0.75\\mathrm{x}$ , and $1\\mathrm{x}$ scales, to measure the accuracy-latency tradeoff.", + "bbox": [ + 75, + 568, + 470, + 718 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our LZU models \"unzoom\" the feature map at each level after the FPN [16]. We adopt the low-cost saliency generators introduced in [22] — a \"fixed\" saliency map exploiting dataset-wide spatial priors, and an \"adaptive\" saliency map exploiting temporal priors by zooming in on detections from the previous frame. When training the adaptive version, we simulate previous detections by jittering the ground truth for the first two epochs. For the last epoch, we jitter detections on the current frame to better simulate previous detections; we call this \" Cascaded\" saliency. To determine saliency hyperparameters, we run grid search at $0.5\\mathrm{x}$ scale on splits of the training set (details in Appendix A.4, A.6). We use", + "bbox": [ + 75, + 719, + 472, + 901 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/f94e72d3b413b540f677b638f6d42b2f84b04d5efb14addee407d755b04b17c9.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ScaleMethodAP\\( AP_{50} \\)\\( AP_{75} \\)\\( AP_S \\)\\( AP_M \\)\\( AP_L \\)Lat (ms)
0.25xUniform10.518.09.90.35.238.623.3
0.25xLZU, fixed12.422.611.21.07.139.223.6
0.25xLZU, adaptive12.322.811.31.46.638.026.4
0.5xUniform22.638.721.73.722.153.136.0
0.5xFOVEA [22]24.940.325.37.127.750.637.9
0.5xLZU, fixed25.242.124.85.526.751.836.4
0.5xLZU, adaptive25.343.024.66.125.952.639.3
0.5xLZU, adaptive w/o cascade sal.22.839.322.35.122.748.939.3
0.75xUniform29.548.429.69.132.455.162.9
0.75xLZU, fixed30.850.431.810.933.554.163.5
0.75xLZU, adaptive26.544.626.78.328.748.766.3
1xUniform31.951.533.111.435.954.598.3
1xLZU, fixed32.652.834.013.236.054.799.3
1xLZU, adaptive32.052.433.112.536.352.9102.0
", + "bbox": [ + 500, + 88, + 888, + 318 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/5844c099818354eeea8bf374406ccfa7a860922849b84f22d2099c1d049d8f82.jpg", + "table_caption": [ + "Table 1. 2D object detection results of RetinaNet [17] on Argoverse-HD [14]. Fixed LZU uses a dataset-wide spatial prior, and adaptive LZU uses a temporal prior based on previous frame detections. LZU consistently outperforms the uniform downsampling baseline and prior work across all scales, with additional latency less than $4\\mathrm{ms}$ . We hypothesize that the drop in $\\mathrm{AP}_L$ is because objects that are already large benefit less from zooming. Still, this drawback is offset by larger improvements on small and medium objects.", + "2D Object Detection" + ], + "table_footnote": [], + "table_body": "
Uniform ResamplingLZU Resampling
ToFromToFrom
0.25x0.5x0.75x1x0.25x0.5x0.75x1x
0.25x10.510.510.510.50.25x11.712.412.412.4
0.5x17.022.622.622.60.5x20.924.824.825.2
0.75x23.528.529.529.50.75x22.529.430.030.8
1x13.528.430.931.91x22.130.731.232.6
", + "bbox": [ + 504, + 465, + 888, + 575 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/2050e13061aa3e02713e47ac8714e935254c6591fd7e971d24503e3b847acb46.jpg", + "table_caption": [ + "Monocular 3D Object Detection" + ], + "table_footnote": [], + "table_body": "
Uniform ResamplingLZU Resampling
ToFromToFrom
0.25x0.5x0.75x1x0.25x0.5x0.75x1x
0.25x21.821.821.821.80.25x22.523.523.423.4
0.5x25.427.527.527.50.5x27.029.229.129.3
0.75x27.630.330.530.50.75x29.031.631.631.8
1x28.430.731.131.21x30.132.532.732.6
", + "bbox": [ + 504, + 592, + 888, + 704 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 2. 2D and 3D object detection results in the upsampling and downsampling regimes, using the \"Uniform\" and \"LZU, fixed\" models from Tables 1 and 5. LZU is surprisingly effective even in the upsampling regime! This demonstrates that simply allocating more pixels to small objects (without retaining extra information) can help performance, suggesting that detectors still struggle with scale invariance for small objects.", + "bbox": [ + 498, + 713, + 893, + 811 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "a learning rate of 0.01 and keep all other training settings identical to the baseline. Latency is measured by timing only the additional operations (the \"zoom\" and \"unzoom\") and adding it to the baseline. This is done to mitigate the impact", + "bbox": [ + 498, + 839, + 893, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "5090", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/0c3fb8ed0214d7550fecb4cca87cd3aef69dac04469aace635c8d12a866bd53c.jpg", + "image_caption": [ + "Figure 6. Examples of the success and failure cases of LZU. Rows A and E show examples where zooming in on the horizon helps the detector pick up smaller objects. On the other hand, sometimes zooming leads to false negatives, such as the black car in Row B and objects near the edge in Row F. For segmentation, LZU consistently improves quality near the center of the image. The last column shows the saliency map used in each case and the resulting spatial magnification ratios. For the Argoverse-HD [14] dataset, the magnification ratio at the center is nearly $2x$ , meaning the \"zoom\" is preserving nearly all information in that region, at the cost of information at the corners." + ], + "image_footnote": [], + "bbox": [ + 106, + 87, + 864, + 699 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "of variance in the latency of the backbone and detector head.", + "bbox": [ + 76, + 805, + 470, + 821 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Results are given in Table 1. We outperform both uniform downsampling and FOVEA in all but one case, while incurring an additional latency of less than $4\\mathrm{ms}$ . The one exception is adaptive LZU at $0.75\\mathrm{x}$ , which is evidence that our adaptive saliency hyperparameters, chosen at $0.5\\mathrm{x}$ scale,", + "bbox": [ + 75, + 824, + 472, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "struggle to generalize to other resolutions. We also confirm that using cascaded saliency to train adaptive LZU is crucial. Although adaptive LZU outperforms fixed LZU at $0.5\\mathrm{x}$ scale, plotting the accuracy-latency curves (Figure 7) reveals that fixed LZU is Pareto optimal at all points.", + "bbox": [ + 498, + 805, + 893, + 881 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Finally, we explore how LZU performs in the upsampling", + "bbox": [ + 517, + 885, + 890, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "5091", + "bbox": [ + 482, + 944, + 513, + 957 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/47cb61a9d3d86735578e14f66ae6a48ca867f9ba4b53a8bf5b61ba7127333c33.jpg", + "image_caption": [ + "Figure 7. Plotting the accuracy-latency/FLOPs tradeoffs reveals the Pareto optimal methods for each task. Fixed LZU is Pareto optimal for both 2D and 3D object detection, outperforming uniform downsampling and FOVEA [22]. For semantic segmentation, we use FLOPs in lieu of latency to enable fair comparisons (ES [19] only reports FLOPS and LDS [11] has an unoptimized implementation). Although LDS boasts large improvements in raw accuracy at each scale, it also incurs a greater cost due to its expensive saliency generator. Overall, the Pareto frontier for segmentation is very competitive, with ES dominating at $64 \\times 64$ , LDS at $128 \\times 128$ , and LZU at $256 \\times 256$ ." + ], + "image_footnote": [], + "bbox": [ + 80, + 89, + 352, + 277 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/0209830e71d049cda9a760af9c79bd5fa6aee3eaed96e415f6df3409450cd518.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 359, + 90, + 617, + 277 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/38cbbd30381daaa68bc2788989d9aca075b735c0a62290ff7fd07211d3409075.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 624, + 90, + 890, + 277 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/dc5aaa8da0e8207bf3b193f2531d945abdb0502e61cd20962abba1851594fcb0.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Crop SizeMethodmIOUIOULatency (ms)
roadswalkbuild.wallfencepoletlightsignveg.terr.skypersonridercartruckbustrainmbikebike
64Uniform26.493.935.668.63.52.90.50.00.172.521.176.026.80.957.18.916.98.00.08.215.5
64LZU, fixed26.793.436.168.95.82.30.40.00.072.623.475.929.41.256.715.010.24.10.011.716.9
128Uniform39.396.354.078.415.07.98.18.516.681.234.486.742.913.874.422.941.624.410.229.616.1
128LZU, fixed41.796.455.278.712.713.48.111.419.081.739.086.545.717.976.821.948.231.711.636.318.0
256Uniform53.697.564.084.720.019.022.134.841.687.041.991.259.333.784.139.262.957.927.749.119.1
256LZU, fixed55.197.767.084.924.424.421.335.242.987.044.590.761.535.785.740.867.952.829.353.421.2
512Uniform63.898.373.388.829.234.340.654.461.690.747.794.072.750.689.145.672.159.144.564.932.3
512LZU, fixed64.298.373.488.630.035.738.856.063.890.447.093.472.443.990.150.576.459.645.465.334.4
", + "bbox": [ + 86, + 375, + 883, + 537 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Full semantic segmentation results of PSPNet [29] on Cityscapes [7]. At each resolution, LZU outperforms uniform downsampling.", + "bbox": [ + 75, + 546, + 892, + 561 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "regime. We reuse the same models trained in our previous experiments, testing them with different pre-resampling resolutions. Results are shown in Table 2. In this regime, LZU consistently outperforms uniform downsampling, even though information retention is no longer a factor.", + "bbox": [ + 75, + 588, + 470, + 664 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.2. Semantic Segmentation", + "text_level": 1, + "bbox": [ + 76, + 679, + 294, + 695 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "For our semantic segmentation experiments, we compare to previous works ES [19] and LDS [11], so we adopt their setup. We test the PSPNet [29] model (with a ResNet-50 backbone [9] and FPN [16]) on Cityscapes [7]. Cityscapes is an urban scene dataset with high resolution $1024 \\times 2048$ images and 19 classes. We perform our experiments at several image scales $(64 \\times 64, 128 \\times 128, 256 \\times 256,$ and $512 \\times 512)$ , taken by resizing a centered square crop of the input image. Our simple baseline trains and tests PSPNet with uniform downsampling. To reduce overfitting, we allot 500 images from the official training set into a mini-validation split. We train our model on the remaining training images and evaluate at 10 equally spaced intervals on the mini-validation", + "bbox": [ + 75, + 704, + 470, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "split. We choose the best performing model and evaluate it on the official validation set.", + "bbox": [ + 498, + 588, + 892, + 618 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "For our LZU model, we unzoom spatial features after the FPN and use a fixed saliency map. Inspired by the idea of zooming on semantic boundaries [19], we generate our fixed saliency by averaging the ground truth semantic boundaries over the train set. Notably, our saliency hyperparameters are chosen qualitatively (for producing a reasonably strong warp) and tested one-shot.", + "bbox": [ + 496, + 623, + 893, + 729 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We report our full results in Table 3 and compare to previous works in Table 4. Since our baseline results are slightly different than reported in previous works [11, 19], we compare results using a percent change relative to the corresponding baseline. We find increased performance over the baseline at all scales, and at $256 \\times 256$ , we beat both previous works with only $2.3\\mathrm{ms}$ of additional latency. Plotting the accuracy-FLOPs tradeoff (Figure 7) reveals that the large improvements of LDS [11] at $64 \\times 64$ and $128 \\times 128$ input scales come at significant cost in FLOPs. In actuality, ES [19] is Pareto optimal at $64 \\times 64$ and $128 \\times 128$ , LDS [11]", + "bbox": [ + 496, + 734, + 893, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "5092", + "bbox": [ + 482, + 944, + 514, + 957 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/b6b82b130ef520de939c6cda0932ba0ee6ae6c4ae038101d90043bdeb4b09872.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodDownsampled Resolution
64 × 64128 × 128256 × 256
Uniform (theirs)294054
Uniform (ours)26.439.353.6
ES [19]32 (+10.3%)43 (+7.5%)54 (+0.0%)
LDS [11]36 (+24.1%)47 (+17.5%)55 (+1.9%)
LZU, fixed26.7 (+1.1%)41.7 (+6.1%)55.1 (+2.9%)
", + "bbox": [ + 84, + 88, + 460, + 203 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/df181fbc45db70b82e65c1465e663b4138e68cda07a6650a3237be3e98100915.jpg", + "table_caption": [ + "Table 4. Semantic segmentation results of PSPNet [29] on Cityscapes [7], in mIOU. Due to differing implementation, the performance of our baseline varies from reported values, so we report relative improvements. At $256 \\times 256$ , we outperform prior works. At $64 \\times 64$ and $128 \\times 128$ , LZU performs worse than prior work, perhaps because \"unzooming\" features at such small scales is more destructive. We posit the performance losses from such aggressive downsampling factors (across all methods) may be too impractical for deployment, and so focus on the $256 \\times 256$ regime." + ], + "table_footnote": [], + "table_body": "
ScaleMethodNDSmAPmATEmASEmAOEmAVEmAAELat (ms)
0.25xUniform21.811.496.732.690.1125.019.854.7
0.25xLZU, fixed23.413.196.831.982.7129.420.055.1
0.5xUniform27.517.590.128.875.5131.617.858.1
0.5xLZU, fixed29.320.188.928.373.9130.616.758.5
0.75xUniform30.521.087.327.967.0132.817.559.2
0.75xLZU, fixed31.822.483.827.567.2134.615.959.7
1xUniform31.222.484.227.470.9129.617.488.7
1xLZU, fixed32.624.884.627.568.2131.618.389.4
", + "bbox": [ + 81, + 356, + 465, + 482 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. 3D object detection results of FCOS3D [26] on nuScenes [2]. Higher NDS and mAP is better, and lower is better on all other metrics. Intuitively, size is an important cue for depth, and image deformations would stifle this signal. Suprisingly, this is not the case. LZU improves upon the uniform downsampling baseline at all scales with less than 1ms of additional latency. Notably, LZU at $0.75\\mathrm{x}$ scale even outperforms uniform downsampling at 1x.", + "bbox": [ + 75, + 494, + 468, + 592 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "at $128 \\times 128$ , and LZU at $256 \\times 256$ . We hypothesize that further improvements might be possible using an adaptive, learned formulation for saliency.", + "bbox": [ + 75, + 619, + 468, + 666 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.3. Monocular 3D Object Detection", + "text_level": 1, + "bbox": [ + 76, + 679, + 357, + 695 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Finally, we evaluate LZU on monocular 3D object detection. To the best of our knowledge, no previous work has applied LZ downsampling to this task. The closest existing solution, FOVEA [22], cannot be extended to 3D object detection, because 3D bounding boxes are amodal and cannot be unwarped in the same manner as 2D bounding boxes. For our base model, we use FCOS3D [26], a fully convolutional model, with a ResNet-50 backbone [9] and FPN [16]. For our dataset, we use nuScenes [2], an autonomous driving dataset with multi-view $1600 \\times 900$ RGB images for 1000 scenes and 3D bounding box annotations for 10 object classes. As is standard practice, we use the nuScenes Detection Score (NDS) metric, which is a combination of", + "bbox": [ + 75, + 703, + 470, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "the usual mAP and measures of translation error (mATE), scale error (mASE), orientation error (mAOE), velocity error (mAVE), and attribute error (mAAE). We run experiments at $0.25\\mathrm{x}$ , $0.5\\mathrm{x}$ , $0.75\\mathrm{x}$ , and $1\\mathrm{x}$ scales and test against a uniform downsampling baseline. We train for 12 epochs with a batch size of 16 with default parameters as in MMDetection3D [5].", + "bbox": [ + 496, + 90, + 893, + 181 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "For our LZU model, again we unzoom post-FPN features and use a fixed saliency map. Inspired by FOVEA [22], our fixed saliency is generated by using kernel density estimation on the set of projected bounding boxes in the image space. We reuse the same saliency hyperparameters from 2D detection. All other training settings are identical to the baseline.", + "bbox": [ + 496, + 181, + 893, + 285 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Results are given in Table 5. LZU performs consistently better than uniform downsampling, with less than 1ms of additional latency. Specifically, LZU improves mAP and the aggregate metric NDS, with mixed results on mATE, mASE, mAOE, mAVE, and mAAE. Since the latter five metrics are computed on only true positives, this demonstrates that LZU increases overall recall, while maintaining about equal performance on true positives. Plotting the accuracy-latency curves (Figure 7) shows that LZU is Pareto optimal. We also repeat the same upsampling experiments as performed in 2D object detection. Results, shown in Table 2, reaffirm the viability of LZU in the upsampling regime.", + "bbox": [ + 496, + 287, + 892, + 468 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 482, + 617, + 497 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We propose LZU, a simple attentional framework consisting of \"zooming\" in on the input image, computing spatial features, and \"unzooming\" to invert any deformations. To unzoom, we approximate the forward warp as a piecewise bilinear mapping and invert each piece. LZU is highly general and can be applied to any task with 2D spatial input and any model with 2D spatial features. We demonstrate the versatility of LZU empirically on a variety of tasks and datasets, including monocular 3D detection which has never been done before. We also show that LZU may even be used when high-resolution sensor data is unavailable. For future work, we can consider alternatives to the \"unzoom\" formulation that are perhaps less destructive than simple resampling of features.", + "bbox": [ + 496, + 507, + 893, + 717 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Broader impact. Our work focuses on increasing the efficiency and accuracy of flagship vision tasks (detection, segmentation, 3D understanding) with high-resolution imagery. We share the same potential harms of the underlying tasks, but our approach may increase privacy concerns as identifiable information may be easier to decode at higher resolutions (e.g., facial identities or license plates). Because our approach is agnostic to the underlying model, it is reproducible with minimal changes to existing codebases.", + "bbox": [ + 496, + 719, + 893, + 854 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements: This work was supported by the CMU Argo AI Center for Autonomous Vehicle Research.", + "bbox": [ + 498, + 869, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "5093", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Thaddeus Beier and Shawn Neely. Feature-based image metamorphosis. ACM SIGGRAPH computer graphics, 26(2):35-42, 1992. 3", + "[2] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In CVPR, 2020. 2, 8, 14", + "[3] Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin. MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155, 2019. 13", + "[4] Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282, 2017. 1", + "[5] MMDetection3D Contributors. MMDetection3D: Open-MMLab next-generation platform for general 3D object detection. https://github.com/open-mmlab/mmdetection3d, 2020.8, 13, 14", + "[6] MMSegmentation Contributors. MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https://github.com/open-mmlab/mmsegmentation, 2020.13", + "[7] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 2, 7, 8, 12, 13", + "[8] Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764-773, 2017. 2", + "[9] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5, 7, 8", + "[10] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. Advances in neural information processing systems, 28, 2015. 2, 3", + "[11] Chen Jin, Ryutaro Tanno, Thomy Mertzanidou, Eleftheria Panagiotaki, and Daniel C Alexander. Learning to downsample for segmentation of ultra-high resolution images. arXiv preprint arXiv:2109.11071, 2021. 1, 2, 3, 4, 7, 8", + "[12] Shu Kong and Charless Fowlkes. Pixel-wise attentional gating for scene parsing. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1024-1033. IEEE, 2019. 2", + "[13] Jun Li, Yongjun Chen, Lei Cai, Ian Davidson, and Shuiwang Ji. Dense transformer networks. arXiv preprint arXiv:1705.08881, 2017. 2" + ], + "bbox": [ + 78, + 116, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] Mengtian Li, Yu-Xiong Wang, and Deva Ramanan. Towards streaming perception. In European Conference on Computer Vision, pages 473-488. Springer, 2020. 2, 5, 6, 11, 13", + "[15] Mengtian Li, Ersin Yumer, and Deva Ramanan. Budgeted training: Rethinking deep neural network training under resource constraints. arXiv preprint arXiv:1905.04753, 2019. 13", + "[16] Tsung-Yi Lin, Piotr Dólar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2117-2125, 2017. 5, 7, 8, 12", + "[17] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dólar. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980-2988, 2017. 2, 5", + "[18] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 2", + "[19] Dmitrii Marin, Zijian He, Peter Vajda, Priyam Chatterjee, Sam Tsai, Fei Yang, and Yuri Boykov. Efficient segmentation: Learning downsampling near semantic boundaries. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2131-2141, 2019. 2, 7, 8", + "[20] Adria Recasens, Petr Kellnhofer, Simon Stent, Wojciech Matusik, and Antonio Torralba. Learning to zoom: a saliency-based sampling layer for neural networks. In Proceedings of the European Conference on Computer Vision (ECCV), pages 51-66, 2018. 1, 2, 3, 4", + "[21] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 2", + "[22] Chittesh Thavamani, Mengtian Li, Nicolas Cebron, and Deva Ramanan. Fovea: Foveated image magnification for autonomous navigation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15539-15548, 2021. 1, 2, 3, 4, 5, 7, 8, 13", + "[23] Vincent Vanhoucke, Andrew Senior, and Mark Z. Mao. Improving the speed of neural networks on cpus. In Deep Learning and Unsupervised Feature Learning Workshop, NIPS 2011, 2011. 1", + "[24] Thomas Verelst and Tinne Tuytelaars. Dynamic convolutions: Exploiting spatial sparsity for faster inference. In Proceedings of the iee/cvf conference on computer vision and pattern recognition, pages 2320-2329, 2020. 2", + "[25] Thomas Verelst and Tinne Tuytelaars. Segblocks: Block-based dynamic resolution networks for real-time segmentation. arXiv preprint arXiv:2011.12025, 2020. 2", + "[26] Tai Wang, Xinge Zhu, Jiangmiao Pang, and Dahua Lin. Fcos3d: Fully convolutional one-stage monocular 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 913-922, 2021. 2, 8", + "[27] George Wolberg. Digital image warping, volume 10662. IEEE computer society press Los Alamitos, CA, 1990. 4, 11" + ], + "bbox": [ + 501, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "5094", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[28] Zhenda Xie, Zheng Zhang, Xizhou Zhu, Gao Huang, and Stephen Lin. Spatially adaptive inference with stochastic feature sampling and interpolation. In European conference on computer vision, pages 531-548. Springer, 2020. 2", + "[29] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881-2890, 2017. 2, 7, 8", + "[30] Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. Deformable convnets v2: More deformable, better results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9308-9316, 2019. 2" + ], + "bbox": [ + 78, + 90, + 468, + 260 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "5095", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning To Zoom and Unzoom/87db51f5-d1a6-44d6-b2f5-c2462a285975_model.json b/2023/Learning To Zoom and Unzoom/87db51f5-d1a6-44d6-b2f5-c2462a285975_model.json new file mode 100644 index 0000000000000000000000000000000000000000..4f69e9a8592bfd2d554c9719441e2ba193457108 --- /dev/null +++ b/2023/Learning To Zoom and Unzoom/87db51f5-d1a6-44d6-b2f5-c2462a285975_model.json @@ -0,0 +1,1969 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.325, + 0.131, + 0.646, + 0.154 + ], + "angle": 0, + "content": "Learning to Zoom and Unzoom" + }, + { + "type": "text", + "bbox": [ + 0.145, + 0.18, + 0.824, + 0.218 + ], + "angle": 0, + "content": "Chittesh Thavamani\\(^{1}\\) Mengtian Li\\(^{†1}\\) Francesco Ferroni\\(^{‡2}\\) Deva Ramanan\\(^{1}\\) \n\\(^{1}\\)Carnegie Mellon University \\(^{2}\\)Argo AI" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.22, + 0.863, + 0.234 + ], + "angle": 0, + "content": "tchittesh@gmail.org mengtial@alumni.cmu.edu ffferroni@nvidia.com deva@cs.cmu.edu" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.269, + 0.314, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.301, + 0.474, + 0.649 + ], + "angle": 0, + "content": "Many perception systems in mobile computing, autonomous navigation, and AR/VR face strict compute constraints that are particularly challenging for high-resolution input images. Previous works propose nonuniform downsamplers that \"learn to zoom\" on salient image regions, reducing compute while retaining task-relevant image information. However, for tasks with spatial labels (such as 2D/3D object detection and semantic segmentation), such distortions may harm performance. In this work (LZU), we \"learn to zoom\" in on the input image, compute spatial features, and then \"unzoom\" to revert any deformations. To enable efficient and differentiable unzooming, we approximate the zooming warp with a piecewise bilinear mapping that is invertible. LZU can be applied to any task with 2D spatial input and any model with 2D spatial features, and we demonstrate this versatility by evaluating on a variety of tasks and datasets: object detection on Argoverse-HD, semantic segmentation on Cityscapes, and monocular 3D object detection on nuScenes. Interestingly, we observe boosts in performance even when high-resolution sensor data is unavailable, implying that LZU can be used to \"learn to up-sample\" as well. Code and additional visuals are available at https://tchittesh.github.io/lzu/." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.676, + 0.21, + 0.691 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.701, + 0.471, + 0.837 + ], + "angle": 0, + "content": "In many applications, the performance of perception systems is bottlenecked by strict inference-time constraints. This can be due to limited compute (as in mobile computing), a need for strong real-time performance (as in autonomous vehicles), or both (as in augmented/virtual reality). These constraints are particularly crippling for settings with high-resolution sensor data. Even with optimizations like model compression [4] and quantization [23], it is common practice to downsample inputs during inference." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.837, + 0.471, + 0.868 + ], + "angle": 0, + "content": "However, running inference at a lower resolution undeniably destroys information. While some information loss is" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.268, + 0.895, + 0.433 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.443, + 0.896, + 0.527 + ], + "angle": 0, + "content": "Figure 1. LZU is characterized by \"zooming\" the input image, computing spatial features, then \"unzooming\" to revert spatial deformations. LZU can be applied to any task and model that makes use of internal 2D features to process 2D inputs. We show visual examples of output tasks including 2D detection, semantic segmentation, and 3D detection from RGB images." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.553, + 0.895, + 0.704 + ], + "angle": 0, + "content": "unavoidable, the usual solution of uniform downsampling assumes that each pixel is equally informative towards the task at hand. To rectify this assumption, Recasens et al. [20] propose Learning to Zoom (LZ), a nonuniform downsampler that samples more densely at salient (task-relevant) image regions. They demonstrate superior performance relative to uniform downsampling on human gaze estimation and fine-grained image classification. However, this formulation warps the input image and thus requires labels to be invariant to such deformations." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.896, + 0.902 + ], + "angle": 0, + "content": "Adapting LZ downsampling to tasks with spatial labels is trickier, but has been accomplished in followup works for semantic segmentation (LDS [11]) and 2D object detection (FOVEA [22]). LDS [11] does not unzoom during learning, and so defines losses in the warped space. This necessitates additional regularization that may not apply to non-pixel-dense tasks like detection. FOVEA [22] does unzoom bounding boxes for 2D detection, but uses a special purpose solution that avoids computing an inverse, making it inapplicable to pixel-dense tasks like semantic segmentation. Despite these otherwise elegant solutions, there doesn't seem to be a general task-agnostic solution for intelligent downsampling." + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.875, + 0.191, + 0.888 + ], + "angle": 0, + "content": "†Now at Waymo." + }, + { + "type": "page_footnote", + "bbox": [ + 0.097, + 0.888, + 0.186, + 0.899 + ], + "angle": 0, + "content": "\\(\\ddagger\\) Now at Nvidia." + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.875, + 0.191, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5086" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.227 + ], + "angle": 0, + "content": "Our primary contribution is a general framework in which we zoom in on an input image, process the zoomed image, and then unzoom the output back with an inverse warp. Learning to Zoom and Unzoom (LZU) can be applied to any network that uses 2D spatial features to process 2D spatial inputs (Figure 1) with no adjustments to the network or loss. To unzoom, we approximate the zooming warp with a piecewise bilinear mapping. This allows efficient and differentiable computation of the forward and inverse warps." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.228, + 0.473, + 0.379 + ], + "angle": 0, + "content": "To demonstrate the generality of LZU, we demonstrate performance a variety of tasks: object detection with RetinaNet [17] on Argoverse-HD [14], semantic segmentation with PSPNet [29] on Cityscapes [7], and monocular 3D detection with FCOS3D [26] on nuScenes [2]. In our experiments, to maintain favorable accuracy-latency tradeoffs, we use cheap sources of saliency (as in [22]) when determining where to zoom. On each task, LZU increases performance over uniform downsampling and prior works with minimal additional latency." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.379, + 0.473, + 0.501 + ], + "angle": 0, + "content": "Interestingly, for both 2D and 3D object detection, we also see performance boosts even when processing low resolution input data. While prior works focus on performance improvements via intelligent downsampling [20, 22], our results show that LZU can also improve performance by intelligently upsampling (suggesting that current networks struggle to remain scale invariant for small objects, a well-known observation in the detection community [18])." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.512, + 0.22, + 0.527 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.537, + 0.472, + 0.614 + ], + "angle": 0, + "content": "We split related work into two sections. The first discusses the broad class of methods aiming to improve efficiency by paying \"attention\" to specific image regions. The second delves into works like LZU that accomplish this by differentiably resampling the input image." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.621, + 0.345, + 0.637 + ], + "angle": 0, + "content": "2.1. Spatial Attentional Processing" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.645, + 0.472, + 0.719 + ], + "angle": 0, + "content": "By design, convolutional neural networks pay equal \"attention\" (perform the same computations) to all regions of the image. In many cases, this is suboptimal, and much work has gone into developing attentional methods that resolve this inefficiency." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.72, + 0.472, + 0.811 + ], + "angle": 0, + "content": "One such method is Dynamic Convolutions [24], which uses sparse convolutions to selectively compute outputs at only the salient regions. Similarly, gated convolutions are used in [12,28]. Notably, these methods implement \"hard\" attention in that the saliency is binary, and non-salient regions are ignored completely." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.472, + 0.902 + ], + "angle": 0, + "content": "Deformable Convolutions [8, 30] provides a softer implementation of spatial attention by learning per pixel offsets when applying convolutions, allowing each output pixel to attend adaptively to pixels in the input image. SegBlocks [25] also provides a softer attention mechanism by splitting the image into blocks and training a lightweight reinforcement" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.198 + ], + "angle": 0, + "content": "learning policy to determine whether each block should be processed at a high or low resolution. This is akin to our method, which also has variable resolution, albeit in a more continuous manner. Our method is also generalizable to tasks in which it's infeasible to \"stitch\" together outputs from different blocks of the image (e.g. in detection where an object can span multiple blocks)." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.216, + 0.892, + 0.248 + ], + "angle": 0, + "content": "2.2. Spatial Attention via Differentiable Image Resampling" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.258, + 0.895, + 0.318 + ], + "angle": 0, + "content": "Spatial Transformer Networks [10] introduces a differentiable method to resample an image. They originally propose this to invert changes in appearance due to viewpoint, thereby enforcing better pose invariance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.321, + 0.895, + 0.426 + ], + "angle": 0, + "content": "Learning to Zoom (LZ) [20] later adapts this resampling operation to \"zoom\" on salient image regions, acting as a spatial attention mechanism. Their key contribution is a transformation parameterized by a saliency map such that regions with higher saliency are more densely sampled. However, this deforms the image, requiring the task to have non-spatial labels." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.43, + 0.897, + 0.763 + ], + "angle": 0, + "content": "Followup works [11, 19, 22] adapt LZ downsampling to detection and semantic segmentation. For object detection, FOVEA [22] exploits the fact that image resampling is implemented via an inverse mapping to map predicted bounding boxes back into the original image space. This allows all processing to be done in the downsampled space and the final bounding box regression loss to be computed in the original space. However, when there are intermediate losses, as is the case with two-stage detectors containing region proposal networks (RPNs) [21], this requires more complex modifications to the usual delta loss formulation, due to the irreversibility of the inverse mapping. For semantic segmentation, Jin et al. [11] apply LZ downsampling to both the input image and the ground truth and computes the loss in the downsampled space. This is elegant and model-agnostic but leads to misalignment between the training objective and the desired evaluation metric. In the extreme case, the model learns degenerate warps that sample \"easy\" parts of the image to reduce the training loss. To address this, they introduce additional regularization on the down sampler. Independently, [19] handcraft an energy minimization formulation to sample more densely at semantic boundaries." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.895, + 0.903 + ], + "angle": 0, + "content": "In terms of warping and unwarping, the closest approach to ours is Dense Transformer Networks [13], which also inverts deformations introduced by nonuniform resampling. However, their warping formulation is not saliency-based, which makes it hard to work with spatial or temporal priors and also makes it time-consuming to produce the warping parameters. Additionally, they only show results for semantic segmentation, whereas we show that our formulation generalizes across spatial vision tasks." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5087" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.08, + 0.089, + 0.177, + 0.164 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.179, + 0.089, + 0.273, + 0.164 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.275, + 0.089, + 0.371, + 0.164 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.373, + 0.089, + 0.47, + 0.164 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.175, + 0.47, + 0.274 + ], + "angle": 0, + "content": "Figure 2. Illustration of \\(\\mathcal{T}_{\\mathrm{LZ}}\\) [20]. Suppose we have a saliency map \\(\\mathbf{S} \\in \\mathbb{R}^{h \\times w}\\) (visualized in the background) and want a warped image of size \\(H' \\times W'\\). (1) We start with a uniform grid of sample locations \\(\\operatorname{Grid}(h, w)\\). (2) Grid points are \"attracted\" to nearby areas with high saliency. (3) Applying this \"force\" yields \\(\\mathcal{T}_{\\mathrm{LZ}}[\\operatorname{Grid}(h, w)]\\). (4) Bilinear upsampling yields \\(\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}[\\operatorname{Grid}(H', W')]\\)." + }, + { + "type": "image", + "bbox": [ + 0.08, + 0.29, + 0.176, + 0.363 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.101, + 0.364, + 0.155, + 0.373 + ], + "angle": 0, + "content": "\\(\\mathcal{T}_{\\mathrm{LZ}}[\\mathrm{Grid}(h,w)]\\)" + }, + { + "type": "image", + "bbox": [ + 0.178, + 0.29, + 0.273, + 0.364 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.195, + 0.364, + 0.257, + 0.373 + ], + "angle": 0, + "content": "\\(\\mathcal{T}_{\\mathrm{LZ,ac}}[\\mathrm{Grid}(h,w)]\\)" + }, + { + "type": "image", + "bbox": [ + 0.275, + 0.29, + 0.371, + 0.364 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.293, + 0.364, + 0.354, + 0.373 + ], + "angle": 0, + "content": "\\(\\mathcal{T}_{\\mathrm{LZ,sep}}[\\mathrm{Grid}(h,w)]\\)" + }, + { + "type": "image", + "bbox": [ + 0.373, + 0.29, + 0.469, + 0.364 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.387, + 0.364, + 0.456, + 0.373 + ], + "angle": 0, + "content": "\\(\\mathcal{T}_{\\mathrm{LZ,sep,ac}}[\\mathrm{Grid}(h,w)]\\)" + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.385, + 0.47, + 0.412 + ], + "angle": 0, + "content": "Figure 3. Examples of the anti-cropping (ac) and separable (sep) variants of \\(\\mathcal{T}_{\\mathrm{LZ}}\\) from [22]." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.439, + 0.206, + 0.456 + ], + "angle": 0, + "content": "3. Background" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.466, + 0.47, + 0.526 + ], + "angle": 0, + "content": "Since our method is a generalization of previous works [11, 20, 22], we include this section as a condensed explanation of prerequisite formulations critical to understanding LZU." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.539, + 0.257, + 0.555 + ], + "angle": 0, + "content": "3.1. Image Resampling" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.563, + 0.471, + 0.804 + ], + "angle": 0, + "content": "Suppose we want to resample an input image \\(\\mathbf{I}(\\mathbf{x})\\) to produce an output image \\(\\mathbf{I}'(\\mathbf{x})\\), both indexed by spatial coordinates \\(\\mathbf{x} \\in [0,1]^2\\). Resampling is typically implemented via an inverse map \\(\\mathcal{T} : [0,1]^2 \\to [0,1]^2\\) from output to input coordinates [1]. For each output coordinate, the inverse map computes the source location from which to \"steal\" the pixel value, i.e. \\(\\mathbf{I}'(\\mathbf{x}) = \\mathbf{I}(\\mathcal{T}(\\mathbf{x}))\\). In practice, we are often given a discretized input image \\(\\mathbf{I} \\in \\mathbb{R}^{H \\times W \\times C}\\) and are interested in computing a discretized output \\(\\mathbf{I}' \\in \\mathbb{R}^{H' \\times W' \\times C}\\). To do so, we compute \\(\\mathbf{I}'(\\mathbf{x})\\) at grid points \\(\\mathbf{x} \\in \\mathrm{Grid}(H',W')\\), where \\(\\mathrm{Grid}(H,W) := \\mathrm{Grid}(H) \\times \\mathrm{Grid}(W)\\) and \\(\\mathrm{Grid}(D) := \\{\\frac{d-1}{D-1} : d \\in [D]\\}\\). However, \\(\\mathcal{T}(\\mathbf{x})\\) may return non-integer pixel locations at which the exact value of \\(\\mathbf{I}\\) is unknown. In such cases, we use bilinear interpolation to compute \\(\\mathbf{I}(\\mathcal{T}(\\mathbf{x}))\\). As proven in [10], such image resampling is differentiable with respect to \\(\\mathcal{T}\\) and \\(\\mathbf{I}\\)." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.817, + 0.36, + 0.833 + ], + "angle": 0, + "content": "3.2. Saliency-Guided Downsampling" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.84, + 0.47, + 0.902 + ], + "angle": 0, + "content": "When using nonuniform downsampling for information retention, it is useful to parameterize \\(\\mathcal{T}\\) with a saliency map \\(\\mathbf{S}(\\mathbf{x})\\) representing the desired sample rate at each spatial location \\(\\mathbf{x} \\in [0,1]^2\\) [20]. Recasens et al. [20] go on" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.894, + 0.169 + ], + "angle": 0, + "content": "to approximate this behavior by having each sample coordinate \\(\\mathcal{T}(\\mathbf{x})\\) be \"attracted\" to nearby areas \\(\\mathbf{x}'\\) with high saliency \\(\\mathbf{S}(\\mathbf{x}')\\) downweighted according to a distance kernel \\(k(\\mathbf{x},\\mathbf{x}')\\), as illustrated in Figure 2. Concretely, \\(\\mathcal{T}_{\\mathrm{LZ}}(\\mathbf{x}) = (\\mathcal{T}_{\\mathrm{LZ},x}(\\mathbf{x}),\\mathcal{T}_{\\mathrm{LZ},y}(\\mathbf{x}))\\), where" + }, + { + "type": "equation", + "bbox": [ + 0.57, + 0.179, + 0.893, + 0.216 + ], + "angle": 0, + "content": "\\[\n\\mathcal {T} _ {\\mathrm {L Z}, x} (\\mathbf {x}) = \\frac {\\int_ {\\mathbf {x} ^ {\\prime}} \\mathbf {S} \\left(\\mathbf {x} ^ {\\prime}\\right) k \\left(\\mathbf {x} , \\mathbf {x} ^ {\\prime}\\right) \\mathbf {x} _ {x} ^ {\\prime} d \\mathbf {x} ^ {\\prime}}{\\int_ {\\mathbf {x} ^ {\\prime}} \\mathbf {S} \\left(\\mathbf {x} ^ {\\prime}\\right) k \\left(\\mathbf {x} , \\mathbf {x} ^ {\\prime}\\right) d \\mathbf {x} ^ {\\prime}}, \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.57, + 0.229, + 0.893, + 0.266 + ], + "angle": 0, + "content": "\\[\n\\mathcal {T} _ {\\mathrm {L Z}, y} (\\mathbf {x}) = \\frac {\\int_ {\\mathbf {x} ^ {\\prime}} \\mathbf {S} \\left(\\mathbf {x} ^ {\\prime}\\right) k \\left(\\mathbf {x} , \\mathbf {x} ^ {\\prime}\\right) \\mathbf {x} _ {y} ^ {\\prime} d \\mathbf {x} ^ {\\prime}}{\\int_ {\\mathbf {x} ^ {\\prime}} \\mathbf {S} \\left(\\mathbf {x} ^ {\\prime}\\right) k \\left(\\mathbf {x} , \\mathbf {x} ^ {\\prime}\\right) d \\mathbf {x} ^ {\\prime}}. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.271, + 0.894, + 0.394 + ], + "angle": 0, + "content": "[22] proposes anti-cropping and separable variants of this down sampler. The anti-cropping variant \\(\\mathcal{T}_{\\mathrm{LZ,ac}}\\) prevents the resampling operation from cropping the image. The separable variant marginalizes the saliency map \\(\\mathbf{S}(\\mathbf{x})\\) into two 1D saliency maps \\(\\mathbf{S}_x(x)\\) and \\(\\mathbf{S}_y(y)\\), and replaces the kernel \\(k(\\mathbf{x},\\mathbf{x}')\\) with a two 1D kernels \\(k_{x}\\) and \\(k_{y}\\) (although generally \\(k_{x} = k_{y}\\)). Then, \\(\\mathcal{T}_{\\mathrm{LZ,sep}}(\\mathbf{x}) = (\\mathcal{T}_{\\mathrm{LZ,sep,x}}(\\mathbf{x}_x),\\mathcal{T}_{\\mathrm{LZ,sep,y}}(\\mathbf{x}_y))\\) where" + }, + { + "type": "equation", + "bbox": [ + 0.554, + 0.404, + 0.893, + 0.441 + ], + "angle": 0, + "content": "\\[\n\\mathcal {T} _ {\\mathrm {L Z}, \\operatorname {s e p}, \\mathrm {x}} (x) = \\frac {\\int_ {x ^ {\\prime}} \\mathbf {S} _ {x} \\left(x ^ {\\prime}\\right) k _ {x} \\left(x , x ^ {\\prime}\\right) x ^ {\\prime} d x ^ {\\prime}}{\\int_ {x ^ {\\prime}} \\mathbf {S} _ {x} \\left(x ^ {\\prime}\\right) k _ {x} \\left(x , x ^ {\\prime}\\right) d x ^ {\\prime}}, \\tag {3}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.558, + 0.454, + 0.893, + 0.493 + ], + "angle": 0, + "content": "\\[\n\\mathcal {T} _ {\\mathrm {L Z}, \\operatorname {s e p}, \\mathrm {y}} (y) = \\frac {\\int_ {y ^ {\\prime}} \\mathbf {S} _ {y} \\left(y ^ {\\prime}\\right) k _ {y} \\left(y , y ^ {\\prime}\\right) y ^ {\\prime} d y ^ {\\prime}}{\\int_ {y ^ {\\prime}} \\mathbf {S} _ {y} \\left(y ^ {\\prime}\\right) k _ {y} \\left(y , y ^ {\\prime}\\right) d y ^ {\\prime}}. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.499, + 0.893, + 0.575 + ], + "angle": 0, + "content": "This preserves axis-alignment of rectangles, which is crucial to object detection where bounding boxes are specified via corners. We refer to the above method and all variants as LZ downsamplers, after the pioneering work \"Learning to Zoom\" [20]. Examples of each variant are shown in Figure 3." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.589, + 0.59, + 0.604 + ], + "angle": 0, + "content": "4. Method" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.615, + 0.892, + 0.66 + ], + "angle": 0, + "content": "We begin by discussing our general technique for warp inversion. Then, we discuss the LZU framework and how we apply warp inversion to efficiently \"unzoom\"." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.67, + 0.842, + 0.687 + ], + "angle": 0, + "content": "4.1. Efficient, Differentiable Warp Inversion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.693, + 0.894, + 0.753 + ], + "angle": 0, + "content": "Suppose we have a continuous map \\(\\mathcal{T}:[0,1]^2\\to [0,1]^2\\). Our primary technical innovation is an efficient and differentiable approximation of \\(\\mathcal{T}^{-1}\\), even in cases where \\(\\mathcal{T}\\) has no closed-form inverse." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.755, + 0.894, + 0.8 + ], + "angle": 0, + "content": "Since \\(\\mathcal{T}\\) is potentially difficult to invert, we first approximate it as \\(\\widetilde{\\mathcal{T}}\\), a piecewise tiling of simpler invertible transforms (illustrated in Figure 4). Formally," + }, + { + "type": "equation", + "bbox": [ + 0.637, + 0.811, + 0.893, + 0.857 + ], + "angle": 0, + "content": "\\[\n\\widetilde {\\mathcal {T}} = \\bigcup_ { \\begin{array}{c} i \\in [ h - 1 ] \\\\ j \\in [ w - 1 ] \\end{array} } \\widetilde {\\mathcal {T}} _ {i j}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.87, + 0.894, + 0.904 + ], + "angle": 0, + "content": "where the \\( ij \\)-th tile \\( \\widetilde{\\mathcal{T}}_{ij} \\) is any bijective map from the rectangle formed by corners \\( R_{ij} = \\left\\{\\frac{i - 1}{h - 1}, \\frac{i}{h - 1}\\right\\} \\times \\left\\{\\frac{j - 1}{w - 1}, \\frac{j}{w - 1}\\right\\} \\)." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5088" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.083, + 0.092, + 0.27, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.282, + 0.091, + 0.468, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.253, + 0.47, + 0.31 + ], + "angle": 0, + "content": "Figure 4. Given a warp \\(\\mathcal{T}\\), we construct an approximation \\(\\widetilde{\\mathcal{T}}\\) designed for efficient inversion. As illustrated, \\(\\widetilde{\\mathcal{T}}\\) is a piecewise tiling of simpler invertible maps. This allows us to approximate the inverse \\(\\widetilde{\\mathcal{T}}^{-1}\\), even when \\(\\mathcal{T}^{-1}\\) lacks a closed form." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.337, + 0.473, + 0.43 + ], + "angle": 0, + "content": "to quadrilateral \\(\\mathcal{T}[R_{ij}]\\). For our purposes, we choose bilinear maps as our tile function, although homographies could work just as well. Then, so long as \\(\\widetilde{\\mathcal{T}}\\) is injective (if each of the tiles \\(\\widetilde{\\mathcal{T}}_{ij}\\) is nondegenerate and no two tiles overlap), we are guaranteed a well-defined left inverse \\(\\widetilde{\\mathcal{T}}^{-1}:[0,1]^2\\rightarrow [0,1]^2\\) given by" + }, + { + "type": "equation", + "bbox": [ + 0.126, + 0.439, + 0.47, + 0.48 + ], + "angle": 0, + "content": "\\[\n\\widetilde {\\mathcal {T}} ^ {- 1} (\\mathbf {x}) = \\left\\{ \\begin{array}{l l} \\widetilde {\\mathcal {T}} _ {i j} ^ {- 1} (\\mathbf {x}) & \\text {i f} \\mathbf {x} \\in \\operatorname {R a n g e} \\left(\\widetilde {\\mathcal {T}} _ {i j}\\right) \\\\ 0 & \\text {e l s e} \\end{array} . \\right. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.49, + 0.472, + 0.657 + ], + "angle": 0, + "content": "Equation 6 is efficient to compute, since determining if \\(\\mathbf{x} \\in \\mathrm{Range}(\\widetilde{\\mathcal{T}}_{ij})\\) simply involves checking if \\(\\mathbf{x}\\) is in the quadrilateral \\(\\mathcal{T}[R_{ij}]\\) and computing the inverse \\(\\widetilde{\\mathcal{T}}_{ij}^{-1}\\) of a bilinear map amounts to solving a quadratic equation [27]. This efficiency is crucial to maintaining favorable accuracy-latency tradeoffs. \\(\\widetilde{\\mathcal{T}}^{-1}\\) is guaranteed to be differentiable with respect to \\(\\mathcal{T}\\), since for each \\(\\mathbf{x} \\in \\widetilde{\\mathcal{T}}[R(i,j)]\\), the inverse bilinear map can be written as a quadratic function of the four corners of tile \\(ij\\) (see Appendix A.1 for exact expression). This allows gradients to flow back into \\(\\mathcal{T}\\), letting us learn the parameters of the warp." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.658, + 0.47, + 0.72 + ], + "angle": 0, + "content": "In the case of LZ warps, \\(\\mathcal{T}_{\\mathrm{LZ}}\\) has no closed form inverse to the best of our knowledge. Because \\(\\mathcal{T}_{\\mathrm{LZ}}[\\mathrm{Grid}(h,w)]\\) has no foldovers [20], \\(\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}\\) must be injective, implying its inverse \\(\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}^{-1}\\) is well-defined." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.72, + 0.473, + 0.902 + ], + "angle": 0, + "content": "When applying an LZ warp, saliency can be learned (with trainable parameters) or unlearned (with fixed parameters), and fixed (invariant across frames) or adaptive (changes every frame). Adaptive saliency maps require efficient warp inversion since a different warp must be applied on every input. Learned saliency maps require differentiability. We note that fixed unlearned saliency maps do not technically require efficiency or differentiability, and most of our current results show that such saliency maps are already quite effective, outperforming prior work. We posit that LZU would shine even more in the learned adaptive setting, where it could make use of temporal priming for top-down saliency." + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.091, + 0.89, + 0.133 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.138, + 0.692, + 0.19 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.696, + 0.139, + 0.888, + 0.19 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.204, + 0.895, + 0.329 + ], + "angle": 0, + "content": "Figure 5. Inverting each axis of a separable warp. LZU first evaluates the forward warp \\(\\mathcal{T}_{\\mathrm{LZ,sep,x}}\\) (solid blue arrows) at a uniform grid of target locations (blue points). The resulting source locations are shown as red points. LZU then approximates the warp in between these samples via a linear transform; this piecewise linear map is \\(\\widetilde{\\mathcal{T}}_{\\mathrm{LZ,sep,x}}\\) (dotted blue arrows). To evaluate the inverse \\(\\widetilde{\\mathcal{T}}_{\\mathrm{LZ,sep,x}}^{-1}\\) (dotted green arrows), we must determine for each green point which red points it falls between and invert the corresponding linear transform. An example is shown in the top-right." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.354, + 0.779, + 0.37 + ], + "angle": 0, + "content": "4.2. Learning to Zoom and Unzoom" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.377, + 0.895, + 0.497 + ], + "angle": 0, + "content": "In the Learning to Zoom and Unzoom (LZU) framework, we use existing LZ downsamplers (see Section 3.2) to \"zoom\" in on the input image, compute spatial features, and then use our warp inversion formulation to \"unzoom\" and revert any deformations in the feature map, as shown in Figure 1. This framework is applicable to all tasks with 2D spatial input and all models with some intermediate 2D spatial representation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.497, + 0.895, + 0.589 + ], + "angle": 0, + "content": "Notice that a poorly approximated inverse warp \\(\\widetilde{\\mathcal{T}}^{-1}\\) would lead to misaligned features and a drop in performance. As a result, we use the approximate forward warp \\(\\widetilde{\\mathcal{T}}\\) instead of the true forward warp \\(\\mathcal{T}\\), so that the composition of forward and inverse warps is actually the identity function. See Appendix A.3 for a discussion of the associated tradeoff." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.589, + 0.895, + 0.725 + ], + "angle": 0, + "content": "To maintain favorable accuracy-latency tradeoffs, we make several optimizations to our forward and inverse warps. As done in previous works [11, 20, 22], for the forward warp or \"zoom,\" instead of computing \\(\\mathcal{T}_{\\mathrm{LZ}}[\\mathrm{Grid}(H',W')]\\), we compute \\(\\mathcal{T}_{\\mathrm{LZ}}[\\mathrm{Grid}(h,w)]\\) for smaller \\(h\\ll H^{\\prime}\\) and \\(w\\ll W^{\\prime}\\) and bilinearly upsample this to get \\(\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}[\\mathrm{Grid}(H',W')]\\). This also reduces the complexity of computing the inverse, by reducing the number of cases in our piecewise bilinear map from \\(H^{\\prime}\\cdot W^{\\prime}\\) to \\(h\\cdot w\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.726, + 0.895, + 0.846 + ], + "angle": 0, + "content": "We explore efficient implementations of both separable and nonseparable warp inversion, but we find experimentally that nonseparable warps perform no better than separable warps for a strictly higher latency cost, so we use separable warps for our experiments. Details for efficiently inverting nonseparable warps are given in Appendix A.2. For separable warps \\(\\mathcal{T}_{\\mathrm{LZ,sep}}\\), we invert each axis separately and take the Cartesian Product:" + }, + { + "type": "equation", + "bbox": [ + 0.504, + 0.854, + 0.894, + 0.876 + ], + "angle": 0, + "content": "\\[\n\\widetilde {\\mathcal {T}} _ {\\mathrm {L Z}, \\operatorname {s e p}} ^ {- 1} [ \\operatorname {G r i d} \\left(H ^ {\\prime}, W ^ {\\prime}\\right) ] = \\tag {7}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.591, + 0.877, + 0.887, + 0.899 + ], + "angle": 0, + "content": "\\[\n\\widetilde {\\mathcal {T}} _ {\\mathrm {L Z , s e p , x}} ^ {- 1} [ \\operatorname {G r i d} (H ^ {\\prime}) ] \\times \\widetilde {\\mathcal {T}} _ {\\mathrm {L Z , s e p , y}} ^ {- 1} [ \\operatorname {G r i d} (W ^ {\\prime}) ].\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5089" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.151 + ], + "angle": 0, + "content": "This further reduces our problem from inverting a piecewise bilinear map with \\( h \\cdot w \\) pieces to inverting two piecewise linear maps with \\( h \\) and \\( w \\) pieces each. Figure 5 visualizes how to invert each axis." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.152, + 0.471, + 0.259 + ], + "angle": 0, + "content": "When unwarping after feature pyramid networks (FPNs) [16], we may have to evaluate the inverse \\(\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}^{-1}\\) at multiple resolutions \\(\\operatorname{Grid}(H', W')\\), \\(\\operatorname{Grid}(H'/2, W'/2)\\), etc. In practice, we evaluate \\(\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}^{-1}[\\operatorname{Grid}(H', W')]\\) and then approximate the inverse at lower resolutions via bilinear downsampling. This is surprisingly effective (see Appendix A.3) and leads to no observable loss in performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.259, + 0.471, + 0.336 + ], + "angle": 0, + "content": "Finally, as introduced in [22], we can also use a fixed warp to exploit dataset-wide spatial priors, such as how objects are concentrated around the horizon in many autonomous driving datasets. This allows us to cache forward and inverse warps, greatly reducing additional latency." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.347, + 0.21, + 0.364 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.372, + 0.473, + 0.538 + ], + "angle": 0, + "content": "First, we compare LZU to naive uniform downsampling and previous works on the tasks of 2D object detection and semantic segmentation. We include ablations to evaluate the effectiveness of training techniques and explore the upsampling regime. Then, we evaluate LZU on monocular 3D object detection, a task which no previous works have applied \"zooming\" to. We perform all timing experiments with a batch size of 1 on a single RTX 2080 Ti GPU. Figure 6 contains qualitative results and analysis across all tasks. Full implementation details and hyperparameters are given in Appendix A.6." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.545, + 0.27, + 0.563 + ], + "angle": 0, + "content": "5.1.2D Object Detection" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.569, + 0.472, + 0.719 + ], + "angle": 0, + "content": "For 2D object detection, we evaluate LZU using RetinaNet [17] (with a ResNet-50 backbone [9] and FPN [16]) on Argoverse-HD [14], an object detection dataset for autonomous driving with high resolution \\(1920 \\times 1200\\) videos. For our baselines, we compare to uniform downsampling and FOVEA [22], a previous work that applies LZ down-sampling to detection by unwarping bounding boxes. We keep the same hyperparameters and setup as in FOVEA [22]. Experiments are run at \\(0.25\\mathrm{x}\\), \\(0.5\\mathrm{x}\\), \\(0.75\\mathrm{x}\\), and \\(1\\mathrm{x}\\) scales, to measure the accuracy-latency tradeoff." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.72, + 0.473, + 0.902 + ], + "angle": 0, + "content": "Our LZU models \"unzoom\" the feature map at each level after the FPN [16]. We adopt the low-cost saliency generators introduced in [22] — a \"fixed\" saliency map exploiting dataset-wide spatial priors, and an \"adaptive\" saliency map exploiting temporal priors by zooming in on detections from the previous frame. When training the adaptive version, we simulate previous detections by jittering the ground truth for the first two epochs. For the last epoch, we jitter detections on the current frame to better simulate previous detections; we call this \" Cascaded\" saliency. To determine saliency hyperparameters, we run grid search at \\(0.5\\mathrm{x}\\) scale on splits of the training set (details in Appendix A.4, A.6). We use" + }, + { + "type": "table", + "bbox": [ + 0.501, + 0.089, + 0.89, + 0.319 + ], + "angle": 0, + "content": "
ScaleMethodAP\\( AP_{50} \\)\\( AP_{75} \\)\\( AP_S \\)\\( AP_M \\)\\( AP_L \\)Lat (ms)
0.25xUniform10.518.09.90.35.238.623.3
0.25xLZU, fixed12.422.611.21.07.139.223.6
0.25xLZU, adaptive12.322.811.31.46.638.026.4
0.5xUniform22.638.721.73.722.153.136.0
0.5xFOVEA [22]24.940.325.37.127.750.637.9
0.5xLZU, fixed25.242.124.85.526.751.836.4
0.5xLZU, adaptive25.343.024.66.125.952.639.3
0.5xLZU, adaptive w/o cascade sal.22.839.322.35.122.748.939.3
0.75xUniform29.548.429.69.132.455.162.9
0.75xLZU, fixed30.850.431.810.933.554.163.5
0.75xLZU, adaptive26.544.626.78.328.748.766.3
1xUniform31.951.533.111.435.954.598.3
1xLZU, fixed32.652.834.013.236.054.799.3
1xLZU, adaptive32.052.433.112.536.352.9102.0
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.328, + 0.895, + 0.44 + ], + "angle": 0, + "content": "Table 1. 2D object detection results of RetinaNet [17] on Argoverse-HD [14]. Fixed LZU uses a dataset-wide spatial prior, and adaptive LZU uses a temporal prior based on previous frame detections. LZU consistently outperforms the uniform downsampling baseline and prior work across all scales, with additional latency less than \\(4\\mathrm{ms}\\). We hypothesize that the drop in \\(\\mathrm{AP}_L\\) is because objects that are already large benefit less from zooming. Still, this drawback is offset by larger improvements on small and medium objects." + }, + { + "type": "table_caption", + "bbox": [ + 0.641, + 0.455, + 0.753, + 0.466 + ], + "angle": 0, + "content": "2D Object Detection" + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.467, + 0.89, + 0.577 + ], + "angle": 0, + "content": "
Uniform ResamplingLZU Resampling
ToFromToFrom
0.25x0.5x0.75x1x0.25x0.5x0.75x1x
0.25x10.510.510.510.50.25x11.712.412.412.4
0.5x17.022.622.622.60.5x20.924.824.825.2
0.75x23.528.529.529.50.75x22.529.430.030.8
1x13.528.430.931.91x22.130.731.232.6
" + }, + { + "type": "table_caption", + "bbox": [ + 0.61, + 0.58, + 0.783, + 0.593 + ], + "angle": 0, + "content": "Monocular 3D Object Detection" + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.593, + 0.89, + 0.705 + ], + "angle": 0, + "content": "
Uniform ResamplingLZU Resampling
ToFromToFrom
0.25x0.5x0.75x1x0.25x0.5x0.75x1x
0.25x21.821.821.821.80.25x22.523.523.423.4
0.5x25.427.527.527.50.5x27.029.229.129.3
0.75x27.630.330.530.50.75x29.031.631.631.8
1x28.430.731.131.21x30.132.532.732.6
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.714, + 0.894, + 0.812 + ], + "angle": 0, + "content": "Table 2. 2D and 3D object detection results in the upsampling and downsampling regimes, using the \"Uniform\" and \"LZU, fixed\" models from Tables 1 and 5. LZU is surprisingly effective even in the upsampling regime! This demonstrates that simply allocating more pixels to small objects (without retaining extra information) can help performance, suggesting that detectors still struggle with scale invariance for small objects." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.84, + 0.894, + 0.903 + ], + "angle": 0, + "content": "a learning rate of 0.01 and keep all other training settings identical to the baseline. Latency is measured by timing only the additional operations (the \"zoom\" and \"unzoom\") and adding it to the baseline. This is done to mitigate the impact" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5090" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.107, + 0.088, + 0.865, + 0.7 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.709, + 0.895, + 0.782 + ], + "angle": 0, + "content": "Figure 6. Examples of the success and failure cases of LZU. Rows A and E show examples where zooming in on the horizon helps the detector pick up smaller objects. On the other hand, sometimes zooming leads to false negatives, such as the black car in Row B and objects near the edge in Row F. For segmentation, LZU consistently improves quality near the center of the image. The last column shows the saliency map used in each case and the resulting spatial magnification ratios. For the Argoverse-HD [14] dataset, the magnification ratio at the center is nearly \\(2x\\), meaning the \"zoom\" is preserving nearly all information in that region, at the cost of information at the corners." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.806, + 0.472, + 0.822 + ], + "angle": 0, + "content": "of variance in the latency of the backbone and detector head." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Results are given in Table 1. We outperform both uniform downsampling and FOVEA in all but one case, while incurring an additional latency of less than \\(4\\mathrm{ms}\\). The one exception is adaptive LZU at \\(0.75\\mathrm{x}\\), which is evidence that our adaptive saliency hyperparameters, chosen at \\(0.5\\mathrm{x}\\) scale," + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.806, + 0.895, + 0.882 + ], + "angle": 0, + "content": "struggle to generalize to other resolutions. We also confirm that using cascaded saliency to train adaptive LZU is crucial. Although adaptive LZU outperforms fixed LZU at \\(0.5\\mathrm{x}\\) scale, plotting the accuracy-latency curves (Figure 7) reveals that fixed LZU is Pareto optimal at all points." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.886, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Finally, we explore how LZU performs in the upsampling" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.958 + ], + "angle": 0, + "content": "5091" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.09, + 0.353, + 0.278 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.361, + 0.091, + 0.619, + 0.278 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.625, + 0.091, + 0.891, + 0.278 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.292, + 0.893, + 0.362 + ], + "angle": 0, + "content": "Figure 7. Plotting the accuracy-latency/FLOPs tradeoffs reveals the Pareto optimal methods for each task. Fixed LZU is Pareto optimal for both 2D and 3D object detection, outperforming uniform downsampling and FOVEA [22]. For semantic segmentation, we use FLOPs in lieu of latency to enable fair comparisons (ES [19] only reports FLOPS and LDS [11] has an unoptimized implementation). Although LDS boasts large improvements in raw accuracy at each scale, it also incurs a greater cost due to its expensive saliency generator. Overall, the Pareto frontier for segmentation is very competitive, with ES dominating at \\(64 \\times 64\\), LDS at \\(128 \\times 128\\), and LZU at \\(256 \\times 256\\)." + }, + { + "type": "table", + "bbox": [ + 0.088, + 0.375, + 0.885, + 0.539 + ], + "angle": 0, + "content": "
Crop SizeMethodmIOUIOULatency (ms)
roadswalkbuild.wallfencepoletlightsignveg.terr.skypersonridercartruckbustrainmbikebike
64Uniform26.493.935.668.63.52.90.50.00.172.521.176.026.80.957.18.916.98.00.08.215.5
64LZU, fixed26.793.436.168.95.82.30.40.00.072.623.475.929.41.256.715.010.24.10.011.716.9
128Uniform39.396.354.078.415.07.98.18.516.681.234.486.742.913.874.422.941.624.410.229.616.1
128LZU, fixed41.796.455.278.712.713.48.111.419.081.739.086.545.717.976.821.948.231.711.636.318.0
256Uniform53.697.564.084.720.019.022.134.841.687.041.991.259.333.784.139.262.957.927.749.119.1
256LZU, fixed55.197.767.084.924.424.421.335.242.987.044.590.761.535.785.740.867.952.829.353.421.2
512Uniform63.898.373.388.829.234.340.654.461.690.747.794.072.750.689.145.672.159.144.564.932.3
512LZU, fixed64.298.373.488.630.035.738.856.063.890.447.093.472.443.990.150.576.459.645.465.334.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.547, + 0.893, + 0.562 + ], + "angle": 0, + "content": "Table 3. Full semantic segmentation results of PSPNet [29] on Cityscapes [7]. At each resolution, LZU outperforms uniform downsampling." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.589, + 0.471, + 0.665 + ], + "angle": 0, + "content": "regime. We reuse the same models trained in our previous experiments, testing them with different pre-resampling resolutions. Results are shown in Table 2. In this regime, LZU consistently outperforms uniform downsampling, even though information retention is no longer a factor." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.68, + 0.295, + 0.696 + ], + "angle": 0, + "content": "5.2. Semantic Segmentation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.705, + 0.472, + 0.901 + ], + "angle": 0, + "content": "For our semantic segmentation experiments, we compare to previous works ES [19] and LDS [11], so we adopt their setup. We test the PSPNet [29] model (with a ResNet-50 backbone [9] and FPN [16]) on Cityscapes [7]. Cityscapes is an urban scene dataset with high resolution \\( 1024 \\times 2048 \\) images and 19 classes. We perform our experiments at several image scales \\( (64 \\times 64, 128 \\times 128, 256 \\times 256, \\) and \\( 512 \\times 512) \\), taken by resizing a centered square crop of the input image. Our simple baseline trains and tests PSPNet with uniform downsampling. To reduce overfitting, we allot 500 images from the official training set into a mini-validation split. We train our model on the remaining training images and evaluate at 10 equally spaced intervals on the mini-validation" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.589, + 0.893, + 0.619 + ], + "angle": 0, + "content": "split. We choose the best performing model and evaluate it on the official validation set." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.624, + 0.894, + 0.73 + ], + "angle": 0, + "content": "For our LZU model, we unzoom spatial features after the FPN and use a fixed saliency map. Inspired by the idea of zooming on semantic boundaries [19], we generate our fixed saliency by averaging the ground truth semantic boundaries over the train set. Notably, our saliency hyperparameters are chosen qualitatively (for producing a reasonably strong warp) and tested one-shot." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.895, + 0.903 + ], + "angle": 0, + "content": "We report our full results in Table 3 and compare to previous works in Table 4. Since our baseline results are slightly different than reported in previous works [11, 19], we compare results using a percent change relative to the corresponding baseline. We find increased performance over the baseline at all scales, and at \\(256 \\times 256\\), we beat both previous works with only \\(2.3\\mathrm{ms}\\) of additional latency. Plotting the accuracy-FLOPs tradeoff (Figure 7) reveals that the large improvements of LDS [11] at \\(64 \\times 64\\) and \\(128 \\times 128\\) input scales come at significant cost in FLOPs. In actuality, ES [19] is Pareto optimal at \\(64 \\times 64\\) and \\(128 \\times 128\\), LDS [11]" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.958 + ], + "angle": 0, + "content": "5092" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.086, + 0.089, + 0.462, + 0.204 + ], + "angle": 0, + "content": "
MethodDownsampled Resolution
64 × 64128 × 128256 × 256
Uniform (theirs)294054
Uniform (ours)26.439.353.6
ES [19]32 (+10.3%)43 (+7.5%)54 (+0.0%)
LDS [11]36 (+24.1%)47 (+17.5%)55 (+1.9%)
LZU, fixed26.7 (+1.1%)41.7 (+6.1%)55.1 (+2.9%)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.215, + 0.47, + 0.34 + ], + "angle": 0, + "content": "Table 4. Semantic segmentation results of PSPNet [29] on Cityscapes [7], in mIOU. Due to differing implementation, the performance of our baseline varies from reported values, so we report relative improvements. At \\(256 \\times 256\\), we outperform prior works. At \\(64 \\times 64\\) and \\(128 \\times 128\\), LZU performs worse than prior work, perhaps because \"unzooming\" features at such small scales is more destructive. We posit the performance losses from such aggressive downsampling factors (across all methods) may be too impractical for deployment, and so focus on the \\(256 \\times 256\\) regime." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.357, + 0.466, + 0.483 + ], + "angle": 0, + "content": "
ScaleMethodNDSmAPmATEmASEmAOEmAVEmAAELat (ms)
0.25xUniform21.811.496.732.690.1125.019.854.7
0.25xLZU, fixed23.413.196.831.982.7129.420.055.1
0.5xUniform27.517.590.128.875.5131.617.858.1
0.5xLZU, fixed29.320.188.928.373.9130.616.758.5
0.75xUniform30.521.087.327.967.0132.817.559.2
0.75xLZU, fixed31.822.483.827.567.2134.615.959.7
1xUniform31.222.484.227.470.9129.617.488.7
1xLZU, fixed32.624.884.627.568.2131.618.389.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.495, + 0.47, + 0.593 + ], + "angle": 0, + "content": "Table 5. 3D object detection results of FCOS3D [26] on nuScenes [2]. Higher NDS and mAP is better, and lower is better on all other metrics. Intuitively, size is an important cue for depth, and image deformations would stifle this signal. Suprisingly, this is not the case. LZU improves upon the uniform downsampling baseline at all scales with less than 1ms of additional latency. Notably, LZU at \\(0.75\\mathrm{x}\\) scale even outperforms uniform downsampling at 1x." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.621, + 0.47, + 0.667 + ], + "angle": 0, + "content": "at \\(128 \\times 128\\), and LZU at \\(256 \\times 256\\). We hypothesize that further improvements might be possible using an adaptive, learned formulation for saliency." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.68, + 0.358, + 0.696 + ], + "angle": 0, + "content": "5.3. Monocular 3D Object Detection" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.704, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Finally, we evaluate LZU on monocular 3D object detection. To the best of our knowledge, no previous work has applied LZ downsampling to this task. The closest existing solution, FOVEA [22], cannot be extended to 3D object detection, because 3D bounding boxes are amodal and cannot be unwarped in the same manner as 2D bounding boxes. For our base model, we use FCOS3D [26], a fully convolutional model, with a ResNet-50 backbone [9] and FPN [16]. For our dataset, we use nuScenes [2], an autonomous driving dataset with multi-view \\(1600 \\times 900\\) RGB images for 1000 scenes and 3D bounding box annotations for 10 object classes. As is standard practice, we use the nuScenes Detection Score (NDS) metric, which is a combination of" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.894, + 0.182 + ], + "angle": 0, + "content": "the usual mAP and measures of translation error (mATE), scale error (mASE), orientation error (mAOE), velocity error (mAVE), and attribute error (mAAE). We run experiments at \\(0.25\\mathrm{x}\\), \\(0.5\\mathrm{x}\\), \\(0.75\\mathrm{x}\\), and \\(1\\mathrm{x}\\) scales and test against a uniform downsampling baseline. We train for 12 epochs with a batch size of 16 with default parameters as in MMDetection3D [5]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.183, + 0.894, + 0.286 + ], + "angle": 0, + "content": "For our LZU model, again we unzoom post-FPN features and use a fixed saliency map. Inspired by FOVEA [22], our fixed saliency is generated by using kernel density estimation on the set of projected bounding boxes in the image space. We reuse the same saliency hyperparameters from 2D detection. All other training settings are identical to the baseline." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.288, + 0.893, + 0.469 + ], + "angle": 0, + "content": "Results are given in Table 5. LZU performs consistently better than uniform downsampling, with less than 1ms of additional latency. Specifically, LZU improves mAP and the aggregate metric NDS, with mixed results on mATE, mASE, mAOE, mAVE, and mAAE. Since the latter five metrics are computed on only true positives, this demonstrates that LZU increases overall recall, while maintaining about equal performance on true positives. Plotting the accuracy-latency curves (Figure 7) shows that LZU is Pareto optimal. We also repeat the same upsampling experiments as performed in 2D object detection. Results, shown in Table 2, reaffirm the viability of LZU in the upsampling regime." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.483, + 0.618, + 0.498 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.508, + 0.895, + 0.718 + ], + "angle": 0, + "content": "We propose LZU, a simple attentional framework consisting of \"zooming\" in on the input image, computing spatial features, and \"unzooming\" to invert any deformations. To unzoom, we approximate the forward warp as a piecewise bilinear mapping and invert each piece. LZU is highly general and can be applied to any task with 2D spatial input and any model with 2D spatial features. We demonstrate the versatility of LZU empirically on a variety of tasks and datasets, including monocular 3D detection which has never been done before. We also show that LZU may even be used when high-resolution sensor data is unavailable. For future work, we can consider alternatives to the \"unzoom\" formulation that are perhaps less destructive than simple resampling of features." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.894, + 0.855 + ], + "angle": 0, + "content": "Broader impact. Our work focuses on increasing the efficiency and accuracy of flagship vision tasks (detection, segmentation, 3D understanding) with high-resolution imagery. We share the same potential harms of the underlying tasks, but our approach may increase privacy concerns as identifiable information may be easier to decode at higher resolutions (e.g., facial identities or license plates). Because our approach is agnostic to the underlying model, it is reproducible with minimal changes to existing codebases." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.87, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Acknowledgements: This work was supported by the CMU Argo AI Center for Autonomous Vehicle Research." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5093" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.117, + 0.472, + 0.158 + ], + "angle": 0, + "content": "[1] Thaddeus Beier and Shawn Neely. Feature-based image metamorphosis. ACM SIGGRAPH computer graphics, 26(2):35-42, 1992. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.161, + 0.472, + 0.218 + ], + "angle": 0, + "content": "[2] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In CVPR, 2020. 2, 8, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.22, + 0.472, + 0.33 + ], + "angle": 0, + "content": "[3] Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin. MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155, 2019. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.334, + 0.472, + 0.375 + ], + "angle": 0, + "content": "[4] Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.378, + 0.472, + 0.433 + ], + "angle": 0, + "content": "[5] MMDetection3D Contributors. MMDetection3D: Open-MMLab next-generation platform for general 3D object detection. https://github.com/open-mmlab/mmdetection3d, 2020.8, 13, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.437, + 0.472, + 0.492 + ], + "angle": 0, + "content": "[6] MMSegmentation Contributors. MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https://github.com/open-mmlab/mmsegmentation, 2020.13" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.495, + 0.472, + 0.579 + ], + "angle": 0, + "content": "[7] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 2, 7, 8, 12, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.581, + 0.472, + 0.637 + ], + "angle": 0, + "content": "[8] Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764-773, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.64, + 0.472, + 0.695 + ], + "angle": 0, + "content": "[9] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.698, + 0.472, + 0.74 + ], + "angle": 0, + "content": "[10] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. Advances in neural information processing systems, 28, 2015. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.743, + 0.472, + 0.798 + ], + "angle": 0, + "content": "[11] Chen Jin, Ryutaro Tanno, Thomy Mertzanidou, Eleftheria Panagiotaki, and Daniel C Alexander. Learning to downsample for segmentation of ultra-high resolution images. arXiv preprint arXiv:2109.11071, 2021. 1, 2, 3, 4, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.802, + 0.472, + 0.856 + ], + "angle": 0, + "content": "[12] Shu Kong and Charless Fowlkes. Pixel-wise attentional gating for scene parsing. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1024-1033. IEEE, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[13] Jun Li, Yongjun Chen, Lei Cai, Ian Davidson, and Shuiwang Ji. Dense transformer networks. arXiv preprint arXiv:1705.08881, 2017. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.117, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.893, + 0.135 + ], + "angle": 0, + "content": "[14] Mengtian Li, Yu-Xiong Wang, and Deva Ramanan. Towards streaming perception. In European Conference on Computer Vision, pages 473-488. Springer, 2020. 2, 5, 6, 11, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.137, + 0.894, + 0.19 + ], + "angle": 0, + "content": "[15] Mengtian Li, Ersin Yumer, and Deva Ramanan. Budgeted training: Rethinking deep neural network training under resource constraints. arXiv preprint arXiv:1905.04753, 2019. 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.193, + 0.893, + 0.261 + ], + "angle": 0, + "content": "[16] Tsung-Yi Lin, Piotr Dólar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2117-2125, 2017. 5, 7, 8, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.264, + 0.894, + 0.319 + ], + "angle": 0, + "content": "[17] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dólar. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980-2988, 2017. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.321, + 0.894, + 0.389 + ], + "angle": 0, + "content": "[18] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.391, + 0.893, + 0.46 + ], + "angle": 0, + "content": "[19] Dmitrii Marin, Zijian He, Peter Vajda, Priyam Chatterjee, Sam Tsai, Fei Yang, and Yuri Boykov. Efficient segmentation: Learning downsampling near semantic boundaries. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2131-2141, 2019. 2, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.462, + 0.894, + 0.531 + ], + "angle": 0, + "content": "[20] Adria Recasens, Petr Kellnhofer, Simon Stent, Wojciech Matusik, and Antonio Torralba. Learning to zoom: a saliency-based sampling layer for neural networks. In Proceedings of the European Conference on Computer Vision (ECCV), pages 51-66, 2018. 1, 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.532, + 0.894, + 0.587 + ], + "angle": 0, + "content": "[21] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.589, + 0.894, + 0.657 + ], + "angle": 0, + "content": "[22] Chittesh Thavamani, Mengtian Li, Nicolas Cebron, and Deva Ramanan. Fovea: Foveated image magnification for autonomous navigation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15539-15548, 2021. 1, 2, 3, 4, 5, 7, 8, 13" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.66, + 0.894, + 0.715 + ], + "angle": 0, + "content": "[23] Vincent Vanhoucke, Andrew Senior, and Mark Z. Mao. Improving the speed of neural networks on cpus. In Deep Learning and Unsupervised Feature Learning Workshop, NIPS 2011, 2011. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.717, + 0.893, + 0.772 + ], + "angle": 0, + "content": "[24] Thomas Verelst and Tinne Tuytelaars. Dynamic convolutions: Exploiting spatial sparsity for faster inference. In Proceedings of the iee/cvf conference on computer vision and pattern recognition, pages 2320-2329, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.774, + 0.893, + 0.815 + ], + "angle": 0, + "content": "[25] Thomas Verelst and Tinne Tuytelaars. Segblocks: Block-based dynamic resolution networks for real-time segmentation. arXiv preprint arXiv:2011.12025, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.817, + 0.893, + 0.872 + ], + "angle": 0, + "content": "[26] Tai Wang, Xinge Zhu, Jiangmiao Pang, and Dahua Lin. Fcos3d: Fully convolutional one-stage monocular 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 913-922, 2021. 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.874, + 0.893, + 0.901 + ], + "angle": 0, + "content": "[27] George Wolberg. Digital image warping, volume 10662. IEEE computer society press Los Alamitos, CA, 1990. 4, 11" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5094" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "[28] Zhenda Xie, Zheng Zhang, Xizhou Zhu, Gao Huang, and Stephen Lin. Spatially adaptive inference with stochastic feature sampling and interpolation. In European conference on computer vision, pages 531-548. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.149, + 0.47, + 0.205 + ], + "angle": 0, + "content": "[29] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881-2890, 2017. 2, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.207, + 0.47, + 0.261 + ], + "angle": 0, + "content": "[30] Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. Deformable convnets v2: More deformable, better results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9308-9316, 2019. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.261 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.515, + 0.957 + ], + "angle": 0, + "content": "5095" + } + ] +] \ No newline at end of file diff --git a/2023/Learning To Zoom and Unzoom/87db51f5-d1a6-44d6-b2f5-c2462a285975_origin.pdf b/2023/Learning To Zoom and Unzoom/87db51f5-d1a6-44d6-b2f5-c2462a285975_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..9d0faf6ca615dfd62a1d4c308fedffc9f6306dd3 --- /dev/null +++ b/2023/Learning To Zoom and Unzoom/87db51f5-d1a6-44d6-b2f5-c2462a285975_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c30af3a7a3478134cc178745bc2958ecd3384b4343d4555247fa4547d562008 +size 9752517 diff --git a/2023/Learning To Zoom and Unzoom/full.md b/2023/Learning To Zoom and Unzoom/full.md new file mode 100644 index 0000000000000000000000000000000000000000..7eaeb33d52453ced82fbd224048ffc6fa0edff30 --- /dev/null +++ b/2023/Learning To Zoom and Unzoom/full.md @@ -0,0 +1,294 @@ +# Learning to Zoom and Unzoom + +Chittesh Thavamani $^{1}$ Mengtian Li $^{†1}$ Francesco Ferroni $^{‡2}$ Deva Ramanan $^{1}$ $^{1}$ Carnegie Mellon University $^{2}$ Argo AI + +tchittesh@gmail.org mengtial@alumni.cmu.edu ffferroni@nvidia.com deva@cs.cmu.edu + +# Abstract + +Many perception systems in mobile computing, autonomous navigation, and AR/VR face strict compute constraints that are particularly challenging for high-resolution input images. Previous works propose nonuniform downsamplers that "learn to zoom" on salient image regions, reducing compute while retaining task-relevant image information. However, for tasks with spatial labels (such as 2D/3D object detection and semantic segmentation), such distortions may harm performance. In this work (LZU), we "learn to zoom" in on the input image, compute spatial features, and then "unzoom" to revert any deformations. To enable efficient and differentiable unzooming, we approximate the zooming warp with a piecewise bilinear mapping that is invertible. LZU can be applied to any task with 2D spatial input and any model with 2D spatial features, and we demonstrate this versatility by evaluating on a variety of tasks and datasets: object detection on Argoverse-HD, semantic segmentation on Cityscapes, and monocular 3D object detection on nuScenes. Interestingly, we observe boosts in performance even when high-resolution sensor data is unavailable, implying that LZU can be used to "learn to up-sample" as well. Code and additional visuals are available at https://tchittesh.github.io/lzu/. + +# 1. Introduction + +In many applications, the performance of perception systems is bottlenecked by strict inference-time constraints. This can be due to limited compute (as in mobile computing), a need for strong real-time performance (as in autonomous vehicles), or both (as in augmented/virtual reality). These constraints are particularly crippling for settings with high-resolution sensor data. Even with optimizations like model compression [4] and quantization [23], it is common practice to downsample inputs during inference. + +However, running inference at a lower resolution undeniably destroys information. While some information loss is + +![](images/0d4321ec9f2a11b55f36bb967f50e5af3fac3dba4d66c2f23fb72028dba75558.jpg) +Figure 1. LZU is characterized by "zooming" the input image, computing spatial features, then "unzooming" to revert spatial deformations. LZU can be applied to any task and model that makes use of internal 2D features to process 2D inputs. We show visual examples of output tasks including 2D detection, semantic segmentation, and 3D detection from RGB images. + +unavoidable, the usual solution of uniform downsampling assumes that each pixel is equally informative towards the task at hand. To rectify this assumption, Recasens et al. [20] propose Learning to Zoom (LZ), a nonuniform downsampler that samples more densely at salient (task-relevant) image regions. They demonstrate superior performance relative to uniform downsampling on human gaze estimation and fine-grained image classification. However, this formulation warps the input image and thus requires labels to be invariant to such deformations. + +Adapting LZ downsampling to tasks with spatial labels is trickier, but has been accomplished in followup works for semantic segmentation (LDS [11]) and 2D object detection (FOVEA [22]). LDS [11] does not unzoom during learning, and so defines losses in the warped space. This necessitates additional regularization that may not apply to non-pixel-dense tasks like detection. FOVEA [22] does unzoom bounding boxes for 2D detection, but uses a special purpose solution that avoids computing an inverse, making it inapplicable to pixel-dense tasks like semantic segmentation. Despite these otherwise elegant solutions, there doesn't seem to be a general task-agnostic solution for intelligent downsampling. + +Our primary contribution is a general framework in which we zoom in on an input image, process the zoomed image, and then unzoom the output back with an inverse warp. Learning to Zoom and Unzoom (LZU) can be applied to any network that uses 2D spatial features to process 2D spatial inputs (Figure 1) with no adjustments to the network or loss. To unzoom, we approximate the zooming warp with a piecewise bilinear mapping. This allows efficient and differentiable computation of the forward and inverse warps. + +To demonstrate the generality of LZU, we demonstrate performance a variety of tasks: object detection with RetinaNet [17] on Argoverse-HD [14], semantic segmentation with PSPNet [29] on Cityscapes [7], and monocular 3D detection with FCOS3D [26] on nuScenes [2]. In our experiments, to maintain favorable accuracy-latency tradeoffs, we use cheap sources of saliency (as in [22]) when determining where to zoom. On each task, LZU increases performance over uniform downsampling and prior works with minimal additional latency. + +Interestingly, for both 2D and 3D object detection, we also see performance boosts even when processing low resolution input data. While prior works focus on performance improvements via intelligent downsampling [20, 22], our results show that LZU can also improve performance by intelligently upsampling (suggesting that current networks struggle to remain scale invariant for small objects, a well-known observation in the detection community [18]). + +# 2. Related Work + +We split related work into two sections. The first discusses the broad class of methods aiming to improve efficiency by paying "attention" to specific image regions. The second delves into works like LZU that accomplish this by differentiably resampling the input image. + +# 2.1. Spatial Attentional Processing + +By design, convolutional neural networks pay equal "attention" (perform the same computations) to all regions of the image. In many cases, this is suboptimal, and much work has gone into developing attentional methods that resolve this inefficiency. + +One such method is Dynamic Convolutions [24], which uses sparse convolutions to selectively compute outputs at only the salient regions. Similarly, gated convolutions are used in [12,28]. Notably, these methods implement "hard" attention in that the saliency is binary, and non-salient regions are ignored completely. + +Deformable Convolutions [8, 30] provides a softer implementation of spatial attention by learning per pixel offsets when applying convolutions, allowing each output pixel to attend adaptively to pixels in the input image. SegBlocks [25] also provides a softer attention mechanism by splitting the image into blocks and training a lightweight reinforcement + +learning policy to determine whether each block should be processed at a high or low resolution. This is akin to our method, which also has variable resolution, albeit in a more continuous manner. Our method is also generalizable to tasks in which it's infeasible to "stitch" together outputs from different blocks of the image (e.g. in detection where an object can span multiple blocks). + +# 2.2. Spatial Attention via Differentiable Image Resampling + +Spatial Transformer Networks [10] introduces a differentiable method to resample an image. They originally propose this to invert changes in appearance due to viewpoint, thereby enforcing better pose invariance. + +Learning to Zoom (LZ) [20] later adapts this resampling operation to "zoom" on salient image regions, acting as a spatial attention mechanism. Their key contribution is a transformation parameterized by a saliency map such that regions with higher saliency are more densely sampled. However, this deforms the image, requiring the task to have non-spatial labels. + +Followup works [11, 19, 22] adapt LZ downsampling to detection and semantic segmentation. For object detection, FOVEA [22] exploits the fact that image resampling is implemented via an inverse mapping to map predicted bounding boxes back into the original image space. This allows all processing to be done in the downsampled space and the final bounding box regression loss to be computed in the original space. However, when there are intermediate losses, as is the case with two-stage detectors containing region proposal networks (RPNs) [21], this requires more complex modifications to the usual delta loss formulation, due to the irreversibility of the inverse mapping. For semantic segmentation, Jin et al. [11] apply LZ downsampling to both the input image and the ground truth and computes the loss in the downsampled space. This is elegant and model-agnostic but leads to misalignment between the training objective and the desired evaluation metric. In the extreme case, the model learns degenerate warps that sample "easy" parts of the image to reduce the training loss. To address this, they introduce additional regularization on the down sampler. Independently, [19] handcraft an energy minimization formulation to sample more densely at semantic boundaries. + +In terms of warping and unwarping, the closest approach to ours is Dense Transformer Networks [13], which also inverts deformations introduced by nonuniform resampling. However, their warping formulation is not saliency-based, which makes it hard to work with spatial or temporal priors and also makes it time-consuming to produce the warping parameters. Additionally, they only show results for semantic segmentation, whereas we show that our formulation generalizes across spatial vision tasks. + +![](images/d3c2ba3fa9c27c6deae0c6b935c6558ad4b7eef275a4a7f725280bcea1f88a2c.jpg) +Figure 2. Illustration of $\mathcal{T}_{\mathrm{LZ}}$ [20]. Suppose we have a saliency map $\mathbf{S} \in \mathbb{R}^{h \times w}$ (visualized in the background) and want a warped image of size $H' \times W'$ . (1) We start with a uniform grid of sample locations $\operatorname{Grid}(h, w)$ . (2) Grid points are "attracted" to nearby areas with high saliency. (3) Applying this "force" yields $\mathcal{T}_{\mathrm{LZ}}[\operatorname{Grid}(h, w)]$ . (4) Bilinear upsampling yields $\widetilde{\mathcal{T}}_{\mathrm{LZ}}[\operatorname{Grid}(H', W')]$ . + +![](images/2663d6d1da4c033e121bd2a7b894434a5c43309751354e9e9c7bfa8144be11c2.jpg) + +![](images/aef3d25fe9bd980e6c54f28bf8238ce7776a059e8291a9ad32ed76f3288700df.jpg) + +![](images/60db0fbde99c6927564346eff99f54e20d7c3d2c46ff3b4ccc3f66975bbda0e8.jpg) + +![](images/1401171ef7546974ed6522b2810fdd0e6cb6000c5d23a19a0235c6d79c742c82.jpg) +$\mathcal{T}_{\mathrm{LZ}}[\mathrm{Grid}(h,w)]$ + +![](images/94c219a72a40cb71ab5abe6e3ba9102ee409c5c7dfa0eb69057b6f912c4d3283.jpg) +$\mathcal{T}_{\mathrm{LZ,ac}}[\mathrm{Grid}(h,w)]$ + +![](images/da8bdb39fa4e1383368d946beed461d3bd5fdbae4a01b00f2dd6c59ba5e04bcc.jpg) +$\mathcal{T}_{\mathrm{LZ,sep}}[\mathrm{Grid}(h,w)]$ +Figure 3. Examples of the anti-cropping (ac) and separable (sep) variants of $\mathcal{T}_{\mathrm{LZ}}$ from [22]. + +![](images/f268d02466bb9a9abe4ddbb9e1201fac731fdfd21933136da19d2b78616b511c.jpg) +$\mathcal{T}_{\mathrm{LZ,sep,ac}}[\mathrm{Grid}(h,w)]$ + +# 3. Background + +Since our method is a generalization of previous works [11, 20, 22], we include this section as a condensed explanation of prerequisite formulations critical to understanding LZU. + +# 3.1. Image Resampling + +Suppose we want to resample an input image $\mathbf{I}(\mathbf{x})$ to produce an output image $\mathbf{I}'(\mathbf{x})$ , both indexed by spatial coordinates $\mathbf{x} \in [0,1]^2$ . Resampling is typically implemented via an inverse map $\mathcal{T} : [0,1]^2 \to [0,1]^2$ from output to input coordinates [1]. For each output coordinate, the inverse map computes the source location from which to "steal" the pixel value, i.e. $\mathbf{I}'(\mathbf{x}) = \mathbf{I}(\mathcal{T}(\mathbf{x}))$ . In practice, we are often given a discretized input image $\mathbf{I} \in \mathbb{R}^{H \times W \times C}$ and are interested in computing a discretized output $\mathbf{I}' \in \mathbb{R}^{H' \times W' \times C}$ . To do so, we compute $\mathbf{I}'(\mathbf{x})$ at grid points $\mathbf{x} \in \mathrm{Grid}(H',W')$ , where $\mathrm{Grid}(H,W) := \mathrm{Grid}(H) \times \mathrm{Grid}(W)$ and $\mathrm{Grid}(D) := \{\frac{d-1}{D-1} : d \in [D]\}$ . However, $\mathcal{T}(\mathbf{x})$ may return non-integer pixel locations at which the exact value of $\mathbf{I}$ is unknown. In such cases, we use bilinear interpolation to compute $\mathbf{I}(\mathcal{T}(\mathbf{x}))$ . As proven in [10], such image resampling is differentiable with respect to $\mathcal{T}$ and $\mathbf{I}$ . + +# 3.2. Saliency-Guided Downsampling + +When using nonuniform downsampling for information retention, it is useful to parameterize $\mathcal{T}$ with a saliency map $\mathbf{S}(\mathbf{x})$ representing the desired sample rate at each spatial location $\mathbf{x} \in [0,1]^2$ [20]. Recasens et al. [20] go on + +to approximate this behavior by having each sample coordinate $\mathcal{T}(\mathbf{x})$ be "attracted" to nearby areas $\mathbf{x}'$ with high saliency $\mathbf{S}(\mathbf{x}')$ downweighted according to a distance kernel $k(\mathbf{x},\mathbf{x}')$ , as illustrated in Figure 2. Concretely, $\mathcal{T}_{\mathrm{LZ}}(\mathbf{x}) = (\mathcal{T}_{\mathrm{LZ},x}(\mathbf{x}),\mathcal{T}_{\mathrm{LZ},y}(\mathbf{x}))$ , where + +$$ +\mathcal {T} _ {\mathrm {L Z}, x} (\mathbf {x}) = \frac {\int_ {\mathbf {x} ^ {\prime}} \mathbf {S} \left(\mathbf {x} ^ {\prime}\right) k \left(\mathbf {x} , \mathbf {x} ^ {\prime}\right) \mathbf {x} _ {x} ^ {\prime} d \mathbf {x} ^ {\prime}}{\int_ {\mathbf {x} ^ {\prime}} \mathbf {S} \left(\mathbf {x} ^ {\prime}\right) k \left(\mathbf {x} , \mathbf {x} ^ {\prime}\right) d \mathbf {x} ^ {\prime}}, \tag {1} +$$ + +$$ +\mathcal {T} _ {\mathrm {L Z}, y} (\mathbf {x}) = \frac {\int_ {\mathbf {x} ^ {\prime}} \mathbf {S} \left(\mathbf {x} ^ {\prime}\right) k \left(\mathbf {x} , \mathbf {x} ^ {\prime}\right) \mathbf {x} _ {y} ^ {\prime} d \mathbf {x} ^ {\prime}}{\int_ {\mathbf {x} ^ {\prime}} \mathbf {S} \left(\mathbf {x} ^ {\prime}\right) k \left(\mathbf {x} , \mathbf {x} ^ {\prime}\right) d \mathbf {x} ^ {\prime}}. \tag {2} +$$ + +[22] proposes anti-cropping and separable variants of this down sampler. The anti-cropping variant $\mathcal{T}_{\mathrm{LZ,ac}}$ prevents the resampling operation from cropping the image. The separable variant marginalizes the saliency map $\mathbf{S}(\mathbf{x})$ into two 1D saliency maps $\mathbf{S}_x(x)$ and $\mathbf{S}_y(y)$ , and replaces the kernel $k(\mathbf{x},\mathbf{x}')$ with a two 1D kernels $k_{x}$ and $k_{y}$ (although generally $k_{x} = k_{y}$ ). Then, $\mathcal{T}_{\mathrm{LZ,sep}}(\mathbf{x}) = (\mathcal{T}_{\mathrm{LZ,sep,x}}(\mathbf{x}_x),\mathcal{T}_{\mathrm{LZ,sep,y}}(\mathbf{x}_y))$ where + +$$ +\mathcal {T} _ {\mathrm {L Z}, \operatorname {s e p}, \mathrm {x}} (x) = \frac {\int_ {x ^ {\prime}} \mathbf {S} _ {x} \left(x ^ {\prime}\right) k _ {x} \left(x , x ^ {\prime}\right) x ^ {\prime} d x ^ {\prime}}{\int_ {x ^ {\prime}} \mathbf {S} _ {x} \left(x ^ {\prime}\right) k _ {x} \left(x , x ^ {\prime}\right) d x ^ {\prime}}, \tag {3} +$$ + +$$ +\mathcal {T} _ {\mathrm {L Z}, \operatorname {s e p}, \mathrm {y}} (y) = \frac {\int_ {y ^ {\prime}} \mathbf {S} _ {y} \left(y ^ {\prime}\right) k _ {y} \left(y , y ^ {\prime}\right) y ^ {\prime} d y ^ {\prime}}{\int_ {y ^ {\prime}} \mathbf {S} _ {y} \left(y ^ {\prime}\right) k _ {y} \left(y , y ^ {\prime}\right) d y ^ {\prime}}. \tag {4} +$$ + +This preserves axis-alignment of rectangles, which is crucial to object detection where bounding boxes are specified via corners. We refer to the above method and all variants as LZ downsamplers, after the pioneering work "Learning to Zoom" [20]. Examples of each variant are shown in Figure 3. + +# 4. Method + +We begin by discussing our general technique for warp inversion. Then, we discuss the LZU framework and how we apply warp inversion to efficiently "unzoom". + +# 4.1. Efficient, Differentiable Warp Inversion + +Suppose we have a continuous map $\mathcal{T}:[0,1]^2\to [0,1]^2$ . Our primary technical innovation is an efficient and differentiable approximation of $\mathcal{T}^{-1}$ , even in cases where $\mathcal{T}$ has no closed-form inverse. + +Since $\mathcal{T}$ is potentially difficult to invert, we first approximate it as $\widetilde{\mathcal{T}}$ , a piecewise tiling of simpler invertible transforms (illustrated in Figure 4). Formally, + +$$ +\widetilde {\mathcal {T}} = \bigcup_ { \begin{array}{c} i \in [ h - 1 ] \\ j \in [ w - 1 ] \end{array} } \widetilde {\mathcal {T}} _ {i j}, \tag {5} +$$ + +where the $ij$ -th tile $\widetilde{\mathcal{T}}_{ij}$ is any bijective map from the rectangle formed by corners $R_{ij} = \left\{\frac{i - 1}{h - 1}, \frac{i}{h - 1}\right\} \times \left\{\frac{j - 1}{w - 1}, \frac{j}{w - 1}\right\}$ . + +![](images/0f32c9995683986f6bed0f1aa3c535863f48d0d66131cbfef006fcf1661c8f9d.jpg) +Figure 4. Given a warp $\mathcal{T}$ , we construct an approximation $\widetilde{\mathcal{T}}$ designed for efficient inversion. As illustrated, $\widetilde{\mathcal{T}}$ is a piecewise tiling of simpler invertible maps. This allows us to approximate the inverse $\widetilde{\mathcal{T}}^{-1}$ , even when $\mathcal{T}^{-1}$ lacks a closed form. + +![](images/331b65f09bdf77c2416e5735c13fa508c452a4050bbd7baeff5eb329e6522d9c.jpg) +Figure 5. Inverting each axis of a separable warp. LZU first evaluates the forward warp $\mathcal{T}_{\mathrm{LZ,sep,x}}$ (solid blue arrows) at a uniform grid of target locations (blue points). The resulting source locations are shown as red points. LZU then approximates the warp in between these samples via a linear transform; this piecewise linear map is $\widetilde{\mathcal{T}}_{\mathrm{LZ,sep,x}}$ (dotted blue arrows). To evaluate the inverse $\widetilde{\mathcal{T}}_{\mathrm{LZ,sep,x}}^{-1}$ (dotted green arrows), we must determine for each green point which red points it falls between and invert the corresponding linear transform. An example is shown in the top-right. + +to quadrilateral $\mathcal{T}[R_{ij}]$ . For our purposes, we choose bilinear maps as our tile function, although homographies could work just as well. Then, so long as $\widetilde{\mathcal{T}}$ is injective (if each of the tiles $\widetilde{\mathcal{T}}_{ij}$ is nondegenerate and no two tiles overlap), we are guaranteed a well-defined left inverse $\widetilde{\mathcal{T}}^{-1}:[0,1]^2\rightarrow [0,1]^2$ given by + +$$ +\widetilde {\mathcal {T}} ^ {- 1} (\mathbf {x}) = \left\{ \begin{array}{l l} \widetilde {\mathcal {T}} _ {i j} ^ {- 1} (\mathbf {x}) & \text {i f} \mathbf {x} \in \operatorname {R a n g e} \left(\widetilde {\mathcal {T}} _ {i j}\right) \\ 0 & \text {e l s e} \end{array} . \right. \tag {6} +$$ + +Equation 6 is efficient to compute, since determining if $\mathbf{x} \in \mathrm{Range}(\widetilde{\mathcal{T}}_{ij})$ simply involves checking if $\mathbf{x}$ is in the quadrilateral $\mathcal{T}[R_{ij}]$ and computing the inverse $\widetilde{\mathcal{T}}_{ij}^{-1}$ of a bilinear map amounts to solving a quadratic equation [27]. This efficiency is crucial to maintaining favorable accuracy-latency tradeoffs. $\widetilde{\mathcal{T}}^{-1}$ is guaranteed to be differentiable with respect to $\mathcal{T}$ , since for each $\mathbf{x} \in \widetilde{\mathcal{T}}[R(i,j)]$ , the inverse bilinear map can be written as a quadratic function of the four corners of tile $ij$ (see Appendix A.1 for exact expression). This allows gradients to flow back into $\mathcal{T}$ , letting us learn the parameters of the warp. + +In the case of LZ warps, $\mathcal{T}_{\mathrm{LZ}}$ has no closed form inverse to the best of our knowledge. Because $\mathcal{T}_{\mathrm{LZ}}[\mathrm{Grid}(h,w)]$ has no foldovers [20], $\widetilde{\mathcal{T}}_{\mathrm{LZ}}$ must be injective, implying its inverse $\widetilde{\mathcal{T}}_{\mathrm{LZ}}^{-1}$ is well-defined. + +When applying an LZ warp, saliency can be learned (with trainable parameters) or unlearned (with fixed parameters), and fixed (invariant across frames) or adaptive (changes every frame). Adaptive saliency maps require efficient warp inversion since a different warp must be applied on every input. Learned saliency maps require differentiability. We note that fixed unlearned saliency maps do not technically require efficiency or differentiability, and most of our current results show that such saliency maps are already quite effective, outperforming prior work. We posit that LZU would shine even more in the learned adaptive setting, where it could make use of temporal priming for top-down saliency. + +![](images/532dc8fd38427997352242a5fce768150e2b8f24a0c102d8d54a7bb501326aff.jpg) + +![](images/f5a411681f9ae66c53ebecb48f023d0ea5afbcc583f9ddb536153b752d196717.jpg) + +![](images/101ce787fa5bfac70cd8eff06aadbb3e420000b961467c8537fe6035289d75f0.jpg) + +# 4.2. Learning to Zoom and Unzoom + +In the Learning to Zoom and Unzoom (LZU) framework, we use existing LZ downsamplers (see Section 3.2) to "zoom" in on the input image, compute spatial features, and then use our warp inversion formulation to "unzoom" and revert any deformations in the feature map, as shown in Figure 1. This framework is applicable to all tasks with 2D spatial input and all models with some intermediate 2D spatial representation. + +Notice that a poorly approximated inverse warp $\widetilde{\mathcal{T}}^{-1}$ would lead to misaligned features and a drop in performance. As a result, we use the approximate forward warp $\widetilde{\mathcal{T}}$ instead of the true forward warp $\mathcal{T}$ , so that the composition of forward and inverse warps is actually the identity function. See Appendix A.3 for a discussion of the associated tradeoff. + +To maintain favorable accuracy-latency tradeoffs, we make several optimizations to our forward and inverse warps. As done in previous works [11, 20, 22], for the forward warp or "zoom," instead of computing $\mathcal{T}_{\mathrm{LZ}}[\mathrm{Grid}(H',W')]$ , we compute $\mathcal{T}_{\mathrm{LZ}}[\mathrm{Grid}(h,w)]$ for smaller $h\ll H^{\prime}$ and $w\ll W^{\prime}$ and bilinearly upsample this to get $\widetilde{\mathcal{T}}_{\mathrm{LZ}}[\mathrm{Grid}(H',W')]$ . This also reduces the complexity of computing the inverse, by reducing the number of cases in our piecewise bilinear map from $H^{\prime}\cdot W^{\prime}$ to $h\cdot w$ . + +We explore efficient implementations of both separable and nonseparable warp inversion, but we find experimentally that nonseparable warps perform no better than separable warps for a strictly higher latency cost, so we use separable warps for our experiments. Details for efficiently inverting nonseparable warps are given in Appendix A.2. For separable warps $\mathcal{T}_{\mathrm{LZ,sep}}$ , we invert each axis separately and take the Cartesian Product: + +$$ +\widetilde {\mathcal {T}} _ {\mathrm {L Z}, \operatorname {s e p}} ^ {- 1} [ \operatorname {G r i d} \left(H ^ {\prime}, W ^ {\prime}\right) ] = \tag {7} +$$ + +$$ +\widetilde {\mathcal {T}} _ {\mathrm {L Z , s e p , x}} ^ {- 1} [ \operatorname {G r i d} (H ^ {\prime}) ] \times \widetilde {\mathcal {T}} _ {\mathrm {L Z , s e p , y}} ^ {- 1} [ \operatorname {G r i d} (W ^ {\prime}) ]. +$$ + +This further reduces our problem from inverting a piecewise bilinear map with $h \cdot w$ pieces to inverting two piecewise linear maps with $h$ and $w$ pieces each. Figure 5 visualizes how to invert each axis. + +When unwarping after feature pyramid networks (FPNs) [16], we may have to evaluate the inverse $\widetilde{\mathcal{T}}_{\mathrm{LZ}}^{-1}$ at multiple resolutions $\operatorname{Grid}(H', W')$ , $\operatorname{Grid}(H'/2, W'/2)$ , etc. In practice, we evaluate $\widetilde{\mathcal{T}}_{\mathrm{LZ}}^{-1}[\operatorname{Grid}(H', W')]$ and then approximate the inverse at lower resolutions via bilinear downsampling. This is surprisingly effective (see Appendix A.3) and leads to no observable loss in performance. + +Finally, as introduced in [22], we can also use a fixed warp to exploit dataset-wide spatial priors, such as how objects are concentrated around the horizon in many autonomous driving datasets. This allows us to cache forward and inverse warps, greatly reducing additional latency. + +# 5. Experiments + +First, we compare LZU to naive uniform downsampling and previous works on the tasks of 2D object detection and semantic segmentation. We include ablations to evaluate the effectiveness of training techniques and explore the upsampling regime. Then, we evaluate LZU on monocular 3D object detection, a task which no previous works have applied "zooming" to. We perform all timing experiments with a batch size of 1 on a single RTX 2080 Ti GPU. Figure 6 contains qualitative results and analysis across all tasks. Full implementation details and hyperparameters are given in Appendix A.6. + +# 5.1.2D Object Detection + +For 2D object detection, we evaluate LZU using RetinaNet [17] (with a ResNet-50 backbone [9] and FPN [16]) on Argoverse-HD [14], an object detection dataset for autonomous driving with high resolution $1920 \times 1200$ videos. For our baselines, we compare to uniform downsampling and FOVEA [22], a previous work that applies LZ down-sampling to detection by unwarping bounding boxes. We keep the same hyperparameters and setup as in FOVEA [22]. Experiments are run at $0.25\mathrm{x}$ , $0.5\mathrm{x}$ , $0.75\mathrm{x}$ , and $1\mathrm{x}$ scales, to measure the accuracy-latency tradeoff. + +Our LZU models "unzoom" the feature map at each level after the FPN [16]. We adopt the low-cost saliency generators introduced in [22] — a "fixed" saliency map exploiting dataset-wide spatial priors, and an "adaptive" saliency map exploiting temporal priors by zooming in on detections from the previous frame. When training the adaptive version, we simulate previous detections by jittering the ground truth for the first two epochs. For the last epoch, we jitter detections on the current frame to better simulate previous detections; we call this " Cascaded" saliency. To determine saliency hyperparameters, we run grid search at $0.5\mathrm{x}$ scale on splits of the training set (details in Appendix A.4, A.6). We use + +
ScaleMethodAP\( AP_{50} \)\( AP_{75} \)\( AP_S \)\( AP_M \)\( AP_L \)Lat (ms)
0.25xUniform10.518.09.90.35.238.623.3
0.25xLZU, fixed12.422.611.21.07.139.223.6
0.25xLZU, adaptive12.322.811.31.46.638.026.4
0.5xUniform22.638.721.73.722.153.136.0
0.5xFOVEA [22]24.940.325.37.127.750.637.9
0.5xLZU, fixed25.242.124.85.526.751.836.4
0.5xLZU, adaptive25.343.024.66.125.952.639.3
0.5xLZU, adaptive w/o cascade sal.22.839.322.35.122.748.939.3
0.75xUniform29.548.429.69.132.455.162.9
0.75xLZU, fixed30.850.431.810.933.554.163.5
0.75xLZU, adaptive26.544.626.78.328.748.766.3
1xUniform31.951.533.111.435.954.598.3
1xLZU, fixed32.652.834.013.236.054.799.3
1xLZU, adaptive32.052.433.112.536.352.9102.0
+ +Table 1. 2D object detection results of RetinaNet [17] on Argoverse-HD [14]. Fixed LZU uses a dataset-wide spatial prior, and adaptive LZU uses a temporal prior based on previous frame detections. LZU consistently outperforms the uniform downsampling baseline and prior work across all scales, with additional latency less than $4\mathrm{ms}$ . We hypothesize that the drop in $\mathrm{AP}_L$ is because objects that are already large benefit less from zooming. Still, this drawback is offset by larger improvements on small and medium objects. +2D Object Detection + +
Uniform ResamplingLZU Resampling
ToFromToFrom
0.25x0.5x0.75x1x0.25x0.5x0.75x1x
0.25x10.510.510.510.50.25x11.712.412.412.4
0.5x17.022.622.622.60.5x20.924.824.825.2
0.75x23.528.529.529.50.75x22.529.430.030.8
1x13.528.430.931.91x22.130.731.232.6
+ +Monocular 3D Object Detection + +
Uniform ResamplingLZU Resampling
ToFromToFrom
0.25x0.5x0.75x1x0.25x0.5x0.75x1x
0.25x21.821.821.821.80.25x22.523.523.423.4
0.5x25.427.527.527.50.5x27.029.229.129.3
0.75x27.630.330.530.50.75x29.031.631.631.8
1x28.430.731.131.21x30.132.532.732.6
+ +Table 2. 2D and 3D object detection results in the upsampling and downsampling regimes, using the "Uniform" and "LZU, fixed" models from Tables 1 and 5. LZU is surprisingly effective even in the upsampling regime! This demonstrates that simply allocating more pixels to small objects (without retaining extra information) can help performance, suggesting that detectors still struggle with scale invariance for small objects. + +a learning rate of 0.01 and keep all other training settings identical to the baseline. Latency is measured by timing only the additional operations (the "zoom" and "unzoom") and adding it to the baseline. This is done to mitigate the impact + +![](images/0c3fb8ed0214d7550fecb4cca87cd3aef69dac04469aace635c8d12a866bd53c.jpg) +Figure 6. Examples of the success and failure cases of LZU. Rows A and E show examples where zooming in on the horizon helps the detector pick up smaller objects. On the other hand, sometimes zooming leads to false negatives, such as the black car in Row B and objects near the edge in Row F. For segmentation, LZU consistently improves quality near the center of the image. The last column shows the saliency map used in each case and the resulting spatial magnification ratios. For the Argoverse-HD [14] dataset, the magnification ratio at the center is nearly $2x$ , meaning the "zoom" is preserving nearly all information in that region, at the cost of information at the corners. + +of variance in the latency of the backbone and detector head. + +Results are given in Table 1. We outperform both uniform downsampling and FOVEA in all but one case, while incurring an additional latency of less than $4\mathrm{ms}$ . The one exception is adaptive LZU at $0.75\mathrm{x}$ , which is evidence that our adaptive saliency hyperparameters, chosen at $0.5\mathrm{x}$ scale, + +struggle to generalize to other resolutions. We also confirm that using cascaded saliency to train adaptive LZU is crucial. Although adaptive LZU outperforms fixed LZU at $0.5\mathrm{x}$ scale, plotting the accuracy-latency curves (Figure 7) reveals that fixed LZU is Pareto optimal at all points. + +Finally, we explore how LZU performs in the upsampling + +![](images/47cb61a9d3d86735578e14f66ae6a48ca867f9ba4b53a8bf5b61ba7127333c33.jpg) +Figure 7. Plotting the accuracy-latency/FLOPs tradeoffs reveals the Pareto optimal methods for each task. Fixed LZU is Pareto optimal for both 2D and 3D object detection, outperforming uniform downsampling and FOVEA [22]. For semantic segmentation, we use FLOPs in lieu of latency to enable fair comparisons (ES [19] only reports FLOPS and LDS [11] has an unoptimized implementation). Although LDS boasts large improvements in raw accuracy at each scale, it also incurs a greater cost due to its expensive saliency generator. Overall, the Pareto frontier for segmentation is very competitive, with ES dominating at $64 \times 64$ , LDS at $128 \times 128$ , and LZU at $256 \times 256$ . + +![](images/0209830e71d049cda9a760af9c79bd5fa6aee3eaed96e415f6df3409450cd518.jpg) + +![](images/38cbbd30381daaa68bc2788989d9aca075b735c0a62290ff7fd07211d3409075.jpg) + +
Crop SizeMethodmIOUIOULatency (ms)
roadswalkbuild.wallfencepoletlightsignveg.terr.skypersonridercartruckbustrainmbikebike
64Uniform26.493.935.668.63.52.90.50.00.172.521.176.026.80.957.18.916.98.00.08.215.5
64LZU, fixed26.793.436.168.95.82.30.40.00.072.623.475.929.41.256.715.010.24.10.011.716.9
128Uniform39.396.354.078.415.07.98.18.516.681.234.486.742.913.874.422.941.624.410.229.616.1
128LZU, fixed41.796.455.278.712.713.48.111.419.081.739.086.545.717.976.821.948.231.711.636.318.0
256Uniform53.697.564.084.720.019.022.134.841.687.041.991.259.333.784.139.262.957.927.749.119.1
256LZU, fixed55.197.767.084.924.424.421.335.242.987.044.590.761.535.785.740.867.952.829.353.421.2
512Uniform63.898.373.388.829.234.340.654.461.690.747.794.072.750.689.145.672.159.144.564.932.3
512LZU, fixed64.298.373.488.630.035.738.856.063.890.447.093.472.443.990.150.576.459.645.465.334.4
+ +Table 3. Full semantic segmentation results of PSPNet [29] on Cityscapes [7]. At each resolution, LZU outperforms uniform downsampling. + +regime. We reuse the same models trained in our previous experiments, testing them with different pre-resampling resolutions. Results are shown in Table 2. In this regime, LZU consistently outperforms uniform downsampling, even though information retention is no longer a factor. + +# 5.2. Semantic Segmentation + +For our semantic segmentation experiments, we compare to previous works ES [19] and LDS [11], so we adopt their setup. We test the PSPNet [29] model (with a ResNet-50 backbone [9] and FPN [16]) on Cityscapes [7]. Cityscapes is an urban scene dataset with high resolution $1024 \times 2048$ images and 19 classes. We perform our experiments at several image scales $(64 \times 64, 128 \times 128, 256 \times 256,$ and $512 \times 512)$ , taken by resizing a centered square crop of the input image. Our simple baseline trains and tests PSPNet with uniform downsampling. To reduce overfitting, we allot 500 images from the official training set into a mini-validation split. We train our model on the remaining training images and evaluate at 10 equally spaced intervals on the mini-validation + +split. We choose the best performing model and evaluate it on the official validation set. + +For our LZU model, we unzoom spatial features after the FPN and use a fixed saliency map. Inspired by the idea of zooming on semantic boundaries [19], we generate our fixed saliency by averaging the ground truth semantic boundaries over the train set. Notably, our saliency hyperparameters are chosen qualitatively (for producing a reasonably strong warp) and tested one-shot. + +We report our full results in Table 3 and compare to previous works in Table 4. Since our baseline results are slightly different than reported in previous works [11, 19], we compare results using a percent change relative to the corresponding baseline. We find increased performance over the baseline at all scales, and at $256 \times 256$ , we beat both previous works with only $2.3\mathrm{ms}$ of additional latency. Plotting the accuracy-FLOPs tradeoff (Figure 7) reveals that the large improvements of LDS [11] at $64 \times 64$ and $128 \times 128$ input scales come at significant cost in FLOPs. In actuality, ES [19] is Pareto optimal at $64 \times 64$ and $128 \times 128$ , LDS [11] + +
MethodDownsampled Resolution
64 × 64128 × 128256 × 256
Uniform (theirs)294054
Uniform (ours)26.439.353.6
ES [19]32 (+10.3%)43 (+7.5%)54 (+0.0%)
LDS [11]36 (+24.1%)47 (+17.5%)55 (+1.9%)
LZU, fixed26.7 (+1.1%)41.7 (+6.1%)55.1 (+2.9%)
+ +Table 4. Semantic segmentation results of PSPNet [29] on Cityscapes [7], in mIOU. Due to differing implementation, the performance of our baseline varies from reported values, so we report relative improvements. At $256 \times 256$ , we outperform prior works. At $64 \times 64$ and $128 \times 128$ , LZU performs worse than prior work, perhaps because "unzooming" features at such small scales is more destructive. We posit the performance losses from such aggressive downsampling factors (across all methods) may be too impractical for deployment, and so focus on the $256 \times 256$ regime. + +
ScaleMethodNDSmAPmATEmASEmAOEmAVEmAAELat (ms)
0.25xUniform21.811.496.732.690.1125.019.854.7
0.25xLZU, fixed23.413.196.831.982.7129.420.055.1
0.5xUniform27.517.590.128.875.5131.617.858.1
0.5xLZU, fixed29.320.188.928.373.9130.616.758.5
0.75xUniform30.521.087.327.967.0132.817.559.2
0.75xLZU, fixed31.822.483.827.567.2134.615.959.7
1xUniform31.222.484.227.470.9129.617.488.7
1xLZU, fixed32.624.884.627.568.2131.618.389.4
+ +Table 5. 3D object detection results of FCOS3D [26] on nuScenes [2]. Higher NDS and mAP is better, and lower is better on all other metrics. Intuitively, size is an important cue for depth, and image deformations would stifle this signal. Suprisingly, this is not the case. LZU improves upon the uniform downsampling baseline at all scales with less than 1ms of additional latency. Notably, LZU at $0.75\mathrm{x}$ scale even outperforms uniform downsampling at 1x. + +at $128 \times 128$ , and LZU at $256 \times 256$ . We hypothesize that further improvements might be possible using an adaptive, learned formulation for saliency. + +# 5.3. Monocular 3D Object Detection + +Finally, we evaluate LZU on monocular 3D object detection. To the best of our knowledge, no previous work has applied LZ downsampling to this task. The closest existing solution, FOVEA [22], cannot be extended to 3D object detection, because 3D bounding boxes are amodal and cannot be unwarped in the same manner as 2D bounding boxes. For our base model, we use FCOS3D [26], a fully convolutional model, with a ResNet-50 backbone [9] and FPN [16]. For our dataset, we use nuScenes [2], an autonomous driving dataset with multi-view $1600 \times 900$ RGB images for 1000 scenes and 3D bounding box annotations for 10 object classes. As is standard practice, we use the nuScenes Detection Score (NDS) metric, which is a combination of + +the usual mAP and measures of translation error (mATE), scale error (mASE), orientation error (mAOE), velocity error (mAVE), and attribute error (mAAE). We run experiments at $0.25\mathrm{x}$ , $0.5\mathrm{x}$ , $0.75\mathrm{x}$ , and $1\mathrm{x}$ scales and test against a uniform downsampling baseline. We train for 12 epochs with a batch size of 16 with default parameters as in MMDetection3D [5]. + +For our LZU model, again we unzoom post-FPN features and use a fixed saliency map. Inspired by FOVEA [22], our fixed saliency is generated by using kernel density estimation on the set of projected bounding boxes in the image space. We reuse the same saliency hyperparameters from 2D detection. All other training settings are identical to the baseline. + +Results are given in Table 5. LZU performs consistently better than uniform downsampling, with less than 1ms of additional latency. Specifically, LZU improves mAP and the aggregate metric NDS, with mixed results on mATE, mASE, mAOE, mAVE, and mAAE. Since the latter five metrics are computed on only true positives, this demonstrates that LZU increases overall recall, while maintaining about equal performance on true positives. Plotting the accuracy-latency curves (Figure 7) shows that LZU is Pareto optimal. We also repeat the same upsampling experiments as performed in 2D object detection. Results, shown in Table 2, reaffirm the viability of LZU in the upsampling regime. + +# 6. Conclusion + +We propose LZU, a simple attentional framework consisting of "zooming" in on the input image, computing spatial features, and "unzooming" to invert any deformations. To unzoom, we approximate the forward warp as a piecewise bilinear mapping and invert each piece. LZU is highly general and can be applied to any task with 2D spatial input and any model with 2D spatial features. We demonstrate the versatility of LZU empirically on a variety of tasks and datasets, including monocular 3D detection which has never been done before. We also show that LZU may even be used when high-resolution sensor data is unavailable. For future work, we can consider alternatives to the "unzoom" formulation that are perhaps less destructive than simple resampling of features. + +Broader impact. Our work focuses on increasing the efficiency and accuracy of flagship vision tasks (detection, segmentation, 3D understanding) with high-resolution imagery. We share the same potential harms of the underlying tasks, but our approach may increase privacy concerns as identifiable information may be easier to decode at higher resolutions (e.g., facial identities or license plates). Because our approach is agnostic to the underlying model, it is reproducible with minimal changes to existing codebases. + +Acknowledgements: This work was supported by the CMU Argo AI Center for Autonomous Vehicle Research. + +# References + +[1] Thaddeus Beier and Shawn Neely. Feature-based image metamorphosis. ACM SIGGRAPH computer graphics, 26(2):35-42, 1992. 3 +[2] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In CVPR, 2020. 2, 8, 14 +[3] Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin. MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155, 2019. 13 +[4] Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282, 2017. 1 +[5] MMDetection3D Contributors. MMDetection3D: Open-MMLab next-generation platform for general 3D object detection. https://github.com/open-mmlab/mmdetection3d, 2020.8, 13, 14 +[6] MMSegmentation Contributors. MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https://github.com/open-mmlab/mmsegmentation, 2020.13 +[7] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 2, 7, 8, 12, 13 +[8] Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764-773, 2017. 2 +[9] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5, 7, 8 +[10] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. Advances in neural information processing systems, 28, 2015. 2, 3 +[11] Chen Jin, Ryutaro Tanno, Thomy Mertzanidou, Eleftheria Panagiotaki, and Daniel C Alexander. Learning to downsample for segmentation of ultra-high resolution images. arXiv preprint arXiv:2109.11071, 2021. 1, 2, 3, 4, 7, 8 +[12] Shu Kong and Charless Fowlkes. Pixel-wise attentional gating for scene parsing. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1024-1033. IEEE, 2019. 2 +[13] Jun Li, Yongjun Chen, Lei Cai, Ian Davidson, and Shuiwang Ji. Dense transformer networks. arXiv preprint arXiv:1705.08881, 2017. 2 + +[14] Mengtian Li, Yu-Xiong Wang, and Deva Ramanan. Towards streaming perception. In European Conference on Computer Vision, pages 473-488. Springer, 2020. 2, 5, 6, 11, 13 +[15] Mengtian Li, Ersin Yumer, and Deva Ramanan. Budgeted training: Rethinking deep neural network training under resource constraints. arXiv preprint arXiv:1905.04753, 2019. 13 +[16] Tsung-Yi Lin, Piotr Dólar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2117-2125, 2017. 5, 7, 8, 12 +[17] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dólar. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980-2988, 2017. 2, 5 +[18] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 2 +[19] Dmitrii Marin, Zijian He, Peter Vajda, Priyam Chatterjee, Sam Tsai, Fei Yang, and Yuri Boykov. Efficient segmentation: Learning downsampling near semantic boundaries. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2131-2141, 2019. 2, 7, 8 +[20] Adria Recasens, Petr Kellnhofer, Simon Stent, Wojciech Matusik, and Antonio Torralba. Learning to zoom: a saliency-based sampling layer for neural networks. In Proceedings of the European Conference on Computer Vision (ECCV), pages 51-66, 2018. 1, 2, 3, 4 +[21] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 2 +[22] Chittesh Thavamani, Mengtian Li, Nicolas Cebron, and Deva Ramanan. Fovea: Foveated image magnification for autonomous navigation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15539-15548, 2021. 1, 2, 3, 4, 5, 7, 8, 13 +[23] Vincent Vanhoucke, Andrew Senior, and Mark Z. Mao. Improving the speed of neural networks on cpus. In Deep Learning and Unsupervised Feature Learning Workshop, NIPS 2011, 2011. 1 +[24] Thomas Verelst and Tinne Tuytelaars. Dynamic convolutions: Exploiting spatial sparsity for faster inference. In Proceedings of the iee/cvf conference on computer vision and pattern recognition, pages 2320-2329, 2020. 2 +[25] Thomas Verelst and Tinne Tuytelaars. Segblocks: Block-based dynamic resolution networks for real-time segmentation. arXiv preprint arXiv:2011.12025, 2020. 2 +[26] Tai Wang, Xinge Zhu, Jiangmiao Pang, and Dahua Lin. Fcos3d: Fully convolutional one-stage monocular 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 913-922, 2021. 2, 8 +[27] George Wolberg. Digital image warping, volume 10662. IEEE computer society press Los Alamitos, CA, 1990. 4, 11 + +[28] Zhenda Xie, Zheng Zhang, Xizhou Zhu, Gao Huang, and Stephen Lin. Spatially adaptive inference with stochastic feature sampling and interpolation. In European conference on computer vision, pages 531-548. Springer, 2020. 2 +[29] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881-2890, 2017. 2, 7, 8 +[30] Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. Deformable convnets v2: More deformable, better results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9308-9316, 2019. 2 \ No newline at end of file diff --git a/2023/Learning To Zoom and Unzoom/images.zip b/2023/Learning To Zoom and Unzoom/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..3f9289d32e38d2b2e89ae522812fa0c681794e9f --- /dev/null +++ b/2023/Learning To Zoom and Unzoom/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4557d375fc7dc4b9b95bcee3e138e8b5c49296e490e7953effdcbb13d65dab4e +size 781463 diff --git a/2023/Learning To Zoom and Unzoom/layout.json b/2023/Learning To Zoom and Unzoom/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..1e94dcef8fa63c984a2ec5661c22c09c14076744 --- /dev/null +++ b/2023/Learning To Zoom and Unzoom/layout.json @@ -0,0 +1,8849 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 198, + 103, + 395, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 198, + 103, + 395, + 121 + ], + "spans": [ + { + "bbox": [ + 198, + 103, + 395, + 121 + ], + "type": "text", + "content": "Learning to Zoom and Unzoom" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "spans": [ + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "type": "text", + "content": "Chittesh Thavamani" + }, + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "type": "text", + "content": " Mengtian Li" + }, + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "type": "inline_equation", + "content": "^{†1}" + }, + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "type": "text", + "content": " Francesco Ferroni" + }, + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "type": "inline_equation", + "content": "^{‡2}" + }, + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "type": "text", + "content": " Deva Ramanan" + }, + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "type": "text", + "content": "Carnegie Mellon University " + }, + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 88, + 142, + 504, + 172 + ], + "type": "text", + "content": "Argo AI" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 174, + 528, + 185 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 174, + 528, + 185 + ], + "spans": [ + { + "bbox": [ + 62, + 174, + 528, + 185 + ], + "type": "text", + "content": "tchittesh@gmail.org mengtial@alumni.cmu.edu ffferroni@nvidia.com deva@cs.cmu.edu" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "spans": [ + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 238, + 290, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 238, + 290, + 514 + ], + "spans": [ + { + "bbox": [ + 46, + 238, + 290, + 514 + ], + "type": "text", + "content": "Many perception systems in mobile computing, autonomous navigation, and AR/VR face strict compute constraints that are particularly challenging for high-resolution input images. Previous works propose nonuniform downsamplers that \"learn to zoom\" on salient image regions, reducing compute while retaining task-relevant image information. However, for tasks with spatial labels (such as 2D/3D object detection and semantic segmentation), such distortions may harm performance. In this work (LZU), we \"learn to zoom\" in on the input image, compute spatial features, and then \"unzoom\" to revert any deformations. To enable efficient and differentiable unzooming, we approximate the zooming warp with a piecewise bilinear mapping that is invertible. LZU can be applied to any task with 2D spatial input and any model with 2D spatial features, and we demonstrate this versatility by evaluating on a variety of tasks and datasets: object detection on Argoverse-HD, semantic segmentation on Cityscapes, and monocular 3D object detection on nuScenes. Interestingly, we observe boosts in performance even when high-resolution sensor data is unavailable, implying that LZU can be used to \"learn to up-sample\" as well. Code and additional visuals are available at https://tchittesh.github.io/lzu/." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 535, + 128, + 547 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 535, + 128, + 547 + ], + "spans": [ + { + "bbox": [ + 47, + 535, + 128, + 547 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 555, + 288, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 555, + 288, + 662 + ], + "spans": [ + { + "bbox": [ + 46, + 555, + 288, + 662 + ], + "type": "text", + "content": "In many applications, the performance of perception systems is bottlenecked by strict inference-time constraints. 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While some information loss is" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 307, + 212, + 547, + 342 + ], + "blocks": [ + { + "bbox": [ + 307, + 212, + 547, + 342 + ], + "lines": [ + { + "bbox": [ + 307, + 212, + 547, + 342 + ], + "spans": [ + { + "bbox": [ + 307, + 212, + 547, + 342 + ], + "type": "image", + "image_path": "0d4321ec9f2a11b55f36bb967f50e5af3fac3dba4d66c2f23fb72028dba75558.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 350, + 548, + 417 + ], + "lines": [ + { + "bbox": [ + 304, + 350, + 548, + 417 + ], + "spans": [ + { + "bbox": [ + 304, + 350, + 548, + 417 + ], + "type": "text", + "content": "Figure 1. LZU is characterized by \"zooming\" the input image, computing spatial features, then \"unzooming\" to revert spatial deformations. LZU can be applied to any task and model that makes use of internal 2D features to process 2D inputs. We show visual examples of output tasks including 2D detection, semantic segmentation, and 3D detection from RGB images." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 437, + 547, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 437, + 547, + 557 + ], + "spans": [ + { + "bbox": [ + 304, + 437, + 547, + 557 + ], + "type": "text", + "content": "unavoidable, the usual solution of uniform downsampling assumes that each pixel is equally informative towards the task at hand. To rectify this assumption, Recasens et al. [20] propose Learning to Zoom (LZ), a nonuniform downsampler that samples more densely at salient (task-relevant) image regions. They demonstrate superior performance relative to uniform downsampling on human gaze estimation and fine-grained image classification. However, this formulation warps the input image and thus requires labels to be invariant to such deformations." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 558, + 548, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 548, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 548, + 714 + ], + "type": "text", + "content": "Adapting LZ downsampling to tasks with spatial labels is trickier, but has been accomplished in followup works for semantic segmentation (LDS [11]) and 2D object detection (FOVEA [22]). LDS [11] does not unzoom during learning, and so defines losses in the warped space. This necessitates additional regularization that may not apply to non-pixel-dense tasks like detection. FOVEA [22] does unzoom bounding boxes for 2D detection, but uses a special purpose solution that avoids computing an inverse, making it inapplicable to pixel-dense tasks like semantic segmentation. Despite these otherwise elegant solutions, there doesn't seem to be a general task-agnostic solution for intelligent downsampling." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 693, + 116, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 693, + 116, + 703 + ], + "spans": [ + { + "bbox": [ + 58, + 693, + 116, + 703 + ], + "type": "text", + "content": "†Now at Waymo." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 59, + 703, + 113, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 703, + 113, + 712 + ], + "spans": [ + { + "bbox": [ + 59, + 703, + 113, + 712 + ], + "type": "inline_equation", + "content": "\\ddagger" + }, + { + "bbox": [ + 59, + 703, + 113, + 712 + ], + "type": "text", + "content": " Now at Nvidia." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5086" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 179 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 179 + ], + "type": "text", + "content": "Our primary contribution is a general framework in which we zoom in on an input image, process the zoomed image, and then unzoom the output back with an inverse warp. Learning to Zoom and Unzoom (LZU) can be applied to any network that uses 2D spatial features to process 2D spatial inputs (Figure 1) with no adjustments to the network or loss. To unzoom, we approximate the zooming warp with a piecewise bilinear mapping. This allows efficient and differentiable computation of the forward and inverse warps." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 180, + 289, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 180, + 289, + 300 + ], + "spans": [ + { + "bbox": [ + 46, + 180, + 289, + 300 + ], + "type": "text", + "content": "To demonstrate the generality of LZU, we demonstrate performance a variety of tasks: object detection with RetinaNet [17] on Argoverse-HD [14], semantic segmentation with PSPNet [29] on Cityscapes [7], and monocular 3D detection with FCOS3D [26] on nuScenes [2]. In our experiments, to maintain favorable accuracy-latency tradeoffs, we use cheap sources of saliency (as in [22]) when determining where to zoom. On each task, LZU increases performance over uniform downsampling and prior works with minimal additional latency." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 300, + 289, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 300, + 289, + 396 + ], + "spans": [ + { + "bbox": [ + 46, + 300, + 289, + 396 + ], + "type": "text", + "content": "Interestingly, for both 2D and 3D object detection, we also see performance boosts even when processing low resolution input data. While prior works focus on performance improvements via intelligent downsampling [20, 22], our results show that LZU can also improve performance by intelligently upsampling (suggesting that current networks struggle to remain scale invariant for small objects, a well-known observation in the detection community [18])." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 405, + 134, + 417 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 405, + 134, + 417 + ], + "spans": [ + { + "bbox": [ + 47, + 405, + 134, + 417 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 425, + 288, + 486 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 425, + 288, + 486 + ], + "spans": [ + { + "bbox": [ + 46, + 425, + 288, + 486 + ], + "type": "text", + "content": "We split related work into two sections. The first discusses the broad class of methods aiming to improve efficiency by paying \"attention\" to specific image regions. The second delves into works like LZU that accomplish this by differentiably resampling the input image." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 491, + 211, + 504 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 491, + 211, + 504 + ], + "spans": [ + { + "bbox": [ + 47, + 491, + 211, + 504 + ], + "type": "text", + "content": "2.1. Spatial Attentional Processing" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 510, + 288, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 510, + 288, + 569 + ], + "spans": [ + { + "bbox": [ + 46, + 510, + 288, + 569 + ], + "type": "text", + "content": "By design, convolutional neural networks pay equal \"attention\" (perform the same computations) to all regions of the image. In many cases, this is suboptimal, and much work has gone into developing attentional methods that resolve this inefficiency." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 570, + 288, + 642 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 570, + 288, + 642 + ], + "spans": [ + { + "bbox": [ + 46, + 570, + 288, + 642 + ], + "type": "text", + "content": "One such method is Dynamic Convolutions [24], which uses sparse convolutions to selectively compute outputs at only the salient regions. Similarly, gated convolutions are used in [12,28]. Notably, these methods implement \"hard\" attention in that the saliency is binary, and non-salient regions are ignored completely." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 642, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 288, + 714 + ], + "type": "text", + "content": "Deformable Convolutions [8, 30] provides a softer implementation of spatial attention by learning per pixel offsets when applying convolutions, allowing each output pixel to attend adaptively to pixels in the input image. SegBlocks [25] also provides a softer attention mechanism by splitting the image into blocks and training a lightweight reinforcement" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "content": "learning policy to determine whether each block should be processed at a high or low resolution. This is akin to our method, which also has variable resolution, albeit in a more continuous manner. Our method is also generalizable to tasks in which it's infeasible to \"stitch\" together outputs from different blocks of the image (e.g. in detection where an object can span multiple blocks)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 171, + 545, + 196 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 171, + 545, + 196 + ], + "spans": [ + { + "bbox": [ + 305, + 171, + 545, + 196 + ], + "type": "text", + "content": "2.2. Spatial Attention via Differentiable Image Resampling" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 204, + 547, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 204, + 547, + 251 + ], + "spans": [ + { + "bbox": [ + 304, + 204, + 547, + 251 + ], + "type": "text", + "content": "Spatial Transformer Networks [10] introduces a differentiable method to resample an image. They originally propose this to invert changes in appearance due to viewpoint, thereby enforcing better pose invariance." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 254, + 547, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 254, + 547, + 337 + ], + "spans": [ + { + "bbox": [ + 304, + 254, + 547, + 337 + ], + "type": "text", + "content": "Learning to Zoom (LZ) [20] later adapts this resampling operation to \"zoom\" on salient image regions, acting as a spatial attention mechanism. Their key contribution is a transformation parameterized by a saliency map such that regions with higher saliency are more densely sampled. However, this deforms the image, requiring the task to have non-spatial labels." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 340, + 548, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 340, + 548, + 604 + ], + "spans": [ + { + "bbox": [ + 304, + 340, + 548, + 604 + ], + "type": "text", + "content": "Followup works [11, 19, 22] adapt LZ downsampling to detection and semantic segmentation. For object detection, FOVEA [22] exploits the fact that image resampling is implemented via an inverse mapping to map predicted bounding boxes back into the original image space. This allows all processing to be done in the downsampled space and the final bounding box regression loss to be computed in the original space. However, when there are intermediate losses, as is the case with two-stage detectors containing region proposal networks (RPNs) [21], this requires more complex modifications to the usual delta loss formulation, due to the irreversibility of the inverse mapping. For semantic segmentation, Jin et al. [11] apply LZ downsampling to both the input image and the ground truth and computes the loss in the downsampled space. This is elegant and model-agnostic but leads to misalignment between the training objective and the desired evaluation metric. In the extreme case, the model learns degenerate warps that sample \"easy\" parts of the image to reduce the training loss. To address this, they introduce additional regularization on the down sampler. Independently, [19] handcraft an energy minimization formulation to sample more densely at semantic boundaries." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 547, + 715 + ], + "type": "text", + "content": "In terms of warping and unwarping, the closest approach to ours is Dense Transformer Networks [13], which also inverts deformations introduced by nonuniform resampling. However, their warping formulation is not saliency-based, which makes it hard to work with spatial or temporal priors and also makes it time-consuming to produce the warping parameters. Additionally, they only show results for semantic segmentation, whereas we show that our formulation generalizes across spatial vision tasks." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5087" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 70, + 108, + 129 + ], + "blocks": [ + { + "bbox": [ + 48, + 70, + 108, + 129 + ], + "lines": [ + { + "bbox": [ + 48, + 70, + 108, + 129 + ], + "spans": [ + { + "bbox": [ + 48, + 70, + 108, + 129 + ], + "type": "image", + "image_path": "d3c2ba3fa9c27c6deae0c6b935c6558ad4b7eef275a4a7f725280bcea1f88a2c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "lines": [ + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "spans": [ + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "type": "text", + "content": "Figure 2. Illustration of " + }, + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ}}" + }, + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "type": "text", + "content": " [20]. Suppose we have a saliency map " + }, + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "type": "inline_equation", + "content": "\\mathbf{S} \\in \\mathbb{R}^{h \\times w}" + }, + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "type": "text", + "content": " (visualized in the background) and want a warped image of size " + }, + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "type": "inline_equation", + "content": "H' \\times W'" + }, + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "type": "text", + "content": ". (1) We start with a uniform grid of sample locations " + }, + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "type": "inline_equation", + "content": "\\operatorname{Grid}(h, w)" + }, + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "type": "text", + "content": ". (2) Grid points are \"attracted\" to nearby areas with high saliency. (3) Applying this \"force\" yields " + }, + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ}}[\\operatorname{Grid}(h, w)]" + }, + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "type": "text", + "content": ". (4) Bilinear upsampling yields " + }, + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}[\\operatorname{Grid}(H', W')]" + }, + { + "bbox": [ + 47, + 138, + 287, + 217 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 109, + 70, + 167, + 129 + ], + "blocks": [ + { + "bbox": [ + 109, + 70, + 167, + 129 + ], + "lines": [ + { + "bbox": [ + 109, + 70, + 167, + 129 + ], + "spans": [ + { + "bbox": [ + 109, + 70, + 167, + 129 + ], + "type": "image", + "image_path": "2663d6d1da4c033e121bd2a7b894434a5c43309751354e9e9c7bfa8144be11c2.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 168, + 70, + 227, + 129 + ], + "blocks": [ + { + "bbox": [ + 168, + 70, + 227, + 129 + ], + "lines": [ + { + "bbox": [ + 168, + 70, + 227, + 129 + ], + "spans": [ + { + "bbox": [ + 168, + 70, + 227, + 129 + ], + "type": "image", + "image_path": "aef3d25fe9bd980e6c54f28bf8238ce7776a059e8291a9ad32ed76f3288700df.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 228, + 70, + 287, + 129 + ], + "blocks": [ + { + "bbox": [ + 228, + 70, + 287, + 129 + ], + "lines": [ + { + "bbox": [ + 228, + 70, + 287, + 129 + ], + "spans": [ + { + "bbox": [ + 228, + 70, + 287, + 129 + ], + "type": "image", + "image_path": "60db0fbde99c6927564346eff99f54e20d7c3d2c46ff3b4ccc3f66975bbda0e8.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 48, + 229, + 107, + 287 + ], + "blocks": [ + { + "bbox": [ + 48, + 229, + 107, + 287 + ], + "lines": [ + { + "bbox": [ + 48, + 229, + 107, + 287 + ], + "spans": [ + { + "bbox": [ + 48, + 229, + 107, + 287 + ], + "type": "image", + "image_path": "1401171ef7546974ed6522b2810fdd0e6cb6000c5d23a19a0235c6d79c742c82.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 61, + 288, + 94, + 295 + ], + "lines": [ + { + "bbox": [ + 61, + 288, + 94, + 295 + ], + "spans": [ + { + "bbox": [ + 61, + 288, + 94, + 295 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ}}[\\mathrm{Grid}(h,w)]" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 108, + 229, + 167, + 288 + ], + "blocks": [ + { + "bbox": [ + 108, + 229, + 167, + 288 + ], + "lines": [ + { + "bbox": [ + 108, + 229, + 167, + 288 + ], + "spans": [ + { + "bbox": [ + 108, + 229, + 167, + 288 + ], + "type": "image", + "image_path": "94c219a72a40cb71ab5abe6e3ba9102ee409c5c7dfa0eb69057b6f912c4d3283.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 119, + 288, + 157, + 295 + ], + "lines": [ + { + "bbox": [ + 119, + 288, + 157, + 295 + ], + "spans": [ + { + "bbox": [ + 119, + 288, + 157, + 295 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ,ac}}[\\mathrm{Grid}(h,w)]" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 168, + 229, + 227, + 288 + ], + "blocks": [ + { + "bbox": [ + 168, + 229, + 227, + 288 + ], + "lines": [ + { + "bbox": [ + 168, + 229, + 227, + 288 + ], + "spans": [ + { + "bbox": [ + 168, + 229, + 227, + 288 + ], + "type": "image", + "image_path": "da8bdb39fa4e1383368d946beed461d3bd5fdbae4a01b00f2dd6c59ba5e04bcc.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 179, + 288, + 216, + 295 + ], + "lines": [ + { + "bbox": [ + 179, + 288, + 216, + 295 + ], + "spans": [ + { + "bbox": [ + 179, + 288, + 216, + 295 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ,sep}}[\\mathrm{Grid}(h,w)]" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 47, + 304, + 287, + 326 + ], + "lines": [ + { + "bbox": [ + 47, + 304, + 287, + 326 + ], + "spans": [ + { + "bbox": [ + 47, + 304, + 287, + 326 + ], + "type": "text", + "content": "Figure 3. Examples of the anti-cropping (ac) and separable (sep) variants of " + }, + { + "bbox": [ + 47, + 304, + 287, + 326 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ}}" + }, + { + "bbox": [ + 47, + 304, + 287, + 326 + ], + "type": "text", + "content": " from [22]." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 228, + 229, + 287, + 288 + ], + "blocks": [ + { + "bbox": [ + 228, + 229, + 287, + 288 + ], + "lines": [ + { + "bbox": [ + 228, + 229, + 287, + 288 + ], + "spans": [ + { + "bbox": [ + 228, + 229, + 287, + 288 + ], + "type": "image", + "image_path": "f268d02466bb9a9abe4ddbb9e1201fac731fdfd21933136da19d2b78616b511c.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 236, + 288, + 279, + 295 + ], + "lines": [ + { + "bbox": [ + 236, + 288, + 279, + 295 + ], + "spans": [ + { + "bbox": [ + 236, + 288, + 279, + 295 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ,sep,ac}}[\\mathrm{Grid}(h,w)]" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 347, + 126, + 361 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 347, + 126, + 361 + ], + "spans": [ + { + "bbox": [ + 47, + 347, + 126, + 361 + ], + "type": "text", + "content": "3. Background" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 47, + 369, + 287, + 416 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 369, + 287, + 416 + ], + "spans": [ + { + "bbox": [ + 47, + 369, + 287, + 416 + ], + "type": "text", + "content": "Since our method is a generalization of previous works [11, 20, 22], we include this section as a condensed explanation of prerequisite formulations critical to understanding LZU." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 47, + 426, + 157, + 439 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 426, + 157, + 439 + ], + "spans": [ + { + "bbox": [ + 47, + 426, + 157, + 439 + ], + "type": "text", + "content": "3.1. Image Resampling" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "spans": [ + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": "Suppose we want to resample an input image " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathbf{I}(\\mathbf{x})" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": " to produce an output image " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathbf{I}'(\\mathbf{x})" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": ", both indexed by spatial coordinates " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathbf{x} \\in [0,1]^2" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": ". Resampling is typically implemented via an inverse map " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathcal{T} : [0,1]^2 \\to [0,1]^2" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": " from output to input coordinates [1]. For each output coordinate, the inverse map computes the source location from which to \"steal\" the pixel value, i.e. " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathbf{I}'(\\mathbf{x}) = \\mathbf{I}(\\mathcal{T}(\\mathbf{x}))" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": ". In practice, we are often given a discretized input image " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathbf{I} \\in \\mathbb{R}^{H \\times W \\times C}" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": " and are interested in computing a discretized output " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathbf{I}' \\in \\mathbb{R}^{H' \\times W' \\times C}" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": ". To do so, we compute " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathbf{I}'(\\mathbf{x})" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": " at grid points " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathbf{x} \\in \\mathrm{Grid}(H',W')" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathrm{Grid}(H,W) := \\mathrm{Grid}(H) \\times \\mathrm{Grid}(W)" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathrm{Grid}(D) := \\{\\frac{d-1}{D-1} : d \\in [D]\\}" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": ". However, " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathcal{T}(\\mathbf{x})" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": " may return non-integer pixel locations at which the exact value of " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathbf{I}" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": " is unknown. In such cases, we use bilinear interpolation to compute " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathbf{I}(\\mathcal{T}(\\mathbf{x}))" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": ". As proven in [10], such image resampling is differentiable with respect to " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "inline_equation", + "content": "\\mathbf{I}" + }, + { + "bbox": [ + 47, + 445, + 288, + 636 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 47, + 647, + 220, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 647, + 220, + 659 + ], + "spans": [ + { + "bbox": [ + 47, + 647, + 220, + 659 + ], + "type": "text", + "content": "3.2. Saliency-Guided Downsampling" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 47, + 665, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 665, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 665, + 287, + 714 + ], + "type": "text", + "content": "When using nonuniform downsampling for information retention, it is useful to parameterize " + }, + { + "bbox": [ + 47, + 665, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 47, + 665, + 287, + 714 + ], + "type": "text", + "content": " with a saliency map " + }, + { + "bbox": [ + 47, + 665, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{S}(\\mathbf{x})" + }, + { + "bbox": [ + 47, + 665, + 287, + 714 + ], + "type": "text", + "content": " representing the desired sample rate at each spatial location " + }, + { + "bbox": [ + 47, + 665, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{x} \\in [0,1]^2" + }, + { + "bbox": [ + 47, + 665, + 287, + 714 + ], + "type": "text", + "content": " [20]. Recasens et al. [20] go on" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "text", + "content": "to approximate this behavior by having each sample coordinate " + }, + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "inline_equation", + "content": "\\mathcal{T}(\\mathbf{x})" + }, + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "text", + "content": " be \"attracted\" to nearby areas " + }, + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "inline_equation", + "content": "\\mathbf{x}'" + }, + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "text", + "content": " with high saliency " + }, + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "inline_equation", + "content": "\\mathbf{S}(\\mathbf{x}')" + }, + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "text", + "content": " downweighted according to a distance kernel " + }, + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "inline_equation", + "content": "k(\\mathbf{x},\\mathbf{x}')" + }, + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "text", + "content": ", as illustrated in Figure 2. Concretely, " + }, + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ}}(\\mathbf{x}) = (\\mathcal{T}_{\\mathrm{LZ},x}(\\mathbf{x}),\\mathcal{T}_{\\mathrm{LZ},y}(\\mathbf{x}))" + }, + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "text", + "content": ", where" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 348, + 141, + 546, + 171 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 348, + 141, + 546, + 171 + ], + "spans": [ + { + "bbox": [ + 348, + 141, + 546, + 171 + ], + "type": "interline_equation", + "content": "\\mathcal {T} _ {\\mathrm {L Z}, x} (\\mathbf {x}) = \\frac {\\int_ {\\mathbf {x} ^ {\\prime}} \\mathbf {S} \\left(\\mathbf {x} ^ {\\prime}\\right) k \\left(\\mathbf {x} , \\mathbf {x} ^ {\\prime}\\right) \\mathbf {x} _ {x} ^ {\\prime} d \\mathbf {x} ^ {\\prime}}{\\int_ {\\mathbf {x} ^ {\\prime}} \\mathbf {S} \\left(\\mathbf {x} ^ {\\prime}\\right) k \\left(\\mathbf {x} , \\mathbf {x} ^ {\\prime}\\right) d \\mathbf {x} ^ {\\prime}}, \\tag {1}", + "image_path": "75a5bdd596b402750227bdc7db5152078ea19f2b04110393cfddb740d58dbedc.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 348, + 181, + 546, + 210 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 348, + 181, + 546, + 210 + ], + "spans": [ + { + "bbox": [ + 348, + 181, + 546, + 210 + ], + "type": "interline_equation", + "content": "\\mathcal {T} _ {\\mathrm {L Z}, y} (\\mathbf {x}) = \\frac {\\int_ {\\mathbf {x} ^ {\\prime}} \\mathbf {S} \\left(\\mathbf {x} ^ {\\prime}\\right) k \\left(\\mathbf {x} , \\mathbf {x} ^ {\\prime}\\right) \\mathbf {x} _ {y} ^ {\\prime} d \\mathbf {x} ^ {\\prime}}{\\int_ {\\mathbf {x} ^ {\\prime}} \\mathbf {S} \\left(\\mathbf {x} ^ {\\prime}\\right) k \\left(\\mathbf {x} , \\mathbf {x} ^ {\\prime}\\right) d \\mathbf {x} ^ {\\prime}}. \\tag {2}", + "image_path": "bd8d215194a03115f83069a10290b4f27787f9364c9e401fa5d0f8fc1eb4989c.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "spans": [ + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "text", + "content": "[22] proposes anti-cropping and separable variants of this down sampler. The anti-cropping variant " + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ,ac}}" + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "text", + "content": " prevents the resampling operation from cropping the image. The separable variant marginalizes the saliency map " + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{S}(\\mathbf{x})" + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "text", + "content": " into two 1D saliency maps " + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_x(x)" + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_y(y)" + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "text", + "content": ", and replaces the kernel " + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "inline_equation", + "content": "k(\\mathbf{x},\\mathbf{x}')" + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "text", + "content": " with a two 1D kernels " + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "inline_equation", + "content": "k_{x}" + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "inline_equation", + "content": "k_{y}" + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "text", + "content": " (although generally " + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "inline_equation", + "content": "k_{x} = k_{y}" + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "text", + "content": "). Then, " + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ,sep}}(\\mathbf{x}) = (\\mathcal{T}_{\\mathrm{LZ,sep,x}}(\\mathbf{x}_x),\\mathcal{T}_{\\mathrm{LZ,sep,y}}(\\mathbf{x}_y))" + }, + { + "bbox": [ + 304, + 214, + 547, + 312 + ], + "type": "text", + "content": " where" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 339, + 319, + 546, + 349 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 339, + 319, + 546, + 349 + ], + "spans": [ + { + "bbox": [ + 339, + 319, + 546, + 349 + ], + "type": "interline_equation", + "content": "\\mathcal {T} _ {\\mathrm {L Z}, \\operatorname {s e p}, \\mathrm {x}} (x) = \\frac {\\int_ {x ^ {\\prime}} \\mathbf {S} _ {x} \\left(x ^ {\\prime}\\right) k _ {x} \\left(x , x ^ {\\prime}\\right) x ^ {\\prime} d x ^ {\\prime}}{\\int_ {x ^ {\\prime}} \\mathbf {S} _ {x} \\left(x ^ {\\prime}\\right) k _ {x} \\left(x , x ^ {\\prime}\\right) d x ^ {\\prime}}, \\tag {3}", + "image_path": "7e48476f1bf49f08837c0c52e38b156a03c9345f418a1bddf98c15953a99bd81.jpg" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 341, + 359, + 546, + 390 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 341, + 359, + 546, + 390 + ], + "spans": [ + { + "bbox": [ + 341, + 359, + 546, + 390 + ], + "type": "interline_equation", + "content": "\\mathcal {T} _ {\\mathrm {L Z}, \\operatorname {s e p}, \\mathrm {y}} (y) = \\frac {\\int_ {y ^ {\\prime}} \\mathbf {S} _ {y} \\left(y ^ {\\prime}\\right) k _ {y} \\left(y , y ^ {\\prime}\\right) y ^ {\\prime} d y ^ {\\prime}}{\\int_ {y ^ {\\prime}} \\mathbf {S} _ {y} \\left(y ^ {\\prime}\\right) k _ {y} \\left(y , y ^ {\\prime}\\right) d y ^ {\\prime}}. \\tag {4}", + "image_path": "b913488f0004313c5e29993f6c94939508d075e540666ddd80f2eb4e4340e941.jpg" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 304, + 395, + 546, + 455 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 395, + 546, + 455 + ], + "spans": [ + { + "bbox": [ + 304, + 395, + 546, + 455 + ], + "type": "text", + "content": "This preserves axis-alignment of rectangles, which is crucial to object detection where bounding boxes are specified via corners. We refer to the above method and all variants as LZ downsamplers, after the pioneering work \"Learning to Zoom\" [20]. Examples of each variant are shown in Figure 3." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 306, + 466, + 361, + 478 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 466, + 361, + 478 + ], + "spans": [ + { + "bbox": [ + 306, + 466, + 361, + 478 + ], + "type": "text", + "content": "4. Method" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 304, + 487, + 545, + 522 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 487, + 545, + 522 + ], + "spans": [ + { + "bbox": [ + 304, + 487, + 545, + 522 + ], + "type": "text", + "content": "We begin by discussing our general technique for warp inversion. Then, we discuss the LZU framework and how we apply warp inversion to efficiently \"unzoom\"." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 306, + 530, + 515, + 544 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 530, + 515, + 544 + ], + "spans": [ + { + "bbox": [ + 306, + 530, + 515, + 544 + ], + "type": "text", + "content": "4.1. Efficient, Differentiable Warp Inversion" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 304, + 548, + 547, + 596 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 548, + 547, + 596 + ], + "spans": [ + { + "bbox": [ + 304, + 548, + 547, + 596 + ], + "type": "text", + "content": "Suppose we have a continuous map " + }, + { + "bbox": [ + 304, + 548, + 547, + 596 + ], + "type": "inline_equation", + "content": "\\mathcal{T}:[0,1]^2\\to [0,1]^2" + }, + { + "bbox": [ + 304, + 548, + 547, + 596 + ], + "type": "text", + "content": ". Our primary technical innovation is an efficient and differentiable approximation of " + }, + { + "bbox": [ + 304, + 548, + 547, + 596 + ], + "type": "inline_equation", + "content": "\\mathcal{T}^{-1}" + }, + { + "bbox": [ + 304, + 548, + 547, + 596 + ], + "type": "text", + "content": ", even in cases where " + }, + { + "bbox": [ + 304, + 548, + 547, + 596 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 304, + 548, + 547, + 596 + ], + "type": "text", + "content": " has no closed-form inverse." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 304, + 597, + 547, + 633 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 597, + 547, + 633 + ], + "spans": [ + { + "bbox": [ + 304, + 597, + 547, + 633 + ], + "type": "text", + "content": "Since " + }, + { + "bbox": [ + 304, + 597, + 547, + 633 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 304, + 597, + 547, + 633 + ], + "type": "text", + "content": " is potentially difficult to invert, we first approximate it as " + }, + { + "bbox": [ + 304, + 597, + 547, + 633 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}" + }, + { + "bbox": [ + 304, + 597, + 547, + 633 + ], + "type": "text", + "content": ", a piecewise tiling of simpler invertible transforms (illustrated in Figure 4). Formally," + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 389, + 642, + 546, + 678 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 389, + 642, + 546, + 678 + ], + "spans": [ + { + "bbox": [ + 389, + 642, + 546, + 678 + ], + "type": "interline_equation", + "content": "\\widetilde {\\mathcal {T}} = \\bigcup_ { \\begin{array}{c} i \\in [ h - 1 ] \\\\ j \\in [ w - 1 ] \\end{array} } \\widetilde {\\mathcal {T}} _ {i j}, \\tag {5}", + "image_path": "bdf4386f1d92963885c11d8ed05473cbc5d7da51bb51e5dc1113ebcb2e14704b.jpg" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 304, + 689, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 689, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 689, + 547, + 715 + ], + "type": "text", + "content": "where the " + }, + { + "bbox": [ + 304, + 689, + 547, + 715 + ], + "type": "inline_equation", + "content": "ij" + }, + { + "bbox": [ + 304, + 689, + 547, + 715 + ], + "type": "text", + "content": "-th tile " + }, + { + "bbox": [ + 304, + 689, + 547, + 715 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}_{ij}" + }, + { + "bbox": [ + 304, + 689, + 547, + 715 + ], + "type": "text", + "content": " is any bijective map from the rectangle formed by corners " + }, + { + "bbox": [ + 304, + 689, + 547, + 715 + ], + "type": "inline_equation", + "content": "R_{ij} = \\left\\{\\frac{i - 1}{h - 1}, \\frac{i}{h - 1}\\right\\} \\times \\left\\{\\frac{j - 1}{w - 1}, \\frac{j}{w - 1}\\right\\}" + }, + { + "bbox": [ + 304, + 689, + 547, + 715 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 33 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5088" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 72, + 165, + 190 + ], + "blocks": [ + { + "bbox": [ + 50, + 72, + 165, + 190 + ], + "lines": [ + { + "bbox": [ + 50, + 72, + 165, + 190 + ], + "spans": [ + { + "bbox": [ + 50, + 72, + 165, + 190 + ], + "type": "image", + "image_path": "0f32c9995683986f6bed0f1aa3c535863f48d0d66131cbfef006fcf1661c8f9d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 200, + 287, + 245 + ], + "lines": [ + { + "bbox": [ + 46, + 200, + 287, + 245 + ], + "spans": [ + { + "bbox": [ + 46, + 200, + 287, + 245 + ], + "type": "text", + "content": "Figure 4. Given a warp " + }, + { + "bbox": [ + 46, + 200, + 287, + 245 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 200, + 287, + 245 + ], + "type": "text", + "content": ", we construct an approximation " + }, + { + "bbox": [ + 46, + 200, + 287, + 245 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}" + }, + { + "bbox": [ + 46, + 200, + 287, + 245 + ], + "type": "text", + "content": " designed for efficient inversion. As illustrated, " + }, + { + "bbox": [ + 46, + 200, + 287, + 245 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}" + }, + { + "bbox": [ + 46, + 200, + 287, + 245 + ], + "type": "text", + "content": " is a piecewise tiling of simpler invertible maps. This allows us to approximate the inverse " + }, + { + "bbox": [ + 46, + 200, + 287, + 245 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}^{-1}" + }, + { + "bbox": [ + 46, + 200, + 287, + 245 + ], + "type": "text", + "content": ", even when " + }, + { + "bbox": [ + 46, + 200, + 287, + 245 + ], + "type": "inline_equation", + "content": "\\mathcal{T}^{-1}" + }, + { + "bbox": [ + 46, + 200, + 287, + 245 + ], + "type": "text", + "content": " lacks a closed form." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 172, + 72, + 286, + 190 + ], + "blocks": [ + { + "bbox": [ + 172, + 72, + 286, + 190 + ], + "lines": [ + { + "bbox": [ + 172, + 72, + 286, + 190 + ], + "spans": [ + { + "bbox": [ + 172, + 72, + 286, + 190 + ], + "type": "image", + "image_path": "331b65f09bdf77c2416e5735c13fa508c452a4050bbd7baeff5eb329e6522d9c.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 161, + 547, + 260 + ], + "lines": [ + { + "bbox": [ + 304, + 161, + 547, + 260 + ], + "spans": [ + { + "bbox": [ + 304, + 161, + 547, + 260 + ], + "type": "text", + "content": "Figure 5. Inverting each axis of a separable warp. LZU first evaluates the forward warp " + }, + { + "bbox": [ + 304, + 161, + 547, + 260 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ,sep,x}}" + }, + { + "bbox": [ + 304, + 161, + 547, + 260 + ], + "type": "text", + "content": " (solid blue arrows) at a uniform grid of target locations (blue points). The resulting source locations are shown as red points. LZU then approximates the warp in between these samples via a linear transform; this piecewise linear map is " + }, + { + "bbox": [ + 304, + 161, + 547, + 260 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}_{\\mathrm{LZ,sep,x}}" + }, + { + "bbox": [ + 304, + 161, + 547, + 260 + ], + "type": "text", + "content": " (dotted blue arrows). To evaluate the inverse " + }, + { + "bbox": [ + 304, + 161, + 547, + 260 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}_{\\mathrm{LZ,sep,x}}^{-1}" + }, + { + "bbox": [ + 304, + 161, + 547, + 260 + ], + "type": "text", + "content": " (dotted green arrows), we must determine for each green point which red points it falls between and invert the corresponding linear transform. An example is shown in the top-right." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 266, + 289, + 340 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 266, + 289, + 340 + ], + "spans": [ + { + "bbox": [ + 46, + 266, + 289, + 340 + ], + "type": "text", + "content": "to quadrilateral " + }, + { + "bbox": [ + 46, + 266, + 289, + 340 + ], + "type": "inline_equation", + "content": "\\mathcal{T}[R_{ij}]" + }, + { + "bbox": [ + 46, + 266, + 289, + 340 + ], + "type": "text", + "content": ". For our purposes, we choose bilinear maps as our tile function, although homographies could work just as well. Then, so long as " + }, + { + "bbox": [ + 46, + 266, + 289, + 340 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}" + }, + { + "bbox": [ + 46, + 266, + 289, + 340 + ], + "type": "text", + "content": " is injective (if each of the tiles " + }, + { + "bbox": [ + 46, + 266, + 289, + 340 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}_{ij}" + }, + { + "bbox": [ + 46, + 266, + 289, + 340 + ], + "type": "text", + "content": " is nondegenerate and no two tiles overlap), we are guaranteed a well-defined left inverse " + }, + { + "bbox": [ + 46, + 266, + 289, + 340 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}^{-1}:[0,1]^2\\rightarrow [0,1]^2" + }, + { + "bbox": [ + 46, + 266, + 289, + 340 + ], + "type": "text", + "content": " given by" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 77, + 347, + 287, + 380 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 347, + 287, + 380 + ], + "spans": [ + { + "bbox": [ + 77, + 347, + 287, + 380 + ], + "type": "interline_equation", + "content": "\\widetilde {\\mathcal {T}} ^ {- 1} (\\mathbf {x}) = \\left\\{ \\begin{array}{l l} \\widetilde {\\mathcal {T}} _ {i j} ^ {- 1} (\\mathbf {x}) & \\text {i f} \\mathbf {x} \\in \\operatorname {R a n g e} \\left(\\widetilde {\\mathcal {T}} _ {i j}\\right) \\\\ 0 & \\text {e l s e} \\end{array} . \\right. \\tag {6}", + "image_path": "6cb57141ac893dab36997851e77df3a6c3874aa8697b3c175736a6228f1b4dd5.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "spans": [ + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "text", + "content": "Equation 6 is efficient to compute, since determining if " + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "inline_equation", + "content": "\\mathbf{x} \\in \\mathrm{Range}(\\widetilde{\\mathcal{T}}_{ij})" + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "text", + "content": " simply involves checking if " + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "text", + "content": " is in the quadrilateral " + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "inline_equation", + "content": "\\mathcal{T}[R_{ij}]" + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "text", + "content": " and computing the inverse " + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}_{ij}^{-1}" + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "text", + "content": " of a bilinear map amounts to solving a quadratic equation [27]. This efficiency is crucial to maintaining favorable accuracy-latency tradeoffs. " + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}^{-1}" + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "text", + "content": " is guaranteed to be differentiable with respect to " + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "text", + "content": ", since for each " + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "inline_equation", + "content": "\\mathbf{x} \\in \\widetilde{\\mathcal{T}}[R(i,j)]" + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "text", + "content": ", the inverse bilinear map can be written as a quadratic function of the four corners of tile " + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "inline_equation", + "content": "ij" + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "text", + "content": " (see Appendix A.1 for exact expression). This allows gradients to flow back into " + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 388, + 288, + 520 + ], + "type": "text", + "content": ", letting us learn the parameters of the warp." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 521, + 287, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 521, + 287, + 570 + ], + "spans": [ + { + "bbox": [ + 47, + 521, + 287, + 570 + ], + "type": "text", + "content": "In the case of LZ warps, " + }, + { + "bbox": [ + 47, + 521, + 287, + 570 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ}}" + }, + { + "bbox": [ + 47, + 521, + 287, + 570 + ], + "type": "text", + "content": " has no closed form inverse to the best of our knowledge. Because " + }, + { + "bbox": [ + 47, + 521, + 287, + 570 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ}}[\\mathrm{Grid}(h,w)]" + }, + { + "bbox": [ + 47, + 521, + 287, + 570 + ], + "type": "text", + "content": " has no foldovers [20], " + }, + { + "bbox": [ + 47, + 521, + 287, + 570 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}" + }, + { + "bbox": [ + 47, + 521, + 287, + 570 + ], + "type": "text", + "content": " must be injective, implying its inverse " + }, + { + "bbox": [ + 47, + 521, + 287, + 570 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}^{-1}" + }, + { + "bbox": [ + 47, + 521, + 287, + 570 + ], + "type": "text", + "content": " is well-defined." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "text", + "content": "When applying an LZ warp, saliency can be learned (with trainable parameters) or unlearned (with fixed parameters), and fixed (invariant across frames) or adaptive (changes every frame). Adaptive saliency maps require efficient warp inversion since a different warp must be applied on every input. Learned saliency maps require differentiability. We note that fixed unlearned saliency maps do not technically require efficiency or differentiability, and most of our current results show that such saliency maps are already quite effective, outperforming prior work. We posit that LZU would shine even more in the learned adaptive setting, where it could make use of temporal priming for top-down saliency." + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 309, + 72, + 544, + 105 + ], + "blocks": [ + { + "bbox": [ + 309, + 72, + 544, + 105 + ], + "lines": [ + { + "bbox": [ + 309, + 72, + 544, + 105 + ], + "spans": [ + { + "bbox": [ + 309, + 72, + 544, + 105 + ], + "type": "image", + "image_path": "532dc8fd38427997352242a5fce768150e2b8f24a0c102d8d54a7bb501326aff.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 308, + 109, + 423, + 150 + ], + "blocks": [ + { + "bbox": [ + 308, + 109, + 423, + 150 + ], + "lines": [ + { + "bbox": [ + 308, + 109, + 423, + 150 + ], + "spans": [ + { + "bbox": [ + 308, + 109, + 423, + 150 + ], + "type": "image", + "image_path": "f5a411681f9ae66c53ebecb48f023d0ea5afbcc583f9ddb536153b752d196717.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 425, + 110, + 543, + 150 + ], + "blocks": [ + { + "bbox": [ + 425, + 110, + 543, + 150 + ], + "lines": [ + { + "bbox": [ + 425, + 110, + 543, + 150 + ], + "spans": [ + { + "bbox": [ + 425, + 110, + 543, + 150 + ], + "type": "image", + "image_path": "101ce787fa5bfac70cd8eff06aadbb3e420000b961467c8537fe6035289d75f0.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 280, + 476, + 293 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 280, + 476, + 293 + ], + "spans": [ + { + "bbox": [ + 305, + 280, + 476, + 293 + ], + "type": "text", + "content": "4.2. Learning to Zoom and Unzoom" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 298, + 547, + 393 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 298, + 547, + 393 + ], + "spans": [ + { + "bbox": [ + 304, + 298, + 547, + 393 + ], + "type": "text", + "content": "In the Learning to Zoom and Unzoom (LZU) framework, we use existing LZ downsamplers (see Section 3.2) to \"zoom\" in on the input image, compute spatial features, and then use our warp inversion formulation to \"unzoom\" and revert any deformations in the feature map, as shown in Figure 1. This framework is applicable to all tasks with 2D spatial input and all models with some intermediate 2D spatial representation." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 393, + 547, + 466 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 393, + 547, + 466 + ], + "spans": [ + { + "bbox": [ + 304, + 393, + 547, + 466 + ], + "type": "text", + "content": "Notice that a poorly approximated inverse warp " + }, + { + "bbox": [ + 304, + 393, + 547, + 466 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}^{-1}" + }, + { + "bbox": [ + 304, + 393, + 547, + 466 + ], + "type": "text", + "content": " would lead to misaligned features and a drop in performance. As a result, we use the approximate forward warp " + }, + { + "bbox": [ + 304, + 393, + 547, + 466 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}" + }, + { + "bbox": [ + 304, + 393, + 547, + 466 + ], + "type": "text", + "content": " instead of the true forward warp " + }, + { + "bbox": [ + 304, + 393, + 547, + 466 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 304, + 393, + 547, + 466 + ], + "type": "text", + "content": ", so that the composition of forward and inverse warps is actually the identity function. See Appendix A.3 for a discussion of the associated tradeoff." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "spans": [ + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "text", + "content": "To maintain favorable accuracy-latency tradeoffs, we make several optimizations to our forward and inverse warps. As done in previous works [11, 20, 22], for the forward warp or \"zoom,\" instead of computing " + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ}}[\\mathrm{Grid}(H',W')]" + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "text", + "content": ", we compute " + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ}}[\\mathrm{Grid}(h,w)]" + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "text", + "content": " for smaller " + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "inline_equation", + "content": "h\\ll H^{\\prime}" + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "inline_equation", + "content": "w\\ll W^{\\prime}" + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "text", + "content": " and bilinearly upsample this to get " + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}[\\mathrm{Grid}(H',W')]" + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "text", + "content": ". This also reduces the complexity of computing the inverse, by reducing the number of cases in our piecewise bilinear map from " + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "inline_equation", + "content": "H^{\\prime}\\cdot W^{\\prime}" + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "inline_equation", + "content": "h\\cdot w" + }, + { + "bbox": [ + 304, + 466, + 547, + 574 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 574, + 547, + 670 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 574, + 547, + 670 + ], + "spans": [ + { + "bbox": [ + 304, + 574, + 547, + 670 + ], + "type": "text", + "content": "We explore efficient implementations of both separable and nonseparable warp inversion, but we find experimentally that nonseparable warps perform no better than separable warps for a strictly higher latency cost, so we use separable warps for our experiments. Details for efficiently inverting nonseparable warps are given in Appendix A.2. For separable warps " + }, + { + "bbox": [ + 304, + 574, + 547, + 670 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_{\\mathrm{LZ,sep}}" + }, + { + "bbox": [ + 304, + 574, + 547, + 670 + ], + "type": "text", + "content": ", we invert each axis separately and take the Cartesian Product:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 676, + 547, + 693 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 676, + 547, + 693 + ], + "spans": [ + { + "bbox": [ + 308, + 676, + 547, + 693 + ], + "type": "interline_equation", + "content": "\\widetilde {\\mathcal {T}} _ {\\mathrm {L Z}, \\operatorname {s e p}} ^ {- 1} [ \\operatorname {G r i d} \\left(H ^ {\\prime}, W ^ {\\prime}\\right) ] = \\tag {7}", + "image_path": "7b8598c03fd43ed8ae80bfd2cc3919be171dadfb270ffa7109e8de12f11a7e87.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 361, + 694, + 542, + 712 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 361, + 694, + 542, + 712 + ], + "spans": [ + { + "bbox": [ + 361, + 694, + 542, + 712 + ], + "type": "interline_equation", + "content": "\\widetilde {\\mathcal {T}} _ {\\mathrm {L Z , s e p , x}} ^ {- 1} [ \\operatorname {G r i d} (H ^ {\\prime}) ] \\times \\widetilde {\\mathcal {T}} _ {\\mathrm {L Z , s e p , y}} ^ {- 1} [ \\operatorname {G r i d} (W ^ {\\prime}) ].", + "image_path": "922f0ebc7030c8d65ffd972d011b4e524301223f68a38bc77e18124e5525edf1.jpg" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5089" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 119 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 119 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 119 + ], + "type": "text", + "content": "This further reduces our problem from inverting a piecewise bilinear map with " + }, + { + "bbox": [ + 46, + 72, + 287, + 119 + ], + "type": "inline_equation", + "content": "h \\cdot w" + }, + { + "bbox": [ + 46, + 72, + 287, + 119 + ], + "type": "text", + "content": " pieces to inverting two piecewise linear maps with " + }, + { + "bbox": [ + 46, + 72, + 287, + 119 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 46, + 72, + 287, + 119 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 72, + 287, + 119 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 46, + 72, + 287, + 119 + ], + "type": "text", + "content": " pieces each. Figure 5 visualizes how to invert each axis." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 120, + 288, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 120, + 288, + 205 + ], + "spans": [ + { + "bbox": [ + 46, + 120, + 288, + 205 + ], + "type": "text", + "content": "When unwarping after feature pyramid networks (FPNs) [16], we may have to evaluate the inverse " + }, + { + "bbox": [ + 46, + 120, + 288, + 205 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}^{-1}" + }, + { + "bbox": [ + 46, + 120, + 288, + 205 + ], + "type": "text", + "content": " at multiple resolutions " + }, + { + "bbox": [ + 46, + 120, + 288, + 205 + ], + "type": "inline_equation", + "content": "\\operatorname{Grid}(H', W')" + }, + { + "bbox": [ + 46, + 120, + 288, + 205 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 120, + 288, + 205 + ], + "type": "inline_equation", + "content": "\\operatorname{Grid}(H'/2, W'/2)" + }, + { + "bbox": [ + 46, + 120, + 288, + 205 + ], + "type": "text", + "content": ", etc. In practice, we evaluate " + }, + { + "bbox": [ + 46, + 120, + 288, + 205 + ], + "type": "inline_equation", + "content": "\\widetilde{\\mathcal{T}}_{\\mathrm{LZ}}^{-1}[\\operatorname{Grid}(H', W')]" + }, + { + "bbox": [ + 46, + 120, + 288, + 205 + ], + "type": "text", + "content": " and then approximate the inverse at lower resolutions via bilinear downsampling. This is surprisingly effective (see Appendix A.3) and leads to no observable loss in performance." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 205, + 288, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 205, + 288, + 266 + ], + "spans": [ + { + "bbox": [ + 46, + 205, + 288, + 266 + ], + "type": "text", + "content": "Finally, as introduced in [22], we can also use a fixed warp to exploit dataset-wide spatial priors, such as how objects are concentrated around the horizon in many autonomous driving datasets. This allows us to cache forward and inverse warps, greatly reducing additional latency." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 274, + 128, + 288 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 274, + 128, + 288 + ], + "spans": [ + { + "bbox": [ + 47, + 274, + 128, + 288 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 294, + 289, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 294, + 289, + 426 + ], + "spans": [ + { + "bbox": [ + 46, + 294, + 289, + 426 + ], + "type": "text", + "content": "First, we compare LZU to naive uniform downsampling and previous works on the tasks of 2D object detection and semantic segmentation. We include ablations to evaluate the effectiveness of training techniques and explore the upsampling regime. Then, we evaluate LZU on monocular 3D object detection, a task which no previous works have applied \"zooming\" to. We perform all timing experiments with a batch size of 1 on a single RTX 2080 Ti GPU. Figure 6 contains qualitative results and analysis across all tasks. Full implementation details and hyperparameters are given in Appendix A.6." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 431, + 165, + 445 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 431, + 165, + 445 + ], + "spans": [ + { + "bbox": [ + 47, + 431, + 165, + 445 + ], + "type": "text", + "content": "5.1.2D Object Detection" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 450, + 288, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 450, + 288, + 569 + ], + "spans": [ + { + "bbox": [ + 46, + 450, + 288, + 569 + ], + "type": "text", + "content": "For 2D object detection, we evaluate LZU using RetinaNet [17] (with a ResNet-50 backbone [9] and FPN [16]) on Argoverse-HD [14], an object detection dataset for autonomous driving with high resolution " + }, + { + "bbox": [ + 46, + 450, + 288, + 569 + ], + "type": "inline_equation", + "content": "1920 \\times 1200" + }, + { + "bbox": [ + 46, + 450, + 288, + 569 + ], + "type": "text", + "content": " videos. For our baselines, we compare to uniform downsampling and FOVEA [22], a previous work that applies LZ down-sampling to detection by unwarping bounding boxes. We keep the same hyperparameters and setup as in FOVEA [22]. Experiments are run at " + }, + { + "bbox": [ + 46, + 450, + 288, + 569 + ], + "type": "inline_equation", + "content": "0.25\\mathrm{x}" + }, + { + "bbox": [ + 46, + 450, + 288, + 569 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 450, + 288, + 569 + ], + "type": "inline_equation", + "content": "0.5\\mathrm{x}" + }, + { + "bbox": [ + 46, + 450, + 288, + 569 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 450, + 288, + 569 + ], + "type": "inline_equation", + "content": "0.75\\mathrm{x}" + }, + { + "bbox": [ + 46, + 450, + 288, + 569 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 450, + 288, + 569 + ], + "type": "inline_equation", + "content": "1\\mathrm{x}" + }, + { + "bbox": [ + 46, + 450, + 288, + 569 + ], + "type": "text", + "content": " scales, to measure the accuracy-latency tradeoff." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "text", + "content": "Our LZU models \"unzoom\" the feature map at each level after the FPN [16]. We adopt the low-cost saliency generators introduced in [22] — a \"fixed\" saliency map exploiting dataset-wide spatial priors, and an \"adaptive\" saliency map exploiting temporal priors by zooming in on detections from the previous frame. When training the adaptive version, we simulate previous detections by jittering the ground truth for the first two epochs. For the last epoch, we jitter detections on the current frame to better simulate previous detections; we call this \" Cascaded\" saliency. To determine saliency hyperparameters, we run grid search at " + }, + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "inline_equation", + "content": "0.5\\mathrm{x}" + }, + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "text", + "content": " scale on splits of the training set (details in Appendix A.4, A.6). We use" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 306, + 70, + 544, + 252 + ], + "blocks": [ + { + "bbox": [ + 306, + 70, + 544, + 252 + ], + "lines": [ + { + "bbox": [ + 306, + 70, + 544, + 252 + ], + "spans": [ + { + "bbox": [ + 306, + 70, + 544, + 252 + ], + "type": "table", + "html": "
ScaleMethodAP\\( AP_{50} \\)\\( AP_{75} \\)\\( AP_S \\)\\( AP_M \\)\\( AP_L \\)Lat (ms)
0.25xUniform10.518.09.90.35.238.623.3
0.25xLZU, fixed12.422.611.21.07.139.223.6
0.25xLZU, adaptive12.322.811.31.46.638.026.4
0.5xUniform22.638.721.73.722.153.136.0
0.5xFOVEA [22]24.940.325.37.127.750.637.9
0.5xLZU, fixed25.242.124.85.526.751.836.4
0.5xLZU, adaptive25.343.024.66.125.952.639.3
0.5xLZU, adaptive w/o cascade sal.22.839.322.35.122.748.939.3
0.75xUniform29.548.429.69.132.455.162.9
0.75xLZU, fixed30.850.431.810.933.554.163.5
0.75xLZU, adaptive26.544.626.78.328.748.766.3
1xUniform31.951.533.111.435.954.598.3
1xLZU, fixed32.652.834.013.236.054.799.3
1xLZU, adaptive32.052.433.112.536.352.9102.0
", + "image_path": "f94e72d3b413b540f677b638f6d42b2f84b04d5efb14addee407d755b04b17c9.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 309, + 369, + 544, + 456 + ], + "blocks": [ + { + "bbox": [ + 305, + 259, + 547, + 348 + ], + "lines": [ + { + "bbox": [ + 305, + 259, + 547, + 348 + ], + "spans": [ + { + "bbox": [ + 305, + 259, + 547, + 348 + ], + "type": "text", + "content": "Table 1. 2D object detection results of RetinaNet [17] on Argoverse-HD [14]. Fixed LZU uses a dataset-wide spatial prior, and adaptive LZU uses a temporal prior based on previous frame detections. LZU consistently outperforms the uniform downsampling baseline and prior work across all scales, with additional latency less than " + }, + { + "bbox": [ + 305, + 259, + 547, + 348 + ], + "type": "inline_equation", + "content": "4\\mathrm{ms}" + }, + { + "bbox": [ + 305, + 259, + 547, + 348 + ], + "type": "text", + "content": ". We hypothesize that the drop in " + }, + { + "bbox": [ + 305, + 259, + 547, + 348 + ], + "type": "inline_equation", + "content": "\\mathrm{AP}_L" + }, + { + "bbox": [ + 305, + 259, + 547, + 348 + ], + "type": "text", + "content": " is because objects that are already large benefit less from zooming. Still, this drawback is offset by larger improvements on small and medium objects." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 392, + 360, + 460, + 369 + ], + "lines": [ + { + "bbox": [ + 392, + 360, + 460, + 369 + ], + "spans": [ + { + "bbox": [ + 392, + 360, + 460, + 369 + ], + "type": "text", + "content": "2D Object Detection" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 369, + 544, + 456 + ], + "lines": [ + { + "bbox": [ + 309, + 369, + 544, + 456 + ], + "spans": [ + { + "bbox": [ + 309, + 369, + 544, + 456 + ], + "type": "table", + "html": "
Uniform ResamplingLZU Resampling
ToFromToFrom
0.25x0.5x0.75x1x0.25x0.5x0.75x1x
0.25x10.510.510.510.50.25x11.712.412.412.4
0.5x17.022.622.622.60.5x20.924.824.825.2
0.75x23.528.529.529.50.75x22.529.430.030.8
1x13.528.430.931.91x22.130.731.232.6
", + "image_path": "5844c099818354eeea8bf374406ccfa7a860922849b84f22d2099c1d049d8f82.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "type": "table", + "bbox": [ + 309, + 469, + 544, + 558 + ], + "blocks": [ + { + "bbox": [ + 373, + 459, + 479, + 469 + ], + "lines": [ + { + "bbox": [ + 373, + 459, + 479, + 469 + ], + "spans": [ + { + "bbox": [ + 373, + 459, + 479, + 469 + ], + "type": "text", + "content": "Monocular 3D Object Detection" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 469, + 544, + 558 + ], + "lines": [ + { + "bbox": [ + 309, + 469, + 544, + 558 + ], + "spans": [ + { + "bbox": [ + 309, + 469, + 544, + 558 + ], + "type": "table", + "html": "
Uniform ResamplingLZU Resampling
ToFromToFrom
0.25x0.5x0.75x1x0.25x0.5x0.75x1x
0.25x21.821.821.821.80.25x22.523.523.423.4
0.5x25.427.527.527.50.5x27.029.229.129.3
0.75x27.630.330.530.50.75x29.031.631.631.8
1x28.430.731.131.21x30.132.532.732.6
", + "image_path": "2050e13061aa3e02713e47ac8714e935254c6591fd7e971d24503e3b847acb46.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 565, + 547, + 643 + ], + "lines": [ + { + "bbox": [ + 305, + 565, + 547, + 643 + ], + "spans": [ + { + "bbox": [ + 305, + 565, + 547, + 643 + ], + "type": "text", + "content": "Table 2. 2D and 3D object detection results in the upsampling and downsampling regimes, using the \"Uniform\" and \"LZU, fixed\" models from Tables 1 and 5. LZU is surprisingly effective even in the upsampling regime! This demonstrates that simply allocating more pixels to small objects (without retaining extra information) can help performance, suggesting that detectors still struggle with scale invariance for small objects." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 305, + 665, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 665, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 305, + 665, + 547, + 715 + ], + "type": "text", + "content": "a learning rate of 0.01 and keep all other training settings identical to the baseline. Latency is measured by timing only the additional operations (the \"zoom\" and \"unzoom\") and adding it to the baseline. This is done to mitigate the impact" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5090" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 69, + 529, + 554 + ], + "blocks": [ + { + "bbox": [ + 65, + 69, + 529, + 554 + ], + "lines": [ + { + "bbox": [ + 65, + 69, + 529, + 554 + ], + "spans": [ + { + "bbox": [ + 65, + 69, + 529, + 554 + ], + "type": "image", + "image_path": "0c3fb8ed0214d7550fecb4cca87cd3aef69dac04469aace635c8d12a866bd53c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 561, + 547, + 619 + ], + "lines": [ + { + "bbox": [ + 46, + 561, + 547, + 619 + ], + "spans": [ + { + "bbox": [ + 46, + 561, + 547, + 619 + ], + "type": "text", + "content": "Figure 6. Examples of the success and failure cases of LZU. Rows A and E show examples where zooming in on the horizon helps the detector pick up smaller objects. On the other hand, sometimes zooming leads to false negatives, such as the black car in Row B and objects near the edge in Row F. For segmentation, LZU consistently improves quality near the center of the image. The last column shows the saliency map used in each case and the resulting spatial magnification ratios. For the Argoverse-HD [14] dataset, the magnification ratio at the center is nearly " + }, + { + "bbox": [ + 46, + 561, + 547, + 619 + ], + "type": "inline_equation", + "content": "2x" + }, + { + "bbox": [ + 46, + 561, + 547, + 619 + ], + "type": "text", + "content": ", meaning the \"zoom\" is preserving nearly all information in that region, at the cost of information at the corners." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 638, + 288, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 638, + 288, + 651 + ], + "spans": [ + { + "bbox": [ + 47, + 638, + 288, + 651 + ], + "type": "text", + "content": "of variance in the latency of the backbone and detector head." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "type": "text", + "content": "Results are given in Table 1. We outperform both uniform downsampling and FOVEA in all but one case, while incurring an additional latency of less than " + }, + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "type": "inline_equation", + "content": "4\\mathrm{ms}" + }, + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "type": "text", + "content": ". The one exception is adaptive LZU at " + }, + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "type": "inline_equation", + "content": "0.75\\mathrm{x}" + }, + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "type": "text", + "content": ", which is evidence that our adaptive saliency hyperparameters, chosen at " + }, + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "type": "inline_equation", + "content": "0.5\\mathrm{x}" + }, + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "type": "text", + "content": " scale," + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 305, + 638, + 547, + 698 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 638, + 547, + 698 + ], + "spans": [ + { + "bbox": [ + 305, + 638, + 547, + 698 + ], + "type": "text", + "content": "struggle to generalize to other resolutions. We also confirm that using cascaded saliency to train adaptive LZU is crucial. Although adaptive LZU outperforms fixed LZU at " + }, + { + "bbox": [ + 305, + 638, + 547, + 698 + ], + "type": "inline_equation", + "content": "0.5\\mathrm{x}" + }, + { + "bbox": [ + 305, + 638, + 547, + 698 + ], + "type": "text", + "content": " scale, plotting the accuracy-latency curves (Figure 7) reveals that fixed LZU is Pareto optimal at all points." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 317, + 701, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 701, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 317, + 701, + 545, + 714 + ], + "type": "text", + "content": "Finally, we explore how LZU performs in the upsampling" + } + ] + } + ], + "index": 5 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 758 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 758 + ], + "type": "text", + "content": "5091" + } + ] + } + ], + "index": 6 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 71, + 216, + 220 + ], + "blocks": [ + { + "bbox": [ + 49, + 71, + 216, + 220 + ], + "lines": [ + { + "bbox": [ + 49, + 71, + 216, + 220 + ], + "spans": [ + { + "bbox": [ + 49, + 71, + 216, + 220 + ], + "type": "image", + "image_path": "47cb61a9d3d86735578e14f66ae6a48ca867f9ba4b53a8bf5b61ba7127333c33.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 231, + 546, + 286 + ], + "lines": [ + { + "bbox": [ + 46, + 231, + 546, + 286 + ], + "spans": [ + { + "bbox": [ + 46, + 231, + 546, + 286 + ], + "type": "text", + "content": "Figure 7. Plotting the accuracy-latency/FLOPs tradeoffs reveals the Pareto optimal methods for each task. Fixed LZU is Pareto optimal for both 2D and 3D object detection, outperforming uniform downsampling and FOVEA [22]. For semantic segmentation, we use FLOPs in lieu of latency to enable fair comparisons (ES [19] only reports FLOPS and LDS [11] has an unoptimized implementation). Although LDS boasts large improvements in raw accuracy at each scale, it also incurs a greater cost due to its expensive saliency generator. Overall, the Pareto frontier for segmentation is very competitive, with ES dominating at " + }, + { + "bbox": [ + 46, + 231, + 546, + 286 + ], + "type": "inline_equation", + "content": "64 \\times 64" + }, + { + "bbox": [ + 46, + 231, + 546, + 286 + ], + "type": "text", + "content": ", LDS at " + }, + { + "bbox": [ + 46, + 231, + 546, + 286 + ], + "type": "inline_equation", + "content": "128 \\times 128" + }, + { + "bbox": [ + 46, + 231, + 546, + 286 + ], + "type": "text", + "content": ", and LZU at " + }, + { + "bbox": [ + 46, + 231, + 546, + 286 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 46, + 231, + 546, + 286 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 220, + 72, + 378, + 220 + ], + "blocks": [ + { + "bbox": [ + 220, + 72, + 378, + 220 + ], + "lines": [ + { + "bbox": [ + 220, + 72, + 378, + 220 + ], + "spans": [ + { + "bbox": [ + 220, + 72, + 378, + 220 + ], + "type": "image", + "image_path": "0209830e71d049cda9a760af9c79bd5fa6aee3eaed96e415f6df3409450cd518.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 382, + 72, + 545, + 220 + ], + "blocks": [ + { + "bbox": [ + 382, + 72, + 545, + 220 + ], + "lines": [ + { + "bbox": [ + 382, + 72, + 545, + 220 + ], + "spans": [ + { + "bbox": [ + 382, + 72, + 545, + 220 + ], + "type": "image", + "image_path": "38cbbd30381daaa68bc2788989d9aca075b735c0a62290ff7fd07211d3409075.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 53, + 297, + 541, + 426 + ], + "blocks": [ + { + "bbox": [ + 53, + 297, + 541, + 426 + ], + "lines": [ + { + "bbox": [ + 53, + 297, + 541, + 426 + ], + "spans": [ + { + "bbox": [ + 53, + 297, + 541, + 426 + ], + "type": "table", + "html": "
Crop SizeMethodmIOUIOULatency (ms)
roadswalkbuild.wallfencepoletlightsignveg.terr.skypersonridercartruckbustrainmbikebike
64Uniform26.493.935.668.63.52.90.50.00.172.521.176.026.80.957.18.916.98.00.08.215.5
64LZU, fixed26.793.436.168.95.82.30.40.00.072.623.475.929.41.256.715.010.24.10.011.716.9
128Uniform39.396.354.078.415.07.98.18.516.681.234.486.742.913.874.422.941.624.410.229.616.1
128LZU, fixed41.796.455.278.712.713.48.111.419.081.739.086.545.717.976.821.948.231.711.636.318.0
256Uniform53.697.564.084.720.019.022.134.841.687.041.991.259.333.784.139.262.957.927.749.119.1
256LZU, fixed55.197.767.084.924.424.421.335.242.987.044.590.761.535.785.740.867.952.829.353.421.2
512Uniform63.898.373.388.829.234.340.654.461.690.747.794.072.750.689.145.672.159.144.564.932.3
512LZU, fixed64.298.373.488.630.035.738.856.063.890.447.093.472.443.990.150.576.459.645.465.334.4
", + "image_path": "dc5aaa8da0e8207bf3b193f2531d945abdb0502e61cd20962abba1851594fcb0.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 433, + 546, + 445 + ], + "lines": [ + { + "bbox": [ + 46, + 433, + 546, + 445 + ], + "spans": [ + { + "bbox": [ + 46, + 433, + 546, + 445 + ], + "type": "text", + "content": "Table 3. Full semantic segmentation results of PSPNet [29] on Cityscapes [7]. At each resolution, LZU outperforms uniform downsampling." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 466, + 288, + 526 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 466, + 288, + 526 + ], + "spans": [ + { + "bbox": [ + 46, + 466, + 288, + 526 + ], + "type": "text", + "content": "regime. We reuse the same models trained in our previous experiments, testing them with different pre-resampling resolutions. Results are shown in Table 2. In this regime, LZU consistently outperforms uniform downsampling, even though information retention is no longer a factor." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 538, + 180, + 551 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 538, + 180, + 551 + ], + "spans": [ + { + "bbox": [ + 47, + 538, + 180, + 551 + ], + "type": "text", + "content": "5.2. Semantic Segmentation" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 558, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 558, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 558, + 288, + 713 + ], + "type": "text", + "content": "For our semantic segmentation experiments, we compare to previous works ES [19] and LDS [11], so we adopt their setup. We test the PSPNet [29] model (with a ResNet-50 backbone [9] and FPN [16]) on Cityscapes [7]. Cityscapes is an urban scene dataset with high resolution " + }, + { + "bbox": [ + 46, + 558, + 288, + 713 + ], + "type": "inline_equation", + "content": "1024 \\times 2048" + }, + { + "bbox": [ + 46, + 558, + 288, + 713 + ], + "type": "text", + "content": " images and 19 classes. We perform our experiments at several image scales " + }, + { + "bbox": [ + 46, + 558, + 288, + 713 + ], + "type": "inline_equation", + "content": "(64 \\times 64, 128 \\times 128, 256 \\times 256," + }, + { + "bbox": [ + 46, + 558, + 288, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 558, + 288, + 713 + ], + "type": "inline_equation", + "content": "512 \\times 512)" + }, + { + "bbox": [ + 46, + 558, + 288, + 713 + ], + "type": "text", + "content": ", taken by resizing a centered square crop of the input image. Our simple baseline trains and tests PSPNet with uniform downsampling. To reduce overfitting, we allot 500 images from the official training set into a mini-validation split. We train our model on the remaining training images and evaluate at 10 equally spaced intervals on the mini-validation" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 466, + 546, + 490 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 466, + 546, + 490 + ], + "spans": [ + { + "bbox": [ + 305, + 466, + 546, + 490 + ], + "type": "text", + "content": "split. We choose the best performing model and evaluate it on the official validation set." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 494, + 547, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 494, + 547, + 578 + ], + "spans": [ + { + "bbox": [ + 304, + 494, + 547, + 578 + ], + "type": "text", + "content": "For our LZU model, we unzoom spatial features after the FPN and use a fixed saliency map. Inspired by the idea of zooming on semantic boundaries [19], we generate our fixed saliency by averaging the ground truth semantic boundaries over the train set. Notably, our saliency hyperparameters are chosen qualitatively (for producing a reasonably strong warp) and tested one-shot." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "content": "We report our full results in Table 3 and compare to previous works in Table 4. Since our baseline results are slightly different than reported in previous works [11, 19], we compare results using a percent change relative to the corresponding baseline. We find increased performance over the baseline at all scales, and at " + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "content": ", we beat both previous works with only " + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "inline_equation", + "content": "2.3\\mathrm{ms}" + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "content": " of additional latency. Plotting the accuracy-FLOPs tradeoff (Figure 7) reveals that the large improvements of LDS [11] at " + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "inline_equation", + "content": "64 \\times 64" + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "inline_equation", + "content": "128 \\times 128" + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "content": " input scales come at significant cost in FLOPs. In actuality, ES [19] is Pareto optimal at " + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "inline_equation", + "content": "64 \\times 64" + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "inline_equation", + "content": "128 \\times 128" + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "content": ", LDS [11]" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "type": "text", + "content": "5092" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 52, + 70, + 282, + 161 + ], + "blocks": [ + { + "bbox": [ + 52, + 70, + 282, + 161 + ], + "lines": [ + { + "bbox": [ + 52, + 70, + 282, + 161 + ], + "spans": [ + { + "bbox": [ + 52, + 70, + 282, + 161 + ], + "type": "table", + "html": "
MethodDownsampled Resolution
64 × 64128 × 128256 × 256
Uniform (theirs)294054
Uniform (ours)26.439.353.6
ES [19]32 (+10.3%)43 (+7.5%)54 (+0.0%)
LDS [11]36 (+24.1%)47 (+17.5%)55 (+1.9%)
LZU, fixed26.7 (+1.1%)41.7 (+6.1%)55.1 (+2.9%)
", + "image_path": "b6b82b130ef520de939c6cda0932ba0ee6ae6c4ae038101d90043bdeb4b09872.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 50, + 282, + 285, + 382 + ], + "blocks": [ + { + "bbox": [ + 46, + 170, + 287, + 269 + ], + "lines": [ + { + "bbox": [ + 46, + 170, + 287, + 269 + ], + "spans": [ + { + "bbox": [ + 46, + 170, + 287, + 269 + ], + "type": "text", + "content": "Table 4. Semantic segmentation results of PSPNet [29] on Cityscapes [7], in mIOU. Due to differing implementation, the performance of our baseline varies from reported values, so we report relative improvements. At " + }, + { + "bbox": [ + 46, + 170, + 287, + 269 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 46, + 170, + 287, + 269 + ], + "type": "text", + "content": ", we outperform prior works. At " + }, + { + "bbox": [ + 46, + 170, + 287, + 269 + ], + "type": "inline_equation", + "content": "64 \\times 64" + }, + { + "bbox": [ + 46, + 170, + 287, + 269 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 170, + 287, + 269 + ], + "type": "inline_equation", + "content": "128 \\times 128" + }, + { + "bbox": [ + 46, + 170, + 287, + 269 + ], + "type": "text", + "content": ", LZU performs worse than prior work, perhaps because \"unzooming\" features at such small scales is more destructive. We posit the performance losses from such aggressive downsampling factors (across all methods) may be too impractical for deployment, and so focus on the " + }, + { + "bbox": [ + 46, + 170, + 287, + 269 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 46, + 170, + 287, + 269 + ], + "type": "text", + "content": " regime." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 282, + 285, + 382 + ], + "lines": [ + { + "bbox": [ + 50, + 282, + 285, + 382 + ], + "spans": [ + { + "bbox": [ + 50, + 282, + 285, + 382 + ], + "type": "table", + "html": "
ScaleMethodNDSmAPmATEmASEmAOEmAVEmAAELat (ms)
0.25xUniform21.811.496.732.690.1125.019.854.7
0.25xLZU, fixed23.413.196.831.982.7129.420.055.1
0.5xUniform27.517.590.128.875.5131.617.858.1
0.5xLZU, fixed29.320.188.928.373.9130.616.758.5
0.75xUniform30.521.087.327.967.0132.817.559.2
0.75xLZU, fixed31.822.483.827.567.2134.615.959.7
1xUniform31.222.484.227.470.9129.617.488.7
1xLZU, fixed32.624.884.627.568.2131.618.389.4
", + "image_path": "df181fbc45db70b82e65c1465e663b4138e68cda07a6650a3237be3e98100915.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 392, + 287, + 469 + ], + "lines": [ + { + "bbox": [ + 46, + 392, + 287, + 469 + ], + "spans": [ + { + "bbox": [ + 46, + 392, + 287, + 469 + ], + "type": "text", + "content": "Table 5. 3D object detection results of FCOS3D [26] on nuScenes [2]. Higher NDS and mAP is better, and lower is better on all other metrics. Intuitively, size is an important cue for depth, and image deformations would stifle this signal. Suprisingly, this is not the case. LZU improves upon the uniform downsampling baseline at all scales with less than 1ms of additional latency. Notably, LZU at " + }, + { + "bbox": [ + 46, + 392, + 287, + 469 + ], + "type": "inline_equation", + "content": "0.75\\mathrm{x}" + }, + { + "bbox": [ + 46, + 392, + 287, + 469 + ], + "type": "text", + "content": " scale even outperforms uniform downsampling at 1x." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 491, + 287, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 491, + 287, + 528 + ], + "spans": [ + { + "bbox": [ + 46, + 491, + 287, + 528 + ], + "type": "text", + "content": "at " + }, + { + "bbox": [ + 46, + 491, + 287, + 528 + ], + "type": "inline_equation", + "content": "128 \\times 128" + }, + { + "bbox": [ + 46, + 491, + 287, + 528 + ], + "type": "text", + "content": ", and LZU at " + }, + { + "bbox": [ + 46, + 491, + 287, + 528 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 46, + 491, + 287, + 528 + ], + "type": "text", + "content": ". We hypothesize that further improvements might be possible using an adaptive, learned formulation for saliency." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 538, + 219, + 551 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 538, + 219, + 551 + ], + "spans": [ + { + "bbox": [ + 47, + 538, + 219, + 551 + ], + "type": "text", + "content": "5.3. Monocular 3D Object Detection" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 557, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 557, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 557, + 288, + 713 + ], + "type": "text", + "content": "Finally, we evaluate LZU on monocular 3D object detection. To the best of our knowledge, no previous work has applied LZ downsampling to this task. The closest existing solution, FOVEA [22], cannot be extended to 3D object detection, because 3D bounding boxes are amodal and cannot be unwarped in the same manner as 2D bounding boxes. For our base model, we use FCOS3D [26], a fully convolutional model, with a ResNet-50 backbone [9] and FPN [16]. For our dataset, we use nuScenes [2], an autonomous driving dataset with multi-view " + }, + { + "bbox": [ + 46, + 557, + 288, + 713 + ], + "type": "inline_equation", + "content": "1600 \\times 900" + }, + { + "bbox": [ + 46, + 557, + 288, + 713 + ], + "type": "text", + "content": " RGB images for 1000 scenes and 3D bounding box annotations for 10 object classes. As is standard practice, we use the nuScenes Detection Score (NDS) metric, which is a combination of" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 72, + 547, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 144 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 144 + ], + "type": "text", + "content": "the usual mAP and measures of translation error (mATE), scale error (mASE), orientation error (mAOE), velocity error (mAVE), and attribute error (mAAE). We run experiments at " + }, + { + "bbox": [ + 304, + 72, + 547, + 144 + ], + "type": "inline_equation", + "content": "0.25\\mathrm{x}" + }, + { + "bbox": [ + 304, + 72, + 547, + 144 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 72, + 547, + 144 + ], + "type": "inline_equation", + "content": "0.5\\mathrm{x}" + }, + { + "bbox": [ + 304, + 72, + 547, + 144 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 72, + 547, + 144 + ], + "type": "inline_equation", + "content": "0.75\\mathrm{x}" + }, + { + "bbox": [ + 304, + 72, + 547, + 144 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 72, + 547, + 144 + ], + "type": "inline_equation", + "content": "1\\mathrm{x}" + }, + { + "bbox": [ + 304, + 72, + 547, + 144 + ], + "type": "text", + "content": " scales and test against a uniform downsampling baseline. We train for 12 epochs with a batch size of 16 with default parameters as in MMDetection3D [5]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 144, + 547, + 226 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 144, + 547, + 226 + ], + "spans": [ + { + "bbox": [ + 304, + 144, + 547, + 226 + ], + "type": "text", + "content": "For our LZU model, again we unzoom post-FPN features and use a fixed saliency map. Inspired by FOVEA [22], our fixed saliency is generated by using kernel density estimation on the set of projected bounding boxes in the image space. We reuse the same saliency hyperparameters from 2D detection. All other training settings are identical to the baseline." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 228, + 546, + 371 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 228, + 546, + 371 + ], + "spans": [ + { + "bbox": [ + 304, + 228, + 546, + 371 + ], + "type": "text", + "content": "Results are given in Table 5. LZU performs consistently better than uniform downsampling, with less than 1ms of additional latency. Specifically, LZU improves mAP and the aggregate metric NDS, with mixed results on mATE, mASE, mAOE, mAVE, and mAAE. Since the latter five metrics are computed on only true positives, this demonstrates that LZU increases overall recall, while maintaining about equal performance on true positives. Plotting the accuracy-latency curves (Figure 7) shows that LZU is Pareto optimal. We also repeat the same upsampling experiments as performed in 2D object detection. Results, shown in Table 2, reaffirm the viability of LZU in the upsampling regime." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 382, + 378, + 394 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 382, + 378, + 394 + ], + "spans": [ + { + "bbox": [ + 306, + 382, + 378, + 394 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 402, + 547, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 402, + 547, + 568 + ], + "spans": [ + { + "bbox": [ + 304, + 402, + 547, + 568 + ], + "type": "text", + "content": "We propose LZU, a simple attentional framework consisting of \"zooming\" in on the input image, computing spatial features, and \"unzooming\" to invert any deformations. To unzoom, we approximate the forward warp as a piecewise bilinear mapping and invert each piece. LZU is highly general and can be applied to any task with 2D spatial input and any model with 2D spatial features. We demonstrate the versatility of LZU empirically on a variety of tasks and datasets, including monocular 3D detection which has never been done before. We also show that LZU may even be used when high-resolution sensor data is unavailable. For future work, we can consider alternatives to the \"unzoom\" formulation that are perhaps less destructive than simple resampling of features." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 570, + 547, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 547, + 677 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 547, + 677 + ], + "type": "text", + "content": "Broader impact. Our work focuses on increasing the efficiency and accuracy of flagship vision tasks (detection, segmentation, 3D understanding) with high-resolution imagery. We share the same potential harms of the underlying tasks, but our approach may increase privacy concerns as identifiable information may be easier to decode at higher resolutions (e.g., facial identities or license plates). Because our approach is agnostic to the underlying model, it is reproducible with minimal changes to existing codebases." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": "Acknowledgements: This work was supported by the CMU Argo AI Center for Autonomous Vehicle Research." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5093" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 92, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 92, + 288, + 125 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 92, + 288, + 125 + ], + "spans": [ + { + "bbox": [ + 53, + 92, + 288, + 125 + ], + "type": "text", + "content": "[1] Thaddeus Beier and Shawn Neely. Feature-based image metamorphosis. ACM SIGGRAPH computer graphics, 26(2):35-42, 1992. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 127, + 288, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 127, + 288, + 172 + ], + "spans": [ + { + "bbox": [ + 53, + 127, + 288, + 172 + ], + "type": "text", + "content": "[2] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In CVPR, 2020. 2, 8, 14" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 174, + 288, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 174, + 288, + 261 + ], + "spans": [ + { + "bbox": [ + 53, + 174, + 288, + 261 + ], + "type": "text", + "content": "[3] Kai Chen, Jiaqi Wang, Jiangmiao Pang, Yuhang Cao, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jiarui Xu, Zheng Zhang, Dazhi Cheng, Chenchen Zhu, Tianheng Cheng, Qijie Zhao, Buyu Li, Xin Lu, Rui Zhu, Yue Wu, Jifeng Dai, Jingdong Wang, Jianping Shi, Wanli Ouyang, Chen Change Loy, and Dahua Lin. MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155, 2019. 13" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 264, + 288, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 264, + 288, + 297 + ], + "spans": [ + { + "bbox": [ + 53, + 264, + 288, + 297 + ], + "type": "text", + "content": "[4] Yu Cheng, Duo Wang, Pan Zhou, and Tao Zhang. A survey of model compression and acceleration for deep neural networks. arXiv preprint arXiv:1710.09282, 2017. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 299, + 288, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 299, + 288, + 342 + ], + "spans": [ + { + "bbox": [ + 53, + 299, + 288, + 342 + ], + "type": "text", + "content": "[5] MMDetection3D Contributors. MMDetection3D: Open-MMLab next-generation platform for general 3D object detection. https://github.com/open-mmlab/mmdetection3d, 2020.8, 13, 14" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 346, + 288, + 389 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 346, + 288, + 389 + ], + "spans": [ + { + "bbox": [ + 53, + 346, + 288, + 389 + ], + "type": "text", + "content": "[6] MMSegmentation Contributors. MMSegmentation: Openmmlab semantic segmentation toolbox and benchmark. https://github.com/open-mmlab/mmsegmentation, 2020.13" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 392, + 288, + 458 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 392, + 288, + 458 + ], + "spans": [ + { + "bbox": [ + 53, + 392, + 288, + 458 + ], + "type": "text", + "content": "[7] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 2, 7, 8, 12, 13" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 460, + 288, + 504 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 460, + 288, + 504 + ], + "spans": [ + { + "bbox": [ + 53, + 460, + 288, + 504 + ], + "type": "text", + "content": "[8] Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764-773, 2017. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 506, + 288, + 550 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 506, + 288, + 550 + ], + "spans": [ + { + "bbox": [ + 53, + 506, + 288, + 550 + ], + "type": "text", + "content": "[9] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 5, 7, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 552, + 288, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 552, + 288, + 586 + ], + "spans": [ + { + "bbox": [ + 48, + 552, + 288, + 586 + ], + "type": "text", + "content": "[10] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. Advances in neural information processing systems, 28, 2015. 2, 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 588, + 288, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 588, + 288, + 632 + ], + "spans": [ + { + "bbox": [ + 48, + 588, + 288, + 632 + ], + "type": "text", + "content": "[11] Chen Jin, Ryutaro Tanno, Thomy Mertzanidou, Eleftheria Panagiotaki, and Daniel C Alexander. Learning to downsample for segmentation of ultra-high resolution images. arXiv preprint arXiv:2109.11071, 2021. 1, 2, 3, 4, 7, 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 635, + 288, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 635, + 288, + 677 + ], + "spans": [ + { + "bbox": [ + 48, + 635, + 288, + 677 + ], + "type": "text", + "content": "[12] Shu Kong and Charless Fowlkes. Pixel-wise attentional gating for scene parsing. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1024-1033. IEEE, 2019. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 680, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 288, + 713 + ], + "type": "text", + "content": "[13] Jun Li, Yongjun Chen, Lei Cai, Ian Davidson, and Shuiwang Ji. Dense transformer networks. arXiv preprint arXiv:1705.08881, 2017. 2" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 307, + 73, + 546, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 546, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 546, + 106 + ], + "type": "text", + "content": "[14] Mengtian Li, Yu-Xiong Wang, and Deva Ramanan. Towards streaming perception. In European Conference on Computer Vision, pages 473-488. Springer, 2020. 2, 5, 6, 11, 13" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 108, + 547, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 108, + 547, + 150 + ], + "spans": [ + { + "bbox": [ + 307, + 108, + 547, + 150 + ], + "type": "text", + "content": "[15] Mengtian Li, Ersin Yumer, and Deva Ramanan. Budgeted training: Rethinking deep neural network training under resource constraints. arXiv preprint arXiv:1905.04753, 2019. 13" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 152, + 546, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 546, + 206 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 546, + 206 + ], + "type": "text", + "content": "[16] Tsung-Yi Lin, Piotr Dólar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2117-2125, 2017. 5, 7, 8, 12" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 209, + 547, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 209, + 547, + 252 + ], + "spans": [ + { + "bbox": [ + 307, + 209, + 547, + 252 + ], + "type": "text", + "content": "[17] Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dólar. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, pages 2980-2988, 2017. 2, 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 254, + 547, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 254, + 547, + 308 + ], + "spans": [ + { + "bbox": [ + 307, + 254, + 547, + 308 + ], + "type": "text", + "content": "[18] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 309, + 546, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 309, + 546, + 364 + ], + "spans": [ + { + "bbox": [ + 307, + 309, + 546, + 364 + ], + "type": "text", + "content": "[19] Dmitrii Marin, Zijian He, Peter Vajda, Priyam Chatterjee, Sam Tsai, Fei Yang, and Yuri Boykov. Efficient segmentation: Learning downsampling near semantic boundaries. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2131-2141, 2019. 2, 7, 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 365, + 547, + 420 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 365, + 547, + 420 + ], + "spans": [ + { + "bbox": [ + 307, + 365, + 547, + 420 + ], + "type": "text", + "content": "[20] Adria Recasens, Petr Kellnhofer, Simon Stent, Wojciech Matusik, and Antonio Torralba. Learning to zoom: a saliency-based sampling layer for neural networks. In Proceedings of the European Conference on Computer Vision (ECCV), pages 51-66, 2018. 1, 2, 3, 4" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 421, + 547, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 421, + 547, + 464 + ], + "spans": [ + { + "bbox": [ + 307, + 421, + 547, + 464 + ], + "type": "text", + "content": "[21] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 2015. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 466, + 547, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 466, + 547, + 520 + ], + "spans": [ + { + "bbox": [ + 307, + 466, + 547, + 520 + ], + "type": "text", + "content": "[22] Chittesh Thavamani, Mengtian Li, Nicolas Cebron, and Deva Ramanan. Fovea: Foveated image magnification for autonomous navigation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15539-15548, 2021. 1, 2, 3, 4, 5, 7, 8, 13" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 522, + 547, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 522, + 547, + 566 + ], + "spans": [ + { + "bbox": [ + 307, + 522, + 547, + 566 + ], + "type": "text", + "content": "[23] Vincent Vanhoucke, Andrew Senior, and Mark Z. Mao. Improving the speed of neural networks on cpus. In Deep Learning and Unsupervised Feature Learning Workshop, NIPS 2011, 2011. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 567, + 546, + 611 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 567, + 546, + 611 + ], + "spans": [ + { + "bbox": [ + 307, + 567, + 546, + 611 + ], + "type": "text", + "content": "[24] Thomas Verelst and Tinne Tuytelaars. Dynamic convolutions: Exploiting spatial sparsity for faster inference. In Proceedings of the iee/cvf conference on computer vision and pattern recognition, pages 2320-2329, 2020. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 613, + 546, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 613, + 546, + 645 + ], + "spans": [ + { + "bbox": [ + 307, + 613, + 546, + 645 + ], + "type": "text", + "content": "[25] Thomas Verelst and Tinne Tuytelaars. Segblocks: Block-based dynamic resolution networks for real-time segmentation. arXiv preprint arXiv:2011.12025, 2020. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 647, + 546, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 647, + 546, + 690 + ], + "spans": [ + { + "bbox": [ + 307, + 647, + 546, + 690 + ], + "type": "text", + "content": "[26] Tai Wang, Xinge Zhu, Jiangmiao Pang, and Dahua Lin. Fcos3d: Fully convolutional one-stage monocular 3d object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 913-922, 2021. 2, 8" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 692, + 546, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 692, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 692, + 546, + 713 + ], + "type": "text", + "content": "[27] George Wolberg. Digital image warping, volume 10662. IEEE computer society press Los Alamitos, CA, 1990. 4, 11" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5094" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 206 + ], + "type": "list", + "angle": 0, + "index": 3, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "text", + "content": "[28] Zhenda Xie, Zheng Zhang, Xizhou Zhu, Gao Huang, and Stephen Lin. Spatially adaptive inference with stochastic feature sampling and interpolation. In European conference on computer vision, pages 531-548. Springer, 2020. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 118, + 287, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 118, + 287, + 162 + ], + "spans": [ + { + "bbox": [ + 48, + 118, + 287, + 162 + ], + "type": "text", + "content": "[29] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881-2890, 2017. 2, 7, 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 163, + 287, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 163, + 287, + 206 + ], + "spans": [ + { + "bbox": [ + 48, + 163, + 287, + 206 + ], + "type": "text", + "content": "[30] Xizhou Zhu, Han Hu, Stephen Lin, and Jifeng Dai. Deformable convnets v2: More deformable, better results. 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Despite some progress, existing methods are mostly limited to highly curated datasets (e.g., K400) and exhibit unsatisfactory out-of-the-box representations. We argue that it is due to the fact that they only capture pixel-level knowledge rather than spatiotemporal semantics, which hinders further progress in video understanding. Inspired by the great success of image-text pre-training (e.g., CLIP), we take the first step to exploit language semantics to boost transferable spatiotemporal representation learning. We introduce a new pretext task, Turning to Video for Transcript Sorting (TVTS), which sorts shuffled ASR scripts by attending to learned video representations. We do not rely on descriptive captions and learn purely from video, i.e., leveraging the natural transcribed speech knowledge to provide noisy but useful semantics over time. Our method enforces the vision model to contextualize what is happening over time so that it can re-organize the narrative transcripts, and can seamlessly apply to large-scale uncurated video data in the real world. Our method demonstrates strong out-of-the-box spatiotemporal representations on diverse benchmarks, e.g., $+13.6\\%$ gains over VideoMAE on SSV2 via linear probing. The code is available at https://github.com/TencentARC/TVTS.", + "bbox": [ + 76, + 393, + 473, + 785 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 814, + 209, + 830 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The aspiration of representation learning is to encode general-purpose representations that transfer well to diverse downstream tasks, where self-supervised methodologies [9, 25] dominate due to their advantage in exploiting", + "bbox": [ + 75, + 839, + 470, + 902 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "large-scale unlabeled data. Despite significant progress in learning representations of still images [23, 43], the real world is dynamic and requires reasoning over time. In this paper, we focus on out-of-the-box spatiotemporal representation learning, a more challenging but practical task towards generic video understanding, which aims to capture hidden representations that can be further used to conduct reasoning on broader tasks, e.g., classification and retrieval.", + "bbox": [ + 496, + 363, + 893, + 484 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "There have been various attempts at self-supervised pretraining on video data from discriminative learning objectives [5, 8, 27] to generative ones [17, 49], where the core is context capturing in spatial and temporal dimensions. Though promising results are achieved when transferring the pre-trained models to downstream video recognition [22, 33, 48] via fine-tuning, the learned representations are still far away from out-of-the-box given the poor linearly probing results (see Figure 1(a)). Moreover, existing works mostly develop video models on the highly curated dataset with particular biases, i.e., K400 [31]. Their applicability in the real world is questioned given the observed performance drops when training on a larger but uncurated dataset, YT-Temporal [55]. We argue that, to address the above issue, the rich spatiotemporal semantics contained in the video itself should be fully exploited. But current video models generally exploit visual-only perception (e.g., pixels) without explicit semantics.", + "bbox": [ + 496, + 488, + 895, + 760 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recently, the success of CLIP [43] has inspired the community to learn semantically aware image representations that are better transferable to downstream tasks and scalable to larger uncurated datasets. It provides a feasible solution for improving spatiotemporal representation learning but remains two key problems. (1) The vision-language contrastive constraints in CLIP mainly encourage the understanding of static objects (noun contrast) and simple motions (verb contrast), while how to enable long-range tem", + "bbox": [ + 496, + 763, + 895, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "23079", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/30311201ad497b9a952207ff0eecbfa161e7a9bb0e3bd4be2eddc34068a44a24.jpg", + "image_caption": [ + "Figure 1. (a) We evaluate the transferability of spatiotemporal representations via linear probing on four video recognition datasets [22, 31, 33, 48], where the state-of-the-art method [49] underperforms. It performs even worse when pre-trained with a large-scale uncurated dataset, YT-Temporal [55]. (b) We encourage complex temporal understanding and advanced spatiotemporal representation learning with a new pretext task of sorting transcripts." + ], + "image_footnote": [], + "bbox": [ + 76, + 88, + 594, + 309 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/7fce2c431215d5060407a73b266ae7173163589f7c5541b0bbab1cba0a068b29.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 607, + 88, + 890, + 309 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "poral understanding with language supervision needs to be studied. (2) The quality of language supervision [47] is critical to the final performance of CLIP, however, it is hard to collect large-scale video data with literal captions that carefully describe the dynamic content over time. The ideal way for self-supervised learning is to learn useful knowledge purely from the data itself, which is also the philosophy followed by previous video pre-training methods [17, 49]. Fortunately, video data is naturally multi-modal with transcribed speech knowledge in the form of text (ASR), providing time-dependent semantics despite some noise.", + "bbox": [ + 75, + 405, + 472, + 571 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To facilitate spatiotemporal understanding in large-scale uncurated data under the supervision of inherent script knowledge, we introduce a new pretext task for video pretraining, namely, Turning to Video for Transcript Sorting (TVTS). Intuitively, people sort out the order of events by temporal reasoning. As illustrated in Figure 1(b), given several unordered transcripts, it is difficult to reorganize the narrative by merely understanding the literal semantics. When the corresponding video is provided, it will be much easier to sort the transcripts by contextualizing what is happening over time. Whereas in neural networks, the temporal inference is embedded in spatiotemporal representations. Thus we believe that if the chronological order of transcripts can be correctly figured out via resorting to the correlated video representations, the video has been well understood.", + "bbox": [ + 75, + 577, + 472, + 819 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We realize the pretext task of TVTS by performing joint attention among the encoded video spatiotemporal representations and the extracted ASR transcript representations. Specifically, given an input video and its successive transcripts, we randomly shuffle the order of the sentences.", + "bbox": [ + 75, + 824, + 472, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Subsequently, we concatenate the encoded script representations and the video representations and perform self-attention to predict the actual orders of the shuffled transcripts by fully understanding the spatiotemporal semantics in the video. The order prediction is cast as a $K$ -way classification task, where $K$ is the number of transcripts. The pretext task indirectly regularizes our model to properly capture contextualized spatiotemporal representations to provide enough knowledge for transcript ordering.", + "bbox": [ + 496, + 405, + 893, + 542 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The usage of language supervision is related to videotext alignment [4, 20] and multimodal representation learning [18, 55] methods, however, we are completely different. (1) Video-text alignment methods focus on retrieval tasks and are devoted to associating the vision patterns with language concepts. They are generally single-frame biased [34] and fail to encode strong out-of-the-box temporal representations. (2) Multimodal representation learning methods aim to learn fused representations across modalities rather than vision-only spatiotemporal representations in our work. Moreover, different from our pretext task that aims to optimize spatiotemporal video representations, [55] sorts video frames by taking the features of individual frames as inputs without temporal modeling, i.e., learning video representations only at the image level. As [55] points out, its ordering pretext task is not critical for downstream tasks (performance even drops) and primarily serves as an interface to query the model about temporal events.", + "bbox": [ + 496, + 547, + 895, + 820 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To summarize, our contributions are three-fold. (i) We exploit the rich semantics from script knowledge which is naturally along with the video, rendering a flexible pretraining method that can easily apply to uncurated video data in the real world. (ii) We introduce a novel pre", + "bbox": [ + 496, + 824, + 893, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "23080", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "text task for video pre-training, namely, Turning to Video for Transcript Sorting (TVTS). It promotes the capability of the model in learning transferable spatiotemporal video representations. (iii) We conduct comprehensive comparisons with advanced methods. Our pre-trained model exhibits strong out-of-the-box spatiotemporal representations on downstream action recognition tasks, especially the relatively large-scale and the most challenging SSV2 [22]. We also achieve state-of-the-art performances on eight common video datasets in terms of fine-tuning.", + "bbox": [ + 75, + 90, + 472, + 243 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 76, + 256, + 218, + 272 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Spatiotemporal representation learning. Dominant video representation learning works have two categories, i.e., discriminative- and generative-based methods. (i) The discriminative-based methods aim at mining unique representations within videos. For example, SVT [44] aligns several views from the same video with different spatial and temporal resolution for video-invariant representations. RSPNet [8], ASCNet [27], and LongShortView [5] utilize the appearance and temporal consistency of videos as the supervision. They use different augmentations of videos to construct positive and negative pairs to learn correspondences along the spatial and temporal dimensions. (ii) The generative-based methods try to reconstruct visual information from corrupted inputs. For example, MAE-based [24] methods [17, 49] use pixel values of video frames as supervision by masking raw videos with an extremely high ratio and reconstructing them.", + "bbox": [ + 75, + 281, + 472, + 537 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Previous works are mainly trained on highly curated datasets, e.g., Kinetics-400, HMDB51, and UCF101, where the temporal motions are not significant [34]. This leads to a \"spatial bias\", thus weakening the transferability to real-world uncurated datasets due to the lack of long-term temporal reasoning. Besides, existing works merely use visual supervision without explicit semantic information. Compared to them, our work leverages natural language derived from the video itself, i.e., the ASR transcripts, as the supervision. Benefiting from the rich spatiotemporal information, our learned video representations have stronger transferability to downstream tasks.", + "bbox": [ + 75, + 537, + 472, + 718 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Video-text pre-training. Existing video-text pre-training work can be divided into two categories. The first category aims to learn video-text alignment for retrieval. For example, Frozen [4], MCQ [20], and MILES [21] generally adopt two separate encoders to extract video and text representations, then align them with contrastive loss. However, they only align videos with a global video caption, thus neglecting the fine-grained temporal information. Furthermore, they rely on clean captions, which are difficult to scale up, and it is actually hard to collect large-scale video data with captions describing the dynamic content over time. The second category works on joint representation learning across", + "bbox": [ + 75, + 719, + 472, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "modalities mainly for VQA. For example, MERLOT [55] adopts a joint encoder to match the captions with the corresponding video frames and put scrambled video frames into the correct order. It aims to match different modalities in the temporal dimension to achieve multi-modality fusion in a joint encoder, rather than learn better spatiotemporal representations.", + "bbox": [ + 496, + 90, + 890, + 196 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Image representation learning by language supervision. Recently, there have been a bunch of successful tries in utilizing language supervision to enhance image representation learning. For example, CLIP [43] utilized 400M imagetext pairs collected from the Internet and adopt the contrastive loss to align the image and its corresponding text. The superior performance on downstream image classification tasks revealed that learning directly from the raw text about images is a promising alternative that leverages a much broader source of supervision. ALIGN [30], uses a larger but noisier uncurated dataset and shows similar results to CLIP. Nevertheless, these methods only utilize language supervision to improve spatial learning, without exploring temporal learning, which hinders them from properly learning out-of-the-box video representations.", + "bbox": [ + 496, + 196, + 890, + 422 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 500, + 434, + 589, + 450 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this work, we introduce a novel pretext task, Turning to Video for Transcript Sorting (TVTS) to learn the transferable spatiotemporal video representation by leveraging the rich semantics from script knowledge. In this section, we first introduce the pretext TVTS in Sec. 3.1 and our pretraining objectives in Sec. 3.2. We then describe the model architecture in Sec. 3.3.", + "bbox": [ + 496, + 460, + 890, + 566 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Turning to Video for Transcript Sorting", + "text_level": 1, + "bbox": [ + 500, + 573, + 841, + 589 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As shown in Fig. 2, we perform the pretext task of TVTS to learn transferable spatiotemporal representations of videos. Given the observation that it will be much easier to sort the ASR transcripts by contextualizing what is happening over time in the video, we first randomly shuffle several consecutive ASR transcripts and extract their representations. We then perform joint attention among the transcript representations and video representations to sort the transcripts in the correct order via capturing contextualized spatiotemporal representations of videos.", + "bbox": [ + 496, + 595, + 890, + 748 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Sample and Shuffle. Given a video $V$ and its corresponding ASR transcripts with word-level timestamps $\\{(w_{i},s_{i})\\}_{i = 1}^{N_{\\mathrm{asr}}}$ , where $N_{\\mathrm{asr}}$ denotes the word number, $w_{i}$ and $s_i$ denote the $i$ -th word and its timestamp respectively, we randomly choose a starting time $s_{\\mathrm{begin}}$ and sample $K$ consecutive transcripts, each with a duration of $l$ (in seconds), and an interval of 1s between adjacent transcripts,", + "bbox": [ + 496, + 755, + 890, + 862 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nS _ {k} = s _ {\\text {b e g i n}} + (k - 1) * (l + 1), \\quad E _ {k} = S _ {k} + l \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 524, + 867, + 890, + 891 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nT _ {k} = \\left\\{w _ {i} | S _ {k} \\leq s _ {i} \\leq E _ {k} \\right\\}, \\quad k \\in \\{1, \\dots , K \\}\n$$\n", + "text_format": "latex", + "bbox": [ + 527, + 888, + 834, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "23081", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/31020a82ce26ae0b37ee895c9df4a3cbaa5a44fdebc34f181c4974aff85faea2.jpg", + "image_caption": [ + "Figure 2. Our pre-training pipeline. We first sample $K$ consecutive ASR transcripts, and a video clip consisting of $M$ frames within the span of the transcripts. We randomly sample frame patches as the input of a vision transformer for the video representations. We then shuffle the transcripts and extract the representation of each transcript. We perform joint attention among the transcript and the video representations to predict the actual order of each transcript, which is optimized with a cross-entropy objective." + ], + "image_footnote": [], + "bbox": [ + 76, + 88, + 893, + 369 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $S_{k}$ and $E_{k}$ denote the beginning and ending time of the $k$ -th transcript. We consecutively sample $K$ transcripts with an interval of 1s and collect all words within $[S_{k}, E_{k}]$ for the $k$ -th transcript. Finally, we randomly shuffle the transcripts as $\\{T_{o_i}\\}_{i=1}^{K}$ , which means that the $i$ -th transcript in this shuffled sequence is actually the $o_i$ -th transcript in the original ordered sequence.", + "bbox": [ + 75, + 459, + 472, + 565 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As for the video, we sample a clip between the beginning and ending time of all $K$ transcripts, i.e., $[S_1,E_K]$ , which contains $M$ frames as $\\{F_i\\}_{i = 1}^M$ . Specifically, we follow TSN [52] to divide $[S_1,E_K]$ into $M$ segments with equal length and randomly sample 1 frame from each segment. After that, we get a video clip with $M$ frames and $K$ shuffled transcripts along the span of the video clip.", + "bbox": [ + 75, + 566, + 468, + 674 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Sorting Transcripts. Given the shuffled transcripts $\\{T_{o_i}\\}_{i = 1}^K$ and the corresponding video clip $\\{F_i\\}_{i = 1}^M$ , we first feed the transcripts in parallel to encode unordered text representations $\\{t_{o_i}\\}_{i = 1}^K$ . We then randomly sample the frame patches by masking a large proportion of the video clip among the spatial and temporal dimension as the input of a vision transformer to encode video representations $\\{v_j\\}_{j = 0}^N$ , where $N$ denotes the number of the unmasked video patches, and $v_0$ is the representation of the [CLS] token. It is worth noting that we do not add the extra [MASK] token, and we have no explicit reconstruction target, which is different from previous works [17, 49]. We sample video frame patches as a means of data augmentation since it provides corrupted knowledge for our model to perform the", + "bbox": [ + 76, + 688, + 472, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "pretext task of TVTS. Such a strategy also reduces the computational cost during pre-training as the attention is calculated on fewer patches.", + "bbox": [ + 496, + 459, + 890, + 503 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We then concatenate the text representations of the shuffled transcripts $\\{t_{o_i}\\}_{i = 1}^K$ and the video representations of the sampled video clip $\\{v_j\\}_{j = 0}^N$ , and perform multi-head selfattention among them. Our model attempts to sort the transcripts in the correct order by attending to the text features of all transcripts and the visual features of the unmasked video clip. We model the prediction of the transcript orders as a $K$ -way classification task for each transcript. The first $K$ output representations after the joint attention are further fed into a linear classifier to predict the order $p\\in \\mathbb{R}^{K}$ where $p_j$ denotes the probability that the transcript is the $j$ -th transcript in the original ordered sequence. For the transcript $T_{o_i}$ , the ground truth classification label should be $o_i$ .", + "bbox": [ + 496, + 505, + 892, + 700 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The pretext task of TVTS regularizes the model to contextualize what is happening over time, so that it can provide enough knowledge for our model to figure out the chronological order of the shuffled transcripts. It improves the capability of the model to learn spatiotemporal representations that can be transferred to downstream tasks. We compare our method with other works that also adopt an ordering-based pretext task for pre-training in Sec. 4.5.", + "bbox": [ + 496, + 700, + 893, + 823 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Pre-training Objectives", + "text_level": 1, + "bbox": [ + 500, + 830, + 718, + 848 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Besides the pretext task of TVTS, we use a global video Transcript contrastive objective. It aligns the features of the video clip and the averaged features of $K$ transcripts so that", + "bbox": [ + 496, + 854, + 893, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "23082", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "the video and transcript representations are in the same feature space for performing the joint attention to predict transcript orders. We combine two objectives to optimize the entire model in an end-to-end manner.", + "bbox": [ + 75, + 90, + 468, + 150 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The first one is a cross-entropy objective $\\mathcal{L}_{\\mathrm{sort}}$ , which supervises our model to predict the correct order of the transcripts, and is formulated as below,", + "bbox": [ + 75, + 151, + 468, + 196 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {s o r t}} = - \\frac {1}{K} \\sum_ {i = 1} ^ {K} \\log \\operatorname {s o f t m a x} \\left(\\hat {p} ^ {i}\\right) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 189, + 204, + 468, + 247 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\ns. t. \\quad \\mathrm {s o f t m a x} (\\hat {p} ^ {i}) = \\frac {\\exp (p _ {o _ {i}} ^ {i})}{\\sum_ {j = 1} ^ {K} \\exp (p _ {j} ^ {i})},\n$$\n", + "text_format": "latex", + "bbox": [ + 156, + 246, + 383, + 279 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $p_j^i$ denotes the probability that the $i$ -th transcript in the shuffled sequence is the $j$ -th transcript in the original ordered sequence and $o_i$ is the ground truth order in the original ordered sequence.", + "bbox": [ + 75, + 286, + 468, + 347 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The second one is the global video-transcript contrastive objective $\\mathcal{L}_{\\mathrm{base}}$ , formulated as a bidirectional InfoNCE [40],", + "bbox": [ + 76, + 347, + 468, + 378 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {b a s e}} = \\mathrm {N C E} (\\hat {t}, \\hat {v}) + \\mathrm {N C E} (\\hat {v}, \\hat {t})\n$$\n", + "text_format": "latex", + "bbox": [ + 163, + 385, + 357, + 401 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\ns. t. \\quad \\mathrm {N C E} (q, k) = - \\log \\frac {\\exp \\left(q ^ {\\top} k _ {+} / \\tau\\right)}{\\sum_ {i = 1} ^ {B} \\exp \\left(q ^ {\\top} k _ {i} / \\tau\\right)}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 129, + 404, + 468, + 438 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\hat{t}$ and $\\hat{v}$ denote the global text and video representation. We average the [CLS] token representation of all $K$ transcripts as $\\hat{t}$ , i.e., $\\hat{t} \\gets \\frac{1}{K} \\sum_{i=1}^{K} t_i$ , and use the [CLS] token representation of the video clip as $\\hat{v}$ , i.e., $\\hat{v} \\gets v_0$ .", + "bbox": [ + 75, + 445, + 468, + 506 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our overall pre-training objective combines the two objectives, i.e., $\\mathcal{L} = \\mathcal{L}_{\\mathrm{base}} + \\lambda \\mathcal{L}_{\\mathrm{sort}}$ , where $\\lambda$ is a hyperparameter to balance the two losses. In our implementation, we set $\\lambda = 2$ to roughly scale the gradient magnitudes of $\\mathcal{L}_{\\mathrm{base}}$ and $\\mathcal{L}_{\\mathrm{sort}}$ to be the same for efficient training.", + "bbox": [ + 75, + 506, + 468, + 580 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Model Architecture", + "text_level": 1, + "bbox": [ + 76, + 590, + 264, + 604 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The vision transformer takes a video clip as input, which consists of $M$ frames of resolution $H\\times W$ , and outputs video representations. We follow [49] to adopt cube embeddings, where each token corresponds to a cube of size $2\\times 16\\times 16$ . This yields $\\frac{M}{2}\\times \\frac{H}{16}\\times \\frac{W}{16} 3D$ tokens. Then we add divided space-time embedding to the token sequence, where tokens within the same frame obtain the same temporal embedding, and tokens within the same spatial location of different frames obtain the same spatial embedding. In this way, the vision transformer learns the positional information of the cubes. Next, we follow BERT [12] to add a learnable [CLS] token at the beginning of the token sequence for global video representations. Then we mask a portion of video tokens without [MASK] token replacement, as stated in Sec. 3.1. We adopt a standard ViT [15] architecture to encode video representations. The unmasked $N$ video tokens as well as the [CLS] token are fed into the vision transformer, and joint space-time attention [3] is performed among the whole unmasked token sequence.", + "bbox": [ + 75, + 613, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We use a DistilBERT [46] to extract the representations of ASR transcripts. We adopt two stacked bidirectional transformer blocks to predict the order of each transcript by performing joint attention among the transcript and the video representations. Within each block, multi-head self-attention is performed among all the video and text tokens, i.e., all transcript-video tokens interact with each other.", + "bbox": [ + 496, + 90, + 890, + 196 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 213, + 633, + 229 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Pre-training Datasets", + "text_level": 1, + "bbox": [ + 500, + 239, + 700, + 256 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We pre-train our model on the large-scale YT-Temporal dataset [55] containing 6M YouTube videos with ASR transcripts and word-level timestamps.", + "bbox": [ + 496, + 263, + 890, + 310 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Downstream Tasks", + "text_level": 1, + "bbox": [ + 500, + 321, + 681, + 337 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Action Recognition. We evaluate our pre-trained model on four common video datasets: (a) Something-Something V2 (SSV2) [22], (b) Kinetics-400 (K400) [31], (c) UCF-101 [48], (d) HMDB-51 [33]. Our evaluation is two-fold: (i) We conduct zero-shot video-to-video retrieval and linear probe classification on SSV2 to evaluate the transferability of the learned video representation. The former aims to retrieve videos of the same category as a query video, and the latter freezes the visual encoder and only optimizes a linear classifier. (ii) We fully fine-tune our pre-trained model on the training set of the four datasets to evaluate the action recognition capability. See Appendix for details.", + "bbox": [ + 496, + 345, + 890, + 527 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Text-to-Video Retrieval. Beyond action recognition, we further evaluate retrieval performance on four benchmarks to see if the improved semantic-aware video representation can benefit retrieval tasks: (a) MSR-VTT [54] (b) DiDeMo [2] (c) MSVD [7] (d) LSMDC [45]. We adopt Recall@K (R@K) and Median Rank (MedR) as the evaluation metric. See Appendix for details.", + "bbox": [ + 496, + 546, + 890, + 652 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Implementation Details", + "text_level": 1, + "bbox": [ + 500, + 664, + 718, + 680 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We follow recent works [4, 20] to adopt the pre-trained DistilBERT [46] to extract transcript representations. The vision transformer is a vanilla ViT-Base [15] with patch size $P = 16$ and hidden state dimension $D = 768$ , and is initialized with ImageMAE-Base [23]. We set the temperature parameter $\\tau$ to be 0.05. We pre-train our model on the YTemporal dataset sampling 16 frames for 20 epochs. We randomly mask $75\\%$ tokens within each frame. The input frame is first resized to $256 \\times 256$ , then we apply RandomCrop during training and CenterCrop during inference. The final input resolution of each frame is $224 \\times 224$ . For downstream tasks, we sample 16 frames for action recognition following [49] and 4 frames for text-to-video retrieval following [4]. More hyper-parameters are listed in Appendix.", + "bbox": [ + 496, + 688, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "23083", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/cef84e40b3fac1623daa6237ccbe1e74531e6aab8ba82fe3d231a2adb17dd17a.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Target →NoneTranscriptVideo
Dataset ↓BaselineOursVCOP [53]MERLOT [55]MERLOT-like
UCF-10181.2 (↓2.2)83.479.1 (↓4.3)74.9 (↓8.5)80.1 (↓3.3)
HMDB-5156.5 (↓1.9)58.454.2 (↓4.2)49.6 (↓8.8)55.4 (↓3.0)
", + "bbox": [ + 186, + 88, + 782, + 171 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/69fba8f99a978819befe6acda4d46eaa35d011322a7e7916b50fe815eb28d252.jpg", + "table_caption": [ + "Table 1. Comparison with methods that use ordering-based pretext tasks for pre-training. We report top-1 accuracy under the linear probe classification protocol on UCF-101 and HMDB-51. The model pre-trained only with $\\mathcal{L}_{\\mathrm{base}}$ serves as the baseline. All models are pre-trained on the YT-Temporal dataset for a fair comparison." + ], + "table_footnote": [], + "table_body": "
Name\\( \\mathcal{L}_{\\text{base}} \\)\\( \\mathcal{L}_{\\text{sort}} \\)sgSSV2Kinetics-400
\\( M_{scratch} \\)××-64.5 (↓4.0)75.4 (↓3.4)
\\( M_{base} \\)✓×-67.0 (↓1.5)77.8 (↓1.0)
\\( M_{sort\\backslash sg} \\)×✓×failedfailed
\\( M_{sort} \\)×✓✓failedfailed
\\( M_{ours\\backslash sg} \\)✓✓×66.2 (↓2.3)76.5 (↓2.3)
\\( M_{ours} \\)✓✓✓68.578.8
", + "bbox": [ + 83, + 247, + 467, + 364 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/e1e77a1a5c280aa17cc32dd3095ea97abdfcacdcd0f91565a8a74bcc42c2b8f1.jpg", + "table_caption": [ + "Table 2. The top-1 accuracy under the fine-tuning protocol on SSV2 and Kinetics-400, w.r.t. different pre-training objectives, where $\\mathcal{L}_{\\mathrm{sort}}$ trains the pretext task of TVTS. sg denotes stopping gradients of $\\mathcal{L}_{\\mathrm{sort}}$ towards encoding transcript representations." + ], + "table_footnote": [], + "table_body": "
DatasetNoneSort Modeling
BaselinePairwiseFactorialK-way
SSV267.0 (↓1.5)67.4 (↓1.1)67.2 (↓1.3)68.5
K40077.8 (↓1.0)78.1 (↓0.7)78.0 (↓0.8)78.8
", + "bbox": [ + 81, + 445, + 467, + 529 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. The top-1 accuracy under the fine-tuning protocol on SSV2 and Kinetics-400, w.r.t. different ways to model the sorting of transcripts. \"Pairwise\" predicts the relative order for all transcript pairs, and \"Factorial\" performs $K!$ -classification for all possible orders. Our method uses \"K-way\" classification to predict the order of each transcript. The model pre-trained with $\\mathcal{L}_{\\mathrm{base}}$ only serves as the baseline.", + "bbox": [ + 75, + 541, + 468, + 638 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.4. Ablation Study", + "text_level": 1, + "bbox": [ + 76, + 665, + 230, + 681 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Pre-training Objectives. To demonstrate the effectiveness of our pretext task TVTS, we pre-train models with different objectives on the YT-Temporal dataset and evaluate them on SSV2 and K400. The results are listed in Table 2, in which $\\mathbf{M}_{\\mathrm{scratch}}$ denotes that we directly fine-tune the Image-MAE [23] initialized model. We have the following observations: (i) $\\mathbf{M}_{\\mathrm{base}}$ outperforms $\\mathbf{M}_{\\mathrm{scratch}}$ , which indicates that the natural language can be a promising supervision for video representation learning. (ii) Compared to $\\mathbf{M}_{\\mathrm{base}}$ , $\\mathbf{M}_{\\mathrm{ours}}$ further boosts performance by $1.5\\%$ , which demonstrates that TVTS can effectively regularize our model to learn transferable spatiotemporal representations. (iii) $\\mathbf{M}_{\\mathrm{ours}\\backslash \\mathrm{sg}}$ drops performance, because when the gradients of $\\mathcal{L}_{\\mathrm{sort}}$ flow towards encoding transcript representations, the model op", + "bbox": [ + 75, + 688, + 470, + 902 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "timizes the transcript representations to ease the ordering task rather than enhance spatiotemporal representations to provide enough knowledge for transcript sorting. (iv) Both $\\mathbf{M}_{\\mathrm{sort}\\backslash \\mathrm{sg}}$ and $\\mathbf{M}_{\\mathrm{sort}}$ failed because the parametric module for sorting hardly converges when feeding the misaligned video and transcript representations from distinct latent spaces.", + "bbox": [ + 496, + 251, + 893, + 343 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Sort Modeling. We explore different ways to model the order prediction of the shuffled transcripts. Besides using $K$ -way classification of each transcript, we also tried Pairwise, which sorts the transcripts by predicting the relative orders of the $K(K - 1) / 2$ transcript pairs, and Factorial, which predicts an overall ordering distribution by performing a $K$ -way classification ( $K!$ possible orders given $K$ transcripts). As listed in Table 3, both Pairwise and Factorial drop performance, because the former ignores the overall relationship among the transcripts while the latter imposes the same penalty on the results when different number of transcripts are sorted incorrectly. But they still outperform the baseline, indicating that sorting transcripts does benefit spatiotemporal representation learning. Our separate $K$ -way classification modeling achieves the best performance.", + "bbox": [ + 496, + 354, + 893, + 582 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.5. Comparison with Ordering-based Pre-training", + "text_level": 1, + "bbox": [ + 498, + 589, + 890, + 607 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "MERLOT [55] also adopts an ordering-based pretext task, but has a totally different approach and purpose. MERLOT reorders scrambled video frames given the representations of every single frame and the ordered ASR transcripts with a joint encoder, and reserves the joint encoder for downstream multimodal tasks such as VQA. Specifically, MERLOT predicts the relative order of two video frames by binary classification. MERLOT aims to promote the joint encoder in learning multi-modal representations rather than spatiotemporal representations of videos. Since the visual encoder takes a single frame as input, it only achieves semantic understanding at the single-frame level without temporal reasoning among frames. As MERLOT points out, the ordering pretext task is not critical for downstream tasks (performance even drops) and primarily serves as an interface to query the model about temporal events. We further tailor MERLOT for our architecture as MERLOT-like, which sorts $K$ shuffled video frames by $K$ -way classification with the knowledge of the ordered tran", + "bbox": [ + 496, + 613, + 893, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "23084", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/ac61bdee5fc3ea606b8268cb1d669f0c0bb8a1e08813e298b953bfdba420dab6.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodVenuePre-train DatasetZero-shot Video-to-video RetrievalLinear Probe
R@1R@5R@10
Spatiotemporal representation learning method(s)
CVRL [42]CVPR'21Kinetics-400---11.4 (↓20.1)
MViT [16]ICCV'21Kinetics-400---19.4 (↓12.1)
SCVRL [14]CVPRW'22Kinetics-400---13.8 (↓17.7)
SVT [44]CVPR'22Kinetics-40011.3 (↓3.4)30.7 (↓7.7)41.1 (↓9.4)18.3 (↓13.2)
SVT† [44]CVPR'22YT-Temporal9.9 (↓4.8)26.2 (↓12.2)36.3 (↓14.2)18.0 (↓13.5)
VideoMAE [49]NeurIPS'22Kinetics-4007.9 (↓6.8)18.6 (↓19.8)26.5 (↓24.0)17.9 (↓13.6)
VideoMAE† [49]NeurIPS'22YT-Temporal7.2 (↓7.5)17.6 (↓20.8)25.6 (↓24.9)15.9 (↓15.6)
Video-text alignment method(s)
Frozen‡ [4]ICCV'21CC3M, WebVid-2M10.4 (↓4.3)28.5 (↓9.9)38.7 (↓11.8)17.5 (↓14.0)
MCQ‡ [20]CVPR'22CC3M, WebVid-2M10.4 (↓4.3)28.6 (↓9.8)38.5 (↓12.0)18.0 (↓13.5)
MILES‡ [21]ECCV'22CC3M, WebVid-2M10.3 (↓4.4)28.4 (↓10.0)38.4 (↓12.1)18.6 (↓12.9)
Image representation learning method(s)
CLIP [43]ICML'21WIT10.5 (↓4.2)28.8 (↓9.6)38.8 (↓11.7)16.4 (↓15.1)
OursCVPR'23YT-Temporal14.738.450.531.5
", + "bbox": [ + 125, + 88, + 846, + 367 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "scripts. Similar to MERLOT, VCOP [53] also predicts the order of the shuffled video clips, but only pre-trains on videos without language semantics.", + "bbox": [ + 75, + 446, + 468, + 491 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "As listed in Table 1, all models that sort shuffled video clips/frames as the pretext task (i.e., VCOP, MERLOT) perform even worse than the baseline counterpart without sorting. It indicates that sorting shuffled videos in pretraining is infeasible and counterintuitive for improving spatiotemporal representations, because it does not regularize the video encoder for spatial and temporal reasoning given only a single frame or a short video segment as input. They mainly regularize an extra module beyond the video encoder to figure out the chronological order of the videos. By contrast, our pretext task regularizes the model to sort transcripts via reasoning among the video representations, which enforces the model to capture contextualized spatiotemporal representations so that it can provide enough knowledge for transcript ordering.", + "bbox": [ + 75, + 492, + 470, + 719 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.6. Main Results", + "text_level": 1, + "bbox": [ + 76, + 729, + 215, + 744 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.6.1 Action Recognition", + "text_level": 1, + "bbox": [ + 76, + 753, + 264, + 768 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Out-of-the-box Representations. To explore the transferability of the learned video representation, we evaluate zero-shot video-to-video retrieval and linear probe classification. We compare our proposed method with seven state-of-the-art methods, including: (a) Five video representation learning methods, i.e., CVRL [42], MViT [16], SCVRL [14], SVT [44], and VideoMAE [49]. (b) Three video-text alignment methods, i.e., Frozen [4], MCQ [20],", + "bbox": [ + 75, + 779, + 468, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/c5c63dbac02fcce9ea009a43c0c55d4690a3a97de4e5b91388f05774e1b9b7af.jpg", + "table_caption": [ + "Table 4. Transferability evaluation on SSV2. We report Recall@K for zero-shot video-to-video retrieval and top-1 accuracy for linear probe classification, where video-to-video retrieval aims to retrieve videos of the same category as a query video. † denotes pre-training on YT-Temporal for a fair comparison, and ‡ denotes the use of official pre-trained weights for evaluation." + ], + "table_footnote": [], + "table_body": "
MethodBackbonePre-train DatasetSSV2K400
TSM [36]R50 × 2ImageNet-1K66.0-
Vi²CLR [13]S3DKinetics-400-71.2
CORP [26]R3D-50Kinetics-40048.8-
MoCo v3 [10]ViT-BKinetics-40062.4-
TANet [39]R50 × 2ImageNet-1K66.0-
MViT [16]ViT-BKinetcis-40064.778.4
TimeSformer [6]ViT-BImageNet-21K59.578.3
RSANet [32]R50ImageNet-1K66.0-
SVT [44]ViT-BKinetics-40059.278.1
VideoMAE† [49]ViT-BYT-Temporal67.978.2
Frozen‡ [4]ViT-BCC3M, WebVid2M55.176.9
MCQ‡ [20]ViT-BCC3M, WebVid2M51.577.8
MILES‡ [21]ViT-BCC3M, WebVid2M54.177.4
OmniVL [50]ViT-B*Enormous Datasets61.679.1
CLIP [43]ViT-BWIT36.375.2
OursViT-BYT-Temporal68.578.8
OursViT-BYT-Temporal CC3M, WebVid2M69.179.8
", + "bbox": [ + 513, + 444, + 880, + 729 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 5. The top-1 accuracy under the fine-tuning protocol on SSV2 and Kinetics-400. OmniVL adopts a mixture of eight datasets. $\\dagger$ denotes pre-training on YT-Temporal, and $\\ddagger$ denotes the use of official pre-trained weights for evaluation.", + "bbox": [ + 496, + 739, + 890, + 797 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "and MILES [21]. (c) One image representation learning method with natural language supervision, i.e., CLIP [43]. We average frame features as its video representation.", + "bbox": [ + 496, + 824, + 890, + 869 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The results are listed in Table 4 and we have the following observations: (i) Our method surpasses all baselines by", + "bbox": [ + 498, + 869, + 890, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "23085", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/fa316cfe4ebe9ff9383e730a88569f7a9f21016c480a6277789cfb19c417b3cb.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MSR-VTTDiDeMoMSVDLSMDC
MethodR@1MedRMethodR@1MedRMethodR@1MedRMethodR@1MedR
MMT [19]26.64.0CE [38]16.18.3NoiseEst [1]20.36.0NoiseEst [1]6.439.0
SupportSet [41]30.13.0ClipBert [35]20.46.0SupportSet [41]28.44.0MMT [19]12.919.3
Frozen [4]31.03.0Frozen [4]31.03.0Frozen [4]45.62.0Frozen [4]15.020.0
Ours34.63.0Ours32.43.0Ours45.92.0Ours17.217.0
", + "bbox": [ + 99, + 88, + 867, + 183 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/d7eb3a26659fcd4c583370af7a8d1fd91a2f612d90272e1a493969a04f5a38e0.jpg", + "table_caption": [ + "Table 6. The R@1 and MedR under the fine-tuning protocol on MSR-VTT, DiDeMo, MSVD, and LSMDC for text-to-video retrieval." + ], + "table_footnote": [], + "table_body": "
MethodBackboneUCF-101HMDB-51
BE [51]I3D87.156.2
CMD [28]R(2+1)D-2685.754.0
Vi²CLR [13]S3D89.155.7
ASCNet [27]S3D-G90.860.5
TEC [29]S3D-G88.263.5
LSFD [5]C3D79.852.1
MCN [37]R3D89.759.3
TCLR [11]R(2+1)D-1884.354.2
SVT [44]ViT-B93.767.2
VideoMAE† [49]ViT-B94.268.4
Frozen‡ [4]ViT-B91.465.6
MCQ‡ [20]ViT-B92.965.1
MILES‡ [21]ViT-B92.166.8
OursViT-B95.170.5
", + "bbox": [ + 98, + 233, + 447, + 470 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 7. The top-1 accuracy under the fine-tuning protocol on UCF-101 and HMDB-51. † denotes pre-training on YT-Temporal, and ‡ denotes the use of official pre-trained weights for evaluation.", + "bbox": [ + 75, + 481, + 468, + 523 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "a large margin under all evaluation metrics, which indicates that our learned video representation has stronger transferability that can be used for out-of-domain video recognition. (ii) Previous video representation learning works yield weak transferability with only visual supervision. It implies that merely exploiting visual-only perception without explicit semantics hinders spatiotemporal understanding. Furthermore, we observe a significant performance drop on VideoMAE when it pre-trains the model on the large-scale uncurated dataset, i.e., YT-Temporal. By contrast, our pretrained model achieves promising results, which indicates that TVTS can successfully apply to real-world uncurated video data by exploiting rich semantics from script knowledge. (iii) Our method also outperforms video-text alignment works by a large margin. We infer that these works only focus on alignment between global video and caption representation without exploring fine-grained temporal information. On the contrary, our proposed TVTS regularizes the model to learn transferable spatiotemporal video representations. (iv) Benefiting from large-scale language supervision, image-based CLIP achieves competitive performance. But it is still worse than our model because we fully exploit the rich semantics from script knowledge.", + "bbox": [ + 73, + 551, + 470, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Fine-tuning Transferability. We evaluate our model under the fine-tuning protocol on SSV2, Kinetics-400, UCF-101, and HMDB-51. Besides pre-training on YT-Temporal, we further follow recent works [20, 21] to jointly post-pretrain our model on Google Conceptual Captions (CC3M) and WebVid-2M. Their texts are harvested from the web in the form of a single caption. Since there is no timestamp-annotated text on CC3M and WebVid-2M, we only adopt the contrastive object. As listed in Table 5 and Table 7, the recognition capability of our model is comparable to previous works as we achieve state-of-the-art or competitive accuracy, while retaining strong transferability. Additionally, the video-text alignment methods show inferior performance on SSV2 since they are devoted to associating the vision patterns with language concepts, without fully exploiting the temporal information. By contrast, our TVTS achieves satisfactory performance via strengthening the learning of spatiotemporal representations.", + "bbox": [ + 496, + 234, + 893, + 508 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.6.2 Text-to-Video Retrieval", + "text_level": 1, + "bbox": [ + 498, + 512, + 718, + 526 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "As we preserve a global video-transcript contrastive loss to ease the ordering task via learning semantically meaningful video representations, it is natural to ask if the semantic-aware video representations can also benefit retrieval. Hence we conduct text-to-video retrieval under the fine-tuning protocol. As reported in Table 6, our model achieves SOTA performance. The promising results show that our TVTS can also learn the association between video patterns and language semantics.", + "bbox": [ + 496, + 529, + 893, + 666 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 671, + 619, + 686 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we for the first time leverage script knowledge that is naturally tied to the video to facilitate spatiotemporal representation learning. We introduce a novel pretext task dubbed Turning to Video for Transcript Sorting (TVTS), which regularizes the model to learn transferable video representations for spatial and temporal reasoning. Extensive evaluations on downstream video tasks show the great superiority of our method.", + "bbox": [ + 496, + 690, + 890, + 811 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement. This work is supported in part by the National Natural Science Foundation of China under grant 62171248, the PCNL KEY project (PCL2021A07), Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies (2022B1212010005), Guangdong Basic and Applied Basic Research Foundation under grant 2021A1515110066, the GXWD 20220811172936001, the Shenzhen Science and Technology Innovation Commission (Research Center for Computer Network (Shenzhen) Ministry of Education), and Shenzhen Science and Technology Program under Grant JCYJ20220818101012025.", + "bbox": [ + 496, + 816, + 893, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "23086", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Elad Amrani, Rami Ben-Ari, Daniel Rotman, and Alex Bronstein. Noise estimation using density estimation for self-supervised multimodal learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 6644-6652, 2021. 8", + "[2] Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. Localizing moments in video with natural language. In Proceedings of the IEEE international conference on computer vision, pages 5803-5812, 2017. 5", + "[3] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6836-6846, 2021. 5", + "[4] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1728-1738, 2021. 2, 3, 5, 7, 8", + "[5] Nadine Behrmann, Mohsen Fayyaz, Juergen Gall, and Mehdi Noroozi. Long short view feature decomposition via contrastive video representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9244-9253, 2021. 1, 3, 8", + "[6] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, volume 2, page 4, 2021. 7", + "[7] David Chen and William B Dolan. Collecting highly parallel data for paraphrase evaluation. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pages 190-200, 2011. 5", + "[8] Peihao Chen, Deng Huang, Dongliang He, Xiang Long, Runhao Zeng, Shilei Wen, Mingkui Tan, and Chuang Gan. Rspnet: Relative speed perception for unsupervised video representation learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 1045-1053, 2021. 1, 3", + "[9] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 1", + "[10] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9640-9649, 2021. 7", + "[11] Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve, and Mubarak Shah. TcIr: Temporal contrastive learning for video representation. Computer Vision and Image Understanding, 219:103406, 2022. 8", + "[12] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 5" + ], + "bbox": [ + 78, + 114, + 470, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Ali Diba, Vivek Sharma, Reza Safdari, Dariush Lotfi, Saquib Sarfraz, Rainer Stiefelhagen, and Luc Van Gool. Vi2cIr: Video and image for visual contrastive learning of representation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1502-1512, 2021. 7, 8", + "[14] Michael Dorkenwald, Fanyi Xiao, Biagio Brattoli, Joseph Tighe, and Davide Modolo. Scvrl: Shuffled contrastive video representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4132-4141, 2022. 7", + "[15] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 5", + "[16] Haoqi Fan, Bo Xiong, Karttikeya Mangalam, Yanghao Li, Zhicheng Yan, Jitendra Malik, and Christoph Feichtenhofer. Multiscale vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6824-6835, 2021. 7", + "[17] Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, and Kaiming He. Masked autoencoders as spatiotemporal learners. arXiv preprint arXiv:2205.09113, 2022. 1, 2, 3, 4", + "[18] Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, and Zicheng Liu. Violet: End-to-end video-language transformers with masked visual-token modeling. arXiv preprint arXiv:2111.12681, 2021. 2", + "[19] Valentin Gabeur, Chen Sun, Karteek Alahari, and Cordelia Schmid. Multi-modal transformer for video retrieval. In European Conference on Computer Vision, pages 214-229. Springer, 2020. 8", + "[20] Yuying Ge, Yixiao Ge, Xihui Liu, Dian Li, Ying Shan, Xiaohu Qie, and Ping Luo. Bridging video-text retrieval with multiple choice questions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16167-16176, 2022. 2, 3, 5, 7, 8", + "[21] Yuying Ge, Yixiao Ge, Xihui Liu, Alex Jinpeng Wang, Jianping Wu, Ying Shan, Xiaohu Qie, and Ping Luo. Miles: Visual bert pre-training with injected language semantics for video-text retrieval. arXiv preprint arXiv:2204.12408, 2022.3,7,8", + "[22] Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al. The\" something something\" video database for learning and evaluating visual common sense. In Proceedings of the IEEE international conference on computer vision, pages 5842-5850, 2017. 1, 2, 3, 5", + "[23] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000-16009, 2022. 1, 5, 6", + "[24] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference" + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "23087", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "on Computer Vision and Pattern Recognition, pages 16000-16009, 2022. 3", + "[25] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 1", + "[26] Kai Hu, Jie Shao, Yuan Liu, Bhiksha Raj, Marios Savvides, and Zhiqiang Shen. Contrast and order representations for video self-supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7939-7949, 2021. 7", + "[27] Deng Huang, Wenhao Wu, Weiwen Hu, Xu Liu, Dongliang He, Zhihua Wu, Xiangmiao Wu, Mingkui Tan, and Errui Ding. Ascnet: Self-supervised video representation learning with appearance-speed consistency. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8096-8105, 2021. 1, 3, 8", + "[28] Lianghua Huang, Yu Liu, Bin Wang, Pan Pan, Yinghui Xu, and Rong Jin. Self-supervised video representation learning by context and motion decoupling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13886-13895, 2021. 8", + "[29] Simon Jenni and Hailin Jin. Time-equivariant contrastive video representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9970-9980, 2021. 8", + "[30] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 3", + "[31] Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017. 1, 2, 5", + "[32] Manjin Kim, Heeseung Kwon, Chunyu Wang, Suha Kwak, and Minsu Cho. Relational self-attention: What's missing in attention for video understanding. Advances in Neural Information Processing Systems, 34:8046-8059, 2021. 7", + "[33] Hildegard Kuehne, Hueihan Jhuang, Estibaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In ICCV, pages 2556-2563. IEEE, 2011. 1, 2, 5", + "[34] Jie Lei, Tamara L Berg, and Mohit Bansal. Revealing single frame bias for video-and-language learning. arXiv preprint arXiv:2206.03428, 2022. 2, 3", + "[35] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7331-7341, 2021. 8", + "[36] Ji Lin, Chuang Gan, and Song Han. Tsm: Temporal shift module for efficient video understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7083-7093, 2019. 7" + ], + "bbox": [ + 78, + 92, + 470, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[37] Yuanze Lin, Xun Guo, and Yan Lu. Self-supervised video representation learning with meta-contrastive network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8239–8249, 2021. 8", + "[38] Yang Liu, Samuel Albanie, Arsha Nagrani, and Andrew Zisserman. Use what you have: Video retrieval using representations from collaborative experts. arXiv preprint arXiv:1907.13487, 2019. 8", + "[39] Zhaoyang Liu, Limin Wang, Wayne Wu, Chen Qian, and Tong Lu. Tam: Temporal adaptive module for video recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13708-13718, 2021. 7", + "[40] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 5", + "[41] Mandela Patrick, Po-Yao Huang, Yuki Asano, Florian Metze, Alexander Hauptmann, Joao Henriques, and Andrea Vedaldi. Support-set bottlenecks for video-text representation learning. arXiv preprint arXiv:2010.02824, 2020. 8", + "[42] Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, and Yin Cui. Spatiotemporal contrastive video representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6964-6974, 2021. 7", + "[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 3, 7", + "[44] Kanchana Ranasinghe, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan, and Michael S Ryoo. Self-supervised video transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2874-2884, 2022. 3, 7, 8", + "[45] Anna Rohrbach, Marcus Rohrbach, Niket Tandon, and Bernt Schiele. A dataset for movie description. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3202-3212, 2015. 5", + "[46] Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019.5", + "[47] Shibani Santurkar, Yann Dubois, Rohan Taori, Percy Liang, and Tatsunori Hashimoto. Is a caption worth a thousand images? a controlled study for representation learning. arXiv preprint arXiv:2207.07635, 2022. 2", + "[48] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012. 1, 2, 5", + "[49] Zhan Tong, Yibing Song, Jue Wang, and Limin Wang. Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training. arXiv preprint arXiv:2203.12602, 2022. 1, 2, 3, 4, 5, 7, 8", + "[50] Junke Wang, Dongdong Chen, Zuxuan Wu, Chong Luo, Luowei Zhou, Yucheng Zhao, Yujia Xie, Ce Liu, Yu-Gang Jiang, and Lu Yuan. Omnil: One foundation model for" + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "23088", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "image-language and video-language tasks. arXiv preprint arXiv:2209.07526, 2022. 7", + "[51] Jinpeng Wang, Yuting Gao, Ke Li, Yiqi Lin, Andy J Ma, Hao Cheng, Pai Peng, Feiyue Huang, Rongrong Ji, and Xing Sun. Removing the background by adding the background: Towards background robust self-supervised video representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11804-11813, 2021. 8", + "[52] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaou Tang, and Luc Van Gool. Temporal segment networks: Towards good practices for deep action recognition. In European conference on computer vision, pages 20-36. Springer, 2016. 4", + "[53] Dejing Xu, Jun Xiao, Zhou Zhao, Jian Shao, Di Xie, and Yueting Zhuang. Self-supervised spatiotemporal learning via video clip order prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10334–10343, 2019. 6, 7", + "[54] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5288-5296, 2016. 5", + "[55] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. Advances in Neural Information Processing Systems, 34:23634-23651, 2021. 1, 2, 3, 5, 6" + ], + "bbox": [ + 78, + 90, + 468, + 484 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "23089", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Learning Transferable Spatiotemporal Representations From Natural Script Knowledge/0f53488e-9219-482a-95c2-382c51f54644_model.json b/2023/Learning Transferable Spatiotemporal Representations From Natural Script Knowledge/0f53488e-9219-482a-95c2-382c51f54644_model.json new file mode 100644 index 0000000000000000000000000000000000000000..16a0e9c0bae249dbf8c5c4cda300e00655ae32a9 --- /dev/null +++ b/2023/Learning Transferable Spatiotemporal Representations From Natural Script Knowledge/0f53488e-9219-482a-95c2-382c51f54644_model.json @@ -0,0 +1,1938 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.205, + 0.131, + 0.768, + 0.177 + ], + "angle": 0, + "content": "Learning Transferable Spatiotemporal Representations from Natural Script Knowledge" + }, + { + "type": "text", + "bbox": [ + 0.182, + 0.202, + 0.79, + 0.293 + ], + "angle": 0, + "content": "Ziyun Zeng\\(^{1,2}\\) * Yuying Ge\\(^{3}\\) * Xihui Liu\\(^{3}\\) \nBin Chen\\(^{4}\\) Ping Luo\\(^{3}\\) Shu-Tao Xia\\(^{1}\\) Yixiao Ge\\(^{2}\\) \n\\(^{1}\\) Tsinghua University \\(^{2}\\) Applied Research Center (ARC), Tencent PCG \n\\(^{3}\\) The University of Hong Kong \\(^{4}\\) Harbin Institute of Technology, Shenzhen \n* equal contribution corresponding authors" + }, + { + "type": "text", + "bbox": [ + 0.104, + 0.296, + 0.858, + 0.328 + ], + "angle": 0, + "content": "zengzy21@mails.tsinghua.edu.cn yuyingge@hku.hk xihuiliu@eee.hku.hk chenbin2021@hit.edu.cn pluo@cs.hku.hk xiast@sz.tsinghua.edu.cn yixiaoge@tencent.com" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.362, + 0.314, + 0.379 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.394, + 0.474, + 0.786 + ], + "angle": 0, + "content": "Pre-training on large-scale video data has become a common recipe for learning transferable spatiotemporal representations in recent years. Despite some progress, existing methods are mostly limited to highly curated datasets (e.g., K400) and exhibit unsatisfactory out-of-the-box representations. We argue that it is due to the fact that they only capture pixel-level knowledge rather than spatiotemporal semantics, which hinders further progress in video understanding. Inspired by the great success of image-text pre-training (e.g., CLIP), we take the first step to exploit language semantics to boost transferable spatiotemporal representation learning. We introduce a new pretext task, Turning to Video for Transcript Sorting (TVTS), which sorts shuffled ASR scripts by attending to learned video representations. We do not rely on descriptive captions and learn purely from video, i.e., leveraging the natural transcribed speech knowledge to provide noisy but useful semantics over time. Our method enforces the vision model to contextualize what is happening over time so that it can re-organize the narrative transcripts, and can seamlessly apply to large-scale uncurated video data in the real world. Our method demonstrates strong out-of-the-box spatiotemporal representations on diverse benchmarks, e.g., \\(+13.6\\%\\) gains over VideoMAE on SSV2 via linear probing. The code is available at https://github.com/TencentARC/TVTS." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.815, + 0.21, + 0.831 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.903 + ], + "angle": 0, + "content": "The aspiration of representation learning is to encode general-purpose representations that transfer well to diverse downstream tasks, where self-supervised methodologies [9, 25] dominate due to their advantage in exploiting" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.364, + 0.895, + 0.485 + ], + "angle": 0, + "content": "large-scale unlabeled data. Despite significant progress in learning representations of still images [23, 43], the real world is dynamic and requires reasoning over time. In this paper, we focus on out-of-the-box spatiotemporal representation learning, a more challenging but practical task towards generic video understanding, which aims to capture hidden representations that can be further used to conduct reasoning on broader tasks, e.g., classification and retrieval." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.489, + 0.896, + 0.761 + ], + "angle": 0, + "content": "There have been various attempts at self-supervised pretraining on video data from discriminative learning objectives [5, 8, 27] to generative ones [17, 49], where the core is context capturing in spatial and temporal dimensions. Though promising results are achieved when transferring the pre-trained models to downstream video recognition [22, 33, 48] via fine-tuning, the learned representations are still far away from out-of-the-box given the poor linearly probing results (see Figure 1(a)). Moreover, existing works mostly develop video models on the highly curated dataset with particular biases, i.e., K400 [31]. Their applicability in the real world is questioned given the observed performance drops when training on a larger but uncurated dataset, YT-Temporal [55]. We argue that, to address the above issue, the rich spatiotemporal semantics contained in the video itself should be fully exploited. But current video models generally exploit visual-only perception (e.g., pixels) without explicit semantics." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.897, + 0.902 + ], + "angle": 0, + "content": "Recently, the success of CLIP [43] has inspired the community to learn semantically aware image representations that are better transferable to downstream tasks and scalable to larger uncurated datasets. It provides a feasible solution for improving spatiotemporal representation learning but remains two key problems. (1) The vision-language contrastive constraints in CLIP mainly encourage the understanding of static objects (noun contrast) and simple motions (verb contrast), while how to enable long-range tem" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "23079" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.078, + 0.089, + 0.596, + 0.31 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.608, + 0.089, + 0.891, + 0.31 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.323, + 0.897, + 0.381 + ], + "angle": 0, + "content": "Figure 1. (a) We evaluate the transferability of spatiotemporal representations via linear probing on four video recognition datasets [22, 31, 33, 48], where the state-of-the-art method [49] underperforms. It performs even worse when pre-trained with a large-scale uncurated dataset, YT-Temporal [55]. (b) We encourage complex temporal understanding and advanced spatiotemporal representation learning with a new pretext task of sorting transcripts." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.406, + 0.473, + 0.572 + ], + "angle": 0, + "content": "poral understanding with language supervision needs to be studied. (2) The quality of language supervision [47] is critical to the final performance of CLIP, however, it is hard to collect large-scale video data with literal captions that carefully describe the dynamic content over time. The ideal way for self-supervised learning is to learn useful knowledge purely from the data itself, which is also the philosophy followed by previous video pre-training methods [17, 49]. Fortunately, video data is naturally multi-modal with transcribed speech knowledge in the form of text (ASR), providing time-dependent semantics despite some noise." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.578, + 0.473, + 0.82 + ], + "angle": 0, + "content": "To facilitate spatiotemporal understanding in large-scale uncurated data under the supervision of inherent script knowledge, we introduce a new pretext task for video pretraining, namely, Turning to Video for Transcript Sorting (TVTS). Intuitively, people sort out the order of events by temporal reasoning. As illustrated in Figure 1(b), given several unordered transcripts, it is difficult to reorganize the narrative by merely understanding the literal semantics. When the corresponding video is provided, it will be much easier to sort the transcripts by contextualizing what is happening over time. Whereas in neural networks, the temporal inference is embedded in spatiotemporal representations. Thus we believe that if the chronological order of transcripts can be correctly figured out via resorting to the correlated video representations, the video has been well understood." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.473, + 0.903 + ], + "angle": 0, + "content": "We realize the pretext task of TVTS by performing joint attention among the encoded video spatiotemporal representations and the extracted ASR transcript representations. Specifically, given an input video and its successive transcripts, we randomly shuffle the order of the sentences." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.406, + 0.895, + 0.543 + ], + "angle": 0, + "content": "Subsequently, we concatenate the encoded script representations and the video representations and perform self-attention to predict the actual orders of the shuffled transcripts by fully understanding the spatiotemporal semantics in the video. The order prediction is cast as a \\( K \\)-way classification task, where \\( K \\) is the number of transcripts. The pretext task indirectly regularizes our model to properly capture contextualized spatiotemporal representations to provide enough knowledge for transcript ordering." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.548, + 0.896, + 0.821 + ], + "angle": 0, + "content": "The usage of language supervision is related to videotext alignment [4, 20] and multimodal representation learning [18, 55] methods, however, we are completely different. (1) Video-text alignment methods focus on retrieval tasks and are devoted to associating the vision patterns with language concepts. They are generally single-frame biased [34] and fail to encode strong out-of-the-box temporal representations. (2) Multimodal representation learning methods aim to learn fused representations across modalities rather than vision-only spatiotemporal representations in our work. Moreover, different from our pretext task that aims to optimize spatiotemporal video representations, [55] sorts video frames by taking the features of individual frames as inputs without temporal modeling, i.e., learning video representations only at the image level. As [55] points out, its ordering pretext task is not critical for downstream tasks (performance even drops) and primarily serves as an interface to query the model about temporal events." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.895, + 0.903 + ], + "angle": 0, + "content": "To summarize, our contributions are three-fold. (i) We exploit the rich semantics from script knowledge which is naturally along with the video, rendering a flexible pretraining method that can easily apply to uncurated video data in the real world. (ii) We introduce a novel pre" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "23080" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.244 + ], + "angle": 0, + "content": "text task for video pre-training, namely, Turning to Video for Transcript Sorting (TVTS). It promotes the capability of the model in learning transferable spatiotemporal video representations. (iii) We conduct comprehensive comparisons with advanced methods. Our pre-trained model exhibits strong out-of-the-box spatiotemporal representations on downstream action recognition tasks, especially the relatively large-scale and the most challenging SSV2 [22]. We also achieve state-of-the-art performances on eight common video datasets in terms of fine-tuning." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.257, + 0.22, + 0.273 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.282, + 0.473, + 0.538 + ], + "angle": 0, + "content": "Spatiotemporal representation learning. Dominant video representation learning works have two categories, i.e., discriminative- and generative-based methods. (i) The discriminative-based methods aim at mining unique representations within videos. For example, SVT [44] aligns several views from the same video with different spatial and temporal resolution for video-invariant representations. RSPNet [8], ASCNet [27], and LongShortView [5] utilize the appearance and temporal consistency of videos as the supervision. They use different augmentations of videos to construct positive and negative pairs to learn correspondences along the spatial and temporal dimensions. (ii) The generative-based methods try to reconstruct visual information from corrupted inputs. For example, MAE-based [24] methods [17, 49] use pixel values of video frames as supervision by masking raw videos with an extremely high ratio and reconstructing them." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.539, + 0.473, + 0.719 + ], + "angle": 0, + "content": "Previous works are mainly trained on highly curated datasets, e.g., Kinetics-400, HMDB51, and UCF101, where the temporal motions are not significant [34]. This leads to a \"spatial bias\", thus weakening the transferability to real-world uncurated datasets due to the lack of long-term temporal reasoning. Besides, existing works merely use visual supervision without explicit semantic information. Compared to them, our work leverages natural language derived from the video itself, i.e., the ASR transcripts, as the supervision. Benefiting from the rich spatiotemporal information, our learned video representations have stronger transferability to downstream tasks." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.72, + 0.473, + 0.902 + ], + "angle": 0, + "content": "Video-text pre-training. Existing video-text pre-training work can be divided into two categories. The first category aims to learn video-text alignment for retrieval. For example, Frozen [4], MCQ [20], and MILES [21] generally adopt two separate encoders to extract video and text representations, then align them with contrastive loss. However, they only align videos with a global video caption, thus neglecting the fine-grained temporal information. Furthermore, they rely on clean captions, which are difficult to scale up, and it is actually hard to collect large-scale video data with captions describing the dynamic content over time. The second category works on joint representation learning across" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.197 + ], + "angle": 0, + "content": "modalities mainly for VQA. For example, MERLOT [55] adopts a joint encoder to match the captions with the corresponding video frames and put scrambled video frames into the correct order. It aims to match different modalities in the temporal dimension to achieve multi-modality fusion in a joint encoder, rather than learn better spatiotemporal representations." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.198, + 0.892, + 0.424 + ], + "angle": 0, + "content": "Image representation learning by language supervision. Recently, there have been a bunch of successful tries in utilizing language supervision to enhance image representation learning. For example, CLIP [43] utilized 400M imagetext pairs collected from the Internet and adopt the contrastive loss to align the image and its corresponding text. The superior performance on downstream image classification tasks revealed that learning directly from the raw text about images is a promising alternative that leverages a much broader source of supervision. ALIGN [30], uses a larger but noisier uncurated dataset and shows similar results to CLIP. Nevertheless, these methods only utilize language supervision to improve spatial learning, without exploring temporal learning, which hinders them from properly learning out-of-the-box video representations." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.435, + 0.591, + 0.451 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.461, + 0.892, + 0.567 + ], + "angle": 0, + "content": "In this work, we introduce a novel pretext task, Turning to Video for Transcript Sorting (TVTS) to learn the transferable spatiotemporal video representation by leveraging the rich semantics from script knowledge. In this section, we first introduce the pretext TVTS in Sec. 3.1 and our pretraining objectives in Sec. 3.2. We then describe the model architecture in Sec. 3.3." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.574, + 0.842, + 0.59 + ], + "angle": 0, + "content": "3.1. Turning to Video for Transcript Sorting" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.597, + 0.892, + 0.749 + ], + "angle": 0, + "content": "As shown in Fig. 2, we perform the pretext task of TVTS to learn transferable spatiotemporal representations of videos. Given the observation that it will be much easier to sort the ASR transcripts by contextualizing what is happening over time in the video, we first randomly shuffle several consecutive ASR transcripts and extract their representations. We then perform joint attention among the transcript representations and video representations to sort the transcripts in the correct order via capturing contextualized spatiotemporal representations of videos." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.756, + 0.892, + 0.863 + ], + "angle": 0, + "content": "Sample and Shuffle. Given a video \\(V\\) and its corresponding ASR transcripts with word-level timestamps \\(\\{(w_{i},s_{i})\\}_{i = 1}^{N_{\\mathrm{asr}}}\\), where \\(N_{\\mathrm{asr}}\\) denotes the word number, \\(w_{i}\\) and \\(s_i\\) denote the \\(i\\)-th word and its timestamp respectively, we randomly choose a starting time \\(s_{\\mathrm{begin}}\\) and sample \\(K\\) consecutive transcripts, each with a duration of \\(l\\) (in seconds), and an interval of 1s between adjacent transcripts," + }, + { + "type": "equation", + "bbox": [ + 0.526, + 0.868, + 0.892, + 0.892 + ], + "angle": 0, + "content": "\\[\nS _ {k} = s _ {\\text {b e g i n}} + (k - 1) * (l + 1), \\quad E _ {k} = S _ {k} + l \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.529, + 0.89, + 0.836, + 0.904 + ], + "angle": 0, + "content": "\\[\nT _ {k} = \\left\\{w _ {i} | S _ {k} \\leq s _ {i} \\leq E _ {k} \\right\\}, \\quad k \\in \\{1, \\dots , K \\}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "23081" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.078, + 0.089, + 0.895, + 0.371 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.075, + 0.377, + 0.895, + 0.437 + ], + "angle": 0, + "content": "Figure 2. Our pre-training pipeline. We first sample \\( K \\) consecutive ASR transcripts, and a video clip consisting of \\( M \\) frames within the span of the transcripts. We randomly sample frame patches as the input of a vision transformer for the video representations. We then shuffle the transcripts and extract the representation of each transcript. We perform joint attention among the transcript and the video representations to predict the actual order of each transcript, which is optimized with a cross-entropy objective." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.46, + 0.473, + 0.566 + ], + "angle": 0, + "content": "where \\( S_{k} \\) and \\( E_{k} \\) denote the beginning and ending time of the \\( k \\)-th transcript. We consecutively sample \\( K \\) transcripts with an interval of 1s and collect all words within \\( [S_{k}, E_{k}] \\) for the \\( k \\)-th transcript. Finally, we randomly shuffle the transcripts as \\( \\{T_{o_i}\\}_{i=1}^{K} \\), which means that the \\( i \\)-th transcript in this shuffled sequence is actually the \\( o_i \\)-th transcript in the original ordered sequence." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.567, + 0.47, + 0.675 + ], + "angle": 0, + "content": "As for the video, we sample a clip between the beginning and ending time of all \\(K\\) transcripts, i.e., \\([S_1,E_K]\\), which contains \\(M\\) frames as \\(\\{F_i\\}_{i = 1}^M\\). Specifically, we follow TSN [52] to divide \\([S_1,E_K]\\) into \\(M\\) segments with equal length and randomly sample 1 frame from each segment. After that, we get a video clip with \\(M\\) frames and \\(K\\) shuffled transcripts along the span of the video clip." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.689, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Sorting Transcripts. Given the shuffled transcripts \\(\\{T_{o_i}\\}_{i = 1}^K\\) and the corresponding video clip \\(\\{F_i\\}_{i = 1}^M\\), we first feed the transcripts in parallel to encode unordered text representations \\(\\{t_{o_i}\\}_{i = 1}^K\\). We then randomly sample the frame patches by masking a large proportion of the video clip among the spatial and temporal dimension as the input of a vision transformer to encode video representations \\(\\{v_j\\}_{j = 0}^N\\), where \\(N\\) denotes the number of the unmasked video patches, and \\(v_0\\) is the representation of the [CLS] token. It is worth noting that we do not add the extra [MASK] token, and we have no explicit reconstruction target, which is different from previous works [17, 49]. We sample video frame patches as a means of data augmentation since it provides corrupted knowledge for our model to perform the" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.46, + 0.892, + 0.505 + ], + "angle": 0, + "content": "pretext task of TVTS. Such a strategy also reduces the computational cost during pre-training as the attention is calculated on fewer patches." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.506, + 0.893, + 0.702 + ], + "angle": 0, + "content": "We then concatenate the text representations of the shuffled transcripts \\(\\{t_{o_i}\\}_{i = 1}^K\\) and the video representations of the sampled video clip \\(\\{v_j\\}_{j = 0}^N\\), and perform multi-head selfattention among them. Our model attempts to sort the transcripts in the correct order by attending to the text features of all transcripts and the visual features of the unmasked video clip. We model the prediction of the transcript orders as a \\(K\\)-way classification task for each transcript. The first \\(K\\) output representations after the joint attention are further fed into a linear classifier to predict the order \\(p\\in \\mathbb{R}^{K}\\) where \\(p_j\\) denotes the probability that the transcript is the \\(j\\)-th transcript in the original ordered sequence. For the transcript \\(T_{o_i}\\), the ground truth classification label should be \\(o_i\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.702, + 0.895, + 0.824 + ], + "angle": 0, + "content": "The pretext task of TVTS regularizes the model to contextualize what is happening over time, so that it can provide enough knowledge for our model to figure out the chronological order of the shuffled transcripts. It improves the capability of the model to learn spatiotemporal representations that can be transferred to downstream tasks. We compare our method with other works that also adopt an ordering-based pretext task for pre-training in Sec. 4.5." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.832, + 0.719, + 0.849 + ], + "angle": 0, + "content": "3.2. Pre-training Objectives" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Besides the pretext task of TVTS, we use a global video Transcript contrastive objective. It aligns the features of the video clip and the averaged features of \\( K \\) transcripts so that" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "23082" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.151 + ], + "angle": 0, + "content": "the video and transcript representations are in the same feature space for performing the joint attention to predict transcript orders. We combine two objectives to optimize the entire model in an end-to-end manner." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.152, + 0.47, + 0.197 + ], + "angle": 0, + "content": "The first one is a cross-entropy objective \\(\\mathcal{L}_{\\mathrm{sort}}\\), which supervises our model to predict the correct order of the transcripts, and is formulated as below," + }, + { + "type": "equation", + "bbox": [ + 0.19, + 0.205, + 0.47, + 0.248 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {s o r t}} = - \\frac {1}{K} \\sum_ {i = 1} ^ {K} \\log \\operatorname {s o f t m a x} \\left(\\hat {p} ^ {i}\\right) \\tag {2}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.157, + 0.247, + 0.384, + 0.28 + ], + "angle": 0, + "content": "\\[\ns. t. \\quad \\mathrm {s o f t m a x} (\\hat {p} ^ {i}) = \\frac {\\exp (p _ {o _ {i}} ^ {i})}{\\sum_ {j = 1} ^ {K} \\exp (p _ {j} ^ {i})},\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.287, + 0.469, + 0.348 + ], + "angle": 0, + "content": "where \\( p_j^i \\) denotes the probability that the \\( i \\)-th transcript in the shuffled sequence is the \\( j \\)-th transcript in the original ordered sequence and \\( o_i \\) is the ground truth order in the original ordered sequence." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.348, + 0.469, + 0.379 + ], + "angle": 0, + "content": "The second one is the global video-transcript contrastive objective \\(\\mathcal{L}_{\\mathrm{base}}\\), formulated as a bidirectional InfoNCE [40]," + }, + { + "type": "equation", + "bbox": [ + 0.164, + 0.386, + 0.359, + 0.402 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {b a s e}} = \\mathrm {N C E} (\\hat {t}, \\hat {v}) + \\mathrm {N C E} (\\hat {v}, \\hat {t})\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.13, + 0.405, + 0.469, + 0.439 + ], + "angle": 0, + "content": "\\[\ns. t. \\quad \\mathrm {N C E} (q, k) = - \\log \\frac {\\exp \\left(q ^ {\\top} k _ {+} / \\tau\\right)}{\\sum_ {i = 1} ^ {B} \\exp \\left(q ^ {\\top} k _ {i} / \\tau\\right)}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.446, + 0.469, + 0.507 + ], + "angle": 0, + "content": "where \\(\\hat{t}\\) and \\(\\hat{v}\\) denote the global text and video representation. We average the [CLS] token representation of all \\(K\\) transcripts as \\(\\hat{t}\\), i.e., \\(\\hat{t} \\gets \\frac{1}{K} \\sum_{i=1}^{K} t_i\\), and use the [CLS] token representation of the video clip as \\(\\hat{v}\\), i.e., \\(\\hat{v} \\gets v_0\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.507, + 0.469, + 0.582 + ], + "angle": 0, + "content": "Our overall pre-training objective combines the two objectives, i.e., \\(\\mathcal{L} = \\mathcal{L}_{\\mathrm{base}} + \\lambda \\mathcal{L}_{\\mathrm{sort}}\\), where \\(\\lambda\\) is a hyperparameter to balance the two losses. In our implementation, we set \\(\\lambda = 2\\) to roughly scale the gradient magnitudes of \\(\\mathcal{L}_{\\mathrm{base}}\\) and \\(\\mathcal{L}_{\\mathrm{sort}}\\) to be the same for efficient training." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.591, + 0.265, + 0.605 + ], + "angle": 0, + "content": "3.3. Model Architecture" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.614, + 0.47, + 0.901 + ], + "angle": 0, + "content": "The vision transformer takes a video clip as input, which consists of \\(M\\) frames of resolution \\(H\\times W\\), and outputs video representations. We follow [49] to adopt cube embeddings, where each token corresponds to a cube of size \\(2\\times 16\\times 16\\). This yields \\(\\frac{M}{2}\\times \\frac{H}{16}\\times \\frac{W}{16} 3D\\) tokens. Then we add divided space-time embedding to the token sequence, where tokens within the same frame obtain the same temporal embedding, and tokens within the same spatial location of different frames obtain the same spatial embedding. In this way, the vision transformer learns the positional information of the cubes. Next, we follow BERT [12] to add a learnable [CLS] token at the beginning of the token sequence for global video representations. Then we mask a portion of video tokens without [MASK] token replacement, as stated in Sec. 3.1. We adopt a standard ViT [15] architecture to encode video representations. The unmasked \\(N\\) video tokens as well as the [CLS] token are fed into the vision transformer, and joint space-time attention [3] is performed among the whole unmasked token sequence." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.198 + ], + "angle": 0, + "content": "We use a DistilBERT [46] to extract the representations of ASR transcripts. We adopt two stacked bidirectional transformer blocks to predict the order of each transcript by performing joint attention among the transcript and the video representations. Within each block, multi-head self-attention is performed among all the video and text tokens, i.e., all transcript-video tokens interact with each other." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.214, + 0.634, + 0.231 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.24, + 0.702, + 0.257 + ], + "angle": 0, + "content": "4.1. Pre-training Datasets" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.264, + 0.892, + 0.311 + ], + "angle": 0, + "content": "We pre-train our model on the large-scale YT-Temporal dataset [55] containing 6M YouTube videos with ASR transcripts and word-level timestamps." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.323, + 0.682, + 0.338 + ], + "angle": 0, + "content": "4.2. Downstream Tasks" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.347, + 0.892, + 0.528 + ], + "angle": 0, + "content": "Action Recognition. We evaluate our pre-trained model on four common video datasets: (a) Something-Something V2 (SSV2) [22], (b) Kinetics-400 (K400) [31], (c) UCF-101 [48], (d) HMDB-51 [33]. Our evaluation is two-fold: (i) We conduct zero-shot video-to-video retrieval and linear probe classification on SSV2 to evaluate the transferability of the learned video representation. The former aims to retrieve videos of the same category as a query video, and the latter freezes the visual encoder and only optimizes a linear classifier. (ii) We fully fine-tune our pre-trained model on the training set of the four datasets to evaluate the action recognition capability. See Appendix for details." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.547, + 0.892, + 0.653 + ], + "angle": 0, + "content": "Text-to-Video Retrieval. Beyond action recognition, we further evaluate retrieval performance on four benchmarks to see if the improved semantic-aware video representation can benefit retrieval tasks: (a) MSR-VTT [54] (b) DiDeMo [2] (c) MSVD [7] (d) LSMDC [45]. We adopt Recall@K (R@K) and Median Rank (MedR) as the evaluation metric. See Appendix for details." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.665, + 0.719, + 0.681 + ], + "angle": 0, + "content": "4.3. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.892, + 0.901 + ], + "angle": 0, + "content": "We follow recent works [4, 20] to adopt the pre-trained DistilBERT [46] to extract transcript representations. The vision transformer is a vanilla ViT-Base [15] with patch size \\( P = 16 \\) and hidden state dimension \\( D = 768 \\), and is initialized with ImageMAE-Base [23]. We set the temperature parameter \\( \\tau \\) to be 0.05. We pre-train our model on the YTemporal dataset sampling 16 frames for 20 epochs. We randomly mask \\( 75\\% \\) tokens within each frame. The input frame is first resized to \\( 256 \\times 256 \\), then we apply RandomCrop during training and CenterCrop during inference. The final input resolution of each frame is \\( 224 \\times 224 \\). For downstream tasks, we sample 16 frames for action recognition following [49] and 4 frames for text-to-video retrieval following [4]. More hyper-parameters are listed in Appendix." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23083" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.187, + 0.089, + 0.784, + 0.172 + ], + "angle": 0, + "content": "
Target →NoneTranscriptVideo
Dataset ↓BaselineOursVCOP [53]MERLOT [55]MERLOT-like
UCF-10181.2 (↓2.2)83.479.1 (↓4.3)74.9 (↓8.5)80.1 (↓3.3)
HMDB-5156.5 (↓1.9)58.454.2 (↓4.2)49.6 (↓8.8)55.4 (↓3.0)
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.183, + 0.895, + 0.226 + ], + "angle": 0, + "content": "Table 1. Comparison with methods that use ordering-based pretext tasks for pre-training. We report top-1 accuracy under the linear probe classification protocol on UCF-101 and HMDB-51. The model pre-trained only with \\( \\mathcal{L}_{\\mathrm{base}} \\) serves as the baseline. All models are pre-trained on the YT-Temporal dataset for a fair comparison." + }, + { + "type": "table", + "bbox": [ + 0.084, + 0.248, + 0.468, + 0.365 + ], + "angle": 0, + "content": "
Name\\( \\mathcal{L}_{\\text{base}} \\)\\( \\mathcal{L}_{\\text{sort}} \\)sgSSV2Kinetics-400
\\( M_{scratch} \\)××-64.5 (↓4.0)75.4 (↓3.4)
\\( M_{base} \\)✓×-67.0 (↓1.5)77.8 (↓1.0)
\\( M_{sort\\backslash sg} \\)×✓×failedfailed
\\( M_{sort} \\)×✓✓failedfailed
\\( M_{ours\\backslash sg} \\)✓✓×66.2 (↓2.3)76.5 (↓2.3)
\\( M_{ours} \\)✓✓✓68.578.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.376, + 0.472, + 0.434 + ], + "angle": 0, + "content": "Table 2. The top-1 accuracy under the fine-tuning protocol on SSV2 and Kinetics-400, w.r.t. different pre-training objectives, where \\(\\mathcal{L}_{\\mathrm{sort}}\\) trains the pretext task of TVTS. sg denotes stopping gradients of \\(\\mathcal{L}_{\\mathrm{sort}}\\) towards encoding transcript representations." + }, + { + "type": "table", + "bbox": [ + 0.083, + 0.446, + 0.468, + 0.53 + ], + "angle": 0, + "content": "
DatasetNoneSort Modeling
BaselinePairwiseFactorialK-way
SSV267.0 (↓1.5)67.4 (↓1.1)67.2 (↓1.3)68.5
K40077.8 (↓1.0)78.1 (↓0.7)78.0 (↓0.8)78.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.542, + 0.47, + 0.64 + ], + "angle": 0, + "content": "Table 3. The top-1 accuracy under the fine-tuning protocol on SSV2 and Kinetics-400, w.r.t. different ways to model the sorting of transcripts. \"Pairwise\" predicts the relative order for all transcript pairs, and \"Factorial\" performs \\( K! \\)-classification for all possible orders. Our method uses \"K-way\" classification to predict the order of each transcript. The model pre-trained with \\( \\mathcal{L}_{\\mathrm{base}} \\) only serves as the baseline." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.666, + 0.231, + 0.683 + ], + "angle": 0, + "content": "4.4. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.689, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Pre-training Objectives. To demonstrate the effectiveness of our pretext task TVTS, we pre-train models with different objectives on the YT-Temporal dataset and evaluate them on SSV2 and K400. The results are listed in Table 2, in which \\( \\mathbf{M}_{\\mathrm{scratch}} \\) denotes that we directly fine-tune the Image-MAE [23] initialized model. We have the following observations: (i) \\( \\mathbf{M}_{\\mathrm{base}} \\) outperforms \\( \\mathbf{M}_{\\mathrm{scratch}} \\), which indicates that the natural language can be a promising supervision for video representation learning. (ii) Compared to \\( \\mathbf{M}_{\\mathrm{base}} \\), \\( \\mathbf{M}_{\\mathrm{ours}} \\) further boosts performance by \\( 1.5\\% \\), which demonstrates that TVTS can effectively regularize our model to learn transferable spatiotemporal representations. (iii) \\( \\mathbf{M}_{\\mathrm{ours}\\backslash \\mathrm{sg}} \\) drops performance, because when the gradients of \\( \\mathcal{L}_{\\mathrm{sort}} \\) flow towards encoding transcript representations, the model op" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.252, + 0.895, + 0.344 + ], + "angle": 0, + "content": "timizes the transcript representations to ease the ordering task rather than enhance spatiotemporal representations to provide enough knowledge for transcript sorting. (iv) Both \\(\\mathbf{M}_{\\mathrm{sort}\\backslash \\mathrm{sg}}\\) and \\(\\mathbf{M}_{\\mathrm{sort}}\\) failed because the parametric module for sorting hardly converges when feeding the misaligned video and transcript representations from distinct latent spaces." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.355, + 0.895, + 0.583 + ], + "angle": 0, + "content": "Sort Modeling. We explore different ways to model the order prediction of the shuffled transcripts. Besides using \\( K \\)-way classification of each transcript, we also tried Pairwise, which sorts the transcripts by predicting the relative orders of the \\( K(K - 1) / 2 \\) transcript pairs, and Factorial, which predicts an overall ordering distribution by performing a \\( K \\)-way classification (\\( K! \\) possible orders given \\( K \\) transcripts). As listed in Table 3, both Pairwise and Factorial drop performance, because the former ignores the overall relationship among the transcripts while the latter imposes the same penalty on the results when different number of transcripts are sorted incorrectly. But they still outperform the baseline, indicating that sorting transcripts does benefit spatiotemporal representation learning. Our separate \\( K \\)-way classification modeling achieves the best performance." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.59, + 0.892, + 0.608 + ], + "angle": 0, + "content": "4.5. Comparison with Ordering-based Pre-training" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.614, + 0.894, + 0.901 + ], + "angle": 0, + "content": "MERLOT [55] also adopts an ordering-based pretext task, but has a totally different approach and purpose. MERLOT reorders scrambled video frames given the representations of every single frame and the ordered ASR transcripts with a joint encoder, and reserves the joint encoder for downstream multimodal tasks such as VQA. Specifically, MERLOT predicts the relative order of two video frames by binary classification. MERLOT aims to promote the joint encoder in learning multi-modal representations rather than spatiotemporal representations of videos. Since the visual encoder takes a single frame as input, it only achieves semantic understanding at the single-frame level without temporal reasoning among frames. As MERLOT points out, the ordering pretext task is not critical for downstream tasks (performance even drops) and primarily serves as an interface to query the model about temporal events. We further tailor MERLOT for our architecture as MERLOT-like, which sorts \\( K \\) shuffled video frames by \\( K \\)-way classification with the knowledge of the ordered tran" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "23084" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.126, + 0.089, + 0.847, + 0.368 + ], + "angle": 0, + "content": "
MethodVenuePre-train DatasetZero-shot Video-to-video RetrievalLinear Probe
R@1R@5R@10
Spatiotemporal representation learning method(s)
CVRL [42]CVPR'21Kinetics-400---11.4 (↓20.1)
MViT [16]ICCV'21Kinetics-400---19.4 (↓12.1)
SCVRL [14]CVPRW'22Kinetics-400---13.8 (↓17.7)
SVT [44]CVPR'22Kinetics-40011.3 (↓3.4)30.7 (↓7.7)41.1 (↓9.4)18.3 (↓13.2)
SVT† [44]CVPR'22YT-Temporal9.9 (↓4.8)26.2 (↓12.2)36.3 (↓14.2)18.0 (↓13.5)
VideoMAE [49]NeurIPS'22Kinetics-4007.9 (↓6.8)18.6 (↓19.8)26.5 (↓24.0)17.9 (↓13.6)
VideoMAE† [49]NeurIPS'22YT-Temporal7.2 (↓7.5)17.6 (↓20.8)25.6 (↓24.9)15.9 (↓15.6)
Video-text alignment method(s)
Frozen‡ [4]ICCV'21CC3M, WebVid-2M10.4 (↓4.3)28.5 (↓9.9)38.7 (↓11.8)17.5 (↓14.0)
MCQ‡ [20]CVPR'22CC3M, WebVid-2M10.4 (↓4.3)28.6 (↓9.8)38.5 (↓12.0)18.0 (↓13.5)
MILES‡ [21]ECCV'22CC3M, WebVid-2M10.3 (↓4.4)28.4 (↓10.0)38.4 (↓12.1)18.6 (↓12.9)
Image representation learning method(s)
CLIP [43]ICML'21WIT10.5 (↓4.2)28.8 (↓9.6)38.8 (↓11.7)16.4 (↓15.1)
OursCVPR'23YT-Temporal14.738.450.531.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.378, + 0.895, + 0.421 + ], + "angle": 0, + "content": "Table 4. Transferability evaluation on SSV2. We report Recall@K for zero-shot video-to-video retrieval and top-1 accuracy for linear probe classification, where video-to-video retrieval aims to retrieve videos of the same category as a query video. † denotes pre-training on YT-Temporal for a fair comparison, and ‡ denotes the use of official pre-trained weights for evaluation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.447, + 0.47, + 0.492 + ], + "angle": 0, + "content": "scripts. Similar to MERLOT, VCOP [53] also predicts the order of the shuffled video clips, but only pre-trains on videos without language semantics." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.493, + 0.471, + 0.72 + ], + "angle": 0, + "content": "As listed in Table 1, all models that sort shuffled video clips/frames as the pretext task (i.e., VCOP, MERLOT) perform even worse than the baseline counterpart without sorting. It indicates that sorting shuffled videos in pretraining is infeasible and counterintuitive for improving spatiotemporal representations, because it does not regularize the video encoder for spatial and temporal reasoning given only a single frame or a short video segment as input. They mainly regularize an extra module beyond the video encoder to figure out the chronological order of the videos. By contrast, our pretext task regularizes the model to sort transcripts via reasoning among the video representations, which enforces the model to capture contextualized spatiotemporal representations so that it can provide enough knowledge for transcript ordering." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.731, + 0.216, + 0.745 + ], + "angle": 0, + "content": "4.6. Main Results" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.755, + 0.265, + 0.77 + ], + "angle": 0, + "content": "4.6.1 Action Recognition" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.47, + 0.903 + ], + "angle": 0, + "content": "Out-of-the-box Representations. To explore the transferability of the learned video representation, we evaluate zero-shot video-to-video retrieval and linear probe classification. We compare our proposed method with seven state-of-the-art methods, including: (a) Five video representation learning methods, i.e., CVRL [42], MViT [16], SCVRL [14], SVT [44], and VideoMAE [49]. (b) Three video-text alignment methods, i.e., Frozen [4], MCQ [20]," + }, + { + "type": "table", + "bbox": [ + 0.514, + 0.445, + 0.882, + 0.731 + ], + "angle": 0, + "content": "
MethodBackbonePre-train DatasetSSV2K400
TSM [36]R50 × 2ImageNet-1K66.0-
Vi²CLR [13]S3DKinetics-400-71.2
CORP [26]R3D-50Kinetics-40048.8-
MoCo v3 [10]ViT-BKinetics-40062.4-
TANet [39]R50 × 2ImageNet-1K66.0-
MViT [16]ViT-BKinetcis-40064.778.4
TimeSformer [6]ViT-BImageNet-21K59.578.3
RSANet [32]R50ImageNet-1K66.0-
SVT [44]ViT-BKinetics-40059.278.1
VideoMAE† [49]ViT-BYT-Temporal67.978.2
Frozen‡ [4]ViT-BCC3M, WebVid2M55.176.9
MCQ‡ [20]ViT-BCC3M, WebVid2M51.577.8
MILES‡ [21]ViT-BCC3M, WebVid2M54.177.4
OmniVL [50]ViT-B*Enormous Datasets61.679.1
CLIP [43]ViT-BWIT36.375.2
OursViT-BYT-Temporal68.578.8
OursViT-BYT-Temporal CC3M, WebVid2M69.179.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.741, + 0.892, + 0.798 + ], + "angle": 0, + "content": "Table 5. The top-1 accuracy under the fine-tuning protocol on SSV2 and Kinetics-400. OmniVL adopts a mixture of eight datasets. \\(\\dagger\\) denotes pre-training on YT-Temporal, and \\(\\ddagger\\) denotes the use of official pre-trained weights for evaluation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.892, + 0.87 + ], + "angle": 0, + "content": "and MILES [21]. (c) One image representation learning method with natural language supervision, i.e., CLIP [43]. We average frame features as its video representation." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.892, + 0.903 + ], + "angle": 0, + "content": "The results are listed in Table 4 and we have the following observations: (i) Our method surpasses all baselines by" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23085" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.1, + 0.089, + 0.868, + 0.184 + ], + "angle": 0, + "content": "
MSR-VTTDiDeMoMSVDLSMDC
MethodR@1MedRMethodR@1MedRMethodR@1MedRMethodR@1MedR
MMT [19]26.64.0CE [38]16.18.3NoiseEst [1]20.36.0NoiseEst [1]6.439.0
SupportSet [41]30.13.0ClipBert [35]20.46.0SupportSet [41]28.44.0MMT [19]12.919.3
Frozen [4]31.03.0Frozen [4]31.03.0Frozen [4]45.62.0Frozen [4]15.020.0
Ours34.63.0Ours32.43.0Ours45.92.0Ours17.217.0
" + }, + { + "type": "table_caption", + "bbox": [ + 0.09, + 0.195, + 0.88, + 0.21 + ], + "angle": 0, + "content": "Table 6. The R@1 and MedR under the fine-tuning protocol on MSR-VTT, DiDeMo, MSVD, and LSMDC for text-to-video retrieval." + }, + { + "type": "table", + "bbox": [ + 0.099, + 0.234, + 0.449, + 0.471 + ], + "angle": 0, + "content": "
MethodBackboneUCF-101HMDB-51
BE [51]I3D87.156.2
CMD [28]R(2+1)D-2685.754.0
Vi²CLR [13]S3D89.155.7
ASCNet [27]S3D-G90.860.5
TEC [29]S3D-G88.263.5
LSFD [5]C3D79.852.1
MCN [37]R3D89.759.3
TCLR [11]R(2+1)D-1884.354.2
SVT [44]ViT-B93.767.2
VideoMAE† [49]ViT-B94.268.4
Frozen‡ [4]ViT-B91.465.6
MCQ‡ [20]ViT-B92.965.1
MILES‡ [21]ViT-B92.166.8
OursViT-B95.170.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.482, + 0.47, + 0.525 + ], + "angle": 0, + "content": "Table 7. The top-1 accuracy under the fine-tuning protocol on UCF-101 and HMDB-51. † denotes pre-training on YT-Temporal, and ‡ denotes the use of official pre-trained weights for evaluation." + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.553, + 0.471, + 0.903 + ], + "angle": 0, + "content": "a large margin under all evaluation metrics, which indicates that our learned video representation has stronger transferability that can be used for out-of-domain video recognition. (ii) Previous video representation learning works yield weak transferability with only visual supervision. It implies that merely exploiting visual-only perception without explicit semantics hinders spatiotemporal understanding. Furthermore, we observe a significant performance drop on VideoMAE when it pre-trains the model on the large-scale uncurated dataset, i.e., YT-Temporal. By contrast, our pretrained model achieves promising results, which indicates that TVTS can successfully apply to real-world uncurated video data by exploiting rich semantics from script knowledge. (iii) Our method also outperforms video-text alignment works by a large margin. We infer that these works only focus on alignment between global video and caption representation without exploring fine-grained temporal information. On the contrary, our proposed TVTS regularizes the model to learn transferable spatiotemporal video representations. (iv) Benefiting from large-scale language supervision, image-based CLIP achieves competitive performance. But it is still worse than our model because we fully exploit the rich semantics from script knowledge." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.236, + 0.895, + 0.509 + ], + "angle": 0, + "content": "Fine-tuning Transferability. We evaluate our model under the fine-tuning protocol on SSV2, Kinetics-400, UCF-101, and HMDB-51. Besides pre-training on YT-Temporal, we further follow recent works [20, 21] to jointly post-pretrain our model on Google Conceptual Captions (CC3M) and WebVid-2M. Their texts are harvested from the web in the form of a single caption. Since there is no timestamp-annotated text on CC3M and WebVid-2M, we only adopt the contrastive object. As listed in Table 5 and Table 7, the recognition capability of our model is comparable to previous works as we achieve state-of-the-art or competitive accuracy, while retaining strong transferability. Additionally, the video-text alignment methods show inferior performance on SSV2 since they are devoted to associating the vision patterns with language concepts, without fully exploiting the temporal information. By contrast, our TVTS achieves satisfactory performance via strengthening the learning of spatiotemporal representations." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.513, + 0.719, + 0.527 + ], + "angle": 0, + "content": "4.6.2 Text-to-Video Retrieval" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.53, + 0.894, + 0.667 + ], + "angle": 0, + "content": "As we preserve a global video-transcript contrastive loss to ease the ordering task via learning semantically meaningful video representations, it is natural to ask if the semantic-aware video representations can also benefit retrieval. Hence we conduct text-to-video retrieval under the fine-tuning protocol. As reported in Table 6, our model achieves SOTA performance. The promising results show that our TVTS can also learn the association between video patterns and language semantics." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.672, + 0.62, + 0.687 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.691, + 0.892, + 0.812 + ], + "angle": 0, + "content": "In this work, we for the first time leverage script knowledge that is naturally tied to the video to facilitate spatiotemporal representation learning. We introduce a novel pretext task dubbed Turning to Video for Transcript Sorting (TVTS), which regularizes the model to learn transferable video representations for spatial and temporal reasoning. Extensive evaluations on downstream video tasks show the great superiority of our method." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.818, + 0.895, + 0.9 + ], + "angle": 0, + "content": "Acknowledgement. This work is supported in part by the National Natural Science Foundation of China under grant 62171248, the PCNL KEY project (PCL2021A07), Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies (2022B1212010005), Guangdong Basic and Applied Basic Research Foundation under grant 2021A1515110066, the GXWD 20220811172936001, the Shenzhen Science and Technology Innovation Commission (Research Center for Computer Network (Shenzhen) Ministry of Education), and Shenzhen Science and Technology Program under Grant JCYJ20220818101012025." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "23086" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.185 + ], + "angle": 0, + "content": "[1] Elad Amrani, Rami Ben-Ari, Daniel Rotman, and Alex Bronstein. Noise estimation using density estimation for self-supervised multimodal learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 6644-6652, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.187, + 0.472, + 0.256 + ], + "angle": 0, + "content": "[2] Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. Localizing moments in video with natural language. In Proceedings of the IEEE international conference on computer vision, pages 5803-5812, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.259, + 0.47, + 0.327 + ], + "angle": 0, + "content": "[3] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6836-6846, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.33, + 0.47, + 0.399 + ], + "angle": 0, + "content": "[4] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1728-1738, 2021. 2, 3, 5, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.402, + 0.47, + 0.471 + ], + "angle": 0, + "content": "[5] Nadine Behrmann, Mohsen Fayyaz, Juergen Gall, and Mehdi Noroozi. Long short view feature decomposition via contrastive video representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9244-9253, 2021. 1, 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.473, + 0.469, + 0.514 + ], + "angle": 0, + "content": "[6] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, volume 2, page 4, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.517, + 0.469, + 0.584 + ], + "angle": 0, + "content": "[7] David Chen and William B Dolan. Collecting highly parallel data for paraphrase evaluation. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pages 190-200, 2011. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.588, + 0.469, + 0.669 + ], + "angle": 0, + "content": "[8] Peihao Chen, Deng Huang, Dongliang He, Xiang Long, Runhao Zeng, Shilei Wen, Mingkui Tan, and Chuang Gan. Rspnet: Relative speed perception for unsupervised video representation learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 1045-1053, 2021. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.673, + 0.469, + 0.729 + ], + "angle": 0, + "content": "[9] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.731, + 0.469, + 0.786 + ], + "angle": 0, + "content": "[10] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9640-9649, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.788, + 0.469, + 0.843 + ], + "angle": 0, + "content": "[11] Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve, and Mubarak Shah. TcIr: Temporal contrastive learning for video representation. Computer Vision and Image Understanding, 219:103406, 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[12] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 5" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.472, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.162 + ], + "angle": 0, + "content": "[13] Ali Diba, Vivek Sharma, Reza Safdari, Dariush Lotfi, Saquib Sarfraz, Rainer Stiefelhagen, and Luc Van Gool. Vi2cIr: Video and image for visual contrastive learning of representation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1502-1512, 2021. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.164, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[14] Michael Dorkenwald, Fanyi Xiao, Biagio Brattoli, Joseph Tighe, and Davide Modolo. Scvrl: Shuffled contrastive video representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4132-4141, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.235, + 0.892, + 0.316 + ], + "angle": 0, + "content": "[15] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.319, + 0.892, + 0.388 + ], + "angle": 0, + "content": "[16] Haoqi Fan, Bo Xiong, Karttikeya Mangalam, Yanghao Li, Zhicheng Yan, Jitendra Malik, and Christoph Feichtenhofer. Multiscale vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6824-6835, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.39, + 0.891, + 0.432 + ], + "angle": 0, + "content": "[17] Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, and Kaiming He. Masked autoencoders as spatiotemporal learners. arXiv preprint arXiv:2205.09113, 2022. 1, 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.434, + 0.892, + 0.489 + ], + "angle": 0, + "content": "[18] Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, and Zicheng Liu. Violet: End-to-end video-language transformers with masked visual-token modeling. arXiv preprint arXiv:2111.12681, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.491, + 0.892, + 0.546 + ], + "angle": 0, + "content": "[19] Valentin Gabeur, Chen Sun, Karteek Alahari, and Cordelia Schmid. Multi-modal transformer for video retrieval. In European Conference on Computer Vision, pages 214-229. Springer, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.548, + 0.892, + 0.617 + ], + "angle": 0, + "content": "[20] Yuying Ge, Yixiao Ge, Xihui Liu, Dian Li, Ying Shan, Xiaohu Qie, and Ping Luo. Bridging video-text retrieval with multiple choice questions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16167-16176, 2022. 2, 3, 5, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.619, + 0.892, + 0.687 + ], + "angle": 0, + "content": "[21] Yuying Ge, Yixiao Ge, Xihui Liu, Alex Jinpeng Wang, Jianping Wu, Ying Shan, Xiaohu Qie, and Ping Luo. Miles: Visual bert pre-training with injected language semantics for video-text retrieval. arXiv preprint arXiv:2204.12408, 2022.3,7,8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.69, + 0.892, + 0.787 + ], + "angle": 0, + "content": "[22] Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al. The\" something something\" video database for learning and evaluating visual common sense. In Proceedings of the IEEE international conference on computer vision, pages 5842-5850, 2017. 1, 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.789, + 0.892, + 0.857 + ], + "angle": 0, + "content": "[23] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000-16009, 2022. 1, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.859, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[24] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23087" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.111, + 0.093, + 0.47, + 0.119 + ], + "angle": 0, + "content": "on Computer Vision and Pattern Recognition, pages 16000-16009, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.47, + 0.189 + ], + "angle": 0, + "content": "[25] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.191, + 0.472, + 0.259 + ], + "angle": 0, + "content": "[26] Kai Hu, Jie Shao, Yuan Liu, Bhiksha Raj, Marios Savvides, and Zhiqiang Shen. Contrast and order representations for video self-supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7939-7949, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.261, + 0.472, + 0.342 + ], + "angle": 0, + "content": "[27] Deng Huang, Wenhao Wu, Weiwen Hu, Xu Liu, Dongliang He, Zhihua Wu, Xiangmiao Wu, Mingkui Tan, and Errui Ding. Ascnet: Self-supervised video representation learning with appearance-speed consistency. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8096-8105, 2021. 1, 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.344, + 0.472, + 0.412 + ], + "angle": 0, + "content": "[28] Lianghua Huang, Yu Liu, Bin Wang, Pan Pan, Yinghui Xu, and Rong Jin. Self-supervised video representation learning by context and motion decoupling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13886-13895, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.414, + 0.47, + 0.468 + ], + "angle": 0, + "content": "[29] Simon Jenni and Hailin Jin. Time-equivariant contrastive video representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9970-9980, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.47, + 0.47, + 0.55 + ], + "angle": 0, + "content": "[30] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.552, + 0.469, + 0.62 + ], + "angle": 0, + "content": "[31] Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.623, + 0.469, + 0.677 + ], + "angle": 0, + "content": "[32] Manjin Kim, Heeseung Kwon, Chunyu Wang, Suha Kwak, and Minsu Cho. Relational self-attention: What's missing in attention for video understanding. Advances in Neural Information Processing Systems, 34:8046-8059, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.679, + 0.469, + 0.732 + ], + "angle": 0, + "content": "[33] Hildegard Kuehne, Hueihan Jhuang, Estibaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In ICCV, pages 2556-2563. IEEE, 2011. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.734, + 0.469, + 0.774 + ], + "angle": 0, + "content": "[34] Jie Lei, Tamara L Berg, and Mohit Bansal. Revealing single frame bias for video-and-language learning. arXiv preprint arXiv:2206.03428, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.776, + 0.469, + 0.844 + ], + "angle": 0, + "content": "[35] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7331-7341, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[36] Ji Lin, Chuang Gan, and Song Han. Tsm: Temporal shift module for efficient video understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7083-7093, 2019. 7" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.093, + 0.472, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[37] Yuanze Lin, Xun Guo, and Yan Lu. Self-supervised video representation learning with meta-contrastive network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8239–8249, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.892, + 0.204 + ], + "angle": 0, + "content": "[38] Yang Liu, Samuel Albanie, Arsha Nagrani, and Andrew Zisserman. Use what you have: Video retrieval using representations from collaborative experts. arXiv preprint arXiv:1907.13487, 2019. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.206, + 0.892, + 0.261 + ], + "angle": 0, + "content": "[39] Zhaoyang Liu, Limin Wang, Wayne Wu, Chen Qian, and Tong Lu. Tam: Temporal adaptive module for video recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13708-13718, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.263, + 0.892, + 0.304 + ], + "angle": 0, + "content": "[40] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.307, + 0.892, + 0.361 + ], + "angle": 0, + "content": "[41] Mandela Patrick, Po-Yao Huang, Yuki Asano, Florian Metze, Alexander Hauptmann, Joao Henriques, and Andrea Vedaldi. Support-set bottlenecks for video-text representation learning. arXiv preprint arXiv:2010.02824, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.363, + 0.892, + 0.432 + ], + "angle": 0, + "content": "[42] Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, and Yin Cui. Spatiotemporal contrastive video representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6964-6974, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.434, + 0.892, + 0.516 + ], + "angle": 0, + "content": "[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.519, + 0.892, + 0.586 + ], + "angle": 0, + "content": "[44] Kanchana Ranasinghe, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan, and Michael S Ryoo. Self-supervised video transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2874-2884, 2022. 3, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.589, + 0.892, + 0.644 + ], + "angle": 0, + "content": "[45] Anna Rohrbach, Marcus Rohrbach, Niket Tandon, and Bernt Schiele. A dataset for movie description. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3202-3212, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.646, + 0.892, + 0.699 + ], + "angle": 0, + "content": "[46] Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.703, + 0.892, + 0.758 + ], + "angle": 0, + "content": "[47] Shibani Santurkar, Yann Dubois, Rohan Taori, Percy Liang, and Tatsunori Hashimoto. Is a caption worth a thousand images? a controlled study for representation learning. arXiv preprint arXiv:2207.07635, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.76, + 0.892, + 0.8 + ], + "angle": 0, + "content": "[48] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.802, + 0.892, + 0.857 + ], + "angle": 0, + "content": "[49] Zhan Tong, Yibing Song, Jue Wang, and Limin Wang. Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training. arXiv preprint arXiv:2203.12602, 2022. 1, 2, 3, 4, 5, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.859, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[50] Junke Wang, Dongdong Chen, Zuxuan Wu, Chong Luo, Luowei Zhou, Yucheng Zhao, Yujia Xie, Ce Liu, Yu-Gang Jiang, and Lu Yuan. Omnil: One foundation model for" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "23088" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.092, + 0.47, + 0.12 + ], + "angle": 0, + "content": "image-language and video-language tasks. arXiv preprint arXiv:2209.07526, 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.47, + 0.218 + ], + "angle": 0, + "content": "[51] Jinpeng Wang, Yuting Gao, Ke Li, Yiqi Lin, Andy J Ma, Hao Cheng, Pai Peng, Feiyue Huang, Rongrong Ji, and Xing Sun. Removing the background by adding the background: Towards background robust self-supervised video representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11804-11813, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.22, + 0.469, + 0.289 + ], + "angle": 0, + "content": "[52] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaou Tang, and Luc Van Gool. Temporal segment networks: Towards good practices for deep action recognition. In European conference on computer vision, pages 20-36. Springer, 2016. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.291, + 0.469, + 0.359 + ], + "angle": 0, + "content": "[53] Dejing Xu, Jun Xiao, Zhou Zhao, Jian Shao, Di Xie, and Yueting Zhuang. Self-supervised spatiotemporal learning via video clip order prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10334–10343, 2019. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.361, + 0.469, + 0.416 + ], + "angle": 0, + "content": "[54] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5288-5296, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.418, + 0.469, + 0.485 + ], + "angle": 0, + "content": "[55] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. Advances in Neural Information Processing Systems, 34:23634-23651, 2021. 1, 2, 3, 5, 6" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.485 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "23089" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Transferable Spatiotemporal Representations From Natural Script Knowledge/0f53488e-9219-482a-95c2-382c51f54644_origin.pdf b/2023/Learning Transferable Spatiotemporal Representations From Natural Script Knowledge/0f53488e-9219-482a-95c2-382c51f54644_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..478afd72fa2ddad065c73893642bfa182dfe6b4c --- /dev/null +++ b/2023/Learning Transferable Spatiotemporal Representations From Natural Script Knowledge/0f53488e-9219-482a-95c2-382c51f54644_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e3ac4f5f101b8250c95121c3f53bc1a70790b791bde37bf4f7c506f1f8b56d93 +size 890443 diff --git a/2023/Learning Transferable Spatiotemporal Representations From Natural Script Knowledge/full.md b/2023/Learning Transferable Spatiotemporal Representations From Natural Script Knowledge/full.md new file mode 100644 index 0000000000000000000000000000000000000000..fc67dfda4f49c713cc810e081770298506df6881 --- /dev/null +++ b/2023/Learning Transferable Spatiotemporal Representations From Natural Script Knowledge/full.md @@ -0,0 +1,272 @@ +# Learning Transferable Spatiotemporal Representations from Natural Script Knowledge + +Ziyun Zeng $^{1,2}$ * Yuying Ge $^{3}$ * Xihui Liu $^{3}$ +Bin Chen $^{4}$ Ping Luo $^{3}$ Shu-Tao Xia $^{1}$ Yixiao Ge $^{2}$ $^{1}$ Tsinghua University $^{2}$ Applied Research Center (ARC), Tencent PCG + $^{3}$ The University of Hong Kong $^{4}$ Harbin Institute of Technology, Shenzhen +* equal contribution corresponding authors + +zengzy21@mails.tsinghua.edu.cn yuyingge@hku.hk xihuiliu@eee.hku.hk chenbin2021@hit.edu.cn pluo@cs.hku.hk xiast@sz.tsinghua.edu.cn yixiaoge@tencent.com + +# Abstract + +Pre-training on large-scale video data has become a common recipe for learning transferable spatiotemporal representations in recent years. Despite some progress, existing methods are mostly limited to highly curated datasets (e.g., K400) and exhibit unsatisfactory out-of-the-box representations. We argue that it is due to the fact that they only capture pixel-level knowledge rather than spatiotemporal semantics, which hinders further progress in video understanding. Inspired by the great success of image-text pre-training (e.g., CLIP), we take the first step to exploit language semantics to boost transferable spatiotemporal representation learning. We introduce a new pretext task, Turning to Video for Transcript Sorting (TVTS), which sorts shuffled ASR scripts by attending to learned video representations. We do not rely on descriptive captions and learn purely from video, i.e., leveraging the natural transcribed speech knowledge to provide noisy but useful semantics over time. Our method enforces the vision model to contextualize what is happening over time so that it can re-organize the narrative transcripts, and can seamlessly apply to large-scale uncurated video data in the real world. Our method demonstrates strong out-of-the-box spatiotemporal representations on diverse benchmarks, e.g., $+13.6\%$ gains over VideoMAE on SSV2 via linear probing. The code is available at https://github.com/TencentARC/TVTS. + +# 1. Introduction + +The aspiration of representation learning is to encode general-purpose representations that transfer well to diverse downstream tasks, where self-supervised methodologies [9, 25] dominate due to their advantage in exploiting + +large-scale unlabeled data. Despite significant progress in learning representations of still images [23, 43], the real world is dynamic and requires reasoning over time. In this paper, we focus on out-of-the-box spatiotemporal representation learning, a more challenging but practical task towards generic video understanding, which aims to capture hidden representations that can be further used to conduct reasoning on broader tasks, e.g., classification and retrieval. + +There have been various attempts at self-supervised pretraining on video data from discriminative learning objectives [5, 8, 27] to generative ones [17, 49], where the core is context capturing in spatial and temporal dimensions. Though promising results are achieved when transferring the pre-trained models to downstream video recognition [22, 33, 48] via fine-tuning, the learned representations are still far away from out-of-the-box given the poor linearly probing results (see Figure 1(a)). Moreover, existing works mostly develop video models on the highly curated dataset with particular biases, i.e., K400 [31]. Their applicability in the real world is questioned given the observed performance drops when training on a larger but uncurated dataset, YT-Temporal [55]. We argue that, to address the above issue, the rich spatiotemporal semantics contained in the video itself should be fully exploited. But current video models generally exploit visual-only perception (e.g., pixels) without explicit semantics. + +Recently, the success of CLIP [43] has inspired the community to learn semantically aware image representations that are better transferable to downstream tasks and scalable to larger uncurated datasets. It provides a feasible solution for improving spatiotemporal representation learning but remains two key problems. (1) The vision-language contrastive constraints in CLIP mainly encourage the understanding of static objects (noun contrast) and simple motions (verb contrast), while how to enable long-range tem + +![](images/30311201ad497b9a952207ff0eecbfa161e7a9bb0e3bd4be2eddc34068a44a24.jpg) +Figure 1. (a) We evaluate the transferability of spatiotemporal representations via linear probing on four video recognition datasets [22, 31, 33, 48], where the state-of-the-art method [49] underperforms. It performs even worse when pre-trained with a large-scale uncurated dataset, YT-Temporal [55]. (b) We encourage complex temporal understanding and advanced spatiotemporal representation learning with a new pretext task of sorting transcripts. + +![](images/7fce2c431215d5060407a73b266ae7173163589f7c5541b0bbab1cba0a068b29.jpg) + +poral understanding with language supervision needs to be studied. (2) The quality of language supervision [47] is critical to the final performance of CLIP, however, it is hard to collect large-scale video data with literal captions that carefully describe the dynamic content over time. The ideal way for self-supervised learning is to learn useful knowledge purely from the data itself, which is also the philosophy followed by previous video pre-training methods [17, 49]. Fortunately, video data is naturally multi-modal with transcribed speech knowledge in the form of text (ASR), providing time-dependent semantics despite some noise. + +To facilitate spatiotemporal understanding in large-scale uncurated data under the supervision of inherent script knowledge, we introduce a new pretext task for video pretraining, namely, Turning to Video for Transcript Sorting (TVTS). Intuitively, people sort out the order of events by temporal reasoning. As illustrated in Figure 1(b), given several unordered transcripts, it is difficult to reorganize the narrative by merely understanding the literal semantics. When the corresponding video is provided, it will be much easier to sort the transcripts by contextualizing what is happening over time. Whereas in neural networks, the temporal inference is embedded in spatiotemporal representations. Thus we believe that if the chronological order of transcripts can be correctly figured out via resorting to the correlated video representations, the video has been well understood. + +We realize the pretext task of TVTS by performing joint attention among the encoded video spatiotemporal representations and the extracted ASR transcript representations. Specifically, given an input video and its successive transcripts, we randomly shuffle the order of the sentences. + +Subsequently, we concatenate the encoded script representations and the video representations and perform self-attention to predict the actual orders of the shuffled transcripts by fully understanding the spatiotemporal semantics in the video. The order prediction is cast as a $K$ -way classification task, where $K$ is the number of transcripts. The pretext task indirectly regularizes our model to properly capture contextualized spatiotemporal representations to provide enough knowledge for transcript ordering. + +The usage of language supervision is related to videotext alignment [4, 20] and multimodal representation learning [18, 55] methods, however, we are completely different. (1) Video-text alignment methods focus on retrieval tasks and are devoted to associating the vision patterns with language concepts. They are generally single-frame biased [34] and fail to encode strong out-of-the-box temporal representations. (2) Multimodal representation learning methods aim to learn fused representations across modalities rather than vision-only spatiotemporal representations in our work. Moreover, different from our pretext task that aims to optimize spatiotemporal video representations, [55] sorts video frames by taking the features of individual frames as inputs without temporal modeling, i.e., learning video representations only at the image level. As [55] points out, its ordering pretext task is not critical for downstream tasks (performance even drops) and primarily serves as an interface to query the model about temporal events. + +To summarize, our contributions are three-fold. (i) We exploit the rich semantics from script knowledge which is naturally along with the video, rendering a flexible pretraining method that can easily apply to uncurated video data in the real world. (ii) We introduce a novel pre + +text task for video pre-training, namely, Turning to Video for Transcript Sorting (TVTS). It promotes the capability of the model in learning transferable spatiotemporal video representations. (iii) We conduct comprehensive comparisons with advanced methods. Our pre-trained model exhibits strong out-of-the-box spatiotemporal representations on downstream action recognition tasks, especially the relatively large-scale and the most challenging SSV2 [22]. We also achieve state-of-the-art performances on eight common video datasets in terms of fine-tuning. + +# 2. Related Work + +Spatiotemporal representation learning. Dominant video representation learning works have two categories, i.e., discriminative- and generative-based methods. (i) The discriminative-based methods aim at mining unique representations within videos. For example, SVT [44] aligns several views from the same video with different spatial and temporal resolution for video-invariant representations. RSPNet [8], ASCNet [27], and LongShortView [5] utilize the appearance and temporal consistency of videos as the supervision. They use different augmentations of videos to construct positive and negative pairs to learn correspondences along the spatial and temporal dimensions. (ii) The generative-based methods try to reconstruct visual information from corrupted inputs. For example, MAE-based [24] methods [17, 49] use pixel values of video frames as supervision by masking raw videos with an extremely high ratio and reconstructing them. + +Previous works are mainly trained on highly curated datasets, e.g., Kinetics-400, HMDB51, and UCF101, where the temporal motions are not significant [34]. This leads to a "spatial bias", thus weakening the transferability to real-world uncurated datasets due to the lack of long-term temporal reasoning. Besides, existing works merely use visual supervision without explicit semantic information. Compared to them, our work leverages natural language derived from the video itself, i.e., the ASR transcripts, as the supervision. Benefiting from the rich spatiotemporal information, our learned video representations have stronger transferability to downstream tasks. + +Video-text pre-training. Existing video-text pre-training work can be divided into two categories. The first category aims to learn video-text alignment for retrieval. For example, Frozen [4], MCQ [20], and MILES [21] generally adopt two separate encoders to extract video and text representations, then align them with contrastive loss. However, they only align videos with a global video caption, thus neglecting the fine-grained temporal information. Furthermore, they rely on clean captions, which are difficult to scale up, and it is actually hard to collect large-scale video data with captions describing the dynamic content over time. The second category works on joint representation learning across + +modalities mainly for VQA. For example, MERLOT [55] adopts a joint encoder to match the captions with the corresponding video frames and put scrambled video frames into the correct order. It aims to match different modalities in the temporal dimension to achieve multi-modality fusion in a joint encoder, rather than learn better spatiotemporal representations. + +Image representation learning by language supervision. Recently, there have been a bunch of successful tries in utilizing language supervision to enhance image representation learning. For example, CLIP [43] utilized 400M imagetext pairs collected from the Internet and adopt the contrastive loss to align the image and its corresponding text. The superior performance on downstream image classification tasks revealed that learning directly from the raw text about images is a promising alternative that leverages a much broader source of supervision. ALIGN [30], uses a larger but noisier uncurated dataset and shows similar results to CLIP. Nevertheless, these methods only utilize language supervision to improve spatial learning, without exploring temporal learning, which hinders them from properly learning out-of-the-box video representations. + +# 3. Method + +In this work, we introduce a novel pretext task, Turning to Video for Transcript Sorting (TVTS) to learn the transferable spatiotemporal video representation by leveraging the rich semantics from script knowledge. In this section, we first introduce the pretext TVTS in Sec. 3.1 and our pretraining objectives in Sec. 3.2. We then describe the model architecture in Sec. 3.3. + +# 3.1. Turning to Video for Transcript Sorting + +As shown in Fig. 2, we perform the pretext task of TVTS to learn transferable spatiotemporal representations of videos. Given the observation that it will be much easier to sort the ASR transcripts by contextualizing what is happening over time in the video, we first randomly shuffle several consecutive ASR transcripts and extract their representations. We then perform joint attention among the transcript representations and video representations to sort the transcripts in the correct order via capturing contextualized spatiotemporal representations of videos. + +Sample and Shuffle. Given a video $V$ and its corresponding ASR transcripts with word-level timestamps $\{(w_{i},s_{i})\}_{i = 1}^{N_{\mathrm{asr}}}$ , where $N_{\mathrm{asr}}$ denotes the word number, $w_{i}$ and $s_i$ denote the $i$ -th word and its timestamp respectively, we randomly choose a starting time $s_{\mathrm{begin}}$ and sample $K$ consecutive transcripts, each with a duration of $l$ (in seconds), and an interval of 1s between adjacent transcripts, + +$$ +S _ {k} = s _ {\text {b e g i n}} + (k - 1) * (l + 1), \quad E _ {k} = S _ {k} + l \tag {1} +$$ + +$$ +T _ {k} = \left\{w _ {i} | S _ {k} \leq s _ {i} \leq E _ {k} \right\}, \quad k \in \{1, \dots , K \} +$$ + +![](images/31020a82ce26ae0b37ee895c9df4a3cbaa5a44fdebc34f181c4974aff85faea2.jpg) +Figure 2. Our pre-training pipeline. We first sample $K$ consecutive ASR transcripts, and a video clip consisting of $M$ frames within the span of the transcripts. We randomly sample frame patches as the input of a vision transformer for the video representations. We then shuffle the transcripts and extract the representation of each transcript. We perform joint attention among the transcript and the video representations to predict the actual order of each transcript, which is optimized with a cross-entropy objective. + +where $S_{k}$ and $E_{k}$ denote the beginning and ending time of the $k$ -th transcript. We consecutively sample $K$ transcripts with an interval of 1s and collect all words within $[S_{k}, E_{k}]$ for the $k$ -th transcript. Finally, we randomly shuffle the transcripts as $\{T_{o_i}\}_{i=1}^{K}$ , which means that the $i$ -th transcript in this shuffled sequence is actually the $o_i$ -th transcript in the original ordered sequence. + +As for the video, we sample a clip between the beginning and ending time of all $K$ transcripts, i.e., $[S_1,E_K]$ , which contains $M$ frames as $\{F_i\}_{i = 1}^M$ . Specifically, we follow TSN [52] to divide $[S_1,E_K]$ into $M$ segments with equal length and randomly sample 1 frame from each segment. After that, we get a video clip with $M$ frames and $K$ shuffled transcripts along the span of the video clip. + +Sorting Transcripts. Given the shuffled transcripts $\{T_{o_i}\}_{i = 1}^K$ and the corresponding video clip $\{F_i\}_{i = 1}^M$ , we first feed the transcripts in parallel to encode unordered text representations $\{t_{o_i}\}_{i = 1}^K$ . We then randomly sample the frame patches by masking a large proportion of the video clip among the spatial and temporal dimension as the input of a vision transformer to encode video representations $\{v_j\}_{j = 0}^N$ , where $N$ denotes the number of the unmasked video patches, and $v_0$ is the representation of the [CLS] token. It is worth noting that we do not add the extra [MASK] token, and we have no explicit reconstruction target, which is different from previous works [17, 49]. We sample video frame patches as a means of data augmentation since it provides corrupted knowledge for our model to perform the + +pretext task of TVTS. Such a strategy also reduces the computational cost during pre-training as the attention is calculated on fewer patches. + +We then concatenate the text representations of the shuffled transcripts $\{t_{o_i}\}_{i = 1}^K$ and the video representations of the sampled video clip $\{v_j\}_{j = 0}^N$ , and perform multi-head selfattention among them. Our model attempts to sort the transcripts in the correct order by attending to the text features of all transcripts and the visual features of the unmasked video clip. We model the prediction of the transcript orders as a $K$ -way classification task for each transcript. The first $K$ output representations after the joint attention are further fed into a linear classifier to predict the order $p\in \mathbb{R}^{K}$ where $p_j$ denotes the probability that the transcript is the $j$ -th transcript in the original ordered sequence. For the transcript $T_{o_i}$ , the ground truth classification label should be $o_i$ . + +The pretext task of TVTS regularizes the model to contextualize what is happening over time, so that it can provide enough knowledge for our model to figure out the chronological order of the shuffled transcripts. It improves the capability of the model to learn spatiotemporal representations that can be transferred to downstream tasks. We compare our method with other works that also adopt an ordering-based pretext task for pre-training in Sec. 4.5. + +# 3.2. Pre-training Objectives + +Besides the pretext task of TVTS, we use a global video Transcript contrastive objective. It aligns the features of the video clip and the averaged features of $K$ transcripts so that + +the video and transcript representations are in the same feature space for performing the joint attention to predict transcript orders. We combine two objectives to optimize the entire model in an end-to-end manner. + +The first one is a cross-entropy objective $\mathcal{L}_{\mathrm{sort}}$ , which supervises our model to predict the correct order of the transcripts, and is formulated as below, + +$$ +\mathcal {L} _ {\text {s o r t}} = - \frac {1}{K} \sum_ {i = 1} ^ {K} \log \operatorname {s o f t m a x} \left(\hat {p} ^ {i}\right) \tag {2} +$$ + +$$ +s. t. \quad \mathrm {s o f t m a x} (\hat {p} ^ {i}) = \frac {\exp (p _ {o _ {i}} ^ {i})}{\sum_ {j = 1} ^ {K} \exp (p _ {j} ^ {i})}, +$$ + +where $p_j^i$ denotes the probability that the $i$ -th transcript in the shuffled sequence is the $j$ -th transcript in the original ordered sequence and $o_i$ is the ground truth order in the original ordered sequence. + +The second one is the global video-transcript contrastive objective $\mathcal{L}_{\mathrm{base}}$ , formulated as a bidirectional InfoNCE [40], + +$$ +\mathcal {L} _ {\text {b a s e}} = \mathrm {N C E} (\hat {t}, \hat {v}) + \mathrm {N C E} (\hat {v}, \hat {t}) +$$ + +$$ +s. t. \quad \mathrm {N C E} (q, k) = - \log \frac {\exp \left(q ^ {\top} k _ {+} / \tau\right)}{\sum_ {i = 1} ^ {B} \exp \left(q ^ {\top} k _ {i} / \tau\right)}, \tag {3} +$$ + +where $\hat{t}$ and $\hat{v}$ denote the global text and video representation. We average the [CLS] token representation of all $K$ transcripts as $\hat{t}$ , i.e., $\hat{t} \gets \frac{1}{K} \sum_{i=1}^{K} t_i$ , and use the [CLS] token representation of the video clip as $\hat{v}$ , i.e., $\hat{v} \gets v_0$ . + +Our overall pre-training objective combines the two objectives, i.e., $\mathcal{L} = \mathcal{L}_{\mathrm{base}} + \lambda \mathcal{L}_{\mathrm{sort}}$ , where $\lambda$ is a hyperparameter to balance the two losses. In our implementation, we set $\lambda = 2$ to roughly scale the gradient magnitudes of $\mathcal{L}_{\mathrm{base}}$ and $\mathcal{L}_{\mathrm{sort}}$ to be the same for efficient training. + +# 3.3. Model Architecture + +The vision transformer takes a video clip as input, which consists of $M$ frames of resolution $H\times W$ , and outputs video representations. We follow [49] to adopt cube embeddings, where each token corresponds to a cube of size $2\times 16\times 16$ . This yields $\frac{M}{2}\times \frac{H}{16}\times \frac{W}{16} 3D$ tokens. Then we add divided space-time embedding to the token sequence, where tokens within the same frame obtain the same temporal embedding, and tokens within the same spatial location of different frames obtain the same spatial embedding. In this way, the vision transformer learns the positional information of the cubes. Next, we follow BERT [12] to add a learnable [CLS] token at the beginning of the token sequence for global video representations. Then we mask a portion of video tokens without [MASK] token replacement, as stated in Sec. 3.1. We adopt a standard ViT [15] architecture to encode video representations. The unmasked $N$ video tokens as well as the [CLS] token are fed into the vision transformer, and joint space-time attention [3] is performed among the whole unmasked token sequence. + +We use a DistilBERT [46] to extract the representations of ASR transcripts. We adopt two stacked bidirectional transformer blocks to predict the order of each transcript by performing joint attention among the transcript and the video representations. Within each block, multi-head self-attention is performed among all the video and text tokens, i.e., all transcript-video tokens interact with each other. + +# 4. Experiments + +# 4.1. Pre-training Datasets + +We pre-train our model on the large-scale YT-Temporal dataset [55] containing 6M YouTube videos with ASR transcripts and word-level timestamps. + +# 4.2. Downstream Tasks + +Action Recognition. We evaluate our pre-trained model on four common video datasets: (a) Something-Something V2 (SSV2) [22], (b) Kinetics-400 (K400) [31], (c) UCF-101 [48], (d) HMDB-51 [33]. Our evaluation is two-fold: (i) We conduct zero-shot video-to-video retrieval and linear probe classification on SSV2 to evaluate the transferability of the learned video representation. The former aims to retrieve videos of the same category as a query video, and the latter freezes the visual encoder and only optimizes a linear classifier. (ii) We fully fine-tune our pre-trained model on the training set of the four datasets to evaluate the action recognition capability. See Appendix for details. + +Text-to-Video Retrieval. Beyond action recognition, we further evaluate retrieval performance on four benchmarks to see if the improved semantic-aware video representation can benefit retrieval tasks: (a) MSR-VTT [54] (b) DiDeMo [2] (c) MSVD [7] (d) LSMDC [45]. We adopt Recall@K (R@K) and Median Rank (MedR) as the evaluation metric. See Appendix for details. + +# 4.3. Implementation Details + +We follow recent works [4, 20] to adopt the pre-trained DistilBERT [46] to extract transcript representations. The vision transformer is a vanilla ViT-Base [15] with patch size $P = 16$ and hidden state dimension $D = 768$ , and is initialized with ImageMAE-Base [23]. We set the temperature parameter $\tau$ to be 0.05. We pre-train our model on the YTemporal dataset sampling 16 frames for 20 epochs. We randomly mask $75\%$ tokens within each frame. The input frame is first resized to $256 \times 256$ , then we apply RandomCrop during training and CenterCrop during inference. The final input resolution of each frame is $224 \times 224$ . For downstream tasks, we sample 16 frames for action recognition following [49] and 4 frames for text-to-video retrieval following [4]. More hyper-parameters are listed in Appendix. + +
Target →NoneTranscriptVideo
Dataset ↓BaselineOursVCOP [53]MERLOT [55]MERLOT-like
UCF-10181.2 (↓2.2)83.479.1 (↓4.3)74.9 (↓8.5)80.1 (↓3.3)
HMDB-5156.5 (↓1.9)58.454.2 (↓4.2)49.6 (↓8.8)55.4 (↓3.0)
+ +Table 1. Comparison with methods that use ordering-based pretext tasks for pre-training. We report top-1 accuracy under the linear probe classification protocol on UCF-101 and HMDB-51. The model pre-trained only with $\mathcal{L}_{\mathrm{base}}$ serves as the baseline. All models are pre-trained on the YT-Temporal dataset for a fair comparison. + +
Name\( \mathcal{L}_{\text{base}} \)\( \mathcal{L}_{\text{sort}} \)sgSSV2Kinetics-400
\( M_{scratch} \)××-64.5 (↓4.0)75.4 (↓3.4)
\( M_{base} \)✓×-67.0 (↓1.5)77.8 (↓1.0)
\( M_{sort\backslash sg} \)×✓×failedfailed
\( M_{sort} \)×✓✓failedfailed
\( M_{ours\backslash sg} \)✓✓×66.2 (↓2.3)76.5 (↓2.3)
\( M_{ours} \)✓✓✓68.578.8
+ +Table 2. The top-1 accuracy under the fine-tuning protocol on SSV2 and Kinetics-400, w.r.t. different pre-training objectives, where $\mathcal{L}_{\mathrm{sort}}$ trains the pretext task of TVTS. sg denotes stopping gradients of $\mathcal{L}_{\mathrm{sort}}$ towards encoding transcript representations. + +
DatasetNoneSort Modeling
BaselinePairwiseFactorialK-way
SSV267.0 (↓1.5)67.4 (↓1.1)67.2 (↓1.3)68.5
K40077.8 (↓1.0)78.1 (↓0.7)78.0 (↓0.8)78.8
+ +Table 3. The top-1 accuracy under the fine-tuning protocol on SSV2 and Kinetics-400, w.r.t. different ways to model the sorting of transcripts. "Pairwise" predicts the relative order for all transcript pairs, and "Factorial" performs $K!$ -classification for all possible orders. Our method uses "K-way" classification to predict the order of each transcript. The model pre-trained with $\mathcal{L}_{\mathrm{base}}$ only serves as the baseline. + +# 4.4. Ablation Study + +Pre-training Objectives. To demonstrate the effectiveness of our pretext task TVTS, we pre-train models with different objectives on the YT-Temporal dataset and evaluate them on SSV2 and K400. The results are listed in Table 2, in which $\mathbf{M}_{\mathrm{scratch}}$ denotes that we directly fine-tune the Image-MAE [23] initialized model. We have the following observations: (i) $\mathbf{M}_{\mathrm{base}}$ outperforms $\mathbf{M}_{\mathrm{scratch}}$ , which indicates that the natural language can be a promising supervision for video representation learning. (ii) Compared to $\mathbf{M}_{\mathrm{base}}$ , $\mathbf{M}_{\mathrm{ours}}$ further boosts performance by $1.5\%$ , which demonstrates that TVTS can effectively regularize our model to learn transferable spatiotemporal representations. (iii) $\mathbf{M}_{\mathrm{ours}\backslash \mathrm{sg}}$ drops performance, because when the gradients of $\mathcal{L}_{\mathrm{sort}}$ flow towards encoding transcript representations, the model op + +timizes the transcript representations to ease the ordering task rather than enhance spatiotemporal representations to provide enough knowledge for transcript sorting. (iv) Both $\mathbf{M}_{\mathrm{sort}\backslash \mathrm{sg}}$ and $\mathbf{M}_{\mathrm{sort}}$ failed because the parametric module for sorting hardly converges when feeding the misaligned video and transcript representations from distinct latent spaces. + +Sort Modeling. We explore different ways to model the order prediction of the shuffled transcripts. Besides using $K$ -way classification of each transcript, we also tried Pairwise, which sorts the transcripts by predicting the relative orders of the $K(K - 1) / 2$ transcript pairs, and Factorial, which predicts an overall ordering distribution by performing a $K$ -way classification ( $K!$ possible orders given $K$ transcripts). As listed in Table 3, both Pairwise and Factorial drop performance, because the former ignores the overall relationship among the transcripts while the latter imposes the same penalty on the results when different number of transcripts are sorted incorrectly. But they still outperform the baseline, indicating that sorting transcripts does benefit spatiotemporal representation learning. Our separate $K$ -way classification modeling achieves the best performance. + +# 4.5. Comparison with Ordering-based Pre-training + +MERLOT [55] also adopts an ordering-based pretext task, but has a totally different approach and purpose. MERLOT reorders scrambled video frames given the representations of every single frame and the ordered ASR transcripts with a joint encoder, and reserves the joint encoder for downstream multimodal tasks such as VQA. Specifically, MERLOT predicts the relative order of two video frames by binary classification. MERLOT aims to promote the joint encoder in learning multi-modal representations rather than spatiotemporal representations of videos. Since the visual encoder takes a single frame as input, it only achieves semantic understanding at the single-frame level without temporal reasoning among frames. As MERLOT points out, the ordering pretext task is not critical for downstream tasks (performance even drops) and primarily serves as an interface to query the model about temporal events. We further tailor MERLOT for our architecture as MERLOT-like, which sorts $K$ shuffled video frames by $K$ -way classification with the knowledge of the ordered tran + +
MethodVenuePre-train DatasetZero-shot Video-to-video RetrievalLinear Probe
R@1R@5R@10
Spatiotemporal representation learning method(s)
CVRL [42]CVPR'21Kinetics-400---11.4 (↓20.1)
MViT [16]ICCV'21Kinetics-400---19.4 (↓12.1)
SCVRL [14]CVPRW'22Kinetics-400---13.8 (↓17.7)
SVT [44]CVPR'22Kinetics-40011.3 (↓3.4)30.7 (↓7.7)41.1 (↓9.4)18.3 (↓13.2)
SVT† [44]CVPR'22YT-Temporal9.9 (↓4.8)26.2 (↓12.2)36.3 (↓14.2)18.0 (↓13.5)
VideoMAE [49]NeurIPS'22Kinetics-4007.9 (↓6.8)18.6 (↓19.8)26.5 (↓24.0)17.9 (↓13.6)
VideoMAE† [49]NeurIPS'22YT-Temporal7.2 (↓7.5)17.6 (↓20.8)25.6 (↓24.9)15.9 (↓15.6)
Video-text alignment method(s)
Frozen‡ [4]ICCV'21CC3M, WebVid-2M10.4 (↓4.3)28.5 (↓9.9)38.7 (↓11.8)17.5 (↓14.0)
MCQ‡ [20]CVPR'22CC3M, WebVid-2M10.4 (↓4.3)28.6 (↓9.8)38.5 (↓12.0)18.0 (↓13.5)
MILES‡ [21]ECCV'22CC3M, WebVid-2M10.3 (↓4.4)28.4 (↓10.0)38.4 (↓12.1)18.6 (↓12.9)
Image representation learning method(s)
CLIP [43]ICML'21WIT10.5 (↓4.2)28.8 (↓9.6)38.8 (↓11.7)16.4 (↓15.1)
OursCVPR'23YT-Temporal14.738.450.531.5
+ +scripts. Similar to MERLOT, VCOP [53] also predicts the order of the shuffled video clips, but only pre-trains on videos without language semantics. + +As listed in Table 1, all models that sort shuffled video clips/frames as the pretext task (i.e., VCOP, MERLOT) perform even worse than the baseline counterpart without sorting. It indicates that sorting shuffled videos in pretraining is infeasible and counterintuitive for improving spatiotemporal representations, because it does not regularize the video encoder for spatial and temporal reasoning given only a single frame or a short video segment as input. They mainly regularize an extra module beyond the video encoder to figure out the chronological order of the videos. By contrast, our pretext task regularizes the model to sort transcripts via reasoning among the video representations, which enforces the model to capture contextualized spatiotemporal representations so that it can provide enough knowledge for transcript ordering. + +# 4.6. Main Results + +# 4.6.1 Action Recognition + +Out-of-the-box Representations. To explore the transferability of the learned video representation, we evaluate zero-shot video-to-video retrieval and linear probe classification. We compare our proposed method with seven state-of-the-art methods, including: (a) Five video representation learning methods, i.e., CVRL [42], MViT [16], SCVRL [14], SVT [44], and VideoMAE [49]. (b) Three video-text alignment methods, i.e., Frozen [4], MCQ [20], + +Table 4. Transferability evaluation on SSV2. We report Recall@K for zero-shot video-to-video retrieval and top-1 accuracy for linear probe classification, where video-to-video retrieval aims to retrieve videos of the same category as a query video. † denotes pre-training on YT-Temporal for a fair comparison, and ‡ denotes the use of official pre-trained weights for evaluation. + +
MethodBackbonePre-train DatasetSSV2K400
TSM [36]R50 × 2ImageNet-1K66.0-
Vi²CLR [13]S3DKinetics-400-71.2
CORP [26]R3D-50Kinetics-40048.8-
MoCo v3 [10]ViT-BKinetics-40062.4-
TANet [39]R50 × 2ImageNet-1K66.0-
MViT [16]ViT-BKinetcis-40064.778.4
TimeSformer [6]ViT-BImageNet-21K59.578.3
RSANet [32]R50ImageNet-1K66.0-
SVT [44]ViT-BKinetics-40059.278.1
VideoMAE† [49]ViT-BYT-Temporal67.978.2
Frozen‡ [4]ViT-BCC3M, WebVid2M55.176.9
MCQ‡ [20]ViT-BCC3M, WebVid2M51.577.8
MILES‡ [21]ViT-BCC3M, WebVid2M54.177.4
OmniVL [50]ViT-B*Enormous Datasets61.679.1
CLIP [43]ViT-BWIT36.375.2
OursViT-BYT-Temporal68.578.8
OursViT-BYT-Temporal CC3M, WebVid2M69.179.8
+ +Table 5. The top-1 accuracy under the fine-tuning protocol on SSV2 and Kinetics-400. OmniVL adopts a mixture of eight datasets. $\dagger$ denotes pre-training on YT-Temporal, and $\ddagger$ denotes the use of official pre-trained weights for evaluation. + +and MILES [21]. (c) One image representation learning method with natural language supervision, i.e., CLIP [43]. We average frame features as its video representation. + +The results are listed in Table 4 and we have the following observations: (i) Our method surpasses all baselines by + +
MSR-VTTDiDeMoMSVDLSMDC
MethodR@1MedRMethodR@1MedRMethodR@1MedRMethodR@1MedR
MMT [19]26.64.0CE [38]16.18.3NoiseEst [1]20.36.0NoiseEst [1]6.439.0
SupportSet [41]30.13.0ClipBert [35]20.46.0SupportSet [41]28.44.0MMT [19]12.919.3
Frozen [4]31.03.0Frozen [4]31.03.0Frozen [4]45.62.0Frozen [4]15.020.0
Ours34.63.0Ours32.43.0Ours45.92.0Ours17.217.0
+ +Table 6. The R@1 and MedR under the fine-tuning protocol on MSR-VTT, DiDeMo, MSVD, and LSMDC for text-to-video retrieval. + +
MethodBackboneUCF-101HMDB-51
BE [51]I3D87.156.2
CMD [28]R(2+1)D-2685.754.0
Vi²CLR [13]S3D89.155.7
ASCNet [27]S3D-G90.860.5
TEC [29]S3D-G88.263.5
LSFD [5]C3D79.852.1
MCN [37]R3D89.759.3
TCLR [11]R(2+1)D-1884.354.2
SVT [44]ViT-B93.767.2
VideoMAE† [49]ViT-B94.268.4
Frozen‡ [4]ViT-B91.465.6
MCQ‡ [20]ViT-B92.965.1
MILES‡ [21]ViT-B92.166.8
OursViT-B95.170.5
+ +Table 7. The top-1 accuracy under the fine-tuning protocol on UCF-101 and HMDB-51. † denotes pre-training on YT-Temporal, and ‡ denotes the use of official pre-trained weights for evaluation. + +a large margin under all evaluation metrics, which indicates that our learned video representation has stronger transferability that can be used for out-of-domain video recognition. (ii) Previous video representation learning works yield weak transferability with only visual supervision. It implies that merely exploiting visual-only perception without explicit semantics hinders spatiotemporal understanding. Furthermore, we observe a significant performance drop on VideoMAE when it pre-trains the model on the large-scale uncurated dataset, i.e., YT-Temporal. By contrast, our pretrained model achieves promising results, which indicates that TVTS can successfully apply to real-world uncurated video data by exploiting rich semantics from script knowledge. (iii) Our method also outperforms video-text alignment works by a large margin. We infer that these works only focus on alignment between global video and caption representation without exploring fine-grained temporal information. On the contrary, our proposed TVTS regularizes the model to learn transferable spatiotemporal video representations. (iv) Benefiting from large-scale language supervision, image-based CLIP achieves competitive performance. But it is still worse than our model because we fully exploit the rich semantics from script knowledge. + +Fine-tuning Transferability. We evaluate our model under the fine-tuning protocol on SSV2, Kinetics-400, UCF-101, and HMDB-51. Besides pre-training on YT-Temporal, we further follow recent works [20, 21] to jointly post-pretrain our model on Google Conceptual Captions (CC3M) and WebVid-2M. Their texts are harvested from the web in the form of a single caption. Since there is no timestamp-annotated text on CC3M and WebVid-2M, we only adopt the contrastive object. As listed in Table 5 and Table 7, the recognition capability of our model is comparable to previous works as we achieve state-of-the-art or competitive accuracy, while retaining strong transferability. Additionally, the video-text alignment methods show inferior performance on SSV2 since they are devoted to associating the vision patterns with language concepts, without fully exploiting the temporal information. By contrast, our TVTS achieves satisfactory performance via strengthening the learning of spatiotemporal representations. + +# 4.6.2 Text-to-Video Retrieval + +As we preserve a global video-transcript contrastive loss to ease the ordering task via learning semantically meaningful video representations, it is natural to ask if the semantic-aware video representations can also benefit retrieval. Hence we conduct text-to-video retrieval under the fine-tuning protocol. As reported in Table 6, our model achieves SOTA performance. The promising results show that our TVTS can also learn the association between video patterns and language semantics. + +# 5. Conclusion + +In this work, we for the first time leverage script knowledge that is naturally tied to the video to facilitate spatiotemporal representation learning. We introduce a novel pretext task dubbed Turning to Video for Transcript Sorting (TVTS), which regularizes the model to learn transferable video representations for spatial and temporal reasoning. Extensive evaluations on downstream video tasks show the great superiority of our method. + +Acknowledgement. This work is supported in part by the National Natural Science Foundation of China under grant 62171248, the PCNL KEY project (PCL2021A07), Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies (2022B1212010005), Guangdong Basic and Applied Basic Research Foundation under grant 2021A1515110066, the GXWD 20220811172936001, the Shenzhen Science and Technology Innovation Commission (Research Center for Computer Network (Shenzhen) Ministry of Education), and Shenzhen Science and Technology Program under Grant JCYJ20220818101012025. + +# References + +[1] Elad Amrani, Rami Ben-Ari, Daniel Rotman, and Alex Bronstein. Noise estimation using density estimation for self-supervised multimodal learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 6644-6652, 2021. 8 +[2] Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. Localizing moments in video with natural language. In Proceedings of the IEEE international conference on computer vision, pages 5803-5812, 2017. 5 +[3] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6836-6846, 2021. 5 +[4] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1728-1738, 2021. 2, 3, 5, 7, 8 +[5] Nadine Behrmann, Mohsen Fayyaz, Juergen Gall, and Mehdi Noroozi. Long short view feature decomposition via contrastive video representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9244-9253, 2021. 1, 3, 8 +[6] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, volume 2, page 4, 2021. 7 +[7] David Chen and William B Dolan. Collecting highly parallel data for paraphrase evaluation. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pages 190-200, 2011. 5 +[8] Peihao Chen, Deng Huang, Dongliang He, Xiang Long, Runhao Zeng, Shilei Wen, Mingkui Tan, and Chuang Gan. Rspnet: Relative speed perception for unsupervised video representation learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 1045-1053, 2021. 1, 3 +[9] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 1 +[10] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9640-9649, 2021. 7 +[11] Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve, and Mubarak Shah. TcIr: Temporal contrastive learning for video representation. Computer Vision and Image Understanding, 219:103406, 2022. 8 +[12] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 5 + +[13] Ali Diba, Vivek Sharma, Reza Safdari, Dariush Lotfi, Saquib Sarfraz, Rainer Stiefelhagen, and Luc Van Gool. Vi2cIr: Video and image for visual contrastive learning of representation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1502-1512, 2021. 7, 8 +[14] Michael Dorkenwald, Fanyi Xiao, Biagio Brattoli, Joseph Tighe, and Davide Modolo. Scvrl: Shuffled contrastive video representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4132-4141, 2022. 7 +[15] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 5 +[16] Haoqi Fan, Bo Xiong, Karttikeya Mangalam, Yanghao Li, Zhicheng Yan, Jitendra Malik, and Christoph Feichtenhofer. Multiscale vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6824-6835, 2021. 7 +[17] Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, and Kaiming He. Masked autoencoders as spatiotemporal learners. arXiv preprint arXiv:2205.09113, 2022. 1, 2, 3, 4 +[18] Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, and Zicheng Liu. Violet: End-to-end video-language transformers with masked visual-token modeling. arXiv preprint arXiv:2111.12681, 2021. 2 +[19] Valentin Gabeur, Chen Sun, Karteek Alahari, and Cordelia Schmid. Multi-modal transformer for video retrieval. In European Conference on Computer Vision, pages 214-229. Springer, 2020. 8 +[20] Yuying Ge, Yixiao Ge, Xihui Liu, Dian Li, Ying Shan, Xiaohu Qie, and Ping Luo. Bridging video-text retrieval with multiple choice questions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16167-16176, 2022. 2, 3, 5, 7, 8 +[21] Yuying Ge, Yixiao Ge, Xihui Liu, Alex Jinpeng Wang, Jianping Wu, Ying Shan, Xiaohu Qie, and Ping Luo. Miles: Visual bert pre-training with injected language semantics for video-text retrieval. arXiv preprint arXiv:2204.12408, 2022.3,7,8 +[22] Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al. The" something something" video database for learning and evaluating visual common sense. In Proceedings of the IEEE international conference on computer vision, pages 5842-5850, 2017. 1, 2, 3, 5 +[23] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000-16009, 2022. 1, 5, 6 +[24] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference + +on Computer Vision and Pattern Recognition, pages 16000-16009, 2022. 3 +[25] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 1 +[26] Kai Hu, Jie Shao, Yuan Liu, Bhiksha Raj, Marios Savvides, and Zhiqiang Shen. Contrast and order representations for video self-supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7939-7949, 2021. 7 +[27] Deng Huang, Wenhao Wu, Weiwen Hu, Xu Liu, Dongliang He, Zhihua Wu, Xiangmiao Wu, Mingkui Tan, and Errui Ding. Ascnet: Self-supervised video representation learning with appearance-speed consistency. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8096-8105, 2021. 1, 3, 8 +[28] Lianghua Huang, Yu Liu, Bin Wang, Pan Pan, Yinghui Xu, and Rong Jin. Self-supervised video representation learning by context and motion decoupling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13886-13895, 2021. 8 +[29] Simon Jenni and Hailin Jin. Time-equivariant contrastive video representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9970-9980, 2021. 8 +[30] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 3 +[31] Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017. 1, 2, 5 +[32] Manjin Kim, Heeseung Kwon, Chunyu Wang, Suha Kwak, and Minsu Cho. Relational self-attention: What's missing in attention for video understanding. Advances in Neural Information Processing Systems, 34:8046-8059, 2021. 7 +[33] Hildegard Kuehne, Hueihan Jhuang, Estibaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In ICCV, pages 2556-2563. IEEE, 2011. 1, 2, 5 +[34] Jie Lei, Tamara L Berg, and Mohit Bansal. Revealing single frame bias for video-and-language learning. arXiv preprint arXiv:2206.03428, 2022. 2, 3 +[35] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7331-7341, 2021. 8 +[36] Ji Lin, Chuang Gan, and Song Han. Tsm: Temporal shift module for efficient video understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7083-7093, 2019. 7 + +[37] Yuanze Lin, Xun Guo, and Yan Lu. Self-supervised video representation learning with meta-contrastive network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8239–8249, 2021. 8 +[38] Yang Liu, Samuel Albanie, Arsha Nagrani, and Andrew Zisserman. Use what you have: Video retrieval using representations from collaborative experts. arXiv preprint arXiv:1907.13487, 2019. 8 +[39] Zhaoyang Liu, Limin Wang, Wayne Wu, Chen Qian, and Tong Lu. Tam: Temporal adaptive module for video recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13708-13718, 2021. 7 +[40] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 5 +[41] Mandela Patrick, Po-Yao Huang, Yuki Asano, Florian Metze, Alexander Hauptmann, Joao Henriques, and Andrea Vedaldi. Support-set bottlenecks for video-text representation learning. arXiv preprint arXiv:2010.02824, 2020. 8 +[42] Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, and Yin Cui. Spatiotemporal contrastive video representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6964-6974, 2021. 7 +[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 3, 7 +[44] Kanchana Ranasinghe, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan, and Michael S Ryoo. Self-supervised video transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2874-2884, 2022. 3, 7, 8 +[45] Anna Rohrbach, Marcus Rohrbach, Niket Tandon, and Bernt Schiele. A dataset for movie description. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3202-3212, 2015. 5 +[46] Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019.5 +[47] Shibani Santurkar, Yann Dubois, Rohan Taori, Percy Liang, and Tatsunori Hashimoto. Is a caption worth a thousand images? a controlled study for representation learning. arXiv preprint arXiv:2207.07635, 2022. 2 +[48] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012. 1, 2, 5 +[49] Zhan Tong, Yibing Song, Jue Wang, and Limin Wang. Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training. arXiv preprint arXiv:2203.12602, 2022. 1, 2, 3, 4, 5, 7, 8 +[50] Junke Wang, Dongdong Chen, Zuxuan Wu, Chong Luo, Luowei Zhou, Yucheng Zhao, Yujia Xie, Ce Liu, Yu-Gang Jiang, and Lu Yuan. Omnil: One foundation model for + +image-language and video-language tasks. arXiv preprint arXiv:2209.07526, 2022. 7 +[51] Jinpeng Wang, Yuting Gao, Ke Li, Yiqi Lin, Andy J Ma, Hao Cheng, Pai Peng, Feiyue Huang, Rongrong Ji, and Xing Sun. Removing the background by adding the background: Towards background robust self-supervised video representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11804-11813, 2021. 8 +[52] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaou Tang, and Luc Van Gool. Temporal segment networks: Towards good practices for deep action recognition. In European conference on computer vision, pages 20-36. Springer, 2016. 4 +[53] Dejing Xu, Jun Xiao, Zhou Zhao, Jian Shao, Di Xie, and Yueting Zhuang. Self-supervised spatiotemporal learning via video clip order prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10334–10343, 2019. 6, 7 +[54] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5288-5296, 2016. 5 +[55] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. 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"text", + "content": " Harbin Institute of Technology, Shenzhen \n* equal contribution corresponding authors" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 63, + 234, + 525, + 259 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 234, + 525, + 259 + ], + "spans": [ + { + "bbox": [ + 63, + 234, + 525, + 259 + ], + "type": "text", + "content": "zengzy21@mails.tsinghua.edu.cn yuyingge@hku.hk xihuiliu@eee.hku.hk chenbin2021@hit.edu.cn pluo@cs.hku.hk xiast@sz.tsinghua.edu.cn yixiaoge@tencent.com" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 143, + 286, + 192, + 300 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 286, + 192, + 300 + ], + "spans": [ + { + "bbox": [ + 143, + 286, + 192, + 300 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 312, + 290, + 622 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 312, + 290, + 622 + ], + "spans": [ + { + "bbox": [ + 47, + 312, + 290, + 622 + ], + "type": "text", + "content": "Pre-training on large-scale video data has become a common recipe for learning transferable spatiotemporal representations in recent years. Despite some progress, existing methods are mostly limited to highly curated datasets (e.g., K400) and exhibit unsatisfactory out-of-the-box representations. We argue that it is due to the fact that they only capture pixel-level knowledge rather than spatiotemporal semantics, which hinders further progress in video understanding. Inspired by the great success of image-text pre-training (e.g., CLIP), we take the first step to exploit language semantics to boost transferable spatiotemporal representation learning. We introduce a new pretext task, Turning to Video for Transcript Sorting (TVTS), which sorts shuffled ASR scripts by attending to learned video representations. We do not rely on descriptive captions and learn purely from video, i.e., leveraging the natural transcribed speech knowledge to provide noisy but useful semantics over time. Our method enforces the vision model to contextualize what is happening over time so that it can re-organize the narrative transcripts, and can seamlessly apply to large-scale uncurated video data in the real world. Our method demonstrates strong out-of-the-box spatiotemporal representations on diverse benchmarks, e.g., " + }, + { + "bbox": [ + 47, + 312, + 290, + 622 + ], + "type": "inline_equation", + "content": "+13.6\\%" + }, + { + "bbox": [ + 47, + 312, + 290, + 622 + ], + "type": "text", + "content": " gains over VideoMAE on SSV2 via linear probing. The code is available at https://github.com/TencentARC/TVTS." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 645, + 128, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 645, + 128, + 658 + ], + "spans": [ + { + "bbox": [ + 47, + 645, + 128, + 658 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "content": "The aspiration of representation learning is to encode general-purpose representations that transfer well to diverse downstream tasks, where self-supervised methodologies [9, 25] dominate due to their advantage in exploiting" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 288, + 547, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 288, + 547, + 384 + ], + "spans": [ + { + "bbox": [ + 304, + 288, + 547, + 384 + ], + "type": "text", + "content": "large-scale unlabeled data. Despite significant progress in learning representations of still images [23, 43], the real world is dynamic and requires reasoning over time. In this paper, we focus on out-of-the-box spatiotemporal representation learning, a more challenging but practical task towards generic video understanding, which aims to capture hidden representations that can be further used to conduct reasoning on broader tasks, e.g., classification and retrieval." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 387, + 548, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 387, + 548, + 602 + ], + "spans": [ + { + "bbox": [ + 304, + 387, + 548, + 602 + ], + "type": "text", + "content": "There have been various attempts at self-supervised pretraining on video data from discriminative learning objectives [5, 8, 27] to generative ones [17, 49], where the core is context capturing in spatial and temporal dimensions. Though promising results are achieved when transferring the pre-trained models to downstream video recognition [22, 33, 48] via fine-tuning, the learned representations are still far away from out-of-the-box given the poor linearly probing results (see Figure 1(a)). Moreover, existing works mostly develop video models on the highly curated dataset with particular biases, i.e., K400 [31]. Their applicability in the real world is questioned given the observed performance drops when training on a larger but uncurated dataset, YT-Temporal [55]. We argue that, to address the above issue, the rich spatiotemporal semantics contained in the video itself should be fully exploited. But current video models generally exploit visual-only perception (e.g., pixels) without explicit semantics." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 605, + 548, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 548, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 548, + 714 + ], + "type": "text", + "content": "Recently, the success of CLIP [43] has inspired the community to learn semantically aware image representations that are better transferable to downstream tasks and scalable to larger uncurated datasets. It provides a feasible solution for improving spatiotemporal representation learning but remains two key problems. (1) The vision-language contrastive constraints in CLIP mainly encourage the understanding of static objects (noun contrast) and simple motions (verb contrast), while how to enable long-range tem" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23079" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 70, + 364, + 245 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 364, + 245 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 364, + 245 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 364, + 245 + ], + "type": "image", + "image_path": "30311201ad497b9a952207ff0eecbfa161e7a9bb0e3bd4be2eddc34068a44a24.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 255, + 548, + 301 + ], + "lines": [ + { + "bbox": [ + 46, + 255, + 548, + 301 + ], + "spans": [ + { + "bbox": [ + 46, + 255, + 548, + 301 + ], + "type": "text", + "content": "Figure 1. (a) We evaluate the transferability of spatiotemporal representations via linear probing on four video recognition datasets [22, 31, 33, 48], where the state-of-the-art method [49] underperforms. It performs even worse when pre-trained with a large-scale uncurated dataset, YT-Temporal [55]. (b) We encourage complex temporal understanding and advanced spatiotemporal representation learning with a new pretext task of sorting transcripts." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 372, + 70, + 545, + 245 + ], + "blocks": [ + { + "bbox": [ + 372, + 70, + 545, + 245 + ], + "lines": [ + { + "bbox": [ + 372, + 70, + 545, + 245 + ], + "spans": [ + { + "bbox": [ + 372, + 70, + 545, + 245 + ], + "type": "image", + "image_path": "7fce2c431215d5060407a73b266ae7173163589f7c5541b0bbab1cba0a068b29.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 321, + 289, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 321, + 289, + 453 + ], + "spans": [ + { + "bbox": [ + 46, + 321, + 289, + 453 + ], + "type": "text", + "content": "poral understanding with language supervision needs to be studied. (2) The quality of language supervision [47] is critical to the final performance of CLIP, however, it is hard to collect large-scale video data with literal captions that carefully describe the dynamic content over time. The ideal way for self-supervised learning is to learn useful knowledge purely from the data itself, which is also the philosophy followed by previous video pre-training methods [17, 49]. Fortunately, video data is naturally multi-modal with transcribed speech knowledge in the form of text (ASR), providing time-dependent semantics despite some noise." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 457, + 289, + 649 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 457, + 289, + 649 + ], + "spans": [ + { + "bbox": [ + 46, + 457, + 289, + 649 + ], + "type": "text", + "content": "To facilitate spatiotemporal understanding in large-scale uncurated data under the supervision of inherent script knowledge, we introduce a new pretext task for video pretraining, namely, Turning to Video for Transcript Sorting (TVTS). Intuitively, people sort out the order of events by temporal reasoning. As illustrated in Figure 1(b), given several unordered transcripts, it is difficult to reorganize the narrative by merely understanding the literal semantics. When the corresponding video is provided, it will be much easier to sort the transcripts by contextualizing what is happening over time. Whereas in neural networks, the temporal inference is embedded in spatiotemporal representations. Thus we believe that if the chronological order of transcripts can be correctly figured out via resorting to the correlated video representations, the video has been well understood." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "type": "text", + "content": "We realize the pretext task of TVTS by performing joint attention among the encoded video spatiotemporal representations and the extracted ASR transcript representations. Specifically, given an input video and its successive transcripts, we randomly shuffle the order of the sentences." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 321, + 547, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 321, + 547, + 430 + ], + "spans": [ + { + "bbox": [ + 304, + 321, + 547, + 430 + ], + "type": "text", + "content": "Subsequently, we concatenate the encoded script representations and the video representations and perform self-attention to predict the actual orders of the shuffled transcripts by fully understanding the spatiotemporal semantics in the video. The order prediction is cast as a " + }, + { + "bbox": [ + 304, + 321, + 547, + 430 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 321, + 547, + 430 + ], + "type": "text", + "content": "-way classification task, where " + }, + { + "bbox": [ + 304, + 321, + 547, + 430 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 321, + 547, + 430 + ], + "type": "text", + "content": " is the number of transcripts. The pretext task indirectly regularizes our model to properly capture contextualized spatiotemporal representations to provide enough knowledge for transcript ordering." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 434, + 548, + 650 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 434, + 548, + 650 + ], + "spans": [ + { + "bbox": [ + 304, + 434, + 548, + 650 + ], + "type": "text", + "content": "The usage of language supervision is related to videotext alignment [4, 20] and multimodal representation learning [18, 55] methods, however, we are completely different. (1) Video-text alignment methods focus on retrieval tasks and are devoted to associating the vision patterns with language concepts. They are generally single-frame biased [34] and fail to encode strong out-of-the-box temporal representations. (2) Multimodal representation learning methods aim to learn fused representations across modalities rather than vision-only spatiotemporal representations in our work. Moreover, different from our pretext task that aims to optimize spatiotemporal video representations, [55] sorts video frames by taking the features of individual frames as inputs without temporal modeling, i.e., learning video representations only at the image level. As [55] points out, its ordering pretext task is not critical for downstream tasks (performance even drops) and primarily serves as an interface to query the model about temporal events." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 547, + 715 + ], + "type": "text", + "content": "To summarize, our contributions are three-fold. (i) We exploit the rich semantics from script knowledge which is naturally along with the video, rendering a flexible pretraining method that can easily apply to uncurated video data in the real world. (ii) We introduce a novel pre" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23080" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "content": "text task for video pre-training, namely, Turning to Video for Transcript Sorting (TVTS). It promotes the capability of the model in learning transferable spatiotemporal video representations. (iii) We conduct comprehensive comparisons with advanced methods. Our pre-trained model exhibits strong out-of-the-box spatiotemporal representations on downstream action recognition tasks, especially the relatively large-scale and the most challenging SSV2 [22]. We also achieve state-of-the-art performances on eight common video datasets in terms of fine-tuning." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 203, + 134, + 216 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 203, + 134, + 216 + ], + "spans": [ + { + "bbox": [ + 47, + 203, + 134, + 216 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 223, + 289, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 223, + 289, + 426 + ], + "spans": [ + { + "bbox": [ + 46, + 223, + 289, + 426 + ], + "type": "text", + "content": "Spatiotemporal representation learning. Dominant video representation learning works have two categories, i.e., discriminative- and generative-based methods. (i) The discriminative-based methods aim at mining unique representations within videos. For example, SVT [44] aligns several views from the same video with different spatial and temporal resolution for video-invariant representations. RSPNet [8], ASCNet [27], and LongShortView [5] utilize the appearance and temporal consistency of videos as the supervision. They use different augmentations of videos to construct positive and negative pairs to learn correspondences along the spatial and temporal dimensions. (ii) The generative-based methods try to reconstruct visual information from corrupted inputs. For example, MAE-based [24] methods [17, 49] use pixel values of video frames as supervision by masking raw videos with an extremely high ratio and reconstructing them." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 426, + 289, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 426, + 289, + 569 + ], + "spans": [ + { + "bbox": [ + 46, + 426, + 289, + 569 + ], + "type": "text", + "content": "Previous works are mainly trained on highly curated datasets, e.g., Kinetics-400, HMDB51, and UCF101, where the temporal motions are not significant [34]. This leads to a \"spatial bias\", thus weakening the transferability to real-world uncurated datasets due to the lack of long-term temporal reasoning. Besides, existing works merely use visual supervision without explicit semantic information. Compared to them, our work leverages natural language derived from the video itself, i.e., the ASR transcripts, as the supervision. Benefiting from the rich spatiotemporal information, our learned video representations have stronger transferability to downstream tasks." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 570, + 289, + 714 + ], + "type": "text", + "content": "Video-text pre-training. Existing video-text pre-training work can be divided into two categories. The first category aims to learn video-text alignment for retrieval. For example, Frozen [4], MCQ [20], and MILES [21] generally adopt two separate encoders to extract video and text representations, then align them with contrastive loss. However, they only align videos with a global video caption, thus neglecting the fine-grained temporal information. Furthermore, they rely on clean captions, which are difficult to scale up, and it is actually hard to collect large-scale video data with captions describing the dynamic content over time. The second category works on joint representation learning across" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "content": "modalities mainly for VQA. For example, MERLOT [55] adopts a joint encoder to match the captions with the corresponding video frames and put scrambled video frames into the correct order. It aims to match different modalities in the temporal dimension to achieve multi-modality fusion in a joint encoder, rather than learn better spatiotemporal representations." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 156, + 545, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 156, + 545, + 335 + ], + "spans": [ + { + "bbox": [ + 304, + 156, + 545, + 335 + ], + "type": "text", + "content": "Image representation learning by language supervision. Recently, there have been a bunch of successful tries in utilizing language supervision to enhance image representation learning. For example, CLIP [43] utilized 400M imagetext pairs collected from the Internet and adopt the contrastive loss to align the image and its corresponding text. The superior performance on downstream image classification tasks revealed that learning directly from the raw text about images is a promising alternative that leverages a much broader source of supervision. ALIGN [30], uses a larger but noisier uncurated dataset and shows similar results to CLIP. Nevertheless, these methods only utilize language supervision to improve spatial learning, without exploring temporal learning, which hinders them from properly learning out-of-the-box video representations." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 344, + 361, + 357 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 344, + 361, + 357 + ], + "spans": [ + { + "bbox": [ + 306, + 344, + 361, + 357 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 365, + 545, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 365, + 545, + 449 + ], + "spans": [ + { + "bbox": [ + 304, + 365, + 545, + 449 + ], + "type": "text", + "content": "In this work, we introduce a novel pretext task, Turning to Video for Transcript Sorting (TVTS) to learn the transferable spatiotemporal video representation by leveraging the rich semantics from script knowledge. In this section, we first introduce the pretext TVTS in Sec. 3.1 and our pretraining objectives in Sec. 3.2. We then describe the model architecture in Sec. 3.3." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 454, + 515, + 467 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 454, + 515, + 467 + ], + "spans": [ + { + "bbox": [ + 306, + 454, + 515, + 467 + ], + "type": "text", + "content": "3.1. Turning to Video for Transcript Sorting" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 472, + 545, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 472, + 545, + 593 + ], + "spans": [ + { + "bbox": [ + 304, + 472, + 545, + 593 + ], + "type": "text", + "content": "As shown in Fig. 2, we perform the pretext task of TVTS to learn transferable spatiotemporal representations of videos. Given the observation that it will be much easier to sort the ASR transcripts by contextualizing what is happening over time in the video, we first randomly shuffle several consecutive ASR transcripts and extract their representations. We then perform joint attention among the transcript representations and video representations to sort the transcripts in the correct order via capturing contextualized spatiotemporal representations of videos." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "spans": [ + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "text", + "content": "Sample and Shuffle. Given a video " + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "text", + "content": " and its corresponding ASR transcripts with word-level timestamps " + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "inline_equation", + "content": "\\{(w_{i},s_{i})\\}_{i = 1}^{N_{\\mathrm{asr}}}" + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "inline_equation", + "content": "N_{\\mathrm{asr}}" + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "text", + "content": " denotes the word number, " + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "inline_equation", + "content": "w_{i}" + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "inline_equation", + "content": "s_i" + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "text", + "content": " denote the " + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "text", + "content": "-th word and its timestamp respectively, we randomly choose a starting time " + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "inline_equation", + "content": "s_{\\mathrm{begin}}" + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "text", + "content": " and sample " + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "text", + "content": " consecutive transcripts, each with a duration of " + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 304, + 598, + 545, + 683 + ], + "type": "text", + "content": " (in seconds), and an interval of 1s between adjacent transcripts," + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 321, + 687, + 545, + 706 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 321, + 687, + 545, + 706 + ], + "spans": [ + { + "bbox": [ + 321, + 687, + 545, + 706 + ], + "type": "interline_equation", + "content": "S _ {k} = s _ {\\text {b e g i n}} + (k - 1) * (l + 1), \\quad E _ {k} = S _ {k} + l \\tag {1}", + "image_path": "d427ce601d5af93edf34b09c6780284cd7d5b2046f519e3da936dfd465bb2c4f.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 323, + 704, + 511, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 323, + 704, + 511, + 715 + ], + "spans": [ + { + "bbox": [ + 323, + 704, + 511, + 715 + ], + "type": "interline_equation", + "content": "T _ {k} = \\left\\{w _ {i} | S _ {k} \\leq s _ {i} \\leq E _ {k} \\right\\}, \\quad k \\in \\{1, \\dots , K \\}", + "image_path": "a444342999aa0fa9191b00a03a815a02e925996f40dc8e9399aa5567b1f1264c.jpg" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23081" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 70, + 547, + 293 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 547, + 293 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 547, + 293 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 547, + 293 + ], + "type": "image", + "image_path": "31020a82ce26ae0b37ee895c9df4a3cbaa5a44fdebc34f181c4974aff85faea2.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 298, + 547, + 346 + ], + "lines": [ + { + "bbox": [ + 45, + 298, + 547, + 346 + ], + "spans": [ + { + "bbox": [ + 45, + 298, + 547, + 346 + ], + "type": "text", + "content": "Figure 2. Our pre-training pipeline. We first sample " + }, + { + "bbox": [ + 45, + 298, + 547, + 346 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 45, + 298, + 547, + 346 + ], + "type": "text", + "content": " consecutive ASR transcripts, and a video clip consisting of " + }, + { + "bbox": [ + 45, + 298, + 547, + 346 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 45, + 298, + 547, + 346 + ], + "type": "text", + "content": " frames within the span of the transcripts. We randomly sample frame patches as the input of a vision transformer for the video representations. We then shuffle the transcripts and extract the representation of each transcript. We perform joint attention among the transcript and the video representations to predict the actual order of each transcript, which is optimized with a cross-entropy objective." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "spans": [ + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "inline_equation", + "content": "S_{k}" + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "inline_equation", + "content": "E_{k}" + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "text", + "content": " denote the beginning and ending time of the " + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "text", + "content": "-th transcript. We consecutively sample " + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "text", + "content": " transcripts with an interval of 1s and collect all words within " + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "inline_equation", + "content": "[S_{k}, E_{k}]" + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "text", + "content": " for the " + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "text", + "content": "-th transcript. Finally, we randomly shuffle the transcripts as " + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "inline_equation", + "content": "\\{T_{o_i}\\}_{i=1}^{K}" + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "text", + "content": ", which means that the " + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "text", + "content": "-th transcript in this shuffled sequence is actually the " + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "inline_equation", + "content": "o_i" + }, + { + "bbox": [ + 46, + 364, + 289, + 448 + ], + "type": "text", + "content": "-th transcript in the original ordered sequence." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "spans": [ + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": "As for the video, we sample a clip between the beginning and ending time of all " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": " transcripts, i.e., " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "[S_1,E_K]" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": ", which contains " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": " frames as " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "\\{F_i\\}_{i = 1}^M" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": ". Specifically, we follow TSN [52] to divide " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "[S_1,E_K]" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": " into " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": " segments with equal length and randomly sample 1 frame from each segment. After that, we get a video clip with " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": " frames and " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": " shuffled transcripts along the span of the video clip." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "text", + "content": "Sorting Transcripts. Given the shuffled transcripts " + }, + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "inline_equation", + "content": "\\{T_{o_i}\\}_{i = 1}^K" + }, + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "text", + "content": " and the corresponding video clip " + }, + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "inline_equation", + "content": "\\{F_i\\}_{i = 1}^M" + }, + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "text", + "content": ", we first feed the transcripts in parallel to encode unordered text representations " + }, + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "inline_equation", + "content": "\\{t_{o_i}\\}_{i = 1}^K" + }, + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "text", + "content": ". We then randomly sample the frame patches by masking a large proportion of the video clip among the spatial and temporal dimension as the input of a vision transformer to encode video representations " + }, + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "inline_equation", + "content": "\\{v_j\\}_{j = 0}^N" + }, + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "text", + "content": " denotes the number of the unmasked video patches, and " + }, + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "inline_equation", + "content": "v_0" + }, + { + "bbox": [ + 47, + 545, + 289, + 715 + ], + "type": "text", + "content": " is the representation of the [CLS] token. It is worth noting that we do not add the extra [MASK] token, and we have no explicit reconstruction target, which is different from previous works [17, 49]. We sample video frame patches as a means of data augmentation since it provides corrupted knowledge for our model to perform the" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 364, + 545, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 364, + 545, + 399 + ], + "spans": [ + { + "bbox": [ + 304, + 364, + 545, + 399 + ], + "type": "text", + "content": "pretext task of TVTS. Such a strategy also reduces the computational cost during pre-training as the attention is calculated on fewer patches." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "spans": [ + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "text", + "content": "We then concatenate the text representations of the shuffled transcripts " + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "inline_equation", + "content": "\\{t_{o_i}\\}_{i = 1}^K" + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "text", + "content": " and the video representations of the sampled video clip " + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "inline_equation", + "content": "\\{v_j\\}_{j = 0}^N" + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "text", + "content": ", and perform multi-head selfattention among them. Our model attempts to sort the transcripts in the correct order by attending to the text features of all transcripts and the visual features of the unmasked video clip. We model the prediction of the transcript orders as a " + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "text", + "content": "-way classification task for each transcript. The first " + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "text", + "content": " output representations after the joint attention are further fed into a linear classifier to predict the order " + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "inline_equation", + "content": "p\\in \\mathbb{R}^{K}" + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "inline_equation", + "content": "p_j" + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "text", + "content": " denotes the probability that the transcript is the " + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "text", + "content": "-th transcript in the original ordered sequence. For the transcript " + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "inline_equation", + "content": "T_{o_i}" + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "text", + "content": ", the ground truth classification label should be " + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "inline_equation", + "content": "o_i" + }, + { + "bbox": [ + 304, + 400, + 546, + 555 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 555, + 547, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 555, + 547, + 652 + ], + "spans": [ + { + "bbox": [ + 304, + 555, + 547, + 652 + ], + "type": "text", + "content": "The pretext task of TVTS regularizes the model to contextualize what is happening over time, so that it can provide enough knowledge for our model to figure out the chronological order of the shuffled transcripts. It improves the capability of the model to learn spatiotemporal representations that can be transferred to downstream tasks. We compare our method with other works that also adopt an ordering-based pretext task for pre-training in Sec. 4.5." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 658, + 440, + 672 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 658, + 440, + 672 + ], + "spans": [ + { + "bbox": [ + 306, + 658, + 440, + 672 + ], + "type": "text", + "content": "3.2. Pre-training Objectives" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "type": "text", + "content": "Besides the pretext task of TVTS, we use a global video Transcript contrastive objective. It aligns the features of the video clip and the averaged features of " + }, + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "type": "text", + "content": " transcripts so that" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23082" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 119 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 119 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 119 + ], + "type": "text", + "content": "the video and transcript representations are in the same feature space for performing the joint attention to predict transcript orders. We combine two objectives to optimize the entire model in an end-to-end manner." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 120, + 287, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 120, + 287, + 156 + ], + "spans": [ + { + "bbox": [ + 46, + 120, + 287, + 156 + ], + "type": "text", + "content": "The first one is a cross-entropy objective " + }, + { + "bbox": [ + 46, + 120, + 287, + 156 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{sort}}" + }, + { + "bbox": [ + 46, + 120, + 287, + 156 + ], + "type": "text", + "content": ", which supervises our model to predict the correct order of the transcripts, and is formulated as below," + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 116, + 162, + 287, + 196 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 162, + 287, + 196 + ], + "spans": [ + { + "bbox": [ + 116, + 162, + 287, + 196 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {s o r t}} = - \\frac {1}{K} \\sum_ {i = 1} ^ {K} \\log \\operatorname {s o f t m a x} \\left(\\hat {p} ^ {i}\\right) \\tag {2}", + "image_path": "69e60b46a77261f891dfa0dfe86c22089049c443947efb77b77625a3b66b3b3d.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 96, + 195, + 235, + 221 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 195, + 235, + 221 + ], + "spans": [ + { + "bbox": [ + 96, + 195, + 235, + 221 + ], + "type": "interline_equation", + "content": "s. t. \\quad \\mathrm {s o f t m a x} (\\hat {p} ^ {i}) = \\frac {\\exp (p _ {o _ {i}} ^ {i})}{\\sum_ {j = 1} ^ {K} \\exp (p _ {j} ^ {i})},", + "image_path": "dc061f32b0bce6601a6b8151b6a4868d7dc79f4a27b954edc4eb6d5c8b04e823.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 227, + 287, + 275 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 227, + 287, + 275 + ], + "spans": [ + { + "bbox": [ + 46, + 227, + 287, + 275 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 227, + 287, + 275 + ], + "type": "inline_equation", + "content": "p_j^i" + }, + { + "bbox": [ + 46, + 227, + 287, + 275 + ], + "type": "text", + "content": " denotes the probability that the " + }, + { + "bbox": [ + 46, + 227, + 287, + 275 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 227, + 287, + 275 + ], + "type": "text", + "content": "-th transcript in the shuffled sequence is the " + }, + { + "bbox": [ + 46, + 227, + 287, + 275 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 46, + 227, + 287, + 275 + ], + "type": "text", + "content": "-th transcript in the original ordered sequence and " + }, + { + "bbox": [ + 46, + 227, + 287, + 275 + ], + "type": "inline_equation", + "content": "o_i" + }, + { + "bbox": [ + 46, + 227, + 287, + 275 + ], + "type": "text", + "content": " is the ground truth order in the original ordered sequence." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 275, + 287, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 275, + 287, + 300 + ], + "spans": [ + { + "bbox": [ + 47, + 275, + 287, + 300 + ], + "type": "text", + "content": "The second one is the global video-transcript contrastive objective " + }, + { + "bbox": [ + 47, + 275, + 287, + 300 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{base}}" + }, + { + "bbox": [ + 47, + 275, + 287, + 300 + ], + "type": "text", + "content": ", formulated as a bidirectional InfoNCE [40]," + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 100, + 305, + 219, + 318 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 100, + 305, + 219, + 318 + ], + "spans": [ + { + "bbox": [ + 100, + 305, + 219, + 318 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {b a s e}} = \\mathrm {N C E} (\\hat {t}, \\hat {v}) + \\mathrm {N C E} (\\hat {v}, \\hat {t})", + "image_path": "dc9e0e407a106420e5356b1e0076d183e1f29832d25260ecce697257ef3ba874.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 79, + 320, + 287, + 347 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 79, + 320, + 287, + 347 + ], + "spans": [ + { + "bbox": [ + 79, + 320, + 287, + 347 + ], + "type": "interline_equation", + "content": "s. t. \\quad \\mathrm {N C E} (q, k) = - \\log \\frac {\\exp \\left(q ^ {\\top} k _ {+} / \\tau\\right)}{\\sum_ {i = 1} ^ {B} \\exp \\left(q ^ {\\top} k _ {i} / \\tau\\right)}, \\tag {3}", + "image_path": "49a92c509b79a59686ebf6df7f7ac5930eb77d1edc202c8b174bd58d31bc94a9.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "spans": [ + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "inline_equation", + "content": "\\hat{t}" + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "inline_equation", + "content": "\\hat{v}" + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "text", + "content": " denote the global text and video representation. We average the [CLS] token representation of all " + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "text", + "content": " transcripts as " + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "inline_equation", + "content": "\\hat{t}" + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "text", + "content": ", i.e., " + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "inline_equation", + "content": "\\hat{t} \\gets \\frac{1}{K} \\sum_{i=1}^{K} t_i" + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "text", + "content": ", and use the [CLS] token representation of the video clip as " + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "inline_equation", + "content": "\\hat{v}" + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "text", + "content": ", i.e., " + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "inline_equation", + "content": "\\hat{v} \\gets v_0" + }, + { + "bbox": [ + 46, + 353, + 287, + 401 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 401, + 287, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 401, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 46, + 401, + 287, + 460 + ], + "type": "text", + "content": "Our overall pre-training objective combines the two objectives, i.e., " + }, + { + "bbox": [ + 46, + 401, + 287, + 460 + ], + "type": "inline_equation", + "content": "\\mathcal{L} = \\mathcal{L}_{\\mathrm{base}} + \\lambda \\mathcal{L}_{\\mathrm{sort}}" + }, + { + "bbox": [ + 46, + 401, + 287, + 460 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 401, + 287, + 460 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 401, + 287, + 460 + ], + "type": "text", + "content": " is a hyperparameter to balance the two losses. In our implementation, we set " + }, + { + "bbox": [ + 46, + 401, + 287, + 460 + ], + "type": "inline_equation", + "content": "\\lambda = 2" + }, + { + "bbox": [ + 46, + 401, + 287, + 460 + ], + "type": "text", + "content": " to roughly scale the gradient magnitudes of " + }, + { + "bbox": [ + 46, + 401, + 287, + 460 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{base}}" + }, + { + "bbox": [ + 46, + 401, + 287, + 460 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 401, + 287, + 460 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{sort}}" + }, + { + "bbox": [ + 46, + 401, + 287, + 460 + ], + "type": "text", + "content": " to be the same for efficient training." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 468, + 162, + 479 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 468, + 162, + 479 + ], + "spans": [ + { + "bbox": [ + 47, + 468, + 162, + 479 + ], + "type": "text", + "content": "3.3. Model Architecture" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 486, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 486, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 486, + 287, + 713 + ], + "type": "text", + "content": "The vision transformer takes a video clip as input, which consists of " + }, + { + "bbox": [ + 46, + 486, + 287, + 713 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 486, + 287, + 713 + ], + "type": "text", + "content": " frames of resolution " + }, + { + "bbox": [ + 46, + 486, + 287, + 713 + ], + "type": "inline_equation", + "content": "H\\times W" + }, + { + "bbox": [ + 46, + 486, + 287, + 713 + ], + "type": "text", + "content": ", and outputs video representations. We follow [49] to adopt cube embeddings, where each token corresponds to a cube of size " + }, + { + "bbox": [ + 46, + 486, + 287, + 713 + ], + "type": "inline_equation", + "content": "2\\times 16\\times 16" + }, + { + "bbox": [ + 46, + 486, + 287, + 713 + ], + "type": "text", + "content": ". This yields " + }, + { + "bbox": [ + 46, + 486, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\frac{M}{2}\\times \\frac{H}{16}\\times \\frac{W}{16} 3D" + }, + { + "bbox": [ + 46, + 486, + 287, + 713 + ], + "type": "text", + "content": " tokens. Then we add divided space-time embedding to the token sequence, where tokens within the same frame obtain the same temporal embedding, and tokens within the same spatial location of different frames obtain the same spatial embedding. In this way, the vision transformer learns the positional information of the cubes. Next, we follow BERT [12] to add a learnable [CLS] token at the beginning of the token sequence for global video representations. Then we mask a portion of video tokens without [MASK] token replacement, as stated in Sec. 3.1. We adopt a standard ViT [15] architecture to encode video representations. The unmasked " + }, + { + "bbox": [ + 46, + 486, + 287, + 713 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 486, + 287, + 713 + ], + "type": "text", + "content": " video tokens as well as the [CLS] token are fed into the vision transformer, and joint space-time attention [3] is performed among the whole unmasked token sequence." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 156 + ], + "type": "text", + "content": "We use a DistilBERT [46] to extract the representations of ASR transcripts. We adopt two stacked bidirectional transformer blocks to predict the order of each transcript by performing joint attention among the transcript and the video representations. Within each block, multi-head self-attention is performed among all the video and text tokens, i.e., all transcript-video tokens interact with each other." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 169, + 388, + 182 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 169, + 388, + 182 + ], + "spans": [ + { + "bbox": [ + 306, + 169, + 388, + 182 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 190, + 429, + 203 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 190, + 429, + 203 + ], + "spans": [ + { + "bbox": [ + 306, + 190, + 429, + 203 + ], + "type": "text", + "content": "4.1. Pre-training Datasets" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 209, + 545, + 246 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 209, + 545, + 246 + ], + "spans": [ + { + "bbox": [ + 304, + 209, + 545, + 246 + ], + "type": "text", + "content": "We pre-train our model on the large-scale YT-Temporal dataset [55] containing 6M YouTube videos with ASR transcripts and word-level timestamps." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 255, + 417, + 267 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 255, + 417, + 267 + ], + "spans": [ + { + "bbox": [ + 306, + 255, + 417, + 267 + ], + "type": "text", + "content": "4.2. Downstream Tasks" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 274, + 545, + 418 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 274, + 545, + 418 + ], + "spans": [ + { + "bbox": [ + 304, + 274, + 545, + 418 + ], + "type": "text", + "content": "Action Recognition. We evaluate our pre-trained model on four common video datasets: (a) Something-Something V2 (SSV2) [22], (b) Kinetics-400 (K400) [31], (c) UCF-101 [48], (d) HMDB-51 [33]. Our evaluation is two-fold: (i) We conduct zero-shot video-to-video retrieval and linear probe classification on SSV2 to evaluate the transferability of the learned video representation. The former aims to retrieve videos of the same category as a query video, and the latter freezes the visual encoder and only optimizes a linear classifier. (ii) We fully fine-tune our pre-trained model on the training set of the four datasets to evaluate the action recognition capability. See Appendix for details." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 433, + 545, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 433, + 545, + 517 + ], + "spans": [ + { + "bbox": [ + 304, + 433, + 545, + 517 + ], + "type": "text", + "content": "Text-to-Video Retrieval. Beyond action recognition, we further evaluate retrieval performance on four benchmarks to see if the improved semantic-aware video representation can benefit retrieval tasks: (a) MSR-VTT [54] (b) DiDeMo [2] (c) MSVD [7] (d) LSMDC [45]. We adopt Recall@K (R@K) and Median Rank (MedR) as the evaluation metric. See Appendix for details." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 526, + 440, + 539 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 526, + 440, + 539 + ], + "spans": [ + { + "bbox": [ + 306, + 526, + 440, + 539 + ], + "type": "text", + "content": "4.3. Implementation Details" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "content": "We follow recent works [4, 20] to adopt the pre-trained DistilBERT [46] to extract transcript representations. The vision transformer is a vanilla ViT-Base [15] with patch size " + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "inline_equation", + "content": "P = 16" + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "content": " and hidden state dimension " + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "inline_equation", + "content": "D = 768" + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "content": ", and is initialized with ImageMAE-Base [23]. We set the temperature parameter " + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "content": " to be 0.05. We pre-train our model on the YTemporal dataset sampling 16 frames for 20 epochs. We randomly mask " + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "inline_equation", + "content": "75\\%" + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "content": " tokens within each frame. The input frame is first resized to " + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "content": ", then we apply RandomCrop during training and CenterCrop during inference. The final input resolution of each frame is " + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "inline_equation", + "content": "224 \\times 224" + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "content": ". For downstream tasks, we sample 16 frames for action recognition following [49] and 4 frames for text-to-video retrieval following [4]. More hyper-parameters are listed in Appendix." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23083" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 114, + 70, + 479, + 136 + ], + "blocks": [ + { + "bbox": [ + 114, + 70, + 479, + 136 + ], + "lines": [ + { + "bbox": [ + 114, + 70, + 479, + 136 + ], + "spans": [ + { + "bbox": [ + 114, + 70, + 479, + 136 + ], + "type": "table", + "html": "
Target →NoneTranscriptVideo
Dataset ↓BaselineOursVCOP [53]MERLOT [55]MERLOT-like
UCF-10181.2 (↓2.2)83.479.1 (↓4.3)74.9 (↓8.5)80.1 (↓3.3)
HMDB-5156.5 (↓1.9)58.454.2 (↓4.2)49.6 (↓8.8)55.4 (↓3.0)
", + "image_path": "cef84e40b3fac1623daa6237ccbe1e74531e6aab8ba82fe3d231a2adb17dd17a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 51, + 196, + 286, + 289 + ], + "blocks": [ + { + "bbox": [ + 46, + 144, + 547, + 178 + ], + "lines": [ + { + "bbox": [ + 46, + 144, + 547, + 178 + ], + "spans": [ + { + "bbox": [ + 46, + 144, + 547, + 178 + ], + "type": "text", + "content": "Table 1. Comparison with methods that use ordering-based pretext tasks for pre-training. We report top-1 accuracy under the linear probe classification protocol on UCF-101 and HMDB-51. The model pre-trained only with " + }, + { + "bbox": [ + 46, + 144, + 547, + 178 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{base}}" + }, + { + "bbox": [ + 46, + 144, + 547, + 178 + ], + "type": "text", + "content": " serves as the baseline. All models are pre-trained on the YT-Temporal dataset for a fair comparison." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 51, + 196, + 286, + 289 + ], + "lines": [ + { + "bbox": [ + 51, + 196, + 286, + 289 + ], + "spans": [ + { + "bbox": [ + 51, + 196, + 286, + 289 + ], + "type": "table", + "html": "
Name\\( \\mathcal{L}_{\\text{base}} \\)\\( \\mathcal{L}_{\\text{sort}} \\)sgSSV2Kinetics-400
\\( M_{scratch} \\)××-64.5 (↓4.0)75.4 (↓3.4)
\\( M_{base} \\)✓×-67.0 (↓1.5)77.8 (↓1.0)
\\( M_{sort\\backslash sg} \\)×✓×failedfailed
\\( M_{sort} \\)×✓✓failedfailed
\\( M_{ours\\backslash sg} \\)✓✓×66.2 (↓2.3)76.5 (↓2.3)
\\( M_{ours} \\)✓✓✓68.578.8
", + "image_path": "69fba8f99a978819befe6acda4d46eaa35d011322a7e7916b50fe815eb28d252.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 50, + 353, + 286, + 419 + ], + "blocks": [ + { + "bbox": [ + 46, + 297, + 288, + 343 + ], + "lines": [ + { + "bbox": [ + 46, + 297, + 288, + 343 + ], + "spans": [ + { + "bbox": [ + 46, + 297, + 288, + 343 + ], + "type": "text", + "content": "Table 2. The top-1 accuracy under the fine-tuning protocol on SSV2 and Kinetics-400, w.r.t. different pre-training objectives, where " + }, + { + "bbox": [ + 46, + 297, + 288, + 343 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{sort}}" + }, + { + "bbox": [ + 46, + 297, + 288, + 343 + ], + "type": "text", + "content": " trains the pretext task of TVTS. sg denotes stopping gradients of " + }, + { + "bbox": [ + 46, + 297, + 288, + 343 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{sort}}" + }, + { + "bbox": [ + 46, + 297, + 288, + 343 + ], + "type": "text", + "content": " towards encoding transcript representations." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 353, + 286, + 419 + ], + "lines": [ + { + "bbox": [ + 50, + 353, + 286, + 419 + ], + "spans": [ + { + "bbox": [ + 50, + 353, + 286, + 419 + ], + "type": "table", + "html": "
DatasetNoneSort Modeling
BaselinePairwiseFactorialK-way
SSV267.0 (↓1.5)67.4 (↓1.1)67.2 (↓1.3)68.5
K40077.8 (↓1.0)78.1 (↓0.7)78.0 (↓0.8)78.8
", + "image_path": "e1e77a1a5c280aa17cc32dd3095ea97abdfcacdcd0f91565a8a74bcc42c2b8f1.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 429, + 287, + 506 + ], + "lines": [ + { + "bbox": [ + 46, + 429, + 287, + 506 + ], + "spans": [ + { + "bbox": [ + 46, + 429, + 287, + 506 + ], + "type": "text", + "content": "Table 3. The top-1 accuracy under the fine-tuning protocol on SSV2 and Kinetics-400, w.r.t. different ways to model the sorting of transcripts. \"Pairwise\" predicts the relative order for all transcript pairs, and \"Factorial\" performs " + }, + { + "bbox": [ + 46, + 429, + 287, + 506 + ], + "type": "inline_equation", + "content": "K!" + }, + { + "bbox": [ + 46, + 429, + 287, + 506 + ], + "type": "text", + "content": "-classification for all possible orders. Our method uses \"K-way\" classification to predict the order of each transcript. The model pre-trained with " + }, + { + "bbox": [ + 46, + 429, + 287, + 506 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{base}}" + }, + { + "bbox": [ + 46, + 429, + 287, + 506 + ], + "type": "text", + "content": " only serves as the baseline." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 47, + 527, + 141, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 527, + 141, + 540 + ], + "spans": [ + { + "bbox": [ + 47, + 527, + 141, + 540 + ], + "type": "text", + "content": "4.4. Ablation Study" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "text", + "content": "Pre-training Objectives. To demonstrate the effectiveness of our pretext task TVTS, we pre-train models with different objectives on the YT-Temporal dataset and evaluate them on SSV2 and K400. The results are listed in Table 2, in which " + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{\\mathrm{scratch}}" + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "text", + "content": " denotes that we directly fine-tune the Image-MAE [23] initialized model. We have the following observations: (i) " + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{\\mathrm{base}}" + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "text", + "content": " outperforms " + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{\\mathrm{scratch}}" + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "text", + "content": ", which indicates that the natural language can be a promising supervision for video representation learning. (ii) Compared to " + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{\\mathrm{base}}" + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{\\mathrm{ours}}" + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "text", + "content": " further boosts performance by " + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "inline_equation", + "content": "1.5\\%" + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "text", + "content": ", which demonstrates that TVTS can effectively regularize our model to learn transferable spatiotemporal representations. (iii) " + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{\\mathrm{ours}\\backslash \\mathrm{sg}}" + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "text", + "content": " drops performance, because when the gradients of " + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{sort}}" + }, + { + "bbox": [ + 46, + 545, + 288, + 715 + ], + "type": "text", + "content": " flow towards encoding transcript representations, the model op" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 199, + 547, + 272 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 199, + 547, + 272 + ], + "spans": [ + { + "bbox": [ + 304, + 199, + 547, + 272 + ], + "type": "text", + "content": "timizes the transcript representations to ease the ordering task rather than enhance spatiotemporal representations to provide enough knowledge for transcript sorting. (iv) Both " + }, + { + "bbox": [ + 304, + 199, + 547, + 272 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{\\mathrm{sort}\\backslash \\mathrm{sg}}" + }, + { + "bbox": [ + 304, + 199, + 547, + 272 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 199, + 547, + 272 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{\\mathrm{sort}}" + }, + { + "bbox": [ + 304, + 199, + 547, + 272 + ], + "type": "text", + "content": " failed because the parametric module for sorting hardly converges when feeding the misaligned video and transcript representations from distinct latent spaces." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "spans": [ + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "text", + "content": "Sort Modeling. We explore different ways to model the order prediction of the shuffled transcripts. Besides using " + }, + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "text", + "content": "-way classification of each transcript, we also tried Pairwise, which sorts the transcripts by predicting the relative orders of the " + }, + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "inline_equation", + "content": "K(K - 1) / 2" + }, + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "text", + "content": " transcript pairs, and Factorial, which predicts an overall ordering distribution by performing a " + }, + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "text", + "content": "-way classification (" + }, + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "inline_equation", + "content": "K!" + }, + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "text", + "content": " possible orders given " + }, + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "text", + "content": " transcripts). As listed in Table 3, both Pairwise and Factorial drop performance, because the former ignores the overall relationship among the transcripts while the latter imposes the same penalty on the results when different number of transcripts are sorted incorrectly. But they still outperform the baseline, indicating that sorting transcripts does benefit spatiotemporal representation learning. Our separate " + }, + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 281, + 547, + 461 + ], + "type": "text", + "content": "-way classification modeling achieves the best performance." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 467, + 545, + 481 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 467, + 545, + 481 + ], + "spans": [ + { + "bbox": [ + 305, + 467, + 545, + 481 + ], + "type": "text", + "content": "4.5. Comparison with Ordering-based Pre-training" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 486, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 486, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 486, + 547, + 713 + ], + "type": "text", + "content": "MERLOT [55] also adopts an ordering-based pretext task, but has a totally different approach and purpose. MERLOT reorders scrambled video frames given the representations of every single frame and the ordered ASR transcripts with a joint encoder, and reserves the joint encoder for downstream multimodal tasks such as VQA. Specifically, MERLOT predicts the relative order of two video frames by binary classification. MERLOT aims to promote the joint encoder in learning multi-modal representations rather than spatiotemporal representations of videos. Since the visual encoder takes a single frame as input, it only achieves semantic understanding at the single-frame level without temporal reasoning among frames. As MERLOT points out, the ordering pretext task is not critical for downstream tasks (performance even drops) and primarily serves as an interface to query the model about temporal events. We further tailor MERLOT for our architecture as MERLOT-like, which sorts " + }, + { + "bbox": [ + 304, + 486, + 547, + 713 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 486, + 547, + 713 + ], + "type": "text", + "content": " shuffled video frames by " + }, + { + "bbox": [ + 304, + 486, + 547, + 713 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 486, + 547, + 713 + ], + "type": "text", + "content": "-way classification with the knowledge of the ordered tran" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23084" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 77, + 70, + 518, + 291 + ], + "blocks": [ + { + "bbox": [ + 77, + 70, + 518, + 291 + ], + "lines": [ + { + "bbox": [ + 77, + 70, + 518, + 291 + ], + "spans": [ + { + "bbox": [ + 77, + 70, + 518, + 291 + ], + "type": "table", + "html": "
MethodVenuePre-train DatasetZero-shot Video-to-video RetrievalLinear Probe
R@1R@5R@10
Spatiotemporal representation learning method(s)
CVRL [42]CVPR'21Kinetics-400---11.4 (↓20.1)
MViT [16]ICCV'21Kinetics-400---19.4 (↓12.1)
SCVRL [14]CVPRW'22Kinetics-400---13.8 (↓17.7)
SVT [44]CVPR'22Kinetics-40011.3 (↓3.4)30.7 (↓7.7)41.1 (↓9.4)18.3 (↓13.2)
SVT† [44]CVPR'22YT-Temporal9.9 (↓4.8)26.2 (↓12.2)36.3 (↓14.2)18.0 (↓13.5)
VideoMAE [49]NeurIPS'22Kinetics-4007.9 (↓6.8)18.6 (↓19.8)26.5 (↓24.0)17.9 (↓13.6)
VideoMAE† [49]NeurIPS'22YT-Temporal7.2 (↓7.5)17.6 (↓20.8)25.6 (↓24.9)15.9 (↓15.6)
Video-text alignment method(s)
Frozen‡ [4]ICCV'21CC3M, WebVid-2M10.4 (↓4.3)28.5 (↓9.9)38.7 (↓11.8)17.5 (↓14.0)
MCQ‡ [20]CVPR'22CC3M, WebVid-2M10.4 (↓4.3)28.6 (↓9.8)38.5 (↓12.0)18.0 (↓13.5)
MILES‡ [21]ECCV'22CC3M, WebVid-2M10.3 (↓4.4)28.4 (↓10.0)38.4 (↓12.1)18.6 (↓12.9)
Image representation learning method(s)
CLIP [43]ICML'21WIT10.5 (↓4.2)28.8 (↓9.6)38.8 (↓11.7)16.4 (↓15.1)
OursCVPR'23YT-Temporal14.738.450.531.5
", + "image_path": "ac61bdee5fc3ea606b8268cb1d669f0c0bb8a1e08813e298b953bfdba420dab6.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 354, + 287, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 354, + 287, + 389 + ], + "spans": [ + { + "bbox": [ + 46, + 354, + 287, + 389 + ], + "type": "text", + "content": "scripts. Similar to MERLOT, VCOP [53] also predicts the order of the shuffled video clips, but only pre-trains on videos without language semantics." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 390, + 288, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 390, + 288, + 570 + ], + "spans": [ + { + "bbox": [ + 46, + 390, + 288, + 570 + ], + "type": "text", + "content": "As listed in Table 1, all models that sort shuffled video clips/frames as the pretext task (i.e., VCOP, MERLOT) perform even worse than the baseline counterpart without sorting. It indicates that sorting shuffled videos in pretraining is infeasible and counterintuitive for improving spatiotemporal representations, because it does not regularize the video encoder for spatial and temporal reasoning given only a single frame or a short video segment as input. They mainly regularize an extra module beyond the video encoder to figure out the chronological order of the videos. By contrast, our pretext task regularizes the model to sort transcripts via reasoning among the video representations, which enforces the model to capture contextualized spatiotemporal representations so that it can provide enough knowledge for transcript ordering." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 578, + 132, + 590 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 578, + 132, + 590 + ], + "spans": [ + { + "bbox": [ + 47, + 578, + 132, + 590 + ], + "type": "text", + "content": "4.6. Main Results" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 597, + 162, + 609 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 597, + 162, + 609 + ], + "spans": [ + { + "bbox": [ + 47, + 597, + 162, + 609 + ], + "type": "text", + "content": "4.6.1 Action Recognition" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 617, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 287, + 715 + ], + "type": "text", + "content": "Out-of-the-box Representations. To explore the transferability of the learned video representation, we evaluate zero-shot video-to-video retrieval and linear probe classification. We compare our proposed method with seven state-of-the-art methods, including: (a) Five video representation learning methods, i.e., CVRL [42], MViT [16], SCVRL [14], SVT [44], and VideoMAE [49]. (b) Three video-text alignment methods, i.e., Frozen [4], MCQ [20]," + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 314, + 352, + 539, + 578 + ], + "blocks": [ + { + "bbox": [ + 46, + 299, + 547, + 333 + ], + "lines": [ + { + "bbox": [ + 46, + 299, + 547, + 333 + ], + "spans": [ + { + "bbox": [ + 46, + 299, + 547, + 333 + ], + "type": "text", + "content": "Table 4. Transferability evaluation on SSV2. We report Recall@K for zero-shot video-to-video retrieval and top-1 accuracy for linear probe classification, where video-to-video retrieval aims to retrieve videos of the same category as a query video. † denotes pre-training on YT-Temporal for a fair comparison, and ‡ denotes the use of official pre-trained weights for evaluation." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 314, + 352, + 539, + 578 + ], + "lines": [ + { + "bbox": [ + 314, + 352, + 539, + 578 + ], + "spans": [ + { + "bbox": [ + 314, + 352, + 539, + 578 + ], + "type": "table", + "html": "
MethodBackbonePre-train DatasetSSV2K400
TSM [36]R50 × 2ImageNet-1K66.0-
Vi²CLR [13]S3DKinetics-400-71.2
CORP [26]R3D-50Kinetics-40048.8-
MoCo v3 [10]ViT-BKinetics-40062.4-
TANet [39]R50 × 2ImageNet-1K66.0-
MViT [16]ViT-BKinetcis-40064.778.4
TimeSformer [6]ViT-BImageNet-21K59.578.3
RSANet [32]R50ImageNet-1K66.0-
SVT [44]ViT-BKinetics-40059.278.1
VideoMAE† [49]ViT-BYT-Temporal67.978.2
Frozen‡ [4]ViT-BCC3M, WebVid2M55.176.9
MCQ‡ [20]ViT-BCC3M, WebVid2M51.577.8
MILES‡ [21]ViT-BCC3M, WebVid2M54.177.4
OmniVL [50]ViT-B*Enormous Datasets61.679.1
CLIP [43]ViT-BWIT36.375.2
OursViT-BYT-Temporal68.578.8
OursViT-BYT-Temporal CC3M, WebVid2M69.179.8
", + "image_path": "c5c63dbac02fcce9ea009a43c0c55d4690a3a97de4e5b91388f05774e1b9b7af.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 586, + 545, + 632 + ], + "lines": [ + { + "bbox": [ + 304, + 586, + 545, + 632 + ], + "spans": [ + { + "bbox": [ + 304, + 586, + 545, + 632 + ], + "type": "text", + "content": "Table 5. The top-1 accuracy under the fine-tuning protocol on SSV2 and Kinetics-400. OmniVL adopts a mixture of eight datasets. " + }, + { + "bbox": [ + 304, + 586, + 545, + 632 + ], + "type": "inline_equation", + "content": "\\dagger" + }, + { + "bbox": [ + 304, + 586, + 545, + 632 + ], + "type": "text", + "content": " denotes pre-training on YT-Temporal, and " + }, + { + "bbox": [ + 304, + 586, + 545, + 632 + ], + "type": "inline_equation", + "content": "\\ddagger" + }, + { + "bbox": [ + 304, + 586, + 545, + 632 + ], + "type": "text", + "content": " denotes the use of official pre-trained weights for evaluation." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 653, + 545, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 545, + 689 + ], + "type": "text", + "content": "and MILES [21]. (c) One image representation learning method with natural language supervision, i.e., CLIP [43]. We average frame features as its video representation." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 689, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 715 + ], + "type": "text", + "content": "The results are listed in Table 4 and we have the following observations: (i) Our method surpasses all baselines by" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "23085" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 61, + 70, + 531, + 145 + ], + "blocks": [ + { + "bbox": [ + 61, + 70, + 531, + 145 + ], + "lines": [ + { + "bbox": [ + 61, + 70, + 531, + 145 + ], + "spans": [ + { + "bbox": [ + 61, + 70, + 531, + 145 + ], + "type": "table", + "html": "
MSR-VTTDiDeMoMSVDLSMDC
MethodR@1MedRMethodR@1MedRMethodR@1MedRMethodR@1MedR
MMT [19]26.64.0CE [38]16.18.3NoiseEst [1]20.36.0NoiseEst [1]6.439.0
SupportSet [41]30.13.0ClipBert [35]20.46.0SupportSet [41]28.44.0MMT [19]12.919.3
Frozen [4]31.03.0Frozen [4]31.03.0Frozen [4]45.62.0Frozen [4]15.020.0
Ours34.63.0Ours32.43.0Ours45.92.0Ours17.217.0
", + "image_path": "fa316cfe4ebe9ff9383e730a88569f7a9f21016c480a6277789cfb19c417b3cb.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 60, + 185, + 274, + 373 + ], + "blocks": [ + { + "bbox": [ + 55, + 154, + 538, + 166 + ], + "lines": [ + { + "bbox": [ + 55, + 154, + 538, + 166 + ], + "spans": [ + { + "bbox": [ + 55, + 154, + 538, + 166 + ], + "type": "text", + "content": "Table 6. The R@1 and MedR under the fine-tuning protocol on MSR-VTT, DiDeMo, MSVD, and LSMDC for text-to-video retrieval." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 60, + 185, + 274, + 373 + ], + "lines": [ + { + "bbox": [ + 60, + 185, + 274, + 373 + ], + "spans": [ + { + "bbox": [ + 60, + 185, + 274, + 373 + ], + "type": "table", + "html": "
MethodBackboneUCF-101HMDB-51
BE [51]I3D87.156.2
CMD [28]R(2+1)D-2685.754.0
Vi²CLR [13]S3D89.155.7
ASCNet [27]S3D-G90.860.5
TEC [29]S3D-G88.263.5
LSFD [5]C3D79.852.1
MCN [37]R3D89.759.3
TCLR [11]R(2+1)D-1884.354.2
SVT [44]ViT-B93.767.2
VideoMAE† [49]ViT-B94.268.4
Frozen‡ [4]ViT-B91.465.6
MCQ‡ [20]ViT-B92.965.1
MILES‡ [21]ViT-B92.166.8
OursViT-B95.170.5
", + "image_path": "d7eb3a26659fcd4c583370af7a8d1fd91a2f612d90272e1a493969a04f5a38e0.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 381, + 287, + 415 + ], + "lines": [ + { + "bbox": [ + 46, + 381, + 287, + 415 + ], + "spans": [ + { + "bbox": [ + 46, + 381, + 287, + 415 + ], + "type": "text", + "content": "Table 7. The top-1 accuracy under the fine-tuning protocol on UCF-101 and HMDB-51. † denotes pre-training on YT-Temporal, and ‡ denotes the use of official pre-trained weights for evaluation." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 45, + 437, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 437, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 45, + 437, + 288, + 715 + ], + "type": "text", + "content": "a large margin under all evaluation metrics, which indicates that our learned video representation has stronger transferability that can be used for out-of-domain video recognition. (ii) Previous video representation learning works yield weak transferability with only visual supervision. It implies that merely exploiting visual-only perception without explicit semantics hinders spatiotemporal understanding. Furthermore, we observe a significant performance drop on VideoMAE when it pre-trains the model on the large-scale uncurated dataset, i.e., YT-Temporal. By contrast, our pretrained model achieves promising results, which indicates that TVTS can successfully apply to real-world uncurated video data by exploiting rich semantics from script knowledge. (iii) Our method also outperforms video-text alignment works by a large margin. We infer that these works only focus on alignment between global video and caption representation without exploring fine-grained temporal information. On the contrary, our proposed TVTS regularizes the model to learn transferable spatiotemporal video representations. (iv) Benefiting from large-scale language supervision, image-based CLIP achieves competitive performance. But it is still worse than our model because we fully exploit the rich semantics from script knowledge." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 186, + 547, + 403 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 186, + 547, + 403 + ], + "spans": [ + { + "bbox": [ + 304, + 186, + 547, + 403 + ], + "type": "text", + "content": "Fine-tuning Transferability. We evaluate our model under the fine-tuning protocol on SSV2, Kinetics-400, UCF-101, and HMDB-51. Besides pre-training on YT-Temporal, we further follow recent works [20, 21] to jointly post-pretrain our model on Google Conceptual Captions (CC3M) and WebVid-2M. Their texts are harvested from the web in the form of a single caption. Since there is no timestamp-annotated text on CC3M and WebVid-2M, we only adopt the contrastive object. As listed in Table 5 and Table 7, the recognition capability of our model is comparable to previous works as we achieve state-of-the-art or competitive accuracy, while retaining strong transferability. Additionally, the video-text alignment methods show inferior performance on SSV2 since they are devoted to associating the vision patterns with language concepts, without fully exploiting the temporal information. By contrast, our TVTS achieves satisfactory performance via strengthening the learning of spatiotemporal representations." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 406, + 440, + 417 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 406, + 440, + 417 + ], + "spans": [ + { + "bbox": [ + 305, + 406, + 440, + 417 + ], + "type": "text", + "content": "4.6.2 Text-to-Video Retrieval" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 419, + 547, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 419, + 547, + 528 + ], + "spans": [ + { + "bbox": [ + 304, + 419, + 547, + 528 + ], + "type": "text", + "content": "As we preserve a global video-transcript contrastive loss to ease the ordering task via learning semantically meaningful video representations, it is natural to ask if the semantic-aware video representations can also benefit retrieval. Hence we conduct text-to-video retrieval under the fine-tuning protocol. As reported in Table 6, our model achieves SOTA performance. The promising results show that our TVTS can also learn the association between video patterns and language semantics." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 532, + 379, + 544 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 532, + 379, + 544 + ], + "spans": [ + { + "bbox": [ + 306, + 532, + 379, + 544 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 547, + 545, + 643 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 547, + 545, + 643 + ], + "spans": [ + { + "bbox": [ + 304, + 547, + 545, + 643 + ], + "type": "text", + "content": "In this work, we for the first time leverage script knowledge that is naturally tied to the video to facilitate spatiotemporal representation learning. We introduce a novel pretext task dubbed Turning to Video for Transcript Sorting (TVTS), which regularizes the model to learn transferable video representations for spatial and temporal reasoning. Extensive evaluations on downstream video tasks show the great superiority of our method." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 647, + 547, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 647, + 547, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 647, + 547, + 712 + ], + "type": "text", + "content": "Acknowledgement. This work is supported in part by the National Natural Science Foundation of China under grant 62171248, the PCNL KEY project (PCL2021A07), Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies (2022B1212010005), Guangdong Basic and Applied Basic Research Foundation under grant 2021A1515110066, the GXWD 20220811172936001, the Shenzhen Science and Technology Innovation Commission (Research Center for Computer Network (Shenzhen) Ministry of Education), and Shenzhen Science and Technology Program under Grant JCYJ20220818101012025." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "23086" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 146 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 146 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 146 + ], + "type": "text", + "content": "[1] Elad Amrani, Rami Ben-Ari, Daniel Rotman, and Alex Bronstein. Noise estimation using density estimation for self-supervised multimodal learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 6644-6652, 2021. 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 148, + 288, + 202 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 148, + 288, + 202 + ], + "spans": [ + { + "bbox": [ + 53, + 148, + 288, + 202 + ], + "type": "text", + "content": "[2] Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, and Bryan Russell. Localizing moments in video with natural language. In Proceedings of the IEEE international conference on computer vision, pages 5803-5812, 2017. 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 205, + 287, + 258 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 205, + 287, + 258 + ], + "spans": [ + { + "bbox": [ + 53, + 205, + 287, + 258 + ], + "type": "text", + "content": "[3] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lucic, and Cordelia Schmid. Vivit: A video vision transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6836-6846, 2021. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 261, + 287, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 261, + 287, + 316 + ], + "spans": [ + { + "bbox": [ + 53, + 261, + 287, + 316 + ], + "type": "text", + "content": "[4] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1728-1738, 2021. 2, 3, 5, 7, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 318, + 287, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 318, + 287, + 373 + ], + "spans": [ + { + "bbox": [ + 53, + 318, + 287, + 373 + ], + "type": "text", + "content": "[5] Nadine Behrmann, Mohsen Fayyaz, Juergen Gall, and Mehdi Noroozi. Long short view feature decomposition via contrastive video representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9244-9253, 2021. 1, 3, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 374, + 287, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 374, + 287, + 407 + ], + "spans": [ + { + "bbox": [ + 53, + 374, + 287, + 407 + ], + "type": "text", + "content": "[6] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, volume 2, page 4, 2021. 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 409, + 287, + 462 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 409, + 287, + 462 + ], + "spans": [ + { + "bbox": [ + 53, + 409, + 287, + 462 + ], + "type": "text", + "content": "[7] David Chen and William B Dolan. Collecting highly parallel data for paraphrase evaluation. In Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pages 190-200, 2011. 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 465, + 287, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 465, + 287, + 529 + ], + "spans": [ + { + "bbox": [ + 53, + 465, + 287, + 529 + ], + "type": "text", + "content": "[8] Peihao Chen, Deng Huang, Dongliang He, Xiang Long, Runhao Zeng, Shilei Wen, Mingkui Tan, and Chuang Gan. Rspnet: Relative speed perception for unsupervised video representation learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 1045-1053, 2021. 1, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 533, + 287, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 533, + 287, + 577 + ], + "spans": [ + { + "bbox": [ + 53, + 533, + 287, + 577 + ], + "type": "text", + "content": "[9] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 578, + 287, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 578, + 287, + 622 + ], + "spans": [ + { + "bbox": [ + 48, + 578, + 287, + 622 + ], + "type": "text", + "content": "[10] Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9640-9649, 2021. 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 624, + 287, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 287, + 667 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 287, + 667 + ], + "type": "text", + "content": "[11] Ishan Dave, Rohit Gupta, Mamshad Nayeem Rizve, and Mubarak Shah. TcIr: Temporal contrastive learning for video representation. Computer Vision and Image Understanding, 219:103406, 2022. 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "text", + "content": "[12] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 5" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "type": "text", + "content": "[13] Ali Diba, Vivek Sharma, Reza Safdari, Dariush Lotfi, Saquib Sarfraz, Rainer Stiefelhagen, and Luc Van Gool. Vi2cIr: Video and image for visual contrastive learning of representation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1502-1512, 2021. 7, 8" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 129, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 545, + 184 + ], + "type": "text", + "content": "[14] Michael Dorkenwald, Fanyi Xiao, Biagio Brattoli, Joseph Tighe, and Davide Modolo. Scvrl: Shuffled contrastive video representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4132-4141, 2022. 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 186, + 545, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 186, + 545, + 250 + ], + "spans": [ + { + "bbox": [ + 308, + 186, + 545, + 250 + ], + "type": "text", + "content": "[15] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 252, + 545, + 307 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 252, + 545, + 307 + ], + "spans": [ + { + "bbox": [ + 308, + 252, + 545, + 307 + ], + "type": "text", + "content": "[16] Haoqi Fan, Bo Xiong, Karttikeya Mangalam, Yanghao Li, Zhicheng Yan, Jitendra Malik, and Christoph Feichtenhofer. Multiscale vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6824-6835, 2021. 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 308, + 545, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 308, + 545, + 342 + ], + "spans": [ + { + "bbox": [ + 308, + 308, + 545, + 342 + ], + "type": "text", + "content": "[17] Christoph Feichtenhofer, Haoqi Fan, Yanghao Li, and Kaiming He. Masked autoencoders as spatiotemporal learners. arXiv preprint arXiv:2205.09113, 2022. 1, 2, 3, 4" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 343, + 545, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 343, + 545, + 387 + ], + "spans": [ + { + "bbox": [ + 308, + 343, + 545, + 387 + ], + "type": "text", + "content": "[18] Tsu-Jui Fu, Linjie Li, Zhe Gan, Kevin Lin, William Yang Wang, Lijuan Wang, and Zicheng Liu. Violet: End-to-end video-language transformers with masked visual-token modeling. arXiv preprint arXiv:2111.12681, 2021. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 388, + 545, + 432 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 388, + 545, + 432 + ], + "spans": [ + { + "bbox": [ + 308, + 388, + 545, + 432 + ], + "type": "text", + "content": "[19] Valentin Gabeur, Chen Sun, Karteek Alahari, and Cordelia Schmid. Multi-modal transformer for video retrieval. In European Conference on Computer Vision, pages 214-229. Springer, 2020. 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 434, + 545, + 488 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 434, + 545, + 488 + ], + "spans": [ + { + "bbox": [ + 308, + 434, + 545, + 488 + ], + "type": "text", + "content": "[20] Yuying Ge, Yixiao Ge, Xihui Liu, Dian Li, Ying Shan, Xiaohu Qie, and Ping Luo. Bridging video-text retrieval with multiple choice questions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16167-16176, 2022. 2, 3, 5, 7, 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 490, + 545, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 490, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 308, + 490, + 545, + 544 + ], + "type": "text", + "content": "[21] Yuying Ge, Yixiao Ge, Xihui Liu, Alex Jinpeng Wang, Jianping Wu, Ying Shan, Xiaohu Qie, and Ping Luo. Miles: Visual bert pre-training with injected language semantics for video-text retrieval. arXiv preprint arXiv:2204.12408, 2022.3,7,8" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 546, + 545, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 546, + 545, + 623 + ], + "spans": [ + { + "bbox": [ + 308, + 546, + 545, + 623 + ], + "type": "text", + "content": "[22] Raghav Goyal, Samira Ebrahimi Kahou, Vincent Michalski, Joanna Materzynska, Susanne Westphal, Heuna Kim, Valentin Haenel, Ingo Fruend, Peter Yianilos, Moritz Mueller-Freitag, et al. The\" something something\" video database for learning and evaluating visual common sense. In Proceedings of the IEEE international conference on computer vision, pages 5842-5850, 2017. 1, 2, 3, 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 624, + 545, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 624, + 545, + 678 + ], + "spans": [ + { + "bbox": [ + 308, + 624, + 545, + 678 + ], + "type": "text", + "content": "[23] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000-16009, 2022. 1, 5, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 680, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 680, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 680, + 545, + 713 + ], + "type": "text", + "content": "[24] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "23087" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 73, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 67, + 73, + 287, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 73, + 287, + 94 + ], + "spans": [ + { + "bbox": [ + 67, + 73, + 287, + 94 + ], + "type": "text", + "content": "on Computer Vision and Pattern Recognition, pages 16000-16009, 2022. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 287, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 287, + 149 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 287, + 149 + ], + "type": "text", + "content": "[25] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 151, + 288, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 151, + 288, + 205 + ], + "spans": [ + { + "bbox": [ + 48, + 151, + 288, + 205 + ], + "type": "text", + "content": "[26] Kai Hu, Jie Shao, Yuan Liu, Bhiksha Raj, Marios Savvides, and Zhiqiang Shen. Contrast and order representations for video self-supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7939-7949, 2021. 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 206, + 288, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 206, + 288, + 270 + ], + "spans": [ + { + "bbox": [ + 48, + 206, + 288, + 270 + ], + "type": "text", + "content": "[27] Deng Huang, Wenhao Wu, Weiwen Hu, Xu Liu, Dongliang He, Zhihua Wu, Xiangmiao Wu, Mingkui Tan, and Errui Ding. Ascnet: Self-supervised video representation learning with appearance-speed consistency. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8096-8105, 2021. 1, 3, 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 272, + 288, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 272, + 288, + 326 + ], + "spans": [ + { + "bbox": [ + 48, + 272, + 288, + 326 + ], + "type": "text", + "content": "[28] Lianghua Huang, Yu Liu, Bin Wang, Pan Pan, Yinghui Xu, and Rong Jin. Self-supervised video representation learning by context and motion decoupling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13886-13895, 2021. 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 327, + 287, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 327, + 287, + 370 + ], + "spans": [ + { + "bbox": [ + 48, + 327, + 287, + 370 + ], + "type": "text", + "content": "[29] Simon Jenni and Hailin Jin. Time-equivariant contrastive video representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9970-9980, 2021. 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 372, + 287, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 372, + 287, + 435 + ], + "spans": [ + { + "bbox": [ + 48, + 372, + 287, + 435 + ], + "type": "text", + "content": "[30] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 437, + 287, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 437, + 287, + 491 + ], + "spans": [ + { + "bbox": [ + 48, + 437, + 287, + 491 + ], + "type": "text", + "content": "[31] Will Kay, Joao Carreira, Karen Simonyan, Brian Zhang, Chloe Hillier, Sudheendra Vijayanarasimhan, Fabio Viola, Tim Green, Trevor Back, Paul Natsev, et al. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017. 1, 2, 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 493, + 287, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 493, + 287, + 536 + ], + "spans": [ + { + "bbox": [ + 48, + 493, + 287, + 536 + ], + "type": "text", + "content": "[32] Manjin Kim, Heeseung Kwon, Chunyu Wang, Suha Kwak, and Minsu Cho. Relational self-attention: What's missing in attention for video understanding. Advances in Neural Information Processing Systems, 34:8046-8059, 2021. 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 537, + 287, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 537, + 287, + 579 + ], + "spans": [ + { + "bbox": [ + 48, + 537, + 287, + 579 + ], + "type": "text", + "content": "[33] Hildegard Kuehne, Hueihan Jhuang, Estibaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In ICCV, pages 2556-2563. IEEE, 2011. 1, 2, 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 581, + 287, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 581, + 287, + 613 + ], + "spans": [ + { + "bbox": [ + 48, + 581, + 287, + 613 + ], + "type": "text", + "content": "[34] Jie Lei, Tamara L Berg, and Mohit Bansal. Revealing single frame bias for video-and-language learning. arXiv preprint arXiv:2206.03428, 2022. 2, 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 614, + 287, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 614, + 287, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 614, + 287, + 668 + ], + "type": "text", + "content": "[35] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7331-7341, 2021. 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "text", + "content": "[36] Ji Lin, Chuang Gan, and Song Han. Tsm: Temporal shift module for efficient video understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7083-7093, 2019. 7" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "text", + "content": "[37] Yuanze Lin, Xun Guo, and Yan Lu. Self-supervised video representation learning with meta-contrastive network. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8239–8249, 2021. 8" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 118, + 545, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 161 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 161 + ], + "type": "text", + "content": "[38] Yang Liu, Samuel Albanie, Arsha Nagrani, and Andrew Zisserman. Use what you have: Video retrieval using representations from collaborative experts. arXiv preprint arXiv:1907.13487, 2019. 8" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 163, + 545, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 163, + 545, + 206 + ], + "spans": [ + { + "bbox": [ + 307, + 163, + 545, + 206 + ], + "type": "text", + "content": "[39] Zhaoyang Liu, Limin Wang, Wayne Wu, Chen Qian, and Tong Lu. Tam: Temporal adaptive module for video recognition. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13708-13718, 2021. 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 208, + 545, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 208, + 545, + 240 + ], + "spans": [ + { + "bbox": [ + 307, + 208, + 545, + 240 + ], + "type": "text", + "content": "[40] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 243, + 545, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 243, + 545, + 285 + ], + "spans": [ + { + "bbox": [ + 307, + 243, + 545, + 285 + ], + "type": "text", + "content": "[41] Mandela Patrick, Po-Yao Huang, Yuki Asano, Florian Metze, Alexander Hauptmann, Joao Henriques, and Andrea Vedaldi. Support-set bottlenecks for video-text representation learning. arXiv preprint arXiv:2010.02824, 2020. 8" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 287, + 545, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 287, + 545, + 342 + ], + "spans": [ + { + "bbox": [ + 307, + 287, + 545, + 342 + ], + "type": "text", + "content": "[42] Rui Qian, Tianjian Meng, Boqing Gong, Ming-Hsuan Yang, Huisheng Wang, Serge Belongie, and Yin Cui. Spatiotemporal contrastive video representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6964-6974, 2021. 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 343, + 545, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 343, + 545, + 408 + ], + "spans": [ + { + "bbox": [ + 307, + 343, + 545, + 408 + ], + "type": "text", + "content": "[43] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 1, 3, 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 411, + 545, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 411, + 545, + 464 + ], + "spans": [ + { + "bbox": [ + 307, + 411, + 545, + 464 + ], + "type": "text", + "content": "[44] Kanchana Ranasinghe, Muzammal Naseer, Salman Khan, Fahad Shahbaz Khan, and Michael S Ryoo. Self-supervised video transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2874-2884, 2022. 3, 7, 8" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 466, + 545, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 466, + 545, + 510 + ], + "spans": [ + { + "bbox": [ + 307, + 466, + 545, + 510 + ], + "type": "text", + "content": "[45] Anna Rohrbach, Marcus Rohrbach, Niket Tandon, and Bernt Schiele. A dataset for movie description. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3202-3212, 2015. 5" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 511, + 545, + 553 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 511, + 545, + 553 + ], + "spans": [ + { + "bbox": [ + 307, + 511, + 545, + 553 + ], + "type": "text", + "content": "[46] Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108, 2019.5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 556, + 545, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 556, + 545, + 600 + ], + "spans": [ + { + "bbox": [ + 307, + 556, + 545, + 600 + ], + "type": "text", + "content": "[47] Shibani Santurkar, Yann Dubois, Rohan Taori, Percy Liang, and Tatsunori Hashimoto. Is a caption worth a thousand images? a controlled study for representation learning. arXiv preprint arXiv:2207.07635, 2022. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 601, + 545, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 601, + 545, + 633 + ], + "spans": [ + { + "bbox": [ + 307, + 601, + 545, + 633 + ], + "type": "text", + "content": "[48] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012. 1, 2, 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 635, + 545, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 635, + 545, + 678 + ], + "spans": [ + { + "bbox": [ + 307, + 635, + 545, + 678 + ], + "type": "text", + "content": "[49] Zhan Tong, Yibing Song, Jue Wang, and Limin Wang. Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training. arXiv preprint arXiv:2203.12602, 2022. 1, 2, 3, 4, 5, 7, 8" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 680, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 680, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 307, + 680, + 545, + 712 + ], + "type": "text", + "content": "[50] Junke Wang, Dongdong Chen, Zuxuan Wu, Chong Luo, Luowei Zhou, Yucheng Zhao, Yujia Xie, Ce Liu, Yu-Gang Jiang, and Lu Yuan. Omnil: One foundation model for" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "23088" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 384 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 67, + 72, + 287, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 72, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 67, + 72, + 287, + 95 + ], + "type": "text", + "content": "image-language and video-language tasks. arXiv preprint arXiv:2209.07526, 2022. 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 287, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 287, + 172 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 287, + 172 + ], + "type": "text", + "content": "[51] Jinpeng Wang, Yuting Gao, Ke Li, Yiqi Lin, Andy J Ma, Hao Cheng, Pai Peng, Feiyue Huang, Rongrong Ji, and Xing Sun. Removing the background by adding the background: Towards background robust self-supervised video representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11804-11813, 2021. 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 174, + 287, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 174, + 287, + 228 + ], + "spans": [ + { + "bbox": [ + 48, + 174, + 287, + 228 + ], + "type": "text", + "content": "[52] Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaou Tang, and Luc Van Gool. Temporal segment networks: Towards good practices for deep action recognition. In European conference on computer vision, pages 20-36. Springer, 2016. 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 230, + 287, + 284 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 230, + 287, + 284 + ], + "spans": [ + { + "bbox": [ + 48, + 230, + 287, + 284 + ], + "type": "text", + "content": "[53] Dejing Xu, Jun Xiao, Zhou Zhao, Jian Shao, Di Xie, and Yueting Zhuang. Self-supervised spatiotemporal learning via video clip order prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10334–10343, 2019. 6, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 285, + 287, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 285, + 287, + 329 + ], + "spans": [ + { + "bbox": [ + 48, + 285, + 287, + 329 + ], + "type": "text", + "content": "[54] Jun Xu, Tao Mei, Ting Yao, and Yong Rui. Msr-vtt: A large video description dataset for bridging video and language. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5288-5296, 2016. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 331, + 287, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 331, + 287, + 384 + ], + "spans": [ + { + "bbox": [ + 48, + 331, + 287, + 384 + ], + "type": "text", + "content": "[55] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. Advances in Neural Information Processing Systems, 34:23634-23651, 2021. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "23089" + } + ] + } + ], + "index": 7 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Transformation-Predictive Representations for Detection and Description of Local Features/aa5b05da-b5ee-4eea-ae47-f2c476393ca4_content_list.json b/2023/Learning Transformation-Predictive Representations for Detection and Description of Local Features/aa5b05da-b5ee-4eea-ae47-f2c476393ca4_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..51524b2fc588af20ba8486a1fc21f2e5e95d408e --- /dev/null +++ b/2023/Learning Transformation-Predictive Representations for Detection and Description of Local Features/aa5b05da-b5ee-4eea-ae47-f2c476393ca4_content_list.json @@ -0,0 +1,1720 @@ +[ + { + "type": "text", + "text": "Learning Transformation-Predictive Representations for Detection and Description of Local Features", + "text_level": 1, + "bbox": [ + 127, + 128, + 843, + 174 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Zihao Wang1 Chunxu Wu2 Yifei Yang2 Zhen Li2*", + "bbox": [ + 207, + 202, + 767, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ School of Intelligence Science and Technology, Peking University $^{2}$ School of Automation, Beijing Institute of Technology", + "bbox": [ + 220, + 220, + 750, + 257 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "zhwang@stu.pku.edu.cn, {3120200960, 3120220892, zhenli}@bit.edu.cn", + "bbox": [ + 191, + 258, + 772, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 308, + 313, + 325 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The task of key-points detection and description is to estimate the stable location and discriminative representation of local features, which is a fundamental task in visual applications. However, either the rough hard positive or negative labels generated from one-to-one correspondences among images may bring indistinguishable samples, like false positives or negatives, which acts as inconsistent supervision. Such resultant false samples mixed with hard samples prevent neural networks from learning descriptions for more accurate matching. To tackle this challenge, we propose to learn the transformation-predictive representations with self-supervised contrastive learning. We maximize the similarity between corresponding views of the same 3D point (landmark) by using none of the negative sample pairs and avoiding collapsing solutions. Furthermore, we adopt self-supervised generation learning and curriculum learning to soften the hard positive labels into soft continuous targets. The aggressively updated soft labels contribute to overcoming the training bottleneck (derived from the label noise of false positives) and facilitating the model training under a stronger transformation paradigm. Our self-supervised training pipeline greatly decreases the computation load and memory usage, and outperforms the sota on the standard image matching benchmarks by noticeable margins, demonstrating excellent generalization capability on multiple downstream tasks.", + "bbox": [ + 76, + 340, + 472, + 734 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 762, + 209, + 777 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Local visual descriptors are fundamental to various computer vision applications such as camera calibration [37], 3D reconstruction [19], visual simultaneous localization and mapping (VSLAM) [33], and image retrieval [38]. The descriptors indicate the representation vector of the patch around the key-points and can be used to generate dense", + "bbox": [ + 75, + 786, + 468, + 878 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "correspondences between images.", + "bbox": [ + 500, + 310, + 725, + 325 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The discriptors are highly dependent on the effective representation, which has been always trained with the Siamese architecture and contrastive learning loss [12, 29, 39]. The core idea of contrastive learning is \"learn to compare\": given an anchor key-point, distinguish a similar (or positive) sample from a set of dissimilar (or negative) samples, in a projected embedding space. The induced representations present two key properties: 1) alignment of features from positive pairs, 2) and uniformity of representation on the hypersphere [51]. Negative samples are thus introduced to keep the uniformity property and avoid model collapse, i.e., preventing the convergence to one constant solution [13]. Therefore, various methods have been proposed to mine hard negatives [6, 21, 55]. However, these methods raise the computational load and memory resources usage heavily [17]. More Importantly, within the hard negatives, some samples are labeled as negatives, but actually have the identical semantics of the anchor (i.e., false negatives). These false negatives act as inconsistent supervision and prevent the learning-based models from achieving higher accuracy [4]. More concretely, the false negatives represent the instance located on the repetitive texture in the structural dataset, as shown in Figure 1. It is challenging to recognize such false negatives from true negatives [39].", + "bbox": [ + 496, + 325, + 893, + 686 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The recent active self-supervised learning methods [5, 8, 9, 15] motivate us to rethink the effectiveness of negatives in descriptors learning. We propose to learn the transformation predictive representations (TPR) for visual descriptors only with the positives and avoids collapsing solutions. Furthermore, using none of negatives greatly improves the training efficiency by reducing the scale of the similarity matrix from $\\mathcal{O}(n^2)$ to $\\mathcal{O}(n)$ , and reduces the computation load and memory usage.", + "bbox": [ + 496, + 688, + 893, + 824 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To further improve the generalization performance of descriptors, hard positives (i.e., corresponding pairs with large-scale transformation) are encouraged as training data to expose novel patterns. However, recent experiments have shown that directly contrastive learning on stronger", + "bbox": [ + 496, + 825, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 94, + 886, + 220, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "11464", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/ed1e3527696e89899ff866bab291c0316b9685e52c570d3670a842ce3758c693.jpg", + "image_caption": [ + "Figure 1. Illustrative schematic of different negative samples. Easy negatives (green) are easy to distinguish from the query (blue) and not sufficient for good performance. Hard negatives (orange) are important for better performance and are still semantically dissimilar from the query. False negatives (red) are practically impossible to distinguish and semantically identical with the query, which is harmful to the performance. The right figure visualizes the embedding of different samples in latent space." + ], + "image_footnote": [], + "bbox": [ + 99, + 87, + 300, + 242 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/2f9f21928dd55645dcbd8d5f038840ae3a668fde5e9c34c352c5b6c338209b5c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 305, + 87, + 468, + 252 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/a47e823a8cf553dd18b7d58c66b3cf1c9753b48bc4db1d290435e216c7f01882.jpg", + "image_caption": [ + "Easy Negatives" + ], + "image_footnote": [], + "bbox": [ + 470, + 88, + 522, + 128 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/ee938beb3247ccf7f3d5756164076229fcdc12021a622c19ebb5b8be032dffae.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 522, + 89, + 591, + 128 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/92a05a4122b31d54882ba49b8af5a7b87163eaf80c869628037d788e981aa824.jpg", + "image_caption": [ + "Hard Negatives" + ], + "image_footnote": [], + "bbox": [ + 472, + 145, + 522, + 184 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/7c8cf1d614d6d8f01310b750816b1bedb96073cc6216fdf06e0dbfe6e4b4c242.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 522, + 146, + 591, + 184 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/eeff306b5559ac672b40e411a7d1c1c16f45c1695d7ef291db776cc03b482b52.jpg", + "image_caption": [ + "False Negatives" + ], + "image_footnote": [], + "bbox": [ + 472, + 200, + 521, + 239 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/928d0c64c22948be70fe1c89aaa93a48023bdc81238cffd154cdd5c6fff06f1f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 522, + 200, + 591, + 239 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/cbcd1163ce6ee8e845ee450893f3031749243bcba8e34b8eab74ae30cac99b7c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 612, + 90, + 661, + 128 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/7692a41aed318eddbfc771ff7d9204ad9a2d309c75a2ada28c0b27600925304b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 612, + 146, + 661, + 184 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/4b14d85b65721f5503547fa8c027303c21d5a05083737bd9e9a4016405b14250.jpg", + "image_caption": [ + "" + ], + "image_footnote": [], + "bbox": [ + 612, + 200, + 661, + 239 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/1ac49b50eea880bd85f83af8189ccc32e8d77d1a1b8c7f5cb435b81670dbc390.jpg", + "image_caption": [ + "t-SNE visualization" + ], + "image_footnote": [], + "bbox": [ + 673, + 90, + 870, + 241 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "transformed images can not learn representations effectively [52]. In addition, current contrastive learning methods label all positives with different transformation strength as coarse “1”, which prevent learning refined representation. We propose to learn transformation predictive representation with soft positive labels from $(0,1]$ instead of “1” to supervise the learning of local descriptors. Furthermore, we propose a self-supervised curriculum learning module to generate controllable stronger positives with gradually refined soft supervision as the network iterative training.", + "bbox": [ + 75, + 344, + 468, + 494 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Finally, our TPR with soft labels is trained on natural images in a fully self-supervised paradigm. Different from previous methods trained on datasets with SfM or camera pose information [20, 29, 39, 49], our training datasets are generally easy to collect and scale up since there is no extra annotation requirement to capture dense correspondences. Experiments show that our self-supervised method outperforms the state-of-the-art on standard image matching benchmarks by noticeable margins and shows excellent generalization capability on multiple downstream tasks (e.g., visual odometry, and localization).", + "bbox": [ + 75, + 496, + 468, + 661 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contributions to this work are as follows: i) we propose to learn transformation-predictive representations for joint local feature learning, using none of the negative sample pairs and avoiding collapsing solutions. ii) We adopt self-supervised generation learning and curriculum learning to soften the hard positives into continuous soft labels, which can alleviate the false positives and train the model with stronger transformation. iii) The overall pipeline is trained with the self-supervised paradigm, and the training data are computed from random affine transformation and augmentation on natural images.", + "bbox": [ + 75, + 662, + 468, + 829 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 76, + 844, + 225, + 859 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our work focuses on the first step of the image matching pipeline, i.e., detection and description of key-points. This", + "bbox": [ + 76, + 869, + 468, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "section gives a brief review of key-points detection and description learning together with contrastive representation learning.", + "bbox": [ + 498, + 344, + 890, + 390 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Key-points Learning. Different from early hand-crafted key-points, e.g., SIFT [27] and SURF [3], the recent effort has been put into learning key-points through the deep neural networks. Many existent works focus on single module optimization with deep learning, such as interest points detection [42], shape estimation [58] and descriptor representation [45, 47]. However, optimizing one single component may not directly enable the improvement of the entire pipeline [44, 57]. Recent works tend to build end-to-end joint learning frameworks for detecting and describing local features [12, 29, 53]. CNN-based methods simultaneously optimize both the detector and descriptor by sharing most parameters with the help of the encoder-decoder pipeline. Compared with the conventional methods only considering low-level features like corners, edges, or blobs in shallow layers, the encoder-decoder pipeline can make full use of deep CNNs for better representations [11, 25, 39]. To maximize the similarity of corresponding pairs, current cutting-edge methods usually take the Siamese structure to train the model with contrastive learning [54].", + "bbox": [ + 496, + 410, + 892, + 712 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Contrastive Learning. Contrastive learning was originated from metric learning and has been widely adopted to supervise the description learning [16, 18, 36, 47]. The critical factor of contrastive learning is to attract the representation of corresponding local descriptions (positive pairs) closer and spread the representations of non-corresponding descriptions (negative pairs) apart. Negative samples are accordingly introduced to keep the uniformity property in order to avoid the representation collapse [13]. Due to expensive manual labeling and the lack of explicit negative signals in most cases, various negative sampling strate-", + "bbox": [ + 496, + 734, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "11465", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "gies were proposed to improve the computation efficiency and promote the training results [56]. However, the sampling of hard negatives highly depends on the large batch size [6, 7, 54] and memory bank [17], so as to increase the computational load and memory resources usage. Recently, some works have been conducted to optimize the contrastive loss without any negatives and avoid the collapse [5, 9, 15]. But all these works were proposed for the holistic representation of whole images and cannot be adapted to the dense prediction tasks. To address this issue, our method uses an exclusive predictor and stop-gradient operation to avoid collapsing and encourage encoding more transformation-aware representations without negatives, which is the first work ever training key-points with only positives.", + "bbox": [ + 75, + 90, + 472, + 316 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 76, + 332, + 168, + 348 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we elaborate on the proposed Transformation Predictive Representations (TPR) for learning detection and description of local features. Given a pair of images $(I_1, I_2)$ , and the correspondences set $\\mathcal{C}$ between them, the key-points learning methods predict the dense 3D description map $D$ and detection heatmap $S \\in [0,1]$ jointly. During the training, the standard Siamese networks are used to deal with the image pairs $(I_1, I_2)$ simultaneously and optimize the parameters with contrastive loss according to $\\mathcal{C}$ . Our approach also follows this pipeline. However, different from the previous approaches using negatives and hard negative sampling for training, TPR is optimized without any negatives. In this way, our model can reduce the computation complexity and memory usage and improves the training efficiency.", + "bbox": [ + 75, + 358, + 472, + 585 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminary", + "text_level": 1, + "bbox": [ + 76, + 595, + 207, + 611 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The architecture in this work is built upon 1) D2-Net [12], and 2) Bootstrap Your Own Latent (BYOL) [15].", + "bbox": [ + 76, + 618, + 468, + 650 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "D2-Net [12] proposes a describe-and-detect strategy to jointly extract descriptions and detections of local features. Over the last feature maps $y \\in \\mathbb{R}^{H \\times W \\times C}$ , D2-Net applies channel-wise L2-normalization to obtain the dense feature descriptors, while the feature detections are derived from 1) the local score and 2) the channel-wise score. Specifically, for each location $(i,j)$ in $y^k (k = 1,\\dots ,C)$ , the final detection score is computed as:", + "bbox": [ + 75, + 670, + 468, + 790 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\ns _ {i j} = \\max _ {t} \\left[ \\frac {\\exp (y _ {i j} ^ {k})}{\\sum_ {(i ^ {\\prime} , j ^ {\\prime}) \\in \\mathcal {N} (i , j)} \\exp (y _ {i ^ {\\prime} j ^ {\\prime}} ^ {k})} \\cdot \\frac {y _ {i j} ^ {k}}{\\max _ {t} y _ {i j} ^ {t}} \\right], \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 91, + 804, + 468, + 844 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathcal{N}(i,j)$ is the neighboring pixels around $(i,j)$ , e.g., 9 neighbours defined by a $3 \\times 3$ kernel. D2-Net adopts the triplet margin ranking loss to optimize the descriptions, and", + "bbox": [ + 76, + 854, + 468, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "is formulated as:", + "bbox": [ + 500, + 90, + 612, + 104 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {t r i p l e t}} = \\sum_ {c \\in \\mathcal {C}} \\max \\left(0, M + d _ {p} ^ {c} - d _ {n} ^ {c}\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 571, + 113, + 890, + 143 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $M$ is the margin, $\\mathcal{C}$ is the set of correspondences, $d_p^c$ and $d_n^c$ represent the distance of positive and selected negative pairs, respectively. Finally, the detection score is used as a weighting term in description loss function.", + "bbox": [ + 498, + 154, + 893, + 215 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "BYOL [15] is a self-supervised image representation learning approach. BYOL uses two neural networks, referred to as the online and target networks, both of which interact and learn from each other. The online network is trained to predict the target network's representation of the same image from an augmented view. The weights of the target network are updated with a slow-moving average of the online network. Different from other contrastive learning methods, BYOL can be trained without any negatives. It hypothesizes that the combination of 1) the addition of a predictor to the online network, and 2) the use of a slow-moving average of the online parameters as the target network encourages encoding more information within the online projection and avoids collapsed solutions.", + "bbox": [ + 496, + 232, + 893, + 445 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Transformation-Predictive Representations", + "text_level": 1, + "bbox": [ + 500, + 452, + 867, + 468 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The Siamese-like structure (i.e., the online and target networks) is used in TPR to learn the representations. From a given representation of the transformed image, referred to as target, the TPR trains a new potentially enhanced representation of the original image, referred to as online, by predicting the target representation. The online network is defined by a set of weights $\\theta_{o}$ and comprises three stages: an online encoder $f_{o}$ , a projector $g_{o}$ , and a predictor $q$ . The target network also has the encoder $f_{t}$ and projector $g_{t}$ in the same structure but with a different set of weights $\\theta_{t}$ . The overall pipeline is described as in Figure 2,", + "bbox": [ + 496, + 474, + 890, + 641 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given the input image $I \\in \\mathbb{R}^{H \\times W \\times 3}$ , a random augmentation $t$ is performed to produce the cropped view $V \\triangleq t(I) \\in \\mathbb{R}^{h \\times w \\times 3}$ . And then, the limited cascaded affine transformation and augmentation $T$ is carried out on the $V$ to produce the transformed view $V' \\triangleq T(V) \\in \\mathbb{R}^{h \\times w \\times 3}$ .", + "bbox": [ + 496, + 641, + 890, + 717 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "From the augmented view $V$ , the online encoder outputs a representation $z \\triangleq f_{o}(V) \\in \\mathbb{R}^{h \\times w \\times c}$ , which is adopted as the objective encouraged to be transformation-predictive. Rather than predicting representations produced by the online encoder, the target representation $z' \\triangleq f_{t}(V') \\in \\mathbb{R}^{h \\times w \\times c}$ is computed by using the target encoder $f_{t}$ , whose parameters $\\theta_{t}$ are an exponential moving average (EMA) of the online encoder parameters $\\theta_{o}$ . Without gradient descent, the update algorithm for $\\theta_{t}$ is formulated as:", + "bbox": [ + 496, + 718, + 890, + 853 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\theta_ {t} \\leftarrow \\tau \\theta_ {t} + (1 - \\tau) \\theta_ {o}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 620, + 862, + 890, + 877 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\tau \\in [0,1)$ is the EMA coefficient.", + "bbox": [ + 500, + 885, + 767, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "11466", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/d970519677a182c89591c0bcc0a5a4a7771db4139930b45a2bcfcf8b51388c45.jpg", + "image_caption": [ + "Figure 2. Illustration of our proposed transformation-predictive representations learning. Top: traditional contrastive learning descriptors based on both positives and negatives and supervised under SfM models. Bottom: our proposed transformation predictive representations learning with only positives and self-supervised loss. In the TPR structure, representations from the online encoder are used in the joint learning of local features task. The target encoder and projection head are defined as an exponential moving average of their online counterparts and are not updated via gradient descent." + ], + "image_footnote": [], + "bbox": [ + 160, + 92, + 803, + 407 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Next, the representation $z$ and $z'$ are fed into the online and target detector to extract the detection score $s$ and $s'$ with (1). We further use the online and target projection heads $g_{o}$ and $g_{t}$ to map the online and target presentations to a latent embedding space[6], and also apply an additional prediction head $q$ to the online projections to predict the target projections[15]:", + "bbox": [ + 75, + 510, + 470, + 616 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {y} \\triangleq q (g _ {o} (z)),\n$$\n", + "text_format": "latex", + "bbox": [ + 227, + 618, + 318, + 633 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ny ^ {\\prime} \\triangleq g _ {t} \\left(z ^ {\\prime}\\right). \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 227, + 633, + 468, + 651 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The target projection head parameters are also given through an EMA of the online projection head parameters by using the same update algorithm as the online and target encoders. Note that the predictor is only applied to online networks so as to make the Siamese architecture asymmetric between the online and target pipeline.", + "bbox": [ + 75, + 652, + 468, + 743 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Next, we compute the transformation predictive loss for TPR by summing over cosine similarities between the predicted and target representations. Different from a holistic representation $\\mathbb{R}^{1\\times 1\\times d}$ of the view $V$ and $V^{\\prime}$ [15], our encoder $f_{o}$ outputs the dense feature maps preserving abundant spatial information with the shape of $h\\times w\\times c$ . The prediction loss is computed on the corresponding location of the dense feature map, i.e., the positive pairs:", + "bbox": [ + 75, + 743, + 470, + 864 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {\\text {p r e d}} ^ {c} = \\left(1 - \\left\\langle y _ {c}, y _ {c} ^ {\\prime} \\right\\rangle\\right) \\tag {5} \\\\ = \\left(1 - \\left\\langle q \\left(g _ {o} (z)\\right) _ {c}, g _ {t} \\left(z ^ {\\prime}\\right) _ {c} \\right\\rangle\\right), \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 155, + 868, + 468, + 904 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\langle \\cdot \\rangle$ denotes the cosine similarity, $\\hat{y}_c$ and $y_c^\\prime$ is the local representation with the $c$ -th correspondence.", + "bbox": [ + 498, + 510, + 890, + 540 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, the detection score is used as a weighting term to formalize the final hard prediction loss function:", + "bbox": [ + 498, + 541, + 890, + 570 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {h a r d}} = \\frac {1}{| \\mathcal {C} |} \\sum_ {c \\in \\mathcal {C}} \\frac {s _ {c} s _ {c} ^ {\\prime}}{\\sum_ {n \\in \\mathcal {C}} s _ {n} s _ {n} ^ {\\prime}} \\mathcal {L} _ {\\text {p r e d}} ^ {c}. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 580, + 580, + 890, + 616 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The transformation-predictive representations follow the predictive nature of the objective and use the exponential moving average target network similar to [15]. Compared with the related works on contrastive representation learning, TPR only uses the positive pairs without negative samples. As a result, there is no necessary need on large buffer to emulate large batch sizes of negatives [6, 17] and complex hard negative sampling strategies. Furthermore, training without negatives can help prevent the inconsistent optimization from the false negatives. Finally, although we do not use the explicit negative samples to prevent collapse while minimizing $\\mathcal{L}_{\\mathrm{hard}}$ , TPR still can avoid converging to a minimum of this loss with respect to $(\\theta_o,\\theta_t)$ (e.g., a collapsed constant representation).", + "bbox": [ + 496, + 627, + 890, + 839 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Learning with Soft Labels", + "text_level": 1, + "bbox": [ + 500, + 847, + 736, + 863 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The TPR method uses carefully designed transformations $T$ to generate the view $V^{\\prime}$ from $V$ . Therefore, the", + "bbox": [ + 498, + 869, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "11467", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "views are not deformed aggressively so that they can still be viewed as the same instance, i.e., positives. Stronger deformation could expose the novel patterns of representations in discriminative contrastive learning. However, directly adopting stronger transformations (e.g., with larger rotation angles, more aggressive color jitting, and cutout) may seriously damage the intrinsic semantics of the original image, which will fail to further improve or even degrade the performance [52]. In this paper, we categorize such positive pairs generated from over-strong deformation into the false positives. The false positives are bound to make the training process degraded in performance because the network cannot effectively distinguish the true positives from the false ones when trained with the existing pipeline [39].", + "bbox": [ + 75, + 90, + 472, + 301 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To tackle the dilemma, where the strong or weak transformations should not have been used to produce the positives, we propose the optimal soft labels to stabilize the training process instead of forcing the labeling coarse hard \"1\" for every positive pair. Accordingly, the prediction loss in (5) is modified as follows.", + "bbox": [ + 75, + 301, + 472, + 393 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {s o f t}} = \\frac {1}{| \\mathcal {C} |} \\sum_ {c \\in \\mathcal {C}} \\frac {s _ {c} s _ {c} ^ {\\prime}}{\\sum_ {n \\in \\mathcal {C}} s _ {n} s _ {n} ^ {\\prime}} \\left(l _ {c} + 1 - \\mathcal {L} _ {\\text {p r e d}} ^ {c}\\right), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 133, + 397, + 468, + 428 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "in which $l_{c}$ represents the computed soft label for the positive pair with $c$ -th correspondence. Therefore, we adopt self-supervised generation learning and curriculum learning to \"adjust\" the inconsistent and incorrect supervision.", + "bbox": [ + 75, + 431, + 472, + 494 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Self-supervised generation learning. We train the network for the first generation following the hard positive label. And then, the network can be assumed to successfully capture most feature distribution of the training data. We propose to utilize the previous generation's similarities to be served as the soft supervision for training the network in a new generation. The generated soft supervisions are gradually refined and sharpened as the network generation progresses. In this way, the system can fully explore the potential of the difficult positives with stronger transformation, and mitigate their label noises with the refined soft confidence. To stabilize the training process, the soft label of the positive pair with the correspondence $c$ in the generation $\\omega = 2,\\dots ,\\Omega$ follows the exponential decay of the transformation strength, which is computed as:", + "bbox": [ + 75, + 511, + 472, + 738 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nl _ {c} = e ^ {- \\left(1 - \\left\\langle y _ {c} ^ {\\omega - 1}, y _ {c} ^ {\\prime \\omega - 1} \\right\\rangle\\right) / \\lambda}, \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 179, + 746, + 468, + 767 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $y^{\\omega -1}$ and $y^{\\prime \\omega -1}$ are generated from the encoder with the last generation parameters $\\theta_{\\omega}$ , and $\\Omega$ is the total number of generations, and $\\lambda$ is the exponential decay constant.", + "bbox": [ + 75, + 775, + 472, + 823 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Curriculum setting for positives generation. The core of curriculum learning is to gradually improve the transformation strength. In particular, we introduce the affine adaptation and data augmentation to generate the transformation", + "bbox": [ + 75, + 839, + 472, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "matrix, which also determines the transformation strength $\\alpha$ . We normalize $\\alpha$ within $[0,1]$ according to the maximum limitation of adaption and augmentation parameters, e.g., rotation and translation values. With the curriculum setting, $\\alpha$ is randomly sampled from a range with gradually relaxed restrictions of the transformation strength. The soft label of the current batch follows the exponential decay of the transformation strength, which is computed as:", + "bbox": [ + 496, + 90, + 893, + 212 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nl _ {c} = e ^ {- \\alpha \\left(1 - \\left\\langle y _ {c} ^ {\\omega - 1}, y _ {c} ^ {\\prime \\omega - 1} \\right\\rangle\\right) / \\lambda}. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 223, + 890, + 244 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To avoid the over-fitting to the soft labels, we also set a maximum operation on the predicted soft loss as $\\max(0, l_c + 1 - \\mathcal{L}_{pred}^c)$ . This setting will help the model avoid divergence through the false positives during the early training stage, and encourage the model to explore more robust representations in the later training stage.", + "bbox": [ + 496, + 257, + 890, + 348 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In the later stage of training, with the increasing difficulty of positive samples, the proportion of false positive samples, which have different semantics with the online query, will increase. Such false positives will be harmful to the generalization performance of the model [52]. To overcome this problem, we introduce a hyperparameter patience $p$ of early stop. When the soft prediction loss no longer decreases, the value of $p$ will decrease. The training will stop until $p = 0$ .", + "bbox": [ + 496, + 349, + 893, + 486 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Implementation Details", + "text_level": 1, + "bbox": [ + 500, + 496, + 718, + 512 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this part, the architectural details of TPR are introduced. Note that at the end of the training, everything but the online encoder $f_{o}$ is discarded. Thus, the online projection head $g_{o}$ , the predictor head $q$ , and the target branch will not introduce the extra computational costs in deployment.", + "bbox": [ + 496, + 520, + 890, + 595 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Encoder, $f_{o}$ , maps the input view $V$ into the dense embeddings $z$ . The encoder can be instanced into the CNN version based on VGG (similar as D2-Net [12]) and DCN (similar as ASLFeat [29]), and Transformer version based on tiny Swin Transformer V1 [26] with only the first two stages.", + "bbox": [ + 496, + 595, + 890, + 672 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Projection head, $g_{o}$ , maps the feature embedding into a 128-dim $l_{2}$ normalization feature vector for the computation of the prediction loss $\\mathcal{L}_{\\mathrm{pred}}$ . $g_{o}$ is implemented as a 3-layer MLP (hidden layer 256-dim), with BN and ReLU on every fully-connected layer except the last output layer.", + "bbox": [ + 496, + 672, + 890, + 748 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Predictor, $q$ , is only applied to the online networks. The predictor is an MLP with two layers. The dimension of the hidden and output layer is 64 and 128, respectively.", + "bbox": [ + 496, + 750, + 890, + 794 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Detection and description head, are adopted on the encoder to simultaneously output the dense description and the detection score map following the D2-Net [12]. Only the locations with higher confidence than 0.7 are selected as the key points. Moreover, a non-maximum suppression (NMS) operation (with a kernel of 5) is applied to remove the key points that are spatially too close.", + "bbox": [ + 496, + 795, + 890, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "11468", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 89, + 209, + 107 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In this section, we first present the details of training our TPR. And then we show the performance across several downstream scenarios, including image matching, visual odometry, and visual localization. It's worthy to NOTE that our methods are not trained on all benchmark datasets, which is essential to show generalization performance.", + "bbox": [ + 75, + 116, + 470, + 207 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 76, + 222, + 267, + 239 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Our TPR method is trained on images from Microsoft COCO dataset[24] and Image Matching Challenge[20], by dropping all the irrelevant annotations. We compute the correspondences between the transformed natural images with the random affine adaption and data augmentation by means of the self-supervised paradigm. For the affine adaption, we uniformly sample the in-plane rotation, shear, translation and scale parameters from $[-45^{\\circ}, +45^{\\circ}]$ , $[-40^{\\circ}, +40^{\\circ}]$ , $[-0.05, +0.05]$ , [0.7,1.4], respectively. For the color jitter, we also uniformly sample the brightness, contrast, saturation, and hue parameters from [0.6,1.4], [0.6,1.4], [0.6,1.4], $[-0.2, +0.2]$ , respectively. The additional random data augmentation is employed on the transformed views $V$ and $V'$ including the grayscale conversion and Gaussian blur.", + "bbox": [ + 75, + 247, + 468, + 458 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We train models using SGD and AdamW optimizer for CNN and Transformer-based models, respectively. During training, we use a mini-batch size of 16 and an initial learning rate of 0.00017 with exponential decay of 0.9. The hyper-parameter of EMA update $\\tau$ , exponential decay constant $\\lambda$ , and early stop patience $p$ is set to 0.99, 10, and 1, which are determined with the grid search to reach the optimum. Our method were trained for 50 epochs, which took 30 hours for training with two NVIDIA-A100 GPUs. We randomly crop the $224 \\times 224$ views for training.", + "bbox": [ + 75, + 460, + 468, + 611 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Moreover, we evaluate the local features learned by TPR across several downstream scenarios, including the image matching on HPatches, visual odometry on KITTI, and visual localization on Aachen Day-Night dataset.", + "bbox": [ + 75, + 612, + 468, + 674 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Image Matching", + "text_level": 1, + "bbox": [ + 76, + 688, + 240, + 705 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We first evaluate the performance of our method in the image matching tasks, i.e., the most extensive key-points applications.", + "bbox": [ + 75, + 713, + 468, + 758 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets. The experiments are taken on HPatches [2] datasets, which include 116 sequences of 6 images with known homography. The datasets are split into two categories according to the illumination and viewpoint changes. The first image in one sequence is taken as reference, and the subsequent images are used to form the pairs with increasing difficulty. By following the previous methods[39], eight high-resolution sequences are excluded.", + "bbox": [ + 75, + 779, + 470, + 902 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/53006f39d28d3235be4fd0e9267c7cc6c600c8a14906f186b9294685dc6def81.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodMMA@3AUC@2AUC@5
SIFT [27]50.139.4949.57
HardNet [32]62.142.6156.85
LF-Net [35]53.238.7448.69
SuperPoint [11]65.744.0859.04
DELF [34]50.744.7349.70
ContextDesc [28]63.247.2358.25
Key.Net [22]72.140.8756.04
R2D2 [39]72.143.3564.17
DISK [49]77.252.3369.80
ALIKE [59]70.551.6569.04
SSL+CAPS [30]69.048.7262.19
LLF [46]74.052.1466.81
MTLDesc [50]78.755.0271.42
PoSFeat [23]75.3450.1669.23
D2-Net [12] (orig.)40.319.4937.78
D2-Net [12] (our impl.)44.522.3543.17
Ours(VGG)49.6 ↑ 9.324.46 ↑ 4.9747.69 ↑ 9.91
ASLFeat [29] (orig.)72.250.1066.93
ASLFeat [29] (our impl.)74.451.8369.24
Ours(DCN)75.5 ↑ 3.252.33 ↑ 2.2370.15 ↑ 3.22
Ours(TR)79.857.1873.00
", + "bbox": [ + 506, + 88, + 867, + 414 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Quantitative results (§4.2) on HPatches.", + "bbox": [ + 555, + 424, + 834, + 438 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation protocols. For a fair comparison, we match the features extracted by each method using the nearest neighbor matcher, accepting only mutual nearest neighbors. A correct match is accordingly considered if its estimated reprojection error is below a given matching threshold. We vary the threshold from 1 pixel to 10 pixels and record the mean matching accuracy (MMA) [31] over all pairs, which represents the ratio of correct matches and possible matches. And then the area is computed under curve (AUC) at 2px and 5px based on the MMA. We report the average scores for overall image pairs.", + "bbox": [ + 496, + 469, + 890, + 636 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Comparisons with other methods. We compare our model with the previous state-of-the-art methods on the HPatches dataset. Unless otherwise specified, we present the results either reported on original papers or derived from authors' public implementations with default parameters. Table. 1 lists different methods' results on HPatches in terms of the MMA area under the overall curve (AUC) up to 2px and 5px. Especially in the low-threshold area, Ours (TR) shows considerable improvements over previous sota methods. For a fair comparison, we show both the performance from the original paper (orig.) and our implementation (our impl.). It is observed that TPR gains a $+6.69\\%$ (44.71% v.s. 38.02%) and $+2.45\\%$ (79.81% v.s. 77.36%) MMA promotion than the leading DISK [49] at 1px and 3px threshold in 128-dimension, respectively. To prove the effectiveness of our training pipeline, we also con", + "bbox": [ + 496, + 657, + 890, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "11469", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/ac65726d1907ebb6fb12c6a890f0cf11c82e00618bf98213040907d6341d27e6.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
NegSample+SSGL+CLMMA@3Mem/GB
w/Random63.1112
Hardest73.4140
Bank78.2↑15.1158
Mixing77.9147
w/oRandom70.179
Random✓77.4↑7.279
Random✓✓79.8↑9.679
", + "bbox": [ + 91, + 88, + 455, + 207 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2. Ablation experiments of the proposed TPR, where Neg, Sample, MMA@3, SSGL, CL and Mem represent whether training w/ Negative samples, Sampling strategy, self-supervised generation learning, curriculum learning, mean matching accuracy at 3px threshold, and GPU Memory usage (GB), respectively.", + "bbox": [ + 75, + 217, + 468, + 287 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "duct experiments based on the D2-Net [12] backbone i.e., Ours (VGG) and ASLFeat [29] backbone, i.e., Ours (DCN). Under the same networks, our retrained model gains $+9.3\\%$ and $+3.2\\%$ improvements on MMA@3, respectively. The t-SNE visualization of descriptions from D2-Net (orig.) and Ours (VGG) is shown in Figure. 3. More importantly, our method is trained in the self-supervised paradigm, requiring no extra annotations, and is not finetuned on the downstream datasets.", + "bbox": [ + 75, + 314, + 467, + 448 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation Study", + "text_level": 1, + "bbox": [ + 76, + 457, + 228, + 472 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We further study the efficacy of our core ideas and essential model designs over HPatches. We train each model from scratch for 50 epochs to perform extensive ablation experiments while keeping other hyper-parameters unchanged.", + "bbox": [ + 75, + 479, + 468, + 555 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablation on different negative sampling strategies. We adopt the standard Siamese networks with transformer blocks as the \"baseline\" model. The baseline model is first trained with both positive and negative pairs, according to the triplet loss in (2). We also evaluate different negative sampling strategies including random negative sampling (Random) [10], in-batch hard negative mining (Hardest) [6], hard negative mining with Memory Bank (Bank) [17], and hard negative mixing (Mixing) [21]. And then we train the model using our TPR method with only positives and evaluate the efficacy of the curriculum learning and self-supervised generation learning. As demonstrated in Table 2, our TPR successfully gets convergent with only positives and avoids the collapsing solution, which proves the effectiveness of our designed components in the Siamese networks. Furthermore, the soft labels effectively alleviate the inconsistent optimization from pseudo positives, especially on the training datasets with stronger transformation and augmentation.", + "bbox": [ + 75, + 564, + 468, + 851 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablation on key components. We also conduct the ablation experiments to evaluate the effectiveness of our core", + "bbox": [ + 76, + 869, + 468, + 898 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7f5167c3299c6824f39640a7780dcbc1ea0e4e290582f64eda429a148d2aab3b.jpg", + "image_caption": [ + "Figure 3. t-SNE visualization of description from different training methods. Left: D2-Net [12] (orig.), Right: Ours(VGG)." + ], + "image_footnote": [], + "bbox": [ + 535, + 89, + 689, + 208 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e692f48a8f8e85f0b81e574e07f3246b25d958e674e0a07f9cdf9452caa484cb.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 702, + 89, + 857, + 208 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "components including training w/o negative samples, self-supervised generation learning (SSGL), and curriculum learning module (CL). The results are also shown in Table 2, which proves the self-supervised curriculum learning with soft labels will improve the contrastive learning performance with only positives.", + "bbox": [ + 496, + 265, + 890, + 354 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Visual Odometry", + "text_level": 1, + "bbox": [ + 500, + 364, + 666, + 381 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Visual Odometry (VO) is the task to estimate a robot's position and orientation from visual measurements. The goal of the system is to estimate the 6-DoF pose of the camera at every frame. In this experiment, we evaluate the visual odometry performance with the learned key-points.", + "bbox": [ + 496, + 388, + 890, + 465 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Datasets. We adopt the KITTI Odometry Datasets [14] to evaluate the downstream localization performance of our method. We only use the grayscale monocular images with a high resolution of $376 \\times 1226$ as input. To output multiscale key-points, a three-layer image pyramid is constructed with the scale factor of 1.2. It is worth noting that in KITTI, there contains many moving objects and changing camera exposure time, which is challenging for accurate and robust key-points matching.", + "bbox": [ + 496, + 481, + 890, + 618 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Evaluation protocols. We take the ORB-SLAM [33] as the benchmark pipeline. We further disable both the local mapping and loop closure thread so as to keep the front-end visual odometry only. To evaluate the key-points fairly, all components in the pipeline are the same except the front-end used key-points. KITTI provides the ground-truth 6-DoF camera poses for sequences 00-10. Since the keypoints are adopted in the monocular VO pipeline, we align their poses with ground-truth to recover the scale of 6-DoF poses. Besides, we use the standard evaluation method, i.e., translational root-mean-square error (RMSE) drift $(\\mathrm{/m})$ provided along with KITTI dataset. To evaluate the efficiency of the learning-based key-points, we also report the mean running frames per second for the methods.", + "bbox": [ + 496, + 621, + 890, + 833 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Results. We report the results of different key-points on different KITTI sequences in Table 3. TPR outperforms the sota learning methods DISK [49] on all sequences even under complex traffic conditions. As for the inference time,", + "bbox": [ + 496, + 839, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "11470", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/3ff86a723490e3376e851a21f8f3a5f104dfce77e4c0183364e6fd355d8ea83b.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodFPSRMSE/m ↓
00010204050607080910Avg.
ORB [40]20.659.46610.3572.6819.26238.6083.4672.7266.06119.2163.52140.53
SuperPoint [11]6.5162.78123.3413.521.066.362.0512.158.668.205.1034.32
D2-Net [12]8.810.44183.04105.332.2914.582.2510.7224.2729.629.6139.22
R2D2 [39]7.849.62515.9660.143.90123.0562.4453.8462.5473.3043.32104.81
SOSNet [48]6.3171.67309.8310.360.4714.684.0715.3510.753.247.6754.81
DISK [49]6.532.77149.9818.670.455.974.3812.8832.854.334.8126.71
Ours (TR)6.27.07164.399.720.233.462.129.997.423.103.7221.12
", + "bbox": [ + 125, + 88, + 849, + 242 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/b345a97e605133e7fee4a811582e5b560ecbf067f4d23888af8949a4a5062f08.jpg", + "table_caption": [ + "Table 3. Visual odometry localization performance based on different key-points in KITTI datasets." + ], + "table_footnote": [], + "table_body": "
MethodFeatAccuracy @ Thresholds (%) ↑
0.25m,2°0.5m,5°5m,10°
RootSIFT [1]11K53.462.372.3
SuperPoint [11]7K68.185.994.8
D2-Net [12]14K67.086.497.4
R2D2 [39]10K70.785.396.9
ASLFeat [29]10K71.285.996.9
DISK [49]10K72.886.497.4
MTLDesc [50]7K74.386.996.9
Ours (TR)10K74.389.098.4
", + "bbox": [ + 88, + 279, + 460, + 465 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 4. Performance on Aachen Day-Night Localization datasets.", + "bbox": [ + 84, + 476, + 460, + 489 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "our method is slower than the hand-crafted ORB [40]. However, our approach adds no additional overhead compared to existing neural network-based methods. We also report the trajectory visualization in the Supplementary Materials.", + "bbox": [ + 75, + 501, + 468, + 563 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.5. Visual Localization", + "text_level": 1, + "bbox": [ + 76, + 571, + 261, + 587 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To further verify the effectiveness of our method confronted with complex tasks, we evaluate it on the task of visual localization, which aims at estimating the camera pose within a given scene using an image sequence. The task was proposed in [41] to evaluate the performance of local features in the context of long-term localization without the need for a specific localization pipeline. All methods are compared on the official evaluation server in a fair manner.", + "bbox": [ + 75, + 595, + 468, + 717 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Datasets. We resort to the Aachen Day-Night dataset [41] to demonstrate the effect on visual localization tasks, which contains images from the old inner city of Aachen, Germany. The key challenge in the dataset lies in matching images with extreme day-night changes for 98 queries.", + "bbox": [ + 75, + 720, + 468, + 797 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Evaluation protocols. The evaluation is conducted by using The Visual Localization Benchmark, which takes a pre-defined visual localization pipeline based on COMLAP [43]. The successful localized images are counted within three error tolerances $(0.25\\mathrm{m}, 2^{\\circ}) / (0.5\\mathrm{m}, 5^{\\circ}) / (5\\mathrm{m}, 10^{\\circ})$ , representing the maximum position error in meters and degrees.", + "bbox": [ + 75, + 801, + 470, + 909 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Results. We compare our model with the typical joint detector and descriptor learning methods. Here, all methods are evaluated with the built-in matching strategy (Nearest Neighbors Search) for a fair comparison. As shown in Table 4, TPR with transformer blocks performs surprisingly well even under challenging illumination changes, about the estimated pose, which demonstrates the effectiveness of self-supervised learning with only positives on the natural dataset.", + "bbox": [ + 496, + 282, + 893, + 417 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.6. Limitations", + "text_level": 1, + "bbox": [ + 498, + 433, + 625, + 449 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "While our approach does not require any additional annotation, e.g., Structure-from-Motion models or optical flow, to produce the corresponding real image pairs. It still needs to receive pairs through homography adaption or image augmentation. Furthermore, despite getting better performance without using negative samples, TPR local features still fail in classical challenging cases such as textured areas, especially in the absence of matching priors.", + "bbox": [ + 496, + 459, + 893, + 582 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 498, + 599, + 619, + 616 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we propose to learn the transformation-predictive representations for the joint detection and description of local features. The model is trained by using none of the negatives and avoids the collapsing solution, which greatly improves the training efficiency. Self-supervised generation learning and curriculum learning are designed to soften the hard positives into continuous soft labels, which can train the model with stronger augmentation. We solve the label noise from false positives and negatives and further improve the performance of local features in the self-supervised paradigm. Experiments show that TPR significantly outperforms state-of-the-art methods.", + "bbox": [ + 496, + 627, + 893, + 809 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 500, + 828, + 658, + 845 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work was supported by the Science and Technology on Space Intelligent Control Laboratory under Grant HTKJ2021KL502019.", + "bbox": [ + 496, + 854, + 893, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "11471", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Relja Arandjelović and Andrew Zisserman. Three things everyone should know to improve object retrieval. In CVPR, 2012. 8", + "[2] Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk. Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors. In CVPR, 2017. 6", + "[3] Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. Surf: Speeded up robust features. In ECCV, 2006. 2", + "[4] Tiffany Tianhui Cai, Jonathan Frankle, David J Schwab, and Ari S Morcos. Are all negatives created equal in contrastive instance discrimination? arXiv preprint arXiv:2010.06682, 2020. 1", + "[5] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. NeurIPS, 2020. 1, 3", + "[6] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In ICML, 2020. 1, 3, 4, 7", + "[7] Ting Chen, Yizhou Sun, Yue Shi, and Liangjie Hong. On sampling strategies for neural network-based collaborative filtering. In ACM SIGKDD, 2017. 3", + "[8] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv:2003.04297, 2020. 1", + "[9] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In CVPR, 2021. 1, 3", + "[10] Peng Cui, Shaowei Liu, and Wenwu Zhu. General knowledge embedded image representation learning. IEEE TMM, 2017. 7", + "[11] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. In CVPR, 2018. 2, 6, 8", + "[12] Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, and Torsten Sattler. D2-net: A trainable cnn for joint detection and description of local features. In CVPR, 2019. 1, 2, 3, 5, 6, 7, 8", + "[13] Tianyu Gao, Xingcheng Yao, and Danqi Chen. Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821, 2021. 1, 2", + "[14] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In CVPR, 2012. 7", + "[15] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. NeurIPS, 2020. 1, 3, 4", + "[16] Michael Gutmann and Aapo Hyvarinen. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In AISTATS, 2010. 2", + "[17] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, 2020. 1, 3, 4, 7", + "[18] Kun He, Yan Lu, and Stan Sclaroff. Local descriptors optimized for average precision. In CVPR, 2018. 2" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[19] Jared Heinly, Johannes L Schonberger, Enrique Dunn, and Jan-Michael Frahm. Reconstructing the world* in six days*(as captured by the yahoo 100 million image dataset). In CVPR, 2015. 1", + "[20] Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, and Eduard Trulls. Image matching across wide baselines: From paper to practice. IJCV, 2021. 2, 6", + "[21] Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus. Hard negative mixing for contrastive learning. NeurIPS, 2020. 1, 7", + "[22] Axel Barroso Laguna and Krystian Mikolajczyk. Key. net: Keypoint detection by handcrafted and learned cnn filters revisited. IEEE TPAMI, 2022. 6", + "[23] K. Li, LongguangWang, L. Liu, Q. Ran, K. Xu, and Y. Guo. Decoupling makes weakly supervised local feature better. In CVPR, 2022. 6", + "[24] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 6", + "[25] Dongfang Liu, Yiming Cui, Liqi Yan, Christos Mousas, Bai-jian Yang, and Yingjie Chen. Densernet: Weakly supervised visual localization using multi-scale feature aggregation. In AAAI, 2021. 2", + "[26] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In CVPR, 2021. 5", + "[27] David G Lowe. Distinctive image features from scale-invariant keypoints. *IJCV*, 2004, 2, 6", + "[28] Zixin Luo, Tianwei Shen, Lei Zhou, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, and Long Quan. Contextdesc: Local descriptor augmentation with cross-modality context. In CVPR, 2019. 6", + "[29] Zixin Luo, Lei Zhou, Xuyang Bai, Hongkai Chen, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, and Long Quan. Aslfeat: Learning local features of accurate shape and localization. In CVPR, 2020. 1, 2, 5, 6, 7, 8", + "[30] Iaroslav Melekhov, Zakaria Laskar, Xiaotian Li, Shuzhe Wang, and Juho Kannala. Digging into self-supervised learning of feature descriptors. In IEEE 3DV, 2021. 6", + "[31] Krystian Mokolajczyk and Cordelia Schmid. A performance evaluation of local descriptors. IEEE TPAMI, 2005. 6", + "[32] Anastasiia Mishchuk, Dmytro Mishkin, Filip Radenovic, and Jiri Matas. Working hard to know your neighbor's margins: Local descriptor learning loss. In NeurIPS, 2017. 6", + "[33] Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics, 2015. 1, 7", + "[34] Hyeonwoo Noh, Andre Araujo, Jack Sim, Tobias Weyand, and Bohyung Han. Large-scale image retrieval with attentive deep local features. In ICCV, 2017. 6", + "[35] Yuki Ono, Eduard Trulls, Pascal Fua, and Kwang Moo Yi. Lf-net: learning local features from images. In NeurIPS, 2018. 6" + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "11472", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[36] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv:1807.03748, 2018. 2", + "[37] Luc Oth, Paul Furgale, Laurent Kneip, and Roland Siegwart. Rolling shutter camera calibration. In CVPR, pages 1360-1367, 2013. 1", + "[38] James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman. Object retrieval with large vocabularies and fast spatial matching. In CVPR, 2007. 1", + "[39] Jerome Revaud, Philippe Weinzaepfel, César De Souza, Noe Pion, Gabriela Csurka, Yohann Cabon, and Martin Humenberger. R2d2: Repeatable and reliable detector and descriptor. In NeurIPS, 2019. 1, 2, 5, 6, 8", + "[40] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In ICCV, 2011. 8", + "[41] Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenberg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, et al. Benchmarking 6dof outdoor visual localization in changing conditions. In CVPR, 2018. 8", + "[42] Nikolay Savinov, Akihito Seki, Lubor Ladicky, Torsten Sattler, and Marc Pollefeys. Quad-networks: unsupervised learning to rank for interest point detection. In CVPR, 2017. 2", + "[43] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In CVPR, 2016. 8", + "[44] Johannes L Schonberger, Hans Hardmeier, Torsten Sattler, and Marc Pollefeys. Comparative evaluation of hand-crafted and learned local features. In CVPR, 2017. 2", + "[45] Edgar Simo-Serra, Eduard Trulls, Luis Ferraz, Iasonas Kokkinos, Pascal Fua, and Francesc Moreno-Noguer. Discriminative learning of deep convolutional feature point descriptors. In ICCV, 2015. 2", + "[46] Suwichaya Suwanwimolkul, Satoshi Komorita, and Kazuyuki Tasaka. Learning of low-level feature keypoints for accurate and robust detection. In WACV, 2021. 6", + "[47] Yurun Tian, Bin Fan, and Fuchao Wu. L2-net: Deep learning of discriminative patch descriptor in euclidean space. In CVPR, 2017. 2", + "[48] Yurun Tian, Xin Yu, Bin Fan, Fuchao Wu, Huub Heijnen, and Vassileios Balntas. Sosnet: Second order similarity regularization for local descriptor learning. In CVPR, 2019. 8", + "[49] Michal Tyszkiiewicz, Pascal Fua, and Eduard Trulls. Disk: Learning local features with policy gradient. NeurIPS, 2020. 2, 6, 7, 8", + "[50] Changwei Wang, Rongtao Xu, Yuyang Zhang, Shibiao Xu, Weiliang Meng, Bin Fan, and Xiaopeng Zhang. Mtldesc: Looking wider to describe better. AAAI, 2022. 6, 8", + "[51] Tongzhou Wang and Phillip Isola. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In ICML, 2020. 1", + "[52] Xiao Wang and Guo-Jun Qi. Contrastive learning with stronger augmentations. arXiv preprint arXiv:2104.07713, 2021. 2.5", + "[53] Zihao Wang, Xueyi Li, and Zhen Li. Local representation is not enough: Soft point-wise transformer for descriptor and detector of local features. In IJCAI, 2021. 2" + ], + "bbox": [ + 78, + 92, + 467, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[54] Zihao Wang, Zhen Li, Xueyi Li, Wenjie Chen, and Xiangdong Liu. Graph-based contrastive learning for description and detection of local features. IEEE TNNLS, 2022. 2, 3", + "[55] Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. Unsupervised feature learning via non-parametric instance discrimination. In CVPR, 2018. 1", + "[56] Lanling Xu, Jianxun Lian, Wayne Xin Zhao, Ming Gong, Linjun Shou, Daxin Jiang, Xing Xie, and Ji-Rong Wen. Negative sampling for contrastive representation learning: A review. arXiv preprint arXiv:2206.00212, 2022. 3", + "[57] Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, and Pascal Fua. Lift: Learned invariant feature transform. In ECCV, 2016. 2", + "[58] Kwang Moo Yi, Yannick Verdie, Pascal Fua, and Vincent Lepetit. Learning to assign orientations to feature points. In CVPR, 2016. 2", + "[59] Xiaoming Zhao, Xingming Wu, Jinyu Miao, Weihai Chen, Peter CY Chen, and Zhengguo Li. Alike: Accurate and lightweight keypoint detection and descriptor extraction. IEEE TMM, 2022. 6" + ], + "bbox": [ + 501, + 92, + 890, + 373 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "11473", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Transformation-Predictive Representations for Detection and Description of Local Features/aa5b05da-b5ee-4eea-ae47-f2c476393ca4_model.json b/2023/Learning Transformation-Predictive Representations for Detection and Description of Local Features/aa5b05da-b5ee-4eea-ae47-f2c476393ca4_model.json new file mode 100644 index 0000000000000000000000000000000000000000..abfcb129d7fec30a7cf74ecf91f4d1de48b69e5b --- /dev/null +++ b/2023/Learning Transformation-Predictive Representations for Detection and Description of Local Features/aa5b05da-b5ee-4eea-ae47-f2c476393ca4_model.json @@ -0,0 +1,2332 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.128, + 0.13, + 0.844, + 0.175 + ], + "angle": 0, + "content": "Learning Transformation-Predictive Representations for Detection and Description of Local Features" + }, + { + "type": "text", + "bbox": [ + 0.208, + 0.203, + 0.768, + 0.221 + ], + "angle": 0, + "content": "Zihao Wang1 Chunxu Wu2 Yifei Yang2 Zhen Li2*" + }, + { + "type": "text", + "bbox": [ + 0.221, + 0.222, + 0.75, + 0.258 + ], + "angle": 0, + "content": "\\(^{1}\\)School of Intelligence Science and Technology, Peking University \\(^{2}\\)School of Automation, Beijing Institute of Technology" + }, + { + "type": "text", + "bbox": [ + 0.192, + 0.26, + 0.773, + 0.275 + ], + "angle": 0, + "content": "zhwang@stu.pku.edu.cn, {3120200960, 3120220892, zhenli}@bit.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.309, + 0.314, + 0.327 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.341, + 0.473, + 0.735 + ], + "angle": 0, + "content": "The task of key-points detection and description is to estimate the stable location and discriminative representation of local features, which is a fundamental task in visual applications. However, either the rough hard positive or negative labels generated from one-to-one correspondences among images may bring indistinguishable samples, like false positives or negatives, which acts as inconsistent supervision. Such resultant false samples mixed with hard samples prevent neural networks from learning descriptions for more accurate matching. To tackle this challenge, we propose to learn the transformation-predictive representations with self-supervised contrastive learning. We maximize the similarity between corresponding views of the same 3D point (landmark) by using none of the negative sample pairs and avoiding collapsing solutions. Furthermore, we adopt self-supervised generation learning and curriculum learning to soften the hard positive labels into soft continuous targets. The aggressively updated soft labels contribute to overcoming the training bottleneck (derived from the label noise of false positives) and facilitating the model training under a stronger transformation paradigm. Our self-supervised training pipeline greatly decreases the computation load and memory usage, and outperforms the sota on the standard image matching benchmarks by noticeable margins, demonstrating excellent generalization capability on multiple downstream tasks." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.763, + 0.21, + 0.779 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.787, + 0.47, + 0.879 + ], + "angle": 0, + "content": "Local visual descriptors are fundamental to various computer vision applications such as camera calibration [37], 3D reconstruction [19], visual simultaneous localization and mapping (VSLAM) [33], and image retrieval [38]. The descriptors indicate the representation vector of the patch around the key-points and can be used to generate dense" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.311, + 0.726, + 0.326 + ], + "angle": 0, + "content": "correspondences between images." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.327, + 0.895, + 0.688 + ], + "angle": 0, + "content": "The discriptors are highly dependent on the effective representation, which has been always trained with the Siamese architecture and contrastive learning loss [12, 29, 39]. The core idea of contrastive learning is \"learn to compare\": given an anchor key-point, distinguish a similar (or positive) sample from a set of dissimilar (or negative) samples, in a projected embedding space. The induced representations present two key properties: 1) alignment of features from positive pairs, 2) and uniformity of representation on the hypersphere [51]. Negative samples are thus introduced to keep the uniformity property and avoid model collapse, i.e., preventing the convergence to one constant solution [13]. Therefore, various methods have been proposed to mine hard negatives [6, 21, 55]. However, these methods raise the computational load and memory resources usage heavily [17]. More Importantly, within the hard negatives, some samples are labeled as negatives, but actually have the identical semantics of the anchor (i.e., false negatives). These false negatives act as inconsistent supervision and prevent the learning-based models from achieving higher accuracy [4]. More concretely, the false negatives represent the instance located on the repetitive texture in the structural dataset, as shown in Figure 1. It is challenging to recognize such false negatives from true negatives [39]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.895, + 0.825 + ], + "angle": 0, + "content": "The recent active self-supervised learning methods [5, 8, 9, 15] motivate us to rethink the effectiveness of negatives in descriptors learning. We propose to learn the transformation predictive representations (TPR) for visual descriptors only with the positives and avoids collapsing solutions. Furthermore, using none of negatives greatly improves the training efficiency by reducing the scale of the similarity matrix from \\(\\mathcal{O}(n^2)\\) to \\(\\mathcal{O}(n)\\), and reduces the computation load and memory usage." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.826, + 0.895, + 0.903 + ], + "angle": 0, + "content": "To further improve the generalization performance of descriptors, hard positives (i.e., corresponding pairs with large-scale transformation) are encouraged as training data to expose novel patterns. However, recent experiments have shown that directly contrastive learning on stronger" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.221, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11464" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.1, + 0.088, + 0.302, + 0.243 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.306, + 0.088, + 0.47, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.472, + 0.089, + 0.523, + 0.13 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.09, + 0.593, + 0.13 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.521, + 0.132, + 0.599, + 0.144 + ], + "angle": 0, + "content": "Easy Negatives" + }, + { + "type": "image", + "bbox": [ + 0.473, + 0.146, + 0.523, + 0.185 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.147, + 0.592, + 0.185 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.521, + 0.187, + 0.599, + 0.199 + ], + "angle": 0, + "content": "Hard Negatives" + }, + { + "type": "image", + "bbox": [ + 0.473, + 0.202, + 0.522, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.202, + 0.592, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.524, + 0.244, + 0.605, + 0.255 + ], + "angle": 0, + "content": "False Negatives" + }, + { + "type": "image", + "bbox": [ + 0.614, + 0.091, + 0.663, + 0.129 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.614, + 0.147, + 0.663, + 0.185 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.614, + 0.202, + 0.663, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.614, + 0.244, + 0.662, + 0.255 + ], + "angle": 0, + "content": "" + }, + { + "type": "image", + "bbox": [ + 0.674, + 0.091, + 0.871, + 0.242 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.729, + 0.244, + 0.829, + 0.255 + ], + "angle": 0, + "content": "t-SNE visualization" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.272, + 0.895, + 0.329 + ], + "angle": 0, + "content": "Figure 1. Illustrative schematic of different negative samples. Easy negatives (green) are easy to distinguish from the query (blue) and not sufficient for good performance. Hard negatives (orange) are important for better performance and are still semantically dissimilar from the query. False negatives (red) are practically impossible to distinguish and semantically identical with the query, which is harmful to the performance. The right figure visualizes the embedding of different samples in latent space." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.345, + 0.469, + 0.495 + ], + "angle": 0, + "content": "transformed images can not learn representations effectively [52]. In addition, current contrastive learning methods label all positives with different transformation strength as coarse “1”, which prevent learning refined representation. We propose to learn transformation predictive representation with soft positive labels from \\((0,1]\\) instead of “1” to supervise the learning of local descriptors. Furthermore, we propose a self-supervised curriculum learning module to generate controllable stronger positives with gradually refined soft supervision as the network iterative training." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.497, + 0.469, + 0.662 + ], + "angle": 0, + "content": "Finally, our TPR with soft labels is trained on natural images in a fully self-supervised paradigm. Different from previous methods trained on datasets with SfM or camera pose information [20, 29, 39, 49], our training datasets are generally easy to collect and scale up since there is no extra annotation requirement to capture dense correspondences. Experiments show that our self-supervised method outperforms the state-of-the-art on standard image matching benchmarks by noticeable margins and shows excellent generalization capability on multiple downstream tasks (e.g., visual odometry, and localization)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.664, + 0.47, + 0.83 + ], + "angle": 0, + "content": "Our contributions to this work are as follows: i) we propose to learn transformation-predictive representations for joint local feature learning, using none of the negative sample pairs and avoiding collapsing solutions. ii) We adopt self-supervised generation learning and curriculum learning to soften the hard positives into continuous soft labels, which can alleviate the false positives and train the model with stronger transformation. iii) The overall pipeline is trained with the self-supervised paradigm, and the training data are computed from random affine transformation and augmentation on natural images." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.845, + 0.227, + 0.86 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.469, + 0.902 + ], + "angle": 0, + "content": "Our work focuses on the first step of the image matching pipeline, i.e., detection and description of key-points. This" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.345, + 0.892, + 0.391 + ], + "angle": 0, + "content": "section gives a brief review of key-points detection and description learning together with contrastive representation learning." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.411, + 0.893, + 0.713 + ], + "angle": 0, + "content": "Key-points Learning. Different from early hand-crafted key-points, e.g., SIFT [27] and SURF [3], the recent effort has been put into learning key-points through the deep neural networks. Many existent works focus on single module optimization with deep learning, such as interest points detection [42], shape estimation [58] and descriptor representation [45, 47]. However, optimizing one single component may not directly enable the improvement of the entire pipeline [44, 57]. Recent works tend to build end-to-end joint learning frameworks for detecting and describing local features [12, 29, 53]. CNN-based methods simultaneously optimize both the detector and descriptor by sharing most parameters with the help of the encoder-decoder pipeline. Compared with the conventional methods only considering low-level features like corners, edges, or blobs in shallow layers, the encoder-decoder pipeline can make full use of deep CNNs for better representations [11, 25, 39]. To maximize the similarity of corresponding pairs, current cutting-edge methods usually take the Siamese structure to train the model with contrastive learning [54]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Contrastive Learning. Contrastive learning was originated from metric learning and has been widely adopted to supervise the description learning [16, 18, 36, 47]. The critical factor of contrastive learning is to attract the representation of corresponding local descriptions (positive pairs) closer and spread the representations of non-corresponding descriptions (negative pairs) apart. Negative samples are accordingly introduced to keep the uniformity property in order to avoid the representation collapse [13]. Due to expensive manual labeling and the lack of explicit negative signals in most cases, various negative sampling strate-" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11465" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.318 + ], + "angle": 0, + "content": "gies were proposed to improve the computation efficiency and promote the training results [56]. However, the sampling of hard negatives highly depends on the large batch size [6, 7, 54] and memory bank [17], so as to increase the computational load and memory resources usage. Recently, some works have been conducted to optimize the contrastive loss without any negatives and avoid the collapse [5, 9, 15]. But all these works were proposed for the holistic representation of whole images and cannot be adapted to the dense prediction tasks. To address this issue, our method uses an exclusive predictor and stop-gradient operation to avoid collapsing and encourage encoding more transformation-aware representations without negatives, which is the first work ever training key-points with only positives." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.333, + 0.169, + 0.349 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.359, + 0.473, + 0.587 + ], + "angle": 0, + "content": "In this section, we elaborate on the proposed Transformation Predictive Representations (TPR) for learning detection and description of local features. Given a pair of images \\((I_1, I_2)\\), and the correspondences set \\(\\mathcal{C}\\) between them, the key-points learning methods predict the dense 3D description map \\(D\\) and detection heatmap \\(S \\in [0,1]\\) jointly. During the training, the standard Siamese networks are used to deal with the image pairs \\((I_1, I_2)\\) simultaneously and optimize the parameters with contrastive loss according to \\(\\mathcal{C}\\). Our approach also follows this pipeline. However, different from the previous approaches using negatives and hard negative sampling for training, TPR is optimized without any negatives. In this way, our model can reduce the computation complexity and memory usage and improves the training efficiency." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.596, + 0.208, + 0.612 + ], + "angle": 0, + "content": "3.1. Preliminary" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.619, + 0.47, + 0.651 + ], + "angle": 0, + "content": "The architecture in this work is built upon 1) D2-Net [12], and 2) Bootstrap Your Own Latent (BYOL) [15]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.671, + 0.47, + 0.791 + ], + "angle": 0, + "content": "D2-Net [12] proposes a describe-and-detect strategy to jointly extract descriptions and detections of local features. Over the last feature maps \\( y \\in \\mathbb{R}^{H \\times W \\times C} \\), D2-Net applies channel-wise L2-normalization to obtain the dense feature descriptors, while the feature detections are derived from 1) the local score and 2) the channel-wise score. Specifically, for each location \\( (i,j) \\) in \\( y^k (k = 1,\\dots ,C) \\), the final detection score is computed as:" + }, + { + "type": "equation", + "bbox": [ + 0.093, + 0.805, + 0.47, + 0.845 + ], + "angle": 0, + "content": "\\[\ns _ {i j} = \\max _ {t} \\left[ \\frac {\\exp (y _ {i j} ^ {k})}{\\sum_ {(i ^ {\\prime} , j ^ {\\prime}) \\in \\mathcal {N} (i , j)} \\exp (y _ {i ^ {\\prime} j ^ {\\prime}} ^ {k})} \\cdot \\frac {y _ {i j} ^ {k}}{\\max _ {t} y _ {i j} ^ {t}} \\right], \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.855, + 0.47, + 0.902 + ], + "angle": 0, + "content": "where \\(\\mathcal{N}(i,j)\\) is the neighboring pixels around \\((i,j)\\), e.g., 9 neighbours defined by a \\(3 \\times 3\\) kernel. D2-Net adopts the triplet margin ranking loss to optimize the descriptions, and" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.092, + 0.614, + 0.105 + ], + "angle": 0, + "content": "is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.573, + 0.114, + 0.892, + 0.145 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {t r i p l e t}} = \\sum_ {c \\in \\mathcal {C}} \\max \\left(0, M + d _ {p} ^ {c} - d _ {n} ^ {c}\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.155, + 0.894, + 0.216 + ], + "angle": 0, + "content": "where \\(M\\) is the margin, \\(\\mathcal{C}\\) is the set of correspondences, \\(d_p^c\\) and \\(d_n^c\\) represent the distance of positive and selected negative pairs, respectively. Finally, the detection score is used as a weighting term in description loss function." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.233, + 0.895, + 0.446 + ], + "angle": 0, + "content": "BYOL [15] is a self-supervised image representation learning approach. BYOL uses two neural networks, referred to as the online and target networks, both of which interact and learn from each other. The online network is trained to predict the target network's representation of the same image from an augmented view. The weights of the target network are updated with a slow-moving average of the online network. Different from other contrastive learning methods, BYOL can be trained without any negatives. It hypothesizes that the combination of 1) the addition of a predictor to the online network, and 2) the use of a slow-moving average of the online parameters as the target network encourages encoding more information within the online projection and avoids collapsed solutions." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.453, + 0.869, + 0.469 + ], + "angle": 0, + "content": "3.2. Transformation-Predictive Representations" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.476, + 0.892, + 0.642 + ], + "angle": 0, + "content": "The Siamese-like structure (i.e., the online and target networks) is used in TPR to learn the representations. From a given representation of the transformed image, referred to as target, the TPR trains a new potentially enhanced representation of the original image, referred to as online, by predicting the target representation. The online network is defined by a set of weights \\(\\theta_{o}\\) and comprises three stages: an online encoder \\(f_{o}\\), a projector \\(g_{o}\\), and a predictor \\(q\\). The target network also has the encoder \\(f_{t}\\) and projector \\(g_{t}\\) in the same structure but with a different set of weights \\(\\theta_{t}\\). The overall pipeline is described as in Figure 2," + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.642, + 0.892, + 0.718 + ], + "angle": 0, + "content": "Given the input image \\(I \\in \\mathbb{R}^{H \\times W \\times 3}\\), a random augmentation \\(t\\) is performed to produce the cropped view \\(V \\triangleq t(I) \\in \\mathbb{R}^{h \\times w \\times 3}\\). And then, the limited cascaded affine transformation and augmentation \\(T\\) is carried out on the \\(V\\) to produce the transformed view \\(V' \\triangleq T(V) \\in \\mathbb{R}^{h \\times w \\times 3}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.719, + 0.892, + 0.854 + ], + "angle": 0, + "content": "From the augmented view \\(V\\), the online encoder outputs a representation \\(z \\triangleq f_{o}(V) \\in \\mathbb{R}^{h \\times w \\times c}\\), which is adopted as the objective encouraged to be transformation-predictive. Rather than predicting representations produced by the online encoder, the target representation \\(z' \\triangleq f_{t}(V') \\in \\mathbb{R}^{h \\times w \\times c}\\) is computed by using the target encoder \\(f_{t}\\), whose parameters \\(\\theta_{t}\\) are an exponential moving average (EMA) of the online encoder parameters \\(\\theta_{o}\\). Without gradient descent, the update algorithm for \\(\\theta_{t}\\) is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.621, + 0.863, + 0.892, + 0.878 + ], + "angle": 0, + "content": "\\[\n\\theta_ {t} \\leftarrow \\tau \\theta_ {t} + (1 - \\tau) \\theta_ {o}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.886, + 0.768, + 0.902 + ], + "angle": 0, + "content": "where \\(\\tau \\in [0,1)\\) is the EMA coefficient." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11466" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.161, + 0.093, + 0.805, + 0.409 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.425, + 0.895, + 0.496 + ], + "angle": 0, + "content": "Figure 2. Illustration of our proposed transformation-predictive representations learning. Top: traditional contrastive learning descriptors based on both positives and negatives and supervised under SfM models. Bottom: our proposed transformation predictive representations learning with only positives and self-supervised loss. In the TPR structure, representations from the online encoder are used in the joint learning of local features task. The target encoder and projection head are defined as an exponential moving average of their online counterparts and are not updated via gradient descent." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.511, + 0.471, + 0.617 + ], + "angle": 0, + "content": "Next, the representation \\( z \\) and \\( z' \\) are fed into the online and target detector to extract the detection score \\( s \\) and \\( s' \\) with (1). We further use the online and target projection heads \\( g_{o} \\) and \\( g_{t} \\) to map the online and target presentations to a latent embedding space[6], and also apply an additional prediction head \\( q \\) to the online projections to predict the target projections[15]:" + }, + { + "type": "equation", + "bbox": [ + 0.228, + 0.619, + 0.32, + 0.635 + ], + "angle": 0, + "content": "\\[\n\\hat {y} \\triangleq q (g _ {o} (z)),\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.228, + 0.635, + 0.47, + 0.652 + ], + "angle": 0, + "content": "\\[\ny ^ {\\prime} \\triangleq g _ {t} \\left(z ^ {\\prime}\\right). \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.654, + 0.47, + 0.744 + ], + "angle": 0, + "content": "The target projection head parameters are also given through an EMA of the online projection head parameters by using the same update algorithm as the online and target encoders. Note that the predictor is only applied to online networks so as to make the Siamese architecture asymmetric between the online and target pipeline." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.744, + 0.471, + 0.866 + ], + "angle": 0, + "content": "Next, we compute the transformation predictive loss for TPR by summing over cosine similarities between the predicted and target representations. Different from a holistic representation \\(\\mathbb{R}^{1\\times 1\\times d}\\) of the view \\(V\\) and \\(V^{\\prime}\\) [15], our encoder \\(f_{o}\\) outputs the dense feature maps preserving abundant spatial information with the shape of \\(h\\times w\\times c\\). The prediction loss is computed on the corresponding location of the dense feature map, i.e., the positive pairs:" + }, + { + "type": "equation", + "bbox": [ + 0.156, + 0.869, + 0.47, + 0.905 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {\\text {p r e d}} ^ {c} = \\left(1 - \\left\\langle y _ {c}, y _ {c} ^ {\\prime} \\right\\rangle\\right) \\tag {5} \\\\ = \\left(1 - \\left\\langle q \\left(g _ {o} (z)\\right) _ {c}, g _ {t} \\left(z ^ {\\prime}\\right) _ {c} \\right\\rangle\\right), \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.511, + 0.892, + 0.541 + ], + "angle": 0, + "content": "where \\(\\langle \\cdot \\rangle\\) denotes the cosine similarity, \\(\\hat{y}_c\\) and \\(y_c^\\prime\\) is the local representation with the \\(c\\)-th correspondence." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.542, + 0.892, + 0.571 + ], + "angle": 0, + "content": "Finally, the detection score is used as a weighting term to formalize the final hard prediction loss function:" + }, + { + "type": "equation", + "bbox": [ + 0.581, + 0.581, + 0.892, + 0.617 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {h a r d}} = \\frac {1}{| \\mathcal {C} |} \\sum_ {c \\in \\mathcal {C}} \\frac {s _ {c} s _ {c} ^ {\\prime}}{\\sum_ {n \\in \\mathcal {C}} s _ {n} s _ {n} ^ {\\prime}} \\mathcal {L} _ {\\text {p r e d}} ^ {c}. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.628, + 0.892, + 0.84 + ], + "angle": 0, + "content": "The transformation-predictive representations follow the predictive nature of the objective and use the exponential moving average target network similar to [15]. Compared with the related works on contrastive representation learning, TPR only uses the positive pairs without negative samples. As a result, there is no necessary need on large buffer to emulate large batch sizes of negatives [6, 17] and complex hard negative sampling strategies. Furthermore, training without negatives can help prevent the inconsistent optimization from the false negatives. Finally, although we do not use the explicit negative samples to prevent collapse while minimizing \\(\\mathcal{L}_{\\mathrm{hard}}\\), TPR still can avoid converging to a minimum of this loss with respect to \\((\\theta_o,\\theta_t)\\) (e.g., a collapsed constant representation)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.848, + 0.738, + 0.864 + ], + "angle": 0, + "content": "3.3. Learning with Soft Labels" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "The TPR method uses carefully designed transformations \\(T\\) to generate the view \\(V^{\\prime}\\) from \\(V\\). Therefore, the" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11467" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.303 + ], + "angle": 0, + "content": "views are not deformed aggressively so that they can still be viewed as the same instance, i.e., positives. Stronger deformation could expose the novel patterns of representations in discriminative contrastive learning. However, directly adopting stronger transformations (e.g., with larger rotation angles, more aggressive color jitting, and cutout) may seriously damage the intrinsic semantics of the original image, which will fail to further improve or even degrade the performance [52]. In this paper, we categorize such positive pairs generated from over-strong deformation into the false positives. The false positives are bound to make the training process degraded in performance because the network cannot effectively distinguish the true positives from the false ones when trained with the existing pipeline [39]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.303, + 0.473, + 0.395 + ], + "angle": 0, + "content": "To tackle the dilemma, where the strong or weak transformations should not have been used to produce the positives, we propose the optimal soft labels to stabilize the training process instead of forcing the labeling coarse hard \"1\" for every positive pair. Accordingly, the prediction loss in (5) is modified as follows." + }, + { + "type": "equation", + "bbox": [ + 0.135, + 0.398, + 0.47, + 0.429 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {s o f t}} = \\frac {1}{| \\mathcal {C} |} \\sum_ {c \\in \\mathcal {C}} \\frac {s _ {c} s _ {c} ^ {\\prime}}{\\sum_ {n \\in \\mathcal {C}} s _ {n} s _ {n} ^ {\\prime}} \\left(l _ {c} + 1 - \\mathcal {L} _ {\\text {p r e d}} ^ {c}\\right), \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.433, + 0.473, + 0.495 + ], + "angle": 0, + "content": "in which \\( l_{c} \\) represents the computed soft label for the positive pair with \\( c \\)-th correspondence. Therefore, we adopt self-supervised generation learning and curriculum learning to \"adjust\" the inconsistent and incorrect supervision." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.512, + 0.473, + 0.739 + ], + "angle": 0, + "content": "Self-supervised generation learning. We train the network for the first generation following the hard positive label. And then, the network can be assumed to successfully capture most feature distribution of the training data. We propose to utilize the previous generation's similarities to be served as the soft supervision for training the network in a new generation. The generated soft supervisions are gradually refined and sharpened as the network generation progresses. In this way, the system can fully explore the potential of the difficult positives with stronger transformation, and mitigate their label noises with the refined soft confidence. To stabilize the training process, the soft label of the positive pair with the correspondence \\( c \\) in the generation \\( \\omega = 2,\\dots ,\\Omega \\) follows the exponential decay of the transformation strength, which is computed as:" + }, + { + "type": "equation", + "bbox": [ + 0.18, + 0.747, + 0.47, + 0.768 + ], + "angle": 0, + "content": "\\[\nl _ {c} = e ^ {- \\left(1 - \\left\\langle y _ {c} ^ {\\omega - 1}, y _ {c} ^ {\\prime \\omega - 1} \\right\\rangle\\right) / \\lambda}, \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.776, + 0.473, + 0.824 + ], + "angle": 0, + "content": "where \\(y^{\\omega -1}\\) and \\(y^{\\prime \\omega -1}\\) are generated from the encoder with the last generation parameters \\(\\theta_{\\omega}\\), and \\(\\Omega\\) is the total number of generations, and \\(\\lambda\\) is the exponential decay constant." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Curriculum setting for positives generation. The core of curriculum learning is to gradually improve the transformation strength. In particular, we introduce the affine adaptation and data augmentation to generate the transformation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.213 + ], + "angle": 0, + "content": "matrix, which also determines the transformation strength \\(\\alpha\\). We normalize \\(\\alpha\\) within \\([0,1]\\) according to the maximum limitation of adaption and augmentation parameters, e.g., rotation and translation values. With the curriculum setting, \\(\\alpha\\) is randomly sampled from a range with gradually relaxed restrictions of the transformation strength. The soft label of the current batch follows the exponential decay of the transformation strength, which is computed as:" + }, + { + "type": "equation", + "bbox": [ + 0.598, + 0.224, + 0.892, + 0.246 + ], + "angle": 0, + "content": "\\[\nl _ {c} = e ^ {- \\alpha \\left(1 - \\left\\langle y _ {c} ^ {\\omega - 1}, y _ {c} ^ {\\prime \\omega - 1} \\right\\rangle\\right) / \\lambda}. \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.258, + 0.892, + 0.349 + ], + "angle": 0, + "content": "To avoid the over-fitting to the soft labels, we also set a maximum operation on the predicted soft loss as \\( \\max(0, l_c + 1 - \\mathcal{L}_{pred}^c) \\). This setting will help the model avoid divergence through the false positives during the early training stage, and encourage the model to explore more robust representations in the later training stage." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.35, + 0.895, + 0.487 + ], + "angle": 0, + "content": "In the later stage of training, with the increasing difficulty of positive samples, the proportion of false positive samples, which have different semantics with the online query, will increase. Such false positives will be harmful to the generalization performance of the model [52]. To overcome this problem, we introduce a hyperparameter patience \\( p \\) of early stop. When the soft prediction loss no longer decreases, the value of \\( p \\) will decrease. The training will stop until \\( p = 0 \\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.497, + 0.719, + 0.513 + ], + "angle": 0, + "content": "3.4. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.521, + 0.892, + 0.596 + ], + "angle": 0, + "content": "In this part, the architectural details of TPR are introduced. Note that at the end of the training, everything but the online encoder \\( f_{o} \\) is discarded. Thus, the online projection head \\( g_{o} \\), the predictor head \\( q \\), and the target branch will not introduce the extra computational costs in deployment." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.597, + 0.892, + 0.673 + ], + "angle": 0, + "content": "Encoder, \\( f_{o} \\), maps the input view \\( V \\) into the dense embeddings \\( z \\). The encoder can be instanced into the CNN version based on VGG (similar as D2-Net [12]) and DCN (similar as ASLFeat [29]), and Transformer version based on tiny Swin Transformer V1 [26] with only the first two stages." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.673, + 0.892, + 0.749 + ], + "angle": 0, + "content": "Projection head, \\( g_{o} \\), maps the feature embedding into a 128-dim \\( l_{2} \\) normalization feature vector for the computation of the prediction loss \\( \\mathcal{L}_{\\mathrm{pred}} \\). \\( g_{o} \\) is implemented as a 3-layer MLP (hidden layer 256-dim), with BN and ReLU on every fully-connected layer except the last output layer." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.892, + 0.795 + ], + "angle": 0, + "content": "Predictor, \\( q \\), is only applied to the online networks. The predictor is an MLP with two layers. The dimension of the hidden and output layer is 64 and 128, respectively." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Detection and description head, are adopted on the encoder to simultaneously output the dense description and the detection score map following the D2-Net [12]. Only the locations with higher confidence than 0.7 are selected as the key points. Moreover, a non-maximum suppression (NMS) operation (with a kernel of 5) is applied to remove the key points that are spatially too close." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11468" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.077, + 0.09, + 0.21, + 0.108 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.117, + 0.471, + 0.208 + ], + "angle": 0, + "content": "In this section, we first present the details of training our TPR. And then we show the performance across several downstream scenarios, including image matching, visual odometry, and visual localization. It's worthy to NOTE that our methods are not trained on all benchmark datasets, which is essential to show generalization performance." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.223, + 0.268, + 0.24 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.248, + 0.47, + 0.459 + ], + "angle": 0, + "content": "Our TPR method is trained on images from Microsoft COCO dataset[24] and Image Matching Challenge[20], by dropping all the irrelevant annotations. We compute the correspondences between the transformed natural images with the random affine adaption and data augmentation by means of the self-supervised paradigm. For the affine adaption, we uniformly sample the in-plane rotation, shear, translation and scale parameters from \\([-45^{\\circ}, +45^{\\circ}]\\), \\([-40^{\\circ}, +40^{\\circ}]\\), \\([-0.05, +0.05]\\), [0.7,1.4], respectively. For the color jitter, we also uniformly sample the brightness, contrast, saturation, and hue parameters from [0.6,1.4], [0.6,1.4], [0.6,1.4], \\([-0.2, +0.2]\\), respectively. The additional random data augmentation is employed on the transformed views \\(V\\) and \\(V'\\) including the grayscale conversion and Gaussian blur." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.461, + 0.47, + 0.612 + ], + "angle": 0, + "content": "We train models using SGD and AdamW optimizer for CNN and Transformer-based models, respectively. During training, we use a mini-batch size of 16 and an initial learning rate of 0.00017 with exponential decay of 0.9. The hyper-parameter of EMA update \\(\\tau\\), exponential decay constant \\(\\lambda\\), and early stop patience \\(p\\) is set to 0.99, 10, and 1, which are determined with the grid search to reach the optimum. Our method were trained for 50 epochs, which took 30 hours for training with two NVIDIA-A100 GPUs. We randomly crop the \\(224 \\times 224\\) views for training." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.613, + 0.47, + 0.675 + ], + "angle": 0, + "content": "Moreover, we evaluate the local features learned by TPR across several downstream scenarios, including the image matching on HPatches, visual odometry on KITTI, and visual localization on Aachen Day-Night dataset." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.689, + 0.241, + 0.706 + ], + "angle": 0, + "content": "4.2. Image Matching" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.714, + 0.47, + 0.76 + ], + "angle": 0, + "content": "We first evaluate the performance of our method in the image matching tasks, i.e., the most extensive key-points applications." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Datasets. The experiments are taken on HPatches [2] datasets, which include 116 sequences of 6 images with known homography. The datasets are split into two categories according to the illumination and viewpoint changes. The first image in one sequence is taken as reference, and the subsequent images are used to form the pairs with increasing difficulty. By following the previous methods[39], eight high-resolution sequences are excluded." + }, + { + "type": "table", + "bbox": [ + 0.508, + 0.089, + 0.868, + 0.415 + ], + "angle": 0, + "content": "
MethodMMA@3AUC@2AUC@5
SIFT [27]50.139.4949.57
HardNet [32]62.142.6156.85
LF-Net [35]53.238.7448.69
SuperPoint [11]65.744.0859.04
DELF [34]50.744.7349.70
ContextDesc [28]63.247.2358.25
Key.Net [22]72.140.8756.04
R2D2 [39]72.143.3564.17
DISK [49]77.252.3369.80
ALIKE [59]70.551.6569.04
SSL+CAPS [30]69.048.7262.19
LLF [46]74.052.1466.81
MTLDesc [50]78.755.0271.42
PoSFeat [23]75.3450.1669.23
D2-Net [12] (orig.)40.319.4937.78
D2-Net [12] (our impl.)44.522.3543.17
Ours(VGG)49.6 ↑ 9.324.46 ↑ 4.9747.69 ↑ 9.91
ASLFeat [29] (orig.)72.250.1066.93
ASLFeat [29] (our impl.)74.451.8369.24
Ours(DCN)75.5 ↑ 3.252.33 ↑ 2.2370.15 ↑ 3.22
Ours(TR)79.857.1873.00
" + }, + { + "type": "table_caption", + "bbox": [ + 0.556, + 0.425, + 0.836, + 0.439 + ], + "angle": 0, + "content": "Table 1. Quantitative results (§4.2) on HPatches." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.47, + 0.892, + 0.637 + ], + "angle": 0, + "content": "Evaluation protocols. For a fair comparison, we match the features extracted by each method using the nearest neighbor matcher, accepting only mutual nearest neighbors. A correct match is accordingly considered if its estimated reprojection error is below a given matching threshold. We vary the threshold from 1 pixel to 10 pixels and record the mean matching accuracy (MMA) [31] over all pairs, which represents the ratio of correct matches and possible matches. And then the area is computed under curve (AUC) at 2px and 5px based on the MMA. We report the average scores for overall image pairs." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.892, + 0.903 + ], + "angle": 0, + "content": "Comparisons with other methods. We compare our model with the previous state-of-the-art methods on the HPatches dataset. Unless otherwise specified, we present the results either reported on original papers or derived from authors' public implementations with default parameters. Table. 1 lists different methods' results on HPatches in terms of the MMA area under the overall curve (AUC) up to 2px and 5px. Especially in the low-threshold area, Ours (TR) shows considerable improvements over previous sota methods. For a fair comparison, we show both the performance from the original paper (orig.) and our implementation (our impl.). It is observed that TPR gains a \\(+6.69\\%\\) (44.71% v.s. 38.02%) and \\(+2.45\\%\\) (79.81% v.s. 77.36%) MMA promotion than the leading DISK [49] at 1px and 3px threshold in 128-dimension, respectively. To prove the effectiveness of our training pipeline, we also con" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11469" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.092, + 0.089, + 0.457, + 0.208 + ], + "angle": 0, + "content": "
NegSample+SSGL+CLMMA@3Mem/GB
w/Random63.1112
Hardest73.4140
Bank78.2↑15.1158
Mixing77.9147
w/oRandom70.179
Random✓77.4↑7.279
Random✓✓79.8↑9.679
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.218, + 0.47, + 0.288 + ], + "angle": 0, + "content": "Table 2. Ablation experiments of the proposed TPR, where Neg, Sample, MMA@3, SSGL, CL and Mem represent whether training w/ Negative samples, Sampling strategy, self-supervised generation learning, curriculum learning, mean matching accuracy at 3px threshold, and GPU Memory usage (GB), respectively." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.315, + 0.468, + 0.449 + ], + "angle": 0, + "content": "duct experiments based on the D2-Net [12] backbone i.e., Ours (VGG) and ASLFeat [29] backbone, i.e., Ours (DCN). Under the same networks, our retrained model gains \\(+9.3\\%\\) and \\(+3.2\\%\\) improvements on MMA@3, respectively. The t-SNE visualization of descriptions from D2-Net (orig.) and Ours (VGG) is shown in Figure. 3. More importantly, our method is trained in the self-supervised paradigm, requiring no extra annotations, and is not finetuned on the downstream datasets." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.458, + 0.229, + 0.473 + ], + "angle": 0, + "content": "4.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.481, + 0.469, + 0.556 + ], + "angle": 0, + "content": "We further study the efficacy of our core ideas and essential model designs over HPatches. We train each model from scratch for 50 epochs to perform extensive ablation experiments while keeping other hyper-parameters unchanged." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.565, + 0.469, + 0.852 + ], + "angle": 0, + "content": "Ablation on different negative sampling strategies. We adopt the standard Siamese networks with transformer blocks as the \"baseline\" model. The baseline model is first trained with both positive and negative pairs, according to the triplet loss in (2). We also evaluate different negative sampling strategies including random negative sampling (Random) [10], in-batch hard negative mining (Hardest) [6], hard negative mining with Memory Bank (Bank) [17], and hard negative mixing (Mixing) [21]. And then we train the model using our TPR method with only positives and evaluate the efficacy of the curriculum learning and self-supervised generation learning. As demonstrated in Table 2, our TPR successfully gets convergent with only positives and avoids the collapsing solution, which proves the effectiveness of our designed components in the Siamese networks. Furthermore, the soft labels effectively alleviate the inconsistent optimization from pseudo positives, especially on the training datasets with stronger transformation and augmentation." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.469, + 0.9 + ], + "angle": 0, + "content": "Ablation on key components. We also conduct the ablation experiments to evaluate the effectiveness of our core" + }, + { + "type": "image", + "bbox": [ + 0.536, + 0.09, + 0.691, + 0.209 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.704, + 0.09, + 0.859, + 0.209 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.22, + 0.892, + 0.246 + ], + "angle": 0, + "content": "Figure 3. t-SNE visualization of description from different training methods. Left: D2-Net [12] (orig.), Right: Ours(VGG)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.266, + 0.892, + 0.356 + ], + "angle": 0, + "content": "components including training w/o negative samples, self-supervised generation learning (SSGL), and curriculum learning module (CL). The results are also shown in Table 2, which proves the self-supervised curriculum learning with soft labels will improve the contrastive learning performance with only positives." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.366, + 0.668, + 0.382 + ], + "angle": 0, + "content": "4.4. Visual Odometry" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.389, + 0.892, + 0.466 + ], + "angle": 0, + "content": "Visual Odometry (VO) is the task to estimate a robot's position and orientation from visual measurements. The goal of the system is to estimate the 6-DoF pose of the camera at every frame. In this experiment, we evaluate the visual odometry performance with the learned key-points." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.482, + 0.892, + 0.619 + ], + "angle": 0, + "content": "Datasets. We adopt the KITTI Odometry Datasets [14] to evaluate the downstream localization performance of our method. We only use the grayscale monocular images with a high resolution of \\(376 \\times 1226\\) as input. To output multiscale key-points, a three-layer image pyramid is constructed with the scale factor of 1.2. It is worth noting that in KITTI, there contains many moving objects and changing camera exposure time, which is challenging for accurate and robust key-points matching." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.622, + 0.892, + 0.834 + ], + "angle": 0, + "content": "Evaluation protocols. We take the ORB-SLAM [33] as the benchmark pipeline. We further disable both the local mapping and loop closure thread so as to keep the front-end visual odometry only. To evaluate the key-points fairly, all components in the pipeline are the same except the front-end used key-points. KITTI provides the ground-truth 6-DoF camera poses for sequences 00-10. Since the keypoints are adopted in the monocular VO pipeline, we align their poses with ground-truth to recover the scale of 6-DoF poses. Besides, we use the standard evaluation method, i.e., translational root-mean-square error (RMSE) drift \\((\\mathrm{/m})\\) provided along with KITTI dataset. To evaluate the efficiency of the learning-based key-points, we also report the mean running frames per second for the methods." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Results. We report the results of different key-points on different KITTI sequences in Table 3. TPR outperforms the sota learning methods DISK [49] on all sequences even under complex traffic conditions. As for the inference time," + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "11470" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.127, + 0.089, + 0.851, + 0.243 + ], + "angle": 0, + "content": "
MethodFPSRMSE/m ↓
00010204050607080910Avg.
ORB [40]20.659.46610.3572.6819.26238.6083.4672.7266.06119.2163.52140.53
SuperPoint [11]6.5162.78123.3413.521.066.362.0512.158.668.205.1034.32
D2-Net [12]8.810.44183.04105.332.2914.582.2510.7224.2729.629.6139.22
R2D2 [39]7.849.62515.9660.143.90123.0562.4453.8462.5473.3043.32104.81
SOSNet [48]6.3171.67309.8310.360.4714.684.0715.3510.753.247.6754.81
DISK [49]6.532.77149.9818.670.455.974.3812.8832.854.334.8126.71
Ours (TR)6.27.07164.399.720.233.462.129.997.423.103.7221.12
" + }, + { + "type": "table_caption", + "bbox": [ + 0.204, + 0.253, + 0.766, + 0.267 + ], + "angle": 0, + "content": "Table 3. Visual odometry localization performance based on different key-points in KITTI datasets." + }, + { + "type": "table", + "bbox": [ + 0.089, + 0.28, + 0.462, + 0.466 + ], + "angle": 0, + "content": "
MethodFeatAccuracy @ Thresholds (%) ↑
0.25m,2°0.5m,5°5m,10°
RootSIFT [1]11K53.462.372.3
SuperPoint [11]7K68.185.994.8
D2-Net [12]14K67.086.497.4
R2D2 [39]10K70.785.396.9
ASLFeat [29]10K71.285.996.9
DISK [49]10K72.886.497.4
MTLDesc [50]7K74.386.996.9
Ours (TR)10K74.389.098.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.085, + 0.477, + 0.461, + 0.49 + ], + "angle": 0, + "content": "Table 4. Performance on Aachen Day-Night Localization datasets." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.502, + 0.47, + 0.564 + ], + "angle": 0, + "content": "our method is slower than the hand-crafted ORB [40]. However, our approach adds no additional overhead compared to existing neural network-based methods. We also report the trajectory visualization in the Supplementary Materials." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.573, + 0.262, + 0.588 + ], + "angle": 0, + "content": "4.5. Visual Localization" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.597, + 0.47, + 0.718 + ], + "angle": 0, + "content": "To further verify the effectiveness of our method confronted with complex tasks, we evaluate it on the task of visual localization, which aims at estimating the camera pose within a given scene using an image sequence. The task was proposed in [41] to evaluate the performance of local features in the context of long-term localization without the need for a specific localization pipeline. All methods are compared on the official evaluation server in a fair manner." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.722, + 0.47, + 0.799 + ], + "angle": 0, + "content": "Datasets. We resort to the Aachen Day-Night dataset [41] to demonstrate the effect on visual localization tasks, which contains images from the old inner city of Aachen, Germany. The key challenge in the dataset lies in matching images with extreme day-night changes for 98 queries." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.803, + 0.471, + 0.91 + ], + "angle": 0, + "content": "Evaluation protocols. The evaluation is conducted by using The Visual Localization Benchmark, which takes a pre-defined visual localization pipeline based on COMLAP [43]. The successful localized images are counted within three error tolerances \\((0.25\\mathrm{m}, 2^{\\circ}) / (0.5\\mathrm{m}, 5^{\\circ}) / (5\\mathrm{m}, 10^{\\circ})\\), representing the maximum position error in meters and degrees." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.283, + 0.895, + 0.419 + ], + "angle": 0, + "content": "Results. We compare our model with the typical joint detector and descriptor learning methods. Here, all methods are evaluated with the built-in matching strategy (Nearest Neighbors Search) for a fair comparison. As shown in Table 4, TPR with transformer blocks performs surprisingly well even under challenging illumination changes, about the estimated pose, which demonstrates the effectiveness of self-supervised learning with only positives on the natural dataset." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.434, + 0.627, + 0.45 + ], + "angle": 0, + "content": "4.6. Limitations" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.46, + 0.895, + 0.583 + ], + "angle": 0, + "content": "While our approach does not require any additional annotation, e.g., Structure-from-Motion models or optical flow, to produce the corresponding real image pairs. It still needs to receive pairs through homography adaption or image augmentation. Furthermore, despite getting better performance without using negative samples, TPR local features still fail in classical challenging cases such as textured areas, especially in the absence of matching priors." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.601, + 0.62, + 0.617 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.628, + 0.894, + 0.81 + ], + "angle": 0, + "content": "In this paper, we propose to learn the transformation-predictive representations for the joint detection and description of local features. The model is trained by using none of the negatives and avoids the collapsing solution, which greatly improves the training efficiency. Self-supervised generation learning and curriculum learning are designed to soften the hard positives into continuous soft labels, which can train the model with stronger augmentation. We solve the label noise from false positives and negatives and further improve the performance of local features in the self-supervised paradigm. Experiments show that TPR significantly outperforms state-of-the-art methods." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.829, + 0.66, + 0.846 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.895, + 0.901 + ], + "angle": 0, + "content": "This work was supported by the Science and Technology on Space Intelligent Control Laboratory under Grant HTKJ2021KL502019." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "11471" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.155 + ], + "angle": 0, + "content": "[1] Relja Arandjelović and Andrew Zisserman. Three things everyone should know to improve object retrieval. In CVPR, 2012. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.157, + 0.472, + 0.199 + ], + "angle": 0, + "content": "[2] Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk. Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors. In CVPR, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.2, + 0.47, + 0.227 + ], + "angle": 0, + "content": "[3] Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. Surf: Speeded up robust features. In ECCV, 2006. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.228, + 0.47, + 0.282 + ], + "angle": 0, + "content": "[4] Tiffany Tianhui Cai, Jonathan Frankle, David J Schwab, and Ari S Morcos. Are all negatives created equal in contrastive instance discrimination? arXiv preprint arXiv:2010.06682, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.284, + 0.468, + 0.338 + ], + "angle": 0, + "content": "[5] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. NeurIPS, 2020. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.34, + 0.468, + 0.381 + ], + "angle": 0, + "content": "[6] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In ICML, 2020. 1, 3, 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.382, + 0.468, + 0.422 + ], + "angle": 0, + "content": "[7] Ting Chen, Yizhou Sun, Yue Shi, and Liangjie Hong. On sampling strategies for neural network-based collaborative filtering. In ACM SIGKDD, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.424, + 0.468, + 0.465 + ], + "angle": 0, + "content": "[8] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv:2003.04297, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.467, + 0.468, + 0.494 + ], + "angle": 0, + "content": "[9] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In CVPR, 2021. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.495, + 0.468, + 0.535 + ], + "angle": 0, + "content": "[10] Peng Cui, Shaowei Liu, and Wenwu Zhu. General knowledge embedded image representation learning. IEEE TMM, 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.537, + 0.468, + 0.577 + ], + "angle": 0, + "content": "[11] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. In CVPR, 2018. 2, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.579, + 0.468, + 0.633 + ], + "angle": 0, + "content": "[12] Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, and Torsten Sattler. D2-net: A trainable cnn for joint detection and description of local features. In CVPR, 2019. 1, 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.635, + 0.468, + 0.676 + ], + "angle": 0, + "content": "[13] Tianyu Gao, Xingcheng Yao, and Danqi Chen. Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.677, + 0.468, + 0.717 + ], + "angle": 0, + "content": "[14] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In CVPR, 2012. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.719, + 0.468, + 0.788 + ], + "angle": 0, + "content": "[15] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. NeurIPS, 2020. 1, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.789, + 0.468, + 0.829 + ], + "angle": 0, + "content": "[16] Michael Gutmann and Aapo Hyvarinen. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In AISTATS, 2010. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.831, + 0.468, + 0.872 + ], + "angle": 0, + "content": "[17] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, 2020. 1, 3, 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.468, + 0.901 + ], + "angle": 0, + "content": "[18] Kun He, Yan Lu, and Stan Sclaroff. Local descriptors optimized for average precision. In CVPR, 2018. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[19] Jared Heinly, Johannes L Schonberger, Enrique Dunn, and Jan-Michael Frahm. Reconstructing the world* in six days*(as captured by the yahoo 100 million image dataset). In CVPR, 2015. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.15, + 0.892, + 0.205 + ], + "angle": 0, + "content": "[20] Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, and Eduard Trulls. Image matching across wide baselines: From paper to practice. IJCV, 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.207, + 0.892, + 0.248 + ], + "angle": 0, + "content": "[21] Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus. Hard negative mixing for contrastive learning. NeurIPS, 2020. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.251, + 0.892, + 0.291 + ], + "angle": 0, + "content": "[22] Axel Barroso Laguna and Krystian Mikolajczyk. Key. net: Keypoint detection by handcrafted and learned cnn filters revisited. IEEE TPAMI, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.295, + 0.892, + 0.335 + ], + "angle": 0, + "content": "[23] K. Li, LongguangWang, L. Liu, Q. Ran, K. Xu, and Y. Guo. Decoupling makes weakly supervised local feature better. In CVPR, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.338, + 0.892, + 0.392 + ], + "angle": 0, + "content": "[24] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.395, + 0.892, + 0.45 + ], + "angle": 0, + "content": "[25] Dongfang Liu, Yiming Cui, Liqi Yan, Christos Mousas, Bai-jian Yang, and Yingjie Chen. Densernet: Weakly supervised visual localization using multi-scale feature aggregation. In AAAI, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.453, + 0.892, + 0.507 + ], + "angle": 0, + "content": "[26] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In CVPR, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.511, + 0.892, + 0.537 + ], + "angle": 0, + "content": "[27] David G Lowe. Distinctive image features from scale-invariant keypoints. *IJCV*, 2004, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.54, + 0.892, + 0.594 + ], + "angle": 0, + "content": "[28] Zixin Luo, Tianwei Shen, Lei Zhou, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, and Long Quan. Contextdesc: Local descriptor augmentation with cross-modality context. In CVPR, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.598, + 0.892, + 0.652 + ], + "angle": 0, + "content": "[29] Zixin Luo, Lei Zhou, Xuyang Bai, Hongkai Chen, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, and Long Quan. Aslfeat: Learning local features of accurate shape and localization. In CVPR, 2020. 1, 2, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.655, + 0.892, + 0.696 + ], + "angle": 0, + "content": "[30] Iaroslav Melekhov, Zakaria Laskar, Xiaotian Li, Shuzhe Wang, and Juho Kannala. Digging into self-supervised learning of feature descriptors. In IEEE 3DV, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.699, + 0.892, + 0.726 + ], + "angle": 0, + "content": "[31] Krystian Mokolajczyk and Cordelia Schmid. A performance evaluation of local descriptors. IEEE TPAMI, 2005. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.729, + 0.892, + 0.77 + ], + "angle": 0, + "content": "[32] Anastasiia Mishchuk, Dmytro Mishkin, Filip Radenovic, and Jiri Matas. Working hard to know your neighbor's margins: Local descriptor learning loss. In NeurIPS, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.772, + 0.892, + 0.813 + ], + "angle": 0, + "content": "[33] Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics, 2015. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.816, + 0.892, + 0.857 + ], + "angle": 0, + "content": "[34] Hyeonwoo Noh, Andre Araujo, Jack Sim, Tobias Weyand, and Bohyung Han. Large-scale image retrieval with attentive deep local features. In ICCV, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.859, + 0.892, + 0.899 + ], + "angle": 0, + "content": "[35] Yuki Ono, Eduard Trulls, Pascal Fua, and Kwang Moo Yi. Lf-net: learning local features from images. In NeurIPS, 2018. 6" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "11472" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.093, + 0.468, + 0.133 + ], + "angle": 0, + "content": "[36] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv:1807.03748, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.134, + 0.468, + 0.175 + ], + "angle": 0, + "content": "[37] Luc Oth, Paul Furgale, Laurent Kneip, and Roland Siegwart. Rolling shutter camera calibration. In CVPR, pages 1360-1367, 2013. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.177, + 0.468, + 0.218 + ], + "angle": 0, + "content": "[38] James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman. Object retrieval with large vocabularies and fast spatial matching. In CVPR, 2007. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.219, + 0.468, + 0.272 + ], + "angle": 0, + "content": "[39] Jerome Revaud, Philippe Weinzaepfel, César De Souza, Noe Pion, Gabriela Csurka, Yohann Cabon, and Martin Humenberger. R2d2: Repeatable and reliable detector and descriptor. In NeurIPS, 2019. 1, 2, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.274, + 0.468, + 0.314 + ], + "angle": 0, + "content": "[40] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In ICCV, 2011. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.315, + 0.468, + 0.383 + ], + "angle": 0, + "content": "[41] Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenberg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, et al. Benchmarking 6dof outdoor visual localization in changing conditions. In CVPR, 2018. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.385, + 0.468, + 0.438 + ], + "angle": 0, + "content": "[42] Nikolay Savinov, Akihito Seki, Lubor Ladicky, Torsten Sattler, and Marc Pollefeys. Quad-networks: unsupervised learning to rank for interest point detection. In CVPR, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.441, + 0.468, + 0.467 + ], + "angle": 0, + "content": "[43] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In CVPR, 2016. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.469, + 0.468, + 0.509 + ], + "angle": 0, + "content": "[44] Johannes L Schonberger, Hans Hardmeier, Torsten Sattler, and Marc Pollefeys. Comparative evaluation of hand-crafted and learned local features. In CVPR, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.511, + 0.468, + 0.566 + ], + "angle": 0, + "content": "[45] Edgar Simo-Serra, Eduard Trulls, Luis Ferraz, Iasonas Kokkinos, Pascal Fua, and Francesc Moreno-Noguer. Discriminative learning of deep convolutional feature point descriptors. In ICCV, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.567, + 0.468, + 0.607 + ], + "angle": 0, + "content": "[46] Suwichaya Suwanwimolkul, Satoshi Komorita, and Kazuyuki Tasaka. Learning of low-level feature keypoints for accurate and robust detection. In WACV, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.609, + 0.468, + 0.648 + ], + "angle": 0, + "content": "[47] Yurun Tian, Bin Fan, and Fuchao Wu. L2-net: Deep learning of discriminative patch descriptor in euclidean space. In CVPR, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.65, + 0.468, + 0.691 + ], + "angle": 0, + "content": "[48] Yurun Tian, Xin Yu, Bin Fan, Fuchao Wu, Huub Heijnen, and Vassileios Balntas. Sosnet: Second order similarity regularization for local descriptor learning. In CVPR, 2019. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.692, + 0.468, + 0.732 + ], + "angle": 0, + "content": "[49] Michal Tyszkiiewicz, Pascal Fua, and Eduard Trulls. Disk: Learning local features with policy gradient. NeurIPS, 2020. 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.734, + 0.468, + 0.774 + ], + "angle": 0, + "content": "[50] Changwei Wang, Rongtao Xu, Yuyang Zhang, Shibiao Xu, Weiliang Meng, Bin Fan, and Xiaopeng Zhang. Mtldesc: Looking wider to describe better. AAAI, 2022. 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.776, + 0.468, + 0.816 + ], + "angle": 0, + "content": "[51] Tongzhou Wang and Phillip Isola. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In ICML, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.818, + 0.468, + 0.857 + ], + "angle": 0, + "content": "[52] Xiao Wang and Guo-Jun Qi. Contrastive learning with stronger augmentations. arXiv preprint arXiv:2104.07713, 2021. 2.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.859, + 0.468, + 0.899 + ], + "angle": 0, + "content": "[53] Zihao Wang, Xueyi Li, and Zhen Li. Local representation is not enough: Soft point-wise transformer for descriptor and detector of local features. In IJCAI, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.093, + 0.468, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.134 + ], + "angle": 0, + "content": "[54] Zihao Wang, Zhen Li, Xueyi Li, Wenjie Chen, and Xiangdong Liu. Graph-based contrastive learning for description and detection of local features. IEEE TNNLS, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.892, + 0.175 + ], + "angle": 0, + "content": "[55] Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. Unsupervised feature learning via non-parametric instance discrimination. In CVPR, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.179, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[56] Lanling Xu, Jianxun Lian, Wayne Xin Zhao, Ming Gong, Linjun Shou, Daxin Jiang, Xing Xie, and Ji-Rong Wen. Negative sampling for contrastive representation learning: A review. arXiv preprint arXiv:2206.00212, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.235, + 0.892, + 0.274 + ], + "angle": 0, + "content": "[57] Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, and Pascal Fua. Lift: Learned invariant feature transform. In ECCV, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.277, + 0.892, + 0.317 + ], + "angle": 0, + "content": "[58] Kwang Moo Yi, Yannick Verdie, Pascal Fua, and Vincent Lepetit. Learning to assign orientations to feature points. In CVPR, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.32, + 0.892, + 0.374 + ], + "angle": 0, + "content": "[59] Xiaoming Zhao, Xingming Wu, Jinyu Miao, Weihai Chen, Peter CY Chen, and Zhengguo Li. Alike: Accurate and lightweight keypoint detection and descriptor extraction. IEEE TMM, 2022. 6" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.374 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.956 + ], + "angle": 0, + "content": "11473" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Transformation-Predictive Representations for Detection and Description of Local Features/aa5b05da-b5ee-4eea-ae47-f2c476393ca4_origin.pdf b/2023/Learning Transformation-Predictive Representations for Detection and Description of Local Features/aa5b05da-b5ee-4eea-ae47-f2c476393ca4_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..fd0122c49ff5ffb1346bbecccead26465b5b382b --- /dev/null +++ b/2023/Learning Transformation-Predictive Representations for Detection and Description of Local Features/aa5b05da-b5ee-4eea-ae47-f2c476393ca4_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0c2ddc754194f5e5a02c8a3922aea53f817f7bad8fbcc65d1415739ce3957261 +size 3527328 diff --git a/2023/Learning Transformation-Predictive Representations for Detection and Description of Local Features/full.md b/2023/Learning Transformation-Predictive Representations for Detection and Description of Local Features/full.md new file mode 100644 index 0000000000000000000000000000000000000000..1590c0b329135dfb07b15e264ed7cf6584360d2b --- /dev/null +++ b/2023/Learning Transformation-Predictive Representations for Detection and Description of Local Features/full.md @@ -0,0 +1,341 @@ +# Learning Transformation-Predictive Representations for Detection and Description of Local Features + +Zihao Wang1 Chunxu Wu2 Yifei Yang2 Zhen Li2* + +$^{1}$ School of Intelligence Science and Technology, Peking University $^{2}$ School of Automation, Beijing Institute of Technology + +zhwang@stu.pku.edu.cn, {3120200960, 3120220892, zhenli}@bit.edu.cn + +# Abstract + +The task of key-points detection and description is to estimate the stable location and discriminative representation of local features, which is a fundamental task in visual applications. However, either the rough hard positive or negative labels generated from one-to-one correspondences among images may bring indistinguishable samples, like false positives or negatives, which acts as inconsistent supervision. Such resultant false samples mixed with hard samples prevent neural networks from learning descriptions for more accurate matching. To tackle this challenge, we propose to learn the transformation-predictive representations with self-supervised contrastive learning. We maximize the similarity between corresponding views of the same 3D point (landmark) by using none of the negative sample pairs and avoiding collapsing solutions. Furthermore, we adopt self-supervised generation learning and curriculum learning to soften the hard positive labels into soft continuous targets. The aggressively updated soft labels contribute to overcoming the training bottleneck (derived from the label noise of false positives) and facilitating the model training under a stronger transformation paradigm. Our self-supervised training pipeline greatly decreases the computation load and memory usage, and outperforms the sota on the standard image matching benchmarks by noticeable margins, demonstrating excellent generalization capability on multiple downstream tasks. + +# 1. Introduction + +Local visual descriptors are fundamental to various computer vision applications such as camera calibration [37], 3D reconstruction [19], visual simultaneous localization and mapping (VSLAM) [33], and image retrieval [38]. The descriptors indicate the representation vector of the patch around the key-points and can be used to generate dense + +correspondences between images. + +The discriptors are highly dependent on the effective representation, which has been always trained with the Siamese architecture and contrastive learning loss [12, 29, 39]. The core idea of contrastive learning is "learn to compare": given an anchor key-point, distinguish a similar (or positive) sample from a set of dissimilar (or negative) samples, in a projected embedding space. The induced representations present two key properties: 1) alignment of features from positive pairs, 2) and uniformity of representation on the hypersphere [51]. Negative samples are thus introduced to keep the uniformity property and avoid model collapse, i.e., preventing the convergence to one constant solution [13]. Therefore, various methods have been proposed to mine hard negatives [6, 21, 55]. However, these methods raise the computational load and memory resources usage heavily [17]. More Importantly, within the hard negatives, some samples are labeled as negatives, but actually have the identical semantics of the anchor (i.e., false negatives). These false negatives act as inconsistent supervision and prevent the learning-based models from achieving higher accuracy [4]. More concretely, the false negatives represent the instance located on the repetitive texture in the structural dataset, as shown in Figure 1. It is challenging to recognize such false negatives from true negatives [39]. + +The recent active self-supervised learning methods [5, 8, 9, 15] motivate us to rethink the effectiveness of negatives in descriptors learning. We propose to learn the transformation predictive representations (TPR) for visual descriptors only with the positives and avoids collapsing solutions. Furthermore, using none of negatives greatly improves the training efficiency by reducing the scale of the similarity matrix from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$ , and reduces the computation load and memory usage. + +To further improve the generalization performance of descriptors, hard positives (i.e., corresponding pairs with large-scale transformation) are encouraged as training data to expose novel patterns. However, recent experiments have shown that directly contrastive learning on stronger + +![](images/ed1e3527696e89899ff866bab291c0316b9685e52c570d3670a842ce3758c693.jpg) +Figure 1. Illustrative schematic of different negative samples. Easy negatives (green) are easy to distinguish from the query (blue) and not sufficient for good performance. Hard negatives (orange) are important for better performance and are still semantically dissimilar from the query. False negatives (red) are practically impossible to distinguish and semantically identical with the query, which is harmful to the performance. The right figure visualizes the embedding of different samples in latent space. + +![](images/2f9f21928dd55645dcbd8d5f038840ae3a668fde5e9c34c352c5b6c338209b5c.jpg) + +![](images/a47e823a8cf553dd18b7d58c66b3cf1c9753b48bc4db1d290435e216c7f01882.jpg) +Easy Negatives + +![](images/ee938beb3247ccf7f3d5756164076229fcdc12021a622c19ebb5b8be032dffae.jpg) + +![](images/92a05a4122b31d54882ba49b8af5a7b87163eaf80c869628037d788e981aa824.jpg) +Hard Negatives + +![](images/7c8cf1d614d6d8f01310b750816b1bedb96073cc6216fdf06e0dbfe6e4b4c242.jpg) + +![](images/eeff306b5559ac672b40e411a7d1c1c16f45c1695d7ef291db776cc03b482b52.jpg) +False Negatives + +![](images/928d0c64c22948be70fe1c89aaa93a48023bdc81238cffd154cdd5c6fff06f1f.jpg) + +![](images/cbcd1163ce6ee8e845ee450893f3031749243bcba8e34b8eab74ae30cac99b7c.jpg) + +![](images/7692a41aed318eddbfc771ff7d9204ad9a2d309c75a2ada28c0b27600925304b.jpg) + +![](images/4b14d85b65721f5503547fa8c027303c21d5a05083737bd9e9a4016405b14250.jpg) + +![](images/1ac49b50eea880bd85f83af8189ccc32e8d77d1a1b8c7f5cb435b81670dbc390.jpg) +t-SNE visualization + +transformed images can not learn representations effectively [52]. In addition, current contrastive learning methods label all positives with different transformation strength as coarse “1”, which prevent learning refined representation. We propose to learn transformation predictive representation with soft positive labels from $(0,1]$ instead of “1” to supervise the learning of local descriptors. Furthermore, we propose a self-supervised curriculum learning module to generate controllable stronger positives with gradually refined soft supervision as the network iterative training. + +Finally, our TPR with soft labels is trained on natural images in a fully self-supervised paradigm. Different from previous methods trained on datasets with SfM or camera pose information [20, 29, 39, 49], our training datasets are generally easy to collect and scale up since there is no extra annotation requirement to capture dense correspondences. Experiments show that our self-supervised method outperforms the state-of-the-art on standard image matching benchmarks by noticeable margins and shows excellent generalization capability on multiple downstream tasks (e.g., visual odometry, and localization). + +Our contributions to this work are as follows: i) we propose to learn transformation-predictive representations for joint local feature learning, using none of the negative sample pairs and avoiding collapsing solutions. ii) We adopt self-supervised generation learning and curriculum learning to soften the hard positives into continuous soft labels, which can alleviate the false positives and train the model with stronger transformation. iii) The overall pipeline is trained with the self-supervised paradigm, and the training data are computed from random affine transformation and augmentation on natural images. + +# 2. Related Works + +Our work focuses on the first step of the image matching pipeline, i.e., detection and description of key-points. This + +section gives a brief review of key-points detection and description learning together with contrastive representation learning. + +Key-points Learning. Different from early hand-crafted key-points, e.g., SIFT [27] and SURF [3], the recent effort has been put into learning key-points through the deep neural networks. Many existent works focus on single module optimization with deep learning, such as interest points detection [42], shape estimation [58] and descriptor representation [45, 47]. However, optimizing one single component may not directly enable the improvement of the entire pipeline [44, 57]. Recent works tend to build end-to-end joint learning frameworks for detecting and describing local features [12, 29, 53]. CNN-based methods simultaneously optimize both the detector and descriptor by sharing most parameters with the help of the encoder-decoder pipeline. Compared with the conventional methods only considering low-level features like corners, edges, or blobs in shallow layers, the encoder-decoder pipeline can make full use of deep CNNs for better representations [11, 25, 39]. To maximize the similarity of corresponding pairs, current cutting-edge methods usually take the Siamese structure to train the model with contrastive learning [54]. + +Contrastive Learning. Contrastive learning was originated from metric learning and has been widely adopted to supervise the description learning [16, 18, 36, 47]. The critical factor of contrastive learning is to attract the representation of corresponding local descriptions (positive pairs) closer and spread the representations of non-corresponding descriptions (negative pairs) apart. Negative samples are accordingly introduced to keep the uniformity property in order to avoid the representation collapse [13]. Due to expensive manual labeling and the lack of explicit negative signals in most cases, various negative sampling strate- + +gies were proposed to improve the computation efficiency and promote the training results [56]. However, the sampling of hard negatives highly depends on the large batch size [6, 7, 54] and memory bank [17], so as to increase the computational load and memory resources usage. Recently, some works have been conducted to optimize the contrastive loss without any negatives and avoid the collapse [5, 9, 15]. But all these works were proposed for the holistic representation of whole images and cannot be adapted to the dense prediction tasks. To address this issue, our method uses an exclusive predictor and stop-gradient operation to avoid collapsing and encourage encoding more transformation-aware representations without negatives, which is the first work ever training key-points with only positives. + +# 3. Method + +In this section, we elaborate on the proposed Transformation Predictive Representations (TPR) for learning detection and description of local features. Given a pair of images $(I_1, I_2)$ , and the correspondences set $\mathcal{C}$ between them, the key-points learning methods predict the dense 3D description map $D$ and detection heatmap $S \in [0,1]$ jointly. During the training, the standard Siamese networks are used to deal with the image pairs $(I_1, I_2)$ simultaneously and optimize the parameters with contrastive loss according to $\mathcal{C}$ . Our approach also follows this pipeline. However, different from the previous approaches using negatives and hard negative sampling for training, TPR is optimized without any negatives. In this way, our model can reduce the computation complexity and memory usage and improves the training efficiency. + +# 3.1. Preliminary + +The architecture in this work is built upon 1) D2-Net [12], and 2) Bootstrap Your Own Latent (BYOL) [15]. + +D2-Net [12] proposes a describe-and-detect strategy to jointly extract descriptions and detections of local features. Over the last feature maps $y \in \mathbb{R}^{H \times W \times C}$ , D2-Net applies channel-wise L2-normalization to obtain the dense feature descriptors, while the feature detections are derived from 1) the local score and 2) the channel-wise score. Specifically, for each location $(i,j)$ in $y^k (k = 1,\dots ,C)$ , the final detection score is computed as: + +$$ +s _ {i j} = \max _ {t} \left[ \frac {\exp (y _ {i j} ^ {k})}{\sum_ {(i ^ {\prime} , j ^ {\prime}) \in \mathcal {N} (i , j)} \exp (y _ {i ^ {\prime} j ^ {\prime}} ^ {k})} \cdot \frac {y _ {i j} ^ {k}}{\max _ {t} y _ {i j} ^ {t}} \right], \tag {1} +$$ + +where $\mathcal{N}(i,j)$ is the neighboring pixels around $(i,j)$ , e.g., 9 neighbours defined by a $3 \times 3$ kernel. D2-Net adopts the triplet margin ranking loss to optimize the descriptions, and + +is formulated as: + +$$ +\mathcal {L} _ {\text {t r i p l e t}} = \sum_ {c \in \mathcal {C}} \max \left(0, M + d _ {p} ^ {c} - d _ {n} ^ {c}\right), \tag {2} +$$ + +where $M$ is the margin, $\mathcal{C}$ is the set of correspondences, $d_p^c$ and $d_n^c$ represent the distance of positive and selected negative pairs, respectively. Finally, the detection score is used as a weighting term in description loss function. + +BYOL [15] is a self-supervised image representation learning approach. BYOL uses two neural networks, referred to as the online and target networks, both of which interact and learn from each other. The online network is trained to predict the target network's representation of the same image from an augmented view. The weights of the target network are updated with a slow-moving average of the online network. Different from other contrastive learning methods, BYOL can be trained without any negatives. It hypothesizes that the combination of 1) the addition of a predictor to the online network, and 2) the use of a slow-moving average of the online parameters as the target network encourages encoding more information within the online projection and avoids collapsed solutions. + +# 3.2. Transformation-Predictive Representations + +The Siamese-like structure (i.e., the online and target networks) is used in TPR to learn the representations. From a given representation of the transformed image, referred to as target, the TPR trains a new potentially enhanced representation of the original image, referred to as online, by predicting the target representation. The online network is defined by a set of weights $\theta_{o}$ and comprises three stages: an online encoder $f_{o}$ , a projector $g_{o}$ , and a predictor $q$ . The target network also has the encoder $f_{t}$ and projector $g_{t}$ in the same structure but with a different set of weights $\theta_{t}$ . The overall pipeline is described as in Figure 2, + +Given the input image $I \in \mathbb{R}^{H \times W \times 3}$ , a random augmentation $t$ is performed to produce the cropped view $V \triangleq t(I) \in \mathbb{R}^{h \times w \times 3}$ . And then, the limited cascaded affine transformation and augmentation $T$ is carried out on the $V$ to produce the transformed view $V' \triangleq T(V) \in \mathbb{R}^{h \times w \times 3}$ . + +From the augmented view $V$ , the online encoder outputs a representation $z \triangleq f_{o}(V) \in \mathbb{R}^{h \times w \times c}$ , which is adopted as the objective encouraged to be transformation-predictive. Rather than predicting representations produced by the online encoder, the target representation $z' \triangleq f_{t}(V') \in \mathbb{R}^{h \times w \times c}$ is computed by using the target encoder $f_{t}$ , whose parameters $\theta_{t}$ are an exponential moving average (EMA) of the online encoder parameters $\theta_{o}$ . Without gradient descent, the update algorithm for $\theta_{t}$ is formulated as: + +$$ +\theta_ {t} \leftarrow \tau \theta_ {t} + (1 - \tau) \theta_ {o}, \tag {3} +$$ + +where $\tau \in [0,1)$ is the EMA coefficient. + +![](images/d970519677a182c89591c0bcc0a5a4a7771db4139930b45a2bcfcf8b51388c45.jpg) +Figure 2. Illustration of our proposed transformation-predictive representations learning. Top: traditional contrastive learning descriptors based on both positives and negatives and supervised under SfM models. Bottom: our proposed transformation predictive representations learning with only positives and self-supervised loss. In the TPR structure, representations from the online encoder are used in the joint learning of local features task. The target encoder and projection head are defined as an exponential moving average of their online counterparts and are not updated via gradient descent. + +Next, the representation $z$ and $z'$ are fed into the online and target detector to extract the detection score $s$ and $s'$ with (1). We further use the online and target projection heads $g_{o}$ and $g_{t}$ to map the online and target presentations to a latent embedding space[6], and also apply an additional prediction head $q$ to the online projections to predict the target projections[15]: + +$$ +\hat {y} \triangleq q (g _ {o} (z)), +$$ + +$$ +y ^ {\prime} \triangleq g _ {t} \left(z ^ {\prime}\right). \tag {4} +$$ + +The target projection head parameters are also given through an EMA of the online projection head parameters by using the same update algorithm as the online and target encoders. Note that the predictor is only applied to online networks so as to make the Siamese architecture asymmetric between the online and target pipeline. + +Next, we compute the transformation predictive loss for TPR by summing over cosine similarities between the predicted and target representations. Different from a holistic representation $\mathbb{R}^{1\times 1\times d}$ of the view $V$ and $V^{\prime}$ [15], our encoder $f_{o}$ outputs the dense feature maps preserving abundant spatial information with the shape of $h\times w\times c$ . The prediction loss is computed on the corresponding location of the dense feature map, i.e., the positive pairs: + +$$ +\begin{array}{l} \mathcal {L} _ {\text {p r e d}} ^ {c} = \left(1 - \left\langle y _ {c}, y _ {c} ^ {\prime} \right\rangle\right) \tag {5} \\ = \left(1 - \left\langle q \left(g _ {o} (z)\right) _ {c}, g _ {t} \left(z ^ {\prime}\right) _ {c} \right\rangle\right), \\ \end{array} +$$ + +where $\langle \cdot \rangle$ denotes the cosine similarity, $\hat{y}_c$ and $y_c^\prime$ is the local representation with the $c$ -th correspondence. + +Finally, the detection score is used as a weighting term to formalize the final hard prediction loss function: + +$$ +\mathcal {L} _ {\text {h a r d}} = \frac {1}{| \mathcal {C} |} \sum_ {c \in \mathcal {C}} \frac {s _ {c} s _ {c} ^ {\prime}}{\sum_ {n \in \mathcal {C}} s _ {n} s _ {n} ^ {\prime}} \mathcal {L} _ {\text {p r e d}} ^ {c}. \tag {6} +$$ + +The transformation-predictive representations follow the predictive nature of the objective and use the exponential moving average target network similar to [15]. Compared with the related works on contrastive representation learning, TPR only uses the positive pairs without negative samples. As a result, there is no necessary need on large buffer to emulate large batch sizes of negatives [6, 17] and complex hard negative sampling strategies. Furthermore, training without negatives can help prevent the inconsistent optimization from the false negatives. Finally, although we do not use the explicit negative samples to prevent collapse while minimizing $\mathcal{L}_{\mathrm{hard}}$ , TPR still can avoid converging to a minimum of this loss with respect to $(\theta_o,\theta_t)$ (e.g., a collapsed constant representation). + +# 3.3. Learning with Soft Labels + +The TPR method uses carefully designed transformations $T$ to generate the view $V^{\prime}$ from $V$ . Therefore, the + +views are not deformed aggressively so that they can still be viewed as the same instance, i.e., positives. Stronger deformation could expose the novel patterns of representations in discriminative contrastive learning. However, directly adopting stronger transformations (e.g., with larger rotation angles, more aggressive color jitting, and cutout) may seriously damage the intrinsic semantics of the original image, which will fail to further improve or even degrade the performance [52]. In this paper, we categorize such positive pairs generated from over-strong deformation into the false positives. The false positives are bound to make the training process degraded in performance because the network cannot effectively distinguish the true positives from the false ones when trained with the existing pipeline [39]. + +To tackle the dilemma, where the strong or weak transformations should not have been used to produce the positives, we propose the optimal soft labels to stabilize the training process instead of forcing the labeling coarse hard "1" for every positive pair. Accordingly, the prediction loss in (5) is modified as follows. + +$$ +\mathcal {L} _ {\text {s o f t}} = \frac {1}{| \mathcal {C} |} \sum_ {c \in \mathcal {C}} \frac {s _ {c} s _ {c} ^ {\prime}}{\sum_ {n \in \mathcal {C}} s _ {n} s _ {n} ^ {\prime}} \left(l _ {c} + 1 - \mathcal {L} _ {\text {p r e d}} ^ {c}\right), \tag {7} +$$ + +in which $l_{c}$ represents the computed soft label for the positive pair with $c$ -th correspondence. Therefore, we adopt self-supervised generation learning and curriculum learning to "adjust" the inconsistent and incorrect supervision. + +Self-supervised generation learning. We train the network for the first generation following the hard positive label. And then, the network can be assumed to successfully capture most feature distribution of the training data. We propose to utilize the previous generation's similarities to be served as the soft supervision for training the network in a new generation. The generated soft supervisions are gradually refined and sharpened as the network generation progresses. In this way, the system can fully explore the potential of the difficult positives with stronger transformation, and mitigate their label noises with the refined soft confidence. To stabilize the training process, the soft label of the positive pair with the correspondence $c$ in the generation $\omega = 2,\dots ,\Omega$ follows the exponential decay of the transformation strength, which is computed as: + +$$ +l _ {c} = e ^ {- \left(1 - \left\langle y _ {c} ^ {\omega - 1}, y _ {c} ^ {\prime \omega - 1} \right\rangle\right) / \lambda}, \tag {8} +$$ + +where $y^{\omega -1}$ and $y^{\prime \omega -1}$ are generated from the encoder with the last generation parameters $\theta_{\omega}$ , and $\Omega$ is the total number of generations, and $\lambda$ is the exponential decay constant. + +Curriculum setting for positives generation. The core of curriculum learning is to gradually improve the transformation strength. In particular, we introduce the affine adaptation and data augmentation to generate the transformation + +matrix, which also determines the transformation strength $\alpha$ . We normalize $\alpha$ within $[0,1]$ according to the maximum limitation of adaption and augmentation parameters, e.g., rotation and translation values. With the curriculum setting, $\alpha$ is randomly sampled from a range with gradually relaxed restrictions of the transformation strength. The soft label of the current batch follows the exponential decay of the transformation strength, which is computed as: + +$$ +l _ {c} = e ^ {- \alpha \left(1 - \left\langle y _ {c} ^ {\omega - 1}, y _ {c} ^ {\prime \omega - 1} \right\rangle\right) / \lambda}. \tag {9} +$$ + +To avoid the over-fitting to the soft labels, we also set a maximum operation on the predicted soft loss as $\max(0, l_c + 1 - \mathcal{L}_{pred}^c)$ . This setting will help the model avoid divergence through the false positives during the early training stage, and encourage the model to explore more robust representations in the later training stage. + +In the later stage of training, with the increasing difficulty of positive samples, the proportion of false positive samples, which have different semantics with the online query, will increase. Such false positives will be harmful to the generalization performance of the model [52]. To overcome this problem, we introduce a hyperparameter patience $p$ of early stop. When the soft prediction loss no longer decreases, the value of $p$ will decrease. The training will stop until $p = 0$ . + +# 3.4. Implementation Details + +In this part, the architectural details of TPR are introduced. Note that at the end of the training, everything but the online encoder $f_{o}$ is discarded. Thus, the online projection head $g_{o}$ , the predictor head $q$ , and the target branch will not introduce the extra computational costs in deployment. + +Encoder, $f_{o}$ , maps the input view $V$ into the dense embeddings $z$ . The encoder can be instanced into the CNN version based on VGG (similar as D2-Net [12]) and DCN (similar as ASLFeat [29]), and Transformer version based on tiny Swin Transformer V1 [26] with only the first two stages. + +Projection head, $g_{o}$ , maps the feature embedding into a 128-dim $l_{2}$ normalization feature vector for the computation of the prediction loss $\mathcal{L}_{\mathrm{pred}}$ . $g_{o}$ is implemented as a 3-layer MLP (hidden layer 256-dim), with BN and ReLU on every fully-connected layer except the last output layer. + +Predictor, $q$ , is only applied to the online networks. The predictor is an MLP with two layers. The dimension of the hidden and output layer is 64 and 128, respectively. + +Detection and description head, are adopted on the encoder to simultaneously output the dense description and the detection score map following the D2-Net [12]. Only the locations with higher confidence than 0.7 are selected as the key points. Moreover, a non-maximum suppression (NMS) operation (with a kernel of 5) is applied to remove the key points that are spatially too close. + +# 4. Experiments + +In this section, we first present the details of training our TPR. And then we show the performance across several downstream scenarios, including image matching, visual odometry, and visual localization. It's worthy to NOTE that our methods are not trained on all benchmark datasets, which is essential to show generalization performance. + +# 4.1. Experimental Setup + +Our TPR method is trained on images from Microsoft COCO dataset[24] and Image Matching Challenge[20], by dropping all the irrelevant annotations. We compute the correspondences between the transformed natural images with the random affine adaption and data augmentation by means of the self-supervised paradigm. For the affine adaption, we uniformly sample the in-plane rotation, shear, translation and scale parameters from $[-45^{\circ}, +45^{\circ}]$ , $[-40^{\circ}, +40^{\circ}]$ , $[-0.05, +0.05]$ , [0.7,1.4], respectively. For the color jitter, we also uniformly sample the brightness, contrast, saturation, and hue parameters from [0.6,1.4], [0.6,1.4], [0.6,1.4], $[-0.2, +0.2]$ , respectively. The additional random data augmentation is employed on the transformed views $V$ and $V'$ including the grayscale conversion and Gaussian blur. + +We train models using SGD and AdamW optimizer for CNN and Transformer-based models, respectively. During training, we use a mini-batch size of 16 and an initial learning rate of 0.00017 with exponential decay of 0.9. The hyper-parameter of EMA update $\tau$ , exponential decay constant $\lambda$ , and early stop patience $p$ is set to 0.99, 10, and 1, which are determined with the grid search to reach the optimum. Our method were trained for 50 epochs, which took 30 hours for training with two NVIDIA-A100 GPUs. We randomly crop the $224 \times 224$ views for training. + +Moreover, we evaluate the local features learned by TPR across several downstream scenarios, including the image matching on HPatches, visual odometry on KITTI, and visual localization on Aachen Day-Night dataset. + +# 4.2. Image Matching + +We first evaluate the performance of our method in the image matching tasks, i.e., the most extensive key-points applications. + +Datasets. The experiments are taken on HPatches [2] datasets, which include 116 sequences of 6 images with known homography. The datasets are split into two categories according to the illumination and viewpoint changes. The first image in one sequence is taken as reference, and the subsequent images are used to form the pairs with increasing difficulty. By following the previous methods[39], eight high-resolution sequences are excluded. + +
MethodMMA@3AUC@2AUC@5
SIFT [27]50.139.4949.57
HardNet [32]62.142.6156.85
LF-Net [35]53.238.7448.69
SuperPoint [11]65.744.0859.04
DELF [34]50.744.7349.70
ContextDesc [28]63.247.2358.25
Key.Net [22]72.140.8756.04
R2D2 [39]72.143.3564.17
DISK [49]77.252.3369.80
ALIKE [59]70.551.6569.04
SSL+CAPS [30]69.048.7262.19
LLF [46]74.052.1466.81
MTLDesc [50]78.755.0271.42
PoSFeat [23]75.3450.1669.23
D2-Net [12] (orig.)40.319.4937.78
D2-Net [12] (our impl.)44.522.3543.17
Ours(VGG)49.6 ↑ 9.324.46 ↑ 4.9747.69 ↑ 9.91
ASLFeat [29] (orig.)72.250.1066.93
ASLFeat [29] (our impl.)74.451.8369.24
Ours(DCN)75.5 ↑ 3.252.33 ↑ 2.2370.15 ↑ 3.22
Ours(TR)79.857.1873.00
+ +Table 1. Quantitative results (§4.2) on HPatches. + +Evaluation protocols. For a fair comparison, we match the features extracted by each method using the nearest neighbor matcher, accepting only mutual nearest neighbors. A correct match is accordingly considered if its estimated reprojection error is below a given matching threshold. We vary the threshold from 1 pixel to 10 pixels and record the mean matching accuracy (MMA) [31] over all pairs, which represents the ratio of correct matches and possible matches. And then the area is computed under curve (AUC) at 2px and 5px based on the MMA. We report the average scores for overall image pairs. + +Comparisons with other methods. We compare our model with the previous state-of-the-art methods on the HPatches dataset. Unless otherwise specified, we present the results either reported on original papers or derived from authors' public implementations with default parameters. Table. 1 lists different methods' results on HPatches in terms of the MMA area under the overall curve (AUC) up to 2px and 5px. Especially in the low-threshold area, Ours (TR) shows considerable improvements over previous sota methods. For a fair comparison, we show both the performance from the original paper (orig.) and our implementation (our impl.). It is observed that TPR gains a $+6.69\%$ (44.71% v.s. 38.02%) and $+2.45\%$ (79.81% v.s. 77.36%) MMA promotion than the leading DISK [49] at 1px and 3px threshold in 128-dimension, respectively. To prove the effectiveness of our training pipeline, we also con + +
NegSample+SSGL+CLMMA@3Mem/GB
w/Random63.1112
Hardest73.4140
Bank78.2↑15.1158
Mixing77.9147
w/oRandom70.179
Random✓77.4↑7.279
Random✓✓79.8↑9.679
+ +Table 2. Ablation experiments of the proposed TPR, where Neg, Sample, MMA@3, SSGL, CL and Mem represent whether training w/ Negative samples, Sampling strategy, self-supervised generation learning, curriculum learning, mean matching accuracy at 3px threshold, and GPU Memory usage (GB), respectively. + +duct experiments based on the D2-Net [12] backbone i.e., Ours (VGG) and ASLFeat [29] backbone, i.e., Ours (DCN). Under the same networks, our retrained model gains $+9.3\%$ and $+3.2\%$ improvements on MMA@3, respectively. The t-SNE visualization of descriptions from D2-Net (orig.) and Ours (VGG) is shown in Figure. 3. More importantly, our method is trained in the self-supervised paradigm, requiring no extra annotations, and is not finetuned on the downstream datasets. + +# 4.3. Ablation Study + +We further study the efficacy of our core ideas and essential model designs over HPatches. We train each model from scratch for 50 epochs to perform extensive ablation experiments while keeping other hyper-parameters unchanged. + +Ablation on different negative sampling strategies. We adopt the standard Siamese networks with transformer blocks as the "baseline" model. The baseline model is first trained with both positive and negative pairs, according to the triplet loss in (2). We also evaluate different negative sampling strategies including random negative sampling (Random) [10], in-batch hard negative mining (Hardest) [6], hard negative mining with Memory Bank (Bank) [17], and hard negative mixing (Mixing) [21]. And then we train the model using our TPR method with only positives and evaluate the efficacy of the curriculum learning and self-supervised generation learning. As demonstrated in Table 2, our TPR successfully gets convergent with only positives and avoids the collapsing solution, which proves the effectiveness of our designed components in the Siamese networks. Furthermore, the soft labels effectively alleviate the inconsistent optimization from pseudo positives, especially on the training datasets with stronger transformation and augmentation. + +Ablation on key components. We also conduct the ablation experiments to evaluate the effectiveness of our core + +![](images/7f5167c3299c6824f39640a7780dcbc1ea0e4e290582f64eda429a148d2aab3b.jpg) +Figure 3. t-SNE visualization of description from different training methods. Left: D2-Net [12] (orig.), Right: Ours(VGG). + +![](images/e692f48a8f8e85f0b81e574e07f3246b25d958e674e0a07f9cdf9452caa484cb.jpg) + +components including training w/o negative samples, self-supervised generation learning (SSGL), and curriculum learning module (CL). The results are also shown in Table 2, which proves the self-supervised curriculum learning with soft labels will improve the contrastive learning performance with only positives. + +# 4.4. Visual Odometry + +Visual Odometry (VO) is the task to estimate a robot's position and orientation from visual measurements. The goal of the system is to estimate the 6-DoF pose of the camera at every frame. In this experiment, we evaluate the visual odometry performance with the learned key-points. + +Datasets. We adopt the KITTI Odometry Datasets [14] to evaluate the downstream localization performance of our method. We only use the grayscale monocular images with a high resolution of $376 \times 1226$ as input. To output multiscale key-points, a three-layer image pyramid is constructed with the scale factor of 1.2. It is worth noting that in KITTI, there contains many moving objects and changing camera exposure time, which is challenging for accurate and robust key-points matching. + +Evaluation protocols. We take the ORB-SLAM [33] as the benchmark pipeline. We further disable both the local mapping and loop closure thread so as to keep the front-end visual odometry only. To evaluate the key-points fairly, all components in the pipeline are the same except the front-end used key-points. KITTI provides the ground-truth 6-DoF camera poses for sequences 00-10. Since the keypoints are adopted in the monocular VO pipeline, we align their poses with ground-truth to recover the scale of 6-DoF poses. Besides, we use the standard evaluation method, i.e., translational root-mean-square error (RMSE) drift $(\mathrm{/m})$ provided along with KITTI dataset. To evaluate the efficiency of the learning-based key-points, we also report the mean running frames per second for the methods. + +Results. We report the results of different key-points on different KITTI sequences in Table 3. TPR outperforms the sota learning methods DISK [49] on all sequences even under complex traffic conditions. As for the inference time, + +
MethodFPSRMSE/m ↓
00010204050607080910Avg.
ORB [40]20.659.46610.3572.6819.26238.6083.4672.7266.06119.2163.52140.53
SuperPoint [11]6.5162.78123.3413.521.066.362.0512.158.668.205.1034.32
D2-Net [12]8.810.44183.04105.332.2914.582.2510.7224.2729.629.6139.22
R2D2 [39]7.849.62515.9660.143.90123.0562.4453.8462.5473.3043.32104.81
SOSNet [48]6.3171.67309.8310.360.4714.684.0715.3510.753.247.6754.81
DISK [49]6.532.77149.9818.670.455.974.3812.8832.854.334.8126.71
Ours (TR)6.27.07164.399.720.233.462.129.997.423.103.7221.12
+ +Table 3. Visual odometry localization performance based on different key-points in KITTI datasets. + +
MethodFeatAccuracy @ Thresholds (%) ↑
0.25m,2°0.5m,5°5m,10°
RootSIFT [1]11K53.462.372.3
SuperPoint [11]7K68.185.994.8
D2-Net [12]14K67.086.497.4
R2D2 [39]10K70.785.396.9
ASLFeat [29]10K71.285.996.9
DISK [49]10K72.886.497.4
MTLDesc [50]7K74.386.996.9
Ours (TR)10K74.389.098.4
+ +Table 4. Performance on Aachen Day-Night Localization datasets. + +our method is slower than the hand-crafted ORB [40]. However, our approach adds no additional overhead compared to existing neural network-based methods. We also report the trajectory visualization in the Supplementary Materials. + +# 4.5. Visual Localization + +To further verify the effectiveness of our method confronted with complex tasks, we evaluate it on the task of visual localization, which aims at estimating the camera pose within a given scene using an image sequence. The task was proposed in [41] to evaluate the performance of local features in the context of long-term localization without the need for a specific localization pipeline. All methods are compared on the official evaluation server in a fair manner. + +Datasets. We resort to the Aachen Day-Night dataset [41] to demonstrate the effect on visual localization tasks, which contains images from the old inner city of Aachen, Germany. The key challenge in the dataset lies in matching images with extreme day-night changes for 98 queries. + +Evaluation protocols. The evaluation is conducted by using The Visual Localization Benchmark, which takes a pre-defined visual localization pipeline based on COMLAP [43]. The successful localized images are counted within three error tolerances $(0.25\mathrm{m}, 2^{\circ}) / (0.5\mathrm{m}, 5^{\circ}) / (5\mathrm{m}, 10^{\circ})$ , representing the maximum position error in meters and degrees. + +Results. We compare our model with the typical joint detector and descriptor learning methods. Here, all methods are evaluated with the built-in matching strategy (Nearest Neighbors Search) for a fair comparison. As shown in Table 4, TPR with transformer blocks performs surprisingly well even under challenging illumination changes, about the estimated pose, which demonstrates the effectiveness of self-supervised learning with only positives on the natural dataset. + +# 4.6. Limitations + +While our approach does not require any additional annotation, e.g., Structure-from-Motion models or optical flow, to produce the corresponding real image pairs. It still needs to receive pairs through homography adaption or image augmentation. Furthermore, despite getting better performance without using negative samples, TPR local features still fail in classical challenging cases such as textured areas, especially in the absence of matching priors. + +# 5. Conclusion + +In this paper, we propose to learn the transformation-predictive representations for the joint detection and description of local features. The model is trained by using none of the negatives and avoids the collapsing solution, which greatly improves the training efficiency. Self-supervised generation learning and curriculum learning are designed to soften the hard positives into continuous soft labels, which can train the model with stronger augmentation. We solve the label noise from false positives and negatives and further improve the performance of local features in the self-supervised paradigm. Experiments show that TPR significantly outperforms state-of-the-art methods. + +# Acknowledgments + +This work was supported by the Science and Technology on Space Intelligent Control Laboratory under Grant HTKJ2021KL502019. + +# References + +[1] Relja Arandjelović and Andrew Zisserman. Three things everyone should know to improve object retrieval. In CVPR, 2012. 8 +[2] Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk. Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors. In CVPR, 2017. 6 +[3] Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. Surf: Speeded up robust features. In ECCV, 2006. 2 +[4] Tiffany Tianhui Cai, Jonathan Frankle, David J Schwab, and Ari S Morcos. Are all negatives created equal in contrastive instance discrimination? arXiv preprint arXiv:2010.06682, 2020. 1 +[5] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. NeurIPS, 2020. 1, 3 +[6] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In ICML, 2020. 1, 3, 4, 7 +[7] Ting Chen, Yizhou Sun, Yue Shi, and Liangjie Hong. On sampling strategies for neural network-based collaborative filtering. In ACM SIGKDD, 2017. 3 +[8] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv:2003.04297, 2020. 1 +[9] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In CVPR, 2021. 1, 3 +[10] Peng Cui, Shaowei Liu, and Wenwu Zhu. General knowledge embedded image representation learning. IEEE TMM, 2017. 7 +[11] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. In CVPR, 2018. 2, 6, 8 +[12] Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, and Torsten Sattler. D2-net: A trainable cnn for joint detection and description of local features. In CVPR, 2019. 1, 2, 3, 5, 6, 7, 8 +[13] Tianyu Gao, Xingcheng Yao, and Danqi Chen. Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821, 2021. 1, 2 +[14] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In CVPR, 2012. 7 +[15] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. NeurIPS, 2020. 1, 3, 4 +[16] Michael Gutmann and Aapo Hyvarinen. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In AISTATS, 2010. 2 +[17] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, 2020. 1, 3, 4, 7 +[18] Kun He, Yan Lu, and Stan Sclaroff. Local descriptors optimized for average precision. In CVPR, 2018. 2 + +[19] Jared Heinly, Johannes L Schonberger, Enrique Dunn, and Jan-Michael Frahm. Reconstructing the world* in six days*(as captured by the yahoo 100 million image dataset). In CVPR, 2015. 1 +[20] Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, and Eduard Trulls. Image matching across wide baselines: From paper to practice. IJCV, 2021. 2, 6 +[21] Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus. Hard negative mixing for contrastive learning. NeurIPS, 2020. 1, 7 +[22] Axel Barroso Laguna and Krystian Mikolajczyk. Key. net: Keypoint detection by handcrafted and learned cnn filters revisited. IEEE TPAMI, 2022. 6 +[23] K. Li, LongguangWang, L. Liu, Q. Ran, K. Xu, and Y. Guo. Decoupling makes weakly supervised local feature better. In CVPR, 2022. 6 +[24] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 6 +[25] Dongfang Liu, Yiming Cui, Liqi Yan, Christos Mousas, Bai-jian Yang, and Yingjie Chen. Densernet: Weakly supervised visual localization using multi-scale feature aggregation. In AAAI, 2021. 2 +[26] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In CVPR, 2021. 5 +[27] David G Lowe. Distinctive image features from scale-invariant keypoints. *IJCV*, 2004, 2, 6 +[28] Zixin Luo, Tianwei Shen, Lei Zhou, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, and Long Quan. Contextdesc: Local descriptor augmentation with cross-modality context. In CVPR, 2019. 6 +[29] Zixin Luo, Lei Zhou, Xuyang Bai, Hongkai Chen, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, and Long Quan. Aslfeat: Learning local features of accurate shape and localization. In CVPR, 2020. 1, 2, 5, 6, 7, 8 +[30] Iaroslav Melekhov, Zakaria Laskar, Xiaotian Li, Shuzhe Wang, and Juho Kannala. Digging into self-supervised learning of feature descriptors. In IEEE 3DV, 2021. 6 +[31] Krystian Mokolajczyk and Cordelia Schmid. A performance evaluation of local descriptors. IEEE TPAMI, 2005. 6 +[32] Anastasiia Mishchuk, Dmytro Mishkin, Filip Radenovic, and Jiri Matas. Working hard to know your neighbor's margins: Local descriptor learning loss. In NeurIPS, 2017. 6 +[33] Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics, 2015. 1, 7 +[34] Hyeonwoo Noh, Andre Araujo, Jack Sim, Tobias Weyand, and Bohyung Han. Large-scale image retrieval with attentive deep local features. In ICCV, 2017. 6 +[35] Yuki Ono, Eduard Trulls, Pascal Fua, and Kwang Moo Yi. Lf-net: learning local features from images. In NeurIPS, 2018. 6 + +[36] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv:1807.03748, 2018. 2 +[37] Luc Oth, Paul Furgale, Laurent Kneip, and Roland Siegwart. Rolling shutter camera calibration. In CVPR, pages 1360-1367, 2013. 1 +[38] James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman. Object retrieval with large vocabularies and fast spatial matching. In CVPR, 2007. 1 +[39] Jerome Revaud, Philippe Weinzaepfel, César De Souza, Noe Pion, Gabriela Csurka, Yohann Cabon, and Martin Humenberger. R2d2: Repeatable and reliable detector and descriptor. In NeurIPS, 2019. 1, 2, 5, 6, 8 +[40] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In ICCV, 2011. 8 +[41] Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenberg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, et al. Benchmarking 6dof outdoor visual localization in changing conditions. In CVPR, 2018. 8 +[42] Nikolay Savinov, Akihito Seki, Lubor Ladicky, Torsten Sattler, and Marc Pollefeys. Quad-networks: unsupervised learning to rank for interest point detection. In CVPR, 2017. 2 +[43] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In CVPR, 2016. 8 +[44] Johannes L Schonberger, Hans Hardmeier, Torsten Sattler, and Marc Pollefeys. Comparative evaluation of hand-crafted and learned local features. In CVPR, 2017. 2 +[45] Edgar Simo-Serra, Eduard Trulls, Luis Ferraz, Iasonas Kokkinos, Pascal Fua, and Francesc Moreno-Noguer. Discriminative learning of deep convolutional feature point descriptors. In ICCV, 2015. 2 +[46] Suwichaya Suwanwimolkul, Satoshi Komorita, and Kazuyuki Tasaka. Learning of low-level feature keypoints for accurate and robust detection. In WACV, 2021. 6 +[47] Yurun Tian, Bin Fan, and Fuchao Wu. L2-net: Deep learning of discriminative patch descriptor in euclidean space. In CVPR, 2017. 2 +[48] Yurun Tian, Xin Yu, Bin Fan, Fuchao Wu, Huub Heijnen, and Vassileios Balntas. Sosnet: Second order similarity regularization for local descriptor learning. In CVPR, 2019. 8 +[49] Michal Tyszkiiewicz, Pascal Fua, and Eduard Trulls. Disk: Learning local features with policy gradient. NeurIPS, 2020. 2, 6, 7, 8 +[50] Changwei Wang, Rongtao Xu, Yuyang Zhang, Shibiao Xu, Weiliang Meng, Bin Fan, and Xiaopeng Zhang. Mtldesc: Looking wider to describe better. AAAI, 2022. 6, 8 +[51] Tongzhou Wang and Phillip Isola. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In ICML, 2020. 1 +[52] Xiao Wang and Guo-Jun Qi. Contrastive learning with stronger augmentations. arXiv preprint arXiv:2104.07713, 2021. 2.5 +[53] Zihao Wang, Xueyi Li, and Zhen Li. Local representation is not enough: Soft point-wise transformer for descriptor and detector of local features. In IJCAI, 2021. 2 + +[54] Zihao Wang, Zhen Li, Xueyi Li, Wenjie Chen, and Xiangdong Liu. Graph-based contrastive learning for description and detection of local features. IEEE TNNLS, 2022. 2, 3 +[55] Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. Unsupervised feature learning via non-parametric instance discrimination. In CVPR, 2018. 1 +[56] Lanling Xu, Jianxun Lian, Wayne Xin Zhao, Ming Gong, Linjun Shou, Daxin Jiang, Xing Xie, and Ji-Rong Wen. Negative sampling for contrastive representation learning: A review. arXiv preprint arXiv:2206.00212, 2022. 3 +[57] Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, and Pascal Fua. Lift: Learned invariant feature transform. In ECCV, 2016. 2 +[58] Kwang Moo Yi, Yannick Verdie, Pascal Fua, and Vincent Lepetit. Learning to assign orientations to feature points. In CVPR, 2016. 2 +[59] Xiaoming Zhao, Xingming Wu, Jinyu Miao, Weihai Chen, Peter CY Chen, and Zhengguo Li. Alike: Accurate and lightweight keypoint detection and descriptor extraction. 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However, either the rough hard positive or negative labels generated from one-to-one correspondences among images may bring indistinguishable samples, like false positives or negatives, which acts as inconsistent supervision. Such resultant false samples mixed with hard samples prevent neural networks from learning descriptions for more accurate matching. To tackle this challenge, we propose to learn the transformation-predictive representations with self-supervised contrastive learning. We maximize the similarity between corresponding views of the same 3D point (landmark) by using none of the negative sample pairs and avoiding collapsing solutions. Furthermore, we adopt self-supervised generation learning and curriculum learning to soften the hard positive labels into soft continuous targets. 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These false negatives act as inconsistent supervision and prevent the learning-based models from achieving higher accuracy [4]. More concretely, the false negatives represent the instance located on the repetitive texture in the structural dataset, as shown in Figure 1. It is challenging to recognize such false negatives from true negatives [39]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 545, + 547, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 547, + 653 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 547, + 653 + ], + "type": "text", + "content": "The recent active self-supervised learning methods [5, 8, 9, 15] motivate us to rethink the effectiveness of negatives in descriptors learning. We propose to learn the transformation predictive representations (TPR) for visual descriptors only with the positives and avoids collapsing solutions. 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In addition, current contrastive learning methods label all positives with different transformation strength as coarse “1”, which prevent learning refined representation. We propose to learn transformation predictive representation with soft positive labels from " + }, + { + "bbox": [ + 46, + 273, + 287, + 392 + ], + "type": "inline_equation", + "content": "(0,1]" + }, + { + "bbox": [ + 46, + 273, + 287, + 392 + ], + "type": "text", + "content": " instead of “1” to supervise the learning of local descriptors. Furthermore, we propose a self-supervised curriculum learning module to generate controllable stronger positives with gradually refined soft supervision as the network iterative training." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 46, + 393, + 287, + 524 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 393, + 287, + 524 + ], + "spans": [ + { + "bbox": [ + 46, + 393, + 287, + 524 + ], + "type": "text", + "content": "Finally, our TPR with soft labels is trained on natural images in a fully self-supervised paradigm. Different from previous methods trained on datasets with SfM or camera pose information [20, 29, 39, 49], our training datasets are generally easy to collect and scale up since there is no extra annotation requirement to capture dense correspondences. Experiments show that our self-supervised method outperforms the state-of-the-art on standard image matching benchmarks by noticeable margins and shows excellent generalization capability on multiple downstream tasks (e.g., visual odometry, and localization)." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 46, + 525, + 287, + 657 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 525, + 287, + 657 + ], + "spans": [ + { + "bbox": [ + 46, + 525, + 287, + 657 + ], + "type": "text", + "content": "Our contributions to this work are as follows: i) we propose to learn transformation-predictive representations for joint local feature learning, using none of the negative sample pairs and avoiding collapsing solutions. ii) We adopt self-supervised generation learning and curriculum learning to soften the hard positives into continuous soft labels, which can alleviate the false positives and train the model with stronger transformation. iii) The overall pipeline is trained with the self-supervised paradigm, and the training data are computed from random affine transformation and augmentation on natural images." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 47, + 669, + 138, + 681 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 669, + 138, + 681 + ], + "spans": [ + { + "bbox": [ + 47, + 669, + 138, + 681 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "text", + "content": "Our work focuses on the first step of the image matching pipeline, i.e., detection and description of key-points. This" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 305, + 273, + 545, + 309 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 273, + 545, + 309 + ], + "spans": [ + { + "bbox": [ + 305, + 273, + 545, + 309 + ], + "type": "text", + "content": "section gives a brief review of key-points detection and description learning together with contrastive representation learning." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 325, + 546, + 564 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 325, + 546, + 564 + ], + "spans": [ + { + "bbox": [ + 304, + 325, + 546, + 564 + ], + "type": "text", + "content": "Key-points Learning. Different from early hand-crafted key-points, e.g., SIFT [27] and SURF [3], the recent effort has been put into learning key-points through the deep neural networks. Many existent works focus on single module optimization with deep learning, such as interest points detection [42], shape estimation [58] and descriptor representation [45, 47]. However, optimizing one single component may not directly enable the improvement of the entire pipeline [44, 57]. Recent works tend to build end-to-end joint learning frameworks for detecting and describing local features [12, 29, 53]. CNN-based methods simultaneously optimize both the detector and descriptor by sharing most parameters with the help of the encoder-decoder pipeline. Compared with the conventional methods only considering low-level features like corners, edges, or blobs in shallow layers, the encoder-decoder pipeline can make full use of deep CNNs for better representations [11, 25, 39]. To maximize the similarity of corresponding pairs, current cutting-edge methods usually take the Siamese structure to train the model with contrastive learning [54]." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "content": "Contrastive Learning. Contrastive learning was originated from metric learning and has been widely adopted to supervise the description learning [16, 18, 36, 47]. The critical factor of contrastive learning is to attract the representation of corresponding local descriptions (positive pairs) closer and spread the representations of non-corresponding descriptions (negative pairs) apart. Negative samples are accordingly introduced to keep the uniformity property in order to avoid the representation collapse [13]. Due to expensive manual labeling and the lack of explicit negative signals in most cases, various negative sampling strate-" + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11465" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 251 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 251 + ], + "type": "text", + "content": "gies were proposed to improve the computation efficiency and promote the training results [56]. However, the sampling of hard negatives highly depends on the large batch size [6, 7, 54] and memory bank [17], so as to increase the computational load and memory resources usage. Recently, some works have been conducted to optimize the contrastive loss without any negatives and avoid the collapse [5, 9, 15]. But all these works were proposed for the holistic representation of whole images and cannot be adapted to the dense prediction tasks. To address this issue, our method uses an exclusive predictor and stop-gradient operation to avoid collapsing and encourage encoding more transformation-aware representations without negatives, which is the first work ever training key-points with only positives." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 263, + 103, + 276 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 263, + 103, + 276 + ], + "spans": [ + { + "bbox": [ + 47, + 263, + 103, + 276 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "spans": [ + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "text", + "content": "In this section, we elaborate on the proposed Transformation Predictive Representations (TPR) for learning detection and description of local features. Given a pair of images " + }, + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "inline_equation", + "content": "(I_1, I_2)" + }, + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "text", + "content": ", and the correspondences set " + }, + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "inline_equation", + "content": "\\mathcal{C}" + }, + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "text", + "content": " between them, the key-points learning methods predict the dense 3D description map " + }, + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "text", + "content": " and detection heatmap " + }, + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "inline_equation", + "content": "S \\in [0,1]" + }, + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "text", + "content": " jointly. During the training, the standard Siamese networks are used to deal with the image pairs " + }, + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "inline_equation", + "content": "(I_1, I_2)" + }, + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "text", + "content": " simultaneously and optimize the parameters with contrastive loss according to " + }, + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "inline_equation", + "content": "\\mathcal{C}" + }, + { + "bbox": [ + 46, + 284, + 289, + 464 + ], + "type": "text", + "content": ". Our approach also follows this pipeline. However, different from the previous approaches using negatives and hard negative sampling for training, TPR is optimized without any negatives. In this way, our model can reduce the computation complexity and memory usage and improves the training efficiency." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 472, + 127, + 484 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 472, + 127, + 484 + ], + "spans": [ + { + "bbox": [ + 47, + 472, + 127, + 484 + ], + "type": "text", + "content": "3.1. Preliminary" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 490, + 287, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 490, + 287, + 515 + ], + "spans": [ + { + "bbox": [ + 47, + 490, + 287, + 515 + ], + "type": "text", + "content": "The architecture in this work is built upon 1) D2-Net [12], and 2) Bootstrap Your Own Latent (BYOL) [15]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 531, + 287, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 531, + 287, + 626 + ], + "spans": [ + { + "bbox": [ + 46, + 531, + 287, + 626 + ], + "type": "text", + "content": "D2-Net [12] proposes a describe-and-detect strategy to jointly extract descriptions and detections of local features. Over the last feature maps " + }, + { + "bbox": [ + 46, + 531, + 287, + 626 + ], + "type": "inline_equation", + "content": "y \\in \\mathbb{R}^{H \\times W \\times C}" + }, + { + "bbox": [ + 46, + 531, + 287, + 626 + ], + "type": "text", + "content": ", D2-Net applies channel-wise L2-normalization to obtain the dense feature descriptors, while the feature detections are derived from 1) the local score and 2) the channel-wise score. Specifically, for each location " + }, + { + "bbox": [ + 46, + 531, + 287, + 626 + ], + "type": "inline_equation", + "content": "(i,j)" + }, + { + "bbox": [ + 46, + 531, + 287, + 626 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 46, + 531, + 287, + 626 + ], + "type": "inline_equation", + "content": "y^k (k = 1,\\dots ,C)" + }, + { + "bbox": [ + 46, + 531, + 287, + 626 + ], + "type": "text", + "content": ", the final detection score is computed as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 637, + 287, + 669 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 637, + 287, + 669 + ], + "spans": [ + { + "bbox": [ + 56, + 637, + 287, + 669 + ], + "type": "interline_equation", + "content": "s _ {i j} = \\max _ {t} \\left[ \\frac {\\exp (y _ {i j} ^ {k})}{\\sum_ {(i ^ {\\prime} , j ^ {\\prime}) \\in \\mathcal {N} (i , j)} \\exp (y _ {i ^ {\\prime} j ^ {\\prime}} ^ {k})} \\cdot \\frac {y _ {i j} ^ {k}}{\\max _ {t} y _ {i j} ^ {t}} \\right], \\tag {1}", + "image_path": "cd35e4a39aa1abb66da3773a3e74aa25eeec065929085180aa84adff8c825113.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{N}(i,j)" + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "text", + "content": " is the neighboring pixels around " + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "inline_equation", + "content": "(i,j)" + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "text", + "content": ", e.g., 9 neighbours defined by a " + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "inline_equation", + "content": "3 \\times 3" + }, + { + "bbox": [ + 47, + 677, + 287, + 714 + ], + "type": "text", + "content": " kernel. D2-Net adopts the triplet margin ranking loss to optimize the descriptions, and" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 72, + 375, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 72, + 375, + 83 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 375, + 83 + ], + "type": "text", + "content": "is formulated as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 350, + 90, + 545, + 114 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 350, + 90, + 545, + 114 + ], + "spans": [ + { + "bbox": [ + 350, + 90, + 545, + 114 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {t r i p l e t}} = \\sum_ {c \\in \\mathcal {C}} \\max \\left(0, M + d _ {p} ^ {c} - d _ {n} ^ {c}\\right), \\tag {2}", + "image_path": "0169fd48df9b120bbc01b441b3bb679846153240ef947af47765c7822768c742.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 122, + 547, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 122, + 547, + 171 + ], + "spans": [ + { + "bbox": [ + 305, + 122, + 547, + 171 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 122, + 547, + 171 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 305, + 122, + 547, + 171 + ], + "type": "text", + "content": " is the margin, " + }, + { + "bbox": [ + 305, + 122, + 547, + 171 + ], + "type": "inline_equation", + "content": "\\mathcal{C}" + }, + { + "bbox": [ + 305, + 122, + 547, + 171 + ], + "type": "text", + "content": " is the set of correspondences, " + }, + { + "bbox": [ + 305, + 122, + 547, + 171 + ], + "type": "inline_equation", + "content": "d_p^c" + }, + { + "bbox": [ + 305, + 122, + 547, + 171 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 122, + 547, + 171 + ], + "type": "inline_equation", + "content": "d_n^c" + }, + { + "bbox": [ + 305, + 122, + 547, + 171 + ], + "type": "text", + "content": " represent the distance of positive and selected negative pairs, respectively. Finally, the detection score is used as a weighting term in description loss function." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 184, + 547, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 184, + 547, + 353 + ], + "spans": [ + { + "bbox": [ + 304, + 184, + 547, + 353 + ], + "type": "text", + "content": "BYOL [15] is a self-supervised image representation learning approach. BYOL uses two neural networks, referred to as the online and target networks, both of which interact and learn from each other. The online network is trained to predict the target network's representation of the same image from an augmented view. The weights of the target network are updated with a slow-moving average of the online network. Different from other contrastive learning methods, BYOL can be trained without any negatives. It hypothesizes that the combination of 1) the addition of a predictor to the online network, and 2) the use of a slow-moving average of the online parameters as the target network encourages encoding more information within the online projection and avoids collapsed solutions." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 358, + 531, + 371 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 358, + 531, + 371 + ], + "spans": [ + { + "bbox": [ + 306, + 358, + 531, + 371 + ], + "type": "text", + "content": "3.2. Transformation-Predictive Representations" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "spans": [ + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "text", + "content": "The Siamese-like structure (i.e., the online and target networks) is used in TPR to learn the representations. From a given representation of the transformed image, referred to as target, the TPR trains a new potentially enhanced representation of the original image, referred to as online, by predicting the target representation. The online network is defined by a set of weights " + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "inline_equation", + "content": "\\theta_{o}" + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "text", + "content": " and comprises three stages: an online encoder " + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "inline_equation", + "content": "f_{o}" + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "text", + "content": ", a projector " + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "inline_equation", + "content": "g_{o}" + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "text", + "content": ", and a predictor " + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "text", + "content": ". The target network also has the encoder " + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "inline_equation", + "content": "f_{t}" + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "text", + "content": " and projector " + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "inline_equation", + "content": "g_{t}" + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "text", + "content": " in the same structure but with a different set of weights " + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "inline_equation", + "content": "\\theta_{t}" + }, + { + "bbox": [ + 304, + 376, + 545, + 508 + ], + "type": "text", + "content": ". The overall pipeline is described as in Figure 2," + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "spans": [ + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "text", + "content": "Given the input image " + }, + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "inline_equation", + "content": "I \\in \\mathbb{R}^{H \\times W \\times 3}" + }, + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "text", + "content": ", a random augmentation " + }, + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "text", + "content": " is performed to produce the cropped view " + }, + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "inline_equation", + "content": "V \\triangleq t(I) \\in \\mathbb{R}^{h \\times w \\times 3}" + }, + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "text", + "content": ". And then, the limited cascaded affine transformation and augmentation " + }, + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "text", + "content": " is carried out on the " + }, + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "text", + "content": " to produce the transformed view " + }, + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "inline_equation", + "content": "V' \\triangleq T(V) \\in \\mathbb{R}^{h \\times w \\times 3}" + }, + { + "bbox": [ + 304, + 508, + 545, + 568 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "spans": [ + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "text", + "content": "From the augmented view " + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "text", + "content": ", the online encoder outputs a representation " + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "inline_equation", + "content": "z \\triangleq f_{o}(V) \\in \\mathbb{R}^{h \\times w \\times c}" + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "text", + "content": ", which is adopted as the objective encouraged to be transformation-predictive. Rather than predicting representations produced by the online encoder, the target representation " + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "inline_equation", + "content": "z' \\triangleq f_{t}(V') \\in \\mathbb{R}^{h \\times w \\times c}" + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "text", + "content": " is computed by using the target encoder " + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "inline_equation", + "content": "f_{t}" + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "text", + "content": ", whose parameters " + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "inline_equation", + "content": "\\theta_{t}" + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "text", + "content": " are an exponential moving average (EMA) of the online encoder parameters " + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "inline_equation", + "content": "\\theta_{o}" + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "text", + "content": ". Without gradient descent, the update algorithm for " + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "inline_equation", + "content": "\\theta_{t}" + }, + { + "bbox": [ + 304, + 569, + 545, + 676 + ], + "type": "text", + "content": " is formulated as:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 380, + 683, + 545, + 695 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 683, + 545, + 695 + ], + "spans": [ + { + "bbox": [ + 380, + 683, + 545, + 695 + ], + "type": "interline_equation", + "content": "\\theta_ {t} \\leftarrow \\tau \\theta_ {t} + (1 - \\tau) \\theta_ {o}, \\tag {3}", + "image_path": "3b2f2608968f72af68ecf40f23935a90387c3dfd32d2eafd553a82515ffd8227.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 701, + 470, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 701, + 470, + 714 + ], + "spans": [ + { + "bbox": [ + 306, + 701, + 470, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 306, + 701, + 470, + 714 + ], + "type": "inline_equation", + "content": "\\tau \\in [0,1)" + }, + { + "bbox": [ + 306, + 701, + 470, + 714 + ], + "type": "text", + "content": " is the EMA coefficient." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11466" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 98, + 73, + 492, + 323 + ], + "blocks": [ + { + "bbox": [ + 98, + 73, + 492, + 323 + ], + "lines": [ + { + "bbox": [ + 98, + 73, + 492, + 323 + ], + "spans": [ + { + "bbox": [ + 98, + 73, + 492, + 323 + ], + "type": "image", + "image_path": "d970519677a182c89591c0bcc0a5a4a7771db4139930b45a2bcfcf8b51388c45.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 336, + 547, + 392 + ], + "lines": [ + { + "bbox": [ + 46, + 336, + 547, + 392 + ], + "spans": [ + { + "bbox": [ + 46, + 336, + 547, + 392 + ], + "type": "text", + "content": "Figure 2. Illustration of our proposed transformation-predictive representations learning. Top: traditional contrastive learning descriptors based on both positives and negatives and supervised under SfM models. Bottom: our proposed transformation predictive representations learning with only positives and self-supervised loss. In the TPR structure, representations from the online encoder are used in the joint learning of local features task. The target encoder and projection head are defined as an exponential moving average of their online counterparts and are not updated via gradient descent." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "spans": [ + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "text", + "content": "Next, the representation " + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "inline_equation", + "content": "z'" + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "text", + "content": " are fed into the online and target detector to extract the detection score " + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "inline_equation", + "content": "s'" + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "text", + "content": " with (1). We further use the online and target projection heads " + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "inline_equation", + "content": "g_{o}" + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "inline_equation", + "content": "g_{t}" + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "text", + "content": " to map the online and target presentations to a latent embedding space[6], and also apply an additional prediction head " + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 46, + 404, + 288, + 488 + ], + "type": "text", + "content": " to the online projections to predict the target projections[15]:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 139, + 490, + 195, + 502 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 139, + 490, + 195, + 502 + ], + "spans": [ + { + "bbox": [ + 139, + 490, + 195, + 502 + ], + "type": "interline_equation", + "content": "\\hat {y} \\triangleq q (g _ {o} (z)),", + "image_path": "06d784c14d29ada935c26db2f90b1009da6ca4cbb9adfc6ab3c349955c06bdb0.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 139, + 502, + 287, + 516 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 139, + 502, + 287, + 516 + ], + "spans": [ + { + "bbox": [ + 139, + 502, + 287, + 516 + ], + "type": "interline_equation", + "content": "y ^ {\\prime} \\triangleq g _ {t} \\left(z ^ {\\prime}\\right). \\tag {4}", + "image_path": "09141f99887279f4cbcb4fe3e201d67a534edc2bf85bcc97e2c331ab66af2caf.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 517, + 287, + 589 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 517, + 287, + 589 + ], + "spans": [ + { + "bbox": [ + 46, + 517, + 287, + 589 + ], + "type": "text", + "content": "The target projection head parameters are also given through an EMA of the online projection head parameters by using the same update algorithm as the online and target encoders. Note that the predictor is only applied to online networks so as to make the Siamese architecture asymmetric between the online and target pipeline." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 589, + 288, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 589, + 288, + 685 + ], + "spans": [ + { + "bbox": [ + 46, + 589, + 288, + 685 + ], + "type": "text", + "content": "Next, we compute the transformation predictive loss for TPR by summing over cosine similarities between the predicted and target representations. Different from a holistic representation " + }, + { + "bbox": [ + 46, + 589, + 288, + 685 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{1\\times 1\\times d}" + }, + { + "bbox": [ + 46, + 589, + 288, + 685 + ], + "type": "text", + "content": " of the view " + }, + { + "bbox": [ + 46, + 589, + 288, + 685 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 46, + 589, + 288, + 685 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 589, + 288, + 685 + ], + "type": "inline_equation", + "content": "V^{\\prime}" + }, + { + "bbox": [ + 46, + 589, + 288, + 685 + ], + "type": "text", + "content": " [15], our encoder " + }, + { + "bbox": [ + 46, + 589, + 288, + 685 + ], + "type": "inline_equation", + "content": "f_{o}" + }, + { + "bbox": [ + 46, + 589, + 288, + 685 + ], + "type": "text", + "content": " outputs the dense feature maps preserving abundant spatial information with the shape of " + }, + { + "bbox": [ + 46, + 589, + 288, + 685 + ], + "type": "inline_equation", + "content": "h\\times w\\times c" + }, + { + "bbox": [ + 46, + 589, + 288, + 685 + ], + "type": "text", + "content": ". The prediction loss is computed on the corresponding location of the dense feature map, i.e., the positive pairs:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 95, + 688, + 287, + 716 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 95, + 688, + 287, + 716 + ], + "spans": [ + { + "bbox": [ + 95, + 688, + 287, + 716 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {\\text {p r e d}} ^ {c} = \\left(1 - \\left\\langle y _ {c}, y _ {c} ^ {\\prime} \\right\\rangle\\right) \\tag {5} \\\\ = \\left(1 - \\left\\langle q \\left(g _ {o} (z)\\right) _ {c}, g _ {t} \\left(z ^ {\\prime}\\right) _ {c} \\right\\rangle\\right), \\\\ \\end{array}", + "image_path": "2a0dd0fbf9bb42968c6ab3e1a8f7c686b3735cdf4d67f0fec1bd1aee544b2667.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 404, + 545, + 428 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 404, + 545, + 428 + ], + "spans": [ + { + "bbox": [ + 305, + 404, + 545, + 428 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 404, + 545, + 428 + ], + "type": "inline_equation", + "content": "\\langle \\cdot \\rangle" + }, + { + "bbox": [ + 305, + 404, + 545, + 428 + ], + "type": "text", + "content": " denotes the cosine similarity, " + }, + { + "bbox": [ + 305, + 404, + 545, + 428 + ], + "type": "inline_equation", + "content": "\\hat{y}_c" + }, + { + "bbox": [ + 305, + 404, + 545, + 428 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 404, + 545, + 428 + ], + "type": "inline_equation", + "content": "y_c^\\prime" + }, + { + "bbox": [ + 305, + 404, + 545, + 428 + ], + "type": "text", + "content": " is the local representation with the " + }, + { + "bbox": [ + 305, + 404, + 545, + 428 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 305, + 404, + 545, + 428 + ], + "type": "text", + "content": "-th correspondence." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 429, + 545, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 429, + 545, + 452 + ], + "spans": [ + { + "bbox": [ + 305, + 429, + 545, + 452 + ], + "type": "text", + "content": "Finally, the detection score is used as a weighting term to formalize the final hard prediction loss function:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 355, + 460, + 545, + 488 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 355, + 460, + 545, + 488 + ], + "spans": [ + { + "bbox": [ + 355, + 460, + 545, + 488 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {h a r d}} = \\frac {1}{| \\mathcal {C} |} \\sum_ {c \\in \\mathcal {C}} \\frac {s _ {c} s _ {c} ^ {\\prime}}{\\sum_ {n \\in \\mathcal {C}} s _ {n} s _ {n} ^ {\\prime}} \\mathcal {L} _ {\\text {p r e d}} ^ {c}. \\tag {6}", + "image_path": "ebe899f58b8a4071022ea6a05fa79282d50c162d1d4ea233fdcd0e30b653318d.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 497, + 545, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 497, + 545, + 665 + ], + "spans": [ + { + "bbox": [ + 304, + 497, + 545, + 665 + ], + "type": "text", + "content": "The transformation-predictive representations follow the predictive nature of the objective and use the exponential moving average target network similar to [15]. Compared with the related works on contrastive representation learning, TPR only uses the positive pairs without negative samples. As a result, there is no necessary need on large buffer to emulate large batch sizes of negatives [6, 17] and complex hard negative sampling strategies. Furthermore, training without negatives can help prevent the inconsistent optimization from the false negatives. Finally, although we do not use the explicit negative samples to prevent collapse while minimizing " + }, + { + "bbox": [ + 304, + 497, + 545, + 665 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{hard}}" + }, + { + "bbox": [ + 304, + 497, + 545, + 665 + ], + "type": "text", + "content": ", TPR still can avoid converging to a minimum of this loss with respect to " + }, + { + "bbox": [ + 304, + 497, + 545, + 665 + ], + "type": "inline_equation", + "content": "(\\theta_o,\\theta_t)" + }, + { + "bbox": [ + 304, + 497, + 545, + 665 + ], + "type": "text", + "content": " (e.g., a collapsed constant representation)." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 671, + 451, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 671, + 451, + 684 + ], + "spans": [ + { + "bbox": [ + 306, + 671, + 451, + 684 + ], + "type": "text", + "content": "3.3. Learning with Soft Labels" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": "The TPR method uses carefully designed transformations " + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": " to generate the view " + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "V^{\\prime}" + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": ". Therefore, the" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "11467" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 239 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 239 + ], + "type": "text", + "content": "views are not deformed aggressively so that they can still be viewed as the same instance, i.e., positives. Stronger deformation could expose the novel patterns of representations in discriminative contrastive learning. However, directly adopting stronger transformations (e.g., with larger rotation angles, more aggressive color jitting, and cutout) may seriously damage the intrinsic semantics of the original image, which will fail to further improve or even degrade the performance [52]. In this paper, we categorize such positive pairs generated from over-strong deformation into the false positives. The false positives are bound to make the training process degraded in performance because the network cannot effectively distinguish the true positives from the false ones when trained with the existing pipeline [39]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 239, + 289, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 239, + 289, + 312 + ], + "spans": [ + { + "bbox": [ + 46, + 239, + 289, + 312 + ], + "type": "text", + "content": "To tackle the dilemma, where the strong or weak transformations should not have been used to produce the positives, we propose the optimal soft labels to stabilize the training process instead of forcing the labeling coarse hard \"1\" for every positive pair. Accordingly, the prediction loss in (5) is modified as follows." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 82, + 315, + 287, + 339 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 315, + 287, + 339 + ], + "spans": [ + { + "bbox": [ + 82, + 315, + 287, + 339 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {s o f t}} = \\frac {1}{| \\mathcal {C} |} \\sum_ {c \\in \\mathcal {C}} \\frac {s _ {c} s _ {c} ^ {\\prime}}{\\sum_ {n \\in \\mathcal {C}} s _ {n} s _ {n} ^ {\\prime}} \\left(l _ {c} + 1 - \\mathcal {L} _ {\\text {p r e d}} ^ {c}\\right), \\tag {7}", + "image_path": "3f3bdd3adf66d904a0be22bdd2c448c25989d256f5143e836ca4222732d0fc3e.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 342, + 289, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 342, + 289, + 392 + ], + "spans": [ + { + "bbox": [ + 46, + 342, + 289, + 392 + ], + "type": "text", + "content": "in which " + }, + { + "bbox": [ + 46, + 342, + 289, + 392 + ], + "type": "inline_equation", + "content": "l_{c}" + }, + { + "bbox": [ + 46, + 342, + 289, + 392 + ], + "type": "text", + "content": " represents the computed soft label for the positive pair with " + }, + { + "bbox": [ + 46, + 342, + 289, + 392 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 46, + 342, + 289, + 392 + ], + "type": "text", + "content": "-th correspondence. Therefore, we adopt self-supervised generation learning and curriculum learning to \"adjust\" the inconsistent and incorrect supervision." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 405, + 289, + 585 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 405, + 289, + 585 + ], + "spans": [ + { + "bbox": [ + 46, + 405, + 289, + 585 + ], + "type": "text", + "content": "Self-supervised generation learning. We train the network for the first generation following the hard positive label. And then, the network can be assumed to successfully capture most feature distribution of the training data. We propose to utilize the previous generation's similarities to be served as the soft supervision for training the network in a new generation. The generated soft supervisions are gradually refined and sharpened as the network generation progresses. In this way, the system can fully explore the potential of the difficult positives with stronger transformation, and mitigate their label noises with the refined soft confidence. To stabilize the training process, the soft label of the positive pair with the correspondence " + }, + { + "bbox": [ + 46, + 405, + 289, + 585 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 46, + 405, + 289, + 585 + ], + "type": "text", + "content": " in the generation " + }, + { + "bbox": [ + 46, + 405, + 289, + 585 + ], + "type": "inline_equation", + "content": "\\omega = 2,\\dots ,\\Omega" + }, + { + "bbox": [ + 46, + 405, + 289, + 585 + ], + "type": "text", + "content": " follows the exponential decay of the transformation strength, which is computed as:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 110, + 591, + 287, + 608 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 110, + 591, + 287, + 608 + ], + "spans": [ + { + "bbox": [ + 110, + 591, + 287, + 608 + ], + "type": "interline_equation", + "content": "l _ {c} = e ^ {- \\left(1 - \\left\\langle y _ {c} ^ {\\omega - 1}, y _ {c} ^ {\\prime \\omega - 1} \\right\\rangle\\right) / \\lambda}, \\tag {8}", + "image_path": "ddf96cc98adbc8cdfd95fae1b309dde3cc0ffd3c296acd45fe97293704edfd78.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 614, + 289, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 614, + 289, + 652 + ], + "spans": [ + { + "bbox": [ + 46, + 614, + 289, + 652 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 614, + 289, + 652 + ], + "type": "inline_equation", + "content": "y^{\\omega -1}" + }, + { + "bbox": [ + 46, + 614, + 289, + 652 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 614, + 289, + 652 + ], + "type": "inline_equation", + "content": "y^{\\prime \\omega -1}" + }, + { + "bbox": [ + 46, + 614, + 289, + 652 + ], + "type": "text", + "content": " are generated from the encoder with the last generation parameters " + }, + { + "bbox": [ + 46, + 614, + 289, + 652 + ], + "type": "inline_equation", + "content": "\\theta_{\\omega}" + }, + { + "bbox": [ + 46, + 614, + 289, + 652 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 614, + 289, + 652 + ], + "type": "inline_equation", + "content": "\\Omega" + }, + { + "bbox": [ + 46, + 614, + 289, + 652 + ], + "type": "text", + "content": " is the total number of generations, and " + }, + { + "bbox": [ + 46, + 614, + 289, + 652 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 614, + 289, + 652 + ], + "type": "text", + "content": " is the exponential decay constant." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 665, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 289, + 715 + ], + "type": "text", + "content": "Curriculum setting for positives generation. The core of curriculum learning is to gradually improve the transformation strength. In particular, we introduce the affine adaptation and data augmentation to generate the transformation" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 72, + 547, + 168 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 168 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 168 + ], + "type": "text", + "content": "matrix, which also determines the transformation strength " + }, + { + "bbox": [ + 304, + 72, + 547, + 168 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 304, + 72, + 547, + 168 + ], + "type": "text", + "content": ". We normalize " + }, + { + "bbox": [ + 304, + 72, + 547, + 168 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 304, + 72, + 547, + 168 + ], + "type": "text", + "content": " within " + }, + { + "bbox": [ + 304, + 72, + 547, + 168 + ], + "type": "inline_equation", + "content": "[0,1]" + }, + { + "bbox": [ + 304, + 72, + 547, + 168 + ], + "type": "text", + "content": " according to the maximum limitation of adaption and augmentation parameters, e.g., rotation and translation values. With the curriculum setting, " + }, + { + "bbox": [ + 304, + 72, + 547, + 168 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 304, + 72, + 547, + 168 + ], + "type": "text", + "content": " is randomly sampled from a range with gradually relaxed restrictions of the transformation strength. The soft label of the current batch follows the exponential decay of the transformation strength, which is computed as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 365, + 177, + 545, + 194 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 177, + 545, + 194 + ], + "spans": [ + { + "bbox": [ + 365, + 177, + 545, + 194 + ], + "type": "interline_equation", + "content": "l _ {c} = e ^ {- \\alpha \\left(1 - \\left\\langle y _ {c} ^ {\\omega - 1}, y _ {c} ^ {\\prime \\omega - 1} \\right\\rangle\\right) / \\lambda}. \\tag {9}", + "image_path": "f34d36c3bb90fd8c49f8b89b94594779a870f7d72368e580dd3797c895d53a50.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 204, + 545, + 276 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 204, + 545, + 276 + ], + "spans": [ + { + "bbox": [ + 304, + 204, + 545, + 276 + ], + "type": "text", + "content": "To avoid the over-fitting to the soft labels, we also set a maximum operation on the predicted soft loss as " + }, + { + "bbox": [ + 304, + 204, + 545, + 276 + ], + "type": "inline_equation", + "content": "\\max(0, l_c + 1 - \\mathcal{L}_{pred}^c)" + }, + { + "bbox": [ + 304, + 204, + 545, + 276 + ], + "type": "text", + "content": ". This setting will help the model avoid divergence through the false positives during the early training stage, and encourage the model to explore more robust representations in the later training stage." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 277, + 547, + 385 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 277, + 547, + 385 + ], + "spans": [ + { + "bbox": [ + 304, + 277, + 547, + 385 + ], + "type": "text", + "content": "In the later stage of training, with the increasing difficulty of positive samples, the proportion of false positive samples, which have different semantics with the online query, will increase. Such false positives will be harmful to the generalization performance of the model [52]. To overcome this problem, we introduce a hyperparameter patience " + }, + { + "bbox": [ + 304, + 277, + 547, + 385 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 304, + 277, + 547, + 385 + ], + "type": "text", + "content": " of early stop. When the soft prediction loss no longer decreases, the value of " + }, + { + "bbox": [ + 304, + 277, + 547, + 385 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 304, + 277, + 547, + 385 + ], + "type": "text", + "content": " will decrease. The training will stop until " + }, + { + "bbox": [ + 304, + 277, + 547, + 385 + ], + "type": "inline_equation", + "content": "p = 0" + }, + { + "bbox": [ + 304, + 277, + 547, + 385 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 393, + 440, + 406 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 393, + 440, + 406 + ], + "spans": [ + { + "bbox": [ + 306, + 393, + 440, + 406 + ], + "type": "text", + "content": "3.4. Implementation Details" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 412, + 545, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 412, + 545, + 472 + ], + "spans": [ + { + "bbox": [ + 304, + 412, + 545, + 472 + ], + "type": "text", + "content": "In this part, the architectural details of TPR are introduced. Note that at the end of the training, everything but the online encoder " + }, + { + "bbox": [ + 304, + 412, + 545, + 472 + ], + "type": "inline_equation", + "content": "f_{o}" + }, + { + "bbox": [ + 304, + 412, + 545, + 472 + ], + "type": "text", + "content": " is discarded. Thus, the online projection head " + }, + { + "bbox": [ + 304, + 412, + 545, + 472 + ], + "type": "inline_equation", + "content": "g_{o}" + }, + { + "bbox": [ + 304, + 412, + 545, + 472 + ], + "type": "text", + "content": ", the predictor head " + }, + { + "bbox": [ + 304, + 412, + 545, + 472 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 304, + 412, + 545, + 472 + ], + "type": "text", + "content": ", and the target branch will not introduce the extra computational costs in deployment." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 472, + 545, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 472, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 304, + 472, + 545, + 533 + ], + "type": "text", + "content": "Encoder, " + }, + { + "bbox": [ + 304, + 472, + 545, + 533 + ], + "type": "inline_equation", + "content": "f_{o}" + }, + { + "bbox": [ + 304, + 472, + 545, + 533 + ], + "type": "text", + "content": ", maps the input view " + }, + { + "bbox": [ + 304, + 472, + 545, + 533 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 304, + 472, + 545, + 533 + ], + "type": "text", + "content": " into the dense embeddings " + }, + { + "bbox": [ + 304, + 472, + 545, + 533 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 304, + 472, + 545, + 533 + ], + "type": "text", + "content": ". The encoder can be instanced into the CNN version based on VGG (similar as D2-Net [12]) and DCN (similar as ASLFeat [29]), and Transformer version based on tiny Swin Transformer V1 [26] with only the first two stages." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "spans": [ + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "type": "text", + "content": "Projection head, " + }, + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "type": "inline_equation", + "content": "g_{o}" + }, + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "type": "text", + "content": ", maps the feature embedding into a 128-dim " + }, + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "type": "inline_equation", + "content": "l_{2}" + }, + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "type": "text", + "content": " normalization feature vector for the computation of the prediction loss " + }, + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{pred}}" + }, + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "type": "inline_equation", + "content": "g_{o}" + }, + { + "bbox": [ + 304, + 533, + 545, + 593 + ], + "type": "text", + "content": " is implemented as a 3-layer MLP (hidden layer 256-dim), with BN and ReLU on every fully-connected layer except the last output layer." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 594, + 545, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 545, + 629 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 545, + 629 + ], + "type": "text", + "content": "Predictor, " + }, + { + "bbox": [ + 304, + 594, + 545, + 629 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 304, + 594, + 545, + 629 + ], + "type": "text", + "content": ", is only applied to the online networks. The predictor is an MLP with two layers. The dimension of the hidden and output layer is 64 and 128, respectively." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 630, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 545, + 714 + ], + "type": "text", + "content": "Detection and description head, are adopted on the encoder to simultaneously output the dense description and the detection score map following the D2-Net [12]. Only the locations with higher confidence than 0.7 are selected as the key points. Moreover, a non-maximum suppression (NMS) operation (with a kernel of 5) is applied to remove the key points that are spatially too close." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11468" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 71, + 128, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 71, + 128, + 85 + ], + "spans": [ + { + "bbox": [ + 47, + 71, + 128, + 85 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 92, + 288, + 164 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 92, + 288, + 164 + ], + "spans": [ + { + "bbox": [ + 46, + 92, + 288, + 164 + ], + "type": "text", + "content": "In this section, we first present the details of training our TPR. And then we show the performance across several downstream scenarios, including image matching, visual odometry, and visual localization. It's worthy to NOTE that our methods are not trained on all benchmark datasets, which is essential to show generalization performance." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 176, + 164, + 190 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 176, + 164, + 190 + ], + "spans": [ + { + "bbox": [ + 47, + 176, + 164, + 190 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "spans": [ + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "text", + "content": "Our TPR method is trained on images from Microsoft COCO dataset[24] and Image Matching Challenge[20], by dropping all the irrelevant annotations. We compute the correspondences between the transformed natural images with the random affine adaption and data augmentation by means of the self-supervised paradigm. For the affine adaption, we uniformly sample the in-plane rotation, shear, translation and scale parameters from " + }, + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "inline_equation", + "content": "[-45^{\\circ}, +45^{\\circ}]" + }, + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "inline_equation", + "content": "[-40^{\\circ}, +40^{\\circ}]" + }, + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "inline_equation", + "content": "[-0.05, +0.05]" + }, + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "text", + "content": ", [0.7,1.4], respectively. For the color jitter, we also uniformly sample the brightness, contrast, saturation, and hue parameters from [0.6,1.4], [0.6,1.4], [0.6,1.4], " + }, + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "inline_equation", + "content": "[-0.2, +0.2]" + }, + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "text", + "content": ", respectively. The additional random data augmentation is employed on the transformed views " + }, + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "inline_equation", + "content": "V'" + }, + { + "bbox": [ + 46, + 196, + 287, + 363 + ], + "type": "text", + "content": " including the grayscale conversion and Gaussian blur." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 365, + 287, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 365, + 287, + 484 + ], + "spans": [ + { + "bbox": [ + 46, + 365, + 287, + 484 + ], + "type": "text", + "content": "We train models using SGD and AdamW optimizer for CNN and Transformer-based models, respectively. During training, we use a mini-batch size of 16 and an initial learning rate of 0.00017 with exponential decay of 0.9. The hyper-parameter of EMA update " + }, + { + "bbox": [ + 46, + 365, + 287, + 484 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 46, + 365, + 287, + 484 + ], + "type": "text", + "content": ", exponential decay constant " + }, + { + "bbox": [ + 46, + 365, + 287, + 484 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 365, + 287, + 484 + ], + "type": "text", + "content": ", and early stop patience " + }, + { + "bbox": [ + 46, + 365, + 287, + 484 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 46, + 365, + 287, + 484 + ], + "type": "text", + "content": " is set to 0.99, 10, and 1, which are determined with the grid search to reach the optimum. Our method were trained for 50 epochs, which took 30 hours for training with two NVIDIA-A100 GPUs. We randomly crop the " + }, + { + "bbox": [ + 46, + 365, + 287, + 484 + ], + "type": "inline_equation", + "content": "224 \\times 224" + }, + { + "bbox": [ + 46, + 365, + 287, + 484 + ], + "type": "text", + "content": " views for training." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 485, + 287, + 534 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 485, + 287, + 534 + ], + "spans": [ + { + "bbox": [ + 46, + 485, + 287, + 534 + ], + "type": "text", + "content": "Moreover, we evaluate the local features learned by TPR across several downstream scenarios, including the image matching on HPatches, visual odometry on KITTI, and visual localization on Aachen Day-Night dataset." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 545, + 147, + 559 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 545, + 147, + 559 + ], + "spans": [ + { + "bbox": [ + 47, + 545, + 147, + 559 + ], + "type": "text", + "content": "4.2. Image Matching" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 565, + 287, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 565, + 287, + 601 + ], + "spans": [ + { + "bbox": [ + 46, + 565, + 287, + 601 + ], + "type": "text", + "content": "We first evaluate the performance of our method in the image matching tasks, i.e., the most extensive key-points applications." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "type": "text", + "content": "Datasets. The experiments are taken on HPatches [2] datasets, which include 116 sequences of 6 images with known homography. The datasets are split into two categories according to the illumination and viewpoint changes. The first image in one sequence is taken as reference, and the subsequent images are used to form the pairs with increasing difficulty. By following the previous methods[39], eight high-resolution sequences are excluded." + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 310, + 70, + 531, + 328 + ], + "blocks": [ + { + "bbox": [ + 310, + 70, + 531, + 328 + ], + "lines": [ + { + "bbox": [ + 310, + 70, + 531, + 328 + ], + "spans": [ + { + "bbox": [ + 310, + 70, + 531, + 328 + ], + "type": "table", + "html": "
MethodMMA@3AUC@2AUC@5
SIFT [27]50.139.4949.57
HardNet [32]62.142.6156.85
LF-Net [35]53.238.7448.69
SuperPoint [11]65.744.0859.04
DELF [34]50.744.7349.70
ContextDesc [28]63.247.2358.25
Key.Net [22]72.140.8756.04
R2D2 [39]72.143.3564.17
DISK [49]77.252.3369.80
ALIKE [59]70.551.6569.04
SSL+CAPS [30]69.048.7262.19
LLF [46]74.052.1466.81
MTLDesc [50]78.755.0271.42
PoSFeat [23]75.3450.1669.23
D2-Net [12] (orig.)40.319.4937.78
D2-Net [12] (our impl.)44.522.3543.17
Ours(VGG)49.6 ↑ 9.324.46 ↑ 4.9747.69 ↑ 9.91
ASLFeat [29] (orig.)72.250.1066.93
ASLFeat [29] (our impl.)74.451.8369.24
Ours(DCN)75.5 ↑ 3.252.33 ↑ 2.2370.15 ↑ 3.22
Ours(TR)79.857.1873.00
", + "image_path": "53006f39d28d3235be4fd0e9267c7cc6c600c8a14906f186b9294685dc6def81.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 340, + 336, + 511, + 347 + ], + "lines": [ + { + "bbox": [ + 340, + 336, + 511, + 347 + ], + "spans": [ + { + "bbox": [ + 340, + 336, + 511, + 347 + ], + "type": "text", + "content": "Table 1. Quantitative results (§4.2) on HPatches." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 372, + 545, + 504 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 372, + 545, + 504 + ], + "spans": [ + { + "bbox": [ + 304, + 372, + 545, + 504 + ], + "type": "text", + "content": "Evaluation protocols. For a fair comparison, we match the features extracted by each method using the nearest neighbor matcher, accepting only mutual nearest neighbors. A correct match is accordingly considered if its estimated reprojection error is below a given matching threshold. We vary the threshold from 1 pixel to 10 pixels and record the mean matching accuracy (MMA) [31] over all pairs, which represents the ratio of correct matches and possible matches. And then the area is computed under curve (AUC) at 2px and 5px based on the MMA. We report the average scores for overall image pairs." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 521, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 545, + 715 + ], + "type": "text", + "content": "Comparisons with other methods. We compare our model with the previous state-of-the-art methods on the HPatches dataset. Unless otherwise specified, we present the results either reported on original papers or derived from authors' public implementations with default parameters. Table. 1 lists different methods' results on HPatches in terms of the MMA area under the overall curve (AUC) up to 2px and 5px. Especially in the low-threshold area, Ours (TR) shows considerable improvements over previous sota methods. For a fair comparison, we show both the performance from the original paper (orig.) and our implementation (our impl.). It is observed that TPR gains a " + }, + { + "bbox": [ + 304, + 521, + 545, + 715 + ], + "type": "inline_equation", + "content": "+6.69\\%" + }, + { + "bbox": [ + 304, + 521, + 545, + 715 + ], + "type": "text", + "content": " (44.71% v.s. 38.02%) and " + }, + { + "bbox": [ + 304, + 521, + 545, + 715 + ], + "type": "inline_equation", + "content": "+2.45\\%" + }, + { + "bbox": [ + 304, + 521, + 545, + 715 + ], + "type": "text", + "content": " (79.81% v.s. 77.36%) MMA promotion than the leading DISK [49] at 1px and 3px threshold in 128-dimension, respectively. To prove the effectiveness of our training pipeline, we also con" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "11469" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 70, + 279, + 164 + ], + "blocks": [ + { + "bbox": [ + 56, + 70, + 279, + 164 + ], + "lines": [ + { + "bbox": [ + 56, + 70, + 279, + 164 + ], + "spans": [ + { + "bbox": [ + 56, + 70, + 279, + 164 + ], + "type": "table", + "html": "
NegSample+SSGL+CLMMA@3Mem/GB
w/Random63.1112
Hardest73.4140
Bank78.2↑15.1158
Mixing77.9147
w/oRandom70.179
Random✓77.4↑7.279
Random✓✓79.8↑9.679
", + "image_path": "ac65726d1907ebb6fb12c6a890f0cf11c82e00618bf98213040907d6341d27e6.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 172, + 287, + 228 + ], + "lines": [ + { + "bbox": [ + 46, + 172, + 287, + 228 + ], + "spans": [ + { + "bbox": [ + 46, + 172, + 287, + 228 + ], + "type": "text", + "content": "Table 2. Ablation experiments of the proposed TPR, where Neg, Sample, MMA@3, SSGL, CL and Mem represent whether training w/ Negative samples, Sampling strategy, self-supervised generation learning, curriculum learning, mean matching accuracy at 3px threshold, and GPU Memory usage (GB), respectively." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 249, + 286, + 355 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 249, + 286, + 355 + ], + "spans": [ + { + "bbox": [ + 46, + 249, + 286, + 355 + ], + "type": "text", + "content": "duct experiments based on the D2-Net [12] backbone i.e., Ours (VGG) and ASLFeat [29] backbone, i.e., Ours (DCN). Under the same networks, our retrained model gains " + }, + { + "bbox": [ + 46, + 249, + 286, + 355 + ], + "type": "inline_equation", + "content": "+9.3\\%" + }, + { + "bbox": [ + 46, + 249, + 286, + 355 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 249, + 286, + 355 + ], + "type": "inline_equation", + "content": "+3.2\\%" + }, + { + "bbox": [ + 46, + 249, + 286, + 355 + ], + "type": "text", + "content": " improvements on MMA@3, respectively. The t-SNE visualization of descriptions from D2-Net (orig.) and Ours (VGG) is shown in Figure. 3. More importantly, our method is trained in the self-supervised paradigm, requiring no extra annotations, and is not finetuned on the downstream datasets." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 362, + 140, + 374 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 362, + 140, + 374 + ], + "spans": [ + { + "bbox": [ + 47, + 362, + 140, + 374 + ], + "type": "text", + "content": "4.3. Ablation Study" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 380, + 287, + 440 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 380, + 287, + 440 + ], + "spans": [ + { + "bbox": [ + 46, + 380, + 287, + 440 + ], + "type": "text", + "content": "We further study the efficacy of our core ideas and essential model designs over HPatches. We train each model from scratch for 50 epochs to perform extensive ablation experiments while keeping other hyper-parameters unchanged." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 447, + 287, + 674 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 447, + 287, + 674 + ], + "spans": [ + { + "bbox": [ + 46, + 447, + 287, + 674 + ], + "type": "text", + "content": "Ablation on different negative sampling strategies. We adopt the standard Siamese networks with transformer blocks as the \"baseline\" model. The baseline model is first trained with both positive and negative pairs, according to the triplet loss in (2). We also evaluate different negative sampling strategies including random negative sampling (Random) [10], in-batch hard negative mining (Hardest) [6], hard negative mining with Memory Bank (Bank) [17], and hard negative mixing (Mixing) [21]. And then we train the model using our TPR method with only positives and evaluate the efficacy of the curriculum learning and self-supervised generation learning. As demonstrated in Table 2, our TPR successfully gets convergent with only positives and avoids the collapsing solution, which proves the effectiveness of our designed components in the Siamese networks. Furthermore, the soft labels effectively alleviate the inconsistent optimization from pseudo positives, especially on the training datasets with stronger transformation and augmentation." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 689, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 712 + ], + "type": "text", + "content": "Ablation on key components. We also conduct the ablation experiments to evaluate the effectiveness of our core" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 328, + 71, + 422, + 165 + ], + "blocks": [ + { + "bbox": [ + 328, + 71, + 422, + 165 + ], + "lines": [ + { + "bbox": [ + 328, + 71, + 422, + 165 + ], + "spans": [ + { + "bbox": [ + 328, + 71, + 422, + 165 + ], + "type": "image", + "image_path": "7f5167c3299c6824f39640a7780dcbc1ea0e4e290582f64eda429a148d2aab3b.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 174, + 545, + 194 + ], + "lines": [ + { + "bbox": [ + 305, + 174, + 545, + 194 + ], + "spans": [ + { + "bbox": [ + 305, + 174, + 545, + 194 + ], + "type": "text", + "content": "Figure 3. t-SNE visualization of description from different training methods. Left: D2-Net [12] (orig.), Right: Ours(VGG)." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 430, + 71, + 525, + 165 + ], + "blocks": [ + { + "bbox": [ + 430, + 71, + 525, + 165 + ], + "lines": [ + { + "bbox": [ + 430, + 71, + 525, + 165 + ], + "spans": [ + { + "bbox": [ + 430, + 71, + 525, + 165 + ], + "type": "image", + "image_path": "e692f48a8f8e85f0b81e574e07f3246b25d958e674e0a07f9cdf9452caa484cb.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 210, + 545, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 210, + 545, + 281 + ], + "spans": [ + { + "bbox": [ + 304, + 210, + 545, + 281 + ], + "type": "text", + "content": "components including training w/o negative samples, self-supervised generation learning (SSGL), and curriculum learning module (CL). The results are also shown in Table 2, which proves the self-supervised curriculum learning with soft labels will improve the contrastive learning performance with only positives." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 289, + 408, + 302 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 289, + 408, + 302 + ], + "spans": [ + { + "bbox": [ + 306, + 289, + 408, + 302 + ], + "type": "text", + "content": "4.4. Visual Odometry" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 308, + 545, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 308, + 545, + 369 + ], + "spans": [ + { + "bbox": [ + 304, + 308, + 545, + 369 + ], + "type": "text", + "content": "Visual Odometry (VO) is the task to estimate a robot's position and orientation from visual measurements. The goal of the system is to estimate the 6-DoF pose of the camera at every frame. In this experiment, we evaluate the visual odometry performance with the learned key-points." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 381, + 545, + 490 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 381, + 545, + 490 + ], + "spans": [ + { + "bbox": [ + 304, + 381, + 545, + 490 + ], + "type": "text", + "content": "Datasets. We adopt the KITTI Odometry Datasets [14] to evaluate the downstream localization performance of our method. We only use the grayscale monocular images with a high resolution of " + }, + { + "bbox": [ + 304, + 381, + 545, + 490 + ], + "type": "inline_equation", + "content": "376 \\times 1226" + }, + { + "bbox": [ + 304, + 381, + 545, + 490 + ], + "type": "text", + "content": " as input. To output multiscale key-points, a three-layer image pyramid is constructed with the scale factor of 1.2. It is worth noting that in KITTI, there contains many moving objects and changing camera exposure time, which is challenging for accurate and robust key-points matching." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 492, + 545, + 660 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 492, + 545, + 660 + ], + "spans": [ + { + "bbox": [ + 304, + 492, + 545, + 660 + ], + "type": "text", + "content": "Evaluation protocols. We take the ORB-SLAM [33] as the benchmark pipeline. We further disable both the local mapping and loop closure thread so as to keep the front-end visual odometry only. To evaluate the key-points fairly, all components in the pipeline are the same except the front-end used key-points. KITTI provides the ground-truth 6-DoF camera poses for sequences 00-10. Since the keypoints are adopted in the monocular VO pipeline, we align their poses with ground-truth to recover the scale of 6-DoF poses. Besides, we use the standard evaluation method, i.e., translational root-mean-square error (RMSE) drift " + }, + { + "bbox": [ + 304, + 492, + 545, + 660 + ], + "type": "inline_equation", + "content": "(\\mathrm{/m})" + }, + { + "bbox": [ + 304, + 492, + 545, + 660 + ], + "type": "text", + "content": " provided along with KITTI dataset. To evaluate the efficiency of the learning-based key-points, we also report the mean running frames per second for the methods." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "content": "Results. We report the results of different key-points on different KITTI sequences in Table 3. TPR outperforms the sota learning methods DISK [49] on all sequences even under complex traffic conditions. As for the inference time," + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11470" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 77, + 70, + 520, + 192 + ], + "blocks": [ + { + "bbox": [ + 77, + 70, + 520, + 192 + ], + "lines": [ + { + "bbox": [ + 77, + 70, + 520, + 192 + ], + "spans": [ + { + "bbox": [ + 77, + 70, + 520, + 192 + ], + "type": "table", + "html": "
MethodFPSRMSE/m ↓
00010204050607080910Avg.
ORB [40]20.659.46610.3572.6819.26238.6083.4672.7266.06119.2163.52140.53
SuperPoint [11]6.5162.78123.3413.521.066.362.0512.158.668.205.1034.32
D2-Net [12]8.810.44183.04105.332.2914.582.2510.7224.2729.629.6139.22
R2D2 [39]7.849.62515.9660.143.90123.0562.4453.8462.5473.3043.32104.81
SOSNet [48]6.3171.67309.8310.360.4714.684.0715.3510.753.247.6754.81
DISK [49]6.532.77149.9818.670.455.974.3812.8832.854.334.8126.71
Ours (TR)6.27.07164.399.720.233.462.129.997.423.103.7221.12
", + "image_path": "3ff86a723490e3376e851a21f8f3a5f104dfce77e4c0183364e6fd355d8ea83b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 54, + 221, + 282, + 369 + ], + "blocks": [ + { + "bbox": [ + 124, + 200, + 468, + 211 + ], + "lines": [ + { + "bbox": [ + 124, + 200, + 468, + 211 + ], + "spans": [ + { + "bbox": [ + 124, + 200, + 468, + 211 + ], + "type": "text", + "content": "Table 3. Visual odometry localization performance based on different key-points in KITTI datasets." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 54, + 221, + 282, + 369 + ], + "lines": [ + { + "bbox": [ + 54, + 221, + 282, + 369 + ], + "spans": [ + { + "bbox": [ + 54, + 221, + 282, + 369 + ], + "type": "table", + "html": "
MethodFeatAccuracy @ Thresholds (%) ↑
0.25m,2°0.5m,5°5m,10°
RootSIFT [1]11K53.462.372.3
SuperPoint [11]7K68.185.994.8
D2-Net [12]14K67.086.497.4
R2D2 [39]10K70.785.396.9
ASLFeat [29]10K71.285.996.9
DISK [49]10K72.886.497.4
MTLDesc [50]7K74.386.996.9
Ours (TR)10K74.389.098.4
", + "image_path": "b345a97e605133e7fee4a811582e5b560ecbf067f4d23888af8949a4a5062f08.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 52, + 377, + 282, + 388 + ], + "lines": [ + { + "bbox": [ + 52, + 377, + 282, + 388 + ], + "spans": [ + { + "bbox": [ + 52, + 377, + 282, + 388 + ], + "type": "text", + "content": "Table 4. Performance on Aachen Day-Night Localization datasets." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 397, + 287, + 446 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 397, + 287, + 446 + ], + "spans": [ + { + "bbox": [ + 46, + 397, + 287, + 446 + ], + "type": "text", + "content": "our method is slower than the hand-crafted ORB [40]. However, our approach adds no additional overhead compared to existing neural network-based methods. We also report the trajectory visualization in the Supplementary Materials." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 453, + 160, + 465 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 453, + 160, + 465 + ], + "spans": [ + { + "bbox": [ + 47, + 453, + 160, + 465 + ], + "type": "text", + "content": "4.5. Visual Localization" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 472, + 287, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 472, + 287, + 568 + ], + "spans": [ + { + "bbox": [ + 46, + 472, + 287, + 568 + ], + "type": "text", + "content": "To further verify the effectiveness of our method confronted with complex tasks, we evaluate it on the task of visual localization, which aims at estimating the camera pose within a given scene using an image sequence. The task was proposed in [41] to evaluate the performance of local features in the context of long-term localization without the need for a specific localization pipeline. All methods are compared on the official evaluation server in a fair manner." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 571, + 287, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 571, + 287, + 632 + ], + "spans": [ + { + "bbox": [ + 46, + 571, + 287, + 632 + ], + "type": "text", + "content": "Datasets. We resort to the Aachen Day-Night dataset [41] to demonstrate the effect on visual localization tasks, which contains images from the old inner city of Aachen, Germany. The key challenge in the dataset lies in matching images with extreme day-night changes for 98 queries." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 635, + 288, + 720 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 635, + 288, + 720 + ], + "spans": [ + { + "bbox": [ + 46, + 635, + 288, + 720 + ], + "type": "text", + "content": "Evaluation protocols. The evaluation is conducted by using The Visual Localization Benchmark, which takes a pre-defined visual localization pipeline based on COMLAP [43]. The successful localized images are counted within three error tolerances " + }, + { + "bbox": [ + 46, + 635, + 288, + 720 + ], + "type": "inline_equation", + "content": "(0.25\\mathrm{m}, 2^{\\circ}) / (0.5\\mathrm{m}, 5^{\\circ}) / (5\\mathrm{m}, 10^{\\circ})" + }, + { + "bbox": [ + 46, + 635, + 288, + 720 + ], + "type": "text", + "content": ", representing the maximum position error in meters and degrees." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 224, + 547, + 331 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 224, + 547, + 331 + ], + "spans": [ + { + "bbox": [ + 304, + 224, + 547, + 331 + ], + "type": "text", + "content": "Results. We compare our model with the typical joint detector and descriptor learning methods. Here, all methods are evaluated with the built-in matching strategy (Nearest Neighbors Search) for a fair comparison. As shown in Table 4, TPR with transformer blocks performs surprisingly well even under challenging illumination changes, about the estimated pose, which demonstrates the effectiveness of self-supervised learning with only positives on the natural dataset." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 343, + 383, + 356 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 343, + 383, + 356 + ], + "spans": [ + { + "bbox": [ + 305, + 343, + 383, + 356 + ], + "type": "text", + "content": "4.6. Limitations" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 364, + 547, + 461 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 364, + 547, + 461 + ], + "spans": [ + { + "bbox": [ + 304, + 364, + 547, + 461 + ], + "type": "text", + "content": "While our approach does not require any additional annotation, e.g., Structure-from-Motion models or optical flow, to produce the corresponding real image pairs. It still needs to receive pairs through homography adaption or image augmentation. Furthermore, despite getting better performance without using negative samples, TPR local features still fail in classical challenging cases such as textured areas, especially in the absence of matching priors." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 475, + 379, + 488 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 475, + 379, + 488 + ], + "spans": [ + { + "bbox": [ + 305, + 475, + 379, + 488 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 497, + 547, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 497, + 547, + 641 + ], + "spans": [ + { + "bbox": [ + 304, + 497, + 547, + 641 + ], + "type": "text", + "content": "In this paper, we propose to learn the transformation-predictive representations for the joint detection and description of local features. The model is trained by using none of the negatives and avoids the collapsing solution, which greatly improves the training efficiency. Self-supervised generation learning and curriculum learning are designed to soften the hard positives into continuous soft labels, which can train the model with stronger augmentation. We solve the label noise from false positives and negatives and further improve the performance of local features in the self-supervised paradigm. Experiments show that TPR significantly outperforms state-of-the-art methods." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 656, + 403, + 670 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 656, + 403, + 670 + ], + "spans": [ + { + "bbox": [ + 306, + 656, + 403, + 670 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "type": "text", + "content": "This work was supported by the Science and Technology on Space Intelligent Control Laboratory under Grant HTKJ2021KL502019." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "11471" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 19, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 122 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 122 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 122 + ], + "type": "text", + "content": "[1] Relja Arandjelović and Andrew Zisserman. Three things everyone should know to improve object retrieval. In CVPR, 2012. 8" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 124, + 288, + 157 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 124, + 288, + 157 + ], + "spans": [ + { + "bbox": [ + 53, + 124, + 288, + 157 + ], + "type": "text", + "content": "[2] Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk. Hpatches: A benchmark and evaluation of handcrafted and learned local descriptors. In CVPR, 2017. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 158, + 287, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 158, + 287, + 179 + ], + "spans": [ + { + "bbox": [ + 53, + 158, + 287, + 179 + ], + "type": "text", + "content": "[3] Herbert Bay, Tinne Tuytelaars, and Luc Van Gool. Surf: Speeded up robust features. In ECCV, 2006. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 180, + 287, + 223 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 180, + 287, + 223 + ], + "spans": [ + { + "bbox": [ + 53, + 180, + 287, + 223 + ], + "type": "text", + "content": "[4] Tiffany Tianhui Cai, Jonathan Frankle, David J Schwab, and Ari S Morcos. Are all negatives created equal in contrastive instance discrimination? arXiv preprint arXiv:2010.06682, 2020. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 224, + 286, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 224, + 286, + 267 + ], + "spans": [ + { + "bbox": [ + 53, + 224, + 286, + 267 + ], + "type": "text", + "content": "[5] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. NeurIPS, 2020. 1, 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 269, + 286, + 301 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 269, + 286, + 301 + ], + "spans": [ + { + "bbox": [ + 53, + 269, + 286, + 301 + ], + "type": "text", + "content": "[6] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In ICML, 2020. 1, 3, 4, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 302, + 286, + 334 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 302, + 286, + 334 + ], + "spans": [ + { + "bbox": [ + 53, + 302, + 286, + 334 + ], + "type": "text", + "content": "[7] Ting Chen, Yizhou Sun, Yue Shi, and Liangjie Hong. On sampling strategies for neural network-based collaborative filtering. In ACM SIGKDD, 2017. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 335, + 286, + 368 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 335, + 286, + 368 + ], + "spans": [ + { + "bbox": [ + 53, + 335, + 286, + 368 + ], + "type": "text", + "content": "[8] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv:2003.04297, 2020. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 369, + 286, + 391 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 369, + 286, + 391 + ], + "spans": [ + { + "bbox": [ + 53, + 369, + 286, + 391 + ], + "type": "text", + "content": "[9] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In CVPR, 2021. 1, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 392, + 286, + 423 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 392, + 286, + 423 + ], + "spans": [ + { + "bbox": [ + 48, + 392, + 286, + 423 + ], + "type": "text", + "content": "[10] Peng Cui, Shaowei Liu, and Wenwu Zhu. General knowledge embedded image representation learning. IEEE TMM, 2017. 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 425, + 286, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 425, + 286, + 456 + ], + "spans": [ + { + "bbox": [ + 48, + 425, + 286, + 456 + ], + "type": "text", + "content": "[11] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. In CVPR, 2018. 2, 6, 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 458, + 286, + 501 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 458, + 286, + 501 + ], + "spans": [ + { + "bbox": [ + 48, + 458, + 286, + 501 + ], + "type": "text", + "content": "[12] Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, and Torsten Sattler. D2-net: A trainable cnn for joint detection and description of local features. In CVPR, 2019. 1, 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 502, + 286, + 535 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 502, + 286, + 535 + ], + "spans": [ + { + "bbox": [ + 48, + 502, + 286, + 535 + ], + "type": "text", + "content": "[13] Tianyu Gao, Xingcheng Yao, and Danqi Chen. Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821, 2021. 1, 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 536, + 286, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 536, + 286, + 567 + ], + "spans": [ + { + "bbox": [ + 48, + 536, + 286, + 567 + ], + "type": "text", + "content": "[14] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In CVPR, 2012. 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 569, + 286, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 569, + 286, + 624 + ], + "spans": [ + { + "bbox": [ + 48, + 569, + 286, + 624 + ], + "type": "text", + "content": "[15] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. NeurIPS, 2020. 1, 3, 4" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 624, + 286, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 286, + 656 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 286, + 656 + ], + "type": "text", + "content": "[16] Michael Gutmann and Aapo Hyvarinen. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In AISTATS, 2010. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 48, + 658, + 286, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 286, + 690 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 286, + 690 + ], + "type": "text", + "content": "[17] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, 2020. 1, 3, 4, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 48, + 691, + 286, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 286, + 713 + ], + "type": "text", + "content": "[18] Kun He, Yan Lu, and Stan Sclaroff. Local descriptors optimized for average precision. In CVPR, 2018. 2" + } + ] + } + ], + "index": 18 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 37, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "text", + "content": "[19] Jared Heinly, Johannes L Schonberger, Enrique Dunn, and Jan-Michael Frahm. Reconstructing the world* in six days*(as captured by the yahoo 100 million image dataset). In CVPR, 2015. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 118, + 545, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 162 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 162 + ], + "type": "text", + "content": "[20] Yuhe Jin, Dmytro Mishkin, Anastasiia Mishchuk, Jiri Matas, Pascal Fua, Kwang Moo Yi, and Eduard Trulls. Image matching across wide baselines: From paper to practice. IJCV, 2021. 2, 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 163, + 545, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 163, + 545, + 196 + ], + "spans": [ + { + "bbox": [ + 307, + 163, + 545, + 196 + ], + "type": "text", + "content": "[21] Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus. Hard negative mixing for contrastive learning. NeurIPS, 2020. 1, 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 198, + 545, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 198, + 545, + 230 + ], + "spans": [ + { + "bbox": [ + 307, + 198, + 545, + 230 + ], + "type": "text", + "content": "[22] Axel Barroso Laguna and Krystian Mikolajczyk. Key. net: Keypoint detection by handcrafted and learned cnn filters revisited. IEEE TPAMI, 2022. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 233, + 545, + 265 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 233, + 545, + 265 + ], + "spans": [ + { + "bbox": [ + 307, + 233, + 545, + 265 + ], + "type": "text", + "content": "[23] K. Li, LongguangWang, L. Liu, Q. Ran, K. Xu, and Y. Guo. Decoupling makes weakly supervised local feature better. In CVPR, 2022. 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 267, + 545, + 310 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 267, + 545, + 310 + ], + "spans": [ + { + "bbox": [ + 307, + 267, + 545, + 310 + ], + "type": "text", + "content": "[24] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In ECCV, 2014. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 312, + 545, + 356 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 312, + 545, + 356 + ], + "spans": [ + { + "bbox": [ + 307, + 312, + 545, + 356 + ], + "type": "text", + "content": "[25] Dongfang Liu, Yiming Cui, Liqi Yan, Christos Mousas, Bai-jian Yang, and Yingjie Chen. Densernet: Weakly supervised visual localization using multi-scale feature aggregation. In AAAI, 2021. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 358, + 545, + 401 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 358, + 545, + 401 + ], + "spans": [ + { + "bbox": [ + 307, + 358, + 545, + 401 + ], + "type": "text", + "content": "[26] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In CVPR, 2021. 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 404, + 545, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 404, + 545, + 425 + ], + "spans": [ + { + "bbox": [ + 307, + 404, + 545, + 425 + ], + "type": "text", + "content": "[27] David G Lowe. Distinctive image features from scale-invariant keypoints. *IJCV*, 2004, 2, 6" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 427, + 545, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 427, + 545, + 470 + ], + "spans": [ + { + "bbox": [ + 307, + 427, + 545, + 470 + ], + "type": "text", + "content": "[28] Zixin Luo, Tianwei Shen, Lei Zhou, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, and Long Quan. Contextdesc: Local descriptor augmentation with cross-modality context. In CVPR, 2019. 6" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 307, + 473, + 545, + 516 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 473, + 545, + 516 + ], + "spans": [ + { + "bbox": [ + 307, + 473, + 545, + 516 + ], + "type": "text", + "content": "[29] Zixin Luo, Lei Zhou, Xuyang Bai, Hongkai Chen, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, and Long Quan. Aslfeat: Learning local features of accurate shape and localization. In CVPR, 2020. 1, 2, 5, 6, 7, 8" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 307, + 518, + 545, + 551 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 518, + 545, + 551 + ], + "spans": [ + { + "bbox": [ + 307, + 518, + 545, + 551 + ], + "type": "text", + "content": "[30] Iaroslav Melekhov, Zakaria Laskar, Xiaotian Li, Shuzhe Wang, and Juho Kannala. Digging into self-supervised learning of feature descriptors. In IEEE 3DV, 2021. 6" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 307, + 553, + 545, + 574 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 553, + 545, + 574 + ], + "spans": [ + { + "bbox": [ + 307, + 553, + 545, + 574 + ], + "type": "text", + "content": "[31] Krystian Mokolajczyk and Cordelia Schmid. A performance evaluation of local descriptors. IEEE TPAMI, 2005. 6" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 307, + 577, + 545, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 577, + 545, + 609 + ], + "spans": [ + { + "bbox": [ + 307, + 577, + 545, + 609 + ], + "type": "text", + "content": "[32] Anastasiia Mishchuk, Dmytro Mishkin, Filip Radenovic, and Jiri Matas. Working hard to know your neighbor's margins: Local descriptor learning loss. In NeurIPS, 2017. 6" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 307, + 611, + 545, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 611, + 545, + 643 + ], + "spans": [ + { + "bbox": [ + 307, + 611, + 545, + 643 + ], + "type": "text", + "content": "[33] Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos. Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics, 2015. 1, 7" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 307, + 646, + 545, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 646, + 545, + 678 + ], + "spans": [ + { + "bbox": [ + 307, + 646, + 545, + 678 + ], + "type": "text", + "content": "[34] Hyeonwoo Noh, Andre Araujo, Jack Sim, Tobias Weyand, and Bohyung Han. Large-scale image retrieval with attentive deep local features. In ICCV, 2017. 6" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 307, + 680, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 680, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 307, + 680, + 545, + 712 + ], + "type": "text", + "content": "[35] Yuki Ono, Eduard Trulls, Pascal Fua, and Kwang Moo Yi. Lf-net: learning local features from images. In NeurIPS, 2018. 6" + } + ] + } + ], + "index": 36 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "11472" + } + ] + } + ], + "index": 38 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 73, + 286, + 712 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 48, + 73, + 286, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 73, + 286, + 105 + ], + "spans": [ + { + "bbox": [ + 48, + 73, + 286, + 105 + ], + "type": "text", + "content": "[36] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv:1807.03748, 2018. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 106, + 286, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 106, + 286, + 138 + ], + "spans": [ + { + "bbox": [ + 49, + 106, + 286, + 138 + ], + "type": "text", + "content": "[37] Luc Oth, Paul Furgale, Laurent Kneip, and Roland Siegwart. Rolling shutter camera calibration. In CVPR, pages 1360-1367, 2013. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 140, + 286, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 140, + 286, + 172 + ], + "spans": [ + { + "bbox": [ + 49, + 140, + 286, + 172 + ], + "type": "text", + "content": "[38] James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman. Object retrieval with large vocabularies and fast spatial matching. In CVPR, 2007. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 173, + 286, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 173, + 286, + 215 + ], + "spans": [ + { + "bbox": [ + 49, + 173, + 286, + 215 + ], + "type": "text", + "content": "[39] Jerome Revaud, Philippe Weinzaepfel, César De Souza, Noe Pion, Gabriela Csurka, Yohann Cabon, and Martin Humenberger. R2d2: Repeatable and reliable detector and descriptor. In NeurIPS, 2019. 1, 2, 5, 6, 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 217, + 286, + 248 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 217, + 286, + 248 + ], + "spans": [ + { + "bbox": [ + 49, + 217, + 286, + 248 + ], + "type": "text", + "content": "[40] Ethan Rublee, Vincent Rabaud, Kurt Konolige, and Gary Bradski. Orb: An efficient alternative to sift or surf. In ICCV, 2011. 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 249, + 286, + 303 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 249, + 286, + 303 + ], + "spans": [ + { + "bbox": [ + 49, + 249, + 286, + 303 + ], + "type": "text", + "content": "[41] Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenberg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, et al. Benchmarking 6dof outdoor visual localization in changing conditions. In CVPR, 2018. 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 304, + 286, + 346 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 304, + 286, + 346 + ], + "spans": [ + { + "bbox": [ + 49, + 304, + 286, + 346 + ], + "type": "text", + "content": "[42] Nikolay Savinov, Akihito Seki, Lubor Ladicky, Torsten Sattler, and Marc Pollefeys. Quad-networks: unsupervised learning to rank for interest point detection. In CVPR, 2017. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 49, + 349, + 286, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 349, + 286, + 369 + ], + "spans": [ + { + "bbox": [ + 49, + 349, + 286, + 369 + ], + "type": "text", + "content": "[43] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In CVPR, 2016. 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 49, + 371, + 286, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 371, + 286, + 403 + ], + "spans": [ + { + "bbox": [ + 49, + 371, + 286, + 403 + ], + "type": "text", + "content": "[44] Johannes L Schonberger, Hans Hardmeier, Torsten Sattler, and Marc Pollefeys. Comparative evaluation of hand-crafted and learned local features. In CVPR, 2017. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 49, + 404, + 286, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 404, + 286, + 448 + ], + "spans": [ + { + "bbox": [ + 49, + 404, + 286, + 448 + ], + "type": "text", + "content": "[45] Edgar Simo-Serra, Eduard Trulls, Luis Ferraz, Iasonas Kokkinos, Pascal Fua, and Francesc Moreno-Noguer. Discriminative learning of deep convolutional feature point descriptors. In ICCV, 2015. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 49, + 449, + 286, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 449, + 286, + 480 + ], + "spans": [ + { + "bbox": [ + 49, + 449, + 286, + 480 + ], + "type": "text", + "content": "[46] Suwichaya Suwanwimolkul, Satoshi Komorita, and Kazuyuki Tasaka. Learning of low-level feature keypoints for accurate and robust detection. In WACV, 2021. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 49, + 482, + 286, + 513 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 482, + 286, + 513 + ], + "spans": [ + { + "bbox": [ + 49, + 482, + 286, + 513 + ], + "type": "text", + "content": "[47] Yurun Tian, Bin Fan, and Fuchao Wu. L2-net: Deep learning of discriminative patch descriptor in euclidean space. In CVPR, 2017. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 49, + 514, + 286, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 514, + 286, + 547 + ], + "spans": [ + { + "bbox": [ + 49, + 514, + 286, + 547 + ], + "type": "text", + "content": "[48] Yurun Tian, Xin Yu, Bin Fan, Fuchao Wu, Huub Heijnen, and Vassileios Balntas. Sosnet: Second order similarity regularization for local descriptor learning. In CVPR, 2019. 8" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 49, + 548, + 286, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 548, + 286, + 579 + ], + "spans": [ + { + "bbox": [ + 49, + 548, + 286, + 579 + ], + "type": "text", + "content": "[49] Michal Tyszkiiewicz, Pascal Fua, and Eduard Trulls. Disk: Learning local features with policy gradient. NeurIPS, 2020. 2, 6, 7, 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 49, + 581, + 286, + 613 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 581, + 286, + 613 + ], + "spans": [ + { + "bbox": [ + 49, + 581, + 286, + 613 + ], + "type": "text", + "content": "[50] Changwei Wang, Rongtao Xu, Yuyang Zhang, Shibiao Xu, Weiliang Meng, Bin Fan, and Xiaopeng Zhang. Mtldesc: Looking wider to describe better. AAAI, 2022. 6, 8" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 49, + 614, + 286, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 614, + 286, + 646 + ], + "spans": [ + { + "bbox": [ + 49, + 614, + 286, + 646 + ], + "type": "text", + "content": "[51] Tongzhou Wang and Phillip Isola. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In ICML, 2020. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 49, + 647, + 286, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 647, + 286, + 678 + ], + "spans": [ + { + "bbox": [ + 49, + 647, + 286, + 678 + ], + "type": "text", + "content": "[52] Xiao Wang and Guo-Jun Qi. Contrastive learning with stronger augmentations. arXiv preprint arXiv:2104.07713, 2021. 2.5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 49, + 680, + 286, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 680, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 49, + 680, + 286, + 712 + ], + "type": "text", + "content": "[53] Zihao Wang, Xueyi Li, and Zhen Li. Local representation is not enough: Soft point-wise transformer for descriptor and detector of local features. In IJCAI, 2021. 2" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 296 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "text", + "content": "[54] Zihao Wang, Zhen Li, Xueyi Li, Wenjie Chen, and Xiangdong Liu. Graph-based contrastive learning for description and detection of local features. IEEE TNNLS, 2022. 2, 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 107, + 545, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 545, + 138 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 545, + 138 + ], + "type": "text", + "content": "[55] Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. Unsupervised feature learning via non-parametric instance discrimination. In CVPR, 2018. 1" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 141, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 141, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 308, + 141, + 545, + 184 + ], + "type": "text", + "content": "[56] Lanling Xu, Jianxun Lian, Wayne Xin Zhao, Ming Gong, Linjun Shou, Daxin Jiang, Xing Xie, and Ji-Rong Wen. Negative sampling for contrastive representation learning: A review. arXiv preprint arXiv:2206.00212, 2022. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 186, + 545, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 186, + 545, + 217 + ], + "spans": [ + { + "bbox": [ + 308, + 186, + 545, + 217 + ], + "type": "text", + "content": "[57] Kwang Moo Yi, Eduard Trulls, Vincent Lepetit, and Pascal Fua. Lift: Learned invariant feature transform. In ECCV, 2016. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 219, + 545, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 219, + 545, + 251 + ], + "spans": [ + { + "bbox": [ + 308, + 219, + 545, + 251 + ], + "type": "text", + "content": "[58] Kwang Moo Yi, Yannick Verdie, Pascal Fua, and Vincent Lepetit. Learning to assign orientations to feature points. In CVPR, 2016. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 253, + 545, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 253, + 545, + 296 + ], + "spans": [ + { + "bbox": [ + 308, + 253, + 545, + 296 + ], + "type": "text", + "content": "[59] Xiaoming Zhao, Xingming Wu, Jinyu Miao, Weihai Chen, Peter CY Chen, and Zhengguo Li. Alike: Accurate and lightweight keypoint detection and descriptor extraction. IEEE TMM, 2022. 6" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "11473" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/69fb3432-f3b6-48da-9de8-e985d9696176_content_list.json b/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/69fb3432-f3b6-48da-9de8-e985d9696176_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..a3fba154938c350bf0495d12a12d1c76278cb60b --- /dev/null +++ b/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/69fb3432-f3b6-48da-9de8-e985d9696176_content_list.json @@ -0,0 +1,1602 @@ +[ + { + "type": "text", + "text": "Learning Transformations To Reduce the Geometric Shift in Object Detection", + "text_level": 1, + "bbox": [ + 93, + 130, + 875, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Vedit Vidit1 Martin Engilberge1 Mathieu Salzmann1,2 \nCVLab, EPFL1, ClearSpace SA2", + "bbox": [ + 267, + 179, + 699, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "firstname_lastname@epfl.ch", + "bbox": [ + 364, + 219, + 598, + 232 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 268, + 313, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between synthetic and real images. Here, by contrast, we tackle geometric shifts emerging from variations in the image capture process, or due to the constraints of the environment causing differences in the apparent geometry of the content itself. We introduce a self-training approach that learns a set of geometric transformations to minimize these shifts without leveraging any labeled data in the new domain, nor any information about the cameras. We evaluate our method on two different shifts, i.e., a camera's field of view (FoV) change and a viewpoint change. Our results evidence that learning geometric transformations helps detectors to perform better in the target domains.", + "bbox": [ + 75, + 300, + 473, + 542 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 571, + 207, + 588 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "While modern object detectors [1, 2, 17, 23, 24] achieve impressive results, their performance decreases when the test data depart from the training distribution. This problem arises in the presence of appearance variations due to, for example, differing illumination or weather conditions. Considering the difficulty and cost of acquiring annotated data in the test (i.e., target) domain, Unsupervised Domain Adaptation (UDA) has emerged as the standard strategy to address such scenarios [3, 4, 9, 26, 38].", + "bbox": [ + 75, + 597, + 468, + 733 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this context, much effort has been made to learn domain invariant features, such that the source and target distributions in this feature space are similar. This has led to great progress in situations where the appearance of the objects changes drastically from one domain to the other, as in case of real-to-sketch adaptation (e.g., Pascal VOC [10] to Comics [15]), or weather adaptation (e.g., Cityscapes [6] to Foggy Cityscapes [27]). Nevertheless, such object appearance changes are not the only sources of domain shifts. They can also have geometric origins. For example, as shown in Fig. 1, they can be due to a change in camera view", + "bbox": [ + 75, + 734, + 470, + 901 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/0ddde06e2387f32ffbdee1691784d1c9c2685302331fc17ca5e0d3c07d7e4427.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 500, + 268, + 893, + 414 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6c249218327b701aa8630044b3c479c23874ad3d691ccc4c158d8a6096b5563c.jpg", + "image_caption": [ + "Figure 1. Geometric shifts. (Left) Due to a different FoV, the cars highlighted in green, undergo different distortions even though they appear in similar image regions. (Right) Different camera viewpoints (front facing vs downward facing) yield different distortions and occlusion patterns for pedestrian detection. (Bottom) The distributions of pedestrian bounding box sizes in Cityscapes [6] and MOT [8] differ significantly as the pedestrians are usually far away or in the periphery in Cityscapes. The top images are taken from Cityscapes [6], and the bottom-left and right ones from KITTI [12] and MOT [8], respectively." + ], + "image_footnote": [], + "bbox": [ + 501, + 415, + 890, + 566 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "point or field-of-view (FoV), or a change of object scale due to different scene setups. In practice, such geometric shifts typically arise from a combination of various factors, including but not limited to the ones mentioned above.", + "bbox": [ + 496, + 747, + 890, + 808 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we introduce a domain adaptation approach tackling such geometric shifts. To the best of our knowledge, the recent work of [13] constitutes the only attempt at considering such geometric distortions. However, it introduces a method solely dedicated to FoV variations, assuming that the target FoV is fixed and known. Here, we de", + "bbox": [ + 496, + 810, + 893, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "17441", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "velop a more general framework able to cope with a much broader family of geometric shifts.", + "bbox": [ + 76, + 90, + 468, + 119 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To this end, we model geometric transformations as a combination of multiple homographies. We show both theoretically and empirically that this representation is sufficient to encompass a broad variety of complex geometric transformations. We then design an aggregator block that can be incorporated to the detector to provide it with the capacity to tackle geometric shifts. We use this modified detector to generate pseudo labels for the target domain, which let us optimize the homographies so as to reduce the geometric shift.", + "bbox": [ + 75, + 121, + 468, + 271 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contributions can be summarized as follows. (i) We tackle the problem of general geometric shifts for object detection. (ii) We learn a set of homographies using unlabeled target data, which alleviates the geometric bias arising in source-only training. (iii) Our method does not require prior information about the target geometric distortions and generalizes to a broad class of geometric shifts. Our experiments demonstrate the benefits of our approach in several scenarios. In the presence of FoV shifts, our approach yields similar performance to the FoV-dedicated framework of [13] but without requiring any camera information. As such, it generalizes better to other FoVs. Furthermore, we show the generality of our method by using it to adapt to a new camera viewpoint in the context of pedestrian detection. Our implementation can be accessed at https://github.com/vidit09/geoshift.", + "bbox": [ + 75, + 273, + 470, + 513 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 76, + 527, + 218, + 542 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Unsupervised Domain Adaptation (UDA). UDA for image recognition [11, 21, 22, 30, 32, 35, 36] and object detection [3, 4, 9, 20, 26, 38] has made a great progress in the past few years. The common trend in both tasks consists of learning domain invariant features. For object detection, this entails aligning the global (e.g., illumination, weather) and local (foreground objects) features in the two domains. In this context, [3, 5, 26, 28] align image- and instance-level features in the two domains via adversarial learning [11]; [33] learns category-specific attention maps to better align specific image regions; [38] clusters the proposed object regions using $k$ -means clustering and uses the centroids for instance-level alignment. While this successfully tackles domain shifts caused by object appearance variations, it fails to account for the presence of shifts due to the image capture process itself, such as changes in camera intrinsics or viewpoint. The only initial step at considering a geometric shift is the work of [13], which shows the existence of an FoV gap in driving datasets [6, 12] and proposes a Position Invariant Transform (PIT) that corrects the distortions caused specifically by an FoV change. In essence, PIT undistorts the images by assuming knowledge of the target FoV. By contrast, here, we introduce an approach that gen", + "bbox": [ + 75, + 551, + 468, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "eralizes to a broad family of geometric shifts by learning transformations without requiring any camera information.", + "bbox": [ + 498, + 90, + 890, + 121 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Self-training. Self-training, generally employed in the semi-supervised setting, offers an alternative to learning domain-invariant features and utilize unlabeled data to improve a detector's performance. In this context, [29] uses a student-teacher architecture where the teacher model is trained with supervised data and generates pseudo-labels on unannotated data. These pseudo-labels are then used to train a student model. While effective in the standard semi-supervised learning scenario, the quality of the pseudo-labels obtained with this approach tends to deteriorate when the labeled and unlabeled data present a distribution shift. [9, 20] have therefore extended this approach to domain adaptation by using the Mean Teacher strategy of [31] to generate reliable pseudo-labels in the target domain. Other approach include the use of CycleGAN [37] generated images to train an unbiased teacher model [9], and that of different augmentation strategies to generate robust pseudo-labels [20]. Our approach also follows a self-training strategy but, while these works focus on object appearance shifts, we incorporate learnable blocks to address geometric shifts. As shown in our experiment, this lets us outperform the state-of-the-art AdaptTeacher [20].", + "bbox": [ + 496, + 128, + 890, + 460 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Learning Geometric Transformations. End-to-end learning of geometric transformations has been used to boost the performance of deep networks. For example, Spatial Transformer Networks (STNs) [16] reduce the classification error by learning to correct for affine transformations; deformable convolutions [7] model geometric transformations by applying the convolution kernels to non-local neighborhoods. These methods work well when annotations are available for supervision, and make the network invariant to the specific geometric transformations seen during training. Here, by contrast, we seek to learn transformations in an unsupervised manner and allow the network to generalize to unknown target transformations.", + "bbox": [ + 496, + 468, + 890, + 664 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Modeling Geometric Transformations", + "text_level": 1, + "bbox": [ + 500, + 679, + 841, + 695 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In the context of UDA, multiple geometric differences can be responsible for the gap between the domains. Some can be characterized by the camera parameters, such as a change in FoV (intrinsic) or viewpoint (extrinsic), whereas others are content specific, such as a difference in road width between different countries. Ultimately, the geometric shift is typically a combination of different geometric operations. Since the parameters of these operations are unknown, we propose to bridge the domain gap by learning a geometric transform. Specifically, we aggregate the results of multiple perspective transforms, i.e., homographies, to obtain a differentiable operation that can emulate a wide variety of geometric transforms.", + "bbox": [ + 496, + 704, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "17442", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3.1. Theoretical Model", + "text_level": 1, + "bbox": [ + 76, + 90, + 256, + 107 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Let us first show that, given sufficiently many homographies, one can perfectly reproduce any mapping between $\\mathbb{R}^2\\setminus (0,0)$ and $\\mathbb{R}^2$", + "bbox": [ + 76, + 113, + 468, + 160 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Single homography for a single point. First, we show that a single homography with 4 degrees of freedom can map a point $p \\in \\mathbb{R}^2 \\setminus (0,0)$ to any other point in $\\mathbb{R}^2$ . To this end, let", + "bbox": [ + 76, + 176, + 468, + 234 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nH = \\left[ \\begin{array}{c c c} s _ {x} & 0 & 0 \\\\ 0 & s _ {y} & 0 \\\\ l _ {x} & l _ {y} & 1 \\end{array} \\right] \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 204, + 234, + 468, + 282 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "be a homography, with $(s_x,s_y)$ the scaling factors on the $x-$ and $y$ -axis, respectively, and $(l_{x},l_{y})$ the perspective factors in $x$ and $y$ , respectively. For any destination point $d\\in \\mathbb{R}^2$ , there exists a set of parameters $(s_x,s_y,l_x,l_y)$ such that $d = H\\times p$ . One such set is $\\left(\\frac{d_x}{p_x},\\frac{d_y}{p_y},0,0\\right)$ .", + "bbox": [ + 76, + 287, + 470, + 371 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Emulating any geometric transformation Now that we have shown that a single homography can move a point to any other point in $\\mathbb{R}^2$ , we describe a simple protocol to emulate any geometric transform. Given an unknown geometric transform $T:\\mathbb{R}^2\\backslash (0,0)\\to \\mathbb{R}^2$ , we aim to emulate $T$ with a set of homographies. In general, for an image $\\mathbf{I}\\in \\mathbb{R}^{3\\times h\\times w}$ we can restrict the domain of $T$ to only image coordinates. To this end, we can define a set of homographies $H_{i}\\in \\mathbb{H}$ for i in $\\{1,2,3,\\dots,h\\times w\\}$ , where the parameters of $H_{i}$ are chosen to mimic the transform $T$ for location $i$ of the image. In this protocol, the aggregation mechanism is trivial since each homography is in charge of remapping a single pixel coordinate of the original space.", + "bbox": [ + 75, + 385, + 468, + 580 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "While this works in theory, this is of course not viable in practice since it would require too many homographies. With a smaller number of homographies, each transform needs to remap multiple points, and a more sophisticated aggregation mechanism is required. Specifically, the aggregation mechanism needs to select which transform is in charge of remapping which point. In the next section, we empirically show that this strategy lets us closely approximate the spherical projection mapping used in PIT [13].", + "bbox": [ + 75, + 580, + 468, + 718 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Approximating PIT with Homographies", + "text_level": 1, + "bbox": [ + 76, + 726, + 421, + 743 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To demonstrate the possibility offered by aggregating multiple homographies, we design an approximation of PIT using only homographies. PIT proposes to correct for an FoV gap by remapping images to a spherical surface. During this transformation, regions further from the center of a scene are compressed with a higher ratio. This variable compression of the space cannot be reproduced by a single homography transformation. To overcome this limitation, we combine the results of multiple homographies that all have different compression rates (scaling parameters). For", + "bbox": [ + 75, + 750, + 470, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "the aggregation mechanism, we use the optimal strategy by selecting for each pixel the homography that approximates best the PIT mapping. As shown in Fig. 2, this combination closely approximates the PIT results with only 5 homographies. Further analysis of these experiments is available in the supplementary material in Fig. A.3.", + "bbox": [ + 496, + 90, + 893, + 183 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/a8585d72f7a753eb330083e477c431b439ebe03778af60c92000cac1274ef772.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 540, + 200, + 849, + 284 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/fd2e8ca6e69a953f98941a93ff1e69b0512e0edce57a48b3c50fe6b63b57795f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 576, + 292, + 823, + 375 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/54fb2a76b33e0be58bb29dfc354ac78951f6602eb23e6f2bbc57d7912813973e.jpg", + "image_caption": [ + "Figure 2. Approximating PIT with homographies. We show the original image (top), the PIT [13] correction (middle), and our approximation of PIT using 5 homographies. Note that 5 homographies are sufficient to closely match the PIT spherical correction." + ], + "image_footnote": [], + "bbox": [ + 576, + 378, + 823, + 460 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.3. Homographies in a Learning Setup", + "text_level": 1, + "bbox": [ + 500, + 553, + 805, + 569 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In the two previous sections, we have demonstrated both theoretically and empirically the flexibility of aggregating homographies. This makes this representation an ideal candidate for domain adaptation since the geometric shift between the domains is unknown and can be a combination of different transforms, such as FoV change, viewpoint change, camera distortion, or appearance distortion. As will be discussed in the next section, by learning jointly the set of perspective transforms and the aggregation mechanism on real data, our model can reduce the geometric shift between the two domains without prior knowledge about this domain gap.", + "bbox": [ + 496, + 575, + 890, + 758 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Method", + "text_level": 1, + "bbox": [ + 500, + 768, + 591, + 784 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Let us now introduce our approach to reducing the geometric shift in object detection. Following the standard UDA setting, let $D_{s} = \\{(I_{s},B_{s},C_{s})\\}$ be a labeled source dataset containing images $I_{s} = \\{I_{s}^{i}\\}_{1}^{N_{s}}$ with corresponding object bounding boxes $B_{s} = \\{b_{s}^{i}\\}_{1}^{N_{s}}$ and object classes $C_s = \\{c_s^i\\}_1^{N_s}$ . Furthermore, let $D_{t} = \\{I_{t}\\}$ denote an unlabeled target dataset for which only images $I_{t} = \\{I_{t}^{i}\\}_{1}^{N_{t}}$", + "bbox": [ + 496, + 795, + 890, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "17443", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/6b23ae9ef0fc1e3a999035fafe024d7628f93faaa004fff9f725f77197b8a824.jpg", + "image_caption": [ + "Figure 3. Architecture: The input image is first transformed by a set of trainable homographies. The feature maps extracted from the transformed images are then unwarped by the inverse homographies to achieve spatial consistency. We then combine the unwarped feature maps using a trainable aggregator, whose output is passed to a detection head. The blocks shown in green correspond to standard FasterRCNN operations. The $\\oplus$ symbol represents the concatenation operation." + ], + "image_footnote": [], + "bbox": [ + 78, + 92, + 893, + 345 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "are available, without annotations. Here, we tackle the case where the two domains differ by geometric shifts but assume no knowledge about the nature of these shifts. Below, we first introduce the architecture we developed to handle this and then our strategy to train this model.", + "bbox": [ + 75, + 438, + 468, + 513 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Model Architecture", + "text_level": 1, + "bbox": [ + 76, + 526, + 264, + 540 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The overall architecture of our approach is depicted in Fig. 3. In essence, and as discussed in Sec. 3, we characterize the geometric changes between the source and target data by a set of transformations $\\mathcal{T} = \\{\\mathcal{H}_i\\}_{1}^N$ . Each $\\mathcal{H}_i$ in $\\mathcal{T}$ is a homography of the same form as in Eq. (1). For our method to remain general, we assume the transformations to be unknown, and our goal, therefore, is to learn $\\mathcal{T}$ to bridge the gap between the domains. This requires differentiability w.r.t. the transformation parameters, which we achieve using the sampling strategy proposed in [16].", + "bbox": [ + 75, + 550, + 468, + 700 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As shown in Fig. 3, the input image is transformed by the individual homographies in $\\mathcal{T}$ , and the transformed images are fed to a modified FasterRCNN [24] detector. Specifically, we extract a feature map $\\mathcal{F}_{\\mathcal{H}_i} \\in \\mathbb{R}^{H \\times W \\times C}$ for each transformed image via a feature extractor shared by all transformations. To enforce spatial correspondence between the different $\\mathcal{F}_{\\mathcal{H}_i}$ s, we unwarp them with $\\mathcal{H}_i^{-1}$ .", + "bbox": [ + 75, + 702, + 468, + 809 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We then introduce an aggregator $\\mathcal{A}_{\\theta_g}$ , parameterized by $\\theta_{g}$ , whose goal is to learn a common representation given a fixed number of unwarped feature maps $\\mathcal{F}_{\\mathcal{H}_i}^{\\prime}$ . To achieve this, the aggregator takes as input", + "bbox": [ + 75, + 810, + 468, + 869 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {G} = \\mathcal {F} _ {\\mathcal {H} _ {1}} ^ {\\prime} \\oplus \\mathcal {F} _ {\\mathcal {H} _ {2}} ^ {\\prime} \\oplus \\dots \\oplus \\mathcal {F} _ {\\mathcal {H} _ {N}} ^ {\\prime} \\in \\mathbb {R} ^ {H \\times W \\times C \\times N}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 882, + 468, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\oplus$ represents concatenation in the channel dimension. The aggregator outputs a feature map $\\mathcal{A}_{\\theta_g}(\\mathcal{G})\\in \\mathbb{R}^{H\\times W\\times C}$ whose dimension is independent of the number of transformations. This output is then passed to a detection head to obtain the objects' bounding boxes and class labels.", + "bbox": [ + 498, + 438, + 890, + 513 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Model Training", + "text_level": 1, + "bbox": [ + 500, + 521, + 656, + 537 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our training procedure relies on three steps: (i) Following common practice in UDA, we first train the Faster-RCNN detector with source-only data; (ii) We then introduce the aggregator and train it so that it learns to combine different homographies using the labeled source data; (iii) Finally, we learn the optimal transformations for adaptation using both the source and target data via a Mean Teacher [31] strategy.", + "bbox": [ + 496, + 544, + 890, + 666 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Aggregator Training. To train the aggregator, we randomly sample a set of homographies $\\mathcal{T} \\in \\mathbb{R}^{N \\times 4}$ in each training iteration. This gives the aggregator the ability to robustly combine diverse input transformations but requires strong supervision to avoid training instabilities. We, therefore, perform this step using the source data.", + "bbox": [ + 496, + 683, + 890, + 773 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The loss function for a set of transformed images $\\mathcal{T}(I_s)$ is then defined as in standard FasterRCNN training with a combination of classification and regression terms [24]. That is, we train the aggregator by solving", + "bbox": [ + 496, + 773, + 890, + 834 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\theta_ {g}} \\mathcal {L} _ {c l s} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) + \\mathcal {L} _ {r e g} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 578, + 842, + 890, + 866 + ], + "page_idx": 3 + }, + { + "type": "page_footnote", + "text": "1As our homographies involve only 4 parameters, with a slight abuse of notation, we say that $\\mathcal{H}_i \\in \\mathbb{R}^4$ .", + "bbox": [ + 500, + 875, + 890, + 898 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "17444", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where", + "bbox": [ + 76, + 92, + 124, + 104 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {c l s} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) = \\mathcal {L} _ {c l s} ^ {r p n} + \\mathcal {L} _ {c l s} ^ {r o i}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 119, + 468, + 137 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {r e g} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) = \\mathcal {L} _ {r e g} ^ {r p n} + \\mathcal {L} _ {r e g} ^ {r o i}. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 140, + 468, + 159 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$\\mathcal{L}^{rpn}$ and $\\mathcal{L}^{roi}$ correspond to the Region Proposal Network (RPN) loss terms and the Region of Interest (RoI) ones, respectively. During this process, we freeze the parameters $\\theta_{b}$ of the base network, i.e., feature extractor and detection head, which were first trained on the source data without aggregator. Ultimately, the aggregator provides the network with the capacity to encode different transformations that are not seen in the source domain. The third training step then aims to learn the best transformation for successful object detection in the target domain.", + "bbox": [ + 75, + 171, + 472, + 325 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Learning the Transformations. As we have no annotations in the target domain, we exploit a Mean Teacher (MT) strategy to learn the optimal transformations. To this end, our starting point is the detector with a trained aggregator and a set of random transformations $\\mathcal{T}$ . The MT strategy is illustrated in Fig. 4. In essence, MT training [31] involves two copies of the model: A student model, with parameters $\\theta^{st} = \\{\\mathcal{T}^{st},\\theta_b^{st},\\theta_g^{st}\\}$ , that will be used during inference, and a teacher model, with parameters $\\theta^{te} = \\{\\mathcal{T}^{te},\\theta_b^{te},\\theta_g^{te}\\}$ , that is updated as an Exponentially Moving Average (EMA) of the student model. That is, the student's parameters are computed with standard backpropagation, whereas the teacher's ones are updated as", + "bbox": [ + 75, + 347, + 472, + 544 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\theta_ {t e} \\leftarrow \\alpha \\theta_ {t e} + (1 - \\alpha) \\theta_ {s t}. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 181, + 556, + 468, + 574 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The student model is trained using both source and target detection losses. Since the target domain does not have annotations, the teacher model is used to generate pseudolabels. These labels might be noisy, and hence we only keep the predictions with a confidence score above a threshold $\\tau$ . Furthermore, non-maxima suppression (NMS) is used to remove the highly-overlapping bounding box predictions.", + "bbox": [ + 75, + 587, + 468, + 694 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Formally, given a source image $I_{s}$ and a target image $I_{t}$ , the student model is trained by solving", + "bbox": [ + 76, + 694, + 468, + 724 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\min _ {\\mathcal {T} ^ {s t}, \\theta_ {g} ^ {s t}, \\theta_ {b} ^ {s t}} \\mathcal {L} _ {d e t} (\\mathcal {T} (I _ {s})) + \\lambda \\mathcal {L} _ {d e t} (\\mathcal {T} (I _ {t})) , \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 135, + 738, + 468, + 763 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\lambda$ controls the target domain contribution and", + "bbox": [ + 76, + 777, + 423, + 792 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {d e t}} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) = \\mathcal {L} _ {\\text {c l s}} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) + \\mathcal {L} _ {\\text {r e g}} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 120, + 806, + 468, + 823 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {d e t}} \\left(\\mathcal {T} \\left(I _ {t}\\right)\\right) = \\mathcal {L} _ {\\text {c l s}} \\left(\\mathcal {T} \\left(I _ {t}\\right)\\right). \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 124, + 825, + 468, + 842 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Similarly to [18,20], we update the student model with only the classification loss in the target domain to help stabilize training.", + "bbox": [ + 75, + 854, + 468, + 901 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/3a1a09bc3237b028b9ced457e72570d6a2cb75a16d94616ff4f8b9174a18c1ea.jpg", + "image_caption": [ + "Figure 4. Mean Teacher formalism. The student model is trained with ground-truth labels in the source domain and pseudo labels in the target one. These pseudo labels are produced by the teacher model, which corresponds to an exponentially moving average (EMA) of the student network." + ], + "image_footnote": [], + "bbox": [ + 506, + 87, + 890, + 252 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 500, + 359, + 633, + 376 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We demonstrate the effectiveness and generality of our method on different geometric shifts. First, to compare to the only other work that modeled a geometric shift [13], we tackle the problem of a change in FoV between the source and target domain. Note that, in contrast to [13], we do not assume knowledge of the target FoV. Furthermore, while [13] was dedicated to FoV adaptation, our approach generalizes to other geometric shifts. We demonstrate this on the task of pedestrian detection under a viewpoint shift. We compare our method with the state-of-the-art AdaptTeacher [20], which also uses a Mean Teacher, but focuses on appearance shifts. In the remainder of this section, we describe our experimental setup and discuss our results.", + "bbox": [ + 496, + 383, + 893, + 582 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1. Datasets", + "text_level": 1, + "bbox": [ + 500, + 590, + 602, + 604 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Cityscapes [6] contains 2975 training and 500 test images with annotations provided for 8 categories (person, car, train, rider, truck, motorcycle, bicycle and bus). The average horizontal (FoVx) and vertical (FoVy) FoVs of the capturing cameras are $50^{\\circ}$ and $26^{\\circ}$ , respectively. We use this dataset as the source domain for both FoV adaptation and viewpoint adaptation.", + "bbox": [ + 496, + 614, + 893, + 720 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "KITTI [12] is also a street-view dataset containing 6684 images annotated with the car category. The horizontal (FoVx) and vertical (FoVy) FoVs of the camera are $90^{\\circ}$ and $34^{\\circ}$ , respectively. We use this dataset as target domain for FoV adaptation, as the viewpoint is similar to that of Cityscapes. Following [13], we use 5684 images for unsupervised training and 1000 images for evaluation.", + "bbox": [ + 496, + 726, + 893, + 834 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "MOT [8] is a multi-object tracking dataset. We use the indoor mall sequence, MOT20-02, consisting of 2782 frames annotated with the person category. We employ this dataset as target domain for viewpoint adaptation. We use the first", + "bbox": [ + 496, + 839, + 893, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "17445", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "2000 frame for unsupervised training and last 782 for evaluation.", + "bbox": [ + 76, + 90, + 468, + 121 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Adaptation Tasks and Metric", + "text_level": 1, + "bbox": [ + 76, + 131, + 339, + 148 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "FoV adaptation. As in [13], we consider the case of an increasing FoV using Cityscapes as source domain and KITTI as target domain. The horizontal and vertical FoVs increase from $(50^{\\circ}, 26^{\\circ})$ in Cityscapes to $(90^{\\circ}, 34^{\\circ})$ in KITTI. Therefore, as can be seen in Fig. 1, the KITTI images have a higher distortion in the corners than the Cityscapes ones. Similarly to PIT [13], we use the car category in our experiments.", + "bbox": [ + 75, + 155, + 468, + 277 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "FoV generalization. Following PIT [13], we study the generalization of our approach to new FoVs by cropping the KITTI images to mimic different FoV changes in the horizontal direction (FoVx). Specifically, we treat FoVx = 50° as the source domain and the cropped images with FoVx = {70°, 80°, 90°} as different target domains. We evaluate our approach on car on these different pairs of domains.", + "bbox": [ + 75, + 284, + 470, + 391 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Viewpoint adaptation. This task entails detecting objects seen from a different viewpoint in the source and target domains. We use the front-facing Cityscapes images as source domain and the downward-facing MOT ones as target one. As the MOT data depicts pedestrians, we use the bounding boxes corresponding to the person category in Cityscapes.[2]", + "bbox": [ + 75, + 398, + 468, + 491 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metric. In all of our experiments, we use the Average Precision (AP) as our metric. Specifically, following [13], we report the AP@0.5, which considers the predictions as true positives if they match the ground-truth label and have an intersection over union (IOU) score of more than 0.5 with the ground-truth bounding boxes.", + "bbox": [ + 75, + 512, + 468, + 603 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.3. Implementation Details", + "text_level": 1, + "bbox": [ + 76, + 613, + 294, + 628 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We use the Detector2 [34] implementation of Faster-RCNN [24] with a ResNet50 [14] backbone as our base architecture. In all of our experiments, the images are resized so that the shorter side has 800 pixels while maintaining the aspect ratio. The base network is first trained on source-only images with random cropping and random flipping augmentation for 24k iterations with batch size 8. We use the Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0.01, scaled down by a 0.1 factor after 18k iterations. We use ImageNet [25] pretrained weights to initialize the ResNet50 backbone.", + "bbox": [ + 75, + 637, + 468, + 801 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We then incorporate the aggregator in the trained base architecture. The aggregator architecture contains three convolutional layers with a kernel size of $3 \\times 3$ , and one $1 \\times 1$ convolutional layer. We first train the aggregator", + "bbox": [ + 76, + 804, + 470, + 864 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/89f3e28d0ea238bb7373db65c9175b6222ffb50691783c497ed59083b6b5b2b3.jpg", + "image_caption": [ + "Figure 5. FoV Adaptation: Qualitative Results. We visualize a car detection result in the Cityscapes-to-KITTI FoV adaptation scenario. The top left image corresponds to the ground truth, the bottom left to the Mean Teacher result, which confuses the orange container with a car, the bottom right to the Mean Teacher adaptation + PIT FoV adaptation result, which also mistakes the orange container for a car and further detects the speed limit on the road. Our approach, on the top right, correctly matches the ground truth." + ], + "image_footnote": [], + "bbox": [ + 501, + 88, + 694, + 186 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/fecc74c15c8496e461f70945446eb786768514a40889ca8c2309e4781aa46481.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 696, + 88, + 888, + 186 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "on the source data with the base frozen and using random transformations $\\mathcal{T}$ . The transformations are generated by randomly sampling each $\\mathcal{H}_i$ parameters as $s_x, s_y \\sim \\mathcal{U}_{[0.5,2.0]}$ , $\\mathcal{U}_{[0.5,2.0]}$ and $l_x, l_y \\sim \\mathcal{U}_{[-0.5,0.5]}$ , $\\mathcal{U}_{[-0.5,0.5]}$ . We train the aggregator for 30k iterations using a batch size of 8 and the SGD optimizer with a learning rate of $1e^{-4}$ .", + "bbox": [ + 496, + 339, + 893, + 429 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The student and teacher models are then initialized with this detector and the random $\\mathcal{T} = \\{\\mathcal{H}_i\\}_{i=1}^N$ . We optimize $\\mathcal{T}$ using Adam [19], while the base and aggregator networks are optimized by SGD. The learning rate is set to $1e^{-3}$ and scaled down by a factor 0.1 after 10k iterations for the SGD optimizer. For the first 10k iterations in FoV adaptation and for 2k iterations for viewpoint adaptation, we only train $\\mathcal{T}$ keeping base and aggregator frozen. The $\\alpha$ coefficient for the EMA update is set to 0.99; the confidence threshold $\\tau = 0.6$ ; $\\lambda = \\{0.01, 0.1\\}$ for FoV and viewpoint adaptation, respectively. The Mean Teacher framework is trained using both the source and target data. We set $N = 5$ , unless otherwise specified, and use a batch size of 4, containing 2 source and 2 target images. We apply random color jittering on both the source and target data as in [20, 31]. All of our models are trained on a single NVIDIA V100 GPU. A detailed hyper-parameter study is provided in the supplementary material.", + "bbox": [ + 496, + 430, + 892, + 702 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.4. Comparison with the State of the Art", + "text_level": 1, + "bbox": [ + 500, + 714, + 821, + 729 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We compare our approach with the following baselines3. FR: FasterRCNN trained only on the source data with random crop augmentation; AT: AdaptTeacher [20]; MT: Mean Teacher initialized with FR and trained with random color jittering on both the source and target data (i.e., this corresponds to our mean teacher setup in Sec. 4.2 but without the aggregator and without transformations $\\mathcal{T}$ ); FR+PIT: Same setup as FR but with the images corrected with PIT [13]; MT+PIT: Same setup as MT but with the", + "bbox": [ + 496, + 737, + 893, + 875 + ], + "page_idx": 5 + }, + { + "type": "page_footnote", + "text": "2In Cityscapes, a person may be labeled as either person or rider. Since the rider label is used for people riding a vehicle, we omit these cases.", + "bbox": [ + 76, + 875, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_footnote", + "text": "3We re-implement PIT baselines with our detector.", + "bbox": [ + 514, + 886, + 787, + 898 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "17446", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/a710b6816092c491e5ea5200d74696d083f0681fedb1e89fa6785e56d783209e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodCar AP@0.5
FR [24]76.1
AT [20]77.2
FR+PIT77.6
MT78.3
MT+PIT [13]79.7
Ours80.4 ± 0.15
", + "bbox": [ + 165, + 88, + 383, + 210 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/75056f449c4c7e19798fbd5eb6e2349548b8382788c216cb419cb33386709cf1.jpg", + "table_caption": [ + "Table 1. FoV Adaptation." + ], + "table_footnote": [], + "table_body": "
MethodCar AP@0.5 for FoVx
50°70°80°90°
FR [24]94.390.286.880.6
FR+PIT [13]93.691.489.285.9
Ours-h94.1±0.1693.1 ± 0.3391.8 ± 0.4088.8 ± 0.21
", + "bbox": [ + 78, + 250, + 491, + 349 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "images corrected with PIT. We refer to our complete approach (Sec. 4.2) as Ours. For the task of FoV generalization, we report our results as Ours- $h$ to indicate that we only optimize the homographies ( $5 \\times 4$ parameters) in $\\mathcal{T}$ to adapt to the new FoVs while keeping the base and aggregator networks frozen. This matches the setup of PIT [13], which also corrects the images according to the new FoVs. As Ours and Ours- $h$ are trained with randomly initialized $\\mathcal{T}$ , we report the average results and standard deviations over three independent runs.", + "bbox": [ + 75, + 400, + 470, + 551 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "FoV adaptation. The results of Cityscapes $\\rightarrow$ KITTI FoV adaptation are provided in Tab. 1. Both MT+PIT and Ours both bridge the FoV gap, outperforming the MT baseline. Note, however, that we achieve this by learning the transformations, without requiring any camera-specific information, which is needed by PIT. Note also that MT outperforms FR by learning a better representation in the target domain, even though FR is trained with strong augmentation, such as random cropping. AT underperforms because its strong augmentation strategy fails to generalize for datasets having prominent geometric shifts. Our improvement over MT evidences that learning transformations helps to overcome geometric shifts. We optimize with $N = 9$ , homographies in this setup. Fig. 5 shows a qualitative example. Different homographies look into different image regions and the aggregator learns how to combine the activations corresponding to objects as depicted in Fig. 7. In supp. material Sec. A.3, we show effectiveness of a learned aggregator over the others. Additionally, we provide results on FoV decreasing adaptation in supp. material Sec. A.5.", + "bbox": [ + 75, + 559, + 468, + 863 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "FoV generalization. Tab. 2 summarizes the results obtained by using different FoVs as target domains while fix-", + "bbox": [ + 75, + 869, + 468, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/34e373ef8912371c335144d334092995319b27a12229ee4f003d1faab09dd3b2.jpg", + "table_caption": [ + "Table 2. FoV Generalization." + ], + "table_footnote": [], + "table_body": "
MethodPedestrian AP@0.5
FR [24]43.7
AT [20]63.5
MT64.7
Ours65.3±0.37
", + "bbox": [ + 584, + 88, + 807, + 180 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3. Viewpoint Adaptation.", + "bbox": [ + 599, + 191, + 790, + 205 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/625516b6d8d8bde88eeea14809c7b05f16fbb5bc488a37cb3b54c5825a083765.jpg", + "image_caption": [ + "Figure 6. Varying the number of homographies. We evaluate the effect of $N$ on the FoV adaptation task." + ], + "image_footnote": [], + "bbox": [ + 542, + 222, + 849, + 382 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "ing the source FoV to $50^{\\circ}$ . Since both the source and target images are taken from KITTI, the domain gap is only caused by a FoV change. Note that the performance of FR drops quickly as the FoV gap increases. Ours-h outperforms FR+PIT by a growing margin as the FoV gap increases. This shows that learning transformations helps to generalize better to different amounts of geometric shifts.", + "bbox": [ + 496, + 450, + 890, + 556 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Viewpoint adaptation. As shown in Fig. 1, a change in the camera viewpoint yields differences in the observed distortions and type of occlusions. The results in Tab. 3 show the benefits of our method over MT in this case. Note that PIT, which was designed for FoV changes, cannot be applied to correct for a viewpoint change. Other baselines outperform FR, as they use pseudo labels to fix the difference in bounding box distribution, as shown in Fig. 1. These results illustrate the generality of our method to different kinds of geometric shifts. Qualitative results for this task can be found in Fig. A.10.", + "bbox": [ + 496, + 564, + 890, + 731 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.5. Additional Analyses", + "text_level": 1, + "bbox": [ + 500, + 739, + 691, + 757 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Variable number of homographies. Let us now study the influence of the number of homographies in $\\mathcal{T}$ . To this end, we vary this number between 1 and 9. In Fig. 6, we plot the resulting APs for the Cityscapes-to-KITTI FoV adaptation task. Increasing the number of transformations results in a steady increase in performance, which nonetheless tends to plateau starting at 4 homographies. This is similar to our theoretical observation in Fig. A.3 of supp. material. Due to limited compute resources, we couldn't run ex", + "bbox": [ + 496, + 763, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "17447", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ab51612655641a63ec4a38dbb2a8b9dd56abbbfde7a2eef5869a89d5ed7e5e82.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 80, + 87, + 269, + 143 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/f1a0b5b1dd972c135881598dee92ebfebcb6cc608dee6e8df5ef48016c0af3b0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 292, + 88, + 470, + 145 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e6071888d47947b532bb2d1b31f0b98df7affdd7577047982c18b4c6eb83177b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 78, + 146, + 267, + 257 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d4a44fbe1381baf2303225fcfba9bd12a67425685692d2a814ed840b2ca6709e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 292, + 146, + 468, + 257 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/45227395d7c88a39fcdcc46fe3450091269972d5ba43bc47eac9cc292567292b.jpg", + "image_caption": [ + "Figure 7. Feature Maps: Top row: predictions of our network and feature map after aggregator. Left column: Image I, transformed by learned homographies; Right Column: Feature maps $F$ warped by corresponding $H^{-1}$ which are input to the aggregator. Each transform distorts the image regions differently. Most of the cars are on the left side and of small size in the image. $H_{1}$ distorts the left side leading to no activation $(H_{1}^{-1}F_{1})$ for the object. $H_{3}$ which causes the zoom-in effect has the strongest activation as the smaller objects are visible better here. These maps are generated by taking maximum over channel dimension." + ], + "image_footnote": [], + "bbox": [ + 83, + 258, + 271, + 313 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/ccf3f483944698569ca6b6b83bed23100f12381d6048b422b9bc29ac13d854be.jpg", + "image_caption": [ + "Figure 8. Diversity in $\\mathcal{T}$ : We train 5 homographies initialized as $\\mathcal{H}_i = I$ . We plot the evolution of $s_x$ for different homograhies as training proceeds. Each homography is shown in a different color. Note that the values for the different homographies become diverse. The best score is achieved at iteration $= 22k$ , indicated with the vertical line." + ], + "image_footnote": [], + "bbox": [ + 292, + 258, + 468, + 314 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "periments with more than 9 homographies. This confirms the intuition that a higher number of perspective transformations can better capture the geometric shift between two domains. Therefore, we conducted all experiments with the maximum number of homographies allowed by our compute resources.", + "bbox": [ + 75, + 494, + 468, + 585 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Only optimizing $\\mathcal{T}$ . We also run the Ours- $h$ baseline in the FoV and viewpoint adaptation scenarios. The resulting APs are 78.2 and 49.8, respectively. By learning only the $20(5\\times 4)$ homography parameters, our approach outperforms FR (in Tab. 1 and Tab. 3, respectively) by a large margin in both cases. This confirms that our training strategy is able to efficiently optimize $\\mathcal{T}$ to bridge the geometric gap between different domains. We visualize in Fig. A.9 in the supplementary material some transformations learned for FoV adaptation by Ours- $h$ . Note that they converge to diverse homographies that mimic a different FoV, correctly reflecting the adaptation task. Additionally in supp. material Sec. A.4, we ablate why the learning transform is useful. Specifically, learning not only provide better performance but provide faster inference w.r.t random transformations.", + "bbox": [ + 75, + 590, + 468, + 819 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Diversity in $\\mathcal{T}$ . To show that our approach can learn a diverse set of transformations that help in the adaptation task, we initialize all the homographies with identity. Fig. 8 depicts the diversity of the learned homographies on the FoV adaptation task. Even though we do not enforce di", + "bbox": [ + 75, + 824, + 470, + 902 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/ccf68a9496c082f1176cb9f7963b35fda52a0f8feae7b67af799ddef9472d20d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 576, + 88, + 816, + 268 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "versity, our approach learns a diverse set of transformations. With these learned homographies, our model achieves 79.5 AP@0.5 score for the FoV adaptation task. We show additional results in the supplementary material Sec. A.6 and Sec. A.7.", + "bbox": [ + 498, + 388, + 893, + 463 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations. Our approach assumes that the geometric gap between two domains can be bridged by a set of perspective transformations. We have shown that with enough transformations this is true. However, using a large number of homographies comes at a computational cost. The computational overhead leads to an increment in the inference time from 0.062s to 0.096s for $N = 5$ on an A100 Nvidia GPU with image dimension $402 \\times 1333$ . Nevertheless, our simple implementation shows promising results, and we will work on reducing this overhead in future work. Moreover since the optimization of the homography set is done at the dataset level, only certain transformations are beneficial to a given image. In the future, we therefore intend to condition the homography on the input image, which would reduce the total number of homographies needed.", + "bbox": [ + 498, + 469, + 892, + 696 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 709, + 619, + 724 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have introduced an approach to bridge the gap between two domains caused by geometric shifts by learning a set of homographies. We have shown the effectiveness our method on two different kinds of shifts, without relying on any annotations in the target domain, including information about the nature of the geometric shifts. Our analyses have evidenced that optimizing the transformations alone brings in improvement over the base detector and increasing the number of learnt homographies helps further. In the future, we plan to learn transformations that are conditioned on the input image to model image-dependent geometric shifts.", + "bbox": [ + 498, + 734, + 893, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "17448", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, 2020. 1", + "[2] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. Detr, https://github.com/facebookresearch/detr, 2020.1", + "[3] Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, and Qi Dou. Harmonizing transferability and discriminability for adapting object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8869-8878, 2020. 1, 2", + "[4] Chaoqi Chen, Zebiao Zheng, Yue Huang, Xinghao Ding, and Yizhou Yu. I3net: Implicit instance-invariant network for adapting one-stage object detectors. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 1, 2", + "[5] Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Domain adaptive faster r-cnn for object detection in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3339-3348, 2018. 2", + "[6] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 1, 2, 5", + "[7] Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764-773, 2017. 2", + "[8] Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, and Laura Leal-Taixe. Mot20: A benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003, 2020. 1, 5", + "[9] Jinhong Deng, Wen Li, Yuhua Chen, and Lixin Duan. Unbiased mean teacher for cross-domain object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4091-4101, 2021. 1, 2", + "[10] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010. 1", + "[11] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. Domain-adversarial training of neural networks. The journal of machine learning research, 17(1):2096-2030, 2016. 2", + "[12] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3354-3361. IEEE, 2012. 1, 2, 5", + "[13] Qiqi Gu, Qianyu Zhou, Minghao Xu, Zhengyang Feng, Guangliang Cheng, Xuequan Lu, Jianping Shi, and Lizhuang" + ], + "bbox": [ + 78, + 114, + 470, + 902 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Ma. Pit: Position-invariant transform for cross-fov domain adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8761-8770, 2021. 1, 2, 3, 5, 6, 7", + "[14] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6", + "[15] Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, and Kiyoharu Aizawa. Cross-domain weakly-supervised object detection through progressive domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5001-5009, 2018. 1", + "[16] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. Advances in neural information processing systems, 28, 2015. 2, 4", + "[17] Glenn Jocher, Alex Stoken, Jirka Borovec, NanoCode012, ChristopherSTAN, Liu Changyu, Laughing, tkianai, Adam Hogan, lorenzomammana, yxNONG, AlexWang1900, Laurentiu Diaconu, Marc, wanghaoyang0106, ml5ah, Doug, Francisco Ingham, Frederik, Guilhien, Hatovix, Jake Poznanski, Jiacong Fang, Lijun Yu, changyu98, Mingyu Wang, Naman Gupta, Osama Akhtar, PetrDvoracek, and Prashant Rai. ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements, Oct. 2020. 1", + "[18] Seunghyeon Kim, Jaehoon Choi, Taekyung Kim, and Chang-ick Kim. Self-training and adversarial background regularization for unsupervised domain adaptive one-stage object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6092–6101, 2019. 5", + "[19] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6", + "[20] Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Kan Chen, Bichen Wu, Zijian He, Kris Kitani, and Peter Vajda. Cross-domain adaptive teacher for object detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 5, 6, 7", + "[21] Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. Learning transferable features with deep adaptation networks. In International conference on machine learning, pages 97-105. PMLR, 2015. 2", + "[22] Zhongyi Pei, Zhangjie Cao, Mingsheng Long, and Jianmin Wang. Multi-adversarial domain adaptation. In Thirty-second AAAI conference on artificial intelligence, 2018. 2", + "[23] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779-788, 2016. 1", + "[24] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 39(6):1137-1149, 2016. 1, 4, 6, 7", + "[25] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. ImageNet Large Scale Visual Recognition Chal" + ], + "bbox": [ + 501, + 92, + 890, + 902 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "17449", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "lenge. International Journal of Computer Vision (IJCV), 115(3):211-252, 2015. 6", + "[26] Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, and Kate Saenko. Strong-weak distribution alignment for adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6956–6965, 2019. 1, 2", + "[27] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126(9):973-992, 2018. 1", + "[28] Zhiqiang Shen, Harsh Maheshwari, Weichen Yao, and Marios Savvides. Scl: Towards accurate domain adaptive object detection via gradient detach based stacked complementary losses. arXiv preprint arXiv:1911.02559, 2019. 2", + "[29] Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li, Han Zhang, Chen-Yu Lee, and Tomas Pfister. A simple semi-supervised learning framework for object detection. arXiv preprint arXiv:2005.04757, 2020. 2", + "[30] Baochen Sun and Kate Saenko. Deep coral: Correlation alignment for deep domain adaptation. In European conference on computer vision, pages 443-450. Springer, 2016. 2", + "[31] Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems, 30, 2017. 2, 4, 5, 6", + "[32] Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. Adversarial discriminative domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7167-7176, 2017. 2", + "[33] Vibashan VS, Vikram Gupta, Poojan Oza, Vishwanath A Sindagi, and Vishal M Patel. Mega-cda: Memory guided attention for category-aware unsupervised domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4516-4526, 2021. 2", + "[34] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detector2. https://github.com/facebookresearch/detectron2, 2019.6", + "[35] Shaoan Xie, Zibin Zheng, Liang Chen, and Chuan Chen. Learning semantic representations for unsupervised domain adaptation. In International conference on machine learning, pages 5423-5432. PMLR, 2018. 2", + "[36] Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang. Adversarial domain adaptation with domain mixup. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 6502-6509, 2020. 2", + "[37] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223-2232, 2017. 2", + "[38] Xinge Zhu, Jiangmiao Pang, Ceyuan Yang, Jianping Shi, and Dahua Lin. Adapting object detectors via selective cross-domain alignment. In Proceedings of the IEEE/CVF Con-" + ], + "bbox": [ + 78, + 90, + 468, + 900 + ], + "page_idx": 9 + }, + { + "type": "text", + "text": "ference on Computer Vision and Pattern Recognition, pages 687-696, 2019. 1, 2", + "bbox": [ + 531, + 90, + 890, + 119 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "17450", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/69fb3432-f3b6-48da-9de8-e985d9696176_model.json b/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/69fb3432-f3b6-48da-9de8-e985d9696176_model.json new file mode 100644 index 0000000000000000000000000000000000000000..88cf0c3e84a126653bf5861eb79f9e6f4bf8e8c4 --- /dev/null +++ b/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/69fb3432-f3b6-48da-9de8-e985d9696176_model.json @@ -0,0 +1,2035 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.094, + 0.131, + 0.877, + 0.153 + ], + "angle": 0, + "content": "Learning Transformations To Reduce the Geometric Shift in Object Detection" + }, + { + "type": "text", + "bbox": [ + 0.269, + 0.18, + 0.7, + 0.217 + ], + "angle": 0, + "content": "Vedit Vidit1 Martin Engilberge1 Mathieu Salzmann1,2 \nCVLab, EPFL1, ClearSpace SA2" + }, + { + "type": "text", + "bbox": [ + 0.366, + 0.22, + 0.599, + 0.233 + ], + "angle": 0, + "content": "firstname_lastname@epfl.ch" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.269, + 0.314, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.301, + 0.474, + 0.543 + ], + "angle": 0, + "content": "The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between synthetic and real images. Here, by contrast, we tackle geometric shifts emerging from variations in the image capture process, or due to the constraints of the environment causing differences in the apparent geometry of the content itself. We introduce a self-training approach that learns a set of geometric transformations to minimize these shifts without leveraging any labeled data in the new domain, nor any information about the cameras. We evaluate our method on two different shifts, i.e., a camera's field of view (FoV) change and a viewpoint change. Our results evidence that learning geometric transformations helps detectors to perform better in the target domains." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.573, + 0.208, + 0.589 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.598, + 0.47, + 0.734 + ], + "angle": 0, + "content": "While modern object detectors [1, 2, 17, 23, 24] achieve impressive results, their performance decreases when the test data depart from the training distribution. This problem arises in the presence of appearance variations due to, for example, differing illumination or weather conditions. Considering the difficulty and cost of acquiring annotated data in the test (i.e., target) domain, Unsupervised Domain Adaptation (UDA) has emerged as the standard strategy to address such scenarios [3, 4, 9, 26, 38]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.471, + 0.902 + ], + "angle": 0, + "content": "In this context, much effort has been made to learn domain invariant features, such that the source and target distributions in this feature space are similar. This has led to great progress in situations where the appearance of the objects changes drastically from one domain to the other, as in case of real-to-sketch adaptation (e.g., Pascal VOC [10] to Comics [15]), or weather adaptation (e.g., Cityscapes [6] to Foggy Cityscapes [27]). Nevertheless, such object appearance changes are not the only sources of domain shifts. They can also have geometric origins. For example, as shown in Fig. 1, they can be due to a change in camera view" + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.27, + 0.895, + 0.415 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.502, + 0.416, + 0.892, + 0.567 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.579, + 0.895, + 0.719 + ], + "angle": 0, + "content": "Figure 1. Geometric shifts. (Left) Due to a different FoV, the cars highlighted in green, undergo different distortions even though they appear in similar image regions. (Right) Different camera viewpoints (front facing vs downward facing) yield different distortions and occlusion patterns for pedestrian detection. (Bottom) The distributions of pedestrian bounding box sizes in Cityscapes [6] and MOT [8] differ significantly as the pedestrians are usually far away or in the periphery in Cityscapes. The top images are taken from Cityscapes [6], and the bottom-left and right ones from KITTI [12] and MOT [8], respectively." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.748, + 0.892, + 0.809 + ], + "angle": 0, + "content": "point or field-of-view (FoV), or a change of object scale due to different scene setups. In practice, such geometric shifts typically arise from a combination of various factors, including but not limited to the ones mentioned above." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.895, + 0.901 + ], + "angle": 0, + "content": "In this paper, we introduce a domain adaptation approach tackling such geometric shifts. To the best of our knowledge, the recent work of [13] constitutes the only attempt at considering such geometric distortions. However, it introduces a method solely dedicated to FoV variations, assuming that the target FoV is fixed and known. Here, we de" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "17441" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.47, + 0.121 + ], + "angle": 0, + "content": "velop a more general framework able to cope with a much broader family of geometric shifts." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.122, + 0.47, + 0.272 + ], + "angle": 0, + "content": "To this end, we model geometric transformations as a combination of multiple homographies. We show both theoretically and empirically that this representation is sufficient to encompass a broad variety of complex geometric transformations. We then design an aggregator block that can be incorporated to the detector to provide it with the capacity to tackle geometric shifts. We use this modified detector to generate pseudo labels for the target domain, which let us optimize the homographies so as to reduce the geometric shift." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.274, + 0.471, + 0.515 + ], + "angle": 0, + "content": "Our contributions can be summarized as follows. (i) We tackle the problem of general geometric shifts for object detection. (ii) We learn a set of homographies using unlabeled target data, which alleviates the geometric bias arising in source-only training. (iii) Our method does not require prior information about the target geometric distortions and generalizes to a broad class of geometric shifts. Our experiments demonstrate the benefits of our approach in several scenarios. In the presence of FoV shifts, our approach yields similar performance to the FoV-dedicated framework of [13] but without requiring any camera information. As such, it generalizes better to other FoVs. Furthermore, we show the generality of our method by using it to adapt to a new camera viewpoint in the context of pedestrian detection. Our implementation can be accessed at https://github.com/vidit09/geoshift." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.528, + 0.219, + 0.543 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.553, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Unsupervised Domain Adaptation (UDA). UDA for image recognition [11, 21, 22, 30, 32, 35, 36] and object detection [3, 4, 9, 20, 26, 38] has made a great progress in the past few years. The common trend in both tasks consists of learning domain invariant features. For object detection, this entails aligning the global (e.g., illumination, weather) and local (foreground objects) features in the two domains. In this context, [3, 5, 26, 28] align image- and instance-level features in the two domains via adversarial learning [11]; [33] learns category-specific attention maps to better align specific image regions; [38] clusters the proposed object regions using \\( k \\)-means clustering and uses the centroids for instance-level alignment. While this successfully tackles domain shifts caused by object appearance variations, it fails to account for the presence of shifts due to the image capture process itself, such as changes in camera intrinsics or viewpoint. The only initial step at considering a geometric shift is the work of [13], which shows the existence of an FoV gap in driving datasets [6, 12] and proposes a Position Invariant Transform (PIT) that corrects the distortions caused specifically by an FoV change. In essence, PIT undistorts the images by assuming knowledge of the target FoV. By contrast, here, we introduce an approach that gen" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.122 + ], + "angle": 0, + "content": "eralizes to a broad family of geometric shifts by learning transformations without requiring any camera information." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.129, + 0.892, + 0.462 + ], + "angle": 0, + "content": "Self-training. Self-training, generally employed in the semi-supervised setting, offers an alternative to learning domain-invariant features and utilize unlabeled data to improve a detector's performance. In this context, [29] uses a student-teacher architecture where the teacher model is trained with supervised data and generates pseudo-labels on unannotated data. These pseudo-labels are then used to train a student model. While effective in the standard semi-supervised learning scenario, the quality of the pseudo-labels obtained with this approach tends to deteriorate when the labeled and unlabeled data present a distribution shift. [9, 20] have therefore extended this approach to domain adaptation by using the Mean Teacher strategy of [31] to generate reliable pseudo-labels in the target domain. Other approach include the use of CycleGAN [37] generated images to train an unbiased teacher model [9], and that of different augmentation strategies to generate robust pseudo-labels [20]. Our approach also follows a self-training strategy but, while these works focus on object appearance shifts, we incorporate learnable blocks to address geometric shifts. As shown in our experiment, this lets us outperform the state-of-the-art AdaptTeacher [20]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.469, + 0.892, + 0.665 + ], + "angle": 0, + "content": "Learning Geometric Transformations. End-to-end learning of geometric transformations has been used to boost the performance of deep networks. For example, Spatial Transformer Networks (STNs) [16] reduce the classification error by learning to correct for affine transformations; deformable convolutions [7] model geometric transformations by applying the convolution kernels to non-local neighborhoods. These methods work well when annotations are available for supervision, and make the network invariant to the specific geometric transformations seen during training. Here, by contrast, we seek to learn transformations in an unsupervised manner and allow the network to generalize to unknown target transformations." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.68, + 0.842, + 0.696 + ], + "angle": 0, + "content": "3. Modeling Geometric Transformations" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.892, + 0.901 + ], + "angle": 0, + "content": "In the context of UDA, multiple geometric differences can be responsible for the gap between the domains. Some can be characterized by the camera parameters, such as a change in FoV (intrinsic) or viewpoint (extrinsic), whereas others are content specific, such as a difference in road width between different countries. Ultimately, the geometric shift is typically a combination of different geometric operations. Since the parameters of these operations are unknown, we propose to bridge the domain gap by learning a geometric transform. Specifically, we aggregate the results of multiple perspective transforms, i.e., homographies, to obtain a differentiable operation that can emulate a wide variety of geometric transforms." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17442" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.078, + 0.091, + 0.258, + 0.108 + ], + "angle": 0, + "content": "3.1. Theoretical Model" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.114, + 0.47, + 0.161 + ], + "angle": 0, + "content": "Let us first show that, given sufficiently many homographies, one can perfectly reproduce any mapping between \\(\\mathbb{R}^2\\setminus (0,0)\\) and \\(\\mathbb{R}^2\\)" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.178, + 0.47, + 0.236 + ], + "angle": 0, + "content": "Single homography for a single point. First, we show that a single homography with 4 degrees of freedom can map a point \\( p \\in \\mathbb{R}^2 \\setminus (0,0) \\) to any other point in \\( \\mathbb{R}^2 \\). To this end, let" + }, + { + "type": "equation", + "bbox": [ + 0.205, + 0.236, + 0.47, + 0.284 + ], + "angle": 0, + "content": "\\[\nH = \\left[ \\begin{array}{c c c} s _ {x} & 0 & 0 \\\\ 0 & s _ {y} & 0 \\\\ l _ {x} & l _ {y} & 1 \\end{array} \\right] \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.289, + 0.472, + 0.372 + ], + "angle": 0, + "content": "be a homography, with \\((s_x,s_y)\\) the scaling factors on the \\(x-\\) and \\(y\\) -axis, respectively, and \\((l_{x},l_{y})\\) the perspective factors in \\(x\\) and \\(y\\), respectively. For any destination point \\(d\\in \\mathbb{R}^2\\), there exists a set of parameters \\((s_x,s_y,l_x,l_y)\\) such that \\(d = H\\times p\\). One such set is \\(\\left(\\frac{d_x}{p_x},\\frac{d_y}{p_y},0,0\\right)\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.386, + 0.47, + 0.581 + ], + "angle": 0, + "content": "Emulating any geometric transformation Now that we have shown that a single homography can move a point to any other point in \\(\\mathbb{R}^2\\), we describe a simple protocol to emulate any geometric transform. Given an unknown geometric transform \\(T:\\mathbb{R}^2\\backslash (0,0)\\to \\mathbb{R}^2\\), we aim to emulate \\(T\\) with a set of homographies. In general, for an image \\(\\mathbf{I}\\in \\mathbb{R}^{3\\times h\\times w}\\) we can restrict the domain of \\(T\\) to only image coordinates. To this end, we can define a set of homographies \\(H_{i}\\in \\mathbb{H}\\) for i in \\(\\{1,2,3,\\dots,h\\times w\\}\\), where the parameters of \\(H_{i}\\) are chosen to mimic the transform \\(T\\) for location \\(i\\) of the image. In this protocol, the aggregation mechanism is trivial since each homography is in charge of remapping a single pixel coordinate of the original space." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.582, + 0.47, + 0.719 + ], + "angle": 0, + "content": "While this works in theory, this is of course not viable in practice since it would require too many homographies. With a smaller number of homographies, each transform needs to remap multiple points, and a more sophisticated aggregation mechanism is required. Specifically, the aggregation mechanism needs to select which transform is in charge of remapping which point. In the next section, we empirically show that this strategy lets us closely approximate the spherical projection mapping used in PIT [13]." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.727, + 0.422, + 0.744 + ], + "angle": 0, + "content": "3.2. Approximating PIT with Homographies" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.471, + 0.902 + ], + "angle": 0, + "content": "To demonstrate the possibility offered by aggregating multiple homographies, we design an approximation of PIT using only homographies. PIT proposes to correct for an FoV gap by remapping images to a spherical surface. During this transformation, regions further from the center of a scene are compressed with a higher ratio. This variable compression of the space cannot be reproduced by a single homography transformation. To overcome this limitation, we combine the results of multiple homographies that all have different compression rates (scaling parameters). For" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.894, + 0.184 + ], + "angle": 0, + "content": "the aggregation mechanism, we use the optimal strategy by selecting for each pixel the homography that approximates best the PIT mapping. As shown in Fig. 2, this combination closely approximates the PIT results with only 5 homographies. Further analysis of these experiments is available in the supplementary material in Fig. A.3." + }, + { + "type": "image", + "bbox": [ + 0.541, + 0.201, + 0.85, + 0.285 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.578, + 0.293, + 0.824, + 0.375 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.578, + 0.38, + 0.824, + 0.462 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.475, + 0.894, + 0.532 + ], + "angle": 0, + "content": "Figure 2. Approximating PIT with homographies. We show the original image (top), the PIT [13] correction (middle), and our approximation of PIT using 5 homographies. Note that 5 homographies are sufficient to closely match the PIT spherical correction." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.554, + 0.807, + 0.57 + ], + "angle": 0, + "content": "3.3. Homographies in a Learning Setup" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.577, + 0.892, + 0.759 + ], + "angle": 0, + "content": "In the two previous sections, we have demonstrated both theoretically and empirically the flexibility of aggregating homographies. This makes this representation an ideal candidate for domain adaptation since the geometric shift between the domains is unknown and can be a combination of different transforms, such as FoV change, viewpoint change, camera distortion, or appearance distortion. As will be discussed in the next section, by learning jointly the set of perspective transforms and the aggregation mechanism on real data, our model can reduce the geometric shift between the two domains without prior knowledge about this domain gap." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.77, + 0.593, + 0.785 + ], + "angle": 0, + "content": "4. Method" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.892, + 0.903 + ], + "angle": 0, + "content": "Let us now introduce our approach to reducing the geometric shift in object detection. Following the standard UDA setting, let \\( D_{s} = \\{(I_{s},B_{s},C_{s})\\} \\) be a labeled source dataset containing images \\( I_{s} = \\{I_{s}^{i}\\}_{1}^{N_{s}} \\) with corresponding object bounding boxes \\( B_{s} = \\{b_{s}^{i}\\}_{1}^{N_{s}} \\) and object classes \\( C_s = \\{c_s^i\\}_1^{N_s} \\). Furthermore, let \\( D_{t} = \\{I_{t}\\} \\) denote an unlabeled target dataset for which only images \\( I_{t} = \\{I_{t}^{i}\\}_{1}^{N_{t}} \\)" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17443" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.08, + 0.093, + 0.895, + 0.346 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.356, + 0.893, + 0.412 + ], + "angle": 0, + "content": "Figure 3. Architecture: The input image is first transformed by a set of trainable homographies. The feature maps extracted from the transformed images are then unwarped by the inverse homographies to achieve spatial consistency. We then combine the unwarped feature maps using a trainable aggregator, whose output is passed to a detection head. The blocks shown in green correspond to standard FasterRCNN operations. The \\(\\oplus\\) symbol represents the concatenation operation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.439, + 0.47, + 0.514 + ], + "angle": 0, + "content": "are available, without annotations. Here, we tackle the case where the two domains differ by geometric shifts but assume no knowledge about the nature of these shifts. Below, we first introduce the architecture we developed to handle this and then our strategy to train this model." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.527, + 0.265, + 0.541 + ], + "angle": 0, + "content": "4.1. Model Architecture" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.551, + 0.47, + 0.702 + ], + "angle": 0, + "content": "The overall architecture of our approach is depicted in Fig. 3. In essence, and as discussed in Sec. 3, we characterize the geometric changes between the source and target data by a set of transformations \\(\\mathcal{T} = \\{\\mathcal{H}_i\\}_{1}^N\\). Each \\(\\mathcal{H}_i\\) in \\(\\mathcal{T}\\) is a homography of the same form as in Eq. (1). For our method to remain general, we assume the transformations to be unknown, and our goal, therefore, is to learn \\(\\mathcal{T}\\) to bridge the gap between the domains. This requires differentiability w.r.t. the transformation parameters, which we achieve using the sampling strategy proposed in [16]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.703, + 0.47, + 0.81 + ], + "angle": 0, + "content": "As shown in Fig. 3, the input image is transformed by the individual homographies in \\(\\mathcal{T}\\), and the transformed images are fed to a modified FasterRCNN [24] detector. Specifically, we extract a feature map \\(\\mathcal{F}_{\\mathcal{H}_i} \\in \\mathbb{R}^{H \\times W \\times C}\\) for each transformed image via a feature extractor shared by all transformations. To enforce spatial correspondence between the different \\(\\mathcal{F}_{\\mathcal{H}_i}\\)s, we unwarp them with \\(\\mathcal{H}_i^{-1}\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.469, + 0.871 + ], + "angle": 0, + "content": "We then introduce an aggregator \\(\\mathcal{A}_{\\theta_g}\\), parameterized by \\(\\theta_{g}\\), whose goal is to learn a common representation given a fixed number of unwarped feature maps \\(\\mathcal{F}_{\\mathcal{H}_i}^{\\prime}\\). To achieve this, the aggregator takes as input" + }, + { + "type": "equation", + "bbox": [ + 0.1, + 0.883, + 0.47, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\mathcal {G} = \\mathcal {F} _ {\\mathcal {H} _ {1}} ^ {\\prime} \\oplus \\mathcal {F} _ {\\mathcal {H} _ {2}} ^ {\\prime} \\oplus \\dots \\oplus \\mathcal {F} _ {\\mathcal {H} _ {N}} ^ {\\prime} \\in \\mathbb {R} ^ {H \\times W \\times C \\times N}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.439, + 0.892, + 0.514 + ], + "angle": 0, + "content": "where \\(\\oplus\\) represents concatenation in the channel dimension. The aggregator outputs a feature map \\(\\mathcal{A}_{\\theta_g}(\\mathcal{G})\\in \\mathbb{R}^{H\\times W\\times C}\\) whose dimension is independent of the number of transformations. This output is then passed to a detection head to obtain the objects' bounding boxes and class labels." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.522, + 0.658, + 0.538 + ], + "angle": 0, + "content": "4.2. Model Training" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.545, + 0.892, + 0.667 + ], + "angle": 0, + "content": "Our training procedure relies on three steps: (i) Following common practice in UDA, we first train the Faster-RCNN detector with source-only data; (ii) We then introduce the aggregator and train it so that it learns to combine different homographies using the labeled source data; (iii) Finally, we learn the optimal transformations for adaptation using both the source and target data via a Mean Teacher [31] strategy." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.684, + 0.892, + 0.774 + ], + "angle": 0, + "content": "Aggregator Training. To train the aggregator, we randomly sample a set of homographies \\(\\mathcal{T} \\in \\mathbb{R}^{N \\times 4}\\) in each training iteration. This gives the aggregator the ability to robustly combine diverse input transformations but requires strong supervision to avoid training instabilities. We, therefore, perform this step using the source data." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.774, + 0.892, + 0.835 + ], + "angle": 0, + "content": "The loss function for a set of transformed images \\(\\mathcal{T}(I_s)\\) is then defined as in standard FasterRCNN training with a combination of classification and regression terms [24]. That is, we train the aggregator by solving" + }, + { + "type": "equation", + "bbox": [ + 0.58, + 0.843, + 0.892, + 0.867 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\theta_ {g}} \\mathcal {L} _ {c l s} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) + \\mathcal {L} _ {r e g} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right), \\tag {3}\n\\]" + }, + { + "type": "page_footnote", + "bbox": [ + 0.5, + 0.875, + 0.892, + 0.9 + ], + "angle": 0, + "content": "1As our homographies involve only 4 parameters, with a slight abuse of notation, we say that \\( \\mathcal{H}_i \\in \\mathbb{R}^4 \\)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17444" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.078, + 0.093, + 0.125, + 0.105 + ], + "angle": 0, + "content": "where" + }, + { + "type": "equation", + "bbox": [ + 0.173, + 0.12, + 0.469, + 0.138 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {c l s} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) = \\mathcal {L} _ {c l s} ^ {r p n} + \\mathcal {L} _ {c l s} ^ {r o i}, \\tag {4}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.173, + 0.141, + 0.469, + 0.16 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {r e g} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) = \\mathcal {L} _ {r e g} ^ {r p n} + \\mathcal {L} _ {r e g} ^ {r o i}. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.172, + 0.473, + 0.326 + ], + "angle": 0, + "content": "\\(\\mathcal{L}^{rpn}\\) and \\(\\mathcal{L}^{roi}\\) correspond to the Region Proposal Network (RPN) loss terms and the Region of Interest (RoI) ones, respectively. During this process, we freeze the parameters \\(\\theta_{b}\\) of the base network, i.e., feature extractor and detection head, which were first trained on the source data without aggregator. Ultimately, the aggregator provides the network with the capacity to encode different transformations that are not seen in the source domain. The third training step then aims to learn the best transformation for successful object detection in the target domain." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.348, + 0.473, + 0.545 + ], + "angle": 0, + "content": "Learning the Transformations. As we have no annotations in the target domain, we exploit a Mean Teacher (MT) strategy to learn the optimal transformations. To this end, our starting point is the detector with a trained aggregator and a set of random transformations \\(\\mathcal{T}\\). The MT strategy is illustrated in Fig. 4. In essence, MT training [31] involves two copies of the model: A student model, with parameters \\(\\theta^{st} = \\{\\mathcal{T}^{st},\\theta_b^{st},\\theta_g^{st}\\}\\), that will be used during inference, and a teacher model, with parameters \\(\\theta^{te} = \\{\\mathcal{T}^{te},\\theta_b^{te},\\theta_g^{te}\\}\\), that is updated as an Exponentially Moving Average (EMA) of the student model. That is, the student's parameters are computed with standard backpropagation, whereas the teacher's ones are updated as" + }, + { + "type": "equation", + "bbox": [ + 0.182, + 0.558, + 0.47, + 0.575 + ], + "angle": 0, + "content": "\\[\n\\theta_ {t e} \\leftarrow \\alpha \\theta_ {t e} + (1 - \\alpha) \\theta_ {s t}. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.588, + 0.47, + 0.695 + ], + "angle": 0, + "content": "The student model is trained using both source and target detection losses. Since the target domain does not have annotations, the teacher model is used to generate pseudolabels. These labels might be noisy, and hence we only keep the predictions with a confidence score above a threshold \\(\\tau\\). Furthermore, non-maxima suppression (NMS) is used to remove the highly-overlapping bounding box predictions." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.695, + 0.47, + 0.726 + ], + "angle": 0, + "content": "Formally, given a source image \\( I_{s} \\) and a target image \\( I_{t} \\), the student model is trained by solving" + }, + { + "type": "equation", + "bbox": [ + 0.136, + 0.739, + 0.47, + 0.765 + ], + "angle": 0, + "content": "\\[\n\\min _ {\\mathcal {T} ^ {s t}, \\theta_ {g} ^ {s t}, \\theta_ {b} ^ {s t}} \\mathcal {L} _ {d e t} (\\mathcal {T} (I _ {s})) + \\lambda \\mathcal {L} _ {d e t} (\\mathcal {T} (I _ {t})) , \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.778, + 0.424, + 0.793 + ], + "angle": 0, + "content": "where \\(\\lambda\\) controls the target domain contribution and" + }, + { + "type": "equation", + "bbox": [ + 0.122, + 0.807, + 0.469, + 0.824 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {d e t}} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) = \\mathcal {L} _ {\\text {c l s}} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) + \\mathcal {L} _ {\\text {r e g}} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right), \\tag {8}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.125, + 0.826, + 0.469, + 0.843 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {d e t}} \\left(\\mathcal {T} \\left(I _ {t}\\right)\\right) = \\mathcal {L} _ {\\text {c l s}} \\left(\\mathcal {T} \\left(I _ {t}\\right)\\right). \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Similarly to [18,20], we update the student model with only the classification loss in the target domain to help stabilize training." + }, + { + "type": "image", + "bbox": [ + 0.507, + 0.088, + 0.891, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.264, + 0.895, + 0.334 + ], + "angle": 0, + "content": "Figure 4. Mean Teacher formalism. The student model is trained with ground-truth labels in the source domain and pseudo labels in the target one. These pseudo labels are produced by the teacher model, which corresponds to an exponentially moving average (EMA) of the student network." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.36, + 0.634, + 0.377 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.385, + 0.895, + 0.583 + ], + "angle": 0, + "content": "We demonstrate the effectiveness and generality of our method on different geometric shifts. First, to compare to the only other work that modeled a geometric shift [13], we tackle the problem of a change in FoV between the source and target domain. Note that, in contrast to [13], we do not assume knowledge of the target FoV. Furthermore, while [13] was dedicated to FoV adaptation, our approach generalizes to other geometric shifts. We demonstrate this on the task of pedestrian detection under a viewpoint shift. We compare our method with the state-of-the-art AdaptTeacher [20], which also uses a Mean Teacher, but focuses on appearance shifts. In the remainder of this section, we describe our experimental setup and discuss our results." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.591, + 0.603, + 0.606 + ], + "angle": 0, + "content": "5.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.615, + 0.894, + 0.722 + ], + "angle": 0, + "content": "Cityscapes [6] contains 2975 training and 500 test images with annotations provided for 8 categories (person, car, train, rider, truck, motorcycle, bicycle and bus). The average horizontal (FoVx) and vertical (FoVy) FoVs of the capturing cameras are \\(50^{\\circ}\\) and \\(26^{\\circ}\\), respectively. We use this dataset as the source domain for both FoV adaptation and viewpoint adaptation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.727, + 0.894, + 0.835 + ], + "angle": 0, + "content": "KITTI [12] is also a street-view dataset containing 6684 images annotated with the car category. The horizontal (FoVx) and vertical (FoVy) FoVs of the camera are \\(90^{\\circ}\\) and \\(34^{\\circ}\\), respectively. We use this dataset as target domain for FoV adaptation, as the viewpoint is similar to that of Cityscapes. Following [13], we use 5684 images for unsupervised training and 1000 images for evaluation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.895, + 0.902 + ], + "angle": 0, + "content": "MOT [8] is a multi-object tracking dataset. We use the indoor mall sequence, MOT20-02, consisting of 2782 frames annotated with the person category. We employ this dataset as target domain for viewpoint adaptation. We use the first" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17445" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.091, + 0.47, + 0.122 + ], + "angle": 0, + "content": "2000 frame for unsupervised training and last 782 for evaluation." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.132, + 0.34, + 0.149 + ], + "angle": 0, + "content": "5.2. Adaptation Tasks and Metric" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.156, + 0.47, + 0.278 + ], + "angle": 0, + "content": "FoV adaptation. As in [13], we consider the case of an increasing FoV using Cityscapes as source domain and KITTI as target domain. The horizontal and vertical FoVs increase from \\((50^{\\circ}, 26^{\\circ})\\) in Cityscapes to \\((90^{\\circ}, 34^{\\circ})\\) in KITTI. Therefore, as can be seen in Fig. 1, the KITTI images have a higher distortion in the corners than the Cityscapes ones. Similarly to PIT [13], we use the car category in our experiments." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.285, + 0.471, + 0.392 + ], + "angle": 0, + "content": "FoV generalization. Following PIT [13], we study the generalization of our approach to new FoVs by cropping the KITTI images to mimic different FoV changes in the horizontal direction (FoVx). Specifically, we treat FoVx = 50° as the source domain and the cropped images with FoVx = {70°, 80°, 90°} as different target domains. We evaluate our approach on car on these different pairs of domains." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.399, + 0.47, + 0.492 + ], + "angle": 0, + "content": "Viewpoint adaptation. This task entails detecting objects seen from a different viewpoint in the source and target domains. We use the front-facing Cityscapes images as source domain and the downward-facing MOT ones as target one. As the MOT data depicts pedestrians, we use the bounding boxes corresponding to the person category in Cityscapes.[2]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.513, + 0.47, + 0.604 + ], + "angle": 0, + "content": "Metric. In all of our experiments, we use the Average Precision (AP) as our metric. Specifically, following [13], we report the AP@0.5, which considers the predictions as true positives if they match the ground-truth label and have an intersection over union (IOU) score of more than 0.5 with the ground-truth bounding boxes." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.614, + 0.295, + 0.63 + ], + "angle": 0, + "content": "5.3. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.638, + 0.47, + 0.803 + ], + "angle": 0, + "content": "We use the Detector2 [34] implementation of Faster-RCNN [24] with a ResNet50 [14] backbone as our base architecture. In all of our experiments, the images are resized so that the shorter side has 800 pixels while maintaining the aspect ratio. The base network is first trained on source-only images with random cropping and random flipping augmentation for 24k iterations with batch size 8. We use the Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0.01, scaled down by a 0.1 factor after 18k iterations. We use ImageNet [25] pretrained weights to initialize the ResNet50 backbone." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.805, + 0.471, + 0.866 + ], + "angle": 0, + "content": "We then incorporate the aggregator in the trained base architecture. The aggregator architecture contains three convolutional layers with a kernel size of \\(3 \\times 3\\), and one \\(1 \\times 1\\) convolutional layer. We first train the aggregator" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.089, + 0.696, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.697, + 0.089, + 0.89, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.2, + 0.894, + 0.312 + ], + "angle": 0, + "content": "Figure 5. FoV Adaptation: Qualitative Results. We visualize a car detection result in the Cityscapes-to-KITTI FoV adaptation scenario. The top left image corresponds to the ground truth, the bottom left to the Mean Teacher result, which confuses the orange container with a car, the bottom right to the Mean Teacher adaptation + PIT FoV adaptation result, which also mistakes the orange container for a car and further detects the speed limit on the road. Our approach, on the top right, correctly matches the ground truth." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.34, + 0.894, + 0.43 + ], + "angle": 0, + "content": "on the source data with the base frozen and using random transformations \\(\\mathcal{T}\\). The transformations are generated by randomly sampling each \\(\\mathcal{H}_i\\) parameters as \\(s_x, s_y \\sim \\mathcal{U}_{[0.5,2.0]}\\), \\(\\mathcal{U}_{[0.5,2.0]}\\) and \\(l_x, l_y \\sim \\mathcal{U}_{[-0.5,0.5]}\\), \\(\\mathcal{U}_{[-0.5,0.5]}\\). We train the aggregator for 30k iterations using a batch size of 8 and the SGD optimizer with a learning rate of \\(1e^{-4}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.431, + 0.893, + 0.703 + ], + "angle": 0, + "content": "The student and teacher models are then initialized with this detector and the random \\(\\mathcal{T} = \\{\\mathcal{H}_i\\}_{i=1}^N\\). We optimize \\(\\mathcal{T}\\) using Adam [19], while the base and aggregator networks are optimized by SGD. The learning rate is set to \\(1e^{-3}\\) and scaled down by a factor 0.1 after 10k iterations for the SGD optimizer. For the first 10k iterations in FoV adaptation and for 2k iterations for viewpoint adaptation, we only train \\(\\mathcal{T}\\) keeping base and aggregator frozen. The \\(\\alpha\\) coefficient for the EMA update is set to 0.99; the confidence threshold \\(\\tau = 0.6\\); \\(\\lambda = \\{0.01, 0.1\\}\\) for FoV and viewpoint adaptation, respectively. The Mean Teacher framework is trained using both the source and target data. We set \\(N = 5\\), unless otherwise specified, and use a batch size of 4, containing 2 source and 2 target images. We apply random color jittering on both the source and target data as in [20, 31]. All of our models are trained on a single NVIDIA V100 GPU. A detailed hyper-parameter study is provided in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.715, + 0.822, + 0.731 + ], + "angle": 0, + "content": "5.4. Comparison with the State of the Art" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.738, + 0.894, + 0.875 + ], + "angle": 0, + "content": "We compare our approach with the following baselines3. FR: FasterRCNN trained only on the source data with random crop augmentation; AT: AdaptTeacher [20]; MT: Mean Teacher initialized with FR and trained with random color jittering on both the source and target data (i.e., this corresponds to our mean teacher setup in Sec. 4.2 but without the aggregator and without transformations \\(\\mathcal{T}\\)); FR+PIT: Same setup as FR but with the images corrected with PIT [13]; MT+PIT: Same setup as MT but with the" + }, + { + "type": "page_footnote", + "bbox": [ + 0.077, + 0.875, + 0.47, + 0.901 + ], + "angle": 0, + "content": "2In Cityscapes, a person may be labeled as either person or rider. Since the rider label is used for people riding a vehicle, we omit these cases." + }, + { + "type": "page_footnote", + "bbox": [ + 0.516, + 0.887, + 0.788, + 0.9 + ], + "angle": 0, + "content": "3We re-implement PIT baselines with our detector." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "17446" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.166, + 0.089, + 0.385, + 0.212 + ], + "angle": 0, + "content": "
MethodCar AP@0.5
FR [24]76.1
AT [20]77.2
FR+PIT77.6
MT78.3
MT+PIT [13]79.7
Ours80.4 ± 0.15
" + }, + { + "type": "table_caption", + "bbox": [ + 0.196, + 0.222, + 0.351, + 0.236 + ], + "angle": 0, + "content": "Table 1. FoV Adaptation." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.25, + 0.493, + 0.35 + ], + "angle": 0, + "content": "
MethodCar AP@0.5 for FoVx
50°70°80°90°
FR [24]94.390.286.880.6
FR+PIT [13]93.691.489.285.9
Ours-h94.1±0.1693.1 ± 0.3391.8 ± 0.4088.8 ± 0.21
" + }, + { + "type": "table_caption", + "bbox": [ + 0.185, + 0.36, + 0.361, + 0.373 + ], + "angle": 0, + "content": "Table 2. FoV Generalization." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.401, + 0.471, + 0.553 + ], + "angle": 0, + "content": "images corrected with PIT. We refer to our complete approach (Sec. 4.2) as Ours. For the task of FoV generalization, we report our results as Ours-\\(h\\) to indicate that we only optimize the homographies (\\(5 \\times 4\\) parameters) in \\(\\mathcal{T}\\) to adapt to the new FoVs while keeping the base and aggregator networks frozen. This matches the setup of PIT [13], which also corrects the images according to the new FoVs. As Ours and Ours-\\(h\\) are trained with randomly initialized \\(\\mathcal{T}\\), we report the average results and standard deviations over three independent runs." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.56, + 0.47, + 0.864 + ], + "angle": 0, + "content": "FoV adaptation. The results of Cityscapes \\(\\rightarrow\\) KITTI FoV adaptation are provided in Tab. 1. Both MT+PIT and Ours both bridge the FoV gap, outperforming the MT baseline. Note, however, that we achieve this by learning the transformations, without requiring any camera-specific information, which is needed by PIT. Note also that MT outperforms FR by learning a better representation in the target domain, even though FR is trained with strong augmentation, such as random cropping. AT underperforms because its strong augmentation strategy fails to generalize for datasets having prominent geometric shifts. Our improvement over MT evidences that learning transformations helps to overcome geometric shifts. We optimize with \\(N = 9\\), homographies in this setup. Fig. 5 shows a qualitative example. Different homographies look into different image regions and the aggregator learns how to combine the activations corresponding to objects as depicted in Fig. 7. In supp. material Sec. A.3, we show effectiveness of a learned aggregator over the others. Additionally, we provide results on FoV decreasing adaptation in supp. material Sec. A.5." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.47, + 0.903 + ], + "angle": 0, + "content": "FoV generalization. Tab. 2 summarizes the results obtained by using different FoVs as target domains while fix-" + }, + { + "type": "table", + "bbox": [ + 0.586, + 0.089, + 0.808, + 0.181 + ], + "angle": 0, + "content": "
MethodPedestrian AP@0.5
FR [24]43.7
AT [20]63.5
MT64.7
Ours65.3±0.37
" + }, + { + "type": "table_caption", + "bbox": [ + 0.601, + 0.192, + 0.792, + 0.206 + ], + "angle": 0, + "content": "Table 3. Viewpoint Adaptation." + }, + { + "type": "image", + "bbox": [ + 0.543, + 0.223, + 0.85, + 0.383 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.396, + 0.892, + 0.424 + ], + "angle": 0, + "content": "Figure 6. Varying the number of homographies. We evaluate the effect of \\( N \\) on the FoV adaptation task." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.451, + 0.892, + 0.558 + ], + "angle": 0, + "content": "ing the source FoV to \\(50^{\\circ}\\). Since both the source and target images are taken from KITTI, the domain gap is only caused by a FoV change. Note that the performance of FR drops quickly as the FoV gap increases. Ours-h outperforms FR+PIT by a growing margin as the FoV gap increases. This shows that learning transformations helps to generalize better to different amounts of geometric shifts." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.565, + 0.892, + 0.732 + ], + "angle": 0, + "content": "Viewpoint adaptation. As shown in Fig. 1, a change in the camera viewpoint yields differences in the observed distortions and type of occlusions. The results in Tab. 3 show the benefits of our method over MT in this case. Note that PIT, which was designed for FoV changes, cannot be applied to correct for a viewpoint change. Other baselines outperform FR, as they use pseudo labels to fix the difference in bounding box distribution, as shown in Fig. 1. These results illustrate the generality of our method to different kinds of geometric shifts. Qualitative results for this task can be found in Fig. A.10." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.741, + 0.692, + 0.758 + ], + "angle": 0, + "content": "5.5. Additional Analyses" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Variable number of homographies. Let us now study the influence of the number of homographies in \\(\\mathcal{T}\\). To this end, we vary this number between 1 and 9. In Fig. 6, we plot the resulting APs for the Cityscapes-to-KITTI FoV adaptation task. Increasing the number of transformations results in a steady increase in performance, which nonetheless tends to plateau starting at 4 homographies. This is similar to our theoretical observation in Fig. A.3 of supp. material. Due to limited compute resources, we couldn't run ex" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17447" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.088, + 0.271, + 0.145 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.293, + 0.089, + 0.471, + 0.146 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.08, + 0.147, + 0.268, + 0.258 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.294, + 0.147, + 0.47, + 0.258 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.084, + 0.26, + 0.272, + 0.314 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.294, + 0.26, + 0.47, + 0.315 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.329, + 0.473, + 0.468 + ], + "angle": 0, + "content": "Figure 7. Feature Maps: Top row: predictions of our network and feature map after aggregator. Left column: Image I, transformed by learned homographies; Right Column: Feature maps \\( F \\) warped by corresponding \\( H^{-1} \\) which are input to the aggregator. Each transform distorts the image regions differently. Most of the cars are on the left side and of small size in the image. \\( H_{1} \\) distorts the left side leading to no activation \\( (H_{1}^{-1}F_{1}) \\) for the object. \\( H_{3} \\) which causes the zoom-in effect has the strongest activation as the smaller objects are visible better here. These maps are generated by taking maximum over channel dimension." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.495, + 0.47, + 0.587 + ], + "angle": 0, + "content": "periments with more than 9 homographies. This confirms the intuition that a higher number of perspective transformations can better capture the geometric shift between two domains. Therefore, we conducted all experiments with the maximum number of homographies allowed by our compute resources." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.592, + 0.47, + 0.82 + ], + "angle": 0, + "content": "Only optimizing \\(\\mathcal{T}\\). We also run the Ours-\\(h\\) baseline in the FoV and viewpoint adaptation scenarios. The resulting APs are 78.2 and 49.8, respectively. By learning only the \\(20(5\\times 4)\\) homography parameters, our approach outperforms FR (in Tab. 1 and Tab. 3, respectively) by a large margin in both cases. This confirms that our training strategy is able to efficiently optimize \\(\\mathcal{T}\\) to bridge the geometric gap between different domains. We visualize in Fig. A.9 in the supplementary material some transformations learned for FoV adaptation by Ours-\\(h\\). Note that they converge to diverse homographies that mimic a different FoV, correctly reflecting the adaptation task. Additionally in supp. material Sec. A.4, we ablate why the learning transform is useful. Specifically, learning not only provide better performance but provide faster inference w.r.t random transformations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Diversity in \\(\\mathcal{T}\\). To show that our approach can learn a diverse set of transformations that help in the adaptation task, we initialize all the homographies with identity. Fig. 8 depicts the diversity of the learned homographies on the FoV adaptation task. Even though we do not enforce di" + }, + { + "type": "image", + "bbox": [ + 0.578, + 0.089, + 0.818, + 0.269 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.279, + 0.895, + 0.363 + ], + "angle": 0, + "content": "Figure 8. Diversity in \\(\\mathcal{T}\\): We train 5 homographies initialized as \\(\\mathcal{H}_i = I\\). We plot the evolution of \\(s_x\\) for different homograhies as training proceeds. Each homography is shown in a different color. Note that the values for the different homographies become diverse. The best score is achieved at iteration \\(= 22k\\), indicated with the vertical line." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.389, + 0.895, + 0.464 + ], + "angle": 0, + "content": "versity, our approach learns a diverse set of transformations. With these learned homographies, our model achieves 79.5 AP@0.5 score for the FoV adaptation task. We show additional results in the supplementary material Sec. A.6 and Sec. A.7." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.47, + 0.893, + 0.697 + ], + "angle": 0, + "content": "Limitations. Our approach assumes that the geometric gap between two domains can be bridged by a set of perspective transformations. We have shown that with enough transformations this is true. However, using a large number of homographies comes at a computational cost. The computational overhead leads to an increment in the inference time from 0.062s to 0.096s for \\( N = 5 \\) on an A100 Nvidia GPU with image dimension \\( 402 \\times 1333 \\). Nevertheless, our simple implementation shows promising results, and we will work on reducing this overhead in future work. Moreover since the optimization of the homography set is done at the dataset level, only certain transformations are beneficial to a given image. In the future, we therefore intend to condition the homography on the input image, which would reduce the total number of homographies needed." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.71, + 0.62, + 0.725 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.735, + 0.895, + 0.903 + ], + "angle": 0, + "content": "We have introduced an approach to bridge the gap between two domains caused by geometric shifts by learning a set of homographies. We have shown the effectiveness our method on two different kinds of shifts, without relying on any annotations in the target domain, including information about the nature of the geometric shifts. Our analyses have evidenced that optimizing the transformations alone brings in improvement over the base detector and increasing the number of learnt homographies helps further. In the future, we plan to learn transformations that are conditioned on the input image to model image-dependent geometric shifts." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "17448" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.157 + ], + "angle": 0, + "content": "[1] Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.157, + 0.47, + 0.2 + ], + "angle": 0, + "content": "[2] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. Detr, https://github.com/facebookresearch/detr, 2020.1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.2, + 0.471, + 0.269 + ], + "angle": 0, + "content": "[3] Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, and Qi Dou. Harmonizing transferability and discriminability for adapting object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8869-8878, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.27, + 0.471, + 0.338 + ], + "angle": 0, + "content": "[4] Chaoqi Chen, Zebiao Zheng, Yue Huang, Xinghao Ding, and Yizhou Yu. I3net: Implicit instance-invariant network for adapting one-stage object detectors. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.34, + 0.471, + 0.408 + ], + "angle": 0, + "content": "[5] Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Domain adaptive faster r-cnn for object detection in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3339-3348, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.41, + 0.471, + 0.493 + ], + "angle": 0, + "content": "[6] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.494, + 0.471, + 0.55 + ], + "angle": 0, + "content": "[7] Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764-773, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.55, + 0.471, + 0.62 + ], + "angle": 0, + "content": "[8] Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, and Laura Leal-Taixe. Mot20: A benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003, 2020. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.621, + 0.471, + 0.689 + ], + "angle": 0, + "content": "[9] Jinhong Deng, Wen Li, Yuhua Chen, and Lixin Duan. Unbiased mean teacher for cross-domain object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4091-4101, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.69, + 0.471, + 0.746 + ], + "angle": 0, + "content": "[10] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.747, + 0.471, + 0.815 + ], + "angle": 0, + "content": "[11] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. Domain-adversarial training of neural networks. The journal of machine learning research, 17(1):2096-2030, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.817, + 0.471, + 0.873 + ], + "angle": 0, + "content": "[12] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3354-3361. IEEE, 2012. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.471, + 0.903 + ], + "angle": 0, + "content": "[13] Qiqi Gu, Qianyu Zhou, Minghao Xu, Zhengyang Feng, Guangliang Cheng, Xuequan Lu, Jianping Shi, and Lizhuang" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.471, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "Ma. Pit: Position-invariant transform for cross-fov domain adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8761-8770, 2021. 1, 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.892, + 0.206 + ], + "angle": 0, + "content": "[14] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.207, + 0.892, + 0.276 + ], + "angle": 0, + "content": "[15] Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, and Kiyoharu Aizawa. Cross-domain weakly-supervised object detection through progressive domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5001-5009, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.277, + 0.892, + 0.319 + ], + "angle": 0, + "content": "[16] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. Advances in neural information processing systems, 28, 2015. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.32, + 0.892, + 0.445 + ], + "angle": 0, + "content": "[17] Glenn Jocher, Alex Stoken, Jirka Borovec, NanoCode012, ChristopherSTAN, Liu Changyu, Laughing, tkianai, Adam Hogan, lorenzomammana, yxNONG, AlexWang1900, Laurentiu Diaconu, Marc, wanghaoyang0106, ml5ah, Doug, Francisco Ingham, Frederik, Guilhien, Hatovix, Jake Poznanski, Jiacong Fang, Lijun Yu, changyu98, Mingyu Wang, Naman Gupta, Osama Akhtar, PetrDvoracek, and Prashant Rai. ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements, Oct. 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.446, + 0.892, + 0.516 + ], + "angle": 0, + "content": "[18] Seunghyeon Kim, Jaehoon Choi, Taekyung Kim, and Chang-ick Kim. Self-training and adversarial background regularization for unsupervised domain adaptive one-stage object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6092–6101, 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.517, + 0.892, + 0.558 + ], + "angle": 0, + "content": "[19] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.56, + 0.892, + 0.63 + ], + "angle": 0, + "content": "[20] Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Kan Chen, Bichen Wu, Zijian He, Kris Kitani, and Peter Vajda. Cross-domain adaptive teacher for object detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.631, + 0.892, + 0.687 + ], + "angle": 0, + "content": "[21] Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. Learning transferable features with deep adaptation networks. In International conference on machine learning, pages 97-105. PMLR, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.688, + 0.892, + 0.731 + ], + "angle": 0, + "content": "[22] Zhongyi Pei, Zhangjie Cao, Mingsheng Long, and Jianmin Wang. Multi-adversarial domain adaptation. In Thirty-second AAAI conference on artificial intelligence, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.731, + 0.892, + 0.788 + ], + "angle": 0, + "content": "[23] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779-788, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.788, + 0.892, + 0.845 + ], + "angle": 0, + "content": "[24] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 39(6):1137-1149, 2016. 1, 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.845, + 0.892, + 0.903 + ], + "angle": 0, + "content": "[25] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. ImageNet Large Scale Visual Recognition Chal" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "17449" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.092, + 0.468, + 0.12 + ], + "angle": 0, + "content": "lenge. International Journal of Computer Vision (IJCV), 115(3):211-252, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.47, + 0.19 + ], + "angle": 0, + "content": "[26] Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, and Kate Saenko. Strong-weak distribution alignment for adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6956–6965, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.192, + 0.469, + 0.246 + ], + "angle": 0, + "content": "[27] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126(9):973-992, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.249, + 0.469, + 0.305 + ], + "angle": 0, + "content": "[28] Zhiqiang Shen, Harsh Maheshwari, Weichen Yao, and Marios Savvides. Scl: Towards accurate domain adaptive object detection via gradient detach based stacked complementary losses. arXiv preprint arXiv:1911.02559, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.307, + 0.469, + 0.361 + ], + "angle": 0, + "content": "[29] Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li, Han Zhang, Chen-Yu Lee, and Tomas Pfister. A simple semi-supervised learning framework for object detection. arXiv preprint arXiv:2005.04757, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.363, + 0.469, + 0.417 + ], + "angle": 0, + "content": "[30] Baochen Sun and Kate Saenko. Deep coral: Correlation alignment for deep domain adaptation. In European conference on computer vision, pages 443-450. Springer, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.42, + 0.469, + 0.475 + ], + "angle": 0, + "content": "[31] Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems, 30, 2017. 2, 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.477, + 0.469, + 0.532 + ], + "angle": 0, + "content": "[32] Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. Adversarial discriminative domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7167-7176, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.534, + 0.469, + 0.616 + ], + "angle": 0, + "content": "[33] Vibashan VS, Vikram Gupta, Poojan Oza, Vishwanath A Sindagi, and Vishal M Patel. Mega-cda: Memory guided attention for category-aware unsupervised domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4516-4526, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.618, + 0.469, + 0.659 + ], + "angle": 0, + "content": "[34] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detector2. https://github.com/facebookresearch/detectron2, 2019.6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.661, + 0.469, + 0.716 + ], + "angle": 0, + "content": "[35] Shaoan Xie, Zibin Zheng, Liang Chen, and Chuan Chen. Learning semantic representations for unsupervised domain adaptation. In International conference on machine learning, pages 5423-5432. PMLR, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.718, + 0.469, + 0.786 + ], + "angle": 0, + "content": "[36] Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang. Adversarial domain adaptation with domain mixup. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 6502-6509, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.789, + 0.469, + 0.856 + ], + "angle": 0, + "content": "[37] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223-2232, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[38] Xinge Zhu, Jiangmiao Pang, Ceyuan Yang, Jianping Shi, and Dahua Lin. Adapting object detectors via selective cross-domain alignment. In Proceedings of the IEEE/CVF Con-" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.532, + 0.092, + 0.892, + 0.12 + ], + "angle": 0, + "content": "ference on Computer Vision and Pattern Recognition, pages 687-696, 2019. 1, 2" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "17450" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/69fb3432-f3b6-48da-9de8-e985d9696176_origin.pdf b/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/69fb3432-f3b6-48da-9de8-e985d9696176_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..784fc528341970ad693834977d19b9f32457a9fd --- /dev/null +++ b/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/69fb3432-f3b6-48da-9de8-e985d9696176_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f691ad0eaed1ab35c8ecd410acd4981220eabf800bd6788baf4f16823a873b73 +size 6400351 diff --git a/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/full.md b/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/full.md new file mode 100644 index 0000000000000000000000000000000000000000..7c161b8f2869b28aec04384acc23a3a9bf5f8f5e --- /dev/null +++ b/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/full.md @@ -0,0 +1,303 @@ +# Learning Transformations To Reduce the Geometric Shift in Object Detection + +Vedit Vidit1 Martin Engilberge1 Mathieu Salzmann1,2 +CVLab, EPFL1, ClearSpace SA2 + +firstname_lastname@epfl.ch + +# Abstract + +The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between synthetic and real images. Here, by contrast, we tackle geometric shifts emerging from variations in the image capture process, or due to the constraints of the environment causing differences in the apparent geometry of the content itself. We introduce a self-training approach that learns a set of geometric transformations to minimize these shifts without leveraging any labeled data in the new domain, nor any information about the cameras. We evaluate our method on two different shifts, i.e., a camera's field of view (FoV) change and a viewpoint change. Our results evidence that learning geometric transformations helps detectors to perform better in the target domains. + +# 1. Introduction + +While modern object detectors [1, 2, 17, 23, 24] achieve impressive results, their performance decreases when the test data depart from the training distribution. This problem arises in the presence of appearance variations due to, for example, differing illumination or weather conditions. Considering the difficulty and cost of acquiring annotated data in the test (i.e., target) domain, Unsupervised Domain Adaptation (UDA) has emerged as the standard strategy to address such scenarios [3, 4, 9, 26, 38]. + +In this context, much effort has been made to learn domain invariant features, such that the source and target distributions in this feature space are similar. This has led to great progress in situations where the appearance of the objects changes drastically from one domain to the other, as in case of real-to-sketch adaptation (e.g., Pascal VOC [10] to Comics [15]), or weather adaptation (e.g., Cityscapes [6] to Foggy Cityscapes [27]). Nevertheless, such object appearance changes are not the only sources of domain shifts. They can also have geometric origins. For example, as shown in Fig. 1, they can be due to a change in camera view + +![](images/0ddde06e2387f32ffbdee1691784d1c9c2685302331fc17ca5e0d3c07d7e4427.jpg) + +![](images/6c249218327b701aa8630044b3c479c23874ad3d691ccc4c158d8a6096b5563c.jpg) +Figure 1. Geometric shifts. (Left) Due to a different FoV, the cars highlighted in green, undergo different distortions even though they appear in similar image regions. (Right) Different camera viewpoints (front facing vs downward facing) yield different distortions and occlusion patterns for pedestrian detection. (Bottom) The distributions of pedestrian bounding box sizes in Cityscapes [6] and MOT [8] differ significantly as the pedestrians are usually far away or in the periphery in Cityscapes. The top images are taken from Cityscapes [6], and the bottom-left and right ones from KITTI [12] and MOT [8], respectively. + +point or field-of-view (FoV), or a change of object scale due to different scene setups. In practice, such geometric shifts typically arise from a combination of various factors, including but not limited to the ones mentioned above. + +In this paper, we introduce a domain adaptation approach tackling such geometric shifts. To the best of our knowledge, the recent work of [13] constitutes the only attempt at considering such geometric distortions. However, it introduces a method solely dedicated to FoV variations, assuming that the target FoV is fixed and known. Here, we de + +velop a more general framework able to cope with a much broader family of geometric shifts. + +To this end, we model geometric transformations as a combination of multiple homographies. We show both theoretically and empirically that this representation is sufficient to encompass a broad variety of complex geometric transformations. We then design an aggregator block that can be incorporated to the detector to provide it with the capacity to tackle geometric shifts. We use this modified detector to generate pseudo labels for the target domain, which let us optimize the homographies so as to reduce the geometric shift. + +Our contributions can be summarized as follows. (i) We tackle the problem of general geometric shifts for object detection. (ii) We learn a set of homographies using unlabeled target data, which alleviates the geometric bias arising in source-only training. (iii) Our method does not require prior information about the target geometric distortions and generalizes to a broad class of geometric shifts. Our experiments demonstrate the benefits of our approach in several scenarios. In the presence of FoV shifts, our approach yields similar performance to the FoV-dedicated framework of [13] but without requiring any camera information. As such, it generalizes better to other FoVs. Furthermore, we show the generality of our method by using it to adapt to a new camera viewpoint in the context of pedestrian detection. Our implementation can be accessed at https://github.com/vidit09/geoshift. + +# 2. Related Work + +Unsupervised Domain Adaptation (UDA). UDA for image recognition [11, 21, 22, 30, 32, 35, 36] and object detection [3, 4, 9, 20, 26, 38] has made a great progress in the past few years. The common trend in both tasks consists of learning domain invariant features. For object detection, this entails aligning the global (e.g., illumination, weather) and local (foreground objects) features in the two domains. In this context, [3, 5, 26, 28] align image- and instance-level features in the two domains via adversarial learning [11]; [33] learns category-specific attention maps to better align specific image regions; [38] clusters the proposed object regions using $k$ -means clustering and uses the centroids for instance-level alignment. While this successfully tackles domain shifts caused by object appearance variations, it fails to account for the presence of shifts due to the image capture process itself, such as changes in camera intrinsics or viewpoint. The only initial step at considering a geometric shift is the work of [13], which shows the existence of an FoV gap in driving datasets [6, 12] and proposes a Position Invariant Transform (PIT) that corrects the distortions caused specifically by an FoV change. In essence, PIT undistorts the images by assuming knowledge of the target FoV. By contrast, here, we introduce an approach that gen + +eralizes to a broad family of geometric shifts by learning transformations without requiring any camera information. + +Self-training. Self-training, generally employed in the semi-supervised setting, offers an alternative to learning domain-invariant features and utilize unlabeled data to improve a detector's performance. In this context, [29] uses a student-teacher architecture where the teacher model is trained with supervised data and generates pseudo-labels on unannotated data. These pseudo-labels are then used to train a student model. While effective in the standard semi-supervised learning scenario, the quality of the pseudo-labels obtained with this approach tends to deteriorate when the labeled and unlabeled data present a distribution shift. [9, 20] have therefore extended this approach to domain adaptation by using the Mean Teacher strategy of [31] to generate reliable pseudo-labels in the target domain. Other approach include the use of CycleGAN [37] generated images to train an unbiased teacher model [9], and that of different augmentation strategies to generate robust pseudo-labels [20]. Our approach also follows a self-training strategy but, while these works focus on object appearance shifts, we incorporate learnable blocks to address geometric shifts. As shown in our experiment, this lets us outperform the state-of-the-art AdaptTeacher [20]. + +Learning Geometric Transformations. End-to-end learning of geometric transformations has been used to boost the performance of deep networks. For example, Spatial Transformer Networks (STNs) [16] reduce the classification error by learning to correct for affine transformations; deformable convolutions [7] model geometric transformations by applying the convolution kernels to non-local neighborhoods. These methods work well when annotations are available for supervision, and make the network invariant to the specific geometric transformations seen during training. Here, by contrast, we seek to learn transformations in an unsupervised manner and allow the network to generalize to unknown target transformations. + +# 3. Modeling Geometric Transformations + +In the context of UDA, multiple geometric differences can be responsible for the gap between the domains. Some can be characterized by the camera parameters, such as a change in FoV (intrinsic) or viewpoint (extrinsic), whereas others are content specific, such as a difference in road width between different countries. Ultimately, the geometric shift is typically a combination of different geometric operations. Since the parameters of these operations are unknown, we propose to bridge the domain gap by learning a geometric transform. Specifically, we aggregate the results of multiple perspective transforms, i.e., homographies, to obtain a differentiable operation that can emulate a wide variety of geometric transforms. + +# 3.1. Theoretical Model + +Let us first show that, given sufficiently many homographies, one can perfectly reproduce any mapping between $\mathbb{R}^2\setminus (0,0)$ and $\mathbb{R}^2$ + +Single homography for a single point. First, we show that a single homography with 4 degrees of freedom can map a point $p \in \mathbb{R}^2 \setminus (0,0)$ to any other point in $\mathbb{R}^2$ . To this end, let + +$$ +H = \left[ \begin{array}{c c c} s _ {x} & 0 & 0 \\ 0 & s _ {y} & 0 \\ l _ {x} & l _ {y} & 1 \end{array} \right] \tag {1} +$$ + +be a homography, with $(s_x,s_y)$ the scaling factors on the $x-$ and $y$ -axis, respectively, and $(l_{x},l_{y})$ the perspective factors in $x$ and $y$ , respectively. For any destination point $d\in \mathbb{R}^2$ , there exists a set of parameters $(s_x,s_y,l_x,l_y)$ such that $d = H\times p$ . One such set is $\left(\frac{d_x}{p_x},\frac{d_y}{p_y},0,0\right)$ . + +Emulating any geometric transformation Now that we have shown that a single homography can move a point to any other point in $\mathbb{R}^2$ , we describe a simple protocol to emulate any geometric transform. Given an unknown geometric transform $T:\mathbb{R}^2\backslash (0,0)\to \mathbb{R}^2$ , we aim to emulate $T$ with a set of homographies. In general, for an image $\mathbf{I}\in \mathbb{R}^{3\times h\times w}$ we can restrict the domain of $T$ to only image coordinates. To this end, we can define a set of homographies $H_{i}\in \mathbb{H}$ for i in $\{1,2,3,\dots,h\times w\}$ , where the parameters of $H_{i}$ are chosen to mimic the transform $T$ for location $i$ of the image. In this protocol, the aggregation mechanism is trivial since each homography is in charge of remapping a single pixel coordinate of the original space. + +While this works in theory, this is of course not viable in practice since it would require too many homographies. With a smaller number of homographies, each transform needs to remap multiple points, and a more sophisticated aggregation mechanism is required. Specifically, the aggregation mechanism needs to select which transform is in charge of remapping which point. In the next section, we empirically show that this strategy lets us closely approximate the spherical projection mapping used in PIT [13]. + +# 3.2. Approximating PIT with Homographies + +To demonstrate the possibility offered by aggregating multiple homographies, we design an approximation of PIT using only homographies. PIT proposes to correct for an FoV gap by remapping images to a spherical surface. During this transformation, regions further from the center of a scene are compressed with a higher ratio. This variable compression of the space cannot be reproduced by a single homography transformation. To overcome this limitation, we combine the results of multiple homographies that all have different compression rates (scaling parameters). For + +the aggregation mechanism, we use the optimal strategy by selecting for each pixel the homography that approximates best the PIT mapping. As shown in Fig. 2, this combination closely approximates the PIT results with only 5 homographies. Further analysis of these experiments is available in the supplementary material in Fig. A.3. + +![](images/a8585d72f7a753eb330083e477c431b439ebe03778af60c92000cac1274ef772.jpg) + +![](images/fd2e8ca6e69a953f98941a93ff1e69b0512e0edce57a48b3c50fe6b63b57795f.jpg) + +![](images/54fb2a76b33e0be58bb29dfc354ac78951f6602eb23e6f2bbc57d7912813973e.jpg) +Figure 2. Approximating PIT with homographies. We show the original image (top), the PIT [13] correction (middle), and our approximation of PIT using 5 homographies. Note that 5 homographies are sufficient to closely match the PIT spherical correction. + +# 3.3. Homographies in a Learning Setup + +In the two previous sections, we have demonstrated both theoretically and empirically the flexibility of aggregating homographies. This makes this representation an ideal candidate for domain adaptation since the geometric shift between the domains is unknown and can be a combination of different transforms, such as FoV change, viewpoint change, camera distortion, or appearance distortion. As will be discussed in the next section, by learning jointly the set of perspective transforms and the aggregation mechanism on real data, our model can reduce the geometric shift between the two domains without prior knowledge about this domain gap. + +# 4. Method + +Let us now introduce our approach to reducing the geometric shift in object detection. Following the standard UDA setting, let $D_{s} = \{(I_{s},B_{s},C_{s})\}$ be a labeled source dataset containing images $I_{s} = \{I_{s}^{i}\}_{1}^{N_{s}}$ with corresponding object bounding boxes $B_{s} = \{b_{s}^{i}\}_{1}^{N_{s}}$ and object classes $C_s = \{c_s^i\}_1^{N_s}$ . Furthermore, let $D_{t} = \{I_{t}\}$ denote an unlabeled target dataset for which only images $I_{t} = \{I_{t}^{i}\}_{1}^{N_{t}}$ + +![](images/6b23ae9ef0fc1e3a999035fafe024d7628f93faaa004fff9f725f77197b8a824.jpg) +Figure 3. Architecture: The input image is first transformed by a set of trainable homographies. The feature maps extracted from the transformed images are then unwarped by the inverse homographies to achieve spatial consistency. We then combine the unwarped feature maps using a trainable aggregator, whose output is passed to a detection head. The blocks shown in green correspond to standard FasterRCNN operations. The $\oplus$ symbol represents the concatenation operation. + +are available, without annotations. Here, we tackle the case where the two domains differ by geometric shifts but assume no knowledge about the nature of these shifts. Below, we first introduce the architecture we developed to handle this and then our strategy to train this model. + +# 4.1. Model Architecture + +The overall architecture of our approach is depicted in Fig. 3. In essence, and as discussed in Sec. 3, we characterize the geometric changes between the source and target data by a set of transformations $\mathcal{T} = \{\mathcal{H}_i\}_{1}^N$ . Each $\mathcal{H}_i$ in $\mathcal{T}$ is a homography of the same form as in Eq. (1). For our method to remain general, we assume the transformations to be unknown, and our goal, therefore, is to learn $\mathcal{T}$ to bridge the gap between the domains. This requires differentiability w.r.t. the transformation parameters, which we achieve using the sampling strategy proposed in [16]. + +As shown in Fig. 3, the input image is transformed by the individual homographies in $\mathcal{T}$ , and the transformed images are fed to a modified FasterRCNN [24] detector. Specifically, we extract a feature map $\mathcal{F}_{\mathcal{H}_i} \in \mathbb{R}^{H \times W \times C}$ for each transformed image via a feature extractor shared by all transformations. To enforce spatial correspondence between the different $\mathcal{F}_{\mathcal{H}_i}$ s, we unwarp them with $\mathcal{H}_i^{-1}$ . + +We then introduce an aggregator $\mathcal{A}_{\theta_g}$ , parameterized by $\theta_{g}$ , whose goal is to learn a common representation given a fixed number of unwarped feature maps $\mathcal{F}_{\mathcal{H}_i}^{\prime}$ . To achieve this, the aggregator takes as input + +$$ +\mathcal {G} = \mathcal {F} _ {\mathcal {H} _ {1}} ^ {\prime} \oplus \mathcal {F} _ {\mathcal {H} _ {2}} ^ {\prime} \oplus \dots \oplus \mathcal {F} _ {\mathcal {H} _ {N}} ^ {\prime} \in \mathbb {R} ^ {H \times W \times C \times N}, \tag {2} +$$ + +where $\oplus$ represents concatenation in the channel dimension. The aggregator outputs a feature map $\mathcal{A}_{\theta_g}(\mathcal{G})\in \mathbb{R}^{H\times W\times C}$ whose dimension is independent of the number of transformations. This output is then passed to a detection head to obtain the objects' bounding boxes and class labels. + +# 4.2. Model Training + +Our training procedure relies on three steps: (i) Following common practice in UDA, we first train the Faster-RCNN detector with source-only data; (ii) We then introduce the aggregator and train it so that it learns to combine different homographies using the labeled source data; (iii) Finally, we learn the optimal transformations for adaptation using both the source and target data via a Mean Teacher [31] strategy. + +Aggregator Training. To train the aggregator, we randomly sample a set of homographies $\mathcal{T} \in \mathbb{R}^{N \times 4}$ in each training iteration. This gives the aggregator the ability to robustly combine diverse input transformations but requires strong supervision to avoid training instabilities. We, therefore, perform this step using the source data. + +The loss function for a set of transformed images $\mathcal{T}(I_s)$ is then defined as in standard FasterRCNN training with a combination of classification and regression terms [24]. That is, we train the aggregator by solving + +$$ +\min _ {\theta_ {g}} \mathcal {L} _ {c l s} \left(\mathcal {T} \left(I _ {s}\right)\right) + \mathcal {L} _ {r e g} \left(\mathcal {T} \left(I _ {s}\right)\right), \tag {3} +$$ + +where + +$$ +\mathcal {L} _ {c l s} \left(\mathcal {T} \left(I _ {s}\right)\right) = \mathcal {L} _ {c l s} ^ {r p n} + \mathcal {L} _ {c l s} ^ {r o i}, \tag {4} +$$ + +$$ +\mathcal {L} _ {r e g} \left(\mathcal {T} \left(I _ {s}\right)\right) = \mathcal {L} _ {r e g} ^ {r p n} + \mathcal {L} _ {r e g} ^ {r o i}. \tag {5} +$$ + +$\mathcal{L}^{rpn}$ and $\mathcal{L}^{roi}$ correspond to the Region Proposal Network (RPN) loss terms and the Region of Interest (RoI) ones, respectively. During this process, we freeze the parameters $\theta_{b}$ of the base network, i.e., feature extractor and detection head, which were first trained on the source data without aggregator. Ultimately, the aggregator provides the network with the capacity to encode different transformations that are not seen in the source domain. The third training step then aims to learn the best transformation for successful object detection in the target domain. + +Learning the Transformations. As we have no annotations in the target domain, we exploit a Mean Teacher (MT) strategy to learn the optimal transformations. To this end, our starting point is the detector with a trained aggregator and a set of random transformations $\mathcal{T}$ . The MT strategy is illustrated in Fig. 4. In essence, MT training [31] involves two copies of the model: A student model, with parameters $\theta^{st} = \{\mathcal{T}^{st},\theta_b^{st},\theta_g^{st}\}$ , that will be used during inference, and a teacher model, with parameters $\theta^{te} = \{\mathcal{T}^{te},\theta_b^{te},\theta_g^{te}\}$ , that is updated as an Exponentially Moving Average (EMA) of the student model. That is, the student's parameters are computed with standard backpropagation, whereas the teacher's ones are updated as + +$$ +\theta_ {t e} \leftarrow \alpha \theta_ {t e} + (1 - \alpha) \theta_ {s t}. \tag {6} +$$ + +The student model is trained using both source and target detection losses. Since the target domain does not have annotations, the teacher model is used to generate pseudolabels. These labels might be noisy, and hence we only keep the predictions with a confidence score above a threshold $\tau$ . Furthermore, non-maxima suppression (NMS) is used to remove the highly-overlapping bounding box predictions. + +Formally, given a source image $I_{s}$ and a target image $I_{t}$ , the student model is trained by solving + +$$ +\min _ {\mathcal {T} ^ {s t}, \theta_ {g} ^ {s t}, \theta_ {b} ^ {s t}} \mathcal {L} _ {d e t} (\mathcal {T} (I _ {s})) + \lambda \mathcal {L} _ {d e t} (\mathcal {T} (I _ {t})) , \tag {7} +$$ + +where $\lambda$ controls the target domain contribution and + +$$ +\mathcal {L} _ {\text {d e t}} \left(\mathcal {T} \left(I _ {s}\right)\right) = \mathcal {L} _ {\text {c l s}} \left(\mathcal {T} \left(I _ {s}\right)\right) + \mathcal {L} _ {\text {r e g}} \left(\mathcal {T} \left(I _ {s}\right)\right), \tag {8} +$$ + +$$ +\mathcal {L} _ {\text {d e t}} \left(\mathcal {T} \left(I _ {t}\right)\right) = \mathcal {L} _ {\text {c l s}} \left(\mathcal {T} \left(I _ {t}\right)\right). \tag {9} +$$ + +Similarly to [18,20], we update the student model with only the classification loss in the target domain to help stabilize training. + +![](images/3a1a09bc3237b028b9ced457e72570d6a2cb75a16d94616ff4f8b9174a18c1ea.jpg) +Figure 4. Mean Teacher formalism. The student model is trained with ground-truth labels in the source domain and pseudo labels in the target one. These pseudo labels are produced by the teacher model, which corresponds to an exponentially moving average (EMA) of the student network. + +# 5. Experiments + +We demonstrate the effectiveness and generality of our method on different geometric shifts. First, to compare to the only other work that modeled a geometric shift [13], we tackle the problem of a change in FoV between the source and target domain. Note that, in contrast to [13], we do not assume knowledge of the target FoV. Furthermore, while [13] was dedicated to FoV adaptation, our approach generalizes to other geometric shifts. We demonstrate this on the task of pedestrian detection under a viewpoint shift. We compare our method with the state-of-the-art AdaptTeacher [20], which also uses a Mean Teacher, but focuses on appearance shifts. In the remainder of this section, we describe our experimental setup and discuss our results. + +# 5.1. Datasets + +Cityscapes [6] contains 2975 training and 500 test images with annotations provided for 8 categories (person, car, train, rider, truck, motorcycle, bicycle and bus). The average horizontal (FoVx) and vertical (FoVy) FoVs of the capturing cameras are $50^{\circ}$ and $26^{\circ}$ , respectively. We use this dataset as the source domain for both FoV adaptation and viewpoint adaptation. + +KITTI [12] is also a street-view dataset containing 6684 images annotated with the car category. The horizontal (FoVx) and vertical (FoVy) FoVs of the camera are $90^{\circ}$ and $34^{\circ}$ , respectively. We use this dataset as target domain for FoV adaptation, as the viewpoint is similar to that of Cityscapes. Following [13], we use 5684 images for unsupervised training and 1000 images for evaluation. + +MOT [8] is a multi-object tracking dataset. We use the indoor mall sequence, MOT20-02, consisting of 2782 frames annotated with the person category. We employ this dataset as target domain for viewpoint adaptation. We use the first + +2000 frame for unsupervised training and last 782 for evaluation. + +# 5.2. Adaptation Tasks and Metric + +FoV adaptation. As in [13], we consider the case of an increasing FoV using Cityscapes as source domain and KITTI as target domain. The horizontal and vertical FoVs increase from $(50^{\circ}, 26^{\circ})$ in Cityscapes to $(90^{\circ}, 34^{\circ})$ in KITTI. Therefore, as can be seen in Fig. 1, the KITTI images have a higher distortion in the corners than the Cityscapes ones. Similarly to PIT [13], we use the car category in our experiments. + +FoV generalization. Following PIT [13], we study the generalization of our approach to new FoVs by cropping the KITTI images to mimic different FoV changes in the horizontal direction (FoVx). Specifically, we treat FoVx = 50° as the source domain and the cropped images with FoVx = {70°, 80°, 90°} as different target domains. We evaluate our approach on car on these different pairs of domains. + +Viewpoint adaptation. This task entails detecting objects seen from a different viewpoint in the source and target domains. We use the front-facing Cityscapes images as source domain and the downward-facing MOT ones as target one. As the MOT data depicts pedestrians, we use the bounding boxes corresponding to the person category in Cityscapes.[2] + +Metric. In all of our experiments, we use the Average Precision (AP) as our metric. Specifically, following [13], we report the AP@0.5, which considers the predictions as true positives if they match the ground-truth label and have an intersection over union (IOU) score of more than 0.5 with the ground-truth bounding boxes. + +# 5.3. Implementation Details + +We use the Detector2 [34] implementation of Faster-RCNN [24] with a ResNet50 [14] backbone as our base architecture. In all of our experiments, the images are resized so that the shorter side has 800 pixels while maintaining the aspect ratio. The base network is first trained on source-only images with random cropping and random flipping augmentation for 24k iterations with batch size 8. We use the Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0.01, scaled down by a 0.1 factor after 18k iterations. We use ImageNet [25] pretrained weights to initialize the ResNet50 backbone. + +We then incorporate the aggregator in the trained base architecture. The aggregator architecture contains three convolutional layers with a kernel size of $3 \times 3$ , and one $1 \times 1$ convolutional layer. We first train the aggregator + +![](images/89f3e28d0ea238bb7373db65c9175b6222ffb50691783c497ed59083b6b5b2b3.jpg) +Figure 5. FoV Adaptation: Qualitative Results. We visualize a car detection result in the Cityscapes-to-KITTI FoV adaptation scenario. The top left image corresponds to the ground truth, the bottom left to the Mean Teacher result, which confuses the orange container with a car, the bottom right to the Mean Teacher adaptation + PIT FoV adaptation result, which also mistakes the orange container for a car and further detects the speed limit on the road. Our approach, on the top right, correctly matches the ground truth. + +![](images/fecc74c15c8496e461f70945446eb786768514a40889ca8c2309e4781aa46481.jpg) + +on the source data with the base frozen and using random transformations $\mathcal{T}$ . The transformations are generated by randomly sampling each $\mathcal{H}_i$ parameters as $s_x, s_y \sim \mathcal{U}_{[0.5,2.0]}$ , $\mathcal{U}_{[0.5,2.0]}$ and $l_x, l_y \sim \mathcal{U}_{[-0.5,0.5]}$ , $\mathcal{U}_{[-0.5,0.5]}$ . We train the aggregator for 30k iterations using a batch size of 8 and the SGD optimizer with a learning rate of $1e^{-4}$ . + +The student and teacher models are then initialized with this detector and the random $\mathcal{T} = \{\mathcal{H}_i\}_{i=1}^N$ . We optimize $\mathcal{T}$ using Adam [19], while the base and aggregator networks are optimized by SGD. The learning rate is set to $1e^{-3}$ and scaled down by a factor 0.1 after 10k iterations for the SGD optimizer. For the first 10k iterations in FoV adaptation and for 2k iterations for viewpoint adaptation, we only train $\mathcal{T}$ keeping base and aggregator frozen. The $\alpha$ coefficient for the EMA update is set to 0.99; the confidence threshold $\tau = 0.6$ ; $\lambda = \{0.01, 0.1\}$ for FoV and viewpoint adaptation, respectively. The Mean Teacher framework is trained using both the source and target data. We set $N = 5$ , unless otherwise specified, and use a batch size of 4, containing 2 source and 2 target images. We apply random color jittering on both the source and target data as in [20, 31]. All of our models are trained on a single NVIDIA V100 GPU. A detailed hyper-parameter study is provided in the supplementary material. + +# 5.4. Comparison with the State of the Art + +We compare our approach with the following baselines3. FR: FasterRCNN trained only on the source data with random crop augmentation; AT: AdaptTeacher [20]; MT: Mean Teacher initialized with FR and trained with random color jittering on both the source and target data (i.e., this corresponds to our mean teacher setup in Sec. 4.2 but without the aggregator and without transformations $\mathcal{T}$ ); FR+PIT: Same setup as FR but with the images corrected with PIT [13]; MT+PIT: Same setup as MT but with the + +
MethodCar AP@0.5
FR [24]76.1
AT [20]77.2
FR+PIT77.6
MT78.3
MT+PIT [13]79.7
Ours80.4 ± 0.15
+ +Table 1. FoV Adaptation. + +
MethodCar AP@0.5 for FoVx
50°70°80°90°
FR [24]94.390.286.880.6
FR+PIT [13]93.691.489.285.9
Ours-h94.1±0.1693.1 ± 0.3391.8 ± 0.4088.8 ± 0.21
+ +images corrected with PIT. We refer to our complete approach (Sec. 4.2) as Ours. For the task of FoV generalization, we report our results as Ours- $h$ to indicate that we only optimize the homographies ( $5 \times 4$ parameters) in $\mathcal{T}$ to adapt to the new FoVs while keeping the base and aggregator networks frozen. This matches the setup of PIT [13], which also corrects the images according to the new FoVs. As Ours and Ours- $h$ are trained with randomly initialized $\mathcal{T}$ , we report the average results and standard deviations over three independent runs. + +FoV adaptation. The results of Cityscapes $\rightarrow$ KITTI FoV adaptation are provided in Tab. 1. Both MT+PIT and Ours both bridge the FoV gap, outperforming the MT baseline. Note, however, that we achieve this by learning the transformations, without requiring any camera-specific information, which is needed by PIT. Note also that MT outperforms FR by learning a better representation in the target domain, even though FR is trained with strong augmentation, such as random cropping. AT underperforms because its strong augmentation strategy fails to generalize for datasets having prominent geometric shifts. Our improvement over MT evidences that learning transformations helps to overcome geometric shifts. We optimize with $N = 9$ , homographies in this setup. Fig. 5 shows a qualitative example. Different homographies look into different image regions and the aggregator learns how to combine the activations corresponding to objects as depicted in Fig. 7. In supp. material Sec. A.3, we show effectiveness of a learned aggregator over the others. Additionally, we provide results on FoV decreasing adaptation in supp. material Sec. A.5. + +FoV generalization. Tab. 2 summarizes the results obtained by using different FoVs as target domains while fix- + +Table 2. FoV Generalization. + +
MethodPedestrian AP@0.5
FR [24]43.7
AT [20]63.5
MT64.7
Ours65.3±0.37
+ +Table 3. Viewpoint Adaptation. + +![](images/625516b6d8d8bde88eeea14809c7b05f16fbb5bc488a37cb3b54c5825a083765.jpg) +Figure 6. Varying the number of homographies. We evaluate the effect of $N$ on the FoV adaptation task. + +ing the source FoV to $50^{\circ}$ . Since both the source and target images are taken from KITTI, the domain gap is only caused by a FoV change. Note that the performance of FR drops quickly as the FoV gap increases. Ours-h outperforms FR+PIT by a growing margin as the FoV gap increases. This shows that learning transformations helps to generalize better to different amounts of geometric shifts. + +Viewpoint adaptation. As shown in Fig. 1, a change in the camera viewpoint yields differences in the observed distortions and type of occlusions. The results in Tab. 3 show the benefits of our method over MT in this case. Note that PIT, which was designed for FoV changes, cannot be applied to correct for a viewpoint change. Other baselines outperform FR, as they use pseudo labels to fix the difference in bounding box distribution, as shown in Fig. 1. These results illustrate the generality of our method to different kinds of geometric shifts. Qualitative results for this task can be found in Fig. A.10. + +# 5.5. Additional Analyses + +Variable number of homographies. Let us now study the influence of the number of homographies in $\mathcal{T}$ . To this end, we vary this number between 1 and 9. In Fig. 6, we plot the resulting APs for the Cityscapes-to-KITTI FoV adaptation task. Increasing the number of transformations results in a steady increase in performance, which nonetheless tends to plateau starting at 4 homographies. This is similar to our theoretical observation in Fig. A.3 of supp. material. Due to limited compute resources, we couldn't run ex + +![](images/ab51612655641a63ec4a38dbb2a8b9dd56abbbfde7a2eef5869a89d5ed7e5e82.jpg) + +![](images/f1a0b5b1dd972c135881598dee92ebfebcb6cc608dee6e8df5ef48016c0af3b0.jpg) + +![](images/e6071888d47947b532bb2d1b31f0b98df7affdd7577047982c18b4c6eb83177b.jpg) + +![](images/d4a44fbe1381baf2303225fcfba9bd12a67425685692d2a814ed840b2ca6709e.jpg) + +![](images/45227395d7c88a39fcdcc46fe3450091269972d5ba43bc47eac9cc292567292b.jpg) +Figure 7. Feature Maps: Top row: predictions of our network and feature map after aggregator. Left column: Image I, transformed by learned homographies; Right Column: Feature maps $F$ warped by corresponding $H^{-1}$ which are input to the aggregator. Each transform distorts the image regions differently. Most of the cars are on the left side and of small size in the image. $H_{1}$ distorts the left side leading to no activation $(H_{1}^{-1}F_{1})$ for the object. $H_{3}$ which causes the zoom-in effect has the strongest activation as the smaller objects are visible better here. These maps are generated by taking maximum over channel dimension. + +![](images/ccf3f483944698569ca6b6b83bed23100f12381d6048b422b9bc29ac13d854be.jpg) +Figure 8. Diversity in $\mathcal{T}$ : We train 5 homographies initialized as $\mathcal{H}_i = I$ . We plot the evolution of $s_x$ for different homograhies as training proceeds. Each homography is shown in a different color. Note that the values for the different homographies become diverse. The best score is achieved at iteration $= 22k$ , indicated with the vertical line. + +periments with more than 9 homographies. This confirms the intuition that a higher number of perspective transformations can better capture the geometric shift between two domains. Therefore, we conducted all experiments with the maximum number of homographies allowed by our compute resources. + +Only optimizing $\mathcal{T}$ . We also run the Ours- $h$ baseline in the FoV and viewpoint adaptation scenarios. The resulting APs are 78.2 and 49.8, respectively. By learning only the $20(5\times 4)$ homography parameters, our approach outperforms FR (in Tab. 1 and Tab. 3, respectively) by a large margin in both cases. This confirms that our training strategy is able to efficiently optimize $\mathcal{T}$ to bridge the geometric gap between different domains. We visualize in Fig. A.9 in the supplementary material some transformations learned for FoV adaptation by Ours- $h$ . Note that they converge to diverse homographies that mimic a different FoV, correctly reflecting the adaptation task. Additionally in supp. material Sec. A.4, we ablate why the learning transform is useful. Specifically, learning not only provide better performance but provide faster inference w.r.t random transformations. + +Diversity in $\mathcal{T}$ . To show that our approach can learn a diverse set of transformations that help in the adaptation task, we initialize all the homographies with identity. Fig. 8 depicts the diversity of the learned homographies on the FoV adaptation task. Even though we do not enforce di + +![](images/ccf68a9496c082f1176cb9f7963b35fda52a0f8feae7b67af799ddef9472d20d.jpg) + +versity, our approach learns a diverse set of transformations. With these learned homographies, our model achieves 79.5 AP@0.5 score for the FoV adaptation task. We show additional results in the supplementary material Sec. A.6 and Sec. A.7. + +Limitations. Our approach assumes that the geometric gap between two domains can be bridged by a set of perspective transformations. We have shown that with enough transformations this is true. However, using a large number of homographies comes at a computational cost. The computational overhead leads to an increment in the inference time from 0.062s to 0.096s for $N = 5$ on an A100 Nvidia GPU with image dimension $402 \times 1333$ . Nevertheless, our simple implementation shows promising results, and we will work on reducing this overhead in future work. Moreover since the optimization of the homography set is done at the dataset level, only certain transformations are beneficial to a given image. In the future, we therefore intend to condition the homography on the input image, which would reduce the total number of homographies needed. + +# 6. Conclusion + +We have introduced an approach to bridge the gap between two domains caused by geometric shifts by learning a set of homographies. We have shown the effectiveness our method on two different kinds of shifts, without relying on any annotations in the target domain, including information about the nature of the geometric shifts. Our analyses have evidenced that optimizing the transformations alone brings in improvement over the base detector and increasing the number of learnt homographies helps further. In the future, we plan to learn transformations that are conditioned on the input image to model image-dependent geometric shifts. + +# References + +[1] Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, 2020. 1 +[2] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. Detr, https://github.com/facebookresearch/detr, 2020.1 +[3] Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, and Qi Dou. Harmonizing transferability and discriminability for adapting object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8869-8878, 2020. 1, 2 +[4] Chaoqi Chen, Zebiao Zheng, Yue Huang, Xinghao Ding, and Yizhou Yu. I3net: Implicit instance-invariant network for adapting one-stage object detectors. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 1, 2 +[5] Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Domain adaptive faster r-cnn for object detection in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3339-3348, 2018. 2 +[6] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 1, 2, 5 +[7] Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764-773, 2017. 2 +[8] Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, and Laura Leal-Taixe. Mot20: A benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003, 2020. 1, 5 +[9] Jinhong Deng, Wen Li, Yuhua Chen, and Lixin Duan. Unbiased mean teacher for cross-domain object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4091-4101, 2021. 1, 2 +[10] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010. 1 +[11] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. Domain-adversarial training of neural networks. The journal of machine learning research, 17(1):2096-2030, 2016. 2 +[12] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3354-3361. IEEE, 2012. 1, 2, 5 +[13] Qiqi Gu, Qianyu Zhou, Minghao Xu, Zhengyang Feng, Guangliang Cheng, Xuequan Lu, Jianping Shi, and Lizhuang + +Ma. Pit: Position-invariant transform for cross-fov domain adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8761-8770, 2021. 1, 2, 3, 5, 6, 7 +[14] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6 +[15] Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, and Kiyoharu Aizawa. Cross-domain weakly-supervised object detection through progressive domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5001-5009, 2018. 1 +[16] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. Advances in neural information processing systems, 28, 2015. 2, 4 +[17] Glenn Jocher, Alex Stoken, Jirka Borovec, NanoCode012, ChristopherSTAN, Liu Changyu, Laughing, tkianai, Adam Hogan, lorenzomammana, yxNONG, AlexWang1900, Laurentiu Diaconu, Marc, wanghaoyang0106, ml5ah, Doug, Francisco Ingham, Frederik, Guilhien, Hatovix, Jake Poznanski, Jiacong Fang, Lijun Yu, changyu98, Mingyu Wang, Naman Gupta, Osama Akhtar, PetrDvoracek, and Prashant Rai. ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements, Oct. 2020. 1 +[18] Seunghyeon Kim, Jaehoon Choi, Taekyung Kim, and Chang-ick Kim. Self-training and adversarial background regularization for unsupervised domain adaptive one-stage object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6092–6101, 2019. 5 +[19] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6 +[20] Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Kan Chen, Bichen Wu, Zijian He, Kris Kitani, and Peter Vajda. Cross-domain adaptive teacher for object detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 5, 6, 7 +[21] Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. Learning transferable features with deep adaptation networks. In International conference on machine learning, pages 97-105. PMLR, 2015. 2 +[22] Zhongyi Pei, Zhangjie Cao, Mingsheng Long, and Jianmin Wang. Multi-adversarial domain adaptation. In Thirty-second AAAI conference on artificial intelligence, 2018. 2 +[23] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779-788, 2016. 1 +[24] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 39(6):1137-1149, 2016. 1, 4, 6, 7 +[25] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. ImageNet Large Scale Visual Recognition Chal + +lenge. International Journal of Computer Vision (IJCV), 115(3):211-252, 2015. 6 +[26] Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, and Kate Saenko. Strong-weak distribution alignment for adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6956–6965, 2019. 1, 2 +[27] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126(9):973-992, 2018. 1 +[28] Zhiqiang Shen, Harsh Maheshwari, Weichen Yao, and Marios Savvides. Scl: Towards accurate domain adaptive object detection via gradient detach based stacked complementary losses. arXiv preprint arXiv:1911.02559, 2019. 2 +[29] Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li, Han Zhang, Chen-Yu Lee, and Tomas Pfister. A simple semi-supervised learning framework for object detection. arXiv preprint arXiv:2005.04757, 2020. 2 +[30] Baochen Sun and Kate Saenko. Deep coral: Correlation alignment for deep domain adaptation. In European conference on computer vision, pages 443-450. Springer, 2016. 2 +[31] Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems, 30, 2017. 2, 4, 5, 6 +[32] Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. Adversarial discriminative domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7167-7176, 2017. 2 +[33] Vibashan VS, Vikram Gupta, Poojan Oza, Vishwanath A Sindagi, and Vishal M Patel. Mega-cda: Memory guided attention for category-aware unsupervised domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4516-4526, 2021. 2 +[34] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detector2. https://github.com/facebookresearch/detectron2, 2019.6 +[35] Shaoan Xie, Zibin Zheng, Liang Chen, and Chuan Chen. Learning semantic representations for unsupervised domain adaptation. In International conference on machine learning, pages 5423-5432. PMLR, 2018. 2 +[36] Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang. Adversarial domain adaptation with domain mixup. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 6502-6509, 2020. 2 +[37] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223-2232, 2017. 2 +[38] Xinge Zhu, Jiangmiao Pang, Ceyuan Yang, Jianping Shi, and Dahua Lin. Adapting object detectors via selective cross-domain alignment. In Proceedings of the IEEE/CVF Con- + +ference on Computer Vision and Pattern Recognition, pages 687-696, 2019. 1, 2 \ No newline at end of file diff --git a/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/images.zip b/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..2560d918f41688fcdc2f90e4a9bc6910fbf92f02 --- /dev/null +++ b/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a2c7a4e06a6c873f84f946b83536b0b48c2e74bfbeae390bc94514f4e7290e1a +size 389707 diff --git a/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/layout.json b/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..73d67f9d783ea996cbe5de9f0bc6cd353251fa57 --- /dev/null +++ b/2023/Learning Transformations To Reduce the Geometric Shift in Object Detection/layout.json @@ -0,0 +1,8500 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 57, + 103, + 536, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 103, + 536, + 121 + ], + "spans": [ + { + "bbox": [ + 57, + 103, + 536, + 121 + ], + "type": "text", + "content": "Learning Transformations To Reduce the Geometric Shift in Object Detection" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 164, + 142, + 428, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 164, + 142, + 428, + 171 + ], + "spans": [ + { + "bbox": [ + 164, + 142, + 428, + 171 + ], + "type": "text", + "content": "Vedit Vidit1 Martin Engilberge1 Mathieu Salzmann1,2 \nCVLab, EPFL1, ClearSpace SA2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 223, + 174, + 366, + 184 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 223, + 174, + 366, + 184 + ], + "spans": [ + { + "bbox": [ + 223, + 174, + 366, + 184 + ], + "type": "text", + "content": "firstname_lastname@epfl.ch" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "spans": [ + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 238, + 290, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 238, + 290, + 430 + ], + "spans": [ + { + "bbox": [ + 46, + 238, + 290, + 430 + ], + "type": "text", + "content": "The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between synthetic and real images. Here, by contrast, we tackle geometric shifts emerging from variations in the image capture process, or due to the constraints of the environment causing differences in the apparent geometry of the content itself. We introduce a self-training approach that learns a set of geometric transformations to minimize these shifts without leveraging any labeled data in the new domain, nor any information about the cameras. We evaluate our method on two different shifts, i.e., a camera's field of view (FoV) change and a viewpoint change. Our results evidence that learning geometric transformations helps detectors to perform better in the target domains." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 453, + 127, + 466 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 453, + 127, + 466 + ], + "spans": [ + { + "bbox": [ + 47, + 453, + 127, + 466 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 473, + 287, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 473, + 287, + 581 + ], + "spans": [ + { + "bbox": [ + 46, + 473, + 287, + 581 + ], + "type": "text", + "content": "While modern object detectors [1, 2, 17, 23, 24] achieve impressive results, their performance decreases when the test data depart from the training distribution. This problem arises in the presence of appearance variations due to, for example, differing illumination or weather conditions. Considering the difficulty and cost of acquiring annotated data in the test (i.e., target) domain, Unsupervised Domain Adaptation (UDA) has emerged as the standard strategy to address such scenarios [3, 4, 9, 26, 38]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "content": "In this context, much effort has been made to learn domain invariant features, such that the source and target distributions in this feature space are similar. This has led to great progress in situations where the appearance of the objects changes drastically from one domain to the other, as in case of real-to-sketch adaptation (e.g., Pascal VOC [10] to Comics [15]), or weather adaptation (e.g., Cityscapes [6] to Foggy Cityscapes [27]). Nevertheless, such object appearance changes are not the only sources of domain shifts. They can also have geometric origins. For example, as shown in Fig. 1, they can be due to a change in camera view" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 306, + 213, + 547, + 328 + ], + "blocks": [ + { + "bbox": [ + 306, + 213, + 547, + 328 + ], + "lines": [ + { + "bbox": [ + 306, + 213, + 547, + 328 + ], + "spans": [ + { + "bbox": [ + 306, + 213, + 547, + 328 + ], + "type": "image", + "image_path": "0ddde06e2387f32ffbdee1691784d1c9c2685302331fc17ca5e0d3c07d7e4427.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 307, + 329, + 545, + 449 + ], + "blocks": [ + { + "bbox": [ + 307, + 329, + 545, + 449 + ], + "lines": [ + { + "bbox": [ + 307, + 329, + 545, + 449 + ], + "spans": [ + { + "bbox": [ + 307, + 329, + 545, + 449 + ], + "type": "image", + "image_path": "6c249218327b701aa8630044b3c479c23874ad3d691ccc4c158d8a6096b5563c.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 458, + 547, + 569 + ], + "lines": [ + { + "bbox": [ + 304, + 458, + 547, + 569 + ], + "spans": [ + { + "bbox": [ + 304, + 458, + 547, + 569 + ], + "type": "text", + "content": "Figure 1. Geometric shifts. (Left) Due to a different FoV, the cars highlighted in green, undergo different distortions even though they appear in similar image regions. (Right) Different camera viewpoints (front facing vs downward facing) yield different distortions and occlusion patterns for pedestrian detection. (Bottom) The distributions of pedestrian bounding box sizes in Cityscapes [6] and MOT [8] differ significantly as the pedestrians are usually far away or in the periphery in Cityscapes. The top images are taken from Cityscapes [6], and the bottom-left and right ones from KITTI [12] and MOT [8], respectively." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 592, + 545, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 592, + 545, + 640 + ], + "spans": [ + { + "bbox": [ + 304, + 592, + 545, + 640 + ], + "type": "text", + "content": "point or field-of-view (FoV), or a change of object scale due to different scene setups. In practice, such geometric shifts typically arise from a combination of various factors, including but not limited to the ones mentioned above." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 642, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 547, + 713 + ], + "type": "text", + "content": "In this paper, we introduce a domain adaptation approach tackling such geometric shifts. To the best of our knowledge, the recent work of [13] constitutes the only attempt at considering such geometric distortions. However, it introduces a method solely dedicated to FoV variations, assuming that the target FoV is fixed and known. Here, we de" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17441" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 95 + ], + "type": "text", + "content": "velop a more general framework able to cope with a much broader family of geometric shifts." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 96, + 287, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 96, + 287, + 215 + ], + "spans": [ + { + "bbox": [ + 46, + 96, + 287, + 215 + ], + "type": "text", + "content": "To this end, we model geometric transformations as a combination of multiple homographies. We show both theoretically and empirically that this representation is sufficient to encompass a broad variety of complex geometric transformations. We then design an aggregator block that can be incorporated to the detector to provide it with the capacity to tackle geometric shifts. We use this modified detector to generate pseudo labels for the target domain, which let us optimize the homographies so as to reduce the geometric shift." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 217, + 288, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 217, + 288, + 407 + ], + "spans": [ + { + "bbox": [ + 46, + 217, + 288, + 407 + ], + "type": "text", + "content": "Our contributions can be summarized as follows. (i) We tackle the problem of general geometric shifts for object detection. (ii) We learn a set of homographies using unlabeled target data, which alleviates the geometric bias arising in source-only training. (iii) Our method does not require prior information about the target geometric distortions and generalizes to a broad class of geometric shifts. Our experiments demonstrate the benefits of our approach in several scenarios. In the presence of FoV shifts, our approach yields similar performance to the FoV-dedicated framework of [13] but without requiring any camera information. As such, it generalizes better to other FoVs. Furthermore, we show the generality of our method by using it to adapt to a new camera viewpoint in the context of pedestrian detection. Our implementation can be accessed at https://github.com/vidit09/geoshift." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 418, + 134, + 430 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 418, + 134, + 430 + ], + "spans": [ + { + "bbox": [ + 47, + 418, + 134, + 430 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 437, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 437, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 437, + 287, + 714 + ], + "type": "text", + "content": "Unsupervised Domain Adaptation (UDA). UDA for image recognition [11, 21, 22, 30, 32, 35, 36] and object detection [3, 4, 9, 20, 26, 38] has made a great progress in the past few years. The common trend in both tasks consists of learning domain invariant features. For object detection, this entails aligning the global (e.g., illumination, weather) and local (foreground objects) features in the two domains. In this context, [3, 5, 26, 28] align image- and instance-level features in the two domains via adversarial learning [11]; [33] learns category-specific attention maps to better align specific image regions; [38] clusters the proposed object regions using " + }, + { + "bbox": [ + 46, + 437, + 287, + 714 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 437, + 287, + 714 + ], + "type": "text", + "content": "-means clustering and uses the centroids for instance-level alignment. While this successfully tackles domain shifts caused by object appearance variations, it fails to account for the presence of shifts due to the image capture process itself, such as changes in camera intrinsics or viewpoint. The only initial step at considering a geometric shift is the work of [13], which shows the existence of an FoV gap in driving datasets [6, 12] and proposes a Position Invariant Transform (PIT) that corrects the distortions caused specifically by an FoV change. In essence, PIT undistorts the images by assuming knowledge of the target FoV. By contrast, here, we introduce an approach that gen" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "text", + "content": "eralizes to a broad family of geometric shifts by learning transformations without requiring any camera information." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 102, + 545, + 365 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 102, + 545, + 365 + ], + "spans": [ + { + "bbox": [ + 304, + 102, + 545, + 365 + ], + "type": "text", + "content": "Self-training. Self-training, generally employed in the semi-supervised setting, offers an alternative to learning domain-invariant features and utilize unlabeled data to improve a detector's performance. In this context, [29] uses a student-teacher architecture where the teacher model is trained with supervised data and generates pseudo-labels on unannotated data. These pseudo-labels are then used to train a student model. While effective in the standard semi-supervised learning scenario, the quality of the pseudo-labels obtained with this approach tends to deteriorate when the labeled and unlabeled data present a distribution shift. [9, 20] have therefore extended this approach to domain adaptation by using the Mean Teacher strategy of [31] to generate reliable pseudo-labels in the target domain. Other approach include the use of CycleGAN [37] generated images to train an unbiased teacher model [9], and that of different augmentation strategies to generate robust pseudo-labels [20]. Our approach also follows a self-training strategy but, while these works focus on object appearance shifts, we incorporate learnable blocks to address geometric shifts. As shown in our experiment, this lets us outperform the state-of-the-art AdaptTeacher [20]." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 371, + 545, + 526 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 371, + 545, + 526 + ], + "spans": [ + { + "bbox": [ + 304, + 371, + 545, + 526 + ], + "type": "text", + "content": "Learning Geometric Transformations. End-to-end learning of geometric transformations has been used to boost the performance of deep networks. For example, Spatial Transformer Networks (STNs) [16] reduce the classification error by learning to correct for affine transformations; deformable convolutions [7] model geometric transformations by applying the convolution kernels to non-local neighborhoods. These methods work well when annotations are available for supervision, and make the network invariant to the specific geometric transformations seen during training. Here, by contrast, we seek to learn transformations in an unsupervised manner and allow the network to generalize to unknown target transformations." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 538, + 515, + 551 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 538, + 515, + 551 + ], + "spans": [ + { + "bbox": [ + 306, + 538, + 515, + 551 + ], + "type": "text", + "content": "3. Modeling Geometric Transformations" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 558, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 545, + 713 + ], + "type": "text", + "content": "In the context of UDA, multiple geometric differences can be responsible for the gap between the domains. Some can be characterized by the camera parameters, such as a change in FoV (intrinsic) or viewpoint (extrinsic), whereas others are content specific, such as a difference in road width between different countries. Ultimately, the geometric shift is typically a combination of different geometric operations. Since the parameters of these operations are unknown, we propose to bridge the domain gap by learning a geometric transform. Specifically, we aggregate the results of multiple perspective transforms, i.e., homographies, to obtain a differentiable operation that can emulate a wide variety of geometric transforms." + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17442" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 157, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 157, + 85 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 157, + 85 + ], + "type": "text", + "content": "3.1. Theoretical Model" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 90, + 287, + 127 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 90, + 287, + 127 + ], + "spans": [ + { + "bbox": [ + 47, + 90, + 287, + 127 + ], + "type": "text", + "content": "Let us first show that, given sufficiently many homographies, one can perfectly reproduce any mapping between " + }, + { + "bbox": [ + 47, + 90, + 287, + 127 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^2\\setminus (0,0)" + }, + { + "bbox": [ + 47, + 90, + 287, + 127 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 90, + 287, + 127 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 140, + 287, + 186 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 140, + 287, + 186 + ], + "spans": [ + { + "bbox": [ + 47, + 140, + 287, + 186 + ], + "type": "text", + "content": "Single homography for a single point. First, we show that a single homography with 4 degrees of freedom can map a point " + }, + { + "bbox": [ + 47, + 140, + 287, + 186 + ], + "type": "inline_equation", + "content": "p \\in \\mathbb{R}^2 \\setminus (0,0)" + }, + { + "bbox": [ + 47, + 140, + 287, + 186 + ], + "type": "text", + "content": " to any other point in " + }, + { + "bbox": [ + 47, + 140, + 287, + 186 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^2" + }, + { + "bbox": [ + 47, + 140, + 287, + 186 + ], + "type": "text", + "content": ". To this end, let" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 125, + 186, + 287, + 224 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 125, + 186, + 287, + 224 + ], + "spans": [ + { + "bbox": [ + 125, + 186, + 287, + 224 + ], + "type": "interline_equation", + "content": "H = \\left[ \\begin{array}{c c c} s _ {x} & 0 & 0 \\\\ 0 & s _ {y} & 0 \\\\ l _ {x} & l _ {y} & 1 \\end{array} \\right] \\tag {1}", + "image_path": "5c4a5f03cf82630a971cd027a09fc4a0ff6db91a2193843b9a499ed184ab7f30.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "spans": [ + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "text", + "content": "be a homography, with " + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "inline_equation", + "content": "(s_x,s_y)" + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "text", + "content": " the scaling factors on the " + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "inline_equation", + "content": "x-" + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "text", + "content": " -axis, respectively, and " + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "inline_equation", + "content": "(l_{x},l_{y})" + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "text", + "content": " the perspective factors in " + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "text", + "content": ", respectively. For any destination point " + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "inline_equation", + "content": "d\\in \\mathbb{R}^2" + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "text", + "content": ", there exists a set of parameters " + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "inline_equation", + "content": "(s_x,s_y,l_x,l_y)" + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "text", + "content": " such that " + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "inline_equation", + "content": "d = H\\times p" + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "text", + "content": ". One such set is " + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "inline_equation", + "content": "\\left(\\frac{d_x}{p_x},\\frac{d_y}{p_y},0,0\\right)" + }, + { + "bbox": [ + 47, + 228, + 288, + 294 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "text", + "content": "Emulating any geometric transformation Now that we have shown that a single homography can move a point to any other point in " + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^2" + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "text", + "content": ", we describe a simple protocol to emulate any geometric transform. Given an unknown geometric transform " + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "inline_equation", + "content": "T:\\mathbb{R}^2\\backslash (0,0)\\to \\mathbb{R}^2" + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "text", + "content": ", we aim to emulate " + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "text", + "content": " with a set of homographies. In general, for an image " + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "inline_equation", + "content": "\\mathbf{I}\\in \\mathbb{R}^{3\\times h\\times w}" + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "text", + "content": " we can restrict the domain of " + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "text", + "content": " to only image coordinates. To this end, we can define a set of homographies " + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "inline_equation", + "content": "H_{i}\\in \\mathbb{H}" + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "text", + "content": " for i in " + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "inline_equation", + "content": "\\{1,2,3,\\dots,h\\times w\\}" + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "text", + "content": ", where the parameters of " + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "inline_equation", + "content": "H_{i}" + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "text", + "content": " are chosen to mimic the transform " + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "text", + "content": " for location " + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 305, + 287, + 460 + ], + "type": "text", + "content": " of the image. In this protocol, the aggregation mechanism is trivial since each homography is in charge of remapping a single pixel coordinate of the original space." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 460, + 287, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 460, + 287, + 569 + ], + "spans": [ + { + "bbox": [ + 46, + 460, + 287, + 569 + ], + "type": "text", + "content": "While this works in theory, this is of course not viable in practice since it would require too many homographies. With a smaller number of homographies, each transform needs to remap multiple points, and a more sophisticated aggregation mechanism is required. Specifically, the aggregation mechanism needs to select which transform is in charge of remapping which point. In the next section, we empirically show that this strategy lets us closely approximate the spherical projection mapping used in PIT [13]." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 575, + 258, + 589 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 575, + 258, + 589 + ], + "spans": [ + { + "bbox": [ + 47, + 575, + 258, + 589 + ], + "type": "text", + "content": "3.2. Approximating PIT with Homographies" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 594, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 288, + 714 + ], + "type": "text", + "content": "To demonstrate the possibility offered by aggregating multiple homographies, we design an approximation of PIT using only homographies. PIT proposes to correct for an FoV gap by remapping images to a spherical surface. During this transformation, regions further from the center of a scene are compressed with a higher ratio. This variable compression of the space cannot be reproduced by a single homography transformation. To overcome this limitation, we combine the results of multiple homographies that all have different compression rates (scaling parameters). For" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 72, + 547, + 145 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 145 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 145 + ], + "type": "text", + "content": "the aggregation mechanism, we use the optimal strategy by selecting for each pixel the homography that approximates best the PIT mapping. As shown in Fig. 2, this combination closely approximates the PIT results with only 5 homographies. Further analysis of these experiments is available in the supplementary material in Fig. A.3." + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 331, + 159, + 520, + 225 + ], + "blocks": [ + { + "bbox": [ + 331, + 159, + 520, + 225 + ], + "lines": [ + { + "bbox": [ + 331, + 159, + 520, + 225 + ], + "spans": [ + { + "bbox": [ + 331, + 159, + 520, + 225 + ], + "type": "image", + "image_path": "a8585d72f7a753eb330083e477c431b439ebe03778af60c92000cac1274ef772.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 353, + 232, + 504, + 297 + ], + "blocks": [ + { + "bbox": [ + 353, + 232, + 504, + 297 + ], + "lines": [ + { + "bbox": [ + 353, + 232, + 504, + 297 + ], + "spans": [ + { + "bbox": [ + 353, + 232, + 504, + 297 + ], + "type": "image", + "image_path": "fd2e8ca6e69a953f98941a93ff1e69b0512e0edce57a48b3c50fe6b63b57795f.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 353, + 300, + 504, + 365 + ], + "blocks": [ + { + "bbox": [ + 353, + 300, + 504, + 365 + ], + "lines": [ + { + "bbox": [ + 353, + 300, + 504, + 365 + ], + "spans": [ + { + "bbox": [ + 353, + 300, + 504, + 365 + ], + "type": "image", + "image_path": "54fb2a76b33e0be58bb29dfc354ac78951f6602eb23e6f2bbc57d7912813973e.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 376, + 547, + 421 + ], + "lines": [ + { + "bbox": [ + 304, + 376, + 547, + 421 + ], + "spans": [ + { + "bbox": [ + 304, + 376, + 547, + 421 + ], + "type": "text", + "content": "Figure 2. Approximating PIT with homographies. We show the original image (top), the PIT [13] correction (middle), and our approximation of PIT using 5 homographies. Note that 5 homographies are sufficient to closely match the PIT spherical correction." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 438, + 493, + 451 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 438, + 493, + 451 + ], + "spans": [ + { + "bbox": [ + 306, + 438, + 493, + 451 + ], + "type": "text", + "content": "3.3. Homographies in a Learning Setup" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 456, + 545, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 456, + 545, + 601 + ], + "spans": [ + { + "bbox": [ + 304, + 456, + 545, + 601 + ], + "type": "text", + "content": "In the two previous sections, we have demonstrated both theoretically and empirically the flexibility of aggregating homographies. This makes this representation an ideal candidate for domain adaptation since the geometric shift between the domains is unknown and can be a combination of different transforms, such as FoV change, viewpoint change, camera distortion, or appearance distortion. As will be discussed in the next section, by learning jointly the set of perspective transforms and the aggregation mechanism on real data, our model can reduce the geometric shift between the two domains without prior knowledge about this domain gap." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 609, + 362, + 621 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 609, + 362, + 621 + ], + "spans": [ + { + "bbox": [ + 306, + 609, + 362, + 621 + ], + "type": "text", + "content": "4. Method" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "type": "text", + "content": "Let us now introduce our approach to reducing the geometric shift in object detection. Following the standard UDA setting, let " + }, + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "type": "inline_equation", + "content": "D_{s} = \\{(I_{s},B_{s},C_{s})\\}" + }, + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "type": "text", + "content": " be a labeled source dataset containing images " + }, + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "type": "inline_equation", + "content": "I_{s} = \\{I_{s}^{i}\\}_{1}^{N_{s}}" + }, + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "type": "text", + "content": " with corresponding object bounding boxes " + }, + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "type": "inline_equation", + "content": "B_{s} = \\{b_{s}^{i}\\}_{1}^{N_{s}}" + }, + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "type": "text", + "content": " and object classes " + }, + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "type": "inline_equation", + "content": "C_s = \\{c_s^i\\}_1^{N_s}" + }, + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "type": "text", + "content": ". Furthermore, let " + }, + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "type": "inline_equation", + "content": "D_{t} = \\{I_{t}\\}" + }, + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "type": "text", + "content": " denote an unlabeled target dataset for which only images " + }, + { + "bbox": [ + 304, + 630, + 545, + 715 + ], + "type": "inline_equation", + "content": "I_{t} = \\{I_{t}^{i}\\}_{1}^{N_{t}}" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17443" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 73, + 547, + 274 + ], + "blocks": [ + { + "bbox": [ + 48, + 73, + 547, + 274 + ], + "lines": [ + { + "bbox": [ + 48, + 73, + 547, + 274 + ], + "spans": [ + { + "bbox": [ + 48, + 73, + 547, + 274 + ], + "type": "image", + "image_path": "6b23ae9ef0fc1e3a999035fafe024d7628f93faaa004fff9f725f77197b8a824.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 281, + 546, + 326 + ], + "lines": [ + { + "bbox": [ + 46, + 281, + 546, + 326 + ], + "spans": [ + { + "bbox": [ + 46, + 281, + 546, + 326 + ], + "type": "text", + "content": "Figure 3. Architecture: The input image is first transformed by a set of trainable homographies. The feature maps extracted from the transformed images are then unwarped by the inverse homographies to achieve spatial consistency. We then combine the unwarped feature maps using a trainable aggregator, whose output is passed to a detection head. The blocks shown in green correspond to standard FasterRCNN operations. The " + }, + { + "bbox": [ + 46, + 281, + 546, + 326 + ], + "type": "inline_equation", + "content": "\\oplus" + }, + { + "bbox": [ + 46, + 281, + 546, + 326 + ], + "type": "text", + "content": " symbol represents the concatenation operation." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 347, + 287, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 347, + 287, + 407 + ], + "spans": [ + { + "bbox": [ + 46, + 347, + 287, + 407 + ], + "type": "text", + "content": "are available, without annotations. Here, we tackle the case where the two domains differ by geometric shifts but assume no knowledge about the nature of these shifts. Below, we first introduce the architecture we developed to handle this and then our strategy to train this model." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 417, + 162, + 428 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 417, + 162, + 428 + ], + "spans": [ + { + "bbox": [ + 47, + 417, + 162, + 428 + ], + "type": "text", + "content": "4.1. Model Architecture" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 436, + 287, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 436, + 287, + 555 + ], + "spans": [ + { + "bbox": [ + 46, + 436, + 287, + 555 + ], + "type": "text", + "content": "The overall architecture of our approach is depicted in Fig. 3. In essence, and as discussed in Sec. 3, we characterize the geometric changes between the source and target data by a set of transformations " + }, + { + "bbox": [ + 46, + 436, + 287, + 555 + ], + "type": "inline_equation", + "content": "\\mathcal{T} = \\{\\mathcal{H}_i\\}_{1}^N" + }, + { + "bbox": [ + 46, + 436, + 287, + 555 + ], + "type": "text", + "content": ". Each " + }, + { + "bbox": [ + 46, + 436, + 287, + 555 + ], + "type": "inline_equation", + "content": "\\mathcal{H}_i" + }, + { + "bbox": [ + 46, + 436, + 287, + 555 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 46, + 436, + 287, + 555 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 436, + 287, + 555 + ], + "type": "text", + "content": " is a homography of the same form as in Eq. (1). For our method to remain general, we assume the transformations to be unknown, and our goal, therefore, is to learn " + }, + { + "bbox": [ + 46, + 436, + 287, + 555 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 436, + 287, + 555 + ], + "type": "text", + "content": " to bridge the gap between the domains. This requires differentiability w.r.t. the transformation parameters, which we achieve using the sampling strategy proposed in [16]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 556, + 287, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 556, + 287, + 641 + ], + "spans": [ + { + "bbox": [ + 46, + 556, + 287, + 641 + ], + "type": "text", + "content": "As shown in Fig. 3, the input image is transformed by the individual homographies in " + }, + { + "bbox": [ + 46, + 556, + 287, + 641 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 556, + 287, + 641 + ], + "type": "text", + "content": ", and the transformed images are fed to a modified FasterRCNN [24] detector. Specifically, we extract a feature map " + }, + { + "bbox": [ + 46, + 556, + 287, + 641 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_{\\mathcal{H}_i} \\in \\mathbb{R}^{H \\times W \\times C}" + }, + { + "bbox": [ + 46, + 556, + 287, + 641 + ], + "type": "text", + "content": " for each transformed image via a feature extractor shared by all transformations. To enforce spatial correspondence between the different " + }, + { + "bbox": [ + 46, + 556, + 287, + 641 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_{\\mathcal{H}_i}" + }, + { + "bbox": [ + 46, + 556, + 287, + 641 + ], + "type": "text", + "content": "s, we unwarp them with " + }, + { + "bbox": [ + 46, + 556, + 287, + 641 + ], + "type": "inline_equation", + "content": "\\mathcal{H}_i^{-1}" + }, + { + "bbox": [ + 46, + 556, + 287, + 641 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 642, + 287, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 287, + 689 + ], + "type": "text", + "content": "We then introduce an aggregator " + }, + { + "bbox": [ + 46, + 642, + 287, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{A}_{\\theta_g}" + }, + { + "bbox": [ + 46, + 642, + 287, + 689 + ], + "type": "text", + "content": ", parameterized by " + }, + { + "bbox": [ + 46, + 642, + 287, + 689 + ], + "type": "inline_equation", + "content": "\\theta_{g}" + }, + { + "bbox": [ + 46, + 642, + 287, + 689 + ], + "type": "text", + "content": ", whose goal is to learn a common representation given a fixed number of unwarped feature maps " + }, + { + "bbox": [ + 46, + 642, + 287, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_{\\mathcal{H}_i}^{\\prime}" + }, + { + "bbox": [ + 46, + 642, + 287, + 689 + ], + "type": "text", + "content": ". To achieve this, the aggregator takes as input" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 699, + 287, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 699, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 61, + 699, + 287, + 715 + ], + "type": "interline_equation", + "content": "\\mathcal {G} = \\mathcal {F} _ {\\mathcal {H} _ {1}} ^ {\\prime} \\oplus \\mathcal {F} _ {\\mathcal {H} _ {2}} ^ {\\prime} \\oplus \\dots \\oplus \\mathcal {F} _ {\\mathcal {H} _ {N}} ^ {\\prime} \\in \\mathbb {R} ^ {H \\times W \\times C \\times N}, \\tag {2}", + "image_path": "287df0cc4139a6ac06df101731dfe8c039051273861e6eac55f0adc04ac2647c.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 347, + 545, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 347, + 545, + 407 + ], + "spans": [ + { + "bbox": [ + 305, + 347, + 545, + 407 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 347, + 545, + 407 + ], + "type": "inline_equation", + "content": "\\oplus" + }, + { + "bbox": [ + 305, + 347, + 545, + 407 + ], + "type": "text", + "content": " represents concatenation in the channel dimension. The aggregator outputs a feature map " + }, + { + "bbox": [ + 305, + 347, + 545, + 407 + ], + "type": "inline_equation", + "content": "\\mathcal{A}_{\\theta_g}(\\mathcal{G})\\in \\mathbb{R}^{H\\times W\\times C}" + }, + { + "bbox": [ + 305, + 347, + 545, + 407 + ], + "type": "text", + "content": " whose dimension is independent of the number of transformations. This output is then passed to a detection head to obtain the objects' bounding boxes and class labels." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 413, + 402, + 426 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 413, + 402, + 426 + ], + "spans": [ + { + "bbox": [ + 306, + 413, + 402, + 426 + ], + "type": "text", + "content": "4.2. Model Training" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 431, + 545, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 431, + 545, + 528 + ], + "spans": [ + { + "bbox": [ + 304, + 431, + 545, + 528 + ], + "type": "text", + "content": "Our training procedure relies on three steps: (i) Following common practice in UDA, we first train the Faster-RCNN detector with source-only data; (ii) We then introduce the aggregator and train it so that it learns to combine different homographies using the labeled source data; (iii) Finally, we learn the optimal transformations for adaptation using both the source and target data via a Mean Teacher [31] strategy." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 541, + 545, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 541, + 545, + 613 + ], + "spans": [ + { + "bbox": [ + 304, + 541, + 545, + 613 + ], + "type": "text", + "content": "Aggregator Training. To train the aggregator, we randomly sample a set of homographies " + }, + { + "bbox": [ + 304, + 541, + 545, + 613 + ], + "type": "inline_equation", + "content": "\\mathcal{T} \\in \\mathbb{R}^{N \\times 4}" + }, + { + "bbox": [ + 304, + 541, + 545, + 613 + ], + "type": "text", + "content": " in each training iteration. This gives the aggregator the ability to robustly combine diverse input transformations but requires strong supervision to avoid training instabilities. We, therefore, perform this step using the source data." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 613, + 545, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 613, + 545, + 661 + ], + "spans": [ + { + "bbox": [ + 304, + 613, + 545, + 661 + ], + "type": "text", + "content": "The loss function for a set of transformed images " + }, + { + "bbox": [ + 304, + 613, + 545, + 661 + ], + "type": "inline_equation", + "content": "\\mathcal{T}(I_s)" + }, + { + "bbox": [ + 304, + 613, + 545, + 661 + ], + "type": "text", + "content": " is then defined as in standard FasterRCNN training with a combination of classification and regression terms [24]. That is, we train the aggregator by solving" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 354, + 667, + 545, + 686 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 354, + 667, + 545, + 686 + ], + "spans": [ + { + "bbox": [ + 354, + 667, + 545, + 686 + ], + "type": "interline_equation", + "content": "\\min _ {\\theta_ {g}} \\mathcal {L} _ {c l s} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) + \\mathcal {L} _ {r e g} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right), \\tag {3}", + "image_path": "e5cadad390975b14a77b0f5b6d9727e95b34d5fdb90800cacc91de7ac27628ff.jpg" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 306, + 693, + 545, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 693, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 306, + 693, + 545, + 712 + ], + "type": "text", + "content": "1As our homographies involve only 4 parameters, with a slight abuse of notation, we say that " + }, + { + "bbox": [ + 306, + 693, + 545, + 712 + ], + "type": "inline_equation", + "content": "\\mathcal{H}_i \\in \\mathbb{R}^4" + }, + { + "bbox": [ + 306, + 693, + 545, + 712 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17444" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 73, + 76, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 73, + 76, + 83 + ], + "spans": [ + { + "bbox": [ + 47, + 73, + 76, + 83 + ], + "type": "text", + "content": "where" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 95, + 287, + 109 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 95, + 287, + 109 + ], + "spans": [ + { + "bbox": [ + 105, + 95, + 287, + 109 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {c l s} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) = \\mathcal {L} _ {c l s} ^ {r p n} + \\mathcal {L} _ {c l s} ^ {r o i}, \\tag {4}", + "image_path": "4db20c7f53c60dc5fb07f597373f2442323221a816564edc6c7ed5a405eebb8b.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 111, + 287, + 126 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 111, + 287, + 126 + ], + "spans": [ + { + "bbox": [ + 105, + 111, + 287, + 126 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {r e g} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) = \\mathcal {L} _ {r e g} ^ {r p n} + \\mathcal {L} _ {r e g} ^ {r o i}. \\tag {5}", + "image_path": "d5385d0a7766e0f7f2118aebba3788af6b5660e991a2021dffd0d6eb1c7b8753.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 136, + 289, + 258 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 136, + 289, + 258 + ], + "spans": [ + { + "bbox": [ + 46, + 136, + 289, + 258 + ], + "type": "inline_equation", + "content": "\\mathcal{L}^{rpn}" + }, + { + "bbox": [ + 46, + 136, + 289, + 258 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 136, + 289, + 258 + ], + "type": "inline_equation", + "content": "\\mathcal{L}^{roi}" + }, + { + "bbox": [ + 46, + 136, + 289, + 258 + ], + "type": "text", + "content": " correspond to the Region Proposal Network (RPN) loss terms and the Region of Interest (RoI) ones, respectively. During this process, we freeze the parameters " + }, + { + "bbox": [ + 46, + 136, + 289, + 258 + ], + "type": "inline_equation", + "content": "\\theta_{b}" + }, + { + "bbox": [ + 46, + 136, + 289, + 258 + ], + "type": "text", + "content": " of the base network, i.e., feature extractor and detection head, which were first trained on the source data without aggregator. Ultimately, the aggregator provides the network with the capacity to encode different transformations that are not seen in the source domain. The third training step then aims to learn the best transformation for successful object detection in the target domain." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 275, + 289, + 431 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 275, + 289, + 431 + ], + "spans": [ + { + "bbox": [ + 46, + 275, + 289, + 431 + ], + "type": "text", + "content": "Learning the Transformations. As we have no annotations in the target domain, we exploit a Mean Teacher (MT) strategy to learn the optimal transformations. To this end, our starting point is the detector with a trained aggregator and a set of random transformations " + }, + { + "bbox": [ + 46, + 275, + 289, + 431 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 275, + 289, + 431 + ], + "type": "text", + "content": ". The MT strategy is illustrated in Fig. 4. In essence, MT training [31] involves two copies of the model: A student model, with parameters " + }, + { + "bbox": [ + 46, + 275, + 289, + 431 + ], + "type": "inline_equation", + "content": "\\theta^{st} = \\{\\mathcal{T}^{st},\\theta_b^{st},\\theta_g^{st}\\}" + }, + { + "bbox": [ + 46, + 275, + 289, + 431 + ], + "type": "text", + "content": ", that will be used during inference, and a teacher model, with parameters " + }, + { + "bbox": [ + 46, + 275, + 289, + 431 + ], + "type": "inline_equation", + "content": "\\theta^{te} = \\{\\mathcal{T}^{te},\\theta_b^{te},\\theta_g^{te}\\}" + }, + { + "bbox": [ + 46, + 275, + 289, + 431 + ], + "type": "text", + "content": ", that is updated as an Exponentially Moving Average (EMA) of the student model. That is, the student's parameters are computed with standard backpropagation, whereas the teacher's ones are updated as" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 111, + 441, + 287, + 455 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 441, + 287, + 455 + ], + "spans": [ + { + "bbox": [ + 111, + 441, + 287, + 455 + ], + "type": "interline_equation", + "content": "\\theta_ {t e} \\leftarrow \\alpha \\theta_ {t e} + (1 - \\alpha) \\theta_ {s t}. \\tag {6}", + "image_path": "bc055250b6dd6091a82825cde3fc8906953c0712905e35d99e8aa3cc300de86d.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 465, + 287, + 550 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 465, + 287, + 550 + ], + "spans": [ + { + "bbox": [ + 46, + 465, + 287, + 550 + ], + "type": "text", + "content": "The student model is trained using both source and target detection losses. Since the target domain does not have annotations, the teacher model is used to generate pseudolabels. These labels might be noisy, and hence we only keep the predictions with a confidence score above a threshold " + }, + { + "bbox": [ + 46, + 465, + 287, + 550 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 46, + 465, + 287, + 550 + ], + "type": "text", + "content": ". Furthermore, non-maxima suppression (NMS) is used to remove the highly-overlapping bounding box predictions." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 550, + 287, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 550, + 287, + 574 + ], + "spans": [ + { + "bbox": [ + 47, + 550, + 287, + 574 + ], + "type": "text", + "content": "Formally, given a source image " + }, + { + "bbox": [ + 47, + 550, + 287, + 574 + ], + "type": "inline_equation", + "content": "I_{s}" + }, + { + "bbox": [ + 47, + 550, + 287, + 574 + ], + "type": "text", + "content": " and a target image " + }, + { + "bbox": [ + 47, + 550, + 287, + 574 + ], + "type": "inline_equation", + "content": "I_{t}" + }, + { + "bbox": [ + 47, + 550, + 287, + 574 + ], + "type": "text", + "content": ", the student model is trained by solving" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 83, + 585, + 287, + 605 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 585, + 287, + 605 + ], + "spans": [ + { + "bbox": [ + 83, + 585, + 287, + 605 + ], + "type": "interline_equation", + "content": "\\min _ {\\mathcal {T} ^ {s t}, \\theta_ {g} ^ {s t}, \\theta_ {b} ^ {s t}} \\mathcal {L} _ {d e t} (\\mathcal {T} (I _ {s})) + \\lambda \\mathcal {L} _ {d e t} (\\mathcal {T} (I _ {t})) , \\tag {7}", + "image_path": "8f08d5399004905c3d09adc4346ad4c65b9883763234d4e4ef93c8b54004275f.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 616, + 259, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 616, + 259, + 628 + ], + "spans": [ + { + "bbox": [ + 47, + 616, + 259, + 628 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 616, + 259, + 628 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 47, + 616, + 259, + 628 + ], + "type": "text", + "content": " controls the target domain contribution and" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 74, + 639, + 287, + 652 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 639, + 287, + 652 + ], + "spans": [ + { + "bbox": [ + 74, + 639, + 287, + 652 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {d e t}} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) = \\mathcal {L} _ {\\text {c l s}} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right) + \\mathcal {L} _ {\\text {r e g}} \\left(\\mathcal {T} \\left(I _ {s}\\right)\\right), \\tag {8}", + "image_path": "a802c4dd8ed3cba2639640324414111dd3e4c101f2cd842330d54df44ce3bb03.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 76, + 654, + 287, + 667 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 76, + 654, + 287, + 667 + ], + "spans": [ + { + "bbox": [ + 76, + 654, + 287, + 667 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {d e t}} \\left(\\mathcal {T} \\left(I _ {t}\\right)\\right) = \\mathcal {L} _ {\\text {c l s}} \\left(\\mathcal {T} \\left(I _ {t}\\right)\\right). \\tag {9}", + "image_path": "40d518c08bb4f61b2fa525fba004bdd2dfa3fb134a99180a49473ecabcd135b8.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 677, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 287, + 714 + ], + "type": "text", + "content": "Similarly to [18,20], we update the student model with only the classification loss in the target domain to help stabilize training." + } + ] + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 310, + 69, + 545, + 200 + ], + "blocks": [ + { + "bbox": [ + 310, + 69, + 545, + 200 + ], + "lines": [ + { + "bbox": [ + 310, + 69, + 545, + 200 + ], + "spans": [ + { + "bbox": [ + 310, + 69, + 545, + 200 + ], + "type": "image", + "image_path": "3a1a09bc3237b028b9ced457e72570d6a2cb75a16d94616ff4f8b9174a18c1ea.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 209, + 547, + 264 + ], + "lines": [ + { + "bbox": [ + 305, + 209, + 547, + 264 + ], + "spans": [ + { + "bbox": [ + 305, + 209, + 547, + 264 + ], + "type": "text", + "content": "Figure 4. Mean Teacher formalism. The student model is trained with ground-truth labels in the source domain and pseudo labels in the target one. These pseudo labels are produced by the teacher model, which corresponds to an exponentially moving average (EMA) of the student network." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 285, + 388, + 298 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 285, + 388, + 298 + ], + "spans": [ + { + "bbox": [ + 306, + 285, + 388, + 298 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 304, + 547, + 461 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 304, + 547, + 461 + ], + "spans": [ + { + "bbox": [ + 304, + 304, + 547, + 461 + ], + "type": "text", + "content": "We demonstrate the effectiveness and generality of our method on different geometric shifts. First, to compare to the only other work that modeled a geometric shift [13], we tackle the problem of a change in FoV between the source and target domain. Note that, in contrast to [13], we do not assume knowledge of the target FoV. Furthermore, while [13] was dedicated to FoV adaptation, our approach generalizes to other geometric shifts. We demonstrate this on the task of pedestrian detection under a viewpoint shift. We compare our method with the state-of-the-art AdaptTeacher [20], which also uses a Mean Teacher, but focuses on appearance shifts. In the remainder of this section, we describe our experimental setup and discuss our results." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 468, + 369, + 479 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 468, + 369, + 479 + ], + "spans": [ + { + "bbox": [ + 306, + 468, + 369, + 479 + ], + "type": "text", + "content": "5.1. Datasets" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 487, + 547, + 571 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 487, + 547, + 571 + ], + "spans": [ + { + "bbox": [ + 304, + 487, + 547, + 571 + ], + "type": "text", + "content": "Cityscapes [6] contains 2975 training and 500 test images with annotations provided for 8 categories (person, car, train, rider, truck, motorcycle, bicycle and bus). The average horizontal (FoVx) and vertical (FoVy) FoVs of the capturing cameras are " + }, + { + "bbox": [ + 304, + 487, + 547, + 571 + ], + "type": "inline_equation", + "content": "50^{\\circ}" + }, + { + "bbox": [ + 304, + 487, + 547, + 571 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 487, + 547, + 571 + ], + "type": "inline_equation", + "content": "26^{\\circ}" + }, + { + "bbox": [ + 304, + 487, + 547, + 571 + ], + "type": "text", + "content": ", respectively. We use this dataset as the source domain for both FoV adaptation and viewpoint adaptation." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 575, + 547, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 575, + 547, + 661 + ], + "spans": [ + { + "bbox": [ + 304, + 575, + 547, + 661 + ], + "type": "text", + "content": "KITTI [12] is also a street-view dataset containing 6684 images annotated with the car category. The horizontal (FoVx) and vertical (FoVy) FoVs of the camera are " + }, + { + "bbox": [ + 304, + 575, + 547, + 661 + ], + "type": "inline_equation", + "content": "90^{\\circ}" + }, + { + "bbox": [ + 304, + 575, + 547, + 661 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 575, + 547, + 661 + ], + "type": "inline_equation", + "content": "34^{\\circ}" + }, + { + "bbox": [ + 304, + 575, + 547, + 661 + ], + "type": "text", + "content": ", respectively. We use this dataset as target domain for FoV adaptation, as the viewpoint is similar to that of Cityscapes. Following [13], we use 5684 images for unsupervised training and 1000 images for evaluation." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "text", + "content": "MOT [8] is a multi-object tracking dataset. We use the indoor mall sequence, MOT20-02, consisting of 2782 frames annotated with the person category. We employ this dataset as target domain for viewpoint adaptation. We use the first" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17445" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "content": "2000 frame for unsupervised training and last 782 for evaluation." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 104, + 208, + 118 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 104, + 208, + 118 + ], + "spans": [ + { + "bbox": [ + 47, + 104, + 208, + 118 + ], + "type": "text", + "content": "5.2. Adaptation Tasks and Metric" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 123, + 287, + 220 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 123, + 287, + 220 + ], + "spans": [ + { + "bbox": [ + 46, + 123, + 287, + 220 + ], + "type": "text", + "content": "FoV adaptation. As in [13], we consider the case of an increasing FoV using Cityscapes as source domain and KITTI as target domain. The horizontal and vertical FoVs increase from " + }, + { + "bbox": [ + 46, + 123, + 287, + 220 + ], + "type": "inline_equation", + "content": "(50^{\\circ}, 26^{\\circ})" + }, + { + "bbox": [ + 46, + 123, + 287, + 220 + ], + "type": "text", + "content": " in Cityscapes to " + }, + { + "bbox": [ + 46, + 123, + 287, + 220 + ], + "type": "inline_equation", + "content": "(90^{\\circ}, 34^{\\circ})" + }, + { + "bbox": [ + 46, + 123, + 287, + 220 + ], + "type": "text", + "content": " in KITTI. Therefore, as can be seen in Fig. 1, the KITTI images have a higher distortion in the corners than the Cityscapes ones. Similarly to PIT [13], we use the car category in our experiments." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 225, + 288, + 310 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 225, + 288, + 310 + ], + "spans": [ + { + "bbox": [ + 46, + 225, + 288, + 310 + ], + "type": "text", + "content": "FoV generalization. Following PIT [13], we study the generalization of our approach to new FoVs by cropping the KITTI images to mimic different FoV changes in the horizontal direction (FoVx). Specifically, we treat FoVx = 50° as the source domain and the cropped images with FoVx = {70°, 80°, 90°} as different target domains. We evaluate our approach on car on these different pairs of domains." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 316, + 287, + 389 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 316, + 287, + 389 + ], + "spans": [ + { + "bbox": [ + 46, + 316, + 287, + 389 + ], + "type": "text", + "content": "Viewpoint adaptation. This task entails detecting objects seen from a different viewpoint in the source and target domains. We use the front-facing Cityscapes images as source domain and the downward-facing MOT ones as target one. As the MOT data depicts pedestrians, we use the bounding boxes corresponding to the person category in Cityscapes.[2]" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 406, + 287, + 478 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 406, + 287, + 478 + ], + "spans": [ + { + "bbox": [ + 46, + 406, + 287, + 478 + ], + "type": "text", + "content": "Metric. In all of our experiments, we use the Average Precision (AP) as our metric. Specifically, following [13], we report the AP@0.5, which considers the predictions as true positives if they match the ground-truth label and have an intersection over union (IOU) score of more than 0.5 with the ground-truth bounding boxes." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 486, + 180, + 498 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 486, + 180, + 498 + ], + "spans": [ + { + "bbox": [ + 47, + 486, + 180, + 498 + ], + "type": "text", + "content": "5.3. Implementation Details" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 505, + 287, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 505, + 287, + 635 + ], + "spans": [ + { + "bbox": [ + 46, + 505, + 287, + 635 + ], + "type": "text", + "content": "We use the Detector2 [34] implementation of Faster-RCNN [24] with a ResNet50 [14] backbone as our base architecture. In all of our experiments, the images are resized so that the shorter side has 800 pixels while maintaining the aspect ratio. The base network is first trained on source-only images with random cropping and random flipping augmentation for 24k iterations with batch size 8. We use the Stochastic Gradient Descent (SGD) optimizer with a learning rate of 0.01, scaled down by a 0.1 factor after 18k iterations. We use ImageNet [25] pretrained weights to initialize the ResNet50 backbone." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 637, + 288, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 637, + 288, + 685 + ], + "spans": [ + { + "bbox": [ + 47, + 637, + 288, + 685 + ], + "type": "text", + "content": "We then incorporate the aggregator in the trained base architecture. The aggregator architecture contains three convolutional layers with a kernel size of " + }, + { + "bbox": [ + 47, + 637, + 288, + 685 + ], + "type": "inline_equation", + "content": "3 \\times 3" + }, + { + "bbox": [ + 47, + 637, + 288, + 685 + ], + "type": "text", + "content": ", and one " + }, + { + "bbox": [ + 47, + 637, + 288, + 685 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 47, + 637, + 288, + 685 + ], + "type": "text", + "content": " convolutional layer. We first train the aggregator" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 307, + 70, + 425, + 148 + ], + "blocks": [ + { + "bbox": [ + 307, + 70, + 425, + 148 + ], + "lines": [ + { + "bbox": [ + 307, + 70, + 425, + 148 + ], + "spans": [ + { + "bbox": [ + 307, + 70, + 425, + 148 + ], + "type": "image", + "image_path": "89f3e28d0ea238bb7373db65c9175b6222ffb50691783c497ed59083b6b5b2b3.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 158, + 547, + 247 + ], + "lines": [ + { + "bbox": [ + 304, + 158, + 547, + 247 + ], + "spans": [ + { + "bbox": [ + 304, + 158, + 547, + 247 + ], + "type": "text", + "content": "Figure 5. FoV Adaptation: Qualitative Results. We visualize a car detection result in the Cityscapes-to-KITTI FoV adaptation scenario. The top left image corresponds to the ground truth, the bottom left to the Mean Teacher result, which confuses the orange container with a car, the bottom right to the Mean Teacher adaptation + PIT FoV adaptation result, which also mistakes the orange container for a car and further detects the speed limit on the road. Our approach, on the top right, correctly matches the ground truth." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 426, + 70, + 544, + 148 + ], + "blocks": [ + { + "bbox": [ + 426, + 70, + 544, + 148 + ], + "lines": [ + { + "bbox": [ + 426, + 70, + 544, + 148 + ], + "spans": [ + { + "bbox": [ + 426, + 70, + 544, + 148 + ], + "type": "image", + "image_path": "fecc74c15c8496e461f70945446eb786768514a40889ca8c2309e4781aa46481.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "spans": [ + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "text", + "content": "on the source data with the base frozen and using random transformations " + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "text", + "content": ". The transformations are generated by randomly sampling each " + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "inline_equation", + "content": "\\mathcal{H}_i" + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "text", + "content": " parameters as " + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "inline_equation", + "content": "s_x, s_y \\sim \\mathcal{U}_{[0.5,2.0]}" + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "inline_equation", + "content": "\\mathcal{U}_{[0.5,2.0]}" + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "inline_equation", + "content": "l_x, l_y \\sim \\mathcal{U}_{[-0.5,0.5]}" + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "inline_equation", + "content": "\\mathcal{U}_{[-0.5,0.5]}" + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "text", + "content": ". We train the aggregator for 30k iterations using a batch size of 8 and the SGD optimizer with a learning rate of " + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "inline_equation", + "content": "1e^{-4}" + }, + { + "bbox": [ + 304, + 269, + 547, + 340 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "spans": [ + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "text", + "content": "The student and teacher models are then initialized with this detector and the random " + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "inline_equation", + "content": "\\mathcal{T} = \\{\\mathcal{H}_i\\}_{i=1}^N" + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "text", + "content": ". We optimize " + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "text", + "content": " using Adam [19], while the base and aggregator networks are optimized by SGD. The learning rate is set to " + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "inline_equation", + "content": "1e^{-3}" + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "text", + "content": " and scaled down by a factor 0.1 after 10k iterations for the SGD optimizer. For the first 10k iterations in FoV adaptation and for 2k iterations for viewpoint adaptation, we only train " + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "text", + "content": " keeping base and aggregator frozen. The " + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "text", + "content": " coefficient for the EMA update is set to 0.99; the confidence threshold " + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "inline_equation", + "content": "\\tau = 0.6" + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "text", + "content": "; " + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "inline_equation", + "content": "\\lambda = \\{0.01, 0.1\\}" + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "text", + "content": " for FoV and viewpoint adaptation, respectively. The Mean Teacher framework is trained using both the source and target data. We set " + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "inline_equation", + "content": "N = 5" + }, + { + "bbox": [ + 304, + 341, + 546, + 556 + ], + "type": "text", + "content": ", unless otherwise specified, and use a batch size of 4, containing 2 source and 2 target images. We apply random color jittering on both the source and target data as in [20, 31]. All of our models are trained on a single NVIDIA V100 GPU. A detailed hyper-parameter study is provided in the supplementary material." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 566, + 503, + 578 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 566, + 503, + 578 + ], + "spans": [ + { + "bbox": [ + 306, + 566, + 503, + 578 + ], + "type": "text", + "content": "5.4. Comparison with the State of the Art" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 584, + 547, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 584, + 547, + 693 + ], + "spans": [ + { + "bbox": [ + 304, + 584, + 547, + 693 + ], + "type": "text", + "content": "We compare our approach with the following baselines3. FR: FasterRCNN trained only on the source data with random crop augmentation; AT: AdaptTeacher [20]; MT: Mean Teacher initialized with FR and trained with random color jittering on both the source and target data (i.e., this corresponds to our mean teacher setup in Sec. 4.2 but without the aggregator and without transformations " + }, + { + "bbox": [ + 304, + 584, + 547, + 693 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 304, + 584, + 547, + 693 + ], + "type": "text", + "content": "); FR+PIT: Same setup as FR but with the images corrected with PIT [13]; MT+PIT: Same setup as MT but with the" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 47, + 693, + 287, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 693, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 693, + 287, + 713 + ], + "type": "text", + "content": "2In Cityscapes, a person may be labeled as either person or rider. Since the rider label is used for people riding a vehicle, we omit these cases." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 315, + 702, + 482, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 702, + 482, + 712 + ], + "spans": [ + { + "bbox": [ + 315, + 702, + 482, + 712 + ], + "type": "text", + "content": "3We re-implement PIT baselines with our detector." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "17446" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 101, + 70, + 235, + 167 + ], + "blocks": [ + { + "bbox": [ + 101, + 70, + 235, + 167 + ], + "lines": [ + { + "bbox": [ + 101, + 70, + 235, + 167 + ], + "spans": [ + { + "bbox": [ + 101, + 70, + 235, + 167 + ], + "type": "table", + "html": "
MethodCar AP@0.5
FR [24]76.1
AT [20]77.2
FR+PIT77.6
MT78.3
MT+PIT [13]79.7
Ours80.4 ± 0.15
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MethodCar AP@0.5 for FoVx
50°70°80°90°
FR [24]94.390.286.880.6
FR+PIT [13]93.691.489.285.9
Ours-h94.1±0.1693.1 ± 0.3391.8 ± 0.4088.8 ± 0.21
", + "image_path": "75056f449c4c7e19798fbd5eb6e2349548b8382788c216cb419cb33386709cf1.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 317, + 288, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 317, + 288, + 437 + ], + "spans": [ + { + "bbox": [ + 46, + 317, + 288, + 437 + ], + "type": "text", + "content": "images corrected with PIT. We refer to our complete approach (Sec. 4.2) as Ours. For the task of FoV generalization, we report our results as Ours-" + }, + { + "bbox": [ + 46, + 317, + 288, + 437 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 46, + 317, + 288, + 437 + ], + "type": "text", + "content": " to indicate that we only optimize the homographies (" + }, + { + "bbox": [ + 46, + 317, + 288, + 437 + ], + "type": "inline_equation", + "content": "5 \\times 4" + }, + { + "bbox": [ + 46, + 317, + 288, + 437 + ], + "type": "text", + "content": " parameters) in " + }, + { + "bbox": [ + 46, + 317, + 288, + 437 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 317, + 288, + 437 + ], + "type": "text", + "content": " to adapt to the new FoVs while keeping the base and aggregator networks frozen. This matches the setup of PIT [13], which also corrects the images according to the new FoVs. As Ours and Ours-" + }, + { + "bbox": [ + 46, + 317, + 288, + 437 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 46, + 317, + 288, + 437 + ], + "type": "text", + "content": " are trained with randomly initialized " + }, + { + "bbox": [ + 46, + 317, + 288, + 437 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 317, + 288, + 437 + ], + "type": "text", + "content": ", we report the average results and standard deviations over three independent runs." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 443, + 287, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 443, + 287, + 684 + ], + "spans": [ + { + "bbox": [ + 46, + 443, + 287, + 684 + ], + "type": "text", + "content": "FoV adaptation. The results of Cityscapes " + }, + { + "bbox": [ + 46, + 443, + 287, + 684 + ], + "type": "inline_equation", + "content": "\\rightarrow" + }, + { + "bbox": [ + 46, + 443, + 287, + 684 + ], + "type": "text", + "content": " KITTI FoV adaptation are provided in Tab. 1. Both MT+PIT and Ours both bridge the FoV gap, outperforming the MT baseline. Note, however, that we achieve this by learning the transformations, without requiring any camera-specific information, which is needed by PIT. Note also that MT outperforms FR by learning a better representation in the target domain, even though FR is trained with strong augmentation, such as random cropping. AT underperforms because its strong augmentation strategy fails to generalize for datasets having prominent geometric shifts. Our improvement over MT evidences that learning transformations helps to overcome geometric shifts. We optimize with " + }, + { + "bbox": [ + 46, + 443, + 287, + 684 + ], + "type": "inline_equation", + "content": "N = 9" + }, + { + "bbox": [ + 46, + 443, + 287, + 684 + ], + "type": "text", + "content": ", homographies in this setup. Fig. 5 shows a qualitative example. Different homographies look into different image regions and the aggregator learns how to combine the activations corresponding to objects as depicted in Fig. 7. In supp. material Sec. A.3, we show effectiveness of a learned aggregator over the others. Additionally, we provide results on FoV decreasing adaptation in supp. material Sec. A.5." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 689, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 287, + 715 + ], + "type": "text", + "content": "FoV generalization. Tab. 2 summarizes the results obtained by using different FoVs as target domains while fix-" + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 358, + 70, + 494, + 143 + ], + "blocks": [ + { + "bbox": [ + 113, + 285, + 220, + 295 + ], + "lines": [ + { + "bbox": [ + 113, + 285, + 220, + 295 + ], + "spans": [ + { + "bbox": [ + 113, + 285, + 220, + 295 + ], + "type": "text", + "content": "Table 2. FoV Generalization." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 358, + 70, + 494, + 143 + ], + "lines": [ + { + "bbox": [ + 358, + 70, + 494, + 143 + ], + "spans": [ + { + "bbox": [ + 358, + 70, + 494, + 143 + ], + "type": "table", + "html": "
MethodPedestrian AP@0.5
FR [24]43.7
AT [20]63.5
MT64.7
Ours65.3±0.37
", + "image_path": "34e373ef8912371c335144d334092995319b27a12229ee4f003d1faab09dd3b2.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 367, + 152, + 484, + 163 + ], + "lines": [ + { + "bbox": [ + 367, + 152, + 484, + 163 + ], + "spans": [ + { + "bbox": [ + 367, + 152, + 484, + 163 + ], + "type": "text", + "content": "Table 3. Viewpoint Adaptation." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 332, + 176, + 520, + 303 + ], + "blocks": [ + { + "bbox": [ + 332, + 176, + 520, + 303 + ], + "lines": [ + { + "bbox": [ + 332, + 176, + 520, + 303 + ], + "spans": [ + { + "bbox": [ + 332, + 176, + 520, + 303 + ], + "type": "image", + "image_path": "625516b6d8d8bde88eeea14809c7b05f16fbb5bc488a37cb3b54c5825a083765.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 313, + 545, + 335 + ], + "lines": [ + { + "bbox": [ + 305, + 313, + 545, + 335 + ], + "spans": [ + { + "bbox": [ + 305, + 313, + 545, + 335 + ], + "type": "text", + "content": "Figure 6. Varying the number of homographies. We evaluate the effect of " + }, + { + "bbox": [ + 305, + 313, + 545, + 335 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 305, + 313, + 545, + 335 + ], + "type": "text", + "content": " on the FoV adaptation task." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 357, + 545, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 357, + 545, + 441 + ], + "spans": [ + { + "bbox": [ + 304, + 357, + 545, + 441 + ], + "type": "text", + "content": "ing the source FoV to " + }, + { + "bbox": [ + 304, + 357, + 545, + 441 + ], + "type": "inline_equation", + "content": "50^{\\circ}" + }, + { + "bbox": [ + 304, + 357, + 545, + 441 + ], + "type": "text", + "content": ". Since both the source and target images are taken from KITTI, the domain gap is only caused by a FoV change. Note that the performance of FR drops quickly as the FoV gap increases. Ours-h outperforms FR+PIT by a growing margin as the FoV gap increases. This shows that learning transformations helps to generalize better to different amounts of geometric shifts." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 447, + 545, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 447, + 545, + 579 + ], + "spans": [ + { + "bbox": [ + 304, + 447, + 545, + 579 + ], + "type": "text", + "content": "Viewpoint adaptation. As shown in Fig. 1, a change in the camera viewpoint yields differences in the observed distortions and type of occlusions. The results in Tab. 3 show the benefits of our method over MT in this case. Note that PIT, which was designed for FoV changes, cannot be applied to correct for a viewpoint change. Other baselines outperform FR, as they use pseudo labels to fix the difference in bounding box distribution, as shown in Fig. 1. These results illustrate the generality of our method to different kinds of geometric shifts. Qualitative results for this task can be found in Fig. A.10." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 586, + 423, + 600 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 586, + 423, + 600 + ], + "spans": [ + { + "bbox": [ + 306, + 586, + 423, + 600 + ], + "type": "text", + "content": "5.5. Additional Analyses" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 605, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 545, + 713 + ], + "type": "text", + "content": "Variable number of homographies. Let us now study the influence of the number of homographies in " + }, + { + "bbox": [ + 304, + 605, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 304, + 605, + 545, + 713 + ], + "type": "text", + "content": ". To this end, we vary this number between 1 and 9. In Fig. 6, we plot the resulting APs for the Cityscapes-to-KITTI FoV adaptation task. Increasing the number of transformations results in a steady increase in performance, which nonetheless tends to plateau starting at 4 homographies. This is similar to our theoretical observation in Fig. A.3 of supp. material. Due to limited compute resources, we couldn't run ex" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17447" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 69, + 165, + 114 + ], + "blocks": [ + { + "bbox": [ + 49, + 69, + 165, + 114 + ], + "lines": [ + { + "bbox": [ + 49, + 69, + 165, + 114 + ], + "spans": [ + { + "bbox": [ + 49, + 69, + 165, + 114 + ], + "type": "image", + "image_path": "ab51612655641a63ec4a38dbb2a8b9dd56abbbfde7a2eef5869a89d5ed7e5e82.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 179, + 70, + 288, + 115 + ], + "blocks": [ + { + "bbox": [ + 179, + 70, + 288, + 115 + ], + "lines": [ + { + "bbox": [ + 179, + 70, + 288, + 115 + ], + "spans": [ + { + "bbox": [ + 179, + 70, + 288, + 115 + ], + "type": "image", + "image_path": "f1a0b5b1dd972c135881598dee92ebfebcb6cc608dee6e8df5ef48016c0af3b0.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 48, + 116, + 164, + 204 + ], + "blocks": [ + { + "bbox": [ + 48, + 116, + 164, + 204 + ], + "lines": [ + { + "bbox": [ + 48, + 116, + 164, + 204 + ], + "spans": [ + { + "bbox": [ + 48, + 116, + 164, + 204 + ], + "type": "image", + "image_path": "e6071888d47947b532bb2d1b31f0b98df7affdd7577047982c18b4c6eb83177b.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 179, + 116, + 287, + 204 + ], + "blocks": [ + { + "bbox": [ + 179, + 116, + 287, + 204 + ], + "lines": [ + { + "bbox": [ + 179, + 116, + 287, + 204 + ], + "spans": [ + { + "bbox": [ + 179, + 116, + 287, + 204 + ], + "type": "image", + "image_path": "d4a44fbe1381baf2303225fcfba9bd12a67425685692d2a814ed840b2ca6709e.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 51, + 205, + 166, + 248 + ], + "blocks": [ + { + "bbox": [ + 51, + 205, + 166, + 248 + ], + "lines": [ + { + "bbox": [ + 51, + 205, + 166, + 248 + ], + "spans": [ + { + "bbox": [ + 51, + 205, + 166, + 248 + ], + "type": "image", + "image_path": "45227395d7c88a39fcdcc46fe3450091269972d5ba43bc47eac9cc292567292b.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 260, + 289, + 370 + ], + "lines": [ + { + "bbox": [ + 46, + 260, + 289, + 370 + ], + "spans": [ + { + "bbox": [ + 46, + 260, + 289, + 370 + ], + "type": "text", + "content": "Figure 7. Feature Maps: Top row: predictions of our network and feature map after aggregator. Left column: Image I, transformed by learned homographies; Right Column: Feature maps " + }, + { + "bbox": [ + 46, + 260, + 289, + 370 + ], + "type": "inline_equation", + "content": "F" + }, + { + "bbox": [ + 46, + 260, + 289, + 370 + ], + "type": "text", + "content": " warped by corresponding " + }, + { + "bbox": [ + 46, + 260, + 289, + 370 + ], + "type": "inline_equation", + "content": "H^{-1}" + }, + { + "bbox": [ + 46, + 260, + 289, + 370 + ], + "type": "text", + "content": " which are input to the aggregator. Each transform distorts the image regions differently. Most of the cars are on the left side and of small size in the image. " + }, + { + "bbox": [ + 46, + 260, + 289, + 370 + ], + "type": "inline_equation", + "content": "H_{1}" + }, + { + "bbox": [ + 46, + 260, + 289, + 370 + ], + "type": "text", + "content": " distorts the left side leading to no activation " + }, + { + "bbox": [ + 46, + 260, + 289, + 370 + ], + "type": "inline_equation", + "content": "(H_{1}^{-1}F_{1})" + }, + { + "bbox": [ + 46, + 260, + 289, + 370 + ], + "type": "text", + "content": " for the object. " + }, + { + "bbox": [ + 46, + 260, + 289, + 370 + ], + "type": "inline_equation", + "content": "H_{3}" + }, + { + "bbox": [ + 46, + 260, + 289, + 370 + ], + "type": "text", + "content": " which causes the zoom-in effect has the strongest activation as the smaller objects are visible better here. These maps are generated by taking maximum over channel dimension." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 179, + 205, + 287, + 249 + ], + "blocks": [ + { + "bbox": [ + 179, + 205, + 287, + 249 + ], + "lines": [ + { + "bbox": [ + 179, + 205, + 287, + 249 + ], + "spans": [ + { + "bbox": [ + 179, + 205, + 287, + 249 + ], + "type": "image", + "image_path": "ccf3f483944698569ca6b6b83bed23100f12381d6048b422b9bc29ac13d854be.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 220, + 547, + 287 + ], + "lines": [ + { + "bbox": [ + 305, + 220, + 547, + 287 + ], + "spans": [ + { + "bbox": [ + 305, + 220, + 547, + 287 + ], + "type": "text", + "content": "Figure 8. Diversity in " + }, + { + "bbox": [ + 305, + 220, + 547, + 287 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 305, + 220, + 547, + 287 + ], + "type": "text", + "content": ": We train 5 homographies initialized as " + }, + { + "bbox": [ + 305, + 220, + 547, + 287 + ], + "type": "inline_equation", + "content": "\\mathcal{H}_i = I" + }, + { + "bbox": [ + 305, + 220, + 547, + 287 + ], + "type": "text", + "content": ". We plot the evolution of " + }, + { + "bbox": [ + 305, + 220, + 547, + 287 + ], + "type": "inline_equation", + "content": "s_x" + }, + { + "bbox": [ + 305, + 220, + 547, + 287 + ], + "type": "text", + "content": " for different homograhies as training proceeds. Each homography is shown in a different color. Note that the values for the different homographies become diverse. The best score is achieved at iteration " + }, + { + "bbox": [ + 305, + 220, + 547, + 287 + ], + "type": "inline_equation", + "content": "= 22k" + }, + { + "bbox": [ + 305, + 220, + 547, + 287 + ], + "type": "text", + "content": ", indicated with the vertical line." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 392, + 287, + 464 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 392, + 287, + 464 + ], + "spans": [ + { + "bbox": [ + 46, + 392, + 287, + 464 + ], + "type": "text", + "content": "periments with more than 9 homographies. This confirms the intuition that a higher number of perspective transformations can better capture the geometric shift between two domains. Therefore, we conducted all experiments with the maximum number of homographies allowed by our compute resources." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 468, + 287, + 649 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 468, + 287, + 649 + ], + "spans": [ + { + "bbox": [ + 46, + 468, + 287, + 649 + ], + "type": "text", + "content": "Only optimizing " + }, + { + "bbox": [ + 46, + 468, + 287, + 649 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 468, + 287, + 649 + ], + "type": "text", + "content": ". We also run the Ours-" + }, + { + "bbox": [ + 46, + 468, + 287, + 649 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 46, + 468, + 287, + 649 + ], + "type": "text", + "content": " baseline in the FoV and viewpoint adaptation scenarios. The resulting APs are 78.2 and 49.8, respectively. By learning only the " + }, + { + "bbox": [ + 46, + 468, + 287, + 649 + ], + "type": "inline_equation", + "content": "20(5\\times 4)" + }, + { + "bbox": [ + 46, + 468, + 287, + 649 + ], + "type": "text", + "content": " homography parameters, our approach outperforms FR (in Tab. 1 and Tab. 3, respectively) by a large margin in both cases. This confirms that our training strategy is able to efficiently optimize " + }, + { + "bbox": [ + 46, + 468, + 287, + 649 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 468, + 287, + 649 + ], + "type": "text", + "content": " to bridge the geometric gap between different domains. We visualize in Fig. A.9 in the supplementary material some transformations learned for FoV adaptation by Ours-" + }, + { + "bbox": [ + 46, + 468, + 287, + 649 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 46, + 468, + 287, + 649 + ], + "type": "text", + "content": ". Note that they converge to diverse homographies that mimic a different FoV, correctly reflecting the adaptation task. Additionally in supp. material Sec. A.4, we ablate why the learning transform is useful. Specifically, learning not only provide better performance but provide faster inference w.r.t random transformations." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "type": "text", + "content": "Diversity in " + }, + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "type": "text", + "content": ". To show that our approach can learn a diverse set of transformations that help in the adaptation task, we initialize all the homographies with identity. Fig. 8 depicts the diversity of the learned homographies on the FoV adaptation task. Even though we do not enforce di" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 353, + 70, + 500, + 213 + ], + "blocks": [ + { + "bbox": [ + 353, + 70, + 500, + 213 + ], + "lines": [ + { + "bbox": [ + 353, + 70, + 500, + 213 + ], + "spans": [ + { + "bbox": [ + 353, + 70, + 500, + 213 + ], + "type": "image", + "image_path": "ccf68a9496c082f1176cb9f7963b35fda52a0f8feae7b67af799ddef9472d20d.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 308, + 547, + 367 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 308, + 547, + 367 + ], + "spans": [ + { + "bbox": [ + 305, + 308, + 547, + 367 + ], + "type": "text", + "content": "versity, our approach learns a diverse set of transformations. With these learned homographies, our model achieves 79.5 AP@0.5 score for the FoV adaptation task. We show additional results in the supplementary material Sec. A.6 and Sec. A.7." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 372, + 546, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 372, + 546, + 552 + ], + "spans": [ + { + "bbox": [ + 305, + 372, + 546, + 552 + ], + "type": "text", + "content": "Limitations. Our approach assumes that the geometric gap between two domains can be bridged by a set of perspective transformations. We have shown that with enough transformations this is true. However, using a large number of homographies comes at a computational cost. The computational overhead leads to an increment in the inference time from 0.062s to 0.096s for " + }, + { + "bbox": [ + 305, + 372, + 546, + 552 + ], + "type": "inline_equation", + "content": "N = 5" + }, + { + "bbox": [ + 305, + 372, + 546, + 552 + ], + "type": "text", + "content": " on an A100 Nvidia GPU with image dimension " + }, + { + "bbox": [ + 305, + 372, + 546, + 552 + ], + "type": "inline_equation", + "content": "402 \\times 1333" + }, + { + "bbox": [ + 305, + 372, + 546, + 552 + ], + "type": "text", + "content": ". Nevertheless, our simple implementation shows promising results, and we will work on reducing this overhead in future work. Moreover since the optimization of the homography set is done at the dataset level, only certain transformations are beneficial to a given image. In the future, we therefore intend to condition the homography on the input image, which would reduce the total number of homographies needed." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 562, + 379, + 574 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 562, + 379, + 574 + ], + "spans": [ + { + "bbox": [ + 306, + 562, + 379, + 574 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 582, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 582, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 305, + 582, + 547, + 715 + ], + "type": "text", + "content": "We have introduced an approach to bridge the gap between two domains caused by geometric shifts by learning a set of homographies. We have shown the effectiveness our method on two different kinds of shifts, without relying on any annotations in the target domain, including information about the nature of the geometric shifts. Our analyses have evidenced that optimizing the transformations alone brings in improvement over the base detector and increasing the number of learnt homographies helps further. In the future, we plan to learn transformations that are conditioned on the input image to model image-dependent geometric shifts." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "17448" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 715 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 124 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 124 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 124 + ], + "type": "text", + "content": "[1] Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934, 2020. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 124, + 287, + 158 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 124, + 287, + 158 + ], + "spans": [ + { + "bbox": [ + 53, + 124, + 287, + 158 + ], + "type": "text", + "content": "[2] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. Detr, https://github.com/facebookresearch/detr, 2020.1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 158, + 288, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 158, + 288, + 213 + ], + "spans": [ + { + "bbox": [ + 53, + 158, + 288, + 213 + ], + "type": "text", + "content": "[3] Chaoqi Chen, Zebiao Zheng, Xinghao Ding, Yue Huang, and Qi Dou. Harmonizing transferability and discriminability for adapting object detectors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8869-8878, 2020. 1, 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 213, + 288, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 213, + 288, + 267 + ], + "spans": [ + { + "bbox": [ + 53, + 213, + 288, + 267 + ], + "type": "text", + "content": "[4] Chaoqi Chen, Zebiao Zheng, Yue Huang, Xinghao Ding, and Yizhou Yu. I3net: Implicit instance-invariant network for adapting one-stage object detectors. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 1, 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 269, + 288, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 269, + 288, + 323 + ], + "spans": [ + { + "bbox": [ + 53, + 269, + 288, + 323 + ], + "type": "text", + "content": "[5] Yuhua Chen, Wen Li, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Domain adaptive faster r-cnn for object detection in the wild. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3339-3348, 2018. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 324, + 288, + 390 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 324, + 288, + 390 + ], + "spans": [ + { + "bbox": [ + 53, + 324, + 288, + 390 + ], + "type": "text", + "content": "[6] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 1, 2, 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 391, + 288, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 391, + 288, + 435 + ], + "spans": [ + { + "bbox": [ + 53, + 391, + 288, + 435 + ], + "type": "text", + "content": "[7] Jifeng Dai, Haozhi Qi, Yuwen Xiong, Yi Li, Guodong Zhang, Han Hu, and Yichen Wei. Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision, pages 764-773, 2017. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 435, + 288, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 435, + 288, + 491 + ], + "spans": [ + { + "bbox": [ + 53, + 435, + 288, + 491 + ], + "type": "text", + "content": "[8] Patrick Dendorfer, Hamid Rezatofighi, Anton Milan, Javen Shi, Daniel Cremers, Ian Reid, Stefan Roth, Konrad Schindler, and Laura Leal-Taixe. Mot20: A benchmark for multi object tracking in crowded scenes. arXiv preprint arXiv:2003.09003, 2020. 1, 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 491, + 288, + 545 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 491, + 288, + 545 + ], + "spans": [ + { + "bbox": [ + 53, + 491, + 288, + 545 + ], + "type": "text", + "content": "[9] Jinhong Deng, Wen Li, Yuhua Chen, and Lixin Duan. Unbiased mean teacher for cross-domain object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4091-4101, 2021. 1, 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 546, + 288, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 546, + 288, + 590 + ], + "spans": [ + { + "bbox": [ + 48, + 546, + 288, + 590 + ], + "type": "text", + "content": "[10] Mark Everingham, Luc Van Gool, Christopher KI Williams, John Winn, and Andrew Zisserman. The pascal visual object classes (voc) challenge. International journal of computer vision, 88(2):303-338, 2010. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 591, + 288, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 591, + 288, + 645 + ], + "spans": [ + { + "bbox": [ + 48, + 591, + 288, + 645 + ], + "type": "text", + "content": "[11] Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. Domain-adversarial training of neural networks. The journal of machine learning research, 17(1):2096-2030, 2016. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 647, + 288, + 691 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 288, + 691 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 288, + 691 + ], + "type": "text", + "content": "[12] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pages 3354-3361. IEEE, 2012. 1, 2, 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 691, + 288, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 288, + 715 + ], + "type": "text", + "content": "[13] Qiqi Gu, Qianyu Zhou, Minghao Xu, Zhengyang Feng, Guangliang Cheng, Xuequan Lu, Jianping Shi, and Lizhuang" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 715 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 116 + ], + "type": "text", + "content": "Ma. Pit: Position-invariant transform for cross-fov domain adaptation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8761-8770, 2021. 1, 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 118, + 545, + 163 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 163 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 163 + ], + "type": "text", + "content": "[14] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 163, + 545, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 163, + 545, + 218 + ], + "spans": [ + { + "bbox": [ + 307, + 163, + 545, + 218 + ], + "type": "text", + "content": "[15] Naoto Inoue, Ryosuke Furuta, Toshihiko Yamasaki, and Kiyoharu Aizawa. Cross-domain weakly-supervised object detection through progressive domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5001-5009, 2018. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 219, + 545, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 219, + 545, + 252 + ], + "spans": [ + { + "bbox": [ + 307, + 219, + 545, + 252 + ], + "type": "text", + "content": "[16] Max Jaderberg, Karen Simonyan, Andrew Zisserman, et al. Spatial transformer networks. Advances in neural information processing systems, 28, 2015. 2, 4" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 253, + 545, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 253, + 545, + 352 + ], + "spans": [ + { + "bbox": [ + 307, + 253, + 545, + 352 + ], + "type": "text", + "content": "[17] Glenn Jocher, Alex Stoken, Jirka Borovec, NanoCode012, ChristopherSTAN, Liu Changyu, Laughing, tkianai, Adam Hogan, lorenzomammana, yxNONG, AlexWang1900, Laurentiu Diaconu, Marc, wanghaoyang0106, ml5ah, Doug, Francisco Ingham, Frederik, Guilhien, Hatovix, Jake Poznanski, Jiacong Fang, Lijun Yu, changyu98, Mingyu Wang, Naman Gupta, Osama Akhtar, PetrDvoracek, and Prashant Rai. ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements, Oct. 2020. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 353, + 545, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 353, + 545, + 408 + ], + "spans": [ + { + "bbox": [ + 307, + 353, + 545, + 408 + ], + "type": "text", + "content": "[18] Seunghyeon Kim, Jaehoon Choi, Taekyung Kim, and Chang-ick Kim. Self-training and adversarial background regularization for unsupervised domain adaptive one-stage object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6092–6101, 2019. 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 409, + 545, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 409, + 545, + 441 + ], + "spans": [ + { + "bbox": [ + 307, + 409, + 545, + 441 + ], + "type": "text", + "content": "[19] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 443, + 545, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 443, + 545, + 498 + ], + "spans": [ + { + "bbox": [ + 307, + 443, + 545, + 498 + ], + "type": "text", + "content": "[20] Yu-Jhe Li, Xiaoliang Dai, Chih-Yao Ma, Yen-Cheng Liu, Kan Chen, Bichen Wu, Zijian He, Kris Kitani, and Peter Vajda. Cross-domain adaptive teacher for object detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 5, 6, 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 499, + 545, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 499, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 307, + 499, + 545, + 544 + ], + "type": "text", + "content": "[21] Mingsheng Long, Yue Cao, Jianmin Wang, and Michael Jordan. Learning transferable features with deep adaptation networks. In International conference on machine learning, pages 97-105. PMLR, 2015. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 544, + 545, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 544, + 545, + 578 + ], + "spans": [ + { + "bbox": [ + 307, + 544, + 545, + 578 + ], + "type": "text", + "content": "[22] Zhongyi Pei, Zhangjie Cao, Mingsheng Long, and Jianmin Wang. Multi-adversarial domain adaptation. In Thirty-second AAAI conference on artificial intelligence, 2018. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 578, + 545, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 578, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 307, + 578, + 545, + 624 + ], + "type": "text", + "content": "[23] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 779-788, 2016. 1" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 624, + 545, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 624, + 545, + 669 + ], + "spans": [ + { + "bbox": [ + 307, + 624, + 545, + 669 + ], + "type": "text", + "content": "[24] Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. Faster r-cnn: towards real-time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 39(6):1137-1149, 2016. 1, 4, 6, 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 669, + 545, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 669, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 307, + 669, + 545, + 715 + ], + "type": "text", + "content": "[25] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. ImageNet Large Scale Visual Recognition Chal" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "17449" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 67, + 72, + 286, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 72, + 286, + 95 + ], + "spans": [ + { + "bbox": [ + 67, + 72, + 286, + 95 + ], + "type": "text", + "content": "lenge. International Journal of Computer Vision (IJCV), 115(3):211-252, 2015. 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 287, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 287, + 150 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 287, + 150 + ], + "type": "text", + "content": "[26] Kuniaki Saito, Yoshitaka Ushiku, Tatsuya Harada, and Kate Saenko. Strong-weak distribution alignment for adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6956–6965, 2019. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 152, + 287, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 152, + 287, + 194 + ], + "spans": [ + { + "bbox": [ + 48, + 152, + 287, + 194 + ], + "type": "text", + "content": "[27] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126(9):973-992, 2018. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 197, + 287, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 197, + 287, + 241 + ], + "spans": [ + { + "bbox": [ + 48, + 197, + 287, + 241 + ], + "type": "text", + "content": "[28] Zhiqiang Shen, Harsh Maheshwari, Weichen Yao, and Marios Savvides. Scl: Towards accurate domain adaptive object detection via gradient detach based stacked complementary losses. arXiv preprint arXiv:1911.02559, 2019. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 243, + 287, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 243, + 287, + 285 + ], + "spans": [ + { + "bbox": [ + 48, + 243, + 287, + 285 + ], + "type": "text", + "content": "[29] Kihyuk Sohn, Zizhao Zhang, Chun-Liang Li, Han Zhang, Chen-Yu Lee, and Tomas Pfister. A simple semi-supervised learning framework for object detection. arXiv preprint arXiv:2005.04757, 2020. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 287, + 287, + 330 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 287, + 287, + 330 + ], + "spans": [ + { + "bbox": [ + 48, + 287, + 287, + 330 + ], + "type": "text", + "content": "[30] Baochen Sun and Kate Saenko. Deep coral: Correlation alignment for deep domain adaptation. In European conference on computer vision, pages 443-450. Springer, 2016. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 332, + 287, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 332, + 287, + 376 + ], + "spans": [ + { + "bbox": [ + 48, + 332, + 287, + 376 + ], + "type": "text", + "content": "[31] Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems, 30, 2017. 2, 4, 5, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 377, + 287, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 377, + 287, + 421 + ], + "spans": [ + { + "bbox": [ + 48, + 377, + 287, + 421 + ], + "type": "text", + "content": "[32] Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell. Adversarial discriminative domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7167-7176, 2017. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 422, + 287, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 422, + 287, + 487 + ], + "spans": [ + { + "bbox": [ + 48, + 422, + 287, + 487 + ], + "type": "text", + "content": "[33] Vibashan VS, Vikram Gupta, Poojan Oza, Vishwanath A Sindagi, and Vishal M Patel. Mega-cda: Memory guided attention for category-aware unsupervised domain adaptive object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4516-4526, 2021. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 489, + 287, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 489, + 287, + 521 + ], + "spans": [ + { + "bbox": [ + 48, + 489, + 287, + 521 + ], + "type": "text", + "content": "[34] Yuxin Wu, Alexander Kirillov, Francisco Massa, Wan-Yen Lo, and Ross Girshick. Detector2. https://github.com/facebookresearch/detectron2, 2019.6" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 523, + 287, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 523, + 287, + 567 + ], + "spans": [ + { + "bbox": [ + 48, + 523, + 287, + 567 + ], + "type": "text", + "content": "[35] Shaoan Xie, Zibin Zheng, Liang Chen, and Chuan Chen. Learning semantic representations for unsupervised domain adaptation. In International conference on machine learning, pages 5423-5432. PMLR, 2018. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 568, + 287, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 568, + 287, + 622 + ], + "spans": [ + { + "bbox": [ + 48, + 568, + 287, + 622 + ], + "type": "text", + "content": "[36] Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang. Adversarial domain adaptation with domain mixup. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 34, pages 6502-6509, 2020. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 624, + 287, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 287, + 677 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 287, + 677 + ], + "type": "text", + "content": "[37] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223-2232, 2017. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "text", + "content": "[38] Xinge Zhu, Jiangmiao Pang, Ceyuan Yang, Jianping Shi, and Dahua Lin. Adapting object detectors via selective cross-domain alignment. In Proceedings of the IEEE/CVF Con-" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 325, + 72, + 545, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 72, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 325, + 72, + 545, + 95 + ], + "type": "text", + "content": "ference on Computer Vision and Pattern Recognition, pages 687-696, 2019. 1, 2" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "17450" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Video Representations From Large Language Models/c00bc946-1a6e-40fd-9b0d-ec2826e3ea29_content_list.json b/2023/Learning Video Representations From Large Language Models/c00bc946-1a6e-40fd-9b0d-ec2826e3ea29_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..9ba7588cb513d0b0ec767eebf067a900b5fc35ab --- /dev/null +++ b/2023/Learning Video Representations From Large Language Models/c00bc946-1a6e-40fd-9b0d-ec2826e3ea29_content_list.json @@ -0,0 +1,1653 @@ +[ + { + "type": "text", + "text": "Learning Video Representations from Large Language Models", + "text_level": 1, + "bbox": [ + 168, + 130, + 802, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yue Zhao $^{1,2*}$ Ishan Misra $^{1}$ Philipp Krahenbuhl $^{2}$ Rohit Girdhar $^{1}$ $^{1}$ FAIR, Meta AI $^{2}$ University of Texas, Austin", + "bbox": [ + 207, + 178, + 756, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "facebookresearch.github.io/LaViLa", + "bbox": [ + 334, + 219, + 630, + 232 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 268, + 313, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We introduce LAVILA, a new approach to learning video-language representations by leveraging Large Language Models (LLMs). We repurpose pre-trained LLMs to be conditioned on visual input, and finetune them to create automatic video narrators. Our auto-generated narrations offer a number of advantages, including dense coverage of long videos, better temporal synchronization of the visual information and text, and much higher diversity of text. The video-language embedding learned contrastively with these narrations outperforms the previous state-of-the-art on multiple first-person and third-person video tasks, both in zero-shot and finetuned setups. Most notably, LAVILA obtains an absolute gain of $10.1\\%$ on EGTEA classification and $5.9\\%$ Epic-Kitchens-100 multi-instance retrieval benchmarks. Furthermore, LAVILA trained with only half the narrations from the Ego4D dataset outperforms models trained on the full set, and shows positive scaling behavior on increasing pre-training data and model size.", + "bbox": [ + 75, + 301, + 473, + 574 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 604, + 209, + 619 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Learning visual representation using web-scale imag-text data is a powerful tool for computer vision. Vision-language approaches [31, 49, 80] have pushed the state-of-the-art across a variety of tasks, including zero-shot classification [49], novel object detection [87], and even image generation [52]. Similar approaches for videos [4, 39, 46], however, have been limited by the small size of paired video-text corpora compared to the billion-scale image-text datasets [31, 49, 84]—even though access to raw video data has exploded in the past decade. In this work, we show it is possible to automatically generate text pairing for such videos by leveraging Large Language Models (LLMs), thus taking full advantage of the massive video data. Learning video-language models with these automatically generated annotations leads to stronger representations, and as Figure 1 shows, sets a new state-of-the-art on six popular first", + "bbox": [ + 75, + 631, + 470, + 875 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/54f3dc189ad3306f4658255c97c4c2a033eb2d199bc9b48e836b3e1a7eadafbd.jpg", + "image_caption": [ + "Figure 1. LAVILA sets a new state-of-the-art across a number of first and third-person video understanding tasks (cf. Table 1 for details), by learning a video-language representation using supervision from large language models as narrators." + ], + "image_footnote": [], + "bbox": [ + 540, + 250, + 857, + 479 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "and third-person video benchmarks.", + "bbox": [ + 500, + 566, + 740, + 580 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Our method, called LAVILA: Language-model augmented Video-Language pre-training, leverages pretrained LLMs, e.g. GPT-2 [50], which encode within their weights a treasure trove of factual knowledge and conversational ability. As shown in Figure 2, we repurpose these LLMs to be \"visually-conditioned narrators\", and finetune on all accessible paired video-text clips. Once trained, we use the model to densely annotate thousands of hours of videos by generating rich textual descriptions. This pseudo-supervision can thus pervade the entire video, in between and beyond the annotated snippets. Paired with another LLM trained to rephrase existing narrations, LAVILA is able to create a much larger and more diverse set of text targets for video-text contrastive learning. In addition to setting a new state-of-the-art as noted earlier, the stronger representation learned by LAVILA even outperforms prior work using only half the groundtruth annotations (Figure 5).", + "bbox": [ + 496, + 580, + 892, + 852 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "LAVILA's strong performance can be attributed to a number of factors. First, LAVILA can provide temporally dense supervision for long-form videos, where the associ", + "bbox": [ + 498, + 854, + 893, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Work done during an internship at Meta.", + "bbox": [ + 94, + 886, + 316, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "6586", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/88a97ec45d22afb394ccf91dab3f7b6b59e031a243109148c2ad200ff9375e7f.jpg", + "image_caption": [ + "Figure 2. LAVILA leverages Large Language Models (LLMs) to densely narrate long videos, and uses those narrations to train strong dual-encoder models. While prior work uses sparsely labeled text by humans, or weakly aligned text transcribed from speech, LAVILA is able to leverage dense, diverse, and well-aligned text generated by a LLM." + ], + "image_footnote": [], + "bbox": [ + 80, + 88, + 472, + 313 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "ated captions are either too sparse, or the video-level \"Alt-Text\" (in the case of web videos) does not describe all the nuanced activities happening in it. Second, the generated text is well-aligned with the visual input. Although prior work has leveraged automatic speech transcription on How-To videos [45] to automatically extract clips paired with text from the speech, such datasets have relatively poor alignment between the visual and textual content ( $\\leq 50\\%$ , cf. [25, 45]), limiting the quality of the learned representations. Third, LAVILA can significantly expand annotations when only a little is available. For instance, videos of mundane day-to-day activities, especially from an egocentric viewpoint, could be very useful for assistive and augmented reality applications. Such videos, however, are rare on the internet, and hence do not readily exist with associated web text. Recent work [24] instead opted to manually capture and narrate such video data. These narrations however required significant manual effort: 250K hours of annotator time spent in narrating 3.6K hours of video. In contrast, LAVILA is able to automatically narrate each video multiple times and far more densely, and hence learns much stronger representations.", + "bbox": [ + 75, + 446, + 468, + 777 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We extensively evaluate LAVILA across multiple videotext pre-training datasets and downstream tasks to validate its effectiveness. Specifically, after being pre-trained on Ego4D, the largest egocentric video datasets with narrations, LAVILA can re-narrate the whole dataset $10 \\times$ over. The resulting model learned on these expanded narrations sets a new state-of-the-art on a wide range of downstream tasks across challenging datasets, including multi-instance", + "bbox": [ + 75, + 779, + 470, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "video retrieval on Epic-Kitchens-100 (5.9% absolute gain on mAP), multiple-choice question answering on Ego4D (5.9% absolute gain on intra-video accuracy), and action recognition on EGTEA (10.1% absolute gain on mean accuracy). It obtains gains both when evaluated for zero-shot transfer to the new dataset, as well as after fine-tuning on that dataset. Similar gains are shown in third-person video data. When training LAVILA after densely re-narrating HowTo100M, we outperform prior work on downstream action classification on UCF-101 and HMDB-51. In a case study of semi-supervised learning, we show that our model, which only ever sees 50% of the human-labeled data, is capable of outperforming the baseline model trained with all the narrations. Moreover, the gains progressively increase as we go to larger data regimes and larger backbones, suggesting the scalability of our method.", + "bbox": [ + 496, + 90, + 893, + 333 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 345, + 640, + 363 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Vision-language representation learning maps visual and textual embeddings into a common space using metric-learning techniques [21, 73]. Recently, different pretext tasks are proposed to learn a finer-grained association between visual and textual modality, e.g. masked language modeling (MLM) [10, 41, 62] and captioning [16, 80]. Another line of research focuses on scaling up both models and pre-training data. For instance, CLIP [49] is pre-trained on 400M image-text pairs with a contrastive loss (InfoNCE [48, 59]) while CoCa [80] unifies contrastive and generative approaches with a single foundation model. Similar trends are also witnessed in the videotext domain [36, 64, 88]. However, collecting high-quality video-text data is more difficult than image-text. Therefore, efforts are made to learn from uncurated videos with machine-generated audio transcripts via contrastive learning [44, 77, 82] or unsupervised alignment [25] while other works focus on either adapting well-performing image-text models to videos [32, 40, 47, 78], or curriculum learning from a single frame to multiple frames [4]. In contrast, our approach leverages language models to generate temporally dense textual supervision on long-form videos.", + "bbox": [ + 496, + 371, + 893, + 704 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Generative Visual Language Models (VLM) were first used for image/video captioning using recurrent networks [17, 68] and Transformer-based architectures [42, 56]. More recently, generative VLMs have unified multiple vision tasks [11, 89] by training multi-modal Transformers on visual-text pairs [30, 81]. Meanwhile, generative VLMs also excel at multimodal tasks via zero-shot or few-shot prompting [1, 65, 83] by leveraging multi-billion-parameter LLMs pre-trained on massive text corpus [7, 28, 50]. In our work, we demonstrate that generative VLMs can narrate long videos and the resulting video-text data benefits video-language representation learning.", + "bbox": [ + 496, + 704, + 893, + 885 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Large-scale multimodal video datasets are crucial for", + "bbox": [ + 500, + 885, + 893, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "6587", + "bbox": [ + 482, + 944, + 514, + 957 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "video understanding tasks but are hard to collect. Conventional video-text datasets [8, 53, 86] either have limited scenarios, e.g. cooking, or are not large enough to learn generic video representation. Miech et al. [45] scrape over 100 million video-text pairs via automatic audio transcription from long-form How-To videos. However, ASR introduces textual noise and visual-text unalignment [25]. WebVid [4] contains 10 million short videos with textual descriptions. But it is still several orders of magnitude smaller than the image counterparts [49, 54] and is harder to scale up since it is sourced from stock footage sites. The recently released Ego4D [24] dataset offers 3,600 hours of egocentric videos in which written sentence narrations are manually annotated every few seconds but requires significant manual effort. In contrast, our method shows a promising alternative by automatically narrating videos using supervision from LLM.", + "bbox": [ + 76, + 90, + 472, + 332 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Data augmentation techniques in NLP, including word-level replacement based on synonyms [72, 85] or nearest-neighbor retrieval [19, 70], improve text classification accuracy. We refer readers to [20] for a comprehensive survey. In this paper, we show that sentence-level paraphrasing based on text-to-text models [51] is helpful for video-language pre-training.", + "bbox": [ + 76, + 333, + 468, + 439 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Preliminaries", + "text_level": 1, + "bbox": [ + 76, + 452, + 215, + 467 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "A video $V$ is a stream of moving images $I$ . The number of frames $|V|$ can be arbitrarily long while video models typically operate on shorter clips, which are often in the range of a few seconds. Therefore, we skim through a long-form video and represent it by a set of $N$ short clips, i.e. $\\mathcal{X}$ . Each clip $x_{i}$ is defined by a specific start and end frame $x_{i} = \\{I_{t_{i}},\\dots ,I_{e_{i}}\\}$ , where $0 < t_i < e_i\\leq |V|$ , and is typically associated with some annotation $y_{i}$ . This annotation could be a class label or free-form textual description of the clip. We denote a video by the set of annotated clips with their corresponding annotations, i.e. $(\\mathcal{X},\\mathcal{Y}) = \\{(x_1,y_1),\\dots ,(x_N,y_N)\\}$ . Note that the annotated clips often cannot densely cover the entire video due to the annotation cost and visual redundancy, i.e. $\\bigcup_{i}[t_{i},e_{i}]\\subsetneq [0,|V|]$ .", + "bbox": [ + 76, + 477, + 468, + 688 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "A typical video model $\\mathcal{F}(\\mathcal{X},\\mathcal{Y})$ learns from these clip-level annotations using a standard training objective such as a cross-entropy loss when the annotations are class labels with a fixed vocabulary. However, more recently, dual-encoder-based contrastive approaches like CLIP [49, 77] have gained popularity. They work with free-form textual annotations which are tokenized [55] into sequences of discrete symbols, i.e. $y = (s_{1},s_{2},\\dots ,s_{L})\\in \\{1,0\\}^{|\\mathbb{S}|\\times L}$ . The model consists of a visual encoder $f_{\\mathrm{v}}:\\mathbb{R}^{T\\times 3\\times H\\times W}\\mapsto \\mathbb{R}^{D_{\\mathrm{v}}}$ plus a projection head $h_\\mathrm{v}:\\mathbb{R}^{D_\\mathrm{v}}\\mapsto \\mathbb{R}^d$ and a textual encoder $f_{\\mathrm{t}}:\\{1,0\\}^{|\\mathbb{S}|\\times L}\\mapsto \\mathbb{R}^{D_{\\mathrm{t}}}$ plus a projection head $h_{\\mathrm{t}}:\\mathbb{R}^{D_{\\mathrm{t}}}\\mapsto \\mathbb{R}^{d}$ in parallel to obtain the global visual and textual embedding respectively:", + "bbox": [ + 76, + 688, + 470, + 872 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {v} = h _ {\\mathrm {v}} \\left(f _ {\\mathrm {v}} (x)\\right), \\quad \\mathbf {u} = h _ {\\mathrm {t}} \\left(f _ {\\mathrm {t}} (y)\\right).\n$$\n", + "text_format": "latex", + "bbox": [ + 166, + 881, + 377, + 897 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/48bec47f8c78bfd006e1421c0b55d8dff0dfb6df0c5855df75587a2dd64b15fb.jpg", + "image_caption": [ + "Narration" + ], + "image_footnote": [], + "bbox": [ + 501, + 89, + 630, + 164 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/0b9cbb002d9bd35f7768f8a5c554b7216fe6131cc5be01ef1455df09d4f4be4f.jpg", + "image_caption": [ + "C separates the yarn" + ], + "image_footnote": [], + "bbox": [ + 598, + 89, + 761, + 164 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/6a835593baf5ce5df53a6de0cfb0eeaee3e05c6195070f035d8d1ce423e5eb0e.jpg", + "image_caption": [ + "NARRATOR", + "C stretches the thread with both hands. \nC pulls out the yarn with her right hand." + ], + "image_footnote": [], + "bbox": [ + 761, + 89, + 890, + 164 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/2bf280c625311a6cb2401d38a528262f3c61bbe14e5dc83c707b97773d677f26.jpg", + "image_caption": [ + "C divides the yarn by a few stitches.", + "Narration", + "C lifts container. C raises the container.", + "Figure 3. Generated samples by NARRATOR and REPHRASER. NARRATOR generates new descriptions of the action taking place, potentially focusing on other objects being interacted with. REPHRASER not only changes the word order of the human narration but also diversifies it by using related verbs or nouns." + ], + "image_footnote": [], + "bbox": [ + 501, + 203, + 630, + 277 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/bca71e97eac0113e025cfec06c5194426b0998028aae966435d8895da9db6aae.jpg", + "image_caption": [ + "NARRATOR" + ], + "image_footnote": [], + "bbox": [ + 699, + 203, + 761, + 277 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/082e7ea4476e6863b6bf528dd276d336086df4344ba5b37a8a282b9092390b67.jpg", + "image_caption": [ + "C wipes the countertop with a sponge. \nCmoves the container." + ], + "image_footnote": [], + "bbox": [ + 761, + 203, + 890, + 277 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "A contrastive loss, such as InfoNCE [48], learns global embeddings that associate corresponding video and text embeddings within a batch of samples $\\mathcal{B}$", + "bbox": [ + 500, + 425, + 890, + 468 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\frac {1}{| \\mathcal {B} |} \\sum_ {(x, y) \\in \\mathcal {B}} \\left(\\operatorname {I n f o N C E} (\\mathbf {v}, \\mathbf {u}) + \\operatorname {I n f o N C E} (\\mathbf {u}, \\mathbf {v})\\right). \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 544, + 477, + 890, + 511 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. LAVILA", + "text_level": 1, + "bbox": [ + 500, + 541, + 596, + 556 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In LAVILA, we leverage large language models (LLMs) as supervision to train the dual-encoder model, where the LLMs serve as vision-conditioned narrators and automatically generate textual descriptions from video clips. In particular, we exploit supervision from two LLMs: (1) NARRATOR ( $\\S 4.1$ ) is a visually-conditioned LLM that pseudolabels existing and new clips with narrations, generating new annotations $(\\mathcal{X}',\\mathcal{Y}')$ . (2) REPHRASER ( $\\S 4.2$ ) is a standard LLM that paraphrases narrations in existing clips, augmenting those annotations to $(\\mathcal{X},\\mathcal{Y}^{\\prime \\prime})$ . As illustrated in Figure 3, NARRATOR generates new descriptions of the action taking place, potentially focusing on other objects being interacted with. REPHRASER serves to augment the text input, e.g., changes word order of the human narration and additionally replaces common verbs or nouns, making annotations more diverse. Finally, we train the dual-encoders ( $\\S 4.3$ ) on all these annotations combined, i.e. $(\\mathcal{X},\\mathcal{Y}) \\cup (\\mathcal{X}',\\mathcal{Y}') \\cup (\\mathcal{X},\\mathcal{Y}^{\\prime \\prime})$ .", + "bbox": [ + 496, + 566, + 890, + 840 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.1. NARRATOR", + "text_level": 1, + "bbox": [ + 500, + 849, + 619, + 863 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Traditional LLMs, such as GPT-2 [50], are trained to generate a sequence of text tokens $(s_1 \\cdots s_L)$ from scratch", + "bbox": [ + 500, + 871, + 890, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "6588", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/9ed857c62160541be695a0c63fac579ca5e1ed59232035ce5c1f0959bc440440.jpg", + "image_caption": [ + "Figure 4. Language supervision from REPHRASER and NARRATOR. REPHRASER(left) takes the narration as input, passes it through a text encoder and uses a text decoder to autoregressively generate the rephrased output. NARRATOR(right) takes video frames as input and obtains the visual embeddings through a video encoder followed by attentional pooling. Equipped with a few additional cross-attention modules, the text decoder autoregressively generates new narrations for those new frames." + ], + "image_footnote": [], + "bbox": [ + 106, + 74, + 408, + 202 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/6774a18f9439c1444f2605f63f81402ad416a2063fcaaf587521748fd165560b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 413, + 74, + 870, + 202 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "by modeling the probability of the next token given all tokens seen so far: $p(s_{l}|s_{$ . We sample from the distribution recursively, i.e. $\\tilde{s}_{\\ell} \\sim p(s|[, \\dots, \\tilde{s}_{\\ell-1}], x)$ until an end-of-sentence token $$ is reached. Following [29], at each step we sample from a subset of tokens that contain the vast majority of the probability mass, which is known as nucleus sampling.", + "bbox": [ + 498, + 446, + 890, + 551 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The effect of nucleus sampling is two-fold. On the one hand, it generates more diverse, open-ended, and human-like text than maximum-likelihood-based methods such as beam search and its variants [67]. On the other hand, the generated text may contain irrelevant or noisy information due to sampling without post-processing based on sentence-level likelihood. To address this, we repeat the sampling process for $K$ times on the same visual input. We later demonstrate that the contrastive pre-training objective is robust to the noise caused by sampling, and the final performance benefits from a more diverse set of narrations.", + "bbox": [ + 496, + 551, + 890, + 717 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To sample video clips for captioning, we start by simply re-captioning the existing clips labeled in the dataset $\\mathcal{X}$ , resulting in expanded annotations. Furthermore, long-form videos are typically sparsely narrated, meaning that the temporal union of all labeled clips cannot cover the entire video. Hence, we use NARRATOR to annotate the remainder of the video to obtain additional annotations by pseudo-captioning. With a simple assumption that video is a stationary process, we uniformly sample clips from the unlabeled intervals. The clip duration is equal to the average of all ground-truth clips, i.e. $\\Delta = \\frac{1}{N} \\sum_{i=1}^{N} (e_i - t_i)$ while the sampling stride is computed likewise. Finally, by", + "bbox": [ + 496, + 719, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "6589", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "combining both re-captioned and pseudo-captioned narrations, we refer to the final set of annotations generated by NARRATOR as $(\\mathcal{X}',\\mathcal{Y}')$", + "bbox": [ + 75, + 90, + 468, + 136 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Post-processing. Exhaustive pseudo-captioning may contain some uninformative visual clips and generate text that is not useful. Thus, we add a filtering process to eliminate low-quality clips and their associated descriptions. We use the baseline dual-encoder model $\\mathcal{F}$ , which is trained on the ground-truth paired clips, to compute the visual and textual embedding of pseudo-labeled pairs and filter based on the similarity score, i.e. $\\mathrm{Filter}(f_{\\mathrm{v}}(x_j')^\\top \\cdot f_{\\mathrm{t}}(y_j'))$ , where $\\mathrm{Filter}(\\cdot)$ can be either top- $k$ of all generated text or a threshold filtering. In the experiments, we use a threshold of 0.5.", + "bbox": [ + 75, + 137, + 468, + 287 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. REPHRASER", + "text_level": 1, + "bbox": [ + 76, + 297, + 202, + 311 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The data generated by NARRATOR is several times larger than the ground-truth pairs. To ensure that we do not overfit the pseudo-labeled data, we increase the number of ground-truth narrations by paraphrasing. In particular, we use a text-to-text LLM which models conditional text likelihood:", + "bbox": [ + 75, + 319, + 468, + 390 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\text {R E P H R A S E R}} \\left(y ^ {\\prime \\prime} | y\\right) = \\prod_ {\\ell = 1} ^ {L} p \\left(s _ {\\ell} ^ {\\prime \\prime} \\mid s _ {< \\ell} ^ {\\prime \\prime}, y\\right).\n$$\n", + "text_format": "latex", + "bbox": [ + 156, + 411, + 387, + 449 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The text-to-text model is implemented by an encoder-decoder architecture, e.g. T5 [51], to auto-regressively generate a new sentence given the original one. We observe that REPHRASER is able to do basic manipulations such as replacing synonyms or changing word order, which serves as an efficient way of automatic data augmentation. The resulting annotations are referred to as $(\\mathcal{X},\\mathcal{Y}^{\\prime \\prime})$", + "bbox": [ + 75, + 457, + 468, + 564 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Training the Dual-Encoders", + "text_level": 1, + "bbox": [ + 76, + 573, + 328, + 589 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We train the dual-encoders as described in Algorithm 1 in Appendix E. In each iteration, we first sample a batch $\\mathcal{B}$ of video clips. It comprises a subset of clips $\\mathcal{B}_l$ with labeled timestamps as well as narrations, and a subset $\\mathcal{B}_u$ whose clips are randomly sampled from videos without narrations. For clip $x_i\\in \\mathcal{B}_u$ , we obtain the pseudo-caption $y_{i}^{\\prime}$ by querying the NARRATOR $y_{i}^{\\prime}\\sim p_{\\mathrm{NARRATOR}}(y^{\\prime}|x)$ , resulting in a set of clips with LLM-generated narrations $\\widetilde{B}_u$ . For clip $(x_{i},y_{i})\\in \\mathcal{B}_{l}$ , the text supervision is obtained from either the REPHRASER or the NARRATOR, with a probability of 0.5. Hence, the effective number of iterations per epoch for LAVILA is the same as that for the baseline Dual-Encoder. We denote the resulting set of pairs to be $\\widetilde{B}_l$ similarly. Following CLIP [49], we use the symmetric cross-entropy loss over the similarity scores of samples in the batch $\\widetilde{B}_l\\cup \\widetilde{B}_u$ .", + "bbox": [ + 75, + 595, + 468, + 824 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In practice, we run REPHRASER and NARRATOR in advance and cache the resulting video-narration pairs so that there is no computational overhead during pre-training. Therefore, training a dual-encoder in LAVILA is as fast as training a standard dual-encoder contrastive model.", + "bbox": [ + 75, + 825, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/327c408407f49155cf9502522662a33457cc6d621bd9e489c9f4852b6cbf5a94.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetsTaskEgo?MetricsEval. Prot.
EK-100 [14]MIR✓mAP, nDCGZS, FT
EK-100 [14]CLS✓top-1 action acc.FT
Ego4D [24]MCQ✓inter-/intra-video acc.ZS
Ego4D [24]NLQ✓Recall@NFT
EGTEA [37]CLS✓top-1, mean acc.ZS, FT
CharadesEgo [58]CLS✓video-level mAPZS, FT
UCF-101 [60]CLS✗mean acc.LP
HMDB-51 [35]CLS✗mean acc.LP
", + "bbox": [ + 504, + 88, + 893, + 209 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 1. Downstream datasets and metrics used for evaluation. We evaluate LAVILA on a wide range of tasks including Multi-Instance Retrieval (MIR), Multiple-Choice Question (MCQ), Natural Language Query (NLQ), and Action Recognition (CLS). The evaluation protocols include zero-shot (ZS), fine-tuning (FT), and linear-probing (LP). Please refer to Appendix C for more details.", + "bbox": [ + 496, + 219, + 893, + 303 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 500, + 329, + 632, + 345 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Dual-Encoder Architecture. The video-language model follows a dual-encoder architecture as CLIP [49]. The Visual encoder is a TimeSformer (TSF) [6], whose spatial attention modules are initialized from a ViT [18] which is contrastively pre-trained on large-scale paired image-text data as in CLIP [49]. We sample 4 frames per clip during pre-training and 16 when finetuning on downstream tasks. The text encoder is a 12-layer Transformer [50, 66]. We use BPE tokenizer [55] to pre-process the full sentence corresponding to the video clip and keep at most 77 tokens.", + "bbox": [ + 496, + 354, + 890, + 505 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "NARRATOR's architecture is a visually conditioned auto-regressive Language Model. The visual encoder is by default TimeSformer-L while the text decoder is a GPT-2 XL. During inference, we use nucleus sampling [29] with $p = 0.95$ and return $K = 10$ candidate outputs.", + "bbox": [ + 496, + 506, + 890, + 580 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "REPHRASER. We use an open-source paraphraseer [23] based on T5-large [51]. It is pre-trained on C4 [51] and then finetuned on a cleaned subset of ParaNMT [74]. During inference, we use Diverse Beam Search [67] with group number the same as beam number $(G = B = 20)$ and set the diversity penalty to be 0.7. We keep 3 candidates per sentence, remove punctuations, and do basic de-duplication.", + "bbox": [ + 496, + 582, + 890, + 686 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Pre-training dataset. We train on the video-narration pairs from Ego4D [13, 24], the largest egocentric video dataset to date. We exclude videos that appear in the validation and test sets of the Ego4D benchmark and determine each clip's interval using the same pairing strategy in [39]. This results in around 4M video-text pairs with an average clip length of 1 second. We also experiment with third-person videos by pre-training on HowTo100M [45] in § 5.2.", + "bbox": [ + 496, + 688, + 890, + 808 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation protocols. We evaluate the learned video-text encoders using three evaluation protocols. (1) Zero-Shot (ZS), meaning that we apply the pre-trained video-text encoders directly on the downstream validation dataset to perform video $\\leftrightarrow$ text retrieval tasks, without any tuning on the downstream dataset. Zero-shot classification is performed", + "bbox": [ + 496, + 810, + 890, + 898 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "6590", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/d42642a82ac8af6ee368307ba15142fc9c987659548a4e24e26c33d6fd01333e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodBackbonemAPnDCG
V→TT→VAvg.V→TT→VAvg.
(ZERO-SHOT)
EgoVLP [39]TSF-B19.413.916.624.122.023.1
EgoVLP* [39]TSF-B26.020.623.328.827.027.9
LAVILATSF-B35.126.630.933.730.432.0
LAVILATSF-L40.032.236.136.133.234.6
(FINETUNED)
MME [75]TBN43.034.038.550.146.948.5
JPoSE [75]TBN49.938.144.055.551.653.5
EgoVLP [39]TSF-B49.940.545.060.957.959.4
LAVILATSF-B55.245.750.566.563.465.0
LAVILATSF-L54.747.150.968.164.966.5
", + "bbox": [ + 78, + 88, + 470, + 258 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "similarly, where we compute the similarity score between the video clip and the textual description of all possible categories. (2) Finetuned (FT), where we take the pre-trained video-text model and perform end-to-end fine-tuning on the training split of the target downstream dataset. (3) LinearProbe (LP), where we compute the video features from a frozen encoder and train a linear SVM on top of the training split of the downstream dataset.", + "bbox": [ + 75, + 383, + 468, + 505 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Downstream benchmarks. We use multiple benchmarks across four first-person (egocentric) and two third-person datasets, as enumerated in Table 1. We summarize them here and refer the reader to Appendix C for details on datasets and metrics. (1) Two tasks on Epic-Kitchens-100: Multi-Instance Retrieval (EK-100 MIR) and Action Recognition (EK-100 CLS) [14]. EK-100 is a very popular and challenging egocentric video recognition benchmark. The MIR task requires retrieving the text given videos $(\\mathrm{V} \\rightarrow \\mathrm{T})$ and videos given text $(\\mathrm{T} \\rightarrow \\mathrm{V})$ . The CLS task requires classifying each video into one of 97 verbs and 300 nouns each, resulting in a combination of 3,806 action categories. (2) Two downstream tasks of Ego4D: Multiple-Choice Questions (EgoMCQ) and Natural Language Query (EgoNLQ). EgoMCQ requires selecting the correct textual description from five choices given a query video clip while EgoNLQ asks the model to output the relevant temporal intervals of video given a text query. We select these two benchmarks because they require reasoning about both visual and textual information. (3) Action Recognition on EGTEA [37]. It requires classifying into 106 classes of fine-grained cooking activities. (4) Action Recognition on CharadesEgo [58]. It requires classification into 157 classes of daily indoor activities. Note that CharadesEgo is very different from EK-100, Ego4D and EGTEA since its videos are captured by head-mounted phone cameras in a crowd-sourcing way.", + "bbox": [ + 75, + 508, + 468, + 901 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/2cacd19a6e1666422b3c3a28fd51a3f7a23a7a4c0c381b7457229d964834a329.jpg", + "table_caption": [ + "Table 2. EK-100 MIR. LAViLA outperforms prior work across all settings, metrics and directions of retrieval, with larger gains when switching to a larger model. Specifically, our best model achieves over $10\\%$ absolute gain in the zero-shot setting and $5.9 \\sim 7.1\\%$ gain in the finetuned setting. EgoVLP* refers to our improved version of [39], details of which are given in Appendix F." + ], + "table_footnote": [], + "table_body": "
MethodEgoMCQEgoNLQ
Accuracy (%)mIOU@0.3mIOU@0.5
Inter-videoIntra-videoR@1R@5R@1R@5
SlowFast [24]--5.4510.743.126.63
EgoVLP [39]90.657.210.8418.846.8113.45
LAVILA (B)93.859.910.5319.136.6913.68
LAVILA (L)94.563.112.0522.387.4315.44
", + "bbox": [ + 506, + 87, + 883, + 181 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. Ego4D EgoMCQ and EgoNLQ. LAVILA outperforms prior work on both Multiple-Choice Questions and Natural Language Questions on Ego4D, with nearly $6\\%$ absolute gain on the challenging intra-video MCQ task that requires reasoning over multiple clips from the same video to answer a question.", + "bbox": [ + 496, + 191, + 890, + 262 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In all tables, we bold and underline the best and second-best performing methods with comparable backbones architectures. We highlight the overall best performing method, which typically uses a larger backbone, if applicable.", + "bbox": [ + 496, + 290, + 890, + 349 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Main Results", + "text_level": 1, + "bbox": [ + 500, + 361, + 637, + 376 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "EK-100. We compare LAVILA with prior works on EK-100 MIR in Table 2. In the zero-shot setup, LAVILA remarkably surpasses an improved version of EgoVLP [39] under similar model complexity: we use TSF-Base+GPT-2 as the dual-encoder architecture while EgoVLP uses TSF-Base+Distil-BERT. With a stronger video encoder, i.e. TSF-Large, the performance improves further. In the fine-tuned setting, LAVILA significantly outperforms all previous supervised approaches including MME, JPOSE [75] and EgoVLP [39]. We also compare LAVILA on EK-100 CLS in Appendix E, and establish a new state-of-the-art.", + "bbox": [ + 496, + 385, + 890, + 551 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Ego4D. We evaluate the pre-trained LAViLA model on EgoMCQ and EgoNLQ tasks and compare the results in Table 3. On EgoMCQ, our method achieves $93.8\\%$ inter-video accuracy and $59.9\\%$ intra-video accuracy, outperforming EgoVLP by a noticeable margin. Note that EgoVLP's performance reported in Table 3 is obtained by using EgoNCE loss [39], a variant of InfoNCE specialized for Ego4D while ours uses a standard InfoNCE loss. EgoVLP with InfoNCE has lower performance (89.4% inter-video and $51.5\\%$ intravideo accuracy). On EgoNLQ, LAViLA achieves comparable results with EgoVLP with similar model complexity.", + "bbox": [ + 496, + 551, + 890, + 718 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "EGTEA. We evaluate the learned video representation by finetuning the video encoder for action classification in Table 4 on another popular egocentric dataset, EGTEA [37]. Our method surpasses the previous state-of-the-art which takes multiple modalities including visual, auditory and textual inputs [33] by a more than $10\\%$ absolute margin on the mean accuracy metric. Since previous methods are based on different backbones, we experiment with a TSF-Base (\"Visual only\") model pre-trained on Kinetics [9] as a fair baseline for LAVILA. We observe that its accuracy is comparable to previous methods but much lower than LAVILA, implying the effectiveness of learning visual representation", + "bbox": [ + 496, + 719, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "6591", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/3701a0799d8659f3af26cedfb1ea1b06ad9ebec884de433afa0ee4e77d915c38.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodBackbonePretrainTop-1 Acc.Mean Acc.
Li et al. [37]I3DK400-53.30
LSTA [63]ConvLSTMIN-1k61.8653.00
IPL [71]I3DK400-60.15
MTCN [33]SlowFast (V+A+T)K400+VGG-Sound73.5965.87
Visual onlyTSF-BIN-21k+K40065.5859.32
LAVILATSF-BWIT+Ego4D77.4570.12
LAVILATSF-LWIT+Ego4D81.7576.00
", + "bbox": [ + 80, + 88, + 467, + 193 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/4d46cf429d0631d247726ec16223c677458f4402a9f885a01d2049312f8e13ca.jpg", + "table_caption": [ + "Table 4. EGTEA Classification. LAVILA obtains significant gains on this task, outperforming prior work with over $10\\%$ mean accuracy. Since the backbones used are not all comparable, we also report a comparable baseline with TSF-B (\"Visual only\")." + ], + "table_footnote": [], + "table_body": "
MethodBackbonemAP (ZS)mAP (FT)
ActorObserverNet [57]ResNet-152-20.0
SSDA [12]I3D-25.8
Ego-Exo [38]SlowFast-R101-30.1
EgoVLP [39]TSF-B25.032.1
LAVILATSF-B26.833.7
LAVILATSF-L28.936.1
", + "bbox": [ + 83, + 275, + 464, + 368 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "on large-scale egocentric videos and using LLM as textual supervision during pre-training.", + "bbox": [ + 75, + 450, + 468, + 479 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "CharadesEgo. Next, we compare LAVILA's representation on the CharadesEgo action classification task. As shown in Table 5, LAVILA's representation excels on this task as well, which is notable as CharadesEgo videos are significantly different compared to Ego4D, being captured by crowdsourced workers using mobile cameras.", + "bbox": [ + 75, + 481, + 468, + 571 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.2. Application to Third-Person Video Pre-training", + "text_level": 1, + "bbox": [ + 76, + 584, + 468, + 599 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We apply LAVILA to third-person videos by experimenting with the HowTo100M [45] dataset. Specifically, we use the temporally aligned subset provided by [25], which contains 3.3M sentences from 247k videos. We evaluate the video representation on two third-person video datasets, i.e.", + "bbox": [ + 75, + 607, + 468, + 684 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/d7c6083fdcfa7dc4337c0b6262e466bc06310a8be8fee9294bd4a116f8663174.jpg", + "table_caption": [ + "Table 5. CharadesEgo Action Recognition. LAVILA sets new state-of-the-art in both zero-shot (ZS) and finetuned (FT) settings. Note that CharadesEgo videos are visually different compared to Ego4D videos, on which LAVILA is pretrained." + ], + "table_footnote": [], + "table_body": "
MethodVis. Enc.UCF-101HMDB-51
MIL-NCE [44]S3D82.754.3
TAN [25]S3D83.256.7
Baseline (w/o LLM)TSF-B86.559.4
LAViLATSF-B87.457.2
LAViLATSF-L88.161.5
", + "bbox": [ + 531, + 88, + 862, + 167 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 6. LAVILA on third-person videos. We measure the linear-probing action classification performance of the video model after pre-training on HowTo100M [45].", + "bbox": [ + 498, + 179, + 890, + 220 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "UCF-101 [60] and HMDB-51 [35] for action classification using the linear probing protocol. For more details, please refer to Appendix D. From Table 6, we see that LAVILA outperforms previous methods such as MIL-NCE [44] and TAN [25] by a large margin. Since we use a different backbone, we report a baseline without LLM and show that LAVILA indeed benefits from the language supervision.", + "bbox": [ + 498, + 250, + 890, + 357 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Application to Semi-supervised Learning", + "text_level": 1, + "bbox": [ + 500, + 369, + 849, + 386 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "While LAVILA is very effective at leveraging existing narrations to augment them, we now show that it is also applicable when only a limited number of narrations are available to begin with. We first divide each long video from Ego4D into 15-second chunks and assume only the annotated clips within every $N$ chunks is available during pre-training, leading to approximately $\\frac{100}{N}\\%$ of the full set, where $N \\in \\{2,5,10\\}$ . This can be considered a practical scenario when we want to annotate as many videos as possible for diversity when the annotation budget is limited. In the remainder $(1 - \\frac{100}{N})\\%$ part that is skipped, we uniformly sample the same number of the clips per chunk with the same clip length as that in the seen chunks. Both the dual-encoder model and NARRATOR are trained on the $100\\%$ available annotations.", + "bbox": [ + 496, + 395, + 890, + 619 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We plot the zero-shot performance curve of pre-training with different proportions in Figure 5. We can see that LAV-ILA consistently outperforms the ground-truth-only baseline at all points (10, 20, 50, and $100\\%$ ). The improvement", + "bbox": [ + 498, + 622, + 890, + 684 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c3f8d35a7515c1bb20c02918ea05d070808b05c9607c308e94319aeb773085c5.jpg", + "image_caption": [ + "(a) EK-100 MIR mAP." + ], + "image_footnote": [], + "bbox": [ + 86, + 724, + 272, + 825 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a99e94a0f49e338898d7ee6d5d41cba9cc2953097107e3b0169b3bca70229447.jpg", + "image_caption": [ + "(b) EK-100 MIR nDCG.", + "Figure 5. LAVILA is effective in a semi-supervised setting where only a limited amount of narrations are given. Comparing zero-shot performance of pre-training, LAVILA consistently outperforms the groundtruth-only baseline when 10, 20, 50, $100\\%$ data is used. We also achieve comparable result with state-of-the-art with only $50\\%$ of the annotated data." + ], + "image_footnote": [], + "bbox": [ + 289, + 703, + 676, + 825 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c6403c1e1dafe166ed69cd617f015dd0bed163ff9c13fc7ba28f23d84b3eba57.jpg", + "image_caption": [ + "(d) EgoMCQ Intra-video accuracy." + ], + "image_footnote": [], + "bbox": [ + 696, + 724, + 880, + 825 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "6592", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/ab53022c098b057542b415759a6667ec4871f1eb7dcd5ae0618e14357cd8136c.jpg", + "table_caption": [], + "table_footnote": [ + "(a) Generation Quality. Using a sufficiently large language model as the text decoder is crucial for good text generation quality and downstream performance." + ], + "table_body": "
Text Dec. arch.Text Dec. init.Freeze LMMETEOR ROUGE-L CIDESAvg. mAP
(baseline)---26.0
GPT-2random×0.2840.5240.88224.3
GPT-2WebText✓0.2700.5050.80424.0
GPT-2 XLWebText✓0.2890.5300.94026.2
", + "bbox": [ + 96, + 88, + 405, + 179 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/8f7aaec3da0d2842abaaa44c377be56746fe80f9264ffa492db9d9e4c1f8be17.jpg", + "table_caption": [], + "table_footnote": [ + "(c) Scaling effect of LAVILA. Gains increase on scaling the video encoder in NARRATOR. Default refers to only using the original narrations.", + "Table 7. Ablations of NARRATOR. We report zero-shot average mAP on EK-100 MIR for comparing downstream performance. We study NARRATOR from the perspective of generation quality (left), sampling techniques (middle), and scaling effect (right)." + ], + "table_body": "
Sampling method# of sentencesAvg. mAP
N/A (baseline)-26.0
Beam search127.9
Nucleus129.6
Nucleus1029.7
", + "bbox": [ + 416, + 98, + 607, + 178 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "(b) Sampling. LAV1LA benefits more from narrations produced by nucleus sampling than beam search.", + "bbox": [ + 413, + 191, + 602, + 223 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/7d8fa188da18a0da78a3340813a546ebcb084ea39047ab291fb0b0ed6a231dbb.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 617, + 97, + 879, + 190 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/d54eaa91999a5dc972dd78f3cb4e3c6bebc9a2bc822829de954854fdb1075516.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Text Dec. arch.Text Dec. init.Freeze LMMETEOR ROUGE1 CIDEsAvg. mAP
(baseline)---26.0
GPT-2randomX0.2840.5240.88224.3
GPT-2WebText✓0.2700.5050.80424.0
GPT-2 XLWebText✓0.2890.5300.94026.2
", + "bbox": [ + 76, + 89, + 405, + 179 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 8. Contributions of different Language Supervision. We can see that (1) using REPHRASER (\"Rephr.\") and NARRATOR (\"Recap.\") improve downstream zero-shot performance complementarily, (2) dense pseudo-captioning further improves performance on 3 out of 6 metrics.", + "bbox": [ + 76, + 378, + 468, + 446 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "tends to be larger when more data is available, indicating the method's scalability as more videos are narrated in the future. Furthermore, we observe our method can achieve a similar level of performance with the baseline often using less than $50\\%$ data. We also achieve a comparable result with the state-of-the-art using much fewer data.", + "bbox": [ + 75, + 474, + 468, + 566 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.4. Ablation Studies", + "text_level": 1, + "bbox": [ + 76, + 574, + 240, + 589 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Contributions of Different Language Supervisions. We ablate different language supervisions in Table 8 on EK-100 MIR (zero-shot), EgoMCQ and EGTEA. Using the text-only REPHRASER (\"rephr.\") or visually conditioned NARRATOR (\"recap.\") separately improves the ground-truth baseline noticeably. Combining both REPHRASER and NARRATOR gives an improvement of $3.5\\%$ average mAP on EK-100 MIR. We see that dense captioning on the entire video (\"pseudo-cap.\") is also helpful. Though the gain on EK-100 MIR is not as significant, it shows nontrivial improvements on EgoMCQ intra-video accuracy and EGTEA mean accuracy. Our conjecture for this marginal gain is that informative clips are mostly covered in Ego4D because all videos are inspected by two annotators.", + "bbox": [ + 75, + 598, + 468, + 809 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Generation Quality of NARRATOR. We study how the NARRATOR's configurations affect the quality of generated text and the downstream performance. The generation quality is measured by standard unsupervised automatic metrics including METEOR, ROUGE, and CIDEr [43]. We use a NARRATOR with a smaller GPT-2 as the text decoder and", + "bbox": [ + 75, + 810, + 470, + 898 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "consider two scenarios in Table 7a: (1) LM is randomly initialized but jointly trained with the gated cross-attention modules, and (2) LM is initialized from the original GPT-2. The generation quality decreases compared to GPT-2 XL in both cases and the zero-shot retrieval result on EK-100 MIR is worse. This indicates that the language model should be sufficiently large and pre-trained on web text data.", + "bbox": [ + 496, + 277, + 890, + 383 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Sampling. In Table 7b, we investigate different sampling methods for text generation from NARRATOR. We see that nucleus sampling works much better than beam search while repetitive sampling shows marginal improvement.", + "bbox": [ + 496, + 387, + 890, + 448 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Scaling effect. In Table 7c, we compare the zero-shot retrieval result by progressively increasing the size of NARRATOR's video encoder from TSF-B to TSF-L and TSFL@HR, which increases the input resolution to be narrated from 224 to 336 while fixing the dual-encoder architecture. The retrieval performance steadily increases while NARRATOR becomes stronger. We conduct this experiment by varying the dual-encoder architecture, namely TSF-Base and TSF-Large, and show similar trends. Both phenomena suggest that LAVILA can scale to larger models.", + "bbox": [ + 496, + 450, + 892, + 602 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion and Future Work", + "text_level": 1, + "bbox": [ + 498, + 626, + 769, + 642 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we proposed LAVILA, a new approach to video-language representation learning by automatically narrating long videos with LLMs. We achieve strong improvements over baselines trained with the same amount of human-narrated videos and set new state-of-the-art on six popular benchmark tasks across first- and third-person video understanding benchmarks. LAVILA also shows positive scaling behavior when adding more training narrations, using larger visual backbones, and using stronger LLMs, all of which are promising areas for future work.", + "bbox": [ + 496, + 655, + 890, + 806 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements: We thank Naman Goyal, Stephen Roller and Susan Zhang for help with language models, Kevin Qinghong Lin for help with EgoVLP, and the Meta AI team for helpful discussions and feedback. This material is based upon work in-part supported by the National Science Foundation under Grant No. IIS-1845485.", + "bbox": [ + 496, + 809, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "6593", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 90, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan. Flamingo: a visual language model for few-shot learning. In NeurIPS, 2022.", + "[2] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid. Vivit: A video vision transformer. In ICCV, 2021.", + "[3] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016.", + "[4] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, 2021.", + "[5] Yoshua Bengio, Réjean Ducharme, and Pascal Vincent. A neural probabilistic language model. In NIPS, 2000.", + "[6] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, 2021.", + "[7] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. NeurIPS, 2020.", + "[8] Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles. Activitynet: A large-scale video benchmark for human activity understanding. In CVPR, 2015.", + "[9] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In CVPR, 2017.", + "[10] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, 2020.", + "[11] Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal. Unifying vision-and-language tasks via text generation. In ICML, 2021.", + "[12] Jinwoo Choi, Gaurav Sharma, Manmohan Chandraker, and Jia-Bin Huang. Unsupervised and semi-supervised domain adaptation for action recognition from drones. In WACV, 2020.", + "[13] Ego4D Consortium. Egocentric live 4d perception (Ego4D) database: A large-scale first-person video database, supporting research in multi-modal machine perception for daily life activity. https://sites.google.com/view/ego4d/home. Accessed: 2022-11-22." + ], + "bbox": [ + 78, + 114, + 468, + 888 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Evangelos Kazakos, Jian Ma, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Rescaling egocentric vision: collection, pipeline and challenges for Epic-Kitchens-100. IJCV, 2022.", + "[15] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In CVPR, 2009.", + "[16] Karan Desai and Justin Johnson. Virtex: Learning visual representations from textual annotations. In CVPR, 2021.", + "[17] Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell. Long-term recurrent convolutional networks for visual recognition and description. In CVPR, 2015.", + "[18] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2020.", + "[19] Marzieh Fadaee, Arianna Bisazza, and Christof Monz. Data augmentation for low-resource neural machine translation. In ACL, 2017.", + "[20] Steven Y Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. A survey of data augmentation approaches for nlp. In ACL Findings, 2021.", + "[21] Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, and Tomas Mikolov. Devise: A deep visual-semantic embedding model. NeurIPS, 2013.", + "[22] Rohit Girdhar, Mannat Singh, Nikhila Ravi, Laurens van der Maaten, Armand Joulin, and Ishan Misra. Omnivore: A single model for many visual modalities. In CVPR, 2022.", + "[23] Ramsri Goutham Golla. High-quality sentence paraphrase using transformers in nlp. https://huggingface.co/ramsrigouthamg/t5-large-paraphraser-diverse-high-quality. Accessed: 2022-06-01.", + "[24] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe" + ], + "bbox": [ + 501, + 92, + 890, + 896 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "6594", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4d: Around the world in 3,000 hours of egocentric video. In CVPR, 2022.", + "[25] Tengda Han, Weidi Xie, and Andrew Zisserman. Temporal alignment networks for long-term video. In CVPR, 2022.", + "[26] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016.", + "[27] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 1997.", + "[28] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre. Training compute-optimal large language models. arXiv preprint arXiv:2203.15556, 2022.", + "[29] Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. The curious case of neural text degeneration. In ICLR, 2020.", + "[30] Xiaowei Hu, Zhe Gan, Jianfeng Wang, Zhengyuan Yang, Zicheng Liu, Yumao Lu, and Lijuan Wang. Scaling up vision-language pre-training for image captioning. In CVPR, 2022.", + "[31] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICML, 2021.", + "[32] Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, and Weidi Xie. Prompting visual-language models for efficient video understanding. In ECCV, 2022.", + "[33] Evangelos Kazakos, Jaesung Huh, Arsha Nagrani, Andrew Zisserman, and Dima Damen. With a little help from my temporal context: Multimodal egocentric action recognition. In BMVC, 2021.", + "[34] Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, and Boqing Gong. Movinets: Mobile video networks for efficient video recognition. In CVPR, 2021.", + "[35] Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In ICCV, 2011.", + "[36] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In CVPR, 2021.", + "[37] Yin Li, Miao Liu, and James M Rehg. In the eye of beholder: Joint learning of gaze and actions in first person video. In ECCV, 2018.", + "[38] Yanghao Li, Tushar Nagarajan, Bo Xiong, and Kristen Grauman. Ego-exo: Transferring visual representations from third-person to first-person videos. In CVPR, 2021.", + "[39] Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan, Michael Wray, Rui Yan, Eric Zhongcong Xu, Difei Gao, Rongcheng Tu, Wenzhe Zhao, Weijie Kong, Hongfa Cai Chengfei, Wang, Dima Damen, Bernard Ghanem, Wei Liu, and Mike Zheng Shou. Egocentric video-language pre" + ], + "bbox": [ + 78, + 90, + 468, + 890 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "training. In NeurIPS, 2022.", + "[40] Ziyi Lin, Shijie Geng, Renrui Zhang, Peng Gao, Gerard de Melo, Xiaogang Wang, Jifeng Dai, Yu Qiao, and Hongsheng Li. Frozen clip models are efficient video learners. In ECCV, 2022.", + "[41] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS, 2019.", + "[42] Huaishao Luo, Lei Ji, Botian Shi, Haoyang Huang, Nan Duan, Tianrui Li, Jason Li, Taroon Bharti, and Ming Zhou. Univl: A unified video and language pre-training model for multimodal understanding and generation. arXiv preprint arXiv:2002.06353, 2020.", + "[43] Maluuba. nlg-eval. https://github.com/Maluuba/nlg-eval. Accessed: 2022-06-01.", + "[44] Antoine Miech, Jean-Baptiste Alayrac, Lucas Smaira, Ivan Laptev, Josef Sivic, and Andrew Zisserman. End-to-end learning of visual representations from uncurated instructional videos. In CVPR, 2020.", + "[45] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic. Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In ICCV, 2019.", + "[46] Arsha Nagrani, Paul Hongsuck Seo, Bryan Seybold, Anja Hauth, Santiago Manen, Chen Sun, and Cordelia Schmid. Learning audio-video modalities from image captions. In ECCV, 2022.", + "[47] Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, and Haibin Ling. Expanding language-image pretrained models for general video recognition. In ECCV, 2022.", + "[48] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018.", + "[49] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In ICML, 2021.", + "[50] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. OpenAI blog, 2019.", + "[51] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR, 2020.", + "[52] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022.", + "[53] Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, and Bernt Schiele. Movie description. *IJCV*, 2017.", + "[54] Christoph Schuhmann, Romain Beaumont, Cade W Gordon, Ross Wightman, Theo Coombes, Aarush Katta, Clayton Mullis, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev. Laion-5b: An open large-scale dataset for training next generation image-text models. In NeurIPS D&B," + ], + "bbox": [ + 503, + 92, + 890, + 890 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "6595", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "2022.", + "[55] Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words with subword units. In ACL, 2016.", + "[56] Paul Hongsuck Seo, Arsha Nagrani, Anurag Arnab, and Cordelia Schmid. End-to-end generative pretraining for multimodal video captioning. In CVPR, 2022.", + "[57] Gunnar A Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, and Karteek Alahari. Actor and observer: Joint modeling of first and third-person videos. In CVPR, 2018.", + "[58] Gunnar A Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, and Karteek Alahari. Charades-ego: A large-scale dataset of paired third and first person videos. arXiv preprint arXiv:1804.09626, 2018.", + "[59] Kihyuk Sohn. Improved deep metric learning with multiclass n-pair loss objective. NeurIPS, 2016.", + "[60] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012.", + "[61] Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer. How to train your vit? data, augmentation, and regularization in vision transformers. TMLR, 2022.", + "[62] Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Vl-bert: Pre-training of generic visual-linguistic representations. In ICLR, 2020.", + "[63] Swathikiran Sudhakaran, Sergio Escalera, and Oswald Lanz. Lsta: Long short-term attention for egocentric action recognition. In CVPR, 2019.", + "[64] Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid. Videobert: A joint model for video and language representation learning. In ICCV, 2019.", + "[65] Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill. Multimodal few-shot learning with frozen language models. In NeurIPS, 2021.", + "[66] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, 2017.", + "[67] Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. Diverse beam search: Decoding diverse solutions from neural sequence models. arXiv preprint arXiv:1610.02424, 2016.", + "[68] Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. Show and tell: A neural image caption generator. In CVPR, 2015.", + "[69] Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, et al. Image as a foreign language: Beit pretraining for all vision and vision-language tasks. arXiv preprint arXiv:2208.10442, 2022.", + "[70] William Yang Wang and Diyi Yang. That's so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets. In EMNLP, 2015.", + "[71] Xiaohan Wang, Linchao Zhu, Heng Wang, and Yi Yang. Interactive prototype learning for egocentric action recognition. In ICCV, 2021.", + "[72] Jason Wei and Kai Zou. Eda: Easy data augmentation techniques for boosting performance on text classification tasks." + ], + "bbox": [ + 78, + 92, + 468, + 898 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "In EMNLP, 2019.", + "[73] Jason Weston, Samy Bengio, and Nicolas Usunier. Large scale image annotation: learning to rank with joint word-image embeddings. Machine learning, 2010.", + "[74] John Wieting and Kevin Gimpel. *Paranmt-50m: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations*. In ACL, 2018.", + "[75] Michael Wray, Diane Larlus, Gabriela Csurka, and Dima Damen. Fine-grained action retrieval through multiple parts-of-speech embeddings. In ICCV, 2019.", + "[76] Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam, Haoqi Fan, Bo Xiong, Jitendra Malik, and Christoph Feichtenhofer. Memvit: Memory-augmented multiscale vision transformer for efficient long-term video recognition. In CVPR, 2022.", + "[77] Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. Videoclip: Contrastive pre-training for zero-shot video-text understanding. In EMNLP, 2021.", + "[78] Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, and Jiebo Luo. Clip-vip: Adapting pretrained image-text model to video-language representation alignment. In ICLR, 2023.", + "[79] Shen Yan, Xuehan Xiong, Anurag Arnab, Zhichao Lu, Mi Zhang, Chen Sun, and Cordelia Schmid. Multiview transformers for video recognition. In CVPR, 2022.", + "[80] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. CoCa: Contrastive captioners are image-text foundation models. TMLR, 2022.", + "[81] Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, Ce Liu, Mengchen Liu, Zicheng Liu, Yumao Lu, Yu Shi, Lijuan Wang, Jianfeng Wang, Bin Xiao, Zhen Xiao, Jianwei Yang, Michael Zeng, Luowei Zhou, and Pengchuan Zhang. Florence: A new foundation model for computer vision. arXiv preprint arXiv:2111.11432, 2021.", + "[82] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. NeurIPS, 2021.", + "[83] Andy Zeng, Adrian Wong, Stefan Welker, Krzysztof Choromanski, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, and Pete Florence. Socratic models: Composing zero-shot multimodal reasoning with language. In ICLR, 2023.", + "[84] Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer. Scaling vision transformers. In CVPR, 2022.", + "[85] Xiang Zhang, Junbo Zhao, and Yann LeCun. Character-level convolutional networks for text classification. In NeurIPS, 2015.", + "[86] Luowei Zhou, Chenliang Xu, and Jason J Corso. Towards automatic learning of procedures from web instructional videos. In AAAI, 2018.", + "[87] Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbihl, and Ishan Misra. Detecting twenty-thousand classes using image-level supervision. In ECCV, 2022.", + "[88] Linchao Zhu and Yi Yang. Actbert: Learning global-local video-text representations. In CVPR, 2020.", + "[89] Xizhou Zhu, Jinguo Zhu, Hao Li, Xiaoshi Wu, Hongsheng Li, Xiaohua Wang, and Jifeng Dai. Uni-perceiver: Pre" + ], + "bbox": [ + 501, + 93, + 890, + 887 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "6596", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 10 + }, + { + "type": "text", + "text": "training unified architecture for generic perception for zero-shot and few-shot tasks. In CVPR, 2022.", + "bbox": [ + 109, + 92, + 467, + 119 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "6597", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/2023/Learning Video Representations From Large Language Models/c00bc946-1a6e-40fd-9b0d-ec2826e3ea29_model.json b/2023/Learning Video Representations From Large Language Models/c00bc946-1a6e-40fd-9b0d-ec2826e3ea29_model.json new file mode 100644 index 0000000000000000000000000000000000000000..9774eb06817b0b05ca7a213f1ac2a7c3130c311f --- /dev/null +++ b/2023/Learning Video Representations From Large Language Models/c00bc946-1a6e-40fd-9b0d-ec2826e3ea29_model.json @@ -0,0 +1,2732 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.0, + 0.812, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.169, + 0.131, + 0.803, + 0.154 + ], + "angle": 0, + "content": "Learning Video Representations from Large Language Models" + }, + { + "type": "text", + "bbox": [ + 0.209, + 0.179, + 0.758, + 0.217 + ], + "angle": 0, + "content": "Yue Zhao\\(^{1,2*}\\) Ishan Misra\\(^{1}\\) Philipp Krahenbuhl\\(^{2}\\) Rohit Girdhar\\(^{1}\\) \n\\(^{1}\\)FAIR, Meta AI \\(^{2}\\)University of Texas, Austin" + }, + { + "type": "text", + "bbox": [ + 0.336, + 0.22, + 0.631, + 0.233 + ], + "angle": 0, + "content": "facebookresearch.github.io/LaViLa" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.269, + 0.314, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.302, + 0.474, + 0.575 + ], + "angle": 0, + "content": "We introduce LAVILA, a new approach to learning video-language representations by leveraging Large Language Models (LLMs). We repurpose pre-trained LLMs to be conditioned on visual input, and finetune them to create automatic video narrators. Our auto-generated narrations offer a number of advantages, including dense coverage of long videos, better temporal synchronization of the visual information and text, and much higher diversity of text. The video-language embedding learned contrastively with these narrations outperforms the previous state-of-the-art on multiple first-person and third-person video tasks, both in zero-shot and finetuned setups. Most notably, LAVILA obtains an absolute gain of \\(10.1\\%\\) on EGTEA classification and \\(5.9\\%\\) Epic-Kitchens-100 multi-instance retrieval benchmarks. Furthermore, LAVILA trained with only half the narrations from the Ego4D dataset outperforms models trained on the full set, and shows positive scaling behavior on increasing pre-training data and model size." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.606, + 0.21, + 0.621 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.632, + 0.471, + 0.875 + ], + "angle": 0, + "content": "Learning visual representation using web-scale imag-text data is a powerful tool for computer vision. Vision-language approaches [31, 49, 80] have pushed the state-of-the-art across a variety of tasks, including zero-shot classification [49], novel object detection [87], and even image generation [52]. Similar approaches for videos [4, 39, 46], however, have been limited by the small size of paired video-text corpora compared to the billion-scale image-text datasets [31, 49, 84]—even though access to raw video data has exploded in the past decade. In this work, we show it is possible to automatically generate text pairing for such videos by leveraging Large Language Models (LLMs), thus taking full advantage of the massive video data. Learning video-language models with these automatically generated annotations leads to stronger representations, and as Figure 1 shows, sets a new state-of-the-art on six popular first" + }, + { + "type": "image", + "bbox": [ + 0.541, + 0.25, + 0.858, + 0.481 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.482, + 0.892, + 0.538 + ], + "angle": 0, + "content": "Figure 1. LAVILA sets a new state-of-the-art across a number of first and third-person video understanding tasks (cf. Table 1 for details), by learning a video-language representation using supervision from large language models as narrators." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.567, + 0.741, + 0.581 + ], + "angle": 0, + "content": "and third-person video benchmarks." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.582, + 0.893, + 0.853 + ], + "angle": 0, + "content": "Our method, called LAVILA: Language-model augmented Video-Language pre-training, leverages pretrained LLMs, e.g. GPT-2 [50], which encode within their weights a treasure trove of factual knowledge and conversational ability. As shown in Figure 2, we repurpose these LLMs to be \"visually-conditioned narrators\", and finetune on all accessible paired video-text clips. Once trained, we use the model to densely annotate thousands of hours of videos by generating rich textual descriptions. This pseudo-supervision can thus pervade the entire video, in between and beyond the annotated snippets. Paired with another LLM trained to rephrase existing narrations, LAVILA is able to create a much larger and more diverse set of text targets for video-text contrastive learning. In addition to setting a new state-of-the-art as noted earlier, the stronger representation learned by LAVILA even outperforms prior work using only half the groundtruth annotations (Figure 5)." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.894, + 0.901 + ], + "angle": 0, + "content": "LAVILA's strong performance can be attributed to a number of factors. First, LAVILA can provide temporally dense supervision for long-form videos, where the associ" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.318, + 0.9 + ], + "angle": 0, + "content": "*Work done during an internship at Meta." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6586" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.089, + 0.473, + 0.314 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.322, + 0.471, + 0.406 + ], + "angle": 0, + "content": "Figure 2. LAVILA leverages Large Language Models (LLMs) to densely narrate long videos, and uses those narrations to train strong dual-encoder models. While prior work uses sparsely labeled text by humans, or weakly aligned text transcribed from speech, LAVILA is able to leverage dense, diverse, and well-aligned text generated by a LLM." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.447, + 0.47, + 0.779 + ], + "angle": 0, + "content": "ated captions are either too sparse, or the video-level \"Alt-Text\" (in the case of web videos) does not describe all the nuanced activities happening in it. Second, the generated text is well-aligned with the visual input. Although prior work has leveraged automatic speech transcription on How-To videos [45] to automatically extract clips paired with text from the speech, such datasets have relatively poor alignment between the visual and textual content (\\(\\leq 50\\%\\), cf. [25, 45]), limiting the quality of the learned representations. Third, LAVILA can significantly expand annotations when only a little is available. For instance, videos of mundane day-to-day activities, especially from an egocentric viewpoint, could be very useful for assistive and augmented reality applications. Such videos, however, are rare on the internet, and hence do not readily exist with associated web text. Recent work [24] instead opted to manually capture and narrate such video data. These narrations however required significant manual effort: 250K hours of annotator time spent in narrating 3.6K hours of video. In contrast, LAVILA is able to automatically narrate each video multiple times and far more densely, and hence learns much stronger representations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.471, + 0.902 + ], + "angle": 0, + "content": "We extensively evaluate LAVILA across multiple videotext pre-training datasets and downstream tasks to validate its effectiveness. Specifically, after being pre-trained on Ego4D, the largest egocentric video datasets with narrations, LAVILA can re-narrate the whole dataset \\(10 \\times\\) over. The resulting model learned on these expanded narrations sets a new state-of-the-art on a wide range of downstream tasks across challenging datasets, including multi-instance" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.334 + ], + "angle": 0, + "content": "video retrieval on Epic-Kitchens-100 (5.9% absolute gain on mAP), multiple-choice question answering on Ego4D (5.9% absolute gain on intra-video accuracy), and action recognition on EGTEA (10.1% absolute gain on mean accuracy). It obtains gains both when evaluated for zero-shot transfer to the new dataset, as well as after fine-tuning on that dataset. Similar gains are shown in third-person video data. When training LAVILA after densely re-narrating HowTo100M, we outperform prior work on downstream action classification on UCF-101 and HMDB-51. In a case study of semi-supervised learning, we show that our model, which only ever sees 50% of the human-labeled data, is capable of outperforming the baseline model trained with all the narrations. Moreover, the gains progressively increase as we go to larger data regimes and larger backbones, suggesting the scalability of our method." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.347, + 0.642, + 0.364 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.372, + 0.895, + 0.705 + ], + "angle": 0, + "content": "Vision-language representation learning maps visual and textual embeddings into a common space using metric-learning techniques [21, 73]. Recently, different pretext tasks are proposed to learn a finer-grained association between visual and textual modality, e.g. masked language modeling (MLM) [10, 41, 62] and captioning [16, 80]. Another line of research focuses on scaling up both models and pre-training data. For instance, CLIP [49] is pre-trained on 400M image-text pairs with a contrastive loss (InfoNCE [48, 59]) while CoCa [80] unifies contrastive and generative approaches with a single foundation model. Similar trends are also witnessed in the videotext domain [36, 64, 88]. However, collecting high-quality video-text data is more difficult than image-text. Therefore, efforts are made to learn from uncurated videos with machine-generated audio transcripts via contrastive learning [44, 77, 82] or unsupervised alignment [25] while other works focus on either adapting well-performing image-text models to videos [32, 40, 47, 78], or curriculum learning from a single frame to multiple frames [4]. In contrast, our approach leverages language models to generate temporally dense textual supervision on long-form videos." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.895, + 0.886 + ], + "angle": 0, + "content": "Generative Visual Language Models (VLM) were first used for image/video captioning using recurrent networks [17, 68] and Transformer-based architectures [42, 56]. More recently, generative VLMs have unified multiple vision tasks [11, 89] by training multi-modal Transformers on visual-text pairs [30, 81]. Meanwhile, generative VLMs also excel at multimodal tasks via zero-shot or few-shot prompting [1, 65, 83] by leveraging multi-billion-parameter LLMs pre-trained on massive text corpus [7, 28, 50]. In our work, we demonstrate that generative VLMs can narrate long videos and the resulting video-text data benefits video-language representation learning." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.886, + 0.894, + 0.901 + ], + "angle": 0, + "content": "Large-scale multimodal video datasets are crucial for" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.958 + ], + "angle": 0, + "content": "6587" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.473, + 0.333 + ], + "angle": 0, + "content": "video understanding tasks but are hard to collect. Conventional video-text datasets [8, 53, 86] either have limited scenarios, e.g. cooking, or are not large enough to learn generic video representation. Miech et al. [45] scrape over 100 million video-text pairs via automatic audio transcription from long-form How-To videos. However, ASR introduces textual noise and visual-text unalignment [25]. WebVid [4] contains 10 million short videos with textual descriptions. But it is still several orders of magnitude smaller than the image counterparts [49, 54] and is harder to scale up since it is sourced from stock footage sites. The recently released Ego4D [24] dataset offers 3,600 hours of egocentric videos in which written sentence narrations are manually annotated every few seconds but requires significant manual effort. In contrast, our method shows a promising alternative by automatically narrating videos using supervision from LLM." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.334, + 0.47, + 0.44 + ], + "angle": 0, + "content": "Data augmentation techniques in NLP, including word-level replacement based on synonyms [72, 85] or nearest-neighbor retrieval [19, 70], improve text classification accuracy. We refer readers to [20] for a comprehensive survey. In this paper, we show that sentence-level paraphrasing based on text-to-text models [51] is helpful for video-language pre-training." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.453, + 0.216, + 0.468 + ], + "angle": 0, + "content": "3. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.478, + 0.47, + 0.689 + ], + "angle": 0, + "content": "A video \\(V\\) is a stream of moving images \\(I\\). The number of frames \\(|V|\\) can be arbitrarily long while video models typically operate on shorter clips, which are often in the range of a few seconds. Therefore, we skim through a long-form video and represent it by a set of \\(N\\) short clips, i.e. \\(\\mathcal{X}\\). Each clip \\(x_{i}\\) is defined by a specific start and end frame \\(x_{i} = \\{I_{t_{i}},\\dots ,I_{e_{i}}\\}\\), where \\(0 < t_i < e_i\\leq |V|\\), and is typically associated with some annotation \\(y_{i}\\). This annotation could be a class label or free-form textual description of the clip. We denote a video by the set of annotated clips with their corresponding annotations, i.e. \\((\\mathcal{X},\\mathcal{Y}) = \\{(x_1,y_1),\\dots ,(x_N,y_N)\\}\\). Note that the annotated clips often cannot densely cover the entire video due to the annotation cost and visual redundancy, i.e. \\(\\bigcup_{i}[t_{i},e_{i}]\\subsetneq [0,|V|]\\)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.689, + 0.471, + 0.873 + ], + "angle": 0, + "content": "A typical video model \\(\\mathcal{F}(\\mathcal{X},\\mathcal{Y})\\) learns from these clip-level annotations using a standard training objective such as a cross-entropy loss when the annotations are class labels with a fixed vocabulary. However, more recently, dual-encoder-based contrastive approaches like CLIP [49, 77] have gained popularity. They work with free-form textual annotations which are tokenized [55] into sequences of discrete symbols, i.e. \\(y = (s_{1},s_{2},\\dots ,s_{L})\\in \\{1,0\\}^{|\\mathbb{S}|\\times L}\\). The model consists of a visual encoder \\(f_{\\mathrm{v}}:\\mathbb{R}^{T\\times 3\\times H\\times W}\\mapsto \\mathbb{R}^{D_{\\mathrm{v}}}\\) plus a projection head \\(h_\\mathrm{v}:\\mathbb{R}^{D_\\mathrm{v}}\\mapsto \\mathbb{R}^d\\) and a textual encoder \\(f_{\\mathrm{t}}:\\{1,0\\}^{|\\mathbb{S}|\\times L}\\mapsto \\mathbb{R}^{D_{\\mathrm{t}}}\\) plus a projection head \\(h_{\\mathrm{t}}:\\mathbb{R}^{D_{\\mathrm{t}}}\\mapsto \\mathbb{R}^{d}\\) in parallel to obtain the global visual and textual embedding respectively:" + }, + { + "type": "equation", + "bbox": [ + 0.167, + 0.882, + 0.379, + 0.898 + ], + "angle": 0, + "content": "\\[\n\\mathbf {v} = h _ {\\mathrm {v}} \\left(f _ {\\mathrm {v}} (x)\\right), \\quad \\mathbf {u} = h _ {\\mathrm {t}} \\left(f _ {\\mathrm {t}} (y)\\right).\n\\]" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.09, + 0.631, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.503, + 0.166, + 0.584, + 0.179 + ], + "angle": 0, + "content": "Narration" + }, + { + "type": "image", + "bbox": [ + 0.599, + 0.09, + 0.762, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.599, + 0.166, + 0.688, + 0.179 + ], + "angle": 0, + "content": "C separates the yarn" + }, + { + "type": "image_caption", + "bbox": [ + 0.503, + 0.181, + 0.68, + 0.203 + ], + "angle": 0, + "content": "C divides the yarn by a few stitches." + }, + { + "type": "image", + "bbox": [ + 0.762, + 0.09, + 0.891, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.7, + 0.166, + 0.767, + 0.196 + ], + "angle": 0, + "content": "NARRATOR" + }, + { + "type": "image_caption", + "bbox": [ + 0.779, + 0.165, + 0.872, + 0.203 + ], + "angle": 0, + "content": "C stretches the thread with both hands. \nC pulls out the yarn with her right hand." + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.204, + 0.631, + 0.279 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.503, + 0.281, + 0.583, + 0.293 + ], + "angle": 0, + "content": "Narration" + }, + { + "type": "image_caption", + "bbox": [ + 0.599, + 0.282, + 0.698, + 0.308 + ], + "angle": 0, + "content": "C lifts container. C raises the container." + }, + { + "type": "image", + "bbox": [ + 0.7, + 0.204, + 0.762, + 0.279 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.7, + 0.282, + 0.768, + 0.306 + ], + "angle": 0, + "content": "NARRATOR" + }, + { + "type": "image", + "bbox": [ + 0.762, + 0.204, + 0.891, + 0.279 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.779, + 0.279, + 0.879, + 0.308 + ], + "angle": 0, + "content": "C wipes the countertop with a sponge. \nCmoves the container." + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.329, + 0.892, + 0.398 + ], + "angle": 0, + "content": "Figure 3. Generated samples by NARRATOR and REPHRASER. NARRATOR generates new descriptions of the action taking place, potentially focusing on other objects being interacted with. REPHRASER not only changes the word order of the human narration but also diversifies it by using related verbs or nouns." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.426, + 0.892, + 0.469 + ], + "angle": 0, + "content": "A contrastive loss, such as InfoNCE [48], learns global embeddings that associate corresponding video and text embeddings within a batch of samples \\(\\mathcal{B}\\)" + }, + { + "type": "equation", + "bbox": [ + 0.545, + 0.478, + 0.892, + 0.512 + ], + "angle": 0, + "content": "\\[\n\\frac {1}{| \\mathcal {B} |} \\sum_ {(x, y) \\in \\mathcal {B}} \\left(\\operatorname {I n f o N C E} (\\mathbf {v}, \\mathbf {u}) + \\operatorname {I n f o N C E} (\\mathbf {u}, \\mathbf {v})\\right). \\tag {1}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.542, + 0.597, + 0.557 + ], + "angle": 0, + "content": "4. LAVILA" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.568, + 0.892, + 0.842 + ], + "angle": 0, + "content": "In LAVILA, we leverage large language models (LLMs) as supervision to train the dual-encoder model, where the LLMs serve as vision-conditioned narrators and automatically generate textual descriptions from video clips. In particular, we exploit supervision from two LLMs: (1) NARRATOR (\\(\\S 4.1\\)) is a visually-conditioned LLM that pseudolabels existing and new clips with narrations, generating new annotations \\((\\mathcal{X}',\\mathcal{Y}')\\). (2) REPHRASER (\\(\\S 4.2\\)) is a standard LLM that paraphrases narrations in existing clips, augmenting those annotations to \\((\\mathcal{X},\\mathcal{Y}^{\\prime \\prime})\\). As illustrated in Figure 3, NARRATOR generates new descriptions of the action taking place, potentially focusing on other objects being interacted with. REPHRASER serves to augment the text input, e.g., changes word order of the human narration and additionally replaces common verbs or nouns, making annotations more diverse. Finally, we train the dual-encoders (\\(\\S 4.3\\)) on all these annotations combined, i.e. \\((\\mathcal{X},\\mathcal{Y}) \\cup (\\mathcal{X}',\\mathcal{Y}') \\cup (\\mathcal{X},\\mathcal{Y}^{\\prime \\prime})\\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.85, + 0.62, + 0.864 + ], + "angle": 0, + "content": "4.1. NARRATOR" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.872, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Traditional LLMs, such as GPT-2 [50], are trained to generate a sequence of text tokens \\((s_1 \\cdots s_L)\\) from scratch" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6588" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.107, + 0.075, + 0.409, + 0.203 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.414, + 0.075, + 0.872, + 0.203 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.075, + 0.215, + 0.894, + 0.271 + ], + "angle": 0, + "content": "Figure 4. Language supervision from REPHRASER and NARRATOR. REPHRASER(left) takes the narration as input, passes it through a text encoder and uses a text decoder to autoregressively generate the rephrased output. NARRATOR(right) takes video frames as input and obtains the visual embeddings through a video encoder followed by attentional pooling. Equipped with a few additional cross-attention modules, the text decoder autoregressively generates new narrations for those new frames." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.297, + 0.47, + 0.408 + ], + "angle": 0, + "content": "by modeling the probability of the next token given all tokens seen so far: \\( p(s_{l}|s_{ \\). We sample from the distribution recursively, i.e. \\( \\tilde{s}_{\\ell} \\sim p(s|[, \\dots, \\tilde{s}_{\\ell-1}], x) \\) until an end-of-sentence token \\( \\) is reached. Following [29], at each step we sample from a subset of tokens that contain the vast majority of the probability mass, which is known as nucleus sampling." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.553, + 0.892, + 0.718 + ], + "angle": 0, + "content": "The effect of nucleus sampling is two-fold. On the one hand, it generates more diverse, open-ended, and human-like text than maximum-likelihood-based methods such as beam search and its variants [67]. On the other hand, the generated text may contain irrelevant or noisy information due to sampling without post-processing based on sentence-level likelihood. To address this, we repeat the sampling process for \\( K \\) times on the same visual input. We later demonstrate that the contrastive pre-training objective is robust to the noise caused by sampling, and the final performance benefits from a more diverse set of narrations." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.892, + 0.901 + ], + "angle": 0, + "content": "To sample video clips for captioning, we start by simply re-captioning the existing clips labeled in the dataset \\(\\mathcal{X}\\), resulting in expanded annotations. Furthermore, long-form videos are typically sparsely narrated, meaning that the temporal union of all labeled clips cannot cover the entire video. Hence, we use NARRATOR to annotate the remainder of the video to obtain additional annotations by pseudo-captioning. With a simple assumption that video is a stationary process, we uniformly sample clips from the unlabeled intervals. The clip duration is equal to the average of all ground-truth clips, i.e. \\(\\Delta = \\frac{1}{N} \\sum_{i=1}^{N} (e_i - t_i)\\) while the sampling stride is computed likewise. Finally, by" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6589" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.137 + ], + "angle": 0, + "content": "combining both re-captioned and pseudo-captioned narrations, we refer to the final set of annotations generated by NARRATOR as \\((\\mathcal{X}',\\mathcal{Y}')\\)" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.138, + 0.47, + 0.289 + ], + "angle": 0, + "content": "Post-processing. Exhaustive pseudo-captioning may contain some uninformative visual clips and generate text that is not useful. Thus, we add a filtering process to eliminate low-quality clips and their associated descriptions. We use the baseline dual-encoder model \\(\\mathcal{F}\\), which is trained on the ground-truth paired clips, to compute the visual and textual embedding of pseudo-labeled pairs and filter based on the similarity score, i.e. \\(\\mathrm{Filter}(f_{\\mathrm{v}}(x_j')^\\top \\cdot f_{\\mathrm{t}}(y_j'))\\), where \\(\\mathrm{Filter}(\\cdot)\\) can be either top-\\(k\\) of all generated text or a threshold filtering. In the experiments, we use a threshold of 0.5." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.298, + 0.204, + 0.312 + ], + "angle": 0, + "content": "4.2. REPHRASER" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.32, + 0.47, + 0.391 + ], + "angle": 0, + "content": "The data generated by NARRATOR is several times larger than the ground-truth pairs. To ensure that we do not overfit the pseudo-labeled data, we increase the number of ground-truth narrations by paraphrasing. In particular, we use a text-to-text LLM which models conditional text likelihood:" + }, + { + "type": "equation", + "bbox": [ + 0.158, + 0.412, + 0.388, + 0.45 + ], + "angle": 0, + "content": "\\[\np _ {\\text {R E P H R A S E R}} \\left(y ^ {\\prime \\prime} | y\\right) = \\prod_ {\\ell = 1} ^ {L} p \\left(s _ {\\ell} ^ {\\prime \\prime} \\mid s _ {< \\ell} ^ {\\prime \\prime}, y\\right).\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.458, + 0.47, + 0.565 + ], + "angle": 0, + "content": "The text-to-text model is implemented by an encoder-decoder architecture, e.g. T5 [51], to auto-regressively generate a new sentence given the original one. We observe that REPHRASER is able to do basic manipulations such as replacing synonyms or changing word order, which serves as an efficient way of automatic data augmentation. The resulting annotations are referred to as \\((\\mathcal{X},\\mathcal{Y}^{\\prime \\prime})\\)" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.574, + 0.329, + 0.59 + ], + "angle": 0, + "content": "4.3. Training the Dual-Encoders" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.597, + 0.47, + 0.825 + ], + "angle": 0, + "content": "We train the dual-encoders as described in Algorithm 1 in Appendix E. In each iteration, we first sample a batch \\(\\mathcal{B}\\) of video clips. It comprises a subset of clips \\(\\mathcal{B}_l\\) with labeled timestamps as well as narrations, and a subset \\(\\mathcal{B}_u\\) whose clips are randomly sampled from videos without narrations. For clip \\(x_i\\in \\mathcal{B}_u\\), we obtain the pseudo-caption \\(y_{i}^{\\prime}\\) by querying the NARRATOR \\(y_{i}^{\\prime}\\sim p_{\\mathrm{NARRATOR}}(y^{\\prime}|x)\\), resulting in a set of clips with LLM-generated narrations \\(\\widetilde{B}_u\\). For clip \\((x_{i},y_{i})\\in \\mathcal{B}_{l}\\), the text supervision is obtained from either the REPHRASER or the NARRATOR, with a probability of 0.5. Hence, the effective number of iterations per epoch for LAVILA is the same as that for the baseline Dual-Encoder. We denote the resulting set of pairs to be \\(\\widetilde{B}_l\\) similarly. Following CLIP [49], we use the symmetric cross-entropy loss over the similarity scores of samples in the batch \\(\\widetilde{B}_l\\cup \\widetilde{B}_u\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.47, + 0.901 + ], + "angle": 0, + "content": "In practice, we run REPHRASER and NARRATOR in advance and cache the resulting video-narration pairs so that there is no computational overhead during pre-training. Therefore, training a dual-encoder in LAVILA is as fast as training a standard dual-encoder contrastive model." + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.089, + 0.895, + 0.21 + ], + "angle": 0, + "content": "
DatasetsTaskEgo?MetricsEval. Prot.
EK-100 [14]MIR✓mAP, nDCGZS, FT
EK-100 [14]CLS✓top-1 action acc.FT
Ego4D [24]MCQ✓inter-/intra-video acc.ZS
Ego4D [24]NLQ✓Recall@NFT
EGTEA [37]CLS✓top-1, mean acc.ZS, FT
CharadesEgo [58]CLS✓video-level mAPZS, FT
UCF-101 [60]CLS✗mean acc.LP
HMDB-51 [35]CLS✗mean acc.LP
" + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.22, + 0.894, + 0.304 + ], + "angle": 0, + "content": "Table 1. Downstream datasets and metrics used for evaluation. We evaluate LAVILA on a wide range of tasks including Multi-Instance Retrieval (MIR), Multiple-Choice Question (MCQ), Natural Language Query (NLQ), and Action Recognition (CLS). The evaluation protocols include zero-shot (ZS), fine-tuning (FT), and linear-probing (LP). Please refer to Appendix C for more details." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.33, + 0.633, + 0.347 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.355, + 0.892, + 0.506 + ], + "angle": 0, + "content": "Dual-Encoder Architecture. The video-language model follows a dual-encoder architecture as CLIP [49]. The Visual encoder is a TimeSformer (TSF) [6], whose spatial attention modules are initialized from a ViT [18] which is contrastively pre-trained on large-scale paired image-text data as in CLIP [49]. We sample 4 frames per clip during pre-training and 16 when finetuning on downstream tasks. The text encoder is a 12-layer Transformer [50, 66]. We use BPE tokenizer [55] to pre-process the full sentence corresponding to the video clip and keep at most 77 tokens." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.507, + 0.892, + 0.582 + ], + "angle": 0, + "content": "NARRATOR's architecture is a visually conditioned auto-regressive Language Model. The visual encoder is by default TimeSformer-L while the text decoder is a GPT-2 XL. During inference, we use nucleus sampling [29] with \\( p = 0.95 \\) and return \\( K = 10 \\) candidate outputs." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.583, + 0.892, + 0.688 + ], + "angle": 0, + "content": "REPHRASER. We use an open-source paraphraseer [23] based on T5-large [51]. It is pre-trained on C4 [51] and then finetuned on a cleaned subset of ParaNMT [74]. During inference, we use Diverse Beam Search [67] with group number the same as beam number \\((G = B = 20)\\) and set the diversity penalty to be 0.7. We keep 3 candidates per sentence, remove punctuations, and do basic de-duplication." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.892, + 0.809 + ], + "angle": 0, + "content": "Pre-training dataset. We train on the video-narration pairs from Ego4D [13, 24], the largest egocentric video dataset to date. We exclude videos that appear in the validation and test sets of the Ego4D benchmark and determine each clip's interval using the same pairing strategy in [39]. This results in around 4M video-text pairs with an average clip length of 1 second. We also experiment with third-person videos by pre-training on HowTo100M [45] in § 5.2." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Evaluation protocols. We evaluate the learned video-text encoders using three evaluation protocols. (1) Zero-Shot (ZS), meaning that we apply the pre-trained video-text encoders directly on the downstream validation dataset to perform video \\(\\leftrightarrow\\) text retrieval tasks, without any tuning on the downstream dataset. Zero-shot classification is performed" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6590" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.08, + 0.089, + 0.472, + 0.259 + ], + "angle": 0, + "content": "
MethodBackbonemAPnDCG
V→TT→VAvg.V→TT→VAvg.
(ZERO-SHOT)
EgoVLP [39]TSF-B19.413.916.624.122.023.1
EgoVLP* [39]TSF-B26.020.623.328.827.027.9
LAVILATSF-B35.126.630.933.730.432.0
LAVILATSF-L40.032.236.136.133.234.6
(FINETUNED)
MME [75]TBN43.034.038.550.146.948.5
JPoSE [75]TBN49.938.144.055.551.653.5
EgoVLP [39]TSF-B49.940.545.060.957.959.4
LAVILATSF-B55.245.750.566.563.465.0
LAVILATSF-L54.747.150.968.164.966.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.269, + 0.47, + 0.354 + ], + "angle": 0, + "content": "Table 2. EK-100 MIR. LAViLA outperforms prior work across all settings, metrics and directions of retrieval, with larger gains when switching to a larger model. Specifically, our best model achieves over \\(10\\%\\) absolute gain in the zero-shot setting and \\(5.9 \\sim 7.1\\%\\) gain in the finetuned setting. EgoVLP* refers to our improved version of [39], details of which are given in Appendix F." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.385, + 0.469, + 0.506 + ], + "angle": 0, + "content": "similarly, where we compute the similarity score between the video clip and the textual description of all possible categories. (2) Finetuned (FT), where we take the pre-trained video-text model and perform end-to-end fine-tuning on the training split of the target downstream dataset. (3) LinearProbe (LP), where we compute the video features from a frozen encoder and train a linear SVM on top of the training split of the downstream dataset." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.509, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Downstream benchmarks. We use multiple benchmarks across four first-person (egocentric) and two third-person datasets, as enumerated in Table 1. We summarize them here and refer the reader to Appendix C for details on datasets and metrics. (1) Two tasks on Epic-Kitchens-100: Multi-Instance Retrieval (EK-100 MIR) and Action Recognition (EK-100 CLS) [14]. EK-100 is a very popular and challenging egocentric video recognition benchmark. The MIR task requires retrieving the text given videos \\((\\mathrm{V} \\rightarrow \\mathrm{T})\\) and videos given text \\((\\mathrm{T} \\rightarrow \\mathrm{V})\\). The CLS task requires classifying each video into one of 97 verbs and 300 nouns each, resulting in a combination of 3,806 action categories. (2) Two downstream tasks of Ego4D: Multiple-Choice Questions (EgoMCQ) and Natural Language Query (EgoNLQ). EgoMCQ requires selecting the correct textual description from five choices given a query video clip while EgoNLQ asks the model to output the relevant temporal intervals of video given a text query. We select these two benchmarks because they require reasoning about both visual and textual information. (3) Action Recognition on EGTEA [37]. It requires classifying into 106 classes of fine-grained cooking activities. (4) Action Recognition on CharadesEgo [58]. It requires classification into 157 classes of daily indoor activities. Note that CharadesEgo is very different from EK-100, Ego4D and EGTEA since its videos are captured by head-mounted phone cameras in a crowd-sourcing way." + }, + { + "type": "table", + "bbox": [ + 0.508, + 0.088, + 0.885, + 0.182 + ], + "angle": 0, + "content": "
MethodEgoMCQEgoNLQ
Accuracy (%)mIOU@0.3mIOU@0.5
Inter-videoIntra-videoR@1R@5R@1R@5
SlowFast [24]--5.4510.743.126.63
EgoVLP [39]90.657.210.8418.846.8113.45
LAVILA (B)93.859.910.5319.136.6913.68
LAVILA (L)94.563.112.0522.387.4315.44
" + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.192, + 0.892, + 0.263 + ], + "angle": 0, + "content": "Table 3. Ego4D EgoMCQ and EgoNLQ. LAVILA outperforms prior work on both Multiple-Choice Questions and Natural Language Questions on Ego4D, with nearly \\(6\\%\\) absolute gain on the challenging intra-video MCQ task that requires reasoning over multiple clips from the same video to answer a question." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.291, + 0.892, + 0.351 + ], + "angle": 0, + "content": "In all tables, we bold and underline the best and second-best performing methods with comparable backbones architectures. We highlight the overall best performing method, which typically uses a larger backbone, if applicable." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.362, + 0.638, + 0.377 + ], + "angle": 0, + "content": "5.1. Main Results" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.386, + 0.892, + 0.552 + ], + "angle": 0, + "content": "EK-100. We compare LAVILA with prior works on EK-100 MIR in Table 2. In the zero-shot setup, LAVILA remarkably surpasses an improved version of EgoVLP [39] under similar model complexity: we use TSF-Base+GPT-2 as the dual-encoder architecture while EgoVLP uses TSF-Base+Distil-BERT. With a stronger video encoder, i.e. TSF-Large, the performance improves further. In the fine-tuned setting, LAVILA significantly outperforms all previous supervised approaches including MME, JPOSE [75] and EgoVLP [39]. We also compare LAVILA on EK-100 CLS in Appendix E, and establish a new state-of-the-art." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.553, + 0.892, + 0.719 + ], + "angle": 0, + "content": "Ego4D. We evaluate the pre-trained LAViLA model on EgoMCQ and EgoNLQ tasks and compare the results in Table 3. On EgoMCQ, our method achieves \\(93.8\\%\\) inter-video accuracy and \\(59.9\\%\\) intra-video accuracy, outperforming EgoVLP by a noticeable margin. Note that EgoVLP's performance reported in Table 3 is obtained by using EgoNCE loss [39], a variant of InfoNCE specialized for Ego4D while ours uses a standard InfoNCE loss. EgoVLP with InfoNCE has lower performance (89.4% inter-video and \\(51.5\\%\\) intravideo accuracy). On EgoNLQ, LAViLA achieves comparable results with EgoVLP with similar model complexity." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.892, + 0.901 + ], + "angle": 0, + "content": "EGTEA. We evaluate the learned video representation by finetuning the video encoder for action classification in Table 4 on another popular egocentric dataset, EGTEA [37]. Our method surpasses the previous state-of-the-art which takes multiple modalities including visual, auditory and textual inputs [33] by a more than \\(10\\%\\) absolute margin on the mean accuracy metric. Since previous methods are based on different backbones, we experiment with a TSF-Base (\"Visual only\") model pre-trained on Kinetics [9] as a fair baseline for LAVILA. We observe that its accuracy is comparable to previous methods but much lower than LAVILA, implying the effectiveness of learning visual representation" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "6591" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.081, + 0.089, + 0.468, + 0.194 + ], + "angle": 0, + "content": "
MethodBackbonePretrainTop-1 Acc.Mean Acc.
Li et al. [37]I3DK400-53.30
LSTA [63]ConvLSTMIN-1k61.8653.00
IPL [71]I3DK400-60.15
MTCN [33]SlowFast (V+A+T)K400+VGG-Sound73.5965.87
Visual onlyTSF-BIN-21k+K40065.5859.32
LAVILATSF-BWIT+Ego4D77.4570.12
LAVILATSF-LWIT+Ego4D81.7576.00
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.205, + 0.471, + 0.262 + ], + "angle": 0, + "content": "Table 4. EGTEA Classification. LAVILA obtains significant gains on this task, outperforming prior work with over \\(10\\%\\) mean accuracy. Since the backbones used are not all comparable, we also report a comparable baseline with TSF-B (\"Visual only\")." + }, + { + "type": "table", + "bbox": [ + 0.084, + 0.276, + 0.465, + 0.369 + ], + "angle": 0, + "content": "
MethodBackbonemAP (ZS)mAP (FT)
ActorObserverNet [57]ResNet-152-20.0
SSDA [12]I3D-25.8
Ego-Exo [38]SlowFast-R101-30.1
EgoVLP [39]TSF-B25.032.1
LAVILATSF-B26.833.7
LAVILATSF-L28.936.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.38, + 0.47, + 0.436 + ], + "angle": 0, + "content": "Table 5. CharadesEgo Action Recognition. LAVILA sets new state-of-the-art in both zero-shot (ZS) and finetuned (FT) settings. Note that CharadesEgo videos are visually different compared to Ego4D videos, on which LAVILA is pretrained." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.452, + 0.469, + 0.481 + ], + "angle": 0, + "content": "on large-scale egocentric videos and using LLM as textual supervision during pre-training." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.482, + 0.47, + 0.573 + ], + "angle": 0, + "content": "CharadesEgo. Next, we compare LAVILA's representation on the CharadesEgo action classification task. As shown in Table 5, LAVILA's representation excels on this task as well, which is notable as CharadesEgo videos are significantly different compared to Ego4D, being captured by crowdsourced workers using mobile cameras." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.585, + 0.47, + 0.601 + ], + "angle": 0, + "content": "5.2. Application to Third-Person Video Pre-training" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.608, + 0.47, + 0.685 + ], + "angle": 0, + "content": "We apply LAVILA to third-person videos by experimenting with the HowTo100M [45] dataset. Specifically, we use the temporally aligned subset provided by [25], which contains 3.3M sentences from 247k videos. We evaluate the video representation on two third-person video datasets, i.e." + }, + { + "type": "table", + "bbox": [ + 0.532, + 0.089, + 0.864, + 0.169 + ], + "angle": 0, + "content": "
MethodVis. Enc.UCF-101HMDB-51
MIL-NCE [44]S3D82.754.3
TAN [25]S3D83.256.7
Baseline (w/o LLM)TSF-B86.559.4
LAViLATSF-B87.457.2
LAViLATSF-L88.161.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.18, + 0.892, + 0.222 + ], + "angle": 0, + "content": "Table 6. LAVILA on third-person videos. We measure the linear-probing action classification performance of the video model after pre-training on HowTo100M [45]." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.251, + 0.892, + 0.358 + ], + "angle": 0, + "content": "UCF-101 [60] and HMDB-51 [35] for action classification using the linear probing protocol. For more details, please refer to Appendix D. From Table 6, we see that LAVILA outperforms previous methods such as MIL-NCE [44] and TAN [25] by a large margin. Since we use a different backbone, we report a baseline without LLM and show that LAVILA indeed benefits from the language supervision." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.371, + 0.851, + 0.387 + ], + "angle": 0, + "content": "5.3. Application to Semi-supervised Learning" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.396, + 0.892, + 0.621 + ], + "angle": 0, + "content": "While LAVILA is very effective at leveraging existing narrations to augment them, we now show that it is also applicable when only a limited number of narrations are available to begin with. We first divide each long video from Ego4D into 15-second chunks and assume only the annotated clips within every \\(N\\) chunks is available during pre-training, leading to approximately \\(\\frac{100}{N}\\%\\) of the full set, where \\(N \\in \\{2,5,10\\}\\). This can be considered a practical scenario when we want to annotate as many videos as possible for diversity when the annotation budget is limited. In the remainder \\((1 - \\frac{100}{N})\\%\\) part that is skipped, we uniformly sample the same number of the clips per chunk with the same clip length as that in the seen chunks. Both the dual-encoder model and NARRATOR are trained on the \\(100\\%\\) available annotations." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.623, + 0.892, + 0.685 + ], + "angle": 0, + "content": "We plot the zero-shot performance curve of pre-training with different proportions in Figure 5. We can see that LAV-ILA consistently outperforms the ground-truth-only baseline at all points (10, 20, 50, and \\(100\\%\\)). The improvement" + }, + { + "type": "image", + "bbox": [ + 0.087, + 0.726, + 0.273, + 0.827 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.128, + 0.832, + 0.234, + 0.842 + ], + "angle": 0, + "content": "(a) EK-100 MIR mAP." + }, + { + "type": "image", + "bbox": [ + 0.29, + 0.704, + 0.677, + 0.827 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.327, + 0.832, + 0.441, + 0.842 + ], + "angle": 0, + "content": "(b) EK-100 MIR nDCG." + }, + { + "type": "image", + "bbox": [ + 0.697, + 0.726, + 0.882, + 0.827 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.71, + 0.832, + 0.871, + 0.843 + ], + "angle": 0, + "content": "(d) EgoMCQ Intra-video accuracy." + }, + { + "type": "image_caption", + "bbox": [ + 0.075, + 0.855, + 0.894, + 0.897 + ], + "angle": 0, + "content": "Figure 5. LAVILA is effective in a semi-supervised setting where only a limited amount of narrations are given. Comparing zero-shot performance of pre-training, LAVILA consistently outperforms the groundtruth-only baseline when 10, 20, 50, \\(100\\%\\) data is used. We also achieve comparable result with state-of-the-art with only \\(50\\%\\) of the annotated data." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6592" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.097, + 0.089, + 0.406, + 0.18 + ], + "angle": 0, + "content": "
Text Dec. arch.Text Dec. init.Freeze LMMETEOR ROUGE-L CIDESAvg. mAP
(baseline)---26.0
GPT-2random×0.2840.5240.88224.3
GPT-2WebText✓0.2700.5050.80424.0
GPT-2 XLWebText✓0.2890.5300.94026.2
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.086, + 0.193, + 0.408, + 0.224 + ], + "angle": 0, + "content": "(a) Generation Quality. Using a sufficiently large language model as the text decoder is crucial for good text generation quality and downstream performance." + }, + { + "type": "table", + "bbox": [ + 0.418, + 0.099, + 0.609, + 0.179 + ], + "angle": 0, + "content": "
Sampling method# of sentencesAvg. mAP
N/A (baseline)-26.0
Beam search127.9
Nucleus129.6
Nucleus1029.7
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.415, + 0.193, + 0.604, + 0.224 + ], + "angle": 0, + "content": "(b) Sampling. LAV1LA benefits more from narrations produced by nucleus sampling than beam search." + }, + { + "type": "image", + "bbox": [ + 0.619, + 0.098, + 0.88, + 0.191 + ], + "angle": 0, + "content": null + }, + { + "type": "table_footnote", + "bbox": [ + 0.61, + 0.193, + 0.882, + 0.224 + ], + "angle": 0, + "content": "(c) Scaling effect of LAVILA. Gains increase on scaling the video encoder in NARRATOR. Default refers to only using the original narrations." + }, + { + "type": "table_footnote", + "bbox": [ + 0.077, + 0.224, + 0.892, + 0.252 + ], + "angle": 0, + "content": "Table 7. Ablations of NARRATOR. We report zero-shot average mAP on EK-100 MIR for comparing downstream performance. We study NARRATOR from the perspective of generation quality (left), sampling techniques (middle), and scaling effect (right)." + }, + { + "type": "table", + "bbox": [ + 0.077, + 0.09, + 0.406, + 0.18 + ], + "angle": 0, + "content": "
Text Dec. arch.Text Dec. init.Freeze LMMETEOR ROUGE1 CIDEsAvg. mAP
(baseline)---26.0
GPT-2randomX0.2840.5240.88224.3
GPT-2WebText✓0.2700.5050.80424.0
GPT-2 XLWebText✓0.2890.5300.94026.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.078, + 0.379, + 0.469, + 0.448 + ], + "angle": 0, + "content": "Table 8. Contributions of different Language Supervision. We can see that (1) using REPHRASER (\"Rephr.\") and NARRATOR (\"Recap.\") improve downstream zero-shot performance complementarily, (2) dense pseudo-captioning further improves performance on 3 out of 6 metrics." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.475, + 0.47, + 0.567 + ], + "angle": 0, + "content": "tends to be larger when more data is available, indicating the method's scalability as more videos are narrated in the future. Furthermore, we observe our method can achieve a similar level of performance with the baseline often using less than \\(50\\%\\) data. We also achieve a comparable result with the state-of-the-art using much fewer data." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.575, + 0.241, + 0.59 + ], + "angle": 0, + "content": "5.4. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.599, + 0.47, + 0.81 + ], + "angle": 0, + "content": "Contributions of Different Language Supervisions. We ablate different language supervisions in Table 8 on EK-100 MIR (zero-shot), EgoMCQ and EGTEA. Using the text-only REPHRASER (\"rephr.\") or visually conditioned NARRATOR (\"recap.\") separately improves the ground-truth baseline noticeably. Combining both REPHRASER and NARRATOR gives an improvement of \\(3.5\\%\\) average mAP on EK-100 MIR. We see that dense captioning on the entire video (\"pseudo-cap.\") is also helpful. Though the gain on EK-100 MIR is not as significant, it shows nontrivial improvements on EgoMCQ intra-video accuracy and EGTEA mean accuracy. Our conjecture for this marginal gain is that informative clips are mostly covered in Ego4D because all videos are inspected by two annotators." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.471, + 0.9 + ], + "angle": 0, + "content": "Generation Quality of NARRATOR. We study how the NARRATOR's configurations affect the quality of generated text and the downstream performance. The generation quality is measured by standard unsupervised automatic metrics including METEOR, ROUGE, and CIDEr [43]. We use a NARRATOR with a smaller GPT-2 as the text decoder and" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.279, + 0.892, + 0.385 + ], + "angle": 0, + "content": "consider two scenarios in Table 7a: (1) LM is randomly initialized but jointly trained with the gated cross-attention modules, and (2) LM is initialized from the original GPT-2. The generation quality decreases compared to GPT-2 XL in both cases and the zero-shot retrieval result on EK-100 MIR is worse. This indicates that the language model should be sufficiently large and pre-trained on web text data." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.388, + 0.892, + 0.449 + ], + "angle": 0, + "content": "Sampling. In Table 7b, we investigate different sampling methods for text generation from NARRATOR. We see that nucleus sampling works much better than beam search while repetitive sampling shows marginal improvement." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.452, + 0.893, + 0.603 + ], + "angle": 0, + "content": "Scaling effect. In Table 7c, we compare the zero-shot retrieval result by progressively increasing the size of NARRATOR's video encoder from TSF-B to TSF-L and TSFL@HR, which increases the input resolution to be narrated from 224 to 336 while fixing the dual-encoder architecture. The retrieval performance steadily increases while NARRATOR becomes stronger. We conduct this experiment by varying the dual-encoder architecture, namely TSF-Base and TSF-Large, and show similar trends. Both phenomena suggest that LAVILA can scale to larger models." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.627, + 0.771, + 0.643 + ], + "angle": 0, + "content": "6. Conclusion and Future Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.656, + 0.892, + 0.807 + ], + "angle": 0, + "content": "In this paper, we proposed LAVILA, a new approach to video-language representation learning by automatically narrating long videos with LLMs. We achieve strong improvements over baselines trained with the same amount of human-narrated videos and set new state-of-the-art on six popular benchmark tasks across first- and third-person video understanding benchmarks. LAVILA also shows positive scaling behavior when adding more training narrations, using larger visual backbones, and using stronger LLMs, all of which are promising areas for future work." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Acknowledgements: We thank Naman Goyal, Stephen Roller and Susan Zhang for help with language models, Kevin Qinghong Lin for help with EgoVLP, and the Meta AI team for helpful discussions and feedback. This material is based upon work in-part supported by the National Science Foundation under Grant No. IIS-1845485." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6593" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.091, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.252 + ], + "angle": 0, + "content": "[1] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan. Flamingo: a visual language model for few-shot learning. In NeurIPS, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.253, + 0.47, + 0.293 + ], + "angle": 0, + "content": "[2] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid. Vivit: A video vision transformer. In ICCV, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.293, + 0.47, + 0.332 + ], + "angle": 0, + "content": "[3] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.334, + 0.47, + 0.374 + ], + "angle": 0, + "content": "[4] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.374, + 0.47, + 0.401 + ], + "angle": 0, + "content": "[5] Yoshua Bengio, Réjean Ducharme, and Pascal Vincent. A neural probabilistic language model. In NIPS, 2000." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.401, + 0.47, + 0.44 + ], + "angle": 0, + "content": "[6] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.44, + 0.47, + 0.577 + ], + "angle": 0, + "content": "[7] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. NeurIPS, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.578, + 0.47, + 0.63 + ], + "angle": 0, + "content": "[8] Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles. Activitynet: A large-scale video benchmark for human activity understanding. In CVPR, 2015." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.631, + 0.47, + 0.671 + ], + "angle": 0, + "content": "[9] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In CVPR, 2017." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.671, + 0.47, + 0.725 + ], + "angle": 0, + "content": "[10] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.726, + 0.47, + 0.765 + ], + "angle": 0, + "content": "[11] Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal. Unifying vision-and-language tasks via text generation. In ICML, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.766, + 0.47, + 0.82 + ], + "angle": 0, + "content": "[12] Jinwoo Choi, Gaurav Sharma, Manmohan Chandraker, and Jia-Bin Huang. Unsupervised and semi-supervised domain adaptation for action recognition from drones. In WACV, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.82, + 0.47, + 0.889 + ], + "angle": 0, + "content": "[13] Ego4D Consortium. Egocentric live 4d perception (Ego4D) database: A large-scale first-person video database, supporting research in multi-modal machine perception for daily life activity. https://sites.google.com/view/ego4d/home. Accessed: 2022-11-22." + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.47, + 0.889 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.161 + ], + "angle": 0, + "content": "[14] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Evangelos Kazakos, Jian Ma, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Rescaling egocentric vision: collection, pipeline and challenges for Epic-Kitchens-100. IJCV, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.161, + 0.892, + 0.202 + ], + "angle": 0, + "content": "[15] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In CVPR, 2009." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.202, + 0.892, + 0.228 + ], + "angle": 0, + "content": "[16] Karan Desai and Justin Johnson. Virtex: Learning visual representations from textual annotations. In CVPR, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.228, + 0.892, + 0.282 + ], + "angle": 0, + "content": "[17] Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell. Long-term recurrent convolutional networks for visual recognition and description. In CVPR, 2015." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.282, + 0.892, + 0.363 + ], + "angle": 0, + "content": "[18] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.364, + 0.892, + 0.403 + ], + "angle": 0, + "content": "[19] Marzieh Fadaee, Arianna Bisazza, and Christof Monz. Data augmentation for low-resource neural machine translation. In ACL, 2017." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.403, + 0.892, + 0.459 + ], + "angle": 0, + "content": "[20] Steven Y Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. A survey of data augmentation approaches for nlp. In ACL Findings, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.459, + 0.892, + 0.512 + ], + "angle": 0, + "content": "[21] Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, and Tomas Mikolov. Devise: A deep visual-semantic embedding model. NeurIPS, 2013." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.512, + 0.892, + 0.553 + ], + "angle": 0, + "content": "[22] Rohit Girdhar, Mannat Singh, Nikhila Ravi, Laurens van der Maaten, Armand Joulin, and Ishan Misra. Omnivore: A single model for many visual modalities. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.553, + 0.892, + 0.607 + ], + "angle": 0, + "content": "[23] Ramsri Goutham Golla. High-quality sentence paraphrase using transformers in nlp. https://huggingface.co/ramsrigouthamg/t5-large-paraphraser-diverse-high-quality. Accessed: 2022-06-01." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.607, + 0.892, + 0.897 + ], + "angle": 0, + "content": "[24] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.897 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6594" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.092, + 0.469, + 0.147 + ], + "angle": 0, + "content": "Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4d: Around the world in 3,000 hours of egocentric video. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.148, + 0.468, + 0.174 + ], + "angle": 0, + "content": "[25] Tengda Han, Weidi Xie, and Andrew Zisserman. Temporal alignment networks for long-term video. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.174, + 0.468, + 0.213 + ], + "angle": 0, + "content": "[26] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.215, + 0.468, + 0.24 + ], + "angle": 0, + "content": "[27] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 1997." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.241, + 0.468, + 0.349 + ], + "angle": 0, + "content": "[28] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre. Training compute-optimal large language models. arXiv preprint arXiv:2203.15556, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.35, + 0.468, + 0.389 + ], + "angle": 0, + "content": "[29] Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. The curious case of neural text degeneration. In ICLR, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.391, + 0.468, + 0.442 + ], + "angle": 0, + "content": "[30] Xiaowei Hu, Zhe Gan, Jianfeng Wang, Zhengyuan Yang, Zicheng Liu, Yumao Lu, and Lijuan Wang. Scaling up vision-language pre-training for image captioning. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.445, + 0.468, + 0.498 + ], + "angle": 0, + "content": "[31] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICML, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.499, + 0.468, + 0.538 + ], + "angle": 0, + "content": "[32] Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, and Weidi Xie. Prompting visual-language models for efficient video understanding. In ECCV, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.539, + 0.468, + 0.592 + ], + "angle": 0, + "content": "[33] Evangelos Kazakos, Jaesung Huh, Arsha Nagrani, Andrew Zisserman, and Dima Damen. With a little help from my temporal context: Multimodal egocentric action recognition. In BMVC, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.593, + 0.468, + 0.646 + ], + "angle": 0, + "content": "[34] Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, and Boqing Gong. Movinets: Mobile video networks for efficient video recognition. In CVPR, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.647, + 0.468, + 0.687 + ], + "angle": 0, + "content": "[35] Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In ICCV, 2011." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.688, + 0.468, + 0.739 + ], + "angle": 0, + "content": "[36] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In CVPR, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.741, + 0.468, + 0.78 + ], + "angle": 0, + "content": "[37] Yin Li, Miao Liu, and James M Rehg. In the eye of beholder: Joint learning of gaze and actions in first person video. In ECCV, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.781, + 0.468, + 0.821 + ], + "angle": 0, + "content": "[38] Yanghao Li, Tushar Nagarajan, Bo Xiong, and Kristen Grauman. Ego-exo: Transferring visual representations from third-person to first-person videos. In CVPR, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.822, + 0.468, + 0.891 + ], + "angle": 0, + "content": "[39] Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan, Michael Wray, Rui Yan, Eric Zhongcong Xu, Difei Gao, Rongcheng Tu, Wenzhe Zhao, Weijie Kong, Hongfa Cai Chengfei, Wang, Dima Damen, Bernard Ghanem, Wei Liu, and Mike Zheng Shou. Egocentric video-language pre" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.469, + 0.891 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.698, + 0.106 + ], + "angle": 0, + "content": "training. In NeurIPS, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.107, + 0.892, + 0.159 + ], + "angle": 0, + "content": "[40] Ziyi Lin, Shijie Geng, Renrui Zhang, Peng Gao, Gerard de Melo, Xiaogang Wang, Jifeng Dai, Yu Qiao, and Hongsheng Li. Frozen clip models are efficient video learners. In ECCV, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.16, + 0.892, + 0.2 + ], + "angle": 0, + "content": "[41] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.201, + 0.892, + 0.268 + ], + "angle": 0, + "content": "[42] Huaishao Luo, Lei Ji, Botian Shi, Haoyang Huang, Nan Duan, Tianrui Li, Jason Li, Taroon Bharti, and Ming Zhou. Univl: A unified video and language pre-training model for multimodal understanding and generation. arXiv preprint arXiv:2002.06353, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.269, + 0.892, + 0.294 + ], + "angle": 0, + "content": "[43] Maluuba. nlg-eval. https://github.com/Maluuba/nlg-eval. Accessed: 2022-06-01." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.295, + 0.892, + 0.348 + ], + "angle": 0, + "content": "[44] Antoine Miech, Jean-Baptiste Alayrac, Lucas Smaira, Ivan Laptev, Josef Sivic, and Andrew Zisserman. End-to-end learning of visual representations from uncurated instructional videos. In CVPR, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.349, + 0.892, + 0.403 + ], + "angle": 0, + "content": "[45] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic. Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In ICCV, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.403, + 0.892, + 0.456 + ], + "angle": 0, + "content": "[46] Arsha Nagrani, Paul Hongsuck Seo, Bryan Seybold, Anja Hauth, Santiago Manen, Chen Sun, and Cordelia Schmid. Learning audio-video modalities from image captions. In ECCV, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.457, + 0.892, + 0.511 + ], + "angle": 0, + "content": "[47] Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, and Haibin Ling. Expanding language-image pretrained models for general video recognition. In ECCV, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.511, + 0.892, + 0.551 + ], + "angle": 0, + "content": "[48] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.552, + 0.892, + 0.619 + ], + "angle": 0, + "content": "[49] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In ICML, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.62, + 0.892, + 0.659 + ], + "angle": 0, + "content": "[50] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. OpenAI blog, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.66, + 0.892, + 0.713 + ], + "angle": 0, + "content": "[51] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.714, + 0.892, + 0.766 + ], + "angle": 0, + "content": "[52] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.767, + 0.892, + 0.808 + ], + "angle": 0, + "content": "[53] Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, and Bernt Schiele. Movie description. *IJCV*, 2017." + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.809, + 0.892, + 0.891 + ], + "angle": 0, + "content": "[54] Christoph Schuhmann, Romain Beaumont, Cade W Gordon, Ross Wightman, Theo Coombes, Aarush Katta, Clayton Mullis, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev. Laion-5b: An open large-scale dataset for training next generation image-text models. In NeurIPS D&B," + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.891 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.515, + 0.957 + ], + "angle": 0, + "content": "6595" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.112, + 0.093, + 0.147, + 0.105 + ], + "angle": 0, + "content": "2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.106, + 0.469, + 0.145 + ], + "angle": 0, + "content": "[55] Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words with subword units. In ACL, 2016." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.147, + 0.469, + 0.186 + ], + "angle": 0, + "content": "[56] Paul Hongsuck Seo, Arsha Nagrani, Anurag Arnab, and Cordelia Schmid. End-to-end generative pretraining for multimodal video captioning. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.187, + 0.469, + 0.227 + ], + "angle": 0, + "content": "[57] Gunnar A Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, and Karteek Alahari. Actor and observer: Joint modeling of first and third-person videos. In CVPR, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.228, + 0.469, + 0.28 + ], + "angle": 0, + "content": "[58] Gunnar A Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, and Karteek Alahari. Charades-ego: A large-scale dataset of paired third and first person videos. arXiv preprint arXiv:1804.09626, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.281, + 0.469, + 0.307 + ], + "angle": 0, + "content": "[59] Kihyuk Sohn. Improved deep metric learning with multiclass n-pair loss objective. NeurIPS, 2016." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.308, + 0.469, + 0.348 + ], + "angle": 0, + "content": "[60] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.349, + 0.469, + 0.401 + ], + "angle": 0, + "content": "[61] Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer. How to train your vit? data, augmentation, and regularization in vision transformers. TMLR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.402, + 0.469, + 0.442 + ], + "angle": 0, + "content": "[62] Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Vl-bert: Pre-training of generic visual-linguistic representations. In ICLR, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.443, + 0.469, + 0.481 + ], + "angle": 0, + "content": "[63] Swathikiran Sudhakaran, Sergio Escalera, and Oswald Lanz. Lsta: Long short-term attention for egocentric action recognition. In CVPR, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.483, + 0.469, + 0.522 + ], + "angle": 0, + "content": "[64] Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid. Videobert: A joint model for video and language representation learning. In ICCV, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.523, + 0.469, + 0.563 + ], + "angle": 0, + "content": "[65] Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill. Multimodal few-shot learning with frozen language models. In NeurIPS, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.564, + 0.469, + 0.603 + ], + "angle": 0, + "content": "[66] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, 2017." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.604, + 0.469, + 0.669 + ], + "angle": 0, + "content": "[67] Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. Diverse beam search: Decoding diverse solutions from neural sequence models. arXiv preprint arXiv:1610.02424, 2016." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.671, + 0.469, + 0.71 + ], + "angle": 0, + "content": "[68] Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. Show and tell: A neural image caption generator. In CVPR, 2015." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.711, + 0.469, + 0.779 + ], + "angle": 0, + "content": "[69] Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, et al. Image as a foreign language: Beit pretraining for all vision and vision-language tasks. arXiv preprint arXiv:2208.10442, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.78, + 0.469, + 0.833 + ], + "angle": 0, + "content": "[70] William Yang Wang and Diyi Yang. That's so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets. In EMNLP, 2015." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.834, + 0.469, + 0.873 + ], + "angle": 0, + "content": "[71] Xiaohan Wang, Linchao Zhu, Heng Wang, and Yi Yang. Interactive prototype learning for egocentric action recognition. In ICCV, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.874, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[72] Jason Wei and Kai Zou. Eda: Easy data augmentation techniques for boosting performance on text classification tasks." + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.093, + 0.469, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.094, + 0.641, + 0.105 + ], + "angle": 0, + "content": "In EMNLP, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.106, + 0.892, + 0.146 + ], + "angle": 0, + "content": "[73] Jason Weston, Samy Bengio, and Nicolas Usunier. Large scale image annotation: learning to rank with joint word-image embeddings. Machine learning, 2010." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.147, + 0.892, + 0.186 + ], + "angle": 0, + "content": "[74] John Wieting and Kevin Gimpel. *Paranmt-50m: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations*. In ACL, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.187, + 0.892, + 0.227 + ], + "angle": 0, + "content": "[75] Michael Wray, Diane Larlus, Gabriela Csurka, and Dima Damen. Fine-grained action retrieval through multiple parts-of-speech embeddings. In ICCV, 2019." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.228, + 0.892, + 0.28 + ], + "angle": 0, + "content": "[76] Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam, Haoqi Fan, Bo Xiong, Jitendra Malik, and Christoph Feichtenhofer. Memvit: Memory-augmented multiscale vision transformer for efficient long-term video recognition. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.281, + 0.892, + 0.335 + ], + "angle": 0, + "content": "[77] Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. Videoclip: Contrastive pre-training for zero-shot video-text understanding. In EMNLP, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.336, + 0.892, + 0.389 + ], + "angle": 0, + "content": "[78] Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, and Jiebo Luo. Clip-vip: Adapting pretrained image-text model to video-language representation alignment. In ICLR, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.39, + 0.892, + 0.43 + ], + "angle": 0, + "content": "[79] Shen Yan, Xuehan Xiong, Anurag Arnab, Zhichao Lu, Mi Zhang, Chen Sun, and Cordelia Schmid. Multiview transformers for video recognition. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.431, + 0.892, + 0.469 + ], + "angle": 0, + "content": "[80] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. CoCa: Contrastive captioners are image-text foundation models. TMLR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.47, + 0.892, + 0.565 + ], + "angle": 0, + "content": "[81] Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, Ce Liu, Mengchen Liu, Zicheng Liu, Yumao Lu, Yu Shi, Lijuan Wang, Jianfeng Wang, Bin Xiao, Zhen Xiao, Jianwei Yang, Michael Zeng, Luowei Zhou, and Pengchuan Zhang. Florence: A new foundation model for computer vision. arXiv preprint arXiv:2111.11432, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.566, + 0.892, + 0.618 + ], + "angle": 0, + "content": "[82] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. NeurIPS, 2021." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.62, + 0.892, + 0.687 + ], + "angle": 0, + "content": "[83] Andy Zeng, Adrian Wong, Stefan Welker, Krzysztof Choromanski, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, and Pete Florence. Socratic models: Composing zero-shot multimodal reasoning with language. In ICLR, 2023." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.688, + 0.892, + 0.713 + ], + "angle": 0, + "content": "[84] Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer. Scaling vision transformers. In CVPR, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.714, + 0.892, + 0.753 + ], + "angle": 0, + "content": "[85] Xiang Zhang, Junbo Zhao, and Yann LeCun. Character-level convolutional networks for text classification. In NeurIPS, 2015." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.755, + 0.892, + 0.793 + ], + "angle": 0, + "content": "[86] Luowei Zhou, Chenliang Xu, and Jason J Corso. Towards automatic learning of procedures from web instructional videos. In AAAI, 2018." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.794, + 0.892, + 0.835 + ], + "angle": 0, + "content": "[87] Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbihl, and Ishan Misra. Detecting twenty-thousand classes using image-level supervision. In ECCV, 2022." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.836, + 0.892, + 0.86 + ], + "angle": 0, + "content": "[88] Linchao Zhu and Yi Yang. Actbert: Learning global-local video-text representations. In CVPR, 2020." + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.861, + 0.892, + 0.888 + ], + "angle": 0, + "content": "[89] Xizhou Zhu, Jinguo Zhu, Hao Li, Xiaoshi Wu, Hongsheng Li, Xiaohua Wang, and Jifeng Dai. Uni-perceiver: Pre" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.094, + 0.892, + 0.888 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6596" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.11, + 0.093, + 0.468, + 0.12 + ], + "angle": 0, + "content": "training unified architecture for generic perception for zero-shot and few-shot tasks. In CVPR, 2022." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.515, + 0.957 + ], + "angle": 0, + "content": "6597" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Video Representations From Large Language Models/c00bc946-1a6e-40fd-9b0d-ec2826e3ea29_origin.pdf b/2023/Learning Video Representations From Large Language Models/c00bc946-1a6e-40fd-9b0d-ec2826e3ea29_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..72d2cf9d76e6934909377f32ed8f3390c3c1c638 --- /dev/null +++ b/2023/Learning Video Representations From Large Language Models/c00bc946-1a6e-40fd-9b0d-ec2826e3ea29_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ae9ba0f580cddc7ec5ecce3321c022eb515a0e46f9d33b8a367a3a34a42a76bf +size 1094244 diff --git a/2023/Learning Video Representations From Large Language Models/full.md b/2023/Learning Video Representations From Large Language Models/full.md new file mode 100644 index 0000000000000000000000000000000000000000..5151604b1c626cf353ed77f1e8da882fe2b609e5 --- /dev/null +++ b/2023/Learning Video Representations From Large Language Models/full.md @@ -0,0 +1,356 @@ +# Learning Video Representations from Large Language Models + +Yue Zhao $^{1,2*}$ Ishan Misra $^{1}$ Philipp Krahenbuhl $^{2}$ Rohit Girdhar $^{1}$ $^{1}$ FAIR, Meta AI $^{2}$ University of Texas, Austin + +facebookresearch.github.io/LaViLa + +# Abstract + +We introduce LAVILA, a new approach to learning video-language representations by leveraging Large Language Models (LLMs). We repurpose pre-trained LLMs to be conditioned on visual input, and finetune them to create automatic video narrators. Our auto-generated narrations offer a number of advantages, including dense coverage of long videos, better temporal synchronization of the visual information and text, and much higher diversity of text. The video-language embedding learned contrastively with these narrations outperforms the previous state-of-the-art on multiple first-person and third-person video tasks, both in zero-shot and finetuned setups. Most notably, LAVILA obtains an absolute gain of $10.1\%$ on EGTEA classification and $5.9\%$ Epic-Kitchens-100 multi-instance retrieval benchmarks. Furthermore, LAVILA trained with only half the narrations from the Ego4D dataset outperforms models trained on the full set, and shows positive scaling behavior on increasing pre-training data and model size. + +# 1. Introduction + +Learning visual representation using web-scale imag-text data is a powerful tool for computer vision. Vision-language approaches [31, 49, 80] have pushed the state-of-the-art across a variety of tasks, including zero-shot classification [49], novel object detection [87], and even image generation [52]. Similar approaches for videos [4, 39, 46], however, have been limited by the small size of paired video-text corpora compared to the billion-scale image-text datasets [31, 49, 84]—even though access to raw video data has exploded in the past decade. In this work, we show it is possible to automatically generate text pairing for such videos by leveraging Large Language Models (LLMs), thus taking full advantage of the massive video data. Learning video-language models with these automatically generated annotations leads to stronger representations, and as Figure 1 shows, sets a new state-of-the-art on six popular first + +![](images/54f3dc189ad3306f4658255c97c4c2a033eb2d199bc9b48e836b3e1a7eadafbd.jpg) +Figure 1. LAVILA sets a new state-of-the-art across a number of first and third-person video understanding tasks (cf. Table 1 for details), by learning a video-language representation using supervision from large language models as narrators. + +and third-person video benchmarks. + +Our method, called LAVILA: Language-model augmented Video-Language pre-training, leverages pretrained LLMs, e.g. GPT-2 [50], which encode within their weights a treasure trove of factual knowledge and conversational ability. As shown in Figure 2, we repurpose these LLMs to be "visually-conditioned narrators", and finetune on all accessible paired video-text clips. Once trained, we use the model to densely annotate thousands of hours of videos by generating rich textual descriptions. This pseudo-supervision can thus pervade the entire video, in between and beyond the annotated snippets. Paired with another LLM trained to rephrase existing narrations, LAVILA is able to create a much larger and more diverse set of text targets for video-text contrastive learning. In addition to setting a new state-of-the-art as noted earlier, the stronger representation learned by LAVILA even outperforms prior work using only half the groundtruth annotations (Figure 5). + +LAVILA's strong performance can be attributed to a number of factors. First, LAVILA can provide temporally dense supervision for long-form videos, where the associ + +![](images/88a97ec45d22afb394ccf91dab3f7b6b59e031a243109148c2ad200ff9375e7f.jpg) +Figure 2. LAVILA leverages Large Language Models (LLMs) to densely narrate long videos, and uses those narrations to train strong dual-encoder models. While prior work uses sparsely labeled text by humans, or weakly aligned text transcribed from speech, LAVILA is able to leverage dense, diverse, and well-aligned text generated by a LLM. + +ated captions are either too sparse, or the video-level "Alt-Text" (in the case of web videos) does not describe all the nuanced activities happening in it. Second, the generated text is well-aligned with the visual input. Although prior work has leveraged automatic speech transcription on How-To videos [45] to automatically extract clips paired with text from the speech, such datasets have relatively poor alignment between the visual and textual content ( $\leq 50\%$ , cf. [25, 45]), limiting the quality of the learned representations. Third, LAVILA can significantly expand annotations when only a little is available. For instance, videos of mundane day-to-day activities, especially from an egocentric viewpoint, could be very useful for assistive and augmented reality applications. Such videos, however, are rare on the internet, and hence do not readily exist with associated web text. Recent work [24] instead opted to manually capture and narrate such video data. These narrations however required significant manual effort: 250K hours of annotator time spent in narrating 3.6K hours of video. In contrast, LAVILA is able to automatically narrate each video multiple times and far more densely, and hence learns much stronger representations. + +We extensively evaluate LAVILA across multiple videotext pre-training datasets and downstream tasks to validate its effectiveness. Specifically, after being pre-trained on Ego4D, the largest egocentric video datasets with narrations, LAVILA can re-narrate the whole dataset $10 \times$ over. The resulting model learned on these expanded narrations sets a new state-of-the-art on a wide range of downstream tasks across challenging datasets, including multi-instance + +video retrieval on Epic-Kitchens-100 (5.9% absolute gain on mAP), multiple-choice question answering on Ego4D (5.9% absolute gain on intra-video accuracy), and action recognition on EGTEA (10.1% absolute gain on mean accuracy). It obtains gains both when evaluated for zero-shot transfer to the new dataset, as well as after fine-tuning on that dataset. Similar gains are shown in third-person video data. When training LAVILA after densely re-narrating HowTo100M, we outperform prior work on downstream action classification on UCF-101 and HMDB-51. In a case study of semi-supervised learning, we show that our model, which only ever sees 50% of the human-labeled data, is capable of outperforming the baseline model trained with all the narrations. Moreover, the gains progressively increase as we go to larger data regimes and larger backbones, suggesting the scalability of our method. + +# 2. Related Work + +Vision-language representation learning maps visual and textual embeddings into a common space using metric-learning techniques [21, 73]. Recently, different pretext tasks are proposed to learn a finer-grained association between visual and textual modality, e.g. masked language modeling (MLM) [10, 41, 62] and captioning [16, 80]. Another line of research focuses on scaling up both models and pre-training data. For instance, CLIP [49] is pre-trained on 400M image-text pairs with a contrastive loss (InfoNCE [48, 59]) while CoCa [80] unifies contrastive and generative approaches with a single foundation model. Similar trends are also witnessed in the videotext domain [36, 64, 88]. However, collecting high-quality video-text data is more difficult than image-text. Therefore, efforts are made to learn from uncurated videos with machine-generated audio transcripts via contrastive learning [44, 77, 82] or unsupervised alignment [25] while other works focus on either adapting well-performing image-text models to videos [32, 40, 47, 78], or curriculum learning from a single frame to multiple frames [4]. In contrast, our approach leverages language models to generate temporally dense textual supervision on long-form videos. + +Generative Visual Language Models (VLM) were first used for image/video captioning using recurrent networks [17, 68] and Transformer-based architectures [42, 56]. More recently, generative VLMs have unified multiple vision tasks [11, 89] by training multi-modal Transformers on visual-text pairs [30, 81]. Meanwhile, generative VLMs also excel at multimodal tasks via zero-shot or few-shot prompting [1, 65, 83] by leveraging multi-billion-parameter LLMs pre-trained on massive text corpus [7, 28, 50]. In our work, we demonstrate that generative VLMs can narrate long videos and the resulting video-text data benefits video-language representation learning. + +Large-scale multimodal video datasets are crucial for + +video understanding tasks but are hard to collect. Conventional video-text datasets [8, 53, 86] either have limited scenarios, e.g. cooking, or are not large enough to learn generic video representation. Miech et al. [45] scrape over 100 million video-text pairs via automatic audio transcription from long-form How-To videos. However, ASR introduces textual noise and visual-text unalignment [25]. WebVid [4] contains 10 million short videos with textual descriptions. But it is still several orders of magnitude smaller than the image counterparts [49, 54] and is harder to scale up since it is sourced from stock footage sites. The recently released Ego4D [24] dataset offers 3,600 hours of egocentric videos in which written sentence narrations are manually annotated every few seconds but requires significant manual effort. In contrast, our method shows a promising alternative by automatically narrating videos using supervision from LLM. + +Data augmentation techniques in NLP, including word-level replacement based on synonyms [72, 85] or nearest-neighbor retrieval [19, 70], improve text classification accuracy. We refer readers to [20] for a comprehensive survey. In this paper, we show that sentence-level paraphrasing based on text-to-text models [51] is helpful for video-language pre-training. + +# 3. Preliminaries + +A video $V$ is a stream of moving images $I$ . The number of frames $|V|$ can be arbitrarily long while video models typically operate on shorter clips, which are often in the range of a few seconds. Therefore, we skim through a long-form video and represent it by a set of $N$ short clips, i.e. $\mathcal{X}$ . Each clip $x_{i}$ is defined by a specific start and end frame $x_{i} = \{I_{t_{i}},\dots ,I_{e_{i}}\}$ , where $0 < t_i < e_i\leq |V|$ , and is typically associated with some annotation $y_{i}$ . This annotation could be a class label or free-form textual description of the clip. We denote a video by the set of annotated clips with their corresponding annotations, i.e. $(\mathcal{X},\mathcal{Y}) = \{(x_1,y_1),\dots ,(x_N,y_N)\}$ . Note that the annotated clips often cannot densely cover the entire video due to the annotation cost and visual redundancy, i.e. $\bigcup_{i}[t_{i},e_{i}]\subsetneq [0,|V|]$ . + +A typical video model $\mathcal{F}(\mathcal{X},\mathcal{Y})$ learns from these clip-level annotations using a standard training objective such as a cross-entropy loss when the annotations are class labels with a fixed vocabulary. However, more recently, dual-encoder-based contrastive approaches like CLIP [49, 77] have gained popularity. They work with free-form textual annotations which are tokenized [55] into sequences of discrete symbols, i.e. $y = (s_{1},s_{2},\dots ,s_{L})\in \{1,0\}^{|\mathbb{S}|\times L}$ . The model consists of a visual encoder $f_{\mathrm{v}}:\mathbb{R}^{T\times 3\times H\times W}\mapsto \mathbb{R}^{D_{\mathrm{v}}}$ plus a projection head $h_\mathrm{v}:\mathbb{R}^{D_\mathrm{v}}\mapsto \mathbb{R}^d$ and a textual encoder $f_{\mathrm{t}}:\{1,0\}^{|\mathbb{S}|\times L}\mapsto \mathbb{R}^{D_{\mathrm{t}}}$ plus a projection head $h_{\mathrm{t}}:\mathbb{R}^{D_{\mathrm{t}}}\mapsto \mathbb{R}^{d}$ in parallel to obtain the global visual and textual embedding respectively: + +$$ +\mathbf {v} = h _ {\mathrm {v}} \left(f _ {\mathrm {v}} (x)\right), \quad \mathbf {u} = h _ {\mathrm {t}} \left(f _ {\mathrm {t}} (y)\right). +$$ + +![](images/48bec47f8c78bfd006e1421c0b55d8dff0dfb6df0c5855df75587a2dd64b15fb.jpg) +Narration + +![](images/0b9cbb002d9bd35f7768f8a5c554b7216fe6131cc5be01ef1455df09d4f4be4f.jpg) +C separates the yarn + +![](images/6a835593baf5ce5df53a6de0cfb0eeaee3e05c6195070f035d8d1ce423e5eb0e.jpg) +NARRATOR +C stretches the thread with both hands. +C pulls out the yarn with her right hand. + +![](images/2bf280c625311a6cb2401d38a528262f3c61bbe14e5dc83c707b97773d677f26.jpg) +C divides the yarn by a few stitches. +Narration +C lifts container. C raises the container. +Figure 3. Generated samples by NARRATOR and REPHRASER. NARRATOR generates new descriptions of the action taking place, potentially focusing on other objects being interacted with. REPHRASER not only changes the word order of the human narration but also diversifies it by using related verbs or nouns. + +![](images/bca71e97eac0113e025cfec06c5194426b0998028aae966435d8895da9db6aae.jpg) +NARRATOR + +![](images/082e7ea4476e6863b6bf528dd276d336086df4344ba5b37a8a282b9092390b67.jpg) +C wipes the countertop with a sponge. +Cmoves the container. + +A contrastive loss, such as InfoNCE [48], learns global embeddings that associate corresponding video and text embeddings within a batch of samples $\mathcal{B}$ + +$$ +\frac {1}{| \mathcal {B} |} \sum_ {(x, y) \in \mathcal {B}} \left(\operatorname {I n f o N C E} (\mathbf {v}, \mathbf {u}) + \operatorname {I n f o N C E} (\mathbf {u}, \mathbf {v})\right). \tag {1} +$$ + +# 4. LAVILA + +In LAVILA, we leverage large language models (LLMs) as supervision to train the dual-encoder model, where the LLMs serve as vision-conditioned narrators and automatically generate textual descriptions from video clips. In particular, we exploit supervision from two LLMs: (1) NARRATOR ( $\S 4.1$ ) is a visually-conditioned LLM that pseudolabels existing and new clips with narrations, generating new annotations $(\mathcal{X}',\mathcal{Y}')$ . (2) REPHRASER ( $\S 4.2$ ) is a standard LLM that paraphrases narrations in existing clips, augmenting those annotations to $(\mathcal{X},\mathcal{Y}^{\prime \prime})$ . As illustrated in Figure 3, NARRATOR generates new descriptions of the action taking place, potentially focusing on other objects being interacted with. REPHRASER serves to augment the text input, e.g., changes word order of the human narration and additionally replaces common verbs or nouns, making annotations more diverse. Finally, we train the dual-encoders ( $\S 4.3$ ) on all these annotations combined, i.e. $(\mathcal{X},\mathcal{Y}) \cup (\mathcal{X}',\mathcal{Y}') \cup (\mathcal{X},\mathcal{Y}^{\prime \prime})$ . + +# 4.1. NARRATOR + +Traditional LLMs, such as GPT-2 [50], are trained to generate a sequence of text tokens $(s_1 \cdots s_L)$ from scratch + +![](images/9ed857c62160541be695a0c63fac579ca5e1ed59232035ce5c1f0959bc440440.jpg) +Figure 4. Language supervision from REPHRASER and NARRATOR. REPHRASER(left) takes the narration as input, passes it through a text encoder and uses a text decoder to autoregressively generate the rephrased output. NARRATOR(right) takes video frames as input and obtains the visual embeddings through a video encoder followed by attentional pooling. Equipped with a few additional cross-attention modules, the text decoder autoregressively generates new narrations for those new frames. + +![](images/6774a18f9439c1444f2605f63f81402ad416a2063fcaaf587521748fd165560b.jpg) + +by modeling the probability of the next token given all tokens seen so far: $p(s_{l}|s_{$ . We sample from the distribution recursively, i.e. $\tilde{s}_{\ell} \sim p(s|[, \dots, \tilde{s}_{\ell-1}], x)$ until an end-of-sentence token $$ is reached. Following [29], at each step we sample from a subset of tokens that contain the vast majority of the probability mass, which is known as nucleus sampling. + +The effect of nucleus sampling is two-fold. On the one hand, it generates more diverse, open-ended, and human-like text than maximum-likelihood-based methods such as beam search and its variants [67]. On the other hand, the generated text may contain irrelevant or noisy information due to sampling without post-processing based on sentence-level likelihood. To address this, we repeat the sampling process for $K$ times on the same visual input. We later demonstrate that the contrastive pre-training objective is robust to the noise caused by sampling, and the final performance benefits from a more diverse set of narrations. + +To sample video clips for captioning, we start by simply re-captioning the existing clips labeled in the dataset $\mathcal{X}$ , resulting in expanded annotations. Furthermore, long-form videos are typically sparsely narrated, meaning that the temporal union of all labeled clips cannot cover the entire video. Hence, we use NARRATOR to annotate the remainder of the video to obtain additional annotations by pseudo-captioning. With a simple assumption that video is a stationary process, we uniformly sample clips from the unlabeled intervals. The clip duration is equal to the average of all ground-truth clips, i.e. $\Delta = \frac{1}{N} \sum_{i=1}^{N} (e_i - t_i)$ while the sampling stride is computed likewise. Finally, by + +combining both re-captioned and pseudo-captioned narrations, we refer to the final set of annotations generated by NARRATOR as $(\mathcal{X}',\mathcal{Y}')$ + +Post-processing. Exhaustive pseudo-captioning may contain some uninformative visual clips and generate text that is not useful. Thus, we add a filtering process to eliminate low-quality clips and their associated descriptions. We use the baseline dual-encoder model $\mathcal{F}$ , which is trained on the ground-truth paired clips, to compute the visual and textual embedding of pseudo-labeled pairs and filter based on the similarity score, i.e. $\mathrm{Filter}(f_{\mathrm{v}}(x_j')^\top \cdot f_{\mathrm{t}}(y_j'))$ , where $\mathrm{Filter}(\cdot)$ can be either top- $k$ of all generated text or a threshold filtering. In the experiments, we use a threshold of 0.5. + +# 4.2. REPHRASER + +The data generated by NARRATOR is several times larger than the ground-truth pairs. To ensure that we do not overfit the pseudo-labeled data, we increase the number of ground-truth narrations by paraphrasing. In particular, we use a text-to-text LLM which models conditional text likelihood: + +$$ +p _ {\text {R E P H R A S E R}} \left(y ^ {\prime \prime} | y\right) = \prod_ {\ell = 1} ^ {L} p \left(s _ {\ell} ^ {\prime \prime} \mid s _ {< \ell} ^ {\prime \prime}, y\right). +$$ + +The text-to-text model is implemented by an encoder-decoder architecture, e.g. T5 [51], to auto-regressively generate a new sentence given the original one. We observe that REPHRASER is able to do basic manipulations such as replacing synonyms or changing word order, which serves as an efficient way of automatic data augmentation. The resulting annotations are referred to as $(\mathcal{X},\mathcal{Y}^{\prime \prime})$ + +# 4.3. Training the Dual-Encoders + +We train the dual-encoders as described in Algorithm 1 in Appendix E. In each iteration, we first sample a batch $\mathcal{B}$ of video clips. It comprises a subset of clips $\mathcal{B}_l$ with labeled timestamps as well as narrations, and a subset $\mathcal{B}_u$ whose clips are randomly sampled from videos without narrations. For clip $x_i\in \mathcal{B}_u$ , we obtain the pseudo-caption $y_{i}^{\prime}$ by querying the NARRATOR $y_{i}^{\prime}\sim p_{\mathrm{NARRATOR}}(y^{\prime}|x)$ , resulting in a set of clips with LLM-generated narrations $\widetilde{B}_u$ . For clip $(x_{i},y_{i})\in \mathcal{B}_{l}$ , the text supervision is obtained from either the REPHRASER or the NARRATOR, with a probability of 0.5. Hence, the effective number of iterations per epoch for LAVILA is the same as that for the baseline Dual-Encoder. We denote the resulting set of pairs to be $\widetilde{B}_l$ similarly. Following CLIP [49], we use the symmetric cross-entropy loss over the similarity scores of samples in the batch $\widetilde{B}_l\cup \widetilde{B}_u$ . + +In practice, we run REPHRASER and NARRATOR in advance and cache the resulting video-narration pairs so that there is no computational overhead during pre-training. Therefore, training a dual-encoder in LAVILA is as fast as training a standard dual-encoder contrastive model. + +
DatasetsTaskEgo?MetricsEval. Prot.
EK-100 [14]MIR✓mAP, nDCGZS, FT
EK-100 [14]CLS✓top-1 action acc.FT
Ego4D [24]MCQ✓inter-/intra-video acc.ZS
Ego4D [24]NLQ✓Recall@NFT
EGTEA [37]CLS✓top-1, mean acc.ZS, FT
CharadesEgo [58]CLS✓video-level mAPZS, FT
UCF-101 [60]CLS✗mean acc.LP
HMDB-51 [35]CLS✗mean acc.LP
+ +Table 1. Downstream datasets and metrics used for evaluation. We evaluate LAVILA on a wide range of tasks including Multi-Instance Retrieval (MIR), Multiple-Choice Question (MCQ), Natural Language Query (NLQ), and Action Recognition (CLS). The evaluation protocols include zero-shot (ZS), fine-tuning (FT), and linear-probing (LP). Please refer to Appendix C for more details. + +# 5. Experiments + +Dual-Encoder Architecture. The video-language model follows a dual-encoder architecture as CLIP [49]. The Visual encoder is a TimeSformer (TSF) [6], whose spatial attention modules are initialized from a ViT [18] which is contrastively pre-trained on large-scale paired image-text data as in CLIP [49]. We sample 4 frames per clip during pre-training and 16 when finetuning on downstream tasks. The text encoder is a 12-layer Transformer [50, 66]. We use BPE tokenizer [55] to pre-process the full sentence corresponding to the video clip and keep at most 77 tokens. + +NARRATOR's architecture is a visually conditioned auto-regressive Language Model. The visual encoder is by default TimeSformer-L while the text decoder is a GPT-2 XL. During inference, we use nucleus sampling [29] with $p = 0.95$ and return $K = 10$ candidate outputs. + +REPHRASER. We use an open-source paraphraseer [23] based on T5-large [51]. It is pre-trained on C4 [51] and then finetuned on a cleaned subset of ParaNMT [74]. During inference, we use Diverse Beam Search [67] with group number the same as beam number $(G = B = 20)$ and set the diversity penalty to be 0.7. We keep 3 candidates per sentence, remove punctuations, and do basic de-duplication. + +Pre-training dataset. We train on the video-narration pairs from Ego4D [13, 24], the largest egocentric video dataset to date. We exclude videos that appear in the validation and test sets of the Ego4D benchmark and determine each clip's interval using the same pairing strategy in [39]. This results in around 4M video-text pairs with an average clip length of 1 second. We also experiment with third-person videos by pre-training on HowTo100M [45] in § 5.2. + +Evaluation protocols. We evaluate the learned video-text encoders using three evaluation protocols. (1) Zero-Shot (ZS), meaning that we apply the pre-trained video-text encoders directly on the downstream validation dataset to perform video $\leftrightarrow$ text retrieval tasks, without any tuning on the downstream dataset. Zero-shot classification is performed + +
MethodBackbonemAPnDCG
V→TT→VAvg.V→TT→VAvg.
(ZERO-SHOT)
EgoVLP [39]TSF-B19.413.916.624.122.023.1
EgoVLP* [39]TSF-B26.020.623.328.827.027.9
LAVILATSF-B35.126.630.933.730.432.0
LAVILATSF-L40.032.236.136.133.234.6
(FINETUNED)
MME [75]TBN43.034.038.550.146.948.5
JPoSE [75]TBN49.938.144.055.551.653.5
EgoVLP [39]TSF-B49.940.545.060.957.959.4
LAVILATSF-B55.245.750.566.563.465.0
LAVILATSF-L54.747.150.968.164.966.5
+ +similarly, where we compute the similarity score between the video clip and the textual description of all possible categories. (2) Finetuned (FT), where we take the pre-trained video-text model and perform end-to-end fine-tuning on the training split of the target downstream dataset. (3) LinearProbe (LP), where we compute the video features from a frozen encoder and train a linear SVM on top of the training split of the downstream dataset. + +Downstream benchmarks. We use multiple benchmarks across four first-person (egocentric) and two third-person datasets, as enumerated in Table 1. We summarize them here and refer the reader to Appendix C for details on datasets and metrics. (1) Two tasks on Epic-Kitchens-100: Multi-Instance Retrieval (EK-100 MIR) and Action Recognition (EK-100 CLS) [14]. EK-100 is a very popular and challenging egocentric video recognition benchmark. The MIR task requires retrieving the text given videos $(\mathrm{V} \rightarrow \mathrm{T})$ and videos given text $(\mathrm{T} \rightarrow \mathrm{V})$ . The CLS task requires classifying each video into one of 97 verbs and 300 nouns each, resulting in a combination of 3,806 action categories. (2) Two downstream tasks of Ego4D: Multiple-Choice Questions (EgoMCQ) and Natural Language Query (EgoNLQ). EgoMCQ requires selecting the correct textual description from five choices given a query video clip while EgoNLQ asks the model to output the relevant temporal intervals of video given a text query. We select these two benchmarks because they require reasoning about both visual and textual information. (3) Action Recognition on EGTEA [37]. It requires classifying into 106 classes of fine-grained cooking activities. (4) Action Recognition on CharadesEgo [58]. It requires classification into 157 classes of daily indoor activities. Note that CharadesEgo is very different from EK-100, Ego4D and EGTEA since its videos are captured by head-mounted phone cameras in a crowd-sourcing way. + +Table 2. EK-100 MIR. LAViLA outperforms prior work across all settings, metrics and directions of retrieval, with larger gains when switching to a larger model. Specifically, our best model achieves over $10\%$ absolute gain in the zero-shot setting and $5.9 \sim 7.1\%$ gain in the finetuned setting. EgoVLP* refers to our improved version of [39], details of which are given in Appendix F. + +
MethodEgoMCQEgoNLQ
Accuracy (%)mIOU@0.3mIOU@0.5
Inter-videoIntra-videoR@1R@5R@1R@5
SlowFast [24]--5.4510.743.126.63
EgoVLP [39]90.657.210.8418.846.8113.45
LAVILA (B)93.859.910.5319.136.6913.68
LAVILA (L)94.563.112.0522.387.4315.44
+ +Table 3. Ego4D EgoMCQ and EgoNLQ. LAVILA outperforms prior work on both Multiple-Choice Questions and Natural Language Questions on Ego4D, with nearly $6\%$ absolute gain on the challenging intra-video MCQ task that requires reasoning over multiple clips from the same video to answer a question. + +In all tables, we bold and underline the best and second-best performing methods with comparable backbones architectures. We highlight the overall best performing method, which typically uses a larger backbone, if applicable. + +# 5.1. Main Results + +EK-100. We compare LAVILA with prior works on EK-100 MIR in Table 2. In the zero-shot setup, LAVILA remarkably surpasses an improved version of EgoVLP [39] under similar model complexity: we use TSF-Base+GPT-2 as the dual-encoder architecture while EgoVLP uses TSF-Base+Distil-BERT. With a stronger video encoder, i.e. TSF-Large, the performance improves further. In the fine-tuned setting, LAVILA significantly outperforms all previous supervised approaches including MME, JPOSE [75] and EgoVLP [39]. We also compare LAVILA on EK-100 CLS in Appendix E, and establish a new state-of-the-art. + +Ego4D. We evaluate the pre-trained LAViLA model on EgoMCQ and EgoNLQ tasks and compare the results in Table 3. On EgoMCQ, our method achieves $93.8\%$ inter-video accuracy and $59.9\%$ intra-video accuracy, outperforming EgoVLP by a noticeable margin. Note that EgoVLP's performance reported in Table 3 is obtained by using EgoNCE loss [39], a variant of InfoNCE specialized for Ego4D while ours uses a standard InfoNCE loss. EgoVLP with InfoNCE has lower performance (89.4% inter-video and $51.5\%$ intravideo accuracy). On EgoNLQ, LAViLA achieves comparable results with EgoVLP with similar model complexity. + +EGTEA. We evaluate the learned video representation by finetuning the video encoder for action classification in Table 4 on another popular egocentric dataset, EGTEA [37]. Our method surpasses the previous state-of-the-art which takes multiple modalities including visual, auditory and textual inputs [33] by a more than $10\%$ absolute margin on the mean accuracy metric. Since previous methods are based on different backbones, we experiment with a TSF-Base ("Visual only") model pre-trained on Kinetics [9] as a fair baseline for LAVILA. We observe that its accuracy is comparable to previous methods but much lower than LAVILA, implying the effectiveness of learning visual representation + +
MethodBackbonePretrainTop-1 Acc.Mean Acc.
Li et al. [37]I3DK400-53.30
LSTA [63]ConvLSTMIN-1k61.8653.00
IPL [71]I3DK400-60.15
MTCN [33]SlowFast (V+A+T)K400+VGG-Sound73.5965.87
Visual onlyTSF-BIN-21k+K40065.5859.32
LAVILATSF-BWIT+Ego4D77.4570.12
LAVILATSF-LWIT+Ego4D81.7576.00
+ +Table 4. EGTEA Classification. LAVILA obtains significant gains on this task, outperforming prior work with over $10\%$ mean accuracy. Since the backbones used are not all comparable, we also report a comparable baseline with TSF-B ("Visual only"). + +
MethodBackbonemAP (ZS)mAP (FT)
ActorObserverNet [57]ResNet-152-20.0
SSDA [12]I3D-25.8
Ego-Exo [38]SlowFast-R101-30.1
EgoVLP [39]TSF-B25.032.1
LAVILATSF-B26.833.7
LAVILATSF-L28.936.1
+ +on large-scale egocentric videos and using LLM as textual supervision during pre-training. + +CharadesEgo. Next, we compare LAVILA's representation on the CharadesEgo action classification task. As shown in Table 5, LAVILA's representation excels on this task as well, which is notable as CharadesEgo videos are significantly different compared to Ego4D, being captured by crowdsourced workers using mobile cameras. + +# 5.2. Application to Third-Person Video Pre-training + +We apply LAVILA to third-person videos by experimenting with the HowTo100M [45] dataset. Specifically, we use the temporally aligned subset provided by [25], which contains 3.3M sentences from 247k videos. We evaluate the video representation on two third-person video datasets, i.e. + +Table 5. CharadesEgo Action Recognition. LAVILA sets new state-of-the-art in both zero-shot (ZS) and finetuned (FT) settings. Note that CharadesEgo videos are visually different compared to Ego4D videos, on which LAVILA is pretrained. + +
MethodVis. Enc.UCF-101HMDB-51
MIL-NCE [44]S3D82.754.3
TAN [25]S3D83.256.7
Baseline (w/o LLM)TSF-B86.559.4
LAViLATSF-B87.457.2
LAViLATSF-L88.161.5
+ +Table 6. LAVILA on third-person videos. We measure the linear-probing action classification performance of the video model after pre-training on HowTo100M [45]. + +UCF-101 [60] and HMDB-51 [35] for action classification using the linear probing protocol. For more details, please refer to Appendix D. From Table 6, we see that LAVILA outperforms previous methods such as MIL-NCE [44] and TAN [25] by a large margin. Since we use a different backbone, we report a baseline without LLM and show that LAVILA indeed benefits from the language supervision. + +# 5.3. Application to Semi-supervised Learning + +While LAVILA is very effective at leveraging existing narrations to augment them, we now show that it is also applicable when only a limited number of narrations are available to begin with. We first divide each long video from Ego4D into 15-second chunks and assume only the annotated clips within every $N$ chunks is available during pre-training, leading to approximately $\frac{100}{N}\%$ of the full set, where $N \in \{2,5,10\}$ . This can be considered a practical scenario when we want to annotate as many videos as possible for diversity when the annotation budget is limited. In the remainder $(1 - \frac{100}{N})\%$ part that is skipped, we uniformly sample the same number of the clips per chunk with the same clip length as that in the seen chunks. Both the dual-encoder model and NARRATOR are trained on the $100\%$ available annotations. + +We plot the zero-shot performance curve of pre-training with different proportions in Figure 5. We can see that LAV-ILA consistently outperforms the ground-truth-only baseline at all points (10, 20, 50, and $100\%$ ). The improvement + +![](images/c3f8d35a7515c1bb20c02918ea05d070808b05c9607c308e94319aeb773085c5.jpg) +(a) EK-100 MIR mAP. + +![](images/a99e94a0f49e338898d7ee6d5d41cba9cc2953097107e3b0169b3bca70229447.jpg) +(b) EK-100 MIR nDCG. +Figure 5. LAVILA is effective in a semi-supervised setting where only a limited amount of narrations are given. Comparing zero-shot performance of pre-training, LAVILA consistently outperforms the groundtruth-only baseline when 10, 20, 50, $100\%$ data is used. We also achieve comparable result with state-of-the-art with only $50\%$ of the annotated data. + +![](images/c6403c1e1dafe166ed69cd617f015dd0bed163ff9c13fc7ba28f23d84b3eba57.jpg) +(d) EgoMCQ Intra-video accuracy. + +
Text Dec. arch.Text Dec. init.Freeze LMMETEOR ROUGE-L CIDESAvg. mAP
(baseline)---26.0
GPT-2random×0.2840.5240.88224.3
GPT-2WebText✓0.2700.5050.80424.0
GPT-2 XLWebText✓0.2890.5300.94026.2
+ +(a) Generation Quality. Using a sufficiently large language model as the text decoder is crucial for good text generation quality and downstream performance. + +
Sampling method# of sentencesAvg. mAP
N/A (baseline)-26.0
Beam search127.9
Nucleus129.6
Nucleus1029.7
+ +(c) Scaling effect of LAVILA. Gains increase on scaling the video encoder in NARRATOR. Default refers to only using the original narrations. +Table 7. Ablations of NARRATOR. We report zero-shot average mAP on EK-100 MIR for comparing downstream performance. We study NARRATOR from the perspective of generation quality (left), sampling techniques (middle), and scaling effect (right). + +(b) Sampling. LAV1LA benefits more from narrations produced by nucleus sampling than beam search. + +![](images/7d8fa188da18a0da78a3340813a546ebcb084ea39047ab291fb0b0ed6a231dbb.jpg) + +
Text Dec. arch.Text Dec. init.Freeze LMMETEOR ROUGE1 CIDEsAvg. mAP
(baseline)---26.0
GPT-2randomX0.2840.5240.88224.3
GPT-2WebText✓0.2700.5050.80424.0
GPT-2 XLWebText✓0.2890.5300.94026.2
+ +Table 8. Contributions of different Language Supervision. We can see that (1) using REPHRASER ("Rephr.") and NARRATOR ("Recap.") improve downstream zero-shot performance complementarily, (2) dense pseudo-captioning further improves performance on 3 out of 6 metrics. + +tends to be larger when more data is available, indicating the method's scalability as more videos are narrated in the future. Furthermore, we observe our method can achieve a similar level of performance with the baseline often using less than $50\%$ data. We also achieve a comparable result with the state-of-the-art using much fewer data. + +# 5.4. Ablation Studies + +Contributions of Different Language Supervisions. We ablate different language supervisions in Table 8 on EK-100 MIR (zero-shot), EgoMCQ and EGTEA. Using the text-only REPHRASER ("rephr.") or visually conditioned NARRATOR ("recap.") separately improves the ground-truth baseline noticeably. Combining both REPHRASER and NARRATOR gives an improvement of $3.5\%$ average mAP on EK-100 MIR. We see that dense captioning on the entire video ("pseudo-cap.") is also helpful. Though the gain on EK-100 MIR is not as significant, it shows nontrivial improvements on EgoMCQ intra-video accuracy and EGTEA mean accuracy. Our conjecture for this marginal gain is that informative clips are mostly covered in Ego4D because all videos are inspected by two annotators. + +Generation Quality of NARRATOR. We study how the NARRATOR's configurations affect the quality of generated text and the downstream performance. The generation quality is measured by standard unsupervised automatic metrics including METEOR, ROUGE, and CIDEr [43]. We use a NARRATOR with a smaller GPT-2 as the text decoder and + +consider two scenarios in Table 7a: (1) LM is randomly initialized but jointly trained with the gated cross-attention modules, and (2) LM is initialized from the original GPT-2. The generation quality decreases compared to GPT-2 XL in both cases and the zero-shot retrieval result on EK-100 MIR is worse. This indicates that the language model should be sufficiently large and pre-trained on web text data. + +Sampling. In Table 7b, we investigate different sampling methods for text generation from NARRATOR. We see that nucleus sampling works much better than beam search while repetitive sampling shows marginal improvement. + +Scaling effect. In Table 7c, we compare the zero-shot retrieval result by progressively increasing the size of NARRATOR's video encoder from TSF-B to TSF-L and TSFL@HR, which increases the input resolution to be narrated from 224 to 336 while fixing the dual-encoder architecture. The retrieval performance steadily increases while NARRATOR becomes stronger. We conduct this experiment by varying the dual-encoder architecture, namely TSF-Base and TSF-Large, and show similar trends. Both phenomena suggest that LAVILA can scale to larger models. + +# 6. Conclusion and Future Work + +In this paper, we proposed LAVILA, a new approach to video-language representation learning by automatically narrating long videos with LLMs. We achieve strong improvements over baselines trained with the same amount of human-narrated videos and set new state-of-the-art on six popular benchmark tasks across first- and third-person video understanding benchmarks. LAVILA also shows positive scaling behavior when adding more training narrations, using larger visual backbones, and using stronger LLMs, all of which are promising areas for future work. + +Acknowledgements: We thank Naman Goyal, Stephen Roller and Susan Zhang for help with language models, Kevin Qinghong Lin for help with EgoVLP, and the Meta AI team for helpful discussions and feedback. This material is based upon work in-part supported by the National Science Foundation under Grant No. IIS-1845485. + +# References + +[1] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan. Flamingo: a visual language model for few-shot learning. In NeurIPS, 2022. +[2] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid. Vivit: A video vision transformer. In ICCV, 2021. +[3] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016. +[4] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, 2021. +[5] Yoshua Bengio, Réjean Ducharme, and Pascal Vincent. A neural probabilistic language model. In NIPS, 2000. +[6] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, 2021. +[7] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. NeurIPS, 2020. +[8] Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles. Activitynet: A large-scale video benchmark for human activity understanding. In CVPR, 2015. +[9] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In CVPR, 2017. +[10] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, 2020. +[11] Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal. Unifying vision-and-language tasks via text generation. In ICML, 2021. +[12] Jinwoo Choi, Gaurav Sharma, Manmohan Chandraker, and Jia-Bin Huang. Unsupervised and semi-supervised domain adaptation for action recognition from drones. In WACV, 2020. +[13] Ego4D Consortium. Egocentric live 4d perception (Ego4D) database: A large-scale first-person video database, supporting research in multi-modal machine perception for daily life activity. https://sites.google.com/view/ego4d/home. Accessed: 2022-11-22. + +[14] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Evangelos Kazakos, Jian Ma, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Rescaling egocentric vision: collection, pipeline and challenges for Epic-Kitchens-100. IJCV, 2022. +[15] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In CVPR, 2009. +[16] Karan Desai and Justin Johnson. Virtex: Learning visual representations from textual annotations. In CVPR, 2021. +[17] Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell. Long-term recurrent convolutional networks for visual recognition and description. In CVPR, 2015. +[18] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2020. +[19] Marzieh Fadaee, Arianna Bisazza, and Christof Monz. Data augmentation for low-resource neural machine translation. In ACL, 2017. +[20] Steven Y Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. A survey of data augmentation approaches for nlp. In ACL Findings, 2021. +[21] Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, and Tomas Mikolov. Devise: A deep visual-semantic embedding model. NeurIPS, 2013. +[22] Rohit Girdhar, Mannat Singh, Nikhila Ravi, Laurens van der Maaten, Armand Joulin, and Ishan Misra. Omnivore: A single model for many visual modalities. In CVPR, 2022. +[23] Ramsri Goutham Golla. High-quality sentence paraphrase using transformers in nlp. https://huggingface.co/ramsrigouthamg/t5-large-paraphraser-diverse-high-quality. Accessed: 2022-06-01. +[24] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe + +Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4d: Around the world in 3,000 hours of egocentric video. In CVPR, 2022. +[25] Tengda Han, Weidi Xie, and Andrew Zisserman. Temporal alignment networks for long-term video. In CVPR, 2022. +[26] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016. +[27] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 1997. +[28] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre. Training compute-optimal large language models. arXiv preprint arXiv:2203.15556, 2022. +[29] Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. The curious case of neural text degeneration. In ICLR, 2020. +[30] Xiaowei Hu, Zhe Gan, Jianfeng Wang, Zhengyuan Yang, Zicheng Liu, Yumao Lu, and Lijuan Wang. Scaling up vision-language pre-training for image captioning. In CVPR, 2022. +[31] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICML, 2021. +[32] Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, and Weidi Xie. Prompting visual-language models for efficient video understanding. In ECCV, 2022. +[33] Evangelos Kazakos, Jaesung Huh, Arsha Nagrani, Andrew Zisserman, and Dima Damen. With a little help from my temporal context: Multimodal egocentric action recognition. In BMVC, 2021. +[34] Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, and Boqing Gong. Movinets: Mobile video networks for efficient video recognition. In CVPR, 2021. +[35] Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In ICCV, 2011. +[36] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In CVPR, 2021. +[37] Yin Li, Miao Liu, and James M Rehg. In the eye of beholder: Joint learning of gaze and actions in first person video. In ECCV, 2018. +[38] Yanghao Li, Tushar Nagarajan, Bo Xiong, and Kristen Grauman. Ego-exo: Transferring visual representations from third-person to first-person videos. In CVPR, 2021. +[39] Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan, Michael Wray, Rui Yan, Eric Zhongcong Xu, Difei Gao, Rongcheng Tu, Wenzhe Zhao, Weijie Kong, Hongfa Cai Chengfei, Wang, Dima Damen, Bernard Ghanem, Wei Liu, and Mike Zheng Shou. Egocentric video-language pre + +training. In NeurIPS, 2022. +[40] Ziyi Lin, Shijie Geng, Renrui Zhang, Peng Gao, Gerard de Melo, Xiaogang Wang, Jifeng Dai, Yu Qiao, and Hongsheng Li. Frozen clip models are efficient video learners. In ECCV, 2022. +[41] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS, 2019. +[42] Huaishao Luo, Lei Ji, Botian Shi, Haoyang Huang, Nan Duan, Tianrui Li, Jason Li, Taroon Bharti, and Ming Zhou. Univl: A unified video and language pre-training model for multimodal understanding and generation. arXiv preprint arXiv:2002.06353, 2020. +[43] Maluuba. nlg-eval. https://github.com/Maluuba/nlg-eval. Accessed: 2022-06-01. +[44] Antoine Miech, Jean-Baptiste Alayrac, Lucas Smaira, Ivan Laptev, Josef Sivic, and Andrew Zisserman. End-to-end learning of visual representations from uncurated instructional videos. In CVPR, 2020. +[45] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic. Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In ICCV, 2019. +[46] Arsha Nagrani, Paul Hongsuck Seo, Bryan Seybold, Anja Hauth, Santiago Manen, Chen Sun, and Cordelia Schmid. Learning audio-video modalities from image captions. In ECCV, 2022. +[47] Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, and Haibin Ling. Expanding language-image pretrained models for general video recognition. In ECCV, 2022. +[48] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. +[49] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In ICML, 2021. +[50] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. OpenAI blog, 2019. +[51] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR, 2020. +[52] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022. +[53] Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, and Bernt Schiele. Movie description. *IJCV*, 2017. +[54] Christoph Schuhmann, Romain Beaumont, Cade W Gordon, Ross Wightman, Theo Coombes, Aarush Katta, Clayton Mullis, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev. Laion-5b: An open large-scale dataset for training next generation image-text models. In NeurIPS D&B, + +2022. +[55] Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words with subword units. In ACL, 2016. +[56] Paul Hongsuck Seo, Arsha Nagrani, Anurag Arnab, and Cordelia Schmid. End-to-end generative pretraining for multimodal video captioning. In CVPR, 2022. +[57] Gunnar A Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, and Karteek Alahari. Actor and observer: Joint modeling of first and third-person videos. In CVPR, 2018. +[58] Gunnar A Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, and Karteek Alahari. Charades-ego: A large-scale dataset of paired third and first person videos. arXiv preprint arXiv:1804.09626, 2018. +[59] Kihyuk Sohn. Improved deep metric learning with multiclass n-pair loss objective. NeurIPS, 2016. +[60] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012. +[61] Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer. How to train your vit? data, augmentation, and regularization in vision transformers. TMLR, 2022. +[62] Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Vl-bert: Pre-training of generic visual-linguistic representations. In ICLR, 2020. +[63] Swathikiran Sudhakaran, Sergio Escalera, and Oswald Lanz. Lsta: Long short-term attention for egocentric action recognition. In CVPR, 2019. +[64] Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid. Videobert: A joint model for video and language representation learning. In ICCV, 2019. +[65] Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill. Multimodal few-shot learning with frozen language models. In NeurIPS, 2021. +[66] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, 2017. +[67] Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. Diverse beam search: Decoding diverse solutions from neural sequence models. arXiv preprint arXiv:1610.02424, 2016. +[68] Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. Show and tell: A neural image caption generator. In CVPR, 2015. +[69] Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, et al. Image as a foreign language: Beit pretraining for all vision and vision-language tasks. arXiv preprint arXiv:2208.10442, 2022. +[70] William Yang Wang and Diyi Yang. That's so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets. In EMNLP, 2015. +[71] Xiaohan Wang, Linchao Zhu, Heng Wang, and Yi Yang. Interactive prototype learning for egocentric action recognition. In ICCV, 2021. +[72] Jason Wei and Kai Zou. Eda: Easy data augmentation techniques for boosting performance on text classification tasks. + +In EMNLP, 2019. +[73] Jason Weston, Samy Bengio, and Nicolas Usunier. Large scale image annotation: learning to rank with joint word-image embeddings. Machine learning, 2010. +[74] John Wieting and Kevin Gimpel. *Paranmt-50m: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations*. In ACL, 2018. +[75] Michael Wray, Diane Larlus, Gabriela Csurka, and Dima Damen. Fine-grained action retrieval through multiple parts-of-speech embeddings. In ICCV, 2019. +[76] Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam, Haoqi Fan, Bo Xiong, Jitendra Malik, and Christoph Feichtenhofer. Memvit: Memory-augmented multiscale vision transformer for efficient long-term video recognition. In CVPR, 2022. +[77] Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. Videoclip: Contrastive pre-training for zero-shot video-text understanding. In EMNLP, 2021. +[78] Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, and Jiebo Luo. Clip-vip: Adapting pretrained image-text model to video-language representation alignment. In ICLR, 2023. +[79] Shen Yan, Xuehan Xiong, Anurag Arnab, Zhichao Lu, Mi Zhang, Chen Sun, and Cordelia Schmid. Multiview transformers for video recognition. In CVPR, 2022. +[80] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. CoCa: Contrastive captioners are image-text foundation models. TMLR, 2022. +[81] Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, Ce Liu, Mengchen Liu, Zicheng Liu, Yumao Lu, Yu Shi, Lijuan Wang, Jianfeng Wang, Bin Xiao, Zhen Xiao, Jianwei Yang, Michael Zeng, Luowei Zhou, and Pengchuan Zhang. Florence: A new foundation model for computer vision. arXiv preprint arXiv:2111.11432, 2021. +[82] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. NeurIPS, 2021. +[83] Andy Zeng, Adrian Wong, Stefan Welker, Krzysztof Choromanski, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, and Pete Florence. Socratic models: Composing zero-shot multimodal reasoning with language. In ICLR, 2023. +[84] Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer. Scaling vision transformers. In CVPR, 2022. +[85] Xiang Zhang, Junbo Zhao, and Yann LeCun. Character-level convolutional networks for text classification. In NeurIPS, 2015. +[86] Luowei Zhou, Chenliang Xu, and Jason J Corso. Towards automatic learning of procedures from web instructional videos. In AAAI, 2018. +[87] Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbihl, and Ishan Misra. Detecting twenty-thousand classes using image-level supervision. In ECCV, 2022. +[88] Linchao Zhu and Yi Yang. Actbert: Learning global-local video-text representations. In CVPR, 2020. +[89] Xizhou Zhu, Jinguo Zhu, Hao Li, Xiaoshi Wu, Hongsheng Li, Xiaohua Wang, and Jifeng Dai. Uni-perceiver: Pre + +training unified architecture for generic perception for zero-shot and few-shot tasks. 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We repurpose pre-trained LLMs to be conditioned on visual input, and finetune them to create automatic video narrators. Our auto-generated narrations offer a number of advantages, including dense coverage of long videos, better temporal synchronization of the visual information and text, and much higher diversity of text. The video-language embedding learned contrastively with these narrations outperforms the previous state-of-the-art on multiple first-person and third-person video tasks, both in zero-shot and finetuned setups. Most notably, LAVILA obtains an absolute gain of " + }, + { + "bbox": [ + 46, + 239, + 290, + 455 + ], + "type": "inline_equation", + "content": "10.1\\%" + }, + { + "bbox": [ + 46, + 239, + 290, + 455 + ], + "type": "text", + "content": " on EGTEA classification and " + }, + { + "bbox": [ + 46, + 239, + 290, + 455 + ], + "type": "inline_equation", + "content": "5.9\\%" + }, + { + "bbox": [ + 46, + 239, + 290, + 455 + ], + "type": "text", + "content": " Epic-Kitchens-100 multi-instance retrieval benchmarks. Furthermore, LAVILA trained with only half the narrations from the Ego4D dataset outperforms models trained on the full set, and shows positive scaling behavior on increasing pre-training data and model size." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 479, + 128, + 491 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 479, + 128, + 491 + ], + "spans": [ + { + "bbox": [ + 47, + 479, + 128, + 491 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 500, + 288, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 500, + 288, + 693 + ], + "spans": [ + { + "bbox": [ + 46, + 500, + 288, + 693 + ], + "type": "text", + "content": "Learning visual representation using web-scale imag-text data is a powerful tool for computer vision. Vision-language approaches [31, 49, 80] have pushed the state-of-the-art across a variety of tasks, including zero-shot classification [49], novel object detection [87], and even image generation [52]. Similar approaches for videos [4, 39, 46], however, have been limited by the small size of paired video-text corpora compared to the billion-scale image-text datasets [31, 49, 84]—even though access to raw video data has exploded in the past decade. In this work, we show it is possible to automatically generate text pairing for such videos by leveraging Large Language Models (LLMs), thus taking full advantage of the massive video data. Learning video-language models with these automatically generated annotations leads to stronger representations, and as Figure 1 shows, sets a new state-of-the-art on six popular first" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 331, + 198, + 525, + 380 + ], + "blocks": [ + { + "bbox": [ + 331, + 198, + 525, + 380 + ], + "lines": [ + { + "bbox": [ + 331, + 198, + 525, + 380 + ], + "spans": [ + { + "bbox": [ + 331, + 198, + 525, + 380 + ], + "type": "image", + "image_path": "54f3dc189ad3306f4658255c97c4c2a033eb2d199bc9b48e836b3e1a7eadafbd.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 381, + 545, + 426 + ], + "lines": [ + { + "bbox": [ + 305, + 381, + 545, + 426 + ], + "spans": [ + { + "bbox": [ + 305, + 381, + 545, + 426 + ], + "type": "text", + "content": "Figure 1. LAVILA sets a new state-of-the-art across a number of first and third-person video understanding tasks (cf. Table 1 for details), by learning a video-language representation using supervision from large language models as narrators." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 449, + 453, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 449, + 453, + 460 + ], + "spans": [ + { + "bbox": [ + 306, + 449, + 453, + 460 + ], + "type": "text", + "content": "and third-person video benchmarks." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 460, + 546, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 460, + 546, + 675 + ], + "spans": [ + { + "bbox": [ + 304, + 460, + 546, + 675 + ], + "type": "text", + "content": "Our method, called LAVILA: Language-model augmented Video-Language pre-training, leverages pretrained LLMs, e.g. GPT-2 [50], which encode within their weights a treasure trove of factual knowledge and conversational ability. As shown in Figure 2, we repurpose these LLMs to be \"visually-conditioned narrators\", and finetune on all accessible paired video-text clips. Once trained, we use the model to densely annotate thousands of hours of videos by generating rich textual descriptions. This pseudo-supervision can thus pervade the entire video, in between and beyond the annotated snippets. Paired with another LLM trained to rephrase existing narrations, LAVILA is able to create a much larger and more diverse set of text targets for video-text contrastive learning. In addition to setting a new state-of-the-art as noted earlier, the stronger representation learned by LAVILA even outperforms prior work using only half the groundtruth annotations (Figure 5)." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 677, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 547, + 713 + ], + "type": "text", + "content": "LAVILA's strong performance can be attributed to a number of factors. First, LAVILA can provide temporally dense supervision for long-form videos, where the associ" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 702, + 194, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 194, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 194, + 712 + ], + "type": "text", + "content": "*Work done during an internship at Meta." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "6586" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 70, + 289, + 248 + ], + "blocks": [ + { + "bbox": [ + 49, + 70, + 289, + 248 + ], + "lines": [ + { + "bbox": [ + 49, + 70, + 289, + 248 + ], + "spans": [ + { + "bbox": [ + 49, + 70, + 289, + 248 + ], + "type": "image", + "image_path": "88a97ec45d22afb394ccf91dab3f7b6b59e031a243109148c2ad200ff9375e7f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 255, + 288, + 321 + ], + "lines": [ + { + "bbox": [ + 46, + 255, + 288, + 321 + ], + "spans": [ + { + "bbox": [ + 46, + 255, + 288, + 321 + ], + "type": "text", + "content": "Figure 2. LAVILA leverages Large Language Models (LLMs) to densely narrate long videos, and uses those narrations to train strong dual-encoder models. While prior work uses sparsely labeled text by humans, or weakly aligned text transcribed from speech, LAVILA is able to leverage dense, diverse, and well-aligned text generated by a LLM." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 354, + 287, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 354, + 287, + 616 + ], + "spans": [ + { + "bbox": [ + 46, + 354, + 287, + 616 + ], + "type": "text", + "content": "ated captions are either too sparse, or the video-level \"Alt-Text\" (in the case of web videos) does not describe all the nuanced activities happening in it. Second, the generated text is well-aligned with the visual input. Although prior work has leveraged automatic speech transcription on How-To videos [45] to automatically extract clips paired with text from the speech, such datasets have relatively poor alignment between the visual and textual content (" + }, + { + "bbox": [ + 46, + 354, + 287, + 616 + ], + "type": "inline_equation", + "content": "\\leq 50\\%" + }, + { + "bbox": [ + 46, + 354, + 287, + 616 + ], + "type": "text", + "content": ", cf. [25, 45]), limiting the quality of the learned representations. Third, LAVILA can significantly expand annotations when only a little is available. For instance, videos of mundane day-to-day activities, especially from an egocentric viewpoint, could be very useful for assistive and augmented reality applications. Such videos, however, are rare on the internet, and hence do not readily exist with associated web text. Recent work [24] instead opted to manually capture and narrate such video data. These narrations however required significant manual effort: 250K hours of annotator time spent in narrating 3.6K hours of video. In contrast, LAVILA is able to automatically narrate each video multiple times and far more densely, and hence learns much stronger representations." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 617, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 288, + 714 + ], + "type": "text", + "content": "We extensively evaluate LAVILA across multiple videotext pre-training datasets and downstream tasks to validate its effectiveness. Specifically, after being pre-trained on Ego4D, the largest egocentric video datasets with narrations, LAVILA can re-narrate the whole dataset " + }, + { + "bbox": [ + 46, + 617, + 288, + 714 + ], + "type": "inline_equation", + "content": "10 \\times" + }, + { + "bbox": [ + 46, + 617, + 288, + 714 + ], + "type": "text", + "content": " over. The resulting model learned on these expanded narrations sets a new state-of-the-art on a wide range of downstream tasks across challenging datasets, including multi-instance" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 72, + 547, + 264 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 264 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 264 + ], + "type": "text", + "content": "video retrieval on Epic-Kitchens-100 (5.9% absolute gain on mAP), multiple-choice question answering on Ego4D (5.9% absolute gain on intra-video accuracy), and action recognition on EGTEA (10.1% absolute gain on mean accuracy). It obtains gains both when evaluated for zero-shot transfer to the new dataset, as well as after fine-tuning on that dataset. Similar gains are shown in third-person video data. When training LAVILA after densely re-narrating HowTo100M, we outperform prior work on downstream action classification on UCF-101 and HMDB-51. In a case study of semi-supervised learning, we show that our model, which only ever sees 50% of the human-labeled data, is capable of outperforming the baseline model trained with all the narrations. Moreover, the gains progressively increase as we go to larger data regimes and larger backbones, suggesting the scalability of our method." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 306, + 274, + 392, + 288 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 274, + 392, + 288 + ], + "spans": [ + { + "bbox": [ + 306, + 274, + 392, + 288 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 294, + 547, + 558 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 294, + 547, + 558 + ], + "spans": [ + { + "bbox": [ + 304, + 294, + 547, + 558 + ], + "type": "text", + "content": "Vision-language representation learning maps visual and textual embeddings into a common space using metric-learning techniques [21, 73]. Recently, different pretext tasks are proposed to learn a finer-grained association between visual and textual modality, e.g. masked language modeling (MLM) [10, 41, 62] and captioning [16, 80]. Another line of research focuses on scaling up both models and pre-training data. For instance, CLIP [49] is pre-trained on 400M image-text pairs with a contrastive loss (InfoNCE [48, 59]) while CoCa [80] unifies contrastive and generative approaches with a single foundation model. Similar trends are also witnessed in the videotext domain [36, 64, 88]. However, collecting high-quality video-text data is more difficult than image-text. Therefore, efforts are made to learn from uncurated videos with machine-generated audio transcripts via contrastive learning [44, 77, 82] or unsupervised alignment [25] while other works focus on either adapting well-performing image-text models to videos [32, 40, 47, 78], or curriculum learning from a single frame to multiple frames [4]. In contrast, our approach leverages language models to generate temporally dense textual supervision on long-form videos." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 558, + 547, + 701 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 547, + 701 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 547, + 701 + ], + "type": "text", + "content": "Generative Visual Language Models (VLM) were first used for image/video captioning using recurrent networks [17, 68] and Transformer-based architectures [42, 56]. More recently, generative VLMs have unified multiple vision tasks [11, 89] by training multi-modal Transformers on visual-text pairs [30, 81]. Meanwhile, generative VLMs also excel at multimodal tasks via zero-shot or few-shot prompting [1, 65, 83] by leveraging multi-billion-parameter LLMs pre-trained on massive text corpus [7, 28, 50]. In our work, we demonstrate that generative VLMs can narrate long videos and the resulting video-text data benefits video-language representation learning." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 701, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 701, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 701, + 547, + 713 + ], + "type": "text", + "content": "Large-scale multimodal video datasets are crucial for" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "type": "text", + "content": "6587" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 289, + 263 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 289, + 263 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 289, + 263 + ], + "type": "text", + "content": "video understanding tasks but are hard to collect. Conventional video-text datasets [8, 53, 86] either have limited scenarios, e.g. cooking, or are not large enough to learn generic video representation. Miech et al. [45] scrape over 100 million video-text pairs via automatic audio transcription from long-form How-To videos. However, ASR introduces textual noise and visual-text unalignment [25]. WebVid [4] contains 10 million short videos with textual descriptions. But it is still several orders of magnitude smaller than the image counterparts [49, 54] and is harder to scale up since it is sourced from stock footage sites. The recently released Ego4D [24] dataset offers 3,600 hours of egocentric videos in which written sentence narrations are manually annotated every few seconds but requires significant manual effort. In contrast, our method shows a promising alternative by automatically narrating videos using supervision from LLM." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 264, + 287, + 348 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 264, + 287, + 348 + ], + "spans": [ + { + "bbox": [ + 47, + 264, + 287, + 348 + ], + "type": "text", + "content": "Data augmentation techniques in NLP, including word-level replacement based on synonyms [72, 85] or nearest-neighbor retrieval [19, 70], improve text classification accuracy. We refer readers to [20] for a comprehensive survey. In this paper, we show that sentence-level paraphrasing based on text-to-text models [51] is helpful for video-language pre-training." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 358, + 132, + 370 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 358, + 132, + 370 + ], + "spans": [ + { + "bbox": [ + 47, + 358, + 132, + 370 + ], + "type": "text", + "content": "3. Preliminaries" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "spans": [ + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "text", + "content": "A video " + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "text", + "content": " is a stream of moving images " + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "text", + "content": ". The number of frames " + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "inline_equation", + "content": "|V|" + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "text", + "content": " can be arbitrarily long while video models typically operate on shorter clips, which are often in the range of a few seconds. Therefore, we skim through a long-form video and represent it by a set of " + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "text", + "content": " short clips, i.e. " + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "inline_equation", + "content": "\\mathcal{X}" + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "text", + "content": ". Each clip " + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "text", + "content": " is defined by a specific start and end frame " + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "inline_equation", + "content": "x_{i} = \\{I_{t_{i}},\\dots ,I_{e_{i}}\\}" + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "inline_equation", + "content": "0 < t_i < e_i\\leq |V|" + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "text", + "content": ", and is typically associated with some annotation " + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "inline_equation", + "content": "y_{i}" + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "text", + "content": ". This annotation could be a class label or free-form textual description of the clip. We denote a video by the set of annotated clips with their corresponding annotations, i.e. " + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "inline_equation", + "content": "(\\mathcal{X},\\mathcal{Y}) = \\{(x_1,y_1),\\dots ,(x_N,y_N)\\}" + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "text", + "content": ". Note that the annotated clips often cannot densely cover the entire video due to the annotation cost and visual redundancy, i.e. " + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "inline_equation", + "content": "\\bigcup_{i}[t_{i},e_{i}]\\subsetneq [0,|V|]" + }, + { + "bbox": [ + 47, + 378, + 287, + 545 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "spans": [ + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "type": "text", + "content": "A typical video model " + }, + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "type": "inline_equation", + "content": "\\mathcal{F}(\\mathcal{X},\\mathcal{Y})" + }, + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "type": "text", + "content": " learns from these clip-level annotations using a standard training objective such as a cross-entropy loss when the annotations are class labels with a fixed vocabulary. However, more recently, dual-encoder-based contrastive approaches like CLIP [49, 77] have gained popularity. They work with free-form textual annotations which are tokenized [55] into sequences of discrete symbols, i.e. " + }, + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "type": "inline_equation", + "content": "y = (s_{1},s_{2},\\dots ,s_{L})\\in \\{1,0\\}^{|\\mathbb{S}|\\times L}" + }, + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "type": "text", + "content": ". The model consists of a visual encoder " + }, + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "type": "inline_equation", + "content": "f_{\\mathrm{v}}:\\mathbb{R}^{T\\times 3\\times H\\times W}\\mapsto \\mathbb{R}^{D_{\\mathrm{v}}}" + }, + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "type": "text", + "content": " plus a projection head " + }, + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "type": "inline_equation", + "content": "h_\\mathrm{v}:\\mathbb{R}^{D_\\mathrm{v}}\\mapsto \\mathbb{R}^d" + }, + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "type": "text", + "content": " and a textual encoder " + }, + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "type": "inline_equation", + "content": "f_{\\mathrm{t}}:\\{1,0\\}^{|\\mathbb{S}|\\times L}\\mapsto \\mathbb{R}^{D_{\\mathrm{t}}}" + }, + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "type": "text", + "content": " plus a projection head " + }, + { + "bbox": [ + 47, + 545, + 288, + 691 + ], + "type": "inline_equation", + 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C raises the container." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 305, + 260, + 545, + 315 + ], + "lines": [ + { + "bbox": [ + 305, + 260, + 545, + 315 + ], + "spans": [ + { + "bbox": [ + 305, + 260, + 545, + 315 + ], + "type": "text", + "content": "Figure 3. Generated samples by NARRATOR and REPHRASER. NARRATOR generates new descriptions of the action taking place, potentially focusing on other objects being interacted with. REPHRASER not only changes the word order of the human narration but also diversifies it by using related verbs or nouns." + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 428, + 161, + 466, + 220 + ], + "blocks": [ + { + "bbox": [ + 428, + 161, + 466, + 220 + ], + "lines": [ + { + "bbox": [ + 428, + 161, + 466, + 220 + ], + "spans": [ + { + "bbox": [ + 428, + 161, + 466, + 220 + ], + "type": "image", + "image_path": "bca71e97eac0113e025cfec06c5194426b0998028aae966435d8895da9db6aae.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 428, + 223, + 470, + 242 + ], + "lines": [ + { + "bbox": [ + 428, + 223, + 470, + 242 + ], + "spans": [ + { + "bbox": [ + 428, + 223, + 470, + 242 + ], + "type": "text", + "content": "NARRATOR" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 17 + }, + { + "type": "image", + "bbox": [ + 466, + 161, + 545, + 220 + ], + "blocks": [ + { + "bbox": [ + 466, + 161, + 545, + 220 + ], + "lines": [ + { + "bbox": [ + 466, + 161, + 545, + 220 + ], + "spans": [ + { + "bbox": [ + 466, + 161, + 545, + 220 + ], + "type": "image", + "image_path": "082e7ea4476e6863b6bf528dd276d336086df4344ba5b37a8a282b9092390b67.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 476, + 220, + 537, + 243 + ], + "lines": [ + { + "bbox": [ + 476, + 220, + 537, + 243 + ], + "spans": [ + { + "bbox": [ + 476, + 220, + 537, + 243 + ], + "type": "text", + "content": "C wipes the countertop with a sponge. \nCmoves the container." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 337, + 545, + 371 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 337, + 545, + 371 + ], + "spans": [ + { + "bbox": [ + 306, + 337, + 545, + 371 + ], + "type": "text", + "content": "A contrastive loss, such as InfoNCE [48], learns global embeddings that associate corresponding video and text embeddings within a batch of samples " + }, + { + "bbox": [ + 306, + 337, + 545, + 371 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 333, + 378, + 545, + 405 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 378, + 545, + 405 + ], + "spans": [ + { + "bbox": [ + 333, + 378, + 545, + 405 + ], + "type": "interline_equation", + "content": "\\frac {1}{| \\mathcal {B} |} \\sum_ {(x, y) \\in \\mathcal {B}} \\left(\\operatorname {I n f o N C E} (\\mathbf {v}, \\mathbf {u}) + \\operatorname {I n f o N C E} (\\mathbf {u}, \\mathbf {v})\\right). \\tag {1}", + "image_path": "5871a7c4184e0706630874004ce6705fb476c01c74ef6b3d99aac88853302e9a.jpg" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 306, + 429, + 365, + 441 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 429, + 365, + 441 + ], + "spans": [ + { + "bbox": [ + 306, + 429, + 365, + 441 + ], + "type": "text", + "content": "4. LAVILA" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "spans": [ + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "text", + "content": "In LAVILA, we leverage large language models (LLMs) as supervision to train the dual-encoder model, where the LLMs serve as vision-conditioned narrators and automatically generate textual descriptions from video clips. In particular, we exploit supervision from two LLMs: (1) NARRATOR (" + }, + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "inline_equation", + "content": "\\S 4.1" + }, + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "text", + "content": ") is a visually-conditioned LLM that pseudolabels existing and new clips with narrations, generating new annotations " + }, + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "inline_equation", + "content": "(\\mathcal{X}',\\mathcal{Y}')" + }, + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "text", + "content": ". (2) REPHRASER (" + }, + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "inline_equation", + "content": "\\S 4.2" + }, + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "text", + "content": ") is a standard LLM that paraphrases narrations in existing clips, augmenting those annotations to " + }, + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "inline_equation", + "content": "(\\mathcal{X},\\mathcal{Y}^{\\prime \\prime})" + }, + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "text", + "content": ". As illustrated in Figure 3, NARRATOR generates new descriptions of the action taking place, potentially focusing on other objects being interacted with. REPHRASER serves to augment the text input, e.g., changes word order of the human narration and additionally replaces common verbs or nouns, making annotations more diverse. Finally, we train the dual-encoders (" + }, + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "inline_equation", + "content": "\\S 4.3" + }, + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "text", + "content": ") on all these annotations combined, i.e. " + }, + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "inline_equation", + "content": "(\\mathcal{X},\\mathcal{Y}) \\cup (\\mathcal{X}',\\mathcal{Y}') \\cup (\\mathcal{X},\\mathcal{Y}^{\\prime \\prime})" + }, + { + "bbox": [ + 304, + 449, + 545, + 666 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 306, + 673, + 379, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 673, + 379, + 684 + ], + "spans": [ + { + "bbox": [ + 306, + 673, + 379, + 684 + ], + "type": "text", + "content": "4.1. NARRATOR" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 306, + 690, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 690, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 306, + 690, + 545, + 714 + ], + "type": "text", + "content": "Traditional LLMs, such as GPT-2 [50], are trained to generate a sequence of text tokens " + }, + { + "bbox": [ + 306, + 690, + 545, + 714 + ], + "type": "inline_equation", + "content": "(s_1 \\cdots s_L)" + }, + { + "bbox": [ + 306, + 690, + 545, + 714 + ], + "type": "text", + "content": " from scratch" + } + ] + } + ], + "index": 27 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6588" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 59, + 250, + 160 + ], + "blocks": [ + { + "bbox": [ + 65, + 59, + 250, + 160 + ], + "lines": [ + { + "bbox": [ + 65, + 59, + 250, + 160 + ], + "spans": [ + { + "bbox": [ + 65, + 59, + 250, + 160 + ], + "type": "image", + "image_path": "9ed857c62160541be695a0c63fac579ca5e1ed59232035ce5c1f0959bc440440.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 170, + 547, + 214 + ], + "lines": [ + { + "bbox": [ + 45, + 170, + 547, + 214 + ], + "spans": [ + { + "bbox": [ + 45, + 170, + 547, + 214 + ], + "type": "text", + "content": "Figure 4. Language supervision from REPHRASER and NARRATOR. REPHRASER(left) takes the narration as input, passes it through a text encoder and uses a text decoder to autoregressively generate the rephrased output. NARRATOR(right) takes video frames as input and obtains the visual embeddings through a video encoder followed by attentional pooling. Equipped with a few additional cross-attention modules, the text decoder autoregressively generates new narrations for those new frames." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 253, + 59, + 533, + 160 + ], + "blocks": [ + { + "bbox": [ + 253, + 59, + 533, + 160 + ], + "lines": [ + { + "bbox": [ + 253, + 59, + 533, + 160 + ], + "spans": [ + { + "bbox": [ + 253, + 59, + 533, + 160 + ], + "type": "image", + "image_path": "6774a18f9439c1444f2605f63f81402ad416a2063fcaaf587521748fd165560b.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 235, + 287, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 235, + 287, + 323 + ], + "spans": [ + { + "bbox": [ + 46, + 235, + 287, + 323 + ], + "type": "text", + "content": "by modeling the probability of the next token given all tokens seen so far: " + }, + { + "bbox": [ + 46, + 235, + 287, + 323 + ], + "type": "inline_equation", + "content": "p(s_{l}|s_{" + }, + { + "bbox": [ + 305, + 354, + 545, + 437 + ], + "type": "text", + "content": ". We sample from the distribution recursively, i.e. " + }, + { + "bbox": [ + 305, + 354, + 545, + 437 + ], + "type": "inline_equation", + "content": "\\tilde{s}_{\\ell} \\sim p(s|[, \\dots, \\tilde{s}_{\\ell-1}], x)" + }, + { + "bbox": [ + 305, + 354, + 545, + 437 + ], + "type": "text", + "content": " until an end-of-sentence token " + }, + { + "bbox": [ + 305, + 354, + 545, + 437 + ], + "type": "inline_equation", + "content": "" + }, + { + "bbox": [ + 305, + 354, + 545, + 437 + ], + "type": "text", + "content": " is reached. Following [29], at each step we sample from a subset of tokens that contain the vast majority of the probability mass, which is known as nucleus sampling." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 437, + 545, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 437, + 545, + 568 + ], + "spans": [ + { + "bbox": [ + 304, + 437, + 545, + 568 + ], + "type": "text", + "content": "The effect of nucleus sampling is two-fold. On the one hand, it generates more diverse, open-ended, and human-like text than maximum-likelihood-based methods such as beam search and its variants [67]. On the other hand, the generated text may contain irrelevant or noisy information due to sampling without post-processing based on sentence-level likelihood. To address this, we repeat the sampling process for " + }, + { + "bbox": [ + 304, + 437, + 545, + 568 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 437, + 545, + 568 + ], + "type": "text", + "content": " times on the same visual input. We later demonstrate that the contrastive pre-training objective is robust to the noise caused by sampling, and the final performance benefits from a more diverse set of narrations." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": "To sample video clips for captioning, we start by simply re-captioning the existing clips labeled in the dataset " + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{X}" + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": ", resulting in expanded annotations. Furthermore, long-form videos are typically sparsely narrated, meaning that the temporal union of all labeled clips cannot cover the entire video. Hence, we use NARRATOR to annotate the remainder of the video to obtain additional annotations by pseudo-captioning. With a simple assumption that video is a stationary process, we uniformly sample clips from the unlabeled intervals. The clip duration is equal to the average of all ground-truth clips, i.e. " + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\Delta = \\frac{1}{N} \\sum_{i=1}^{N} (e_i - t_i)" + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": " while the sampling stride is computed likewise. Finally, by" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6589" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 108 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 108 + ], + "type": "text", + "content": "combining both re-captioned and pseudo-captioned narrations, we refer to the final set of annotations generated by NARRATOR as " + }, + { + "bbox": [ + 46, + 72, + 287, + 108 + ], + "type": "inline_equation", + "content": "(\\mathcal{X}',\\mathcal{Y}')" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 109, + 287, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 109, + 287, + 228 + ], + "spans": [ + { + "bbox": [ + 46, + 109, + 287, + 228 + ], + "type": "text", + "content": "Post-processing. Exhaustive pseudo-captioning may contain some uninformative visual clips and generate text that is not useful. Thus, we add a filtering process to eliminate low-quality clips and their associated descriptions. We use the baseline dual-encoder model " + }, + { + "bbox": [ + 46, + 109, + 287, + 228 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 46, + 109, + 287, + 228 + ], + "type": "text", + "content": ", which is trained on the ground-truth paired clips, to compute the visual and textual embedding of pseudo-labeled pairs and filter based on the similarity score, i.e. " + }, + { + "bbox": [ + 46, + 109, + 287, + 228 + ], + "type": "inline_equation", + "content": "\\mathrm{Filter}(f_{\\mathrm{v}}(x_j')^\\top \\cdot f_{\\mathrm{t}}(y_j'))" + }, + { + "bbox": [ + 46, + 109, + 287, + 228 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 109, + 287, + 228 + ], + "type": "inline_equation", + "content": "\\mathrm{Filter}(\\cdot)" + }, + { + "bbox": [ + 46, + 109, + 287, + 228 + ], + "type": "text", + "content": " can be either top-" + }, + { + "bbox": [ + 46, + 109, + 287, + 228 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 109, + 287, + 228 + ], + "type": "text", + "content": " of all generated text or a threshold filtering. In the experiments, we use a threshold of 0.5." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 236, + 124, + 247 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 236, + 124, + 247 + ], + "spans": [ + { + "bbox": [ + 47, + 236, + 124, + 247 + ], + "type": "text", + "content": "4.2. REPHRASER" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 253, + 287, + 309 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 253, + 287, + 309 + ], + "spans": [ + { + "bbox": [ + 46, + 253, + 287, + 309 + ], + "type": "text", + "content": "The data generated by NARRATOR is several times larger than the ground-truth pairs. To ensure that we do not overfit the pseudo-labeled data, we increase the number of ground-truth narrations by paraphrasing. In particular, we use a text-to-text LLM which models conditional text likelihood:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 96, + 326, + 237, + 356 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 326, + 237, + 356 + ], + "spans": [ + { + "bbox": [ + 96, + 326, + 237, + 356 + ], + "type": "interline_equation", + "content": "p _ {\\text {R E P H R A S E R}} \\left(y ^ {\\prime \\prime} | y\\right) = \\prod_ {\\ell = 1} ^ {L} p \\left(s _ {\\ell} ^ {\\prime \\prime} \\mid s _ {< \\ell} ^ {\\prime \\prime}, y\\right).", + "image_path": "d3cec74c926c1a079900facdadae91f5abcd4dfcd8a580534d57c81fc2d19728.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 362, + 287, + 447 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 362, + 287, + 447 + ], + "spans": [ + { + "bbox": [ + 46, + 362, + 287, + 447 + ], + "type": "text", + "content": "The text-to-text model is implemented by an encoder-decoder architecture, e.g. T5 [51], to auto-regressively generate a new sentence given the original one. We observe that REPHRASER is able to do basic manipulations such as replacing synonyms or changing word order, which serves as an efficient way of automatic data augmentation. The resulting annotations are referred to as " + }, + { + "bbox": [ + 46, + 362, + 287, + 447 + ], + "type": "inline_equation", + "content": "(\\mathcal{X},\\mathcal{Y}^{\\prime \\prime})" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 454, + 201, + 467 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 454, + 201, + 467 + ], + "spans": [ + { + "bbox": [ + 47, + 454, + 201, + 467 + ], + "type": "text", + "content": "4.3. Training the Dual-Encoders" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "spans": [ + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "text", + "content": "We train the dual-encoders as described in Algorithm 1 in Appendix E. In each iteration, we first sample a batch " + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "text", + "content": " of video clips. It comprises a subset of clips " + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_l" + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "text", + "content": " with labeled timestamps as well as narrations, and a subset " + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "inline_equation", + "content": "\\mathcal{B}_u" + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "text", + "content": " whose clips are randomly sampled from videos without narrations. For clip " + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "inline_equation", + "content": "x_i\\in \\mathcal{B}_u" + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "text", + "content": ", we obtain the pseudo-caption " + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "inline_equation", + "content": "y_{i}^{\\prime}" + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "text", + "content": " by querying the NARRATOR " + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "inline_equation", + "content": "y_{i}^{\\prime}\\sim p_{\\mathrm{NARRATOR}}(y^{\\prime}|x)" + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "text", + "content": ", resulting in a set of clips with LLM-generated narrations " + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "inline_equation", + "content": "\\widetilde{B}_u" + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "text", + "content": ". For clip " + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "inline_equation", + "content": "(x_{i},y_{i})\\in \\mathcal{B}_{l}" + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "text", + "content": ", the text supervision is obtained from either the REPHRASER or the NARRATOR, with a probability of 0.5. Hence, the effective number of iterations per epoch for LAVILA is the same as that for the baseline Dual-Encoder. We denote the resulting set of pairs to be " + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "inline_equation", + "content": "\\widetilde{B}_l" + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "text", + "content": " similarly. Following CLIP [49], we use the symmetric cross-entropy loss over the similarity scores of samples in the batch " + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "inline_equation", + "content": "\\widetilde{B}_l\\cup \\widetilde{B}_u" + }, + { + "bbox": [ + 46, + 472, + 287, + 653 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 654, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 287, + 713 + ], + "type": "text", + "content": "In practice, we run REPHRASER and NARRATOR in advance and cache the resulting video-narration pairs so that there is no computational overhead during pre-training. Therefore, training a dual-encoder in LAVILA is as fast as training a standard dual-encoder contrastive model." + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 309, + 70, + 547, + 166 + ], + "blocks": [ + { + "bbox": [ + 309, + 70, + 547, + 166 + ], + "lines": [ + { + "bbox": [ + 309, + 70, + 547, + 166 + ], + "spans": [ + { + "bbox": [ + 309, + 70, + 547, + 166 + ], + "type": "table", + "html": "
DatasetsTaskEgo?MetricsEval. Prot.
EK-100 [14]MIR✓mAP, nDCGZS, FT
EK-100 [14]CLS✓top-1 action acc.FT
Ego4D [24]MCQ✓inter-/intra-video acc.ZS
Ego4D [24]NLQ✓Recall@NFT
EGTEA [37]CLS✓top-1, mean acc.ZS, FT
CharadesEgo [58]CLS✓video-level mAPZS, FT
UCF-101 [60]CLS✗mean acc.LP
HMDB-51 [35]CLS✗mean acc.LP
", + "image_path": "327c408407f49155cf9502522662a33457cc6d621bd9e489c9f4852b6cbf5a94.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 174, + 547, + 240 + ], + "lines": [ + { + "bbox": [ + 304, + 174, + 547, + 240 + ], + "spans": [ + { + "bbox": [ + 304, + 174, + 547, + 240 + ], + "type": "text", + "content": "Table 1. Downstream datasets and metrics used for evaluation. We evaluate LAVILA on a wide range of tasks including Multi-Instance Retrieval (MIR), Multiple-Choice Question (MCQ), Natural Language Query (NLQ), and Action Recognition (CLS). The evaluation protocols include zero-shot (ZS), fine-tuning (FT), and linear-probing (LP). Please refer to Appendix C for more details." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 306, + 261, + 387, + 274 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 261, + 387, + 274 + ], + "spans": [ + { + "bbox": [ + 306, + 261, + 387, + 274 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 281, + 545, + 400 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 281, + 545, + 400 + ], + "spans": [ + { + "bbox": [ + 304, + 281, + 545, + 400 + ], + "type": "text", + "content": "Dual-Encoder Architecture. The video-language model follows a dual-encoder architecture as CLIP [49]. The Visual encoder is a TimeSformer (TSF) [6], whose spatial attention modules are initialized from a ViT [18] which is contrastively pre-trained on large-scale paired image-text data as in CLIP [49]. We sample 4 frames per clip during pre-training and 16 when finetuning on downstream tasks. The text encoder is a 12-layer Transformer [50, 66]. We use BPE tokenizer [55] to pre-process the full sentence corresponding to the video clip and keep at most 77 tokens." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 401, + 545, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 401, + 545, + 460 + ], + "spans": [ + { + "bbox": [ + 304, + 401, + 545, + 460 + ], + "type": "text", + "content": "NARRATOR's architecture is a visually conditioned auto-regressive Language Model. The visual encoder is by default TimeSformer-L while the text decoder is a GPT-2 XL. During inference, we use nucleus sampling [29] with " + }, + { + "bbox": [ + 304, + 401, + 545, + 460 + ], + "type": "inline_equation", + "content": "p = 0.95" + }, + { + "bbox": [ + 304, + 401, + 545, + 460 + ], + "type": "text", + "content": " and return " + }, + { + "bbox": [ + 304, + 401, + 545, + 460 + ], + "type": "inline_equation", + "content": "K = 10" + }, + { + "bbox": [ + 304, + 401, + 545, + 460 + ], + "type": "text", + "content": " candidate outputs." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 461, + 545, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 461, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 304, + 461, + 545, + 544 + ], + "type": "text", + "content": "REPHRASER. We use an open-source paraphraseer [23] based on T5-large [51]. It is pre-trained on C4 [51] and then finetuned on a cleaned subset of ParaNMT [74]. During inference, we use Diverse Beam Search [67] with group number the same as beam number " + }, + { + "bbox": [ + 304, + 461, + 545, + 544 + ], + "type": "inline_equation", + "content": "(G = B = 20)" + }, + { + "bbox": [ + 304, + 461, + 545, + 544 + ], + "type": "text", + "content": " and set the diversity penalty to be 0.7. We keep 3 candidates per sentence, remove punctuations, and do basic de-duplication." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 545, + 545, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 545, + 640 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 545, + 640 + ], + "type": "text", + "content": "Pre-training dataset. We train on the video-narration pairs from Ego4D [13, 24], the largest egocentric video dataset to date. We exclude videos that appear in the validation and test sets of the Ego4D benchmark and determine each clip's interval using the same pairing strategy in [39]. This results in around 4M video-text pairs with an average clip length of 1 second. We also experiment with third-person videos by pre-training on HowTo100M [45] in § 5.2." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "type": "text", + "content": "Evaluation protocols. We evaluate the learned video-text encoders using three evaluation protocols. (1) Zero-Shot (ZS), meaning that we apply the pre-trained video-text encoders directly on the downstream validation dataset to perform video " + }, + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "type": "inline_equation", + "content": "\\leftrightarrow" + }, + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "type": "text", + "content": " text retrieval tasks, without any tuning on the downstream dataset. Zero-shot classification is performed" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6590" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 48, + 70, + 288, + 205 + ], + "blocks": [ + { + "bbox": [ + 48, + 70, + 288, + 205 + ], + "lines": [ + { + "bbox": [ + 48, + 70, + 288, + 205 + ], + "spans": [ + { + "bbox": [ + 48, + 70, + 288, + 205 + ], + "type": "table", + "html": "
MethodBackbonemAPnDCG
V→TT→VAvg.V→TT→VAvg.
(ZERO-SHOT)
EgoVLP [39]TSF-B19.413.916.624.122.023.1
EgoVLP* [39]TSF-B26.020.623.328.827.027.9
LAVILATSF-B35.126.630.933.730.432.0
LAVILATSF-L40.032.236.136.133.234.6
(FINETUNED)
MME [75]TBN43.034.038.550.146.948.5
JPoSE [75]TBN49.938.144.055.551.653.5
EgoVLP [39]TSF-B49.940.545.060.957.959.4
LAVILATSF-B55.245.750.566.563.465.0
LAVILATSF-L54.747.150.968.164.966.5
", + "image_path": "d42642a82ac8af6ee368307ba15142fc9c987659548a4e24e26c33d6fd01333e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 304, + 287, + 400 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 304, + 287, + 400 + ], + "spans": [ + { + "bbox": [ + 46, + 304, + 287, + 400 + ], + "type": "text", + "content": "similarly, where we compute the similarity score between the video clip and the textual description of all possible categories. (2) Finetuned (FT), where we take the pre-trained video-text model and perform end-to-end fine-tuning on the training split of the target downstream dataset. (3) LinearProbe (LP), where we compute the video features from a frozen encoder and train a linear SVM on top of the training split of the downstream dataset." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 403, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 403, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 403, + 287, + 714 + ], + "type": "text", + "content": "Downstream benchmarks. We use multiple benchmarks across four first-person (egocentric) and two third-person datasets, as enumerated in Table 1. We summarize them here and refer the reader to Appendix C for details on datasets and metrics. (1) Two tasks on Epic-Kitchens-100: Multi-Instance Retrieval (EK-100 MIR) and Action Recognition (EK-100 CLS) [14]. EK-100 is a very popular and challenging egocentric video recognition benchmark. The MIR task requires retrieving the text given videos " + }, + { + "bbox": [ + 46, + 403, + 287, + 714 + ], + "type": "inline_equation", + "content": "(\\mathrm{V} \\rightarrow \\mathrm{T})" + }, + { + "bbox": [ + 46, + 403, + 287, + 714 + ], + "type": "text", + "content": " and videos given text " + }, + { + "bbox": [ + 46, + 403, + 287, + 714 + ], + "type": "inline_equation", + "content": "(\\mathrm{T} \\rightarrow \\mathrm{V})" + }, + { + "bbox": [ + 46, + 403, + 287, + 714 + ], + "type": "text", + "content": ". The CLS task requires classifying each video into one of 97 verbs and 300 nouns each, resulting in a combination of 3,806 action categories. (2) Two downstream tasks of Ego4D: Multiple-Choice Questions (EgoMCQ) and Natural Language Query (EgoNLQ). EgoMCQ requires selecting the correct textual description from five choices given a query video clip while EgoNLQ asks the model to output the relevant temporal intervals of video given a text query. We select these two benchmarks because they require reasoning about both visual and textual information. (3) Action Recognition on EGTEA [37]. It requires classifying into 106 classes of fine-grained cooking activities. (4) Action Recognition on CharadesEgo [58]. It requires classification into 157 classes of daily indoor activities. Note that CharadesEgo is very different from EK-100, Ego4D and EGTEA since its videos are captured by head-mounted phone cameras in a crowd-sourcing way." + } + ] + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 310, + 69, + 541, + 144 + ], + "blocks": [ + { + "bbox": [ + 46, + 213, + 287, + 280 + ], + "lines": [ + { + "bbox": [ + 46, + 213, + 287, + 280 + ], + "spans": [ + { + "bbox": [ + 46, + 213, + 287, + 280 + ], + "type": "text", + "content": "Table 2. EK-100 MIR. LAViLA outperforms prior work across all settings, metrics and directions of retrieval, with larger gains when switching to a larger model. Specifically, our best model achieves over " + }, + { + "bbox": [ + 46, + 213, + 287, + 280 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 213, + 287, + 280 + ], + "type": "text", + "content": " absolute gain in the zero-shot setting and " + }, + { + "bbox": [ + 46, + 213, + 287, + 280 + ], + "type": "inline_equation", + "content": "5.9 \\sim 7.1\\%" + }, + { + "bbox": [ + 46, + 213, + 287, + 280 + ], + "type": "text", + "content": " gain in the finetuned setting. EgoVLP* refers to our improved version of [39], details of which are given in Appendix F." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 310, + 69, + 541, + 144 + ], + "lines": [ + { + "bbox": [ + 310, + 69, + 541, + 144 + ], + "spans": [ + { + "bbox": [ + 310, + 69, + 541, + 144 + ], + "type": "table", + "html": "
MethodEgoMCQEgoNLQ
Accuracy (%)mIOU@0.3mIOU@0.5
Inter-videoIntra-videoR@1R@5R@1R@5
SlowFast [24]--5.4510.743.126.63
EgoVLP [39]90.657.210.8418.846.8113.45
LAVILA (B)93.859.910.5319.136.6913.68
LAVILA (L)94.563.112.0522.387.4315.44
", + "image_path": "2cacd19a6e1666422b3c3a28fd51a3f7a23a7a4c0c381b7457229d964834a329.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 152, + 545, + 208 + ], + "lines": [ + { + "bbox": [ + 304, + 152, + 545, + 208 + ], + "spans": [ + { + "bbox": [ + 304, + 152, + 545, + 208 + ], + "type": "text", + "content": "Table 3. Ego4D EgoMCQ and EgoNLQ. LAVILA outperforms prior work on both Multiple-Choice Questions and Natural Language Questions on Ego4D, with nearly " + }, + { + "bbox": [ + 304, + 152, + 545, + 208 + ], + "type": "inline_equation", + "content": "6\\%" + }, + { + "bbox": [ + 304, + 152, + 545, + 208 + ], + "type": "text", + "content": " absolute gain on the challenging intra-video MCQ task that requires reasoning over multiple clips from the same video to answer a question." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 230, + 545, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 230, + 545, + 277 + ], + "spans": [ + { + "bbox": [ + 304, + 230, + 545, + 277 + ], + "type": "text", + "content": "In all tables, we bold and underline the best and second-best performing methods with comparable backbones architectures. We highlight the overall best performing method, which typically uses a larger backbone, if applicable." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 286, + 390, + 298 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 286, + 390, + 298 + ], + "spans": [ + { + "bbox": [ + 306, + 286, + 390, + 298 + ], + "type": "text", + "content": "5.1. Main Results" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 305, + 545, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 305, + 545, + 437 + ], + "spans": [ + { + "bbox": [ + 304, + 305, + 545, + 437 + ], + "type": "text", + "content": "EK-100. We compare LAVILA with prior works on EK-100 MIR in Table 2. In the zero-shot setup, LAVILA remarkably surpasses an improved version of EgoVLP [39] under similar model complexity: we use TSF-Base+GPT-2 as the dual-encoder architecture while EgoVLP uses TSF-Base+Distil-BERT. With a stronger video encoder, i.e. TSF-Large, the performance improves further. In the fine-tuned setting, LAVILA significantly outperforms all previous supervised approaches including MME, JPOSE [75] and EgoVLP [39]. We also compare LAVILA on EK-100 CLS in Appendix E, and establish a new state-of-the-art." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 437, + 545, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 437, + 545, + 569 + ], + "spans": [ + { + "bbox": [ + 304, + 437, + 545, + 569 + ], + "type": "text", + "content": "Ego4D. We evaluate the pre-trained LAViLA model on EgoMCQ and EgoNLQ tasks and compare the results in Table 3. On EgoMCQ, our method achieves " + }, + { + "bbox": [ + 304, + 437, + 545, + 569 + ], + "type": "inline_equation", + "content": "93.8\\%" + }, + { + "bbox": [ + 304, + 437, + 545, + 569 + ], + "type": "text", + "content": " inter-video accuracy and " + }, + { + "bbox": [ + 304, + 437, + 545, + 569 + ], + "type": "inline_equation", + "content": "59.9\\%" + }, + { + "bbox": [ + 304, + 437, + 545, + 569 + ], + "type": "text", + "content": " intra-video accuracy, outperforming EgoVLP by a noticeable margin. Note that EgoVLP's performance reported in Table 3 is obtained by using EgoNCE loss [39], a variant of InfoNCE specialized for Ego4D while ours uses a standard InfoNCE loss. EgoVLP with InfoNCE has lower performance (89.4% inter-video and " + }, + { + "bbox": [ + 304, + 437, + 545, + 569 + ], + "type": "inline_equation", + "content": "51.5\\%" + }, + { + "bbox": [ + 304, + 437, + 545, + 569 + ], + "type": "text", + "content": " intravideo accuracy). On EgoNLQ, LAViLA achieves comparable results with EgoVLP with similar model complexity." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": "EGTEA. We evaluate the learned video representation by finetuning the video encoder for action classification in Table 4 on another popular egocentric dataset, EGTEA [37]. Our method surpasses the previous state-of-the-art which takes multiple modalities including visual, auditory and textual inputs [33] by a more than " + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": " absolute margin on the mean accuracy metric. Since previous methods are based on different backbones, we experiment with a TSF-Base (\"Visual only\") model pre-trained on Kinetics [9] as a fair baseline for LAVILA. We observe that its accuracy is comparable to previous methods but much lower than LAVILA, implying the effectiveness of learning visual representation" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "6591" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 49, + 70, + 286, + 153 + ], + "blocks": [ + { + "bbox": [ + 49, + 70, + 286, + 153 + ], + "lines": [ + { + "bbox": [ + 49, + 70, + 286, + 153 + ], + "spans": [ + { + "bbox": [ + 49, + 70, + 286, + 153 + ], + "type": "table", + "html": "
MethodBackbonePretrainTop-1 Acc.Mean Acc.
Li et al. [37]I3DK400-53.30
LSTA [63]ConvLSTMIN-1k61.8653.00
IPL [71]I3DK400-60.15
MTCN [33]SlowFast (V+A+T)K400+VGG-Sound73.5965.87
Visual onlyTSF-BIN-21k+K40065.5859.32
LAVILATSF-BWIT+Ego4D77.4570.12
LAVILATSF-LWIT+Ego4D81.7576.00
", + "image_path": "3701a0799d8659f3af26cedfb1ea1b06ad9ebec884de433afa0ee4e77d915c38.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 51, + 218, + 284, + 292 + ], + "blocks": [ + { + "bbox": [ + 46, + 162, + 288, + 207 + ], + "lines": [ + { + "bbox": [ + 46, + 162, + 288, + 207 + ], + "spans": [ + { + "bbox": [ + 46, + 162, + 288, + 207 + ], + "type": "text", + "content": "Table 4. EGTEA Classification. LAVILA obtains significant gains on this task, outperforming prior work with over " + }, + { + "bbox": [ + 46, + 162, + 288, + 207 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 162, + 288, + 207 + ], + "type": "text", + "content": " mean accuracy. Since the backbones used are not all comparable, we also report a comparable baseline with TSF-B (\"Visual only\")." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 51, + 218, + 284, + 292 + ], + "lines": [ + { + "bbox": [ + 51, + 218, + 284, + 292 + ], + "spans": [ + { + "bbox": [ + 51, + 218, + 284, + 292 + ], + "type": "table", + "html": "
MethodBackbonemAP (ZS)mAP (FT)
ActorObserverNet [57]ResNet-152-20.0
SSDA [12]I3D-25.8
Ego-Exo [38]SlowFast-R101-30.1
EgoVLP [39]TSF-B25.032.1
LAVILATSF-B26.833.7
LAVILATSF-L28.936.1
", + "image_path": "4d46cf429d0631d247726ec16223c677458f4402a9f885a01d2049312f8e13ca.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 357, + 287, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 357, + 287, + 380 + ], + "spans": [ + { + "bbox": [ + 46, + 357, + 287, + 380 + ], + "type": "text", + "content": "on large-scale egocentric videos and using LLM as textual supervision during pre-training." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 381, + 287, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 381, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 46, + 381, + 287, + 453 + ], + "type": "text", + "content": "CharadesEgo. Next, we compare LAVILA's representation on the CharadesEgo action classification task. As shown in Table 5, LAVILA's representation excels on this task as well, which is notable as CharadesEgo videos are significantly different compared to Ego4D, being captured by crowdsourced workers using mobile cameras." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 463, + 287, + 475 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 463, + 287, + 475 + ], + "spans": [ + { + "bbox": [ + 47, + 463, + 287, + 475 + ], + "type": "text", + "content": "5.2. Application to Third-Person Video Pre-training" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 481, + 287, + 542 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 481, + 287, + 542 + ], + "spans": [ + { + "bbox": [ + 46, + 481, + 287, + 542 + ], + "type": "text", + "content": "We apply LAVILA to third-person videos by experimenting with the HowTo100M [45] dataset. Specifically, we use the temporally aligned subset provided by [25], which contains 3.3M sentences from 247k videos. We evaluate the video representation on two third-person video datasets, i.e." + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 325, + 70, + 528, + 133 + ], + "blocks": [ + { + "bbox": [ + 46, + 300, + 287, + 345 + ], + "lines": [ + { + "bbox": [ + 46, + 300, + 287, + 345 + ], + "spans": [ + { + "bbox": [ + 46, + 300, + 287, + 345 + ], + "type": "text", + "content": "Table 5. CharadesEgo Action Recognition. LAVILA sets new state-of-the-art in both zero-shot (ZS) and finetuned (FT) settings. Note that CharadesEgo videos are visually different compared to Ego4D videos, on which LAVILA is pretrained." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 325, + 70, + 528, + 133 + ], + "lines": [ + { + "bbox": [ + 325, + 70, + 528, + 133 + ], + "spans": [ + { + "bbox": [ + 325, + 70, + 528, + 133 + ], + "type": "table", + "html": "
MethodVis. Enc.UCF-101HMDB-51
MIL-NCE [44]S3D82.754.3
TAN [25]S3D83.256.7
Baseline (w/o LLM)TSF-B86.559.4
LAViLATSF-B87.457.2
LAViLATSF-L88.161.5
", + "image_path": "d7c6083fdcfa7dc4337c0b6262e466bc06310a8be8fee9294bd4a116f8663174.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 142, + 545, + 175 + ], + "lines": [ + { + "bbox": [ + 305, + 142, + 545, + 175 + ], + "spans": [ + { + "bbox": [ + 305, + 142, + 545, + 175 + ], + "type": "text", + "content": "Table 6. LAVILA on third-person videos. We measure the linear-probing action classification performance of the video model after pre-training on HowTo100M [45]." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 305, + 198, + 545, + 283 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 198, + 545, + 283 + ], + "spans": [ + { + "bbox": [ + 305, + 198, + 545, + 283 + ], + "type": "text", + "content": "UCF-101 [60] and HMDB-51 [35] for action classification using the linear probing protocol. For more details, please refer to Appendix D. From Table 6, we see that LAVILA outperforms previous methods such as MIL-NCE [44] and TAN [25] by a large margin. Since we use a different backbone, we report a baseline without LLM and show that LAVILA indeed benefits from the language supervision." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 293, + 520, + 306 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 293, + 520, + 306 + ], + "spans": [ + { + "bbox": [ + 306, + 293, + 520, + 306 + ], + "type": "text", + "content": "5.3. Application to Semi-supervised Learning" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 313, + 545, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 313, + 545, + 491 + ], + "spans": [ + { + "bbox": [ + 304, + 313, + 545, + 491 + ], + "type": "text", + "content": "While LAVILA is very effective at leveraging existing narrations to augment them, we now show that it is also applicable when only a limited number of narrations are available to begin with. We first divide each long video from Ego4D into 15-second chunks and assume only the annotated clips within every " + }, + { + "bbox": [ + 304, + 313, + 545, + 491 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 304, + 313, + 545, + 491 + ], + "type": "text", + "content": " chunks is available during pre-training, leading to approximately " + }, + { + "bbox": [ + 304, + 313, + 545, + 491 + ], + "type": "inline_equation", + "content": "\\frac{100}{N}\\%" + }, + { + "bbox": [ + 304, + 313, + 545, + 491 + ], + "type": "text", + "content": " of the full set, where " + }, + { + "bbox": [ + 304, + 313, + 545, + 491 + ], + "type": "inline_equation", + "content": "N \\in \\{2,5,10\\}" + }, + { + "bbox": [ + 304, + 313, + 545, + 491 + ], + "type": "text", + "content": ". This can be considered a practical scenario when we want to annotate as many videos as possible for diversity when the annotation budget is limited. In the remainder " + }, + { + "bbox": [ + 304, + 313, + 545, + 491 + ], + "type": "inline_equation", + "content": "(1 - \\frac{100}{N})\\%" + }, + { + "bbox": [ + 304, + 313, + 545, + 491 + ], + "type": "text", + "content": " part that is skipped, we uniformly sample the same number of the clips per chunk with the same clip length as that in the seen chunks. Both the dual-encoder model and NARRATOR are trained on the " + }, + { + "bbox": [ + 304, + 313, + 545, + 491 + ], + "type": "inline_equation", + "content": "100\\%" + }, + { + "bbox": [ + 304, + 313, + 545, + 491 + ], + "type": "text", + "content": " available annotations." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 493, + 545, + 542 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 493, + 545, + 542 + ], + "spans": [ + { + "bbox": [ + 305, + 493, + 545, + 542 + ], + "type": "text", + "content": "We plot the zero-shot performance curve of pre-training with different proportions in Figure 5. We can see that LAV-ILA consistently outperforms the ground-truth-only baseline at all points (10, 20, 50, and " + }, + { + "bbox": [ + 305, + 493, + 545, + 542 + ], + "type": "inline_equation", + "content": "100\\%" + }, + { + "bbox": [ + 305, + 493, + 545, + 542 + ], + "type": "text", + "content": "). The improvement" + } + ] + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 53, + 574, + 167, + 654 + ], + "blocks": [ + { + "bbox": [ + 53, + 574, + 167, + 654 + ], + "lines": [ + { + "bbox": [ + 53, + 574, + 167, + 654 + ], + "spans": [ + { + "bbox": [ + 53, + 574, + 167, + 654 + ], + "type": "image", + "image_path": "c3f8d35a7515c1bb20c02918ea05d070808b05c9607c308e94319aeb773085c5.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 78, + 658, + 143, + 666 + ], + "lines": [ + { + "bbox": [ + 78, + 658, + 143, + 666 + ], + "spans": [ + { + "bbox": [ + 78, + 658, + 143, + 666 + ], + "type": "text", + "content": "(a) EK-100 MIR mAP." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 177, + 557, + 414, + 654 + ], + "blocks": [ + { + "bbox": [ + 177, + 557, + 414, + 654 + ], + "lines": [ + { + "bbox": [ + 177, + 557, + 414, + 654 + ], + "spans": [ + { + "bbox": [ + 177, + 557, + 414, + 654 + ], + "type": "image", + "image_path": "a99e94a0f49e338898d7ee6d5d41cba9cc2953097107e3b0169b3bca70229447.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 200, + 658, + 269, + 666 + ], + "lines": [ + { + "bbox": [ + 200, + 658, + 269, + 666 + ], + "spans": [ + { + "bbox": [ + 200, + 658, + 269, + 666 + ], + "type": "text", + "content": "(b) EK-100 MIR nDCG." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 45, + 677, + 547, + 710 + ], + "lines": [ + { + "bbox": [ + 45, + 677, + 547, + 710 + ], + "spans": [ + { + "bbox": [ + 45, + 677, + 547, + 710 + ], + "type": "text", + "content": "Figure 5. LAVILA is effective in a semi-supervised setting where only a limited amount of narrations are given. Comparing zero-shot performance of pre-training, LAVILA consistently outperforms the groundtruth-only baseline when 10, 20, 50, " + }, + { + "bbox": [ + 45, + 677, + 547, + 710 + ], + "type": "inline_equation", + "content": "100\\%" + }, + { + "bbox": [ + 45, + 677, + 547, + 710 + ], + "type": "text", + "content": " data is used. We also achieve comparable result with state-of-the-art with only " + }, + { + "bbox": [ + 45, + 677, + 547, + 710 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 45, + 677, + 547, + 710 + ], + "type": "text", + "content": " of the annotated data." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 426, + 574, + 539, + 654 + ], + "blocks": [ + { + "bbox": [ + 426, + 574, + 539, + 654 + ], + "lines": [ + { + "bbox": [ + 426, + 574, + 539, + 654 + ], + "spans": [ + { + "bbox": [ + 426, + 574, + 539, + 654 + ], + "type": "image", + "image_path": "c6403c1e1dafe166ed69cd617f015dd0bed163ff9c13fc7ba28f23d84b3eba57.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 434, + 658, + 533, + 667 + ], + "lines": [ + { + "bbox": [ + 434, + 658, + 533, + 667 + ], + "spans": [ + { + "bbox": [ + 434, + 658, + 533, + 667 + ], + "type": "text", + "content": "(d) EgoMCQ Intra-video accuracy." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6592" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 59, + 70, + 248, + 142 + ], + "blocks": [ + { + "bbox": [ + 59, + 70, + 248, + 142 + ], + "lines": [ + { + "bbox": [ + 59, + 70, + 248, + 142 + ], + "spans": [ + { + "bbox": [ + 59, + 70, + 248, + 142 + ], + "type": "table", + "html": "
Text Dec. arch.Text Dec. init.Freeze LMMETEOR ROUGE-L CIDESAvg. mAP
(baseline)---26.0
GPT-2random×0.2840.5240.88224.3
GPT-2WebText✓0.2700.5050.80424.0
GPT-2 XLWebText✓0.2890.5300.94026.2
", + "image_path": "ab53022c098b057542b415759a6667ec4871f1eb7dcd5ae0618e14357cd8136c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 52, + 152, + 249, + 177 + ], + "lines": [ + { + "bbox": [ + 52, + 152, + 249, + 177 + ], + "spans": [ + { + "bbox": [ + 52, + 152, + 249, + 177 + ], + "type": "text", + "content": "(a) Generation Quality. Using a sufficiently large language model as the text decoder is crucial for good text generation quality and downstream performance." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 255, + 78, + 372, + 141 + ], + "blocks": [ + { + "bbox": [ + 255, + 78, + 372, + 141 + ], + "lines": [ + { + "bbox": [ + 255, + 78, + 372, + 141 + ], + "spans": [ + { + "bbox": [ + 255, + 78, + 372, + 141 + ], + "type": "table", + "html": "
Sampling method# of sentencesAvg. mAP
N/A (baseline)-26.0
Beam search127.9
Nucleus129.6
Nucleus1029.7
", + "image_path": "8f7aaec3da0d2842abaaa44c377be56746fe80f9264ffa492db9d9e4c1f8be17.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 373, + 152, + 539, + 177 + ], + "lines": [ + { + "bbox": [ + 373, + 152, + 539, + 177 + ], + "spans": [ + { + "bbox": [ + 373, + 152, + 539, + 177 + ], + "type": "text", + "content": "(c) Scaling effect of LAVILA. Gains increase on scaling the video encoder in NARRATOR. Default refers to only using the original narrations." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_footnote" + }, + { + "bbox": [ + 47, + 177, + 545, + 199 + ], + "lines": [ + { + "bbox": [ + 47, + 177, + 545, + 199 + ], + "spans": [ + { + "bbox": [ + 47, + 177, + 545, + 199 + ], + "type": "text", + "content": "Table 7. Ablations of NARRATOR. We report zero-shot average mAP on EK-100 MIR for comparing downstream performance. We study NARRATOR from the perspective of generation quality (left), sampling techniques (middle), and scaling effect (right)." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 2 + }, + { + "bbox": [ + 253, + 152, + 369, + 177 + ], + "lines": [ + { + "bbox": [ + 253, + 152, + 369, + 177 + ], + "spans": [ + { + "bbox": [ + 253, + 152, + 369, + 177 + ], + "type": "text", + "content": "(b) Sampling. LAV1LA benefits more from narrations produced by nucleus sampling than beam search." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 378, + 77, + 538, + 151 + ], + "blocks": [ + { + "bbox": [ + 378, + 77, + 538, + 151 + ], + "lines": [ + { + "bbox": [ + 378, + 77, + 538, + 151 + ], + "spans": [ + { + "bbox": [ + 378, + 77, + 538, + 151 + ], + "type": "image", + "image_path": "7d8fa188da18a0da78a3340813a546ebcb084ea39047ab291fb0b0ed6a231dbb.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 47, + 71, + 248, + 142 + ], + "blocks": [ + { + "bbox": [ + 47, + 71, + 248, + 142 + ], + "lines": [ + { + "bbox": [ + 47, + 71, + 248, + 142 + ], + "spans": [ + { + "bbox": [ + 47, + 71, + 248, + 142 + ], + "type": "table", + "html": "
Text Dec. arch.Text Dec. init.Freeze LMMETEOR ROUGE1 CIDEsAvg. mAP
(baseline)---26.0
GPT-2randomX0.2840.5240.88224.3
GPT-2WebText✓0.2700.5050.80424.0
GPT-2 XLWebText✓0.2890.5300.94026.2
", + "image_path": "d54eaa91999a5dc972dd78f3cb4e3c6bebc9a2bc822829de954854fdb1075516.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 300, + 287, + 354 + ], + "lines": [ + { + "bbox": [ + 47, + 300, + 287, + 354 + ], + "spans": [ + { + "bbox": [ + 47, + 300, + 287, + 354 + ], + "type": "text", + "content": "Table 8. Contributions of different Language Supervision. We can see that (1) using REPHRASER (\"Rephr.\") and NARRATOR (\"Recap.\") improve downstream zero-shot performance complementarily, (2) dense pseudo-captioning further improves performance on 3 out of 6 metrics." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 376, + 287, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 376, + 287, + 449 + ], + "spans": [ + { + "bbox": [ + 46, + 376, + 287, + 449 + ], + "type": "text", + "content": "tends to be larger when more data is available, indicating the method's scalability as more videos are narrated in the future. Furthermore, we observe our method can achieve a similar level of performance with the baseline often using less than " + }, + { + "bbox": [ + 46, + 376, + 287, + 449 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 46, + 376, + 287, + 449 + ], + "type": "text", + "content": " data. We also achieve a comparable result with the state-of-the-art using much fewer data." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 455, + 147, + 467 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 455, + 147, + 467 + ], + "spans": [ + { + "bbox": [ + 47, + 455, + 147, + 467 + ], + "type": "text", + "content": "5.4. Ablation Studies" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 474, + 287, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 474, + 287, + 641 + ], + "spans": [ + { + "bbox": [ + 46, + 474, + 287, + 641 + ], + "type": "text", + "content": "Contributions of Different Language Supervisions. We ablate different language supervisions in Table 8 on EK-100 MIR (zero-shot), EgoMCQ and EGTEA. Using the text-only REPHRASER (\"rephr.\") or visually conditioned NARRATOR (\"recap.\") separately improves the ground-truth baseline noticeably. Combining both REPHRASER and NARRATOR gives an improvement of " + }, + { + "bbox": [ + 46, + 474, + 287, + 641 + ], + "type": "inline_equation", + "content": "3.5\\%" + }, + { + "bbox": [ + 46, + 474, + 287, + 641 + ], + "type": "text", + "content": " average mAP on EK-100 MIR. We see that dense captioning on the entire video (\"pseudo-cap.\") is also helpful. Though the gain on EK-100 MIR is not as significant, it shows nontrivial improvements on EgoMCQ intra-video accuracy and EGTEA mean accuracy. Our conjecture for this marginal gain is that informative clips are mostly covered in Ego4D because all videos are inspected by two annotators." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 642, + 288, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 288, + 712 + ], + "type": "text", + "content": "Generation Quality of NARRATOR. We study how the NARRATOR's configurations affect the quality of generated text and the downstream performance. The generation quality is measured by standard unsupervised automatic metrics including METEOR, ROUGE, and CIDEr [43]. We use a NARRATOR with a smaller GPT-2 as the text decoder and" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 220, + 545, + 304 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 220, + 545, + 304 + ], + "spans": [ + { + "bbox": [ + 304, + 220, + 545, + 304 + ], + "type": "text", + "content": "consider two scenarios in Table 7a: (1) LM is randomly initialized but jointly trained with the gated cross-attention modules, and (2) LM is initialized from the original GPT-2. The generation quality decreases compared to GPT-2 XL in both cases and the zero-shot retrieval result on EK-100 MIR is worse. This indicates that the language model should be sufficiently large and pre-trained on web text data." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 307, + 545, + 355 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 307, + 545, + 355 + ], + "spans": [ + { + "bbox": [ + 304, + 307, + 545, + 355 + ], + "type": "text", + "content": "Sampling. In Table 7b, we investigate different sampling methods for text generation from NARRATOR. We see that nucleus sampling works much better than beam search while repetitive sampling shows marginal improvement." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 357, + 546, + 477 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 357, + 546, + 477 + ], + "spans": [ + { + "bbox": [ + 304, + 357, + 546, + 477 + ], + "type": "text", + "content": "Scaling effect. In Table 7c, we compare the zero-shot retrieval result by progressively increasing the size of NARRATOR's video encoder from TSF-B to TSF-L and TSFL@HR, which increases the input resolution to be narrated from 224 to 336 while fixing the dual-encoder architecture. The retrieval performance steadily increases while NARRATOR becomes stronger. We conduct this experiment by varying the dual-encoder architecture, namely TSF-Base and TSF-Large, and show similar trends. Both phenomena suggest that LAVILA can scale to larger models." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 496, + 471, + 509 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 496, + 471, + 509 + ], + "spans": [ + { + "bbox": [ + 305, + 496, + 471, + 509 + ], + "type": "text", + "content": "6. Conclusion and Future Work" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 519, + 545, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 519, + 545, + 639 + ], + "spans": [ + { + "bbox": [ + 304, + 519, + 545, + 639 + ], + "type": "text", + "content": "In this paper, we proposed LAVILA, a new approach to video-language representation learning by automatically narrating long videos with LLMs. We achieve strong improvements over baselines trained with the same amount of human-narrated videos and set new state-of-the-art on six popular benchmark tasks across first- and third-person video understanding benchmarks. LAVILA also shows positive scaling behavior when adding more training narrations, using larger visual backbones, and using stronger LLMs, all of which are promising areas for future work." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 641, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 641, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 641, + 545, + 712 + ], + "type": "text", + "content": "Acknowledgements: We thank Naman Goyal, Stephen Roller and Susan Zhang for help with language models, Kevin Qinghong Lin for help with EgoVLP, and the Meta AI team for helpful discussions and feedback. This material is based upon work in-part supported by the National Science Foundation under Grant No. IIS-1845485." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6593" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 287, + 704 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 199 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 199 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 199 + ], + "type": "text", + "content": "[1] Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katie Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob Menick, Sebastian Borgeaud, Andrew Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikolaj Binkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, and Karen Simonyan. Flamingo: a visual language model for few-shot learning. In NeurIPS, 2022." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 200, + 287, + 232 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 200, + 287, + 232 + ], + "spans": [ + { + "bbox": [ + 53, + 200, + 287, + 232 + ], + "type": "text", + "content": "[2] Anurag Arnab, Mostafa Dehghani, Georg Heigold, Chen Sun, Mario Lučić, and Cordelia Schmid. Vivit: A video vision transformer. In ICCV, 2021." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 232, + 287, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 232, + 287, + 262 + ], + "spans": [ + { + "bbox": [ + 53, + 232, + 287, + 262 + ], + "type": "text", + "content": "[3] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 264, + 287, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 264, + 287, + 296 + ], + "spans": [ + { + "bbox": [ + 53, + 264, + 287, + 296 + ], + "type": "text", + "content": "[4] Max Bain, Arsha Nagrani, Gül Varol, and Andrew Zisserman. Frozen in time: A joint video and image encoder for end-to-end retrieval. In ICCV, 2021." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 296, + 287, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 296, + 287, + 317 + ], + "spans": [ + { + "bbox": [ + 53, + 296, + 287, + 317 + ], + "type": "text", + "content": "[5] Yoshua Bengio, Réjean Ducharme, and Pascal Vincent. A neural probabilistic language model. In NIPS, 2000." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 317, + 287, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 317, + 287, + 348 + ], + "spans": [ + { + "bbox": [ + 53, + 317, + 287, + 348 + ], + "type": "text", + "content": "[6] Gedas Bertasius, Heng Wang, and Lorenzo Torresani. Is space-time attention all you need for video understanding? In ICML, 2021." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 348, + 287, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 348, + 287, + 456 + ], + "spans": [ + { + "bbox": [ + 53, + 348, + 287, + 456 + ], + "type": "text", + "content": "[7] Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language models are few-shot learners. NeurIPS, 2020." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 457, + 287, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 457, + 287, + 498 + ], + "spans": [ + { + "bbox": [ + 53, + 457, + 287, + 498 + ], + "type": "text", + "content": "[8] Fabian Caba Heilbron, Victor Escorcia, Bernard Ghanem, and Juan Carlos Niebles. Activitynet: A large-scale video benchmark for human activity understanding. In CVPR, 2015." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 499, + 287, + 531 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 499, + 287, + 531 + ], + "spans": [ + { + "bbox": [ + 53, + 499, + 287, + 531 + ], + "type": "text", + "content": "[9] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In CVPR, 2017." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 531, + 287, + 574 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 531, + 287, + 574 + ], + "spans": [ + { + "bbox": [ + 48, + 531, + 287, + 574 + ], + "type": "text", + "content": "[10] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In ECCV, 2020." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 574, + 287, + 605 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 574, + 287, + 605 + ], + "spans": [ + { + "bbox": [ + 48, + 574, + 287, + 605 + ], + "type": "text", + "content": "[11] Jaemin Cho, Jie Lei, Hao Tan, and Mohit Bansal. Unifying vision-and-language tasks via text generation. In ICML, 2021." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 606, + 287, + 649 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 606, + 287, + 649 + ], + "spans": [ + { + "bbox": [ + 48, + 606, + 287, + 649 + ], + "type": "text", + "content": "[12] Jinwoo Choi, Gaurav Sharma, Manmohan Chandraker, and Jia-Bin Huang. Unsupervised and semi-supervised domain adaptation for action recognition from drones. In WACV, 2020." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 649, + 287, + 704 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 649, + 287, + 704 + ], + "spans": [ + { + "bbox": [ + 48, + 649, + 287, + 704 + ], + "type": "text", + "content": "[13] Ego4D Consortium. Egocentric live 4d perception (Ego4D) database: A large-scale first-person video database, supporting research in multi-modal machine perception for daily life activity. https://sites.google.com/view/ego4d/home. Accessed: 2022-11-22." + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 710 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 127 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 127 + ], + "type": "text", + "content": "[14] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Evangelos Kazakos, Jian Ma, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Rescaling egocentric vision: collection, pipeline and challenges for Epic-Kitchens-100. IJCV, 2022." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 127, + 545, + 159 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 127, + 545, + 159 + ], + "spans": [ + { + "bbox": [ + 307, + 127, + 545, + 159 + ], + "type": "text", + "content": "[15] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In CVPR, 2009." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 159, + 545, + 180 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 159, + 545, + 180 + ], + "spans": [ + { + "bbox": [ + 307, + 159, + 545, + 180 + ], + "type": "text", + "content": "[16] Karan Desai and Justin Johnson. Virtex: Learning visual representations from textual annotations. In CVPR, 2021." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 180, + 545, + 223 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 180, + 545, + 223 + ], + "spans": [ + { + "bbox": [ + 307, + 180, + 545, + 223 + ], + "type": "text", + "content": "[17] Jeffrey Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, and Trevor Darrell. Long-term recurrent convolutional networks for visual recognition and description. In CVPR, 2015." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 223, + 545, + 287 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 223, + 545, + 287 + ], + "spans": [ + { + "bbox": [ + 307, + 223, + 545, + 287 + ], + "type": "text", + "content": "[18] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2020." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 288, + 545, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 288, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 307, + 288, + 545, + 319 + ], + "type": "text", + "content": "[19] Marzieh Fadaee, Arianna Bisazza, and Christof Monz. Data augmentation for low-resource neural machine translation. In ACL, 2017." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 319, + 545, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 319, + 545, + 363 + ], + "spans": [ + { + "bbox": [ + 307, + 319, + 545, + 363 + ], + "type": "text", + "content": "[20] Steven Y Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard Hovy. A survey of data augmentation approaches for nlp. In ACL Findings, 2021." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 363, + 545, + 405 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 363, + 545, + 405 + ], + "spans": [ + { + "bbox": [ + 307, + 363, + 545, + 405 + ], + "type": "text", + "content": "[21] Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc'Aurelio Ranzato, and Tomas Mikolov. Devise: A deep visual-semantic embedding model. NeurIPS, 2013." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 405, + 545, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 405, + 545, + 437 + ], + "spans": [ + { + "bbox": [ + 307, + 405, + 545, + 437 + ], + "type": "text", + "content": "[22] Rohit Girdhar, Mannat Singh, Nikhila Ravi, Laurens van der Maaten, Armand Joulin, and Ishan Misra. Omnivore: A single model for many visual modalities. In CVPR, 2022." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 437, + 545, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 437, + 545, + 480 + ], + "spans": [ + { + "bbox": [ + 307, + 437, + 545, + 480 + ], + "type": "text", + "content": "[23] Ramsri Goutham Golla. High-quality sentence paraphrase using transformers in nlp. https://huggingface.co/ramsrigouthamg/t5-large-paraphraser-diverse-high-quality. Accessed: 2022-06-01." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 480, + 545, + 710 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 480, + 545, + 710 + ], + "spans": [ + { + "bbox": [ + 307, + 480, + 545, + 710 + ], + "type": "text", + "content": "[24] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonino Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Ilija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Abrham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Ziwei Zhao, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Christian Fuegen, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "6594" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 705 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 67, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 67, + 72, + 287, + 116 + ], + "type": "text", + "content": "Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4d: Around the world in 3,000 hours of egocentric video. In CVPR, 2022." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 117, + 286, + 137 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 117, + 286, + 137 + ], + "spans": [ + { + "bbox": [ + 48, + 117, + 286, + 137 + ], + "type": "text", + "content": "[25] Tengda Han, Weidi Xie, and Andrew Zisserman. Temporal alignment networks for long-term video. In CVPR, 2022." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 137, + 286, + 168 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 137, + 286, + 168 + ], + "spans": [ + { + "bbox": [ + 49, + 137, + 286, + 168 + ], + "type": "text", + "content": "[26] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, 2016." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 170, + 286, + 190 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 170, + 286, + 190 + ], + "spans": [ + { + "bbox": [ + 49, + 170, + 286, + 190 + ], + "type": "text", + "content": "[27] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 1997." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 190, + 286, + 276 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 190, + 286, + 276 + ], + "spans": [ + { + "bbox": [ + 49, + 190, + 286, + 276 + ], + "type": "text", + "content": "[28] Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Jack W. Rae, Oriol Vinyals, and Laurent Sifre. Training compute-optimal large language models. arXiv preprint arXiv:2203.15556, 2022." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 277, + 286, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 277, + 286, + 308 + ], + "spans": [ + { + "bbox": [ + 49, + 277, + 286, + 308 + ], + "type": "text", + "content": "[29] Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. The curious case of neural text degeneration. In ICLR, 2020." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 309, + 286, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 309, + 286, + 350 + ], + "spans": [ + { + "bbox": [ + 49, + 309, + 286, + 350 + ], + "type": "text", + "content": "[30] Xiaowei Hu, Zhe Gan, Jianfeng Wang, Zhengyuan Yang, Zicheng Liu, Yumao Lu, and Lijuan Wang. Scaling up vision-language pre-training for image captioning. In CVPR, 2022." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 352, + 286, + 394 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 352, + 286, + 394 + ], + "spans": [ + { + "bbox": [ + 48, + 352, + 286, + 394 + ], + "type": "text", + "content": "[31] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In ICML, 2021." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 395, + 286, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 395, + 286, + 426 + ], + "spans": [ + { + "bbox": [ + 48, + 395, + 286, + 426 + ], + "type": "text", + "content": "[32] Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, and Weidi Xie. Prompting visual-language models for efficient video understanding. In ECCV, 2022." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 426, + 286, + 468 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 426, + 286, + 468 + ], + "spans": [ + { + "bbox": [ + 48, + 426, + 286, + 468 + ], + "type": "text", + "content": "[33] Evangelos Kazakos, Jaesung Huh, Arsha Nagrani, Andrew Zisserman, and Dima Damen. With a little help from my temporal context: Multimodal egocentric action recognition. In BMVC, 2021." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 469, + 286, + 511 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 469, + 286, + 511 + ], + "spans": [ + { + "bbox": [ + 48, + 469, + 286, + 511 + ], + "type": "text", + "content": "[34] Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, and Boqing Gong. Movinets: Mobile video networks for efficient video recognition. In CVPR, 2021." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 512, + 286, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 512, + 286, + 544 + ], + "spans": [ + { + "bbox": [ + 48, + 512, + 286, + 544 + ], + "type": "text", + "content": "[35] Hildegard Kuehne, Hueihan Jhuang, Estíbaliz Garrote, Tomaso Poggio, and Thomas Serre. Hmdb: a large video database for human motion recognition. In ICCV, 2011." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 544, + 286, + 585 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 544, + 286, + 585 + ], + "spans": [ + { + "bbox": [ + 48, + 544, + 286, + 585 + ], + "type": "text", + "content": "[36] Jie Lei, Linjie Li, Luowei Zhou, Zhe Gan, Tamara L Berg, Mohit Bansal, and Jingjing Liu. Less is more: Clipbert for video-and-language learning via sparse sampling. In CVPR, 2021." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 586, + 286, + 617 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 586, + 286, + 617 + ], + "spans": [ + { + "bbox": [ + 48, + 586, + 286, + 617 + ], + "type": "text", + "content": "[37] Yin Li, Miao Liu, and James M Rehg. In the eye of beholder: Joint learning of gaze and actions in first person video. In ECCV, 2018." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 618, + 286, + 650 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 618, + 286, + 650 + ], + "spans": [ + { + "bbox": [ + 48, + 618, + 286, + 650 + ], + "type": "text", + "content": "[38] Yanghao Li, Tushar Nagarajan, Bo Xiong, and Kristen Grauman. Ego-exo: Transferring visual representations from third-person to first-person videos. In CVPR, 2021." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 651, + 286, + 705 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 651, + 286, + 705 + ], + "spans": [ + { + "bbox": [ + 48, + 651, + 286, + 705 + ], + "type": "text", + "content": "[39] Kevin Qinghong Lin, Alex Jinpeng Wang, Mattia Soldan, Michael Wray, Rui Yan, Eric Zhongcong Xu, Difei Gao, Rongcheng Tu, Wenzhe Zhao, Weijie Kong, Hongfa Cai Chengfei, Wang, Dima Damen, Bernard Ghanem, Wei Liu, and Mike Zheng Shou. Egocentric video-language pre" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 705 + ], + "type": "list", + "angle": 0, + "index": 33, + "blocks": [ + { + "bbox": [ + 327, + 73, + 427, + 83 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 427, + 83 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 427, + 83 + ], + "type": "text", + "content": "training. In NeurIPS, 2022." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 84, + 545, + 125 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 84, + 545, + 125 + ], + "spans": [ + { + "bbox": [ + 308, + 84, + 545, + 125 + ], + "type": "text", + "content": "[40] Ziyi Lin, Shijie Geng, Renrui Zhang, Peng Gao, Gerard de Melo, Xiaogang Wang, Jifeng Dai, Yu Qiao, and Hongsheng Li. Frozen clip models are efficient video learners. In ECCV, 2022." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 126, + 545, + 158 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 126, + 545, + 158 + ], + "spans": [ + { + "bbox": [ + 308, + 126, + 545, + 158 + ], + "type": "text", + "content": "[41] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. NeurIPS, 2019." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 159, + 545, + 212 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 159, + 545, + 212 + ], + "spans": [ + { + "bbox": [ + 308, + 159, + 545, + 212 + ], + "type": "text", + "content": "[42] Huaishao Luo, Lei Ji, Botian Shi, Haoyang Huang, Nan Duan, Tianrui Li, Jason Li, Taroon Bharti, and Ming Zhou. Univl: A unified video and language pre-training model for multimodal understanding and generation. arXiv preprint arXiv:2002.06353, 2020." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 213, + 545, + 232 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 213, + 545, + 232 + ], + "spans": [ + { + "bbox": [ + 308, + 213, + 545, + 232 + ], + "type": "text", + "content": "[43] Maluuba. nlg-eval. https://github.com/Maluuba/nlg-eval. Accessed: 2022-06-01." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 233, + 545, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 233, + 545, + 275 + ], + "spans": [ + { + "bbox": [ + 308, + 233, + 545, + 275 + ], + "type": "text", + "content": "[44] Antoine Miech, Jean-Baptiste Alayrac, Lucas Smaira, Ivan Laptev, Josef Sivic, and Andrew Zisserman. End-to-end learning of visual representations from uncurated instructional videos. In CVPR, 2020." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 276, + 545, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 276, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 308, + 276, + 545, + 319 + ], + "type": "text", + "content": "[45] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic. Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In ICCV, 2019." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 319, + 545, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 319, + 545, + 361 + ], + "spans": [ + { + "bbox": [ + 308, + 319, + 545, + 361 + ], + "type": "text", + "content": "[46] Arsha Nagrani, Paul Hongsuck Seo, Bryan Seybold, Anja Hauth, Santiago Manen, Chen Sun, and Cordelia Schmid. Learning audio-video modalities from image captions. In ECCV, 2022." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 361, + 545, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 361, + 545, + 404 + ], + "spans": [ + { + "bbox": [ + 308, + 361, + 545, + 404 + ], + "type": "text", + "content": "[47] Bolin Ni, Houwen Peng, Minghao Chen, Songyang Zhang, Gaofeng Meng, Jianlong Fu, Shiming Xiang, and Haibin Ling. Expanding language-image pretrained models for general video recognition. In ECCV, 2022." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 404, + 545, + 436 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 404, + 545, + 436 + ], + "spans": [ + { + "bbox": [ + 308, + 404, + 545, + 436 + ], + "type": "text", + "content": "[48] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 437, + 545, + 490 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 437, + 545, + 490 + ], + "spans": [ + { + "bbox": [ + 308, + 437, + 545, + 490 + ], + "type": "text", + "content": "[49] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In ICML, 2021." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 491, + 545, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 491, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 308, + 491, + 545, + 521 + ], + "type": "text", + "content": "[50] Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. Language models are unsupervised multitask learners. OpenAI blog, 2019." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 522, + 545, + 564 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 522, + 545, + 564 + ], + "spans": [ + { + "bbox": [ + 308, + 522, + 545, + 564 + ], + "type": "text", + "content": "[51] Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. Exploring the limits of transfer learning with a unified text-to-text transformer. JMLR, 2020." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 565, + 545, + 606 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 565, + 545, + 606 + ], + "spans": [ + { + "bbox": [ + 308, + 565, + 545, + 606 + ], + "type": "text", + "content": "[52] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 607, + 545, + 639 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 607, + 545, + 639 + ], + "spans": [ + { + "bbox": [ + 308, + 607, + 545, + 639 + ], + "type": "text", + "content": "[53] Anna Rohrbach, Atousa Torabi, Marcus Rohrbach, Niket Tandon, Christopher Pal, Hugo Larochelle, Aaron Courville, and Bernt Schiele. Movie description. *IJCV*, 2017." + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 308, + 640, + 545, + 705 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 640, + 545, + 705 + ], + "spans": [ + { + "bbox": [ + 308, + 640, + 545, + 705 + ], + "type": "text", + "content": "[54] Christoph Schuhmann, Romain Beaumont, Cade W Gordon, Ross Wightman, Theo Coombes, Aarush Katta, Clayton Mullis, Patrick Schramowski, Srivatsa R Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev. Laion-5b: An open large-scale dataset for training next generation image-text models. In NeurIPS D&B," + } + ] + } + ], + "index": 32 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "6595" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 73, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 19, + "blocks": [ + { + "bbox": [ + 68, + 73, + 89, + 83 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 68, + 73, + 89, + 83 + ], + "spans": [ + { + "bbox": [ + 68, + 73, + 89, + 83 + ], + "type": "text", + "content": "2022." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 83, + 287, + 114 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 83, + 287, + 114 + ], + "spans": [ + { + "bbox": [ + 48, + 83, + 287, + 114 + ], + "type": "text", + "content": "[55] Rico Sennrich, Barry Haddow, and Alexandra Birch. Neural machine translation of rare words with subword units. In ACL, 2016." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 116, + 287, + 147 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 116, + 287, + 147 + ], + "spans": [ + { + "bbox": [ + 49, + 116, + 287, + 147 + ], + "type": "text", + "content": "[56] Paul Hongsuck Seo, Arsha Nagrani, Anurag Arnab, and Cordelia Schmid. End-to-end generative pretraining for multimodal video captioning. In CVPR, 2022." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 148, + 287, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 148, + 287, + 179 + ], + "spans": [ + { + "bbox": [ + 49, + 148, + 287, + 179 + ], + "type": "text", + "content": "[57] Gunnar A Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, and Karteek Alahari. Actor and observer: Joint modeling of first and third-person videos. In CVPR, 2018." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 180, + 287, + 221 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 180, + 287, + 221 + ], + "spans": [ + { + "bbox": [ + 49, + 180, + 287, + 221 + ], + "type": "text", + "content": "[58] Gunnar A Sigurdsson, Abhinav Gupta, Cordelia Schmid, Ali Farhadi, and Karteek Alahari. Charades-ego: A large-scale dataset of paired third and first person videos. arXiv preprint arXiv:1804.09626, 2018." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 222, + 287, + 243 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 222, + 287, + 243 + ], + "spans": [ + { + "bbox": [ + 49, + 222, + 287, + 243 + ], + "type": "text", + "content": "[59] Kihyuk Sohn. Improved deep metric learning with multiclass n-pair loss objective. NeurIPS, 2016." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 243, + 287, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 243, + 287, + 275 + ], + "spans": [ + { + "bbox": [ + 49, + 243, + 287, + 275 + ], + "type": "text", + "content": "[60] Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. Ucf101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402, 2012." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 49, + 276, + 287, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 276, + 287, + 317 + ], + "spans": [ + { + "bbox": [ + 49, + 276, + 287, + 317 + ], + "type": "text", + "content": "[61] Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer. How to train your vit? data, augmentation, and regularization in vision transformers. TMLR, 2022." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 49, + 318, + 287, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 318, + 287, + 350 + ], + "spans": [ + { + "bbox": [ + 49, + 318, + 287, + 350 + ], + "type": "text", + "content": "[62] Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Vl-bert: Pre-training of generic visual-linguistic representations. In ICLR, 2020." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 49, + 350, + 287, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 350, + 287, + 380 + ], + "spans": [ + { + "bbox": [ + 49, + 350, + 287, + 380 + ], + "type": "text", + "content": "[63] Swathikiran Sudhakaran, Sergio Escalera, and Oswald Lanz. Lsta: Long short-term attention for egocentric action recognition. In CVPR, 2019." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 49, + 382, + 287, + 413 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 382, + 287, + 413 + ], + "spans": [ + { + "bbox": [ + 49, + 382, + 287, + 413 + ], + "type": "text", + "content": "[64] Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, and Cordelia Schmid. Videobert: A joint model for video and language representation learning. In ICCV, 2019." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 49, + 414, + 287, + 445 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 414, + 287, + 445 + ], + "spans": [ + { + "bbox": [ + 49, + 414, + 287, + 445 + ], + "type": "text", + "content": "[65] Maria Tsimpoukelli, Jacob L Menick, Serkan Cabi, SM Eslami, Oriol Vinyals, and Felix Hill. Multimodal few-shot learning with frozen language models. In NeurIPS, 2021." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 49, + 446, + 287, + 477 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 446, + 287, + 477 + ], + "spans": [ + { + "bbox": [ + 49, + 446, + 287, + 477 + ], + "type": "text", + "content": "[66] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. In NeurIPS, 2017." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 49, + 478, + 287, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 478, + 287, + 529 + ], + "spans": [ + { + "bbox": [ + 49, + 478, + 287, + 529 + ], + "type": "text", + "content": "[67] Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. Diverse beam search: Decoding diverse solutions from neural sequence models. arXiv preprint arXiv:1610.02424, 2016." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 49, + 531, + 287, + 562 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 531, + 287, + 562 + ], + "spans": [ + { + "bbox": [ + 49, + 531, + 287, + 562 + ], + "type": "text", + "content": "[68] Oriol Vinyals, Alexander Toshev, Samy Bengio, and Dumitru Erhan. Show and tell: A neural image caption generator. In CVPR, 2015." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 49, + 563, + 287, + 616 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 563, + 287, + 616 + ], + "spans": [ + { + "bbox": [ + 49, + 563, + 287, + 616 + ], + "type": "text", + "content": "[69] Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, et al. Image as a foreign language: Beit pretraining for all vision and vision-language tasks. arXiv preprint arXiv:2208.10442, 2022." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 49, + 617, + 287, + 659 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 617, + 287, + 659 + ], + "spans": [ + { + "bbox": [ + 49, + 617, + 287, + 659 + ], + "type": "text", + "content": "[70] William Yang Wang and Diyi Yang. That's so annoying!!!: A lexical and frame-semantic embedding based data augmentation approach to automatic categorization of annoying behaviors using# petpeeve tweets. In EMNLP, 2015." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 49, + 660, + 287, + 691 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 660, + 287, + 691 + ], + "spans": [ + { + "bbox": [ + 49, + 660, + 287, + 691 + ], + "type": "text", + "content": "[71] Xiaohan Wang, Linchao Zhu, Heng Wang, and Yi Yang. Interactive prototype learning for egocentric action recognition. In ICCV, 2021." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 49, + 692, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 692, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 49, + 692, + 287, + 712 + ], + "type": "text", + "content": "[72] Jason Wei and Kai Zou. Eda: Easy data augmentation techniques for boosting performance on text classification tasks." + } + ] + } + ], + "index": 18 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 74, + 545, + 703 + ], + "type": "list", + "angle": 0, + "index": 38, + "blocks": [ + { + "bbox": [ + 327, + 74, + 392, + 83 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 74, + 392, + 83 + ], + "spans": [ + { + "bbox": [ + 327, + 74, + 392, + 83 + ], + "type": "text", + "content": "In EMNLP, 2019." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 83, + 545, + 115 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 83, + 545, + 115 + ], + "spans": [ + { + "bbox": [ + 307, + 83, + 545, + 115 + ], + "type": "text", + "content": "[73] Jason Weston, Samy Bengio, and Nicolas Usunier. Large scale image annotation: learning to rank with joint word-image embeddings. Machine learning, 2010." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 116, + 545, + 147 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 116, + 545, + 147 + ], + "spans": [ + { + "bbox": [ + 307, + 116, + 545, + 147 + ], + "type": "text", + "content": "[74] John Wieting and Kevin Gimpel. *Paranmt-50m: Pushing the limits of paraphrastic sentence embeddings with millions of machine translations*. In ACL, 2018." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 148, + 545, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 148, + 545, + 179 + ], + "spans": [ + { + "bbox": [ + 307, + 148, + 545, + 179 + ], + "type": "text", + "content": "[75] Michael Wray, Diane Larlus, Gabriela Csurka, and Dima Damen. Fine-grained action retrieval through multiple parts-of-speech embeddings. In ICCV, 2019." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 180, + 545, + 221 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 180, + 545, + 221 + ], + "spans": [ + { + "bbox": [ + 307, + 180, + 545, + 221 + ], + "type": "text", + "content": "[76] Chao-Yuan Wu, Yanghao Li, Karttikeya Mangalam, Haoqi Fan, Bo Xiong, Jitendra Malik, and Christoph Feichtenhofer. Memvit: Memory-augmented multiscale vision transformer for efficient long-term video recognition. In CVPR, 2022." + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 222, + 545, + 265 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 222, + 545, + 265 + ], + "spans": [ + { + "bbox": [ + 307, + 222, + 545, + 265 + ], + "type": "text", + "content": "[77] Hu Xu, Gargi Ghosh, Po-Yao Huang, Dmytro Okhonko, Armen Aghajanyan, Florian Metze, Luke Zettlemoyer, and Christoph Feichtenhofer. Videoclip: Contrastive pre-training for zero-shot video-text understanding. In EMNLP, 2021." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 266, + 545, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 266, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 307, + 266, + 545, + 308 + ], + "type": "text", + "content": "[78] Hongwei Xue, Yuchong Sun, Bei Liu, Jianlong Fu, Ruihua Song, Houqiang Li, and Jiebo Luo. Clip-vip: Adapting pretrained image-text model to video-language representation alignment. In ICLR, 2023." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 308, + 545, + 340 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 308, + 545, + 340 + ], + "spans": [ + { + "bbox": [ + 307, + 308, + 545, + 340 + ], + "type": "text", + "content": "[79] Shen Yan, Xuehan Xiong, Anurag Arnab, Zhichao Lu, Mi Zhang, Chen Sun, and Cordelia Schmid. Multiview transformers for video recognition. In CVPR, 2022." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 341, + 545, + 371 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 341, + 545, + 371 + ], + "spans": [ + { + "bbox": [ + 307, + 341, + 545, + 371 + ], + "type": "text", + "content": "[80] Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu. CoCa: Contrastive captioners are image-text foundation models. TMLR, 2022." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 372, + 545, + 447 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 372, + 545, + 447 + ], + "spans": [ + { + "bbox": [ + 307, + 372, + 545, + 447 + ], + "type": "text", + "content": "[81] Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, Ce Liu, Mengchen Liu, Zicheng Liu, Yumao Lu, Yu Shi, Lijuan Wang, Jianfeng Wang, Bin Xiao, Zhen Xiao, Jianwei Yang, Michael Zeng, Luowei Zhou, and Pengchuan Zhang. Florence: A new foundation model for computer vision. arXiv preprint arXiv:2111.11432, 2021." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 307, + 448, + 545, + 489 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 448, + 545, + 489 + ], + "spans": [ + { + "bbox": [ + 307, + 448, + 545, + 489 + ], + "type": "text", + "content": "[82] Rowan Zellers, Ximing Lu, Jack Hessel, Youngjae Yu, Jae Sung Park, Jize Cao, Ali Farhadi, and Yejin Choi. Merlot: Multimodal neural script knowledge models. NeurIPS, 2021." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 307, + 491, + 545, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 491, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 307, + 491, + 545, + 544 + ], + "type": "text", + "content": "[83] Andy Zeng, Adrian Wong, Stefan Welker, Krzysztof Choromanski, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, and Pete Florence. Socratic models: Composing zero-shot multimodal reasoning with language. In ICLR, 2023." + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 307, + 544, + 545, + 564 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 544, + 545, + 564 + ], + "spans": [ + { + "bbox": [ + 307, + 544, + 545, + 564 + ], + "type": "text", + "content": "[84] Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer. Scaling vision transformers. In CVPR, 2022." + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 307, + 565, + 545, + 596 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 565, + 545, + 596 + ], + "spans": [ + { + "bbox": [ + 307, + 565, + 545, + 596 + ], + "type": "text", + "content": "[85] Xiang Zhang, Junbo Zhao, and Yann LeCun. Character-level convolutional networks for text classification. In NeurIPS, 2015." + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 307, + 597, + 545, + 628 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 597, + 545, + 628 + ], + "spans": [ + { + "bbox": [ + 307, + 597, + 545, + 628 + ], + "type": "text", + "content": "[86] Luowei Zhou, Chenliang Xu, and Jason J Corso. Towards automatic learning of procedures from web instructional videos. In AAAI, 2018." + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 307, + 628, + 545, + 661 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 628, + 545, + 661 + ], + "spans": [ + { + "bbox": [ + 307, + 628, + 545, + 661 + ], + "type": "text", + "content": "[87] Xingyi Zhou, Rohit Girdhar, Armand Joulin, Philipp Krahenbihl, and Ishan Misra. Detecting twenty-thousand classes using image-level supervision. In ECCV, 2022." + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 307, + 662, + 545, + 681 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 662, + 545, + 681 + ], + "spans": [ + { + "bbox": [ + 307, + 662, + 545, + 681 + ], + "type": "text", + "content": "[88] Linchao Zhu and Yi Yang. Actbert: Learning global-local video-text representations. In CVPR, 2020." + } + ] + } + ], + "index": 36 + }, + { + "bbox": [ + 307, + 681, + 545, + 703 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 681, + 545, + 703 + ], + "spans": [ + { + "bbox": [ + 307, + 681, + 545, + 703 + ], + "type": "text", + "content": "[89] Xizhou Zhu, Jinguo Zhu, Hao Li, Xiaoshi Wu, Hongsheng Li, Xiaohua Wang, and Jifeng Dai. Uni-perceiver: Pre" + } + ] + } + ], + "index": 37 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "6596" + } + ] + } + ], + "index": 39 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 67, + 73, + 286, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 73, + 286, + 95 + ], + "spans": [ + { + "bbox": [ + 67, + 73, + 286, + 95 + ], + "type": "text", + "content": "training unified architecture for generic perception for zero-shot and few-shot tasks. 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Given sparse-view RGBD input (left), our method reconstructs detailed geometry (middle left), albedo (middle right), and visibility for realistic 3D human relighting under different illuminations (right)." + ], + "image_footnote": [], + "bbox": [ + 122, + 234, + 851, + 411 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 462, + 313, + 478 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Detailed 3D reconstruction and photo-realistic relighting of digital humans are essential for various applications. To this end, we propose a novel sparse-view 3d human reconstruction framework that closely incorporates the occupancy field and albedo field with an additional visibility field—it not only resolves occlusion ambiguity in multiview feature aggregation, but can also be used to evaluate light attenuation for self-shadowed relighting. To enhance its training viability and efficiency, we discretize visibility onto a fixed set of sample directions and supply it with coupled geometric 3D depth feature and local 2D image feature. We further propose a novel rendering-inspired loss, namely TransferLoss, to implicitly enforce the alignment between visibility and occupancy field, enabling end-to-end joint training. Results and extensive experiments demonstrate the effectiveness of the proposed method, as it surpasses state-of-the-art in terms of reconstruction accuracy while achieving comparably accurate relighting to ray-traced ground truth.", + "bbox": [ + 75, + 494, + 472, + 782 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 814, + 209, + 829 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "3D reconstruction and relighting are of great importance in human digitization, especially in supporting realistic rendering in varying virtual environments, that can be widely applied in AR/VR [32, 37], holographic communi", + "bbox": [ + 75, + 839, + 468, + 902 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "cation [24,63], movie and gaming industry [7].", + "bbox": [ + 500, + 464, + 812, + 479 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Traditional methods often require dense camera setups using multi-view stereo, non-rigid registration and texture mapping [9, 13]. To enhance capture realism, researchers have extended them with additional synchronous variable illumination systems, which aid photometric stereo for detail reconstruction and material acquisition [12]. However, these systems are often too complex, expensive and difficult to maintain, thus preventing widespread applications.", + "bbox": [ + 496, + 483, + 890, + 604 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "By leveraging deep prior and neural representation, sophisticated dense camera setups can be reduced to a single camera, leading to blossoms in learning-based human reconstruction. In particular, encoding human geometry and appearance as continuous fields using Multi-Layer Perceptron (MLP) has emerged as a promising lead. Starting from Siclope [36] and PIFu [43], a series of methods [15] improve the reconstruction performance in speed [11, 25], quality [44], robustness [65, 66] and light decoupling [1]. However, single-view reconstruction quality is restricted by its inherent depth ambiguity, thus limiting its application under view-consistent high-quality requirements.", + "bbox": [ + 496, + 609, + 892, + 790 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Therefore, as the trade-off between view coverage and system accessibility, sparse-view reconstruction has become a research hotspot. The predominant practice is to project the query point onto each view to interpolate local features, which are then aggregated and fed to MLP for inference [6, 16, 43, 47, 52, 61]. This method suffers from occlusion ambiguity, where some views may well", + "bbox": [ + 496, + 795, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "216", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "be occluded, and mixing their features with visible ones causes inefficient feature utilization, thus penalizing the reconstruction quality [43]. A natural solution is to filter features based on view visibility. Human templates such as SMPL [29] can serve as effective guidance [2,40,56,64], but introduce additional template alignment errors and therefore cannot guarantee complete occlusion awareness. Function4D [61] leverages the truncated Projective Signed Distance Function (PSDF) for visibility indication, but its level of details is susceptible to depth noise.", + "bbox": [ + 75, + 90, + 472, + 241 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To this end, we directly model a continuous visibility field, which can be efficiently learned with our proposed framework and discretization technique using sparse-view RGB-D input. The visibility field enables efficient visibility query, which effectively guides multi-view feature aggregation for more accurate occupancy and albedo inference. Moreover, visibility can also be directly used for light attenuation evaluation—the key ingredient in achieving realistic self-shadowing. When supervising jointly with our novel TransferLoss, the alignment between the visibility field and occupancy field can be implicitly enforced without between-field constraints, such as matching visibility with occupancy ray integral. We train our framework end-to-end and demonstrate its effectiveness in detailed 3D human reconstruction by quantity and quality comparison with the state-of-the-art. We directly relight the reconstructed geometry with inferred visibility using diffuse Bidirectional Reflectance Distribution Function (BRDF) as in Fig. 1, which achieves photo-realistic self-shadowing without any post ray-tracing steps. To conclude, our contributions include:", + "bbox": [ + 75, + 242, + 472, + 544 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- An end-to-end framework for sparse-view detailed 3D human reconstruction that also supports direct selfshadowed relighting.", + "- A novel method of visibility field learning, with the specifically designed TransferLoss significantly improves field alignment.", + "- A visibility-guided multi-view feature aggregation strategy that guarantees occlusion awareness." + ], + "bbox": [ + 94, + 555, + 468, + 698 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Background and Related Work", + "text_level": 1, + "bbox": [ + 76, + 723, + 362, + 739 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Neural human synthesis The literature on human reconstruction is vast and rapidly growing. Here, we only review and contrast with closely related work and refer readers to surveys [55, 59] for comprehensive reviews.", + "bbox": [ + 75, + 750, + 468, + 809 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Neural implicit representation encodes geometry as a function of spatial coordinate using MLP. This representation is appealing as it is naturally differentiable, has exceptional expressiveness yet maintains compact memory footprint. The pioneer work follows an encoder-decoder-like architecture, where the globally encoded feature is applied", + "bbox": [ + 75, + 810, + 470, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "to condition spatial coordinates to infer low-level geometric details [5, 33, 34, 38]. This highly unbalanced information flow limits its capability to represent mere simple shapes [39]. PIFu [43] proposes to replace the global feature with pixel-aligned local features, which captures convolutional inductive bias and achieves highly detailed human reconstruction. It inspires a variety of works, ranging from quality improvement [44], parametric model extension [2, 65], animation support [14, 17], light estimation and relighting [1].", + "bbox": [ + 496, + 90, + 890, + 241 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "By averaging local features across views, the pixel-aligned framework can be easily extended to multi-view settings [43]. However, simple averaging diminishes high frequency details, yielding overly smoothed geometry. Moreover, it treats all views as equally visible even for partially occluded regions, resulting in inaccurate and erroneous reconstruction. Zhang et al. [64] addresses the occlusion ambiguity by incorporating the attention mechanism, which weights features by their learned cross-view correlations. Despite its promising performance, self-attention introduces substantial memory and computation overhead. Yu et al. [61] additionally leverages global depth information, namely PSDF, to annihilate the ambiguity but is sensitive to depth noise. In contrast, we use robustly learned visibility to weight per-view contribution, which handles occlusion in a physically plausible manner while being more memory efficient to compute.", + "bbox": [ + 496, + 242, + 892, + 498 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Relighting boils down to substituting the incident environment radiance and re-evaluating the rendering equation [19]. For a scene of reflectors, the outgoing radiance reflected at the surface point $x$ in direction $\\omega_{o}$ can be described as:", + "bbox": [ + 496, + 500, + 890, + 573 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\nL (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {o}) = \\int_ {\\Omega^ {+}} L (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}) \\rho (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}, \\boldsymbol {\\omega} _ {o}) V (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}) (\\boldsymbol {n} \\cdot \\boldsymbol {\\omega} _ {i}) d \\boldsymbol {\\omega} _ {i} \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 500, + 580, + 890, + 625 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "where $\\pmb{n}$ is unit surface normal at $\\pmb{x}$ , $\\Omega^{+}$ is hemisphere of possible directions $\\omega_{i}$ , $L(\\pmb{x},\\omega_{i})$ is incident radiance arriving $\\pmb{x}$ along $\\omega_{i}$ , directly from environment or indirectly reflected. $\\rho$ is the BRDF that models the surface reflectance. $V$ is the visibility function that describes whether light $\\pmb{x}$ is attenuated along $\\omega_{i}$ . As the main contributor to realistic self-shadowing, visibility evaluation requires ray tracing geometry over all sample directions $\\omega_{i}$ per fragment [18], which is expensive and usually omitted [45] or baked offline using Precomputed Radiance Transfer (PRT) [48]. PRT rewrites Eq. (1) with the following transfer function", + "bbox": [ + 496, + 625, + 893, + 790 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\nT (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}, \\boldsymbol {\\omega} _ {o}) = \\rho (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}, \\boldsymbol {\\omega} _ {o}) V (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}) (\\boldsymbol {n} \\cdot \\boldsymbol {\\omega} _ {i}) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 539, + 799, + 890, + 815 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "which is independent of light and can be precomputed and projected on the Spherical Harmonics (SH) basis as coefficients [42] ahead of time to save rendering cost.", + "bbox": [ + 496, + 824, + 890, + 869 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "However, PRT does not ameliorate visibility calculation complexity and requires recompute after deformation. Its", + "bbox": [ + 496, + 869, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "217", + "bbox": [ + 485, + 944, + 509, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/1efd1488a911874576f86c560028be3e37cd1b20765f1cc89bd667d20b0b0e9c.jpg", + "image_caption": [ + "Figure 2. Method overview. Given sparse RGB-D frames as input, our framework first infer visibility, which is then applied to guide multiview 2D feature aggregation for joint occupancy and albedo inference. Our framework is end-to-end trainable and produces high-fidelity human reconstruction that supports direct self-shadowed relighting without using any post ray-tracing steps." + ], + "image_footnote": [], + "bbox": [ + 89, + 88, + 893, + 289 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "limitation has been addressed by several learning-based approaches [20, 23, 26, 41, 53], which predict transfer coefficients using deep neural networks. Instead of its SH parameterization, we predict raw visibility, as it (1) can be used for feature aggregation, and (2) is also well defined in free space that can be learned without surface evaluation.", + "bbox": [ + 75, + 366, + 470, + 455 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "NeRF [35] models the surface density and employs volume rendering for high fidelity novel view synthesis. It has been extended with reflectance [3] and transfer function [31] to support relighting. Visibility is naturally defined in the density field, but its evaluation requires the integration of multiple density queries along the ray, which is equally inefficient and needs to be accelerated, such as using occlusion map [4] or MLP [50]. Although sharing similar ends, we achieve this with different means: (1) We emphasize accuracy by supervising with ground truth and use the TransferLoss to regularize the field alignment, rather than attempting to directly align the fields by matching inferred visibility with accumulated transmittance. (2) We extensively train our model on human scan dataset to ensure fast inference, rather than fitting it per-scene in order to render arbitrary within-scene objects.", + "bbox": [ + 75, + 468, + 468, + 710 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 76, + 757, + 166, + 773 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To introduce our method, we first define the visibility field and its discretization process, which prioritizes efficiency without compromising performance. We then outline our framework and visibility learning procedure. Finally, we showcase its application in sparse-view 3D human reconstruction, where it guides feature aggregation and enables direct self-shadowed relighting.", + "bbox": [ + 75, + 794, + 468, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Visibility Field", + "text_level": 1, + "bbox": [ + 498, + 364, + 650, + 382 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For any point $X\\in \\mathbb{R}^3$ , whether it is visible $V\\in \\{0,1\\}$ along any view direction $\\omega \\in \\mathbb{R}^3$ of the unit sphere $S$ forms a continuous field and can be parameterized using MLP:", + "bbox": [ + 498, + 388, + 890, + 434 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {M L P} _ {\\phi}: (\\boldsymbol {X}, \\omega) \\rightarrow V _ {\\phi} \\in [ 0, 1 ], \\omega \\in S \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 563, + 445, + 890, + 462 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\phi$ is the MLP weights. Although Eq. (3) helps mitigate the cost of the ray integral, querying visibility over $s$ directions still requires $s$ calls per point of interest. This cost is exacerbated in pixel-aligned settings due to excessive point-wise feature $F$ duplication as illustrated in Fig. 3a (top), leading to substantially high memory overhead.", + "bbox": [ + 496, + 470, + 890, + 561 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We observe that, by uniformly sampling a discrete set of directions, visibility along any direction can be interpolated by Eq. (5) (Fig. 3c), with accuracy capped by the sample size. Thus, the $O(s)$ complexity can be reduced to a single MLP call plus an additional interpolation cost. To this end, we propose an alternative definition, by treating visibility as a function of $\\mathbf{X}$ conditioned on a fixed set of $n$ sample directions $\\omega^{(n)}$ as in Fig. 3a (bottom).", + "bbox": [ + 496, + 561, + 892, + 684 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {M L P} _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}: (\\boldsymbol {X}, \\boldsymbol {F}) \\rightarrow V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}} \\in [ 0, 1 ] ^ {n}, \\boldsymbol {\\omega} ^ {(n)} \\in S \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 511, + 691, + 890, + 712 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In practice, we interpolate by top $k$ closest cosine distance with respect to $\\omega^{(n)}$ :", + "bbox": [ + 498, + 720, + 890, + 750 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\left\\{\\boldsymbol {\\omega} ^ {(i)} \\right\\} _ {i = 1 \\dots k} = \\operatorname {t o p k} \\left(\\boldsymbol {\\omega} ^ {(n)} \\cdot \\boldsymbol {\\omega}\\right) \\\\ V (\\boldsymbol {X}, \\omega) = \\sum_ {i = 1} ^ {k} \\frac {V _ {\\omega^ {(i)}} (\\boldsymbol {X}) \\left(\\boldsymbol {\\omega} ^ {(i)} \\cdot \\boldsymbol {\\omega}\\right)}{\\sum_ {j = 1} ^ {k} \\left(\\boldsymbol {\\omega} ^ {(j)} \\cdot \\boldsymbol {\\omega}\\right)} \\tag {5} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 571, + 760, + 890, + 825 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The simplification in Eq. (4) leads to a surprisingly simple implementation-visibility prediction can be treated as $n - D$ binary classification supervised by BCE loss:", + "bbox": [ + 498, + 829, + 890, + 875 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nL _ {\\mathrm {V i s}} = V _ {\\mathrm {G T}} \\cdot \\log V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}} + (1 - V _ {\\mathrm {G T}}) \\cdot \\log \\left(1 - V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}\\right) \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 506, + 883, + 890, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "218", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/23906dda58aec08f6733eb70d526b47876c6be120ee4b366315dd31e51e2be08.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 135, + 89, + 251, + 195 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/3e8adf8208effac05162c76a8ae392814efeabad3babb96c3e385273fcc4a5da.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 269, + 95, + 467, + 191 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/bfc4224654ed9a784783ed054f7f4a85edeb690f79c3fe9a3692adff808c4c58.jpg", + "image_caption": [ + "(c)" + ], + "image_footnote": [], + "bbox": [ + 491, + 99, + 589, + 175 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/afd6fbb0fd0e1f5d389e3345bec6d47e93b2d8d1baeb5ecbf4e98a8318bb373d.jpg", + "image_caption": [ + "(d)" + ], + "image_footnote": [], + "bbox": [ + 602, + 94, + 740, + 191 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/fd68b0a244955ff333a30f8209829656e43dd6c81868d0f214567eddc1039415.jpg", + "image_caption": [ + "(e)", + "Figure 3. (a) Visibility as a continuous function of both point position $X$ and sample direction $\\omega$ (top) leads to multiple queries and pointwise feature $F$ duplication. In contrast, by fixing the sampling directions $\\omega^{(n)}$ (bottom), it only requires a single query. We extensively analyze our visibility field simplification strategy over $10\\%$ of training data and report average accuracy over the sample size $n$ in (b) with the following procedure: For each sample size decision, we sample 10k query points per model (same sampling distribution as training data) with visibility evaluated over $\\omega^{(n)}$ , that we apply cosine distance interpolation (c) to infer 10k randomly sampled validation directions per point. For aggregating multi-view features (d), we apply the same interpolation method as in (c) for each camera view direction to evaluate visibility along the back projection ray. (e) Directional visibility naturally constraints on the presence of surface." + ], + "image_footnote": [], + "bbox": [ + 754, + 90, + 856, + 196 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/41ac1dddfa6b4e299b9ce1e03aa8b03a914938ede647c87e8c90fc0c3fe4ddea.jpg", + "image_caption": [ + "Figure 4. TransferLoss ablation: From left to right: w/o, w TransferLoss, ground truth. TransferLoss significantly mitigates rendering defects and improves near-surface visibility prediction accuracy (27.5% → 88.4% for synthesized testset, 20.8% → 84.7% for real-captured data)." + ], + "image_footnote": [], + "bbox": [ + 101, + 349, + 274, + 472 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/cfa3a32372e2b983bc13719a2738b67709c1ddbdd17c8f69b6f566472b48cf53.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 292, + 349, + 439, + 472 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Framework Overview", + "text_level": 1, + "bbox": [ + 76, + 556, + 282, + 571 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As shown in Fig. 2, our framework represents all fields using MLPs. Given sparse multi-view RGB-D frames $\\{(\\mathcal{I}_i,\\mathcal{D}_i),i = 1\\dots m\\}$ with known camera projection matrices $\\pi_{i}$ , we first extract depth point cloud voxelized 3D feature and pixel-aligned RGB 2D feature to directly predict visibility. Then we utilize inferred visibility to guide the aggregation of multi-view 2D feature, which are paired with 3D feature for joint geometry and albedo inference.", + "bbox": [ + 75, + 580, + 468, + 702 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Hybrid Feature Extraction", + "text_level": 1, + "bbox": [ + 76, + 710, + 318, + 726 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Conventionally, evaluating visibility requires tracing ray along the direction of interest and checking surface hits. In other words, it requires reasoning about the geometric feature of surfaces near that direction. Since we model both geometry (occupancy) and visibility, it is crucial to also bridge the two fields to incorporate their interconnection.", + "bbox": [ + 75, + 734, + 468, + 824 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To this end, we adopt a hybrid feature extraction procedure, by separating depth from RGB and encoding it as 3D feature. Specifically, we follow [39] to unproject depth image into pointcloud, voxelize and filter using 3D convolutional as coarse feature volume. For a query point $X$ ,", + "bbox": [ + 75, + 825, + 468, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "we acquire its local 3D feature $F_{\\mathcal{D}}$ by trilinear interpolation based on point coordinates and then share it with both visibility and occupancy MLPs. Intuitively, the 3D feature originates from voxelized point cloud, which can be viewed as noisy samples of the underlying surface (decision boundary of the occupancy field). By applying 3D convolution, the network reasons the coarse geometric surface feature over a sufficiently large receptive field, consequently aiding visibility inference. 3D feature is necessary for accurate visibility prediction as ablated in Fig. 8.", + "bbox": [ + 496, + 347, + 890, + 497 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For multi-view RGB frames, we directly filter them using HRNet [51] to acquire 2D feature maps $F_{i}$ . The local 2D features are then extracted in the pixel-aligned fashion as in [43], by projecting the point coordinate $X$ onto each view as the image coordinate $\\pi (X)$ , then bilinearly interpolating the corresponding feature maps $F_{i}(\\pi (X))$ . Compared to feature volume, 2D feature maps have much higher resolution and thus grant better details.", + "bbox": [ + 496, + 500, + 890, + 619 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Follow Eq. (4), we infer $V_{\\phi|\\omega^{(n)}}$ by providing 3D feature $F_{\\mathcal{D}}$ and averaged 2D feature $F_{avg}$ :", + "bbox": [ + 500, + 621, + 890, + 652 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {M L P} _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}: \\left(\\boldsymbol {X}, F _ {\\mathcal {D}}, F _ {a v g}\\right) \\in [ 0, 1 ] ^ {n} \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 568, + 666, + 890, + 685 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Field Alignment Regularization", + "text_level": 1, + "bbox": [ + 500, + 709, + 777, + 726 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Given similar formulations, it is natural to train the visibility field and occupancy field together. However, as the point moves across the surface (occupancy classification boundary), its visibility changes drastically, ranging from fully occluded to partially visible. A slight misalignment between the two fields could cause inner visibility leakage and introduce conspicuous rendering defects as in Fig. 4. To enforce their alignment, a common practice is to explicitly constrain their correspondence, by matching visibility with surface queried along the ray [50], but at the expense of substantially large training overhead.", + "bbox": [ + 496, + 734, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "219", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/86c7bf2641ae982a58cdcf4509551ee358ee71fcc259d017859b38cefc422314.jpg", + "image_caption": [ + "Figure 5. Qualitative comparisons on THUman2.0 [61]. We show (a) shaded color reference, geometry of (b) MV-PIFu [43], (c) Function4D [61], (d) MV-PIFuHD [44], (e) ours, (f) our albedo, (g-i) from left to right: our irradiance, our relighting and ground truth." + ], + "image_footnote": [], + "bbox": [ + 81, + 85, + 890, + 545 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Since we bridge the two fields with the 3D feature reasoning about their correlation, we exploit the constraint of visibility over occupancy (Fig. 3e) by emphasizing the accuracy of near-surface visibility prediction, so that their alignment can be implicitly enforced. To this end, we propose a novel TransferLoss inspired by radiance transfer in Eq. (2):", + "bbox": [ + 75, + 609, + 470, + 715 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL _ {\\text {T r a n s f e r}} = \\sum_ {i} | \\hat {T} (\\boldsymbol {X}, V _ {\\phi | \\boldsymbol {\\omega} ^ {(i)}}) - T (\\boldsymbol {X}, V _ {\\mathrm {G T}}) | \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 122, + 724, + 468, + 756 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "It supervises visibility, but (1) prioritizes the normal-facing directions in contrast to equal weighting in BCE (Eq. (6)) loss, and (2) follows the same parameterization for diffuse BRDF evaluation, which intuitively renders the light-independent part of the scene and perceptually penalizes the difference.", + "bbox": [ + 75, + 763, + 468, + 853 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Since occupancy and visibility are jointly supervised, we directly sample query points on the ground truth mesh surface and use their normals to assist Eq. (8) evaluation for", + "bbox": [ + 75, + 854, + 470, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "inferred visibility. TransferLoss effectively enforces fields alignment, resulting in significantly less rendering defects and higher relighting fidelity as ablated in Sec. 4.3 and Fig. 4.", + "bbox": [ + 496, + 609, + 892, + 670 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.5. Visibility-Guided Feature Aggregation", + "text_level": 1, + "bbox": [ + 498, + 679, + 831, + 695 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For each view, we trace back to its camera center and evaluate the directional visibility as described in Eq. (5). We then prioritize visible features over occluded ones using clamped negative log weighted average:", + "bbox": [ + 496, + 702, + 892, + 762 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} F _ {a g g} = \\sum_ {i = 1} ^ {m} W _ {V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}}) F _ {i} (\\pi_ {i} (\\boldsymbol {X})) \\tag {9} \\\\ W _ {V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}} (\\boldsymbol {X}, \\boldsymbol {\\omega}) = \\max (- \\log (1 - V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)})}, 1 0 0) \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 771, + 890, + 834 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Thus, occupancy and albedo are represented as:", + "bbox": [ + 517, + 842, + 834, + 857 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {M L P} _ {\\text {o c c}}: \\left(\\boldsymbol {X}, F _ {\\mathcal {D}}, F _ {\\text {a g g}}\\right)\\rightarrow [ 0, 1 ] \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 581, + 864, + 890, + 890 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {M L P} _ {a l b e d o}: \\left(\\boldsymbol {X}, F _ {\\mathcal {D}}, F _ {a g g}\\right)\\rightarrow \\mathbb {R} ^ {3} \\tag {16}\n$$\n", + "text_format": "latex", + "bbox": [ + 581, + 885, + 890, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "220", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.6. Reconstruction and Relighting", + "text_level": 1, + "bbox": [ + 76, + 90, + 349, + 107 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "During inference, we first apply visibility-guided aggregation to sample a grid of occupancy for surface mesh extraction [30]. We then pass the mesh vertices to obtain albedo and visibility using the same technique. Following Eq. (2), we compute per-vertex transfer coefficients and directly apply them for rasterized self-shadowed relighting.", + "bbox": [ + 75, + 113, + 468, + 205 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 218, + 209, + 234 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Experimental Settings", + "text_level": 1, + "bbox": [ + 76, + 242, + 284, + 258 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Training details. For training, we collect 400 high-quality clothed human scans from THUman2.0 [61], rotate each one around the yaw axis, and apply random shifts to obtain 60 views. For each view, we render $512 \\times 512$ images of albedo, color using diffuse BRDF and depth fused with TOF depth sensors noise [10]. To simulate the incomplete depth caused by capture insensitivity for materials such as hair, we use [60] to mask hair out.", + "bbox": [ + 75, + 266, + 468, + 385 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For each scanned mesh, we sample total 5k points for occupancy and visibility, with 4k near surface and 1k uniformly within the bounding volume. Near-surface points are sampled using normal distribution with standard deviation of 0.05, and we ensure that half of them with distances less than the standard deviation are used for albedo training. As described in Sec. 3.1 and Fig. 3b, we uniformly sample 64 fixed directions using the Fibonacci lattice and keep it consistent throughout the experiment. We use Embree [57] for ground truth visibility evaluation.", + "bbox": [ + 75, + 387, + 468, + 537 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In addition to visibility, we supervise per-sample occupancy with BCE loss and albedo with $L_{1}$ loss. We also extract patches using depth-guided raymarching [27] and supervise its albedo using VGG perceptual loss.", + "bbox": [ + 75, + 537, + 468, + 599 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We set the view number to 4 and train our framework using Adam [21] optimizer and Cyclic learning rate (lr) scheduler [49] over 600 epochs. The lr ranges from $5e - 5$ to $5e - 4$ every 5 epochs, with the max lr halved every 100 epochs.", + "bbox": [ + 75, + 599, + 468, + 657 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation details. For evaluation, we prepare 100 training-excluded scans from THUman2.0, and an additional 100 scans from Twindom [46]. We follow the same rendering procedure as for training and report metrics averaged over the two sets. We further prepare real-captured RGB-D video sequences from a synchronized multi-Kinect capturing system that we leverage RVM [28] for foreground mask segmentation. For all experiments, we keep the number of views to 4 except for the view-number ablation. All meshes are extracted from $512^{3}$ voxel using Marching Cube [30]. The relighting results are rendered using diffuse BRDF with inferred visibility and SH order of 2.", + "bbox": [ + 75, + 657, + 468, + 839 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "All experiments run on a PC with an Nvidia GeForce RTX3090 GPU and an Intel i7-8700k CPU. Our framework requires 50 ms for 3D and 100 ms for 2D feature extraction, and 200 ms per 2 million point queries for each of", + "bbox": [ + 75, + 839, + 468, + 901 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/2e96b1ed34828383acf01b1a781513db3bd8086d1fca804bf7090d5054ed40cc.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodsMetrics
NC ↑CD (L1) ↓F-score (0.5%)↑
MV-PIFu [43]0.9120.1450.624
MV-PIFuHD [44]0.9060.1350.690
Function4D [61]0.8930.1290.704
Ours0.9170.1220.736
", + "bbox": [ + 504, + 89, + 893, + 188 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Quantitative comparisons on reconstruction quality.", + "bbox": [ + 514, + 198, + 875, + 213 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "the 4-layer ResNet MLP decoders. The total reconstruction process takes approximately 3 seconds. After experimenting with TensorRT conversion, 3D and 2D feature extraction can be reduced to 10 and 7 ms, respectively. Since they can be performed in parallel, our framework has the potential to achieve real-time performance under heavy decoder optimization.", + "bbox": [ + 496, + 241, + 890, + 345 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metrics. We report normal consistency (NC) [33], $L_{1}$ Chamfer Distance $(\\mathrm{CD} L_{1})$ [8] and F-score [22] for geometric quality evaluation. Specifically, NC is calculated as the mean absolute dot product of normals of points sampled from reconstructed mesh and their closest neighbours' ones on ground truth mesh. We follow [33] to use 1/10 of the maximum bounding box edge as unit 1 for CD $L_{1}$ and $0.5\\%$ as the F-score distance threshold as suggested by [54].", + "bbox": [ + 496, + 347, + 890, + 469 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To evaluate the quality of relighting, we adopt the peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM) [58] and the learned perceptual image patch similarity (LPIPS) [62]. We mask out renderings with ground truth alpha channel and only report the average contributions of valid pixels.", + "bbox": [ + 496, + 469, + 890, + 560 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Comparisons", + "text_level": 1, + "bbox": [ + 500, + 573, + 638, + 589 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Reconstruction. We compare our framework with the state-of-the-art prior-free sparse-view reconstruction approaches, namely multi-view PIFu (MV-PIFu) [43], multiview PIFuHD (MV-PIFuHD) [44] and Function4D [61]. MV-PIFu takes RGB inputs and aggregates using averaging, which we adapt with additional depth input to ensure comparison consistency. PIFuHD integrates normal information, coarse-to-fine two-stage inference, and higher $1024 \\times 1024$ resolution input. We self-implement multiview RGB-D variants (MV-PIFuHD) with the same averaging aggregation as MV-PIFu. Normal maps are still inferred rather than being computed from depth maps due to noise concern. Function4D uses averaging in geometry inference as well, but its integration of the truncated PSDF serves as a strong signal to identify visible features and has been shown to generalize well on real captured data. For a fair comparison, we re-render and train all three approaches on our dataset until converge. Regrettably, we could only compare the geometry, since none of them estimates surface albedo.", + "bbox": [ + 496, + 597, + 890, + 883 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Relighting. Limited by the dataset, which maps human into", + "bbox": [ + 500, + 885, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "221", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/e9e372c27f22586eec956d76a3d65f51e7666cf541ecf1cb4d5d238912294728.jpg", + "image_caption": [ + "", + "", + "(c)", + "(d)", + "(e)", + "Figure 6. Qualitative comparisons on Twindom. From left to right: (a) shaded color reference, geometry of (b) MV-PIFu [43], (c) Function4D [61], (d) MV-PIFuHD [44], (e) ours, (f) our albedo, (g-i) from left to right: our irradiance, our relighting and ground truth." + ], + "image_footnote": [], + "bbox": [ + 83, + 88, + 459, + 564 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "(a)", + "bbox": [ + 107, + 570, + 125, + 582 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "(b)", + "bbox": [ + 174, + 570, + 192, + 582 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "(c)", + "bbox": [ + 233, + 570, + 250, + 582 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "）", + "bbox": [ + 398, + 570, + 403, + 580 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2a8df9529a498ef36e378ce1929c0a3467e2b4732d5176b2fbeaa8c466c4c440.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 460, + 88, + 660, + 215 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ffd13b85fc506976c7e28bf7eb5e6e9160fedbacb6bcf7554528d6a192b2badc.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 464, + 220, + 663, + 316 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/97ccf92a8684aafc2a32667c1d8ba1ba8627983788336bc383b9d6ace1ec6637.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 669, + 88, + 756, + 215 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/30485eee588c706f4efefcecdfc5aa29eadbfbfcfb7270730a01ed217c039b60.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 674, + 219, + 763, + 316 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/1e3a93c65bfc20ef0a498d30ced46fcb78703b028dab55cf956c4b10eef6b56f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 774, + 89, + 875, + 215 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/678769f3b53fc2c48a091d2919316972c13ed9c22d88809aee17c83bc8cd806f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 777, + 218, + 877, + 316 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d29d8daec0ae9787e3539214968258329734e5da74875cc87a241df73ba875ac.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 460, + 323, + 678, + 431 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/299681713c1abbf616e9b543b519c4c547098a38a0761dd99feb54c732600e4d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 665, + 324, + 885, + 431 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "", + "bbox": [ + 84, + 435, + 138, + 564 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "(a)", + "bbox": [ + 107, + 570, + 125, + 582 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "", + "bbox": [ + 153, + 435, + 209, + 563 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "(b)", + "bbox": [ + 174, + 570, + 192, + 582 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "", + "bbox": [ + 214, + 435, + 263, + 563 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "", + "bbox": [ + 271, + 435, + 328, + 563 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "", + "bbox": [ + 339, + 435, + 387, + 563 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "", + "bbox": [ + 385, + 436, + 446, + 564 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/3e8fbea81c3c11a834f76ed2569f775ad0189344136dcaf7b1792853dfa8762b.jpg", + "image_caption": [ + "(f)" + ], + "image_footnote": [], + "bbox": [ + 473, + 438, + 521, + 563 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/878eb40c6fdc02a1506b02a70c1b5ac7e7f8b9b658a1150aaac66ef041a44f8e.jpg", + "image_caption": [ + "(g)" + ], + "image_footnote": [], + "bbox": [ + 532, + 438, + 599, + 563 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/18be21b31dad78e3699e78d4fed6456ebd6a3504fc0b1a3f6f25b0554220c982.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 599, + 438, + 669, + 563 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7734dd7d91f4c833e4ac044766f218ac515220d84dd08a1ed14b577a48c62ba9.jpg", + "image_caption": [ + "(h)" + ], + "image_footnote": [], + "bbox": [ + 678, + 438, + 751, + 564 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/05a4e30cd3d50e0c1e19cda9d32b706239f45dc5dd22c0800607c49823bee37e.jpg", + "image_caption": [ + "(i)" + ], + "image_footnote": [], + "bbox": [ + 754, + 440, + 888, + 564 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8cc8ec28d27e9235738f8088000fa6bc76b140cb91bf67f027325274bf07898d.jpg", + "image_caption": [ + "Figure 7. Visualization comparison between Function4D [61] (green) and our method (red) on data captured by Kinect." + ], + "image_footnote": [], + "bbox": [ + 91, + 646, + 454, + 731 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "single albedo texture without differentiating specular components and coefficients, we were only able to render using diffuse BRDF. This also limits our relighting comparison with NeRF-like method [3,4,50], where the view-dependent specular term is explicitly modeled and is crucial for rendering fidelity. Therefore, we directly compare our results to ray-traced ground truth to demonstrate our relighting per", + "bbox": [ + 75, + 794, + 470, + 902 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5fee874a2fd77b88b8383408e38d48e1d91c3fd0aecc1aa107cd35544116d991.jpg", + "image_caption": [ + "Figure 8. 3D Feature ablation. (a) We compare the visibility error maps using 2D RGB input (left half of first two), 2D RGB-D input (left half of last two) and ours (right half of all four). (b) We count per-vertex number of wrong directional visibility predictions (out of 64) and plot the normalized histogram." + ], + "image_footnote": [], + "bbox": [ + 521, + 650, + 586, + 734 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e046787c00c98442cd8e628337a18c03537f25aa8350cc1442d5c692166f9d35.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 598, + 651, + 663, + 734 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2c34bd6b5e5b55b0bc18911439edaf7fb137ebf6dcc0b46a3aeed1c7d7dae0ba.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 671, + 652, + 691, + 731 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2009bb1c93ed7ec49c4bf7409f7bf0046689101ddb7d3d68b0728e2da8ec5418.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 700, + 652, + 867, + 729 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "formance.", + "bbox": [ + 500, + 867, + 570, + 880 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Qualitative Comparison. Fig. 5 shows the reconstruction", + "bbox": [ + 500, + 885, + 890, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "222", + "bbox": [ + 485, + 945, + 511, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/e21ef781b513022335b21ed9139c7a97e89fd6922f99f3c996a68da055bce9b2.jpg", + "table_caption": [ + "Table 2. Feature aggregation ablation." + ], + "table_footnote": [], + "table_body": "
MethodNC↑Geometry MetricRelit Rendering
CD (L1)↓F-score (0.5%)↑PSNR↑SSIM↑LPIPS↓
Average0.9110.1290.69617.9330.6500.327
Attention0.9090.1270.71019.1480.7130.259
Ours0.9170.1220.73623.4360.8090.196
", + "bbox": [ + 81, + 114, + 470, + 178 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/98dd0b9b5b0b1d9296e8e055cade04255229031e1005a103ace243891e900c6f.jpg", + "image_caption": [ + "Figure 9. Feature aggregation ablation in geometry. From left to right: color reference, the results of average, attention and ours." + ], + "image_footnote": [], + "bbox": [ + 81, + 193, + 467, + 260 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/a77878132e7974e0b19d4fc0020fe1f85ea21c63815009257d0685ad6e90a512.jpg", + "image_caption": [ + "Figure 10. Feature aggregation ablation in rendering quality. From left to right: average, attention, and ours and ground truth." + ], + "image_footnote": [], + "bbox": [ + 106, + 311, + 437, + 397 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "on the synthetic datasets. Benefiting from visibility-guided feature aggregation, our method produces at least comparable details to MV-PIFuHD, but with simpler architecture, less input information and visual cues. Compared to oversmoothed geometry from Function4D, our hybrid features relax the dominant contribution of noisy depth input, leading to improved facial details. We further demonstrate our generalizability on real captured data in comparison with Function4D. As shown in Fig. 7, our method evidently produces more complete and detailed reconstructions, especially in regions of the eyes, glasses and hair.", + "bbox": [ + 75, + 455, + 468, + 619 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Quantitative Comparison summarized in Tab. 1 is consistent with qualitative analysis, and our method outperforms others in all metrics.", + "bbox": [ + 75, + 622, + 468, + 666 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.3. Ablation Study", + "text_level": 1, + "bbox": [ + 76, + 679, + 228, + 695 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "3D Feature. We ablate our hybrid feature with 2D RGB and 2D RGB-D variants. The results validate the necessity of the 3D feature, as it predicts visibility with higher accuracy (Fig. 8a) and lower error variance (Fig. 8b).", + "bbox": [ + 75, + 702, + 468, + 762 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Visibility-guided Feature Aggregation. In comparison with other aggregation techniques, namely averaging [43] and self-attention [61], we implement them in our framework. Our strategy surpasses others in metrics (Tab. 2) and achieves sharper geometric details (Fig. 9), better rendering fidelity (Fig. 10), even near heavily folded clothing thanks to our occlusion-aware aggregation.", + "bbox": [ + 75, + 763, + 468, + 869 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "View number. Though we train our model with 4 views, it generalizes well across different view numbers and achieves", + "bbox": [ + 76, + 869, + 468, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/355149894fc6d873ad5f9ce29674cb09ee04df6dd2c174a368bd2160fe5befba.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 506, + 92, + 630, + 236 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/49eea60cad0e18d314bd98ec4bca5d2b6a9b118ffa920b81da90af174f03b44e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 632, + 92, + 758, + 236 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/48a46947c27b76ad6277524c232a3602c6e0043f53847f09ab7441fa638ea8b6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 759, + 92, + 885, + 236 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/efea5f367fa7c78129c6c3f9c95715e20c2dd213fb71a79029cc6268182ec04f.jpg", + "image_caption": [ + "Figure 11. View number ablation." + ], + "image_footnote": [], + "bbox": [ + 511, + 239, + 630, + 383 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/14c95b618aac2468a8a5ceaf1377fa6c747ef1a593d5a8efd7a53cba8191b9ff.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 632, + 239, + 758, + 383 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/70dc15fc91a094ea33359510bfa1e80a4c503022d56ea4f894af5f5be6d6cd67.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 759, + 239, + 885, + 383 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "similar performance under sufficient view coverage as ablated in Fig. 11. As view number increases, compared to degraded reconstruction quality from average and attention, our aggregation technique mitigates visibility ambiguity to leverage additional visual cues, resulting in better geometry.", + "bbox": [ + 496, + 431, + 890, + 507 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "TransferLoss. Finally, we verify the effectiveness of our proposed TransferLoss in Fig. 4. It enforces the alignment of the fields and significantly alleviates rendering defects.", + "bbox": [ + 496, + 508, + 890, + 554 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 570, + 617, + 585 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations. Our method cannot achieve real-time performance for interactive applications. We leave its acceleration using TensorRT and CUDA for future work. Moreover, our framework relies on depth input for accurate visibility prediction, it would be interesting to see if it can be extended to simpler RGB setup.", + "bbox": [ + 496, + 595, + 890, + 686 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Conclusion. In this work, we integrate visibility into sparse-view reconstruction framework by exploiting its effective guidance in multi-view feature aggregation and direct support for self-shadowed relighting. Our discretization strategy and novel TransferLoss enable visibility to be learned jointly alongside occupancy in an end-to-end manner. Our paper demonstrates the effectiveness of visibility in simultaneous reconstruction and relighting and provides a good baseline for future work.", + "bbox": [ + 496, + 688, + 890, + 823 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement. This work was supported by NSFC No.62171255, National Key R&D Program of China No.2022YFF0902201, Guoqiang Institute of Tsinghua University No.2021GQG0001 and Tsinghua-Toyota Joint Research Fund.", + "bbox": [ + 496, + 824, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "223", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Thiemo Alldieck, Mihai Zanfir, and Cristian Sminchisescu. Photorealistic monocular 3d reconstruction of humans wearing clothing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1506-1515, 2022. 1, 2", + "[2] Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Combining implicit function learning and parametric models for 3d human reconstruction. In European Conference on Computer Vision, pages 311-329. Springer, 2020. 2", + "[3] Mark Boss, Raphael Braun, Varun Jampani, Jonathan T Barron, Ce Liu, and Hendrik Lensch. Nerd: Neural reflectance decomposition from image collections. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12684-12694, 2021. 3, 7", + "[4] Zhaoxi Chen and Ziwei Liu. Relighting4d: Neural relightable human from videos. In European Conference on Computer Vision, pages 606-623. Springer, 2022. 3, 7", + "[5] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5939-5948, 2019. 2", + "[6] Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll. Implicit functions in feature space for 3d shape reconstruction and completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6970-6981, 2020. 1", + "[7] Paul Debevec. Pursuing reality with image-based modeling, rendering, and lighting. In European Workshop on 3D Structure from Multiple Images of Large-Scale Environments, pages 1-16. Springer, 2000. 1", + "[8] Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 605-613, 2017. 6", + "[9] Sean Ryan Fanello, Cem Keskin, Shahram Izadi, Pushmeet Kohli, David Kim, David Sweeney, Antonio Criminisi, Jamie Shotton, Sing Bing Kang, and Tim Paek. Learning to be a depth camera for close-range human capture and interaction. ACM Transactions on Graphics (TOG), 33(4):1-11, 2014. 1", + "[10] Péter Fankhauser, Michael Bloesch, Diego Rodriguez, Ralf Kaestner, Marco Hutter, and Roland Siegwart. Kinect v2 for mobile robot navigation: Evaluation and modeling. In 2015 International Conference on Advanced Robotics (ICAR), pages 388-394. IEEE, 2015. 6", + "[11] Qiao Feng, Yebin Liu, Yu-Kun Lai, Jingyu Yang, and Kun Li. Fof: learning fourier occupancy field for monocular real-time human reconstruction. arXiv preprint arXiv:2206.02194, 2022. 1", + "[12] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, et al. The relightables: Volumetric performance capture of humans with realistic relighting. ACM Transactions on Graphics (ToG), 38(6):1–19, 2019. 1" + ], + "bbox": [ + 78, + 114, + 470, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Kaiwen Guo, Feng Xu, Yangang Wang, Yebin Liu, and Qionghai Dai. Robust non-rigid motion tracking and surface reconstruction using 10 regularization. In Proceedings of the IEEE International Conference on Computer Vision, pages 3083-3091, 2015. 1", + "[14] Tong He, Yuanlu Xu, Shunsuke Saito, Stefano Soatto, and Tony Tung. Arch++: Animation-ready clothed human reconstruction revisited. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11046-11056, 2021. 2", + "[15] Yang Hong, Juyong Zhang, Boyi Jiang, Yudong Guo, Ligang Liu, and Hujun Bao. Stereopifu: Depth aware clothed human digitization via stereo vision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 535-545, 2021. 1", + "[16] Buzhen Huang, Yuan Shu, Tianshu Zhang, and Yangang Wang. Dynamic multi-person mesh recovery from uncalibrated multi-view cameras. In 2021 International Conference on 3D Vision (3DV), pages 710-720. IEEE, 2021. 1", + "[17] Zeng Huang, Yuanlu Xu, Christoph Lassner, Hao Li, and Tony Tung. Arch: Animatable reconstruction of clothed humans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3093-3102, 2020. 2", + "[18] Chaonan Ji, Tao Yu, Kaiwen Guo, Jingxin Liu, and Yebin Liu. Geometry-aware single-image full-body human relighting. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVI, pages 388-405. Springer, 2022. 2", + "[19] James T Kajiya. The rendering equation. In Proceedings of the 13th annual conference on Computer graphics and interactive techniques, pages 143-150, 1986. 2", + "[20] Yoshihiro Kanamori and Yuki Endo. Relighting humans: occlusion-aware inverse rendering for full-body human images. arXiv preprint arXiv:1908.02714, 2019. 3", + "[21] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6", + "[22] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics (ToG), 36(4):1-13, 2017. 6", + "[23] Manuel Lagunas, Xin Sun, Jimei Yang, Ruben Villegas, Jianming Zhang, Zhixin Shu, Belen Masia, and Diego Gutierrez. Single-image full-body human relighting. arXiv preprint arXiv:2107.07259, 2021. 3", + "[24] Jason Lawrence, Dan B Goldman, Supreeth Achar, Gregory Major Blascovich, Joseph G Desloge, Tommy Fortes, Eric M Gomez, Sascha Haberling, Hugues Hoppe, Andy Huibers, et al. Project starline: A high-fidelity telepresence system. 2021. 1", + "[25] Ruilong Li, Yuliang Xiu, Shunsuke Saito, Zeng Huang, Kyle Olszewski, and Hao Li. Monocular real-time volumetric performance capture. In European Conference on Computer Vision, pages 49-67. Springer, 2020. 1", + "[26] Yue Li, Pablo Wiedemann, and Kenny Mitchell. Deep precomputed radiance transfer for deformable objects. Pro" + ], + "bbox": [ + 501, + 92, + 893, + 901 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "224", + "bbox": [ + 485, + 945, + 509, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ceedings of the ACM on Computer Graphics and Interactive Techniques, 2(1):1-16, 2019. 3", + "[27] Haotong Lin, Sida Peng, Zhen Xu, Yunzhi Yan, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Efficient neural radiance fields for interactive free-viewpoint video. In SIGGRAPH Asia 2022 Conference Papers, pages 1-9, 2022. 6", + "[28] Shanchuan Lin, Linjie Yang, Imran Saleemi, and Soumyadip Sengupta. Robust high-resolution video matting with temporal guidance. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 238-247, 2022. 6", + "[29] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. Smpl: A skinned multiperson linear model. ACM transactions on graphics (TOG), 34(6):1-16, 2015. 2", + "[30] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. ACM siggraph computer graphics, 21(4):163-169, 1987. 6", + "[31] Linjie Lyu, Ayush Tewari, Thomas Leimkuhler, Marc Habermann, and Christian Theobalt. Neural radiance transfer fields for relightable novel-view synthesis with global illumination. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVII, pages 153-169. Springer, 2022. 3", + "[32] Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando De La Torre, and Yaser Sheikh. Pixel codec avatars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 64-73, 2021. 1", + "[33] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4460-4470, 2019. 2, 6", + "[34] Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson. Deep level sets: Implicit surface representations for 3d shape inference. arXiv preprint arXiv:1901.06802, 2019. 2", + "[35] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 3", + "[36] Ryota Natsume, Shunsuke Saito, Zeng Huang, Weikai Chen, Chongyang Ma, Hao Li, and Shigeo Morishima. *Siclope: Silhouette-based clothed people. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pages 4480–4490, 2019. 1", + "[37] Sergio Orts-Escalano, Christoph Rhemann, Sean Fanello, Wayne Chang, Adarsh Kowdle, Yury Degtyarev, David Kim, Philip L Davidson, Sameh Khamis, Mingsong Dou, et al. Holoportation: Virtual 3d teleportation in real-time. In Proceedings of the 29th annual symposium on user interface software and technology, pages 741-754, 2016. 1", + "[38] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation." + ], + "bbox": [ + 78, + 92, + 468, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019. 2", + "[39] Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In European Conference on Computer Vision, pages 523-540. Springer, 2020. 2, 4", + "[40] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9054-9063, 2021. 2", + "[41] Gilles Rainer, Adrien Bousseau, Tobias Ritschel, and George Drettakis. Neural precomputed radiance transfer. In Computer Graphics Forum, volume 41, pages 365-378. Wiley Online Library, 2022. 3", + "[42] Ravi Ramamoorthi and Pat Hanrahan. An efficient representation for irradiance environment maps. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 497-500, 2001. 2", + "[43] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2304-2314, 2019. 1, 2, 4, 5, 6, 7, 8", + "[44] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo. Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 84-93, 2020. 1, 2, 5, 6, 7", + "[45] Soumyadip Sengupta, Angjoo Kanazawa, Carlos D Castillo, and David W Jacobs. Sfsnet: Learning shape, reflectance and illuminance of facesin the wild'. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6296-6305, 2018. 2", + "[46] Ruizhi Shao, Hongwen Zhang, He Zhang, Mingjia Chen, Yan-Pei Cao, Tao Yu, and Yebin Liu. Doublefield: Bridging the neural surface and radiance fields for high-fidelity human reconstruction and rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15872-15882, 2022. 6", + "[47] Ruizhi Shao, Zerong Zheng, Hongwen Zhang, Jingxiang Sun, and Yebin Liu. Diffustereo: High quality human reconstruction via diffusion-based stereo using sparse cameras. arXiv preprint arXiv:2207.08000, 2022. 1", + "[48] Peter-Pike Sloan, Jan Kautz, and John Snyder. Precomputed radiance transfer for real-time rendering in dynamic, low-frequency lighting environments. In Proceedings of the 29th annual conference on Computer graphics and interactive techniques, pages 527-536, 2002. 2", + "[49] Leslie N Smith. Cyclical learning rates for training neural networks. In 2017 IEEE winter conference on applications of computer vision (WACV), pages 464-472. IEEE, 2017. 6", + "[50] Pratul P Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan T Barron. Nerv: Neural reflectance and visibility fields for relighting" + ], + "bbox": [ + 503, + 92, + 890, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "225", + "bbox": [ + 486, + 945, + 509, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "and view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7495-7504, 2021. 3, 4, 7", + "[51] Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning for human pose estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5693-5703, 2019. 4", + "[52] Xin Suo, Yuheng Jiang, Pei Lin, Yingliang Zhang, Minye Wu, Kaiwen Guo, and Lan Xu. Neuralhumanfvv: Real-time neural volumetric human performance rendering using rgb cameras. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6226-6237, 2021. 1", + "[53] Daichi Tajima, Yoshihiro Kanamori, and Yuki Endo. Relighting humans in the wild: Monocular full-body human relighting with domain adaptation. In Computer Graphics Forum, volume 40, pages 205-216. Wiley Online Library, 2021. 3", + "[54] Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox. What do single-view 3d reconstruction networks learn? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3405-3414, 2019. 6", + "[55] Yating Tian, Hongwen Zhang, Yebin Liu, and Limin Wang. Recovering 3d human mesh from monocular images: A survey. arXiv preprint arXiv:2203.01923, 2022. 2", + "[56] Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T Barron, and Pratul P Srinivasan. Ref-nerf: Structured view-dependent appearance for neural radiance fields. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5481-5490. IEEE, 2022. 2", + "[57] Ingo Wald, Sven Woop, Carsten Benthin, Gregory S Johnson, and Manfred Ernst. Embree: a kernel framework for efficient cpu ray tracing. ACM Transactions on Graphics (TOG), 33(4):1-8, 2014. 6", + "[58] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004. 6", + "[59] Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. Neural fields in visual computing and beyond. In Computer Graphics Forum, volume 41, pages 641-676. Wiley Online Library, 2022. 2", + "[60] YBIGTA. Hair-segmentation. https://pytorchhair.gitbook.io/project/.6", + "[61] Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, and Yebin Liu. Function4d: Real-time human volumetric capture from very sparse consumer rgbd sensors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5746-5756, 2021. 1, 2, 5, 6, 7, 8", + "[62] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 2018. 6" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[63] Yizhong Zhang, Jiaolong Yang, Zhen Liu, Ruicheng Wang, Guojun Chen, Xin Tong, and Baining Guo. Virtualcube: An immersive 3d video communication system. IEEE Transactions on Visualization and Computer Graphics, 28(5):2146-2156, 2022. 1", + "[64] Yang Zheng, Ruizhi Shao, Yuxiang Zhang, Tao Yu, Zerong Zheng, Qionghai Dai, and Yebin Liu. Deepmulticap: Performance capture of multiple characters using sparse multiview cameras. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6239-6249, 2021. 2", + "[65] Zerong Zheng, Tao Yu, Yebin Liu, and Qionghai Dai. Pamir: Parametric model-conditioned implicit representation for image-based human reconstruction. IEEE transactions on pattern analysis and machine intelligence, 44(6):3170-3184, 2021. 1, 2", + "[66] Zerong Zheng, Tao Yu, Yixuan Wei, Qionghai Dai, and Yebin Liu. Deephuman: 3d human reconstruction from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7739-7749, 2019. 1" + ], + "bbox": [ + 501, + 90, + 892, + 373 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "226", + "bbox": [ + 486, + 945, + 511, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting/ad55c0fb-3962-4c84-be1b-ade77062812f_model.json b/2023/Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting/ad55c0fb-3962-4c84-be1b-ade77062812f_model.json new file mode 100644 index 0000000000000000000000000000000000000000..7d1f561b38da298b530d6c684e297f879afadb0f --- /dev/null +++ b/2023/Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting/ad55c0fb-3962-4c84-be1b-ade77062812f_model.json @@ -0,0 +1,2917 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.0, + 0.812, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.084, + 0.131, + 0.888, + 0.154 + ], + "angle": 0, + "content": "Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting" + }, + { + "type": "text", + "bbox": [ + 0.253, + 0.18, + 0.713, + 0.218 + ], + "angle": 0, + "content": "Ruichen Zheng*,1,2, Peng Li*,1, Haoqian Wang1, Tao Yu1 1Tsinghua University, China 2Weilan Tech, Beijing, China" + }, + { + "type": "image", + "bbox": [ + 0.124, + 0.236, + 0.852, + 0.412 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.422, + 0.893, + 0.451 + ], + "angle": 0, + "content": "Figure 1. Given sparse-view RGBD input (left), our method reconstructs detailed geometry (middle left), albedo (middle right), and visibility for realistic 3D human relighting under different illuminations (right)." + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.463, + 0.314, + 0.479 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.496, + 0.473, + 0.784 + ], + "angle": 0, + "content": "Detailed 3D reconstruction and photo-realistic relighting of digital humans are essential for various applications. To this end, we propose a novel sparse-view 3d human reconstruction framework that closely incorporates the occupancy field and albedo field with an additional visibility field—it not only resolves occlusion ambiguity in multiview feature aggregation, but can also be used to evaluate light attenuation for self-shadowed relighting. To enhance its training viability and efficiency, we discretize visibility onto a fixed set of sample directions and supply it with coupled geometric 3D depth feature and local 2D image feature. We further propose a novel rendering-inspired loss, namely TransferLoss, to implicitly enforce the alignment between visibility and occupancy field, enabling end-to-end joint training. Results and extensive experiments demonstrate the effectiveness of the proposed method, as it surpasses state-of-the-art in terms of reconstruction accuracy while achieving comparably accurate relighting to ray-traced ground truth." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.815, + 0.21, + 0.83 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.47, + 0.903 + ], + "angle": 0, + "content": "3D reconstruction and relighting are of great importance in human digitization, especially in supporting realistic rendering in varying virtual environments, that can be widely applied in AR/VR [32, 37], holographic communi" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.465, + 0.813, + 0.48 + ], + "angle": 0, + "content": "cation [24,63], movie and gaming industry [7]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.484, + 0.892, + 0.606 + ], + "angle": 0, + "content": "Traditional methods often require dense camera setups using multi-view stereo, non-rigid registration and texture mapping [9, 13]. To enhance capture realism, researchers have extended them with additional synchronous variable illumination systems, which aid photometric stereo for detail reconstruction and material acquisition [12]. However, these systems are often too complex, expensive and difficult to maintain, thus preventing widespread applications." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.61, + 0.893, + 0.791 + ], + "angle": 0, + "content": "By leveraging deep prior and neural representation, sophisticated dense camera setups can be reduced to a single camera, leading to blossoms in learning-based human reconstruction. In particular, encoding human geometry and appearance as continuous fields using Multi-Layer Perceptron (MLP) has emerged as a promising lead. Starting from Siclope [36] and PIFu [43], a series of methods [15] improve the reconstruction performance in speed [11, 25], quality [44], robustness [65, 66] and light decoupling [1]. However, single-view reconstruction quality is restricted by its inherent depth ambiguity, thus limiting its application under view-consistent high-quality requirements." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.894, + 0.903 + ], + "angle": 0, + "content": "Therefore, as the trade-off between view coverage and system accessibility, sparse-view reconstruction has become a research hotspot. The predominant practice is to project the query point onto each view to interpolate local features, which are then aggregated and fed to MLP for inference [6, 16, 43, 47, 52, 61]. This method suffers from occlusion ambiguity, where some views may well" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "216" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.242 + ], + "angle": 0, + "content": "be occluded, and mixing their features with visible ones causes inefficient feature utilization, thus penalizing the reconstruction quality [43]. A natural solution is to filter features based on view visibility. Human templates such as SMPL [29] can serve as effective guidance [2,40,56,64], but introduce additional template alignment errors and therefore cannot guarantee complete occlusion awareness. Function4D [61] leverages the truncated Projective Signed Distance Function (PSDF) for visibility indication, but its level of details is susceptible to depth noise." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.243, + 0.473, + 0.545 + ], + "angle": 0, + "content": "To this end, we directly model a continuous visibility field, which can be efficiently learned with our proposed framework and discretization technique using sparse-view RGB-D input. The visibility field enables efficient visibility query, which effectively guides multi-view feature aggregation for more accurate occupancy and albedo inference. Moreover, visibility can also be directly used for light attenuation evaluation—the key ingredient in achieving realistic self-shadowing. When supervising jointly with our novel TransferLoss, the alignment between the visibility field and occupancy field can be implicitly enforced without between-field constraints, such as matching visibility with occupancy ray integral. We train our framework end-to-end and demonstrate its effectiveness in detailed 3D human reconstruction by quantity and quality comparison with the state-of-the-art. We directly relight the reconstructed geometry with inferred visibility using diffuse Bidirectional Reflectance Distribution Function (BRDF) as in Fig. 1, which achieves photo-realistic self-shadowing without any post ray-tracing steps. To conclude, our contributions include:" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.556, + 0.47, + 0.601 + ], + "angle": 0, + "content": "- An end-to-end framework for sparse-view detailed 3D human reconstruction that also supports direct selfshadowed relighting." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.612, + 0.47, + 0.657 + ], + "angle": 0, + "content": "- A novel method of visibility field learning, with the specifically designed TransferLoss significantly improves field alignment." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.669, + 0.47, + 0.699 + ], + "angle": 0, + "content": "- A visibility-guided multi-view feature aggregation strategy that guarantees occlusion awareness." + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.556, + 0.47, + 0.699 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.724, + 0.364, + 0.74 + ], + "angle": 0, + "content": "2. Background and Related Work" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.47, + 0.81 + ], + "angle": 0, + "content": "Neural human synthesis The literature on human reconstruction is vast and rapidly growing. Here, we only review and contrast with closely related work and refer readers to surveys [55, 59] for comprehensive reviews." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Neural implicit representation encodes geometry as a function of spatial coordinate using MLP. This representation is appealing as it is naturally differentiable, has exceptional expressiveness yet maintains compact memory footprint. The pioneer work follows an encoder-decoder-like architecture, where the globally encoded feature is applied" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.242 + ], + "angle": 0, + "content": "to condition spatial coordinates to infer low-level geometric details [5, 33, 34, 38]. This highly unbalanced information flow limits its capability to represent mere simple shapes [39]. PIFu [43] proposes to replace the global feature with pixel-aligned local features, which captures convolutional inductive bias and achieves highly detailed human reconstruction. It inspires a variety of works, ranging from quality improvement [44], parametric model extension [2, 65], animation support [14, 17], light estimation and relighting [1]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.243, + 0.893, + 0.499 + ], + "angle": 0, + "content": "By averaging local features across views, the pixel-aligned framework can be easily extended to multi-view settings [43]. However, simple averaging diminishes high frequency details, yielding overly smoothed geometry. Moreover, it treats all views as equally visible even for partially occluded regions, resulting in inaccurate and erroneous reconstruction. Zhang et al. [64] addresses the occlusion ambiguity by incorporating the attention mechanism, which weights features by their learned cross-view correlations. Despite its promising performance, self-attention introduces substantial memory and computation overhead. Yu et al. [61] additionally leverages global depth information, namely PSDF, to annihilate the ambiguity but is sensitive to depth noise. In contrast, we use robustly learned visibility to weight per-view contribution, which handles occlusion in a physically plausible manner while being more memory efficient to compute." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.5, + 0.892, + 0.574 + ], + "angle": 0, + "content": "Relighting boils down to substituting the incident environment radiance and re-evaluating the rendering equation [19]. For a scene of reflectors, the outgoing radiance reflected at the surface point \\( x \\) in direction \\( \\omega_{o} \\) can be described as:" + }, + { + "type": "equation", + "bbox": [ + 0.5, + 0.581, + 0.892, + 0.625 + ], + "angle": 0, + "content": "\\[\nL (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {o}) = \\int_ {\\Omega^ {+}} L (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}) \\rho (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}, \\boldsymbol {\\omega} _ {o}) V (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}) (\\boldsymbol {n} \\cdot \\boldsymbol {\\omega} _ {i}) d \\boldsymbol {\\omega} _ {i} \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.625, + 0.894, + 0.791 + ], + "angle": 0, + "content": "where \\(\\pmb{n}\\) is unit surface normal at \\(\\pmb{x}\\), \\(\\Omega^{+}\\) is hemisphere of possible directions \\(\\omega_{i}\\), \\(L(\\pmb{x},\\omega_{i})\\) is incident radiance arriving \\(\\pmb{x}\\) along \\(\\omega_{i}\\), directly from environment or indirectly reflected. \\(\\rho\\) is the BRDF that models the surface reflectance. \\(V\\) is the visibility function that describes whether light \\(\\pmb{x}\\) is attenuated along \\(\\omega_{i}\\). As the main contributor to realistic self-shadowing, visibility evaluation requires ray tracing geometry over all sample directions \\(\\omega_{i}\\) per fragment [18], which is expensive and usually omitted [45] or baked offline using Precomputed Radiance Transfer (PRT) [48]. PRT rewrites Eq. (1) with the following transfer function" + }, + { + "type": "equation", + "bbox": [ + 0.54, + 0.8, + 0.892, + 0.816 + ], + "angle": 0, + "content": "\\[\nT (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}, \\boldsymbol {\\omega} _ {o}) = \\rho (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}, \\boldsymbol {\\omega} _ {o}) V (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}) (\\boldsymbol {n} \\cdot \\boldsymbol {\\omega} _ {i}) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.892, + 0.87 + ], + "angle": 0, + "content": "which is independent of light and can be precomputed and projected on the Spherical Harmonics (SH) basis as coefficients [42] ahead of time to save rendering cost." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "However, PRT does not ameliorate visibility calculation complexity and requires recompute after deformation. Its" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.945, + 0.511, + 0.957 + ], + "angle": 0, + "content": "217" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.091, + 0.089, + 0.895, + 0.29 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.298, + 0.895, + 0.342 + ], + "angle": 0, + "content": "Figure 2. Method overview. Given sparse RGB-D frames as input, our framework first infer visibility, which is then applied to guide multiview 2D feature aggregation for joint occupancy and albedo inference. Our framework is end-to-end trainable and produces high-fidelity human reconstruction that supports direct self-shadowed relighting without using any post ray-tracing steps." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.367, + 0.471, + 0.457 + ], + "angle": 0, + "content": "limitation has been addressed by several learning-based approaches [20, 23, 26, 41, 53], which predict transfer coefficients using deep neural networks. Instead of its SH parameterization, we predict raw visibility, as it (1) can be used for feature aggregation, and (2) is also well defined in free space that can be learned without surface evaluation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.469, + 0.47, + 0.712 + ], + "angle": 0, + "content": "NeRF [35] models the surface density and employs volume rendering for high fidelity novel view synthesis. It has been extended with reflectance [3] and transfer function [31] to support relighting. Visibility is naturally defined in the density field, but its evaluation requires the integration of multiple density queries along the ray, which is equally inefficient and needs to be accelerated, such as using occlusion map [4] or MLP [50]. Although sharing similar ends, we achieve this with different means: (1) We emphasize accuracy by supervising with ground truth and use the TransferLoss to regularize the field alignment, rather than attempting to directly align the fields by matching inferred visibility with accumulated transmittance. (2) We extensively train our model on human scan dataset to ensure fast inference, rather than fitting it per-scene in order to render arbitrary within-scene objects." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.758, + 0.168, + 0.774 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.795, + 0.469, + 0.903 + ], + "angle": 0, + "content": "To introduce our method, we first define the visibility field and its discretization process, which prioritizes efficiency without compromising performance. We then outline our framework and visibility learning procedure. Finally, we showcase its application in sparse-view 3D human reconstruction, where it guides feature aggregation and enables direct self-shadowed relighting." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.366, + 0.651, + 0.383 + ], + "angle": 0, + "content": "3.1. Visibility Field" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.389, + 0.892, + 0.435 + ], + "angle": 0, + "content": "For any point \\(X\\in \\mathbb{R}^3\\) , whether it is visible \\(V\\in \\{0,1\\}\\) along any view direction \\(\\omega \\in \\mathbb{R}^3\\) of the unit sphere \\(S\\) forms a continuous field and can be parameterized using MLP:" + }, + { + "type": "equation", + "bbox": [ + 0.565, + 0.446, + 0.892, + 0.463 + ], + "angle": 0, + "content": "\\[\n\\operatorname {M L P} _ {\\phi}: (\\boldsymbol {X}, \\omega) \\rightarrow V _ {\\phi} \\in [ 0, 1 ], \\omega \\in S \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.472, + 0.892, + 0.563 + ], + "angle": 0, + "content": "where \\(\\phi\\) is the MLP weights. Although Eq. (3) helps mitigate the cost of the ray integral, querying visibility over \\(s\\) directions still requires \\(s\\) calls per point of interest. This cost is exacerbated in pixel-aligned settings due to excessive point-wise feature \\(F\\) duplication as illustrated in Fig. 3a (top), leading to substantially high memory overhead." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.563, + 0.893, + 0.685 + ], + "angle": 0, + "content": "We observe that, by uniformly sampling a discrete set of directions, visibility along any direction can be interpolated by Eq. (5) (Fig. 3c), with accuracy capped by the sample size. Thus, the \\(O(s)\\) complexity can be reduced to a single MLP call plus an additional interpolation cost. To this end, we propose an alternative definition, by treating visibility as a function of \\(\\mathbf{X}\\) conditioned on a fixed set of \\(n\\) sample directions \\(\\omega^{(n)}\\) as in Fig. 3a (bottom)." + }, + { + "type": "equation", + "bbox": [ + 0.512, + 0.693, + 0.892, + 0.713 + ], + "angle": 0, + "content": "\\[\n\\operatorname {M L P} _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}: (\\boldsymbol {X}, \\boldsymbol {F}) \\rightarrow V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}} \\in [ 0, 1 ] ^ {n}, \\boldsymbol {\\omega} ^ {(n)} \\in S \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.721, + 0.892, + 0.751 + ], + "angle": 0, + "content": "In practice, we interpolate by top \\(k\\) closest cosine distance with respect to \\(\\omega^{(n)}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.572, + 0.761, + 0.892, + 0.826 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\left\\{\\boldsymbol {\\omega} ^ {(i)} \\right\\} _ {i = 1 \\dots k} = \\operatorname {t o p k} \\left(\\boldsymbol {\\omega} ^ {(n)} \\cdot \\boldsymbol {\\omega}\\right) \\\\ V (\\boldsymbol {X}, \\omega) = \\sum_ {i = 1} ^ {k} \\frac {V _ {\\omega^ {(i)}} (\\boldsymbol {X}) \\left(\\boldsymbol {\\omega} ^ {(i)} \\cdot \\boldsymbol {\\omega}\\right)}{\\sum_ {j = 1} ^ {k} \\left(\\boldsymbol {\\omega} ^ {(j)} \\cdot \\boldsymbol {\\omega}\\right)} \\tag {5} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.83, + 0.892, + 0.875 + ], + "angle": 0, + "content": "The simplification in Eq. (4) leads to a surprisingly simple implementation-visibility prediction can be treated as \\( n - D \\) binary classification supervised by BCE loss:" + }, + { + "type": "equation", + "bbox": [ + 0.508, + 0.885, + 0.892, + 0.903 + ], + "angle": 0, + "content": "\\[\nL _ {\\mathrm {V i s}} = V _ {\\mathrm {G T}} \\cdot \\log V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}} + (1 - V _ {\\mathrm {G T}}) \\cdot \\log \\left(1 - V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}\\right) \\tag {6}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "218" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.137, + 0.09, + 0.252, + 0.196 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.163, + 0.2, + 0.18, + 0.212 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.27, + 0.096, + 0.468, + 0.193 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.338, + 0.199, + 0.357, + 0.212 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.492, + 0.101, + 0.59, + 0.176 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.535, + 0.199, + 0.553, + 0.212 + ], + "angle": 0, + "content": "(c)" + }, + { + "type": "image", + "bbox": [ + 0.604, + 0.095, + 0.741, + 0.193 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.665, + 0.199, + 0.684, + 0.212 + ], + "angle": 0, + "content": "(d)" + }, + { + "type": "image", + "bbox": [ + 0.756, + 0.091, + 0.857, + 0.197 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.791, + 0.199, + 0.808, + 0.211 + ], + "angle": 0, + "content": "(e)" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.224, + 0.894, + 0.321 + ], + "angle": 0, + "content": "Figure 3. (a) Visibility as a continuous function of both point position \\( X \\) and sample direction \\( \\omega \\) (top) leads to multiple queries and pointwise feature \\( F \\) duplication. In contrast, by fixing the sampling directions \\( \\omega^{(n)} \\) (bottom), it only requires a single query. We extensively analyze our visibility field simplification strategy over \\( 10\\% \\) of training data and report average accuracy over the sample size \\( n \\) in (b) with the following procedure: For each sample size decision, we sample 10k query points per model (same sampling distribution as training data) with visibility evaluated over \\( \\omega^{(n)} \\), that we apply cosine distance interpolation (c) to infer 10k randomly sampled validation directions per point. For aggregating multi-view features (d), we apply the same interpolation method as in (c) for each camera view direction to evaluate visibility along the back projection ray. (e) Directional visibility naturally constraints on the presence of surface." + }, + { + "type": "image", + "bbox": [ + 0.102, + 0.35, + 0.275, + 0.473 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.293, + 0.35, + 0.441, + 0.473 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.48, + 0.47, + 0.55 + ], + "angle": 0, + "content": "Figure 4. TransferLoss ablation: From left to right: w/o, w TransferLoss, ground truth. TransferLoss significantly mitigates rendering defects and improves near-surface visibility prediction accuracy (27.5% → 88.4% for synthesized testset, 20.8% → 84.7% for real-captured data)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.558, + 0.283, + 0.573 + ], + "angle": 0, + "content": "3.2. Framework Overview" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.581, + 0.47, + 0.703 + ], + "angle": 0, + "content": "As shown in Fig. 2, our framework represents all fields using MLPs. Given sparse multi-view RGB-D frames \\(\\{(\\mathcal{I}_i,\\mathcal{D}_i),i = 1\\dots m\\}\\) with known camera projection matrices \\(\\pi_{i}\\), we first extract depth point cloud voxelized 3D feature and pixel-aligned RGB 2D feature to directly predict visibility. Then we utilize inferred visibility to guide the aggregation of multi-view 2D feature, which are paired with 3D feature for joint geometry and albedo inference." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.711, + 0.32, + 0.727 + ], + "angle": 0, + "content": "3.3. Hybrid Feature Extraction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.469, + 0.825 + ], + "angle": 0, + "content": "Conventionally, evaluating visibility requires tracing ray along the direction of interest and checking surface hits. In other words, it requires reasoning about the geometric feature of surfaces near that direction. Since we model both geometry (occupancy) and visibility, it is crucial to also bridge the two fields to incorporate their interconnection." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.469, + 0.902 + ], + "angle": 0, + "content": "To this end, we adopt a hybrid feature extraction procedure, by separating depth from RGB and encoding it as 3D feature. Specifically, we follow [39] to unproject depth image into pointcloud, voxelize and filter using 3D convolutional as coarse feature volume. For a query point \\(X\\)," + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.348, + 0.892, + 0.498 + ], + "angle": 0, + "content": "we acquire its local 3D feature \\( F_{\\mathcal{D}} \\) by trilinear interpolation based on point coordinates and then share it with both visibility and occupancy MLPs. Intuitively, the 3D feature originates from voxelized point cloud, which can be viewed as noisy samples of the underlying surface (decision boundary of the occupancy field). By applying 3D convolution, the network reasons the coarse geometric surface feature over a sufficiently large receptive field, consequently aiding visibility inference. 3D feature is necessary for accurate visibility prediction as ablated in Fig. 8." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.5, + 0.892, + 0.621 + ], + "angle": 0, + "content": "For multi-view RGB frames, we directly filter them using HRNet [51] to acquire 2D feature maps \\( F_{i} \\). The local 2D features are then extracted in the pixel-aligned fashion as in [43], by projecting the point coordinate \\( X \\) onto each view as the image coordinate \\( \\pi (X) \\), then bilinearly interpolating the corresponding feature maps \\( F_{i}(\\pi (X)) \\). Compared to feature volume, 2D feature maps have much higher resolution and thus grant better details." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.622, + 0.892, + 0.654 + ], + "angle": 0, + "content": "Follow Eq. (4), we infer \\( V_{\\phi|\\omega^{(n)}} \\) by providing 3D feature \\( F_{\\mathcal{D}} \\) and averaged 2D feature \\( F_{avg} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.57, + 0.667, + 0.892, + 0.686 + ], + "angle": 0, + "content": "\\[\n\\operatorname {M L P} _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}: \\left(\\boldsymbol {X}, F _ {\\mathcal {D}}, F _ {a v g}\\right) \\in [ 0, 1 ] ^ {n} \\tag {7}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.71, + 0.779, + 0.727 + ], + "angle": 0, + "content": "3.4. Field Alignment Regularization" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Given similar formulations, it is natural to train the visibility field and occupancy field together. However, as the point moves across the surface (occupancy classification boundary), its visibility changes drastically, ranging from fully occluded to partially visible. A slight misalignment between the two fields could cause inner visibility leakage and introduce conspicuous rendering defects as in Fig. 4. To enforce their alignment, a common practice is to explicitly constrain their correspondence, by matching visibility with surface queried along the ray [50], but at the expense of substantially large training overhead." + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "219" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.086, + 0.892, + 0.546 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.556, + 0.893, + 0.585 + ], + "angle": 0, + "content": "Figure 5. Qualitative comparisons on THUman2.0 [61]. We show (a) shaded color reference, geometry of (b) MV-PIFu [43], (c) Function4D [61], (d) MV-PIFuHD [44], (e) ours, (f) our albedo, (g-i) from left to right: our irradiance, our relighting and ground truth." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.611, + 0.471, + 0.717 + ], + "angle": 0, + "content": "Since we bridge the two fields with the 3D feature reasoning about their correlation, we exploit the constraint of visibility over occupancy (Fig. 3e) by emphasizing the accuracy of near-surface visibility prediction, so that their alignment can be implicitly enforced. To this end, we propose a novel TransferLoss inspired by radiance transfer in Eq. (2):" + }, + { + "type": "equation", + "bbox": [ + 0.123, + 0.726, + 0.47, + 0.757 + ], + "angle": 0, + "content": "\\[\nL _ {\\text {T r a n s f e r}} = \\sum_ {i} | \\hat {T} (\\boldsymbol {X}, V _ {\\phi | \\boldsymbol {\\omega} ^ {(i)}}) - T (\\boldsymbol {X}, V _ {\\mathrm {G T}}) | \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.765, + 0.47, + 0.854 + ], + "angle": 0, + "content": "It supervises visibility, but (1) prioritizes the normal-facing directions in contrast to equal weighting in BCE (Eq. (6)) loss, and (2) follows the same parameterization for diffuse BRDF evaluation, which intuitively renders the light-independent part of the scene and perceptually penalizes the difference." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Since occupancy and visibility are jointly supervised, we directly sample query points on the ground truth mesh surface and use their normals to assist Eq. (8) evaluation for" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.611, + 0.893, + 0.671 + ], + "angle": 0, + "content": "inferred visibility. TransferLoss effectively enforces fields alignment, resulting in significantly less rendering defects and higher relighting fidelity as ablated in Sec. 4.3 and Fig. 4." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.68, + 0.833, + 0.696 + ], + "angle": 0, + "content": "3.5. Visibility-Guided Feature Aggregation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.703, + 0.893, + 0.763 + ], + "angle": 0, + "content": "For each view, we trace back to its camera center and evaluate the directional visibility as described in Eq. (5). We then prioritize visible features over occluded ones using clamped negative log weighted average:" + }, + { + "type": "equation", + "bbox": [ + 0.518, + 0.772, + 0.892, + 0.835 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} F _ {a g g} = \\sum_ {i = 1} ^ {m} W _ {V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}}) F _ {i} (\\pi_ {i} (\\boldsymbol {X})) \\tag {9} \\\\ W _ {V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}} (\\boldsymbol {X}, \\boldsymbol {\\omega}) = \\max (- \\log (1 - V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)})}, 1 0 0) \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.843, + 0.835, + 0.858 + ], + "angle": 0, + "content": "Thus, occupancy and albedo are represented as:" + }, + { + "type": "equation", + "bbox": [ + 0.583, + 0.866, + 0.892, + 0.891 + ], + "angle": 0, + "content": "\\[\n\\mathrm {M L P} _ {\\text {o c c}}: \\left(\\boldsymbol {X}, F _ {\\mathcal {D}}, F _ {\\text {a g g}}\\right)\\rightarrow [ 0, 1 ] \\tag {10}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.583, + 0.886, + 0.892, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\mathbf {M L P} _ {a l b e d o}: \\left(\\boldsymbol {X}, F _ {\\mathcal {D}}, F _ {a g g}\\right)\\rightarrow \\mathbb {R} ^ {3} \\tag {16}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "220" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.078, + 0.091, + 0.35, + 0.108 + ], + "angle": 0, + "content": "3.6. Reconstruction and Relighting" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.114, + 0.47, + 0.206 + ], + "angle": 0, + "content": "During inference, we first apply visibility-guided aggregation to sample a grid of occupancy for surface mesh extraction [30]. We then pass the mesh vertices to obtain albedo and visibility using the same technique. Following Eq. (2), we compute per-vertex transfer coefficients and directly apply them for rasterized self-shadowed relighting." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.219, + 0.21, + 0.236 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.243, + 0.285, + 0.26 + ], + "angle": 0, + "content": "4.1. Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.267, + 0.47, + 0.386 + ], + "angle": 0, + "content": "Training details. For training, we collect 400 high-quality clothed human scans from THUman2.0 [61], rotate each one around the yaw axis, and apply random shifts to obtain 60 views. For each view, we render \\(512 \\times 512\\) images of albedo, color using diffuse BRDF and depth fused with TOF depth sensors noise [10]. To simulate the incomplete depth caused by capture insensitivity for materials such as hair, we use [60] to mask hair out." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.388, + 0.47, + 0.539 + ], + "angle": 0, + "content": "For each scanned mesh, we sample total 5k points for occupancy and visibility, with 4k near surface and 1k uniformly within the bounding volume. Near-surface points are sampled using normal distribution with standard deviation of 0.05, and we ensure that half of them with distances less than the standard deviation are used for albedo training. As described in Sec. 3.1 and Fig. 3b, we uniformly sample 64 fixed directions using the Fibonacci lattice and keep it consistent throughout the experiment. We use Embree [57] for ground truth visibility evaluation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.539, + 0.47, + 0.6 + ], + "angle": 0, + "content": "In addition to visibility, we supervise per-sample occupancy with BCE loss and albedo with \\( L_{1} \\) loss. We also extract patches using depth-guided raymarching [27] and supervise its albedo using VGG perceptual loss." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.6, + 0.47, + 0.659 + ], + "angle": 0, + "content": "We set the view number to 4 and train our framework using Adam [21] optimizer and Cyclic learning rate (lr) scheduler [49] over 600 epochs. The lr ranges from \\(5e - 5\\) to \\(5e - 4\\) every 5 epochs, with the max lr halved every 100 epochs." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.659, + 0.47, + 0.84 + ], + "angle": 0, + "content": "Evaluation details. For evaluation, we prepare 100 training-excluded scans from THUman2.0, and an additional 100 scans from Twindom [46]. We follow the same rendering procedure as for training and report metrics averaged over the two sets. We further prepare real-captured RGB-D video sequences from a synchronized multi-Kinect capturing system that we leverage RVM [28] for foreground mask segmentation. For all experiments, we keep the number of views to 4 except for the view-number ablation. All meshes are extracted from \\(512^{3}\\) voxel using Marching Cube [30]. The relighting results are rendered using diffuse BRDF with inferred visibility and SH order of 2." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.47, + 0.902 + ], + "angle": 0, + "content": "All experiments run on a PC with an Nvidia GeForce RTX3090 GPU and an Intel i7-8700k CPU. Our framework requires 50 ms for 3D and 100 ms for 2D feature extraction, and 200 ms per 2 million point queries for each of" + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.09, + 0.895, + 0.189 + ], + "angle": 0, + "content": "
MethodsMetrics
NC ↑CD (L1) ↓F-score (0.5%)↑
MV-PIFu [43]0.9120.1450.624
MV-PIFuHD [44]0.9060.1350.690
Function4D [61]0.8930.1290.704
Ours0.9170.1220.736
" + }, + { + "type": "table_caption", + "bbox": [ + 0.516, + 0.199, + 0.877, + 0.214 + ], + "angle": 0, + "content": "Table 1. Quantitative comparisons on reconstruction quality." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.242, + 0.892, + 0.346 + ], + "angle": 0, + "content": "the 4-layer ResNet MLP decoders. The total reconstruction process takes approximately 3 seconds. After experimenting with TensorRT conversion, 3D and 2D feature extraction can be reduced to 10 and 7 ms, respectively. Since they can be performed in parallel, our framework has the potential to achieve real-time performance under heavy decoder optimization." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.348, + 0.892, + 0.47 + ], + "angle": 0, + "content": "Metrics. We report normal consistency (NC) [33], \\(L_{1}\\) Chamfer Distance \\((\\mathrm{CD} L_{1})\\) [8] and F-score [22] for geometric quality evaluation. Specifically, NC is calculated as the mean absolute dot product of normals of points sampled from reconstructed mesh and their closest neighbours' ones on ground truth mesh. We follow [33] to use 1/10 of the maximum bounding box edge as unit 1 for CD \\(L_{1}\\) and \\(0.5\\%\\) as the F-score distance threshold as suggested by [54]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.47, + 0.892, + 0.561 + ], + "angle": 0, + "content": "To evaluate the quality of relighting, we adopt the peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM) [58] and the learned perceptual image patch similarity (LPIPS) [62]. We mask out renderings with ground truth alpha channel and only report the average contributions of valid pixels." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.574, + 0.64, + 0.59 + ], + "angle": 0, + "content": "4.2. Comparisons" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.598, + 0.892, + 0.884 + ], + "angle": 0, + "content": "Reconstruction. We compare our framework with the state-of-the-art prior-free sparse-view reconstruction approaches, namely multi-view PIFu (MV-PIFu) [43], multiview PIFuHD (MV-PIFuHD) [44] and Function4D [61]. MV-PIFu takes RGB inputs and aggregates using averaging, which we adapt with additional depth input to ensure comparison consistency. PIFuHD integrates normal information, coarse-to-fine two-stage inference, and higher \\(1024 \\times 1024\\) resolution input. We self-implement multiview RGB-D variants (MV-PIFuHD) with the same averaging aggregation as MV-PIFu. Normal maps are still inferred rather than being computed from depth maps due to noise concern. Function4D uses averaging in geometry inference as well, but its integration of the truncated PSDF serves as a strong signal to identify visible features and has been shown to generalize well on real captured data. For a fair comparison, we re-render and train all three approaches on our dataset until converge. Regrettably, we could only compare the geometry, since none of them estimates surface albedo." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.886, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Relighting. Limited by the dataset, which maps human into" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "221" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.089, + 0.46, + 0.565 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.109, + 0.571, + 0.127, + 0.583 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image_caption", + "bbox": [ + 0.176, + 0.571, + 0.194, + 0.583 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image_caption", + "bbox": [ + 0.234, + 0.571, + 0.25, + 0.583 + ], + "angle": 0, + "content": "(c)" + }, + { + "type": "image_caption", + "bbox": [ + 0.272, + 0.568, + 0.28, + 0.581 + ], + "angle": 0, + "content": "" + }, + { + "type": "image_caption", + "bbox": [ + 0.339, + 0.568, + 0.359, + 0.581 + ], + "angle": 0, + "content": "" + }, + { + "type": "image_caption", + "bbox": [ + 0.399, + 0.571, + 0.405, + 0.582 + ], + "angle": 0, + "content": "）" + }, + { + "type": "image", + "bbox": [ + 0.462, + 0.089, + 0.661, + 0.217 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.465, + 0.221, + 0.665, + 0.318 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.671, + 0.089, + 0.758, + 0.216 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.675, + 0.22, + 0.764, + 0.318 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.775, + 0.09, + 0.877, + 0.216 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.779, + 0.219, + 0.879, + 0.318 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.462, + 0.324, + 0.679, + 0.433 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.666, + 0.325, + 0.886, + 0.432 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.086, + 0.436, + 0.139, + 0.565 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.109, + 0.571, + 0.127, + 0.583 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.154, + 0.436, + 0.21, + 0.564 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.175, + 0.571, + 0.193, + 0.583 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.215, + 0.436, + 0.264, + 0.564 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.233, + 0.571, + 0.25, + 0.583 + ], + "angle": 0, + "content": "(c)" + }, + { + "type": "image", + "bbox": [ + 0.272, + 0.436, + 0.329, + 0.564 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.291, + 0.571, + 0.308, + 0.582 + ], + "angle": 0, + "content": "(d)" + }, + { + "type": "image", + "bbox": [ + 0.34, + 0.436, + 0.388, + 0.564 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.386, + 0.438, + 0.447, + 0.565 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.386, + 0.571, + 0.404, + 0.583 + ], + "angle": 0, + "content": "(e)" + }, + { + "type": "image", + "bbox": [ + 0.475, + 0.439, + 0.522, + 0.564 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.493, + 0.571, + 0.508, + 0.583 + ], + "angle": 0, + "content": "(f)" + }, + { + "type": "image", + "bbox": [ + 0.534, + 0.439, + 0.6, + 0.564 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.55, + 0.571, + 0.569, + 0.583 + ], + "angle": 0, + "content": "(g)" + }, + { + "type": "image", + "bbox": [ + 0.6, + 0.439, + 0.67, + 0.564 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.679, + 0.439, + 0.752, + 0.565 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.679, + 0.571, + 0.698, + 0.583 + ], + "angle": 0, + "content": "(h)" + }, + { + "type": "image", + "bbox": [ + 0.755, + 0.441, + 0.889, + 0.565 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.796, + 0.571, + 0.812, + 0.583 + ], + "angle": 0, + "content": "(i)" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.586, + 0.893, + 0.615 + ], + "angle": 0, + "content": "Figure 6. Qualitative comparisons on Twindom. From left to right: (a) shaded color reference, geometry of (b) MV-PIFu [43], (c) Function4D [61], (d) MV-PIFuHD [44], (e) ours, (f) our albedo, (g-i) from left to right: our irradiance, our relighting and ground truth." + }, + { + "type": "image", + "bbox": [ + 0.093, + 0.647, + 0.455, + 0.732 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.754, + 0.47, + 0.782 + ], + "angle": 0, + "content": "Figure 7. Visualization comparison between Function4D [61] (green) and our method (red) on data captured by Kinect." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.795, + 0.471, + 0.903 + ], + "angle": 0, + "content": "single albedo texture without differentiating specular components and coefficients, we were only able to render using diffuse BRDF. This also limits our relighting comparison with NeRF-like method [3,4,50], where the view-dependent specular term is explicitly modeled and is crucial for rendering fidelity. Therefore, we directly compare our results to ray-traced ground truth to demonstrate our relighting per" + }, + { + "type": "image", + "bbox": [ + 0.522, + 0.651, + 0.587, + 0.736 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.599, + 0.652, + 0.664, + 0.736 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.672, + 0.653, + 0.692, + 0.732 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.6, + 0.74, + 0.617, + 0.751 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.701, + 0.653, + 0.868, + 0.731 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.777, + 0.739, + 0.795, + 0.751 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.763, + 0.894, + 0.834 + ], + "angle": 0, + "content": "Figure 8. 3D Feature ablation. (a) We compare the visibility error maps using 2D RGB input (left half of first two), 2D RGB-D input (left half of last two) and ours (right half of all four). (b) We count per-vertex number of wrong directional visibility predictions (out of 64) and plot the normalized histogram." + }, + { + "type": "text", + "bbox": [ + 0.501, + 0.868, + 0.571, + 0.881 + ], + "angle": 0, + "content": "formance." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.886, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Qualitative Comparison. Fig. 5 shows the reconstruction" + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "222" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.16, + 0.09, + 0.387, + 0.104 + ], + "angle": 0, + "content": "Table 2. Feature aggregation ablation." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.116, + 0.472, + 0.179 + ], + "angle": 0, + "content": "
MethodNC↑Geometry MetricRelit Rendering
CD (L1)↓F-score (0.5%)↑PSNR↑SSIM↑LPIPS↓
Average0.9110.1290.69617.9330.6500.327
Attention0.9090.1270.71019.1480.7130.259
Ours0.9170.1220.73623.4360.8090.196
" + }, + { + "type": "image", + "bbox": [ + 0.082, + 0.194, + 0.468, + 0.261 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.271, + 0.469, + 0.3 + ], + "angle": 0, + "content": "Figure 9. Feature aggregation ablation in geometry. From left to right: color reference, the results of average, attention and ours." + }, + { + "type": "image", + "bbox": [ + 0.107, + 0.312, + 0.439, + 0.398 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.4, + 0.469, + 0.428 + ], + "angle": 0, + "content": "Figure 10. Feature aggregation ablation in rendering quality. From left to right: average, attention, and ours and ground truth." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.456, + 0.469, + 0.621 + ], + "angle": 0, + "content": "on the synthetic datasets. Benefiting from visibility-guided feature aggregation, our method produces at least comparable details to MV-PIFuHD, but with simpler architecture, less input information and visual cues. Compared to oversmoothed geometry from Function4D, our hybrid features relax the dominant contribution of noisy depth input, leading to improved facial details. We further demonstrate our generalizability on real captured data in comparison with Function4D. As shown in Fig. 7, our method evidently produces more complete and detailed reconstructions, especially in regions of the eyes, glasses and hair." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.623, + 0.469, + 0.667 + ], + "angle": 0, + "content": "Quantitative Comparison summarized in Tab. 1 is consistent with qualitative analysis, and our method outperforms others in all metrics." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.68, + 0.229, + 0.696 + ], + "angle": 0, + "content": "4.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.703, + 0.469, + 0.763 + ], + "angle": 0, + "content": "3D Feature. We ablate our hybrid feature with 2D RGB and 2D RGB-D variants. The results validate the necessity of the 3D feature, as it predicts visibility with higher accuracy (Fig. 8a) and lower error variance (Fig. 8b)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.764, + 0.469, + 0.87 + ], + "angle": 0, + "content": "Visibility-guided Feature Aggregation. In comparison with other aggregation techniques, namely averaging [43] and self-attention [61], we implement them in our framework. Our strategy surpasses others in metrics (Tab. 2) and achieves sharper geometric details (Fig. 9), better rendering fidelity (Fig. 10), even near heavily folded clothing thanks to our occlusion-aware aggregation." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.469, + 0.901 + ], + "angle": 0, + "content": "View number. Though we train our model with 4 views, it generalizes well across different view numbers and achieves" + }, + { + "type": "image", + "bbox": [ + 0.507, + 0.093, + 0.632, + 0.237 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.633, + 0.093, + 0.759, + 0.237 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.761, + 0.093, + 0.887, + 0.237 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.513, + 0.24, + 0.632, + 0.385 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.633, + 0.24, + 0.759, + 0.385 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.761, + 0.24, + 0.887, + 0.385 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.594, + 0.391, + 0.797, + 0.404 + ], + "angle": 0, + "content": "Figure 11. View number ablation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.433, + 0.892, + 0.508 + ], + "angle": 0, + "content": "similar performance under sufficient view coverage as ablated in Fig. 11. As view number increases, compared to degraded reconstruction quality from average and attention, our aggregation technique mitigates visibility ambiguity to leverage additional visual cues, resulting in better geometry." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.51, + 0.892, + 0.555 + ], + "angle": 0, + "content": "TransferLoss. Finally, we verify the effectiveness of our proposed TransferLoss in Fig. 4. It enforces the alignment of the fields and significantly alleviates rendering defects." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.571, + 0.619, + 0.587 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.597, + 0.892, + 0.687 + ], + "angle": 0, + "content": "Limitations. Our method cannot achieve real-time performance for interactive applications. We leave its acceleration using TensorRT and CUDA for future work. Moreover, our framework relies on depth input for accurate visibility prediction, it would be interesting to see if it can be extended to simpler RGB setup." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.892, + 0.824 + ], + "angle": 0, + "content": "Conclusion. In this work, we integrate visibility into sparse-view reconstruction framework by exploiting its effective guidance in multi-view feature aggregation and direct support for self-shadowed relighting. Our discretization strategy and novel TransferLoss enable visibility to be learned jointly alongside occupancy in an end-to-end manner. Our paper demonstrates the effectiveness of visibility in simultaneous reconstruction and relighting and provides a good baseline for future work." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.892, + 0.899 + ], + "angle": 0, + "content": "Acknowledgement. This work was supported by NSFC No.62171255, National Key R&D Program of China No.2022YFF0902201, Guoqiang Institute of Tsinghua University No.2021GQG0001 and Tsinghua-Toyota Joint Research Fund." + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "223" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.184 + ], + "angle": 0, + "content": "[1] Thiemo Alldieck, Mihai Zanfir, and Cristian Sminchisescu. Photorealistic monocular 3d reconstruction of humans wearing clothing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1506-1515, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.186, + 0.472, + 0.255 + ], + "angle": 0, + "content": "[2] Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Combining implicit function learning and parametric models for 3d human reconstruction. In European Conference on Computer Vision, pages 311-329. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.256, + 0.472, + 0.325 + ], + "angle": 0, + "content": "[3] Mark Boss, Raphael Braun, Varun Jampani, Jonathan T Barron, Ce Liu, and Hendrik Lensch. Nerd: Neural reflectance decomposition from image collections. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12684-12694, 2021. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.326, + 0.47, + 0.368 + ], + "angle": 0, + "content": "[4] Zhaoxi Chen and Ziwei Liu. Relighting4d: Neural relightable human from videos. In European Conference on Computer Vision, pages 606-623. Springer, 2022. 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.369, + 0.47, + 0.424 + ], + "angle": 0, + "content": "[5] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5939-5948, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.425, + 0.47, + 0.493 + ], + "angle": 0, + "content": "[6] Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll. Implicit functions in feature space for 3d shape reconstruction and completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6970-6981, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.495, + 0.47, + 0.55 + ], + "angle": 0, + "content": "[7] Paul Debevec. Pursuing reality with image-based modeling, rendering, and lighting. In European Workshop on 3D Structure from Multiple Images of Large-Scale Environments, pages 1-16. Springer, 2000. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.551, + 0.47, + 0.607 + ], + "angle": 0, + "content": "[8] Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 605-613, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.608, + 0.47, + 0.689 + ], + "angle": 0, + "content": "[9] Sean Ryan Fanello, Cem Keskin, Shahram Izadi, Pushmeet Kohli, David Kim, David Sweeney, Antonio Criminisi, Jamie Shotton, Sing Bing Kang, and Tim Paek. Learning to be a depth camera for close-range human capture and interaction. ACM Transactions on Graphics (TOG), 33(4):1-11, 2014. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.691, + 0.47, + 0.761 + ], + "angle": 0, + "content": "[10] Péter Fankhauser, Michael Bloesch, Diego Rodriguez, Ralf Kaestner, Marco Hutter, and Roland Siegwart. Kinect v2 for mobile robot navigation: Evaluation and modeling. In 2015 International Conference on Advanced Robotics (ICAR), pages 388-394. IEEE, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.761, + 0.47, + 0.816 + ], + "angle": 0, + "content": "[11] Qiao Feng, Yebin Liu, Yu-Kun Lai, Jingyu Yang, and Kun Li. Fof: learning fourier occupancy field for monocular real-time human reconstruction. arXiv preprint arXiv:2206.02194, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.818, + 0.47, + 0.9 + ], + "angle": 0, + "content": "[12] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, et al. The relightables: Volumetric performance capture of humans with realistic relighting. ACM Transactions on Graphics (ToG), 38(6):1–19, 2019. 1" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.472, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.162 + ], + "angle": 0, + "content": "[13] Kaiwen Guo, Feng Xu, Yangang Wang, Yebin Liu, and Qionghai Dai. Robust non-rigid motion tracking and surface reconstruction using 10 regularization. In Proceedings of the IEEE International Conference on Computer Vision, pages 3083-3091, 2015. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.163, + 0.892, + 0.232 + ], + "angle": 0, + "content": "[14] Tong He, Yuanlu Xu, Shunsuke Saito, Stefano Soatto, and Tony Tung. Arch++: Animation-ready clothed human reconstruction revisited. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11046-11056, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.234, + 0.894, + 0.303 + ], + "angle": 0, + "content": "[15] Yang Hong, Juyong Zhang, Boyi Jiang, Yudong Guo, Ligang Liu, and Hujun Bao. Stereopifu: Depth aware clothed human digitization via stereo vision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 535-545, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.304, + 0.892, + 0.36 + ], + "angle": 0, + "content": "[16] Buzhen Huang, Yuan Shu, Tianshu Zhang, and Yangang Wang. Dynamic multi-person mesh recovery from uncalibrated multi-view cameras. In 2021 International Conference on 3D Vision (3DV), pages 710-720. IEEE, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.361, + 0.892, + 0.43 + ], + "angle": 0, + "content": "[17] Zeng Huang, Yuanlu Xu, Christoph Lassner, Hao Li, and Tony Tung. Arch: Animatable reconstruction of clothed humans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3093-3102, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.432, + 0.892, + 0.502 + ], + "angle": 0, + "content": "[18] Chaonan Ji, Tao Yu, Kaiwen Guo, Jingxin Liu, and Yebin Liu. Geometry-aware single-image full-body human relighting. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVI, pages 388-405. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.502, + 0.892, + 0.545 + ], + "angle": 0, + "content": "[19] James T Kajiya. The rendering equation. In Proceedings of the 13th annual conference on Computer graphics and interactive techniques, pages 143-150, 1986. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.546, + 0.892, + 0.588 + ], + "angle": 0, + "content": "[20] Yoshihiro Kanamori and Yuki Endo. Relighting humans: occlusion-aware inverse rendering for full-body human images. arXiv preprint arXiv:1908.02714, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.589, + 0.892, + 0.629 + ], + "angle": 0, + "content": "[21] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.631, + 0.892, + 0.687 + ], + "angle": 0, + "content": "[22] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics (ToG), 36(4):1-13, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.689, + 0.892, + 0.743 + ], + "angle": 0, + "content": "[23] Manuel Lagunas, Xin Sun, Jimei Yang, Ruben Villegas, Jianming Zhang, Zhixin Shu, Belen Masia, and Diego Gutierrez. Single-image full-body human relighting. arXiv preprint arXiv:2107.07259, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.745, + 0.892, + 0.814 + ], + "angle": 0, + "content": "[24] Jason Lawrence, Dan B Goldman, Supreeth Achar, Gregory Major Blascovich, Joseph G Desloge, Tommy Fortes, Eric M Gomez, Sascha Haberling, Hugues Hoppe, Andy Huibers, et al. Project starline: A high-fidelity telepresence system. 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.816, + 0.892, + 0.872 + ], + "angle": 0, + "content": "[25] Ruilong Li, Yuliang Xiu, Shunsuke Saito, Zeng Huang, Kyle Olszewski, and Hao Li. Monocular real-time volumetric performance capture. In European Conference on Computer Vision, pages 49-67. Springer, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.873, + 0.892, + 0.902 + ], + "angle": 0, + "content": "[26] Yue Li, Pablo Wiedemann, and Kenny Mitchell. Deep precomputed radiance transfer for deformable objects. Pro" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.486, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "224" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.111, + 0.093, + 0.469, + 0.12 + ], + "angle": 0, + "content": "ceedings of the ACM on Computer Graphics and Interactive Techniques, 2(1):1-16, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.47, + 0.177 + ], + "angle": 0, + "content": "[27] Haotong Lin, Sida Peng, Zhen Xu, Yunzhi Yan, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Efficient neural radiance fields for interactive free-viewpoint video. In SIGGRAPH Asia 2022 Conference Papers, pages 1-9, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.179, + 0.469, + 0.247 + ], + "angle": 0, + "content": "[28] Shanchuan Lin, Linjie Yang, Imran Saleemi, and Soumyadip Sengupta. Robust high-resolution video matting with temporal guidance. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 238-247, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.25, + 0.469, + 0.305 + ], + "angle": 0, + "content": "[29] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. Smpl: A skinned multiperson linear model. ACM transactions on graphics (TOG), 34(6):1-16, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.307, + 0.469, + 0.348 + ], + "angle": 0, + "content": "[30] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. ACM siggraph computer graphics, 21(4):163-169, 1987. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.35, + 0.469, + 0.433 + ], + "angle": 0, + "content": "[31] Linjie Lyu, Ayush Tewari, Thomas Leimkuhler, Marc Habermann, and Christian Theobalt. Neural radiance transfer fields for relightable novel-view synthesis with global illumination. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVII, pages 153-169. Springer, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.435, + 0.469, + 0.503 + ], + "angle": 0, + "content": "[32] Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando De La Torre, and Yaser Sheikh. Pixel codec avatars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 64-73, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.505, + 0.469, + 0.575 + ], + "angle": 0, + "content": "[33] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4460-4470, 2019. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.577, + 0.469, + 0.631 + ], + "angle": 0, + "content": "[34] Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson. Deep level sets: Implicit surface representations for 3d shape inference. arXiv preprint arXiv:1901.06802, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.633, + 0.469, + 0.7 + ], + "angle": 0, + "content": "[35] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.704, + 0.47, + 0.773 + ], + "angle": 0, + "content": "[36] Ryota Natsume, Shunsuke Saito, Zeng Huang, Weikai Chen, Chongyang Ma, Hao Li, and Shigeo Morishima. *Siclope: Silhouette-based clothed people. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pages 4480–4490, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.775, + 0.469, + 0.858 + ], + "angle": 0, + "content": "[37] Sergio Orts-Escalano, Christoph Rhemann, Sean Fanello, Wayne Chang, Adarsh Kowdle, Yury Degtyarev, David Kim, Philip L Davidson, Sameh Khamis, Mingsong Dou, et al. Holoportation: Virtual 3d teleportation in real-time. In Proceedings of the 29th annual symposium on user interface software and technology, pages 741-754, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.86, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[38] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation." + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.093, + 0.47, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.892, + 0.177 + ], + "angle": 0, + "content": "[39] Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In European Conference on Computer Vision, pages 523-540. Springer, 2020. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.179, + 0.892, + 0.262 + ], + "angle": 0, + "content": "[40] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9054-9063, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.264, + 0.892, + 0.319 + ], + "angle": 0, + "content": "[41] Gilles Rainer, Adrien Bousseau, Tobias Ritschel, and George Drettakis. Neural precomputed radiance transfer. In Computer Graphics Forum, volume 41, pages 365-378. Wiley Online Library, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.321, + 0.892, + 0.376 + ], + "angle": 0, + "content": "[42] Ravi Ramamoorthi and Pat Hanrahan. An efficient representation for irradiance environment maps. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 497-500, 2001. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.378, + 0.892, + 0.459 + ], + "angle": 0, + "content": "[43] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2304-2314, 2019. 1, 2, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.462, + 0.892, + 0.532 + ], + "angle": 0, + "content": "[44] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo. Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 84-93, 2020. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.533, + 0.892, + 0.602 + ], + "angle": 0, + "content": "[45] Soumyadip Sengupta, Angjoo Kanazawa, Carlos D Castillo, and David W Jacobs. Sfsnet: Learning shape, reflectance and illuminance of facesin the wild'. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6296-6305, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.604, + 0.892, + 0.687 + ], + "angle": 0, + "content": "[46] Ruizhi Shao, Hongwen Zhang, He Zhang, Mingjia Chen, Yan-Pei Cao, Tao Yu, and Yebin Liu. Doublefield: Bridging the neural surface and radiance fields for high-fidelity human reconstruction and rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15872-15882, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.689, + 0.892, + 0.744 + ], + "angle": 0, + "content": "[47] Ruizhi Shao, Zerong Zheng, Hongwen Zhang, Jingxiang Sun, and Yebin Liu. Diffustereo: High quality human reconstruction via diffusion-based stereo using sparse cameras. arXiv preprint arXiv:2207.08000, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.746, + 0.892, + 0.814 + ], + "angle": 0, + "content": "[48] Peter-Pike Sloan, Jan Kautz, and John Snyder. Precomputed radiance transfer for real-time rendering in dynamic, low-frequency lighting environments. In Proceedings of the 29th annual conference on Computer graphics and interactive techniques, pages 527-536, 2002. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.817, + 0.892, + 0.858 + ], + "angle": 0, + "content": "[49] Leslie N Smith. Cyclical learning rates for training neural networks. In 2017 IEEE winter conference on applications of computer vision (WACV), pages 464-472. IEEE, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.86, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[50] Pratul P Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan T Barron. Nerv: Neural reflectance and visibility fields for relighting" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.487, + 0.946, + 0.511, + 0.957 + ], + "angle": 0, + "content": "225" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.092, + 0.47, + 0.134 + ], + "angle": 0, + "content": "and view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7495-7504, 2021. 3, 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.135, + 0.47, + 0.203 + ], + "angle": 0, + "content": "[51] Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning for human pose estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5693-5703, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.204, + 0.471, + 0.286 + ], + "angle": 0, + "content": "[52] Xin Suo, Yuheng Jiang, Pei Lin, Yingliang Zhang, Minye Wu, Kaiwen Guo, and Lan Xu. Neuralhumanfvv: Real-time neural volumetric human performance rendering using rgb cameras. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6226-6237, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.288, + 0.471, + 0.356 + ], + "angle": 0, + "content": "[53] Daichi Tajima, Yoshihiro Kanamori, and Yuki Endo. Relighting humans in the wild: Monocular full-body human relighting with domain adaptation. In Computer Graphics Forum, volume 40, pages 205-216. Wiley Online Library, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.357, + 0.471, + 0.427 + ], + "angle": 0, + "content": "[54] Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox. What do single-view 3d reconstruction networks learn? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3405-3414, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.427, + 0.471, + 0.468 + ], + "angle": 0, + "content": "[55] Yating Tian, Hongwen Zhang, Yebin Liu, and Limin Wang. Recovering 3d human mesh from monocular images: A survey. arXiv preprint arXiv:2203.01923, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.469, + 0.471, + 0.539 + ], + "angle": 0, + "content": "[56] Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T Barron, and Pratul P Srinivasan. Ref-nerf: Structured view-dependent appearance for neural radiance fields. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5481-5490. IEEE, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.539, + 0.471, + 0.594 + ], + "angle": 0, + "content": "[57] Ingo Wald, Sven Woop, Carsten Benthin, Gregory S Johnson, and Manfred Ernst. Embree: a kernel framework for efficient cpu ray tracing. ACM Transactions on Graphics (TOG), 33(4):1-8, 2014. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.595, + 0.471, + 0.649 + ], + "angle": 0, + "content": "[58] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.65, + 0.471, + 0.72 + ], + "angle": 0, + "content": "[59] Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. Neural fields in visual computing and beyond. In Computer Graphics Forum, volume 41, pages 641-676. Wiley Online Library, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.72, + 0.468, + 0.748 + ], + "angle": 0, + "content": "[60] YBIGTA. Hair-segmentation. https://pytorchhair.gitbook.io/project/.6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.749, + 0.471, + 0.83 + ], + "angle": 0, + "content": "[61] Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, and Yebin Liu. Function4d: Real-time human volumetric capture from very sparse consumer rgbd sensors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5746-5756, 2021. 1, 2, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.832, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[62] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 2018. 6" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.471, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.092, + 0.893, + 0.161 + ], + "angle": 0, + "content": "[63] Yizhong Zhang, Jiaolong Yang, Zhen Liu, Ruicheng Wang, Guojun Chen, Xin Tong, and Baining Guo. Virtualcube: An immersive 3d video communication system. IEEE Transactions on Visualization and Computer Graphics, 28(5):2146-2156, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.163, + 0.893, + 0.245 + ], + "angle": 0, + "content": "[64] Yang Zheng, Ruizhi Shao, Yuxiang Zhang, Tao Yu, Zerong Zheng, Qionghai Dai, and Yebin Liu. Deepmulticap: Performance capture of multiple characters using sparse multiview cameras. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6239-6249, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.247, + 0.893, + 0.316 + ], + "angle": 0, + "content": "[65] Zerong Zheng, Tao Yu, Yebin Liu, and Qionghai Dai. Pamir: Parametric model-conditioned implicit representation for image-based human reconstruction. IEEE transactions on pattern analysis and machine intelligence, 44(6):3170-3184, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.318, + 0.893, + 0.374 + ], + "angle": 0, + "content": "[66] Zerong Zheng, Tao Yu, Yixuan Wei, Qionghai Dai, and Yebin Liu. Deephuman: 3d human reconstruction from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7739-7749, 2019. 1" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.092, + 0.893, + 0.374 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.487, + 0.946, + 0.512, + 0.957 + ], + "angle": 0, + "content": "226" + } + ] +] \ No newline at end of file diff --git a/2023/Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting/ad55c0fb-3962-4c84-be1b-ade77062812f_origin.pdf b/2023/Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting/ad55c0fb-3962-4c84-be1b-ade77062812f_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b7f4eb1c8b95923d37d2d7a1e68863a859d269f2 --- /dev/null +++ b/2023/Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting/ad55c0fb-3962-4c84-be1b-ade77062812f_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef1e4b390e041bfb6ffb033900780ee7efeabfc852d80aaa60ef63fd580b8847 +size 7728249 diff --git a/2023/Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting/full.md b/2023/Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting/full.md new file mode 100644 index 0000000000000000000000000000000000000000..09546a94163fd6a6c9a9038da4b520dbf7d28941 --- /dev/null +++ b/2023/Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting/full.md @@ -0,0 +1,403 @@ +# Learning Visibility Field for Detailed 3D Human Reconstruction and Relighting + +Ruichen Zheng*,1,2, Peng Li*,1, Haoqian Wang1, Tao Yu1 1Tsinghua University, China 2Weilan Tech, Beijing, China + +![](images/0f9458a4cf8e63f493a896d7950d32e444f483c6edb8855cbe23905692a016ee.jpg) +Figure 1. Given sparse-view RGBD input (left), our method reconstructs detailed geometry (middle left), albedo (middle right), and visibility for realistic 3D human relighting under different illuminations (right). + +# Abstract + +Detailed 3D reconstruction and photo-realistic relighting of digital humans are essential for various applications. To this end, we propose a novel sparse-view 3d human reconstruction framework that closely incorporates the occupancy field and albedo field with an additional visibility field—it not only resolves occlusion ambiguity in multiview feature aggregation, but can also be used to evaluate light attenuation for self-shadowed relighting. To enhance its training viability and efficiency, we discretize visibility onto a fixed set of sample directions and supply it with coupled geometric 3D depth feature and local 2D image feature. We further propose a novel rendering-inspired loss, namely TransferLoss, to implicitly enforce the alignment between visibility and occupancy field, enabling end-to-end joint training. Results and extensive experiments demonstrate the effectiveness of the proposed method, as it surpasses state-of-the-art in terms of reconstruction accuracy while achieving comparably accurate relighting to ray-traced ground truth. + +# 1. Introduction + +3D reconstruction and relighting are of great importance in human digitization, especially in supporting realistic rendering in varying virtual environments, that can be widely applied in AR/VR [32, 37], holographic communi + +cation [24,63], movie and gaming industry [7]. + +Traditional methods often require dense camera setups using multi-view stereo, non-rigid registration and texture mapping [9, 13]. To enhance capture realism, researchers have extended them with additional synchronous variable illumination systems, which aid photometric stereo for detail reconstruction and material acquisition [12]. However, these systems are often too complex, expensive and difficult to maintain, thus preventing widespread applications. + +By leveraging deep prior and neural representation, sophisticated dense camera setups can be reduced to a single camera, leading to blossoms in learning-based human reconstruction. In particular, encoding human geometry and appearance as continuous fields using Multi-Layer Perceptron (MLP) has emerged as a promising lead. Starting from Siclope [36] and PIFu [43], a series of methods [15] improve the reconstruction performance in speed [11, 25], quality [44], robustness [65, 66] and light decoupling [1]. However, single-view reconstruction quality is restricted by its inherent depth ambiguity, thus limiting its application under view-consistent high-quality requirements. + +Therefore, as the trade-off between view coverage and system accessibility, sparse-view reconstruction has become a research hotspot. The predominant practice is to project the query point onto each view to interpolate local features, which are then aggregated and fed to MLP for inference [6, 16, 43, 47, 52, 61]. This method suffers from occlusion ambiguity, where some views may well + +be occluded, and mixing their features with visible ones causes inefficient feature utilization, thus penalizing the reconstruction quality [43]. A natural solution is to filter features based on view visibility. Human templates such as SMPL [29] can serve as effective guidance [2,40,56,64], but introduce additional template alignment errors and therefore cannot guarantee complete occlusion awareness. Function4D [61] leverages the truncated Projective Signed Distance Function (PSDF) for visibility indication, but its level of details is susceptible to depth noise. + +To this end, we directly model a continuous visibility field, which can be efficiently learned with our proposed framework and discretization technique using sparse-view RGB-D input. The visibility field enables efficient visibility query, which effectively guides multi-view feature aggregation for more accurate occupancy and albedo inference. Moreover, visibility can also be directly used for light attenuation evaluation—the key ingredient in achieving realistic self-shadowing. When supervising jointly with our novel TransferLoss, the alignment between the visibility field and occupancy field can be implicitly enforced without between-field constraints, such as matching visibility with occupancy ray integral. We train our framework end-to-end and demonstrate its effectiveness in detailed 3D human reconstruction by quantity and quality comparison with the state-of-the-art. We directly relight the reconstructed geometry with inferred visibility using diffuse Bidirectional Reflectance Distribution Function (BRDF) as in Fig. 1, which achieves photo-realistic self-shadowing without any post ray-tracing steps. To conclude, our contributions include: + +- An end-to-end framework for sparse-view detailed 3D human reconstruction that also supports direct selfshadowed relighting. +- A novel method of visibility field learning, with the specifically designed TransferLoss significantly improves field alignment. +- A visibility-guided multi-view feature aggregation strategy that guarantees occlusion awareness. + +# 2. Background and Related Work + +Neural human synthesis The literature on human reconstruction is vast and rapidly growing. Here, we only review and contrast with closely related work and refer readers to surveys [55, 59] for comprehensive reviews. + +Neural implicit representation encodes geometry as a function of spatial coordinate using MLP. This representation is appealing as it is naturally differentiable, has exceptional expressiveness yet maintains compact memory footprint. The pioneer work follows an encoder-decoder-like architecture, where the globally encoded feature is applied + +to condition spatial coordinates to infer low-level geometric details [5, 33, 34, 38]. This highly unbalanced information flow limits its capability to represent mere simple shapes [39]. PIFu [43] proposes to replace the global feature with pixel-aligned local features, which captures convolutional inductive bias and achieves highly detailed human reconstruction. It inspires a variety of works, ranging from quality improvement [44], parametric model extension [2, 65], animation support [14, 17], light estimation and relighting [1]. + +By averaging local features across views, the pixel-aligned framework can be easily extended to multi-view settings [43]. However, simple averaging diminishes high frequency details, yielding overly smoothed geometry. Moreover, it treats all views as equally visible even for partially occluded regions, resulting in inaccurate and erroneous reconstruction. Zhang et al. [64] addresses the occlusion ambiguity by incorporating the attention mechanism, which weights features by their learned cross-view correlations. Despite its promising performance, self-attention introduces substantial memory and computation overhead. Yu et al. [61] additionally leverages global depth information, namely PSDF, to annihilate the ambiguity but is sensitive to depth noise. In contrast, we use robustly learned visibility to weight per-view contribution, which handles occlusion in a physically plausible manner while being more memory efficient to compute. + +Relighting boils down to substituting the incident environment radiance and re-evaluating the rendering equation [19]. For a scene of reflectors, the outgoing radiance reflected at the surface point $x$ in direction $\omega_{o}$ can be described as: + +$$ +L (\boldsymbol {x}, \boldsymbol {\omega} _ {o}) = \int_ {\Omega^ {+}} L (\boldsymbol {x}, \boldsymbol {\omega} _ {i}) \rho (\boldsymbol {x}, \boldsymbol {\omega} _ {i}, \boldsymbol {\omega} _ {o}) V (\boldsymbol {x}, \boldsymbol {\omega} _ {i}) (\boldsymbol {n} \cdot \boldsymbol {\omega} _ {i}) d \boldsymbol {\omega} _ {i} \tag {1} +$$ + +where $\pmb{n}$ is unit surface normal at $\pmb{x}$ , $\Omega^{+}$ is hemisphere of possible directions $\omega_{i}$ , $L(\pmb{x},\omega_{i})$ is incident radiance arriving $\pmb{x}$ along $\omega_{i}$ , directly from environment or indirectly reflected. $\rho$ is the BRDF that models the surface reflectance. $V$ is the visibility function that describes whether light $\pmb{x}$ is attenuated along $\omega_{i}$ . As the main contributor to realistic self-shadowing, visibility evaluation requires ray tracing geometry over all sample directions $\omega_{i}$ per fragment [18], which is expensive and usually omitted [45] or baked offline using Precomputed Radiance Transfer (PRT) [48]. PRT rewrites Eq. (1) with the following transfer function + +$$ +T (\boldsymbol {x}, \boldsymbol {\omega} _ {i}, \boldsymbol {\omega} _ {o}) = \rho (\boldsymbol {x}, \boldsymbol {\omega} _ {i}, \boldsymbol {\omega} _ {o}) V (\boldsymbol {x}, \boldsymbol {\omega} _ {i}) (\boldsymbol {n} \cdot \boldsymbol {\omega} _ {i}) \tag {2} +$$ + +which is independent of light and can be precomputed and projected on the Spherical Harmonics (SH) basis as coefficients [42] ahead of time to save rendering cost. + +However, PRT does not ameliorate visibility calculation complexity and requires recompute after deformation. Its + +![](images/1efd1488a911874576f86c560028be3e37cd1b20765f1cc89bd667d20b0b0e9c.jpg) +Figure 2. Method overview. Given sparse RGB-D frames as input, our framework first infer visibility, which is then applied to guide multiview 2D feature aggregation for joint occupancy and albedo inference. Our framework is end-to-end trainable and produces high-fidelity human reconstruction that supports direct self-shadowed relighting without using any post ray-tracing steps. + +limitation has been addressed by several learning-based approaches [20, 23, 26, 41, 53], which predict transfer coefficients using deep neural networks. Instead of its SH parameterization, we predict raw visibility, as it (1) can be used for feature aggregation, and (2) is also well defined in free space that can be learned without surface evaluation. + +NeRF [35] models the surface density and employs volume rendering for high fidelity novel view synthesis. It has been extended with reflectance [3] and transfer function [31] to support relighting. Visibility is naturally defined in the density field, but its evaluation requires the integration of multiple density queries along the ray, which is equally inefficient and needs to be accelerated, such as using occlusion map [4] or MLP [50]. Although sharing similar ends, we achieve this with different means: (1) We emphasize accuracy by supervising with ground truth and use the TransferLoss to regularize the field alignment, rather than attempting to directly align the fields by matching inferred visibility with accumulated transmittance. (2) We extensively train our model on human scan dataset to ensure fast inference, rather than fitting it per-scene in order to render arbitrary within-scene objects. + +# 3. Method + +To introduce our method, we first define the visibility field and its discretization process, which prioritizes efficiency without compromising performance. We then outline our framework and visibility learning procedure. Finally, we showcase its application in sparse-view 3D human reconstruction, where it guides feature aggregation and enables direct self-shadowed relighting. + +# 3.1. Visibility Field + +For any point $X\in \mathbb{R}^3$ , whether it is visible $V\in \{0,1\}$ along any view direction $\omega \in \mathbb{R}^3$ of the unit sphere $S$ forms a continuous field and can be parameterized using MLP: + +$$ +\operatorname {M L P} _ {\phi}: (\boldsymbol {X}, \omega) \rightarrow V _ {\phi} \in [ 0, 1 ], \omega \in S \tag {3} +$$ + +where $\phi$ is the MLP weights. Although Eq. (3) helps mitigate the cost of the ray integral, querying visibility over $s$ directions still requires $s$ calls per point of interest. This cost is exacerbated in pixel-aligned settings due to excessive point-wise feature $F$ duplication as illustrated in Fig. 3a (top), leading to substantially high memory overhead. + +We observe that, by uniformly sampling a discrete set of directions, visibility along any direction can be interpolated by Eq. (5) (Fig. 3c), with accuracy capped by the sample size. Thus, the $O(s)$ complexity can be reduced to a single MLP call plus an additional interpolation cost. To this end, we propose an alternative definition, by treating visibility as a function of $\mathbf{X}$ conditioned on a fixed set of $n$ sample directions $\omega^{(n)}$ as in Fig. 3a (bottom). + +$$ +\operatorname {M L P} _ {\phi | \boldsymbol {\omega} ^ {(n)}}: (\boldsymbol {X}, \boldsymbol {F}) \rightarrow V _ {\phi | \boldsymbol {\omega} ^ {(n)}} \in [ 0, 1 ] ^ {n}, \boldsymbol {\omega} ^ {(n)} \in S \tag {4} +$$ + +In practice, we interpolate by top $k$ closest cosine distance with respect to $\omega^{(n)}$ : + +$$ +\begin{array}{l} \left\{\boldsymbol {\omega} ^ {(i)} \right\} _ {i = 1 \dots k} = \operatorname {t o p k} \left(\boldsymbol {\omega} ^ {(n)} \cdot \boldsymbol {\omega}\right) \\ V (\boldsymbol {X}, \omega) = \sum_ {i = 1} ^ {k} \frac {V _ {\omega^ {(i)}} (\boldsymbol {X}) \left(\boldsymbol {\omega} ^ {(i)} \cdot \boldsymbol {\omega}\right)}{\sum_ {j = 1} ^ {k} \left(\boldsymbol {\omega} ^ {(j)} \cdot \boldsymbol {\omega}\right)} \tag {5} \\ \end{array} +$$ + +The simplification in Eq. (4) leads to a surprisingly simple implementation-visibility prediction can be treated as $n - D$ binary classification supervised by BCE loss: + +$$ +L _ {\mathrm {V i s}} = V _ {\mathrm {G T}} \cdot \log V _ {\phi | \boldsymbol {\omega} ^ {(n)}} + (1 - V _ {\mathrm {G T}}) \cdot \log \left(1 - V _ {\phi | \boldsymbol {\omega} ^ {(n)}}\right) \tag {6} +$$ + +![](images/23906dda58aec08f6733eb70d526b47876c6be120ee4b366315dd31e51e2be08.jpg) +(a) + +![](images/3e8adf8208effac05162c76a8ae392814efeabad3babb96c3e385273fcc4a5da.jpg) +(b) + +![](images/bfc4224654ed9a784783ed054f7f4a85edeb690f79c3fe9a3692adff808c4c58.jpg) +(c) + +![](images/afd6fbb0fd0e1f5d389e3345bec6d47e93b2d8d1baeb5ecbf4e98a8318bb373d.jpg) +(d) + +![](images/fd68b0a244955ff333a30f8209829656e43dd6c81868d0f214567eddc1039415.jpg) +(e) +Figure 3. (a) Visibility as a continuous function of both point position $X$ and sample direction $\omega$ (top) leads to multiple queries and pointwise feature $F$ duplication. In contrast, by fixing the sampling directions $\omega^{(n)}$ (bottom), it only requires a single query. We extensively analyze our visibility field simplification strategy over $10\%$ of training data and report average accuracy over the sample size $n$ in (b) with the following procedure: For each sample size decision, we sample 10k query points per model (same sampling distribution as training data) with visibility evaluated over $\omega^{(n)}$ , that we apply cosine distance interpolation (c) to infer 10k randomly sampled validation directions per point. For aggregating multi-view features (d), we apply the same interpolation method as in (c) for each camera view direction to evaluate visibility along the back projection ray. (e) Directional visibility naturally constraints on the presence of surface. + +![](images/41ac1dddfa6b4e299b9ce1e03aa8b03a914938ede647c87e8c90fc0c3fe4ddea.jpg) +Figure 4. TransferLoss ablation: From left to right: w/o, w TransferLoss, ground truth. TransferLoss significantly mitigates rendering defects and improves near-surface visibility prediction accuracy (27.5% → 88.4% for synthesized testset, 20.8% → 84.7% for real-captured data). + +![](images/cfa3a32372e2b983bc13719a2738b67709c1ddbdd17c8f69b6f566472b48cf53.jpg) + +# 3.2. Framework Overview + +As shown in Fig. 2, our framework represents all fields using MLPs. Given sparse multi-view RGB-D frames $\{(\mathcal{I}_i,\mathcal{D}_i),i = 1\dots m\}$ with known camera projection matrices $\pi_{i}$ , we first extract depth point cloud voxelized 3D feature and pixel-aligned RGB 2D feature to directly predict visibility. Then we utilize inferred visibility to guide the aggregation of multi-view 2D feature, which are paired with 3D feature for joint geometry and albedo inference. + +# 3.3. Hybrid Feature Extraction + +Conventionally, evaluating visibility requires tracing ray along the direction of interest and checking surface hits. In other words, it requires reasoning about the geometric feature of surfaces near that direction. Since we model both geometry (occupancy) and visibility, it is crucial to also bridge the two fields to incorporate their interconnection. + +To this end, we adopt a hybrid feature extraction procedure, by separating depth from RGB and encoding it as 3D feature. Specifically, we follow [39] to unproject depth image into pointcloud, voxelize and filter using 3D convolutional as coarse feature volume. For a query point $X$ , + +we acquire its local 3D feature $F_{\mathcal{D}}$ by trilinear interpolation based on point coordinates and then share it with both visibility and occupancy MLPs. Intuitively, the 3D feature originates from voxelized point cloud, which can be viewed as noisy samples of the underlying surface (decision boundary of the occupancy field). By applying 3D convolution, the network reasons the coarse geometric surface feature over a sufficiently large receptive field, consequently aiding visibility inference. 3D feature is necessary for accurate visibility prediction as ablated in Fig. 8. + +For multi-view RGB frames, we directly filter them using HRNet [51] to acquire 2D feature maps $F_{i}$ . The local 2D features are then extracted in the pixel-aligned fashion as in [43], by projecting the point coordinate $X$ onto each view as the image coordinate $\pi (X)$ , then bilinearly interpolating the corresponding feature maps $F_{i}(\pi (X))$ . Compared to feature volume, 2D feature maps have much higher resolution and thus grant better details. + +Follow Eq. (4), we infer $V_{\phi|\omega^{(n)}}$ by providing 3D feature $F_{\mathcal{D}}$ and averaged 2D feature $F_{avg}$ : + +$$ +\operatorname {M L P} _ {\phi | \boldsymbol {\omega} ^ {(n)}}: \left(\boldsymbol {X}, F _ {\mathcal {D}}, F _ {a v g}\right) \in [ 0, 1 ] ^ {n} \tag {7} +$$ + +# 3.4. Field Alignment Regularization + +Given similar formulations, it is natural to train the visibility field and occupancy field together. However, as the point moves across the surface (occupancy classification boundary), its visibility changes drastically, ranging from fully occluded to partially visible. A slight misalignment between the two fields could cause inner visibility leakage and introduce conspicuous rendering defects as in Fig. 4. To enforce their alignment, a common practice is to explicitly constrain their correspondence, by matching visibility with surface queried along the ray [50], but at the expense of substantially large training overhead. + +![](images/86c7bf2641ae982a58cdcf4509551ee358ee71fcc259d017859b38cefc422314.jpg) +Figure 5. Qualitative comparisons on THUman2.0 [61]. We show (a) shaded color reference, geometry of (b) MV-PIFu [43], (c) Function4D [61], (d) MV-PIFuHD [44], (e) ours, (f) our albedo, (g-i) from left to right: our irradiance, our relighting and ground truth. + +Since we bridge the two fields with the 3D feature reasoning about their correlation, we exploit the constraint of visibility over occupancy (Fig. 3e) by emphasizing the accuracy of near-surface visibility prediction, so that their alignment can be implicitly enforced. To this end, we propose a novel TransferLoss inspired by radiance transfer in Eq. (2): + +$$ +L _ {\text {T r a n s f e r}} = \sum_ {i} | \hat {T} (\boldsymbol {X}, V _ {\phi | \boldsymbol {\omega} ^ {(i)}}) - T (\boldsymbol {X}, V _ {\mathrm {G T}}) | \tag {8} +$$ + +It supervises visibility, but (1) prioritizes the normal-facing directions in contrast to equal weighting in BCE (Eq. (6)) loss, and (2) follows the same parameterization for diffuse BRDF evaluation, which intuitively renders the light-independent part of the scene and perceptually penalizes the difference. + +Since occupancy and visibility are jointly supervised, we directly sample query points on the ground truth mesh surface and use their normals to assist Eq. (8) evaluation for + +inferred visibility. TransferLoss effectively enforces fields alignment, resulting in significantly less rendering defects and higher relighting fidelity as ablated in Sec. 4.3 and Fig. 4. + +# 3.5. Visibility-Guided Feature Aggregation + +For each view, we trace back to its camera center and evaluate the directional visibility as described in Eq. (5). We then prioritize visible features over occluded ones using clamped negative log weighted average: + +$$ +\begin{array}{l} F _ {a g g} = \sum_ {i = 1} ^ {m} W _ {V _ {\phi | \boldsymbol {\omega} ^ {(n)}}}) F _ {i} (\pi_ {i} (\boldsymbol {X})) \tag {9} \\ W _ {V _ {\phi | \boldsymbol {\omega} ^ {(n)}}} (\boldsymbol {X}, \boldsymbol {\omega}) = \max (- \log (1 - V _ {\phi | \boldsymbol {\omega} ^ {(n)})}, 1 0 0) \\ \end{array} +$$ + +Thus, occupancy and albedo are represented as: + +$$ +\mathrm {M L P} _ {\text {o c c}}: \left(\boldsymbol {X}, F _ {\mathcal {D}}, F _ {\text {a g g}}\right)\rightarrow [ 0, 1 ] \tag {10} +$$ + +$$ +\mathbf {M L P} _ {a l b e d o}: \left(\boldsymbol {X}, F _ {\mathcal {D}}, F _ {a g g}\right)\rightarrow \mathbb {R} ^ {3} \tag {16} +$$ + +# 3.6. Reconstruction and Relighting + +During inference, we first apply visibility-guided aggregation to sample a grid of occupancy for surface mesh extraction [30]. We then pass the mesh vertices to obtain albedo and visibility using the same technique. Following Eq. (2), we compute per-vertex transfer coefficients and directly apply them for rasterized self-shadowed relighting. + +# 4. Experiments + +# 4.1. Experimental Settings + +Training details. For training, we collect 400 high-quality clothed human scans from THUman2.0 [61], rotate each one around the yaw axis, and apply random shifts to obtain 60 views. For each view, we render $512 \times 512$ images of albedo, color using diffuse BRDF and depth fused with TOF depth sensors noise [10]. To simulate the incomplete depth caused by capture insensitivity for materials such as hair, we use [60] to mask hair out. + +For each scanned mesh, we sample total 5k points for occupancy and visibility, with 4k near surface and 1k uniformly within the bounding volume. Near-surface points are sampled using normal distribution with standard deviation of 0.05, and we ensure that half of them with distances less than the standard deviation are used for albedo training. As described in Sec. 3.1 and Fig. 3b, we uniformly sample 64 fixed directions using the Fibonacci lattice and keep it consistent throughout the experiment. We use Embree [57] for ground truth visibility evaluation. + +In addition to visibility, we supervise per-sample occupancy with BCE loss and albedo with $L_{1}$ loss. We also extract patches using depth-guided raymarching [27] and supervise its albedo using VGG perceptual loss. + +We set the view number to 4 and train our framework using Adam [21] optimizer and Cyclic learning rate (lr) scheduler [49] over 600 epochs. The lr ranges from $5e - 5$ to $5e - 4$ every 5 epochs, with the max lr halved every 100 epochs. + +Evaluation details. For evaluation, we prepare 100 training-excluded scans from THUman2.0, and an additional 100 scans from Twindom [46]. We follow the same rendering procedure as for training and report metrics averaged over the two sets. We further prepare real-captured RGB-D video sequences from a synchronized multi-Kinect capturing system that we leverage RVM [28] for foreground mask segmentation. For all experiments, we keep the number of views to 4 except for the view-number ablation. All meshes are extracted from $512^{3}$ voxel using Marching Cube [30]. The relighting results are rendered using diffuse BRDF with inferred visibility and SH order of 2. + +All experiments run on a PC with an Nvidia GeForce RTX3090 GPU and an Intel i7-8700k CPU. Our framework requires 50 ms for 3D and 100 ms for 2D feature extraction, and 200 ms per 2 million point queries for each of + +
MethodsMetrics
NC ↑CD (L1) ↓F-score (0.5%)↑
MV-PIFu [43]0.9120.1450.624
MV-PIFuHD [44]0.9060.1350.690
Function4D [61]0.8930.1290.704
Ours0.9170.1220.736
+ +Table 1. Quantitative comparisons on reconstruction quality. + +the 4-layer ResNet MLP decoders. The total reconstruction process takes approximately 3 seconds. After experimenting with TensorRT conversion, 3D and 2D feature extraction can be reduced to 10 and 7 ms, respectively. Since they can be performed in parallel, our framework has the potential to achieve real-time performance under heavy decoder optimization. + +Metrics. We report normal consistency (NC) [33], $L_{1}$ Chamfer Distance $(\mathrm{CD} L_{1})$ [8] and F-score [22] for geometric quality evaluation. Specifically, NC is calculated as the mean absolute dot product of normals of points sampled from reconstructed mesh and their closest neighbours' ones on ground truth mesh. We follow [33] to use 1/10 of the maximum bounding box edge as unit 1 for CD $L_{1}$ and $0.5\%$ as the F-score distance threshold as suggested by [54]. + +To evaluate the quality of relighting, we adopt the peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM) [58] and the learned perceptual image patch similarity (LPIPS) [62]. We mask out renderings with ground truth alpha channel and only report the average contributions of valid pixels. + +# 4.2. Comparisons + +Reconstruction. We compare our framework with the state-of-the-art prior-free sparse-view reconstruction approaches, namely multi-view PIFu (MV-PIFu) [43], multiview PIFuHD (MV-PIFuHD) [44] and Function4D [61]. MV-PIFu takes RGB inputs and aggregates using averaging, which we adapt with additional depth input to ensure comparison consistency. PIFuHD integrates normal information, coarse-to-fine two-stage inference, and higher $1024 \times 1024$ resolution input. We self-implement multiview RGB-D variants (MV-PIFuHD) with the same averaging aggregation as MV-PIFu. Normal maps are still inferred rather than being computed from depth maps due to noise concern. Function4D uses averaging in geometry inference as well, but its integration of the truncated PSDF serves as a strong signal to identify visible features and has been shown to generalize well on real captured data. For a fair comparison, we re-render and train all three approaches on our dataset until converge. Regrettably, we could only compare the geometry, since none of them estimates surface albedo. + +Relighting. Limited by the dataset, which maps human into + +![](images/e9e372c27f22586eec956d76a3d65f51e7666cf541ecf1cb4d5d238912294728.jpg) + + +(c) +(d) +(e) +Figure 6. Qualitative comparisons on Twindom. From left to right: (a) shaded color reference, geometry of (b) MV-PIFu [43], (c) Function4D [61], (d) MV-PIFuHD [44], (e) ours, (f) our albedo, (g-i) from left to right: our irradiance, our relighting and ground truth. + +(a) + +(b) + +(c) + +） + +![](images/2a8df9529a498ef36e378ce1929c0a3467e2b4732d5176b2fbeaa8c466c4c440.jpg) + +![](images/ffd13b85fc506976c7e28bf7eb5e6e9160fedbacb6bcf7554528d6a192b2badc.jpg) + +![](images/97ccf92a8684aafc2a32667c1d8ba1ba8627983788336bc383b9d6ace1ec6637.jpg) + +![](images/30485eee588c706f4efefcecdfc5aa29eadbfbfcfb7270730a01ed217c039b60.jpg) + +![](images/1e3a93c65bfc20ef0a498d30ced46fcb78703b028dab55cf956c4b10eef6b56f.jpg) + +![](images/678769f3b53fc2c48a091d2919316972c13ed9c22d88809aee17c83bc8cd806f.jpg) + +![](images/d29d8daec0ae9787e3539214968258329734e5da74875cc87a241df73ba875ac.jpg) + +![](images/299681713c1abbf616e9b543b519c4c547098a38a0761dd99feb54c732600e4d.jpg) + +(a) + +(b) + +![](images/3e8fbea81c3c11a834f76ed2569f775ad0189344136dcaf7b1792853dfa8762b.jpg) +(f) + +![](images/878eb40c6fdc02a1506b02a70c1b5ac7e7f8b9b658a1150aaac66ef041a44f8e.jpg) +(g) + +![](images/18be21b31dad78e3699e78d4fed6456ebd6a3504fc0b1a3f6f25b0554220c982.jpg) + +![](images/7734dd7d91f4c833e4ac044766f218ac515220d84dd08a1ed14b577a48c62ba9.jpg) +(h) + +![](images/05a4e30cd3d50e0c1e19cda9d32b706239f45dc5dd22c0800607c49823bee37e.jpg) +(i) + +![](images/8cc8ec28d27e9235738f8088000fa6bc76b140cb91bf67f027325274bf07898d.jpg) +Figure 7. Visualization comparison between Function4D [61] (green) and our method (red) on data captured by Kinect. + +single albedo texture without differentiating specular components and coefficients, we were only able to render using diffuse BRDF. This also limits our relighting comparison with NeRF-like method [3,4,50], where the view-dependent specular term is explicitly modeled and is crucial for rendering fidelity. Therefore, we directly compare our results to ray-traced ground truth to demonstrate our relighting per + +![](images/5fee874a2fd77b88b8383408e38d48e1d91c3fd0aecc1aa107cd35544116d991.jpg) +Figure 8. 3D Feature ablation. (a) We compare the visibility error maps using 2D RGB input (left half of first two), 2D RGB-D input (left half of last two) and ours (right half of all four). (b) We count per-vertex number of wrong directional visibility predictions (out of 64) and plot the normalized histogram. + +![](images/e046787c00c98442cd8e628337a18c03537f25aa8350cc1442d5c692166f9d35.jpg) +(a) + +![](images/2c34bd6b5e5b55b0bc18911439edaf7fb137ebf6dcc0b46a3aeed1c7d7dae0ba.jpg) + +![](images/2009bb1c93ed7ec49c4bf7409f7bf0046689101ddb7d3d68b0728e2da8ec5418.jpg) +(b) + +formance. + +Qualitative Comparison. Fig. 5 shows the reconstruction + +Table 2. Feature aggregation ablation. + +
MethodNC↑Geometry MetricRelit Rendering
CD (L1)↓F-score (0.5%)↑PSNR↑SSIM↑LPIPS↓
Average0.9110.1290.69617.9330.6500.327
Attention0.9090.1270.71019.1480.7130.259
Ours0.9170.1220.73623.4360.8090.196
+ +![](images/98dd0b9b5b0b1d9296e8e055cade04255229031e1005a103ace243891e900c6f.jpg) +Figure 9. Feature aggregation ablation in geometry. From left to right: color reference, the results of average, attention and ours. + +![](images/a77878132e7974e0b19d4fc0020fe1f85ea21c63815009257d0685ad6e90a512.jpg) +Figure 10. Feature aggregation ablation in rendering quality. From left to right: average, attention, and ours and ground truth. + +on the synthetic datasets. Benefiting from visibility-guided feature aggregation, our method produces at least comparable details to MV-PIFuHD, but with simpler architecture, less input information and visual cues. Compared to oversmoothed geometry from Function4D, our hybrid features relax the dominant contribution of noisy depth input, leading to improved facial details. We further demonstrate our generalizability on real captured data in comparison with Function4D. As shown in Fig. 7, our method evidently produces more complete and detailed reconstructions, especially in regions of the eyes, glasses and hair. + +Quantitative Comparison summarized in Tab. 1 is consistent with qualitative analysis, and our method outperforms others in all metrics. + +# 4.3. Ablation Study + +3D Feature. We ablate our hybrid feature with 2D RGB and 2D RGB-D variants. The results validate the necessity of the 3D feature, as it predicts visibility with higher accuracy (Fig. 8a) and lower error variance (Fig. 8b). + +Visibility-guided Feature Aggregation. In comparison with other aggregation techniques, namely averaging [43] and self-attention [61], we implement them in our framework. Our strategy surpasses others in metrics (Tab. 2) and achieves sharper geometric details (Fig. 9), better rendering fidelity (Fig. 10), even near heavily folded clothing thanks to our occlusion-aware aggregation. + +View number. Though we train our model with 4 views, it generalizes well across different view numbers and achieves + +![](images/355149894fc6d873ad5f9ce29674cb09ee04df6dd2c174a368bd2160fe5befba.jpg) + +![](images/49eea60cad0e18d314bd98ec4bca5d2b6a9b118ffa920b81da90af174f03b44e.jpg) + +![](images/48a46947c27b76ad6277524c232a3602c6e0043f53847f09ab7441fa638ea8b6.jpg) + +![](images/efea5f367fa7c78129c6c3f9c95715e20c2dd213fb71a79029cc6268182ec04f.jpg) +Figure 11. View number ablation. + +![](images/14c95b618aac2468a8a5ceaf1377fa6c747ef1a593d5a8efd7a53cba8191b9ff.jpg) + +![](images/70dc15fc91a094ea33359510bfa1e80a4c503022d56ea4f894af5f5be6d6cd67.jpg) + +similar performance under sufficient view coverage as ablated in Fig. 11. As view number increases, compared to degraded reconstruction quality from average and attention, our aggregation technique mitigates visibility ambiguity to leverage additional visual cues, resulting in better geometry. + +TransferLoss. Finally, we verify the effectiveness of our proposed TransferLoss in Fig. 4. It enforces the alignment of the fields and significantly alleviates rendering defects. + +# 5. Conclusion + +Limitations. Our method cannot achieve real-time performance for interactive applications. We leave its acceleration using TensorRT and CUDA for future work. Moreover, our framework relies on depth input for accurate visibility prediction, it would be interesting to see if it can be extended to simpler RGB setup. + +Conclusion. In this work, we integrate visibility into sparse-view reconstruction framework by exploiting its effective guidance in multi-view feature aggregation and direct support for self-shadowed relighting. Our discretization strategy and novel TransferLoss enable visibility to be learned jointly alongside occupancy in an end-to-end manner. Our paper demonstrates the effectiveness of visibility in simultaneous reconstruction and relighting and provides a good baseline for future work. + +Acknowledgement. This work was supported by NSFC No.62171255, National Key R&D Program of China No.2022YFF0902201, Guoqiang Institute of Tsinghua University No.2021GQG0001 and Tsinghua-Toyota Joint Research Fund. + +# References + +[1] Thiemo Alldieck, Mihai Zanfir, and Cristian Sminchisescu. Photorealistic monocular 3d reconstruction of humans wearing clothing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1506-1515, 2022. 1, 2 +[2] Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Combining implicit function learning and parametric models for 3d human reconstruction. In European Conference on Computer Vision, pages 311-329. Springer, 2020. 2 +[3] Mark Boss, Raphael Braun, Varun Jampani, Jonathan T Barron, Ce Liu, and Hendrik Lensch. Nerd: Neural reflectance decomposition from image collections. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12684-12694, 2021. 3, 7 +[4] Zhaoxi Chen and Ziwei Liu. Relighting4d: Neural relightable human from videos. In European Conference on Computer Vision, pages 606-623. Springer, 2022. 3, 7 +[5] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5939-5948, 2019. 2 +[6] Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll. Implicit functions in feature space for 3d shape reconstruction and completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6970-6981, 2020. 1 +[7] Paul Debevec. Pursuing reality with image-based modeling, rendering, and lighting. In European Workshop on 3D Structure from Multiple Images of Large-Scale Environments, pages 1-16. Springer, 2000. 1 +[8] Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 605-613, 2017. 6 +[9] Sean Ryan Fanello, Cem Keskin, Shahram Izadi, Pushmeet Kohli, David Kim, David Sweeney, Antonio Criminisi, Jamie Shotton, Sing Bing Kang, and Tim Paek. Learning to be a depth camera for close-range human capture and interaction. ACM Transactions on Graphics (TOG), 33(4):1-11, 2014. 1 +[10] Péter Fankhauser, Michael Bloesch, Diego Rodriguez, Ralf Kaestner, Marco Hutter, and Roland Siegwart. Kinect v2 for mobile robot navigation: Evaluation and modeling. In 2015 International Conference on Advanced Robotics (ICAR), pages 388-394. IEEE, 2015. 6 +[11] Qiao Feng, Yebin Liu, Yu-Kun Lai, Jingyu Yang, and Kun Li. Fof: learning fourier occupancy field for monocular real-time human reconstruction. arXiv preprint arXiv:2206.02194, 2022. 1 +[12] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, et al. The relightables: Volumetric performance capture of humans with realistic relighting. ACM Transactions on Graphics (ToG), 38(6):1–19, 2019. 1 + +[13] Kaiwen Guo, Feng Xu, Yangang Wang, Yebin Liu, and Qionghai Dai. Robust non-rigid motion tracking and surface reconstruction using 10 regularization. In Proceedings of the IEEE International Conference on Computer Vision, pages 3083-3091, 2015. 1 +[14] Tong He, Yuanlu Xu, Shunsuke Saito, Stefano Soatto, and Tony Tung. Arch++: Animation-ready clothed human reconstruction revisited. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11046-11056, 2021. 2 +[15] Yang Hong, Juyong Zhang, Boyi Jiang, Yudong Guo, Ligang Liu, and Hujun Bao. Stereopifu: Depth aware clothed human digitization via stereo vision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 535-545, 2021. 1 +[16] Buzhen Huang, Yuan Shu, Tianshu Zhang, and Yangang Wang. Dynamic multi-person mesh recovery from uncalibrated multi-view cameras. In 2021 International Conference on 3D Vision (3DV), pages 710-720. IEEE, 2021. 1 +[17] Zeng Huang, Yuanlu Xu, Christoph Lassner, Hao Li, and Tony Tung. Arch: Animatable reconstruction of clothed humans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3093-3102, 2020. 2 +[18] Chaonan Ji, Tao Yu, Kaiwen Guo, Jingxin Liu, and Yebin Liu. Geometry-aware single-image full-body human relighting. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVI, pages 388-405. Springer, 2022. 2 +[19] James T Kajiya. The rendering equation. In Proceedings of the 13th annual conference on Computer graphics and interactive techniques, pages 143-150, 1986. 2 +[20] Yoshihiro Kanamori and Yuki Endo. Relighting humans: occlusion-aware inverse rendering for full-body human images. arXiv preprint arXiv:1908.02714, 2019. 3 +[21] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6 +[22] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics (ToG), 36(4):1-13, 2017. 6 +[23] Manuel Lagunas, Xin Sun, Jimei Yang, Ruben Villegas, Jianming Zhang, Zhixin Shu, Belen Masia, and Diego Gutierrez. Single-image full-body human relighting. arXiv preprint arXiv:2107.07259, 2021. 3 +[24] Jason Lawrence, Dan B Goldman, Supreeth Achar, Gregory Major Blascovich, Joseph G Desloge, Tommy Fortes, Eric M Gomez, Sascha Haberling, Hugues Hoppe, Andy Huibers, et al. Project starline: A high-fidelity telepresence system. 2021. 1 +[25] Ruilong Li, Yuliang Xiu, Shunsuke Saito, Zeng Huang, Kyle Olszewski, and Hao Li. Monocular real-time volumetric performance capture. In European Conference on Computer Vision, pages 49-67. Springer, 2020. 1 +[26] Yue Li, Pablo Wiedemann, and Kenny Mitchell. Deep precomputed radiance transfer for deformable objects. Pro + +ceedings of the ACM on Computer Graphics and Interactive Techniques, 2(1):1-16, 2019. 3 +[27] Haotong Lin, Sida Peng, Zhen Xu, Yunzhi Yan, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Efficient neural radiance fields for interactive free-viewpoint video. In SIGGRAPH Asia 2022 Conference Papers, pages 1-9, 2022. 6 +[28] Shanchuan Lin, Linjie Yang, Imran Saleemi, and Soumyadip Sengupta. Robust high-resolution video matting with temporal guidance. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 238-247, 2022. 6 +[29] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. Smpl: A skinned multiperson linear model. ACM transactions on graphics (TOG), 34(6):1-16, 2015. 2 +[30] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. ACM siggraph computer graphics, 21(4):163-169, 1987. 6 +[31] Linjie Lyu, Ayush Tewari, Thomas Leimkuhler, Marc Habermann, and Christian Theobalt. Neural radiance transfer fields for relightable novel-view synthesis with global illumination. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVII, pages 153-169. Springer, 2022. 3 +[32] Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando De La Torre, and Yaser Sheikh. Pixel codec avatars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 64-73, 2021. 1 +[33] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4460-4470, 2019. 2, 6 +[34] Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson. Deep level sets: Implicit surface representations for 3d shape inference. arXiv preprint arXiv:1901.06802, 2019. 2 +[35] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 3 +[36] Ryota Natsume, Shunsuke Saito, Zeng Huang, Weikai Chen, Chongyang Ma, Hao Li, and Shigeo Morishima. *Siclope: Silhouette-based clothed people. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pages 4480–4490, 2019. 1 +[37] Sergio Orts-Escalano, Christoph Rhemann, Sean Fanello, Wayne Chang, Adarsh Kowdle, Yury Degtyarev, David Kim, Philip L Davidson, Sameh Khamis, Mingsong Dou, et al. Holoportation: Virtual 3d teleportation in real-time. In Proceedings of the 29th annual symposium on user interface software and technology, pages 741-754, 2016. 1 +[38] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. + +In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019. 2 +[39] Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In European Conference on Computer Vision, pages 523-540. Springer, 2020. 2, 4 +[40] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9054-9063, 2021. 2 +[41] Gilles Rainer, Adrien Bousseau, Tobias Ritschel, and George Drettakis. Neural precomputed radiance transfer. In Computer Graphics Forum, volume 41, pages 365-378. Wiley Online Library, 2022. 3 +[42] Ravi Ramamoorthi and Pat Hanrahan. An efficient representation for irradiance environment maps. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 497-500, 2001. 2 +[43] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2304-2314, 2019. 1, 2, 4, 5, 6, 7, 8 +[44] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo. Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 84-93, 2020. 1, 2, 5, 6, 7 +[45] Soumyadip Sengupta, Angjoo Kanazawa, Carlos D Castillo, and David W Jacobs. Sfsnet: Learning shape, reflectance and illuminance of facesin the wild'. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6296-6305, 2018. 2 +[46] Ruizhi Shao, Hongwen Zhang, He Zhang, Mingjia Chen, Yan-Pei Cao, Tao Yu, and Yebin Liu. Doublefield: Bridging the neural surface and radiance fields for high-fidelity human reconstruction and rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15872-15882, 2022. 6 +[47] Ruizhi Shao, Zerong Zheng, Hongwen Zhang, Jingxiang Sun, and Yebin Liu. Diffustereo: High quality human reconstruction via diffusion-based stereo using sparse cameras. arXiv preprint arXiv:2207.08000, 2022. 1 +[48] Peter-Pike Sloan, Jan Kautz, and John Snyder. Precomputed radiance transfer for real-time rendering in dynamic, low-frequency lighting environments. In Proceedings of the 29th annual conference on Computer graphics and interactive techniques, pages 527-536, 2002. 2 +[49] Leslie N Smith. Cyclical learning rates for training neural networks. In 2017 IEEE winter conference on applications of computer vision (WACV), pages 464-472. IEEE, 2017. 6 +[50] Pratul P Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan T Barron. Nerv: Neural reflectance and visibility fields for relighting + +and view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7495-7504, 2021. 3, 4, 7 +[51] Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning for human pose estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5693-5703, 2019. 4 +[52] Xin Suo, Yuheng Jiang, Pei Lin, Yingliang Zhang, Minye Wu, Kaiwen Guo, and Lan Xu. Neuralhumanfvv: Real-time neural volumetric human performance rendering using rgb cameras. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6226-6237, 2021. 1 +[53] Daichi Tajima, Yoshihiro Kanamori, and Yuki Endo. Relighting humans in the wild: Monocular full-body human relighting with domain adaptation. In Computer Graphics Forum, volume 40, pages 205-216. Wiley Online Library, 2021. 3 +[54] Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox. What do single-view 3d reconstruction networks learn? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3405-3414, 2019. 6 +[55] Yating Tian, Hongwen Zhang, Yebin Liu, and Limin Wang. Recovering 3d human mesh from monocular images: A survey. arXiv preprint arXiv:2203.01923, 2022. 2 +[56] Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T Barron, and Pratul P Srinivasan. Ref-nerf: Structured view-dependent appearance for neural radiance fields. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5481-5490. IEEE, 2022. 2 +[57] Ingo Wald, Sven Woop, Carsten Benthin, Gregory S Johnson, and Manfred Ernst. Embree: a kernel framework for efficient cpu ray tracing. ACM Transactions on Graphics (TOG), 33(4):1-8, 2014. 6 +[58] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004. 6 +[59] Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. Neural fields in visual computing and beyond. In Computer Graphics Forum, volume 41, pages 641-676. Wiley Online Library, 2022. 2 +[60] YBIGTA. Hair-segmentation. https://pytorchhair.gitbook.io/project/.6 +[61] Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, and Yebin Liu. Function4d: Real-time human volumetric capture from very sparse consumer rgbd sensors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5746-5756, 2021. 1, 2, 5, 6, 7, 8 +[62] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 2018. 6 + +[63] Yizhong Zhang, Jiaolong Yang, Zhen Liu, Ruicheng Wang, Guojun Chen, Xin Tong, and Baining Guo. Virtualcube: An immersive 3d video communication system. IEEE Transactions on Visualization and Computer Graphics, 28(5):2146-2156, 2022. 1 +[64] Yang Zheng, Ruizhi Shao, Yuxiang Zhang, Tao Yu, Zerong Zheng, Qionghai Dai, and Yebin Liu. Deepmulticap: Performance capture of multiple characters using sparse multiview cameras. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6239-6249, 2021. 2 +[65] Zerong Zheng, Tao Yu, Yebin Liu, and Qionghai Dai. Pamir: Parametric model-conditioned implicit representation for image-based human reconstruction. IEEE transactions on pattern analysis and machine intelligence, 44(6):3170-3184, 2021. 1, 2 +[66] Zerong Zheng, Tao Yu, Yixuan Wei, Qionghai Dai, and Yebin Liu. Deephuman: 3d human reconstruction from a single image. 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Given sparse-view RGBD input (left), our method reconstructs detailed geometry (middle left), albedo (middle right), and visibility for realistic 3D human relighting under different illuminations (right)." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 143, + 366, + 192, + 379 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 366, + 192, + 379 + ], + "spans": [ + { + "bbox": [ + 143, + 366, + 192, + 379 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 392, + 289, + 620 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 392, + 289, + 620 + ], + "spans": [ + { + "bbox": [ + 46, + 392, + 289, + 620 + ], + "type": "text", + "content": "Detailed 3D reconstruction and photo-realistic relighting of digital humans are essential for various applications. To this end, we propose a novel sparse-view 3d human reconstruction framework that closely incorporates the occupancy field and albedo field with an additional visibility field—it not only resolves occlusion ambiguity in multiview feature aggregation, but can also be used to evaluate light attenuation for self-shadowed relighting. To enhance its training viability and efficiency, we discretize visibility onto a fixed set of sample directions and supply it with coupled geometric 3D depth feature and local 2D image feature. We further propose a novel rendering-inspired loss, namely TransferLoss, to implicitly enforce the alignment between visibility and occupancy field, enabling end-to-end joint training. Results and extensive experiments demonstrate the effectiveness of the proposed method, as it surpasses state-of-the-art in terms of reconstruction accuracy while achieving comparably accurate relighting to ray-traced ground truth." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 645, + 128, + 657 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 645, + 128, + 657 + ], + "spans": [ + { + "bbox": [ + 47, + 645, + 128, + 657 + ], + "type": "text", + "content": "1. 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To enhance capture realism, researchers have extended them with additional synchronous variable illumination systems, which aid photometric stereo for detail reconstruction and material acquisition [12]. However, these systems are often too complex, expensive and difficult to maintain, thus preventing widespread applications." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 483, + 546, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 483, + 546, + 626 + ], + "spans": [ + { + "bbox": [ + 304, + 483, + 546, + 626 + ], + "type": "text", + "content": "By leveraging deep prior and neural representation, sophisticated dense camera setups can be reduced to a single camera, leading to blossoms in learning-based human reconstruction. In particular, encoding human geometry and appearance as continuous fields using Multi-Layer Perceptron (MLP) has emerged as a promising lead. Starting from Siclope [36] and PIFu [43], a series of methods [15] improve the reconstruction performance in speed [11, 25], quality [44], robustness [65, 66] and light decoupling [1]. However, single-view reconstruction quality is restricted by its inherent depth ambiguity, thus limiting its application under view-consistent high-quality requirements." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 630, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 547, + 715 + ], + "type": "text", + "content": "Therefore, as the trade-off between view coverage and system accessibility, sparse-view reconstruction has become a research hotspot. The predominant practice is to project the query point onto each view to interpolate local features, which are then aggregated and fed to MLP for inference [6, 16, 43, 47, 52, 61]. This method suffers from occlusion ambiguity, where some views may well" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "216" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 191 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 191 + ], + "type": "text", + "content": "be occluded, and mixing their features with visible ones causes inefficient feature utilization, thus penalizing the reconstruction quality [43]. A natural solution is to filter features based on view visibility. Human templates such as SMPL [29] can serve as effective guidance [2,40,56,64], but introduce additional template alignment errors and therefore cannot guarantee complete occlusion awareness. Function4D [61] leverages the truncated Projective Signed Distance Function (PSDF) for visibility indication, but its level of details is susceptible to depth noise." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 192, + 289, + 431 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 192, + 289, + 431 + ], + "spans": [ + { + "bbox": [ + 46, + 192, + 289, + 431 + ], + "type": "text", + "content": "To this end, we directly model a continuous visibility field, which can be efficiently learned with our proposed framework and discretization technique using sparse-view RGB-D input. The visibility field enables efficient visibility query, which effectively guides multi-view feature aggregation for more accurate occupancy and albedo inference. Moreover, visibility can also be directly used for light attenuation evaluation—the key ingredient in achieving realistic self-shadowing. When supervising jointly with our novel TransferLoss, the alignment between the visibility field and occupancy field can be implicitly enforced without between-field constraints, such as matching visibility with occupancy ray integral. We train our framework end-to-end and demonstrate its effectiveness in detailed 3D human reconstruction by quantity and quality comparison with the state-of-the-art. We directly relight the reconstructed geometry with inferred visibility using diffuse Bidirectional Reflectance Distribution Function (BRDF) as in Fig. 1, which achieves photo-realistic self-shadowing without any post ray-tracing steps. To conclude, our contributions include:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 440, + 287, + 553 + ], + "type": "list", + "angle": 0, + "index": 5, + "blocks": [ + { + "bbox": [ + 58, + 440, + 287, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 440, + 287, + 475 + ], + "spans": [ + { + "bbox": [ + 58, + 440, + 287, + 475 + ], + "type": "text", + "content": "- An end-to-end framework for sparse-view detailed 3D human reconstruction that also supports direct selfshadowed relighting." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 58, + 484, + 287, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 484, + 287, + 520 + ], + "spans": [ + { + "bbox": [ + 58, + 484, + 287, + 520 + ], + "type": "text", + "content": "- A novel method of visibility field learning, with the specifically designed TransferLoss significantly improves field alignment." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 58, + 529, + 287, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 529, + 287, + 553 + ], + "spans": [ + { + "bbox": [ + 58, + 529, + 287, + 553 + ], + "type": "text", + "content": "- A visibility-guided multi-view feature aggregation strategy that guarantees occlusion awareness." + } + ] + } + ], + "index": 4 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 47, + 573, + 222, + 586 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 573, + 222, + 586 + ], + "spans": [ + { + "bbox": [ + 47, + 573, + 222, + 586 + ], + "type": "text", + "content": "2. Background and Related Work" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 594, + 287, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 287, + 641 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 287, + 641 + ], + "type": "text", + "content": "Neural human synthesis The literature on human reconstruction is vast and rapidly growing. Here, we only review and contrast with closely related work and refer readers to surveys [55, 59] for comprehensive reviews." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 642, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 288, + 714 + ], + "type": "text", + "content": "Neural implicit representation encodes geometry as a function of spatial coordinate using MLP. This representation is appealing as it is naturally differentiable, has exceptional expressiveness yet maintains compact memory footprint. The pioneer work follows an encoder-decoder-like architecture, where the globally encoded feature is applied" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 72, + 545, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 191 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 191 + ], + "type": "text", + "content": "to condition spatial coordinates to infer low-level geometric details [5, 33, 34, 38]. This highly unbalanced information flow limits its capability to represent mere simple shapes [39]. PIFu [43] proposes to replace the global feature with pixel-aligned local features, which captures convolutional inductive bias and achieves highly detailed human reconstruction. It inspires a variety of works, ranging from quality improvement [44], parametric model extension [2, 65], animation support [14, 17], light estimation and relighting [1]." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 192, + 546, + 395 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 192, + 546, + 395 + ], + "spans": [ + { + "bbox": [ + 304, + 192, + 546, + 395 + ], + "type": "text", + "content": "By averaging local features across views, the pixel-aligned framework can be easily extended to multi-view settings [43]. However, simple averaging diminishes high frequency details, yielding overly smoothed geometry. Moreover, it treats all views as equally visible even for partially occluded regions, resulting in inaccurate and erroneous reconstruction. Zhang et al. [64] addresses the occlusion ambiguity by incorporating the attention mechanism, which weights features by their learned cross-view correlations. Despite its promising performance, self-attention introduces substantial memory and computation overhead. Yu et al. [61] additionally leverages global depth information, namely PSDF, to annihilate the ambiguity but is sensitive to depth noise. In contrast, we use robustly learned visibility to weight per-view contribution, which handles occlusion in a physically plausible manner while being more memory efficient to compute." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 396, + 545, + 454 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 396, + 545, + 454 + ], + "spans": [ + { + "bbox": [ + 304, + 396, + 545, + 454 + ], + "type": "text", + "content": "Relighting boils down to substituting the incident environment radiance and re-evaluating the rendering equation [19]. For a scene of reflectors, the outgoing radiance reflected at the surface point " + }, + { + "bbox": [ + 304, + 396, + 545, + 454 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 304, + 396, + 545, + 454 + ], + "type": "text", + "content": " in direction " + }, + { + "bbox": [ + 304, + 396, + 545, + 454 + ], + "type": "inline_equation", + "content": "\\omega_{o}" + }, + { + "bbox": [ + 304, + 396, + 545, + 454 + ], + "type": "text", + "content": " can be described as:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 460, + 545, + 495 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 460, + 545, + 495 + ], + "spans": [ + { + "bbox": [ + 306, + 460, + 545, + 495 + ], + "type": "interline_equation", + "content": "L (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {o}) = \\int_ {\\Omega^ {+}} L (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}) \\rho (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}, \\boldsymbol {\\omega} _ {o}) V (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}) (\\boldsymbol {n} \\cdot \\boldsymbol {\\omega} _ {i}) d \\boldsymbol {\\omega} _ {i} \\tag {1}", + "image_path": "245866f9e6da14a70801573306c5334401a03d6d0b53875eebfb62c3d1d7b31f.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "spans": [ + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "inline_equation", + "content": "\\pmb{n}" + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "content": " is unit surface normal at " + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "inline_equation", + "content": "\\pmb{x}" + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "inline_equation", + "content": "\\Omega^{+}" + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "content": " is hemisphere of possible directions " + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "inline_equation", + "content": "\\omega_{i}" + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "inline_equation", + "content": "L(\\pmb{x},\\omega_{i})" + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "content": " is incident radiance arriving " + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "inline_equation", + "content": "\\pmb{x}" + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "content": " along " + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "inline_equation", + "content": "\\omega_{i}" + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "content": ", directly from environment or indirectly reflected. " + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "content": " is the BRDF that models the surface reflectance. " + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "content": " is the visibility function that describes whether light " + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "inline_equation", + "content": "\\pmb{x}" + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "content": " is attenuated along " + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "inline_equation", + "content": "\\omega_{i}" + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "content": ". As the main contributor to realistic self-shadowing, visibility evaluation requires ray tracing geometry over all sample directions " + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "inline_equation", + "content": "\\omega_{i}" + }, + { + "bbox": [ + 304, + 495, + 547, + 626 + ], + "type": "text", + "content": " per fragment [18], which is expensive and usually omitted [45] or baked offline using Precomputed Radiance Transfer (PRT) [48]. PRT rewrites Eq. (1) with the following transfer function" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 330, + 633, + 545, + 646 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 330, + 633, + 545, + 646 + ], + "spans": [ + { + "bbox": [ + 330, + 633, + 545, + 646 + ], + "type": "interline_equation", + "content": "T (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}, \\boldsymbol {\\omega} _ {o}) = \\rho (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}, \\boldsymbol {\\omega} _ {o}) V (\\boldsymbol {x}, \\boldsymbol {\\omega} _ {i}) (\\boldsymbol {n} \\cdot \\boldsymbol {\\omega} _ {i}) \\tag {2}", + "image_path": "657e345ac2da430b41a636c426460805b32ac44b6eb6921326e361705e7ba0aa.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 653, + 545, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 545, + 689 + ], + "type": "text", + "content": "which is independent of light and can be precomputed and projected on the Spherical Harmonics (SH) basis as coefficients [42] ahead of time to save rendering cost." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "text", + "content": "However, PRT does not ameliorate visibility calculation complexity and requires recompute after deformation. Its" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 748, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 748, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 748, + 312, + 757 + ], + "type": "text", + "content": "217" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 55, + 70, + 547, + 229 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 547, + 229 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 547, + 229 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 547, + 229 + ], + "type": "image", + "image_path": "1efd1488a911874576f86c560028be3e37cd1b20765f1cc89bd667d20b0b0e9c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 236, + 547, + 270 + ], + "lines": [ + { + "bbox": [ + 46, + 236, + 547, + 270 + ], + "spans": [ + { + "bbox": [ + 46, + 236, + 547, + 270 + ], + "type": "text", + "content": "Figure 2. Method overview. Given sparse RGB-D frames as input, our framework first infer visibility, which is then applied to guide multiview 2D feature aggregation for joint occupancy and albedo inference. Our framework is end-to-end trainable and produces high-fidelity human reconstruction that supports direct self-shadowed relighting without using any post ray-tracing steps." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 290, + 288, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 290, + 288, + 361 + ], + "spans": [ + { + "bbox": [ + 46, + 290, + 288, + 361 + ], + "type": "text", + "content": "limitation has been addressed by several learning-based approaches [20, 23, 26, 41, 53], which predict transfer coefficients using deep neural networks. Instead of its SH parameterization, we predict raw visibility, as it (1) can be used for feature aggregation, and (2) is also well defined in free space that can be learned without surface evaluation." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 371, + 287, + 563 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 371, + 287, + 563 + ], + "spans": [ + { + "bbox": [ + 46, + 371, + 287, + 563 + ], + "type": "text", + "content": "NeRF [35] models the surface density and employs volume rendering for high fidelity novel view synthesis. It has been extended with reflectance [3] and transfer function [31] to support relighting. Visibility is naturally defined in the density field, but its evaluation requires the integration of multiple density queries along the ray, which is equally inefficient and needs to be accelerated, such as using occlusion map [4] or MLP [50]. Although sharing similar ends, we achieve this with different means: (1) We emphasize accuracy by supervising with ground truth and use the TransferLoss to regularize the field alignment, rather than attempting to directly align the fields by matching inferred visibility with accumulated transmittance. (2) We extensively train our model on human scan dataset to ensure fast inference, rather than fitting it per-scene in order to render arbitrary within-scene objects." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 600, + 102, + 613 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 600, + 102, + 613 + ], + "spans": [ + { + "bbox": [ + 47, + 600, + 102, + 613 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 629, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 629, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 629, + 287, + 715 + ], + "type": "text", + "content": "To introduce our method, we first define the visibility field and its discretization process, which prioritizes efficiency without compromising performance. We then outline our framework and visibility learning procedure. Finally, we showcase its application in sparse-view 3D human reconstruction, where it guides feature aggregation and enables direct self-shadowed relighting." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 289, + 398, + 303 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 289, + 398, + 303 + ], + "spans": [ + { + "bbox": [ + 305, + 289, + 398, + 303 + ], + "type": "text", + "content": "3.1. Visibility Field" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 308, + 545, + 344 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 308, + 545, + 344 + ], + "spans": [ + { + "bbox": [ + 305, + 308, + 545, + 344 + ], + "type": "text", + "content": "For any point " + }, + { + "bbox": [ + 305, + 308, + 545, + 344 + ], + "type": "inline_equation", + "content": "X\\in \\mathbb{R}^3" + }, + { + "bbox": [ + 305, + 308, + 545, + 344 + ], + "type": "text", + "content": " , whether it is visible " + }, + { + "bbox": [ + 305, + 308, + 545, + 344 + ], + "type": "inline_equation", + "content": "V\\in \\{0,1\\}" + }, + { + "bbox": [ + 305, + 308, + 545, + 344 + ], + "type": "text", + "content": " along any view direction " + }, + { + "bbox": [ + 305, + 308, + 545, + 344 + ], + "type": "inline_equation", + "content": "\\omega \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 305, + 308, + 545, + 344 + ], + "type": "text", + "content": " of the unit sphere " + }, + { + "bbox": [ + 305, + 308, + 545, + 344 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 305, + 308, + 545, + 344 + ], + "type": "text", + "content": " forms a continuous field and can be parameterized using MLP:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 345, + 353, + 545, + 366 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 353, + 545, + 366 + ], + "spans": [ + { + "bbox": [ + 345, + 353, + 545, + 366 + ], + "type": "interline_equation", + "content": "\\operatorname {M L P} _ {\\phi}: (\\boldsymbol {X}, \\omega) \\rightarrow V _ {\\phi} \\in [ 0, 1 ], \\omega \\in S \\tag {3}", + "image_path": "8183c7ec20ba74d9d8363bd1f1684becb3e2f62376980c08477f6f8f5c44634c.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 373, + 545, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 373, + 545, + 445 + ], + "spans": [ + { + "bbox": [ + 304, + 373, + 545, + 445 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 373, + 545, + 445 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 304, + 373, + 545, + 445 + ], + "type": "text", + "content": " is the MLP weights. Although Eq. (3) helps mitigate the cost of the ray integral, querying visibility over " + }, + { + "bbox": [ + 304, + 373, + 545, + 445 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 373, + 545, + 445 + ], + "type": "text", + "content": " directions still requires " + }, + { + "bbox": [ + 304, + 373, + 545, + 445 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 373, + 545, + 445 + ], + "type": "text", + "content": " calls per point of interest. This cost is exacerbated in pixel-aligned settings due to excessive point-wise feature " + }, + { + "bbox": [ + 304, + 373, + 545, + 445 + ], + "type": "inline_equation", + "content": "F" + }, + { + "bbox": [ + 304, + 373, + 545, + 445 + ], + "type": "text", + "content": " duplication as illustrated in Fig. 3a (top), leading to substantially high memory overhead." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 445, + 546, + 542 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 445, + 546, + 542 + ], + "spans": [ + { + "bbox": [ + 304, + 445, + 546, + 542 + ], + "type": "text", + "content": "We observe that, by uniformly sampling a discrete set of directions, visibility along any direction can be interpolated by Eq. (5) (Fig. 3c), with accuracy capped by the sample size. Thus, the " + }, + { + "bbox": [ + 304, + 445, + 546, + 542 + ], + "type": "inline_equation", + "content": "O(s)" + }, + { + "bbox": [ + 304, + 445, + 546, + 542 + ], + "type": "text", + "content": " complexity can be reduced to a single MLP call plus an additional interpolation cost. To this end, we propose an alternative definition, by treating visibility as a function of " + }, + { + "bbox": [ + 304, + 445, + 546, + 542 + ], + "type": "inline_equation", + "content": "\\mathbf{X}" + }, + { + "bbox": [ + 304, + 445, + 546, + 542 + ], + "type": "text", + "content": " conditioned on a fixed set of " + }, + { + "bbox": [ + 304, + 445, + 546, + 542 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 304, + 445, + 546, + 542 + ], + "type": "text", + "content": " sample directions " + }, + { + "bbox": [ + 304, + 445, + 546, + 542 + ], + "type": "inline_equation", + "content": "\\omega^{(n)}" + }, + { + "bbox": [ + 304, + 445, + 546, + 542 + ], + "type": "text", + "content": " as in Fig. 3a (bottom)." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 313, + 548, + 545, + 564 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 548, + 545, + 564 + ], + "spans": [ + { + "bbox": [ + 313, + 548, + 545, + 564 + ], + "type": "interline_equation", + "content": "\\operatorname {M L P} _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}: (\\boldsymbol {X}, \\boldsymbol {F}) \\rightarrow V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}} \\in [ 0, 1 ] ^ {n}, \\boldsymbol {\\omega} ^ {(n)} \\in S \\tag {4}", + "image_path": "f1fdd3445aecebde46096bd05f76aeb13cbad19fd9e082d3736a0ac36c0ae101.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 571, + 545, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 571, + 545, + 594 + ], + "spans": [ + { + "bbox": [ + 305, + 571, + 545, + 594 + ], + "type": "text", + "content": "In practice, we interpolate by top " + }, + { + "bbox": [ + 305, + 571, + 545, + 594 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 305, + 571, + 545, + 594 + ], + "type": "text", + "content": " closest cosine distance with respect to " + }, + { + "bbox": [ + 305, + 571, + 545, + 594 + ], + "type": "inline_equation", + "content": "\\omega^{(n)}" + }, + { + "bbox": [ + 305, + 571, + 545, + 594 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 350, + 602, + 545, + 654 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 350, + 602, + 545, + 654 + ], + "spans": [ + { + "bbox": [ + 350, + 602, + 545, + 654 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\left\\{\\boldsymbol {\\omega} ^ {(i)} \\right\\} _ {i = 1 \\dots k} = \\operatorname {t o p k} \\left(\\boldsymbol {\\omega} ^ {(n)} \\cdot \\boldsymbol {\\omega}\\right) \\\\ V (\\boldsymbol {X}, \\omega) = \\sum_ {i = 1} ^ {k} \\frac {V _ {\\omega^ {(i)}} (\\boldsymbol {X}) \\left(\\boldsymbol {\\omega} ^ {(i)} \\cdot \\boldsymbol {\\omega}\\right)}{\\sum_ {j = 1} ^ {k} \\left(\\boldsymbol {\\omega} ^ {(j)} \\cdot \\boldsymbol {\\omega}\\right)} \\tag {5} \\\\ \\end{array}", + "image_path": "0e0ccb49efc5b3df85eb6446bd4dc676071d54785d525d56ac8561a01478afab.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 657, + 545, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 657, + 545, + 693 + ], + "spans": [ + { + "bbox": [ + 305, + 657, + 545, + 693 + ], + "type": "text", + "content": "The simplification in Eq. (4) leads to a surprisingly simple implementation-visibility prediction can be treated as " + }, + { + "bbox": [ + 305, + 657, + 545, + 693 + ], + "type": "inline_equation", + "content": "n - D" + }, + { + "bbox": [ + 305, + 657, + 545, + 693 + ], + "type": "text", + "content": " binary classification supervised by BCE loss:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 310, + 700, + 545, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 310, + 700, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 310, + 700, + 545, + 715 + ], + "type": "interline_equation", + "content": "L _ {\\mathrm {V i s}} = V _ {\\mathrm {G T}} \\cdot \\log V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}} + (1 - V _ {\\mathrm {G T}}) \\cdot \\log \\left(1 - V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}\\right) \\tag {6}", + "image_path": "96d1d628bf11b1e2c1bf241c68556abc25a1f73950fd159988d34c3dc0777d8f.jpg" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + 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(a) Visibility as a continuous function of both point position " + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "text", + "content": " and sample direction " + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "inline_equation", + "content": "\\omega" + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "text", + "content": " (top) leads to multiple queries and pointwise feature " + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "inline_equation", + "content": "F" + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "text", + "content": " duplication. In contrast, by fixing the sampling directions " + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "inline_equation", + "content": "\\omega^{(n)}" + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "text", + "content": " (bottom), it only requires a single query. We extensively analyze our visibility field simplification strategy over " + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "text", + "content": " of training data and report average accuracy over the sample size " + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "text", + "content": " in (b) with the following procedure: For each sample size decision, we sample 10k query points per model (same sampling distribution as training data) with visibility evaluated over " + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "inline_equation", + "content": "\\omega^{(n)}" + }, + { + "bbox": [ + 46, + 177, + 547, + 254 + ], + "type": "text", + "content": ", that we apply cosine distance interpolation (c) to infer 10k randomly sampled validation directions per point. For aggregating multi-view features (d), we apply the same interpolation method as in (c) for each camera view direction to evaluate visibility along the back projection ray. (e) Directional visibility naturally constraints on the presence of surface." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 62, + 277, + 168, + 374 + ], + "blocks": [ + { + "bbox": [ + 62, + 277, + 168, + 374 + ], + "lines": [ + { + "bbox": [ + 62, + 277, + 168, + 374 + ], + "spans": [ + { + "bbox": [ + 62, + 277, + 168, + 374 + ], + "type": "image", + "image_path": "41ac1dddfa6b4e299b9ce1e03aa8b03a914938ede647c87e8c90fc0c3fe4ddea.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 380, + 287, + 435 + ], + "lines": [ + { + "bbox": [ + 46, + 380, + 287, + 435 + ], + "spans": [ + { + "bbox": [ + 46, + 380, + 287, + 435 + ], + "type": "text", + "content": "Figure 4. TransferLoss ablation: From left to right: w/o, w TransferLoss, ground truth. TransferLoss significantly mitigates rendering defects and improves near-surface visibility prediction accuracy (27.5% → 88.4% for synthesized testset, 20.8% → 84.7% for real-captured data)." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 179, + 277, + 269, + 374 + ], + "blocks": [ + { + "bbox": [ + 179, + 277, + 269, + 374 + ], + "lines": [ + { + "bbox": [ + 179, + 277, + 269, + 374 + ], + "spans": [ + { + "bbox": [ + 179, + 277, + 269, + 374 + ], + "type": "image", + "image_path": "cfa3a32372e2b983bc13719a2738b67709c1ddbdd17c8f69b6f566472b48cf53.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 47, + 441, + 173, + 453 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 441, + 173, + 453 + ], + "spans": [ + { + "bbox": [ + 47, + 441, + 173, + 453 + ], + "type": "text", + "content": "3.2. Framework Overview" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "spans": [ + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "content": "As shown in Fig. 2, our framework represents all fields using MLPs. Given sparse multi-view RGB-D frames " + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "inline_equation", + "content": "\\{(\\mathcal{I}_i,\\mathcal{D}_i),i = 1\\dots m\\}" + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "content": " with known camera projection matrices " + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "inline_equation", + "content": "\\pi_{i}" + }, + { + "bbox": [ + 46, + 460, + 287, + 556 + ], + "type": "text", + "content": ", we first extract depth point cloud voxelized 3D feature and pixel-aligned RGB 2D feature to directly predict visibility. Then we utilize inferred visibility to guide the aggregation of multi-view 2D feature, which are paired with 3D feature for joint geometry and albedo inference." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 47, + 563, + 195, + 575 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 563, + 195, + 575 + ], + "spans": [ + { + "bbox": [ + 47, + 563, + 195, + 575 + ], + "type": "text", + "content": "3.3. Hybrid Feature Extraction" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 46, + 582, + 287, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 287, + 653 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 287, + 653 + ], + "type": "text", + "content": "Conventionally, evaluating visibility requires tracing ray along the direction of interest and checking surface hits. In other words, it requires reasoning about the geometric feature of surfaces near that direction. Since we model both geometry (occupancy) and visibility, it is crucial to also bridge the two fields to incorporate their interconnection." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 46, + 654, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 287, + 714 + ], + "type": "text", + "content": "To this end, we adopt a hybrid feature extraction procedure, by separating depth from RGB and encoding it as 3D feature. Specifically, we follow [39] to unproject depth image into pointcloud, voxelize and filter using 3D convolutional as coarse feature volume. For a query point " + }, + { + "bbox": [ + 46, + 654, + 287, + 714 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 46, + 654, + 287, + 714 + ], + "type": "text", + "content": "," + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 275, + 545, + 394 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 275, + 545, + 394 + ], + "spans": [ + { + "bbox": [ + 304, + 275, + 545, + 394 + ], + "type": "text", + "content": "we acquire its local 3D feature " + }, + { + "bbox": [ + 304, + 275, + 545, + 394 + ], + "type": "inline_equation", + "content": "F_{\\mathcal{D}}" + }, + { + "bbox": [ + 304, + 275, + 545, + 394 + ], + "type": "text", + "content": " by trilinear interpolation based on point coordinates and then share it with both visibility and occupancy MLPs. Intuitively, the 3D feature originates from voxelized point cloud, which can be viewed as noisy samples of the underlying surface (decision boundary of the occupancy field). By applying 3D convolution, the network reasons the coarse geometric surface feature over a sufficiently large receptive field, consequently aiding visibility inference. 3D feature is necessary for accurate visibility prediction as ablated in Fig. 8." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 396, + 545, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 396, + 545, + 491 + ], + "spans": [ + { + "bbox": [ + 304, + 396, + 545, + 491 + ], + "type": "text", + "content": "For multi-view RGB frames, we directly filter them using HRNet [51] to acquire 2D feature maps " + }, + { + "bbox": [ + 304, + 396, + 545, + 491 + ], + "type": "inline_equation", + "content": "F_{i}" + }, + { + "bbox": [ + 304, + 396, + 545, + 491 + ], + "type": "text", + "content": ". The local 2D features are then extracted in the pixel-aligned fashion as in [43], by projecting the point coordinate " + }, + { + "bbox": [ + 304, + 396, + 545, + 491 + ], + "type": "inline_equation", + "content": "X" + }, + { + "bbox": [ + 304, + 396, + 545, + 491 + ], + "type": "text", + "content": " onto each view as the image coordinate " + }, + { + "bbox": [ + 304, + 396, + 545, + 491 + ], + "type": "inline_equation", + "content": "\\pi (X)" + }, + { + "bbox": [ + 304, + 396, + 545, + 491 + ], + "type": "text", + "content": ", then bilinearly interpolating the corresponding feature maps " + }, + { + "bbox": [ + 304, + 396, + 545, + 491 + ], + "type": "inline_equation", + "content": "F_{i}(\\pi (X))" + }, + { + "bbox": [ + 304, + 396, + 545, + 491 + ], + "type": "text", + "content": ". Compared to feature volume, 2D feature maps have much higher resolution and thus grant better details." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 306, + 492, + 545, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 492, + 545, + 517 + ], + "spans": [ + { + "bbox": [ + 306, + 492, + 545, + 517 + ], + "type": "text", + "content": "Follow Eq. (4), we infer " + }, + { + "bbox": [ + 306, + 492, + 545, + 517 + ], + "type": "inline_equation", + "content": "V_{\\phi|\\omega^{(n)}}" + }, + { + "bbox": [ + 306, + 492, + 545, + 517 + ], + "type": "text", + "content": " by providing 3D feature " + }, + { + "bbox": [ + 306, + 492, + 545, + 517 + ], + "type": "inline_equation", + "content": "F_{\\mathcal{D}}" + }, + { + "bbox": [ + 306, + 492, + 545, + 517 + ], + "type": "text", + "content": " and averaged 2D feature " + }, + { + "bbox": [ + 306, + 492, + 545, + 517 + ], + "type": "inline_equation", + "content": "F_{avg}" + }, + { + "bbox": [ + 306, + 492, + 545, + 517 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 348, + 528, + 545, + 543 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 348, + 528, + 545, + 543 + ], + "spans": [ + { + "bbox": [ + 348, + 528, + 545, + 543 + ], + "type": "interline_equation", + "content": "\\operatorname {M L P} _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}: \\left(\\boldsymbol {X}, F _ {\\mathcal {D}}, F _ {a v g}\\right) \\in [ 0, 1 ] ^ {n} \\tag {7}", + "image_path": "0b71fedf92fae92e4d5c521daf0f05cc7401496bf1567c9bb7e016c36b095dc7.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 306, + 562, + 476, + 575 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 562, + 476, + 575 + ], + "spans": [ + { + "bbox": [ + 306, + 562, + 476, + 575 + ], + "type": "text", + "content": "3.4. Field Alignment Regularization" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 545, + 713 + ], + "type": "text", + "content": "Given similar formulations, it is natural to train the visibility field and occupancy field together. However, as the point moves across the surface (occupancy classification boundary), its visibility changes drastically, ranging from fully occluded to partially visible. A slight misalignment between the two fields could cause inner visibility leakage and introduce conspicuous rendering defects as in Fig. 4. To enforce their alignment, a common practice is to explicitly constrain their correspondence, by matching visibility with surface queried along the ray [50], but at the expense of substantially large training overhead." + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "219" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 68, + 545, + 432 + ], + "blocks": [ + { + "bbox": [ + 50, + 68, + 545, + 432 + ], + "lines": [ + { + "bbox": [ + 50, + 68, + 545, + 432 + ], + "spans": [ + { + "bbox": [ + 50, + 68, + 545, + 432 + ], + "type": "image", + "image_path": "86c7bf2641ae982a58cdcf4509551ee358ee71fcc259d017859b38cefc422314.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 440, + 546, + 463 + ], + "lines": [ + { + "bbox": [ + 46, + 440, + 546, + 463 + ], + "spans": [ + { + "bbox": [ + 46, + 440, + 546, + 463 + ], + "type": "text", + "content": "Figure 5. Qualitative comparisons on THUman2.0 [61]. We show (a) shaded color reference, geometry of (b) MV-PIFu [43], (c) Function4D [61], (d) MV-PIFuHD [44], (e) ours, (f) our albedo, (g-i) from left to right: our irradiance, our relighting and ground truth." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 483, + 288, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 483, + 288, + 567 + ], + "spans": [ + { + "bbox": [ + 46, + 483, + 288, + 567 + ], + "type": "text", + "content": "Since we bridge the two fields with the 3D feature reasoning about their correlation, we exploit the constraint of visibility over occupancy (Fig. 3e) by emphasizing the accuracy of near-surface visibility prediction, so that their alignment can be implicitly enforced. To this end, we propose a novel TransferLoss inspired by radiance transfer in Eq. (2):" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 75, + 574, + 287, + 599 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 75, + 574, + 287, + 599 + ], + "spans": [ + { + "bbox": [ + 75, + 574, + 287, + 599 + ], + "type": "interline_equation", + "content": "L _ {\\text {T r a n s f e r}} = \\sum_ {i} | \\hat {T} (\\boldsymbol {X}, V _ {\\phi | \\boldsymbol {\\omega} ^ {(i)}}) - T (\\boldsymbol {X}, V _ {\\mathrm {G T}}) | \\tag {8}", + "image_path": "ce99fbe7c92c6db06e6f66e1063fa52a6171cc367d6eb9af2c587f81f9111a22.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 605, + 287, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 287, + 676 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 287, + 676 + ], + "type": "text", + "content": "It supervises visibility, but (1) prioritizes the normal-facing directions in contrast to equal weighting in BCE (Eq. (6)) loss, and (2) follows the same parameterization for diffuse BRDF evaluation, which intuitively renders the light-independent part of the scene and perceptually penalizes the difference." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "content": "Since occupancy and visibility are jointly supervised, we directly sample query points on the ground truth mesh surface and use their normals to assist Eq. (8) evaluation for" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 483, + 546, + 531 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 483, + 546, + 531 + ], + "spans": [ + { + "bbox": [ + 304, + 483, + 546, + 531 + ], + "type": "text", + "content": "inferred visibility. TransferLoss effectively enforces fields alignment, resulting in significantly less rendering defects and higher relighting fidelity as ablated in Sec. 4.3 and Fig. 4." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 538, + 509, + 551 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 538, + 509, + 551 + ], + "spans": [ + { + "bbox": [ + 305, + 538, + 509, + 551 + ], + "type": "text", + "content": "3.5. Visibility-Guided Feature Aggregation" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 556, + 546, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 556, + 546, + 604 + ], + "spans": [ + { + "bbox": [ + 304, + 556, + 546, + 604 + ], + "type": "text", + "content": "For each view, we trace back to its camera center and evaluate the directional visibility as described in Eq. (5). We then prioritize visible features over occluded ones using clamped negative log weighted average:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 317, + 611, + 545, + 661 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 611, + 545, + 661 + ], + "spans": [ + { + "bbox": [ + 317, + 611, + 545, + 661 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} F _ {a g g} = \\sum_ {i = 1} ^ {m} W _ {V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}}) F _ {i} (\\pi_ {i} (\\boldsymbol {X})) \\tag {9} \\\\ W _ {V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)}}} (\\boldsymbol {X}, \\boldsymbol {\\omega}) = \\max (- \\log (1 - V _ {\\phi | \\boldsymbol {\\omega} ^ {(n)})}, 1 0 0) \\\\ \\end{array}", + "image_path": "b03a28ff2c6a5a3acf2481065f3d7a2fc635fd990271d78d89c41695ca7e9253.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 317, + 667, + 511, + 679 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 667, + 511, + 679 + ], + "spans": [ + { + "bbox": [ + 317, + 667, + 511, + 679 + ], + "type": "text", + "content": "Thus, occupancy and albedo are represented as:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 356, + 685, + 545, + 705 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 356, + 685, + 545, + 705 + ], + "spans": [ + { + "bbox": [ + 356, + 685, + 545, + 705 + ], + "type": "interline_equation", + "content": "\\mathrm {M L P} _ {\\text {o c c}}: \\left(\\boldsymbol {X}, F _ {\\mathcal {D}}, F _ {\\text {a g g}}\\right)\\rightarrow [ 0, 1 ] \\tag {10}", + "image_path": "67ee6cf3bbfa6cdc54d99765fd1350295e595bf7f91eb7c5f032316a7e535829.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 356, + 701, + 545, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 356, + 701, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 356, + 701, + 545, + 715 + ], + "type": "interline_equation", + "content": "\\mathbf {M L P} _ {a l b e d o}: \\left(\\boldsymbol {X}, F _ {\\mathcal {D}}, F _ {a g g}\\right)\\rightarrow \\mathbb {R} ^ {3} \\tag {16}", + "image_path": "e3094587217b36110dc6bfef2f189d077e3f3e7574afcc11288d8cc52c9f718b.jpg" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "220" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 214, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 214, + 85 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 214, + 85 + ], + "type": "text", + "content": "3.6. Reconstruction and Relighting" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 90, + 287, + 163 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 90, + 287, + 163 + ], + "spans": [ + { + "bbox": [ + 46, + 90, + 287, + 163 + ], + "type": "text", + "content": "During inference, we first apply visibility-guided aggregation to sample a grid of occupancy for surface mesh extraction [30]. We then pass the mesh vertices to obtain albedo and visibility using the same technique. Following Eq. (2), we compute per-vertex transfer coefficients and directly apply them for rasterized self-shadowed relighting." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 173, + 128, + 186 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 173, + 128, + 186 + ], + "spans": [ + { + "bbox": [ + 47, + 173, + 128, + 186 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 192, + 174, + 205 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 192, + 174, + 205 + ], + "spans": [ + { + "bbox": [ + 47, + 192, + 174, + 205 + ], + "type": "text", + "content": "4.1. Experimental Settings" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 211, + 287, + 305 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 211, + 287, + 305 + ], + "spans": [ + { + "bbox": [ + 46, + 211, + 287, + 305 + ], + "type": "text", + "content": "Training details. For training, we collect 400 high-quality clothed human scans from THUman2.0 [61], rotate each one around the yaw axis, and apply random shifts to obtain 60 views. For each view, we render " + }, + { + "bbox": [ + 46, + 211, + 287, + 305 + ], + "type": "inline_equation", + "content": "512 \\times 512" + }, + { + "bbox": [ + 46, + 211, + 287, + 305 + ], + "type": "text", + "content": " images of albedo, color using diffuse BRDF and depth fused with TOF depth sensors noise [10]. To simulate the incomplete depth caused by capture insensitivity for materials such as hair, we use [60] to mask hair out." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 307, + 287, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 307, + 287, + 426 + ], + "spans": [ + { + "bbox": [ + 46, + 307, + 287, + 426 + ], + "type": "text", + "content": "For each scanned mesh, we sample total 5k points for occupancy and visibility, with 4k near surface and 1k uniformly within the bounding volume. Near-surface points are sampled using normal distribution with standard deviation of 0.05, and we ensure that half of them with distances less than the standard deviation are used for albedo training. As described in Sec. 3.1 and Fig. 3b, we uniformly sample 64 fixed directions using the Fibonacci lattice and keep it consistent throughout the experiment. We use Embree [57] for ground truth visibility evaluation." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 426, + 287, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 426, + 287, + 475 + ], + "spans": [ + { + "bbox": [ + 46, + 426, + 287, + 475 + ], + "type": "text", + "content": "In addition to visibility, we supervise per-sample occupancy with BCE loss and albedo with " + }, + { + "bbox": [ + 46, + 426, + 287, + 475 + ], + "type": "inline_equation", + "content": "L_{1}" + }, + { + "bbox": [ + 46, + 426, + 287, + 475 + ], + "type": "text", + "content": " loss. We also extract patches using depth-guided raymarching [27] and supervise its albedo using VGG perceptual loss." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 475, + 287, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 475, + 287, + 521 + ], + "spans": [ + { + "bbox": [ + 46, + 475, + 287, + 521 + ], + "type": "text", + "content": "We set the view number to 4 and train our framework using Adam [21] optimizer and Cyclic learning rate (lr) scheduler [49] over 600 epochs. The lr ranges from " + }, + { + "bbox": [ + 46, + 475, + 287, + 521 + ], + "type": "inline_equation", + "content": "5e - 5" + }, + { + "bbox": [ + 46, + 475, + 287, + 521 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 46, + 475, + 287, + 521 + ], + "type": "inline_equation", + "content": "5e - 4" + }, + { + "bbox": [ + 46, + 475, + 287, + 521 + ], + "type": "text", + "content": " every 5 epochs, with the max lr halved every 100 epochs." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "spans": [ + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "type": "text", + "content": "Evaluation details. For evaluation, we prepare 100 training-excluded scans from THUman2.0, and an additional 100 scans from Twindom [46]. We follow the same rendering procedure as for training and report metrics averaged over the two sets. We further prepare real-captured RGB-D video sequences from a synchronized multi-Kinect capturing system that we leverage RVM [28] for foreground mask segmentation. For all experiments, we keep the number of views to 4 except for the view-number ablation. All meshes are extracted from " + }, + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "type": "inline_equation", + "content": "512^{3}" + }, + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "type": "text", + "content": " voxel using Marching Cube [30]. The relighting results are rendered using diffuse BRDF with inferred visibility and SH order of 2." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": "All experiments run on a PC with an Nvidia GeForce RTX3090 GPU and an Intel i7-8700k CPU. Our framework requires 50 ms for 3D and 100 ms for 2D feature extraction, and 200 ms per 2 million point queries for each of" + } + ] + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 309, + 71, + 547, + 149 + ], + "blocks": [ + { + "bbox": [ + 309, + 71, + 547, + 149 + ], + "lines": [ + { + "bbox": [ + 309, + 71, + 547, + 149 + ], + "spans": [ + { + "bbox": [ + 309, + 71, + 547, + 149 + ], + "type": "table", + "html": "
MethodsMetrics
NC ↑CD (L1) ↓F-score (0.5%)↑
MV-PIFu [43]0.9120.1450.624
MV-PIFuHD [44]0.9060.1350.690
Function4D [61]0.8930.1290.704
Ours0.9170.1220.736
", + "image_path": "2e96b1ed34828383acf01b1a781513db3bd8086d1fca804bf7090d5054ed40cc.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "bbox": [ + 315, + 157, + 536, + 169 + ], + "lines": [ + { + "bbox": [ + 315, + 157, + 536, + 169 + ], + "spans": [ + { + "bbox": [ + 315, + 157, + 536, + 169 + ], + "type": "text", + "content": "Table 1. Quantitative comparisons on reconstruction quality." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 191, + 545, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 191, + 545, + 274 + ], + "spans": [ + { + "bbox": [ + 304, + 191, + 545, + 274 + ], + "type": "text", + "content": "the 4-layer ResNet MLP decoders. The total reconstruction process takes approximately 3 seconds. After experimenting with TensorRT conversion, 3D and 2D feature extraction can be reduced to 10 and 7 ms, respectively. Since they can be performed in parallel, our framework has the potential to achieve real-time performance under heavy decoder optimization." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 275, + 545, + 372 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 275, + 545, + 372 + ], + "spans": [ + { + "bbox": [ + 304, + 275, + 545, + 372 + ], + "type": "text", + "content": "Metrics. We report normal consistency (NC) [33], " + }, + { + "bbox": [ + 304, + 275, + 545, + 372 + ], + "type": "inline_equation", + "content": "L_{1}" + }, + { + "bbox": [ + 304, + 275, + 545, + 372 + ], + "type": "text", + "content": " Chamfer Distance " + }, + { + "bbox": [ + 304, + 275, + 545, + 372 + ], + "type": "inline_equation", + "content": "(\\mathrm{CD} L_{1})" + }, + { + "bbox": [ + 304, + 275, + 545, + 372 + ], + "type": "text", + "content": " [8] and F-score [22] for geometric quality evaluation. Specifically, NC is calculated as the mean absolute dot product of normals of points sampled from reconstructed mesh and their closest neighbours' ones on ground truth mesh. We follow [33] to use 1/10 of the maximum bounding box edge as unit 1 for CD " + }, + { + "bbox": [ + 304, + 275, + 545, + 372 + ], + "type": "inline_equation", + "content": "L_{1}" + }, + { + "bbox": [ + 304, + 275, + 545, + 372 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 275, + 545, + 372 + ], + "type": "inline_equation", + "content": "0.5\\%" + }, + { + "bbox": [ + 304, + 275, + 545, + 372 + ], + "type": "text", + "content": " as the F-score distance threshold as suggested by [54]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 372, + 545, + 444 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 372, + 545, + 444 + ], + "spans": [ + { + "bbox": [ + 304, + 372, + 545, + 444 + ], + "type": "text", + "content": "To evaluate the quality of relighting, we adopt the peak signal-to-noise ratio (PSNR), the structural similarity index measure (SSIM) [58] and the learned perceptual image patch similarity (LPIPS) [62]. We mask out renderings with ground truth alpha channel and only report the average contributions of valid pixels." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 454, + 391, + 467 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 454, + 391, + 467 + ], + "spans": [ + { + "bbox": [ + 306, + 454, + 391, + 467 + ], + "type": "text", + "content": "4.2. Comparisons" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 473, + 545, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 473, + 545, + 700 + ], + "spans": [ + { + "bbox": [ + 304, + 473, + 545, + 700 + ], + "type": "text", + "content": "Reconstruction. We compare our framework with the state-of-the-art prior-free sparse-view reconstruction approaches, namely multi-view PIFu (MV-PIFu) [43], multiview PIFuHD (MV-PIFuHD) [44] and Function4D [61]. MV-PIFu takes RGB inputs and aggregates using averaging, which we adapt with additional depth input to ensure comparison consistency. PIFuHD integrates normal information, coarse-to-fine two-stage inference, and higher " + }, + { + "bbox": [ + 304, + 473, + 545, + 700 + ], + "type": "inline_equation", + "content": "1024 \\times 1024" + }, + { + "bbox": [ + 304, + 473, + 545, + 700 + ], + "type": "text", + "content": " resolution input. We self-implement multiview RGB-D variants (MV-PIFuHD) with the same averaging aggregation as MV-PIFu. Normal maps are still inferred rather than being computed from depth maps due to noise concern. Function4D uses averaging in geometry inference as well, but its integration of the truncated PSDF serves as a strong signal to identify visible features and has been shown to generalize well on real captured data. For a fair comparison, we re-render and train all three approaches on our dataset until converge. Regrettably, we could only compare the geometry, since none of them estimates surface albedo." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 701, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 701, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 701, + 545, + 713 + ], + "type": "text", + "content": "Relighting. 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Visualization comparison between Function4D [61] (green) and our method (red) on data captured by Kinect." + } + ] + } + ], + "index": 37, + "angle": 0, + "type": "image_caption" + } + ], + "index": 36 + }, + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "type": "text", + "content": "single albedo texture without differentiating specular components and coefficients, we were only able to render using diffuse BRDF. This also limits our relighting comparison with NeRF-like method [3,4,50], where the view-dependent specular term is explicitly modeled and is crucial for rendering fidelity. Therefore, we directly compare our results to ray-traced ground truth to demonstrate our relighting per" + } + ] + } + ], + "index": 38 + }, + { + "type": "image", + "bbox": [ + 319, + 515, + 359, + 582 + ], + "blocks": [ + { + "bbox": [ + 319, + 515, + 359, + 582 + ], + "lines": [ + { + "bbox": [ + 319, + 515, + 359, + 582 + ], + "spans": [ + { + "bbox": [ + 319, + 515, + 359, + 582 + ], + "type": "image", + "image_path": "5fee874a2fd77b88b8383408e38d48e1d91c3fd0aecc1aa107cd35544116d991.jpg" + } + ] + } + ], + "index": 39, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 604, + 547, + 660 + ], + "lines": [ + { + "bbox": [ + 305, + 604, + 547, + 660 + ], + "spans": [ + { + "bbox": [ + 305, + 604, + 547, + 660 + ], + "type": "text", + "content": "Figure 8. 3D Feature ablation. (a) We compare the visibility error maps using 2D RGB input (left half of first two), 2D RGB-D input (left half of last two) and ours (right half of all four). (b) We count per-vertex number of wrong directional visibility predictions (out of 64) and plot the normalized histogram." + } + ] + } + ], + "index": 45, + "angle": 0, + "type": "image_caption" + } + ], + "index": 39 + }, + { + "type": "image", + "bbox": [ + 366, + 516, + 406, + 582 + ], + "blocks": [ + { + "bbox": [ + 366, + 516, + 406, + 582 + ], + "lines": [ + { + "bbox": [ + 366, + 516, + 406, + 582 + ], + "spans": [ + { + "bbox": [ + 366, + 516, + 406, + 582 + ], + "type": "image", + "image_path": "e046787c00c98442cd8e628337a18c03537f25aa8350cc1442d5c692166f9d35.jpg" + } + ] + } + ], + "index": 40, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 367, + 586, + 377, + 594 + ], + "lines": [ + { + "bbox": [ + 367, + 586, + 377, + 594 + ], + "spans": [ + { + "bbox": [ + 367, + 586, + 377, + 594 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 42, + "angle": 0, + "type": "image_caption" + } + ], + "index": 40 + }, + { + "type": "image", + "bbox": [ + 411, + 517, + 423, + 579 + ], + "blocks": [ + { + "bbox": [ + 411, + 517, + 423, + 579 + ], + "lines": [ + { + "bbox": [ + 411, + 517, + 423, + 579 + ], + "spans": [ + { + "bbox": [ + 411, + 517, + 423, + 579 + ], + "type": "image", + "image_path": "2c34bd6b5e5b55b0bc18911439edaf7fb137ebf6dcc0b46a3aeed1c7d7dae0ba.jpg" + } + ] + } + ], + "index": 41, + "angle": 0, + "type": "image_body" + } + ], + "index": 41 + }, + { + "type": "image", + "bbox": [ + 429, + 517, + 531, + 578 + ], + "blocks": [ + { + "bbox": [ + 429, + 517, + 531, + 578 + ], + "lines": [ + { + "bbox": [ + 429, + 517, + 531, + 578 + ], + "spans": [ + { + "bbox": [ + 429, + 517, + 531, + 578 + ], + "type": "image", + "image_path": "2009bb1c93ed7ec49c4bf7409f7bf0046689101ddb7d3d68b0728e2da8ec5418.jpg" + } + ] + } + ], + "index": 43, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 475, + 585, + 486, + 594 + ], + "lines": [ + { + "bbox": [ + 475, + 585, + 486, + 594 + ], + "spans": [ + { + "bbox": [ + 475, + 585, + 486, + 594 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 44, + "angle": 0, + "type": "image_caption" + } + ], + "index": 43 + }, + { + "bbox": [ + 306, + 687, + 349, + 697 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 687, + 349, + 697 + ], + "spans": [ + { + "bbox": [ + 306, + 687, + 349, + 697 + ], + "type": "text", + "content": "formance." + } + ] + } + ], + "index": 46 + }, + { + "bbox": [ + 306, + 701, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 701, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 306, + 701, + 545, + 714 + ], + "type": "text", + "content": "Qualitative Comparison. Fig. 5 shows the reconstruction" + } + ] + } + ], + "index": 47 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 313, + 757 + ], + "type": "text", + "content": "222" + } + ] + } + ], + "index": 48 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 50, + 91, + 288, + 141 + ], + "blocks": [ + { + "bbox": [ + 97, + 71, + 236, + 82 + ], + "lines": [ + { + "bbox": [ + 97, + 71, + 236, + 82 + ], + "spans": [ + { + "bbox": [ + 97, + 71, + 236, + 82 + ], + "type": "text", + "content": "Table 2. Feature aggregation ablation." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 91, + 288, + 141 + ], + "lines": [ + { + "bbox": [ + 50, + 91, + 288, + 141 + ], + "spans": [ + { + "bbox": [ + 50, + 91, + 288, + 141 + ], + "type": "table", + "html": "
MethodNC↑Geometry MetricRelit Rendering
CD (L1)↓F-score (0.5%)↑PSNR↑SSIM↑LPIPS↓
Average0.9110.1290.69617.9330.6500.327
Attention0.9090.1270.71019.1480.7130.259
Ours0.9170.1220.73623.4360.8090.196
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From left to right: color reference, the results of average, attention and ours." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 65, + 247, + 268, + 315 + ], + "blocks": [ + { + "bbox": [ + 65, + 247, + 268, + 315 + ], + "lines": [ + { + "bbox": [ + 65, + 247, + 268, + 315 + ], + "spans": [ + { + "bbox": [ + 65, + 247, + 268, + 315 + ], + "type": "image", + "image_path": "a77878132e7974e0b19d4fc0020fe1f85ea21c63815009257d0685ad6e90a512.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 47, + 316, + 287, + 338 + ], + "lines": [ + { + "bbox": [ + 47, + 316, + 287, + 338 + ], + "spans": [ + { + "bbox": [ + 47, + 316, + 287, + 338 + ], + "type": "text", + "content": "Figure 10. Feature aggregation ablation in rendering quality. From left to right: average, attention, and ours and ground truth." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 361, + 287, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 361, + 287, + 491 + ], + "spans": [ + { + "bbox": [ + 46, + 361, + 287, + 491 + ], + "type": "text", + "content": "on the synthetic datasets. Benefiting from visibility-guided feature aggregation, our method produces at least comparable details to MV-PIFuHD, but with simpler architecture, less input information and visual cues. Compared to oversmoothed geometry from Function4D, our hybrid features relax the dominant contribution of noisy depth input, leading to improved facial details. We further demonstrate our generalizability on real captured data in comparison with Function4D. As shown in Fig. 7, our method evidently produces more complete and detailed reconstructions, especially in regions of the eyes, glasses and hair." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 493, + 287, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 493, + 287, + 528 + ], + "spans": [ + { + "bbox": [ + 46, + 493, + 287, + 528 + ], + "type": "text", + "content": "Quantitative Comparison summarized in Tab. 1 is consistent with qualitative analysis, and our method outperforms others in all metrics." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 538, + 140, + 551 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 538, + 140, + 551 + ], + "spans": [ + { + "bbox": [ + 47, + 538, + 140, + 551 + ], + "type": "text", + "content": "4.3. Ablation Study" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 556, + 287, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 556, + 287, + 604 + ], + "spans": [ + { + "bbox": [ + 46, + 556, + 287, + 604 + ], + "type": "text", + "content": "3D Feature. We ablate our hybrid feature with 2D RGB and 2D RGB-D variants. The results validate the necessity of the 3D feature, as it predicts visibility with higher accuracy (Fig. 8a) and lower error variance (Fig. 8b)." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 287, + 689 + ], + "type": "text", + "content": "Visibility-guided Feature Aggregation. In comparison with other aggregation techniques, namely averaging [43] and self-attention [61], we implement them in our framework. Our strategy surpasses others in metrics (Tab. 2) and achieves sharper geometric details (Fig. 9), better rendering fidelity (Fig. 10), even near heavily folded clothing thanks to our occlusion-aware aggregation." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": "View number. Though we train our model with 4 views, it generalizes well across different view numbers and achieves" + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 310, + 73, + 386, + 187 + ], + "blocks": [ + { + "bbox": [ + 310, + 73, + 386, + 187 + ], + "lines": [ + { + "bbox": [ + 310, + 73, + 386, + 187 + ], + "spans": [ + { + "bbox": [ + 310, + 73, + 386, + 187 + ], + "type": "image", + "image_path": "355149894fc6d873ad5f9ce29674cb09ee04df6dd2c174a368bd2160fe5befba.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 387, + 73, + 464, + 187 + ], + "blocks": [ + { + "bbox": [ + 387, + 73, + 464, + 187 + ], + "lines": [ + { + "bbox": [ + 387, + 73, + 464, + 187 + ], + "spans": [ + { + "bbox": [ + 387, + 73, + 464, + 187 + ], + "type": "image", + "image_path": "49eea60cad0e18d314bd98ec4bca5d2b6a9b118ffa920b81da90af174f03b44e.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 465, + 73, + 542, + 187 + ], + "blocks": [ + { + "bbox": [ + 465, + 73, + 542, + 187 + ], + "lines": [ + { + "bbox": [ + 465, + 73, + 542, + 187 + ], + "spans": [ + { + "bbox": [ + 465, + 73, + 542, + 187 + ], + "type": "image", + "image_path": "48a46947c27b76ad6277524c232a3602c6e0043f53847f09ab7441fa638ea8b6.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 313, + 190, + 386, + 304 + ], + "blocks": [ + { + "bbox": [ + 313, + 190, + 386, + 304 + ], + "lines": [ + { + "bbox": [ + 313, + 190, + 386, + 304 + ], + "spans": [ + { + "bbox": [ + 313, + 190, + 386, + 304 + ], + "type": "image", + "image_path": "efea5f367fa7c78129c6c3f9c95715e20c2dd213fb71a79029cc6268182ec04f.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 363, + 309, + 487, + 319 + ], + "lines": [ + { + "bbox": [ + 363, + 309, + 487, + 319 + ], + "spans": [ + { + "bbox": [ + 363, + 309, + 487, + 319 + ], + "type": "text", + "content": "Figure 11. View number ablation." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 387, + 190, + 464, + 304 + ], + "blocks": [ + { + "bbox": [ + 387, + 190, + 464, + 304 + ], + "lines": [ + { + "bbox": [ + 387, + 190, + 464, + 304 + ], + "spans": [ + { + "bbox": [ + 387, + 190, + 464, + 304 + ], + "type": "image", + "image_path": "14c95b618aac2468a8a5ceaf1377fa6c747ef1a593d5a8efd7a53cba8191b9ff.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 465, + 190, + 542, + 304 + ], + "blocks": [ + { + "bbox": [ + 465, + 190, + 542, + 304 + ], + "lines": [ + { + "bbox": [ + 465, + 190, + 542, + 304 + ], + "spans": [ + { + "bbox": [ + 465, + 190, + 542, + 304 + ], + "type": "image", + "image_path": "70dc15fc91a094ea33359510bfa1e80a4c503022d56ea4f894af5f5be6d6cd67.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 342, + 545, + 402 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 342, + 545, + 402 + ], + "spans": [ + { + "bbox": [ + 304, + 342, + 545, + 402 + ], + "type": "text", + "content": "similar performance under sufficient view coverage as ablated in Fig. 11. As view number increases, compared to degraded reconstruction quality from average and attention, our aggregation technique mitigates visibility ambiguity to leverage additional visual cues, resulting in better geometry." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 403, + 545, + 439 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 403, + 545, + 439 + ], + "spans": [ + { + "bbox": [ + 304, + 403, + 545, + 439 + ], + "type": "text", + "content": "TransferLoss. Finally, we verify the effectiveness of our proposed TransferLoss in Fig. 4. It enforces the alignment of the fields and significantly alleviates rendering defects." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 306, + 452, + 378, + 464 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 452, + 378, + 464 + ], + "spans": [ + { + "bbox": [ + 306, + 452, + 378, + 464 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 472, + 545, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 472, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 304, + 472, + 545, + 544 + ], + "type": "text", + "content": "Limitations. Our method cannot achieve real-time performance for interactive applications. We leave its acceleration using TensorRT and CUDA for future work. Moreover, our framework relies on depth input for accurate visibility prediction, it would be interesting to see if it can be extended to simpler RGB setup." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 545, + 545, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 545, + 652 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 545, + 652 + ], + "type": "text", + "content": "Conclusion. In this work, we integrate visibility into sparse-view reconstruction framework by exploiting its effective guidance in multi-view feature aggregation and direct support for self-shadowed relighting. Our discretization strategy and novel TransferLoss enable visibility to be learned jointly alongside occupancy in an end-to-end manner. Our paper demonstrates the effectiveness of visibility in simultaneous reconstruction and relighting and provides a good baseline for future work." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 653, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 545, + 712 + ], + "type": "text", + "content": "Acknowledgement. This work was supported by NSFC No.62171255, National Key R&D Program of China No.2022YFF0902201, Guoqiang Institute of Tsinghua University No.2021GQG0001 and Tsinghua-Toyota Joint Research Fund." + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "223" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "text", + "content": "[1] Thiemo Alldieck, Mihai Zanfir, and Cristian Sminchisescu. Photorealistic monocular 3d reconstruction of humans wearing clothing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1506-1515, 2022. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 147, + 288, + 201 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 147, + 288, + 201 + ], + "spans": [ + { + "bbox": [ + 53, + 147, + 288, + 201 + ], + "type": "text", + "content": "[2] Bharat Lal Bhatnagar, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Combining implicit function learning and parametric models for 3d human reconstruction. In European Conference on Computer Vision, pages 311-329. Springer, 2020. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 202, + 288, + 257 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 202, + 288, + 257 + ], + "spans": [ + { + "bbox": [ + 53, + 202, + 288, + 257 + ], + "type": "text", + "content": "[3] Mark Boss, Raphael Braun, Varun Jampani, Jonathan T Barron, Ce Liu, and Hendrik Lensch. Nerd: Neural reflectance decomposition from image collections. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12684-12694, 2021. 3, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 258, + 287, + 291 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 258, + 287, + 291 + ], + "spans": [ + { + "bbox": [ + 53, + 258, + 287, + 291 + ], + "type": "text", + "content": "[4] Zhaoxi Chen and Ziwei Liu. Relighting4d: Neural relightable human from videos. In European Conference on Computer Vision, pages 606-623. Springer, 2022. 3, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 292, + 287, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 292, + 287, + 335 + ], + "spans": [ + { + "bbox": [ + 53, + 292, + 287, + 335 + ], + "type": "text", + "content": "[5] Zhiqin Chen and Hao Zhang. Learning implicit fields for generative shape modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5939-5948, 2019. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 336, + 287, + 390 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 336, + 287, + 390 + ], + "spans": [ + { + "bbox": [ + 53, + 336, + 287, + 390 + ], + "type": "text", + "content": "[6] Julian Chibane, Thiemo Alldieck, and Gerard Pons-Moll. Implicit functions in feature space for 3d shape reconstruction and completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6970-6981, 2020. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 392, + 287, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 392, + 287, + 435 + ], + "spans": [ + { + "bbox": [ + 53, + 392, + 287, + 435 + ], + "type": "text", + "content": "[7] Paul Debevec. Pursuing reality with image-based modeling, rendering, and lighting. In European Workshop on 3D Structure from Multiple Images of Large-Scale Environments, pages 1-16. Springer, 2000. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 436, + 287, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 436, + 287, + 480 + ], + "spans": [ + { + "bbox": [ + 53, + 436, + 287, + 480 + ], + "type": "text", + "content": "[8] Haoqiang Fan, Hao Su, and Leonidas J Guibas. A point set generation network for 3d object reconstruction from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 605-613, 2017. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 481, + 287, + 545 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 481, + 287, + 545 + ], + "spans": [ + { + "bbox": [ + 53, + 481, + 287, + 545 + ], + "type": "text", + "content": "[9] Sean Ryan Fanello, Cem Keskin, Shahram Izadi, Pushmeet Kohli, David Kim, David Sweeney, Antonio Criminisi, Jamie Shotton, Sing Bing Kang, and Tim Paek. Learning to be a depth camera for close-range human capture and interaction. ACM Transactions on Graphics (TOG), 33(4):1-11, 2014. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 547, + 287, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 547, + 287, + 602 + ], + "spans": [ + { + "bbox": [ + 48, + 547, + 287, + 602 + ], + "type": "text", + "content": "[10] Péter Fankhauser, Michael Bloesch, Diego Rodriguez, Ralf Kaestner, Marco Hutter, and Roland Siegwart. Kinect v2 for mobile robot navigation: Evaluation and modeling. In 2015 International Conference on Advanced Robotics (ICAR), pages 388-394. IEEE, 2015. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 602, + 287, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 602, + 287, + 646 + ], + "spans": [ + { + "bbox": [ + 48, + 602, + 287, + 646 + ], + "type": "text", + "content": "[11] Qiao Feng, Yebin Liu, Yu-Kun Lai, Jingyu Yang, and Kun Li. Fof: learning fourier occupancy field for monocular real-time human reconstruction. arXiv preprint arXiv:2206.02194, 2022. 1" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 647, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 287, + 712 + ], + "type": "text", + "content": "[12] Kaiwen Guo, Peter Lincoln, Philip Davidson, Jay Busch, Xueming Yu, Matt Whalen, Geoff Harvey, Sergio Orts-Escolano, Rohit Pandey, Jason Dourgarian, et al. The relightables: Volumetric performance capture of humans with realistic relighting. ACM Transactions on Graphics (ToG), 38(6):1–19, 2019. 1" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 714 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 128 + ], + "type": "text", + "content": "[13] Kaiwen Guo, Feng Xu, Yangang Wang, Yebin Liu, and Qionghai Dai. Robust non-rigid motion tracking and surface reconstruction using 10 regularization. In Proceedings of the IEEE International Conference on Computer Vision, pages 3083-3091, 2015. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 129, + 545, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 545, + 183 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 545, + 183 + ], + "type": "text", + "content": "[14] Tong He, Yuanlu Xu, Shunsuke Saito, Stefano Soatto, and Tony Tung. Arch++: Animation-ready clothed human reconstruction revisited. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11046-11056, 2021. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 185, + 547, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 185, + 547, + 239 + ], + "spans": [ + { + "bbox": [ + 308, + 185, + 547, + 239 + ], + "type": "text", + "content": "[15] Yang Hong, Juyong Zhang, Boyi Jiang, Yudong Guo, Ligang Liu, and Hujun Bao. Stereopifu: Depth aware clothed human digitization via stereo vision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 535-545, 2021. 1" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 240, + 545, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 240, + 545, + 285 + ], + "spans": [ + { + "bbox": [ + 308, + 240, + 545, + 285 + ], + "type": "text", + "content": "[16] Buzhen Huang, Yuan Shu, Tianshu Zhang, and Yangang Wang. Dynamic multi-person mesh recovery from uncalibrated multi-view cameras. In 2021 International Conference on 3D Vision (3DV), pages 710-720. IEEE, 2021. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 285, + 545, + 340 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 285, + 545, + 340 + ], + "spans": [ + { + "bbox": [ + 308, + 285, + 545, + 340 + ], + "type": "text", + "content": "[17] Zeng Huang, Yuanlu Xu, Christoph Lassner, Hao Li, and Tony Tung. Arch: Animatable reconstruction of clothed humans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3093-3102, 2020. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 342, + 545, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 342, + 545, + 397 + ], + "spans": [ + { + "bbox": [ + 308, + 342, + 545, + 397 + ], + "type": "text", + "content": "[18] Chaonan Ji, Tao Yu, Kaiwen Guo, Jingxin Liu, and Yebin Liu. Geometry-aware single-image full-body human relighting. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVI, pages 388-405. Springer, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 397, + 545, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 397, + 545, + 431 + ], + "spans": [ + { + "bbox": [ + 308, + 397, + 545, + 431 + ], + "type": "text", + "content": "[19] James T Kajiya. The rendering equation. In Proceedings of the 13th annual conference on Computer graphics and interactive techniques, pages 143-150, 1986. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 432, + 545, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 432, + 545, + 465 + ], + "spans": [ + { + "bbox": [ + 308, + 432, + 545, + 465 + ], + "type": "text", + "content": "[20] Yoshihiro Kanamori and Yuki Endo. Relighting humans: occlusion-aware inverse rendering for full-body human images. arXiv preprint arXiv:1908.02714, 2019. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 466, + 545, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 466, + 545, + 498 + ], + "spans": [ + { + "bbox": [ + 308, + 466, + 545, + 498 + ], + "type": "text", + "content": "[21] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 499, + 545, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 499, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 308, + 499, + 545, + 544 + ], + "type": "text", + "content": "[22] Arno Knapitsch, Jaesik Park, Qian-Yi Zhou, and Vladlen Koltun. Tanks and temples: Benchmarking large-scale scene reconstruction. ACM Transactions on Graphics (ToG), 36(4):1-13, 2017. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 545, + 545, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 545, + 545, + 588 + ], + "spans": [ + { + "bbox": [ + 308, + 545, + 545, + 588 + ], + "type": "text", + "content": "[23] Manuel Lagunas, Xin Sun, Jimei Yang, Ruben Villegas, Jianming Zhang, Zhixin Shu, Belen Masia, and Diego Gutierrez. Single-image full-body human relighting. arXiv preprint arXiv:2107.07259, 2021. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 590, + 545, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 590, + 545, + 644 + ], + "spans": [ + { + "bbox": [ + 308, + 590, + 545, + 644 + ], + "type": "text", + "content": "[24] Jason Lawrence, Dan B Goldman, Supreeth Achar, Gregory Major Blascovich, Joseph G Desloge, Tommy Fortes, Eric M Gomez, Sascha Haberling, Hugues Hoppe, Andy Huibers, et al. Project starline: A high-fidelity telepresence system. 2021. 1" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 646, + 545, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 646, + 545, + 690 + ], + "spans": [ + { + "bbox": [ + 308, + 646, + 545, + 690 + ], + "type": "text", + "content": "[25] Ruilong Li, Yuliang Xiu, Shunsuke Saito, Zeng Huang, Kyle Olszewski, and Hao Li. Monocular real-time volumetric performance capture. In European Conference on Computer Vision, pages 49-67. Springer, 2020. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 691, + 545, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 691, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 308, + 691, + 545, + 714 + ], + "type": "text", + "content": "[26] Yue Li, Pablo Wiedemann, and Kenny Mitchell. Deep precomputed radiance transfer for deformable objects. Pro" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 297, + 749, + 312, + 757 + ], + "type": "text", + "content": "224" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 73, + 287, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 67, + 73, + 287, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 73, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 67, + 73, + 287, + 95 + ], + "type": "text", + "content": "ceedings of the ACM on Computer Graphics and Interactive Techniques, 2(1):1-16, 2019. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 287, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 287, + 140 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 287, + 140 + ], + "type": "text", + "content": "[27] Haotong Lin, Sida Peng, Zhen Xu, Yunzhi Yan, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Efficient neural radiance fields for interactive free-viewpoint video. In SIGGRAPH Asia 2022 Conference Papers, pages 1-9, 2022. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 141, + 287, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 141, + 287, + 195 + ], + "spans": [ + { + "bbox": [ + 48, + 141, + 287, + 195 + ], + "type": "text", + "content": "[28] Shanchuan Lin, Linjie Yang, Imran Saleemi, and Soumyadip Sengupta. Robust high-resolution video matting with temporal guidance. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 238-247, 2022. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 198, + 287, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 198, + 287, + 241 + ], + "spans": [ + { + "bbox": [ + 48, + 198, + 287, + 241 + ], + "type": "text", + "content": "[29] Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. Smpl: A skinned multiperson linear model. ACM transactions on graphics (TOG), 34(6):1-16, 2015. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 243, + 287, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 243, + 287, + 275 + ], + "spans": [ + { + "bbox": [ + 48, + 243, + 287, + 275 + ], + "type": "text", + "content": "[30] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. ACM siggraph computer graphics, 21(4):163-169, 1987. 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 277, + 287, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 277, + 287, + 342 + ], + "spans": [ + { + "bbox": [ + 48, + 277, + 287, + 342 + ], + "type": "text", + "content": "[31] Linjie Lyu, Ayush Tewari, Thomas Leimkuhler, Marc Habermann, and Christian Theobalt. Neural radiance transfer fields for relightable novel-view synthesis with global illumination. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XVII, pages 153-169. Springer, 2022. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 344, + 287, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 344, + 287, + 398 + ], + "spans": [ + { + "bbox": [ + 48, + 344, + 287, + 398 + ], + "type": "text", + "content": "[32] Shugao Ma, Tomas Simon, Jason Saragih, Dawei Wang, Yuecheng Li, Fernando De La Torre, and Yaser Sheikh. Pixel codec avatars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 64-73, 2021. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 399, + 287, + 455 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 399, + 287, + 455 + ], + "spans": [ + { + "bbox": [ + 48, + 399, + 287, + 455 + ], + "type": "text", + "content": "[33] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4460-4470, 2019. 2, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 456, + 287, + 499 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 456, + 287, + 499 + ], + "spans": [ + { + "bbox": [ + 48, + 456, + 287, + 499 + ], + "type": "text", + "content": "[34] Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson. Deep level sets: Implicit surface representations for 3d shape inference. arXiv preprint arXiv:1901.06802, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 501, + 287, + 554 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 501, + 287, + 554 + ], + "spans": [ + { + "bbox": [ + 48, + 501, + 287, + 554 + ], + "type": "text", + "content": "[35] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 557, + 287, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 557, + 287, + 612 + ], + "spans": [ + { + "bbox": [ + 48, + 557, + 287, + 612 + ], + "type": "text", + "content": "[36] Ryota Natsume, Shunsuke Saito, Zeng Huang, Weikai Chen, Chongyang Ma, Hao Li, and Shigeo Morishima. *Siclope: Silhouette-based clothed people. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition*, pages 4480–4490, 2019. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 613, + 287, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 613, + 287, + 679 + ], + "spans": [ + { + "bbox": [ + 48, + 613, + 287, + 679 + ], + "type": "text", + "content": "[37] Sergio Orts-Escalano, Christoph Rhemann, Sean Fanello, Wayne Chang, Adarsh Kowdle, Yury Degtyarev, David Kim, Philip L Davidson, Sameh Khamis, Mingsong Dou, et al. Holoportation: Virtual 3d teleportation in real-time. In Proceedings of the 29th annual symposium on user interface software and technology, pages 741-754, 2016. 1" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 681, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 681, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 681, + 287, + 713 + ], + "type": "text", + "content": "[38] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation." + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "text", + "content": "In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 165-174, 2019. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 308, + 96, + 545, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 545, + 140 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 545, + 140 + ], + "type": "text", + "content": "[39] Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In European Conference on Computer Vision, pages 523-540. Springer, 2020. 2, 4" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 141, + 545, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 141, + 545, + 207 + ], + "spans": [ + { + "bbox": [ + 308, + 141, + 545, + 207 + ], + "type": "text", + "content": "[40] Sida Peng, Yuanqing Zhang, Yinghao Xu, Qianqian Wang, Qing Shuai, Hujun Bao, and Xiaowei Zhou. Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9054-9063, 2021. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 209, + 545, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 209, + 545, + 252 + ], + "spans": [ + { + "bbox": [ + 308, + 209, + 545, + 252 + ], + "type": "text", + "content": "[41] Gilles Rainer, Adrien Bousseau, Tobias Ritschel, and George Drettakis. Neural precomputed radiance transfer. In Computer Graphics Forum, volume 41, pages 365-378. Wiley Online Library, 2022. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 254, + 545, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 254, + 545, + 297 + ], + "spans": [ + { + "bbox": [ + 308, + 254, + 545, + 297 + ], + "type": "text", + "content": "[42] Ravi Ramamoorthi and Pat Hanrahan. An efficient representation for irradiance environment maps. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 497-500, 2001. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 299, + 545, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 299, + 545, + 363 + ], + "spans": [ + { + "bbox": [ + 308, + 299, + 545, + 363 + ], + "type": "text", + "content": "[43] Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 2304-2314, 2019. 1, 2, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 365, + 545, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 365, + 545, + 421 + ], + "spans": [ + { + "bbox": [ + 308, + 365, + 545, + 421 + ], + "type": "text", + "content": "[44] Shunsuke Saito, Tomas Simon, Jason Saragih, and Hanbyul Joo. Pifuhd: Multi-level pixel-aligned implicit function for high-resolution 3d human digitization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 84-93, 2020. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 422, + 545, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 422, + 545, + 476 + ], + "spans": [ + { + "bbox": [ + 308, + 422, + 545, + 476 + ], + "type": "text", + "content": "[45] Soumyadip Sengupta, Angjoo Kanazawa, Carlos D Castillo, and David W Jacobs. Sfsnet: Learning shape, reflectance and illuminance of facesin the wild'. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 6296-6305, 2018. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 478, + 545, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 478, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 308, + 478, + 545, + 544 + ], + "type": "text", + "content": "[46] Ruizhi Shao, Hongwen Zhang, He Zhang, Mingjia Chen, Yan-Pei Cao, Tao Yu, and Yebin Liu. Doublefield: Bridging the neural surface and radiance fields for high-fidelity human reconstruction and rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15872-15882, 2022. 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 545, + 545, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 545, + 545, + 589 + ], + "spans": [ + { + "bbox": [ + 308, + 545, + 545, + 589 + ], + "type": "text", + "content": "[47] Ruizhi Shao, Zerong Zheng, Hongwen Zhang, Jingxiang Sun, and Yebin Liu. Diffustereo: High quality human reconstruction via diffusion-based stereo using sparse cameras. arXiv preprint arXiv:2207.08000, 2022. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 590, + 545, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 590, + 545, + 644 + ], + "spans": [ + { + "bbox": [ + 308, + 590, + 545, + 644 + ], + "type": "text", + "content": "[48] Peter-Pike Sloan, Jan Kautz, and John Snyder. Precomputed radiance transfer for real-time rendering in dynamic, low-frequency lighting environments. In Proceedings of the 29th annual conference on Computer graphics and interactive techniques, pages 527-536, 2002. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 647, + 545, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 647, + 545, + 679 + ], + "spans": [ + { + "bbox": [ + 308, + 647, + 545, + 679 + ], + "type": "text", + "content": "[49] Leslie N Smith. Cyclical learning rates for training neural networks. In 2017 IEEE winter conference on applications of computer vision (WACV), pages 464-472. IEEE, 2017. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "type": "text", + "content": "[50] Pratul P Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan T Barron. Nerv: Neural reflectance and visibility fields for relighting" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "spans": [ + { + "bbox": [ + 298, + 749, + 312, + 757 + ], + "type": "text", + "content": "225" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 66, + 72, + 287, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 72, + 287, + 106 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 287, + 106 + ], + "type": "text", + "content": "and view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7495-7504, 2021. 3, 4, 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 106, + 287, + 160 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 106, + 287, + 160 + ], + "spans": [ + { + "bbox": [ + 48, + 106, + 287, + 160 + ], + "type": "text", + "content": "[51] Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning for human pose estimation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5693-5703, 2019. 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 161, + 288, + 226 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 161, + 288, + 226 + ], + "spans": [ + { + "bbox": [ + 48, + 161, + 288, + 226 + ], + "type": "text", + "content": "[52] Xin Suo, Yuheng Jiang, Pei Lin, Yingliang Zhang, Minye Wu, Kaiwen Guo, and Lan Xu. Neuralhumanfvv: Real-time neural volumetric human performance rendering using rgb cameras. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6226-6237, 2021. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 228, + 288, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 228, + 288, + 281 + ], + "spans": [ + { + "bbox": [ + 48, + 228, + 288, + 281 + ], + "type": "text", + "content": "[53] Daichi Tajima, Yoshihiro Kanamori, and Yuki Endo. Relighting humans in the wild: Monocular full-body human relighting with domain adaptation. In Computer Graphics Forum, volume 40, pages 205-216. Wiley Online Library, 2021. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 282, + 288, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 282, + 288, + 338 + ], + "spans": [ + { + "bbox": [ + 48, + 282, + 288, + 338 + ], + "type": "text", + "content": "[54] Maxim Tatarchenko, Stephan R Richter, René Ranftl, Zhuwen Li, Vladlen Koltun, and Thomas Brox. What do single-view 3d reconstruction networks learn? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3405-3414, 2019. 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 338, + 288, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 338, + 288, + 370 + ], + "spans": [ + { + "bbox": [ + 48, + 338, + 288, + 370 + ], + "type": "text", + "content": "[55] Yating Tian, Hongwen Zhang, Yebin Liu, and Limin Wang. Recovering 3d human mesh from monocular images: A survey. arXiv preprint arXiv:2203.01923, 2022. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 371, + 288, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 371, + 288, + 426 + ], + "spans": [ + { + "bbox": [ + 48, + 371, + 288, + 426 + ], + "type": "text", + "content": "[56] Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan T Barron, and Pratul P Srinivasan. Ref-nerf: Structured view-dependent appearance for neural radiance fields. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 5481-5490. IEEE, 2022. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 426, + 288, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 426, + 288, + 470 + ], + "spans": [ + { + "bbox": [ + 48, + 426, + 288, + 470 + ], + "type": "text", + "content": "[57] Ingo Wald, Sven Woop, Carsten Benthin, Gregory S Johnson, and Manfred Ernst. Embree: a kernel framework for efficient cpu ray tracing. ACM Transactions on Graphics (TOG), 33(4):1-8, 2014. 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 471, + 288, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 471, + 288, + 514 + ], + "spans": [ + { + "bbox": [ + 48, + 471, + 288, + 514 + ], + "type": "text", + "content": "[58] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing, 13(4):600-612, 2004. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 514, + 288, + 570 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 514, + 288, + 570 + ], + "spans": [ + { + "bbox": [ + 48, + 514, + 288, + 570 + ], + "type": "text", + "content": "[59] Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar. Neural fields in visual computing and beyond. In Computer Graphics Forum, volume 41, pages 641-676. Wiley Online Library, 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 570, + 286, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 570, + 286, + 592 + ], + "spans": [ + { + "bbox": [ + 48, + 570, + 286, + 592 + ], + "type": "text", + "content": "[60] YBIGTA. Hair-segmentation. https://pytorchhair.gitbook.io/project/.6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 593, + 288, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 593, + 288, + 657 + ], + "spans": [ + { + "bbox": [ + 48, + 593, + 288, + 657 + ], + "type": "text", + "content": "[61] Tao Yu, Zerong Zheng, Kaiwen Guo, Pengpeng Liu, Qionghai Dai, and Yebin Liu. Function4d: Real-time human volumetric capture from very sparse consumer rgbd sensors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5746-5756, 2021. 1, 2, 5, 6, 7, 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 658, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 288, + 713 + ], + "type": "text", + "content": "[62] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 586-595, 2018. 6" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 72, + 546, + 296 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 307, + 72, + 546, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 72, + 546, + 127 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 546, + 127 + ], + "type": "text", + "content": "[63] Yizhong Zhang, Jiaolong Yang, Zhen Liu, Ruicheng Wang, Guojun Chen, Xin Tong, and Baining Guo. Virtualcube: An immersive 3d video communication system. IEEE Transactions on Visualization and Computer Graphics, 28(5):2146-2156, 2022. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 129, + 546, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 546, + 194 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 546, + 194 + ], + "type": "text", + "content": "[64] Yang Zheng, Ruizhi Shao, Yuxiang Zhang, Tao Yu, Zerong Zheng, Qionghai Dai, and Yebin Liu. Deepmulticap: Performance capture of multiple characters using sparse multiview cameras. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6239-6249, 2021. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 195, + 546, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 195, + 546, + 250 + ], + "spans": [ + { + "bbox": [ + 307, + 195, + 546, + 250 + ], + "type": "text", + "content": "[65] Zerong Zheng, Tao Yu, Yebin Liu, and Qionghai Dai. Pamir: Parametric model-conditioned implicit representation for image-based human reconstruction. IEEE transactions on pattern analysis and machine intelligence, 44(6):3170-3184, 2021. 1, 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 251, + 546, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 251, + 546, + 296 + ], + "spans": [ + { + "bbox": [ + 307, + 251, + 546, + 296 + ], + "type": "text", + "content": "[66] Zerong Zheng, Tao Yu, Yixuan Wei, Qionghai Dai, and Yebin Liu. Deephuman: 3d human reconstruction from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7739-7749, 2019. 1" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "spans": [ + { + "bbox": [ + 298, + 749, + 313, + 757 + ], + "type": "text", + "content": "226" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning Visual Representations via Language-Guided Sampling/c0b46ea1-1183-4a23-a0d4-68578b0842d6_content_list.json b/2023/Learning Visual Representations via Language-Guided Sampling/c0b46ea1-1183-4a23-a0d4-68578b0842d6_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..ff1951fae75c42734dda6153a2c1b1027a80411b --- /dev/null +++ b/2023/Learning Visual Representations via Language-Guided Sampling/c0b46ea1-1183-4a23-a0d4-68578b0842d6_content_list.json @@ -0,0 +1,1423 @@ +[ + { + "type": "text", + "text": "Learning Visual Representations via Language-Guided Sampling", + "text_level": 1, + "bbox": [ + 156, + 130, + 815, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Mohamed El Banani Karan Desai Justin Johnson University of Michigan", + "bbox": [ + 269, + 180, + 697, + 219 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{mbanani, kdexd, justincj}@umich.edu", + "bbox": [ + 356, + 222, + 609, + 236 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 272, + 313, + 287 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Although an object may appear in numerous contexts, we often describe it in a limited number of ways. Language allows us to abstract away visual variation to represent and communicate concepts. Building on this intuition, we propose an alternative approach to visual representation learning: using language similarity to sample semantically similar image pairs for contrastive learning. Our approach diverges from image-based contrastive learning by sampling view pairs using language similarity instead of handcrafted augmentations or learned clusters. Our approach also differs from image-text contrastive learning by relying on pre-trained language models to guide the learning rather than directly minimizing a cross-modal loss. Through a series of experiments, we show that language-guided learning yields better features than image-based and image-text representation learning approaches.", + "bbox": [ + 75, + 304, + 473, + 547 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 573, + 209, + 588 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Consider the images in Fig. 1, is the center image more similar to its left or right neighbor? Despite the difference in background and pose, it is clear that the right pair captures the same concept: a flying snow owl. Nevertheless, a self-supervised image model will judge the left pair as more similar. Human perception and language abstract away appearance differences to capture conceptual similarity rather than just visual similarity. Ideally, we could learn visual features that capture conceptual similarity and generalize effectively to other visual tasks. In this work, we show how language can be a proxy for conceptual similarity; allowing us to sample better pairs for contrastive learning and train more generalizable visual models.", + "bbox": [ + 75, + 598, + 468, + 794 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Image-only contrastive learning uses visual similarity as a proxy for conceptual similarity. This is based on the observation that discriminative approaches can discover interclass similarity-e.g., cheetahs are similar to lions- without requiring explicit annotations [106]. The core idea is to train a discriminative model where each instance is treated as a separate class, and the model is trained to map augmented", + "bbox": [ + 75, + 795, + 470, + 901 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/2a5f83ca8ed7251c5a75201146ebbfb71ede11bdb330ed1f7735e41f79e3778a.jpg", + "image_caption": [ + "Visual Embedding", + "Language Embedding", + "Figure 1. Language allows us to find conceptually similar image pairs even if they are visually dissimilar. We use those pairs for contrastive learning to learn generalizable visual features." + ], + "image_footnote": [], + "bbox": [ + 522, + 285, + 870, + 484 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "views of the same image to similar features [12-15, 106]. While successful, instance discrimination ignores the similarity between different instances as it assumes all other images are unrelated. Later work focused on inter-image relationships by estimating clusters [3, 9, 10] or finding nearest neighbors [28]. However, those relationships are estimated using visual embeddings; resulting in visually, rather than conceptually, similar pairs.", + "bbox": [ + 496, + 580, + 890, + 702 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Language similarity is a strong proxy for semantic relationships. Consider the example in Fig. 1; images that depict the same concept are often described similarly. Radford et al. [76] propose language-image contrastive learning by mapping images and text to a shared representation space and achieve impressive generalization capabilities. However, it is unclear whether forcing models to map onto a shared space is optimal for visual learning. Although linguistic and visual similarity might align for similar instances, it is unclear whether all distances in one space should map exactly to the other. Instead of learning a joint vision-and-language representations, we argue that it is better to use linguistic similarity to guide visual learning.", + "bbox": [ + 496, + 704, + 892, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "19208", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "To this end, we propose language-guided contrastive learning: a simple adaptation to contrastive learning that uses language models to find conceptually-similar image pairs for visual learning. Our approach is motivated by the observation that language models, despite never training on visual data, can still be used to sample caption pairs that belong to conceptually similar images, as seen in Fig. 2. Such sampled images exhibit desirable variations in pose, lightning, and context which are very different from hand-crafted augmentations which can be ill-suited to downstream tasks [108] or too focused on background textures [81]. We use the sampled pairs instead of image augmentations within standard self-supervised visual learning approaches such as SimCLR [12], SimSiam [15], and SLIP [67]. Our approach departs from image-only contrastive learning by relying on conceptually-similar image pairs rather than visually similar augmentations or cluster-assignment. We also depart from image-text pre-training by allowing the model to be guided by language similarity rather than learning a joint embedding space.", + "bbox": [ + 80, + 90, + 467, + 392 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We conduct a series of controlled experiments to analyze our approach and compare it to commonly used representation learning paradigms on generalization to downstream classification tasks. In controlled settings, our approach outperforms all baselines on linear probe and few-shot classification on a range of downstream classification datasets. Our analysis suggests that while learning multimodal joint embeddings can result in good representations, it is better to use one modality to guide the training of the other. Furthermore, we find that our approach is robust to the specific choice of sampling strategy or language model. Our code and pre-trained models are available at https://github.com/mbanani/lgssl.", + "bbox": [ + 80, + 393, + 467, + 588 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 80, + 604, + 215, + 618 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Visual Representation Learning aims to learn visual embedding spaces that capture semantics, with a typical focus on learning from scalable data sources. Broadly speaking, there are two general approaches: generative and discriminative. Generative approaches hypothesize that a model that can capture the image distribution will learn semantically relevant features [26, 31, 37, 70, 98, 115]. In contrast, discriminative approaches posit that differentiating between images will give rise to better features. This idea can be traced by to early work on metric learning [18] and dimensionality reduction [35], and is clearly seen for supervised classification models [84]. More recently, Wu et al. [106] proposed treating each image as a separate class and using augmented images as class instances to relieve the need for human annotation. This was followed by papers that simplified this approach [12-14,38] and proposed non-contrastive variants [15, 34]. While those approaches have been successful, the utility of augmentation-based self-supervised", + "bbox": [ + 80, + 628, + 467, + 898 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "learning has been questioned [68, 108] with follow-up work proposing the use of objectness [66, 75] and saliency [81] to alleviate some of those concerns. While we share the goal of visual representation learning, we question the reliance on image augmentations for training and propose using language models to learn for conceptually-similar images.", + "bbox": [ + 503, + 90, + 888, + 181 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Language-supervised vision pre-training aims to learn visual representations from language data. Early work of Li et al. [57] trained n-gram models using YFCC [93] images and user-tag metadata. While some works learn joint vision-and-language representations for tasks like visual question answering [2, 33, 45, 118], visual reasoning [50, 89, 113], and retrieval [72, 112], we are interested in using language to learn better visual representations [23, 23, 76, 80, 88]. Early works used language modeling as a pretext task for visual learning [23, 80], but contrastive approaches quickly gained more popularity due to their relative simplicity and generalization capabilities [47, 76]. Follow-up work extended the contrastive formulation to learn dense features [109, 111] or used additional self-supervised losses to improve performance and data efficiency [21, 56, 59, 67]. While we share the motivation of using language for visual learning, we focus on learning visual representations by using linguistic guidance from pre-trained language models.", + "bbox": [ + 503, + 194, + 888, + 479 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Leveraging structure in the data. This is commonly done in dense feature learning, where optical flow [36, 46, 82, 101] or 3D transformations [29, 44, 83, 90, 105] provide natural associations between image patches. For images, prior approaches used class names [51, 79], class hierarchies [58, 110], meta data [32, 48, 57] or clustering [3, 9, 10, 94, 117] to improve learning and inference. Within contrastive learning, clustering has been a popular choice for leveraging dataset structure. The intuition is that natural clusters emerge in feature spaces that can provide an additional training signal or useful pseudo-labels. While such approaches work well on curated datasets (e.g., ImageNet) where the label set provides an estimate of the number of clusters, it struggles with imbalanced and uncurated data [4]. Other approaches sample nearest neighbors as a feature-driven within-domain augmentation [28, 59]. While these approaches differ in how they extract inter-instance relationships, they all use within-domain feature similarity to sample positive pairs or clusters and hence do not leverage the rich cross-modal relationships. Closest to our work is Han et al. [36] who propose a co-training [6] scheme for jointly learning image and optical flow representations. We share their motivation of using similarity in one space (language) to learn in another (vision). Furthermore, instead of relying on co-training on the same dataset, we extract distances from a text-only language model, allowing us to leverage unaligned data.", + "bbox": [ + 503, + 493, + 888, + 898 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "19209", + "bbox": [ + 482, + 946, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/6ac66ff458ae5d20b4632a624cc94593bf71b2b70fae9662fed97b44d3b8393f.jpg", + "image_caption": [ + "Figure 2. Language sampling yields semantically-similar and visually-diverse image pairs. We sample the three nearest neighbors using a self-supervised visual model [13], an ImageNet supervised model [27], and a self-supervised language model [78]. While visual sampling yields visually similar pairs, language sampling yields semantically relevant and visually diverse images. We argue that the combination of semantic consistency and visual diversity are better for learning generalizable features." + ], + "image_footnote": [], + "bbox": [ + 80, + 89, + 405, + 338 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/a230684098129931190e4cc85dd800f83a851afdfd13bf174ff13d743cbd1b8c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 416, + 90, + 647, + 338 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/a553430611a5e26e5dcb81f6bb1ea8e688c7a49a7f341eefc3ace81c47b95c5d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 658, + 90, + 888, + 338 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 76, + 426, + 166, + 441 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The goal of this work is to learn visual representations that can generalize to other datasets. We extend image-only contrastive learning beyond hand-crafted augmentations and visually-sampled clusters to learn from conceptually similar images. Through learning to associate images that depict the same visual concept, models can learn visual invariances that more closely capture human semantics. To achieve this, we propose sampling image pairs that have similar captions using a pre-trained sentence encoder [78] and using them for contrastive learning. This work does not propose a new model or loss but rather a novel way of sampling image views that is applicable to a variety of approaches and losses for learning visual representations.", + "bbox": [ + 75, + 453, + 472, + 650 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Learning from Conceptual Similarity", + "text_level": 1, + "bbox": [ + 76, + 664, + 398, + 680 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Instance discrimination has been the dominant task for visual representation learning. Its core intuition is that visual similarity is a good proxy for semantic similarity. The standard approach generates positive view pairs using image augmentations and maximizes their embedding similarity, with or without negative views. While there has been a large number of contrastive learning approaches, view pair generation has largely remained the same. Other methods use visual feature similarity to learn prototypes [3, 9, 10] or sample previously seen instances [28] for contrastive learning. While these approaches extend beyond instances and consider relations in the dataset, they still rely on visual similarity to generate their contrastive pairs. This limits the visual invariances that they can learn [108].", + "bbox": [ + 75, + 689, + 468, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We propose training models to identify the same visual concept instead of the same instance. Our key observation is simple: images that have similar captions often depict similar concepts regardless of the actual appearance similarity. This can be clearly seen in Fig. 2. Nearest neighbors in visual representation space depict objects in similar scenes and poses, with self-supervised models showing some color invariances due to color augmentation. Conversely, similarly captioned images depict objects in different colors, poses, and contexts. This makes language-sampled images an excellent source for visual representation learning as they implicitly capture human-like visual invariances.", + "bbox": [ + 496, + 428, + 892, + 609 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Sampling Image Pairs using Language", + "text_level": 1, + "bbox": [ + 500, + 619, + 831, + 636 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given a captioned image dataset, we want to sample image pairs that have very similar captions. While caption similarity may be a good proxy for conceptual similarity, measuring caption similarity is a challenge on its own. Traditional metrics such as BLEU [71] and CIDER [96] rely on n-gram overlap, which can be too sensitive to phrasing and sentence structure. This makes them ill-suited for our needs. Other metrics such as SPICE [1] account for such variety by comparing parse trees; however, they still can not account for different wording choices. Inspired by advances in language models as well as approaches like BERTScore [116] and CLIPScore [43], we use a pre-trained sentence encoder to compute caption similarity.", + "bbox": [ + 496, + 643, + 890, + 839 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Sentence encoders are trained to extract sentence-level features [52, 61, 78]. We use SBERT [78], which finetunes a pre-trained language model to allow it to better capture semantic similarity using feature cosine distance.", + "bbox": [ + 496, + 840, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "19210", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/1397f6941cc01f862fc25d1a2bc7b3e9bc24852c452e9147f4bdb8d7fe166b04.jpg", + "image_caption": [ + "Image Augmentations" + ], + "image_footnote": [], + "bbox": [ + 81, + 89, + 230, + 220 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/c021b8fa370d5c17757a95ebc197718a14e336d0a6056498327ba74d5bb9d22c.jpg", + "image_caption": [ + "Image-Caption" + ], + "image_footnote": [], + "bbox": [ + 245, + 89, + 395, + 220 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/415f2d0674e29c314b0d7041e97c25e5cca5c7d7b006322ac306c25b445effa2.jpg", + "image_caption": [ + "Nearest Neighbor Memory Bank", + "Figure 3. Contrasting Contrastive Formulations. While image-only and image-text contrastive learning directly extract views from the instance, nearest-neighbor methods rely on a memory bank of previously extracted features for training. In contrast, our approach samples nearest neighbors in caption embedding space using a pretrained language model and use the associated image for contrastive learning." + ], + "image_footnote": [], + "bbox": [ + 413, + 88, + 630, + 220 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/3e7b900b88c395b4d6e7d4ad93a1c1082eff483f77887ba015d666e7c8ec58af.jpg", + "image_caption": [ + "Language-Guided Sampling" + ], + "image_footnote": [], + "bbox": [ + 645, + 88, + 890, + 220 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "SBERT is trained in two stages: first, a language backbone is trained using a standard self-supervised task such as masked [25, 65] or permuted [87] language modeling; second, the language modeled is fine-tuned via contrastive learning on a large combined dataset of 1 billion sentence pairs. Fine-tuning the model in a contrastive way simplifies downstream usage as it allows features to be compared directly using cosine similarity. We use an SBERT [78] model with an MPNet [87] backbone. However, we find that our formulation is not sensitive to the choice of language encoder, as shown in Tab. 5c.", + "bbox": [ + 75, + 313, + 468, + 479 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, we sample the nearest neighbors for all captions in the language embedding space. We leverage modern similarity search libraries [49] to perform the nearest neighbor search quickly, despite the large dataset size. For example, nearest neighbor sampling runs in under 3 hours for RedCaps (12 million instances) on 4 GPUs, with 43 minutes spent on feature extraction and 117 minutes on nearest neighbor search. Furthermore, we find that we could further reduce the complexity of the sampling by only searching within subsets of the data as shown in Appendix E.", + "bbox": [ + 75, + 482, + 468, + 633 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Language-Guided Visual Learning", + "text_level": 1, + "bbox": [ + 76, + 648, + 380, + 666 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our approach is applicable to several representation learning methods as it only changes the training view pairs. We focus on contrastive learning since its fairly minimal setting allows us to analyze the impact of language guidance with minimal confounding factors. We train SimCLR with the language-sampled pairs and refer to it as LGSimCLR. We also evaluate the impact of language guidance on SimSiam [15] and SLIP [67], and find that they can similarly benefit from language guidance. We only use random cropping for image augmentations since language-sampled pairs are naturally augmented versions of each other and find that additional augmentations are not helpful. For LGSLIP, we match their setup by applying the CLIP loss only between the source's image and caption, ignoring an additional loss between the nearest neighbor image and its caption.", + "bbox": [ + 75, + 674, + 470, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 311, + 632, + 330 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our experiments evaluate the efficacy of learning visual features from conceptually similar images. We hypothesize that a model trained with language guidance will learn useful visual invariances and better generalize to downstream tasks. We are interested in answering these questions: Does language guidance improve generalization over other pretraining approaches? Does language guidance generalize to other datasets and pre-training approaches? How can language be used for visual pre-training?", + "bbox": [ + 496, + 338, + 890, + 474 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 500, + 484, + 689, + 500 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We formulate our experimental setup to compare the efficacy of different learning signals. We train models with language-guided sampling and compare them with image-only self-supervised models and image-text contrastive models. We are interested in conducting controlled experiments for a fair comparison.", + "bbox": [ + 496, + 508, + 890, + 598 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Recent work in self-supervised learning has demonstrated the impressive impact of scaling [12, 76, 114]. While such work has shown impressive performance, it has complicated the evaluation as different models are trained on different pretext tasks on different datasets using varying amounts of compute and training recipes. Furthermore, replication is difficult, if not impossible, due to the unavailability of training data or prohibitive compute requirements. Fortunately, several papers report results that indicate that performance patterns often hold at smaller scales [10, 12, 21, 67, 76]. Hence, we conduct our experiments at a scale that allows us to perform a comprehensive evaluation and permits replication by others.", + "bbox": [ + 496, + 599, + 892, + 794 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We conduct our experiments with a standard backbone [39] on publicly available datasets [11, 24, 85]. To account for variation in training recipes, we retrain all methods from scratch using the same training recipe. We scale down experiments to a level that permits fair comparisons and replication. We also provide system-level comparisons in Tab. 4 and scaling results in App. D.", + "bbox": [ + 496, + 795, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "19211", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Training details: We use a ResNet-50 backbone and train all models using the AdamW optimizer [63] with a learning rate of $10^{-3}$ and a weight decay of $10^{-2}$ . We use a cosine learning scheduler [62] with 5000 warm-up steps. Models are trained using a batch size of 512 for 250k steps; this corresponds to 10.5 epochs on RedCaps. We use a constant number of steps to permit meaningful comparisons between models trained on different datasets.", + "bbox": [ + 75, + 90, + 472, + 210 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation setup: We evaluate all approaches using linear probe and fewshot classification on 15 classification datasets inspired by [53,76]. We use the linear probe evaluation proposed by [53] and learn a single linear layer using logistic regression. We sweep over a range of cost values and choose the value with the best validation performance. We retrain a classifier on both train and validation splits and report test performance. We also evaluate all approaches on fewshot classification to understand their generalization ability. We use a weighted kNN classifier on frozen support features inspired by prior work showing its effectiveness for fewshot classification [102]. Please see Appendices A and B for more details on evaluation datasets and tasks.", + "bbox": [ + 75, + 214, + 472, + 411 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Baselines: While there have been many proposed visual representation learning approaches, they can be grouped into several key directions that differ in the pretext task. We focus our comparison on a few representative approaches to explore the impact of the learning signal. We overview the baselines here and provide more details in Appendix C.", + "bbox": [ + 75, + 417, + 468, + 508 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Many of our baselines are variants of contrastive learning as shown in Fig. 3. Contrastive approaches operate over paired source and target feature embeddings: $z^s$ and $z^t$ . The goal is to maximize the similarity between the paired embeddings and minimize it with respect to all other embeddings. Given a batch size $N$ and embedding dimension $F$ , $z^s, z^t \\in \\mathbb{R}^{N \\times F}$ . The contrastive loss [86] is:", + "bbox": [ + 75, + 508, + 470, + 614 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} \\left(z ^ {s}, z ^ {t}\\right) = - \\log \\frac {\\exp \\left(\\operatorname {s i m} \\left(\\boldsymbol {z} _ {i} ^ {s} , \\boldsymbol {z} _ {i} ^ {t}\\right) / \\tau\\right)}{\\sum_ {k = 1} ^ {N} \\exp \\left(\\operatorname {s i m} \\left(\\boldsymbol {z} _ {i} ^ {s} , \\boldsymbol {z} _ {k} ^ {t}\\right) / \\tau\\right)}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 104, + 623, + 468, + 661 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\tau$ is a scaling parameter and $\\mathrm{sim}(\\cdot ,\\cdot)$ is cosine similarity. Contrastive approaches primarily differ in how the embeddings are computed.", + "bbox": [ + 75, + 667, + 468, + 714 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Image-Only Contrastive Learning contrasts features extracted from two randomly augmented views of the same image to perform instance discrimination [106]. We use SimCLR [12] as a representative approach due to its simplicity and strong performance.", + "bbox": [ + 75, + 715, + 468, + 792 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Image-Text Contrastive Learning learns by contrasting features extracted from images and their captions. Unlike image-only approaches, this approach can learn semantics from the captions. Radford et al. [76] first proposed this approach and has had several follow-ups that augment it with additional self-supervised losses losses [56, 59, 67]. We use CLIP [76] and SLIP [67] due to their simplicity.", + "bbox": [ + 75, + 795, + 470, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Nearest Neighbor Contrastive Learning contrast source embeddings with retrieved embeddings from a memory bank. The target features are used to retrieve the nearest neighbor embedding from a memory bank of previous batches. Dwibedi et al. [28] proposed this approach for image-only contrastive learning, while Li et al. [59] proposed adapting this loss for language embeddings. We use NNCLR [28] as Visual NNCLR and DeCLIP [59] with the CLIP and the language NNS losses as Language NNCLR.", + "bbox": [ + 496, + 90, + 890, + 227 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Image-Only Non-Contrastive Learning deviates from the typical contrastive setup by learning without negative samples [15, 34]. We use SimSiam as a representative approach due to its simplicity and strong performance.", + "bbox": [ + 496, + 236, + 893, + 297 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Cluster-based Contrastive Learning learn by contrasting image features with learned prototypes [3,9,10]. Prototypes are estimated via clustering or learned jointly with the feature encoder. Caron et al. [10] report that different cluster-based approaches perform similarly when provided with the same algorithmic advances. We use an adapted SwAV without the multi-crop augmentation strategy as it is equally applicable to other methods. We also compare against a pretrained SwAV checkpoint in Tab. 4.", + "bbox": [ + 496, + 306, + 890, + 441 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Results", + "text_level": 1, + "bbox": [ + 500, + 458, + 591, + 472 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We train all approaches with a ResNet-50 backbone on RedCaps and report results in Tabs. 1 and 3. Our model outperforms all baselines with a significant margin for both evaluations. We analyze the results below through a series of questions.", + "bbox": [ + 496, + 483, + 890, + 559 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Does language-guided sampling provide better training pairs than image augmentations? LGSimCLR greatly outperforms SimCLR despite using the same learning objective. By using language sampled pairs instead of image augmentations, LGSimCLR learns stronger invariances. We find that the largest gains arise in fine-grained datasets: Cars, CUB, and Food101. The performance gains can be explained by considering the critique of Xiao et al. [108]: the training augmentations dictate the invariances learned by SimCLR as shown in nearest neighbor samples in Fig. 2. Consider the third row of Fig. 2, while language sampling depicts three Aston Martin cars in different spots, visual nearest neighbors are sports cars in different poses and colors, closely resembling the flip and color augmentations used for training. Similarly in the first row of Fig. 2, visual nearest neighbors depict owls from different species in similar poses, while language sampling retrieves three great horned owls from different viewpoints. These trends are further amplified when features are used directly for fewshot classification. Language guidance allows us to capture relationships that go beyond visual similarity by training on image pairs that capture human semantics.", + "bbox": [ + 496, + 568, + 892, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "19212", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/d1f4c4c26c7493ba59537abcc0614d57abbde81dfde0aedcab0a86d16130f62a.jpg", + "table_caption": [ + "Table 1. Linear Probe Evaluations. We train ResNet-50 models on RedCaps and report performance of a linear probe using frozen features on 15 downstream tasks. Models are split based on whether or not they require caption images for training. LGSimCLR outperforms all previous approaches with strong performance gains for fine-grained classification datasets." + ], + "table_footnote": [], + "table_body": "
ModelFood-101CIFAR-10CIFAR-100CUBSUN397CarsAircraftDTDPetsCaltech-101FlowersSTL-10EuroSATRESISC45PCAMMean
SwAV63.681.357.521.647.522.935.468.161.170.578.087.794.379.984.363.6
SimSiam64.179.956.128.248.329.541.266.269.173.683.685.794.482.183.365.7
Visual NNCLR65.482.860.226.650.026.640.968.065.275.483.588.595.382.283.866.3
SimCLR69.082.961.630.652.633.743.769.870.574.186.988.095.484.684.468.5
Language NNCLR81.283.161.948.656.545.137.268.878.182.090.293.492.581.180.772.0
CLIP80.984.762.750.457.445.836.767.679.884.091.093.593.982.282.672.9
SLIP77.787.267.042.458.148.745.272.379.582.792.192.795.685.583.474.0
LGSimCLR (Ours)83.287.869.059.360.362.353.471.281.889.495.994.095.688.081.178.2
", + "bbox": [ + 76, + 138, + 893, + 333 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/92f71a19b956769e81e37cfb6a4e1c482d022d9c27185ea664c3f1ae677b185b.jpg", + "image_caption": [ + "Figure 4. Nearest Neighbor methods are limited by the memory bank size. Even with a large memory bank, the nearest embedding can still be unrelated to the source image while language sampling provides us with conceptually similar pairs." + ], + "image_footnote": [], + "bbox": [ + 89, + 356, + 459, + 534 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Can we just sample nearest neighbors from previous batches? LGSimCLR outperforms NNCLR despite both relying on nearest neighbors. NNCLR uses the nearest feature embedding from a memory bank in the same modality. The quality of their retrieved samples is limited by the size of the memory bank. To demonstrate this, we visualize the nearest neighbors retrieved by NNCLR for different memory bank sizes in Fig. 4. We find that the retrieval quality is poor even for larger queues. Interestingly, we note that NNCLR also underperforms SimCLR on RedCaps, despite performing better on ImageNet. We posit that ImageNet's curated distribution explains this: a queue of $16k$ will most probably contain instances from each class, resulting in both visually and conceptually similar retrievals. Additionally, the quality of nearest neighbors is affected by the sampling feature space; features that are only trained on image augmentations will have limited invariances as shown in Fig. 2. We further explore the impact of sampling space on training in Sec. 4.3.", + "bbox": [ + 75, + 613, + 472, + 902 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/092766732b0b2a305b5e33a5559d819b92d9550e6a8b51aa1cf87d060c368f37.jpg", + "table_caption": [ + "Table 2. Language-guided contrastive learning outperforms image-text contrastive learning, regardless of text encoder." + ], + "table_footnote": [], + "table_body": "
ObjectiveText EncoderLinearFewshot
Image-TextRandomly- Initialized72.977.5
Frozen SBERT71.877.1
Image-ImageFrozen CLIP (RedCaps)78.382.4
Frozen SBERT78.282.5
", + "bbox": [ + 500, + 388, + 890, + 472 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Can cluster-based approaches learn better features? Similar to nearest-neighbor sampling, clustering is performed using visual similarity. Furthermore, it is based on an estimated number of clusters in the training dataset. Although this can be determined for ImageNet due to its known class structure, the number of clusters in an arbitrary uncurated dataset is unknown. This results in a large performance drop, as seen in Tab. 1 and Tab. 3. On the other hand, sampling related pairs assumes no global structure within the data and hence is able to better capture inter-instance similarity. This results in nearest-neighbor sampling outperforming clustering and both being outperformed by contrastive learning and language-guided contrastive learning.", + "bbox": [ + 496, + 500, + 890, + 696 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Should we use language for guidance or supervision? Our experiments indicate that LGSimCLR outperforms both CLIP and SLIP. We consider two possible explanations: (a) SBERT extracts better language embeddings than CLIP can learn from the data, or (b) language-guided contrastive learning is a better training objective than imag-text contrastive learning. To evaluate this, we compare four models in Tab. 2. The first two models use CLIP's training objective: the first model uses a randomly initialized language encoder, similar to CLIP. The second model uses a frozen SBERT model as the language encoder and only trains the projection layers. The second two models use LGSimCLR's training objective but sample pairs using a", + "bbox": [ + 496, + 704, + 890, + 901 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "19213", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/1336d8a77e1a78060af2c5dd4c1e9bc56a688a62d17f888a72f860abbc6a56d9.jpg", + "table_caption": [ + "Table 3. Few-Shot Evaluations. We train ResNet-50 models on RedCaps and report 5-way, 5-shot classification performance. We observe that language results in huge performance gains as shown by the performance of CLIP and LGSimCLR. Furthermore, the use of any augmentations hurts performance as seen by SLIP's drop in performance." + ], + "table_footnote": [], + "table_body": "
ModelFood-101CIFAR-10CIFAR-100CUBSUN397CarsAircraftDTDPetsCaltech-101FlowersSTL-10EuroSATRESISC45Mean
SwAV64.554.061.845.884.936.534.174.866.578.175.572.680.472.964.5
SimSiam63.949.957.249.584.539.337.975.767.879.781.569.680.679.465.5
Visual NNCLR65.654.161.745.885.337.934.975.267.381.175.474.383.676.765.6
SimCLR66.945.751.051.587.144.038.477.670.180.086.969.683.581.366.7
Language NNCLR89.365.373.478.690.868.440.475.278.890.994.389.675.271.977.3
CLIP88.964.673.178.390.969.740.775.777.591.694.789.875.374.877.5
SLIP81.563.570.863.191.362.942.179.676.488.492.283.482.780.875.6
LGSimCLR (Ours)90.366.375.583.192.777.650.681.184.195.497.686.585.089.082.5
", + "bbox": [ + 76, + 138, + 893, + 333 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "pre-trained language-only SBERT or the language encoder from a CLIP model trained on RedCaps. We find that image-image contrastive learning yields better visual features for both setups. While CLIP does not benefit from an SBERT backbone, LGSimCLR benefits from sampling using a language encoder trained on the same dataset. This suggests that learning joint embeddings results in worse visual features than language-guided learning.", + "bbox": [ + 75, + 356, + 470, + 478 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "System-level comparisons: We compare LGSimCLR with publicly-available checkpoints of prior approaches; see Appendix C for details. We emphasize that while the experiments reported in Tabs. 1 and 3 were done in a controlled setup (same batch size, training data, optimizer), the system level comparisons are trained on different datasets with different training recipes and enhancements to further boost performance; e.g., large batch sizes, longer training, multi-crop augmentation. Furthermore, it has been shown that models trained on ImageNet implicitly benefit from its curated nature [4, 67]. Nevertheless, our approach still outperforms prior self-supervised approaches. We fall short of CLIP's ResNet-50 due to its training scale; $64 \\times$ larger batch, $32 \\times$ larger dataset, and $75 \\times$ . We also observe that ImageNet-supervised ResNet-50 achieves better few-shot performance. Examining the performance breakdown in Tab. 10, we find the improvement mainly comes from CIFAR10, CIFAR100, and Pets. We posit that this can be explained by ImageNet's class structure: mostly pets with a large overlap with CIFAR's classes.", + "bbox": [ + 75, + 484, + 470, + 787 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Analysis", + "text_level": 1, + "bbox": [ + 76, + 800, + 181, + 816 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We now analyze language-guided contrastive learning by evaluating the impact of pre-training data, the choice of embedding space, and the pretext task. By understanding the impact of those choices, we can better understand what the model is learning.", + "bbox": [ + 75, + 824, + 468, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/d7e86ccea587475fd2f6689b4d1d916bb626f8fba9fc906898514fb331d9865a.jpg", + "table_caption": [ + "Table 4. ResNet-50 System Level Comparisons. We outperform prior self-supervised approaches despite them benefiting from ImageNet's curation for training and using larger batch sizes. CLIP outperforms us due to the scale of its training." + ], + "table_footnote": [], + "table_body": "
Batch#Img UpdatesDatasetLinearFewshot
Supervised [104]10241.3×108ImageNet78.085.7
SimSiam [15]5121.3×108ImageNet72.978.7
SimCLR [13]40961.0×109ImageNet75.477.4
MoCo [16]40961.3×108ImageNet77.780.1
SwAV [10]40961.3×108ImageNet78.278.5
CLIP [76]327681.0×1010CLIP81.887.8
LGSimCLR5121.3×108RedCaps78.282.5
", + "bbox": [ + 501, + 417, + 890, + 529 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Approach generality: We extend language guidance to other contrastive approaches: SimSiam and SLIP. We observe that language guidance uniformly improves performance for all methods, as shown in Tab. 5a. Furthermore, the difference between SimCLR and SLIP shrinks when adding language guidance. This suggests that language guidance provides the model with similar semantics to the ones learned from an image-text contrastive loss, resulting in diminished gains from the additional image-text loss.", + "bbox": [ + 496, + 558, + 893, + 696 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Impact of training dataset: We train our model on four datasets: CC3M [85], CC12M [11], RedCaps-2020, and RedCaps [24]. In Tab. 5b, we observe that larger datasets result in stronger performance, indicating that our approach could scale well with even larger datasets. Furthermore, we observe that RedCaps results in better performance than Conceptual Captions. This may be attributed to the higher quality of captions in RedCaps; while the alt-text captions CC3M and CC12M can be short and contain image metadata, RedCaps captions are diverse, longer, and more descriptive. This allows our model to sample more interesting visual pairs that capture more visual diversity. We provide qualitative results in Appendix F to support this.", + "bbox": [ + 496, + 703, + 893, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "19214", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/f90185898f69db080285d7f9bad2ed26f0f3294fa290c74fffb10bddba32cd19.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Image Aug.Language
LinearFewshotLinearFewshot
SimSiam65.765.571.275.7
SimCLR68.566.778.282.5
SLIP74.075.678.882.8
", + "bbox": [ + 86, + 108, + 359, + 185 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/24d6888625cec079092ae956ad3791b70a7fc66c01c19d322fd741d334ab28f2.jpg", + "table_caption": [ + "(a) Approach Generality" + ], + "table_footnote": [], + "table_body": "
SizeLinearFewshot
CC3M2.7M71.576.3
CC12M10.9M76.881.9
RedCaps 20203.2M73.878.8
RedCaps12.0M78.282.5
", + "bbox": [ + 372, + 114, + 620, + 186 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/a88fde085caf1509c7ec5e88146e83faac4917e791640a770e77fd9025e26fea.jpg", + "table_caption": [ + "(b) Impact of training dataset" + ], + "table_footnote": [], + "table_body": "
LinearFewshot
SBERT (MPNet)78.282.5
SBERT (MiniLM)78.683.3
CLIP Language (ViT-B/32)78.383.1
FastText BoW76.180.9
ImageNet-supervised78.381.8
SimCLR (ImageNet)73.174.6
", + "bbox": [ + 635, + 88, + 888, + 185 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "(c) Impact of sampling space", + "bbox": [ + 676, + 188, + 836, + 200 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Analysis Experiments. We conduct a series of analysis experiments to understand language-guided contrastive learning. The results indicate that language sampling is beneficial to several formulations and scales well with larger datasets. Furthermore, while language sampling consistently results in good pairs for training, visual sampling only helps if it has access to semantics through supervision.", + "bbox": [ + 75, + 205, + 892, + 248 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Impact of sampling space: The idea of using offline nearest-neighbor sampling does not require a specific language model or even a specific modality. We explore other choices for embedding space: four sentence encoders and two image models. In our experiments, we use SBERT's MPNet model [78, 87]; the highest performing SBERT model for sentence similarity. We compare it to two other sentence transformers: a smaller SBERT model, MiniLM [100], and the language encoder from CLIP [76]. We also compared against a bag-of-words (BoW) sentence encoder that uses FastText [7] embeddings. Results are in Tab. 5c. While we expected that using CLIP for sampling would improve performance due to its multimodal training, we were surprised that MiniLM also improved performance despite its lower performance on language tasks. We find that pairs obtained using a BoW model result in a weaker performance which might hint at the importance of contextual sentence embeddings. Nevertheless, the BoW-sampled pairs still result in higher performance than all the other baselines on RedCaps.", + "bbox": [ + 75, + 266, + 472, + 569 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We also consider training with pairs sampled using two visual models: ImageNet-supervised ResNet-50 [104] and ImageNet-trained SimCLR [13]. We find that using a visual model for sampling is only beneficial if the visual model captures semantic relations; e.g., through supervised training. Using a self-supervised language model results in a strong drop in performance relative to the other sampling spaces. Nevertheless, it still allows the model to achieve better performance than using a self-supervised visual approach on the same data. This indicates that while language is a better modality to use, \"sample-guided\" contrastive learning can still achieve a stronger performance than only using self-supervised learning.", + "bbox": [ + 75, + 574, + 468, + 772 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations: We observe a few limitations in our approach. Image captions can be noisy, vague, and often omit obvious relations in the image [5]. While this broadly affects image-language models, it can result in us retrieving unrelated image pairs. For example, captions like \"I found this in the garden\" or \"Photo from our family trip\" could describe a large range of images, some of which are unrelated. We expand on this in Appendix F. Image descriptions also de", + "bbox": [ + 75, + 779, + 470, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "pend on the context and the perceiver; e.g., a tourist and an art curator will describe artwork in very different ways. We observe that descriptions in topic-focused subreddits (e.g., r/birdpics and r/woodworking) are more specific than in generic subreddits (e.g., r/itookapicture and r/pics). Our experiments in Appendix E support this observation. Since a caption only captures one aspect of the image, sampled pairs can be similar for a variety of reasons. Allowing the model to condition the feature extraction or similarity calculation on captions could alleviate this issue.", + "bbox": [ + 496, + 267, + 890, + 417 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 430, + 617, + 446 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We propose using language to find conceptually similar images for contrastive learning. This is based on a simple observation: people describe an object in similar ways even when it appears in different contexts. We use pre-trained language models to sample similar captions and use the captioned images for contrastive learning. We hypothesize that using language guidance instead of image augmentations would result in learning more human-like invariances.", + "bbox": [ + 496, + 455, + 890, + 575 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We evaluate our approach on multiple train and test datasets and find that it outperforms previous self-supervised and image-text contrastive models. Our analysis demonstrates the utility of using nearest-neighbor instances for training and the superiority of language sampling over other approaches for unlabeled datasets. Our findings align with prior work that critiques the use of image augmentations [81, 108] and shows the utility of cross-modal guidance [36] and intra-instance relationships [28, 51]. Our results demonstrate the potential of incorporating language guidance in contrastive learning. We hope that future work will explore scaling up our approach to larger and more diverse datasets, as well as modeling approaches that further integrate language into the learning process.", + "bbox": [ + 496, + 575, + 890, + 787 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments: We thank Richard Higgins, Ashkan Kazemi, and Santiago Castro for many helpful discussions, as well as David Fouhey, Ziyang Chen, Chenhao Zheng, Fahad Kamran, and Dandan Shan for their feedback on early drafts. This project was funded under the Ford-UM Alliance partnership; we thank Alireza Rahimpour, Devesh Upadhyay, and Ali Hassani from Ford Research for their support and discussion.", + "bbox": [ + 496, + 795, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "19215", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. SPICE: Semantic propositional image caption evaluation. In Proceedings of European Conference on Computer Vision (ECCV), 2016. 3", + "[2] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. VQA: Visual question answering. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2", + "[3] Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi. Self-labelling via simultaneous clustering and representation learning. In International Conference on Learning Representations, 2019. 1, 2, 3, 5", + "[4] Mido Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, and Nicolas Ballas. The hidden uniform cluster prior in self-supervised learning. In Proceedings of the International Conference on Learning Representations (ICLR), 2023. 2, 7", + "[5] Hessam Bagherinezhad, Hannaneh Hajishirzi, Yejin Choi, and Ali Farhadi. Are elephants bigger than butterflies? reasoning about sizes of objects. In AAAI Conference on Artificial Intelligence, 2016. 8", + "[6] Avrim Blum and Tom Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of Annual Conference on Computational learning theory, 1998. 2", + "[7] Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606, 2016. 8", + "[8] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101 - mining discriminative components with random forests. In European Conference on Computer Vision, 2014. 15", + "[9] Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. Deep clustering for unsupervised learning of visual features. In Proceedings of the European conference on computer vision (ECCV), pages 132-149, 2018. 1, 2, 3, 5", + "[10] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In Advances in Neural Information Processing Systems (NeurIPS), volume 33, 2020. 1, 2, 3, 4, 5, 7, 15, 21, 22, 23", + "[11] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 4, 7", + "[12] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the International Conference on Machine Learning (ICML), 2020. 1, 2, 4, 5, 15, 16", + "[13] Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton. Big self-supervised" + ], + "bbox": [ + 86, + 114, + 470, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "models are strong semi-supervised learners. In Advances in Neural Information Processing Systems (NeurIPS), volume 33, 2020. 1, 2, 3, 7, 8, 15, 16, 20, 21, 22, 23", + "[14] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297, 2020. 1, 2", + "[15] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 1, 2, 4, 5, 7, 21, 22, 23", + "[16] Xinlei Chen*, Saining Xie*, and Kaiming He. An empirical study of training self-supervised vision transformers. arXiv preprint arXiv:2104.02057, 2021. 7, 15, 21, 22, 23", + "[17] Gong Cheng, Junwei Han, and Xiaoqiang Lu. Remote sensing image scene classification: Benchmark and state of the art. Proceedings of the IEEE, 105(10):1865-1883, Oct 2017. 15", + "[18] Sumit Chopra, Raia Hadsell, and Yann LeCun. Learning a similarity metric discriminatively, with application to face verification. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005. 2", + "[19] M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi. Describing textures in the wild. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. 15", + "[20] Adam Coates, Andrew Ng, and Honglak Lee. An Analysis of Single Layer Networks in Unsupervised Feature Learning. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2011. https://cs.stanford.edu/~acoates/papers/coatesleeng_iastats_2011.pdf. 15", + "[21] Yufeng Cui, Lichen Zhao, Feng Liang, Yangguang Li, and Jing Shao. Democratizing contrastive language-image pretraining: A CLIP benchmark of data, model, and supervision. In ICML Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward, 2022. 2, 4", + "[22] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009. 15, 18", + "[23] Karan Desai and Justin Johnson. Virtex: Learning visual representations from textual annotations. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 11162-11173, 2021. 2", + "[24] Karan Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson. RedCaps: Web-curated image-text data created by the people, for the people. In NeurIPS Datasets and Benchmarks, 2021. 4, 7", + "[25] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT), 2018. 4", + "[26] Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2" + ], + "bbox": [ + 509, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "19216", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[27] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In Proceedings of the International Conference on Learning Representations (ICLR), 2021. 3, 15, 20", + "[28] Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. With a little help from my friends: Nearest-neighbor contrastive learning of visual representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 9588–9597, October 2021. 1, 2, 3, 5, 8, 16", + "[29] Mohamed El Banani, Luya Gao, and Justin Johnson. Unsupervisedr&r: Unsupervised point cloud registration via differentiable rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7129-7139, 2021. 2", + "[30] Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Pattern Recognition Workshop, 2004. 14, 15", + "[31] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In Proceedings of the International Conference on Learning Representations (ICLR), 2018. 2", + "[32] Yunchao Gong, Qifa Ke, Michael Isard, and Svetlana Lazebnik. A multi-view embedding space for modeling internet images, tags, and their semantics. In IJCV, 2014. 2", + "[33] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. Making the V in VQA matter: Elevating the role of image understanding in visual question answering. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 2", + "[34] Jean-Bastien Grill, Florian Strub, Florent Alché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in Neural Information Processing Systems (NeurIPS), 2020. 2, 5", + "[35] Raia Hadsell, Sumit Chopra, and Yann LeCun. Dimensionality reduction by learning an invariant mapping. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2006. 2", + "[36] Tengda Han, Weidi Xie, and Andrew Zisserman. Self-supervised co-training for video representation learning. In Advances in Neural Information Processing Systems (NeurIPS), 2020. 2, 8", + "[37] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2", + "[38] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 2" + ], + "bbox": [ + 86, + 92, + 470, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[39] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 4, 15", + "[40] Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019. 15", + "[41] Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer. The many faces of robustness: A critical analysis of out-of-distribution generalization. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 15, 18", + "[42] Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song. Natural adversarial examples. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 15, 18", + "[43] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021. 3", + "[44] Ji Hou, Saining Xie, Benjamin Graham, Angela Dai, and Matthias Nießner. Pri3d: Can 3d priors help 2d representation learning? In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 2", + "[45] Drew A Hudson and Christopher D Manning. GQA: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2", + "[46] Allan Jabri, Andrew Owens, and Alexei Efros. Space-time correspondence as a contrastive random walk. Advances in Neural Information Processing Systems (NeurIPS), 2020. 2", + "[47] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In Proceedings of the International Conference on Machine Learning (ICML), 2021. 2", + "[48] Justin Johnson, Lamberto Ballan, and Li Fei-Fei. Love thy neighbors: Image annotation by exploiting image metadata. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2", + "[49] Jeff Johnson, Matthijs Douze, and Hervé Jégou. Billionscale similarity search with GPUs. IEEE Transactions on Big Data, 2019. 4", + "[50] Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg. Referitgame: Referring to objects in photographs of natural scenes. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014. 2", + "[51] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning." + ], + "bbox": [ + 509, + 92, + 893, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "19217", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "In Advances in Neural Information Processing Systems (NeurIPS), 2020. 2, 8", + "[52] Ryan Kiros, Yukun Zhu, Russ R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. Skip-thought vectors. In Advances in Neural Information Processing Systems (NeurIPS), 2015. 3", + "[53] Simon Kornblith, Jonathon Shlens, and Quoc V Le. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2661-2671, 2019. 5, 14", + "[54] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), Sydney, Australia, 2013. 15", + "[55] Alex Krizhevsky. Learning multiple layers of features from tiny images, 2009. 15", + "[56] Janghyeon Lee, Jongsuk Kim, Hyounguk Shon, Bumsoo Kim, Seung Hwan Kim, Honglak Lee, and Junmo Kim. Uniclip: Unified framework for contrastive language-image pre-training. In Advances in Neural Information Processing Systems (NeurIPS), 2022. 2, 5", + "[57] Ang Li, Allan Jabri, Armand Joulin, and Laurens van der Maaten. Learning visual n-grams from web data. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017. 2", + "[58] Aoxue Li, Tiange Luo, Zhiwu Lu, Tao Xiang, and Liwei Wang. Large-scale few-shot learning: Knowledge transfer with class hierarchy. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2", + "[59] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. SupervisionexistsEverywhere:A Data Efficient Contrastive Language-Image Pre-training Paradigm.In Proceedings of the International Conference on Learning Representations (ICLR),2022.2,5,16", + "[60] Dong C Liu and Jorge Nocedal. On the limited memory bfgs method for large scale optimization. Mathematical programming, 1989. 14", + "[61] Lajanugen Logeswaran and Honglak Lee. An efficient framework for learning sentence representations. In Proceedings of the International Conference on Learning Representations (ICLR), 2018. 3", + "[62] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016.5", + "[63] Ilya Loshchilov and Frank Hutter. Decoupled Weight Decay Regularization. In Proceedings of the International Conference on Learning Representations (ICLR), 2019. 5", + "[64] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, 2013. 15", + "[65] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (NeurIPS), 2013. 4" + ], + "bbox": [ + 86, + 90, + 470, + 898 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[66] Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Abhishek Sharma, David Jacobs, and Dilip Krishnan. Object-aware cropping for self-supervised learning. arXiv preprint arXiv:2112.00319, 2021. 2", + "[67] Norman Mu, Alexander Kirillov, David Wagner, and Saining Xie. Slip: Self-supervision meets language-image pretraining. In Proceedings of European Conference on Computer Vision (ECCV), 2022. 2, 4, 5, 7, 15, 16", + "[68] Alejandro Newell and Jia Deng. How useful is self-supervised pretraining for visual tasks? In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 2", + "[69] M-E. Nilsback and A. Zisserman. Automated flower classification over a large number of classes. In Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, Dec 2008. 15", + "[70] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 2", + "[71] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the Annual Meeting on Association for Computational Linguistics (ACL), 2002. 3", + "[72] Yookoon Park, Mahmoud Azab, Bo Xiong, Seungwhan Moon, Florian Metze, Gourab Kundu, and Kirmani Ahmed. Normalized contrastive learning for text-video retrieval. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022. 2", + "[73] O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar. Cats and dogs. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. 15", + "[74] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (NeurIPS), 2019. 14", + "[75] Xiangyu Peng, Kai Wang, Zheng Zhu, Mang Wang, and Yang You. Crafting better contrastive views for siamese representation learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 16031-16040, 2022. 2", + "[76] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference on Machine Learning (ICML), 2021. 1, 2, 4, 5, 7, 8, 14, 16, 21, 22, 23", + "[77] Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. Do imagenet classifiers generalize to imageNet? In Proceedings of the International Conference on Machine Learning (ICML), 2019. 15, 18", + "[78] Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural" + ], + "bbox": [ + 509, + 92, + 893, + 900 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "19218", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Language Processing. Association for Computational Linguistics, 11 2019. 3, 4, 8, 14, 20", + "[79] Karsten Roth, Oriol Vinyls, and Zeynep Akata. Integrating Language Guidance into Vision-based Deep Metric Learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2", + "[80] Mert Bulent Sariyildiz, Julien Perez, and Diane Larlus. Learning visual representations with caption annotations. In Proceedings of European Conference on Computer Vision (ECCV), 2020. 2", + "[81] Ramprasaath R Selvaraju, Karan Desai, Justin Johnson, and Nikhil Naik. Casting your model: Learning to localize improves self-supervised representations. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 11058-11067, 2021. 2, 8", + "[82] Dandan Shan, Richard Ely Locke Higgins, and David Fouhey. Cohesiv: Contrastive object and hand embedding segmentation in video. In Advances in Neural Information Processing Systems (NeurIPS), 2021. 2", + "[83] Jinghuan Shang, Srijan Das, and Michael S Ryoo. Learning viewpoint-agnostic visual representations by recovering tokens in 3d space. Advances in Neural Information Processing Systems (NeurIPS), 2022. 2", + "[84] Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson. Cnn features off-the-shelf: an astounding baseline for recognition. In CVPRW, 2014. 2", + "[85] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual Captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2018. 4, 7", + "[86] Kihyuk Sohn. Improved deep metric learning with multiclass n-pair loss objective. In Advances in Neural Information Processing Systems (NeurIPS), 2016. 5", + "[87] Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. MPNet: Masked and Permuted Pre-training for language understanding. Advances in Neural Information Processing Systems (NeurIPS), 2020. 4, 8, 14", + "[88] Jonathan C Stroud, Zhichao Lu, Chen Sun, Jia Deng, Rahul Sukthankar, Cordelia Schmid, and David A Ross. Learning video representations from textual web supervision. arXiv preprint arXiv:2007.14937, 2020. 2", + "[89] Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Hua-jun Bai, and Yoav Artzi. A corpus for reasoning about natural language grounded in photographs. In Proceedings of the Annual Meeting on Association for Computational Linguistics (ACL), 2019. 2", + "[90] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. LoFTR: Detector-free local feature matching with transformers. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 2", + "[91] Igor Susmelj, Matthias Heller, Philipp Wirth, Prescott Jeremy, and Malte Ebner. Lightly. GitHub. Note: https://github.com/lightly-ai/lightly, 2020. 16", + "[92] TensorFlow Datasets, a collection of ready-to-use datasets. https://www.tensorflow.org/datasets.14" + ], + "bbox": [ + 86, + 92, + 470, + 898 + ], + "page_idx": 11 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[93] Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. YFCC100M: The new data in multimedia research. Communications of the ACM, 2016. 2", + "[94] Yonglong Tian, Olivier J Henaff, and Aaron van den Oord. Divide and contrast: Self-supervised learning from uncu-rated data. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021. 2", + "[95] Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola. Rethinking few-shot image classification: a good embedding is all you need? In Proceedings of European Conference on Computer Vision (ECCV), 2020. 14", + "[96] Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. CIDEr: Consensus-based image description evaluation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 3", + "[97] Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling. Rotation equivariant cnns for digital pathology. In International Conference on Medical image computing and computer-assisted intervention, 2018. 15", + "[98] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the International Conference on Machine Learning (ICML), 2008. 2", + "[99] Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing. Learning robust global representations by penalizing local predictive power. In Advances in Neural Information Processing Systems (NeurIPS), 2019. 15, 18", + "[100] Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. Advances in Neural Information Processing Systems (NeurIPS), 2020. 8, 14", + "[101] Xiaolong Wang, Allan Jabri, and Alexei A Efros. Learning correspondence from the cycle-consistency of time. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2", + "[102] Yan Wang, Wei-Lun Chao, Kilian Q. Weinberger, and Laurens van der Maaten. Simpleshot: Revisiting nearest-neighbor classification for few-shot learning. arXiv preprint arXiv:1911.04623, 2019. 5, 14", + "[103] P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona. Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology, 2010. 15", + "[104] Ross Wightman, Hugo Touvron, and Herve Jegou. Resnet strikes back: An improved training procedure in timm. In NeurIPS Workshop on ImageNet: Past, Present, and Future, 2021. 7, 8, 15, 21, 22, 23", + "[105] Xiaoshi Wu, Hadar Averbuch-Elor, Jin Sun, and Noah Snavely. Towers of babel: Combining images, language, and 3d geometry for learning multimodal vision. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 2" + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "19219", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 11 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[106] Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. Unsupervised feature learning via non-parametric instance discrimination. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3733-3742, 2018. 1, 2, 5", + "[107] J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. SUN database: Large-scale scene recognition from abbey to zoo. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. 15", + "[108] Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell. What should not be contrastive in contrastive learning. In International Conference on Learning Representations, 2020. 2, 3, 5, 8", + "[109] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2", + "[110] Zhicheng Yan, Hao Zhang, Robinson Piramuthu, Vignesh Jagadeesh, Dennis DeCoste, Wei Di, and Yizhou Yu. Hd-cnn: hierarchical deep convolutional neural networks for large scale visual recognition. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2", + "[111] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. FILIP: Fine-grained interactive language-image pre-training. In Proceedings of the International Conference on Learning Representations (ICLR), 2022. 2", + "[112] Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. TACL, 2014. 2", + "[113] Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. From recognition to cognition: Visual commonsense reasoning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2", + "[114] Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer. Scaling vision transformers. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 4", + "[115] Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful image colorization. In Proceedings of European Conference on Computer Vision (ECCV), 2016. 2", + "[116] Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. Bertscore: Evaluating text generation with bert. In Proceedings of the International Conference on Learning Representations (ICLR), 2019. 3", + "[117] Mingkai Zheng, Fei Wang, Shan You, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu. Weakly supervised contrastive learning. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 2", + "[118] Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. Visual7w: Grounded question answering in images. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 2" + ], + "bbox": [ + 78, + 90, + 470, + 893 + ], + "page_idx": 12 + }, + { + "type": "page_number", + "text": "19220", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 12 + } +] \ No newline at end of file diff --git a/2023/Learning Visual Representations via Language-Guided Sampling/c0b46ea1-1183-4a23-a0d4-68578b0842d6_model.json b/2023/Learning Visual Representations via Language-Guided Sampling/c0b46ea1-1183-4a23-a0d4-68578b0842d6_model.json new file mode 100644 index 0000000000000000000000000000000000000000..a398f82da963520f8c77fb6ceffae71bf954b092 --- /dev/null +++ b/2023/Learning Visual Representations via Language-Guided Sampling/c0b46ea1-1183-4a23-a0d4-68578b0842d6_model.json @@ -0,0 +1,2734 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.157, + 0.131, + 0.816, + 0.154 + ], + "angle": 0, + "content": "Learning Visual Representations via Language-Guided Sampling" + }, + { + "type": "text", + "bbox": [ + 0.271, + 0.181, + 0.699, + 0.22 + ], + "angle": 0, + "content": "Mohamed El Banani Karan Desai Justin Johnson University of Michigan" + }, + { + "type": "text", + "bbox": [ + 0.357, + 0.223, + 0.61, + 0.237 + ], + "angle": 0, + "content": "{mbanani, kdexd, justincj}@umich.edu" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.273, + 0.314, + 0.289 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.305, + 0.474, + 0.548 + ], + "angle": 0, + "content": "Although an object may appear in numerous contexts, we often describe it in a limited number of ways. Language allows us to abstract away visual variation to represent and communicate concepts. Building on this intuition, we propose an alternative approach to visual representation learning: using language similarity to sample semantically similar image pairs for contrastive learning. Our approach diverges from image-based contrastive learning by sampling view pairs using language similarity instead of handcrafted augmentations or learned clusters. Our approach also differs from image-text contrastive learning by relying on pre-trained language models to guide the learning rather than directly minimizing a cross-modal loss. Through a series of experiments, we show that language-guided learning yields better features than image-based and image-text representation learning approaches." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.574, + 0.21, + 0.589 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.599, + 0.47, + 0.795 + ], + "angle": 0, + "content": "Consider the images in Fig. 1, is the center image more similar to its left or right neighbor? Despite the difference in background and pose, it is clear that the right pair captures the same concept: a flying snow owl. Nevertheless, a self-supervised image model will judge the left pair as more similar. Human perception and language abstract away appearance differences to capture conceptual similarity rather than just visual similarity. Ideally, we could learn visual features that capture conceptual similarity and generalize effectively to other visual tasks. In this work, we show how language can be a proxy for conceptual similarity; allowing us to sample better pairs for contrastive learning and train more generalizable visual models." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.796, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Image-only contrastive learning uses visual similarity as a proxy for conceptual similarity. This is based on the observation that discriminative approaches can discover interclass similarity-e.g., cheetahs are similar to lions- without requiring explicit annotations [106]. The core idea is to train a discriminative model where each instance is treated as a separate class, and the model is trained to map augmented" + }, + { + "type": "image_caption", + "bbox": [ + 0.634, + 0.271, + 0.762, + 0.286 + ], + "angle": 0, + "content": "Visual Embedding" + }, + { + "type": "image", + "bbox": [ + 0.523, + 0.286, + 0.871, + 0.486 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.621, + 0.488, + 0.774, + 0.502 + ], + "angle": 0, + "content": "Language Embedding" + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.509, + 0.895, + 0.551 + ], + "angle": 0, + "content": "Figure 1. Language allows us to find conceptually similar image pairs even if they are visually dissimilar. We use those pairs for contrastive learning to learn generalizable visual features." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.581, + 0.892, + 0.703 + ], + "angle": 0, + "content": "views of the same image to similar features [12-15, 106]. While successful, instance discrimination ignores the similarity between different instances as it assumes all other images are unrelated. Later work focused on inter-image relationships by estimating clusters [3, 9, 10] or finding nearest neighbors [28]. However, those relationships are estimated using visual embeddings; resulting in visually, rather than conceptually, similar pairs." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Language similarity is a strong proxy for semantic relationships. Consider the example in Fig. 1; images that depict the same concept are often described similarly. Radford et al. [76] propose language-image contrastive learning by mapping images and text to a shared representation space and achieve impressive generalization capabilities. However, it is unclear whether forcing models to map onto a shared space is optimal for visual learning. Although linguistic and visual similarity might align for similar instances, it is unclear whether all distances in one space should map exactly to the other. Instead of learning a joint vision-and-language representations, we argue that it is better to use linguistic similarity to guide visual learning." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19208" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.081, + 0.092, + 0.468, + 0.393 + ], + "angle": 0, + "content": "To this end, we propose language-guided contrastive learning: a simple adaptation to contrastive learning that uses language models to find conceptually-similar image pairs for visual learning. Our approach is motivated by the observation that language models, despite never training on visual data, can still be used to sample caption pairs that belong to conceptually similar images, as seen in Fig. 2. Such sampled images exhibit desirable variations in pose, lightning, and context which are very different from hand-crafted augmentations which can be ill-suited to downstream tasks [108] or too focused on background textures [81]. We use the sampled pairs instead of image augmentations within standard self-supervised visual learning approaches such as SimCLR [12], SimSiam [15], and SLIP [67]. Our approach departs from image-only contrastive learning by relying on conceptually-similar image pairs rather than visually similar augmentations or cluster-assignment. We also depart from image-text pre-training by allowing the model to be guided by language similarity rather than learning a joint embedding space." + }, + { + "type": "text", + "bbox": [ + 0.081, + 0.395, + 0.468, + 0.589 + ], + "angle": 0, + "content": "We conduct a series of controlled experiments to analyze our approach and compare it to commonly used representation learning paradigms on generalization to downstream classification tasks. In controlled settings, our approach outperforms all baselines on linear probe and few-shot classification on a range of downstream classification datasets. Our analysis suggests that while learning multimodal joint embeddings can result in good representations, it is better to use one modality to guide the training of the other. Furthermore, we find that our approach is robust to the specific choice of sampling strategy or language model. Our code and pre-trained models are available at https://github.com/mbanani/lgssl." + }, + { + "type": "title", + "bbox": [ + 0.081, + 0.605, + 0.217, + 0.619 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.081, + 0.63, + 0.468, + 0.9 + ], + "angle": 0, + "content": "Visual Representation Learning aims to learn visual embedding spaces that capture semantics, with a typical focus on learning from scalable data sources. Broadly speaking, there are two general approaches: generative and discriminative. Generative approaches hypothesize that a model that can capture the image distribution will learn semantically relevant features [26, 31, 37, 70, 98, 115]. In contrast, discriminative approaches posit that differentiating between images will give rise to better features. This idea can be traced by to early work on metric learning [18] and dimensionality reduction [35], and is clearly seen for supervised classification models [84]. More recently, Wu et al. [106] proposed treating each image as a separate class and using augmented images as class instances to relieve the need for human annotation. This was followed by papers that simplified this approach [12-14,38] and proposed non-contrastive variants [15, 34]. While those approaches have been successful, the utility of augmentation-based self-supervised" + }, + { + "type": "text", + "bbox": [ + 0.504, + 0.092, + 0.89, + 0.182 + ], + "angle": 0, + "content": "learning has been questioned [68, 108] with follow-up work proposing the use of objectness [66, 75] and saliency [81] to alleviate some of those concerns. While we share the goal of visual representation learning, we question the reliance on image augmentations for training and propose using language models to learn for conceptually-similar images." + }, + { + "type": "text", + "bbox": [ + 0.504, + 0.195, + 0.89, + 0.481 + ], + "angle": 0, + "content": "Language-supervised vision pre-training aims to learn visual representations from language data. Early work of Li et al. [57] trained n-gram models using YFCC [93] images and user-tag metadata. While some works learn joint vision-and-language representations for tasks like visual question answering [2, 33, 45, 118], visual reasoning [50, 89, 113], and retrieval [72, 112], we are interested in using language to learn better visual representations [23, 23, 76, 80, 88]. Early works used language modeling as a pretext task for visual learning [23, 80], but contrastive approaches quickly gained more popularity due to their relative simplicity and generalization capabilities [47, 76]. Follow-up work extended the contrastive formulation to learn dense features [109, 111] or used additional self-supervised losses to improve performance and data efficiency [21, 56, 59, 67]. While we share the motivation of using language for visual learning, we focus on learning visual representations by using linguistic guidance from pre-trained language models." + }, + { + "type": "text", + "bbox": [ + 0.504, + 0.494, + 0.89, + 0.9 + ], + "angle": 0, + "content": "Leveraging structure in the data. This is commonly done in dense feature learning, where optical flow [36, 46, 82, 101] or 3D transformations [29, 44, 83, 90, 105] provide natural associations between image patches. For images, prior approaches used class names [51, 79], class hierarchies [58, 110], meta data [32, 48, 57] or clustering [3, 9, 10, 94, 117] to improve learning and inference. Within contrastive learning, clustering has been a popular choice for leveraging dataset structure. The intuition is that natural clusters emerge in feature spaces that can provide an additional training signal or useful pseudo-labels. While such approaches work well on curated datasets (e.g., ImageNet) where the label set provides an estimate of the number of clusters, it struggles with imbalanced and uncurated data [4]. Other approaches sample nearest neighbors as a feature-driven within-domain augmentation [28, 59]. While these approaches differ in how they extract inter-instance relationships, they all use within-domain feature similarity to sample positive pairs or clusters and hence do not leverage the rich cross-modal relationships. Closest to our work is Han et al. [36] who propose a co-training [6] scheme for jointly learning image and optical flow representations. We share their motivation of using similarity in one space (language) to learn in another (vision). Furthermore, instead of relying on co-training on the same dataset, we extract distances from a text-only language model, allowing us to leverage unaligned data." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.947, + 0.518, + 0.957 + ], + "angle": 0, + "content": "19209" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.09, + 0.406, + 0.339 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.418, + 0.091, + 0.648, + 0.339 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.659, + 0.092, + 0.89, + 0.339 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.346, + 0.893, + 0.404 + ], + "angle": 0, + "content": "Figure 2. Language sampling yields semantically-similar and visually-diverse image pairs. We sample the three nearest neighbors using a self-supervised visual model [13], an ImageNet supervised model [27], and a self-supervised language model [78]. While visual sampling yields visually similar pairs, language sampling yields semantically relevant and visually diverse images. We argue that the combination of semantic consistency and visual diversity are better for learning generalizable features." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.428, + 0.168, + 0.442 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.454, + 0.473, + 0.651 + ], + "angle": 0, + "content": "The goal of this work is to learn visual representations that can generalize to other datasets. We extend image-only contrastive learning beyond hand-crafted augmentations and visually-sampled clusters to learn from conceptually similar images. Through learning to associate images that depict the same visual concept, models can learn visual invariances that more closely capture human semantics. To achieve this, we propose sampling image pairs that have similar captions using a pre-trained sentence encoder [78] and using them for contrastive learning. This work does not propose a new model or loss but rather a novel way of sampling image views that is applicable to a variety of approaches and losses for learning visual representations." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.665, + 0.4, + 0.681 + ], + "angle": 0, + "content": "3.1. Learning from Conceptual Similarity" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.69, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Instance discrimination has been the dominant task for visual representation learning. Its core intuition is that visual similarity is a good proxy for semantic similarity. The standard approach generates positive view pairs using image augmentations and maximizes their embedding similarity, with or without negative views. While there has been a large number of contrastive learning approaches, view pair generation has largely remained the same. Other methods use visual feature similarity to learn prototypes [3, 9, 10] or sample previously seen instances [28] for contrastive learning. While these approaches extend beyond instances and consider relations in the dataset, they still rely on visual similarity to generate their contrastive pairs. This limits the visual invariances that they can learn [108]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.429, + 0.893, + 0.61 + ], + "angle": 0, + "content": "We propose training models to identify the same visual concept instead of the same instance. Our key observation is simple: images that have similar captions often depict similar concepts regardless of the actual appearance similarity. This can be clearly seen in Fig. 2. Nearest neighbors in visual representation space depict objects in similar scenes and poses, with self-supervised models showing some color invariances due to color augmentation. Conversely, similarly captioned images depict objects in different colors, poses, and contexts. This makes language-sampled images an excellent source for visual representation learning as they implicitly capture human-like visual invariances." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.62, + 0.833, + 0.637 + ], + "angle": 0, + "content": "3.2. Sampling Image Pairs using Language" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.644, + 0.892, + 0.84 + ], + "angle": 0, + "content": "Given a captioned image dataset, we want to sample image pairs that have very similar captions. While caption similarity may be a good proxy for conceptual similarity, measuring caption similarity is a challenge on its own. Traditional metrics such as BLEU [71] and CIDER [96] rely on n-gram overlap, which can be too sensitive to phrasing and sentence structure. This makes them ill-suited for our needs. Other metrics such as SPICE [1] account for such variety by comparing parse trees; however, they still can not account for different wording choices. Inspired by advances in language models as well as approaches like BERTScore [116] and CLIPScore [43], we use a pre-trained sentence encoder to compute caption similarity." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.841, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Sentence encoders are trained to extract sentence-level features [52, 61, 78]. We use SBERT [78], which finetunes a pre-trained language model to allow it to better capture semantic similarity using feature cosine distance." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19210" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.09, + 0.231, + 0.221 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.093, + 0.226, + 0.218, + 0.237 + ], + "angle": 0, + "content": "Image Augmentations" + }, + { + "type": "image", + "bbox": [ + 0.246, + 0.09, + 0.397, + 0.221 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.279, + 0.226, + 0.365, + 0.237 + ], + "angle": 0, + "content": "Image-Caption" + }, + { + "type": "image", + "bbox": [ + 0.414, + 0.089, + 0.631, + 0.221 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.43, + 0.226, + 0.614, + 0.237 + ], + "angle": 0, + "content": "Nearest Neighbor Memory Bank" + }, + { + "type": "image", + "bbox": [ + 0.647, + 0.089, + 0.891, + 0.221 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.689, + 0.226, + 0.848, + 0.237 + ], + "angle": 0, + "content": "Language-Guided Sampling" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.246, + 0.894, + 0.289 + ], + "angle": 0, + "content": "Figure 3. Contrasting Contrastive Formulations. While image-only and image-text contrastive learning directly extract views from the instance, nearest-neighbor methods rely on a memory bank of previously extracted features for training. In contrast, our approach samples nearest neighbors in caption embedding space using a pretrained language model and use the associated image for contrastive learning." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.314, + 0.47, + 0.48 + ], + "angle": 0, + "content": "SBERT is trained in two stages: first, a language backbone is trained using a standard self-supervised task such as masked [25, 65] or permuted [87] language modeling; second, the language modeled is fine-tuned via contrastive learning on a large combined dataset of 1 billion sentence pairs. Fine-tuning the model in a contrastive way simplifies downstream usage as it allows features to be compared directly using cosine similarity. We use an SBERT [78] model with an MPNet [87] backbone. However, we find that our formulation is not sensitive to the choice of language encoder, as shown in Tab. 5c." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.483, + 0.47, + 0.634 + ], + "angle": 0, + "content": "Finally, we sample the nearest neighbors for all captions in the language embedding space. We leverage modern similarity search libraries [49] to perform the nearest neighbor search quickly, despite the large dataset size. For example, nearest neighbor sampling runs in under 3 hours for RedCaps (12 million instances) on 4 GPUs, with 43 minutes spent on feature extraction and 117 minutes on nearest neighbor search. Furthermore, we find that we could further reduce the complexity of the sampling by only searching within subsets of the data as shown in Appendix E." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.65, + 0.382, + 0.667 + ], + "angle": 0, + "content": "3.3. Language-Guided Visual Learning" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.675, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Our approach is applicable to several representation learning methods as it only changes the training view pairs. We focus on contrastive learning since its fairly minimal setting allows us to analyze the impact of language guidance with minimal confounding factors. We train SimCLR with the language-sampled pairs and refer to it as LGSimCLR. We also evaluate the impact of language guidance on SimSiam [15] and SLIP [67], and find that they can similarly benefit from language guidance. We only use random cropping for image augmentations since language-sampled pairs are naturally augmented versions of each other and find that additional augmentations are not helpful. For LGSLIP, we match their setup by applying the CLIP loss only between the source's image and caption, ignoring an additional loss between the nearest neighbor image and its caption." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.313, + 0.633, + 0.331 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.339, + 0.892, + 0.476 + ], + "angle": 0, + "content": "Our experiments evaluate the efficacy of learning visual features from conceptually similar images. We hypothesize that a model trained with language guidance will learn useful visual invariances and better generalize to downstream tasks. We are interested in answering these questions: Does language guidance improve generalization over other pretraining approaches? Does language guidance generalize to other datasets and pre-training approaches? How can language be used for visual pre-training?" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.485, + 0.691, + 0.501 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.509, + 0.892, + 0.599 + ], + "angle": 0, + "content": "We formulate our experimental setup to compare the efficacy of different learning signals. We train models with language-guided sampling and compare them with image-only self-supervised models and image-text contrastive models. We are interested in conducting controlled experiments for a fair comparison." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.6, + 0.893, + 0.795 + ], + "angle": 0, + "content": "Recent work in self-supervised learning has demonstrated the impressive impact of scaling [12, 76, 114]. While such work has shown impressive performance, it has complicated the evaluation as different models are trained on different pretext tasks on different datasets using varying amounts of compute and training recipes. Furthermore, replication is difficult, if not impossible, due to the unavailability of training data or prohibitive compute requirements. Fortunately, several papers report results that indicate that performance patterns often hold at smaller scales [10, 12, 21, 67, 76]. Hence, we conduct our experiments at a scale that allows us to perform a comprehensive evaluation and permits replication by others." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.892, + 0.901 + ], + "angle": 0, + "content": "We conduct our experiments with a standard backbone [39] on publicly available datasets [11, 24, 85]. To account for variation in training recipes, we retrain all methods from scratch using the same training recipe. We scale down experiments to a level that permits fair comparisons and replication. We also provide system-level comparisons in Tab. 4 and scaling results in App. D." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "19211" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.212 + ], + "angle": 0, + "content": "Training details: We use a ResNet-50 backbone and train all models using the AdamW optimizer [63] with a learning rate of \\(10^{-3}\\) and a weight decay of \\(10^{-2}\\). We use a cosine learning scheduler [62] with 5000 warm-up steps. Models are trained using a batch size of 512 for 250k steps; this corresponds to 10.5 epochs on RedCaps. We use a constant number of steps to permit meaningful comparisons between models trained on different datasets." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.215, + 0.473, + 0.412 + ], + "angle": 0, + "content": "Evaluation setup: We evaluate all approaches using linear probe and fewshot classification on 15 classification datasets inspired by [53,76]. We use the linear probe evaluation proposed by [53] and learn a single linear layer using logistic regression. We sweep over a range of cost values and choose the value with the best validation performance. We retrain a classifier on both train and validation splits and report test performance. We also evaluate all approaches on fewshot classification to understand their generalization ability. We use a weighted kNN classifier on frozen support features inspired by prior work showing its effectiveness for fewshot classification [102]. Please see Appendices A and B for more details on evaluation datasets and tasks." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.418, + 0.47, + 0.509 + ], + "angle": 0, + "content": "Baselines: While there have been many proposed visual representation learning approaches, they can be grouped into several key directions that differ in the pretext task. We focus our comparison on a few representative approaches to explore the impact of the learning signal. We overview the baselines here and provide more details in Appendix C." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.509, + 0.472, + 0.616 + ], + "angle": 0, + "content": "Many of our baselines are variants of contrastive learning as shown in Fig. 3. Contrastive approaches operate over paired source and target feature embeddings: \\( z^s \\) and \\( z^t \\). The goal is to maximize the similarity between the paired embeddings and minimize it with respect to all other embeddings. Given a batch size \\( N \\) and embedding dimension \\( F \\), \\( z^s, z^t \\in \\mathbb{R}^{N \\times F} \\). The contrastive loss [86] is:" + }, + { + "type": "equation", + "bbox": [ + 0.105, + 0.624, + 0.47, + 0.662 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} \\left(z ^ {s}, z ^ {t}\\right) = - \\log \\frac {\\exp \\left(\\operatorname {s i m} \\left(\\boldsymbol {z} _ {i} ^ {s} , \\boldsymbol {z} _ {i} ^ {t}\\right) / \\tau\\right)}{\\sum_ {k = 1} ^ {N} \\exp \\left(\\operatorname {s i m} \\left(\\boldsymbol {z} _ {i} ^ {s} , \\boldsymbol {z} _ {k} ^ {t}\\right) / \\tau\\right)}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.669, + 0.47, + 0.715 + ], + "angle": 0, + "content": "where \\(\\tau\\) is a scaling parameter and \\(\\mathrm{sim}(\\cdot ,\\cdot)\\) is cosine similarity. Contrastive approaches primarily differ in how the embeddings are computed." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.717, + 0.47, + 0.793 + ], + "angle": 0, + "content": "Image-Only Contrastive Learning contrasts features extracted from two randomly augmented views of the same image to perform instance discrimination [106]. We use SimCLR [12] as a representative approach due to its simplicity and strong performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.796, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Image-Text Contrastive Learning learns by contrasting features extracted from images and their captions. Unlike image-only approaches, this approach can learn semantics from the captions. Radford et al. [76] first proposed this approach and has had several follow-ups that augment it with additional self-supervised losses losses [56, 59, 67]. We use CLIP [76] and SLIP [67] due to their simplicity." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.228 + ], + "angle": 0, + "content": "Nearest Neighbor Contrastive Learning contrast source embeddings with retrieved embeddings from a memory bank. The target features are used to retrieve the nearest neighbor embedding from a memory bank of previous batches. Dwibedi et al. [28] proposed this approach for image-only contrastive learning, while Li et al. [59] proposed adapting this loss for language embeddings. We use NNCLR [28] as Visual NNCLR and DeCLIP [59] with the CLIP and the language NNS losses as Language NNCLR." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.237, + 0.894, + 0.299 + ], + "angle": 0, + "content": "Image-Only Non-Contrastive Learning deviates from the typical contrastive setup by learning without negative samples [15, 34]. We use SimSiam as a representative approach due to its simplicity and strong performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.307, + 0.892, + 0.443 + ], + "angle": 0, + "content": "Cluster-based Contrastive Learning learn by contrasting image features with learned prototypes [3,9,10]. Prototypes are estimated via clustering or learned jointly with the feature encoder. Caron et al. [10] report that different cluster-based approaches perform similarly when provided with the same algorithmic advances. We use an adapted SwAV without the multi-crop augmentation strategy as it is equally applicable to other methods. We also compare against a pretrained SwAV checkpoint in Tab. 4." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.459, + 0.593, + 0.473 + ], + "angle": 0, + "content": "4.2. Results" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.484, + 0.892, + 0.56 + ], + "angle": 0, + "content": "We train all approaches with a ResNet-50 backbone on RedCaps and report results in Tabs. 1 and 3. Our model outperforms all baselines with a significant margin for both evaluations. We analyze the results below through a series of questions." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.569, + 0.893, + 0.903 + ], + "angle": 0, + "content": "Does language-guided sampling provide better training pairs than image augmentations? LGSimCLR greatly outperforms SimCLR despite using the same learning objective. By using language sampled pairs instead of image augmentations, LGSimCLR learns stronger invariances. We find that the largest gains arise in fine-grained datasets: Cars, CUB, and Food101. The performance gains can be explained by considering the critique of Xiao et al. [108]: the training augmentations dictate the invariances learned by SimCLR as shown in nearest neighbor samples in Fig. 2. Consider the third row of Fig. 2, while language sampling depicts three Aston Martin cars in different spots, visual nearest neighbors are sports cars in different poses and colors, closely resembling the flip and color augmentations used for training. Similarly in the first row of Fig. 2, visual nearest neighbors depict owls from different species in similar poses, while language sampling retrieves three great horned owls from different viewpoints. These trends are further amplified when features are used directly for fewshot classification. Language guidance allows us to capture relationships that go beyond visual similarity by training on image pairs that capture human semantics." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "19212" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.089, + 0.895, + 0.133 + ], + "angle": 0, + "content": "Table 1. Linear Probe Evaluations. We train ResNet-50 models on RedCaps and report performance of a linear probe using frozen features on 15 downstream tasks. Models are split based on whether or not they require caption images for training. LGSimCLR outperforms all previous approaches with strong performance gains for fine-grained classification datasets." + }, + { + "type": "table", + "bbox": [ + 0.078, + 0.139, + 0.895, + 0.334 + ], + "angle": 0, + "content": "
ModelFood-101CIFAR-10CIFAR-100CUBSUN397CarsAircraftDTDPetsCaltech-101FlowersSTL-10EuroSATRESISC45PCAMMean
SwAV63.681.357.521.647.522.935.468.161.170.578.087.794.379.984.363.6
SimSiam64.179.956.128.248.329.541.266.269.173.683.685.794.482.183.365.7
Visual NNCLR65.482.860.226.650.026.640.968.065.275.483.588.595.382.283.866.3
SimCLR69.082.961.630.652.633.743.769.870.574.186.988.095.484.684.468.5
Language NNCLR81.283.161.948.656.545.137.268.878.182.090.293.492.581.180.772.0
CLIP80.984.762.750.457.445.836.767.679.884.091.093.593.982.282.672.9
SLIP77.787.267.042.458.148.745.272.379.582.792.192.795.685.583.474.0
LGSimCLR (Ours)83.287.869.059.360.362.353.471.281.889.495.994.095.688.081.178.2
" + }, + { + "type": "image", + "bbox": [ + 0.09, + 0.357, + 0.46, + 0.535 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.541, + 0.472, + 0.598 + ], + "angle": 0, + "content": "Figure 4. Nearest Neighbor methods are limited by the memory bank size. Even with a large memory bank, the nearest embedding can still be unrelated to the source image while language sampling provides us with conceptually similar pairs." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.614, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Can we just sample nearest neighbors from previous batches? LGSimCLR outperforms NNCLR despite both relying on nearest neighbors. NNCLR uses the nearest feature embedding from a memory bank in the same modality. The quality of their retrieved samples is limited by the size of the memory bank. To demonstrate this, we visualize the nearest neighbors retrieved by NNCLR for different memory bank sizes in Fig. 4. We find that the retrieval quality is poor even for larger queues. Interestingly, we note that NNCLR also underperforms SimCLR on RedCaps, despite performing better on ImageNet. We posit that ImageNet's curated distribution explains this: a queue of \\(16k\\) will most probably contain instances from each class, resulting in both visually and conceptually similar retrievals. Additionally, the quality of nearest neighbors is affected by the sampling feature space; features that are only trained on image augmentations will have limited invariances as shown in Fig. 2. We further explore the impact of sampling space on training in Sec. 4.3." + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.355, + 0.892, + 0.383 + ], + "angle": 0, + "content": "Table 2. Language-guided contrastive learning outperforms image-text contrastive learning, regardless of text encoder." + }, + { + "type": "table", + "bbox": [ + 0.501, + 0.389, + 0.892, + 0.473 + ], + "angle": 0, + "content": "
ObjectiveText EncoderLinearFewshot
Image-TextRandomly- Initialized72.977.5
Frozen SBERT71.877.1
Image-ImageFrozen CLIP (RedCaps)78.382.4
Frozen SBERT78.282.5
" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.5, + 0.892, + 0.698 + ], + "angle": 0, + "content": "Can cluster-based approaches learn better features? Similar to nearest-neighbor sampling, clustering is performed using visual similarity. Furthermore, it is based on an estimated number of clusters in the training dataset. Although this can be determined for ImageNet due to its known class structure, the number of clusters in an arbitrary uncurated dataset is unknown. This results in a large performance drop, as seen in Tab. 1 and Tab. 3. On the other hand, sampling related pairs assumes no global structure within the data and hence is able to better capture inter-instance similarity. This results in nearest-neighbor sampling outperforming clustering and both being outperformed by contrastive learning and language-guided contrastive learning." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Should we use language for guidance or supervision? Our experiments indicate that LGSimCLR outperforms both CLIP and SLIP. We consider two possible explanations: (a) SBERT extracts better language embeddings than CLIP can learn from the data, or (b) language-guided contrastive learning is a better training objective than imag-text contrastive learning. To evaluate this, we compare four models in Tab. 2. The first two models use CLIP's training objective: the first model uses a randomly initialized language encoder, similar to CLIP. The second model uses a frozen SBERT model as the language encoder and only trains the projection layers. The second two models use LGSimCLR's training objective but sample pairs using a" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19213" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.089, + 0.895, + 0.133 + ], + "angle": 0, + "content": "Table 3. Few-Shot Evaluations. We train ResNet-50 models on RedCaps and report 5-way, 5-shot classification performance. We observe that language results in huge performance gains as shown by the performance of CLIP and LGSimCLR. Furthermore, the use of any augmentations hurts performance as seen by SLIP's drop in performance." + }, + { + "type": "table", + "bbox": [ + 0.078, + 0.139, + 0.895, + 0.334 + ], + "angle": 0, + "content": "
ModelFood-101CIFAR-10CIFAR-100CUBSUN397CarsAircraftDTDPetsCaltech-101FlowersSTL-10EuroSATRESISC45Mean
SwAV64.554.061.845.884.936.534.174.866.578.175.572.680.472.964.5
SimSiam63.949.957.249.584.539.337.975.767.879.781.569.680.679.465.5
Visual NNCLR65.654.161.745.885.337.934.975.267.381.175.474.383.676.765.6
SimCLR66.945.751.051.587.144.038.477.670.180.086.969.683.581.366.7
Language NNCLR89.365.373.478.690.868.440.475.278.890.994.389.675.271.977.3
CLIP88.964.673.178.390.969.740.775.777.591.694.789.875.374.877.5
SLIP81.563.570.863.191.362.942.179.676.488.492.283.482.780.875.6
LGSimCLR (Ours)90.366.375.583.192.777.650.681.184.195.497.686.585.089.082.5
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.357, + 0.471, + 0.479 + ], + "angle": 0, + "content": "pre-trained language-only SBERT or the language encoder from a CLIP model trained on RedCaps. We find that image-image contrastive learning yields better visual features for both setups. While CLIP does not benefit from an SBERT backbone, LGSimCLR benefits from sampling using a language encoder trained on the same dataset. This suggests that learning joint embeddings results in worse visual features than language-guided learning." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.485, + 0.471, + 0.789 + ], + "angle": 0, + "content": "System-level comparisons: We compare LGSimCLR with publicly-available checkpoints of prior approaches; see Appendix C for details. We emphasize that while the experiments reported in Tabs. 1 and 3 were done in a controlled setup (same batch size, training data, optimizer), the system level comparisons are trained on different datasets with different training recipes and enhancements to further boost performance; e.g., large batch sizes, longer training, multi-crop augmentation. Furthermore, it has been shown that models trained on ImageNet implicitly benefit from its curated nature [4, 67]. Nevertheless, our approach still outperforms prior self-supervised approaches. We fall short of CLIP's ResNet-50 due to its training scale; \\(64 \\times\\) larger batch, \\(32 \\times\\) larger dataset, and \\(75 \\times\\). We also observe that ImageNet-supervised ResNet-50 achieves better few-shot performance. Examining the performance breakdown in Tab. 10, we find the improvement mainly comes from CIFAR10, CIFAR100, and Pets. We posit that this can be explained by ImageNet's class structure: mostly pets with a large overlap with CIFAR's classes." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.801, + 0.182, + 0.817 + ], + "angle": 0, + "content": "4.3. Analysis" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.47, + 0.903 + ], + "angle": 0, + "content": "We now analyze language-guided contrastive learning by evaluating the impact of pre-training data, the choice of embedding space, and the pretext task. By understanding the impact of those choices, we can better understand what the model is learning." + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.355, + 0.895, + 0.412 + ], + "angle": 0, + "content": "Table 4. ResNet-50 System Level Comparisons. We outperform prior self-supervised approaches despite them benefiting from ImageNet's curation for training and using larger batch sizes. CLIP outperforms us due to the scale of its training." + }, + { + "type": "table", + "bbox": [ + 0.502, + 0.418, + 0.892, + 0.53 + ], + "angle": 0, + "content": "
Batch#Img UpdatesDatasetLinearFewshot
Supervised [104]10241.3×108ImageNet78.085.7
SimSiam [15]5121.3×108ImageNet72.978.7
SimCLR [13]40961.0×109ImageNet75.477.4
MoCo [16]40961.3×108ImageNet77.780.1
SwAV [10]40961.3×108ImageNet78.278.5
CLIP [76]327681.0×1010CLIP81.887.8
LGSimCLR5121.3×108RedCaps78.282.5
" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.559, + 0.895, + 0.697 + ], + "angle": 0, + "content": "Approach generality: We extend language guidance to other contrastive approaches: SimSiam and SLIP. We observe that language guidance uniformly improves performance for all methods, as shown in Tab. 5a. Furthermore, the difference between SimCLR and SLIP shrinks when adding language guidance. This suggests that language guidance provides the model with similar semantics to the ones learned from an image-text contrastive loss, resulting in diminished gains from the additional image-text loss." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.704, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Impact of training dataset: We train our model on four datasets: CC3M [85], CC12M [11], RedCaps-2020, and RedCaps [24]. In Tab. 5b, we observe that larger datasets result in stronger performance, indicating that our approach could scale well with even larger datasets. Furthermore, we observe that RedCaps results in better performance than Conceptual Captions. This may be attributed to the higher quality of captions in RedCaps; while the alt-text captions CC3M and CC12M can be short and contain image metadata, RedCaps captions are diverse, longer, and more descriptive. This allows our model to sample more interesting visual pairs that capture more visual diversity. We provide qualitative results in Appendix F to support this." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "19214" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.088, + 0.109, + 0.36, + 0.186 + ], + "angle": 0, + "content": "
Image Aug.Language
LinearFewshotLinearFewshot
SimSiam65.765.571.275.7
SimCLR68.566.778.282.5
SLIP74.075.678.882.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.149, + 0.189, + 0.288, + 0.201 + ], + "angle": 0, + "content": "(a) Approach Generality" + }, + { + "type": "table", + "bbox": [ + 0.374, + 0.116, + 0.621, + 0.187 + ], + "angle": 0, + "content": "
SizeLinearFewshot
CC3M2.7M71.576.3
CC12M10.9M76.881.9
RedCaps 20203.2M73.878.8
RedCaps12.0M78.282.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.41, + 0.189, + 0.575, + 0.201 + ], + "angle": 0, + "content": "(b) Impact of training dataset" + }, + { + "type": "table", + "bbox": [ + 0.636, + 0.089, + 0.89, + 0.186 + ], + "angle": 0, + "content": "
LinearFewshot
SBERT (MPNet)78.282.5
SBERT (MiniLM)78.683.3
CLIP Language (ViT-B/32)78.383.1
FastText BoW76.180.9
ImageNet-supervised78.381.8
SimCLR (ImageNet)73.174.6
" + }, + { + "type": "table_caption", + "bbox": [ + 0.677, + 0.189, + 0.838, + 0.201 + ], + "angle": 0, + "content": "(c) Impact of sampling space" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.207, + 0.893, + 0.249 + ], + "angle": 0, + "content": "Table 5. Analysis Experiments. We conduct a series of analysis experiments to understand language-guided contrastive learning. The results indicate that language sampling is beneficial to several formulations and scales well with larger datasets. Furthermore, while language sampling consistently results in good pairs for training, visual sampling only helps if it has access to semantics through supervision." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.267, + 0.473, + 0.57 + ], + "angle": 0, + "content": "Impact of sampling space: The idea of using offline nearest-neighbor sampling does not require a specific language model or even a specific modality. We explore other choices for embedding space: four sentence encoders and two image models. In our experiments, we use SBERT's MPNet model [78, 87]; the highest performing SBERT model for sentence similarity. We compare it to two other sentence transformers: a smaller SBERT model, MiniLM [100], and the language encoder from CLIP [76]. We also compared against a bag-of-words (BoW) sentence encoder that uses FastText [7] embeddings. Results are in Tab. 5c. While we expected that using CLIP for sampling would improve performance due to its multimodal training, we were surprised that MiniLM also improved performance despite its lower performance on language tasks. We find that pairs obtained using a BoW model result in a weaker performance which might hint at the importance of contextual sentence embeddings. Nevertheless, the BoW-sampled pairs still result in higher performance than all the other baselines on RedCaps." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.575, + 0.47, + 0.773 + ], + "angle": 0, + "content": "We also consider training with pairs sampled using two visual models: ImageNet-supervised ResNet-50 [104] and ImageNet-trained SimCLR [13]. We find that using a visual model for sampling is only beneficial if the visual model captures semantic relations; e.g., through supervised training. Using a self-supervised language model results in a strong drop in performance relative to the other sampling spaces. Nevertheless, it still allows the model to achieve better performance than using a self-supervised visual approach on the same data. This indicates that while language is a better modality to use, \"sample-guided\" contrastive learning can still achieve a stronger performance than only using self-supervised learning." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Limitations: We observe a few limitations in our approach. Image captions can be noisy, vague, and often omit obvious relations in the image [5]. While this broadly affects image-language models, it can result in us retrieving unrelated image pairs. For example, captions like \"I found this in the garden\" or \"Photo from our family trip\" could describe a large range of images, some of which are unrelated. We expand on this in Appendix F. Image descriptions also de" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.268, + 0.892, + 0.419 + ], + "angle": 0, + "content": "pend on the context and the perceiver; e.g., a tourist and an art curator will describe artwork in very different ways. We observe that descriptions in topic-focused subreddits (e.g., r/birdpics and r/woodworking) are more specific than in generic subreddits (e.g., r/itookapicture and r/pics). Our experiments in Appendix E support this observation. Since a caption only captures one aspect of the image, sampled pairs can be similar for a variety of reasons. Allowing the model to condition the feature extraction or similarity calculation on captions could alleviate this issue." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.431, + 0.619, + 0.447 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.456, + 0.892, + 0.576 + ], + "angle": 0, + "content": "We propose using language to find conceptually similar images for contrastive learning. This is based on a simple observation: people describe an object in similar ways even when it appears in different contexts. We use pre-trained language models to sample similar captions and use the captioned images for contrastive learning. We hypothesize that using language guidance instead of image augmentations would result in learning more human-like invariances." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.577, + 0.892, + 0.789 + ], + "angle": 0, + "content": "We evaluate our approach on multiple train and test datasets and find that it outperforms previous self-supervised and image-text contrastive models. Our analysis demonstrates the utility of using nearest-neighbor instances for training and the superiority of language sampling over other approaches for unlabeled datasets. Our findings align with prior work that critiques the use of image augmentations [81, 108] and shows the utility of cross-modal guidance [36] and intra-instance relationships [28, 51]. Our results demonstrate the potential of incorporating language guidance in contrastive learning. We hope that future work will explore scaling up our approach to larger and more diverse datasets, as well as modeling approaches that further integrate language into the learning process." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Acknowledgments: We thank Richard Higgins, Ashkan Kazemi, and Santiago Castro for many helpful discussions, as well as David Fouhey, Ziyang Chen, Chenhao Zheng, Fahad Kamran, and Dandan Shan for their feedback on early drafts. This project was funded under the Ford-UM Alliance partnership; we thank Alireza Rahimpour, Devesh Upadhyay, and Ali Hassani from Ford Research for their support and discussion." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19215" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.176, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.115, + 0.47, + 0.17 + ], + "angle": 0, + "content": "[1] Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. SPICE: Semantic propositional image caption evaluation. In Proceedings of European Conference on Computer Vision (ECCV), 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.171, + 0.472, + 0.24 + ], + "angle": 0, + "content": "[2] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. VQA: Visual question answering. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.242, + 0.471, + 0.297 + ], + "angle": 0, + "content": "[3] Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi. Self-labelling via simultaneous clustering and representation learning. In International Conference on Learning Representations, 2019. 1, 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.298, + 0.472, + 0.38 + ], + "angle": 0, + "content": "[4] Mido Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, and Nicolas Ballas. The hidden uniform cluster prior in self-supervised learning. In Proceedings of the International Conference on Learning Representations (ICLR), 2023. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.381, + 0.471, + 0.437 + ], + "angle": 0, + "content": "[5] Hessam Bagherinezhad, Hannaneh Hajishirzi, Yejin Choi, and Ali Farhadi. Are elephants bigger than butterflies? reasoning about sizes of objects. In AAAI Conference on Artificial Intelligence, 2016. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.438, + 0.471, + 0.48 + ], + "angle": 0, + "content": "[6] Avrim Blum and Tom Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of Annual Conference on Computational learning theory, 1998. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.481, + 0.471, + 0.522 + ], + "angle": 0, + "content": "[7] Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606, 2016. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.523, + 0.471, + 0.577 + ], + "angle": 0, + "content": "[8] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101 - mining discriminative components with random forests. In European Conference on Computer Vision, 2014. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.579, + 0.471, + 0.647 + ], + "angle": 0, + "content": "[9] Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. Deep clustering for unsupervised learning of visual features. In Proceedings of the European conference on computer vision (ECCV), pages 132-149, 2018. 1, 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.649, + 0.47, + 0.731 + ], + "angle": 0, + "content": "[10] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In Advances in Neural Information Processing Systems (NeurIPS), volume 33, 2020. 1, 2, 3, 4, 5, 7, 15, 21, 22, 23" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.733, + 0.471, + 0.802 + ], + "angle": 0, + "content": "[11] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.803, + 0.471, + 0.871 + ], + "angle": 0, + "content": "[12] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the International Conference on Machine Learning (ICML), 2020. 1, 2, 4, 5, 15, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.873, + 0.471, + 0.902 + ], + "angle": 0, + "content": "[13] Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton. Big self-supervised" + }, + { + "type": "list", + "bbox": [ + 0.087, + 0.115, + 0.472, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.515, + 0.093, + 0.893, + 0.134 + ], + "angle": 0, + "content": "models are strong semi-supervised learners. In Advances in Neural Information Processing Systems (NeurIPS), volume 33, 2020. 1, 2, 3, 7, 8, 15, 16, 20, 21, 22, 23" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.135, + 0.892, + 0.176 + ], + "angle": 0, + "content": "[14] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.177, + 0.893, + 0.232 + ], + "angle": 0, + "content": "[15] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 1, 2, 4, 5, 7, 21, 22, 23" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.233, + 0.893, + 0.274 + ], + "angle": 0, + "content": "[16] Xinlei Chen*, Saining Xie*, and Kaiming He. An empirical study of training self-supervised vision transformers. arXiv preprint arXiv:2104.02057, 2021. 7, 15, 21, 22, 23" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.275, + 0.893, + 0.328 + ], + "angle": 0, + "content": "[17] Gong Cheng, Junwei Han, and Xiaoqiang Lu. Remote sensing image scene classification: Benchmark and state of the art. Proceedings of the IEEE, 105(10):1865-1883, Oct 2017. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.33, + 0.893, + 0.385 + ], + "angle": 0, + "content": "[18] Sumit Chopra, Raia Hadsell, and Yann LeCun. Learning a similarity metric discriminatively, with application to face verification. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.386, + 0.894, + 0.441 + ], + "angle": 0, + "content": "[19] M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi. Describing textures in the wild. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.442, + 0.893, + 0.524 + ], + "angle": 0, + "content": "[20] Adam Coates, Andrew Ng, and Honglak Lee. An Analysis of Single Layer Networks in Unsupervised Feature Learning. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2011. https://cs.stanford.edu/~acoates/papers/coatesleeng_iastats_2011.pdf. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.525, + 0.893, + 0.594 + ], + "angle": 0, + "content": "[21] Yufeng Cui, Lichen Zhao, Feng Liang, Yangguang Li, and Jing Shao. Democratizing contrastive language-image pretraining: A CLIP benchmark of data, model, and supervision. In ICML Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward, 2022. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.595, + 0.893, + 0.65 + ], + "angle": 0, + "content": "[22] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009. 15, 18" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.651, + 0.894, + 0.706 + ], + "angle": 0, + "content": "[23] Karan Desai and Justin Johnson. Virtex: Learning visual representations from textual annotations. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 11162-11173, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.707, + 0.893, + 0.761 + ], + "angle": 0, + "content": "[24] Karan Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson. RedCaps: Web-curated image-text data created by the people, for the people. In NeurIPS Datasets and Benchmarks, 2021. 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.762, + 0.893, + 0.845 + ], + "angle": 0, + "content": "[25] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT), 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.846, + 0.893, + 0.901 + ], + "angle": 0, + "content": "[26] Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2" + }, + { + "type": "list", + "bbox": [ + 0.51, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "19216" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.093, + 0.472, + 0.189 + ], + "angle": 0, + "content": "[27] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In Proceedings of the International Conference on Learning Representations (ICLR), 2021. 3, 15, 20" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.192, + 0.472, + 0.274 + ], + "angle": 0, + "content": "[28] Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. With a little help from my friends: Nearest-neighbor contrastive learning of visual representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 9588–9597, October 2021. 1, 2, 3, 5, 8, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.276, + 0.472, + 0.346 + ], + "angle": 0, + "content": "[29] Mohamed El Banani, Luya Gao, and Justin Johnson. Unsupervisedr&r: Unsupervised point cloud registration via differentiable rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7129-7139, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.347, + 0.472, + 0.415 + ], + "angle": 0, + "content": "[30] Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Pattern Recognition Workshop, 2004. 14, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.418, + 0.472, + 0.473 + ], + "angle": 0, + "content": "[31] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In Proceedings of the International Conference on Learning Representations (ICLR), 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.476, + 0.472, + 0.517 + ], + "angle": 0, + "content": "[32] Yunchao Gong, Qifa Ke, Michael Isard, and Svetlana Lazebnik. A multi-view embedding space for modeling internet images, tags, and their semantics. In IJCV, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.518, + 0.472, + 0.587 + ], + "angle": 0, + "content": "[33] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. Making the V in VQA matter: Elevating the role of image understanding in visual question answering. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.589, + 0.472, + 0.672 + ], + "angle": 0, + "content": "[34] Jean-Bastien Grill, Florian Strub, Florent Alché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in Neural Information Processing Systems (NeurIPS), 2020. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.674, + 0.472, + 0.729 + ], + "angle": 0, + "content": "[35] Raia Hadsell, Sumit Chopra, and Yann LeCun. Dimensionality reduction by learning an invariant mapping. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2006. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.731, + 0.472, + 0.786 + ], + "angle": 0, + "content": "[36] Tengda Han, Weidi Xie, and Andrew Zisserman. Self-supervised co-training for video representation learning. In Advances in Neural Information Processing Systems (NeurIPS), 2020. 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.788, + 0.472, + 0.844 + ], + "angle": 0, + "content": "[37] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.845, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[38] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 2" + }, + { + "type": "list", + "bbox": [ + 0.087, + 0.093, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[39] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 4, 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.15, + 0.894, + 0.219 + ], + "angle": 0, + "content": "[40] Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.221, + 0.894, + 0.316 + ], + "angle": 0, + "content": "[41] Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer. The many faces of robustness: A critical analysis of out-of-distribution generalization. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 15, 18" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.319, + 0.892, + 0.374 + ], + "angle": 0, + "content": "[42] Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song. Natural adversarial examples. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 15, 18" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.376, + 0.892, + 0.445 + ], + "angle": 0, + "content": "[43] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.447, + 0.892, + 0.502 + ], + "angle": 0, + "content": "[44] Ji Hou, Saining Xie, Benjamin Graham, Angela Dai, and Matthias Nießner. Pri3d: Can 3d priors help 2d representation learning? In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.504, + 0.892, + 0.559 + ], + "angle": 0, + "content": "[45] Drew A Hudson and Christopher D Manning. GQA: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.561, + 0.892, + 0.602 + ], + "angle": 0, + "content": "[46] Allan Jabri, Andrew Owens, and Alexei Efros. Space-time correspondence as a contrastive random walk. Advances in Neural Information Processing Systems (NeurIPS), 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.604, + 0.892, + 0.687 + ], + "angle": 0, + "content": "[47] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In Proceedings of the International Conference on Machine Learning (ICML), 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.689, + 0.892, + 0.744 + ], + "angle": 0, + "content": "[48] Justin Johnson, Lamberto Ballan, and Li Fei-Fei. Love thy neighbors: Image annotation by exploiting image metadata. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.746, + 0.892, + 0.787 + ], + "angle": 0, + "content": "[49] Jeff Johnson, Matthijs Douze, and Hervé Jégou. Billionscale similarity search with GPUs. IEEE Transactions on Big Data, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.789, + 0.892, + 0.857 + ], + "angle": 0, + "content": "[50] Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg. Referitgame: Referring to objects in photographs of natural scenes. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.859, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[51] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning." + }, + { + "type": "list", + "bbox": [ + 0.51, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "19217" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.118, + 0.092, + 0.47, + 0.12 + ], + "angle": 0, + "content": "In Advances in Neural Information Processing Systems (NeurIPS), 2020. 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.123, + 0.472, + 0.178 + ], + "angle": 0, + "content": "[52] Ryan Kiros, Yukun Zhu, Russ R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. Skip-thought vectors. In Advances in Neural Information Processing Systems (NeurIPS), 2015. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.181, + 0.472, + 0.236 + ], + "angle": 0, + "content": "[53] Simon Kornblith, Jonathon Shlens, and Quoc V Le. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2661-2671, 2019. 5, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.239, + 0.47, + 0.294 + ], + "angle": 0, + "content": "[54] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), Sydney, Australia, 2013. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.297, + 0.47, + 0.323 + ], + "angle": 0, + "content": "[55] Alex Krizhevsky. Learning multiple layers of features from tiny images, 2009. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.326, + 0.47, + 0.395 + ], + "angle": 0, + "content": "[56] Janghyeon Lee, Jongsuk Kim, Hyounguk Shon, Bumsoo Kim, Seung Hwan Kim, Honglak Lee, and Junmo Kim. Uniclip: Unified framework for contrastive language-image pre-training. In Advances in Neural Information Processing Systems (NeurIPS), 2022. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.398, + 0.47, + 0.453 + ], + "angle": 0, + "content": "[57] Ang Li, Allan Jabri, Armand Joulin, and Laurens van der Maaten. Learning visual n-grams from web data. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.456, + 0.47, + 0.511 + ], + "angle": 0, + "content": "[58] Aoxue Li, Tiange Luo, Zhiwu Lu, Tao Xiang, and Liwei Wang. Large-scale few-shot learning: Knowledge transfer with class hierarchy. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.513, + 0.471, + 0.595 + ], + "angle": 0, + "content": "[59] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. SupervisionexistsEverywhere:A Data Efficient Contrastive Language-Image Pre-training Paradigm.In Proceedings of the International Conference on Learning Representations (ICLR),2022.2,5,16" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.599, + 0.47, + 0.64 + ], + "angle": 0, + "content": "[60] Dong C Liu and Jorge Nocedal. On the limited memory bfgs method for large scale optimization. Mathematical programming, 1989. 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.643, + 0.47, + 0.697 + ], + "angle": 0, + "content": "[61] Lajanugen Logeswaran and Honglak Lee. An efficient framework for learning sentence representations. In Proceedings of the International Conference on Learning Representations (ICLR), 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.7, + 0.47, + 0.74 + ], + "angle": 0, + "content": "[62] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.744, + 0.47, + 0.785 + ], + "angle": 0, + "content": "[63] Ilya Loshchilov and Frank Hutter. Decoupled Weight Decay Regularization. In Proceedings of the International Conference on Learning Representations (ICLR), 2019. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.788, + 0.47, + 0.841 + ], + "angle": 0, + "content": "[64] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, 2013. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.845, + 0.47, + 0.9 + ], + "angle": 0, + "content": "[65] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (NeurIPS), 2013. 4" + }, + { + "type": "list", + "bbox": [ + 0.088, + 0.092, + 0.472, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[66] Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Abhishek Sharma, David Jacobs, and Dilip Krishnan. Object-aware cropping for self-supervised learning. arXiv preprint arXiv:2112.00319, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.15, + 0.892, + 0.205 + ], + "angle": 0, + "content": "[67] Norman Mu, Alexander Kirillov, David Wagner, and Saining Xie. Slip: Self-supervision meets language-image pretraining. In Proceedings of European Conference on Computer Vision (ECCV), 2022. 2, 4, 5, 7, 15, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.207, + 0.894, + 0.261 + ], + "angle": 0, + "content": "[68] Alejandro Newell and Jia Deng. How useful is self-supervised pretraining for visual tasks? In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.264, + 0.895, + 0.319 + ], + "angle": 0, + "content": "[69] M-E. Nilsback and A. Zisserman. Automated flower classification over a large number of classes. In Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, Dec 2008. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.321, + 0.892, + 0.362 + ], + "angle": 0, + "content": "[70] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.364, + 0.892, + 0.419 + ], + "angle": 0, + "content": "[71] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the Annual Meeting on Association for Computational Linguistics (ACL), 2002. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.421, + 0.892, + 0.49 + ], + "angle": 0, + "content": "[72] Yookoon Park, Mahmoud Azab, Bo Xiong, Seungwhan Moon, Florian Metze, Gourab Kundu, and Kirmani Ahmed. Normalized contrastive learning for text-video retrieval. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.492, + 0.892, + 0.545 + ], + "angle": 0, + "content": "[73] O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar. Cats and dogs. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.549, + 0.892, + 0.631 + ], + "angle": 0, + "content": "[74] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (NeurIPS), 2019. 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.633, + 0.892, + 0.702 + ], + "angle": 0, + "content": "[75] Xiangyu Peng, Kai Wang, Zheng Zhu, Mang Wang, and Yang You. Crafting better contrastive views for siamese representation learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 16031-16040, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.704, + 0.892, + 0.799 + ], + "angle": 0, + "content": "[76] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference on Machine Learning (ICML), 2021. 1, 2, 4, 5, 7, 8, 14, 16, 21, 22, 23" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.803, + 0.892, + 0.858 + ], + "angle": 0, + "content": "[77] Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. Do imagenet classifiers generalize to imageNet? In Proceedings of the International Conference on Machine Learning (ICML), 2019. 15, 18" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.86, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[78] Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural" + }, + { + "type": "list", + "bbox": [ + 0.511, + 0.093, + 0.895, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "19218" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.118, + 0.093, + 0.468, + 0.12 + ], + "angle": 0, + "content": "Language Processing. Association for Computational Linguistics, 11 2019. 3, 4, 8, 14, 20" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.123, + 0.47, + 0.178 + ], + "angle": 0, + "content": "[79] Karsten Roth, Oriol Vinyls, and Zeynep Akata. Integrating Language Guidance into Vision-based Deep Metric Learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.181, + 0.468, + 0.235 + ], + "angle": 0, + "content": "[80] Mert Bulent Sariyildiz, Julien Perez, and Diane Larlus. Learning visual representations with caption annotations. In Proceedings of European Conference on Computer Vision (ECCV), 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.239, + 0.471, + 0.307 + ], + "angle": 0, + "content": "[81] Ramprasaath R Selvaraju, Karan Desai, Justin Johnson, and Nikhil Naik. Casting your model: Learning to localize improves self-supervised representations. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 11058-11067, 2021. 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.31, + 0.468, + 0.365 + ], + "angle": 0, + "content": "[82] Dandan Shan, Richard Ely Locke Higgins, and David Fouhey. Cohesiv: Contrastive object and hand embedding segmentation in video. In Advances in Neural Information Processing Systems (NeurIPS), 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.368, + 0.468, + 0.422 + ], + "angle": 0, + "content": "[83] Jinghuan Shang, Srijan Das, and Michael S Ryoo. Learning viewpoint-agnostic visual representations by recovering tokens in 3d space. Advances in Neural Information Processing Systems (NeurIPS), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.425, + 0.468, + 0.467 + ], + "angle": 0, + "content": "[84] Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson. Cnn features off-the-shelf: an astounding baseline for recognition. In CVPRW, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.469, + 0.468, + 0.538 + ], + "angle": 0, + "content": "[85] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual Captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2018. 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.541, + 0.468, + 0.582 + ], + "angle": 0, + "content": "[86] Kihyuk Sohn. Improved deep metric learning with multiclass n-pair loss objective. In Advances in Neural Information Processing Systems (NeurIPS), 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.585, + 0.468, + 0.64 + ], + "angle": 0, + "content": "[87] Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. MPNet: Masked and Permuted Pre-training for language understanding. Advances in Neural Information Processing Systems (NeurIPS), 2020. 4, 8, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.643, + 0.468, + 0.697 + ], + "angle": 0, + "content": "[88] Jonathan C Stroud, Zhichao Lu, Chen Sun, Jia Deng, Rahul Sukthankar, Cordelia Schmid, and David A Ross. Learning video representations from textual web supervision. arXiv preprint arXiv:2007.14937, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.7, + 0.47, + 0.769 + ], + "angle": 0, + "content": "[89] Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Hua-jun Bai, and Yoav Artzi. A corpus for reasoning about natural language grounded in photographs. In Proceedings of the Annual Meeting on Association for Computational Linguistics (ACL), 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.772, + 0.468, + 0.827 + ], + "angle": 0, + "content": "[90] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. LoFTR: Detector-free local feature matching with transformers. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.829, + 0.468, + 0.871 + ], + "angle": 0, + "content": "[91] Igor Susmelj, Matthias Heller, Philipp Wirth, Prescott Jeremy, and Malte Ebner. Lightly. GitHub. Note: https://github.com/lightly-ai/lightly, 2020. 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.873, + 0.468, + 0.9 + ], + "angle": 0, + "content": "[92] TensorFlow Datasets, a collection of ready-to-use datasets. https://www.tensorflow.org/datasets.14" + }, + { + "type": "list", + "bbox": [ + 0.088, + 0.093, + 0.471, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[93] Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. YFCC100M: The new data in multimedia research. Communications of the ACM, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.15, + 0.892, + 0.205 + ], + "angle": 0, + "content": "[94] Yonglong Tian, Olivier J Henaff, and Aaron van den Oord. Divide and contrast: Self-supervised learning from uncu-rated data. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.207, + 0.892, + 0.275 + ], + "angle": 0, + "content": "[95] Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola. Rethinking few-shot image classification: a good embedding is all you need? In Proceedings of European Conference on Computer Vision (ECCV), 2020. 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.278, + 0.892, + 0.332 + ], + "angle": 0, + "content": "[96] Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. CIDEr: Consensus-based image description evaluation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.334, + 0.892, + 0.403 + ], + "angle": 0, + "content": "[97] Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling. Rotation equivariant cnns for digital pathology. In International Conference on Medical image computing and computer-assisted intervention, 2018. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.406, + 0.892, + 0.473 + ], + "angle": 0, + "content": "[98] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the International Conference on Machine Learning (ICML), 2008. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.511, + 0.476, + 0.892, + 0.532 + ], + "angle": 0, + "content": "[99] Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing. Learning robust global representations by penalizing local predictive power. In Advances in Neural Information Processing Systems (NeurIPS), 2019. 15, 18" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.533, + 0.892, + 0.602 + ], + "angle": 0, + "content": "[100] Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. Advances in Neural Information Processing Systems (NeurIPS), 2020. 8, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.604, + 0.892, + 0.659 + ], + "angle": 0, + "content": "[101] Xiaolong Wang, Allan Jabri, and Alexei A Efros. Learning correspondence from the cycle-consistency of time. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.661, + 0.892, + 0.716 + ], + "angle": 0, + "content": "[102] Yan Wang, Wei-Lun Chao, Kilian Q. Weinberger, and Laurens van der Maaten. Simpleshot: Revisiting nearest-neighbor classification for few-shot learning. arXiv preprint arXiv:1911.04623, 2019. 5, 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.718, + 0.892, + 0.773 + ], + "angle": 0, + "content": "[103] P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona. Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology, 2010. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.775, + 0.892, + 0.829 + ], + "angle": 0, + "content": "[104] Ross Wightman, Hugo Touvron, and Herve Jegou. Resnet strikes back: An improved training procedure in timm. In NeurIPS Workshop on ImageNet: Past, Present, and Future, 2021. 7, 8, 15, 21, 22, 23" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.832, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[105] Xiaoshi Wu, Hadar Averbuch-Elor, Jin Sun, and Noah Snavely. Towers of babel: Combining images, language, and 3d geometry for learning multimodal vision. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "19219" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.161 + ], + "angle": 0, + "content": "[106] Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. Unsupervised feature learning via non-parametric instance discrimination. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3733-3742, 2018. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.163, + 0.472, + 0.219 + ], + "angle": 0, + "content": "[107] J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. SUN database: Large-scale scene recognition from abbey to zoo. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.22, + 0.47, + 0.275 + ], + "angle": 0, + "content": "[108] Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell. What should not be contrastive in contrastive learning. In International Conference on Learning Representations, 2020. 2, 3, 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.276, + 0.47, + 0.345 + ], + "angle": 0, + "content": "[109] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.346, + 0.47, + 0.428 + ], + "angle": 0, + "content": "[110] Zhicheng Yan, Hao Zhang, Robinson Piramuthu, Vignesh Jagadeesh, Dennis DeCoste, Wei Di, and Yizhou Yu. Hd-cnn: hierarchical deep convolutional neural networks for large scale visual recognition. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.43, + 0.47, + 0.5 + ], + "angle": 0, + "content": "[111] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. FILIP: Fine-grained interactive language-image pre-training. In Proceedings of the International Conference on Learning Representations (ICLR), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.5, + 0.47, + 0.556 + ], + "angle": 0, + "content": "[112] Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. TACL, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.557, + 0.47, + 0.612 + ], + "angle": 0, + "content": "[113] Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. From recognition to cognition: Visual commonsense reasoning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.613, + 0.47, + 0.669 + ], + "angle": 0, + "content": "[114] Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer. Scaling vision transformers. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.669, + 0.47, + 0.711 + ], + "angle": 0, + "content": "[115] Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful image colorization. In Proceedings of European Conference on Computer Vision (ECCV), 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.712, + 0.47, + 0.768 + ], + "angle": 0, + "content": "[116] Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. Bertscore: Evaluating text generation with bert. In Proceedings of the International Conference on Learning Representations (ICLR), 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.769, + 0.47, + 0.837 + ], + "angle": 0, + "content": "[117] Mingkai Zheng, Fei Wang, Shan You, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu. Weakly supervised contrastive learning. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.839, + 0.47, + 0.895 + ], + "angle": 0, + "content": "[118] Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. Visual7w: Grounded question answering in images. 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Language allows us to abstract away visual variation to represent and communicate concepts. Building on this intuition, we propose an alternative approach to visual representation learning: using language similarity to sample semantically similar image pairs for contrastive learning. Our approach diverges from image-based contrastive learning by sampling view pairs using language similarity instead of handcrafted augmentations or learned clusters. Our approach also differs from image-text contrastive learning by relying on pre-trained language models to guide the learning rather than directly minimizing a cross-modal loss. Through a series of experiments, we show that language-guided learning yields better features than image-based and image-text representation learning approaches. + +# 1. Introduction + +Consider the images in Fig. 1, is the center image more similar to its left or right neighbor? Despite the difference in background and pose, it is clear that the right pair captures the same concept: a flying snow owl. Nevertheless, a self-supervised image model will judge the left pair as more similar. Human perception and language abstract away appearance differences to capture conceptual similarity rather than just visual similarity. Ideally, we could learn visual features that capture conceptual similarity and generalize effectively to other visual tasks. In this work, we show how language can be a proxy for conceptual similarity; allowing us to sample better pairs for contrastive learning and train more generalizable visual models. + +Image-only contrastive learning uses visual similarity as a proxy for conceptual similarity. This is based on the observation that discriminative approaches can discover interclass similarity-e.g., cheetahs are similar to lions- without requiring explicit annotations [106]. The core idea is to train a discriminative model where each instance is treated as a separate class, and the model is trained to map augmented + +![](images/2a5f83ca8ed7251c5a75201146ebbfb71ede11bdb330ed1f7735e41f79e3778a.jpg) +Visual Embedding +Language Embedding +Figure 1. Language allows us to find conceptually similar image pairs even if they are visually dissimilar. We use those pairs for contrastive learning to learn generalizable visual features. + +views of the same image to similar features [12-15, 106]. While successful, instance discrimination ignores the similarity between different instances as it assumes all other images are unrelated. Later work focused on inter-image relationships by estimating clusters [3, 9, 10] or finding nearest neighbors [28]. However, those relationships are estimated using visual embeddings; resulting in visually, rather than conceptually, similar pairs. + +Language similarity is a strong proxy for semantic relationships. Consider the example in Fig. 1; images that depict the same concept are often described similarly. Radford et al. [76] propose language-image contrastive learning by mapping images and text to a shared representation space and achieve impressive generalization capabilities. However, it is unclear whether forcing models to map onto a shared space is optimal for visual learning. Although linguistic and visual similarity might align for similar instances, it is unclear whether all distances in one space should map exactly to the other. Instead of learning a joint vision-and-language representations, we argue that it is better to use linguistic similarity to guide visual learning. + +To this end, we propose language-guided contrastive learning: a simple adaptation to contrastive learning that uses language models to find conceptually-similar image pairs for visual learning. Our approach is motivated by the observation that language models, despite never training on visual data, can still be used to sample caption pairs that belong to conceptually similar images, as seen in Fig. 2. Such sampled images exhibit desirable variations in pose, lightning, and context which are very different from hand-crafted augmentations which can be ill-suited to downstream tasks [108] or too focused on background textures [81]. We use the sampled pairs instead of image augmentations within standard self-supervised visual learning approaches such as SimCLR [12], SimSiam [15], and SLIP [67]. Our approach departs from image-only contrastive learning by relying on conceptually-similar image pairs rather than visually similar augmentations or cluster-assignment. We also depart from image-text pre-training by allowing the model to be guided by language similarity rather than learning a joint embedding space. + +We conduct a series of controlled experiments to analyze our approach and compare it to commonly used representation learning paradigms on generalization to downstream classification tasks. In controlled settings, our approach outperforms all baselines on linear probe and few-shot classification on a range of downstream classification datasets. Our analysis suggests that while learning multimodal joint embeddings can result in good representations, it is better to use one modality to guide the training of the other. Furthermore, we find that our approach is robust to the specific choice of sampling strategy or language model. Our code and pre-trained models are available at https://github.com/mbanani/lgssl. + +# 2. Related Work + +Visual Representation Learning aims to learn visual embedding spaces that capture semantics, with a typical focus on learning from scalable data sources. Broadly speaking, there are two general approaches: generative and discriminative. Generative approaches hypothesize that a model that can capture the image distribution will learn semantically relevant features [26, 31, 37, 70, 98, 115]. In contrast, discriminative approaches posit that differentiating between images will give rise to better features. This idea can be traced by to early work on metric learning [18] and dimensionality reduction [35], and is clearly seen for supervised classification models [84]. More recently, Wu et al. [106] proposed treating each image as a separate class and using augmented images as class instances to relieve the need for human annotation. This was followed by papers that simplified this approach [12-14,38] and proposed non-contrastive variants [15, 34]. While those approaches have been successful, the utility of augmentation-based self-supervised + +learning has been questioned [68, 108] with follow-up work proposing the use of objectness [66, 75] and saliency [81] to alleviate some of those concerns. While we share the goal of visual representation learning, we question the reliance on image augmentations for training and propose using language models to learn for conceptually-similar images. + +Language-supervised vision pre-training aims to learn visual representations from language data. Early work of Li et al. [57] trained n-gram models using YFCC [93] images and user-tag metadata. While some works learn joint vision-and-language representations for tasks like visual question answering [2, 33, 45, 118], visual reasoning [50, 89, 113], and retrieval [72, 112], we are interested in using language to learn better visual representations [23, 23, 76, 80, 88]. Early works used language modeling as a pretext task for visual learning [23, 80], but contrastive approaches quickly gained more popularity due to their relative simplicity and generalization capabilities [47, 76]. Follow-up work extended the contrastive formulation to learn dense features [109, 111] or used additional self-supervised losses to improve performance and data efficiency [21, 56, 59, 67]. While we share the motivation of using language for visual learning, we focus on learning visual representations by using linguistic guidance from pre-trained language models. + +Leveraging structure in the data. This is commonly done in dense feature learning, where optical flow [36, 46, 82, 101] or 3D transformations [29, 44, 83, 90, 105] provide natural associations between image patches. For images, prior approaches used class names [51, 79], class hierarchies [58, 110], meta data [32, 48, 57] or clustering [3, 9, 10, 94, 117] to improve learning and inference. Within contrastive learning, clustering has been a popular choice for leveraging dataset structure. The intuition is that natural clusters emerge in feature spaces that can provide an additional training signal or useful pseudo-labels. While such approaches work well on curated datasets (e.g., ImageNet) where the label set provides an estimate of the number of clusters, it struggles with imbalanced and uncurated data [4]. Other approaches sample nearest neighbors as a feature-driven within-domain augmentation [28, 59]. While these approaches differ in how they extract inter-instance relationships, they all use within-domain feature similarity to sample positive pairs or clusters and hence do not leverage the rich cross-modal relationships. Closest to our work is Han et al. [36] who propose a co-training [6] scheme for jointly learning image and optical flow representations. We share their motivation of using similarity in one space (language) to learn in another (vision). Furthermore, instead of relying on co-training on the same dataset, we extract distances from a text-only language model, allowing us to leverage unaligned data. + +![](images/6ac66ff458ae5d20b4632a624cc94593bf71b2b70fae9662fed97b44d3b8393f.jpg) +Figure 2. Language sampling yields semantically-similar and visually-diverse image pairs. We sample the three nearest neighbors using a self-supervised visual model [13], an ImageNet supervised model [27], and a self-supervised language model [78]. While visual sampling yields visually similar pairs, language sampling yields semantically relevant and visually diverse images. We argue that the combination of semantic consistency and visual diversity are better for learning generalizable features. + +![](images/a230684098129931190e4cc85dd800f83a851afdfd13bf174ff13d743cbd1b8c.jpg) + +![](images/a553430611a5e26e5dcb81f6bb1ea8e688c7a49a7f341eefc3ace81c47b95c5d.jpg) + +# 3. Method + +The goal of this work is to learn visual representations that can generalize to other datasets. We extend image-only contrastive learning beyond hand-crafted augmentations and visually-sampled clusters to learn from conceptually similar images. Through learning to associate images that depict the same visual concept, models can learn visual invariances that more closely capture human semantics. To achieve this, we propose sampling image pairs that have similar captions using a pre-trained sentence encoder [78] and using them for contrastive learning. This work does not propose a new model or loss but rather a novel way of sampling image views that is applicable to a variety of approaches and losses for learning visual representations. + +# 3.1. Learning from Conceptual Similarity + +Instance discrimination has been the dominant task for visual representation learning. Its core intuition is that visual similarity is a good proxy for semantic similarity. The standard approach generates positive view pairs using image augmentations and maximizes their embedding similarity, with or without negative views. While there has been a large number of contrastive learning approaches, view pair generation has largely remained the same. Other methods use visual feature similarity to learn prototypes [3, 9, 10] or sample previously seen instances [28] for contrastive learning. While these approaches extend beyond instances and consider relations in the dataset, they still rely on visual similarity to generate their contrastive pairs. This limits the visual invariances that they can learn [108]. + +We propose training models to identify the same visual concept instead of the same instance. Our key observation is simple: images that have similar captions often depict similar concepts regardless of the actual appearance similarity. This can be clearly seen in Fig. 2. Nearest neighbors in visual representation space depict objects in similar scenes and poses, with self-supervised models showing some color invariances due to color augmentation. Conversely, similarly captioned images depict objects in different colors, poses, and contexts. This makes language-sampled images an excellent source for visual representation learning as they implicitly capture human-like visual invariances. + +# 3.2. Sampling Image Pairs using Language + +Given a captioned image dataset, we want to sample image pairs that have very similar captions. While caption similarity may be a good proxy for conceptual similarity, measuring caption similarity is a challenge on its own. Traditional metrics such as BLEU [71] and CIDER [96] rely on n-gram overlap, which can be too sensitive to phrasing and sentence structure. This makes them ill-suited for our needs. Other metrics such as SPICE [1] account for such variety by comparing parse trees; however, they still can not account for different wording choices. Inspired by advances in language models as well as approaches like BERTScore [116] and CLIPScore [43], we use a pre-trained sentence encoder to compute caption similarity. + +Sentence encoders are trained to extract sentence-level features [52, 61, 78]. We use SBERT [78], which finetunes a pre-trained language model to allow it to better capture semantic similarity using feature cosine distance. + +![](images/1397f6941cc01f862fc25d1a2bc7b3e9bc24852c452e9147f4bdb8d7fe166b04.jpg) +Image Augmentations + +![](images/c021b8fa370d5c17757a95ebc197718a14e336d0a6056498327ba74d5bb9d22c.jpg) +Image-Caption + +![](images/415f2d0674e29c314b0d7041e97c25e5cca5c7d7b006322ac306c25b445effa2.jpg) +Nearest Neighbor Memory Bank +Figure 3. Contrasting Contrastive Formulations. While image-only and image-text contrastive learning directly extract views from the instance, nearest-neighbor methods rely on a memory bank of previously extracted features for training. In contrast, our approach samples nearest neighbors in caption embedding space using a pretrained language model and use the associated image for contrastive learning. + +![](images/3e7b900b88c395b4d6e7d4ad93a1c1082eff483f77887ba015d666e7c8ec58af.jpg) +Language-Guided Sampling + +SBERT is trained in two stages: first, a language backbone is trained using a standard self-supervised task such as masked [25, 65] or permuted [87] language modeling; second, the language modeled is fine-tuned via contrastive learning on a large combined dataset of 1 billion sentence pairs. Fine-tuning the model in a contrastive way simplifies downstream usage as it allows features to be compared directly using cosine similarity. We use an SBERT [78] model with an MPNet [87] backbone. However, we find that our formulation is not sensitive to the choice of language encoder, as shown in Tab. 5c. + +Finally, we sample the nearest neighbors for all captions in the language embedding space. We leverage modern similarity search libraries [49] to perform the nearest neighbor search quickly, despite the large dataset size. For example, nearest neighbor sampling runs in under 3 hours for RedCaps (12 million instances) on 4 GPUs, with 43 minutes spent on feature extraction and 117 minutes on nearest neighbor search. Furthermore, we find that we could further reduce the complexity of the sampling by only searching within subsets of the data as shown in Appendix E. + +# 3.3. Language-Guided Visual Learning + +Our approach is applicable to several representation learning methods as it only changes the training view pairs. We focus on contrastive learning since its fairly minimal setting allows us to analyze the impact of language guidance with minimal confounding factors. We train SimCLR with the language-sampled pairs and refer to it as LGSimCLR. We also evaluate the impact of language guidance on SimSiam [15] and SLIP [67], and find that they can similarly benefit from language guidance. We only use random cropping for image augmentations since language-sampled pairs are naturally augmented versions of each other and find that additional augmentations are not helpful. For LGSLIP, we match their setup by applying the CLIP loss only between the source's image and caption, ignoring an additional loss between the nearest neighbor image and its caption. + +# 4. Experiments + +Our experiments evaluate the efficacy of learning visual features from conceptually similar images. We hypothesize that a model trained with language guidance will learn useful visual invariances and better generalize to downstream tasks. We are interested in answering these questions: Does language guidance improve generalization over other pretraining approaches? Does language guidance generalize to other datasets and pre-training approaches? How can language be used for visual pre-training? + +# 4.1. Experimental Setup + +We formulate our experimental setup to compare the efficacy of different learning signals. We train models with language-guided sampling and compare them with image-only self-supervised models and image-text contrastive models. We are interested in conducting controlled experiments for a fair comparison. + +Recent work in self-supervised learning has demonstrated the impressive impact of scaling [12, 76, 114]. While such work has shown impressive performance, it has complicated the evaluation as different models are trained on different pretext tasks on different datasets using varying amounts of compute and training recipes. Furthermore, replication is difficult, if not impossible, due to the unavailability of training data or prohibitive compute requirements. Fortunately, several papers report results that indicate that performance patterns often hold at smaller scales [10, 12, 21, 67, 76]. Hence, we conduct our experiments at a scale that allows us to perform a comprehensive evaluation and permits replication by others. + +We conduct our experiments with a standard backbone [39] on publicly available datasets [11, 24, 85]. To account for variation in training recipes, we retrain all methods from scratch using the same training recipe. We scale down experiments to a level that permits fair comparisons and replication. We also provide system-level comparisons in Tab. 4 and scaling results in App. D. + +Training details: We use a ResNet-50 backbone and train all models using the AdamW optimizer [63] with a learning rate of $10^{-3}$ and a weight decay of $10^{-2}$ . We use a cosine learning scheduler [62] with 5000 warm-up steps. Models are trained using a batch size of 512 for 250k steps; this corresponds to 10.5 epochs on RedCaps. We use a constant number of steps to permit meaningful comparisons between models trained on different datasets. + +Evaluation setup: We evaluate all approaches using linear probe and fewshot classification on 15 classification datasets inspired by [53,76]. We use the linear probe evaluation proposed by [53] and learn a single linear layer using logistic regression. We sweep over a range of cost values and choose the value with the best validation performance. We retrain a classifier on both train and validation splits and report test performance. We also evaluate all approaches on fewshot classification to understand their generalization ability. We use a weighted kNN classifier on frozen support features inspired by prior work showing its effectiveness for fewshot classification [102]. Please see Appendices A and B for more details on evaluation datasets and tasks. + +Baselines: While there have been many proposed visual representation learning approaches, they can be grouped into several key directions that differ in the pretext task. We focus our comparison on a few representative approaches to explore the impact of the learning signal. We overview the baselines here and provide more details in Appendix C. + +Many of our baselines are variants of contrastive learning as shown in Fig. 3. Contrastive approaches operate over paired source and target feature embeddings: $z^s$ and $z^t$ . The goal is to maximize the similarity between the paired embeddings and minimize it with respect to all other embeddings. Given a batch size $N$ and embedding dimension $F$ , $z^s, z^t \in \mathbb{R}^{N \times F}$ . The contrastive loss [86] is: + +$$ +\mathcal {L} \left(z ^ {s}, z ^ {t}\right) = - \log \frac {\exp \left(\operatorname {s i m} \left(\boldsymbol {z} _ {i} ^ {s} , \boldsymbol {z} _ {i} ^ {t}\right) / \tau\right)}{\sum_ {k = 1} ^ {N} \exp \left(\operatorname {s i m} \left(\boldsymbol {z} _ {i} ^ {s} , \boldsymbol {z} _ {k} ^ {t}\right) / \tau\right)}, \tag {1} +$$ + +where $\tau$ is a scaling parameter and $\mathrm{sim}(\cdot ,\cdot)$ is cosine similarity. Contrastive approaches primarily differ in how the embeddings are computed. + +Image-Only Contrastive Learning contrasts features extracted from two randomly augmented views of the same image to perform instance discrimination [106]. We use SimCLR [12] as a representative approach due to its simplicity and strong performance. + +Image-Text Contrastive Learning learns by contrasting features extracted from images and their captions. Unlike image-only approaches, this approach can learn semantics from the captions. Radford et al. [76] first proposed this approach and has had several follow-ups that augment it with additional self-supervised losses losses [56, 59, 67]. We use CLIP [76] and SLIP [67] due to their simplicity. + +Nearest Neighbor Contrastive Learning contrast source embeddings with retrieved embeddings from a memory bank. The target features are used to retrieve the nearest neighbor embedding from a memory bank of previous batches. Dwibedi et al. [28] proposed this approach for image-only contrastive learning, while Li et al. [59] proposed adapting this loss for language embeddings. We use NNCLR [28] as Visual NNCLR and DeCLIP [59] with the CLIP and the language NNS losses as Language NNCLR. + +Image-Only Non-Contrastive Learning deviates from the typical contrastive setup by learning without negative samples [15, 34]. We use SimSiam as a representative approach due to its simplicity and strong performance. + +Cluster-based Contrastive Learning learn by contrasting image features with learned prototypes [3,9,10]. Prototypes are estimated via clustering or learned jointly with the feature encoder. Caron et al. [10] report that different cluster-based approaches perform similarly when provided with the same algorithmic advances. We use an adapted SwAV without the multi-crop augmentation strategy as it is equally applicable to other methods. We also compare against a pretrained SwAV checkpoint in Tab. 4. + +# 4.2. Results + +We train all approaches with a ResNet-50 backbone on RedCaps and report results in Tabs. 1 and 3. Our model outperforms all baselines with a significant margin for both evaluations. We analyze the results below through a series of questions. + +Does language-guided sampling provide better training pairs than image augmentations? LGSimCLR greatly outperforms SimCLR despite using the same learning objective. By using language sampled pairs instead of image augmentations, LGSimCLR learns stronger invariances. We find that the largest gains arise in fine-grained datasets: Cars, CUB, and Food101. The performance gains can be explained by considering the critique of Xiao et al. [108]: the training augmentations dictate the invariances learned by SimCLR as shown in nearest neighbor samples in Fig. 2. Consider the third row of Fig. 2, while language sampling depicts three Aston Martin cars in different spots, visual nearest neighbors are sports cars in different poses and colors, closely resembling the flip and color augmentations used for training. Similarly in the first row of Fig. 2, visual nearest neighbors depict owls from different species in similar poses, while language sampling retrieves three great horned owls from different viewpoints. These trends are further amplified when features are used directly for fewshot classification. Language guidance allows us to capture relationships that go beyond visual similarity by training on image pairs that capture human semantics. + +Table 1. Linear Probe Evaluations. We train ResNet-50 models on RedCaps and report performance of a linear probe using frozen features on 15 downstream tasks. Models are split based on whether or not they require caption images for training. LGSimCLR outperforms all previous approaches with strong performance gains for fine-grained classification datasets. + +
ModelFood-101CIFAR-10CIFAR-100CUBSUN397CarsAircraftDTDPetsCaltech-101FlowersSTL-10EuroSATRESISC45PCAMMean
SwAV63.681.357.521.647.522.935.468.161.170.578.087.794.379.984.363.6
SimSiam64.179.956.128.248.329.541.266.269.173.683.685.794.482.183.365.7
Visual NNCLR65.482.860.226.650.026.640.968.065.275.483.588.595.382.283.866.3
SimCLR69.082.961.630.652.633.743.769.870.574.186.988.095.484.684.468.5
Language NNCLR81.283.161.948.656.545.137.268.878.182.090.293.492.581.180.772.0
CLIP80.984.762.750.457.445.836.767.679.884.091.093.593.982.282.672.9
SLIP77.787.267.042.458.148.745.272.379.582.792.192.795.685.583.474.0
LGSimCLR (Ours)83.287.869.059.360.362.353.471.281.889.495.994.095.688.081.178.2
+ +![](images/92f71a19b956769e81e37cfb6a4e1c482d022d9c27185ea664c3f1ae677b185b.jpg) +Figure 4. Nearest Neighbor methods are limited by the memory bank size. Even with a large memory bank, the nearest embedding can still be unrelated to the source image while language sampling provides us with conceptually similar pairs. + +Can we just sample nearest neighbors from previous batches? LGSimCLR outperforms NNCLR despite both relying on nearest neighbors. NNCLR uses the nearest feature embedding from a memory bank in the same modality. The quality of their retrieved samples is limited by the size of the memory bank. To demonstrate this, we visualize the nearest neighbors retrieved by NNCLR for different memory bank sizes in Fig. 4. We find that the retrieval quality is poor even for larger queues. Interestingly, we note that NNCLR also underperforms SimCLR on RedCaps, despite performing better on ImageNet. We posit that ImageNet's curated distribution explains this: a queue of $16k$ will most probably contain instances from each class, resulting in both visually and conceptually similar retrievals. Additionally, the quality of nearest neighbors is affected by the sampling feature space; features that are only trained on image augmentations will have limited invariances as shown in Fig. 2. We further explore the impact of sampling space on training in Sec. 4.3. + +Table 2. Language-guided contrastive learning outperforms image-text contrastive learning, regardless of text encoder. + +
ObjectiveText EncoderLinearFewshot
Image-TextRandomly- Initialized72.977.5
Frozen SBERT71.877.1
Image-ImageFrozen CLIP (RedCaps)78.382.4
Frozen SBERT78.282.5
+ +Can cluster-based approaches learn better features? Similar to nearest-neighbor sampling, clustering is performed using visual similarity. Furthermore, it is based on an estimated number of clusters in the training dataset. Although this can be determined for ImageNet due to its known class structure, the number of clusters in an arbitrary uncurated dataset is unknown. This results in a large performance drop, as seen in Tab. 1 and Tab. 3. On the other hand, sampling related pairs assumes no global structure within the data and hence is able to better capture inter-instance similarity. This results in nearest-neighbor sampling outperforming clustering and both being outperformed by contrastive learning and language-guided contrastive learning. + +Should we use language for guidance or supervision? Our experiments indicate that LGSimCLR outperforms both CLIP and SLIP. We consider two possible explanations: (a) SBERT extracts better language embeddings than CLIP can learn from the data, or (b) language-guided contrastive learning is a better training objective than imag-text contrastive learning. To evaluate this, we compare four models in Tab. 2. The first two models use CLIP's training objective: the first model uses a randomly initialized language encoder, similar to CLIP. The second model uses a frozen SBERT model as the language encoder and only trains the projection layers. The second two models use LGSimCLR's training objective but sample pairs using a + +Table 3. Few-Shot Evaluations. We train ResNet-50 models on RedCaps and report 5-way, 5-shot classification performance. We observe that language results in huge performance gains as shown by the performance of CLIP and LGSimCLR. Furthermore, the use of any augmentations hurts performance as seen by SLIP's drop in performance. + +
ModelFood-101CIFAR-10CIFAR-100CUBSUN397CarsAircraftDTDPetsCaltech-101FlowersSTL-10EuroSATRESISC45Mean
SwAV64.554.061.845.884.936.534.174.866.578.175.572.680.472.964.5
SimSiam63.949.957.249.584.539.337.975.767.879.781.569.680.679.465.5
Visual NNCLR65.654.161.745.885.337.934.975.267.381.175.474.383.676.765.6
SimCLR66.945.751.051.587.144.038.477.670.180.086.969.683.581.366.7
Language NNCLR89.365.373.478.690.868.440.475.278.890.994.389.675.271.977.3
CLIP88.964.673.178.390.969.740.775.777.591.694.789.875.374.877.5
SLIP81.563.570.863.191.362.942.179.676.488.492.283.482.780.875.6
LGSimCLR (Ours)90.366.375.583.192.777.650.681.184.195.497.686.585.089.082.5
+ +pre-trained language-only SBERT or the language encoder from a CLIP model trained on RedCaps. We find that image-image contrastive learning yields better visual features for both setups. While CLIP does not benefit from an SBERT backbone, LGSimCLR benefits from sampling using a language encoder trained on the same dataset. This suggests that learning joint embeddings results in worse visual features than language-guided learning. + +System-level comparisons: We compare LGSimCLR with publicly-available checkpoints of prior approaches; see Appendix C for details. We emphasize that while the experiments reported in Tabs. 1 and 3 were done in a controlled setup (same batch size, training data, optimizer), the system level comparisons are trained on different datasets with different training recipes and enhancements to further boost performance; e.g., large batch sizes, longer training, multi-crop augmentation. Furthermore, it has been shown that models trained on ImageNet implicitly benefit from its curated nature [4, 67]. Nevertheless, our approach still outperforms prior self-supervised approaches. We fall short of CLIP's ResNet-50 due to its training scale; $64 \times$ larger batch, $32 \times$ larger dataset, and $75 \times$ . We also observe that ImageNet-supervised ResNet-50 achieves better few-shot performance. Examining the performance breakdown in Tab. 10, we find the improvement mainly comes from CIFAR10, CIFAR100, and Pets. We posit that this can be explained by ImageNet's class structure: mostly pets with a large overlap with CIFAR's classes. + +# 4.3. Analysis + +We now analyze language-guided contrastive learning by evaluating the impact of pre-training data, the choice of embedding space, and the pretext task. By understanding the impact of those choices, we can better understand what the model is learning. + +Table 4. ResNet-50 System Level Comparisons. We outperform prior self-supervised approaches despite them benefiting from ImageNet's curation for training and using larger batch sizes. CLIP outperforms us due to the scale of its training. + +
Batch#Img UpdatesDatasetLinearFewshot
Supervised [104]10241.3×108ImageNet78.085.7
SimSiam [15]5121.3×108ImageNet72.978.7
SimCLR [13]40961.0×109ImageNet75.477.4
MoCo [16]40961.3×108ImageNet77.780.1
SwAV [10]40961.3×108ImageNet78.278.5
CLIP [76]327681.0×1010CLIP81.887.8
LGSimCLR5121.3×108RedCaps78.282.5
+ +Approach generality: We extend language guidance to other contrastive approaches: SimSiam and SLIP. We observe that language guidance uniformly improves performance for all methods, as shown in Tab. 5a. Furthermore, the difference between SimCLR and SLIP shrinks when adding language guidance. This suggests that language guidance provides the model with similar semantics to the ones learned from an image-text contrastive loss, resulting in diminished gains from the additional image-text loss. + +Impact of training dataset: We train our model on four datasets: CC3M [85], CC12M [11], RedCaps-2020, and RedCaps [24]. In Tab. 5b, we observe that larger datasets result in stronger performance, indicating that our approach could scale well with even larger datasets. Furthermore, we observe that RedCaps results in better performance than Conceptual Captions. This may be attributed to the higher quality of captions in RedCaps; while the alt-text captions CC3M and CC12M can be short and contain image metadata, RedCaps captions are diverse, longer, and more descriptive. This allows our model to sample more interesting visual pairs that capture more visual diversity. We provide qualitative results in Appendix F to support this. + +
Image Aug.Language
LinearFewshotLinearFewshot
SimSiam65.765.571.275.7
SimCLR68.566.778.282.5
SLIP74.075.678.882.8
+ +(a) Approach Generality + +
SizeLinearFewshot
CC3M2.7M71.576.3
CC12M10.9M76.881.9
RedCaps 20203.2M73.878.8
RedCaps12.0M78.282.5
+ +(b) Impact of training dataset + +
LinearFewshot
SBERT (MPNet)78.282.5
SBERT (MiniLM)78.683.3
CLIP Language (ViT-B/32)78.383.1
FastText BoW76.180.9
ImageNet-supervised78.381.8
SimCLR (ImageNet)73.174.6
+ +(c) Impact of sampling space + +Table 5. Analysis Experiments. We conduct a series of analysis experiments to understand language-guided contrastive learning. The results indicate that language sampling is beneficial to several formulations and scales well with larger datasets. Furthermore, while language sampling consistently results in good pairs for training, visual sampling only helps if it has access to semantics through supervision. + +Impact of sampling space: The idea of using offline nearest-neighbor sampling does not require a specific language model or even a specific modality. We explore other choices for embedding space: four sentence encoders and two image models. In our experiments, we use SBERT's MPNet model [78, 87]; the highest performing SBERT model for sentence similarity. We compare it to two other sentence transformers: a smaller SBERT model, MiniLM [100], and the language encoder from CLIP [76]. We also compared against a bag-of-words (BoW) sentence encoder that uses FastText [7] embeddings. Results are in Tab. 5c. While we expected that using CLIP for sampling would improve performance due to its multimodal training, we were surprised that MiniLM also improved performance despite its lower performance on language tasks. We find that pairs obtained using a BoW model result in a weaker performance which might hint at the importance of contextual sentence embeddings. Nevertheless, the BoW-sampled pairs still result in higher performance than all the other baselines on RedCaps. + +We also consider training with pairs sampled using two visual models: ImageNet-supervised ResNet-50 [104] and ImageNet-trained SimCLR [13]. We find that using a visual model for sampling is only beneficial if the visual model captures semantic relations; e.g., through supervised training. Using a self-supervised language model results in a strong drop in performance relative to the other sampling spaces. Nevertheless, it still allows the model to achieve better performance than using a self-supervised visual approach on the same data. This indicates that while language is a better modality to use, "sample-guided" contrastive learning can still achieve a stronger performance than only using self-supervised learning. + +Limitations: We observe a few limitations in our approach. Image captions can be noisy, vague, and often omit obvious relations in the image [5]. While this broadly affects image-language models, it can result in us retrieving unrelated image pairs. For example, captions like "I found this in the garden" or "Photo from our family trip" could describe a large range of images, some of which are unrelated. We expand on this in Appendix F. Image descriptions also de + +pend on the context and the perceiver; e.g., a tourist and an art curator will describe artwork in very different ways. We observe that descriptions in topic-focused subreddits (e.g., r/birdpics and r/woodworking) are more specific than in generic subreddits (e.g., r/itookapicture and r/pics). Our experiments in Appendix E support this observation. Since a caption only captures one aspect of the image, sampled pairs can be similar for a variety of reasons. Allowing the model to condition the feature extraction or similarity calculation on captions could alleviate this issue. + +# 5. Conclusion + +We propose using language to find conceptually similar images for contrastive learning. This is based on a simple observation: people describe an object in similar ways even when it appears in different contexts. We use pre-trained language models to sample similar captions and use the captioned images for contrastive learning. We hypothesize that using language guidance instead of image augmentations would result in learning more human-like invariances. + +We evaluate our approach on multiple train and test datasets and find that it outperforms previous self-supervised and image-text contrastive models. Our analysis demonstrates the utility of using nearest-neighbor instances for training and the superiority of language sampling over other approaches for unlabeled datasets. Our findings align with prior work that critiques the use of image augmentations [81, 108] and shows the utility of cross-modal guidance [36] and intra-instance relationships [28, 51]. Our results demonstrate the potential of incorporating language guidance in contrastive learning. We hope that future work will explore scaling up our approach to larger and more diverse datasets, as well as modeling approaches that further integrate language into the learning process. + +Acknowledgments: We thank Richard Higgins, Ashkan Kazemi, and Santiago Castro for many helpful discussions, as well as David Fouhey, Ziyang Chen, Chenhao Zheng, Fahad Kamran, and Dandan Shan for their feedback on early drafts. This project was funded under the Ford-UM Alliance partnership; we thank Alireza Rahimpour, Devesh Upadhyay, and Ali Hassani from Ford Research for their support and discussion. + +# References + +[1] Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. SPICE: Semantic propositional image caption evaluation. In Proceedings of European Conference on Computer Vision (ECCV), 2016. 3 +[2] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. VQA: Visual question answering. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2 +[3] Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi. Self-labelling via simultaneous clustering and representation learning. In International Conference on Learning Representations, 2019. 1, 2, 3, 5 +[4] Mido Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, and Nicolas Ballas. The hidden uniform cluster prior in self-supervised learning. In Proceedings of the International Conference on Learning Representations (ICLR), 2023. 2, 7 +[5] Hessam Bagherinezhad, Hannaneh Hajishirzi, Yejin Choi, and Ali Farhadi. Are elephants bigger than butterflies? reasoning about sizes of objects. In AAAI Conference on Artificial Intelligence, 2016. 8 +[6] Avrim Blum and Tom Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of Annual Conference on Computational learning theory, 1998. 2 +[7] Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606, 2016. 8 +[8] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101 - mining discriminative components with random forests. In European Conference on Computer Vision, 2014. 15 +[9] Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. Deep clustering for unsupervised learning of visual features. In Proceedings of the European conference on computer vision (ECCV), pages 132-149, 2018. 1, 2, 3, 5 +[10] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In Advances in Neural Information Processing Systems (NeurIPS), volume 33, 2020. 1, 2, 3, 4, 5, 7, 15, 21, 22, 23 +[11] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 4, 7 +[12] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the International Conference on Machine Learning (ICML), 2020. 1, 2, 4, 5, 15, 16 +[13] Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton. Big self-supervised + +models are strong semi-supervised learners. In Advances in Neural Information Processing Systems (NeurIPS), volume 33, 2020. 1, 2, 3, 7, 8, 15, 16, 20, 21, 22, 23 +[14] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297, 2020. 1, 2 +[15] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 1, 2, 4, 5, 7, 21, 22, 23 +[16] Xinlei Chen*, Saining Xie*, and Kaiming He. An empirical study of training self-supervised vision transformers. arXiv preprint arXiv:2104.02057, 2021. 7, 15, 21, 22, 23 +[17] Gong Cheng, Junwei Han, and Xiaoqiang Lu. Remote sensing image scene classification: Benchmark and state of the art. Proceedings of the IEEE, 105(10):1865-1883, Oct 2017. 15 +[18] Sumit Chopra, Raia Hadsell, and Yann LeCun. Learning a similarity metric discriminatively, with application to face verification. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005. 2 +[19] M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi. Describing textures in the wild. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. 15 +[20] Adam Coates, Andrew Ng, and Honglak Lee. An Analysis of Single Layer Networks in Unsupervised Feature Learning. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2011. https://cs.stanford.edu/~acoates/papers/coatesleeng_iastats_2011.pdf. 15 +[21] Yufeng Cui, Lichen Zhao, Feng Liang, Yangguang Li, and Jing Shao. Democratizing contrastive language-image pretraining: A CLIP benchmark of data, model, and supervision. In ICML Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward, 2022. 2, 4 +[22] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009. 15, 18 +[23] Karan Desai and Justin Johnson. Virtex: Learning visual representations from textual annotations. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 11162-11173, 2021. 2 +[24] Karan Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson. RedCaps: Web-curated image-text data created by the people, for the people. In NeurIPS Datasets and Benchmarks, 2021. 4, 7 +[25] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT), 2018. 4 +[26] Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2 + +[27] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In Proceedings of the International Conference on Learning Representations (ICLR), 2021. 3, 15, 20 +[28] Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. With a little help from my friends: Nearest-neighbor contrastive learning of visual representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 9588–9597, October 2021. 1, 2, 3, 5, 8, 16 +[29] Mohamed El Banani, Luya Gao, and Justin Johnson. Unsupervisedr&r: Unsupervised point cloud registration via differentiable rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7129-7139, 2021. 2 +[30] Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Pattern Recognition Workshop, 2004. 14, 15 +[31] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In Proceedings of the International Conference on Learning Representations (ICLR), 2018. 2 +[32] Yunchao Gong, Qifa Ke, Michael Isard, and Svetlana Lazebnik. A multi-view embedding space for modeling internet images, tags, and their semantics. In IJCV, 2014. 2 +[33] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. Making the V in VQA matter: Elevating the role of image understanding in visual question answering. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 2 +[34] Jean-Bastien Grill, Florian Strub, Florent Alché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in Neural Information Processing Systems (NeurIPS), 2020. 2, 5 +[35] Raia Hadsell, Sumit Chopra, and Yann LeCun. Dimensionality reduction by learning an invariant mapping. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2006. 2 +[36] Tengda Han, Weidi Xie, and Andrew Zisserman. Self-supervised co-training for video representation learning. In Advances in Neural Information Processing Systems (NeurIPS), 2020. 2, 8 +[37] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2 +[38] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 2 + +[39] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 4, 15 +[40] Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019. 15 +[41] Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer. The many faces of robustness: A critical analysis of out-of-distribution generalization. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 15, 18 +[42] Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song. Natural adversarial examples. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 15, 18 +[43] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021. 3 +[44] Ji Hou, Saining Xie, Benjamin Graham, Angela Dai, and Matthias Nießner. Pri3d: Can 3d priors help 2d representation learning? In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 2 +[45] Drew A Hudson and Christopher D Manning. GQA: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2 +[46] Allan Jabri, Andrew Owens, and Alexei Efros. Space-time correspondence as a contrastive random walk. Advances in Neural Information Processing Systems (NeurIPS), 2020. 2 +[47] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In Proceedings of the International Conference on Machine Learning (ICML), 2021. 2 +[48] Justin Johnson, Lamberto Ballan, and Li Fei-Fei. Love thy neighbors: Image annotation by exploiting image metadata. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2 +[49] Jeff Johnson, Matthijs Douze, and Hervé Jégou. Billionscale similarity search with GPUs. IEEE Transactions on Big Data, 2019. 4 +[50] Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg. Referitgame: Referring to objects in photographs of natural scenes. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014. 2 +[51] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. + +In Advances in Neural Information Processing Systems (NeurIPS), 2020. 2, 8 +[52] Ryan Kiros, Yukun Zhu, Russ R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. Skip-thought vectors. In Advances in Neural Information Processing Systems (NeurIPS), 2015. 3 +[53] Simon Kornblith, Jonathon Shlens, and Quoc V Le. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2661-2671, 2019. 5, 14 +[54] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), Sydney, Australia, 2013. 15 +[55] Alex Krizhevsky. Learning multiple layers of features from tiny images, 2009. 15 +[56] Janghyeon Lee, Jongsuk Kim, Hyounguk Shon, Bumsoo Kim, Seung Hwan Kim, Honglak Lee, and Junmo Kim. Uniclip: Unified framework for contrastive language-image pre-training. In Advances in Neural Information Processing Systems (NeurIPS), 2022. 2, 5 +[57] Ang Li, Allan Jabri, Armand Joulin, and Laurens van der Maaten. Learning visual n-grams from web data. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017. 2 +[58] Aoxue Li, Tiange Luo, Zhiwu Lu, Tao Xiang, and Liwei Wang. Large-scale few-shot learning: Knowledge transfer with class hierarchy. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2 +[59] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. SupervisionexistsEverywhere:A Data Efficient Contrastive Language-Image Pre-training Paradigm.In Proceedings of the International Conference on Learning Representations (ICLR),2022.2,5,16 +[60] Dong C Liu and Jorge Nocedal. On the limited memory bfgs method for large scale optimization. Mathematical programming, 1989. 14 +[61] Lajanugen Logeswaran and Honglak Lee. An efficient framework for learning sentence representations. In Proceedings of the International Conference on Learning Representations (ICLR), 2018. 3 +[62] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016.5 +[63] Ilya Loshchilov and Frank Hutter. Decoupled Weight Decay Regularization. In Proceedings of the International Conference on Learning Representations (ICLR), 2019. 5 +[64] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, 2013. 15 +[65] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (NeurIPS), 2013. 4 + +[66] Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Abhishek Sharma, David Jacobs, and Dilip Krishnan. Object-aware cropping for self-supervised learning. arXiv preprint arXiv:2112.00319, 2021. 2 +[67] Norman Mu, Alexander Kirillov, David Wagner, and Saining Xie. Slip: Self-supervision meets language-image pretraining. In Proceedings of European Conference on Computer Vision (ECCV), 2022. 2, 4, 5, 7, 15, 16 +[68] Alejandro Newell and Jia Deng. How useful is self-supervised pretraining for visual tasks? In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 2 +[69] M-E. Nilsback and A. Zisserman. Automated flower classification over a large number of classes. In Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, Dec 2008. 15 +[70] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 2 +[71] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the Annual Meeting on Association for Computational Linguistics (ACL), 2002. 3 +[72] Yookoon Park, Mahmoud Azab, Bo Xiong, Seungwhan Moon, Florian Metze, Gourab Kundu, and Kirmani Ahmed. Normalized contrastive learning for text-video retrieval. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022. 2 +[73] O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar. Cats and dogs. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. 15 +[74] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (NeurIPS), 2019. 14 +[75] Xiangyu Peng, Kai Wang, Zheng Zhu, Mang Wang, and Yang You. Crafting better contrastive views for siamese representation learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 16031-16040, 2022. 2 +[76] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference on Machine Learning (ICML), 2021. 1, 2, 4, 5, 7, 8, 14, 16, 21, 22, 23 +[77] Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. Do imagenet classifiers generalize to imageNet? In Proceedings of the International Conference on Machine Learning (ICML), 2019. 15, 18 +[78] Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural + +Language Processing. Association for Computational Linguistics, 11 2019. 3, 4, 8, 14, 20 +[79] Karsten Roth, Oriol Vinyls, and Zeynep Akata. Integrating Language Guidance into Vision-based Deep Metric Learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2 +[80] Mert Bulent Sariyildiz, Julien Perez, and Diane Larlus. Learning visual representations with caption annotations. In Proceedings of European Conference on Computer Vision (ECCV), 2020. 2 +[81] Ramprasaath R Selvaraju, Karan Desai, Justin Johnson, and Nikhil Naik. Casting your model: Learning to localize improves self-supervised representations. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 11058-11067, 2021. 2, 8 +[82] Dandan Shan, Richard Ely Locke Higgins, and David Fouhey. Cohesiv: Contrastive object and hand embedding segmentation in video. In Advances in Neural Information Processing Systems (NeurIPS), 2021. 2 +[83] Jinghuan Shang, Srijan Das, and Michael S Ryoo. Learning viewpoint-agnostic visual representations by recovering tokens in 3d space. Advances in Neural Information Processing Systems (NeurIPS), 2022. 2 +[84] Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson. Cnn features off-the-shelf: an astounding baseline for recognition. In CVPRW, 2014. 2 +[85] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual Captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2018. 4, 7 +[86] Kihyuk Sohn. Improved deep metric learning with multiclass n-pair loss objective. In Advances in Neural Information Processing Systems (NeurIPS), 2016. 5 +[87] Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. MPNet: Masked and Permuted Pre-training for language understanding. Advances in Neural Information Processing Systems (NeurIPS), 2020. 4, 8, 14 +[88] Jonathan C Stroud, Zhichao Lu, Chen Sun, Jia Deng, Rahul Sukthankar, Cordelia Schmid, and David A Ross. Learning video representations from textual web supervision. arXiv preprint arXiv:2007.14937, 2020. 2 +[89] Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Hua-jun Bai, and Yoav Artzi. A corpus for reasoning about natural language grounded in photographs. In Proceedings of the Annual Meeting on Association for Computational Linguistics (ACL), 2019. 2 +[90] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. LoFTR: Detector-free local feature matching with transformers. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 2 +[91] Igor Susmelj, Matthias Heller, Philipp Wirth, Prescott Jeremy, and Malte Ebner. Lightly. GitHub. Note: https://github.com/lightly-ai/lightly, 2020. 16 +[92] TensorFlow Datasets, a collection of ready-to-use datasets. https://www.tensorflow.org/datasets.14 + +[93] Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. YFCC100M: The new data in multimedia research. Communications of the ACM, 2016. 2 +[94] Yonglong Tian, Olivier J Henaff, and Aaron van den Oord. Divide and contrast: Self-supervised learning from uncu-rated data. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021. 2 +[95] Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola. Rethinking few-shot image classification: a good embedding is all you need? In Proceedings of European Conference on Computer Vision (ECCV), 2020. 14 +[96] Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. CIDEr: Consensus-based image description evaluation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 3 +[97] Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling. Rotation equivariant cnns for digital pathology. In International Conference on Medical image computing and computer-assisted intervention, 2018. 15 +[98] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the International Conference on Machine Learning (ICML), 2008. 2 +[99] Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing. Learning robust global representations by penalizing local predictive power. In Advances in Neural Information Processing Systems (NeurIPS), 2019. 15, 18 +[100] Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. Advances in Neural Information Processing Systems (NeurIPS), 2020. 8, 14 +[101] Xiaolong Wang, Allan Jabri, and Alexei A Efros. Learning correspondence from the cycle-consistency of time. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2 +[102] Yan Wang, Wei-Lun Chao, Kilian Q. Weinberger, and Laurens van der Maaten. Simpleshot: Revisiting nearest-neighbor classification for few-shot learning. arXiv preprint arXiv:1911.04623, 2019. 5, 14 +[103] P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona. Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology, 2010. 15 +[104] Ross Wightman, Hugo Touvron, and Herve Jegou. Resnet strikes back: An improved training procedure in timm. In NeurIPS Workshop on ImageNet: Past, Present, and Future, 2021. 7, 8, 15, 21, 22, 23 +[105] Xiaoshi Wu, Hadar Averbuch-Elor, Jin Sun, and Noah Snavely. Towers of babel: Combining images, language, and 3d geometry for learning multimodal vision. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 2 + +[106] Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. Unsupervised feature learning via non-parametric instance discrimination. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3733-3742, 2018. 1, 2, 5 +[107] J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. SUN database: Large-scale scene recognition from abbey to zoo. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. 15 +[108] Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell. What should not be contrastive in contrastive learning. In International Conference on Learning Representations, 2020. 2, 3, 5, 8 +[109] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2 +[110] Zhicheng Yan, Hao Zhang, Robinson Piramuthu, Vignesh Jagadeesh, Dennis DeCoste, Wei Di, and Yizhou Yu. Hd-cnn: hierarchical deep convolutional neural networks for large scale visual recognition. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2 +[111] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. FILIP: Fine-grained interactive language-image pre-training. In Proceedings of the International Conference on Learning Representations (ICLR), 2022. 2 +[112] Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. TACL, 2014. 2 +[113] Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. From recognition to cognition: Visual commonsense reasoning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2 +[114] Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer. Scaling vision transformers. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 4 +[115] Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful image colorization. In Proceedings of European Conference on Computer Vision (ECCV), 2016. 2 +[116] Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. Bertscore: Evaluating text generation with bert. In Proceedings of the International Conference on Learning Representations (ICLR), 2019. 3 +[117] Mingkai Zheng, Fei Wang, Shan You, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu. Weakly supervised contrastive learning. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 2 +[118] Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. Visual7w: Grounded question answering in images. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 2 \ No newline at end of file diff --git a/2023/Learning Visual Representations via Language-Guided Sampling/images.zip b/2023/Learning Visual Representations via Language-Guided Sampling/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..d1f7f8c9ebc09d034b7f21976ea23c7687e374e3 --- /dev/null +++ b/2023/Learning Visual Representations via Language-Guided Sampling/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f8983f22b145f30f2f2ab42e7d107ade6268f127e09cee2536ae6de99ac05c99 +size 613239 diff --git a/2023/Learning Visual Representations via Language-Guided Sampling/layout.json b/2023/Learning Visual Representations via Language-Guided Sampling/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..f25e76b6abb844be2f0de802736a63aea4c0f393 --- /dev/null +++ b/2023/Learning Visual Representations via Language-Guided Sampling/layout.json @@ -0,0 +1,8556 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 96, + 103, + 499, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 103, + 499, + 121 + ], + "spans": [ + { + "bbox": [ + 96, + 103, + 499, + 121 + ], + "type": "text", + "content": "Learning Visual Representations via Language-Guided Sampling" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 165, + 143, + 427, + 174 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 165, + 143, + 427, + 174 + ], + "spans": [ + { + "bbox": [ + 165, + 143, + 427, + 174 + ], + "type": "text", + "content": "Mohamed El Banani Karan Desai Justin Johnson University of Michigan" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 218, + 176, + 373, + 187 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 218, + 176, + 373, + 187 + ], + "spans": [ + { + "bbox": [ + 218, + 176, + 373, + 187 + ], + "type": "text", + "content": "{mbanani, kdexd, justincj}@umich.edu" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 143, + 216, + 192, + 228 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 216, + 192, + 228 + ], + "spans": [ + { + "bbox": [ + 143, + 216, + 192, + 228 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 241, + 290, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 241, + 290, + 434 + ], + "spans": [ + { + "bbox": [ + 46, + 241, + 290, + 434 + ], + "type": "text", + "content": "Although an object may appear in numerous contexts, we often describe it in a limited number of ways. Language allows us to abstract away visual variation to represent and communicate concepts. Building on this intuition, we propose an alternative approach to visual representation learning: using language similarity to sample semantically similar image pairs for contrastive learning. Our approach diverges from image-based contrastive learning by sampling view pairs using language similarity instead of handcrafted augmentations or learned clusters. Our approach also differs from image-text contrastive learning by relying on pre-trained language models to guide the learning rather than directly minimizing a cross-modal loss. Through a series of experiments, we show that language-guided learning yields better features than image-based and image-text representation learning approaches." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 454, + 128, + 466 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 454, + 128, + 466 + ], + "spans": [ + { + "bbox": [ + 47, + 454, + 128, + 466 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 474, + 287, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 474, + 287, + 629 + ], + "spans": [ + { + "bbox": [ + 46, + 474, + 287, + 629 + ], + "type": "text", + "content": "Consider the images in Fig. 1, is the center image more similar to its left or right neighbor? Despite the difference in background and pose, it is clear that the right pair captures the same concept: a flying snow owl. Nevertheless, a self-supervised image model will judge the left pair as more similar. Human perception and language abstract away appearance differences to capture conceptual similarity rather than just visual similarity. Ideally, we could learn visual features that capture conceptual similarity and generalize effectively to other visual tasks. In this work, we show how language can be a proxy for conceptual similarity; allowing us to sample better pairs for contrastive learning and train more generalizable visual models." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 630, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 630, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 630, + 288, + 714 + ], + "type": "text", + "content": "Image-only contrastive learning uses visual similarity as a proxy for conceptual similarity. This is based on the observation that discriminative approaches can discover interclass similarity-e.g., cheetahs are similar to lions- without requiring explicit annotations [106]. The core idea is to train a discriminative model where each instance is treated as a separate class, and the model is trained to map augmented" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 320, + 226, + 533, + 384 + ], + "blocks": [ + { + "bbox": [ + 388, + 214, + 466, + 226 + ], + "lines": [ + { + "bbox": [ + 388, + 214, + 466, + 226 + ], + "spans": [ + { + "bbox": [ + 388, + 214, + 466, + 226 + ], + "type": "text", + "content": "Visual Embedding" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 320, + 226, + 533, + 384 + ], + "lines": [ + { + "bbox": [ + 320, + 226, + 533, + 384 + ], + "spans": [ + { + "bbox": [ + 320, + 226, + 533, + 384 + ], + "type": "image", + "image_path": "2a5f83ca8ed7251c5a75201146ebbfb71ede11bdb330ed1f7735e41f79e3778a.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 380, + 386, + 473, + 397 + ], + "lines": [ + { + "bbox": [ + 380, + 386, + 473, + 397 + ], + "spans": [ + { + "bbox": [ + 380, + 386, + 473, + 397 + ], + "type": "text", + "content": "Language Embedding" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 305, + 403, + 547, + 436 + ], + "lines": [ + { + "bbox": [ + 305, + 403, + 547, + 436 + ], + "spans": [ + { + "bbox": [ + 305, + 403, + 547, + 436 + ], + "type": "text", + "content": "Figure 1. Language allows us to find conceptually similar image pairs even if they are visually dissimilar. We use those pairs for contrastive learning to learn generalizable visual features." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 460, + 545, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 460, + 545, + 556 + ], + "spans": [ + { + "bbox": [ + 304, + 460, + 545, + 556 + ], + "type": "text", + "content": "views of the same image to similar features [12-15, 106]. While successful, instance discrimination ignores the similarity between different instances as it assumes all other images are unrelated. Later work focused on inter-image relationships by estimating clusters [3, 9, 10] or finding nearest neighbors [28]. However, those relationships are estimated using visual embeddings; resulting in visually, rather than conceptually, similar pairs." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 558, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 546, + 713 + ], + "type": "text", + "content": "Language similarity is a strong proxy for semantic relationships. Consider the example in Fig. 1; images that depict the same concept are often described similarly. Radford et al. [76] propose language-image contrastive learning by mapping images and text to a shared representation space and achieve impressive generalization capabilities. However, it is unclear whether forcing models to map onto a shared space is optimal for visual learning. Although linguistic and visual similarity might align for similar instances, it is unclear whether all distances in one space should map exactly to the other. Instead of learning a joint vision-and-language representations, we argue that it is better to use linguistic similarity to guide visual learning." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19208" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 49, + 72, + 286, + 311 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 72, + 286, + 311 + ], + "spans": [ + { + "bbox": [ + 49, + 72, + 286, + 311 + ], + "type": "text", + "content": "To this end, we propose language-guided contrastive learning: a simple adaptation to contrastive learning that uses language models to find conceptually-similar image pairs for visual learning. Our approach is motivated by the observation that language models, despite never training on visual data, can still be used to sample caption pairs that belong to conceptually similar images, as seen in Fig. 2. Such sampled images exhibit desirable variations in pose, lightning, and context which are very different from hand-crafted augmentations which can be ill-suited to downstream tasks [108] or too focused on background textures [81]. We use the sampled pairs instead of image augmentations within standard self-supervised visual learning approaches such as SimCLR [12], SimSiam [15], and SLIP [67]. Our approach departs from image-only contrastive learning by relying on conceptually-similar image pairs rather than visually similar augmentations or cluster-assignment. We also depart from image-text pre-training by allowing the model to be guided by language similarity rather than learning a joint embedding space." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 312, + 286, + 466 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 312, + 286, + 466 + ], + "spans": [ + { + "bbox": [ + 49, + 312, + 286, + 466 + ], + "type": "text", + "content": "We conduct a series of controlled experiments to analyze our approach and compare it to commonly used representation learning paradigms on generalization to downstream classification tasks. In controlled settings, our approach outperforms all baselines on linear probe and few-shot classification on a range of downstream classification datasets. Our analysis suggests that while learning multimodal joint embeddings can result in good representations, it is better to use one modality to guide the training of the other. Furthermore, we find that our approach is robust to the specific choice of sampling strategy or language model. Our code and pre-trained models are available at https://github.com/mbanani/lgssl." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 479, + 132, + 490 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 479, + 132, + 490 + ], + "spans": [ + { + "bbox": [ + 49, + 479, + 132, + 490 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 498, + 286, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 498, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 49, + 498, + 286, + 712 + ], + "type": "text", + "content": "Visual Representation Learning aims to learn visual embedding spaces that capture semantics, with a typical focus on learning from scalable data sources. Broadly speaking, there are two general approaches: generative and discriminative. Generative approaches hypothesize that a model that can capture the image distribution will learn semantically relevant features [26, 31, 37, 70, 98, 115]. In contrast, discriminative approaches posit that differentiating between images will give rise to better features. This idea can be traced by to early work on metric learning [18] and dimensionality reduction [35], and is clearly seen for supervised classification models [84]. More recently, Wu et al. [106] proposed treating each image as a separate class and using augmented images as class instances to relieve the need for human annotation. This was followed by papers that simplified this approach [12-14,38] and proposed non-contrastive variants [15, 34]. While those approaches have been successful, the utility of augmentation-based self-supervised" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 308, + 72, + 544, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 72, + 544, + 144 + ], + "spans": [ + { + "bbox": [ + 308, + 72, + 544, + 144 + ], + "type": "text", + "content": "learning has been questioned [68, 108] with follow-up work proposing the use of objectness [66, 75] and saliency [81] to alleviate some of those concerns. While we share the goal of visual representation learning, we question the reliance on image augmentations for training and propose using language models to learn for conceptually-similar images." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 308, + 154, + 544, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 154, + 544, + 380 + ], + "spans": [ + { + "bbox": [ + 308, + 154, + 544, + 380 + ], + "type": "text", + "content": "Language-supervised vision pre-training aims to learn visual representations from language data. Early work of Li et al. [57] trained n-gram models using YFCC [93] images and user-tag metadata. While some works learn joint vision-and-language representations for tasks like visual question answering [2, 33, 45, 118], visual reasoning [50, 89, 113], and retrieval [72, 112], we are interested in using language to learn better visual representations [23, 23, 76, 80, 88]. Early works used language modeling as a pretext task for visual learning [23, 80], but contrastive approaches quickly gained more popularity due to their relative simplicity and generalization capabilities [47, 76]. Follow-up work extended the contrastive formulation to learn dense features [109, 111] or used additional self-supervised losses to improve performance and data efficiency [21, 56, 59, 67]. While we share the motivation of using language for visual learning, we focus on learning visual representations by using linguistic guidance from pre-trained language models." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 308, + 391, + 544, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 391, + 544, + 712 + ], + "spans": [ + { + "bbox": [ + 308, + 391, + 544, + 712 + ], + "type": "text", + "content": "Leveraging structure in the data. This is commonly done in dense feature learning, where optical flow [36, 46, 82, 101] or 3D transformations [29, 44, 83, 90, 105] provide natural associations between image patches. For images, prior approaches used class names [51, 79], class hierarchies [58, 110], meta data [32, 48, 57] or clustering [3, 9, 10, 94, 117] to improve learning and inference. Within contrastive learning, clustering has been a popular choice for leveraging dataset structure. The intuition is that natural clusters emerge in feature spaces that can provide an additional training signal or useful pseudo-labels. While such approaches work well on curated datasets (e.g., ImageNet) where the label set provides an estimate of the number of clusters, it struggles with imbalanced and uncurated data [4]. Other approaches sample nearest neighbors as a feature-driven within-domain augmentation [28, 59]. While these approaches differ in how they extract inter-instance relationships, they all use within-domain feature similarity to sample positive pairs or clusters and hence do not leverage the rich cross-modal relationships. Closest to our work is Han et al. [36] who propose a co-training [6] scheme for jointly learning image and optical flow representations. We share their motivation of using similarity in one space (language) to learn in another (vision). Furthermore, instead of relying on co-training on the same dataset, we extract distances from a text-only language model, allowing us to leverage unaligned data." + } + ] + } + ], + "index": 6 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 750, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 750, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 750, + 317, + 757 + ], + "type": "text", + "content": "19209" + } + ] + } + ], + "index": 7 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 71, + 248, + 268 + ], + "blocks": [ + { + "bbox": [ + 49, + 71, + 248, + 268 + ], + "lines": [ + { + "bbox": [ + 49, + 71, + 248, + 268 + ], + "spans": [ + { + "bbox": [ + 49, + 71, + 248, + 268 + ], + "type": "image", + "image_path": "6ac66ff458ae5d20b4632a624cc94593bf71b2b70fae9662fed97b44d3b8393f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 274, + 546, + 319 + ], + "lines": [ + { + "bbox": [ + 46, + 274, + 546, + 319 + ], + "spans": [ + { + "bbox": [ + 46, + 274, + 546, + 319 + ], + "type": "text", + "content": "Figure 2. Language sampling yields semantically-similar and visually-diverse image pairs. We sample the three nearest neighbors using a self-supervised visual model [13], an ImageNet supervised model [27], and a self-supervised language model [78]. While visual sampling yields visually similar pairs, language sampling yields semantically relevant and visually diverse images. We argue that the combination of semantic consistency and visual diversity are better for learning generalizable features." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 255, + 72, + 396, + 268 + ], + "blocks": [ + { + "bbox": [ + 255, + 72, + 396, + 268 + ], + "lines": [ + { + "bbox": [ + 255, + 72, + 396, + 268 + ], + "spans": [ + { + "bbox": [ + 255, + 72, + 396, + 268 + ], + "type": "image", + "image_path": "a230684098129931190e4cc85dd800f83a851afdfd13bf174ff13d743cbd1b8c.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 403, + 72, + 544, + 268 + ], + "blocks": [ + { + "bbox": [ + 403, + 72, + 544, + 268 + ], + "lines": [ + { + "bbox": [ + 403, + 72, + 544, + 268 + ], + "spans": [ + { + "bbox": [ + 403, + 72, + 544, + 268 + ], + "type": "image", + "image_path": "a553430611a5e26e5dcb81f6bb1ea8e688c7a49a7f341eefc3ace81c47b95c5d.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 338, + 102, + 350 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 338, + 102, + 350 + ], + "spans": [ + { + "bbox": [ + 47, + 338, + 102, + 350 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 359, + 289, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 359, + 289, + 515 + ], + "spans": [ + { + "bbox": [ + 46, + 359, + 289, + 515 + ], + "type": "text", + "content": "The goal of this work is to learn visual representations that can generalize to other datasets. We extend image-only contrastive learning beyond hand-crafted augmentations and visually-sampled clusters to learn from conceptually similar images. Through learning to associate images that depict the same visual concept, models can learn visual invariances that more closely capture human semantics. To achieve this, we propose sampling image pairs that have similar captions using a pre-trained sentence encoder [78] and using them for contrastive learning. This work does not propose a new model or loss but rather a novel way of sampling image views that is applicable to a variety of approaches and losses for learning visual representations." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 526, + 244, + 539 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 526, + 244, + 539 + ], + "spans": [ + { + "bbox": [ + 47, + 526, + 244, + 539 + ], + "type": "text", + "content": "3.1. Learning from Conceptual Similarity" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 546, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 546, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 546, + 287, + 713 + ], + "type": "text", + "content": "Instance discrimination has been the dominant task for visual representation learning. Its core intuition is that visual similarity is a good proxy for semantic similarity. The standard approach generates positive view pairs using image augmentations and maximizes their embedding similarity, with or without negative views. While there has been a large number of contrastive learning approaches, view pair generation has largely remained the same. Other methods use visual feature similarity to learn prototypes [3, 9, 10] or sample previously seen instances [28] for contrastive learning. While these approaches extend beyond instances and consider relations in the dataset, they still rely on visual similarity to generate their contrastive pairs. This limits the visual invariances that they can learn [108]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 339, + 546, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 339, + 546, + 483 + ], + "spans": [ + { + "bbox": [ + 304, + 339, + 546, + 483 + ], + "type": "text", + "content": "We propose training models to identify the same visual concept instead of the same instance. Our key observation is simple: images that have similar captions often depict similar concepts regardless of the actual appearance similarity. This can be clearly seen in Fig. 2. Nearest neighbors in visual representation space depict objects in similar scenes and poses, with self-supervised models showing some color invariances due to color augmentation. Conversely, similarly captioned images depict objects in different colors, poses, and contexts. This makes language-sampled images an excellent source for visual representation learning as they implicitly capture human-like visual invariances." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 491, + 509, + 504 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 491, + 509, + 504 + ], + "spans": [ + { + "bbox": [ + 306, + 491, + 509, + 504 + ], + "type": "text", + "content": "3.2. Sampling Image Pairs using Language" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 510, + 545, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 510, + 545, + 665 + ], + "spans": [ + { + "bbox": [ + 304, + 510, + 545, + 665 + ], + "type": "text", + "content": "Given a captioned image dataset, we want to sample image pairs that have very similar captions. While caption similarity may be a good proxy for conceptual similarity, measuring caption similarity is a challenge on its own. Traditional metrics such as BLEU [71] and CIDER [96] rely on n-gram overlap, which can be too sensitive to phrasing and sentence structure. This makes them ill-suited for our needs. Other metrics such as SPICE [1] account for such variety by comparing parse trees; however, they still can not account for different wording choices. Inspired by advances in language models as well as approaches like BERTScore [116] and CLIPScore [43], we use a pre-trained sentence encoder to compute caption similarity." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "type": "text", + "content": "Sentence encoders are trained to extract sentence-level features [52, 61, 78]. We use SBERT [78], which finetunes a pre-trained language model to allow it to better capture semantic similarity using feature cosine distance." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19210" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 71, + 141, + 175 + ], + "blocks": [ + { + "bbox": [ + 50, + 71, + 141, + 175 + ], + "lines": [ + { + "bbox": [ + 50, + 71, + 141, + 175 + ], + "spans": [ + { + "bbox": [ + 50, + 71, + 141, + 175 + ], + "type": "image", + "image_path": "1397f6941cc01f862fc25d1a2bc7b3e9bc24852c452e9147f4bdb8d7fe166b04.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 56, + 178, + 133, + 187 + ], + "lines": [ + { + "bbox": [ + 56, + 178, + 133, + 187 + ], + "spans": [ + { + "bbox": [ + 56, + 178, + 133, + 187 + ], + "type": "text", + "content": "Image Augmentations" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 150, + 71, + 242, + 175 + ], + "blocks": [ + { + "bbox": [ + 150, + 71, + 242, + 175 + ], + "lines": [ + { + "bbox": [ + 150, + 71, + 242, + 175 + ], + "spans": [ + { + "bbox": [ + 150, + 71, + 242, + 175 + ], + "type": "image", + "image_path": "c021b8fa370d5c17757a95ebc197718a14e336d0a6056498327ba74d5bb9d22c.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 170, + 178, + 223, + 187 + ], + "lines": [ + { + "bbox": [ + 170, + 178, + 223, + 187 + ], + "spans": [ + { + "bbox": [ + 170, + 178, + 223, + 187 + ], + "type": "text", + "content": "Image-Caption" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 253, + 70, + 386, + 175 + ], + "blocks": [ + { + "bbox": [ + 253, + 70, + 386, + 175 + ], + "lines": [ + { + "bbox": [ + 253, + 70, + 386, + 175 + ], + "spans": [ + { + "bbox": [ + 253, + 70, + 386, + 175 + ], + "type": "image", + "image_path": "415f2d0674e29c314b0d7041e97c25e5cca5c7d7b006322ac306c25b445effa2.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 263, + 178, + 375, + 187 + ], + "lines": [ + { + "bbox": [ + 263, + 178, + 375, + 187 + ], + "spans": [ + { + "bbox": [ + 263, + 178, + 375, + 187 + ], + "type": "text", + "content": "Nearest Neighbor Memory Bank" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 46, + 194, + 547, + 228 + ], + "lines": [ + { + "bbox": [ + 46, + 194, + 547, + 228 + ], + "spans": [ + { + "bbox": [ + 46, + 194, + 547, + 228 + ], + "type": "text", + "content": "Figure 3. Contrasting Contrastive Formulations. While image-only and image-text contrastive learning directly extract views from the instance, nearest-neighbor methods rely on a memory bank of previously extracted features for training. In contrast, our approach samples nearest neighbors in caption embedding space using a pretrained language model and use the associated image for contrastive learning." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 395, + 70, + 545, + 175 + ], + "blocks": [ + { + "bbox": [ + 395, + 70, + 545, + 175 + ], + "lines": [ + { + "bbox": [ + 395, + 70, + 545, + 175 + ], + "spans": [ + { + "bbox": [ + 395, + 70, + 545, + 175 + ], + "type": "image", + "image_path": "3e7b900b88c395b4d6e7d4ad93a1c1082eff483f77887ba015d666e7c8ec58af.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 421, + 178, + 518, + 187 + ], + "lines": [ + { + "bbox": [ + 421, + 178, + 518, + 187 + ], + "spans": [ + { + "bbox": [ + 421, + 178, + 518, + 187 + ], + "type": "text", + "content": "Language-Guided Sampling" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 248, + 287, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 248, + 287, + 380 + ], + "spans": [ + { + "bbox": [ + 46, + 248, + 287, + 380 + ], + "type": "text", + "content": "SBERT is trained in two stages: first, a language backbone is trained using a standard self-supervised task such as masked [25, 65] or permuted [87] language modeling; second, the language modeled is fine-tuned via contrastive learning on a large combined dataset of 1 billion sentence pairs. Fine-tuning the model in a contrastive way simplifies downstream usage as it allows features to be compared directly using cosine similarity. We use an SBERT [78] model with an MPNet [87] backbone. However, we find that our formulation is not sensitive to the choice of language encoder, as shown in Tab. 5c." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 382, + 287, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 382, + 287, + 502 + ], + "spans": [ + { + "bbox": [ + 46, + 382, + 287, + 502 + ], + "type": "text", + "content": "Finally, we sample the nearest neighbors for all captions in the language embedding space. We leverage modern similarity search libraries [49] to perform the nearest neighbor search quickly, despite the large dataset size. For example, nearest neighbor sampling runs in under 3 hours for RedCaps (12 million instances) on 4 GPUs, with 43 minutes spent on feature extraction and 117 minutes on nearest neighbor search. Furthermore, we find that we could further reduce the complexity of the sampling by only searching within subsets of the data as shown in Appendix E." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 514, + 233, + 528 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 514, + 233, + 528 + ], + "spans": [ + { + "bbox": [ + 47, + 514, + 233, + 528 + ], + "type": "text", + "content": "3.3. Language-Guided Visual Learning" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 534, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 534, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 534, + 288, + 713 + ], + "type": "text", + "content": "Our approach is applicable to several representation learning methods as it only changes the training view pairs. We focus on contrastive learning since its fairly minimal setting allows us to analyze the impact of language guidance with minimal confounding factors. We train SimCLR with the language-sampled pairs and refer to it as LGSimCLR. We also evaluate the impact of language guidance on SimSiam [15] and SLIP [67], and find that they can similarly benefit from language guidance. We only use random cropping for image augmentations since language-sampled pairs are naturally augmented versions of each other and find that additional augmentations are not helpful. For LGSLIP, we match their setup by applying the CLIP loss only between the source's image and caption, ignoring an additional loss between the nearest neighbor image and its caption." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 247, + 387, + 262 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 247, + 387, + 262 + ], + "spans": [ + { + "bbox": [ + 306, + 247, + 387, + 262 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 268, + 545, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 268, + 545, + 376 + ], + "spans": [ + { + "bbox": [ + 304, + 268, + 545, + 376 + ], + "type": "text", + "content": "Our experiments evaluate the efficacy of learning visual features from conceptually similar images. We hypothesize that a model trained with language guidance will learn useful visual invariances and better generalize to downstream tasks. We are interested in answering these questions: Does language guidance improve generalization over other pretraining approaches? Does language guidance generalize to other datasets and pre-training approaches? How can language be used for visual pre-training?" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 384, + 422, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 384, + 422, + 396 + ], + "spans": [ + { + "bbox": [ + 306, + 384, + 422, + 396 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 403, + 545, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 403, + 545, + 474 + ], + "spans": [ + { + "bbox": [ + 304, + 403, + 545, + 474 + ], + "type": "text", + "content": "We formulate our experimental setup to compare the efficacy of different learning signals. We train models with language-guided sampling and compare them with image-only self-supervised models and image-text contrastive models. We are interested in conducting controlled experiments for a fair comparison." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 475, + 546, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 475, + 546, + 629 + ], + "spans": [ + { + "bbox": [ + 304, + 475, + 546, + 629 + ], + "type": "text", + "content": "Recent work in self-supervised learning has demonstrated the impressive impact of scaling [12, 76, 114]. While such work has shown impressive performance, it has complicated the evaluation as different models are trained on different pretext tasks on different datasets using varying amounts of compute and training recipes. Furthermore, replication is difficult, if not impossible, due to the unavailability of training data or prohibitive compute requirements. Fortunately, several papers report results that indicate that performance patterns often hold at smaller scales [10, 12, 21, 67, 76]. Hence, we conduct our experiments at a scale that allows us to perform a comprehensive evaluation and permits replication by others." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": "We conduct our experiments with a standard backbone [39] on publicly available datasets [11, 24, 85]. To account for variation in training recipes, we retrain all methods from scratch using the same training recipe. We scale down experiments to a level that permits fair comparisons and replication. We also provide system-level comparisons in Tab. 4 and scaling results in App. D." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19211" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 167 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 167 + ], + "type": "text", + "content": "Training details: We use a ResNet-50 backbone and train all models using the AdamW optimizer [63] with a learning rate of " + }, + { + "bbox": [ + 46, + 72, + 289, + 167 + ], + "type": "inline_equation", + "content": "10^{-3}" + }, + { + "bbox": [ + 46, + 72, + 289, + 167 + ], + "type": "text", + "content": " and a weight decay of " + }, + { + "bbox": [ + 46, + 72, + 289, + 167 + ], + "type": "inline_equation", + "content": "10^{-2}" + }, + { + "bbox": [ + 46, + 72, + 289, + 167 + ], + "type": "text", + "content": ". We use a cosine learning scheduler [62] with 5000 warm-up steps. Models are trained using a batch size of 512 for 250k steps; this corresponds to 10.5 epochs on RedCaps. We use a constant number of steps to permit meaningful comparisons between models trained on different datasets." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 170, + 289, + 326 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 170, + 289, + 326 + ], + "spans": [ + { + "bbox": [ + 46, + 170, + 289, + 326 + ], + "type": "text", + "content": "Evaluation setup: We evaluate all approaches using linear probe and fewshot classification on 15 classification datasets inspired by [53,76]. We use the linear probe evaluation proposed by [53] and learn a single linear layer using logistic regression. We sweep over a range of cost values and choose the value with the best validation performance. We retrain a classifier on both train and validation splits and report test performance. We also evaluate all approaches on fewshot classification to understand their generalization ability. We use a weighted kNN classifier on frozen support features inspired by prior work showing its effectiveness for fewshot classification [102]. Please see Appendices A and B for more details on evaluation datasets and tasks." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 331, + 287, + 403 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 331, + 287, + 403 + ], + "spans": [ + { + "bbox": [ + 46, + 331, + 287, + 403 + ], + "type": "text", + "content": "Baselines: While there have been many proposed visual representation learning approaches, they can be grouped into several key directions that differ in the pretext task. We focus our comparison on a few representative approaches to explore the impact of the learning signal. We overview the baselines here and provide more details in Appendix C." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 403, + 288, + 487 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 403, + 288, + 487 + ], + "spans": [ + { + "bbox": [ + 46, + 403, + 288, + 487 + ], + "type": "text", + "content": "Many of our baselines are variants of contrastive learning as shown in Fig. 3. Contrastive approaches operate over paired source and target feature embeddings: " + }, + { + "bbox": [ + 46, + 403, + 288, + 487 + ], + "type": "inline_equation", + "content": "z^s" + }, + { + "bbox": [ + 46, + 403, + 288, + 487 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 403, + 288, + 487 + ], + "type": "inline_equation", + "content": "z^t" + }, + { + "bbox": [ + 46, + 403, + 288, + 487 + ], + "type": "text", + "content": ". The goal is to maximize the similarity between the paired embeddings and minimize it with respect to all other embeddings. Given a batch size " + }, + { + "bbox": [ + 46, + 403, + 288, + 487 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 403, + 288, + 487 + ], + "type": "text", + "content": " and embedding dimension " + }, + { + "bbox": [ + 46, + 403, + 288, + 487 + ], + "type": "inline_equation", + "content": "F" + }, + { + "bbox": [ + 46, + 403, + 288, + 487 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 403, + 288, + 487 + ], + "type": "inline_equation", + "content": "z^s, z^t \\in \\mathbb{R}^{N \\times F}" + }, + { + "bbox": [ + 46, + 403, + 288, + 487 + ], + "type": "text", + "content": ". The contrastive loss [86] is:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 64, + 494, + 287, + 524 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 64, + 494, + 287, + 524 + ], + "spans": [ + { + "bbox": [ + 64, + 494, + 287, + 524 + ], + "type": "interline_equation", + "content": "\\mathcal {L} \\left(z ^ {s}, z ^ {t}\\right) = - \\log \\frac {\\exp \\left(\\operatorname {s i m} \\left(\\boldsymbol {z} _ {i} ^ {s} , \\boldsymbol {z} _ {i} ^ {t}\\right) / \\tau\\right)}{\\sum_ {k = 1} ^ {N} \\exp \\left(\\operatorname {s i m} \\left(\\boldsymbol {z} _ {i} ^ {s} , \\boldsymbol {z} _ {k} ^ {t}\\right) / \\tau\\right)}, \\tag {1}", + "image_path": "cdb5fe83cec0fd96d9e0275d85c387159faf246a38dfc7ed9626f2740db6e742.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 529, + 287, + 566 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 529, + 287, + 566 + ], + "spans": [ + { + "bbox": [ + 46, + 529, + 287, + 566 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 529, + 287, + 566 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 46, + 529, + 287, + 566 + ], + "type": "text", + "content": " is a scaling parameter and " + }, + { + "bbox": [ + 46, + 529, + 287, + 566 + ], + "type": "inline_equation", + "content": "\\mathrm{sim}(\\cdot ,\\cdot)" + }, + { + "bbox": [ + 46, + 529, + 287, + 566 + ], + "type": "text", + "content": " is cosine similarity. Contrastive approaches primarily differ in how the embeddings are computed." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 567, + 287, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 567, + 287, + 628 + ], + "spans": [ + { + "bbox": [ + 46, + 567, + 287, + 628 + ], + "type": "text", + "content": "Image-Only Contrastive Learning contrasts features extracted from two randomly augmented views of the same image to perform instance discrimination [106]. We use SimCLR [12] as a representative approach due to its simplicity and strong performance." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 630, + 288, + 715 + ], + "type": "text", + "content": "Image-Text Contrastive Learning learns by contrasting features extracted from images and their captions. Unlike image-only approaches, this approach can learn semantics from the captions. Radford et al. [76] first proposed this approach and has had several follow-ups that augment it with additional self-supervised losses losses [56, 59, 67]. We use CLIP [76] and SLIP [67] due to their simplicity." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 180 + ], + "type": "text", + "content": "Nearest Neighbor Contrastive Learning contrast source embeddings with retrieved embeddings from a memory bank. The target features are used to retrieve the nearest neighbor embedding from a memory bank of previous batches. Dwibedi et al. [28] proposed this approach for image-only contrastive learning, while Li et al. [59] proposed adapting this loss for language embeddings. We use NNCLR [28] as Visual NNCLR and DeCLIP [59] with the CLIP and the language NNS losses as Language NNCLR." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 187, + 547, + 236 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 187, + 547, + 236 + ], + "spans": [ + { + "bbox": [ + 304, + 187, + 547, + 236 + ], + "type": "text", + "content": "Image-Only Non-Contrastive Learning deviates from the typical contrastive setup by learning without negative samples [15, 34]. We use SimSiam as a representative approach due to its simplicity and strong performance." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 243, + 545, + 350 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 243, + 545, + 350 + ], + "spans": [ + { + "bbox": [ + 304, + 243, + 545, + 350 + ], + "type": "text", + "content": "Cluster-based Contrastive Learning learn by contrasting image features with learned prototypes [3,9,10]. Prototypes are estimated via clustering or learned jointly with the feature encoder. Caron et al. [10] report that different cluster-based approaches perform similarly when provided with the same algorithmic advances. We use an adapted SwAV without the multi-crop augmentation strategy as it is equally applicable to other methods. We also compare against a pretrained SwAV checkpoint in Tab. 4." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 363, + 362, + 374 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 363, + 362, + 374 + ], + "spans": [ + { + "bbox": [ + 306, + 363, + 362, + 374 + ], + "type": "text", + "content": "4.2. Results" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 383, + 545, + 443 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 383, + 545, + 443 + ], + "spans": [ + { + "bbox": [ + 304, + 383, + 545, + 443 + ], + "type": "text", + "content": "We train all approaches with a ResNet-50 backbone on RedCaps and report results in Tabs. 1 and 3. Our model outperforms all baselines with a significant margin for both evaluations. We analyze the results below through a series of questions." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 450, + 546, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 450, + 546, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 450, + 546, + 715 + ], + "type": "text", + "content": "Does language-guided sampling provide better training pairs than image augmentations? LGSimCLR greatly outperforms SimCLR despite using the same learning objective. By using language sampled pairs instead of image augmentations, LGSimCLR learns stronger invariances. We find that the largest gains arise in fine-grained datasets: Cars, CUB, and Food101. The performance gains can be explained by considering the critique of Xiao et al. [108]: the training augmentations dictate the invariances learned by SimCLR as shown in nearest neighbor samples in Fig. 2. Consider the third row of Fig. 2, while language sampling depicts three Aston Martin cars in different spots, visual nearest neighbors are sports cars in different poses and colors, closely resembling the flip and color augmentations used for training. Similarly in the first row of Fig. 2, visual nearest neighbors depict owls from different species in similar poses, while language sampling retrieves three great horned owls from different viewpoints. These trends are further amplified when features are used directly for fewshot classification. Language guidance allows us to capture relationships that go beyond visual similarity by training on image pairs that capture human semantics." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "19212" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 47, + 110, + 547, + 264 + ], + "blocks": [ + { + "bbox": [ + 46, + 70, + 547, + 105 + ], + "lines": [ + { + "bbox": [ + 46, + 70, + 547, + 105 + ], + "spans": [ + { + "bbox": [ + 46, + 70, + 547, + 105 + ], + "type": "text", + "content": "Table 1. Linear Probe Evaluations. We train ResNet-50 models on RedCaps and report performance of a linear probe using frozen features on 15 downstream tasks. Models are split based on whether or not they require caption images for training. LGSimCLR outperforms all previous approaches with strong performance gains for fine-grained classification datasets." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 47, + 110, + 547, + 264 + ], + "lines": [ + { + "bbox": [ + 47, + 110, + 547, + 264 + ], + "spans": [ + { + "bbox": [ + 47, + 110, + 547, + 264 + ], + "type": "table", + "html": "
ModelFood-101CIFAR-10CIFAR-100CUBSUN397CarsAircraftDTDPetsCaltech-101FlowersSTL-10EuroSATRESISC45PCAMMean
SwAV63.681.357.521.647.522.935.468.161.170.578.087.794.379.984.363.6
SimSiam64.179.956.128.248.329.541.266.269.173.683.685.794.482.183.365.7
Visual NNCLR65.482.860.226.650.026.640.968.065.275.483.588.595.382.283.866.3
SimCLR69.082.961.630.652.633.743.769.870.574.186.988.095.484.684.468.5
Language NNCLR81.283.161.948.656.545.137.268.878.182.090.293.492.581.180.772.0
CLIP80.984.762.750.457.445.836.767.679.884.091.093.593.982.282.672.9
SLIP77.787.267.042.458.148.745.272.379.582.792.192.795.685.583.474.0
LGSimCLR (Ours)83.287.869.059.360.362.353.471.281.889.495.994.095.688.081.178.2
", + "image_path": "d1f4c4c26c7493ba59537abcc0614d57abbde81dfde0aedcab0a86d16130f62a.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 55, + 282, + 281, + 423 + ], + "blocks": [ + { + "bbox": [ + 55, + 282, + 281, + 423 + ], + "lines": [ + { + "bbox": [ + 55, + 282, + 281, + 423 + ], + "spans": [ + { + "bbox": [ + 55, + 282, + 281, + 423 + ], + "type": "image", + "image_path": "92f71a19b956769e81e37cfb6a4e1c482d022d9c27185ea664c3f1ae677b185b.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 428, + 288, + 473 + ], + "lines": [ + { + "bbox": [ + 46, + 428, + 288, + 473 + ], + "spans": [ + { + "bbox": [ + 46, + 428, + 288, + 473 + ], + "type": "text", + "content": "Figure 4. Nearest Neighbor methods are limited by the memory bank size. Even with a large memory bank, the nearest embedding can still be unrelated to the source image while language sampling provides us with conceptually similar pairs." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 486, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 486, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 486, + 289, + 715 + ], + "type": "text", + "content": "Can we just sample nearest neighbors from previous batches? LGSimCLR outperforms NNCLR despite both relying on nearest neighbors. NNCLR uses the nearest feature embedding from a memory bank in the same modality. The quality of their retrieved samples is limited by the size of the memory bank. To demonstrate this, we visualize the nearest neighbors retrieved by NNCLR for different memory bank sizes in Fig. 4. We find that the retrieval quality is poor even for larger queues. Interestingly, we note that NNCLR also underperforms SimCLR on RedCaps, despite performing better on ImageNet. We posit that ImageNet's curated distribution explains this: a queue of " + }, + { + "bbox": [ + 46, + 486, + 289, + 715 + ], + "type": "inline_equation", + "content": "16k" + }, + { + "bbox": [ + 46, + 486, + 289, + 715 + ], + "type": "text", + "content": " will most probably contain instances from each class, resulting in both visually and conceptually similar retrievals. Additionally, the quality of nearest neighbors is affected by the sampling feature space; features that are only trained on image augmentations will have limited invariances as shown in Fig. 2. We further explore the impact of sampling space on training in Sec. 4.3." + } + ] + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 306, + 308, + 545, + 374 + ], + "blocks": [ + { + "bbox": [ + 305, + 281, + 545, + 303 + ], + "lines": [ + { + "bbox": [ + 305, + 281, + 545, + 303 + ], + "spans": [ + { + "bbox": [ + 305, + 281, + 545, + 303 + ], + "type": "text", + "content": "Table 2. Language-guided contrastive learning outperforms image-text contrastive learning, regardless of text encoder." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 306, + 308, + 545, + 374 + ], + "lines": [ + { + "bbox": [ + 306, + 308, + 545, + 374 + ], + "spans": [ + { + "bbox": [ + 306, + 308, + 545, + 374 + ], + "type": "table", + "html": "
ObjectiveText EncoderLinearFewshot
Image-TextRandomly- Initialized72.977.5
Frozen SBERT71.877.1
Image-ImageFrozen CLIP (RedCaps)78.382.4
Frozen SBERT78.282.5
", + "image_path": "092766732b0b2a305b5e33a5559d819b92d9550e6a8b51aa1cf87d060c368f37.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 396, + 545, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 396, + 545, + 552 + ], + "spans": [ + { + "bbox": [ + 304, + 396, + 545, + 552 + ], + "type": "text", + "content": "Can cluster-based approaches learn better features? Similar to nearest-neighbor sampling, clustering is performed using visual similarity. Furthermore, it is based on an estimated number of clusters in the training dataset. Although this can be determined for ImageNet due to its known class structure, the number of clusters in an arbitrary uncurated dataset is unknown. This results in a large performance drop, as seen in Tab. 1 and Tab. 3. On the other hand, sampling related pairs assumes no global structure within the data and hence is able to better capture inter-instance similarity. This results in nearest-neighbor sampling outperforming clustering and both being outperformed by contrastive learning and language-guided contrastive learning." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 558, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 545, + 714 + ], + "type": "text", + "content": "Should we use language for guidance or supervision? Our experiments indicate that LGSimCLR outperforms both CLIP and SLIP. We consider two possible explanations: (a) SBERT extracts better language embeddings than CLIP can learn from the data, or (b) language-guided contrastive learning is a better training objective than imag-text contrastive learning. To evaluate this, we compare four models in Tab. 2. The first two models use CLIP's training objective: the first model uses a randomly initialized language encoder, similar to CLIP. The second model uses a frozen SBERT model as the language encoder and only trains the projection layers. The second two models use LGSimCLR's training objective but sample pairs using a" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19213" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 47, + 110, + 547, + 264 + ], + "blocks": [ + { + "bbox": [ + 46, + 70, + 547, + 105 + ], + "lines": [ + { + "bbox": [ + 46, + 70, + 547, + 105 + ], + "spans": [ + { + "bbox": [ + 46, + 70, + 547, + 105 + ], + "type": "text", + "content": "Table 3. Few-Shot Evaluations. We train ResNet-50 models on RedCaps and report 5-way, 5-shot classification performance. We observe that language results in huge performance gains as shown by the performance of CLIP and LGSimCLR. Furthermore, the use of any augmentations hurts performance as seen by SLIP's drop in performance." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 47, + 110, + 547, + 264 + ], + "lines": [ + { + "bbox": [ + 47, + 110, + 547, + 264 + ], + "spans": [ + { + "bbox": [ + 47, + 110, + 547, + 264 + ], + "type": "table", + "html": "
ModelFood-101CIFAR-10CIFAR-100CUBSUN397CarsAircraftDTDPetsCaltech-101FlowersSTL-10EuroSATRESISC45Mean
SwAV64.554.061.845.884.936.534.174.866.578.175.572.680.472.964.5
SimSiam63.949.957.249.584.539.337.975.767.879.781.569.680.679.465.5
Visual NNCLR65.654.161.745.885.337.934.975.267.381.175.474.383.676.765.6
SimCLR66.945.751.051.587.144.038.477.670.180.086.969.683.581.366.7
Language NNCLR89.365.373.478.690.868.440.475.278.890.994.389.675.271.977.3
CLIP88.964.673.178.390.969.740.775.777.591.694.789.875.374.877.5
SLIP81.563.570.863.191.362.942.179.676.488.492.283.482.780.875.6
LGSimCLR (Ours)90.366.375.583.192.777.650.681.184.195.497.686.585.089.082.5
", + "image_path": "1336d8a77e1a78060af2c5dd4c1e9bc56a688a62d17f888a72f860abbc6a56d9.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 282, + 288, + 379 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 282, + 288, + 379 + ], + "spans": [ + { + "bbox": [ + 46, + 282, + 288, + 379 + ], + "type": "text", + "content": "pre-trained language-only SBERT or the language encoder from a CLIP model trained on RedCaps. We find that image-image contrastive learning yields better visual features for both setups. While CLIP does not benefit from an SBERT backbone, LGSimCLR benefits from sampling using a language encoder trained on the same dataset. This suggests that learning joint embeddings results in worse visual features than language-guided learning." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 384, + 288, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 384, + 288, + 624 + ], + "spans": [ + { + "bbox": [ + 46, + 384, + 288, + 624 + ], + "type": "text", + "content": "System-level comparisons: We compare LGSimCLR with publicly-available checkpoints of prior approaches; see Appendix C for details. We emphasize that while the experiments reported in Tabs. 1 and 3 were done in a controlled setup (same batch size, training data, optimizer), the system level comparisons are trained on different datasets with different training recipes and enhancements to further boost performance; e.g., large batch sizes, longer training, multi-crop augmentation. Furthermore, it has been shown that models trained on ImageNet implicitly benefit from its curated nature [4, 67]. Nevertheless, our approach still outperforms prior self-supervised approaches. We fall short of CLIP's ResNet-50 due to its training scale; " + }, + { + "bbox": [ + 46, + 384, + 288, + 624 + ], + "type": "inline_equation", + "content": "64 \\times" + }, + { + "bbox": [ + 46, + 384, + 288, + 624 + ], + "type": "text", + "content": " larger batch, " + }, + { + "bbox": [ + 46, + 384, + 288, + 624 + ], + "type": "inline_equation", + "content": "32 \\times" + }, + { + "bbox": [ + 46, + 384, + 288, + 624 + ], + "type": "text", + "content": " larger dataset, and " + }, + { + "bbox": [ + 46, + 384, + 288, + 624 + ], + "type": "inline_equation", + "content": "75 \\times" + }, + { + "bbox": [ + 46, + 384, + 288, + 624 + ], + "type": "text", + "content": ". We also observe that ImageNet-supervised ResNet-50 achieves better few-shot performance. Examining the performance breakdown in Tab. 10, we find the improvement mainly comes from CIFAR10, CIFAR100, and Pets. We posit that this can be explained by ImageNet's class structure: mostly pets with a large overlap with CIFAR's classes." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 634, + 111, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 634, + 111, + 647 + ], + "spans": [ + { + "bbox": [ + 47, + 634, + 111, + 647 + ], + "type": "text", + "content": "4.3. Analysis" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "text", + "content": "We now analyze language-guided contrastive learning by evaluating the impact of pre-training data, the choice of embedding space, and the pretext task. By understanding the impact of those choices, we can better understand what the model is learning." + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 307, + 331, + 545, + 419 + ], + "blocks": [ + { + "bbox": [ + 304, + 281, + 547, + 326 + ], + "lines": [ + { + "bbox": [ + 304, + 281, + 547, + 326 + ], + "spans": [ + { + "bbox": [ + 304, + 281, + 547, + 326 + ], + "type": "text", + "content": "Table 4. ResNet-50 System Level Comparisons. We outperform prior self-supervised approaches despite them benefiting from ImageNet's curation for training and using larger batch sizes. CLIP outperforms us due to the scale of its training." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 307, + 331, + 545, + 419 + ], + "lines": [ + { + "bbox": [ + 307, + 331, + 545, + 419 + ], + "spans": [ + { + "bbox": [ + 307, + 331, + 545, + 419 + ], + "type": "table", + "html": "
Batch#Img UpdatesDatasetLinearFewshot
Supervised [104]10241.3×108ImageNet78.085.7
SimSiam [15]5121.3×108ImageNet72.978.7
SimCLR [13]40961.0×109ImageNet75.477.4
MoCo [16]40961.3×108ImageNet77.780.1
SwAV [10]40961.3×108ImageNet78.278.5
CLIP [76]327681.0×1010CLIP81.887.8
LGSimCLR5121.3×108RedCaps78.282.5
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Image Aug.Language
LinearFewshotLinearFewshot
SimSiam65.765.571.275.7
SimCLR68.566.778.282.5
SLIP74.075.678.882.8
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SizeLinearFewshot
CC3M2.7M71.576.3
CC12M10.9M76.881.9
RedCaps 20203.2M73.878.8
RedCaps12.0M78.282.5
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LinearFewshot
SBERT (MPNet)78.282.5
SBERT (MiniLM)78.683.3
CLIP Language (ViT-B/32)78.383.1
FastText BoW76.180.9
ImageNet-supervised78.381.8
SimCLR (ImageNet)73.174.6
", + "image_path": "a88fde085caf1509c7ec5e88146e83faac4917e791640a770e77fd9025e26fea.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 414, + 149, + 512, + 159 + ], + "lines": [ + { + "bbox": [ + 414, + 149, + 512, + 159 + ], + "spans": [ + { + "bbox": [ + 414, + 149, + 512, + 159 + ], + "type": "text", + "content": "(c) Impact of sampling space" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 163, + 546, + 197 + ], + "lines": [ + { + "bbox": [ + 46, + 163, + 546, + 197 + ], + "spans": [ + { + "bbox": [ + 46, + 163, + 546, + 197 + ], + "type": "text", + "content": "Table 5. Analysis Experiments. We conduct a series of analysis experiments to understand language-guided contrastive learning. The results indicate that language sampling is beneficial to several formulations and scales well with larger datasets. Furthermore, while language sampling consistently results in good pairs for training, visual sampling only helps if it has access to semantics through supervision." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 211, + 289, + 451 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 211, + 289, + 451 + ], + "spans": [ + { + "bbox": [ + 46, + 211, + 289, + 451 + ], + "type": "text", + "content": "Impact of sampling space: The idea of using offline nearest-neighbor sampling does not require a specific language model or even a specific modality. We explore other choices for embedding space: four sentence encoders and two image models. In our experiments, we use SBERT's MPNet model [78, 87]; the highest performing SBERT model for sentence similarity. We compare it to two other sentence transformers: a smaller SBERT model, MiniLM [100], and the language encoder from CLIP [76]. We also compared against a bag-of-words (BoW) sentence encoder that uses FastText [7] embeddings. Results are in Tab. 5c. While we expected that using CLIP for sampling would improve performance due to its multimodal training, we were surprised that MiniLM also improved performance despite its lower performance on language tasks. We find that pairs obtained using a BoW model result in a weaker performance which might hint at the importance of contextual sentence embeddings. Nevertheless, the BoW-sampled pairs still result in higher performance than all the other baselines on RedCaps." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 455, + 287, + 612 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 455, + 287, + 612 + ], + "spans": [ + { + "bbox": [ + 46, + 455, + 287, + 612 + ], + "type": "text", + "content": "We also consider training with pairs sampled using two visual models: ImageNet-supervised ResNet-50 [104] and ImageNet-trained SimCLR [13]. We find that using a visual model for sampling is only beneficial if the visual model captures semantic relations; e.g., through supervised training. Using a self-supervised language model results in a strong drop in performance relative to the other sampling spaces. Nevertheless, it still allows the model to achieve better performance than using a self-supervised visual approach on the same data. This indicates that while language is a better modality to use, \"sample-guided\" contrastive learning can still achieve a stronger performance than only using self-supervised learning." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "type": "text", + "content": "Limitations: We observe a few limitations in our approach. Image captions can be noisy, vague, and often omit obvious relations in the image [5]. While this broadly affects image-language models, it can result in us retrieving unrelated image pairs. For example, captions like \"I found this in the garden\" or \"Photo from our family trip\" could describe a large range of images, some of which are unrelated. We expand on this in Appendix F. Image descriptions also de" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 212, + 545, + 331 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 212, + 545, + 331 + ], + "spans": [ + { + "bbox": [ + 304, + 212, + 545, + 331 + ], + "type": "text", + "content": "pend on the context and the perceiver; e.g., a tourist and an art curator will describe artwork in very different ways. We observe that descriptions in topic-focused subreddits (e.g., r/birdpics and r/woodworking) are more specific than in generic subreddits (e.g., r/itookapicture and r/pics). Our experiments in Appendix E support this observation. Since a caption only captures one aspect of the image, sampled pairs can be similar for a variety of reasons. Allowing the model to condition the feature extraction or similarity calculation on captions could alleviate this issue." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 341, + 378, + 354 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 341, + 378, + 354 + ], + "spans": [ + { + "bbox": [ + 306, + 341, + 378, + 354 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 361, + 545, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 361, + 545, + 456 + ], + "spans": [ + { + "bbox": [ + 304, + 361, + 545, + 456 + ], + "type": "text", + "content": "We propose using language to find conceptually similar images for contrastive learning. This is based on a simple observation: people describe an object in similar ways even when it appears in different contexts. We use pre-trained language models to sample similar captions and use the captioned images for contrastive learning. We hypothesize that using language guidance instead of image augmentations would result in learning more human-like invariances." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 456, + 545, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 456, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 304, + 456, + 545, + 624 + ], + "type": "text", + "content": "We evaluate our approach on multiple train and test datasets and find that it outperforms previous self-supervised and image-text contrastive models. Our analysis demonstrates the utility of using nearest-neighbor instances for training and the superiority of language sampling over other approaches for unlabeled datasets. Our findings align with prior work that critiques the use of image augmentations [81, 108] and shows the utility of cross-modal guidance [36] and intra-instance relationships [28, 51]. Our results demonstrate the potential of incorporating language guidance in contrastive learning. We hope that future work will explore scaling up our approach to larger and more diverse datasets, as well as modeling approaches that further integrate language into the learning process." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": "Acknowledgments: We thank Richard Higgins, Ashkan Kazemi, and Santiago Castro for many helpful discussions, as well as David Fouhey, Ziyang Chen, Chenhao Zheng, Fahad Kamran, and Dandan Shan for their feedback on early drafts. This project was funded under the Ford-UM Alliance partnership; we thank Alireza Rahimpour, Devesh Upadhyay, and Ali Hassani from Ford Research for their support and discussion." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19215" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 91, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 56, + 91, + 287, + 134 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 91, + 287, + 134 + ], + "spans": [ + { + "bbox": [ + 56, + 91, + 287, + 134 + ], + "type": "text", + "content": "[1] Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. SPICE: Semantic propositional image caption evaluation. In Proceedings of European Conference on Computer Vision (ECCV), 2016. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 135, + 288, + 190 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 135, + 288, + 190 + ], + "spans": [ + { + "bbox": [ + 56, + 135, + 288, + 190 + ], + "type": "text", + "content": "[2] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. VQA: Visual question answering. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 191, + 288, + 235 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 191, + 288, + 235 + ], + "spans": [ + { + "bbox": [ + 56, + 191, + 288, + 235 + ], + "type": "text", + "content": "[3] Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi. Self-labelling via simultaneous clustering and representation learning. In International Conference on Learning Representations, 2019. 1, 2, 3, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 236, + 288, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 236, + 288, + 300 + ], + "spans": [ + { + "bbox": [ + 56, + 236, + 288, + 300 + ], + "type": "text", + "content": "[4] Mido Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, and Nicolas Ballas. The hidden uniform cluster prior in self-supervised learning. In Proceedings of the International Conference on Learning Representations (ICLR), 2023. 2, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 301, + 288, + 346 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 301, + 288, + 346 + ], + "spans": [ + { + "bbox": [ + 56, + 301, + 288, + 346 + ], + "type": "text", + "content": "[5] Hessam Bagherinezhad, Hannaneh Hajishirzi, Yejin Choi, and Ali Farhadi. Are elephants bigger than butterflies? reasoning about sizes of objects. In AAAI Conference on Artificial Intelligence, 2016. 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 346, + 288, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 346, + 288, + 380 + ], + "spans": [ + { + "bbox": [ + 56, + 346, + 288, + 380 + ], + "type": "text", + "content": "[6] Avrim Blum and Tom Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of Annual Conference on Computational learning theory, 1998. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 380, + 288, + 413 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 380, + 288, + 413 + ], + "spans": [ + { + "bbox": [ + 56, + 380, + 288, + 413 + ], + "type": "text", + "content": "[7] Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. Enriching word vectors with subword information. arXiv preprint arXiv:1607.04606, 2016. 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 414, + 288, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 414, + 288, + 456 + ], + "spans": [ + { + "bbox": [ + 56, + 414, + 288, + 456 + ], + "type": "text", + "content": "[8] Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool. Food-101 - mining discriminative components with random forests. In European Conference on Computer Vision, 2014. 15" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 458, + 288, + 512 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 458, + 288, + 512 + ], + "spans": [ + { + "bbox": [ + 56, + 458, + 288, + 512 + ], + "type": "text", + "content": "[9] Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. Deep clustering for unsupervised learning of visual features. In Proceedings of the European conference on computer vision (ECCV), pages 132-149, 2018. 1, 2, 3, 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 514, + 287, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 514, + 287, + 578 + ], + "spans": [ + { + "bbox": [ + 53, + 514, + 287, + 578 + ], + "type": "text", + "content": "[10] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In Advances in Neural Information Processing Systems (NeurIPS), volume 33, 2020. 1, 2, 3, 4, 5, 7, 15, 21, 22, 23" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 53, + 580, + 288, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 580, + 288, + 635 + ], + "spans": [ + { + "bbox": [ + 53, + 580, + 288, + 635 + ], + "type": "text", + "content": "[11] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 4, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 53, + 635, + 288, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 635, + 288, + 689 + ], + "spans": [ + { + "bbox": [ + 53, + 635, + 288, + 689 + ], + "type": "text", + "content": "[12] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A Simple Framework for Contrastive Learning of Visual Representations. In Proceedings of the International Conference on Machine Learning (ICML), 2020. 1, 2, 4, 5, 15, 16" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 53, + 691, + 288, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 691, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 53, + 691, + 288, + 714 + ], + "type": "text", + "content": "[13] Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton. Big self-supervised" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 312, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 315, + 73, + 546, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 73, + 546, + 106 + ], + "spans": [ + { + "bbox": [ + 315, + 73, + 546, + 106 + ], + "type": "text", + "content": "models are strong semi-supervised learners. In Advances in Neural Information Processing Systems (NeurIPS), volume 33, 2020. 1, 2, 3, 7, 8, 15, 16, 20, 21, 22, 23" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 312, + 106, + 545, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 106, + 545, + 139 + ], + "spans": [ + { + "bbox": [ + 312, + 106, + 545, + 139 + ], + "type": "text", + "content": "[14] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297, 2020. 1, 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 312, + 140, + 546, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 140, + 546, + 183 + ], + "spans": [ + { + "bbox": [ + 312, + 140, + 546, + 183 + ], + "type": "text", + "content": "[15] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 1, 2, 4, 5, 7, 21, 22, 23" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 312, + 184, + 546, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 184, + 546, + 217 + ], + "spans": [ + { + "bbox": [ + 312, + 184, + 546, + 217 + ], + "type": "text", + "content": "[16] Xinlei Chen*, Saining Xie*, and Kaiming He. An empirical study of training self-supervised vision transformers. arXiv preprint arXiv:2104.02057, 2021. 7, 15, 21, 22, 23" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 312, + 217, + 546, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 217, + 546, + 259 + ], + "spans": [ + { + "bbox": [ + 312, + 217, + 546, + 259 + ], + "type": "text", + "content": "[17] Gong Cheng, Junwei Han, and Xiaoqiang Lu. Remote sensing image scene classification: Benchmark and state of the art. Proceedings of the IEEE, 105(10):1865-1883, Oct 2017. 15" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 312, + 261, + 546, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 261, + 546, + 304 + ], + "spans": [ + { + "bbox": [ + 312, + 261, + 546, + 304 + ], + "type": "text", + "content": "[18] Sumit Chopra, Raia Hadsell, and Yann LeCun. Learning a similarity metric discriminatively, with application to face verification. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 312, + 305, + 547, + 349 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 305, + 547, + 349 + ], + "spans": [ + { + "bbox": [ + 312, + 305, + 547, + 349 + ], + "type": "text", + "content": "[19] M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi. Describing textures in the wild. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. 15" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 312, + 350, + 546, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 350, + 546, + 415 + ], + "spans": [ + { + "bbox": [ + 312, + 350, + 546, + 415 + ], + "type": "text", + "content": "[20] Adam Coates, Andrew Ng, and Honglak Lee. An Analysis of Single Layer Networks in Unsupervised Feature Learning. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS), 2011. https://cs.stanford.edu/~acoates/papers/coatesleeng_iastats_2011.pdf. 15" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 312, + 415, + 546, + 470 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 415, + 546, + 470 + ], + "spans": [ + { + "bbox": [ + 312, + 415, + 546, + 470 + ], + "type": "text", + "content": "[21] Yufeng Cui, Lichen Zhao, Feng Liang, Yangguang Li, and Jing Shao. Democratizing contrastive language-image pretraining: A CLIP benchmark of data, model, and supervision. In ICML Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward, 2022. 2, 4" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 312, + 471, + 546, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 471, + 546, + 514 + ], + "spans": [ + { + "bbox": [ + 312, + 471, + 546, + 514 + ], + "type": "text", + "content": "[22] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009. 15, 18" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 312, + 515, + 547, + 559 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 515, + 547, + 559 + ], + "spans": [ + { + "bbox": [ + 312, + 515, + 547, + 559 + ], + "type": "text", + "content": "[23] Karan Desai and Justin Johnson. Virtex: Learning visual representations from textual annotations. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 11162-11173, 2021. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 312, + 559, + 546, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 559, + 546, + 602 + ], + "spans": [ + { + "bbox": [ + 312, + 559, + 546, + 602 + ], + "type": "text", + "content": "[24] Karan Desai, Gaurav Kaul, Zubin Aysola, and Justin Johnson. RedCaps: Web-curated image-text data created by the people, for the people. In NeurIPS Datasets and Benchmarks, 2021. 4, 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 312, + 603, + 546, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 603, + 546, + 669 + ], + "spans": [ + { + "bbox": [ + 312, + 603, + 546, + 669 + ], + "type": "text", + "content": "[25] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT), 2018. 4" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 312, + 670, + 546, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 670, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 312, + 670, + 546, + 713 + ], + "type": "text", + "content": "[26] Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "19216" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 53, + 73, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 53, + 73, + 288, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 73, + 288, + 149 + ], + "spans": [ + { + "bbox": [ + 53, + 73, + 288, + 149 + ], + "type": "text", + "content": "[27] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In Proceedings of the International Conference on Learning Representations (ICLR), 2021. 3, 15, 20" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 152, + 288, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 152, + 288, + 217 + ], + "spans": [ + { + "bbox": [ + 53, + 152, + 288, + 217 + ], + "type": "text", + "content": "[28] Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. With a little help from my friends: Nearest-neighbor contrastive learning of visual representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 9588–9597, October 2021. 1, 2, 3, 5, 8, 16" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 218, + 288, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 218, + 288, + 274 + ], + "spans": [ + { + "bbox": [ + 53, + 218, + 288, + 274 + ], + "type": "text", + "content": "[29] Mohamed El Banani, Luya Gao, and Justin Johnson. Unsupervisedr&r: Unsupervised point cloud registration via differentiable rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7129-7139, 2021. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 274, + 288, + 328 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 274, + 288, + 328 + ], + "spans": [ + { + "bbox": [ + 53, + 274, + 288, + 328 + ], + "type": "text", + "content": "[30] Li Fei-Fei, Rob Fergus, and Pietro Perona. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. Computer Vision and Pattern Recognition Workshop, 2004. 14, 15" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 331, + 288, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 331, + 288, + 374 + ], + "spans": [ + { + "bbox": [ + 53, + 331, + 288, + 374 + ], + "type": "text", + "content": "[31] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In Proceedings of the International Conference on Learning Representations (ICLR), 2018. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 376, + 288, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 376, + 288, + 409 + ], + "spans": [ + { + "bbox": [ + 53, + 376, + 288, + 409 + ], + "type": "text", + "content": "[32] Yunchao Gong, Qifa Ke, Michael Isard, and Svetlana Lazebnik. A multi-view embedding space for modeling internet images, tags, and their semantics. In IJCV, 2014. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 410, + 288, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 410, + 288, + 464 + ], + "spans": [ + { + "bbox": [ + 53, + 410, + 288, + 464 + ], + "type": "text", + "content": "[33] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra, and Devi Parikh. Making the V in VQA matter: Elevating the role of image understanding in visual question answering. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 466, + 288, + 532 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 466, + 288, + 532 + ], + "spans": [ + { + "bbox": [ + 53, + 466, + 288, + 532 + ], + "type": "text", + "content": "[34] Jean-Bastien Grill, Florian Strub, Florent Alché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in Neural Information Processing Systems (NeurIPS), 2020. 2, 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 533, + 288, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 533, + 288, + 577 + ], + "spans": [ + { + "bbox": [ + 53, + 533, + 288, + 577 + ], + "type": "text", + "content": "[35] Raia Hadsell, Sumit Chopra, and Yann LeCun. Dimensionality reduction by learning an invariant mapping. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2006. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 578, + 288, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 578, + 288, + 622 + ], + "spans": [ + { + "bbox": [ + 53, + 578, + 288, + 622 + ], + "type": "text", + "content": "[36] Tengda Han, Weidi Xie, and Andrew Zisserman. Self-supervised co-training for video representation learning. In Advances in Neural Information Processing Systems (NeurIPS), 2020. 2, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 624, + 288, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 624, + 288, + 668 + ], + "spans": [ + { + "bbox": [ + 53, + 624, + 288, + 668 + ], + "type": "text", + "content": "[37] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 53, + 669, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 669, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 53, + 669, + 288, + 713 + ], + "type": "text", + "content": "[38] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 2" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 312, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 312, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 312, + 73, + 545, + 116 + ], + "type": "text", + "content": "[39] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 4, 15" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 312, + 118, + 547, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 118, + 547, + 173 + ], + "spans": [ + { + "bbox": [ + 312, + 118, + 547, + 173 + ], + "type": "text", + "content": "[40] Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019. 15" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 312, + 175, + 547, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 175, + 547, + 250 + ], + "spans": [ + { + "bbox": [ + 312, + 175, + 547, + 250 + ], + "type": "text", + "content": "[41] Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, Dawn Song, Jacob Steinhardt, and Justin Gilmer. The many faces of robustness: A critical analysis of out-of-distribution generalization. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 15, 18" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 312, + 252, + 545, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 252, + 545, + 296 + ], + "spans": [ + { + "bbox": [ + 312, + 252, + 545, + 296 + ], + "type": "text", + "content": "[42] Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song. Natural adversarial examples. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 15, 18" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 312, + 297, + 545, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 297, + 545, + 352 + ], + "spans": [ + { + "bbox": [ + 312, + 297, + 545, + 352 + ], + "type": "text", + "content": "[43] Jack Hessel, Ari Holtzman, Maxwell Forbes, Ronan Le Bras, and Yejin Choi. Clipscore: A reference-free evaluation metric for image captioning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 312, + 354, + 545, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 354, + 545, + 397 + ], + "spans": [ + { + "bbox": [ + 312, + 354, + 545, + 397 + ], + "type": "text", + "content": "[44] Ji Hou, Saining Xie, Benjamin Graham, Angela Dai, and Matthias Nießner. Pri3d: Can 3d priors help 2d representation learning? In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 312, + 399, + 545, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 399, + 545, + 442 + ], + "spans": [ + { + "bbox": [ + 312, + 399, + 545, + 442 + ], + "type": "text", + "content": "[45] Drew A Hudson and Christopher D Manning. GQA: A new dataset for real-world visual reasoning and compositional question answering. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 312, + 444, + 545, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 444, + 545, + 476 + ], + "spans": [ + { + "bbox": [ + 312, + 444, + 545, + 476 + ], + "type": "text", + "content": "[46] Allan Jabri, Andrew Owens, and Alexei Efros. Space-time correspondence as a contrastive random walk. Advances in Neural Information Processing Systems (NeurIPS), 2020. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 312, + 478, + 545, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 478, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 312, + 478, + 545, + 544 + ], + "type": "text", + "content": "[47] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In Proceedings of the International Conference on Machine Learning (ICML), 2021. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 312, + 545, + 545, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 545, + 545, + 589 + ], + "spans": [ + { + "bbox": [ + 312, + 545, + 545, + 589 + ], + "type": "text", + "content": "[48] Justin Johnson, Lamberto Ballan, and Li Fei-Fei. Love thy neighbors: Image annotation by exploiting image metadata. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 312, + 590, + 545, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 590, + 545, + 623 + ], + "spans": [ + { + "bbox": [ + 312, + 590, + 545, + 623 + ], + "type": "text", + "content": "[49] Jeff Johnson, Matthijs Douze, and Hervé Jégou. Billionscale similarity search with GPUs. IEEE Transactions on Big Data, 2019. 4" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 312, + 624, + 545, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 624, + 545, + 678 + ], + "spans": [ + { + "bbox": [ + 312, + 624, + 545, + 678 + ], + "type": "text", + "content": "[50] Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, and Tamara Berg. Referitgame: Referring to objects in photographs of natural scenes. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 312, + 680, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 680, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 312, + 680, + 545, + 713 + ], + "type": "text", + "content": "[51] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning." + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "19217" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 53, + 72, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 72, + 72, + 287, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 72, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 72, + 72, + 287, + 95 + ], + "type": "text", + "content": "In Advances in Neural Information Processing Systems (NeurIPS), 2020. 2, 8" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 97, + 288, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 97, + 288, + 140 + ], + "spans": [ + { + "bbox": [ + 53, + 97, + 288, + 140 + ], + "type": "text", + "content": "[52] Ryan Kiros, Yukun Zhu, Russ R Salakhutdinov, Richard Zemel, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. Skip-thought vectors. In Advances in Neural Information Processing Systems (NeurIPS), 2015. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 143, + 288, + 186 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 143, + 288, + 186 + ], + "spans": [ + { + "bbox": [ + 53, + 143, + 288, + 186 + ], + "type": "text", + "content": "[53] Simon Kornblith, Jonathon Shlens, and Quoc V Le. Do better imagenet models transfer better? In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2661-2671, 2019. 5, 14" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 189, + 287, + 232 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 189, + 287, + 232 + ], + "spans": [ + { + "bbox": [ + 53, + 189, + 287, + 232 + ], + "type": "text", + "content": "[54] Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei. 3d object representations for fine-grained categorization. In 4th International IEEE Workshop on 3D Representation and Recognition (3dRR-13), Sydney, Australia, 2013. 15" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 235, + 287, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 235, + 287, + 255 + ], + "spans": [ + { + "bbox": [ + 53, + 235, + 287, + 255 + ], + "type": "text", + "content": "[55] Alex Krizhevsky. Learning multiple layers of features from tiny images, 2009. 15" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 258, + 287, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 258, + 287, + 312 + ], + "spans": [ + { + "bbox": [ + 53, + 258, + 287, + 312 + ], + "type": "text", + "content": "[56] Janghyeon Lee, Jongsuk Kim, Hyounguk Shon, Bumsoo Kim, Seung Hwan Kim, Honglak Lee, and Junmo Kim. Uniclip: Unified framework for contrastive language-image pre-training. In Advances in Neural Information Processing Systems (NeurIPS), 2022. 2, 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 315, + 287, + 358 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 315, + 287, + 358 + ], + "spans": [ + { + "bbox": [ + 53, + 315, + 287, + 358 + ], + "type": "text", + "content": "[57] Ang Li, Allan Jabri, Armand Joulin, and Laurens van der Maaten. Learning visual n-grams from web data. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2017. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 361, + 287, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 361, + 287, + 404 + ], + "spans": [ + { + "bbox": [ + 53, + 361, + 287, + 404 + ], + "type": "text", + "content": "[58] Aoxue Li, Tiange Luo, Zhiwu Lu, Tao Xiang, and Liwei Wang. Large-scale few-shot learning: Knowledge transfer with class hierarchy. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 406, + 288, + 471 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 406, + 288, + 471 + ], + "spans": [ + { + "bbox": [ + 53, + 406, + 288, + 471 + ], + "type": "text", + "content": "[59] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. SupervisionexistsEverywhere:A Data Efficient Contrastive Language-Image Pre-training Paradigm.In Proceedings of the International Conference on Learning Representations (ICLR),2022.2,5,16" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 474, + 287, + 506 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 474, + 287, + 506 + ], + "spans": [ + { + "bbox": [ + 53, + 474, + 287, + 506 + ], + "type": "text", + "content": "[60] Dong C Liu and Jorge Nocedal. On the limited memory bfgs method for large scale optimization. Mathematical programming, 1989. 14" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 509, + 287, + 552 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 509, + 287, + 552 + ], + "spans": [ + { + "bbox": [ + 53, + 509, + 287, + 552 + ], + "type": "text", + "content": "[61] Lajanugen Logeswaran and Honglak Lee. An efficient framework for learning sentence representations. In Proceedings of the International Conference on Learning Representations (ICLR), 2018. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 53, + 554, + 287, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 554, + 287, + 586 + ], + "spans": [ + { + "bbox": [ + 53, + 554, + 287, + 586 + ], + "type": "text", + "content": "[62] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016.5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 53, + 589, + 287, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 589, + 287, + 621 + ], + "spans": [ + { + "bbox": [ + 53, + 589, + 287, + 621 + ], + "type": "text", + "content": "[63] Ilya Loshchilov and Frank Hutter. Decoupled Weight Decay Regularization. In Proceedings of the International Conference on Learning Representations (ICLR), 2019. 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 53, + 624, + 287, + 666 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 624, + 287, + 666 + ], + "spans": [ + { + "bbox": [ + 53, + 624, + 287, + 666 + ], + "type": "text", + "content": "[64] Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi. Fine-grained visual classification of aircraft. arXiv preprint arXiv:1306.5151, 2013. 15" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 53, + 669, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 669, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 53, + 669, + 287, + 712 + ], + "type": "text", + "content": "[65] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (NeurIPS), 2013. 4" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 312, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 312, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 312, + 73, + 545, + 116 + ], + "type": "text", + "content": "[66] Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Abhishek Sharma, David Jacobs, and Dilip Krishnan. Object-aware cropping for self-supervised learning. arXiv preprint arXiv:2112.00319, 2021. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 312, + 118, + 545, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 118, + 545, + 162 + ], + "spans": [ + { + "bbox": [ + 312, + 118, + 545, + 162 + ], + "type": "text", + "content": "[67] Norman Mu, Alexander Kirillov, David Wagner, and Saining Xie. Slip: Self-supervision meets language-image pretraining. In Proceedings of European Conference on Computer Vision (ECCV), 2022. 2, 4, 5, 7, 15, 16" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 312, + 163, + 547, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 163, + 547, + 206 + ], + "spans": [ + { + "bbox": [ + 312, + 163, + 547, + 206 + ], + "type": "text", + "content": "[68] Alejandro Newell and Jia Deng. How useful is self-supervised pretraining for visual tasks? In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 312, + 209, + 547, + 252 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 209, + 547, + 252 + ], + "spans": [ + { + "bbox": [ + 312, + 209, + 547, + 252 + ], + "type": "text", + "content": "[69] M-E. Nilsback and A. Zisserman. Automated flower classification over a large number of classes. In Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, Dec 2008. 15" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 312, + 254, + 545, + 286 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 254, + 545, + 286 + ], + "spans": [ + { + "bbox": [ + 312, + 254, + 545, + 286 + ], + "type": "text", + "content": "[70] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748, 2018. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 312, + 288, + 545, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 288, + 545, + 331 + ], + "spans": [ + { + "bbox": [ + 312, + 288, + 545, + 331 + ], + "type": "text", + "content": "[71] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. Bleu: a method for automatic evaluation of machine translation. In Proceedings of the Annual Meeting on Association for Computational Linguistics (ACL), 2002. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 312, + 333, + 545, + 388 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 333, + 545, + 388 + ], + "spans": [ + { + "bbox": [ + 312, + 333, + 545, + 388 + ], + "type": "text", + "content": "[72] Yookoon Park, Mahmoud Azab, Bo Xiong, Seungwhan Moon, Florian Metze, Gourab Kundu, and Kirmani Ahmed. Normalized contrastive learning for text-video retrieval. Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 312, + 389, + 545, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 389, + 545, + 431 + ], + "spans": [ + { + "bbox": [ + 312, + 389, + 545, + 431 + ], + "type": "text", + "content": "[73] O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar. Cats and dogs. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012. 15" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 312, + 434, + 545, + 499 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 434, + 545, + 499 + ], + "spans": [ + { + "bbox": [ + 312, + 434, + 545, + 499 + ], + "type": "text", + "content": "[74] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (NeurIPS), 2019. 14" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 312, + 501, + 545, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 501, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 312, + 501, + 545, + 555 + ], + "type": "text", + "content": "[75] Xiangyu Peng, Kai Wang, Zheng Zhu, Mang Wang, and Yang You. Crafting better contrastive views for siamese representation learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 16031-16040, 2022. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 312, + 557, + 545, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 557, + 545, + 632 + ], + "spans": [ + { + "bbox": [ + 312, + 557, + 545, + 632 + ], + "type": "text", + "content": "[76] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In Proceedings of the International Conference on Machine Learning (ICML), 2021. 1, 2, 4, 5, 7, 8, 14, 16, 21, 22, 23" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 312, + 635, + 545, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 635, + 545, + 679 + ], + "spans": [ + { + "bbox": [ + 312, + 635, + 545, + 679 + ], + "type": "text", + "content": "[77] Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. Do imagenet classifiers generalize to imageNet? In Proceedings of the International Conference on Machine Learning (ICML), 2019. 15, 18" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 312, + 681, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 681, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 312, + 681, + 545, + 713 + ], + "type": "text", + "content": "[78] Nils Reimers and Iryna Gurevych. Sentence-bert: Sentence embeddings using siamese bert-networks. In Proceedings of the 2019 Conference on Empirical Methods in Natural" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "19218" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 53, + 73, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 72, + 73, + 286, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 73, + 286, + 95 + ], + "spans": [ + { + "bbox": [ + 72, + 73, + 286, + 95 + ], + "type": "text", + "content": "Language Processing. Association for Computational Linguistics, 11 2019. 3, 4, 8, 14, 20" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 97, + 287, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 97, + 287, + 140 + ], + "spans": [ + { + "bbox": [ + 53, + 97, + 287, + 140 + ], + "type": "text", + "content": "[79] Karsten Roth, Oriol Vinyls, and Zeynep Akata. Integrating Language Guidance into Vision-based Deep Metric Learning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 143, + 286, + 186 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 143, + 286, + 186 + ], + "spans": [ + { + "bbox": [ + 53, + 143, + 286, + 186 + ], + "type": "text", + "content": "[80] Mert Bulent Sariyildiz, Julien Perez, and Diane Larlus. Learning visual representations with caption annotations. In Proceedings of European Conference on Computer Vision (ECCV), 2020. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 189, + 288, + 243 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 189, + 288, + 243 + ], + "spans": [ + { + "bbox": [ + 53, + 189, + 288, + 243 + ], + "type": "text", + "content": "[81] Ramprasaath R Selvaraju, Karan Desai, Justin Johnson, and Nikhil Naik. Casting your model: Learning to localize improves self-supervised representations. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 11058-11067, 2021. 2, 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 245, + 286, + 289 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 245, + 286, + 289 + ], + "spans": [ + { + "bbox": [ + 53, + 245, + 286, + 289 + ], + "type": "text", + "content": "[82] Dandan Shan, Richard Ely Locke Higgins, and David Fouhey. Cohesiv: Contrastive object and hand embedding segmentation in video. In Advances in Neural Information Processing Systems (NeurIPS), 2021. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 291, + 286, + 334 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 291, + 286, + 334 + ], + "spans": [ + { + "bbox": [ + 53, + 291, + 286, + 334 + ], + "type": "text", + "content": "[83] Jinghuan Shang, Srijan Das, and Michael S Ryoo. Learning viewpoint-agnostic visual representations by recovering tokens in 3d space. Advances in Neural Information Processing Systems (NeurIPS), 2022. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 336, + 286, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 336, + 286, + 369 + ], + "spans": [ + { + "bbox": [ + 53, + 336, + 286, + 369 + ], + "type": "text", + "content": "[84] Ali Sharif Razavian, Hossein Azizpour, Josephine Sullivan, and Stefan Carlsson. Cnn features off-the-shelf: an astounding baseline for recognition. In CVPRW, 2014. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 371, + 286, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 371, + 286, + 426 + ], + "spans": [ + { + "bbox": [ + 53, + 371, + 286, + 426 + ], + "type": "text", + "content": "[85] Piyush Sharma, Nan Ding, Sebastian Goodman, and Radu Soricut. Conceptual Captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning. In Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2018. 4, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 428, + 286, + 460 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 428, + 286, + 460 + ], + "spans": [ + { + "bbox": [ + 53, + 428, + 286, + 460 + ], + "type": "text", + "content": "[86] Kihyuk Sohn. Improved deep metric learning with multiclass n-pair loss objective. In Advances in Neural Information Processing Systems (NeurIPS), 2016. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 463, + 286, + 506 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 463, + 286, + 506 + ], + "spans": [ + { + "bbox": [ + 53, + 463, + 286, + 506 + ], + "type": "text", + "content": "[87] Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, and Tie-Yan Liu. MPNet: Masked and Permuted Pre-training for language understanding. Advances in Neural Information Processing Systems (NeurIPS), 2020. 4, 8, 14" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 509, + 286, + 552 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 509, + 286, + 552 + ], + "spans": [ + { + "bbox": [ + 53, + 509, + 286, + 552 + ], + "type": "text", + "content": "[88] Jonathan C Stroud, Zhichao Lu, Chen Sun, Jia Deng, Rahul Sukthankar, Cordelia Schmid, and David A Ross. Learning video representations from textual web supervision. arXiv preprint arXiv:2007.14937, 2020. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 53, + 554, + 287, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 554, + 287, + 609 + ], + "spans": [ + { + "bbox": [ + 53, + 554, + 287, + 609 + ], + "type": "text", + "content": "[89] Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Hua-jun Bai, and Yoav Artzi. A corpus for reasoning about natural language grounded in photographs. In Proceedings of the Annual Meeting on Association for Computational Linguistics (ACL), 2019. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 53, + 611, + 286, + 654 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 611, + 286, + 654 + ], + "spans": [ + { + "bbox": [ + 53, + 611, + 286, + 654 + ], + "type": "text", + "content": "[90] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. LoFTR: Detector-free local feature matching with transformers. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 53, + 656, + 286, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 656, + 286, + 689 + ], + "spans": [ + { + "bbox": [ + 53, + 656, + 286, + 689 + ], + "type": "text", + "content": "[91] Igor Susmelj, Matthias Heller, Philipp Wirth, Prescott Jeremy, and Malte Ebner. Lightly. GitHub. Note: https://github.com/lightly-ai/lightly, 2020. 16" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 53, + 691, + 286, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 691, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 53, + 691, + 286, + 712 + ], + "type": "text", + "content": "[92] TensorFlow Datasets, a collection of ready-to-use datasets. https://www.tensorflow.org/datasets.14" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 312, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 312, + 73, + 545, + 116 + ], + "type": "text", + "content": "[93] Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. YFCC100M: The new data in multimedia research. Communications of the ACM, 2016. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 312, + 118, + 545, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 118, + 545, + 162 + ], + "spans": [ + { + "bbox": [ + 312, + 118, + 545, + 162 + ], + "type": "text", + "content": "[94] Yonglong Tian, Olivier J Henaff, and Aaron van den Oord. Divide and contrast: Self-supervised learning from uncu-rated data. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 312, + 163, + 545, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 163, + 545, + 217 + ], + "spans": [ + { + "bbox": [ + 312, + 163, + 545, + 217 + ], + "type": "text", + "content": "[95] Yonglong Tian, Yue Wang, Dilip Krishnan, Joshua B Tenenbaum, and Phillip Isola. Rethinking few-shot image classification: a good embedding is all you need? In Proceedings of European Conference on Computer Vision (ECCV), 2020. 14" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 312, + 220, + 545, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 220, + 545, + 262 + ], + "spans": [ + { + "bbox": [ + 312, + 220, + 545, + 262 + ], + "type": "text", + "content": "[96] Ramakrishna Vedantam, C Lawrence Zitnick, and Devi Parikh. CIDEr: Consensus-based image description evaluation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 312, + 264, + 545, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 264, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 312, + 264, + 545, + 319 + ], + "type": "text", + "content": "[97] Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling. Rotation equivariant cnns for digital pathology. In International Conference on Medical image computing and computer-assisted intervention, 2018. 15" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 312, + 321, + 545, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 321, + 545, + 374 + ], + "spans": [ + { + "bbox": [ + 312, + 321, + 545, + 374 + ], + "type": "text", + "content": "[98] Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the International Conference on Machine Learning (ICML), 2008. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 312, + 376, + 545, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 376, + 545, + 421 + ], + "spans": [ + { + "bbox": [ + 312, + 376, + 545, + 421 + ], + "type": "text", + "content": "[99] Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing. Learning robust global representations by penalizing local predictive power. In Advances in Neural Information Processing Systems (NeurIPS), 2019. 15, 18" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 422, + 545, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 422, + 545, + 476 + ], + "spans": [ + { + "bbox": [ + 307, + 422, + 545, + 476 + ], + "type": "text", + "content": "[100] Wenhui Wang, Furu Wei, Li Dong, Hangbo Bao, Nan Yang, and Ming Zhou. MiniLM: Deep self-attention distillation for task-agnostic compression of pre-trained transformers. Advances in Neural Information Processing Systems (NeurIPS), 2020. 8, 14" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 478, + 545, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 478, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 308, + 478, + 545, + 521 + ], + "type": "text", + "content": "[101] Xiaolong Wang, Allan Jabri, and Alexei A Efros. Learning correspondence from the cycle-consistency of time. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 523, + 545, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 523, + 545, + 567 + ], + "spans": [ + { + "bbox": [ + 308, + 523, + 545, + 567 + ], + "type": "text", + "content": "[102] Yan Wang, Wei-Lun Chao, Kilian Q. Weinberger, and Laurens van der Maaten. Simpleshot: Revisiting nearest-neighbor classification for few-shot learning. arXiv preprint arXiv:1911.04623, 2019. 5, 14" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 568, + 545, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 568, + 545, + 612 + ], + "spans": [ + { + "bbox": [ + 308, + 568, + 545, + 612 + ], + "type": "text", + "content": "[103] P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona. Caltech-UCSD Birds 200. Technical Report CNS-TR-2010-001, California Institute of Technology, 2010. 15" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 613, + 545, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 613, + 545, + 656 + ], + "spans": [ + { + "bbox": [ + 308, + 613, + 545, + 656 + ], + "type": "text", + "content": "[104] Ross Wightman, Hugo Touvron, and Herve Jegou. Resnet strikes back: An improved training procedure in timm. In NeurIPS Workshop on ImageNet: Past, Present, and Future, 2021. 7, 8, 15, 21, 22, 23" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 658, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 658, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 308, + 658, + 545, + 712 + ], + "type": "text", + "content": "[105] Xiaoshi Wu, Hadar Averbuch-Elor, Jin Sun, and Noah Snavely. Towers of babel: Combining images, language, and 3d geometry for learning multimodal vision. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 2" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "19219" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 11 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 708 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "text", + "content": "[106] Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. Unsupervised feature learning via non-parametric instance discrimination. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3733-3742, 2018. 1, 2, 5" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 129, + 288, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 129, + 288, + 173 + ], + "spans": [ + { + "bbox": [ + 48, + 129, + 288, + 173 + ], + "type": "text", + "content": "[107] J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba. SUN database: Large-scale scene recognition from abbey to zoo. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010. 15" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 174, + 287, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 174, + 287, + 217 + ], + "spans": [ + { + "bbox": [ + 48, + 174, + 287, + 217 + ], + "type": "text", + "content": "[108] Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell. What should not be contrastive in contrastive learning. In International Conference on Learning Representations, 2020. 2, 3, 5, 8" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 218, + 287, + 273 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 218, + 287, + 273 + ], + "spans": [ + { + "bbox": [ + 48, + 218, + 287, + 273 + ], + "type": "text", + "content": "[109] Jiarui Xu, Shalini De Mello, Sifei Liu, Wonmin Byeon, Thomas Breuel, Jan Kautz, and Xiaolong Wang. Groupvit: Semantic segmentation emerges from text supervision. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 274, + 287, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 274, + 287, + 338 + ], + "spans": [ + { + "bbox": [ + 48, + 274, + 287, + 338 + ], + "type": "text", + "content": "[110] Zhicheng Yan, Hao Zhang, Robinson Piramuthu, Vignesh Jagadeesh, Dennis DeCoste, Wei Di, and Yizhou Yu. Hd-cnn: hierarchical deep convolutional neural networks for large scale visual recognition. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2015. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 340, + 287, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 340, + 287, + 396 + ], + "spans": [ + { + "bbox": [ + 48, + 340, + 287, + 396 + ], + "type": "text", + "content": "[111] Lewei Yao, Runhui Huang, Lu Hou, Guansong Lu, Minzhe Niu, Hang Xu, Xiaodan Liang, Zhenguo Li, Xin Jiang, and Chunjing Xu. FILIP: Fine-grained interactive language-image pre-training. In Proceedings of the International Conference on Learning Representations (ICLR), 2022. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 396, + 287, + 440 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 396, + 287, + 440 + ], + "spans": [ + { + "bbox": [ + 48, + 396, + 287, + 440 + ], + "type": "text", + "content": "[112] Peter Young, Alice Lai, Micah Hodosh, and Julia Hockenmaier. From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions. TACL, 2014. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 441, + 287, + 484 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 441, + 287, + 484 + ], + "spans": [ + { + "bbox": [ + 48, + 441, + 287, + 484 + ], + "type": "text", + "content": "[113] Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. From recognition to cognition: Visual commonsense reasoning. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 485, + 287, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 485, + 287, + 529 + ], + "spans": [ + { + "bbox": [ + 48, + 485, + 287, + 529 + ], + "type": "text", + "content": "[114] Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer. Scaling vision transformers. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 4" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 529, + 287, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 529, + 287, + 563 + ], + "spans": [ + { + "bbox": [ + 48, + 529, + 287, + 563 + ], + "type": "text", + "content": "[115] Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful image colorization. In Proceedings of European Conference on Computer Vision (ECCV), 2016. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 563, + 287, + 608 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 563, + 287, + 608 + ], + "spans": [ + { + "bbox": [ + 48, + 563, + 287, + 608 + ], + "type": "text", + "content": "[116] Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. Bertscore: Evaluating text generation with bert. In Proceedings of the International Conference on Learning Representations (ICLR), 2019. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 609, + 287, + 662 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 609, + 287, + 662 + ], + "spans": [ + { + "bbox": [ + 48, + 609, + 287, + 662 + ], + "type": "text", + "content": "[117] Mingkai Zheng, Fei Wang, Shan You, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu. Weakly supervised contrastive learning. In Proceedings of IEEE International Conference on Computer Vision (ICCV), 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 664, + 287, + 708 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 664, + 287, + 708 + ], + "spans": [ + { + "bbox": [ + 48, + 664, + 287, + 708 + ], + "type": "text", + "content": "[118] Yuke Zhu, Oliver Groth, Michael Bernstein, and Li Fei-Fei. Visual7w: Grounded question answering in images. 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In this paper, we find that image degradations under different weather conditions contain general characteristics as well as their specific characteristics. Inspired by this observation, we design an efficient unified framework with a two-stage training strategy to explore the weather-general and weather-specific features. The first training stage aims to learn the weather-general features by taking the images under various weather conditions as inputs and outputting the coarsely restored results. The second training stage aims to learn to adaptively expand the specific parameters for each weather type in the deep model, where the requisite positions for expanding weather-specific parameters are automatically learned. Hence, we can obtain an efficient and unified model for image restoration under multiple adverse weather conditions. Moreover, we build the first real-world benchmark dataset with multiple weather conditions to better deal with real-world weather scenarios. Experimental results show that our method achieves superior performance on all the synthetic and real-world benchmarks. Codes and datasets are available at this repository.", + "bbox": [ + 75, + 368, + 473, + 717 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 742, + 209, + 758 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Adverse weather conditions, such as rain, haze, and snow, are common climatic phenomena in our daily life. They often lead to the poor visual quality of captured images and primarily deteriorate the performance of many outdoor vision systems, such as outdoor security cameras", + "bbox": [ + 75, + 768, + 468, + 844 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/b7e3f5454ba282cdb45f2d9a34f409c16a5b8e9f3066fa4ef80c4d0498a263a1.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 504, + 335, + 694, + 496 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/fa4b63e70da508e5cbff3fe3e4614e23a0747837bc8b088322c3a16e0552f9bc.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 702, + 335, + 890, + 496 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/7bc2ef2e65a41690ccd587766c48d949a2ed99a58b5a88ca2518b9e695a0119c.jpg", + "image_caption": [ + "Figure 1. Illustration of the proposed method and the currently existing solutions. (a) The weather-specific methods; (b) the method of [41]; (c) methods of [6, 69]; (d) our method, which learns the weather-specific and weather-general features in an efficient manner to remove multiple weather-related artifacts." + ], + "image_footnote": [ + ":Convolution Layer", + ": Specific Parameters for Deraining", + "Specific Parameters for Dehazing" + ], + "bbox": [ + 503, + 500, + 694, + 661 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d4b397b70c6dc0a7426b934c9b8bfbf6e05f5070582ed06d9db877b586944c06.jpg", + "image_caption": [], + "image_footnote": [ + "Specific Parameters for Desnowing", + ":General Parameters for All Weather", + "NULL : No Parameters at This Layer" + ], + "bbox": [ + 702, + 500, + 890, + 661 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "and automatic driving [53, 107]. To make these systems more robust to various adverse weather conditions, many restoration solutions have been proposed, such as deraining [16, 17, 25, 40, 72, 73, 82, 83], dehazing [1, 23, 49, 65, 80, 84], desnowing [4, 51, 97], and raindrop removal [22, 59, 96].", + "bbox": [ + 500, + 824, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation.", + "bbox": [ + 236, + 0, + 810, + 18 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "Except for this watermark, it is identical to the accepted version;", + "bbox": [ + 323, + 16, + 723, + 31 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 292, + 31, + 754, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "$\\star$ : This work was done during their internship at Shanghai Artificial Intelligence Laboratory.", + "bbox": [ + 75, + 851, + 468, + 875 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "†: Co-first authors contributed equally.", + "bbox": [ + 76, + 876, + 287, + 888 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "$\\boxtimes$ : Corresponding authors.", + "bbox": [ + 76, + 888, + 227, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "21747", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Albeit these approaches exhibit promising performance in the given weather situation, they are only applicable to certain typical weather scenarios. However, it is inevitable to tackle various kinds of weather in the application of outdoor vision intelligent systems. Consequently, as shown in Fig. 1 (a), multiple sets of weather-specific model parameters are required to deal with various conditions, which brings additional computational and storage burdens. Hence, it is vitally requisite to develop a unified model capable of addressing various types of adverse weather conditions via a single set of network parameters.", + "bbox": [ + 75, + 90, + 472, + 257 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Recently, several methods [6, 41, 69] adopt a single set of network parameters to remove different weather-related artifacts. However, these solutions contain limitations for practical deployment and applications. Firstly, some methods [6, 69] fail to consider the specific characteristics of each weather condition in their proposed unified models, limiting their restoration performance on specific weather conditions. Secondly, as shown in Fig. 1 (b), although Li et al. [41] tackle the differences and similarities of weather degradation with multiple individual encoders and a shared decoder. Such multiple fixed encoders may largely increase network parameters. Thirdly, existing unified models [6, 41, 69] often require a large number of parameters, limiting the model efficiency. Lastly, current state-of-the-art methods [6, 41, 69] mainly employ synthesized datasets in their training phase, causing apparent performance drops in real-world scenarios.", + "bbox": [ + 75, + 260, + 472, + 517 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we argue that images with different weather distortions contain general characteristics as well as their specific characteristics. According to the atmosphere scattering model [55, 57], due to the attenuation and scattering effects, these weather disturbances often share some similar visual degradation appearances, e.g., low contrast and color degradation. Meanwhile, the typical type of weather distortion has its unique characteristics. For example, rainy images often suffer from occlusion by rain steaks with different shapes and scales [32, 82]; haze exhibits global distortions on the entire images [34, 65]. Pioneer works also have devised many specific priors [23, 72, 80, 83, 97] for different weather conditions, which motivates us to explore weather-general and weather-specific features to perform image restoration under multiple weather conditions.", + "bbox": [ + 75, + 520, + 472, + 747 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To achieve this, we design an efficient unified framework for multiple adverse weather-related artifacts removal by exploring both weather-general and weather-specific features. The training procedure of our framework consists of two stages. The first training stage aims to learn the general features by taking various images under different weather conditions as the inputs and outputting coarse results for multiple weather conditions. In the second training stage, we devise a regularization-based optimization scheme, which learns to adaptively expand the specific pa", + "bbox": [ + 75, + 750, + 472, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "rameters for each weather type in the deep model. Note that these requisite positions to expand weather-specific parameters could be learned automatically, thus avoiding redundant parameters pre-designed by researchers. Hence, we are able to obtain an efficient and unified model for image restoration under multiple adverse weather conditions. Furthermore, we newly construct the first real-world benchmark dataset with multiple weather conditions to better deal with various weather-related artifacts in real-world scenarios.", + "bbox": [ + 496, + 90, + 893, + 226 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The contributions of this paper could be summarized as:", + "bbox": [ + 517, + 227, + 890, + 243 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We reveal that image degradations under different weather conditions contain both general and specific characteristics, which motivates us to design a unified deep model by exploring the weather-general and weather-specific features for removing weather-related artifacts under multiple weather conditions.", + "- We present a two-stage training strategy to learn the weather-general and weather-specific features automatically. Moreover, the weather-specific features are adaptively added at the learned positions, which makes our model efficient and effective.", + "- In order to better deal with real-world weather conditions, we construct the first real-world benchmark dataset with multiple weather conditions. Additionally, experimental results validate the superiority of our proposed method on various benchmarks." + ], + "bbox": [ + 517, + 255, + 890, + 523 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 546, + 640, + 561 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Single image restoration under different weather conditions have been extensively studied in previous literatures, including deraining [16, 17, 19, 25, 26, 32, 40, 43, 72, 73, 78, 79, 82, 83, 86, 95, 104], dehazing [1, 10, 12, 23, 45, 49, 50, 65, 80, 84, 90, 98], desnowing [4, 51, 97], and raindrop removal [22, 59, 62, 96].", + "bbox": [ + 496, + 571, + 890, + 662 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Rain removal. The effects of rainy weather on images are usually divided into rain streaks and rain drops. For rain streak removal, [16] first applies the convolutional neural network on deraining, followed by GAN [94] and recurrent network [43]. Researchers look for better ways to understand and represent rain streaks [2, 7, 32, 40, 74, 81, 91, 101, 106], for more delicate detail recovery [11, 89], and for wider applicability under severe weather condition [40, 82]. Besides, many studies introduce the adversarial learning [40], transfer learning [27, 87], frequency priors [21, 28, 29, 103] and data generation to improve the models' performance.", + "bbox": [ + 496, + 670, + 890, + 851 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The degradation of raindrops is different from that of streaks. Thus, they are usually treated separately. There are approaches including CNN [15], attention map [59], and mathematical descriptions [22, 63] on raindrop removal.", + "bbox": [ + 496, + 852, + 890, + 912 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "21748", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Haze removal. [23] proposes a simple prior of hazy images. [1, 65, 93] applies various methods for dehazing, followed by GAN [61, 84] and fusion-based strategy [66], attention-based model with trainable pre-treatment part [49], dense feature fusion [13], and the combination of CNN and transformer [20]. [38, 80] consider various priors and modeling of raindrops. Proposed learning methods include contrastive regularization [77] and multi-guided bilateral learning [102]. [44] pays attention to colorful haze removal. There are also works pursuing stronger utilization and generalization on datasets [8, 46, 85].", + "bbox": [ + 75, + 90, + 472, + 257 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Snow removal. [51] designs a two-stage network to deal with different types of snow, while [4] considers the veiling effect. [31] proposes a pyramid-structure model with lateral connections. [5] uses dual-tree wavelet transform. [97] introduces semantics and depth prior to snowfall removal.", + "bbox": [ + 75, + 258, + 468, + 335 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Multiple weather-related artifacts removal. Different from the above typical single weather removal task, all-weather-removal is required to recover the clear images with a single set of network parameters under multiple kinds of weather conditions. Moreover, due to the differences among different tasks, all-weather-removal is obviously more challenging than the single image restoration task. Li et al. [41] explore the differences and similarities of each weather degradation by designing an all-in-one network with multiple encoders. [6] combines the two-stage knowledge and multi-contrastive learning strategy to handle the weather removal problem. [69] propose a transformer-based network with learnable specific weather queries to handle the weather removal in a unified model. The method [41] and our method both consider the differences and similarities among different weathers. However, unlike [41] using the specific weather parameters at the fixed encoder positions, our method could adaptively expand the specific parameters for a certain weather type at the learned network positions, which is more flexible and efficient.", + "bbox": [ + 75, + 337, + 472, + 640 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Multi-task learning (MTL). MTL mainly focuses on handling multiple tasks in a single network, which is extensively studied in the high-level [48, 71, 100, 105] and low-level vision tasks [3, 42]. Recently, methods [3, 42] attempt to exploit the transformers and pre-training scheme to handle multiple image restoration tasks, including deraining, denoising, and super-resolution. However, the correlation among these tasks is limited, while different weather degradations exist with apparent similarities to a certain extent.", + "bbox": [ + 75, + 642, + 472, + 779 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Motivation", + "text_level": 1, + "bbox": [ + 76, + 782, + 194, + 797 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Weather-distorted images are often captured in outdoor environments. Here, we would like to further analyze the general and specific characteristics of different weather conditions from the following two perspectives.", + "bbox": [ + 75, + 801, + 468, + 863 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The degradation in bad weathers. Different weather effects, i.e., rain, haze, and snow, are mainly caused by the", + "bbox": [ + 75, + 869, + 468, + 901 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/8f33d9fc7f2a5e5744d778d26dc783799376ba0500df6eabb220415ba6ba8806.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 506, + 90, + 712, + 167 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/0960278d652dbb8aeba0b43bf4600b3332f0880903b878d7eda4a72adc0e3026.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 715, + 90, + 802, + 169 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/923b76cd613ecd8f89ed95ac5d43d4c28e2b9cade776e4f30151f2d291702132.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 802, + 90, + 888, + 169 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/e4924970f34eddc184ad7e04d00d29c6c670bddf7404573410f71c236633a90b.jpg", + "image_caption": [ + "Figure 2. (a) Illustration of the atmosphere scattering model [54, 56] in various weather conditions. (b) Real-world scenes of different weather conditions.", + "(a)" + ], + "image_footnote": [], + "bbox": [ + 501, + 234, + 620, + 321 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/a2e29f6ca4dea54da11947fd30c954e1d0d621279c10c81cb4621e29d2f021cc.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 625, + 234, + 741, + 321 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/40cf16dd341740ceee36cacaa08662189a51112be765529529df8da1df59876e.jpg", + "image_caption": [ + "(c)", + "Figure 3. Similarity visualizations among different layers of multiple pre-trained models for deraining, dehazing, and desnowing. (a) The similarity of Rain and Snow models; (b) the similarity of Rain and Haze models; (c) similarity of Haze and Snow models. The values on two axes (0-80) indicate the position order of different network layers, where we uniformly sampled 80 layers." + ], + "image_footnote": [], + "bbox": [ + 746, + 234, + 864, + 321 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "various particles involved in the transparent medium (air space) based on the atmosphere optics. The atmosphere scattering model [54, 56] describes the imaging formulation in bad weather conditions, which reveals that the received irradiance of a certain scene point from the photographic sensor is the summation of the direct transmission (attenuation) and the airlight, respectively:", + "bbox": [ + 496, + 431, + 892, + 539 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nE = \\underbrace {E _ {\\infty} \\rho e ^ {- \\beta d}} _ {\\text {D i r e c t t r a n s m i s s i o n}} + \\underbrace {E _ {\\infty} \\left(1 - e ^ {- \\beta d}\\right)} _ {\\text {A i r l i g h t}}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 560, + 547, + 890, + 587 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $E_{\\infty}$ denotes the sky intensity; $\\rho$ denotes the normalized radiance of the certain scene point; $\\beta d$ denotes the optical distance of a certain scene point. Here we refer readers to [55-57] for more details. In Fig. 2 (a), we also provide the illustration of Eq. (1), in which the former term (direct transmission) describes the attenuation effect of the object light travel through the atmosphere, and the latter term (airlight) reveals the scattering of illumination by different involved particles in the atmosphere space.", + "bbox": [ + 496, + 598, + 890, + 733 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "According to the above physical imaging model, different weather effects share general characteristics. In detail, during the imaging process, particles of different weathers unavoidably modulate the light transmitting from the certain scene point to the sight of observers. The scattering light integration along the light propagation path [54] may unavoidably change the brightness of different weather conditions (Fig. 2 (b)). Thus, images under different adverse weathers exist common degradation, such as low contrast and subtle color distortion.", + "bbox": [ + 496, + 734, + 890, + 883 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Besides, there also exist weather-specific characteristics", + "bbox": [ + 517, + 885, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "21749", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/dad3f2f362e540f5277690cbaf1f60bd66e7b5c36c82162a8963001b3d17c93c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 84, + 90, + 517, + 151 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/e12ba783769f1de1b45fe1699db9c90e12649e040704935d34c442238008fe5d.jpg", + "image_caption": [ + "Figure 4. The overall illustration of our method. (a) and (b) illustrate the first and second training stages of our framework, respectively; (c) illustrates the inference stage of our framework. For simplicity, skip connections are omitted." + ], + "image_footnote": [], + "bbox": [ + 86, + 154, + 521, + 325 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/0cb52ec3f861a5dab225e599f8a2f75b6e8c9e9a218b12ba6e15624773320661.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 529, + 90, + 890, + 220 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/f8c116b5c946904839cf0c4d4ec1d53671e698eeb567109e5fb4d886213e1981.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 529, + 224, + 890, + 325 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "in each weather condition. For instance, the particle type of haze is aerosol, and haze is assumed as the entire image corruption [1,68]. Rain is constituted of water drops, and rainy images suffer from occlusion by the large motion-blurred particles with different shapes and scales. In contrast, irregular particle trajectories and opaque snowflakes are commonly seen in snowfall images. These differences among multiple weather types will cause interference when removing multiple weather-related artifacts. Hence, many more accurate physical models [4, 39, 75, 81] of various weather conditions based on Eqn. 1 are appeared, which further incorporate the typical weather priors into their models.", + "bbox": [ + 75, + 364, + 472, + 547 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Analysis of feature representations. To study the feature representations in deep networks for different weather-related artifacts removal, we individually train the models on different weather datasets using the UNet-based structure network [67]. Then, we adopt centered kernel alignment (CKA) [9, 36, 37, 42, 58] to measure the similarity of feature representations in deep networks, which have been pre-trained on the different weather datasets.", + "bbox": [ + 75, + 561, + 468, + 683 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The similarity visualizations across hidden layers of different networks are shown in Fig. 3. The internal representations learned on the rain dataset seem similar to the counterparts on the snow dataset. One plausible reason is that many snowfall images often resemble rainy images regarding appearances of degradation. While from Fig. 3 (b) and (c), the layers of the dehazing model have more similar patterns to the layers of deraining or desnowing models at the shallow and deep layers. Although networks are trained with different weather datasets, similar representations still exist at the sub-set of network positions.", + "bbox": [ + 75, + 686, + 468, + 852 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Above observations motivate us to expand parameters for different weather types in an adaptive manner, which is flexible and efficient for multiple weather artifacts removal.", + "bbox": [ + 76, + 854, + 470, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Methodology", + "text_level": 1, + "bbox": [ + 498, + 363, + 635, + 381 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To explore weather-specific and weather-general features for multiple weather removal tasks, we construct a unified network architecture based on the U-Net [67]. Additionally, as shown in Fig. 4, we devise a novel two-stage training strategy, where we separately learn weather-general features and weather-specific features in the first and second training stages. Then, we elaborate the implementation details of our method. Without loss of generality, we assume that weather types include rain, snow, and haze.", + "bbox": [ + 496, + 388, + 892, + 526 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Weather-General Feature Learning", + "text_level": 1, + "bbox": [ + 498, + 532, + 808, + 549 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We employ the shared backbone network to learn the weather-general features. Specifically, various types of weather images are adopted to jointly update the shared backbone network, which helps the network to learn the weather-general feature representations. The forward process at this stage could be formulated as", + "bbox": [ + 496, + 556, + 893, + 647 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {X} _ {\\rho} = f \\left(X _ {\\rho}; \\theta_ {\\text {s h a r e}}\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 614, + 652, + 890, + 672 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $X_{\\rho}$ and $\\hat{X}_{\\rho}$ denote the input and recovered images of weather type $\\rho$ ; $\\theta_{share}$ denotes the general sharing parameters for all weather types, which are jointly optimized by multiple weather datasets.", + "bbox": [ + 496, + 680, + 892, + 739 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In addition, the aforementioned Eq. (1) reveals that the bad weather degradation is largely related to the depth of corresponding scenes. Hence, we introduce the depth information in our framework to optimize the network. We adopt the depth consistency loss $L_{depth}$ , which fully exploits the general depth auxiliary information among different adverse weathers. Hence, the final loss function of the first training stage is formulated as follows:", + "bbox": [ + 496, + 739, + 893, + 862 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {s 1} = L _ {\\text {c o n t e n t}} \\left(\\hat {X} _ {\\rho}, Y _ {\\rho}\\right) + \\lambda_ {\\text {d e p t h}} \\| D \\left(\\hat {X} _ {\\rho}\\right) - D \\left(Y _ {\\rho}\\right) \\| _ {1}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 503, + 868, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "21750", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $L_{content}$ denotes the L1 and perceptual losses [33]; $\\hat{X}_{\\rho}$ and $Y_{\\rho}$ indicate the outputs and labels of the weather $\\rho$ ; $D(\\cdot)$ denotes the pre-trained depth estimation network [47], and $\\lambda_{depth}$ denotes the balanced weight.", + "bbox": [ + 76, + 90, + 468, + 154 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Weather-Specific Feature Learning", + "text_level": 1, + "bbox": [ + 76, + 159, + 382, + 176 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Adopting the shared parameters to deal with different weather conditions often degrades the restoration performance on each weather due to the interference issues of different sub-tasks. A straightforward solution is to insert the specific parameters for each weather condition across the network layers. Without loss of generality, such network expansion manner of the specific parameter $\\theta_{\\rho}$ for the weather type $\\rho$ ( $\\rho \\in [Rain, Haze, Snow]$ ) is defined as", + "bbox": [ + 75, + 183, + 470, + 305 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\theta_ {\\rho} ^ {i} = \\theta_ {s h a r e} ^ {i} + \\Delta \\theta_ {\\rho} ^ {i}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 196, + 311, + 468, + 330 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\theta_{\\text{share}}^i$ denotes the general sharing weights at the network layer $i$ ; $\\Delta \\theta_{\\rho}^{i}$ denotes the specific weights of weather type $\\rho$ at the layer $i$ ( $i = 1,2,\\dots,N$ , $N$ is the total number of network layers).", + "bbox": [ + 75, + 339, + 468, + 398 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "However, such a simple and fixed network expansion paradigm exists limitations. First, according to the analysis in Sec. 3, the fixed form of expansion for different weathers is redundant and inflexible, leading to heavy computation overhead. Second, when the weather types increase, the fixed expansion manner will introduce the massive network parameters. A problem naturally arises: how to efficiently and flexibly explore weather-specific features to boost the network performance on a certain weather type?", + "bbox": [ + 75, + 400, + 468, + 535 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this regard, we devise the regularization-based optimization scheme to achieve adaptive weather-specific parameters expansion for different weather types. In detail, we take the pre-trained model at the first stage as the basic model, where the parameters $(\\theta_{\\text{share}})$ are shared in different weather conditions. To learn the weather-specific features, we first keep the parameters $\\theta_{\\text{share}}$ frozen during this second training stage. Then, we introduce the weather-specific parameters $\\Delta \\theta_{\\rho}$ and learnable scoring variable $S_{\\rho}^{i}$ for the typical weather $\\rho$ at the network layer $i$ . Hence, in our method, the parameters of the typical weather type $\\rho$ at the layer $i$ could be reorganized as", + "bbox": [ + 75, + 536, + 470, + 717 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\theta_ {\\rho} ^ {i} = \\theta_ {s h a r e} ^ {i} + F _ {\\tau} \\left(S _ {\\rho} ^ {i}\\right) * \\Delta \\theta_ {\\rho} ^ {i}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 169, + 724, + 468, + 744 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $S_{\\rho}^{i}$ is the learnable scoring variable, which is used to assess the necessity to expand parameters at the layer $i$ . An indicator function $F_{\\tau}(\\cdot)$ is designed to determine whether to add the new parameters or not, which is defined as", + "bbox": [ + 75, + 752, + 468, + 813 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nF _ {\\tau} \\left(S _ {\\rho} ^ {i}\\right) = \\left\\{ \\begin{array}{l l} 1 & \\text {i f} S _ {\\rho} ^ {i} \\geq \\tau , \\\\ 0 & \\text {o t h e r w i s e}, \\end{array} \\right. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 176, + 821, + 468, + 861 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\tau$ is the threshold hyperparameter. Only if $S_{\\rho}^{i}$ is larger than the threshold $\\tau$ , the new parameters will be al", + "bbox": [ + 76, + 869, + 468, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "owed to add to the deep network. Here, we study the convolution operators with the kernel sizes of $1 \\times 1$ or $3 \\times 3$ as the newly added weather-specific parameters (please see Sec. 6.4 for the detailed ablation study).", + "bbox": [ + 496, + 90, + 890, + 151 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Lastly, the overall loss function of the second training stage could be formulated as follows:", + "bbox": [ + 498, + 152, + 890, + 181 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {s 2} = \\sum_ {\\rho} \\left(L _ {\\text {c o n t e n t}} \\left(\\hat {X} _ {\\rho}, Y _ {\\rho}\\right) + \\lambda_ {r e g} \\sum_ {i = 1} ^ {N} \\left| S _ {\\rho} ^ {i} \\right| _ {1}\\right), \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 514, + 191, + 890, + 247 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $L_{content}$ denotes the MAE and perceptual losses [33]; $\\hat{X}_{\\rho}$ and $Y_{\\rho}$ indicates the restored outputs and ground truths of the weather $\\rho$ ; $\\lambda_{reg}$ denotes the hyperparameter of the regularization term. As described in Eq. (7), we introduce the sparsity regularization of $S_{\\rho}^{i}$ in the objective functions, which guides the network to automatically discover the network positions where we need to add weather-specific parameters. Experimental results also reveal that adding relatively small weather-specific parameters to the learned positions will significantly improve model performance (see Sec. 6.4 for details). Therefore, our network could flexibly learn the weather-specific parameters and efficiently handle various weather conditions.", + "bbox": [ + 496, + 248, + 890, + 444 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Inference Stage", + "text_level": 1, + "bbox": [ + 498, + 446, + 656, + 463 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Fig. 4 (c) illustrates the inference stage of our method. The learned model comprises the weather-general parameters for different weathers and the adaptive weather-specific parameters for each typical weather at the sub-set of network positions. For example, taking an image with a certain type of weather (e.g., haze) as the input, the image will pass through the parameters in black and blue color. Note that these positions marked \"NULL\" indicates that these parameters are unnecessary and the forward propagation will not pass through these layers.", + "bbox": [ + 496, + 464, + 890, + 616 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Real-World Datasets for Multiple Weather-Related Artifacts Removal", + "text_level": 1, + "bbox": [ + 498, + 619, + 890, + 652 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Current all-weather-removal approaches rely on synthetic datasets to train their models, which limits their generalization capability to deal with real-world scenarios. To this regard, we construct the first real-world benchmark datasets under various weather conditions, including haze, rain, and snow. For dehazing, we directly employ the existing real-world dehazing dataset [99]. For deraining, we construct the SPA+ dataset based on the previous SPA [73] dataset. SPA is the first large-scale real-world dataset with paired images. However, SPA still contains two evident issues: (i) many images with sparse rain steaks and repeated background scenes; and (ii) existing images with the same scenes in both training and testing sets. To solve these issues, we first merge the images with repeated background scenes and densify the rain streaks. Then, we remove the images with the same scenes from the training set.", + "bbox": [ + 496, + 659, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "21751", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/96693cfa8d5acb9a6a56a76c274f6db7fdd97297c36d01a7ce49c89c0edd40d0.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [], + "bbox": [ + 101, + 89, + 282, + 119 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/8de56c55143211c1e010ce30ab4353ec42c8f6b361462c35f031011843e428d1.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 282, + 89, + 468, + 119 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/6f0314376df57c1a43ad5825bbf01cc16ea332c5b2c86a7694cc8d16d8406d1b.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [], + "bbox": [ + 99, + 121, + 282, + 151 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/ceaffa7cc743cce8f40678511fcb3435408be8b710e176621a1d4e7097a7f636.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 284, + 121, + 468, + 151 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/38b5e7909aeef03ae964aefaf497d10f021e75d2f94ed236c780285f6453ebdb.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 99, + 151, + 189, + 191 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/a21d97fac08dd8b0b45d581ac6f65d8bb32e0c1c7c8bc5669f4d4768cc6936b6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 191, + 151, + 305, + 191 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/c5d9c60a7b743815bfbd54c3cbbd496eea8dcef52f730a72b38ef71b4b265825.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 310, + 151, + 375, + 191 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/d97e6a75f17cff70af178966d8ba54d280b5d2886c0a3ddf1698f23fd9466045.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 375, + 151, + 467, + 191 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/38fd98d55bd545c1e881bb13a9b3c5300095950c0a1b55bc092e73971f4e5943.jpg", + "image_caption": [ + "(c)" + ], + "image_footnote": [], + "bbox": [ + 101, + 193, + 171, + 250 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/18425b205c95ee6216f8adf7c14f462d0bd4e2f7dabb93147d73504b83d783d5.jpg", + "image_caption": [ + "SPA SPA+" + ], + "image_footnote": [], + "bbox": [ + 173, + 193, + 248, + 250 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/14057568c4d09d156301a9934d668404568ebc9db4311b4c966b6fd7c0e70637.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 250, + 193, + 316, + 223 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/7a81094653b22c63b4d0a90f7f84cbfcab9e0a248b834455ff5412363ade566c.jpg", + "image_caption": [ + "Figure 5. (a) and (b) illustrate the visual samples of the real-world dehazing dataset [99] and our proposed RealSnow, respectively. (c) exhibits the same scenes with sparse rains occur multiple times in SPA and corresponding merged images in our proposed SPA+." + ], + "image_footnote": [], + "bbox": [ + 320, + 193, + 395, + 250 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/aca3c4449af284ccea2c18b2c890ee0131e44403e5a95bd9fe14a6a2cca6374d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 395, + 193, + 467, + 250 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/636552707e180abf2b51c57932ac50f0e9f31944fe92210943044b123ce19cb7.jpg", + "table_caption": [ + "Table 1. Illustration of three dataset settings for the weather removal task (R: Rain; RD: RainDrop; S: Snow; H: Haze)." + ], + "table_footnote": [], + "table_body": "
SettingWeather TypesDatasetsTraining Configurations
[Setting 1] (Synthetic)(R,RD,H)Outdoor-Rain [40] RainDrop [59] Snow100K [51]Uniformly sampling 9000 images pairs
[Setting 2] (Synthetic)(R,S,H)Rain1400 [17] RESIDE [39] Snow100K [51]Uniformly sampling 5000 images pairs
[Setting 3] (Real)(R,S,H)SPA+ RealSnow REVIDE [99]Uniformly sampling 160000 images patches
", + "bbox": [ + 81, + 356, + 468, + 474 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In addition, inspired by [73], we build the first real-world desnowing dataset by using the background-static videos to acquire real-world snowing image pairs. The proposed real-world snow dataset, named RealSnow, has 1890 image pairs in total, where 1650 image pairs are used for training, and 240 image pairs are for evaluation. These images are acquired from 126 background-static videos with a wide variety of urban or natural background scenarios, e.g., buildings, cars, statues, trees, and roads. These scenes include varying degrees of snowfall densities and illuminations (e.g., day and night). RealSnow also contains various resolutions, and the average resolution is $1208 \\times 646$ . The example images from our dataset are shown in Fig. 5.", + "bbox": [ + 75, + 493, + 468, + 689 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "6. Experiments", + "text_level": 1, + "bbox": [ + 76, + 708, + 207, + 724 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "6.1. Implementation Details", + "text_level": 1, + "bbox": [ + 76, + 732, + 292, + 748 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We implement our method on Pytorch platform. Adam optimizer [35] is adopted. At the first stage, our model is trained for 100 epochs, and the initial learning rate is $2 \\times 10^{-4}$ , which is adjusted with the cosine annealing scheme [52]. In the second stage, our model is trained for another 100 epochs, and the initial learning rate is $1 \\times 10^{-4}$ , which is also adjusted with the cosine annealing scheme. The patch size is set as $224 \\times 224$ . Additionally, hyperparameters are empirically set as: $\\tau = 0.1$ , $\\lambda_{depth} = 0.02$ , and $\\lambda_{reg} = 0.08$ .", + "bbox": [ + 75, + 752, + 468, + 902 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/780c78105bc50c0c65ad193536f19c7e0a8fb9d2a421fe9796187aa2666198f2.jpg", + "table_caption": [ + "Table 2. [Setting 1] Comparisons on the Outdoor-Rain [40]." + ], + "table_footnote": [], + "table_body": "
TypeMethodVenuePSNR ↑SSIM ↑
Derainingpix2pix [30]CVPR'1719.090.71
HRGAN [40]CVPR'1921.560.86
MPRNet [92]CVPR'2121.900.85
Multi-TasksAll-in-One [41]CVPR'2024.710.90
TransWeather [69]CVPR'2223.180.84
Chen et al. [6]CVPR'2223.940.85
Ours-25.310.90
", + "bbox": [ + 504, + 104, + 890, + 227 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/e0232451f5f328e115b194aa4bbc1f3b656661510a33f1f32056768b6f5d9068.jpg", + "table_caption": [ + "Table 3. [Setting 1] Comparisons on the RainDrop dataset [59]." + ], + "table_footnote": [], + "table_body": "
TypeMethodVenuePSNR ↑SSIM ↑
RainDrop RemovalPix2pix [30]CVPR'1728.020.85
Attn.GAN [59]CVPR'1830.550.90
Quan et al. [63]ICCV'1931.440.93
CCN [62]CVPR'2131.340.95
Multi-TasksAll-in-One [41]CVPR'2031.120.93
TransWeather [69]CVPR'2228.980.90
Chen et al. [6]CVPR'2230.750.91
Ours-31.310.93
", + "bbox": [ + 504, + 244, + 890, + 381 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/b59c440c57b51c8877dec79a648459127a4294685b9314b00f3ea46755ce549b.jpg", + "table_caption": [ + "Table 4. [Setting 1] & [Setting 2] Comparisons on the SnowTest100k-L (S-L) dataset [51]." + ], + "table_footnote": [], + "table_body": "
TypeMethodVenuePSNR ↑SSIM ↑
DesnowingDetailsNet [17]CVPR ' 1719.180.75
DesnowNet [51]TIP ' 1827.170.90
JSTASR [4]ECCV ' 2025.320.81
DDMSNET [97]TIP ' 2128.850.88
Multi-Tasks(Setting 1)All-in-One [41]CVPR ' 2028.330.88
TransWeather [69]CVPR ' 2227.800.85
Chen et al. [6]CVPR ' 2229.270.88
Ours-29.710.89
Multi-Tasks(Setting 2)TransWeather [69]CVPR ' 2226.170.88
Chen et al. [6]CVPR ' 2228.710.88
Ours-29.420.89
", + "bbox": [ + 504, + 419, + 890, + 595 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/02a8e07785030ed08fde50087710f8c0d8a7c8bb84d40616494aaecd4e3e99b0.jpg", + "table_caption": [ + "Table 5. [Setting 2] Comparisons on the RESIDE dataset [39]." + ], + "table_footnote": [], + "table_body": "
TypeMethodVenuePSNR ↑SSIM ↑
DehazingEPDN [61]CVPR' 1923.820.89
PFDN [14]ECCV' 2031.450.97
KDDN [24]CVPR' 2033.490.97
MSBDN [13]CVPR' 2033.790.98
FFA-Net [60]AAAI' 2034.980.99
AECRNet [77]CVPR' 2135.610.98
MPRNet [92]CVPR' 2131.310.97
Multi-TasksAll-in-One [41]CVPR' 2030.490.95
TransWeather [69]CVPR' 2227.660.95
Chen et al. [6]CVPR' 2230.760.97
Ours-30.850.98
", + "bbox": [ + 504, + 614, + 890, + 792 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "6.2. Benchmark Datasets", + "text_level": 1, + "bbox": [ + 500, + 806, + 694, + 821 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To verify the effectiveness of our method, we employ three dataset settings, which are presented in Tab. 1. Previous all-weather-removal methods [6,41,69] only perform their methods on the settings of the synthetic datasets, e.g., [Setting 1] and [Setting 2]. Moreover, we construct the first", + "bbox": [ + 496, + 824, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "21752", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/573adc5e733020f972e8b1029e915a5f1ce2f0f092da2a66b2a4a4c2917803d2.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 84, + 89, + 184, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a15fe6c69f02d8df4f8482cf2e4ad4cfcaf1488512ae4ba8ef204dabfc0698ee.jpg", + "image_caption": [ + "Input" + ], + "image_footnote": [], + "bbox": [ + 84, + 146, + 184, + 196 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/360bd1d6cde5cd86befc574405ea0d43dec668641259cc7a3e3a576381c9ee7c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 184, + 90, + 285, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/ac2a8c76793a66d23e3938ac7c61a9e6be8ff1dac2974b2aa5804390870f8995.jpg", + "image_caption": [ + "Chen et.al" + ], + "image_footnote": [], + "bbox": [ + 184, + 146, + 285, + 196 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/cd4c66fb3c86bd8ed2b9fe80de69a3c20c7ab077cda1840ac08897e67a518ab9.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 285, + 90, + 383, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2640aac55a31796a050f7c4c0f55a6732cd0b83acf5101cd7e8bba242391cdde.jpg", + "image_caption": [ + "TransWeather", + "Figure 6. Visualization comparisons with previous all-weather-removal methods under multiple real-world weather conditions." + ], + "image_footnote": [], + "bbox": [ + 285, + 146, + 383, + 196 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8f01c37b8927eb0f89de2044bd34c336d8fa8896ffad0863ba457d0346d79b85.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 385, + 90, + 485, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/1ebfa02a26970c172486e5d8430153ae5793ad70aff4b4202d2e92b466fa9383.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 385, + 146, + 485, + 196 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/81ff5b4986ceb2d1297c4c396253b777abd35eaaa032f846adc818c4e294858c.jpg", + "table_caption": [ + "Table 6. [Setting 2] Comparisons on the Rain1400 dataset [17]." + ], + "table_footnote": [], + "table_body": "
TypeMethodVenuePSNR ↑SSIM ↑
DerainingJORDER [82]CVPR' 1731.280.92
PReNet [64]CVPR' 1931.880.93
DRD-Net [11]CVPR' 2029.650.88
MSPFN [32]CVPR' 2029.240.88
DualGCN [18]AAAI' 2130.500.91
JRJG [88]CVPR' 2131.180.91
MPRNet [92]CVPR' 2131.530.96
Multi TaskAll-in-One [41]CVPR' 2030.820.90
TransWeather [69]CVPR' 2229.140.89
Chen et al. [6]CVPR' 2231.750.91
Ours-32.490.93
", + "bbox": [ + 81, + 252, + 468, + 429 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/0b10896438c6b0d098912840a7c32841153f7661690c95676783d2d722a0ecf6.jpg", + "table_caption": [ + "Table 7. Comparisons results on the datasets of [Setting 3]." + ], + "table_footnote": [], + "table_body": "
DatasetsMethodVenuePSNR ↑SSIM ↑
SPA+TransWeather [69]CVPR' 2233.640.93
Chen et al. [6]CVPR' 2237.320.97
Ours-38.940.98
RealSnowTransWeather [69]CVPR' 2229.160.82
Chen et al. [6]CVPR' 2229.370.88
Ours-29.460.85
REVIDE [99]TransWeather [69]CVPR' 2217.330.82
Chen et al. [6]CVPR' 2220.100.85
Ours-20.440.87
", + "bbox": [ + 81, + 446, + 468, + 583 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/83174b8feefdfff08135fb16aa4344fa7585ed10c8d773ecc6c22b3519708f6b.jpg", + "table_caption": [ + "Table 8. Ablation study of our training strategy using the PNSR metric on the Datasets of [Setting 1]." + ], + "table_footnote": [], + "table_body": "
ModelsExperiment settingsRainRainDropS-L
Model-1Ours-Stage 1 (4.98 M)25.0430.8829.13
Model-2Ours-Stage 2 (5.97 M)25.3131.3129.71
Model-3Random Expansion of specific #Param.25.1430.9629.28
Model-4Enlarging #Param. of Model-1 (6.79 M)25.2230.9929.42
", + "bbox": [ + 80, + 614, + 468, + 696 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/f5865611974d9467bf29e2f1d247cd54f3565cde1b4ffd7e03c4b8077a38c147.jpg", + "table_caption": [ + "Table 9. Ablation study of the parameters expansion types." + ], + "table_footnote": [], + "table_body": "
TypeSetting 1Setting 2
RainRainDropS-LRain1400RESIDES-L
1 × 130.3131.3129.7132.4930.8529.43
3 × 330.5231.5229.8632.7530.9029.69
", + "bbox": [ + 81, + 715, + 468, + 785 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "real-world benchmark dataset for image restoration under multiple weather conditions of real scenes ([Setting 3]).", + "bbox": [ + 75, + 794, + 468, + 824 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "For fair comparisons, we follow [6,41] to uniformly sample images/patches from the dataset for network training. Meanwhile, except for RealSnow in [Setting 3], we adopt the corresponding test datasets that have already been predivided by the pioneer works [17,39,40,51,59,73].", + "bbox": [ + 75, + 825, + 468, + 900 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e73e8e997b367b9ae393161fa2434e686f4102852c630cd67f6cc0cd638b6c44.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 486, + 90, + 586, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8854d9e2899b829c0d3b9de5ab0c16d88e0502f6c2b2ab21e67d7b45f8307ffc.jpg", + "image_caption": [ + "Input" + ], + "image_footnote": [], + "bbox": [ + 486, + 145, + 586, + 196 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8bc742818822e30f7810581ec6e3fbb1b0eff0901f8350a5943d53e02e9b9871.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 586, + 90, + 686, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/590576d86bdf4fd8135d01530d9983a866e220f7fe8868ac8688d03046d90432.jpg", + "image_caption": [ + "Chen et.al" + ], + "image_footnote": [], + "bbox": [ + 586, + 145, + 686, + 196 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/76cfa3d7d7f6cf1fac09eded16d8fd2eb1caaf7fda4cdf746252fbb5c07a1842.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 687, + 90, + 785, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/53d3a6b8b6a8447f8ec3f91238e750ddde07f5edd702a18f1446bb42ce9ab14b.jpg", + "image_caption": [ + "TransWeather" + ], + "image_footnote": [], + "bbox": [ + 687, + 145, + 785, + 196 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7356f0914a2b139bdeb929adf67d219a4f38ecd5cbd5d6db3c4bc3cf13a828ea.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 787, + 90, + 885, + 143 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/deff61cd9e1e45e802158afcd298e11a1d6468a050b53c720540dbf364552c67.jpg", + "image_caption": [ + "Ours" + ], + "image_footnote": [], + "bbox": [ + 787, + 145, + 885, + 196 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/eaa5af2c5504eb8543b1b043bfd7c7a2e29fb2a7e2e1784c7c666bbf40d933d7.jpg", + "table_caption": [ + "Table 10. Ablation study of losses on the Datasets of [Setting 1]." + ], + "table_footnote": [], + "table_body": "
lcontentldepthOutdoor-RainRainDropSnow100K-L
Stage 1✓24.9630.7929.00
✓✓25.0430.8829.13
", + "bbox": [ + 504, + 252, + 890, + 301 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/722948a63a52f42f33e4f4de922e6d1a89b2d97be9da798fc5f0cb29f49c0caa.jpg", + "table_caption": [ + "Table 11. Model efficiency comparisons." + ], + "table_footnote": [], + "table_body": "
MethodsAll-in-OneChen et al.TransWeatherOurs
#Param (M: 106)44.0028.7138.055.97
Inference Time (s)-0.0670.0260.030
", + "bbox": [ + 504, + 319, + 890, + 359 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7f4c68093d602cc22f4fe3c557f09ce21933de1dcae9d397879a721f884a7db9.jpg", + "image_caption": [ + "Figure 7. Ablation study of the regularization term $\\lambda_{reg}$ ." + ], + "image_footnote": [], + "bbox": [ + 506, + 369, + 696, + 448 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/27db97bed94ad2c9449a02e7b88922b18bd851e6a4075d07c2f88909d1e335c3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 700, + 371, + 890, + 448 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "6.3. Comparison with the State-of-the-art Methods", + "text_level": 1, + "bbox": [ + 500, + 479, + 890, + 494 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Similar to [41, 69], we adopt the Peak Signal-to-Noise Ratio (PSNR) and the structural similarity (SSIM) [76] to evaluate the restoration performance of different models. PSNR and SSIM are calculated along the RGB channels.", + "bbox": [ + 498, + 500, + 890, + 559 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In Tables 2, 3, 4, 5, 6, 7, we report the qualitative comparison results among different methods, including both all-weather-removal and typical-weather-removal methods. Obviously, our method achieves superior performance in terms of all metrics on all datasets compared with all-weather-removal methods. In particular, our method surpasses TransWeather [69] by nearly two dB on multiple datasets under different weather conditions.", + "bbox": [ + 496, + 560, + 890, + 679 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Moreover, we provide visual comparisons on the real-world scenarios of various kinds of weather in Fig. 6. It is evident that our results successfully preserve background details and remove multiple weather artifacts. In contrast, the apparent artifacts still exist in the results of [69].", + "bbox": [ + 496, + 681, + 890, + 755 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "6.4. Ablation Study", + "text_level": 1, + "bbox": [ + 500, + 763, + 651, + 777 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We further conduct ablation studies to validate the effects of each component in our method.", + "bbox": [ + 498, + 784, + 890, + 813 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablation study of the two-stage training strategy. We set up three models with the following configurations: (i) Model-1 adopts the first training stage, and the network only learns the weather-general parameters; (ii) Model-2", + "bbox": [ + 496, + 816, + 890, + 878 + ], + "page_idx": 6 + }, + { + "type": "page_footnote", + "text": "1The authors have updated their results in the new arxiv version.", + "bbox": [ + 517, + 887, + 857, + 898 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "21753", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c7aae7bbdb13a0ac0b13495164af28fde33179e93897abc8d7b192ba3ec5f651.jpg", + "image_caption": [ + "Figure 8. Visualization of the learned positions of expanded parameters. '1': expansion and '0': non-expansion." + ], + "image_footnote": [], + "bbox": [ + 81, + 87, + 467, + 227 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/3772e38d89810b04a8057eef41234c7653719f4f8895f3777e9d0ae81bf77aad.jpg", + "image_caption": [ + "(a) t-SNE from weather-general features" + ], + "image_footnote": [], + "bbox": [ + 83, + 270, + 274, + 378 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/513693ad9b4d2af2576ec7e86b5892419155243f4124753bf9d0641644357f4f.jpg", + "image_caption": [ + "(b) t-SNE from weather-specific features", + "Figure 9. Visualization of t-SNE from learned weather-general and weather-specific features with our proposed training strategy." + ], + "image_footnote": [], + "bbox": [ + 277, + 270, + 467, + 378 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "adopts two training stages, and we fix the weather-general parameters and learn the weather-specific parameters; (iii) Model-4 expands the weather-specific parameters with the random sampling positions strategy. (iv) Model-4 enlarges the parameters of Model-1, and the network only learns the weather-general parameters.", + "bbox": [ + 75, + 438, + 468, + 527 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "As reported in Tab. 8, the performance of Model-2 performs is significantly improved after the second training stage, showing the effectiveness of our training strategy. For fair comparisons, note that Model-3 enlarges the network parameters by directly increasing the channel dimensions. Compared to Model-3 and Model-4, Model-2 adopts the adaptive parameters expansion strategy with fewer parameters but delivers better restoration performance.", + "bbox": [ + 75, + 529, + 468, + 650 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation study of expansion ratios. As described in Sec. 4.1, we introduce a sparsity regularization in the objective function during the second training stage. It guides our network to discover effective positions where to expand the weather-specific parameters. Consequently, the expansion ratio is defined as the ratio of the number of learned positions to the total number of positions. The expansion ratios mainly depend on the coefficient of the regularization term $\\lambda_{reg}$ . Consequently, we could adjust the $\\lambda_{reg}$ to achieve a trade-off between the model efficiency and the number of expansions. Using smaller $\\lambda_{reg}$ usually achieves better model performance while leading to larger expansion ratios. Meanwhile, larger expansion ratios often require more weather-specific parameters, bringing more computational overhead. In Fig.7, we present the model performance under different values of $\\lambda_{reg}$ . Considering the model effi", + "bbox": [ + 75, + 659, + 468, + 901 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "ciency, we set the default value of $\\lambda_{reg}$ as 0.08. Moreover, we visualize the learned positions in Fig.8. The middle layers have a stronger response to haze, since the middle features of the haze model are largely different from other weather types from Fig.3.", + "bbox": [ + 496, + 90, + 890, + 167 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation study of the parameters expansion types. We also study two weather-specific parameter types: convolution layers with the kernel size of $3 \\times 3$ and $1 \\times 1$ . As shown in Tab. 9, we report the performance of these two types under a similar expansion ratio condition. Obviously, given the similar expansion ratios of parameters, using $3 \\times 3$ convolutions slightly performs better than the $1 \\times 1$ convolutions but requires more network parameters. Hence, we adopt convolutions with kernel size of $1 \\times 1$ as the default throughout the experiments.", + "bbox": [ + 496, + 172, + 892, + 323 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation study of losses. We validate the effectiveness of loss functions in Tab. 10, indicating that the auxiliary depth information improves the restoration performance.", + "bbox": [ + 496, + 329, + 892, + 375 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Ablation study of feature representations. Fig. 9 visualizes the t-SNE results [70] of the weather-general and weather-specific features from the last layer of our network. It indicates that our strategy can learn uniformly weather-general and distinctively weather-specific features.", + "bbox": [ + 496, + 380, + 890, + 455 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Efficiency comparisons. In Tab. 11, we further conduct comparisons of the network parameters and the average inference times among all-weather-removal methods. Our method uses much fewer network parameters than others but achieves better performance, as shown in Sec. 6.3. Moreover, our method only takes 0.03 seconds to process an image with the resolution of $256 \\times 256$ on a single NVIDIA Geforce GTX 1080 Ti GPU.", + "bbox": [ + 496, + 460, + 892, + 580 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "7. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 589, + 617, + 604 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work formulates an efficiently unified model to remove weather-related artifacts under multiple adverse weather conditions. To achieve this, we design a two-stage training strategy, which first optimizes the network to learn weather-general features and then to learn the weather-specific features. More importantly, our method expands the network parameters to generate weather-specific features adaptively, thus reducing the computation overhead of our model. Moreover, we construct the first real-world dataset with multiple adverse weather conditions to promote the further research on the real-world scenarios. Various experiments demonstrate the superiority of our method over other state-of-the-art methods.", + "bbox": [ + 496, + 609, + 890, + 803 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgment. This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62276243, in part by the National Key R&D Program of China(NO.2022ZD0160100), and in part by Shanghai Committee of Science and Technology (Grant No. 21DZ1100100).", + "bbox": [ + 496, + 810, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "21754", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing, and Dacheng Tao. DehazeNet: An end-to-end system for single image haze removal. IEEE Trans. Image Process., 25(11):5187-5198, 2016. 1, 2, 3, 4", + "[2] Chenghao Chen and Hao Li. Robust representation learning with feedback for single image deraining. In IEEE Conf. Comput. Vis. Pattern Recog., pages 7742-7751, 2021. 2", + "[3] Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao. Pre-trained image processing transformer. In IEEE Conf. Comput. Vis. Pattern Recog., pages 12299-12310, 2021. 3", + "[4] Wei-Ting Chen, Hao-Yu Fang, Jian-Jiun Ding, Cheng-Che Tsai, and Sy-Yen Kuo. JSTASR: Joint size and transparency-aware snow removal algorithm based on modified partial convolution and veiling effect removal. In IEEE Eur. Conf. Comput. Vis., pages 754-770, 2020. 1, 2, 3, 4, 6", + "[5] Wei-Ting Chen, Hao-Yu Fang, Cheng-Lin Hsieh, Cheng-Che Tsai, I-Hsiang Chen, Jian-Jiun Ding, Sy-Yen Kuo, et al. All snow removed: Single image desnowing algorithm using hierarchical dual-tree complex wavelet representation and contradict channel loss. In IEEE Int. Conf. Comput. Vis., pages 4196-4205, 2021. 3", + "[6] Wei-Ting Chen, Zhi-Kai Huang, Cheng-Che Tsai, Hao-Hsiang Yang, Jian-Jiun Ding, and Sy-Yen Kuo. Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model. In IEEE Conf. Comput. Vis. Pattern Recog., pages 17653–17662, 2022. 1, 2, 3, 6, 7", + "[7] Xiang Chen, Jinshan Pan, Kui Jiang, Yufeng Li, Yufeng Huang, Caihua Kong, Longgang Dai, and Zhentao Fan. Unpaired deep image deraining using dual contrastive learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2017-2026, 2022. 2", + "[8] Zeyuan Chen, Yangchao Wang, Yang Yang, and Dong Liu. PSD: Principled synthetic-to-real dehazing guided by physical priors. In IEEE Conf. Comput. Vis. Pattern Recog., pages 7180-7189, 2021. 3", + "[9] Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh. Algorithms for learning kernels based on centered alignment. J. Mach. Learn. Res., 13:795-828, 2012. 4", + "[10] Qili Deng, Ziling Huang, Chung-Chi Tsai, and Chia-Wen Lin. Hardgan: A haze-aware representation distillation gan for single image dehazing. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part VI 16. Springer, 2020. 2", + "[11] Sen Deng, Mingqiang Wei, Jun Wang, Yidan Feng, Luming Liang, Haoran Xie, Fu Lee Wang, and Meng Wang. Detail-recovery image deraining via context aggregation networks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 14560-14569, 2020. 2, 7", + "[12] Zijun Deng, Lei Zhu, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Qing Zhang, Jing Qin, and Pheng-Ann Heng. Deep multi-model fusion for single-image dehazing. In IEEE Int. Conf. Comput. Vis., pages 2453–2462, 2019. 2" + ], + "bbox": [ + 86, + 114, + 470, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, and Ming-Hsuan Yang. Multi-scale boosted de-hazing network with dense feature fusion. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2157-2167, 2020. 3, 6", + "[14] Jiangxin Dong and Jinshan Pan. Physics-based feature dehazing networks. In IEEE Eur. Conf. Comput. Vis., pages 188-204. Springer, 2020. 6", + "[15] David Eigen, Dilip Krishnan, and Rob Fergus. Restoring an image taken through a window covered with dirt or rain. In IEEE Int. Conf. Comput. Vis., pages 633-640, 2013. 2", + "[16] Xueyang Fu, Jiabin Huang, Xinghao Ding, Yinghao Liao, and John Paisley. Clearing the skies: A deep network architecture for single-image rain removal. IEEE Trans. Image Process., 26(6):2944-2956, 2017. 1, 2", + "[17] Xueyang Fu, Jiabin Huang, Delu Zeng, Yue Huang, Xing-hao Ding, and John Paisley. Removing rain from single images via a deep detail network. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3855-3863, 2017. 1, 2, 6, 7", + "[18] Xueyang Fu, Qi Qi, Zheng-Jun Zha, Yurui Zhu, and Xing-hao Ding. Rain streak removal via dual graph convolutional network. In Proc. AAAI Conf. Artif. Intell., volume 35, pages 1352-1360, 2021. 7", + "[19] Xueyang Fu, Jie Xiao, Yurui Zhu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Continual image deraining with hypergraph convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 2", + "[20] Chun-Le Guo, Qixin Yan, Saeed Anwar, Runmin Cong, Wenqi Ren, and Chongyi Li. Image dehazing transformer with transmission-aware 3D position embedding. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5812-5820, 2022. 3", + "[21] Xin Guo, Xueyang Fu, Man Zhou, Zhen Huang, Jialun Peng, and Zheng-Jun Zha. Exploring fourier prior for single image rain removal. In Proceedings of the 30th International Joint Conferences on Artificial Intelligence, pages 935–941, 2022. 2", + "[22] Zhixiang Hao, Shaodi You, Yu Li, Kunming Li, and Feng Lu. Learning from synthetic photorealistic raindrop for single image raindrop removal. In IEEE Int. Conf. Comput. Vis., Oct 2019, 1, 2", + "[23] Kaiming He, Jian Sun, and Xiaou Tang. Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell., 33(12):2341-2353, 2010. 1, 2, 3", + "[24] Ming Hong, Yuan Xie, Cuihua Li, and Yanyun Qu. Distilling image dehazing with heterogeneous task imitation. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3462-3471, 2020. 6", + "[25] Xiaowei Hu, Chi-Wing Fu, Lei Zhu, and Pheng-Ann Heng. Depth-attentional features for single-image rain removal. In IEEE Conf. Comput. Vis. Pattern Recog., pages 8022–8031, 2019. 1, 2", + "[26] Xiaowei Hu, Lei Zhu, Tianyu Wang, Chi-Wing Fu, and Pheng-Ann Heng. Single-image real-time rain removal based on depth-guided non-local features. IEEE Trans. Image Process., 30:1759-1770, 2021. 2", + "[27] Huaibo Huang, Aijing Yu, and Ran He. Memory oriented transfer learning for semi-supervised image deraining. In" + ], + "bbox": [ + 509, + 92, + 890, + 901 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "21755", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "IEEE Conf. Comput. Vis. Pattern Recog., pages 7732-7741, 2021. 2", + "[28] Jie Huang, Yajing Liu, Feng Zhao, Keyu Yan, Jinghao Zhang, Yukun Huang, Man Zhou, and Zhiwei Xiong. Deep fourier-based exposure correction network with spatial-frequency interaction. In ECCV. Springer, 2022. 2", + "[29] Jie Huang, Man Zhou, Yajing Liu, Mingde Yao, Feng Zhao, and Zhiwei Xiong. Exposure-consistency representation learning for exposure correction. In Proceedings of the 30th ACM International Conference on Multimedia, page 6309-6317, 2022. 2", + "[30] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation with conditional adversarial networks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1125-1134, 2017. 6", + "[31] Da-Wei Jaw, Shih-Chia Huang, and Sy-Yen Kuo. Desnow-Gan: An efficient single image snow removal framework using cross-resolution lateral connection and GANs. IEEE Trans. Circuits Syst. Video Technol., 31(4):1342-1350, 2020. 3", + "[32] Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, and Junjun Jiang. Multi-scale progressive fusion network for single image deraining. In IEEE Conf. Comput. Vis. Pattern Recog., pages 8346-8355, 2020. 2, 7", + "[33] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In IEEE Eur. Conf. Comput. Vis., pages 694–711. Springer, 2016. 5", + "[34] Jin-Hwan Kim, Jae-Young Sim, and Chang-Su Kim. Single image dehazing based on contrast enhancement. In 2011 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pages 1273–1276. IEEE, 2011. 2", + "[35] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2015. 6", + "[36] Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton. Similarity of neural network representations revisited. In Proc. Int. Conf. Mach. Learn., pages 3519-3529. PMLR, 2019. 4", + "[37] Simon Kornblith, Jonathon Shlens, and Quoc V Le. Do better ImageNet models transfer better? In IEEE Conf. Comput. Vis. Pattern Recog., pages 2661-2671, 2019. 4", + "[38] Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, and Dan Feng. Aod-net: All-in-one dehazing network. In IEEE Int. Conf. Comput. Vis., pages 4770-4778, 2017. 3", + "[39] Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process., 28(1):492-505, 2019. 4, 6, 7", + "[40] Ruoteng Li, Loong-Fah Cheong, and Robby T. Tan. Heavy rain image restoration: Integrating physics model and conditional adversarial learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1633-1642, 2019. 1, 2, 6, 7", + "[41] Ruoteng Li, Robby T. Tan, and Loong-Fah Cheong. All in one bad weather removal using architectural search. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3175-3185, 2020. 1, 2, 3, 6, 7" + ], + "bbox": [ + 86, + 90, + 470, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[42] Wenbo Li, Xin Lu, Jiangbo Lu, Xiangyu Zhang, and Jiaya Jia. On efficient transformer and image pre-training for low-level vision. arXiv preprint arXiv:2112.10175, 2021.3, 4", + "[43] Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, and Hongbin Zha. Recurrent squeeze-and-excitation context aggregation net for single image deraining. In IEEE Eur. Conf. Comput. Vis., pages 254-269, 2018. 2", + "[44] Yi Li, Yi Chang, Yan Gao, Changfeng Yu, and Luxin Yan. Physically disentangled intra-and inter-domain adaptation for varicolored haze removal. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5841-5850, 2022. 3", + "[45] Yunan Li, Qiguang Miao, Wanli Ouyang, Zhenxin Ma, Huijuan Fang, Chao Dong, and Yining Quan. Lap-net: Level-aware progressive network for image dehazing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. 2", + "[46] Huan Liu, Zijun Wu, Liangyan Li, Sadaf Salehkalaibar, Jun Chen, and Keyan Wang. Towards multi-domain single image dehazing via test-time training. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5831-5840, 2022. 3", + "[47] Lina Liu, Xibin Song, Mengmeng Wang, Yong Liu, and Liangjun Zhang. Self-supervised monocular depth estimation for all day images using domain separation. In IEEE Int. Conf. Comput. Vis., pages 12737-12746, 2021. 5", + "[48] Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101, 2016. 3", + "[49] Xiaohong Liu, Yongrui Ma, Zhihao Shi, and Jun Chen. GridDehazeNet: Attention-based multi-scale network for image dehazing. In IEEE Int. Conf. Comput. Vis., pages 7314-7323, 2019. 1, 2, 3", + "[50] Yang Liu, Jinshan Pan, Jimmy Ren, and Zhixun Su. Learning deep priors for image dehazing. In Proceedings of the IEEE/CVF international conference on computer vision, 2019. 2", + "[51] Yun-Fu Liu, Da-Wei Jaw, Shih-Chia Huang, and Jenq-Neng Hwang. DesnowNet: Context-aware deep network for snow removal. IEEE Trans. Image Process., 27(6):3064–3073, 2018. 1, 2, 3, 6, 7", + "[52] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. In International Conference on Learning Representations, 2017. 6", + "[53] Jiayuan Mao, Tete Xiao, Yuning Jiang, and Zhimin Cao. What can help pedestrian detection? In IEEE Conf. Comput. Vis. Pattern Recog., 2017. 1", + "[54] Srinivasa G. Narasimhan. Models and algorithms for vision through the atmosphere. Columbia University, 2004. 3", + "[55] Srinivasa G. Narasimhan and Shree K. Nayar. Vision and the atmosphere. Int. J. Comput. Vis., 48(3):233-254, 2002. 2, 3", + "[56] Shree K Nayar and Srinivasa G. Narasimhan. Vision in bad weather. In IEEE Int. Conf. Comput. Vis., volume 2, pages 820-827. IEEE, 1999. 3", + "[57] Shree K. Nayar and Srinivasa G. Narasimhan. Seeing through bad weather. In Int. Symp. Robot. Res., pages 335-350. Springer, 2005. 2, 3" + ], + "bbox": [ + 509, + 90, + 890, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "21756", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[58] Thao Nguyen, Maithra Raghu, and Simon Kornblith. Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth. arXiv preprint arXiv:2010.15327, 2020. 4", + "[59] Rui Qian, Robby T. Tan, Wenhan Yang, Jiajun Su, and Jiaying Liu. Attentive generative adversarial network for raindrop removal from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2482-2491, 2018. 1, 2, 6, 7", + "[60] Xu Qin, Zhilin Wang, Yuanchao Bai, Xiaodong Xie, and Huizhu Jia. FFA-Net: Feature fusion attention network for single image dehazing. In Proc. AAAI Conf. Artif. Intell., volume 34, pages 11908–11915, 2020. 6", + "[61] Yanyun Qu, Yizi Chen, Jingying Huang, and Yuan Xie. Enhanced pix2pix dehazing network. In IEEE Conf. Comput. Vis. Pattern Recog., pages 8160-8168, 2019. 3, 6", + "[62] Ruijie Quan, Xin Yu, Yuanzhi Liang, and Yi Yang. Removing raindrops and rain streaks in one go. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9147-9156, 2021. 2, 6", + "[63] Yuhui Quan, Shijie Deng, Yixin Chen, and Hui Ji. Deep learning for seeing through window with raindrops. In IEEE Int. Conf. Comput. Vis., pages 2463-2471, 2019. 2, 6", + "[64] Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng. Progressive image deraining networks: A better and simpler baseline. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3937-3946, 2019. 7", + "[65] Wenqi Ren, Si Liu, Hua Zhang, Jinshan Pan, Xiaochun Cao, and Ming-Hsuan Yang. Single image dehazing via multi-scale convolutional neural networks. In IEEE Eur. Conf. Comput. Vis., pages 154-169. Springer, 2016. 1, 2, 3", + "[66] Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. Gated fusion network for single image dehazing. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3253-3261, 2018. 3", + "[67] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks for biomedical image segmentation. In Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervention, pages 234-241. Springer, 2015. 4", + "[68] Robby T. Tan. Visibility in bad weather from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1-8. IEEE, 2008. 4", + "[69] Jeya Maria Jose Valanarasu, Rajeev Yasarla, and Vishal M. Patel. TransWeather: Transformer-based restoration of images degraded by adverse weather conditions. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2353-2363, 2022. 1, 2, 3, 6, 7", + "[70] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. J. Mach. Learn. Res., 2008. 8", + "[71] Matthew Wallingford, Hao Li, Alessandro Achille, Avinash Ravichandran, Charless Fowlkes, Rahul Bhotika, and Stefano Soatto. Task adaptive parameter sharing for multi-task learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 7561-7570, 2022. 3", + "[72] Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng. A model-driven deep neural network for single image rain removal. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3103-3112, 2020. 1, 2" + ], + "bbox": [ + 86, + 90, + 470, + 898 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[73] Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson W.H. Lau. Spatial attentive single-image deraining with a high quality real rain dataset. In IEEE Conf. Comput. Vis. Pattern Recog., pages 12270-12279, 2019. 1, 2, 5, 6, 7", + "[74] Yinglong Wang, Chao Ma, and Bing Zeng. Multi-decoding deraining network and quasi-sparsity based training. In IEEE Conf. Comput. Vis. Pattern Recog., pages 13375-13384, 2021. 2", + "[75] Yinglong Wang, Yibing Song, Chao Ma, and Bing Zeng. Rethinking image deraining via rain streaks and vapors. In IEEE Eur. Conf. Comput. Vis., pages 367-382. Springer, 2020. 4", + "[76] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600-612, 2004. 7", + "[77] Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, and Lizhuang Ma. Contrastive learning for compact single image dehazing. In IEEE Conf. Comput. Vis. Pattern Recog., pages 10551-10560, 2021. 3, 6", + "[78] Jie Xiao, Xueyang Fu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Image de-raining transformer. IEEE Trans. Pattern Anal. Mach. Intell., pages 1-18, 2022. 2", + "[79] Jie Xiao, Man Zhou, Xueyang Fu, Aiping Liu, and Zheng-Jun Zha. Improving de-raining generalization via neural re-organization. In IEEE Int. Conf. Comput. Vis., pages 4987–4996, 2021. 2", + "[80] Dong Yang and Jian Sun. Proximal Dehaze-Net: A prior learning-based deep network for single image dehazing. In IEEE Eur. Conf. Comput. Vis., pages 702-717, 2018. 1, 2, 3", + "[81] Wenhan Yang, Robby T. Tan, Jiashi Feng, Zongming Guo, Shuicheng Yan, and Jiaying Liu. Joint rain detection and removal from a single image with contextualized deep networks. IEEE Trans. Pattern Anal. Mach. Intell., 42(6):1377-1393, 2019. 2, 4", + "[82] Wenhan Yang, Robby T. Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan. Deep joint rain detection and removal from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1357-1366, 2017. 1, 2, 7", + "[83] Wenhan Yang, Robby T. Tan, Shiqi Wang, Yuming Fang, and Jiaying Liu. Single image deraining: From model-based to data-driven and beyond. IEEE Trans. Pattern Anal. Mach. Intell., 43(11):4059-4077, 2020. 1, 2", + "[84] Xitong Yang, Zheng Xu, and Jiebo Luo. Towards perceptual image dehazing by physics-based disentanglement and adversarial training. In Proc. AAAI Conf. Artif. Intell., volume 32, 2018. 1, 2, 3", + "[85] Yang Yang, Chaoyue Wang, Risheng Liu, Lin Zhang, Xiaojie Guo, and Dacheng Tao. Self-augmented unpaired image dehazing via density and depth decomposition. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2037-2046, 2022. 3", + "[86] Zizheng Yang, Mingde Yao, Jie Huang, Man Zhou, and Feng Zhao. Sir-former: Stereo image restoration using" + ], + "bbox": [ + 509, + 90, + 893, + 902 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "21757", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "transformer. In Proceedings of the 30th ACM International Conference on Multimedia, page 6377-6385, 2022. 2", + "[87] Rajeev Yasarla, Vishwanath A. Sindagi, and Vishal M. Patel. Syn2real transfer learning for image deraining using gaussian processes. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2726-2736, 2020. 2", + "[88] Yuntong Ye, Yi Chang, Hanyu Zhou, and Luxin Yan. Closing the loop: Joint rain generation and removal via disentangled image translation. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2053-2062, 2021. 7", + "[89] Qiaosi Yi, Juncheng Li, Qinyan Dai, Faming Fang, Guixu Zhang, and Tieyong Zeng. Structure-preserving deraining with residue channel prior guidance. In IEEE Int. Conf. Comput. Vis., pages 4238-4247, 2021. 2", + "[90] Hu Yu, Jie Huang, Yajing Liu, Qi Zhu, Man Zhou, and Feng Zhao. Source-free domain adaptation for real-world image dehazing. In Proceedings of the 30th ACM International Conference on Multimedia, page 6645-6654, 2022. 2", + "[91] Ye Yuntong, Yu Changfeng, Chang Yi, Zhu Lin, Zhao Xile, Yan Luxin, and Tian Yonghong. Unsupervised deraining: Where contrastive learning meets self-similarity. arXiv preprint arXiv:2203.11509, 2022. 2", + "[92] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In IEEE Conf. Comput. Vis. Pattern Recog., pages 14821-14831, 2021. 6, 7", + "[93] He Zhang and Vishal M. Patel. Densely connected pyramid dehazing network. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3194-3203, 2018. 3", + "[94] He Zhang, Vishwanath Sindagi, and Vishal M. Patel. Image de-raining using a conditional generative adversarial network. IEEE Trans. Circuits Syst. Video Technol., 30(11):3943-3956, 2019. 2", + "[95] Jinghao Zhang, Jie Huang, Mingde Yao, Man Zhou, and Feng Zhao. Structure- and texture-aware learning for low-light image enhancement. In Proceedings of the 30th ACM International Conference on Multimedia, page 6483–6492, 2022. 2", + "[96] Kaihao Zhang, Dongxu Li, Wenhan Luo, and Wenqi Ren. Dual attention-in-attention model for joint rain streak and raindrop removal. IEEE Trans. Image Process., 30:7608-7619, 2021. 1, 2", + "[97] Kaihao Zhang, Rongqing Li, Yanjiang Yu, Wenhan Luo, and Changsheng Li. Deep dense multi-scale network for snow removal using semantic and depth priors. IEEE Trans. Image Process., 30:7419-7431, 2021. 1, 2, 3, 6", + "[98] Kaihao Zhang, Wenhan Luo, Wenqi Ren, Jingwen Wang, Fang Zhao, Lin Ma, and Hongdong Li. Beyond monocular deraining: Stereo image deraining via semantic understanding. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXVII 16. Springer, 2020. 2", + "[99] Xinyi Zhang, Hang Dong, Jinshan Pan, Chao Zhu, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, and Fei Wang. Learning to restore hazy video: A new real-world dataset and a new method. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9239-9248, June 2021. 5, 6, 7" + ], + "bbox": [ + 86, + 90, + 470, + 900 + ], + "page_idx": 11 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[100] Yu Zhang and Qiang Yang. An overview of multi-task learning. Natl. Sci, 5(1):30-43, 2018. 3", + "[101] Zheyu Zhang, Yurui Zhu, Xueyang Fu, Zhiwei Xiong, Zheng-Jun Zha, and Feng Wu. Multifocal attention-based cross-scale network for image de-raining. In Proceedings of the 29th ACM International Conference on Multimedia, pages 3673-3681, 2021. 2", + "[102] Zhuoran Zheng, Wenqi Ren, Xiaochun Cao, Xiaobin Hu, Tao Wang, Fenglong Song, and Xiuyi Jia. Ultra-high-definition image dehazing via multi-guided bilateral learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 16180-16189. IEEE, 2021. 3", + "[103] Man Zhou, Jie Huang, Keyu Yan, Hu Yu, Xueyang Fu, Aiping Liu, Xian Wei, and Feng Zhao. Spatial-frequency domain information integration for pan-sharpening. In ECCV. Springer, 2022. 2", + "[104] Man Zhou, Jie Xiao, Yifan Chang, Xueyang Fu, Aiping Liu, Jinshan Pan, and Zheng-Jun Zha. Image de-raining via continual learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4907-4916, 2021. 2", + "[105] Kai Zhu, Wei Zhai, Yang Cao, Jiebo Luo, and Zheng-Jun Zha. Self-sustaining representation expansion for nonexemplar class-incremental learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9296-9305, 2022. 3", + "[106] Lei Zhu, Zijun Deng, Xiaowei Hu, Haoran Xie, Xuemiao Xu, Jing Qin, and Pheng-Ann Heng. Learning gated non-local residual for single-image rain streak removal. IEEE Trans. Circuits Syst. Video Technol., 31(6):2147-2159, 2020. 2", + "[107] Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, and Shimin Hu. Traffic-sign detection and classification in the wild. In IEEE Conf. Comput. Vis. Pattern Recog., 2016. 1" + ], + "bbox": [ + 501, + 92, + 890, + 556 + ], + "page_idx": 11 + }, + { + "type": "page_number", + "text": "21758", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 11 + } +] \ No newline at end of file diff --git a/2023/Learning Weather-General and Weather-Specific Features for Image Restoration Under Multiple Adverse Weather Conditions/af74b836-ff73-4675-8c73-1cc8c534f9c3_model.json b/2023/Learning Weather-General and Weather-Specific Features for Image Restoration Under Multiple Adverse Weather Conditions/af74b836-ff73-4675-8c73-1cc8c534f9c3_model.json new file mode 100644 index 0000000000000000000000000000000000000000..6b711d970c2643b64e330c3c2020c2a59e60c650 --- /dev/null +++ b/2023/Learning Weather-General and Weather-Specific Features for Image Restoration Under Multiple Adverse Weather Conditions/af74b836-ff73-4675-8c73-1cc8c534f9c3_model.json @@ -0,0 +1,3733 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.02 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation." + }, + { + "type": "header", + "bbox": [ + 0.325, + 0.017, + 0.725, + 0.032 + ], + "angle": 0, + "content": "Except for this watermark, it is identical to the accepted version;" + }, + { + "type": "header", + "bbox": [ + 0.294, + 0.032, + 0.756, + 0.047 + ], + "angle": 0, + "content": "the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.162, + 0.131, + 0.812, + 0.177 + ], + "angle": 0, + "content": "Learning Weather-General and Weather-Specific Features for Image Restoration Under Multiple Adverse Weather Conditions" + }, + { + "type": "text", + "bbox": [ + 0.206, + 0.209, + 0.764, + 0.249 + ], + "angle": 0, + "content": "Yurui Zhu \\(^{1,3,\\star,\\dagger}\\), Tianyu Wang \\(^{2,\\dagger}\\), Xueyang Fu \\(^{1,\\boxtimes}\\), Xuanyu Yang \\(^{3,\\star}\\), Xin Guo \\(^{1}\\), Jifeng Dai \\(^{4,3}\\), Yu Qiao \\(^{3}\\), Xiaowei Hu \\(^{3,\\boxtimes}\\)" + }, + { + "type": "text", + "bbox": [ + 0.122, + 0.249, + 0.848, + 0.286 + ], + "angle": 0, + "content": "1 University of Science and Technology of China 2 The Chinese University of Hong Kong \n3 Shanghai Artificial Intelligence Laboratory 4 Tsinghua University" + }, + { + "type": "text", + "bbox": [ + 0.203, + 0.288, + 0.754, + 0.303 + ], + "angle": 0, + "content": "zyr@mail.ustc.edu.cn, xyfu@ustc.edu.cn, huxiaowei@pjlab.org.cn" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.337, + 0.314, + 0.354 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.369, + 0.474, + 0.718 + ], + "angle": 0, + "content": "Image restoration under multiple adverse weather conditions aims to remove weather-related artifacts by using a single set of network parameters. In this paper, we find that image degradations under different weather conditions contain general characteristics as well as their specific characteristics. Inspired by this observation, we design an efficient unified framework with a two-stage training strategy to explore the weather-general and weather-specific features. The first training stage aims to learn the weather-general features by taking the images under various weather conditions as inputs and outputting the coarsely restored results. The second training stage aims to learn to adaptively expand the specific parameters for each weather type in the deep model, where the requisite positions for expanding weather-specific parameters are automatically learned. Hence, we can obtain an efficient and unified model for image restoration under multiple adverse weather conditions. Moreover, we build the first real-world benchmark dataset with multiple weather conditions to better deal with real-world weather scenarios. Experimental results show that our method achieves superior performance on all the synthetic and real-world benchmarks. Codes and datasets are available at this repository." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.743, + 0.21, + 0.759 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.769, + 0.47, + 0.845 + ], + "angle": 0, + "content": "Adverse weather conditions, such as rain, haze, and snow, are common climatic phenomena in our daily life. They often lead to the poor visual quality of captured images and primarily deteriorate the performance of many outdoor vision systems, such as outdoor security cameras" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.336, + 0.695, + 0.497 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.703, + 0.337, + 0.891, + 0.497 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.5, + 0.695, + 0.662 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.703, + 0.5, + 0.891, + 0.662 + ], + "angle": 0, + "content": null + }, + { + "type": "image_footnote", + "bbox": [ + 0.511, + 0.667, + 0.627, + 0.682 + ], + "angle": 0, + "content": ":Convolution Layer" + }, + { + "type": "image_footnote", + "bbox": [ + 0.512, + 0.687, + 0.688, + 0.704 + ], + "angle": 0, + "content": ": Specific Parameters for Deraining" + }, + { + "type": "image_footnote", + "bbox": [ + 0.512, + 0.709, + 0.688, + 0.726 + ], + "angle": 0, + "content": "Specific Parameters for Dehazing" + }, + { + "type": "list", + "bbox": [ + 0.511, + 0.667, + 0.688, + 0.726 + ], + "angle": 0, + "content": null + }, + { + "type": "image_footnote", + "bbox": [ + 0.708, + 0.666, + 0.89, + 0.684 + ], + "angle": 0, + "content": "Specific Parameters for Desnowing" + }, + { + "type": "image_footnote", + "bbox": [ + 0.708, + 0.688, + 0.89, + 0.704 + ], + "angle": 0, + "content": ":General Parameters for All Weather" + }, + { + "type": "image_footnote", + "bbox": [ + 0.708, + 0.709, + 0.861, + 0.726 + ], + "angle": 0, + "content": "NULL : No Parameters at This Layer" + }, + { + "type": "list", + "bbox": [ + 0.708, + 0.666, + 0.89, + 0.726 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.733, + 0.894, + 0.803 + ], + "angle": 0, + "content": "Figure 1. Illustration of the proposed method and the currently existing solutions. (a) The weather-specific methods; (b) the method of [41]; (c) methods of [6, 69]; (d) our method, which learns the weather-specific and weather-general features in an efficient manner to remove multiple weather-related artifacts." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.825, + 0.894, + 0.903 + ], + "angle": 0, + "content": "and automatic driving [53, 107]. To make these systems more robust to various adverse weather conditions, many restoration solutions have been proposed, such as deraining [16, 17, 25, 40, 72, 73, 82, 83], dehazing [1, 23, 49, 65, 80, 84], desnowing [4, 51, 97], and raindrop removal [22, 59, 96]." + }, + { + "type": "page_footnote", + "bbox": [ + 0.076, + 0.852, + 0.47, + 0.876 + ], + "angle": 0, + "content": "\\(\\star\\) : This work was done during their internship at Shanghai Artificial Intelligence Laboratory." + }, + { + "type": "page_footnote", + "bbox": [ + 0.078, + 0.877, + 0.288, + 0.889 + ], + "angle": 0, + "content": "†: Co-first authors contributed equally." + }, + { + "type": "page_footnote", + "bbox": [ + 0.078, + 0.889, + 0.228, + 0.9 + ], + "angle": 0, + "content": "\\(\\boxtimes\\) : Corresponding authors." + }, + { + "type": "list", + "bbox": [ + 0.076, + 0.852, + 0.47, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "21747" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.258 + ], + "angle": 0, + "content": "Albeit these approaches exhibit promising performance in the given weather situation, they are only applicable to certain typical weather scenarios. However, it is inevitable to tackle various kinds of weather in the application of outdoor vision intelligent systems. Consequently, as shown in Fig. 1 (a), multiple sets of weather-specific model parameters are required to deal with various conditions, which brings additional computational and storage burdens. Hence, it is vitally requisite to develop a unified model capable of addressing various types of adverse weather conditions via a single set of network parameters." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.261, + 0.473, + 0.518 + ], + "angle": 0, + "content": "Recently, several methods [6, 41, 69] adopt a single set of network parameters to remove different weather-related artifacts. However, these solutions contain limitations for practical deployment and applications. Firstly, some methods [6, 69] fail to consider the specific characteristics of each weather condition in their proposed unified models, limiting their restoration performance on specific weather conditions. Secondly, as shown in Fig. 1 (b), although Li et al. [41] tackle the differences and similarities of weather degradation with multiple individual encoders and a shared decoder. Such multiple fixed encoders may largely increase network parameters. Thirdly, existing unified models [6, 41, 69] often require a large number of parameters, limiting the model efficiency. Lastly, current state-of-the-art methods [6, 41, 69] mainly employ synthesized datasets in their training phase, causing apparent performance drops in real-world scenarios." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.521, + 0.473, + 0.748 + ], + "angle": 0, + "content": "In this paper, we argue that images with different weather distortions contain general characteristics as well as their specific characteristics. According to the atmosphere scattering model [55, 57], due to the attenuation and scattering effects, these weather disturbances often share some similar visual degradation appearances, e.g., low contrast and color degradation. Meanwhile, the typical type of weather distortion has its unique characteristics. For example, rainy images often suffer from occlusion by rain steaks with different shapes and scales [32, 82]; haze exhibits global distortions on the entire images [34, 65]. Pioneer works also have devised many specific priors [23, 72, 80, 83, 97] for different weather conditions, which motivates us to explore weather-general and weather-specific features to perform image restoration under multiple weather conditions." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.473, + 0.903 + ], + "angle": 0, + "content": "To achieve this, we design an efficient unified framework for multiple adverse weather-related artifacts removal by exploring both weather-general and weather-specific features. The training procedure of our framework consists of two stages. The first training stage aims to learn the general features by taking various images under different weather conditions as the inputs and outputting coarse results for multiple weather conditions. In the second training stage, we devise a regularization-based optimization scheme, which learns to adaptively expand the specific pa" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.227 + ], + "angle": 0, + "content": "rameters for each weather type in the deep model. Note that these requisite positions to expand weather-specific parameters could be learned automatically, thus avoiding redundant parameters pre-designed by researchers. Hence, we are able to obtain an efficient and unified model for image restoration under multiple adverse weather conditions. Furthermore, we newly construct the first real-world benchmark dataset with multiple weather conditions to better deal with various weather-related artifacts in real-world scenarios." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.228, + 0.892, + 0.244 + ], + "angle": 0, + "content": "The contributions of this paper could be summarized as:" + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.256, + 0.892, + 0.348 + ], + "angle": 0, + "content": "- We reveal that image degradations under different weather conditions contain both general and specific characteristics, which motivates us to design a unified deep model by exploring the weather-general and weather-specific features for removing weather-related artifacts under multiple weather conditions." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.36, + 0.892, + 0.435 + ], + "angle": 0, + "content": "- We present a two-stage training strategy to learn the weather-general and weather-specific features automatically. Moreover, the weather-specific features are adaptively added at the learned positions, which makes our model efficient and effective." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.449, + 0.892, + 0.524 + ], + "angle": 0, + "content": "- In order to better deal with real-world weather conditions, we construct the first real-world benchmark dataset with multiple weather conditions. Additionally, experimental results validate the superiority of our proposed method on various benchmarks." + }, + { + "type": "list", + "bbox": [ + 0.519, + 0.256, + 0.892, + 0.524 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.547, + 0.642, + 0.563 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.573, + 0.892, + 0.664 + ], + "angle": 0, + "content": "Single image restoration under different weather conditions have been extensively studied in previous literatures, including deraining [16, 17, 19, 25, 26, 32, 40, 43, 72, 73, 78, 79, 82, 83, 86, 95, 104], dehazing [1, 10, 12, 23, 45, 49, 50, 65, 80, 84, 90, 98], desnowing [4, 51, 97], and raindrop removal [22, 59, 62, 96]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.671, + 0.892, + 0.852 + ], + "angle": 0, + "content": "Rain removal. The effects of rainy weather on images are usually divided into rain streaks and rain drops. For rain streak removal, [16] first applies the convolutional neural network on deraining, followed by GAN [94] and recurrent network [43]. Researchers look for better ways to understand and represent rain streaks [2, 7, 32, 40, 74, 81, 91, 101, 106], for more delicate detail recovery [11, 89], and for wider applicability under severe weather condition [40, 82]. Besides, many studies introduce the adversarial learning [40], transfer learning [27, 87], frequency priors [21, 28, 29, 103] and data generation to improve the models' performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.853, + 0.892, + 0.914 + ], + "angle": 0, + "content": "The degradation of raindrops is different from that of streaks. Thus, they are usually treated separately. There are approaches including CNN [15], attention map [59], and mathematical descriptions [22, 63] on raindrop removal." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "21748" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.258 + ], + "angle": 0, + "content": "Haze removal. [23] proposes a simple prior of hazy images. [1, 65, 93] applies various methods for dehazing, followed by GAN [61, 84] and fusion-based strategy [66], attention-based model with trainable pre-treatment part [49], dense feature fusion [13], and the combination of CNN and transformer [20]. [38, 80] consider various priors and modeling of raindrops. Proposed learning methods include contrastive regularization [77] and multi-guided bilateral learning [102]. [44] pays attention to colorful haze removal. There are also works pursuing stronger utilization and generalization on datasets [8, 46, 85]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.26, + 0.47, + 0.336 + ], + "angle": 0, + "content": "Snow removal. [51] designs a two-stage network to deal with different types of snow, while [4] considers the veiling effect. [31] proposes a pyramid-structure model with lateral connections. [5] uses dual-tree wavelet transform. [97] introduces semantics and depth prior to snowfall removal." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.338, + 0.473, + 0.641 + ], + "angle": 0, + "content": "Multiple weather-related artifacts removal. Different from the above typical single weather removal task, all-weather-removal is required to recover the clear images with a single set of network parameters under multiple kinds of weather conditions. Moreover, due to the differences among different tasks, all-weather-removal is obviously more challenging than the single image restoration task. Li et al. [41] explore the differences and similarities of each weather degradation by designing an all-in-one network with multiple encoders. [6] combines the two-stage knowledge and multi-contrastive learning strategy to handle the weather removal problem. [69] propose a transformer-based network with learnable specific weather queries to handle the weather removal in a unified model. The method [41] and our method both consider the differences and similarities among different weathers. However, unlike [41] using the specific weather parameters at the fixed encoder positions, our method could adaptively expand the specific parameters for a certain weather type at the learned network positions, which is more flexible and efficient." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.643, + 0.473, + 0.78 + ], + "angle": 0, + "content": "Multi-task learning (MTL). MTL mainly focuses on handling multiple tasks in a single network, which is extensively studied in the high-level [48, 71, 100, 105] and low-level vision tasks [3, 42]. Recently, methods [3, 42] attempt to exploit the transformers and pre-training scheme to handle multiple image restoration tasks, including deraining, denoising, and super-resolution. However, the correlation among these tasks is limited, while different weather degradations exist with apparent similarities to a certain extent." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.783, + 0.195, + 0.798 + ], + "angle": 0, + "content": "3. Motivation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.802, + 0.47, + 0.864 + ], + "angle": 0, + "content": "Weather-distorted images are often captured in outdoor environments. Here, we would like to further analyze the general and specific characteristics of different weather conditions from the following two perspectives." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.47, + 0.902 + ], + "angle": 0, + "content": "The degradation in bad weathers. Different weather effects, i.e., rain, haze, and snow, are mainly caused by the" + }, + { + "type": "image", + "bbox": [ + 0.507, + 0.091, + 0.714, + 0.169 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.605, + 0.171, + 0.622, + 0.182 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.717, + 0.091, + 0.803, + 0.17 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.803, + 0.091, + 0.889, + 0.17 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.794, + 0.171, + 0.812, + 0.182 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.193, + 0.894, + 0.233 + ], + "angle": 0, + "content": "Figure 2. (a) Illustration of the atmosphere scattering model [54, 56] in various weather conditions. (b) Real-world scenes of different weather conditions." + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.235, + 0.621, + 0.323 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.564, + 0.323, + 0.578, + 0.332 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.626, + 0.236, + 0.743, + 0.322 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.683, + 0.323, + 0.697, + 0.332 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.748, + 0.236, + 0.865, + 0.322 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.808, + 0.323, + 0.822, + 0.332 + ], + "angle": 0, + "content": "(c)" + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.336, + 0.894, + 0.42 + ], + "angle": 0, + "content": "Figure 3. Similarity visualizations among different layers of multiple pre-trained models for deraining, dehazing, and desnowing. (a) The similarity of Rain and Snow models; (b) the similarity of Rain and Haze models; (c) similarity of Haze and Snow models. The values on two axes (0-80) indicate the position order of different network layers, where we uniformly sampled 80 layers." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.433, + 0.893, + 0.54 + ], + "angle": 0, + "content": "various particles involved in the transparent medium (air space) based on the atmosphere optics. The atmosphere scattering model [54, 56] describes the imaging formulation in bad weather conditions, which reveals that the received irradiance of a certain scene point from the photographic sensor is the summation of the direct transmission (attenuation) and the airlight, respectively:" + }, + { + "type": "equation", + "bbox": [ + 0.561, + 0.549, + 0.892, + 0.588 + ], + "angle": 0, + "content": "\\[\nE = \\underbrace {E _ {\\infty} \\rho e ^ {- \\beta d}} _ {\\text {D i r e c t t r a n s m i s s i o n}} + \\underbrace {E _ {\\infty} \\left(1 - e ^ {- \\beta d}\\right)} _ {\\text {A i r l i g h t}}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.599, + 0.892, + 0.734 + ], + "angle": 0, + "content": "where \\( E_{\\infty} \\) denotes the sky intensity; \\( \\rho \\) denotes the normalized radiance of the certain scene point; \\( \\beta d \\) denotes the optical distance of a certain scene point. Here we refer readers to [55-57] for more details. In Fig. 2 (a), we also provide the illustration of Eq. (1), in which the former term (direct transmission) describes the attenuation effect of the object light travel through the atmosphere, and the latter term (airlight) reveals the scattering of illumination by different involved particles in the atmosphere space." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.892, + 0.884 + ], + "angle": 0, + "content": "According to the above physical imaging model, different weather effects share general characteristics. In detail, during the imaging process, particles of different weathers unavoidably modulate the light transmitting from the certain scene point to the sight of observers. The scattering light integration along the light propagation path [54] may unavoidably change the brightness of different weather conditions (Fig. 2 (b)). Thus, images under different adverse weathers exist common degradation, such as low contrast and subtle color distortion." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.886, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Besides, there also exist weather-specific characteristics" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "21749" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.086, + 0.092, + 0.518, + 0.152 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.087, + 0.155, + 0.522, + 0.327 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.53, + 0.091, + 0.891, + 0.222 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.53, + 0.226, + 0.891, + 0.327 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.334, + 0.893, + 0.363 + ], + "angle": 0, + "content": "Figure 4. The overall illustration of our method. (a) and (b) illustrate the first and second training stages of our framework, respectively; (c) illustrates the inference stage of our framework. For simplicity, skip connections are omitted." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.366, + 0.473, + 0.548 + ], + "angle": 0, + "content": "in each weather condition. For instance, the particle type of haze is aerosol, and haze is assumed as the entire image corruption [1,68]. Rain is constituted of water drops, and rainy images suffer from occlusion by the large motion-blurred particles with different shapes and scales. In contrast, irregular particle trajectories and opaque snowflakes are commonly seen in snowfall images. These differences among multiple weather types will cause interference when removing multiple weather-related artifacts. Hence, many more accurate physical models [4, 39, 75, 81] of various weather conditions based on Eqn. 1 are appeared, which further incorporate the typical weather priors into their models." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.563, + 0.47, + 0.684 + ], + "angle": 0, + "content": "Analysis of feature representations. To study the feature representations in deep networks for different weather-related artifacts removal, we individually train the models on different weather datasets using the UNet-based structure network [67]. Then, we adopt centered kernel alignment (CKA) [9, 36, 37, 42, 58] to measure the similarity of feature representations in deep networks, which have been pre-trained on the different weather datasets." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.687, + 0.47, + 0.853 + ], + "angle": 0, + "content": "The similarity visualizations across hidden layers of different networks are shown in Fig. 3. The internal representations learned on the rain dataset seem similar to the counterparts on the snow dataset. One plausible reason is that many snowfall images often resemble rainy images regarding appearances of degradation. While from Fig. 3 (b) and (c), the layers of the dehazing model have more similar patterns to the layers of deraining or desnowing models at the shallow and deep layers. Although networks are trained with different weather datasets, similar representations still exist at the sub-set of network positions." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.856, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Above observations motivate us to expand parameters for different weather types in an adaptive manner, which is flexible and efficient for multiple weather artifacts removal." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.364, + 0.637, + 0.382 + ], + "angle": 0, + "content": "4. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.39, + 0.893, + 0.527 + ], + "angle": 0, + "content": "To explore weather-specific and weather-general features for multiple weather removal tasks, we construct a unified network architecture based on the U-Net [67]. Additionally, as shown in Fig. 4, we devise a novel two-stage training strategy, where we separately learn weather-general features and weather-specific features in the first and second training stages. Then, we elaborate the implementation details of our method. Without loss of generality, we assume that weather types include rain, snow, and haze." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.534, + 0.81, + 0.55 + ], + "angle": 0, + "content": "4.1. Weather-General Feature Learning" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.557, + 0.894, + 0.648 + ], + "angle": 0, + "content": "We employ the shared backbone network to learn the weather-general features. Specifically, various types of weather images are adopted to jointly update the shared backbone network, which helps the network to learn the weather-general feature representations. The forward process at this stage could be formulated as" + }, + { + "type": "equation", + "bbox": [ + 0.615, + 0.654, + 0.892, + 0.673 + ], + "angle": 0, + "content": "\\[\n\\hat {X} _ {\\rho} = f \\left(X _ {\\rho}; \\theta_ {\\text {s h a r e}}\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.681, + 0.893, + 0.741 + ], + "angle": 0, + "content": "where \\(X_{\\rho}\\) and \\(\\hat{X}_{\\rho}\\) denote the input and recovered images of weather type \\(\\rho\\); \\(\\theta_{share}\\) denotes the general sharing parameters for all weather types, which are jointly optimized by multiple weather datasets." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.741, + 0.894, + 0.863 + ], + "angle": 0, + "content": "In addition, the aforementioned Eq. (1) reveals that the bad weather degradation is largely related to the depth of corresponding scenes. Hence, we introduce the depth information in our framework to optimize the network. We adopt the depth consistency loss \\( L_{depth} \\), which fully exploits the general depth auxiliary information among different adverse weathers. Hence, the final loss function of the first training stage is formulated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.504, + 0.869, + 0.892, + 0.901 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {s 1} = L _ {\\text {c o n t e n t}} \\left(\\hat {X} _ {\\rho}, Y _ {\\rho}\\right) + \\lambda_ {\\text {d e p t h}} \\| D \\left(\\hat {X} _ {\\rho}\\right) - D \\left(Y _ {\\rho}\\right) \\| _ {1}, \\tag {3}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21750" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.47, + 0.155 + ], + "angle": 0, + "content": "where \\(L_{content}\\) denotes the L1 and perceptual losses [33]; \\(\\hat{X}_{\\rho}\\) and \\(Y_{\\rho}\\) indicate the outputs and labels of the weather \\(\\rho\\); \\(D(\\cdot)\\) denotes the pre-trained depth estimation network [47], and \\(\\lambda_{depth}\\) denotes the balanced weight." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.16, + 0.383, + 0.178 + ], + "angle": 0, + "content": "4.2. Weather-Specific Feature Learning" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.184, + 0.471, + 0.306 + ], + "angle": 0, + "content": "Adopting the shared parameters to deal with different weather conditions often degrades the restoration performance on each weather due to the interference issues of different sub-tasks. A straightforward solution is to insert the specific parameters for each weather condition across the network layers. Without loss of generality, such network expansion manner of the specific parameter \\(\\theta_{\\rho}\\) for the weather type \\(\\rho\\) (\\(\\rho \\in [Rain, Haze, Snow]\\)) is defined as" + }, + { + "type": "equation", + "bbox": [ + 0.197, + 0.313, + 0.47, + 0.332 + ], + "angle": 0, + "content": "\\[\n\\theta_ {\\rho} ^ {i} = \\theta_ {s h a r e} ^ {i} + \\Delta \\theta_ {\\rho} ^ {i}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.34, + 0.47, + 0.4 + ], + "angle": 0, + "content": "where \\(\\theta_{\\text{share}}^i\\) denotes the general sharing weights at the network layer \\(i\\); \\(\\Delta \\theta_{\\rho}^{i}\\) denotes the specific weights of weather type \\(\\rho\\) at the layer \\(i\\) (\\(i = 1,2,\\dots,N\\), \\(N\\) is the total number of network layers)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.401, + 0.47, + 0.536 + ], + "angle": 0, + "content": "However, such a simple and fixed network expansion paradigm exists limitations. First, according to the analysis in Sec. 3, the fixed form of expansion for different weathers is redundant and inflexible, leading to heavy computation overhead. Second, when the weather types increase, the fixed expansion manner will introduce the massive network parameters. A problem naturally arises: how to efficiently and flexibly explore weather-specific features to boost the network performance on a certain weather type?" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.537, + 0.471, + 0.718 + ], + "angle": 0, + "content": "In this regard, we devise the regularization-based optimization scheme to achieve adaptive weather-specific parameters expansion for different weather types. In detail, we take the pre-trained model at the first stage as the basic model, where the parameters \\((\\theta_{\\text{share}})\\) are shared in different weather conditions. To learn the weather-specific features, we first keep the parameters \\(\\theta_{\\text{share}}\\) frozen during this second training stage. Then, we introduce the weather-specific parameters \\(\\Delta \\theta_{\\rho}\\) and learnable scoring variable \\(S_{\\rho}^{i}\\) for the typical weather \\(\\rho\\) at the network layer \\(i\\). Hence, in our method, the parameters of the typical weather type \\(\\rho\\) at the layer \\(i\\) could be reorganized as" + }, + { + "type": "equation", + "bbox": [ + 0.17, + 0.725, + 0.469, + 0.745 + ], + "angle": 0, + "content": "\\[\n\\theta_ {\\rho} ^ {i} = \\theta_ {s h a r e} ^ {i} + F _ {\\tau} \\left(S _ {\\rho} ^ {i}\\right) * \\Delta \\theta_ {\\rho} ^ {i}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.753, + 0.47, + 0.814 + ], + "angle": 0, + "content": "where \\( S_{\\rho}^{i} \\) is the learnable scoring variable, which is used to assess the necessity to expand parameters at the layer \\( i \\). An indicator function \\( F_{\\tau}(\\cdot) \\) is designed to determine whether to add the new parameters or not, which is defined as" + }, + { + "type": "equation", + "bbox": [ + 0.177, + 0.822, + 0.469, + 0.862 + ], + "angle": 0, + "content": "\\[\nF _ {\\tau} \\left(S _ {\\rho} ^ {i}\\right) = \\left\\{ \\begin{array}{l l} 1 & \\text {i f} S _ {\\rho} ^ {i} \\geq \\tau , \\\\ 0 & \\text {o t h e r w i s e}, \\end{array} \\right. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.87, + 0.47, + 0.902 + ], + "angle": 0, + "content": "where \\(\\tau\\) is the threshold hyperparameter. Only if \\(S_{\\rho}^{i}\\) is larger than the threshold \\(\\tau\\), the new parameters will be al" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.152 + ], + "angle": 0, + "content": "owed to add to the deep network. Here, we study the convolution operators with the kernel sizes of \\( 1 \\times 1 \\) or \\( 3 \\times 3 \\) as the newly added weather-specific parameters (please see Sec. 6.4 for the detailed ablation study)." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.153, + 0.892, + 0.182 + ], + "angle": 0, + "content": "Lastly, the overall loss function of the second training stage could be formulated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.515, + 0.192, + 0.892, + 0.248 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {s 2} = \\sum_ {\\rho} \\left(L _ {\\text {c o n t e n t}} \\left(\\hat {X} _ {\\rho}, Y _ {\\rho}\\right) + \\lambda_ {r e g} \\sum_ {i = 1} ^ {N} \\left| S _ {\\rho} ^ {i} \\right| _ {1}\\right), \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.249, + 0.892, + 0.445 + ], + "angle": 0, + "content": "where \\( L_{content} \\) denotes the MAE and perceptual losses [33]; \\( \\hat{X}_{\\rho} \\) and \\( Y_{\\rho} \\) indicates the restored outputs and ground truths of the weather \\( \\rho \\); \\( \\lambda_{reg} \\) denotes the hyperparameter of the regularization term. As described in Eq. (7), we introduce the sparsity regularization of \\( S_{\\rho}^{i} \\) in the objective functions, which guides the network to automatically discover the network positions where we need to add weather-specific parameters. Experimental results also reveal that adding relatively small weather-specific parameters to the learned positions will significantly improve model performance (see Sec. 6.4 for details). Therefore, our network could flexibly learn the weather-specific parameters and efficiently handle various weather conditions." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.448, + 0.657, + 0.464 + ], + "angle": 0, + "content": "4.3. Inference Stage" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.465, + 0.892, + 0.617 + ], + "angle": 0, + "content": "Fig. 4 (c) illustrates the inference stage of our method. The learned model comprises the weather-general parameters for different weathers and the adaptive weather-specific parameters for each typical weather at the sub-set of network positions. For example, taking an image with a certain type of weather (e.g., haze) as the input, the image will pass through the parameters in black and blue color. Note that these positions marked \"NULL\" indicates that these parameters are unnecessary and the forward propagation will not pass through these layers." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.62, + 0.892, + 0.654 + ], + "angle": 0, + "content": "5. Real-World Datasets for Multiple Weather-Related Artifacts Removal" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.66, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Current all-weather-removal approaches rely on synthetic datasets to train their models, which limits their generalization capability to deal with real-world scenarios. To this regard, we construct the first real-world benchmark datasets under various weather conditions, including haze, rain, and snow. For dehazing, we directly employ the existing real-world dehazing dataset [99]. For deraining, we construct the SPA+ dataset based on the previous SPA [73] dataset. SPA is the first large-scale real-world dataset with paired images. However, SPA still contains two evident issues: (i) many images with sparse rain steaks and repeated background scenes; and (ii) existing images with the same scenes in both training and testing sets. To solve these issues, we first merge the images with repeated background scenes and densify the rain streaks. Then, we remove the images with the same scenes from the training set." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "21751" + } + ], + [ + { + "type": "image_caption", + "bbox": [ + 0.082, + 0.099, + 0.097, + 0.112 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.102, + 0.09, + 0.283, + 0.121 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.284, + 0.09, + 0.469, + 0.121 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.101, + 0.122, + 0.283, + 0.152 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.285, + 0.122, + 0.469, + 0.152 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.082, + 0.151, + 0.097, + 0.16 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.101, + 0.152, + 0.19, + 0.192 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.192, + 0.152, + 0.307, + 0.192 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.312, + 0.152, + 0.376, + 0.192 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.377, + 0.152, + 0.468, + 0.192 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.082, + 0.219, + 0.097, + 0.229 + ], + "angle": 0, + "content": "(c)" + }, + { + "type": "image", + "bbox": [ + 0.102, + 0.194, + 0.172, + 0.251 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.174, + 0.194, + 0.249, + 0.251 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.251, + 0.194, + 0.318, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.251, + 0.225, + 0.318, + 0.233 + ], + "angle": 0, + "content": "SPA SPA+" + }, + { + "type": "image", + "bbox": [ + 0.321, + 0.194, + 0.396, + 0.251 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.397, + 0.194, + 0.468, + 0.251 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.259, + 0.469, + 0.315 + ], + "angle": 0, + "content": "Figure 5. (a) and (b) illustrate the visual samples of the real-world dehazing dataset [99] and our proposed RealSnow, respectively. (c) exhibits the same scenes with sparse rains occur multiple times in SPA and corresponding merged images in our proposed SPA+." + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.328, + 0.468, + 0.356 + ], + "angle": 0, + "content": "Table 1. Illustration of three dataset settings for the weather removal task (R: Rain; RD: RainDrop; S: Snow; H: Haze)." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.357, + 0.469, + 0.476 + ], + "angle": 0, + "content": "
SettingWeather TypesDatasetsTraining Configurations
[Setting 1] (Synthetic)(R,RD,H)Outdoor-Rain [40] RainDrop [59] Snow100K [51]Uniformly sampling 9000 images pairs
[Setting 2] (Synthetic)(R,S,H)Rain1400 [17] RESIDE [39] Snow100K [51]Uniformly sampling 5000 images pairs
[Setting 3] (Real)(R,S,H)SPA+ RealSnow REVIDE [99]Uniformly sampling 160000 images patches
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.494, + 0.469, + 0.69 + ], + "angle": 0, + "content": "In addition, inspired by [73], we build the first real-world desnowing dataset by using the background-static videos to acquire real-world snowing image pairs. The proposed real-world snow dataset, named RealSnow, has 1890 image pairs in total, where 1650 image pairs are used for training, and 240 image pairs are for evaluation. These images are acquired from 126 background-static videos with a wide variety of urban or natural background scenarios, e.g., buildings, cars, statues, trees, and roads. These scenes include varying degrees of snowfall densities and illuminations (e.g., day and night). RealSnow also contains various resolutions, and the average resolution is \\(1208 \\times 646\\). The example images from our dataset are shown in Fig. 5." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.709, + 0.209, + 0.725 + ], + "angle": 0, + "content": "6. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.733, + 0.294, + 0.749 + ], + "angle": 0, + "content": "6.1. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.753, + 0.469, + 0.904 + ], + "angle": 0, + "content": "We implement our method on Pytorch platform. Adam optimizer [35] is adopted. At the first stage, our model is trained for 100 epochs, and the initial learning rate is \\(2 \\times 10^{-4}\\), which is adjusted with the cosine annealing scheme [52]. In the second stage, our model is trained for another 100 epochs, and the initial learning rate is \\(1 \\times 10^{-4}\\), which is also adjusted with the cosine annealing scheme. The patch size is set as \\(224 \\times 224\\). Additionally, hyperparameters are empirically set as: \\(\\tau = 0.1\\), \\(\\lambda_{depth} = 0.02\\), and \\(\\lambda_{reg} = 0.08\\)." + }, + { + "type": "table_caption", + "bbox": [ + 0.517, + 0.09, + 0.874, + 0.103 + ], + "angle": 0, + "content": "Table 2. [Setting 1] Comparisons on the Outdoor-Rain [40]." + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.105, + 0.892, + 0.228 + ], + "angle": 0, + "content": "
TypeMethodVenuePSNR ↑SSIM ↑
Derainingpix2pix [30]CVPR'1719.090.71
HRGAN [40]CVPR'1921.560.86
MPRNet [92]CVPR'2121.900.85
Multi-TasksAll-in-One [41]CVPR'2024.710.90
TransWeather [69]CVPR'2223.180.84
Chen et al. [6]CVPR'2223.940.85
Ours-25.310.90
" + }, + { + "type": "table_caption", + "bbox": [ + 0.507, + 0.23, + 0.885, + 0.244 + ], + "angle": 0, + "content": "Table 3. [Setting 1] Comparisons on the RainDrop dataset [59]." + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.246, + 0.892, + 0.382 + ], + "angle": 0, + "content": "
TypeMethodVenuePSNR ↑SSIM ↑
RainDrop RemovalPix2pix [30]CVPR'1728.020.85
Attn.GAN [59]CVPR'1830.550.90
Quan et al. [63]ICCV'1931.440.93
CCN [62]CVPR'2131.340.95
Multi-TasksAll-in-One [41]CVPR'2031.120.93
TransWeather [69]CVPR'2228.980.90
Chen et al. [6]CVPR'2230.750.91
Ours-31.310.93
" + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.39, + 0.891, + 0.418 + ], + "angle": 0, + "content": "Table 4. [Setting 1] & [Setting 2] Comparisons on the SnowTest100k-L (S-L) dataset [51]." + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.42, + 0.892, + 0.596 + ], + "angle": 0, + "content": "
TypeMethodVenuePSNR ↑SSIM ↑
DesnowingDetailsNet [17]CVPR ' 1719.180.75
DesnowNet [51]TIP ' 1827.170.90
JSTASR [4]ECCV ' 2025.320.81
DDMSNET [97]TIP ' 2128.850.88
Multi-Tasks(Setting 1)All-in-One [41]CVPR ' 2028.330.88
TransWeather [69]CVPR ' 2227.800.85
Chen et al. [6]CVPR ' 2229.270.88
Ours-29.710.89
Multi-Tasks(Setting 2)TransWeather [69]CVPR ' 2226.170.88
Chen et al. [6]CVPR ' 2228.710.88
Ours-29.420.89
" + }, + { + "type": "table_caption", + "bbox": [ + 0.51, + 0.599, + 0.88, + 0.613 + ], + "angle": 0, + "content": "Table 5. [Setting 2] Comparisons on the RESIDE dataset [39]." + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.615, + 0.892, + 0.793 + ], + "angle": 0, + "content": "
TypeMethodVenuePSNR ↑SSIM ↑
DehazingEPDN [61]CVPR' 1923.820.89
PFDN [14]ECCV' 2031.450.97
KDDN [24]CVPR' 2033.490.97
MSBDN [13]CVPR' 2033.790.98
FFA-Net [60]AAAI' 2034.980.99
AECRNet [77]CVPR' 2135.610.98
MPRNet [92]CVPR' 2131.310.97
Multi-TasksAll-in-One [41]CVPR' 2030.490.95
TransWeather [69]CVPR' 2227.660.95
Chen et al. [6]CVPR' 2230.760.97
Ours-30.850.98
" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.808, + 0.696, + 0.822 + ], + "angle": 0, + "content": "6.2. Benchmark Datasets" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.892, + 0.901 + ], + "angle": 0, + "content": "To verify the effectiveness of our method, we employ three dataset settings, which are presented in Tab. 1. Previous all-weather-removal methods [6,41,69] only perform their methods on the settings of the synthetic datasets, e.g., [Setting 1] and [Setting 2]. Moreover, we construct the first" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "21752" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.086, + 0.09, + 0.185, + 0.145 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.086, + 0.147, + 0.185, + 0.198 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.119, + 0.2, + 0.15, + 0.212 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image", + "bbox": [ + 0.186, + 0.091, + 0.286, + 0.145 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.186, + 0.147, + 0.286, + 0.198 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.208, + 0.199, + 0.264, + 0.21 + ], + "angle": 0, + "content": "Chen et.al" + }, + { + "type": "image", + "bbox": [ + 0.286, + 0.091, + 0.385, + 0.145 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.286, + 0.147, + 0.385, + 0.198 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.298, + 0.2, + 0.374, + 0.212 + ], + "angle": 0, + "content": "TransWeather" + }, + { + "type": "image", + "bbox": [ + 0.387, + 0.091, + 0.486, + 0.145 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.387, + 0.147, + 0.486, + 0.198 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.42, + 0.2, + 0.448, + 0.211 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.11, + 0.216, + 0.858, + 0.23 + ], + "angle": 0, + "content": "Figure 6. Visualization comparisons with previous all-weather-removal methods under multiple real-world weather conditions." + }, + { + "type": "table_caption", + "bbox": [ + 0.084, + 0.238, + 0.461, + 0.251 + ], + "angle": 0, + "content": "Table 6. [Setting 2] Comparisons on the Rain1400 dataset [17]." + }, + { + "type": "table", + "bbox": [ + 0.083, + 0.253, + 0.469, + 0.43 + ], + "angle": 0, + "content": "
TypeMethodVenuePSNR ↑SSIM ↑
DerainingJORDER [82]CVPR' 1731.280.92
PReNet [64]CVPR' 1931.880.93
DRD-Net [11]CVPR' 2029.650.88
MSPFN [32]CVPR' 2029.240.88
DualGCN [18]AAAI' 2130.500.91
JRJG [88]CVPR' 2131.180.91
MPRNet [92]CVPR' 2131.530.96
Multi TaskAll-in-One [41]CVPR' 2030.820.90
TransWeather [69]CVPR' 2229.140.89
Chen et al. [6]CVPR' 2231.750.91
Ours-32.490.93
" + }, + { + "type": "table_caption", + "bbox": [ + 0.097, + 0.433, + 0.449, + 0.447 + ], + "angle": 0, + "content": "Table 7. Comparisons results on the datasets of [Setting 3]." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.448, + 0.469, + 0.584 + ], + "angle": 0, + "content": "
DatasetsMethodVenuePSNR ↑SSIM ↑
SPA+TransWeather [69]CVPR' 2233.640.93
Chen et al. [6]CVPR' 2237.320.97
Ours-38.940.98
RealSnowTransWeather [69]CVPR' 2229.160.82
Chen et al. [6]CVPR' 2229.370.88
Ours-29.460.85
REVIDE [99]TransWeather [69]CVPR' 2217.330.82
Chen et al. [6]CVPR' 2220.100.85
Ours-20.440.87
" + }, + { + "type": "table_caption", + "bbox": [ + 0.078, + 0.586, + 0.468, + 0.613 + ], + "angle": 0, + "content": "Table 8. Ablation study of our training strategy using the PNSR metric on the Datasets of [Setting 1]." + }, + { + "type": "table", + "bbox": [ + 0.081, + 0.615, + 0.469, + 0.697 + ], + "angle": 0, + "content": "
ModelsExperiment settingsRainRainDropS-L
Model-1Ours-Stage 1 (4.98 M)25.0430.8829.13
Model-2Ours-Stage 2 (5.97 M)25.3131.3129.71
Model-3Random Expansion of specific #Param.25.1430.9629.28
Model-4Enlarging #Param. of Model-1 (6.79 M)25.2230.9929.42
" + }, + { + "type": "table_caption", + "bbox": [ + 0.099, + 0.701, + 0.447, + 0.715 + ], + "angle": 0, + "content": "Table 9. Ablation study of the parameters expansion types." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.716, + 0.469, + 0.786 + ], + "angle": 0, + "content": "
TypeSetting 1Setting 2
RainRainDropS-LRain1400RESIDES-L
1 × 130.3131.3129.7132.4930.8529.43
3 × 330.5231.5229.8632.7530.9029.69
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.795, + 0.469, + 0.825 + ], + "angle": 0, + "content": "real-world benchmark dataset for image restoration under multiple weather conditions of real scenes ([Setting 3])." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.469, + 0.901 + ], + "angle": 0, + "content": "For fair comparisons, we follow [6,41] to uniformly sample images/patches from the dataset for network training. Meanwhile, except for RealSnow in [Setting 3], we adopt the corresponding test datasets that have already been predivided by the pioneer works [17,39,40,51,59,73]." + }, + { + "type": "image", + "bbox": [ + 0.488, + 0.091, + 0.587, + 0.144 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.488, + 0.146, + 0.587, + 0.198 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.522, + 0.2, + 0.553, + 0.212 + ], + "angle": 0, + "content": "Input" + }, + { + "type": "image", + "bbox": [ + 0.588, + 0.091, + 0.687, + 0.144 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.588, + 0.146, + 0.687, + 0.198 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.61, + 0.2, + 0.666, + 0.211 + ], + "angle": 0, + "content": "Chen et.al" + }, + { + "type": "image", + "bbox": [ + 0.688, + 0.091, + 0.787, + 0.144 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.688, + 0.146, + 0.787, + 0.198 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.702, + 0.2, + 0.779, + 0.212 + ], + "angle": 0, + "content": "TransWeather" + }, + { + "type": "image", + "bbox": [ + 0.788, + 0.091, + 0.887, + 0.144 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.788, + 0.146, + 0.887, + 0.198 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.825, + 0.2, + 0.853, + 0.211 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "table_caption", + "bbox": [ + 0.503, + 0.238, + 0.889, + 0.251 + ], + "angle": 0, + "content": "Table 10. Ablation study of losses on the Datasets of [Setting 1]." + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.253, + 0.892, + 0.302 + ], + "angle": 0, + "content": "
lcontentldepthOutdoor-RainRainDropSnow100K-L
Stage 1✓24.9630.7929.00
✓✓25.0430.8829.13
" + }, + { + "type": "table_caption", + "bbox": [ + 0.575, + 0.306, + 0.817, + 0.319 + ], + "angle": 0, + "content": "Table 11. Model efficiency comparisons." + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.32, + 0.892, + 0.361 + ], + "angle": 0, + "content": "
MethodsAll-in-OneChen et al.TransWeatherOurs
#Param (M: 106)44.0028.7138.055.97
Inference Time (s)-0.0670.0260.030
" + }, + { + "type": "image", + "bbox": [ + 0.508, + 0.371, + 0.697, + 0.449 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.702, + 0.372, + 0.892, + 0.449 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.525, + 0.453, + 0.865, + 0.467 + ], + "angle": 0, + "content": "Figure 7. Ablation study of the regularization term \\(\\lambda_{reg}\\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.481, + 0.891, + 0.496 + ], + "angle": 0, + "content": "6.3. Comparison with the State-of-the-art Methods" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.5, + 0.891, + 0.56 + ], + "angle": 0, + "content": "Similar to [41, 69], we adopt the Peak Signal-to-Noise Ratio (PSNR) and the structural similarity (SSIM) [76] to evaluate the restoration performance of different models. PSNR and SSIM are calculated along the RGB channels." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.561, + 0.892, + 0.68 + ], + "angle": 0, + "content": "In Tables 2, 3, 4, 5, 6, 7, we report the qualitative comparison results among different methods, including both all-weather-removal and typical-weather-removal methods. Obviously, our method achieves superior performance in terms of all metrics on all datasets compared with all-weather-removal methods. In particular, our method surpasses TransWeather [69] by nearly two dB on multiple datasets under different weather conditions." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.682, + 0.892, + 0.756 + ], + "angle": 0, + "content": "Moreover, we provide visual comparisons on the real-world scenarios of various kinds of weather in Fig. 6. It is evident that our results successfully preserve background details and remove multiple weather artifacts. In contrast, the apparent artifacts still exist in the results of [69]." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.764, + 0.652, + 0.779 + ], + "angle": 0, + "content": "6.4. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.785, + 0.891, + 0.814 + ], + "angle": 0, + "content": "We further conduct ablation studies to validate the effects of each component in our method." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.818, + 0.892, + 0.879 + ], + "angle": 0, + "content": "Ablation study of the two-stage training strategy. We set up three models with the following configurations: (i) Model-1 adopts the first training stage, and the network only learns the weather-general parameters; (ii) Model-2" + }, + { + "type": "page_footnote", + "bbox": [ + 0.518, + 0.888, + 0.858, + 0.9 + ], + "angle": 0, + "content": "1The authors have updated their results in the new arxiv version." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "21753" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.088, + 0.468, + 0.228 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.236, + 0.47, + 0.264 + ], + "angle": 0, + "content": "Figure 8. Visualization of the learned positions of expanded parameters. '1': expansion and '0': non-expansion." + }, + { + "type": "image", + "bbox": [ + 0.084, + 0.271, + 0.276, + 0.38 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.1, + 0.382, + 0.258, + 0.392 + ], + "angle": 0, + "content": "(a) t-SNE from weather-general features" + }, + { + "type": "image", + "bbox": [ + 0.278, + 0.271, + 0.468, + 0.38 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.295, + 0.382, + 0.454, + 0.392 + ], + "angle": 0, + "content": "(b) t-SNE from weather-specific features" + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.399, + 0.47, + 0.427 + ], + "angle": 0, + "content": "Figure 9. Visualization of t-SNE from learned weather-general and weather-specific features with our proposed training strategy." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.439, + 0.469, + 0.528 + ], + "angle": 0, + "content": "adopts two training stages, and we fix the weather-general parameters and learn the weather-specific parameters; (iii) Model-4 expands the weather-specific parameters with the random sampling positions strategy. (iv) Model-4 enlarges the parameters of Model-1, and the network only learns the weather-general parameters." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.53, + 0.469, + 0.651 + ], + "angle": 0, + "content": "As reported in Tab. 8, the performance of Model-2 performs is significantly improved after the second training stage, showing the effectiveness of our training strategy. For fair comparisons, note that Model-3 enlarges the network parameters by directly increasing the channel dimensions. Compared to Model-3 and Model-4, Model-2 adopts the adaptive parameters expansion strategy with fewer parameters but delivers better restoration performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.66, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Ablation study of expansion ratios. As described in Sec. 4.1, we introduce a sparsity regularization in the objective function during the second training stage. It guides our network to discover effective positions where to expand the weather-specific parameters. Consequently, the expansion ratio is defined as the ratio of the number of learned positions to the total number of positions. The expansion ratios mainly depend on the coefficient of the regularization term \\(\\lambda_{reg}\\). Consequently, we could adjust the \\(\\lambda_{reg}\\) to achieve a trade-off between the model efficiency and the number of expansions. Using smaller \\(\\lambda_{reg}\\) usually achieves better model performance while leading to larger expansion ratios. Meanwhile, larger expansion ratios often require more weather-specific parameters, bringing more computational overhead. In Fig.7, we present the model performance under different values of \\(\\lambda_{reg}\\). Considering the model effi" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.168 + ], + "angle": 0, + "content": "ciency, we set the default value of \\(\\lambda_{reg}\\) as 0.08. Moreover, we visualize the learned positions in Fig.8. The middle layers have a stronger response to haze, since the middle features of the haze model are largely different from other weather types from Fig.3." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.173, + 0.893, + 0.324 + ], + "angle": 0, + "content": "Ablation study of the parameters expansion types. We also study two weather-specific parameter types: convolution layers with the kernel size of \\(3 \\times 3\\) and \\(1 \\times 1\\). As shown in Tab. 9, we report the performance of these two types under a similar expansion ratio condition. Obviously, given the similar expansion ratios of parameters, using \\(3 \\times 3\\) convolutions slightly performs better than the \\(1 \\times 1\\) convolutions but requires more network parameters. Hence, we adopt convolutions with kernel size of \\(1 \\times 1\\) as the default throughout the experiments." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.33, + 0.893, + 0.375 + ], + "angle": 0, + "content": "Ablation study of losses. We validate the effectiveness of loss functions in Tab. 10, indicating that the auxiliary depth information improves the restoration performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.381, + 0.892, + 0.457 + ], + "angle": 0, + "content": "Ablation study of feature representations. Fig. 9 visualizes the t-SNE results [70] of the weather-general and weather-specific features from the last layer of our network. It indicates that our strategy can learn uniformly weather-general and distinctively weather-specific features." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.462, + 0.893, + 0.581 + ], + "angle": 0, + "content": "Efficiency comparisons. In Tab. 11, we further conduct comparisons of the network parameters and the average inference times among all-weather-removal methods. Our method uses much fewer network parameters than others but achieves better performance, as shown in Sec. 6.3. Moreover, our method only takes 0.03 seconds to process an image with the resolution of \\(256 \\times 256\\) on a single NVIDIA Geforce GTX 1080 Ti GPU." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.59, + 0.619, + 0.605 + ], + "angle": 0, + "content": "7. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.61, + 0.892, + 0.804 + ], + "angle": 0, + "content": "This work formulates an efficiently unified model to remove weather-related artifacts under multiple adverse weather conditions. To achieve this, we design a two-stage training strategy, which first optimizes the network to learn weather-general features and then to learn the weather-specific features. More importantly, our method expands the network parameters to generate weather-specific features adaptively, thus reducing the computation overhead of our model. Moreover, we construct the first real-world dataset with multiple adverse weather conditions to promote the further research on the real-world scenarios. Various experiments demonstrate the superiority of our method over other state-of-the-art methods." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Acknowledgment. This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62276243, in part by the National Key R&D Program of China(NO.2022ZD0160100), and in part by Shanghai Committee of Science and Technology (Grant No. 21DZ1100100)." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "21754" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.176, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.116, + 0.47, + 0.171 + ], + "angle": 0, + "content": "[1] Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing, and Dacheng Tao. DehazeNet: An end-to-end system for single image haze removal. IEEE Trans. Image Process., 25(11):5187-5198, 2016. 1, 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.173, + 0.47, + 0.216 + ], + "angle": 0, + "content": "[2] Chenghao Chen and Hao Li. Robust representation learning with feedback for single image deraining. In IEEE Conf. Comput. Vis. Pattern Recog., pages 7742-7751, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.218, + 0.471, + 0.285 + ], + "angle": 0, + "content": "[3] Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao. Pre-trained image processing transformer. In IEEE Conf. Comput. Vis. Pattern Recog., pages 12299-12310, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.288, + 0.471, + 0.358 + ], + "angle": 0, + "content": "[4] Wei-Ting Chen, Hao-Yu Fang, Jian-Jiun Ding, Cheng-Che Tsai, and Sy-Yen Kuo. JSTASR: Joint size and transparency-aware snow removal algorithm based on modified partial convolution and veiling effect removal. In IEEE Eur. Conf. Comput. Vis., pages 754-770, 2020. 1, 2, 3, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.36, + 0.47, + 0.442 + ], + "angle": 0, + "content": "[5] Wei-Ting Chen, Hao-Yu Fang, Cheng-Lin Hsieh, Cheng-Che Tsai, I-Hsiang Chen, Jian-Jiun Ding, Sy-Yen Kuo, et al. All snow removed: Single image desnowing algorithm using hierarchical dual-tree complex wavelet representation and contradict channel loss. In IEEE Int. Conf. Comput. Vis., pages 4196-4205, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.445, + 0.47, + 0.528 + ], + "angle": 0, + "content": "[6] Wei-Ting Chen, Zhi-Kai Huang, Cheng-Che Tsai, Hao-Hsiang Yang, Jian-Jiun Ding, and Sy-Yen Kuo. Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model. In IEEE Conf. Comput. Vis. Pattern Recog., pages 17653–17662, 2022. 1, 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.53, + 0.47, + 0.598 + ], + "angle": 0, + "content": "[7] Xiang Chen, Jinshan Pan, Kui Jiang, Yufeng Li, Yufeng Huang, Caihua Kong, Longgang Dai, and Zhentao Fan. Unpaired deep image deraining using dual contrastive learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2017-2026, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.601, + 0.47, + 0.657 + ], + "angle": 0, + "content": "[8] Zeyuan Chen, Yangchao Wang, Yang Yang, and Dong Liu. PSD: Principled synthetic-to-real dehazing guided by physical priors. In IEEE Conf. Comput. Vis. Pattern Recog., pages 7180-7189, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.093, + 0.659, + 0.469, + 0.7 + ], + "angle": 0, + "content": "[9] Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh. Algorithms for learning kernels based on centered alignment. J. Mach. Learn. Res., 13:795-828, 2012. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.702, + 0.471, + 0.773 + ], + "angle": 0, + "content": "[10] Qili Deng, Ziling Huang, Chung-Chi Tsai, and Chia-Wen Lin. Hardgan: A haze-aware representation distillation gan for single image dehazing. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part VI 16. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.774, + 0.471, + 0.843 + ], + "angle": 0, + "content": "[11] Sen Deng, Mingqiang Wei, Jun Wang, Yidan Feng, Luming Liang, Haoran Xie, Fu Lee Wang, and Meng Wang. Detail-recovery image deraining via context aggregation networks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 14560-14569, 2020. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.845, + 0.47, + 0.902 + ], + "angle": 0, + "content": "[12] Zijun Deng, Lei Zhu, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Qing Zhang, Jing Qin, and Pheng-Ann Heng. Deep multi-model fusion for single-image dehazing. In IEEE Int. Conf. Comput. Vis., pages 2453–2462, 2019. 2" + }, + { + "type": "list", + "bbox": [ + 0.087, + 0.116, + 0.471, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.093, + 0.892, + 0.148 + ], + "angle": 0, + "content": "[13] Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, and Ming-Hsuan Yang. Multi-scale boosted de-hazing network with dense feature fusion. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2157-2167, 2020. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.149, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[14] Jiangxin Dong and Jinshan Pan. Physics-based feature dehazing networks. In IEEE Eur. Conf. Comput. Vis., pages 188-204. Springer, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.192, + 0.892, + 0.234 + ], + "angle": 0, + "content": "[15] David Eigen, Dilip Krishnan, and Rob Fergus. Restoring an image taken through a window covered with dirt or rain. In IEEE Int. Conf. Comput. Vis., pages 633-640, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.235, + 0.892, + 0.29 + ], + "angle": 0, + "content": "[16] Xueyang Fu, Jiabin Huang, Xinghao Ding, Yinghao Liao, and John Paisley. Clearing the skies: A deep network architecture for single-image rain removal. IEEE Trans. Image Process., 26(6):2944-2956, 2017. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.292, + 0.892, + 0.347 + ], + "angle": 0, + "content": "[17] Xueyang Fu, Jiabin Huang, Delu Zeng, Yue Huang, Xing-hao Ding, and John Paisley. Removing rain from single images via a deep detail network. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3855-3863, 2017. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.349, + 0.892, + 0.404 + ], + "angle": 0, + "content": "[18] Xueyang Fu, Qi Qi, Zheng-Jun Zha, Yurui Zhu, and Xing-hao Ding. Rain streak removal via dual graph convolutional network. In Proc. AAAI Conf. Artif. Intell., volume 35, pages 1352-1360, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.406, + 0.892, + 0.461 + ], + "angle": 0, + "content": "[19] Xueyang Fu, Jie Xiao, Yurui Zhu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Continual image deraining with hypergraph convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.462, + 0.892, + 0.53 + ], + "angle": 0, + "content": "[20] Chun-Le Guo, Qixin Yan, Saeed Anwar, Runmin Cong, Wenqi Ren, and Chongyi Li. Image dehazing transformer with transmission-aware 3D position embedding. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5812-5820, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.532, + 0.892, + 0.6 + ], + "angle": 0, + "content": "[21] Xin Guo, Xueyang Fu, Man Zhou, Zhen Huang, Jialun Peng, and Zheng-Jun Zha. Exploring fourier prior for single image rain removal. In Proceedings of the 30th International Joint Conferences on Artificial Intelligence, pages 935–941, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.602, + 0.892, + 0.657 + ], + "angle": 0, + "content": "[22] Zhixiang Hao, Shaodi You, Yu Li, Kunming Li, and Feng Lu. Learning from synthetic photorealistic raindrop for single image raindrop removal. In IEEE Int. Conf. Comput. Vis., Oct 2019, 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.66, + 0.892, + 0.702 + ], + "angle": 0, + "content": "[23] Kaiming He, Jian Sun, and Xiaou Tang. Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell., 33(12):2341-2353, 2010. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.703, + 0.892, + 0.757 + ], + "angle": 0, + "content": "[24] Ming Hong, Yuan Xie, Cuihua Li, and Yanyun Qu. Distilling image dehazing with heterogeneous task imitation. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3462-3471, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.759, + 0.892, + 0.814 + ], + "angle": 0, + "content": "[25] Xiaowei Hu, Chi-Wing Fu, Lei Zhu, and Pheng-Ann Heng. Depth-attentional features for single-image rain removal. In IEEE Conf. Comput. Vis. Pattern Recog., pages 8022–8031, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.816, + 0.892, + 0.871 + ], + "angle": 0, + "content": "[26] Xiaowei Hu, Lei Zhu, Tianyu Wang, Chi-Wing Fu, and Pheng-Ann Heng. Single-image real-time rain removal based on depth-guided non-local features. IEEE Trans. Image Process., 30:1759-1770, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.873, + 0.892, + 0.902 + ], + "angle": 0, + "content": "[27] Huaibo Huang, Aijing Yu, and Ran He. Memory oriented transfer learning for semi-supervised image deraining. In" + }, + { + "type": "list", + "bbox": [ + 0.51, + 0.093, + 0.892, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "21755" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.118, + 0.092, + 0.468, + 0.119 + ], + "angle": 0, + "content": "IEEE Conf. Comput. Vis. Pattern Recog., pages 7732-7741, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.122, + 0.47, + 0.178 + ], + "angle": 0, + "content": "[28] Jie Huang, Yajing Liu, Feng Zhao, Keyu Yan, Jinghao Zhang, Yukun Huang, Man Zhou, and Zhiwei Xiong. Deep fourier-based exposure correction network with spatial-frequency interaction. In ECCV. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.179, + 0.471, + 0.247 + ], + "angle": 0, + "content": "[29] Jie Huang, Man Zhou, Yajing Liu, Mingde Yao, Feng Zhao, and Zhiwei Xiong. Exposure-consistency representation learning for exposure correction. In Proceedings of the 30th ACM International Conference on Multimedia, page 6309-6317, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.249, + 0.471, + 0.305 + ], + "angle": 0, + "content": "[30] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation with conditional adversarial networks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1125-1134, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.306, + 0.471, + 0.374 + ], + "angle": 0, + "content": "[31] Da-Wei Jaw, Shih-Chia Huang, and Sy-Yen Kuo. Desnow-Gan: An efficient single image snow removal framework using cross-resolution lateral connection and GANs. IEEE Trans. Circuits Syst. Video Technol., 31(4):1342-1350, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.376, + 0.471, + 0.445 + ], + "angle": 0, + "content": "[32] Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, and Junjun Jiang. Multi-scale progressive fusion network for single image deraining. In IEEE Conf. Comput. Vis. Pattern Recog., pages 8346-8355, 2020. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.447, + 0.471, + 0.502 + ], + "angle": 0, + "content": "[33] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In IEEE Eur. Conf. Comput. Vis., pages 694–711. Springer, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.504, + 0.471, + 0.56 + ], + "angle": 0, + "content": "[34] Jin-Hwan Kim, Jae-Young Sim, and Chang-Su Kim. Single image dehazing based on contrast enhancement. In 2011 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pages 1273–1276. IEEE, 2011. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.561, + 0.471, + 0.588 + ], + "angle": 0, + "content": "[35] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.59, + 0.471, + 0.645 + ], + "angle": 0, + "content": "[36] Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton. Similarity of neural network representations revisited. In Proc. Int. Conf. Mach. Learn., pages 3519-3529. PMLR, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.647, + 0.471, + 0.688 + ], + "angle": 0, + "content": "[37] Simon Kornblith, Jonathon Shlens, and Quoc V Le. Do better ImageNet models transfer better? In IEEE Conf. Comput. Vis. Pattern Recog., pages 2661-2671, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.689, + 0.471, + 0.731 + ], + "angle": 0, + "content": "[38] Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, and Dan Feng. Aod-net: All-in-one dehazing network. In IEEE Int. Conf. Comput. Vis., pages 4770-4778, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.732, + 0.471, + 0.788 + ], + "angle": 0, + "content": "[39] Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process., 28(1):492-505, 2019. 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.789, + 0.471, + 0.844 + ], + "angle": 0, + "content": "[40] Ruoteng Li, Loong-Fah Cheong, and Robby T. Tan. Heavy rain image restoration: Integrating physics model and conditional adversarial learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1633-1642, 2019. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.846, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[41] Ruoteng Li, Robby T. Tan, and Loong-Fah Cheong. All in one bad weather removal using architectural search. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3175-3185, 2020. 1, 2, 3, 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.088, + 0.092, + 0.471, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.092, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[42] Wenbo Li, Xin Lu, Jiangbo Lu, Xiangyu Zhang, and Jiaya Jia. On efficient transformer and image pre-training for low-level vision. arXiv preprint arXiv:2112.10175, 2021.3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.149, + 0.892, + 0.205 + ], + "angle": 0, + "content": "[43] Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, and Hongbin Zha. Recurrent squeeze-and-excitation context aggregation net for single image deraining. In IEEE Eur. Conf. Comput. Vis., pages 254-269, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.207, + 0.892, + 0.262 + ], + "angle": 0, + "content": "[44] Yi Li, Yi Chang, Yan Gao, Changfeng Yu, and Luxin Yan. Physically disentangled intra-and inter-domain adaptation for varicolored haze removal. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5841-5850, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.263, + 0.892, + 0.331 + ], + "angle": 0, + "content": "[45] Yunan Li, Qiguang Miao, Wanli Ouyang, Zhenxin Ma, Huijuan Fang, Chao Dong, and Yining Quan. Lap-net: Level-aware progressive network for image dehazing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.333, + 0.892, + 0.389 + ], + "angle": 0, + "content": "[46] Huan Liu, Zijun Wu, Liangyan Li, Sadaf Salehkalaibar, Jun Chen, and Keyan Wang. Towards multi-domain single image dehazing via test-time training. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5831-5840, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.39, + 0.892, + 0.446 + ], + "angle": 0, + "content": "[47] Lina Liu, Xibin Song, Mengmeng Wang, Yong Liu, and Liangjun Zhang. Self-supervised monocular depth estimation for all day images using domain separation. In IEEE Int. Conf. Comput. Vis., pages 12737-12746, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.447, + 0.892, + 0.489 + ], + "angle": 0, + "content": "[48] Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.49, + 0.892, + 0.544 + ], + "angle": 0, + "content": "[49] Xiaohong Liu, Yongrui Ma, Zhihao Shi, and Jun Chen. GridDehazeNet: Attention-based multi-scale network for image dehazing. In IEEE Int. Conf. Comput. Vis., pages 7314-7323, 2019. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.546, + 0.892, + 0.6 + ], + "angle": 0, + "content": "[50] Yang Liu, Jinshan Pan, Jimmy Ren, and Zhixun Su. Learning deep priors for image dehazing. In Proceedings of the IEEE/CVF international conference on computer vision, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.602, + 0.892, + 0.657 + ], + "angle": 0, + "content": "[51] Yun-Fu Liu, Da-Wei Jaw, Shih-Chia Huang, and Jenq-Neng Hwang. DesnowNet: Context-aware deep network for snow removal. IEEE Trans. Image Process., 27(6):3064–3073, 2018. 1, 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.659, + 0.892, + 0.701 + ], + "angle": 0, + "content": "[52] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. In International Conference on Learning Representations, 2017. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.702, + 0.892, + 0.744 + ], + "angle": 0, + "content": "[53] Jiayuan Mao, Tete Xiao, Yuning Jiang, and Zhimin Cao. What can help pedestrian detection? In IEEE Conf. Comput. Vis. Pattern Recog., 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.745, + 0.892, + 0.773 + ], + "angle": 0, + "content": "[54] Srinivasa G. Narasimhan. Models and algorithms for vision through the atmosphere. Columbia University, 2004. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.774, + 0.892, + 0.814 + ], + "angle": 0, + "content": "[55] Srinivasa G. Narasimhan and Shree K. Nayar. Vision and the atmosphere. Int. J. Comput. Vis., 48(3):233-254, 2002. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.817, + 0.892, + 0.857 + ], + "angle": 0, + "content": "[56] Shree K Nayar and Srinivasa G. Narasimhan. Vision in bad weather. In IEEE Int. Conf. Comput. Vis., volume 2, pages 820-827. IEEE, 1999. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.859, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[57] Shree K. Nayar and Srinivasa G. Narasimhan. Seeing through bad weather. In Int. Symp. Robot. Res., pages 335-350. Springer, 2005. 2, 3" + }, + { + "type": "list", + "bbox": [ + 0.51, + 0.092, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "21756" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.092, + 0.47, + 0.148 + ], + "angle": 0, + "content": "[58] Thao Nguyen, Maithra Raghu, and Simon Kornblith. Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth. arXiv preprint arXiv:2010.15327, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.149, + 0.47, + 0.205 + ], + "angle": 0, + "content": "[59] Rui Qian, Robby T. Tan, Wenhan Yang, Jiajun Su, and Jiaying Liu. Attentive generative adversarial network for raindrop removal from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2482-2491, 2018. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.206, + 0.471, + 0.262 + ], + "angle": 0, + "content": "[60] Xu Qin, Zhilin Wang, Yuanchao Bai, Xiaodong Xie, and Huizhu Jia. FFA-Net: Feature fusion attention network for single image dehazing. In Proc. AAAI Conf. Artif. Intell., volume 34, pages 11908–11915, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.263, + 0.471, + 0.305 + ], + "angle": 0, + "content": "[61] Yanyun Qu, Yizi Chen, Jingying Huang, and Yuan Xie. Enhanced pix2pix dehazing network. In IEEE Conf. Comput. Vis. Pattern Recog., pages 8160-8168, 2019. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.306, + 0.471, + 0.348 + ], + "angle": 0, + "content": "[62] Ruijie Quan, Xin Yu, Yuanzhi Liang, and Yi Yang. Removing raindrops and rain streaks in one go. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9147-9156, 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.349, + 0.471, + 0.391 + ], + "angle": 0, + "content": "[63] Yuhui Quan, Shijie Deng, Yixin Chen, and Hui Ji. Deep learning for seeing through window with raindrops. In IEEE Int. Conf. Comput. Vis., pages 2463-2471, 2019. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.392, + 0.471, + 0.447 + ], + "angle": 0, + "content": "[64] Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng. Progressive image deraining networks: A better and simpler baseline. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3937-3946, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.449, + 0.471, + 0.504 + ], + "angle": 0, + "content": "[65] Wenqi Ren, Si Liu, Hua Zhang, Jinshan Pan, Xiaochun Cao, and Ming-Hsuan Yang. Single image dehazing via multi-scale convolutional neural networks. In IEEE Eur. Conf. Comput. Vis., pages 154-169. Springer, 2016. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.505, + 0.471, + 0.56 + ], + "angle": 0, + "content": "[66] Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. Gated fusion network for single image dehazing. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3253-3261, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.562, + 0.471, + 0.63 + ], + "angle": 0, + "content": "[67] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks for biomedical image segmentation. In Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervention, pages 234-241. Springer, 2015. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.632, + 0.471, + 0.673 + ], + "angle": 0, + "content": "[68] Robby T. Tan. Visibility in bad weather from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1-8. IEEE, 2008. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.675, + 0.471, + 0.743 + ], + "angle": 0, + "content": "[69] Jeya Maria Jose Valanarasu, Rajeev Yasarla, and Vishal M. Patel. TransWeather: Transformer-based restoration of images degraded by adverse weather conditions. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2353-2363, 2022. 1, 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.746, + 0.471, + 0.773 + ], + "angle": 0, + "content": "[70] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. J. Mach. Learn. Res., 2008. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.775, + 0.471, + 0.843 + ], + "angle": 0, + "content": "[71] Matthew Wallingford, Hao Li, Alessandro Achille, Avinash Ravichandran, Charless Fowlkes, Rahul Bhotika, and Stefano Soatto. Task adaptive parameter sharing for multi-task learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 7561-7570, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.845, + 0.471, + 0.9 + ], + "angle": 0, + "content": "[72] Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng. A model-driven deep neural network for single image rain removal. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3103-3112, 2020. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.087, + 0.092, + 0.471, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.092, + 0.894, + 0.161 + ], + "angle": 0, + "content": "[73] Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson W.H. Lau. Spatial attentive single-image deraining with a high quality real rain dataset. In IEEE Conf. Comput. Vis. Pattern Recog., pages 12270-12279, 2019. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.162, + 0.894, + 0.217 + ], + "angle": 0, + "content": "[74] Yinglong Wang, Chao Ma, and Bing Zeng. Multi-decoding deraining network and quasi-sparsity based training. In IEEE Conf. Comput. Vis. Pattern Recog., pages 13375-13384, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.218, + 0.894, + 0.272 + ], + "angle": 0, + "content": "[75] Yinglong Wang, Yibing Song, Chao Ma, and Bing Zeng. Rethinking image deraining via rain streaks and vapors. In IEEE Eur. Conf. Comput. Vis., pages 367-382. Springer, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.274, + 0.894, + 0.328 + ], + "angle": 0, + "content": "[76] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600-612, 2004. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.33, + 0.894, + 0.398 + ], + "angle": 0, + "content": "[77] Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, and Lizhuang Ma. Contrastive learning for compact single image dehazing. In IEEE Conf. Comput. Vis. Pattern Recog., pages 10551-10560, 2021. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.4, + 0.894, + 0.44 + ], + "angle": 0, + "content": "[78] Jie Xiao, Xueyang Fu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Image de-raining transformer. IEEE Trans. Pattern Anal. Mach. Intell., pages 1-18, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.442, + 0.894, + 0.496 + ], + "angle": 0, + "content": "[79] Jie Xiao, Man Zhou, Xueyang Fu, Aiping Liu, and Zheng-Jun Zha. Improving de-raining generalization via neural re-organization. In IEEE Int. Conf. Comput. Vis., pages 4987–4996, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.498, + 0.894, + 0.551 + ], + "angle": 0, + "content": "[80] Dong Yang and Jian Sun. Proximal Dehaze-Net: A prior learning-based deep network for single image dehazing. In IEEE Eur. Conf. Comput. Vis., pages 702-717, 2018. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.553, + 0.894, + 0.621 + ], + "angle": 0, + "content": "[81] Wenhan Yang, Robby T. Tan, Jiashi Feng, Zongming Guo, Shuicheng Yan, and Jiaying Liu. Joint rain detection and removal from a single image with contextualized deep networks. IEEE Trans. Pattern Anal. Mach. Intell., 42(6):1377-1393, 2019. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.622, + 0.894, + 0.691 + ], + "angle": 0, + "content": "[82] Wenhan Yang, Robby T. Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan. Deep joint rain detection and removal from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1357-1366, 2017. 1, 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.692, + 0.894, + 0.747 + ], + "angle": 0, + "content": "[83] Wenhan Yang, Robby T. Tan, Shiqi Wang, Yuming Fang, and Jiaying Liu. Single image deraining: From model-based to data-driven and beyond. IEEE Trans. Pattern Anal. Mach. Intell., 43(11):4059-4077, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.748, + 0.894, + 0.802 + ], + "angle": 0, + "content": "[84] Xitong Yang, Zheng Xu, and Jiebo Luo. Towards perceptual image dehazing by physics-based disentanglement and adversarial training. In Proc. AAAI Conf. Artif. Intell., volume 32, 2018. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.804, + 0.894, + 0.871 + ], + "angle": 0, + "content": "[85] Yang Yang, Chaoyue Wang, Risheng Liu, Lin Zhang, Xiaojie Guo, and Dacheng Tao. Self-augmented unpaired image dehazing via density and depth decomposition. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2037-2046, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.51, + 0.873, + 0.894, + 0.903 + ], + "angle": 0, + "content": "[86] Zizheng Yang, Mingde Yao, Jie Huang, Man Zhou, and Feng Zhao. Sir-former: Stereo image restoration using" + }, + { + "type": "list", + "bbox": [ + 0.51, + 0.092, + 0.894, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "21757" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.118, + 0.092, + 0.47, + 0.12 + ], + "angle": 0, + "content": "transformer. In Proceedings of the 30th ACM International Conference on Multimedia, page 6377-6385, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.121, + 0.472, + 0.176 + ], + "angle": 0, + "content": "[87] Rajeev Yasarla, Vishwanath A. Sindagi, and Vishal M. Patel. Syn2real transfer learning for image deraining using gaussian processes. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2726-2736, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.089, + 0.177, + 0.471, + 0.233 + ], + "angle": 0, + "content": "[88] Yuntong Ye, Yi Chang, Hanyu Zhou, and Luxin Yan. Closing the loop: Joint rain generation and removal via disentangled image translation. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2053-2062, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.234, + 0.471, + 0.288 + ], + "angle": 0, + "content": "[89] Qiaosi Yi, Juncheng Li, Qinyan Dai, Faming Fang, Guixu Zhang, and Tieyong Zeng. Structure-preserving deraining with residue channel prior guidance. In IEEE Int. Conf. Comput. Vis., pages 4238-4247, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.289, + 0.471, + 0.343 + ], + "angle": 0, + "content": "[90] Hu Yu, Jie Huang, Yajing Liu, Qi Zhu, Man Zhou, and Feng Zhao. Source-free domain adaptation for real-world image dehazing. In Proceedings of the 30th ACM International Conference on Multimedia, page 6645-6654, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.344, + 0.471, + 0.399 + ], + "angle": 0, + "content": "[91] Ye Yuntong, Yu Changfeng, Chang Yi, Zhu Lin, Zhao Xile, Yan Luxin, and Tian Yonghong. Unsupervised deraining: Where contrastive learning meets self-similarity. arXiv preprint arXiv:2203.11509, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.401, + 0.471, + 0.468 + ], + "angle": 0, + "content": "[92] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In IEEE Conf. Comput. Vis. Pattern Recog., pages 14821-14831, 2021. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.47, + 0.471, + 0.511 + ], + "angle": 0, + "content": "[93] He Zhang and Vishal M. Patel. Densely connected pyramid dehazing network. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3194-3203, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.511, + 0.471, + 0.566 + ], + "angle": 0, + "content": "[94] He Zhang, Vishwanath Sindagi, and Vishal M. Patel. Image de-raining using a conditional generative adversarial network. IEEE Trans. Circuits Syst. Video Technol., 30(11):3943-3956, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.567, + 0.471, + 0.635 + ], + "angle": 0, + "content": "[95] Jinghao Zhang, Jie Huang, Mingde Yao, Man Zhou, and Feng Zhao. Structure- and texture-aware learning for low-light image enhancement. In Proceedings of the 30th ACM International Conference on Multimedia, page 6483–6492, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.637, + 0.471, + 0.691 + ], + "angle": 0, + "content": "[96] Kaihao Zhang, Dongxu Li, Wenhan Luo, and Wenqi Ren. Dual attention-in-attention model for joint rain streak and raindrop removal. IEEE Trans. Image Process., 30:7608-7619, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.693, + 0.471, + 0.748 + ], + "angle": 0, + "content": "[97] Kaihao Zhang, Rongqing Li, Yanjiang Yu, Wenhan Luo, and Changsheng Li. Deep dense multi-scale network for snow removal using semantic and depth priors. IEEE Trans. Image Process., 30:7419-7431, 2021. 1, 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.749, + 0.471, + 0.831 + ], + "angle": 0, + "content": "[98] Kaihao Zhang, Wenhan Luo, Wenqi Ren, Jingwen Wang, Fang Zhao, Lin Ma, and Hongdong Li. Beyond monocular deraining: Stereo image deraining via semantic understanding. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXVII 16. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.088, + 0.832, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[99] Xinyi Zhang, Hang Dong, Jinshan Pan, Chao Zhu, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, and Fei Wang. Learning to restore hazy video: A new real-world dataset and a new method. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9239-9248, June 2021. 5, 6, 7" + }, + { + "type": "list", + "bbox": [ + 0.088, + 0.092, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "[100] Yu Zhang and Qiang Yang. An overview of multi-task learning. Natl. Sci, 5(1):30-43, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.122, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[101] Zheyu Zhang, Yurui Zhu, Xueyang Fu, Zhiwei Xiong, Zheng-Jun Zha, and Feng Wu. Multifocal attention-based cross-scale network for image de-raining. In Proceedings of the 29th ACM International Conference on Multimedia, pages 3673-3681, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.192, + 0.892, + 0.261 + ], + "angle": 0, + "content": "[102] Zhuoran Zheng, Wenqi Ren, Xiaochun Cao, Xiaobin Hu, Tao Wang, Fenglong Song, and Xiuyi Jia. Ultra-high-definition image dehazing via multi-guided bilateral learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 16180-16189. IEEE, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.262, + 0.892, + 0.318 + ], + "angle": 0, + "content": "[103] Man Zhou, Jie Huang, Keyu Yan, Hu Yu, Xueyang Fu, Aiping Liu, Xian Wei, and Feng Zhao. Spatial-frequency domain information integration for pan-sharpening. In ECCV. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.32, + 0.892, + 0.375 + ], + "angle": 0, + "content": "[104] Man Zhou, Jie Xiao, Yifan Chang, Xueyang Fu, Aiping Liu, Jinshan Pan, and Zheng-Jun Zha. Image de-raining via continual learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4907-4916, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.376, + 0.892, + 0.432 + ], + "angle": 0, + "content": "[105] Kai Zhu, Wei Zhai, Yang Cao, Jiebo Luo, and Zheng-Jun Zha. Self-sustaining representation expansion for nonexemplar class-incremental learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9296-9305, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.433, + 0.892, + 0.501 + ], + "angle": 0, + "content": "[106] Lei Zhu, Zijun Deng, Xiaowei Hu, Haoran Xie, Xuemiao Xu, Jing Qin, and Pheng-Ann Heng. Learning gated non-local residual for single-image rain streak removal. IEEE Trans. Circuits Syst. Video Technol., 31(6):2147-2159, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.503, + 0.892, + 0.557 + ], + "angle": 0, + "content": "[107] Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, and Shimin Hu. Traffic-sign detection and classification in the wild. In IEEE Conf. Comput. Vis. 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In this paper, we find that image degradations under different weather conditions contain general characteristics as well as their specific characteristics. Inspired by this observation, we design an efficient unified framework with a two-stage training strategy to explore the weather-general and weather-specific features. The first training stage aims to learn the weather-general features by taking the images under various weather conditions as inputs and outputting the coarsely restored results. The second training stage aims to learn to adaptively expand the specific parameters for each weather type in the deep model, where the requisite positions for expanding weather-specific parameters are automatically learned. Hence, we can obtain an efficient and unified model for image restoration under multiple adverse weather conditions. Moreover, we build the first real-world benchmark dataset with multiple weather conditions to better deal with real-world weather scenarios. Experimental results show that our method achieves superior performance on all the synthetic and real-world benchmarks. Codes and datasets are available at this repository. + +# 1. Introduction + +Adverse weather conditions, such as rain, haze, and snow, are common climatic phenomena in our daily life. They often lead to the poor visual quality of captured images and primarily deteriorate the performance of many outdoor vision systems, such as outdoor security cameras + +![](images/b7e3f5454ba282cdb45f2d9a34f409c16a5b8e9f3066fa4ef80c4d0498a263a1.jpg) + +![](images/fa4b63e70da508e5cbff3fe3e4614e23a0747837bc8b088322c3a16e0552f9bc.jpg) + +Figure 1. Illustration of the proposed method and the currently existing solutions. (a) The weather-specific methods; (b) the method of [41]; (c) methods of [6, 69]; (d) our method, which learns the weather-specific and weather-general features in an efficient manner to remove multiple weather-related artifacts. +![](images/7bc2ef2e65a41690ccd587766c48d949a2ed99a58b5a88ca2518b9e695a0119c.jpg) +:Convolution Layer +: Specific Parameters for Deraining +Specific Parameters for Dehazing + +![](images/d4b397b70c6dc0a7426b934c9b8bfbf6e05f5070582ed06d9db877b586944c06.jpg) +Specific Parameters for Desnowing +:General Parameters for All Weather +NULL : No Parameters at This Layer + +and automatic driving [53, 107]. To make these systems more robust to various adverse weather conditions, many restoration solutions have been proposed, such as deraining [16, 17, 25, 40, 72, 73, 82, 83], dehazing [1, 23, 49, 65, 80, 84], desnowing [4, 51, 97], and raindrop removal [22, 59, 96]. + +Albeit these approaches exhibit promising performance in the given weather situation, they are only applicable to certain typical weather scenarios. However, it is inevitable to tackle various kinds of weather in the application of outdoor vision intelligent systems. Consequently, as shown in Fig. 1 (a), multiple sets of weather-specific model parameters are required to deal with various conditions, which brings additional computational and storage burdens. Hence, it is vitally requisite to develop a unified model capable of addressing various types of adverse weather conditions via a single set of network parameters. + +Recently, several methods [6, 41, 69] adopt a single set of network parameters to remove different weather-related artifacts. However, these solutions contain limitations for practical deployment and applications. Firstly, some methods [6, 69] fail to consider the specific characteristics of each weather condition in their proposed unified models, limiting their restoration performance on specific weather conditions. Secondly, as shown in Fig. 1 (b), although Li et al. [41] tackle the differences and similarities of weather degradation with multiple individual encoders and a shared decoder. Such multiple fixed encoders may largely increase network parameters. Thirdly, existing unified models [6, 41, 69] often require a large number of parameters, limiting the model efficiency. Lastly, current state-of-the-art methods [6, 41, 69] mainly employ synthesized datasets in their training phase, causing apparent performance drops in real-world scenarios. + +In this paper, we argue that images with different weather distortions contain general characteristics as well as their specific characteristics. According to the atmosphere scattering model [55, 57], due to the attenuation and scattering effects, these weather disturbances often share some similar visual degradation appearances, e.g., low contrast and color degradation. Meanwhile, the typical type of weather distortion has its unique characteristics. For example, rainy images often suffer from occlusion by rain steaks with different shapes and scales [32, 82]; haze exhibits global distortions on the entire images [34, 65]. Pioneer works also have devised many specific priors [23, 72, 80, 83, 97] for different weather conditions, which motivates us to explore weather-general and weather-specific features to perform image restoration under multiple weather conditions. + +To achieve this, we design an efficient unified framework for multiple adverse weather-related artifacts removal by exploring both weather-general and weather-specific features. The training procedure of our framework consists of two stages. The first training stage aims to learn the general features by taking various images under different weather conditions as the inputs and outputting coarse results for multiple weather conditions. In the second training stage, we devise a regularization-based optimization scheme, which learns to adaptively expand the specific pa + +rameters for each weather type in the deep model. Note that these requisite positions to expand weather-specific parameters could be learned automatically, thus avoiding redundant parameters pre-designed by researchers. Hence, we are able to obtain an efficient and unified model for image restoration under multiple adverse weather conditions. Furthermore, we newly construct the first real-world benchmark dataset with multiple weather conditions to better deal with various weather-related artifacts in real-world scenarios. + +The contributions of this paper could be summarized as: + +- We reveal that image degradations under different weather conditions contain both general and specific characteristics, which motivates us to design a unified deep model by exploring the weather-general and weather-specific features for removing weather-related artifacts under multiple weather conditions. +- We present a two-stage training strategy to learn the weather-general and weather-specific features automatically. Moreover, the weather-specific features are adaptively added at the learned positions, which makes our model efficient and effective. +- In order to better deal with real-world weather conditions, we construct the first real-world benchmark dataset with multiple weather conditions. Additionally, experimental results validate the superiority of our proposed method on various benchmarks. + +# 2. Related Work + +Single image restoration under different weather conditions have been extensively studied in previous literatures, including deraining [16, 17, 19, 25, 26, 32, 40, 43, 72, 73, 78, 79, 82, 83, 86, 95, 104], dehazing [1, 10, 12, 23, 45, 49, 50, 65, 80, 84, 90, 98], desnowing [4, 51, 97], and raindrop removal [22, 59, 62, 96]. + +Rain removal. The effects of rainy weather on images are usually divided into rain streaks and rain drops. For rain streak removal, [16] first applies the convolutional neural network on deraining, followed by GAN [94] and recurrent network [43]. Researchers look for better ways to understand and represent rain streaks [2, 7, 32, 40, 74, 81, 91, 101, 106], for more delicate detail recovery [11, 89], and for wider applicability under severe weather condition [40, 82]. Besides, many studies introduce the adversarial learning [40], transfer learning [27, 87], frequency priors [21, 28, 29, 103] and data generation to improve the models' performance. + +The degradation of raindrops is different from that of streaks. Thus, they are usually treated separately. There are approaches including CNN [15], attention map [59], and mathematical descriptions [22, 63] on raindrop removal. + +Haze removal. [23] proposes a simple prior of hazy images. [1, 65, 93] applies various methods for dehazing, followed by GAN [61, 84] and fusion-based strategy [66], attention-based model with trainable pre-treatment part [49], dense feature fusion [13], and the combination of CNN and transformer [20]. [38, 80] consider various priors and modeling of raindrops. Proposed learning methods include contrastive regularization [77] and multi-guided bilateral learning [102]. [44] pays attention to colorful haze removal. There are also works pursuing stronger utilization and generalization on datasets [8, 46, 85]. + +Snow removal. [51] designs a two-stage network to deal with different types of snow, while [4] considers the veiling effect. [31] proposes a pyramid-structure model with lateral connections. [5] uses dual-tree wavelet transform. [97] introduces semantics and depth prior to snowfall removal. + +Multiple weather-related artifacts removal. Different from the above typical single weather removal task, all-weather-removal is required to recover the clear images with a single set of network parameters under multiple kinds of weather conditions. Moreover, due to the differences among different tasks, all-weather-removal is obviously more challenging than the single image restoration task. Li et al. [41] explore the differences and similarities of each weather degradation by designing an all-in-one network with multiple encoders. [6] combines the two-stage knowledge and multi-contrastive learning strategy to handle the weather removal problem. [69] propose a transformer-based network with learnable specific weather queries to handle the weather removal in a unified model. The method [41] and our method both consider the differences and similarities among different weathers. However, unlike [41] using the specific weather parameters at the fixed encoder positions, our method could adaptively expand the specific parameters for a certain weather type at the learned network positions, which is more flexible and efficient. + +Multi-task learning (MTL). MTL mainly focuses on handling multiple tasks in a single network, which is extensively studied in the high-level [48, 71, 100, 105] and low-level vision tasks [3, 42]. Recently, methods [3, 42] attempt to exploit the transformers and pre-training scheme to handle multiple image restoration tasks, including deraining, denoising, and super-resolution. However, the correlation among these tasks is limited, while different weather degradations exist with apparent similarities to a certain extent. + +# 3. Motivation + +Weather-distorted images are often captured in outdoor environments. Here, we would like to further analyze the general and specific characteristics of different weather conditions from the following two perspectives. + +The degradation in bad weathers. Different weather effects, i.e., rain, haze, and snow, are mainly caused by the + +![](images/8f33d9fc7f2a5e5744d778d26dc783799376ba0500df6eabb220415ba6ba8806.jpg) +(a) + +![](images/0960278d652dbb8aeba0b43bf4600b3332f0880903b878d7eda4a72adc0e3026.jpg) +(b) + +![](images/923b76cd613ecd8f89ed95ac5d43d4c28e2b9cade776e4f30151f2d291702132.jpg) + +![](images/e4924970f34eddc184ad7e04d00d29c6c670bddf7404573410f71c236633a90b.jpg) +Figure 2. (a) Illustration of the atmosphere scattering model [54, 56] in various weather conditions. (b) Real-world scenes of different weather conditions. +(a) + +![](images/a2e29f6ca4dea54da11947fd30c954e1d0d621279c10c81cb4621e29d2f021cc.jpg) +(b) + +![](images/40cf16dd341740ceee36cacaa08662189a51112be765529529df8da1df59876e.jpg) +(c) +Figure 3. Similarity visualizations among different layers of multiple pre-trained models for deraining, dehazing, and desnowing. (a) The similarity of Rain and Snow models; (b) the similarity of Rain and Haze models; (c) similarity of Haze and Snow models. The values on two axes (0-80) indicate the position order of different network layers, where we uniformly sampled 80 layers. + +various particles involved in the transparent medium (air space) based on the atmosphere optics. The atmosphere scattering model [54, 56] describes the imaging formulation in bad weather conditions, which reveals that the received irradiance of a certain scene point from the photographic sensor is the summation of the direct transmission (attenuation) and the airlight, respectively: + +$$ +E = \underbrace {E _ {\infty} \rho e ^ {- \beta d}} _ {\text {D i r e c t t r a n s m i s s i o n}} + \underbrace {E _ {\infty} \left(1 - e ^ {- \beta d}\right)} _ {\text {A i r l i g h t}}, \tag {1} +$$ + +where $E_{\infty}$ denotes the sky intensity; $\rho$ denotes the normalized radiance of the certain scene point; $\beta d$ denotes the optical distance of a certain scene point. Here we refer readers to [55-57] for more details. In Fig. 2 (a), we also provide the illustration of Eq. (1), in which the former term (direct transmission) describes the attenuation effect of the object light travel through the atmosphere, and the latter term (airlight) reveals the scattering of illumination by different involved particles in the atmosphere space. + +According to the above physical imaging model, different weather effects share general characteristics. In detail, during the imaging process, particles of different weathers unavoidably modulate the light transmitting from the certain scene point to the sight of observers. The scattering light integration along the light propagation path [54] may unavoidably change the brightness of different weather conditions (Fig. 2 (b)). Thus, images under different adverse weathers exist common degradation, such as low contrast and subtle color distortion. + +Besides, there also exist weather-specific characteristics + +![](images/dad3f2f362e540f5277690cbaf1f60bd66e7b5c36c82162a8963001b3d17c93c.jpg) + +![](images/e12ba783769f1de1b45fe1699db9c90e12649e040704935d34c442238008fe5d.jpg) +Figure 4. The overall illustration of our method. (a) and (b) illustrate the first and second training stages of our framework, respectively; (c) illustrates the inference stage of our framework. For simplicity, skip connections are omitted. + +![](images/0cb52ec3f861a5dab225e599f8a2f75b6e8c9e9a218b12ba6e15624773320661.jpg) + +![](images/f8c116b5c946904839cf0c4d4ec1d53671e698eeb567109e5fb4d886213e1981.jpg) + +in each weather condition. For instance, the particle type of haze is aerosol, and haze is assumed as the entire image corruption [1,68]. Rain is constituted of water drops, and rainy images suffer from occlusion by the large motion-blurred particles with different shapes and scales. In contrast, irregular particle trajectories and opaque snowflakes are commonly seen in snowfall images. These differences among multiple weather types will cause interference when removing multiple weather-related artifacts. Hence, many more accurate physical models [4, 39, 75, 81] of various weather conditions based on Eqn. 1 are appeared, which further incorporate the typical weather priors into their models. + +Analysis of feature representations. To study the feature representations in deep networks for different weather-related artifacts removal, we individually train the models on different weather datasets using the UNet-based structure network [67]. Then, we adopt centered kernel alignment (CKA) [9, 36, 37, 42, 58] to measure the similarity of feature representations in deep networks, which have been pre-trained on the different weather datasets. + +The similarity visualizations across hidden layers of different networks are shown in Fig. 3. The internal representations learned on the rain dataset seem similar to the counterparts on the snow dataset. One plausible reason is that many snowfall images often resemble rainy images regarding appearances of degradation. While from Fig. 3 (b) and (c), the layers of the dehazing model have more similar patterns to the layers of deraining or desnowing models at the shallow and deep layers. Although networks are trained with different weather datasets, similar representations still exist at the sub-set of network positions. + +Above observations motivate us to expand parameters for different weather types in an adaptive manner, which is flexible and efficient for multiple weather artifacts removal. + +# 4. Methodology + +To explore weather-specific and weather-general features for multiple weather removal tasks, we construct a unified network architecture based on the U-Net [67]. Additionally, as shown in Fig. 4, we devise a novel two-stage training strategy, where we separately learn weather-general features and weather-specific features in the first and second training stages. Then, we elaborate the implementation details of our method. Without loss of generality, we assume that weather types include rain, snow, and haze. + +# 4.1. Weather-General Feature Learning + +We employ the shared backbone network to learn the weather-general features. Specifically, various types of weather images are adopted to jointly update the shared backbone network, which helps the network to learn the weather-general feature representations. The forward process at this stage could be formulated as + +$$ +\hat {X} _ {\rho} = f \left(X _ {\rho}; \theta_ {\text {s h a r e}}\right), \tag {2} +$$ + +where $X_{\rho}$ and $\hat{X}_{\rho}$ denote the input and recovered images of weather type $\rho$ ; $\theta_{share}$ denotes the general sharing parameters for all weather types, which are jointly optimized by multiple weather datasets. + +In addition, the aforementioned Eq. (1) reveals that the bad weather degradation is largely related to the depth of corresponding scenes. Hence, we introduce the depth information in our framework to optimize the network. We adopt the depth consistency loss $L_{depth}$ , which fully exploits the general depth auxiliary information among different adverse weathers. Hence, the final loss function of the first training stage is formulated as follows: + +$$ +\mathcal {L} _ {s 1} = L _ {\text {c o n t e n t}} \left(\hat {X} _ {\rho}, Y _ {\rho}\right) + \lambda_ {\text {d e p t h}} \| D \left(\hat {X} _ {\rho}\right) - D \left(Y _ {\rho}\right) \| _ {1}, \tag {3} +$$ + +where $L_{content}$ denotes the L1 and perceptual losses [33]; $\hat{X}_{\rho}$ and $Y_{\rho}$ indicate the outputs and labels of the weather $\rho$ ; $D(\cdot)$ denotes the pre-trained depth estimation network [47], and $\lambda_{depth}$ denotes the balanced weight. + +# 4.2. Weather-Specific Feature Learning + +Adopting the shared parameters to deal with different weather conditions often degrades the restoration performance on each weather due to the interference issues of different sub-tasks. A straightforward solution is to insert the specific parameters for each weather condition across the network layers. Without loss of generality, such network expansion manner of the specific parameter $\theta_{\rho}$ for the weather type $\rho$ ( $\rho \in [Rain, Haze, Snow]$ ) is defined as + +$$ +\theta_ {\rho} ^ {i} = \theta_ {s h a r e} ^ {i} + \Delta \theta_ {\rho} ^ {i}, \tag {4} +$$ + +where $\theta_{\text{share}}^i$ denotes the general sharing weights at the network layer $i$ ; $\Delta \theta_{\rho}^{i}$ denotes the specific weights of weather type $\rho$ at the layer $i$ ( $i = 1,2,\dots,N$ , $N$ is the total number of network layers). + +However, such a simple and fixed network expansion paradigm exists limitations. First, according to the analysis in Sec. 3, the fixed form of expansion for different weathers is redundant and inflexible, leading to heavy computation overhead. Second, when the weather types increase, the fixed expansion manner will introduce the massive network parameters. A problem naturally arises: how to efficiently and flexibly explore weather-specific features to boost the network performance on a certain weather type? + +In this regard, we devise the regularization-based optimization scheme to achieve adaptive weather-specific parameters expansion for different weather types. In detail, we take the pre-trained model at the first stage as the basic model, where the parameters $(\theta_{\text{share}})$ are shared in different weather conditions. To learn the weather-specific features, we first keep the parameters $\theta_{\text{share}}$ frozen during this second training stage. Then, we introduce the weather-specific parameters $\Delta \theta_{\rho}$ and learnable scoring variable $S_{\rho}^{i}$ for the typical weather $\rho$ at the network layer $i$ . Hence, in our method, the parameters of the typical weather type $\rho$ at the layer $i$ could be reorganized as + +$$ +\theta_ {\rho} ^ {i} = \theta_ {s h a r e} ^ {i} + F _ {\tau} \left(S _ {\rho} ^ {i}\right) * \Delta \theta_ {\rho} ^ {i}, \tag {5} +$$ + +where $S_{\rho}^{i}$ is the learnable scoring variable, which is used to assess the necessity to expand parameters at the layer $i$ . An indicator function $F_{\tau}(\cdot)$ is designed to determine whether to add the new parameters or not, which is defined as + +$$ +F _ {\tau} \left(S _ {\rho} ^ {i}\right) = \left\{ \begin{array}{l l} 1 & \text {i f} S _ {\rho} ^ {i} \geq \tau , \\ 0 & \text {o t h e r w i s e}, \end{array} \right. \tag {6} +$$ + +where $\tau$ is the threshold hyperparameter. Only if $S_{\rho}^{i}$ is larger than the threshold $\tau$ , the new parameters will be al + +owed to add to the deep network. Here, we study the convolution operators with the kernel sizes of $1 \times 1$ or $3 \times 3$ as the newly added weather-specific parameters (please see Sec. 6.4 for the detailed ablation study). + +Lastly, the overall loss function of the second training stage could be formulated as follows: + +$$ +\mathcal {L} _ {s 2} = \sum_ {\rho} \left(L _ {\text {c o n t e n t}} \left(\hat {X} _ {\rho}, Y _ {\rho}\right) + \lambda_ {r e g} \sum_ {i = 1} ^ {N} \left| S _ {\rho} ^ {i} \right| _ {1}\right), \tag {7} +$$ + +where $L_{content}$ denotes the MAE and perceptual losses [33]; $\hat{X}_{\rho}$ and $Y_{\rho}$ indicates the restored outputs and ground truths of the weather $\rho$ ; $\lambda_{reg}$ denotes the hyperparameter of the regularization term. As described in Eq. (7), we introduce the sparsity regularization of $S_{\rho}^{i}$ in the objective functions, which guides the network to automatically discover the network positions where we need to add weather-specific parameters. Experimental results also reveal that adding relatively small weather-specific parameters to the learned positions will significantly improve model performance (see Sec. 6.4 for details). Therefore, our network could flexibly learn the weather-specific parameters and efficiently handle various weather conditions. + +# 4.3. Inference Stage + +Fig. 4 (c) illustrates the inference stage of our method. The learned model comprises the weather-general parameters for different weathers and the adaptive weather-specific parameters for each typical weather at the sub-set of network positions. For example, taking an image with a certain type of weather (e.g., haze) as the input, the image will pass through the parameters in black and blue color. Note that these positions marked "NULL" indicates that these parameters are unnecessary and the forward propagation will not pass through these layers. + +# 5. Real-World Datasets for Multiple Weather-Related Artifacts Removal + +Current all-weather-removal approaches rely on synthetic datasets to train their models, which limits their generalization capability to deal with real-world scenarios. To this regard, we construct the first real-world benchmark datasets under various weather conditions, including haze, rain, and snow. For dehazing, we directly employ the existing real-world dehazing dataset [99]. For deraining, we construct the SPA+ dataset based on the previous SPA [73] dataset. SPA is the first large-scale real-world dataset with paired images. However, SPA still contains two evident issues: (i) many images with sparse rain steaks and repeated background scenes; and (ii) existing images with the same scenes in both training and testing sets. To solve these issues, we first merge the images with repeated background scenes and densify the rain streaks. Then, we remove the images with the same scenes from the training set. + +![](images/96693cfa8d5acb9a6a56a76c274f6db7fdd97297c36d01a7ce49c89c0edd40d0.jpg) +(a) + +![](images/8de56c55143211c1e010ce30ab4353ec42c8f6b361462c35f031011843e428d1.jpg) + +![](images/6f0314376df57c1a43ad5825bbf01cc16ea332c5b2c86a7694cc8d16d8406d1b.jpg) +(b) + +![](images/ceaffa7cc743cce8f40678511fcb3435408be8b710e176621a1d4e7097a7f636.jpg) + +![](images/38b5e7909aeef03ae964aefaf497d10f021e75d2f94ed236c780285f6453ebdb.jpg) + +![](images/a21d97fac08dd8b0b45d581ac6f65d8bb32e0c1c7c8bc5669f4d4768cc6936b6.jpg) + +![](images/c5d9c60a7b743815bfbd54c3cbbd496eea8dcef52f730a72b38ef71b4b265825.jpg) + +![](images/d97e6a75f17cff70af178966d8ba54d280b5d2886c0a3ddf1698f23fd9466045.jpg) + +![](images/38fd98d55bd545c1e881bb13a9b3c5300095950c0a1b55bc092e73971f4e5943.jpg) +(c) + +![](images/18425b205c95ee6216f8adf7c14f462d0bd4e2f7dabb93147d73504b83d783d5.jpg) +SPA SPA+ + +![](images/14057568c4d09d156301a9934d668404568ebc9db4311b4c966b6fd7c0e70637.jpg) + +![](images/7a81094653b22c63b4d0a90f7f84cbfcab9e0a248b834455ff5412363ade566c.jpg) +Figure 5. (a) and (b) illustrate the visual samples of the real-world dehazing dataset [99] and our proposed RealSnow, respectively. (c) exhibits the same scenes with sparse rains occur multiple times in SPA and corresponding merged images in our proposed SPA+. + +![](images/aca3c4449af284ccea2c18b2c890ee0131e44403e5a95bd9fe14a6a2cca6374d.jpg) + +Table 1. Illustration of three dataset settings for the weather removal task (R: Rain; RD: RainDrop; S: Snow; H: Haze). + +
SettingWeather TypesDatasetsTraining Configurations
[Setting 1] (Synthetic)(R,RD,H)Outdoor-Rain [40] RainDrop [59] Snow100K [51]Uniformly sampling 9000 images pairs
[Setting 2] (Synthetic)(R,S,H)Rain1400 [17] RESIDE [39] Snow100K [51]Uniformly sampling 5000 images pairs
[Setting 3] (Real)(R,S,H)SPA+ RealSnow REVIDE [99]Uniformly sampling 160000 images patches
+ +In addition, inspired by [73], we build the first real-world desnowing dataset by using the background-static videos to acquire real-world snowing image pairs. The proposed real-world snow dataset, named RealSnow, has 1890 image pairs in total, where 1650 image pairs are used for training, and 240 image pairs are for evaluation. These images are acquired from 126 background-static videos with a wide variety of urban or natural background scenarios, e.g., buildings, cars, statues, trees, and roads. These scenes include varying degrees of snowfall densities and illuminations (e.g., day and night). RealSnow also contains various resolutions, and the average resolution is $1208 \times 646$ . The example images from our dataset are shown in Fig. 5. + +# 6. Experiments + +# 6.1. Implementation Details + +We implement our method on Pytorch platform. Adam optimizer [35] is adopted. At the first stage, our model is trained for 100 epochs, and the initial learning rate is $2 \times 10^{-4}$ , which is adjusted with the cosine annealing scheme [52]. In the second stage, our model is trained for another 100 epochs, and the initial learning rate is $1 \times 10^{-4}$ , which is also adjusted with the cosine annealing scheme. The patch size is set as $224 \times 224$ . Additionally, hyperparameters are empirically set as: $\tau = 0.1$ , $\lambda_{depth} = 0.02$ , and $\lambda_{reg} = 0.08$ . + +Table 2. [Setting 1] Comparisons on the Outdoor-Rain [40]. + +
TypeMethodVenuePSNR ↑SSIM ↑
Derainingpix2pix [30]CVPR'1719.090.71
HRGAN [40]CVPR'1921.560.86
MPRNet [92]CVPR'2121.900.85
Multi-TasksAll-in-One [41]CVPR'2024.710.90
TransWeather [69]CVPR'2223.180.84
Chen et al. [6]CVPR'2223.940.85
Ours-25.310.90
+ +Table 3. [Setting 1] Comparisons on the RainDrop dataset [59]. + +
TypeMethodVenuePSNR ↑SSIM ↑
RainDrop RemovalPix2pix [30]CVPR'1728.020.85
Attn.GAN [59]CVPR'1830.550.90
Quan et al. [63]ICCV'1931.440.93
CCN [62]CVPR'2131.340.95
Multi-TasksAll-in-One [41]CVPR'2031.120.93
TransWeather [69]CVPR'2228.980.90
Chen et al. [6]CVPR'2230.750.91
Ours-31.310.93
+ +Table 4. [Setting 1] & [Setting 2] Comparisons on the SnowTest100k-L (S-L) dataset [51]. + +
TypeMethodVenuePSNR ↑SSIM ↑
DesnowingDetailsNet [17]CVPR ' 1719.180.75
DesnowNet [51]TIP ' 1827.170.90
JSTASR [4]ECCV ' 2025.320.81
DDMSNET [97]TIP ' 2128.850.88
Multi-Tasks(Setting 1)All-in-One [41]CVPR ' 2028.330.88
TransWeather [69]CVPR ' 2227.800.85
Chen et al. [6]CVPR ' 2229.270.88
Ours-29.710.89
Multi-Tasks(Setting 2)TransWeather [69]CVPR ' 2226.170.88
Chen et al. [6]CVPR ' 2228.710.88
Ours-29.420.89
+ +Table 5. [Setting 2] Comparisons on the RESIDE dataset [39]. + +
TypeMethodVenuePSNR ↑SSIM ↑
DehazingEPDN [61]CVPR' 1923.820.89
PFDN [14]ECCV' 2031.450.97
KDDN [24]CVPR' 2033.490.97
MSBDN [13]CVPR' 2033.790.98
FFA-Net [60]AAAI' 2034.980.99
AECRNet [77]CVPR' 2135.610.98
MPRNet [92]CVPR' 2131.310.97
Multi-TasksAll-in-One [41]CVPR' 2030.490.95
TransWeather [69]CVPR' 2227.660.95
Chen et al. [6]CVPR' 2230.760.97
Ours-30.850.98
+ +# 6.2. Benchmark Datasets + +To verify the effectiveness of our method, we employ three dataset settings, which are presented in Tab. 1. Previous all-weather-removal methods [6,41,69] only perform their methods on the settings of the synthetic datasets, e.g., [Setting 1] and [Setting 2]. Moreover, we construct the first + +![](images/573adc5e733020f972e8b1029e915a5f1ce2f0f092da2a66b2a4a4c2917803d2.jpg) + +![](images/a15fe6c69f02d8df4f8482cf2e4ad4cfcaf1488512ae4ba8ef204dabfc0698ee.jpg) +Input + +![](images/360bd1d6cde5cd86befc574405ea0d43dec668641259cc7a3e3a576381c9ee7c.jpg) + +![](images/ac2a8c76793a66d23e3938ac7c61a9e6be8ff1dac2974b2aa5804390870f8995.jpg) +Chen et.al + +![](images/cd4c66fb3c86bd8ed2b9fe80de69a3c20c7ab077cda1840ac08897e67a518ab9.jpg) + +![](images/2640aac55a31796a050f7c4c0f55a6732cd0b83acf5101cd7e8bba242391cdde.jpg) +TransWeather +Figure 6. Visualization comparisons with previous all-weather-removal methods under multiple real-world weather conditions. + +![](images/8f01c37b8927eb0f89de2044bd34c336d8fa8896ffad0863ba457d0346d79b85.jpg) + +![](images/1ebfa02a26970c172486e5d8430153ae5793ad70aff4b4202d2e92b466fa9383.jpg) +Ours + +Table 6. [Setting 2] Comparisons on the Rain1400 dataset [17]. + +
TypeMethodVenuePSNR ↑SSIM ↑
DerainingJORDER [82]CVPR' 1731.280.92
PReNet [64]CVPR' 1931.880.93
DRD-Net [11]CVPR' 2029.650.88
MSPFN [32]CVPR' 2029.240.88
DualGCN [18]AAAI' 2130.500.91
JRJG [88]CVPR' 2131.180.91
MPRNet [92]CVPR' 2131.530.96
Multi TaskAll-in-One [41]CVPR' 2030.820.90
TransWeather [69]CVPR' 2229.140.89
Chen et al. [6]CVPR' 2231.750.91
Ours-32.490.93
+ +Table 7. Comparisons results on the datasets of [Setting 3]. + +
DatasetsMethodVenuePSNR ↑SSIM ↑
SPA+TransWeather [69]CVPR' 2233.640.93
Chen et al. [6]CVPR' 2237.320.97
Ours-38.940.98
RealSnowTransWeather [69]CVPR' 2229.160.82
Chen et al. [6]CVPR' 2229.370.88
Ours-29.460.85
REVIDE [99]TransWeather [69]CVPR' 2217.330.82
Chen et al. [6]CVPR' 2220.100.85
Ours-20.440.87
+ +Table 8. Ablation study of our training strategy using the PNSR metric on the Datasets of [Setting 1]. + +
ModelsExperiment settingsRainRainDropS-L
Model-1Ours-Stage 1 (4.98 M)25.0430.8829.13
Model-2Ours-Stage 2 (5.97 M)25.3131.3129.71
Model-3Random Expansion of specific #Param.25.1430.9629.28
Model-4Enlarging #Param. of Model-1 (6.79 M)25.2230.9929.42
+ +Table 9. Ablation study of the parameters expansion types. + +
TypeSetting 1Setting 2
RainRainDropS-LRain1400RESIDES-L
1 × 130.3131.3129.7132.4930.8529.43
3 × 330.5231.5229.8632.7530.9029.69
+ +real-world benchmark dataset for image restoration under multiple weather conditions of real scenes ([Setting 3]). + +For fair comparisons, we follow [6,41] to uniformly sample images/patches from the dataset for network training. Meanwhile, except for RealSnow in [Setting 3], we adopt the corresponding test datasets that have already been predivided by the pioneer works [17,39,40,51,59,73]. + +![](images/e73e8e997b367b9ae393161fa2434e686f4102852c630cd67f6cc0cd638b6c44.jpg) + +![](images/8854d9e2899b829c0d3b9de5ab0c16d88e0502f6c2b2ab21e67d7b45f8307ffc.jpg) +Input + +![](images/8bc742818822e30f7810581ec6e3fbb1b0eff0901f8350a5943d53e02e9b9871.jpg) + +![](images/590576d86bdf4fd8135d01530d9983a866e220f7fe8868ac8688d03046d90432.jpg) +Chen et.al + +![](images/76cfa3d7d7f6cf1fac09eded16d8fd2eb1caaf7fda4cdf746252fbb5c07a1842.jpg) + +![](images/53d3a6b8b6a8447f8ec3f91238e750ddde07f5edd702a18f1446bb42ce9ab14b.jpg) +TransWeather + +![](images/7356f0914a2b139bdeb929adf67d219a4f38ecd5cbd5d6db3c4bc3cf13a828ea.jpg) + +![](images/deff61cd9e1e45e802158afcd298e11a1d6468a050b53c720540dbf364552c67.jpg) +Ours + +Table 10. Ablation study of losses on the Datasets of [Setting 1]. + +
lcontentldepthOutdoor-RainRainDropSnow100K-L
Stage 1✓24.9630.7929.00
✓✓25.0430.8829.13
+ +Table 11. Model efficiency comparisons. + +
MethodsAll-in-OneChen et al.TransWeatherOurs
#Param (M: 106)44.0028.7138.055.97
Inference Time (s)-0.0670.0260.030
+ +![](images/7f4c68093d602cc22f4fe3c557f09ce21933de1dcae9d397879a721f884a7db9.jpg) +Figure 7. Ablation study of the regularization term $\lambda_{reg}$ . + +![](images/27db97bed94ad2c9449a02e7b88922b18bd851e6a4075d07c2f88909d1e335c3.jpg) + +# 6.3. Comparison with the State-of-the-art Methods + +Similar to [41, 69], we adopt the Peak Signal-to-Noise Ratio (PSNR) and the structural similarity (SSIM) [76] to evaluate the restoration performance of different models. PSNR and SSIM are calculated along the RGB channels. + +In Tables 2, 3, 4, 5, 6, 7, we report the qualitative comparison results among different methods, including both all-weather-removal and typical-weather-removal methods. Obviously, our method achieves superior performance in terms of all metrics on all datasets compared with all-weather-removal methods. In particular, our method surpasses TransWeather [69] by nearly two dB on multiple datasets under different weather conditions. + +Moreover, we provide visual comparisons on the real-world scenarios of various kinds of weather in Fig. 6. It is evident that our results successfully preserve background details and remove multiple weather artifacts. In contrast, the apparent artifacts still exist in the results of [69]. + +# 6.4. Ablation Study + +We further conduct ablation studies to validate the effects of each component in our method. + +Ablation study of the two-stage training strategy. We set up three models with the following configurations: (i) Model-1 adopts the first training stage, and the network only learns the weather-general parameters; (ii) Model-2 + +![](images/c7aae7bbdb13a0ac0b13495164af28fde33179e93897abc8d7b192ba3ec5f651.jpg) +Figure 8. Visualization of the learned positions of expanded parameters. '1': expansion and '0': non-expansion. + +![](images/3772e38d89810b04a8057eef41234c7653719f4f8895f3777e9d0ae81bf77aad.jpg) +(a) t-SNE from weather-general features + +![](images/513693ad9b4d2af2576ec7e86b5892419155243f4124753bf9d0641644357f4f.jpg) +(b) t-SNE from weather-specific features +Figure 9. Visualization of t-SNE from learned weather-general and weather-specific features with our proposed training strategy. + +adopts two training stages, and we fix the weather-general parameters and learn the weather-specific parameters; (iii) Model-4 expands the weather-specific parameters with the random sampling positions strategy. (iv) Model-4 enlarges the parameters of Model-1, and the network only learns the weather-general parameters. + +As reported in Tab. 8, the performance of Model-2 performs is significantly improved after the second training stage, showing the effectiveness of our training strategy. For fair comparisons, note that Model-3 enlarges the network parameters by directly increasing the channel dimensions. Compared to Model-3 and Model-4, Model-2 adopts the adaptive parameters expansion strategy with fewer parameters but delivers better restoration performance. + +Ablation study of expansion ratios. As described in Sec. 4.1, we introduce a sparsity regularization in the objective function during the second training stage. It guides our network to discover effective positions where to expand the weather-specific parameters. Consequently, the expansion ratio is defined as the ratio of the number of learned positions to the total number of positions. The expansion ratios mainly depend on the coefficient of the regularization term $\lambda_{reg}$ . Consequently, we could adjust the $\lambda_{reg}$ to achieve a trade-off between the model efficiency and the number of expansions. Using smaller $\lambda_{reg}$ usually achieves better model performance while leading to larger expansion ratios. Meanwhile, larger expansion ratios often require more weather-specific parameters, bringing more computational overhead. In Fig.7, we present the model performance under different values of $\lambda_{reg}$ . Considering the model effi + +ciency, we set the default value of $\lambda_{reg}$ as 0.08. Moreover, we visualize the learned positions in Fig.8. The middle layers have a stronger response to haze, since the middle features of the haze model are largely different from other weather types from Fig.3. + +Ablation study of the parameters expansion types. We also study two weather-specific parameter types: convolution layers with the kernel size of $3 \times 3$ and $1 \times 1$ . As shown in Tab. 9, we report the performance of these two types under a similar expansion ratio condition. Obviously, given the similar expansion ratios of parameters, using $3 \times 3$ convolutions slightly performs better than the $1 \times 1$ convolutions but requires more network parameters. Hence, we adopt convolutions with kernel size of $1 \times 1$ as the default throughout the experiments. + +Ablation study of losses. We validate the effectiveness of loss functions in Tab. 10, indicating that the auxiliary depth information improves the restoration performance. + +Ablation study of feature representations. Fig. 9 visualizes the t-SNE results [70] of the weather-general and weather-specific features from the last layer of our network. It indicates that our strategy can learn uniformly weather-general and distinctively weather-specific features. + +Efficiency comparisons. In Tab. 11, we further conduct comparisons of the network parameters and the average inference times among all-weather-removal methods. Our method uses much fewer network parameters than others but achieves better performance, as shown in Sec. 6.3. Moreover, our method only takes 0.03 seconds to process an image with the resolution of $256 \times 256$ on a single NVIDIA Geforce GTX 1080 Ti GPU. + +# 7. Conclusion + +This work formulates an efficiently unified model to remove weather-related artifacts under multiple adverse weather conditions. To achieve this, we design a two-stage training strategy, which first optimizes the network to learn weather-general features and then to learn the weather-specific features. More importantly, our method expands the network parameters to generate weather-specific features adaptively, thus reducing the computation overhead of our model. Moreover, we construct the first real-world dataset with multiple adverse weather conditions to promote the further research on the real-world scenarios. Various experiments demonstrate the superiority of our method over other state-of-the-art methods. + +Acknowledgment. This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62276243, in part by the National Key R&D Program of China(NO.2022ZD0160100), and in part by Shanghai Committee of Science and Technology (Grant No. 21DZ1100100). + +# References + +[1] Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing, and Dacheng Tao. DehazeNet: An end-to-end system for single image haze removal. IEEE Trans. Image Process., 25(11):5187-5198, 2016. 1, 2, 3, 4 +[2] Chenghao Chen and Hao Li. Robust representation learning with feedback for single image deraining. In IEEE Conf. Comput. Vis. Pattern Recog., pages 7742-7751, 2021. 2 +[3] Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao. Pre-trained image processing transformer. In IEEE Conf. Comput. Vis. Pattern Recog., pages 12299-12310, 2021. 3 +[4] Wei-Ting Chen, Hao-Yu Fang, Jian-Jiun Ding, Cheng-Che Tsai, and Sy-Yen Kuo. JSTASR: Joint size and transparency-aware snow removal algorithm based on modified partial convolution and veiling effect removal. In IEEE Eur. Conf. Comput. Vis., pages 754-770, 2020. 1, 2, 3, 4, 6 +[5] Wei-Ting Chen, Hao-Yu Fang, Cheng-Lin Hsieh, Cheng-Che Tsai, I-Hsiang Chen, Jian-Jiun Ding, Sy-Yen Kuo, et al. All snow removed: Single image desnowing algorithm using hierarchical dual-tree complex wavelet representation and contradict channel loss. In IEEE Int. Conf. Comput. Vis., pages 4196-4205, 2021. 3 +[6] Wei-Ting Chen, Zhi-Kai Huang, Cheng-Che Tsai, Hao-Hsiang Yang, Jian-Jiun Ding, and Sy-Yen Kuo. Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model. In IEEE Conf. Comput. Vis. Pattern Recog., pages 17653–17662, 2022. 1, 2, 3, 6, 7 +[7] Xiang Chen, Jinshan Pan, Kui Jiang, Yufeng Li, Yufeng Huang, Caihua Kong, Longgang Dai, and Zhentao Fan. Unpaired deep image deraining using dual contrastive learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2017-2026, 2022. 2 +[8] Zeyuan Chen, Yangchao Wang, Yang Yang, and Dong Liu. PSD: Principled synthetic-to-real dehazing guided by physical priors. In IEEE Conf. Comput. Vis. Pattern Recog., pages 7180-7189, 2021. 3 +[9] Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh. Algorithms for learning kernels based on centered alignment. J. Mach. Learn. Res., 13:795-828, 2012. 4 +[10] Qili Deng, Ziling Huang, Chung-Chi Tsai, and Chia-Wen Lin. Hardgan: A haze-aware representation distillation gan for single image dehazing. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part VI 16. Springer, 2020. 2 +[11] Sen Deng, Mingqiang Wei, Jun Wang, Yidan Feng, Luming Liang, Haoran Xie, Fu Lee Wang, and Meng Wang. Detail-recovery image deraining via context aggregation networks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 14560-14569, 2020. 2, 7 +[12] Zijun Deng, Lei Zhu, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Qing Zhang, Jing Qin, and Pheng-Ann Heng. Deep multi-model fusion for single-image dehazing. In IEEE Int. Conf. Comput. Vis., pages 2453–2462, 2019. 2 + +[13] Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, and Ming-Hsuan Yang. Multi-scale boosted de-hazing network with dense feature fusion. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2157-2167, 2020. 3, 6 +[14] Jiangxin Dong and Jinshan Pan. Physics-based feature dehazing networks. In IEEE Eur. Conf. Comput. Vis., pages 188-204. Springer, 2020. 6 +[15] David Eigen, Dilip Krishnan, and Rob Fergus. Restoring an image taken through a window covered with dirt or rain. In IEEE Int. Conf. Comput. Vis., pages 633-640, 2013. 2 +[16] Xueyang Fu, Jiabin Huang, Xinghao Ding, Yinghao Liao, and John Paisley. Clearing the skies: A deep network architecture for single-image rain removal. IEEE Trans. Image Process., 26(6):2944-2956, 2017. 1, 2 +[17] Xueyang Fu, Jiabin Huang, Delu Zeng, Yue Huang, Xing-hao Ding, and John Paisley. Removing rain from single images via a deep detail network. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3855-3863, 2017. 1, 2, 6, 7 +[18] Xueyang Fu, Qi Qi, Zheng-Jun Zha, Yurui Zhu, and Xing-hao Ding. Rain streak removal via dual graph convolutional network. In Proc. AAAI Conf. Artif. Intell., volume 35, pages 1352-1360, 2021. 7 +[19] Xueyang Fu, Jie Xiao, Yurui Zhu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Continual image deraining with hypergraph convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 2 +[20] Chun-Le Guo, Qixin Yan, Saeed Anwar, Runmin Cong, Wenqi Ren, and Chongyi Li. Image dehazing transformer with transmission-aware 3D position embedding. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5812-5820, 2022. 3 +[21] Xin Guo, Xueyang Fu, Man Zhou, Zhen Huang, Jialun Peng, and Zheng-Jun Zha. Exploring fourier prior for single image rain removal. In Proceedings of the 30th International Joint Conferences on Artificial Intelligence, pages 935–941, 2022. 2 +[22] Zhixiang Hao, Shaodi You, Yu Li, Kunming Li, and Feng Lu. Learning from synthetic photorealistic raindrop for single image raindrop removal. In IEEE Int. Conf. Comput. Vis., Oct 2019, 1, 2 +[23] Kaiming He, Jian Sun, and Xiaou Tang. Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell., 33(12):2341-2353, 2010. 1, 2, 3 +[24] Ming Hong, Yuan Xie, Cuihua Li, and Yanyun Qu. Distilling image dehazing with heterogeneous task imitation. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3462-3471, 2020. 6 +[25] Xiaowei Hu, Chi-Wing Fu, Lei Zhu, and Pheng-Ann Heng. Depth-attentional features for single-image rain removal. In IEEE Conf. Comput. Vis. Pattern Recog., pages 8022–8031, 2019. 1, 2 +[26] Xiaowei Hu, Lei Zhu, Tianyu Wang, Chi-Wing Fu, and Pheng-Ann Heng. Single-image real-time rain removal based on depth-guided non-local features. IEEE Trans. Image Process., 30:1759-1770, 2021. 2 +[27] Huaibo Huang, Aijing Yu, and Ran He. Memory oriented transfer learning for semi-supervised image deraining. In + +IEEE Conf. Comput. Vis. Pattern Recog., pages 7732-7741, 2021. 2 +[28] Jie Huang, Yajing Liu, Feng Zhao, Keyu Yan, Jinghao Zhang, Yukun Huang, Man Zhou, and Zhiwei Xiong. Deep fourier-based exposure correction network with spatial-frequency interaction. In ECCV. Springer, 2022. 2 +[29] Jie Huang, Man Zhou, Yajing Liu, Mingde Yao, Feng Zhao, and Zhiwei Xiong. Exposure-consistency representation learning for exposure correction. In Proceedings of the 30th ACM International Conference on Multimedia, page 6309-6317, 2022. 2 +[30] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation with conditional adversarial networks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1125-1134, 2017. 6 +[31] Da-Wei Jaw, Shih-Chia Huang, and Sy-Yen Kuo. Desnow-Gan: An efficient single image snow removal framework using cross-resolution lateral connection and GANs. IEEE Trans. Circuits Syst. Video Technol., 31(4):1342-1350, 2020. 3 +[32] Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, and Junjun Jiang. Multi-scale progressive fusion network for single image deraining. In IEEE Conf. Comput. Vis. Pattern Recog., pages 8346-8355, 2020. 2, 7 +[33] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In IEEE Eur. Conf. Comput. Vis., pages 694–711. Springer, 2016. 5 +[34] Jin-Hwan Kim, Jae-Young Sim, and Chang-Su Kim. Single image dehazing based on contrast enhancement. In 2011 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pages 1273–1276. IEEE, 2011. 2 +[35] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2015. 6 +[36] Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton. Similarity of neural network representations revisited. In Proc. Int. Conf. Mach. Learn., pages 3519-3529. PMLR, 2019. 4 +[37] Simon Kornblith, Jonathon Shlens, and Quoc V Le. Do better ImageNet models transfer better? In IEEE Conf. Comput. Vis. Pattern Recog., pages 2661-2671, 2019. 4 +[38] Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, and Dan Feng. Aod-net: All-in-one dehazing network. In IEEE Int. Conf. Comput. Vis., pages 4770-4778, 2017. 3 +[39] Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process., 28(1):492-505, 2019. 4, 6, 7 +[40] Ruoteng Li, Loong-Fah Cheong, and Robby T. Tan. Heavy rain image restoration: Integrating physics model and conditional adversarial learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1633-1642, 2019. 1, 2, 6, 7 +[41] Ruoteng Li, Robby T. Tan, and Loong-Fah Cheong. All in one bad weather removal using architectural search. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3175-3185, 2020. 1, 2, 3, 6, 7 + +[42] Wenbo Li, Xin Lu, Jiangbo Lu, Xiangyu Zhang, and Jiaya Jia. On efficient transformer and image pre-training for low-level vision. arXiv preprint arXiv:2112.10175, 2021.3, 4 +[43] Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, and Hongbin Zha. Recurrent squeeze-and-excitation context aggregation net for single image deraining. In IEEE Eur. Conf. Comput. Vis., pages 254-269, 2018. 2 +[44] Yi Li, Yi Chang, Yan Gao, Changfeng Yu, and Luxin Yan. Physically disentangled intra-and inter-domain adaptation for varicolored haze removal. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5841-5850, 2022. 3 +[45] Yunan Li, Qiguang Miao, Wanli Ouyang, Zhenxin Ma, Huijuan Fang, Chao Dong, and Yining Quan. Lap-net: Level-aware progressive network for image dehazing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. 2 +[46] Huan Liu, Zijun Wu, Liangyan Li, Sadaf Salehkalaibar, Jun Chen, and Keyan Wang. Towards multi-domain single image dehazing via test-time training. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5831-5840, 2022. 3 +[47] Lina Liu, Xibin Song, Mengmeng Wang, Yong Liu, and Liangjun Zhang. Self-supervised monocular depth estimation for all day images using domain separation. In IEEE Int. Conf. Comput. Vis., pages 12737-12746, 2021. 5 +[48] Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101, 2016. 3 +[49] Xiaohong Liu, Yongrui Ma, Zhihao Shi, and Jun Chen. GridDehazeNet: Attention-based multi-scale network for image dehazing. In IEEE Int. Conf. Comput. Vis., pages 7314-7323, 2019. 1, 2, 3 +[50] Yang Liu, Jinshan Pan, Jimmy Ren, and Zhixun Su. Learning deep priors for image dehazing. In Proceedings of the IEEE/CVF international conference on computer vision, 2019. 2 +[51] Yun-Fu Liu, Da-Wei Jaw, Shih-Chia Huang, and Jenq-Neng Hwang. DesnowNet: Context-aware deep network for snow removal. IEEE Trans. Image Process., 27(6):3064–3073, 2018. 1, 2, 3, 6, 7 +[52] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. In International Conference on Learning Representations, 2017. 6 +[53] Jiayuan Mao, Tete Xiao, Yuning Jiang, and Zhimin Cao. What can help pedestrian detection? In IEEE Conf. Comput. Vis. Pattern Recog., 2017. 1 +[54] Srinivasa G. Narasimhan. Models and algorithms for vision through the atmosphere. Columbia University, 2004. 3 +[55] Srinivasa G. Narasimhan and Shree K. Nayar. Vision and the atmosphere. Int. J. Comput. Vis., 48(3):233-254, 2002. 2, 3 +[56] Shree K Nayar and Srinivasa G. Narasimhan. Vision in bad weather. In IEEE Int. Conf. Comput. Vis., volume 2, pages 820-827. IEEE, 1999. 3 +[57] Shree K. Nayar and Srinivasa G. Narasimhan. Seeing through bad weather. In Int. Symp. Robot. Res., pages 335-350. Springer, 2005. 2, 3 + +[58] Thao Nguyen, Maithra Raghu, and Simon Kornblith. Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth. arXiv preprint arXiv:2010.15327, 2020. 4 +[59] Rui Qian, Robby T. Tan, Wenhan Yang, Jiajun Su, and Jiaying Liu. Attentive generative adversarial network for raindrop removal from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2482-2491, 2018. 1, 2, 6, 7 +[60] Xu Qin, Zhilin Wang, Yuanchao Bai, Xiaodong Xie, and Huizhu Jia. FFA-Net: Feature fusion attention network for single image dehazing. In Proc. AAAI Conf. Artif. Intell., volume 34, pages 11908–11915, 2020. 6 +[61] Yanyun Qu, Yizi Chen, Jingying Huang, and Yuan Xie. Enhanced pix2pix dehazing network. In IEEE Conf. Comput. Vis. Pattern Recog., pages 8160-8168, 2019. 3, 6 +[62] Ruijie Quan, Xin Yu, Yuanzhi Liang, and Yi Yang. Removing raindrops and rain streaks in one go. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9147-9156, 2021. 2, 6 +[63] Yuhui Quan, Shijie Deng, Yixin Chen, and Hui Ji. Deep learning for seeing through window with raindrops. In IEEE Int. Conf. Comput. Vis., pages 2463-2471, 2019. 2, 6 +[64] Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng. Progressive image deraining networks: A better and simpler baseline. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3937-3946, 2019. 7 +[65] Wenqi Ren, Si Liu, Hua Zhang, Jinshan Pan, Xiaochun Cao, and Ming-Hsuan Yang. Single image dehazing via multi-scale convolutional neural networks. In IEEE Eur. Conf. Comput. Vis., pages 154-169. Springer, 2016. 1, 2, 3 +[66] Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. Gated fusion network for single image dehazing. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3253-3261, 2018. 3 +[67] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks for biomedical image segmentation. In Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervention, pages 234-241. Springer, 2015. 4 +[68] Robby T. Tan. Visibility in bad weather from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1-8. IEEE, 2008. 4 +[69] Jeya Maria Jose Valanarasu, Rajeev Yasarla, and Vishal M. Patel. TransWeather: Transformer-based restoration of images degraded by adverse weather conditions. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2353-2363, 2022. 1, 2, 3, 6, 7 +[70] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. J. Mach. Learn. Res., 2008. 8 +[71] Matthew Wallingford, Hao Li, Alessandro Achille, Avinash Ravichandran, Charless Fowlkes, Rahul Bhotika, and Stefano Soatto. Task adaptive parameter sharing for multi-task learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 7561-7570, 2022. 3 +[72] Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng. A model-driven deep neural network for single image rain removal. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3103-3112, 2020. 1, 2 + +[73] Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson W.H. Lau. Spatial attentive single-image deraining with a high quality real rain dataset. In IEEE Conf. Comput. Vis. Pattern Recog., pages 12270-12279, 2019. 1, 2, 5, 6, 7 +[74] Yinglong Wang, Chao Ma, and Bing Zeng. Multi-decoding deraining network and quasi-sparsity based training. In IEEE Conf. Comput. Vis. Pattern Recog., pages 13375-13384, 2021. 2 +[75] Yinglong Wang, Yibing Song, Chao Ma, and Bing Zeng. Rethinking image deraining via rain streaks and vapors. In IEEE Eur. Conf. Comput. Vis., pages 367-382. Springer, 2020. 4 +[76] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600-612, 2004. 7 +[77] Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, and Lizhuang Ma. Contrastive learning for compact single image dehazing. In IEEE Conf. Comput. Vis. Pattern Recog., pages 10551-10560, 2021. 3, 6 +[78] Jie Xiao, Xueyang Fu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Image de-raining transformer. IEEE Trans. Pattern Anal. Mach. Intell., pages 1-18, 2022. 2 +[79] Jie Xiao, Man Zhou, Xueyang Fu, Aiping Liu, and Zheng-Jun Zha. Improving de-raining generalization via neural re-organization. In IEEE Int. Conf. Comput. Vis., pages 4987–4996, 2021. 2 +[80] Dong Yang and Jian Sun. Proximal Dehaze-Net: A prior learning-based deep network for single image dehazing. In IEEE Eur. Conf. Comput. Vis., pages 702-717, 2018. 1, 2, 3 +[81] Wenhan Yang, Robby T. Tan, Jiashi Feng, Zongming Guo, Shuicheng Yan, and Jiaying Liu. Joint rain detection and removal from a single image with contextualized deep networks. IEEE Trans. Pattern Anal. Mach. Intell., 42(6):1377-1393, 2019. 2, 4 +[82] Wenhan Yang, Robby T. Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan. Deep joint rain detection and removal from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1357-1366, 2017. 1, 2, 7 +[83] Wenhan Yang, Robby T. Tan, Shiqi Wang, Yuming Fang, and Jiaying Liu. Single image deraining: From model-based to data-driven and beyond. IEEE Trans. Pattern Anal. Mach. Intell., 43(11):4059-4077, 2020. 1, 2 +[84] Xitong Yang, Zheng Xu, and Jiebo Luo. Towards perceptual image dehazing by physics-based disentanglement and adversarial training. In Proc. AAAI Conf. Artif. Intell., volume 32, 2018. 1, 2, 3 +[85] Yang Yang, Chaoyue Wang, Risheng Liu, Lin Zhang, Xiaojie Guo, and Dacheng Tao. Self-augmented unpaired image dehazing via density and depth decomposition. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2037-2046, 2022. 3 +[86] Zizheng Yang, Mingde Yao, Jie Huang, Man Zhou, and Feng Zhao. Sir-former: Stereo image restoration using + +transformer. In Proceedings of the 30th ACM International Conference on Multimedia, page 6377-6385, 2022. 2 +[87] Rajeev Yasarla, Vishwanath A. Sindagi, and Vishal M. Patel. Syn2real transfer learning for image deraining using gaussian processes. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2726-2736, 2020. 2 +[88] Yuntong Ye, Yi Chang, Hanyu Zhou, and Luxin Yan. Closing the loop: Joint rain generation and removal via disentangled image translation. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2053-2062, 2021. 7 +[89] Qiaosi Yi, Juncheng Li, Qinyan Dai, Faming Fang, Guixu Zhang, and Tieyong Zeng. Structure-preserving deraining with residue channel prior guidance. In IEEE Int. Conf. Comput. Vis., pages 4238-4247, 2021. 2 +[90] Hu Yu, Jie Huang, Yajing Liu, Qi Zhu, Man Zhou, and Feng Zhao. Source-free domain adaptation for real-world image dehazing. In Proceedings of the 30th ACM International Conference on Multimedia, page 6645-6654, 2022. 2 +[91] Ye Yuntong, Yu Changfeng, Chang Yi, Zhu Lin, Zhao Xile, Yan Luxin, and Tian Yonghong. Unsupervised deraining: Where contrastive learning meets self-similarity. arXiv preprint arXiv:2203.11509, 2022. 2 +[92] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In IEEE Conf. Comput. Vis. Pattern Recog., pages 14821-14831, 2021. 6, 7 +[93] He Zhang and Vishal M. Patel. Densely connected pyramid dehazing network. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3194-3203, 2018. 3 +[94] He Zhang, Vishwanath Sindagi, and Vishal M. Patel. Image de-raining using a conditional generative adversarial network. IEEE Trans. Circuits Syst. Video Technol., 30(11):3943-3956, 2019. 2 +[95] Jinghao Zhang, Jie Huang, Mingde Yao, Man Zhou, and Feng Zhao. Structure- and texture-aware learning for low-light image enhancement. In Proceedings of the 30th ACM International Conference on Multimedia, page 6483–6492, 2022. 2 +[96] Kaihao Zhang, Dongxu Li, Wenhan Luo, and Wenqi Ren. Dual attention-in-attention model for joint rain streak and raindrop removal. IEEE Trans. Image Process., 30:7608-7619, 2021. 1, 2 +[97] Kaihao Zhang, Rongqing Li, Yanjiang Yu, Wenhan Luo, and Changsheng Li. Deep dense multi-scale network for snow removal using semantic and depth priors. IEEE Trans. Image Process., 30:7419-7431, 2021. 1, 2, 3, 6 +[98] Kaihao Zhang, Wenhan Luo, Wenqi Ren, Jingwen Wang, Fang Zhao, Lin Ma, and Hongdong Li. Beyond monocular deraining: Stereo image deraining via semantic understanding. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXVII 16. Springer, 2020. 2 +[99] Xinyi Zhang, Hang Dong, Jinshan Pan, Chao Zhu, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, and Fei Wang. Learning to restore hazy video: A new real-world dataset and a new method. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9239-9248, June 2021. 5, 6, 7 + +[100] Yu Zhang and Qiang Yang. An overview of multi-task learning. Natl. Sci, 5(1):30-43, 2018. 3 +[101] Zheyu Zhang, Yurui Zhu, Xueyang Fu, Zhiwei Xiong, Zheng-Jun Zha, and Feng Wu. Multifocal attention-based cross-scale network for image de-raining. In Proceedings of the 29th ACM International Conference on Multimedia, pages 3673-3681, 2021. 2 +[102] Zhuoran Zheng, Wenqi Ren, Xiaochun Cao, Xiaobin Hu, Tao Wang, Fenglong Song, and Xiuyi Jia. Ultra-high-definition image dehazing via multi-guided bilateral learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 16180-16189. IEEE, 2021. 3 +[103] Man Zhou, Jie Huang, Keyu Yan, Hu Yu, Xueyang Fu, Aiping Liu, Xian Wei, and Feng Zhao. Spatial-frequency domain information integration for pan-sharpening. In ECCV. Springer, 2022. 2 +[104] Man Zhou, Jie Xiao, Yifan Chang, Xueyang Fu, Aiping Liu, Jinshan Pan, and Zheng-Jun Zha. Image de-raining via continual learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4907-4916, 2021. 2 +[105] Kai Zhu, Wei Zhai, Yang Cao, Jiebo Luo, and Zheng-Jun Zha. Self-sustaining representation expansion for nonexemplar class-incremental learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9296-9305, 2022. 3 +[106] Lei Zhu, Zijun Deng, Xiaowei Hu, Haoran Xie, Xuemiao Xu, Jing Qin, and Pheng-Ann Heng. Learning gated non-local residual for single-image rain streak removal. IEEE Trans. Circuits Syst. Video Technol., 31(6):2147-2159, 2020. 2 +[107] Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, and Shimin Hu. Traffic-sign detection and classification in the wild. In IEEE Conf. Comput. Vis. 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In this paper, we find that image degradations under different weather conditions contain general characteristics as well as their specific characteristics. Inspired by this observation, we design an efficient unified framework with a two-stage training strategy to explore the weather-general and weather-specific features. The first training stage aims to learn the weather-general features by taking the images under various weather conditions as inputs and outputting the coarsely restored results. The second training stage aims to learn to adaptively expand the specific parameters for each weather type in the deep model, where the requisite positions for expanding weather-specific parameters are automatically learned. Hence, we can obtain an efficient and unified model for image restoration under multiple adverse weather conditions. Moreover, we build the first real-world benchmark dataset with multiple weather conditions to better deal with real-world weather scenarios. 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Illustration of the proposed method and the currently existing solutions. (a) The weather-specific methods; (b) the method of [41]; (c) methods of [6, 69]; (d) our method, which learns the weather-specific and weather-general features in an efficient manner to remove multiple weather-related artifacts." + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 430, + 396, + 545, + 524 + ], + "blocks": [ + { + "bbox": [ + 430, + 396, + 545, + 524 + ], + "lines": [ + { + "bbox": [ + 430, + 396, + 545, + 524 + ], + "spans": [ + { + "bbox": [ + 430, + 396, + 545, + 524 + ], + "type": "image", + "image_path": "d4b397b70c6dc0a7426b934c9b8bfbf6e05f5070582ed06d9db877b586944c06.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 433, + 527, + 544, + 541 + ], + "lines": [ + { + "bbox": [ + 433, + 527, + 544, + 541 + ], + "spans": [ + { + "bbox": [ + 433, + 527, + 544, + 541 + ], + "type": "text", + "content": "Specific Parameters for Desnowing" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_footnote" + }, + { + "bbox": [ + 433, + 544, + 544, + 557 + ], + "lines": [ + { + "bbox": [ + 433, + 544, + 544, + 557 + ], + "spans": [ + { + "bbox": [ + 433, + 544, + 544, + 557 + ], + "type": "text", + "content": ":General Parameters for All Weather" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_footnote" + }, + { + "bbox": [ + 433, + 561, + 526, + 574 + ], + "lines": [ + { + "bbox": [ + 433, + 561, + 526, + 574 + ], + "spans": [ + { + "bbox": [ + 433, + 561, + 526, + 574 + ], + "type": "text", + "content": "NULL : No Parameters at This Layer" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_footnote" + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 653, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 653, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 306, + 653, + 547, + 715 + ], + "type": "text", + "content": "and automatic driving [53, 107]. To make these systems more robust to various adverse weather conditions, many restoration solutions have been proposed, such as deraining [16, 17, 25, 40, 72, 73, 82, 83], dehazing [1, 23, 49, 65, 80, 84], desnowing [4, 51, 97], and raindrop removal [22, 59, 96]." + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 15 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 15 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 15 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 198, + 13, + 443, + 25 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 198, + 13, + 443, + 25 + ], + "spans": [ + { + "bbox": [ + 198, + 13, + 443, + 25 + ], + "type": "text", + "content": "Except for this watermark, it is identical to the accepted version;" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 179, + 25, + 462, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 179, + 25, + 462, + 37 + ], + "spans": [ + { + "bbox": [ + 179, + 25, + 462, + 37 + ], + "type": "text", + "content": "the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 674, + 287, + 693 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 674, + 287, + 693 + ], + "spans": [ + { + "bbox": [ + 46, + 674, + 287, + 693 + ], + "type": "inline_equation", + "content": "\\star" + }, + { + "bbox": [ + 46, + 674, + 287, + 693 + ], + "type": "text", + "content": " : This work was done during their internship at Shanghai Artificial Intelligence Laboratory." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 47, + 694, + 176, + 704 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 694, + 176, + 704 + ], + "spans": [ + { + "bbox": [ + 47, + 694, + 176, + 704 + ], + "type": "text", + "content": "†: Co-first authors contributed equally." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 47, + 704, + 139, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 704, + 139, + 712 + ], + "spans": [ + { + "bbox": [ + 47, + 704, + 139, + 712 + ], + "type": "inline_equation", + "content": "\\boxtimes" + }, + { + "bbox": [ + 47, + 704, + 139, + 712 + ], + "type": "text", + "content": " : Corresponding authors." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "21747" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "content": "Albeit these approaches exhibit promising performance in the given weather situation, they are only applicable to certain typical weather scenarios. However, it is inevitable to tackle various kinds of weather in the application of outdoor vision intelligent systems. Consequently, as shown in Fig. 1 (a), multiple sets of weather-specific model parameters are required to deal with various conditions, which brings additional computational and storage burdens. Hence, it is vitally requisite to develop a unified model capable of addressing various types of adverse weather conditions via a single set of network parameters." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 206, + 289, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 206, + 289, + 410 + ], + "spans": [ + { + "bbox": [ + 46, + 206, + 289, + 410 + ], + "type": "text", + "content": "Recently, several methods [6, 41, 69] adopt a single set of network parameters to remove different weather-related artifacts. However, these solutions contain limitations for practical deployment and applications. Firstly, some methods [6, 69] fail to consider the specific characteristics of each weather condition in their proposed unified models, limiting their restoration performance on specific weather conditions. Secondly, as shown in Fig. 1 (b), although Li et al. [41] tackle the differences and similarities of weather degradation with multiple individual encoders and a shared decoder. Such multiple fixed encoders may largely increase network parameters. Thirdly, existing unified models [6, 41, 69] often require a large number of parameters, limiting the model efficiency. Lastly, current state-of-the-art methods [6, 41, 69] mainly employ synthesized datasets in their training phase, causing apparent performance drops in real-world scenarios." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 412, + 289, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 412, + 289, + 592 + ], + "spans": [ + { + "bbox": [ + 46, + 412, + 289, + 592 + ], + "type": "text", + "content": "In this paper, we argue that images with different weather distortions contain general characteristics as well as their specific characteristics. According to the atmosphere scattering model [55, 57], due to the attenuation and scattering effects, these weather disturbances often share some similar visual degradation appearances, e.g., low contrast and color degradation. Meanwhile, the typical type of weather distortion has its unique characteristics. For example, rainy images often suffer from occlusion by rain steaks with different shapes and scales [32, 82]; haze exhibits global distortions on the entire images [34, 65]. Pioneer works also have devised many specific priors [23, 72, 80, 83, 97] for different weather conditions, which motivates us to explore weather-general and weather-specific features to perform image restoration under multiple weather conditions." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 289, + 715 + ], + "type": "text", + "content": "To achieve this, we design an efficient unified framework for multiple adverse weather-related artifacts removal by exploring both weather-general and weather-specific features. The training procedure of our framework consists of two stages. The first training stage aims to learn the general features by taking various images under different weather conditions as the inputs and outputting coarse results for multiple weather conditions. In the second training stage, we devise a regularization-based optimization scheme, which learns to adaptively expand the specific pa" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 72, + 547, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 179 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 179 + ], + "type": "text", + "content": "rameters for each weather type in the deep model. Note that these requisite positions to expand weather-specific parameters could be learned automatically, thus avoiding redundant parameters pre-designed by researchers. Hence, we are able to obtain an efficient and unified model for image restoration under multiple adverse weather conditions. Furthermore, we newly construct the first real-world benchmark dataset with multiple weather conditions to better deal with various weather-related artifacts in real-world scenarios." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 317, + 180, + 545, + 193 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 180, + 545, + 193 + ], + "spans": [ + { + "bbox": [ + 317, + 180, + 545, + 193 + ], + "type": "text", + "content": "The contributions of this paper could be summarized as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 317, + 202, + 545, + 415 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 317, + 202, + 545, + 275 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 202, + 545, + 275 + ], + "spans": [ + { + "bbox": [ + 317, + 202, + 545, + 275 + ], + "type": "text", + "content": "- We reveal that image degradations under different weather conditions contain both general and specific characteristics, which motivates us to design a unified deep model by exploring the weather-general and weather-specific features for removing weather-related artifacts under multiple weather conditions." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 317, + 285, + 545, + 344 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 285, + 545, + 344 + ], + "spans": [ + { + "bbox": [ + 317, + 285, + 545, + 344 + ], + "type": "text", + "content": "- We present a two-stage training strategy to learn the weather-general and weather-specific features automatically. Moreover, the weather-specific features are adaptively added at the learned positions, which makes our model efficient and effective." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 317, + 355, + 545, + 415 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 355, + 545, + 415 + ], + "spans": [ + { + "bbox": [ + 317, + 355, + 545, + 415 + ], + "type": "text", + "content": "- In order to better deal with real-world weather conditions, we construct the first real-world benchmark dataset with multiple weather conditions. Additionally, experimental results validate the superiority of our proposed method on various benchmarks." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 433, + 392, + 445 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 433, + 392, + 445 + ], + "spans": [ + { + "bbox": [ + 306, + 433, + 392, + 445 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 453, + 545, + 525 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 453, + 545, + 525 + ], + "spans": [ + { + "bbox": [ + 304, + 453, + 545, + 525 + ], + "type": "text", + "content": "Single image restoration under different weather conditions have been extensively studied in previous literatures, including deraining [16, 17, 19, 25, 26, 32, 40, 43, 72, 73, 78, 79, 82, 83, 86, 95, 104], dehazing [1, 10, 12, 23, 45, 49, 50, 65, 80, 84, 90, 98], desnowing [4, 51, 97], and raindrop removal [22, 59, 62, 96]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 531, + 545, + 674 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 531, + 545, + 674 + ], + "spans": [ + { + "bbox": [ + 304, + 531, + 545, + 674 + ], + "type": "text", + "content": "Rain removal. The effects of rainy weather on images are usually divided into rain streaks and rain drops. For rain streak removal, [16] first applies the convolutional neural network on deraining, followed by GAN [94] and recurrent network [43]. Researchers look for better ways to understand and represent rain streaks [2, 7, 32, 40, 74, 81, 91, 101, 106], for more delicate detail recovery [11, 89], and for wider applicability under severe weather condition [40, 82]. Besides, many studies introduce the adversarial learning [40], transfer learning [27, 87], frequency priors [21, 28, 29, 103] and data generation to improve the models' performance." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 675, + 545, + 723 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 675, + 545, + 723 + ], + "spans": [ + { + "bbox": [ + 304, + 675, + 545, + 723 + ], + "type": "text", + "content": "The degradation of raindrops is different from that of streaks. Thus, they are usually treated separately. There are approaches including CNN [15], attention map [59], and mathematical descriptions [22, 63] on raindrop removal." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "21748" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "content": "Haze removal. [23] proposes a simple prior of hazy images. [1, 65, 93] applies various methods for dehazing, followed by GAN [61, 84] and fusion-based strategy [66], attention-based model with trainable pre-treatment part [49], dense feature fusion [13], and the combination of CNN and transformer [20]. [38, 80] consider various priors and modeling of raindrops. Proposed learning methods include contrastive regularization [77] and multi-guided bilateral learning [102]. [44] pays attention to colorful haze removal. There are also works pursuing stronger utilization and generalization on datasets [8, 46, 85]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 205, + 287, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 205, + 287, + 266 + ], + "spans": [ + { + "bbox": [ + 46, + 205, + 287, + 266 + ], + "type": "text", + "content": "Snow removal. [51] designs a two-stage network to deal with different types of snow, while [4] considers the veiling effect. [31] proposes a pyramid-structure model with lateral connections. [5] uses dual-tree wavelet transform. [97] introduces semantics and depth prior to snowfall removal." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 267, + 289, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 267, + 289, + 507 + ], + "spans": [ + { + "bbox": [ + 46, + 267, + 289, + 507 + ], + "type": "text", + "content": "Multiple weather-related artifacts removal. Different from the above typical single weather removal task, all-weather-removal is required to recover the clear images with a single set of network parameters under multiple kinds of weather conditions. Moreover, due to the differences among different tasks, all-weather-removal is obviously more challenging than the single image restoration task. Li et al. [41] explore the differences and similarities of each weather degradation by designing an all-in-one network with multiple encoders. [6] combines the two-stage knowledge and multi-contrastive learning strategy to handle the weather removal problem. [69] propose a transformer-based network with learnable specific weather queries to handle the weather removal in a unified model. The method [41] and our method both consider the differences and similarities among different weathers. However, unlike [41] using the specific weather parameters at the fixed encoder positions, our method could adaptively expand the specific parameters for a certain weather type at the learned network positions, which is more flexible and efficient." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 509, + 289, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 509, + 289, + 617 + ], + "spans": [ + { + "bbox": [ + 46, + 509, + 289, + 617 + ], + "type": "text", + "content": "Multi-task learning (MTL). MTL mainly focuses on handling multiple tasks in a single network, which is extensively studied in the high-level [48, 71, 100, 105] and low-level vision tasks [3, 42]. Recently, methods [3, 42] attempt to exploit the transformers and pre-training scheme to handle multiple image restoration tasks, including deraining, denoising, and super-resolution. However, the correlation among these tasks is limited, while different weather degradations exist with apparent similarities to a certain extent." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 620, + 119, + 632 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 620, + 119, + 632 + ], + "spans": [ + { + "bbox": [ + 47, + 620, + 119, + 632 + ], + "type": "text", + "content": "3. Motivation" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 635, + 287, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 635, + 287, + 684 + ], + "spans": [ + { + "bbox": [ + 46, + 635, + 287, + 684 + ], + "type": "text", + "content": "Weather-distorted images are often captured in outdoor environments. Here, we would like to further analyze the general and specific characteristics of different weather conditions from the following two perspectives." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 689, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 287, + 714 + ], + "type": "text", + "content": "The degradation in bad weathers. Different weather effects, i.e., rain, haze, and snow, are mainly caused by the" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 310, + 72, + 436, + 133 + ], + "blocks": [ + { + "bbox": [ + 310, + 72, + 436, + 133 + ], + "lines": [ + { + "bbox": [ + 310, + 72, + 436, + 133 + ], + "spans": [ + { + "bbox": [ + 310, + 72, + 436, + 133 + ], + "type": "image", + "image_path": "8f33d9fc7f2a5e5744d778d26dc783799376ba0500df6eabb220415ba6ba8806.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 370, + 135, + 380, + 144 + ], + "lines": [ + { + "bbox": [ + 370, + 135, + 380, + 144 + ], + "spans": [ + { + "bbox": [ + 370, + 135, + 380, + 144 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 438, + 72, + 491, + 134 + ], + "blocks": [ + { + "bbox": [ + 438, + 72, + 491, + 134 + ], + "lines": [ + { + "bbox": [ + 438, + 72, + 491, + 134 + ], + "spans": [ + { + "bbox": [ + 438, + 72, + 491, + 134 + ], + "type": "image", + "image_path": "0960278d652dbb8aeba0b43bf4600b3332f0880903b878d7eda4a72adc0e3026.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 485, + 135, + 496, + 144 + ], + "lines": [ + { + "bbox": [ + 485, + 135, + 496, + 144 + ], + "spans": [ + { + "bbox": [ + 485, + 135, + 496, + 144 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 491, + 72, + 544, + 134 + ], + "blocks": [ + { + "bbox": [ + 491, + 72, + 544, + 134 + ], + "lines": [ + { + "bbox": [ + 491, + 72, + 544, + 134 + ], + "spans": [ + { + "bbox": [ + 491, + 72, + 544, + 134 + ], + "type": "image", + "image_path": "923b76cd613ecd8f89ed95ac5d43d4c28e2b9cade776e4f30151f2d291702132.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 307, + 186, + 380, + 255 + ], + "blocks": [ + { + "bbox": [ + 306, + 152, + 547, + 184 + ], + "lines": [ + { + "bbox": [ + 306, + 152, + 547, + 184 + ], + "spans": [ + { + "bbox": [ + 306, + 152, + 547, + 184 + ], + "type": "text", + "content": "Figure 2. (a) Illustration of the atmosphere scattering model [54, 56] in various weather conditions. (b) Real-world scenes of different weather conditions." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 307, + 186, + 380, + 255 + ], + "lines": [ + { + "bbox": [ + 307, + 186, + 380, + 255 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 380, + 255 + ], + "type": "image", + "image_path": "e4924970f34eddc184ad7e04d00d29c6c670bddf7404573410f71c236633a90b.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 345, + 255, + 353, + 262 + ], + "lines": [ + { + "bbox": [ + 345, + 255, + 353, + 262 + ], + "spans": [ + { + "bbox": [ + 345, + 255, + 353, + 262 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 383, + 186, + 454, + 255 + ], + "blocks": [ + { + "bbox": [ + 383, + 186, + 454, + 255 + ], + "lines": [ + { + "bbox": [ + 383, + 186, + 454, + 255 + ], + "spans": [ + { + "bbox": [ + 383, + 186, + 454, + 255 + ], + "type": "image", + "image_path": "a2e29f6ca4dea54da11947fd30c954e1d0d621279c10c81cb4621e29d2f021cc.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 417, + 255, + 426, + 262 + ], + "lines": [ + { + "bbox": [ + 417, + 255, + 426, + 262 + ], + "spans": [ + { + "bbox": [ + 417, + 255, + 426, + 262 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 457, + 186, + 529, + 255 + ], + "blocks": [ + { + "bbox": [ + 457, + 186, + 529, + 255 + ], + "lines": [ + { + "bbox": [ + 457, + 186, + 529, + 255 + ], + "spans": [ + { + "bbox": [ + 457, + 186, + 529, + 255 + ], + "type": "image", + "image_path": "40cf16dd341740ceee36cacaa08662189a51112be765529529df8da1df59876e.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 494, + 255, + 503, + 262 + ], + "lines": [ + { + "bbox": [ + 494, + 255, + 503, + 262 + ], + "spans": [ + { + "bbox": [ + 494, + 255, + 503, + 262 + ], + "type": "text", + "content": "(c)" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 304, + 266, + 547, + 332 + ], + "lines": [ + { + "bbox": [ + 304, + 266, + 547, + 332 + ], + "spans": [ + { + "bbox": [ + 304, + 266, + 547, + 332 + ], + "type": "text", + "content": "Figure 3. Similarity visualizations among different layers of multiple pre-trained models for deraining, dehazing, and desnowing. (a) The similarity of Rain and Snow models; (b) the similarity of Rain and Haze models; (c) similarity of Haze and Snow models. The values on two axes (0-80) indicate the position order of different network layers, where we uniformly sampled 80 layers." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_caption" + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 342, + 546, + 427 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 342, + 546, + 427 + ], + "spans": [ + { + "bbox": [ + 304, + 342, + 546, + 427 + ], + "type": "text", + "content": "various particles involved in the transparent medium (air space) based on the atmosphere optics. The atmosphere scattering model [54, 56] describes the imaging formulation in bad weather conditions, which reveals that the received irradiance of a certain scene point from the photographic sensor is the summation of the direct transmission (attenuation) and the airlight, respectively:" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 343, + 434, + 545, + 465 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 434, + 545, + 465 + ], + "spans": [ + { + "bbox": [ + 343, + 434, + 545, + 465 + ], + "type": "interline_equation", + "content": "E = \\underbrace {E _ {\\infty} \\rho e ^ {- \\beta d}} _ {\\text {D i r e c t t r a n s m i s s i o n}} + \\underbrace {E _ {\\infty} \\left(1 - e ^ {- \\beta d}\\right)} _ {\\text {A i r l i g h t}}, \\tag {1}", + "image_path": "61a6b76db2074f2d709f0f94146de643aeb4622d74c5ea8752d98d9f816080fb.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 474, + 545, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 474, + 545, + 581 + ], + "spans": [ + { + "bbox": [ + 304, + 474, + 545, + 581 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 474, + 545, + 581 + ], + "type": "inline_equation", + "content": "E_{\\infty}" + }, + { + "bbox": [ + 304, + 474, + 545, + 581 + ], + "type": "text", + "content": " denotes the sky intensity; " + }, + { + "bbox": [ + 304, + 474, + 545, + 581 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 304, + 474, + 545, + 581 + ], + "type": "text", + "content": " denotes the normalized radiance of the certain scene point; " + }, + { + "bbox": [ + 304, + 474, + 545, + 581 + ], + "type": "inline_equation", + "content": "\\beta d" + }, + { + "bbox": [ + 304, + 474, + 545, + 581 + ], + "type": "text", + "content": " denotes the optical distance of a certain scene point. Here we refer readers to [55-57] for more details. In Fig. 2 (a), we also provide the illustration of Eq. (1), in which the former term (direct transmission) describes the attenuation effect of the object light travel through the atmosphere, and the latter term (airlight) reveals the scattering of illumination by different involved particles in the atmosphere space." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 582, + 545, + 700 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 545, + 700 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 545, + 700 + ], + "type": "text", + "content": "According to the above physical imaging model, different weather effects share general characteristics. In detail, during the imaging process, particles of different weathers unavoidably modulate the light transmitting from the certain scene point to the sight of observers. The scattering light integration along the light propagation path [54] may unavoidably change the brightness of different weather conditions (Fig. 2 (b)). Thus, images under different adverse weathers exist common degradation, such as low contrast and subtle color distortion." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 317, + 701, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 701, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 701, + 545, + 713 + ], + "type": "text", + "content": "Besides, there also exist weather-specific characteristics" + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "21749" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 52, + 72, + 317, + 120 + ], + "blocks": [ + { + "bbox": [ + 52, + 72, + 317, + 120 + ], + "lines": [ + { + "bbox": [ + 52, + 72, + 317, + 120 + ], + "spans": [ + { + "bbox": [ + 52, + 72, + 317, + 120 + ], + "type": "image", + "image_path": "dad3f2f362e540f5277690cbaf1f60bd66e7b5c36c82162a8963001b3d17c93c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 53, + 122, + 319, + 258 + ], + "blocks": [ + { + "bbox": [ + 53, + 122, + 319, + 258 + ], + "lines": [ + { + "bbox": [ + 53, + 122, + 319, + 258 + ], + "spans": [ + { + "bbox": [ + 53, + 122, + 319, + 258 + ], + "type": "image", + "image_path": "e12ba783769f1de1b45fe1699db9c90e12649e040704935d34c442238008fe5d.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 264, + 546, + 287 + ], + "lines": [ + { + "bbox": [ + 46, + 264, + 546, + 287 + ], + "spans": [ + { + "bbox": [ + 46, + 264, + 546, + 287 + ], + "type": "text", + "content": "Figure 4. The overall illustration of our method. (a) and (b) illustrate the first and second training stages of our framework, respectively; (c) illustrates the inference stage of our framework. For simplicity, skip connections are omitted." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 324, + 72, + 545, + 175 + ], + "blocks": [ + { + "bbox": [ + 324, + 72, + 545, + 175 + ], + "lines": [ + { + "bbox": [ + 324, + 72, + 545, + 175 + ], + "spans": [ + { + "bbox": [ + 324, + 72, + 545, + 175 + ], + "type": "image", + "image_path": "0cb52ec3f861a5dab225e599f8a2f75b6e8c9e9a218b12ba6e15624773320661.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 324, + 178, + 545, + 258 + ], + "blocks": [ + { + "bbox": [ + 324, + 178, + 545, + 258 + ], + "lines": [ + { + "bbox": [ + 324, + 178, + 545, + 258 + ], + "spans": [ + { + "bbox": [ + 324, + 178, + 545, + 258 + ], + "type": "image", + "image_path": "f8c116b5c946904839cf0c4d4ec1d53671e698eeb567109e5fb4d886213e1981.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 289, + 289, + 434 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 289, + 289, + 434 + ], + "spans": [ + { + "bbox": [ + 46, + 289, + 289, + 434 + ], + "type": "text", + "content": "in each weather condition. For instance, the particle type of haze is aerosol, and haze is assumed as the entire image corruption [1,68]. Rain is constituted of water drops, and rainy images suffer from occlusion by the large motion-blurred particles with different shapes and scales. In contrast, irregular particle trajectories and opaque snowflakes are commonly seen in snowfall images. These differences among multiple weather types will cause interference when removing multiple weather-related artifacts. Hence, many more accurate physical models [4, 39, 75, 81] of various weather conditions based on Eqn. 1 are appeared, which further incorporate the typical weather priors into their models." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 445, + 287, + 541 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 445, + 287, + 541 + ], + "spans": [ + { + "bbox": [ + 46, + 445, + 287, + 541 + ], + "type": "text", + "content": "Analysis of feature representations. To study the feature representations in deep networks for different weather-related artifacts removal, we individually train the models on different weather datasets using the UNet-based structure network [67]. Then, we adopt centered kernel alignment (CKA) [9, 36, 37, 42, 58] to measure the similarity of feature representations in deep networks, which have been pre-trained on the different weather datasets." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 544, + 287, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 544, + 287, + 675 + ], + "spans": [ + { + "bbox": [ + 46, + 544, + 287, + 675 + ], + "type": "text", + "content": "The similarity visualizations across hidden layers of different networks are shown in Fig. 3. The internal representations learned on the rain dataset seem similar to the counterparts on the snow dataset. One plausible reason is that many snowfall images often resemble rainy images regarding appearances of degradation. While from Fig. 3 (b) and (c), the layers of the dehazing model have more similar patterns to the layers of deraining or desnowing models at the shallow and deep layers. Although networks are trained with different weather datasets, similar representations still exist at the sub-set of network positions." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 677, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 677, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 677, + 288, + 713 + ], + "type": "text", + "content": "Above observations motivate us to expand parameters for different weather types in an adaptive manner, which is flexible and efficient for multiple weather artifacts removal." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 288, + 389, + 302 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 288, + 389, + 302 + ], + "spans": [ + { + "bbox": [ + 305, + 288, + 389, + 302 + ], + "type": "text", + "content": "4. Methodology" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 308, + 546, + 417 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 308, + 546, + 417 + ], + "spans": [ + { + "bbox": [ + 304, + 308, + 546, + 417 + ], + "type": "text", + "content": "To explore weather-specific and weather-general features for multiple weather removal tasks, we construct a unified network architecture based on the U-Net [67]. Additionally, as shown in Fig. 4, we devise a novel two-stage training strategy, where we separately learn weather-general features and weather-specific features in the first and second training stages. Then, we elaborate the implementation details of our method. Without loss of generality, we assume that weather types include rain, snow, and haze." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 422, + 495, + 435 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 422, + 495, + 435 + ], + "spans": [ + { + "bbox": [ + 305, + 422, + 495, + 435 + ], + "type": "text", + "content": "4.1. Weather-General Feature Learning" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 441, + 547, + 513 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 441, + 547, + 513 + ], + "spans": [ + { + "bbox": [ + 304, + 441, + 547, + 513 + ], + "type": "text", + "content": "We employ the shared backbone network to learn the weather-general features. Specifically, various types of weather images are adopted to jointly update the shared backbone network, which helps the network to learn the weather-general feature representations. The forward process at this stage could be formulated as" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 376, + 517, + 545, + 533 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 376, + 517, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 376, + 517, + 545, + 533 + ], + "type": "interline_equation", + "content": "\\hat {X} _ {\\rho} = f \\left(X _ {\\rho}; \\theta_ {\\text {s h a r e}}\\right), \\tag {2}", + "image_path": "d8ed2a3e82ff91de8f53d3858edc9d8632c73f17691cc8f3cf46c7d2f549d296.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 539, + 546, + 586 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 539, + 546, + 586 + ], + "spans": [ + { + "bbox": [ + 304, + 539, + 546, + 586 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 539, + 546, + 586 + ], + "type": "inline_equation", + "content": "X_{\\rho}" + }, + { + "bbox": [ + 304, + 539, + 546, + 586 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 539, + 546, + 586 + ], + "type": "inline_equation", + "content": "\\hat{X}_{\\rho}" + }, + { + "bbox": [ + 304, + 539, + 546, + 586 + ], + "type": "text", + "content": " denote the input and recovered images of weather type " + }, + { + "bbox": [ + 304, + 539, + 546, + 586 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 304, + 539, + 546, + 586 + ], + "type": "text", + "content": "; " + }, + { + "bbox": [ + 304, + 539, + 546, + 586 + ], + "type": "inline_equation", + "content": "\\theta_{share}" + }, + { + "bbox": [ + 304, + 539, + 546, + 586 + ], + "type": "text", + "content": " denotes the general sharing parameters for all weather types, which are jointly optimized by multiple weather datasets." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 586, + 547, + 683 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 586, + 547, + 683 + ], + "spans": [ + { + "bbox": [ + 304, + 586, + 547, + 683 + ], + "type": "text", + "content": "In addition, the aforementioned Eq. (1) reveals that the bad weather degradation is largely related to the depth of corresponding scenes. Hence, we introduce the depth information in our framework to optimize the network. We adopt the depth consistency loss " + }, + { + "bbox": [ + 304, + 586, + 547, + 683 + ], + "type": "inline_equation", + "content": "L_{depth}" + }, + { + "bbox": [ + 304, + 586, + 547, + 683 + ], + "type": "text", + "content": ", which fully exploits the general depth auxiliary information among different adverse weathers. Hence, the final loss function of the first training stage is formulated as follows:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 688, + 545, + 713 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 688, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 688, + 545, + 713 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {s 1} = L _ {\\text {c o n t e n t}} \\left(\\hat {X} _ {\\rho}, Y _ {\\rho}\\right) + \\lambda_ {\\text {d e p t h}} \\| D \\left(\\hat {X} _ {\\rho}\\right) - D \\left(Y _ {\\rho}\\right) \\| _ {1}, \\tag {3}", + "image_path": "8028ac5d87d38f2917cd721fb4847884f249f4a83e3ebf55c316abf1d1fc660e.jpg" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21750" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "inline_equation", + "content": "L_{content}" + }, + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "text", + "content": " denotes the L1 and perceptual losses [33]; " + }, + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "inline_equation", + "content": "\\hat{X}_{\\rho}" + }, + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "inline_equation", + "content": "Y_{\\rho}" + }, + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "text", + "content": " indicate the outputs and labels of the weather " + }, + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "text", + "content": "; " + }, + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "inline_equation", + "content": "D(\\cdot)" + }, + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "text", + "content": " denotes the pre-trained depth estimation network [47], and " + }, + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "inline_equation", + "content": "\\lambda_{depth}" + }, + { + "bbox": [ + 47, + 72, + 287, + 122 + ], + "type": "text", + "content": " denotes the balanced weight." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 126, + 234, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 126, + 234, + 140 + ], + "spans": [ + { + "bbox": [ + 47, + 126, + 234, + 140 + ], + "type": "text", + "content": "4.2. Weather-Specific Feature Learning" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 145, + 288, + 242 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 145, + 288, + 242 + ], + "spans": [ + { + "bbox": [ + 46, + 145, + 288, + 242 + ], + "type": "text", + "content": "Adopting the shared parameters to deal with different weather conditions often degrades the restoration performance on each weather due to the interference issues of different sub-tasks. A straightforward solution is to insert the specific parameters for each weather condition across the network layers. Without loss of generality, such network expansion manner of the specific parameter " + }, + { + "bbox": [ + 46, + 145, + 288, + 242 + ], + "type": "inline_equation", + "content": "\\theta_{\\rho}" + }, + { + "bbox": [ + 46, + 145, + 288, + 242 + ], + "type": "text", + "content": " for the weather type " + }, + { + "bbox": [ + 46, + 145, + 288, + 242 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 46, + 145, + 288, + 242 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 46, + 145, + 288, + 242 + ], + "type": "inline_equation", + "content": "\\rho \\in [Rain, Haze, Snow]" + }, + { + "bbox": [ + 46, + 145, + 288, + 242 + ], + "type": "text", + "content": ") is defined as" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 120, + 247, + 287, + 262 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 120, + 247, + 287, + 262 + ], + "spans": [ + { + "bbox": [ + 120, + 247, + 287, + 262 + ], + "type": "interline_equation", + "content": "\\theta_ {\\rho} ^ {i} = \\theta_ {s h a r e} ^ {i} + \\Delta \\theta_ {\\rho} ^ {i}, \\tag {4}", + "image_path": "82a47a5a719862da9d08166622d295ac5d1f684544192da1ec4fff879b0f14b5.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "spans": [ + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "inline_equation", + "content": "\\theta_{\\text{share}}^i" + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "text", + "content": " denotes the general sharing weights at the network layer " + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "text", + "content": "; " + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "inline_equation", + "content": "\\Delta \\theta_{\\rho}^{i}" + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "text", + "content": " denotes the specific weights of weather type " + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "text", + "content": " at the layer " + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "inline_equation", + "content": "i = 1,2,\\dots,N" + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 269, + 287, + 316 + ], + "type": "text", + "content": " is the total number of network layers)." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 317, + 287, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 317, + 287, + 424 + ], + "spans": [ + { + "bbox": [ + 46, + 317, + 287, + 424 + ], + "type": "text", + "content": "However, such a simple and fixed network expansion paradigm exists limitations. First, according to the analysis in Sec. 3, the fixed form of expansion for different weathers is redundant and inflexible, leading to heavy computation overhead. Second, when the weather types increase, the fixed expansion manner will introduce the massive network parameters. A problem naturally arises: how to efficiently and flexibly explore weather-specific features to boost the network performance on a certain weather type?" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "spans": [ + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "text", + "content": "In this regard, we devise the regularization-based optimization scheme to achieve adaptive weather-specific parameters expansion for different weather types. In detail, we take the pre-trained model at the first stage as the basic model, where the parameters " + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "inline_equation", + "content": "(\\theta_{\\text{share}})" + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "text", + "content": " are shared in different weather conditions. To learn the weather-specific features, we first keep the parameters " + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "inline_equation", + "content": "\\theta_{\\text{share}}" + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "text", + "content": " frozen during this second training stage. Then, we introduce the weather-specific parameters " + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "inline_equation", + "content": "\\Delta \\theta_{\\rho}" + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "text", + "content": " and learnable scoring variable " + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "inline_equation", + "content": "S_{\\rho}^{i}" + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "text", + "content": " for the typical weather " + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "text", + "content": " at the network layer " + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "text", + "content": ". Hence, in our method, the parameters of the typical weather type " + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "text", + "content": " at the layer " + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 425, + 288, + 568 + ], + "type": "text", + "content": " could be reorganized as" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 104, + 574, + 287, + 590 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 574, + 287, + 590 + ], + "spans": [ + { + "bbox": [ + 104, + 574, + 287, + 590 + ], + "type": "interline_equation", + "content": "\\theta_ {\\rho} ^ {i} = \\theta_ {s h a r e} ^ {i} + F _ {\\tau} \\left(S _ {\\rho} ^ {i}\\right) * \\Delta \\theta_ {\\rho} ^ {i}, \\tag {5}", + "image_path": "3c281a443d41077bcda84cdf15f8c739c57cffa9dbdc2da5230761f278e04e1d.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 596, + 287, + 644 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 596, + 287, + 644 + ], + "spans": [ + { + "bbox": [ + 46, + 596, + 287, + 644 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 596, + 287, + 644 + ], + "type": "inline_equation", + "content": "S_{\\rho}^{i}" + }, + { + "bbox": [ + 46, + 596, + 287, + 644 + ], + "type": "text", + "content": " is the learnable scoring variable, which is used to assess the necessity to expand parameters at the layer " + }, + { + "bbox": [ + 46, + 596, + 287, + 644 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 596, + 287, + 644 + ], + "type": "text", + "content": ". An indicator function " + }, + { + "bbox": [ + 46, + 596, + 287, + 644 + ], + "type": "inline_equation", + "content": "F_{\\tau}(\\cdot)" + }, + { + "bbox": [ + 46, + 596, + 287, + 644 + ], + "type": "text", + "content": " is designed to determine whether to add the new parameters or not, which is defined as" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 108, + 651, + 287, + 682 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 108, + 651, + 287, + 682 + ], + "spans": [ + { + "bbox": [ + 108, + 651, + 287, + 682 + ], + "type": "interline_equation", + "content": "F _ {\\tau} \\left(S _ {\\rho} ^ {i}\\right) = \\left\\{ \\begin{array}{l l} 1 & \\text {i f} S _ {\\rho} ^ {i} \\geq \\tau , \\\\ 0 & \\text {o t h e r w i s e}, \\end{array} \\right. \\tag {6}", + "image_path": "a7eef3e14f6cf40b4bd3e522c5f9345af83e57bc08d60148cb457cc9396190d6.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "text", + "content": " is the threshold hyperparameter. Only if " + }, + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "inline_equation", + "content": "S_{\\rho}^{i}" + }, + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "text", + "content": " is larger than the threshold " + }, + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "text", + "content": ", the new parameters will be al" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "text", + "content": "owed to add to the deep network. Here, we study the convolution operators with the kernel sizes of " + }, + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "text", + "content": " or " + }, + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "3 \\times 3" + }, + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "text", + "content": " as the newly added weather-specific parameters (please see Sec. 6.4 for the detailed ablation study)." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 121, + 545, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 121, + 545, + 144 + ], + "spans": [ + { + "bbox": [ + 305, + 121, + 545, + 144 + ], + "type": "text", + "content": "Lastly, the overall loss function of the second training stage could be formulated as follows:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 315, + 152, + 545, + 196 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 152, + 545, + 196 + ], + "spans": [ + { + "bbox": [ + 315, + 152, + 545, + 196 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {s 2} = \\sum_ {\\rho} \\left(L _ {\\text {c o n t e n t}} \\left(\\hat {X} _ {\\rho}, Y _ {\\rho}\\right) + \\lambda_ {r e g} \\sum_ {i = 1} ^ {N} \\left| S _ {\\rho} ^ {i} \\right| _ {1}\\right), \\tag {7}", + "image_path": "a9425b729a3a66fdaeb6d6567e07680eeecd3fe969aee4b95148a79b388ca841.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "spans": [ + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "inline_equation", + "content": "L_{content}" + }, + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "text", + "content": " denotes the MAE and perceptual losses [33]; " + }, + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "inline_equation", + "content": "\\hat{X}_{\\rho}" + }, + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "inline_equation", + "content": "Y_{\\rho}" + }, + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "text", + "content": " indicates the restored outputs and ground truths of the weather " + }, + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "text", + "content": "; " + }, + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "inline_equation", + "content": "\\lambda_{reg}" + }, + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "text", + "content": " denotes the hyperparameter of the regularization term. As described in Eq. (7), we introduce the sparsity regularization of " + }, + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "inline_equation", + "content": "S_{\\rho}^{i}" + }, + { + "bbox": [ + 304, + 197, + 545, + 352 + ], + "type": "text", + "content": " in the objective functions, which guides the network to automatically discover the network positions where we need to add weather-specific parameters. Experimental results also reveal that adding relatively small weather-specific parameters to the learned positions will significantly improve model performance (see Sec. 6.4 for details). Therefore, our network could flexibly learn the weather-specific parameters and efficiently handle various weather conditions." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 354, + 402, + 367 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 354, + 402, + 367 + ], + "spans": [ + { + "bbox": [ + 305, + 354, + 402, + 367 + ], + "type": "text", + "content": "4.3. Inference Stage" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 368, + 545, + 488 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 368, + 545, + 488 + ], + "spans": [ + { + "bbox": [ + 304, + 368, + 545, + 488 + ], + "type": "text", + "content": "Fig. 4 (c) illustrates the inference stage of our method. The learned model comprises the weather-general parameters for different weathers and the adaptive weather-specific parameters for each typical weather at the sub-set of network positions. For example, taking an image with a certain type of weather (e.g., haze) as the input, the image will pass through the parameters in black and blue color. Note that these positions marked \"NULL\" indicates that these parameters are unnecessary and the forward propagation will not pass through these layers." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 491, + 545, + 517 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 491, + 545, + 517 + ], + "spans": [ + { + "bbox": [ + 305, + 491, + 545, + 517 + ], + "type": "text", + "content": "5. Real-World Datasets for Multiple Weather-Related Artifacts Removal" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 522, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 522, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 522, + 545, + 713 + ], + "type": "text", + "content": "Current all-weather-removal approaches rely on synthetic datasets to train their models, which limits their generalization capability to deal with real-world scenarios. To this regard, we construct the first real-world benchmark datasets under various weather conditions, including haze, rain, and snow. For dehazing, we directly employ the existing real-world dehazing dataset [99]. For deraining, we construct the SPA+ dataset based on the previous SPA [73] dataset. SPA is the first large-scale real-world dataset with paired images. However, SPA still contains two evident issues: (i) many images with sparse rain steaks and repeated background scenes; and (ii) existing images with the same scenes in both training and testing sets. To solve these issues, we first merge the images with repeated background scenes and densify the rain streaks. 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+ "index": 15, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 47, + 205, + 287, + 249 + ], + "lines": [ + { + "bbox": [ + 47, + 205, + 287, + 249 + ], + "spans": [ + { + "bbox": [ + 47, + 205, + 287, + 249 + ], + "type": "text", + "content": "Figure 5. (a) and (b) illustrate the visual samples of the real-world dehazing dataset [99] and our proposed RealSnow, respectively. (c) exhibits the same scenes with sparse rains occur multiple times in SPA and corresponding merged images in our proposed SPA+." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 242, + 153, + 286, + 198 + ], + "blocks": [ + { + "bbox": [ + 242, + 153, + 286, + 198 + ], + "lines": [ + { + "bbox": [ + 242, + 153, + 286, + 198 + ], + "spans": [ + { + "bbox": [ + 242, + 153, + 286, + 198 + ], + "type": "image", + "image_path": "aca3c4449af284ccea2c18b2c890ee0131e44403e5a95bd9fe14a6a2cca6374d.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + } + ], + "index": 16 + }, + { + "type": "table", + "bbox": [ + 50, + 282, + 287, + 376 + ], + "blocks": [ + { + "bbox": [ + 47, + 259, + 286, + 281 + ], + "lines": [ + { + "bbox": [ + 47, + 259, + 286, + 281 + ], + "spans": [ + { + "bbox": [ + 47, + 259, + 286, + 281 + ], + "type": "text", + "content": "Table 1. Illustration of three dataset settings for the weather removal task (R: Rain; RD: RainDrop; S: Snow; H: Haze)." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 282, + 287, + 376 + ], + "lines": [ + { + "bbox": [ + 50, + 282, + 287, + 376 + ], + "spans": [ + { + "bbox": [ + 50, + 282, + 287, + 376 + ], + "type": "table", + "html": "
SettingWeather TypesDatasetsTraining Configurations
[Setting 1] (Synthetic)(R,RD,H)Outdoor-Rain [40] RainDrop [59] Snow100K [51]Uniformly sampling 9000 images pairs
[Setting 2] (Synthetic)(R,S,H)Rain1400 [17] RESIDE [39] Snow100K [51]Uniformly sampling 5000 images pairs
[Setting 3] (Real)(R,S,H)SPA+ RealSnow REVIDE [99]Uniformly sampling 160000 images patches
", + "image_path": "636552707e180abf2b51c57932ac50f0e9f31944fe92210943044b123ce19cb7.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "table_body" + } + ], + "index": 19 + }, + { + "bbox": [ + 46, + 391, + 287, + 546 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 391, + 287, + 546 + ], + "spans": [ + { + "bbox": [ + 46, + 391, + 287, + 546 + ], + "type": "text", + "content": "In addition, inspired by [73], we build the first real-world desnowing dataset by using the background-static videos to acquire real-world snowing image pairs. The proposed real-world snow dataset, named RealSnow, has 1890 image pairs in total, where 1650 image pairs are used for training, and 240 image pairs are for evaluation. These images are acquired from 126 background-static videos with a wide variety of urban or natural background scenarios, e.g., buildings, cars, statues, trees, and roads. These scenes include varying degrees of snowfall densities and illuminations (e.g., day and night). RealSnow also contains various resolutions, and the average resolution is " + }, + { + "bbox": [ + 46, + 391, + 287, + 546 + ], + "type": "inline_equation", + "content": "1208 \\times 646" + }, + { + "bbox": [ + 46, + 391, + 287, + 546 + ], + "type": "text", + "content": ". The example images from our dataset are shown in Fig. 5." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 47, + 561, + 127, + 574 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 561, + 127, + 574 + ], + "spans": [ + { + "bbox": [ + 47, + 561, + 127, + 574 + ], + "type": "text", + "content": "6. Experiments" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 47, + 580, + 179, + 593 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 580, + 179, + 593 + ], + "spans": [ + { + "bbox": [ + 47, + 580, + 179, + 593 + ], + "type": "text", + "content": "6.1. Implementation Details" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "text", + "content": "We implement our method on Pytorch platform. Adam optimizer [35] is adopted. At the first stage, our model is trained for 100 epochs, and the initial learning rate is " + }, + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "inline_equation", + "content": "2 \\times 10^{-4}" + }, + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "text", + "content": ", which is adjusted with the cosine annealing scheme [52]. In the second stage, our model is trained for another 100 epochs, and the initial learning rate is " + }, + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "inline_equation", + "content": "1 \\times 10^{-4}" + }, + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "text", + "content": ", which is also adjusted with the cosine annealing scheme. The patch size is set as " + }, + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "inline_equation", + "content": "224 \\times 224" + }, + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "text", + "content": ". Additionally, hyperparameters are empirically set as: " + }, + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "inline_equation", + "content": "\\tau = 0.1" + }, + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "inline_equation", + "content": "\\lambda_{depth} = 0.02" + }, + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "inline_equation", + "content": "\\lambda_{reg} = 0.08" + }, + { + "bbox": [ + 46, + 596, + 287, + 715 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 23 + }, + { + "type": "table", + "bbox": [ + 309, + 83, + 545, + 180 + ], + "blocks": [ + { + "bbox": [ + 316, + 71, + 534, + 81 + ], + "lines": [ + { + "bbox": [ + 316, + 71, + 534, + 81 + ], + "spans": [ + { + "bbox": [ + 316, + 71, + 534, + 81 + ], + "type": "text", + "content": "Table 2. [Setting 1] Comparisons on the Outdoor-Rain [40]." + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 83, + 545, + 180 + ], + "lines": [ + { + "bbox": [ + 309, + 83, + 545, + 180 + ], + "spans": [ + { + "bbox": [ + 309, + 83, + 545, + 180 + ], + "type": "table", + "html": "
TypeMethodVenuePSNR ↑SSIM ↑
Derainingpix2pix [30]CVPR'1719.090.71
HRGAN [40]CVPR'1921.560.86
MPRNet [92]CVPR'2121.900.85
Multi-TasksAll-in-One [41]CVPR'2024.710.90
TransWeather [69]CVPR'2223.180.84
Chen et al. [6]CVPR'2223.940.85
Ours-25.310.90
", + "image_path": "780c78105bc50c0c65ad193536f19c7e0a8fb9d2a421fe9796187aa2666198f2.jpg" + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "table_body" + } + ], + "index": 25 + }, + { + "type": "table", + "bbox": [ + 309, + 194, + 545, + 302 + ], + "blocks": [ + { + "bbox": [ + 310, + 182, + 541, + 193 + ], + "lines": [ + { + "bbox": [ + 310, + 182, + 541, + 193 + ], + "spans": [ + { + "bbox": [ + 310, + 182, + 541, + 193 + ], + "type": "text", + "content": "Table 3. [Setting 1] Comparisons on the RainDrop dataset [59]." + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 194, + 545, + 302 + ], + "lines": [ + { + "bbox": [ + 309, + 194, + 545, + 302 + ], + "spans": [ + { + "bbox": [ + 309, + 194, + 545, + 302 + ], + "type": "table", + "html": "
TypeMethodVenuePSNR ↑SSIM ↑
RainDrop RemovalPix2pix [30]CVPR'1728.020.85
Attn.GAN [59]CVPR'1830.550.90
Quan et al. [63]ICCV'1931.440.93
CCN [62]CVPR'2131.340.95
Multi-TasksAll-in-One [41]CVPR'2031.120.93
TransWeather [69]CVPR'2228.980.90
Chen et al. [6]CVPR'2230.750.91
Ours-31.310.93
", + "image_path": "e0232451f5f328e115b194aa4bbc1f3b656661510a33f1f32056768b6f5d9068.jpg" + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "table_body" + } + ], + "index": 27 + }, + { + "type": "table", + "bbox": [ + 309, + 332, + 545, + 472 + ], + "blocks": [ + { + "bbox": [ + 306, + 308, + 545, + 331 + ], + "lines": [ + { + "bbox": [ + 306, + 308, + 545, + 331 + ], + "spans": [ + { + "bbox": [ + 306, + 308, + 545, + 331 + ], + "type": "text", + "content": "Table 4. [Setting 1] & [Setting 2] Comparisons on the SnowTest100k-L (S-L) dataset [51]." + } + ] + } + ], + "index": 28, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 332, + 545, + 472 + ], + "lines": [ + { + "bbox": [ + 309, + 332, + 545, + 472 + ], + "spans": [ + { + "bbox": [ + 309, + 332, + 545, + 472 + ], + "type": "table", + "html": "
TypeMethodVenuePSNR ↑SSIM ↑
DesnowingDetailsNet [17]CVPR ' 1719.180.75
DesnowNet [51]TIP ' 1827.170.90
JSTASR [4]ECCV ' 2025.320.81
DDMSNET [97]TIP ' 2128.850.88
Multi-Tasks(Setting 1)All-in-One [41]CVPR ' 2028.330.88
TransWeather [69]CVPR ' 2227.800.85
Chen et al. [6]CVPR ' 2229.270.88
Ours-29.710.89
Multi-Tasks(Setting 2)TransWeather [69]CVPR ' 2226.170.88
Chen et al. [6]CVPR ' 2228.710.88
Ours-29.420.89
", + "image_path": "b59c440c57b51c8877dec79a648459127a4294685b9314b00f3ea46755ce549b.jpg" + } + ] + } + ], + "index": 29, + "angle": 0, + "type": "table_body" + } + ], + "index": 29 + }, + { + "type": "table", + "bbox": [ + 309, + 487, + 545, + 628 + ], + "blocks": [ + { + "bbox": [ + 312, + 474, + 538, + 485 + ], + "lines": [ + { + "bbox": [ + 312, + 474, + 538, + 485 + ], + "spans": [ + { + "bbox": [ + 312, + 474, + 538, + 485 + ], + "type": "text", + "content": "Table 5. [Setting 2] Comparisons on the RESIDE dataset [39]." + } + ] + } + ], + "index": 30, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 487, + 545, + 628 + ], + "lines": [ + { + "bbox": [ + 309, + 487, + 545, + 628 + ], + "spans": [ + { + "bbox": [ + 309, + 487, + 545, + 628 + ], + "type": "table", + "html": "
TypeMethodVenuePSNR ↑SSIM ↑
DehazingEPDN [61]CVPR' 1923.820.89
PFDN [14]ECCV' 2031.450.97
KDDN [24]CVPR' 2033.490.97
MSBDN [13]CVPR' 2033.790.98
FFA-Net [60]AAAI' 2034.980.99
AECRNet [77]CVPR' 2135.610.98
MPRNet [92]CVPR' 2131.310.97
Multi-TasksAll-in-One [41]CVPR' 2030.490.95
TransWeather [69]CVPR' 2227.660.95
Chen et al. [6]CVPR' 2230.760.97
Ours-30.850.98
", + "image_path": "02a8e07785030ed08fde50087710f8c0d8a7c8bb84d40616494aaecd4e3e99b0.jpg" + } + ] + } + ], + "index": 31, + "angle": 0, + "type": "table_body" + } + ], + "index": 31 + }, + { + "bbox": [ + 306, + 639, + 425, + 651 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 639, + 425, + 651 + ], + "spans": [ + { + "bbox": [ + 306, + 639, + 425, + 651 + ], + "type": "text", + "content": "6.2. Benchmark Datasets" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "content": "To verify the effectiveness of our method, we employ three dataset settings, which are presented in Tab. 1. Previous all-weather-removal methods [6,41,69] only perform their methods on the settings of the synthetic datasets, e.g., [Setting 1] and [Setting 2]. Moreover, we construct the first" + } + ] + } + ], + "index": 33 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21752" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 52, + 71, + 113, + 114 + ], + "blocks": [ + { + "bbox": [ + 52, + 71, + 113, + 114 + ], + "lines": [ + { + "bbox": [ + 52, + 71, + 113, + 114 + ], + "spans": [ + { + "bbox": [ + 52, + 71, + 113, + 114 + ], + "type": "image", + "image_path": "573adc5e733020f972e8b1029e915a5f1ce2f0f092da2a66b2a4a4c2917803d2.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 52, + 116, + 113, + 156 + ], + "blocks": [ + { + "bbox": [ + 52, + 116, + 113, + 156 + ], + "lines": [ + { + "bbox": [ + 52, + 116, + 113, + 156 + ], + "spans": [ + { + "bbox": [ + 52, + 116, + 113, + 156 + ], + "type": "image", + "image_path": "a15fe6c69f02d8df4f8482cf2e4ad4cfcaf1488512ae4ba8ef204dabfc0698ee.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 72, + 158, + 91, + 167 + ], + "lines": [ + { + "bbox": [ + 72, + 158, + 91, + 167 + ], + "spans": [ + { + "bbox": [ + 72, + 158, + 91, + 167 + ], + "type": "text", + "content": "Input" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 113, + 72, + 175, + 114 + ], + "blocks": [ + { + "bbox": [ + 113, + 72, + 175, + 114 + ], + "lines": [ + { + "bbox": [ + 113, + 72, + 175, + 114 + ], + "spans": [ + { + "bbox": [ + 113, + 72, + 175, + 114 + ], + "type": "image", + "image_path": "360bd1d6cde5cd86befc574405ea0d43dec668641259cc7a3e3a576381c9ee7c.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 113, + 116, + 175, + 156 + ], + "blocks": [ + { + "bbox": [ + 113, + 116, + 175, + 156 + ], + "lines": [ + { + "bbox": [ + 113, + 116, + 175, + 156 + ], + "spans": [ + { + "bbox": [ + 113, + 116, + 175, + 156 + ], + "type": "image", + "image_path": "ac2a8c76793a66d23e3938ac7c61a9e6be8ff1dac2974b2aa5804390870f8995.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 127, + 157, + 161, + 166 + ], + "lines": [ + { + "bbox": [ + 127, + 157, + 161, + 166 + ], + "spans": [ + { + "bbox": [ + 127, + 157, + 161, + 166 + ], + "type": "text", + "content": "Chen et.al" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 175, + 72, + 235, + 114 + ], + "blocks": [ + { + "bbox": [ + 175, + 72, + 235, + 114 + ], + "lines": [ + { + "bbox": [ + 175, + 72, + 235, + 114 + ], + "spans": [ + { + "bbox": [ + 175, + 72, + 235, + 114 + ], + "type": "image", + "image_path": "cd4c66fb3c86bd8ed2b9fe80de69a3c20c7ab077cda1840ac08897e67a518ab9.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 175, + 116, + 235, + 156 + ], + "blocks": [ + { + "bbox": [ + 175, + 116, + 235, + 156 + ], + "lines": [ + { + "bbox": [ + 175, + 116, + 235, + 156 + ], + "spans": [ + { + "bbox": [ + 175, + 116, + 235, + 156 + ], + "type": "image", + "image_path": "2640aac55a31796a050f7c4c0f55a6732cd0b83acf5101cd7e8bba242391cdde.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 182, + 158, + 228, + 167 + ], + "lines": [ + { + "bbox": [ + 182, + 158, + 228, + 167 + ], + "spans": [ + { + "bbox": [ + 182, + 158, + 228, + 167 + ], + "type": "text", + "content": "TransWeather" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 67, + 171, + 525, + 182 + ], + "lines": [ + { + "bbox": [ + 67, + 171, + 525, + 182 + ], + "spans": [ + { + "bbox": [ + 67, + 171, + 525, + 182 + ], + "type": "text", + "content": "Figure 6. Visualization comparisons with previous all-weather-removal methods under multiple real-world weather conditions." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 236, + 72, + 297, + 114 + ], + "blocks": [ + { + "bbox": [ + 236, + 72, + 297, + 114 + ], + "lines": [ + { + "bbox": [ + 236, + 72, + 297, + 114 + ], + "spans": [ + { + "bbox": [ + 236, + 72, + 297, + 114 + ], + "type": "image", + "image_path": "8f01c37b8927eb0f89de2044bd34c336d8fa8896ffad0863ba457d0346d79b85.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 236, + 116, + 297, + 156 + ], + "blocks": [ + { + "bbox": [ + 236, + 116, + 297, + 156 + ], + "lines": [ + { + "bbox": [ + 236, + 116, + 297, + 156 + ], + "spans": [ + { + "bbox": [ + 236, + 116, + 297, + 156 + ], + "type": "image", + "image_path": "1ebfa02a26970c172486e5d8430153ae5793ad70aff4b4202d2e92b466fa9383.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 257, + 158, + 274, + 167 + ], + "lines": [ + { + "bbox": [ + 257, + 158, + 274, + 167 + ], + "spans": [ + { + "bbox": [ + 257, + 158, + 274, + 167 + ], + "type": "text", + "content": "Ours" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 50, + 200, + 287, + 340 + ], + "blocks": [ + { + "bbox": [ + 51, + 188, + 282, + 198 + ], + "lines": [ + { + "bbox": [ + 51, + 188, + 282, + 198 + ], + "spans": [ + { + "bbox": [ + 51, + 188, + 282, + 198 + ], + "type": "text", + "content": "Table 6. [Setting 2] Comparisons on the Rain1400 dataset [17]." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 200, + 287, + 340 + ], + "lines": [ + { + "bbox": [ + 50, + 200, + 287, + 340 + ], + "spans": [ + { + "bbox": [ + 50, + 200, + 287, + 340 + ], + "type": "table", + "html": "
TypeMethodVenuePSNR ↑SSIM ↑
DerainingJORDER [82]CVPR' 1731.280.92
PReNet [64]CVPR' 1931.880.93
DRD-Net [11]CVPR' 2029.650.88
MSPFN [32]CVPR' 2029.240.88
DualGCN [18]AAAI' 2130.500.91
JRJG [88]CVPR' 2131.180.91
MPRNet [92]CVPR' 2131.530.96
Multi TaskAll-in-One [41]CVPR' 2030.820.90
TransWeather [69]CVPR' 2229.140.89
Chen et al. [6]CVPR' 2231.750.91
Ours-32.490.93
", + "image_path": "81ff5b4986ceb2d1297c4c396253b777abd35eaaa032f846adc818c4e294858c.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "table_body" + } + ], + "index": 14 + }, + { + "type": "table", + "bbox": [ + 50, + 354, + 287, + 462 + ], + "blocks": [ + { + "bbox": [ + 59, + 342, + 274, + 354 + ], + "lines": [ + { + "bbox": [ + 59, + 342, + 274, + 354 + ], + "spans": [ + { + "bbox": [ + 59, + 342, + 274, + 354 + ], + "type": "text", + "content": "Table 7. Comparisons results on the datasets of [Setting 3]." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 354, + 287, + 462 + ], + "lines": [ + { + "bbox": [ + 50, + 354, + 287, + 462 + ], + "spans": [ + { + "bbox": [ + 50, + 354, + 287, + 462 + ], + "type": "table", + "html": "
DatasetsMethodVenuePSNR ↑SSIM ↑
SPA+TransWeather [69]CVPR' 2233.640.93
Chen et al. [6]CVPR' 2237.320.97
Ours-38.940.98
RealSnowTransWeather [69]CVPR' 2229.160.82
Chen et al. [6]CVPR' 2229.370.88
Ours-29.460.85
REVIDE [99]TransWeather [69]CVPR' 2217.330.82
Chen et al. [6]CVPR' 2220.100.85
Ours-20.440.87
", + "image_path": "0b10896438c6b0d098912840a7c32841153f7661690c95676783d2d722a0ecf6.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "table_body" + } + ], + "index": 16 + }, + { + "type": "table", + "bbox": [ + 49, + 487, + 287, + 552 + ], + "blocks": [ + { + "bbox": [ + 47, + 464, + 286, + 485 + ], + "lines": [ + { + "bbox": [ + 47, + 464, + 286, + 485 + ], + "spans": [ + { + "bbox": [ + 47, + 464, + 286, + 485 + ], + "type": "text", + "content": "Table 8. Ablation study of our training strategy using the PNSR metric on the Datasets of [Setting 1]." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 49, + 487, + 287, + 552 + ], + "lines": [ + { + "bbox": [ + 49, + 487, + 287, + 552 + ], + "spans": [ + { + "bbox": [ + 49, + 487, + 287, + 552 + ], + "type": "table", + "html": "
ModelsExperiment settingsRainRainDropS-L
Model-1Ours-Stage 1 (4.98 M)25.0430.8829.13
Model-2Ours-Stage 2 (5.97 M)25.3131.3129.71
Model-3Random Expansion of specific #Param.25.1430.9629.28
Model-4Enlarging #Param. of Model-1 (6.79 M)25.2230.9929.42
", + "image_path": "83174b8feefdfff08135fb16aa4344fa7585ed10c8d773ecc6c22b3519708f6b.jpg" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "table_body" + } + ], + "index": 18 + }, + { + "type": "table", + "bbox": [ + 50, + 567, + 287, + 622 + ], + "blocks": [ + { + "bbox": [ + 60, + 555, + 273, + 566 + ], + "lines": [ + { + "bbox": [ + 60, + 555, + 273, + 566 + ], + "spans": [ + { + "bbox": [ + 60, + 555, + 273, + 566 + ], + "type": "text", + "content": "Table 9. Ablation study of the parameters expansion types." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 567, + 287, + 622 + ], + "lines": [ + { + "bbox": [ + 50, + 567, + 287, + 622 + ], + "spans": [ + { + "bbox": [ + 50, + 567, + 287, + 622 + ], + "type": "table", + "html": "
TypeSetting 1Setting 2
RainRainDropS-LRain1400RESIDES-L
1 × 130.3131.3129.7132.4930.8529.43
3 × 330.5231.5229.8632.7530.9029.69
", + "image_path": "f5865611974d9467bf29e2f1d247cd54f3565cde1b4ffd7e03c4b8077a38c147.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "table_body" + } + ], + "index": 20 + }, + { + "bbox": [ + 46, + 629, + 287, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 629, + 287, + 653 + ], + "spans": [ + { + "bbox": [ + 46, + 629, + 287, + 653 + ], + "type": "text", + "content": "real-world benchmark dataset for image restoration under multiple weather conditions of real scenes ([Setting 3])." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 46, + 654, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 287, + 713 + ], + "type": "text", + "content": "For fair comparisons, we follow [6,41] to uniformly sample images/patches from the dataset for network training. Meanwhile, except for RealSnow in [Setting 3], we adopt the corresponding test datasets that have already been predivided by the pioneer works [17,39,40,51,59,73]." + } + ] + } + ], + "index": 22 + }, + { + "type": "image", + "bbox": [ + 298, + 72, + 359, + 114 + ], + "blocks": [ + { + "bbox": [ + 298, + 72, + 359, + 114 + ], + "lines": [ + { + "bbox": [ + 298, + 72, + 359, + 114 + ], + "spans": [ + { + "bbox": [ + 298, + 72, + 359, + 114 + ], + "type": "image", + "image_path": "e73e8e997b367b9ae393161fa2434e686f4102852c630cd67f6cc0cd638b6c44.jpg" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_body" + } + ], + "index": 23 + }, + { + "type": "image", + "bbox": [ + 298, + 115, + 359, + 156 + ], + "blocks": [ + { + "bbox": [ + 298, + 115, + 359, + 156 + ], + "lines": [ + { + "bbox": [ + 298, + 115, + 359, + 156 + ], + "spans": [ + { + "bbox": [ + 298, + 115, + 359, + 156 + ], + "type": "image", + "image_path": 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"image_caption" + } + ], + "index": 33 + }, + { + "type": "table", + "bbox": [ + 309, + 200, + 545, + 239 + ], + "blocks": [ + { + "bbox": [ + 307, + 188, + 544, + 198 + ], + "lines": [ + { + "bbox": [ + 307, + 188, + 544, + 198 + ], + "spans": [ + { + "bbox": [ + 307, + 188, + 544, + 198 + ], + "type": "text", + "content": "Table 10. Ablation study of losses on the Datasets of [Setting 1]." + } + ] + } + ], + "index": 35, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 200, + 545, + 239 + ], + "lines": [ + { + "bbox": [ + 309, + 200, + 545, + 239 + ], + "spans": [ + { + "bbox": [ + 309, + 200, + 545, + 239 + ], + "type": "table", + "html": "
lcontentldepthOutdoor-RainRainDropSnow100K-L
Stage 1✓24.9630.7929.00
✓✓25.0430.8829.13
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MethodsAll-in-OneChen et al.TransWeatherOurs
#Param (M: 106)44.0028.7138.055.97
Inference Time (s)-0.0670.0260.030
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Ablation study of the regularization term " + }, + { + "bbox": [ + 321, + 358, + 529, + 369 + ], + "type": "inline_equation", + "content": "\\lambda_{reg}" + }, + { + "bbox": [ + 321, + 358, + 529, + 369 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 41, + "angle": 0, + "type": "image_caption" + } + ], + "index": 39 + }, + { + "type": "image", + "bbox": [ + 429, + 294, + 545, + 355 + ], + "blocks": [ + { + "bbox": [ + 429, + 294, + 545, + 355 + ], + "lines": [ + { + "bbox": [ + 429, + 294, + 545, + 355 + ], + "spans": [ + { + "bbox": [ + 429, + 294, + 545, + 355 + ], + "type": "image", + "image_path": "27db97bed94ad2c9449a02e7b88922b18bd851e6a4075d07c2f88909d1e335c3.jpg" + } + ] + } + ], + "index": 40, + "angle": 0, + "type": "image_body" + } + ], + "index": 40 + }, + { + "bbox": [ + 306, + 380, + 545, + 392 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 380, + 545, + 392 + ], + "spans": [ + { + "bbox": [ + 306, + 380, + 545, + 392 + ], + "type": "text", + "content": "6.3. Comparison with the State-of-the-art Methods" + } + ] + } + ], + "index": 42 + }, + { + "bbox": [ + 305, + 396, + 545, + 443 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 396, + 545, + 443 + ], + "spans": [ + { + "bbox": [ + 305, + 396, + 545, + 443 + ], + "type": "text", + "content": "Similar to [41, 69], we adopt the Peak Signal-to-Noise Ratio (PSNR) and the structural similarity (SSIM) [76] to evaluate the restoration performance of different models. PSNR and SSIM are calculated along the RGB channels." + } + ] + } + ], + "index": 43 + }, + { + "bbox": [ + 304, + 444, + 545, + 538 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 444, + 545, + 538 + ], + "spans": [ + { + "bbox": [ + 304, + 444, + 545, + 538 + ], + "type": "text", + "content": "In Tables 2, 3, 4, 5, 6, 7, we report the qualitative comparison results among different methods, including both all-weather-removal and typical-weather-removal methods. Obviously, our method achieves superior performance in terms of all metrics on all datasets compared with all-weather-removal methods. In particular, our method surpasses TransWeather [69] by nearly two dB on multiple datasets under different weather conditions." + } + ] + } + ], + "index": 44 + }, + { + "bbox": [ + 304, + 540, + 545, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 540, + 545, + 598 + ], + "spans": [ + { + "bbox": [ + 304, + 540, + 545, + 598 + ], + "type": "text", + "content": "Moreover, we provide visual comparisons on the real-world scenarios of various kinds of weather in Fig. 6. It is evident that our results successfully preserve background details and remove multiple weather artifacts. In contrast, the apparent artifacts still exist in the results of [69]." + } + ] + } + ], + "index": 45 + }, + { + "bbox": [ + 306, + 605, + 399, + 616 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 605, + 399, + 616 + ], + "spans": [ + { + "bbox": [ + 306, + 605, + 399, + 616 + ], + "type": "text", + "content": "6.4. Ablation Study" + } + ] + } + ], + "index": 46 + }, + { + "bbox": [ + 305, + 621, + 545, + 644 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 621, + 545, + 644 + ], + "spans": [ + { + "bbox": [ + 305, + 621, + 545, + 644 + ], + "type": "text", + "content": "We further conduct ablation studies to validate the effects of each component in our method." + } + ] + } + ], + "index": 47 + }, + { + "bbox": [ + 304, + 647, + 545, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 647, + 545, + 696 + ], + "spans": [ + { + "bbox": [ + 304, + 647, + 545, + 696 + ], + "type": "text", + "content": "Ablation study of the two-stage training strategy. We set up three models with the following configurations: (i) Model-1 adopts the first training stage, and the network only learns the weather-general parameters; (ii) Model-2" + } + ] + } + ], + "index": 48 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 317, + 703, + 525, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 703, + 525, + 712 + ], + "spans": [ + { + "bbox": [ + 317, + 703, + 525, + 712 + ], + "type": "text", + "content": "1The authors have updated their results in the new arxiv version." + } + ] + } + ], + "index": 49 + }, + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21753" + } + ] + } + ], + "index": 50 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 69, + 286, + 180 + ], + "blocks": [ + { + "bbox": [ + 50, + 69, + 286, + 180 + ], + "lines": [ + { + "bbox": [ + 50, + 69, + 286, + 180 + ], + "spans": [ + { + "bbox": [ + 50, + 69, + 286, + 180 + ], + "type": "image", + "image_path": "c7aae7bbdb13a0ac0b13495164af28fde33179e93897abc8d7b192ba3ec5f651.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 47, + 186, + 287, + 209 + ], + "lines": [ + { + "bbox": [ + 47, + 186, + 287, + 209 + ], + "spans": [ + { + "bbox": [ + 47, + 186, + 287, + 209 + ], + "type": "text", + "content": "Figure 8. Visualization of the learned positions of expanded parameters. '1': expansion and '0': non-expansion." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 51, + 214, + 168, + 300 + ], + "blocks": [ + { + "bbox": [ + 51, + 214, + 168, + 300 + ], + "lines": [ + { + "bbox": [ + 51, + 214, + 168, + 300 + ], + "spans": [ + { + "bbox": [ + 51, + 214, + 168, + 300 + ], + "type": "image", + "image_path": "3772e38d89810b04a8057eef41234c7653719f4f8895f3777e9d0ae81bf77aad.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 61, + 302, + 157, + 310 + ], + "lines": [ + { + "bbox": [ + 61, + 302, + 157, + 310 + ], + "spans": [ + { + "bbox": [ + 61, + 302, + 157, + 310 + ], + "type": "text", + "content": "(a) t-SNE from weather-general features" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 170, + 214, + 286, + 300 + ], + "blocks": [ + { + "bbox": [ + 170, + 214, + 286, + 300 + ], + "lines": [ + { + "bbox": [ + 170, + 214, + 286, + 300 + ], + "spans": [ + { + "bbox": [ + 170, + 214, + 286, + 300 + ], + "type": "image", + "image_path": "513693ad9b4d2af2576ec7e86b5892419155243f4124753bf9d0641644357f4f.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 180, + 302, + 277, + 310 + ], + "lines": [ + { + "bbox": [ + 180, + 302, + 277, + 310 + ], + "spans": [ + { + "bbox": [ + 180, + 302, + 277, + 310 + ], + "type": "text", + "content": "(b) t-SNE from weather-specific features" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 47, + 316, + 287, + 338 + ], + "lines": [ + { + "bbox": [ + 47, + 316, + 287, + 338 + ], + "spans": [ + { + "bbox": [ + 47, + 316, + 287, + 338 + ], + "type": "text", + "content": "Figure 9. Visualization of t-SNE from learned weather-general and weather-specific features with our proposed training strategy." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 347, + 287, + 418 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 347, + 287, + 418 + ], + "spans": [ + { + "bbox": [ + 46, + 347, + 287, + 418 + ], + "type": "text", + "content": "adopts two training stages, and we fix the weather-general parameters and learn the weather-specific parameters; (iii) Model-4 expands the weather-specific parameters with the random sampling positions strategy. (iv) Model-4 enlarges the parameters of Model-1, and the network only learns the weather-general parameters." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 419, + 287, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 419, + 287, + 515 + ], + "spans": [ + { + "bbox": [ + 46, + 419, + 287, + 515 + ], + "type": "text", + "content": "As reported in Tab. 8, the performance of Model-2 performs is significantly improved after the second training stage, showing the effectiveness of our training strategy. For fair comparisons, note that Model-3 enlarges the network parameters by directly increasing the channel dimensions. Compared to Model-3 and Model-4, Model-2 adopts the adaptive parameters expansion strategy with fewer parameters but delivers better restoration performance." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 522, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 522, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 522, + 287, + 714 + ], + "type": "text", + "content": "Ablation study of expansion ratios. As described in Sec. 4.1, we introduce a sparsity regularization in the objective function during the second training stage. It guides our network to discover effective positions where to expand the weather-specific parameters. Consequently, the expansion ratio is defined as the ratio of the number of learned positions to the total number of positions. The expansion ratios mainly depend on the coefficient of the regularization term " + }, + { + "bbox": [ + 46, + 522, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\lambda_{reg}" + }, + { + "bbox": [ + 46, + 522, + 287, + 714 + ], + "type": "text", + "content": ". Consequently, we could adjust the " + }, + { + "bbox": [ + 46, + 522, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\lambda_{reg}" + }, + { + "bbox": [ + 46, + 522, + 287, + 714 + ], + "type": "text", + "content": " to achieve a trade-off between the model efficiency and the number of expansions. Using smaller " + }, + { + "bbox": [ + 46, + 522, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\lambda_{reg}" + }, + { + "bbox": [ + 46, + 522, + 287, + 714 + ], + "type": "text", + "content": " usually achieves better model performance while leading to larger expansion ratios. Meanwhile, larger expansion ratios often require more weather-specific parameters, bringing more computational overhead. In Fig.7, we present the model performance under different values of " + }, + { + "bbox": [ + 46, + 522, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\lambda_{reg}" + }, + { + "bbox": [ + 46, + 522, + 287, + 714 + ], + "type": "text", + "content": ". Considering the model effi" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "content": "ciency, we set the default value of " + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "inline_equation", + "content": "\\lambda_{reg}" + }, + { + "bbox": [ + 304, + 72, + 545, + 133 + ], + "type": "text", + "content": " as 0.08. Moreover, we visualize the learned positions in Fig.8. The middle layers have a stronger response to haze, since the middle features of the haze model are largely different from other weather types from Fig.3." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 137, + 546, + 256 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 137, + 546, + 256 + ], + "spans": [ + { + "bbox": [ + 304, + 137, + 546, + 256 + ], + "type": "text", + "content": "Ablation study of the parameters expansion types. We also study two weather-specific parameter types: convolution layers with the kernel size of " + }, + { + "bbox": [ + 304, + 137, + 546, + 256 + ], + "type": "inline_equation", + "content": "3 \\times 3" + }, + { + "bbox": [ + 304, + 137, + 546, + 256 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 137, + 546, + 256 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 304, + 137, + 546, + 256 + ], + "type": "text", + "content": ". As shown in Tab. 9, we report the performance of these two types under a similar expansion ratio condition. Obviously, given the similar expansion ratios of parameters, using " + }, + { + "bbox": [ + 304, + 137, + 546, + 256 + ], + "type": "inline_equation", + "content": "3 \\times 3" + }, + { + "bbox": [ + 304, + 137, + 546, + 256 + ], + "type": "text", + "content": " convolutions slightly performs better than the " + }, + { + "bbox": [ + 304, + 137, + 546, + 256 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 304, + 137, + 546, + 256 + ], + "type": "text", + "content": " convolutions but requires more network parameters. Hence, we adopt convolutions with kernel size of " + }, + { + "bbox": [ + 304, + 137, + 546, + 256 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 304, + 137, + 546, + 256 + ], + "type": "text", + "content": " as the default throughout the experiments." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 261, + 546, + 297 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 261, + 546, + 297 + ], + "spans": [ + { + "bbox": [ + 304, + 261, + 546, + 297 + ], + "type": "text", + "content": "Ablation study of losses. We validate the effectiveness of loss functions in Tab. 10, indicating that the auxiliary depth information improves the restoration performance." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 301, + 545, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 301, + 545, + 361 + ], + "spans": [ + { + "bbox": [ + 304, + 301, + 545, + 361 + ], + "type": "text", + "content": "Ablation study of feature representations. Fig. 9 visualizes the t-SNE results [70] of the weather-general and weather-specific features from the last layer of our network. It indicates that our strategy can learn uniformly weather-general and distinctively weather-specific features." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 365, + 546, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 365, + 546, + 460 + ], + "spans": [ + { + "bbox": [ + 304, + 365, + 546, + 460 + ], + "type": "text", + "content": "Efficiency comparisons. In Tab. 11, we further conduct comparisons of the network parameters and the average inference times among all-weather-removal methods. Our method uses much fewer network parameters than others but achieves better performance, as shown in Sec. 6.3. Moreover, our method only takes 0.03 seconds to process an image with the resolution of " + }, + { + "bbox": [ + 304, + 365, + 546, + 460 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 304, + 365, + 546, + 460 + ], + "type": "text", + "content": " on a single NVIDIA Geforce GTX 1080 Ti GPU." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 467, + 378, + 479 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 467, + 378, + 479 + ], + "spans": [ + { + "bbox": [ + 306, + 467, + 378, + 479 + ], + "type": "text", + "content": "7. Conclusion" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 483, + 545, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 483, + 545, + 636 + ], + "spans": [ + { + "bbox": [ + 304, + 483, + 545, + 636 + ], + "type": "text", + "content": "This work formulates an efficiently unified model to remove weather-related artifacts under multiple adverse weather conditions. To achieve this, we design a two-stage training strategy, which first optimizes the network to learn weather-general features and then to learn the weather-specific features. More importantly, our method expands the network parameters to generate weather-specific features adaptively, thus reducing the computation overhead of our model. Moreover, we construct the first real-world dataset with multiple adverse weather conditions to promote the further research on the real-world scenarios. Various experiments demonstrate the superiority of our method over other state-of-the-art methods." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "type": "text", + "content": "Acknowledgment. This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62276243, in part by the National Key R&D Program of China(NO.2022ZD0160100), and in part by Shanghai Committee of Science and Technology (Grant No. 21DZ1100100)." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "21754" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 91, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 56, + 91, + 287, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 91, + 287, + 135 + ], + "spans": [ + { + "bbox": [ + 56, + 91, + 287, + 135 + ], + "type": "text", + "content": "[1] Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing, and Dacheng Tao. DehazeNet: An end-to-end system for single image haze removal. IEEE Trans. Image Process., 25(11):5187-5198, 2016. 1, 2, 3, 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 56, + 137, + 287, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 137, + 287, + 171 + ], + "spans": [ + { + "bbox": [ + 56, + 137, + 287, + 171 + ], + "type": "text", + "content": "[2] Chenghao Chen and Hao Li. Robust representation learning with feedback for single image deraining. In IEEE Conf. Comput. Vis. Pattern Recog., pages 7742-7751, 2021. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 172, + 288, + 225 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 172, + 288, + 225 + ], + "spans": [ + { + "bbox": [ + 56, + 172, + 288, + 225 + ], + "type": "text", + "content": "[3] Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao. Pre-trained image processing transformer. In IEEE Conf. Comput. Vis. Pattern Recog., pages 12299-12310, 2021. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 56, + 228, + 288, + 283 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 228, + 288, + 283 + ], + "spans": [ + { + "bbox": [ + 56, + 228, + 288, + 283 + ], + "type": "text", + "content": "[4] Wei-Ting Chen, Hao-Yu Fang, Jian-Jiun Ding, Cheng-Che Tsai, and Sy-Yen Kuo. JSTASR: Joint size and transparency-aware snow removal algorithm based on modified partial convolution and veiling effect removal. In IEEE Eur. Conf. Comput. Vis., pages 754-770, 2020. 1, 2, 3, 4, 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 56, + 285, + 287, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 285, + 287, + 350 + ], + "spans": [ + { + "bbox": [ + 56, + 285, + 287, + 350 + ], + "type": "text", + "content": "[5] Wei-Ting Chen, Hao-Yu Fang, Cheng-Lin Hsieh, Cheng-Che Tsai, I-Hsiang Chen, Jian-Jiun Ding, Sy-Yen Kuo, et al. All snow removed: Single image desnowing algorithm using hierarchical dual-tree complex wavelet representation and contradict channel loss. In IEEE Int. Conf. Comput. Vis., pages 4196-4205, 2021. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 56, + 352, + 287, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 352, + 287, + 418 + ], + "spans": [ + { + "bbox": [ + 56, + 352, + 287, + 418 + ], + "type": "text", + "content": "[6] Wei-Ting Chen, Zhi-Kai Huang, Cheng-Che Tsai, Hao-Hsiang Yang, Jian-Jiun Ding, and Sy-Yen Kuo. Learning multiple adverse weather removal via two-stage knowledge learning and multi-contrastive regularization: Toward a unified model. In IEEE Conf. Comput. Vis. Pattern Recog., pages 17653–17662, 2022. 1, 2, 3, 6, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 56, + 419, + 287, + 473 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 419, + 287, + 473 + ], + "spans": [ + { + "bbox": [ + 56, + 419, + 287, + 473 + ], + "type": "text", + "content": "[7] Xiang Chen, Jinshan Pan, Kui Jiang, Yufeng Li, Yufeng Huang, Caihua Kong, Longgang Dai, and Zhentao Fan. Unpaired deep image deraining using dual contrastive learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2017-2026, 2022. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 56, + 475, + 287, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 475, + 287, + 520 + ], + "spans": [ + { + "bbox": [ + 56, + 475, + 287, + 520 + ], + "type": "text", + "content": "[8] Zeyuan Chen, Yangchao Wang, Yang Yang, and Dong Liu. PSD: Principled synthetic-to-real dehazing guided by physical priors. In IEEE Conf. Comput. Vis. Pattern Recog., pages 7180-7189, 2021. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 56, + 521, + 287, + 554 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 56, + 521, + 287, + 554 + ], + "spans": [ + { + "bbox": [ + 56, + 521, + 287, + 554 + ], + "type": "text", + "content": "[9] Corinna Cortes, Mehryar Mohri, and Afshin Rostamizadeh. Algorithms for learning kernels based on centered alignment. J. Mach. Learn. Res., 13:795-828, 2012. 4" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 555, + 288, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 555, + 288, + 612 + ], + "spans": [ + { + "bbox": [ + 53, + 555, + 288, + 612 + ], + "type": "text", + "content": "[10] Qili Deng, Ziling Huang, Chung-Chi Tsai, and Chia-Wen Lin. Hardgan: A haze-aware representation distillation gan for single image dehazing. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part VI 16. Springer, 2020. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 53, + 613, + 288, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 613, + 288, + 667 + ], + "spans": [ + { + "bbox": [ + 53, + 613, + 288, + 667 + ], + "type": "text", + "content": "[11] Sen Deng, Mingqiang Wei, Jun Wang, Yidan Feng, Luming Liang, Haoran Xie, Fu Lee Wang, and Meng Wang. Detail-recovery image deraining via context aggregation networks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 14560-14569, 2020. 2, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 53, + 669, + 287, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 669, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 53, + 669, + 287, + 714 + ], + "type": "text", + "content": "[12] Zijun Deng, Lei Zhu, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Qing Zhang, Jing Qin, and Pheng-Ann Heng. Deep multi-model fusion for single-image dehazing. In IEEE Int. Conf. Comput. Vis., pages 2453–2462, 2019. 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 312, + 73, + 545, + 714 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 312, + 73, + 545, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 73, + 545, + 117 + ], + "spans": [ + { + "bbox": [ + 312, + 73, + 545, + 117 + ], + "type": "text", + "content": "[13] Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, and Ming-Hsuan Yang. Multi-scale boosted de-hazing network with dense feature fusion. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2157-2167, 2020. 3, 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 312, + 118, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 118, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 312, + 118, + 545, + 150 + ], + "type": "text", + "content": "[14] Jiangxin Dong and Jinshan Pan. Physics-based feature dehazing networks. In IEEE Eur. Conf. Comput. Vis., pages 188-204. Springer, 2020. 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 312, + 152, + 545, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 152, + 545, + 185 + ], + "spans": [ + { + "bbox": [ + 312, + 152, + 545, + 185 + ], + "type": "text", + "content": "[15] David Eigen, Dilip Krishnan, and Rob Fergus. Restoring an image taken through a window covered with dirt or rain. In IEEE Int. Conf. Comput. Vis., pages 633-640, 2013. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 312, + 186, + 545, + 229 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 186, + 545, + 229 + ], + "spans": [ + { + "bbox": [ + 312, + 186, + 545, + 229 + ], + "type": "text", + "content": "[16] Xueyang Fu, Jiabin Huang, Xinghao Ding, Yinghao Liao, and John Paisley. Clearing the skies: A deep network architecture for single-image rain removal. IEEE Trans. Image Process., 26(6):2944-2956, 2017. 1, 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 312, + 231, + 545, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 231, + 545, + 274 + ], + "spans": [ + { + "bbox": [ + 312, + 231, + 545, + 274 + ], + "type": "text", + "content": "[17] Xueyang Fu, Jiabin Huang, Delu Zeng, Yue Huang, Xing-hao Ding, and John Paisley. Removing rain from single images via a deep detail network. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3855-3863, 2017. 1, 2, 6, 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 312, + 276, + 545, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 276, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 312, + 276, + 545, + 319 + ], + "type": "text", + "content": "[18] Xueyang Fu, Qi Qi, Zheng-Jun Zha, Yurui Zhu, and Xing-hao Ding. Rain streak removal via dual graph convolutional network. In Proc. AAAI Conf. Artif. Intell., volume 35, pages 1352-1360, 2021. 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 312, + 321, + 545, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 321, + 545, + 365 + ], + "spans": [ + { + "bbox": [ + 312, + 321, + 545, + 365 + ], + "type": "text", + "content": "[19] Xueyang Fu, Jie Xiao, Yurui Zhu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Continual image deraining with hypergraph convolutional networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 312, + 365, + 545, + 419 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 365, + 545, + 419 + ], + "spans": [ + { + "bbox": [ + 312, + 365, + 545, + 419 + ], + "type": "text", + "content": "[20] Chun-Le Guo, Qixin Yan, Saeed Anwar, Runmin Cong, Wenqi Ren, and Chongyi Li. Image dehazing transformer with transmission-aware 3D position embedding. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5812-5820, 2022. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 312, + 421, + 545, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 421, + 545, + 475 + ], + "spans": [ + { + "bbox": [ + 312, + 421, + 545, + 475 + ], + "type": "text", + "content": "[21] Xin Guo, Xueyang Fu, Man Zhou, Zhen Huang, Jialun Peng, and Zheng-Jun Zha. Exploring fourier prior for single image rain removal. In Proceedings of the 30th International Joint Conferences on Artificial Intelligence, pages 935–941, 2022. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 312, + 476, + 545, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 476, + 545, + 520 + ], + "spans": [ + { + "bbox": [ + 312, + 476, + 545, + 520 + ], + "type": "text", + "content": "[22] Zhixiang Hao, Shaodi You, Yu Li, Kunming Li, and Feng Lu. Learning from synthetic photorealistic raindrop for single image raindrop removal. In IEEE Int. Conf. Comput. Vis., Oct 2019, 1, 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 312, + 522, + 545, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 522, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 312, + 522, + 545, + 555 + ], + "type": "text", + "content": "[23] Kaiming He, Jian Sun, and Xiaou Tang. Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell., 33(12):2341-2353, 2010. 1, 2, 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 312, + 556, + 545, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 556, + 545, + 599 + ], + "spans": [ + { + "bbox": [ + 312, + 556, + 545, + 599 + ], + "type": "text", + "content": "[24] Ming Hong, Yuan Xie, Cuihua Li, and Yanyun Qu. Distilling image dehazing with heterogeneous task imitation. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3462-3471, 2020. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 312, + 601, + 545, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 601, + 545, + 644 + ], + "spans": [ + { + "bbox": [ + 312, + 601, + 545, + 644 + ], + "type": "text", + "content": "[25] Xiaowei Hu, Chi-Wing Fu, Lei Zhu, and Pheng-Ann Heng. Depth-attentional features for single-image rain removal. In IEEE Conf. Comput. Vis. Pattern Recog., pages 8022–8031, 2019. 1, 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 312, + 646, + 545, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 646, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 312, + 646, + 545, + 689 + ], + "type": "text", + "content": "[26] Xiaowei Hu, Lei Zhu, Tianyu Wang, Chi-Wing Fu, and Pheng-Ann Heng. Single-image real-time rain removal based on depth-guided non-local features. IEEE Trans. Image Process., 30:1759-1770, 2021. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 312, + 691, + 545, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 691, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 312, + 691, + 545, + 714 + ], + "type": "text", + "content": "[27] Huaibo Huang, Aijing Yu, and Ran He. Memory oriented transfer learning for semi-supervised image deraining. In" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "21755" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 53, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 72, + 72, + 286, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 72, + 286, + 94 + ], + "spans": [ + { + "bbox": [ + 72, + 72, + 286, + 94 + ], + "type": "text", + "content": "IEEE Conf. Comput. Vis. Pattern Recog., pages 7732-7741, 2021. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 96, + 287, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 96, + 287, + 140 + ], + "spans": [ + { + "bbox": [ + 53, + 96, + 287, + 140 + ], + "type": "text", + "content": "[28] Jie Huang, Yajing Liu, Feng Zhao, Keyu Yan, Jinghao Zhang, Yukun Huang, Man Zhou, and Zhiwei Xiong. Deep fourier-based exposure correction network with spatial-frequency interaction. In ECCV. Springer, 2022. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 141, + 288, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 141, + 288, + 195 + ], + "spans": [ + { + "bbox": [ + 53, + 141, + 288, + 195 + ], + "type": "text", + "content": "[29] Jie Huang, Man Zhou, Yajing Liu, Mingde Yao, Feng Zhao, and Zhiwei Xiong. Exposure-consistency representation learning for exposure correction. In Proceedings of the 30th ACM International Conference on Multimedia, page 6309-6317, 2022. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 197, + 288, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 197, + 288, + 241 + ], + "spans": [ + { + "bbox": [ + 53, + 197, + 288, + 241 + ], + "type": "text", + "content": "[30] Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. Image-to-image translation with conditional adversarial networks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1125-1134, 2017. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 242, + 288, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 242, + 288, + 296 + ], + "spans": [ + { + "bbox": [ + 53, + 242, + 288, + 296 + ], + "type": "text", + "content": "[31] Da-Wei Jaw, Shih-Chia Huang, and Sy-Yen Kuo. Desnow-Gan: An efficient single image snow removal framework using cross-resolution lateral connection and GANs. IEEE Trans. Circuits Syst. Video Technol., 31(4):1342-1350, 2020. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 297, + 288, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 297, + 288, + 352 + ], + "spans": [ + { + "bbox": [ + 53, + 297, + 288, + 352 + ], + "type": "text", + "content": "[32] Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, and Junjun Jiang. Multi-scale progressive fusion network for single image deraining. In IEEE Conf. Comput. Vis. Pattern Recog., pages 8346-8355, 2020. 2, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 354, + 288, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 354, + 288, + 397 + ], + "spans": [ + { + "bbox": [ + 53, + 354, + 288, + 397 + ], + "type": "text", + "content": "[33] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In IEEE Eur. Conf. Comput. Vis., pages 694–711. Springer, 2016. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 399, + 288, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 399, + 288, + 443 + ], + "spans": [ + { + "bbox": [ + 53, + 399, + 288, + 443 + ], + "type": "text", + "content": "[34] Jin-Hwan Kim, Jae-Young Sim, and Chang-Su Kim. Single image dehazing based on contrast enhancement. In 2011 IEEE Int. Conf. on Acoustics, Speech and Signal Processing, pages 1273–1276. IEEE, 2011. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 444, + 288, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 444, + 288, + 465 + ], + "spans": [ + { + "bbox": [ + 53, + 444, + 288, + 465 + ], + "type": "text", + "content": "[35] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2015. 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 467, + 288, + 510 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 467, + 288, + 510 + ], + "spans": [ + { + "bbox": [ + 53, + 467, + 288, + 510 + ], + "type": "text", + "content": "[36] Simon Kornblith, Mohammad Norouzi, Honglak Lee, and Geoffrey Hinton. Similarity of neural network representations revisited. In Proc. Int. Conf. Mach. Learn., pages 3519-3529. PMLR, 2019. 4" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 512, + 288, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 512, + 288, + 544 + ], + "spans": [ + { + "bbox": [ + 53, + 512, + 288, + 544 + ], + "type": "text", + "content": "[37] Simon Kornblith, Jonathon Shlens, and Quoc V Le. Do better ImageNet models transfer better? In IEEE Conf. Comput. Vis. Pattern Recog., pages 2661-2671, 2019. 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 53, + 545, + 288, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 545, + 288, + 578 + ], + "spans": [ + { + "bbox": [ + 53, + 545, + 288, + 578 + ], + "type": "text", + "content": "[38] Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, and Dan Feng. Aod-net: All-in-one dehazing network. In IEEE Int. Conf. Comput. Vis., pages 4770-4778, 2017. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 53, + 579, + 288, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 579, + 288, + 624 + ], + "spans": [ + { + "bbox": [ + 53, + 579, + 288, + 624 + ], + "type": "text", + "content": "[39] Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process., 28(1):492-505, 2019. 4, 6, 7" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 53, + 624, + 288, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 624, + 288, + 668 + ], + "spans": [ + { + "bbox": [ + 53, + 624, + 288, + 668 + ], + "type": "text", + "content": "[40] Ruoteng Li, Loong-Fah Cheong, and Robby T. Tan. Heavy rain image restoration: Integrating physics model and conditional adversarial learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1633-1642, 2019. 1, 2, 6, 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 53, + 670, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 670, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 53, + 670, + 288, + 713 + ], + "type": "text", + "content": "[41] Ruoteng Li, Robby T. Tan, and Loong-Fah Cheong. All in one bad weather removal using architectural search. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3175-3185, 2020. 1, 2, 3, 6, 7" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 312, + 72, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 32, + "blocks": [ + { + "bbox": [ + 312, + 72, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 72, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 312, + 72, + 545, + 116 + ], + "type": "text", + "content": "[42] Wenbo Li, Xin Lu, Jiangbo Lu, Xiangyu Zhang, and Jiaya Jia. On efficient transformer and image pre-training for low-level vision. arXiv preprint arXiv:2112.10175, 2021.3, 4" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 312, + 118, + 545, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 118, + 545, + 162 + ], + "spans": [ + { + "bbox": [ + 312, + 118, + 545, + 162 + ], + "type": "text", + "content": "[43] Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, and Hongbin Zha. Recurrent squeeze-and-excitation context aggregation net for single image deraining. In IEEE Eur. Conf. Comput. Vis., pages 254-269, 2018. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 312, + 163, + 545, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 163, + 545, + 207 + ], + "spans": [ + { + "bbox": [ + 312, + 163, + 545, + 207 + ], + "type": "text", + "content": "[44] Yi Li, Yi Chang, Yan Gao, Changfeng Yu, and Luxin Yan. Physically disentangled intra-and inter-domain adaptation for varicolored haze removal. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5841-5850, 2022. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 312, + 208, + 545, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 208, + 545, + 262 + ], + "spans": [ + { + "bbox": [ + 312, + 208, + 545, + 262 + ], + "type": "text", + "content": "[45] Yunan Li, Qiguang Miao, Wanli Ouyang, Zhenxin Ma, Huijuan Fang, Chao Dong, and Yining Quan. Lap-net: Level-aware progressive network for image dehazing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 312, + 263, + 545, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 263, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 312, + 263, + 545, + 308 + ], + "type": "text", + "content": "[46] Huan Liu, Zijun Wu, Liangyan Li, Sadaf Salehkalaibar, Jun Chen, and Keyan Wang. Towards multi-domain single image dehazing via test-time training. In IEEE Conf. Comput. Vis. Pattern Recog., pages 5831-5840, 2022. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 312, + 308, + 545, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 308, + 545, + 353 + ], + "spans": [ + { + "bbox": [ + 312, + 308, + 545, + 353 + ], + "type": "text", + "content": "[47] Lina Liu, Xibin Song, Mengmeng Wang, Yong Liu, and Liangjun Zhang. Self-supervised monocular depth estimation for all day images using domain separation. In IEEE Int. Conf. Comput. Vis., pages 12737-12746, 2021. 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 312, + 354, + 545, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 354, + 545, + 387 + ], + "spans": [ + { + "bbox": [ + 312, + 354, + 545, + 387 + ], + "type": "text", + "content": "[48] Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101, 2016. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 312, + 388, + 545, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 388, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 312, + 388, + 545, + 430 + ], + "type": "text", + "content": "[49] Xiaohong Liu, Yongrui Ma, Zhihao Shi, and Jun Chen. GridDehazeNet: Attention-based multi-scale network for image dehazing. In IEEE Int. Conf. Comput. Vis., pages 7314-7323, 2019. 1, 2, 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 312, + 432, + 545, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 432, + 545, + 475 + ], + "spans": [ + { + "bbox": [ + 312, + 432, + 545, + 475 + ], + "type": "text", + "content": "[50] Yang Liu, Jinshan Pan, Jimmy Ren, and Zhixun Su. Learning deep priors for image dehazing. In Proceedings of the IEEE/CVF international conference on computer vision, 2019. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 312, + 476, + 545, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 476, + 545, + 520 + ], + "spans": [ + { + "bbox": [ + 312, + 476, + 545, + 520 + ], + "type": "text", + "content": "[51] Yun-Fu Liu, Da-Wei Jaw, Shih-Chia Huang, and Jenq-Neng Hwang. DesnowNet: Context-aware deep network for snow removal. IEEE Trans. Image Process., 27(6):3064–3073, 2018. 1, 2, 3, 6, 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 312, + 521, + 545, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 521, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 312, + 521, + 545, + 555 + ], + "type": "text", + "content": "[52] Ilya Loshchilov and Frank Hutter. SGDR: Stochastic gradient descent with warm restarts. In International Conference on Learning Representations, 2017. 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 312, + 555, + 545, + 589 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 555, + 545, + 589 + ], + "spans": [ + { + "bbox": [ + 312, + 555, + 545, + 589 + ], + "type": "text", + "content": "[53] Jiayuan Mao, Tete Xiao, Yuning Jiang, and Zhimin Cao. What can help pedestrian detection? In IEEE Conf. Comput. Vis. Pattern Recog., 2017. 1" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 312, + 590, + 545, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 590, + 545, + 612 + ], + "spans": [ + { + "bbox": [ + 312, + 590, + 545, + 612 + ], + "type": "text", + "content": "[54] Srinivasa G. Narasimhan. Models and algorithms for vision through the atmosphere. Columbia University, 2004. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 312, + 613, + 545, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 613, + 545, + 644 + ], + "spans": [ + { + "bbox": [ + 312, + 613, + 545, + 644 + ], + "type": "text", + "content": "[55] Srinivasa G. Narasimhan and Shree K. Nayar. Vision and the atmosphere. Int. J. Comput. Vis., 48(3):233-254, 2002. 2, 3" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 312, + 647, + 545, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 647, + 545, + 678 + ], + "spans": [ + { + "bbox": [ + 312, + 647, + 545, + 678 + ], + "type": "text", + "content": "[56] Shree K Nayar and Srinivasa G. Narasimhan. Vision in bad weather. In IEEE Int. Conf. Comput. Vis., volume 2, pages 820-827. IEEE, 1999. 3" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 312, + 680, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 680, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 312, + 680, + 545, + 713 + ], + "type": "text", + "content": "[57] Shree K. Nayar and Srinivasa G. Narasimhan. Seeing through bad weather. In Int. Symp. Robot. Res., pages 335-350. Springer, 2005. 2, 3" + } + ] + } + ], + "index": 31 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "21756" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 53, + 72, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 53, + 72, + 287, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 72, + 287, + 117 + ], + "spans": [ + { + "bbox": [ + 53, + 72, + 287, + 117 + ], + "type": "text", + "content": "[58] Thao Nguyen, Maithra Raghu, and Simon Kornblith. Do wide and deep networks learn the same things? uncovering how neural network representations vary with width and depth. arXiv preprint arXiv:2010.15327, 2020. 4" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 118, + 287, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 118, + 287, + 162 + ], + "spans": [ + { + "bbox": [ + 53, + 118, + 287, + 162 + ], + "type": "text", + "content": "[59] Rui Qian, Robby T. Tan, Wenhan Yang, Jiajun Su, and Jiaying Liu. Attentive generative adversarial network for raindrop removal from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2482-2491, 2018. 1, 2, 6, 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 163, + 288, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 163, + 288, + 207 + ], + "spans": [ + { + "bbox": [ + 53, + 163, + 288, + 207 + ], + "type": "text", + "content": "[60] Xu Qin, Zhilin Wang, Yuanchao Bai, Xiaodong Xie, and Huizhu Jia. FFA-Net: Feature fusion attention network for single image dehazing. In Proc. AAAI Conf. Artif. Intell., volume 34, pages 11908–11915, 2020. 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 208, + 288, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 208, + 288, + 241 + ], + "spans": [ + { + "bbox": [ + 53, + 208, + 288, + 241 + ], + "type": "text", + "content": "[61] Yanyun Qu, Yizi Chen, Jingying Huang, and Yuan Xie. Enhanced pix2pix dehazing network. In IEEE Conf. Comput. Vis. Pattern Recog., pages 8160-8168, 2019. 3, 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 242, + 288, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 242, + 288, + 275 + ], + "spans": [ + { + "bbox": [ + 53, + 242, + 288, + 275 + ], + "type": "text", + "content": "[62] Ruijie Quan, Xin Yu, Yuanzhi Liang, and Yi Yang. Removing raindrops and rain streaks in one go. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9147-9156, 2021. 2, 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 276, + 288, + 309 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 276, + 288, + 309 + ], + "spans": [ + { + "bbox": [ + 53, + 276, + 288, + 309 + ], + "type": "text", + "content": "[63] Yuhui Quan, Shijie Deng, Yixin Chen, and Hui Ji. Deep learning for seeing through window with raindrops. In IEEE Int. Conf. Comput. Vis., pages 2463-2471, 2019. 2, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 310, + 288, + 354 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 310, + 288, + 354 + ], + "spans": [ + { + "bbox": [ + 53, + 310, + 288, + 354 + ], + "type": "text", + "content": "[64] Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng. Progressive image deraining networks: A better and simpler baseline. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3937-3946, 2019. 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 355, + 288, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 355, + 288, + 399 + ], + "spans": [ + { + "bbox": [ + 53, + 355, + 288, + 399 + ], + "type": "text", + "content": "[65] Wenqi Ren, Si Liu, Hua Zhang, Jinshan Pan, Xiaochun Cao, and Ming-Hsuan Yang. Single image dehazing via multi-scale convolutional neural networks. In IEEE Eur. Conf. Comput. Vis., pages 154-169. Springer, 2016. 1, 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 399, + 288, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 399, + 288, + 443 + ], + "spans": [ + { + "bbox": [ + 53, + 399, + 288, + 443 + ], + "type": "text", + "content": "[66] Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. Gated fusion network for single image dehazing. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3253-3261, 2018. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 445, + 288, + 498 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 445, + 288, + 498 + ], + "spans": [ + { + "bbox": [ + 53, + 445, + 288, + 498 + ], + "type": "text", + "content": "[67] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks for biomedical image segmentation. In Proc. Int. Conf. Med. Image Comput. Comput.-Assisted Intervention, pages 234-241. Springer, 2015. 4" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 500, + 288, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 500, + 288, + 533 + ], + "spans": [ + { + "bbox": [ + 53, + 500, + 288, + 533 + ], + "type": "text", + "content": "[68] Robby T. Tan. Visibility in bad weather from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1-8. IEEE, 2008. 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 53, + 534, + 288, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 534, + 288, + 588 + ], + "spans": [ + { + "bbox": [ + 53, + 534, + 288, + 588 + ], + "type": "text", + "content": "[69] Jeya Maria Jose Valanarasu, Rajeev Yasarla, and Vishal M. Patel. TransWeather: Transformer-based restoration of images degraded by adverse weather conditions. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2353-2363, 2022. 1, 2, 3, 6, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 53, + 590, + 288, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 590, + 288, + 612 + ], + "spans": [ + { + "bbox": [ + 53, + 590, + 288, + 612 + ], + "type": "text", + "content": "[70] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-SNE. J. Mach. Learn. Res., 2008. 8" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 53, + 613, + 288, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 613, + 288, + 667 + ], + "spans": [ + { + "bbox": [ + 53, + 613, + 288, + 667 + ], + "type": "text", + "content": "[71] Matthew Wallingford, Hao Li, Alessandro Achille, Avinash Ravichandran, Charless Fowlkes, Rahul Bhotika, and Stefano Soatto. Task adaptive parameter sharing for multi-task learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 7561-7570, 2022. 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 53, + 669, + 288, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 669, + 288, + 712 + ], + "spans": [ + { + "bbox": [ + 53, + 669, + 288, + 712 + ], + "type": "text", + "content": "[72] Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng. A model-driven deep neural network for single image rain removal. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3103-3112, 2020. 1, 2" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 312, + 72, + 547, + 715 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 312, + 72, + 547, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 72, + 547, + 127 + ], + "spans": [ + { + "bbox": [ + 312, + 72, + 547, + 127 + ], + "type": "text", + "content": "[73] Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson W.H. Lau. Spatial attentive single-image deraining with a high quality real rain dataset. In IEEE Conf. Comput. Vis. Pattern Recog., pages 12270-12279, 2019. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 312, + 128, + 547, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 128, + 547, + 171 + ], + "spans": [ + { + "bbox": [ + 312, + 128, + 547, + 171 + ], + "type": "text", + "content": "[74] Yinglong Wang, Chao Ma, and Bing Zeng. Multi-decoding deraining network and quasi-sparsity based training. In IEEE Conf. Comput. Vis. Pattern Recog., pages 13375-13384, 2021. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 312, + 172, + 547, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 172, + 547, + 215 + ], + "spans": [ + { + "bbox": [ + 312, + 172, + 547, + 215 + ], + "type": "text", + "content": "[75] Yinglong Wang, Yibing Song, Chao Ma, and Bing Zeng. Rethinking image deraining via rain streaks and vapors. In IEEE Eur. Conf. Comput. Vis., pages 367-382. Springer, 2020. 4" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 312, + 217, + 547, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 217, + 547, + 259 + ], + "spans": [ + { + "bbox": [ + 312, + 217, + 547, + 259 + ], + "type": "text", + "content": "[76] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600-612, 2004. 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 312, + 261, + 547, + 315 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 261, + 547, + 315 + ], + "spans": [ + { + "bbox": [ + 312, + 261, + 547, + 315 + ], + "type": "text", + "content": "[77] Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, and Lizhuang Ma. Contrastive learning for compact single image dehazing. In IEEE Conf. Comput. Vis. Pattern Recog., pages 10551-10560, 2021. 3, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 312, + 316, + 547, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 316, + 547, + 348 + ], + "spans": [ + { + "bbox": [ + 312, + 316, + 547, + 348 + ], + "type": "text", + "content": "[78] Jie Xiao, Xueyang Fu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Image de-raining transformer. IEEE Trans. Pattern Anal. Mach. Intell., pages 1-18, 2022. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 312, + 350, + 547, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 350, + 547, + 392 + ], + "spans": [ + { + "bbox": [ + 312, + 350, + 547, + 392 + ], + "type": "text", + "content": "[79] Jie Xiao, Man Zhou, Xueyang Fu, Aiping Liu, and Zheng-Jun Zha. Improving de-raining generalization via neural re-organization. In IEEE Int. Conf. Comput. Vis., pages 4987–4996, 2021. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 312, + 394, + 547, + 436 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 394, + 547, + 436 + ], + "spans": [ + { + "bbox": [ + 312, + 394, + 547, + 436 + ], + "type": "text", + "content": "[80] Dong Yang and Jian Sun. Proximal Dehaze-Net: A prior learning-based deep network for single image dehazing. In IEEE Eur. Conf. Comput. Vis., pages 702-717, 2018. 1, 2, 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 312, + 437, + 547, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 437, + 547, + 491 + ], + "spans": [ + { + "bbox": [ + 312, + 437, + 547, + 491 + ], + "type": "text", + "content": "[81] Wenhan Yang, Robby T. Tan, Jiashi Feng, Zongming Guo, Shuicheng Yan, and Jiaying Liu. Joint rain detection and removal from a single image with contextualized deep networks. IEEE Trans. Pattern Anal. Mach. Intell., 42(6):1377-1393, 2019. 2, 4" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 312, + 492, + 547, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 492, + 547, + 547 + ], + "spans": [ + { + "bbox": [ + 312, + 492, + 547, + 547 + ], + "type": "text", + "content": "[82] Wenhan Yang, Robby T. Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan. Deep joint rain detection and removal from a single image. In IEEE Conf. Comput. Vis. Pattern Recog., pages 1357-1366, 2017. 1, 2, 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 312, + 548, + 547, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 548, + 547, + 591 + ], + "spans": [ + { + "bbox": [ + 312, + 548, + 547, + 591 + ], + "type": "text", + "content": "[83] Wenhan Yang, Robby T. Tan, Shiqi Wang, Yuming Fang, and Jiaying Liu. Single image deraining: From model-based to data-driven and beyond. IEEE Trans. Pattern Anal. Mach. Intell., 43(11):4059-4077, 2020. 1, 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 312, + 592, + 547, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 592, + 547, + 635 + ], + "spans": [ + { + "bbox": [ + 312, + 592, + 547, + 635 + ], + "type": "text", + "content": "[84] Xitong Yang, Zheng Xu, and Jiebo Luo. Towards perceptual image dehazing by physics-based disentanglement and adversarial training. In Proc. AAAI Conf. Artif. Intell., volume 32, 2018. 1, 2, 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 312, + 636, + 547, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 636, + 547, + 689 + ], + "spans": [ + { + "bbox": [ + 312, + 636, + 547, + 689 + ], + "type": "text", + "content": "[85] Yang Yang, Chaoyue Wang, Risheng Liu, Lin Zhang, Xiaojie Guo, and Dacheng Tao. Self-augmented unpaired image dehazing via density and depth decomposition. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2037-2046, 2022. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 312, + 691, + 547, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 312, + 691, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 312, + 691, + 547, + 715 + ], + "type": "text", + "content": "[86] Zizheng Yang, Mingde Yao, Jie Huang, Man Zhou, and Feng Zhao. Sir-former: Stereo image restoration using" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "21757" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + }, + { + "para_blocks": [ + { + "bbox": [ + 53, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 72, + 72, + 287, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 72, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 72, + 72, + 287, + 95 + ], + "type": "text", + "content": "transformer. In Proceedings of the 30th ACM International Conference on Multimedia, page 6377-6385, 2022. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 95, + 288, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 95, + 288, + 139 + ], + "spans": [ + { + "bbox": [ + 53, + 95, + 288, + 139 + ], + "type": "text", + "content": "[87] Rajeev Yasarla, Vishwanath A. Sindagi, and Vishal M. Patel. Syn2real transfer learning for image deraining using gaussian processes. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2726-2736, 2020. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 54, + 140, + 288, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 54, + 140, + 288, + 184 + ], + "spans": [ + { + "bbox": [ + 54, + 140, + 288, + 184 + ], + "type": "text", + "content": "[88] Yuntong Ye, Yi Chang, Hanyu Zhou, and Luxin Yan. Closing the loop: Joint rain generation and removal via disentangled image translation. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2053-2062, 2021. 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 185, + 288, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 185, + 288, + 228 + ], + "spans": [ + { + "bbox": [ + 53, + 185, + 288, + 228 + ], + "type": "text", + "content": "[89] Qiaosi Yi, Juncheng Li, Qinyan Dai, Faming Fang, Guixu Zhang, and Tieyong Zeng. Structure-preserving deraining with residue channel prior guidance. In IEEE Int. Conf. Comput. Vis., pages 4238-4247, 2021. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 228, + 288, + 271 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 228, + 288, + 271 + ], + "spans": [ + { + "bbox": [ + 53, + 228, + 288, + 271 + ], + "type": "text", + "content": "[90] Hu Yu, Jie Huang, Yajing Liu, Qi Zhu, Man Zhou, and Feng Zhao. Source-free domain adaptation for real-world image dehazing. In Proceedings of the 30th ACM International Conference on Multimedia, page 6645-6654, 2022. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 272, + 288, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 272, + 288, + 316 + ], + "spans": [ + { + "bbox": [ + 53, + 272, + 288, + 316 + ], + "type": "text", + "content": "[91] Ye Yuntong, Yu Changfeng, Chang Yi, Zhu Lin, Zhao Xile, Yan Luxin, and Tian Yonghong. Unsupervised deraining: Where contrastive learning meets self-similarity. arXiv preprint arXiv:2203.11509, 2022. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 317, + 288, + 370 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 317, + 288, + 370 + ], + "spans": [ + { + "bbox": [ + 53, + 317, + 288, + 370 + ], + "type": "text", + "content": "[92] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In IEEE Conf. Comput. Vis. Pattern Recog., pages 14821-14831, 2021. 6, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 372, + 288, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 372, + 288, + 404 + ], + "spans": [ + { + "bbox": [ + 53, + 372, + 288, + 404 + ], + "type": "text", + "content": "[93] He Zhang and Vishal M. Patel. Densely connected pyramid dehazing network. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3194-3203, 2018. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 404, + 288, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 404, + 288, + 448 + ], + "spans": [ + { + "bbox": [ + 53, + 404, + 288, + 448 + ], + "type": "text", + "content": "[94] He Zhang, Vishwanath Sindagi, and Vishal M. Patel. Image de-raining using a conditional generative adversarial network. IEEE Trans. Circuits Syst. Video Technol., 30(11):3943-3956, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 449, + 288, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 449, + 288, + 502 + ], + "spans": [ + { + "bbox": [ + 53, + 449, + 288, + 502 + ], + "type": "text", + "content": "[95] Jinghao Zhang, Jie Huang, Mingde Yao, Man Zhou, and Feng Zhao. Structure- and texture-aware learning for low-light image enhancement. In Proceedings of the 30th ACM International Conference on Multimedia, page 6483–6492, 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 53, + 504, + 288, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 504, + 288, + 547 + ], + "spans": [ + { + "bbox": [ + 53, + 504, + 288, + 547 + ], + "type": "text", + "content": "[96] Kaihao Zhang, Dongxu Li, Wenhan Luo, and Wenqi Ren. Dual attention-in-attention model for joint rain streak and raindrop removal. IEEE Trans. Image Process., 30:7608-7619, 2021. 1, 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 53, + 548, + 288, + 592 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 548, + 288, + 592 + ], + "spans": [ + { + "bbox": [ + 53, + 548, + 288, + 592 + ], + "type": "text", + "content": "[97] Kaihao Zhang, Rongqing Li, Yanjiang Yu, Wenhan Luo, and Changsheng Li. Deep dense multi-scale network for snow removal using semantic and depth priors. IEEE Trans. Image Process., 30:7419-7431, 2021. 1, 2, 3, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 53, + 593, + 288, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 593, + 288, + 658 + ], + "spans": [ + { + "bbox": [ + 53, + 593, + 288, + 658 + ], + "type": "text", + "content": "[98] Kaihao Zhang, Wenhan Luo, Wenqi Ren, Jingwen Wang, Fang Zhao, Lin Ma, and Hongdong Li. Beyond monocular deraining: Stereo image deraining via semantic understanding. In Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXVII 16. Springer, 2020. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 53, + 658, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 658, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 53, + 658, + 288, + 713 + ], + "type": "text", + "content": "[99] Xinyi Zhang, Hang Dong, Jinshan Pan, Chao Zhu, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, and Fei Wang. Learning to restore hazy video: A new real-world dataset and a new method. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9239-9248, June 2021. 5, 6, 7" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 441 + ], + "type": "list", + "angle": 0, + "index": 23, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "type": "text", + "content": "[100] Yu Zhang and Qiang Yang. An overview of multi-task learning. Natl. Sci, 5(1):30-43, 2018. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 96, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 96, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 307, + 96, + 545, + 150 + ], + "type": "text", + "content": "[101] Zheyu Zhang, Yurui Zhu, Xueyang Fu, Zhiwei Xiong, Zheng-Jun Zha, and Feng Wu. Multifocal attention-based cross-scale network for image de-raining. In Proceedings of the 29th ACM International Conference on Multimedia, pages 3673-3681, 2021. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 152, + 545, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 206 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 206 + ], + "type": "text", + "content": "[102] Zhuoran Zheng, Wenqi Ren, Xiaochun Cao, Xiaobin Hu, Tao Wang, Fenglong Song, and Xiuyi Jia. Ultra-high-definition image dehazing via multi-guided bilateral learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 16180-16189. IEEE, 2021. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 207, + 545, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 207, + 545, + 251 + ], + "spans": [ + { + "bbox": [ + 307, + 207, + 545, + 251 + ], + "type": "text", + "content": "[103] Man Zhou, Jie Huang, Keyu Yan, Hu Yu, Xueyang Fu, Aiping Liu, Xian Wei, and Feng Zhao. Spatial-frequency domain information integration for pan-sharpening. In ECCV. Springer, 2022. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 253, + 545, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 253, + 545, + 297 + ], + "spans": [ + { + "bbox": [ + 307, + 253, + 545, + 297 + ], + "type": "text", + "content": "[104] Man Zhou, Jie Xiao, Yifan Chang, Xueyang Fu, Aiping Liu, Jinshan Pan, and Zheng-Jun Zha. Image de-raining via continual learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4907-4916, 2021. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 297, + 545, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 297, + 545, + 342 + ], + "spans": [ + { + "bbox": [ + 307, + 297, + 545, + 342 + ], + "type": "text", + "content": "[105] Kai Zhu, Wei Zhai, Yang Cao, Jiebo Luo, and Zheng-Jun Zha. Self-sustaining representation expansion for nonexemplar class-incremental learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9296-9305, 2022. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 342, + 545, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 342, + 545, + 396 + ], + "spans": [ + { + "bbox": [ + 307, + 342, + 545, + 396 + ], + "type": "text", + "content": "[106] Lei Zhu, Zijun Deng, Xiaowei Hu, Haoran Xie, Xuemiao Xu, Jing Qin, and Pheng-Ann Heng. Learning gated non-local residual for single-image rain streak removal. IEEE Trans. Circuits Syst. Video Technol., 31(6):2147-2159, 2020. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 398, + 545, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 398, + 545, + 441 + ], + "spans": [ + { + "bbox": [ + 307, + 398, + 545, + 441 + ], + "type": "text", + "content": "[107] Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, and Shimin Hu. Traffic-sign detection and classification in the wild. In IEEE Conf. Comput. Vis. Pattern Recog., 2016. 1" + } + ] + } + ], + "index": 22 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "21758" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 11 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning With Fantasy_ Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning/5de50010-4b67-476f-aff9-6ea2c799f91f_content_list.json b/2023/Learning With Fantasy_ Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning/5de50010-4b67-476f-aff9-6ea2c799f91f_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..115dea81b57b34060a09d72c1029d1d702cae19c --- /dev/null +++ b/2023/Learning With Fantasy_ Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning/5de50010-4b67-476f-aff9-6ea2c799f91f_content_list.json @@ -0,0 +1,1818 @@ +[ + { + "type": "text", + "text": "Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning", + "text_level": 1, + "bbox": [ + 104, + 128, + 867, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Zeyin Song $^{1\\dagger}$", + "bbox": [ + 84, + 200, + 197, + 222 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yifan Zhao $^{2\\dagger}$", + "bbox": [ + 236, + 202, + 341, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yujun Shi", + "bbox": [ + 383, + 203, + 470, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Peixi Peng $^{2,4*}$", + "bbox": [ + 511, + 203, + 622, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Li Yuan $^{1,4}$", + "bbox": [ + 658, + 203, + 743, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yonghong Tian $^{1,2,4*}$", + "bbox": [ + 784, + 203, + 941, + 222 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ School of Electronic and Computer Engineering, Peking University", + "bbox": [ + 243, + 220, + 784, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ School of Computer Science, Peking University", + "bbox": [ + 325, + 238, + 709, + 256 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{3}$ National University of Singapore", + "bbox": [ + 259, + 256, + 531, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{4}$ Peng Cheng Laboratory", + "bbox": [ + 571, + 257, + 769, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "zysong@stu.pku.edu.cn, {zhaoyf, pxpeng, yuanli-ece, yhtian}@pku.edu.cn, shi.yujun@u.nus.edu", + "bbox": [ + 112, + 277, + 916, + 292 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 325, + 313, + 342 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes continually from limited samples without forgetting the old classes. The mainstream framework tackling FSCIL is first to adopt the cross-entropy (CE) loss for training at the base session, then freeze the feature extractor to adapt to new classes. However, in this work, we find that the CE loss is not ideal for the base session training as it suffers poor class separation in terms of representations, which further degrades generalization to novel classes. One tempting method to mitigate this problem is to apply an additional naive supervised contrastive learning (SCL) in the base session. Unfortunately, we find that although SCL can create a slightly better representation separation among different base classes, it still struggles to separate base classes and new classes. Inspired by the observations made, we propose Semantic-Aware Virtual Contrastive model (SAVC), a novel method that facilitates separation between new classes and base classes by introducing virtual classes to SCL. These virtual classes, which are generated via pre-defined transformations, not only act as placeholders for unseen classes in the representation space, but also provide diverse semantic information. By learning to recognize and contrast in the fantasy space fostered by virtual classes, our SAVC significantly boosts base class separation and novel class generalization, achieving new state-of-the-art performance on the three widely-used FSCIL benchmark datasets. Code is available at: https://github.com/zysong0113/SAVC.", + "bbox": [ + 75, + 358, + 473, + 782 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 808, + 209, + 824 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Like humans, the modern artificial intelligence models are expected to be equipped with the ability of learning continually. For instance, a face recognition system needs", + "bbox": [ + 75, + 833, + 468, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/ff8c3fa845e51ec82292227df3deea822baa9c489fa71cbb4e98cb2be210d679.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 506, + 325, + 658, + 398 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/ef88fcb962d627a462ae94ab8dab191df63a2382ff20fcf69607071fd969e333.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 665, + 325, + 769, + 398 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/3c0616c201007dcd2f2db08d5e9167dacbfb0ada4e2f4a944716f2da3591ba72.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 776, + 325, + 883, + 398 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/22fb51011b3e6849bf4a050ad44e3729637ca4a54cd70f3289ce1dd76d342db2.jpg", + "image_caption": [], + "image_footnote": [ + "(a) Comparison of the embedding spaces trained with different methods." + ], + "bbox": [ + 506, + 402, + 658, + 459 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d3faa184b5b5b0b9117b693d9821b80595320ab9aab97711da9b395ead1fb52a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 665, + 402, + 769, + 459 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/a3ed8cd245fa55c71b7ff77d283ec8116d67de6991a5761cd2b27c2dfc0cc1fb.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 776, + 402, + 883, + 459 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9fc842bb2b9d483274269eebc66ad2e8f11c1c97ca552b1d6b5a50a958bd355e.jpg", + "image_caption": [ + "Figure 1. The motivation of the proposed approach. Under the incremental-frozen framework in FSCIL, our SAVC learns to recognize and contrast in the fantasy space, which leads to better base class separation and novel class generalization than CE and SCL." + ], + "image_footnote": [ + "(b) Illustration of our Semantic-Aware Virtual Contrastive model (SAVC)." + ], + "bbox": [ + 504, + 476, + 883, + 599 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "to continually learn to recognize new faces without forgetting those has already kept in mind. In this case, Class-Incremental Learning (CIL) [19,25,30,37,50] draws attention extensively, that sufficient and disjoint data are coming session-by-session. However, it is unrealistic to always rely on accessing of multitudinous data. Taking the case of face recognition system again, whether it can quickly learn a new concept with just a few images, is an important criterion for evaluating its performance. As a result, designing effective and efficient algorithms to resolve Few-Shot Class-Incremental Learning (FSCIL) problem has drawn increasing attention [6,8,12,40,44,51,55]. Unlike CIL, only a few data are provided for FSCIL at incremental session", + "bbox": [ + 496, + 703, + 893, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "†: authors contributed equally. *: corresponding author.", + "bbox": [ + 99, + 886, + 397, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "24183", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "while sufficient samples are only visible at the base session.", + "bbox": [ + 76, + 90, + 467, + 104 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The core challenge of CIL is to strike a balance of stability and plasticity, i.e., suppressing forgetting on old knowledge while adapting smoothly to new knowledge. Previous works on FSCIL have demonstrated that an incrementally trained model is prone to overfit on limited new data and suffer catastrophic forgetting [8, 12, 44, 54]. Thus the mainstream FSCIL framework [40, 51, 58] is to first use the cross-entropy (CE) loss for training in the base session, then freeze the backbone to adapt to new classes. Given such baseline framework, the main problem that needs to be taken into consideration is: What makes a good base-session-model that can generalize well to new classes with limited data?", + "bbox": [ + 75, + 108, + 467, + 303 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Intuitively, if all representations of base classes concentrate around their own clustering centers (i.e., prototypes), and all prototypes are far away from each other, then novel classes should be easily incorporated into the current space without overlapping. Therefore, we conjecture that base class separation facilitates novel class generalization. However, the current widely adopted CE loss in FSCIL cannot well separate the class margins, which causes poor generalization to new classes. This motivates our attempt at methods with better clustering effects, such as supervised contrastive learning (SCL) [21]. We conduct experiments to validate our conjecture by training models optimized by only CE loss, and both CE and SCL. We empirically find that CE leads to lower base class separation and classification accuracy than SCL. Although SCL indeed slightly improves the base class separation with higher accuracy, we find it implicitly decreases the inter-class distance. Therefore, the two methods both leave inadequate room for future updates, which leads to the inevitable overlapping between novel classes and old classes in the representation space (as shown in the left and middle columns of Fig. 1a).", + "bbox": [ + 75, + 306, + 467, + 625 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The unsatisfactory performance of SCL implies that the limited semantic information base session provided may need our fantasy remedy. Although we have no access to future classes in the base session, we can create many virtual classes with various semantics filling the unallocated representation space, which not only holds enough space for future classes, but also enables the learning of diverse semantics for better inference and generalization.", + "bbox": [ + 75, + 626, + 467, + 746 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To this end, we propose a Semantic-Aware Virtual Contrastive (SAVC) model to improve the FSCIL by boosting base class separation (see Fig. 1b). Our fantasy space is constructed with pre-defined transformations [11, 15, 35, 52], which can be regarded as the semantic extension of the original space in finer grains. The type of transformations represents the richness of our imagination, i.e., how compact we want the embedding space to be and how fine the semantic grains we want to acquire. Learning to contrast and recognize in the fantasy space not only brings a better class", + "bbox": [ + 75, + 750, + 467, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "separation effect, but also provides richer semantic information to improve the accuracy. In contrast to the CE baseline and SCL, our SAVC can simultaneously decrease intraclass distance and increase inter-class distance, which leads to better base class separation and novel class generalization (as given in the right of Fig. 1a). Extensive experiments on various benchmark datasets further verify the superiority of our method.", + "bbox": [ + 496, + 90, + 890, + 210 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our main contributions are summarized in three folds:", + "bbox": [ + 517, + 212, + 877, + 224 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "1) We empirically discover that it is crucial to boost class separation degree in base session for the incremental-frozen framework in FSCIL, which helps fast generalization for novel classes with only a few samples.", + "2) We propose a novel Semantic-Aware Virtual Contrastive (SAVC) framework, enhancing the FSCIL learner by learning to recognize and contrast in the fantasy space, which preserves enough place for novel generalization and enables a multi-semantic aggregated inference effect.", + "3) Extensive experiments on three FSCIL benchmarks, i.e., CIFAR100, miniImageNet and CUB200, demonstrate that our SAVC outperforms all approaches by a large margin and achieves the state-of-the-art performance." + ], + "bbox": [ + 496, + 227, + 890, + 422 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 436, + 638, + 452 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Few-Shot Learning. FSL focuses on fast adaptation to unseen classes through few learnable samples. Current FSL methods are mainly divided into two lines: optimization-based methods [14, 26, 33, 36] and metric-based methods [34, 42, 43, 45]. Optimization-based methods aim to encourage the model to obtain generalizable representations by using meta-learning frameworks. Our method has more connection with metric-based methods, which use a pretrained backbone to extract feature, and compute score by measuring the distance between query and support. Nevertheless, FSL aims to adapt to novel classes via few samples and cannot be directly applied to FSCIL, without maintaining classification performance over base classes.", + "bbox": [ + 496, + 462, + 890, + 657 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Class Incremental Learning. CIL is the process of learning new knowledge continually without forgetting. Classical works fall into three streams. The first stream rehearsals former samples selectively to maintain discrimination over old classes [5, 37, 38]. The second stream regularizes the important parameters from being dramatically changed [25, 29]. The third stream dynamically expands the network architecture to adjust the model's representation ability on new tasks [49, 50]. Recent methods boost performance by encouraging the model to learn to recognize more classes [41, 57], which are relative to our method. CIL methods build on the basis of the large number of training examples, which is unsuitable for FSCIL.", + "bbox": [ + 496, + 659, + 890, + 853 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Few-Shot Class-Incremental Learning. FSCIL [1, 2, 6, 9, 10, 18, 27, 31] aims at undertaking CIL task in insufficient data settings. TOPIC [44] first proposes this new task", + "bbox": [ + 496, + 854, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "24184", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "and solves it by neural gas structure. Current FSCIL methods can be roughly divided into two groups. The first group updates the backbone in incremental session [8, 12, 44, 54], while the second group fixes the backbone in incremental sessions [40, 51, 58]. Built on the latter incremental-frozen framework, other works balance the stability and plasticity trade-off from different perspectives, such as finding flatten minima [40], episodic training [51, 56, 58], ensemble learning [20], forward compatibility [55], and so on.", + "bbox": [ + 75, + 90, + 472, + 226 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Self-supervised learning. SSL aims at learning representations without human annotations. A common way of SSL is to design pretext tasks and teach the model how to solve these prediction tasks [11, 15, 35, 52]. [24] proposes to utilize these transformations under full-supervised datasets and trains a unified classifier, which motivates the construction of our fantasy space. Another commonly used pretext task is instance discrimination represented by contrastive learning [3, 7, 16]. The methodology is to teach the model to aggregate positives and exclude negatives. SupCon [21] converted to \"class discrimination\" task, leveraging the label information to decide positive pairs.", + "bbox": [ + 75, + 227, + 472, + 409 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 76, + 420, + 210, + 436 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminary", + "text_level": 1, + "bbox": [ + 76, + 444, + 205, + 460 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In FSCIL, we learn a sequence of tasks continually. There is a constant stream of training data denoting $\\mathcal{D}_{train} = \\{\\mathcal{D}_{train}^t\\}_{t=0}^T$ , where $\\mathcal{D}_{train}^t = \\{(\\mathbf{x}_i, y_i)\\}_{i=0}^{N_t}$ represents the training samples from session $t$ , $\\mathbf{x}_i$ and $y_i$ are the $i$ -th image and corresponding label respectively. Assuming that the label space of the $t$ -th task is $\\mathcal{C}^t$ , the label spaces from different tasks have no overlap, i.e., the model trained on the $\\mathcal{D}_{train}^t$ should be evaluated on $\\mathcal{D}_{test}^t$ which contains all encountered classes $\\mathcal{C}^0 \\cup \\mathcal{C}^1 \\dots \\cup \\mathcal{C}^t$ in the $t$ -th session. Specifically, sufficient data can be provided in base session (also called initial session), while the model only has access to a few samples in all following sessions (also called incremental sessions). In particular, the incremental data are always organized as $N$ -way $K$ -shot format, i.e., there are $N$ classes, and each class contains $K$ training images.", + "bbox": [ + 75, + 468, + 468, + 696 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Several provident works [40, 51] recommend an incremental-frozen framework that leverages sufficient data in base session to train the model by optimizing the persample loss:", + "bbox": [ + 75, + 696, + 470, + 756 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {c l s} (\\phi ; \\mathbf {x}, y) = \\mathcal {L} _ {c e} (\\phi (\\mathbf {x}), y), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 165, + 765, + 468, + 781 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{ce}(\\cdot ,\\cdot)$ is cross-entropy loss (CE), $\\phi (\\mathbf{x})$ is the model which can be decomposed into feature extractor and classifier: $\\phi (\\mathbf{x}) = W^{T}f(\\mathbf{x})$ where $\\phi (\\mathbf{x})\\in \\mathbb{R}^{|C^0 | \\times 1}$ $W\\in \\mathbb{R}^{d\\times |\\mathcal{C}^0 |}$ and $f(\\mathbf{x})\\in \\mathbb{R}^{d\\times 1}$", + "bbox": [ + 75, + 787, + 468, + 854 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "After the base training stage, the backbone will be completely fixed, and the classifier is extended in each incremental session: $W = \\{ \\boldsymbol{w}_1^0, \\boldsymbol{w}_2^0, \\dots, \\boldsymbol{w}_{|\\mathcal{C}^0|}^0 \\} \\cup \\dots \\cup$", + "bbox": [ + 75, + 854, + 468, + 902 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/faf96d51778bb96680dfa63a1a6d55c55be965d072fd4dda109dbb01fd02f1f0.jpg", + "image_caption": [ + "(a) Cumulative distribution function of inter-class distance." + ], + "image_footnote": [], + "bbox": [ + 506, + 90, + 700, + 219 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/43c02d3690f74a98993de4df6d81e74868108c51bce3609f36f6a9a1b5684456.jpg", + "image_caption": [ + "(b) Cumulative distribution function of intra-class distance." + ], + "image_footnote": [], + "bbox": [ + 702, + 90, + 903, + 219 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/da973bb00d7a1eab0662992ed15b1ec6a4a42f6e3cd9ca7acb3457ab24f5daf1.jpg", + "image_caption": [ + "(c) Base, novel and mutual class separation degree.", + "Figure 2. The relationship between base class separation and novel class generalization. (a) Inter-class distance is defined in Eq. (5). A curve lies more right on the figure denotes greater inter-class distance. (b) Intra-class distance is defined in Eq. (6). A curve lies more left on the figure denotes smaller intra-class distance. (c) Class separation degree $(R^2)$ is defined in Eq. (7)." + ], + "image_footnote": [], + "bbox": [ + 503, + 252, + 700, + 378 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/96832395515a657f966dda532deee5318799826668cb4bc299652597230b4146.jpg", + "image_caption": [ + "(d) Base, novel and total classification accuracy." + ], + "image_footnote": [], + "bbox": [ + 700, + 252, + 898, + 377 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "$\\{\\pmb{w}_1^t,\\dots ,\\pmb{w}_{|\\mathcal{C}^t |}^t\\}$ . The classifier weight is parameterized by the average embeddings of each class (i.e., prototype), represented as $\\pmb {w}_c^t = \\frac{1}{n_c^t}\\sum_{i = 1}^{n_c^t}f(\\mathbf{x}_{c,i})$", + "bbox": [ + 498, + 515, + 890, + 566 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In each session, the inference is conducted by the nearest class mean (NCM) algorithm [32] to evaluate accuracy of all encountered classes. Specifically, we measure the distance between $f(\\mathbf{x})$ and all prototypes in classifiers:", + "bbox": [ + 498, + 566, + 890, + 626 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nc _ {\\mathbf {x}} = \\underset {c, t} {\\arg \\max } \\operatorname {s i m} \\left(f (\\mathbf {x}), \\boldsymbol {w} _ {c} ^ {t}\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 588, + 636, + 890, + 657 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\operatorname{sim}(\\mathbf{x},\\mathbf{y}) = \\mathbf{x}^{\\top}\\mathbf{y} / (\\| \\mathbf{x}\\| \\| \\mathbf{y}\\|)$ is cosine similarity between two vectors.", + "bbox": [ + 498, + 671, + 888, + 700 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Base Class Separation Facilitates Novel Class Generalization", + "text_level": 1, + "bbox": [ + 500, + 710, + 890, + 739 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The incremental-frozen baseline outperforms a lot of incremental trainable methods [40], demonstrating that preserving old knowledge may matter more than accommodating new knowledge in FSCIL. The guarantee of base performance by the freezing strategy motivates our consideration of what our model should learn in the base training stage for future few-shot generalization.", + "bbox": [ + 496, + 750, + 890, + 854 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "An intuitive hypothesis is that, base class separation facilitates novel class generalization. If all prototypes are far away from each other, and the features of the same class", + "bbox": [ + 498, + 854, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "24185", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/368365ae657be6bba49ddc45ef81f21a2932c44c26c15f9b41cb4de946e03e71.jpg", + "image_caption": [ + "Figure 3. Overall pipeline of our Semantic-Aware Virtual Contrastive (SAVC) framework, which is composed of a classification branch and a contrastive learning branch. The backbone is shared for classification and contrastive query encoding, while the key backbone is updated with a large momentum ('//' means stop gradient). Both classification and contrastive loss are optimized in fantasy space, while the inference results of different transformations are aggregated and then projected into the original label space." + ], + "image_footnote": [], + "bbox": [ + 107, + 88, + 851, + 375 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "concentrate around their prototypes, there would be enough place for future class incorporation. Please note that the novel class generalization contains two meanings: without overlapping among novel class sets and with base class embeddings. Previous work demonstrated CE always cannot generalize well due to the poor class margins [13, 28] and may not be the optimal strategy for FSCIL, which inspires our attempt of supervised contrastive learning (SCL) [21], whose clustering effect has been demonstrated [53].", + "bbox": [ + 75, + 463, + 468, + 598 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We adopt MoCo [7, 16] framework to implement SCL in our method, for the sake of lower memory budget and storing slowly evolving features of recent batches. For an given image-label pair $(\\mathbf{x},y)$ , we generate a query view $\\mathbf{x}_q = \\mathrm{Aug}_q(\\mathbf{x})$ and a key view $\\mathbf{x}_k = \\mathrm{Aug}_k(\\mathbf{x})$ by random augmentations. Then the two augmented images are fed into the query network $g_{q}$ and key network $g_{k}$ , getting two $L_{2}$ -normalized representations $\\pmb{q}$ and $\\pmb{k}$ , where $g = h\\circ f$ is composed of a feature extractor $f$ and a projector $h$ . The two networks share the same architectures, while the key network updates itself with a large momentum. We maintain a feature queue and a label queue with the same length, storing the most current key embeddings and corresponding labels. To this end, given an image $\\mathbf{x}$ , we have the following per-sample supervised contrastive loss:", + "bbox": [ + 75, + 599, + 473, + 828 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {c o n t} (g; \\mathbf {x}, \\tau , A) \\\\ = - \\frac {1}{| P (\\mathbf {x}) |} \\sum_ {\\boldsymbol {k} _ {+} \\in P (\\mathbf {x})} \\log \\frac {\\exp \\left(\\boldsymbol {q} ^ {\\mathrm {T}} \\boldsymbol {k} _ {+} / \\tau\\right)}{\\sum_ {\\boldsymbol {k} ^ {\\prime} \\in A (\\mathbf {x})} \\exp \\left(\\boldsymbol {q} ^ {\\mathrm {T}} \\boldsymbol {k} ^ {\\prime} / \\tau\\right)}, \\tag {3} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 102, + 835, + 468, + 902 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $A(\\mathbf{x}) = \\mathbf{k} \\cup Q$ is the concatenation of the key embedding $\\mathbf{k}$ of $\\mathbf{x}$ and feature queue $Q$ , $P(\\mathbf{x})$ denotes positive set, i.e., those in $A(\\mathbf{x})$ belonging to the same class with $\\mathbf{x}$ .", + "bbox": [ + 496, + 463, + 890, + 508 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In our practice, the feature extractor for query encoding and classification is shared. Put the training of the classification branch and contrastive learning branch together, we form a unified end-to-end framework, and the overall training objective can be concluded as:", + "bbox": [ + 496, + 508, + 892, + 583 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {c l s} + \\alpha \\mathcal {L} _ {\\text {c o n t}}. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 625, + 593, + 890, + 609 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We would like to refer the model trained with Eq. (1) as CE, and the model trained with Eq. (4) as $\\mathrm{CE + SCL}$ .", + "bbox": [ + 496, + 618, + 890, + 647 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We then introduce and define some metrics to measure class separation and generalization, including:", + "bbox": [ + 496, + 648, + 890, + 678 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "1) Inter-class distance. Given two prototypes $\\pmb{p}_m$ and $\\pmb{p}_k$ of class $m$ and $k$ , we compute their distance and get $|\\mathcal{C}^0|(|\\mathcal{C}^0| - 1)/2$ inter-class distances in base session.", + "bbox": [ + 498, + 679, + 890, + 724 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nd _ {i n t e r} ^ {m, k} = 1 - \\operatorname {s i m} \\left(\\boldsymbol {p} _ {m}, \\boldsymbol {p} _ {k}\\right). \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 599, + 733, + 890, + 753 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "2) Intra-class distance. Given a class $k$ , we compute the average Euclidean distance between all test samples belonging to class $k$ and prototype $p_k$ to measure the discrepancy within this class. For $|\\mathcal{C}^0|$ classes in base session, we have:", + "bbox": [ + 496, + 761, + 890, + 823 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nd _ {i n t r a} ^ {k} = 1 - \\frac {1}{n _ {k}} \\sum_ {i = 1} ^ {n _ {k}} \\operatorname {s i m} (f (\\mathbf {x} _ {k, i}), \\boldsymbol {p} _ {k}). \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 550, + 830, + 890, + 861 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3) Class Separation $(R^2)$ . On the one hand, we refer to Eq. (11) in [22] to measure the base $(K = |\\mathcal{C}^0|)$ and novel", + "bbox": [ + 498, + 869, + 890, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "24186", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$(K = \\sum_{t=1}^{T} |\\mathcal{C}^t|)$ class separation degree respectively:", + "bbox": [ + 76, + 88, + 436, + 108 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} R ^ {2} = 1 - \\bar {d} _ {\\text {w i t h i n}} / \\bar {d} _ {\\text {t o t a l}}, \\\\ \\bar {d} _ {w i t h i n} = \\sum_ {k = 1} ^ {K} \\sum_ {i = 1} ^ {n _ {k}} \\sum_ {j = 1} ^ {n _ {k}} \\frac {1 - \\mathrm {s i m} (f (\\mathbf {x} _ {k , i}) , f (\\mathbf {x} _ {k , j}))}{K n _ {k} ^ {2}}, \\\\ \\bar {d} _ {\\text {t o t a l}} = \\sum_ {k = 1} ^ {K} \\sum_ {m = 1} ^ {K} \\sum_ {i = 1} ^ {n _ {j}} \\sum_ {j = 1} ^ {n _ {k}} \\frac {1 - \\operatorname {s i m} \\left(f \\left(\\mathbf {x} _ {k , i}\\right) , f \\left(\\mathbf {x} _ {m , j}\\right)\\right)}{K ^ {2} n _ {k} n _ {j}}. \\tag {7} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 86, + 112, + 468, + 238 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "On the other hand, we propose a novel metric to evaluate the mutual separation degree between novel and base classes, which is referred to as 'mutual class separation $(R_{mul}^{2})$ . We only need to let $K = \\sum_{t=0}^{T} |\\mathcal{C}^t|$ in $\\bar{d}_{within}$ , and $K = |\\mathcal{C}^0|$ and $\\sum_{t=1}^{T} |\\mathcal{C}^t|$ respectively in $\\bar{d}_{total}$ , to compute $R_{mul}^{2}$ .", + "bbox": [ + 75, + 244, + 467, + 325 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Greater $d_{inter}$ and $R_{base}^{2}$ , smaller $d_{intra}$ represents more compact the base embedding space is. Under the incremental-frozen framework, we compare the result of CE and $\\mathrm{CE + SCL}$ in Fig. 2 on CIFAR100 benchmark, while $d_{inter}$ and $d_{intra}$ are shown in the cumulative distribution function (CDF) style. We can observe that although $\\mathrm{CE + SCL}$ gets greater class separation degree and classification accuracy than CE on all classes, it implicitly decreases inter-class distance while decreasing intra-class distance (the $\\mathrm{CE + SCL}$ curve lies on the left of the CE curve both in Fig. 2a and Fig. 2b). Especially, SCL only provides minor improvement of the mutual class separation $R_{mul}^{2}$ , which implies that SCL cannot separate the novel representations from the old distributions (i.e., poor novel class generalization), and is also far from perfect for our objective (see Fig. 2c and Fig. 2d).", + "bbox": [ + 75, + 327, + 468, + 568 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Semantic-Aware Contrastive Class Fantasy", + "text_level": 1, + "bbox": [ + 76, + 575, + 442, + 590 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The unsatisfactory performance of SCL, especially the inappreciable improvements of mutual class separation may convey that the semantic information base session provides is not diversified enough, which makes the learned decision boundary very sharp and close to data, resulting in poor base class separation and novel class generalization. Although the rests contained in new classes are invisible in base session, a provident model may be self-sufficient: imagining what would happen in the future and making adequate preparations for it. In other words, our model should use its fantasy to generate many virtual classes with various semantics and fill the unallocated embedding space, all by itself. These virtual classes act as not only class placeholders which preserve enough space for future updates, but also semantic knowledge providers which encourage extensive learning of different semantics for better generalization.", + "bbox": [ + 75, + 598, + 468, + 839 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Semantic-aware class fantasy. In our semantic-aware contrastive class fantasy (SAVC) framework, we take inspiration from [24] and generate virtual classes by applying pre-defined transformations in self-supervised learning", + "bbox": [ + 75, + 839, + 468, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "(SSL) [11, 15, 35, 52]. In detail, we first define a discrete transformation (i.e., fantasy) set $\\mathcal{F}$ and supposing there are $M$ elements in it, i.e., the number of transformations. Then, we can generate $M$ transformed samples for an image-label pair $(\\mathbf{x}, y)$ , denoting $\\mathcal{F}(\\mathbf{x}, y) = \\{(\\mathbf{x}_m, y_m)\\}_{m=1}^M$ , where $y_m = y \\times M + m$ . The subscript of $(\\mathbf{x}_m, y_m)$ denotes $m$ -th transformation is applied to $(\\mathbf{x}, y)$ , while $(\\mathbf{x}_1, y_1)$ represents the original image-label pair.", + "bbox": [ + 498, + 90, + 890, + 210 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this way, we enlarge our label space by $M$ times, and each virtual class can be regarded as a 'fine-grained' class derived from the original one. The number and type of the elements in $\\mathcal{F}$ convey not only how compact we expect the embedding space to be, but also how fine the semantic grains we want to acquire. Hence we conduct the classification and contrasting training process in finer gains:", + "bbox": [ + 496, + 210, + 892, + 318 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {c l s} \\left(\\phi ; \\mathbf {x}, y, \\mathcal {F}\\right) = \\frac {1}{M} \\sum_ {i = 1} ^ {M} \\mathcal {L} _ {c e} \\left(\\phi \\left(\\mathbf {x} _ {m}\\right), y _ {m}\\right), \\\\ \\mathcal {L} _ {\\text {c o n t}} (g; \\mathbf {x}, \\tau , A, \\mathcal {F}) = \\\\ - \\frac {1}{M} \\sum_ {m = 1} ^ {M} \\frac {1}{| P (\\mathbf {x} _ {m}) |} \\sum_ {\\mathbf {k} _ {+} \\in P (\\mathbf {x} _ {m})} \\log \\frac {\\exp \\left(\\boldsymbol {q} ^ {\\mathrm {T}} \\mathbf {k} _ {+} / \\tau\\right)}{\\sum_ {\\boldsymbol {k} ^ {\\prime} \\in A (\\mathbf {x} _ {m})} \\exp \\left(\\boldsymbol {q} ^ {\\mathrm {T}} \\boldsymbol {k} ^ {\\prime} / \\tau\\right)}. \\tag {8} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 500, + 327, + 905, + 450 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Multi-semantic aggregated inference. With the increase of class, we can acquire various derived class information in finer gains, which enables extending of classifiers with the prototypes of original and pseudo classes: $W = \\{\\pmb{w}_{11}^{0},\\pmb{w}_{12}^{0},\\dots ,\\pmb{w}_{1M}^{0},\\dots ,\\pmb{w}_{|\\mathcal{C}^{0}|M}^{0}\\} \\cup \\dots \\cup \\{\\pmb{w}_{11}^{t},\\dots ,\\pmb{w}_{|\\mathcal{C}^{t}|M}^{t}\\}$ . Prototypes with the same second subscript can naturally form a conditional prototype subset, which denotes a group of clustering centers aware of the same type of fine-grained semantics from the original classes: $W_{m} = \\{\\pmb{w}_{1m}^{0},\\pmb{w}_{2m}^{0},\\dots ,\\pmb{w}_{|\\mathcal{C}^{0}|m}^{0}\\} \\cup \\dots \\cup \\{\\pmb{w}_{1m}^{t},\\dots ,\\pmb{w}_{|\\mathcal{C}^{t}|m}^{t}\\}$ . In contrast to the traditional single inference in Eq. (2), we first specify the test sample $\\mathbf{x}$ into various fine-grained semantics: $\\mathcal{F}(\\mathbf{x}) = \\{\\mathbf{x}_m\\}_{m = 1}^M$ . Then we aggregate the inference results from different conditional prototype subsets, which improves accuracy significantly:", + "bbox": [ + 498, + 460, + 892, + 691 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nc _ {\\mathbf {x}} = \\arg \\max _ {c, t} \\sum_ {m = 1} ^ {M} \\sin \\left(f \\left(\\mathbf {x} _ {m}\\right), \\boldsymbol {w} _ {c m} ^ {t}\\right). \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 558, + 702, + 890, + 743 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To this end, We compare our SAVC with CE and $\\mathrm{CE + SCL}$ to verify its effectiveness. As we expected, SAVC effectively increases inter-class distance and decreases intraclass distance in base session, resulting in the best base class separation and novel class generalization (see Fig. 2).", + "bbox": [ + 496, + 747, + 890, + 824 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Discussion", + "text_level": 1, + "bbox": [ + 500, + 832, + 616, + 845 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Our training pipeline is depicted in Fig. 3. Besides the training regime demonstrated in Sec. 3.3, the proposed SAVC also benefits from the following aspects:", + "bbox": [ + 498, + 854, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "24187", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/c5c93e1eac13950d68fd4d43ecd49a1f732a4ba0406216a56571fb189022e4be.jpg", + "table_caption": [ + "Table 1. Comparison with SOTA methods on CUB200 dataset for FSCIL. $\\ast$ : Performances reported by [44]. $\\Delta_{\\mathrm{last}}$ : Relative improvements of the last session compared to the Finetune baseline. More results on other datasets can be found in the appendix." + ], + "table_footnote": [], + "table_body": "
MethodAcc. in each session (%) ↑Δlast
012345678910
Finetune* [44]68.6843.7025.0517.7218.0816.9515.1010.068.938.938.47-
iCaRL* [37]68.6852.6548.6144.1636.6229.5227.8326.2624.0123.8921.16+12.69
EEIL* [4]68.6853.6347.9144.2036.3027.4625.9324.7023.9524.1322.11+13.64
TOPIC* [44]68.6862.4954.8149.9945.2541.4038.3535.3632.2228.3126.28+17.81
Rebalancing* [19]68.6857.1244.2128.7826.7125.6624.6221.5220.1220.0619.87+11.40
SPPR [58]68.6861.8557.4352.6850.1946.8844.6543.0740.1739.6337.33+28.86
F2M [40]81.0778.1675.5772.8970.8668.1767.0165.2663.3661.7660.26+51.79
CEC [51]75.8571.9468.5063.5062.4358.2757.7355.8154.8353.5252.28+43.81
FACT [55]75.9073.2370.8466.1365.5662.1561.7459.8358.4157.8956.94+48.47
SAVC (Ours)81.8577.9274.9570.2169.9667.0266.1665.3063.8463.1562.50+54.03
", + "bbox": [ + 119, + 128, + 849, + 286 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/5d4d70728da69a44e02dd132c75451bbbc4c5a99992914cd4d1f8481039f0d72.jpg", + "image_caption": [ + "(a) CIFAR100" + ], + "image_footnote": [ + "Finetune Rebalancing iCaRL EEIL TOPIC SPPR CEC FACT F2M SAVC" + ], + "bbox": [ + 119, + 287, + 364, + 424 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/df24679c1a50e745c83ce38f90cd79fa397095a5d61294e57980eee6badddabc.jpg", + "image_caption": [ + "(b) miniImagenet" + ], + "image_footnote": [], + "bbox": [ + 364, + 287, + 606, + 424 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/342e3c2b190ea5d7cb366cc43a454740736c8f318bbd3c49071c1b521df90ae9.jpg", + "image_caption": [ + "(c) CUB200", + "Figure 4. Comparison with SOTA methods on CIFAR100, miniImageNet, and CUB200 benchmarks." + ], + "image_footnote": [], + "bbox": [ + 607, + 289, + 849, + 424 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/d934ea4d3f6251ac659bae36f5bb93ea60a45af0e9febd326e6f5f9bcb40fec2.jpg", + "table_caption": [ + "Table 2. Statistics of benchmarks datasets. $\\mathcal{C}^{base}$ : number of classes in base session. $\\mathcal{C}^{inc}$ : total number of classes in incremental sessions. #Inc.: number of incremental sessions. Shots: training shots for incremental sessions." + ], + "table_footnote": [], + "table_body": "
DatasetCbaseCinc#Inc.ShotsResolution
CIFAR100 [23]60408532×32
miniImageNet [39]60408584×84
CUB200 [48]100100105224×224
", + "bbox": [ + 81, + 571, + 468, + 640 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Multi-Crop strategy. Motivated by [3, 47], we add $N$ constraint small crops to capture more local information, which is enforced to overlap with the global crops over a pre-defined threshold ensuring information sharing. These $N$ smaller crops are only served as query views and not fed into the key encoder. In practice, we find optimizing the global and local contrastive loss respectively is helpful, and the overall training objective can be rewritten as:", + "bbox": [ + 75, + 648, + 467, + 770 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {c l s} + \\alpha \\mathcal {L} _ {\\text {c o n t - g l o b a l}} + \\beta \\mathcal {L} _ {\\text {c o n t - l o c a l}}. \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 133, + 782, + 468, + 799 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Incremental finetuning. Although there are only a few novel samples available in incremental sessions, it is essential to finetune the pretrained model by Eq. (10) in each incremental session. Fortunately, we can contrast novel query samples with these slowly evolving key embeddings of base session from the feature queue, which helps push the novel", + "bbox": [ + 75, + 809, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "samples away from the distributions of the old classes and benefits novel class generalization.", + "bbox": [ + 498, + 506, + 890, + 537 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 556, + 633, + 574 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 500, + 585, + 689, + 602 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets. Following the benchmark setting [51], we conduct experiments on three datasets, i.e., CIFAR100 [23], miniImageNet [39] and CUB200 [48], to show the performance of our method. Their statistic characteristics are listed in Tab. 2.", + "bbox": [ + 496, + 611, + 890, + 686 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation details. Following [51], we adopt ResNet18 [17] backbones for miniImageNet and CUB200, and ResNet20 [17] for experiments on CIFAR100. We use SGD with 0.9 momentum to optimize the model. The initial learning rate is 0.1 for CIFAR100 and miniImageNet, and 0.002 for CUB200 in base session. For each incremental learning stage, we only finetune the model for 10 epochs in case of overfitting. We only consider 3 transformation sets $\\mathcal{F}$ , including 2-fold rotations, 4-fold rotations and 12-augmentations, and their concrete meanings are listed in Tab. 4. We use 12 augmentations for miniImageNet, and 2-fold rotation for CUB200 and CIFAR100, to fancy virtual classes. We set $\\alpha$ as 0.2 and $\\beta$ as 0.8 for all datasets to control the impact of global and local contrastive loss.", + "bbox": [ + 496, + 688, + 892, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "24188", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/2030b4331445b9d4181b9a68acc26ad702ff72700fe6a9778e404739acf00290.jpg", + "table_caption": [ + "Table 3. Ablation studies on miniImageNet benchmark. SCL, F, MC, FT denote supervised contrastive learning, virtual classes fantasy, multi-crop, incremental finetuning, respectively. ${\\Delta }_{\\text{last }}$ : Relative improvements of the last sessions compared to the CE baseline." + ], + "table_footnote": [], + "table_body": "
SCLFMCFTAcc. in each session (%) ↑Δlast
012345678
70.2765.1761.1357.8054.8051.8449.1447.0345.54-
✓72.7767.4263.2659.5656.5153.6250.8748.9147.10+1.56
✓✓80.7275.8271.4667.6564.6361.3158.2756.2054.52+8.98
✓✓✓81.1276.0271.8168.4165.4662.1159.3357.4255.80+10.26
✓✓✓✓81.1276.1472.4368.9266.4862.9559.9258.3957.11+11.57
", + "bbox": [ + 119, + 128, + 849, + 229 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/b48770e89d37c8b87ec4a047ed8ef11ba253ef427cfc6e7f09ebecc251dcf4d1.jpg", + "table_caption": [ + "Table 4. The concrete meaning of different types of transformation set $\\mathcal{F}$ . 'ALL' denotes using all $0^{\\circ}$ , $90^{\\circ}$ , $180^{\\circ}$ , $270^{\\circ}$ degrees." + ], + "table_footnote": [], + "table_body": "
FRotationColor permutationM
2-fold rorations0°, 180°RGB2
4-fold rorationsALLRGB4
12-augmentationsALLRGB, GBR, BRG12
", + "bbox": [ + 78, + 277, + 470, + 354 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.2. Comparison with State of The Arts", + "text_level": 1, + "bbox": [ + 76, + 369, + 382, + 386 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We first compare our proposed SAVC with prevailing methods on three FSCIL benchmarks. These methods include: classical CIL methods, i.e., iCaRL [37], EEIL [4], Rebalancing [19], incremental-trainable FSCIL methods, i.e., TOPIC [44], and incremental-frozen FSCIL methods, i.e., SPPR [58], F2M [40], CEC [51], and FACT [55]. We also show a naive baseline that directly finetunes the model with limited data as 'finetune'. We report the performance curve in Fig. 4 and the detailed values on CUB200 in Tab. 1.", + "bbox": [ + 75, + 393, + 468, + 542 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "It is observed that 'finetune' leaves out preserving old knowledge and gets inferior performance. Classical CIL methods only consider extending the model with sufficient instances and are not suitable for FSCIL. Incremental-trainable FSCIL methods suffer from overfitting phenomena and perform poorly in incremental sessions. Incremental-frozen FSCIL methods can continually adapt to new classes without any training, which achieve better performance. Under the incremental-frozen framework, our SAVC effectively boosts both base class separation and novel class generalization by recognizing and contrasting semantic-aware virtual classes, consistently achieving the highest average accuracy over all sessions on three datasets. e.g., our approach gets the final accuracies of $53.12\\%$ , $62.50\\%$ and $57.11\\%$ for CIFAR100, CUB200 and miniImageNet, outperforming the current SOTA method FACT by $1.02\\%$ , $5.56\\%$ and $6.62\\%$ , respectively.", + "bbox": [ + 75, + 546, + 470, + 803 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation study", + "text_level": 1, + "bbox": [ + 76, + 815, + 225, + 832 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We conduct ablation studies to prove the importance of our proposed components. Built on the incremental-frozen framework, we adopt only CE loss as the baseline, while adding SCL (SCL) and observe the influence this attempt", + "bbox": [ + 75, + 839, + 470, + 900 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/3c2c8ac452922e3ffe8ea8fa8c5ff8aea2f1676012b7bc1dd0c9d22606279800.jpg", + "image_caption": [ + "(a) CE.", + "Figure 5. Comparison of the confusion matrices of CE and SAVC methods on CUB200 dataset." + ], + "image_footnote": [], + "bbox": [ + 504, + 239, + 679, + 369 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/1fa5fbe11afbe8302a1a9acca5148cac2320d8b2a6451a2bfbd6f2cd39dd243e.jpg", + "image_caption": [ + "(b)SAVC." + ], + "image_footnote": [], + "bbox": [ + 683, + 239, + 888, + 369 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "brings (i.e., CE and $\\mathrm{CE + SCL}$ defined in Sec. 3.2). Then we fantasy (F) contrastive virtual class in $\\mathrm{CE + SCL}$ and verify its effectiveness. In addition, we consider the influence of the two components mentioned in Sec. 3.4, i.e., multi-crop strategy (MC) and finetune (FT) the pre-trained model. We report the results for miniImageNet dataset in Tab. 3.", + "bbox": [ + 496, + 441, + 890, + 532 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Compared with the baseline, SCL shows limited influences on improving mutual class separation and lightly improves the performance by $1.56\\%$ in the last session. The fantasy of virtual classes (F) plays a vital part in preventing overlap among old and new distributions, which brings $8.98\\%$ performance improvement. When attending more local information (MC), our model works better, i.e., $10.26\\%$ gains. Lastly, it is essential to fine-tune the model in incremental session (T), which achieves $11.57\\%$ accuracy gain.", + "bbox": [ + 496, + 532, + 890, + 669 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Visualization", + "text_level": 1, + "bbox": [ + 500, + 680, + 635, + 695 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Confusion Matrix. We visualize the confusion matrix generated by CE and our SAVC on CUB200 dataset after the training of the last session in Fig. 5. Bright diagonal and dim background indicates higher classification accuracy. It is observed that CE draws a relatively distinct diagonal, but it has poor performance on new classes. In contrast, our method shows better performance on both base and new classes, which proves that our method adapts to novel classes without confusing the previous decision boundaries.", + "bbox": [ + 496, + 703, + 890, + 839 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Visualization of class separation. We visualize the embedding space on CIFAR100 dataset with t-SNE [46] in Fig. 7. We randomly choose 6 classes from base classes and 3 classes from novel classes respectively. We can ob-", + "bbox": [ + 496, + 840, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "24189", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8ac243e755214eb67a72cd3b42a88e6ada87b1f025da3af4d9e7e969066ff144.jpg", + "image_caption": [ + "(a) Influence of fantasy methods." + ], + "image_footnote": [], + "bbox": [ + 81, + 95, + 321, + 251 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/9d8a5bc729f0fe9b5d748a00a97a1bf7d37082e530481a49af64f5d28ae18d71.jpg", + "image_caption": [ + "(b) Influence of incremental trainable layers." + ], + "image_footnote": [], + "bbox": [ + 356, + 97, + 598, + 251 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/cfba7eb682b88ade55f2629fac9a9c69661ebd1dd49a18c44f59b17aed723d01.jpg", + "image_caption": [ + "(c) Influence of hyper-parameters." + ], + "image_footnote": [], + "bbox": [ + 635, + 92, + 885, + 251 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/635547371ba45edbbb84ca4e45e5e3e29f4a8aaf4ad52b5645dfdadd071842ca.jpg", + "image_caption": [ + "Figure 6. Analysis of detailed hyper configurations of our method: fantasy methods, trainable layers, and hyper-parameters.", + "(a) CE." + ], + "image_footnote": [], + "bbox": [ + 86, + 311, + 212, + 407 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/dfcde936ae1e080a8156df750a468988eab708ab1ce1a1f8b45f9cc85b18f5a2.jpg", + "image_caption": [ + "(b) $\\mathrm{CE + SCL}$", + "Figure 7. The t-SNE [46] visualization on CIFAR100 dataset of the embeddings learned by various methods. Class 0-5 represent the base classes while class 6-8 represent the novel classes. Our SAVC gets the best class separation degree." + ], + "image_footnote": [], + "bbox": [ + 217, + 311, + 344, + 409 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4438a30d9abed828f711432d5c5b16c6bee078c7f611d6e9b07a2231dc3ad96d.jpg", + "image_caption": [ + "(c)SAVC." + ], + "image_footnote": [], + "bbox": [ + 349, + 311, + 475, + 409 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "serve that CE has limited clustering effects. Naïve SCL slightly improves the class separation degree, but implicitly decreases inter-class distance and remains the mutual class confusion problems unresolved. With the help of virtual classes, Our proposed SAVC can clearly separate base classes and incorporate novel classes with the least overlap.", + "bbox": [ + 75, + 511, + 467, + 602 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.5. Further Analysis", + "text_level": 1, + "bbox": [ + 76, + 617, + 243, + 633 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Analysis of fantasy method. We consider 3 transformation sets $\\mathcal{F}$ and show their impacts on our method in Fig. 6a. We observe that miniImageNet benefits from complex transformation more, while it would hurt the class separation on CUB200 and CIFAR100. We suppose that CUB200 is already a fine-grained dataset, and the learning process would be hindered by overly fine grains. As for CIFAR100, the image resolutions are too small, and it may be hard to make a distinction between the original and virtual classes. We put discussions on more types of fantasy methods in the appendix.", + "bbox": [ + 75, + 642, + 467, + 808 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Analysis of trainable layers in incremental sessions. We update the trainable layers in incremental sessions on miniImageNet benchmark. The results are reported in Fig. 6b. The gray horizontal line indicates without incremental finetuning. It is observed that finetuning fewer and deeper layers gets higher accuracy, while finetuning too", + "bbox": [ + 75, + 810, + 467, + 901 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "many layers harms the former knowledge and shows poorer performance. We infer that the low-level characteristics extracted by shallow layers are shared between classes, so we only need to update high layers to adapt to new classes.", + "bbox": [ + 496, + 310, + 890, + 369 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Analysis of hyper-parameter. There are two main hyper-parameters in our loss, i.e., coefficient $\\alpha$ and $\\beta$ which control the impact of global and local supervised contrastive loss in Eq. (10). We report the accuracy in the last session on miniImageNet dataset varying $\\alpha$ and $\\beta$ in Fig. 6c. Our method is robust to the choices of hyper-parameters, and it achieves the best performance when $\\alpha = 0.2$ and $\\beta = 0.8$ . For convenience, we fix the value of $\\alpha$ and $\\beta$ on all datasets.", + "bbox": [ + 496, + 369, + 890, + 489 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 503, + 617, + 518 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we solve the FSCIL problems by Semantic-Aware Virtual Contrastive framework (SAVC). Based on the mainstream incremental-frozen framework, both CE and SCL fail to separate base classes and new classes well. We propose to introduce more semantic knowledge by imagining virtual classes. These virtual classes not only hold enough feature space for future updates, but enable a multisemantic aggregated inference effect and significantly improve classification accuracy. By learning to recognize and contrast in the fantasy space, our method can maintain the separation of all classes and achieve SOTA performance on the three popular FSCIL benchmarks.", + "bbox": [ + 496, + 529, + 890, + 710 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments", + "text_level": 1, + "bbox": [ + 500, + 724, + 658, + 739 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work is supported by grants from the KeyArea Research and Development Program of Guangdong Province (2020B0101380001), the National Key R&D Program of China (2022ZD0118101), the National Natural Science Foundation of China (61825101, 62202010, 62202014), Shenzhen Basic Research Program (JCYJ20220813151736001), China Postdoctoral Science Foundation (2022M710212), and Huawei Technologies Co., Ltd. The computing resources of Pengcheng Cloudbrain are used in this research.", + "bbox": [ + 496, + 750, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "24190", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, and Ethan Fetaya. Gp-tree: A gaussian process classifier for few-shot incremental learning. In ICML, pages 54-65. PMLR, 2021. 2", + "[2] Touqueer Ahmad, Akshay Raj Dhamija, Steve Cruz, Ryan Rabinowitz, Chunchun Li, Mohsen Jafarzadeh, and Terrance E Boult. Few-shot class incremental learning leveraging self-supervised features. In CVPRW, pages 3900-3910, 2022. 2", + "[3] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In NeurIPS, pages 9912-9924, 2020. 3, 6", + "[4] Francisco M Castro, Manuel J Marín-Jiménez, Nicolas Guil, Cordelia Schmid, and Karteek Alahari. End-to-end incremental learning. In ECCV, pages 233-248, 2018. 6, 7", + "[5] Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny. Efficient lifelong learning with a gem. arXiv preprint arXiv:1812.00420, 2018. 2", + "[6] Kuilin Chen and Chi-Guhn Lee. Incremental few-shot learning via vector quantization in deep embedded space. In ICLR, 2020. 1, 2", + "[7] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297, 2020. 3, 4", + "[8] Ali Cheraghian, Shafin Rahman, Pengfei Fang, Soumava Kumar Roy, Lars Petersson, and Mehrtash Harandi. Semantic-aware knowledge distillation for few-shot class-incremental learning. In CVPR, pages 2534-2543, 2021. 1, 2, 3", + "[9] Ali Cheraghian, Shafin Rahman, Sameera Ramasinghe, Pengfei Fang, Christian Simon, Lars Petersson, and Mehrtash Harandi. Synthesized feature based few-shot class incremental learning on a mixture of subspaces. In ICCV, pages 8661-8670, 2021. 2", + "[10] Zhixiang Chi, Li Gu, Huan Liu, Yang Wang, Yuanhao Yu, and Jin Tang. Meta-scil: A meta-learning approach for few-shot class incremental learning. In CVPR, pages 14166-14175, 2022. 2", + "[11] Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In ICCV, pages 1422-1430, 2015. 2, 3, 5", + "[12] Songlin Dong, Xiaopeng Hong, Xiaoyu Tao, Xinyuan Chang, Xing Wei, and Yihong Gong. Few-shot class-incremental learning via relation knowledge distillation. In AAAI, volume 35, pages 1255-1263, 2021. 1, 2, 3", + "[13] Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, and Samy Bengio. Large margin deep networks for classification. In NeurIPS, 2018. 4", + "[14] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, pages 1126–1135. PMLR, 2017. 2", + "[15] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728, 2018. 2, 3, 5" + ], + "bbox": [ + 78, + 116, + 470, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[16] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9729-9738, 2020. 3, 4", + "[17] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016. 6", + "[18] Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, and Abbas Rahimi. Constrained few-shot class-incremental learning. In CVPR, pages 9057-9067, 2022. 2", + "[19] Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin. Learning a unified classifier incrementally via rebalancing. In CVPR, pages 831-839, 2019. 1, 6, 7", + "[20] Zhong Ji, Zhishen Hou, Xiyao Liu, Yanwei Pang, and Xuelong Li. Memorizing complementation network for few-shot class-incremental learning. TIP, 2023. 3", + "[21] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. In NeurIPS, pages 18661-18673, 2020. 2, 3, 4", + "[22] Simon Kornblith, Ting Chen, Honglak Lee, and Mohammad Norouzi. Why do better loss functions lead to less transferable features? In NeurIPS, pages 28648-28662, 2021. 4", + "[23] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 6", + "[24] Hankook Lee, Sung Ju Hwang, and Jinwoo Shin. Self-supervised label augmentation via input transformations. In ICML, pages 5714-5724. PMLR, 2020. 3, 5", + "[25] Zhizhong Li and Derek Hoiem. Learning without forgetting. TPAMI, 40(12):2935-2947, 2017. 1, 2", + "[26] Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li. Metasgd: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835, 2017. 2", + "[27] Huan Liu, Li Gu, Zhixiang Chi, Yang Wang, Yuanhao Yu, Jun Chen, and Jin Tang. Few-shot class-incremental learning via entropy-regularized data-free replay. In ECCV, pages 146–162. Springer, 2022. 2", + "[28] Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang. Large-margin softmax loss for convolutional neural networks. arXiv preprint arXiv:1612.02295, 2016. 4", + "[29] Xialei Liu, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio M Lopez, and Andrew D Bagdanov. Rotate your networks: Better weight consolidation and less catastrophic forgetting. In ICPR, pages 2262-2268. IEEE, 2018. 2", + "[30] Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D Bagdanov, and Joost van de Weijer. Class incremental learning: survey and performance evaluation on image classification. arXiv preprint arXiv:2010.15277, 2020.1", + "[31] Pratik Mazumder, Pravendra Singh, and Piyush Rai. Few-shot lifelong learning. In AAAI, volume 35, pages 2337-2345, 2021. 2", + "[32] Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka. Distance-based image classification: Generalizing to new classes at near-zero cost. TPAMI, 35(11):2624-2637, 2013. 3" + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "24191", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[33] Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018. 2", + "[34] Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste. Tadam: Task dependent adaptive metric for improved few-shot learning. In NeurIPS, 2018. 2", + "[35] Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. Context encoders: Feature learning by inpainting. In CVPR, pages 2536-2544, 2016. 2, 3, 5", + "[36] Sachin Ravi and Hugo Larochelle. Optimization as a model for few-shot learning. In ICLR, 2017. 2", + "[37] Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. icarl: Incremental classifier and representation learning. In CVPR, pages 2001–2010, 2017. 1, 2, 6, 7", + "[38] David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne. Experience replay for continual learning. In NeurIPS, 2019. 2", + "[39] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. IJCV, 115:211-252, 2015. 6", + "[40] Guangyuan Shi, Jiaxin Chen, Wenlong Zhang, Li-Ming Zhan, and Xiao-Ming Wu. Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima. In NeurIPS, pages 6747-6761, 2021. 1, 2, 3, 6, 7", + "[41] Yujun Shi, Kuangqi Zhou, Jian Liang, Zihang Jiang, Jiashi Feng, Philip HS Torr, Song Bai, and Vincent YF Tan. Mimicking the oracle: an initial phase decorrelation approach for class incremental learning. In CVPR, pages 16722-16731, 2022. 2", + "[42] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In NeurIPS, 2017. 2", + "[43] Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales. Learning to compare: Relation network for few-shot learning. In CVPR, pages 1199–1208, 2018. 2", + "[44] Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Songlin Dong, Xing Wei, and Yihong Gong. Few-shot class incremental learning. In CVPR, pages 12183-12192, 2020. 1, 2, 3, 6, 7", + "[45] Eleni Triantafillou, Richard Zemel, and Raquel Urtasun. Few-shot learning through an information retrieval lens. In NeurIPS, 2017. 2", + "[46] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008. 7, 8", + "[47] Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc V Gool. Revisiting contrastive methods for unsupervised learning of visual representations. In NeurIPS, pages 16238-16250, 2021. 6", + "[48] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200-2011 dataset. 2011. 6" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[49] Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister. Learning to prompt for continual learning. In CVPR, pages 139-149, 2022. 2", + "[50] Shipeng Yan, Jiangwei Xie, and Xuming He. Der: Dynamically expandable representation for class incremental learning. In CVPR, pages 3014-3023, 2021. 1, 2", + "[51] Chi Zhang, Nan Song, Guosheng Lin, Yun Zheng, Pan Pan, and Yinghui Xu. Few-shot incremental learning with continually evolved classifiers. In CVPR, pages 12455-12464, 2021. 1, 2, 3, 6, 7", + "[52] Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful image colorization. In ECCV, pages 649-666. Springer, 2016. 2, 3, 5", + "[53] Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, and Jiashi Feng. Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning. In NeurIPS, pages 29848–29860, 2021. 4", + "[54] Hanbin Zhao, Yongjian Fu, Mintong Kang, Qi Tian, Fei Wu, and Xi Li. Mgsvf: Multi-grained slow vs. fast framework for few-shot class-incremental learning. TPAMI, 2021. 2, 3", + "[55] Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, and De-Chuan Zhan. Forward compatible few-shot class-incremental learning. In CVPR, pages 9046–9056, 2022. 1, 3, 6, 7", + "[56] Da-Wei Zhou, Han-Jia Ye, Liang Ma, Di Xie, Shiliang Pu, and De-Chuan Zhan. Few-shot class-incremental learning by sampling multi-phase tasks. TPAMI, 2022. 3", + "[57] Fei Zhu, Zhen Cheng, Xu-Yao Zhang, and Cheng-lin Liu. Class-incremental learning via dual augmentation. In NeurIPS, pages 14306-14318, 2021. 2", + "[58] Kai Zhu, Yang Cao, Wei Zhai, Jie Cheng, and Zheng-Jun Zha. Self-promoted prototype refinement for few-shot class incremental learning. In CVPR, pages 6801-6810, 2021. 2, 3, 6, 7" + ], + "bbox": [ + 501, + 90, + 890, + 585 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "24192", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning With Fantasy_ Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning/5de50010-4b67-476f-aff9-6ea2c799f91f_model.json b/2023/Learning With Fantasy_ Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning/5de50010-4b67-476f-aff9-6ea2c799f91f_model.json new file mode 100644 index 0000000000000000000000000000000000000000..72bfbf0f3b600550be1c54c598722a2fd9c05474 --- /dev/null +++ b/2023/Learning With Fantasy_ Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning/5de50010-4b67-476f-aff9-6ea2c799f91f_model.json @@ -0,0 +1,2618 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.105, + 0.13, + 0.868, + 0.177 + ], + "angle": 0, + "content": "Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning" + }, + { + "type": "text", + "bbox": [ + 0.086, + 0.202, + 0.198, + 0.223 + ], + "angle": 0, + "content": "Zeyin Song\\(^{1\\dagger}\\)" + }, + { + "type": "text", + "bbox": [ + 0.237, + 0.203, + 0.343, + 0.222 + ], + "angle": 0, + "content": "Yifan Zhao\\(^{2\\dagger}\\)" + }, + { + "type": "text", + "bbox": [ + 0.385, + 0.204, + 0.472, + 0.222 + ], + "angle": 0, + "content": "Yujun Shi" + }, + { + "type": "text", + "bbox": [ + 0.512, + 0.204, + 0.624, + 0.222 + ], + "angle": 0, + "content": "Peixi Peng\\(^{2,4*}\\)" + }, + { + "type": "text", + "bbox": [ + 0.66, + 0.204, + 0.744, + 0.222 + ], + "angle": 0, + "content": "Li Yuan\\(^{1,4}\\)" + }, + { + "type": "text", + "bbox": [ + 0.785, + 0.204, + 0.942, + 0.223 + ], + "angle": 0, + "content": "Yonghong Tian\\(^{1,2,4*}\\)" + }, + { + "type": "text", + "bbox": [ + 0.245, + 0.222, + 0.785, + 0.24 + ], + "angle": 0, + "content": "\\(^{1}\\)School of Electronic and Computer Engineering, Peking University" + }, + { + "type": "text", + "bbox": [ + 0.326, + 0.239, + 0.71, + 0.257 + ], + "angle": 0, + "content": "\\(^{2}\\)School of Computer Science, Peking University" + }, + { + "type": "text", + "bbox": [ + 0.261, + 0.257, + 0.532, + 0.275 + ], + "angle": 0, + "content": "\\(^{3}\\)National University of Singapore" + }, + { + "type": "text", + "bbox": [ + 0.572, + 0.258, + 0.77, + 0.275 + ], + "angle": 0, + "content": "\\(^{4}\\)Peng Cheng Laboratory" + }, + { + "type": "text", + "bbox": [ + 0.113, + 0.278, + 0.918, + 0.293 + ], + "angle": 0, + "content": "zysong@stu.pku.edu.cn, {zhaoyf, pxpeng, yuanli-ece, yhtian}@pku.edu.cn, shi.yujun@u.nus.edu" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.327, + 0.314, + 0.343 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.359, + 0.474, + 0.784 + ], + "angle": 0, + "content": "Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes continually from limited samples without forgetting the old classes. The mainstream framework tackling FSCIL is first to adopt the cross-entropy (CE) loss for training at the base session, then freeze the feature extractor to adapt to new classes. However, in this work, we find that the CE loss is not ideal for the base session training as it suffers poor class separation in terms of representations, which further degrades generalization to novel classes. One tempting method to mitigate this problem is to apply an additional naive supervised contrastive learning (SCL) in the base session. Unfortunately, we find that although SCL can create a slightly better representation separation among different base classes, it still struggles to separate base classes and new classes. Inspired by the observations made, we propose Semantic-Aware Virtual Contrastive model (SAVC), a novel method that facilitates separation between new classes and base classes by introducing virtual classes to SCL. These virtual classes, which are generated via pre-defined transformations, not only act as placeholders for unseen classes in the representation space, but also provide diverse semantic information. By learning to recognize and contrast in the fantasy space fostered by virtual classes, our SAVC significantly boosts base class separation and novel class generalization, achieving new state-of-the-art performance on the three widely-used FSCIL benchmark datasets. Code is available at: https://github.com/zysong0113/SAVC." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.809, + 0.21, + 0.825 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.834, + 0.47, + 0.88 + ], + "angle": 0, + "content": "Like humans, the modern artificial intelligence models are expected to be equipped with the ability of learning continually. For instance, a face recognition system needs" + }, + { + "type": "image", + "bbox": [ + 0.507, + 0.327, + 0.66, + 0.4 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.666, + 0.327, + 0.771, + 0.399 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.777, + 0.327, + 0.884, + 0.399 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.508, + 0.404, + 0.659, + 0.46 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.666, + 0.404, + 0.771, + 0.46 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.777, + 0.404, + 0.884, + 0.46 + ], + "angle": 0, + "content": null + }, + { + "type": "image_footnote", + "bbox": [ + 0.506, + 0.462, + 0.886, + 0.474 + ], + "angle": 0, + "content": "(a) Comparison of the embedding spaces trained with different methods." + }, + { + "type": "image", + "bbox": [ + 0.506, + 0.477, + 0.884, + 0.6 + ], + "angle": 0, + "content": null + }, + { + "type": "image_footnote", + "bbox": [ + 0.501, + 0.601, + 0.892, + 0.614 + ], + "angle": 0, + "content": "(b) Illustration of our Semantic-Aware Virtual Contrastive model (SAVC)." + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.626, + 0.894, + 0.682 + ], + "angle": 0, + "content": "Figure 1. The motivation of the proposed approach. Under the incremental-frozen framework in FSCIL, our SAVC learns to recognize and contrast in the fantasy space, which leads to better base class separation and novel class generalization than CE and SCL." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.704, + 0.895, + 0.901 + ], + "angle": 0, + "content": "to continually learn to recognize new faces without forgetting those has already kept in mind. In this case, Class-Incremental Learning (CIL) [19,25,30,37,50] draws attention extensively, that sufficient and disjoint data are coming session-by-session. However, it is unrealistic to always rely on accessing of multitudinous data. Taking the case of face recognition system again, whether it can quickly learn a new concept with just a few images, is an important criterion for evaluating its performance. As a result, designing effective and efficient algorithms to resolve Few-Shot Class-Incremental Learning (FSCIL) problem has drawn increasing attention [6,8,12,40,44,51,55]. Unlike CIL, only a few data are provided for FSCIL at incremental session" + }, + { + "type": "page_footnote", + "bbox": [ + 0.101, + 0.887, + 0.398, + 0.901 + ], + "angle": 0, + "content": "†: authors contributed equally. *: corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "24183" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.078, + 0.092, + 0.468, + 0.106 + ], + "angle": 0, + "content": "while sufficient samples are only visible at the base session." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.109, + 0.468, + 0.304 + ], + "angle": 0, + "content": "The core challenge of CIL is to strike a balance of stability and plasticity, i.e., suppressing forgetting on old knowledge while adapting smoothly to new knowledge. Previous works on FSCIL have demonstrated that an incrementally trained model is prone to overfit on limited new data and suffer catastrophic forgetting [8, 12, 44, 54]. Thus the mainstream FSCIL framework [40, 51, 58] is to first use the cross-entropy (CE) loss for training in the base session, then freeze the backbone to adapt to new classes. Given such baseline framework, the main problem that needs to be taken into consideration is: What makes a good base-session-model that can generalize well to new classes with limited data?" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.308, + 0.468, + 0.625 + ], + "angle": 0, + "content": "Intuitively, if all representations of base classes concentrate around their own clustering centers (i.e., prototypes), and all prototypes are far away from each other, then novel classes should be easily incorporated into the current space without overlapping. Therefore, we conjecture that base class separation facilitates novel class generalization. However, the current widely adopted CE loss in FSCIL cannot well separate the class margins, which causes poor generalization to new classes. This motivates our attempt at methods with better clustering effects, such as supervised contrastive learning (SCL) [21]. We conduct experiments to validate our conjecture by training models optimized by only CE loss, and both CE and SCL. We empirically find that CE leads to lower base class separation and classification accuracy than SCL. Although SCL indeed slightly improves the base class separation with higher accuracy, we find it implicitly decreases the inter-class distance. Therefore, the two methods both leave inadequate room for future updates, which leads to the inevitable overlapping between novel classes and old classes in the representation space (as shown in the left and middle columns of Fig. 1a)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.627, + 0.468, + 0.747 + ], + "angle": 0, + "content": "The unsatisfactory performance of SCL implies that the limited semantic information base session provided may need our fantasy remedy. Although we have no access to future classes in the base session, we can create many virtual classes with various semantics filling the unallocated representation space, which not only holds enough space for future classes, but also enables the learning of diverse semantics for better inference and generalization." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.468, + 0.901 + ], + "angle": 0, + "content": "To this end, we propose a Semantic-Aware Virtual Contrastive (SAVC) model to improve the FSCIL by boosting base class separation (see Fig. 1b). Our fantasy space is constructed with pre-defined transformations [11, 15, 35, 52], which can be regarded as the semantic extension of the original space in finer grains. The type of transformations represents the richness of our imagination, i.e., how compact we want the embedding space to be and how fine the semantic grains we want to acquire. Learning to contrast and recognize in the fantasy space not only brings a better class" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.211 + ], + "angle": 0, + "content": "separation effect, but also provides richer semantic information to improve the accuracy. In contrast to the CE baseline and SCL, our SAVC can simultaneously decrease intraclass distance and increase inter-class distance, which leads to better base class separation and novel class generalization (as given in the right of Fig. 1a). Extensive experiments on various benchmark datasets further verify the superiority of our method." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.213, + 0.878, + 0.226 + ], + "angle": 0, + "content": "Our main contributions are summarized in three folds:" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.228, + 0.892, + 0.287 + ], + "angle": 0, + "content": "1) We empirically discover that it is crucial to boost class separation degree in base session for the incremental-frozen framework in FSCIL, which helps fast generalization for novel classes with only a few samples." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.289, + 0.892, + 0.363 + ], + "angle": 0, + "content": "2) We propose a novel Semantic-Aware Virtual Contrastive (SAVC) framework, enhancing the FSCIL learner by learning to recognize and contrast in the fantasy space, which preserves enough place for novel generalization and enables a multi-semantic aggregated inference effect." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.364, + 0.892, + 0.424 + ], + "angle": 0, + "content": "3) Extensive experiments on three FSCIL benchmarks, i.e., CIFAR100, miniImageNet and CUB200, demonstrate that our SAVC outperforms all approaches by a large margin and achieves the state-of-the-art performance." + }, + { + "type": "list", + "bbox": [ + 0.498, + 0.228, + 0.892, + 0.424 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.438, + 0.64, + 0.453 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.463, + 0.892, + 0.658 + ], + "angle": 0, + "content": "Few-Shot Learning. FSL focuses on fast adaptation to unseen classes through few learnable samples. Current FSL methods are mainly divided into two lines: optimization-based methods [14, 26, 33, 36] and metric-based methods [34, 42, 43, 45]. Optimization-based methods aim to encourage the model to obtain generalizable representations by using meta-learning frameworks. Our method has more connection with metric-based methods, which use a pretrained backbone to extract feature, and compute score by measuring the distance between query and support. Nevertheless, FSL aims to adapt to novel classes via few samples and cannot be directly applied to FSCIL, without maintaining classification performance over base classes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.66, + 0.892, + 0.854 + ], + "angle": 0, + "content": "Class Incremental Learning. CIL is the process of learning new knowledge continually without forgetting. Classical works fall into three streams. The first stream rehearsals former samples selectively to maintain discrimination over old classes [5, 37, 38]. The second stream regularizes the important parameters from being dramatically changed [25, 29]. The third stream dynamically expands the network architecture to adjust the model's representation ability on new tasks [49, 50]. Recent methods boost performance by encouraging the model to learn to recognize more classes [41, 57], which are relative to our method. CIL methods build on the basis of the large number of training examples, which is unsuitable for FSCIL." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Few-Shot Class-Incremental Learning. FSCIL [1, 2, 6, 9, 10, 18, 27, 31] aims at undertaking CIL task in insufficient data settings. TOPIC [44] first proposes this new task" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "24184" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.227 + ], + "angle": 0, + "content": "and solves it by neural gas structure. Current FSCIL methods can be roughly divided into two groups. The first group updates the backbone in incremental session [8, 12, 44, 54], while the second group fixes the backbone in incremental sessions [40, 51, 58]. Built on the latter incremental-frozen framework, other works balance the stability and plasticity trade-off from different perspectives, such as finding flatten minima [40], episodic training [51, 56, 58], ensemble learning [20], forward compatibility [55], and so on." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.228, + 0.473, + 0.41 + ], + "angle": 0, + "content": "Self-supervised learning. SSL aims at learning representations without human annotations. A common way of SSL is to design pretext tasks and teach the model how to solve these prediction tasks [11, 15, 35, 52]. [24] proposes to utilize these transformations under full-supervised datasets and trains a unified classifier, which motivates the construction of our fantasy space. Another commonly used pretext task is instance discrimination represented by contrastive learning [3, 7, 16]. The methodology is to teach the model to aggregate positives and exclude negatives. SupCon [21] converted to \"class discrimination\" task, leveraging the label information to decide positive pairs." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.421, + 0.212, + 0.437 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.445, + 0.206, + 0.461 + ], + "angle": 0, + "content": "3.1. Preliminary" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.469, + 0.47, + 0.697 + ], + "angle": 0, + "content": "In FSCIL, we learn a sequence of tasks continually. There is a constant stream of training data denoting \\(\\mathcal{D}_{train} = \\{\\mathcal{D}_{train}^t\\}_{t=0}^T\\), where \\(\\mathcal{D}_{train}^t = \\{(\\mathbf{x}_i, y_i)\\}_{i=0}^{N_t}\\) represents the training samples from session \\(t\\), \\(\\mathbf{x}_i\\) and \\(y_i\\) are the \\(i\\)-th image and corresponding label respectively. Assuming that the label space of the \\(t\\)-th task is \\(\\mathcal{C}^t\\), the label spaces from different tasks have no overlap, i.e., the model trained on the \\(\\mathcal{D}_{train}^t\\) should be evaluated on \\(\\mathcal{D}_{test}^t\\) which contains all encountered classes \\(\\mathcal{C}^0 \\cup \\mathcal{C}^1 \\dots \\cup \\mathcal{C}^t\\) in the \\(t\\)-th session. Specifically, sufficient data can be provided in base session (also called initial session), while the model only has access to a few samples in all following sessions (also called incremental sessions). In particular, the incremental data are always organized as \\(N\\)-way \\(K\\)-shot format, i.e., there are \\(N\\) classes, and each class contains \\(K\\) training images." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.697, + 0.471, + 0.757 + ], + "angle": 0, + "content": "Several provident works [40, 51] recommend an incremental-frozen framework that leverages sufficient data in base session to train the model by optimizing the persample loss:" + }, + { + "type": "equation", + "bbox": [ + 0.166, + 0.766, + 0.469, + 0.782 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {c l s} (\\phi ; \\mathbf {x}, y) = \\mathcal {L} _ {c e} (\\phi (\\mathbf {x}), y), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.789, + 0.469, + 0.856 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{ce}(\\cdot ,\\cdot)\\) is cross-entropy loss (CE), \\(\\phi (\\mathbf{x})\\) is the model which can be decomposed into feature extractor and classifier: \\(\\phi (\\mathbf{x}) = W^{T}f(\\mathbf{x})\\) where \\(\\phi (\\mathbf{x})\\in \\mathbb{R}^{|C^0 | \\times 1}\\) \\(W\\in \\mathbb{R}^{d\\times |\\mathcal{C}^0 |}\\) and \\(f(\\mathbf{x})\\in \\mathbb{R}^{d\\times 1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.47, + 0.904 + ], + "angle": 0, + "content": "After the base training stage, the backbone will be completely fixed, and the classifier is extended in each incremental session: \\( W = \\{ \\boldsymbol{w}_1^0, \\boldsymbol{w}_2^0, \\dots, \\boldsymbol{w}_{|\\mathcal{C}^0|}^0 \\} \\cup \\dots \\cup \\)" + }, + { + "type": "image", + "bbox": [ + 0.507, + 0.092, + 0.702, + 0.22 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.704, + 0.092, + 0.905, + 0.22 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.501, + 0.226, + 0.693, + 0.249 + ], + "angle": 0, + "content": "(a) Cumulative distribution function of inter-class distance." + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.253, + 0.701, + 0.379 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.7, + 0.226, + 0.892, + 0.249 + ], + "angle": 0, + "content": "(b) Cumulative distribution function of intra-class distance." + }, + { + "type": "image", + "bbox": [ + 0.702, + 0.253, + 0.899, + 0.378 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.501, + 0.383, + 0.693, + 0.407 + ], + "angle": 0, + "content": "(c) Base, novel and mutual class separation degree." + }, + { + "type": "image_caption", + "bbox": [ + 0.698, + 0.383, + 0.892, + 0.407 + ], + "angle": 0, + "content": "(d) Base, novel and total classification accuracy." + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.42, + 0.892, + 0.504 + ], + "angle": 0, + "content": "Figure 2. The relationship between base class separation and novel class generalization. (a) Inter-class distance is defined in Eq. (5). A curve lies more right on the figure denotes greater inter-class distance. (b) Intra-class distance is defined in Eq. (6). A curve lies more left on the figure denotes smaller intra-class distance. (c) Class separation degree \\((R^2)\\) is defined in Eq. (7)." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.516, + 0.892, + 0.568 + ], + "angle": 0, + "content": "\\(\\{\\pmb{w}_1^t,\\dots ,\\pmb{w}_{|\\mathcal{C}^t |}^t\\}\\) . The classifier weight is parameterized by the average embeddings of each class (i.e., prototype), represented as \\(\\pmb {w}_c^t = \\frac{1}{n_c^t}\\sum_{i = 1}^{n_c^t}f(\\mathbf{x}_{c,i})\\)" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.568, + 0.892, + 0.627 + ], + "angle": 0, + "content": "In each session, the inference is conducted by the nearest class mean (NCM) algorithm [32] to evaluate accuracy of all encountered classes. Specifically, we measure the distance between \\( f(\\mathbf{x}) \\) and all prototypes in classifiers:" + }, + { + "type": "equation", + "bbox": [ + 0.589, + 0.637, + 0.891, + 0.659 + ], + "angle": 0, + "content": "\\[\nc _ {\\mathbf {x}} = \\underset {c, t} {\\arg \\max } \\operatorname {s i m} \\left(f (\\mathbf {x}), \\boldsymbol {w} _ {c} ^ {t}\\right), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.672, + 0.89, + 0.702 + ], + "angle": 0, + "content": "where \\(\\operatorname{sim}(\\mathbf{x},\\mathbf{y}) = \\mathbf{x}^{\\top}\\mathbf{y} / (\\| \\mathbf{x}\\| \\| \\mathbf{y}\\|)\\) is cosine similarity between two vectors." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.712, + 0.892, + 0.741 + ], + "angle": 0, + "content": "3.2. Base Class Separation Facilitates Novel Class Generalization" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.892, + 0.856 + ], + "angle": 0, + "content": "The incremental-frozen baseline outperforms a lot of incremental trainable methods [40], demonstrating that preserving old knowledge may matter more than accommodating new knowledge in FSCIL. The guarantee of base performance by the freezing strategy motivates our consideration of what our model should learn in the base training stage for future few-shot generalization." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "An intuitive hypothesis is that, base class separation facilitates novel class generalization. If all prototypes are far away from each other, and the features of the same class" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "24185" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.109, + 0.089, + 0.852, + 0.375 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.388, + 0.895, + 0.445 + ], + "angle": 0, + "content": "Figure 3. Overall pipeline of our Semantic-Aware Virtual Contrastive (SAVC) framework, which is composed of a classification branch and a contrastive learning branch. The backbone is shared for classification and contrastive query encoding, while the key backbone is updated with a large momentum ('//' means stop gradient). Both classification and contrastive loss are optimized in fantasy space, while the inference results of different transformations are aggregated and then projected into the original label space." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.464, + 0.47, + 0.599 + ], + "angle": 0, + "content": "concentrate around their prototypes, there would be enough place for future class incorporation. Please note that the novel class generalization contains two meanings: without overlapping among novel class sets and with base class embeddings. Previous work demonstrated CE always cannot generalize well due to the poor class margins [13, 28] and may not be the optimal strategy for FSCIL, which inspires our attempt of supervised contrastive learning (SCL) [21], whose clustering effect has been demonstrated [53]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.6, + 0.474, + 0.829 + ], + "angle": 0, + "content": "We adopt MoCo [7, 16] framework to implement SCL in our method, for the sake of lower memory budget and storing slowly evolving features of recent batches. For an given image-label pair \\((\\mathbf{x},y)\\), we generate a query view \\(\\mathbf{x}_q = \\mathrm{Aug}_q(\\mathbf{x})\\) and a key view \\(\\mathbf{x}_k = \\mathrm{Aug}_k(\\mathbf{x})\\) by random augmentations. Then the two augmented images are fed into the query network \\(g_{q}\\) and key network \\(g_{k}\\), getting two \\(L_{2}\\)-normalized representations \\(\\pmb{q}\\) and \\(\\pmb{k}\\), where \\(g = h\\circ f\\) is composed of a feature extractor \\(f\\) and a projector \\(h\\). The two networks share the same architectures, while the key network updates itself with a large momentum. We maintain a feature queue and a label queue with the same length, storing the most current key embeddings and corresponding labels. To this end, given an image \\(\\mathbf{x}\\), we have the following per-sample supervised contrastive loss:" + }, + { + "type": "equation", + "bbox": [ + 0.104, + 0.837, + 0.47, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {c o n t} (g; \\mathbf {x}, \\tau , A) \\\\ = - \\frac {1}{| P (\\mathbf {x}) |} \\sum_ {\\boldsymbol {k} _ {+} \\in P (\\mathbf {x})} \\log \\frac {\\exp \\left(\\boldsymbol {q} ^ {\\mathrm {T}} \\boldsymbol {k} _ {+} / \\tau\\right)}{\\sum_ {\\boldsymbol {k} ^ {\\prime} \\in A (\\mathbf {x})} \\exp \\left(\\boldsymbol {q} ^ {\\mathrm {T}} \\boldsymbol {k} ^ {\\prime} / \\tau\\right)}, \\tag {3} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.464, + 0.892, + 0.509 + ], + "angle": 0, + "content": "where \\( A(\\mathbf{x}) = \\mathbf{k} \\cup Q \\) is the concatenation of the key embedding \\( \\mathbf{k} \\) of \\( \\mathbf{x} \\) and feature queue \\( Q \\), \\( P(\\mathbf{x}) \\) denotes positive set, i.e., those in \\( A(\\mathbf{x}) \\) belonging to the same class with \\( \\mathbf{x} \\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.51, + 0.893, + 0.584 + ], + "angle": 0, + "content": "In our practice, the feature extractor for query encoding and classification is shared. Put the training of the classification branch and contrastive learning branch together, we form a unified end-to-end framework, and the overall training objective can be concluded as:" + }, + { + "type": "equation", + "bbox": [ + 0.627, + 0.594, + 0.892, + 0.61 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathcal {L} _ {c l s} + \\alpha \\mathcal {L} _ {\\text {c o n t}}. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.619, + 0.892, + 0.648 + ], + "angle": 0, + "content": "We would like to refer the model trained with Eq. (1) as CE, and the model trained with Eq. (4) as \\(\\mathrm{CE + SCL}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.649, + 0.892, + 0.679 + ], + "angle": 0, + "content": "We then introduce and define some metrics to measure class separation and generalization, including:" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.68, + 0.892, + 0.726 + ], + "angle": 0, + "content": "1) Inter-class distance. Given two prototypes \\( \\pmb{p}_m \\) and \\( \\pmb{p}_k \\) of class \\( m \\) and \\( k \\), we compute their distance and get \\( |\\mathcal{C}^0|(|\\mathcal{C}^0| - 1)/2 \\) inter-class distances in base session." + }, + { + "type": "equation", + "bbox": [ + 0.601, + 0.734, + 0.892, + 0.754 + ], + "angle": 0, + "content": "\\[\nd _ {i n t e r} ^ {m, k} = 1 - \\operatorname {s i m} \\left(\\boldsymbol {p} _ {m}, \\boldsymbol {p} _ {k}\\right). \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.762, + 0.892, + 0.824 + ], + "angle": 0, + "content": "2) Intra-class distance. Given a class \\( k \\), we compute the average Euclidean distance between all test samples belonging to class \\( k \\) and prototype \\( p_k \\) to measure the discrepancy within this class. For \\( |\\mathcal{C}^0| \\) classes in base session, we have:" + }, + { + "type": "equation", + "bbox": [ + 0.551, + 0.831, + 0.892, + 0.862 + ], + "angle": 0, + "content": "\\[\nd _ {i n t r a} ^ {k} = 1 - \\frac {1}{n _ {k}} \\sum_ {i = 1} ^ {n _ {k}} \\operatorname {s i m} (f (\\mathbf {x} _ {k, i}), \\boldsymbol {p} _ {k}). \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.892, + 0.903 + ], + "angle": 0, + "content": "3) Class Separation \\((R^2)\\). On the one hand, we refer to Eq. (11) in [22] to measure the base \\((K = |\\mathcal{C}^0|)\\) and novel" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "24186" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.089, + 0.437, + 0.109 + ], + "angle": 0, + "content": "\\((K = \\sum_{t=1}^{T} |\\mathcal{C}^t|)\\) class separation degree respectively:" + }, + { + "type": "equation", + "bbox": [ + 0.088, + 0.113, + 0.469, + 0.239 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} R ^ {2} = 1 - \\bar {d} _ {\\text {w i t h i n}} / \\bar {d} _ {\\text {t o t a l}}, \\\\ \\bar {d} _ {w i t h i n} = \\sum_ {k = 1} ^ {K} \\sum_ {i = 1} ^ {n _ {k}} \\sum_ {j = 1} ^ {n _ {k}} \\frac {1 - \\mathrm {s i m} (f (\\mathbf {x} _ {k , i}) , f (\\mathbf {x} _ {k , j}))}{K n _ {k} ^ {2}}, \\\\ \\bar {d} _ {\\text {t o t a l}} = \\sum_ {k = 1} ^ {K} \\sum_ {m = 1} ^ {K} \\sum_ {i = 1} ^ {n _ {j}} \\sum_ {j = 1} ^ {n _ {k}} \\frac {1 - \\operatorname {s i m} \\left(f \\left(\\mathbf {x} _ {k , i}\\right) , f \\left(\\mathbf {x} _ {m , j}\\right)\\right)}{K ^ {2} n _ {k} n _ {j}}. \\tag {7} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.246, + 0.468, + 0.327 + ], + "angle": 0, + "content": "On the other hand, we propose a novel metric to evaluate the mutual separation degree between novel and base classes, which is referred to as 'mutual class separation \\((R_{mul}^{2})\\). We only need to let \\(K = \\sum_{t=0}^{T} |\\mathcal{C}^t|\\) in \\(\\bar{d}_{within}\\), and \\(K = |\\mathcal{C}^0|\\) and \\(\\sum_{t=1}^{T} |\\mathcal{C}^t|\\) respectively in \\(\\bar{d}_{total}\\), to compute \\(R_{mul}^{2}\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.328, + 0.469, + 0.569 + ], + "angle": 0, + "content": "Greater \\(d_{inter}\\) and \\(R_{base}^{2}\\), smaller \\(d_{intra}\\) represents more compact the base embedding space is. Under the incremental-frozen framework, we compare the result of CE and \\(\\mathrm{CE + SCL}\\) in Fig. 2 on CIFAR100 benchmark, while \\(d_{inter}\\) and \\(d_{intra}\\) are shown in the cumulative distribution function (CDF) style. We can observe that although \\(\\mathrm{CE + SCL}\\) gets greater class separation degree and classification accuracy than CE on all classes, it implicitly decreases inter-class distance while decreasing intra-class distance (the \\(\\mathrm{CE + SCL}\\) curve lies on the left of the CE curve both in Fig. 2a and Fig. 2b). Especially, SCL only provides minor improvement of the mutual class separation \\(R_{mul}^{2}\\), which implies that SCL cannot separate the novel representations from the old distributions (i.e., poor novel class generalization), and is also far from perfect for our objective (see Fig. 2c and Fig. 2d)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.576, + 0.443, + 0.592 + ], + "angle": 0, + "content": "3.3. Semantic-Aware Contrastive Class Fantasy" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.599, + 0.469, + 0.84 + ], + "angle": 0, + "content": "The unsatisfactory performance of SCL, especially the inappreciable improvements of mutual class separation may convey that the semantic information base session provides is not diversified enough, which makes the learned decision boundary very sharp and close to data, resulting in poor base class separation and novel class generalization. Although the rests contained in new classes are invisible in base session, a provident model may be self-sufficient: imagining what would happen in the future and making adequate preparations for it. In other words, our model should use its fantasy to generate many virtual classes with various semantics and fill the unallocated embedding space, all by itself. These virtual classes act as not only class placeholders which preserve enough space for future updates, but also semantic knowledge providers which encourage extensive learning of different semantics for better generalization." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.469, + 0.902 + ], + "angle": 0, + "content": "Semantic-aware class fantasy. In our semantic-aware contrastive class fantasy (SAVC) framework, we take inspiration from [24] and generate virtual classes by applying pre-defined transformations in self-supervised learning" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.091, + 0.892, + 0.212 + ], + "angle": 0, + "content": "(SSL) [11, 15, 35, 52]. In detail, we first define a discrete transformation (i.e., fantasy) set \\(\\mathcal{F}\\) and supposing there are \\(M\\) elements in it, i.e., the number of transformations. Then, we can generate \\(M\\) transformed samples for an image-label pair \\((\\mathbf{x}, y)\\), denoting \\(\\mathcal{F}(\\mathbf{x}, y) = \\{(\\mathbf{x}_m, y_m)\\}_{m=1}^M\\), where \\(y_m = y \\times M + m\\). The subscript of \\((\\mathbf{x}_m, y_m)\\) denotes \\(m\\)-th transformation is applied to \\((\\mathbf{x}, y)\\), while \\((\\mathbf{x}_1, y_1)\\) represents the original image-label pair." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.212, + 0.893, + 0.319 + ], + "angle": 0, + "content": "In this way, we enlarge our label space by \\(M\\) times, and each virtual class can be regarded as a 'fine-grained' class derived from the original one. The number and type of the elements in \\(\\mathcal{F}\\) convey not only how compact we expect the embedding space to be, but also how fine the semantic grains we want to acquire. Hence we conduct the classification and contrasting training process in finer gains:" + }, + { + "type": "equation", + "bbox": [ + 0.5, + 0.328, + 0.906, + 0.451 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {c l s} \\left(\\phi ; \\mathbf {x}, y, \\mathcal {F}\\right) = \\frac {1}{M} \\sum_ {i = 1} ^ {M} \\mathcal {L} _ {c e} \\left(\\phi \\left(\\mathbf {x} _ {m}\\right), y _ {m}\\right), \\\\ \\mathcal {L} _ {\\text {c o n t}} (g; \\mathbf {x}, \\tau , A, \\mathcal {F}) = \\\\ - \\frac {1}{M} \\sum_ {m = 1} ^ {M} \\frac {1}{| P (\\mathbf {x} _ {m}) |} \\sum_ {\\mathbf {k} _ {+} \\in P (\\mathbf {x} _ {m})} \\log \\frac {\\exp \\left(\\boldsymbol {q} ^ {\\mathrm {T}} \\mathbf {k} _ {+} / \\tau\\right)}{\\sum_ {\\boldsymbol {k} ^ {\\prime} \\in A (\\mathbf {x} _ {m})} \\exp \\left(\\boldsymbol {q} ^ {\\mathrm {T}} \\boldsymbol {k} ^ {\\prime} / \\tau\\right)}. \\tag {8} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.461, + 0.893, + 0.693 + ], + "angle": 0, + "content": "Multi-semantic aggregated inference. With the increase of class, we can acquire various derived class information in finer gains, which enables extending of classifiers with the prototypes of original and pseudo classes: \\( W = \\{\\pmb{w}_{11}^{0},\\pmb{w}_{12}^{0},\\dots ,\\pmb{w}_{1M}^{0},\\dots ,\\pmb{w}_{|\\mathcal{C}^{0}|M}^{0}\\} \\cup \\dots \\cup \\{\\pmb{w}_{11}^{t},\\dots ,\\pmb{w}_{|\\mathcal{C}^{t}|M}^{t}\\} \\). Prototypes with the same second subscript can naturally form a conditional prototype subset, which denotes a group of clustering centers aware of the same type of fine-grained semantics from the original classes: \\( W_{m} = \\{\\pmb{w}_{1m}^{0},\\pmb{w}_{2m}^{0},\\dots ,\\pmb{w}_{|\\mathcal{C}^{0}|m}^{0}\\} \\cup \\dots \\cup \\{\\pmb{w}_{1m}^{t},\\dots ,\\pmb{w}_{|\\mathcal{C}^{t}|m}^{t}\\} \\). In contrast to the traditional single inference in Eq. (2), we first specify the test sample \\( \\mathbf{x} \\) into various fine-grained semantics: \\( \\mathcal{F}(\\mathbf{x}) = \\{\\mathbf{x}_m\\}_{m = 1}^M \\). Then we aggregate the inference results from different conditional prototype subsets, which improves accuracy significantly:" + }, + { + "type": "equation", + "bbox": [ + 0.559, + 0.703, + 0.891, + 0.744 + ], + "angle": 0, + "content": "\\[\nc _ {\\mathbf {x}} = \\arg \\max _ {c, t} \\sum_ {m = 1} ^ {M} \\sin \\left(f \\left(\\mathbf {x} _ {m}\\right), \\boldsymbol {w} _ {c m} ^ {t}\\right). \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.748, + 0.892, + 0.825 + ], + "angle": 0, + "content": "To this end, We compare our SAVC with CE and \\(\\mathrm{CE + SCL}\\) to verify its effectiveness. As we expected, SAVC effectively increases inter-class distance and decreases intraclass distance in base session, resulting in the best base class separation and novel class generalization (see Fig. 2)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.833, + 0.617, + 0.847 + ], + "angle": 0, + "content": "3.4. Discussion" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Our training pipeline is depicted in Fig. 3. Besides the training regime demonstrated in Sec. 3.3, the proposed SAVC also benefits from the following aspects:" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "24187" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.089, + 0.892, + 0.118 + ], + "angle": 0, + "content": "Table 1. Comparison with SOTA methods on CUB200 dataset for FSCIL. \\(\\ast\\): Performances reported by [44]. \\(\\Delta_{\\mathrm{last}}\\): Relative improvements of the last session compared to the Finetune baseline. More results on other datasets can be found in the appendix." + }, + { + "type": "table", + "bbox": [ + 0.12, + 0.129, + 0.85, + 0.287 + ], + "angle": 0, + "content": "
MethodAcc. in each session (%) ↑Δlast
012345678910
Finetune* [44]68.6843.7025.0517.7218.0816.9515.1010.068.938.938.47-
iCaRL* [37]68.6852.6548.6144.1636.6229.5227.8326.2624.0123.8921.16+12.69
EEIL* [4]68.6853.6347.9144.2036.3027.4625.9324.7023.9524.1322.11+13.64
TOPIC* [44]68.6862.4954.8149.9945.2541.4038.3535.3632.2228.3126.28+17.81
Rebalancing* [19]68.6857.1244.2128.7826.7125.6624.6221.5220.1220.0619.87+11.40
SPPR [58]68.6861.8557.4352.6850.1946.8844.6543.0740.1739.6337.33+28.86
F2M [40]81.0778.1675.5772.8970.8668.1767.0165.2663.3661.7660.26+51.79
CEC [51]75.8571.9468.5063.5062.4358.2757.7355.8154.8353.5252.28+43.81
FACT [55]75.9073.2370.8466.1365.5662.1561.7459.8358.4157.8956.94+48.47
SAVC (Ours)81.8577.9274.9570.2169.9667.0266.1665.3063.8463.1562.50+54.03
" + }, + { + "type": "image", + "bbox": [ + 0.12, + 0.289, + 0.365, + 0.425 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.227, + 0.428, + 0.289, + 0.438 + ], + "angle": 0, + "content": "(a) CIFAR100" + }, + { + "type": "image", + "bbox": [ + 0.365, + 0.289, + 0.607, + 0.425 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.464, + 0.428, + 0.537, + 0.438 + ], + "angle": 0, + "content": "(b) miniImagenet" + }, + { + "type": "image", + "bbox": [ + 0.608, + 0.29, + 0.85, + 0.425 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.716, + 0.427, + 0.769, + 0.437 + ], + "angle": 0, + "content": "(c) CUB200" + }, + { + "type": "image_footnote", + "bbox": [ + 0.147, + 0.446, + 0.822, + 0.457 + ], + "angle": 0, + "content": "Finetune Rebalancing iCaRL EEIL TOPIC SPPR CEC FACT F2M SAVC" + }, + { + "type": "image_caption", + "bbox": [ + 0.185, + 0.472, + 0.782, + 0.487 + ], + "angle": 0, + "content": "Figure 4. Comparison with SOTA methods on CIFAR100, miniImageNet, and CUB200 benchmarks." + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.505, + 0.47, + 0.561 + ], + "angle": 0, + "content": "Table 2. Statistics of benchmarks datasets. \\( \\mathcal{C}^{base} \\): number of classes in base session. \\( \\mathcal{C}^{inc} \\): total number of classes in incremental sessions. #Inc.: number of incremental sessions. Shots: training shots for incremental sessions." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.573, + 0.47, + 0.641 + ], + "angle": 0, + "content": "
DatasetCbaseCinc#Inc.ShotsResolution
CIFAR100 [23]60408532×32
miniImageNet [39]60408584×84
CUB200 [48]100100105224×224
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.65, + 0.468, + 0.771 + ], + "angle": 0, + "content": "Multi-Crop strategy. Motivated by [3, 47], we add \\(N\\) constraint small crops to capture more local information, which is enforced to overlap with the global crops over a pre-defined threshold ensuring information sharing. These \\(N\\) smaller crops are only served as query views and not fed into the key encoder. In practice, we find optimizing the global and local contrastive loss respectively is helpful, and the overall training objective can be rewritten as:" + }, + { + "type": "equation", + "bbox": [ + 0.135, + 0.783, + 0.469, + 0.8 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathcal {L} _ {c l s} + \\alpha \\mathcal {L} _ {\\text {c o n t - g l o b a l}} + \\beta \\mathcal {L} _ {\\text {c o n t - l o c a l}}. \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Incremental finetuning. Although there are only a few novel samples available in incremental sessions, it is essential to finetune the pretrained model by Eq. (10) in each incremental session. Fortunately, we can contrast novel query samples with these slowly evolving key embeddings of base session from the feature queue, which helps push the novel" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.507, + 0.892, + 0.539 + ], + "angle": 0, + "content": "samples away from the distributions of the old classes and benefits novel class generalization." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.558, + 0.634, + 0.575 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.586, + 0.691, + 0.603 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.612, + 0.892, + 0.687 + ], + "angle": 0, + "content": "Datasets. Following the benchmark setting [51], we conduct experiments on three datasets, i.e., CIFAR100 [23], miniImageNet [39] and CUB200 [48], to show the performance of our method. Their statistic characteristics are listed in Tab. 2." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Implementation details. Following [51], we adopt ResNet18 [17] backbones for miniImageNet and CUB200, and ResNet20 [17] for experiments on CIFAR100. We use SGD with 0.9 momentum to optimize the model. The initial learning rate is 0.1 for CIFAR100 and miniImageNet, and 0.002 for CUB200 in base session. For each incremental learning stage, we only finetune the model for 10 epochs in case of overfitting. We only consider 3 transformation sets \\(\\mathcal{F}\\), including 2-fold rotations, 4-fold rotations and 12-augmentations, and their concrete meanings are listed in Tab. 4. We use 12 augmentations for miniImageNet, and 2-fold rotation for CUB200 and CIFAR100, to fancy virtual classes. We set \\(\\alpha\\) as 0.2 and \\(\\beta\\) as 0.8 for all datasets to control the impact of global and local contrastive loss." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "24188" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.089, + 0.892, + 0.119 + ], + "angle": 0, + "content": "Table 3. Ablation studies on miniImageNet benchmark. SCL, F, MC, FT denote supervised contrastive learning, virtual classes fantasy, multi-crop, incremental finetuning, respectively. \\( {\\Delta }_{\\text{last }} \\) : Relative improvements of the last sessions compared to the CE baseline." + }, + { + "type": "table", + "bbox": [ + 0.12, + 0.129, + 0.85, + 0.23 + ], + "angle": 0, + "content": "
SCLFMCFTAcc. in each session (%) ↑Δlast
012345678
70.2765.1761.1357.8054.8051.8449.1447.0345.54-
✓72.7767.4263.2659.5656.5153.6250.8748.9147.10+1.56
✓✓80.7275.8271.4667.6564.6361.3158.2756.2054.52+8.98
✓✓✓81.1276.0271.8168.4165.4662.1159.3357.4255.80+10.26
✓✓✓✓81.1276.1472.4368.9266.4862.9559.9258.3957.11+11.57
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.239, + 0.47, + 0.268 + ], + "angle": 0, + "content": "Table 4. The concrete meaning of different types of transformation set \\(\\mathcal{F}\\). 'ALL' denotes using all \\(0^{\\circ}\\), \\(90^{\\circ}\\), \\(180^{\\circ}\\), \\(270^{\\circ}\\) degrees." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.278, + 0.472, + 0.356 + ], + "angle": 0, + "content": "
FRotationColor permutationM
2-fold rorations0°, 180°RGB2
4-fold rorationsALLRGB4
12-augmentationsALLRGB, GBR, BRG12
" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.37, + 0.383, + 0.387 + ], + "angle": 0, + "content": "4.2. Comparison with State of The Arts" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.394, + 0.47, + 0.544 + ], + "angle": 0, + "content": "We first compare our proposed SAVC with prevailing methods on three FSCIL benchmarks. These methods include: classical CIL methods, i.e., iCaRL [37], EEIL [4], Rebalancing [19], incremental-trainable FSCIL methods, i.e., TOPIC [44], and incremental-frozen FSCIL methods, i.e., SPPR [58], F2M [40], CEC [51], and FACT [55]. We also show a naive baseline that directly finetunes the model with limited data as 'finetune'. We report the performance curve in Fig. 4 and the detailed values on CUB200 in Tab. 1." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.547, + 0.471, + 0.804 + ], + "angle": 0, + "content": "It is observed that 'finetune' leaves out preserving old knowledge and gets inferior performance. Classical CIL methods only consider extending the model with sufficient instances and are not suitable for FSCIL. Incremental-trainable FSCIL methods suffer from overfitting phenomena and perform poorly in incremental sessions. Incremental-frozen FSCIL methods can continually adapt to new classes without any training, which achieve better performance. Under the incremental-frozen framework, our SAVC effectively boosts both base class separation and novel class generalization by recognizing and contrasting semantic-aware virtual classes, consistently achieving the highest average accuracy over all sessions on three datasets. e.g., our approach gets the final accuracies of \\(53.12\\%\\), \\(62.50\\%\\) and \\(57.11\\%\\) for CIFAR100, CUB200 and miniImageNet, outperforming the current SOTA method FACT by \\(1.02\\%\\), \\(5.56\\%\\) and \\(6.62\\%\\), respectively." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.816, + 0.227, + 0.833 + ], + "angle": 0, + "content": "4.3. Ablation study" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.901 + ], + "angle": 0, + "content": "We conduct ablation studies to prove the importance of our proposed components. Built on the incremental-frozen framework, we adopt only CE loss as the baseline, while adding SCL (SCL) and observe the influence this attempt" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.241, + 0.68, + 0.37 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.572, + 0.376, + 0.612, + 0.387 + ], + "angle": 0, + "content": "(a) CE." + }, + { + "type": "image", + "bbox": [ + 0.684, + 0.24, + 0.89, + 0.37 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.759, + 0.374, + 0.816, + 0.387 + ], + "angle": 0, + "content": "(b)SAVC." + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.399, + 0.892, + 0.427 + ], + "angle": 0, + "content": "Figure 5. Comparison of the confusion matrices of CE and SAVC methods on CUB200 dataset." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.442, + 0.892, + 0.533 + ], + "angle": 0, + "content": "brings (i.e., CE and \\(\\mathrm{CE + SCL}\\) defined in Sec. 3.2). Then we fantasy (F) contrastive virtual class in \\(\\mathrm{CE + SCL}\\) and verify its effectiveness. In addition, we consider the influence of the two components mentioned in Sec. 3.4, i.e., multi-crop strategy (MC) and finetune (FT) the pre-trained model. We report the results for miniImageNet dataset in Tab. 3." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.534, + 0.892, + 0.67 + ], + "angle": 0, + "content": "Compared with the baseline, SCL shows limited influences on improving mutual class separation and lightly improves the performance by \\(1.56\\%\\) in the last session. The fantasy of virtual classes (F) plays a vital part in preventing overlap among old and new distributions, which brings \\(8.98\\%\\) performance improvement. When attending more local information (MC), our model works better, i.e., \\(10.26\\%\\) gains. Lastly, it is essential to fine-tune the model in incremental session (T), which achieves \\(11.57\\%\\) accuracy gain." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.681, + 0.637, + 0.696 + ], + "angle": 0, + "content": "4.4. Visualization" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.704, + 0.892, + 0.84 + ], + "angle": 0, + "content": "Confusion Matrix. We visualize the confusion matrix generated by CE and our SAVC on CUB200 dataset after the training of the last session in Fig. 5. Bright diagonal and dim background indicates higher classification accuracy. It is observed that CE draws a relatively distinct diagonal, but it has poor performance on new classes. In contrast, our method shows better performance on both base and new classes, which proves that our method adapts to novel classes without confusing the previous decision boundaries." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.841, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Visualization of class separation. We visualize the embedding space on CIFAR100 dataset with t-SNE [46] in Fig. 7. We randomly choose 6 classes from base classes and 3 classes from novel classes respectively. We can ob-" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "24189" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.096, + 0.322, + 0.252 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.115, + 0.258, + 0.29, + 0.27 + ], + "angle": 0, + "content": "(a) Influence of fantasy methods." + }, + { + "type": "image", + "bbox": [ + 0.357, + 0.098, + 0.599, + 0.252 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.36, + 0.257, + 0.594, + 0.27 + ], + "angle": 0, + "content": "(b) Influence of incremental trainable layers." + }, + { + "type": "image", + "bbox": [ + 0.637, + 0.093, + 0.887, + 0.252 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.671, + 0.258, + 0.849, + 0.27 + ], + "angle": 0, + "content": "(c) Influence of hyper-parameters." + }, + { + "type": "image_caption", + "bbox": [ + 0.119, + 0.282, + 0.848, + 0.297 + ], + "angle": 0, + "content": "Figure 6. Analysis of detailed hyper configurations of our method: fantasy methods, trainable layers, and hyper-parameters." + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.312, + 0.213, + 0.408 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.124, + 0.418, + 0.162, + 0.429 + ], + "angle": 0, + "content": "(a) CE." + }, + { + "type": "image", + "bbox": [ + 0.218, + 0.312, + 0.345, + 0.41 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.238, + 0.417, + 0.308, + 0.429 + ], + "angle": 0, + "content": "(b) \\(\\mathrm{CE + SCL}\\)" + }, + { + "type": "image", + "bbox": [ + 0.351, + 0.312, + 0.477, + 0.41 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.377, + 0.417, + 0.433, + 0.429 + ], + "angle": 0, + "content": "(c)SAVC." + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.442, + 0.47, + 0.498 + ], + "angle": 0, + "content": "Figure 7. The t-SNE [46] visualization on CIFAR100 dataset of the embeddings learned by various methods. Class 0-5 represent the base classes while class 6-8 represent the novel classes. Our SAVC gets the best class separation degree." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.512, + 0.468, + 0.603 + ], + "angle": 0, + "content": "serve that CE has limited clustering effects. Naïve SCL slightly improves the class separation degree, but implicitly decreases inter-class distance and remains the mutual class confusion problems unresolved. With the help of virtual classes, Our proposed SAVC can clearly separate base classes and incorporate novel classes with the least overlap." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.618, + 0.245, + 0.634 + ], + "angle": 0, + "content": "4.5. Further Analysis" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.643, + 0.468, + 0.809 + ], + "angle": 0, + "content": "Analysis of fantasy method. We consider 3 transformation sets \\(\\mathcal{F}\\) and show their impacts on our method in Fig. 6a. We observe that miniImageNet benefits from complex transformation more, while it would hurt the class separation on CUB200 and CIFAR100. We suppose that CUB200 is already a fine-grained dataset, and the learning process would be hindered by overly fine grains. As for CIFAR100, the image resolutions are too small, and it may be hard to make a distinction between the original and virtual classes. We put discussions on more types of fantasy methods in the appendix." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.468, + 0.902 + ], + "angle": 0, + "content": "Analysis of trainable layers in incremental sessions. We update the trainable layers in incremental sessions on miniImageNet benchmark. The results are reported in Fig. 6b. The gray horizontal line indicates without incremental finetuning. It is observed that finetuning fewer and deeper layers gets higher accuracy, while finetuning too" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.311, + 0.892, + 0.37 + ], + "angle": 0, + "content": "many layers harms the former knowledge and shows poorer performance. We infer that the low-level characteristics extracted by shallow layers are shared between classes, so we only need to update high layers to adapt to new classes." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.371, + 0.892, + 0.491 + ], + "angle": 0, + "content": "Analysis of hyper-parameter. There are two main hyper-parameters in our loss, i.e., coefficient \\(\\alpha\\) and \\(\\beta\\) which control the impact of global and local supervised contrastive loss in Eq. (10). We report the accuracy in the last session on miniImageNet dataset varying \\(\\alpha\\) and \\(\\beta\\) in Fig. 6c. Our method is robust to the choices of hyper-parameters, and it achieves the best performance when \\(\\alpha = 0.2\\) and \\(\\beta = 0.8\\). For convenience, we fix the value of \\(\\alpha\\) and \\(\\beta\\) on all datasets." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.505, + 0.619, + 0.52 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.53, + 0.892, + 0.711 + ], + "angle": 0, + "content": "In this paper, we solve the FSCIL problems by Semantic-Aware Virtual Contrastive framework (SAVC). Based on the mainstream incremental-frozen framework, both CE and SCL fail to separate base classes and new classes well. We propose to introduce more semantic knowledge by imagining virtual classes. These virtual classes not only hold enough feature space for future updates, but enable a multisemantic aggregated inference effect and significantly improve classification accuracy. By learning to recognize and contrast in the fantasy space, our method can maintain the separation of all classes and achieve SOTA performance on the three popular FSCIL benchmarks." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.725, + 0.659, + 0.741 + ], + "angle": 0, + "content": "Acknowledgments" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.892, + 0.9 + ], + "angle": 0, + "content": "This work is supported by grants from the KeyArea Research and Development Program of Guangdong Province (2020B0101380001), the National Key R&D Program of China (2022ZD0118101), the National Natural Science Foundation of China (61825101, 62202010, 62202014), Shenzhen Basic Research Program (JCYJ20220813151736001), China Postdoctoral Science Foundation (2022M710212), and Huawei Technologies Co., Ltd. The computing resources of Pengcheng Cloudbrain are used in this research." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "24190" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.105 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.117, + 0.47, + 0.171 + ], + "angle": 0, + "content": "[1] Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, and Ethan Fetaya. Gp-tree: A gaussian process classifier for few-shot incremental learning. In ICML, pages 54-65. PMLR, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.175, + 0.472, + 0.232 + ], + "angle": 0, + "content": "[2] Touqueer Ahmad, Akshay Raj Dhamija, Steve Cruz, Ryan Rabinowitz, Chunchun Li, Mohsen Jafarzadeh, and Terrance E Boult. Few-shot class incremental learning leveraging self-supervised features. In CVPRW, pages 3900-3910, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.233, + 0.471, + 0.288 + ], + "angle": 0, + "content": "[3] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In NeurIPS, pages 9912-9924, 2020. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.291, + 0.47, + 0.332 + ], + "angle": 0, + "content": "[4] Francisco M Castro, Manuel J Marín-Jiménez, Nicolas Guil, Cordelia Schmid, and Karteek Alahari. End-to-end incremental learning. In ECCV, pages 233-248, 2018. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.334, + 0.47, + 0.377 + ], + "angle": 0, + "content": "[5] Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny. Efficient lifelong learning with a gem. arXiv preprint arXiv:1812.00420, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.379, + 0.47, + 0.42 + ], + "angle": 0, + "content": "[6] Kuilin Chen and Chi-Guhn Lee. Incremental few-shot learning via vector quantization in deep embedded space. In ICLR, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.423, + 0.47, + 0.465 + ], + "angle": 0, + "content": "[7] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297, 2020. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.468, + 0.47, + 0.535 + ], + "angle": 0, + "content": "[8] Ali Cheraghian, Shafin Rahman, Pengfei Fang, Soumava Kumar Roy, Lars Petersson, and Mehrtash Harandi. Semantic-aware knowledge distillation for few-shot class-incremental learning. In CVPR, pages 2534-2543, 2021. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.539, + 0.47, + 0.609 + ], + "angle": 0, + "content": "[9] Ali Cheraghian, Shafin Rahman, Sameera Ramasinghe, Pengfei Fang, Christian Simon, Lars Petersson, and Mehrtash Harandi. Synthesized feature based few-shot class incremental learning on a mixture of subspaces. In ICCV, pages 8661-8670, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.611, + 0.47, + 0.665 + ], + "angle": 0, + "content": "[10] Zhixiang Chi, Li Gu, Huan Liu, Yang Wang, Yuanhao Yu, and Jin Tang. Meta-scil: A meta-learning approach for few-shot class incremental learning. In CVPR, pages 14166-14175, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.669, + 0.47, + 0.711 + ], + "angle": 0, + "content": "[11] Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In ICCV, pages 1422-1430, 2015. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.713, + 0.47, + 0.769 + ], + "angle": 0, + "content": "[12] Songlin Dong, Xiaopeng Hong, Xiaoyu Tao, Xinyuan Chang, Xing Wei, and Yihong Gong. Few-shot class-incremental learning via relation knowledge distillation. In AAAI, volume 35, pages 1255-1263, 2021. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.771, + 0.469, + 0.812 + ], + "angle": 0, + "content": "[13] Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, and Samy Bengio. Large margin deep networks for classification. In NeurIPS, 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.814, + 0.47, + 0.857 + ], + "angle": 0, + "content": "[14] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, pages 1126–1135. PMLR, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.47, + 0.902 + ], + "angle": 0, + "content": "[15] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728, 2018. 2, 3, 5" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.117, + 0.472, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[16] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9729-9738, 2020. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.892, + 0.191 + ], + "angle": 0, + "content": "[17] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.193, + 0.892, + 0.247 + ], + "angle": 0, + "content": "[18] Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, and Abbas Rahimi. Constrained few-shot class-incremental learning. In CVPR, pages 9057-9067, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.249, + 0.892, + 0.291 + ], + "angle": 0, + "content": "[19] Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin. Learning a unified classifier incrementally via rebalancing. In CVPR, pages 831-839, 2019. 1, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.293, + 0.892, + 0.333 + ], + "angle": 0, + "content": "[20] Zhong Ji, Zhishen Hou, Xiyao Liu, Yanwei Pang, and Xuelong Li. Memorizing complementation network for few-shot class-incremental learning. TIP, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.334, + 0.892, + 0.389 + ], + "angle": 0, + "content": "[21] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. In NeurIPS, pages 18661-18673, 2020. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.391, + 0.892, + 0.432 + ], + "angle": 0, + "content": "[22] Simon Kornblith, Ting Chen, Honglak Lee, and Mohammad Norouzi. Why do better loss functions lead to less transferable features? In NeurIPS, pages 28648-28662, 2021. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.434, + 0.892, + 0.461 + ], + "angle": 0, + "content": "[23] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.463, + 0.892, + 0.504 + ], + "angle": 0, + "content": "[24] Hankook Lee, Sung Ju Hwang, and Jinwoo Shin. Self-supervised label augmentation via input transformations. In ICML, pages 5714-5724. PMLR, 2020. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.505, + 0.892, + 0.533 + ], + "angle": 0, + "content": "[25] Zhizhong Li and Derek Hoiem. Learning without forgetting. TPAMI, 40(12):2935-2947, 2017. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.535, + 0.892, + 0.575 + ], + "angle": 0, + "content": "[26] Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li. Metasgd: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.577, + 0.892, + 0.632 + ], + "angle": 0, + "content": "[27] Huan Liu, Li Gu, Zhixiang Chi, Yang Wang, Yuanhao Yu, Jun Chen, and Jin Tang. Few-shot class-incremental learning via entropy-regularized data-free replay. In ECCV, pages 146–162. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.634, + 0.892, + 0.675 + ], + "angle": 0, + "content": "[28] Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang. Large-margin softmax loss for convolutional neural networks. arXiv preprint arXiv:1612.02295, 2016. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.677, + 0.892, + 0.732 + ], + "angle": 0, + "content": "[29] Xialei Liu, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio M Lopez, and Andrew D Bagdanov. Rotate your networks: Better weight consolidation and less catastrophic forgetting. In ICPR, pages 2262-2268. IEEE, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.734, + 0.892, + 0.801 + ], + "angle": 0, + "content": "[30] Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D Bagdanov, and Joost van de Weijer. Class incremental learning: survey and performance evaluation on image classification. arXiv preprint arXiv:2010.15277, 2020.1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.803, + 0.892, + 0.844 + ], + "angle": 0, + "content": "[31] Pratik Mazumder, Pravendra Singh, and Piyush Rai. Few-shot lifelong learning. In AAAI, volume 35, pages 2337-2345, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.846, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[32] Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka. Distance-based image classification: Generalizing to new classes at near-zero cost. TPAMI, 35(11):2624-2637, 2013. 3" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "24191" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.134 + ], + "angle": 0, + "content": "[33] Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.136, + 0.47, + 0.178 + ], + "angle": 0, + "content": "[34] Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste. Tadam: Task dependent adaptive metric for improved few-shot learning. In NeurIPS, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.18, + 0.469, + 0.234 + ], + "angle": 0, + "content": "[35] Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. Context encoders: Feature learning by inpainting. In CVPR, pages 2536-2544, 2016. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.238, + 0.469, + 0.265 + ], + "angle": 0, + "content": "[36] Sachin Ravi and Hugo Larochelle. Optimization as a model for few-shot learning. In ICLR, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.268, + 0.469, + 0.322 + ], + "angle": 0, + "content": "[37] Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. icarl: Incremental classifier and representation learning. In CVPR, pages 2001–2010, 2017. 1, 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.325, + 0.469, + 0.367 + ], + "angle": 0, + "content": "[38] David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne. Experience replay for continual learning. In NeurIPS, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.369, + 0.469, + 0.436 + ], + "angle": 0, + "content": "[39] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. IJCV, 115:211-252, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.44, + 0.469, + 0.495 + ], + "angle": 0, + "content": "[40] Guangyuan Shi, Jiaxin Chen, Wenlong Zhang, Li-Ming Zhan, and Xiao-Ming Wu. Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima. In NeurIPS, pages 6747-6761, 2021. 1, 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.498, + 0.469, + 0.566 + ], + "angle": 0, + "content": "[41] Yujun Shi, Kuangqi Zhou, Jian Liang, Zihang Jiang, Jiashi Feng, Philip HS Torr, Song Bai, and Vincent YF Tan. Mimicking the oracle: an initial phase decorrelation approach for class incremental learning. In CVPR, pages 16722-16731, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.569, + 0.469, + 0.597 + ], + "angle": 0, + "content": "[42] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In NeurIPS, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.599, + 0.469, + 0.653 + ], + "angle": 0, + "content": "[43] Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales. Learning to compare: Relation network for few-shot learning. In CVPR, pages 1199–1208, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.656, + 0.469, + 0.711 + ], + "angle": 0, + "content": "[44] Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Songlin Dong, Xing Wei, and Yihong Gong. Few-shot class incremental learning. In CVPR, pages 12183-12192, 2020. 1, 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.714, + 0.469, + 0.755 + ], + "angle": 0, + "content": "[45] Eleni Triantafillou, Richard Zemel, and Raquel Urtasun. Few-shot learning through an information retrieval lens. In NeurIPS, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.758, + 0.469, + 0.799 + ], + "angle": 0, + "content": "[46] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.802, + 0.469, + 0.857 + ], + "angle": 0, + "content": "[47] Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc V Gool. Revisiting contrastive methods for unsupervised learning of visual representations. In NeurIPS, pages 16238-16250, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[48] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200-2011 dataset. 2011. 6" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.092, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[49] Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister. Learning to prompt for continual learning. In CVPR, pages 139-149, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[50] Shipeng Yan, Jiangwei Xie, and Xuming He. Der: Dynamically expandable representation for class incremental learning. In CVPR, pages 3014-3023, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.192, + 0.892, + 0.247 + ], + "angle": 0, + "content": "[51] Chi Zhang, Nan Song, Guosheng Lin, Yun Zheng, Pan Pan, and Yinghui Xu. Few-shot incremental learning with continually evolved classifiers. In CVPR, pages 12455-12464, 2021. 1, 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.249, + 0.892, + 0.289 + ], + "angle": 0, + "content": "[52] Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful image colorization. In ECCV, pages 649-666. Springer, 2016. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.291, + 0.892, + 0.346 + ], + "angle": 0, + "content": "[53] Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, and Jiashi Feng. Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning. In NeurIPS, pages 29848–29860, 2021. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.348, + 0.892, + 0.389 + ], + "angle": 0, + "content": "[54] Hanbin Zhao, Yongjian Fu, Mintong Kang, Qi Tian, Fei Wu, and Xi Li. Mgsvf: Multi-grained slow vs. fast framework for few-shot class-incremental learning. TPAMI, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.391, + 0.892, + 0.445 + ], + "angle": 0, + "content": "[55] Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, and De-Chuan Zhan. Forward compatible few-shot class-incremental learning. In CVPR, pages 9046–9056, 2022. 1, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.448, + 0.892, + 0.489 + ], + "angle": 0, + "content": "[56] Da-Wei Zhou, Han-Jia Ye, Liang Ma, Di Xie, Shiliang Pu, and De-Chuan Zhan. Few-shot class-incremental learning by sampling multi-phase tasks. TPAMI, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.49, + 0.892, + 0.532 + ], + "angle": 0, + "content": "[57] Fei Zhu, Zhen Cheng, Xu-Yao Zhang, and Cheng-lin Liu. Class-incremental learning via dual augmentation. In NeurIPS, pages 14306-14318, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.533, + 0.892, + 0.587 + ], + "angle": 0, + "content": "[58] Kai Zhu, Yang Cao, Wei Zhai, Jie Cheng, and Zheng-Jun Zha. Self-promoted prototype refinement for few-shot class incremental learning. 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The mainstream framework tackling FSCIL is first to adopt the cross-entropy (CE) loss for training at the base session, then freeze the feature extractor to adapt to new classes. However, in this work, we find that the CE loss is not ideal for the base session training as it suffers poor class separation in terms of representations, which further degrades generalization to novel classes. One tempting method to mitigate this problem is to apply an additional naive supervised contrastive learning (SCL) in the base session. Unfortunately, we find that although SCL can create a slightly better representation separation among different base classes, it still struggles to separate base classes and new classes. Inspired by the observations made, we propose Semantic-Aware Virtual Contrastive model (SAVC), a novel method that facilitates separation between new classes and base classes by introducing virtual classes to SCL. These virtual classes, which are generated via pre-defined transformations, not only act as placeholders for unseen classes in the representation space, but also provide diverse semantic information. By learning to recognize and contrast in the fantasy space fostered by virtual classes, our SAVC significantly boosts base class separation and novel class generalization, achieving new state-of-the-art performance on the three widely-used FSCIL benchmark datasets. Code is available at: https://github.com/zysong0113/SAVC. + +# 1. Introduction + +Like humans, the modern artificial intelligence models are expected to be equipped with the ability of learning continually. For instance, a face recognition system needs + +![](images/ff8c3fa845e51ec82292227df3deea822baa9c489fa71cbb4e98cb2be210d679.jpg) + +![](images/ef88fcb962d627a462ae94ab8dab191df63a2382ff20fcf69607071fd969e333.jpg) + +![](images/3c0616c201007dcd2f2db08d5e9167dacbfb0ada4e2f4a944716f2da3591ba72.jpg) + +![](images/22fb51011b3e6849bf4a050ad44e3729637ca4a54cd70f3289ce1dd76d342db2.jpg) +(a) Comparison of the embedding spaces trained with different methods. + +![](images/d3faa184b5b5b0b9117b693d9821b80595320ab9aab97711da9b395ead1fb52a.jpg) + +![](images/a3ed8cd245fa55c71b7ff77d283ec8116d67de6991a5761cd2b27c2dfc0cc1fb.jpg) + +Figure 1. The motivation of the proposed approach. Under the incremental-frozen framework in FSCIL, our SAVC learns to recognize and contrast in the fantasy space, which leads to better base class separation and novel class generalization than CE and SCL. +![](images/9fc842bb2b9d483274269eebc66ad2e8f11c1c97ca552b1d6b5a50a958bd355e.jpg) +(b) Illustration of our Semantic-Aware Virtual Contrastive model (SAVC). + +to continually learn to recognize new faces without forgetting those has already kept in mind. In this case, Class-Incremental Learning (CIL) [19,25,30,37,50] draws attention extensively, that sufficient and disjoint data are coming session-by-session. However, it is unrealistic to always rely on accessing of multitudinous data. Taking the case of face recognition system again, whether it can quickly learn a new concept with just a few images, is an important criterion for evaluating its performance. As a result, designing effective and efficient algorithms to resolve Few-Shot Class-Incremental Learning (FSCIL) problem has drawn increasing attention [6,8,12,40,44,51,55]. Unlike CIL, only a few data are provided for FSCIL at incremental session + +while sufficient samples are only visible at the base session. + +The core challenge of CIL is to strike a balance of stability and plasticity, i.e., suppressing forgetting on old knowledge while adapting smoothly to new knowledge. Previous works on FSCIL have demonstrated that an incrementally trained model is prone to overfit on limited new data and suffer catastrophic forgetting [8, 12, 44, 54]. Thus the mainstream FSCIL framework [40, 51, 58] is to first use the cross-entropy (CE) loss for training in the base session, then freeze the backbone to adapt to new classes. Given such baseline framework, the main problem that needs to be taken into consideration is: What makes a good base-session-model that can generalize well to new classes with limited data? + +Intuitively, if all representations of base classes concentrate around their own clustering centers (i.e., prototypes), and all prototypes are far away from each other, then novel classes should be easily incorporated into the current space without overlapping. Therefore, we conjecture that base class separation facilitates novel class generalization. However, the current widely adopted CE loss in FSCIL cannot well separate the class margins, which causes poor generalization to new classes. This motivates our attempt at methods with better clustering effects, such as supervised contrastive learning (SCL) [21]. We conduct experiments to validate our conjecture by training models optimized by only CE loss, and both CE and SCL. We empirically find that CE leads to lower base class separation and classification accuracy than SCL. Although SCL indeed slightly improves the base class separation with higher accuracy, we find it implicitly decreases the inter-class distance. Therefore, the two methods both leave inadequate room for future updates, which leads to the inevitable overlapping between novel classes and old classes in the representation space (as shown in the left and middle columns of Fig. 1a). + +The unsatisfactory performance of SCL implies that the limited semantic information base session provided may need our fantasy remedy. Although we have no access to future classes in the base session, we can create many virtual classes with various semantics filling the unallocated representation space, which not only holds enough space for future classes, but also enables the learning of diverse semantics for better inference and generalization. + +To this end, we propose a Semantic-Aware Virtual Contrastive (SAVC) model to improve the FSCIL by boosting base class separation (see Fig. 1b). Our fantasy space is constructed with pre-defined transformations [11, 15, 35, 52], which can be regarded as the semantic extension of the original space in finer grains. The type of transformations represents the richness of our imagination, i.e., how compact we want the embedding space to be and how fine the semantic grains we want to acquire. Learning to contrast and recognize in the fantasy space not only brings a better class + +separation effect, but also provides richer semantic information to improve the accuracy. In contrast to the CE baseline and SCL, our SAVC can simultaneously decrease intraclass distance and increase inter-class distance, which leads to better base class separation and novel class generalization (as given in the right of Fig. 1a). Extensive experiments on various benchmark datasets further verify the superiority of our method. + +Our main contributions are summarized in three folds: + +1) We empirically discover that it is crucial to boost class separation degree in base session for the incremental-frozen framework in FSCIL, which helps fast generalization for novel classes with only a few samples. +2) We propose a novel Semantic-Aware Virtual Contrastive (SAVC) framework, enhancing the FSCIL learner by learning to recognize and contrast in the fantasy space, which preserves enough place for novel generalization and enables a multi-semantic aggregated inference effect. +3) Extensive experiments on three FSCIL benchmarks, i.e., CIFAR100, miniImageNet and CUB200, demonstrate that our SAVC outperforms all approaches by a large margin and achieves the state-of-the-art performance. + +# 2. Related Work + +Few-Shot Learning. FSL focuses on fast adaptation to unseen classes through few learnable samples. Current FSL methods are mainly divided into two lines: optimization-based methods [14, 26, 33, 36] and metric-based methods [34, 42, 43, 45]. Optimization-based methods aim to encourage the model to obtain generalizable representations by using meta-learning frameworks. Our method has more connection with metric-based methods, which use a pretrained backbone to extract feature, and compute score by measuring the distance between query and support. Nevertheless, FSL aims to adapt to novel classes via few samples and cannot be directly applied to FSCIL, without maintaining classification performance over base classes. + +Class Incremental Learning. CIL is the process of learning new knowledge continually without forgetting. Classical works fall into three streams. The first stream rehearsals former samples selectively to maintain discrimination over old classes [5, 37, 38]. The second stream regularizes the important parameters from being dramatically changed [25, 29]. The third stream dynamically expands the network architecture to adjust the model's representation ability on new tasks [49, 50]. Recent methods boost performance by encouraging the model to learn to recognize more classes [41, 57], which are relative to our method. CIL methods build on the basis of the large number of training examples, which is unsuitable for FSCIL. + +Few-Shot Class-Incremental Learning. FSCIL [1, 2, 6, 9, 10, 18, 27, 31] aims at undertaking CIL task in insufficient data settings. TOPIC [44] first proposes this new task + +and solves it by neural gas structure. Current FSCIL methods can be roughly divided into two groups. The first group updates the backbone in incremental session [8, 12, 44, 54], while the second group fixes the backbone in incremental sessions [40, 51, 58]. Built on the latter incremental-frozen framework, other works balance the stability and plasticity trade-off from different perspectives, such as finding flatten minima [40], episodic training [51, 56, 58], ensemble learning [20], forward compatibility [55], and so on. + +Self-supervised learning. SSL aims at learning representations without human annotations. A common way of SSL is to design pretext tasks and teach the model how to solve these prediction tasks [11, 15, 35, 52]. [24] proposes to utilize these transformations under full-supervised datasets and trains a unified classifier, which motivates the construction of our fantasy space. Another commonly used pretext task is instance discrimination represented by contrastive learning [3, 7, 16]. The methodology is to teach the model to aggregate positives and exclude negatives. SupCon [21] converted to "class discrimination" task, leveraging the label information to decide positive pairs. + +# 3. Methodology + +# 3.1. Preliminary + +In FSCIL, we learn a sequence of tasks continually. There is a constant stream of training data denoting $\mathcal{D}_{train} = \{\mathcal{D}_{train}^t\}_{t=0}^T$ , where $\mathcal{D}_{train}^t = \{(\mathbf{x}_i, y_i)\}_{i=0}^{N_t}$ represents the training samples from session $t$ , $\mathbf{x}_i$ and $y_i$ are the $i$ -th image and corresponding label respectively. Assuming that the label space of the $t$ -th task is $\mathcal{C}^t$ , the label spaces from different tasks have no overlap, i.e., the model trained on the $\mathcal{D}_{train}^t$ should be evaluated on $\mathcal{D}_{test}^t$ which contains all encountered classes $\mathcal{C}^0 \cup \mathcal{C}^1 \dots \cup \mathcal{C}^t$ in the $t$ -th session. Specifically, sufficient data can be provided in base session (also called initial session), while the model only has access to a few samples in all following sessions (also called incremental sessions). In particular, the incremental data are always organized as $N$ -way $K$ -shot format, i.e., there are $N$ classes, and each class contains $K$ training images. + +Several provident works [40, 51] recommend an incremental-frozen framework that leverages sufficient data in base session to train the model by optimizing the persample loss: + +$$ +\mathcal {L} _ {c l s} (\phi ; \mathbf {x}, y) = \mathcal {L} _ {c e} (\phi (\mathbf {x}), y), \tag {1} +$$ + +where $\mathcal{L}_{ce}(\cdot ,\cdot)$ is cross-entropy loss (CE), $\phi (\mathbf{x})$ is the model which can be decomposed into feature extractor and classifier: $\phi (\mathbf{x}) = W^{T}f(\mathbf{x})$ where $\phi (\mathbf{x})\in \mathbb{R}^{|C^0 | \times 1}$ $W\in \mathbb{R}^{d\times |\mathcal{C}^0 |}$ and $f(\mathbf{x})\in \mathbb{R}^{d\times 1}$ + +After the base training stage, the backbone will be completely fixed, and the classifier is extended in each incremental session: $W = \{ \boldsymbol{w}_1^0, \boldsymbol{w}_2^0, \dots, \boldsymbol{w}_{|\mathcal{C}^0|}^0 \} \cup \dots \cup$ + +![](images/faf96d51778bb96680dfa63a1a6d55c55be965d072fd4dda109dbb01fd02f1f0.jpg) +(a) Cumulative distribution function of inter-class distance. + +![](images/43c02d3690f74a98993de4df6d81e74868108c51bce3609f36f6a9a1b5684456.jpg) +(b) Cumulative distribution function of intra-class distance. + +![](images/da973bb00d7a1eab0662992ed15b1ec6a4a42f6e3cd9ca7acb3457ab24f5daf1.jpg) +(c) Base, novel and mutual class separation degree. +Figure 2. The relationship between base class separation and novel class generalization. (a) Inter-class distance is defined in Eq. (5). A curve lies more right on the figure denotes greater inter-class distance. (b) Intra-class distance is defined in Eq. (6). A curve lies more left on the figure denotes smaller intra-class distance. (c) Class separation degree $(R^2)$ is defined in Eq. (7). + +![](images/96832395515a657f966dda532deee5318799826668cb4bc299652597230b4146.jpg) +(d) Base, novel and total classification accuracy. + +$\{\pmb{w}_1^t,\dots ,\pmb{w}_{|\mathcal{C}^t |}^t\}$ . The classifier weight is parameterized by the average embeddings of each class (i.e., prototype), represented as $\pmb {w}_c^t = \frac{1}{n_c^t}\sum_{i = 1}^{n_c^t}f(\mathbf{x}_{c,i})$ + +In each session, the inference is conducted by the nearest class mean (NCM) algorithm [32] to evaluate accuracy of all encountered classes. Specifically, we measure the distance between $f(\mathbf{x})$ and all prototypes in classifiers: + +$$ +c _ {\mathbf {x}} = \underset {c, t} {\arg \max } \operatorname {s i m} \left(f (\mathbf {x}), \boldsymbol {w} _ {c} ^ {t}\right), \tag {2} +$$ + +where $\operatorname{sim}(\mathbf{x},\mathbf{y}) = \mathbf{x}^{\top}\mathbf{y} / (\| \mathbf{x}\| \| \mathbf{y}\|)$ is cosine similarity between two vectors. + +# 3.2. Base Class Separation Facilitates Novel Class Generalization + +The incremental-frozen baseline outperforms a lot of incremental trainable methods [40], demonstrating that preserving old knowledge may matter more than accommodating new knowledge in FSCIL. The guarantee of base performance by the freezing strategy motivates our consideration of what our model should learn in the base training stage for future few-shot generalization. + +An intuitive hypothesis is that, base class separation facilitates novel class generalization. If all prototypes are far away from each other, and the features of the same class + +![](images/368365ae657be6bba49ddc45ef81f21a2932c44c26c15f9b41cb4de946e03e71.jpg) +Figure 3. Overall pipeline of our Semantic-Aware Virtual Contrastive (SAVC) framework, which is composed of a classification branch and a contrastive learning branch. The backbone is shared for classification and contrastive query encoding, while the key backbone is updated with a large momentum ('//' means stop gradient). Both classification and contrastive loss are optimized in fantasy space, while the inference results of different transformations are aggregated and then projected into the original label space. + +concentrate around their prototypes, there would be enough place for future class incorporation. Please note that the novel class generalization contains two meanings: without overlapping among novel class sets and with base class embeddings. Previous work demonstrated CE always cannot generalize well due to the poor class margins [13, 28] and may not be the optimal strategy for FSCIL, which inspires our attempt of supervised contrastive learning (SCL) [21], whose clustering effect has been demonstrated [53]. + +We adopt MoCo [7, 16] framework to implement SCL in our method, for the sake of lower memory budget and storing slowly evolving features of recent batches. For an given image-label pair $(\mathbf{x},y)$ , we generate a query view $\mathbf{x}_q = \mathrm{Aug}_q(\mathbf{x})$ and a key view $\mathbf{x}_k = \mathrm{Aug}_k(\mathbf{x})$ by random augmentations. Then the two augmented images are fed into the query network $g_{q}$ and key network $g_{k}$ , getting two $L_{2}$ -normalized representations $\pmb{q}$ and $\pmb{k}$ , where $g = h\circ f$ is composed of a feature extractor $f$ and a projector $h$ . The two networks share the same architectures, while the key network updates itself with a large momentum. We maintain a feature queue and a label queue with the same length, storing the most current key embeddings and corresponding labels. To this end, given an image $\mathbf{x}$ , we have the following per-sample supervised contrastive loss: + +$$ +\begin{array}{l} \mathcal {L} _ {c o n t} (g; \mathbf {x}, \tau , A) \\ = - \frac {1}{| P (\mathbf {x}) |} \sum_ {\boldsymbol {k} _ {+} \in P (\mathbf {x})} \log \frac {\exp \left(\boldsymbol {q} ^ {\mathrm {T}} \boldsymbol {k} _ {+} / \tau\right)}{\sum_ {\boldsymbol {k} ^ {\prime} \in A (\mathbf {x})} \exp \left(\boldsymbol {q} ^ {\mathrm {T}} \boldsymbol {k} ^ {\prime} / \tau\right)}, \tag {3} \\ \end{array} +$$ + +where $A(\mathbf{x}) = \mathbf{k} \cup Q$ is the concatenation of the key embedding $\mathbf{k}$ of $\mathbf{x}$ and feature queue $Q$ , $P(\mathbf{x})$ denotes positive set, i.e., those in $A(\mathbf{x})$ belonging to the same class with $\mathbf{x}$ . + +In our practice, the feature extractor for query encoding and classification is shared. Put the training of the classification branch and contrastive learning branch together, we form a unified end-to-end framework, and the overall training objective can be concluded as: + +$$ +\mathcal {L} = \mathcal {L} _ {c l s} + \alpha \mathcal {L} _ {\text {c o n t}}. \tag {4} +$$ + +We would like to refer the model trained with Eq. (1) as CE, and the model trained with Eq. (4) as $\mathrm{CE + SCL}$ . + +We then introduce and define some metrics to measure class separation and generalization, including: + +1) Inter-class distance. Given two prototypes $\pmb{p}_m$ and $\pmb{p}_k$ of class $m$ and $k$ , we compute their distance and get $|\mathcal{C}^0|(|\mathcal{C}^0| - 1)/2$ inter-class distances in base session. + +$$ +d _ {i n t e r} ^ {m, k} = 1 - \operatorname {s i m} \left(\boldsymbol {p} _ {m}, \boldsymbol {p} _ {k}\right). \tag {5} +$$ + +2) Intra-class distance. Given a class $k$ , we compute the average Euclidean distance between all test samples belonging to class $k$ and prototype $p_k$ to measure the discrepancy within this class. For $|\mathcal{C}^0|$ classes in base session, we have: + +$$ +d _ {i n t r a} ^ {k} = 1 - \frac {1}{n _ {k}} \sum_ {i = 1} ^ {n _ {k}} \operatorname {s i m} (f (\mathbf {x} _ {k, i}), \boldsymbol {p} _ {k}). \tag {6} +$$ + +3) Class Separation $(R^2)$ . On the one hand, we refer to Eq. (11) in [22] to measure the base $(K = |\mathcal{C}^0|)$ and novel + +$(K = \sum_{t=1}^{T} |\mathcal{C}^t|)$ class separation degree respectively: + +$$ +\begin{array}{l} R ^ {2} = 1 - \bar {d} _ {\text {w i t h i n}} / \bar {d} _ {\text {t o t a l}}, \\ \bar {d} _ {w i t h i n} = \sum_ {k = 1} ^ {K} \sum_ {i = 1} ^ {n _ {k}} \sum_ {j = 1} ^ {n _ {k}} \frac {1 - \mathrm {s i m} (f (\mathbf {x} _ {k , i}) , f (\mathbf {x} _ {k , j}))}{K n _ {k} ^ {2}}, \\ \bar {d} _ {\text {t o t a l}} = \sum_ {k = 1} ^ {K} \sum_ {m = 1} ^ {K} \sum_ {i = 1} ^ {n _ {j}} \sum_ {j = 1} ^ {n _ {k}} \frac {1 - \operatorname {s i m} \left(f \left(\mathbf {x} _ {k , i}\right) , f \left(\mathbf {x} _ {m , j}\right)\right)}{K ^ {2} n _ {k} n _ {j}}. \tag {7} \\ \end{array} +$$ + +On the other hand, we propose a novel metric to evaluate the mutual separation degree between novel and base classes, which is referred to as 'mutual class separation $(R_{mul}^{2})$ . We only need to let $K = \sum_{t=0}^{T} |\mathcal{C}^t|$ in $\bar{d}_{within}$ , and $K = |\mathcal{C}^0|$ and $\sum_{t=1}^{T} |\mathcal{C}^t|$ respectively in $\bar{d}_{total}$ , to compute $R_{mul}^{2}$ . + +Greater $d_{inter}$ and $R_{base}^{2}$ , smaller $d_{intra}$ represents more compact the base embedding space is. Under the incremental-frozen framework, we compare the result of CE and $\mathrm{CE + SCL}$ in Fig. 2 on CIFAR100 benchmark, while $d_{inter}$ and $d_{intra}$ are shown in the cumulative distribution function (CDF) style. We can observe that although $\mathrm{CE + SCL}$ gets greater class separation degree and classification accuracy than CE on all classes, it implicitly decreases inter-class distance while decreasing intra-class distance (the $\mathrm{CE + SCL}$ curve lies on the left of the CE curve both in Fig. 2a and Fig. 2b). Especially, SCL only provides minor improvement of the mutual class separation $R_{mul}^{2}$ , which implies that SCL cannot separate the novel representations from the old distributions (i.e., poor novel class generalization), and is also far from perfect for our objective (see Fig. 2c and Fig. 2d). + +# 3.3. Semantic-Aware Contrastive Class Fantasy + +The unsatisfactory performance of SCL, especially the inappreciable improvements of mutual class separation may convey that the semantic information base session provides is not diversified enough, which makes the learned decision boundary very sharp and close to data, resulting in poor base class separation and novel class generalization. Although the rests contained in new classes are invisible in base session, a provident model may be self-sufficient: imagining what would happen in the future and making adequate preparations for it. In other words, our model should use its fantasy to generate many virtual classes with various semantics and fill the unallocated embedding space, all by itself. These virtual classes act as not only class placeholders which preserve enough space for future updates, but also semantic knowledge providers which encourage extensive learning of different semantics for better generalization. + +Semantic-aware class fantasy. In our semantic-aware contrastive class fantasy (SAVC) framework, we take inspiration from [24] and generate virtual classes by applying pre-defined transformations in self-supervised learning + +(SSL) [11, 15, 35, 52]. In detail, we first define a discrete transformation (i.e., fantasy) set $\mathcal{F}$ and supposing there are $M$ elements in it, i.e., the number of transformations. Then, we can generate $M$ transformed samples for an image-label pair $(\mathbf{x}, y)$ , denoting $\mathcal{F}(\mathbf{x}, y) = \{(\mathbf{x}_m, y_m)\}_{m=1}^M$ , where $y_m = y \times M + m$ . The subscript of $(\mathbf{x}_m, y_m)$ denotes $m$ -th transformation is applied to $(\mathbf{x}, y)$ , while $(\mathbf{x}_1, y_1)$ represents the original image-label pair. + +In this way, we enlarge our label space by $M$ times, and each virtual class can be regarded as a 'fine-grained' class derived from the original one. The number and type of the elements in $\mathcal{F}$ convey not only how compact we expect the embedding space to be, but also how fine the semantic grains we want to acquire. Hence we conduct the classification and contrasting training process in finer gains: + +$$ +\begin{array}{l} \mathcal {L} _ {c l s} \left(\phi ; \mathbf {x}, y, \mathcal {F}\right) = \frac {1}{M} \sum_ {i = 1} ^ {M} \mathcal {L} _ {c e} \left(\phi \left(\mathbf {x} _ {m}\right), y _ {m}\right), \\ \mathcal {L} _ {\text {c o n t}} (g; \mathbf {x}, \tau , A, \mathcal {F}) = \\ - \frac {1}{M} \sum_ {m = 1} ^ {M} \frac {1}{| P (\mathbf {x} _ {m}) |} \sum_ {\mathbf {k} _ {+} \in P (\mathbf {x} _ {m})} \log \frac {\exp \left(\boldsymbol {q} ^ {\mathrm {T}} \mathbf {k} _ {+} / \tau\right)}{\sum_ {\boldsymbol {k} ^ {\prime} \in A (\mathbf {x} _ {m})} \exp \left(\boldsymbol {q} ^ {\mathrm {T}} \boldsymbol {k} ^ {\prime} / \tau\right)}. \tag {8} \\ \end{array} +$$ + +Multi-semantic aggregated inference. With the increase of class, we can acquire various derived class information in finer gains, which enables extending of classifiers with the prototypes of original and pseudo classes: $W = \{\pmb{w}_{11}^{0},\pmb{w}_{12}^{0},\dots ,\pmb{w}_{1M}^{0},\dots ,\pmb{w}_{|\mathcal{C}^{0}|M}^{0}\} \cup \dots \cup \{\pmb{w}_{11}^{t},\dots ,\pmb{w}_{|\mathcal{C}^{t}|M}^{t}\}$ . Prototypes with the same second subscript can naturally form a conditional prototype subset, which denotes a group of clustering centers aware of the same type of fine-grained semantics from the original classes: $W_{m} = \{\pmb{w}_{1m}^{0},\pmb{w}_{2m}^{0},\dots ,\pmb{w}_{|\mathcal{C}^{0}|m}^{0}\} \cup \dots \cup \{\pmb{w}_{1m}^{t},\dots ,\pmb{w}_{|\mathcal{C}^{t}|m}^{t}\}$ . In contrast to the traditional single inference in Eq. (2), we first specify the test sample $\mathbf{x}$ into various fine-grained semantics: $\mathcal{F}(\mathbf{x}) = \{\mathbf{x}_m\}_{m = 1}^M$ . Then we aggregate the inference results from different conditional prototype subsets, which improves accuracy significantly: + +$$ +c _ {\mathbf {x}} = \arg \max _ {c, t} \sum_ {m = 1} ^ {M} \sin \left(f \left(\mathbf {x} _ {m}\right), \boldsymbol {w} _ {c m} ^ {t}\right). \tag {9} +$$ + +To this end, We compare our SAVC with CE and $\mathrm{CE + SCL}$ to verify its effectiveness. As we expected, SAVC effectively increases inter-class distance and decreases intraclass distance in base session, resulting in the best base class separation and novel class generalization (see Fig. 2). + +# 3.4. Discussion + +Our training pipeline is depicted in Fig. 3. Besides the training regime demonstrated in Sec. 3.3, the proposed SAVC also benefits from the following aspects: + +Table 1. Comparison with SOTA methods on CUB200 dataset for FSCIL. $\ast$ : Performances reported by [44]. $\Delta_{\mathrm{last}}$ : Relative improvements of the last session compared to the Finetune baseline. More results on other datasets can be found in the appendix. + +
MethodAcc. in each session (%) ↑Δlast
012345678910
Finetune* [44]68.6843.7025.0517.7218.0816.9515.1010.068.938.938.47-
iCaRL* [37]68.6852.6548.6144.1636.6229.5227.8326.2624.0123.8921.16+12.69
EEIL* [4]68.6853.6347.9144.2036.3027.4625.9324.7023.9524.1322.11+13.64
TOPIC* [44]68.6862.4954.8149.9945.2541.4038.3535.3632.2228.3126.28+17.81
Rebalancing* [19]68.6857.1244.2128.7826.7125.6624.6221.5220.1220.0619.87+11.40
SPPR [58]68.6861.8557.4352.6850.1946.8844.6543.0740.1739.6337.33+28.86
F2M [40]81.0778.1675.5772.8970.8668.1767.0165.2663.3661.7660.26+51.79
CEC [51]75.8571.9468.5063.5062.4358.2757.7355.8154.8353.5252.28+43.81
FACT [55]75.9073.2370.8466.1365.5662.1561.7459.8358.4157.8956.94+48.47
SAVC (Ours)81.8577.9274.9570.2169.9667.0266.1665.3063.8463.1562.50+54.03
+ +(a) CIFAR100 +![](images/5d4d70728da69a44e02dd132c75451bbbc4c5a99992914cd4d1f8481039f0d72.jpg) +Finetune Rebalancing iCaRL EEIL TOPIC SPPR CEC FACT F2M SAVC + +![](images/df24679c1a50e745c83ce38f90cd79fa397095a5d61294e57980eee6badddabc.jpg) +(b) miniImagenet + +![](images/342e3c2b190ea5d7cb366cc43a454740736c8f318bbd3c49071c1b521df90ae9.jpg) +(c) CUB200 +Figure 4. Comparison with SOTA methods on CIFAR100, miniImageNet, and CUB200 benchmarks. + +Table 2. Statistics of benchmarks datasets. $\mathcal{C}^{base}$ : number of classes in base session. $\mathcal{C}^{inc}$ : total number of classes in incremental sessions. #Inc.: number of incremental sessions. Shots: training shots for incremental sessions. + +
DatasetCbaseCinc#Inc.ShotsResolution
CIFAR100 [23]60408532×32
miniImageNet [39]60408584×84
CUB200 [48]100100105224×224
+ +Multi-Crop strategy. Motivated by [3, 47], we add $N$ constraint small crops to capture more local information, which is enforced to overlap with the global crops over a pre-defined threshold ensuring information sharing. These $N$ smaller crops are only served as query views and not fed into the key encoder. In practice, we find optimizing the global and local contrastive loss respectively is helpful, and the overall training objective can be rewritten as: + +$$ +\mathcal {L} = \mathcal {L} _ {c l s} + \alpha \mathcal {L} _ {\text {c o n t - g l o b a l}} + \beta \mathcal {L} _ {\text {c o n t - l o c a l}}. \tag {10} +$$ + +Incremental finetuning. Although there are only a few novel samples available in incremental sessions, it is essential to finetune the pretrained model by Eq. (10) in each incremental session. Fortunately, we can contrast novel query samples with these slowly evolving key embeddings of base session from the feature queue, which helps push the novel + +samples away from the distributions of the old classes and benefits novel class generalization. + +# 4. Experiments + +# 4.1. Experimental Setup + +Datasets. Following the benchmark setting [51], we conduct experiments on three datasets, i.e., CIFAR100 [23], miniImageNet [39] and CUB200 [48], to show the performance of our method. Their statistic characteristics are listed in Tab. 2. + +Implementation details. Following [51], we adopt ResNet18 [17] backbones for miniImageNet and CUB200, and ResNet20 [17] for experiments on CIFAR100. We use SGD with 0.9 momentum to optimize the model. The initial learning rate is 0.1 for CIFAR100 and miniImageNet, and 0.002 for CUB200 in base session. For each incremental learning stage, we only finetune the model for 10 epochs in case of overfitting. We only consider 3 transformation sets $\mathcal{F}$ , including 2-fold rotations, 4-fold rotations and 12-augmentations, and their concrete meanings are listed in Tab. 4. We use 12 augmentations for miniImageNet, and 2-fold rotation for CUB200 and CIFAR100, to fancy virtual classes. We set $\alpha$ as 0.2 and $\beta$ as 0.8 for all datasets to control the impact of global and local contrastive loss. + +Table 3. Ablation studies on miniImageNet benchmark. SCL, F, MC, FT denote supervised contrastive learning, virtual classes fantasy, multi-crop, incremental finetuning, respectively. ${\Delta }_{\text{last }}$ : Relative improvements of the last sessions compared to the CE baseline. + +
SCLFMCFTAcc. in each session (%) ↑Δlast
012345678
70.2765.1761.1357.8054.8051.8449.1447.0345.54-
✓72.7767.4263.2659.5656.5153.6250.8748.9147.10+1.56
✓✓80.7275.8271.4667.6564.6361.3158.2756.2054.52+8.98
✓✓✓81.1276.0271.8168.4165.4662.1159.3357.4255.80+10.26
✓✓✓✓81.1276.1472.4368.9266.4862.9559.9258.3957.11+11.57
+ +Table 4. The concrete meaning of different types of transformation set $\mathcal{F}$ . 'ALL' denotes using all $0^{\circ}$ , $90^{\circ}$ , $180^{\circ}$ , $270^{\circ}$ degrees. + +
FRotationColor permutationM
2-fold rorations0°, 180°RGB2
4-fold rorationsALLRGB4
12-augmentationsALLRGB, GBR, BRG12
+ +# 4.2. Comparison with State of The Arts + +We first compare our proposed SAVC with prevailing methods on three FSCIL benchmarks. These methods include: classical CIL methods, i.e., iCaRL [37], EEIL [4], Rebalancing [19], incremental-trainable FSCIL methods, i.e., TOPIC [44], and incremental-frozen FSCIL methods, i.e., SPPR [58], F2M [40], CEC [51], and FACT [55]. We also show a naive baseline that directly finetunes the model with limited data as 'finetune'. We report the performance curve in Fig. 4 and the detailed values on CUB200 in Tab. 1. + +It is observed that 'finetune' leaves out preserving old knowledge and gets inferior performance. Classical CIL methods only consider extending the model with sufficient instances and are not suitable for FSCIL. Incremental-trainable FSCIL methods suffer from overfitting phenomena and perform poorly in incremental sessions. Incremental-frozen FSCIL methods can continually adapt to new classes without any training, which achieve better performance. Under the incremental-frozen framework, our SAVC effectively boosts both base class separation and novel class generalization by recognizing and contrasting semantic-aware virtual classes, consistently achieving the highest average accuracy over all sessions on three datasets. e.g., our approach gets the final accuracies of $53.12\%$ , $62.50\%$ and $57.11\%$ for CIFAR100, CUB200 and miniImageNet, outperforming the current SOTA method FACT by $1.02\%$ , $5.56\%$ and $6.62\%$ , respectively. + +# 4.3. Ablation study + +We conduct ablation studies to prove the importance of our proposed components. Built on the incremental-frozen framework, we adopt only CE loss as the baseline, while adding SCL (SCL) and observe the influence this attempt + +![](images/3c2c8ac452922e3ffe8ea8fa8c5ff8aea2f1676012b7bc1dd0c9d22606279800.jpg) +(a) CE. +Figure 5. Comparison of the confusion matrices of CE and SAVC methods on CUB200 dataset. + +![](images/1fa5fbe11afbe8302a1a9acca5148cac2320d8b2a6451a2bfbd6f2cd39dd243e.jpg) +(b)SAVC. + +brings (i.e., CE and $\mathrm{CE + SCL}$ defined in Sec. 3.2). Then we fantasy (F) contrastive virtual class in $\mathrm{CE + SCL}$ and verify its effectiveness. In addition, we consider the influence of the two components mentioned in Sec. 3.4, i.e., multi-crop strategy (MC) and finetune (FT) the pre-trained model. We report the results for miniImageNet dataset in Tab. 3. + +Compared with the baseline, SCL shows limited influences on improving mutual class separation and lightly improves the performance by $1.56\%$ in the last session. The fantasy of virtual classes (F) plays a vital part in preventing overlap among old and new distributions, which brings $8.98\%$ performance improvement. When attending more local information (MC), our model works better, i.e., $10.26\%$ gains. Lastly, it is essential to fine-tune the model in incremental session (T), which achieves $11.57\%$ accuracy gain. + +# 4.4. Visualization + +Confusion Matrix. We visualize the confusion matrix generated by CE and our SAVC on CUB200 dataset after the training of the last session in Fig. 5. Bright diagonal and dim background indicates higher classification accuracy. It is observed that CE draws a relatively distinct diagonal, but it has poor performance on new classes. In contrast, our method shows better performance on both base and new classes, which proves that our method adapts to novel classes without confusing the previous decision boundaries. + +Visualization of class separation. We visualize the embedding space on CIFAR100 dataset with t-SNE [46] in Fig. 7. We randomly choose 6 classes from base classes and 3 classes from novel classes respectively. We can ob- + +![](images/8ac243e755214eb67a72cd3b42a88e6ada87b1f025da3af4d9e7e969066ff144.jpg) +(a) Influence of fantasy methods. + +![](images/9d8a5bc729f0fe9b5d748a00a97a1bf7d37082e530481a49af64f5d28ae18d71.jpg) +(b) Influence of incremental trainable layers. + +![](images/cfba7eb682b88ade55f2629fac9a9c69661ebd1dd49a18c44f59b17aed723d01.jpg) +(c) Influence of hyper-parameters. + +![](images/635547371ba45edbbb84ca4e45e5e3e29f4a8aaf4ad52b5645dfdadd071842ca.jpg) +Figure 6. Analysis of detailed hyper configurations of our method: fantasy methods, trainable layers, and hyper-parameters. +(a) CE. + +![](images/dfcde936ae1e080a8156df750a468988eab708ab1ce1a1f8b45f9cc85b18f5a2.jpg) +(b) $\mathrm{CE + SCL}$ +Figure 7. The t-SNE [46] visualization on CIFAR100 dataset of the embeddings learned by various methods. Class 0-5 represent the base classes while class 6-8 represent the novel classes. Our SAVC gets the best class separation degree. + +![](images/4438a30d9abed828f711432d5c5b16c6bee078c7f611d6e9b07a2231dc3ad96d.jpg) +(c)SAVC. + +serve that CE has limited clustering effects. Naïve SCL slightly improves the class separation degree, but implicitly decreases inter-class distance and remains the mutual class confusion problems unresolved. With the help of virtual classes, Our proposed SAVC can clearly separate base classes and incorporate novel classes with the least overlap. + +# 4.5. Further Analysis + +Analysis of fantasy method. We consider 3 transformation sets $\mathcal{F}$ and show their impacts on our method in Fig. 6a. We observe that miniImageNet benefits from complex transformation more, while it would hurt the class separation on CUB200 and CIFAR100. We suppose that CUB200 is already a fine-grained dataset, and the learning process would be hindered by overly fine grains. As for CIFAR100, the image resolutions are too small, and it may be hard to make a distinction between the original and virtual classes. We put discussions on more types of fantasy methods in the appendix. + +Analysis of trainable layers in incremental sessions. We update the trainable layers in incremental sessions on miniImageNet benchmark. The results are reported in Fig. 6b. The gray horizontal line indicates without incremental finetuning. It is observed that finetuning fewer and deeper layers gets higher accuracy, while finetuning too + +many layers harms the former knowledge and shows poorer performance. We infer that the low-level characteristics extracted by shallow layers are shared between classes, so we only need to update high layers to adapt to new classes. + +Analysis of hyper-parameter. There are two main hyper-parameters in our loss, i.e., coefficient $\alpha$ and $\beta$ which control the impact of global and local supervised contrastive loss in Eq. (10). We report the accuracy in the last session on miniImageNet dataset varying $\alpha$ and $\beta$ in Fig. 6c. Our method is robust to the choices of hyper-parameters, and it achieves the best performance when $\alpha = 0.2$ and $\beta = 0.8$ . For convenience, we fix the value of $\alpha$ and $\beta$ on all datasets. + +# 5. Conclusion + +In this paper, we solve the FSCIL problems by Semantic-Aware Virtual Contrastive framework (SAVC). Based on the mainstream incremental-frozen framework, both CE and SCL fail to separate base classes and new classes well. We propose to introduce more semantic knowledge by imagining virtual classes. These virtual classes not only hold enough feature space for future updates, but enable a multisemantic aggregated inference effect and significantly improve classification accuracy. By learning to recognize and contrast in the fantasy space, our method can maintain the separation of all classes and achieve SOTA performance on the three popular FSCIL benchmarks. + +# Acknowledgments + +This work is supported by grants from the KeyArea Research and Development Program of Guangdong Province (2020B0101380001), the National Key R&D Program of China (2022ZD0118101), the National Natural Science Foundation of China (61825101, 62202010, 62202014), Shenzhen Basic Research Program (JCYJ20220813151736001), China Postdoctoral Science Foundation (2022M710212), and Huawei Technologies Co., Ltd. The computing resources of Pengcheng Cloudbrain are used in this research. + +# References + +[1] Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, and Ethan Fetaya. Gp-tree: A gaussian process classifier for few-shot incremental learning. In ICML, pages 54-65. PMLR, 2021. 2 +[2] Touqueer Ahmad, Akshay Raj Dhamija, Steve Cruz, Ryan Rabinowitz, Chunchun Li, Mohsen Jafarzadeh, and Terrance E Boult. Few-shot class incremental learning leveraging self-supervised features. In CVPRW, pages 3900-3910, 2022. 2 +[3] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In NeurIPS, pages 9912-9924, 2020. 3, 6 +[4] Francisco M Castro, Manuel J Marín-Jiménez, Nicolas Guil, Cordelia Schmid, and Karteek Alahari. End-to-end incremental learning. In ECCV, pages 233-248, 2018. 6, 7 +[5] Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny. Efficient lifelong learning with a gem. arXiv preprint arXiv:1812.00420, 2018. 2 +[6] Kuilin Chen and Chi-Guhn Lee. Incremental few-shot learning via vector quantization in deep embedded space. In ICLR, 2020. 1, 2 +[7] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297, 2020. 3, 4 +[8] Ali Cheraghian, Shafin Rahman, Pengfei Fang, Soumava Kumar Roy, Lars Petersson, and Mehrtash Harandi. Semantic-aware knowledge distillation for few-shot class-incremental learning. In CVPR, pages 2534-2543, 2021. 1, 2, 3 +[9] Ali Cheraghian, Shafin Rahman, Sameera Ramasinghe, Pengfei Fang, Christian Simon, Lars Petersson, and Mehrtash Harandi. Synthesized feature based few-shot class incremental learning on a mixture of subspaces. In ICCV, pages 8661-8670, 2021. 2 +[10] Zhixiang Chi, Li Gu, Huan Liu, Yang Wang, Yuanhao Yu, and Jin Tang. Meta-scil: A meta-learning approach for few-shot class incremental learning. In CVPR, pages 14166-14175, 2022. 2 +[11] Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In ICCV, pages 1422-1430, 2015. 2, 3, 5 +[12] Songlin Dong, Xiaopeng Hong, Xiaoyu Tao, Xinyuan Chang, Xing Wei, and Yihong Gong. Few-shot class-incremental learning via relation knowledge distillation. In AAAI, volume 35, pages 1255-1263, 2021. 1, 2, 3 +[13] Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, and Samy Bengio. Large margin deep networks for classification. In NeurIPS, 2018. 4 +[14] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, pages 1126–1135. PMLR, 2017. 2 +[15] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728, 2018. 2, 3, 5 + +[16] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9729-9738, 2020. 3, 4 +[17] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016. 6 +[18] Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, and Abbas Rahimi. Constrained few-shot class-incremental learning. In CVPR, pages 9057-9067, 2022. 2 +[19] Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin. Learning a unified classifier incrementally via rebalancing. In CVPR, pages 831-839, 2019. 1, 6, 7 +[20] Zhong Ji, Zhishen Hou, Xiyao Liu, Yanwei Pang, and Xuelong Li. Memorizing complementation network for few-shot class-incremental learning. TIP, 2023. 3 +[21] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. In NeurIPS, pages 18661-18673, 2020. 2, 3, 4 +[22] Simon Kornblith, Ting Chen, Honglak Lee, and Mohammad Norouzi. Why do better loss functions lead to less transferable features? In NeurIPS, pages 28648-28662, 2021. 4 +[23] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 6 +[24] Hankook Lee, Sung Ju Hwang, and Jinwoo Shin. Self-supervised label augmentation via input transformations. In ICML, pages 5714-5724. PMLR, 2020. 3, 5 +[25] Zhizhong Li and Derek Hoiem. Learning without forgetting. TPAMI, 40(12):2935-2947, 2017. 1, 2 +[26] Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li. Metasgd: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835, 2017. 2 +[27] Huan Liu, Li Gu, Zhixiang Chi, Yang Wang, Yuanhao Yu, Jun Chen, and Jin Tang. Few-shot class-incremental learning via entropy-regularized data-free replay. In ECCV, pages 146–162. Springer, 2022. 2 +[28] Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang. Large-margin softmax loss for convolutional neural networks. arXiv preprint arXiv:1612.02295, 2016. 4 +[29] Xialei Liu, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio M Lopez, and Andrew D Bagdanov. Rotate your networks: Better weight consolidation and less catastrophic forgetting. In ICPR, pages 2262-2268. IEEE, 2018. 2 +[30] Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D Bagdanov, and Joost van de Weijer. Class incremental learning: survey and performance evaluation on image classification. arXiv preprint arXiv:2010.15277, 2020.1 +[31] Pratik Mazumder, Pravendra Singh, and Piyush Rai. Few-shot lifelong learning. In AAAI, volume 35, pages 2337-2345, 2021. 2 +[32] Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka. Distance-based image classification: Generalizing to new classes at near-zero cost. TPAMI, 35(11):2624-2637, 2013. 3 + +[33] Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018. 2 +[34] Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste. Tadam: Task dependent adaptive metric for improved few-shot learning. In NeurIPS, 2018. 2 +[35] Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. Context encoders: Feature learning by inpainting. In CVPR, pages 2536-2544, 2016. 2, 3, 5 +[36] Sachin Ravi and Hugo Larochelle. Optimization as a model for few-shot learning. In ICLR, 2017. 2 +[37] Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. icarl: Incremental classifier and representation learning. In CVPR, pages 2001–2010, 2017. 1, 2, 6, 7 +[38] David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne. Experience replay for continual learning. In NeurIPS, 2019. 2 +[39] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. IJCV, 115:211-252, 2015. 6 +[40] Guangyuan Shi, Jiaxin Chen, Wenlong Zhang, Li-Ming Zhan, and Xiao-Ming Wu. Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima. In NeurIPS, pages 6747-6761, 2021. 1, 2, 3, 6, 7 +[41] Yujun Shi, Kuangqi Zhou, Jian Liang, Zihang Jiang, Jiashi Feng, Philip HS Torr, Song Bai, and Vincent YF Tan. Mimicking the oracle: an initial phase decorrelation approach for class incremental learning. In CVPR, pages 16722-16731, 2022. 2 +[42] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In NeurIPS, 2017. 2 +[43] Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales. Learning to compare: Relation network for few-shot learning. In CVPR, pages 1199–1208, 2018. 2 +[44] Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Songlin Dong, Xing Wei, and Yihong Gong. Few-shot class incremental learning. In CVPR, pages 12183-12192, 2020. 1, 2, 3, 6, 7 +[45] Eleni Triantafillou, Richard Zemel, and Raquel Urtasun. Few-shot learning through an information retrieval lens. In NeurIPS, 2017. 2 +[46] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008. 7, 8 +[47] Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc V Gool. Revisiting contrastive methods for unsupervised learning of visual representations. In NeurIPS, pages 16238-16250, 2021. 6 +[48] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200-2011 dataset. 2011. 6 + +[49] Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister. Learning to prompt for continual learning. In CVPR, pages 139-149, 2022. 2 +[50] Shipeng Yan, Jiangwei Xie, and Xuming He. Der: Dynamically expandable representation for class incremental learning. In CVPR, pages 3014-3023, 2021. 1, 2 +[51] Chi Zhang, Nan Song, Guosheng Lin, Yun Zheng, Pan Pan, and Yinghui Xu. Few-shot incremental learning with continually evolved classifiers. In CVPR, pages 12455-12464, 2021. 1, 2, 3, 6, 7 +[52] Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful image colorization. In ECCV, pages 649-666. Springer, 2016. 2, 3, 5 +[53] Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, and Jiashi Feng. Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning. In NeurIPS, pages 29848–29860, 2021. 4 +[54] Hanbin Zhao, Yongjian Fu, Mintong Kang, Qi Tian, Fei Wu, and Xi Li. Mgsvf: Multi-grained slow vs. fast framework for few-shot class-incremental learning. TPAMI, 2021. 2, 3 +[55] Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, and De-Chuan Zhan. Forward compatible few-shot class-incremental learning. In CVPR, pages 9046–9056, 2022. 1, 3, 6, 7 +[56] Da-Wei Zhou, Han-Jia Ye, Liang Ma, Di Xie, Shiliang Pu, and De-Chuan Zhan. Few-shot class-incremental learning by sampling multi-phase tasks. TPAMI, 2022. 3 +[57] Fei Zhu, Zhen Cheng, Xu-Yao Zhang, and Cheng-lin Liu. Class-incremental learning via dual augmentation. In NeurIPS, pages 14306-14318, 2021. 2 +[58] Kai Zhu, Yang Cao, Wei Zhai, Jie Cheng, and Zheng-Jun Zha. Self-promoted prototype refinement for few-shot class incremental learning. 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The motivation of the proposed approach. Under the incremental-frozen framework in FSCIL, our SAVC learns to recognize and contrast in the fantasy space, which leads to better base class separation and novel class generalization than CE and SCL." + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_caption" + } + ], + "index": 25 + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": "to continually learn to recognize new faces without forgetting those has already kept in mind. In this case, Class-Incremental Learning (CIL) [19,25,30,37,50] draws attention extensively, that sufficient and disjoint data are coming session-by-session. However, it is unrealistic to always rely on accessing of multitudinous data. Taking the case of face recognition system again, whether it can quickly learn a new concept with just a few images, is an important criterion for evaluating its performance. As a result, designing effective and efficient algorithms to resolve Few-Shot Class-Incremental Learning (FSCIL) problem has drawn increasing attention [6,8,12,40,44,51,55]. Unlike CIL, only a few data are provided for FSCIL at incremental session" + } + ] + } + ], + "index": 28 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 702, + 243, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 702, + 243, + 713 + ], + "spans": [ + { + "bbox": [ + 61, + 702, + 243, + 713 + ], + "type": "text", + "content": "†: authors contributed equally. *: corresponding author." + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "24183" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 286, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 286, + 83 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 286, + 83 + ], + "type": "text", + "content": "while sufficient samples are only visible at the base session." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 86, + 286, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 86, + 286, + 240 + ], + "spans": [ + { + "bbox": [ + 46, + 86, + 286, + 240 + ], + "type": "text", + "content": "The core challenge of CIL is to strike a balance of stability and plasticity, i.e., suppressing forgetting on old knowledge while adapting smoothly to new knowledge. Previous works on FSCIL have demonstrated that an incrementally trained model is prone to overfit on limited new data and suffer catastrophic forgetting [8, 12, 44, 54]. Thus the mainstream FSCIL framework [40, 51, 58] is to first use the cross-entropy (CE) loss for training in the base session, then freeze the backbone to adapt to new classes. Given such baseline framework, the main problem that needs to be taken into consideration is: What makes a good base-session-model that can generalize well to new classes with limited data?" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 243, + 286, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 243, + 286, + 495 + ], + "spans": [ + { + "bbox": [ + 46, + 243, + 286, + 495 + ], + "type": "text", + "content": "Intuitively, if all representations of base classes concentrate around their own clustering centers (i.e., prototypes), and all prototypes are far away from each other, then novel classes should be easily incorporated into the current space without overlapping. Therefore, we conjecture that base class separation facilitates novel class generalization. However, the current widely adopted CE loss in FSCIL cannot well separate the class margins, which causes poor generalization to new classes. This motivates our attempt at methods with better clustering effects, such as supervised contrastive learning (SCL) [21]. We conduct experiments to validate our conjecture by training models optimized by only CE loss, and both CE and SCL. We empirically find that CE leads to lower base class separation and classification accuracy than SCL. Although SCL indeed slightly improves the base class separation with higher accuracy, we find it implicitly decreases the inter-class distance. Therefore, the two methods both leave inadequate room for future updates, which leads to the inevitable overlapping between novel classes and old classes in the representation space (as shown in the left and middle columns of Fig. 1a)." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 496, + 286, + 591 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 496, + 286, + 591 + ], + "spans": [ + { + "bbox": [ + 46, + 496, + 286, + 591 + ], + "type": "text", + "content": "The unsatisfactory performance of SCL implies that the limited semantic information base session provided may need our fantasy remedy. Although we have no access to future classes in the base session, we can create many virtual classes with various semantics filling the unallocated representation space, which not only holds enough space for future classes, but also enables the learning of diverse semantics for better inference and generalization." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 594, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 286, + 713 + ], + "type": "text", + "content": "To this end, we propose a Semantic-Aware Virtual Contrastive (SAVC) model to improve the FSCIL by boosting base class separation (see Fig. 1b). Our fantasy space is constructed with pre-defined transformations [11, 15, 35, 52], which can be regarded as the semantic extension of the original space in finer grains. The type of transformations represents the richness of our imagination, i.e., how compact we want the embedding space to be and how fine the semantic grains we want to acquire. Learning to contrast and recognize in the fantasy space not only brings a better class" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 72, + 545, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 167 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 167 + ], + "type": "text", + "content": "separation effect, but also provides richer semantic information to improve the accuracy. In contrast to the CE baseline and SCL, our SAVC can simultaneously decrease intraclass distance and increase inter-class distance, which leads to better base class separation and novel class generalization (as given in the right of Fig. 1a). Extensive experiments on various benchmark datasets further verify the superiority of our method." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 317, + 168, + 537, + 178 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 168, + 537, + 178 + ], + "spans": [ + { + "bbox": [ + 317, + 168, + 537, + 178 + ], + "type": "text", + "content": "Our main contributions are summarized in three folds:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 180, + 545, + 335 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 304, + 180, + 545, + 227 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 180, + 545, + 227 + ], + "spans": [ + { + "bbox": [ + 304, + 180, + 545, + 227 + ], + "type": "text", + "content": "1) We empirically discover that it is crucial to boost class separation degree in base session for the incremental-frozen framework in FSCIL, which helps fast generalization for novel classes with only a few samples." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 228, + 545, + 287 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 228, + 545, + 287 + ], + "spans": [ + { + "bbox": [ + 304, + 228, + 545, + 287 + ], + "type": "text", + "content": "2) We propose a novel Semantic-Aware Virtual Contrastive (SAVC) framework, enhancing the FSCIL learner by learning to recognize and contrast in the fantasy space, which preserves enough place for novel generalization and enables a multi-semantic aggregated inference effect." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 288, + 545, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 288, + 545, + 335 + ], + "spans": [ + { + "bbox": [ + 304, + 288, + 545, + 335 + ], + "type": "text", + "content": "3) Extensive experiments on three FSCIL benchmarks, i.e., CIFAR100, miniImageNet and CUB200, demonstrate that our SAVC outperforms all approaches by a large margin and achieves the state-of-the-art performance." + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 346, + 391, + 358 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 346, + 391, + 358 + ], + "spans": [ + { + "bbox": [ + 306, + 346, + 391, + 358 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 366, + 545, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 366, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 304, + 366, + 545, + 521 + ], + "type": "text", + "content": "Few-Shot Learning. FSL focuses on fast adaptation to unseen classes through few learnable samples. Current FSL methods are mainly divided into two lines: optimization-based methods [14, 26, 33, 36] and metric-based methods [34, 42, 43, 45]. Optimization-based methods aim to encourage the model to obtain generalizable representations by using meta-learning frameworks. Our method has more connection with metric-based methods, which use a pretrained backbone to extract feature, and compute score by measuring the distance between query and support. Nevertheless, FSL aims to adapt to novel classes via few samples and cannot be directly applied to FSCIL, without maintaining classification performance over base classes." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 522, + 545, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 522, + 545, + 676 + ], + "spans": [ + { + "bbox": [ + 304, + 522, + 545, + 676 + ], + "type": "text", + "content": "Class Incremental Learning. CIL is the process of learning new knowledge continually without forgetting. Classical works fall into three streams. The first stream rehearsals former samples selectively to maintain discrimination over old classes [5, 37, 38]. The second stream regularizes the important parameters from being dramatically changed [25, 29]. The third stream dynamically expands the network architecture to adjust the model's representation ability on new tasks [49, 50]. Recent methods boost performance by encouraging the model to learn to recognize more classes [41, 57], which are relative to our method. CIL methods build on the basis of the large number of training examples, which is unsuitable for FSCIL." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 545, + 713 + ], + "type": "text", + "content": "Few-Shot Class-Incremental Learning. FSCIL [1, 2, 6, 9, 10, 18, 27, 31] aims at undertaking CIL task in insufficient data settings. TOPIC [44] first proposes this new task" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24184" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 179 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 179 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 179 + ], + "type": "text", + "content": "and solves it by neural gas structure. Current FSCIL methods can be roughly divided into two groups. The first group updates the backbone in incremental session [8, 12, 44, 54], while the second group fixes the backbone in incremental sessions [40, 51, 58]. Built on the latter incremental-frozen framework, other works balance the stability and plasticity trade-off from different perspectives, such as finding flatten minima [40], episodic training [51, 56, 58], ensemble learning [20], forward compatibility [55], and so on." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 180, + 289, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 180, + 289, + 324 + ], + "spans": [ + { + "bbox": [ + 46, + 180, + 289, + 324 + ], + "type": "text", + "content": "Self-supervised learning. SSL aims at learning representations without human annotations. A common way of SSL is to design pretext tasks and teach the model how to solve these prediction tasks [11, 15, 35, 52]. [24] proposes to utilize these transformations under full-supervised datasets and trains a unified classifier, which motivates the construction of our fantasy space. Another commonly used pretext task is instance discrimination represented by contrastive learning [3, 7, 16]. The methodology is to teach the model to aggregate positives and exclude negatives. SupCon [21] converted to \"class discrimination\" task, leveraging the label information to decide positive pairs." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 333, + 129, + 346 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 333, + 129, + 346 + ], + "spans": [ + { + "bbox": [ + 47, + 333, + 129, + 346 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 352, + 126, + 365 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 352, + 126, + 365 + ], + "spans": [ + { + "bbox": [ + 47, + 352, + 126, + 365 + ], + "type": "text", + "content": "3.1. Preliminary" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "spans": [ + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": "In FSCIL, we learn a sequence of tasks continually. There is a constant stream of training data denoting " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{train} = \\{\\mathcal{D}_{train}^t\\}_{t=0}^T" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{train}^t = \\{(\\mathbf{x}_i, y_i)\\}_{i=0}^{N_t}" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": " represents the training samples from session " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_i" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "y_i" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": " are the " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": "-th image and corresponding label respectively. Assuming that the label space of the " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": "-th task is " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "\\mathcal{C}^t" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": ", the label spaces from different tasks have no overlap, i.e., the model trained on the " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{train}^t" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": " should be evaluated on " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "\\mathcal{D}_{test}^t" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": " which contains all encountered classes " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "\\mathcal{C}^0 \\cup \\mathcal{C}^1 \\dots \\cup \\mathcal{C}^t" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": " in the " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": "-th session. Specifically, sufficient data can be provided in base session (also called initial session), while the model only has access to a few samples in all following sessions (also called incremental sessions). In particular, the incremental data are always organized as " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": "-way " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": "-shot format, i.e., there are " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": " classes, and each class contains " + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 371, + 287, + 552 + ], + "type": "text", + "content": " training images." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 552, + 288, + 599 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 552, + 288, + 599 + ], + "spans": [ + { + "bbox": [ + 46, + 552, + 288, + 599 + ], + "type": "text", + "content": "Several provident works [40, 51] recommend an incremental-frozen framework that leverages sufficient data in base session to train the model by optimizing the persample loss:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 101, + 606, + 287, + 619 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 101, + 606, + 287, + 619 + ], + "spans": [ + { + "bbox": [ + 101, + 606, + 287, + 619 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {c l s} (\\phi ; \\mathbf {x}, y) = \\mathcal {L} _ {c e} (\\phi (\\mathbf {x}), y), \\tag {1}", + "image_path": "79b82e6407dfee8444adc7b59b95b93502096b63baa4b89404c414d937f00104.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 624, + 287, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 624, + 287, + 677 + ], + "spans": [ + { + "bbox": [ + 46, + 624, + 287, + 677 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 624, + 287, + 677 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{ce}(\\cdot ,\\cdot)" + }, + { + "bbox": [ + 46, + 624, + 287, + 677 + ], + "type": "text", + "content": " is cross-entropy loss (CE), " + }, + { + "bbox": [ + 46, + 624, + 287, + 677 + ], + "type": "inline_equation", + "content": "\\phi (\\mathbf{x})" + }, + { + "bbox": [ + 46, + 624, + 287, + 677 + ], + "type": "text", + "content": " is the model which can be decomposed into feature extractor and classifier: " + }, + { + "bbox": [ + 46, + 624, + 287, + 677 + ], + "type": "inline_equation", + "content": "\\phi (\\mathbf{x}) = W^{T}f(\\mathbf{x})" + }, + { + "bbox": [ + 46, + 624, + 287, + 677 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 46, + 624, + 287, + 677 + ], + "type": "inline_equation", + "content": "\\phi (\\mathbf{x})\\in \\mathbb{R}^{|C^0 | \\times 1}" + }, + { + "bbox": [ + 46, + 624, + 287, + 677 + ], + "type": "inline_equation", + "content": "W\\in \\mathbb{R}^{d\\times |\\mathcal{C}^0 |}" + }, + { + "bbox": [ + 46, + 624, + 287, + 677 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 624, + 287, + 677 + ], + "type": "inline_equation", + "content": "f(\\mathbf{x})\\in \\mathbb{R}^{d\\times 1}" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 677, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 287, + 715 + ], + "type": "text", + "content": "After the base training stage, the backbone will be completely fixed, and the classifier is extended in each incremental session: " + }, + { + "bbox": [ + 46, + 677, + 287, + 715 + ], + "type": "inline_equation", + "content": "W = \\{ \\boldsymbol{w}_1^0, \\boldsymbol{w}_2^0, \\dots, \\boldsymbol{w}_{|\\mathcal{C}^0|}^0 \\} \\cup \\dots \\cup" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 310, + 72, + 429, + 174 + ], + "blocks": [ + { + "bbox": [ + 310, + 72, + 429, + 174 + ], + "lines": [ + { + "bbox": [ + 310, + 72, + 429, + 174 + ], + "spans": [ + { + "bbox": [ + 310, + 72, + 429, + 174 + ], + "type": "image", + "image_path": "faf96d51778bb96680dfa63a1a6d55c55be965d072fd4dda109dbb01fd02f1f0.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 306, + 178, + 424, + 197 + ], + "lines": [ + { + "bbox": [ + 306, + 178, + 424, + 197 + ], + "spans": [ + { + "bbox": [ + 306, + 178, + 424, + 197 + ], + "type": "text", + "content": "(a) Cumulative distribution function of inter-class distance." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 430, + 72, + 553, + 174 + ], + "blocks": [ + { + "bbox": [ + 430, + 72, + 553, + 174 + ], + "lines": [ + { + "bbox": [ + 430, + 72, + 553, + 174 + ], + "spans": [ + { + "bbox": [ + 430, + 72, + 553, + 174 + ], + "type": "image", + "image_path": "43c02d3690f74a98993de4df6d81e74868108c51bce3609f36f6a9a1b5684456.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 428, + 178, + 545, + 197 + ], + "lines": [ + { + "bbox": [ + 428, + 178, + 545, + 197 + ], + "spans": [ + { + "bbox": [ + 428, + 178, + 545, + 197 + ], + "type": "text", + "content": "(b) Cumulative distribution function of intra-class distance." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 308, + 200, + 429, + 300 + ], + "blocks": [ + { + "bbox": [ + 308, + 200, + 429, + 300 + ], + "lines": [ + { + "bbox": [ + 308, + 200, + 429, + 300 + ], + "spans": [ + { + "bbox": [ + 308, + 200, + 429, + 300 + ], + "type": "image", + "image_path": "da973bb00d7a1eab0662992ed15b1ec6a4a42f6e3cd9ca7acb3457ab24f5daf1.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 306, + 303, + 424, + 322 + ], + "lines": [ + { + "bbox": [ + 306, + 303, + 424, + 322 + ], + "spans": [ + { + "bbox": [ + 306, + 303, + 424, + 322 + ], + "type": "text", + "content": "(c) Base, novel and mutual class separation degree." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 305, + 332, + 545, + 399 + ], + "lines": [ + { + "bbox": [ + 305, + 332, + 545, + 399 + ], + "spans": [ + { + "bbox": [ + 305, + 332, + 545, + 399 + ], + "type": "text", + "content": "Figure 2. The relationship between base class separation and novel class generalization. (a) Inter-class distance is defined in Eq. (5). A curve lies more right on the figure denotes greater inter-class distance. (b) Intra-class distance is defined in Eq. (6). A curve lies more left on the figure denotes smaller intra-class distance. (c) Class separation degree " + }, + { + "bbox": [ + 305, + 332, + 545, + 399 + ], + "type": "inline_equation", + "content": "(R^2)" + }, + { + "bbox": [ + 305, + 332, + 545, + 399 + ], + "type": "text", + "content": " is defined in Eq. (7)." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 429, + 200, + 550, + 299 + ], + "blocks": [ + { + "bbox": [ + 429, + 200, + 550, + 299 + ], + "lines": [ + { + "bbox": [ + 429, + 200, + 550, + 299 + ], + "spans": [ + { + "bbox": [ + 429, + 200, + 550, + 299 + ], + "type": "image", + "image_path": "96832395515a657f966dda532deee5318799826668cb4bc299652597230b4146.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 427, + 303, + 545, + 322 + ], + "lines": [ + { + "bbox": [ + 427, + 303, + 545, + 322 + ], + "spans": [ + { + "bbox": [ + 427, + 303, + 545, + 322 + ], + "type": "text", + "content": "(d) Base, novel and total classification accuracy." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 408, + 545, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 408, + 545, + 449 + ], + "spans": [ + { + "bbox": [ + 305, + 408, + 545, + 449 + ], + "type": "inline_equation", + "content": "\\{\\pmb{w}_1^t,\\dots ,\\pmb{w}_{|\\mathcal{C}^t |}^t\\}" + }, + { + "bbox": [ + 305, + 408, + 545, + 449 + ], + "type": "text", + "content": " . The classifier weight is parameterized by the average embeddings of each class (i.e., prototype), represented as " + }, + { + "bbox": [ + 305, + 408, + 545, + 449 + ], + "type": "inline_equation", + "content": "\\pmb {w}_c^t = \\frac{1}{n_c^t}\\sum_{i = 1}^{n_c^t}f(\\mathbf{x}_{c,i})" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 305, + 449, + 545, + 496 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 449, + 545, + 496 + ], + "spans": [ + { + "bbox": [ + 305, + 449, + 545, + 496 + ], + "type": "text", + "content": "In each session, the inference is conducted by the nearest class mean (NCM) algorithm [32] to evaluate accuracy of all encountered classes. Specifically, we measure the distance between " + }, + { + "bbox": [ + 305, + 449, + 545, + 496 + ], + "type": "inline_equation", + "content": "f(\\mathbf{x})" + }, + { + "bbox": [ + 305, + 449, + 545, + 496 + ], + "type": "text", + "content": " and all prototypes in classifiers:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 360, + 504, + 545, + 521 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 360, + 504, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 360, + 504, + 545, + 521 + ], + "type": "interline_equation", + "content": "c _ {\\mathbf {x}} = \\underset {c, t} {\\arg \\max } \\operatorname {s i m} \\left(f (\\mathbf {x}), \\boldsymbol {w} _ {c} ^ {t}\\right), \\tag {2}", + "image_path": "7905ccd541f1e670ee000f000df7077faf509d4378ae27c8e65679544fee604e.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 305, + 532, + 544, + 555 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 532, + 544, + 555 + ], + "spans": [ + { + "bbox": [ + 305, + 532, + 544, + 555 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 532, + 544, + 555 + ], + "type": "inline_equation", + "content": "\\operatorname{sim}(\\mathbf{x},\\mathbf{y}) = \\mathbf{x}^{\\top}\\mathbf{y} / (\\| \\mathbf{x}\\| \\| \\mathbf{y}\\|)" + }, + { + "bbox": [ + 305, + 532, + 544, + 555 + ], + "type": "text", + "content": " is cosine similarity between two vectors." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 306, + 563, + 545, + 586 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 563, + 545, + 586 + ], + "spans": [ + { + "bbox": [ + 306, + 563, + 545, + 586 + ], + "type": "text", + "content": "3.2. Base Class Separation Facilitates Novel Class Generalization" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 594, + 545, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 545, + 677 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 545, + 677 + ], + "type": "text", + "content": "The incremental-frozen baseline outperforms a lot of incremental trainable methods [40], demonstrating that preserving old knowledge may matter more than accommodating new knowledge in FSCIL. The guarantee of base performance by the freezing strategy motivates our consideration of what our model should learn in the base training stage for future few-shot generalization." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": "An intuitive hypothesis is that, base class separation facilitates novel class generalization. If all prototypes are far away from each other, and the features of the same class" + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24185" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 66, + 70, + 521, + 297 + ], + "blocks": [ + { + "bbox": [ + 66, + 70, + 521, + 297 + ], + "lines": [ + { + "bbox": [ + 66, + 70, + 521, + 297 + ], + "spans": [ + { + "bbox": [ + 66, + 70, + 521, + 297 + ], + "type": "image", + "image_path": "368365ae657be6bba49ddc45ef81f21a2932c44c26c15f9b41cb4de946e03e71.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 307, + 547, + 352 + ], + "lines": [ + { + "bbox": [ + 46, + 307, + 547, + 352 + ], + "spans": [ + { + "bbox": [ + 46, + 307, + 547, + 352 + ], + "type": "text", + "content": "Figure 3. Overall pipeline of our Semantic-Aware Virtual Contrastive (SAVC) framework, which is composed of a classification branch and a contrastive learning branch. The backbone is shared for classification and contrastive query encoding, while the key backbone is updated with a large momentum ('//' means stop gradient). Both classification and contrastive loss are optimized in fantasy space, while the inference results of different transformations are aggregated and then projected into the original label space." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 367, + 287, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 367, + 287, + 474 + ], + "spans": [ + { + "bbox": [ + 46, + 367, + 287, + 474 + ], + "type": "text", + "content": "concentrate around their prototypes, there would be enough place for future class incorporation. Please note that the novel class generalization contains two meanings: without overlapping among novel class sets and with base class embeddings. Previous work demonstrated CE always cannot generalize well due to the poor class margins [13, 28] and may not be the optimal strategy for FSCIL, which inspires our attempt of supervised contrastive learning (SCL) [21], whose clustering effect has been demonstrated [53]." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "spans": [ + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "content": "We adopt MoCo [7, 16] framework to implement SCL in our method, for the sake of lower memory budget and storing slowly evolving features of recent batches. For an given image-label pair " + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "inline_equation", + "content": "(\\mathbf{x},y)" + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "content": ", we generate a query view " + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_q = \\mathrm{Aug}_q(\\mathbf{x})" + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "content": " and a key view " + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_k = \\mathrm{Aug}_k(\\mathbf{x})" + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "content": " by random augmentations. Then the two augmented images are fed into the query network " + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "inline_equation", + "content": "g_{q}" + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "content": " and key network " + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "inline_equation", + "content": "g_{k}" + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "content": ", getting two " + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "inline_equation", + "content": "L_{2}" + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "content": "-normalized representations " + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "inline_equation", + "content": "\\pmb{q}" + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "inline_equation", + "content": "\\pmb{k}" + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "inline_equation", + "content": "g = h\\circ f" + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "content": " is composed of a feature extractor " + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "content": " and a projector " + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "inline_equation", + "content": "h" + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "content": ". The two networks share the same architectures, while the key network updates itself with a large momentum. We maintain a feature queue and a label queue with the same length, storing the most current key embeddings and corresponding labels. To this end, given an image " + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 46, + 475, + 290, + 656 + ], + "type": "text", + "content": ", we have the following per-sample supervised contrastive loss:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 63, + 662, + 287, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 662, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 63, + 662, + 287, + 715 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {c o n t} (g; \\mathbf {x}, \\tau , A) \\\\ = - \\frac {1}{| P (\\mathbf {x}) |} \\sum_ {\\boldsymbol {k} _ {+} \\in P (\\mathbf {x})} \\log \\frac {\\exp \\left(\\boldsymbol {q} ^ {\\mathrm {T}} \\boldsymbol {k} _ {+} / \\tau\\right)}{\\sum_ {\\boldsymbol {k} ^ {\\prime} \\in A (\\mathbf {x})} \\exp \\left(\\boldsymbol {q} ^ {\\mathrm {T}} \\boldsymbol {k} ^ {\\prime} / \\tau\\right)}, \\tag {3} \\\\ \\end{array}", + "image_path": "b213a31b8f17933ea2160f12105439b01fb8444ea1f756ddeebadef49c61ee09.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "spans": [ + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "inline_equation", + "content": "A(\\mathbf{x}) = \\mathbf{k} \\cup Q" + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "text", + "content": " is the concatenation of the key embedding " + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "inline_equation", + "content": "\\mathbf{k}" + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "text", + "content": " and feature queue " + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "inline_equation", + "content": "P(\\mathbf{x})" + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "text", + "content": " denotes positive set, i.e., those in " + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "inline_equation", + "content": "A(\\mathbf{x})" + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "text", + "content": " belonging to the same class with " + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 367, + 545, + 403 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 403, + 546, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 403, + 546, + 462 + ], + "spans": [ + { + "bbox": [ + 304, + 403, + 546, + 462 + ], + "type": "text", + "content": "In our practice, the feature extractor for query encoding and classification is shared. Put the training of the classification branch and contrastive learning branch together, we form a unified end-to-end framework, and the overall training objective can be concluded as:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 383, + 470, + 545, + 483 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 383, + 470, + 545, + 483 + ], + "spans": [ + { + "bbox": [ + 383, + 470, + 545, + 483 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathcal {L} _ {c l s} + \\alpha \\mathcal {L} _ {\\text {c o n t}}. \\tag {4}", + "image_path": "d5f13be67dcb75e7f445e36f88affee26e26a0a63fe3585966f8f1004af12271.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 490, + 545, + 513 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 490, + 545, + 513 + ], + "spans": [ + { + "bbox": [ + 304, + 490, + 545, + 513 + ], + "type": "text", + "content": "We would like to refer the model trained with Eq. (1) as CE, and the model trained with Eq. (4) as " + }, + { + "bbox": [ + 304, + 490, + 545, + 513 + ], + "type": "inline_equation", + "content": "\\mathrm{CE + SCL}" + }, + { + "bbox": [ + 304, + 490, + 545, + 513 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 514, + 545, + 537 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 514, + 545, + 537 + ], + "spans": [ + { + "bbox": [ + 304, + 514, + 545, + 537 + ], + "type": "text", + "content": "We then introduce and define some metrics to measure class separation and generalization, including:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 538, + 545, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 538, + 545, + 574 + ], + "spans": [ + { + "bbox": [ + 305, + 538, + 545, + 574 + ], + "type": "text", + "content": "1) Inter-class distance. Given two prototypes " + }, + { + "bbox": [ + 305, + 538, + 545, + 574 + ], + "type": "inline_equation", + "content": "\\pmb{p}_m" + }, + { + "bbox": [ + 305, + 538, + 545, + 574 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 538, + 545, + 574 + ], + "type": "inline_equation", + "content": "\\pmb{p}_k" + }, + { + "bbox": [ + 305, + 538, + 545, + 574 + ], + "type": "text", + "content": " of class " + }, + { + "bbox": [ + 305, + 538, + 545, + 574 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 305, + 538, + 545, + 574 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 538, + 545, + 574 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 305, + 538, + 545, + 574 + ], + "type": "text", + "content": ", we compute their distance and get " + }, + { + "bbox": [ + 305, + 538, + 545, + 574 + ], + "type": "inline_equation", + "content": "|\\mathcal{C}^0|(|\\mathcal{C}^0| - 1)/2" + }, + { + "bbox": [ + 305, + 538, + 545, + 574 + ], + "type": "text", + "content": " inter-class distances in base session." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 367, + 581, + 545, + 597 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 367, + 581, + 545, + 597 + ], + "spans": [ + { + "bbox": [ + 367, + 581, + 545, + 597 + ], + "type": "interline_equation", + "content": "d _ {i n t e r} ^ {m, k} = 1 - \\operatorname {s i m} \\left(\\boldsymbol {p} _ {m}, \\boldsymbol {p} _ {k}\\right). \\tag {5}", + "image_path": "cbc1f8f618d2bd8a372d436fdf5ca38cf6bd09237a8379ddacd646246db95a9c.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 603, + 545, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 603, + 545, + 652 + ], + "spans": [ + { + "bbox": [ + 304, + 603, + 545, + 652 + ], + "type": "text", + "content": "2) Intra-class distance. Given a class " + }, + { + "bbox": [ + 304, + 603, + 545, + 652 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 603, + 545, + 652 + ], + "type": "text", + "content": ", we compute the average Euclidean distance between all test samples belonging to class " + }, + { + "bbox": [ + 304, + 603, + 545, + 652 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 603, + 545, + 652 + ], + "type": "text", + "content": " and prototype " + }, + { + "bbox": [ + 304, + 603, + 545, + 652 + ], + "type": "inline_equation", + "content": "p_k" + }, + { + "bbox": [ + 304, + 603, + 545, + 652 + ], + "type": "text", + "content": " to measure the discrepancy within this class. For " + }, + { + "bbox": [ + 304, + 603, + 545, + 652 + ], + "type": "inline_equation", + "content": "|\\mathcal{C}^0|" + }, + { + "bbox": [ + 304, + 603, + 545, + 652 + ], + "type": "text", + "content": " classes in base session, we have:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 337, + 658, + 545, + 682 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 337, + 658, + 545, + 682 + ], + "spans": [ + { + "bbox": [ + 337, + 658, + 545, + 682 + ], + "type": "interline_equation", + "content": "d _ {i n t r a} ^ {k} = 1 - \\frac {1}{n _ {k}} \\sum_ {i = 1} ^ {n _ {k}} \\operatorname {s i m} (f (\\mathbf {x} _ {k, i}), \\boldsymbol {p} _ {k}). \\tag {6}", + "image_path": "53edeebc821681fdae99ab4d4f675b2cbb252174726ed42839f67638b18e3dd0.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 689, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 715 + ], + "type": "text", + "content": "3) Class Separation " + }, + { + "bbox": [ + 305, + 689, + 545, + 715 + ], + "type": "inline_equation", + "content": "(R^2)" + }, + { + "bbox": [ + 305, + 689, + 545, + 715 + ], + "type": "text", + "content": ". On the one hand, we refer to Eq. (11) in [22] to measure the base " + }, + { + "bbox": [ + 305, + 689, + 545, + 715 + ], + "type": "inline_equation", + "content": "(K = |\\mathcal{C}^0|)" + }, + { + "bbox": [ + 305, + 689, + 545, + 715 + ], + "type": "text", + "content": " and novel" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "24186" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 70, + 267, + 86 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 70, + 267, + 86 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 267, + 86 + ], + "type": "inline_equation", + "content": "(K = \\sum_{t=1}^{T} |\\mathcal{C}^t|)" + }, + { + "bbox": [ + 47, + 70, + 267, + 86 + ], + "type": "text", + "content": " class separation degree respectively:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 53, + 89, + 287, + 189 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 89, + 287, + 189 + ], + "spans": [ + { + "bbox": [ + 53, + 89, + 287, + 189 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} R ^ {2} = 1 - \\bar {d} _ {\\text {w i t h i n}} / \\bar {d} _ {\\text {t o t a l}}, \\\\ \\bar {d} _ {w i t h i n} = \\sum_ {k = 1} ^ {K} \\sum_ {i = 1} ^ {n _ {k}} \\sum_ {j = 1} ^ {n _ {k}} \\frac {1 - \\mathrm {s i m} (f (\\mathbf {x} _ {k , i}) , f (\\mathbf {x} _ {k , j}))}{K n _ {k} ^ {2}}, \\\\ \\bar {d} _ {\\text {t o t a l}} = \\sum_ {k = 1} ^ {K} \\sum_ {m = 1} ^ {K} \\sum_ {i = 1} ^ {n _ {j}} \\sum_ {j = 1} ^ {n _ {k}} \\frac {1 - \\operatorname {s i m} \\left(f \\left(\\mathbf {x} _ {k , i}\\right) , f \\left(\\mathbf {x} _ {m , j}\\right)\\right)}{K ^ {2} n _ {k} n _ {j}}. \\tag {7} \\\\ \\end{array}", + "image_path": "f1c1d0ddc7f9279598f7733dd1a281640f7f079ff9867380baf31f30cdc9a5e8.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "spans": [ + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "text", + "content": "On the other hand, we propose a novel metric to evaluate the mutual separation degree between novel and base classes, which is referred to as 'mutual class separation " + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "inline_equation", + "content": "(R_{mul}^{2})" + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "text", + "content": ". We only need to let " + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "inline_equation", + "content": "K = \\sum_{t=0}^{T} |\\mathcal{C}^t|" + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "inline_equation", + "content": "\\bar{d}_{within}" + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "inline_equation", + "content": "K = |\\mathcal{C}^0|" + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "inline_equation", + "content": "\\sum_{t=1}^{T} |\\mathcal{C}^t|" + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "text", + "content": " respectively in " + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "inline_equation", + "content": "\\bar{d}_{total}" + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "text", + "content": ", to compute " + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "inline_equation", + "content": "R_{mul}^{2}" + }, + { + "bbox": [ + 46, + 194, + 286, + 258 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "spans": [ + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "text", + "content": "Greater " + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "inline_equation", + "content": "d_{inter}" + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "inline_equation", + "content": "R_{base}^{2}" + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "text", + "content": ", smaller " + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "inline_equation", + "content": "d_{intra}" + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "text", + "content": " represents more compact the base embedding space is. Under the incremental-frozen framework, we compare the result of CE and " + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "inline_equation", + "content": "\\mathrm{CE + SCL}" + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "text", + "content": " in Fig. 2 on CIFAR100 benchmark, while " + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "inline_equation", + "content": "d_{inter}" + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "inline_equation", + "content": "d_{intra}" + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "text", + "content": " are shown in the cumulative distribution function (CDF) style. We can observe that although " + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "inline_equation", + "content": "\\mathrm{CE + SCL}" + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "text", + "content": " gets greater class separation degree and classification accuracy than CE on all classes, it implicitly decreases inter-class distance while decreasing intra-class distance (the " + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "inline_equation", + "content": "\\mathrm{CE + SCL}" + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "text", + "content": " curve lies on the left of the CE curve both in Fig. 2a and Fig. 2b). Especially, SCL only provides minor improvement of the mutual class separation " + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "inline_equation", + "content": "R_{mul}^{2}" + }, + { + "bbox": [ + 46, + 259, + 287, + 450 + ], + "type": "text", + "content": ", which implies that SCL cannot separate the novel representations from the old distributions (i.e., poor novel class generalization), and is also far from perfect for our objective (see Fig. 2c and Fig. 2d)." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 456, + 271, + 468 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 456, + 271, + 468 + ], + "spans": [ + { + "bbox": [ + 47, + 456, + 271, + 468 + ], + "type": "text", + "content": "3.3. Semantic-Aware Contrastive Class Fantasy" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 474, + 287, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 474, + 287, + 665 + ], + "spans": [ + { + "bbox": [ + 46, + 474, + 287, + 665 + ], + "type": "text", + "content": "The unsatisfactory performance of SCL, especially the inappreciable improvements of mutual class separation may convey that the semantic information base session provides is not diversified enough, which makes the learned decision boundary very sharp and close to data, resulting in poor base class separation and novel class generalization. Although the rests contained in new classes are invisible in base session, a provident model may be self-sufficient: imagining what would happen in the future and making adequate preparations for it. In other words, our model should use its fantasy to generate many virtual classes with various semantics and fill the unallocated embedding space, all by itself. These virtual classes act as not only class placeholders which preserve enough space for future updates, but also semantic knowledge providers which encourage extensive learning of different semantics for better generalization." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": "Semantic-aware class fantasy. In our semantic-aware contrastive class fantasy (SAVC) framework, we take inspiration from [24] and generate virtual classes by applying pre-defined transformations in self-supervised learning" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "text", + "content": "(SSL) [11, 15, 35, 52]. In detail, we first define a discrete transformation (i.e., fantasy) set " + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "text", + "content": " and supposing there are " + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "text", + "content": " elements in it, i.e., the number of transformations. Then, we can generate " + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "text", + "content": " transformed samples for an image-label pair " + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "inline_equation", + "content": "(\\mathbf{x}, y)" + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "text", + "content": ", denoting " + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "inline_equation", + "content": "\\mathcal{F}(\\mathbf{x}, y) = \\{(\\mathbf{x}_m, y_m)\\}_{m=1}^M" + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "inline_equation", + "content": "y_m = y \\times M + m" + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "text", + "content": ". The subscript of " + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "inline_equation", + "content": "(\\mathbf{x}_m, y_m)" + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "text", + "content": " denotes " + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "text", + "content": "-th transformation is applied to " + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "inline_equation", + "content": "(\\mathbf{x}, y)" + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "text", + "content": ", while " + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "inline_equation", + "content": "(\\mathbf{x}_1, y_1)" + }, + { + "bbox": [ + 305, + 72, + 545, + 167 + ], + "type": "text", + "content": " represents the original image-label pair." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 167, + 546, + 252 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 167, + 546, + 252 + ], + "spans": [ + { + "bbox": [ + 304, + 167, + 546, + 252 + ], + "type": "text", + "content": "In this way, we enlarge our label space by " + }, + { + "bbox": [ + 304, + 167, + 546, + 252 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 167, + 546, + 252 + ], + "type": "text", + "content": " times, and each virtual class can be regarded as a 'fine-grained' class derived from the original one. The number and type of the elements in " + }, + { + "bbox": [ + 304, + 167, + 546, + 252 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 304, + 167, + 546, + 252 + ], + "type": "text", + "content": " convey not only how compact we expect the embedding space to be, but also how fine the semantic grains we want to acquire. Hence we conduct the classification and contrasting training process in finer gains:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 259, + 554, + 357 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 259, + 554, + 357 + ], + "spans": [ + { + "bbox": [ + 306, + 259, + 554, + 357 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {c l s} \\left(\\phi ; \\mathbf {x}, y, \\mathcal {F}\\right) = \\frac {1}{M} \\sum_ {i = 1} ^ {M} \\mathcal {L} _ {c e} \\left(\\phi \\left(\\mathbf {x} _ {m}\\right), y _ {m}\\right), \\\\ \\mathcal {L} _ {\\text {c o n t}} (g; \\mathbf {x}, \\tau , A, \\mathcal {F}) = \\\\ - \\frac {1}{M} \\sum_ {m = 1} ^ {M} \\frac {1}{| P (\\mathbf {x} _ {m}) |} \\sum_ {\\mathbf {k} _ {+} \\in P (\\mathbf {x} _ {m})} \\log \\frac {\\exp \\left(\\boldsymbol {q} ^ {\\mathrm {T}} \\mathbf {k} _ {+} / \\tau\\right)}{\\sum_ {\\boldsymbol {k} ^ {\\prime} \\in A (\\mathbf {x} _ {m})} \\exp \\left(\\boldsymbol {q} ^ {\\mathrm {T}} \\boldsymbol {k} ^ {\\prime} / \\tau\\right)}. \\tag {8} \\\\ \\end{array}", + "image_path": "fc2197094f5ecd3e33fc570e584e1bb87142a673ad750c279e0c02253be27813.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 365, + 546, + 548 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 365, + 546, + 548 + ], + "spans": [ + { + "bbox": [ + 305, + 365, + 546, + 548 + ], + "type": "text", + "content": "Multi-semantic aggregated inference. With the increase of class, we can acquire various derived class information in finer gains, which enables extending of classifiers with the prototypes of original and pseudo classes: " + }, + { + "bbox": [ + 305, + 365, + 546, + 548 + ], + "type": "inline_equation", + "content": "W = \\{\\pmb{w}_{11}^{0},\\pmb{w}_{12}^{0},\\dots ,\\pmb{w}_{1M}^{0},\\dots ,\\pmb{w}_{|\\mathcal{C}^{0}|M}^{0}\\} \\cup \\dots \\cup \\{\\pmb{w}_{11}^{t},\\dots ,\\pmb{w}_{|\\mathcal{C}^{t}|M}^{t}\\}" + }, + { + "bbox": [ + 305, + 365, + 546, + 548 + ], + "type": "text", + "content": ". Prototypes with the same second subscript can naturally form a conditional prototype subset, which denotes a group of clustering centers aware of the same type of fine-grained semantics from the original classes: " + }, + { + "bbox": [ + 305, + 365, + 546, + 548 + ], + "type": "inline_equation", + "content": "W_{m} = \\{\\pmb{w}_{1m}^{0},\\pmb{w}_{2m}^{0},\\dots ,\\pmb{w}_{|\\mathcal{C}^{0}|m}^{0}\\} \\cup \\dots \\cup \\{\\pmb{w}_{1m}^{t},\\dots ,\\pmb{w}_{|\\mathcal{C}^{t}|m}^{t}\\}" + }, + { + "bbox": [ + 305, + 365, + 546, + 548 + ], + "type": "text", + "content": ". In contrast to the traditional single inference in Eq. (2), we first specify the test sample " + }, + { + "bbox": [ + 305, + 365, + 546, + 548 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 305, + 365, + 546, + 548 + ], + "type": "text", + "content": " into various fine-grained semantics: " + }, + { + "bbox": [ + 305, + 365, + 546, + 548 + ], + "type": "inline_equation", + "content": "\\mathcal{F}(\\mathbf{x}) = \\{\\mathbf{x}_m\\}_{m = 1}^M" + }, + { + "bbox": [ + 305, + 365, + 546, + 548 + ], + "type": "text", + "content": ". Then we aggregate the inference results from different conditional prototype subsets, which improves accuracy significantly:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 342, + 556, + 545, + 589 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 342, + 556, + 545, + 589 + ], + "spans": [ + { + "bbox": [ + 342, + 556, + 545, + 589 + ], + "type": "interline_equation", + "content": "c _ {\\mathbf {x}} = \\arg \\max _ {c, t} \\sum_ {m = 1} ^ {M} \\sin \\left(f \\left(\\mathbf {x} _ {m}\\right), \\boldsymbol {w} _ {c m} ^ {t}\\right). \\tag {9}", + "image_path": "b23493dc8daf8fe4d0f57effb3074f7ec2de14f08f793d4735ef249811255da6.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 592, + 545, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 592, + 545, + 653 + ], + "spans": [ + { + "bbox": [ + 304, + 592, + 545, + 653 + ], + "type": "text", + "content": "To this end, We compare our SAVC with CE and " + }, + { + "bbox": [ + 304, + 592, + 545, + 653 + ], + "type": "inline_equation", + "content": "\\mathrm{CE + SCL}" + }, + { + "bbox": [ + 304, + 592, + 545, + 653 + ], + "type": "text", + "content": " to verify its effectiveness. As we expected, SAVC effectively increases inter-class distance and decreases intraclass distance in base session, resulting in the best base class separation and novel class generalization (see Fig. 2)." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 659, + 377, + 670 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 659, + 377, + 670 + ], + "spans": [ + { + "bbox": [ + 306, + 659, + 377, + 670 + ], + "type": "text", + "content": "3.4. Discussion" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": "Our training pipeline is depicted in Fig. 3. Besides the training regime demonstrated in Sec. 3.3, the proposed SAVC also benefits from the following aspects:" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24187" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 73, + 102, + 520, + 227 + ], + "blocks": [ + { + "bbox": [ + 46, + 70, + 545, + 93 + ], + "lines": [ + { + "bbox": [ + 46, + 70, + 545, + 93 + ], + "spans": [ + { + "bbox": [ + 46, + 70, + 545, + 93 + ], + "type": "text", + "content": "Table 1. Comparison with SOTA methods on CUB200 dataset for FSCIL. " + }, + { + "bbox": [ + 46, + 70, + 545, + 93 + ], + "type": "inline_equation", + "content": "\\ast" + }, + { + "bbox": [ + 46, + 70, + 545, + 93 + ], + "type": "text", + "content": ": Performances reported by [44]. " + }, + { + "bbox": [ + 46, + 70, + 545, + 93 + ], + "type": "inline_equation", + "content": "\\Delta_{\\mathrm{last}}" + }, + { + "bbox": [ + 46, + 70, + 545, + 93 + ], + "type": "text", + "content": ": Relative improvements of the last session compared to the Finetune baseline. More results on other datasets can be found in the appendix." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 73, + 102, + 520, + 227 + ], + "lines": [ + { + "bbox": [ + 73, + 102, + 520, + 227 + ], + "spans": [ + { + "bbox": [ + 73, + 102, + 520, + 227 + ], + "type": "table", + "html": "
MethodAcc. in each session (%) ↑Δlast
012345678910
Finetune* [44]68.6843.7025.0517.7218.0816.9515.1010.068.938.938.47-
iCaRL* [37]68.6852.6548.6144.1636.6229.5227.8326.2624.0123.8921.16+12.69
EEIL* [4]68.6853.6347.9144.2036.3027.4625.9324.7023.9524.1322.11+13.64
TOPIC* [44]68.6862.4954.8149.9945.2541.4038.3535.3632.2228.3126.28+17.81
Rebalancing* [19]68.6857.1244.2128.7826.7125.6624.6221.5220.1220.0619.87+11.40
SPPR [58]68.6861.8557.4352.6850.1946.8844.6543.0740.1739.6337.33+28.86
F2M [40]81.0778.1675.5772.8970.8668.1767.0165.2663.3661.7660.26+51.79
CEC [51]75.8571.9468.5063.5062.4358.2757.7355.8154.8353.5252.28+43.81
FACT [55]75.9073.2370.8466.1365.5662.1561.7459.8358.4157.8956.94+48.47
SAVC (Ours)81.8577.9274.9570.2169.9667.0266.1665.3063.8463.1562.50+54.03
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DatasetCbaseCinc#Inc.ShotsResolution
CIFAR100 [23]60408532×32
miniImageNet [39]60408584×84
CUB200 [48]100100105224×224
", + "image_path": "d934ea4d3f6251ac659bae36f5bb93ea60a45af0e9febd326e6f5f9bcb40fec2.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 514, + 286, + 610 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 514, + 286, + 610 + ], + "spans": [ + { + "bbox": [ + 46, + 514, + 286, + 610 + ], + "type": "text", + "content": "Multi-Crop strategy. Motivated by [3, 47], we add " + }, + { + "bbox": [ + 46, + 514, + 286, + 610 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 514, + 286, + 610 + ], + "type": "text", + "content": " constraint small crops to capture more local information, which is enforced to overlap with the global crops over a pre-defined threshold ensuring information sharing. These " + }, + { + "bbox": [ + 46, + 514, + 286, + 610 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 514, + 286, + 610 + ], + "type": "text", + "content": " smaller crops are only served as query views and not fed into the key encoder. In practice, we find optimizing the global and local contrastive loss respectively is helpful, and the overall training objective can be rewritten as:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 82, + 620, + 287, + 633 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 620, + 287, + 633 + ], + "spans": [ + { + "bbox": [ + 82, + 620, + 287, + 633 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathcal {L} _ {c l s} + \\alpha \\mathcal {L} _ {\\text {c o n t - g l o b a l}} + \\beta \\mathcal {L} _ {\\text {c o n t - l o c a l}}. \\tag {10}", + "image_path": "715361f100b39da2b5b529774fc1c71783b1c79777a5a9786f47a876029d7f5a.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 46, + 641, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 287, + 713 + ], + "type": "text", + "content": "Incremental finetuning. Although there are only a few novel samples available in incremental sessions, it is essential to finetune the pretrained model by Eq. (10) in each incremental session. Fortunately, we can contrast novel query samples with these slowly evolving key embeddings of base session from the feature queue, which helps push the novel" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 401, + 545, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 401, + 545, + 426 + ], + "spans": [ + { + "bbox": [ + 305, + 401, + 545, + 426 + ], + "type": "text", + "content": "samples away from the distributions of the old classes and benefits novel class generalization." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 441, + 388, + 455 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 441, + 388, + 455 + ], + "spans": [ + { + "bbox": [ + 306, + 441, + 388, + 455 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 464, + 422, + 477 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 464, + 422, + 477 + ], + "spans": [ + { + "bbox": [ + 306, + 464, + 422, + 477 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 484, + 545, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 484, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 304, + 484, + 545, + 544 + ], + "type": "text", + "content": "Datasets. Following the benchmark setting [51], we conduct experiments on three datasets, i.e., CIFAR100 [23], miniImageNet [39] and CUB200 [48], to show the performance of our method. Their statistic characteristics are listed in Tab. 2." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 545, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 546, + 713 + ], + "type": "text", + "content": "Implementation details. Following [51], we adopt ResNet18 [17] backbones for miniImageNet and CUB200, and ResNet20 [17] for experiments on CIFAR100. We use SGD with 0.9 momentum to optimize the model. The initial learning rate is 0.1 for CIFAR100 and miniImageNet, and 0.002 for CUB200 in base session. For each incremental learning stage, we only finetune the model for 10 epochs in case of overfitting. We only consider 3 transformation sets " + }, + { + "bbox": [ + 304, + 545, + 546, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 304, + 545, + 546, + 713 + ], + "type": "text", + "content": ", including 2-fold rotations, 4-fold rotations and 12-augmentations, and their concrete meanings are listed in Tab. 4. We use 12 augmentations for miniImageNet, and 2-fold rotation for CUB200 and CIFAR100, to fancy virtual classes. We set " + }, + { + "bbox": [ + 304, + 545, + 546, + 713 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 304, + 545, + 546, + 713 + ], + "type": "text", + "content": " as 0.2 and " + }, + { + "bbox": [ + 304, + 545, + 546, + 713 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 304, + 545, + 546, + 713 + ], + "type": "text", + "content": " as 0.8 for all datasets to control the impact of global and local contrastive loss." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24188" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 73, + 102, + 520, + 182 + ], + "blocks": [ + { + "bbox": [ + 46, + 70, + 545, + 94 + ], + "lines": [ + { + "bbox": [ + 46, + 70, + 545, + 94 + ], + "spans": [ + { + "bbox": [ + 46, + 70, + 545, + 94 + ], + "type": "text", + "content": "Table 3. Ablation studies on miniImageNet benchmark. SCL, F, MC, FT denote supervised contrastive learning, virtual classes fantasy, multi-crop, incremental finetuning, respectively. " + }, + { + "bbox": [ + 46, + 70, + 545, + 94 + ], + "type": "inline_equation", + "content": "{\\Delta }_{\\text{last }}" + }, + { + "bbox": [ + 46, + 70, + 545, + 94 + ], + "type": "text", + "content": " : Relative improvements of the last sessions compared to the CE baseline." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 73, + 102, + 520, + 182 + ], + "lines": [ + { + "bbox": [ + 73, + 102, + 520, + 182 + ], + "spans": [ + { + "bbox": [ + 73, + 102, + 520, + 182 + ], + "type": "table", + "html": "
SCLFMCFTAcc. in each session (%) ↑Δlast
012345678
70.2765.1761.1357.8054.8051.8449.1447.0345.54-
✓72.7767.4263.2659.5656.5153.6250.8748.9147.10+1.56
✓✓80.7275.8271.4667.6564.6361.3158.2756.2054.52+8.98
✓✓✓81.1276.0271.8168.4165.4662.1159.3357.4255.80+10.26
✓✓✓✓81.1276.1472.4368.9266.4862.9559.9258.3957.11+11.57
", + "image_path": "2030b4331445b9d4181b9a68acc26ad702ff72700fe6a9778e404739acf00290.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 48, + 220, + 288, + 281 + ], + "blocks": [ + { + "bbox": [ + 47, + 189, + 287, + 212 + ], + "lines": [ + { + "bbox": [ + 47, + 189, + 287, + 212 + ], + "spans": [ + { + "bbox": [ + 47, + 189, + 287, + 212 + ], + "type": "text", + "content": "Table 4. The concrete meaning of different types of transformation set " + }, + { + "bbox": [ + 47, + 189, + 287, + 212 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 47, + 189, + 287, + 212 + ], + "type": "text", + "content": ". 'ALL' denotes using all " + }, + { + "bbox": [ + 47, + 189, + 287, + 212 + ], + "type": "inline_equation", + "content": "0^{\\circ}" + }, + { + "bbox": [ + 47, + 189, + 287, + 212 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 189, + 287, + 212 + ], + "type": "inline_equation", + "content": "90^{\\circ}" + }, + { + "bbox": [ + 47, + 189, + 287, + 212 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 189, + 287, + 212 + ], + "type": "inline_equation", + "content": "180^{\\circ}" + }, + { + "bbox": [ + 47, + 189, + 287, + 212 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 189, + 287, + 212 + ], + "type": "inline_equation", + "content": "270^{\\circ}" + }, + { + "bbox": [ + 47, + 189, + 287, + 212 + ], + "type": "text", + "content": " degrees." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 220, + 288, + 281 + ], + "lines": [ + { + "bbox": [ + 48, + 220, + 288, + 281 + ], + "spans": [ + { + "bbox": [ + 48, + 220, + 288, + 281 + ], + "type": "table", + "html": "
FRotationColor permutationM
2-fold rorations0°, 180°RGB2
4-fold rorationsALLRGB4
12-augmentationsALLRGB, GBR, BRG12
", + "image_path": "b48770e89d37c8b87ec4a047ed8ef11ba253ef427cfc6e7f09ebecc251dcf4d1.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 293, + 234, + 306 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 293, + 234, + 306 + ], + "spans": [ + { + "bbox": [ + 47, + 293, + 234, + 306 + ], + "type": "text", + "content": "4.2. Comparison with State of The Arts" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 312, + 287, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 312, + 287, + 430 + ], + "spans": [ + { + "bbox": [ + 46, + 312, + 287, + 430 + ], + "type": "text", + "content": "We first compare our proposed SAVC with prevailing methods on three FSCIL benchmarks. These methods include: classical CIL methods, i.e., iCaRL [37], EEIL [4], Rebalancing [19], incremental-trainable FSCIL methods, i.e., TOPIC [44], and incremental-frozen FSCIL methods, i.e., SPPR [58], F2M [40], CEC [51], and FACT [55]. We also show a naive baseline that directly finetunes the model with limited data as 'finetune'. We report the performance curve in Fig. 4 and the detailed values on CUB200 in Tab. 1." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "spans": [ + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "text", + "content": "It is observed that 'finetune' leaves out preserving old knowledge and gets inferior performance. Classical CIL methods only consider extending the model with sufficient instances and are not suitable for FSCIL. Incremental-trainable FSCIL methods suffer from overfitting phenomena and perform poorly in incremental sessions. Incremental-frozen FSCIL methods can continually adapt to new classes without any training, which achieve better performance. Under the incremental-frozen framework, our SAVC effectively boosts both base class separation and novel class generalization by recognizing and contrasting semantic-aware virtual classes, consistently achieving the highest average accuracy over all sessions on three datasets. e.g., our approach gets the final accuracies of " + }, + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "inline_equation", + "content": "53.12\\%" + }, + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "inline_equation", + "content": "62.50\\%" + }, + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "inline_equation", + "content": "57.11\\%" + }, + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "text", + "content": " for CIFAR100, CUB200 and miniImageNet, outperforming the current SOTA method FACT by " + }, + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "inline_equation", + "content": "1.02\\%" + }, + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "inline_equation", + "content": "5.56\\%" + }, + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "inline_equation", + "content": "6.62\\%" + }, + { + "bbox": [ + 46, + 433, + 288, + 636 + ], + "type": "text", + "content": ", respectively." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 646, + 138, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 646, + 138, + 659 + ], + "spans": [ + { + "bbox": [ + 47, + 646, + 138, + 659 + ], + "type": "text", + "content": "4.3. Ablation study" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 665, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 713 + ], + "type": "text", + "content": "We conduct ablation studies to prove the importance of our proposed components. Built on the incremental-frozen framework, we adopt only CE loss as the baseline, while adding SCL (SCL) and observe the influence this attempt" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 309, + 190, + 416, + 293 + ], + "blocks": [ + { + "bbox": [ + 309, + 190, + 416, + 293 + ], + "lines": [ + { + "bbox": [ + 309, + 190, + 416, + 293 + ], + "spans": [ + { + "bbox": [ + 309, + 190, + 416, + 293 + ], + "type": "image", + "image_path": "3c2c8ac452922e3ffe8ea8fa8c5ff8aea2f1676012b7bc1dd0c9d22606279800.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 350, + 297, + 374, + 306 + ], + "lines": [ + { + "bbox": [ + 350, + 297, + 374, + 306 + ], + "spans": [ + { + "bbox": [ + 350, + 297, + 374, + 306 + ], + "type": "text", + "content": "(a) CE." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 305, + 316, + 545, + 338 + ], + "lines": [ + { + "bbox": [ + 305, + 316, + 545, + 338 + ], + "spans": [ + { + "bbox": [ + 305, + 316, + 545, + 338 + ], + "type": "text", + "content": "Figure 5. Comparison of the confusion matrices of CE and SAVC methods on CUB200 dataset." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 418, + 190, + 544, + 293 + ], + "blocks": [ + { + "bbox": [ + 418, + 190, + 544, + 293 + ], + "lines": [ + { + "bbox": [ + 418, + 190, + 544, + 293 + ], + "spans": [ + { + "bbox": [ + 418, + 190, + 544, + 293 + ], + "type": "image", + "image_path": "1fa5fbe11afbe8302a1a9acca5148cac2320d8b2a6451a2bfbd6f2cd39dd243e.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 464, + 296, + 499, + 306 + ], + "lines": [ + { + "bbox": [ + 464, + 296, + 499, + 306 + ], + "spans": [ + { + "bbox": [ + 464, + 296, + 499, + 306 + ], + "type": "text", + "content": "(b)SAVC." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "spans": [ + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "text", + "content": "brings (i.e., CE and " + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "inline_equation", + "content": "\\mathrm{CE + SCL}" + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "text", + "content": " defined in Sec. 3.2). Then we fantasy (F) contrastive virtual class in " + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "inline_equation", + "content": "\\mathrm{CE + SCL}" + }, + { + "bbox": [ + 304, + 350, + 545, + 422 + ], + "type": "text", + "content": " and verify its effectiveness. In addition, we consider the influence of the two components mentioned in Sec. 3.4, i.e., multi-crop strategy (MC) and finetune (FT) the pre-trained model. We report the results for miniImageNet dataset in Tab. 3." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 422, + 545, + 530 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 422, + 545, + 530 + ], + "spans": [ + { + "bbox": [ + 304, + 422, + 545, + 530 + ], + "type": "text", + "content": "Compared with the baseline, SCL shows limited influences on improving mutual class separation and lightly improves the performance by " + }, + { + "bbox": [ + 304, + 422, + 545, + 530 + ], + "type": "inline_equation", + "content": "1.56\\%" + }, + { + "bbox": [ + 304, + 422, + 545, + 530 + ], + "type": "text", + "content": " in the last session. The fantasy of virtual classes (F) plays a vital part in preventing overlap among old and new distributions, which brings " + }, + { + "bbox": [ + 304, + 422, + 545, + 530 + ], + "type": "inline_equation", + "content": "8.98\\%" + }, + { + "bbox": [ + 304, + 422, + 545, + 530 + ], + "type": "text", + "content": " performance improvement. When attending more local information (MC), our model works better, i.e., " + }, + { + "bbox": [ + 304, + 422, + 545, + 530 + ], + "type": "inline_equation", + "content": "10.26\\%" + }, + { + "bbox": [ + 304, + 422, + 545, + 530 + ], + "type": "text", + "content": " gains. Lastly, it is essential to fine-tune the model in incremental session (T), which achieves " + }, + { + "bbox": [ + 304, + 422, + 545, + 530 + ], + "type": "inline_equation", + "content": "11.57\\%" + }, + { + "bbox": [ + 304, + 422, + 545, + 530 + ], + "type": "text", + "content": " accuracy gain." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 539, + 389, + 551 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 539, + 389, + 551 + ], + "spans": [ + { + "bbox": [ + 306, + 539, + 389, + 551 + ], + "type": "text", + "content": "4.4. Visualization" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 557, + 545, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 557, + 545, + 665 + ], + "spans": [ + { + "bbox": [ + 304, + 557, + 545, + 665 + ], + "type": "text", + "content": "Confusion Matrix. We visualize the confusion matrix generated by CE and our SAVC on CUB200 dataset after the training of the last session in Fig. 5. Bright diagonal and dim background indicates higher classification accuracy. It is observed that CE draws a relatively distinct diagonal, but it has poor performance on new classes. In contrast, our method shows better performance on both base and new classes, which proves that our method adapts to novel classes without confusing the previous decision boundaries." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "type": "text", + "content": "Visualization of class separation. We visualize the embedding space on CIFAR100 dataset with t-SNE [46] in Fig. 7. We randomly choose 6 classes from base classes and 3 classes from novel classes respectively. We can ob-" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24189" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 76, + 197, + 199 + ], + "blocks": [ + { + "bbox": [ + 50, + 76, + 197, + 199 + ], + "lines": [ + { + "bbox": [ + 50, + 76, + 197, + 199 + ], + "spans": [ + { + "bbox": [ + 50, + 76, + 197, + 199 + ], + "type": "image", + "image_path": "8ac243e755214eb67a72cd3b42a88e6ada87b1f025da3af4d9e7e969066ff144.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 70, + 204, + 177, + 213 + ], + "lines": [ + { + "bbox": [ + 70, + 204, + 177, + 213 + ], + "spans": [ + { + "bbox": [ + 70, + 204, + 177, + 213 + ], + "type": "text", + "content": "(a) Influence of fantasy methods." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 218, + 77, + 366, + 199 + ], + "blocks": [ + { + "bbox": [ + 218, + 77, + 366, + 199 + ], + "lines": [ + { + "bbox": [ + 218, + 77, + 366, + 199 + ], + "spans": [ + { + "bbox": [ + 218, + 77, + 366, + 199 + ], + "type": "image", + "image_path": "9d8a5bc729f0fe9b5d748a00a97a1bf7d37082e530481a49af64f5d28ae18d71.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 220, + 203, + 363, + 213 + ], + "lines": [ + { + "bbox": [ + 220, + 203, + 363, + 213 + ], + "spans": [ + { + "bbox": [ + 220, + 203, + 363, + 213 + ], + "type": "text", + "content": "(b) Influence of incremental trainable layers." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 389, + 73, + 542, + 199 + ], + "blocks": [ + { + "bbox": [ + 389, + 73, + 542, + 199 + ], + "lines": [ + { + "bbox": [ + 389, + 73, + 542, + 199 + ], + "spans": [ + { + "bbox": [ + 389, + 73, + 542, + 199 + ], + "type": "image", + "image_path": "cfba7eb682b88ade55f2629fac9a9c69661ebd1dd49a18c44f59b17aed723d01.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 410, + 204, + 519, + 213 + ], + "lines": [ + { + "bbox": [ + 410, + 204, + 519, + 213 + ], + "spans": [ + { + "bbox": [ + 410, + 204, + 519, + 213 + ], + "type": "text", + "content": "(c) Influence of hyper-parameters." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 53, + 247, + 130, + 323 + ], + "blocks": [ + { + "bbox": [ + 72, + 223, + 518, + 235 + ], + "lines": [ + { + "bbox": [ + 72, + 223, + 518, + 235 + ], + "spans": [ + { + "bbox": [ + 72, + 223, + 518, + 235 + ], + "type": "text", + "content": "Figure 6. Analysis of detailed hyper configurations of our method: fantasy methods, trainable layers, and hyper-parameters." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 53, + 247, + 130, + 323 + ], + "lines": [ + { + "bbox": [ + 53, + 247, + 130, + 323 + ], + "spans": [ + { + "bbox": [ + 53, + 247, + 130, + 323 + ], + "type": "image", + "image_path": "635547371ba45edbbb84ca4e45e5e3e29f4a8aaf4ad52b5645dfdadd071842ca.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 75, + 331, + 99, + 339 + ], + "lines": [ + { + "bbox": [ + 75, + 331, + 99, + 339 + ], + "spans": [ + { + "bbox": [ + 75, + 331, + 99, + 339 + ], + "type": "text", + "content": "(a) CE." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 133, + 247, + 211, + 324 + ], + "blocks": [ + { + "bbox": [ + 133, + 247, + 211, + 324 + ], + "lines": [ + { + "bbox": [ + 133, + 247, + 211, + 324 + ], + "spans": [ + { + "bbox": [ + 133, + 247, + 211, + 324 + ], + "type": "image", + "image_path": "dfcde936ae1e080a8156df750a468988eab708ab1ce1a1f8b45f9cc85b18f5a2.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 145, + 330, + 188, + 339 + ], + "lines": [ + { + "bbox": [ + 145, + 330, + 188, + 339 + ], + "spans": [ + { + "bbox": [ + 145, + 330, + 188, + 339 + ], + "type": "text", + "content": "(b) " + }, + { + "bbox": [ + 145, + 330, + 188, + 339 + ], + "type": "inline_equation", + "content": "\\mathrm{CE + SCL}" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 46, + 350, + 287, + 394 + ], + "lines": [ + { + "bbox": [ + 46, + 350, + 287, + 394 + ], + "spans": [ + { + "bbox": [ + 46, + 350, + 287, + 394 + ], + "type": "text", + "content": "Figure 7. The t-SNE [46] visualization on CIFAR100 dataset of the embeddings learned by various methods. Class 0-5 represent the base classes while class 6-8 represent the novel classes. Our SAVC gets the best class separation degree." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 214, + 247, + 291, + 324 + ], + "blocks": [ + { + "bbox": [ + 214, + 247, + 291, + 324 + ], + "lines": [ + { + "bbox": [ + 214, + 247, + 291, + 324 + ], + "spans": [ + { + "bbox": [ + 214, + 247, + 291, + 324 + ], + "type": "image", + "image_path": "4438a30d9abed828f711432d5c5b16c6bee078c7f611d6e9b07a2231dc3ad96d.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 230, + 330, + 264, + 339 + ], + "lines": [ + { + "bbox": [ + 230, + 330, + 264, + 339 + ], + "spans": [ + { + "bbox": [ + 230, + 330, + 264, + 339 + ], + "type": "text", + "content": "(c)SAVC." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 405, + 286, + 477 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 405, + 286, + 477 + ], + "spans": [ + { + "bbox": [ + 46, + 405, + 286, + 477 + ], + "type": "text", + "content": "serve that CE has limited clustering effects. Naïve SCL slightly improves the class separation degree, but implicitly decreases inter-class distance and remains the mutual class confusion problems unresolved. With the help of virtual classes, Our proposed SAVC can clearly separate base classes and incorporate novel classes with the least overlap." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 47, + 489, + 149, + 502 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 489, + 149, + 502 + ], + "spans": [ + { + "bbox": [ + 47, + 489, + 149, + 502 + ], + "type": "text", + "content": "4.5. Further Analysis" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 46, + 509, + 286, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 509, + 286, + 640 + ], + "spans": [ + { + "bbox": [ + 46, + 509, + 286, + 640 + ], + "type": "text", + "content": "Analysis of fantasy method. We consider 3 transformation sets " + }, + { + "bbox": [ + 46, + 509, + 286, + 640 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 46, + 509, + 286, + 640 + ], + "type": "text", + "content": " and show their impacts on our method in Fig. 6a. We observe that miniImageNet benefits from complex transformation more, while it would hurt the class separation on CUB200 and CIFAR100. We suppose that CUB200 is already a fine-grained dataset, and the learning process would be hindered by overly fine grains. As for CIFAR100, the image resolutions are too small, and it may be hard to make a distinction between the original and virtual classes. We put discussions on more types of fantasy methods in the appendix." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 46, + 642, + 286, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 286, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 286, + 714 + ], + "type": "text", + "content": "Analysis of trainable layers in incremental sessions. We update the trainable layers in incremental sessions on miniImageNet benchmark. The results are reported in Fig. 6b. The gray horizontal line indicates without incremental finetuning. It is observed that finetuning fewer and deeper layers gets higher accuracy, while finetuning too" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 246, + 545, + 293 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 246, + 545, + 293 + ], + "spans": [ + { + "bbox": [ + 304, + 246, + 545, + 293 + ], + "type": "text", + "content": "many layers harms the former knowledge and shows poorer performance. We infer that the low-level characteristics extracted by shallow layers are shared between classes, so we only need to update high layers to adapt to new classes." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "spans": [ + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "text", + "content": "Analysis of hyper-parameter. There are two main hyper-parameters in our loss, i.e., coefficient " + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "text", + "content": " which control the impact of global and local supervised contrastive loss in Eq. (10). We report the accuracy in the last session on miniImageNet dataset varying " + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "text", + "content": " in Fig. 6c. Our method is robust to the choices of hyper-parameters, and it achieves the best performance when " + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "inline_equation", + "content": "\\alpha = 0.2" + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "inline_equation", + "content": "\\beta = 0.8" + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "text", + "content": ". For convenience, we fix the value of " + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 304, + 293, + 545, + 388 + ], + "type": "text", + "content": " on all datasets." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 399, + 378, + 411 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 399, + 378, + 411 + ], + "spans": [ + { + "bbox": [ + 306, + 399, + 378, + 411 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 419, + 545, + 563 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 419, + 545, + 563 + ], + "spans": [ + { + "bbox": [ + 304, + 419, + 545, + 563 + ], + "type": "text", + "content": "In this paper, we solve the FSCIL problems by Semantic-Aware Virtual Contrastive framework (SAVC). Based on the mainstream incremental-frozen framework, both CE and SCL fail to separate base classes and new classes well. We propose to introduce more semantic knowledge by imagining virtual classes. These virtual classes not only hold enough feature space for future updates, but enable a multisemantic aggregated inference effect and significantly improve classification accuracy. By learning to recognize and contrast in the fantasy space, our method can maintain the separation of all classes and achieve SOTA performance on the three popular FSCIL benchmarks." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 306, + 574, + 403, + 586 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 574, + 403, + 586 + ], + "spans": [ + { + "bbox": [ + 306, + 574, + 403, + 586 + ], + "type": "text", + "content": "Acknowledgments" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 594, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 545, + 712 + ], + "type": "text", + "content": "This work is supported by grants from the KeyArea Research and Development Program of Guangdong Province (2020B0101380001), the National Key R&D Program of China (2022ZD0118101), the National Natural Science Foundation of China (61825101, 62202010, 62202014), Shenzhen Basic Research Program (JCYJ20220813151736001), China Postdoctoral Science Foundation (2022M710212), and Huawei Technologies Co., Ltd. The computing resources of Pengcheng Cloudbrain are used in this research." + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24190" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 92, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 53, + 92, + 287, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 92, + 287, + 135 + ], + "spans": [ + { + "bbox": [ + 53, + 92, + 287, + 135 + ], + "type": "text", + "content": "[1] Idan Achituve, Aviv Navon, Yochai Yemini, Gal Chechik, and Ethan Fetaya. Gp-tree: A gaussian process classifier for few-shot incremental learning. In ICML, pages 54-65. PMLR, 2021. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 138, + 288, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 138, + 288, + 183 + ], + "spans": [ + { + "bbox": [ + 53, + 138, + 288, + 183 + ], + "type": "text", + "content": "[2] Touqueer Ahmad, Akshay Raj Dhamija, Steve Cruz, Ryan Rabinowitz, Chunchun Li, Mohsen Jafarzadeh, and Terrance E Boult. Few-shot class incremental learning leveraging self-supervised features. In CVPRW, pages 3900-3910, 2022. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 184, + 288, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 184, + 288, + 228 + ], + "spans": [ + { + "bbox": [ + 53, + 184, + 288, + 228 + ], + "type": "text", + "content": "[3] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. In NeurIPS, pages 9912-9924, 2020. 3, 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 230, + 287, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 230, + 287, + 262 + ], + "spans": [ + { + "bbox": [ + 53, + 230, + 287, + 262 + ], + "type": "text", + "content": "[4] Francisco M Castro, Manuel J Marín-Jiménez, Nicolas Guil, Cordelia Schmid, and Karteek Alahari. End-to-end incremental learning. In ECCV, pages 233-248, 2018. 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 264, + 287, + 298 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 264, + 287, + 298 + ], + "spans": [ + { + "bbox": [ + 53, + 264, + 287, + 298 + ], + "type": "text", + "content": "[5] Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny. Efficient lifelong learning with a gem. arXiv preprint arXiv:1812.00420, 2018. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 300, + 287, + 332 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 300, + 287, + 332 + ], + "spans": [ + { + "bbox": [ + 53, + 300, + 287, + 332 + ], + "type": "text", + "content": "[6] Kuilin Chen and Chi-Guhn Lee. Incremental few-shot learning via vector quantization in deep embedded space. In ICLR, 2020. 1, 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 335, + 287, + 368 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 335, + 287, + 368 + ], + "spans": [ + { + "bbox": [ + 53, + 335, + 287, + 368 + ], + "type": "text", + "content": "[7] Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297, 2020. 3, 4" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 370, + 287, + 423 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 370, + 287, + 423 + ], + "spans": [ + { + "bbox": [ + 53, + 370, + 287, + 423 + ], + "type": "text", + "content": "[8] Ali Cheraghian, Shafin Rahman, Pengfei Fang, Soumava Kumar Roy, Lars Petersson, and Mehrtash Harandi. Semantic-aware knowledge distillation for few-shot class-incremental learning. In CVPR, pages 2534-2543, 2021. 1, 2, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 426, + 287, + 482 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 426, + 287, + 482 + ], + "spans": [ + { + "bbox": [ + 53, + 426, + 287, + 482 + ], + "type": "text", + "content": "[9] Ali Cheraghian, Shafin Rahman, Sameera Ramasinghe, Pengfei Fang, Christian Simon, Lars Petersson, and Mehrtash Harandi. Synthesized feature based few-shot class incremental learning on a mixture of subspaces. In ICCV, pages 8661-8670, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 483, + 287, + 526 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 483, + 287, + 526 + ], + "spans": [ + { + "bbox": [ + 48, + 483, + 287, + 526 + ], + "type": "text", + "content": "[10] Zhixiang Chi, Li Gu, Huan Liu, Yang Wang, Yuanhao Yu, and Jin Tang. Meta-scil: A meta-learning approach for few-shot class incremental learning. In CVPR, pages 14166-14175, 2022. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 529, + 287, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 529, + 287, + 563 + ], + "spans": [ + { + "bbox": [ + 48, + 529, + 287, + 563 + ], + "type": "text", + "content": "[11] Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In ICCV, pages 1422-1430, 2015. 2, 3, 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 564, + 287, + 609 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 564, + 287, + 609 + ], + "spans": [ + { + "bbox": [ + 48, + 564, + 287, + 609 + ], + "type": "text", + "content": "[12] Songlin Dong, Xiaopeng Hong, Xiaoyu Tao, Xinyuan Chang, Xing Wei, and Yihong Gong. Few-shot class-incremental learning via relation knowledge distillation. In AAAI, volume 35, pages 1255-1263, 2021. 1, 2, 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 610, + 287, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 610, + 287, + 643 + ], + "spans": [ + { + "bbox": [ + 48, + 610, + 287, + 643 + ], + "type": "text", + "content": "[13] Gamaleldin Elsayed, Dilip Krishnan, Hossein Mobahi, Kevin Regan, and Samy Bengio. Large margin deep networks for classification. In NeurIPS, 2018. 4" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 644, + 287, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 644, + 287, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 644, + 287, + 678 + ], + "type": "text", + "content": "[14] Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, pages 1126–1135. PMLR, 2017. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 680, + 287, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 287, + 714 + ], + "type": "text", + "content": "[15] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728, 2018. 2, 3, 5" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 34, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "text", + "content": "[16] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In CVPR, pages 9729-9738, 2020. 3, 4" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 118, + 545, + 151 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 151 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 151 + ], + "type": "text", + "content": "[17] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR, pages 770-778, 2016. 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "text", + "content": "[18] Michael Hersche, Geethan Karunaratne, Giovanni Cherubini, Luca Benini, Abu Sebastian, and Abbas Rahimi. Constrained few-shot class-incremental learning. In CVPR, pages 9057-9067, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 197, + 545, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 197, + 545, + 230 + ], + "spans": [ + { + "bbox": [ + 307, + 197, + 545, + 230 + ], + "type": "text", + "content": "[19] Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin. Learning a unified classifier incrementally via rebalancing. In CVPR, pages 831-839, 2019. 1, 6, 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 232, + 545, + 263 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 232, + 545, + 263 + ], + "spans": [ + { + "bbox": [ + 307, + 232, + 545, + 263 + ], + "type": "text", + "content": "[20] Zhong Ji, Zhishen Hou, Xiyao Liu, Yanwei Pang, and Xuelong Li. Memorizing complementation network for few-shot class-incremental learning. TIP, 2023. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 264, + 545, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 264, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 307, + 264, + 545, + 308 + ], + "type": "text", + "content": "[21] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. In NeurIPS, pages 18661-18673, 2020. 2, 3, 4" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 309, + 545, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 309, + 545, + 342 + ], + "spans": [ + { + "bbox": [ + 307, + 309, + 545, + 342 + ], + "type": "text", + "content": "[22] Simon Kornblith, Ting Chen, Honglak Lee, and Mohammad Norouzi. Why do better loss functions lead to less transferable features? In NeurIPS, pages 28648-28662, 2021. 4" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 343, + 545, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 343, + 545, + 365 + ], + "spans": [ + { + "bbox": [ + 307, + 343, + 545, + 365 + ], + "type": "text", + "content": "[23] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 366, + 545, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 366, + 545, + 399 + ], + "spans": [ + { + "bbox": [ + 307, + 366, + 545, + 399 + ], + "type": "text", + "content": "[24] Hankook Lee, Sung Ju Hwang, and Jinwoo Shin. Self-supervised label augmentation via input transformations. In ICML, pages 5714-5724. PMLR, 2020. 3, 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 399, + 545, + 422 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 399, + 545, + 422 + ], + "spans": [ + { + "bbox": [ + 307, + 399, + 545, + 422 + ], + "type": "text", + "content": "[25] Zhizhong Li and Derek Hoiem. Learning without forgetting. TPAMI, 40(12):2935-2947, 2017. 1, 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 423, + 545, + 455 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 423, + 545, + 455 + ], + "spans": [ + { + "bbox": [ + 307, + 423, + 545, + 455 + ], + "type": "text", + "content": "[26] Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li. Metasgd: Learning to learn quickly for few-shot learning. arXiv preprint arXiv:1707.09835, 2017. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 456, + 545, + 500 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 456, + 545, + 500 + ], + "spans": [ + { + "bbox": [ + 307, + 456, + 545, + 500 + ], + "type": "text", + "content": "[27] Huan Liu, Li Gu, Zhixiang Chi, Yang Wang, Yuanhao Yu, Jun Chen, and Jin Tang. Few-shot class-incremental learning via entropy-regularized data-free replay. In ECCV, pages 146–162. Springer, 2022. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 502, + 545, + 534 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 502, + 545, + 534 + ], + "spans": [ + { + "bbox": [ + 307, + 502, + 545, + 534 + ], + "type": "text", + "content": "[28] Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang. Large-margin softmax loss for convolutional neural networks. arXiv preprint arXiv:1612.02295, 2016. 4" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 307, + 536, + 545, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 536, + 545, + 579 + ], + "spans": [ + { + "bbox": [ + 307, + 536, + 545, + 579 + ], + "type": "text", + "content": "[29] Xialei Liu, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio M Lopez, and Andrew D Bagdanov. Rotate your networks: Better weight consolidation and less catastrophic forgetting. In ICPR, pages 2262-2268. IEEE, 2018. 2" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 307, + 581, + 545, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 581, + 545, + 634 + ], + "spans": [ + { + "bbox": [ + 307, + 581, + 545, + 634 + ], + "type": "text", + "content": "[30] Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D Bagdanov, and Joost van de Weijer. Class incremental learning: survey and performance evaluation on image classification. arXiv preprint arXiv:2010.15277, 2020.1" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 307, + 635, + 545, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 635, + 545, + 668 + ], + "spans": [ + { + "bbox": [ + 307, + 635, + 545, + 668 + ], + "type": "text", + "content": "[31] Pratik Mazumder, Pravendra Singh, and Piyush Rai. Few-shot lifelong learning. In AAAI, volume 35, pages 2337-2345, 2021. 2" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 307, + 670, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 670, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 307, + 670, + 545, + 712 + ], + "type": "text", + "content": "[32] Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka. Distance-based image classification: Generalizing to new classes at near-zero cost. TPAMI, 35(11):2624-2637, 2013. 3" + } + ] + } + ], + "index": 33 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "24191" + } + ] + } + ], + "index": 35 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "type": "text", + "content": "[33] Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 107, + 287, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 107, + 287, + 140 + ], + "spans": [ + { + "bbox": [ + 48, + 107, + 287, + 140 + ], + "type": "text", + "content": "[34] Boris Oreshkin, Pau Rodríguez López, and Alexandre Lacoste. Tadam: Task dependent adaptive metric for improved few-shot learning. In NeurIPS, 2018. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 142, + 287, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 142, + 287, + 185 + ], + "spans": [ + { + "bbox": [ + 48, + 142, + 287, + 185 + ], + "type": "text", + "content": "[35] Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. Context encoders: Feature learning by inpainting. In CVPR, pages 2536-2544, 2016. 2, 3, 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 188, + 287, + 209 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 188, + 287, + 209 + ], + "spans": [ + { + "bbox": [ + 48, + 188, + 287, + 209 + ], + "type": "text", + "content": "[36] Sachin Ravi and Hugo Larochelle. Optimization as a model for few-shot learning. In ICLR, 2017. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 212, + 287, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 212, + 287, + 255 + ], + "spans": [ + { + "bbox": [ + 48, + 212, + 287, + 255 + ], + "type": "text", + "content": "[37] Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert. icarl: Incremental classifier and representation learning. In CVPR, pages 2001–2010, 2017. 1, 2, 6, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 257, + 287, + 290 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 257, + 287, + 290 + ], + "spans": [ + { + "bbox": [ + 48, + 257, + 287, + 290 + ], + "type": "text", + "content": "[38] David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne. Experience replay for continual learning. In NeurIPS, 2019. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 292, + 287, + 345 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 292, + 287, + 345 + ], + "spans": [ + { + "bbox": [ + 48, + 292, + 287, + 345 + ], + "type": "text", + "content": "[39] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. IJCV, 115:211-252, 2015. 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 348, + 287, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 348, + 287, + 392 + ], + "spans": [ + { + "bbox": [ + 48, + 348, + 287, + 392 + ], + "type": "text", + "content": "[40] Guangyuan Shi, Jiaxin Chen, Wenlong Zhang, Li-Ming Zhan, and Xiao-Ming Wu. Overcoming catastrophic forgetting in incremental few-shot learning by finding flat minima. In NeurIPS, pages 6747-6761, 2021. 1, 2, 3, 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 394, + 287, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 394, + 287, + 448 + ], + "spans": [ + { + "bbox": [ + 48, + 394, + 287, + 448 + ], + "type": "text", + "content": "[41] Yujun Shi, Kuangqi Zhou, Jian Liang, Zihang Jiang, Jiashi Feng, Philip HS Torr, Song Bai, and Vincent YF Tan. Mimicking the oracle: an initial phase decorrelation approach for class incremental learning. In CVPR, pages 16722-16731, 2022. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 450, + 287, + 472 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 450, + 287, + 472 + ], + "spans": [ + { + "bbox": [ + 48, + 450, + 287, + 472 + ], + "type": "text", + "content": "[42] Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In NeurIPS, 2017. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 474, + 287, + 517 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 474, + 287, + 517 + ], + "spans": [ + { + "bbox": [ + 48, + 474, + 287, + 517 + ], + "type": "text", + "content": "[43] Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales. Learning to compare: Relation network for few-shot learning. In CVPR, pages 1199–1208, 2018. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 519, + 287, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 519, + 287, + 563 + ], + "spans": [ + { + "bbox": [ + 48, + 519, + 287, + 563 + ], + "type": "text", + "content": "[44] Xiaoyu Tao, Xiaopeng Hong, Xinyuan Chang, Songlin Dong, Xing Wei, and Yihong Gong. Few-shot class incremental learning. In CVPR, pages 12183-12192, 2020. 1, 2, 3, 6, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 565, + 287, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 565, + 287, + 597 + ], + "spans": [ + { + "bbox": [ + 48, + 565, + 287, + 597 + ], + "type": "text", + "content": "[45] Eleni Triantafillou, Richard Zemel, and Raquel Urtasun. Few-shot learning through an information retrieval lens. In NeurIPS, 2017. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 600, + 287, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 600, + 287, + 632 + ], + "spans": [ + { + "bbox": [ + 48, + 600, + 287, + 632 + ], + "type": "text", + "content": "[46] Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research, 9(11), 2008. 7, 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 635, + 287, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 635, + 287, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 635, + 287, + 678 + ], + "type": "text", + "content": "[47] Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc V Gool. Revisiting contrastive methods for unsupervised learning of visual representations. In NeurIPS, pages 16238-16250, 2021. 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 680, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 287, + 712 + ], + "type": "text", + "content": "[48] Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie. The caltech-ucsd birds-200-2011 dataset. 2011. 6" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 72, + 545, + 464 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 307, + 72, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 72, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 545, + 116 + ], + "type": "text", + "content": "[49] Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, and Tomas Pfister. Learning to prompt for continual learning. In CVPR, pages 139-149, 2022. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 118, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 150 + ], + "type": "text", + "content": "[50] Shipeng Yan, Jiangwei Xie, and Xuming He. Der: Dynamically expandable representation for class incremental learning. In CVPR, pages 3014-3023, 2021. 1, 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "text", + "content": "[51] Chi Zhang, Nan Song, Guosheng Lin, Yun Zheng, Pan Pan, and Yinghui Xu. Few-shot incremental learning with continually evolved classifiers. In CVPR, pages 12455-12464, 2021. 1, 2, 3, 6, 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 197, + 545, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 197, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 307, + 197, + 545, + 228 + ], + "type": "text", + "content": "[52] Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful image colorization. In ECCV, pages 649-666. Springer, 2016. 2, 3, 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 230, + 545, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 230, + 545, + 274 + ], + "spans": [ + { + "bbox": [ + 307, + 230, + 545, + 274 + ], + "type": "text", + "content": "[53] Yifan Zhang, Bryan Hooi, Dapeng Hu, Jian Liang, and Jiashi Feng. Unleashing the power of contrastive self-supervised visual models via contrast-regularized fine-tuning. In NeurIPS, pages 29848–29860, 2021. 4" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 275, + 545, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 275, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 307, + 275, + 545, + 308 + ], + "type": "text", + "content": "[54] Hanbin Zhao, Yongjian Fu, Mintong Kang, Qi Tian, Fei Wu, and Xi Li. Mgsvf: Multi-grained slow vs. fast framework for few-shot class-incremental learning. TPAMI, 2021. 2, 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 309, + 545, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 309, + 545, + 352 + ], + "spans": [ + { + "bbox": [ + 307, + 309, + 545, + 352 + ], + "type": "text", + "content": "[55] Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye, Liang Ma, Shiliang Pu, and De-Chuan Zhan. Forward compatible few-shot class-incremental learning. In CVPR, pages 9046–9056, 2022. 1, 3, 6, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 354, + 545, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 354, + 545, + 387 + ], + "spans": [ + { + "bbox": [ + 307, + 354, + 545, + 387 + ], + "type": "text", + "content": "[56] Da-Wei Zhou, Han-Jia Ye, Liang Ma, Di Xie, Shiliang Pu, and De-Chuan Zhan. Few-shot class-incremental learning by sampling multi-phase tasks. TPAMI, 2022. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 388, + 545, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 388, + 545, + 421 + ], + "spans": [ + { + "bbox": [ + 307, + 388, + 545, + 421 + ], + "type": "text", + "content": "[57] Fei Zhu, Zhen Cheng, Xu-Yao Zhang, and Cheng-lin Liu. Class-incremental learning via dual augmentation. In NeurIPS, pages 14306-14318, 2021. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 422, + 545, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 422, + 545, + 464 + ], + "spans": [ + { + "bbox": [ + 307, + 422, + 545, + 464 + ], + "type": "text", + "content": "[58] Kai Zhu, Yang Cao, Wei Zhai, Jie Cheng, and Zheng-Jun Zha. Self-promoted prototype refinement for few-shot class incremental learning. In CVPR, pages 6801-6810, 2021. 2, 3, 6, 7" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "24192" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking/fee95f26-a8a8-4c1d-9f44-c06f33970b8b_content_list.json b/2023/Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking/fee95f26-a8a8-4c1d-9f44-c06f33970b8b_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..92e72c51c8f8aa4c62d2fcc279e3ff04e093bbfe --- /dev/null +++ b/2023/Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking/fee95f26-a8a8-4c1d-9f44-c06f33970b8b_content_list.json @@ -0,0 +1,1817 @@ +[ + { + "type": "text", + "text": "Learning with Noisy labels via Self-supervised Adversarial Noisy Masking", + "text_level": 1, + "bbox": [ + 111, + 130, + 856, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yuanpeng $\\mathrm{Tu}^{1*}$ Boshen Zhang $^{2*}$ Yuxi Li $^{2}$ Liang Liu $^{2}$ Jian Li $^{2}$", + "bbox": [ + 202, + 179, + 766, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Jiangning Zhang² Yabiao Wang²† Chengjie Wang²,³ Cai Rong Zhao¹", + "bbox": [ + 181, + 198, + 785, + 217 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1Dept. of Electronic and Information Engineering, Tongji University, Shanghai", + "bbox": [ + 176, + 215, + 794, + 234 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "2YouTu Lab, Tencent, Shanghai, 3Shanghai Jiao Tong University", + "bbox": [ + 220, + 234, + 750, + 252 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{2030809，zhaocairong}@tongji.edu.cn", + "bbox": [ + 326, + 253, + 643, + 268 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{boshenzhang, yukiyxli, leoneliu, swordli, vtzhang, caseywang, jasoncjwang}@tencent.com", + "bbox": [ + 101, + 272, + 867, + 286 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 321, + 312, + 335 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate this problem via identifying and removing noisy samples or correcting their labels according to the statistical properties (e.g., loss values) among training samples. In this paper, we aim to tackle this problem from a new perspective, delving into the deep feature maps, we empirically find that models trained with clean and mistrabeled samples manifest distinguishable activation feature distributions. From this observation, a novel robust training approach termed adversarial noisy masking is proposed. The idea is to regularize deep features with a label quality guided masking scheme, which adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. Further, an auxiliary task is designed to reconstruct input data, it naturally provides noise-free self-supervised signals to reinforce the generalization ability of models. The proposed method is simple yet effective, it is tested on synthetic and real-world noisy datasets, where significant improvements are obtained over previous methods. Code is available at https://github.com/yuanpengtu/SANM.", + "bbox": [ + 75, + 354, + 470, + 700 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 731, + 207, + 746 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Deep learning has achieved remarkable success, relying on large-scale datasets with human-annotated accurate labels. However, collecting such high-quality labels is time-consuming and expensive. As an alternative, inexpensive strategies are usually used for generating labels for large-scale samples, such as web crawling, leveraging search engines, or using machine-generated annotations. All these", + "bbox": [ + 75, + 757, + 468, + 863 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/8d4bd35fab85ba2e38ae07afd0eb4dd0865f3e85b92a1da01d00f955cf066a03.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 504, + 321, + 558, + 363 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/c5a3240a76f155b7daf58ba9769642981be2a78478f39ad0e7116b0fe6e3c53a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 563, + 321, + 616, + 364 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/0a33b35fe9dc6f958e6718d55aebb889fec3bce05d13a15f4abc67c6bd540ea1.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 504, + 364, + 557, + 407 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/969ee81f27629586b384a274ecd4f6a7755d06e9c04b89dd54c30ffe41f880a0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 563, + 367, + 616, + 407 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/96a6e70c829cfea2ddfb3011b41890d279bca7d2b134071a1514dd0d76bee9e4.jpg", + "image_caption": [ + "(a) Clean-trained Mis-predicted" + ], + "image_footnote": [], + "bbox": [ + 504, + 410, + 555, + 450 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/ea2c1fe08c589a9b65509289c4d06b2e65ecaa15d04c73a0a79b956f7b07451d.jpg", + "image_caption": [ + "Figure 1. Activation maps of mis-predicted (a-b) and correctly-predicted (c) samples when training PreAct ResNet-18 with clean (i.e., clean-trained) and noisy (i.e., noisy-trained) data on CIFAR-10 (1st and 2nd row) and Clothing1M [38] (3rd row)." + ], + "image_footnote": [], + "bbox": [ + 563, + 410, + 616, + 449 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/1655adbf0782bbefedfd52e0342ac0ed1650deca53d21d23c94b2c8e1d56c225.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 638, + 321, + 689, + 363 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/109f164380a852ec6f9c25573c55cf06933ebfb63cb41fb02fc55faffd524a91.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 696, + 321, + 750, + 363 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/2f7a8b025beca001b4aa424f13c1c95a573fddb0a630e41f7309e32e1f03a24d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 638, + 367, + 689, + 407 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/072f7ba010ad9cfe4d3e0e244210b6f4a5ca0ee31b239afb79b416d2f651adef.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 696, + 367, + 750, + 407 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/3da8e92445975de0b70d38cb559abb194e3c5860930e9551161407b368bee1a8.jpg", + "image_caption": [ + "(b) Noisy-trained Mis-predicted" + ], + "image_footnote": [], + "bbox": [ + 638, + 410, + 689, + 449 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/07f1f8ea1049e309594bf4212bfb0592289ad084177490b60f06cb088b89548f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 696, + 410, + 750, + 448 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d2ed929b9fdc3b2cd0ba998f011c1d3079ea1b7beeb78eed8aec056b697f8621.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 772, + 321, + 823, + 363 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/ef52a15d1265a9306d7c35570248901526ba7c220a805be4116db916f5adadc7.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 828, + 321, + 882, + 363 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/c9a9b6a87e31d1b7897e240c4230e42404443a362e40882d4511b618af12a561.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 772, + 367, + 823, + 407 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/fad8268d672a74f6efb28e34d3c67e8993f941a238232158d13da19deec7d10a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 830, + 367, + 882, + 407 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/0febccb808759dc20ff5db891783eb283d73a14938fdbe3d28ebda19499cc4e2.jpg", + "image_caption": [ + "(c) Noisy-trained Correctly-predicted" + ], + "image_footnote": [], + "bbox": [ + 772, + 410, + 823, + 449 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/3320e84ca9307b47f48886e3fafd5ac9bbde467f069b9c44a2cc2655625a2d60.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 828, + 410, + 882, + 449 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "alternative methods inevitably yield numerous noisy samples. However, previous research [2] has revealed that deep networks can easily overfit to noisy labels and suffer from dramatic degradation in the generalization performance.", + "bbox": [ + 496, + 536, + 890, + 595 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Towards this problem, numerous Learning with Noisy labels (LNL) approaches have been proposed. Sample selection methods [3, 8, 34, 41] are the most straightforward methods, which attempt to select clean samples based on certain criterion (e.g., loss value) and then reweight or discard the noisy instances to reduce the interference of mislabeled training samples. However, these methods fail to leverage the potential information of the discarded samples. Similarly, label correction based methods [32, 40, 48] try to correct labels, which often impose assumptions on the existence of a small correctly-labeled validation subset or directly utilize predictions of deep models. However, such assumptions can not be always full-filled in real-world noisy datasets [27], leading to limited application scenarios. Besides, the predictions of deep model tend to fluctuate when training with mislabeled samples, making the label correction process unstable and sub-optimal [36]. On the contrary, regularization based methods [2, 6, 15, 20, 26, 29, 43, 47, 50] aim to alleviate the side effect of label noise by preventing the deep models from overfitting to all training sam", + "bbox": [ + 496, + 598, + 892, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* Equal contribution.", + "bbox": [ + 99, + 875, + 214, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "† Corresponding author.", + "bbox": [ + 102, + 888, + 230, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "16186", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "ples, which is flexible and can work collaboratively with other LNL methods. Both explicit and implicit regularization techniques were proposed, the former generally calibrates the parameter update of networks by modifying the expected training loss, i.e., dropout. The latter focuses on improving the generalization by utilizing the stochasticity, i.e., data augmentation strategy and stochastic gradient descent [29]. However, most of these methods are designed for general fully-supervised learning tasks with correctly-labeled samples, and poor generalization ability could be obtained when the label noise is severe [29].", + "bbox": [ + 75, + 90, + 472, + 257 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we present a novel regularization based method specialized for LNL problem. Our method is inspired by an observation that some differences exist in activation maps between the predictions of deep models trained with clean and noisy labels. As shown in Fig. 1 (a), activation maps of mis-predicted samples by the model trained with clean labels are always focused on their foreground areas. By contrast, when the model is trained with noisy labels, it tends to generate results focused on the meaningless background area for the mis-predicted samples (Fig. 1 (b)). And even for cases that the prediction is correct, i.e., Fig. 1 (c), the high-activated region drifts to irrelevant (i.e., edge) areas of the object, rather than the main regions of the object. This indicates that model trained with mistrabeled samples is likely to overfit to some corner parts of the object from noisy data or remember the less-informative regions (i.e., background). Therefore, from this observation, we propose a novel Self-supervised Adversarial Noisy Masking (SANM) method for LNL. The idea is to explicitly regularize the model to generate more diverse activation maps through masking the certain region (e.g., max-activated) of the input image and thus alleviates the confirmation bias [8].", + "bbox": [ + 75, + 260, + 472, + 607 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Moreover, to investigate how the masking strategies affect the performance of training deep models with noisy labels, we conduct a simple experiment, where different adversarial masking strategies (i.e., masking the max-activated region of input images with fixed, random, and noise-aware mask ratio) were applied to the popular LNL framework DivideMix [15]. As shown in Fig. 2, we find that the performance gain is much more sensitive to the masking strategy. A fixed or random mask ratio obtain only marginal gain, but when dealing with the correctly-labeled and mislabeled samples with different mask ratios, a significant performance gain is achieved. This indicates that how to design an effective adversarial masking method in the case of LNL is non-trivial and not well addressed.", + "bbox": [ + 75, + 609, + 472, + 821 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Towards the problem above, a label quality guided adaptive masking strategy is proposed, which modulates the image label and the mask ratio of image masking simultaneously. The label is updated via first leveraging the soft-distributed model prediction and then reducing the proba", + "bbox": [ + 75, + 824, + 472, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "bility of max-activated class with the noise-aware masking ratio, while at the same time lifting the probability of other classes. Intuitively, a sample with a high probability of being mislabeled will possess a larger masking ratio, leading to a more strictly regularized input image and modulated label. Therefore, the negative impact of noisy labels can be greatly reduced. As for the correctly-labeled samples, the masking ratio is relatively small, which plays the role of general regularization strategy and improves the model generalization ability by preventing the model from overfitting training samples. Further, we specially customized an auxiliary decode branch to reconstruct the original input image, which provides noise-free self-supervised information for learning a robust deep model. The proposed SANM is flexible and can further boost existing LNL methods. It is tested on synthetic and real-world large-scale noisy datasets, and elaborately ablative experiments were conducted to verify each design component of the proposed method. In a nutshell, the key contributions of this work are:", + "bbox": [ + 496, + 90, + 893, + 378 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a novel self-supervised adversarial noisy masking method named SANM to explicitly impose regularization for LNL problem, preventing the model from overfitting to some corner parts of the object or less-informative regions from noisy data;", + "- A label quality guided masking strategy is proposed to differently adjust the process for clean and noisy samples according to the label quality estimation. This strategy modulates the image label and the ratio of image masking simultaneously;", + "- A self-supervised mask reconstruction auxiliary task is designed to reconstruct the original images based on the features of masked ones, which aims at enhancing generalization by providing noise-free supervision signals." + ], + "bbox": [ + 496, + 378, + 893, + 592 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 608, + 640, + 625 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Learning with Noisy Labels", + "text_level": 1, + "bbox": [ + 500, + 633, + 750, + 650 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "A variety of methods have been proposed to improve the robustness of DNNs on noisy datasets, which consist of regularization, label correction, and noisy sample selection.", + "bbox": [ + 496, + 657, + 890, + 703 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Regularization based methods help resist memorizing noisy data since previous works [2] have revealed that networks always firstly learn simple patterns before memorizing noisy hard data. Previous research [29] claims that these methods can be roughly divided into two categories: explicit/implicit regularization. Explicit regularization based methods generally modify the losses for the training samples. Among them, ELR [20] tries to alleviate the influence of noisy data by regularizing the loss gradient. SR [50] proposes a spare regularization method to restrict network output to the permutation set over a one-hot vector. Meta learning based methods [26] impose regularization on the sample gradient directions by bi-level optimization with a small un-", + "bbox": [ + 496, + 704, + 893, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "16187", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/d9f7e7760fc27be3fb34c6179dbf7fc31648c8ce4fe0933b6266a37d6f9bf39e.jpg", + "image_caption": [ + "Figure 2. A experiment for masking the max-activated region with different mask ratios. The performance gains of different mask strategies under $50\\%$ and $80\\%$ symmetric noise of CIFAR-10/100 [14] are reported, where DivideMix [15] is adopted as the baseline. \"Fixed(0.2/0.3)\" denotes masking all the images with the same mask ratio of 0.2/0.3. \"Random\" represents masking images with a random mask ratio between 0.2 and 0.4. \"Noise-aware\" is masking noisy samples with a mask ratio of 0.3 while the ratio for clean ones is 0.2." + ], + "image_footnote": [], + "bbox": [ + 80, + 88, + 282, + 209 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/0ae134af4b1e650eb094c2b8960f9b00e1ddc14124550f7990b4a3e3c4759f42.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 282, + 88, + 483, + 209 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/f57a73def6e20f2d396e0ee9762818de7533667b15fead30d3824fde8a990273.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 483, + 88, + 686, + 209 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/61afd408f6f53f57f3faa14b04f3d1f56a9ef09e1834e6239af837af8238c418.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 686, + 88, + 888, + 209 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "biased validation. In addition to these regularizers, example re-weighting strategies are also popular explicit regularization solutions to the over-fitting problem. However, these methods often need sensitive hyper-parameters or deeper networks to improve the model capacity. For implicit regularization methods, they generally utilize the stochasticity effect, i.e., augmentation and stochastic gradient descent. Among them, semi-supervised based LNL methods [15,47] generally use mixup [43] as an effective regularization strategy to hinder over-fitting, which makes the network favor simple linear behavior in-between training examples. Adversarial samples based methods [6] improve the robustness by helping the model classify original and perturbed inputs. Since the model is unable to distinguish right from wrong by itself, regularization can generally work surprisingly well in certain synthetic noisy cases.", + "bbox": [ + 75, + 271, + 472, + 513 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Label correction methods seek to transform noisy labels to correct ones directly. The pseudo labeling strategy is commonly adopted by these methods, which utilizes the confident output of the network. Joint [32] optimizes labels and network parameters in an alternating strategy. PENCIL [40] corrects and updates labels probabilistically through the back-propagation mechanism. MLC [48] trains a label correction network through meta learning to generate pseudo labels for the training of main models. However, these approaches either need extra clean validation data or suffer from significant performance drop under high noise settings due to the low quality of generated pseudo labels.", + "bbox": [ + 75, + 518, + 468, + 699 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Noisy sample selection approaches mostly exploit the small-loss criterion, which is based on the observation that samples with noisy labels tend to have much higher losses than the clean ones. Among these methods, MentorNet uses a pre-trained teacher model to select clean samples for the training of a student model. Co-teaching based methods [8, 34, 41, 42] update the parameters of two parallel networks with clean instances chosen from their peer networks, which target avoiding the confirmation bias problem. INCV [3] attempts to separate clean data from the noisy training set by using cross-validation and removing high-loss samples. Though these methods may reduce the risk of error correction, they fail to use the potential infor", + "bbox": [ + 75, + 704, + 470, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "mation of the abandoned samples. Moreover, most of them need prior knowledge, i.e., the noise rate.", + "bbox": [ + 498, + 272, + 890, + 301 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Masking Regularization Techniques", + "text_level": 1, + "bbox": [ + 500, + 316, + 812, + 333 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "As a commonly adopted strategy in weakly-supervised semantic segmentation, adversarial masking regularization aims at helping models discover less apparent semantic objects. Specifically, a Class Activation feature Map (CAM) [49] branch is first used to generate initial attention maps and selectively mask the discovered areas in the images through a threshold. Then the masked images are used for the training of another network. AE-PSL [35] iteratively erases the images and expands target object regions with the erased areas to produce final contiguous segmentation masks. Self-erasing [12] utilizes the background prior to make models generate more refined and focused attention on the foreground area. GAIN [18] tries to impose constraints on the activation maps in a regularized style to generate more complete attention regions. PSA [30] trains two networks with opposing losses to eliminate the drawbacks of certain mask strategies. In addition to weakly-supervised semantic segmentation, adversarial masking is also used in fine-grained visual classification. DB [31] designs a diversification block to mask out the most salient features, aiming at forcing networks to mine subtle differences between images. WS-DAN [13] randomly crop and drop one of the discriminative regions in the attention maps to encourage models to mine local features. Several adversarial masking strategies are proposed in [7, 39] to preserve human privacy by masking the minimal number of pixels required for identification. [33] designs an adversarial discriminator for mask generation at inference to correct segmentation errors. However, the purpose of these methods is quite different from our approach. Based on the assumption that the samples are all correctly-labeled, previous methods generally retain the original most salient regions while helping models mine more subtle information through masking regularization. By contrast, our method aims to regularize the model through adversarial masking to make the activation map more dispersed rather than concentrated to specific regions, and a similar masking strategy is applied to gener", + "bbox": [ + 496, + 340, + 890, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "16188", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "ate more smooth pseudo labels, thus avoiding over-fitting to noisy labels and alleviating the confirmation bias.", + "bbox": [ + 76, + 90, + 468, + 121 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 76, + 135, + 166, + 150 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Overview. For convenience, we begin by introducing the notations. Consider LNL as a classification task, the class number is $c$ . The noisily-labeled training dataset is denoted as $D = \\{(x_i,y_i)\\}_{i=1}^N$ , where $x_i \\in \\mathbb{R}^{H_i \\times W_i \\times 3}$ and $y_i \\in \\{0,1\\}^c$ are the $i$ -th sample and its noisy label, $H_i, W_i$ are the height and width of samples. $N$ represents the dataset size. The encoder and decoder models are denoted as $M_E$ and $M_D$ respectively. We firstly introduce the overall framework of the proposed SANM. As shown in Fig. 3, a label quality guided masking scheme is proposed to adaptively perform adversarial noisy masking for each sample where a contrastive regularization strategy is imposed on the noisy predictions as well. Afterward, the representations of the masked images are used for the self-supervised reconstruction of original images, which aims at preventing models from overfitting to the noisy labels by providing noise-free signals. For application, SANM can be easily integrated with existing LNL frameworks to further boost their performance.", + "bbox": [ + 75, + 161, + 472, + 449 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.1. Adversarial Noisy Masking", + "text_level": 1, + "bbox": [ + 76, + 458, + 323, + 474 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Adversarial Masking Generation. To highlight the semantic regions of correctly labeled samples and keep the captured attention areas of the noisy samples from expanding to meaningless background regions, we propose a novel Adversarial noisy Masking Generation (AMG) strategy during the training process. Specifically, we begin by extracting the feature of image $x_{i}$ through $M_E$ and the obtained feature map and prediction are denoted as $F_{i} \\in \\mathbb{R}^{H_{F} \\times W_{F} \\times N_{C}}$ and $\\widetilde{y}_{i}$ respectively, where $H_{F}, W_{F}, N_{C}$ are the height, width and channel number of the feature layer. For each sample, a two-component Gaussian Mixture Model (GMM) [25] is fit to the cross-entropy loss with the Expectation-Maximization algorithm, where the generated weight for $x_{i}$ is denoted as $G_{i}$ . Afterward, $F_{i}$ is used to generate the activation maps [49] $A_{i} \\in \\mathbb{R}^{H_{i} \\times W_{i}}$ of the category with the largest prediction, which is derived as:", + "bbox": [ + 75, + 481, + 468, + 723 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nA _ {i} = \\operatorname {C A M} \\left(F _ {i}, \\arg \\max \\left(\\tilde {y} _ {i}\\right)\\right) \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 736, + 468, + 753 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathrm{CAM}(\\cdot)$ is the activation map generation function. Then the coordinates with the maximum and minimum activation values for $x_{i}$ are recorded as $\\{h_i^{\\max},w_i^{\\max}\\}$ and $\\{h_i^{\\min},w_i^{\\min}\\}$ respectively. For the $\\{h_i^{\\max},w_i^{\\max}\\}$ , assuming that the GMM division is overall correct, it is believed that the activation maps of correct-labeled samples are mainly focused on the target area of the foreground, while due to the interference of noisy labels, the max-activated region in the activation maps of mislabeled sam", + "bbox": [ + 76, + 763, + 468, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "plies are mostly concentrated in meaningless background areas. To breakdown the over-fitting to noisy labels, we intend to adversarially augment training samples via a novel making strategy. In particular, denote the loss function as $\\mathcal{L}(x_i,y_i)$ , we try to maximize $\\mathcal{L}$ by adaptively generating region perturbations for noisy and clean samples across the training process. The objective can be derived as $\\max \\mathcal{L}(x_i + \\sigma ,y_i)$ , where $\\sigma$ is the generated perturbations. Specifically, we generate masks with larger ratios for the max-activated region of noisy samples to force the model to reduce its response to these areas and pay more attention to the rest area, where the target is more likely to be found. For the correct-labeled ones, high response areas in their activation maps are likely to exist in the edge area of the object since they are affected by the gradient of noisy samples. Thus smaller mask ratios are used for the max-activated regions of them, which keep the model from ignoring the masked area and force it to mine discriminative information from the rest areas of the target since the correct labels help limit the network to pay more attention to the foreground area. However, it will make the activation map of the well-trained model scattered to the meaningless area when filtering out nearly all the peak locations for each sample through a threshold like existing methods, which violates our purpose. Therefore, we choose to mask random rectangular areas based on recorded coordinates. Specifically, the mask ratio $r_i$ for $x_{i}$ is calculated as follows:", + "bbox": [ + 496, + 90, + 893, + 500 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nr _ {i} = \\mu \\times (1 - G _ {i}) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 630, + 511, + 890, + 527 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mu$ is the basic mask ratio. The more likely a sample is to be noisy, the smaller the $G_{i}$ is. Based on Eq. (2), the greater mask ratio will be adopted for this sample, thus stronger regularization will be imposed [15]. Subsequently, the aspect of the masked area $\\delta_{i}$ is generated as follows:", + "bbox": [ + 496, + 539, + 893, + 614 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\delta_ {i} \\sim \\operatorname {U n i f o r m} (\\delta , \\frac {1}{\\delta}) \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 622, + 626, + 890, + 655 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\delta$ is a hyper-parameter for generating the aspect. Based on $r_i$ and $\\delta_{i}$ , the height and width of the masked area are given by:", + "bbox": [ + 496, + 664, + 890, + 710 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\left\\{ \\begin{array}{r l} h _ {i} ^ {u p} & = \\max \\left(h _ {i} ^ {\\max } - \\sqrt {\\frac {\\left(H _ {x} \\times W _ {x}\\right) \\times r _ {i} \\times \\delta_ {i}}{4}}, 0\\right) \\\\ h _ {i} ^ {\\mathrm {d n}} & = \\min \\left(h _ {i} ^ {\\max } + \\sqrt {\\frac {\\left(H _ {x} \\times W _ {x}\\right) \\times r _ {i} \\times \\delta_ {i}}{4}}, H _ {x}\\right) \\\\ w _ {i} ^ {\\mathrm {l t}} & = \\max \\left(w _ {i} ^ {\\max } - \\sqrt {\\frac {\\left(H _ {x} \\times W _ {x}\\right) \\times r _ {i}}{4 \\delta_ {i}}}, 0\\right) \\\\ w _ {i} ^ {\\mathrm {r t}} & = \\min \\left(w _ {i} ^ {\\max } + \\sqrt {\\frac {\\left(H _ {x} \\times W _ {x}\\right) \\times r _ {i}}{4 \\delta_ {i}}}, W _ {x}\\right) \\end{array} \\right. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 720, + 890, + 847 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The selected locations in the masked area of $x_{i}$ are assigned random values generated from a uniform distribution. Therefore, the adversarial noisy masked image $x_{i}^{m}$", + "bbox": [ + 496, + 854, + 890, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "16189", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/abe64279038ef48d4752e743d5b013142dd0b4c8895a0b6547c17d9310e8f72b.jpg", + "image_caption": [ + "Figure 3. The framework of SANM. Three components are included: AMG (Adversarial Masking Generation), NLR (Noisy Label Regularization), SMR (Self-supervised Masking Reconstruction). In AMG and NLR, firstly feed the images to an encoder and generate activation maps. And a label quality guided adversarial masking strategy is proposed to modulate the images and noisy labels simultaneously. Further, an auxiliary decode branch is designed in SMR to reconstruct input images from the features of masked images. Finally, the generated modulated images and labels of SANM together with the reconstruction loss can be directly adopted for existing LNL frameworks." + ], + "image_footnote": [], + "bbox": [ + 98, + 89, + 872, + 280 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "can be derived as follows:", + "bbox": [ + 76, + 362, + 251, + 376 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nx _ {i} ^ {m} (m, n) = \\left\\{ \\begin{array}{l l} U (0, 1), & \\text {i f} m \\in \\left[ h _ {i} ^ {u p}, h _ {i} ^ {d n} \\right], n \\in \\left[ w _ {i} ^ {l t}, w _ {i} ^ {r t} \\right], \\\\ x _ {i} (m, n), & \\text {o t h e r w i s e .} \\end{array} \\right. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 76, + 387, + 483, + 433 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $U$ denotes the Uniform distribution. Similar operation is performed on the regions of minimum activation value with the coordinates $\\{h_i^{min},w_i^{min}\\}$ as well. Different from Eq. (2), since the regions of minimum activation value for correct-labeled samples are mainly focused on the background, a smaller mask ratio is adopted for them, helping models narrow the search scope for targets. Therefore, we set the mask ratio of the regions with minimum activation values as $\\mu \\times G_{i}$ .", + "bbox": [ + 75, + 433, + 468, + 569 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Noisy Label Regularization. To penalize the overconfident predictions of the network, a contrastive Noisy Label Regularization (NLR) strategy is designed. Specifically, the generated $x_{i}^{m}$ is fed into $M_{E}$ to obtain the prediction $\\widetilde{y_{i}^{m}}$ and feature $\\mathbf{f}_{\\mathbf{i}}^{m}$ as follows:", + "bbox": [ + 75, + 570, + 468, + 648 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {f} _ {\\mathbf {i}} ^ {m} = M _ {E} \\left(x _ {i} ^ {m}; \\theta_ {E}\\right) \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 202, + 661, + 468, + 679 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\theta_{E}$ denotes the parameters of the encoder $M_{E}$ . Specifically, we give the pseudo labels of $x_{i}^{m}$ with the predictions of $x_{i}$ . Since the mask generation is based on the activation maps of the class with the largest predictions, the corresponding prediction is penalized with the mask ratio $r_{i}$ . Besides, there exists an equal possibility for the masked region to be predicted as each class. Thus the prediction values for all the classes are increased uniformly. To sum up, the regularized pseudo label $y_{i}^{r}$ for $x_{i}^{m}$ can be derived as follows:", + "bbox": [ + 75, + 691, + 470, + 843 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\ny _ {i} ^ {r} (j) = \\left\\{ \\begin{array}{l l} y _ {i} (j) - r _ {i} + r _ {i} / c, & \\text {i f} j = \\operatorname {a r g m a x} \\left(y _ {i} (j)\\right) \\\\ y _ {i} (j) + r _ {i} / c, & \\text {o t h e r w i s e} \\end{array} , \\right. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 84, + 854, + 468, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $0 \\leq j < c$ . Afterward, $y_i^r$ and $\\widetilde{y_i^{m}}$ are used for supervised loss calculation as:", + "bbox": [ + 498, + 359, + 890, + 391 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {c}} = \\mathcal {L} _ {\\mathrm {c e}} \\left(\\widetilde {y _ {i} ^ {m}}, y _ {i} ^ {r}\\right) \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 633, + 402, + 890, + 422 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{\\mathrm{ce}}$ denotes the cross-entropy loss.", + "bbox": [ + 503, + 434, + 779, + 450 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.2. Self-supervised Masking Reconstruction", + "text_level": 1, + "bbox": [ + 500, + 453, + 844, + 469 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Inspired by [11], a Self-supervised Masking Reconstruction branch (SMR) is designed to impose implicit regularization by providing extra noise-free supervised signal. Specifically, the decoder $M_D$ reconstructs the original image from the latent representation of the masked image $x_i^m$ by predicting the pixel values for the masked patch, which is defined as:", + "bbox": [ + 496, + 477, + 890, + 583 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nx _ {i} ^ {r} = M _ {D} \\left(\\mathbf {f} _ {\\mathbf {i}} ^ {m}; \\theta_ {D}\\right) \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 625, + 595, + 890, + 612 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\theta_{D}$ is the parameters of the decoder model $M_{D}$ . Then the mean squared error (MSE) loss is computed between the reconstructed image $x_{i}^{r}$ and the original unmasked image $x_{i}$ in the pixel space, which is shown as follows:", + "bbox": [ + 496, + 625, + 890, + 686 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {r}} = \\left\\| x _ {i} ^ {r} - x _ {i} \\right\\| ^ {2} \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 635, + 698, + 890, + 715 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this way, a challenging self-supervised pretext task that requires holistic understanding beyond low-level image statistics is created for the model. This supervision signal is not only free of interference from noisy labels but can also enhance the model generalization ability. Therefore the overall loss function for SANM is constructed as follows:", + "bbox": [ + 496, + 728, + 890, + 818 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {t r a i n}} = \\mathcal {L} _ {\\mathrm {c}} + \\beta \\mathcal {L} _ {\\mathrm {r}} \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 629, + 832, + 890, + 848 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\beta$ is a parameter to balance the weight of two loss terms. Putting this all together, Algorithm 1 delineates the proposed SANM. Firstly, the original images are masked", + "bbox": [ + 496, + 854, + 890, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "16190", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/8483af1a979678825e0a235e7908889a7b1aa6b957fe878c357cf8683a60c01b.jpg", + "table_caption": [ + "Table 1. Comparison with state-of-the-art methods on CIFAR-10/100 datasets with symmetric noise." + ], + "table_footnote": [], + "table_body": "
DatasetCIFAR-10CIFAR-100
Method/Noise ratio20%50%80%90%20%50%80%90%
Cross-Entropy (CE)86.879.462.942.762.046.719.910.1
Co-teaching+ [41]89.585.767.447.965.651.827.913.7
Mixup [43]95.687.171.652.267.857.330.814.6
PENCIL [40]92.489.177.558.969.457.531.115.3
Meta-Learning [16]92.989.377.458.768.559.242.419.5
M-correction [1]94.092.086.869.173.966.148.224.3
DivideMix [15]96.194.693.276.077.374.660.231.5
C2D [47]96.395.294.493.578.676.467.758.7
AugDesc [22]96.395.493.891.979.577.266.441.2
GCE [5]90.089.373.936.568.153.322.18.9
Sel-CL+ [19]95.593.989.281.976.572.459.648.8
MOIT+ [23]94.191.881.174.775.970.647.641.8
SANM(DivideMix)96.495.894.692.381.278.268.743.5
SANM(C2D)96.696.495.795.181.979.371.661.9
", + "bbox": [ + 143, + 104, + 820, + 380 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "with the label quality guided masking scheme, where mislabeled and correct-labeled samples are modulated adaptively. Meanwhile, the predictions of the original images are used for regularized label generation. Then the regularized features of the masked images are utilized for self-supervised masking reconstruction, which provides noise-free supervisions. Finally, SANM can be plugged into existing LNL frameworks to further boost performance.", + "bbox": [ + 75, + 392, + 468, + 512 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 537, + 207, + 555 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Datasets and Implementation Details", + "text_level": 1, + "bbox": [ + 76, + 566, + 395, + 583 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Simulated noisy datasets. We evaluate on two simulated noisy datasets: CIFAR-10/100 [14], which consists of 50k training samples and 10k test samples. Following previous work [15], two simulated noisy settings are considered: symmetric and asymmetric noise. Following noise rates are considered: $\\tau \\in \\{20\\%, 50\\%, 80\\%, 90\\}$ for symmetric noise and $\\tau \\in \\{20\\%, 40\\%$ for asymmetric noise. An 18-layer PreAct ResNet is used and we train for 300 epochs with a batch size of 64 and SGD optimizer. The initial learning rate is set as 0.02 and is reduced by a factor of 10 after 150 epochs. $\\beta$ is empirically set as 1.", + "bbox": [ + 75, + 594, + 468, + 760 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Real-world noisy datasets. We also validate our method on real-world noisy datasets Clothing-1M [38] and Animal10N [28], the former contains 1 million training samples with nearly $40\\%$ mislabeled samples, the latter contains 55,000 human-labeled online images for 10 confusing animals. It includes approximately $8\\%$ noisy-labeled samples. The original protocol is used to split the training and test images. We strictly follow the previous experimental settings, more details can be found in the supplementary materials.", + "bbox": [ + 75, + 763, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Algorithm 1 The proposed SANM framework", + "text_level": 1, + "bbox": [ + 500, + 391, + 808, + 407 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Input: Noisy training set $D$ , encoder model $M_E(\\cdot;\\theta_E)$ , decoder model $M_D(\\cdot;\\theta_D)$ , batch size $b$ , max iterations $m$ , basic mask ratio $\\mu$ .", + "bbox": [ + 500, + 411, + 890, + 436 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Procedure:", + "text_level": 1, + "bbox": [ + 504, + 436, + 568, + 446 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "1: for $i = 1$ to $m$ do", + "bbox": [ + 506, + 448, + 622, + 458 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "2: $\\{x_{i},y_{i}\\}_{i = 1}^{b}\\gets$ SampleMiniBatch(D,b).", + "bbox": [ + 506, + 459, + 758, + 473 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "3: Feed $\\{x_i\\}_{i=1}^b$ into $M_E$ and generate feature maps $\\{F_i\\}_{i=1}^b$ and predictions $\\{\\widetilde{y}_i\\}_{i=1}^b$ .", + "bbox": [ + 506, + 473, + 890, + 498 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4: Generate activation maps $\\{A_{i}\\}_{i = 1}^{b}$ by Eq. (1).", + "bbox": [ + 506, + 500, + 785, + 512 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5: Calculate mask ratios $\\{r_i\\}_{i = 1}^b$ and adversarial masked images $\\{x_{i}^{m}\\}_{i = 1}^{b}$ by Eq. (2-5).", + "bbox": [ + 506, + 512, + 890, + 539 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "6: Feed $\\{x_i^m\\}_{i = 1}^b$ into $M_E$ and generate predictions $\\{y_i^{\\overline{m}}\\}_{i = 1}^b$ and features $\\{\\mathbf{f}_i^m\\}_{i = 1}^b$ by Eq. (6).", + "bbox": [ + 506, + 539, + 890, + 566 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "7: Calculate the regularized labels $\\{y_i^r\\}_{i = 1}^b$ by Eq. (7).", + "bbox": [ + 506, + 566, + 813, + 580 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "8: Calculate cross-entropy loss $\\mathcal{L}_c$ by Eq. (8).", + "bbox": [ + 506, + 580, + 766, + 590 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "9: Feed $\\{\\mathbf{f}_i^m\\}_{i = 1}^b$ into $M_D$ and generate reconstructed images $\\{x_i^r\\}_{i = 1}^b$ by Eq. (9).", + "bbox": [ + 506, + 590, + 888, + 617 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "10: Calculate self-supervised reconstruction loss $\\mathcal{L}_r$ by Eq. (10).", + "bbox": [ + 501, + 617, + 859, + 628 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "11: Update parameter $\\theta_{E},\\theta_{D}$ in backward process.", + "bbox": [ + 501, + 628, + 792, + 641 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "12: end for", + "bbox": [ + 501, + 641, + 566, + 651 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Output: The final encoder $M_E(\\cdot ;\\theta_E)$", + "bbox": [ + 501, + 652, + 712, + 665 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Experimental Results", + "text_level": 1, + "bbox": [ + 500, + 699, + 700, + 715 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "- Results on Simulated Noisy Datasets. Here we compare the proposed method with multiple baselines on CIFAR-10/100 using different symmetric noise levels. As shown in Tab. 1, by integrating with C2D [47], our SANM outperforms the naive C2D significantly and achieves state-of-the-art performance among these methods. Specifically, an increase of around $3 \\sim 4\\%$ across all the symmetric noise cases on CIFAR-100 can be observed. Besides, when combined with DivideMix [15], SANM can bring greater improvement to its performance, especially for severe label noise cases. Under $90\\%$ symmetric noise setting, our SANM (DivideMix) outperforms DivideMix by a large", + "bbox": [ + 496, + 719, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "16191", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/7cd3c1ac0418f4df3b9c8eb4cd05a8325f4475ba3754ab6ef5fc8d1ec5c5749e.jpg", + "image_caption": [ + "Figure 4. Investigation of the basic mask ratio $\\mu$ under $50\\%$ and $80\\%$ symmetric noise of CIFAR-10/100." + ], + "image_footnote": [], + "bbox": [ + 117, + 89, + 477, + 251 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/82daa868d10f0be5af35a2f4d572c77e34617669c5846b5b27057394e0d1e903.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 480, + 89, + 846, + 251 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/5fc218044a5aca407e5fb9dde65696805ca8e9b0cb33fec486f37ab6bd82ad5e.jpg", + "table_caption": [ + "Table 2. Asymmetric noise on CIFAR-10." + ], + "table_footnote": [], + "table_body": "
MethodNoisy ratio
20%40%
Joint-Optim [32]92.891.7
PENCIL [40]92.491.2
F-correction [24]89.9-
Distilling [46]92.790.2
Meta-Learning [16]-88.6
M-correction [1]-86.3
Iterative-CV [3]-88.0
DivideMix [15]93.493.4
REED [44]95.092.3
C2D [47]93.893.4
Sel-CL+ [19]95.293.4
GCE [5]87.378.1
RRL [17]-92.4
SANM(DivideMix)95.494.8
", + "bbox": [ + 91, + 289, + 315, + 503 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/f17edfcc0e340dae9a6212a99941e46d1b3f54a7fc985b869773e0f18684ed12.jpg", + "table_caption": [ + "Table 3. Testing accuracy on Clothing-1M." + ], + "table_footnote": [], + "table_body": "
MethodAcc
CrossEntropy69.21
F-correction [24]69.84
M-correction [1]71.00
Joint-Optim [32]72.16
Meta-Cleaner [45]72.50
Meta-Learning [16]73.47
PENCIL [40]73.49
Self-Learning [10]74.45
DivideMix [15]74.76
Nested [4]74.90
AugDesc [22]75.11
RRL [17]74.90
GCE [5]73.30
C2D [47]74.30
SANM(DivideMix)75.63
", + "bbox": [ + 393, + 284, + 570, + 507 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/249ba0d1c371fb95a2a888c78950bee2f818cd9b783d6a76673115e7313467a8.jpg", + "table_caption": [ + "Table 4. Comparison with masks generated from pre-trained backbones on CIFAR10/100. M: Method. P: Pretrained Backbone. S: SANM. D: DivideMix. C: C2D." + ], + "table_footnote": [], + "table_body": "
DatasetCIFAR-10
MPS20%50%80%90%
D✓X96.095.193.781.5
X✓96.495.894.692.3
C✓X96.395.495.093.9
X✓96.696.495.795.1
DatasetCIFAR-100
MPS20%50%80%90%
D✓X78.876.664.936.4
X✓81.278.268.743.5
C✓X80.077.169.258.4
X✓81.979.371.661.9
", + "bbox": [ + 627, + 325, + 888, + 507 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "margin of $12\\%$ . A similar performance boost can be observed on the CIFAR-100 as well, especially in the high noise case. When compared with the implicit regularization method AugDesc [22], our SANM (DivideMix) can also achieve much better accuracy across all the settings. Tab. 2 shows comparisons of the test accuracy on CIFAR-10 with varied asymmetric noise levels. SANM shows a consistent improvement with respect to the state-of-the-art methods, demonstrating the ability of SANM in enhancing the robustness of the model to semantic noise.", + "bbox": [ + 75, + 520, + 468, + 670 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "- Results on Real-world Noisy Datasets. The results on Clothing1M dataset are shown in Tab. 3, one can see that SANM can bring around $0.87\\%$ improvement in accuracy to the baseline method DivideMix, and get much better result than other state-of-the-art methods, verifying that SANM is also effective in real-world noise scenarios. Moreover, SANM obtains $89.3\\%$ accuracy on Animal-10N (detailed comparison on Animal-10N is in supplementary materials), which sets the new state-of-the-art, indicating that SANM can handle fine-grained real-world noisy datasets as well.", + "bbox": [ + 75, + 672, + 468, + 823 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Generality of SANM. To further verify the effectiveness and generalization of SANM, we adopt vanilla CE, Co-teaching [9], CDR [37] and ELR+ [21], other than DivideMix and C2D, to work collaboratively with SANM. As shown in Tab. 6, SANM brings consistent performance", + "bbox": [ + 75, + 824, + 468, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "boost to all the corresponding baselines across all the noisy cases. For example, when seamlessly combined with SANM, an accuracy increase of around $14.9\\%$ on the symmetric $80\\%$ noise setting (CIFAR-100) can be achieved by vanilla CE, demonstrating that SANM is capable of effectively imposing adaptive regularization on noisy and clean samples respectively and thus alleviates over-fitting to noisy labels. Therefore, all the results indicate the generalization ability of SANM to boost existing LNL approaches.", + "bbox": [ + 496, + 520, + 890, + 656 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation Study", + "text_level": 1, + "bbox": [ + 500, + 665, + 653, + 681 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Component Analysis. To explore the impact of each component in SANM, i.e., AMG-adversarial noisy masking generation, NLR-noisy label regularization, SMR-self-supervised masking reconstruction, we conduct an ablation study on CIFAR-10 and CIFAR-100. As shown in Tab. 5, we report test accuracy for all the noisy settings for SANM where the contribution of each component is clearly shown. Specifically, it can be observed that AMG is crucial to performance, especially under severe label noisy cases. Besides, the accuracy is further improved when further combined with NLR. By introducing noise-free self-supervised signals, SMR can also boost the performance of the baseline across all the settings. Best accuracy can be achieved by SANM when all the components are included. This ver", + "bbox": [ + 496, + 688, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "16192", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/21fdc55a5d5826e1b1550400673f081bb47d329fe3ff3eb56431b9c08ead9f16.jpg", + "table_caption": [ + "Table 5. Ablation study for the effectiveness of each key component. AMG: adversarial noisy masking generation, NLR: noisy label regularization, SMR: self-supervised masking reconstruction." + ], + "table_footnote": [], + "table_body": "
ComponentCIFAR-10CIFAR-100
AMGNLRSMR20%50%80%90%20%50%80%90%
XXXBest96.194.693.276.077.374.660.231.5
Last95.794.492.975.476.974.259.631.0
✓XXBest96.395.394.091.480.277.368.042.7
Last96.295.193.690.879.777.067.542.5
✓✓XBest96.495.594.291.680.577.568.343.0
Last96.395.494.091.580.277.168.242.7
✓✓✓Best96.495.894.692.381.278.268.743.5
Last96.395.694.392.180.478.068.343.0
", + "bbox": [ + 215, + 119, + 754, + 244 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/ddcdd97eb50cbfa3b26cddbcf223662682da1383908b295c2b84b3063abf2c7e.jpg", + "table_caption": [ + "Table 6. Comparison between the LNL methods and their SANM applications with symmetric noise on CIFAR-10/100. Specifically, the 9-layer CNN is adopted as the backbone network of Co-teaching." + ], + "table_footnote": [], + "table_body": "
Dataset\nMethod/Noise ratioCIFAR-10CIFAR-100
20%50%80%90%20%50%80%90%
CEBest86.879.462.942.762.046.719.910.1
SANM(CE)Best92.489.772.151.570.953.134.818.6
Co-teaching [9]Best82.673.024.014.650.538.211.84.9
SANM(Co-teaching)Best89.278.236.420.758.251.319.413.4
CDR [37]Best90.485.047.212.363.339.529.28.0
SANM(CDR)Best92.691.655.316.772.756.436.620.8
ELR+ [21]Best94.693.891.175.277.572.458.230.8
SANM(ELR+)Best96.395.794.182.979.877.365.038.7
", + "bbox": [ + 171, + 287, + 794, + 465 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "ifies the effectiveness of all three key designs in SANM.", + "bbox": [ + 76, + 469, + 449, + 484 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Parameter Sensitivity. The basic mask ratio $\\mu$ in Eq. (2) is a important parameter within the proposed method. To investigate how $\\mu$ affects the performance, we conduct parameter analysis experiments under $50\\% / 80\\%$ symmetric noise levels on CIFAR-10/100. Best test accuracy with respect to different $\\mu$ is shown in Fig. 4. Compared with the results of baseline methods, i.e., masking the images with fixed ratios and random ratios (sampled from Uniform(0, $\\mu$ ) for each sample, SANM achieves superior performance. Moreover, the results show that keeping $\\mu$ between 0.1 and 0.3 leads to better performance than without adversarial noisy masking $(\\mu = 0)$ . This is mainly because large $\\mu$ makes the masked training images and the test images differ significantly in image style, and thus the model is unable to learn class-specific discriminative information when trained with such strong regularization. However, an extremely small $\\mu$ results in encoder overfit to noisy labels more severely. Therefore, we set $\\mu$ as 0.2 to obtain the best performance.", + "bbox": [ + 75, + 488, + 472, + 760 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Comparison with Masks from Pre-trained Backbones. The effectiveness of the proposed adversarial masking generation is evaluated in Tab. 5. And further we conduct experiments that utilizing a strong backbone (i.e., pretrained ResNet-18 with DivideMix and C2D) to generate more object-centric masks as comparison to our method. The results shown in Tab. 4 indicate that even compared with the mask generation strategy by well-trained strong backbones, our adversarial masking generation can consis", + "bbox": [ + 75, + 763, + 470, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "tently performs better since the mask generation strategy of fixed well-trained backbones cannot adaptively adjust the mask strategy according to the training state of the base model, leading to sub-optimal results.", + "bbox": [ + 496, + 469, + 893, + 530 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 539, + 619, + 554 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we propose SANM, a novel self-supervised adversarial noisy masking LNL method. SANM prevents models from overfitting noisy instances by explicitly imposing regularization on the features. A label quality guided adversarial masking scheme is designed to adaptively modify the inputs and noisy labels. Moreover, an auxiliary decoder is introduced to reconstruct images based on the modulated features, which enhances the generalization by providing extra noise-free self-supervised signals. Besides, SANM is flexible and can be easily plugged into existing LNL frameworks. State-of-the-art performance is obtained by SANM on both synthetic and real-world noisy datasets.", + "bbox": [ + 496, + 560, + 893, + 742 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement", + "text_level": 1, + "bbox": [ + 500, + 753, + 660, + 771 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work was supported by National Natural Science Fund of China (62076184, 61976158, 61976160, 62076182, 62276190), in part by Fundamental Research Funds for the Central Universities and State Key Laboratory of Integrated Services Networks (Xidian University), in part by Shanghai Innovation Action Project of Science and Technology (20511100700) and Shanghai Natural Science Foundation (22ZR1466700).", + "bbox": [ + 496, + 779, + 893, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "16193", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Eric Arazo, Diego Ortego, Paul Albert, Noel E O'Connor, and Kevin McGuinness. Unsupervised label noise modeling and loss correction. In Proc. International Conference on Machine Learning (ICML), 2019. 6, 7", + "[2] Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al. A closer look at memorization in deep networks. In Proc. International Conference on Machine Learning (ICML), pages 233-242. PMLR, 2017. 1, 2", + "[3] Pengfei Chen, Benben Liao, Guangyong Chen, and Shengyu Zhang. Understanding and utilizing deep neural networks trained with noisy labels. In Proc. International Conference on Machine Learning (ICML), 2019. 1, 3, 7", + "[4] Yingyi Chen, Xi Shen, Shell Xu Hu, and Johan A. K. Suykens. Boosting co-teaching with compression regularization for label noise. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 2688-2692, June 2021. 7", + "[5] Aritra Ghosh and Andrew Lan. Contrastive learning improves model robustness under label noise. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2703-2708, 2021. 6, 7", + "[6] Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014. 1, 3", + "[7] Aayush Gupta, Ayush Jaiswal, Yue Wu, Vivek Yadav, and Pradeep Natarajan. Adversarial mask generation for preserving visual privacy. In 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), pages 1-5. IEEE, 2021. 3", + "[8] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Co-teaching: Robust training of deep neural networks with extremely noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), volume 31, 2018. 1, 2, 3", + "[9] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Coteaching: Robust training of deep neural networks with extremely noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), 2018. 7, 8", + "[10] Jiangfan Han, Ping Luo, and Xiaogang Wang. Deep self-learning from noisy labels. In Proc. IEEE International Conference on Computer Vision (ICCV), 2019. 7", + "[11] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377, 2021.5", + "[12] Qibin Hou, PengTao Jiang, Yunchao Wei, and Ming-Ming Cheng. Self-erasing network for integral object attention. In Proc. Advances in Neural Information Processing Systems (NeurIPS), volume 31, 2018. 3", + "[13] Tao Hu, Honggang Qi, Qingming Huang, and Yan Lu. See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification. arXiv preprint arXiv:1901.09891, 2019. 3" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 3, 6", + "[15] Junnan Li, Richard Socher, and Steven CH Hoi. Dividemix: Learning with noisy labels as semi-supervised learning. In Proc. International Conference on Learning Representations (ICLR), 2020. 1, 2, 3, 4, 6, 7", + "[16] Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S. Kankanhalli. Learning to learn from noisy labeled data. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 6, 7", + "[17] Junnan Li, Caiming Xiong, and Steven CH Hoi. Learning from noisy data with robust representation learning. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 9485-9494, 2021. 7", + "[18] Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, and Yun Fu. Tell me where to look: Guided attention inference network. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 9215-9223, 2018. 3", + "[19] Shikun Li, Xiaobo Xia, Shiming Ge, and Tongliang Liu. Selective-supervised contrastive learning with noisy labels. arXiv preprint arXiv:2203.04181, 2022. 6, 7", + "[20] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), volume 33, pages 20331–20342, 2020. 1, 2", + "[21] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), 2020. 7, 8", + "[22] Kento Nishi, Yi Ding, Alex Rich, and Tobias Hollerer. Augmentation strategies for learning with noisy labels. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8022-8031, 2021. 6, 7", + "[23] Diego Ortego, Eric Arazo, Paul Albert, Noel E O'Connor, and Kevin McGuinness. Multi-objective interpolation training for robustness to label noise. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6606-6615, 2021. 6", + "[24] Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. Making deep neural networks robust to label noise: A loss correction approach. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 7", + "[25] Haim Permuter, Joseph Francos, and Ian Jermyn. A study of gaussian mixture models of color and texture features for image classification and segmentation. Pattern recognition, 39(4):695-706, 2006. 4", + "[26] Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. Learning to reweight examples for robust deep learning. In Proc. International Conference on Machine Learning (ICML), pages 4334-4343. PMLR, 2018. 1, 2", + "[27] Hwanjun Song, Minseok Kim, and Jae-Gil Lee. Selfie: Refurbishing unclean samples for robust deep learning. In Proc. International Conference on Machine Learning (ICML), 2019. 1" + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "16194", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[28] Hwanjun Song, Minseok Kim, and Jae-Gil Lee. SELFIE: Refurbishing unclean samples for robust deep learning. In Proc. International Conference on Machine Learning (ICML), pages 5907-5915, 2019. 6", + "[29] Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, 2022. 1, 2", + "[30] Erik Stammes, Tom FH Runia, Michael Hofmann, and Mohsen Ghafoorian. Find it if you can: end-to-end adversarial erasing for weakly-supervised semantic segmentation. In Thirteenth International Conference on Digital Image Processing (ICDIP 2021), volume 11878, page 1187825. International Society for Optics and Photonics, 2021. 3", + "[31] Guolei Sun, Hisham Cholakkal, Salman Khan, Fahad Khan, and Ling Shao. Fine-grained recognition: Accounting for subtle differences between similar classes. In Association for the Advancement of Artificial Intelligence (AAAI), volume 34, pages 12047–12054, 2020. 3", + "[32] Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa. Joint optimization framework for learning with noisy labels. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 1, 3, 7", + "[33] Gabriele Valvano, Andrea Leo, and Sotirios A Tsaftaris. Reusing adversarial mask discriminators for test-time training under distribution shifts. arXiv preprint arXiv:2108.11926, 2021. 3", + "[34] Hongxin Wei, Lei Feng, Xiangyu Chen, and Bo An. Combating noisy labels by agreement: A joint training method with co-regularization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 13726–13735, 2020. 1, 3", + "[35] Yunchao Wei, Jiashi Feng, Xiaodan Liang, Ming-Ming Cheng, Yao Zhao, and Shuicheng Yan. Object region mining with adversarial erasing: A simple classification to semantic segmentation approach. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1568-1576, 2017. 3", + "[36] Yichen Wu, Jun Shu, Qi Xie, Qian Zhao, and Deyu Meng. Learning to purify noisy labels via meta soft label corrector. In Association for the Advancement of Artificial Intelligence (AAAI), 2021. 1", + "[37] Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang. Robust early-learning: Hindering the memorization of noisy labels. In Proc. International Conference on Learning Representations (ICLR), 2021. 7, 8", + "[38] Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang. Learning from massive noisy labeled data for image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 1, 6", + "[39] Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu, Yuefeng Chen, and Hui Xue. Towards face encryption by generating adversarial identity masks. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 3897-3907, 2021. 3", + "[40] Kun Yi and Jianxin Wu. Probabilistic end-to-end noise correction for learning with noisy labels. In Proc. IEEE Confer-" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "ence on Computer Vision and Pattern Recognition (CVPR), pages 7017-7025, 2019. 1, 3, 6, 7", + "[41] Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W Tsang, and Masashi Sugiyama. How does disagreement help generalization against label corruption? In Proc. International Conference on Machine Learning (ICML), 2019. 1, 3, 6", + "[42] Boshen Zhang, Yuxi Li, Yuanpeng Tu, Jinlong Peng, Yabiao Wang, Cunlin Wu, Yang Xiao, and Cairong Zhao. Learning from noisy labels with coarse-to-fine sample credibility modeling. In Computer Vision-ECCV 2022 Workshops: Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part II, pages 21-38. Springer, 2023. 3", + "[43] Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412, 2017. 1, 3, 6", + "[44] Hui Zhang and Quanming Yao. Decoupling representation and classifier for noisy label learning. arXiv preprint arXiv:2011.08145, 2020. 7", + "[45] Weihe Zhang, Yali Wang, and Yu Qiao. Metacleaner: Learning to hallucinate clean representations for noisy-labeled visual recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 7", + "[46] Z. Zhang, H. Zhang, S. Arik, H. Lee, and T. Pfister. Distilling effective supervision from severe label noise. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 7", + "[47] Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M Bronstein, and Or Litany. Contrast to divide: Self-supervised pre-training for learning with noisy labels. In Proc. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 1657-1667, 2022. 1, 3, 6, 7", + "[48] Guoqing Zheng, Ahmed Hassan Awadallah, and Susan Dumas. Meta label correction for noisy label learning. In Association for the Advancement of Artificial Intelligence (AAAI), 2021. 1, 3", + "[49] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2921-2929, 2016. 3, 4", + "[50] Xiong Zhou, Xianming Liu, Chenyang Wang, Deming Zhai, Junjun Jiang, and Xiangyang Ji. Learning with noisy labels via sparse regularization. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 72-81, 2021. 1, 2" + ], + "bbox": [ + 503, + 92, + 890, + 739 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "16195", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking/fee95f26-a8a8-4c1d-9f44-c06f33970b8b_model.json b/2023/Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking/fee95f26-a8a8-4c1d-9f44-c06f33970b8b_model.json new file mode 100644 index 0000000000000000000000000000000000000000..77feb1fc309377011c45d7d046441721c3272135 --- /dev/null +++ b/2023/Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking/fee95f26-a8a8-4c1d-9f44-c06f33970b8b_model.json @@ -0,0 +1,2398 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.112, + 0.131, + 0.857, + 0.153 + ], + "angle": 0, + "content": "Learning with Noisy labels via Self-supervised Adversarial Noisy Masking" + }, + { + "type": "text", + "bbox": [ + 0.204, + 0.18, + 0.767, + 0.199 + ], + "angle": 0, + "content": "Yuanpeng \\(\\mathrm{Tu}^{1*}\\) Boshen Zhang\\(^{2*}\\) Yuxi Li\\(^{2}\\) Liang Liu\\(^{2}\\) Jian Li\\(^{2}\\)" + }, + { + "type": "text", + "bbox": [ + 0.182, + 0.199, + 0.786, + 0.218 + ], + "angle": 0, + "content": "Jiangning Zhang² Yabiao Wang²† Chengjie Wang²,³ Cai Rong Zhao¹" + }, + { + "type": "text", + "bbox": [ + 0.177, + 0.217, + 0.795, + 0.235 + ], + "angle": 0, + "content": "1Dept. of Electronic and Information Engineering, Tongji University, Shanghai" + }, + { + "type": "text", + "bbox": [ + 0.221, + 0.235, + 0.75, + 0.253 + ], + "angle": 0, + "content": "2YouTu Lab, Tencent, Shanghai, 3Shanghai Jiao Tong University" + }, + { + "type": "text", + "bbox": [ + 0.327, + 0.255, + 0.644, + 0.269 + ], + "angle": 0, + "content": "{2030809，zhaocairong}@tongji.edu.cn" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.273, + 0.868, + 0.287 + ], + "angle": 0, + "content": "{boshenzhang, yukiyxli, leoneliu, swordli, vtzhang, caseywang, jasoncjwang}@tencent.com" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.322, + 0.313, + 0.337 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.355, + 0.471, + 0.701 + ], + "angle": 0, + "content": "Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate this problem via identifying and removing noisy samples or correcting their labels according to the statistical properties (e.g., loss values) among training samples. In this paper, we aim to tackle this problem from a new perspective, delving into the deep feature maps, we empirically find that models trained with clean and mistrabeled samples manifest distinguishable activation feature distributions. From this observation, a novel robust training approach termed adversarial noisy masking is proposed. The idea is to regularize deep features with a label quality guided masking scheme, which adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. Further, an auxiliary task is designed to reconstruct input data, it naturally provides noise-free self-supervised signals to reinforce the generalization ability of models. The proposed method is simple yet effective, it is tested on synthetic and real-world noisy datasets, where significant improvements are obtained over previous methods. Code is available at https://github.com/yuanpengtu/SANM." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.732, + 0.208, + 0.747 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.758, + 0.469, + 0.864 + ], + "angle": 0, + "content": "Deep learning has achieved remarkable success, relying on large-scale datasets with human-annotated accurate labels. However, collecting such high-quality labels is time-consuming and expensive. As an alternative, inexpensive strategies are usually used for generating labels for large-scale samples, such as web crawling, leveraging search engines, or using machine-generated annotations. All these" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.322, + 0.559, + 0.364 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.565, + 0.323, + 0.617, + 0.365 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.506, + 0.366, + 0.558, + 0.408 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.565, + 0.368, + 0.617, + 0.408 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.506, + 0.411, + 0.556, + 0.451 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.565, + 0.411, + 0.617, + 0.45 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.521, + 0.453, + 0.606, + 0.476 + ], + "angle": 0, + "content": "(a) Clean-trained Mis-predicted" + }, + { + "type": "image", + "bbox": [ + 0.639, + 0.323, + 0.691, + 0.364 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.697, + 0.323, + 0.75, + 0.364 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.639, + 0.368, + 0.691, + 0.409 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.697, + 0.368, + 0.751, + 0.409 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.639, + 0.411, + 0.691, + 0.45 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.697, + 0.411, + 0.751, + 0.449 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.658, + 0.453, + 0.739, + 0.475 + ], + "angle": 0, + "content": "(b) Noisy-trained Mis-predicted" + }, + { + "type": "image", + "bbox": [ + 0.773, + 0.323, + 0.825, + 0.364 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.83, + 0.323, + 0.883, + 0.364 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.773, + 0.368, + 0.825, + 0.408 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.831, + 0.368, + 0.883, + 0.408 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.773, + 0.411, + 0.825, + 0.45 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.83, + 0.411, + 0.883, + 0.45 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.791, + 0.453, + 0.88, + 0.476 + ], + "angle": 0, + "content": "(c) Noisy-trained Correctly-predicted" + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.477, + 0.891, + 0.531 + ], + "angle": 0, + "content": "Figure 1. Activation maps of mis-predicted (a-b) and correctly-predicted (c) samples when training PreAct ResNet-18 with clean (i.e., clean-trained) and noisy (i.e., noisy-trained) data on CIFAR-10 (1st and 2nd row) and Clothing1M [38] (3rd row)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.537, + 0.892, + 0.597 + ], + "angle": 0, + "content": "alternative methods inevitably yield numerous noisy samples. However, previous research [2] has revealed that deep networks can easily overfit to noisy labels and suffer from dramatic degradation in the generalization performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.599, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Towards this problem, numerous Learning with Noisy labels (LNL) approaches have been proposed. Sample selection methods [3, 8, 34, 41] are the most straightforward methods, which attempt to select clean samples based on certain criterion (e.g., loss value) and then reweight or discard the noisy instances to reduce the interference of mislabeled training samples. However, these methods fail to leverage the potential information of the discarded samples. Similarly, label correction based methods [32, 40, 48] try to correct labels, which often impose assumptions on the existence of a small correctly-labeled validation subset or directly utilize predictions of deep models. However, such assumptions can not be always full-filled in real-world noisy datasets [27], leading to limited application scenarios. Besides, the predictions of deep model tend to fluctuate when training with mislabeled samples, making the label correction process unstable and sub-optimal [36]. On the contrary, regularization based methods [2, 6, 15, 20, 26, 29, 43, 47, 50] aim to alleviate the side effect of label noise by preventing the deep models from overfitting to all training sam" + }, + { + "type": "page_footnote", + "bbox": [ + 0.101, + 0.876, + 0.215, + 0.888 + ], + "angle": 0, + "content": "* Equal contribution." + }, + { + "type": "page_footnote", + "bbox": [ + 0.104, + 0.889, + 0.231, + 0.9 + ], + "angle": 0, + "content": "† Corresponding author." + }, + { + "type": "list", + "bbox": [ + 0.101, + 0.876, + 0.231, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16186" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.258 + ], + "angle": 0, + "content": "ples, which is flexible and can work collaboratively with other LNL methods. Both explicit and implicit regularization techniques were proposed, the former generally calibrates the parameter update of networks by modifying the expected training loss, i.e., dropout. The latter focuses on improving the generalization by utilizing the stochasticity, i.e., data augmentation strategy and stochastic gradient descent [29]. However, most of these methods are designed for general fully-supervised learning tasks with correctly-labeled samples, and poor generalization ability could be obtained when the label noise is severe [29]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.261, + 0.473, + 0.608 + ], + "angle": 0, + "content": "In this paper, we present a novel regularization based method specialized for LNL problem. Our method is inspired by an observation that some differences exist in activation maps between the predictions of deep models trained with clean and noisy labels. As shown in Fig. 1 (a), activation maps of mis-predicted samples by the model trained with clean labels are always focused on their foreground areas. By contrast, when the model is trained with noisy labels, it tends to generate results focused on the meaningless background area for the mis-predicted samples (Fig. 1 (b)). And even for cases that the prediction is correct, i.e., Fig. 1 (c), the high-activated region drifts to irrelevant (i.e., edge) areas of the object, rather than the main regions of the object. This indicates that model trained with mistrabeled samples is likely to overfit to some corner parts of the object from noisy data or remember the less-informative regions (i.e., background). Therefore, from this observation, we propose a novel Self-supervised Adversarial Noisy Masking (SANM) method for LNL. The idea is to explicitly regularize the model to generate more diverse activation maps through masking the certain region (e.g., max-activated) of the input image and thus alleviates the confirmation bias [8]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.611, + 0.473, + 0.822 + ], + "angle": 0, + "content": "Moreover, to investigate how the masking strategies affect the performance of training deep models with noisy labels, we conduct a simple experiment, where different adversarial masking strategies (i.e., masking the max-activated region of input images with fixed, random, and noise-aware mask ratio) were applied to the popular LNL framework DivideMix [15]. As shown in Fig. 2, we find that the performance gain is much more sensitive to the masking strategy. A fixed or random mask ratio obtain only marginal gain, but when dealing with the correctly-labeled and mislabeled samples with different mask ratios, a significant performance gain is achieved. This indicates that how to design an effective adversarial masking method in the case of LNL is non-trivial and not well addressed." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Towards the problem above, a label quality guided adaptive masking strategy is proposed, which modulates the image label and the mask ratio of image masking simultaneously. The label is updated via first leveraging the soft-distributed model prediction and then reducing the proba" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.379 + ], + "angle": 0, + "content": "bility of max-activated class with the noise-aware masking ratio, while at the same time lifting the probability of other classes. Intuitively, a sample with a high probability of being mislabeled will possess a larger masking ratio, leading to a more strictly regularized input image and modulated label. Therefore, the negative impact of noisy labels can be greatly reduced. As for the correctly-labeled samples, the masking ratio is relatively small, which plays the role of general regularization strategy and improves the model generalization ability by preventing the model from overfitting training samples. Further, we specially customized an auxiliary decode branch to reconstruct the original input image, which provides noise-free self-supervised information for learning a robust deep model. The proposed SANM is flexible and can further boost existing LNL methods. It is tested on synthetic and real-world large-scale noisy datasets, and elaborately ablative experiments were conducted to verify each design component of the proposed method. In a nutshell, the key contributions of this work are:" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.38, + 0.895, + 0.456 + ], + "angle": 0, + "content": "- We propose a novel self-supervised adversarial noisy masking method named SANM to explicitly impose regularization for LNL problem, preventing the model from overfitting to some corner parts of the object or less-informative regions from noisy data;" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.456, + 0.895, + 0.532 + ], + "angle": 0, + "content": "- A label quality guided masking strategy is proposed to differently adjust the process for clean and noisy samples according to the label quality estimation. This strategy modulates the image label and the ratio of image masking simultaneously;" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.532, + 0.895, + 0.593 + ], + "angle": 0, + "content": "- A self-supervised mask reconstruction auxiliary task is designed to reconstruct the original images based on the features of masked ones, which aims at enhancing generalization by providing noise-free supervision signals." + }, + { + "type": "list", + "bbox": [ + 0.498, + 0.38, + 0.895, + 0.593 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.609, + 0.642, + 0.625 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.634, + 0.75, + 0.651 + ], + "angle": 0, + "content": "2.1. Learning with Noisy Labels" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.892, + 0.704 + ], + "angle": 0, + "content": "A variety of methods have been proposed to improve the robustness of DNNs on noisy datasets, which consist of regularization, label correction, and noisy sample selection." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Regularization based methods help resist memorizing noisy data since previous works [2] have revealed that networks always firstly learn simple patterns before memorizing noisy hard data. Previous research [29] claims that these methods can be roughly divided into two categories: explicit/implicit regularization. Explicit regularization based methods generally modify the losses for the training samples. Among them, ELR [20] tries to alleviate the influence of noisy data by regularizing the loss gradient. SR [50] proposes a spare regularization method to restrict network output to the permutation set over a one-hot vector. Meta learning based methods [26] impose regularization on the sample gradient directions by bi-level optimization with a small un-" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "16187" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.089, + 0.283, + 0.21 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.284, + 0.089, + 0.485, + 0.21 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.485, + 0.089, + 0.687, + 0.21 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.687, + 0.089, + 0.889, + 0.21 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.211, + 0.894, + 0.266 + ], + "angle": 0, + "content": "Figure 2. A experiment for masking the max-activated region with different mask ratios. The performance gains of different mask strategies under \\(50\\%\\) and \\(80\\%\\) symmetric noise of CIFAR-10/100 [14] are reported, where DivideMix [15] is adopted as the baseline. \"Fixed(0.2/0.3)\" denotes masking all the images with the same mask ratio of 0.2/0.3. \"Random\" represents masking images with a random mask ratio between 0.2 and 0.4. \"Noise-aware\" is masking noisy samples with a mask ratio of 0.3 while the ratio for clean ones is 0.2." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.272, + 0.473, + 0.514 + ], + "angle": 0, + "content": "biased validation. In addition to these regularizers, example re-weighting strategies are also popular explicit regularization solutions to the over-fitting problem. However, these methods often need sensitive hyper-parameters or deeper networks to improve the model capacity. For implicit regularization methods, they generally utilize the stochasticity effect, i.e., augmentation and stochastic gradient descent. Among them, semi-supervised based LNL methods [15,47] generally use mixup [43] as an effective regularization strategy to hinder over-fitting, which makes the network favor simple linear behavior in-between training examples. Adversarial samples based methods [6] improve the robustness by helping the model classify original and perturbed inputs. Since the model is unable to distinguish right from wrong by itself, regularization can generally work surprisingly well in certain synthetic noisy cases." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.519, + 0.47, + 0.7 + ], + "angle": 0, + "content": "Label correction methods seek to transform noisy labels to correct ones directly. The pseudo labeling strategy is commonly adopted by these methods, which utilizes the confident output of the network. Joint [32] optimizes labels and network parameters in an alternating strategy. PENCIL [40] corrects and updates labels probabilistically through the back-propagation mechanism. MLC [48] trains a label correction network through meta learning to generate pseudo labels for the training of main models. However, these approaches either need extra clean validation data or suffer from significant performance drop under high noise settings due to the low quality of generated pseudo labels." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.705, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Noisy sample selection approaches mostly exploit the small-loss criterion, which is based on the observation that samples with noisy labels tend to have much higher losses than the clean ones. Among these methods, MentorNet uses a pre-trained teacher model to select clean samples for the training of a student model. Co-teaching based methods [8, 34, 41, 42] update the parameters of two parallel networks with clean instances chosen from their peer networks, which target avoiding the confirmation bias problem. INCV [3] attempts to separate clean data from the noisy training set by using cross-validation and removing high-loss samples. Though these methods may reduce the risk of error correction, they fail to use the potential infor" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.273, + 0.892, + 0.303 + ], + "angle": 0, + "content": "mation of the abandoned samples. Moreover, most of them need prior knowledge, i.e., the noise rate." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.318, + 0.813, + 0.334 + ], + "angle": 0, + "content": "2.2. Masking Regularization Techniques" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.342, + 0.892, + 0.903 + ], + "angle": 0, + "content": "As a commonly adopted strategy in weakly-supervised semantic segmentation, adversarial masking regularization aims at helping models discover less apparent semantic objects. Specifically, a Class Activation feature Map (CAM) [49] branch is first used to generate initial attention maps and selectively mask the discovered areas in the images through a threshold. Then the masked images are used for the training of another network. AE-PSL [35] iteratively erases the images and expands target object regions with the erased areas to produce final contiguous segmentation masks. Self-erasing [12] utilizes the background prior to make models generate more refined and focused attention on the foreground area. GAIN [18] tries to impose constraints on the activation maps in a regularized style to generate more complete attention regions. PSA [30] trains two networks with opposing losses to eliminate the drawbacks of certain mask strategies. In addition to weakly-supervised semantic segmentation, adversarial masking is also used in fine-grained visual classification. DB [31] designs a diversification block to mask out the most salient features, aiming at forcing networks to mine subtle differences between images. WS-DAN [13] randomly crop and drop one of the discriminative regions in the attention maps to encourage models to mine local features. Several adversarial masking strategies are proposed in [7, 39] to preserve human privacy by masking the minimal number of pixels required for identification. [33] designs an adversarial discriminator for mask generation at inference to correct segmentation errors. However, the purpose of these methods is quite different from our approach. Based on the assumption that the samples are all correctly-labeled, previous methods generally retain the original most salient regions while helping models mine more subtle information through masking regularization. By contrast, our method aims to regularize the model through adversarial masking to make the activation map more dispersed rather than concentrated to specific regions, and a similar masking strategy is applied to gener" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16188" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.47, + 0.122 + ], + "angle": 0, + "content": "ate more smooth pseudo labels, thus avoiding over-fitting to noisy labels and alleviating the confirmation bias." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.136, + 0.168, + 0.151 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.162, + 0.473, + 0.45 + ], + "angle": 0, + "content": "Overview. For convenience, we begin by introducing the notations. Consider LNL as a classification task, the class number is \\( c \\). The noisily-labeled training dataset is denoted as \\( D = \\{(x_i,y_i)\\}_{i=1}^N \\), where \\( x_i \\in \\mathbb{R}^{H_i \\times W_i \\times 3} \\) and \\( y_i \\in \\{0,1\\}^c \\) are the \\( i \\)-th sample and its noisy label, \\( H_i, W_i \\) are the height and width of samples. \\( N \\) represents the dataset size. The encoder and decoder models are denoted as \\( M_E \\) and \\( M_D \\) respectively. We firstly introduce the overall framework of the proposed SANM. As shown in Fig. 3, a label quality guided masking scheme is proposed to adaptively perform adversarial noisy masking for each sample where a contrastive regularization strategy is imposed on the noisy predictions as well. Afterward, the representations of the masked images are used for the self-supervised reconstruction of original images, which aims at preventing models from overfitting to the noisy labels by providing noise-free signals. For application, SANM can be easily integrated with existing LNL frameworks to further boost their performance." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.459, + 0.325, + 0.475 + ], + "angle": 0, + "content": "3.1. Adversarial Noisy Masking" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.482, + 0.47, + 0.724 + ], + "angle": 0, + "content": "Adversarial Masking Generation. To highlight the semantic regions of correctly labeled samples and keep the captured attention areas of the noisy samples from expanding to meaningless background regions, we propose a novel Adversarial noisy Masking Generation (AMG) strategy during the training process. Specifically, we begin by extracting the feature of image \\( x_{i} \\) through \\( M_E \\) and the obtained feature map and prediction are denoted as \\( F_{i} \\in \\mathbb{R}^{H_{F} \\times W_{F} \\times N_{C}} \\) and \\( \\widetilde{y}_{i} \\) respectively, where \\( H_{F}, W_{F}, N_{C} \\) are the height, width and channel number of the feature layer. For each sample, a two-component Gaussian Mixture Model (GMM) [25] is fit to the cross-entropy loss with the Expectation-Maximization algorithm, where the generated weight for \\( x_{i} \\) is denoted as \\( G_{i} \\). Afterward, \\( F_{i} \\) is used to generate the activation maps [49] \\( A_{i} \\in \\mathbb{R}^{H_{i} \\times W_{i}} \\) of the category with the largest prediction, which is derived as:" + }, + { + "type": "equation", + "bbox": [ + 0.173, + 0.737, + 0.469, + 0.754 + ], + "angle": 0, + "content": "\\[\nA _ {i} = \\operatorname {C A M} \\left(F _ {i}, \\arg \\max \\left(\\tilde {y} _ {i}\\right)\\right) \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.765, + 0.47, + 0.901 + ], + "angle": 0, + "content": "where \\(\\mathrm{CAM}(\\cdot)\\) is the activation map generation function. Then the coordinates with the maximum and minimum activation values for \\(x_{i}\\) are recorded as \\(\\{h_i^{\\max},w_i^{\\max}\\}\\) and \\(\\{h_i^{\\min},w_i^{\\min}\\}\\) respectively. For the \\(\\{h_i^{\\max},w_i^{\\max}\\}\\), assuming that the GMM division is overall correct, it is believed that the activation maps of correct-labeled samples are mainly focused on the target area of the foreground, while due to the interference of noisy labels, the max-activated region in the activation maps of mislabeled sam" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.5 + ], + "angle": 0, + "content": "plies are mostly concentrated in meaningless background areas. To breakdown the over-fitting to noisy labels, we intend to adversarially augment training samples via a novel making strategy. In particular, denote the loss function as \\(\\mathcal{L}(x_i,y_i)\\), we try to maximize \\(\\mathcal{L}\\) by adaptively generating region perturbations for noisy and clean samples across the training process. The objective can be derived as \\(\\max \\mathcal{L}(x_i + \\sigma ,y_i)\\), where \\(\\sigma\\) is the generated perturbations. Specifically, we generate masks with larger ratios for the max-activated region of noisy samples to force the model to reduce its response to these areas and pay more attention to the rest area, where the target is more likely to be found. For the correct-labeled ones, high response areas in their activation maps are likely to exist in the edge area of the object since they are affected by the gradient of noisy samples. Thus smaller mask ratios are used for the max-activated regions of them, which keep the model from ignoring the masked area and force it to mine discriminative information from the rest areas of the target since the correct labels help limit the network to pay more attention to the foreground area. However, it will make the activation map of the well-trained model scattered to the meaningless area when filtering out nearly all the peak locations for each sample through a threshold like existing methods, which violates our purpose. Therefore, we choose to mask random rectangular areas based on recorded coordinates. Specifically, the mask ratio \\(r_i\\) for \\(x_{i}\\) is calculated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.631, + 0.512, + 0.892, + 0.528 + ], + "angle": 0, + "content": "\\[\nr _ {i} = \\mu \\times (1 - G _ {i}) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.54, + 0.894, + 0.616 + ], + "angle": 0, + "content": "where \\(\\mu\\) is the basic mask ratio. The more likely a sample is to be noisy, the smaller the \\(G_{i}\\) is. Based on Eq. (2), the greater mask ratio will be adopted for this sample, thus stronger regularization will be imposed [15]. Subsequently, the aspect of the masked area \\(\\delta_{i}\\) is generated as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.624, + 0.627, + 0.892, + 0.656 + ], + "angle": 0, + "content": "\\[\n\\delta_ {i} \\sim \\operatorname {U n i f o r m} (\\delta , \\frac {1}{\\delta}) \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.665, + 0.892, + 0.711 + ], + "angle": 0, + "content": "where \\(\\delta\\) is a hyper-parameter for generating the aspect. Based on \\(r_i\\) and \\(\\delta_{i}\\) , the height and width of the masked area are given by:" + }, + { + "type": "equation", + "bbox": [ + 0.518, + 0.721, + 0.892, + 0.848 + ], + "angle": 0, + "content": "\\[\n\\left\\{ \\begin{array}{r l} h _ {i} ^ {u p} & = \\max \\left(h _ {i} ^ {\\max } - \\sqrt {\\frac {\\left(H _ {x} \\times W _ {x}\\right) \\times r _ {i} \\times \\delta_ {i}}{4}}, 0\\right) \\\\ h _ {i} ^ {\\mathrm {d n}} & = \\min \\left(h _ {i} ^ {\\max } + \\sqrt {\\frac {\\left(H _ {x} \\times W _ {x}\\right) \\times r _ {i} \\times \\delta_ {i}}{4}}, H _ {x}\\right) \\\\ w _ {i} ^ {\\mathrm {l t}} & = \\max \\left(w _ {i} ^ {\\max } - \\sqrt {\\frac {\\left(H _ {x} \\times W _ {x}\\right) \\times r _ {i}}{4 \\delta_ {i}}}, 0\\right) \\\\ w _ {i} ^ {\\mathrm {r t}} & = \\min \\left(w _ {i} ^ {\\max } + \\sqrt {\\frac {\\left(H _ {x} \\times W _ {x}\\right) \\times r _ {i}}{4 \\delta_ {i}}}, W _ {x}\\right) \\end{array} \\right. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.892, + 0.903 + ], + "angle": 0, + "content": "The selected locations in the masked area of \\( x_{i} \\) are assigned random values generated from a uniform distribution. Therefore, the adversarial noisy masked image \\( x_{i}^{m} \\)" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16189" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.099, + 0.09, + 0.873, + 0.281 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.285, + 0.895, + 0.356 + ], + "angle": 0, + "content": "Figure 3. The framework of SANM. Three components are included: AMG (Adversarial Masking Generation), NLR (Noisy Label Regularization), SMR (Self-supervised Masking Reconstruction). In AMG and NLR, firstly feed the images to an encoder and generate activation maps. And a label quality guided adversarial masking strategy is proposed to modulate the images and noisy labels simultaneously. Further, an auxiliary decode branch is designed in SMR to reconstruct input images from the features of masked images. Finally, the generated modulated images and labels of SANM together with the reconstruction loss can be directly adopted for existing LNL frameworks." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.363, + 0.253, + 0.377 + ], + "angle": 0, + "content": "can be derived as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.077, + 0.388, + 0.484, + 0.434 + ], + "angle": 0, + "content": "\\[\nx _ {i} ^ {m} (m, n) = \\left\\{ \\begin{array}{l l} U (0, 1), & \\text {i f} m \\in \\left[ h _ {i} ^ {u p}, h _ {i} ^ {d n} \\right], n \\in \\left[ w _ {i} ^ {l t}, w _ {i} ^ {r t} \\right], \\\\ x _ {i} (m, n), & \\text {o t h e r w i s e .} \\end{array} \\right. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.434, + 0.47, + 0.57 + ], + "angle": 0, + "content": "where \\(U\\) denotes the Uniform distribution. Similar operation is performed on the regions of minimum activation value with the coordinates \\(\\{h_i^{min},w_i^{min}\\}\\) as well. Different from Eq. (2), since the regions of minimum activation value for correct-labeled samples are mainly focused on the background, a smaller mask ratio is adopted for them, helping models narrow the search scope for targets. Therefore, we set the mask ratio of the regions with minimum activation values as \\(\\mu \\times G_{i}\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.571, + 0.47, + 0.649 + ], + "angle": 0, + "content": "Noisy Label Regularization. To penalize the overconfident predictions of the network, a contrastive Noisy Label Regularization (NLR) strategy is designed. Specifically, the generated \\( x_{i}^{m} \\) is fed into \\( M_{E} \\) to obtain the prediction \\( \\widetilde{y_{i}^{m}} \\) and feature \\( \\mathbf{f}_{\\mathbf{i}}^{m} \\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.204, + 0.662, + 0.47, + 0.68 + ], + "angle": 0, + "content": "\\[\n\\mathbf {f} _ {\\mathbf {i}} ^ {m} = M _ {E} \\left(x _ {i} ^ {m}; \\theta_ {E}\\right) \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.693, + 0.471, + 0.844 + ], + "angle": 0, + "content": "where \\(\\theta_{E}\\) denotes the parameters of the encoder \\(M_{E}\\). Specifically, we give the pseudo labels of \\(x_{i}^{m}\\) with the predictions of \\(x_{i}\\). Since the mask generation is based on the activation maps of the class with the largest predictions, the corresponding prediction is penalized with the mask ratio \\(r_{i}\\). Besides, there exists an equal possibility for the masked region to be predicted as each class. Thus the prediction values for all the classes are increased uniformly. To sum up, the regularized pseudo label \\(y_{i}^{r}\\) for \\(x_{i}^{m}\\) can be derived as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.086, + 0.855, + 0.47, + 0.902 + ], + "angle": 0, + "content": "\\[\ny _ {i} ^ {r} (j) = \\left\\{ \\begin{array}{l l} y _ {i} (j) - r _ {i} + r _ {i} / c, & \\text {i f} j = \\operatorname {a r g m a x} \\left(y _ {i} (j)\\right) \\\\ y _ {i} (j) + r _ {i} / c, & \\text {o t h e r w i s e} \\end{array} , \\right. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.361, + 0.892, + 0.392 + ], + "angle": 0, + "content": "where \\(0 \\leq j < c\\). Afterward, \\(y_i^r\\) and \\(\\widetilde{y_i^{m}}\\) are used for supervised loss calculation as:" + }, + { + "type": "equation", + "bbox": [ + 0.634, + 0.404, + 0.892, + 0.423 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {c}} = \\mathcal {L} _ {\\mathrm {c e}} \\left(\\widetilde {y _ {i} ^ {m}}, y _ {i} ^ {r}\\right) \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.504, + 0.435, + 0.78, + 0.451 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{\\mathrm{ce}}\\) denotes the cross-entropy loss." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.454, + 0.846, + 0.47 + ], + "angle": 0, + "content": "3.2. Self-supervised Masking Reconstruction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.478, + 0.892, + 0.584 + ], + "angle": 0, + "content": "Inspired by [11], a Self-supervised Masking Reconstruction branch (SMR) is designed to impose implicit regularization by providing extra noise-free supervised signal. Specifically, the decoder \\( M_D \\) reconstructs the original image from the latent representation of the masked image \\( x_i^m \\) by predicting the pixel values for the masked patch, which is defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.626, + 0.597, + 0.891, + 0.613 + ], + "angle": 0, + "content": "\\[\nx _ {i} ^ {r} = M _ {D} \\left(\\mathbf {f} _ {\\mathbf {i}} ^ {m}; \\theta_ {D}\\right) \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.626, + 0.892, + 0.687 + ], + "angle": 0, + "content": "where \\(\\theta_{D}\\) is the parameters of the decoder model \\(M_{D}\\). Then the mean squared error (MSE) loss is computed between the reconstructed image \\(x_{i}^{r}\\) and the original unmasked image \\(x_{i}\\) in the pixel space, which is shown as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.636, + 0.699, + 0.891, + 0.716 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {r}} = \\left\\| x _ {i} ^ {r} - x _ {i} \\right\\| ^ {2} \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.729, + 0.892, + 0.819 + ], + "angle": 0, + "content": "In this way, a challenging self-supervised pretext task that requires holistic understanding beyond low-level image statistics is created for the model. This supervision signal is not only free of interference from noisy labels but can also enhance the model generalization ability. Therefore the overall loss function for SANM is constructed as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.63, + 0.833, + 0.891, + 0.849 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {t r a i n}} = \\mathcal {L} _ {\\mathrm {c}} + \\beta \\mathcal {L} _ {\\mathrm {r}} \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.892, + 0.902 + ], + "angle": 0, + "content": "where \\(\\beta\\) is a parameter to balance the weight of two loss terms. Putting this all together, Algorithm 1 delineates the proposed SANM. Firstly, the original images are masked" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "16190" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.186, + 0.09, + 0.782, + 0.103 + ], + "angle": 0, + "content": "Table 1. Comparison with state-of-the-art methods on CIFAR-10/100 datasets with symmetric noise." + }, + { + "type": "table", + "bbox": [ + 0.144, + 0.105, + 0.821, + 0.381 + ], + "angle": 0, + "content": "
DatasetCIFAR-10CIFAR-100
Method/Noise ratio20%50%80%90%20%50%80%90%
Cross-Entropy (CE)86.879.462.942.762.046.719.910.1
Co-teaching+ [41]89.585.767.447.965.651.827.913.7
Mixup [43]95.687.171.652.267.857.330.814.6
PENCIL [40]92.489.177.558.969.457.531.115.3
Meta-Learning [16]92.989.377.458.768.559.242.419.5
M-correction [1]94.092.086.869.173.966.148.224.3
DivideMix [15]96.194.693.276.077.374.660.231.5
C2D [47]96.395.294.493.578.676.467.758.7
AugDesc [22]96.395.493.891.979.577.266.441.2
GCE [5]90.089.373.936.568.153.322.18.9
Sel-CL+ [19]95.593.989.281.976.572.459.648.8
MOIT+ [23]94.191.881.174.775.970.647.641.8
SANM(DivideMix)96.495.894.692.381.278.268.743.5
SANM(C2D)96.696.495.795.181.979.371.661.9
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.393, + 0.47, + 0.513 + ], + "angle": 0, + "content": "with the label quality guided masking scheme, where mislabeled and correct-labeled samples are modulated adaptively. Meanwhile, the predictions of the original images are used for regularized label generation. Then the regularized features of the masked images are utilized for self-supervised masking reconstruction, which provides noise-free supervisions. Finally, SANM can be plugged into existing LNL frameworks to further boost performance." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.539, + 0.209, + 0.556 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.568, + 0.396, + 0.584 + ], + "angle": 0, + "content": "4.1. Datasets and Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.595, + 0.469, + 0.761 + ], + "angle": 0, + "content": "Simulated noisy datasets. We evaluate on two simulated noisy datasets: CIFAR-10/100 [14], which consists of 50k training samples and 10k test samples. Following previous work [15], two simulated noisy settings are considered: symmetric and asymmetric noise. Following noise rates are considered: \\(\\tau \\in \\{20\\%, 50\\%, 80\\%, 90\\}\\) for symmetric noise and \\(\\tau \\in \\{20\\%, 40\\%\\) for asymmetric noise. An 18-layer PreAct ResNet is used and we train for 300 epochs with a batch size of 64 and SGD optimizer. The initial learning rate is set as 0.02 and is reduced by a factor of 10 after 150 epochs. \\(\\beta\\) is empirically set as 1." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.765, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Real-world noisy datasets. We also validate our method on real-world noisy datasets Clothing-1M [38] and Animal10N [28], the former contains 1 million training samples with nearly \\(40\\%\\) mislabeled samples, the latter contains 55,000 human-labeled online images for 10 confusing animals. It includes approximately \\(8\\%\\) noisy-labeled samples. The original protocol is used to split the training and test images. We strictly follow the previous experimental settings, more details can be found in the supplementary materials." + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.392, + 0.81, + 0.408 + ], + "angle": 0, + "content": "Algorithm 1 The proposed SANM framework" + }, + { + "type": "text", + "bbox": [ + 0.501, + 0.412, + 0.892, + 0.437 + ], + "angle": 0, + "content": "Input: Noisy training set \\(D\\), encoder model \\(M_E(\\cdot;\\theta_E)\\), decoder model \\(M_D(\\cdot;\\theta_D)\\), batch size \\(b\\), max iterations \\(m\\), basic mask ratio \\(\\mu\\)." + }, + { + "type": "title", + "bbox": [ + 0.506, + 0.438, + 0.569, + 0.447 + ], + "angle": 0, + "content": "Procedure:" + }, + { + "type": "text", + "bbox": [ + 0.508, + 0.449, + 0.623, + 0.459 + ], + "angle": 0, + "content": "1: for \\( i = 1 \\) to \\( m \\) do" + }, + { + "type": "text", + "bbox": [ + 0.508, + 0.46, + 0.759, + 0.474 + ], + "angle": 0, + "content": "2: \\(\\{x_{i},y_{i}\\}_{i = 1}^{b}\\gets\\) SampleMiniBatch(D,b)." + }, + { + "type": "text", + "bbox": [ + 0.508, + 0.474, + 0.891, + 0.499 + ], + "angle": 0, + "content": "3: Feed \\(\\{x_i\\}_{i=1}^b\\) into \\(M_E\\) and generate feature maps \\(\\{F_i\\}_{i=1}^b\\) and predictions \\(\\{\\widetilde{y}_i\\}_{i=1}^b\\)." + }, + { + "type": "text", + "bbox": [ + 0.508, + 0.5, + 0.787, + 0.513 + ], + "angle": 0, + "content": "4: Generate activation maps \\(\\{A_{i}\\}_{i = 1}^{b}\\) by Eq. (1)." + }, + { + "type": "text", + "bbox": [ + 0.508, + 0.513, + 0.891, + 0.54 + ], + "angle": 0, + "content": "5: Calculate mask ratios \\(\\{r_i\\}_{i = 1}^b\\) and adversarial masked images \\(\\{x_{i}^{m}\\}_{i = 1}^{b}\\) by Eq. (2-5)." + }, + { + "type": "text", + "bbox": [ + 0.508, + 0.54, + 0.891, + 0.568 + ], + "angle": 0, + "content": "6: Feed \\(\\{x_i^m\\}_{i = 1}^b\\) into \\(M_E\\) and generate predictions \\(\\{y_i^{\\overline{m}}\\}_{i = 1}^b\\) and features \\(\\{\\mathbf{f}_i^m\\}_{i = 1}^b\\) by Eq. (6)." + }, + { + "type": "text", + "bbox": [ + 0.508, + 0.568, + 0.815, + 0.581 + ], + "angle": 0, + "content": "7: Calculate the regularized labels \\(\\{y_i^r\\}_{i = 1}^b\\) by Eq. (7)." + }, + { + "type": "text", + "bbox": [ + 0.508, + 0.581, + 0.767, + 0.592 + ], + "angle": 0, + "content": "8: Calculate cross-entropy loss \\(\\mathcal{L}_c\\) by Eq. (8)." + }, + { + "type": "text", + "bbox": [ + 0.508, + 0.592, + 0.89, + 0.618 + ], + "angle": 0, + "content": "9: Feed \\(\\{\\mathbf{f}_i^m\\}_{i = 1}^b\\) into \\(M_D\\) and generate reconstructed images \\(\\{x_i^r\\}_{i = 1}^b\\) by Eq. (9)." + }, + { + "type": "text", + "bbox": [ + 0.502, + 0.618, + 0.861, + 0.63 + ], + "angle": 0, + "content": "10: Calculate self-supervised reconstruction loss \\(\\mathcal{L}_r\\) by Eq. (10)." + }, + { + "type": "text", + "bbox": [ + 0.503, + 0.63, + 0.793, + 0.642 + ], + "angle": 0, + "content": "11: Update parameter \\(\\theta_{E},\\theta_{D}\\) in backward process." + }, + { + "type": "text", + "bbox": [ + 0.503, + 0.642, + 0.568, + 0.652 + ], + "angle": 0, + "content": "12: end for" + }, + { + "type": "text", + "bbox": [ + 0.502, + 0.653, + 0.714, + 0.666 + ], + "angle": 0, + "content": "Output: The final encoder \\(M_E(\\cdot ;\\theta_E)\\)" + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.7, + 0.701, + 0.716 + ], + "angle": 0, + "content": "4.2. Experimental Results" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.892, + 0.901 + ], + "angle": 0, + "content": "- Results on Simulated Noisy Datasets. Here we compare the proposed method with multiple baselines on CIFAR-10/100 using different symmetric noise levels. As shown in Tab. 1, by integrating with C2D [47], our SANM outperforms the naive C2D significantly and achieves state-of-the-art performance among these methods. Specifically, an increase of around \\(3 \\sim 4\\%\\) across all the symmetric noise cases on CIFAR-100 can be observed. Besides, when combined with DivideMix [15], SANM can bring greater improvement to its performance, especially for severe label noise cases. Under \\(90\\%\\) symmetric noise setting, our SANM (DivideMix) outperforms DivideMix by a large" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "16191" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.118, + 0.09, + 0.478, + 0.252 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.481, + 0.09, + 0.848, + 0.252 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.173, + 0.253, + 0.794, + 0.266 + ], + "angle": 0, + "content": "Figure 4. Investigation of the basic mask ratio \\(\\mu\\) under \\(50\\%\\) and \\(80\\%\\) symmetric noise of CIFAR-10/100." + }, + { + "type": "table_caption", + "bbox": [ + 0.081, + 0.274, + 0.33, + 0.287 + ], + "angle": 0, + "content": "Table 2. Asymmetric noise on CIFAR-10." + }, + { + "type": "table", + "bbox": [ + 0.093, + 0.29, + 0.316, + 0.504 + ], + "angle": 0, + "content": "
MethodNoisy ratio
20%40%
Joint-Optim [32]92.891.7
PENCIL [40]92.491.2
F-correction [24]89.9-
Distilling [46]92.790.2
Meta-Learning [16]-88.6
M-correction [1]-86.3
Iterative-CV [3]-88.0
DivideMix [15]93.493.4
REED [44]95.092.3
C2D [47]93.893.4
Sel-CL+ [19]95.293.4
GCE [5]87.378.1
RRL [17]-92.4
SANM(DivideMix)95.494.8
" + }, + { + "type": "table_caption", + "bbox": [ + 0.356, + 0.271, + 0.611, + 0.284 + ], + "angle": 0, + "content": "Table 3. Testing accuracy on Clothing-1M." + }, + { + "type": "table", + "bbox": [ + 0.395, + 0.285, + 0.571, + 0.508 + ], + "angle": 0, + "content": "
MethodAcc
CrossEntropy69.21
F-correction [24]69.84
M-correction [1]71.00
Joint-Optim [32]72.16
Meta-Cleaner [45]72.50
Meta-Learning [16]73.47
PENCIL [40]73.49
Self-Learning [10]74.45
DivideMix [15]74.76
Nested [4]74.90
AugDesc [22]75.11
RRL [17]74.90
GCE [5]73.30
C2D [47]74.30
SANM(DivideMix)75.63
" + }, + { + "type": "table_caption", + "bbox": [ + 0.627, + 0.271, + 0.894, + 0.325 + ], + "angle": 0, + "content": "Table 4. Comparison with masks generated from pre-trained backbones on CIFAR10/100. M: Method. P: Pretrained Backbone. S: SANM. D: DivideMix. C: C2D." + }, + { + "type": "table", + "bbox": [ + 0.629, + 0.326, + 0.89, + 0.508 + ], + "angle": 0, + "content": "
DatasetCIFAR-10
MPS20%50%80%90%
D✓X96.095.193.781.5
X✓96.495.894.692.3
C✓X96.395.495.093.9
X✓96.696.495.795.1
DatasetCIFAR-100
MPS20%50%80%90%
D✓X78.876.664.936.4
X✓81.278.268.743.5
C✓X80.077.169.258.4
X✓81.979.371.661.9
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.521, + 0.47, + 0.671 + ], + "angle": 0, + "content": "margin of \\(12\\%\\). A similar performance boost can be observed on the CIFAR-100 as well, especially in the high noise case. When compared with the implicit regularization method AugDesc [22], our SANM (DivideMix) can also achieve much better accuracy across all the settings. Tab. 2 shows comparisons of the test accuracy on CIFAR-10 with varied asymmetric noise levels. SANM shows a consistent improvement with respect to the state-of-the-art methods, demonstrating the ability of SANM in enhancing the robustness of the model to semantic noise." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.673, + 0.47, + 0.824 + ], + "angle": 0, + "content": "- Results on Real-world Noisy Datasets. The results on Clothing1M dataset are shown in Tab. 3, one can see that SANM can bring around \\(0.87\\%\\) improvement in accuracy to the baseline method DivideMix, and get much better result than other state-of-the-art methods, verifying that SANM is also effective in real-world noise scenarios. Moreover, SANM obtains \\(89.3\\%\\) accuracy on Animal-10N (detailed comparison on Animal-10N is in supplementary materials), which sets the new state-of-the-art, indicating that SANM can handle fine-grained real-world noisy datasets as well." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Generality of SANM. To further verify the effectiveness and generalization of SANM, we adopt vanilla CE, Co-teaching [9], CDR [37] and ELR+ [21], other than DivideMix and C2D, to work collaboratively with SANM. As shown in Tab. 6, SANM brings consistent performance" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.521, + 0.892, + 0.657 + ], + "angle": 0, + "content": "boost to all the corresponding baselines across all the noisy cases. For example, when seamlessly combined with SANM, an accuracy increase of around \\(14.9\\%\\) on the symmetric \\(80\\%\\) noise setting (CIFAR-100) can be achieved by vanilla CE, demonstrating that SANM is capable of effectively imposing adaptive regularization on noisy and clean samples respectively and thus alleviates over-fitting to noisy labels. Therefore, all the results indicate the generalization ability of SANM to boost existing LNL approaches." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.666, + 0.655, + 0.683 + ], + "angle": 0, + "content": "4.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Component Analysis. To explore the impact of each component in SANM, i.e., AMG-adversarial noisy masking generation, NLR-noisy label regularization, SMR-self-supervised masking reconstruction, we conduct an ablation study on CIFAR-10 and CIFAR-100. As shown in Tab. 5, we report test accuracy for all the noisy settings for SANM where the contribution of each component is clearly shown. Specifically, it can be observed that AMG is crucial to performance, especially under severe label noisy cases. Besides, the accuracy is further improved when further combined with NLR. By introducing noise-free self-supervised signals, SMR can also boost the performance of the baseline across all the settings. Best accuracy can be achieved by SANM when all the components are included. This ver" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "16192" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.089, + 0.895, + 0.118 + ], + "angle": 0, + "content": "Table 5. Ablation study for the effectiveness of each key component. AMG: adversarial noisy masking generation, NLR: noisy label regularization, SMR: self-supervised masking reconstruction." + }, + { + "type": "table", + "bbox": [ + 0.217, + 0.12, + 0.756, + 0.246 + ], + "angle": 0, + "content": "
ComponentCIFAR-10CIFAR-100
AMGNLRSMR20%50%80%90%20%50%80%90%
XXXBest96.194.693.276.077.374.660.231.5
Last95.794.492.975.476.974.259.631.0
✓XXBest96.395.394.091.480.277.368.042.7
Last96.295.193.690.879.777.067.542.5
✓✓XBest96.495.594.291.680.577.568.343.0
Last96.395.494.091.580.277.168.242.7
✓✓✓Best96.495.894.692.381.278.268.743.5
Last96.395.694.392.180.478.068.343.0
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.257, + 0.895, + 0.286 + ], + "angle": 0, + "content": "Table 6. Comparison between the LNL methods and their SANM applications with symmetric noise on CIFAR-10/100. Specifically, the 9-layer CNN is adopted as the backbone network of Co-teaching." + }, + { + "type": "table", + "bbox": [ + 0.172, + 0.288, + 0.795, + 0.466 + ], + "angle": 0, + "content": "
Dataset\nMethod/Noise ratioCIFAR-10CIFAR-100
20%50%80%90%20%50%80%90%
CEBest86.879.462.942.762.046.719.910.1
SANM(CE)Best92.489.772.151.570.953.134.818.6
Co-teaching [9]Best82.673.024.014.650.538.211.84.9
SANM(Co-teaching)Best89.278.236.420.758.251.319.413.4
CDR [37]Best90.485.047.212.363.339.529.28.0
SANM(CDR)Best92.691.655.316.772.756.436.620.8
ELR+ [21]Best94.693.891.175.277.572.458.230.8
SANM(ELR+)Best96.395.794.182.979.877.365.038.7
" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.47, + 0.45, + 0.485 + ], + "angle": 0, + "content": "ifies the effectiveness of all three key designs in SANM." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.489, + 0.473, + 0.761 + ], + "angle": 0, + "content": "Parameter Sensitivity. The basic mask ratio \\(\\mu\\) in Eq. (2) is a important parameter within the proposed method. To investigate how \\(\\mu\\) affects the performance, we conduct parameter analysis experiments under \\(50\\% / 80\\%\\) symmetric noise levels on CIFAR-10/100. Best test accuracy with respect to different \\(\\mu\\) is shown in Fig. 4. Compared with the results of baseline methods, i.e., masking the images with fixed ratios and random ratios (sampled from Uniform(0, \\(\\mu\\)) for each sample, SANM achieves superior performance. Moreover, the results show that keeping \\(\\mu\\) between 0.1 and 0.3 leads to better performance than without adversarial noisy masking \\((\\mu = 0)\\). This is mainly because large \\(\\mu\\) makes the masked training images and the test images differ significantly in image style, and thus the model is unable to learn class-specific discriminative information when trained with such strong regularization. However, an extremely small \\(\\mu\\) results in encoder overfit to noisy labels more severely. Therefore, we set \\(\\mu\\) as 0.2 to obtain the best performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.765, + 0.472, + 0.903 + ], + "angle": 0, + "content": "Comparison with Masks from Pre-trained Backbones. The effectiveness of the proposed adversarial masking generation is evaluated in Tab. 5. And further we conduct experiments that utilizing a strong backbone (i.e., pretrained ResNet-18 with DivideMix and C2D) to generate more object-centric masks as comparison to our method. The results shown in Tab. 4 indicate that even compared with the mask generation strategy by well-trained strong backbones, our adversarial masking generation can consis" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.47, + 0.895, + 0.531 + ], + "angle": 0, + "content": "tently performs better since the mask generation strategy of fixed well-trained backbones cannot adaptively adjust the mask strategy according to the training state of the base model, leading to sub-optimal results." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.54, + 0.62, + 0.555 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.561, + 0.895, + 0.743 + ], + "angle": 0, + "content": "In this paper, we propose SANM, a novel self-supervised adversarial noisy masking LNL method. SANM prevents models from overfitting noisy instances by explicitly imposing regularization on the features. A label quality guided adversarial masking scheme is designed to adaptively modify the inputs and noisy labels. Moreover, an auxiliary decoder is introduced to reconstruct images based on the modulated features, which enhances the generalization by providing extra noise-free self-supervised signals. Besides, SANM is flexible and can be easily plugged into existing LNL frameworks. State-of-the-art performance is obtained by SANM on both synthetic and real-world noisy datasets." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.755, + 0.661, + 0.772 + ], + "angle": 0, + "content": "Acknowledgement" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.895, + 0.903 + ], + "angle": 0, + "content": "This work was supported by National Natural Science Fund of China (62076184, 61976158, 61976160, 62076182, 62276190), in part by Fundamental Research Funds for the Central Universities and State Key Laboratory of Integrated Services Networks (Xidian University), in part by Shanghai Innovation Action Project of Science and Technology (20511100700) and Shanghai Natural Science Foundation (22ZR1466700)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "16193" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.176, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.17 + ], + "angle": 0, + "content": "[1] Eric Arazo, Diego Ortego, Paul Albert, Noel E O'Connor, and Kevin McGuinness. Unsupervised label noise modeling and loss correction. In Proc. International Conference on Machine Learning (ICML), 2019. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.171, + 0.472, + 0.255 + ], + "angle": 0, + "content": "[2] Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al. A closer look at memorization in deep networks. In Proc. International Conference on Machine Learning (ICML), pages 233-242. PMLR, 2017. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.256, + 0.471, + 0.311 + ], + "angle": 0, + "content": "[3] Pengfei Chen, Benben Liao, Guangyong Chen, and Shengyu Zhang. Understanding and utilizing deep neural networks trained with noisy labels. In Proc. International Conference on Machine Learning (ICML), 2019. 1, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.311, + 0.471, + 0.382 + ], + "angle": 0, + "content": "[4] Yingyi Chen, Xi Shen, Shell Xu Hu, and Johan A. K. Suykens. Boosting co-teaching with compression regularization for label noise. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 2688-2692, June 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.382, + 0.471, + 0.437 + ], + "angle": 0, + "content": "[5] Aritra Ghosh and Andrew Lan. Contrastive learning improves model robustness under label noise. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2703-2708, 2021. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.438, + 0.471, + 0.48 + ], + "angle": 0, + "content": "[6] Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.481, + 0.471, + 0.55 + ], + "angle": 0, + "content": "[7] Aayush Gupta, Ayush Jaiswal, Yue Wu, Vivek Yadav, and Pradeep Natarajan. Adversarial mask generation for preserving visual privacy. In 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), pages 1-5. IEEE, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.55, + 0.471, + 0.632 + ], + "angle": 0, + "content": "[8] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Co-teaching: Robust training of deep neural networks with extremely noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), volume 31, 2018. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.634, + 0.471, + 0.704 + ], + "angle": 0, + "content": "[9] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Coteaching: Robust training of deep neural networks with extremely noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), 2018. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.704, + 0.471, + 0.746 + ], + "angle": 0, + "content": "[10] Jiangfan Han, Ping Luo, and Xiaogang Wang. Deep self-learning from noisy labels. In Proc. IEEE International Conference on Computer Vision (ICCV), 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.746, + 0.471, + 0.789 + ], + "angle": 0, + "content": "[11] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377, 2021.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.789, + 0.471, + 0.844 + ], + "angle": 0, + "content": "[12] Qibin Hou, PengTao Jiang, Yunchao Wei, and Ming-Ming Cheng. Self-erasing network for integral object attention. In Proc. Advances in Neural Information Processing Systems (NeurIPS), volume 31, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.845, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[13] Tao Hu, Honggang Qi, Qingming Huang, and Yan Lu. See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification. arXiv preprint arXiv:1901.09891, 2019. 3" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.115, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.121 + ], + "angle": 0, + "content": "[14] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.122, + 0.892, + 0.178 + ], + "angle": 0, + "content": "[15] Junnan Li, Richard Socher, and Steven CH Hoi. Dividemix: Learning with noisy labels as semi-supervised learning. In Proc. International Conference on Learning Representations (ICLR), 2020. 1, 2, 3, 4, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.179, + 0.892, + 0.234 + ], + "angle": 0, + "content": "[16] Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S. Kankanhalli. Learning to learn from noisy labeled data. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.235, + 0.892, + 0.291 + ], + "angle": 0, + "content": "[17] Junnan Li, Caiming Xiong, and Steven CH Hoi. Learning from noisy data with robust representation learning. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 9485-9494, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.292, + 0.892, + 0.348 + ], + "angle": 0, + "content": "[18] Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, and Yun Fu. Tell me where to look: Guided attention inference network. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 9215-9223, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.349, + 0.892, + 0.391 + ], + "angle": 0, + "content": "[19] Shikun Li, Xiaobo Xia, Shiming Ge, and Tongliang Liu. Selective-supervised contrastive learning with noisy labels. arXiv preprint arXiv:2203.04181, 2022. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.392, + 0.892, + 0.462 + ], + "angle": 0, + "content": "[20] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), volume 33, pages 20331–20342, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.463, + 0.892, + 0.532 + ], + "angle": 0, + "content": "[21] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), 2020. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.533, + 0.892, + 0.589 + ], + "angle": 0, + "content": "[22] Kento Nishi, Yi Ding, Alex Rich, and Tobias Hollerer. Augmentation strategies for learning with noisy labels. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8022-8031, 2021. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.59, + 0.892, + 0.659 + ], + "angle": 0, + "content": "[23] Diego Ortego, Eric Arazo, Paul Albert, Noel E O'Connor, and Kevin McGuinness. Multi-objective interpolation training for robustness to label noise. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6606-6615, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.66, + 0.892, + 0.731 + ], + "angle": 0, + "content": "[24] Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. Making deep neural networks robust to label noise: A loss correction approach. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.731, + 0.892, + 0.787 + ], + "angle": 0, + "content": "[25] Haim Permuter, Joseph Francos, and Ian Jermyn. A study of gaussian mixture models of color and texture features for image classification and segmentation. Pattern recognition, 39(4):695-706, 2006. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.788, + 0.892, + 0.845 + ], + "angle": 0, + "content": "[26] Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. Learning to reweight examples for robust deep learning. In Proc. International Conference on Machine Learning (ICML), pages 4334-4343. PMLR, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.845, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[27] Hwanjun Song, Minseok Kim, and Jae-Gil Lee. Selfie: Refurbishing unclean samples for robust deep learning. In Proc. International Conference on Machine Learning (ICML), 2019. 1" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "16194" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "[28] Hwanjun Song, Minseok Kim, and Jae-Gil Lee. SELFIE: Refurbishing unclean samples for robust deep learning. In Proc. International Conference on Machine Learning (ICML), pages 5907-5915, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.149, + 0.47, + 0.204 + ], + "angle": 0, + "content": "[29] Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.205, + 0.471, + 0.287 + ], + "angle": 0, + "content": "[30] Erik Stammes, Tom FH Runia, Michael Hofmann, and Mohsen Ghafoorian. Find it if you can: end-to-end adversarial erasing for weakly-supervised semantic segmentation. In Thirteenth International Conference on Digital Image Processing (ICDIP 2021), volume 11878, page 1187825. International Society for Optics and Photonics, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.288, + 0.471, + 0.357 + ], + "angle": 0, + "content": "[31] Guolei Sun, Hisham Cholakkal, Salman Khan, Fahad Khan, and Ling Shao. Fine-grained recognition: Accounting for subtle differences between similar classes. In Association for the Advancement of Artificial Intelligence (AAAI), volume 34, pages 12047–12054, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.358, + 0.47, + 0.412 + ], + "angle": 0, + "content": "[32] Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa. Joint optimization framework for learning with noisy labels. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 1, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.414, + 0.47, + 0.467 + ], + "angle": 0, + "content": "[33] Gabriele Valvano, Andrea Leo, and Sotirios A Tsaftaris. Reusing adversarial mask discriminators for test-time training under distribution shifts. arXiv preprint arXiv:2108.11926, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.469, + 0.47, + 0.537 + ], + "angle": 0, + "content": "[34] Hongxin Wei, Lei Feng, Xiangyu Chen, and Bo An. Combating noisy labels by agreement: A joint training method with co-regularization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 13726–13735, 2020. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.539, + 0.47, + 0.621 + ], + "angle": 0, + "content": "[35] Yunchao Wei, Jiashi Feng, Xiaodan Liang, Ming-Ming Cheng, Yao Zhao, and Shuicheng Yan. Object region mining with adversarial erasing: A simple classification to semantic segmentation approach. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1568-1576, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.623, + 0.469, + 0.677 + ], + "angle": 0, + "content": "[36] Yichen Wu, Jun Shu, Qi Xie, Qian Zhao, and Deyu Meng. Learning to purify noisy labels via meta soft label corrector. In Association for the Advancement of Artificial Intelligence (AAAI), 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.679, + 0.469, + 0.746 + ], + "angle": 0, + "content": "[37] Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang. Robust early-learning: Hindering the memorization of noisy labels. In Proc. International Conference on Learning Representations (ICLR), 2021. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.748, + 0.469, + 0.803 + ], + "angle": 0, + "content": "[38] Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang. Learning from massive noisy labeled data for image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.805, + 0.469, + 0.871 + ], + "angle": 0, + "content": "[39] Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu, Yuefeng Chen, and Hui Xue. Towards face encryption by generating adversarial identity masks. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 3897-3907, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.874, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[40] Kun Yi and Jianxin Wu. Probabilistic end-to-end noise correction for learning with noisy labels. In Proc. IEEE Confer-" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.471, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "ence on Computer Vision and Pattern Recognition (CVPR), pages 7017-7025, 2019. 1, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.892, + 0.189 + ], + "angle": 0, + "content": "[41] Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W Tsang, and Masashi Sugiyama. How does disagreement help generalization against label corruption? In Proc. International Conference on Machine Learning (ICML), 2019. 1, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.192, + 0.892, + 0.275 + ], + "angle": 0, + "content": "[42] Boshen Zhang, Yuxi Li, Yuanpeng Tu, Jinlong Peng, Yabiao Wang, Cunlin Wu, Yang Xiao, and Cairong Zhao. Learning from noisy labels with coarse-to-fine sample credibility modeling. In Computer Vision-ECCV 2022 Workshops: Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part II, pages 21-38. Springer, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.277, + 0.892, + 0.318 + ], + "angle": 0, + "content": "[43] Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412, 2017. 1, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.32, + 0.892, + 0.359 + ], + "angle": 0, + "content": "[44] Hui Zhang and Quanming Yao. Decoupling representation and classifier for noisy label learning. arXiv preprint arXiv:2011.08145, 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.362, + 0.892, + 0.416 + ], + "angle": 0, + "content": "[45] Weihe Zhang, Yali Wang, and Yu Qiao. Metacleaner: Learning to hallucinate clean representations for noisy-labeled visual recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.419, + 0.892, + 0.473 + ], + "angle": 0, + "content": "[46] Z. Zhang, H. Zhang, S. Arik, H. Lee, and T. Pfister. Distilling effective supervision from severe label noise. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.476, + 0.892, + 0.545 + ], + "angle": 0, + "content": "[47] Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M Bronstein, and Or Litany. Contrast to divide: Self-supervised pre-training for learning with noisy labels. In Proc. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 1657-1667, 2022. 1, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.547, + 0.892, + 0.599 + ], + "angle": 0, + "content": "[48] Guoqing Zheng, Ahmed Hassan Awadallah, and Susan Dumas. Meta label correction for noisy label learning. In Association for the Advancement of Artificial Intelligence (AAAI), 2021. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.602, + 0.892, + 0.669 + ], + "angle": 0, + "content": "[49] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2921-2929, 2016. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.672, + 0.892, + 0.74 + ], + "angle": 0, + "content": "[50] Xiong Zhou, Xianming Liu, Chenyang Wang, Deming Zhai, Junjun Jiang, and Xiangyang Ji. Learning with noisy labels via sparse regularization. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 72-81, 2021. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.74 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "16195" + } + ] +] \ No newline at end of file diff --git a/2023/Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking/fee95f26-a8a8-4c1d-9f44-c06f33970b8b_origin.pdf b/2023/Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking/fee95f26-a8a8-4c1d-9f44-c06f33970b8b_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..36c95f893b9581610010299f7edffd17505d37e8 --- /dev/null +++ b/2023/Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking/fee95f26-a8a8-4c1d-9f44-c06f33970b8b_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:1df5c70d7017f5721e1a705095d87fd7b212737fb534c903d06d1f31448c5799 +size 3304299 diff --git a/2023/Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking/full.md b/2023/Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking/full.md new file mode 100644 index 0000000000000000000000000000000000000000..2147826b9602994d1aa16f0bec0449965a582fc0 --- /dev/null +++ b/2023/Learning With Noisy Labels via Self-Supervised Adversarial Noisy Masking/full.md @@ -0,0 +1,358 @@ +# Learning with Noisy labels via Self-supervised Adversarial Noisy Masking + +Yuanpeng $\mathrm{Tu}^{1*}$ Boshen Zhang $^{2*}$ Yuxi Li $^{2}$ Liang Liu $^{2}$ Jian Li $^{2}$ + +Jiangning Zhang² Yabiao Wang²† Chengjie Wang²,³ Cai Rong Zhao¹ + +1Dept. of Electronic and Information Engineering, Tongji University, Shanghai + +2YouTu Lab, Tencent, Shanghai, 3Shanghai Jiao Tong University + +{2030809，zhaocairong}@tongji.edu.cn + +{boshenzhang, yukiyxli, leoneliu, swordli, vtzhang, caseywang, jasoncjwang}@tencent.com + +# Abstract + +Collecting large-scale datasets is crucial for training deep models, annotating the data, however, inevitably yields noisy labels, which poses challenges to deep learning algorithms. Previous efforts tend to mitigate this problem via identifying and removing noisy samples or correcting their labels according to the statistical properties (e.g., loss values) among training samples. In this paper, we aim to tackle this problem from a new perspective, delving into the deep feature maps, we empirically find that models trained with clean and mistrabeled samples manifest distinguishable activation feature distributions. From this observation, a novel robust training approach termed adversarial noisy masking is proposed. The idea is to regularize deep features with a label quality guided masking scheme, which adaptively modulates the input data and label simultaneously, preventing the model to overfit noisy samples. Further, an auxiliary task is designed to reconstruct input data, it naturally provides noise-free self-supervised signals to reinforce the generalization ability of models. The proposed method is simple yet effective, it is tested on synthetic and real-world noisy datasets, where significant improvements are obtained over previous methods. Code is available at https://github.com/yuanpengtu/SANM. + +# 1. Introduction + +Deep learning has achieved remarkable success, relying on large-scale datasets with human-annotated accurate labels. However, collecting such high-quality labels is time-consuming and expensive. As an alternative, inexpensive strategies are usually used for generating labels for large-scale samples, such as web crawling, leveraging search engines, or using machine-generated annotations. All these + +![](images/8d4bd35fab85ba2e38ae07afd0eb4dd0865f3e85b92a1da01d00f955cf066a03.jpg) + +![](images/c5a3240a76f155b7daf58ba9769642981be2a78478f39ad0e7116b0fe6e3c53a.jpg) + +![](images/0a33b35fe9dc6f958e6718d55aebb889fec3bce05d13a15f4abc67c6bd540ea1.jpg) + +![](images/969ee81f27629586b384a274ecd4f6a7755d06e9c04b89dd54c30ffe41f880a0.jpg) + +![](images/96a6e70c829cfea2ddfb3011b41890d279bca7d2b134071a1514dd0d76bee9e4.jpg) +(a) Clean-trained Mis-predicted + +![](images/ea2c1fe08c589a9b65509289c4d06b2e65ecaa15d04c73a0a79b956f7b07451d.jpg) +Figure 1. Activation maps of mis-predicted (a-b) and correctly-predicted (c) samples when training PreAct ResNet-18 with clean (i.e., clean-trained) and noisy (i.e., noisy-trained) data on CIFAR-10 (1st and 2nd row) and Clothing1M [38] (3rd row). + +![](images/1655adbf0782bbefedfd52e0342ac0ed1650deca53d21d23c94b2c8e1d56c225.jpg) + +![](images/109f164380a852ec6f9c25573c55cf06933ebfb63cb41fb02fc55faffd524a91.jpg) + +![](images/2f7a8b025beca001b4aa424f13c1c95a573fddb0a630e41f7309e32e1f03a24d.jpg) + +![](images/072f7ba010ad9cfe4d3e0e244210b6f4a5ca0ee31b239afb79b416d2f651adef.jpg) + +![](images/3da8e92445975de0b70d38cb559abb194e3c5860930e9551161407b368bee1a8.jpg) +(b) Noisy-trained Mis-predicted + +![](images/07f1f8ea1049e309594bf4212bfb0592289ad084177490b60f06cb088b89548f.jpg) + +![](images/d2ed929b9fdc3b2cd0ba998f011c1d3079ea1b7beeb78eed8aec056b697f8621.jpg) + +![](images/ef52a15d1265a9306d7c35570248901526ba7c220a805be4116db916f5adadc7.jpg) + +![](images/c9a9b6a87e31d1b7897e240c4230e42404443a362e40882d4511b618af12a561.jpg) + +![](images/fad8268d672a74f6efb28e34d3c67e8993f941a238232158d13da19deec7d10a.jpg) + +![](images/0febccb808759dc20ff5db891783eb283d73a14938fdbe3d28ebda19499cc4e2.jpg) +(c) Noisy-trained Correctly-predicted + +![](images/3320e84ca9307b47f48886e3fafd5ac9bbde467f069b9c44a2cc2655625a2d60.jpg) + +alternative methods inevitably yield numerous noisy samples. However, previous research [2] has revealed that deep networks can easily overfit to noisy labels and suffer from dramatic degradation in the generalization performance. + +Towards this problem, numerous Learning with Noisy labels (LNL) approaches have been proposed. Sample selection methods [3, 8, 34, 41] are the most straightforward methods, which attempt to select clean samples based on certain criterion (e.g., loss value) and then reweight or discard the noisy instances to reduce the interference of mislabeled training samples. However, these methods fail to leverage the potential information of the discarded samples. Similarly, label correction based methods [32, 40, 48] try to correct labels, which often impose assumptions on the existence of a small correctly-labeled validation subset or directly utilize predictions of deep models. However, such assumptions can not be always full-filled in real-world noisy datasets [27], leading to limited application scenarios. Besides, the predictions of deep model tend to fluctuate when training with mislabeled samples, making the label correction process unstable and sub-optimal [36]. On the contrary, regularization based methods [2, 6, 15, 20, 26, 29, 43, 47, 50] aim to alleviate the side effect of label noise by preventing the deep models from overfitting to all training sam + +ples, which is flexible and can work collaboratively with other LNL methods. Both explicit and implicit regularization techniques were proposed, the former generally calibrates the parameter update of networks by modifying the expected training loss, i.e., dropout. The latter focuses on improving the generalization by utilizing the stochasticity, i.e., data augmentation strategy and stochastic gradient descent [29]. However, most of these methods are designed for general fully-supervised learning tasks with correctly-labeled samples, and poor generalization ability could be obtained when the label noise is severe [29]. + +In this paper, we present a novel regularization based method specialized for LNL problem. Our method is inspired by an observation that some differences exist in activation maps between the predictions of deep models trained with clean and noisy labels. As shown in Fig. 1 (a), activation maps of mis-predicted samples by the model trained with clean labels are always focused on their foreground areas. By contrast, when the model is trained with noisy labels, it tends to generate results focused on the meaningless background area for the mis-predicted samples (Fig. 1 (b)). And even for cases that the prediction is correct, i.e., Fig. 1 (c), the high-activated region drifts to irrelevant (i.e., edge) areas of the object, rather than the main regions of the object. This indicates that model trained with mistrabeled samples is likely to overfit to some corner parts of the object from noisy data or remember the less-informative regions (i.e., background). Therefore, from this observation, we propose a novel Self-supervised Adversarial Noisy Masking (SANM) method for LNL. The idea is to explicitly regularize the model to generate more diverse activation maps through masking the certain region (e.g., max-activated) of the input image and thus alleviates the confirmation bias [8]. + +Moreover, to investigate how the masking strategies affect the performance of training deep models with noisy labels, we conduct a simple experiment, where different adversarial masking strategies (i.e., masking the max-activated region of input images with fixed, random, and noise-aware mask ratio) were applied to the popular LNL framework DivideMix [15]. As shown in Fig. 2, we find that the performance gain is much more sensitive to the masking strategy. A fixed or random mask ratio obtain only marginal gain, but when dealing with the correctly-labeled and mislabeled samples with different mask ratios, a significant performance gain is achieved. This indicates that how to design an effective adversarial masking method in the case of LNL is non-trivial and not well addressed. + +Towards the problem above, a label quality guided adaptive masking strategy is proposed, which modulates the image label and the mask ratio of image masking simultaneously. The label is updated via first leveraging the soft-distributed model prediction and then reducing the proba + +bility of max-activated class with the noise-aware masking ratio, while at the same time lifting the probability of other classes. Intuitively, a sample with a high probability of being mislabeled will possess a larger masking ratio, leading to a more strictly regularized input image and modulated label. Therefore, the negative impact of noisy labels can be greatly reduced. As for the correctly-labeled samples, the masking ratio is relatively small, which plays the role of general regularization strategy and improves the model generalization ability by preventing the model from overfitting training samples. Further, we specially customized an auxiliary decode branch to reconstruct the original input image, which provides noise-free self-supervised information for learning a robust deep model. The proposed SANM is flexible and can further boost existing LNL methods. It is tested on synthetic and real-world large-scale noisy datasets, and elaborately ablative experiments were conducted to verify each design component of the proposed method. In a nutshell, the key contributions of this work are: + +- We propose a novel self-supervised adversarial noisy masking method named SANM to explicitly impose regularization for LNL problem, preventing the model from overfitting to some corner parts of the object or less-informative regions from noisy data; +- A label quality guided masking strategy is proposed to differently adjust the process for clean and noisy samples according to the label quality estimation. This strategy modulates the image label and the ratio of image masking simultaneously; +- A self-supervised mask reconstruction auxiliary task is designed to reconstruct the original images based on the features of masked ones, which aims at enhancing generalization by providing noise-free supervision signals. + +# 2. Related Work + +# 2.1. Learning with Noisy Labels + +A variety of methods have been proposed to improve the robustness of DNNs on noisy datasets, which consist of regularization, label correction, and noisy sample selection. + +Regularization based methods help resist memorizing noisy data since previous works [2] have revealed that networks always firstly learn simple patterns before memorizing noisy hard data. Previous research [29] claims that these methods can be roughly divided into two categories: explicit/implicit regularization. Explicit regularization based methods generally modify the losses for the training samples. Among them, ELR [20] tries to alleviate the influence of noisy data by regularizing the loss gradient. SR [50] proposes a spare regularization method to restrict network output to the permutation set over a one-hot vector. Meta learning based methods [26] impose regularization on the sample gradient directions by bi-level optimization with a small un- + +![](images/d9f7e7760fc27be3fb34c6179dbf7fc31648c8ce4fe0933b6266a37d6f9bf39e.jpg) +Figure 2. A experiment for masking the max-activated region with different mask ratios. The performance gains of different mask strategies under $50\%$ and $80\%$ symmetric noise of CIFAR-10/100 [14] are reported, where DivideMix [15] is adopted as the baseline. "Fixed(0.2/0.3)" denotes masking all the images with the same mask ratio of 0.2/0.3. "Random" represents masking images with a random mask ratio between 0.2 and 0.4. "Noise-aware" is masking noisy samples with a mask ratio of 0.3 while the ratio for clean ones is 0.2. + +![](images/0ae134af4b1e650eb094c2b8960f9b00e1ddc14124550f7990b4a3e3c4759f42.jpg) + +![](images/f57a73def6e20f2d396e0ee9762818de7533667b15fead30d3824fde8a990273.jpg) + +![](images/61afd408f6f53f57f3faa14b04f3d1f56a9ef09e1834e6239af837af8238c418.jpg) + +biased validation. In addition to these regularizers, example re-weighting strategies are also popular explicit regularization solutions to the over-fitting problem. However, these methods often need sensitive hyper-parameters or deeper networks to improve the model capacity. For implicit regularization methods, they generally utilize the stochasticity effect, i.e., augmentation and stochastic gradient descent. Among them, semi-supervised based LNL methods [15,47] generally use mixup [43] as an effective regularization strategy to hinder over-fitting, which makes the network favor simple linear behavior in-between training examples. Adversarial samples based methods [6] improve the robustness by helping the model classify original and perturbed inputs. Since the model is unable to distinguish right from wrong by itself, regularization can generally work surprisingly well in certain synthetic noisy cases. + +Label correction methods seek to transform noisy labels to correct ones directly. The pseudo labeling strategy is commonly adopted by these methods, which utilizes the confident output of the network. Joint [32] optimizes labels and network parameters in an alternating strategy. PENCIL [40] corrects and updates labels probabilistically through the back-propagation mechanism. MLC [48] trains a label correction network through meta learning to generate pseudo labels for the training of main models. However, these approaches either need extra clean validation data or suffer from significant performance drop under high noise settings due to the low quality of generated pseudo labels. + +Noisy sample selection approaches mostly exploit the small-loss criterion, which is based on the observation that samples with noisy labels tend to have much higher losses than the clean ones. Among these methods, MentorNet uses a pre-trained teacher model to select clean samples for the training of a student model. Co-teaching based methods [8, 34, 41, 42] update the parameters of two parallel networks with clean instances chosen from their peer networks, which target avoiding the confirmation bias problem. INCV [3] attempts to separate clean data from the noisy training set by using cross-validation and removing high-loss samples. Though these methods may reduce the risk of error correction, they fail to use the potential infor + +mation of the abandoned samples. Moreover, most of them need prior knowledge, i.e., the noise rate. + +# 2.2. Masking Regularization Techniques + +As a commonly adopted strategy in weakly-supervised semantic segmentation, adversarial masking regularization aims at helping models discover less apparent semantic objects. Specifically, a Class Activation feature Map (CAM) [49] branch is first used to generate initial attention maps and selectively mask the discovered areas in the images through a threshold. Then the masked images are used for the training of another network. AE-PSL [35] iteratively erases the images and expands target object regions with the erased areas to produce final contiguous segmentation masks. Self-erasing [12] utilizes the background prior to make models generate more refined and focused attention on the foreground area. GAIN [18] tries to impose constraints on the activation maps in a regularized style to generate more complete attention regions. PSA [30] trains two networks with opposing losses to eliminate the drawbacks of certain mask strategies. In addition to weakly-supervised semantic segmentation, adversarial masking is also used in fine-grained visual classification. DB [31] designs a diversification block to mask out the most salient features, aiming at forcing networks to mine subtle differences between images. WS-DAN [13] randomly crop and drop one of the discriminative regions in the attention maps to encourage models to mine local features. Several adversarial masking strategies are proposed in [7, 39] to preserve human privacy by masking the minimal number of pixels required for identification. [33] designs an adversarial discriminator for mask generation at inference to correct segmentation errors. However, the purpose of these methods is quite different from our approach. Based on the assumption that the samples are all correctly-labeled, previous methods generally retain the original most salient regions while helping models mine more subtle information through masking regularization. By contrast, our method aims to regularize the model through adversarial masking to make the activation map more dispersed rather than concentrated to specific regions, and a similar masking strategy is applied to gener + +ate more smooth pseudo labels, thus avoiding over-fitting to noisy labels and alleviating the confirmation bias. + +# 3. Method + +Overview. For convenience, we begin by introducing the notations. Consider LNL as a classification task, the class number is $c$ . The noisily-labeled training dataset is denoted as $D = \{(x_i,y_i)\}_{i=1}^N$ , where $x_i \in \mathbb{R}^{H_i \times W_i \times 3}$ and $y_i \in \{0,1\}^c$ are the $i$ -th sample and its noisy label, $H_i, W_i$ are the height and width of samples. $N$ represents the dataset size. The encoder and decoder models are denoted as $M_E$ and $M_D$ respectively. We firstly introduce the overall framework of the proposed SANM. As shown in Fig. 3, a label quality guided masking scheme is proposed to adaptively perform adversarial noisy masking for each sample where a contrastive regularization strategy is imposed on the noisy predictions as well. Afterward, the representations of the masked images are used for the self-supervised reconstruction of original images, which aims at preventing models from overfitting to the noisy labels by providing noise-free signals. For application, SANM can be easily integrated with existing LNL frameworks to further boost their performance. + +# 3.1. Adversarial Noisy Masking + +Adversarial Masking Generation. To highlight the semantic regions of correctly labeled samples and keep the captured attention areas of the noisy samples from expanding to meaningless background regions, we propose a novel Adversarial noisy Masking Generation (AMG) strategy during the training process. Specifically, we begin by extracting the feature of image $x_{i}$ through $M_E$ and the obtained feature map and prediction are denoted as $F_{i} \in \mathbb{R}^{H_{F} \times W_{F} \times N_{C}}$ and $\widetilde{y}_{i}$ respectively, where $H_{F}, W_{F}, N_{C}$ are the height, width and channel number of the feature layer. For each sample, a two-component Gaussian Mixture Model (GMM) [25] is fit to the cross-entropy loss with the Expectation-Maximization algorithm, where the generated weight for $x_{i}$ is denoted as $G_{i}$ . Afterward, $F_{i}$ is used to generate the activation maps [49] $A_{i} \in \mathbb{R}^{H_{i} \times W_{i}}$ of the category with the largest prediction, which is derived as: + +$$ +A _ {i} = \operatorname {C A M} \left(F _ {i}, \arg \max \left(\tilde {y} _ {i}\right)\right) \tag {1} +$$ + +where $\mathrm{CAM}(\cdot)$ is the activation map generation function. Then the coordinates with the maximum and minimum activation values for $x_{i}$ are recorded as $\{h_i^{\max},w_i^{\max}\}$ and $\{h_i^{\min},w_i^{\min}\}$ respectively. For the $\{h_i^{\max},w_i^{\max}\}$ , assuming that the GMM division is overall correct, it is believed that the activation maps of correct-labeled samples are mainly focused on the target area of the foreground, while due to the interference of noisy labels, the max-activated region in the activation maps of mislabeled sam + +plies are mostly concentrated in meaningless background areas. To breakdown the over-fitting to noisy labels, we intend to adversarially augment training samples via a novel making strategy. In particular, denote the loss function as $\mathcal{L}(x_i,y_i)$ , we try to maximize $\mathcal{L}$ by adaptively generating region perturbations for noisy and clean samples across the training process. The objective can be derived as $\max \mathcal{L}(x_i + \sigma ,y_i)$ , where $\sigma$ is the generated perturbations. Specifically, we generate masks with larger ratios for the max-activated region of noisy samples to force the model to reduce its response to these areas and pay more attention to the rest area, where the target is more likely to be found. For the correct-labeled ones, high response areas in their activation maps are likely to exist in the edge area of the object since they are affected by the gradient of noisy samples. Thus smaller mask ratios are used for the max-activated regions of them, which keep the model from ignoring the masked area and force it to mine discriminative information from the rest areas of the target since the correct labels help limit the network to pay more attention to the foreground area. However, it will make the activation map of the well-trained model scattered to the meaningless area when filtering out nearly all the peak locations for each sample through a threshold like existing methods, which violates our purpose. Therefore, we choose to mask random rectangular areas based on recorded coordinates. Specifically, the mask ratio $r_i$ for $x_{i}$ is calculated as follows: + +$$ +r _ {i} = \mu \times (1 - G _ {i}) \tag {2} +$$ + +where $\mu$ is the basic mask ratio. The more likely a sample is to be noisy, the smaller the $G_{i}$ is. Based on Eq. (2), the greater mask ratio will be adopted for this sample, thus stronger regularization will be imposed [15]. Subsequently, the aspect of the masked area $\delta_{i}$ is generated as follows: + +$$ +\delta_ {i} \sim \operatorname {U n i f o r m} (\delta , \frac {1}{\delta}) \tag {3} +$$ + +where $\delta$ is a hyper-parameter for generating the aspect. Based on $r_i$ and $\delta_{i}$ , the height and width of the masked area are given by: + +$$ +\left\{ \begin{array}{r l} h _ {i} ^ {u p} & = \max \left(h _ {i} ^ {\max } - \sqrt {\frac {\left(H _ {x} \times W _ {x}\right) \times r _ {i} \times \delta_ {i}}{4}}, 0\right) \\ h _ {i} ^ {\mathrm {d n}} & = \min \left(h _ {i} ^ {\max } + \sqrt {\frac {\left(H _ {x} \times W _ {x}\right) \times r _ {i} \times \delta_ {i}}{4}}, H _ {x}\right) \\ w _ {i} ^ {\mathrm {l t}} & = \max \left(w _ {i} ^ {\max } - \sqrt {\frac {\left(H _ {x} \times W _ {x}\right) \times r _ {i}}{4 \delta_ {i}}}, 0\right) \\ w _ {i} ^ {\mathrm {r t}} & = \min \left(w _ {i} ^ {\max } + \sqrt {\frac {\left(H _ {x} \times W _ {x}\right) \times r _ {i}}{4 \delta_ {i}}}, W _ {x}\right) \end{array} \right. \tag {4} +$$ + +The selected locations in the masked area of $x_{i}$ are assigned random values generated from a uniform distribution. Therefore, the adversarial noisy masked image $x_{i}^{m}$ + +![](images/abe64279038ef48d4752e743d5b013142dd0b4c8895a0b6547c17d9310e8f72b.jpg) +Figure 3. The framework of SANM. Three components are included: AMG (Adversarial Masking Generation), NLR (Noisy Label Regularization), SMR (Self-supervised Masking Reconstruction). In AMG and NLR, firstly feed the images to an encoder and generate activation maps. And a label quality guided adversarial masking strategy is proposed to modulate the images and noisy labels simultaneously. Further, an auxiliary decode branch is designed in SMR to reconstruct input images from the features of masked images. Finally, the generated modulated images and labels of SANM together with the reconstruction loss can be directly adopted for existing LNL frameworks. + +can be derived as follows: + +$$ +x _ {i} ^ {m} (m, n) = \left\{ \begin{array}{l l} U (0, 1), & \text {i f} m \in \left[ h _ {i} ^ {u p}, h _ {i} ^ {d n} \right], n \in \left[ w _ {i} ^ {l t}, w _ {i} ^ {r t} \right], \\ x _ {i} (m, n), & \text {o t h e r w i s e .} \end{array} \right. \tag {5} +$$ + +where $U$ denotes the Uniform distribution. Similar operation is performed on the regions of minimum activation value with the coordinates $\{h_i^{min},w_i^{min}\}$ as well. Different from Eq. (2), since the regions of minimum activation value for correct-labeled samples are mainly focused on the background, a smaller mask ratio is adopted for them, helping models narrow the search scope for targets. Therefore, we set the mask ratio of the regions with minimum activation values as $\mu \times G_{i}$ . + +Noisy Label Regularization. To penalize the overconfident predictions of the network, a contrastive Noisy Label Regularization (NLR) strategy is designed. Specifically, the generated $x_{i}^{m}$ is fed into $M_{E}$ to obtain the prediction $\widetilde{y_{i}^{m}}$ and feature $\mathbf{f}_{\mathbf{i}}^{m}$ as follows: + +$$ +\mathbf {f} _ {\mathbf {i}} ^ {m} = M _ {E} \left(x _ {i} ^ {m}; \theta_ {E}\right) \tag {6} +$$ + +where $\theta_{E}$ denotes the parameters of the encoder $M_{E}$ . Specifically, we give the pseudo labels of $x_{i}^{m}$ with the predictions of $x_{i}$ . Since the mask generation is based on the activation maps of the class with the largest predictions, the corresponding prediction is penalized with the mask ratio $r_{i}$ . Besides, there exists an equal possibility for the masked region to be predicted as each class. Thus the prediction values for all the classes are increased uniformly. To sum up, the regularized pseudo label $y_{i}^{r}$ for $x_{i}^{m}$ can be derived as follows: + +$$ +y _ {i} ^ {r} (j) = \left\{ \begin{array}{l l} y _ {i} (j) - r _ {i} + r _ {i} / c, & \text {i f} j = \operatorname {a r g m a x} \left(y _ {i} (j)\right) \\ y _ {i} (j) + r _ {i} / c, & \text {o t h e r w i s e} \end{array} , \right. \tag {7} +$$ + +where $0 \leq j < c$ . Afterward, $y_i^r$ and $\widetilde{y_i^{m}}$ are used for supervised loss calculation as: + +$$ +\mathcal {L} _ {\mathrm {c}} = \mathcal {L} _ {\mathrm {c e}} \left(\widetilde {y _ {i} ^ {m}}, y _ {i} ^ {r}\right) \tag {8} +$$ + +where $\mathcal{L}_{\mathrm{ce}}$ denotes the cross-entropy loss. + +# 3.2. Self-supervised Masking Reconstruction + +Inspired by [11], a Self-supervised Masking Reconstruction branch (SMR) is designed to impose implicit regularization by providing extra noise-free supervised signal. Specifically, the decoder $M_D$ reconstructs the original image from the latent representation of the masked image $x_i^m$ by predicting the pixel values for the masked patch, which is defined as: + +$$ +x _ {i} ^ {r} = M _ {D} \left(\mathbf {f} _ {\mathbf {i}} ^ {m}; \theta_ {D}\right) \tag {9} +$$ + +where $\theta_{D}$ is the parameters of the decoder model $M_{D}$ . Then the mean squared error (MSE) loss is computed between the reconstructed image $x_{i}^{r}$ and the original unmasked image $x_{i}$ in the pixel space, which is shown as follows: + +$$ +\mathcal {L} _ {\mathrm {r}} = \left\| x _ {i} ^ {r} - x _ {i} \right\| ^ {2} \tag {10} +$$ + +In this way, a challenging self-supervised pretext task that requires holistic understanding beyond low-level image statistics is created for the model. This supervision signal is not only free of interference from noisy labels but can also enhance the model generalization ability. Therefore the overall loss function for SANM is constructed as follows: + +$$ +\mathcal {L} _ {\text {t r a i n}} = \mathcal {L} _ {\mathrm {c}} + \beta \mathcal {L} _ {\mathrm {r}} \tag {11} +$$ + +where $\beta$ is a parameter to balance the weight of two loss terms. Putting this all together, Algorithm 1 delineates the proposed SANM. Firstly, the original images are masked + +Table 1. Comparison with state-of-the-art methods on CIFAR-10/100 datasets with symmetric noise. + +
DatasetCIFAR-10CIFAR-100
Method/Noise ratio20%50%80%90%20%50%80%90%
Cross-Entropy (CE)86.879.462.942.762.046.719.910.1
Co-teaching+ [41]89.585.767.447.965.651.827.913.7
Mixup [43]95.687.171.652.267.857.330.814.6
PENCIL [40]92.489.177.558.969.457.531.115.3
Meta-Learning [16]92.989.377.458.768.559.242.419.5
M-correction [1]94.092.086.869.173.966.148.224.3
DivideMix [15]96.194.693.276.077.374.660.231.5
C2D [47]96.395.294.493.578.676.467.758.7
AugDesc [22]96.395.493.891.979.577.266.441.2
GCE [5]90.089.373.936.568.153.322.18.9
Sel-CL+ [19]95.593.989.281.976.572.459.648.8
MOIT+ [23]94.191.881.174.775.970.647.641.8
SANM(DivideMix)96.495.894.692.381.278.268.743.5
SANM(C2D)96.696.495.795.181.979.371.661.9
+ +with the label quality guided masking scheme, where mislabeled and correct-labeled samples are modulated adaptively. Meanwhile, the predictions of the original images are used for regularized label generation. Then the regularized features of the masked images are utilized for self-supervised masking reconstruction, which provides noise-free supervisions. Finally, SANM can be plugged into existing LNL frameworks to further boost performance. + +# 4. Experiments + +# 4.1. Datasets and Implementation Details + +Simulated noisy datasets. We evaluate on two simulated noisy datasets: CIFAR-10/100 [14], which consists of 50k training samples and 10k test samples. Following previous work [15], two simulated noisy settings are considered: symmetric and asymmetric noise. Following noise rates are considered: $\tau \in \{20\%, 50\%, 80\%, 90\}$ for symmetric noise and $\tau \in \{20\%, 40\%$ for asymmetric noise. An 18-layer PreAct ResNet is used and we train for 300 epochs with a batch size of 64 and SGD optimizer. The initial learning rate is set as 0.02 and is reduced by a factor of 10 after 150 epochs. $\beta$ is empirically set as 1. + +Real-world noisy datasets. We also validate our method on real-world noisy datasets Clothing-1M [38] and Animal10N [28], the former contains 1 million training samples with nearly $40\%$ mislabeled samples, the latter contains 55,000 human-labeled online images for 10 confusing animals. It includes approximately $8\%$ noisy-labeled samples. The original protocol is used to split the training and test images. We strictly follow the previous experimental settings, more details can be found in the supplementary materials. + +# Algorithm 1 The proposed SANM framework + +Input: Noisy training set $D$ , encoder model $M_E(\cdot;\theta_E)$ , decoder model $M_D(\cdot;\theta_D)$ , batch size $b$ , max iterations $m$ , basic mask ratio $\mu$ . + +# Procedure: + +1: for $i = 1$ to $m$ do + +2: $\{x_{i},y_{i}\}_{i = 1}^{b}\gets$ SampleMiniBatch(D,b). + +3: Feed $\{x_i\}_{i=1}^b$ into $M_E$ and generate feature maps $\{F_i\}_{i=1}^b$ and predictions $\{\widetilde{y}_i\}_{i=1}^b$ . + +4: Generate activation maps $\{A_{i}\}_{i = 1}^{b}$ by Eq. (1). + +5: Calculate mask ratios $\{r_i\}_{i = 1}^b$ and adversarial masked images $\{x_{i}^{m}\}_{i = 1}^{b}$ by Eq. (2-5). + +6: Feed $\{x_i^m\}_{i = 1}^b$ into $M_E$ and generate predictions $\{y_i^{\overline{m}}\}_{i = 1}^b$ and features $\{\mathbf{f}_i^m\}_{i = 1}^b$ by Eq. (6). + +7: Calculate the regularized labels $\{y_i^r\}_{i = 1}^b$ by Eq. (7). + +8: Calculate cross-entropy loss $\mathcal{L}_c$ by Eq. (8). + +9: Feed $\{\mathbf{f}_i^m\}_{i = 1}^b$ into $M_D$ and generate reconstructed images $\{x_i^r\}_{i = 1}^b$ by Eq. (9). + +10: Calculate self-supervised reconstruction loss $\mathcal{L}_r$ by Eq. (10). + +11: Update parameter $\theta_{E},\theta_{D}$ in backward process. + +12: end for + +Output: The final encoder $M_E(\cdot ;\theta_E)$ + +# 4.2. Experimental Results + +- Results on Simulated Noisy Datasets. Here we compare the proposed method with multiple baselines on CIFAR-10/100 using different symmetric noise levels. As shown in Tab. 1, by integrating with C2D [47], our SANM outperforms the naive C2D significantly and achieves state-of-the-art performance among these methods. Specifically, an increase of around $3 \sim 4\%$ across all the symmetric noise cases on CIFAR-100 can be observed. Besides, when combined with DivideMix [15], SANM can bring greater improvement to its performance, especially for severe label noise cases. Under $90\%$ symmetric noise setting, our SANM (DivideMix) outperforms DivideMix by a large + +![](images/7cd3c1ac0418f4df3b9c8eb4cd05a8325f4475ba3754ab6ef5fc8d1ec5c5749e.jpg) +Figure 4. Investigation of the basic mask ratio $\mu$ under $50\%$ and $80\%$ symmetric noise of CIFAR-10/100. + +![](images/82daa868d10f0be5af35a2f4d572c77e34617669c5846b5b27057394e0d1e903.jpg) + +Table 2. Asymmetric noise on CIFAR-10. + +
MethodNoisy ratio
20%40%
Joint-Optim [32]92.891.7
PENCIL [40]92.491.2
F-correction [24]89.9-
Distilling [46]92.790.2
Meta-Learning [16]-88.6
M-correction [1]-86.3
Iterative-CV [3]-88.0
DivideMix [15]93.493.4
REED [44]95.092.3
C2D [47]93.893.4
Sel-CL+ [19]95.293.4
GCE [5]87.378.1
RRL [17]-92.4
SANM(DivideMix)95.494.8
+ +Table 3. Testing accuracy on Clothing-1M. + +
MethodAcc
CrossEntropy69.21
F-correction [24]69.84
M-correction [1]71.00
Joint-Optim [32]72.16
Meta-Cleaner [45]72.50
Meta-Learning [16]73.47
PENCIL [40]73.49
Self-Learning [10]74.45
DivideMix [15]74.76
Nested [4]74.90
AugDesc [22]75.11
RRL [17]74.90
GCE [5]73.30
C2D [47]74.30
SANM(DivideMix)75.63
+ +Table 4. Comparison with masks generated from pre-trained backbones on CIFAR10/100. M: Method. P: Pretrained Backbone. S: SANM. D: DivideMix. C: C2D. + +
DatasetCIFAR-10
MPS20%50%80%90%
D✓X96.095.193.781.5
X✓96.495.894.692.3
C✓X96.395.495.093.9
X✓96.696.495.795.1
DatasetCIFAR-100
MPS20%50%80%90%
D✓X78.876.664.936.4
X✓81.278.268.743.5
C✓X80.077.169.258.4
X✓81.979.371.661.9
+ +margin of $12\%$ . A similar performance boost can be observed on the CIFAR-100 as well, especially in the high noise case. When compared with the implicit regularization method AugDesc [22], our SANM (DivideMix) can also achieve much better accuracy across all the settings. Tab. 2 shows comparisons of the test accuracy on CIFAR-10 with varied asymmetric noise levels. SANM shows a consistent improvement with respect to the state-of-the-art methods, demonstrating the ability of SANM in enhancing the robustness of the model to semantic noise. + +- Results on Real-world Noisy Datasets. The results on Clothing1M dataset are shown in Tab. 3, one can see that SANM can bring around $0.87\%$ improvement in accuracy to the baseline method DivideMix, and get much better result than other state-of-the-art methods, verifying that SANM is also effective in real-world noise scenarios. Moreover, SANM obtains $89.3\%$ accuracy on Animal-10N (detailed comparison on Animal-10N is in supplementary materials), which sets the new state-of-the-art, indicating that SANM can handle fine-grained real-world noisy datasets as well. + +Generality of SANM. To further verify the effectiveness and generalization of SANM, we adopt vanilla CE, Co-teaching [9], CDR [37] and ELR+ [21], other than DivideMix and C2D, to work collaboratively with SANM. As shown in Tab. 6, SANM brings consistent performance + +boost to all the corresponding baselines across all the noisy cases. For example, when seamlessly combined with SANM, an accuracy increase of around $14.9\%$ on the symmetric $80\%$ noise setting (CIFAR-100) can be achieved by vanilla CE, demonstrating that SANM is capable of effectively imposing adaptive regularization on noisy and clean samples respectively and thus alleviates over-fitting to noisy labels. Therefore, all the results indicate the generalization ability of SANM to boost existing LNL approaches. + +# 4.3. Ablation Study + +Component Analysis. To explore the impact of each component in SANM, i.e., AMG-adversarial noisy masking generation, NLR-noisy label regularization, SMR-self-supervised masking reconstruction, we conduct an ablation study on CIFAR-10 and CIFAR-100. As shown in Tab. 5, we report test accuracy for all the noisy settings for SANM where the contribution of each component is clearly shown. Specifically, it can be observed that AMG is crucial to performance, especially under severe label noisy cases. Besides, the accuracy is further improved when further combined with NLR. By introducing noise-free self-supervised signals, SMR can also boost the performance of the baseline across all the settings. Best accuracy can be achieved by SANM when all the components are included. This ver + +Table 5. Ablation study for the effectiveness of each key component. AMG: adversarial noisy masking generation, NLR: noisy label regularization, SMR: self-supervised masking reconstruction. + +
ComponentCIFAR-10CIFAR-100
AMGNLRSMR20%50%80%90%20%50%80%90%
XXXBest96.194.693.276.077.374.660.231.5
Last95.794.492.975.476.974.259.631.0
✓XXBest96.395.394.091.480.277.368.042.7
Last96.295.193.690.879.777.067.542.5
✓✓XBest96.495.594.291.680.577.568.343.0
Last96.395.494.091.580.277.168.242.7
✓✓✓Best96.495.894.692.381.278.268.743.5
Last96.395.694.392.180.478.068.343.0
+ +Table 6. Comparison between the LNL methods and their SANM applications with symmetric noise on CIFAR-10/100. Specifically, the 9-layer CNN is adopted as the backbone network of Co-teaching. + +
Dataset +Method/Noise ratioCIFAR-10CIFAR-100
20%50%80%90%20%50%80%90%
CEBest86.879.462.942.762.046.719.910.1
SANM(CE)Best92.489.772.151.570.953.134.818.6
Co-teaching [9]Best82.673.024.014.650.538.211.84.9
SANM(Co-teaching)Best89.278.236.420.758.251.319.413.4
CDR [37]Best90.485.047.212.363.339.529.28.0
SANM(CDR)Best92.691.655.316.772.756.436.620.8
ELR+ [21]Best94.693.891.175.277.572.458.230.8
SANM(ELR+)Best96.395.794.182.979.877.365.038.7
+ +ifies the effectiveness of all three key designs in SANM. + +Parameter Sensitivity. The basic mask ratio $\mu$ in Eq. (2) is a important parameter within the proposed method. To investigate how $\mu$ affects the performance, we conduct parameter analysis experiments under $50\% / 80\%$ symmetric noise levels on CIFAR-10/100. Best test accuracy with respect to different $\mu$ is shown in Fig. 4. Compared with the results of baseline methods, i.e., masking the images with fixed ratios and random ratios (sampled from Uniform(0, $\mu$ ) for each sample, SANM achieves superior performance. Moreover, the results show that keeping $\mu$ between 0.1 and 0.3 leads to better performance than without adversarial noisy masking $(\mu = 0)$ . This is mainly because large $\mu$ makes the masked training images and the test images differ significantly in image style, and thus the model is unable to learn class-specific discriminative information when trained with such strong regularization. However, an extremely small $\mu$ results in encoder overfit to noisy labels more severely. Therefore, we set $\mu$ as 0.2 to obtain the best performance. + +Comparison with Masks from Pre-trained Backbones. The effectiveness of the proposed adversarial masking generation is evaluated in Tab. 5. And further we conduct experiments that utilizing a strong backbone (i.e., pretrained ResNet-18 with DivideMix and C2D) to generate more object-centric masks as comparison to our method. The results shown in Tab. 4 indicate that even compared with the mask generation strategy by well-trained strong backbones, our adversarial masking generation can consis + +tently performs better since the mask generation strategy of fixed well-trained backbones cannot adaptively adjust the mask strategy according to the training state of the base model, leading to sub-optimal results. + +# 5. Conclusion + +In this paper, we propose SANM, a novel self-supervised adversarial noisy masking LNL method. SANM prevents models from overfitting noisy instances by explicitly imposing regularization on the features. A label quality guided adversarial masking scheme is designed to adaptively modify the inputs and noisy labels. Moreover, an auxiliary decoder is introduced to reconstruct images based on the modulated features, which enhances the generalization by providing extra noise-free self-supervised signals. Besides, SANM is flexible and can be easily plugged into existing LNL frameworks. State-of-the-art performance is obtained by SANM on both synthetic and real-world noisy datasets. + +# Acknowledgement + +This work was supported by National Natural Science Fund of China (62076184, 61976158, 61976160, 62076182, 62276190), in part by Fundamental Research Funds for the Central Universities and State Key Laboratory of Integrated Services Networks (Xidian University), in part by Shanghai Innovation Action Project of Science and Technology (20511100700) and Shanghai Natural Science Foundation (22ZR1466700). + +# References + +[1] Eric Arazo, Diego Ortego, Paul Albert, Noel E O'Connor, and Kevin McGuinness. Unsupervised label noise modeling and loss correction. In Proc. International Conference on Machine Learning (ICML), 2019. 6, 7 +[2] Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al. A closer look at memorization in deep networks. In Proc. International Conference on Machine Learning (ICML), pages 233-242. PMLR, 2017. 1, 2 +[3] Pengfei Chen, Benben Liao, Guangyong Chen, and Shengyu Zhang. Understanding and utilizing deep neural networks trained with noisy labels. In Proc. International Conference on Machine Learning (ICML), 2019. 1, 3, 7 +[4] Yingyi Chen, Xi Shen, Shell Xu Hu, and Johan A. K. Suykens. Boosting co-teaching with compression regularization for label noise. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 2688-2692, June 2021. 7 +[5] Aritra Ghosh and Andrew Lan. Contrastive learning improves model robustness under label noise. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2703-2708, 2021. 6, 7 +[6] Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014. 1, 3 +[7] Aayush Gupta, Ayush Jaiswal, Yue Wu, Vivek Yadav, and Pradeep Natarajan. Adversarial mask generation for preserving visual privacy. In 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), pages 1-5. IEEE, 2021. 3 +[8] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Co-teaching: Robust training of deep neural networks with extremely noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), volume 31, 2018. 1, 2, 3 +[9] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Coteaching: Robust training of deep neural networks with extremely noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), 2018. 7, 8 +[10] Jiangfan Han, Ping Luo, and Xiaogang Wang. Deep self-learning from noisy labels. In Proc. IEEE International Conference on Computer Vision (ICCV), 2019. 7 +[11] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377, 2021.5 +[12] Qibin Hou, PengTao Jiang, Yunchao Wei, and Ming-Ming Cheng. Self-erasing network for integral object attention. In Proc. Advances in Neural Information Processing Systems (NeurIPS), volume 31, 2018. 3 +[13] Tao Hu, Honggang Qi, Qingming Huang, and Yan Lu. See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification. arXiv preprint arXiv:1901.09891, 2019. 3 + +[14] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 3, 6 +[15] Junnan Li, Richard Socher, and Steven CH Hoi. Dividemix: Learning with noisy labels as semi-supervised learning. In Proc. International Conference on Learning Representations (ICLR), 2020. 1, 2, 3, 4, 6, 7 +[16] Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S. Kankanhalli. Learning to learn from noisy labeled data. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 6, 7 +[17] Junnan Li, Caiming Xiong, and Steven CH Hoi. Learning from noisy data with robust representation learning. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 9485-9494, 2021. 7 +[18] Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, and Yun Fu. Tell me where to look: Guided attention inference network. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 9215-9223, 2018. 3 +[19] Shikun Li, Xiaobo Xia, Shiming Ge, and Tongliang Liu. Selective-supervised contrastive learning with noisy labels. arXiv preprint arXiv:2203.04181, 2022. 6, 7 +[20] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), volume 33, pages 20331–20342, 2020. 1, 2 +[21] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), 2020. 7, 8 +[22] Kento Nishi, Yi Ding, Alex Rich, and Tobias Hollerer. Augmentation strategies for learning with noisy labels. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8022-8031, 2021. 6, 7 +[23] Diego Ortego, Eric Arazo, Paul Albert, Noel E O'Connor, and Kevin McGuinness. Multi-objective interpolation training for robustness to label noise. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6606-6615, 2021. 6 +[24] Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. Making deep neural networks robust to label noise: A loss correction approach. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 7 +[25] Haim Permuter, Joseph Francos, and Ian Jermyn. A study of gaussian mixture models of color and texture features for image classification and segmentation. Pattern recognition, 39(4):695-706, 2006. 4 +[26] Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. Learning to reweight examples for robust deep learning. In Proc. International Conference on Machine Learning (ICML), pages 4334-4343. PMLR, 2018. 1, 2 +[27] Hwanjun Song, Minseok Kim, and Jae-Gil Lee. Selfie: Refurbishing unclean samples for robust deep learning. In Proc. International Conference on Machine Learning (ICML), 2019. 1 + +[28] Hwanjun Song, Minseok Kim, and Jae-Gil Lee. SELFIE: Refurbishing unclean samples for robust deep learning. In Proc. International Conference on Machine Learning (ICML), pages 5907-5915, 2019. 6 +[29] Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, 2022. 1, 2 +[30] Erik Stammes, Tom FH Runia, Michael Hofmann, and Mohsen Ghafoorian. Find it if you can: end-to-end adversarial erasing for weakly-supervised semantic segmentation. In Thirteenth International Conference on Digital Image Processing (ICDIP 2021), volume 11878, page 1187825. International Society for Optics and Photonics, 2021. 3 +[31] Guolei Sun, Hisham Cholakkal, Salman Khan, Fahad Khan, and Ling Shao. Fine-grained recognition: Accounting for subtle differences between similar classes. In Association for the Advancement of Artificial Intelligence (AAAI), volume 34, pages 12047–12054, 2020. 3 +[32] Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa. Joint optimization framework for learning with noisy labels. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 1, 3, 7 +[33] Gabriele Valvano, Andrea Leo, and Sotirios A Tsaftaris. Reusing adversarial mask discriminators for test-time training under distribution shifts. arXiv preprint arXiv:2108.11926, 2021. 3 +[34] Hongxin Wei, Lei Feng, Xiangyu Chen, and Bo An. Combating noisy labels by agreement: A joint training method with co-regularization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 13726–13735, 2020. 1, 3 +[35] Yunchao Wei, Jiashi Feng, Xiaodan Liang, Ming-Ming Cheng, Yao Zhao, and Shuicheng Yan. Object region mining with adversarial erasing: A simple classification to semantic segmentation approach. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1568-1576, 2017. 3 +[36] Yichen Wu, Jun Shu, Qi Xie, Qian Zhao, and Deyu Meng. Learning to purify noisy labels via meta soft label corrector. In Association for the Advancement of Artificial Intelligence (AAAI), 2021. 1 +[37] Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang. Robust early-learning: Hindering the memorization of noisy labels. In Proc. International Conference on Learning Representations (ICLR), 2021. 7, 8 +[38] Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang. Learning from massive noisy labeled data for image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 1, 6 +[39] Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu, Yuefeng Chen, and Hui Xue. Towards face encryption by generating adversarial identity masks. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 3897-3907, 2021. 3 +[40] Kun Yi and Jianxin Wu. Probabilistic end-to-end noise correction for learning with noisy labels. In Proc. IEEE Confer- + +ence on Computer Vision and Pattern Recognition (CVPR), pages 7017-7025, 2019. 1, 3, 6, 7 +[41] Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W Tsang, and Masashi Sugiyama. How does disagreement help generalization against label corruption? In Proc. International Conference on Machine Learning (ICML), 2019. 1, 3, 6 +[42] Boshen Zhang, Yuxi Li, Yuanpeng Tu, Jinlong Peng, Yabiao Wang, Cunlin Wu, Yang Xiao, and Cairong Zhao. Learning from noisy labels with coarse-to-fine sample credibility modeling. In Computer Vision-ECCV 2022 Workshops: Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part II, pages 21-38. Springer, 2023. 3 +[43] Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412, 2017. 1, 3, 6 +[44] Hui Zhang and Quanming Yao. Decoupling representation and classifier for noisy label learning. arXiv preprint arXiv:2011.08145, 2020. 7 +[45] Weihe Zhang, Yali Wang, and Yu Qiao. Metacleaner: Learning to hallucinate clean representations for noisy-labeled visual recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 7 +[46] Z. Zhang, H. Zhang, S. Arik, H. Lee, and T. Pfister. Distilling effective supervision from severe label noise. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 7 +[47] Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M Bronstein, and Or Litany. Contrast to divide: Self-supervised pre-training for learning with noisy labels. In Proc. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 1657-1667, 2022. 1, 3, 6, 7 +[48] Guoqing Zheng, Ahmed Hassan Awadallah, and Susan Dumas. Meta label correction for noisy label learning. In Association for the Advancement of Artificial Intelligence (AAAI), 2021. 1, 3 +[49] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2921-2929, 2016. 3, 4 +[50] Xiong Zhou, Xianming Liu, Chenyang Wang, Deming Zhai, Junjun Jiang, and Xiangyang Ji. Learning with noisy labels via sparse regularization. In Proc. 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"bbox": [ + 507, + 325, + 540, + 356 + ], + "blocks": [ + { + "bbox": [ + 507, + 325, + 540, + 356 + ], + "lines": [ + { + "bbox": [ + 507, + 325, + 540, + 356 + ], + "spans": [ + { + "bbox": [ + 507, + 325, + 540, + 356 + ], + "type": "image", + "image_path": "3320e84ca9307b47f48886e3fafd5ac9bbde467f069b9c44a2cc2655625a2d60.jpg" + } + ] + } + ], + "index": 32, + "angle": 0, + "type": "image_body" + } + ], + "index": 32 + }, + { + "bbox": [ + 304, + 425, + 545, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 425, + 545, + 472 + ], + "spans": [ + { + "bbox": [ + 304, + 425, + 545, + 472 + ], + "type": "text", + "content": "alternative methods inevitably yield numerous noisy samples. However, previous research [2] has revealed that deep networks can easily overfit to noisy labels and suffer from dramatic degradation in the generalization performance." + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 304, + 474, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 474, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 474, + 546, + 713 + ], + "type": "text", + "content": "Towards this problem, numerous Learning with Noisy labels (LNL) approaches have been proposed. Sample selection methods [3, 8, 34, 41] are the most straightforward methods, which attempt to select clean samples based on certain criterion (e.g., loss value) and then reweight or discard the noisy instances to reduce the interference of mislabeled training samples. However, these methods fail to leverage the potential information of the discarded samples. Similarly, label correction based methods [32, 40, 48] try to correct labels, which often impose assumptions on the existence of a small correctly-labeled validation subset or directly utilize predictions of deep models. However, such assumptions can not be always full-filled in real-world noisy datasets [27], leading to limited application scenarios. Besides, the predictions of deep model tend to fluctuate when training with mislabeled samples, making the label correction process unstable and sub-optimal [36]. On the contrary, regularization based methods [2, 6, 15, 20, 26, 29, 43, 47, 50] aim to alleviate the side effect of label noise by preventing the deep models from overfitting to all training sam" + } + ] + } + ], + "index": 36 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 693, + 131, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 693, + 131, + 703 + ], + "spans": [ + { + "bbox": [ + 61, + 693, + 131, + 703 + ], + "type": "text", + "content": "* Equal contribution." + } + ] + } + ], + "index": 37 + }, + { + "bbox": [ + 63, + 704, + 141, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 704, + 141, + 712 + ], + "spans": [ + { + "bbox": [ + 63, + 704, + 141, + 712 + ], + "type": "text", + "content": "† Corresponding author." + } + ] + } + ], + "index": 38 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16186" + } + ] + } + ], + "index": 40 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 204 + ], + "type": "text", + "content": "ples, which is flexible and can work collaboratively with other LNL methods. Both explicit and implicit regularization techniques were proposed, the former generally calibrates the parameter update of networks by modifying the expected training loss, i.e., dropout. The latter focuses on improving the generalization by utilizing the stochasticity, i.e., data augmentation strategy and stochastic gradient descent [29]. However, most of these methods are designed for general fully-supervised learning tasks with correctly-labeled samples, and poor generalization ability could be obtained when the label noise is severe [29]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 206, + 289, + 481 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 206, + 289, + 481 + ], + "spans": [ + { + "bbox": [ + 46, + 206, + 289, + 481 + ], + "type": "text", + "content": "In this paper, we present a novel regularization based method specialized for LNL problem. Our method is inspired by an observation that some differences exist in activation maps between the predictions of deep models trained with clean and noisy labels. As shown in Fig. 1 (a), activation maps of mis-predicted samples by the model trained with clean labels are always focused on their foreground areas. By contrast, when the model is trained with noisy labels, it tends to generate results focused on the meaningless background area for the mis-predicted samples (Fig. 1 (b)). And even for cases that the prediction is correct, i.e., Fig. 1 (c), the high-activated region drifts to irrelevant (i.e., edge) areas of the object, rather than the main regions of the object. This indicates that model trained with mistrabeled samples is likely to overfit to some corner parts of the object from noisy data or remember the less-informative regions (i.e., background). Therefore, from this observation, we propose a novel Self-supervised Adversarial Noisy Masking (SANM) method for LNL. The idea is to explicitly regularize the model to generate more diverse activation maps through masking the certain region (e.g., max-activated) of the input image and thus alleviates the confirmation bias [8]." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 483, + 289, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 483, + 289, + 651 + ], + "spans": [ + { + "bbox": [ + 46, + 483, + 289, + 651 + ], + "type": "text", + "content": "Moreover, to investigate how the masking strategies affect the performance of training deep models with noisy labels, we conduct a simple experiment, where different adversarial masking strategies (i.e., masking the max-activated region of input images with fixed, random, and noise-aware mask ratio) were applied to the popular LNL framework DivideMix [15]. As shown in Fig. 2, we find that the performance gain is much more sensitive to the masking strategy. A fixed or random mask ratio obtain only marginal gain, but when dealing with the correctly-labeled and mislabeled samples with different mask ratios, a significant performance gain is achieved. This indicates that how to design an effective adversarial masking method in the case of LNL is non-trivial and not well addressed." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 289, + 715 + ], + "type": "text", + "content": "Towards the problem above, a label quality guided adaptive masking strategy is proposed, which modulates the image label and the mask ratio of image masking simultaneously. The label is updated via first leveraging the soft-distributed model prediction and then reducing the proba" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 304, + 72, + 547, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 300 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 300 + ], + "type": "text", + "content": "bility of max-activated class with the noise-aware masking ratio, while at the same time lifting the probability of other classes. Intuitively, a sample with a high probability of being mislabeled will possess a larger masking ratio, leading to a more strictly regularized input image and modulated label. Therefore, the negative impact of noisy labels can be greatly reduced. As for the correctly-labeled samples, the masking ratio is relatively small, which plays the role of general regularization strategy and improves the model generalization ability by preventing the model from overfitting training samples. Further, we specially customized an auxiliary decode branch to reconstruct the original input image, which provides noise-free self-supervised information for learning a robust deep model. The proposed SANM is flexible and can further boost existing LNL methods. It is tested on synthetic and real-world large-scale noisy datasets, and elaborately ablative experiments were conducted to verify each design component of the proposed method. In a nutshell, the key contributions of this work are:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 300, + 547, + 469 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 304, + 300, + 547, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 300, + 547, + 361 + ], + "spans": [ + { + "bbox": [ + 304, + 300, + 547, + 361 + ], + "type": "text", + "content": "- We propose a novel self-supervised adversarial noisy masking method named SANM to explicitly impose regularization for LNL problem, preventing the model from overfitting to some corner parts of the object or less-informative regions from noisy data;" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 361, + 547, + 421 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 361, + 547, + 421 + ], + "spans": [ + { + "bbox": [ + 304, + 361, + 547, + 421 + ], + "type": "text", + "content": "- A label quality guided masking strategy is proposed to differently adjust the process for clean and noisy samples according to the label quality estimation. This strategy modulates the image label and the ratio of image masking simultaneously;" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 421, + 547, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 421, + 547, + 469 + ], + "spans": [ + { + "bbox": [ + 304, + 421, + 547, + 469 + ], + "type": "text", + "content": "- A self-supervised mask reconstruction auxiliary task is designed to reconstruct the original images based on the features of masked ones, which aims at enhancing generalization by providing noise-free supervision signals." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 482, + 392, + 495 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 482, + 392, + 495 + ], + "spans": [ + { + "bbox": [ + 306, + 482, + 392, + 495 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 502, + 459, + 515 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 502, + 459, + 515 + ], + "spans": [ + { + "bbox": [ + 306, + 502, + 459, + 515 + ], + "type": "text", + "content": "2.1. Learning with Noisy Labels" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 521, + 545, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 545, + 557 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 545, + 557 + ], + "type": "text", + "content": "A variety of methods have been proposed to improve the robustness of DNNs on noisy datasets, which consist of regularization, label correction, and noisy sample selection." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 558, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 547, + 715 + ], + "type": "text", + "content": "Regularization based methods help resist memorizing noisy data since previous works [2] have revealed that networks always firstly learn simple patterns before memorizing noisy hard data. Previous research [29] claims that these methods can be roughly divided into two categories: explicit/implicit regularization. Explicit regularization based methods generally modify the losses for the training samples. Among them, ELR [20] tries to alleviate the influence of noisy data by regularizing the loss gradient. SR [50] proposes a spare regularization method to restrict network output to the permutation set over a one-hot vector. Meta learning based methods [26] impose regularization on the sample gradient directions by bi-level optimization with a small un-" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "16187" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 70, + 173, + 166 + ], + "blocks": [ + { + "bbox": [ + 49, + 70, + 173, + 166 + ], + "lines": [ + { + "bbox": [ + 49, + 70, + 173, + 166 + ], + "spans": [ + { + "bbox": [ + 49, + 70, + 173, + 166 + ], + "type": "image", + "image_path": "d9f7e7760fc27be3fb34c6179dbf7fc31648c8ce4fe0933b6266a37d6f9bf39e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 167, + 547, + 210 + ], + "lines": [ + { + "bbox": [ + 46, + 167, + 547, + 210 + ], + "spans": [ + { + "bbox": [ + 46, + 167, + 547, + 210 + ], + "type": "text", + "content": "Figure 2. A experiment for masking the max-activated region with different mask ratios. The performance gains of different mask strategies under " + }, + { + "bbox": [ + 46, + 167, + 547, + 210 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 46, + 167, + 547, + 210 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 167, + 547, + 210 + ], + "type": "inline_equation", + "content": "80\\%" + }, + { + "bbox": [ + 46, + 167, + 547, + 210 + ], + "type": "text", + "content": " symmetric noise of CIFAR-10/100 [14] are reported, where DivideMix [15] is adopted as the baseline. \"Fixed(0.2/0.3)\" denotes masking all the images with the same mask ratio of 0.2/0.3. \"Random\" represents masking images with a random mask ratio between 0.2 and 0.4. \"Noise-aware\" is masking noisy samples with a mask ratio of 0.3 while the ratio for clean ones is 0.2." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 173, + 70, + 296, + 166 + ], + "blocks": [ + { + "bbox": [ + 173, + 70, + 296, + 166 + ], + "lines": [ + { + "bbox": [ + 173, + 70, + 296, + 166 + ], + "spans": [ + { + "bbox": [ + 173, + 70, + 296, + 166 + ], + "type": "image", + "image_path": "0ae134af4b1e650eb094c2b8960f9b00e1ddc14124550f7990b4a3e3c4759f42.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 296, + 70, + 420, + 166 + ], + "blocks": [ + { + "bbox": [ + 296, + 70, + 420, + 166 + ], + "lines": [ + { + "bbox": [ + 296, + 70, + 420, + 166 + ], + "spans": [ + { + "bbox": [ + 296, + 70, + 420, + 166 + ], + "type": "image", + "image_path": "f57a73def6e20f2d396e0ee9762818de7533667b15fead30d3824fde8a990273.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 420, + 70, + 544, + 166 + ], + "blocks": [ + { + "bbox": [ + 420, + 70, + 544, + 166 + ], + "lines": [ + { + "bbox": [ + 420, + 70, + 544, + 166 + ], + "spans": [ + { + "bbox": [ + 420, + 70, + 544, + 166 + ], + "type": "image", + "image_path": "61afd408f6f53f57f3faa14b04f3d1f56a9ef09e1834e6239af837af8238c418.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 215, + 289, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 215, + 289, + 407 + ], + "spans": [ + { + "bbox": [ + 46, + 215, + 289, + 407 + ], + "type": "text", + "content": "biased validation. In addition to these regularizers, example re-weighting strategies are also popular explicit regularization solutions to the over-fitting problem. However, these methods often need sensitive hyper-parameters or deeper networks to improve the model capacity. For implicit regularization methods, they generally utilize the stochasticity effect, i.e., augmentation and stochastic gradient descent. Among them, semi-supervised based LNL methods [15,47] generally use mixup [43] as an effective regularization strategy to hinder over-fitting, which makes the network favor simple linear behavior in-between training examples. Adversarial samples based methods [6] improve the robustness by helping the model classify original and perturbed inputs. Since the model is unable to distinguish right from wrong by itself, regularization can generally work surprisingly well in certain synthetic noisy cases." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 411, + 287, + 554 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 411, + 287, + 554 + ], + "spans": [ + { + "bbox": [ + 46, + 411, + 287, + 554 + ], + "type": "text", + "content": "Label correction methods seek to transform noisy labels to correct ones directly. The pseudo labeling strategy is commonly adopted by these methods, which utilizes the confident output of the network. Joint [32] optimizes labels and network parameters in an alternating strategy. PENCIL [40] corrects and updates labels probabilistically through the back-propagation mechanism. MLC [48] trains a label correction network through meta learning to generate pseudo labels for the training of main models. However, these approaches either need extra clean validation data or suffer from significant performance drop under high noise settings due to the low quality of generated pseudo labels." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 558, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 558, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 558, + 288, + 714 + ], + "type": "text", + "content": "Noisy sample selection approaches mostly exploit the small-loss criterion, which is based on the observation that samples with noisy labels tend to have much higher losses than the clean ones. Among these methods, MentorNet uses a pre-trained teacher model to select clean samples for the training of a student model. Co-teaching based methods [8, 34, 41, 42] update the parameters of two parallel networks with clean instances chosen from their peer networks, which target avoiding the confirmation bias problem. INCV [3] attempts to separate clean data from the noisy training set by using cross-validation and removing high-loss samples. Though these methods may reduce the risk of error correction, they fail to use the potential infor" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 216, + 545, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 216, + 545, + 239 + ], + "spans": [ + { + "bbox": [ + 305, + 216, + 545, + 239 + ], + "type": "text", + "content": "mation of the abandoned samples. Moreover, most of them need prior knowledge, i.e., the noise rate." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 251, + 497, + 264 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 251, + 497, + 264 + ], + "spans": [ + { + "bbox": [ + 306, + 251, + 497, + 264 + ], + "type": "text", + "content": "2.2. Masking Regularization Techniques" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 270, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 270, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 270, + 545, + 715 + ], + "type": "text", + "content": "As a commonly adopted strategy in weakly-supervised semantic segmentation, adversarial masking regularization aims at helping models discover less apparent semantic objects. Specifically, a Class Activation feature Map (CAM) [49] branch is first used to generate initial attention maps and selectively mask the discovered areas in the images through a threshold. Then the masked images are used for the training of another network. AE-PSL [35] iteratively erases the images and expands target object regions with the erased areas to produce final contiguous segmentation masks. Self-erasing [12] utilizes the background prior to make models generate more refined and focused attention on the foreground area. GAIN [18] tries to impose constraints on the activation maps in a regularized style to generate more complete attention regions. PSA [30] trains two networks with opposing losses to eliminate the drawbacks of certain mask strategies. In addition to weakly-supervised semantic segmentation, adversarial masking is also used in fine-grained visual classification. DB [31] designs a diversification block to mask out the most salient features, aiming at forcing networks to mine subtle differences between images. WS-DAN [13] randomly crop and drop one of the discriminative regions in the attention maps to encourage models to mine local features. Several adversarial masking strategies are proposed in [7, 39] to preserve human privacy by masking the minimal number of pixels required for identification. [33] designs an adversarial discriminator for mask generation at inference to correct segmentation errors. However, the purpose of these methods is quite different from our approach. Based on the assumption that the samples are all correctly-labeled, previous methods generally retain the original most salient regions while helping models mine more subtle information through masking regularization. By contrast, our method aims to regularize the model through adversarial masking to make the activation map more dispersed rather than concentrated to specific regions, and a similar masking strategy is applied to gener" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16188" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 96 + ], + "type": "text", + "content": "ate more smooth pseudo labels, thus avoiding over-fitting to noisy labels and alleviating the confirmation bias." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 107, + 102, + 119 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 107, + 102, + 119 + ], + "spans": [ + { + "bbox": [ + 47, + 107, + 102, + 119 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "spans": [ + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "text", + "content": "Overview. For convenience, we begin by introducing the notations. Consider LNL as a classification task, the class number is " + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "text", + "content": ". The noisily-labeled training dataset is denoted as " + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "inline_equation", + "content": "D = \\{(x_i,y_i)\\}_{i=1}^N" + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "inline_equation", + "content": "x_i \\in \\mathbb{R}^{H_i \\times W_i \\times 3}" + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "inline_equation", + "content": "y_i \\in \\{0,1\\}^c" + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "text", + "content": " are the " + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "text", + "content": "-th sample and its noisy label, " + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "inline_equation", + "content": "H_i, W_i" + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "text", + "content": " are the height and width of samples. " + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "text", + "content": " represents the dataset size. The encoder and decoder models are denoted as " + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "inline_equation", + "content": "M_E" + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "inline_equation", + "content": "M_D" + }, + { + "bbox": [ + 46, + 128, + 289, + 356 + ], + "type": "text", + "content": " respectively. We firstly introduce the overall framework of the proposed SANM. As shown in Fig. 3, a label quality guided masking scheme is proposed to adaptively perform adversarial noisy masking for each sample where a contrastive regularization strategy is imposed on the noisy predictions as well. Afterward, the representations of the masked images are used for the self-supervised reconstruction of original images, which aims at preventing models from overfitting to the noisy labels by providing noise-free signals. For application, SANM can be easily integrated with existing LNL frameworks to further boost their performance." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 363, + 198, + 376 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 363, + 198, + 376 + ], + "spans": [ + { + "bbox": [ + 47, + 363, + 198, + 376 + ], + "type": "text", + "content": "3.1. Adversarial Noisy Masking" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "spans": [ + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "text", + "content": "Adversarial Masking Generation. To highlight the semantic regions of correctly labeled samples and keep the captured attention areas of the noisy samples from expanding to meaningless background regions, we propose a novel Adversarial noisy Masking Generation (AMG) strategy during the training process. Specifically, we begin by extracting the feature of image " + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "text", + "content": " through " + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "inline_equation", + "content": "M_E" + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "text", + "content": " and the obtained feature map and prediction are denoted as " + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "inline_equation", + "content": "F_{i} \\in \\mathbb{R}^{H_{F} \\times W_{F} \\times N_{C}}" + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "inline_equation", + "content": "\\widetilde{y}_{i}" + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "text", + "content": " respectively, where " + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "inline_equation", + "content": "H_{F}, W_{F}, N_{C}" + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "text", + "content": " are the height, width and channel number of the feature layer. For each sample, a two-component Gaussian Mixture Model (GMM) [25] is fit to the cross-entropy loss with the Expectation-Maximization algorithm, where the generated weight for " + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "text", + "content": " is denoted as " + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "inline_equation", + "content": "G_{i}" + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "text", + "content": ". Afterward, " + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "inline_equation", + "content": "F_{i}" + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "text", + "content": " is used to generate the activation maps [49] " + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "inline_equation", + "content": "A_{i} \\in \\mathbb{R}^{H_{i} \\times W_{i}}" + }, + { + "bbox": [ + 46, + 381, + 287, + 573 + ], + "type": "text", + "content": " of the category with the largest prediction, which is derived as:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 583, + 287, + 597 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 583, + 287, + 597 + ], + "spans": [ + { + "bbox": [ + 105, + 583, + 287, + 597 + ], + "type": "interline_equation", + "content": "A _ {i} = \\operatorname {C A M} \\left(F _ {i}, \\arg \\max \\left(\\tilde {y} _ {i}\\right)\\right) \\tag {1}", + "image_path": "06b5cdafe3f71684bc4534c97c40b6b9af415e9df00dbe62b0ce8b9ed64b718a.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 605, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 605, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 605, + 287, + 713 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 605, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{CAM}(\\cdot)" + }, + { + "bbox": [ + 47, + 605, + 287, + 713 + ], + "type": "text", + "content": " is the activation map generation function. Then the coordinates with the maximum and minimum activation values for " + }, + { + "bbox": [ + 47, + 605, + 287, + 713 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 47, + 605, + 287, + 713 + ], + "type": "text", + "content": " are recorded as " + }, + { + "bbox": [ + 47, + 605, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\{h_i^{\\max},w_i^{\\max}\\}" + }, + { + "bbox": [ + 47, + 605, + 287, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 605, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\{h_i^{\\min},w_i^{\\min}\\}" + }, + { + "bbox": [ + 47, + 605, + 287, + 713 + ], + "type": "text", + "content": " respectively. For the " + }, + { + "bbox": [ + 47, + 605, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\{h_i^{\\max},w_i^{\\max}\\}" + }, + { + "bbox": [ + 47, + 605, + 287, + 713 + ], + "type": "text", + "content": ", assuming that the GMM division is overall correct, it is believed that the activation maps of correct-labeled samples are mainly focused on the target area of the foreground, while due to the interference of noisy labels, the max-activated region in the activation maps of mislabeled sam" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "text", + "content": "plies are mostly concentrated in meaningless background areas. To breakdown the over-fitting to noisy labels, we intend to adversarially augment training samples via a novel making strategy. In particular, denote the loss function as " + }, + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "inline_equation", + "content": "\\mathcal{L}(x_i,y_i)" + }, + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "text", + "content": ", we try to maximize " + }, + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "text", + "content": " by adaptively generating region perturbations for noisy and clean samples across the training process. The objective can be derived as " + }, + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "inline_equation", + "content": "\\max \\mathcal{L}(x_i + \\sigma ,y_i)" + }, + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "text", + "content": " is the generated perturbations. Specifically, we generate masks with larger ratios for the max-activated region of noisy samples to force the model to reduce its response to these areas and pay more attention to the rest area, where the target is more likely to be found. For the correct-labeled ones, high response areas in their activation maps are likely to exist in the edge area of the object since they are affected by the gradient of noisy samples. Thus smaller mask ratios are used for the max-activated regions of them, which keep the model from ignoring the masked area and force it to mine discriminative information from the rest areas of the target since the correct labels help limit the network to pay more attention to the foreground area. However, it will make the activation map of the well-trained model scattered to the meaningless area when filtering out nearly all the peak locations for each sample through a threshold like existing methods, which violates our purpose. Therefore, we choose to mask random rectangular areas based on recorded coordinates. Specifically, the mask ratio " + }, + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "inline_equation", + "content": "r_i" + }, + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 304, + 72, + 547, + 396 + ], + "type": "text", + "content": " is calculated as follows:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 386, + 405, + 545, + 418 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 386, + 405, + 545, + 418 + ], + "spans": [ + { + "bbox": [ + 386, + 405, + 545, + 418 + ], + "type": "interline_equation", + "content": "r _ {i} = \\mu \\times (1 - G _ {i}) \\tag {2}", + "image_path": "9d4e99a84e6a0c8d14017f20a48e7cad75cbc55c2f0ec088b636586dad9bb2de.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 427, + 547, + 487 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 427, + 547, + 487 + ], + "spans": [ + { + "bbox": [ + 304, + 427, + 547, + 487 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 427, + 547, + 487 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 304, + 427, + 547, + 487 + ], + "type": "text", + "content": " is the basic mask ratio. The more likely a sample is to be noisy, the smaller the " + }, + { + "bbox": [ + 304, + 427, + 547, + 487 + ], + "type": "inline_equation", + "content": "G_{i}" + }, + { + "bbox": [ + 304, + 427, + 547, + 487 + ], + "type": "text", + "content": " is. Based on Eq. (2), the greater mask ratio will be adopted for this sample, thus stronger regularization will be imposed [15]. Subsequently, the aspect of the masked area " + }, + { + "bbox": [ + 304, + 427, + 547, + 487 + ], + "type": "inline_equation", + "content": "\\delta_{i}" + }, + { + "bbox": [ + 304, + 427, + 547, + 487 + ], + "type": "text", + "content": " is generated as follows:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 381, + 496, + 545, + 519 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 381, + 496, + 545, + 519 + ], + "spans": [ + { + "bbox": [ + 381, + 496, + 545, + 519 + ], + "type": "interline_equation", + "content": "\\delta_ {i} \\sim \\operatorname {U n i f o r m} (\\delta , \\frac {1}{\\delta}) \\tag {3}", + "image_path": "2be3fb34abeacd4311ef337483a75ab81105e688d78792cc80c0528b407ef632.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 526, + 545, + 563 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 526, + 545, + 563 + ], + "spans": [ + { + "bbox": [ + 304, + 526, + 545, + 563 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 526, + 545, + 563 + ], + "type": "inline_equation", + "content": "\\delta" + }, + { + "bbox": [ + 304, + 526, + 545, + 563 + ], + "type": "text", + "content": " is a hyper-parameter for generating the aspect. Based on " + }, + { + "bbox": [ + 304, + 526, + 545, + 563 + ], + "type": "inline_equation", + "content": "r_i" + }, + { + "bbox": [ + 304, + 526, + 545, + 563 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 526, + 545, + 563 + ], + "type": "inline_equation", + "content": "\\delta_{i}" + }, + { + "bbox": [ + 304, + 526, + 545, + 563 + ], + "type": "text", + "content": " , the height and width of the masked area are given by:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 317, + 571, + 545, + 671 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 571, + 545, + 671 + ], + "spans": [ + { + "bbox": [ + 317, + 571, + 545, + 671 + ], + "type": "interline_equation", + "content": "\\left\\{ \\begin{array}{r l} h _ {i} ^ {u p} & = \\max \\left(h _ {i} ^ {\\max } - \\sqrt {\\frac {\\left(H _ {x} \\times W _ {x}\\right) \\times r _ {i} \\times \\delta_ {i}}{4}}, 0\\right) \\\\ h _ {i} ^ {\\mathrm {d n}} & = \\min \\left(h _ {i} ^ {\\max } + \\sqrt {\\frac {\\left(H _ {x} \\times W _ {x}\\right) \\times r _ {i} \\times \\delta_ {i}}{4}}, H _ {x}\\right) \\\\ w _ {i} ^ {\\mathrm {l t}} & = \\max \\left(w _ {i} ^ {\\max } - \\sqrt {\\frac {\\left(H _ {x} \\times W _ {x}\\right) \\times r _ {i}}{4 \\delta_ {i}}}, 0\\right) \\\\ w _ {i} ^ {\\mathrm {r t}} & = \\min \\left(w _ {i} ^ {\\max } + \\sqrt {\\frac {\\left(H _ {x} \\times W _ {x}\\right) \\times r _ {i}}{4 \\delta_ {i}}}, W _ {x}\\right) \\end{array} \\right. \\tag {4}", + "image_path": "f04c52b1ee40723d47071bfe8d9b30270bde981a8bc7e9e6bb371b350cd739f8.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "text", + "content": "The selected locations in the masked area of " + }, + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "text", + "content": " are assigned random values generated from a uniform distribution. Therefore, the adversarial noisy masked image " + }, + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "inline_equation", + "content": "x_{i}^{m}" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16189" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 60, + 71, + 534, + 222 + ], + "blocks": [ + { + "bbox": [ + 60, + 71, + 534, + 222 + ], + "lines": [ + { + "bbox": [ + 60, + 71, + 534, + 222 + ], + "spans": [ + { + "bbox": [ + 60, + 71, + 534, + 222 + ], + "type": "image", + "image_path": "abe64279038ef48d4752e743d5b013142dd0b4c8895a0b6547c17d9310e8f72b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 225, + 547, + 281 + ], + "lines": [ + { + "bbox": [ + 46, + 225, + 547, + 281 + ], + "spans": [ + { + "bbox": [ + 46, + 225, + 547, + 281 + ], + "type": "text", + "content": "Figure 3. The framework of SANM. Three components are included: AMG (Adversarial Masking Generation), NLR (Noisy Label Regularization), SMR (Self-supervised Masking Reconstruction). In AMG and NLR, firstly feed the images to an encoder and generate activation maps. And a label quality guided adversarial masking strategy is proposed to modulate the images and noisy labels simultaneously. Further, an auxiliary decode branch is designed in SMR to reconstruct input images from the features of masked images. Finally, the generated modulated images and labels of SANM together with the reconstruction loss can be directly adopted for existing LNL frameworks." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 287, + 154, + 298 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 287, + 154, + 298 + ], + "spans": [ + { + "bbox": [ + 47, + 287, + 154, + 298 + ], + "type": "text", + "content": "can be derived as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 307, + 296, + 343 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 307, + 296, + 343 + ], + "spans": [ + { + "bbox": [ + 47, + 307, + 296, + 343 + ], + "type": "interline_equation", + "content": "x _ {i} ^ {m} (m, n) = \\left\\{ \\begin{array}{l l} U (0, 1), & \\text {i f} m \\in \\left[ h _ {i} ^ {u p}, h _ {i} ^ {d n} \\right], n \\in \\left[ w _ {i} ^ {l t}, w _ {i} ^ {r t} \\right], \\\\ x _ {i} (m, n), & \\text {o t h e r w i s e .} \\end{array} \\right. \\tag {5}", + "image_path": "fee002a350c56627e158b99170989c375eb2136431772c62a87520c1294bb272.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 343, + 287, + 451 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 343, + 287, + 451 + ], + "spans": [ + { + "bbox": [ + 46, + 343, + 287, + 451 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 343, + 287, + 451 + ], + "type": "inline_equation", + "content": "U" + }, + { + "bbox": [ + 46, + 343, + 287, + 451 + ], + "type": "text", + "content": " denotes the Uniform distribution. Similar operation is performed on the regions of minimum activation value with the coordinates " + }, + { + "bbox": [ + 46, + 343, + 287, + 451 + ], + "type": "inline_equation", + "content": "\\{h_i^{min},w_i^{min}\\}" + }, + { + "bbox": [ + 46, + 343, + 287, + 451 + ], + "type": "text", + "content": " as well. Different from Eq. (2), since the regions of minimum activation value for correct-labeled samples are mainly focused on the background, a smaller mask ratio is adopted for them, helping models narrow the search scope for targets. Therefore, we set the mask ratio of the regions with minimum activation values as " + }, + { + "bbox": [ + 46, + 343, + 287, + 451 + ], + "type": "inline_equation", + "content": "\\mu \\times G_{i}" + }, + { + "bbox": [ + 46, + 343, + 287, + 451 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 452, + 287, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 452, + 287, + 514 + ], + "spans": [ + { + "bbox": [ + 46, + 452, + 287, + 514 + ], + "type": "text", + "content": "Noisy Label Regularization. To penalize the overconfident predictions of the network, a contrastive Noisy Label Regularization (NLR) strategy is designed. Specifically, the generated " + }, + { + "bbox": [ + 46, + 452, + 287, + 514 + ], + "type": "inline_equation", + "content": "x_{i}^{m}" + }, + { + "bbox": [ + 46, + 452, + 287, + 514 + ], + "type": "text", + "content": " is fed into " + }, + { + "bbox": [ + 46, + 452, + 287, + 514 + ], + "type": "inline_equation", + "content": "M_{E}" + }, + { + "bbox": [ + 46, + 452, + 287, + 514 + ], + "type": "text", + "content": " to obtain the prediction " + }, + { + "bbox": [ + 46, + 452, + 287, + 514 + ], + "type": "inline_equation", + "content": "\\widetilde{y_{i}^{m}}" + }, + { + "bbox": [ + 46, + 452, + 287, + 514 + ], + "type": "text", + "content": " and feature " + }, + { + "bbox": [ + 46, + 452, + 287, + 514 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{\\mathbf{i}}^{m}" + }, + { + "bbox": [ + 46, + 452, + 287, + 514 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 124, + 524, + 287, + 538 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 124, + 524, + 287, + 538 + ], + "spans": [ + { + "bbox": [ + 124, + 524, + 287, + 538 + ], + "type": "interline_equation", + "content": "\\mathbf {f} _ {\\mathbf {i}} ^ {m} = M _ {E} \\left(x _ {i} ^ {m}; \\theta_ {E}\\right) \\tag {6}", + "image_path": "45a1c41c6942c70a080096e32388142b8ea30b7067916eee361046bff719a220.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "spans": [ + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "inline_equation", + "content": "\\theta_{E}" + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "text", + "content": " denotes the parameters of the encoder " + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "inline_equation", + "content": "M_{E}" + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "text", + "content": ". Specifically, we give the pseudo labels of " + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "inline_equation", + "content": "x_{i}^{m}" + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "text", + "content": " with the predictions of " + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "text", + "content": ". Since the mask generation is based on the activation maps of the class with the largest predictions, the corresponding prediction is penalized with the mask ratio " + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "inline_equation", + "content": "r_{i}" + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "text", + "content": ". Besides, there exists an equal possibility for the masked region to be predicted as each class. Thus the prediction values for all the classes are increased uniformly. To sum up, the regularized pseudo label " + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "inline_equation", + "content": "y_{i}^{r}" + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "inline_equation", + "content": "x_{i}^{m}" + }, + { + "bbox": [ + 46, + 548, + 288, + 668 + ], + "type": "text", + "content": " can be derived as follows:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 52, + 677, + 287, + 714 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 52, + 677, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 52, + 677, + 287, + 714 + ], + "type": "interline_equation", + "content": "y _ {i} ^ {r} (j) = \\left\\{ \\begin{array}{l l} y _ {i} (j) - r _ {i} + r _ {i} / c, & \\text {i f} j = \\operatorname {a r g m a x} \\left(y _ {i} (j)\\right) \\\\ y _ {i} (j) + r _ {i} / c, & \\text {o t h e r w i s e} \\end{array} , \\right. \\tag {7}", + "image_path": "442ffb7d6eb329655f7bbbc4bec85d217d1528036404a29f5cc318583c41cc4f.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 285, + 545, + 310 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 285, + 545, + 310 + ], + "spans": [ + { + "bbox": [ + 305, + 285, + 545, + 310 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 285, + 545, + 310 + ], + "type": "inline_equation", + "content": "0 \\leq j < c" + }, + { + "bbox": [ + 305, + 285, + 545, + 310 + ], + "type": "text", + "content": ". Afterward, " + }, + { + "bbox": [ + 305, + 285, + 545, + 310 + ], + "type": "inline_equation", + "content": "y_i^r" + }, + { + "bbox": [ + 305, + 285, + 545, + 310 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 285, + 545, + 310 + ], + "type": "inline_equation", + "content": "\\widetilde{y_i^{m}}" + }, + { + "bbox": [ + 305, + 285, + 545, + 310 + ], + "type": "text", + "content": " are used for supervised loss calculation as:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 388, + 319, + 545, + 335 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 388, + 319, + 545, + 335 + ], + "spans": [ + { + "bbox": [ + 388, + 319, + 545, + 335 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {c}} = \\mathcal {L} _ {\\mathrm {c e}} \\left(\\widetilde {y _ {i} ^ {m}}, y _ {i} ^ {r}\\right) \\tag {8}", + "image_path": "cf1d204b415d66f6d1532dc354e78cb80286f457edbf39b16ff58333e680db28.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 308, + 344, + 477, + 357 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 344, + 477, + 357 + ], + "spans": [ + { + "bbox": [ + 308, + 344, + 477, + 357 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 308, + 344, + 477, + 357 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{ce}}" + }, + { + "bbox": [ + 308, + 344, + 477, + 357 + ], + "type": "text", + "content": " denotes the cross-entropy loss." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 359, + 517, + 372 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 359, + 517, + 372 + ], + "spans": [ + { + "bbox": [ + 306, + 359, + 517, + 372 + ], + "type": "text", + "content": "3.2. Self-supervised Masking Reconstruction" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 378, + 545, + 462 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 378, + 545, + 462 + ], + "spans": [ + { + "bbox": [ + 304, + 378, + 545, + 462 + ], + "type": "text", + "content": "Inspired by [11], a Self-supervised Masking Reconstruction branch (SMR) is designed to impose implicit regularization by providing extra noise-free supervised signal. Specifically, the decoder " + }, + { + "bbox": [ + 304, + 378, + 545, + 462 + ], + "type": "inline_equation", + "content": "M_D" + }, + { + "bbox": [ + 304, + 378, + 545, + 462 + ], + "type": "text", + "content": " reconstructs the original image from the latent representation of the masked image " + }, + { + "bbox": [ + 304, + 378, + 545, + 462 + ], + "type": "inline_equation", + "content": "x_i^m" + }, + { + "bbox": [ + 304, + 378, + 545, + 462 + ], + "type": "text", + "content": " by predicting the pixel values for the masked patch, which is defined as:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 383, + 472, + 545, + 485 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 383, + 472, + 545, + 485 + ], + "spans": [ + { + "bbox": [ + 383, + 472, + 545, + 485 + ], + "type": "interline_equation", + "content": "x _ {i} ^ {r} = M _ {D} \\left(\\mathbf {f} _ {\\mathbf {i}} ^ {m}; \\theta_ {D}\\right) \\tag {9}", + "image_path": "14ff657d125699296da44e702c8b2874822e866665165d06bfb1cb49d4401957.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 495, + 545, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 495, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 304, + 495, + 545, + 544 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 495, + 545, + 544 + ], + "type": "inline_equation", + "content": "\\theta_{D}" + }, + { + "bbox": [ + 304, + 495, + 545, + 544 + ], + "type": "text", + "content": " is the parameters of the decoder model " + }, + { + "bbox": [ + 304, + 495, + 545, + 544 + ], + "type": "inline_equation", + "content": "M_{D}" + }, + { + "bbox": [ + 304, + 495, + 545, + 544 + ], + "type": "text", + "content": ". Then the mean squared error (MSE) loss is computed between the reconstructed image " + }, + { + "bbox": [ + 304, + 495, + 545, + 544 + ], + "type": "inline_equation", + "content": "x_{i}^{r}" + }, + { + "bbox": [ + 304, + 495, + 545, + 544 + ], + "type": "text", + "content": " and the original unmasked image " + }, + { + "bbox": [ + 304, + 495, + 545, + 544 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 304, + 495, + 545, + 544 + ], + "type": "text", + "content": " in the pixel space, which is shown as follows:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 389, + 553, + 545, + 567 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 389, + 553, + 545, + 567 + ], + "spans": [ + { + "bbox": [ + 389, + 553, + 545, + 567 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {r}} = \\left\\| x _ {i} ^ {r} - x _ {i} \\right\\| ^ {2} \\tag {10}", + "image_path": "6b6db18c23e2cae8f22ac3ac7db2ab3e1f5b83e35bcd09222ae17faed04ea570.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 577, + 545, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 577, + 545, + 648 + ], + "spans": [ + { + "bbox": [ + 304, + 577, + 545, + 648 + ], + "type": "text", + "content": "In this way, a challenging self-supervised pretext task that requires holistic understanding beyond low-level image statistics is created for the model. This supervision signal is not only free of interference from noisy labels but can also enhance the model generalization ability. Therefore the overall loss function for SANM is constructed as follows:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 385, + 659, + 545, + 672 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 385, + 659, + 545, + 672 + ], + "spans": [ + { + "bbox": [ + 385, + 659, + 545, + 672 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {t r a i n}} = \\mathcal {L} _ {\\mathrm {c}} + \\beta \\mathcal {L} _ {\\mathrm {r}} \\tag {11}", + "image_path": "2f1af71afa146edd339d3796c64e1d3357183404e8c336369460c416775e87ab.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 677, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 545, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 677, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 304, + 677, + 545, + 714 + ], + "type": "text", + "content": " is a parameter to balance the weight of two loss terms. Putting this all together, Algorithm 1 delineates the proposed SANM. Firstly, the original images are masked" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "16190" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 88, + 83, + 502, + 301 + ], + "blocks": [ + { + "bbox": [ + 113, + 71, + 478, + 81 + ], + "lines": [ + { + "bbox": [ + 113, + 71, + 478, + 81 + ], + "spans": [ + { + "bbox": [ + 113, + 71, + 478, + 81 + ], + "type": "text", + "content": "Table 1. Comparison with state-of-the-art methods on CIFAR-10/100 datasets with symmetric noise." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 88, + 83, + 502, + 301 + ], + "lines": [ + { + "bbox": [ + 88, + 83, + 502, + 301 + ], + "spans": [ + { + "bbox": [ + 88, + 83, + 502, + 301 + ], + "type": "table", + "html": "
DatasetCIFAR-10CIFAR-100
Method/Noise ratio20%50%80%90%20%50%80%90%
Cross-Entropy (CE)86.879.462.942.762.046.719.910.1
Co-teaching+ [41]89.585.767.447.965.651.827.913.7
Mixup [43]95.687.171.652.267.857.330.814.6
PENCIL [40]92.489.177.558.969.457.531.115.3
Meta-Learning [16]92.989.377.458.768.559.242.419.5
M-correction [1]94.092.086.869.173.966.148.224.3
DivideMix [15]96.194.693.276.077.374.660.231.5
C2D [47]96.395.294.493.578.676.467.758.7
AugDesc [22]96.395.493.891.979.577.266.441.2
GCE [5]90.089.373.936.568.153.322.18.9
Sel-CL+ [19]95.593.989.281.976.572.459.648.8
MOIT+ [23]94.191.881.174.775.970.647.641.8
SANM(DivideMix)96.495.894.692.381.278.268.743.5
SANM(C2D)96.696.495.795.181.979.371.661.9
", + "image_path": "8483af1a979678825e0a235e7908889a7b1aa6b957fe878c357cf8683a60c01b.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 311, + 287, + 406 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 311, + 287, + 406 + ], + "spans": [ + { + "bbox": [ + 46, + 311, + 287, + 406 + ], + "type": "text", + "content": "with the label quality guided masking scheme, where mislabeled and correct-labeled samples are modulated adaptively. Meanwhile, the predictions of the original images are used for regularized label generation. Then the regularized features of the masked images are utilized for self-supervised masking reconstruction, which provides noise-free supervisions. Finally, SANM can be plugged into existing LNL frameworks to further boost performance." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 426, + 127, + 440 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 426, + 127, + 440 + ], + "spans": [ + { + "bbox": [ + 47, + 426, + 127, + 440 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 449, + 242, + 462 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 449, + 242, + 462 + ], + "spans": [ + { + "bbox": [ + 47, + 449, + 242, + 462 + ], + "type": "text", + "content": "4.1. Datasets and Implementation Details" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 471, + 287, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 471, + 287, + 602 + ], + "spans": [ + { + "bbox": [ + 46, + 471, + 287, + 602 + ], + "type": "text", + "content": "Simulated noisy datasets. We evaluate on two simulated noisy datasets: CIFAR-10/100 [14], which consists of 50k training samples and 10k test samples. Following previous work [15], two simulated noisy settings are considered: symmetric and asymmetric noise. Following noise rates are considered: " + }, + { + "bbox": [ + 46, + 471, + 287, + 602 + ], + "type": "inline_equation", + "content": "\\tau \\in \\{20\\%, 50\\%, 80\\%, 90\\}" + }, + { + "bbox": [ + 46, + 471, + 287, + 602 + ], + "type": "text", + "content": " for symmetric noise and " + }, + { + "bbox": [ + 46, + 471, + 287, + 602 + ], + "type": "inline_equation", + "content": "\\tau \\in \\{20\\%, 40\\%" + }, + { + "bbox": [ + 46, + 471, + 287, + 602 + ], + "type": "text", + "content": " for asymmetric noise. An 18-layer PreAct ResNet is used and we train for 300 epochs with a batch size of 64 and SGD optimizer. The initial learning rate is set as 0.02 and is reduced by a factor of 10 after 150 epochs. " + }, + { + "bbox": [ + 46, + 471, + 287, + 602 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 46, + 471, + 287, + 602 + ], + "type": "text", + "content": " is empirically set as 1." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 605, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 287, + 713 + ], + "type": "text", + "content": "Real-world noisy datasets. We also validate our method on real-world noisy datasets Clothing-1M [38] and Animal10N [28], the former contains 1 million training samples with nearly " + }, + { + "bbox": [ + 46, + 605, + 287, + 713 + ], + "type": "inline_equation", + "content": "40\\%" + }, + { + "bbox": [ + 46, + 605, + 287, + 713 + ], + "type": "text", + "content": " mislabeled samples, the latter contains 55,000 human-labeled online images for 10 confusing animals. It includes approximately " + }, + { + "bbox": [ + 46, + 605, + 287, + 713 + ], + "type": "inline_equation", + "content": "8\\%" + }, + { + "bbox": [ + 46, + 605, + 287, + 713 + ], + "type": "text", + "content": " noisy-labeled samples. The original protocol is used to split the training and test images. We strictly follow the previous experimental settings, more details can be found in the supplementary materials." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 310, + 495, + 323 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 310, + 495, + 323 + ], + "spans": [ + { + "bbox": [ + 306, + 310, + 495, + 323 + ], + "type": "text", + "content": "Algorithm 1 The proposed SANM framework" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 326, + 545, + 346 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 326, + 545, + 346 + ], + "spans": [ + { + "bbox": [ + 306, + 326, + 545, + 346 + ], + "type": "text", + "content": "Input: Noisy training set " + }, + { + "bbox": [ + 306, + 326, + 545, + 346 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 306, + 326, + 545, + 346 + ], + "type": "text", + "content": ", encoder model " + }, + { + "bbox": [ + 306, + 326, + 545, + 346 + ], + "type": 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Experimental Results" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": "- Results on Simulated Noisy Datasets. Here we compare the proposed method with multiple baselines on CIFAR-10/100 using different symmetric noise levels. As shown in Tab. 1, by integrating with C2D [47], our SANM outperforms the naive C2D significantly and achieves state-of-the-art performance among these methods. Specifically, an increase of around " + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "inline_equation", + "content": "3 \\sim 4\\%" + }, + { + "bbox": [ + 304, + 570, + 545, + 713 + ], + "type": "text", + "content": " across all the symmetric noise cases on CIFAR-100 can be observed. 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Investigation of the basic mask ratio " + }, + { + "bbox": [ + 105, + 200, + 485, + 210 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 105, + 200, + 485, + 210 + ], + "type": "text", + "content": " under " + }, + { + "bbox": [ + 105, + 200, + 485, + 210 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 105, + 200, + 485, + 210 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 105, + 200, + 485, + 210 + ], + "type": "inline_equation", + "content": "80\\%" + }, + { + "bbox": [ + 105, + 200, + 485, + 210 + ], + "type": "text", + "content": " symmetric noise of CIFAR-10/100." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 294, + 71, + 518, + 199 + ], + "blocks": [ + { + "bbox": [ + 294, + 71, + 518, + 199 + ], + "lines": [ + { + "bbox": [ + 294, + 71, + 518, + 199 + ], + "spans": [ + { + "bbox": [ + 294, + 71, + 518, + 199 + ], + "type": "image", + "image_path": "82daa868d10f0be5af35a2f4d572c77e34617669c5846b5b27057394e0d1e903.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 56, + 229, + 193, + 399 + ], + "blocks": [ + { + "bbox": [ + 49, + 217, + 201, + 227 + ], + "lines": [ + { + "bbox": [ + 49, + 217, + 201, + 227 + ], + "spans": [ + { + "bbox": [ + 49, + 217, + 201, + 227 + ], + "type": "text", + "content": "Table 2. Asymmetric noise on CIFAR-10." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 229, + 193, + 399 + ], + "lines": [ + { + "bbox": [ + 56, + 229, + 193, + 399 + ], + "spans": [ + { + "bbox": [ + 56, + 229, + 193, + 399 + ], + "type": "table", + "html": "
MethodNoisy ratio
20%40%
Joint-Optim [32]92.891.7
PENCIL [40]92.491.2
F-correction [24]89.9-
Distilling [46]92.790.2
Meta-Learning [16]-88.6
M-correction [1]-86.3
Iterative-CV [3]-88.0
DivideMix [15]93.493.4
REED [44]95.092.3
C2D [47]93.893.4
Sel-CL+ [19]95.293.4
GCE [5]87.378.1
RRL [17]-92.4
SANM(DivideMix)95.494.8
", + "image_path": "5fc218044a5aca407e5fb9dde65696805ca8e9b0cb33fec486f37ab6bd82ad5e.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 241, + 225, + 349, + 402 + ], + "blocks": [ + { + "bbox": [ + 217, + 214, + 373, + 224 + ], + "lines": [ + { + "bbox": [ + 217, + 214, + 373, + 224 + ], + "spans": [ + { + "bbox": [ + 217, + 214, + 373, + 224 + ], + "type": "text", + "content": "Table 3. Testing accuracy on Clothing-1M." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 241, + 225, + 349, + 402 + ], + "lines": [ + { + "bbox": [ + 241, + 225, + 349, + 402 + ], + "spans": [ + { + "bbox": [ + 241, + 225, + 349, + 402 + ], + "type": "table", + "html": "
MethodAcc
CrossEntropy69.21
F-correction [24]69.84
M-correction [1]71.00
Joint-Optim [32]72.16
Meta-Cleaner [45]72.50
Meta-Learning [16]73.47
PENCIL [40]73.49
Self-Learning [10]74.45
DivideMix [15]74.76
Nested [4]74.90
AugDesc [22]75.11
RRL [17]74.90
GCE [5]73.30
C2D [47]74.30
SANM(DivideMix)75.63
", + "image_path": "f17edfcc0e340dae9a6212a99941e46d1b3f54a7fc985b869773e0f18684ed12.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 384, + 258, + 544, + 402 + ], + "blocks": [ + { + "bbox": [ + 383, + 214, + 547, + 257 + ], + "lines": [ + { + "bbox": [ + 383, + 214, + 547, + 257 + ], + "spans": [ + { + "bbox": [ + 383, + 214, + 547, + 257 + ], + "type": "text", + "content": "Table 4. Comparison with masks generated from pre-trained backbones on CIFAR10/100. M: Method. P: Pretrained Backbone. S: SANM. D: DivideMix. C: C2D." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 384, + 258, + 544, + 402 + ], + "lines": [ + { + "bbox": [ + 384, + 258, + 544, + 402 + ], + "spans": [ + { + "bbox": [ + 384, + 258, + 544, + 402 + ], + "type": "table", + "html": "
DatasetCIFAR-10
MPS20%50%80%90%
D✓X96.095.193.781.5
X✓96.495.894.692.3
C✓X96.395.495.093.9
X✓96.696.495.795.1
DatasetCIFAR-100
MPS20%50%80%90%
D✓X78.876.664.936.4
X✓81.278.268.743.5
C✓X80.077.169.258.4
X✓81.979.371.661.9
", + "image_path": "249ba0d1c371fb95a2a888c78950bee2f818cd9b783d6a76673115e7313467a8.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 412, + 287, + 531 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 412, + 287, + 531 + ], + "spans": [ + { + "bbox": [ + 46, + 412, + 287, + 531 + ], + "type": "text", + "content": "margin of " + }, + { + "bbox": [ + 46, + 412, + 287, + 531 + ], + "type": "inline_equation", + "content": "12\\%" + }, + { + "bbox": [ + 46, + 412, + 287, + 531 + ], + "type": "text", + "content": ". A similar performance boost can be observed on the CIFAR-100 as well, especially in the high noise case. When compared with the implicit regularization method AugDesc [22], our SANM (DivideMix) can also achieve much better accuracy across all the settings. Tab. 2 shows comparisons of the test accuracy on CIFAR-10 with varied asymmetric noise levels. SANM shows a consistent improvement with respect to the state-of-the-art methods, demonstrating the ability of SANM in enhancing the robustness of the model to semantic noise." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 533, + 287, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 533, + 287, + 652 + ], + "spans": [ + { + "bbox": [ + 46, + 533, + 287, + 652 + ], + "type": "text", + "content": "- Results on Real-world Noisy Datasets. The results on Clothing1M dataset are shown in Tab. 3, one can see that SANM can bring around " + }, + { + "bbox": [ + 46, + 533, + 287, + 652 + ], + "type": "inline_equation", + "content": "0.87\\%" + }, + { + "bbox": [ + 46, + 533, + 287, + 652 + ], + "type": "text", + "content": " improvement in accuracy to the baseline method DivideMix, and get much better result than other state-of-the-art methods, verifying that SANM is also effective in real-world noise scenarios. Moreover, SANM obtains " + }, + { + "bbox": [ + 46, + 533, + 287, + 652 + ], + "type": "inline_equation", + "content": "89.3\\%" + }, + { + "bbox": [ + 46, + 533, + 287, + 652 + ], + "type": "text", + "content": " accuracy on Animal-10N (detailed comparison on Animal-10N is in supplementary materials), which sets the new state-of-the-art, indicating that SANM can handle fine-grained real-world noisy datasets as well." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 653, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 287, + 714 + ], + "type": "text", + "content": "Generality of SANM. To further verify the effectiveness and generalization of SANM, we adopt vanilla CE, Co-teaching [9], CDR [37] and ELR+ [21], other than DivideMix and C2D, to work collaboratively with SANM. As shown in Tab. 6, SANM brings consistent performance" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 412, + 545, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 412, + 545, + 520 + ], + "spans": [ + { + "bbox": [ + 304, + 412, + 545, + 520 + ], + "type": "text", + "content": "boost to all the corresponding baselines across all the noisy cases. For example, when seamlessly combined with SANM, an accuracy increase of around " + }, + { + "bbox": [ + 304, + 412, + 545, + 520 + ], + "type": "inline_equation", + "content": "14.9\\%" + }, + { + "bbox": [ + 304, + 412, + 545, + 520 + ], + "type": "text", + "content": " on the symmetric " + }, + { + "bbox": [ + 304, + 412, + 545, + 520 + ], + "type": "inline_equation", + "content": "80\\%" + }, + { + "bbox": [ + 304, + 412, + 545, + 520 + ], + "type": "text", + "content": " noise setting (CIFAR-100) can be achieved by vanilla CE, demonstrating that SANM is capable of effectively imposing adaptive regularization on noisy and clean samples respectively and thus alleviates over-fitting to noisy labels. Therefore, all the results indicate the generalization ability of SANM to boost existing LNL approaches." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 527, + 400, + 540 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 527, + 400, + 540 + ], + "spans": [ + { + "bbox": [ + 306, + 527, + 400, + 540 + ], + "type": "text", + "content": "4.3. Ablation Study" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "content": "Component Analysis. To explore the impact of each component in SANM, i.e., AMG-adversarial noisy masking generation, NLR-noisy label regularization, SMR-self-supervised masking reconstruction, we conduct an ablation study on CIFAR-10 and CIFAR-100. As shown in Tab. 5, we report test accuracy for all the noisy settings for SANM where the contribution of each component is clearly shown. Specifically, it can be observed that AMG is crucial to performance, especially under severe label noisy cases. Besides, the accuracy is further improved when further combined with NLR. By introducing noise-free self-supervised signals, SMR can also boost the performance of the baseline across all the settings. Best accuracy can be achieved by SANM when all the components are included. This ver" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "16192" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 132, + 95, + 462, + 194 + ], + "blocks": [ + { + "bbox": [ + 46, + 70, + 547, + 93 + ], + "lines": [ + { + "bbox": [ + 46, + 70, + 547, + 93 + ], + "spans": [ + { + "bbox": [ + 46, + 70, + 547, + 93 + ], + "type": "text", + "content": "Table 5. Ablation study for the effectiveness of each key component. AMG: adversarial noisy masking generation, NLR: noisy label regularization, SMR: self-supervised masking reconstruction." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 132, + 95, + 462, + 194 + ], + "lines": [ + { + "bbox": [ + 132, + 95, + 462, + 194 + ], + "spans": [ + { + "bbox": [ + 132, + 95, + 462, + 194 + ], + "type": "table", + "html": "
ComponentCIFAR-10CIFAR-100
AMGNLRSMR20%50%80%90%20%50%80%90%
XXXBest96.194.693.276.077.374.660.231.5
Last95.794.492.975.476.974.259.631.0
✓XXBest96.395.394.091.480.277.368.042.7
Last96.295.193.690.879.777.067.542.5
✓✓XBest96.495.594.291.680.577.568.343.0
Last96.395.494.091.580.277.168.242.7
✓✓✓Best96.495.894.692.381.278.268.743.5
Last96.395.694.392.180.478.068.343.0
", + "image_path": "21fdc55a5d5826e1b1550400673f081bb47d329fe3ff3eb56431b9c08ead9f16.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "table", + "bbox": [ + 105, + 228, + 486, + 369 + ], + "blocks": [ + { + "bbox": [ + 46, + 203, + 547, + 226 + ], + "lines": [ + { + "bbox": [ + 46, + 203, + 547, + 226 + ], + "spans": [ + { + "bbox": [ + 46, + 203, + 547, + 226 + ], + "type": "text", + "content": "Table 6. Comparison between the LNL methods and their SANM applications with symmetric noise on CIFAR-10/100. Specifically, the 9-layer CNN is adopted as the backbone network of Co-teaching." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 105, + 228, + 486, + 369 + ], + "lines": [ + { + "bbox": [ + 105, + 228, + 486, + 369 + ], + "spans": [ + { + "bbox": [ + 105, + 228, + 486, + 369 + ], + "type": "table", + "html": "
Dataset\nMethod/Noise ratioCIFAR-10CIFAR-100
20%50%80%90%20%50%80%90%
CEBest86.879.462.942.762.046.719.910.1
SANM(CE)Best92.489.772.151.570.953.134.818.6
Co-teaching [9]Best82.673.024.014.650.538.211.84.9
SANM(Co-teaching)Best89.278.236.420.758.251.319.413.4
CDR [37]Best90.485.047.212.363.339.529.28.0
SANM(CDR)Best92.691.655.316.772.756.436.620.8
ELR+ [21]Best94.693.891.175.277.572.458.230.8
SANM(ELR+)Best96.395.794.182.979.877.365.038.7
", + "image_path": "ddcdd97eb50cbfa3b26cddbcf223662682da1383908b295c2b84b3063abf2c7e.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 372, + 275, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 372, + 275, + 384 + ], + "spans": [ + { + "bbox": [ + 47, + 372, + 275, + 384 + ], + "type": "text", + "content": "ifies the effectiveness of all three key designs in SANM." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "spans": [ + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "text", + "content": "Parameter Sensitivity. The basic mask ratio " + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "text", + "content": " in Eq. (2) is a important parameter within the proposed method. To investigate how " + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "text", + "content": " affects the performance, we conduct parameter analysis experiments under " + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "inline_equation", + "content": "50\\% / 80\\%" + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "text", + "content": " symmetric noise levels on CIFAR-10/100. Best test accuracy with respect to different " + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "text", + "content": " is shown in Fig. 4. Compared with the results of baseline methods, i.e., masking the images with fixed ratios and random ratios (sampled from Uniform(0, " + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "text", + "content": ") for each sample, SANM achieves superior performance. Moreover, the results show that keeping " + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "text", + "content": " between 0.1 and 0.3 leads to better performance than without adversarial noisy masking " + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "inline_equation", + "content": "(\\mu = 0)" + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "text", + "content": ". This is mainly because large " + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "text", + "content": " makes the masked training images and the test images differ significantly in image style, and thus the model is unable to learn class-specific discriminative information when trained with such strong regularization. However, an extremely small " + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "text", + "content": " results in encoder overfit to noisy labels more severely. Therefore, we set " + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "inline_equation", + "content": "\\mu" + }, + { + "bbox": [ + 46, + 387, + 289, + 602 + ], + "type": "text", + "content": " as 0.2 to obtain the best performance." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 605, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 288, + 715 + ], + "type": "text", + "content": "Comparison with Masks from Pre-trained Backbones. The effectiveness of the proposed adversarial masking generation is evaluated in Tab. 5. And further we conduct experiments that utilizing a strong backbone (i.e., pretrained ResNet-18 with DivideMix and C2D) to generate more object-centric masks as comparison to our method. The results shown in Tab. 4 indicate that even compared with the mask generation strategy by well-trained strong backbones, our adversarial masking generation can consis" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 372, + 547, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 372, + 547, + 420 + ], + "spans": [ + { + "bbox": [ + 304, + 372, + 547, + 420 + ], + "type": "text", + "content": "tently performs better since the mask generation strategy of fixed well-trained backbones cannot adaptively adjust the mask strategy according to the training state of the base model, leading to sub-optimal results." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 427, + 379, + 439 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 427, + 379, + 439 + ], + "spans": [ + { + "bbox": [ + 306, + 427, + 379, + 439 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 444, + 547, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 444, + 547, + 588 + ], + "spans": [ + { + "bbox": [ + 304, + 444, + 547, + 588 + ], + "type": "text", + "content": "In this paper, we propose SANM, a novel self-supervised adversarial noisy masking LNL method. SANM prevents models from overfitting noisy instances by explicitly imposing regularization on the features. A label quality guided adversarial masking scheme is designed to adaptively modify the inputs and noisy labels. Moreover, an auxiliary decoder is introduced to reconstruct images based on the modulated features, which enhances the generalization by providing extra noise-free self-supervised signals. Besides, SANM is flexible and can be easily plugged into existing LNL frameworks. State-of-the-art performance is obtained by SANM on both synthetic and real-world noisy datasets." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 597, + 404, + 611 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 597, + 404, + 611 + ], + "spans": [ + { + "bbox": [ + 306, + 597, + 404, + 611 + ], + "type": "text", + "content": "Acknowledgement" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 617, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 547, + 715 + ], + "type": "text", + "content": "This work was supported by National Natural Science Fund of China (62076184, 61976158, 61976160, 62076182, 62276190), in part by Fundamental Research Funds for the Central Universities and State Key Laboratory of Integrated Services Networks (Xidian University), in part by Shanghai Innovation Action Project of Science and Technology (20511100700) and Shanghai Natural Science Foundation (22ZR1466700)." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "16193" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "type": "text", + "content": "[1] Eric Arazo, Diego Ortego, Paul Albert, Noel E O'Connor, and Kevin McGuinness. Unsupervised label noise modeling and loss correction. In Proc. International Conference on Machine Learning (ICML), 2019. 6, 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 135, + 288, + 201 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 135, + 288, + 201 + ], + "spans": [ + { + "bbox": [ + 53, + 135, + 288, + 201 + ], + "type": "text", + "content": "[2] Devansh Arpit, Stanisław Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al. A closer look at memorization in deep networks. In Proc. International Conference on Machine Learning (ICML), pages 233-242. PMLR, 2017. 1, 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 202, + 288, + 246 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 202, + 288, + 246 + ], + "spans": [ + { + "bbox": [ + 53, + 202, + 288, + 246 + ], + "type": "text", + "content": "[3] Pengfei Chen, Benben Liao, Guangyong Chen, and Shengyu Zhang. Understanding and utilizing deep neural networks trained with noisy labels. In Proc. International Conference on Machine Learning (ICML), 2019. 1, 3, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 246, + 288, + 302 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 246, + 288, + 302 + ], + "spans": [ + { + "bbox": [ + 53, + 246, + 288, + 302 + ], + "type": "text", + "content": "[4] Yingyi Chen, Xi Shen, Shell Xu Hu, and Johan A. K. Suykens. Boosting co-teaching with compression regularization for label noise. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pages 2688-2692, June 2021. 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 302, + 288, + 346 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 302, + 288, + 346 + ], + "spans": [ + { + "bbox": [ + 53, + 302, + 288, + 346 + ], + "type": "text", + "content": "[5] Aritra Ghosh and Andrew Lan. Contrastive learning improves model robustness under label noise. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2703-2708, 2021. 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 346, + 288, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 346, + 288, + 380 + ], + "spans": [ + { + "bbox": [ + 53, + 346, + 288, + 380 + ], + "type": "text", + "content": "[6] Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572, 2014. 1, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 380, + 288, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 380, + 288, + 435 + ], + "spans": [ + { + "bbox": [ + 53, + 380, + 288, + 435 + ], + "type": "text", + "content": "[7] Aayush Gupta, Ayush Jaiswal, Yue Wu, Vivek Yadav, and Pradeep Natarajan. Adversarial mask generation for preserving visual privacy. In 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021), pages 1-5. IEEE, 2021. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 435, + 288, + 500 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 435, + 288, + 500 + ], + "spans": [ + { + "bbox": [ + 53, + 435, + 288, + 500 + ], + "type": "text", + "content": "[8] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Co-teaching: Robust training of deep neural networks with extremely noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), volume 31, 2018. 1, 2, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 502, + 288, + 557 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 502, + 288, + 557 + ], + "spans": [ + { + "bbox": [ + 53, + 502, + 288, + 557 + ], + "type": "text", + "content": "[9] Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. Coteaching: Robust training of deep neural networks with extremely noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), 2018. 7, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 557, + 288, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 557, + 288, + 590 + ], + "spans": [ + { + "bbox": [ + 48, + 557, + 288, + 590 + ], + "type": "text", + "content": "[10] Jiangfan Han, Ping Luo, and Xiaogang Wang. Deep self-learning from noisy labels. In Proc. IEEE International Conference on Computer Vision (ICCV), 2019. 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 590, + 288, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 590, + 288, + 624 + ], + "spans": [ + { + "bbox": [ + 48, + 590, + 288, + 624 + ], + "type": "text", + "content": "[11] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377, 2021.5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 624, + 288, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 288, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 288, + 668 + ], + "type": "text", + "content": "[12] Qibin Hou, PengTao Jiang, Yunchao Wei, and Ming-Ming Cheng. Self-erasing network for integral object attention. In Proc. Advances in Neural Information Processing Systems (NeurIPS), volume 31, 2018. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 669, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 669, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 669, + 288, + 713 + ], + "type": "text", + "content": "[13] Tao Hu, Honggang Qi, Qingming Huang, and Yan Lu. See better before looking closer: Weakly supervised data augmentation network for fine-grained visual classification. arXiv preprint arXiv:1901.09891, 2019. 3" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 95 + ], + "type": "text", + "content": "[14] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. 2009. 3, 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 96, + 545, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 96, + 545, + 140 + ], + "spans": [ + { + "bbox": [ + 307, + 96, + 545, + 140 + ], + "type": "text", + "content": "[15] Junnan Li, Richard Socher, and Steven CH Hoi. Dividemix: Learning with noisy labels as semi-supervised learning. In Proc. International Conference on Learning Representations (ICLR), 2020. 1, 2, 3, 4, 6, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 141, + 545, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 141, + 545, + 185 + ], + "spans": [ + { + "bbox": [ + 307, + 141, + 545, + 185 + ], + "type": "text", + "content": "[16] Junnan Li, Yongkang Wong, Qi Zhao, and Mohan S. Kankanhalli. Learning to learn from noisy labeled data. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 6, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 186, + 545, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 545, + 230 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 545, + 230 + ], + "type": "text", + "content": "[17] Junnan Li, Caiming Xiong, and Steven CH Hoi. Learning from noisy data with robust representation learning. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 9485-9494, 2021. 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 231, + 545, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 231, + 545, + 275 + ], + "spans": [ + { + "bbox": [ + 307, + 231, + 545, + 275 + ], + "type": "text", + "content": "[18] Kunpeng Li, Ziyan Wu, Kuan-Chuan Peng, Jan Ernst, and Yun Fu. Tell me where to look: Guided attention inference network. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 9215-9223, 2018. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 276, + 545, + 309 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 276, + 545, + 309 + ], + "spans": [ + { + "bbox": [ + 307, + 276, + 545, + 309 + ], + "type": "text", + "content": "[19] Shikun Li, Xiaobo Xia, Shiming Ge, and Tongliang Liu. Selective-supervised contrastive learning with noisy labels. arXiv preprint arXiv:2203.04181, 2022. 6, 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 310, + 545, + 365 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 310, + 545, + 365 + ], + "spans": [ + { + "bbox": [ + 307, + 310, + 545, + 365 + ], + "type": "text", + "content": "[20] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), volume 33, pages 20331–20342, 2020. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 366, + 545, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 366, + 545, + 421 + ], + "spans": [ + { + "bbox": [ + 307, + 366, + 545, + 421 + ], + "type": "text", + "content": "[21] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. In Proc. Advances in Neural Information Processing Systems (NeurIPS), 2020. 7, 8" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 422, + 545, + 466 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 422, + 545, + 466 + ], + "spans": [ + { + "bbox": [ + 307, + 422, + 545, + 466 + ], + "type": "text", + "content": "[22] Kento Nishi, Yi Ding, Alex Rich, and Tobias Hollerer. Augmentation strategies for learning with noisy labels. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8022-8031, 2021. 6, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 467, + 545, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 467, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 307, + 467, + 545, + 521 + ], + "type": "text", + "content": "[23] Diego Ortego, Eric Arazo, Paul Albert, Noel E O'Connor, and Kevin McGuinness. Multi-objective interpolation training for robustness to label noise. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6606-6615, 2021. 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 522, + 545, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 522, + 545, + 578 + ], + "spans": [ + { + "bbox": [ + 307, + 522, + 545, + 578 + ], + "type": "text", + "content": "[24] Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu. Making deep neural networks robust to label noise: A loss correction approach. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 578, + 545, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 578, + 545, + 623 + ], + "spans": [ + { + "bbox": [ + 307, + 578, + 545, + 623 + ], + "type": "text", + "content": "[25] Haim Permuter, Joseph Francos, and Ian Jermyn. A study of gaussian mixture models of color and texture features for image classification and segmentation. Pattern recognition, 39(4):695-706, 2006. 4" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 624, + 545, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 624, + 545, + 669 + ], + "spans": [ + { + "bbox": [ + 307, + 624, + 545, + 669 + ], + "type": "text", + "content": "[26] Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. Learning to reweight examples for robust deep learning. In Proc. International Conference on Machine Learning (ICML), pages 4334-4343. PMLR, 2018. 1, 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 669, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 669, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 669, + 545, + 713 + ], + "type": "text", + "content": "[27] Hwanjun Song, Minseok Kim, and Jae-Gil Lee. Selfie: Refurbishing unclean samples for robust deep learning. In Proc. International Conference on Machine Learning (ICML), 2019. 1" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "16194" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "text", + "content": "[28] Hwanjun Song, Minseok Kim, and Jae-Gil Lee. SELFIE: Refurbishing unclean samples for robust deep learning. In Proc. International Conference on Machine Learning (ICML), pages 5907-5915, 2019. 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 118, + 287, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 118, + 287, + 161 + ], + "spans": [ + { + "bbox": [ + 49, + 118, + 287, + 161 + ], + "type": "text", + "content": "[29] Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. Learning from noisy labels with deep neural networks: A survey. IEEE Transactions on Neural Networks and Learning Systems, 2022. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 162, + 288, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 162, + 288, + 227 + ], + "spans": [ + { + "bbox": [ + 49, + 162, + 288, + 227 + ], + "type": "text", + "content": "[30] Erik Stammes, Tom FH Runia, Michael Hofmann, and Mohsen Ghafoorian. Find it if you can: end-to-end adversarial erasing for weakly-supervised semantic segmentation. In Thirteenth International Conference on Digital Image Processing (ICDIP 2021), volume 11878, page 1187825. International Society for Optics and Photonics, 2021. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 228, + 288, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 228, + 288, + 282 + ], + "spans": [ + { + "bbox": [ + 49, + 228, + 288, + 282 + ], + "type": "text", + "content": "[31] Guolei Sun, Hisham Cholakkal, Salman Khan, Fahad Khan, and Ling Shao. Fine-grained recognition: Accounting for subtle differences between similar classes. In Association for the Advancement of Artificial Intelligence (AAAI), volume 34, pages 12047–12054, 2020. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 283, + 287, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 283, + 287, + 326 + ], + "spans": [ + { + "bbox": [ + 49, + 283, + 287, + 326 + ], + "type": "text", + "content": "[32] Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa. Joint optimization framework for learning with noisy labels. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 1, 3, 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 327, + 287, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 327, + 287, + 369 + ], + "spans": [ + { + "bbox": [ + 49, + 327, + 287, + 369 + ], + "type": "text", + "content": "[33] Gabriele Valvano, Andrea Leo, and Sotirios A Tsaftaris. Reusing adversarial mask discriminators for test-time training under distribution shifts. arXiv preprint arXiv:2108.11926, 2021. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 371, + 287, + 425 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 371, + 287, + 425 + ], + "spans": [ + { + "bbox": [ + 49, + 371, + 287, + 425 + ], + "type": "text", + "content": "[34] Hongxin Wei, Lei Feng, Xiangyu Chen, and Bo An. Combating noisy labels by agreement: A joint training method with co-regularization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 13726–13735, 2020. 1, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 49, + 426, + 287, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 426, + 287, + 491 + ], + "spans": [ + { + "bbox": [ + 49, + 426, + 287, + 491 + ], + "type": "text", + "content": "[35] Yunchao Wei, Jiashi Feng, Xiaodan Liang, Ming-Ming Cheng, Yao Zhao, and Shuicheng Yan. Object region mining with adversarial erasing: A simple classification to semantic segmentation approach. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1568-1576, 2017. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 49, + 493, + 287, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 493, + 287, + 536 + ], + "spans": [ + { + "bbox": [ + 49, + 493, + 287, + 536 + ], + "type": "text", + "content": "[36] Yichen Wu, Jun Shu, Qi Xie, Qian Zhao, and Deyu Meng. Learning to purify noisy labels via meta soft label corrector. In Association for the Advancement of Artificial Intelligence (AAAI), 2021. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 49, + 537, + 287, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 537, + 287, + 590 + ], + "spans": [ + { + "bbox": [ + 49, + 537, + 287, + 590 + ], + "type": "text", + "content": "[37] Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang. Robust early-learning: Hindering the memorization of noisy labels. In Proc. International Conference on Learning Representations (ICLR), 2021. 7, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 49, + 592, + 287, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 592, + 287, + 635 + ], + "spans": [ + { + "bbox": [ + 49, + 592, + 287, + 635 + ], + "type": "text", + "content": "[38] Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang. Learning from massive noisy labeled data for image classification. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. 1, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 49, + 637, + 287, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 637, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 49, + 637, + 287, + 689 + ], + "type": "text", + "content": "[39] Xiao Yang, Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu, Yuefeng Chen, and Hui Xue. Towards face encryption by generating adversarial identity masks. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 3897-3907, 2021. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 49, + 692, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 692, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 49, + 692, + 287, + 713 + ], + "type": "text", + "content": "[40] Kun Yi and Jianxin Wu. Probabilistic end-to-end noise correction for learning with noisy labels. In Proc. IEEE Confer-" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 586 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "text", + "content": "ence on Computer Vision and Pattern Recognition (CVPR), pages 7017-7025, 2019. 1, 3, 6, 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 308, + 96, + 545, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 545, + 149 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 545, + 149 + ], + "type": "text", + "content": "[41] Xingrui Yu, Bo Han, Jiangchao Yao, Gang Niu, Ivor W Tsang, and Masashi Sugiyama. How does disagreement help generalization against label corruption? In Proc. International Conference on Machine Learning (ICML), 2019. 1, 3, 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 152, + 545, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 152, + 545, + 217 + ], + "spans": [ + { + "bbox": [ + 308, + 152, + 545, + 217 + ], + "type": "text", + "content": "[42] Boshen Zhang, Yuxi Li, Yuanpeng Tu, Jinlong Peng, Yabiao Wang, Cunlin Wu, Yang Xiao, and Cairong Zhao. Learning from noisy labels with coarse-to-fine sample credibility modeling. In Computer Vision-ECCV 2022 Workshops: Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part II, pages 21-38. Springer, 2023. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 219, + 545, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 219, + 545, + 251 + ], + "spans": [ + { + "bbox": [ + 308, + 219, + 545, + 251 + ], + "type": "text", + "content": "[43] Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz. mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412, 2017. 1, 3, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 253, + 545, + 284 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 253, + 545, + 284 + ], + "spans": [ + { + "bbox": [ + 308, + 253, + 545, + 284 + ], + "type": "text", + "content": "[44] Hui Zhang and Quanming Yao. Decoupling representation and classifier for noisy label learning. arXiv preprint arXiv:2011.08145, 2020. 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 286, + 545, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 286, + 545, + 329 + ], + "spans": [ + { + "bbox": [ + 308, + 286, + 545, + 329 + ], + "type": "text", + "content": "[45] Weihe Zhang, Yali Wang, and Yu Qiao. Metacleaner: Learning to hallucinate clean representations for noisy-labeled visual recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 331, + 545, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 331, + 545, + 374 + ], + "spans": [ + { + "bbox": [ + 308, + 331, + 545, + 374 + ], + "type": "text", + "content": "[46] Z. Zhang, H. Zhang, S. Arik, H. Lee, and T. Pfister. Distilling effective supervision from severe label noise. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 376, + 545, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 376, + 545, + 431 + ], + "spans": [ + { + "bbox": [ + 308, + 376, + 545, + 431 + ], + "type": "text", + "content": "[47] Evgenii Zheltonozhskii, Chaim Baskin, Avi Mendelson, Alex M Bronstein, and Or Litany. Contrast to divide: Self-supervised pre-training for learning with noisy labels. In Proc. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pages 1657-1667, 2022. 1, 3, 6, 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 433, + 545, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 433, + 545, + 474 + ], + "spans": [ + { + "bbox": [ + 308, + 433, + 545, + 474 + ], + "type": "text", + "content": "[48] Guoqing Zheng, Ahmed Hassan Awadallah, and Susan Dumas. Meta label correction for noisy label learning. In Association for the Advancement of Artificial Intelligence (AAAI), 2021. 1, 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 476, + 545, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 476, + 545, + 529 + ], + "spans": [ + { + "bbox": [ + 308, + 476, + 545, + 529 + ], + "type": "text", + "content": "[49] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2921-2929, 2016. 3, 4" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 532, + 545, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 532, + 545, + 586 + ], + "spans": [ + { + "bbox": [ + 308, + 532, + 545, + 586 + ], + "type": "text", + "content": "[50] Xiong Zhou, Xianming Liu, Chenyang Wang, Deming Zhai, Junjun Jiang, and Xiangyang Ji. Learning with noisy labels via sparse regularization. In Proc. IEEE International Conference on Computer Vision (ICCV), pages 72-81, 2021. 1, 2" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "16195" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning a 3D Morphable Face Reflectance Model From Low-Cost Data/bfac72ff-9a91-49c0-91b2-40f3beec823c_content_list.json b/2023/Learning a 3D Morphable Face Reflectance Model From Low-Cost Data/bfac72ff-9a91-49c0-91b2-40f3beec823c_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..5275042c84f5be0c3b5e622703d78c4822939ceb --- /dev/null +++ b/2023/Learning a 3D Morphable Face Reflectance Model From Low-Cost Data/bfac72ff-9a91-49c0-91b2-40f3beec823c_content_list.json @@ -0,0 +1,1701 @@ +[ + { + "type": "text", + "text": "Learning a 3D Morphable Face Reflectance Model from Low-cost Data", + "text_level": 1, + "bbox": [ + 127, + 130, + 841, + 151 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yuxuan Han", + "bbox": [ + 267, + 180, + 375, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Zhibo Wang", + "bbox": [ + 442, + 180, + 550, + 199 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Feng Xu1", + "bbox": [ + 616, + 180, + 694, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ School of Software and BNrist, Tsinghua University", + "bbox": [ + 163, + 209, + 591, + 227 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ SenseTime Research", + "bbox": [ + 625, + 209, + 800, + 226 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/22051f689b25b3193c24cbb907a605cb7c56a8f115d097a7138a4eecef6f8447.jpg", + "image_caption": [ + "Training" + ], + "image_footnote": [], + "bbox": [ + 83, + 267, + 232, + 325 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/a054de928c00833303237d0be7d0f365e17376644a540406900ff67d93f1f6f7.jpg", + "image_caption": [ + "Low-cost Data" + ], + "image_footnote": [], + "bbox": [ + 83, + 327, + 232, + 386 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/8e29ece3d56c0839ee18ae2313bf6964c1ef0a6448e712aa918d40d9f04d65d6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 236, + 267, + 367, + 345 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/206aa70f6d31e24d95c02e6a9968da648264d81686136f1732b9197c0c1592d3.jpg", + "image_caption": [ + "Diffuse Albedo" + ], + "image_footnote": [], + "bbox": [ + 236, + 345, + 321, + 385 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/6d6c9ef04bc5c7901e18113642358f0a4252a592b84b53d0d81b3c96317f55db.jpg", + "image_caption": [ + "Morphable Face Reflectance Model", + "Linear Combination Weights for Different Specular Exponents", + "Figure 1. We propose the first 3D morphable face reflectance model with spatially varying BRDF and a technique to train the model with low-cost publicly-available data. We represent face reflectance as a Lambertian BRDF combined with the linear combination of Blinn-Phong BRDFs with different predefined specular exponents. The reflectance parameters for each face vertex are the diffuse albedo and a set of weights. We show the first 3 principal components of diffuse albedo and spatially varying weights here in nonlinear sRGB space." + ], + "image_footnote": [], + "bbox": [ + 367, + 267, + 888, + 386 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 483, + 313, + 500 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Modeling non-Lambertian effects such as facial specularity leads to a more realistic 3D Morphable Face Model. Existing works build parametric models for diffuse and specular albedo using Light Stage data. However, only diffuse and specular albedo cannot determine the full BRDF. In addition, the requirement of Light Stage data is hard to fulfill for the research communities. This paper proposes the first 3D morphable face reflectance model with spatially varying BRDF using only low-cost publicly-available data. We apply linear shiness weighting into parametric modeling to represent spatially varying specular intensity and shiness. Then an inverse rendering algorithm is developed to reconstruct the reflectance parameters from nonLight Stage data, which are used to train an initial morphable reflectance model. To enhance the model's generalization capability and expressive power, we further propose an update-by-reconstruction strategy to finetune it on an in-the-wild dataset. Experimental results show that our method obtains decent rendering results with plausible facial peculiarities. Our code is released here.", + "bbox": [ + 73, + 513, + 472, + 818 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 844, + 207, + 859 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "3D Morphable Face Models (3DMM) [4, 19] have attracted much attention in the past two decades, as it pro", + "bbox": [ + 75, + 869, + 468, + 902 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "vides a powerful and compact statistical prior of 3D face geometry and appearance with dense point-to-point correspondence to various downstream applications like face reconstruction [14,22,52,55,56], rendering [13,53,57,58,70], and animation [3,7,9,20,21,68]. Existing works [52,54,55] have demonstrated promising results for improving the generalization capability and expressive power of 3DMM under the assumption that faces are Lambertian surfaces. However, it is still challenging to model non-Lambertian effects such as facial specularity in 3DMM, which can lead to a more realistic face model.", + "bbox": [ + 496, + 484, + 890, + 650 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "A few recent works [37, 50] involve non-Lambertian facial reflectance in the morphable face model. Using a Light Stage [11, 24, 39], they capture diffuse and specular albedo maps of tens of participants. Then, they model the diffuse and specular albedo by training a PCA model [50] or a deep generative network [37] on the acquired data. However, only the diffuse and specular albedo cannot determine the complete Bidirectional Reflectance Distribution Function (BRDF). Thus, other works [15-17] set the remaining reflectance parameters (e.g. specular exponent for the Blinn-Phong BRDF [5], roughness for the Torrance-Sparrow BRDF [59]) of all face vertices to a reasonable value to characterize specular shiness and obtain the complete BRDF. As shown in Figure 7, these spatially uniform parameters lead to unpleasant rendering results since face reflectance is inherently spatially varying [65]. Besides, the", + "bbox": [ + 496, + 657, + 892, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "8598", + "bbox": [ + 482, + 944, + 514, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "requirement of Light Stage data is hard to fulfill since building a Light Stage is quite difficult, and no publicly available Light Stage dataset is sufficient to construct a 3DMM.", + "bbox": [ + 76, + 90, + 468, + 136 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To overcome these limitations, we propose and train the first morphable face reflectance model with spatially varying BRDF from low-cost publicly-available data. Inspired by previous works [41, 42], we represent face reflectance as a Lambertian BRDF combined with the linear combination of several Blinn-Phong BRDFs corresponding to different predefined specular exponents. Thus, the reflectance parameters of each face vertex include an RGB color for the Lambertian BRDF and a set of weights for the Blinn-Phong BRDFs. As illustrated in Figure 2, our representation can naturally modulate specular intensity and shiness by adjusting the absolute and relative scales of the linear combination weights, respectively. Compared to previous works [37, 50] not modeling specular shiness, we define a complete BRDF by this representation in 3DMM. Compared to the traditional Blinn-Phong BRDF that models specular intensity and shiness in a nonlinear formulation [5], our linear representation (Equation (2)) is much easier to reconstruct the reflectance parameters from recorded images. With this linear reflectance representation, we develop an inverse rendering approach to estimate the spatially varying reflectance parameters for the 128 selected identities in Multi-PIE [25], a public dataset with face images captured under controlled camera views and light directions. Then, we learn a PCA model for the estimated reflectance parameters as our initial morphable face reflectance model.", + "bbox": [ + 76, + 141, + 472, + 532 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Considering that the Multi-PIE dataset only contains 128 identities which is far from sufficient to capture the variability of human faces, we propose to finetune the initial model on a large-scale in-the-wild dataset, FFHQ [29], to improve its generalization capability and expressive power. As the inputs are in-the-wild images with unknown lighting information, it is not easy to reconstruct accurate reflectance from them. Our key observation is that, on the one hand, we already have an initial parametric reflectance model that can better formulate the reflectance reconstruction from in-the-wild images. On the other hand, the reconstructed reflectance from in-the-wild data could provide feedback to enhance the face prior knowledge in our morphable reflectance model. Based on this observation, we jointly reconstruct the face reflectance coefficients and update the parameters of our morphable face reflectance model (the mean and bases). Another challenge here is to predict high-order spherical harmonics (SH) lighting [44] for in-the-wild images, which is crucial for updating the high-frequency information of our non-Lambertian reflectance model [45]. To solve this problem, we build another PCA model for real-world environment lighting in SH coefficients space, which largely reduces the searching space of the high-order SH coefficients. During face reconstruction, we first predict", + "bbox": [ + 76, + 537, + 472, + 902 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "the parameters of the PCA lighting model and then retrieve the high-order SH coefficients from it. Finally, the in-the-wild images are well reconstructed with our parametric reflectance model, and the model itself is also updated gradually in this process to achieve high generalization capability and expressive power.", + "bbox": [ + 496, + 90, + 890, + 181 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In summary, our contributions include:", + "bbox": [ + 517, + 181, + 777, + 196 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose the first 3D morphable face reflectance model with spatially varying BRDF and a technique to train the model with low-cost publicly-available data.", + "- We apply linear shiness weighting into parametric face modeling to represent spatially varying specular shiness and intensity and ease the process of reconstructing reflectance from images.", + "- We propose an update-by-reconstruction strategy to finetune our face reflectance model on an in-the-wild dataset, improving its generalization capability and expressive power." + ], + "bbox": [ + 517, + 196, + 890, + 362 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 377, + 640, + 393 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3D Morphable Face Model The origin 3DMM, proposed by Blanz and Vetter [4], learns a PCA model to represent 3D face shape and texture from 200 scans. This seminal work has motivated substantial follow-ups in the past two decades [19]. Paysan et al. [43] propose the Basel Face Model (BFM), the first 3DMM available to the public. However, BFM and the original 3DMM can only model neutral faces. To handle expression variation, Li et al. [38] propose the FLAME model with additive expression bases trained from 4D scans. Cao et al. [8] build a bilinear expression model from a database with multi-expression scans of the same person. Another class of works attempts to better capture human face variation by scaling up the number of scans for 3DMM training. Dai et al. [10] and Booth et al. [6] learn large-scale 3DMM from $1.2\\mathrm{k}$ and $10\\mathrm{k}$ subjects, respectively. However, all of these previous works approximate face as Lambertian surface and ignore the modeling of non-Lambertian reflectance. Recently, some works [37,50] build morphable models for diffuse and specular albedo using Light Stage scans [24,39,51]. However, specular shiness is ignored in their model. In addition, their requirement on Light Stage scans is hard to fulfill for the research community. Our method can represent both spatially varying specular intensity and shiness while only using low-cost publicly-available data as the training set.", + "bbox": [ + 496, + 401, + 890, + 779 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "More recently, some works [52, 54, 55, 60, 61] propose to learn 3DMM from large-scale 2D datasets by jointly performing face reconstruction and face model learning. These methods can learn a 3DMM that generalizes well across the population. Tewari et al. [52] learn linear face shape and texture models from videos by designing novel loss functions to handle depth ambiguity. A follow-up work [54] learns a complete 3DMM, including shape, expression, and", + "bbox": [ + 496, + 780, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "8599", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "texture, from videos and neutral face images. Tran et al. [60,61] learn a non-linear 3DMM from 2D image collections, using deep neural networks to model face shape and texture. Inspired by these works, we finetune our initial face reflectance model on an in-the-wild face image dataset to improve its generalization capability and expressive power.", + "bbox": [ + 75, + 90, + 472, + 183 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Face Appearance Capture Existing methods for face appearance capture [32] fall into two categories: the image-based method and the model-based method. The key idea of the image-based method is to capture a set of images to sample the light transport function, and then novel appearances can be obtained by linearly recombining these images. To fulfill this, Debevec et al. [11] construct the Light Stage to capture the light transport function by programmatically activating One-Light-At-a-Time (OLAT). Using the captured OLAT data as the training set, Mallikarjun et al. [40] propose a learning-based method to infer the whole light transport function from a monocular input face image, and Kumar et al. [33] build a statistical model for the light transport function at a fixed frontal viewpoint. By directly modeling the light transport function, these image-based methods can represent peculiarities, sub-surface scattering, and other high-order effects caused by the complex interaction between light and face surface. However, they cannot export geometry or material assets for further usages like material editing or animation.", + "bbox": [ + 75, + 204, + 472, + 505 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Model-based methods capture the parameters of a reflectance model and utilize the rendering equation [27] to synthesize novel appearances. Previous works [24, 39, 51] use polarised illumination to directly capture the diffuse and specular albedo of human face. Using the captured data from [24, 39, 51], recent works train a neural network to map a monocular face image into its diffuse and specular albedo map [35, 67] or build a morphable model for this maps [37, 50]. Another class of works adopts an inverse rendering framework to estimate the face reflectance parameters from images. Weyrich et al. [65] develop a novel reflectance model for face and estimate its parameter from dense OLAT images captured by a Light Stage [11]. Riviere et al. [47] leverage images captured by a lightweight single-shot system for inverse rendering. In our method, we estimate the reflectance parameters for each identity in the Multi-PIE dataset from the provided OLAT images via an inverse rendering framework and use these parameters to build an initial morphable face reflectance model.", + "bbox": [ + 75, + 507, + 472, + 792 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "More recently, some works attempt to capture face appearance using a low-cost setup, such as a single selfie video of the subject rotating under the sun [62], recorded video of the subject illuminated by a desktop monitor [48], and colocated captured sequence [2, 49]. However, all of these previous works are person-specific. Our goal is to build a generic morphable reflectance model using low-cost data.", + "bbox": [ + 75, + 795, + 472, + 902 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/e93f214ee0fc2ba1c8d4b95ed2059f3d6070bddaae1a0990ad3878a4e08ddf98.jpg", + "image_caption": [ + "(a) $w_{1} = 0.4, w_{2} = 0.1$" + ], + "image_footnote": [], + "bbox": [ + 514, + 88, + 602, + 133 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/47f1d5aa776f1d4689ccf585f3b5a1c07537568533452876e44501ea623198e0.jpg", + "image_caption": [ + "(b) $w_{1} = 0.8, w_{2} = 0.2$", + "Figure 2. Our reflectance representation can naturally modulate specular intensity and shiness by adjusting the absolute or relative scales of the linear combination weights. Here we show an example using the linear combination of 2 Blinn-Phong BRDFs with specular exponents $p_1 = 8$ and $p_2 = 64$ , respectively. Note the changes in specular intensity and shiness under different linear combination weights $w_1$ and $w_2$ ." + ], + "image_footnote": [], + "bbox": [ + 663, + 88, + 723, + 133 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/4b2f3a4b849ee277b50b9434e47e7b960d4e2c3d63c2247777b7855ceefd5d41.jpg", + "image_caption": [ + "(c) $w_{1} = 0.1, w_{2} = 0.4$" + ], + "image_footnote": [], + "bbox": [ + 790, + 88, + 851, + 133 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 500, + 270, + 589, + 286 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this Section, we first introduce the representation of our morphable face reflectance model (Section 3.1), then propose a method to learn this model from low-cost publicly-available data. Specifically, we first learn an initial model from the Multi-PIE dataset (Section 3.2) and then finetune it on the FFHQ dataset to improve its generalization capability and expressive power (Section 3.3).", + "bbox": [ + 496, + 295, + 890, + 402 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Morphable Face Reflectance Model", + "text_level": 1, + "bbox": [ + 500, + 409, + 808, + 425 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our goal is to design a morphable model to represent the spatially varying BRDF of the human face across the population. To this end, we build our model upon the BFM09 [43] geometry model and assign the spatially varying reflectance parameters to its vertices. We employ a linear model for the reflectance parameters of the human face:", + "bbox": [ + 496, + 431, + 890, + 523 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nR = \\bar {R} + \\mathrm {M} _ {\\mathrm {R}} \\cdot \\beta \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 635, + 531, + 890, + 547 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Here, $\\bar{R} \\in \\mathbb{R}^{kV}$ and $\\mathrm{M}_{\\mathrm{R}} \\in \\mathbb{R}^{kV \\times N_{R}}$ are the mean and bases of face reflectance parameters, respectively; $N_{R}$ is the number of bases; $k$ is the number of reflectance model parameters for each vertex; $V$ is the number of vertices of the BFM09 geometry model; $\\beta \\in \\mathbb{R}^{N_{R}}$ is the morphable model coefficients. Note that previous works [23, 52, 54] represent face reflectance as the Lambertian BRDF. So in their scenario $k = 3$ to represent the RGB diffuse color.", + "bbox": [ + 496, + 553, + 893, + 675 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Next, we first introduce our reflectance representation. Then, we illustrate our efficient shading technique for this representation under directional or environmental illumination, which can accelerate the model learning process detailed in later Sections.", + "bbox": [ + 496, + 676, + 893, + 751 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Reflectance Representation To model non-Lambertian effects such as facial specularity, we incorporate a diffuse term and a specular term in our face reflectance representation $f_{r}$ . We instantiate them as the Lambertian BRDF and the linear combination of several Blinn-Phong BRDFs [5] with different predefined specular exponents, respectively:", + "bbox": [ + 496, + 763, + 893, + 854 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nf _ {r} (\\mathbf {l}, \\mathbf {v}, \\mathbf {n}) = \\frac {c}{\\pi} + \\sum_ {i = 1} ^ {k _ {b p}} w _ {i} \\cdot f _ {i} \\cdot \\frac {\\langle \\mathbf {h} , \\mathbf {n} \\rangle^ {p _ {i}}}{\\langle \\mathbf {l} , \\mathbf {n} \\rangle} \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 563, + 861, + 890, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "8600", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Here, $\\mathbf{l}$ , $\\mathbf{v}$ , and $\\mathbf{n}$ indicate the incident light direction, viewing direction, and normal direction, respectively; $c$ is the RGB diffuse color; $w_{i}$ are the linear combination weights; $p_{i}$ are the predefined specular exponents; $k_{bp}$ is the number of Blinn-Phong BRDFs; $f_{i} = \\frac{p_{i} + 2}{4\\pi \\cdot (2 - 2^{-\\frac{p_{i}}{2}})}$ are the energy normalization factor [1] such that the corresponding Blinn-Phong lobe integrates to 1; $\\langle \\cdot, \\cdot \\rangle$ is the clamped cosine function; $\\mathbf{h} = \\frac{\\mathbf{v} + \\mathbf{l}}{||\\mathbf{v} + \\mathbf{l}||_2}$ is the half vector [1].", + "bbox": [ + 75, + 90, + 468, + 219 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In our scenario, the reflectance parameters for each face vertex are the diffuse color $c$ and $k_{bp}$ linear combination weights $w_{i}$ . Thus, each face vertex has $k = k_{bp} + 3$ reflectance parameters attached to it. Note that the specular exponents $p_{i}$ are predefined and shared by each face vertex; they are hyper-parameters in our model. Our representation can naturally modulate the specular intensity and shiness. As illustrated in Figure 2, doubling all the weights would double the specular intensity, while adjusting the aspect ratio between weights would change the specular shiness. Moreover, our reflectance representation generalizes to previous work [15-17] with spatially varying specular albedo and a global specular exponent when $k_{bp} = 1$ .", + "bbox": [ + 75, + 219, + 470, + 416 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Efficient Shading For directional illumination, by denoting the incoming irradiance from direction $\\mathbf{l}$ as $E$ , we can directly obtain the shading:", + "bbox": [ + 76, + 426, + 468, + 472 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ns = E \\cdot \\left(\\frac {c}{\\pi} \\cdot \\langle \\mathbf {l}, \\mathbf {n} \\rangle + \\sum_ {i = 1} ^ {k _ {b p}} w _ {i} \\cdot f _ {i} \\cdot \\langle \\mathbf {h}, \\mathbf {n} \\rangle^ {p _ {i}}\\right) \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 130, + 478, + 468, + 520 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For environmental illumination, we denote the incoming radiance from direction $\\mathbf{l}$ as $E(\\mathbf{l})$ . According to the rendering equation [27], we have:", + "bbox": [ + 76, + 526, + 468, + 571 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ns = \\int_ {\\mathbf {l} \\in \\Omega^ {+}} E (\\mathbf {l}) \\cdot f _ {r} (\\mathbf {l}, \\mathbf {v}, \\mathbf {n}) \\cdot \\langle \\mathbf {l}, \\mathbf {n} \\rangle \\mathrm {d} \\mathbf {l} \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 151, + 577, + 468, + 611 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here, $\\Omega^{+}$ is the upper hemisphere centered by $\\mathbf{n}$ . Substituting Equation (2) into Equation (4), we then separate the shading $s$ into a diffuse part $s_d$ and a specular part $s_s$ :", + "bbox": [ + 76, + 616, + 468, + 662 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ns = s _ {d} + s _ {s}, \\text {w h e r e} \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 150, + 669, + 468, + 684 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ns _ {d} = \\int_ {\\mathbf {l} \\in \\Omega^ {+}} \\frac {c}{\\pi} \\cdot E (\\mathbf {l}) \\cdot \\langle \\mathbf {l}, \\mathbf {n} \\rangle d \\mathbf {l} \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 142, + 688, + 468, + 720 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ns _ {s} = \\int_ {\\mathbf {l} \\in \\Omega^ {+}} \\sum_ {i = 1} ^ {k _ {b p}} w _ {i} \\cdot f _ {i} \\langle \\mathbf {h}, \\mathbf {n} \\rangle^ {p _ {i}} \\cdot E (\\mathbf {l}) d \\mathbf {l} \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 143, + 723, + 468, + 765 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We can efficiently compute $s_d$ and $s_s$ in the frequency space [45]:", + "bbox": [ + 76, + 771, + 468, + 801 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ns _ {d} = \\frac {c}{\\pi} \\cdot \\sum_ {l = 0} ^ {L} \\sum_ {m = - l} ^ {l} A _ {l} \\cdot K _ {l m} \\cdot Y _ {l m} (\\mathbf {n}), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 130, + 806, + 468, + 849 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ns _ {s} = \\sum_ {i = 1} ^ {k _ {b p}} \\sum_ {l = 0} ^ {L} \\sum_ {m = - l} ^ {l} w _ {i} \\cdot B _ {l} \\cdot K _ {l m} \\cdot Y _ {l m} (\\mathbf {r}). \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 130, + 854, + 468, + 896 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here, $L$ is the SH order, $A_{l}$ , $B_{l}^{i}$ , and $K_{lm}$ are the SH coefficients of the Lambertian BRDF, Blinn-Phong BRDF with specular exponent $p_i$ , and the environmental illumination, respectively; $Y_{lm}(\\cdot)$ are the SH basis functions; $\\mathbf{r} = \\frac{2(\\mathbf{n} \\cdot \\mathbf{v}) \\mathbf{n} - \\mathbf{v}}{||2(\\mathbf{n} \\cdot \\mathbf{v}) \\mathbf{n} - \\mathbf{v}||_2}$ is the specular reflect direction [1].", + "bbox": [ + 496, + 90, + 890, + 172 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Initial Model Learning", + "text_level": 1, + "bbox": [ + 500, + 180, + 712, + 196 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this part, we propose a method to learn the mean $\\bar{R}$ and bases $\\mathrm{M}_{\\mathrm{R}}$ of our morphable face reflectance model from the publicly-available Multi-PIE dataset [25]. Specifically, we first estimate the reflectance parameters for each identity in the dataset via inverse rendering, and then train a PCA model for them.", + "bbox": [ + 496, + 204, + 890, + 294 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Dataset Preprocessing The Multi-PIE dataset contains 337 identities captured under 15 viewpoints and 19 illuminations. We first exclude objects with facial accessories or hair occlusions, resulting in 128 identities. Then, we manually select 9 viewpoints and 12 illuminations (including 11 directional flash images and 1 room light image) with well color consistency to train our model. By removing the room light effect in the flash images, we obtain 11 OLAT images per viewpoint. We adopt a simple model-based approach to reconstruct the BFM09 geometry coefficients of each identity, camera parameters of each viewpoint, and the position of each flash simultaneously. See more implementation details in our Supplementary Material.", + "bbox": [ + 496, + 306, + 892, + 503 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Reflectance Parameter Estimation For a specific identity, we estimate all the reflectance parameters in UV space, including the diffuse color map $C \\in \\mathbb{R}^{3 \\times H \\times W}$ and the linear combination weight map $W \\in \\mathbb{R}^{k_{bp} \\times H \\times W}$ . We unwarp all the OLAT images into UV space and denote the one captured under the $i$ -th viewpoint and the $j$ -th directional flash illumination as $I_{ij}^{uv} \\in \\mathbb{R}^{3 \\times H \\times W}$ . From the reconstructed face geometry and scene information, we precompute the incident light direction $\\mathbf{l}_j^{uv}$ for each flash, view direction $\\mathbf{v}_i^{uv}$ for each camera, normal direction $\\mathbf{n}^{uv}$ for the face geometry, and shadow mask1 $M_{ij}^{uv}$ for each OLAT image in the UV space. By predefining a reasonable incoming irradiance $E$ from the directional flash2, we obtain the reconstructed OLAT image $\\hat{I}_{ij}^{uv}$ using the efficient shading technique under directional illumination presented in Equation (3):", + "bbox": [ + 496, + 517, + 890, + 744 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {I} _ {i j} ^ {u v} = E \\cdot \\left(\\frac {C}{\\pi} \\cdot \\langle \\mathbf {l} _ {j} ^ {u v}, \\mathbf {n} ^ {u v} \\rangle + \\sum_ {k = 1} ^ {k _ {b p}} W _ {k} \\cdot f _ {k} \\cdot \\langle \\mathbf {h} _ {i j} ^ {u v}, \\mathbf {n} ^ {u v} \\rangle^ {p _ {k}}\\right) \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 506, + 756, + 890, + 799 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Here, $\\mathbf{h}_{ij}^{uv}$ is the half vector UV map obtained by $\\mathbf{l}_j^{uv}$ and $\\mathbf{v}_i^{uv}$ . We optimize the diffuse color map $C$ and linear com", + "bbox": [ + 496, + 809, + 890, + 840 + ], + "page_idx": 3 + }, + { + "type": "page_footnote", + "text": "1We obtained shadow mask via ray tracing.", + "bbox": [ + 517, + 849, + 750, + 863 + ], + "page_idx": 3 + }, + { + "type": "page_footnote", + "text": "There is an inevitably global scale between the reflectance parameters estimated by the inverse rendering method and the ground truth, if lighting unknown. See more theoretical analysis in [45].", + "bbox": [ + 500, + 863, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "8601", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/6e3ab599aa40599de8d393e0453afb2a500c2fe1c13fe440fcf8e8ec4c5a5996.jpg", + "image_caption": [ + "Figure 3. Model finetuning pipeline overview. Given a single input face image, we apply an encoder $E_{\\theta}$ to estimate its lighting scale $z$ , lighting coefficients $\\gamma$ , and reflectance coefficients $\\beta$ . Combined with the precompute geometry parameters $\\alpha$ , $\\delta$ , $\\mathbf{R}$ , $\\mathbf{t}$ , and $\\Pi$ , we can obtain the reconstructed face image via a differentiable renderer to compute self-supervised loss and jointly update $E_{\\theta}$ and our reflectance model." + ], + "image_footnote": [], + "bbox": [ + 178, + 85, + 790, + 226 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "bination weights map $W$ with loss:", + "bbox": [ + 76, + 305, + 313, + 320 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\underset {C, W} {\\arg \\min } \\mathcal {L} _ {\\text {r e c o n}} + w _ {\\text {r e g}} \\mathcal {L} _ {\\text {r e g}} \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 176, + 334, + 468, + 358 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$\\mathcal{L}_{\\text{recon}}$ is the weighted L1 reconstruction loss:", + "bbox": [ + 76, + 371, + 385, + 388 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {r e c o n}} = \\sum_ {i, j} \\left\\langle \\mathbf {l} _ {j} ^ {u v}, \\mathbf {n} ^ {u v} \\right\\rangle \\cdot \\left\\| M _ {i j} ^ {u v} \\cdot \\left(\\hat {I} _ {i j} ^ {u v} - I _ {i j} ^ {u v}\\right) \\right\\| _ {1}, \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 398, + 468, + 433 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$\\mathcal{L}_{reg}$ is designed to restrict the reflectance parameters to be non-negative:", + "bbox": [ + 76, + 445, + 468, + 474 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {r e g}} = - M _ {C} \\cdot C - M _ {W} \\cdot W \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 489, + 468, + 506 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Here, $M_C$ and $M_W$ are the masks that indicate negative values in $C$ and $W$ , respectively. During parameter estimation, we randomly horizontal flip $C$ and $W$ to introduce symmetric constraints [66] to the reflectance parameter map.", + "bbox": [ + 76, + 518, + 468, + 579 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Compared to the traditional way that uses specular intensity and exponents to parameterize the Blinn-Phong BRDF, our linear representation is much easier for parameter estimation in practice.", + "bbox": [ + 75, + 580, + 468, + 641 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Model Learning With the estimated reflectance parameter maps for each identity, we can build our initial morphable face reflectance model. Similar to AlbedoMM [50], we learn a PCA model only for the diffuse albedo. Then, we transfer it to the specular weights by using the same linear combination of the training samples to form the bases. Thus, we can use the same coefficients $\\beta$ for the diffuse and specular reflectance parameters as Equation (1) while keeping the orthonormality of the diffuse bases so that the user can use our diffuse model independently.", + "bbox": [ + 75, + 656, + 468, + 808 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. Model Finetuning", + "text_level": 1, + "bbox": [ + 76, + 819, + 251, + 835 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To improve the generalization capability and expressive power of our initial morphable face reflecatance model, we finetune it on an in-the-wild face image dataset, FFHQ [29], by jointly doing face reconstruction and model updating.", + "bbox": [ + 76, + 843, + 468, + 905 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Dataset Preprocessing Before model finetuning, we use an off-the-shelf [14] method to estimate the BFM09 geometry coefficients and head pose for each image in the dataset. To further improve the geometry reconstruction accuracy, we apply an offline optimization using the same loss functions as [14]. Finally, we obtain the shape coefficients $\\alpha$ , expression coefficients $\\delta^3$ , and head pose $\\mathbf{R}$ , $\\mathbf{t}$ for each image. Similar to [14], we use the perspective camera model with a reasonable predefined focal length to represent the 3D-2D projection $\\Pi$ .", + "bbox": [ + 496, + 305, + 893, + 455 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Network Architecture As illustrated in Figure 3, given a single face image $I$ as input, our face reconstruction network $E_{\\theta}(\\cdot)$ predicts the reflectance model coefficients $\\beta$ and the SH lighting. Combined with the geometry parameters $\\alpha, \\delta$ , head pose $\\mathbf{R}, \\mathbf{t}$ , and the projection $\\Pi$ , we can obtain the reconstructed image $\\hat{I}$ via a differentiable rasterizer [34, 46] using the efficient shading technique presented in Equation (8) and (9).", + "bbox": [ + 496, + 470, + 893, + 590 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To update the high-frequency information in our non-Lambertian reflectance representation, we need to predict high-order SH lighting [45]. We adopt 8-order SH lighting with 273 parameters in our method as [17,36]. However, if handled naively, the network cannot predict reasonable SH lighting due to the large searching space of high-order SH coefficients, as shown in Figure 5. To constrain the searching space, we build a PCA model for the real-world environmental lighting in SH coefficient space inspired by [18]. Specifically, we utilize a real-world HDRI environment map dataset [64] and apply rotation augmentation to it. For each environment map, we compute its SH coefficients up to the 8-th order. We then divide them by the 0-th order coefficient for normalization. Note that each color channel is normalized independently. Next, we learn a PCA model for these normalized SH coefficients. We use the first $N_{L}$ bases for lighting prediction.", + "bbox": [ + 496, + 592, + 893, + 848 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "During finetuning, together with the reflectance model", + "bbox": [ + 517, + 849, + 890, + 864 + ], + "page_idx": 4 + }, + { + "type": "page_footnote", + "text": "3The expression bases are adapted from FaceWarehouse [8] since BFM09 does not model expression. See more details in [14].", + "bbox": [ + 500, + 875, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "8602", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "coefficients $\\beta$ , our network predicts $\\gamma \\in \\mathbb{R}^{N_L}$ as the lighting PCA model coefficients and $z \\in \\mathbb{R}^3$ to represent the scale of 0-th order SH coefficient for each color channel. From this, we first use $\\gamma$ to recover the SH coefficients from the PCA lighting model and then apply the predicted scale $z$ to them. We adopt the ResNet-50 [26] architecture as the reconstruction network $E_{\\theta}(\\cdot)$ and modify the last fully-connected layer to $N_R + N_L + 3$ neurons. We adopt the Softplus activation for $z$ to ensure a non-negative prediction and linear activation for $\\beta$ and $\\gamma$ .", + "bbox": [ + 75, + 90, + 472, + 243 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Loss Function In model finetuning, the learnable parameters are the morphable model parameters, including the mean $\\bar{R}$ and bases $\\mathrm{M}_{\\mathrm{R}}$ , and face reconstruction network parameters $\\theta$ . We optimize them with the combination of a reconstruction loss $\\mathcal{L}_{rec}$ and a regularization loss $\\mathcal{L}_{reg}$ :", + "bbox": [ + 75, + 257, + 470, + 335 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\underset {\\bar {R}, \\mathrm {M} _ {\\mathrm {R}}, \\theta} {\\arg \\min } \\mathcal {L} _ {r e c} + \\mathcal {L} _ {r e g} \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 200, + 345, + 468, + 375 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "$\\mathcal{L}_{rec}$ is the combination of a L1 term $\\mathcal{L}_{l1}$ and a perceptual term $\\mathcal{L}_{per}$ ; see more details in our Supplementary Material. In our regularization loss $\\mathcal{L}_{reg}$ , we design $\\mathcal{L}_{upd}$ to constrain the updating of our morphable reflectance model:", + "bbox": [ + 76, + 385, + 470, + 446 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {u p d} = \\left| \\left| \\bar {R} - \\bar {R} _ {0} \\right| \\right| _ {1} + \\left| \\left| \\mathrm {M} _ {\\mathrm {R}} - \\mathrm {M} _ {\\mathrm {R} _ {0}} \\right| \\right| _ {1} \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 142, + 458, + 468, + 477 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In addition, we adopt $\\mathcal{L}_{\\text {light }}$ to encourage monochromatic environment lighting as [12] to resolve the color ambiguity between albedo and lighting and $\\mathcal{L}_{\\text {coef }}$ to constrain the predicted PCA coefficients $\\beta$ and $\\gamma$ ; see more details in our Supplementary Material.", + "bbox": [ + 75, + 488, + 470, + 565 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 579, + 209, + 595 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Implementation Details", + "text_level": 1, + "bbox": [ + 76, + 604, + 294, + 619 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Initial Model Learning For reflectance parameter estimation, we set $w_{reg} = 100$ and adopt Adam [31] optimizer to minimize the loss function, with learning rate 5e-3. We use 3 Blinn-Phong BRDFs with specular exponents 1, 8, and 64 in our method, i.e. $k_{bp} = 3$ . Thus, there are 6 reflectance parameters for each face vertex. We use the first 80 PCA bases in our initial model, i.e. $N_R = 80$ .", + "bbox": [ + 75, + 628, + 468, + 733 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Model Finetuning For the lighting PCA model, we also use the first 80 PCA bases, i.e. $N_{L} = 80$ . The weights for $\\mathcal{L}_{l1}, \\mathcal{L}_{per}, \\mathcal{L}_{coef}, \\mathcal{L}_{upd}, \\mathcal{L}_{light}$ are set to 2, 0.1, 0.001, 10, 10, respectively. We finetune our model on the FFHQ dataset [30], with 70000 high-fidelity single-view face images, and crop them to $224 \\times 224$ when input to our reconstruction network $E_{\\theta}$ . We first pretrain $E_{\\theta}$ using $\\mathcal{L}_{l1}, \\mathcal{L}_{per}, \\mathcal{L}_{coef}$ for 20 epochs to ensure it can output reasonable reflectance and lighting coefficients prediction, with learning rate 1e-4. Then, we use the full loss function to", + "bbox": [ + 75, + 750, + 470, + 900 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/21f0fece86385e8124d4e2f5c8992098083c86064007760e2322021d503fd369.jpg", + "image_caption": [ + "Figure 4. Qualitative comparison of face rendering results on the Multi-PIE dataset between our spatially varying reflectance representation and previous works with a global specular exponent. Here, $Exp$ stands for the specular exponent in the Blinn-Phong BRDF. A large global exponent (e.g. 32, 64) leads to over-shiness artifacts around the cheek and chin, while a small one (e.g. 8, 16) cannot represent peculiarities appear in the forehead and tip of the nose. Our representation can well model the spatially varying specular intensity and shiness on the face." + ], + "image_footnote": [], + "bbox": [ + 511, + 88, + 879, + 314 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/a1968f8139e26ce0ff4f05967823f2f9cf7dee41545ef4843537d91927179f93.jpg", + "table_caption": [ + "Table 1. Quantitative comparison of face rendering results on the Multi-PIE dataset between our method and previous works with a global specular exponent." + ], + "table_footnote": [], + "table_body": "
LPIPS ↓SSIM ↑PSNR ↑
Exp=80.0970.93724.56
Exp=160.0980.93624.54
Exp=320.0970.93724.72
Exp=640.0960.93724.82
Ours0.0830.94225.00
", + "bbox": [ + 578, + 506, + 810, + 583 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "simultaneously update the parameters of $E_{\\theta}$ and our morphable face reflectance model for 2 epochs, with learning rate 1e-5. We adopt Adam optimizer [31].", + "bbox": [ + 498, + 609, + 890, + 655 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Evaluations", + "text_level": 1, + "bbox": [ + 500, + 665, + 627, + 680 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Reflectance Parameter Estimation In our reflectance representation, we use the linear combination of 3 Blinn-Phong BRDFs with specular exponents 1, 8, and 64 as the specular term. In Figure 4, we compare the face rendering results between our reflectance representation and previous works [15-17] with spatially varying specular albedo and a global specular exponent. As discussed in Section 3.1, we implement the reflectance representation of previous work by setting $k_{bp} = 1$ , and use the same inverse rendering pipeline described in Section 3.2. The results illustrate that our representation obtains more realistic rendering results than the previous work. For example, the specular shiness is more significant at the tip of nose while vanishing around the cheek; previous works with global specular", + "bbox": [ + 496, + 688, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "8603", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/801532b480b755e0e9c9b27fc09f2871c36c5f06b50f84584eac3be2753923c3.jpg", + "table_caption": [ + "Table 2. Photometric face reconstruction comparison between our method and competitors on 1000 images randomly sampled from the CelebA-HQ dataset." + ], + "table_footnote": [], + "table_body": "
LPIPS ↓SSIM ↑PSNR ↑
BFM090.1140.89323.51
AlbedoMM0.1160.90123.69
Ours0.1100.89624.21
Ours w/o finetune0.12560.88623.26
", + "bbox": [ + 133, + 135, + 411, + 209 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/622d032bf4b740119a07d07f2c08fa4097549fdba856ba4dbd9435d549f9484b.jpg", + "image_caption": [ + "Figure 5. Qualitative ablation study of model finetuning and the use of our lighting PCA model." + ], + "image_footnote": [], + "bbox": [ + 86, + 222, + 455, + 354 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "exponent cannot capture this phenomenon. With the spatially varying linear combination weights of different Blinn-Phong BRDFs, our method can naturally represent spatially varying specular intensity and shiness. In Table 1, we report the SSIM [63], PSNR, and LPIPS [69] scores in the face region to quantitatively measure the discrepancy between the re-rendered face and the ground truth. Again, our method achieves better results than the global exponent counterpart.", + "bbox": [ + 75, + 414, + 467, + 535 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Model Visualization We visualize the first 3 principal components of our morphable model in Figure 1, including the diffuse albedo, and the weights for Blinn-Phong BRDF with specular exponents 1, 8, and 64, from left to right; we multiply the weights by 3 for better visualization. It shows that our model learns to assign a large specular shiness to the tip of the nose while a small value to the cheek. See more visualizations in our Supplementary Material.", + "bbox": [ + 75, + 549, + 467, + 670 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Ablation Study As shown in Figure 5 and Table 2, the proposed finetuning strategy can improve the generalization capability and expressive power of our initial morphable face reflectance model, leading to better face reconstruction quality, especially around mouth and eyes. We then verify the effectiveness of our lighting PCA model. As illustrated in Figure 5, directly predicts 273 coefficients of the 8-order SH (w/o Light PCA) leads to unreasonable results; our lighting PCA model obtains better lighting predictions by constraining the searching space of the SH coefficients.", + "bbox": [ + 75, + 684, + 467, + 835 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Comparisons", + "text_level": 1, + "bbox": [ + 76, + 845, + 215, + 862 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Baselines We compare our method with BFM09 [43] and AlbedoMM [50]. BFM09 is a diffuse-only model built from", + "bbox": [ + 76, + 869, + 467, + 900 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/6096b35492eef67711ba16dc2ee992523e2234b63c4c812b3cb363189fe62b28.jpg", + "image_caption": [ + "Figure 6. Qualitative comparison of face reconstruction and shading contributions between our method and AlbedoMM." + ], + "image_footnote": [], + "bbox": [ + 509, + 87, + 877, + 369 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "3D scans; AlbedoMM is a morphable model for diffuse and specular albedo built from Light Stage data. To ensure a fair comparison, we use the same CNN-based framework (see Section 3.3) to implement the competitors. We train the reconstruction network for them on the FFHQ dataset, but we do not update their morphable model parameters. As we only focus on appearance, the reconstruction network only predicts reflectance coefficients and lighting parameters and uses fixed precomputed geometry during training. For BFM09, we adopt the same geometry parameters as ours. For AlbedoMM, we use the same steps as mentioned in Section 3.3 to obtain its geometry parameters. Akin to [50], we adopt the Blinn-Phong BRDF for AlbedoMM and set the global shiness to 20.", + "bbox": [ + 496, + 429, + 890, + 641 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Face Reconstruction We evaluate our method and the competitors on the CelebA-HQ [28] dataset. As illustrated in Table 2, our method obtains better photometric face reconstruction quantitative scores than the competitors since we finetune it on an in-the-wild dataset to improve its generalization capability and expressive power while the competitors are built from a limited number of scans. As shown in Figure 6, our method can reconstruct the input image well; compared to AlbedoMM trained from Light Stage scans, our method trained from low-cost data can also disentangle the diffuse and specular shading in a plausible way.", + "bbox": [ + 496, + 660, + 890, + 827 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Face Relighting We evaluate the relighting performance of our method and the competitors on the Multi-PIE dataset [25]. Specifically, given an input image, we first obtain its geometry parameters as described in Section 3.2 and", + "bbox": [ + 496, + 839, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "8604", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/607cf050064d8db1e14017a3715d8173efb65ee6e09f358aac2225a23a22a0af.jpg", + "image_caption": [ + "Figure 7. Qualitative comparison of face relighting results under point light source between our method and the competitors." + ], + "image_footnote": [], + "bbox": [ + 86, + 85, + 455, + 378 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "reconstruct its reflectance parameters using our CNN-based face reconstruction network (columns 1 and 3 in Figure 7). Then, we re-render the image under a new point light source and compare it to the corresponding OLAT image with exactly the same light position obtained from the preprocessed Multi-PIE dataset in Section 3.2 (columns 2 and 4 in Figure 7). Since we do not have the ground truth light color and intensity information of the Multi-PIE dataset, we render our method and the competitors using the same white point light source for a fair comparison; please ignore the color difference and only focus on the distribution of facial peculiarities in Figure 7. Compared to BFM09, our method successfully render plausible facial peculiarities since we adopt a non-Lambertian reflectance representation. Compared to AlbedoMM, our method achieves more realistic results especially around the tip of the nose since we can model both spatially varying specular intensity and shiness. See the video comparisons on our project page for a better demonstration.", + "bbox": [ + 75, + 431, + 472, + 720 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Reflectance Reconstruction Although our goal is not to model physically-accurate reflectance parameters, we compare our method with AlbedoMM on 23 Light Stage scans with ground truth diffuse and specular albedo captured under neutral expression from the 3D-RFE database [51]. We adopt the sum of 3 linear combination weights in our reflectance representation as the specular albedo; this quantity shares the same meaning as the specular albedo, i.e. the specular shading under a spatially-uniform environment lighting with unit radiance. As shown in Figure 8, our method can reconstruct plausible reflectance maps. How", + "bbox": [ + 76, + 734, + 472, + 902 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/df0ccdd51c9f9982ef3cfe1f9ce53c5dbf19604db60dcd44d668a37a0b70d690.jpg", + "table_caption": [ + "Table 3. Quantitative diffuse and specular albedo reconstruction results on 23 Light Stage scans from the 3D-RFE dataset (PSNR↑)." + ], + "table_footnote": [], + "table_body": "
Diffuse AlbedoSpecular Albedo
AlbedoMM19.3126.51
Ours20.1319.14
", + "bbox": [ + 563, + 121, + 826, + 170 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/11b3699eb099456d9807e3406f69fe3efe1fa18b7a73013a0aa7c4bdc1dfa936.jpg", + "image_caption": [ + "Figure 8. Qualitative comparison of diffuse and specular albedo reconstruction on the 3D-RFE dataset." + ], + "image_footnote": [], + "bbox": [ + 509, + 179, + 877, + 299 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "ever, as shown in Table 3, our method obtains inferior quantitative results than AlbedoMM on specular albedo reconstruction. We attribute this to two reasons: $i)$ AlbedoMM uses the 3D-RFE database to build their model [50] while our method has never seen these scans, and $ii)$ our method is built from low-cost data without lighting information, so there exists a global scale between our reflectance parameters and the ground truth although we try to mitigate it by setting a reasonable lighting color in Section 3.2.", + "bbox": [ + 496, + 353, + 890, + 489 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 503, + 619, + 518 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We propose the first 3D morphable face reflectance model with spatially varying BRDF, using only low-cost publicly-available data. To represent spatially varying reflectance, we apply linear shiness weighting into parametric face modeling. We develop an inverse rendering algorithm to reconstruct the reflectance parameters from the Multi-PIE dataset, from which we build an initial model. We propose a strategy that jointly learns the face reconstruction network and updates the morphable model parameters on the FFHQ dataset to improve its generalization capability and expressive power. Our method obtains decent rendering results with plausible facial peculiarities. We discuss the limitations of our method in the Supplementary Material.", + "bbox": [ + 496, + 527, + 890, + 726 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement", + "text_level": 1, + "bbox": [ + 500, + 739, + 660, + 757 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work was supported by the National Key R&D Program of China (2018YFA0704000), Beijing Natural Science Foundation (M22024), the NSFC (No.62021002), and the Key Research and Development Project of Tibet Autonomous Region (XZ202101ZY0019G). This work was also supported by THUIBCS, Tsinghua University, and BLBCI, Beijing Municipal Education Commission. We thank the anonymous reviewers for their constructive suggestions. Feng Xu is the corresponding author.", + "bbox": [ + 496, + 763, + 890, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "8605", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Tomas Akenine-Moller, Eric Haines, and Naty Hoffman. Real-time rendering. AK Peters/crc Press, 2019. 4", + "[2] Dejan Azinović, Olivier Maury, Christophe Hery, Mathias Niessner, and Justus Thies. High-res facial appearance capture from polarized smartphone images. ArXiv, abs/2212.01160, 2022. 3", + "[3] Ziqian Bai, Zhaopeng Cui, Xiaoming Liu, and Ping Tan. Riggable 3d face reconstruction via in-network optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6216-6225, 2021. 1", + "[4] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 187-194, 1999. 1, 2", + "[5] James F Blinn. Models of light reflection for computer synthesized pictures. In Proceedings of the 4th annual conference on Computer graphics and interactive techniques, pages 192-198, 1977. 1, 2, 3", + "[6] James Booth, Anastasios Roussos, Stefanos Zafeiriou, Allan Ponniah, and David J. Dunaway. A 3d morphable model learnt from 10,000 faces. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5543-5552, 2016. 2", + "[7] Chen Cao, Yanlin Weng, Stephen Lin, and Kun Zhou. 3d shape regression for real-time facial animation. ACM Transactions on Graphics (TOG), 32(4):1-10, 2013. 1", + "[8] Chen Cao, Yanlin Weng, Shun Zhou, Y. Tong, and Kun Zhou. Facewarehouse: A 3d facial expression database for visual computing. IEEE Transactions on Visualization and Computer Graphics, 20:413-425, 2014. 2, 5", + "[9] Bindita Chaudhuri, Noranart Vesdapunt, Linda Shapiro, and Baoyuan Wang. Personalized face modeling for improved face reconstruction and motion retargeting. In European Conference on Computer Vision, pages 142-160. Springer, 2020. 1", + "[10] Hang Dai, Nick E. Pears, William Smith, and Christian Duncan. A 3d morphable model of craniofacial shape and texture variation. 2017 IEEE International Conference on Computer Vision (ICCV), pages 3104-3112, 2017. 2", + "[11] Paul Debevec, Tim Hawkins, Chris Tchou, Haarm-Pieter Duiker, Westley Sarokin, and Mark Sagar. Acquiring the reflectance field of a human face. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pages 145-156, 2000. 1, 3", + "[12] Yu Deng. Deep3dfacereconstruction, 2019. https://github.com/microsoft/Deep3DFaceReconstruction. 6", + "[13] Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, and Xin Tong. Disentangled and controllable face image generation via 3d imitative-contrastive learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5154–5163, 2020. 1", + "[14] Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set. In Proceedings of the IEEE/CVF Conference on Computer" + ], + "bbox": [ + 78, + 114, + 467, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Vision and Pattern Recognition Workshops, pages 0-0, 2019. 1, 5", + "[15] Abdallah Dib, Junghyun Ahn, Cedric Thebault, Philippe-Henri Gosselin, and Louis Chevallier. S2f2: Self-supervised high fidelity face reconstruction from monocular image. arXiv preprint arXiv:2203.07732, 2022. 1, 4, 6", + "[16] Abdallah Dib, Gaurav Bharaj, Junghyun Ahn, Cédric Thébault, Philippe Gosselin, Marco Romeo, and Louis Chevallier. Practical face reconstruction via differentiable ray tracing. In Computer Graphics Forum, volume 40, pages 153-164. Wiley Online Library, 2021. 1, 4, 6", + "[17] Abdallah Dib, Cédric Thébault, Junghyun Ahn, Philippe-Henri Gosselin, Christian Theobalt, and Louis Chevallier. Towards high fidelity monocular face reconstruction with rich reflectance using self-supervised learning and ray tracing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12819-12829, 2021. 1, 4, 5, 6", + "[18] Bernhard Egger, Sandro Schonborn, Andreas Schneider, Adam Kortylewski, Andreas Morel-Forster, Clemens Blumer, and Thomas Vetter. Occlusion-aware 3d morphable models and an illumination prior for face image analysis. International Journal of Computer Vision, 126(12):1269-1287, 2018. 5", + "[19] Bernhard Egger, William AP Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani, et al. 3d morphable face models—past, present, and future. ACM Transactions on Graphics (TOG), 39(5):1-38, 2020. 1, 2", + "[20] Yao Feng, Haiwen Feng, Michael J Black, and Timo Bolkart. Learning an animatable detailed 3d face model from inthe-wild images. ACM Transactions on Graphics (ToG), 40(4):1-13, 2021. 1", + "[21] Pablo Garrido, Michael Zollhöfer, Dan Casas, Levi Valgaerts, Kiran Varanasi, Patrick Pérez, and Christian Theobalt. Reconstruction of personalized 3d face rigs from monocular video. ACM Transactions on Graphics (TOG), 35(3):1-15, 2016. 1", + "[22] Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, and William T Freeman. Unsupervised training for 3d morphable model regression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8377-8386, 2018. 1", + "[23] Thomas Gereg, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel Luthi, Sandro Schonborn, and Thomas Vetter. Morphable face models—an open framework. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pages 75–82. IEEE, 2018. 3", + "[24] Abhijeet Ghosh, Graham Fyffe, Borom Tunwattanapong, Jay Busch, Xueming Yu, and Paul Debevec. Multiview face capture using polarized spherical gradient illumination. In Proceedings of the 2011 SIGGRAPH Asia Conference, pages 1-10, 2011. 1, 2, 3", + "[25] Ralph Gross, Iain Matthews, Jeffrey Cohn, Takeo Kanade, and Simon Baker. Multi-pie. Image and vision computing, 28(5):807-813, 2010. 2, 4, 7" + ], + "bbox": [ + 503, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "8606", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[26] Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016. 6", + "[27] James T Kajiya. The rendering equation. In Proceedings of the 13th annual conference on Computer graphics and interactive techniques, pages 143-150, 1986. 3, 4", + "[28] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. ArXiv, abs/1710.10196, 2018. 7", + "[29] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4396-4405, 2018. 2, 5", + "[30] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110-8119, 2020. 6", + "[31] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2015. 6", + "[32] Oliver Klehm, Fabrice Rousselle, Marios Papas, Derek Bradley, Christophe Hery, B. Bickel, Wojciech Jarosz, and Thabo Beeler. Recent advances in facial appearance capture. Computer Graphics Forum, 34, 2015. 3", + "[33] Ritwik K. Kumar, Michael J. Jones, and Tim K. Marks. Morphable reflectance fields for enhancing face recognition. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 2606-2613, 2010. 3", + "[34] Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, and Timo Aila. Modular primitives for high-performance differentiable rendering. ACM Transactions on Graphics (TOG), 39:1-14, 2020. 5", + "[35] Alexandros Lattas, Stylianos Moschoglou, Baris Gecer, Stylianos Ploumpis, Vasileios Triantafyllou, Abhijeet Ghosh, and Stefanos Zafeiriou. Avatarme: Realistically renderable 3d facial reconstruction \"in-the-wild\". 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 757-766, 2020. 3", + "[36] Chen Li, Kun Zhou, and Stephen Lin. Intrinsic face image decomposition with human face priors. In European Conference on Computer Vision, 2014. 5", + "[37] Ruilong Li, Kalle Bladin, Yajie Zhao, Chinmay Chinara, Owen Ingraham, Pengda Xiang, Xinglei Ren, Pratusha Bhuvana Prasad, Bipin Kishore, Jun Xing, and Hao Li. Learning formation of physically-based face attributes. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3407-3416, 2020. 1, 2, 3", + "[38] Tianye Li, Timo Bolkart, Michael J Black, Hao Li, and Javier Romero. Learning a model of facial shape and expression from 4d scans. ACM Trans. Graph., 36(6):194-1, 2017. 2", + "[39] Wan-Chun Ma, Tim Hawkins, Pieter Peers, Charles-Felix Chabert, Malte Weiss, Paul E Debevec, et al. Rapid acquisition of specular and diffuse normal maps from polarized spherical gradient illumination. Rendering Techniques, 2007(9):10, 2007. 1, 2, 3", + "[40] R. MallikarjunB., Ayush Tewari, Tae-Hyun Oh, Tim Weyrich, B. Bickel, Hans-Peter Seidel, Hanspeter Pfister," + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Wojciech Matusik, Mohamed A. Elgharib, and Christian Theobalt. Monocular reconstruction of neural face reflectance fields. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4789-4798, 2021. 3", + "[41] Wojciech Matusik. A data-driven reflectance model. PhD thesis, Massachusetts Institute of Technology, 2003. 2", + "[42] Rohit Pandey, Sergio Orts Escolano, Chloe Legendre, Christian Haene, Sofien Bouaziz, Christoph Rhemann, Paul Debevec, and Sean Fanello. Total relighting: learning to relight portraits for background replacement. ACM Transactions on Graphics (TOG), 40(4):1-21, 2021. 2", + "[43] Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pages 296-301, 2009. 2, 3, 7", + "[44] Ravi Ramamoorthi and Pat Hanrahan. An efficient representation for irradiance environment maps. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 497-500, 2001. 2", + "[45] Ravi Ramamoorthi and Pat Hanrahan. A signal-processing framework for inverse rendering. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 117-128, 2001. 2, 4, 5", + "[46] Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari. Accelerating 3d deep learning with pytorch3d. SIGGRAPH Asia 2020 Courses, 2020. 5", + "[47] Jérémy Riviere, Paulo F. U. Gotardo, Derek Bradley, Abhijeet Ghosh, and Thabo Beeler. Single-shot high-quality facial geometry and skin appearance capture. ACM Transactions on Graphics (TOG), 39:81:1-81:12, 2020. 3", + "[48] Soumyadip Sengupta, Brian Curless, Ira Kemelmacher-Shlizerman, and Steven M. Seitz. A light stage on every desk. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 2400-2409, 2021. 3", + "[49] Artem Sevastopolsky, SA Ignatiev, Gonzalo Ferrer, Evgeny Burnaev, and Victor S. Lempitsky. Relightable 3d head portraits from a smartphone video. ArXiv, abs/2012.09963, 2020. 3", + "[50] William AP Smith, Alassane Seck, Hannah Dee, Bernard Tiddeman, Joshua B Tenenbaum, and Bernhard Egger. A morphable face albedo model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5011-5020, 2020. 1, 2, 3, 5, 7, 8", + "[51] Giota Stratou, Abhijeet Ghosh, Paul E. Debevec, and Louis-Philippe Morency. Effect of illumination on automatic expression recognition: A novel 3d reconfigurable facial database. Face and Gesture 2011, pages 611–618, 2011. 2, 3, 8", + "[52] Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Fml: Face model learning from videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10812-10822, 2019. 1, 2, 3" + ], + "bbox": [ + 503, + 92, + 893, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "8607", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[53] Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Stylerig: Rigging stylegan for 3d control over portrait images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6142-6151, 2020. 1", + "[54] Ayush Tewari, Hans-Peter Seidel, Mohamed Elgharib, Christian Theobalt, et al. Learning complete 3d morphable face models from images and videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3361-3371, 2021. 1, 2, 3", + "[55] Ayush Tewari, Michael Zollhöfer, Pablo Garrido, Florian Bernard, Hyeongwoo Kim, Patrick Pérez, and Christian Theobalt. Self-supervised multi-level face model learning for monocular reconstruction at over 250 hz. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2549-2559, 2018. 1, 2", + "[56] Ayush Tewari, Michael Zollhofer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, Patrick Perez, and Christian Theobalt. Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 1274-1283, 2017. 1", + "[57] Justus Thies, Mohamed Elgharib, Ayush Tewari, Christian Theobalt, and Matthias Nießner. Neural voice puppetry: Audio-driven facial reenactment. In European conference on computer vision, pages 716-731. Springer, 2020. 1", + "[58] Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Nießner. Face2face: Real-time face capture and reenactment of rgb videos. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2387-2395, 2016. 1", + "[59] Kenneth E Torrance and Ephraim M Sparrow. Theory for off-specular reflection from roughened surfaces. Josa, 57(9):1105-1114, 1967. 1", + "[60] Luan Tran, Feng Liu, and Xiaoming Liu. Towards high-fidelity nonlinear 3d face morphable model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1126-1135, 2019. 2, 3", + "[61] Luan Tran and Xiaoming Liu. Nonlinear 3d face morphable model. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7346-7355, 2018. 2, 3", + "[62] Yifan Wang, Aleksander Holynski, Xiuming Zhang, and Xuaner Cecilia Zhang. Sunstage: Portrait reconstruction and relighting using the sun as a light stage. ArXiv, abs/2204.03648, 2022. 3", + "[63] Zhou Wang, Alan Conrad Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13:600-612, 2004. 7", + "[64] Zhibo Wang, Xin Yu, Ming Lu, Quan Wang, Chen Qian, and Feng Xu. Single image portrait relighting via explicit multiple reflectance channel modeling. ACM Transactions on Graphics (TOG), 39:1-13, 2020. 5", + "[65] Tim Weyrich, Wojciech Matusik, Hanspeter Pfister, Bernd Bickel, Craig Donner, Chien Tu, Janet McAndless, Jinho Lee, Addy Ngan, Henrik Wann Jensen, et al. Analysis of" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "human faces using a measurement-based skin reflectance model. ACM Transactions on Graphics (ToG), 25(3):1013-1024, 2006. 1, 3", + "[66] Shangzhe Wu, Christian Rupprecht, and Andrea Vedaldi. Unsupervised learning of probably symmetric deformable 3d objects from images in the wild. In CVPR, 2020. 5", + "[67] Shugo Yamaguchi, Shunsuke Saito, Koki Nagano, Yajie Zhao, Weikai Chen, Kyle Olszewski, Shigeo Morishima, and Hao Li. High-fidelity facial reflectance and geometry inference from an unconstrained image. ACM Transactions on Graphics (TOG), 37:1-14, 2018. 3", + "[68] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. Facescape: a large-scale high quality 3d face dataset and detailed riggable 3d face prediction. In Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, pages 601-610, 2020. 1", + "[69] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 586-595, 2018. 7", + "[70] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. Im avatar: Implicit morphable head avatars from videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13545-13555, 2022. 1" + ], + "bbox": [ + 501, + 92, + 890, + 472 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "8608", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Learning a 3D Morphable Face Reflectance Model From Low-Cost Data/bfac72ff-9a91-49c0-91b2-40f3beec823c_model.json b/2023/Learning a 3D Morphable Face Reflectance Model From Low-Cost Data/bfac72ff-9a91-49c0-91b2-40f3beec823c_model.json new file mode 100644 index 0000000000000000000000000000000000000000..bf60cd89a9db918fcc0e0b3de35d004a858e08df --- /dev/null +++ b/2023/Learning a 3D Morphable Face Reflectance Model From Low-Cost Data/bfac72ff-9a91-49c0-91b2-40f3beec823c_model.json @@ -0,0 +1,2587 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.128, + 0.131, + 0.843, + 0.152 + ], + "angle": 0, + "content": "Learning a 3D Morphable Face Reflectance Model from Low-cost Data" + }, + { + "type": "text", + "bbox": [ + 0.269, + 0.181, + 0.377, + 0.198 + ], + "angle": 0, + "content": "Yuxuan Han" + }, + { + "type": "text", + "bbox": [ + 0.443, + 0.181, + 0.551, + 0.2 + ], + "angle": 0, + "content": "Zhibo Wang" + }, + { + "type": "text", + "bbox": [ + 0.617, + 0.181, + 0.695, + 0.199 + ], + "angle": 0, + "content": "Feng Xu1" + }, + { + "type": "text", + "bbox": [ + 0.164, + 0.21, + 0.593, + 0.228 + ], + "angle": 0, + "content": "\\(^{1}\\)School of Software and BNrist, Tsinghua University" + }, + { + "type": "text", + "bbox": [ + 0.627, + 0.21, + 0.802, + 0.227 + ], + "angle": 0, + "content": "\\(^{2}\\)SenseTime Research" + }, + { + "type": "image_caption", + "bbox": [ + 0.128, + 0.253, + 0.189, + 0.267 + ], + "angle": 0, + "content": "Training" + }, + { + "type": "image", + "bbox": [ + 0.084, + 0.268, + 0.233, + 0.326 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.084, + 0.328, + 0.233, + 0.387 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.117, + 0.388, + 0.202, + 0.397 + ], + "angle": 0, + "content": "Low-cost Data" + }, + { + "type": "image", + "bbox": [ + 0.238, + 0.268, + 0.368, + 0.347 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.238, + 0.347, + 0.323, + 0.386 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.259, + 0.388, + 0.343, + 0.397 + ], + "angle": 0, + "content": "Diffuse Albedo" + }, + { + "type": "image_caption", + "bbox": [ + 0.416, + 0.253, + 0.672, + 0.267 + ], + "angle": 0, + "content": "Morphable Face Reflectance Model" + }, + { + "type": "image", + "bbox": [ + 0.368, + 0.268, + 0.89, + 0.387 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.459, + 0.388, + 0.805, + 0.4 + ], + "angle": 0, + "content": "Linear Combination Weights for Different Specular Exponents" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.415, + 0.894, + 0.472 + ], + "angle": 0, + "content": "Figure 1. We propose the first 3D morphable face reflectance model with spatially varying BRDF and a technique to train the model with low-cost publicly-available data. We represent face reflectance as a Lambertian BRDF combined with the linear combination of Blinn-Phong BRDFs with different predefined specular exponents. The reflectance parameters for each face vertex are the diffuse albedo and a set of weights. We show the first 3 principal components of diffuse albedo and spatially varying weights here in nonlinear sRGB space." + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.484, + 0.314, + 0.5 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.515, + 0.473, + 0.819 + ], + "angle": 0, + "content": "Modeling non-Lambertian effects such as facial specularity leads to a more realistic 3D Morphable Face Model. Existing works build parametric models for diffuse and specular albedo using Light Stage data. However, only diffuse and specular albedo cannot determine the full BRDF. In addition, the requirement of Light Stage data is hard to fulfill for the research communities. This paper proposes the first 3D morphable face reflectance model with spatially varying BRDF using only low-cost publicly-available data. We apply linear shiness weighting into parametric modeling to represent spatially varying specular intensity and shiness. Then an inverse rendering algorithm is developed to reconstruct the reflectance parameters from nonLight Stage data, which are used to train an initial morphable reflectance model. To enhance the model's generalization capability and expressive power, we further propose an update-by-reconstruction strategy to finetune it on an in-the-wild dataset. Experimental results show that our method obtains decent rendering results with plausible facial peculiarities. Our code is released here." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.845, + 0.208, + 0.861 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.87, + 0.47, + 0.903 + ], + "angle": 0, + "content": "3D Morphable Face Models (3DMM) [4, 19] have attracted much attention in the past two decades, as it pro" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.485, + 0.892, + 0.651 + ], + "angle": 0, + "content": "vides a powerful and compact statistical prior of 3D face geometry and appearance with dense point-to-point correspondence to various downstream applications like face reconstruction [14,22,52,55,56], rendering [13,53,57,58,70], and animation [3,7,9,20,21,68]. Existing works [52,54,55] have demonstrated promising results for improving the generalization capability and expressive power of 3DMM under the assumption that faces are Lambertian surfaces. However, it is still challenging to model non-Lambertian effects such as facial specularity in 3DMM, which can lead to a more realistic face model." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.893, + 0.903 + ], + "angle": 0, + "content": "A few recent works [37, 50] involve non-Lambertian facial reflectance in the morphable face model. Using a Light Stage [11, 24, 39], they capture diffuse and specular albedo maps of tens of participants. Then, they model the diffuse and specular albedo by training a PCA model [50] or a deep generative network [37] on the acquired data. However, only the diffuse and specular albedo cannot determine the complete Bidirectional Reflectance Distribution Function (BRDF). Thus, other works [15-17] set the remaining reflectance parameters (e.g. specular exponent for the Blinn-Phong BRDF [5], roughness for the Torrance-Sparrow BRDF [59]) of all face vertices to a reasonable value to characterize specular shiness and obtain the complete BRDF. As shown in Figure 7, these spatially uniform parameters lead to unpleasant rendering results since face reflectance is inherently spatially varying [65]. Besides, the" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.958 + ], + "angle": 0, + "content": "8598" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.47, + 0.137 + ], + "angle": 0, + "content": "requirement of Light Stage data is hard to fulfill since building a Light Stage is quite difficult, and no publicly available Light Stage dataset is sufficient to construct a 3DMM." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.142, + 0.473, + 0.534 + ], + "angle": 0, + "content": "To overcome these limitations, we propose and train the first morphable face reflectance model with spatially varying BRDF from low-cost publicly-available data. Inspired by previous works [41, 42], we represent face reflectance as a Lambertian BRDF combined with the linear combination of several Blinn-Phong BRDFs corresponding to different predefined specular exponents. Thus, the reflectance parameters of each face vertex include an RGB color for the Lambertian BRDF and a set of weights for the Blinn-Phong BRDFs. As illustrated in Figure 2, our representation can naturally modulate specular intensity and shiness by adjusting the absolute and relative scales of the linear combination weights, respectively. Compared to previous works [37, 50] not modeling specular shiness, we define a complete BRDF by this representation in 3DMM. Compared to the traditional Blinn-Phong BRDF that models specular intensity and shiness in a nonlinear formulation [5], our linear representation (Equation (2)) is much easier to reconstruct the reflectance parameters from recorded images. With this linear reflectance representation, we develop an inverse rendering approach to estimate the spatially varying reflectance parameters for the 128 selected identities in Multi-PIE [25], a public dataset with face images captured under controlled camera views and light directions. Then, we learn a PCA model for the estimated reflectance parameters as our initial morphable face reflectance model." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.539, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Considering that the Multi-PIE dataset only contains 128 identities which is far from sufficient to capture the variability of human faces, we propose to finetune the initial model on a large-scale in-the-wild dataset, FFHQ [29], to improve its generalization capability and expressive power. As the inputs are in-the-wild images with unknown lighting information, it is not easy to reconstruct accurate reflectance from them. Our key observation is that, on the one hand, we already have an initial parametric reflectance model that can better formulate the reflectance reconstruction from in-the-wild images. On the other hand, the reconstructed reflectance from in-the-wild data could provide feedback to enhance the face prior knowledge in our morphable reflectance model. Based on this observation, we jointly reconstruct the face reflectance coefficients and update the parameters of our morphable face reflectance model (the mean and bases). Another challenge here is to predict high-order spherical harmonics (SH) lighting [44] for in-the-wild images, which is crucial for updating the high-frequency information of our non-Lambertian reflectance model [45]. To solve this problem, we build another PCA model for real-world environment lighting in SH coefficients space, which largely reduces the searching space of the high-order SH coefficients. During face reconstruction, we first predict" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.182 + ], + "angle": 0, + "content": "the parameters of the PCA lighting model and then retrieve the high-order SH coefficients from it. Finally, the in-the-wild images are well reconstructed with our parametric reflectance model, and the model itself is also updated gradually in this process to achieve high generalization capability and expressive power." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.183, + 0.778, + 0.197 + ], + "angle": 0, + "content": "In summary, our contributions include:" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.198, + 0.891, + 0.242 + ], + "angle": 0, + "content": "- We propose the first 3D morphable face reflectance model with spatially varying BRDF and a technique to train the model with low-cost publicly-available data." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.243, + 0.891, + 0.303 + ], + "angle": 0, + "content": "- We apply linear shiness weighting into parametric face modeling to represent spatially varying specular shiness and intensity and ease the process of reconstructing reflectance from images." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.304, + 0.892, + 0.363 + ], + "angle": 0, + "content": "- We propose an update-by-reconstruction strategy to finetune our face reflectance model on an in-the-wild dataset, improving its generalization capability and expressive power." + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.198, + 0.892, + 0.363 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.378, + 0.642, + 0.394 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.402, + 0.892, + 0.78 + ], + "angle": 0, + "content": "3D Morphable Face Model The origin 3DMM, proposed by Blanz and Vetter [4], learns a PCA model to represent 3D face shape and texture from 200 scans. This seminal work has motivated substantial follow-ups in the past two decades [19]. Paysan et al. [43] propose the Basel Face Model (BFM), the first 3DMM available to the public. However, BFM and the original 3DMM can only model neutral faces. To handle expression variation, Li et al. [38] propose the FLAME model with additive expression bases trained from 4D scans. Cao et al. [8] build a bilinear expression model from a database with multi-expression scans of the same person. Another class of works attempts to better capture human face variation by scaling up the number of scans for 3DMM training. Dai et al. [10] and Booth et al. [6] learn large-scale 3DMM from \\(1.2\\mathrm{k}\\) and \\(10\\mathrm{k}\\) subjects, respectively. However, all of these previous works approximate face as Lambertian surface and ignore the modeling of non-Lambertian reflectance. Recently, some works [37,50] build morphable models for diffuse and specular albedo using Light Stage scans [24,39,51]. However, specular shiness is ignored in their model. In addition, their requirement on Light Stage scans is hard to fulfill for the research community. Our method can represent both spatially varying specular intensity and shiness while only using low-cost publicly-available data as the training set." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.781, + 0.892, + 0.901 + ], + "angle": 0, + "content": "More recently, some works [52, 54, 55, 60, 61] propose to learn 3DMM from large-scale 2D datasets by jointly performing face reconstruction and face model learning. These methods can learn a 3DMM that generalizes well across the population. Tewari et al. [52] learn linear face shape and texture models from videos by designing novel loss functions to handle depth ambiguity. A follow-up work [54] learns a complete 3DMM, including shape, expression, and" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8599" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.184 + ], + "angle": 0, + "content": "texture, from videos and neutral face images. Tran et al. [60,61] learn a non-linear 3DMM from 2D image collections, using deep neural networks to model face shape and texture. Inspired by these works, we finetune our initial face reflectance model on an in-the-wild face image dataset to improve its generalization capability and expressive power." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.205, + 0.473, + 0.506 + ], + "angle": 0, + "content": "Face Appearance Capture Existing methods for face appearance capture [32] fall into two categories: the image-based method and the model-based method. The key idea of the image-based method is to capture a set of images to sample the light transport function, and then novel appearances can be obtained by linearly recombining these images. To fulfill this, Debevec et al. [11] construct the Light Stage to capture the light transport function by programmatically activating One-Light-At-a-Time (OLAT). Using the captured OLAT data as the training set, Mallikarjun et al. [40] propose a learning-based method to infer the whole light transport function from a monocular input face image, and Kumar et al. [33] build a statistical model for the light transport function at a fixed frontal viewpoint. By directly modeling the light transport function, these image-based methods can represent peculiarities, sub-surface scattering, and other high-order effects caused by the complex interaction between light and face surface. However, they cannot export geometry or material assets for further usages like material editing or animation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.508, + 0.473, + 0.794 + ], + "angle": 0, + "content": "Model-based methods capture the parameters of a reflectance model and utilize the rendering equation [27] to synthesize novel appearances. Previous works [24, 39, 51] use polarised illumination to directly capture the diffuse and specular albedo of human face. Using the captured data from [24, 39, 51], recent works train a neural network to map a monocular face image into its diffuse and specular albedo map [35, 67] or build a morphable model for this maps [37, 50]. Another class of works adopts an inverse rendering framework to estimate the face reflectance parameters from images. Weyrich et al. [65] develop a novel reflectance model for face and estimate its parameter from dense OLAT images captured by a Light Stage [11]. Riviere et al. [47] leverage images captured by a lightweight single-shot system for inverse rendering. In our method, we estimate the reflectance parameters for each identity in the Multi-PIE dataset from the provided OLAT images via an inverse rendering framework and use these parameters to build an initial morphable face reflectance model." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.796, + 0.473, + 0.904 + ], + "angle": 0, + "content": "More recently, some works attempt to capture face appearance using a low-cost setup, such as a single selfie video of the subject rotating under the sun [62], recorded video of the subject illuminated by a desktop monitor [48], and colocated captured sequence [2, 49]. However, all of these previous works are person-specific. Our goal is to build a generic morphable reflectance model using low-cost data." + }, + { + "type": "image", + "bbox": [ + 0.515, + 0.089, + 0.603, + 0.135 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.512, + 0.136, + 0.626, + 0.147 + ], + "angle": 0, + "content": "(a) \\(w_{1} = 0.4, w_{2} = 0.1\\)" + }, + { + "type": "image", + "bbox": [ + 0.665, + 0.089, + 0.725, + 0.135 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.639, + 0.136, + 0.752, + 0.146 + ], + "angle": 0, + "content": "(b) \\(w_{1} = 0.8, w_{2} = 0.2\\)" + }, + { + "type": "image", + "bbox": [ + 0.792, + 0.089, + 0.852, + 0.135 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.765, + 0.135, + 0.878, + 0.146 + ], + "angle": 0, + "content": "(c) \\( w_{1} = 0.1, w_{2} = 0.4 \\)" + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.152, + 0.895, + 0.249 + ], + "angle": 0, + "content": "Figure 2. Our reflectance representation can naturally modulate specular intensity and shiness by adjusting the absolute or relative scales of the linear combination weights. Here we show an example using the linear combination of 2 Blinn-Phong BRDFs with specular exponents \\( p_1 = 8 \\) and \\( p_2 = 64 \\), respectively. Note the changes in specular intensity and shiness under different linear combination weights \\( w_1 \\) and \\( w_2 \\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.271, + 0.591, + 0.287 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.296, + 0.892, + 0.403 + ], + "angle": 0, + "content": "In this Section, we first introduce the representation of our morphable face reflectance model (Section 3.1), then propose a method to learn this model from low-cost publicly-available data. Specifically, we first learn an initial model from the Multi-PIE dataset (Section 3.2) and then finetune it on the FFHQ dataset to improve its generalization capability and expressive power (Section 3.3)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.41, + 0.809, + 0.426 + ], + "angle": 0, + "content": "3.1. Morphable Face Reflectance Model" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.433, + 0.892, + 0.524 + ], + "angle": 0, + "content": "Our goal is to design a morphable model to represent the spatially varying BRDF of the human face across the population. To this end, we build our model upon the BFM09 [43] geometry model and assign the spatially varying reflectance parameters to its vertices. We employ a linear model for the reflectance parameters of the human face:" + }, + { + "type": "equation", + "bbox": [ + 0.637, + 0.532, + 0.892, + 0.548 + ], + "angle": 0, + "content": "\\[\nR = \\bar {R} + \\mathrm {M} _ {\\mathrm {R}} \\cdot \\beta \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.554, + 0.895, + 0.676 + ], + "angle": 0, + "content": "Here, \\(\\bar{R} \\in \\mathbb{R}^{kV}\\) and \\(\\mathrm{M}_{\\mathrm{R}} \\in \\mathbb{R}^{kV \\times N_{R}}\\) are the mean and bases of face reflectance parameters, respectively; \\(N_{R}\\) is the number of bases; \\(k\\) is the number of reflectance model parameters for each vertex; \\(V\\) is the number of vertices of the BFM09 geometry model; \\(\\beta \\in \\mathbb{R}^{N_{R}}\\) is the morphable model coefficients. Note that previous works [23, 52, 54] represent face reflectance as the Lambertian BRDF. So in their scenario \\(k = 3\\) to represent the RGB diffuse color." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.677, + 0.894, + 0.752 + ], + "angle": 0, + "content": "Next, we first introduce our reflectance representation. Then, we illustrate our efficient shading technique for this representation under directional or environmental illumination, which can accelerate the model learning process detailed in later Sections." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.764, + 0.894, + 0.856 + ], + "angle": 0, + "content": "Reflectance Representation To model non-Lambertian effects such as facial specularity, we incorporate a diffuse term and a specular term in our face reflectance representation \\( f_{r} \\). We instantiate them as the Lambertian BRDF and the linear combination of several Blinn-Phong BRDFs [5] with different predefined specular exponents, respectively:" + }, + { + "type": "equation", + "bbox": [ + 0.565, + 0.862, + 0.892, + 0.904 + ], + "angle": 0, + "content": "\\[\nf _ {r} (\\mathbf {l}, \\mathbf {v}, \\mathbf {n}) = \\frac {c}{\\pi} + \\sum_ {i = 1} ^ {k _ {b p}} w _ {i} \\cdot f _ {i} \\cdot \\frac {\\langle \\mathbf {h} , \\mathbf {n} \\rangle^ {p _ {i}}}{\\langle \\mathbf {l} , \\mathbf {n} \\rangle} \\tag {2}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8600" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.22 + ], + "angle": 0, + "content": "Here, \\(\\mathbf{l}\\), \\(\\mathbf{v}\\), and \\(\\mathbf{n}\\) indicate the incident light direction, viewing direction, and normal direction, respectively; \\(c\\) is the RGB diffuse color; \\(w_{i}\\) are the linear combination weights; \\(p_{i}\\) are the predefined specular exponents; \\(k_{bp}\\) is the number of Blinn-Phong BRDFs; \\(f_{i} = \\frac{p_{i} + 2}{4\\pi \\cdot (2 - 2^{-\\frac{p_{i}}{2}})}\\) are the energy normalization factor [1] such that the corresponding Blinn-Phong lobe integrates to 1; \\(\\langle \\cdot, \\cdot \\rangle\\) is the clamped cosine function; \\(\\mathbf{h} = \\frac{\\mathbf{v} + \\mathbf{l}}{||\\mathbf{v} + \\mathbf{l}||_2}\\) is the half vector [1]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.22, + 0.471, + 0.417 + ], + "angle": 0, + "content": "In our scenario, the reflectance parameters for each face vertex are the diffuse color \\( c \\) and \\( k_{bp} \\) linear combination weights \\( w_{i} \\). Thus, each face vertex has \\( k = k_{bp} + 3 \\) reflectance parameters attached to it. Note that the specular exponents \\( p_{i} \\) are predefined and shared by each face vertex; they are hyper-parameters in our model. Our representation can naturally modulate the specular intensity and shiness. As illustrated in Figure 2, doubling all the weights would double the specular intensity, while adjusting the aspect ratio between weights would change the specular shiness. Moreover, our reflectance representation generalizes to previous work [15-17] with spatially varying specular albedo and a global specular exponent when \\( k_{bp} = 1 \\)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.427, + 0.47, + 0.473 + ], + "angle": 0, + "content": "Efficient Shading For directional illumination, by denoting the incoming irradiance from direction \\(\\mathbf{l}\\) as \\(E\\), we can directly obtain the shading:" + }, + { + "type": "equation", + "bbox": [ + 0.131, + 0.479, + 0.47, + 0.521 + ], + "angle": 0, + "content": "\\[\ns = E \\cdot \\left(\\frac {c}{\\pi} \\cdot \\langle \\mathbf {l}, \\mathbf {n} \\rangle + \\sum_ {i = 1} ^ {k _ {b p}} w _ {i} \\cdot f _ {i} \\cdot \\langle \\mathbf {h}, \\mathbf {n} \\rangle^ {p _ {i}}\\right) \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.527, + 0.47, + 0.573 + ], + "angle": 0, + "content": "For environmental illumination, we denote the incoming radiance from direction \\(\\mathbf{l}\\) as \\(E(\\mathbf{l})\\). According to the rendering equation [27], we have:" + }, + { + "type": "equation", + "bbox": [ + 0.152, + 0.578, + 0.47, + 0.612 + ], + "angle": 0, + "content": "\\[\ns = \\int_ {\\mathbf {l} \\in \\Omega^ {+}} E (\\mathbf {l}) \\cdot f _ {r} (\\mathbf {l}, \\mathbf {v}, \\mathbf {n}) \\cdot \\langle \\mathbf {l}, \\mathbf {n} \\rangle \\mathrm {d} \\mathbf {l} \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.617, + 0.47, + 0.663 + ], + "angle": 0, + "content": "Here, \\(\\Omega^{+}\\) is the upper hemisphere centered by \\(\\mathbf{n}\\). Substituting Equation (2) into Equation (4), we then separate the shading \\(s\\) into a diffuse part \\(s_d\\) and a specular part \\(s_s\\):" + }, + { + "type": "equation", + "bbox": [ + 0.151, + 0.67, + 0.469, + 0.685 + ], + "angle": 0, + "content": "\\[\ns = s _ {d} + s _ {s}, \\text {w h e r e} \\tag {5}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.143, + 0.689, + 0.47, + 0.721 + ], + "angle": 0, + "content": "\\[\ns _ {d} = \\int_ {\\mathbf {l} \\in \\Omega^ {+}} \\frac {c}{\\pi} \\cdot E (\\mathbf {l}) \\cdot \\langle \\mathbf {l}, \\mathbf {n} \\rangle d \\mathbf {l} \\tag {6}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.144, + 0.724, + 0.469, + 0.766 + ], + "angle": 0, + "content": "\\[\ns _ {s} = \\int_ {\\mathbf {l} \\in \\Omega^ {+}} \\sum_ {i = 1} ^ {k _ {b p}} w _ {i} \\cdot f _ {i} \\langle \\mathbf {h}, \\mathbf {n} \\rangle^ {p _ {i}} \\cdot E (\\mathbf {l}) d \\mathbf {l} \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.772, + 0.469, + 0.803 + ], + "angle": 0, + "content": "We can efficiently compute \\( s_d \\) and \\( s_s \\) in the frequency space [45]:" + }, + { + "type": "equation", + "bbox": [ + 0.131, + 0.808, + 0.47, + 0.85 + ], + "angle": 0, + "content": "\\[\ns _ {d} = \\frac {c}{\\pi} \\cdot \\sum_ {l = 0} ^ {L} \\sum_ {m = - l} ^ {l} A _ {l} \\cdot K _ {l m} \\cdot Y _ {l m} (\\mathbf {n}), \\tag {8}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.132, + 0.855, + 0.469, + 0.897 + ], + "angle": 0, + "content": "\\[\ns _ {s} = \\sum_ {i = 1} ^ {k _ {b p}} \\sum_ {l = 0} ^ {L} \\sum_ {m = - l} ^ {l} w _ {i} \\cdot B _ {l} \\cdot K _ {l m} \\cdot Y _ {l m} (\\mathbf {r}). \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.173 + ], + "angle": 0, + "content": "Here, \\(L\\) is the SH order, \\(A_{l}\\), \\(B_{l}^{i}\\), and \\(K_{lm}\\) are the SH coefficients of the Lambertian BRDF, Blinn-Phong BRDF with specular exponent \\(p_i\\), and the environmental illumination, respectively; \\(Y_{lm}(\\cdot)\\) are the SH basis functions; \\(\\mathbf{r} = \\frac{2(\\mathbf{n} \\cdot \\mathbf{v}) \\mathbf{n} - \\mathbf{v}}{||2(\\mathbf{n} \\cdot \\mathbf{v}) \\mathbf{n} - \\mathbf{v}||_2}\\) is the specular reflect direction [1]." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.181, + 0.714, + 0.198 + ], + "angle": 0, + "content": "3.2. Initial Model Learning" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.205, + 0.892, + 0.295 + ], + "angle": 0, + "content": "In this part, we propose a method to learn the mean \\(\\bar{R}\\) and bases \\(\\mathrm{M}_{\\mathrm{R}}\\) of our morphable face reflectance model from the publicly-available Multi-PIE dataset [25]. Specifically, we first estimate the reflectance parameters for each identity in the dataset via inverse rendering, and then train a PCA model for them." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.308, + 0.893, + 0.505 + ], + "angle": 0, + "content": "Dataset Preprocessing The Multi-PIE dataset contains 337 identities captured under 15 viewpoints and 19 illuminations. We first exclude objects with facial accessories or hair occlusions, resulting in 128 identities. Then, we manually select 9 viewpoints and 12 illuminations (including 11 directional flash images and 1 room light image) with well color consistency to train our model. By removing the room light effect in the flash images, we obtain 11 OLAT images per viewpoint. We adopt a simple model-based approach to reconstruct the BFM09 geometry coefficients of each identity, camera parameters of each viewpoint, and the position of each flash simultaneously. See more implementation details in our Supplementary Material." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.518, + 0.892, + 0.745 + ], + "angle": 0, + "content": "Reflectance Parameter Estimation For a specific identity, we estimate all the reflectance parameters in UV space, including the diffuse color map \\(C \\in \\mathbb{R}^{3 \\times H \\times W}\\) and the linear combination weight map \\(W \\in \\mathbb{R}^{k_{bp} \\times H \\times W}\\). We unwarp all the OLAT images into UV space and denote the one captured under the \\(i\\)-th viewpoint and the \\(j\\)-th directional flash illumination as \\(I_{ij}^{uv} \\in \\mathbb{R}^{3 \\times H \\times W}\\). From the reconstructed face geometry and scene information, we precompute the incident light direction \\(\\mathbf{l}_j^{uv}\\) for each flash, view direction \\(\\mathbf{v}_i^{uv}\\) for each camera, normal direction \\(\\mathbf{n}^{uv}\\) for the face geometry, and shadow mask1 \\(M_{ij}^{uv}\\) for each OLAT image in the UV space. By predefining a reasonable incoming irradiance \\(E\\) from the directional flash2, we obtain the reconstructed OLAT image \\(\\hat{I}_{ij}^{uv}\\) using the efficient shading technique under directional illumination presented in Equation (3):" + }, + { + "type": "equation", + "bbox": [ + 0.508, + 0.757, + 0.892, + 0.8 + ], + "angle": 0, + "content": "\\[\n\\hat {I} _ {i j} ^ {u v} = E \\cdot \\left(\\frac {C}{\\pi} \\cdot \\langle \\mathbf {l} _ {j} ^ {u v}, \\mathbf {n} ^ {u v} \\rangle + \\sum_ {k = 1} ^ {k _ {b p}} W _ {k} \\cdot f _ {k} \\cdot \\langle \\mathbf {h} _ {i j} ^ {u v}, \\mathbf {n} ^ {u v} \\rangle^ {p _ {k}}\\right) \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.892, + 0.842 + ], + "angle": 0, + "content": "Here, \\(\\mathbf{h}_{ij}^{uv}\\) is the half vector UV map obtained by \\(\\mathbf{l}_j^{uv}\\) and \\(\\mathbf{v}_i^{uv}\\). We optimize the diffuse color map \\(C\\) and linear com" + }, + { + "type": "page_footnote", + "bbox": [ + 0.518, + 0.851, + 0.75, + 0.864 + ], + "angle": 0, + "content": "1We obtained shadow mask via ray tracing." + }, + { + "type": "page_footnote", + "bbox": [ + 0.5, + 0.864, + 0.892, + 0.901 + ], + "angle": 0, + "content": "There is an inevitably global scale between the reflectance parameters estimated by the inverse rendering method and the ground truth, if lighting unknown. See more theoretical analysis in [45]." + }, + { + "type": "list", + "bbox": [ + 0.5, + 0.851, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "8601" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.179, + 0.087, + 0.791, + 0.227 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.237, + 0.895, + 0.281 + ], + "angle": 0, + "content": "Figure 3. Model finetuning pipeline overview. Given a single input face image, we apply an encoder \\( E_{\\theta} \\) to estimate its lighting scale \\( z \\), lighting coefficients \\( \\gamma \\), and reflectance coefficients \\( \\beta \\). Combined with the precompute geometry parameters \\( \\alpha \\), \\( \\delta \\), \\( \\mathbf{R} \\), \\( \\mathbf{t} \\), and \\( \\Pi \\), we can obtain the reconstructed face image via a differentiable renderer to compute self-supervised loss and jointly update \\( E_{\\theta} \\) and our reflectance model." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.306, + 0.314, + 0.321 + ], + "angle": 0, + "content": "bination weights map \\(W\\) with loss:" + }, + { + "type": "equation", + "bbox": [ + 0.178, + 0.335, + 0.47, + 0.359 + ], + "angle": 0, + "content": "\\[\n\\underset {C, W} {\\arg \\min } \\mathcal {L} _ {\\text {r e c o n}} + w _ {\\text {r e g}} \\mathcal {L} _ {\\text {r e g}} \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.372, + 0.387, + 0.389 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{\\text{recon}}\\) is the weighted L1 reconstruction loss:" + }, + { + "type": "equation", + "bbox": [ + 0.096, + 0.4, + 0.47, + 0.434 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {r e c o n}} = \\sum_ {i, j} \\left\\langle \\mathbf {l} _ {j} ^ {u v}, \\mathbf {n} ^ {u v} \\right\\rangle \\cdot \\left\\| M _ {i j} ^ {u v} \\cdot \\left(\\hat {I} _ {i j} ^ {u v} - I _ {i j} ^ {u v}\\right) \\right\\| _ {1}, \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.446, + 0.47, + 0.476 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{reg}\\) is designed to restrict the reflectance parameters to be non-negative:" + }, + { + "type": "equation", + "bbox": [ + 0.172, + 0.49, + 0.47, + 0.507 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {r e g}} = - M _ {C} \\cdot C - M _ {W} \\cdot W \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.519, + 0.469, + 0.58 + ], + "angle": 0, + "content": "Here, \\(M_C\\) and \\(M_W\\) are the masks that indicate negative values in \\(C\\) and \\(W\\), respectively. During parameter estimation, we randomly horizontal flip \\(C\\) and \\(W\\) to introduce symmetric constraints [66] to the reflectance parameter map." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.581, + 0.47, + 0.642 + ], + "angle": 0, + "content": "Compared to the traditional way that uses specular intensity and exponents to parameterize the Blinn-Phong BRDF, our linear representation is much easier for parameter estimation in practice." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.657, + 0.47, + 0.809 + ], + "angle": 0, + "content": "Model Learning With the estimated reflectance parameter maps for each identity, we can build our initial morphable face reflectance model. Similar to AlbedoMM [50], we learn a PCA model only for the diffuse albedo. Then, we transfer it to the specular weights by using the same linear combination of the training samples to form the bases. Thus, we can use the same coefficients \\(\\beta\\) for the diffuse and specular reflectance parameters as Equation (1) while keeping the orthonormality of the diffuse bases so that the user can use our diffuse model independently." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.82, + 0.253, + 0.837 + ], + "angle": 0, + "content": "3.3. Model Finetuning" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.844, + 0.47, + 0.906 + ], + "angle": 0, + "content": "To improve the generalization capability and expressive power of our initial morphable face reflecatance model, we finetune it on an in-the-wild face image dataset, FFHQ [29], by jointly doing face reconstruction and model updating." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.306, + 0.895, + 0.457 + ], + "angle": 0, + "content": "Dataset Preprocessing Before model finetuning, we use an off-the-shelf [14] method to estimate the BFM09 geometry coefficients and head pose for each image in the dataset. To further improve the geometry reconstruction accuracy, we apply an offline optimization using the same loss functions as [14]. Finally, we obtain the shape coefficients \\(\\alpha\\), expression coefficients \\(\\delta^3\\), and head pose \\(\\mathbf{R}\\), \\(\\mathbf{t}\\) for each image. Similar to [14], we use the perspective camera model with a reasonable predefined focal length to represent the 3D-2D projection \\(\\Pi\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.471, + 0.895, + 0.592 + ], + "angle": 0, + "content": "Network Architecture As illustrated in Figure 3, given a single face image \\(I\\) as input, our face reconstruction network \\(E_{\\theta}(\\cdot)\\) predicts the reflectance model coefficients \\(\\beta\\) and the SH lighting. Combined with the geometry parameters \\(\\alpha, \\delta\\), head pose \\(\\mathbf{R}, \\mathbf{t}\\), and the projection \\(\\Pi\\), we can obtain the reconstructed image \\(\\hat{I}\\) via a differentiable rasterizer [34, 46] using the efficient shading technique presented in Equation (8) and (9)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.593, + 0.895, + 0.849 + ], + "angle": 0, + "content": "To update the high-frequency information in our non-Lambertian reflectance representation, we need to predict high-order SH lighting [45]. We adopt 8-order SH lighting with 273 parameters in our method as [17,36]. However, if handled naively, the network cannot predict reasonable SH lighting due to the large searching space of high-order SH coefficients, as shown in Figure 5. To constrain the searching space, we build a PCA model for the real-world environmental lighting in SH coefficient space inspired by [18]. Specifically, we utilize a real-world HDRI environment map dataset [64] and apply rotation augmentation to it. For each environment map, we compute its SH coefficients up to the 8-th order. We then divide them by the 0-th order coefficient for normalization. Note that each color channel is normalized independently. Next, we learn a PCA model for these normalized SH coefficients. We use the first \\( N_{L} \\) bases for lighting prediction." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.85, + 0.892, + 0.866 + ], + "angle": 0, + "content": "During finetuning, together with the reflectance model" + }, + { + "type": "page_footnote", + "bbox": [ + 0.5, + 0.875, + 0.892, + 0.901 + ], + "angle": 0, + "content": "3The expression bases are adapted from FaceWarehouse [8] since BFM09 does not model expression. See more details in [14]." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8602" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.091, + 0.473, + 0.244 + ], + "angle": 0, + "content": "coefficients \\(\\beta\\), our network predicts \\(\\gamma \\in \\mathbb{R}^{N_L}\\) as the lighting PCA model coefficients and \\(z \\in \\mathbb{R}^3\\) to represent the scale of 0-th order SH coefficient for each color channel. From this, we first use \\(\\gamma\\) to recover the SH coefficients from the PCA lighting model and then apply the predicted scale \\(z\\) to them. We adopt the ResNet-50 [26] architecture as the reconstruction network \\(E_{\\theta}(\\cdot)\\) and modify the last fully-connected layer to \\(N_R + N_L + 3\\) neurons. We adopt the Softplus activation for \\(z\\) to ensure a non-negative prediction and linear activation for \\(\\beta\\) and \\(\\gamma\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.258, + 0.472, + 0.336 + ], + "angle": 0, + "content": "Loss Function In model finetuning, the learnable parameters are the morphable model parameters, including the mean \\(\\bar{R}\\) and bases \\(\\mathrm{M}_{\\mathrm{R}}\\), and face reconstruction network parameters \\(\\theta\\). We optimize them with the combination of a reconstruction loss \\(\\mathcal{L}_{rec}\\) and a regularization loss \\(\\mathcal{L}_{reg}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.201, + 0.347, + 0.47, + 0.375 + ], + "angle": 0, + "content": "\\[\n\\underset {\\bar {R}, \\mathrm {M} _ {\\mathrm {R}}, \\theta} {\\arg \\min } \\mathcal {L} _ {r e c} + \\mathcal {L} _ {r e g} \\tag {14}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.386, + 0.471, + 0.448 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{rec}\\) is the combination of a L1 term \\(\\mathcal{L}_{l1}\\) and a perceptual term \\(\\mathcal{L}_{per}\\); see more details in our Supplementary Material. In our regularization loss \\(\\mathcal{L}_{reg}\\), we design \\(\\mathcal{L}_{upd}\\) to constrain the updating of our morphable reflectance model:" + }, + { + "type": "equation", + "bbox": [ + 0.143, + 0.459, + 0.47, + 0.478 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {u p d} = \\left| \\left| \\bar {R} - \\bar {R} _ {0} \\right| \\right| _ {1} + \\left| \\left| \\mathrm {M} _ {\\mathrm {R}} - \\mathrm {M} _ {\\mathrm {R} _ {0}} \\right| \\right| _ {1} \\tag {15}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.489, + 0.471, + 0.566 + ], + "angle": 0, + "content": "In addition, we adopt \\(\\mathcal{L}_{\\text {light }}\\) to encourage monochromatic environment lighting as [12] to resolve the color ambiguity between albedo and lighting and \\(\\mathcal{L}_{\\text {coef }}\\) to constrain the predicted PCA coefficients \\(\\beta\\) and \\(\\gamma\\); see more details in our Supplementary Material." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.58, + 0.21, + 0.597 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.606, + 0.295, + 0.621 + ], + "angle": 0, + "content": "4.1. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.629, + 0.47, + 0.734 + ], + "angle": 0, + "content": "Initial Model Learning For reflectance parameter estimation, we set \\( w_{reg} = 100 \\) and adopt Adam [31] optimizer to minimize the loss function, with learning rate 5e-3. We use 3 Blinn-Phong BRDFs with specular exponents 1, 8, and 64 in our method, i.e. \\( k_{bp} = 3 \\). Thus, there are 6 reflectance parameters for each face vertex. We use the first 80 PCA bases in our initial model, i.e. \\( N_R = 80 \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Model Finetuning For the lighting PCA model, we also use the first 80 PCA bases, i.e. \\( N_{L} = 80 \\). The weights for \\( \\mathcal{L}_{l1}, \\mathcal{L}_{per}, \\mathcal{L}_{coef}, \\mathcal{L}_{upd}, \\mathcal{L}_{light} \\) are set to 2, 0.1, 0.001, 10, 10, respectively. We finetune our model on the FFHQ dataset [30], with 70000 high-fidelity single-view face images, and crop them to \\( 224 \\times 224 \\) when input to our reconstruction network \\( E_{\\theta} \\). We first pretrain \\( E_{\\theta} \\) using \\( \\mathcal{L}_{l1}, \\mathcal{L}_{per}, \\mathcal{L}_{coef} \\) for 20 epochs to ensure it can output reasonable reflectance and lighting coefficients prediction, with learning rate 1e-4. Then, we use the full loss function to" + }, + { + "type": "image", + "bbox": [ + 0.513, + 0.089, + 0.88, + 0.315 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.319, + 0.895, + 0.445 + ], + "angle": 0, + "content": "Figure 4. Qualitative comparison of face rendering results on the Multi-PIE dataset between our spatially varying reflectance representation and previous works with a global specular exponent. Here, \\(Exp\\) stands for the specular exponent in the Blinn-Phong BRDF. A large global exponent (e.g. 32, 64) leads to over-shiness artifacts around the cheek and chin, while a small one (e.g. 8, 16) cannot represent peculiarities appear in the forehead and tip of the nose. Our representation can well model the spatially varying specular intensity and shiness on the face." + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.453, + 0.894, + 0.496 + ], + "angle": 0, + "content": "Table 1. Quantitative comparison of face rendering results on the Multi-PIE dataset between our method and previous works with a global specular exponent." + }, + { + "type": "table", + "bbox": [ + 0.58, + 0.507, + 0.812, + 0.584 + ], + "angle": 0, + "content": "
LPIPS ↓SSIM ↑PSNR ↑
Exp=80.0970.93724.56
Exp=160.0980.93624.54
Exp=320.0970.93724.72
Exp=640.0960.93724.82
Ours0.0830.94225.00
" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.61, + 0.892, + 0.656 + ], + "angle": 0, + "content": "simultaneously update the parameters of \\( E_{\\theta} \\) and our morphable face reflectance model for 2 epochs, with learning rate 1e-5. We adopt Adam optimizer [31]." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.666, + 0.628, + 0.681 + ], + "angle": 0, + "content": "4.2. Evaluations" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Reflectance Parameter Estimation In our reflectance representation, we use the linear combination of 3 Blinn-Phong BRDFs with specular exponents 1, 8, and 64 as the specular term. In Figure 4, we compare the face rendering results between our reflectance representation and previous works [15-17] with spatially varying specular albedo and a global specular exponent. As discussed in Section 3.1, we implement the reflectance representation of previous work by setting \\( k_{bp} = 1 \\), and use the same inverse rendering pipeline described in Section 3.2. The results illustrate that our representation obtains more realistic rendering results than the previous work. For example, the specular shiness is more significant at the tip of nose while vanishing around the cheek; previous works with global specular" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8603" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.083, + 0.47, + 0.125 + ], + "angle": 0, + "content": "Table 2. Photometric face reconstruction comparison between our method and competitors on 1000 images randomly sampled from the CelebA-HQ dataset." + }, + { + "type": "table", + "bbox": [ + 0.134, + 0.136, + 0.412, + 0.21 + ], + "angle": 0, + "content": "
LPIPS ↓SSIM ↑PSNR ↑
BFM090.1140.89323.51
AlbedoMM0.1160.90123.69
Ours0.1100.89624.21
Ours w/o finetune0.12560.88623.26
" + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.223, + 0.456, + 0.355 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.359, + 0.468, + 0.387 + ], + "angle": 0, + "content": "Figure 5. Qualitative ablation study of model finetuning and the use of our lighting PCA model." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.415, + 0.468, + 0.536 + ], + "angle": 0, + "content": "exponent cannot capture this phenomenon. With the spatially varying linear combination weights of different Blinn-Phong BRDFs, our method can naturally represent spatially varying specular intensity and shiness. In Table 1, we report the SSIM [63], PSNR, and LPIPS [69] scores in the face region to quantitatively measure the discrepancy between the re-rendered face and the ground truth. Again, our method achieves better results than the global exponent counterpart." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.55, + 0.468, + 0.671 + ], + "angle": 0, + "content": "Model Visualization We visualize the first 3 principal components of our morphable model in Figure 1, including the diffuse albedo, and the weights for Blinn-Phong BRDF with specular exponents 1, 8, and 64, from left to right; we multiply the weights by 3 for better visualization. It shows that our model learns to assign a large specular shiness to the tip of the nose while a small value to the cheek. See more visualizations in our Supplementary Material." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.685, + 0.468, + 0.837 + ], + "angle": 0, + "content": "Ablation Study As shown in Figure 5 and Table 2, the proposed finetuning strategy can improve the generalization capability and expressive power of our initial morphable face reflectance model, leading to better face reconstruction quality, especially around mouth and eyes. We then verify the effectiveness of our lighting PCA model. As illustrated in Figure 5, directly predicts 273 coefficients of the 8-order SH (w/o Light PCA) leads to unreasonable results; our lighting PCA model obtains better lighting predictions by constraining the searching space of the SH coefficients." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.847, + 0.216, + 0.863 + ], + "angle": 0, + "content": "4.3. Comparisons" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.468, + 0.901 + ], + "angle": 0, + "content": "Baselines We compare our method with BFM09 [43] and AlbedoMM [50]. BFM09 is a diffuse-only model built from" + }, + { + "type": "image", + "bbox": [ + 0.511, + 0.088, + 0.878, + 0.37 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.375, + 0.89, + 0.403 + ], + "angle": 0, + "content": "Figure 6. Qualitative comparison of face reconstruction and shading contributions between our method and AlbedoMM." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.43, + 0.892, + 0.642 + ], + "angle": 0, + "content": "3D scans; AlbedoMM is a morphable model for diffuse and specular albedo built from Light Stage data. To ensure a fair comparison, we use the same CNN-based framework (see Section 3.3) to implement the competitors. We train the reconstruction network for them on the FFHQ dataset, but we do not update their morphable model parameters. As we only focus on appearance, the reconstruction network only predicts reflectance coefficients and lighting parameters and uses fixed precomputed geometry during training. For BFM09, we adopt the same geometry parameters as ours. For AlbedoMM, we use the same steps as mentioned in Section 3.3 to obtain its geometry parameters. Akin to [50], we adopt the Blinn-Phong BRDF for AlbedoMM and set the global shiness to 20." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.661, + 0.892, + 0.828 + ], + "angle": 0, + "content": "Face Reconstruction We evaluate our method and the competitors on the CelebA-HQ [28] dataset. As illustrated in Table 2, our method obtains better photometric face reconstruction quantitative scores than the competitors since we finetune it on an in-the-wild dataset to improve its generalization capability and expressive power while the competitors are built from a limited number of scans. As shown in Figure 6, our method can reconstruct the input image well; compared to AlbedoMM trained from Light Stage scans, our method trained from low-cost data can also disentangle the diffuse and specular shading in a plausible way." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Face Relighting We evaluate the relighting performance of our method and the competitors on the Multi-PIE dataset [25]. Specifically, given an input image, we first obtain its geometry parameters as described in Section 3.2 and" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8604" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.088, + 0.087, + 0.457, + 0.38 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.384, + 0.47, + 0.414 + ], + "angle": 0, + "content": "Figure 7. Qualitative comparison of face relighting results under point light source between our method and the competitors." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.433, + 0.473, + 0.721 + ], + "angle": 0, + "content": "reconstruct its reflectance parameters using our CNN-based face reconstruction network (columns 1 and 3 in Figure 7). Then, we re-render the image under a new point light source and compare it to the corresponding OLAT image with exactly the same light position obtained from the preprocessed Multi-PIE dataset in Section 3.2 (columns 2 and 4 in Figure 7). Since we do not have the ground truth light color and intensity information of the Multi-PIE dataset, we render our method and the competitors using the same white point light source for a fair comparison; please ignore the color difference and only focus on the distribution of facial peculiarities in Figure 7. Compared to BFM09, our method successfully render plausible facial peculiarities since we adopt a non-Lambertian reflectance representation. Compared to AlbedoMM, our method achieves more realistic results especially around the tip of the nose since we can model both spatially varying specular intensity and shiness. See the video comparisons on our project page for a better demonstration." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.735, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Reflectance Reconstruction Although our goal is not to model physically-accurate reflectance parameters, we compare our method with AlbedoMM on 23 Light Stage scans with ground truth diffuse and specular albedo captured under neutral expression from the 3D-RFE database [51]. We adopt the sum of 3 linear combination weights in our reflectance representation as the specular albedo; this quantity shares the same meaning as the specular albedo, i.e. the specular shading under a spatially-uniform environment lighting with unit radiance. As shown in Figure 8, our method can reconstruct plausible reflectance maps. How" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.083, + 0.892, + 0.112 + ], + "angle": 0, + "content": "Table 3. Quantitative diffuse and specular albedo reconstruction results on 23 Light Stage scans from the 3D-RFE dataset (PSNR↑)." + }, + { + "type": "table", + "bbox": [ + 0.564, + 0.122, + 0.828, + 0.171 + ], + "angle": 0, + "content": "
Diffuse AlbedoSpecular Albedo
AlbedoMM19.3126.51
Ours20.1319.14
" + }, + { + "type": "image", + "bbox": [ + 0.51, + 0.18, + 0.878, + 0.3 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.305, + 0.892, + 0.333 + ], + "angle": 0, + "content": "Figure 8. Qualitative comparison of diffuse and specular albedo reconstruction on the 3D-RFE dataset." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.354, + 0.892, + 0.49 + ], + "angle": 0, + "content": "ever, as shown in Table 3, our method obtains inferior quantitative results than AlbedoMM on specular albedo reconstruction. We attribute this to two reasons: \\(i)\\) AlbedoMM uses the 3D-RFE database to build their model [50] while our method has never seen these scans, and \\(ii)\\) our method is built from low-cost data without lighting information, so there exists a global scale between our reflectance parameters and the ground truth although we try to mitigate it by setting a reasonable lighting color in Section 3.2." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.504, + 0.62, + 0.52 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.529, + 0.892, + 0.727 + ], + "angle": 0, + "content": "We propose the first 3D morphable face reflectance model with spatially varying BRDF, using only low-cost publicly-available data. To represent spatially varying reflectance, we apply linear shiness weighting into parametric face modeling. We develop an inverse rendering algorithm to reconstruct the reflectance parameters from the Multi-PIE dataset, from which we build an initial model. We propose a strategy that jointly learns the face reconstruction network and updates the morphable model parameters on the FFHQ dataset to improve its generalization capability and expressive power. Our method obtains decent rendering results with plausible facial peculiarities. We discuss the limitations of our method in the Supplementary Material." + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.74, + 0.661, + 0.758 + ], + "angle": 0, + "content": "Acknowledgement" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.892, + 0.903 + ], + "angle": 0, + "content": "This work was supported by the National Key R&D Program of China (2018YFA0704000), Beijing Natural Science Foundation (M22024), the NSFC (No.62021002), and the Key Research and Development Project of Tibet Autonomous Region (XZ202101ZY0019G). This work was also supported by THUIBCS, Tsinghua University, and BLBCI, Beijing Municipal Education Commission. We thank the anonymous reviewers for their constructive suggestions. Feng Xu is the corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "8605" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.175, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.468, + 0.143 + ], + "angle": 0, + "content": "[1] Tomas Akenine-Moller, Eric Haines, and Naty Hoffman. Real-time rendering. AK Peters/crc Press, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.146, + 0.468, + 0.2 + ], + "angle": 0, + "content": "[2] Dejan Azinović, Olivier Maury, Christophe Hery, Mathias Niessner, and Justus Thies. High-res facial appearance capture from polarized smartphone images. ArXiv, abs/2212.01160, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.203, + 0.468, + 0.258 + ], + "angle": 0, + "content": "[3] Ziqian Bai, Zhaopeng Cui, Xiaoming Liu, and Ping Tan. Riggable 3d face reconstruction via in-network optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6216-6225, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.26, + 0.468, + 0.315 + ], + "angle": 0, + "content": "[4] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 187-194, 1999. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.317, + 0.468, + 0.373 + ], + "angle": 0, + "content": "[5] James F Blinn. Models of light reflection for computer synthesized pictures. In Proceedings of the 4th annual conference on Computer graphics and interactive techniques, pages 192-198, 1977. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.375, + 0.468, + 0.442 + ], + "angle": 0, + "content": "[6] James Booth, Anastasios Roussos, Stefanos Zafeiriou, Allan Ponniah, and David J. Dunaway. A 3d morphable model learnt from 10,000 faces. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5543-5552, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.446, + 0.468, + 0.487 + ], + "angle": 0, + "content": "[7] Chen Cao, Yanlin Weng, Stephen Lin, and Kun Zhou. 3d shape regression for real-time facial animation. ACM Transactions on Graphics (TOG), 32(4):1-10, 2013. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.489, + 0.468, + 0.544 + ], + "angle": 0, + "content": "[8] Chen Cao, Yanlin Weng, Shun Zhou, Y. Tong, and Kun Zhou. Facewarehouse: A 3d facial expression database for visual computing. IEEE Transactions on Visualization and Computer Graphics, 20:413-425, 2014. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.546, + 0.468, + 0.613 + ], + "angle": 0, + "content": "[9] Bindita Chaudhuri, Noranart Vesdapunt, Linda Shapiro, and Baoyuan Wang. Personalized face modeling for improved face reconstruction and motion retargeting. In European Conference on Computer Vision, pages 142-160. Springer, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.617, + 0.468, + 0.672 + ], + "angle": 0, + "content": "[10] Hang Dai, Nick E. Pears, William Smith, and Christian Duncan. A 3d morphable model of craniofacial shape and texture variation. 2017 IEEE International Conference on Computer Vision (ICCV), pages 3104-3112, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.674, + 0.468, + 0.743 + ], + "angle": 0, + "content": "[11] Paul Debevec, Tim Hawkins, Chris Tchou, Haarm-Pieter Duiker, Westley Sarokin, and Mark Sagar. Acquiring the reflectance field of a human face. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pages 145-156, 2000. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.746, + 0.468, + 0.773 + ], + "angle": 0, + "content": "[12] Yu Deng. Deep3dfacereconstruction, 2019. https://github.com/microsoft/Deep3DFaceReconstruction. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.775, + 0.468, + 0.843 + ], + "angle": 0, + "content": "[13] Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, and Xin Tong. Disentangled and controllable face image generation via 3d imitative-contrastive learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5154–5163, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.468, + 0.901 + ], + "angle": 0, + "content": "[14] Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set. In Proceedings of the IEEE/CVF Conference on Computer" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.468, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.89, + 0.119 + ], + "angle": 0, + "content": "Vision and Pattern Recognition Workshops, pages 0-0, 2019. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.892, + 0.178 + ], + "angle": 0, + "content": "[15] Abdallah Dib, Junghyun Ahn, Cedric Thebault, Philippe-Henri Gosselin, and Louis Chevallier. S2f2: Self-supervised high fidelity face reconstruction from monocular image. arXiv preprint arXiv:2203.07732, 2022. 1, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.18, + 0.892, + 0.248 + ], + "angle": 0, + "content": "[16] Abdallah Dib, Gaurav Bharaj, Junghyun Ahn, Cédric Thébault, Philippe Gosselin, Marco Romeo, and Louis Chevallier. Practical face reconstruction via differentiable ray tracing. In Computer Graphics Forum, volume 40, pages 153-164. Wiley Online Library, 2021. 1, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.251, + 0.892, + 0.346 + ], + "angle": 0, + "content": "[17] Abdallah Dib, Cédric Thébault, Junghyun Ahn, Philippe-Henri Gosselin, Christian Theobalt, and Louis Chevallier. Towards high fidelity monocular face reconstruction with rich reflectance using self-supervised learning and ray tracing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12819-12829, 2021. 1, 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.349, + 0.892, + 0.431 + ], + "angle": 0, + "content": "[18] Bernhard Egger, Sandro Schonborn, Andreas Schneider, Adam Kortylewski, Andreas Morel-Forster, Clemens Blumer, and Thomas Vetter. Occlusion-aware 3d morphable models and an illumination prior for face image analysis. International Journal of Computer Vision, 126(12):1269-1287, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.434, + 0.892, + 0.503 + ], + "angle": 0, + "content": "[19] Bernhard Egger, William AP Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani, et al. 3d morphable face models—past, present, and future. ACM Transactions on Graphics (TOG), 39(5):1-38, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.505, + 0.892, + 0.56 + ], + "angle": 0, + "content": "[20] Yao Feng, Haiwen Feng, Michael J Black, and Timo Bolkart. Learning an animatable detailed 3d face model from inthe-wild images. ACM Transactions on Graphics (ToG), 40(4):1-13, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.562, + 0.892, + 0.631 + ], + "angle": 0, + "content": "[21] Pablo Garrido, Michael Zollhöfer, Dan Casas, Levi Valgaerts, Kiran Varanasi, Patrick Pérez, and Christian Theobalt. Reconstruction of personalized 3d face rigs from monocular video. ACM Transactions on Graphics (TOG), 35(3):1-15, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.633, + 0.892, + 0.702 + ], + "angle": 0, + "content": "[22] Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, and William T Freeman. Unsupervised training for 3d morphable model regression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8377-8386, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.704, + 0.892, + 0.785 + ], + "angle": 0, + "content": "[23] Thomas Gereg, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel Luthi, Sandro Schonborn, and Thomas Vetter. Morphable face models—an open framework. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pages 75–82. IEEE, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.789, + 0.892, + 0.857 + ], + "angle": 0, + "content": "[24] Abhijeet Ghosh, Graham Fyffe, Borom Tunwattanapong, Jay Busch, Xueming Yu, and Paul Debevec. Multiview face capture using polarized spherical gradient illumination. In Proceedings of the 2011 SIGGRAPH Asia Conference, pages 1-10, 2011. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.86, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[25] Ralph Gross, Iain Matthews, Jeffrey Cohn, Takeo Kanade, and Simon Baker. Multi-pie. Image and vision computing, 28(5):807-813, 2010. 2, 4, 7" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8606" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "[26] Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.149, + 0.472, + 0.189 + ], + "angle": 0, + "content": "[27] James T Kajiya. The rendering equation. In Proceedings of the 13th annual conference on Computer graphics and interactive techniques, pages 143-150, 1986. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.191, + 0.469, + 0.231 + ], + "angle": 0, + "content": "[28] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. ArXiv, abs/1710.10196, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.233, + 0.469, + 0.287 + ], + "angle": 0, + "content": "[29] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4396-4405, 2018. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.289, + 0.47, + 0.356 + ], + "angle": 0, + "content": "[30] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110-8119, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.358, + 0.469, + 0.384 + ], + "angle": 0, + "content": "[31] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.386, + 0.469, + 0.44 + ], + "angle": 0, + "content": "[32] Oliver Klehm, Fabrice Rousselle, Marios Papas, Derek Bradley, Christophe Hery, B. Bickel, Wojciech Jarosz, and Thabo Beeler. Recent advances in facial appearance capture. Computer Graphics Forum, 34, 2015. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.442, + 0.469, + 0.496 + ], + "angle": 0, + "content": "[33] Ritwik K. Kumar, Michael J. Jones, and Tim K. Marks. Morphable reflectance fields for enhancing face recognition. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 2606-2613, 2010. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.498, + 0.469, + 0.552 + ], + "angle": 0, + "content": "[34] Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, and Timo Aila. Modular primitives for high-performance differentiable rendering. ACM Transactions on Graphics (TOG), 39:1-14, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.554, + 0.469, + 0.635 + ], + "angle": 0, + "content": "[35] Alexandros Lattas, Stylianos Moschoglou, Baris Gecer, Stylianos Ploumpis, Vasileios Triantafyllou, Abhijeet Ghosh, and Stefanos Zafeiriou. Avatarme: Realistically renderable 3d facial reconstruction \"in-the-wild\". 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 757-766, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.637, + 0.469, + 0.677 + ], + "angle": 0, + "content": "[36] Chen Li, Kun Zhou, and Stephen Lin. Intrinsic face image decomposition with human face priors. In European Conference on Computer Vision, 2014. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.679, + 0.469, + 0.761 + ], + "angle": 0, + "content": "[37] Ruilong Li, Kalle Bladin, Yajie Zhao, Chinmay Chinara, Owen Ingraham, Pengda Xiang, Xinglei Ren, Pratusha Bhuvana Prasad, Bipin Kishore, Jun Xing, and Hao Li. Learning formation of physically-based face attributes. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3407-3416, 2020. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.763, + 0.469, + 0.802 + ], + "angle": 0, + "content": "[38] Tianye Li, Timo Bolkart, Michael J Black, Hao Li, and Javier Romero. Learning a model of facial shape and expression from 4d scans. ACM Trans. Graph., 36(6):194-1, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.804, + 0.469, + 0.871 + ], + "angle": 0, + "content": "[39] Wan-Chun Ma, Tim Hawkins, Pieter Peers, Charles-Felix Chabert, Malte Weiss, Paul E Debevec, et al. Rapid acquisition of specular and diffuse normal maps from polarized spherical gradient illumination. Rendering Techniques, 2007(9):10, 2007. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.873, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[40] R. MallikarjunB., Ayush Tewari, Tae-Hyun Oh, Tim Weyrich, B. Bickel, Hans-Peter Seidel, Hanspeter Pfister," + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.16 + ], + "angle": 0, + "content": "Wojciech Matusik, Mohamed A. Elgharib, and Christian Theobalt. Monocular reconstruction of neural face reflectance fields. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4789-4798, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.165, + 0.892, + 0.192 + ], + "angle": 0, + "content": "[41] Wojciech Matusik. A data-driven reflectance model. PhD thesis, Massachusetts Institute of Technology, 2003. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.195, + 0.892, + 0.264 + ], + "angle": 0, + "content": "[42] Rohit Pandey, Sergio Orts Escolano, Chloe Legendre, Christian Haene, Sofien Bouaziz, Christoph Rhemann, Paul Debevec, and Sean Fanello. Total relighting: learning to relight portraits for background replacement. ACM Transactions on Graphics (TOG), 40(4):1-21, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.267, + 0.892, + 0.336 + ], + "angle": 0, + "content": "[43] Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pages 296-301, 2009. 2, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.339, + 0.894, + 0.394 + ], + "angle": 0, + "content": "[44] Ravi Ramamoorthi and Pat Hanrahan. An efficient representation for irradiance environment maps. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 497-500, 2001. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.397, + 0.892, + 0.453 + ], + "angle": 0, + "content": "[45] Ravi Ramamoorthi and Pat Hanrahan. A signal-processing framework for inverse rendering. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 117-128, 2001. 2, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.455, + 0.892, + 0.509 + ], + "angle": 0, + "content": "[46] Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari. Accelerating 3d deep learning with pytorch3d. SIGGRAPH Asia 2020 Courses, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.513, + 0.892, + 0.568 + ], + "angle": 0, + "content": "[47] Jérémy Riviere, Paulo F. U. Gotardo, Derek Bradley, Abhijeet Ghosh, and Thabo Beeler. Single-shot high-quality facial geometry and skin appearance capture. ACM Transactions on Graphics (TOG), 39:81:1-81:12, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.572, + 0.892, + 0.627 + ], + "angle": 0, + "content": "[48] Soumyadip Sengupta, Brian Curless, Ira Kemelmacher-Shlizerman, and Steven M. Seitz. A light stage on every desk. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 2400-2409, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.63, + 0.892, + 0.683 + ], + "angle": 0, + "content": "[49] Artem Sevastopolsky, SA Ignatiev, Gonzalo Ferrer, Evgeny Burnaev, and Victor S. Lempitsky. Relightable 3d head portraits from a smartphone video. ArXiv, abs/2012.09963, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.687, + 0.892, + 0.757 + ], + "angle": 0, + "content": "[50] William AP Smith, Alassane Seck, Hannah Dee, Bernard Tiddeman, Joshua B Tenenbaum, and Bernhard Egger. A morphable face albedo model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5011-5020, 2020. 1, 2, 3, 5, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.76, + 0.892, + 0.814 + ], + "angle": 0, + "content": "[51] Giota Stratou, Abhijeet Ghosh, Paul E. Debevec, and Louis-Philippe Morency. Effect of illumination on automatic expression recognition: A novel 3d reconfigurable facial database. Face and Gesture 2011, pages 611–618, 2011. 2, 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.818, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[52] Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Fml: Face model learning from videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10812-10822, 2019. 1, 2, 3" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "8607" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.176 + ], + "angle": 0, + "content": "[53] Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Stylerig: Rigging stylegan for 3d control over portrait images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6142-6151, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.178, + 0.472, + 0.247 + ], + "angle": 0, + "content": "[54] Ayush Tewari, Hans-Peter Seidel, Mohamed Elgharib, Christian Theobalt, et al. Learning complete 3d morphable face models from images and videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3361-3371, 2021. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.249, + 0.472, + 0.331 + ], + "angle": 0, + "content": "[55] Ayush Tewari, Michael Zollhöfer, Pablo Garrido, Florian Bernard, Hyeongwoo Kim, Patrick Pérez, and Christian Theobalt. Self-supervised multi-level face model learning for monocular reconstruction at over 250 hz. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2549-2559, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.333, + 0.472, + 0.416 + ], + "angle": 0, + "content": "[56] Ayush Tewari, Michael Zollhofer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, Patrick Perez, and Christian Theobalt. Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 1274-1283, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.418, + 0.472, + 0.473 + ], + "angle": 0, + "content": "[57] Justus Thies, Mohamed Elgharib, Ayush Tewari, Christian Theobalt, and Matthias Nießner. Neural voice puppetry: Audio-driven facial reenactment. In European conference on computer vision, pages 716-731. Springer, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.475, + 0.472, + 0.544 + ], + "angle": 0, + "content": "[58] Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Nießner. Face2face: Real-time face capture and reenactment of rgb videos. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2387-2395, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.546, + 0.472, + 0.586 + ], + "angle": 0, + "content": "[59] Kenneth E Torrance and Ephraim M Sparrow. Theory for off-specular reflection from roughened surfaces. Josa, 57(9):1105-1114, 1967. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.589, + 0.472, + 0.643 + ], + "angle": 0, + "content": "[60] Luan Tran, Feng Liu, and Xiaoming Liu. Towards high-fidelity nonlinear 3d face morphable model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1126-1135, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.646, + 0.472, + 0.687 + ], + "angle": 0, + "content": "[61] Luan Tran and Xiaoming Liu. Nonlinear 3d face morphable model. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7346-7355, 2018. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.689, + 0.472, + 0.743 + ], + "angle": 0, + "content": "[62] Yifan Wang, Aleksander Holynski, Xiuming Zhang, and Xuaner Cecilia Zhang. Sunstage: Portrait reconstruction and relighting using the sun as a light stage. ArXiv, abs/2204.03648, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.746, + 0.472, + 0.801 + ], + "angle": 0, + "content": "[63] Zhou Wang, Alan Conrad Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13:600-612, 2004. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.803, + 0.472, + 0.857 + ], + "angle": 0, + "content": "[64] Zhibo Wang, Xin Yu, Ming Lu, Quan Wang, Chen Qian, and Feng Xu. Single image portrait relighting via explicit multiple reflectance channel modeling. ACM Transactions on Graphics (TOG), 39:1-13, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.86, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[65] Tim Weyrich, Wojciech Matusik, Hanspeter Pfister, Bernd Bickel, Craig Donner, Chien Tu, Janet McAndless, Jinho Lee, Addy Ngan, Henrik Wann Jensen, et al. Analysis of" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.134 + ], + "angle": 0, + "content": "human faces using a measurement-based skin reflectance model. ACM Transactions on Graphics (ToG), 25(3):1013-1024, 2006. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.892, + 0.177 + ], + "angle": 0, + "content": "[66] Shangzhe Wu, Christian Rupprecht, and Andrea Vedaldi. Unsupervised learning of probably symmetric deformable 3d objects from images in the wild. In CVPR, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.179, + 0.892, + 0.247 + ], + "angle": 0, + "content": "[67] Shugo Yamaguchi, Shunsuke Saito, Koki Nagano, Yajie Zhao, Weikai Chen, Kyle Olszewski, Shigeo Morishima, and Hao Li. High-fidelity facial reflectance and geometry inference from an unconstrained image. ACM Transactions on Graphics (TOG), 37:1-14, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.249, + 0.892, + 0.33 + ], + "angle": 0, + "content": "[68] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. Facescape: a large-scale high quality 3d face dataset and detailed riggable 3d face prediction. In Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, pages 601-610, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.333, + 0.892, + 0.401 + ], + "angle": 0, + "content": "[69] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 586-595, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.404, + 0.892, + 0.473 + ], + "angle": 0, + "content": "[70] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. Im avatar: Implicit morphable head avatars from videos. 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We propose the first 3D morphable face reflectance model with spatially varying BRDF and a technique to train the model with low-cost publicly-available data. We represent face reflectance as a Lambertian BRDF combined with the linear combination of Blinn-Phong BRDFs with different predefined specular exponents. The reflectance parameters for each face vertex are the diffuse albedo and a set of weights. We show the first 3 principal components of diffuse albedo and spatially varying weights here in nonlinear sRGB space. + +# Abstract + +Modeling non-Lambertian effects such as facial specularity leads to a more realistic 3D Morphable Face Model. Existing works build parametric models for diffuse and specular albedo using Light Stage data. However, only diffuse and specular albedo cannot determine the full BRDF. In addition, the requirement of Light Stage data is hard to fulfill for the research communities. This paper proposes the first 3D morphable face reflectance model with spatially varying BRDF using only low-cost publicly-available data. We apply linear shiness weighting into parametric modeling to represent spatially varying specular intensity and shiness. Then an inverse rendering algorithm is developed to reconstruct the reflectance parameters from nonLight Stage data, which are used to train an initial morphable reflectance model. To enhance the model's generalization capability and expressive power, we further propose an update-by-reconstruction strategy to finetune it on an in-the-wild dataset. Experimental results show that our method obtains decent rendering results with plausible facial peculiarities. Our code is released here. + +# 1. Introduction + +3D Morphable Face Models (3DMM) [4, 19] have attracted much attention in the past two decades, as it pro + +vides a powerful and compact statistical prior of 3D face geometry and appearance with dense point-to-point correspondence to various downstream applications like face reconstruction [14,22,52,55,56], rendering [13,53,57,58,70], and animation [3,7,9,20,21,68]. Existing works [52,54,55] have demonstrated promising results for improving the generalization capability and expressive power of 3DMM under the assumption that faces are Lambertian surfaces. However, it is still challenging to model non-Lambertian effects such as facial specularity in 3DMM, which can lead to a more realistic face model. + +A few recent works [37, 50] involve non-Lambertian facial reflectance in the morphable face model. Using a Light Stage [11, 24, 39], they capture diffuse and specular albedo maps of tens of participants. Then, they model the diffuse and specular albedo by training a PCA model [50] or a deep generative network [37] on the acquired data. However, only the diffuse and specular albedo cannot determine the complete Bidirectional Reflectance Distribution Function (BRDF). Thus, other works [15-17] set the remaining reflectance parameters (e.g. specular exponent for the Blinn-Phong BRDF [5], roughness for the Torrance-Sparrow BRDF [59]) of all face vertices to a reasonable value to characterize specular shiness and obtain the complete BRDF. As shown in Figure 7, these spatially uniform parameters lead to unpleasant rendering results since face reflectance is inherently spatially varying [65]. Besides, the + +requirement of Light Stage data is hard to fulfill since building a Light Stage is quite difficult, and no publicly available Light Stage dataset is sufficient to construct a 3DMM. + +To overcome these limitations, we propose and train the first morphable face reflectance model with spatially varying BRDF from low-cost publicly-available data. Inspired by previous works [41, 42], we represent face reflectance as a Lambertian BRDF combined with the linear combination of several Blinn-Phong BRDFs corresponding to different predefined specular exponents. Thus, the reflectance parameters of each face vertex include an RGB color for the Lambertian BRDF and a set of weights for the Blinn-Phong BRDFs. As illustrated in Figure 2, our representation can naturally modulate specular intensity and shiness by adjusting the absolute and relative scales of the linear combination weights, respectively. Compared to previous works [37, 50] not modeling specular shiness, we define a complete BRDF by this representation in 3DMM. Compared to the traditional Blinn-Phong BRDF that models specular intensity and shiness in a nonlinear formulation [5], our linear representation (Equation (2)) is much easier to reconstruct the reflectance parameters from recorded images. With this linear reflectance representation, we develop an inverse rendering approach to estimate the spatially varying reflectance parameters for the 128 selected identities in Multi-PIE [25], a public dataset with face images captured under controlled camera views and light directions. Then, we learn a PCA model for the estimated reflectance parameters as our initial morphable face reflectance model. + +Considering that the Multi-PIE dataset only contains 128 identities which is far from sufficient to capture the variability of human faces, we propose to finetune the initial model on a large-scale in-the-wild dataset, FFHQ [29], to improve its generalization capability and expressive power. As the inputs are in-the-wild images with unknown lighting information, it is not easy to reconstruct accurate reflectance from them. Our key observation is that, on the one hand, we already have an initial parametric reflectance model that can better formulate the reflectance reconstruction from in-the-wild images. On the other hand, the reconstructed reflectance from in-the-wild data could provide feedback to enhance the face prior knowledge in our morphable reflectance model. Based on this observation, we jointly reconstruct the face reflectance coefficients and update the parameters of our morphable face reflectance model (the mean and bases). Another challenge here is to predict high-order spherical harmonics (SH) lighting [44] for in-the-wild images, which is crucial for updating the high-frequency information of our non-Lambertian reflectance model [45]. To solve this problem, we build another PCA model for real-world environment lighting in SH coefficients space, which largely reduces the searching space of the high-order SH coefficients. During face reconstruction, we first predict + +the parameters of the PCA lighting model and then retrieve the high-order SH coefficients from it. Finally, the in-the-wild images are well reconstructed with our parametric reflectance model, and the model itself is also updated gradually in this process to achieve high generalization capability and expressive power. + +In summary, our contributions include: + +- We propose the first 3D morphable face reflectance model with spatially varying BRDF and a technique to train the model with low-cost publicly-available data. +- We apply linear shiness weighting into parametric face modeling to represent spatially varying specular shiness and intensity and ease the process of reconstructing reflectance from images. +- We propose an update-by-reconstruction strategy to finetune our face reflectance model on an in-the-wild dataset, improving its generalization capability and expressive power. + +# 2. Related Work + +3D Morphable Face Model The origin 3DMM, proposed by Blanz and Vetter [4], learns a PCA model to represent 3D face shape and texture from 200 scans. This seminal work has motivated substantial follow-ups in the past two decades [19]. Paysan et al. [43] propose the Basel Face Model (BFM), the first 3DMM available to the public. However, BFM and the original 3DMM can only model neutral faces. To handle expression variation, Li et al. [38] propose the FLAME model with additive expression bases trained from 4D scans. Cao et al. [8] build a bilinear expression model from a database with multi-expression scans of the same person. Another class of works attempts to better capture human face variation by scaling up the number of scans for 3DMM training. Dai et al. [10] and Booth et al. [6] learn large-scale 3DMM from $1.2\mathrm{k}$ and $10\mathrm{k}$ subjects, respectively. However, all of these previous works approximate face as Lambertian surface and ignore the modeling of non-Lambertian reflectance. Recently, some works [37,50] build morphable models for diffuse and specular albedo using Light Stage scans [24,39,51]. However, specular shiness is ignored in their model. In addition, their requirement on Light Stage scans is hard to fulfill for the research community. Our method can represent both spatially varying specular intensity and shiness while only using low-cost publicly-available data as the training set. + +More recently, some works [52, 54, 55, 60, 61] propose to learn 3DMM from large-scale 2D datasets by jointly performing face reconstruction and face model learning. These methods can learn a 3DMM that generalizes well across the population. Tewari et al. [52] learn linear face shape and texture models from videos by designing novel loss functions to handle depth ambiguity. A follow-up work [54] learns a complete 3DMM, including shape, expression, and + +texture, from videos and neutral face images. Tran et al. [60,61] learn a non-linear 3DMM from 2D image collections, using deep neural networks to model face shape and texture. Inspired by these works, we finetune our initial face reflectance model on an in-the-wild face image dataset to improve its generalization capability and expressive power. + +Face Appearance Capture Existing methods for face appearance capture [32] fall into two categories: the image-based method and the model-based method. The key idea of the image-based method is to capture a set of images to sample the light transport function, and then novel appearances can be obtained by linearly recombining these images. To fulfill this, Debevec et al. [11] construct the Light Stage to capture the light transport function by programmatically activating One-Light-At-a-Time (OLAT). Using the captured OLAT data as the training set, Mallikarjun et al. [40] propose a learning-based method to infer the whole light transport function from a monocular input face image, and Kumar et al. [33] build a statistical model for the light transport function at a fixed frontal viewpoint. By directly modeling the light transport function, these image-based methods can represent peculiarities, sub-surface scattering, and other high-order effects caused by the complex interaction between light and face surface. However, they cannot export geometry or material assets for further usages like material editing or animation. + +Model-based methods capture the parameters of a reflectance model and utilize the rendering equation [27] to synthesize novel appearances. Previous works [24, 39, 51] use polarised illumination to directly capture the diffuse and specular albedo of human face. Using the captured data from [24, 39, 51], recent works train a neural network to map a monocular face image into its diffuse and specular albedo map [35, 67] or build a morphable model for this maps [37, 50]. Another class of works adopts an inverse rendering framework to estimate the face reflectance parameters from images. Weyrich et al. [65] develop a novel reflectance model for face and estimate its parameter from dense OLAT images captured by a Light Stage [11]. Riviere et al. [47] leverage images captured by a lightweight single-shot system for inverse rendering. In our method, we estimate the reflectance parameters for each identity in the Multi-PIE dataset from the provided OLAT images via an inverse rendering framework and use these parameters to build an initial morphable face reflectance model. + +More recently, some works attempt to capture face appearance using a low-cost setup, such as a single selfie video of the subject rotating under the sun [62], recorded video of the subject illuminated by a desktop monitor [48], and colocated captured sequence [2, 49]. However, all of these previous works are person-specific. Our goal is to build a generic morphable reflectance model using low-cost data. + +![](images/e93f214ee0fc2ba1c8d4b95ed2059f3d6070bddaae1a0990ad3878a4e08ddf98.jpg) +(a) $w_{1} = 0.4, w_{2} = 0.1$ + +![](images/47f1d5aa776f1d4689ccf585f3b5a1c07537568533452876e44501ea623198e0.jpg) +(b) $w_{1} = 0.8, w_{2} = 0.2$ +Figure 2. Our reflectance representation can naturally modulate specular intensity and shiness by adjusting the absolute or relative scales of the linear combination weights. Here we show an example using the linear combination of 2 Blinn-Phong BRDFs with specular exponents $p_1 = 8$ and $p_2 = 64$ , respectively. Note the changes in specular intensity and shiness under different linear combination weights $w_1$ and $w_2$ . + +![](images/4b2f3a4b849ee277b50b9434e47e7b960d4e2c3d63c2247777b7855ceefd5d41.jpg) +(c) $w_{1} = 0.1, w_{2} = 0.4$ + +# 3. Method + +In this Section, we first introduce the representation of our morphable face reflectance model (Section 3.1), then propose a method to learn this model from low-cost publicly-available data. Specifically, we first learn an initial model from the Multi-PIE dataset (Section 3.2) and then finetune it on the FFHQ dataset to improve its generalization capability and expressive power (Section 3.3). + +# 3.1. Morphable Face Reflectance Model + +Our goal is to design a morphable model to represent the spatially varying BRDF of the human face across the population. To this end, we build our model upon the BFM09 [43] geometry model and assign the spatially varying reflectance parameters to its vertices. We employ a linear model for the reflectance parameters of the human face: + +$$ +R = \bar {R} + \mathrm {M} _ {\mathrm {R}} \cdot \beta \tag {1} +$$ + +Here, $\bar{R} \in \mathbb{R}^{kV}$ and $\mathrm{M}_{\mathrm{R}} \in \mathbb{R}^{kV \times N_{R}}$ are the mean and bases of face reflectance parameters, respectively; $N_{R}$ is the number of bases; $k$ is the number of reflectance model parameters for each vertex; $V$ is the number of vertices of the BFM09 geometry model; $\beta \in \mathbb{R}^{N_{R}}$ is the morphable model coefficients. Note that previous works [23, 52, 54] represent face reflectance as the Lambertian BRDF. So in their scenario $k = 3$ to represent the RGB diffuse color. + +Next, we first introduce our reflectance representation. Then, we illustrate our efficient shading technique for this representation under directional or environmental illumination, which can accelerate the model learning process detailed in later Sections. + +Reflectance Representation To model non-Lambertian effects such as facial specularity, we incorporate a diffuse term and a specular term in our face reflectance representation $f_{r}$ . We instantiate them as the Lambertian BRDF and the linear combination of several Blinn-Phong BRDFs [5] with different predefined specular exponents, respectively: + +$$ +f _ {r} (\mathbf {l}, \mathbf {v}, \mathbf {n}) = \frac {c}{\pi} + \sum_ {i = 1} ^ {k _ {b p}} w _ {i} \cdot f _ {i} \cdot \frac {\langle \mathbf {h} , \mathbf {n} \rangle^ {p _ {i}}}{\langle \mathbf {l} , \mathbf {n} \rangle} \tag {2} +$$ + +Here, $\mathbf{l}$ , $\mathbf{v}$ , and $\mathbf{n}$ indicate the incident light direction, viewing direction, and normal direction, respectively; $c$ is the RGB diffuse color; $w_{i}$ are the linear combination weights; $p_{i}$ are the predefined specular exponents; $k_{bp}$ is the number of Blinn-Phong BRDFs; $f_{i} = \frac{p_{i} + 2}{4\pi \cdot (2 - 2^{-\frac{p_{i}}{2}})}$ are the energy normalization factor [1] such that the corresponding Blinn-Phong lobe integrates to 1; $\langle \cdot, \cdot \rangle$ is the clamped cosine function; $\mathbf{h} = \frac{\mathbf{v} + \mathbf{l}}{||\mathbf{v} + \mathbf{l}||_2}$ is the half vector [1]. + +In our scenario, the reflectance parameters for each face vertex are the diffuse color $c$ and $k_{bp}$ linear combination weights $w_{i}$ . Thus, each face vertex has $k = k_{bp} + 3$ reflectance parameters attached to it. Note that the specular exponents $p_{i}$ are predefined and shared by each face vertex; they are hyper-parameters in our model. Our representation can naturally modulate the specular intensity and shiness. As illustrated in Figure 2, doubling all the weights would double the specular intensity, while adjusting the aspect ratio between weights would change the specular shiness. Moreover, our reflectance representation generalizes to previous work [15-17] with spatially varying specular albedo and a global specular exponent when $k_{bp} = 1$ . + +Efficient Shading For directional illumination, by denoting the incoming irradiance from direction $\mathbf{l}$ as $E$ , we can directly obtain the shading: + +$$ +s = E \cdot \left(\frac {c}{\pi} \cdot \langle \mathbf {l}, \mathbf {n} \rangle + \sum_ {i = 1} ^ {k _ {b p}} w _ {i} \cdot f _ {i} \cdot \langle \mathbf {h}, \mathbf {n} \rangle^ {p _ {i}}\right) \tag {3} +$$ + +For environmental illumination, we denote the incoming radiance from direction $\mathbf{l}$ as $E(\mathbf{l})$ . According to the rendering equation [27], we have: + +$$ +s = \int_ {\mathbf {l} \in \Omega^ {+}} E (\mathbf {l}) \cdot f _ {r} (\mathbf {l}, \mathbf {v}, \mathbf {n}) \cdot \langle \mathbf {l}, \mathbf {n} \rangle \mathrm {d} \mathbf {l} \tag {4} +$$ + +Here, $\Omega^{+}$ is the upper hemisphere centered by $\mathbf{n}$ . Substituting Equation (2) into Equation (4), we then separate the shading $s$ into a diffuse part $s_d$ and a specular part $s_s$ : + +$$ +s = s _ {d} + s _ {s}, \text {w h e r e} \tag {5} +$$ + +$$ +s _ {d} = \int_ {\mathbf {l} \in \Omega^ {+}} \frac {c}{\pi} \cdot E (\mathbf {l}) \cdot \langle \mathbf {l}, \mathbf {n} \rangle d \mathbf {l} \tag {6} +$$ + +$$ +s _ {s} = \int_ {\mathbf {l} \in \Omega^ {+}} \sum_ {i = 1} ^ {k _ {b p}} w _ {i} \cdot f _ {i} \langle \mathbf {h}, \mathbf {n} \rangle^ {p _ {i}} \cdot E (\mathbf {l}) d \mathbf {l} \tag {7} +$$ + +We can efficiently compute $s_d$ and $s_s$ in the frequency space [45]: + +$$ +s _ {d} = \frac {c}{\pi} \cdot \sum_ {l = 0} ^ {L} \sum_ {m = - l} ^ {l} A _ {l} \cdot K _ {l m} \cdot Y _ {l m} (\mathbf {n}), \tag {8} +$$ + +$$ +s _ {s} = \sum_ {i = 1} ^ {k _ {b p}} \sum_ {l = 0} ^ {L} \sum_ {m = - l} ^ {l} w _ {i} \cdot B _ {l} \cdot K _ {l m} \cdot Y _ {l m} (\mathbf {r}). \tag {9} +$$ + +Here, $L$ is the SH order, $A_{l}$ , $B_{l}^{i}$ , and $K_{lm}$ are the SH coefficients of the Lambertian BRDF, Blinn-Phong BRDF with specular exponent $p_i$ , and the environmental illumination, respectively; $Y_{lm}(\cdot)$ are the SH basis functions; $\mathbf{r} = \frac{2(\mathbf{n} \cdot \mathbf{v}) \mathbf{n} - \mathbf{v}}{||2(\mathbf{n} \cdot \mathbf{v}) \mathbf{n} - \mathbf{v}||_2}$ is the specular reflect direction [1]. + +# 3.2. Initial Model Learning + +In this part, we propose a method to learn the mean $\bar{R}$ and bases $\mathrm{M}_{\mathrm{R}}$ of our morphable face reflectance model from the publicly-available Multi-PIE dataset [25]. Specifically, we first estimate the reflectance parameters for each identity in the dataset via inverse rendering, and then train a PCA model for them. + +Dataset Preprocessing The Multi-PIE dataset contains 337 identities captured under 15 viewpoints and 19 illuminations. We first exclude objects with facial accessories or hair occlusions, resulting in 128 identities. Then, we manually select 9 viewpoints and 12 illuminations (including 11 directional flash images and 1 room light image) with well color consistency to train our model. By removing the room light effect in the flash images, we obtain 11 OLAT images per viewpoint. We adopt a simple model-based approach to reconstruct the BFM09 geometry coefficients of each identity, camera parameters of each viewpoint, and the position of each flash simultaneously. See more implementation details in our Supplementary Material. + +Reflectance Parameter Estimation For a specific identity, we estimate all the reflectance parameters in UV space, including the diffuse color map $C \in \mathbb{R}^{3 \times H \times W}$ and the linear combination weight map $W \in \mathbb{R}^{k_{bp} \times H \times W}$ . We unwarp all the OLAT images into UV space and denote the one captured under the $i$ -th viewpoint and the $j$ -th directional flash illumination as $I_{ij}^{uv} \in \mathbb{R}^{3 \times H \times W}$ . From the reconstructed face geometry and scene information, we precompute the incident light direction $\mathbf{l}_j^{uv}$ for each flash, view direction $\mathbf{v}_i^{uv}$ for each camera, normal direction $\mathbf{n}^{uv}$ for the face geometry, and shadow mask1 $M_{ij}^{uv}$ for each OLAT image in the UV space. By predefining a reasonable incoming irradiance $E$ from the directional flash2, we obtain the reconstructed OLAT image $\hat{I}_{ij}^{uv}$ using the efficient shading technique under directional illumination presented in Equation (3): + +$$ +\hat {I} _ {i j} ^ {u v} = E \cdot \left(\frac {C}{\pi} \cdot \langle \mathbf {l} _ {j} ^ {u v}, \mathbf {n} ^ {u v} \rangle + \sum_ {k = 1} ^ {k _ {b p}} W _ {k} \cdot f _ {k} \cdot \langle \mathbf {h} _ {i j} ^ {u v}, \mathbf {n} ^ {u v} \rangle^ {p _ {k}}\right) \tag {10} +$$ + +Here, $\mathbf{h}_{ij}^{uv}$ is the half vector UV map obtained by $\mathbf{l}_j^{uv}$ and $\mathbf{v}_i^{uv}$ . We optimize the diffuse color map $C$ and linear com + +![](images/6e3ab599aa40599de8d393e0453afb2a500c2fe1c13fe440fcf8e8ec4c5a5996.jpg) +Figure 3. Model finetuning pipeline overview. Given a single input face image, we apply an encoder $E_{\theta}$ to estimate its lighting scale $z$ , lighting coefficients $\gamma$ , and reflectance coefficients $\beta$ . Combined with the precompute geometry parameters $\alpha$ , $\delta$ , $\mathbf{R}$ , $\mathbf{t}$ , and $\Pi$ , we can obtain the reconstructed face image via a differentiable renderer to compute self-supervised loss and jointly update $E_{\theta}$ and our reflectance model. + +bination weights map $W$ with loss: + +$$ +\underset {C, W} {\arg \min } \mathcal {L} _ {\text {r e c o n}} + w _ {\text {r e g}} \mathcal {L} _ {\text {r e g}} \tag {11} +$$ + +$\mathcal{L}_{\text{recon}}$ is the weighted L1 reconstruction loss: + +$$ +\mathcal {L} _ {\text {r e c o n}} = \sum_ {i, j} \left\langle \mathbf {l} _ {j} ^ {u v}, \mathbf {n} ^ {u v} \right\rangle \cdot \left\| M _ {i j} ^ {u v} \cdot \left(\hat {I} _ {i j} ^ {u v} - I _ {i j} ^ {u v}\right) \right\| _ {1}, \tag {12} +$$ + +$\mathcal{L}_{reg}$ is designed to restrict the reflectance parameters to be non-negative: + +$$ +\mathcal {L} _ {\text {r e g}} = - M _ {C} \cdot C - M _ {W} \cdot W \tag {13} +$$ + +Here, $M_C$ and $M_W$ are the masks that indicate negative values in $C$ and $W$ , respectively. During parameter estimation, we randomly horizontal flip $C$ and $W$ to introduce symmetric constraints [66] to the reflectance parameter map. + +Compared to the traditional way that uses specular intensity and exponents to parameterize the Blinn-Phong BRDF, our linear representation is much easier for parameter estimation in practice. + +Model Learning With the estimated reflectance parameter maps for each identity, we can build our initial morphable face reflectance model. Similar to AlbedoMM [50], we learn a PCA model only for the diffuse albedo. Then, we transfer it to the specular weights by using the same linear combination of the training samples to form the bases. Thus, we can use the same coefficients $\beta$ for the diffuse and specular reflectance parameters as Equation (1) while keeping the orthonormality of the diffuse bases so that the user can use our diffuse model independently. + +# 3.3. Model Finetuning + +To improve the generalization capability and expressive power of our initial morphable face reflecatance model, we finetune it on an in-the-wild face image dataset, FFHQ [29], by jointly doing face reconstruction and model updating. + +Dataset Preprocessing Before model finetuning, we use an off-the-shelf [14] method to estimate the BFM09 geometry coefficients and head pose for each image in the dataset. To further improve the geometry reconstruction accuracy, we apply an offline optimization using the same loss functions as [14]. Finally, we obtain the shape coefficients $\alpha$ , expression coefficients $\delta^3$ , and head pose $\mathbf{R}$ , $\mathbf{t}$ for each image. Similar to [14], we use the perspective camera model with a reasonable predefined focal length to represent the 3D-2D projection $\Pi$ . + +Network Architecture As illustrated in Figure 3, given a single face image $I$ as input, our face reconstruction network $E_{\theta}(\cdot)$ predicts the reflectance model coefficients $\beta$ and the SH lighting. Combined with the geometry parameters $\alpha, \delta$ , head pose $\mathbf{R}, \mathbf{t}$ , and the projection $\Pi$ , we can obtain the reconstructed image $\hat{I}$ via a differentiable rasterizer [34, 46] using the efficient shading technique presented in Equation (8) and (9). + +To update the high-frequency information in our non-Lambertian reflectance representation, we need to predict high-order SH lighting [45]. We adopt 8-order SH lighting with 273 parameters in our method as [17,36]. However, if handled naively, the network cannot predict reasonable SH lighting due to the large searching space of high-order SH coefficients, as shown in Figure 5. To constrain the searching space, we build a PCA model for the real-world environmental lighting in SH coefficient space inspired by [18]. Specifically, we utilize a real-world HDRI environment map dataset [64] and apply rotation augmentation to it. For each environment map, we compute its SH coefficients up to the 8-th order. We then divide them by the 0-th order coefficient for normalization. Note that each color channel is normalized independently. Next, we learn a PCA model for these normalized SH coefficients. We use the first $N_{L}$ bases for lighting prediction. + +During finetuning, together with the reflectance model + +coefficients $\beta$ , our network predicts $\gamma \in \mathbb{R}^{N_L}$ as the lighting PCA model coefficients and $z \in \mathbb{R}^3$ to represent the scale of 0-th order SH coefficient for each color channel. From this, we first use $\gamma$ to recover the SH coefficients from the PCA lighting model and then apply the predicted scale $z$ to them. We adopt the ResNet-50 [26] architecture as the reconstruction network $E_{\theta}(\cdot)$ and modify the last fully-connected layer to $N_R + N_L + 3$ neurons. We adopt the Softplus activation for $z$ to ensure a non-negative prediction and linear activation for $\beta$ and $\gamma$ . + +Loss Function In model finetuning, the learnable parameters are the morphable model parameters, including the mean $\bar{R}$ and bases $\mathrm{M}_{\mathrm{R}}$ , and face reconstruction network parameters $\theta$ . We optimize them with the combination of a reconstruction loss $\mathcal{L}_{rec}$ and a regularization loss $\mathcal{L}_{reg}$ : + +$$ +\underset {\bar {R}, \mathrm {M} _ {\mathrm {R}}, \theta} {\arg \min } \mathcal {L} _ {r e c} + \mathcal {L} _ {r e g} \tag {14} +$$ + +$\mathcal{L}_{rec}$ is the combination of a L1 term $\mathcal{L}_{l1}$ and a perceptual term $\mathcal{L}_{per}$ ; see more details in our Supplementary Material. In our regularization loss $\mathcal{L}_{reg}$ , we design $\mathcal{L}_{upd}$ to constrain the updating of our morphable reflectance model: + +$$ +\mathcal {L} _ {u p d} = \left| \left| \bar {R} - \bar {R} _ {0} \right| \right| _ {1} + \left| \left| \mathrm {M} _ {\mathrm {R}} - \mathrm {M} _ {\mathrm {R} _ {0}} \right| \right| _ {1} \tag {15} +$$ + +In addition, we adopt $\mathcal{L}_{\text {light }}$ to encourage monochromatic environment lighting as [12] to resolve the color ambiguity between albedo and lighting and $\mathcal{L}_{\text {coef }}$ to constrain the predicted PCA coefficients $\beta$ and $\gamma$ ; see more details in our Supplementary Material. + +# 4. Experiments + +# 4.1. Implementation Details + +Initial Model Learning For reflectance parameter estimation, we set $w_{reg} = 100$ and adopt Adam [31] optimizer to minimize the loss function, with learning rate 5e-3. We use 3 Blinn-Phong BRDFs with specular exponents 1, 8, and 64 in our method, i.e. $k_{bp} = 3$ . Thus, there are 6 reflectance parameters for each face vertex. We use the first 80 PCA bases in our initial model, i.e. $N_R = 80$ . + +Model Finetuning For the lighting PCA model, we also use the first 80 PCA bases, i.e. $N_{L} = 80$ . The weights for $\mathcal{L}_{l1}, \mathcal{L}_{per}, \mathcal{L}_{coef}, \mathcal{L}_{upd}, \mathcal{L}_{light}$ are set to 2, 0.1, 0.001, 10, 10, respectively. We finetune our model on the FFHQ dataset [30], with 70000 high-fidelity single-view face images, and crop them to $224 \times 224$ when input to our reconstruction network $E_{\theta}$ . We first pretrain $E_{\theta}$ using $\mathcal{L}_{l1}, \mathcal{L}_{per}, \mathcal{L}_{coef}$ for 20 epochs to ensure it can output reasonable reflectance and lighting coefficients prediction, with learning rate 1e-4. Then, we use the full loss function to + +![](images/21f0fece86385e8124d4e2f5c8992098083c86064007760e2322021d503fd369.jpg) +Figure 4. Qualitative comparison of face rendering results on the Multi-PIE dataset between our spatially varying reflectance representation and previous works with a global specular exponent. Here, $Exp$ stands for the specular exponent in the Blinn-Phong BRDF. A large global exponent (e.g. 32, 64) leads to over-shiness artifacts around the cheek and chin, while a small one (e.g. 8, 16) cannot represent peculiarities appear in the forehead and tip of the nose. Our representation can well model the spatially varying specular intensity and shiness on the face. + +Table 1. Quantitative comparison of face rendering results on the Multi-PIE dataset between our method and previous works with a global specular exponent. + +
LPIPS ↓SSIM ↑PSNR ↑
Exp=80.0970.93724.56
Exp=160.0980.93624.54
Exp=320.0970.93724.72
Exp=640.0960.93724.82
Ours0.0830.94225.00
+ +simultaneously update the parameters of $E_{\theta}$ and our morphable face reflectance model for 2 epochs, with learning rate 1e-5. We adopt Adam optimizer [31]. + +# 4.2. Evaluations + +Reflectance Parameter Estimation In our reflectance representation, we use the linear combination of 3 Blinn-Phong BRDFs with specular exponents 1, 8, and 64 as the specular term. In Figure 4, we compare the face rendering results between our reflectance representation and previous works [15-17] with spatially varying specular albedo and a global specular exponent. As discussed in Section 3.1, we implement the reflectance representation of previous work by setting $k_{bp} = 1$ , and use the same inverse rendering pipeline described in Section 3.2. The results illustrate that our representation obtains more realistic rendering results than the previous work. For example, the specular shiness is more significant at the tip of nose while vanishing around the cheek; previous works with global specular + +Table 2. Photometric face reconstruction comparison between our method and competitors on 1000 images randomly sampled from the CelebA-HQ dataset. + +
LPIPS ↓SSIM ↑PSNR ↑
BFM090.1140.89323.51
AlbedoMM0.1160.90123.69
Ours0.1100.89624.21
Ours w/o finetune0.12560.88623.26
+ +![](images/622d032bf4b740119a07d07f2c08fa4097549fdba856ba4dbd9435d549f9484b.jpg) +Figure 5. Qualitative ablation study of model finetuning and the use of our lighting PCA model. + +exponent cannot capture this phenomenon. With the spatially varying linear combination weights of different Blinn-Phong BRDFs, our method can naturally represent spatially varying specular intensity and shiness. In Table 1, we report the SSIM [63], PSNR, and LPIPS [69] scores in the face region to quantitatively measure the discrepancy between the re-rendered face and the ground truth. Again, our method achieves better results than the global exponent counterpart. + +Model Visualization We visualize the first 3 principal components of our morphable model in Figure 1, including the diffuse albedo, and the weights for Blinn-Phong BRDF with specular exponents 1, 8, and 64, from left to right; we multiply the weights by 3 for better visualization. It shows that our model learns to assign a large specular shiness to the tip of the nose while a small value to the cheek. See more visualizations in our Supplementary Material. + +Ablation Study As shown in Figure 5 and Table 2, the proposed finetuning strategy can improve the generalization capability and expressive power of our initial morphable face reflectance model, leading to better face reconstruction quality, especially around mouth and eyes. We then verify the effectiveness of our lighting PCA model. As illustrated in Figure 5, directly predicts 273 coefficients of the 8-order SH (w/o Light PCA) leads to unreasonable results; our lighting PCA model obtains better lighting predictions by constraining the searching space of the SH coefficients. + +# 4.3. Comparisons + +Baselines We compare our method with BFM09 [43] and AlbedoMM [50]. BFM09 is a diffuse-only model built from + +![](images/6096b35492eef67711ba16dc2ee992523e2234b63c4c812b3cb363189fe62b28.jpg) +Figure 6. Qualitative comparison of face reconstruction and shading contributions between our method and AlbedoMM. + +3D scans; AlbedoMM is a morphable model for diffuse and specular albedo built from Light Stage data. To ensure a fair comparison, we use the same CNN-based framework (see Section 3.3) to implement the competitors. We train the reconstruction network for them on the FFHQ dataset, but we do not update their morphable model parameters. As we only focus on appearance, the reconstruction network only predicts reflectance coefficients and lighting parameters and uses fixed precomputed geometry during training. For BFM09, we adopt the same geometry parameters as ours. For AlbedoMM, we use the same steps as mentioned in Section 3.3 to obtain its geometry parameters. Akin to [50], we adopt the Blinn-Phong BRDF for AlbedoMM and set the global shiness to 20. + +Face Reconstruction We evaluate our method and the competitors on the CelebA-HQ [28] dataset. As illustrated in Table 2, our method obtains better photometric face reconstruction quantitative scores than the competitors since we finetune it on an in-the-wild dataset to improve its generalization capability and expressive power while the competitors are built from a limited number of scans. As shown in Figure 6, our method can reconstruct the input image well; compared to AlbedoMM trained from Light Stage scans, our method trained from low-cost data can also disentangle the diffuse and specular shading in a plausible way. + +Face Relighting We evaluate the relighting performance of our method and the competitors on the Multi-PIE dataset [25]. Specifically, given an input image, we first obtain its geometry parameters as described in Section 3.2 and + +![](images/607cf050064d8db1e14017a3715d8173efb65ee6e09f358aac2225a23a22a0af.jpg) +Figure 7. Qualitative comparison of face relighting results under point light source between our method and the competitors. + +reconstruct its reflectance parameters using our CNN-based face reconstruction network (columns 1 and 3 in Figure 7). Then, we re-render the image under a new point light source and compare it to the corresponding OLAT image with exactly the same light position obtained from the preprocessed Multi-PIE dataset in Section 3.2 (columns 2 and 4 in Figure 7). Since we do not have the ground truth light color and intensity information of the Multi-PIE dataset, we render our method and the competitors using the same white point light source for a fair comparison; please ignore the color difference and only focus on the distribution of facial peculiarities in Figure 7. Compared to BFM09, our method successfully render plausible facial peculiarities since we adopt a non-Lambertian reflectance representation. Compared to AlbedoMM, our method achieves more realistic results especially around the tip of the nose since we can model both spatially varying specular intensity and shiness. See the video comparisons on our project page for a better demonstration. + +Reflectance Reconstruction Although our goal is not to model physically-accurate reflectance parameters, we compare our method with AlbedoMM on 23 Light Stage scans with ground truth diffuse and specular albedo captured under neutral expression from the 3D-RFE database [51]. We adopt the sum of 3 linear combination weights in our reflectance representation as the specular albedo; this quantity shares the same meaning as the specular albedo, i.e. the specular shading under a spatially-uniform environment lighting with unit radiance. As shown in Figure 8, our method can reconstruct plausible reflectance maps. How + +Table 3. Quantitative diffuse and specular albedo reconstruction results on 23 Light Stage scans from the 3D-RFE dataset (PSNR↑). + +
Diffuse AlbedoSpecular Albedo
AlbedoMM19.3126.51
Ours20.1319.14
+ +![](images/11b3699eb099456d9807e3406f69fe3efe1fa18b7a73013a0aa7c4bdc1dfa936.jpg) +Figure 8. Qualitative comparison of diffuse and specular albedo reconstruction on the 3D-RFE dataset. + +ever, as shown in Table 3, our method obtains inferior quantitative results than AlbedoMM on specular albedo reconstruction. We attribute this to two reasons: $i)$ AlbedoMM uses the 3D-RFE database to build their model [50] while our method has never seen these scans, and $ii)$ our method is built from low-cost data without lighting information, so there exists a global scale between our reflectance parameters and the ground truth although we try to mitigate it by setting a reasonable lighting color in Section 3.2. + +# 5. Conclusion + +We propose the first 3D morphable face reflectance model with spatially varying BRDF, using only low-cost publicly-available data. To represent spatially varying reflectance, we apply linear shiness weighting into parametric face modeling. We develop an inverse rendering algorithm to reconstruct the reflectance parameters from the Multi-PIE dataset, from which we build an initial model. We propose a strategy that jointly learns the face reconstruction network and updates the morphable model parameters on the FFHQ dataset to improve its generalization capability and expressive power. Our method obtains decent rendering results with plausible facial peculiarities. We discuss the limitations of our method in the Supplementary Material. + +# Acknowledgement + +This work was supported by the National Key R&D Program of China (2018YFA0704000), Beijing Natural Science Foundation (M22024), the NSFC (No.62021002), and the Key Research and Development Project of Tibet Autonomous Region (XZ202101ZY0019G). This work was also supported by THUIBCS, Tsinghua University, and BLBCI, Beijing Municipal Education Commission. We thank the anonymous reviewers for their constructive suggestions. Feng Xu is the corresponding author. + +# References + +[1] Tomas Akenine-Moller, Eric Haines, and Naty Hoffman. Real-time rendering. AK Peters/crc Press, 2019. 4 +[2] Dejan Azinović, Olivier Maury, Christophe Hery, Mathias Niessner, and Justus Thies. High-res facial appearance capture from polarized smartphone images. ArXiv, abs/2212.01160, 2022. 3 +[3] Ziqian Bai, Zhaopeng Cui, Xiaoming Liu, and Ping Tan. Riggable 3d face reconstruction via in-network optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6216-6225, 2021. 1 +[4] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 187-194, 1999. 1, 2 +[5] James F Blinn. Models of light reflection for computer synthesized pictures. In Proceedings of the 4th annual conference on Computer graphics and interactive techniques, pages 192-198, 1977. 1, 2, 3 +[6] James Booth, Anastasios Roussos, Stefanos Zafeiriou, Allan Ponniah, and David J. Dunaway. A 3d morphable model learnt from 10,000 faces. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5543-5552, 2016. 2 +[7] Chen Cao, Yanlin Weng, Stephen Lin, and Kun Zhou. 3d shape regression for real-time facial animation. ACM Transactions on Graphics (TOG), 32(4):1-10, 2013. 1 +[8] Chen Cao, Yanlin Weng, Shun Zhou, Y. Tong, and Kun Zhou. Facewarehouse: A 3d facial expression database for visual computing. IEEE Transactions on Visualization and Computer Graphics, 20:413-425, 2014. 2, 5 +[9] Bindita Chaudhuri, Noranart Vesdapunt, Linda Shapiro, and Baoyuan Wang. Personalized face modeling for improved face reconstruction and motion retargeting. In European Conference on Computer Vision, pages 142-160. Springer, 2020. 1 +[10] Hang Dai, Nick E. Pears, William Smith, and Christian Duncan. A 3d morphable model of craniofacial shape and texture variation. 2017 IEEE International Conference on Computer Vision (ICCV), pages 3104-3112, 2017. 2 +[11] Paul Debevec, Tim Hawkins, Chris Tchou, Haarm-Pieter Duiker, Westley Sarokin, and Mark Sagar. Acquiring the reflectance field of a human face. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pages 145-156, 2000. 1, 3 +[12] Yu Deng. Deep3dfacereconstruction, 2019. https://github.com/microsoft/Deep3DFaceReconstruction. 6 +[13] Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, and Xin Tong. Disentangled and controllable face image generation via 3d imitative-contrastive learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5154–5163, 2020. 1 +[14] Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set. In Proceedings of the IEEE/CVF Conference on Computer + +Vision and Pattern Recognition Workshops, pages 0-0, 2019. 1, 5 +[15] Abdallah Dib, Junghyun Ahn, Cedric Thebault, Philippe-Henri Gosselin, and Louis Chevallier. S2f2: Self-supervised high fidelity face reconstruction from monocular image. arXiv preprint arXiv:2203.07732, 2022. 1, 4, 6 +[16] Abdallah Dib, Gaurav Bharaj, Junghyun Ahn, Cédric Thébault, Philippe Gosselin, Marco Romeo, and Louis Chevallier. Practical face reconstruction via differentiable ray tracing. In Computer Graphics Forum, volume 40, pages 153-164. Wiley Online Library, 2021. 1, 4, 6 +[17] Abdallah Dib, Cédric Thébault, Junghyun Ahn, Philippe-Henri Gosselin, Christian Theobalt, and Louis Chevallier. Towards high fidelity monocular face reconstruction with rich reflectance using self-supervised learning and ray tracing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12819-12829, 2021. 1, 4, 5, 6 +[18] Bernhard Egger, Sandro Schonborn, Andreas Schneider, Adam Kortylewski, Andreas Morel-Forster, Clemens Blumer, and Thomas Vetter. Occlusion-aware 3d morphable models and an illumination prior for face image analysis. International Journal of Computer Vision, 126(12):1269-1287, 2018. 5 +[19] Bernhard Egger, William AP Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani, et al. 3d morphable face models—past, present, and future. ACM Transactions on Graphics (TOG), 39(5):1-38, 2020. 1, 2 +[20] Yao Feng, Haiwen Feng, Michael J Black, and Timo Bolkart. Learning an animatable detailed 3d face model from inthe-wild images. ACM Transactions on Graphics (ToG), 40(4):1-13, 2021. 1 +[21] Pablo Garrido, Michael Zollhöfer, Dan Casas, Levi Valgaerts, Kiran Varanasi, Patrick Pérez, and Christian Theobalt. Reconstruction of personalized 3d face rigs from monocular video. ACM Transactions on Graphics (TOG), 35(3):1-15, 2016. 1 +[22] Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, and William T Freeman. Unsupervised training for 3d morphable model regression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8377-8386, 2018. 1 +[23] Thomas Gereg, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel Luthi, Sandro Schonborn, and Thomas Vetter. Morphable face models—an open framework. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pages 75–82. IEEE, 2018. 3 +[24] Abhijeet Ghosh, Graham Fyffe, Borom Tunwattanapong, Jay Busch, Xueming Yu, and Paul Debevec. Multiview face capture using polarized spherical gradient illumination. In Proceedings of the 2011 SIGGRAPH Asia Conference, pages 1-10, 2011. 1, 2, 3 +[25] Ralph Gross, Iain Matthews, Jeffrey Cohn, Takeo Kanade, and Simon Baker. Multi-pie. Image and vision computing, 28(5):807-813, 2010. 2, 4, 7 + +[26] Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016. 6 +[27] James T Kajiya. The rendering equation. In Proceedings of the 13th annual conference on Computer graphics and interactive techniques, pages 143-150, 1986. 3, 4 +[28] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. ArXiv, abs/1710.10196, 2018. 7 +[29] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4396-4405, 2018. 2, 5 +[30] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110-8119, 2020. 6 +[31] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2015. 6 +[32] Oliver Klehm, Fabrice Rousselle, Marios Papas, Derek Bradley, Christophe Hery, B. Bickel, Wojciech Jarosz, and Thabo Beeler. Recent advances in facial appearance capture. Computer Graphics Forum, 34, 2015. 3 +[33] Ritwik K. Kumar, Michael J. Jones, and Tim K. Marks. Morphable reflectance fields for enhancing face recognition. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 2606-2613, 2010. 3 +[34] Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, and Timo Aila. Modular primitives for high-performance differentiable rendering. ACM Transactions on Graphics (TOG), 39:1-14, 2020. 5 +[35] Alexandros Lattas, Stylianos Moschoglou, Baris Gecer, Stylianos Ploumpis, Vasileios Triantafyllou, Abhijeet Ghosh, and Stefanos Zafeiriou. Avatarme: Realistically renderable 3d facial reconstruction "in-the-wild". 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 757-766, 2020. 3 +[36] Chen Li, Kun Zhou, and Stephen Lin. Intrinsic face image decomposition with human face priors. In European Conference on Computer Vision, 2014. 5 +[37] Ruilong Li, Kalle Bladin, Yajie Zhao, Chinmay Chinara, Owen Ingraham, Pengda Xiang, Xinglei Ren, Pratusha Bhuvana Prasad, Bipin Kishore, Jun Xing, and Hao Li. Learning formation of physically-based face attributes. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3407-3416, 2020. 1, 2, 3 +[38] Tianye Li, Timo Bolkart, Michael J Black, Hao Li, and Javier Romero. Learning a model of facial shape and expression from 4d scans. ACM Trans. Graph., 36(6):194-1, 2017. 2 +[39] Wan-Chun Ma, Tim Hawkins, Pieter Peers, Charles-Felix Chabert, Malte Weiss, Paul E Debevec, et al. Rapid acquisition of specular and diffuse normal maps from polarized spherical gradient illumination. Rendering Techniques, 2007(9):10, 2007. 1, 2, 3 +[40] R. MallikarjunB., Ayush Tewari, Tae-Hyun Oh, Tim Weyrich, B. Bickel, Hans-Peter Seidel, Hanspeter Pfister, + +Wojciech Matusik, Mohamed A. Elgharib, and Christian Theobalt. Monocular reconstruction of neural face reflectance fields. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4789-4798, 2021. 3 +[41] Wojciech Matusik. A data-driven reflectance model. PhD thesis, Massachusetts Institute of Technology, 2003. 2 +[42] Rohit Pandey, Sergio Orts Escolano, Chloe Legendre, Christian Haene, Sofien Bouaziz, Christoph Rhemann, Paul Debevec, and Sean Fanello. Total relighting: learning to relight portraits for background replacement. ACM Transactions on Graphics (TOG), 40(4):1-21, 2021. 2 +[43] Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pages 296-301, 2009. 2, 3, 7 +[44] Ravi Ramamoorthi and Pat Hanrahan. An efficient representation for irradiance environment maps. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 497-500, 2001. 2 +[45] Ravi Ramamoorthi and Pat Hanrahan. A signal-processing framework for inverse rendering. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 117-128, 2001. 2, 4, 5 +[46] Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari. Accelerating 3d deep learning with pytorch3d. SIGGRAPH Asia 2020 Courses, 2020. 5 +[47] Jérémy Riviere, Paulo F. U. Gotardo, Derek Bradley, Abhijeet Ghosh, and Thabo Beeler. Single-shot high-quality facial geometry and skin appearance capture. ACM Transactions on Graphics (TOG), 39:81:1-81:12, 2020. 3 +[48] Soumyadip Sengupta, Brian Curless, Ira Kemelmacher-Shlizerman, and Steven M. Seitz. A light stage on every desk. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 2400-2409, 2021. 3 +[49] Artem Sevastopolsky, SA Ignatiev, Gonzalo Ferrer, Evgeny Burnaev, and Victor S. Lempitsky. Relightable 3d head portraits from a smartphone video. ArXiv, abs/2012.09963, 2020. 3 +[50] William AP Smith, Alassane Seck, Hannah Dee, Bernard Tiddeman, Joshua B Tenenbaum, and Bernhard Egger. A morphable face albedo model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5011-5020, 2020. 1, 2, 3, 5, 7, 8 +[51] Giota Stratou, Abhijeet Ghosh, Paul E. Debevec, and Louis-Philippe Morency. Effect of illumination on automatic expression recognition: A novel 3d reconfigurable facial database. Face and Gesture 2011, pages 611–618, 2011. 2, 3, 8 +[52] Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Fml: Face model learning from videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10812-10822, 2019. 1, 2, 3 + +[53] Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Stylerig: Rigging stylegan for 3d control over portrait images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6142-6151, 2020. 1 +[54] Ayush Tewari, Hans-Peter Seidel, Mohamed Elgharib, Christian Theobalt, et al. Learning complete 3d morphable face models from images and videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3361-3371, 2021. 1, 2, 3 +[55] Ayush Tewari, Michael Zollhöfer, Pablo Garrido, Florian Bernard, Hyeongwoo Kim, Patrick Pérez, and Christian Theobalt. Self-supervised multi-level face model learning for monocular reconstruction at over 250 hz. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2549-2559, 2018. 1, 2 +[56] Ayush Tewari, Michael Zollhofer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, Patrick Perez, and Christian Theobalt. Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 1274-1283, 2017. 1 +[57] Justus Thies, Mohamed Elgharib, Ayush Tewari, Christian Theobalt, and Matthias Nießner. Neural voice puppetry: Audio-driven facial reenactment. In European conference on computer vision, pages 716-731. Springer, 2020. 1 +[58] Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Nießner. Face2face: Real-time face capture and reenactment of rgb videos. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2387-2395, 2016. 1 +[59] Kenneth E Torrance and Ephraim M Sparrow. Theory for off-specular reflection from roughened surfaces. Josa, 57(9):1105-1114, 1967. 1 +[60] Luan Tran, Feng Liu, and Xiaoming Liu. Towards high-fidelity nonlinear 3d face morphable model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1126-1135, 2019. 2, 3 +[61] Luan Tran and Xiaoming Liu. Nonlinear 3d face morphable model. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7346-7355, 2018. 2, 3 +[62] Yifan Wang, Aleksander Holynski, Xiuming Zhang, and Xuaner Cecilia Zhang. Sunstage: Portrait reconstruction and relighting using the sun as a light stage. ArXiv, abs/2204.03648, 2022. 3 +[63] Zhou Wang, Alan Conrad Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13:600-612, 2004. 7 +[64] Zhibo Wang, Xin Yu, Ming Lu, Quan Wang, Chen Qian, and Feng Xu. Single image portrait relighting via explicit multiple reflectance channel modeling. ACM Transactions on Graphics (TOG), 39:1-13, 2020. 5 +[65] Tim Weyrich, Wojciech Matusik, Hanspeter Pfister, Bernd Bickel, Craig Donner, Chien Tu, Janet McAndless, Jinho Lee, Addy Ngan, Henrik Wann Jensen, et al. Analysis of + +human faces using a measurement-based skin reflectance model. ACM Transactions on Graphics (ToG), 25(3):1013-1024, 2006. 1, 3 +[66] Shangzhe Wu, Christian Rupprecht, and Andrea Vedaldi. Unsupervised learning of probably symmetric deformable 3d objects from images in the wild. In CVPR, 2020. 5 +[67] Shugo Yamaguchi, Shunsuke Saito, Koki Nagano, Yajie Zhao, Weikai Chen, Kyle Olszewski, Shigeo Morishima, and Hao Li. High-fidelity facial reflectance and geometry inference from an unconstrained image. ACM Transactions on Graphics (TOG), 37:1-14, 2018. 3 +[68] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. Facescape: a large-scale high quality 3d face dataset and detailed riggable 3d face prediction. In Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, pages 601-610, 2020. 1 +[69] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 586-595, 2018. 7 +[70] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. Im avatar: Implicit morphable head avatars from videos. 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We propose the first 3D morphable face reflectance model with spatially varying BRDF and a technique to train the model with low-cost publicly-available data. We represent face reflectance as a Lambertian BRDF combined with the linear combination of Blinn-Phong BRDFs with different predefined specular exponents. The reflectance parameters for each face vertex are the diffuse albedo and a set of weights. We show the first 3 principal components of diffuse albedo and spatially varying weights here in nonlinear sRGB space." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "bbox": [ + 143, + 383, + 192, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 383, + 192, + 396 + ], + "spans": [ + { + "bbox": [ + 143, + 383, + 192, + 396 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 45, + 407, + 289, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 407, + 289, + 648 + ], + "spans": [ + { + "bbox": [ + 45, + 407, + 289, + 648 + ], + "type": "text", + "content": "Modeling non-Lambertian effects such as facial specularity leads to a more realistic 3D Morphable Face Model. Existing works build parametric models for diffuse and specular albedo using Light Stage data. However, only diffuse and specular albedo cannot determine the full BRDF. In addition, the requirement of Light Stage data is hard to fulfill for the research communities. This paper proposes the first 3D morphable face reflectance model with spatially varying BRDF using only low-cost publicly-available data. We apply linear shiness weighting into parametric modeling to represent spatially varying specular intensity and shiness. Then an inverse rendering algorithm is developed to reconstruct the reflectance parameters from nonLight Stage data, which are used to train an initial morphable reflectance model. To enhance the model's generalization capability and expressive power, we further propose an update-by-reconstruction strategy to finetune it on an in-the-wild dataset. Experimental results show that our method obtains decent rendering results with plausible facial peculiarities. Our code is released here." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 47, + 669, + 127, + 681 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 669, + 127, + 681 + ], + "spans": [ + { + "bbox": [ + 47, + 669, + 127, + 681 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 46, + 689, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 287, + 715 + ], + "type": "text", + "content": "3D Morphable Face Models (3DMM) [4, 19] have attracted much attention in the past two decades, as it pro" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 384, + 545, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 384, + 545, + 515 + ], + "spans": [ + { + "bbox": [ + 304, + 384, + 545, + 515 + ], + "type": "text", + "content": "vides a powerful and compact statistical prior of 3D face geometry and appearance with dense point-to-point correspondence to various downstream applications like face reconstruction [14,22,52,55,56], rendering [13,53,57,58,70], and animation [3,7,9,20,21,68]. Existing works [52,54,55] have demonstrated promising results for improving the generalization capability and expressive power of 3DMM under the assumption that faces are Lambertian surfaces. However, it is still challenging to model non-Lambertian effects such as facial specularity in 3DMM, which can lead to a more realistic face model." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 521, + 546, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 546, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 546, + 715 + ], + "type": "text", + "content": "A few recent works [37, 50] involve non-Lambertian facial reflectance in the morphable face model. Using a Light Stage [11, 24, 39], they capture diffuse and specular albedo maps of tens of participants. Then, they model the diffuse and specular albedo by training a PCA model [50] or a deep generative network [37] on the acquired data. However, only the diffuse and specular albedo cannot determine the complete Bidirectional Reflectance Distribution Function (BRDF). Thus, other works [15-17] set the remaining reflectance parameters (e.g. specular exponent for the Blinn-Phong BRDF [5], roughness for the Torrance-Sparrow BRDF [59]) of all face vertices to a reasonable value to characterize specular shiness and obtain the complete BRDF. As shown in Figure 7, these spatially uniform parameters lead to unpleasant rendering results since face reflectance is inherently spatially varying [65]. Besides, the" + } + ] + } + ], + "index": 24 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 758 + ], + "type": "text", + "content": "8598" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 108 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 108 + ], + "type": "text", + "content": "requirement of Light Stage data is hard to fulfill since building a Light Stage is quite difficult, and no publicly available Light Stage dataset is sufficient to construct a 3DMM." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 112, + 289, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 112, + 289, + 422 + ], + "spans": [ + { + "bbox": [ + 47, + 112, + 289, + 422 + ], + "type": "text", + "content": "To overcome these limitations, we propose and train the first morphable face reflectance model with spatially varying BRDF from low-cost publicly-available data. Inspired by previous works [41, 42], we represent face reflectance as a Lambertian BRDF combined with the linear combination of several Blinn-Phong BRDFs corresponding to different predefined specular exponents. Thus, the reflectance parameters of each face vertex include an RGB color for the Lambertian BRDF and a set of weights for the Blinn-Phong BRDFs. As illustrated in Figure 2, our representation can naturally modulate specular intensity and shiness by adjusting the absolute and relative scales of the linear combination weights, respectively. Compared to previous works [37, 50] not modeling specular shiness, we define a complete BRDF by this representation in 3DMM. Compared to the traditional Blinn-Phong BRDF that models specular intensity and shiness in a nonlinear formulation [5], our linear representation (Equation (2)) is much easier to reconstruct the reflectance parameters from recorded images. With this linear reflectance representation, we develop an inverse rendering approach to estimate the spatially varying reflectance parameters for the 128 selected identities in Multi-PIE [25], a public dataset with face images captured under controlled camera views and light directions. Then, we learn a PCA model for the estimated reflectance parameters as our initial morphable face reflectance model." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 426, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 426, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 426, + 289, + 715 + ], + "type": "text", + "content": "Considering that the Multi-PIE dataset only contains 128 identities which is far from sufficient to capture the variability of human faces, we propose to finetune the initial model on a large-scale in-the-wild dataset, FFHQ [29], to improve its generalization capability and expressive power. As the inputs are in-the-wild images with unknown lighting information, it is not easy to reconstruct accurate reflectance from them. Our key observation is that, on the one hand, we already have an initial parametric reflectance model that can better formulate the reflectance reconstruction from in-the-wild images. On the other hand, the reconstructed reflectance from in-the-wild data could provide feedback to enhance the face prior knowledge in our morphable reflectance model. Based on this observation, we jointly reconstruct the face reflectance coefficients and update the parameters of our morphable face reflectance model (the mean and bases). Another challenge here is to predict high-order spherical harmonics (SH) lighting [44] for in-the-wild images, which is crucial for updating the high-frequency information of our non-Lambertian reflectance model [45]. To solve this problem, we build another PCA model for real-world environment lighting in SH coefficients space, which largely reduces the searching space of the high-order SH coefficients. During face reconstruction, we first predict" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 144 + ], + "type": "text", + "content": "the parameters of the PCA lighting model and then retrieve the high-order SH coefficients from it. Finally, the in-the-wild images are well reconstructed with our parametric reflectance model, and the model itself is also updated gradually in this process to achieve high generalization capability and expressive power." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 317, + 144, + 476, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 144, + 476, + 156 + ], + "spans": [ + { + "bbox": [ + 317, + 144, + 476, + 156 + ], + "type": "text", + "content": "In summary, our contributions include:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 317, + 156, + 545, + 287 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 317, + 156, + 545, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 156, + 545, + 191 + ], + "spans": [ + { + "bbox": [ + 317, + 156, + 545, + 191 + ], + "type": "text", + "content": "- We propose the first 3D morphable face reflectance model with spatially varying BRDF and a technique to train the model with low-cost publicly-available data." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 317, + 192, + 545, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 192, + 545, + 239 + ], + "spans": [ + { + "bbox": [ + 317, + 192, + 545, + 239 + ], + "type": "text", + "content": "- We apply linear shiness weighting into parametric face modeling to represent spatially varying specular shiness and intensity and ease the process of reconstructing reflectance from images." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 317, + 240, + 545, + 287 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 240, + 545, + 287 + ], + "spans": [ + { + "bbox": [ + 317, + 240, + 545, + 287 + ], + "type": "text", + "content": "- We propose an update-by-reconstruction strategy to finetune our face reflectance model on an in-the-wild dataset, improving its generalization capability and expressive power." + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 299, + 392, + 312 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 299, + 392, + 312 + ], + "spans": [ + { + "bbox": [ + 306, + 299, + 392, + 312 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 318, + 545, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 318, + 545, + 617 + ], + "spans": [ + { + "bbox": [ + 304, + 318, + 545, + 617 + ], + "type": "text", + "content": "3D Morphable Face Model The origin 3DMM, proposed by Blanz and Vetter [4], learns a PCA model to represent 3D face shape and texture from 200 scans. This seminal work has motivated substantial follow-ups in the past two decades [19]. Paysan et al. [43] propose the Basel Face Model (BFM), the first 3DMM available to the public. However, BFM and the original 3DMM can only model neutral faces. To handle expression variation, Li et al. [38] propose the FLAME model with additive expression bases trained from 4D scans. Cao et al. [8] build a bilinear expression model from a database with multi-expression scans of the same person. Another class of works attempts to better capture human face variation by scaling up the number of scans for 3DMM training. Dai et al. [10] and Booth et al. [6] learn large-scale 3DMM from " + }, + { + "bbox": [ + 304, + 318, + 545, + 617 + ], + "type": "inline_equation", + "content": "1.2\\mathrm{k}" + }, + { + "bbox": [ + 304, + 318, + 545, + 617 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 318, + 545, + 617 + ], + "type": "inline_equation", + "content": "10\\mathrm{k}" + }, + { + "bbox": [ + 304, + 318, + 545, + 617 + ], + "type": "text", + "content": " subjects, respectively. However, all of these previous works approximate face as Lambertian surface and ignore the modeling of non-Lambertian reflectance. Recently, some works [37,50] build morphable models for diffuse and specular albedo using Light Stage scans [24,39,51]. However, specular shiness is ignored in their model. In addition, their requirement on Light Stage scans is hard to fulfill for the research community. Our method can represent both spatially varying specular intensity and shiness while only using low-cost publicly-available data as the training set." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 618, + 545, + 713 + ], + "type": "text", + "content": "More recently, some works [52, 54, 55, 60, 61] propose to learn 3DMM from large-scale 2D datasets by jointly performing face reconstruction and face model learning. These methods can learn a 3DMM that generalizes well across the population. Tewari et al. [52] learn linear face shape and texture models from videos by designing novel loss functions to handle depth ambiguity. A follow-up work [54] learns a complete 3DMM, including shape, expression, and" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "8599" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 145 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 145 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 145 + ], + "type": "text", + "content": "texture, from videos and neutral face images. Tran et al. [60,61] learn a non-linear 3DMM from 2D image collections, using deep neural networks to model face shape and texture. Inspired by these works, we finetune our initial face reflectance model on an in-the-wild face image dataset to improve its generalization capability and expressive power." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 162, + 289, + 400 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 162, + 289, + 400 + ], + "spans": [ + { + "bbox": [ + 46, + 162, + 289, + 400 + ], + "type": "text", + "content": "Face Appearance Capture Existing methods for face appearance capture [32] fall into two categories: the image-based method and the model-based method. The key idea of the image-based method is to capture a set of images to sample the light transport function, and then novel appearances can be obtained by linearly recombining these images. To fulfill this, Debevec et al. [11] construct the Light Stage to capture the light transport function by programmatically activating One-Light-At-a-Time (OLAT). Using the captured OLAT data as the training set, Mallikarjun et al. [40] propose a learning-based method to infer the whole light transport function from a monocular input face image, and Kumar et al. [33] build a statistical model for the light transport function at a fixed frontal viewpoint. By directly modeling the light transport function, these image-based methods can represent peculiarities, sub-surface scattering, and other high-order effects caused by the complex interaction between light and face surface. However, they cannot export geometry or material assets for further usages like material editing or animation." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 402, + 289, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 402, + 289, + 628 + ], + "spans": [ + { + "bbox": [ + 46, + 402, + 289, + 628 + ], + "type": "text", + "content": "Model-based methods capture the parameters of a reflectance model and utilize the rendering equation [27] to synthesize novel appearances. Previous works [24, 39, 51] use polarised illumination to directly capture the diffuse and specular albedo of human face. Using the captured data from [24, 39, 51], recent works train a neural network to map a monocular face image into its diffuse and specular albedo map [35, 67] or build a morphable model for this maps [37, 50]. Another class of works adopts an inverse rendering framework to estimate the face reflectance parameters from images. Weyrich et al. [65] develop a novel reflectance model for face and estimate its parameter from dense OLAT images captured by a Light Stage [11]. Riviere et al. [47] leverage images captured by a lightweight single-shot system for inverse rendering. In our method, we estimate the reflectance parameters for each identity in the Multi-PIE dataset from the provided OLAT images via an inverse rendering framework and use these parameters to build an initial morphable face reflectance model." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 630, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 630, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 630, + 289, + 715 + ], + "type": "text", + "content": "More recently, some works attempt to capture face appearance using a low-cost setup, such as a single selfie video of the subject rotating under the sun [62], recorded video of the subject illuminated by a desktop monitor [48], and colocated captured sequence [2, 49]. However, all of these previous works are person-specific. Our goal is to build a generic morphable reflectance model using low-cost data." + } + ] + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 315, + 70, + 369, + 106 + ], + "blocks": [ + { + "bbox": [ + 315, + 70, + 369, + 106 + ], + "lines": [ + { + "bbox": [ + 315, + 70, + 369, + 106 + ], + "spans": [ + { + "bbox": [ + 315, + 70, + 369, + 106 + ], + "type": "image", + "image_path": "e93f214ee0fc2ba1c8d4b95ed2059f3d6070bddaae1a0990ad3878a4e08ddf98.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 313, + 107, + 383, + 116 + ], + "lines": [ + { + "bbox": [ + 313, + 107, + 383, + 116 + ], + "spans": [ + { + "bbox": [ + 313, + 107, + 383, + 116 + ], + "type": "text", + "content": "(a) " + }, + { + "bbox": [ + 313, + 107, + 383, + 116 + ], + "type": "inline_equation", + "content": "w_{1} = 0.4, w_{2} = 0.1" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 406, + 70, + 443, + 106 + ], + "blocks": [ + { + "bbox": [ + 406, + 70, + 443, + 106 + ], + "lines": [ + { + "bbox": [ + 406, + 70, + 443, + 106 + ], + "spans": [ + { + "bbox": [ + 406, + 70, + 443, + 106 + ], + "type": "image", + "image_path": "47f1d5aa776f1d4689ccf585f3b5a1c07537568533452876e44501ea623198e0.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 391, + 107, + 460, + 115 + ], + "lines": [ + { + "bbox": [ + 391, + 107, + 460, + 115 + ], + "spans": [ + { + "bbox": [ + 391, + 107, + 460, + 115 + ], + "type": "text", + "content": "(b) " + }, + { + "bbox": [ + 391, + 107, + 460, + 115 + ], + "type": "inline_equation", + "content": "w_{1} = 0.8, w_{2} = 0.2" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 304, + 120, + 547, + 197 + ], + "lines": [ + { + "bbox": [ + 304, + 120, + 547, + 197 + ], + "spans": [ + { + "bbox": [ + 304, + 120, + 547, + 197 + ], + "type": "text", + "content": "Figure 2. Our reflectance representation can naturally modulate specular intensity and shiness by adjusting the absolute or relative scales of the linear combination weights. Here we show an example using the linear combination of 2 Blinn-Phong BRDFs with specular exponents " + }, + { + "bbox": [ + 304, + 120, + 547, + 197 + ], + "type": "inline_equation", + "content": "p_1 = 8" + }, + { + "bbox": [ + 304, + 120, + 547, + 197 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 120, + 547, + 197 + ], + "type": "inline_equation", + "content": "p_2 = 64" + }, + { + "bbox": [ + 304, + 120, + 547, + 197 + ], + "type": "text", + "content": ", respectively. Note the changes in specular intensity and shiness under different linear combination weights " + }, + { + "bbox": [ + 304, + 120, + 547, + 197 + ], + "type": "inline_equation", + "content": "w_1" + }, + { + "bbox": [ + 304, + 120, + 547, + 197 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 120, + 547, + 197 + ], + "type": "inline_equation", + "content": "w_2" + }, + { + "bbox": [ + 304, + 120, + 547, + 197 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 484, + 70, + 521, + 106 + ], + "blocks": [ + { + "bbox": [ + 484, + 70, + 521, + 106 + ], + "lines": [ + { + "bbox": [ + 484, + 70, + 521, + 106 + ], + "spans": [ + { + "bbox": [ + 484, + 70, + 521, + 106 + ], + "type": "image", + "image_path": "4b2f3a4b849ee277b50b9434e47e7b960d4e2c3d63c2247777b7855ceefd5d41.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 468, + 106, + 537, + 115 + ], + "lines": [ + { + "bbox": [ + 468, + 106, + 537, + 115 + ], + "spans": [ + { + "bbox": [ + 468, + 106, + 537, + 115 + ], + "type": "text", + "content": "(c) " + }, + { + "bbox": [ + 468, + 106, + 537, + 115 + ], + "type": "inline_equation", + "content": "w_{1} = 0.1, w_{2} = 0.4" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 214, + 361, + 227 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 214, + 361, + 227 + ], + "spans": [ + { + "bbox": [ + 306, + 214, + 361, + 227 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 234, + 545, + 319 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 234, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 304, + 234, + 545, + 319 + ], + "type": "text", + "content": "In this Section, we first introduce the representation of our morphable face reflectance model (Section 3.1), then propose a method to learn this model from low-cost publicly-available data. Specifically, we first learn an initial model from the Multi-PIE dataset (Section 3.2) and then finetune it on the FFHQ dataset to improve its generalization capability and expressive power (Section 3.3)." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 324, + 495, + 337 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 324, + 495, + 337 + ], + "spans": [ + { + "bbox": [ + 306, + 324, + 495, + 337 + ], + "type": "text", + "content": "3.1. Morphable Face Reflectance Model" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 342, + 545, + 415 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 342, + 545, + 415 + ], + "spans": [ + { + "bbox": [ + 304, + 342, + 545, + 415 + ], + "type": "text", + "content": "Our goal is to design a morphable model to represent the spatially varying BRDF of the human face across the population. To this end, we build our model upon the BFM09 [43] geometry model and assign the spatially varying reflectance parameters to its vertices. We employ a linear model for the reflectance parameters of the human face:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 389, + 421, + 545, + 434 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 389, + 421, + 545, + 434 + ], + "spans": [ + { + "bbox": [ + 389, + 421, + 545, + 434 + ], + "type": "interline_equation", + "content": "R = \\bar {R} + \\mathrm {M} _ {\\mathrm {R}} \\cdot \\beta \\tag {1}", + "image_path": "8f2135b83d08c1c1ce91e063f86f43b367f6c94164187d6ec3362fd5dd630db2.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "spans": [ + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "inline_equation", + "content": "\\bar{R} \\in \\mathbb{R}^{kV}" + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "inline_equation", + "content": "\\mathrm{M}_{\\mathrm{R}} \\in \\mathbb{R}^{kV \\times N_{R}}" + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "text", + "content": " are the mean and bases of face reflectance parameters, respectively; " + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "inline_equation", + "content": "N_{R}" + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "text", + "content": " is the number of bases; " + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "text", + "content": " is the number of reflectance model parameters for each vertex; " + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "text", + "content": " is the number of vertices of the BFM09 geometry model; " + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "inline_equation", + "content": "\\beta \\in \\mathbb{R}^{N_{R}}" + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "text", + "content": " is the morphable model coefficients. Note that previous works [23, 52, 54] represent face reflectance as the Lambertian BRDF. So in their scenario " + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "inline_equation", + "content": "k = 3" + }, + { + "bbox": [ + 304, + 438, + 547, + 535 + ], + "type": "text", + "content": " to represent the RGB diffuse color." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 536, + 547, + 595 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 536, + 547, + 595 + ], + "spans": [ + { + "bbox": [ + 304, + 536, + 547, + 595 + ], + "type": "text", + "content": "Next, we first introduce our reflectance representation. Then, we illustrate our efficient shading technique for this representation under directional or environmental illumination, which can accelerate the model learning process detailed in later Sections." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 605, + 547, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 547, + 677 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 547, + 677 + ], + "type": "text", + "content": "Reflectance Representation To model non-Lambertian effects such as facial specularity, we incorporate a diffuse term and a specular term in our face reflectance representation " + }, + { + "bbox": [ + 304, + 605, + 547, + 677 + ], + "type": "inline_equation", + "content": "f_{r}" + }, + { + "bbox": [ + 304, + 605, + 547, + 677 + ], + "type": "text", + "content": ". We instantiate them as the Lambertian BRDF and the linear combination of several Blinn-Phong BRDFs [5] with different predefined specular exponents, respectively:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 345, + 682, + 545, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 345, + 682, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 345, + 682, + 545, + 715 + ], + "type": "interline_equation", + "content": "f _ {r} (\\mathbf {l}, \\mathbf {v}, \\mathbf {n}) = \\frac {c}{\\pi} + \\sum_ {i = 1} ^ {k _ {b p}} w _ {i} \\cdot f _ {i} \\cdot \\frac {\\langle \\mathbf {h} , \\mathbf {n} \\rangle^ {p _ {i}}}{\\langle \\mathbf {l} , \\mathbf {n} \\rangle} \\tag {2}", + "image_path": "b356dd9ea2a0e0fde5d634e2bd618554792067fc99a57161fa8fe0b67d2d6d26.jpg" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8600" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "inline_equation", + "content": "\\mathbf{l}" + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "inline_equation", + "content": "\\mathbf{v}" + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "inline_equation", + "content": "\\mathbf{n}" + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "text", + "content": " indicate the incident light direction, viewing direction, and normal direction, respectively; " + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "text", + "content": " is the RGB diffuse color; " + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "inline_equation", + "content": "w_{i}" + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "text", + "content": " are the linear combination weights; " + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "inline_equation", + "content": "p_{i}" + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "text", + "content": " are the predefined specular exponents; " + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "inline_equation", + "content": "k_{bp}" + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "text", + "content": " is the number of Blinn-Phong BRDFs; " + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "inline_equation", + "content": "f_{i} = \\frac{p_{i} + 2}{4\\pi \\cdot (2 - 2^{-\\frac{p_{i}}{2}})}" + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "text", + "content": " are the energy normalization factor [1] such that the corresponding Blinn-Phong lobe integrates to 1; " + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "inline_equation", + "content": "\\langle \\cdot, \\cdot \\rangle" + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "text", + "content": " is the clamped cosine function; " + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "inline_equation", + "content": "\\mathbf{h} = \\frac{\\mathbf{v} + \\mathbf{l}}{||\\mathbf{v} + \\mathbf{l}||_2}" + }, + { + "bbox": [ + 46, + 72, + 287, + 174 + ], + "type": "text", + "content": " is the half vector [1]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "spans": [ + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "text", + "content": "In our scenario, the reflectance parameters for each face vertex are the diffuse color " + }, + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "inline_equation", + "content": "k_{bp}" + }, + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "text", + "content": " linear combination weights " + }, + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "inline_equation", + "content": "w_{i}" + }, + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "text", + "content": ". Thus, each face vertex has " + }, + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "inline_equation", + "content": "k = k_{bp} + 3" + }, + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "text", + "content": " reflectance parameters attached to it. Note that the specular exponents " + }, + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "inline_equation", + "content": "p_{i}" + }, + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "text", + "content": " are predefined and shared by each face vertex; they are hyper-parameters in our model. Our representation can naturally modulate the specular intensity and shiness. As illustrated in Figure 2, doubling all the weights would double the specular intensity, while adjusting the aspect ratio between weights would change the specular shiness. Moreover, our reflectance representation generalizes to previous work [15-17] with spatially varying specular albedo and a global specular exponent when " + }, + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "inline_equation", + "content": "k_{bp} = 1" + }, + { + "bbox": [ + 46, + 174, + 288, + 330 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 338, + 287, + 374 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 338, + 287, + 374 + ], + "spans": [ + { + "bbox": [ + 47, + 338, + 287, + 374 + ], + "type": "text", + "content": "Efficient Shading For directional illumination, by denoting the incoming irradiance from direction " + }, + { + "bbox": [ + 47, + 338, + 287, + 374 + ], + "type": "inline_equation", + "content": "\\mathbf{l}" + }, + { + "bbox": [ + 47, + 338, + 287, + 374 + ], + "type": "text", + "content": " as " + }, + { + "bbox": [ + 47, + 338, + 287, + 374 + ], + "type": "inline_equation", + "content": "E" + }, + { + "bbox": [ + 47, + 338, + 287, + 374 + ], + "type": "text", + "content": ", we can directly obtain the shading:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 80, + 379, + 287, + 412 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 80, + 379, + 287, + 412 + ], + "spans": [ + { + "bbox": [ + 80, + 379, + 287, + 412 + ], + "type": "interline_equation", + "content": "s = E \\cdot \\left(\\frac {c}{\\pi} \\cdot \\langle \\mathbf {l}, \\mathbf {n} \\rangle + \\sum_ {i = 1} ^ {k _ {b p}} w _ {i} \\cdot f _ {i} \\cdot \\langle \\mathbf {h}, \\mathbf {n} \\rangle^ {p _ {i}}\\right) \\tag {3}", + "image_path": "973eaf4b3afa002a9a35cb79d108cc7f553c3921c231a48d0624c4938b5e23dd.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 417, + 287, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 417, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 47, + 417, + 287, + 453 + ], + "type": "text", + "content": "For environmental illumination, we denote the incoming radiance from direction " + }, + { + "bbox": [ + 47, + 417, + 287, + 453 + ], + "type": "inline_equation", + "content": "\\mathbf{l}" + }, + { + "bbox": [ + 47, + 417, + 287, + 453 + ], + "type": "text", + "content": " as " + }, + { + "bbox": [ + 47, + 417, + 287, + 453 + ], + "type": "inline_equation", + "content": "E(\\mathbf{l})" + }, + { + "bbox": [ + 47, + 417, + 287, + 453 + ], + "type": "text", + "content": ". According to the rendering equation [27], we have:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 93, + 457, + 287, + 484 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 93, + 457, + 287, + 484 + ], + "spans": [ + { + "bbox": [ + 93, + 457, + 287, + 484 + ], + "type": "interline_equation", + "content": "s = \\int_ {\\mathbf {l} \\in \\Omega^ {+}} E (\\mathbf {l}) \\cdot f _ {r} (\\mathbf {l}, \\mathbf {v}, \\mathbf {n}) \\cdot \\langle \\mathbf {l}, \\mathbf {n} \\rangle \\mathrm {d} \\mathbf {l} \\tag {4}", + "image_path": "aae458bd569976be8c69d35a0f9d626ab4145b75e4b1063264d5c839d2c34a17.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 488, + 287, + 525 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 488, + 287, + 525 + ], + "spans": [ + { + "bbox": [ + 47, + 488, + 287, + 525 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 47, + 488, + 287, + 525 + ], + "type": "inline_equation", + "content": "\\Omega^{+}" + }, + { + "bbox": [ + 47, + 488, + 287, + 525 + ], + "type": "text", + "content": " is the upper hemisphere centered by " + }, + { + "bbox": [ + 47, + 488, + 287, + 525 + ], + "type": "inline_equation", + "content": "\\mathbf{n}" + }, + { + "bbox": [ + 47, + 488, + 287, + 525 + ], + "type": "text", + "content": ". Substituting Equation (2) into Equation (4), we then separate the shading " + }, + { + "bbox": [ + 47, + 488, + 287, + 525 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 47, + 488, + 287, + 525 + ], + "type": "text", + "content": " into a diffuse part " + }, + { + "bbox": [ + 47, + 488, + 287, + 525 + ], + "type": "inline_equation", + "content": "s_d" + }, + { + "bbox": [ + 47, + 488, + 287, + 525 + ], + "type": "text", + "content": " and a specular part " + }, + { + "bbox": [ + 47, + 488, + 287, + 525 + ], + "type": "inline_equation", + "content": "s_s" + }, + { + "bbox": [ + 47, + 488, + 287, + 525 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 92, + 530, + 287, + 542 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 92, + 530, + 287, + 542 + ], + "spans": [ + { + "bbox": [ + 92, + 530, + 287, + 542 + ], + "type": "interline_equation", + "content": "s = s _ {d} + s _ {s}, \\text {w h e r e} \\tag {5}", + "image_path": "5d8fd11c6a48dd4e6242350cdf223331f789f319d3c38c09dd226cf586c9891e.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 87, + 545, + 287, + 571 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 545, + 287, + 571 + ], + "spans": [ + { + "bbox": [ + 87, + 545, + 287, + 571 + ], + "type": "interline_equation", + "content": "s _ {d} = \\int_ {\\mathbf {l} \\in \\Omega^ {+}} \\frac {c}{\\pi} \\cdot E (\\mathbf {l}) \\cdot \\langle \\mathbf {l}, \\mathbf {n} \\rangle d \\mathbf {l} \\tag {6}", + "image_path": "53988fac51e04dbe707037e5e91f189ecb823ada4fc08960cd92128366253261.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 88, + 573, + 287, + 606 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 88, + 573, + 287, + 606 + ], + "spans": [ + { + "bbox": [ + 88, + 573, + 287, + 606 + ], + "type": "interline_equation", + "content": "s _ {s} = \\int_ {\\mathbf {l} \\in \\Omega^ {+}} \\sum_ {i = 1} ^ {k _ {b p}} w _ {i} \\cdot f _ {i} \\langle \\mathbf {h}, \\mathbf {n} \\rangle^ {p _ {i}} \\cdot E (\\mathbf {l}) d \\mathbf {l} \\tag {7}", + "image_path": "35c3f16c15c799de4f9a087fbb797dac1cb03c5c59276348bab155e6f3414c14.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 611, + 287, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 611, + 287, + 635 + ], + "spans": [ + { + "bbox": [ + 47, + 611, + 287, + 635 + ], + "type": "text", + "content": "We can efficiently compute " + }, + { + "bbox": [ + 47, + 611, + 287, + 635 + ], + "type": "inline_equation", + "content": "s_d" + }, + { + "bbox": [ + 47, + 611, + 287, + 635 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 611, + 287, + 635 + ], + "type": "inline_equation", + "content": "s_s" + }, + { + "bbox": [ + 47, + 611, + 287, + 635 + ], + "type": "text", + "content": " in the frequency space [45]:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 80, + 639, + 287, + 673 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 80, + 639, + 287, + 673 + ], + "spans": [ + { + "bbox": [ + 80, + 639, + 287, + 673 + ], + "type": "interline_equation", + "content": "s _ {d} = \\frac {c}{\\pi} \\cdot \\sum_ {l = 0} ^ {L} \\sum_ {m = - l} ^ {l} A _ {l} \\cdot K _ {l m} \\cdot Y _ {l m} (\\mathbf {n}), \\tag {8}", + "image_path": "9a1dc3d54ade653b611d4abf765fd0370ae88b927074428df648dff950ce4e7b.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 80, + 677, + 287, + 710 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 80, + 677, + 287, + 710 + ], + "spans": [ + { + "bbox": [ + 80, + 677, + 287, + 710 + ], + "type": "interline_equation", + "content": "s _ {s} = \\sum_ {i = 1} ^ {k _ {b p}} \\sum_ {l = 0} ^ {L} \\sum_ {m = - l} ^ {l} w _ {i} \\cdot B _ {l} \\cdot K _ {l m} \\cdot Y _ {l m} (\\mathbf {r}). \\tag {9}", + "image_path": "62fbd9dbdeece41bac0c8d0a2ab10a668f3a4f2298d3084b39fb4300b5a31fdc.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "text", + "content": " is the SH order, " + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "inline_equation", + "content": "A_{l}" + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "inline_equation", + "content": "B_{l}^{i}" + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "inline_equation", + "content": "K_{lm}" + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "text", + "content": " are the SH coefficients of the Lambertian BRDF, Blinn-Phong BRDF with specular exponent " + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "inline_equation", + "content": "p_i" + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "text", + "content": ", and the environmental illumination, respectively; " + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "inline_equation", + "content": "Y_{lm}(\\cdot)" + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "text", + "content": " are the SH basis functions; " + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "inline_equation", + "content": "\\mathbf{r} = \\frac{2(\\mathbf{n} \\cdot \\mathbf{v}) \\mathbf{n} - \\mathbf{v}}{||2(\\mathbf{n} \\cdot \\mathbf{v}) \\mathbf{n} - \\mathbf{v}||_2}" + }, + { + "bbox": [ + 304, + 72, + 545, + 137 + ], + "type": "text", + "content": " is the specular reflect direction [1]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 143, + 436, + 156 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 143, + 436, + 156 + ], + "spans": [ + { + "bbox": [ + 306, + 143, + 436, + 156 + ], + "type": "text", + "content": "3.2. Initial Model Learning" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 162, + 545, + 233 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 162, + 545, + 233 + ], + "spans": [ + { + "bbox": [ + 304, + 162, + 545, + 233 + ], + "type": "text", + "content": "In this part, we propose a method to learn the mean " + }, + { + "bbox": [ + 304, + 162, + 545, + 233 + ], + "type": "inline_equation", + "content": "\\bar{R}" + }, + { + "bbox": [ + 304, + 162, + 545, + 233 + ], + "type": "text", + "content": " and bases " + }, + { + "bbox": [ + 304, + 162, + 545, + 233 + ], + "type": "inline_equation", + "content": "\\mathrm{M}_{\\mathrm{R}}" + }, + { + "bbox": [ + 304, + 162, + 545, + 233 + ], + "type": "text", + "content": " of our morphable face reflectance model from the publicly-available Multi-PIE dataset [25]. Specifically, we first estimate the reflectance parameters for each identity in the dataset via inverse rendering, and then train a PCA model for them." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 243, + 546, + 399 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 243, + 546, + 399 + ], + "spans": [ + { + "bbox": [ + 304, + 243, + 546, + 399 + ], + "type": "text", + "content": "Dataset Preprocessing The Multi-PIE dataset contains 337 identities captured under 15 viewpoints and 19 illuminations. We first exclude objects with facial accessories or hair occlusions, resulting in 128 identities. Then, we manually select 9 viewpoints and 12 illuminations (including 11 directional flash images and 1 room light image) with well color consistency to train our model. By removing the room light effect in the flash images, we obtain 11 OLAT images per viewpoint. We adopt a simple model-based approach to reconstruct the BFM09 geometry coefficients of each identity, camera parameters of each viewpoint, and the position of each flash simultaneously. See more implementation details in our Supplementary Material." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "spans": [ + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "text", + "content": "Reflectance Parameter Estimation For a specific identity, we estimate all the reflectance parameters in UV space, including the diffuse color map " + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "inline_equation", + "content": "C \\in \\mathbb{R}^{3 \\times H \\times W}" + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "text", + "content": " and the linear combination weight map " + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "inline_equation", + "content": "W \\in \\mathbb{R}^{k_{bp} \\times H \\times W}" + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "text", + "content": ". We unwarp all the OLAT images into UV space and denote the one captured under the " + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "text", + "content": "-th viewpoint and the " + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "text", + "content": "-th directional flash illumination as " + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "inline_equation", + "content": "I_{ij}^{uv} \\in \\mathbb{R}^{3 \\times H \\times W}" + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "text", + "content": ". From the reconstructed face geometry and scene information, we precompute the incident light direction " + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{l}_j^{uv}" + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "text", + "content": " for each flash, view direction " + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{v}_i^{uv}" + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "text", + "content": " for each camera, normal direction " + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{n}^{uv}" + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "text", + "content": " for the face geometry, and shadow mask1 " + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "inline_equation", + "content": "M_{ij}^{uv}" + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "text", + "content": " for each OLAT image in the UV space. By predefining a reasonable incoming irradiance " + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "inline_equation", + "content": "E" + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "text", + "content": " from the directional flash2, we obtain the reconstructed OLAT image " + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "inline_equation", + "content": "\\hat{I}_{ij}^{uv}" + }, + { + "bbox": [ + 304, + 410, + 545, + 590 + ], + "type": "text", + "content": " using the efficient shading technique under directional illumination presented in Equation (3):" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 310, + 599, + 545, + 633 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 310, + 599, + 545, + 633 + ], + "spans": [ + { + "bbox": [ + 310, + 599, + 545, + 633 + ], + "type": "interline_equation", + "content": "\\hat {I} _ {i j} ^ {u v} = E \\cdot \\left(\\frac {C}{\\pi} \\cdot \\langle \\mathbf {l} _ {j} ^ {u v}, \\mathbf {n} ^ {u v} \\rangle + \\sum_ {k = 1} ^ {k _ {b p}} W _ {k} \\cdot f _ {k} \\cdot \\langle \\mathbf {h} _ {i j} ^ {u v}, \\mathbf {n} ^ {u v} \\rangle^ {p _ {k}}\\right) \\tag {10}", + "image_path": "bdbd4ff92f8e7ba8a68572d668921f00e1380a853a317a29103a6eca2eb74508.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 641, + 545, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 641, + 545, + 666 + ], + "spans": [ + { + "bbox": [ + 304, + 641, + 545, + 666 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 304, + 641, + 545, + 666 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{ij}^{uv}" + }, + { + "bbox": [ + 304, + 641, + 545, + 666 + ], + "type": "text", + "content": " is the half vector UV map obtained by " + }, + { + "bbox": [ + 304, + 641, + 545, + 666 + ], + "type": "inline_equation", + "content": "\\mathbf{l}_j^{uv}" + }, + { + "bbox": [ + 304, + 641, + 545, + 666 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 641, + 545, + 666 + ], + "type": "inline_equation", + "content": "\\mathbf{v}_i^{uv}" + }, + { + "bbox": [ + 304, + 641, + 545, + 666 + ], + "type": "text", + "content": ". We optimize the diffuse color map " + }, + { + "bbox": [ + 304, + 641, + 545, + 666 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 304, + 641, + 545, + 666 + ], + "type": "text", + "content": " and linear com" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 317, + 673, + 459, + 684 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 673, + 459, + 684 + ], + "spans": [ + { + "bbox": [ + 317, + 673, + 459, + 684 + ], + "type": "text", + "content": "1We obtained shadow mask via ray tracing." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 306, + 684, + 545, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 684, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 684, + 545, + 713 + ], + "type": "text", + "content": "There is an inevitably global scale between the reflectance parameters estimated by the inverse rendering method and the ground truth, if lighting unknown. See more theoretical analysis in [45]." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "8601" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 109, + 68, + 484, + 179 + ], + "blocks": [ + { + "bbox": [ + 109, + 68, + 484, + 179 + ], + "lines": [ + { + "bbox": [ + 109, + 68, + 484, + 179 + ], + "spans": [ + { + "bbox": [ + 109, + 68, + 484, + 179 + ], + "type": "image", + "image_path": "6e3ab599aa40599de8d393e0453afb2a500c2fe1c13fe440fcf8e8ec4c5a5996.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 187, + 547, + 222 + ], + "lines": [ + { + "bbox": [ + 46, + 187, + 547, + 222 + ], + "spans": [ + { + "bbox": [ + 46, + 187, + 547, + 222 + ], + "type": "text", + "content": "Figure 3. Model finetuning pipeline overview. Given a single input face image, we apply an encoder " + }, + { + "bbox": [ + 46, + 187, + 547, + 222 + ], + "type": "inline_equation", + "content": "E_{\\theta}" + }, + { + "bbox": [ + 46, + 187, + 547, + 222 + ], + "type": "text", + "content": " to estimate its lighting scale " + }, + { + "bbox": [ + 46, + 187, + 547, + 222 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 46, + 187, + 547, + 222 + ], + "type": "text", + "content": ", lighting coefficients " + }, + { + "bbox": [ + 46, + 187, + 547, + 222 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 46, + 187, + 547, + 222 + ], + "type": "text", + "content": ", and reflectance coefficients " + }, + { + "bbox": [ + 46, + 187, + 547, + 222 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 46, + 187, + 547, + 222 + ], + "type": "text", + "content": ". 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I _ {i j} ^ {u v}\\right) \\right\\| _ {1}, \\tag {12}", + "image_path": "d27319a1f4145106f27ee07388a8d20dc1ff865709332d26372b84df73234997.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 353, + 287, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 353, + 287, + 376 + ], + "spans": [ + { + "bbox": [ + 47, + 353, + 287, + 376 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{reg}" + }, + { + "bbox": [ + 47, + 353, + 287, + 376 + ], + "type": "text", + "content": " is designed to restrict the reflectance parameters to be non-negative:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 388, + 287, + 401 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 388, + 287, + 401 + ], + "spans": [ + { + "bbox": [ + 105, + 388, + 287, + 401 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {r e g}} = - M _ {C} \\cdot C - M _ {W} \\cdot W \\tag {13}", + "image_path": "2b41760e90dd0a6825456c78bf273dd3bce61e04cba88eef068feaf064cab747.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "spans": [ + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "text", + "content": "Here, " + }, + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "inline_equation", + "content": "M_C" + }, + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "inline_equation", + "content": "M_W" + }, + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "text", + "content": " are the masks that indicate negative values in " + }, + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "text", + "content": ", respectively. During parameter estimation, we randomly horizontal flip " + }, + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 47, + 411, + 287, + 459 + ], + "type": "text", + "content": " to introduce symmetric constraints [66] to the reflectance parameter map." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 460, + 287, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 460, + 287, + 508 + ], + "spans": [ + { + "bbox": [ + 46, + 460, + 287, + 508 + ], + "type": "text", + "content": "Compared to the traditional way that uses specular intensity and exponents to parameterize the Blinn-Phong BRDF, our linear representation is much easier for parameter estimation in practice." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 520, + 287, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 520, + 287, + 640 + ], + "spans": [ + { + "bbox": [ + 46, + 520, + 287, + 640 + ], + "type": "text", + "content": "Model Learning With the estimated reflectance parameter maps for each identity, we can build our initial morphable face reflectance model. Similar to AlbedoMM [50], we learn a PCA model only for the diffuse albedo. Then, we transfer it to the specular weights by using the same linear combination of the training samples to form the bases. Thus, we can use the same coefficients " + }, + { + "bbox": [ + 46, + 520, + 287, + 640 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 46, + 520, + 287, + 640 + ], + "type": "text", + "content": " for the diffuse and specular reflectance parameters as Equation (1) while keeping the orthonormality of the diffuse bases so that the user can use our diffuse model independently." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 649, + 154, + 662 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 649, + 154, + 662 + ], + "spans": [ + { + "bbox": [ + 47, + 649, + 154, + 662 + ], + "type": "text", + "content": "3.3. Model Finetuning" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 668, + 287, + 717 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 668, + 287, + 717 + ], + "spans": [ + { + "bbox": [ + 47, + 668, + 287, + 717 + ], + "type": "text", + "content": "To improve the generalization capability and expressive power of our initial morphable face reflecatance model, we finetune it on an in-the-wild face image dataset, FFHQ [29], by jointly doing face reconstruction and model updating." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 242, + 547, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 242, + 547, + 361 + ], + "spans": [ + { + "bbox": [ + 304, + 242, + 547, + 361 + ], + "type": "text", + "content": "Dataset Preprocessing Before model finetuning, we use an off-the-shelf [14] method to estimate the BFM09 geometry coefficients and head pose for each image in the dataset. To further improve the geometry reconstruction accuracy, we apply an offline optimization using the same loss functions as [14]. Finally, we obtain the shape coefficients " + }, + { + "bbox": [ + 304, + 242, + 547, + 361 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 304, + 242, + 547, + 361 + ], + "type": "text", + "content": ", expression coefficients " + }, + { + "bbox": [ + 304, + 242, + 547, + 361 + ], + "type": "inline_equation", + "content": "\\delta^3" + }, + { + "bbox": [ + 304, + 242, + 547, + 361 + ], + "type": "text", + "content": ", and head pose " + }, + { + "bbox": [ + 304, + 242, + 547, + 361 + ], + "type": "inline_equation", + "content": "\\mathbf{R}" + }, + { + "bbox": [ + 304, + 242, + 547, + 361 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 242, + 547, + 361 + ], + "type": "inline_equation", + "content": "\\mathbf{t}" + }, + { + "bbox": [ + 304, + 242, + 547, + 361 + ], + "type": "text", + "content": " for each image. Similar to [14], we use the perspective camera model with a reasonable predefined focal length to represent the 3D-2D projection " + }, + { + "bbox": [ + 304, + 242, + 547, + 361 + ], + "type": "inline_equation", + "content": "\\Pi" + }, + { + "bbox": [ + 304, + 242, + 547, + 361 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "spans": [ + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "text", + "content": "Network Architecture As illustrated in Figure 3, given a single face image " + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "text", + "content": " as input, our face reconstruction network " + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "inline_equation", + "content": "E_{\\theta}(\\cdot)" + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "text", + "content": " predicts the reflectance model coefficients " + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "text", + "content": " and the SH lighting. Combined with the geometry parameters " + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "inline_equation", + "content": "\\alpha, \\delta" + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "text", + "content": ", head pose " + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "inline_equation", + "content": "\\mathbf{R}, \\mathbf{t}" + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "text", + "content": ", and the projection " + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "inline_equation", + "content": "\\Pi" + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "text", + "content": ", we can obtain the reconstructed image " + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "inline_equation", + "content": "\\hat{I}" + }, + { + "bbox": [ + 304, + 373, + 547, + 468 + ], + "type": "text", + "content": " via a differentiable rasterizer [34, 46] using the efficient shading technique presented in Equation (8) and (9)." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 469, + 547, + 672 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 469, + 547, + 672 + ], + "spans": [ + { + "bbox": [ + 304, + 469, + 547, + 672 + ], + "type": "text", + "content": "To update the high-frequency information in our non-Lambertian reflectance representation, we need to predict high-order SH lighting [45]. We adopt 8-order SH lighting with 273 parameters in our method as [17,36]. However, if handled naively, the network cannot predict reasonable SH lighting due to the large searching space of high-order SH coefficients, as shown in Figure 5. To constrain the searching space, we build a PCA model for the real-world environmental lighting in SH coefficient space inspired by [18]. Specifically, we utilize a real-world HDRI environment map dataset [64] and apply rotation augmentation to it. For each environment map, we compute its SH coefficients up to the 8-th order. We then divide them by the 0-th order coefficient for normalization. Note that each color channel is normalized independently. Next, we learn a PCA model for these normalized SH coefficients. We use the first " + }, + { + "bbox": [ + 304, + 469, + 547, + 672 + ], + "type": "inline_equation", + "content": "N_{L}" + }, + { + "bbox": [ + 304, + 469, + 547, + 672 + ], + "type": "text", + "content": " bases for lighting prediction." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 673, + 545, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 673, + 545, + 685 + ], + "spans": [ + { + "bbox": [ + 317, + 673, + 545, + 685 + ], + "type": "text", + "content": "During finetuning, together with the reflectance model" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 306, + 693, + 545, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 693, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 693, + 545, + 713 + ], + "type": "text", + "content": "3The expression bases are adapted from FaceWarehouse [8] since BFM09 does not model expression. See more details in [14]." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8602" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "content": "coefficients " + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "content": ", our network predicts " + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "inline_equation", + "content": "\\gamma \\in \\mathbb{R}^{N_L}" + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "content": " as the lighting PCA model coefficients and " + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "inline_equation", + "content": "z \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "content": " to represent the scale of 0-th order SH coefficient for each color channel. From this, we first use " + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "content": " to recover the SH coefficients from the PCA lighting model and then apply the predicted scale " + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "content": " to them. We adopt the ResNet-50 [26] architecture as the reconstruction network " + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "inline_equation", + "content": "E_{\\theta}(\\cdot)" + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "content": " and modify the last fully-connected layer to " + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "inline_equation", + "content": "N_R + N_L + 3" + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "content": " neurons. We adopt the Softplus activation for " + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "content": " to ensure a non-negative prediction and linear activation for " + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 46, + 72, + 289, + 193 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 204, + 288, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 204, + 288, + 266 + ], + "spans": [ + { + "bbox": [ + 46, + 204, + 288, + 266 + ], + "type": "text", + "content": "Loss Function In model finetuning, the learnable parameters are the morphable model parameters, including the mean " + }, + { + "bbox": [ + 46, + 204, + 288, + 266 + ], + "type": "inline_equation", + "content": "\\bar{R}" + }, + { + "bbox": [ + 46, + 204, + 288, + 266 + ], + "type": "text", + "content": " and bases " + }, + { + "bbox": [ + 46, + 204, + 288, + 266 + ], + "type": "inline_equation", + "content": "\\mathrm{M}_{\\mathrm{R}}" + }, + { + "bbox": [ + 46, + 204, + 288, + 266 + ], + "type": "text", + "content": ", and face reconstruction network parameters " + }, + { + "bbox": [ + 46, + 204, + 288, + 266 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 46, + 204, + 288, + 266 + ], + "type": "text", + "content": ". We optimize them with the combination of a reconstruction loss " + }, + { + "bbox": [ + 46, + 204, + 288, + 266 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{rec}" + }, + { + "bbox": [ + 46, + 204, + 288, + 266 + ], + "type": "text", + "content": " and a regularization loss " + }, + { + "bbox": [ + 46, + 204, + 288, + 266 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{reg}" + }, + { + "bbox": [ + 46, + 204, + 288, + 266 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 123, + 274, + 287, + 297 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 123, + 274, + 287, + 297 + ], + "spans": [ + { + "bbox": [ + 123, + 274, + 287, + 297 + ], + "type": "interline_equation", + "content": "\\underset {\\bar {R}, \\mathrm {M} _ {\\mathrm {R}}, \\theta} {\\arg \\min } \\mathcal {L} _ {r e c} + \\mathcal {L} _ {r e g} \\tag {14}", + "image_path": "751d833b019bba0d412a5d0b02f17d336c361fa4e1d303f857227563e207bb93.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 305, + 288, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 305, + 288, + 354 + ], + "spans": [ + { + "bbox": [ + 47, + 305, + 288, + 354 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{rec}" + }, + { + "bbox": [ + 47, + 305, + 288, + 354 + ], + "type": "text", + "content": " is the combination of a L1 term " + }, + { + "bbox": [ + 47, + 305, + 288, + 354 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{l1}" + }, + { + "bbox": [ + 47, + 305, + 288, + 354 + ], + "type": "text", + "content": " and a perceptual term " + }, + { + "bbox": [ + 47, + 305, + 288, + 354 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{per}" + }, + { + "bbox": [ + 47, + 305, + 288, + 354 + ], + "type": "text", + "content": "; see more details in our Supplementary Material. In our regularization loss " + }, + { + "bbox": [ + 47, + 305, + 288, + 354 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{reg}" + }, + { + "bbox": [ + 47, + 305, + 288, + 354 + ], + "type": "text", + "content": ", we design " + }, + { + "bbox": [ + 47, + 305, + 288, + 354 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{upd}" + }, + { + "bbox": [ + 47, + 305, + 288, + 354 + ], + "type": "text", + "content": " to constrain the updating of our morphable reflectance model:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 87, + 363, + 287, + 378 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 363, + 287, + 378 + ], + "spans": [ + { + "bbox": [ + 87, + 363, + 287, + 378 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {u p d} = \\left| \\left| \\bar {R} - \\bar {R} _ {0} \\right| \\right| _ {1} + \\left| \\left| \\mathrm {M} _ {\\mathrm {R}} - \\mathrm {M} _ {\\mathrm {R} _ {0}} \\right| \\right| _ {1} \\tag {15}", + "image_path": "d915bb904479b078cced812d9c392e4cebefac7c16b19aa5acb5c2906e4f03dc.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 387, + 288, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 387, + 288, + 448 + ], + "spans": [ + { + "bbox": [ + 46, + 387, + 288, + 448 + ], + "type": "text", + "content": "In addition, we adopt " + }, + { + "bbox": [ + 46, + 387, + 288, + 448 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text {light }}" + }, + { + "bbox": [ + 46, + 387, + 288, + 448 + ], + "type": "text", + "content": " to encourage monochromatic environment lighting as [12] to resolve the color ambiguity between albedo and lighting and " + }, + { + "bbox": [ + 46, + 387, + 288, + 448 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text {coef }}" + }, + { + "bbox": [ + 46, + 387, + 288, + 448 + ], + "type": "text", + "content": " to constrain the predicted PCA coefficients " + }, + { + "bbox": [ + 46, + 387, + 288, + 448 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 46, + 387, + 288, + 448 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 387, + 288, + 448 + ], + "type": "inline_equation", + "content": "\\gamma" + }, + { + "bbox": [ + 46, + 387, + 288, + 448 + ], + "type": "text", + "content": "; see more details in our Supplementary Material." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 459, + 128, + 472 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 459, + 128, + 472 + ], + "spans": [ + { + "bbox": [ + 47, + 459, + 128, + 472 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 479, + 180, + 491 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 479, + 180, + 491 + ], + "spans": [ + { + "bbox": [ + 47, + 479, + 180, + 491 + ], + "type": "text", + "content": "4.1. Implementation Details" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 498, + 287, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 498, + 287, + 581 + ], + "spans": [ + { + "bbox": [ + 46, + 498, + 287, + 581 + ], + "type": "text", + "content": "Initial Model Learning For reflectance parameter estimation, we set " + }, + { + "bbox": [ + 46, + 498, + 287, + 581 + ], + "type": "inline_equation", + "content": "w_{reg} = 100" + }, + { + "bbox": [ + 46, + 498, + 287, + 581 + ], + "type": "text", + "content": " and adopt Adam [31] optimizer to minimize the loss function, with learning rate 5e-3. We use 3 Blinn-Phong BRDFs with specular exponents 1, 8, and 64 in our method, i.e. " + }, + { + "bbox": [ + 46, + 498, + 287, + 581 + ], + "type": "inline_equation", + "content": "k_{bp} = 3" + }, + { + "bbox": [ + 46, + 498, + 287, + 581 + ], + "type": "text", + "content": ". Thus, there are 6 reflectance parameters for each face vertex. We use the first 80 PCA bases in our initial model, i.e. " + }, + { + "bbox": [ + 46, + 498, + 287, + 581 + ], + "type": "inline_equation", + "content": "N_R = 80" + }, + { + "bbox": [ + 46, + 498, + 287, + 581 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": "Model Finetuning For the lighting PCA model, we also use the first 80 PCA bases, i.e. " + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "inline_equation", + "content": "N_{L} = 80" + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": ". The weights for " + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{l1}, \\mathcal{L}_{per}, \\mathcal{L}_{coef}, \\mathcal{L}_{upd}, \\mathcal{L}_{light}" + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": " are set to 2, 0.1, 0.001, 10, 10, respectively. We finetune our model on the FFHQ dataset [30], with 70000 high-fidelity single-view face images, and crop them to " + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "inline_equation", + "content": "224 \\times 224" + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": " when input to our reconstruction network " + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "inline_equation", + "content": "E_{\\theta}" + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": ". We first pretrain " + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "inline_equation", + "content": "E_{\\theta}" + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": " using " + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{l1}, \\mathcal{L}_{per}, \\mathcal{L}_{coef}" + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": " for 20 epochs to ensure it can output reasonable reflectance and lighting coefficients prediction, with learning rate 1e-4. Then, we use the full loss function to" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 313, + 70, + 538, + 249 + ], + "blocks": [ + { + "bbox": [ + 313, + 70, + 538, + 249 + ], + "lines": [ + { + "bbox": [ + 313, + 70, + 538, + 249 + ], + "spans": [ + { + "bbox": [ + 313, + 70, + 538, + 249 + ], + "type": "image", + "image_path": "21f0fece86385e8124d4e2f5c8992098083c86064007760e2322021d503fd369.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 252, + 547, + 352 + ], + "lines": [ + { + "bbox": [ + 305, + 252, + 547, + 352 + ], + "spans": [ + { + "bbox": [ + 305, + 252, + 547, + 352 + ], + "type": "text", + "content": "Figure 4. Qualitative comparison of face rendering results on the Multi-PIE dataset between our spatially varying reflectance representation and previous works with a global specular exponent. Here, " + }, + { + "bbox": [ + 305, + 252, + 547, + 352 + ], + "type": "inline_equation", + "content": "Exp" + }, + { + "bbox": [ + 305, + 252, + 547, + 352 + ], + "type": "text", + "content": " stands for the specular exponent in the Blinn-Phong BRDF. A large global exponent (e.g. 32, 64) leads to over-shiness artifacts around the cheek and chin, while a small one (e.g. 8, 16) cannot represent peculiarities appear in the forehead and tip of the nose. Our representation can well model the spatially varying specular intensity and shiness on the face." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 354, + 401, + 496, + 462 + ], + "blocks": [ + { + "bbox": [ + 305, + 358, + 547, + 392 + ], + "lines": [ + { + "bbox": [ + 305, + 358, + 547, + 392 + ], + "spans": [ + { + "bbox": [ + 305, + 358, + 547, + 392 + ], + "type": "text", + "content": "Table 1. Quantitative comparison of face rendering results on the Multi-PIE dataset between our method and previous works with a global specular exponent." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 354, + 401, + 496, + 462 + ], + "lines": [ + { + "bbox": [ + 354, + 401, + 496, + 462 + ], + "spans": [ + { + "bbox": [ + 354, + 401, + 496, + 462 + ], + "type": "table", + "html": "
LPIPS ↓SSIM ↑PSNR ↑
Exp=80.0970.93724.56
Exp=160.0980.93624.54
Exp=320.0970.93724.72
Exp=640.0960.93724.82
Ours0.0830.94225.00
", + "image_path": "a1968f8139e26ce0ff4f05967823f2f9cf7dee41545ef4843537d91927179f93.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 483, + 545, + 519 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 483, + 545, + 519 + ], + "spans": [ + { + "bbox": [ + 305, + 483, + 545, + 519 + ], + "type": "text", + "content": "simultaneously update the parameters of " + }, + { + "bbox": [ + 305, + 483, + 545, + 519 + ], + "type": "inline_equation", + "content": "E_{\\theta}" + }, + { + "bbox": [ + 305, + 483, + 545, + 519 + ], + "type": "text", + "content": " and our morphable face reflectance model for 2 epochs, with learning rate 1e-5. We adopt Adam optimizer [31]." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 527, + 384, + 539 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 527, + 384, + 539 + ], + "spans": [ + { + "bbox": [ + 306, + 527, + 384, + 539 + ], + "type": "text", + "content": "4.2. Evaluations" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "content": "Reflectance Parameter Estimation In our reflectance representation, we use the linear combination of 3 Blinn-Phong BRDFs with specular exponents 1, 8, and 64 as the specular term. In Figure 4, we compare the face rendering results between our reflectance representation and previous works [15-17] with spatially varying specular albedo and a global specular exponent. As discussed in Section 3.1, we implement the reflectance representation of previous work by setting " + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "inline_equation", + "content": "k_{bp} = 1" + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "content": ", and use the same inverse rendering pipeline described in Section 3.2. The results illustrate that our representation obtains more realistic rendering results than the previous work. For example, the specular shiness is more significant at the tip of nose while vanishing around the cheek; previous works with global specular" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8603" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 82, + 107, + 252, + 166 + ], + "blocks": [ + { + "bbox": [ + 47, + 65, + 287, + 99 + ], + "lines": [ + { + "bbox": [ + 47, + 65, + 287, + 99 + ], + "spans": [ + { + "bbox": [ + 47, + 65, + 287, + 99 + ], + "type": "text", + "content": "Table 2. Photometric face reconstruction comparison between our method and competitors on 1000 images randomly sampled from the CelebA-HQ dataset." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 82, + 107, + 252, + 166 + ], + "lines": [ + { + "bbox": [ + 82, + 107, + 252, + 166 + ], + "spans": [ + { + "bbox": [ + 82, + 107, + 252, + 166 + ], + "type": "table", + "html": "
LPIPS ↓SSIM ↑PSNR ↑
BFM090.1140.89323.51
AlbedoMM0.1160.90123.69
Ours0.1100.89624.21
Ours w/o finetune0.12560.88623.26
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Qualitative ablation study of model finetuning and the use of our lighting PCA model." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 328, + 286, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 328, + 286, + 424 + ], + "spans": [ + { + "bbox": [ + 46, + 328, + 286, + 424 + ], + "type": "text", + "content": "exponent cannot capture this phenomenon. With the spatially varying linear combination weights of different Blinn-Phong BRDFs, our method can naturally represent spatially varying specular intensity and shiness. In Table 1, we report the SSIM [63], PSNR, and LPIPS [69] scores in the face region to quantitatively measure the discrepancy between the re-rendered face and the ground truth. Again, our method achieves better results than the global exponent counterpart." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 435, + 286, + 531 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 435, + 286, + 531 + ], + "spans": [ + { + "bbox": [ + 46, + 435, + 286, + 531 + ], + "type": "text", + "content": "Model Visualization We visualize the first 3 principal components of our morphable model in Figure 1, including the diffuse albedo, and the weights for Blinn-Phong BRDF with specular exponents 1, 8, and 64, from left to right; we multiply the weights by 3 for better visualization. It shows that our model learns to assign a large specular shiness to the tip of the nose while a small value to the cheek. See more visualizations in our Supplementary Material." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 542, + 286, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 542, + 286, + 662 + ], + "spans": [ + { + "bbox": [ + 46, + 542, + 286, + 662 + ], + "type": "text", + "content": "Ablation Study As shown in Figure 5 and Table 2, the proposed finetuning strategy can improve the generalization capability and expressive power of our initial morphable face reflectance model, leading to better face reconstruction quality, especially around mouth and eyes. We then verify the effectiveness of our lighting PCA model. As illustrated in Figure 5, directly predicts 273 coefficients of the 8-order SH (w/o Light PCA) leads to unreasonable results; our lighting PCA model obtains better lighting predictions by constraining the searching space of the SH coefficients." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 670, + 132, + 683 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 670, + 132, + 683 + ], + "spans": [ + { + "bbox": [ + 47, + 670, + 132, + 683 + ], + "type": "text", + "content": "4.3. Comparisons" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 689, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 286, + 713 + ], + "type": "text", + "content": "Baselines We compare our method with BFM09 [43] and AlbedoMM [50]. BFM09 is a diffuse-only model built from" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 312, + 69, + 537, + 293 + ], + "blocks": [ + { + "bbox": [ + 312, + 69, + 537, + 293 + ], + "lines": [ + { + "bbox": [ + 312, + 69, + 537, + 293 + ], + "spans": [ + { + "bbox": [ + 312, + 69, + 537, + 293 + ], + "type": "image", + "image_path": "6096b35492eef67711ba16dc2ee992523e2234b63c4c812b3cb363189fe62b28.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 306, + 297, + 544, + 319 + ], + "lines": [ + { + "bbox": [ + 306, + 297, + 544, + 319 + ], + "spans": [ + { + "bbox": [ + 306, + 297, + 544, + 319 + ], + "type": "text", + "content": "Figure 6. Qualitative comparison of face reconstruction and shading contributions between our method and AlbedoMM." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 340, + 545, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 340, + 545, + 508 + ], + "spans": [ + { + "bbox": [ + 304, + 340, + 545, + 508 + ], + "type": "text", + "content": "3D scans; AlbedoMM is a morphable model for diffuse and specular albedo built from Light Stage data. To ensure a fair comparison, we use the same CNN-based framework (see Section 3.3) to implement the competitors. We train the reconstruction network for them on the FFHQ dataset, but we do not update their morphable model parameters. As we only focus on appearance, the reconstruction network only predicts reflectance coefficients and lighting parameters and uses fixed precomputed geometry during training. For BFM09, we adopt the same geometry parameters as ours. For AlbedoMM, we use the same steps as mentioned in Section 3.3 to obtain its geometry parameters. Akin to [50], we adopt the Blinn-Phong BRDF for AlbedoMM and set the global shiness to 20." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 523, + 545, + 655 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 523, + 545, + 655 + ], + "spans": [ + { + "bbox": [ + 304, + 523, + 545, + 655 + ], + "type": "text", + "content": "Face Reconstruction We evaluate our method and the competitors on the CelebA-HQ [28] dataset. As illustrated in Table 2, our method obtains better photometric face reconstruction quantitative scores than the competitors since we finetune it on an in-the-wild dataset to improve its generalization capability and expressive power while the competitors are built from a limited number of scans. As shown in Figure 6, our method can reconstruct the input image well; compared to AlbedoMM trained from Light Stage scans, our method trained from low-cost data can also disentangle the diffuse and specular shading in a plausible way." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 545, + 713 + ], + "type": "text", + "content": "Face Relighting We evaluate the relighting performance of our method and the competitors on the Multi-PIE dataset [25]. Specifically, given an input image, we first obtain its geometry parameters as described in Section 3.2 and" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8604" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 68, + 279, + 300 + ], + "blocks": [ + { + "bbox": [ + 53, + 68, + 279, + 300 + ], + "lines": [ + { + "bbox": [ + 53, + 68, + 279, + 300 + ], + "spans": [ + { + "bbox": [ + 53, + 68, + 279, + 300 + ], + "type": "image", + "image_path": "607cf050064d8db1e14017a3715d8173efb65ee6e09f358aac2225a23a22a0af.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 47, + 304, + 287, + 327 + ], + "lines": [ + { + "bbox": [ + 47, + 304, + 287, + 327 + ], + "spans": [ + { + "bbox": [ + 47, + 304, + 287, + 327 + ], + "type": "text", + "content": "Figure 7. Qualitative comparison of face relighting results under point light source between our method and the competitors." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 342, + 289, + 571 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 342, + 289, + 571 + ], + "spans": [ + { + "bbox": [ + 46, + 342, + 289, + 571 + ], + "type": "text", + "content": "reconstruct its reflectance parameters using our CNN-based face reconstruction network (columns 1 and 3 in Figure 7). Then, we re-render the image under a new point light source and compare it to the corresponding OLAT image with exactly the same light position obtained from the preprocessed Multi-PIE dataset in Section 3.2 (columns 2 and 4 in Figure 7). Since we do not have the ground truth light color and intensity information of the Multi-PIE dataset, we render our method and the competitors using the same white point light source for a fair comparison; please ignore the color difference and only focus on the distribution of facial peculiarities in Figure 7. Compared to BFM09, our method successfully render plausible facial peculiarities since we adopt a non-Lambertian reflectance representation. Compared to AlbedoMM, our method achieves more realistic results especially around the tip of the nose since we can model both spatially varying specular intensity and shiness. See the video comparisons on our project page for a better demonstration." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 582, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 582, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 582, + 289, + 715 + ], + "type": "text", + "content": "Reflectance Reconstruction Although our goal is not to model physically-accurate reflectance parameters, we compare our method with AlbedoMM on 23 Light Stage scans with ground truth diffuse and specular albedo captured under neutral expression from the 3D-RFE database [51]. We adopt the sum of 3 linear combination weights in our reflectance representation as the specular albedo; this quantity shares the same meaning as the specular albedo, i.e. the specular shading under a spatially-uniform environment lighting with unit radiance. As shown in Figure 8, our method can reconstruct plausible reflectance maps. How" + } + ] + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 345, + 96, + 506, + 135 + ], + "blocks": [ + { + "bbox": [ + 305, + 65, + 545, + 88 + ], + "lines": [ + { + "bbox": [ + 305, + 65, + 545, + 88 + ], + "spans": [ + { + "bbox": [ + 305, + 65, + 545, + 88 + ], + "type": "text", + "content": "Table 3. Quantitative diffuse and specular albedo reconstruction results on 23 Light Stage scans from the 3D-RFE dataset (PSNR↑)." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 345, + 96, + 506, + 135 + ], + "lines": [ + { + "bbox": [ + 345, + 96, + 506, + 135 + ], + "spans": [ + { + "bbox": [ + 345, + 96, + 506, + 135 + ], + "type": "table", + "html": "
Diffuse AlbedoSpecular Albedo
AlbedoMM19.3126.51
Ours20.1319.14
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Qualitative comparison of diffuse and specular albedo reconstruction on the 3D-RFE dataset." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 280, + 545, + 388 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 280, + 545, + 388 + ], + "spans": [ + { + "bbox": [ + 304, + 280, + 545, + 388 + ], + "type": "text", + "content": "ever, as shown in Table 3, our method obtains inferior quantitative results than AlbedoMM on specular albedo reconstruction. We attribute this to two reasons: " + }, + { + "bbox": [ + 304, + 280, + 545, + 388 + ], + "type": "inline_equation", + "content": "i)" + }, + { + "bbox": [ + 304, + 280, + 545, + 388 + ], + "type": "text", + "content": " AlbedoMM uses the 3D-RFE database to build their model [50] while our method has never seen these scans, and " + }, + { + "bbox": [ + 304, + 280, + 545, + 388 + ], + "type": "inline_equation", + "content": "ii)" + }, + { + "bbox": [ + 304, + 280, + 545, + 388 + ], + "type": "text", + "content": " our method is built from low-cost data without lighting information, so there exists a global scale between our reflectance parameters and the ground truth although we try to mitigate it by setting a reasonable lighting color in Section 3.2." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 399, + 379, + 411 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 399, + 379, + 411 + ], + "spans": [ + { + "bbox": [ + 306, + 399, + 379, + 411 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 418, + 545, + 575 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 418, + 545, + 575 + ], + "spans": [ + { + "bbox": [ + 304, + 418, + 545, + 575 + ], + "type": "text", + "content": "We propose the first 3D morphable face reflectance model with spatially varying BRDF, using only low-cost publicly-available data. To represent spatially varying reflectance, we apply linear shiness weighting into parametric face modeling. We develop an inverse rendering algorithm to reconstruct the reflectance parameters from the Multi-PIE dataset, from which we build an initial model. We propose a strategy that jointly learns the face reconstruction network and updates the morphable model parameters on the FFHQ dataset to improve its generalization capability and expressive power. Our method obtains decent rendering results with plausible facial peculiarities. We discuss the limitations of our method in the Supplementary Material." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 586, + 404, + 600 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 586, + 404, + 600 + ], + "spans": [ + { + "bbox": [ + 306, + 586, + 404, + 600 + ], + "type": "text", + "content": "Acknowledgement" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 605, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 545, + 715 + ], + "type": "text", + "content": "This work was supported by the National Key R&D Program of China (2018YFA0704000), Beijing Natural Science Foundation (M22024), the NSFC (No.62021002), and the Key Research and Development Project of Tibet Autonomous Region (XZ202101ZY0019G). This work was also supported by THUIBCS, Tsinghua University, and BLBCI, Beijing Municipal Education Commission. We thank the anonymous reviewers for their constructive suggestions. Feng Xu is the corresponding author." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8605" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 286, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 53, + 91, + 286, + 113 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 286, + 113 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 286, + 113 + ], + "type": "text", + "content": "[1] Tomas Akenine-Moller, Eric Haines, and Naty Hoffman. Real-time rendering. AK Peters/crc Press, 2019. 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 115, + 286, + 158 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 115, + 286, + 158 + ], + "spans": [ + { + "bbox": [ + 53, + 115, + 286, + 158 + ], + "type": "text", + "content": "[2] Dejan Azinović, Olivier Maury, Christophe Hery, Mathias Niessner, and Justus Thies. High-res facial appearance capture from polarized smartphone images. ArXiv, abs/2212.01160, 2022. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 160, + 286, + 204 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 160, + 286, + 204 + ], + "spans": [ + { + "bbox": [ + 53, + 160, + 286, + 204 + ], + "type": "text", + "content": "[3] Ziqian Bai, Zhaopeng Cui, Xiaoming Liu, and Ping Tan. Riggable 3d face reconstruction via in-network optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6216-6225, 2021. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 205, + 286, + 249 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 205, + 286, + 249 + ], + "spans": [ + { + "bbox": [ + 53, + 205, + 286, + 249 + ], + "type": "text", + "content": "[4] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pages 187-194, 1999. 1, 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 251, + 286, + 295 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 251, + 286, + 295 + ], + "spans": [ + { + "bbox": [ + 53, + 251, + 286, + 295 + ], + "type": "text", + "content": "[5] James F Blinn. Models of light reflection for computer synthesized pictures. In Proceedings of the 4th annual conference on Computer graphics and interactive techniques, pages 192-198, 1977. 1, 2, 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 297, + 286, + 350 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 297, + 286, + 350 + ], + "spans": [ + { + "bbox": [ + 53, + 297, + 286, + 350 + ], + "type": "text", + "content": "[6] James Booth, Anastasios Roussos, Stefanos Zafeiriou, Allan Ponniah, and David J. Dunaway. A 3d morphable model learnt from 10,000 faces. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5543-5552, 2016. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 353, + 286, + 385 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 353, + 286, + 385 + ], + "spans": [ + { + "bbox": [ + 53, + 353, + 286, + 385 + ], + "type": "text", + "content": "[7] Chen Cao, Yanlin Weng, Stephen Lin, and Kun Zhou. 3d shape regression for real-time facial animation. ACM Transactions on Graphics (TOG), 32(4):1-10, 2013. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 387, + 286, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 387, + 286, + 430 + ], + "spans": [ + { + "bbox": [ + 53, + 387, + 286, + 430 + ], + "type": "text", + "content": "[8] Chen Cao, Yanlin Weng, Shun Zhou, Y. Tong, and Kun Zhou. Facewarehouse: A 3d facial expression database for visual computing. IEEE Transactions on Visualization and Computer Graphics, 20:413-425, 2014. 2, 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 432, + 286, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 432, + 286, + 485 + ], + "spans": [ + { + "bbox": [ + 53, + 432, + 286, + 485 + ], + "type": "text", + "content": "[9] Bindita Chaudhuri, Noranart Vesdapunt, Linda Shapiro, and Baoyuan Wang. Personalized face modeling for improved face reconstruction and motion retargeting. In European Conference on Computer Vision, pages 142-160. Springer, 2020. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 488, + 286, + 532 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 488, + 286, + 532 + ], + "spans": [ + { + "bbox": [ + 48, + 488, + 286, + 532 + ], + "type": "text", + "content": "[10] Hang Dai, Nick E. Pears, William Smith, and Christian Duncan. A 3d morphable model of craniofacial shape and texture variation. 2017 IEEE International Conference on Computer Vision (ICCV), pages 3104-3112, 2017. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 533, + 286, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 533, + 286, + 588 + ], + "spans": [ + { + "bbox": [ + 48, + 533, + 286, + 588 + ], + "type": "text", + "content": "[11] Paul Debevec, Tim Hawkins, Chris Tchou, Haarm-Pieter Duiker, Westley Sarokin, and Mark Sagar. Acquiring the reflectance field of a human face. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pages 145-156, 2000. 1, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 590, + 286, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 590, + 286, + 612 + ], + "spans": [ + { + "bbox": [ + 48, + 590, + 286, + 612 + ], + "type": "text", + "content": "[12] Yu Deng. Deep3dfacereconstruction, 2019. https://github.com/microsoft/Deep3DFaceReconstruction. 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 613, + 286, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 613, + 286, + 667 + ], + "spans": [ + { + "bbox": [ + 48, + 613, + 286, + 667 + ], + "type": "text", + "content": "[13] Yu Deng, Jiaolong Yang, Dong Chen, Fang Wen, and Xin Tong. Disentangled and controllable face image generation via 3d imitative-contrastive learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5154–5163, 2020. 1" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 670, + 286, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 286, + 713 + ], + "type": "text", + "content": "[14] Yu Deng, Jiaolong Yang, Sicheng Xu, Dong Chen, Yunde Jia, and Xin Tong. Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set. In Proceedings of the IEEE/CVF Conference on Computer" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 327, + 73, + 544, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 544, + 94 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 544, + 94 + ], + "type": "text", + "content": "Vision and Pattern Recognition Workshops, pages 0-0, 2019. 1, 5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 96, + 545, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 545, + 140 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 545, + 140 + ], + "type": "text", + "content": "[15] Abdallah Dib, Junghyun Ahn, Cedric Thebault, Philippe-Henri Gosselin, and Louis Chevallier. S2f2: Self-supervised high fidelity face reconstruction from monocular image. arXiv preprint arXiv:2203.07732, 2022. 1, 4, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 142, + 545, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 142, + 545, + 196 + ], + "spans": [ + { + "bbox": [ + 308, + 142, + 545, + 196 + ], + "type": "text", + "content": "[16] Abdallah Dib, Gaurav Bharaj, Junghyun Ahn, Cédric Thébault, Philippe Gosselin, Marco Romeo, and Louis Chevallier. Practical face reconstruction via differentiable ray tracing. In Computer Graphics Forum, volume 40, pages 153-164. Wiley Online Library, 2021. 1, 4, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 198, + 545, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 198, + 545, + 274 + ], + "spans": [ + { + "bbox": [ + 308, + 198, + 545, + 274 + ], + "type": "text", + "content": "[17] Abdallah Dib, Cédric Thébault, Junghyun Ahn, Philippe-Henri Gosselin, Christian Theobalt, and Louis Chevallier. Towards high fidelity monocular face reconstruction with rich reflectance using self-supervised learning and ray tracing. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 12819-12829, 2021. 1, 4, 5, 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 276, + 545, + 341 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 276, + 545, + 341 + ], + "spans": [ + { + "bbox": [ + 308, + 276, + 545, + 341 + ], + "type": "text", + "content": "[18] Bernhard Egger, Sandro Schonborn, Andreas Schneider, Adam Kortylewski, Andreas Morel-Forster, Clemens Blumer, and Thomas Vetter. Occlusion-aware 3d morphable models and an illumination prior for face image analysis. International Journal of Computer Vision, 126(12):1269-1287, 2018. 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 343, + 545, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 343, + 545, + 398 + ], + "spans": [ + { + "bbox": [ + 308, + 343, + 545, + 398 + ], + "type": "text", + "content": "[19] Bernhard Egger, William AP Smith, Ayush Tewari, Stefanie Wuhrer, Michael Zollhoefer, Thabo Beeler, Florian Bernard, Timo Bolkart, Adam Kortylewski, Sami Romdhani, et al. 3d morphable face models—past, present, and future. ACM Transactions on Graphics (TOG), 39(5):1-38, 2020. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 399, + 545, + 443 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 399, + 545, + 443 + ], + "spans": [ + { + "bbox": [ + 308, + 399, + 545, + 443 + ], + "type": "text", + "content": "[20] Yao Feng, Haiwen Feng, Michael J Black, and Timo Bolkart. Learning an animatable detailed 3d face model from inthe-wild images. ACM Transactions on Graphics (ToG), 40(4):1-13, 2021. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 445, + 545, + 499 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 445, + 545, + 499 + ], + "spans": [ + { + "bbox": [ + 308, + 445, + 545, + 499 + ], + "type": "text", + "content": "[21] Pablo Garrido, Michael Zollhöfer, Dan Casas, Levi Valgaerts, Kiran Varanasi, Patrick Pérez, and Christian Theobalt. Reconstruction of personalized 3d face rigs from monocular video. ACM Transactions on Graphics (TOG), 35(3):1-15, 2016. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 501, + 545, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 501, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 308, + 501, + 545, + 555 + ], + "type": "text", + "content": "[22] Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, and William T Freeman. Unsupervised training for 3d morphable model regression. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8377-8386, 2018. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 557, + 545, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 557, + 545, + 621 + ], + "spans": [ + { + "bbox": [ + 308, + 557, + 545, + 621 + ], + "type": "text", + "content": "[23] Thomas Gereg, Andreas Morel-Forster, Clemens Blumer, Bernhard Egger, Marcel Luthi, Sandro Schonborn, and Thomas Vetter. Morphable face models—an open framework. In 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), pages 75–82. IEEE, 2018. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 624, + 545, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 624, + 545, + 678 + ], + "spans": [ + { + "bbox": [ + 308, + 624, + 545, + 678 + ], + "type": "text", + "content": "[24] Abhijeet Ghosh, Graham Fyffe, Borom Tunwattanapong, Jay Busch, Xueming Yu, and Paul Debevec. Multiview face capture using polarized spherical gradient illumination. In Proceedings of the 2011 SIGGRAPH Asia Conference, pages 1-10, 2011. 1, 2, 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "type": "text", + "content": "[25] Ralph Gross, Iain Matthews, Jeffrey Cohn, Takeo Kanade, and Simon Baker. Multi-pie. Image and vision computing, 28(5):807-813, 2010. 2, 4, 7" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "8606" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "text", + "content": "[26] Kaiming He, X. Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, 2016. 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 118, + 288, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 118, + 288, + 149 + ], + "spans": [ + { + "bbox": [ + 49, + 118, + 288, + 149 + ], + "type": "text", + "content": "[27] James T Kajiya. The rendering equation. In Proceedings of the 13th annual conference on Computer graphics and interactive techniques, pages 143-150, 1986. 3, 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 151, + 287, + 182 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 151, + 287, + 182 + ], + "spans": [ + { + "bbox": [ + 49, + 151, + 287, + 182 + ], + "type": "text", + "content": "[28] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. ArXiv, abs/1710.10196, 2018. 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 184, + 287, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 184, + 287, + 227 + ], + "spans": [ + { + "bbox": [ + 49, + 184, + 287, + 227 + ], + "type": "text", + "content": "[29] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4396-4405, 2018. 2, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 228, + 287, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 228, + 287, + 281 + ], + "spans": [ + { + "bbox": [ + 49, + 228, + 287, + 281 + ], + "type": "text", + "content": "[30] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110-8119, 2020. 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 283, + 287, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 283, + 287, + 304 + ], + "spans": [ + { + "bbox": [ + 49, + 283, + 287, + 304 + ], + "type": "text", + "content": "[31] Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2015. 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 305, + 287, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 305, + 287, + 348 + ], + "spans": [ + { + "bbox": [ + 49, + 305, + 287, + 348 + ], + "type": "text", + "content": "[32] Oliver Klehm, Fabrice Rousselle, Marios Papas, Derek Bradley, Christophe Hery, B. Bickel, Wojciech Jarosz, and Thabo Beeler. Recent advances in facial appearance capture. Computer Graphics Forum, 34, 2015. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 49, + 350, + 287, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 350, + 287, + 392 + ], + "spans": [ + { + "bbox": [ + 49, + 350, + 287, + 392 + ], + "type": "text", + "content": "[33] Ritwik K. Kumar, Michael J. Jones, and Tim K. Marks. Morphable reflectance fields for enhancing face recognition. 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 2606-2613, 2010. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 49, + 394, + 287, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 394, + 287, + 437 + ], + "spans": [ + { + "bbox": [ + 49, + 394, + 287, + 437 + ], + "type": "text", + "content": "[34] Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, and Timo Aila. Modular primitives for high-performance differentiable rendering. ACM Transactions on Graphics (TOG), 39:1-14, 2020. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 49, + 438, + 287, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 438, + 287, + 502 + ], + "spans": [ + { + "bbox": [ + 49, + 438, + 287, + 502 + ], + "type": "text", + "content": "[35] Alexandros Lattas, Stylianos Moschoglou, Baris Gecer, Stylianos Ploumpis, Vasileios Triantafyllou, Abhijeet Ghosh, and Stefanos Zafeiriou. Avatarme: Realistically renderable 3d facial reconstruction \"in-the-wild\". 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 757-766, 2020. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 49, + 504, + 287, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 504, + 287, + 536 + ], + "spans": [ + { + "bbox": [ + 49, + 504, + 287, + 536 + ], + "type": "text", + "content": "[36] Chen Li, Kun Zhou, and Stephen Lin. Intrinsic face image decomposition with human face priors. In European Conference on Computer Vision, 2014. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 49, + 537, + 287, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 537, + 287, + 602 + ], + "spans": [ + { + "bbox": [ + 49, + 537, + 287, + 602 + ], + "type": "text", + "content": "[37] Ruilong Li, Kalle Bladin, Yajie Zhao, Chinmay Chinara, Owen Ingraham, Pengda Xiang, Xinglei Ren, Pratusha Bhuvana Prasad, Bipin Kishore, Jun Xing, and Hao Li. Learning formation of physically-based face attributes. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3407-3416, 2020. 1, 2, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 49, + 604, + 287, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 604, + 287, + 635 + ], + "spans": [ + { + "bbox": [ + 49, + 604, + 287, + 635 + ], + "type": "text", + "content": "[38] Tianye Li, Timo Bolkart, Michael J Black, Hao Li, and Javier Romero. Learning a model of facial shape and expression from 4d scans. ACM Trans. Graph., 36(6):194-1, 2017. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 49, + 636, + 287, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 636, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 49, + 636, + 287, + 689 + ], + "type": "text", + "content": "[39] Wan-Chun Ma, Tim Hawkins, Pieter Peers, Charles-Felix Chabert, Malte Weiss, Paul E Debevec, et al. Rapid acquisition of specular and diffuse normal maps from polarized spherical gradient illumination. Rendering Techniques, 2007(9):10, 2007. 1, 2, 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 49, + 691, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 691, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 49, + 691, + 287, + 713 + ], + "type": "text", + "content": "[40] R. MallikarjunB., Ayush Tewari, Tae-Hyun Oh, Tim Weyrich, B. Bickel, Hans-Peter Seidel, Hanspeter Pfister," + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 126 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 126 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 126 + ], + "type": "text", + "content": "Wojciech Matusik, Mohamed A. Elgharib, and Christian Theobalt. Monocular reconstruction of neural face reflectance fields. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4789-4798, 2021. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 130, + 545, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 130, + 545, + 152 + ], + "spans": [ + { + "bbox": [ + 308, + 130, + 545, + 152 + ], + "type": "text", + "content": "[41] Wojciech Matusik. A data-driven reflectance model. PhD thesis, Massachusetts Institute of Technology, 2003. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 154, + 545, + 209 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 154, + 545, + 209 + ], + "spans": [ + { + "bbox": [ + 308, + 154, + 545, + 209 + ], + "type": "text", + "content": "[42] Rohit Pandey, Sergio Orts Escolano, Chloe Legendre, Christian Haene, Sofien Bouaziz, Christoph Rhemann, Paul Debevec, and Sean Fanello. Total relighting: learning to relight portraits for background replacement. ACM Transactions on Graphics (TOG), 40(4):1-21, 2021. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 211, + 545, + 266 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 211, + 545, + 266 + ], + "spans": [ + { + "bbox": [ + 308, + 211, + 545, + 266 + ], + "type": "text", + "content": "[43] Pascal Paysan, Reinhard Knothe, Brian Amberg, Sami Romdhani, and Thomas Vetter. A 3d face model for pose and illumination invariant face recognition. 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, pages 296-301, 2009. 2, 3, 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 268, + 547, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 268, + 547, + 312 + ], + "spans": [ + { + "bbox": [ + 308, + 268, + 547, + 312 + ], + "type": "text", + "content": "[44] Ravi Ramamoorthi and Pat Hanrahan. An efficient representation for irradiance environment maps. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 497-500, 2001. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 314, + 545, + 358 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 314, + 545, + 358 + ], + "spans": [ + { + "bbox": [ + 308, + 314, + 545, + 358 + ], + "type": "text", + "content": "[45] Ravi Ramamoorthi and Pat Hanrahan. A signal-processing framework for inverse rendering. In Proceedings of the 28th annual conference on Computer graphics and interactive techniques, pages 117-128, 2001. 2, 4, 5" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 360, + 545, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 360, + 545, + 403 + ], + "spans": [ + { + "bbox": [ + 308, + 360, + 545, + 403 + ], + "type": "text", + "content": "[46] Nikhila Ravi, Jeremy Reizenstein, David Novotny, Taylor Gordon, Wan-Yen Lo, Justin Johnson, and Georgia Gkioxari. Accelerating 3d deep learning with pytorch3d. SIGGRAPH Asia 2020 Courses, 2020. 5" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 406, + 545, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 406, + 545, + 449 + ], + "spans": [ + { + "bbox": [ + 308, + 406, + 545, + 449 + ], + "type": "text", + "content": "[47] Jérémy Riviere, Paulo F. U. Gotardo, Derek Bradley, Abhijeet Ghosh, and Thabo Beeler. Single-shot high-quality facial geometry and skin appearance capture. ACM Transactions on Graphics (TOG), 39:81:1-81:12, 2020. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 453, + 545, + 496 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 453, + 545, + 496 + ], + "spans": [ + { + "bbox": [ + 308, + 453, + 545, + 496 + ], + "type": "text", + "content": "[48] Soumyadip Sengupta, Brian Curless, Ira Kemelmacher-Shlizerman, and Steven M. Seitz. A light stage on every desk. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 2400-2409, 2021. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 498, + 545, + 540 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 498, + 545, + 540 + ], + "spans": [ + { + "bbox": [ + 308, + 498, + 545, + 540 + ], + "type": "text", + "content": "[49] Artem Sevastopolsky, SA Ignatiev, Gonzalo Ferrer, Evgeny Burnaev, and Victor S. Lempitsky. Relightable 3d head portraits from a smartphone video. ArXiv, abs/2012.09963, 2020. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 544, + 545, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 544, + 545, + 599 + ], + "spans": [ + { + "bbox": [ + 308, + 544, + 545, + 599 + ], + "type": "text", + "content": "[50] William AP Smith, Alassane Seck, Hannah Dee, Bernard Tiddeman, Joshua B Tenenbaum, and Bernhard Egger. A morphable face albedo model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5011-5020, 2020. 1, 2, 3, 5, 7, 8" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 601, + 545, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 601, + 545, + 644 + ], + "spans": [ + { + "bbox": [ + 308, + 601, + 545, + 644 + ], + "type": "text", + "content": "[51] Giota Stratou, Abhijeet Ghosh, Paul E. Debevec, and Louis-Philippe Morency. Effect of illumination on automatic expression recognition: A novel 3d reconfigurable facial database. Face and Gesture 2011, pages 611–618, 2011. 2, 3, 8" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 647, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 647, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 647, + 545, + 713 + ], + "type": "text", + "content": "[52] Ayush Tewari, Florian Bernard, Pablo Garrido, Gaurav Bharaj, Mohamed Elgharib, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Fml: Face model learning from videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10812-10822, 2019. 1, 2, 3" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "8607" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 288, + 139 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 288, + 139 + ], + "type": "text", + "content": "[53] Ayush Tewari, Mohamed Elgharib, Gaurav Bharaj, Florian Bernard, Hans-Peter Seidel, Patrick Pérez, Michael Zollhofer, and Christian Theobalt. Stylerig: Rigging stylegan for 3d control over portrait images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6142-6151, 2020. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 140, + 288, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 140, + 288, + 195 + ], + "spans": [ + { + "bbox": [ + 48, + 140, + 288, + 195 + ], + "type": "text", + "content": "[54] Ayush Tewari, Hans-Peter Seidel, Mohamed Elgharib, Christian Theobalt, et al. Learning complete 3d morphable face models from images and videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3361-3371, 2021. 1, 2, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 197, + 288, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 197, + 288, + 262 + ], + "spans": [ + { + "bbox": [ + 48, + 197, + 288, + 262 + ], + "type": "text", + "content": "[55] Ayush Tewari, Michael Zollhöfer, Pablo Garrido, Florian Bernard, Hyeongwoo Kim, Patrick Pérez, and Christian Theobalt. Self-supervised multi-level face model learning for monocular reconstruction at over 250 hz. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2549-2559, 2018. 1, 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 263, + 288, + 329 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 263, + 288, + 329 + ], + "spans": [ + { + "bbox": [ + 48, + 263, + 288, + 329 + ], + "type": "text", + "content": "[56] Ayush Tewari, Michael Zollhofer, Hyeongwoo Kim, Pablo Garrido, Florian Bernard, Patrick Perez, and Christian Theobalt. Mofa: Model-based deep convolutional face autoencoder for unsupervised monocular reconstruction. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 1274-1283, 2017. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 331, + 288, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 331, + 288, + 374 + ], + "spans": [ + { + "bbox": [ + 48, + 331, + 288, + 374 + ], + "type": "text", + "content": "[57] Justus Thies, Mohamed Elgharib, Ayush Tewari, Christian Theobalt, and Matthias Nießner. Neural voice puppetry: Audio-driven facial reenactment. In European conference on computer vision, pages 716-731. Springer, 2020. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 376, + 288, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 376, + 288, + 430 + ], + "spans": [ + { + "bbox": [ + 48, + 376, + 288, + 430 + ], + "type": "text", + "content": "[58] Justus Thies, Michael Zollhofer, Marc Stamminger, Christian Theobalt, and Matthias Nießner. Face2face: Real-time face capture and reenactment of rgb videos. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2387-2395, 2016. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 432, + 288, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 432, + 288, + 464 + ], + "spans": [ + { + "bbox": [ + 48, + 432, + 288, + 464 + ], + "type": "text", + "content": "[59] Kenneth E Torrance and Ephraim M Sparrow. Theory for off-specular reflection from roughened surfaces. Josa, 57(9):1105-1114, 1967. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 466, + 288, + 509 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 466, + 288, + 509 + ], + "spans": [ + { + "bbox": [ + 48, + 466, + 288, + 509 + ], + "type": "text", + "content": "[60] Luan Tran, Feng Liu, and Xiaoming Liu. Towards high-fidelity nonlinear 3d face morphable model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1126-1135, 2019. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 511, + 288, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 511, + 288, + 544 + ], + "spans": [ + { + "bbox": [ + 48, + 511, + 288, + 544 + ], + "type": "text", + "content": "[61] Luan Tran and Xiaoming Liu. Nonlinear 3d face morphable model. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7346-7355, 2018. 2, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 545, + 288, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 545, + 288, + 588 + ], + "spans": [ + { + "bbox": [ + 48, + 545, + 288, + 588 + ], + "type": "text", + "content": "[62] Yifan Wang, Aleksander Holynski, Xiuming Zhang, and Xuaner Cecilia Zhang. Sunstage: Portrait reconstruction and relighting using the sun as a light stage. ArXiv, abs/2204.03648, 2022. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 590, + 288, + 634 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 590, + 288, + 634 + ], + "spans": [ + { + "bbox": [ + 48, + 590, + 288, + 634 + ], + "type": "text", + "content": "[63] Zhou Wang, Alan Conrad Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13:600-612, 2004. 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 635, + 288, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 635, + 288, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 635, + 288, + 678 + ], + "type": "text", + "content": "[64] Zhibo Wang, Xin Yu, Ming Lu, Quan Wang, Chen Qian, and Feng Xu. Single image portrait relighting via explicit multiple reflectance channel modeling. ACM Transactions on Graphics (TOG), 39:1-13, 2020. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 681, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 681, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 681, + 288, + 713 + ], + "type": "text", + "content": "[65] Tim Weyrich, Wojciech Matusik, Hanspeter Pfister, Bernd Bickel, Craig Donner, Chien Tu, Janet McAndless, Jinho Lee, Addy Ngan, Henrik Wann Jensen, et al. Analysis of" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 374 + ], + "type": "list", + "angle": 0, + "index": 20, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 106 + ], + "type": "text", + "content": "human faces using a measurement-based skin reflectance model. ACM Transactions on Graphics (ToG), 25(3):1013-1024, 2006. 1, 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 107, + 545, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 545, + 140 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 545, + 140 + ], + "type": "text", + "content": "[66] Shangzhe Wu, Christian Rupprecht, and Andrea Vedaldi. Unsupervised learning of probably symmetric deformable 3d objects from images in the wild. In CVPR, 2020. 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 141, + 545, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 141, + 545, + 195 + ], + "spans": [ + { + "bbox": [ + 307, + 141, + 545, + 195 + ], + "type": "text", + "content": "[67] Shugo Yamaguchi, Shunsuke Saito, Koki Nagano, Yajie Zhao, Weikai Chen, Kyle Olszewski, Shigeo Morishima, and Hao Li. High-fidelity facial reflectance and geometry inference from an unconstrained image. ACM Transactions on Graphics (TOG), 37:1-14, 2018. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 197, + 545, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 197, + 545, + 261 + ], + "spans": [ + { + "bbox": [ + 307, + 197, + 545, + 261 + ], + "type": "text", + "content": "[68] Haotian Yang, Hao Zhu, Yanru Wang, Mingkai Huang, Qiu Shen, Ruigang Yang, and Xun Cao. Facescape: a large-scale high quality 3d face dataset and detailed riggable 3d face prediction. In Proceedings of the IEEE/cvf conference on computer vision and pattern recognition, pages 601-610, 2020. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 263, + 545, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 263, + 545, + 317 + ], + "spans": [ + { + "bbox": [ + 307, + 263, + 545, + 317 + ], + "type": "text", + "content": "[69] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 586-595, 2018. 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 319, + 545, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 319, + 545, + 374 + ], + "spans": [ + { + "bbox": [ + 307, + 319, + 545, + 374 + ], + "type": "text", + "content": "[70] Yufeng Zheng, Victoria Fernández Abrevaya, Marcel C Bühler, Xu Chen, Michael J Black, and Otmar Hilliges. Im avatar: Implicit morphable head avatars from videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13545-13555, 2022. 1" + } + ] + } + ], + "index": 19 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "8608" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning a Deep Color Difference Metric for Photographic Images/dbc258de-351a-4a82-83a7-8b31f7a409d8_content_list.json b/2023/Learning a Deep Color Difference Metric for Photographic Images/dbc258de-351a-4a82-83a7-8b31f7a409d8_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..4354c444228e6559c10e48045dc0b22b3d029a74 --- /dev/null +++ b/2023/Learning a Deep Color Difference Metric for Photographic Images/dbc258de-351a-4a82-83a7-8b31f7a409d8_content_list.json @@ -0,0 +1,1502 @@ +[ + { + "type": "text", + "text": "Learning a Deep Color Difference Metric for Photographic Images", + "text_level": 1, + "bbox": [ + 148, + 130, + 821, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Haoyu Chen $^{1}$ Zhihua Wang $^{1,2,*}$ Yang Yang $^{3}$ Qilin Sun $^{4}$ Kede Ma $^{1}$", + "bbox": [ + 197, + 179, + 767, + 199 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1City University of Hong Kong 2Shenzhen MSU-BIT University", + "bbox": [ + 264, + 200, + 705, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "3Shenzhen Transsion Holdings Co., Ltd. 4The Chinese University of Hong Kong (Shenzhen)", + "bbox": [ + 173, + 217, + 795, + 233 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{haoychen3-c,zhihua.wang}@my.cityu.edu.hk, kede.ma@cityu.edu.hk,", + "bbox": [ + 202, + 237, + 764, + 251 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "yang.yang6@transsion.com, sunqilin@cuhk.edu.cn", + "bbox": [ + 279, + 255, + 687, + 268 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 304, + 312, + 319 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Most well-established and widely used color difference (CD) metrics are handcrafted and subject-calibrated against uniformly colored patches, which do not generalize well to photographic images characterized by natural scene complexities. Constructing CD formulae for photographic images is still an active research topic in imaging/illumination, vision science, and color science communities. In this paper, we aim to learn a deep CD metric for photographic images with four desirable properties. First, it well aligns with the observations in vision science that color and form are linked inextricably in visual cortical processing. Second, it is a proper metric in the mathematical sense. Third, it computes accurate CDs between photographic images, differing mainly in color appearances. Fourth, it is robust to mild geometric distortions (e.g., translation or due to parallax), which are often present in photographic images of the same scene captured by different digital cameras. We show that all four properties can be satisfied at once by learning a multi-scale autoregressive normalizing flow for feature transform, followed by the Euclidean distance which is linearly proportional to the human perceptual CD. Quantitative and qualitative experiments on the large-scale SPCD dataset demonstrate the promise of the learned CD metric. Source code is available at https://github.com/haoychen3/CD-Flow.", + "bbox": [ + 76, + 335, + 472, + 714 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 743, + 209, + 758 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "For a long time in vision science community, the modular and segregated view of cortical color processing predominated [46]: the visual perception/processing of color-related quantities is separate from and in parallel with the perception/processing of form (i.e., object shape and structure), motion direction, and depth order in natural scenes. As a result, vision scientists preferred to investigate color", + "bbox": [ + 76, + 768, + 468, + 876 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "perception under minimal conditions on form [20, 46], for example, using uniformly colored patches.", + "bbox": [ + 500, + 305, + 890, + 335 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The idea that color as a visual sensation can be analyzed separately had a profound impact on the development of computational formulae for color difference (CD) assessment. Till now, the most well-established and widely used CD metrics are primarily built upon the three-dimensional spatially-isotropic CIELAB coordinate system [32], recommended by the International Commission on Illumination (abbreviated as CIE from its French name Commission Internationale de l'Éclairage) in 1976. However, the uniformity1 of the CIELAB space is not as ideal as intended [28], even for uniformly colored patches. Thus, more complex and parametric formulae are proposed to rectify different aspects of perceptual non-uniformity. Representative methods include JPC79 [33], $\\mathrm{CMC}(l:c)^2$ [9], BFD(l:c) [31], CIE94 [34], and CIEDE2000 [29], in which the parameters are calibrated by fitting the human perceptual CD measurements of uniformly colored patches. A naive application of these metrics to photographic images is to compute the mean of the CDs between co-located pixels, which has been empirically shown to correlate poorly to human perception of CDs [38].", + "bbox": [ + 496, + 335, + 892, + 654 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Back to the vision science community, with more supporting evidence from psychophysical and perceptual studies [2, 6, 27, 47], vision scientists have gradually come to agree on an alternative and more persuasive view of color perception: color and form (and motion) are inextricably interdependent as a unitary process of perceptual organization [19, 46]. Even the primary visual cortex (i.e., V1) plays a significant role in color perception through two types of color-sensitive neurons: single-opponent and double-opponent cells. The single-opponent cells are sensitive to large areas of color, while the double-opponent cells respond to color patterns, textures, and boundaries [24, 46].", + "bbox": [ + 496, + 655, + 893, + 838 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "1A system is perceptually uniform if a small perturbation to a component value is approximately equally perceptible across the range of that value [43].", + "bbox": [ + 500, + 849, + 890, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "$2l$ and $c$ are two multiplicative parameters in the model to be fitted.", + "bbox": [ + 517, + 887, + 870, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author.", + "bbox": [ + 94, + 886, + 220, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "22242", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "At later stages, color is transformed to more complex and abstract features, which represent the integral properties of objects, and remain consistent against the changes of the environmental illumination [17, 37].", + "bbox": [ + 75, + 90, + 468, + 151 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Inspired by these scientific findings, researchers and engineers began to take spatial context (i.e., local surrounding regions) into account, when designing CD formulae. Representative strategies include low-pass spatial filtering [57], histograming [18], patch-based comparison [49], and texture-based segmentation [38]. Most recently, Wang et al. [51] established the largest photographic image dataset, SPCD, for perceptual CD assessment. They further trained a lightweight deep neural network (DNN) for CD assessment of photographic images in a data-driven fashion, as a generalization of several existing CD metrics built on the CIE colorimetry. Nonetheless, the learned formula may not be a proper metric, due to reliance on the possibly surjective mapping for feature transform.", + "bbox": [ + 75, + 152, + 467, + 362 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this work, we further pursue the data-driven approach. We aim to learn a deep CD metric for photographic images with four desirable properties.", + "bbox": [ + 76, + 363, + 467, + 407 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- It is conceptually inspired by color perception in the visual cortex. The design of our approach should respect the view that color and form interact inextricably through all stages of visual cortical processing.", + "- It is a proper metric that satisfies non-negativity, symmetry, identity of indiscernibles, and triangle inequality. Such design has been proven useful for perceptual optimization of image processing systems [11].", + "- It is accurate in predicting the human perceptual CDs of photographic images, with good generalization to uniformly colored patches.", + "- It is robust to mild geometric distortions (e.g., translation and dilation), which are often present in photographic images of the same scene captured with different camera settings or along different lines of sight." + ], + "bbox": [ + 94, + 416, + 467, + 650 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We show that all the four desirable properties can be satisfied at once by learning a multi-scale autoregressive normalizing flow (a variant of RealNVP [13] to be specific) for feature transform, followed by Euclidean distance measure in the transformed space. More specifically, we achieve the first property by the squeezing operation (also known as invertible downsampling) in the normalizing flow, which trades space size for channel dimension. The second property is a direct consequence of the bijectivity of the normalizing flow and the Euclidean distance measure. We achieve the third property by optimizing the model parameters to explain the human perceptual CDs in SPCD [51]. We achieve the fourth property by enforcing the normalizing flow to be multi-scale and autoregressive, in which the features at a particular scale are conditioned on those at a higher (i.e., coarser) scale. By doing so, our metric automatically learns", + "bbox": [ + 75, + 659, + 467, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "to preferentially rely on coarse-scale feature representations with more built-in tolerance to geometric distortions for CD assessment.", + "bbox": [ + 498, + 90, + 890, + 133 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We conduct extensive experiments on the large-scale SPCD dataset [51], and find that our proposed metric, termed as CD-Flow, outperforms 15 CD formulae in assessing CDs of photographic images, produces competitive multi-scale local CD maps without any dense supervision, and is more robust to geometric distortions. Moreover, we empirically verify the perceptual uniformity of the learned color image representation from multiple aspects.", + "bbox": [ + 496, + 136, + 890, + 257 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 271, + 640, + 286 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this section, we first review CD formulae in a broader context, and then discuss normalizing flow-based models, which are core to the proposed CD metric.", + "bbox": [ + 496, + 297, + 890, + 340 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "CD Formulae. The CD assessment is necessary for day-to-day color control, and is indispensable for color matching in color industries. Admittedly, CD formulae have accelerated the instrumental pass/fail devices for color judgments, but much still needs to be done for complete satisfaction. The scientific investigation of perceptual CDs can be dated at least back to Young and Helmholtz, who proposed and developed the trichromatic theory of color, which is the foundation of the metameric color matching experiment. CIELAB [32] is one of the most successful CD metrics recommended by CIE in 1976, and has been widely adopted in industry for a long time. However, the CIELAB color space is not perceptually uniform [30], which motivates the development of CIE94 [34] and CIEDE2000 [29] through the introduction of application-specific parameters. Other CIELAB-based CD metrics include JPC79 [33], BFD(l:c) [31], and $\\mathrm{CMC}(l:c)$ [9]. The introduced parameters are primarily calibrated using uniformly colored patches, digital or printed, which are statistically and semantically different from photographic images. Thus, the generalization of these metrics to photographic images is somewhat limited, especially when misalignment due to geometric distortions is present.", + "bbox": [ + 496, + 342, + 890, + 688 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To incorporate spatial context into CD assessment, Zhang and Wandell [57] presented S-CIELAB, which extends CIELAB by adding spatial low-pass filtering as preprocessing. Similarly, Ouni et al. [39] provided a spatial extension of CIEDE2000. Lee et al. [26] re-examined histogram intersection, which is widely used in color image index, for the purpose of color image similarity assessment. Hong and Luo [18] chose to give larger weights to areas with spatially homogeneous colors and pixels with larger CDs. This method was later augmented by spatial filtering [41]. Lee and Plataniotis [25] built upon the philosophy of color structural similarity, and gave the hue component careful treatment with circular statistics. Jaramillo et al. [38] grouped the same texture areas for human-like CD", + "bbox": [ + 496, + 688, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "22243", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/9b62db9181548ec0173451c49935e05b805ca83459b562d3ab054b74c2fa6486.jpg", + "image_caption": [ + "Figure 1. Feature transform of the proposed CD-Flow for perceptual CD assessment. The coordinate transform module consists of six scales. The leftmost yellow cube represents the input image with dimension $s \\times s \\times 3$ . The six blue cubes represent the six scales of representations, respectively. Each red cube represents a cascade of squeezing operation, multiple flow steps, and a splitting operation, in which each flow step is composed of an actnorm operation, an invertible $1 \\times 1$ convolution, and an affine coupling layer. The splitting operation is excluded in the last red cube." + ], + "image_footnote": [], + "bbox": [ + 106, + 90, + 867, + 251 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "assessment, using local binary patterns as texture descriptors.", + "bbox": [ + 75, + 356, + 468, + 386 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "General-purpose image quality models, including full-reference ones - SSIM [49], VSI [55], LPIPS [56] and DISTS [10], reduced-reference ones - Wang05 [50] and Yu09 [54], and no-reference ones - BRISQUE [35], NIQE [36] and Gao13 [14] can be directly adopted for CD assessment, regarding CDs as a particular form of \"visual degradations.\" Meanwhile, just-noticeable difference (JND) methods, e.g., Butteraugli [1] and FLLIP [3], also attempt to characterize visually indistinguishable color changes between two images. In the era of deep learning, due to the lack of sufficient human-labeled training data, DNN-based CD formulae are rarely proposed. Wang et al. [51] created the first largest image dataset, SPCD, for perceptual CD assessment, and made one of the first attempts to train a DNN-based CD measure for photographic images. However, the underlying feature transform is not mathematically bijective.", + "bbox": [ + 75, + 386, + 470, + 643 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Normalizing Flow-based Models. Normalizing flow-based generative models are constructed by bijective functions $f: \\mathbb{R}^D \\to \\mathbb{R}^D$ , with typically easy-to-compute analytical inverse $f^{-1}: \\mathbb{R}^D \\to \\mathbb{R}^D$ . The primary goal of $f$ is to map raw data $\\pmb{x}$ to samples $\\pmb{z} = f(\\pmb{x})$ from a simple probability distribution $p_{\\mathcal{Z}}(\\pmb{z})$ . Many classic machine learning algorithms can be cast in this framework, such as principal component analysis (PCA, where $f$ is a linear transform and $p_{\\mathcal{Z}}(\\pmb{z})$ is standard Gaussian) and independent component analysis (ICA, where $f$ is again linear and $p_{\\mathcal{Z}}$ is factorized and heavy-tailed).", + "bbox": [ + 75, + 643, + 470, + 809 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In 2014, Dinh et al. [12] proposed non-linear independent component estimation (NICE), as a generalization of ICA. NICE is considered the first normalizing flow with the introduction of the additive coupling to ease the calculation of the Jacobian determinant. To make flow-based models more suitable for image-related tasks, Dinh et", + "bbox": [ + 75, + 810, + 470, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "al. [13] extended NICE to RealNVP, which admits a multiscale autoregressive architecture, implemented by squeezing and affine coupling. Kingma and Dhariwal [22] introduced the invertible $1 \\times 1$ convolution (i.e., the linear transform in PCA) to replace the fixed random permutation for splitting the channel dimension during multiscale processing. The batch normalization in RealNVP is also replaced with activation normalization (i.e., actnorm). To allow unconstrained architectural design, Grathwohl et al. [16] leveraged the Hutchinson's trace estimator for scalable and unbiased estimation of the log-density. Similarly, Behrmann et al. [4] proposed invertible residual networks (i-ResNet), introducing a tractable estimation to the Jacobian log-determinant of a residual block. Other representative normalizing flow work includes hierarchical recursive coupling [23] for increasing flow expressiveness, Wavelet Flow [53] for scaling flow to ultra-high dimensional data, and Discrete Flow [48] for discrete data modeling. In this paper, we do not use normalizing flow for generative modeling, but for invertible feature transform.", + "bbox": [ + 496, + 356, + 893, + 657 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Proposed CD-Flow", + "text_level": 1, + "bbox": [ + 500, + 672, + 684, + 690 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we detail our proposed CD metric, CD-Flow, consisting of two key components: the feature transform and the distance measure [29, 32]. The feature transform is built upon a variant of RealNVP [13], which is a learnable invertible transformation between input data and samples from a pre-fixed latent distribution. The CD distance is then computed using the Euclidean distance.", + "bbox": [ + 496, + 699, + 893, + 806 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Problem Definition", + "text_level": 1, + "bbox": [ + 500, + 816, + 683, + 830 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We denote the RGB image space as $\\mathcal{X}$ with an unknown distribution $p_{\\mathcal{X}}$ and the transformed representation space as $\\mathcal{Z}$ with a latent distribution $p_{\\mathcal{Z}}$ . We are given a training dataset $\\mathcal{D} = \\{(x^{(i)},y^{(i)}),\\varDelta V^{(i)}\\}_{i = 1}^{M}$ , where", + "bbox": [ + 496, + 839, + 893, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "22244", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/7a77d0b7f59a6340b78e40334f953c28eae652f9b94fd5ecf4f80b2acba94c4f.jpg", + "image_caption": [ + "Figure 2. Architecture of the feature transform, adapted from [22]. The three yellow blocks constitute one step of flow." + ], + "image_footnote": [], + "bbox": [ + 81, + 89, + 467, + 223 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$\\pmb{x}^{(i)}, \\pmb{y}^{(i)} \\in \\mathcal{X}$ form the $i$ -th image pair of the same visual content but different color appearances, $\\Delta V^{(i)}$ represents the corresponding human perceptual CD collected from a subjective experiment, and $M$ is the number of training pairs. Our goal is to learn a normalizing flow-based invertible and differentiable transform $f$ , which maps RGB images to latent representations with Gaussian conditionals for CD assessment.", + "bbox": [ + 75, + 284, + 468, + 406 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. CD-Flow", + "text_level": 1, + "bbox": [ + 76, + 415, + 181, + 429 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Feature Transform. Figure 1 illustrates the system diagram of the multi-scale autoregressive normalizing flow for the feature transform, which consists of $K$ scales of flow processing: $f = f_{1} \\circ f_{2} \\circ \\dots \\circ f_{K}$ for multi-scale color and form interaction and abstraction. At the $k$ -th scale, $z_{2(k-1)}$ is processed and split into $z_{2k-1}$ and $z_{2k}$ , the latter of which further undergoes the $k+1$ -th scale of processing and splitting. At the final $K$ -th scale, we only process $z_{2(K-1)}$ to $z_{2K-1}$ without splitting. By this notation, we assume that $z_{0} = x$ is the input RGB image. The probability density of the latent representation $z = \\{z_{1}, z_{3}, \\ldots, z_{2K-1}\\}$ can then be conditionally factorized as", + "bbox": [ + 75, + 438, + 468, + 619 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} p (\\boldsymbol {z}) = \\prod_ {k = 1} ^ {K - 1} p \\left(\\boldsymbol {z} _ {2 k - 1} \\mid \\left\\{\\boldsymbol {z} _ {\\geq (2 k + 1)} \\right\\}\\right) p (\\boldsymbol {z} _ {2 K - 1}) \\\\ = \\prod_ {k = 1} ^ {K - 1} p \\left(\\boldsymbol {z} _ {2 k - 1} \\mid \\boldsymbol {z} _ {2 k}\\right) p \\left(\\boldsymbol {z} _ {2 K - 1}\\right). \\tag {1} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 114, + 627, + 468, + 712 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The second equality is due to the bijectivity of the normalizing flow. $p(z_{2k-1}|z_{2k})$ , for $k \\in \\{1, 2, \\dots, K-1\\}$ , can conveniently be modeled as conditionally independent Gaussians. That is, the mean vector and the diagonal covariance matrix are computed from $z_{2k}$ through say a tiny neural network [13]. Similarly, $p(z_{2K-1})$ is modeled as (unconditionally) independent Gaussians, where the parameters are directly estimated via backpropagation. As shown in Figure 2, each scale of flow processing consists of a squeezing operation, multiple flow steps, and a splitting operation. Each flow step is further decomposed into three operations: actnorm, invertible $1 \\times 1$ convolution, and affine coupling.", + "bbox": [ + 75, + 719, + 467, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "- Actnorm is introduced to replace the batch normalization to avoid degenerated performance when the minibatch size is small. Given an input $z$ of size $c \\times h \\times w$ , the output of the same size can be computed by", + "bbox": [ + 517, + 90, + 890, + 151 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {z} ^ {\\prime} = \\boldsymbol {s} \\odot \\boldsymbol {z} + \\boldsymbol {t}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 656, + 160, + 890, + 175 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "with the log-determinant $h\\cdot w\\cdot \\log |\\operatorname *{det}(\\operatorname {diag}(s))|$ $\\{s,t\\}$ are learnable scaling and bias parameters.", + "bbox": [ + 531, + 183, + 888, + 214 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "- Invertible $1 \\times 1$ Convolution is a learnable linear transform for channel mixing. Given a $c \\times h \\times w$ input $\\mathbf{z}$ and a $c \\times c$ weight matrix $\\mathbf{W}$ , we have", + "bbox": [ + 517, + 222, + 890, + 267 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nz ^ {\\prime} = W z, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 673, + 276, + 890, + 291 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "with the log-determinant $h \\cdot w \\cdot \\log |\\operatorname{det}(\\mathbf{W})|$ .", + "bbox": [ + 531, + 300, + 839, + 316 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "- Affine Coupling constrains its Jacobian to be a triangular matrix, which facilitates the log-determinant computation. Specifically, the input $z$ of $D$ dimensions is first split into two non-overlapping subsets: $z_{1:d}$ and $z_{d+1:D}$ , where $d < D$ . The output of the same dimension can be computed by", + "bbox": [ + 517, + 323, + 890, + 414 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {z} _ {1: d} ^ {\\prime} = \\boldsymbol {z} _ {1: d},\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 422, + 687, + 438 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {z} _ {d + 1: D} ^ {\\prime} = \\boldsymbol {z} _ {d + 1: D} \\odot e ^ {s \\left(\\boldsymbol {z} _ {1: d}\\right)} + t \\left(\\boldsymbol {z} _ {1: d}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 584, + 434, + 890, + 460 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $s(\\cdot)$ and $t(\\cdot)$ are the scaling and translation functions, respectively, which are not necessarily invertible. The inverse is easily and analytically derived as", + "bbox": [ + 531, + 468, + 890, + 513 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {z} _ {1: d} = \\boldsymbol {z} _ {1: d} ^ {\\prime},\n$$\n", + "text_format": "latex", + "bbox": [ + 581, + 521, + 663, + 537 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {z} _ {d + 1: D} = \\left(\\boldsymbol {z} _ {d + 1: D} ^ {\\prime} - t \\left(\\boldsymbol {z} _ {1: d} ^ {\\prime}\\right)\\right) \\odot e ^ {- s \\left(\\boldsymbol {z} _ {1: d} ^ {\\prime}\\right)}. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 562, + 534, + 890, + 560 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "CD Distance. We adopt the Euclidean distance, i.e., the square root of mean squared differences between two latent color representations $f(\\pmb{x})$ and $f(\\pmb{y})$ , as the CD distance between two input images $\\pmb{x}$ and $\\pmb{y}$ :", + "bbox": [ + 500, + 570, + 890, + 631 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\Delta E (\\boldsymbol {x}, \\boldsymbol {y}) = \\sqrt {\\frac {(f (\\boldsymbol {x}) - f (\\boldsymbol {y})) ^ {T} (f (\\boldsymbol {x}) - f (\\boldsymbol {y}))}{D}}. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 522, + 638, + 890, + 672 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Loss Function", + "text_level": 1, + "bbox": [ + 500, + 686, + 643, + 700 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "It is straightforward to measure the $\\ell_p$ -norm induced distance between the predicted CD computed by Eq. (6) and the perceptual CD of the given image pair $(x, y)$ :", + "bbox": [ + 498, + 710, + 890, + 756 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\ell (\\boldsymbol {x}, \\boldsymbol {y}) = \\| \\Delta E (\\boldsymbol {x}, \\boldsymbol {y}) - \\Delta V (\\boldsymbol {x}, \\boldsymbol {y}) \\| _ {p}. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 763, + 890, + 780 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To encourage the robustness of CD-Flow to mild geometric distortions, which are often unavoidable in practice, we introduce a multi-scale version of Eq. (7) to put more emphasis on coarser-scale latent representations:", + "bbox": [ + 498, + 787, + 890, + 848 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\ell_ {\\mathrm {m s}} (\\boldsymbol {x}, \\boldsymbol {y}) = \\sum_ {k = 1} ^ {K} \\| \\Delta E _ {k} (\\boldsymbol {x}, \\boldsymbol {y}) - \\Delta V (\\boldsymbol {x}, \\boldsymbol {y}) \\| _ {p}, \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 540, + 854, + 890, + 896 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "22245", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/11a575541e1826ad60862ffc9e915d1ad56860ae9773953309b409dbc6a72c35.jpg", + "table_caption": [ + "Table 1. STRESS, PLCC, and SRCC between predicted CDs $(\\Delta E)$ and perceptual CDs $(\\Delta V)$ in SPCD. The top section lists representative CD formulae developed from homogeneous color patches. The second section contains CD measures adapted for natural images. The third section includes general-purpose image quality models. The fourth section consists of JND measures. Closest to ours, CD-Net is a DNN-based data-driven CD measure for natural images. The top two methods are highlighted in boldface" + ], + "table_footnote": [ + "1 The spatial extension of CIEDE2000." + ], + "table_body": "
MethodPerfectly aligned pairsNon-perfectly aligned pairsAll
\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)
CIELAB [45]31.2440.7930.77529.6390.6900.57931.8720.7160.666
CIE94 [34]34.7210.7900.77229.9160.6930.57234.3260.7100.654
CIEDE2000 [29]29.9750.8250.82130.3470.6670.56331.4390.7260.686
S-CIELAB [57]30.0940.8220.81931.8040.6310.52232.7800.7000.657
Hong06 [18]60.5570.7940.81057.0700.5430.46161.2270.6450.632
Ouni081[39]29.9770.8260.82130.3550.6680.56331.4440.7260.685
Jaramillo19 [38]43.4190.5140.50650.2990.0810.04168.8050.3210.329
SSIM [49]39.3930.5890.54953.0350.0770.04448.0250.3090.324
FLLIP [3]29.3180.7450.71527.1580.7340.64029.0990.7180.663
PieAPP [44]29.0440.7370.73737.5280.5220.45932.3540.6520.643
LPIPS [56]44.8110.6950.68853.1320.2190.17164.1450.4390.490
DISTS [10]31.4090.7450.74635.0430.5280.44732.9950.6400.626
Chou07 [8]50.7210.7870.78536.1840.6030.45949.5450.6120.557
Butteraugli [1]42.6200.6060.59348.2170.2580.24554.7370.3710.359
CD-Net [51]20.8910.8670.87022.5430.8180.77621.4310.8460.842
CD-Flow16.6130.8960.90421.3740.8560.79418.4730.8710.865
", + "bbox": [ + 80, + 157, + 890, + 444 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where", + "bbox": [ + 76, + 500, + 122, + 512 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\Delta E _ {k} (\\boldsymbol {x}, \\boldsymbol {y}) = \\sqrt {\\frac {\\left(f _ {k :} (\\boldsymbol {x}) - f _ {k :} (\\boldsymbol {y})\\right) ^ {T} \\left(f _ {k :} (\\boldsymbol {x}) - f _ {k :} (\\boldsymbol {y})\\right)}{D _ {k}}}. \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 86, + 537, + 468, + 593 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$f_{k}(\\pmb {x}) = [z_{2k - 1}^{T},\\dots ,z_{2K - 1}^{T}]^{T}$ and $D_{k}$ is the number of feature dimensions of $f_{k}(\\pmb {x})$ . That is, $\\Delta E_k(\\pmb {x},\\pmb {y})$ makes an estimate of $\\Delta V(\\pmb {x},\\pmb {y})$ using the $k$ -th to $K$ -th scale latent representations, and we have $\\Delta E_1$ equal to $\\Delta E$ in Eq. (6).", + "bbox": [ + 75, + 619, + 468, + 684 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "It is noteworthy that if the transform $f$ is supervised by the CD loss in Eq. (8) solely, the bijectivity of $f$ may not be necessarily guaranteed. This is because the adopted normalizing flow is not bijective by design. For example, the \"invertible\" $1 \\times 1$ convolution as the channel mixer becomes non-invertible when $W$ is degenerate. Empirically, we observe that the scaling factor of the affine-coupling layer is close to zero after optimizing Eq. (8). Since the inverse transform includes operations of dividing by the scaling factor, there will easily result in the exploding inverse problem [5]. Thus, we incorporate the commonly used maximum likelihood objective in normalizing flow [40] into our training objective. We work with the negative log-likelihood loss:", + "bbox": [ + 75, + 688, + 470, + 900 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\ell_ {\\mathrm {n l}} (\\boldsymbol {x}) = - \\log p _ {\\mathcal {X}} (\\boldsymbol {x}) \\\\ = - \\log p _ {\\mathcal {Z}} (f (\\boldsymbol {x})) - \\log \\left| \\det \\left(\\frac {\\partial f (\\boldsymbol {x})}{\\partial \\boldsymbol {x}}\\right) \\right|. \\tag {10} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 518, + 890, + 573 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "During training, we randomly sample a mini-batch $\\mathcal{B}$ from the training dataset $\\mathcal{D}$ in each iteration, and make use of a variant of stochastic gradient descent to optimize the parameters in CD-Flow:", + "bbox": [ + 498, + 579, + 893, + 638 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\ell (\\mathcal {B}) = \\frac {1}{| \\mathcal {B} |} \\sum_ {(x, y) \\in \\mathcal {B}} \\left(\\ell_ {\\mathrm {m s}} (\\boldsymbol {x}, \\boldsymbol {y}) + \\lambda \\left(\\ell_ {\\mathrm {n l}} (\\boldsymbol {x}) + \\ell_ {\\mathrm {n l}} (\\boldsymbol {y})\\right)\\right), \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 506, + 645, + 890, + 700 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $|\\mathcal{B}|$ denotes the cardinality of $\\mathcal{B}$ , and $\\lambda$ is the trade-off to balance the magnitudes of different loss terms.", + "bbox": [ + 498, + 709, + 893, + 742 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 753, + 633, + 770 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we begin by presenting the experimental setups. We then compare the proposed CD-Flow with 15 CD measures. We last conduct extensive ablation studies to justify the key designs of CD-Flow.", + "bbox": [ + 496, + 777, + 893, + 839 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental Setups", + "text_level": 1, + "bbox": [ + 500, + 847, + 697, + 864 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "CD Dataset. We conduct experiments on SPCD [51], so far the largest natural image dataset tailored for perceptual CD", + "bbox": [ + 498, + 869, + 893, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "22246", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/62d45d80728ba9b6a21269af35b50a566d63d3be43f5689fc942362215b02735.jpg", + "table_caption": [ + "Table 2. STRESS, PLCC, and SRCC between predicted CDs $(\\Delta E)$ and perceptual CDs $(\\Delta V)$ in SPCD [51] under geometric distortions. Translation means randomly shifting one image with respect to the other image in an image pair (by up to $5\\%$ of pixels) in both spacial directions. Rotation means randomly rotating one image (by up to $3^{\\circ}$ ) around the center point. Dilation means zooming in (and cropping) one image without changing the image size (by a factor of 1.05)" + ], + "table_footnote": [], + "table_body": "
MethodTranslationRotationDilation
\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)
CIELAB [45]29.4140.6200.57732.6330.5290.49531.5110.5190.467
CIE94 [34]29.1410.6450.59631.9430.5660.51930.3230.5670.505
CIEDE2000 [29]28.0350.6540.61331.2550.5660.52729.9280.5660.512
CD-Net [51]19.8250.8450.84222.4630.7840.77221.7040.7870.773
CD-Flow19.3110.8520.85620.1390.8370.81621.3520.8270.797
", + "bbox": [ + 80, + 157, + 890, + 277 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "assessment. SPCD consists of 15,335 color images out of 1,000 distinct natural scenes spanning a variety of realistic shooting scenarios. A total of 30,000 image pairs are sampled for human labeling, where 10,005 are non-perfectly aligned image pairs. For each image pair, there are 20 human ratings. We randomly split $70\\%$ , $10\\%$ , and $20\\%$ of image pairs in SPCD as training, validation, and test sets, respectively, according to the image content to ensure content independence.", + "bbox": [ + 75, + 313, + 472, + 449 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Network Architecture. We employ a variant of RealNVP [22], which consists of $K = 6$ scales, and each scale includes a squeezing operation, $L = 8$ steps of flow, and a splitting operation. We do not split at the last scale. The squeezing operation trades the spatial size for the channel number by transforming an $s \\times s \\times c$ tensor to an $\\frac{s}{2} \\times \\frac{s}{2} \\times 4c$ tensor, where $s$ is the spatial size and $c$ is the number of channels. The splitting operation divides the latent representation of the same scale into two halves along the channel dimension. One is used as the color features for CD assessment, and the other is subject to further processing.", + "bbox": [ + 75, + 454, + 472, + 621 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Training and Testing Details. We employ the Adam as the stochastic optimizer, where the batch size is 4, and the initial learning rate is $10^{-5}$ with a decay factor of 2 for every 5 epochs. We train CD-Flow for 50 epochs in total.", + "bbox": [ + 76, + 625, + 470, + 686 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation Criteria. We use three standard criteria to quantify the performance of CD-Flow against existing CD measures, including the standardized residual sum of squares (STRESS) [15], Pearson linear correlation coefficient (PLCC), and Spearman's rank correlation coefficient (SRCC). STRESS measures both prediction accuracy and statistical significance, which is defined by", + "bbox": [ + 75, + 690, + 470, + 797 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\text {S T R E S S} = 1 0 0 \\sqrt {\\frac {\\sum_ {i = 1} ^ {M} \\left(\\Delta E _ {i} - F \\Delta V _ {i}\\right) ^ {2}}{F ^ {2} \\sum_ {i = 1} ^ {M} \\Delta V _ {i} ^ {2}}}, \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 112, + 816, + 468, + 864 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $M$ is the number of test pairs and $F$ is the scale cor", + "bbox": [ + 76, + 885, + 470, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "resection factor between $\\Delta E$ and $\\Delta V$ , defined as", + "bbox": [ + 498, + 313, + 815, + 329 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nF = \\frac {\\sum_ {i = 1} ^ {M} \\Delta E _ {i} ^ {2}}{\\sum_ {i = 1} ^ {M} \\Delta E _ {i} \\Delta V _ {i}}. \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 620, + 337, + 890, + 377 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "STRESS generally ranges from 0 to 100, where a small value suggests a tight-fitting between model predictions and ground truths. SRCC and PLCC measure the prediction monotonicity and prediction linearity, respectively. Before calculating PLCC, we linearize model predictions by regressing a four-parameter monotonic function.", + "bbox": [ + 496, + 385, + 893, + 476 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Main Results", + "text_level": 1, + "bbox": [ + 498, + 484, + 638, + 500 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "SPCD Results. We compare the proposed CD-Flow with 15 existing CD measures which can be grouped into four categories: 1) CD measures for homogeneous color patches: CIELAB [45], CIE94 [34] and CIEDE2000 [29], 2) CD measures for natural images: S-CIELAB [57], Hong06 [18], Ouni08 [39], Jaramillo19 [38] and CD-Net [51], 3) full-reference image quality model: SSIM [49], FLIP [3], PieAPP [44], LPIPS [56] and DISTS [10], and 4) JND methods: Chou07 [8] and Butteraugli [1]. We employ official implementations of Butteraugli and FLIP, and retrain PieAPP, LPIPS, and DISTS on the same training set as CD-Flow. The rest methods are implemented and made publicly available by Jaramillo et al. [38].", + "bbox": [ + 496, + 508, + 890, + 703 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The comparison results are documented in Table 1. Several interesting observations merit attention. First, CIE94 and CIEDE2000 rectify the non-uniformity of CIELAB on color patches by incorporating weight coefficients. Nonetheless, when it comes to natural photographic images, these approaches do not show noticeable improvements compared to the original CIELAB formula. This provides a strong indication that humans perceive CDs of uniformly colored patches and natural images in drastically different manners. Naive extensions such as spatial filtering and texture grouping may not be sufficient to bridge the gap as evidenced by the results in the second section of Table 1. Second, despite being retrained on the same training", + "bbox": [ + 496, + 704, + 893, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "22247", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/d97aae6a138ee8d4a309ffd1f7df149ad703cd98338a207437c4c97e599d0046.jpg", + "table_caption": [ + "Table 3. Generalizability evaluation of CD-Flow on the COM dataset and its four subsets: BFD-P, Leeds, Witt, and RIT-DuPont. PLCC on RIT-DuPont is indicated by “—” since it is not computable" + ], + "table_footnote": [], + "table_body": "
MethodBFD-P [31]Leeds [21]Witt [52]RIT-DuPont [7]COM dataset [29]
\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)
CIELAB [45]45.0540.74940.0930.29551.6890.56530.348—45.2020.693
CIE94 [34]35.7980.83030.4940.58431.8570.79320.982—33.2350.814
CIEDE2000 [29]31.9350.86119.2470.77230.3580.82520.239—28.9790.862
CD-Net39.3120.79138.5580.44933.6400.82842.999—38.8720.786
CD-Flow34.6610.83334.2750.47631.9650.82036.504—35.0610.801
", + "bbox": [ + 76, + 130, + 893, + 237 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "set as CD-Flow, general-purpose DNN-based image quality models PieAPP [44], LPIPS [56], and DISTS [10] do not deliver comparable performance. We believe this arises for PieAPP because it requires a much larger labeled set to support training from scratch, and 21,000 training pairs, even with data augmentation, are less likely to overcome overfitting. LPIPS and DISTS rely on pre-trained VGG features, which are empirically proven to be more structure and texture inductive. As a result, they may be less ideal for assessing CDs. Third, DNN-based CD formulae, i.e., CD-Net [51] and CD-Flow, outperform all other competing models, which are primarily attributed to their superior representation learning capabilities. This observation confirms the potential of employing DNNs in the field of color science. Fourth, CD-Flow achieves the most remarkable results, thereby demonstrating the promise of the multi-scale autoregressive normalizing flow for CD assessment.", + "bbox": [ + 75, + 273, + 472, + 530 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Robustness Results to Geometric Distortions. We conduct experiments to evaluate the robustness of CD-Flow to mild geometric distortions, including translation, rotation, and dilation. Specifically, we translate randomly one image with respect to the other image in an image pair by up to $5\\%$ pixels in both horizontal and vertical directions, rotate randomly one image clockwise by up to $3^{\\circ}$ , and zoom in one image by a factor of 1.05. The corresponding results are presented in Table 2. We find that the performance of all models degrades due to the presence of geometric distortions. CIELAB-based formulae are particularly sensitive to geometric distortions because of pixel-by-pixel CD computation. In contrast, CD-Flow demonstrates the best robustness results, which is as expected because it is designed to abstract color features, characterized by the interdependence between color and form for CD assessment.", + "bbox": [ + 75, + 534, + 470, + 776 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Generalizability Results on Other Datasets. We examine the generalizability of CD-Flow on the COM dataset [29] and the TID2013 subset [42]. The COM dataset consists of four subsets: BFD-P [31], Leeds [21], Witt [52], and RIT-DuPont [7], which include uniformly colored patches used for the development of CIEDE2000. The comparison results are reported in Table 3. It is clear that two DNN-based CD models perform better than CIELAB on", + "bbox": [ + 75, + 779, + 470, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/ca403d846bcf1801c47301bf09ccd50c2e6f9f006b7b11086534475f270afe8f.jpg", + "table_caption": [ + "Table 4. Generalizability evaluation of CD-Flow on the TID2013 subset, containing quantization noise, image color quantization with dither, and chromatic aberration artifacts" + ], + "table_footnote": [], + "table_body": "
Method\\( STRESS\\downarrow \\)PLCC↑SRCC↑
CIEDE2000 [29]18.2030.7300.751
PieAPP [44]20.9180.6200.653
LPIPS [56]15.4200.8160.804
DISTS [10]15.2350.8210.805
CD-Net [51]15.9620.8010.826
CD-Flow14.1100.8370.832
", + "bbox": [ + 529, + 325, + 864, + 436 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "the COM dataset, despite not being exposed to color patch data during training. Although underperforming CIE94 and CIEDE2000, CD-Flow exhibits better generalization compared to CD-Net. We then test the generalizability of the TID2013 subset, which contains three types of color-related distortions: quantization noise, image color quantization with dither, and chromatic aberration. Table 4 lists the comparison results. We find that CD-Flow performs much better than the best-performing CIELAB-based CD formula CIEDE2000, and is on par with general-purpose image quality methods LPIPS and DISTS, which may have been exposed to similar distortion appearances during training. All these results constitute supportive evidence that the multi-scale autoregressive normalizing flow provides a compelling embodiment of the hypothesis: \"color and form are inextricably interdependent as a unitray process of perceptual organization [19, 46].\"", + "bbox": [ + 496, + 476, + 893, + 734 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Visualization of Local CD Maps. We further visualize the multi-scale local CD maps generated by CD-Flow, as illustrated in Figure 3. Our observations are as follows. First, local CDs are generally small at the first three scales compared to those at later scales, indicating that CD-Flow indeed relies more on coarser-scale representations to assess perceptual CDs, as encouraged by the multi-scale training objective in Eq. (8). Second, interestingly, the effects of single-opponent and double-opponent cells emerge in the computation of CD-Flow. At coarser scales, local CDs are evaluated over a large image area, and exhibit reason-", + "bbox": [ + 496, + 734, + 895, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "22248", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/1b90dc0ed4886c5ba4b8e3ab4ada4784068b756dd788d53896a3bbba408d89a0.jpg", + "image_caption": [ + "Figure 3. Multi-scale local CD maps generated by CD-Flow. First two columns: Input image pair $(\\pmb{x},\\pmb{y})$ . Third to the eighth column: Six scales of CD maps generated by CD-Flow, with a warmer color indicating a larger local CD." + ], + "image_footnote": [], + "bbox": [ + 83, + 90, + 888, + 244 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/01babba2c80dc525310c5601d4791d28c19da6b4e2816fd51dc93bd7e04e3000.jpg", + "table_caption": [ + "Table 5. Ablation analysis of the number of flow steps in CD-Flow (i.e., the hyperparameter $L$ ), where the number of scales is fixed to six. The default setting is highlighted in boldface" + ], + "table_footnote": [], + "table_body": "
# of flow stepsSTRESS↓PLCC↑SRCC↑
L = 221.6330.8370.826
L = 420.9940.8380.825
L = 818.4730.8710.865
L = 1617.7920.8790.870
", + "bbox": [ + 101, + 364, + 444, + 452 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/ecad96595d094d447478057a683bd801c29241448fbc0db0df23014acb07fda6.jpg", + "table_caption": [ + "Table 6. Ablation analysis of the number of scales in CD-Flow (i.e., the hyperparameter $K$ ),where the number of flow steps is fixed to eight. The default setting is highlighted in boldface" + ], + "table_footnote": [], + "table_body": "
# of scalesSTRESS↓PLCC↑SRCC↑
K = 224.6860.7600.732
K = 419.7300.8570.850
K = 618.4730.8710.865
K = 818.5240.8740.861
", + "bbox": [ + 114, + 532, + 431, + 619 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "able sensitivity, resembling single-opponent cells in V1. At finer scales, local CDs are more responsive to textures and boundaries, consistent with the response characteristics of double-opponent cells in V1.", + "bbox": [ + 75, + 656, + 468, + 717 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.3. Ablation Studies", + "text_level": 1, + "bbox": [ + 76, + 726, + 240, + 739 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We conduct two ablation experiments to evaluate the key design choices of CD-Flow. First, we study the impact of the number of flow steps (i.e., the hyperparameter $L$ ) on the model performance. In Table 5, we find that the performance of CD-Flow increases with the number of flow steps, which in turn expands the non-linear representational capacity of CD-Flow. Nevertheless, too many steps may substantially increase the computational complexity, and negatively impact generalization as well due to potential overfitting. We select $L = 8$ as the default setting to strike a", + "bbox": [ + 75, + 750, + 470, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "good balance between model performance and complexity. Next, we examine the effect of the number of scales (i.e., the hyperparameter $K$ ) on the model performance. Table 6 reports the results. We find that increasing the number of scales from 2 to 6 significantly improves the CD assessment performance. However, further increasing $K$ does not bring extra performance improvements, not as the case of increasing $L$ . We attribute this to the setting of the current training input resolution (i.e., $768 \\times 768 \\times 3$ ). When $K = 8$ and at the coarsest scale, we are essentially comparing the difference of two \"globally averaged\" color values over the entire images, which is less biologically plausible and less practically meaningful.", + "bbox": [ + 496, + 311, + 893, + 508 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 523, + 617, + 539 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have introduced CD-Flow, a normalizing flow-based CD metric for photographic images. Our approach is inspired by the scientific evidence that humans perceive color based on the interdependence of color and form in images. We utilized a multi-scale autoregressive normalizing flow to learn a coordinate transform, followed by computing the Euclidean distance in the transformed space. Remarkably, the learned feature transform enjoys four desirable properties: 1) consistent with the working mechanism of human color perception, 2) proper as a mathematical metric, 3) accurate to explain human data of perceptual CDs, and 4) robust to slight geometric distortions. We hope that the proposed CD-Flow can benefit related fields in imaging/illumination, vision science, and color science.", + "bbox": [ + 496, + 550, + 890, + 762 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgement", + "text_level": 1, + "bbox": [ + 500, + 777, + 660, + 794 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We would like to thank Keshuo Xu for the technical support and code sharing. This work was supported in part by the National Natural Science Foundation of China under Grant 62071407, the Hong Kong ITC Innovation and Technology Fund (9440288), and the CUHKSZ University Development Fund (UDF01002437).", + "bbox": [ + 496, + 801, + 890, + 887 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "22249", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Jyrki Alakuijala, Robert Obryk, Ostap Stoliarchuk, Zoltan Szabadka, Lode Vandevenne, and Jan Wassenberg. Guetzli: Perceptually guided JPEG encoder. arXiv preprint arXiv:1703.04421, 2017. 3, 5, 6", + "[2] Liliana Albertazzi, Gert J. Van Tonder, and Dhanraj Vishwanath. Perception Beyond Inference: The Information Content of Visual Processes. MIT Press, 2011. 1", + "[3] Pontus Andersson, Jim Nilsson, Tomas Akenine-Moller, Magnus Oskarsson, Kalle Åström, and Mark D. Fairchild. FLIP: A difference evaluator for alternating images. ACM on Computer Graphics and Interactive Techniques, 3(2):1-23, 2020. 3, 5, 6", + "[4] Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, and Jorn-Henrik Jacobsen. Invertible residual networks. In International Conference on Machine Learning, pages 573-582, 2019. 3", + "[5] Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger Grosse, and Jorn-Henrik Jacobsen. Understanding and mitigating exploding inverses in invertible neural networks. In International Conference on Artificial Intelligence and Statistics, pages 1792-1800, 2021. 5", + "[6] Ohad Ben-Shahar and Steven W. Zucker. Hue geometry and horizontal connections. *Neural Networks*, 17(5-6):753–771, 2004. 1", + "[7] Roy S. Berns, David H. Alman, Lisa Reniff, Gregory D. Snyder, and Mitchell R. Balonon-Rosen. Visual determination of suprathreshold color-difference tolerances using probit analysis. Color Research & Application, 16(5):297-316, 1991.", + "[8] Chun-Hsien Chou and Kuo-Cheng Liu. A fidelity metric for assessing visual quality of color images. In International Conference on Computer Communications and Networks, pages 1154-1159, 2007. 5, 6", + "[9] Frank J. J. Clarke, Roderick McDonald, and Bryan Rigg. Modification to the JPC79 colour-difference formula. Journal of the Society of Dyers and Colourists, 100(4):128-132, 1984. 1, 2", + "[10] Keyan Ding, Kede Ma, Shiqi Wang, and Eero P. Simoncelli. Image quality assessment: Unifying structure and texture similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5):2567-2581, 2020. 3, 5, 6, 7", + "[11] Keyan Ding, Kede Ma, Shiqi Wang, and Eero P. Simoncelli. Comparison of full-reference image quality models for optimization of image processing systems. International Journal of Computer Vision, 129(4):1258-1281, 2021. 2", + "[12] Laurent Dinh, David Krueger, and Yoshua Bengio. NICE: Non-linear independent components estimation. In International Conference on Representations Workshops, 2015. 3", + "[13] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real NVP. In International Conference on Learning Representations, 2017. 2, 3, 4", + "[14] Chen Gao, Karen Panetta, and Sos Agaian. No reference color image quality measures. In IEEE International Conference on Cybernetics, pages 243-248, 2013. 3", + "[15] Pedro A. Garcia, Rafael Huertas, Manuel Melgosa, and Guihua Cui. Measurement of the relationship between perceived" + ], + "bbox": [ + 78, + 114, + 470, + 901 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "and computed color differences. Journal of the Optical Society of America A, 24(7):1823-1829, 2007. 6", + "[16] Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud. FFJORD: Free-form continuous dynamics for scalable reversible generative models. International Conference on Learning Representations, 2019. 3", + "[17] Thorsten Hansen, Maria Olkkonen, Sebastian Walter, and Karl R. Gegenfurtner. Memory modulates color appearance. Nature Neuroscience, 9(11):1367-1368, 2006. 2", + "[18] Guowei Hong and Ming Ronnier Luo. New algorithm for calculating perceived colour difference of images. The Imaging Science Journal, 54(2):86-91, 2006. 2, 5, 6", + "[19] Gaetano Kanizsa. Organization in Vision: Essays on Gestalt Perception. Praeger Publishers, 1979. 1, 7", + "[20] David Katz. The World of Colour. Routledge, 1935. 1", + "[21] Dong-Ho Kim. New weighting functions for the modified CIELAB colour-difference formulae. Textile Coloration and Finishing, 9(6):51-57, 1997. 7", + "[22] Durk P. Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. Neural Information Processing Systems, 2018. 3, 4, 6", + "[23] Jakob Kruse, Gianluca Detommaso, Ullrich Kothe, and Robert Scheicl. HINT: Hierarchical invertible neural transport for density estimation and bayesian inference. AAAI Conference on Artificial Intelligence, pages 8191-8199, 2021. 3", + "[24] Edwin H. Land. The retina theory of color vision. Scientific American, 237(6):108-129, 1977. 1", + "[25] Dohyoung Lee and Konstantinos N. Plataniotis. Towards a novel perceptual color difference metric using circular processing of hue components. In IEEE International Conference on Acoustics, Speech, & Signal Processing, pages 166-170, 2014. 2", + "[26] Sang-Mi Lee, John Haozhong Xin, and Stephen Westland. Evaluation of image similarity by histogram intersection. Color Research & Application, 30(4):265-274, 2005. 2", + "[27] Peter Lennie. Color coding in the cortex. Color Vision: From Genes to Perception, pages 235-247, 1999. 1", + "[28] Ming Ronnier Luo. Colour science: Past, present and future. Colour Imaging Vision and Technology, pages 381-404, 1999. 1", + "[29] Ming Ronnier Luo, Guihua Cui, and Bryan Rigg. The development of the CIE 2000 colour-difference formula: CIEDE2000. Color Research & Application, 26(5):340-350, 2001. 1, 2, 3, 5, 6, 7", + "[30] Ming Ronnier Luo and Bryan Rigg. Chromaticity-discrimination ellipses for surface colours. Color Research & Application, 11(1):25-42, 1986. 2", + "[31] Ming Ronnier Luo and Bryan Rigg. BFD(l:c) colour-difference formula. Journal of the Society of Dyers and Colourists, 103(2):86-94, 1987. 1, 2, 7", + "[32] Marc Mahy, Luc Van Eycken, and André Oosterlinck. Evaluation of uniform color spaces developed after the adoption of CIELAB and CIELUV. Color Research & Application, 19(2):105-121, 1994. 1, 2, 3" + ], + "bbox": [ + 503, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "22250", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[33] Roderick McDonald. Industrial pass/fail colour matching. Journal of the Society of Dyers and Colourists, 96(7):372-376, 1980. 1, 2", + "[34] Roderick McDonald and Kenneth J. Smith. CIE94-A new colour-difference formula. Journal of the Society of Dyers and Colourists, 111(12):376-379, 1995. 1, 2, 5, 6, 7", + "[35] Anish Mittal, Anush Krishna Moorthy, and Alan C. Bovik. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12):4695-4708, 2012. 3", + "[36] Anish Mittal, Rajiv Soundararajan, and Alan C. Bovik. Making a \"completely blind\" image quality analyzer. IEEE Signal Processing Letters, 20(3):209-212, 2012. 3", + "[37] Ken Nakayama and Shinsuke Shimojo. Experiencing and perceiving visual surfaces. Science, 257(5075):1357-1363, 1992. 2", + "[38] Benhur Ortiz-Jaramillo, Asli Kumcu, Ljiljana Platisa, and Wilfried Philips. Evaluation of color differences in natural scene color images. Signal Processing: Image Communication, 71:128-137, 2019. 1, 2, 5, 6", + "[39] Sonia Ouni, Ezzeddine Zagrouba, Majed Chambah, and Michel Herbin. A new spatial colour metric for perceptual comparison. In International Conference on E-Systems Engineering, Communication and Information, pages 413–428, 2008. 2, 5, 6", + "[40] George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. Normalizing flows for probabilistic modeling and inference. Journal of Machine Learning Research, 22(57):1-64, 2021. 5", + "[41] Marius Pedersen and Jon Yngve Hardeberg. A new spatial hue angle metric for perceptual image difference. In International Workshop on Computational Color Imaging, pages 81-90, 2009. 2", + "[42] Nikolay Ponomarenko, Lina Jin, Oleg Ieremeiev, Vladimir Lukin, Karen Egiazarian, Jaakko Astola, Benoit Vozel, Kacem Chehdi, Marco Carli, Federica Battisti, and Chung-Chieh Jay Kuo. Image database TID2013: Peculiarities, results and perspectives. Signal Processing: Image Communication, 30:57-77, 2015. 7", + "[43] Charles A. Poynton. A Technical Introduction to Digital Video. John Wiley & Sons, Inc., 1996. 1", + "[44] Ekta Prashnani, Hong Cai, Yasamin Mostofi, and Pradeep Sen. PieAPP: Perceptual image-error assessment through pairwise preference. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1808-1817, 2018. 5, 6, 7", + "[45] Alan R. Robertson. The CIE 1976 color-difference formulae. Color Research & Application, 2(1):7-11, 1977. 5, 6, 7", + "[46] Robert Shapley and Michael J. Hawken. Color in the cortex: Single-and double-opponent cells. Vision Research, 51(7):701-717, 2011. 1, 7", + "[47] Steven K. Shevell and Frederick A. A. Kingdom. Color in complex scenes. Annual Review of Psychology, 59:143-166, 2008. 1", + "[48] Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, and Ben Poole. Discrete flows: Invertible generative models" + ], + "bbox": [ + 78, + 92, + 468, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "of discrete data. Advances in Neural Information Processing Systems, 2019. 3", + "[49] Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004. 2, 3, 5, 6", + "[50] Zhou Wang and Eero P. Simoncelli. Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In Human Vision and Electronic Imaging X, pages 149-159, 2005. 3", + "[51] Zhihua Wang, Keshuo Xu, Yang Yang, Jianlei Dong, Shuhang Gu, Lihao Xu, Yuming Fang, and Kede Ma. Measuring perceptual color differences of smartphone photography. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 2, 3, 5, 6, 7", + "[52] Klaus Witt. Geometric relations between scales of small colour differences. Color Research & Application, 24(2):78-92, 1999. 7", + "[53] Jason J. Yu, Konstantinos G. Derpanis, and Marcus A. Brubaker. Wavelet flow: Fast training of high resolution normalizing flows. Advances in Neural Information Processing Systems, pages 6184-6196, 2020. 3", + "[54] Ming Yu, Huijuan Liu, Yingchun Guo, and Dongming Zhao. A method for reduced-reference color image quality assessment. In International Conference on Image and Signal Processing, pages 1-5, 2009. 3", + "[55] Lin Zhang, Ying Shen, and Hongyu Li. VSI: A visual saliency-induced index for perceptual image quality assessment. IEEE Transactions on Image Processing, 23(10):4270-4281, 2014. 3", + "[56] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In IEEE Conference on Computer Vision and Pattern Recognition, pages 586-595, 2018. 3, 5, 6, 7", + "[57] Xuemei Zhang and Brian A. Wandell. A spatial extension of CIELAB for digital color-image reproduction. Journal of the Society for Information Display, 5(1):61-63, 1997. 2, 5, 6" + ], + "bbox": [ + 501, + 92, + 890, + 641 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "22251", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning a Deep Color Difference Metric for Photographic Images/dbc258de-351a-4a82-83a7-8b31f7a409d8_model.json b/2023/Learning a Deep Color Difference Metric for Photographic Images/dbc258de-351a-4a82-83a7-8b31f7a409d8_model.json new file mode 100644 index 0000000000000000000000000000000000000000..9bd81aad2ee9ef89d7cb306c9da5d3a4bfde4363 --- /dev/null +++ b/2023/Learning a Deep Color Difference Metric for Photographic Images/dbc258de-351a-4a82-83a7-8b31f7a409d8_model.json @@ -0,0 +1,2189 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.149, + 0.131, + 0.823, + 0.154 + ], + "angle": 0, + "content": "Learning a Deep Color Difference Metric for Photographic Images" + }, + { + "type": "text", + "bbox": [ + 0.199, + 0.18, + 0.768, + 0.2 + ], + "angle": 0, + "content": "Haoyu Chen\\(^{1}\\) Zhihua Wang\\(^{1,2,*}\\) Yang Yang\\(^{3}\\) Qilin Sun\\(^{4}\\) Kede Ma\\(^{1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.265, + 0.201, + 0.706, + 0.217 + ], + "angle": 0, + "content": "1City University of Hong Kong 2Shenzhen MSU-BIT University" + }, + { + "type": "text", + "bbox": [ + 0.174, + 0.218, + 0.797, + 0.234 + ], + "angle": 0, + "content": "3Shenzhen Transsion Holdings Co., Ltd. 4The Chinese University of Hong Kong (Shenzhen)" + }, + { + "type": "text", + "bbox": [ + 0.204, + 0.238, + 0.765, + 0.252 + ], + "angle": 0, + "content": "{haoychen3-c,zhihua.wang}@my.cityu.edu.hk, kede.ma@cityu.edu.hk," + }, + { + "type": "text", + "bbox": [ + 0.281, + 0.256, + 0.689, + 0.269 + ], + "angle": 0, + "content": "yang.yang6@transsion.com, sunqilin@cuhk.edu.cn" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.305, + 0.313, + 0.32 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.337, + 0.473, + 0.715 + ], + "angle": 0, + "content": "Most well-established and widely used color difference (CD) metrics are handcrafted and subject-calibrated against uniformly colored patches, which do not generalize well to photographic images characterized by natural scene complexities. Constructing CD formulae for photographic images is still an active research topic in imaging/illumination, vision science, and color science communities. In this paper, we aim to learn a deep CD metric for photographic images with four desirable properties. First, it well aligns with the observations in vision science that color and form are linked inextricably in visual cortical processing. Second, it is a proper metric in the mathematical sense. Third, it computes accurate CDs between photographic images, differing mainly in color appearances. Fourth, it is robust to mild geometric distortions (e.g., translation or due to parallax), which are often present in photographic images of the same scene captured by different digital cameras. We show that all four properties can be satisfied at once by learning a multi-scale autoregressive normalizing flow for feature transform, followed by the Euclidean distance which is linearly proportional to the human perceptual CD. Quantitative and qualitative experiments on the large-scale SPCD dataset demonstrate the promise of the learned CD metric. Source code is available at https://github.com/haoychen3/CD-Flow." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.744, + 0.21, + 0.76 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.77, + 0.47, + 0.877 + ], + "angle": 0, + "content": "For a long time in vision science community, the modular and segregated view of cortical color processing predominated [46]: the visual perception/processing of color-related quantities is separate from and in parallel with the perception/processing of form (i.e., object shape and structure), motion direction, and depth order in natural scenes. As a result, vision scientists preferred to investigate color" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.306, + 0.892, + 0.336 + ], + "angle": 0, + "content": "perception under minimal conditions on form [20, 46], for example, using uniformly colored patches." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.337, + 0.893, + 0.655 + ], + "angle": 0, + "content": "The idea that color as a visual sensation can be analyzed separately had a profound impact on the development of computational formulae for color difference (CD) assessment. Till now, the most well-established and widely used CD metrics are primarily built upon the three-dimensional spatially-isotropic CIELAB coordinate system [32], recommended by the International Commission on Illumination (abbreviated as CIE from its French name Commission Internationale de l'Éclairage) in 1976. However, the uniformity1 of the CIELAB space is not as ideal as intended [28], even for uniformly colored patches. Thus, more complex and parametric formulae are proposed to rectify different aspects of perceptual non-uniformity. Representative methods include JPC79 [33], \\(\\mathrm{CMC}(l:c)^2\\) [9], BFD(l:c) [31], CIE94 [34], and CIEDE2000 [29], in which the parameters are calibrated by fitting the human perceptual CD measurements of uniformly colored patches. A naive application of these metrics to photographic images is to compute the mean of the CDs between co-located pixels, which has been empirically shown to correlate poorly to human perception of CDs [38]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.656, + 0.895, + 0.839 + ], + "angle": 0, + "content": "Back to the vision science community, with more supporting evidence from psychophysical and perceptual studies [2, 6, 27, 47], vision scientists have gradually come to agree on an alternative and more persuasive view of color perception: color and form (and motion) are inextricably interdependent as a unitary process of perceptual organization [19, 46]. Even the primary visual cortex (i.e., V1) plays a significant role in color perception through two types of color-sensitive neurons: single-opponent and double-opponent cells. The single-opponent cells are sensitive to large areas of color, while the double-opponent cells respond to color patterns, textures, and boundaries [24, 46]." + }, + { + "type": "page_footnote", + "bbox": [ + 0.5, + 0.851, + 0.892, + 0.888 + ], + "angle": 0, + "content": "1A system is perceptually uniform if a small perturbation to a component value is approximately equally perceptible across the range of that value [43]." + }, + { + "type": "page_footnote", + "bbox": [ + 0.518, + 0.888, + 0.871, + 0.9 + ], + "angle": 0, + "content": "\\(2l\\) and \\(c\\) are two multiplicative parameters in the model to be fitted." + }, + { + "type": "list", + "bbox": [ + 0.5, + 0.851, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.222, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22242" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.152 + ], + "angle": 0, + "content": "At later stages, color is transformed to more complex and abstract features, which represent the integral properties of objects, and remain consistent against the changes of the environmental illumination [17, 37]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.153, + 0.468, + 0.363 + ], + "angle": 0, + "content": "Inspired by these scientific findings, researchers and engineers began to take spatial context (i.e., local surrounding regions) into account, when designing CD formulae. Representative strategies include low-pass spatial filtering [57], histograming [18], patch-based comparison [49], and texture-based segmentation [38]. Most recently, Wang et al. [51] established the largest photographic image dataset, SPCD, for perceptual CD assessment. They further trained a lightweight deep neural network (DNN) for CD assessment of photographic images in a data-driven fashion, as a generalization of several existing CD metrics built on the CIE colorimetry. Nonetheless, the learned formula may not be a proper metric, due to reliance on the possibly surjective mapping for feature transform." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.364, + 0.468, + 0.409 + ], + "angle": 0, + "content": "In this work, we further pursue the data-driven approach. We aim to learn a deep CD metric for photographic images with four desirable properties." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.417, + 0.468, + 0.477 + ], + "angle": 0, + "content": "- It is conceptually inspired by color perception in the visual cortex. The design of our approach should respect the view that color and form interact inextricably through all stages of visual cortical processing." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.481, + 0.468, + 0.54 + ], + "angle": 0, + "content": "- It is a proper metric that satisfies non-negativity, symmetry, identity of indiscernibles, and triangle inequality. Such design has been proven useful for perceptual optimization of image processing systems [11]." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.544, + 0.468, + 0.588 + ], + "angle": 0, + "content": "- It is accurate in predicting the human perceptual CDs of photographic images, with good generalization to uniformly colored patches." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.591, + 0.468, + 0.651 + ], + "angle": 0, + "content": "- It is robust to mild geometric distortions (e.g., translation and dilation), which are often present in photographic images of the same scene captured with different camera settings or along different lines of sight." + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.417, + 0.468, + 0.651 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.66, + 0.468, + 0.901 + ], + "angle": 0, + "content": "We show that all the four desirable properties can be satisfied at once by learning a multi-scale autoregressive normalizing flow (a variant of RealNVP [13] to be specific) for feature transform, followed by Euclidean distance measure in the transformed space. More specifically, we achieve the first property by the squeezing operation (also known as invertible downsampling) in the normalizing flow, which trades space size for channel dimension. The second property is a direct consequence of the bijectivity of the normalizing flow and the Euclidean distance measure. We achieve the third property by optimizing the model parameters to explain the human perceptual CDs in SPCD [51]. We achieve the fourth property by enforcing the normalizing flow to be multi-scale and autoregressive, in which the features at a particular scale are conditioned on those at a higher (i.e., coarser) scale. By doing so, our metric automatically learns" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.891, + 0.135 + ], + "angle": 0, + "content": "to preferentially rely on coarse-scale feature representations with more built-in tolerance to geometric distortions for CD assessment." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.137, + 0.892, + 0.258 + ], + "angle": 0, + "content": "We conduct extensive experiments on the large-scale SPCD dataset [51], and find that our proposed metric, termed as CD-Flow, outperforms 15 CD formulae in assessing CDs of photographic images, produces competitive multi-scale local CD maps without any dense supervision, and is more robust to geometric distortions. Moreover, we empirically verify the perceptual uniformity of the learned color image representation from multiple aspects." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.272, + 0.642, + 0.287 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.298, + 0.891, + 0.342 + ], + "angle": 0, + "content": "In this section, we first review CD formulae in a broader context, and then discuss normalizing flow-based models, which are core to the proposed CD metric." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.343, + 0.892, + 0.689 + ], + "angle": 0, + "content": "CD Formulae. The CD assessment is necessary for day-to-day color control, and is indispensable for color matching in color industries. Admittedly, CD formulae have accelerated the instrumental pass/fail devices for color judgments, but much still needs to be done for complete satisfaction. The scientific investigation of perceptual CDs can be dated at least back to Young and Helmholtz, who proposed and developed the trichromatic theory of color, which is the foundation of the metameric color matching experiment. CIELAB [32] is one of the most successful CD metrics recommended by CIE in 1976, and has been widely adopted in industry for a long time. However, the CIELAB color space is not perceptually uniform [30], which motivates the development of CIE94 [34] and CIEDE2000 [29] through the introduction of application-specific parameters. Other CIELAB-based CD metrics include JPC79 [33], BFD(l:c) [31], and \\(\\mathrm{CMC}(l:c)\\) [9]. The introduced parameters are primarily calibrated using uniformly colored patches, digital or printed, which are statistically and semantically different from photographic images. Thus, the generalization of these metrics to photographic images is somewhat limited, especially when misalignment due to geometric distortions is present." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.892, + 0.901 + ], + "angle": 0, + "content": "To incorporate spatial context into CD assessment, Zhang and Wandell [57] presented S-CIELAB, which extends CIELAB by adding spatial low-pass filtering as preprocessing. Similarly, Ouni et al. [39] provided a spatial extension of CIEDE2000. Lee et al. [26] re-examined histogram intersection, which is widely used in color image index, for the purpose of color image similarity assessment. Hong and Luo [18] chose to give larger weights to areas with spatially homogeneous colors and pixels with larger CDs. This method was later augmented by spatial filtering [41]. Lee and Plataniotis [25] built upon the philosophy of color structural similarity, and gave the hue component careful treatment with circular statistics. Jaramillo et al. [38] grouped the same texture areas for human-like CD" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22243" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.107, + 0.091, + 0.868, + 0.252 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.26, + 0.895, + 0.332 + ], + "angle": 0, + "content": "Figure 1. Feature transform of the proposed CD-Flow for perceptual CD assessment. The coordinate transform module consists of six scales. The leftmost yellow cube represents the input image with dimension \\( s \\times s \\times 3 \\). The six blue cubes represent the six scales of representations, respectively. Each red cube represents a cascade of squeezing operation, multiple flow steps, and a splitting operation, in which each flow step is composed of an actnorm operation, an invertible \\( 1 \\times 1 \\) convolution, and an affine coupling layer. The splitting operation is excluded in the last red cube." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.357, + 0.47, + 0.387 + ], + "angle": 0, + "content": "assessment, using local binary patterns as texture descriptors." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.387, + 0.471, + 0.644 + ], + "angle": 0, + "content": "General-purpose image quality models, including full-reference ones - SSIM [49], VSI [55], LPIPS [56] and DISTS [10], reduced-reference ones - Wang05 [50] and Yu09 [54], and no-reference ones - BRISQUE [35], NIQE [36] and Gao13 [14] can be directly adopted for CD assessment, regarding CDs as a particular form of \"visual degradations.\" Meanwhile, just-noticeable difference (JND) methods, e.g., Butteraugli [1] and FLLIP [3], also attempt to characterize visually indistinguishable color changes between two images. In the era of deep learning, due to the lack of sufficient human-labeled training data, DNN-based CD formulae are rarely proposed. Wang et al. [51] created the first largest image dataset, SPCD, for perceptual CD assessment, and made one of the first attempts to train a DNN-based CD measure for photographic images. However, the underlying feature transform is not mathematically bijective." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.644, + 0.471, + 0.81 + ], + "angle": 0, + "content": "Normalizing Flow-based Models. Normalizing flow-based generative models are constructed by bijective functions \\( f: \\mathbb{R}^D \\to \\mathbb{R}^D \\), with typically easy-to-compute analytical inverse \\( f^{-1}: \\mathbb{R}^D \\to \\mathbb{R}^D \\). The primary goal of \\( f \\) is to map raw data \\( \\pmb{x} \\) to samples \\( \\pmb{z} = f(\\pmb{x}) \\) from a simple probability distribution \\( p_{\\mathcal{Z}}(\\pmb{z}) \\). Many classic machine learning algorithms can be cast in this framework, such as principal component analysis (PCA, where \\( f \\) is a linear transform and \\( p_{\\mathcal{Z}}(\\pmb{z}) \\) is standard Gaussian) and independent component analysis (ICA, where \\( f \\) is again linear and \\( p_{\\mathcal{Z}} \\) is factorized and heavy-tailed)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.472, + 0.903 + ], + "angle": 0, + "content": "In 2014, Dinh et al. [12] proposed non-linear independent component estimation (NICE), as a generalization of ICA. NICE is considered the first normalizing flow with the introduction of the additive coupling to ease the calculation of the Jacobian determinant. To make flow-based models more suitable for image-related tasks, Dinh et" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.357, + 0.895, + 0.659 + ], + "angle": 0, + "content": "al. [13] extended NICE to RealNVP, which admits a multiscale autoregressive architecture, implemented by squeezing and affine coupling. Kingma and Dhariwal [22] introduced the invertible \\( 1 \\times 1 \\) convolution (i.e., the linear transform in PCA) to replace the fixed random permutation for splitting the channel dimension during multiscale processing. The batch normalization in RealNVP is also replaced with activation normalization (i.e., actnorm). To allow unconstrained architectural design, Grathwohl et al. [16] leveraged the Hutchinson's trace estimator for scalable and unbiased estimation of the log-density. Similarly, Behrmann et al. [4] proposed invertible residual networks (i-ResNet), introducing a tractable estimation to the Jacobian log-determinant of a residual block. Other representative normalizing flow work includes hierarchical recursive coupling [23] for increasing flow expressiveness, Wavelet Flow [53] for scaling flow to ultra-high dimensional data, and Discrete Flow [48] for discrete data modeling. In this paper, we do not use normalizing flow for generative modeling, but for invertible feature transform." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.674, + 0.685, + 0.691 + ], + "angle": 0, + "content": "3. Proposed CD-Flow" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.7, + 0.894, + 0.807 + ], + "angle": 0, + "content": "In this section, we detail our proposed CD metric, CD-Flow, consisting of two key components: the feature transform and the distance measure [29, 32]. The feature transform is built upon a variant of RealNVP [13], which is a learnable invertible transformation between input data and samples from a pre-fixed latent distribution. The CD distance is then computed using the Euclidean distance." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.817, + 0.684, + 0.832 + ], + "angle": 0, + "content": "3.1. Problem Definition" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.895, + 0.903 + ], + "angle": 0, + "content": "We denote the RGB image space as \\(\\mathcal{X}\\) with an unknown distribution \\(p_{\\mathcal{X}}\\) and the transformed representation space as \\(\\mathcal{Z}\\) with a latent distribution \\(p_{\\mathcal{Z}}\\). We are given a training dataset \\(\\mathcal{D} = \\{(x^{(i)},y^{(i)}),\\varDelta V^{(i)}\\}_{i = 1}^{M}\\), where" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22244" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.09, + 0.468, + 0.224 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.235, + 0.468, + 0.263 + ], + "angle": 0, + "content": "Figure 2. Architecture of the feature transform, adapted from [22]. The three yellow blocks constitute one step of flow." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.285, + 0.47, + 0.407 + ], + "angle": 0, + "content": "\\(\\pmb{x}^{(i)}, \\pmb{y}^{(i)} \\in \\mathcal{X}\\) form the \\(i\\)-th image pair of the same visual content but different color appearances, \\(\\Delta V^{(i)}\\) represents the corresponding human perceptual CD collected from a subjective experiment, and \\(M\\) is the number of training pairs. Our goal is to learn a normalizing flow-based invertible and differentiable transform \\(f\\), which maps RGB images to latent representations with Gaussian conditionals for CD assessment." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.416, + 0.183, + 0.43 + ], + "angle": 0, + "content": "3.2. CD-Flow" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.439, + 0.47, + 0.62 + ], + "angle": 0, + "content": "Feature Transform. Figure 1 illustrates the system diagram of the multi-scale autoregressive normalizing flow for the feature transform, which consists of \\( K \\) scales of flow processing: \\( f = f_{1} \\circ f_{2} \\circ \\dots \\circ f_{K} \\) for multi-scale color and form interaction and abstraction. At the \\( k \\)-th scale, \\( z_{2(k-1)} \\) is processed and split into \\( z_{2k-1} \\) and \\( z_{2k} \\), the latter of which further undergoes the \\( k+1 \\)-th scale of processing and splitting. At the final \\( K \\)-th scale, we only process \\( z_{2(K-1)} \\) to \\( z_{2K-1} \\) without splitting. By this notation, we assume that \\( z_{0} = x \\) is the input RGB image. The probability density of the latent representation \\( z = \\{z_{1}, z_{3}, \\ldots, z_{2K-1}\\} \\) can then be conditionally factorized as" + }, + { + "type": "equation", + "bbox": [ + 0.116, + 0.628, + 0.469, + 0.713 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} p (\\boldsymbol {z}) = \\prod_ {k = 1} ^ {K - 1} p \\left(\\boldsymbol {z} _ {2 k - 1} \\mid \\left\\{\\boldsymbol {z} _ {\\geq (2 k + 1)} \\right\\}\\right) p (\\boldsymbol {z} _ {2 K - 1}) \\\\ = \\prod_ {k = 1} ^ {K - 1} p \\left(\\boldsymbol {z} _ {2 k - 1} \\mid \\boldsymbol {z} _ {2 k}\\right) p \\left(\\boldsymbol {z} _ {2 K - 1}\\right). \\tag {1} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.72, + 0.468, + 0.901 + ], + "angle": 0, + "content": "The second equality is due to the bijectivity of the normalizing flow. \\( p(z_{2k-1}|z_{2k}) \\), for \\( k \\in \\{1, 2, \\dots, K-1\\} \\), can conveniently be modeled as conditionally independent Gaussians. That is, the mean vector and the diagonal covariance matrix are computed from \\( z_{2k} \\) through say a tiny neural network [13]. Similarly, \\( p(z_{2K-1}) \\) is modeled as (unconditionally) independent Gaussians, where the parameters are directly estimated via backpropagation. As shown in Figure 2, each scale of flow processing consists of a squeezing operation, multiple flow steps, and a splitting operation. Each flow step is further decomposed into three operations: actnorm, invertible \\( 1 \\times 1 \\) convolution, and affine coupling." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.092, + 0.891, + 0.152 + ], + "angle": 0, + "content": "- Actnorm is introduced to replace the batch normalization to avoid degenerated performance when the minibatch size is small. Given an input \\( z \\) of size \\( c \\times h \\times w \\), the output of the same size can be computed by" + }, + { + "type": "equation", + "bbox": [ + 0.657, + 0.161, + 0.891, + 0.176 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {z} ^ {\\prime} = \\boldsymbol {s} \\odot \\boldsymbol {z} + \\boldsymbol {t}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.532, + 0.184, + 0.89, + 0.215 + ], + "angle": 0, + "content": "with the log-determinant \\(h\\cdot w\\cdot \\log |\\operatorname *{det}(\\operatorname {diag}(s))|\\) \\(\\{s,t\\}\\) are learnable scaling and bias parameters." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.223, + 0.891, + 0.268 + ], + "angle": 0, + "content": "- Invertible \\(1 \\times 1\\) Convolution is a learnable linear transform for channel mixing. Given a \\(c \\times h \\times w\\) input \\(\\mathbf{z}\\) and a \\(c \\times c\\) weight matrix \\(\\mathbf{W}\\), we have" + }, + { + "type": "equation", + "bbox": [ + 0.674, + 0.277, + 0.891, + 0.292 + ], + "angle": 0, + "content": "\\[\nz ^ {\\prime} = W z, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.532, + 0.301, + 0.84, + 0.317 + ], + "angle": 0, + "content": "with the log-determinant \\( h \\cdot w \\cdot \\log |\\operatorname{det}(\\mathbf{W})| \\)." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.324, + 0.891, + 0.415 + ], + "angle": 0, + "content": "- Affine Coupling constrains its Jacobian to be a triangular matrix, which facilitates the log-determinant computation. Specifically, the input \\( z \\) of \\( D \\) dimensions is first split into two non-overlapping subsets: \\( z_{1:d} \\) and \\( z_{d+1:D} \\), where \\( d < D \\). The output of the same dimension can be computed by" + }, + { + "type": "equation", + "bbox": [ + 0.604, + 0.424, + 0.688, + 0.439 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {z} _ {1: d} ^ {\\prime} = \\boldsymbol {z} _ {1: d},\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.585, + 0.435, + 0.891, + 0.461 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {z} _ {d + 1: D} ^ {\\prime} = \\boldsymbol {z} _ {d + 1: D} \\odot e ^ {s \\left(\\boldsymbol {z} _ {1: d}\\right)} + t \\left(\\boldsymbol {z} _ {1: d}\\right), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.532, + 0.469, + 0.891, + 0.514 + ], + "angle": 0, + "content": "where \\( s(\\cdot) \\) and \\( t(\\cdot) \\) are the scaling and translation functions, respectively, which are not necessarily invertible. The inverse is easily and analytically derived as" + }, + { + "type": "equation", + "bbox": [ + 0.583, + 0.522, + 0.665, + 0.538 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {z} _ {1: d} = \\boldsymbol {z} _ {1: d} ^ {\\prime},\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.563, + 0.535, + 0.891, + 0.561 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {z} _ {d + 1: D} = \\left(\\boldsymbol {z} _ {d + 1: D} ^ {\\prime} - t \\left(\\boldsymbol {z} _ {1: d} ^ {\\prime}\\right)\\right) \\odot e ^ {- s \\left(\\boldsymbol {z} _ {1: d} ^ {\\prime}\\right)}. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.571, + 0.891, + 0.632 + ], + "angle": 0, + "content": "CD Distance. We adopt the Euclidean distance, i.e., the square root of mean squared differences between two latent color representations \\( f(\\pmb{x}) \\) and \\( f(\\pmb{y}) \\), as the CD distance between two input images \\( \\pmb{x} \\) and \\( \\pmb{y} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.524, + 0.64, + 0.891, + 0.673 + ], + "angle": 0, + "content": "\\[\n\\Delta E (\\boldsymbol {x}, \\boldsymbol {y}) = \\sqrt {\\frac {(f (\\boldsymbol {x}) - f (\\boldsymbol {y})) ^ {T} (f (\\boldsymbol {x}) - f (\\boldsymbol {y}))}{D}}. \\tag {6}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.688, + 0.644, + 0.701 + ], + "angle": 0, + "content": "3.3. Loss Function" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.711, + 0.891, + 0.757 + ], + "angle": 0, + "content": "It is straightforward to measure the \\(\\ell_p\\)-norm induced distance between the predicted CD computed by Eq. (6) and the perceptual CD of the given image pair \\((x, y)\\):" + }, + { + "type": "equation", + "bbox": [ + 0.567, + 0.764, + 0.891, + 0.781 + ], + "angle": 0, + "content": "\\[\n\\ell (\\boldsymbol {x}, \\boldsymbol {y}) = \\| \\Delta E (\\boldsymbol {x}, \\boldsymbol {y}) - \\Delta V (\\boldsymbol {x}, \\boldsymbol {y}) \\| _ {p}. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.789, + 0.891, + 0.849 + ], + "angle": 0, + "content": "To encourage the robustness of CD-Flow to mild geometric distortions, which are often unavoidable in practice, we introduce a multi-scale version of Eq. (7) to put more emphasis on coarser-scale latent representations:" + }, + { + "type": "equation", + "bbox": [ + 0.541, + 0.856, + 0.891, + 0.897 + ], + "angle": 0, + "content": "\\[\n\\ell_ {\\mathrm {m s}} (\\boldsymbol {x}, \\boldsymbol {y}) = \\sum_ {k = 1} ^ {K} \\| \\Delta E _ {k} (\\boldsymbol {x}, \\boldsymbol {y}) - \\Delta V (\\boldsymbol {x}, \\boldsymbol {y}) \\| _ {p}, \\tag {8}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22245" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.089, + 0.895, + 0.149 + ], + "angle": 0, + "content": "Table 1. STRESS, PLCC, and SRCC between predicted CDs \\((\\Delta E)\\) and perceptual CDs \\((\\Delta V)\\) in SPCD. The top section lists representative CD formulae developed from homogeneous color patches. The second section contains CD measures adapted for natural images. The third section includes general-purpose image quality models. The fourth section consists of JND measures. Closest to ours, CD-Net is a DNN-based data-driven CD measure for natural images. The top two methods are highlighted in boldface" + }, + { + "type": "table", + "bbox": [ + 0.081, + 0.159, + 0.892, + 0.445 + ], + "angle": 0, + "content": "
MethodPerfectly aligned pairsNon-perfectly aligned pairsAll
\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)
CIELAB [45]31.2440.7930.77529.6390.6900.57931.8720.7160.666
CIE94 [34]34.7210.7900.77229.9160.6930.57234.3260.7100.654
CIEDE2000 [29]29.9750.8250.82130.3470.6670.56331.4390.7260.686
S-CIELAB [57]30.0940.8220.81931.8040.6310.52232.7800.7000.657
Hong06 [18]60.5570.7940.81057.0700.5430.46161.2270.6450.632
Ouni081[39]29.9770.8260.82130.3550.6680.56331.4440.7260.685
Jaramillo19 [38]43.4190.5140.50650.2990.0810.04168.8050.3210.329
SSIM [49]39.3930.5890.54953.0350.0770.04448.0250.3090.324
FLLIP [3]29.3180.7450.71527.1580.7340.64029.0990.7180.663
PieAPP [44]29.0440.7370.73737.5280.5220.45932.3540.6520.643
LPIPS [56]44.8110.6950.68853.1320.2190.17164.1450.4390.490
DISTS [10]31.4090.7450.74635.0430.5280.44732.9950.6400.626
Chou07 [8]50.7210.7870.78536.1840.6030.45949.5450.6120.557
Butteraugli [1]42.6200.6060.59348.2170.2580.24554.7370.3710.359
CD-Net [51]20.8910.8670.87022.5430.8180.77621.4310.8460.842
CD-Flow16.6130.8960.90421.3740.8560.79418.4730.8710.865
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.093, + 0.446, + 0.355, + 0.461 + ], + "angle": 0, + "content": "1 The spatial extension of CIEDE2000." + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.5, + 0.124, + 0.513 + ], + "angle": 0, + "content": "where" + }, + { + "type": "equation", + "bbox": [ + 0.087, + 0.538, + 0.47, + 0.594 + ], + "angle": 0, + "content": "\\[\n\\Delta E _ {k} (\\boldsymbol {x}, \\boldsymbol {y}) = \\sqrt {\\frac {\\left(f _ {k :} (\\boldsymbol {x}) - f _ {k :} (\\boldsymbol {y})\\right) ^ {T} \\left(f _ {k :} (\\boldsymbol {x}) - f _ {k :} (\\boldsymbol {y})\\right)}{D _ {k}}}. \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.62, + 0.47, + 0.685 + ], + "angle": 0, + "content": "\\(f_{k}(\\pmb {x}) = [z_{2k - 1}^{T},\\dots ,z_{2K - 1}^{T}]^{T}\\) and \\(D_{k}\\) is the number of feature dimensions of \\(f_{k}(\\pmb {x})\\) . That is, \\(\\Delta E_k(\\pmb {x},\\pmb {y})\\) makes an estimate of \\(\\Delta V(\\pmb {x},\\pmb {y})\\) using the \\(k\\) -th to \\(K\\) -th scale latent representations, and we have \\(\\Delta E_1\\) equal to \\(\\Delta E\\) in Eq. (6)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.689, + 0.471, + 0.901 + ], + "angle": 0, + "content": "It is noteworthy that if the transform \\( f \\) is supervised by the CD loss in Eq. (8) solely, the bijectivity of \\( f \\) may not be necessarily guaranteed. This is because the adopted normalizing flow is not bijective by design. For example, the \"invertible\" \\( 1 \\times 1 \\) convolution as the channel mixer becomes non-invertible when \\( W \\) is degenerate. Empirically, we observe that the scaling factor of the affine-coupling layer is close to zero after optimizing Eq. (8). Since the inverse transform includes operations of dividing by the scaling factor, there will easily result in the exploding inverse problem [5]. Thus, we incorporate the commonly used maximum likelihood objective in normalizing flow [40] into our training objective. We work with the negative log-likelihood loss:" + }, + { + "type": "equation", + "bbox": [ + 0.518, + 0.52, + 0.892, + 0.574 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\ell_ {\\mathrm {n l}} (\\boldsymbol {x}) = - \\log p _ {\\mathcal {X}} (\\boldsymbol {x}) \\\\ = - \\log p _ {\\mathcal {Z}} (f (\\boldsymbol {x})) - \\log \\left| \\det \\left(\\frac {\\partial f (\\boldsymbol {x})}{\\partial \\boldsymbol {x}}\\right) \\right|. \\tag {10} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.58, + 0.895, + 0.64 + ], + "angle": 0, + "content": "During training, we randomly sample a mini-batch \\(\\mathcal{B}\\) from the training dataset \\(\\mathcal{D}\\) in each iteration, and make use of a variant of stochastic gradient descent to optimize the parameters in CD-Flow:" + }, + { + "type": "equation", + "bbox": [ + 0.508, + 0.646, + 0.892, + 0.702 + ], + "angle": 0, + "content": "\\[\n\\ell (\\mathcal {B}) = \\frac {1}{| \\mathcal {B} |} \\sum_ {(x, y) \\in \\mathcal {B}} \\left(\\ell_ {\\mathrm {m s}} (\\boldsymbol {x}, \\boldsymbol {y}) + \\lambda \\left(\\ell_ {\\mathrm {n l}} (\\boldsymbol {x}) + \\ell_ {\\mathrm {n l}} (\\boldsymbol {y})\\right)\\right), \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.71, + 0.895, + 0.743 + ], + "angle": 0, + "content": "where \\(|\\mathcal{B}|\\) denotes the cardinality of \\(\\mathcal{B}\\), and \\(\\lambda\\) is the trade-off to balance the magnitudes of different loss terms." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.754, + 0.634, + 0.771 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.779, + 0.894, + 0.84 + ], + "angle": 0, + "content": "In this section, we begin by presenting the experimental setups. We then compare the proposed CD-Flow with 15 CD measures. We last conduct extensive ablation studies to justify the key designs of CD-Flow." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.848, + 0.698, + 0.865 + ], + "angle": 0, + "content": "4.1. Experimental Setups" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.895, + 0.903 + ], + "angle": 0, + "content": "CD Dataset. We conduct experiments on SPCD [51], so far the largest natural image dataset tailored for perceptual CD" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22246" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.089, + 0.895, + 0.149 + ], + "angle": 0, + "content": "Table 2. STRESS, PLCC, and SRCC between predicted CDs \\((\\Delta E)\\) and perceptual CDs \\((\\Delta V)\\) in SPCD [51] under geometric distortions. Translation means randomly shifting one image with respect to the other image in an image pair (by up to \\(5\\%\\) of pixels) in both spacial directions. Rotation means randomly rotating one image (by up to \\(3^{\\circ}\\)) around the center point. Dilation means zooming in (and cropping) one image without changing the image size (by a factor of 1.05)" + }, + { + "type": "table", + "bbox": [ + 0.081, + 0.159, + 0.891, + 0.278 + ], + "angle": 0, + "content": "
MethodTranslationRotationDilation
\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)
CIELAB [45]29.4140.6200.57732.6330.5290.49531.5110.5190.467
CIE94 [34]29.1410.6450.59631.9430.5660.51930.3230.5670.505
CIEDE2000 [29]28.0350.6540.61331.2550.5660.52729.9280.5660.512
CD-Net [51]19.8250.8450.84222.4630.7840.77221.7040.7870.773
CD-Flow19.3110.8520.85620.1390.8370.81621.3520.8270.797
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.314, + 0.473, + 0.45 + ], + "angle": 0, + "content": "assessment. SPCD consists of 15,335 color images out of 1,000 distinct natural scenes spanning a variety of realistic shooting scenarios. A total of 30,000 image pairs are sampled for human labeling, where 10,005 are non-perfectly aligned image pairs. For each image pair, there are 20 human ratings. We randomly split \\(70\\%\\), \\(10\\%\\), and \\(20\\%\\) of image pairs in SPCD as training, validation, and test sets, respectively, according to the image content to ensure content independence." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.455, + 0.473, + 0.622 + ], + "angle": 0, + "content": "Network Architecture. We employ a variant of RealNVP [22], which consists of \\( K = 6 \\) scales, and each scale includes a squeezing operation, \\( L = 8 \\) steps of flow, and a splitting operation. We do not split at the last scale. The squeezing operation trades the spatial size for the channel number by transforming an \\( s \\times s \\times c \\) tensor to an \\( \\frac{s}{2} \\times \\frac{s}{2} \\times 4c \\) tensor, where \\( s \\) is the spatial size and \\( c \\) is the number of channels. The splitting operation divides the latent representation of the same scale into two halves along the channel dimension. One is used as the color features for CD assessment, and the other is subject to further processing." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.626, + 0.471, + 0.687 + ], + "angle": 0, + "content": "Training and Testing Details. We employ the Adam as the stochastic optimizer, where the batch size is 4, and the initial learning rate is \\(10^{-5}\\) with a decay factor of 2 for every 5 epochs. We train CD-Flow for 50 epochs in total." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.691, + 0.472, + 0.798 + ], + "angle": 0, + "content": "Evaluation Criteria. We use three standard criteria to quantify the performance of CD-Flow against existing CD measures, including the standardized residual sum of squares (STRESS) [15], Pearson linear correlation coefficient (PLCC), and Spearman's rank correlation coefficient (SRCC). STRESS measures both prediction accuracy and statistical significance, which is defined by" + }, + { + "type": "equation", + "bbox": [ + 0.114, + 0.818, + 0.47, + 0.866 + ], + "angle": 0, + "content": "\\[\n\\text {S T R E S S} = 1 0 0 \\sqrt {\\frac {\\sum_ {i = 1} ^ {M} \\left(\\Delta E _ {i} - F \\Delta V _ {i}\\right) ^ {2}}{F ^ {2} \\sum_ {i = 1} ^ {M} \\Delta V _ {i} ^ {2}}}, \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.886, + 0.471, + 0.902 + ], + "angle": 0, + "content": "where \\(M\\) is the number of test pairs and \\(F\\) is the scale cor" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.314, + 0.816, + 0.33 + ], + "angle": 0, + "content": "resection factor between \\(\\Delta E\\) and \\(\\Delta V\\), defined as" + }, + { + "type": "equation", + "bbox": [ + 0.621, + 0.338, + 0.892, + 0.378 + ], + "angle": 0, + "content": "\\[\nF = \\frac {\\sum_ {i = 1} ^ {M} \\Delta E _ {i} ^ {2}}{\\sum_ {i = 1} ^ {M} \\Delta E _ {i} \\Delta V _ {i}}. \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.386, + 0.895, + 0.477 + ], + "angle": 0, + "content": "STRESS generally ranges from 0 to 100, where a small value suggests a tight-fitting between model predictions and ground truths. SRCC and PLCC measure the prediction monotonicity and prediction linearity, respectively. Before calculating PLCC, we linearize model predictions by regressing a four-parameter monotonic function." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.485, + 0.64, + 0.5 + ], + "angle": 0, + "content": "4.2. Main Results" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.509, + 0.892, + 0.704 + ], + "angle": 0, + "content": "SPCD Results. We compare the proposed CD-Flow with 15 existing CD measures which can be grouped into four categories: 1) CD measures for homogeneous color patches: CIELAB [45], CIE94 [34] and CIEDE2000 [29], 2) CD measures for natural images: S-CIELAB [57], Hong06 [18], Ouni08 [39], Jaramillo19 [38] and CD-Net [51], 3) full-reference image quality model: SSIM [49], FLIP [3], PieAPP [44], LPIPS [56] and DISTS [10], and 4) JND methods: Chou07 [8] and Butteraugli [1]. We employ official implementations of Butteraugli and FLIP, and retrain PieAPP, LPIPS, and DISTS on the same training set as CD-Flow. The rest methods are implemented and made publicly available by Jaramillo et al. [38]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.895, + 0.903 + ], + "angle": 0, + "content": "The comparison results are documented in Table 1. Several interesting observations merit attention. First, CIE94 and CIEDE2000 rectify the non-uniformity of CIELAB on color patches by incorporating weight coefficients. Nonetheless, when it comes to natural photographic images, these approaches do not show noticeable improvements compared to the original CIELAB formula. This provides a strong indication that humans perceive CDs of uniformly colored patches and natural images in drastically different manners. Naive extensions such as spatial filtering and texture grouping may not be sufficient to bridge the gap as evidenced by the results in the second section of Table 1. Second, despite being retrained on the same training" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22247" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.089, + 0.892, + 0.118 + ], + "angle": 0, + "content": "Table 3. Generalizability evaluation of CD-Flow on the COM dataset and its four subsets: BFD-P, Leeds, Witt, and RIT-DuPont. PLCC on RIT-DuPont is indicated by “—” since it is not computable" + }, + { + "type": "table", + "bbox": [ + 0.078, + 0.131, + 0.894, + 0.238 + ], + "angle": 0, + "content": "
MethodBFD-P [31]Leeds [21]Witt [52]RIT-DuPont [7]COM dataset [29]
\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)
CIELAB [45]45.0540.74940.0930.29551.6890.56530.348—45.2020.693
CIE94 [34]35.7980.83030.4940.58431.8570.79320.982—33.2350.814
CIEDE2000 [29]31.9350.86119.2470.77230.3580.82520.239—28.9790.862
CD-Net39.3120.79138.5580.44933.6400.82842.999—38.8720.786
CD-Flow34.6610.83334.2750.47631.9650.82036.504—35.0610.801
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.274, + 0.473, + 0.531 + ], + "angle": 0, + "content": "set as CD-Flow, general-purpose DNN-based image quality models PieAPP [44], LPIPS [56], and DISTS [10] do not deliver comparable performance. We believe this arises for PieAPP because it requires a much larger labeled set to support training from scratch, and 21,000 training pairs, even with data augmentation, are less likely to overcome overfitting. LPIPS and DISTS rely on pre-trained VGG features, which are empirically proven to be more structure and texture inductive. As a result, they may be less ideal for assessing CDs. Third, DNN-based CD formulae, i.e., CD-Net [51] and CD-Flow, outperform all other competing models, which are primarily attributed to their superior representation learning capabilities. This observation confirms the potential of employing DNNs in the field of color science. Fourth, CD-Flow achieves the most remarkable results, thereby demonstrating the promise of the multi-scale autoregressive normalizing flow for CD assessment." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.535, + 0.472, + 0.777 + ], + "angle": 0, + "content": "Robustness Results to Geometric Distortions. We conduct experiments to evaluate the robustness of CD-Flow to mild geometric distortions, including translation, rotation, and dilation. Specifically, we translate randomly one image with respect to the other image in an image pair by up to \\(5\\%\\) pixels in both horizontal and vertical directions, rotate randomly one image clockwise by up to \\(3^{\\circ}\\), and zoom in one image by a factor of 1.05. The corresponding results are presented in Table 2. We find that the performance of all models degrades due to the presence of geometric distortions. CIELAB-based formulae are particularly sensitive to geometric distortions because of pixel-by-pixel CD computation. In contrast, CD-Flow demonstrates the best robustness results, which is as expected because it is designed to abstract color features, characterized by the interdependence between color and form for CD assessment." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.472, + 0.903 + ], + "angle": 0, + "content": "Generalizability Results on Other Datasets. We examine the generalizability of CD-Flow on the COM dataset [29] and the TID2013 subset [42]. The COM dataset consists of four subsets: BFD-P [31], Leeds [21], Witt [52], and RIT-DuPont [7], which include uniformly colored patches used for the development of CIEDE2000. The comparison results are reported in Table 3. It is clear that two DNN-based CD models perform better than CIELAB on" + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.272, + 0.895, + 0.314 + ], + "angle": 0, + "content": "Table 4. Generalizability evaluation of CD-Flow on the TID2013 subset, containing quantization noise, image color quantization with dither, and chromatic aberration artifacts" + }, + { + "type": "table", + "bbox": [ + 0.53, + 0.327, + 0.866, + 0.438 + ], + "angle": 0, + "content": "
Method\\( STRESS\\downarrow \\)PLCC↑SRCC↑
CIEDE2000 [29]18.2030.7300.751
PieAPP [44]20.9180.6200.653
LPIPS [56]15.4200.8160.804
DISTS [10]15.2350.8210.805
CD-Net [51]15.9620.8010.826
CD-Flow14.1100.8370.832
" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.477, + 0.895, + 0.735 + ], + "angle": 0, + "content": "the COM dataset, despite not being exposed to color patch data during training. Although underperforming CIE94 and CIEDE2000, CD-Flow exhibits better generalization compared to CD-Net. We then test the generalizability of the TID2013 subset, which contains three types of color-related distortions: quantization noise, image color quantization with dither, and chromatic aberration. Table 4 lists the comparison results. We find that CD-Flow performs much better than the best-performing CIELAB-based CD formula CIEDE2000, and is on par with general-purpose image quality methods LPIPS and DISTS, which may have been exposed to similar distortion appearances during training. All these results constitute supportive evidence that the multi-scale autoregressive normalizing flow provides a compelling embodiment of the hypothesis: \"color and form are inextricably interdependent as a unitray process of perceptual organization [19, 46].\"" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.897, + 0.903 + ], + "angle": 0, + "content": "Visualization of Local CD Maps. We further visualize the multi-scale local CD maps generated by CD-Flow, as illustrated in Figure 3. Our observations are as follows. First, local CDs are generally small at the first three scales compared to those at later scales, indicating that CD-Flow indeed relies more on coarser-scale representations to assess perceptual CDs, as encouraged by the multi-scale training objective in Eq. (8). Second, interestingly, the effects of single-opponent and double-opponent cells emerge in the computation of CD-Flow. At coarser scales, local CDs are evaluated over a large image area, and exhibit reason-" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22248" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.091, + 0.89, + 0.245 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.257, + 0.893, + 0.288 + ], + "angle": 0, + "content": "Figure 3. Multi-scale local CD maps generated by CD-Flow. First two columns: Input image pair \\((\\pmb{x},\\pmb{y})\\). Third to the eighth column: Six scales of CD maps generated by CD-Flow, with a warmer color indicating a larger local CD." + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.311, + 0.471, + 0.354 + ], + "angle": 0, + "content": "Table 5. Ablation analysis of the number of flow steps in CD-Flow (i.e., the hyperparameter \\( L \\)), where the number of scales is fixed to six. The default setting is highlighted in boldface" + }, + { + "type": "table", + "bbox": [ + 0.102, + 0.366, + 0.445, + 0.453 + ], + "angle": 0, + "content": "
# of flow stepsSTRESS↓PLCC↑SRCC↑
L = 221.6330.8370.826
L = 420.9940.8380.825
L = 818.4730.8710.865
L = 1617.7920.8790.870
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.478, + 0.47, + 0.521 + ], + "angle": 0, + "content": "Table 6. Ablation analysis of the number of scales in CD-Flow (i.e., the hyperparameter \\( K \\) ),where the number of flow steps is fixed to eight. The default setting is highlighted in boldface" + }, + { + "type": "table", + "bbox": [ + 0.116, + 0.533, + 0.432, + 0.62 + ], + "angle": 0, + "content": "
# of scalesSTRESS↓PLCC↑SRCC↑
K = 224.6860.7600.732
K = 419.7300.8570.850
K = 618.4730.8710.865
K = 818.5240.8740.861
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.657, + 0.47, + 0.718 + ], + "angle": 0, + "content": "able sensitivity, resembling single-opponent cells in V1. At finer scales, local CDs are more responsive to textures and boundaries, consistent with the response characteristics of double-opponent cells in V1." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.727, + 0.241, + 0.74 + ], + "angle": 0, + "content": "4.3. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.471, + 0.901 + ], + "angle": 0, + "content": "We conduct two ablation experiments to evaluate the key design choices of CD-Flow. First, we study the impact of the number of flow steps (i.e., the hyperparameter \\( L \\)) on the model performance. In Table 5, we find that the performance of CD-Flow increases with the number of flow steps, which in turn expands the non-linear representational capacity of CD-Flow. Nevertheless, too many steps may substantially increase the computational complexity, and negatively impact generalization as well due to potential overfitting. We select \\( L = 8 \\) as the default setting to strike a" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.313, + 0.895, + 0.51 + ], + "angle": 0, + "content": "good balance between model performance and complexity. Next, we examine the effect of the number of scales (i.e., the hyperparameter \\( K \\)) on the model performance. Table 6 reports the results. We find that increasing the number of scales from 2 to 6 significantly improves the CD assessment performance. However, further increasing \\( K \\) does not bring extra performance improvements, not as the case of increasing \\( L \\). We attribute this to the setting of the current training input resolution (i.e., \\( 768 \\times 768 \\times 3 \\)). When \\( K = 8 \\) and at the coarsest scale, we are essentially comparing the difference of two \"globally averaged\" color values over the entire images, which is less biologically plausible and less practically meaningful." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.525, + 0.619, + 0.54 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.551, + 0.892, + 0.763 + ], + "angle": 0, + "content": "We have introduced CD-Flow, a normalizing flow-based CD metric for photographic images. Our approach is inspired by the scientific evidence that humans perceive color based on the interdependence of color and form in images. We utilized a multi-scale autoregressive normalizing flow to learn a coordinate transform, followed by computing the Euclidean distance in the transformed space. Remarkably, the learned feature transform enjoys four desirable properties: 1) consistent with the working mechanism of human color perception, 2) proper as a mathematical metric, 3) accurate to explain human data of perceptual CDs, and 4) robust to slight geometric distortions. We hope that the proposed CD-Flow can benefit related fields in imaging/illumination, vision science, and color science." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.779, + 0.661, + 0.795 + ], + "angle": 0, + "content": "Acknowledgement" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.803, + 0.892, + 0.888 + ], + "angle": 0, + "content": "We would like to thank Keshuo Xu for the technical support and code sharing. This work was supported in part by the National Natural Science Foundation of China under Grant 62071407, the Hong Kong ITC Innovation and Technology Fund (9440288), and the CUHKSZ University Development Fund (UDF01002437)." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.957 + ], + "angle": 0, + "content": "22249" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.105 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.17 + ], + "angle": 0, + "content": "[1] Jyrki Alakuijala, Robert Obryk, Ostap Stoliarchuk, Zoltan Szabadka, Lode Vandevenne, and Jan Wassenberg. Guetzli: Perceptually guided JPEG encoder. arXiv preprint arXiv:1703.04421, 2017. 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.171, + 0.47, + 0.213 + ], + "angle": 0, + "content": "[2] Liliana Albertazzi, Gert J. Van Tonder, and Dhanraj Vishwanath. Perception Beyond Inference: The Information Content of Visual Processes. MIT Press, 2011. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.213, + 0.471, + 0.282 + ], + "angle": 0, + "content": "[3] Pontus Andersson, Jim Nilsson, Tomas Akenine-Moller, Magnus Oskarsson, Kalle Åström, and Mark D. Fairchild. FLIP: A difference evaluator for alternating images. ACM on Computer Graphics and Interactive Techniques, 3(2):1-23, 2020. 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.283, + 0.471, + 0.338 + ], + "angle": 0, + "content": "[4] Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, and Jorn-Henrik Jacobsen. Invertible residual networks. In International Conference on Machine Learning, pages 573-582, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.339, + 0.471, + 0.408 + ], + "angle": 0, + "content": "[5] Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger Grosse, and Jorn-Henrik Jacobsen. Understanding and mitigating exploding inverses in invertible neural networks. In International Conference on Artificial Intelligence and Statistics, pages 1792-1800, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.41, + 0.47, + 0.45 + ], + "angle": 0, + "content": "[6] Ohad Ben-Shahar and Steven W. Zucker. Hue geometry and horizontal connections. *Neural Networks*, 17(5-6):753–771, 2004. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.451, + 0.471, + 0.519 + ], + "angle": 0, + "content": "[7] Roy S. Berns, David H. Alman, Lisa Reniff, Gregory D. Snyder, and Mitchell R. Balonon-Rosen. Visual determination of suprathreshold color-difference tolerances using probit analysis. Color Research & Application, 16(5):297-316, 1991." + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.521, + 0.471, + 0.577 + ], + "angle": 0, + "content": "[8] Chun-Hsien Chou and Kuo-Cheng Liu. A fidelity metric for assessing visual quality of color images. In International Conference on Computer Communications and Networks, pages 1154-1159, 2007. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.578, + 0.47, + 0.632 + ], + "angle": 0, + "content": "[9] Frank J. J. Clarke, Roderick McDonald, and Bryan Rigg. Modification to the JPC79 colour-difference formula. Journal of the Society of Dyers and Colourists, 100(4):128-132, 1984. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.634, + 0.47, + 0.689 + ], + "angle": 0, + "content": "[10] Keyan Ding, Kede Ma, Shiqi Wang, and Eero P. Simoncelli. Image quality assessment: Unifying structure and texture similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5):2567-2581, 2020. 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.69, + 0.47, + 0.746 + ], + "angle": 0, + "content": "[11] Keyan Ding, Kede Ma, Shiqi Wang, and Eero P. Simoncelli. Comparison of full-reference image quality models for optimization of image processing systems. International Journal of Computer Vision, 129(4):1258-1281, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.746, + 0.47, + 0.788 + ], + "angle": 0, + "content": "[12] Laurent Dinh, David Krueger, and Yoshua Bengio. NICE: Non-linear independent components estimation. In International Conference on Representations Workshops, 2015. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.789, + 0.47, + 0.83 + ], + "angle": 0, + "content": "[13] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real NVP. In International Conference on Learning Representations, 2017. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.831, + 0.47, + 0.872 + ], + "angle": 0, + "content": "[14] Chen Gao, Karen Panetta, and Sos Agaian. No reference color image quality measures. In IEEE International Conference on Cybernetics, pages 243-248, 2013. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.873, + 0.47, + 0.902 + ], + "angle": 0, + "content": "[15] Pedro A. Garcia, Rafael Huertas, Manuel Melgosa, and Guihua Cui. Measurement of the relationship between perceived" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.115, + 0.471, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "and computed color differences. Journal of the Optical Society of America A, 24(7):1823-1829, 2007. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[16] Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud. FFJORD: Free-form continuous dynamics for scalable reversible generative models. International Conference on Learning Representations, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.192, + 0.892, + 0.234 + ], + "angle": 0, + "content": "[17] Thorsten Hansen, Maria Olkkonen, Sebastian Walter, and Karl R. Gegenfurtner. Memory modulates color appearance. Nature Neuroscience, 9(11):1367-1368, 2006. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.236, + 0.892, + 0.278 + ], + "angle": 0, + "content": "[18] Guowei Hong and Ming Ronnier Luo. New algorithm for calculating perceived colour difference of images. The Imaging Science Journal, 54(2):86-91, 2006. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.281, + 0.892, + 0.308 + ], + "angle": 0, + "content": "[19] Gaetano Kanizsa. Organization in Vision: Essays on Gestalt Perception. Praeger Publishers, 1979. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.31, + 0.857, + 0.324 + ], + "angle": 0, + "content": "[20] David Katz. The World of Colour. Routledge, 1935. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.326, + 0.892, + 0.367 + ], + "angle": 0, + "content": "[21] Dong-Ho Kim. New weighting functions for the modified CIELAB colour-difference formulae. Textile Coloration and Finishing, 9(6):51-57, 1997. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.368, + 0.892, + 0.411 + ], + "angle": 0, + "content": "[22] Durk P. Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. Neural Information Processing Systems, 2018. 3, 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.412, + 0.892, + 0.481 + ], + "angle": 0, + "content": "[23] Jakob Kruse, Gianluca Detommaso, Ullrich Kothe, and Robert Scheicl. HINT: Hierarchical invertible neural transport for density estimation and bayesian inference. AAAI Conference on Artificial Intelligence, pages 8191-8199, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.483, + 0.892, + 0.511 + ], + "angle": 0, + "content": "[24] Edwin H. Land. The retina theory of color vision. Scientific American, 237(6):108-129, 1977. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.513, + 0.892, + 0.581 + ], + "angle": 0, + "content": "[25] Dohyoung Lee and Konstantinos N. Plataniotis. Towards a novel perceptual color difference metric using circular processing of hue components. In IEEE International Conference on Acoustics, Speech, & Signal Processing, pages 166-170, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.584, + 0.892, + 0.627 + ], + "angle": 0, + "content": "[26] Sang-Mi Lee, John Haozhong Xin, and Stephen Westland. Evaluation of image similarity by histogram intersection. Color Research & Application, 30(4):265-274, 2005. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.628, + 0.892, + 0.656 + ], + "angle": 0, + "content": "[27] Peter Lennie. Color coding in the cortex. Color Vision: From Genes to Perception, pages 235-247, 1999. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.658, + 0.892, + 0.699 + ], + "angle": 0, + "content": "[28] Ming Ronnier Luo. Colour science: Past, present and future. Colour Imaging Vision and Technology, pages 381-404, 1999. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.701, + 0.892, + 0.756 + ], + "angle": 0, + "content": "[29] Ming Ronnier Luo, Guihua Cui, and Bryan Rigg. The development of the CIE 2000 colour-difference formula: CIEDE2000. Color Research & Application, 26(5):340-350, 2001. 1, 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.758, + 0.892, + 0.8 + ], + "angle": 0, + "content": "[30] Ming Ronnier Luo and Bryan Rigg. Chromaticity-discrimination ellipses for surface colours. Color Research & Application, 11(1):25-42, 1986. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.802, + 0.892, + 0.843 + ], + "angle": 0, + "content": "[31] Ming Ronnier Luo and Bryan Rigg. BFD(l:c) colour-difference formula. Journal of the Society of Dyers and Colourists, 103(2):86-94, 1987. 1, 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.845, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[32] Marc Mahy, Luc Van Eycken, and André Oosterlinck. Evaluation of uniform color spaces developed after the adoption of CIELAB and CIELUV. Color Research & Application, 19(2):105-121, 1994. 1, 2, 3" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "22250" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.093, + 0.47, + 0.133 + ], + "angle": 0, + "content": "[33] Roderick McDonald. Industrial pass/fail colour matching. Journal of the Society of Dyers and Colourists, 96(7):372-376, 1980. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.136, + 0.47, + 0.177 + ], + "angle": 0, + "content": "[34] Roderick McDonald and Kenneth J. Smith. CIE94-A new colour-difference formula. Journal of the Society of Dyers and Colourists, 111(12):376-379, 1995. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.179, + 0.469, + 0.233 + ], + "angle": 0, + "content": "[35] Anish Mittal, Anush Krishna Moorthy, and Alan C. Bovik. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12):4695-4708, 2012. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.235, + 0.469, + 0.276 + ], + "angle": 0, + "content": "[36] Anish Mittal, Rajiv Soundararajan, and Alan C. Bovik. Making a \"completely blind\" image quality analyzer. IEEE Signal Processing Letters, 20(3):209-212, 2012. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.278, + 0.469, + 0.318 + ], + "angle": 0, + "content": "[37] Ken Nakayama and Shinsuke Shimojo. Experiencing and perceiving visual surfaces. Science, 257(5075):1357-1363, 1992. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.321, + 0.469, + 0.375 + ], + "angle": 0, + "content": "[38] Benhur Ortiz-Jaramillo, Asli Kumcu, Ljiljana Platisa, and Wilfried Philips. Evaluation of color differences in natural scene color images. Signal Processing: Image Communication, 71:128-137, 2019. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.378, + 0.469, + 0.445 + ], + "angle": 0, + "content": "[39] Sonia Ouni, Ezzeddine Zagrouba, Majed Chambah, and Michel Herbin. A new spatial colour metric for perceptual comparison. In International Conference on E-Systems Engineering, Communication and Information, pages 413–428, 2008. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.448, + 0.469, + 0.516 + ], + "angle": 0, + "content": "[40] George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. Normalizing flows for probabilistic modeling and inference. Journal of Machine Learning Research, 22(57):1-64, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.519, + 0.469, + 0.572 + ], + "angle": 0, + "content": "[41] Marius Pedersen and Jon Yngve Hardeberg. A new spatial hue angle metric for perceptual image difference. In International Workshop on Computational Color Imaging, pages 81-90, 2009. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.575, + 0.469, + 0.657 + ], + "angle": 0, + "content": "[42] Nikolay Ponomarenko, Lina Jin, Oleg Ieremeiev, Vladimir Lukin, Karen Egiazarian, Jaakko Astola, Benoit Vozel, Kacem Chehdi, Marco Carli, Federica Battisti, and Chung-Chieh Jay Kuo. Image database TID2013: Peculiarities, results and perspectives. Signal Processing: Image Communication, 30:57-77, 2015. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.66, + 0.469, + 0.687 + ], + "angle": 0, + "content": "[43] Charles A. Poynton. A Technical Introduction to Digital Video. John Wiley & Sons, Inc., 1996. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.689, + 0.469, + 0.755 + ], + "angle": 0, + "content": "[44] Ekta Prashnani, Hong Cai, Yasamin Mostofi, and Pradeep Sen. PieAPP: Perceptual image-error assessment through pairwise preference. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1808-1817, 2018. 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.759, + 0.469, + 0.786 + ], + "angle": 0, + "content": "[45] Alan R. Robertson. The CIE 1976 color-difference formulae. Color Research & Application, 2(1):7-11, 1977. 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.789, + 0.469, + 0.828 + ], + "angle": 0, + "content": "[46] Robert Shapley and Michael J. Hawken. Color in the cortex: Single-and double-opponent cells. Vision Research, 51(7):701-717, 2011. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.831, + 0.469, + 0.87 + ], + "angle": 0, + "content": "[47] Steven K. Shevell and Frederick A. A. Kingdom. Color in complex scenes. Annual Review of Psychology, 59:143-166, 2008. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.874, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[48] Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, and Ben Poole. Discrete flows: Invertible generative models" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.093, + 0.47, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "of discrete data. Advances in Neural Information Processing Systems, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.122, + 0.892, + 0.177 + ], + "angle": 0, + "content": "[49] Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004. 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.179, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[50] Zhou Wang and Eero P. Simoncelli. Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In Human Vision and Electronic Imaging X, pages 149-159, 2005. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.235, + 0.892, + 0.303 + ], + "angle": 0, + "content": "[51] Zhihua Wang, Keshuo Xu, Yang Yang, Jianlei Dong, Shuhang Gu, Lihao Xu, Yuming Fang, and Kede Ma. Measuring perceptual color differences of smartphone photography. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.305, + 0.892, + 0.346 + ], + "angle": 0, + "content": "[52] Klaus Witt. Geometric relations between scales of small colour differences. Color Research & Application, 24(2):78-92, 1999. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.349, + 0.892, + 0.403 + ], + "angle": 0, + "content": "[53] Jason J. Yu, Konstantinos G. Derpanis, and Marcus A. Brubaker. Wavelet flow: Fast training of high resolution normalizing flows. Advances in Neural Information Processing Systems, pages 6184-6196, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.405, + 0.892, + 0.46 + ], + "angle": 0, + "content": "[54] Ming Yu, Huijuan Liu, Yingchun Guo, and Dongming Zhao. A method for reduced-reference color image quality assessment. In International Conference on Image and Signal Processing, pages 1-5, 2009. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.462, + 0.892, + 0.516 + ], + "angle": 0, + "content": "[55] Lin Zhang, Ying Shen, and Hongyu Li. VSI: A visual saliency-induced index for perceptual image quality assessment. IEEE Transactions on Image Processing, 23(10):4270-4281, 2014. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.518, + 0.892, + 0.586 + ], + "angle": 0, + "content": "[56] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In IEEE Conference on Computer Vision and Pattern Recognition, pages 586-595, 2018. 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.589, + 0.892, + 0.642 + ], + "angle": 0, + "content": "[57] Xuemei Zhang and Brian A. Wandell. A spatial extension of CIELAB for digital color-image reproduction. Journal of the Society for Information Display, 5(1):61-63, 1997. 2, 5, 6" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.642 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22251" + } + ] +] \ No newline at end of file diff --git a/2023/Learning a Deep Color Difference Metric for Photographic Images/dbc258de-351a-4a82-83a7-8b31f7a409d8_origin.pdf b/2023/Learning a Deep Color Difference Metric for Photographic Images/dbc258de-351a-4a82-83a7-8b31f7a409d8_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b70f6486b0724f6c62f9b8e3bfc09f415fea4bbc --- /dev/null +++ b/2023/Learning a Deep Color Difference Metric for Photographic Images/dbc258de-351a-4a82-83a7-8b31f7a409d8_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:b05d8d1834b124d9a3679fa2054c3035ffeba3f984ef3c9fecf3637609b533e2 +size 1018909 diff --git a/2023/Learning a Deep Color Difference Metric for Photographic Images/full.md b/2023/Learning a Deep Color Difference Metric for Photographic Images/full.md new file mode 100644 index 0000000000000000000000000000000000000000..3238b80014ad38cac6c5e47404b78ac0693e5a32 --- /dev/null +++ b/2023/Learning a Deep Color Difference Metric for Photographic Images/full.md @@ -0,0 +1,320 @@ +# Learning a Deep Color Difference Metric for Photographic Images + +Haoyu Chen $^{1}$ Zhihua Wang $^{1,2,*}$ Yang Yang $^{3}$ Qilin Sun $^{4}$ Kede Ma $^{1}$ + +1City University of Hong Kong 2Shenzhen MSU-BIT University + +3Shenzhen Transsion Holdings Co., Ltd. 4The Chinese University of Hong Kong (Shenzhen) + +{haoychen3-c,zhihua.wang}@my.cityu.edu.hk, kede.ma@cityu.edu.hk, + +yang.yang6@transsion.com, sunqilin@cuhk.edu.cn + +# Abstract + +Most well-established and widely used color difference (CD) metrics are handcrafted and subject-calibrated against uniformly colored patches, which do not generalize well to photographic images characterized by natural scene complexities. Constructing CD formulae for photographic images is still an active research topic in imaging/illumination, vision science, and color science communities. In this paper, we aim to learn a deep CD metric for photographic images with four desirable properties. First, it well aligns with the observations in vision science that color and form are linked inextricably in visual cortical processing. Second, it is a proper metric in the mathematical sense. Third, it computes accurate CDs between photographic images, differing mainly in color appearances. Fourth, it is robust to mild geometric distortions (e.g., translation or due to parallax), which are often present in photographic images of the same scene captured by different digital cameras. We show that all four properties can be satisfied at once by learning a multi-scale autoregressive normalizing flow for feature transform, followed by the Euclidean distance which is linearly proportional to the human perceptual CD. Quantitative and qualitative experiments on the large-scale SPCD dataset demonstrate the promise of the learned CD metric. Source code is available at https://github.com/haoychen3/CD-Flow. + +# 1. Introduction + +For a long time in vision science community, the modular and segregated view of cortical color processing predominated [46]: the visual perception/processing of color-related quantities is separate from and in parallel with the perception/processing of form (i.e., object shape and structure), motion direction, and depth order in natural scenes. As a result, vision scientists preferred to investigate color + +perception under minimal conditions on form [20, 46], for example, using uniformly colored patches. + +The idea that color as a visual sensation can be analyzed separately had a profound impact on the development of computational formulae for color difference (CD) assessment. Till now, the most well-established and widely used CD metrics are primarily built upon the three-dimensional spatially-isotropic CIELAB coordinate system [32], recommended by the International Commission on Illumination (abbreviated as CIE from its French name Commission Internationale de l'Éclairage) in 1976. However, the uniformity1 of the CIELAB space is not as ideal as intended [28], even for uniformly colored patches. Thus, more complex and parametric formulae are proposed to rectify different aspects of perceptual non-uniformity. Representative methods include JPC79 [33], $\mathrm{CMC}(l:c)^2$ [9], BFD(l:c) [31], CIE94 [34], and CIEDE2000 [29], in which the parameters are calibrated by fitting the human perceptual CD measurements of uniformly colored patches. A naive application of these metrics to photographic images is to compute the mean of the CDs between co-located pixels, which has been empirically shown to correlate poorly to human perception of CDs [38]. + +Back to the vision science community, with more supporting evidence from psychophysical and perceptual studies [2, 6, 27, 47], vision scientists have gradually come to agree on an alternative and more persuasive view of color perception: color and form (and motion) are inextricably interdependent as a unitary process of perceptual organization [19, 46]. Even the primary visual cortex (i.e., V1) plays a significant role in color perception through two types of color-sensitive neurons: single-opponent and double-opponent cells. The single-opponent cells are sensitive to large areas of color, while the double-opponent cells respond to color patterns, textures, and boundaries [24, 46]. + +At later stages, color is transformed to more complex and abstract features, which represent the integral properties of objects, and remain consistent against the changes of the environmental illumination [17, 37]. + +Inspired by these scientific findings, researchers and engineers began to take spatial context (i.e., local surrounding regions) into account, when designing CD formulae. Representative strategies include low-pass spatial filtering [57], histograming [18], patch-based comparison [49], and texture-based segmentation [38]. Most recently, Wang et al. [51] established the largest photographic image dataset, SPCD, for perceptual CD assessment. They further trained a lightweight deep neural network (DNN) for CD assessment of photographic images in a data-driven fashion, as a generalization of several existing CD metrics built on the CIE colorimetry. Nonetheless, the learned formula may not be a proper metric, due to reliance on the possibly surjective mapping for feature transform. + +In this work, we further pursue the data-driven approach. We aim to learn a deep CD metric for photographic images with four desirable properties. + +- It is conceptually inspired by color perception in the visual cortex. The design of our approach should respect the view that color and form interact inextricably through all stages of visual cortical processing. +- It is a proper metric that satisfies non-negativity, symmetry, identity of indiscernibles, and triangle inequality. Such design has been proven useful for perceptual optimization of image processing systems [11]. +- It is accurate in predicting the human perceptual CDs of photographic images, with good generalization to uniformly colored patches. +- It is robust to mild geometric distortions (e.g., translation and dilation), which are often present in photographic images of the same scene captured with different camera settings or along different lines of sight. + +We show that all the four desirable properties can be satisfied at once by learning a multi-scale autoregressive normalizing flow (a variant of RealNVP [13] to be specific) for feature transform, followed by Euclidean distance measure in the transformed space. More specifically, we achieve the first property by the squeezing operation (also known as invertible downsampling) in the normalizing flow, which trades space size for channel dimension. The second property is a direct consequence of the bijectivity of the normalizing flow and the Euclidean distance measure. We achieve the third property by optimizing the model parameters to explain the human perceptual CDs in SPCD [51]. We achieve the fourth property by enforcing the normalizing flow to be multi-scale and autoregressive, in which the features at a particular scale are conditioned on those at a higher (i.e., coarser) scale. By doing so, our metric automatically learns + +to preferentially rely on coarse-scale feature representations with more built-in tolerance to geometric distortions for CD assessment. + +We conduct extensive experiments on the large-scale SPCD dataset [51], and find that our proposed metric, termed as CD-Flow, outperforms 15 CD formulae in assessing CDs of photographic images, produces competitive multi-scale local CD maps without any dense supervision, and is more robust to geometric distortions. Moreover, we empirically verify the perceptual uniformity of the learned color image representation from multiple aspects. + +# 2. Related Work + +In this section, we first review CD formulae in a broader context, and then discuss normalizing flow-based models, which are core to the proposed CD metric. + +CD Formulae. The CD assessment is necessary for day-to-day color control, and is indispensable for color matching in color industries. Admittedly, CD formulae have accelerated the instrumental pass/fail devices for color judgments, but much still needs to be done for complete satisfaction. The scientific investigation of perceptual CDs can be dated at least back to Young and Helmholtz, who proposed and developed the trichromatic theory of color, which is the foundation of the metameric color matching experiment. CIELAB [32] is one of the most successful CD metrics recommended by CIE in 1976, and has been widely adopted in industry for a long time. However, the CIELAB color space is not perceptually uniform [30], which motivates the development of CIE94 [34] and CIEDE2000 [29] through the introduction of application-specific parameters. Other CIELAB-based CD metrics include JPC79 [33], BFD(l:c) [31], and $\mathrm{CMC}(l:c)$ [9]. The introduced parameters are primarily calibrated using uniformly colored patches, digital or printed, which are statistically and semantically different from photographic images. Thus, the generalization of these metrics to photographic images is somewhat limited, especially when misalignment due to geometric distortions is present. + +To incorporate spatial context into CD assessment, Zhang and Wandell [57] presented S-CIELAB, which extends CIELAB by adding spatial low-pass filtering as preprocessing. Similarly, Ouni et al. [39] provided a spatial extension of CIEDE2000. Lee et al. [26] re-examined histogram intersection, which is widely used in color image index, for the purpose of color image similarity assessment. Hong and Luo [18] chose to give larger weights to areas with spatially homogeneous colors and pixels with larger CDs. This method was later augmented by spatial filtering [41]. Lee and Plataniotis [25] built upon the philosophy of color structural similarity, and gave the hue component careful treatment with circular statistics. Jaramillo et al. [38] grouped the same texture areas for human-like CD + +![](images/9b62db9181548ec0173451c49935e05b805ca83459b562d3ab054b74c2fa6486.jpg) +Figure 1. Feature transform of the proposed CD-Flow for perceptual CD assessment. The coordinate transform module consists of six scales. The leftmost yellow cube represents the input image with dimension $s \times s \times 3$ . The six blue cubes represent the six scales of representations, respectively. Each red cube represents a cascade of squeezing operation, multiple flow steps, and a splitting operation, in which each flow step is composed of an actnorm operation, an invertible $1 \times 1$ convolution, and an affine coupling layer. The splitting operation is excluded in the last red cube. + +assessment, using local binary patterns as texture descriptors. + +General-purpose image quality models, including full-reference ones - SSIM [49], VSI [55], LPIPS [56] and DISTS [10], reduced-reference ones - Wang05 [50] and Yu09 [54], and no-reference ones - BRISQUE [35], NIQE [36] and Gao13 [14] can be directly adopted for CD assessment, regarding CDs as a particular form of "visual degradations." Meanwhile, just-noticeable difference (JND) methods, e.g., Butteraugli [1] and FLLIP [3], also attempt to characterize visually indistinguishable color changes between two images. In the era of deep learning, due to the lack of sufficient human-labeled training data, DNN-based CD formulae are rarely proposed. Wang et al. [51] created the first largest image dataset, SPCD, for perceptual CD assessment, and made one of the first attempts to train a DNN-based CD measure for photographic images. However, the underlying feature transform is not mathematically bijective. + +Normalizing Flow-based Models. Normalizing flow-based generative models are constructed by bijective functions $f: \mathbb{R}^D \to \mathbb{R}^D$ , with typically easy-to-compute analytical inverse $f^{-1}: \mathbb{R}^D \to \mathbb{R}^D$ . The primary goal of $f$ is to map raw data $\pmb{x}$ to samples $\pmb{z} = f(\pmb{x})$ from a simple probability distribution $p_{\mathcal{Z}}(\pmb{z})$ . Many classic machine learning algorithms can be cast in this framework, such as principal component analysis (PCA, where $f$ is a linear transform and $p_{\mathcal{Z}}(\pmb{z})$ is standard Gaussian) and independent component analysis (ICA, where $f$ is again linear and $p_{\mathcal{Z}}$ is factorized and heavy-tailed). + +In 2014, Dinh et al. [12] proposed non-linear independent component estimation (NICE), as a generalization of ICA. NICE is considered the first normalizing flow with the introduction of the additive coupling to ease the calculation of the Jacobian determinant. To make flow-based models more suitable for image-related tasks, Dinh et + +al. [13] extended NICE to RealNVP, which admits a multiscale autoregressive architecture, implemented by squeezing and affine coupling. Kingma and Dhariwal [22] introduced the invertible $1 \times 1$ convolution (i.e., the linear transform in PCA) to replace the fixed random permutation for splitting the channel dimension during multiscale processing. The batch normalization in RealNVP is also replaced with activation normalization (i.e., actnorm). To allow unconstrained architectural design, Grathwohl et al. [16] leveraged the Hutchinson's trace estimator for scalable and unbiased estimation of the log-density. Similarly, Behrmann et al. [4] proposed invertible residual networks (i-ResNet), introducing a tractable estimation to the Jacobian log-determinant of a residual block. Other representative normalizing flow work includes hierarchical recursive coupling [23] for increasing flow expressiveness, Wavelet Flow [53] for scaling flow to ultra-high dimensional data, and Discrete Flow [48] for discrete data modeling. In this paper, we do not use normalizing flow for generative modeling, but for invertible feature transform. + +# 3. Proposed CD-Flow + +In this section, we detail our proposed CD metric, CD-Flow, consisting of two key components: the feature transform and the distance measure [29, 32]. The feature transform is built upon a variant of RealNVP [13], which is a learnable invertible transformation between input data and samples from a pre-fixed latent distribution. The CD distance is then computed using the Euclidean distance. + +# 3.1. Problem Definition + +We denote the RGB image space as $\mathcal{X}$ with an unknown distribution $p_{\mathcal{X}}$ and the transformed representation space as $\mathcal{Z}$ with a latent distribution $p_{\mathcal{Z}}$ . We are given a training dataset $\mathcal{D} = \{(x^{(i)},y^{(i)}),\varDelta V^{(i)}\}_{i = 1}^{M}$ , where + +![](images/7a77d0b7f59a6340b78e40334f953c28eae652f9b94fd5ecf4f80b2acba94c4f.jpg) +Figure 2. Architecture of the feature transform, adapted from [22]. The three yellow blocks constitute one step of flow. + +$\pmb{x}^{(i)}, \pmb{y}^{(i)} \in \mathcal{X}$ form the $i$ -th image pair of the same visual content but different color appearances, $\Delta V^{(i)}$ represents the corresponding human perceptual CD collected from a subjective experiment, and $M$ is the number of training pairs. Our goal is to learn a normalizing flow-based invertible and differentiable transform $f$ , which maps RGB images to latent representations with Gaussian conditionals for CD assessment. + +# 3.2. CD-Flow + +Feature Transform. Figure 1 illustrates the system diagram of the multi-scale autoregressive normalizing flow for the feature transform, which consists of $K$ scales of flow processing: $f = f_{1} \circ f_{2} \circ \dots \circ f_{K}$ for multi-scale color and form interaction and abstraction. At the $k$ -th scale, $z_{2(k-1)}$ is processed and split into $z_{2k-1}$ and $z_{2k}$ , the latter of which further undergoes the $k+1$ -th scale of processing and splitting. At the final $K$ -th scale, we only process $z_{2(K-1)}$ to $z_{2K-1}$ without splitting. By this notation, we assume that $z_{0} = x$ is the input RGB image. The probability density of the latent representation $z = \{z_{1}, z_{3}, \ldots, z_{2K-1}\}$ can then be conditionally factorized as + +$$ +\begin{array}{l} p (\boldsymbol {z}) = \prod_ {k = 1} ^ {K - 1} p \left(\boldsymbol {z} _ {2 k - 1} \mid \left\{\boldsymbol {z} _ {\geq (2 k + 1)} \right\}\right) p (\boldsymbol {z} _ {2 K - 1}) \\ = \prod_ {k = 1} ^ {K - 1} p \left(\boldsymbol {z} _ {2 k - 1} \mid \boldsymbol {z} _ {2 k}\right) p \left(\boldsymbol {z} _ {2 K - 1}\right). \tag {1} \\ \end{array} +$$ + +The second equality is due to the bijectivity of the normalizing flow. $p(z_{2k-1}|z_{2k})$ , for $k \in \{1, 2, \dots, K-1\}$ , can conveniently be modeled as conditionally independent Gaussians. That is, the mean vector and the diagonal covariance matrix are computed from $z_{2k}$ through say a tiny neural network [13]. Similarly, $p(z_{2K-1})$ is modeled as (unconditionally) independent Gaussians, where the parameters are directly estimated via backpropagation. As shown in Figure 2, each scale of flow processing consists of a squeezing operation, multiple flow steps, and a splitting operation. Each flow step is further decomposed into three operations: actnorm, invertible $1 \times 1$ convolution, and affine coupling. + +- Actnorm is introduced to replace the batch normalization to avoid degenerated performance when the minibatch size is small. Given an input $z$ of size $c \times h \times w$ , the output of the same size can be computed by + +$$ +\boldsymbol {z} ^ {\prime} = \boldsymbol {s} \odot \boldsymbol {z} + \boldsymbol {t}, \tag {2} +$$ + +with the log-determinant $h\cdot w\cdot \log |\operatorname *{det}(\operatorname {diag}(s))|$ $\{s,t\}$ are learnable scaling and bias parameters. + +- Invertible $1 \times 1$ Convolution is a learnable linear transform for channel mixing. Given a $c \times h \times w$ input $\mathbf{z}$ and a $c \times c$ weight matrix $\mathbf{W}$ , we have + +$$ +z ^ {\prime} = W z, \tag {3} +$$ + +with the log-determinant $h \cdot w \cdot \log |\operatorname{det}(\mathbf{W})|$ . + +- Affine Coupling constrains its Jacobian to be a triangular matrix, which facilitates the log-determinant computation. Specifically, the input $z$ of $D$ dimensions is first split into two non-overlapping subsets: $z_{1:d}$ and $z_{d+1:D}$ , where $d < D$ . The output of the same dimension can be computed by + +$$ +\boldsymbol {z} _ {1: d} ^ {\prime} = \boldsymbol {z} _ {1: d}, +$$ + +$$ +\boldsymbol {z} _ {d + 1: D} ^ {\prime} = \boldsymbol {z} _ {d + 1: D} \odot e ^ {s \left(\boldsymbol {z} _ {1: d}\right)} + t \left(\boldsymbol {z} _ {1: d}\right), \tag {4} +$$ + +where $s(\cdot)$ and $t(\cdot)$ are the scaling and translation functions, respectively, which are not necessarily invertible. The inverse is easily and analytically derived as + +$$ +\boldsymbol {z} _ {1: d} = \boldsymbol {z} _ {1: d} ^ {\prime}, +$$ + +$$ +\boldsymbol {z} _ {d + 1: D} = \left(\boldsymbol {z} _ {d + 1: D} ^ {\prime} - t \left(\boldsymbol {z} _ {1: d} ^ {\prime}\right)\right) \odot e ^ {- s \left(\boldsymbol {z} _ {1: d} ^ {\prime}\right)}. \tag {5} +$$ + +CD Distance. We adopt the Euclidean distance, i.e., the square root of mean squared differences between two latent color representations $f(\pmb{x})$ and $f(\pmb{y})$ , as the CD distance between two input images $\pmb{x}$ and $\pmb{y}$ : + +$$ +\Delta E (\boldsymbol {x}, \boldsymbol {y}) = \sqrt {\frac {(f (\boldsymbol {x}) - f (\boldsymbol {y})) ^ {T} (f (\boldsymbol {x}) - f (\boldsymbol {y}))}{D}}. \tag {6} +$$ + +# 3.3. Loss Function + +It is straightforward to measure the $\ell_p$ -norm induced distance between the predicted CD computed by Eq. (6) and the perceptual CD of the given image pair $(x, y)$ : + +$$ +\ell (\boldsymbol {x}, \boldsymbol {y}) = \| \Delta E (\boldsymbol {x}, \boldsymbol {y}) - \Delta V (\boldsymbol {x}, \boldsymbol {y}) \| _ {p}. \tag {7} +$$ + +To encourage the robustness of CD-Flow to mild geometric distortions, which are often unavoidable in practice, we introduce a multi-scale version of Eq. (7) to put more emphasis on coarser-scale latent representations: + +$$ +\ell_ {\mathrm {m s}} (\boldsymbol {x}, \boldsymbol {y}) = \sum_ {k = 1} ^ {K} \| \Delta E _ {k} (\boldsymbol {x}, \boldsymbol {y}) - \Delta V (\boldsymbol {x}, \boldsymbol {y}) \| _ {p}, \tag {8} +$$ + +Table 1. STRESS, PLCC, and SRCC between predicted CDs $(\Delta E)$ and perceptual CDs $(\Delta V)$ in SPCD. The top section lists representative CD formulae developed from homogeneous color patches. The second section contains CD measures adapted for natural images. The third section includes general-purpose image quality models. The fourth section consists of JND measures. Closest to ours, CD-Net is a DNN-based data-driven CD measure for natural images. The top two methods are highlighted in boldface + +
MethodPerfectly aligned pairsNon-perfectly aligned pairsAll
\( STRESS\downarrow \)\( PLCC\uparrow \)\( SRCC\uparrow \)\( STRESS\downarrow \)\( PLCC\uparrow \)\( SRCC\uparrow \)\( STRESS\downarrow \)\( PLCC\uparrow \)\( SRCC\uparrow \)
CIELAB [45]31.2440.7930.77529.6390.6900.57931.8720.7160.666
CIE94 [34]34.7210.7900.77229.9160.6930.57234.3260.7100.654
CIEDE2000 [29]29.9750.8250.82130.3470.6670.56331.4390.7260.686
S-CIELAB [57]30.0940.8220.81931.8040.6310.52232.7800.7000.657
Hong06 [18]60.5570.7940.81057.0700.5430.46161.2270.6450.632
Ouni081[39]29.9770.8260.82130.3550.6680.56331.4440.7260.685
Jaramillo19 [38]43.4190.5140.50650.2990.0810.04168.8050.3210.329
SSIM [49]39.3930.5890.54953.0350.0770.04448.0250.3090.324
FLLIP [3]29.3180.7450.71527.1580.7340.64029.0990.7180.663
PieAPP [44]29.0440.7370.73737.5280.5220.45932.3540.6520.643
LPIPS [56]44.8110.6950.68853.1320.2190.17164.1450.4390.490
DISTS [10]31.4090.7450.74635.0430.5280.44732.9950.6400.626
Chou07 [8]50.7210.7870.78536.1840.6030.45949.5450.6120.557
Butteraugli [1]42.6200.6060.59348.2170.2580.24554.7370.3710.359
CD-Net [51]20.8910.8670.87022.5430.8180.77621.4310.8460.842
CD-Flow16.6130.8960.90421.3740.8560.79418.4730.8710.865
+ +1 The spatial extension of CIEDE2000. + +where + +$$ +\Delta E _ {k} (\boldsymbol {x}, \boldsymbol {y}) = \sqrt {\frac {\left(f _ {k :} (\boldsymbol {x}) - f _ {k :} (\boldsymbol {y})\right) ^ {T} \left(f _ {k :} (\boldsymbol {x}) - f _ {k :} (\boldsymbol {y})\right)}{D _ {k}}}. \tag {9} +$$ + +$f_{k}(\pmb {x}) = [z_{2k - 1}^{T},\dots ,z_{2K - 1}^{T}]^{T}$ and $D_{k}$ is the number of feature dimensions of $f_{k}(\pmb {x})$ . That is, $\Delta E_k(\pmb {x},\pmb {y})$ makes an estimate of $\Delta V(\pmb {x},\pmb {y})$ using the $k$ -th to $K$ -th scale latent representations, and we have $\Delta E_1$ equal to $\Delta E$ in Eq. (6). + +It is noteworthy that if the transform $f$ is supervised by the CD loss in Eq. (8) solely, the bijectivity of $f$ may not be necessarily guaranteed. This is because the adopted normalizing flow is not bijective by design. For example, the "invertible" $1 \times 1$ convolution as the channel mixer becomes non-invertible when $W$ is degenerate. Empirically, we observe that the scaling factor of the affine-coupling layer is close to zero after optimizing Eq. (8). Since the inverse transform includes operations of dividing by the scaling factor, there will easily result in the exploding inverse problem [5]. Thus, we incorporate the commonly used maximum likelihood objective in normalizing flow [40] into our training objective. We work with the negative log-likelihood loss: + +$$ +\begin{array}{l} \ell_ {\mathrm {n l}} (\boldsymbol {x}) = - \log p _ {\mathcal {X}} (\boldsymbol {x}) \\ = - \log p _ {\mathcal {Z}} (f (\boldsymbol {x})) - \log \left| \det \left(\frac {\partial f (\boldsymbol {x})}{\partial \boldsymbol {x}}\right) \right|. \tag {10} \\ \end{array} +$$ + +During training, we randomly sample a mini-batch $\mathcal{B}$ from the training dataset $\mathcal{D}$ in each iteration, and make use of a variant of stochastic gradient descent to optimize the parameters in CD-Flow: + +$$ +\ell (\mathcal {B}) = \frac {1}{| \mathcal {B} |} \sum_ {(x, y) \in \mathcal {B}} \left(\ell_ {\mathrm {m s}} (\boldsymbol {x}, \boldsymbol {y}) + \lambda \left(\ell_ {\mathrm {n l}} (\boldsymbol {x}) + \ell_ {\mathrm {n l}} (\boldsymbol {y})\right)\right), \tag {11} +$$ + +where $|\mathcal{B}|$ denotes the cardinality of $\mathcal{B}$ , and $\lambda$ is the trade-off to balance the magnitudes of different loss terms. + +# 4. Experiments + +In this section, we begin by presenting the experimental setups. We then compare the proposed CD-Flow with 15 CD measures. We last conduct extensive ablation studies to justify the key designs of CD-Flow. + +# 4.1. Experimental Setups + +CD Dataset. We conduct experiments on SPCD [51], so far the largest natural image dataset tailored for perceptual CD + +Table 2. STRESS, PLCC, and SRCC between predicted CDs $(\Delta E)$ and perceptual CDs $(\Delta V)$ in SPCD [51] under geometric distortions. Translation means randomly shifting one image with respect to the other image in an image pair (by up to $5\%$ of pixels) in both spacial directions. Rotation means randomly rotating one image (by up to $3^{\circ}$ ) around the center point. Dilation means zooming in (and cropping) one image without changing the image size (by a factor of 1.05) + +
MethodTranslationRotationDilation
\( STRESS\downarrow \)\( PLCC\uparrow \)\( SRCC\uparrow \)\( STRESS\downarrow \)\( PLCC\uparrow \)\( SRCC\uparrow \)\( STRESS\downarrow \)\( PLCC\uparrow \)\( SRCC\uparrow \)
CIELAB [45]29.4140.6200.57732.6330.5290.49531.5110.5190.467
CIE94 [34]29.1410.6450.59631.9430.5660.51930.3230.5670.505
CIEDE2000 [29]28.0350.6540.61331.2550.5660.52729.9280.5660.512
CD-Net [51]19.8250.8450.84222.4630.7840.77221.7040.7870.773
CD-Flow19.3110.8520.85620.1390.8370.81621.3520.8270.797
+ +assessment. SPCD consists of 15,335 color images out of 1,000 distinct natural scenes spanning a variety of realistic shooting scenarios. A total of 30,000 image pairs are sampled for human labeling, where 10,005 are non-perfectly aligned image pairs. For each image pair, there are 20 human ratings. We randomly split $70\%$ , $10\%$ , and $20\%$ of image pairs in SPCD as training, validation, and test sets, respectively, according to the image content to ensure content independence. + +Network Architecture. We employ a variant of RealNVP [22], which consists of $K = 6$ scales, and each scale includes a squeezing operation, $L = 8$ steps of flow, and a splitting operation. We do not split at the last scale. The squeezing operation trades the spatial size for the channel number by transforming an $s \times s \times c$ tensor to an $\frac{s}{2} \times \frac{s}{2} \times 4c$ tensor, where $s$ is the spatial size and $c$ is the number of channels. The splitting operation divides the latent representation of the same scale into two halves along the channel dimension. One is used as the color features for CD assessment, and the other is subject to further processing. + +Training and Testing Details. We employ the Adam as the stochastic optimizer, where the batch size is 4, and the initial learning rate is $10^{-5}$ with a decay factor of 2 for every 5 epochs. We train CD-Flow for 50 epochs in total. + +Evaluation Criteria. We use three standard criteria to quantify the performance of CD-Flow against existing CD measures, including the standardized residual sum of squares (STRESS) [15], Pearson linear correlation coefficient (PLCC), and Spearman's rank correlation coefficient (SRCC). STRESS measures both prediction accuracy and statistical significance, which is defined by + +$$ +\text {S T R E S S} = 1 0 0 \sqrt {\frac {\sum_ {i = 1} ^ {M} \left(\Delta E _ {i} - F \Delta V _ {i}\right) ^ {2}}{F ^ {2} \sum_ {i = 1} ^ {M} \Delta V _ {i} ^ {2}}}, \tag {12} +$$ + +where $M$ is the number of test pairs and $F$ is the scale cor + +resection factor between $\Delta E$ and $\Delta V$ , defined as + +$$ +F = \frac {\sum_ {i = 1} ^ {M} \Delta E _ {i} ^ {2}}{\sum_ {i = 1} ^ {M} \Delta E _ {i} \Delta V _ {i}}. \tag {13} +$$ + +STRESS generally ranges from 0 to 100, where a small value suggests a tight-fitting between model predictions and ground truths. SRCC and PLCC measure the prediction monotonicity and prediction linearity, respectively. Before calculating PLCC, we linearize model predictions by regressing a four-parameter monotonic function. + +# 4.2. Main Results + +SPCD Results. We compare the proposed CD-Flow with 15 existing CD measures which can be grouped into four categories: 1) CD measures for homogeneous color patches: CIELAB [45], CIE94 [34] and CIEDE2000 [29], 2) CD measures for natural images: S-CIELAB [57], Hong06 [18], Ouni08 [39], Jaramillo19 [38] and CD-Net [51], 3) full-reference image quality model: SSIM [49], FLIP [3], PieAPP [44], LPIPS [56] and DISTS [10], and 4) JND methods: Chou07 [8] and Butteraugli [1]. We employ official implementations of Butteraugli and FLIP, and retrain PieAPP, LPIPS, and DISTS on the same training set as CD-Flow. The rest methods are implemented and made publicly available by Jaramillo et al. [38]. + +The comparison results are documented in Table 1. Several interesting observations merit attention. First, CIE94 and CIEDE2000 rectify the non-uniformity of CIELAB on color patches by incorporating weight coefficients. Nonetheless, when it comes to natural photographic images, these approaches do not show noticeable improvements compared to the original CIELAB formula. This provides a strong indication that humans perceive CDs of uniformly colored patches and natural images in drastically different manners. Naive extensions such as spatial filtering and texture grouping may not be sufficient to bridge the gap as evidenced by the results in the second section of Table 1. Second, despite being retrained on the same training + +Table 3. Generalizability evaluation of CD-Flow on the COM dataset and its four subsets: BFD-P, Leeds, Witt, and RIT-DuPont. PLCC on RIT-DuPont is indicated by “—” since it is not computable + +
MethodBFD-P [31]Leeds [21]Witt [52]RIT-DuPont [7]COM dataset [29]
\( STRESS\downarrow \)\( PLCC\uparrow \)\( STRESS\downarrow \)\( PLCC\uparrow \)\( STRESS\downarrow \)\( PLCC\uparrow \)\( STRESS\downarrow \)\( PLCC\uparrow \)\( STRESS\downarrow \)\( PLCC\uparrow \)
CIELAB [45]45.0540.74940.0930.29551.6890.56530.348—45.2020.693
CIE94 [34]35.7980.83030.4940.58431.8570.79320.982—33.2350.814
CIEDE2000 [29]31.9350.86119.2470.77230.3580.82520.239—28.9790.862
CD-Net39.3120.79138.5580.44933.6400.82842.999—38.8720.786
CD-Flow34.6610.83334.2750.47631.9650.82036.504—35.0610.801
+ +set as CD-Flow, general-purpose DNN-based image quality models PieAPP [44], LPIPS [56], and DISTS [10] do not deliver comparable performance. We believe this arises for PieAPP because it requires a much larger labeled set to support training from scratch, and 21,000 training pairs, even with data augmentation, are less likely to overcome overfitting. LPIPS and DISTS rely on pre-trained VGG features, which are empirically proven to be more structure and texture inductive. As a result, they may be less ideal for assessing CDs. Third, DNN-based CD formulae, i.e., CD-Net [51] and CD-Flow, outperform all other competing models, which are primarily attributed to their superior representation learning capabilities. This observation confirms the potential of employing DNNs in the field of color science. Fourth, CD-Flow achieves the most remarkable results, thereby demonstrating the promise of the multi-scale autoregressive normalizing flow for CD assessment. + +Robustness Results to Geometric Distortions. We conduct experiments to evaluate the robustness of CD-Flow to mild geometric distortions, including translation, rotation, and dilation. Specifically, we translate randomly one image with respect to the other image in an image pair by up to $5\%$ pixels in both horizontal and vertical directions, rotate randomly one image clockwise by up to $3^{\circ}$ , and zoom in one image by a factor of 1.05. The corresponding results are presented in Table 2. We find that the performance of all models degrades due to the presence of geometric distortions. CIELAB-based formulae are particularly sensitive to geometric distortions because of pixel-by-pixel CD computation. In contrast, CD-Flow demonstrates the best robustness results, which is as expected because it is designed to abstract color features, characterized by the interdependence between color and form for CD assessment. + +Generalizability Results on Other Datasets. We examine the generalizability of CD-Flow on the COM dataset [29] and the TID2013 subset [42]. The COM dataset consists of four subsets: BFD-P [31], Leeds [21], Witt [52], and RIT-DuPont [7], which include uniformly colored patches used for the development of CIEDE2000. The comparison results are reported in Table 3. It is clear that two DNN-based CD models perform better than CIELAB on + +Table 4. Generalizability evaluation of CD-Flow on the TID2013 subset, containing quantization noise, image color quantization with dither, and chromatic aberration artifacts + +
Method\( STRESS\downarrow \)PLCC↑SRCC↑
CIEDE2000 [29]18.2030.7300.751
PieAPP [44]20.9180.6200.653
LPIPS [56]15.4200.8160.804
DISTS [10]15.2350.8210.805
CD-Net [51]15.9620.8010.826
CD-Flow14.1100.8370.832
+ +the COM dataset, despite not being exposed to color patch data during training. Although underperforming CIE94 and CIEDE2000, CD-Flow exhibits better generalization compared to CD-Net. We then test the generalizability of the TID2013 subset, which contains three types of color-related distortions: quantization noise, image color quantization with dither, and chromatic aberration. Table 4 lists the comparison results. We find that CD-Flow performs much better than the best-performing CIELAB-based CD formula CIEDE2000, and is on par with general-purpose image quality methods LPIPS and DISTS, which may have been exposed to similar distortion appearances during training. All these results constitute supportive evidence that the multi-scale autoregressive normalizing flow provides a compelling embodiment of the hypothesis: "color and form are inextricably interdependent as a unitray process of perceptual organization [19, 46]." + +Visualization of Local CD Maps. We further visualize the multi-scale local CD maps generated by CD-Flow, as illustrated in Figure 3. Our observations are as follows. First, local CDs are generally small at the first three scales compared to those at later scales, indicating that CD-Flow indeed relies more on coarser-scale representations to assess perceptual CDs, as encouraged by the multi-scale training objective in Eq. (8). Second, interestingly, the effects of single-opponent and double-opponent cells emerge in the computation of CD-Flow. At coarser scales, local CDs are evaluated over a large image area, and exhibit reason- + +![](images/1b90dc0ed4886c5ba4b8e3ab4ada4784068b756dd788d53896a3bbba408d89a0.jpg) +Figure 3. Multi-scale local CD maps generated by CD-Flow. First two columns: Input image pair $(\pmb{x},\pmb{y})$ . Third to the eighth column: Six scales of CD maps generated by CD-Flow, with a warmer color indicating a larger local CD. + +Table 5. Ablation analysis of the number of flow steps in CD-Flow (i.e., the hyperparameter $L$ ), where the number of scales is fixed to six. The default setting is highlighted in boldface + +
# of flow stepsSTRESS↓PLCC↑SRCC↑
L = 221.6330.8370.826
L = 420.9940.8380.825
L = 818.4730.8710.865
L = 1617.7920.8790.870
+ +Table 6. Ablation analysis of the number of scales in CD-Flow (i.e., the hyperparameter $K$ ),where the number of flow steps is fixed to eight. The default setting is highlighted in boldface + +
# of scalesSTRESS↓PLCC↑SRCC↑
K = 224.6860.7600.732
K = 419.7300.8570.850
K = 618.4730.8710.865
K = 818.5240.8740.861
+ +able sensitivity, resembling single-opponent cells in V1. At finer scales, local CDs are more responsive to textures and boundaries, consistent with the response characteristics of double-opponent cells in V1. + +# 4.3. Ablation Studies + +We conduct two ablation experiments to evaluate the key design choices of CD-Flow. First, we study the impact of the number of flow steps (i.e., the hyperparameter $L$ ) on the model performance. In Table 5, we find that the performance of CD-Flow increases with the number of flow steps, which in turn expands the non-linear representational capacity of CD-Flow. Nevertheless, too many steps may substantially increase the computational complexity, and negatively impact generalization as well due to potential overfitting. We select $L = 8$ as the default setting to strike a + +good balance between model performance and complexity. Next, we examine the effect of the number of scales (i.e., the hyperparameter $K$ ) on the model performance. Table 6 reports the results. We find that increasing the number of scales from 2 to 6 significantly improves the CD assessment performance. However, further increasing $K$ does not bring extra performance improvements, not as the case of increasing $L$ . We attribute this to the setting of the current training input resolution (i.e., $768 \times 768 \times 3$ ). When $K = 8$ and at the coarsest scale, we are essentially comparing the difference of two "globally averaged" color values over the entire images, which is less biologically plausible and less practically meaningful. + +# 5. Conclusion + +We have introduced CD-Flow, a normalizing flow-based CD metric for photographic images. Our approach is inspired by the scientific evidence that humans perceive color based on the interdependence of color and form in images. We utilized a multi-scale autoregressive normalizing flow to learn a coordinate transform, followed by computing the Euclidean distance in the transformed space. Remarkably, the learned feature transform enjoys four desirable properties: 1) consistent with the working mechanism of human color perception, 2) proper as a mathematical metric, 3) accurate to explain human data of perceptual CDs, and 4) robust to slight geometric distortions. We hope that the proposed CD-Flow can benefit related fields in imaging/illumination, vision science, and color science. + +# Acknowledgement + +We would like to thank Keshuo Xu for the technical support and code sharing. This work was supported in part by the National Natural Science Foundation of China under Grant 62071407, the Hong Kong ITC Innovation and Technology Fund (9440288), and the CUHKSZ University Development Fund (UDF01002437). + +# References + +[1] Jyrki Alakuijala, Robert Obryk, Ostap Stoliarchuk, Zoltan Szabadka, Lode Vandevenne, and Jan Wassenberg. Guetzli: Perceptually guided JPEG encoder. arXiv preprint arXiv:1703.04421, 2017. 3, 5, 6 +[2] Liliana Albertazzi, Gert J. Van Tonder, and Dhanraj Vishwanath. Perception Beyond Inference: The Information Content of Visual Processes. MIT Press, 2011. 1 +[3] Pontus Andersson, Jim Nilsson, Tomas Akenine-Moller, Magnus Oskarsson, Kalle Åström, and Mark D. Fairchild. FLIP: A difference evaluator for alternating images. ACM on Computer Graphics and Interactive Techniques, 3(2):1-23, 2020. 3, 5, 6 +[4] Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, and Jorn-Henrik Jacobsen. Invertible residual networks. In International Conference on Machine Learning, pages 573-582, 2019. 3 +[5] Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger Grosse, and Jorn-Henrik Jacobsen. Understanding and mitigating exploding inverses in invertible neural networks. In International Conference on Artificial Intelligence and Statistics, pages 1792-1800, 2021. 5 +[6] Ohad Ben-Shahar and Steven W. Zucker. Hue geometry and horizontal connections. *Neural Networks*, 17(5-6):753–771, 2004. 1 +[7] Roy S. Berns, David H. Alman, Lisa Reniff, Gregory D. Snyder, and Mitchell R. Balonon-Rosen. Visual determination of suprathreshold color-difference tolerances using probit analysis. Color Research & Application, 16(5):297-316, 1991. +[8] Chun-Hsien Chou and Kuo-Cheng Liu. A fidelity metric for assessing visual quality of color images. In International Conference on Computer Communications and Networks, pages 1154-1159, 2007. 5, 6 +[9] Frank J. J. Clarke, Roderick McDonald, and Bryan Rigg. Modification to the JPC79 colour-difference formula. Journal of the Society of Dyers and Colourists, 100(4):128-132, 1984. 1, 2 +[10] Keyan Ding, Kede Ma, Shiqi Wang, and Eero P. Simoncelli. Image quality assessment: Unifying structure and texture similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5):2567-2581, 2020. 3, 5, 6, 7 +[11] Keyan Ding, Kede Ma, Shiqi Wang, and Eero P. Simoncelli. Comparison of full-reference image quality models for optimization of image processing systems. International Journal of Computer Vision, 129(4):1258-1281, 2021. 2 +[12] Laurent Dinh, David Krueger, and Yoshua Bengio. NICE: Non-linear independent components estimation. In International Conference on Representations Workshops, 2015. 3 +[13] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real NVP. In International Conference on Learning Representations, 2017. 2, 3, 4 +[14] Chen Gao, Karen Panetta, and Sos Agaian. No reference color image quality measures. In IEEE International Conference on Cybernetics, pages 243-248, 2013. 3 +[15] Pedro A. Garcia, Rafael Huertas, Manuel Melgosa, and Guihua Cui. Measurement of the relationship between perceived + +and computed color differences. Journal of the Optical Society of America A, 24(7):1823-1829, 2007. 6 +[16] Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud. FFJORD: Free-form continuous dynamics for scalable reversible generative models. International Conference on Learning Representations, 2019. 3 +[17] Thorsten Hansen, Maria Olkkonen, Sebastian Walter, and Karl R. Gegenfurtner. Memory modulates color appearance. Nature Neuroscience, 9(11):1367-1368, 2006. 2 +[18] Guowei Hong and Ming Ronnier Luo. New algorithm for calculating perceived colour difference of images. The Imaging Science Journal, 54(2):86-91, 2006. 2, 5, 6 +[19] Gaetano Kanizsa. Organization in Vision: Essays on Gestalt Perception. Praeger Publishers, 1979. 1, 7 +[20] David Katz. The World of Colour. Routledge, 1935. 1 +[21] Dong-Ho Kim. New weighting functions for the modified CIELAB colour-difference formulae. Textile Coloration and Finishing, 9(6):51-57, 1997. 7 +[22] Durk P. Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. Neural Information Processing Systems, 2018. 3, 4, 6 +[23] Jakob Kruse, Gianluca Detommaso, Ullrich Kothe, and Robert Scheicl. HINT: Hierarchical invertible neural transport for density estimation and bayesian inference. AAAI Conference on Artificial Intelligence, pages 8191-8199, 2021. 3 +[24] Edwin H. Land. The retina theory of color vision. Scientific American, 237(6):108-129, 1977. 1 +[25] Dohyoung Lee and Konstantinos N. Plataniotis. Towards a novel perceptual color difference metric using circular processing of hue components. In IEEE International Conference on Acoustics, Speech, & Signal Processing, pages 166-170, 2014. 2 +[26] Sang-Mi Lee, John Haozhong Xin, and Stephen Westland. Evaluation of image similarity by histogram intersection. Color Research & Application, 30(4):265-274, 2005. 2 +[27] Peter Lennie. Color coding in the cortex. Color Vision: From Genes to Perception, pages 235-247, 1999. 1 +[28] Ming Ronnier Luo. Colour science: Past, present and future. Colour Imaging Vision and Technology, pages 381-404, 1999. 1 +[29] Ming Ronnier Luo, Guihua Cui, and Bryan Rigg. The development of the CIE 2000 colour-difference formula: CIEDE2000. Color Research & Application, 26(5):340-350, 2001. 1, 2, 3, 5, 6, 7 +[30] Ming Ronnier Luo and Bryan Rigg. Chromaticity-discrimination ellipses for surface colours. Color Research & Application, 11(1):25-42, 1986. 2 +[31] Ming Ronnier Luo and Bryan Rigg. BFD(l:c) colour-difference formula. Journal of the Society of Dyers and Colourists, 103(2):86-94, 1987. 1, 2, 7 +[32] Marc Mahy, Luc Van Eycken, and André Oosterlinck. Evaluation of uniform color spaces developed after the adoption of CIELAB and CIELUV. Color Research & Application, 19(2):105-121, 1994. 1, 2, 3 + +[33] Roderick McDonald. Industrial pass/fail colour matching. Journal of the Society of Dyers and Colourists, 96(7):372-376, 1980. 1, 2 +[34] Roderick McDonald and Kenneth J. Smith. CIE94-A new colour-difference formula. Journal of the Society of Dyers and Colourists, 111(12):376-379, 1995. 1, 2, 5, 6, 7 +[35] Anish Mittal, Anush Krishna Moorthy, and Alan C. Bovik. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12):4695-4708, 2012. 3 +[36] Anish Mittal, Rajiv Soundararajan, and Alan C. Bovik. Making a "completely blind" image quality analyzer. IEEE Signal Processing Letters, 20(3):209-212, 2012. 3 +[37] Ken Nakayama and Shinsuke Shimojo. Experiencing and perceiving visual surfaces. Science, 257(5075):1357-1363, 1992. 2 +[38] Benhur Ortiz-Jaramillo, Asli Kumcu, Ljiljana Platisa, and Wilfried Philips. Evaluation of color differences in natural scene color images. Signal Processing: Image Communication, 71:128-137, 2019. 1, 2, 5, 6 +[39] Sonia Ouni, Ezzeddine Zagrouba, Majed Chambah, and Michel Herbin. A new spatial colour metric for perceptual comparison. In International Conference on E-Systems Engineering, Communication and Information, pages 413–428, 2008. 2, 5, 6 +[40] George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. Normalizing flows for probabilistic modeling and inference. Journal of Machine Learning Research, 22(57):1-64, 2021. 5 +[41] Marius Pedersen and Jon Yngve Hardeberg. A new spatial hue angle metric for perceptual image difference. In International Workshop on Computational Color Imaging, pages 81-90, 2009. 2 +[42] Nikolay Ponomarenko, Lina Jin, Oleg Ieremeiev, Vladimir Lukin, Karen Egiazarian, Jaakko Astola, Benoit Vozel, Kacem Chehdi, Marco Carli, Federica Battisti, and Chung-Chieh Jay Kuo. Image database TID2013: Peculiarities, results and perspectives. Signal Processing: Image Communication, 30:57-77, 2015. 7 +[43] Charles A. Poynton. A Technical Introduction to Digital Video. John Wiley & Sons, Inc., 1996. 1 +[44] Ekta Prashnani, Hong Cai, Yasamin Mostofi, and Pradeep Sen. PieAPP: Perceptual image-error assessment through pairwise preference. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1808-1817, 2018. 5, 6, 7 +[45] Alan R. Robertson. The CIE 1976 color-difference formulae. Color Research & Application, 2(1):7-11, 1977. 5, 6, 7 +[46] Robert Shapley and Michael J. Hawken. Color in the cortex: Single-and double-opponent cells. Vision Research, 51(7):701-717, 2011. 1, 7 +[47] Steven K. Shevell and Frederick A. A. Kingdom. Color in complex scenes. Annual Review of Psychology, 59:143-166, 2008. 1 +[48] Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, and Ben Poole. Discrete flows: Invertible generative models + +of discrete data. Advances in Neural Information Processing Systems, 2019. 3 +[49] Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004. 2, 3, 5, 6 +[50] Zhou Wang and Eero P. Simoncelli. Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In Human Vision and Electronic Imaging X, pages 149-159, 2005. 3 +[51] Zhihua Wang, Keshuo Xu, Yang Yang, Jianlei Dong, Shuhang Gu, Lihao Xu, Yuming Fang, and Kede Ma. Measuring perceptual color differences of smartphone photography. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 2, 3, 5, 6, 7 +[52] Klaus Witt. Geometric relations between scales of small colour differences. Color Research & Application, 24(2):78-92, 1999. 7 +[53] Jason J. Yu, Konstantinos G. Derpanis, and Marcus A. Brubaker. Wavelet flow: Fast training of high resolution normalizing flows. Advances in Neural Information Processing Systems, pages 6184-6196, 2020. 3 +[54] Ming Yu, Huijuan Liu, Yingchun Guo, and Dongming Zhao. A method for reduced-reference color image quality assessment. In International Conference on Image and Signal Processing, pages 1-5, 2009. 3 +[55] Lin Zhang, Ying Shen, and Hongyu Li. VSI: A visual saliency-induced index for perceptual image quality assessment. IEEE Transactions on Image Processing, 23(10):4270-4281, 2014. 3 +[56] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In IEEE Conference on Computer Vision and Pattern Recognition, pages 586-595, 2018. 3, 5, 6, 7 +[57] Xuemei Zhang and Brian A. Wandell. A spatial extension of CIELAB for digital color-image reproduction. 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Constructing CD formulae for photographic images is still an active research topic in imaging/illumination, vision science, and color science communities. In this paper, we aim to learn a deep CD metric for photographic images with four desirable properties. First, it well aligns with the observations in vision science that color and form are linked inextricably in visual cortical processing. Second, it is a proper metric in the mathematical sense. Third, it computes accurate CDs between photographic images, differing mainly in color appearances. Fourth, it is robust to mild geometric distortions (e.g., translation or due to parallax), which are often present in photographic images of the same scene captured by different digital cameras. We show that all four properties can be satisfied at once by learning a multi-scale autoregressive normalizing flow for feature transform, followed by the Euclidean distance which is linearly proportional to the human perceptual CD. 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Introduction" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 609, + 287, + 694 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 609, + 287, + 694 + ], + "spans": [ + { + "bbox": [ + 47, + 609, + 287, + 694 + ], + "type": "text", + "content": "For a long time in vision science community, the modular and segregated view of cortical color processing predominated [46]: the visual perception/processing of color-related quantities is separate from and in parallel with the perception/processing of form (i.e., object shape and structure), motion direction, and depth order in natural scenes. As a result, vision scientists preferred to investigate color" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 242, + 545, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 242, + 545, + 266 + ], + "spans": [ + { + "bbox": [ + 306, + 242, + 545, + 266 + ], + "type": "text", + "content": "perception under minimal conditions on form [20, 46], for example, using uniformly colored patches." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 266, + 546, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 266, + 546, + 518 + ], + "spans": [ + { + "bbox": [ + 304, + 266, + 546, + 518 + ], + "type": "text", + "content": "The idea that color as a visual sensation can be analyzed separately had a profound impact on the development of computational formulae for color difference (CD) assessment. Till now, the most well-established and widely used CD metrics are primarily built upon the three-dimensional spatially-isotropic CIELAB coordinate system [32], recommended by the International Commission on Illumination (abbreviated as CIE from its French name Commission Internationale de l'Éclairage) in 1976. However, the uniformity1 of the CIELAB space is not as ideal as intended [28], even for uniformly colored patches. Thus, more complex and parametric formulae are proposed to rectify different aspects of perceptual non-uniformity. Representative methods include JPC79 [33], " + }, + { + "bbox": [ + 304, + 266, + 546, + 518 + ], + "type": "inline_equation", + "content": "\\mathrm{CMC}(l:c)^2" + }, + { + "bbox": [ + 304, + 266, + 546, + 518 + ], + "type": "text", + "content": " [9], BFD(l:c) [31], CIE94 [34], and CIEDE2000 [29], in which the parameters are calibrated by fitting the human perceptual CD measurements of uniformly colored patches. A naive application of these metrics to photographic images is to compute the mean of the CDs between co-located pixels, which has been empirically shown to correlate poorly to human perception of CDs [38]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 519, + 547, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 519, + 547, + 664 + ], + "spans": [ + { + "bbox": [ + 304, + 519, + 547, + 664 + ], + "type": "text", + "content": "Back to the vision science community, with more supporting evidence from psychophysical and perceptual studies [2, 6, 27, 47], vision scientists have gradually come to agree on an alternative and more persuasive view of color perception: color and form (and motion) are inextricably interdependent as a unitary process of perceptual organization [19, 46]. Even the primary visual cortex (i.e., V1) plays a significant role in color perception through two types of color-sensitive neurons: single-opponent and double-opponent cells. The single-opponent cells are sensitive to large areas of color, while the double-opponent cells respond to color patterns, textures, and boundaries [24, 46]." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 306, + 673, + 545, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 673, + 545, + 703 + ], + "spans": [ + { + "bbox": [ + 306, + 673, + 545, + 703 + ], + "type": "text", + "content": "1A system is perceptually uniform if a small perturbation to a component value is approximately equally perceptible across the range of that value [43]." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 317, + 703, + 533, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 703, + 533, + 712 + ], + "spans": [ + { + "bbox": [ + 317, + 703, + 533, + 712 + ], + "type": "inline_equation", + "content": "2l" + }, + { + "bbox": [ + 317, + 703, + 533, + 712 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 317, + 703, + 533, + 712 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 317, + 703, + 533, + 712 + ], + "type": "text", + "content": " are two multiplicative parameters in the model to be fitted." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "type": "text", + "content": "*Corresponding author." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22242" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "type": "text", + "content": "At later stages, color is transformed to more complex and abstract features, which represent the integral properties of objects, and remain consistent against the changes of the environmental illumination [17, 37]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 121, + 286, + 287 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 121, + 286, + 287 + ], + "spans": [ + { + "bbox": [ + 46, + 121, + 286, + 287 + ], + "type": "text", + "content": "Inspired by these scientific findings, researchers and engineers began to take spatial context (i.e., local surrounding regions) into account, when designing CD formulae. Representative strategies include low-pass spatial filtering [57], histograming [18], patch-based comparison [49], and texture-based segmentation [38]. Most recently, Wang et al. [51] established the largest photographic image dataset, SPCD, for perceptual CD assessment. They further trained a lightweight deep neural network (DNN) for CD assessment of photographic images in a data-driven fashion, as a generalization of several existing CD metrics built on the CIE colorimetry. Nonetheless, the learned formula may not be a proper metric, due to reliance on the possibly surjective mapping for feature transform." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 288, + 286, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 288, + 286, + 323 + ], + "spans": [ + { + "bbox": [ + 47, + 288, + 286, + 323 + ], + "type": "text", + "content": "In this work, we further pursue the data-driven approach. We aim to learn a deep CD metric for photographic images with four desirable properties." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 58, + 330, + 286, + 515 + ], + "type": "list", + "angle": 0, + "index": 7, + "blocks": [ + { + "bbox": [ + 58, + 330, + 286, + 377 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 330, + 286, + 377 + ], + "spans": [ + { + "bbox": [ + 58, + 330, + 286, + 377 + ], + "type": "text", + "content": "- It is conceptually inspired by color perception in the visual cortex. The design of our approach should respect the view that color and form interact inextricably through all stages of visual cortical processing." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 58, + 380, + 286, + 427 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 380, + 286, + 427 + ], + "spans": [ + { + "bbox": [ + 58, + 380, + 286, + 427 + ], + "type": "text", + "content": "- It is a proper metric that satisfies non-negativity, symmetry, identity of indiscernibles, and triangle inequality. Such design has been proven useful for perceptual optimization of image processing systems [11]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 58, + 430, + 286, + 465 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 430, + 286, + 465 + ], + "spans": [ + { + "bbox": [ + 58, + 430, + 286, + 465 + ], + "type": "text", + "content": "- It is accurate in predicting the human perceptual CDs of photographic images, with good generalization to uniformly colored patches." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 58, + 468, + 286, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 468, + 286, + 515 + ], + "spans": [ + { + "bbox": [ + 58, + 468, + 286, + 515 + ], + "type": "text", + "content": "- It is robust to mild geometric distortions (e.g., translation and dilation), which are often present in photographic images of the same scene captured with different camera settings or along different lines of sight." + } + ] + } + ], + "index": 6 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 46, + 522, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 522, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 522, + 286, + 713 + ], + "type": "text", + "content": "We show that all the four desirable properties can be satisfied at once by learning a multi-scale autoregressive normalizing flow (a variant of RealNVP [13] to be specific) for feature transform, followed by Euclidean distance measure in the transformed space. More specifically, we achieve the first property by the squeezing operation (also known as invertible downsampling) in the normalizing flow, which trades space size for channel dimension. The second property is a direct consequence of the bijectivity of the normalizing flow and the Euclidean distance measure. We achieve the third property by optimizing the model parameters to explain the human perceptual CDs in SPCD [51]. We achieve the fourth property by enforcing the normalizing flow to be multi-scale and autoregressive, in which the features at a particular scale are conditioned on those at a higher (i.e., coarser) scale. By doing so, our metric automatically learns" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 72, + 545, + 106 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 106 + ], + "type": "text", + "content": "to preferentially rely on coarse-scale feature representations with more built-in tolerance to geometric distortions for CD assessment." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 108, + 545, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 108, + 545, + 204 + ], + "spans": [ + { + "bbox": [ + 304, + 108, + 545, + 204 + ], + "type": "text", + "content": "We conduct extensive experiments on the large-scale SPCD dataset [51], and find that our proposed metric, termed as CD-Flow, outperforms 15 CD formulae in assessing CDs of photographic images, produces competitive multi-scale local CD maps without any dense supervision, and is more robust to geometric distortions. Moreover, we empirically verify the perceptual uniformity of the learned color image representation from multiple aspects." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 215, + 392, + 227 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 215, + 392, + 227 + ], + "spans": [ + { + "bbox": [ + 306, + 215, + 392, + 227 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 236, + 545, + 270 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 236, + 545, + 270 + ], + "spans": [ + { + "bbox": [ + 304, + 236, + 545, + 270 + ], + "type": "text", + "content": "In this section, we first review CD formulae in a broader context, and then discuss normalizing flow-based models, which are core to the proposed CD metric." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 271, + 545, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 271, + 545, + 545 + ], + "spans": [ + { + "bbox": [ + 304, + 271, + 545, + 545 + ], + "type": "text", + "content": "CD Formulae. The CD assessment is necessary for day-to-day color control, and is indispensable for color matching in color industries. Admittedly, CD formulae have accelerated the instrumental pass/fail devices for color judgments, but much still needs to be done for complete satisfaction. The scientific investigation of perceptual CDs can be dated at least back to Young and Helmholtz, who proposed and developed the trichromatic theory of color, which is the foundation of the metameric color matching experiment. CIELAB [32] is one of the most successful CD metrics recommended by CIE in 1976, and has been widely adopted in industry for a long time. However, the CIELAB color space is not perceptually uniform [30], which motivates the development of CIE94 [34] and CIEDE2000 [29] through the introduction of application-specific parameters. Other CIELAB-based CD metrics include JPC79 [33], BFD(l:c) [31], and " + }, + { + "bbox": [ + 304, + 271, + 545, + 545 + ], + "type": "inline_equation", + "content": "\\mathrm{CMC}(l:c)" + }, + { + "bbox": [ + 304, + 271, + 545, + 545 + ], + "type": "text", + "content": " [9]. The introduced parameters are primarily calibrated using uniformly colored patches, digital or printed, which are statistically and semantically different from photographic images. Thus, the generalization of these metrics to photographic images is somewhat limited, especially when misalignment due to geometric distortions is present." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 545, + 713 + ], + "type": "text", + "content": "To incorporate spatial context into CD assessment, Zhang and Wandell [57] presented S-CIELAB, which extends CIELAB by adding spatial low-pass filtering as preprocessing. Similarly, Ouni et al. [39] provided a spatial extension of CIEDE2000. Lee et al. [26] re-examined histogram intersection, which is widely used in color image index, for the purpose of color image similarity assessment. Hong and Luo [18] chose to give larger weights to areas with spatially homogeneous colors and pixels with larger CDs. This method was later augmented by spatial filtering [41]. Lee and Plataniotis [25] built upon the philosophy of color structural similarity, and gave the hue component careful treatment with circular statistics. Jaramillo et al. [38] grouped the same texture areas for human-like CD" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22243" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 72, + 531, + 199 + ], + "blocks": [ + { + "bbox": [ + 65, + 72, + 531, + 199 + ], + "lines": [ + { + "bbox": [ + 65, + 72, + 531, + 199 + ], + "spans": [ + { + "bbox": [ + 65, + 72, + 531, + 199 + ], + "type": "image", + "image_path": "9b62db9181548ec0173451c49935e05b805ca83459b562d3ab054b74c2fa6486.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 205, + 547, + 262 + ], + "lines": [ + { + "bbox": [ + 46, + 205, + 547, + 262 + ], + "spans": [ + { + "bbox": [ + 46, + 205, + 547, + 262 + ], + "type": "text", + "content": "Figure 1. Feature transform of the proposed CD-Flow for perceptual CD assessment. The coordinate transform module consists of six scales. The leftmost yellow cube represents the input image with dimension " + }, + { + "bbox": [ + 46, + 205, + 547, + 262 + ], + "type": "inline_equation", + "content": "s \\times s \\times 3" + }, + { + "bbox": [ + 46, + 205, + 547, + 262 + ], + "type": "text", + "content": ". The six blue cubes represent the six scales of representations, respectively. Each red cube represents a cascade of squeezing operation, multiple flow steps, and a splitting operation, in which each flow step is composed of an actnorm operation, an invertible " + }, + { + "bbox": [ + 46, + 205, + 547, + 262 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 46, + 205, + 547, + 262 + ], + "type": "text", + "content": " convolution, and an affine coupling layer. The splitting operation is excluded in the last red cube." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 282, + 287, + 306 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 282, + 287, + 306 + ], + "spans": [ + { + "bbox": [ + 46, + 282, + 287, + 306 + ], + "type": "text", + "content": "assessment, using local binary patterns as texture descriptors." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 306, + 288, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 306, + 288, + 510 + ], + "spans": [ + { + "bbox": [ + 46, + 306, + 288, + 510 + ], + "type": "text", + "content": "General-purpose image quality models, including full-reference ones - SSIM [49], VSI [55], LPIPS [56] and DISTS [10], reduced-reference ones - Wang05 [50] and Yu09 [54], and no-reference ones - BRISQUE [35], NIQE [36] and Gao13 [14] can be directly adopted for CD assessment, regarding CDs as a particular form of \"visual degradations.\" Meanwhile, just-noticeable difference (JND) methods, e.g., Butteraugli [1] and FLLIP [3], also attempt to characterize visually indistinguishable color changes between two images. In the era of deep learning, due to the lack of sufficient human-labeled training data, DNN-based CD formulae are rarely proposed. Wang et al. [51] created the first largest image dataset, SPCD, for perceptual CD assessment, and made one of the first attempts to train a DNN-based CD measure for photographic images. However, the underlying feature transform is not mathematically bijective." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "spans": [ + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "text", + "content": "Normalizing Flow-based Models. Normalizing flow-based generative models are constructed by bijective functions " + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "inline_equation", + "content": "f: \\mathbb{R}^D \\to \\mathbb{R}^D" + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "text", + "content": ", with typically easy-to-compute analytical inverse " + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "inline_equation", + "content": "f^{-1}: \\mathbb{R}^D \\to \\mathbb{R}^D" + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "text", + "content": ". The primary goal of " + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "text", + "content": " is to map raw data " + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "inline_equation", + "content": "\\pmb{x}" + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "text", + "content": " to samples " + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "inline_equation", + "content": "\\pmb{z} = f(\\pmb{x})" + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "text", + "content": " from a simple probability distribution " + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{Z}}(\\pmb{z})" + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "text", + "content": ". Many classic machine learning algorithms can be cast in this framework, such as principal component analysis (PCA, where " + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "text", + "content": " is a linear transform and " + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{Z}}(\\pmb{z})" + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "text", + "content": " is standard Gaussian) and independent component analysis (ICA, where " + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "text", + "content": " is again linear and " + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{Z}}" + }, + { + "bbox": [ + 46, + 510, + 288, + 641 + ], + "type": "text", + "content": " is factorized and heavy-tailed)." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 642, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 288, + 715 + ], + "type": "text", + "content": "In 2014, Dinh et al. [12] proposed non-linear independent component estimation (NICE), as a generalization of ICA. NICE is considered the first normalizing flow with the introduction of the additive coupling to ease the calculation of the Jacobian determinant. To make flow-based models more suitable for image-related tasks, Dinh et" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 282, + 547, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 282, + 547, + 521 + ], + "spans": [ + { + "bbox": [ + 304, + 282, + 547, + 521 + ], + "type": "text", + "content": "al. [13] extended NICE to RealNVP, which admits a multiscale autoregressive architecture, implemented by squeezing and affine coupling. Kingma and Dhariwal [22] introduced the invertible " + }, + { + "bbox": [ + 304, + 282, + 547, + 521 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 304, + 282, + 547, + 521 + ], + "type": "text", + "content": " convolution (i.e., the linear transform in PCA) to replace the fixed random permutation for splitting the channel dimension during multiscale processing. The batch normalization in RealNVP is also replaced with activation normalization (i.e., actnorm). To allow unconstrained architectural design, Grathwohl et al. [16] leveraged the Hutchinson's trace estimator for scalable and unbiased estimation of the log-density. Similarly, Behrmann et al. [4] proposed invertible residual networks (i-ResNet), introducing a tractable estimation to the Jacobian log-determinant of a residual block. Other representative normalizing flow work includes hierarchical recursive coupling [23] for increasing flow expressiveness, Wavelet Flow [53] for scaling flow to ultra-high dimensional data, and Discrete Flow [48] for discrete data modeling. In this paper, we do not use normalizing flow for generative modeling, but for invertible feature transform." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 533, + 419, + 547 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 533, + 419, + 547 + ], + "spans": [ + { + "bbox": [ + 306, + 533, + 419, + 547 + ], + "type": "text", + "content": "3. Proposed CD-Flow" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 554, + 547, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 554, + 547, + 639 + ], + "spans": [ + { + "bbox": [ + 304, + 554, + 547, + 639 + ], + "type": "text", + "content": "In this section, we detail our proposed CD metric, CD-Flow, consisting of two key components: the feature transform and the distance measure [29, 32]. The feature transform is built upon a variant of RealNVP [13], which is a learnable invertible transformation between input data and samples from a pre-fixed latent distribution. The CD distance is then computed using the Euclidean distance." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 647, + 418, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 647, + 418, + 658 + ], + "spans": [ + { + "bbox": [ + 306, + 647, + 418, + 658 + ], + "type": "text", + "content": "3.1. Problem Definition" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "content": "We denote the RGB image space as " + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{X}" + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "content": " with an unknown distribution " + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{X}}" + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "content": " and the transformed representation space as " + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{Z}" + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "content": " with a latent distribution " + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "inline_equation", + "content": "p_{\\mathcal{Z}}" + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "content": ". We are given a training dataset " + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{D} = \\{(x^{(i)},y^{(i)}),\\varDelta V^{(i)}\\}_{i = 1}^{M}" + }, + { + "bbox": [ + 304, + 665, + 547, + 715 + ], + "type": "text", + "content": ", where" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22244" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 71, + 286, + 177 + ], + "blocks": [ + { + "bbox": [ + 50, + 71, + 286, + 177 + ], + "lines": [ + { + "bbox": [ + 50, + 71, + 286, + 177 + ], + "spans": [ + { + "bbox": [ + 50, + 71, + 286, + 177 + ], + "type": "image", + "image_path": "7a77d0b7f59a6340b78e40334f953c28eae652f9b94fd5ecf4f80b2acba94c4f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 47, + 186, + 286, + 208 + ], + "lines": [ + { + "bbox": [ + 47, + 186, + 286, + 208 + ], + "spans": [ + { + "bbox": [ + 47, + 186, + 286, + 208 + ], + "type": "text", + "content": "Figure 2. Architecture of the feature transform, adapted from [22]. The three yellow blocks constitute one step of flow." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 225, + 287, + 322 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 225, + 287, + 322 + ], + "spans": [ + { + "bbox": [ + 46, + 225, + 287, + 322 + ], + "type": "inline_equation", + "content": "\\pmb{x}^{(i)}, \\pmb{y}^{(i)} \\in \\mathcal{X}" + }, + { + "bbox": [ + 46, + 225, + 287, + 322 + ], + "type": "text", + "content": " form the " + }, + { + "bbox": [ + 46, + 225, + 287, + 322 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 225, + 287, + 322 + ], + "type": "text", + "content": "-th image pair of the same visual content but different color appearances, " + }, + { + "bbox": [ + 46, + 225, + 287, + 322 + ], + "type": "inline_equation", + "content": "\\Delta V^{(i)}" + }, + { + "bbox": [ + 46, + 225, + 287, + 322 + ], + "type": "text", + "content": " represents the corresponding human perceptual CD collected from a subjective experiment, and " + }, + { + "bbox": [ + 46, + 225, + 287, + 322 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 225, + 287, + 322 + ], + "type": "text", + "content": " is the number of training pairs. Our goal is to learn a normalizing flow-based invertible and differentiable transform " + }, + { + "bbox": [ + 46, + 225, + 287, + 322 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 46, + 225, + 287, + 322 + ], + "type": "text", + "content": ", which maps RGB images to latent representations with Gaussian conditionals for CD assessment." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 329, + 111, + 340 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 329, + 111, + 340 + ], + "spans": [ + { + "bbox": [ + 47, + 329, + 111, + 340 + ], + "type": "text", + "content": "3.2. CD-Flow" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "spans": [ + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "content": "Feature Transform. Figure 1 illustrates the system diagram of the multi-scale autoregressive normalizing flow for the feature transform, which consists of " + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "content": " scales of flow processing: " + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "inline_equation", + "content": "f = f_{1} \\circ f_{2} \\circ \\dots \\circ f_{K}" + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "content": " for multi-scale color and form interaction and abstraction. At the " + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "content": "-th scale, " + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "inline_equation", + "content": "z_{2(k-1)}" + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "content": " is processed and split into " + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "inline_equation", + "content": "z_{2k-1}" + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "inline_equation", + "content": "z_{2k}" + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "content": ", the latter of which further undergoes the " + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "inline_equation", + "content": "k+1" + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "content": "-th scale of processing and splitting. At the final " + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "content": "-th scale, we only process " + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "inline_equation", + "content": "z_{2(K-1)}" + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "inline_equation", + "content": "z_{2K-1}" + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "content": " without splitting. By this notation, we assume that " + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "inline_equation", + "content": "z_{0} = x" + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "content": " is the input RGB image. The probability density of the latent representation " + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "inline_equation", + "content": "z = \\{z_{1}, z_{3}, \\ldots, z_{2K-1}\\}" + }, + { + "bbox": [ + 46, + 347, + 287, + 491 + ], + "type": "text", + "content": " can then be conditionally factorized as" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 70, + 497, + 287, + 564 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 497, + 287, + 564 + ], + "spans": [ + { + "bbox": [ + 70, + 497, + 287, + 564 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} p (\\boldsymbol {z}) = \\prod_ {k = 1} ^ {K - 1} p \\left(\\boldsymbol {z} _ {2 k - 1} \\mid \\left\\{\\boldsymbol {z} _ {\\geq (2 k + 1)} \\right\\}\\right) p (\\boldsymbol {z} _ {2 K - 1}) \\\\ = \\prod_ {k = 1} ^ {K - 1} p \\left(\\boldsymbol {z} _ {2 k - 1} \\mid \\boldsymbol {z} _ {2 k}\\right) p \\left(\\boldsymbol {z} _ {2 K - 1}\\right). \\tag {1} \\\\ \\end{array}", + "image_path": "82bd290092d9a38879f9ff060b021a1a0c9fec626df3950e6abd1d63b0f2478b.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 570, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 570, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 570, + 286, + 713 + ], + "type": "text", + "content": "The second equality is due to the bijectivity of the normalizing flow. " + }, + { + "bbox": [ + 46, + 570, + 286, + 713 + ], + "type": "inline_equation", + "content": "p(z_{2k-1}|z_{2k})" + }, + { + "bbox": [ + 46, + 570, + 286, + 713 + ], + "type": "text", + "content": ", for " + }, + { + "bbox": [ + 46, + 570, + 286, + 713 + ], + "type": "inline_equation", + "content": "k \\in \\{1, 2, \\dots, K-1\\}" + }, + { + "bbox": [ + 46, + 570, + 286, + 713 + ], + "type": "text", + "content": ", can conveniently be modeled as conditionally independent Gaussians. That is, the mean vector and the diagonal covariance matrix are computed from " + }, + { + "bbox": [ + 46, + 570, + 286, + 713 + ], + "type": "inline_equation", + "content": "z_{2k}" + }, + { + "bbox": [ + 46, + 570, + 286, + 713 + ], + "type": "text", + "content": " through say a tiny neural network [13]. Similarly, " + }, + { + "bbox": [ + 46, + 570, + 286, + 713 + ], + "type": "inline_equation", + "content": "p(z_{2K-1})" + }, + { + "bbox": [ + 46, + 570, + 286, + 713 + ], + "type": "text", + "content": " is modeled as (unconditionally) independent Gaussians, where the parameters are directly estimated via backpropagation. As shown in Figure 2, each scale of flow processing consists of a squeezing operation, multiple flow steps, and a splitting operation. Each flow step is further decomposed into three operations: actnorm, invertible " + }, + { + "bbox": [ + 46, + 570, + 286, + 713 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 46, + 570, + 286, + 713 + ], + "type": "text", + "content": " convolution, and affine coupling." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 317, + 72, + 545, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 72, + 545, + 120 + ], + "spans": [ + { + "bbox": [ + 317, + 72, + 545, + 120 + ], + "type": "text", + "content": "- Actnorm is introduced to replace the batch normalization to avoid degenerated performance when the minibatch size is small. Given an input " + }, + { + "bbox": [ + 317, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 317, + 72, + 545, + 120 + ], + "type": "text", + "content": " of size " + }, + { + "bbox": [ + 317, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "c \\times h \\times w" + }, + { + "bbox": [ + 317, + 72, + 545, + 120 + ], + "type": "text", + "content": ", the output of the same size can be computed by" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 402, + 127, + 545, + 139 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 402, + 127, + 545, + 139 + ], + "spans": [ + { + "bbox": [ + 402, + 127, + 545, + 139 + ], + "type": "interline_equation", + "content": "\\boldsymbol {z} ^ {\\prime} = \\boldsymbol {s} \\odot \\boldsymbol {z} + \\boldsymbol {t}, \\tag {2}", + "image_path": "f36656b37d87701950a78674465a5190e9e1634dd977e4abbf53ae674d6fe6cf.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 325, + 145, + 544, + 170 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 145, + 544, + 170 + ], + "spans": [ + { + "bbox": [ + 325, + 145, + 544, + 170 + ], + "type": "text", + "content": "with the log-determinant " + }, + { + "bbox": [ + 325, + 145, + 544, + 170 + ], + "type": "inline_equation", + "content": "h\\cdot w\\cdot \\log |\\operatorname *{det}(\\operatorname {diag}(s))|" + }, + { + "bbox": [ + 325, + 145, + 544, + 170 + ], + "type": "inline_equation", + "content": "\\{s,t\\}" + }, + { + "bbox": [ + 325, + 145, + 544, + 170 + ], + "type": "text", + "content": " are learnable scaling and bias parameters." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 317, + 176, + 545, + 212 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 176, + 545, + 212 + ], + "spans": [ + { + "bbox": [ + 317, + 176, + 545, + 212 + ], + "type": "text", + "content": "- Invertible " + }, + { + "bbox": [ + 317, + 176, + 545, + 212 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 317, + 176, + 545, + 212 + ], + "type": "text", + "content": " Convolution is a learnable linear transform for channel mixing. Given a " + }, + { + "bbox": [ + 317, + 176, + 545, + 212 + ], + "type": "inline_equation", + "content": "c \\times h \\times w" + }, + { + "bbox": [ + 317, + 176, + 545, + 212 + ], + "type": "text", + "content": " input " + }, + { + "bbox": [ + 317, + 176, + 545, + 212 + ], + "type": "inline_equation", + "content": "\\mathbf{z}" + }, + { + "bbox": [ + 317, + 176, + 545, + 212 + ], + "type": "text", + "content": " and a " + }, + { + "bbox": [ + 317, + 176, + 545, + 212 + ], + "type": "inline_equation", + "content": "c \\times c" + }, + { + "bbox": [ + 317, + 176, + 545, + 212 + ], + "type": "text", + "content": " weight matrix " + }, + { + "bbox": [ + 317, + 176, + 545, + 212 + ], + "type": "inline_equation", + "content": "\\mathbf{W}" + }, + { + "bbox": [ + 317, + 176, + 545, + 212 + ], + "type": "text", + "content": ", we have" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 412, + 219, + 545, + 231 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 412, + 219, + 545, + 231 + ], + "spans": [ + { + "bbox": [ + 412, + 219, + 545, + 231 + ], + "type": "interline_equation", + "content": "z ^ {\\prime} = W z, \\tag {3}", + "image_path": "4ba7b7cdd27ede74ca064e1e411ca41da8b616aeabe5ac93b94e35464a3b5a05.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 325, + 238, + 514, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 238, + 514, + 251 + ], + "spans": [ + { + "bbox": [ + 325, + 238, + 514, + 251 + ], + "type": "text", + "content": "with the log-determinant " + }, + { + "bbox": [ + 325, + 238, + 514, + 251 + ], + "type": "inline_equation", + "content": "h \\cdot w \\cdot \\log |\\operatorname{det}(\\mathbf{W})|" + }, + { + "bbox": [ + 325, + 238, + 514, + 251 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 317, + 256, + 545, + 328 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 256, + 545, + 328 + ], + "spans": [ + { + "bbox": [ + 317, + 256, + 545, + 328 + ], + "type": "text", + "content": "- Affine Coupling constrains its Jacobian to be a triangular matrix, which facilitates the log-determinant computation. Specifically, the input " + }, + { + "bbox": [ + 317, + 256, + 545, + 328 + ], + "type": "inline_equation", + "content": "z" + }, + { + "bbox": [ + 317, + 256, + 545, + 328 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 317, + 256, + 545, + 328 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 317, + 256, + 545, + 328 + ], + "type": "text", + "content": " dimensions is first split into two non-overlapping subsets: " + }, + { + "bbox": [ + 317, + 256, + 545, + 328 + ], + "type": "inline_equation", + "content": "z_{1:d}" + }, + { + "bbox": [ + 317, + 256, + 545, + 328 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 317, + 256, + 545, + 328 + ], + "type": "inline_equation", + "content": "z_{d+1:D}" + }, + { + "bbox": [ + 317, + 256, + 545, + 328 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 317, + 256, + 545, + 328 + ], + "type": "inline_equation", + "content": "d < D" + }, + { + "bbox": [ + 317, + 256, + 545, + 328 + ], + "type": "text", + "content": ". The output of the same dimension can be computed by" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 369, + 335, + 421, + 347 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 335, + 421, + 347 + ], + "spans": [ + { + "bbox": [ + 369, + 335, + 421, + 347 + ], + "type": "interline_equation", + "content": "\\boldsymbol {z} _ {1: d} ^ {\\prime} = \\boldsymbol {z} _ {1: d},", + "image_path": "d8f840f756d2bc3540a2c0131c7d2f00f32000fcb99eb3059d6b94fb0c2c8e67.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 358, + 344, + 545, + 365 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 358, + 344, + 545, + 365 + ], + "spans": [ + { + "bbox": [ + 358, + 344, + 545, + 365 + ], + "type": "interline_equation", + "content": "\\boldsymbol {z} _ {d + 1: D} ^ {\\prime} = \\boldsymbol {z} _ {d + 1: D} \\odot e ^ {s \\left(\\boldsymbol {z} _ {1: d}\\right)} + t \\left(\\boldsymbol {z} _ {1: d}\\right), \\tag {4}", + "image_path": "26f69fc6089d11609acf09d577c7b9be6c442111637fa8beadaa23d36855c4e1.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 325, + 371, + 545, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 371, + 545, + 407 + ], + "spans": [ + { + "bbox": [ + 325, + 371, + 545, + 407 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 325, + 371, + 545, + 407 + ], + "type": "inline_equation", + "content": "s(\\cdot)" + }, + { + "bbox": [ + 325, + 371, + 545, + 407 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 325, + 371, + 545, + 407 + ], + "type": "inline_equation", + "content": "t(\\cdot)" + }, + { + "bbox": [ + 325, + 371, + 545, + 407 + ], + "type": "text", + "content": " are the scaling and translation functions, respectively, which are not necessarily invertible. The inverse is easily and analytically derived as" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 356, + 413, + 406, + 426 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 356, + 413, + 406, + 426 + ], + "spans": [ + { + "bbox": [ + 356, + 413, + 406, + 426 + ], + "type": "interline_equation", + "content": "\\boldsymbol {z} _ {1: d} = \\boldsymbol {z} _ {1: d} ^ {\\prime},", + "image_path": "ba448e3f61c9375f6d563429665125984a9b9baa0aad209e013c47674560a9ee.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 344, + 423, + 545, + 444 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 344, + 423, + 545, + 444 + ], + "spans": [ + { + "bbox": [ + 344, + 423, + 545, + 444 + ], + "type": "interline_equation", + "content": "\\boldsymbol {z} _ {d + 1: D} = \\left(\\boldsymbol {z} _ {d + 1: D} ^ {\\prime} - t \\left(\\boldsymbol {z} _ {1: d} ^ {\\prime}\\right)\\right) \\odot e ^ {- s \\left(\\boldsymbol {z} _ {1: d} ^ {\\prime}\\right)}. \\tag {5}", + "image_path": "869c97247f37dfca98d384e77df29ded15a36b2affc100ce9189b278fea71b68.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 452, + 545, + 500 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 452, + 545, + 500 + ], + "spans": [ + { + "bbox": [ + 306, + 452, + 545, + 500 + ], + "type": "text", + "content": "CD Distance. We adopt the Euclidean distance, i.e., the square root of mean squared differences between two latent color representations " + }, + { + "bbox": [ + 306, + 452, + 545, + 500 + ], + "type": "inline_equation", + "content": "f(\\pmb{x})" + }, + { + "bbox": [ + 306, + 452, + 545, + 500 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 306, + 452, + 545, + 500 + ], + "type": "inline_equation", + "content": "f(\\pmb{y})" + }, + { + "bbox": [ + 306, + 452, + 545, + 500 + ], + "type": "text", + "content": ", as the CD distance between two input images " + }, + { + "bbox": [ + 306, + 452, + 545, + 500 + ], + "type": "inline_equation", + "content": "\\pmb{x}" + }, + { + "bbox": [ + 306, + 452, + 545, + 500 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 306, + 452, + 545, + 500 + ], + "type": "inline_equation", + "content": "\\pmb{y}" + }, + { + "bbox": [ + 306, + 452, + 545, + 500 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 320, + 506, + 545, + 533 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 506, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 320, + 506, + 545, + 533 + ], + "type": "interline_equation", + "content": "\\Delta E (\\boldsymbol {x}, \\boldsymbol {y}) = \\sqrt {\\frac {(f (\\boldsymbol {x}) - f (\\boldsymbol {y})) ^ {T} (f (\\boldsymbol {x}) - f (\\boldsymbol {y}))}{D}}. \\tag {6}", + "image_path": "87314c4600b0d76bd8c8c296d4a2905554289eee7fb55dd37bc24358f3578334.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 306, + 544, + 394, + 555 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 544, + 394, + 555 + ], + "spans": [ + { + "bbox": [ + 306, + 544, + 394, + 555 + ], + "type": "text", + "content": "3.3. Loss Function" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 305, + 563, + 545, + 599 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 563, + 545, + 599 + ], + "spans": [ + { + "bbox": [ + 305, + 563, + 545, + 599 + ], + "type": "text", + "content": "It is straightforward to measure the " + }, + { + "bbox": [ + 305, + 563, + 545, + 599 + ], + "type": "inline_equation", + "content": "\\ell_p" + }, + { + "bbox": [ + 305, + 563, + 545, + 599 + ], + "type": "text", + "content": "-norm induced distance between the predicted CD computed by Eq. (6) and the perceptual CD of the given image pair " + }, + { + "bbox": [ + 305, + 563, + 545, + 599 + ], + "type": "inline_equation", + "content": "(x, y)" + }, + { + "bbox": [ + 305, + 563, + 545, + 599 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 347, + 605, + 545, + 618 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 605, + 545, + 618 + ], + "spans": [ + { + "bbox": [ + 347, + 605, + 545, + 618 + ], + "type": "interline_equation", + "content": "\\ell (\\boldsymbol {x}, \\boldsymbol {y}) = \\| \\Delta E (\\boldsymbol {x}, \\boldsymbol {y}) - \\Delta V (\\boldsymbol {x}, \\boldsymbol {y}) \\| _ {p}. \\tag {7}", + "image_path": "ca41b6998cbfc88e5007f24b8e9d7c156ca064fb249d0f148f482a086be9cf82.jpg" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 305, + 624, + 545, + 672 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 624, + 545, + 672 + ], + "spans": [ + { + "bbox": [ + 305, + 624, + 545, + 672 + ], + "type": "text", + "content": "To encourage the robustness of CD-Flow to mild geometric distortions, which are often unavoidable in practice, we introduce a multi-scale version of Eq. (7) to put more emphasis on coarser-scale latent representations:" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 331, + 677, + 545, + 710 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 331, + 677, + 545, + 710 + ], + "spans": [ + { + "bbox": [ + 331, + 677, + 545, + 710 + ], + "type": "interline_equation", + "content": "\\ell_ {\\mathrm {m s}} (\\boldsymbol {x}, \\boldsymbol {y}) = \\sum_ {k = 1} ^ {K} \\| \\Delta E _ {k} (\\boldsymbol {x}, \\boldsymbol {y}) - \\Delta V (\\boldsymbol {x}, \\boldsymbol {y}) \\| _ {p}, \\tag {8}", + "image_path": "6145642def9b7655fcf1e4211e879126c4602788981dd1510ba0c79b79dbfa7a.jpg" + } + ] + } + ], + "index": 25 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22245" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 49, + 125, + 545, + 352 + ], + "blocks": [ + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "lines": [ + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "spans": [ + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "text", + "content": "Table 1. STRESS, PLCC, and SRCC between predicted CDs " + }, + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "inline_equation", + "content": "(\\Delta E)" + }, + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "text", + "content": " and perceptual CDs " + }, + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "inline_equation", + "content": "(\\Delta V)" + }, + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "text", + "content": " in SPCD. The top section lists representative CD formulae developed from homogeneous color patches. The second section contains CD measures adapted for natural images. The third section includes general-purpose image quality models. The fourth section consists of JND measures. Closest to ours, CD-Net is a DNN-based data-driven CD measure for natural images. The top two methods are highlighted in boldface" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 49, + 125, + 545, + 352 + ], + "lines": [ + { + "bbox": [ + 49, + 125, + 545, + 352 + ], + "spans": [ + { + "bbox": [ + 49, + 125, + 545, + 352 + ], + "type": "table", + "html": "
MethodPerfectly aligned pairsNon-perfectly aligned pairsAll
\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)
CIELAB [45]31.2440.7930.77529.6390.6900.57931.8720.7160.666
CIE94 [34]34.7210.7900.77229.9160.6930.57234.3260.7100.654
CIEDE2000 [29]29.9750.8250.82130.3470.6670.56331.4390.7260.686
S-CIELAB [57]30.0940.8220.81931.8040.6310.52232.7800.7000.657
Hong06 [18]60.5570.7940.81057.0700.5430.46161.2270.6450.632
Ouni081[39]29.9770.8260.82130.3550.6680.56331.4440.7260.685
Jaramillo19 [38]43.4190.5140.50650.2990.0810.04168.8050.3210.329
SSIM [49]39.3930.5890.54953.0350.0770.04448.0250.3090.324
FLLIP [3]29.3180.7450.71527.1580.7340.64029.0990.7180.663
PieAPP [44]29.0440.7370.73737.5280.5220.45932.3540.6520.643
LPIPS [56]44.8110.6950.68853.1320.2190.17164.1450.4390.490
DISTS [10]31.4090.7450.74635.0430.5280.44732.9950.6400.626
Chou07 [8]50.7210.7870.78536.1840.6030.45949.5450.6120.557
Butteraugli [1]42.6200.6060.59348.2170.2580.24554.7370.3710.359
CD-Net [51]20.8910.8670.87022.5430.8180.77621.4310.8460.842
CD-Flow16.6130.8960.90421.3740.8560.79418.4730.8710.865
", + "image_path": "11a575541e1826ad60862ffc9e915d1ad56860ae9773953309b409dbc6a72c35.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 56, + 353, + 217, + 365 + ], + "lines": [ + { + "bbox": [ + 56, + 353, + 217, + 365 + ], + "spans": [ + { + "bbox": [ + 56, + 353, + 217, + 365 + ], + "type": "text", + "content": "1 The spatial extension of CIEDE2000." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 396, + 75, + 406 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 396, + 75, + 406 + ], + "spans": [ + { + "bbox": [ + 47, + 396, + 75, + 406 + ], + "type": "text", + "content": "where" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 426, + 287, + 470 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 426, + 287, + 470 + ], + "spans": [ + { + "bbox": [ + 53, + 426, + 287, + 470 + ], + "type": "interline_equation", + "content": "\\Delta E _ {k} (\\boldsymbol {x}, \\boldsymbol {y}) = \\sqrt {\\frac {\\left(f _ {k :} (\\boldsymbol {x}) - f _ {k :} (\\boldsymbol {y})\\right) ^ {T} \\left(f _ {k :} (\\boldsymbol {x}) - f _ {k :} (\\boldsymbol {y})\\right)}{D _ {k}}}. \\tag {9}", + "image_path": "84bbfebf09f9b3c48d98ec1c10dfeea63b4b606fe3cd3fd9fdc26553ac7fd2f4.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "spans": [ + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "inline_equation", + "content": "f_{k}(\\pmb {x}) = [z_{2k - 1}^{T},\\dots ,z_{2K - 1}^{T}]^{T}" + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "inline_equation", + "content": "D_{k}" + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "text", + "content": " is the number of feature dimensions of " + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "inline_equation", + "content": "f_{k}(\\pmb {x})" + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "text", + "content": " . That is, " + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "inline_equation", + "content": "\\Delta E_k(\\pmb {x},\\pmb {y})" + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "text", + "content": " makes an estimate of " + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "inline_equation", + "content": "\\Delta V(\\pmb {x},\\pmb {y})" + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "text", + "content": " using the " + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "text", + "content": " -th to " + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "text", + "content": " -th scale latent representations, and we have " + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "inline_equation", + "content": "\\Delta E_1" + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "text", + "content": " equal to " + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "inline_equation", + "content": "\\Delta E" + }, + { + "bbox": [ + 46, + 491, + 287, + 542 + ], + "type": "text", + "content": " in Eq. (6)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "type": "text", + "content": "It is noteworthy that if the transform " + }, + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "type": "text", + "content": " is supervised by the CD loss in Eq. (8) solely, the bijectivity of " + }, + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "type": "text", + "content": " may not be necessarily guaranteed. This is because the adopted normalizing flow is not bijective by design. For example, the \"invertible\" " + }, + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "type": "text", + "content": " convolution as the channel mixer becomes non-invertible when " + }, + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "type": "inline_equation", + "content": "W" + }, + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "type": "text", + "content": " is degenerate. Empirically, we observe that the scaling factor of the affine-coupling layer is close to zero after optimizing Eq. (8). Since the inverse transform includes operations of dividing by the scaling factor, there will easily result in the exploding inverse problem [5]. Thus, we incorporate the commonly used maximum likelihood objective in normalizing flow [40] into our training objective. We work with the negative log-likelihood loss:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 317, + 411, + 545, + 454 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 411, + 545, + 454 + ], + "spans": [ + { + "bbox": [ + 317, + 411, + 545, + 454 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\ell_ {\\mathrm {n l}} (\\boldsymbol {x}) = - \\log p _ {\\mathcal {X}} (\\boldsymbol {x}) \\\\ = - \\log p _ {\\mathcal {Z}} (f (\\boldsymbol {x})) - \\log \\left| \\det \\left(\\frac {\\partial f (\\boldsymbol {x})}{\\partial \\boldsymbol {x}}\\right) \\right|. \\tag {10} \\\\ \\end{array}", + "image_path": "7b87cafd385a9309bbd92232b90751f66bf4359cbbf892d5cd524d32570339fa.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 459, + 547, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 459, + 547, + 506 + ], + "spans": [ + { + "bbox": [ + 305, + 459, + 547, + 506 + ], + "type": "text", + "content": "During training, we randomly sample a mini-batch " + }, + { + "bbox": [ + 305, + 459, + 547, + 506 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 305, + 459, + 547, + 506 + ], + "type": "text", + "content": " from the training dataset " + }, + { + "bbox": [ + 305, + 459, + 547, + 506 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 305, + 459, + 547, + 506 + ], + "type": "text", + "content": " in each iteration, and make use of a variant of stochastic gradient descent to optimize the parameters in CD-Flow:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 310, + 511, + 545, + 555 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 310, + 511, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 310, + 511, + 545, + 555 + ], + "type": "interline_equation", + "content": "\\ell (\\mathcal {B}) = \\frac {1}{| \\mathcal {B} |} \\sum_ {(x, y) \\in \\mathcal {B}} \\left(\\ell_ {\\mathrm {m s}} (\\boldsymbol {x}, \\boldsymbol {y}) + \\lambda \\left(\\ell_ {\\mathrm {n l}} (\\boldsymbol {x}) + \\ell_ {\\mathrm {n l}} (\\boldsymbol {y})\\right)\\right), \\tag {11}", + "image_path": "069c727f3c5583f0c0b373988b2b96ba5400bf39be37db42cae4182c09f2d6ce.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 562, + 547, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 562, + 547, + 588 + ], + "spans": [ + { + "bbox": [ + 305, + 562, + 547, + 588 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 562, + 547, + 588 + ], + "type": "inline_equation", + "content": "|\\mathcal{B}|" + }, + { + "bbox": [ + 305, + 562, + 547, + 588 + ], + "type": "text", + "content": " denotes the cardinality of " + }, + { + "bbox": [ + 305, + 562, + 547, + 588 + ], + "type": "inline_equation", + "content": "\\mathcal{B}" + }, + { + "bbox": [ + 305, + 562, + 547, + 588 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 305, + 562, + 547, + 588 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 305, + 562, + 547, + 588 + ], + "type": "text", + "content": " is the trade-off to balance the magnitudes of different loss terms." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 597, + 388, + 610 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 597, + 388, + 610 + ], + "spans": [ + { + "bbox": [ + 306, + 597, + 388, + 610 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 616, + 547, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 616, + 547, + 665 + ], + "spans": [ + { + "bbox": [ + 304, + 616, + 547, + 665 + ], + "type": "text", + "content": "In this section, we begin by presenting the experimental setups. We then compare the proposed CD-Flow with 15 CD measures. We last conduct extensive ablation studies to justify the key designs of CD-Flow." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 671, + 427, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 671, + 427, + 685 + ], + "spans": [ + { + "bbox": [ + 306, + 671, + 427, + 685 + ], + "type": "text", + "content": "4.1. Experimental Setups" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 689, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 547, + 715 + ], + "type": "text", + "content": "CD Dataset. We conduct experiments on SPCD [51], so far the largest natural image dataset tailored for perceptual CD" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22246" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 49, + 125, + 545, + 220 + ], + "blocks": [ + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "lines": [ + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "spans": [ + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "text", + "content": "Table 2. STRESS, PLCC, and SRCC between predicted CDs " + }, + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "inline_equation", + "content": "(\\Delta E)" + }, + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "text", + "content": " and perceptual CDs " + }, + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "inline_equation", + "content": "(\\Delta V)" + }, + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "text", + "content": " in SPCD [51] under geometric distortions. Translation means randomly shifting one image with respect to the other image in an image pair (by up to " + }, + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "text", + "content": " of pixels) in both spacial directions. Rotation means randomly rotating one image (by up to " + }, + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "inline_equation", + "content": "3^{\\circ}" + }, + { + "bbox": [ + 46, + 70, + 547, + 118 + ], + "type": "text", + "content": ") around the center point. Dilation means zooming in (and cropping) one image without changing the image size (by a factor of 1.05)" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 49, + 125, + 545, + 220 + ], + "lines": [ + { + "bbox": [ + 49, + 125, + 545, + 220 + ], + "spans": [ + { + "bbox": [ + 49, + 125, + 545, + 220 + ], + "type": "table", + "html": "
MethodTranslationRotationDilation
\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( SRCC\\uparrow \\)
CIELAB [45]29.4140.6200.57732.6330.5290.49531.5110.5190.467
CIE94 [34]29.1410.6450.59631.9430.5660.51930.3230.5670.505
CIEDE2000 [29]28.0350.6540.61331.2550.5660.52729.9280.5660.512
CD-Net [51]19.8250.8450.84222.4630.7840.77221.7040.7870.773
CD-Flow19.3110.8520.85620.1390.8370.81621.3520.8270.797
", + "image_path": "62d45d80728ba9b6a21269af35b50a566d63d3be43f5689fc942362215b02735.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 248, + 289, + 356 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 248, + 289, + 356 + ], + "spans": [ + { + "bbox": [ + 46, + 248, + 289, + 356 + ], + "type": "text", + "content": "assessment. SPCD consists of 15,335 color images out of 1,000 distinct natural scenes spanning a variety of realistic shooting scenarios. A total of 30,000 image pairs are sampled for human labeling, where 10,005 are non-perfectly aligned image pairs. For each image pair, there are 20 human ratings. We randomly split " + }, + { + "bbox": [ + 46, + 248, + 289, + 356 + ], + "type": "inline_equation", + "content": "70\\%" + }, + { + "bbox": [ + 46, + 248, + 289, + 356 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 248, + 289, + 356 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 248, + 289, + 356 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 248, + 289, + 356 + ], + "type": "inline_equation", + "content": "20\\%" + }, + { + "bbox": [ + 46, + 248, + 289, + 356 + ], + "type": "text", + "content": " of image pairs in SPCD as training, validation, and test sets, respectively, according to the image content to ensure content independence." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "spans": [ + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "text", + "content": "Network Architecture. We employ a variant of RealNVP [22], which consists of " + }, + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "inline_equation", + "content": "K = 6" + }, + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "text", + "content": " scales, and each scale includes a squeezing operation, " + }, + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "inline_equation", + "content": "L = 8" + }, + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "text", + "content": " steps of flow, and a splitting operation. We do not split at the last scale. The squeezing operation trades the spatial size for the channel number by transforming an " + }, + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "inline_equation", + "content": "s \\times s \\times c" + }, + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "text", + "content": " tensor to an " + }, + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "inline_equation", + "content": "\\frac{s}{2} \\times \\frac{s}{2} \\times 4c" + }, + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "text", + "content": " tensor, where " + }, + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "text", + "content": " is the spatial size and " + }, + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 46, + 360, + 289, + 492 + ], + "type": "text", + "content": " is the number of channels. The splitting operation divides the latent representation of the same scale into two halves along the channel dimension. One is used as the color features for CD assessment, and the other is subject to further processing." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 495, + 288, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 495, + 288, + 544 + ], + "spans": [ + { + "bbox": [ + 47, + 495, + 288, + 544 + ], + "type": "text", + "content": "Training and Testing Details. We employ the Adam as the stochastic optimizer, where the batch size is 4, and the initial learning rate is " + }, + { + "bbox": [ + 47, + 495, + 288, + 544 + ], + "type": "inline_equation", + "content": "10^{-5}" + }, + { + "bbox": [ + 47, + 495, + 288, + 544 + ], + "type": "text", + "content": " with a decay factor of 2 for every 5 epochs. We train CD-Flow for 50 epochs in total." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 547, + 288, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 547, + 288, + 632 + ], + "spans": [ + { + "bbox": [ + 46, + 547, + 288, + 632 + ], + "type": "text", + "content": "Evaluation Criteria. We use three standard criteria to quantify the performance of CD-Flow against existing CD measures, including the standardized residual sum of squares (STRESS) [15], Pearson linear correlation coefficient (PLCC), and Spearman's rank correlation coefficient (SRCC). STRESS measures both prediction accuracy and statistical significance, which is defined by" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 69, + 647, + 287, + 685 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 647, + 287, + 685 + ], + "spans": [ + { + "bbox": [ + 69, + 647, + 287, + 685 + ], + "type": "interline_equation", + "content": "\\text {S T R E S S} = 1 0 0 \\sqrt {\\frac {\\sum_ {i = 1} ^ {M} \\left(\\Delta E _ {i} - F \\Delta V _ {i}\\right) ^ {2}}{F ^ {2} \\sum_ {i = 1} ^ {M} \\Delta V _ {i} ^ {2}}}, \\tag {12}", + "image_path": "aea2015d117cf62f63bb156eaaa98db7deaeb8fc24cedd9c879740bc0fe05e9f.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 701, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 701, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 701, + 288, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 701, + 288, + 714 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 47, + 701, + 288, + 714 + ], + "type": "text", + "content": " is the number of test pairs and " + }, + { + "bbox": [ + 47, + 701, + 288, + 714 + ], + "type": "inline_equation", + "content": "F" + }, + { + "bbox": [ + 47, + 701, + 288, + 714 + ], + "type": "text", + "content": " is the scale cor" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 248, + 499, + 261 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 248, + 499, + 261 + ], + "spans": [ + { + "bbox": [ + 305, + 248, + 499, + 261 + ], + "type": "text", + "content": "resection factor between " + }, + { + "bbox": [ + 305, + 248, + 499, + 261 + ], + "type": "inline_equation", + "content": "\\Delta E" + }, + { + "bbox": [ + 305, + 248, + 499, + 261 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 248, + 499, + 261 + ], + "type": "inline_equation", + "content": "\\Delta V" + }, + { + "bbox": [ + 305, + 248, + 499, + 261 + ], + "type": "text", + "content": ", defined as" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 380, + 267, + 545, + 299 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 267, + 545, + 299 + ], + "spans": [ + { + "bbox": [ + 380, + 267, + 545, + 299 + ], + "type": "interline_equation", + "content": "F = \\frac {\\sum_ {i = 1} ^ {M} \\Delta E _ {i} ^ {2}}{\\sum_ {i = 1} ^ {M} \\Delta E _ {i} \\Delta V _ {i}}. \\tag {13}", + "image_path": "d3ba5745e186a14c07b3c9d80a5592d9f0118d35083c30302b42c496fda2fdda.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 305, + 547, + 377 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 305, + 547, + 377 + ], + "spans": [ + { + "bbox": [ + 304, + 305, + 547, + 377 + ], + "type": "text", + "content": "STRESS generally ranges from 0 to 100, where a small value suggests a tight-fitting between model predictions and ground truths. SRCC and PLCC measure the prediction monotonicity and prediction linearity, respectively. Before calculating PLCC, we linearize model predictions by regressing a four-parameter monotonic function." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 384, + 391, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 384, + 391, + 396 + ], + "spans": [ + { + "bbox": [ + 305, + 384, + 391, + 396 + ], + "type": "text", + "content": "4.2. Main Results" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 403, + 545, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 403, + 545, + 557 + ], + "spans": [ + { + "bbox": [ + 304, + 403, + 545, + 557 + ], + "type": "text", + "content": "SPCD Results. We compare the proposed CD-Flow with 15 existing CD measures which can be grouped into four categories: 1) CD measures for homogeneous color patches: CIELAB [45], CIE94 [34] and CIEDE2000 [29], 2) CD measures for natural images: S-CIELAB [57], Hong06 [18], Ouni08 [39], Jaramillo19 [38] and CD-Net [51], 3) full-reference image quality model: SSIM [49], FLIP [3], PieAPP [44], LPIPS [56] and DISTS [10], and 4) JND methods: Chou07 [8] and Butteraugli [1]. We employ official implementations of Butteraugli and FLIP, and retrain PieAPP, LPIPS, and DISTS on the same training set as CD-Flow. The rest methods are implemented and made publicly available by Jaramillo et al. [38]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 558, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 547, + 715 + ], + "type": "text", + "content": "The comparison results are documented in Table 1. Several interesting observations merit attention. First, CIE94 and CIEDE2000 rectify the non-uniformity of CIELAB on color patches by incorporating weight coefficients. Nonetheless, when it comes to natural photographic images, these approaches do not show noticeable improvements compared to the original CIELAB formula. This provides a strong indication that humans perceive CDs of uniformly colored patches and natural images in drastically different manners. Naive extensions such as spatial filtering and texture grouping may not be sufficient to bridge the gap as evidenced by the results in the second section of Table 1. Second, despite being retrained on the same training" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22247" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 47, + 103, + 547, + 188 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 545, + 93 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 545, + 93 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 545, + 93 + ], + "type": "text", + "content": "Table 3. Generalizability evaluation of CD-Flow on the COM dataset and its four subsets: BFD-P, Leeds, Witt, and RIT-DuPont. PLCC on RIT-DuPont is indicated by “—” since it is not computable" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 47, + 103, + 547, + 188 + ], + "lines": [ + { + "bbox": [ + 47, + 103, + 547, + 188 + ], + "spans": [ + { + "bbox": [ + 47, + 103, + 547, + 188 + ], + "type": "table", + "html": "
MethodBFD-P [31]Leeds [21]Witt [52]RIT-DuPont [7]COM dataset [29]
\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)\\( STRESS\\downarrow \\)\\( PLCC\\uparrow \\)
CIELAB [45]45.0540.74940.0930.29551.6890.56530.348—45.2020.693
CIE94 [34]35.7980.83030.4940.58431.8570.79320.982—33.2350.814
CIEDE2000 [29]31.9350.86119.2470.77230.3580.82520.239—28.9790.862
CD-Net39.3120.79138.5580.44933.6400.82842.999—38.8720.786
CD-Flow34.6610.83334.2750.47631.9650.82036.504—35.0610.801
", + "image_path": "d97aae6a138ee8d4a309ffd1f7df149ad703cd98338a207437c4c97e599d0046.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 217, + 289, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 217, + 289, + 420 + ], + "spans": [ + { + "bbox": [ + 46, + 217, + 289, + 420 + ], + "type": "text", + "content": "set as CD-Flow, general-purpose DNN-based image quality models PieAPP [44], LPIPS [56], and DISTS [10] do not deliver comparable performance. We believe this arises for PieAPP because it requires a much larger labeled set to support training from scratch, and 21,000 training pairs, even with data augmentation, are less likely to overcome overfitting. LPIPS and DISTS rely on pre-trained VGG features, which are empirically proven to be more structure and texture inductive. As a result, they may be less ideal for assessing CDs. Third, DNN-based CD formulae, i.e., CD-Net [51] and CD-Flow, outperform all other competing models, which are primarily attributed to their superior representation learning capabilities. This observation confirms the potential of employing DNNs in the field of color science. Fourth, CD-Flow achieves the most remarkable results, thereby demonstrating the promise of the multi-scale autoregressive normalizing flow for CD assessment." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 423, + 288, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 423, + 288, + 615 + ], + "spans": [ + { + "bbox": [ + 46, + 423, + 288, + 615 + ], + "type": "text", + "content": "Robustness Results to Geometric Distortions. We conduct experiments to evaluate the robustness of CD-Flow to mild geometric distortions, including translation, rotation, and dilation. Specifically, we translate randomly one image with respect to the other image in an image pair by up to " + }, + { + "bbox": [ + 46, + 423, + 288, + 615 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 46, + 423, + 288, + 615 + ], + "type": "text", + "content": " pixels in both horizontal and vertical directions, rotate randomly one image clockwise by up to " + }, + { + "bbox": [ + 46, + 423, + 288, + 615 + ], + "type": "inline_equation", + "content": "3^{\\circ}" + }, + { + "bbox": [ + 46, + 423, + 288, + 615 + ], + "type": "text", + "content": ", and zoom in one image by a factor of 1.05. The corresponding results are presented in Table 2. We find that the performance of all models degrades due to the presence of geometric distortions. CIELAB-based formulae are particularly sensitive to geometric distortions because of pixel-by-pixel CD computation. In contrast, CD-Flow demonstrates the best robustness results, which is as expected because it is designed to abstract color features, characterized by the interdependence between color and form for CD assessment." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 288, + 715 + ], + "type": "text", + "content": "Generalizability Results on Other Datasets. We examine the generalizability of CD-Flow on the COM dataset [29] and the TID2013 subset [42]. The COM dataset consists of four subsets: BFD-P [31], Leeds [21], Witt [52], and RIT-DuPont [7], which include uniformly colored patches used for the development of CIEDE2000. The comparison results are reported in Table 3. It is clear that two DNN-based CD models perform better than CIELAB on" + } + ] + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 324, + 258, + 529, + 346 + ], + "blocks": [ + { + "bbox": [ + 304, + 215, + 547, + 248 + ], + "lines": [ + { + "bbox": [ + 304, + 215, + 547, + 248 + ], + "spans": [ + { + "bbox": [ + 304, + 215, + 547, + 248 + ], + "type": "text", + "content": "Table 4. Generalizability evaluation of CD-Flow on the TID2013 subset, containing quantization noise, image color quantization with dither, and chromatic aberration artifacts" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 324, + 258, + 529, + 346 + ], + "lines": [ + { + "bbox": [ + 324, + 258, + 529, + 346 + ], + "spans": [ + { + "bbox": [ + 324, + 258, + 529, + 346 + ], + "type": "table", + "html": "
Method\\( STRESS\\downarrow \\)PLCC↑SRCC↑
CIEDE2000 [29]18.2030.7300.751
PieAPP [44]20.9180.6200.653
LPIPS [56]15.4200.8160.804
DISTS [10]15.2350.8210.805
CD-Net [51]15.9620.8010.826
CD-Flow14.1100.8370.832
", + "image_path": "ca403d846bcf1801c47301bf09ccd50c2e6f9f006b7b11086534475f270afe8f.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 377, + 547, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 377, + 547, + 582 + ], + "spans": [ + { + "bbox": [ + 304, + 377, + 547, + 582 + ], + "type": "text", + "content": "the COM dataset, despite not being exposed to color patch data during training. Although underperforming CIE94 and CIEDE2000, CD-Flow exhibits better generalization compared to CD-Net. We then test the generalizability of the TID2013 subset, which contains three types of color-related distortions: quantization noise, image color quantization with dither, and chromatic aberration. Table 4 lists the comparison results. We find that CD-Flow performs much better than the best-performing CIELAB-based CD formula CIEDE2000, and is on par with general-purpose image quality methods LPIPS and DISTS, which may have been exposed to similar distortion appearances during training. All these results constitute supportive evidence that the multi-scale autoregressive normalizing flow provides a compelling embodiment of the hypothesis: \"color and form are inextricably interdependent as a unitray process of perceptual organization [19, 46].\"" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 548, + 715 + ], + "type": "text", + "content": "Visualization of Local CD Maps. We further visualize the multi-scale local CD maps generated by CD-Flow, as illustrated in Figure 3. Our observations are as follows. First, local CDs are generally small at the first three scales compared to those at later scales, indicating that CD-Flow indeed relies more on coarser-scale representations to assess perceptual CDs, as encouraged by the multi-scale training objective in Eq. (8). Second, interestingly, the effects of single-opponent and double-opponent cells emerge in the computation of CD-Flow. At coarser scales, local CDs are evaluated over a large image area, and exhibit reason-" + } + ] + } + ], + "index": 8 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22248" + } + ] + } + ], + "index": 9 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 51, + 72, + 544, + 194 + ], + "blocks": [ + { + "bbox": [ + 51, + 72, + 544, + 194 + ], + "lines": [ + { + "bbox": [ + 51, + 72, + 544, + 194 + ], + "spans": [ + { + "bbox": [ + 51, + 72, + 544, + 194 + ], + "type": "image", + "image_path": "1b90dc0ed4886c5ba4b8e3ab4ada4784068b756dd788d53896a3bbba408d89a0.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 203, + 546, + 228 + ], + "lines": [ + { + "bbox": [ + 46, + 203, + 546, + 228 + ], + "spans": [ + { + "bbox": [ + 46, + 203, + 546, + 228 + ], + "type": "text", + "content": "Figure 3. Multi-scale local CD maps generated by CD-Flow. First two columns: Input image pair " + }, + { + "bbox": [ + 46, + 203, + 546, + 228 + ], + "type": "inline_equation", + "content": "(\\pmb{x},\\pmb{y})" + }, + { + "bbox": [ + 46, + 203, + 546, + 228 + ], + "type": "text", + "content": ". Third to the eighth column: Six scales of CD maps generated by CD-Flow, with a warmer color indicating a larger local CD." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 62, + 289, + 272, + 358 + ], + "blocks": [ + { + "bbox": [ + 46, + 246, + 288, + 280 + ], + "lines": [ + { + "bbox": [ + 46, + 246, + 288, + 280 + ], + "spans": [ + { + "bbox": [ + 46, + 246, + 288, + 280 + ], + "type": "text", + "content": "Table 5. Ablation analysis of the number of flow steps in CD-Flow (i.e., the hyperparameter " + }, + { + "bbox": [ + 46, + 246, + 288, + 280 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 46, + 246, + 288, + 280 + ], + "type": "text", + "content": "), where the number of scales is fixed to six. The default setting is highlighted in boldface" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 62, + 289, + 272, + 358 + ], + "lines": [ + { + "bbox": [ + 62, + 289, + 272, + 358 + ], + "spans": [ + { + "bbox": [ + 62, + 289, + 272, + 358 + ], + "type": "table", + "html": "
# of flow stepsSTRESS↓PLCC↑SRCC↑
L = 221.6330.8370.826
L = 420.9940.8380.825
L = 818.4730.8710.865
L = 1617.7920.8790.870
", + "image_path": "01babba2c80dc525310c5601d4791d28c19da6b4e2816fd51dc93bd7e04e3000.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 70, + 422, + 264, + 491 + ], + "blocks": [ + { + "bbox": [ + 46, + 378, + 287, + 412 + ], + "lines": [ + { + "bbox": [ + 46, + 378, + 287, + 412 + ], + "spans": [ + { + "bbox": [ + 46, + 378, + 287, + 412 + ], + "type": "text", + "content": "Table 6. Ablation analysis of the number of scales in CD-Flow (i.e., the hyperparameter " + }, + { + "bbox": [ + 46, + 378, + 287, + 412 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 378, + 287, + 412 + ], + "type": "text", + "content": " ),where the number of flow steps is fixed to eight. The default setting is highlighted in boldface" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 70, + 422, + 264, + 491 + ], + "lines": [ + { + "bbox": [ + 70, + 422, + 264, + 491 + ], + "spans": [ + { + "bbox": [ + 70, + 422, + 264, + 491 + ], + "type": "table", + "html": "
# of scalesSTRESS↓PLCC↑SRCC↑
K = 224.6860.7600.732
K = 419.7300.8570.850
K = 618.4730.8710.865
K = 818.5240.8740.861
", + "image_path": "ecad96595d094d447478057a683bd801c29241448fbc0db0df23014acb07fda6.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 520, + 287, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 520, + 287, + 568 + ], + "spans": [ + { + "bbox": [ + 46, + 520, + 287, + 568 + ], + "type": "text", + "content": "able sensitivity, resembling single-opponent cells in V1. At finer scales, local CDs are more responsive to textures and boundaries, consistent with the response characteristics of double-opponent cells in V1." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 575, + 147, + 586 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 575, + 147, + 586 + ], + "spans": [ + { + "bbox": [ + 47, + 575, + 147, + 586 + ], + "type": "text", + "content": "4.3. Ablation Studies" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": "We conduct two ablation experiments to evaluate the key design choices of CD-Flow. First, we study the impact of the number of flow steps (i.e., the hyperparameter " + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": ") on the model performance. In Table 5, we find that the performance of CD-Flow increases with the number of flow steps, which in turn expands the non-linear representational capacity of CD-Flow. Nevertheless, too many steps may substantially increase the computational complexity, and negatively impact generalization as well due to potential overfitting. We select " + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "inline_equation", + "content": "L = 8" + }, + { + "bbox": [ + 46, + 594, + 288, + 713 + ], + "type": "text", + "content": " as the default setting to strike a" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 247, + 547, + 403 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 247, + 547, + 403 + ], + "spans": [ + { + "bbox": [ + 304, + 247, + 547, + 403 + ], + "type": "text", + "content": "good balance between model performance and complexity. Next, we examine the effect of the number of scales (i.e., the hyperparameter " + }, + { + "bbox": [ + 304, + 247, + 547, + 403 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 247, + 547, + 403 + ], + "type": "text", + "content": ") on the model performance. Table 6 reports the results. We find that increasing the number of scales from 2 to 6 significantly improves the CD assessment performance. However, further increasing " + }, + { + "bbox": [ + 304, + 247, + 547, + 403 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 247, + 547, + 403 + ], + "type": "text", + "content": " does not bring extra performance improvements, not as the case of increasing " + }, + { + "bbox": [ + 304, + 247, + 547, + 403 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 304, + 247, + 547, + 403 + ], + "type": "text", + "content": ". We attribute this to the setting of the current training input resolution (i.e., " + }, + { + "bbox": [ + 304, + 247, + 547, + 403 + ], + "type": "inline_equation", + "content": "768 \\times 768 \\times 3" + }, + { + "bbox": [ + 304, + 247, + 547, + 403 + ], + "type": "text", + "content": "). When " + }, + { + "bbox": [ + 304, + 247, + 547, + 403 + ], + "type": "inline_equation", + "content": "K = 8" + }, + { + "bbox": [ + 304, + 247, + 547, + 403 + ], + "type": "text", + "content": " and at the coarsest scale, we are essentially comparing the difference of two \"globally averaged\" color values over the entire images, which is less biologically plausible and less practically meaningful." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 415, + 378, + 427 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 415, + 378, + 427 + ], + "spans": [ + { + "bbox": [ + 306, + 415, + 378, + 427 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 436, + 545, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 436, + 545, + 604 + ], + "spans": [ + { + "bbox": [ + 304, + 436, + 545, + 604 + ], + "type": "text", + "content": "We have introduced CD-Flow, a normalizing flow-based CD metric for photographic images. Our approach is inspired by the scientific evidence that humans perceive color based on the interdependence of color and form in images. We utilized a multi-scale autoregressive normalizing flow to learn a coordinate transform, followed by computing the Euclidean distance in the transformed space. Remarkably, the learned feature transform enjoys four desirable properties: 1) consistent with the working mechanism of human color perception, 2) proper as a mathematical metric, 3) accurate to explain human data of perceptual CDs, and 4) robust to slight geometric distortions. We hope that the proposed CD-Flow can benefit related fields in imaging/illumination, vision science, and color science." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 616, + 404, + 629 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 616, + 404, + 629 + ], + "spans": [ + { + "bbox": [ + 306, + 616, + 404, + 629 + ], + "type": "text", + "content": "Acknowledgement" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 635, + 545, + 703 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 635, + 545, + 703 + ], + "spans": [ + { + "bbox": [ + 304, + 635, + 545, + 703 + ], + "type": "text", + "content": "We would like to thank Keshuo Xu for the technical support and code sharing. This work was supported in part by the National Natural Science Foundation of China under Grant 62071407, the Hong Kong ITC Innovation and Technology Fund (9440288), and the CUHKSZ University Development Fund (UDF01002437)." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "22249" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 134 + ], + "type": "text", + "content": "[1] Jyrki Alakuijala, Robert Obryk, Ostap Stoliarchuk, Zoltan Szabadka, Lode Vandevenne, and Jan Wassenberg. Guetzli: Perceptually guided JPEG encoder. arXiv preprint arXiv:1703.04421, 2017. 3, 5, 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 135, + 287, + 168 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 135, + 287, + 168 + ], + "spans": [ + { + "bbox": [ + 53, + 135, + 287, + 168 + ], + "type": "text", + "content": "[2] Liliana Albertazzi, Gert J. Van Tonder, and Dhanraj Vishwanath. Perception Beyond Inference: The Information Content of Visual Processes. MIT Press, 2011. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 168, + 288, + 223 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 168, + 288, + 223 + ], + "spans": [ + { + "bbox": [ + 53, + 168, + 288, + 223 + ], + "type": "text", + "content": "[3] Pontus Andersson, Jim Nilsson, Tomas Akenine-Moller, Magnus Oskarsson, Kalle Åström, and Mark D. Fairchild. FLIP: A difference evaluator for alternating images. ACM on Computer Graphics and Interactive Techniques, 3(2):1-23, 2020. 3, 5, 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 224, + 288, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 224, + 288, + 267 + ], + "spans": [ + { + "bbox": [ + 53, + 224, + 288, + 267 + ], + "type": "text", + "content": "[4] Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, and Jorn-Henrik Jacobsen. Invertible residual networks. In International Conference on Machine Learning, pages 573-582, 2019. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 268, + 288, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 268, + 288, + 323 + ], + "spans": [ + { + "bbox": [ + 53, + 268, + 288, + 323 + ], + "type": "text", + "content": "[5] Jens Behrmann, Paul Vicol, Kuan-Chieh Wang, Roger Grosse, and Jorn-Henrik Jacobsen. Understanding and mitigating exploding inverses in invertible neural networks. In International Conference on Artificial Intelligence and Statistics, pages 1792-1800, 2021. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 324, + 287, + 356 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 324, + 287, + 356 + ], + "spans": [ + { + "bbox": [ + 53, + 324, + 287, + 356 + ], + "type": "text", + "content": "[6] Ohad Ben-Shahar and Steven W. Zucker. Hue geometry and horizontal connections. *Neural Networks*, 17(5-6):753–771, 2004. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 357, + 288, + 411 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 357, + 288, + 411 + ], + "spans": [ + { + "bbox": [ + 53, + 357, + 288, + 411 + ], + "type": "text", + "content": "[7] Roy S. Berns, David H. Alman, Lisa Reniff, Gregory D. Snyder, and Mitchell R. Balonon-Rosen. Visual determination of suprathreshold color-difference tolerances using probit analysis. Color Research & Application, 16(5):297-316, 1991." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 412, + 288, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 412, + 288, + 456 + ], + "spans": [ + { + "bbox": [ + 53, + 412, + 288, + 456 + ], + "type": "text", + "content": "[8] Chun-Hsien Chou and Kuo-Cheng Liu. A fidelity metric for assessing visual quality of color images. In International Conference on Computer Communications and Networks, pages 1154-1159, 2007. 5, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 457, + 287, + 500 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 457, + 287, + 500 + ], + "spans": [ + { + "bbox": [ + 53, + 457, + 287, + 500 + ], + "type": "text", + "content": "[9] Frank J. J. Clarke, Roderick McDonald, and Bryan Rigg. Modification to the JPC79 colour-difference formula. Journal of the Society of Dyers and Colourists, 100(4):128-132, 1984. 1, 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 502, + 287, + 545 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 502, + 287, + 545 + ], + "spans": [ + { + "bbox": [ + 48, + 502, + 287, + 545 + ], + "type": "text", + "content": "[10] Keyan Ding, Kede Ma, Shiqi Wang, and Eero P. Simoncelli. Image quality assessment: Unifying structure and texture similarity. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(5):2567-2581, 2020. 3, 5, 6, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 546, + 287, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 546, + 287, + 590 + ], + "spans": [ + { + "bbox": [ + 48, + 546, + 287, + 590 + ], + "type": "text", + "content": "[11] Keyan Ding, Kede Ma, Shiqi Wang, and Eero P. Simoncelli. Comparison of full-reference image quality models for optimization of image processing systems. International Journal of Computer Vision, 129(4):1258-1281, 2021. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 590, + 287, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 590, + 287, + 624 + ], + "spans": [ + { + "bbox": [ + 48, + 590, + 287, + 624 + ], + "type": "text", + "content": "[12] Laurent Dinh, David Krueger, and Yoshua Bengio. NICE: Non-linear independent components estimation. In International Conference on Representations Workshops, 2015. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 624, + 287, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 287, + 657 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 287, + 657 + ], + "type": "text", + "content": "[13] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real NVP. In International Conference on Learning Representations, 2017. 2, 3, 4" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 658, + 287, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 287, + 690 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 287, + 690 + ], + "type": "text", + "content": "[14] Chen Gao, Karen Panetta, and Sos Agaian. No reference color image quality measures. In IEEE International Conference on Cybernetics, pages 243-248, 2013. 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 691, + 287, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 287, + 714 + ], + "type": "text", + "content": "[15] Pedro A. Garcia, Rafael Huertas, Manuel Melgosa, and Guihua Cui. Measurement of the relationship between perceived" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 35, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "and computed color differences. Journal of the Optical Society of America A, 24(7):1823-1829, 2007. 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 96, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 545, + 150 + ], + "type": "text", + "content": "[16] Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, and David Duvenaud. FFJORD: Free-form continuous dynamics for scalable reversible generative models. International Conference on Learning Representations, 2019. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 152, + 545, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 152, + 545, + 185 + ], + "spans": [ + { + "bbox": [ + 308, + 152, + 545, + 185 + ], + "type": "text", + "content": "[17] Thorsten Hansen, Maria Olkkonen, Sebastian Walter, and Karl R. Gegenfurtner. Memory modulates color appearance. Nature Neuroscience, 9(11):1367-1368, 2006. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 186, + 545, + 220 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 186, + 545, + 220 + ], + "spans": [ + { + "bbox": [ + 308, + 186, + 545, + 220 + ], + "type": "text", + "content": "[18] Guowei Hong and Ming Ronnier Luo. New algorithm for calculating perceived colour difference of images. The Imaging Science Journal, 54(2):86-91, 2006. 2, 5, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 222, + 545, + 243 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 222, + 545, + 243 + ], + "spans": [ + { + "bbox": [ + 308, + 222, + 545, + 243 + ], + "type": "text", + "content": "[19] Gaetano Kanizsa. Organization in Vision: Essays on Gestalt Perception. Praeger Publishers, 1979. 1, 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 245, + 524, + 256 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 245, + 524, + 256 + ], + "spans": [ + { + "bbox": [ + 308, + 245, + 524, + 256 + ], + "type": "text", + "content": "[20] David Katz. The World of Colour. Routledge, 1935. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 258, + 545, + 290 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 258, + 545, + 290 + ], + "spans": [ + { + "bbox": [ + 308, + 258, + 545, + 290 + ], + "type": "text", + "content": "[21] Dong-Ho Kim. New weighting functions for the modified CIELAB colour-difference formulae. Textile Coloration and Finishing, 9(6):51-57, 1997. 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 291, + 545, + 325 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 291, + 545, + 325 + ], + "spans": [ + { + "bbox": [ + 308, + 291, + 545, + 325 + ], + "type": "text", + "content": "[22] Durk P. Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 convolutions. Neural Information Processing Systems, 2018. 3, 4, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 326, + 545, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 326, + 545, + 380 + ], + "spans": [ + { + "bbox": [ + 308, + 326, + 545, + 380 + ], + "type": "text", + "content": "[23] Jakob Kruse, Gianluca Detommaso, Ullrich Kothe, and Robert Scheicl. HINT: Hierarchical invertible neural transport for density estimation and bayesian inference. AAAI Conference on Artificial Intelligence, pages 8191-8199, 2021. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 382, + 545, + 404 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 382, + 545, + 404 + ], + "spans": [ + { + "bbox": [ + 308, + 382, + 545, + 404 + ], + "type": "text", + "content": "[24] Edwin H. Land. The retina theory of color vision. Scientific American, 237(6):108-129, 1977. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 406, + 545, + 460 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 406, + 545, + 460 + ], + "spans": [ + { + "bbox": [ + 308, + 406, + 545, + 460 + ], + "type": "text", + "content": "[25] Dohyoung Lee and Konstantinos N. Plataniotis. Towards a novel perceptual color difference metric using circular processing of hue components. In IEEE International Conference on Acoustics, Speech, & Signal Processing, pages 166-170, 2014. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 462, + 545, + 496 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 462, + 545, + 496 + ], + "spans": [ + { + "bbox": [ + 308, + 462, + 545, + 496 + ], + "type": "text", + "content": "[26] Sang-Mi Lee, John Haozhong Xin, and Stephen Westland. Evaluation of image similarity by histogram intersection. Color Research & Application, 30(4):265-274, 2005. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 497, + 545, + 519 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 497, + 545, + 519 + ], + "spans": [ + { + "bbox": [ + 308, + 497, + 545, + 519 + ], + "type": "text", + "content": "[27] Peter Lennie. Color coding in the cortex. Color Vision: From Genes to Perception, pages 235-247, 1999. 1" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 521, + 545, + 553 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 521, + 545, + 553 + ], + "spans": [ + { + "bbox": [ + 308, + 521, + 545, + 553 + ], + "type": "text", + "content": "[28] Ming Ronnier Luo. Colour science: Past, present and future. Colour Imaging Vision and Technology, pages 381-404, 1999. 1" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 555, + 545, + 598 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 555, + 545, + 598 + ], + "spans": [ + { + "bbox": [ + 308, + 555, + 545, + 598 + ], + "type": "text", + "content": "[29] Ming Ronnier Luo, Guihua Cui, and Bryan Rigg. The development of the CIE 2000 colour-difference formula: CIEDE2000. Color Research & Application, 26(5):340-350, 2001. 1, 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 308, + 600, + 545, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 600, + 545, + 633 + ], + "spans": [ + { + "bbox": [ + 308, + 600, + 545, + 633 + ], + "type": "text", + "content": "[30] Ming Ronnier Luo and Bryan Rigg. Chromaticity-discrimination ellipses for surface colours. Color Research & Application, 11(1):25-42, 1986. 2" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 308, + 635, + 545, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 635, + 545, + 667 + ], + "spans": [ + { + "bbox": [ + 308, + 635, + 545, + 667 + ], + "type": "text", + "content": "[31] Ming Ronnier Luo and Bryan Rigg. BFD(l:c) colour-difference formula. Journal of the Society of Dyers and Colourists, 103(2):86-94, 1987. 1, 2, 7" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 308, + 669, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 669, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 669, + 545, + 713 + ], + "type": "text", + "content": "[32] Marc Mahy, Luc Van Eycken, and André Oosterlinck. Evaluation of uniform color spaces developed after the adoption of CIELAB and CIELUV. Color Research & Application, 19(2):105-121, 1994. 1, 2, 3" + } + ] + } + ], + "index": 34 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "22250" + } + ] + } + ], + "index": 36 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 73, + 287, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 48, + 73, + 287, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 73, + 287, + 105 + ], + "spans": [ + { + "bbox": [ + 48, + 73, + 287, + 105 + ], + "type": "text", + "content": "[33] Roderick McDonald. Industrial pass/fail colour matching. Journal of the Society of Dyers and Colourists, 96(7):372-376, 1980. 1, 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 107, + 287, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 107, + 287, + 140 + ], + "spans": [ + { + "bbox": [ + 48, + 107, + 287, + 140 + ], + "type": "text", + "content": "[34] Roderick McDonald and Kenneth J. Smith. CIE94-A new colour-difference formula. Journal of the Society of Dyers and Colourists, 111(12):376-379, 1995. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 141, + 287, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 141, + 287, + 184 + ], + "spans": [ + { + "bbox": [ + 48, + 141, + 287, + 184 + ], + "type": "text", + "content": "[35] Anish Mittal, Anush Krishna Moorthy, and Alan C. Bovik. No-reference image quality assessment in the spatial domain. IEEE Transactions on Image Processing, 21(12):4695-4708, 2012. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 186, + 287, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 186, + 287, + 218 + ], + "spans": [ + { + "bbox": [ + 48, + 186, + 287, + 218 + ], + "type": "text", + "content": "[36] Anish Mittal, Rajiv Soundararajan, and Alan C. Bovik. Making a \"completely blind\" image quality analyzer. IEEE Signal Processing Letters, 20(3):209-212, 2012. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 220, + 287, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 220, + 287, + 251 + ], + "spans": [ + { + "bbox": [ + 48, + 220, + 287, + 251 + ], + "type": "text", + "content": "[37] Ken Nakayama and Shinsuke Shimojo. Experiencing and perceiving visual surfaces. Science, 257(5075):1357-1363, 1992. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 254, + 287, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 254, + 287, + 297 + ], + "spans": [ + { + "bbox": [ + 48, + 254, + 287, + 297 + ], + "type": "text", + "content": "[38] Benhur Ortiz-Jaramillo, Asli Kumcu, Ljiljana Platisa, and Wilfried Philips. Evaluation of color differences in natural scene color images. Signal Processing: Image Communication, 71:128-137, 2019. 1, 2, 5, 6" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 299, + 287, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 299, + 287, + 352 + ], + "spans": [ + { + "bbox": [ + 48, + 299, + 287, + 352 + ], + "type": "text", + "content": "[39] Sonia Ouni, Ezzeddine Zagrouba, Majed Chambah, and Michel Herbin. A new spatial colour metric for perceptual comparison. In International Conference on E-Systems Engineering, Communication and Information, pages 413–428, 2008. 2, 5, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 354, + 287, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 354, + 287, + 408 + ], + "spans": [ + { + "bbox": [ + 48, + 354, + 287, + 408 + ], + "type": "text", + "content": "[40] George Papamakarios, Eric T. Nalisnick, Danilo Jimenez Rezende, Shakir Mohamed, and Balaji Lakshminarayanan. Normalizing flows for probabilistic modeling and inference. Journal of Machine Learning Research, 22(57):1-64, 2021. 5" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 411, + 287, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 411, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 48, + 411, + 287, + 453 + ], + "type": "text", + "content": "[41] Marius Pedersen and Jon Yngve Hardeberg. A new spatial hue angle metric for perceptual image difference. In International Workshop on Computational Color Imaging, pages 81-90, 2009. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 455, + 287, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 455, + 287, + 520 + ], + "spans": [ + { + "bbox": [ + 48, + 455, + 287, + 520 + ], + "type": "text", + "content": "[42] Nikolay Ponomarenko, Lina Jin, Oleg Ieremeiev, Vladimir Lukin, Karen Egiazarian, Jaakko Astola, Benoit Vozel, Kacem Chehdi, Marco Carli, Federica Battisti, and Chung-Chieh Jay Kuo. Image database TID2013: Peculiarities, results and perspectives. Signal Processing: Image Communication, 30:57-77, 2015. 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 522, + 287, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 522, + 287, + 544 + ], + "spans": [ + { + "bbox": [ + 48, + 522, + 287, + 544 + ], + "type": "text", + "content": "[43] Charles A. Poynton. A Technical Introduction to Digital Video. John Wiley & Sons, Inc., 1996. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 545, + 287, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 545, + 287, + 597 + ], + "spans": [ + { + "bbox": [ + 48, + 545, + 287, + 597 + ], + "type": "text", + "content": "[44] Ekta Prashnani, Hong Cai, Yasamin Mostofi, and Pradeep Sen. PieAPP: Perceptual image-error assessment through pairwise preference. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1808-1817, 2018. 5, 6, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 601, + 287, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 601, + 287, + 622 + ], + "spans": [ + { + "bbox": [ + 48, + 601, + 287, + 622 + ], + "type": "text", + "content": "[45] Alan R. Robertson. The CIE 1976 color-difference formulae. Color Research & Application, 2(1):7-11, 1977. 5, 6, 7" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 624, + 287, + 655 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 287, + 655 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 287, + 655 + ], + "type": "text", + "content": "[46] Robert Shapley and Michael J. Hawken. Color in the cortex: Single-and double-opponent cells. Vision Research, 51(7):701-717, 2011. 1, 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 658, + 287, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 287, + 689 + ], + "type": "text", + "content": "[47] Steven K. Shevell and Frederick A. A. Kingdom. Color in complex scenes. Annual Review of Psychology, 59:143-166, 2008. 1" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 692, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 692, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 692, + 287, + 713 + ], + "type": "text", + "content": "[48] Dustin Tran, Keyon Vafa, Kumar Agrawal, Laurent Dinh, and Ben Poole. Discrete flows: Invertible generative models" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 508 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 545, + 95 + ], + "type": "text", + "content": "of discrete data. Advances in Neural Information Processing Systems, 2019. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 96, + 545, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 96, + 545, + 140 + ], + "spans": [ + { + "bbox": [ + 307, + 96, + 545, + 140 + ], + "type": "text", + "content": "[49] Zhou Wang, Alan C. Bovik, Hamid R. Sheikh, and Eero P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing, 13(4):600-612, 2004. 2, 3, 5, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 141, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 141, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 141, + 545, + 184 + ], + "type": "text", + "content": "[50] Zhou Wang and Eero P. Simoncelli. Reduced-reference image quality assessment using a wavelet-domain natural image statistic model. In Human Vision and Electronic Imaging X, pages 149-159, 2005. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 186, + 545, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 545, + 239 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 545, + 239 + ], + "type": "text", + "content": "[51] Zhihua Wang, Keshuo Xu, Yang Yang, Jianlei Dong, Shuhang Gu, Lihao Xu, Yuming Fang, and Kede Ma. Measuring perceptual color differences of smartphone photography. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023. 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 241, + 545, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 241, + 545, + 274 + ], + "spans": [ + { + "bbox": [ + 307, + 241, + 545, + 274 + ], + "type": "text", + "content": "[52] Klaus Witt. Geometric relations between scales of small colour differences. Color Research & Application, 24(2):78-92, 1999. 7" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 276, + 545, + 319 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 276, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 307, + 276, + 545, + 319 + ], + "type": "text", + "content": "[53] Jason J. Yu, Konstantinos G. Derpanis, and Marcus A. Brubaker. Wavelet flow: Fast training of high resolution normalizing flows. Advances in Neural Information Processing Systems, pages 6184-6196, 2020. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 320, + 545, + 364 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 320, + 545, + 364 + ], + "spans": [ + { + "bbox": [ + 307, + 320, + 545, + 364 + ], + "type": "text", + "content": "[54] Ming Yu, Huijuan Liu, Yingchun Guo, and Dongming Zhao. A method for reduced-reference color image quality assessment. In International Conference on Image and Signal Processing, pages 1-5, 2009. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 365, + 545, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 365, + 545, + 408 + ], + "spans": [ + { + "bbox": [ + 307, + 365, + 545, + 408 + ], + "type": "text", + "content": "[55] Lin Zhang, Ying Shen, and Hongyu Li. VSI: A visual saliency-induced index for perceptual image quality assessment. IEEE Transactions on Image Processing, 23(10):4270-4281, 2014. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 410, + 545, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 410, + 545, + 464 + ], + "spans": [ + { + "bbox": [ + 307, + 410, + 545, + 464 + ], + "type": "text", + "content": "[56] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In IEEE Conference on Computer Vision and Pattern Recognition, pages 586-595, 2018. 3, 5, 6, 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 466, + 545, + 508 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 466, + 545, + 508 + ], + "spans": [ + { + "bbox": [ + 307, + 466, + 545, + 508 + ], + "type": "text", + "content": "[57] Xuemei Zhang and Brian A. Wandell. A spatial extension of CIELAB for digital color-image reproduction. Journal of the Society for Information Display, 5(1):61-63, 1997. 2, 5, 6" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22251" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning a Depth Covariance Function/ce47874e-679a-4e09-bfc4-c70c4939d294_content_list.json b/2023/Learning a Depth Covariance Function/ce47874e-679a-4e09-bfc4-c70c4939d294_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..77c19733f9d7db2a3ba9bf347dbbdab91b0e29da --- /dev/null +++ b/2023/Learning a Depth Covariance Function/ce47874e-679a-4e09-bfc4-c70c4939d294_content_list.json @@ -0,0 +1,1572 @@ +[ + { + "type": "text", + "text": "Learning a Depth Covariance Function", + "text_level": 1, + "bbox": [ + 285, + 130, + 684, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Eric Dexheimer and Andrew J. Davison Dyson Robotics Lab, Imperial College London {e.dexheimer21, a.davison}@imperial.ac.uk", + "bbox": [ + 272, + 181, + 643, + 233 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 268, + 313, + 284 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We propose learning a depth covariance function with applications to geometric vision tasks. Given RGB images as input, the covariance function can be flexibly used to define priors over depth functions, predictive distributions given observations, and methods for active point selection. We leverage these techniques for a selection of downstream tasks: depth completion, bundle adjustment, and monocular dense visual odometry.", + "bbox": [ + 75, + 300, + 473, + 422 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 450, + 209, + 467 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Inferring the 3D structure of the world from 2D images is an essential computer vision task. In recent years, there has been significant interest in combining principled multiple view geometry with data-driven priors. Learning-based methods that predict geometry provide a prior directly over the latent variables, which avoids the ill-posed configurations of traditional methods. However, direct, overconfident priors may prevent realization of the true 3D structure when multi-view geometry is well-defined. For example, data-driven methods have shown tremendous promise in monocular depth estimation, but often lack consistency when fusing information into 3D space.", + "bbox": [ + 75, + 477, + 468, + 657 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Thus far, designing a unified framework that combines the best of learning and optimization methods has proven challenging. Recent data-driven methods have attempted to relax rigid geometric constraints by also predicting low-dimensional subspaces or per-pixel uncertainties. Fixing capacity during training is often wasteful or inflexible at test time, while per-pixel residual distributions typically explain away the limitations of the model instead of the relationship between errors. In reality, the 3D world is anywhere from simple to complex, and an ideal system should explicitly adapt its capacity and correlation based on the scene.", + "bbox": [ + 75, + 657, + 468, + 824 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, rather than directly predicting geometry from images, we propose learning a depth covariance function. Given an RGB image, our method predicts how the depths of any two pixels relate. To achieve this, a neural network transforms color information to a feature space, and", + "bbox": [ + 75, + 825, + 468, + 902 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/bcc7a3f45e349900d139d1bd5b7122baaa2c5bcf9bc01e4fe28c8f565124e518.jpg", + "image_caption": [ + "Figure 1. Example monocular reconstruction using the depth covariance for bundle adjustment and dense depth prediction from three seconds (100 frames) of TUM data. Three representative images and the mesh from TSDF fusion of the depth predictions are shown. Each frame leverages the learned covariance function to model geometric correlation between pairs of scene points." + ], + "image_footnote": [], + "bbox": [ + 506, + 267, + 890, + 518 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "a Gaussian process (GP) models a prior distribution given these features and a base kernel function. The distinction between image processing and the prior enables promoting locality and granting flexible capacity at test time. Locality avoids over-correlating pixels on distinct structures, while adaptive capacity permits tuning the complexity of our subspace to the content of the viewed scene.", + "bbox": [ + 496, + 642, + 893, + 747 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Learning this flexible, high-level prior allows for balancing data-driven methods with test-time optimization that can be applied to a variety of geometric vision tasks. Furthermore, the covariance function is agnostic to the 3D representation as it does not directly learn a geometric output. Depth maps may be requested by conditioning on observations, but the prior may also be leveraged for inferring the desired latent 3D representation. In Figure 1, we illustrate depth covariance along with an example of bundle adjustment, dense depth prediction, and multi-view fusion.", + "bbox": [ + 496, + 750, + 893, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "13122", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In summary, our key contributions are:", + "bbox": [ + 96, + 90, + 354, + 104 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- A framework for learning the covariance function by selecting a depth representation, a base kernel function, and a scalable optimization objective", + "- Application of the prior to depth completion, bundle adjustment, and monocular dense visual odometry" + ], + "bbox": [ + 94, + 114, + 467, + 198 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 76, + 210, + 218, + 226 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "View-based Priors for Geometric Inference Traditionally, sparse reconstruction methods have ignored per-image correlations [37], [26], while dense methods [27], [13] have used naive priors based on neighboring pixels. With the availability of depth data and the rise of deep learning, a variety of learned depth priors have been proposed. Although monocular depth estimation methods that directly predict geometry [12], [15], [32] have demonstrated great progress, they may predict irreparable mistakes and do not allow for handling ambiguities. Utilizing surface normals may refine these depth estimates [1], but the improvement is still subject to errors from the initial depth prediction. For this reason, we focus on approaches that balance learned priors and test-time optimization. Popular approaches include predicting low-dimensional latent codes [3], [9] and generating a basis of depth maps [42], [16]. While these methods utilize single image information to reduce the dimensionality of depth map inference, they fix capacity during training, produce overly smooth depth maps, and create over-correlated global changes of the depth map. Predicting mesh vertices in the image plane [4] permits distribution of capacity and optimizing depth, but the number of latent variables is still fixed and an explicit mesh representation cannot easily model complex scenes.", + "bbox": [ + 76, + 236, + 468, + 598 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Instead of predicting geometry directly, we focus on predicting geometric correlation. By decoupling the pipeline into an image processing network and a GP, the network is not responsible for learning geometry directly and we do not need to fix the geometric capacity. The dimensionality of the subspace may be adapted for representing low-rank scenes more compactly and complex geometry with high-fidelity. Lastly, we use a covariance function with locality to bias learning towards local appearance information.", + "bbox": [ + 75, + 598, + 467, + 733 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Learning Covariance Functions Choosing covariance functions and performing model selection for GPs is a well-studied topic [34]. While stationary kernels have been explored for merging LiDAR observations with monocular depth estimates [49], more expressive kernels are required for sparse observations. Nonstationary covariance functions using local Gaussian parameterizations [29] have shown potential in robotic terrain mapping [22]. However, since GP model selection occurs per data example, optimizing hyperparameters is challenging, and novel nonstationary kernels have been proposed to limit flexibility [5].", + "bbox": [ + 75, + 734, + 467, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Rather than performing model selection per image, we are inspired by deep kernel learning (DKL) [47], which leverages training data to predict kernel hyperparameters. Alternatively, this may be viewed as meta-learning, where each task is an RGB-depth example [31]. To mitigate overcorrelation when using stationary kernels in feature space as proposed in DKL [28], we utilize local information in pixel space [29] to model surface geometry. While deep nonstationary kernel regression has been explored for depth completion [23], GPs balance data fit and model complexity during training, and the uncertainty estimates are conducive to decision-making and inference in optimization frameworks. Residual Covariances in Computer Vision Uncertainties in machine learning are often divided between two types: model uncertainty and data uncertainty [19]. For vision problems, model uncertainty is often ignored due to the availability of data and for tractable optimization. Residual uncertainty is usually predicted from a network as a per-pixel variance due to high-dimensionality, but the assumption of independence ignores the correlation present in images. For example, variational autoencoders (VAEs) [20] with a diagonal likelihood output overly smooth mean predictions and unnatural samples with salt-and-pepper noise. To introduce correlation in the likelihood, structured uncertainty prediction networks (SUPNs) predict a full information matrix by defining a graph topology consisting of specific neighbors [11]. Since this matrix is sparse, it can be efficiently inverted to obtain the covariance matrix. This has also been leveraged to distill monocular depth prediction ensembles into a single SUPN [40].", + "bbox": [ + 496, + 90, + 890, + 542 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Similar to SUPNs, we move beyond diagonal covariance approximations that lack correlation. However, we learn a covariance function which does not require a predefined graph topology and allows for long-range correlations beyond neighboring pixels. Furthermore, the marginal distribution of any set of variables may be examined without constructing the full joint distribution, which is of great interest for compact inference in geometric vision.", + "bbox": [ + 496, + 542, + 890, + 664 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Learning a Depth Covariance Function", + "text_level": 1, + "bbox": [ + 498, + 676, + 852, + 694 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Given an image, we model a distribution over possible depth functions via a Gaussian process (GP) [34]. With the input domain being normalized image coordinates $\\mathbf{x}_i = (u_i,v_i)$ where $u_{i},v_{i}\\in [-1,1]$ , the outputs for any finite set of inputs are jointly Gaussian. The GP is then defined by a mean function $m(\\mathbf{x})$ and a covariance function $k(\\mathbf{x},\\mathbf{x}^{\\prime})$ :", + "bbox": [ + 496, + 702, + 890, + 792 + ], + "page_idx": 1 + }, + { + "type": "equation", + "text": "\n$$\nf (\\mathbf {x}) \\sim \\mathcal {G P} (m (\\mathbf {x}), k (\\mathbf {x}, \\mathbf {x} ^ {\\prime})). \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 591, + 800, + 890, + 816 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "For learning the parameters of this GP, we need to select the representation of depth, the mean and covariance functions, and the optimization objective. We will outline our decisions in the following sections, but there exist many ways to define this prior.", + "bbox": [ + 496, + 824, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "13123", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3.1. Depth Representation", + "text_level": 1, + "bbox": [ + 76, + 90, + 281, + 107 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "When predicting depth, we often want to attenuate errors further from the camera view, so that nearby structures are prioritized. Examples of such representations include inverse depth [7], disparity [32], and log-depth [12]. We select log-depth for two reasons. First, the log-normal distribution is more suitable for skewed distributions [35], as the range of the GP is unbounded. Representing depth or inverse depth with a normal distribution would require ad hoc truncation, as depth functions could move behind the camera. Second, we represent depth with relative scale, as the absolute scale ambiguity in monocular images accounts for much of the error in learned depth prediction [12]. We can then focus on learning the relationship between predictions. In the log-depth formulation, a constant mean $m(\\mathbf{x}_i) = m$ directly corresponds to a scale variable. Given a log-depth observation $\\mathbf{y}_i = \\log \\mathbf{d}_i$ , we can adjust its scale via this mean:", + "bbox": [ + 75, + 113, + 473, + 369 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\ne ^ {\\mathbf {y} _ {i} - m} = e ^ {\\mathbf {y} _ {i}} e ^ {- m} = C e ^ {\\mathbf {y} _ {i}} = C \\mathbf {d} _ {i}. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 156, + 383, + 468, + 400 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In other words, the median of the depth distribution is controlled by the mean of the log-normal distribution. During training, similar to [12], we may find the optimal scale $m$ that minimizes our data loss. In addition, the log-depth representation allows covariance to be in relative scale. At test time, the scale may be fixed if known, or jointly optimized with other variables. Learning a more expressive mean function per image would be useful in the absence of any depth observations, but introduces many degrees of freedom. For GPs, generalization is largely dependent on the covariance function, so we leave the mean to be a constant per image.", + "bbox": [ + 75, + 412, + 470, + 594 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Covariance Function", + "text_level": 1, + "bbox": [ + 76, + 604, + 274, + 619 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We would like to learn the parameters of a depth covariance function using pairs of RGB and depth images. By definition, the covariance function must be positive semidefinite (PSD). In our framework, an RGB image is fed into a convolutional neural network (CNN), which outputs features for a base covariance function. We model the depth observations as being jointly Gaussian so that the CNN and base kernel hyperparameters may be jointly learned.", + "bbox": [ + 75, + 628, + 468, + 750 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For the base covariance function, we would like to avoid over-correlation of independent structures. To achieve local influence, we use the family of nonstationary kernels described in [29]. Each point $\\mathbf{x}_i$ in an image can be viewed as a Gaussian distribution with a 2D kernel matrix $\\Sigma_i$ . Then, the covariance between two points is the convolution of these two densities over the input domain with a normalizing constant. Alternatively, this can be viewed as the similarity between distributions via the Bhattacharyya kernel [18]. Given an isotropic covariance function $R^S (\\sqrt{q})$ ,", + "bbox": [ + 75, + 750, + 470, + 902 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/08b72017400e437045c8a1d0b6e4fd6db6ed5424056f9787f748da7b2e06cb99.jpg", + "image_caption": [ + "Figure 2. Visualizing our depth covariance function: for every pixel of an input image, the trained network predicts a 2D kernel matrix. Here we show the covariance function between pairs of pixels in both matrix form and as edges in a graph, with the line thickness representing the magnitude of covariance." + ], + "image_footnote": [], + "bbox": [ + 501, + 87, + 893, + 314 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "the closed-form expression is:", + "bbox": [ + 500, + 419, + 702, + 434 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nk \\left(\\mathbf {x} _ {i}, \\mathbf {x} _ {j}\\right) = \\sigma_ {f} ^ {2} \\frac {\\left| \\Sigma_ {i} \\right| ^ {1 / 4} \\left| \\Sigma_ {j} \\right| ^ {1 / 4}}{\\left| \\Sigma_ {i} + \\Sigma_ {j} \\right| ^ {1 / 2}} R ^ {S} (\\sqrt {q}), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 526, + 441, + 890, + 476 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nq = \\left(\\mathbf {x} _ {i} - \\mathbf {x} _ {j}\\right) ^ {T} \\left(\\Sigma_ {i} + \\Sigma_ {j}\\right) ^ {- 1} \\left(\\mathbf {x} _ {i} - \\mathbf {x} _ {j}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 580, + 478, + 890, + 497 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\sigma_f^2$ is a learnable signal variance. To better handle discontinuities, we select the Matérn function as our isotropic covariance function. With the base covariance function requiring a 2D PSD matrix for each pixel, the CNN outputs three channels $(c_1, c_2, c_3)$ , which are parameterized given the positive diagonal and determinant constraints:", + "bbox": [ + 496, + 503, + 893, + 595 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\Sigma_ {i} = \\left[ \\begin{array}{c c} e ^ {c _ {1}} & \\tanh (c _ {3}) \\sqrt {e ^ {c _ {1}} * e ^ {c _ {2}}} \\\\ \\tanh (c _ {3}) \\sqrt {e ^ {c _ {1}} * e ^ {c _ {2}}} & e ^ {c _ {2}} \\end{array} \\right]. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 511, + 602, + 890, + 638 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "An example of five kernel matrices and the marginal covariance in matrix and graph form is shown in Figure 2. We use a UNet architecture [36] and output features at different levels for a multi-scale loss. Each scale also has its own GP hyperparameters, signal variance $\\sigma_f^2$ and noise variance $\\sigma_n^2$ . Depth maps are coarsened for lower-levels, so finer levels are weighted higher during the total loss calculation.", + "bbox": [ + 496, + 643, + 893, + 750 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "While a more global covariance function, such as a squared-exponential over output features, would provide additional flexibility, we avoid this for two reasons. First, changes in depths on one part of the scene may have significant influence on a completely unrelated part, as seen in [3]. Second, deep kernel learning using this setup is unstable and biased towards over-correlating the input domain [28]. By directly using pixel coordinates in our covariance function, we restrict changes in geometry to be local and bias the network to learn local appearance information.", + "bbox": [ + 496, + 750, + 893, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "13124", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.3. Optimization Objective", + "text_level": 1, + "bbox": [ + 76, + 90, + 294, + 107 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In GP literature, model selection is performed by minimizing the negative log marginal likelihood (NLML)", + "bbox": [ + 76, + 113, + 468, + 143 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} - \\log p (\\mathbf {y} | X) = \\frac {1}{2} (\\mathbf {y} - m) ^ {T} \\left(K _ {\\mathrm {f f}} + \\sigma_ {n} ^ {2} I\\right) ^ {- 1} (\\mathbf {y} - m) \\\\ + \\frac {n}{2} \\log 2 \\pi + \\frac {1}{2} \\log | K _ {\\mathrm {f f}} + \\sigma_ {n} ^ {2} I |, \\tag {6} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 96, + 152, + 468, + 212 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where the matrix $K_{\\mathrm{ff}}$ is a PSD matrix with entries defined by the covariance function. The first term is often referred to as the \"data fit\", while the third term is the \"complexity penalty\". In our scenario, the complexity term is minimized by correlating points, while the data term ensures the ground-truth depth map is plausible given the mean and predicted covariance.", + "bbox": [ + 75, + 218, + 468, + 323 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "However, the marginal likelihood for a GP requires a $O(N^3)$ matrix inversion. For pixels in an image, this becomes intractable. Since depth images are relatively low-rank, we use a sparse GP approximation [2] during training by randomly sampling inducing points for a Nystrom approximation to the full covariance matrix. Given the covariance matrix for the inducing points $K_{\\mathrm{uu}}$ and the cross-covariance between the inducing points and the entire domain $K_{\\mathrm{uf}}$ , we have:", + "bbox": [ + 75, + 324, + 468, + 460 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nK _ {\\mathrm {f f}} \\approx \\tilde {K} _ {\\mathrm {f f}} \\triangleq K _ {\\mathrm {f u}} K _ {\\mathrm {u u}} ^ {- 1} K _ {\\mathrm {u f}}. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 183, + 468, + 468, + 486 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Specifically, we use the variational free energy (VFE) framework [45], which defines our training loss as", + "bbox": [ + 76, + 496, + 468, + 526 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {F} = \\frac {1}{2} (\\mathbf {y} - m) ^ {T} \\left(\\tilde {K} _ {\\mathrm {f f}} + \\sigma_ {n} ^ {2} I\\right) ^ {- 1} (\\mathbf {y} - m) + \\frac {n}{2} \\log 2 \\pi \\\\ + \\frac {1}{2} \\log | \\tilde {K} _ {\\mathrm {f f}} + \\sigma_ {n} ^ {2} I | + \\frac {1}{2 \\sigma_ {n} ^ {2}} \\operatorname {t r} \\left(K _ {\\mathrm {f f}} - \\tilde {K} _ {\\mathrm {f f}}\\right). \\tag {8} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 94, + 534, + 468, + 595 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The first three terms are the same as the original NLML, but using the approximate covariance, while the last term penalizes the conditional variances at all inputs given the inducing points, which only requires the diagonal of the full covariance matrix. Note that we also assume homoscedastic observation noise variance $\\sigma_{n}^{2}$ across the entire dataset.", + "bbox": [ + 75, + 604, + 468, + 696 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Leveraging a Depth Covariance Function", + "text_level": 1, + "bbox": [ + 76, + 707, + 446, + 724 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Given an image and its corresponding CNN output maps, we may leverage the covariance function by defining a prior over depth functions, conditioning on depth observations to yield a predictive distribution, and actively sampling pixel locations that minimize the variance of depth predictions.", + "bbox": [ + 75, + 733, + 468, + 808 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Gaussian Process Prior", + "text_level": 1, + "bbox": [ + 76, + 816, + 290, + 830 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Given a finite set of input points $X_{*}$ , we may define a Gaussian prior over their log-depths:", + "bbox": [ + 76, + 840, + 468, + 869 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {f} _ {*} \\sim \\mathcal {N} (m, K (X _ {*}, X _ {*})). \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 181, + 880, + 468, + 896 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/f8a1e88a7239dbf8b964ae433baa7caa90e786cfe230bbfaa0f7f271ef19accf.jpg", + "image_caption": [ + "Figure 3. Conditioning example for 128 samples. The posterior variance is high around edges and in areas lacking samples. The columns of $K_{\\mathrm{fn}}$ , or correlation maps, are shown for select points." + ], + "image_footnote": [], + "bbox": [ + 500, + 87, + 888, + 258 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The GP defines a prior over functions, and for any number of input points, we obtain a Gaussian prior. For geometric vision tasks, this prior can be leveraged for anything from sparse to dense methods. Due to the marginalization property of GPs, adding points to the indexing set will not change the marginal distribution of the existing set. Alternatively, the prior may be viewed as an image-conditioned regularizer of any desired latent geometry parameterization.", + "bbox": [ + 496, + 340, + 890, + 460 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Predictive Distribution", + "text_level": 1, + "bbox": [ + 500, + 469, + 710, + 483 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In many cases, log-depth observations $\\mathbf{y}$ , such as from RGB-D sensors or LiDAR, may be available. We can explicitly condition the prior on these observations to obtain a posterior distribution. The predictive mean $\\mathbf{f}_{*}$ and covariance $\\Sigma_{*}$ given $N$ samples is:", + "bbox": [ + 498, + 493, + 890, + 568 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {f} _ {*} = m + K _ {\\mathrm {f n}} \\left(K _ {\\mathrm {n n}} + \\sigma_ {n} ^ {2} I\\right) ^ {- 1} (\\mathbf {y} - m), \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 578, + 890, + 595 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\Sigma_ {*} = K _ {\\mathrm {f f}} - K _ {\\mathrm {f n}} \\left(K _ {\\mathrm {n n}} + \\sigma_ {n} ^ {2} I\\right) ^ {- 1} K _ {\\mathrm {n f}}. \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 560, + 599, + 890, + 616 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The predictive mean is a linear function in terms of the observations $\\mathbf{y}$ , so test-time inference of latent depths is efficient. If a full covariance is not required, only certain blocks or per-pixel variances need to be calculated. Note that the covariance depends only on the observation coordinates and not the observed values. We visualize the predictive mean, variance, and correlation maps for an example in Figure 3.", + "bbox": [ + 496, + 626, + 890, + 732 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.3. Active Point Selection", + "text_level": 1, + "bbox": [ + 500, + 741, + 702, + 755 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For dense reconstruction, it is beneficial to construct a compact representation that can achieve high-fidelity results. As mentioned previously, the predictive covariance depends only on the RGB image and locations of depth observations, but not on the observations themselves. By viewing the CNN as a meta-learned initialization of the nonstationary kernel parameters, we may use 2D observations as a proxy for the complexity of 3D geometry. Active selection of informative pixels up to a desired variance permits", + "bbox": [ + 496, + 763, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "13125", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/c43530ae22fbf80585b1faac80725a27dea23510509d4cdbe690419100b89743.jpg", + "image_caption": [ + "Figure 4. Qualitative comparison of random and active sampling given 32 sample selections. Random sampling misrepresents depth at the top of the image, while active sampling focuses on the chair geometry and avoids redundant samples on the floor." + ], + "image_footnote": [], + "bbox": [ + 83, + 87, + 464, + 252 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "representing less complex scenes with fewer samples and allocating capacity towards high-frequency geometry.", + "bbox": [ + 75, + 345, + 468, + 377 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Inspired by sensor placement literature [17], an entropy-based criterion is used to select informative pixels. In the greedy-case, this simplifies to selecting the input point with the highest conditional variance at each step. Since this requires computing the conditional variance for each newly added point, we leverage incremental updates to the variance and Cholesky factorization of the training covariance matrix [33]. By decoupling the neural network and GP, we do not require any additional network passes.", + "bbox": [ + 75, + 377, + 468, + 513 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "A qualitative example of active sampling is shown in Figure 4. Random sampling severely misrepresents depth of table and chairs, which all appear at similar depths. Active sampling focus samples around the thin chair edge near the image, while also avoiding oversampling on the floor. Furthermore, active sampling avoids missing the top section of the image, so that the table and chairs are well-represented.", + "bbox": [ + 75, + 512, + 468, + 617 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Applications", + "text_level": 1, + "bbox": [ + 76, + 631, + 207, + 648 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We apply depth covariance to three fundamental geometric vision tasks: depth completion, bundle adjustment, and monocular dense visual odometry (DVO). For depth completion, we use the NYUv2 benchmark [39] and the train-test splits from [25]. For bundle adjustment and DVO, we train the covariance function on ScanNet [10], and evaluate on the TUM RGB-D dataset [41]. For the UNet, we use an input size of $256 \\times 192$ , 16 channels after the first layer, 5 downsampling steps, and 4 output levels. This results in roughly 9 million parameters.", + "bbox": [ + 75, + 656, + 468, + 808 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1. Depth Completion", + "text_level": 1, + "bbox": [ + 76, + 816, + 254, + 833 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Depth completion is a fundamental task that will be leveraged for additional applications. We may directly condition on sparse observations as described in Section 4.2 to obtain a dense depth map and uncertainties.", + "bbox": [ + 75, + 839, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1.1 Completion Accuracy and Error", + "text_level": 1, + "bbox": [ + 500, + 90, + 781, + 106 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Since our method does not predict a specific instance of geometry, we report errors with respect to the GP posterior mean. We compare to the foundational Sparse-to-Dense (S2D) [25], deep kernel regression with (KernelNet+R) and without refinement (KernelNet) [23], and a recent state-of-the-art network RigNet [48]. Results on the validation set with 500 random samples are shown in Table 1.", + "bbox": [ + 496, + 114, + 890, + 220 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/5f0a7178d64fc7d9eab34e319939895f1292d58d10bee279b18f6a7d3c5b3f86.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodError (↓) RMSEAccuracy (↑)
δ1.02δ1.05δ1.10δ1.25δ1.252
S2D [25]0.204---97.899.6
KernelNet [23]0.19865.582.991.697.799.8
KernelNet+R [23]0.11184.894.197.499.399.9
RigNet [48]0.090---99.799.9
Ours0.15781.292.697.199.499.9
", + "bbox": [ + 501, + 229, + 890, + 340 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "KernelNet also predicts three feature maps for 2D covariance parameters, but we achieve better performance under the GP framework. We achieve comparable accuracy to state-of-the-art methods KernelNet $^+$ R and RigNet while using fewer parameters. We also do not convert the problem into classification [23] or have complex forward passes with iterative layers [48]. The depth covariance outperforms methods with similar UNet architectures in RMSE. While the error is not state-of-the-art, we use a lightweight network and do not train specifically for the single task of depth completion with the number of samples known a priori.", + "bbox": [ + 496, + 378, + 890, + 545 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We also explore varying sparsity, as depth completion networks are trained for a specific number of samples. A comparison of RMSE for a varying number of test samples is shown in Table 2. Depth covariance outperforms traditional depth completion methods for sparse inputs, and is competitive with SpAgNet [8] which is designed specifically for these cases and contains an additional non-local spatial propagation layer. We note that for error metrics, we simply use the posterior mean for comparison. However, the GP provides a distribution over depths, not just a single instance, and we will demonstrate additional capabilities such as active decision-making and inferring latent geometry when direct observations are not present.", + "bbox": [ + 496, + 545, + 890, + 742 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/76877701ba3558dcdad372c0540b063b7b8e6c65415bb05bbb4a4bda4a2723e1.jpg", + "table_caption": [ + "Table 1. Depth completion on NYUv2 with 500 sampled points." + ], + "table_footnote": [], + "table_body": "
# Samples550100200
CSPN [6]2.0630.8840.3880.177
NLSPN [30]1.0330.4230.2460.142
SpAgNet [8]0.4670.2720.2090.155
Ours0.7170.2980.2360.193
", + "bbox": [ + 535, + 751, + 856, + 830 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 2. Depth completion RMSE (m) on NYUv2 with a varying number of input samples. The best result is in bold, while the second best is underlined.", + "bbox": [ + 496, + 840, + 890, + 882 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "13126", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/746e05b8afe5e74e2a427f7552750362009c18c9a72536950066bd0208c83985.jpg", + "image_caption": [ + "Figure 5. Calibration plots of varying posterior marginal covariance block dimensions $D$ on NYUv2 depth completion. The ideal calibration is $y = x$ , where the observed confidence matches expected confidence. The region above the line indicates model under-confidence, while the area below signals over-confidence." + ], + "image_footnote": [], + "bbox": [ + 102, + 88, + 444, + 287 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1.2 Posterior Uncertainty", + "text_level": 1, + "bbox": [ + 76, + 393, + 282, + 409 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Calibrated uncertainties are beneficial for balancing multiple views and sensors in optimization. Under-confidence does not account for the full information given by the constraints, while over-confidence may bias the solution. Given that the GP provides uncertainties in addition to the mean, we evaluate the calibration properties for the depth completion setup from Section 5.1.", + "bbox": [ + 75, + 417, + 468, + 523 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Since most methods typically estimate per-pixel observation noise, we assess the effect of including off-diagonal terms. Calibration plots measure how well the model's predicted confidence matches the expected confidence. To extend calibration plots for regression beyond variance [21], we extract marginal covariances of varying dimension $D$ and calculate Mahalanobis distances using this block-diagonal approximation to the full covariance matrix. We then count the frequency of distances below varying chi-square thresholds that define the expected confidence. In the variance case with $D = 1$ , residuals do not affect each other. We plot the results in Figure 5 along with the RMSE against the ideal calibration, where the empirical confidence matches the observed confidence. Including more off-diagonal terms improves the model calibration, and significantly reduces the under-confidence of the model.", + "bbox": [ + 75, + 523, + 468, + 765 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1.3 Active Sampling Evaluation", + "text_level": 1, + "bbox": [ + 76, + 785, + 326, + 801 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "While the location of depth samples is often not a free variable, such as from LiDAR or depth sensors, in monocular vision, we may wish to actively estimate the depths of certain pixels to improve downstream tasks such as dense reconstruction. We investigate whether the meta-learned covariance parameters may be leveraged for selecting more", + "bbox": [ + 75, + 809, + 468, + 901 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/7d04aae8cc617b7ff11d6f6c42c59d6fd6a049d674224b878299be7d13d6e7c4.jpg", + "image_caption": [ + "Figure 6. Mean depth completion RMSE for random and active sampling with a varying number of samples. Green line shows mean percent improvement of active over random sampling." + ], + "image_footnote": [], + "bbox": [ + 501, + 89, + 890, + 232 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "informative pixels. We compare randomly sampling pixels uniformly against the greedy conditional variance as described in Section 4.3. The effect on depth error and the mean percentage improvement for a varying number of samples is shown in Figure 6. Note that the greedy active sampling consistently outperforms random sampling. For a large number of samples, the relative improvement of active sampling decreases as there is a sufficient number of observations. For a few samples, only coarse depth structure is retained, and the greedy selection occasionally encourages samples near the image boundaries. Beyond greedy selection, other sampling methods that explicitly minimize uncertainty over the whole domain may demonstrate improved performance at the expense of computational cost.", + "bbox": [ + 496, + 309, + 892, + 521 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Bundle Adjustment", + "text_level": 1, + "bbox": [ + 500, + 529, + 687, + 544 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Bundle adjustment is the foundation for many vision pipelines, and when successful, it produces very accurate camera poses. However, the standard formulation of assuming independence between observations proves challenging for monocular SLAM systems, which may fail during initialization or when translation is negligible compared to rotation. Many realistic scenarios require robust and fast initialization with little motion. Therefore, we evaluate the use of the depth covariance in small baseline scenarios. We also show that the depth covariance is not limited to 2D depth map inference, but can also be used to infer 3D structure.", + "bbox": [ + 496, + 551, + 890, + 717 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Bundle adjustment jointly optimizes camera poses and point landmarks given pixel correspondences. Traditionally, the cost involves the sum of reprojection errors, as well as pose and scale priors on the first pose to fix gauge freedom which we omit for brevity. We also add our depth prior factor per camera, so for landmarks $\\mathbf{P}$ in the world frame, and poses $T_{cW}$ for $c = 1,\\dots,C$ , we have:", + "bbox": [ + 496, + 718, + 890, + 824 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} E = \\sum_ {c} \\sum_ {i} | | \\pi (\\mathbf {T} _ {c W}, \\mathbf {P} _ {i}) - \\mathbf {x} _ {c, i} | | _ {\\sigma_ {r} ^ {2} I} ^ {2} \\\\ + \\sum_ {c} | | [ T _ {c W} \\mathbf {P} _ {c} ] _ {z} - m _ {c} | | _ {K _ {c}} ^ {2}, \\tag {12} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 832, + 890, + 896 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "13127", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/d288ae9e3be58612930368dab42d0a04082df55a9596cd66ac2f622d15872dee.jpg", + "image_caption": [ + "Figure 7. Factor graphs for bundle adjustment and monocular dense visual odometry. Pose and scale priors to constrain gauge freedom are omitted for simplicity." + ], + "image_footnote": [], + "bbox": [ + 89, + 88, + 459, + 276 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "where each frame also has a scale variable $m_{c}$ and depth covariance $K_{c}$ for vectorized observed landmarks $\\mathbf{P}_c$ in that frame. The projection function $\\pi$ projects 3D landmarks into the image plane. To reduce the influence of outliers, we use the Huber cost function for reprojection errors. The least-squares cost is optimized using Levenberg-Marquardt via GTSAM [14]. The corresponding factor graph is shown on the left of Figure 7.", + "bbox": [ + 75, + 354, + 468, + 474 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We divide all of the Freiburg 1 sequences from the TUM RGB-D dataset into small baseline 5-frame windows. This yields 1544 sequences, emulating initializations to visual odometry systems. For the frontend, Shi-Tomasi corners [38] are detected and tracked using Lucas-Kanade tracking [24], and outliers are filtered out via essential matrix RANSAC. Poses are initialized using the motion capture data closest to the current RGB frame timestamps, while landmarks are triangulated if sufficiently constrained. We optimize each sequence with and without the GP depth prior to evaluate its effect. Using the corresponding depth frames, we compare the error of the reprojected sparse landmarks where valid depths occur, as well as the dense depth map error by conditioning on the sparse landmarks. To handle monocular scale ambiguity, we align depth maps with the optimal scale before computing the error. A boxplot of errors between the two methods is shown in Figure 8. By exploiting the correlation between observations, significantly more coherent geometry can be generated, as visualized by an example in Figure 9. Note that the prior is able to achieve consistent geometry despite the low baseline and only optimizing sparse landmarks. The shadows reveal the complete chair legs and table. We also show a longer example with over three seconds of data fused into a TSDF in Figure 1.", + "bbox": [ + 75, + 476, + 470, + 838 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Monocular Dense Visual Odometry", + "text_level": 1, + "bbox": [ + 76, + 847, + 383, + 864 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We propose a monocular dense visual odometry system based on the depth covariance and conditioning on per", + "bbox": [ + 76, + 869, + 470, + 902 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/2aabe93867af65324b952751c2bc362fbd1943cc304705dedc76b21f8febf1b9.jpg", + "image_caption": [ + "Figure 8. Boxplot of sparse and dense depth errors across all 5-frame windows, with and without the GP depth prior included in the optimization. The median RMSE is written next to the plots." + ], + "image_footnote": [], + "bbox": [ + 511, + 88, + 883, + 224 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5b3ff60e2f75a97d292928e763a2ad9d3d77f63092b9b82177d15d78aef67622.jpg", + "image_caption": [ + "Figure 9. Qualitative example of small baseline bundle adjustment with and without the GP depth factor. Both examples fuse 5 predicted depth maps densified from the optimized landmarks into a TSDF, and are visualized with identical lighting." + ], + "image_footnote": [], + "bbox": [ + 513, + 290, + 879, + 428 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "frame latent depth points. We perform sliding window optimization over a fixed number of keyframes, where poses, scales, and inducing depths are optimized per-frame. To regularize the depths, we may condition on the current estimates of the inducing depths as in Section 4.2, project depths into neighboring keyframes, and apply relative photometric error constraints. The photometric error for pixels $n$ across all directed edges $E$ is:", + "bbox": [ + 496, + 518, + 890, + 640 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\nE = \\sum_ {i, j \\in E} \\sum_ {n} \\| I _ {i} (\\mathbf {x} _ {n}) - I _ {j} (w (\\mathbf {x} _ {n}, \\mathbf {T} _ {i}, \\mathbf {T} _ {j}, \\mathbf {y} _ {\\mathbf {i}}, m _ {i}) \\| _ {\\sigma_ {r} ^ {2} I} ^ {2}, \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 506, + 647, + 890, + 695 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "where $I$ is the image intensity, $\\mathbf{x}$ are pixel coordinates, $w$ is the warping function that queries a depth map and projects the corresponding point into the neighboring image, $\\mathbf{T}$ are camera poses, $\\mathbf{y}$ are estimated log-depth observations, and $m$ is the mean log depth. The corresponding factor graph with the depth priors included is shown in the right of Figure 7. To initialize a new keyframe, we first run the RGB through the network to get the covariance feature maps and sample inducing points. We perform active sampling since this is independent of whether log-depths are known as mentioned in 4.3, and stop sampling if a maximum variance threshold is achieved. The log-depths $\\mathbf{y}$ of a new keyframe are initialized by minimizing the least-squares cost of the", + "bbox": [ + 496, + 703, + 893, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "13128", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/48c1b432172f6e286f6885af0042f774ac187ff4157b3ab5bb492120679bbeeb.jpg", + "image_caption": [ + "Figure 10. Example depth maps of keyframes while running the dense odometry on ScanNet validation sequences and TUM." + ], + "image_footnote": [], + "bbox": [ + 83, + 89, + 478, + 258 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c1ec712c25799e814247361dbb3d1d4f906a1d3094c9a37414cbbe3de30ec3e3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 490, + 89, + 887, + 258 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "prior at the sampled locations and a predictive data term over the reprojected depths $\\mathbf{d}$ from the latest keyframe:", + "bbox": [ + 75, + 311, + 470, + 342 + ], + "page_idx": 7 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {y} _ {\\text {i n i t}} = \\underset {\\mathbf {y}} {\\arg \\min } \\| \\mathbf {y} - m \\| _ {K} ^ {2} + \\| \\log \\mathbf {d} - \\mathbf {f} _ {*} \\| _ {\\operatorname {d i a g} \\left[ \\Sigma_ {*} \\right]}. \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 86, + 351, + 468, + 377 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "When the window size is full, the last keyframe is dropped, and scale and pose priors are added to the current estimates of the oldest keyframe still in the window. Initialization of the system is achieved via two-frame SfM, where the first frame's depth map is optimized along with the relative pose to another frame. Tracking is performed using coarse-to-fine photometric tracking against the last keyframe. We also optimize affine brightness parameters for varying exposure.", + "bbox": [ + 75, + 386, + 468, + 508 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "For quantitative evaluation, we compare our odometry to other learning-based dense systems on Freiburg 1 sequences in the TUM dataset. Since our method is purely a sliding-window odometry system, we compare against the comparable monocular methods evaluated in [44] that lack global optimization and bundle adjustment. We omit the floor sequence which drops a significant number of frames. The absolute trajectory errors (ATE) are shown in Table 3. Since our dense odometry does not have global scale, we align to the ground-truth trajectory's scale.", + "bbox": [ + 75, + 508, + 468, + 657 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Despite the simplicity of our system, it is first or second in 6 of 8 sequences, and achieves the best mean error. We only use photometric error compared to the additional depth and keypoint factors in DeepFactors. While we lack many features in our simple sliding window formulation, we believe that including additional components will further improve the results. Qualitative examples of inferred depth maps from the odometry on ScanNet validation sequences and TUM are shown in Figure 10. Note that our representation can predict a variety of thin structures and regularize flat ones, even in the case of high-frequency texture.", + "bbox": [ + 75, + 659, + 468, + 824 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Odometry runs at an average of $7 - 10\\mathrm{Hz}$ on $256\\times 192$ images with 8 keyframes on a single RTX 3080. The code is not optimized and alternates between tracking and mapping in a single thread, so we believe significant speed-ups are possible given additional optimization or compute.", + "bbox": [ + 75, + 824, + 468, + 902 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/96b71c3ae7c1b1f1f0219593fbbcd370496e1449dd2b68033c72a2a6eb75851c.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
SequenceTartanVO [46]DeepV2D [43]DeepFactors [9]Ours
3600.1780.2430.1590.128
desk0.1250.1660.1700.056
desk20.1220.3790.2530.048
plant0.2970.2030.3050.261
room0.3330.2460.3640.257
ryp0.0490.1050.0430.052
teddy0.3390.3160.6010.475
xyz0.0620.0640.0350.056
mean0.1880.2150.2410.167
", + "bbox": [ + 514, + 308, + 879, + 479 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 3. Absolute trajectory error (m) on TUM Freiburg1. The best result is in bold, while the second best is underlined.", + "bbox": [ + 498, + 489, + 890, + 517 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion and Future Work", + "text_level": 1, + "bbox": [ + 500, + 544, + 769, + 561 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have proposed learning a depth covariance function, and provided selections of the depth representation, the base covariance function, and the optimization objective. While we selected a specific nonstationary kernel function, exploration of alternatives is a valuable direction. There is great potential to draw additional connections to the literature on Bayesian methods and GPs. For example, geometry is low-rank and spatially local, so active selection and sparse kernels will permit scalability to higher resolutions and larger capacities. Lastly, we demonstrated the utility of the covariance function on a selection of geometric vision tasks: depth completion, bundle adjustment, and monocular dense visual odometry. Beyond these applications, the prior can be tightly integrated into existing geometric vision frameworks and serve as a foundation for novel formulations.", + "bbox": [ + 496, + 571, + 890, + 797 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "7. Acknowledgements", + "text_level": 1, + "bbox": [ + 500, + 814, + 687, + 830 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Research presented in this paper has been supported by Dyson Technology Ltd. We would like to thank Tristan Laidlow, Hidenobu Matsuki, Riku Murai, and members of the Dyson Robotics Lab for insightful discussions.", + "bbox": [ + 498, + 839, + 893, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "13129", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Gwangbin Bae, Ignas Budvytis, and Roberto Cipolla. Iron-Depth: Iterative refinement of single-view depth using surface normal and its uncertainty. In Proceedings of the British Machine Vision Conference (BMVC), 2022. 2", + "[2] Matthias Bauer, Mark van der Wilk, and Carl Edward Rasmussen. Understanding probabilistic sparse Gaussian process approximations. In Neural Information Processing Systems (NeurIPS), 2016. 4", + "[3] Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, and Andrew J. Davison. CodeSLAM - learning a compact, optimisable representation for dense visual SLAM. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 2, 3", + "[4] Michael Bloesch, Tristan Laidlow, Ronald Clark, Stefan Leutenegger, and Andrew Davison. Learning meshes for dense visual SLAM. In Proceedings of the International Conference on Computer Vision (ICCV), 2019. 2", + "[5] Weizhe Chen, Roni Khardon, and Lantao Liu. AK: Attentive kernel for information gathering. In Proceedings of Robotics: Science and Systems (RSS), 2022. 2", + "[6] Xinjing Cheng, Peng Wang, and Ruigang Yang. Depth estimation via affinity learned with convolutional spatial propagation network. In Proceedings of the European Conference on Computer Vision (ECCV), 2018. 5", + "[7] Javier Civera, Andrew J. Davison, and J. M. Martínez Montiel. Inverse depth parametrization for monocular SLAM. IEEE Transactions on Robotics (T-RO), 2008. 3", + "[8] Andrea Conti, Matteo Poggi, and Stefano Mattoccia. Sparsity agnostic depth completion. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023. 5", + "[9] Jan Czarnowski, Tristan Laidlow, Ronald Clark, and Andrew J. Davison. DeepFactors: Real-time probabilistic dense monocular SLAM. IEEE Robotics and Automation Letters (RA-L), 2020. 2, 8", + "[10] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. ScanNet: Richly-annotated 3D reconstructions of indoor scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 5", + "[11] Garoe Dorta, Sara Vicente, Lourdes Agapito, Neill D. F. Campbell, and Ivor Simpson. Structured uncertainty prediction networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 2", + "[12] David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. In Neural Information Processing Systems (NeurIPS), 2014. 2, 3", + "[13] Jakob Engel, Thomas Schöps, and Daniel Cremers. LSD-SLAM: Large-scale direct monocular SLAM. In Proceedings of the European Conference on Computer Vision (ECCV), 2014. 2", + "[14] Frank Dellaert et al. borglab/gtsam, May 2022. 7", + "[15] Clement Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow. Digging into self-supervised monocular" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "depth estimation. In Proceedings of the International Conference on Computer Vision (ICCV), 2019. 2", + "[16] Benjamin Graham and David Novotny. RidgeSfM: Structure from motion via robust pairwise matching under depth uncertainty. In Proceedings of the International Conference on 3D Vision (3DV), 2020. 2", + "[17] Carlos Guestrin, Andreas Krause, and Ajit Paul Singh. Nearoptimal sensor placements in Gaussian processes. In Proceedings of the International Conference on Machine Learning (ICML), 2005. 5", + "[18] Tony Jebara, Risi Kondor, and Andrew Howard. Probability product kernels. The Journal of Machine Learning Research, 2004. 3", + "[19] Alex Kendall and Yarin Gal. What uncertainties do we need in Bayesian deep learning for computer vision? In Neural Information Processing Systems (NeurIPS), 2017. 2", + "[20] Diederik P. Kingma and Max Welling. Auto-encoding variational Bayes. In Proceedings of the International Conference on Learning Representations (ICLR), 2014. 2", + "[21] Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon. Accurate uncertainties for deep learning using calibrated regression. In Proceedings of the International Conference on Machine Learning (ICML), 2018. 6", + "[22] Tobias Lang, Christian Plagemann, and Wolfram Burgard. Adaptive non-stationary kernel regression for terrain modeling. In Proceedings of Robotics: Science and Systems (RSS), 2007. 2", + "[23] Lina Liu, Yiyi Liao, Yue Wang, Andreas Geiger, and Yong Liu. Learning steering kernels for guided depth completion. IEEE Transactions on Image Processing, 2021. 2, 5", + "[24] Bruce D. Lucas and Takeo Kanade. An iterative image registration technique with an application to stereo vision. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1981. 7", + "[25] Fangchang Ma and Sertac Karaman. Sparse-to-dense: Depth prediction from sparse depth samples and a single image. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2018. 5", + "[26] Raul Mur-Artal and Juan D. Tardós. ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Transactions on Robotics (T-RO), 2017. 2", + "[27] Richard. A. Newcombe, Steven J. Lovegrove, and Andrew J. Davison. DTAM: Dense tracking and mapping in real-time. In Proceedings of the International Conference on Computer Vision (ICCV), 2011. 2", + "[28] Sebastian W. Ober, Carl E. Rasmussen, and Mark van der Wilk. The promises and pitfalls of deep kernel learning. In Conference on Uncertainty in Artificial Intelligence, 2021. 2, 3", + "[29] Christopher Paciorek and Mark Schervish. Nonstationary covariance functions for Gaussian process regression. In Neural Information Processing Systems (NeurIPS), 2003. 2, 3", + "[30] Jinsun Park, Kyungdon Joo, Zhe Hu, Chi-Kuei Liu, and In So Kweon. Non-local spatial propagation network for depth completion. In Proceedings of the European Conference on Computer Vision (ECCV), 2020. 5" + ], + "bbox": [ + 503, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "13130", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[31] Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O' Boyle, and Amos J Storkey. Bayesian meta-learning for the few-shot setting via deep kernels. In Neural Information Processing Systems (NeurIPS), 2020. 2", + "[32] Rene Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2022. 2, 3", + "[33] Ananth Ranganathan, Ming-Hsuan Yang, and Jeffrey Ho. Online sparse Gaussian process regression and its applications. IEEE Transactions on Image Processing, 2011. 5", + "[34] Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2005. 2", + "[35] Jean-Michel M. Rendu. Normal and lognormal estimation. Journal of the International Association for Mathematical Geology, 1979. 3", + "[36] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015. 3", + "[37] Johannes L. Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 2", + "[38] J. Shi and C. Tomasi. Good features to track. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1994. 7", + "[39] Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from RGBD images. In Proceedings of the European Conference on Computer Vision (ECCV), 2012. 5", + "[40] Ivor J. A. Simpson, Sara Vicente, and Neill D. F. Campbell. Learning structured Gaussians to approximate deep ensembles. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2", + "[41] Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evaluation of RGB-D SLAM systems. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2012. 5", + "[42] Chengzhou Tang and Ping Tan. BA-Net: Dense bundle adjustment networks. In Proceedings of the International Conference on Learning Representations (ICLR), 2019. 2", + "[43] Zachary Teed and Jia Deng. DeepV2D: Video to depth with differentiable structure from motion. In Proceedings of the International Conference on Learning Representations (ICLR), 2020. 8", + "[44] Zachary Teed and Jia Deng. DROID-SLAM: Deep visual SLAM for monocular, stereo, and RGB-D cameras. In Neural Information Processing Systems (NeurIPS), 2021. 8", + "[45] Michalis Titsias. Variational learning of inducing variables in sparse Gaussian processes. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2009. 4" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[46] Wenshan Wang, Yaoyu Hu, and Sebastian Scherer. TartanVO: A generalizable learning-based VO. In Conference on Robot Learning (CoRL), 2021. 8", + "[47] Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P. Xing. Deep kernel learning. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. 2", + "[48] Zhiqiang Yan, Kun Wang, Xiang Li, Zhenyu Zhang, Jun Li, and Jian Yang. RigNet: Repetitive image guided network for depth completion. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 5", + "[49] Sungho Yoon and Ayoung Kim. Balanced depth completion between dense depth inference and sparse range measurements via KISS-GP. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2020. 2" + ], + "bbox": [ + 501, + 92, + 890, + 303 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "13131", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning a Depth Covariance Function/ce47874e-679a-4e09-bfc4-c70c4939d294_model.json b/2023/Learning a Depth Covariance Function/ce47874e-679a-4e09-bfc4-c70c4939d294_model.json new file mode 100644 index 0000000000000000000000000000000000000000..082116da355eb306907cba11bba17e30ca6c1b64 --- /dev/null +++ b/2023/Learning a Depth Covariance Function/ce47874e-679a-4e09-bfc4-c70c4939d294_model.json @@ -0,0 +1,2134 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.287, + 0.131, + 0.685, + 0.154 + ], + "angle": 0, + "content": "Learning a Depth Covariance Function" + }, + { + "type": "text", + "bbox": [ + 0.273, + 0.182, + 0.644, + 0.234 + ], + "angle": 0, + "content": "Eric Dexheimer and Andrew J. Davison Dyson Robotics Lab, Imperial College London {e.dexheimer21, a.davison}@imperial.ac.uk" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.269, + 0.314, + 0.285 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.301, + 0.474, + 0.424 + ], + "angle": 0, + "content": "We propose learning a depth covariance function with applications to geometric vision tasks. Given RGB images as input, the covariance function can be flexibly used to define priors over depth functions, predictive distributions given observations, and methods for active point selection. We leverage these techniques for a selection of downstream tasks: depth completion, bundle adjustment, and monocular dense visual odometry." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.452, + 0.21, + 0.468 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.478, + 0.47, + 0.658 + ], + "angle": 0, + "content": "Inferring the 3D structure of the world from 2D images is an essential computer vision task. In recent years, there has been significant interest in combining principled multiple view geometry with data-driven priors. Learning-based methods that predict geometry provide a prior directly over the latent variables, which avoids the ill-posed configurations of traditional methods. However, direct, overconfident priors may prevent realization of the true 3D structure when multi-view geometry is well-defined. For example, data-driven methods have shown tremendous promise in monocular depth estimation, but often lack consistency when fusing information into 3D space." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.659, + 0.47, + 0.825 + ], + "angle": 0, + "content": "Thus far, designing a unified framework that combines the best of learning and optimization methods has proven challenging. Recent data-driven methods have attempted to relax rigid geometric constraints by also predicting low-dimensional subspaces or per-pixel uncertainties. Fixing capacity during training is often wasteful or inflexible at test time, while per-pixel residual distributions typically explain away the limitations of the model instead of the relationship between errors. In reality, the 3D world is anywhere from simple to complex, and an ideal system should explicitly adapt its capacity and correlation based on the scene." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.47, + 0.903 + ], + "angle": 0, + "content": "In this paper, rather than directly predicting geometry from images, we propose learning a depth covariance function. Given an RGB image, our method predicts how the depths of any two pixels relate. To achieve this, a neural network transforms color information to a feature space, and" + }, + { + "type": "image", + "bbox": [ + 0.508, + 0.268, + 0.891, + 0.52 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.529, + 0.895, + 0.614 + ], + "angle": 0, + "content": "Figure 1. Example monocular reconstruction using the depth covariance for bundle adjustment and dense depth prediction from three seconds (100 frames) of TUM data. Three representative images and the mesh from TSDF fusion of the depth predictions are shown. Each frame leverages the learned covariance function to model geometric correlation between pairs of scene points." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.643, + 0.894, + 0.748 + ], + "angle": 0, + "content": "a Gaussian process (GP) models a prior distribution given these features and a base kernel function. The distinction between image processing and the prior enables promoting locality and granting flexible capacity at test time. Locality avoids over-correlating pixels on distinct structures, while adaptive capacity permits tuning the complexity of our subspace to the content of the viewed scene." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.895, + 0.902 + ], + "angle": 0, + "content": "Learning this flexible, high-level prior allows for balancing data-driven methods with test-time optimization that can be applied to a variety of geometric vision tasks. Furthermore, the covariance function is agnostic to the 3D representation as it does not directly learn a geometric output. Depth maps may be requested by conditioning on observations, but the prior may also be leveraged for inferring the desired latent 3D representation. In Figure 1, we illustrate depth covariance along with an example of bundle adjustment, dense depth prediction, and multi-view fusion." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "13122" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.097, + 0.092, + 0.355, + 0.106 + ], + "angle": 0, + "content": "In summary, our key contributions are:" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.115, + 0.468, + 0.16 + ], + "angle": 0, + "content": "- A framework for learning the covariance function by selecting a depth representation, a base kernel function, and a scalable optimization objective" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.169, + 0.468, + 0.199 + ], + "angle": 0, + "content": "- Application of the prior to depth completion, bundle adjustment, and monocular dense visual odometry" + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.115, + 0.468, + 0.199 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.211, + 0.22, + 0.227 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.237, + 0.47, + 0.599 + ], + "angle": 0, + "content": "View-based Priors for Geometric Inference Traditionally, sparse reconstruction methods have ignored per-image correlations [37], [26], while dense methods [27], [13] have used naive priors based on neighboring pixels. With the availability of depth data and the rise of deep learning, a variety of learned depth priors have been proposed. Although monocular depth estimation methods that directly predict geometry [12], [15], [32] have demonstrated great progress, they may predict irreparable mistakes and do not allow for handling ambiguities. Utilizing surface normals may refine these depth estimates [1], but the improvement is still subject to errors from the initial depth prediction. For this reason, we focus on approaches that balance learned priors and test-time optimization. Popular approaches include predicting low-dimensional latent codes [3], [9] and generating a basis of depth maps [42], [16]. While these methods utilize single image information to reduce the dimensionality of depth map inference, they fix capacity during training, produce overly smooth depth maps, and create over-correlated global changes of the depth map. Predicting mesh vertices in the image plane [4] permits distribution of capacity and optimizing depth, but the number of latent variables is still fixed and an explicit mesh representation cannot easily model complex scenes." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.599, + 0.468, + 0.734 + ], + "angle": 0, + "content": "Instead of predicting geometry directly, we focus on predicting geometric correlation. By decoupling the pipeline into an image processing network and a GP, the network is not responsible for learning geometry directly and we do not need to fix the geometric capacity. The dimensionality of the subspace may be adapted for representing low-rank scenes more compactly and complex geometry with high-fidelity. Lastly, we use a covariance function with locality to bias learning towards local appearance information." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.468, + 0.902 + ], + "angle": 0, + "content": "Learning Covariance Functions Choosing covariance functions and performing model selection for GPs is a well-studied topic [34]. While stationary kernels have been explored for merging LiDAR observations with monocular depth estimates [49], more expressive kernels are required for sparse observations. Nonstationary covariance functions using local Gaussian parameterizations [29] have shown potential in robotic terrain mapping [22]. However, since GP model selection occurs per data example, optimizing hyperparameters is challenging, and novel nonstationary kernels have been proposed to limit flexibility [5]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.544 + ], + "angle": 0, + "content": "Rather than performing model selection per image, we are inspired by deep kernel learning (DKL) [47], which leverages training data to predict kernel hyperparameters. Alternatively, this may be viewed as meta-learning, where each task is an RGB-depth example [31]. To mitigate overcorrelation when using stationary kernels in feature space as proposed in DKL [28], we utilize local information in pixel space [29] to model surface geometry. While deep nonstationary kernel regression has been explored for depth completion [23], GPs balance data fit and model complexity during training, and the uncertainty estimates are conducive to decision-making and inference in optimization frameworks. Residual Covariances in Computer Vision Uncertainties in machine learning are often divided between two types: model uncertainty and data uncertainty [19]. For vision problems, model uncertainty is often ignored due to the availability of data and for tractable optimization. Residual uncertainty is usually predicted from a network as a per-pixel variance due to high-dimensionality, but the assumption of independence ignores the correlation present in images. For example, variational autoencoders (VAEs) [20] with a diagonal likelihood output overly smooth mean predictions and unnatural samples with salt-and-pepper noise. To introduce correlation in the likelihood, structured uncertainty prediction networks (SUPNs) predict a full information matrix by defining a graph topology consisting of specific neighbors [11]. Since this matrix is sparse, it can be efficiently inverted to obtain the covariance matrix. This has also been leveraged to distill monocular depth prediction ensembles into a single SUPN [40]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.544, + 0.892, + 0.665 + ], + "angle": 0, + "content": "Similar to SUPNs, we move beyond diagonal covariance approximations that lack correlation. However, we learn a covariance function which does not require a predefined graph topology and allows for long-range correlations beyond neighboring pixels. Furthermore, the marginal distribution of any set of variables may be examined without constructing the full joint distribution, which is of great interest for compact inference in geometric vision." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.677, + 0.854, + 0.695 + ], + "angle": 0, + "content": "3. Learning a Depth Covariance Function" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.703, + 0.892, + 0.794 + ], + "angle": 0, + "content": "Given an image, we model a distribution over possible depth functions via a Gaussian process (GP) [34]. With the input domain being normalized image coordinates \\(\\mathbf{x}_i = (u_i,v_i)\\) where \\(u_{i},v_{i}\\in [-1,1]\\), the outputs for any finite set of inputs are jointly Gaussian. The GP is then defined by a mean function \\(m(\\mathbf{x})\\) and a covariance function \\(k(\\mathbf{x},\\mathbf{x}^{\\prime})\\):" + }, + { + "type": "equation", + "bbox": [ + 0.593, + 0.801, + 0.891, + 0.818 + ], + "angle": 0, + "content": "\\[\nf (\\mathbf {x}) \\sim \\mathcal {G P} (m (\\mathbf {x}), k (\\mathbf {x}, \\mathbf {x} ^ {\\prime})). \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.892, + 0.901 + ], + "angle": 0, + "content": "For learning the parameters of this GP, we need to select the representation of depth, the mean and covariance functions, and the optimization objective. We will outline our decisions in the following sections, but there exist many ways to define this prior." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13123" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.078, + 0.091, + 0.282, + 0.108 + ], + "angle": 0, + "content": "3.1. Depth Representation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.114, + 0.474, + 0.371 + ], + "angle": 0, + "content": "When predicting depth, we often want to attenuate errors further from the camera view, so that nearby structures are prioritized. Examples of such representations include inverse depth [7], disparity [32], and log-depth [12]. We select log-depth for two reasons. First, the log-normal distribution is more suitable for skewed distributions [35], as the range of the GP is unbounded. Representing depth or inverse depth with a normal distribution would require ad hoc truncation, as depth functions could move behind the camera. Second, we represent depth with relative scale, as the absolute scale ambiguity in monocular images accounts for much of the error in learned depth prediction [12]. We can then focus on learning the relationship between predictions. In the log-depth formulation, a constant mean \\( m(\\mathbf{x}_i) = m \\) directly corresponds to a scale variable. Given a log-depth observation \\( \\mathbf{y}_i = \\log \\mathbf{d}_i \\), we can adjust its scale via this mean:" + }, + { + "type": "equation", + "bbox": [ + 0.157, + 0.384, + 0.47, + 0.401 + ], + "angle": 0, + "content": "\\[\ne ^ {\\mathbf {y} _ {i} - m} = e ^ {\\mathbf {y} _ {i}} e ^ {- m} = C e ^ {\\mathbf {y} _ {i}} = C \\mathbf {d} _ {i}. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.413, + 0.471, + 0.595 + ], + "angle": 0, + "content": "In other words, the median of the depth distribution is controlled by the mean of the log-normal distribution. During training, similar to [12], we may find the optimal scale \\( m \\) that minimizes our data loss. In addition, the log-depth representation allows covariance to be in relative scale. At test time, the scale may be fixed if known, or jointly optimized with other variables. Learning a more expressive mean function per image would be useful in the absence of any depth observations, but introduces many degrees of freedom. For GPs, generalization is largely dependent on the covariance function, so we leave the mean to be a constant per image." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.606, + 0.276, + 0.62 + ], + "angle": 0, + "content": "3.2. Covariance Function" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.629, + 0.47, + 0.75 + ], + "angle": 0, + "content": "We would like to learn the parameters of a depth covariance function using pairs of RGB and depth images. By definition, the covariance function must be positive semidefinite (PSD). In our framework, an RGB image is fed into a convolutional neural network (CNN), which outputs features for a base covariance function. We model the depth observations as being jointly Gaussian so that the CNN and base kernel hyperparameters may be jointly learned." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.471, + 0.904 + ], + "angle": 0, + "content": "For the base covariance function, we would like to avoid over-correlation of independent structures. To achieve local influence, we use the family of nonstationary kernels described in [29]. Each point \\(\\mathbf{x}_i\\) in an image can be viewed as a Gaussian distribution with a 2D kernel matrix \\(\\Sigma_i\\). Then, the covariance between two points is the convolution of these two densities over the input domain with a normalizing constant. Alternatively, this can be viewed as the similarity between distributions via the Bhattacharyya kernel [18]. Given an isotropic covariance function \\(R^S (\\sqrt{q})\\)," + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.088, + 0.895, + 0.315 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.325, + 0.895, + 0.396 + ], + "angle": 0, + "content": "Figure 2. Visualizing our depth covariance function: for every pixel of an input image, the trained network predicts a 2D kernel matrix. Here we show the covariance function between pairs of pixels in both matrix form and as edges in a graph, with the line thickness representing the magnitude of covariance." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.42, + 0.704, + 0.435 + ], + "angle": 0, + "content": "the closed-form expression is:" + }, + { + "type": "equation", + "bbox": [ + 0.527, + 0.442, + 0.892, + 0.477 + ], + "angle": 0, + "content": "\\[\nk \\left(\\mathbf {x} _ {i}, \\mathbf {x} _ {j}\\right) = \\sigma_ {f} ^ {2} \\frac {\\left| \\Sigma_ {i} \\right| ^ {1 / 4} \\left| \\Sigma_ {j} \\right| ^ {1 / 4}}{\\left| \\Sigma_ {i} + \\Sigma_ {j} \\right| ^ {1 / 2}} R ^ {S} (\\sqrt {q}), \\tag {3}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.581, + 0.479, + 0.892, + 0.498 + ], + "angle": 0, + "content": "\\[\nq = \\left(\\mathbf {x} _ {i} - \\mathbf {x} _ {j}\\right) ^ {T} \\left(\\Sigma_ {i} + \\Sigma_ {j}\\right) ^ {- 1} \\left(\\mathbf {x} _ {i} - \\mathbf {x} _ {j}\\right), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.504, + 0.894, + 0.597 + ], + "angle": 0, + "content": "where \\(\\sigma_f^2\\) is a learnable signal variance. To better handle discontinuities, we select the Matérn function as our isotropic covariance function. With the base covariance function requiring a 2D PSD matrix for each pixel, the CNN outputs three channels \\((c_1, c_2, c_3)\\), which are parameterized given the positive diagonal and determinant constraints:" + }, + { + "type": "equation", + "bbox": [ + 0.513, + 0.603, + 0.892, + 0.639 + ], + "angle": 0, + "content": "\\[\n\\Sigma_ {i} = \\left[ \\begin{array}{c c} e ^ {c _ {1}} & \\tanh (c _ {3}) \\sqrt {e ^ {c _ {1}} * e ^ {c _ {2}}} \\\\ \\tanh (c _ {3}) \\sqrt {e ^ {c _ {1}} * e ^ {c _ {2}}} & e ^ {c _ {2}} \\end{array} \\right]. \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.644, + 0.894, + 0.75 + ], + "angle": 0, + "content": "An example of five kernel matrices and the marginal covariance in matrix and graph form is shown in Figure 2. We use a UNet architecture [36] and output features at different levels for a multi-scale loss. Each scale also has its own GP hyperparameters, signal variance \\(\\sigma_f^2\\) and noise variance \\(\\sigma_n^2\\). Depth maps are coarsened for lower-levels, so finer levels are weighted higher during the total loss calculation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.895, + 0.902 + ], + "angle": 0, + "content": "While a more global covariance function, such as a squared-exponential over output features, would provide additional flexibility, we avoid this for two reasons. First, changes in depths on one part of the scene may have significant influence on a completely unrelated part, as seen in [3]. Second, deep kernel learning using this setup is unstable and biased towards over-correlating the input domain [28]. By directly using pixel coordinates in our covariance function, we restrict changes in geometry to be local and bias the network to learn local appearance information." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "13124" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.078, + 0.091, + 0.295, + 0.108 + ], + "angle": 0, + "content": "3.3. Optimization Objective" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.114, + 0.47, + 0.145 + ], + "angle": 0, + "content": "In GP literature, model selection is performed by minimizing the negative log marginal likelihood (NLML)" + }, + { + "type": "equation", + "bbox": [ + 0.098, + 0.153, + 0.469, + 0.213 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} - \\log p (\\mathbf {y} | X) = \\frac {1}{2} (\\mathbf {y} - m) ^ {T} \\left(K _ {\\mathrm {f f}} + \\sigma_ {n} ^ {2} I\\right) ^ {- 1} (\\mathbf {y} - m) \\\\ + \\frac {n}{2} \\log 2 \\pi + \\frac {1}{2} \\log | K _ {\\mathrm {f f}} + \\sigma_ {n} ^ {2} I |, \\tag {6} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.219, + 0.469, + 0.324 + ], + "angle": 0, + "content": "where the matrix \\( K_{\\mathrm{ff}} \\) is a PSD matrix with entries defined by the covariance function. The first term is often referred to as the \"data fit\", while the third term is the \"complexity penalty\". In our scenario, the complexity term is minimized by correlating points, while the data term ensures the ground-truth depth map is plausible given the mean and predicted covariance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.325, + 0.469, + 0.461 + ], + "angle": 0, + "content": "However, the marginal likelihood for a GP requires a \\( O(N^3) \\) matrix inversion. For pixels in an image, this becomes intractable. Since depth images are relatively low-rank, we use a sparse GP approximation [2] during training by randomly sampling inducing points for a Nystrom approximation to the full covariance matrix. Given the covariance matrix for the inducing points \\( K_{\\mathrm{uu}} \\) and the cross-covariance between the inducing points and the entire domain \\( K_{\\mathrm{uf}} \\), we have:" + }, + { + "type": "equation", + "bbox": [ + 0.184, + 0.469, + 0.469, + 0.487 + ], + "angle": 0, + "content": "\\[\nK _ {\\mathrm {f f}} \\approx \\tilde {K} _ {\\mathrm {f f}} \\triangleq K _ {\\mathrm {f u}} K _ {\\mathrm {u u}} ^ {- 1} K _ {\\mathrm {u f}}. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.497, + 0.469, + 0.527 + ], + "angle": 0, + "content": "Specifically, we use the variational free energy (VFE) framework [45], which defines our training loss as" + }, + { + "type": "equation", + "bbox": [ + 0.095, + 0.535, + 0.469, + 0.597 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {F} = \\frac {1}{2} (\\mathbf {y} - m) ^ {T} \\left(\\tilde {K} _ {\\mathrm {f f}} + \\sigma_ {n} ^ {2} I\\right) ^ {- 1} (\\mathbf {y} - m) + \\frac {n}{2} \\log 2 \\pi \\\\ + \\frac {1}{2} \\log | \\tilde {K} _ {\\mathrm {f f}} + \\sigma_ {n} ^ {2} I | + \\frac {1}{2 \\sigma_ {n} ^ {2}} \\operatorname {t r} \\left(K _ {\\mathrm {f f}} - \\tilde {K} _ {\\mathrm {f f}}\\right). \\tag {8} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.605, + 0.469, + 0.697 + ], + "angle": 0, + "content": "The first three terms are the same as the original NLML, but using the approximate covariance, while the last term penalizes the conditional variances at all inputs given the inducing points, which only requires the diagonal of the full covariance matrix. Note that we also assume homoscedastic observation noise variance \\(\\sigma_{n}^{2}\\) across the entire dataset." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.708, + 0.447, + 0.725 + ], + "angle": 0, + "content": "4. Leveraging a Depth Covariance Function" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.734, + 0.469, + 0.809 + ], + "angle": 0, + "content": "Given an image and its corresponding CNN output maps, we may leverage the covariance function by defining a prior over depth functions, conditioning on depth observations to yield a predictive distribution, and actively sampling pixel locations that minimize the variance of depth predictions." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.818, + 0.292, + 0.832 + ], + "angle": 0, + "content": "4.1. Gaussian Process Prior" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.841, + 0.469, + 0.871 + ], + "angle": 0, + "content": "Given a finite set of input points \\( X_{*} \\), we may define a Gaussian prior over their log-depths:" + }, + { + "type": "equation", + "bbox": [ + 0.182, + 0.881, + 0.469, + 0.897 + ], + "angle": 0, + "content": "\\[\n\\mathbf {f} _ {*} \\sim \\mathcal {N} (m, K (X _ {*}, X _ {*})). \\tag {9}\n\\]" + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.088, + 0.89, + 0.26 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.273, + 0.892, + 0.316 + ], + "angle": 0, + "content": "Figure 3. Conditioning example for 128 samples. The posterior variance is high around edges and in areas lacking samples. The columns of \\( K_{\\mathrm{fn}} \\), or correlation maps, are shown for select points." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.341, + 0.892, + 0.462 + ], + "angle": 0, + "content": "The GP defines a prior over functions, and for any number of input points, we obtain a Gaussian prior. For geometric vision tasks, this prior can be leveraged for anything from sparse to dense methods. Due to the marginalization property of GPs, adding points to the indexing set will not change the marginal distribution of the existing set. Alternatively, the prior may be viewed as an image-conditioned regularizer of any desired latent geometry parameterization." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.47, + 0.712, + 0.484 + ], + "angle": 0, + "content": "4.2. Predictive Distribution" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.494, + 0.891, + 0.569 + ], + "angle": 0, + "content": "In many cases, log-depth observations \\(\\mathbf{y}\\), such as from RGB-D sensors or LiDAR, may be available. We can explicitly condition the prior on these observations to obtain a posterior distribution. The predictive mean \\(\\mathbf{f}_{*}\\) and covariance \\(\\Sigma_{*}\\) given \\(N\\) samples is:" + }, + { + "type": "equation", + "bbox": [ + 0.567, + 0.579, + 0.891, + 0.597 + ], + "angle": 0, + "content": "\\[\n\\mathbf {f} _ {*} = m + K _ {\\mathrm {f n}} \\left(K _ {\\mathrm {n n}} + \\sigma_ {n} ^ {2} I\\right) ^ {- 1} (\\mathbf {y} - m), \\tag {10}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.562, + 0.6, + 0.891, + 0.617 + ], + "angle": 0, + "content": "\\[\n\\Sigma_ {*} = K _ {\\mathrm {f f}} - K _ {\\mathrm {f n}} \\left(K _ {\\mathrm {n n}} + \\sigma_ {n} ^ {2} I\\right) ^ {- 1} K _ {\\mathrm {n f}}. \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.627, + 0.892, + 0.733 + ], + "angle": 0, + "content": "The predictive mean is a linear function in terms of the observations \\( \\mathbf{y} \\), so test-time inference of latent depths is efficient. If a full covariance is not required, only certain blocks or per-pixel variances need to be calculated. Note that the covariance depends only on the observation coordinates and not the observed values. We visualize the predictive mean, variance, and correlation maps for an example in Figure 3." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.742, + 0.704, + 0.756 + ], + "angle": 0, + "content": "4.3. Active Point Selection" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.892, + 0.901 + ], + "angle": 0, + "content": "For dense reconstruction, it is beneficial to construct a compact representation that can achieve high-fidelity results. As mentioned previously, the predictive covariance depends only on the RGB image and locations of depth observations, but not on the observations themselves. By viewing the CNN as a meta-learned initialization of the nonstationary kernel parameters, we may use 2D observations as a proxy for the complexity of 3D geometry. Active selection of informative pixels up to a desired variance permits" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "13125" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.088, + 0.465, + 0.253 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.265, + 0.47, + 0.321 + ], + "angle": 0, + "content": "Figure 4. Qualitative comparison of random and active sampling given 32 sample selections. Random sampling misrepresents depth at the top of the image, while active sampling focuses on the chair geometry and avoids redundant samples on the floor." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.347, + 0.469, + 0.378 + ], + "angle": 0, + "content": "representing less complex scenes with fewer samples and allocating capacity towards high-frequency geometry." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.378, + 0.469, + 0.514 + ], + "angle": 0, + "content": "Inspired by sensor placement literature [17], an entropy-based criterion is used to select informative pixels. In the greedy-case, this simplifies to selecting the input point with the highest conditional variance at each step. Since this requires computing the conditional variance for each newly added point, we leverage incremental updates to the variance and Cholesky factorization of the training covariance matrix [33]. By decoupling the neural network and GP, we do not require any additional network passes." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.513, + 0.47, + 0.618 + ], + "angle": 0, + "content": "A qualitative example of active sampling is shown in Figure 4. Random sampling severely misrepresents depth of table and chairs, which all appear at similar depths. Active sampling focus samples around the thin chair edge near the image, while also avoiding oversampling on the floor. Furthermore, active sampling avoids missing the top section of the image, so that the table and chairs are well-represented." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.632, + 0.208, + 0.649 + ], + "angle": 0, + "content": "5. Applications" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.657, + 0.469, + 0.809 + ], + "angle": 0, + "content": "We apply depth covariance to three fundamental geometric vision tasks: depth completion, bundle adjustment, and monocular dense visual odometry (DVO). For depth completion, we use the NYUv2 benchmark [39] and the train-test splits from [25]. For bundle adjustment and DVO, we train the covariance function on ScanNet [10], and evaluate on the TUM RGB-D dataset [41]. For the UNet, we use an input size of \\( 256 \\times 192 \\), 16 channels after the first layer, 5 downsampling steps, and 4 output levels. This results in roughly 9 million parameters." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.817, + 0.255, + 0.834 + ], + "angle": 0, + "content": "5.1. Depth Completion" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Depth completion is a fundamental task that will be leveraged for additional applications. We may directly condition on sparse observations as described in Section 4.2 to obtain a dense depth map and uncertainties." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.092, + 0.782, + 0.107 + ], + "angle": 0, + "content": "5.1.1 Completion Accuracy and Error" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.115, + 0.892, + 0.221 + ], + "angle": 0, + "content": "Since our method does not predict a specific instance of geometry, we report errors with respect to the GP posterior mean. We compare to the foundational Sparse-to-Dense (S2D) [25], deep kernel regression with (KernelNet+R) and without refinement (KernelNet) [23], and a recent state-of-the-art network RigNet [48]. Results on the validation set with 500 random samples are shown in Table 1." + }, + { + "type": "table", + "bbox": [ + 0.502, + 0.231, + 0.892, + 0.342 + ], + "angle": 0, + "content": "
MethodError (↓) RMSEAccuracy (↑)
δ1.02δ1.05δ1.10δ1.25δ1.252
S2D [25]0.204---97.899.6
KernelNet [23]0.19865.582.991.697.799.8
KernelNet+R [23]0.11184.894.197.499.399.9
RigNet [48]0.090---99.799.9
Ours0.15781.292.697.199.499.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.504, + 0.351, + 0.886, + 0.366 + ], + "angle": 0, + "content": "Table 1. Depth completion on NYUv2 with 500 sampled points." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.379, + 0.892, + 0.546 + ], + "angle": 0, + "content": "KernelNet also predicts three feature maps for 2D covariance parameters, but we achieve better performance under the GP framework. We achieve comparable accuracy to state-of-the-art methods KernelNet \\(^+\\) R and RigNet while using fewer parameters. We also do not convert the problem into classification [23] or have complex forward passes with iterative layers [48]. The depth covariance outperforms methods with similar UNet architectures in RMSE. While the error is not state-of-the-art, we use a lightweight network and do not train specifically for the single task of depth completion with the number of samples known a priori." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.546, + 0.892, + 0.743 + ], + "angle": 0, + "content": "We also explore varying sparsity, as depth completion networks are trained for a specific number of samples. A comparison of RMSE for a varying number of test samples is shown in Table 2. Depth covariance outperforms traditional depth completion methods for sparse inputs, and is competitive with SpAgNet [8] which is designed specifically for these cases and contains an additional non-local spatial propagation layer. We note that for error metrics, we simply use the posterior mean for comparison. However, the GP provides a distribution over depths, not just a single instance, and we will demonstrate additional capabilities such as active decision-making and inferring latent geometry when direct observations are not present." + }, + { + "type": "table", + "bbox": [ + 0.537, + 0.752, + 0.857, + 0.832 + ], + "angle": 0, + "content": "
# Samples550100200
CSPN [6]2.0630.8840.3880.177
NLSPN [30]1.0330.4230.2460.142
SpAgNet [8]0.4670.2720.2090.155
Ours0.7170.2980.2360.193
" + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.842, + 0.892, + 0.883 + ], + "angle": 0, + "content": "Table 2. Depth completion RMSE (m) on NYUv2 with a varying number of input samples. The best result is in bold, while the second best is underlined." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13126" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.104, + 0.089, + 0.445, + 0.288 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.298, + 0.471, + 0.368 + ], + "angle": 0, + "content": "Figure 5. Calibration plots of varying posterior marginal covariance block dimensions \\( D \\) on NYUv2 depth completion. The ideal calibration is \\( y = x \\), where the observed confidence matches expected confidence. The region above the line indicates model under-confidence, while the area below signals over-confidence." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.395, + 0.283, + 0.41 + ], + "angle": 0, + "content": "5.1.2 Posterior Uncertainty" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.418, + 0.469, + 0.524 + ], + "angle": 0, + "content": "Calibrated uncertainties are beneficial for balancing multiple views and sensors in optimization. Under-confidence does not account for the full information given by the constraints, while over-confidence may bias the solution. Given that the GP provides uncertainties in addition to the mean, we evaluate the calibration properties for the depth completion setup from Section 5.1." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.525, + 0.47, + 0.766 + ], + "angle": 0, + "content": "Since most methods typically estimate per-pixel observation noise, we assess the effect of including off-diagonal terms. Calibration plots measure how well the model's predicted confidence matches the expected confidence. To extend calibration plots for regression beyond variance [21], we extract marginal covariances of varying dimension \\(D\\) and calculate Mahalanobis distances using this block-diagonal approximation to the full covariance matrix. We then count the frequency of distances below varying chi-square thresholds that define the expected confidence. In the variance case with \\(D = 1\\), residuals do not affect each other. We plot the results in Figure 5 along with the RMSE against the ideal calibration, where the empirical confidence matches the observed confidence. Including more off-diagonal terms improves the model calibration, and significantly reduces the under-confidence of the model." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.786, + 0.327, + 0.803 + ], + "angle": 0, + "content": "5.1.3 Active Sampling Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.469, + 0.902 + ], + "angle": 0, + "content": "While the location of depth samples is often not a free variable, such as from LiDAR or depth sensors, in monocular vision, we may wish to actively estimate the depths of certain pixels to improve downstream tasks such as dense reconstruction. We investigate whether the meta-learned covariance parameters may be leveraged for selecting more" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.09, + 0.891, + 0.233 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.244, + 0.892, + 0.287 + ], + "angle": 0, + "content": "Figure 6. Mean depth completion RMSE for random and active sampling with a varying number of samples. Green line shows mean percent improvement of active over random sampling." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.31, + 0.893, + 0.522 + ], + "angle": 0, + "content": "informative pixels. We compare randomly sampling pixels uniformly against the greedy conditional variance as described in Section 4.3. The effect on depth error and the mean percentage improvement for a varying number of samples is shown in Figure 6. Note that the greedy active sampling consistently outperforms random sampling. For a large number of samples, the relative improvement of active sampling decreases as there is a sufficient number of observations. For a few samples, only coarse depth structure is retained, and the greedy selection occasionally encourages samples near the image boundaries. Beyond greedy selection, other sampling methods that explicitly minimize uncertainty over the whole domain may demonstrate improved performance at the expense of computational cost." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.53, + 0.688, + 0.545 + ], + "angle": 0, + "content": "5.2. Bundle Adjustment" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.553, + 0.892, + 0.718 + ], + "angle": 0, + "content": "Bundle adjustment is the foundation for many vision pipelines, and when successful, it produces very accurate camera poses. However, the standard formulation of assuming independence between observations proves challenging for monocular SLAM systems, which may fail during initialization or when translation is negligible compared to rotation. Many realistic scenarios require robust and fast initialization with little motion. Therefore, we evaluate the use of the depth covariance in small baseline scenarios. We also show that the depth covariance is not limited to 2D depth map inference, but can also be used to infer 3D structure." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.719, + 0.892, + 0.825 + ], + "angle": 0, + "content": "Bundle adjustment jointly optimizes camera poses and point landmarks given pixel correspondences. Traditionally, the cost involves the sum of reprojection errors, as well as pose and scale priors on the first pose to fix gauge freedom which we omit for brevity. We also add our depth prior factor per camera, so for landmarks \\(\\mathbf{P}\\) in the world frame, and poses \\(T_{cW}\\) for \\(c = 1,\\dots,C\\), we have:" + }, + { + "type": "equation", + "bbox": [ + 0.567, + 0.833, + 0.891, + 0.897 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} E = \\sum_ {c} \\sum_ {i} | | \\pi (\\mathbf {T} _ {c W}, \\mathbf {P} _ {i}) - \\mathbf {x} _ {c, i} | | _ {\\sigma_ {r} ^ {2} I} ^ {2} \\\\ + \\sum_ {c} | | [ T _ {c W} \\mathbf {P} _ {c} ] _ {z} - m _ {c} | | _ {K _ {c}} ^ {2}, \\tag {12} \\\\ \\end{array}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13127" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.09, + 0.089, + 0.46, + 0.277 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.288, + 0.471, + 0.332 + ], + "angle": 0, + "content": "Figure 7. Factor graphs for bundle adjustment and monocular dense visual odometry. Pose and scale priors to constrain gauge freedom are omitted for simplicity." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.356, + 0.47, + 0.476 + ], + "angle": 0, + "content": "where each frame also has a scale variable \\( m_{c} \\) and depth covariance \\( K_{c} \\) for vectorized observed landmarks \\( \\mathbf{P}_c \\) in that frame. The projection function \\( \\pi \\) projects 3D landmarks into the image plane. To reduce the influence of outliers, we use the Huber cost function for reprojection errors. The least-squares cost is optimized using Levenberg-Marquardt via GTSAM [14]. The corresponding factor graph is shown on the left of Figure 7." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.477, + 0.471, + 0.839 + ], + "angle": 0, + "content": "We divide all of the Freiburg 1 sequences from the TUM RGB-D dataset into small baseline 5-frame windows. This yields 1544 sequences, emulating initializations to visual odometry systems. For the frontend, Shi-Tomasi corners [38] are detected and tracked using Lucas-Kanade tracking [24], and outliers are filtered out via essential matrix RANSAC. Poses are initialized using the motion capture data closest to the current RGB frame timestamps, while landmarks are triangulated if sufficiently constrained. We optimize each sequence with and without the GP depth prior to evaluate its effect. Using the corresponding depth frames, we compare the error of the reprojected sparse landmarks where valid depths occur, as well as the dense depth map error by conditioning on the sparse landmarks. To handle monocular scale ambiguity, we align depth maps with the optimal scale before computing the error. A boxplot of errors between the two methods is shown in Figure 8. By exploiting the correlation between observations, significantly more coherent geometry can be generated, as visualized by an example in Figure 9. Note that the prior is able to achieve consistent geometry despite the low baseline and only optimizing sparse landmarks. The shadows reveal the complete chair legs and table. We also show a longer example with over three seconds of data fused into a TSDF in Figure 1." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.848, + 0.385, + 0.865 + ], + "angle": 0, + "content": "5.3. Monocular Dense Visual Odometry" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.471, + 0.903 + ], + "angle": 0, + "content": "We propose a monocular dense visual odometry system based on the depth covariance and conditioning on per" + }, + { + "type": "image", + "bbox": [ + 0.513, + 0.089, + 0.885, + 0.226 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.235, + 0.895, + 0.278 + ], + "angle": 0, + "content": "Figure 8. Boxplot of sparse and dense depth errors across all 5-frame windows, with and without the GP depth prior included in the optimization. The median RMSE is written next to the plots." + }, + { + "type": "image", + "bbox": [ + 0.514, + 0.291, + 0.88, + 0.429 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.439, + 0.894, + 0.496 + ], + "angle": 0, + "content": "Figure 9. Qualitative example of small baseline bundle adjustment with and without the GP depth factor. Both examples fuse 5 predicted depth maps densified from the optimized landmarks into a TSDF, and are visualized with identical lighting." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.519, + 0.892, + 0.641 + ], + "angle": 0, + "content": "frame latent depth points. We perform sliding window optimization over a fixed number of keyframes, where poses, scales, and inducing depths are optimized per-frame. To regularize the depths, we may condition on the current estimates of the inducing depths as in Section 4.2, project depths into neighboring keyframes, and apply relative photometric error constraints. The photometric error for pixels \\( n \\) across all directed edges \\( E \\) is:" + }, + { + "type": "equation", + "bbox": [ + 0.507, + 0.648, + 0.892, + 0.696 + ], + "angle": 0, + "content": "\\[\nE = \\sum_ {i, j \\in E} \\sum_ {n} \\| I _ {i} (\\mathbf {x} _ {n}) - I _ {j} (w (\\mathbf {x} _ {n}, \\mathbf {T} _ {i}, \\mathbf {T} _ {j}, \\mathbf {y} _ {\\mathbf {i}}, m _ {i}) \\| _ {\\sigma_ {r} ^ {2} I} ^ {2}, \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.704, + 0.895, + 0.901 + ], + "angle": 0, + "content": "where \\(I\\) is the image intensity, \\(\\mathbf{x}\\) are pixel coordinates, \\(w\\) is the warping function that queries a depth map and projects the corresponding point into the neighboring image, \\(\\mathbf{T}\\) are camera poses, \\(\\mathbf{y}\\) are estimated log-depth observations, and \\(m\\) is the mean log depth. The corresponding factor graph with the depth priors included is shown in the right of Figure 7. To initialize a new keyframe, we first run the RGB through the network to get the covariance feature maps and sample inducing points. We perform active sampling since this is independent of whether log-depths are known as mentioned in 4.3, and stop sampling if a maximum variance threshold is achieved. The log-depths \\(\\mathbf{y}\\) of a new keyframe are initialized by minimizing the least-squares cost of the" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "13128" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.09, + 0.48, + 0.259 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.491, + 0.09, + 0.888, + 0.26 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.115, + 0.27, + 0.855, + 0.285 + ], + "angle": 0, + "content": "Figure 10. Example depth maps of keyframes while running the dense odometry on ScanNet validation sequences and TUM." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.312, + 0.471, + 0.343 + ], + "angle": 0, + "content": "prior at the sampled locations and a predictive data term over the reprojected depths \\(\\mathbf{d}\\) from the latest keyframe:" + }, + { + "type": "equation", + "bbox": [ + 0.088, + 0.352, + 0.47, + 0.378 + ], + "angle": 0, + "content": "\\[\n\\mathbf {y} _ {\\text {i n i t}} = \\underset {\\mathbf {y}} {\\arg \\min } \\| \\mathbf {y} - m \\| _ {K} ^ {2} + \\| \\log \\mathbf {d} - \\mathbf {f} _ {*} \\| _ {\\operatorname {d i a g} \\left[ \\Sigma_ {*} \\right]}. \\tag {14}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.387, + 0.47, + 0.509 + ], + "angle": 0, + "content": "When the window size is full, the last keyframe is dropped, and scale and pose priors are added to the current estimates of the oldest keyframe still in the window. Initialization of the system is achieved via two-frame SfM, where the first frame's depth map is optimized along with the relative pose to another frame. Tracking is performed using coarse-to-fine photometric tracking against the last keyframe. We also optimize affine brightness parameters for varying exposure." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.509, + 0.47, + 0.659 + ], + "angle": 0, + "content": "For quantitative evaluation, we compare our odometry to other learning-based dense systems on Freiburg 1 sequences in the TUM dataset. Since our method is purely a sliding-window odometry system, we compare against the comparable monocular methods evaluated in [44] that lack global optimization and bundle adjustment. We omit the floor sequence which drops a significant number of frames. The absolute trajectory errors (ATE) are shown in Table 3. Since our dense odometry does not have global scale, we align to the ground-truth trajectory's scale." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.66, + 0.47, + 0.825 + ], + "angle": 0, + "content": "Despite the simplicity of our system, it is first or second in 6 of 8 sequences, and achieves the best mean error. We only use photometric error compared to the additional depth and keypoint factors in DeepFactors. While we lack many features in our simple sliding window formulation, we believe that including additional components will further improve the results. Qualitative examples of inferred depth maps from the odometry on ScanNet validation sequences and TUM are shown in Figure 10. Note that our representation can predict a variety of thin structures and regularize flat ones, even in the case of high-frequency texture." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.47, + 0.903 + ], + "angle": 0, + "content": "Odometry runs at an average of \\(7 - 10\\mathrm{Hz}\\) on \\(256\\times 192\\) images with 8 keyframes on a single RTX 3080. The code is not optimized and alternates between tracking and mapping in a single thread, so we believe significant speed-ups are possible given additional optimization or compute." + }, + { + "type": "table", + "bbox": [ + 0.515, + 0.309, + 0.88, + 0.48 + ], + "angle": 0, + "content": "
SequenceTartanVO [46]DeepV2D [43]DeepFactors [9]Ours
3600.1780.2430.1590.128
desk0.1250.1660.1700.056
desk20.1220.3790.2530.048
plant0.2970.2030.3050.261
room0.3330.2460.3640.257
ryp0.0490.1050.0430.052
teddy0.3390.3160.6010.475
xyz0.0620.0640.0350.056
mean0.1880.2150.2410.167
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.49, + 0.892, + 0.518 + ], + "angle": 0, + "content": "Table 3. Absolute trajectory error (m) on TUM Freiburg1. The best result is in bold, while the second best is underlined." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.545, + 0.771, + 0.562 + ], + "angle": 0, + "content": "6. Conclusion and Future Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.572, + 0.892, + 0.798 + ], + "angle": 0, + "content": "We have proposed learning a depth covariance function, and provided selections of the depth representation, the base covariance function, and the optimization objective. While we selected a specific nonstationary kernel function, exploration of alternatives is a valuable direction. There is great potential to draw additional connections to the literature on Bayesian methods and GPs. For example, geometry is low-rank and spatially local, so active selection and sparse kernels will permit scalability to higher resolutions and larger capacities. Lastly, we demonstrated the utility of the covariance function on a selection of geometric vision tasks: depth completion, bundle adjustment, and monocular dense visual odometry. Beyond these applications, the prior can be tightly integrated into existing geometric vision frameworks and serve as a foundation for novel formulations." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.815, + 0.688, + 0.832 + ], + "angle": 0, + "content": "7. Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.84, + 0.894, + 0.901 + ], + "angle": 0, + "content": "Research presented in this paper has been supported by Dyson Technology Ltd. We would like to thank Tristan Laidlow, Hidenobu Matsuki, Riku Murai, and members of the Dyson Robotics Lab for insightful discussions." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "13129" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.171 + ], + "angle": 0, + "content": "[1] Gwangbin Bae, Ignas Budvytis, and Roberto Cipolla. Iron-Depth: Iterative refinement of single-view depth using surface normal and its uncertainty. In Proceedings of the British Machine Vision Conference (BMVC), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.173, + 0.472, + 0.227 + ], + "angle": 0, + "content": "[2] Matthias Bauer, Mark van der Wilk, and Carl Edward Rasmussen. Understanding probabilistic sparse Gaussian process approximations. In Neural Information Processing Systems (NeurIPS), 2016. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.23, + 0.471, + 0.299 + ], + "angle": 0, + "content": "[3] Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, and Andrew J. Davison. CodeSLAM - learning a compact, optimisable representation for dense visual SLAM. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.301, + 0.471, + 0.357 + ], + "angle": 0, + "content": "[4] Michael Bloesch, Tristan Laidlow, Ronald Clark, Stefan Leutenegger, and Andrew Davison. Learning meshes for dense visual SLAM. In Proceedings of the International Conference on Computer Vision (ICCV), 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.359, + 0.472, + 0.399 + ], + "angle": 0, + "content": "[5] Weizhe Chen, Roni Khardon, and Lantao Liu. AK: Attentive kernel for information gathering. In Proceedings of Robotics: Science and Systems (RSS), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.402, + 0.471, + 0.457 + ], + "angle": 0, + "content": "[6] Xinjing Cheng, Peng Wang, and Ruigang Yang. Depth estimation via affinity learned with convolutional spatial propagation network. In Proceedings of the European Conference on Computer Vision (ECCV), 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.458, + 0.47, + 0.5 + ], + "angle": 0, + "content": "[7] Javier Civera, Andrew J. Davison, and J. M. Martínez Montiel. Inverse depth parametrization for monocular SLAM. IEEE Transactions on Robotics (T-RO), 2008. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.502, + 0.471, + 0.557 + ], + "angle": 0, + "content": "[8] Andrea Conti, Matteo Poggi, and Stefano Mattoccia. Sparsity agnostic depth completion. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.559, + 0.471, + 0.613 + ], + "angle": 0, + "content": "[9] Jan Czarnowski, Tristan Laidlow, Ronald Clark, and Andrew J. Davison. DeepFactors: Real-time probabilistic dense monocular SLAM. IEEE Robotics and Automation Letters (RA-L), 2020. 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.616, + 0.47, + 0.685 + ], + "angle": 0, + "content": "[10] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. ScanNet: Richly-annotated 3D reconstructions of indoor scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.687, + 0.47, + 0.741 + ], + "angle": 0, + "content": "[11] Garoe Dorta, Sara Vicente, Lourdes Agapito, Neill D. F. Campbell, and Ivor Simpson. Structured uncertainty prediction networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.743, + 0.469, + 0.798 + ], + "angle": 0, + "content": "[12] David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. In Neural Information Processing Systems (NeurIPS), 2014. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.801, + 0.469, + 0.855 + ], + "angle": 0, + "content": "[13] Jakob Engel, Thomas Schöps, and Daniel Cremers. LSD-SLAM: Large-scale direct monocular SLAM. In Proceedings of the European Conference on Computer Vision (ECCV), 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.858, + 0.405, + 0.871 + ], + "angle": 0, + "content": "[14] Frank Dellaert et al. borglab/gtsam, May 2022. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[15] Clement Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow. Digging into self-supervised monocular" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.12 + ], + "angle": 0, + "content": "depth estimation. In Proceedings of the International Conference on Computer Vision (ICCV), 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.892, + 0.176 + ], + "angle": 0, + "content": "[16] Benjamin Graham and David Novotny. RidgeSfM: Structure from motion via robust pairwise matching under depth uncertainty. In Proceedings of the International Conference on 3D Vision (3DV), 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.178, + 0.892, + 0.232 + ], + "angle": 0, + "content": "[17] Carlos Guestrin, Andreas Krause, and Ajit Paul Singh. Nearoptimal sensor placements in Gaussian processes. In Proceedings of the International Conference on Machine Learning (ICML), 2005. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.234, + 0.892, + 0.272 + ], + "angle": 0, + "content": "[18] Tony Jebara, Risi Kondor, and Andrew Howard. Probability product kernels. The Journal of Machine Learning Research, 2004. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.274, + 0.892, + 0.315 + ], + "angle": 0, + "content": "[19] Alex Kendall and Yarin Gal. What uncertainties do we need in Bayesian deep learning for computer vision? In Neural Information Processing Systems (NeurIPS), 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.317, + 0.892, + 0.357 + ], + "angle": 0, + "content": "[20] Diederik P. Kingma and Max Welling. Auto-encoding variational Bayes. In Proceedings of the International Conference on Learning Representations (ICLR), 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.359, + 0.892, + 0.413 + ], + "angle": 0, + "content": "[21] Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon. Accurate uncertainties for deep learning using calibrated regression. In Proceedings of the International Conference on Machine Learning (ICML), 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.415, + 0.892, + 0.467 + ], + "angle": 0, + "content": "[22] Tobias Lang, Christian Plagemann, and Wolfram Burgard. Adaptive non-stationary kernel regression for terrain modeling. In Proceedings of Robotics: Science and Systems (RSS), 2007. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.47, + 0.892, + 0.51 + ], + "angle": 0, + "content": "[23] Lina Liu, Yiyi Liao, Yue Wang, Andreas Geiger, and Yong Liu. Learning steering kernels for guided depth completion. IEEE Transactions on Image Processing, 2021. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.512, + 0.892, + 0.567 + ], + "angle": 0, + "content": "[24] Bruce D. Lucas and Takeo Kanade. An iterative image registration technique with an application to stereo vision. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1981. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.568, + 0.892, + 0.621 + ], + "angle": 0, + "content": "[25] Fangchang Ma and Sertac Karaman. Sparse-to-dense: Depth prediction from sparse depth samples and a single image. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.623, + 0.892, + 0.676 + ], + "angle": 0, + "content": "[26] Raul Mur-Artal and Juan D. Tardós. ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Transactions on Robotics (T-RO), 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.679, + 0.892, + 0.734 + ], + "angle": 0, + "content": "[27] Richard. A. Newcombe, Steven J. Lovegrove, and Andrew J. Davison. DTAM: Dense tracking and mapping in real-time. In Proceedings of the International Conference on Computer Vision (ICCV), 2011. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.735, + 0.892, + 0.788 + ], + "angle": 0, + "content": "[28] Sebastian W. Ober, Carl E. Rasmussen, and Mark van der Wilk. The promises and pitfalls of deep kernel learning. In Conference on Uncertainty in Artificial Intelligence, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.79, + 0.892, + 0.843 + ], + "angle": 0, + "content": "[29] Christopher Paciorek and Mark Schervish. Nonstationary covariance functions for Gaussian process regression. In Neural Information Processing Systems (NeurIPS), 2003. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.846, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[30] Jinsun Park, Kyungdon Joo, Zhe Hu, Chi-Kuei Liu, and In So Kweon. Non-local spatial propagation network for depth completion. In Proceedings of the European Conference on Computer Vision (ECCV), 2020. 5" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "13130" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.148 + ], + "angle": 0, + "content": "[31] Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O' Boyle, and Amos J Storkey. Bayesian meta-learning for the few-shot setting via deep kernels. In Neural Information Processing Systems (NeurIPS), 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.151, + 0.472, + 0.22 + ], + "angle": 0, + "content": "[32] Rene Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.222, + 0.47, + 0.263 + ], + "angle": 0, + "content": "[33] Ananth Ranganathan, Ming-Hsuan Yang, and Jeffrey Ho. Online sparse Gaussian process regression and its applications. IEEE Transactions on Image Processing, 2011. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.266, + 0.47, + 0.305 + ], + "angle": 0, + "content": "[34] Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2005. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.31, + 0.47, + 0.351 + ], + "angle": 0, + "content": "[35] Jean-Michel M. Rendu. Normal and lognormal estimation. Journal of the International Association for Mathematical Geology, 1979. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.354, + 0.47, + 0.422 + ], + "angle": 0, + "content": "[36] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.425, + 0.47, + 0.479 + ], + "angle": 0, + "content": "[37] Johannes L. Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.483, + 0.47, + 0.524 + ], + "angle": 0, + "content": "[38] J. Shi and C. Tomasi. Good features to track. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1994. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.527, + 0.47, + 0.581 + ], + "angle": 0, + "content": "[39] Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from RGBD images. In Proceedings of the European Conference on Computer Vision (ECCV), 2012. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.584, + 0.47, + 0.64 + ], + "angle": 0, + "content": "[40] Ivor J. A. Simpson, Sara Vicente, and Neill D. F. Campbell. Learning structured Gaussians to approximate deep ensembles. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.643, + 0.47, + 0.711 + ], + "angle": 0, + "content": "[41] Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evaluation of RGB-D SLAM systems. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2012. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.714, + 0.47, + 0.755 + ], + "angle": 0, + "content": "[42] Chengzhou Tang and Ping Tan. BA-Net: Dense bundle adjustment networks. In Proceedings of the International Conference on Learning Representations (ICLR), 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.758, + 0.47, + 0.813 + ], + "angle": 0, + "content": "[43] Zachary Teed and Jia Deng. DeepV2D: Video to depth with differentiable structure from motion. In Proceedings of the International Conference on Learning Representations (ICLR), 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.816, + 0.47, + 0.857 + ], + "angle": 0, + "content": "[44] Zachary Teed and Jia Deng. DROID-SLAM: Deep visual SLAM for monocular, stereo, and RGB-D cameras. In Neural Information Processing Systems (NeurIPS), 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[45] Michalis Titsias. Variational learning of inducing variables in sparse Gaussian processes. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2009. 4" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.134 + ], + "angle": 0, + "content": "[46] Wenshan Wang, Yaoyu Hu, and Sebastian Scherer. TartanVO: A generalizable learning-based VO. In Conference on Robot Learning (CoRL), 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.892, + 0.189 + ], + "angle": 0, + "content": "[47] Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P. Xing. Deep kernel learning. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.193, + 0.892, + 0.247 + ], + "angle": 0, + "content": "[48] Zhiqiang Yan, Kun Wang, Xiang Li, Zhenyu Zhang, Jun Li, and Jian Yang. RigNet: Repetitive image guided network for depth completion. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.25, + 0.892, + 0.304 + ], + "angle": 0, + "content": "[49] Sungho Yoon and Ayoung Kim. Balanced depth completion between dense depth inference and sparse range measurements via KISS-GP. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2020. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.304 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "13131" + } + ] +] \ No newline at end of file diff --git a/2023/Learning a Depth Covariance Function/ce47874e-679a-4e09-bfc4-c70c4939d294_origin.pdf b/2023/Learning a Depth Covariance Function/ce47874e-679a-4e09-bfc4-c70c4939d294_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..d0f597238508a73f075bf768266132f67b493592 --- /dev/null +++ b/2023/Learning a Depth Covariance Function/ce47874e-679a-4e09-bfc4-c70c4939d294_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ab48f053b08aa690d39f8fcb56a7e4db263b7b98543acceef694411a78ecc1e6 +size 9613245 diff --git a/2023/Learning a Depth Covariance Function/full.md b/2023/Learning a Depth Covariance Function/full.md new file mode 100644 index 0000000000000000000000000000000000000000..1bed300ce3a8edbea27452da267c9dc6e3d8b5eb --- /dev/null +++ b/2023/Learning a Depth Covariance Function/full.md @@ -0,0 +1,320 @@ +# Learning a Depth Covariance Function + +Eric Dexheimer and Andrew J. Davison Dyson Robotics Lab, Imperial College London {e.dexheimer21, a.davison}@imperial.ac.uk + +# Abstract + +We propose learning a depth covariance function with applications to geometric vision tasks. Given RGB images as input, the covariance function can be flexibly used to define priors over depth functions, predictive distributions given observations, and methods for active point selection. We leverage these techniques for a selection of downstream tasks: depth completion, bundle adjustment, and monocular dense visual odometry. + +# 1. Introduction + +Inferring the 3D structure of the world from 2D images is an essential computer vision task. In recent years, there has been significant interest in combining principled multiple view geometry with data-driven priors. Learning-based methods that predict geometry provide a prior directly over the latent variables, which avoids the ill-posed configurations of traditional methods. However, direct, overconfident priors may prevent realization of the true 3D structure when multi-view geometry is well-defined. For example, data-driven methods have shown tremendous promise in monocular depth estimation, but often lack consistency when fusing information into 3D space. + +Thus far, designing a unified framework that combines the best of learning and optimization methods has proven challenging. Recent data-driven methods have attempted to relax rigid geometric constraints by also predicting low-dimensional subspaces or per-pixel uncertainties. Fixing capacity during training is often wasteful or inflexible at test time, while per-pixel residual distributions typically explain away the limitations of the model instead of the relationship between errors. In reality, the 3D world is anywhere from simple to complex, and an ideal system should explicitly adapt its capacity and correlation based on the scene. + +In this paper, rather than directly predicting geometry from images, we propose learning a depth covariance function. Given an RGB image, our method predicts how the depths of any two pixels relate. To achieve this, a neural network transforms color information to a feature space, and + +![](images/bcc7a3f45e349900d139d1bd5b7122baaa2c5bcf9bc01e4fe28c8f565124e518.jpg) +Figure 1. Example monocular reconstruction using the depth covariance for bundle adjustment and dense depth prediction from three seconds (100 frames) of TUM data. Three representative images and the mesh from TSDF fusion of the depth predictions are shown. Each frame leverages the learned covariance function to model geometric correlation between pairs of scene points. + +a Gaussian process (GP) models a prior distribution given these features and a base kernel function. The distinction between image processing and the prior enables promoting locality and granting flexible capacity at test time. Locality avoids over-correlating pixels on distinct structures, while adaptive capacity permits tuning the complexity of our subspace to the content of the viewed scene. + +Learning this flexible, high-level prior allows for balancing data-driven methods with test-time optimization that can be applied to a variety of geometric vision tasks. Furthermore, the covariance function is agnostic to the 3D representation as it does not directly learn a geometric output. Depth maps may be requested by conditioning on observations, but the prior may also be leveraged for inferring the desired latent 3D representation. In Figure 1, we illustrate depth covariance along with an example of bundle adjustment, dense depth prediction, and multi-view fusion. + +In summary, our key contributions are: + +- A framework for learning the covariance function by selecting a depth representation, a base kernel function, and a scalable optimization objective +- Application of the prior to depth completion, bundle adjustment, and monocular dense visual odometry + +# 2. Related Work + +View-based Priors for Geometric Inference Traditionally, sparse reconstruction methods have ignored per-image correlations [37], [26], while dense methods [27], [13] have used naive priors based on neighboring pixels. With the availability of depth data and the rise of deep learning, a variety of learned depth priors have been proposed. Although monocular depth estimation methods that directly predict geometry [12], [15], [32] have demonstrated great progress, they may predict irreparable mistakes and do not allow for handling ambiguities. Utilizing surface normals may refine these depth estimates [1], but the improvement is still subject to errors from the initial depth prediction. For this reason, we focus on approaches that balance learned priors and test-time optimization. Popular approaches include predicting low-dimensional latent codes [3], [9] and generating a basis of depth maps [42], [16]. While these methods utilize single image information to reduce the dimensionality of depth map inference, they fix capacity during training, produce overly smooth depth maps, and create over-correlated global changes of the depth map. Predicting mesh vertices in the image plane [4] permits distribution of capacity and optimizing depth, but the number of latent variables is still fixed and an explicit mesh representation cannot easily model complex scenes. + +Instead of predicting geometry directly, we focus on predicting geometric correlation. By decoupling the pipeline into an image processing network and a GP, the network is not responsible for learning geometry directly and we do not need to fix the geometric capacity. The dimensionality of the subspace may be adapted for representing low-rank scenes more compactly and complex geometry with high-fidelity. Lastly, we use a covariance function with locality to bias learning towards local appearance information. + +Learning Covariance Functions Choosing covariance functions and performing model selection for GPs is a well-studied topic [34]. While stationary kernels have been explored for merging LiDAR observations with monocular depth estimates [49], more expressive kernels are required for sparse observations. Nonstationary covariance functions using local Gaussian parameterizations [29] have shown potential in robotic terrain mapping [22]. However, since GP model selection occurs per data example, optimizing hyperparameters is challenging, and novel nonstationary kernels have been proposed to limit flexibility [5]. + +Rather than performing model selection per image, we are inspired by deep kernel learning (DKL) [47], which leverages training data to predict kernel hyperparameters. Alternatively, this may be viewed as meta-learning, where each task is an RGB-depth example [31]. To mitigate overcorrelation when using stationary kernels in feature space as proposed in DKL [28], we utilize local information in pixel space [29] to model surface geometry. While deep nonstationary kernel regression has been explored for depth completion [23], GPs balance data fit and model complexity during training, and the uncertainty estimates are conducive to decision-making and inference in optimization frameworks. Residual Covariances in Computer Vision Uncertainties in machine learning are often divided between two types: model uncertainty and data uncertainty [19]. For vision problems, model uncertainty is often ignored due to the availability of data and for tractable optimization. Residual uncertainty is usually predicted from a network as a per-pixel variance due to high-dimensionality, but the assumption of independence ignores the correlation present in images. For example, variational autoencoders (VAEs) [20] with a diagonal likelihood output overly smooth mean predictions and unnatural samples with salt-and-pepper noise. To introduce correlation in the likelihood, structured uncertainty prediction networks (SUPNs) predict a full information matrix by defining a graph topology consisting of specific neighbors [11]. Since this matrix is sparse, it can be efficiently inverted to obtain the covariance matrix. This has also been leveraged to distill monocular depth prediction ensembles into a single SUPN [40]. + +Similar to SUPNs, we move beyond diagonal covariance approximations that lack correlation. However, we learn a covariance function which does not require a predefined graph topology and allows for long-range correlations beyond neighboring pixels. Furthermore, the marginal distribution of any set of variables may be examined without constructing the full joint distribution, which is of great interest for compact inference in geometric vision. + +# 3. Learning a Depth Covariance Function + +Given an image, we model a distribution over possible depth functions via a Gaussian process (GP) [34]. With the input domain being normalized image coordinates $\mathbf{x}_i = (u_i,v_i)$ where $u_{i},v_{i}\in [-1,1]$ , the outputs for any finite set of inputs are jointly Gaussian. The GP is then defined by a mean function $m(\mathbf{x})$ and a covariance function $k(\mathbf{x},\mathbf{x}^{\prime})$ : + +$$ +f (\mathbf {x}) \sim \mathcal {G P} (m (\mathbf {x}), k (\mathbf {x}, \mathbf {x} ^ {\prime})). \tag {1} +$$ + +For learning the parameters of this GP, we need to select the representation of depth, the mean and covariance functions, and the optimization objective. We will outline our decisions in the following sections, but there exist many ways to define this prior. + +# 3.1. Depth Representation + +When predicting depth, we often want to attenuate errors further from the camera view, so that nearby structures are prioritized. Examples of such representations include inverse depth [7], disparity [32], and log-depth [12]. We select log-depth for two reasons. First, the log-normal distribution is more suitable for skewed distributions [35], as the range of the GP is unbounded. Representing depth or inverse depth with a normal distribution would require ad hoc truncation, as depth functions could move behind the camera. Second, we represent depth with relative scale, as the absolute scale ambiguity in monocular images accounts for much of the error in learned depth prediction [12]. We can then focus on learning the relationship between predictions. In the log-depth formulation, a constant mean $m(\mathbf{x}_i) = m$ directly corresponds to a scale variable. Given a log-depth observation $\mathbf{y}_i = \log \mathbf{d}_i$ , we can adjust its scale via this mean: + +$$ +e ^ {\mathbf {y} _ {i} - m} = e ^ {\mathbf {y} _ {i}} e ^ {- m} = C e ^ {\mathbf {y} _ {i}} = C \mathbf {d} _ {i}. \tag {2} +$$ + +In other words, the median of the depth distribution is controlled by the mean of the log-normal distribution. During training, similar to [12], we may find the optimal scale $m$ that minimizes our data loss. In addition, the log-depth representation allows covariance to be in relative scale. At test time, the scale may be fixed if known, or jointly optimized with other variables. Learning a more expressive mean function per image would be useful in the absence of any depth observations, but introduces many degrees of freedom. For GPs, generalization is largely dependent on the covariance function, so we leave the mean to be a constant per image. + +# 3.2. Covariance Function + +We would like to learn the parameters of a depth covariance function using pairs of RGB and depth images. By definition, the covariance function must be positive semidefinite (PSD). In our framework, an RGB image is fed into a convolutional neural network (CNN), which outputs features for a base covariance function. We model the depth observations as being jointly Gaussian so that the CNN and base kernel hyperparameters may be jointly learned. + +For the base covariance function, we would like to avoid over-correlation of independent structures. To achieve local influence, we use the family of nonstationary kernels described in [29]. Each point $\mathbf{x}_i$ in an image can be viewed as a Gaussian distribution with a 2D kernel matrix $\Sigma_i$ . Then, the covariance between two points is the convolution of these two densities over the input domain with a normalizing constant. Alternatively, this can be viewed as the similarity between distributions via the Bhattacharyya kernel [18]. Given an isotropic covariance function $R^S (\sqrt{q})$ , + +![](images/08b72017400e437045c8a1d0b6e4fd6db6ed5424056f9787f748da7b2e06cb99.jpg) +Figure 2. Visualizing our depth covariance function: for every pixel of an input image, the trained network predicts a 2D kernel matrix. Here we show the covariance function between pairs of pixels in both matrix form and as edges in a graph, with the line thickness representing the magnitude of covariance. + +the closed-form expression is: + +$$ +k \left(\mathbf {x} _ {i}, \mathbf {x} _ {j}\right) = \sigma_ {f} ^ {2} \frac {\left| \Sigma_ {i} \right| ^ {1 / 4} \left| \Sigma_ {j} \right| ^ {1 / 4}}{\left| \Sigma_ {i} + \Sigma_ {j} \right| ^ {1 / 2}} R ^ {S} (\sqrt {q}), \tag {3} +$$ + +$$ +q = \left(\mathbf {x} _ {i} - \mathbf {x} _ {j}\right) ^ {T} \left(\Sigma_ {i} + \Sigma_ {j}\right) ^ {- 1} \left(\mathbf {x} _ {i} - \mathbf {x} _ {j}\right), \tag {4} +$$ + +where $\sigma_f^2$ is a learnable signal variance. To better handle discontinuities, we select the Matérn function as our isotropic covariance function. With the base covariance function requiring a 2D PSD matrix for each pixel, the CNN outputs three channels $(c_1, c_2, c_3)$ , which are parameterized given the positive diagonal and determinant constraints: + +$$ +\Sigma_ {i} = \left[ \begin{array}{c c} e ^ {c _ {1}} & \tanh (c _ {3}) \sqrt {e ^ {c _ {1}} * e ^ {c _ {2}}} \\ \tanh (c _ {3}) \sqrt {e ^ {c _ {1}} * e ^ {c _ {2}}} & e ^ {c _ {2}} \end{array} \right]. \tag {5} +$$ + +An example of five kernel matrices and the marginal covariance in matrix and graph form is shown in Figure 2. We use a UNet architecture [36] and output features at different levels for a multi-scale loss. Each scale also has its own GP hyperparameters, signal variance $\sigma_f^2$ and noise variance $\sigma_n^2$ . Depth maps are coarsened for lower-levels, so finer levels are weighted higher during the total loss calculation. + +While a more global covariance function, such as a squared-exponential over output features, would provide additional flexibility, we avoid this for two reasons. First, changes in depths on one part of the scene may have significant influence on a completely unrelated part, as seen in [3]. Second, deep kernel learning using this setup is unstable and biased towards over-correlating the input domain [28]. By directly using pixel coordinates in our covariance function, we restrict changes in geometry to be local and bias the network to learn local appearance information. + +# 3.3. Optimization Objective + +In GP literature, model selection is performed by minimizing the negative log marginal likelihood (NLML) + +$$ +\begin{array}{l} - \log p (\mathbf {y} | X) = \frac {1}{2} (\mathbf {y} - m) ^ {T} \left(K _ {\mathrm {f f}} + \sigma_ {n} ^ {2} I\right) ^ {- 1} (\mathbf {y} - m) \\ + \frac {n}{2} \log 2 \pi + \frac {1}{2} \log | K _ {\mathrm {f f}} + \sigma_ {n} ^ {2} I |, \tag {6} \\ \end{array} +$$ + +where the matrix $K_{\mathrm{ff}}$ is a PSD matrix with entries defined by the covariance function. The first term is often referred to as the "data fit", while the third term is the "complexity penalty". In our scenario, the complexity term is minimized by correlating points, while the data term ensures the ground-truth depth map is plausible given the mean and predicted covariance. + +However, the marginal likelihood for a GP requires a $O(N^3)$ matrix inversion. For pixels in an image, this becomes intractable. Since depth images are relatively low-rank, we use a sparse GP approximation [2] during training by randomly sampling inducing points for a Nystrom approximation to the full covariance matrix. Given the covariance matrix for the inducing points $K_{\mathrm{uu}}$ and the cross-covariance between the inducing points and the entire domain $K_{\mathrm{uf}}$ , we have: + +$$ +K _ {\mathrm {f f}} \approx \tilde {K} _ {\mathrm {f f}} \triangleq K _ {\mathrm {f u}} K _ {\mathrm {u u}} ^ {- 1} K _ {\mathrm {u f}}. \tag {7} +$$ + +Specifically, we use the variational free energy (VFE) framework [45], which defines our training loss as + +$$ +\begin{array}{l} \mathcal {F} = \frac {1}{2} (\mathbf {y} - m) ^ {T} \left(\tilde {K} _ {\mathrm {f f}} + \sigma_ {n} ^ {2} I\right) ^ {- 1} (\mathbf {y} - m) + \frac {n}{2} \log 2 \pi \\ + \frac {1}{2} \log | \tilde {K} _ {\mathrm {f f}} + \sigma_ {n} ^ {2} I | + \frac {1}{2 \sigma_ {n} ^ {2}} \operatorname {t r} \left(K _ {\mathrm {f f}} - \tilde {K} _ {\mathrm {f f}}\right). \tag {8} \\ \end{array} +$$ + +The first three terms are the same as the original NLML, but using the approximate covariance, while the last term penalizes the conditional variances at all inputs given the inducing points, which only requires the diagonal of the full covariance matrix. Note that we also assume homoscedastic observation noise variance $\sigma_{n}^{2}$ across the entire dataset. + +# 4. Leveraging a Depth Covariance Function + +Given an image and its corresponding CNN output maps, we may leverage the covariance function by defining a prior over depth functions, conditioning on depth observations to yield a predictive distribution, and actively sampling pixel locations that minimize the variance of depth predictions. + +# 4.1. Gaussian Process Prior + +Given a finite set of input points $X_{*}$ , we may define a Gaussian prior over their log-depths: + +$$ +\mathbf {f} _ {*} \sim \mathcal {N} (m, K (X _ {*}, X _ {*})). \tag {9} +$$ + +![](images/f8a1e88a7239dbf8b964ae433baa7caa90e786cfe230bbfaa0f7f271ef19accf.jpg) +Figure 3. Conditioning example for 128 samples. The posterior variance is high around edges and in areas lacking samples. The columns of $K_{\mathrm{fn}}$ , or correlation maps, are shown for select points. + +The GP defines a prior over functions, and for any number of input points, we obtain a Gaussian prior. For geometric vision tasks, this prior can be leveraged for anything from sparse to dense methods. Due to the marginalization property of GPs, adding points to the indexing set will not change the marginal distribution of the existing set. Alternatively, the prior may be viewed as an image-conditioned regularizer of any desired latent geometry parameterization. + +# 4.2. Predictive Distribution + +In many cases, log-depth observations $\mathbf{y}$ , such as from RGB-D sensors or LiDAR, may be available. We can explicitly condition the prior on these observations to obtain a posterior distribution. The predictive mean $\mathbf{f}_{*}$ and covariance $\Sigma_{*}$ given $N$ samples is: + +$$ +\mathbf {f} _ {*} = m + K _ {\mathrm {f n}} \left(K _ {\mathrm {n n}} + \sigma_ {n} ^ {2} I\right) ^ {- 1} (\mathbf {y} - m), \tag {10} +$$ + +$$ +\Sigma_ {*} = K _ {\mathrm {f f}} - K _ {\mathrm {f n}} \left(K _ {\mathrm {n n}} + \sigma_ {n} ^ {2} I\right) ^ {- 1} K _ {\mathrm {n f}}. \tag {11} +$$ + +The predictive mean is a linear function in terms of the observations $\mathbf{y}$ , so test-time inference of latent depths is efficient. If a full covariance is not required, only certain blocks or per-pixel variances need to be calculated. Note that the covariance depends only on the observation coordinates and not the observed values. We visualize the predictive mean, variance, and correlation maps for an example in Figure 3. + +# 4.3. Active Point Selection + +For dense reconstruction, it is beneficial to construct a compact representation that can achieve high-fidelity results. As mentioned previously, the predictive covariance depends only on the RGB image and locations of depth observations, but not on the observations themselves. By viewing the CNN as a meta-learned initialization of the nonstationary kernel parameters, we may use 2D observations as a proxy for the complexity of 3D geometry. Active selection of informative pixels up to a desired variance permits + +![](images/c43530ae22fbf80585b1faac80725a27dea23510509d4cdbe690419100b89743.jpg) +Figure 4. Qualitative comparison of random and active sampling given 32 sample selections. Random sampling misrepresents depth at the top of the image, while active sampling focuses on the chair geometry and avoids redundant samples on the floor. + +representing less complex scenes with fewer samples and allocating capacity towards high-frequency geometry. + +Inspired by sensor placement literature [17], an entropy-based criterion is used to select informative pixels. In the greedy-case, this simplifies to selecting the input point with the highest conditional variance at each step. Since this requires computing the conditional variance for each newly added point, we leverage incremental updates to the variance and Cholesky factorization of the training covariance matrix [33]. By decoupling the neural network and GP, we do not require any additional network passes. + +A qualitative example of active sampling is shown in Figure 4. Random sampling severely misrepresents depth of table and chairs, which all appear at similar depths. Active sampling focus samples around the thin chair edge near the image, while also avoiding oversampling on the floor. Furthermore, active sampling avoids missing the top section of the image, so that the table and chairs are well-represented. + +# 5. Applications + +We apply depth covariance to three fundamental geometric vision tasks: depth completion, bundle adjustment, and monocular dense visual odometry (DVO). For depth completion, we use the NYUv2 benchmark [39] and the train-test splits from [25]. For bundle adjustment and DVO, we train the covariance function on ScanNet [10], and evaluate on the TUM RGB-D dataset [41]. For the UNet, we use an input size of $256 \times 192$ , 16 channels after the first layer, 5 downsampling steps, and 4 output levels. This results in roughly 9 million parameters. + +# 5.1. Depth Completion + +Depth completion is a fundamental task that will be leveraged for additional applications. We may directly condition on sparse observations as described in Section 4.2 to obtain a dense depth map and uncertainties. + +# 5.1.1 Completion Accuracy and Error + +Since our method does not predict a specific instance of geometry, we report errors with respect to the GP posterior mean. We compare to the foundational Sparse-to-Dense (S2D) [25], deep kernel regression with (KernelNet+R) and without refinement (KernelNet) [23], and a recent state-of-the-art network RigNet [48]. Results on the validation set with 500 random samples are shown in Table 1. + +
MethodError (↓) RMSEAccuracy (↑)
δ1.02δ1.05δ1.10δ1.25δ1.252
S2D [25]0.204---97.899.6
KernelNet [23]0.19865.582.991.697.799.8
KernelNet+R [23]0.11184.894.197.499.399.9
RigNet [48]0.090---99.799.9
Ours0.15781.292.697.199.499.9
+ +KernelNet also predicts three feature maps for 2D covariance parameters, but we achieve better performance under the GP framework. We achieve comparable accuracy to state-of-the-art methods KernelNet $^+$ R and RigNet while using fewer parameters. We also do not convert the problem into classification [23] or have complex forward passes with iterative layers [48]. The depth covariance outperforms methods with similar UNet architectures in RMSE. While the error is not state-of-the-art, we use a lightweight network and do not train specifically for the single task of depth completion with the number of samples known a priori. + +We also explore varying sparsity, as depth completion networks are trained for a specific number of samples. A comparison of RMSE for a varying number of test samples is shown in Table 2. Depth covariance outperforms traditional depth completion methods for sparse inputs, and is competitive with SpAgNet [8] which is designed specifically for these cases and contains an additional non-local spatial propagation layer. We note that for error metrics, we simply use the posterior mean for comparison. However, the GP provides a distribution over depths, not just a single instance, and we will demonstrate additional capabilities such as active decision-making and inferring latent geometry when direct observations are not present. + +Table 1. Depth completion on NYUv2 with 500 sampled points. + +
# Samples550100200
CSPN [6]2.0630.8840.3880.177
NLSPN [30]1.0330.4230.2460.142
SpAgNet [8]0.4670.2720.2090.155
Ours0.7170.2980.2360.193
+ +Table 2. Depth completion RMSE (m) on NYUv2 with a varying number of input samples. The best result is in bold, while the second best is underlined. + +![](images/746e05b8afe5e74e2a427f7552750362009c18c9a72536950066bd0208c83985.jpg) +Figure 5. Calibration plots of varying posterior marginal covariance block dimensions $D$ on NYUv2 depth completion. The ideal calibration is $y = x$ , where the observed confidence matches expected confidence. The region above the line indicates model under-confidence, while the area below signals over-confidence. + +# 5.1.2 Posterior Uncertainty + +Calibrated uncertainties are beneficial for balancing multiple views and sensors in optimization. Under-confidence does not account for the full information given by the constraints, while over-confidence may bias the solution. Given that the GP provides uncertainties in addition to the mean, we evaluate the calibration properties for the depth completion setup from Section 5.1. + +Since most methods typically estimate per-pixel observation noise, we assess the effect of including off-diagonal terms. Calibration plots measure how well the model's predicted confidence matches the expected confidence. To extend calibration plots for regression beyond variance [21], we extract marginal covariances of varying dimension $D$ and calculate Mahalanobis distances using this block-diagonal approximation to the full covariance matrix. We then count the frequency of distances below varying chi-square thresholds that define the expected confidence. In the variance case with $D = 1$ , residuals do not affect each other. We plot the results in Figure 5 along with the RMSE against the ideal calibration, where the empirical confidence matches the observed confidence. Including more off-diagonal terms improves the model calibration, and significantly reduces the under-confidence of the model. + +# 5.1.3 Active Sampling Evaluation + +While the location of depth samples is often not a free variable, such as from LiDAR or depth sensors, in monocular vision, we may wish to actively estimate the depths of certain pixels to improve downstream tasks such as dense reconstruction. We investigate whether the meta-learned covariance parameters may be leveraged for selecting more + +![](images/7d04aae8cc617b7ff11d6f6c42c59d6fd6a049d674224b878299be7d13d6e7c4.jpg) +Figure 6. Mean depth completion RMSE for random and active sampling with a varying number of samples. Green line shows mean percent improvement of active over random sampling. + +informative pixels. We compare randomly sampling pixels uniformly against the greedy conditional variance as described in Section 4.3. The effect on depth error and the mean percentage improvement for a varying number of samples is shown in Figure 6. Note that the greedy active sampling consistently outperforms random sampling. For a large number of samples, the relative improvement of active sampling decreases as there is a sufficient number of observations. For a few samples, only coarse depth structure is retained, and the greedy selection occasionally encourages samples near the image boundaries. Beyond greedy selection, other sampling methods that explicitly minimize uncertainty over the whole domain may demonstrate improved performance at the expense of computational cost. + +# 5.2. Bundle Adjustment + +Bundle adjustment is the foundation for many vision pipelines, and when successful, it produces very accurate camera poses. However, the standard formulation of assuming independence between observations proves challenging for monocular SLAM systems, which may fail during initialization or when translation is negligible compared to rotation. Many realistic scenarios require robust and fast initialization with little motion. Therefore, we evaluate the use of the depth covariance in small baseline scenarios. We also show that the depth covariance is not limited to 2D depth map inference, but can also be used to infer 3D structure. + +Bundle adjustment jointly optimizes camera poses and point landmarks given pixel correspondences. Traditionally, the cost involves the sum of reprojection errors, as well as pose and scale priors on the first pose to fix gauge freedom which we omit for brevity. We also add our depth prior factor per camera, so for landmarks $\mathbf{P}$ in the world frame, and poses $T_{cW}$ for $c = 1,\dots,C$ , we have: + +$$ +\begin{array}{l} E = \sum_ {c} \sum_ {i} | | \pi (\mathbf {T} _ {c W}, \mathbf {P} _ {i}) - \mathbf {x} _ {c, i} | | _ {\sigma_ {r} ^ {2} I} ^ {2} \\ + \sum_ {c} | | [ T _ {c W} \mathbf {P} _ {c} ] _ {z} - m _ {c} | | _ {K _ {c}} ^ {2}, \tag {12} \\ \end{array} +$$ + +![](images/d288ae9e3be58612930368dab42d0a04082df55a9596cd66ac2f622d15872dee.jpg) +Figure 7. Factor graphs for bundle adjustment and monocular dense visual odometry. Pose and scale priors to constrain gauge freedom are omitted for simplicity. + +where each frame also has a scale variable $m_{c}$ and depth covariance $K_{c}$ for vectorized observed landmarks $\mathbf{P}_c$ in that frame. The projection function $\pi$ projects 3D landmarks into the image plane. To reduce the influence of outliers, we use the Huber cost function for reprojection errors. The least-squares cost is optimized using Levenberg-Marquardt via GTSAM [14]. The corresponding factor graph is shown on the left of Figure 7. + +We divide all of the Freiburg 1 sequences from the TUM RGB-D dataset into small baseline 5-frame windows. This yields 1544 sequences, emulating initializations to visual odometry systems. For the frontend, Shi-Tomasi corners [38] are detected and tracked using Lucas-Kanade tracking [24], and outliers are filtered out via essential matrix RANSAC. Poses are initialized using the motion capture data closest to the current RGB frame timestamps, while landmarks are triangulated if sufficiently constrained. We optimize each sequence with and without the GP depth prior to evaluate its effect. Using the corresponding depth frames, we compare the error of the reprojected sparse landmarks where valid depths occur, as well as the dense depth map error by conditioning on the sparse landmarks. To handle monocular scale ambiguity, we align depth maps with the optimal scale before computing the error. A boxplot of errors between the two methods is shown in Figure 8. By exploiting the correlation between observations, significantly more coherent geometry can be generated, as visualized by an example in Figure 9. Note that the prior is able to achieve consistent geometry despite the low baseline and only optimizing sparse landmarks. The shadows reveal the complete chair legs and table. We also show a longer example with over three seconds of data fused into a TSDF in Figure 1. + +# 5.3. Monocular Dense Visual Odometry + +We propose a monocular dense visual odometry system based on the depth covariance and conditioning on per + +![](images/2aabe93867af65324b952751c2bc362fbd1943cc304705dedc76b21f8febf1b9.jpg) +Figure 8. Boxplot of sparse and dense depth errors across all 5-frame windows, with and without the GP depth prior included in the optimization. The median RMSE is written next to the plots. + +![](images/5b3ff60e2f75a97d292928e763a2ad9d3d77f63092b9b82177d15d78aef67622.jpg) +Figure 9. Qualitative example of small baseline bundle adjustment with and without the GP depth factor. Both examples fuse 5 predicted depth maps densified from the optimized landmarks into a TSDF, and are visualized with identical lighting. + +frame latent depth points. We perform sliding window optimization over a fixed number of keyframes, where poses, scales, and inducing depths are optimized per-frame. To regularize the depths, we may condition on the current estimates of the inducing depths as in Section 4.2, project depths into neighboring keyframes, and apply relative photometric error constraints. The photometric error for pixels $n$ across all directed edges $E$ is: + +$$ +E = \sum_ {i, j \in E} \sum_ {n} \| I _ {i} (\mathbf {x} _ {n}) - I _ {j} (w (\mathbf {x} _ {n}, \mathbf {T} _ {i}, \mathbf {T} _ {j}, \mathbf {y} _ {\mathbf {i}}, m _ {i}) \| _ {\sigma_ {r} ^ {2} I} ^ {2}, \tag {13} +$$ + +where $I$ is the image intensity, $\mathbf{x}$ are pixel coordinates, $w$ is the warping function that queries a depth map and projects the corresponding point into the neighboring image, $\mathbf{T}$ are camera poses, $\mathbf{y}$ are estimated log-depth observations, and $m$ is the mean log depth. The corresponding factor graph with the depth priors included is shown in the right of Figure 7. To initialize a new keyframe, we first run the RGB through the network to get the covariance feature maps and sample inducing points. We perform active sampling since this is independent of whether log-depths are known as mentioned in 4.3, and stop sampling if a maximum variance threshold is achieved. The log-depths $\mathbf{y}$ of a new keyframe are initialized by minimizing the least-squares cost of the + +![](images/48c1b432172f6e286f6885af0042f774ac187ff4157b3ab5bb492120679bbeeb.jpg) +Figure 10. Example depth maps of keyframes while running the dense odometry on ScanNet validation sequences and TUM. + +![](images/c1ec712c25799e814247361dbb3d1d4f906a1d3094c9a37414cbbe3de30ec3e3.jpg) + +prior at the sampled locations and a predictive data term over the reprojected depths $\mathbf{d}$ from the latest keyframe: + +$$ +\mathbf {y} _ {\text {i n i t}} = \underset {\mathbf {y}} {\arg \min } \| \mathbf {y} - m \| _ {K} ^ {2} + \| \log \mathbf {d} - \mathbf {f} _ {*} \| _ {\operatorname {d i a g} \left[ \Sigma_ {*} \right]}. \tag {14} +$$ + +When the window size is full, the last keyframe is dropped, and scale and pose priors are added to the current estimates of the oldest keyframe still in the window. Initialization of the system is achieved via two-frame SfM, where the first frame's depth map is optimized along with the relative pose to another frame. Tracking is performed using coarse-to-fine photometric tracking against the last keyframe. We also optimize affine brightness parameters for varying exposure. + +For quantitative evaluation, we compare our odometry to other learning-based dense systems on Freiburg 1 sequences in the TUM dataset. Since our method is purely a sliding-window odometry system, we compare against the comparable monocular methods evaluated in [44] that lack global optimization and bundle adjustment. We omit the floor sequence which drops a significant number of frames. The absolute trajectory errors (ATE) are shown in Table 3. Since our dense odometry does not have global scale, we align to the ground-truth trajectory's scale. + +Despite the simplicity of our system, it is first or second in 6 of 8 sequences, and achieves the best mean error. We only use photometric error compared to the additional depth and keypoint factors in DeepFactors. While we lack many features in our simple sliding window formulation, we believe that including additional components will further improve the results. Qualitative examples of inferred depth maps from the odometry on ScanNet validation sequences and TUM are shown in Figure 10. Note that our representation can predict a variety of thin structures and regularize flat ones, even in the case of high-frequency texture. + +Odometry runs at an average of $7 - 10\mathrm{Hz}$ on $256\times 192$ images with 8 keyframes on a single RTX 3080. The code is not optimized and alternates between tracking and mapping in a single thread, so we believe significant speed-ups are possible given additional optimization or compute. + +
SequenceTartanVO [46]DeepV2D [43]DeepFactors [9]Ours
3600.1780.2430.1590.128
desk0.1250.1660.1700.056
desk20.1220.3790.2530.048
plant0.2970.2030.3050.261
room0.3330.2460.3640.257
ryp0.0490.1050.0430.052
teddy0.3390.3160.6010.475
xyz0.0620.0640.0350.056
mean0.1880.2150.2410.167
+ +Table 3. Absolute trajectory error (m) on TUM Freiburg1. The best result is in bold, while the second best is underlined. + +# 6. Conclusion and Future Work + +We have proposed learning a depth covariance function, and provided selections of the depth representation, the base covariance function, and the optimization objective. While we selected a specific nonstationary kernel function, exploration of alternatives is a valuable direction. There is great potential to draw additional connections to the literature on Bayesian methods and GPs. For example, geometry is low-rank and spatially local, so active selection and sparse kernels will permit scalability to higher resolutions and larger capacities. Lastly, we demonstrated the utility of the covariance function on a selection of geometric vision tasks: depth completion, bundle adjustment, and monocular dense visual odometry. Beyond these applications, the prior can be tightly integrated into existing geometric vision frameworks and serve as a foundation for novel formulations. + +# 7. Acknowledgements + +Research presented in this paper has been supported by Dyson Technology Ltd. We would like to thank Tristan Laidlow, Hidenobu Matsuki, Riku Murai, and members of the Dyson Robotics Lab for insightful discussions. + +# References + +[1] Gwangbin Bae, Ignas Budvytis, and Roberto Cipolla. Iron-Depth: Iterative refinement of single-view depth using surface normal and its uncertainty. In Proceedings of the British Machine Vision Conference (BMVC), 2022. 2 +[2] Matthias Bauer, Mark van der Wilk, and Carl Edward Rasmussen. Understanding probabilistic sparse Gaussian process approximations. In Neural Information Processing Systems (NeurIPS), 2016. 4 +[3] Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, and Andrew J. Davison. CodeSLAM - learning a compact, optimisable representation for dense visual SLAM. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 2, 3 +[4] Michael Bloesch, Tristan Laidlow, Ronald Clark, Stefan Leutenegger, and Andrew Davison. Learning meshes for dense visual SLAM. In Proceedings of the International Conference on Computer Vision (ICCV), 2019. 2 +[5] Weizhe Chen, Roni Khardon, and Lantao Liu. AK: Attentive kernel for information gathering. In Proceedings of Robotics: Science and Systems (RSS), 2022. 2 +[6] Xinjing Cheng, Peng Wang, and Ruigang Yang. Depth estimation via affinity learned with convolutional spatial propagation network. In Proceedings of the European Conference on Computer Vision (ECCV), 2018. 5 +[7] Javier Civera, Andrew J. Davison, and J. M. Martínez Montiel. Inverse depth parametrization for monocular SLAM. IEEE Transactions on Robotics (T-RO), 2008. 3 +[8] Andrea Conti, Matteo Poggi, and Stefano Mattoccia. Sparsity agnostic depth completion. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023. 5 +[9] Jan Czarnowski, Tristan Laidlow, Ronald Clark, and Andrew J. Davison. DeepFactors: Real-time probabilistic dense monocular SLAM. IEEE Robotics and Automation Letters (RA-L), 2020. 2, 8 +[10] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. ScanNet: Richly-annotated 3D reconstructions of indoor scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 5 +[11] Garoe Dorta, Sara Vicente, Lourdes Agapito, Neill D. F. Campbell, and Ivor Simpson. Structured uncertainty prediction networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 2 +[12] David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. In Neural Information Processing Systems (NeurIPS), 2014. 2, 3 +[13] Jakob Engel, Thomas Schöps, and Daniel Cremers. LSD-SLAM: Large-scale direct monocular SLAM. In Proceedings of the European Conference on Computer Vision (ECCV), 2014. 2 +[14] Frank Dellaert et al. borglab/gtsam, May 2022. 7 +[15] Clement Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow. Digging into self-supervised monocular + +depth estimation. In Proceedings of the International Conference on Computer Vision (ICCV), 2019. 2 +[16] Benjamin Graham and David Novotny. RidgeSfM: Structure from motion via robust pairwise matching under depth uncertainty. In Proceedings of the International Conference on 3D Vision (3DV), 2020. 2 +[17] Carlos Guestrin, Andreas Krause, and Ajit Paul Singh. Nearoptimal sensor placements in Gaussian processes. In Proceedings of the International Conference on Machine Learning (ICML), 2005. 5 +[18] Tony Jebara, Risi Kondor, and Andrew Howard. Probability product kernels. The Journal of Machine Learning Research, 2004. 3 +[19] Alex Kendall and Yarin Gal. What uncertainties do we need in Bayesian deep learning for computer vision? In Neural Information Processing Systems (NeurIPS), 2017. 2 +[20] Diederik P. Kingma and Max Welling. Auto-encoding variational Bayes. In Proceedings of the International Conference on Learning Representations (ICLR), 2014. 2 +[21] Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon. Accurate uncertainties for deep learning using calibrated regression. In Proceedings of the International Conference on Machine Learning (ICML), 2018. 6 +[22] Tobias Lang, Christian Plagemann, and Wolfram Burgard. Adaptive non-stationary kernel regression for terrain modeling. In Proceedings of Robotics: Science and Systems (RSS), 2007. 2 +[23] Lina Liu, Yiyi Liao, Yue Wang, Andreas Geiger, and Yong Liu. Learning steering kernels for guided depth completion. IEEE Transactions on Image Processing, 2021. 2, 5 +[24] Bruce D. Lucas and Takeo Kanade. An iterative image registration technique with an application to stereo vision. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1981. 7 +[25] Fangchang Ma and Sertac Karaman. Sparse-to-dense: Depth prediction from sparse depth samples and a single image. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2018. 5 +[26] Raul Mur-Artal and Juan D. Tardós. ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Transactions on Robotics (T-RO), 2017. 2 +[27] Richard. A. Newcombe, Steven J. Lovegrove, and Andrew J. Davison. DTAM: Dense tracking and mapping in real-time. In Proceedings of the International Conference on Computer Vision (ICCV), 2011. 2 +[28] Sebastian W. Ober, Carl E. Rasmussen, and Mark van der Wilk. The promises and pitfalls of deep kernel learning. In Conference on Uncertainty in Artificial Intelligence, 2021. 2, 3 +[29] Christopher Paciorek and Mark Schervish. Nonstationary covariance functions for Gaussian process regression. In Neural Information Processing Systems (NeurIPS), 2003. 2, 3 +[30] Jinsun Park, Kyungdon Joo, Zhe Hu, Chi-Kuei Liu, and In So Kweon. Non-local spatial propagation network for depth completion. In Proceedings of the European Conference on Computer Vision (ECCV), 2020. 5 + +[31] Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O' Boyle, and Amos J Storkey. Bayesian meta-learning for the few-shot setting via deep kernels. In Neural Information Processing Systems (NeurIPS), 2020. 2 +[32] Rene Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2022. 2, 3 +[33] Ananth Ranganathan, Ming-Hsuan Yang, and Jeffrey Ho. Online sparse Gaussian process regression and its applications. IEEE Transactions on Image Processing, 2011. 5 +[34] Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2005. 2 +[35] Jean-Michel M. Rendu. Normal and lognormal estimation. Journal of the International Association for Mathematical Geology, 1979. 3 +[36] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015. 3 +[37] Johannes L. Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 2 +[38] J. Shi and C. Tomasi. Good features to track. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1994. 7 +[39] Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from RGBD images. In Proceedings of the European Conference on Computer Vision (ECCV), 2012. 5 +[40] Ivor J. A. Simpson, Sara Vicente, and Neill D. F. Campbell. Learning structured Gaussians to approximate deep ensembles. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2 +[41] Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evaluation of RGB-D SLAM systems. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2012. 5 +[42] Chengzhou Tang and Ping Tan. BA-Net: Dense bundle adjustment networks. In Proceedings of the International Conference on Learning Representations (ICLR), 2019. 2 +[43] Zachary Teed and Jia Deng. DeepV2D: Video to depth with differentiable structure from motion. In Proceedings of the International Conference on Learning Representations (ICLR), 2020. 8 +[44] Zachary Teed and Jia Deng. DROID-SLAM: Deep visual SLAM for monocular, stereo, and RGB-D cameras. In Neural Information Processing Systems (NeurIPS), 2021. 8 +[45] Michalis Titsias. Variational learning of inducing variables in sparse Gaussian processes. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2009. 4 + +[46] Wenshan Wang, Yaoyu Hu, and Sebastian Scherer. TartanVO: A generalizable learning-based VO. In Conference on Robot Learning (CoRL), 2021. 8 +[47] Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P. Xing. Deep kernel learning. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. 2 +[48] Zhiqiang Yan, Kun Wang, Xiang Li, Zhenyu Zhang, Jun Li, and Jian Yang. RigNet: Repetitive image guided network for depth completion. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 5 +[49] Sungho Yoon and Ayoung Kim. Balanced depth completion between dense depth inference and sparse range measurements via KISS-GP. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2020. 2 \ No newline at end of file diff --git a/2023/Learning a Depth Covariance Function/images.zip b/2023/Learning a Depth Covariance Function/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..c44969d60abbd24e0e23c257617171830c6603fc --- /dev/null +++ b/2023/Learning a Depth Covariance Function/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:fc87649e391460312e87daca9ef7eaecd92bfa8889e86f442e586989090fb692 +size 561472 diff --git a/2023/Learning a Depth Covariance Function/layout.json b/2023/Learning a Depth Covariance Function/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..e10400c5de1d196f061a61b409940926062d3925 --- /dev/null +++ b/2023/Learning a Depth Covariance Function/layout.json @@ -0,0 +1,7562 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 175, + 103, + 419, + 121 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 175, + 103, + 419, + 121 + ], + "spans": [ + { + "bbox": [ + 175, + 103, + 419, + 121 + ], + "type": "text", + "content": "Learning a Depth Covariance Function" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 167, + 144, + 394, + 185 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 167, + 144, + 394, + 185 + ], + "spans": [ + { + "bbox": [ + 167, + 144, + 394, + 185 + ], + "type": "text", + "content": "Eric Dexheimer and Andrew J. Davison Dyson Robotics Lab, Imperial College London {e.dexheimer21, a.davison}@imperial.ac.uk" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "spans": [ + { + "bbox": [ + 143, + 213, + 192, + 225 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 238, + 290, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 238, + 290, + 335 + ], + "spans": [ + { + "bbox": [ + 46, + 238, + 290, + 335 + ], + "type": "text", + "content": "We propose learning a depth covariance function with applications to geometric vision tasks. Given RGB images as input, the covariance function can be flexibly used to define priors over depth functions, predictive distributions given observations, and methods for active point selection. We leverage these techniques for a selection of downstream tasks: depth completion, bundle adjustment, and monocular dense visual odometry." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 357, + 128, + 370 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 357, + 128, + 370 + ], + "spans": [ + { + "bbox": [ + 47, + 357, + 128, + 370 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 378, + 287, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 378, + 287, + 521 + ], + "spans": [ + { + "bbox": [ + 46, + 378, + 287, + 521 + ], + "type": "text", + "content": "Inferring the 3D structure of the world from 2D images is an essential computer vision task. In recent years, there has been significant interest in combining principled multiple view geometry with data-driven priors. Learning-based methods that predict geometry provide a prior directly over the latent variables, which avoids the ill-posed configurations of traditional methods. However, direct, overconfident priors may prevent realization of the true 3D structure when multi-view geometry is well-defined. For example, data-driven methods have shown tremendous promise in monocular depth estimation, but often lack consistency when fusing information into 3D space." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 521, + 287, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 521, + 287, + 653 + ], + "spans": [ + { + "bbox": [ + 46, + 521, + 287, + 653 + ], + "type": "text", + "content": "Thus far, designing a unified framework that combines the best of learning and optimization methods has proven challenging. Recent data-driven methods have attempted to relax rigid geometric constraints by also predicting low-dimensional subspaces or per-pixel uncertainties. Fixing capacity during training is often wasteful or inflexible at test time, while per-pixel residual distributions typically explain away the limitations of the model instead of the relationship between errors. In reality, the 3D world is anywhere from simple to complex, and an ideal system should explicitly adapt its capacity and correlation based on the scene." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 654, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 287, + 715 + ], + "type": "text", + "content": "In this paper, rather than directly predicting geometry from images, we propose learning a depth covariance function. Given an RGB image, our method predicts how the depths of any two pixels relate. To achieve this, a neural network transforms color information to a feature space, and" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 310, + 212, + 545, + 411 + ], + "blocks": [ + { + "bbox": [ + 310, + 212, + 545, + 411 + ], + "lines": [ + { + "bbox": [ + 310, + 212, + 545, + 411 + ], + "spans": [ + { + "bbox": [ + 310, + 212, + 545, + 411 + ], + "type": "image", + "image_path": "bcc7a3f45e349900d139d1bd5b7122baaa2c5bcf9bc01e4fe28c8f565124e518.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 418, + 547, + 486 + ], + "lines": [ + { + "bbox": [ + 304, + 418, + 547, + 486 + ], + "spans": [ + { + "bbox": [ + 304, + 418, + 547, + 486 + ], + "type": "text", + "content": "Figure 1. Example monocular reconstruction using the depth covariance for bundle adjustment and dense depth prediction from three seconds (100 frames) of TUM data. Three representative images and the mesh from TSDF fusion of the depth predictions are shown. Each frame leverages the learned covariance function to model geometric correlation between pairs of scene points." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 509, + 547, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 509, + 547, + 592 + ], + "spans": [ + { + "bbox": [ + 304, + 509, + 547, + 592 + ], + "type": "text", + "content": "a Gaussian process (GP) models a prior distribution given these features and a base kernel function. The distinction between image processing and the prior enables promoting locality and granting flexible capacity at test time. Locality avoids over-correlating pixels on distinct structures, while adaptive capacity permits tuning the complexity of our subspace to the content of the viewed scene." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 594, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 547, + 714 + ], + "type": "text", + "content": "Learning this flexible, high-level prior allows for balancing data-driven methods with test-time optimization that can be applied to a variety of geometric vision tasks. Furthermore, the covariance function is agnostic to the 3D representation as it does not directly learn a geometric output. Depth maps may be requested by conditioning on observations, but the prior may also be leveraged for inferring the desired latent 3D representation. In Figure 1, we illustrate depth covariance along with an example of bundle adjustment, dense depth prediction, and multi-view fusion." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "13122" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 59, + 72, + 217, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 72, + 217, + 83 + ], + "spans": [ + { + "bbox": [ + 59, + 72, + 217, + 83 + ], + "type": "text", + "content": "In summary, our key contributions are:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 58, + 91, + 286, + 157 + ], + "type": "list", + "angle": 0, + "index": 3, + "blocks": [ + { + "bbox": [ + 58, + 91, + 286, + 126 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 91, + 286, + 126 + ], + "spans": [ + { + "bbox": [ + 58, + 91, + 286, + 126 + ], + "type": "text", + "content": "- A framework for learning the covariance function by selecting a depth representation, a base kernel function, and a scalable optimization objective" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 133, + 286, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 133, + 286, + 157 + ], + "spans": [ + { + "bbox": [ + 58, + 133, + 286, + 157 + ], + "type": "text", + "content": "- Application of the prior to depth completion, bundle adjustment, and monocular dense visual odometry" + } + ] + } + ], + "index": 2 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 47, + 167, + 134, + 179 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 167, + 134, + 179 + ], + "spans": [ + { + "bbox": [ + 47, + 167, + 134, + 179 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 187, + 287, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 187, + 287, + 474 + ], + "spans": [ + { + "bbox": [ + 47, + 187, + 287, + 474 + ], + "type": "text", + "content": "View-based Priors for Geometric Inference Traditionally, sparse reconstruction methods have ignored per-image correlations [37], [26], while dense methods [27], [13] have used naive priors based on neighboring pixels. With the availability of depth data and the rise of deep learning, a variety of learned depth priors have been proposed. Although monocular depth estimation methods that directly predict geometry [12], [15], [32] have demonstrated great progress, they may predict irreparable mistakes and do not allow for handling ambiguities. Utilizing surface normals may refine these depth estimates [1], but the improvement is still subject to errors from the initial depth prediction. For this reason, we focus on approaches that balance learned priors and test-time optimization. Popular approaches include predicting low-dimensional latent codes [3], [9] and generating a basis of depth maps [42], [16]. While these methods utilize single image information to reduce the dimensionality of depth map inference, they fix capacity during training, produce overly smooth depth maps, and create over-correlated global changes of the depth map. Predicting mesh vertices in the image plane [4] permits distribution of capacity and optimizing depth, but the number of latent variables is still fixed and an explicit mesh representation cannot easily model complex scenes." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 474, + 286, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 474, + 286, + 581 + ], + "spans": [ + { + "bbox": [ + 46, + 474, + 286, + 581 + ], + "type": "text", + "content": "Instead of predicting geometry directly, we focus on predicting geometric correlation. By decoupling the pipeline into an image processing network and a GP, the network is not responsible for learning geometry directly and we do not need to fix the geometric capacity. The dimensionality of the subspace may be adapted for representing low-rank scenes more compactly and complex geometry with high-fidelity. Lastly, we use a covariance function with locality to bias learning towards local appearance information." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 582, + 286, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 286, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 286, + 714 + ], + "type": "text", + "content": "Learning Covariance Functions Choosing covariance functions and performing model selection for GPs is a well-studied topic [34]. While stationary kernels have been explored for merging LiDAR observations with monocular depth estimates [49], more expressive kernels are required for sparse observations. Nonstationary covariance functions using local Gaussian parameterizations [29] have shown potential in robotic terrain mapping [22]. However, since GP model selection occurs per data example, optimizing hyperparameters is challenging, and novel nonstationary kernels have been proposed to limit flexibility [5]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 72, + 545, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 430 + ], + "type": "text", + "content": "Rather than performing model selection per image, we are inspired by deep kernel learning (DKL) [47], which leverages training data to predict kernel hyperparameters. Alternatively, this may be viewed as meta-learning, where each task is an RGB-depth example [31]. To mitigate overcorrelation when using stationary kernels in feature space as proposed in DKL [28], we utilize local information in pixel space [29] to model surface geometry. While deep nonstationary kernel regression has been explored for depth completion [23], GPs balance data fit and model complexity during training, and the uncertainty estimates are conducive to decision-making and inference in optimization frameworks. Residual Covariances in Computer Vision Uncertainties in machine learning are often divided between two types: model uncertainty and data uncertainty [19]. For vision problems, model uncertainty is often ignored due to the availability of data and for tractable optimization. Residual uncertainty is usually predicted from a network as a per-pixel variance due to high-dimensionality, but the assumption of independence ignores the correlation present in images. For example, variational autoencoders (VAEs) [20] with a diagonal likelihood output overly smooth mean predictions and unnatural samples with salt-and-pepper noise. To introduce correlation in the likelihood, structured uncertainty prediction networks (SUPNs) predict a full information matrix by defining a graph topology consisting of specific neighbors [11]. Since this matrix is sparse, it can be efficiently inverted to obtain the covariance matrix. This has also been leveraged to distill monocular depth prediction ensembles into a single SUPN [40]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 430, + 545, + 526 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 430, + 545, + 526 + ], + "spans": [ + { + "bbox": [ + 304, + 430, + 545, + 526 + ], + "type": "text", + "content": "Similar to SUPNs, we move beyond diagonal covariance approximations that lack correlation. However, we learn a covariance function which does not require a predefined graph topology and allows for long-range correlations beyond neighboring pixels. Furthermore, the marginal distribution of any set of variables may be examined without constructing the full joint distribution, which is of great interest for compact inference in geometric vision." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 536, + 522, + 550 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 536, + 522, + 550 + ], + "spans": [ + { + "bbox": [ + 305, + 536, + 522, + 550 + ], + "type": "text", + "content": "3. Learning a Depth Covariance Function" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 556, + 545, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 556, + 545, + 628 + ], + "spans": [ + { + "bbox": [ + 304, + 556, + 545, + 628 + ], + "type": "text", + "content": "Given an image, we model a distribution over possible depth functions via a Gaussian process (GP) [34]. With the input domain being normalized image coordinates " + }, + { + "bbox": [ + 304, + 556, + 545, + 628 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_i = (u_i,v_i)" + }, + { + "bbox": [ + 304, + 556, + 545, + 628 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 304, + 556, + 545, + 628 + ], + "type": "inline_equation", + "content": "u_{i},v_{i}\\in [-1,1]" + }, + { + "bbox": [ + 304, + 556, + 545, + 628 + ], + "type": "text", + "content": ", the outputs for any finite set of inputs are jointly Gaussian. The GP is then defined by a mean function " + }, + { + "bbox": [ + 304, + 556, + 545, + 628 + ], + "type": "inline_equation", + "content": "m(\\mathbf{x})" + }, + { + "bbox": [ + 304, + 556, + 545, + 628 + ], + "type": "text", + "content": " and a covariance function " + }, + { + "bbox": [ + 304, + 556, + 545, + 628 + ], + "type": "inline_equation", + "content": "k(\\mathbf{x},\\mathbf{x}^{\\prime})" + }, + { + "bbox": [ + 304, + 556, + 545, + 628 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 362, + 634, + 545, + 647 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 362, + 634, + 545, + 647 + ], + "spans": [ + { + "bbox": [ + 362, + 634, + 545, + 647 + ], + "type": "interline_equation", + "content": "f (\\mathbf {x}) \\sim \\mathcal {G P} (m (\\mathbf {x}), k (\\mathbf {x}, \\mathbf {x} ^ {\\prime})). \\tag {1}", + "image_path": "bfd6b98e14b8fe3bf898505f41b9931cb929caea81e39defe0a7b7f30f517dc0.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "content": "For learning the parameters of this GP, we need to select the representation of depth, the mean and covariance functions, and the optimization objective. We will outline our decisions in the following sections, but there exist many ways to define this prior." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13123" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 172, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 172, + 85 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 172, + 85 + ], + "type": "text", + "content": "3.1. Depth Representation" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 90, + 290, + 293 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 90, + 290, + 293 + ], + "spans": [ + { + "bbox": [ + 46, + 90, + 290, + 293 + ], + "type": "text", + "content": "When predicting depth, we often want to attenuate errors further from the camera view, so that nearby structures are prioritized. Examples of such representations include inverse depth [7], disparity [32], and log-depth [12]. We select log-depth for two reasons. First, the log-normal distribution is more suitable for skewed distributions [35], as the range of the GP is unbounded. Representing depth or inverse depth with a normal distribution would require ad hoc truncation, as depth functions could move behind the camera. Second, we represent depth with relative scale, as the absolute scale ambiguity in monocular images accounts for much of the error in learned depth prediction [12]. We can then focus on learning the relationship between predictions. In the log-depth formulation, a constant mean " + }, + { + "bbox": [ + 46, + 90, + 290, + 293 + ], + "type": "inline_equation", + "content": "m(\\mathbf{x}_i) = m" + }, + { + "bbox": [ + 46, + 90, + 290, + 293 + ], + "type": "text", + "content": " directly corresponds to a scale variable. Given a log-depth observation " + }, + { + "bbox": [ + 46, + 90, + 290, + 293 + ], + "type": "inline_equation", + "content": "\\mathbf{y}_i = \\log \\mathbf{d}_i" + }, + { + "bbox": [ + 46, + 90, + 290, + 293 + ], + "type": "text", + "content": ", we can adjust its scale via this mean:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 96, + 304, + 287, + 317 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 304, + 287, + 317 + ], + "spans": [ + { + "bbox": [ + 96, + 304, + 287, + 317 + ], + "type": "interline_equation", + "content": "e ^ {\\mathbf {y} _ {i} - m} = e ^ {\\mathbf {y} _ {i}} e ^ {- m} = C e ^ {\\mathbf {y} _ {i}} = C \\mathbf {d} _ {i}. \\tag {2}", + "image_path": "202d4157a26700b4e805633936729b4292316d5681783654fd4857ad56199540.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 327, + 288, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 327, + 288, + 471 + ], + "spans": [ + { + "bbox": [ + 46, + 327, + 288, + 471 + ], + "type": "text", + "content": "In other words, the median of the depth distribution is controlled by the mean of the log-normal distribution. During training, similar to [12], we may find the optimal scale " + }, + { + "bbox": [ + 46, + 327, + 288, + 471 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 46, + 327, + 288, + 471 + ], + "type": "text", + "content": " that minimizes our data loss. In addition, the log-depth representation allows covariance to be in relative scale. At test time, the scale may be fixed if known, or jointly optimized with other variables. Learning a more expressive mean function per image would be useful in the absence of any depth observations, but introduces many degrees of freedom. For GPs, generalization is largely dependent on the covariance function, so we leave the mean to be a constant per image." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 479, + 168, + 491 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 479, + 168, + 491 + ], + "spans": [ + { + "bbox": [ + 47, + 479, + 168, + 491 + ], + "type": "text", + "content": "3.2. Covariance Function" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 498, + 287, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 498, + 287, + 594 + ], + "spans": [ + { + "bbox": [ + 46, + 498, + 287, + 594 + ], + "type": "text", + "content": "We would like to learn the parameters of a depth covariance function using pairs of RGB and depth images. By definition, the covariance function must be positive semidefinite (PSD). In our framework, an RGB image is fed into a convolutional neural network (CNN), which outputs features for a base covariance function. We model the depth observations as being jointly Gaussian so that the CNN and base kernel hyperparameters may be jointly learned." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": "For the base covariance function, we would like to avoid over-correlation of independent structures. To achieve local influence, we use the family of nonstationary kernels described in [29]. Each point " + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_i" + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": " in an image can be viewed as a Gaussian distribution with a 2D kernel matrix " + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\Sigma_i" + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": ". Then, the covariance between two points is the convolution of these two densities over the input domain with a normalizing constant. Alternatively, this can be viewed as the similarity between distributions via the Bhattacharyya kernel [18]. Given an isotropic covariance function " + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "inline_equation", + "content": "R^S (\\sqrt{q})" + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": "," + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 307, + 69, + 547, + 249 + ], + "blocks": [ + { + "bbox": [ + 307, + 69, + 547, + 249 + ], + "lines": [ + { + "bbox": [ + 307, + 69, + 547, + 249 + ], + "spans": [ + { + "bbox": [ + 307, + 69, + 547, + 249 + ], + "type": "image", + "image_path": "08b72017400e437045c8a1d0b6e4fd6db6ed5424056f9787f748da7b2e06cb99.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 257, + 547, + 313 + ], + "lines": [ + { + "bbox": [ + 305, + 257, + 547, + 313 + ], + "spans": [ + { + "bbox": [ + 305, + 257, + 547, + 313 + ], + "type": "text", + "content": "Figure 2. Visualizing our depth covariance function: for every pixel of an input image, the trained network predicts a 2D kernel matrix. Here we show the covariance function between pairs of pixels in both matrix form and as edges in a graph, with the line thickness representing the magnitude of covariance." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 332, + 430, + 344 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 332, + 430, + 344 + ], + "spans": [ + { + "bbox": [ + 306, + 332, + 430, + 344 + ], + "type": "text", + "content": "the closed-form expression is:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 322, + 350, + 545, + 377 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 322, + 350, + 545, + 377 + ], + "spans": [ + { + "bbox": [ + 322, + 350, + 545, + 377 + ], + "type": "interline_equation", + "content": "k \\left(\\mathbf {x} _ {i}, \\mathbf {x} _ {j}\\right) = \\sigma_ {f} ^ {2} \\frac {\\left| \\Sigma_ {i} \\right| ^ {1 / 4} \\left| \\Sigma_ {j} \\right| ^ {1 / 4}}{\\left| \\Sigma_ {i} + \\Sigma_ {j} \\right| ^ {1 / 2}} R ^ {S} (\\sqrt {q}), \\tag {3}", + "image_path": "328e530b5c6c5179ea1665470f4e20e21d74cfc1f6c6145a48aa42b247a8d2a0.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 355, + 379, + 545, + 394 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 355, + 379, + 545, + 394 + ], + "spans": [ + { + "bbox": [ + 355, + 379, + 545, + 394 + ], + "type": "interline_equation", + "content": "q = \\left(\\mathbf {x} _ {i} - \\mathbf {x} _ {j}\\right) ^ {T} \\left(\\Sigma_ {i} + \\Sigma_ {j}\\right) ^ {- 1} \\left(\\mathbf {x} _ {i} - \\mathbf {x} _ {j}\\right), \\tag {4}", + "image_path": "b4c4560de92e1741d75ef826d077d7be41007440caefa31f40c54e7cb61b7af2.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 399, + 547, + 472 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 399, + 547, + 472 + ], + "spans": [ + { + "bbox": [ + 304, + 399, + 547, + 472 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 399, + 547, + 472 + ], + "type": "inline_equation", + "content": "\\sigma_f^2" + }, + { + "bbox": [ + 304, + 399, + 547, + 472 + ], + "type": "text", + "content": " is a learnable signal variance. To better handle discontinuities, we select the Matérn function as our isotropic covariance function. With the base covariance function requiring a 2D PSD matrix for each pixel, the CNN outputs three channels " + }, + { + "bbox": [ + 304, + 399, + 547, + 472 + ], + "type": "inline_equation", + "content": "(c_1, c_2, c_3)" + }, + { + "bbox": [ + 304, + 399, + 547, + 472 + ], + "type": "text", + "content": ", which are parameterized given the positive diagonal and determinant constraints:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 313, + 477, + 545, + 506 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 477, + 545, + 506 + ], + "spans": [ + { + "bbox": [ + 313, + 477, + 545, + 506 + ], + "type": "interline_equation", + "content": "\\Sigma_ {i} = \\left[ \\begin{array}{c c} e ^ {c _ {1}} & \\tanh (c _ {3}) \\sqrt {e ^ {c _ {1}} * e ^ {c _ {2}}} \\\\ \\tanh (c _ {3}) \\sqrt {e ^ {c _ {1}} * e ^ {c _ {2}}} & e ^ {c _ {2}} \\end{array} \\right]. \\tag {5}", + "image_path": "34b494a1ba526155c20985c4625cba5be80d11e99851a77ed03d6cd468f77a95.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 510, + 547, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 510, + 547, + 594 + ], + "spans": [ + { + "bbox": [ + 304, + 510, + 547, + 594 + ], + "type": "text", + "content": "An example of five kernel matrices and the marginal covariance in matrix and graph form is shown in Figure 2. We use a UNet architecture [36] and output features at different levels for a multi-scale loss. Each scale also has its own GP hyperparameters, signal variance " + }, + { + "bbox": [ + 304, + 510, + 547, + 594 + ], + "type": "inline_equation", + "content": "\\sigma_f^2" + }, + { + "bbox": [ + 304, + 510, + 547, + 594 + ], + "type": "text", + "content": " and noise variance " + }, + { + "bbox": [ + 304, + 510, + 547, + 594 + ], + "type": "inline_equation", + "content": "\\sigma_n^2" + }, + { + "bbox": [ + 304, + 510, + 547, + 594 + ], + "type": "text", + "content": ". Depth maps are coarsened for lower-levels, so finer levels are weighted higher during the total loss calculation." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 594, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 547, + 714 + ], + "type": "text", + "content": "While a more global covariance function, such as a squared-exponential over output features, would provide additional flexibility, we avoid this for two reasons. First, changes in depths on one part of the scene may have significant influence on a completely unrelated part, as seen in [3]. Second, deep kernel learning using this setup is unstable and biased towards over-correlating the input domain [28]. By directly using pixel coordinates in our covariance function, we restrict changes in geometry to be local and bias the network to learn local appearance information." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "13124" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 180, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 180, + 85 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 180, + 85 + ], + "type": "text", + "content": "3.3. Optimization Objective" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 90, + 287, + 114 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 90, + 287, + 114 + ], + "spans": [ + { + "bbox": [ + 47, + 90, + 287, + 114 + ], + "type": "text", + "content": "In GP literature, model selection is performed by minimizing the negative log marginal likelihood (NLML)" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 59, + 121, + 287, + 168 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 121, + 287, + 168 + ], + "spans": [ + { + "bbox": [ + 59, + 121, + 287, + 168 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} - \\log p (\\mathbf {y} | X) = \\frac {1}{2} (\\mathbf {y} - m) ^ {T} \\left(K _ {\\mathrm {f f}} + \\sigma_ {n} ^ {2} I\\right) ^ {- 1} (\\mathbf {y} - m) \\\\ + \\frac {n}{2} \\log 2 \\pi + \\frac {1}{2} \\log | K _ {\\mathrm {f f}} + \\sigma_ {n} ^ {2} I |, \\tag {6} \\\\ \\end{array}", + "image_path": "0465fed90a7af9f8ca60b30a3dd0180b91eaad3057c8b1f44d186da5ce8db579.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 173, + 287, + 256 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 173, + 287, + 256 + ], + "spans": [ + { + "bbox": [ + 46, + 173, + 287, + 256 + ], + "type": "text", + "content": "where the matrix " + }, + { + "bbox": [ + 46, + 173, + 287, + 256 + ], + "type": "inline_equation", + "content": "K_{\\mathrm{ff}}" + }, + { + "bbox": [ + 46, + 173, + 287, + 256 + ], + "type": "text", + "content": " is a PSD matrix with entries defined by the covariance function. The first term is often referred to as the \"data fit\", while the third term is the \"complexity penalty\". In our scenario, the complexity term is minimized by correlating points, while the data term ensures the ground-truth depth map is plausible given the mean and predicted covariance." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 257, + 287, + 365 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 257, + 287, + 365 + ], + "spans": [ + { + "bbox": [ + 46, + 257, + 287, + 365 + ], + "type": "text", + "content": "However, the marginal likelihood for a GP requires a " + }, + { + "bbox": [ + 46, + 257, + 287, + 365 + ], + "type": "inline_equation", + "content": "O(N^3)" + }, + { + "bbox": [ + 46, + 257, + 287, + 365 + ], + "type": "text", + "content": " matrix inversion. For pixels in an image, this becomes intractable. Since depth images are relatively low-rank, we use a sparse GP approximation [2] during training by randomly sampling inducing points for a Nystrom approximation to the full covariance matrix. Given the covariance matrix for the inducing points " + }, + { + "bbox": [ + 46, + 257, + 287, + 365 + ], + "type": "inline_equation", + "content": "K_{\\mathrm{uu}}" + }, + { + "bbox": [ + 46, + 257, + 287, + 365 + ], + "type": "text", + "content": " and the cross-covariance between the inducing points and the entire domain " + }, + { + "bbox": [ + 46, + 257, + 287, + 365 + ], + "type": "inline_equation", + "content": "K_{\\mathrm{uf}}" + }, + { + "bbox": [ + 46, + 257, + 287, + 365 + ], + "type": "text", + "content": ", we have:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 112, + 371, + 287, + 385 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 112, + 371, + 287, + 385 + ], + "spans": [ + { + "bbox": [ + 112, + 371, + 287, + 385 + ], + "type": "interline_equation", + "content": "K _ {\\mathrm {f f}} \\approx \\tilde {K} _ {\\mathrm {f f}} \\triangleq K _ {\\mathrm {f u}} K _ {\\mathrm {u u}} ^ {- 1} K _ {\\mathrm {u f}}. \\tag {7}", + "image_path": "a09b3ffb11a457bdc0d19ef0e5967ca3537e44ce993ed77538ee4b64f3a5708d.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 393, + 287, + 417 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 393, + 287, + 417 + ], + "spans": [ + { + "bbox": [ + 47, + 393, + 287, + 417 + ], + "type": "text", + "content": "Specifically, we use the variational free energy (VFE) framework [45], which defines our training loss as" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 58, + 423, + 287, + 472 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 423, + 287, + 472 + ], + "spans": [ + { + "bbox": [ + 58, + 423, + 287, + 472 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {F} = \\frac {1}{2} (\\mathbf {y} - m) ^ {T} \\left(\\tilde {K} _ {\\mathrm {f f}} + \\sigma_ {n} ^ {2} I\\right) ^ {- 1} (\\mathbf {y} - m) + \\frac {n}{2} \\log 2 \\pi \\\\ + \\frac {1}{2} \\log | \\tilde {K} _ {\\mathrm {f f}} + \\sigma_ {n} ^ {2} I | + \\frac {1}{2 \\sigma_ {n} ^ {2}} \\operatorname {t r} \\left(K _ {\\mathrm {f f}} - \\tilde {K} _ {\\mathrm {f f}}\\right). \\tag {8} \\\\ \\end{array}", + "image_path": "9a43047f5b17b043dfef152836da206b6d22b3f79d39c38b68b7e9ba47cf9de7.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 479, + 287, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 479, + 287, + 552 + ], + "spans": [ + { + "bbox": [ + 46, + 479, + 287, + 552 + ], + "type": "text", + "content": "The first three terms are the same as the original NLML, but using the approximate covariance, while the last term penalizes the conditional variances at all inputs given the inducing points, which only requires the diagonal of the full covariance matrix. Note that we also assume homoscedastic observation noise variance " + }, + { + "bbox": [ + 46, + 479, + 287, + 552 + ], + "type": "inline_equation", + "content": "\\sigma_{n}^{2}" + }, + { + "bbox": [ + 46, + 479, + 287, + 552 + ], + "type": "text", + "content": " across the entire dataset." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 560, + 273, + 574 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 560, + 273, + 574 + ], + "spans": [ + { + "bbox": [ + 47, + 560, + 273, + 574 + ], + "type": "text", + "content": "4. Leveraging a Depth Covariance Function" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 581, + 287, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 581, + 287, + 640 + ], + "spans": [ + { + "bbox": [ + 46, + 581, + 287, + 640 + ], + "type": "text", + "content": "Given an image and its corresponding CNN output maps, we may leverage the covariance function by defining a prior over depth functions, conditioning on depth observations to yield a predictive distribution, and actively sampling pixel locations that minimize the variance of depth predictions." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 647, + 178, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 647, + 178, + 658 + ], + "spans": [ + { + "bbox": [ + 47, + 647, + 178, + 658 + ], + "type": "text", + "content": "4.1. Gaussian Process Prior" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 666, + 287, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 666, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 47, + 666, + 287, + 689 + ], + "type": "text", + "content": "Given a finite set of input points " + }, + { + "bbox": [ + 47, + 666, + 287, + 689 + ], + "type": "inline_equation", + "content": "X_{*}" + }, + { + "bbox": [ + 47, + 666, + 287, + 689 + ], + "type": "text", + "content": ", we may define a Gaussian prior over their log-depths:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 111, + 697, + 287, + 710 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 697, + 287, + 710 + ], + "spans": [ + { + "bbox": [ + 111, + 697, + 287, + 710 + ], + "type": "interline_equation", + "content": "\\mathbf {f} _ {*} \\sim \\mathcal {N} (m, K (X _ {*}, X _ {*})). \\tag {9}", + "image_path": "a6d77efee368137ef229e8fd961c1c5599d9976df9afddb14bf21508fe8afbd9.jpg" + } + ] + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 306, + 69, + 544, + 205 + ], + "blocks": [ + { + "bbox": [ + 306, + 69, + 544, + 205 + ], + "lines": [ + { + "bbox": [ + 306, + 69, + 544, + 205 + ], + "spans": [ + { + "bbox": [ + 306, + 69, + 544, + 205 + ], + "type": "image", + "image_path": "f8a1e88a7239dbf8b964ae433baa7caa90e786cfe230bbfaa0f7f271ef19accf.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 216, + 545, + 250 + ], + "lines": [ + { + "bbox": [ + 305, + 216, + 545, + 250 + ], + "spans": [ + { + "bbox": [ + 305, + 216, + 545, + 250 + ], + "type": "text", + "content": "Figure 3. Conditioning example for 128 samples. The posterior variance is high around edges and in areas lacking samples. The columns of " + }, + { + "bbox": [ + 305, + 216, + 545, + 250 + ], + "type": "inline_equation", + "content": "K_{\\mathrm{fn}}" + }, + { + "bbox": [ + 305, + 216, + 545, + 250 + ], + "type": "text", + "content": ", or correlation maps, are shown for select points." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 270, + 545, + 365 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 270, + 545, + 365 + ], + "spans": [ + { + "bbox": [ + 304, + 270, + 545, + 365 + ], + "type": "text", + "content": "The GP defines a prior over functions, and for any number of input points, we obtain a Gaussian prior. For geometric vision tasks, this prior can be leveraged for anything from sparse to dense methods. Due to the marginalization property of GPs, adding points to the indexing set will not change the marginal distribution of the existing set. Alternatively, the prior may be viewed as an image-conditioned regularizer of any desired latent geometry parameterization." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 372, + 435, + 383 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 372, + 435, + 383 + ], + "spans": [ + { + "bbox": [ + 306, + 372, + 435, + 383 + ], + "type": "text", + "content": "4.2. Predictive Distribution" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 391, + 545, + 450 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 391, + 545, + 450 + ], + "spans": [ + { + "bbox": [ + 305, + 391, + 545, + 450 + ], + "type": "text", + "content": "In many cases, log-depth observations " + }, + { + "bbox": [ + 305, + 391, + 545, + 450 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 305, + 391, + 545, + 450 + ], + "type": "text", + "content": ", such as from RGB-D sensors or LiDAR, may be available. We can explicitly condition the prior on these observations to obtain a posterior distribution. The predictive mean " + }, + { + "bbox": [ + 305, + 391, + 545, + 450 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_{*}" + }, + { + "bbox": [ + 305, + 391, + 545, + 450 + ], + "type": "text", + "content": " and covariance " + }, + { + "bbox": [ + 305, + 391, + 545, + 450 + ], + "type": "inline_equation", + "content": "\\Sigma_{*}" + }, + { + "bbox": [ + 305, + 391, + 545, + 450 + ], + "type": "text", + "content": " given " + }, + { + "bbox": [ + 305, + 391, + 545, + 450 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 305, + 391, + 545, + 450 + ], + "type": "text", + "content": " samples is:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 347, + 458, + 545, + 472 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 458, + 545, + 472 + ], + "spans": [ + { + "bbox": [ + 347, + 458, + 545, + 472 + ], + "type": "interline_equation", + "content": "\\mathbf {f} _ {*} = m + K _ {\\mathrm {f n}} \\left(K _ {\\mathrm {n n}} + \\sigma_ {n} ^ {2} I\\right) ^ {- 1} (\\mathbf {y} - m), \\tag {10}", + "image_path": "20243e30fb09290dabc6dee8521837b8169c7775b1f6e7801ff7375e353b9d2a.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 343, + 475, + 545, + 488 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 343, + 475, + 545, + 488 + ], + "spans": [ + { + "bbox": [ + 343, + 475, + 545, + 488 + ], + "type": "interline_equation", + "content": "\\Sigma_ {*} = K _ {\\mathrm {f f}} - K _ {\\mathrm {f n}} \\left(K _ {\\mathrm {n n}} + \\sigma_ {n} ^ {2} I\\right) ^ {- 1} K _ {\\mathrm {n f}}. \\tag {11}", + "image_path": "82c90a0ddcd24ed323a44ec9a165ca652ee14edf4c1c411d606ee48c663facb7.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 496, + 545, + 580 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 496, + 545, + 580 + ], + "spans": [ + { + "bbox": [ + 304, + 496, + 545, + 580 + ], + "type": "text", + "content": "The predictive mean is a linear function in terms of the observations " + }, + { + "bbox": [ + 304, + 496, + 545, + 580 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 304, + 496, + 545, + 580 + ], + "type": "text", + "content": ", so test-time inference of latent depths is efficient. If a full covariance is not required, only certain blocks or per-pixel variances need to be calculated. Note that the covariance depends only on the observation coordinates and not the observed values. We visualize the predictive mean, variance, and correlation maps for an example in Figure 3." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 306, + 587, + 430, + 598 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 587, + 430, + 598 + ], + "spans": [ + { + "bbox": [ + 306, + 587, + 430, + 598 + ], + "type": "text", + "content": "4.3. Active Point Selection" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 605, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 545, + 713 + ], + "type": "text", + "content": "For dense reconstruction, it is beneficial to construct a compact representation that can achieve high-fidelity results. As mentioned previously, the predictive covariance depends only on the RGB image and locations of depth observations, but not on the observations themselves. By viewing the CNN as a meta-learned initialization of the nonstationary kernel parameters, we may use 2D observations as a proxy for the complexity of 3D geometry. Active selection of informative pixels up to a desired variance permits" + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13125" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 51, + 69, + 284, + 200 + ], + "blocks": [ + { + "bbox": [ + 51, + 69, + 284, + 200 + ], + "lines": [ + { + "bbox": [ + 51, + 69, + 284, + 200 + ], + "spans": [ + { + "bbox": [ + 51, + 69, + 284, + 200 + ], + "type": "image", + "image_path": "c43530ae22fbf80585b1faac80725a27dea23510509d4cdbe690419100b89743.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 209, + 287, + 254 + ], + "lines": [ + { + "bbox": [ + 46, + 209, + 287, + 254 + ], + "spans": [ + { + "bbox": [ + 46, + 209, + 287, + 254 + ], + "type": "text", + "content": "Figure 4. Qualitative comparison of random and active sampling given 32 sample selections. Random sampling misrepresents depth at the top of the image, while active sampling focuses on the chair geometry and avoids redundant samples on the floor." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 274, + 287, + 299 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 274, + 287, + 299 + ], + "spans": [ + { + "bbox": [ + 46, + 274, + 287, + 299 + ], + "type": "text", + "content": "representing less complex scenes with fewer samples and allocating capacity towards high-frequency geometry." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 299, + 287, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 299, + 287, + 407 + ], + "spans": [ + { + "bbox": [ + 46, + 299, + 287, + 407 + ], + "type": "text", + "content": "Inspired by sensor placement literature [17], an entropy-based criterion is used to select informative pixels. In the greedy-case, this simplifies to selecting the input point with the highest conditional variance at each step. Since this requires computing the conditional variance for each newly added point, we leverage incremental updates to the variance and Cholesky factorization of the training covariance matrix [33]. By decoupling the neural network and GP, we do not require any additional network passes." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 406, + 287, + 489 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 406, + 287, + 489 + ], + "spans": [ + { + "bbox": [ + 46, + 406, + 287, + 489 + ], + "type": "text", + "content": "A qualitative example of active sampling is shown in Figure 4. Random sampling severely misrepresents depth of table and chairs, which all appear at similar depths. Active sampling focus samples around the thin chair edge near the image, while also avoiding oversampling on the floor. Furthermore, active sampling avoids missing the top section of the image, so that the table and chairs are well-represented." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 500, + 127, + 514 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 500, + 127, + 514 + ], + "spans": [ + { + "bbox": [ + 47, + 500, + 127, + 514 + ], + "type": "text", + "content": "5. Applications" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 520, + 287, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 520, + 287, + 640 + ], + "spans": [ + { + "bbox": [ + 46, + 520, + 287, + 640 + ], + "type": "text", + "content": "We apply depth covariance to three fundamental geometric vision tasks: depth completion, bundle adjustment, and monocular dense visual odometry (DVO). For depth completion, we use the NYUv2 benchmark [39] and the train-test splits from [25]. For bundle adjustment and DVO, we train the covariance function on ScanNet [10], and evaluate on the TUM RGB-D dataset [41]. For the UNet, we use an input size of " + }, + { + "bbox": [ + 46, + 520, + 287, + 640 + ], + "type": "inline_equation", + "content": "256 \\times 192" + }, + { + "bbox": [ + 46, + 520, + 287, + 640 + ], + "type": "text", + "content": ", 16 channels after the first layer, 5 downsampling steps, and 4 output levels. This results in roughly 9 million parameters." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 647, + 156, + 660 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 647, + 156, + 660 + ], + "spans": [ + { + "bbox": [ + 47, + 647, + 156, + 660 + ], + "type": "text", + "content": "5.1. Depth Completion" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "type": "text", + "content": "Depth completion is a fundamental task that will be leveraged for additional applications. We may directly condition on sparse observations as described in Section 4.2 to obtain a dense depth map and uncertainties." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 72, + 478, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 72, + 478, + 84 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 478, + 84 + ], + "type": "text", + "content": "5.1.1 Completion Accuracy and Error" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 91, + 545, + 175 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 91, + 545, + 175 + ], + "spans": [ + { + "bbox": [ + 304, + 91, + 545, + 175 + ], + "type": "text", + "content": "Since our method does not predict a specific instance of geometry, we report errors with respect to the GP posterior mean. We compare to the foundational Sparse-to-Dense (S2D) [25], deep kernel regression with (KernelNet+R) and without refinement (KernelNet) [23], and a recent state-of-the-art network RigNet [48]. Results on the validation set with 500 random samples are shown in Table 1." + } + ] + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 307, + 182, + 545, + 270 + ], + "blocks": [ + { + "bbox": [ + 307, + 182, + 545, + 270 + ], + "lines": [ + { + "bbox": [ + 307, + 182, + 545, + 270 + ], + "spans": [ + { + "bbox": [ + 307, + 182, + 545, + 270 + ], + "type": "table", + "html": "
MethodError (↓) RMSEAccuracy (↑)
δ1.02δ1.05δ1.10δ1.25δ1.252
S2D [25]0.204---97.899.6
KernelNet [23]0.19865.582.991.697.799.8
KernelNet+R [23]0.11184.894.197.499.399.9
RigNet [48]0.090---99.799.9
Ours0.15781.292.697.199.499.9
", + "image_path": "5f0a7178d64fc7d9eab34e319939895f1292d58d10bee279b18f6a7d3c5b3f86.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 300, + 545, + 432 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 300, + 545, + 432 + ], + "spans": [ + { + "bbox": [ + 304, + 300, + 545, + 432 + ], + "type": "text", + "content": "KernelNet also predicts three feature maps for 2D covariance parameters, but we achieve better performance under the GP framework. We achieve comparable accuracy to state-of-the-art methods KernelNet " + }, + { + "bbox": [ + 304, + 300, + 545, + 432 + ], + "type": "inline_equation", + "content": "^+" + }, + { + "bbox": [ + 304, + 300, + 545, + 432 + ], + "type": "text", + "content": " R and RigNet while using fewer parameters. We also do not convert the problem into classification [23] or have complex forward passes with iterative layers [48]. The depth covariance outperforms methods with similar UNet architectures in RMSE. While the error is not state-of-the-art, we use a lightweight network and do not train specifically for the single task of depth completion with the number of samples known a priori." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 432, + 545, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 432, + 545, + 588 + ], + "spans": [ + { + "bbox": [ + 304, + 432, + 545, + 588 + ], + "type": "text", + "content": "We also explore varying sparsity, as depth completion networks are trained for a specific number of samples. A comparison of RMSE for a varying number of test samples is shown in Table 2. Depth covariance outperforms traditional depth completion methods for sparse inputs, and is competitive with SpAgNet [8] which is designed specifically for these cases and contains an additional non-local spatial propagation layer. We note that for error metrics, we simply use the posterior mean for comparison. However, the GP provides a distribution over depths, not just a single instance, and we will demonstrate additional capabilities such as active decision-making and inferring latent geometry when direct observations are not present." + } + ] + } + ], + "index": 14 + }, + { + "type": "table", + "bbox": [ + 328, + 595, + 524, + 658 + ], + "blocks": [ + { + "bbox": [ + 308, + 277, + 542, + 289 + ], + "lines": [ + { + "bbox": [ + 308, + 277, + 542, + 289 + ], + "spans": [ + { + "bbox": [ + 308, + 277, + 542, + 289 + ], + "type": "text", + "content": "Table 1. Depth completion on NYUv2 with 500 sampled points." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 328, + 595, + 524, + 658 + ], + "lines": [ + { + "bbox": [ + 328, + 595, + 524, + 658 + ], + "spans": [ + { + "bbox": [ + 328, + 595, + 524, + 658 + ], + "type": "table", + "html": "
# Samples550100200
CSPN [6]2.0630.8840.3880.177
NLSPN [30]1.0330.4230.2460.142
SpAgNet [8]0.4670.2720.2090.155
Ours0.7170.2980.2360.193
", + "image_path": "76877701ba3558dcdad372c0540b063b7b8e6c65415bb05bbb4a4bda4a2723e1.jpg" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "table_body" + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 666, + 545, + 699 + ], + "lines": [ + { + "bbox": [ + 304, + 666, + 545, + 699 + ], + "spans": [ + { + "bbox": [ + 304, + 666, + 545, + 699 + ], + "type": "text", + "content": "Table 2. Depth completion RMSE (m) on NYUv2 with a varying number of input samples. The best result is in bold, while the second best is underlined." + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13126" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 63, + 70, + 272, + 228 + ], + "blocks": [ + { + "bbox": [ + 63, + 70, + 272, + 228 + ], + "lines": [ + { + "bbox": [ + 63, + 70, + 272, + 228 + ], + "spans": [ + { + "bbox": [ + 63, + 70, + 272, + 228 + ], + "type": "image", + "image_path": "746e05b8afe5e74e2a427f7552750362009c18c9a72536950066bd0208c83985.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 236, + 288, + 291 + ], + "lines": [ + { + "bbox": [ + 46, + 236, + 288, + 291 + ], + "spans": [ + { + "bbox": [ + 46, + 236, + 288, + 291 + ], + "type": "text", + "content": "Figure 5. Calibration plots of varying posterior marginal covariance block dimensions " + }, + { + "bbox": [ + 46, + 236, + 288, + 291 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 46, + 236, + 288, + 291 + ], + "type": "text", + "content": " on NYUv2 depth completion. The ideal calibration is " + }, + { + "bbox": [ + 46, + 236, + 288, + 291 + ], + "type": "inline_equation", + "content": "y = x" + }, + { + "bbox": [ + 46, + 236, + 288, + 291 + ], + "type": "text", + "content": ", where the observed confidence matches expected confidence. The region above the line indicates model under-confidence, while the area below signals over-confidence." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 312, + 173, + 324 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 312, + 173, + 324 + ], + "spans": [ + { + "bbox": [ + 47, + 312, + 173, + 324 + ], + "type": "text", + "content": "5.1.2 Posterior Uncertainty" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 331, + 287, + 415 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 331, + 287, + 415 + ], + "spans": [ + { + "bbox": [ + 46, + 331, + 287, + 415 + ], + "type": "text", + "content": "Calibrated uncertainties are beneficial for balancing multiple views and sensors in optimization. Under-confidence does not account for the full information given by the constraints, while over-confidence may bias the solution. Given that the GP provides uncertainties in addition to the mean, we evaluate the calibration properties for the depth completion setup from Section 5.1." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 415, + 287, + 606 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 415, + 287, + 606 + ], + "spans": [ + { + "bbox": [ + 46, + 415, + 287, + 606 + ], + "type": "text", + "content": "Since most methods typically estimate per-pixel observation noise, we assess the effect of including off-diagonal terms. Calibration plots measure how well the model's predicted confidence matches the expected confidence. To extend calibration plots for regression beyond variance [21], we extract marginal covariances of varying dimension " + }, + { + "bbox": [ + 46, + 415, + 287, + 606 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 46, + 415, + 287, + 606 + ], + "type": "text", + "content": " and calculate Mahalanobis distances using this block-diagonal approximation to the full covariance matrix. We then count the frequency of distances below varying chi-square thresholds that define the expected confidence. In the variance case with " + }, + { + "bbox": [ + 46, + 415, + 287, + 606 + ], + "type": "inline_equation", + "content": "D = 1" + }, + { + "bbox": [ + 46, + 415, + 287, + 606 + ], + "type": "text", + "content": ", residuals do not affect each other. We plot the results in Figure 5 along with the RMSE against the ideal calibration, where the empirical confidence matches the observed confidence. Including more off-diagonal terms improves the model calibration, and significantly reduces the under-confidence of the model." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 622, + 200, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 622, + 200, + 635 + ], + "spans": [ + { + "bbox": [ + 47, + 622, + 200, + 635 + ], + "type": "text", + "content": "5.1.3 Active Sampling Evaluation" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "content": "While the location of depth samples is often not a free variable, such as from LiDAR or depth sensors, in monocular vision, we may wish to actively estimate the depths of certain pixels to improve downstream tasks such as dense reconstruction. We investigate whether the meta-learned covariance parameters may be leveraged for selecting more" + } + ] + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 307, + 71, + 545, + 184 + ], + "blocks": [ + { + "bbox": [ + 307, + 71, + 545, + 184 + ], + "lines": [ + { + "bbox": [ + 307, + 71, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 71, + 545, + 184 + ], + "type": "image", + "image_path": "7d04aae8cc617b7ff11d6f6c42c59d6fd6a049d674224b878299be7d13d6e7c4.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 193, + 545, + 227 + ], + "lines": [ + { + "bbox": [ + 305, + 193, + 545, + 227 + ], + "spans": [ + { + "bbox": [ + 305, + 193, + 545, + 227 + ], + "type": "text", + "content": "Figure 6. Mean depth completion RMSE for random and active sampling with a varying number of samples. Green line shows mean percent improvement of active over random sampling." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_caption" + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 245, + 546, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 245, + 546, + 413 + ], + "spans": [ + { + "bbox": [ + 304, + 245, + 546, + 413 + ], + "type": "text", + "content": "informative pixels. We compare randomly sampling pixels uniformly against the greedy conditional variance as described in Section 4.3. The effect on depth error and the mean percentage improvement for a varying number of samples is shown in Figure 6. Note that the greedy active sampling consistently outperforms random sampling. For a large number of samples, the relative improvement of active sampling decreases as there is a sufficient number of observations. For a few samples, only coarse depth structure is retained, and the greedy selection occasionally encourages samples near the image boundaries. Beyond greedy selection, other sampling methods that explicitly minimize uncertainty over the whole domain may demonstrate improved performance at the expense of computational cost." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 419, + 421, + 431 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 419, + 421, + 431 + ], + "spans": [ + { + "bbox": [ + 306, + 419, + 421, + 431 + ], + "type": "text", + "content": "5.2. Bundle Adjustment" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 437, + 545, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 437, + 545, + 568 + ], + "spans": [ + { + "bbox": [ + 304, + 437, + 545, + 568 + ], + "type": "text", + "content": "Bundle adjustment is the foundation for many vision pipelines, and when successful, it produces very accurate camera poses. However, the standard formulation of assuming independence between observations proves challenging for monocular SLAM systems, which may fail during initialization or when translation is negligible compared to rotation. Many realistic scenarios require robust and fast initialization with little motion. Therefore, we evaluate the use of the depth covariance in small baseline scenarios. We also show that the depth covariance is not limited to 2D depth map inference, but can also be used to infer 3D structure." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 569, + 545, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 569, + 545, + 653 + ], + "spans": [ + { + "bbox": [ + 304, + 569, + 545, + 653 + ], + "type": "text", + "content": "Bundle adjustment jointly optimizes camera poses and point landmarks given pixel correspondences. Traditionally, the cost involves the sum of reprojection errors, as well as pose and scale priors on the first pose to fix gauge freedom which we omit for brevity. We also add our depth prior factor per camera, so for landmarks " + }, + { + "bbox": [ + 304, + 569, + 545, + 653 + ], + "type": "inline_equation", + "content": "\\mathbf{P}" + }, + { + "bbox": [ + 304, + 569, + 545, + 653 + ], + "type": "text", + "content": " in the world frame, and poses " + }, + { + "bbox": [ + 304, + 569, + 545, + 653 + ], + "type": "inline_equation", + "content": "T_{cW}" + }, + { + "bbox": [ + 304, + 569, + 545, + 653 + ], + "type": "text", + "content": " for " + }, + { + "bbox": [ + 304, + 569, + 545, + 653 + ], + "type": "inline_equation", + "content": "c = 1,\\dots,C" + }, + { + "bbox": [ + 304, + 569, + 545, + 653 + ], + "type": "text", + "content": ", we have:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 347, + 659, + 545, + 710 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 659, + 545, + 710 + ], + "spans": [ + { + "bbox": [ + 347, + 659, + 545, + 710 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} E = \\sum_ {c} \\sum_ {i} | | \\pi (\\mathbf {T} _ {c W}, \\mathbf {P} _ {i}) - \\mathbf {x} _ {c, i} | | _ {\\sigma_ {r} ^ {2} I} ^ {2} \\\\ + \\sum_ {c} | | [ T _ {c W} \\mathbf {P} _ {c} ] _ {z} - m _ {c} | | _ {K _ {c}} ^ {2}, \\tag {12} \\\\ \\end{array}", + "image_path": "2675f887266660808bd9b6c639bdf93a8948f6120b83e1750a1a606fab832163.jpg" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "13127" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 55, + 70, + 281, + 219 + ], + "blocks": [ + { + "bbox": [ + 55, + 70, + 281, + 219 + ], + "lines": [ + { + "bbox": [ + 55, + 70, + 281, + 219 + ], + "spans": [ + { + "bbox": [ + 55, + 70, + 281, + 219 + ], + "type": "image", + "image_path": "d288ae9e3be58612930368dab42d0a04082df55a9596cd66ac2f622d15872dee.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 228, + 288, + 262 + ], + "lines": [ + { + "bbox": [ + 46, + 228, + 288, + 262 + ], + "spans": [ + { + "bbox": [ + 46, + 228, + 288, + 262 + ], + "type": "text", + "content": "Figure 7. Factor graphs for bundle adjustment and monocular dense visual odometry. Pose and scale priors to constrain gauge freedom are omitted for simplicity." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 281, + 287, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 281, + 287, + 376 + ], + "spans": [ + { + "bbox": [ + 46, + 281, + 287, + 376 + ], + "type": "text", + "content": "where each frame also has a scale variable " + }, + { + "bbox": [ + 46, + 281, + 287, + 376 + ], + "type": "inline_equation", + "content": "m_{c}" + }, + { + "bbox": [ + 46, + 281, + 287, + 376 + ], + "type": "text", + "content": " and depth covariance " + }, + { + "bbox": [ + 46, + 281, + 287, + 376 + ], + "type": "inline_equation", + "content": "K_{c}" + }, + { + "bbox": [ + 46, + 281, + 287, + 376 + ], + "type": "text", + "content": " for vectorized observed landmarks " + }, + { + "bbox": [ + 46, + 281, + 287, + 376 + ], + "type": "inline_equation", + "content": "\\mathbf{P}_c" + }, + { + "bbox": [ + 46, + 281, + 287, + 376 + ], + "type": "text", + "content": " in that frame. The projection function " + }, + { + "bbox": [ + 46, + 281, + 287, + 376 + ], + "type": "inline_equation", + "content": "\\pi" + }, + { + "bbox": [ + 46, + 281, + 287, + 376 + ], + "type": "text", + "content": " projects 3D landmarks into the image plane. To reduce the influence of outliers, we use the Huber cost function for reprojection errors. The least-squares cost is optimized using Levenberg-Marquardt via GTSAM [14]. The corresponding factor graph is shown on the left of Figure 7." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 377, + 288, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 377, + 288, + 664 + ], + "spans": [ + { + "bbox": [ + 46, + 377, + 288, + 664 + ], + "type": "text", + "content": "We divide all of the Freiburg 1 sequences from the TUM RGB-D dataset into small baseline 5-frame windows. This yields 1544 sequences, emulating initializations to visual odometry systems. For the frontend, Shi-Tomasi corners [38] are detected and tracked using Lucas-Kanade tracking [24], and outliers are filtered out via essential matrix RANSAC. Poses are initialized using the motion capture data closest to the current RGB frame timestamps, while landmarks are triangulated if sufficiently constrained. We optimize each sequence with and without the GP depth prior to evaluate its effect. Using the corresponding depth frames, we compare the error of the reprojected sparse landmarks where valid depths occur, as well as the dense depth map error by conditioning on the sparse landmarks. To handle monocular scale ambiguity, we align depth maps with the optimal scale before computing the error. A boxplot of errors between the two methods is shown in Figure 8. By exploiting the correlation between observations, significantly more coherent geometry can be generated, as visualized by an example in Figure 9. Note that the prior is able to achieve consistent geometry despite the low baseline and only optimizing sparse landmarks. The shadows reveal the complete chair legs and table. We also show a longer example with over three seconds of data fused into a TSDF in Figure 1." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 671, + 235, + 685 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 671, + 235, + 685 + ], + "spans": [ + { + "bbox": [ + 47, + 671, + 235, + 685 + ], + "type": "text", + "content": "5.3. Monocular Dense Visual Odometry" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 689, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 288, + 715 + ], + "type": "text", + "content": "We propose a monocular dense visual odometry system based on the depth covariance and conditioning on per" + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 313, + 70, + 541, + 178 + ], + "blocks": [ + { + "bbox": [ + 313, + 70, + 541, + 178 + ], + "lines": [ + { + "bbox": [ + 313, + 70, + 541, + 178 + ], + "spans": [ + { + "bbox": [ + 313, + 70, + 541, + 178 + ], + "type": "image", + "image_path": "2aabe93867af65324b952751c2bc362fbd1943cc304705dedc76b21f8febf1b9.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 186, + 547, + 220 + ], + "lines": [ + { + "bbox": [ + 305, + 186, + 547, + 220 + ], + "spans": [ + { + "bbox": [ + 305, + 186, + 547, + 220 + ], + "type": "text", + "content": "Figure 8. Boxplot of sparse and dense depth errors across all 5-frame windows, with and without the GP depth prior included in the optimization. The median RMSE is written next to the plots." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 314, + 230, + 538, + 339 + ], + "blocks": [ + { + "bbox": [ + 314, + 230, + 538, + 339 + ], + "lines": [ + { + "bbox": [ + 314, + 230, + 538, + 339 + ], + "spans": [ + { + "bbox": [ + 314, + 230, + 538, + 339 + ], + "type": "image", + "image_path": "5b3ff60e2f75a97d292928e763a2ad9d3d77f63092b9b82177d15d78aef67622.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 347, + 547, + 392 + ], + "lines": [ + { + "bbox": [ + 305, + 347, + 547, + 392 + ], + "spans": [ + { + "bbox": [ + 305, + 347, + 547, + 392 + ], + "type": "text", + "content": "Figure 9. Qualitative example of small baseline bundle adjustment with and without the GP depth factor. Both examples fuse 5 predicted depth maps densified from the optimized landmarks into a TSDF, and are visualized with identical lighting." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "spans": [ + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "text", + "content": "frame latent depth points. We perform sliding window optimization over a fixed number of keyframes, where poses, scales, and inducing depths are optimized per-frame. To regularize the depths, we may condition on the current estimates of the inducing depths as in Section 4.2, project depths into neighboring keyframes, and apply relative photometric error constraints. The photometric error for pixels " + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "text", + "content": " across all directed edges " + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "inline_equation", + "content": "E" + }, + { + "bbox": [ + 304, + 411, + 545, + 507 + ], + "type": "text", + "content": " is:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 310, + 513, + 545, + 551 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 310, + 513, + 545, + 551 + ], + "spans": [ + { + "bbox": [ + 310, + 513, + 545, + 551 + ], + "type": "interline_equation", + "content": "E = \\sum_ {i, j \\in E} \\sum_ {n} \\| I _ {i} (\\mathbf {x} _ {n}) - I _ {j} (w (\\mathbf {x} _ {n}, \\mathbf {T} _ {i}, \\mathbf {T} _ {j}, \\mathbf {y} _ {\\mathbf {i}}, m _ {i}) \\| _ {\\sigma_ {r} ^ {2} I} ^ {2}, \\tag {13}", + "image_path": "393370b98da9630cc37df691eb83f2b8719a78ce2a47a7d0e6f23c72f74719d9.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " is the image intensity, " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " are pixel coordinates, " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "w" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " is the warping function that queries a depth map and projects the corresponding point into the neighboring image, " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{T}" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " are camera poses, " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " are estimated log-depth observations, and " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " is the mean log depth. The corresponding factor graph with the depth priors included is shown in the right of Figure 7. To initialize a new keyframe, we first run the RGB through the network to get the covariance feature maps and sample inducing points. We perform active sampling since this is independent of whether log-depths are known as mentioned in 4.3, and stop sampling if a maximum variance threshold is achieved. The log-depths " + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{y}" + }, + { + "bbox": [ + 304, + 557, + 547, + 713 + ], + "type": "text", + "content": " of a new keyframe are initialized by minimizing the least-squares cost of the" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "13128" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 51, + 71, + 293, + 205 + ], + "blocks": [ + { + "bbox": [ + 51, + 71, + 293, + 205 + ], + "lines": [ + { + "bbox": [ + 51, + 71, + 293, + 205 + ], + "spans": [ + { + "bbox": [ + 51, + 71, + 293, + 205 + ], + "type": "image", + "image_path": "48c1b432172f6e286f6885af0042f774ac187ff4157b3ab5bb492120679bbeeb.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 70, + 213, + 523, + 225 + ], + "lines": [ + { + "bbox": [ + 70, + 213, + 523, + 225 + ], + "spans": [ + { + "bbox": [ + 70, + 213, + 523, + 225 + ], + "type": "text", + "content": "Figure 10. Example depth maps of keyframes while running the dense odometry on ScanNet validation sequences and TUM." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 300, + 71, + 543, + 205 + ], + "blocks": [ + { + "bbox": [ + 300, + 71, + 543, + 205 + ], + "lines": [ + { + "bbox": [ + 300, + 71, + 543, + 205 + ], + "spans": [ + { + "bbox": [ + 300, + 71, + 543, + 205 + ], + "type": "image", + "image_path": "c1ec712c25799e814247361dbb3d1d4f906a1d3094c9a37414cbbe3de30ec3e3.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 247, + 288, + 271 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 247, + 288, + 271 + ], + "spans": [ + { + "bbox": [ + 46, + 247, + 288, + 271 + ], + "type": "text", + "content": "prior at the sampled locations and a predictive data term over the reprojected depths " + }, + { + "bbox": [ + 46, + 247, + 288, + 271 + ], + "type": "inline_equation", + "content": "\\mathbf{d}" + }, + { + "bbox": [ + 46, + 247, + 288, + 271 + ], + "type": "text", + "content": " from the latest keyframe:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 278, + 287, + 299 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 278, + 287, + 299 + ], + "spans": [ + { + "bbox": [ + 53, + 278, + 287, + 299 + ], + "type": "interline_equation", + "content": "\\mathbf {y} _ {\\text {i n i t}} = \\underset {\\mathbf {y}} {\\arg \\min } \\| \\mathbf {y} - m \\| _ {K} ^ {2} + \\| \\log \\mathbf {d} - \\mathbf {f} _ {*} \\| _ {\\operatorname {d i a g} \\left[ \\Sigma_ {*} \\right]}. \\tag {14}", + "image_path": "5f098d8026551a1b82b5d759c32e08cdbfdd14c48d0df776c37865de55a3c16a.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 306, + 287, + 403 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 306, + 287, + 403 + ], + "spans": [ + { + "bbox": [ + 46, + 306, + 287, + 403 + ], + "type": "text", + "content": "When the window size is full, the last keyframe is dropped, and scale and pose priors are added to the current estimates of the oldest keyframe still in the window. Initialization of the system is achieved via two-frame SfM, where the first frame's depth map is optimized along with the relative pose to another frame. Tracking is performed using coarse-to-fine photometric tracking against the last keyframe. We also optimize affine brightness parameters for varying exposure." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "spans": [ + { + "bbox": [ + 46, + 403, + 287, + 521 + ], + "type": "text", + "content": "For quantitative evaluation, we compare our odometry to other learning-based dense systems on Freiburg 1 sequences in the TUM dataset. Since our method is purely a sliding-window odometry system, we compare against the comparable monocular methods evaluated in [44] that lack global optimization and bundle adjustment. We omit the floor sequence which drops a significant number of frames. The absolute trajectory errors (ATE) are shown in Table 3. Since our dense odometry does not have global scale, we align to the ground-truth trajectory's scale." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 522, + 287, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 522, + 287, + 653 + ], + "spans": [ + { + "bbox": [ + 46, + 522, + 287, + 653 + ], + "type": "text", + "content": "Despite the simplicity of our system, it is first or second in 6 of 8 sequences, and achieves the best mean error. We only use photometric error compared to the additional depth and keypoint factors in DeepFactors. While we lack many features in our simple sliding window formulation, we believe that including additional components will further improve the results. Qualitative examples of inferred depth maps from the odometry on ScanNet validation sequences and TUM are shown in Figure 10. Note that our representation can predict a variety of thin structures and regularize flat ones, even in the case of high-frequency texture." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "text", + "content": "Odometry runs at an average of " + }, + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "inline_equation", + "content": "7 - 10\\mathrm{Hz}" + }, + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "text", + "content": " on " + }, + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "inline_equation", + "content": "256\\times 192" + }, + { + "bbox": [ + 46, + 653, + 287, + 715 + ], + "type": "text", + "content": " images with 8 keyframes on a single RTX 3080. The code is not optimized and alternates between tracking and mapping in a single thread, so we believe significant speed-ups are possible given additional optimization or compute." + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 315, + 244, + 538, + 380 + ], + "blocks": [ + { + "bbox": [ + 315, + 244, + 538, + 380 + ], + "lines": [ + { + "bbox": [ + 315, + 244, + 538, + 380 + ], + "spans": [ + { + "bbox": [ + 315, + 244, + 538, + 380 + ], + "type": "table", + "html": "
SequenceTartanVO [46]DeepV2D [43]DeepFactors [9]Ours
3600.1780.2430.1590.128
desk0.1250.1660.1700.056
desk20.1220.3790.2530.048
plant0.2970.2030.3050.261
room0.3330.2460.3640.257
ryp0.0490.1050.0430.052
teddy0.3390.3160.6010.475
xyz0.0620.0640.0350.056
mean0.1880.2150.2410.167
", + "image_path": "96b71c3ae7c1b1f1f0219593fbbcd370496e1449dd2b68033c72a2a6eb75851c.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 388, + 545, + 410 + ], + "lines": [ + { + "bbox": [ + 305, + 388, + 545, + 410 + ], + "spans": [ + { + "bbox": [ + 305, + 388, + 545, + 410 + ], + "type": "text", + "content": "Table 3. Absolute trajectory error (m) on TUM Freiburg1. The best result is in bold, while the second best is underlined." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 306, + 431, + 471, + 445 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 431, + 471, + 445 + ], + "spans": [ + { + "bbox": [ + 306, + 431, + 471, + 445 + ], + "type": "text", + "content": "6. Conclusion and Future Work" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 453, + 545, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 453, + 545, + 632 + ], + "spans": [ + { + "bbox": [ + 304, + 453, + 545, + 632 + ], + "type": "text", + "content": "We have proposed learning a depth covariance function, and provided selections of the depth representation, the base covariance function, and the optimization objective. While we selected a specific nonstationary kernel function, exploration of alternatives is a valuable direction. There is great potential to draw additional connections to the literature on Bayesian methods and GPs. For example, geometry is low-rank and spatially local, so active selection and sparse kernels will permit scalability to higher resolutions and larger capacities. Lastly, we demonstrated the utility of the covariance function on a selection of geometric vision tasks: depth completion, bundle adjustment, and monocular dense visual odometry. Beyond these applications, the prior can be tightly integrated into existing geometric vision frameworks and serve as a foundation for novel formulations." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 645, + 421, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 645, + 421, + 658 + ], + "spans": [ + { + "bbox": [ + 306, + 645, + 421, + 658 + ], + "type": "text", + "content": "7. Acknowledgements" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 665, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 665, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 665, + 547, + 713 + ], + "type": "text", + "content": "Research presented in this paper has been supported by Dyson Technology Ltd. We would like to thank Tristan Laidlow, Hidenobu Matsuki, Riku Murai, and members of the Dyson Robotics Lab for insightful discussions." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "13129" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "text", + "content": "[1] Gwangbin Bae, Ignas Budvytis, and Roberto Cipolla. Iron-Depth: Iterative refinement of single-view depth using surface normal and its uncertainty. In Proceedings of the British Machine Vision Conference (BMVC), 2022. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 137, + 288, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 137, + 288, + 179 + ], + "spans": [ + { + "bbox": [ + 53, + 137, + 288, + 179 + ], + "type": "text", + "content": "[2] Matthias Bauer, Mark van der Wilk, and Carl Edward Rasmussen. Understanding probabilistic sparse Gaussian process approximations. In Neural Information Processing Systems (NeurIPS), 2016. 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 182, + 288, + 236 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 182, + 288, + 236 + ], + "spans": [ + { + "bbox": [ + 53, + 182, + 288, + 236 + ], + "type": "text", + "content": "[3] Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, and Andrew J. Davison. CodeSLAM - learning a compact, optimisable representation for dense visual SLAM. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 2, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 238, + 288, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 238, + 288, + 282 + ], + "spans": [ + { + "bbox": [ + 53, + 238, + 288, + 282 + ], + "type": "text", + "content": "[4] Michael Bloesch, Tristan Laidlow, Ronald Clark, Stefan Leutenegger, and Andrew Davison. Learning meshes for dense visual SLAM. In Proceedings of the International Conference on Computer Vision (ICCV), 2019. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 284, + 288, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 284, + 288, + 316 + ], + "spans": [ + { + "bbox": [ + 53, + 284, + 288, + 316 + ], + "type": "text", + "content": "[5] Weizhe Chen, Roni Khardon, and Lantao Liu. AK: Attentive kernel for information gathering. In Proceedings of Robotics: Science and Systems (RSS), 2022. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 318, + 288, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 318, + 288, + 361 + ], + "spans": [ + { + "bbox": [ + 53, + 318, + 288, + 361 + ], + "type": "text", + "content": "[6] Xinjing Cheng, Peng Wang, and Ruigang Yang. Depth estimation via affinity learned with convolutional spatial propagation network. In Proceedings of the European Conference on Computer Vision (ECCV), 2018. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 362, + 287, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 362, + 287, + 396 + ], + "spans": [ + { + "bbox": [ + 53, + 362, + 287, + 396 + ], + "type": "text", + "content": "[7] Javier Civera, Andrew J. Davison, and J. M. Martínez Montiel. Inverse depth parametrization for monocular SLAM. IEEE Transactions on Robotics (T-RO), 2008. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 397, + 288, + 441 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 397, + 288, + 441 + ], + "spans": [ + { + "bbox": [ + 53, + 397, + 288, + 441 + ], + "type": "text", + "content": "[8] Andrea Conti, Matteo Poggi, and Stefano Mattoccia. Sparsity agnostic depth completion. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 442, + 288, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 442, + 288, + 485 + ], + "spans": [ + { + "bbox": [ + 53, + 442, + 288, + 485 + ], + "type": "text", + "content": "[9] Jan Czarnowski, Tristan Laidlow, Ronald Clark, and Andrew J. Davison. DeepFactors: Real-time probabilistic dense monocular SLAM. IEEE Robotics and Automation Letters (RA-L), 2020. 2, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 487, + 287, + 542 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 487, + 287, + 542 + ], + "spans": [ + { + "bbox": [ + 48, + 487, + 287, + 542 + ], + "type": "text", + "content": "[10] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. ScanNet: Richly-annotated 3D reconstructions of indoor scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 544, + 287, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 544, + 287, + 586 + ], + "spans": [ + { + "bbox": [ + 48, + 544, + 287, + 586 + ], + "type": "text", + "content": "[11] Garoe Dorta, Sara Vicente, Lourdes Agapito, Neill D. F. Campbell, and Ivor Simpson. Structured uncertainty prediction networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 588, + 287, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 588, + 287, + 632 + ], + "spans": [ + { + "bbox": [ + 48, + 588, + 287, + 632 + ], + "type": "text", + "content": "[12] David Eigen, Christian Puhrsch, and Rob Fergus. Depth map prediction from a single image using a multi-scale deep network. In Neural Information Processing Systems (NeurIPS), 2014. 2, 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 634, + 287, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 634, + 287, + 677 + ], + "spans": [ + { + "bbox": [ + 48, + 634, + 287, + 677 + ], + "type": "text", + "content": "[13] Jakob Engel, Thomas Schöps, and Daniel Cremers. LSD-SLAM: Large-scale direct monocular SLAM. In Proceedings of the European Conference on Computer Vision (ECCV), 2014. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 679, + 247, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 679, + 247, + 689 + ], + "spans": [ + { + "bbox": [ + 48, + 679, + 247, + 689 + ], + "type": "text", + "content": "[14] Frank Dellaert et al. borglab/gtsam, May 2022. 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "text", + "content": "[15] Clement Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow. Digging into self-supervised monocular" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 33, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "depth estimation. In Proceedings of the International Conference on Computer Vision (ICCV), 2019. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 96, + 545, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 545, + 139 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 545, + 139 + ], + "type": "text", + "content": "[16] Benjamin Graham and David Novotny. RidgeSfM: Structure from motion via robust pairwise matching under depth uncertainty. In Proceedings of the International Conference on 3D Vision (3DV), 2020. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 140, + 545, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 140, + 545, + 183 + ], + "spans": [ + { + "bbox": [ + 308, + 140, + 545, + 183 + ], + "type": "text", + "content": "[17] Carlos Guestrin, Andreas Krause, and Ajit Paul Singh. Nearoptimal sensor placements in Gaussian processes. In Proceedings of the International Conference on Machine Learning (ICML), 2005. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 185, + 545, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 185, + 545, + 215 + ], + "spans": [ + { + "bbox": [ + 308, + 185, + 545, + 215 + ], + "type": "text", + "content": "[18] Tony Jebara, Risi Kondor, and Andrew Howard. Probability product kernels. The Journal of Machine Learning Research, 2004. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 217, + 545, + 249 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 217, + 545, + 249 + ], + "spans": [ + { + "bbox": [ + 308, + 217, + 545, + 249 + ], + "type": "text", + "content": "[19] Alex Kendall and Yarin Gal. What uncertainties do we need in Bayesian deep learning for computer vision? In Neural Information Processing Systems (NeurIPS), 2017. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 251, + 545, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 251, + 545, + 282 + ], + "spans": [ + { + "bbox": [ + 308, + 251, + 545, + 282 + ], + "type": "text", + "content": "[20] Diederik P. Kingma and Max Welling. Auto-encoding variational Bayes. In Proceedings of the International Conference on Learning Representations (ICLR), 2014. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 284, + 545, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 284, + 545, + 327 + ], + "spans": [ + { + "bbox": [ + 308, + 284, + 545, + 327 + ], + "type": "text", + "content": "[21] Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon. Accurate uncertainties for deep learning using calibrated regression. In Proceedings of the International Conference on Machine Learning (ICML), 2018. 6" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 328, + 545, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 328, + 545, + 369 + ], + "spans": [ + { + "bbox": [ + 308, + 328, + 545, + 369 + ], + "type": "text", + "content": "[22] Tobias Lang, Christian Plagemann, and Wolfram Burgard. Adaptive non-stationary kernel regression for terrain modeling. In Proceedings of Robotics: Science and Systems (RSS), 2007. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 372, + 545, + 403 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 372, + 545, + 403 + ], + "spans": [ + { + "bbox": [ + 308, + 372, + 545, + 403 + ], + "type": "text", + "content": "[23] Lina Liu, Yiyi Liao, Yue Wang, Andreas Geiger, and Yong Liu. Learning steering kernels for guided depth completion. IEEE Transactions on Image Processing, 2021. 2, 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 405, + 545, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 405, + 545, + 449 + ], + "spans": [ + { + "bbox": [ + 308, + 405, + 545, + 449 + ], + "type": "text", + "content": "[24] Bruce D. Lucas and Takeo Kanade. An iterative image registration technique with an application to stereo vision. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1981. 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 449, + 545, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 449, + 545, + 491 + ], + "spans": [ + { + "bbox": [ + 308, + 449, + 545, + 491 + ], + "type": "text", + "content": "[25] Fangchang Ma and Sertac Karaman. Sparse-to-dense: Depth prediction from sparse depth samples and a single image. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 2018. 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 493, + 545, + 535 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 493, + 545, + 535 + ], + "spans": [ + { + "bbox": [ + 308, + 493, + 545, + 535 + ], + "type": "text", + "content": "[26] Raul Mur-Artal and Juan D. Tardós. ORB-SLAM2: An open-source SLAM system for monocular, stereo, and RGB-D cameras. IEEE Transactions on Robotics (T-RO), 2017. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 537, + 545, + 581 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 537, + 545, + 581 + ], + "spans": [ + { + "bbox": [ + 308, + 537, + 545, + 581 + ], + "type": "text", + "content": "[27] Richard. A. Newcombe, Steven J. Lovegrove, and Andrew J. Davison. DTAM: Dense tracking and mapping in real-time. In Proceedings of the International Conference on Computer Vision (ICCV), 2011. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 582, + 545, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 582, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 308, + 582, + 545, + 624 + ], + "type": "text", + "content": "[28] Sebastian W. Ober, Carl E. Rasmussen, and Mark van der Wilk. The promises and pitfalls of deep kernel learning. In Conference on Uncertainty in Artificial Intelligence, 2021. 2, 3" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 625, + 545, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 625, + 545, + 667 + ], + "spans": [ + { + "bbox": [ + 308, + 625, + 545, + 667 + ], + "type": "text", + "content": "[29] Christopher Paciorek and Mark Schervish. Nonstationary covariance functions for Gaussian process regression. In Neural Information Processing Systems (NeurIPS), 2003. 2, 3" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 670, + 545, + 713 + ], + "type": "text", + "content": "[30] Jinsun Park, Kyungdon Joo, Zhe Hu, Chi-Kuei Liu, and In So Kweon. Non-local spatial propagation network for depth completion. In Proceedings of the European Conference on Computer Vision (ECCV), 2020. 5" + } + ] + } + ], + "index": 32 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "13130" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 117 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 117 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 117 + ], + "type": "text", + "content": "[31] Massimiliano Patacchiola, Jack Turner, Elliot J. Crowley, Michael O' Boyle, and Amos J Storkey. Bayesian meta-learning for the few-shot setting via deep kernels. In Neural Information Processing Systems (NeurIPS), 2020. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 119, + 288, + 174 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 119, + 288, + 174 + ], + "spans": [ + { + "bbox": [ + 48, + 119, + 288, + 174 + ], + "type": "text", + "content": "[32] Rene Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2022. 2, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 175, + 287, + 208 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 175, + 287, + 208 + ], + "spans": [ + { + "bbox": [ + 48, + 175, + 287, + 208 + ], + "type": "text", + "content": "[33] Ananth Ranganathan, Ming-Hsuan Yang, and Jeffrey Ho. Online sparse Gaussian process regression and its applications. IEEE Transactions on Image Processing, 2011. 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 210, + 287, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 210, + 287, + 241 + ], + "spans": [ + { + "bbox": [ + 48, + 210, + 287, + 241 + ], + "type": "text", + "content": "[34] Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning. MIT Press, 2005. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 245, + 287, + 277 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 245, + 287, + 277 + ], + "spans": [ + { + "bbox": [ + 48, + 245, + 287, + 277 + ], + "type": "text", + "content": "[35] Jean-Michel M. Rendu. Normal and lognormal estimation. Journal of the International Association for Mathematical Geology, 1979. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 280, + 287, + 334 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 280, + 287, + 334 + ], + "spans": [ + { + "bbox": [ + 48, + 280, + 287, + 334 + ], + "type": "text", + "content": "[36] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2015. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 336, + 287, + 379 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 336, + 287, + 379 + ], + "spans": [ + { + "bbox": [ + 48, + 336, + 287, + 379 + ], + "type": "text", + "content": "[37] Johannes L. Schonberger and Jan-Michael Frahm. Structure-from-motion revisited. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 382, + 287, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 382, + 287, + 415 + ], + "spans": [ + { + "bbox": [ + 48, + 382, + 287, + 415 + ], + "type": "text", + "content": "[38] J. Shi and C. Tomasi. Good features to track. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1994. 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 417, + 287, + 460 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 417, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 48, + 417, + 287, + 460 + ], + "type": "text", + "content": "[39] Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from RGBD images. In Proceedings of the European Conference on Computer Vision (ECCV), 2012. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 462, + 287, + 506 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 462, + 287, + 506 + ], + "spans": [ + { + "bbox": [ + 48, + 462, + 287, + 506 + ], + "type": "text", + "content": "[40] Ivor J. A. Simpson, Sara Vicente, and Neill D. F. Campbell. Learning structured Gaussians to approximate deep ensembles. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 509, + 287, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 509, + 287, + 563 + ], + "spans": [ + { + "bbox": [ + 48, + 509, + 287, + 563 + ], + "type": "text", + "content": "[41] Jürgen Sturm, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. A benchmark for the evaluation of RGB-D SLAM systems. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2012. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 565, + 287, + 597 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 565, + 287, + 597 + ], + "spans": [ + { + "bbox": [ + 48, + 565, + 287, + 597 + ], + "type": "text", + "content": "[42] Chengzhou Tang and Ping Tan. BA-Net: Dense bundle adjustment networks. In Proceedings of the International Conference on Learning Representations (ICLR), 2019. 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 600, + 287, + 643 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 600, + 287, + 643 + ], + "spans": [ + { + "bbox": [ + 48, + 600, + 287, + 643 + ], + "type": "text", + "content": "[43] Zachary Teed and Jia Deng. DeepV2D: Video to depth with differentiable structure from motion. In Proceedings of the International Conference on Learning Representations (ICLR), 2020. 8" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 646, + 287, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 646, + 287, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 646, + 287, + 678 + ], + "type": "text", + "content": "[44] Zachary Teed and Jia Deng. DROID-SLAM: Deep visual SLAM for monocular, stereo, and RGB-D cameras. In Neural Information Processing Systems (NeurIPS), 2021. 8" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "text", + "content": "[45] Michalis Titsias. Variational learning of inducing variables in sparse Gaussian processes. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2009. 4" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 240 + ], + "type": "list", + "angle": 0, + "index": 20, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "text", + "content": "[46] Wenshan Wang, Yaoyu Hu, and Sebastian Scherer. TartanVO: A generalizable learning-based VO. In Conference on Robot Learning (CoRL), 2021. 8" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 107, + 545, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 545, + 149 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 545, + 149 + ], + "type": "text", + "content": "[47] Andrew Gordon Wilson, Zhiting Hu, Ruslan Salakhutdinov, and Eric P. Xing. Deep kernel learning. In International Conference on Artificial Intelligence and Statistics (AISTATS), 2016. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "text", + "content": "[48] Zhiqiang Yan, Kun Wang, Xiang Li, Zhenyu Zhang, Jun Li, and Jian Yang. RigNet: Repetitive image guided network for depth completion. In Proceedings of the European Conference on Computer Vision (ECCV), 2022. 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 198, + 545, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 198, + 545, + 240 + ], + "spans": [ + { + "bbox": [ + 307, + 198, + 545, + 240 + ], + "type": "text", + "content": "[49] Sungho Yoon and Ayoung Kim. Balanced depth completion between dense depth inference and sparse range measurements via KISS-GP. 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Up-converting SDR content for HDR-WCG TV display. To ensure better viewing experience [1], we use (1) $3D$ Yxy chromaticity diagram (vertical axis $Y$ for HDR/lumiannce, $xy$ plane for WCG/color) to see how HDRTV's advance on HDR&WCG volume is recovered, and (2) detailed visuals (yellow and blue boxes) to assess method's recover ability. Note that HDR is dimer here in print version since its large luminance&color container is interpreted by small SDR capacity, and will appear normal if correctly visualized (top Fig.7). Still, from (3) 'Brightness Index' & 'Saturation Level' we know that result from current methods is more dim and desaturated than GT." + ], + "image_footnote": [], + "bbox": [ + 98, + 400, + 209, + 481 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/48262841fb43cf3ed8d2494f736ef84a2c466e82584a4f58dc7d2d50579d6bc1.jpg", + "image_caption": [ + "1 0" + ], + "image_footnote": [], + "bbox": [ + 210, + 400, + 320, + 481 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/2d79477cdfc769ca8d7570a879887ed21598a194be7b8bd25848689d1134d0ce.jpg", + "image_caption": [ + "28 0.8 0.8 0.7" + ], + "image_footnote": [], + "bbox": [ + 321, + 400, + 429, + 479 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/b1f7d02ccc112727cc86e661ae0e556cffd5cadbe56ce77726a5f7badaf43f34.jpg", + "image_caption": [ + "A 0" + ], + "image_footnote": [], + "bbox": [ + 431, + 400, + 539, + 479 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/54c7d365dfa578c8014a81fbf3a02deda0b9f37b4d1931a2937dbc2e2fad1f52.jpg", + "image_caption": [ + "15 10 \nY 10" + ], + "image_footnote": [], + "bbox": [ + 540, + 400, + 651, + 479 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d93747b1ceee6bec62d8ff5ecbbe8e6a69e37a9f9118cd62d033c7f6993fec0d.jpg", + "image_caption": [ + "0.8 0.8 0.7 0.8 0.8" + ], + "image_footnote": [], + "bbox": [ + 653, + 401, + 761, + 479 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/38dcad6372fc6a25407a2d3f77cfb126feea422e33f7eb9540ce601729b7dcb2.jpg", + "image_caption": [ + "0.8 0.7 0.6 0.5" + ], + "image_footnote": [], + "bbox": [ + 761, + 401, + 870, + 479 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 585, + 313, + 599 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In media industry, the demand of SDR-to-HDRTV upconversion arises when users possess HDR-WCG (high dynamic range-wide color gamut) TVs while most off-the-shelf footage is still in SDR (standard dynamic range). The research community has started tackling this low-level vision task by learning-based approaches. When applied to real SDR, yet, current methods tend to produce dim and desaturated result, making nearly no improvement on viewing experience. Different from other network-oriented methods, we attribute such deficiency to training set (HDR-SDR pair). Consequently, we propose new HDRTV dataset (dubbed HDRTV4K) and new HDR-to-SDR degradation models. Then, it's used to train a luminance-segmented network (LSN) consisting of a global mapping trunk, and two Transformer branches on bright and dark luminance range. We also update assessment criteria by tailored metrics and subjective experiment. Finally, ablation studies are conducted to prove the effectiveness. Our work is available", + "bbox": [ + 73, + 628, + 472, + 901 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "at: https://github.com/AndreGuo/HDRTVDM.", + "bbox": [ + 500, + 587, + 875, + 601 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 500, + 632, + 632, + 647 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The dynamic range of image is defined as the maximum recorded luminance to the minimum. Larger luminance container endows high dynamic range (HDR) a better expressiveness of scene. In media and film industry, the superiority of HDR is further boosted by advanced electro-optical transfer function (EOTF) e.g. PQ/HLG [2], and wide color-gamut (WCG) RGB primaries e.g. BT.2020 [3].", + "bbox": [ + 496, + 657, + 890, + 763 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "While WCG-HDR displays are becoming more readily available in consumer market, most commercial footage is still in standard dynamic range (SDR) since WCG-HDR version is yet scarce due to exorbitant production workflow. Hence, there raise the demand of converting vast existing SDR content for HDRTV service. Such SDR may carry irreproducible scenes, but more likely, imperfections brought by old imaging system and transmission. This indicates that SDR-to-HDRTV up-conversion is an ill-posed low-level vi", + "bbox": [ + 496, + 763, + 892, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "22231", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "sion task, and research community has therefore begun involving learning-based methods ([4-9] etc.).", + "bbox": [ + 76, + 90, + 468, + 119 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Yet, versatile networks they use (§2.1), we find current methods' result dim and desaturated when feeding real SDR images (Fig.1), conflicting with the perceptual motive of SDR-to-HDRTV up-conversion. As reported by CVPR22-1st Workshop on Vision Dataset Understanding [10], most methods are network-oriented and understate the impact of training set. For restoration-like low-level vision, there are 2 ingredients of a training set: the quality of label GT itself, and the GT-to-LQ degradation model (DM) i.e. what the network learns to restore. Such neglect is getting remedied in other low-level vision tasks [11-16], but still pervasive in learning-based SDR-to-HDRTV up-conversion.", + "bbox": [ + 75, + 122, + 468, + 303 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Not serendipitously, we find dataset the reason why current methods underperform. We exploit several HDRTV-tailored metrics (Tab.4) to assess current training set:(1) by measuring label HDR's extent of $HDR/WCG$ etc. (Tab.5), we notice that its quality and diversity are inadequate to incentive the network to produce appealing result, (2) via the statistics of degraded SDR, we find current HDR-to-SDR DMs' tendency to exaggeratedly alter the saturation and brightness (see Tab.6) thus network will learn a SDR-to-HDR deterioration. Hence, we propose HDRTV4K dataset ( $\\S 3.2$ ) consisting of high-quality and diversified (Fig.4) HDRTV frames as label. Then exploit 3 new HDRTV-to-SDR DMs ( $\\S 3.3$ ) avoiding above insufficiency, meanwhile possessing appropriate degradation capability (Tab.6) so the network can learn reasonable restoration ability.", + "bbox": [ + 75, + 304, + 468, + 530 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Afterwards, we formulate the task as the combination of global mapping on the full luminance range and recovery of low/high luminance range. Correspondingly, we propose Luminance Segmented Network (LSN, §3.1) where a global trunk and two Transformer-style UNet [17] branches are assigned to respectively execute divergent operations required in different segmented luminance ranges (areas).", + "bbox": [ + 75, + 531, + 468, + 637 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Lastly, as found by [18, 19], conventional distance-based metrics well-performed in solely-reconstruction task (e.g. denoising) fail for perceptual-motivated HDR reconstruction, we therefore update the assessment criteria with fine-grained metrics (§4.2) and subjective experiment (§4.3) etc.", + "bbox": [ + 75, + 637, + 468, + 713 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contributions are three-fold: (1) Emphasizing & verifying the impact of dataset on SDR-to-HDRTV task, which has long been understated. (2) Exploiting novel HDRTV dataset and HDR-to-SDR degradation models for network to learn. (3) Introducing new problem formulation, and accordingly proposing novel luminance segmented network.", + "bbox": [ + 75, + 713, + 468, + 804 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 76, + 820, + 225, + 835 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Nomenclature and our scope", + "text_level": 1, + "bbox": [ + 76, + 845, + 333, + 862 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Plenty of HDR-related learning-based methods [20] have been proposed, yet, they markedly differ in intended appli", + "bbox": [ + 76, + 869, + 468, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "cation. As in Tab.1, 'linear HDR' means scene-referred HDR images dedicating to record the linear radiance for graphics application e.g. image-based lighting [21,22], and 'HDRTV' stands for our display-referred WCG-HDR [2] format. Clearly, ① synthesize HDR view on SDR display, ② emphasize dealing with misalignment between multiple SDRs and is designed for new imaging pipeline, while ③ and ④ are all oriented for existing SDR, but for different application. Since many works are proposed before community's clarity on above nomenclature, we classify a method as SDR-to-HDRTV up-conversion (④, our task) if its target HDR is claimed in PQ/HLG EOTF and BT.2020 WCG.", + "bbox": [ + 496, + 90, + 890, + 272 + ], + "page_idx": 1 + }, + { + "type": "table", + "img_path": "images/f68ccc343aeab65f916031d1d1a41b704338275168b45864a7e6a3966e4da74d.jpg", + "table_caption": [], + "table_footnote": [ + "*: SI-HDR: Single-Image HDR reconstruction, iTM: inverse Tone-Mapping." + ], + "table_body": "
Task nameFromTo singleMethods
①HDR-style enhancementsingle SDRenhanced SDRe.g. [23-26]
②multi-exposure HDR imagingmultiple SDRlinear HDRe.g. [27-32]
③SI-HDR*single SDRe.g. [33-37]
④ (ours)iTM* or up-conversionHDRTV frame[4-9] [38-53]
", + "bbox": [ + 509, + 281, + 883, + 381 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Table 1. Various learning-based HDR-related tasks. Green/cyan/magenta terms are respectively from [18]/our/ [7] nomenclature.", + "bbox": [ + 500, + 404, + 890, + 431 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In our scope (4), methods designed their networks with distinct philosophy: Except e.g. semantic-requiring highlight recovery, main process of our task belongs to global mapping resembling image retouching/enhancement. Following this, [7] formulates the task as 3 steps and use $1 \\times 1$ convolution for global part, [43] learn the mapping between small sorted image patches, while [51] conducts explicitly-defined per-pixel mapping using the learned latent vector.", + "bbox": [ + 496, + 446, + 890, + 568 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Feature modulation is also popular to involve global prior information. Compared with [7], they change: prior's type [47, 49], modulation tensor's shape [48-50], modulation mechanism [9] and modulation's position [50].", + "bbox": [ + 496, + 568, + 890, + 628 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To coordinate consecutive video frames, [42] applies 3D convolution with extra temporal-D, [8] take 3 frames as input and deploy multi-frame interaction module at UNet bottleneck. Also, for alignment, [44,49] use deformable convolution whose offset map is predicted by different modules.", + "bbox": [ + 496, + 628, + 890, + 703 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Due to the resolution discrepancy between SDRTV and HDRTV, some methods [4-6, 46, 48, 52] jointly conduct super-resolution and up-conversion. Also, some methods are assisted by non-learning pre-processing [53] or bypass [43, 45], while [41] is trained for badly-exposed SDR.", + "bbox": [ + 496, + 704, + 890, + 779 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. HDRTV dataset", + "text_level": 1, + "bbox": [ + 500, + 786, + 660, + 801 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Diversified networks they use, there're currently only 3 open HDRTV training set (Tab.2). All datasets (including ours) contain HDRTV frames in (D65 white) BT.2020 [3] RGB primaries (gamut), PQ [54] EOTF and 1000nit peak luminance, and SDR counterpart in BT.709 [55] gamut, gamma EOTF and 100nit peak luminance.", + "bbox": [ + 496, + 810, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "22232", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "table", + "img_path": "images/118c9e0019274aa44a9169f81d2b15651fd2245ff47716c3ffc0f352ad6f409b.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Dataset (Usage)#pairResolutionHDR format
KAIST [4] ([5,48,52])39840160×160uint16 MATLAB .mat YUV
Zeng20 [6]23229UHDH.265 main10 YUV
HDRTV1K [7] ([9,47,50])123516bit .png RGB
HDRTV4K (ours new)3878HD&UHD16bit lossless .tif RGB
", + "bbox": [ + 81, + 88, + 465, + 143 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.3. HDRTV-to-SDR degradation model", + "text_level": 1, + "bbox": [ + 76, + 266, + 387, + 282 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Most methods follow the common practice to degrade label HDR(GT) to input SDR(LQ). Degradation model (DM) matters since it determined what network can learn, and is relatively paid more attention even in SI-HDR [16, 33, 35]. Yet, few discussion is made in SDR-to-HDRTV (Tab.3):", + "bbox": [ + 75, + 290, + 468, + 364 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/fbedbc53198d6a83f4d53ca902111e76855a0e4702f3aad7e7a9174abc0aef0e.jpg", + "table_caption": [ + "Table 2. Status of different HDRTV dataset: training set part. Our HDR frames sized both HD $(1920\\times 1080)$ and UHD $(3840\\times 2160)$ are manually chosen from $>220$ different videos clips, and encapsulated in lossless (LZW or deflate) TIFF. Others are respectively extracted from only 7 [4]/18 [7] TV demos and 1 graded movie [6], their quality and diversity are quantified later in Tab.5 & Fig.4." + ], + "table_footnote": [], + "table_body": "
DMYouTubeReinhardother DMs
Usage[4,5,7-9,40,44,46-50,52][6,38,41-43]2446a [45]etc. [51,53,56]
DatasetKAIST & HDRTV1KZeng20
", + "bbox": [ + 81, + 378, + 465, + 414 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Table 3. Current HDRTV-to-SDR degradation models (DMs). 'Dataset' means SDR there is degraded from HDR using that DM.", + "bbox": [ + 76, + 425, + 468, + 453 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Youtube stands for the default conversion YouTube applied to BT.2020/PQ1000 HDR content to produce its SDR-applicable version, Reinhard/2446a means tonemapming HDR to SDR using Reinhard TMO [57]/BT.2446 [58]Method A. [51]/[53]/[56] respectively degrade HDR to SDR by grading/Habel TMO/another learned network.", + "bbox": [ + 75, + 472, + 468, + 561 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In other restoration tasks [11-16], DMs are designed to have proper extent and diversity of degradation so network can learn appropriate restore capability and good generalization. Accordingly, we argue that current DMs are not favorable for training. Specifically, the motive of YouTube is to synthesize HDR view for user possessing only SDR display, it tends to enhance/increase the brightness and saturation so network will, vise-versa, learn to deteriorate/decline them. Also, tone-mapping e.g. Reinhard and 2446a dedicate to preserve as much information from HDR, they have monotonically increasing mapping curves (Fig.5) without highlight clipping, therefore the trained network is not likely to recover much information in over-exposed areas. Above observations are later proven in Tab.6, Fig.1&6 etc.", + "bbox": [ + 75, + 564, + 468, + 773 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Proposed Method", + "text_level": 1, + "bbox": [ + 76, + 792, + 250, + 808 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The overview of our method is illustrated in Fig.2.", + "bbox": [ + 96, + 818, + 428, + 832 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Network structure", + "text_level": 1, + "bbox": [ + 76, + 845, + 254, + 859 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Problem formulation helps the researcher to clarify what degradation the network should restore. Given $x$ the", + "bbox": [ + 76, + 869, + 468, + 898 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "SDR, y the HDR. Previous methods [7, 47, 49] hypothesis that x and y are from single RAW file of same camera.", + "bbox": [ + 498, + 90, + 890, + 119 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "This applies to SDR-HDR synchronous production where $\\mathbf{x}$ is also of high quality, thus up-conversion $(\\mathbf{y} = f(\\mathbf{x}))$ is only supposed to follow specific style and recover less missing information. However, since our method is for existing SDR, we assume that SDR $(\\mathbf{x})$ and it imaginary HDR counterpart $(\\mathbf{y})$ were simultaneously shot by different camera and later pipeline, similar to [18]. The imperfection of SDR pipeline makes existing SDR susceptible to limited latitude, narrow gamut and other degradations $(\\mathbf{x} = d(\\mathbf{y}))$ .", + "bbox": [ + 496, + 121, + 890, + 257 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Some works [7, 35] suppose $d(\\cdot)$ are introduced orderly, and assign hierarchical sub-networks to for $f(\\cdot)$ . Since cascaded networks are bulky for real application, we instead formulate that specific degradation is more visible in different luminance range. Concretely, over-exposure occurs in high-luminance range in SDR $(\\mathbf{x}_h)$ , low-luminance range $(\\mathbf{x}_l)$ is more susceptible to noise etc., while the quality of mid-luminance range $(\\mathbf{x}_m)$ is relatively higher. Segmented luminance ranges are treated separately by some traditional up-conversion operators ([59] etc.), and here we introduce this idea to our deep neural network (DNN)—LSN:", + "bbox": [ + 496, + 257, + 890, + 421 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Luminance Segmented Network (LSN) consists of an trunk on full-luminance range $(\\mathbf{x})$ , and 2 branches respectively on $\\mathbf{x}_l$ and $\\mathbf{x}_h$ which are segmented by:", + "bbox": [ + 496, + 422, + 890, + 468 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {x} _ {l} = \\max (0, \\frac {t - \\mathbf {x}}{t}), \\mathbf {x} _ {h} = \\max (0, \\frac {\\mathbf {x} - 1}{t} + 1) \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 522, + 473, + 890, + 502 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathbf{x} \\in [0,1]$ . That is, luminance range lower/higher than threshold $t$ (empirically set to 0.05) is linearly mapped to more significant value [0, 1], as in top-right Fig.2.", + "bbox": [ + 496, + 507, + 890, + 553 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "After segmentation, $\\mathbf{x}$ , $\\mathbf{x}_l$ and $\\mathbf{x}_h$ require distinct DNN operation. First, as found by [7] etc. (§2.1), the majority of $f(\\cdot)$ belongs to global (pixel-independent) operation similar to image enhancement/retouching. Therefore, we assign 5 cascaded receptive-field-free $1 \\times 1$ convolution layers on full luminance range $(\\mathbf{x})$ as the Global Mapping Trunk. Similar to [7, 9, 47-50], we append a modulation branch (Fig.3 left) to involve $\\mathbf{x}$ 's global prior into DNN's decision.", + "bbox": [ + 496, + 553, + 890, + 672 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "On the other hand, $f(\\cdot)$ still involves non-global restoration where local semantic should be aggregated. Specifically, DNN's mission in $\\mathbf{x}_l$ is similar to denoising and low-light enhancement, while that in $\\mathbf{x}_h$ resembles image inpainting (claimed by [60, 61]) where lost content is hallucinated. These low-level vision tasks require larger receptive-filed, we hence choose Transformer blocks [17] specializing long-distance dependency, and arranged them as encoder-decoder with skip-connections (UNet). Transformer-style UNet branches on $\\mathbf{x}_l$ and $\\mathbf{x}_h$ share the same structure but different parameters. The detail of LSN is depicted in Fig.3.", + "bbox": [ + 496, + 672, + 890, + 839 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. HDRTV4K dataset", + "text_level": 1, + "bbox": [ + 500, + 847, + 681, + 861 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "After designing LSN, we need better training set to unleash its capability. We start with the quality of label HDR:", + "bbox": [ + 500, + 869, + 890, + 898 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "22233", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/c20142dbb6852e2a501f5d763c1255840dfdff6d08db00ab4631e52264969431.jpg", + "image_caption": [ + "Figure 2. Overview of this work. Our Luminance Segmented Network (LSN, §3.1) is designed based on novel problem formulation, then supervisedly trained with label HDR from the proposed HDRTV4K dataset (§3.2), and input SDR degraded by novel degradation models (DMs, §3.3). The major concerns of our LSN, HDRTV4K dataset, and DMs are respectively: recovering dark&bright areas, improving the quality (Tab.5) and diversity (Fig.4) of label GT-HDR, and ensuring the LQ-SDR is with proper style and degradation (Tab.6 & Fig.5)." + ], + "image_footnote": [], + "bbox": [ + 122, + 85, + 851, + 303 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/5b640231ea36dc6f53047156974a794bcfefddbfa43f39bbe1036865df08dd0c.jpg", + "image_caption": [ + "Figure 3. Detailed structure of LSN modules. 'k3s1n16' means convolution layer with $3 \\times 3$ kernel, stride=1, out nc=16, and 'FC32' is fully-connected layer with out nc=32. We deploy only 1 Transformer block [17] at each en/decode level of UNet to lighten LSN (#param=325k) for its application on 4K resolution." + ], + "image_footnote": [], + "bbox": [ + 78, + 393, + 468, + 551 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Motivation. In §1, we argue the quality of label HDR in current datasets. Here, it is assessed from 3 aspects by 10 metrics in Tab.4, based on following considerations:", + "bbox": [ + 75, + 657, + 468, + 703 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "First, greater extent of HDR/WCG stands more probability for network to learn pixel in advanced WCG-HDR volume beyond SDR's capability. Meanwhile, higher intra-frame diversity means better generalization the network can learn within small batch-size, i.e. bigger SI/CF/stdL indicate more diversified high-frequency patterns/richer color volume/greater luminance contrast for network to learn.", + "bbox": [ + 73, + 704, + 468, + 809 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Also, style distillation [51] has become a major contributor to method's performance in similar task e.g. image enhancement/retouching, we therefore append 3 metrics quantifying the overall-style of label HDR. Note that network's learned style will be, substantially, affected more by degradation model (DM) which will be discussed later.", + "bbox": [ + 75, + 810, + 470, + 902 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/28ebdfdc556a000988b8326015d02dd28170e7765a289d1cdd5479294f0e440c.jpg", + "table_caption": [], + "table_footnote": [ + "1: EHL is to compensate cases e.g. an all-101 n it HDR frame with $100\\%$ FHLP but less extent of highlight.", + "2: You can find the formulation of gamut hard-clipping $(HC(\\cdot))$ at Eq.5." + ], + "table_body": "
Metrics on the extent of HDR/WCG ↓
FHLPFraction of HighLight Pixel: Spatial ratio of ‘highlight’ pixel i.e. whose normalized luminance Y = 0.2627R + 0.6780G +0.0593B > 0.01 (100nit, SDR's peak luminance.)
EHLExtent of HighLight: Average pixel (i) distance between the luminance of HDR and its clip-to-100nit version1: 1/n ∑i=1n√[Yi - clip(Yi)]2, clip(x) = clamp(x, 0, 0.01)
FWGPFraction of Wide-Gamut Pixel: Spatial ratio of WCG pixel i.e. whose [x, y] coordinates fall inside BT.2020 but outside SDR's BT.709 gamut in Yxy chromaticity diagram.
EWGExtent of Wide-Gamut [62]: Average pixel-distance between WCG-HDR and its gamut-hard-clipped [63] version2: 1/n ∑i=1n||Si - HC(Si)||2, S = [X, Y, Z]T
Metrics on intra-frame diversity ↓ (all variance-based)
SISpatial Information: Standard deviation over the pixels of Sobel-filtered frame, defined in Annex 6 of [64].
CFColorFulness: Defined in [65].
stdLstandard deviation of Luminance, over all pixels of a frame.
Metrics on overall-style ↓
ASLAverage Saturation Level: Normalized pixel-average length of HDR chrominance component C = [Ct, Cp]T [66]: 1/n ∑i=1n||Ci||2, C ∈ [−0.5, 0.5]
ALLAverage Luminance Level: Pixel-average of Y in FHLP.
HDRBQHDR Brightness Quantification [67], visual salience involved.
", + "bbox": [ + 501, + 393, + 890, + 652 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Table 4. The quality and diversity of label HDR is measured from 3 aspects by 10 metrics above (both in positive correlation), results are in Tab.5. See supplementary material for full illustration.", + "bbox": [ + 500, + 683, + 890, + 726 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our work. Statistics in Tab.5 confirms the deficiency of current datasets, i.e. lower intra-frame diversity, and most importantly, less extent of HDR/WCG preventing their network from producing true HDR-WCG volume. To this end, we propose a new dataset HDRTV4K consisting of 3878 BT.2020/PQ1000 (Tab.2) HDR frames with higher quality.", + "bbox": [ + 496, + 760, + 893, + 852 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Specifically, these frames are manually extracted and aligned from various open content ([68-72] etc.) with greater extent of HDR/WCG, higher intra-frame diversity", + "bbox": [ + 496, + 854, + 893, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "22234", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/ad8b707c75fd66a7b486e135ae9b334521177756761e38d6f50e4da3f2c2f56a.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Metrics \nDatasetExtent of HDRExtent of WCGIntra-frame diversityOverall-style
FHLPEHLFWGPEWGSICFstdLASLALLHDRBQ
KAIST [4]1.52500.20255.47710.11041.93725.94850.95978.908717.28541.8597
Zeng20 [6]0.01970.00120.47920.00340.12314.20480.31463.80616.08050.3781
HDRTV1K [7]1.28430.19712.90890.16332.237811.07221.800610.941415.16262.7970
HDRTV4K (ours)5.30830.95952.63690.51233.550810.58823.48379.827421.19965.1593
", + "bbox": [ + 94, + 88, + 877, + 176 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "and reasonable style (Tab. 5). Note that we re-grade some original footage using DaVinci Resolve to add some perturbation on the diversity. The thumbnails of our dataset and its frame distribution comparison are visualized in Fig.4.", + "bbox": [ + 75, + 258, + 470, + 319 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/9631aecb115177faf01a396f7ed46936d73d2152aff7e94ae585c283bfd011c0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 81, + 330, + 207, + 388 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/15e19d47db101494bc31b9a7b7927b4ceefcf491276f3a52d4139a0291074d6a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 210, + 330, + 336, + 388 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/e326d4d45e45a1ebcad6839aa8d6e78dbd503cf9bef3ee84966bf8f7c813ba5e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 339, + 330, + 468, + 388 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/e09ffe26fd4c0512b298d9c93d30156daa2f268d1c1737fa4d227caa0a741a71.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 81, + 388, + 207, + 445 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/13c9957352cd0340e6dfc3ec5700ff0a18d138bd895ef1ae4aa9fa68d2b3b77a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 210, + 388, + 336, + 445 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/6e2507c9eabfb26de4419477ce8d581c615e9b8ba1b49ad20306260bc67c0046.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 339, + 388, + 468, + 445 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/766f292c0519f767e5c7346e87b981493f6652dc39fba0a56a0be88280dd28e1.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 80, + 449, + 467, + 609 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/09a9ba62bf1a4dd233389f8825951695bca6a5f2024aaa03e03b0832c0969522.jpg", + "image_caption": [ + "Figure 4. Diversity comparison: our HDRTV4K dataset vs. others. Here, each 2D-coordinate is the projection of single frame's 10-D vector (containing 10 metrics from Tab.5) using t-SNE [73] $(\\mathrm{exg} = 3,\\mathrm{prep} = 50)$ . We also depict thumbnail HDR frames from our dataset (1)-(9) with their corresponding 2D-coordinates highlighted with gray circle. As seen, our dataset provides wider frame distribution i.e. more diversified scenes for network to learn." + ], + "image_footnote": [], + "bbox": [ + 80, + 611, + 209, + 667 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/0427cd22e78ba264d2a946b0eff5e427922b93fbf097087796be0b81a239f0f6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 210, + 611, + 336, + 667 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/661d59522178f90cecde33e03b82d01fae034286b370d19d3d447646eae3f3df.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 339, + 611, + 467, + 667 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.3. New degradation models", + "text_level": 1, + "bbox": [ + 76, + 801, + 303, + 816 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "After obtaining label HDR with higher quality, we focus on degradation model (DM) which determines the restoration network will learn. As claimed in §2.3, current DMs fail for undue saturation & brightness change and meager over-exposure degradation, which are verified by ALL,", + "bbox": [ + 75, + 824, + 470, + 902 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "ASL, FOEP in Tab.6. That is, YouTube/2446a tend to increase/decline both ASL & ALL so network will learn to simultaneously de/over-saturate and unduly-darken/brighten. Also, 2446a and Rienhard provide small FOEP which is adverse to network's over-exposure hallucination ability.", + "bbox": [ + 498, + 257, + 890, + 334 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/c7cc6ea70a41559553ac6c2adb8a2f1774c744f14bc4d1c7430387b8ecf5ca2f.jpg", + "table_caption": [ + "Table 5. Quality of label HDR frames, manifested in the frame-average of 10 metrics from Tab.4. Greater extent of HDR/WCG encourages network to produce more pixels in non-SDR volume, while intra-frame diversity is helpful for network's generalization ability. All numbers are in percentage (\\%), and we highlight those the most favorable to training. Their diversity is further demonstrated in Fig.4." + ], + "table_footnote": [ + "1: FOEP: Fraction of Over-exposed Pixels: Spatial ratio of pixels whose normalized luminance $Y = 1$ .", + "2: For SDR, ASL is also calculated as Tab.4, but with $\\mathbf{C} = [C_h, C_r]^{\\mathrm{T}}$ [55] rather $[C_t, C_p]^{\\mathrm{T}}$ .", + "3: Assuming that HDR's highlight $(>100n it)$ part should all be clipped to 1 (over-exposure) in SDR.", + "4: Gray means container discrepancy i.e. HDR/SDR's metric should be different even for well-degraded SDR." + ], + "table_body": "
SDR Degraded byFOEP1ALLASL2SICF
CurrentDM2446a0.1815.8803.6246.0416.573
Reinhard1.26421.8877.44214.14712.568
YouTube5.43928.21914.64118.54525.225
OursDM2446c+GM1.73911.66910.18312.50319.391
HC+GM4.25214.06210.37715.09020.146
OCIO21.58018.8879.97713.57818.052
criteria↑: better whenkept3samllerkept--
Source HDR (Tab.5)45.30821.2009.8273.55110.588
", + "bbox": [ + 503, + 345, + 890, + 443 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 6. Given label HDR from HDRTV4K dataset, SDR's statistics alters when degraded by different DMs. Results are in percentage, we mark those unfavorable / neutral / beneficial for training based on the observation in §2.3 & 3.3. As seen, our DMs provide adequate over-exposure (FOEP) and no undue brightness (ALL) & saturation (ASL) change for network to learn. Example of different degraded SDR can be found in supplementary material.", + "bbox": [ + 498, + 494, + 890, + 593 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "This motivate us to utilize/exploit new DMs with proper extent of degradation and no deficiencies on style:", + "bbox": [ + 498, + 609, + 890, + 638 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "(1) OCIO2: OpenColorIO v2 [74], commercial method from BT.2020/PQ1000 HDR container to BT.709/gamma SDR, implemented by 3D look-up table (LUT) here.", + "(2) $2446c + GM$ : Modified Method $C$ tone-mapping from Rec.2446 [58], followed by Gamut Mapping. Here, each HDR encoded value $\\mathbf{E}' = [R', G', B']^{\\mathrm{T}}$ (we use superscript ' for non-linearity) is linearize to $\\mathbf{E}$ by PQ EOTF, and then transferred to $Yxy$ . Then $Y$ is globally mapped to $Y_{SDR}$ , while $xy$ are kept and then recomposed with $Y_{SDR}$ to form $\\mathbf{E}_{SDR}$ and later $\\mathbf{E}_{SDR_{709}}'$ . Our modification lies in:" + ], + "bbox": [ + 498, + 638, + 890, + 791 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nY _ {S D R} = \\operatorname {c l a m p} \\left(T M _ {2 4 4 6 c} (Y), 0, 1 0 0 \\text {n i t}\\right) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 555, + 799, + 890, + 815 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $TM_{2446c}(\\cdot)\\in [0,118\\text{nit} ]$ is the original global mapping curve in [58] (Fig.5 dashed line). That is, we clip the original output range to 100nit to produce more FOEP, rather than linear-scale which share same shortcoming with DM 2446a. Gamut mapping will be introduced in Eq.4&5.", + "bbox": [ + 496, + 824, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "22235", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/6d83014d1cad1b53747f3913acb162ca84587da10788be6ba467824e474603e8.jpg", + "image_caption": [ + "Figure 5. One of the key ideas of the proposed degradation models (DMs, §3.3): HDR's luminance (left) and color (right) volume should be properly clipped, so that network will learn corresponding restoration. Another idea is keeping a sensible brightness & color/saturation style during degradation, please refer to Tab.6." + ], + "image_footnote": [], + "bbox": [ + 78, + 87, + 472, + 220 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "(3) $HC + GM$ : Hard-Clipping and Gamut Mapping, a container conversion with SDR part unchanged and all luminance/color in HDR/WCG volume hard-clipped. For luminance part, we clip all $>100\\mathrm{nit}$ pixels to $100\\mathrm{nit}$ :", + "bbox": [ + 75, + 324, + 470, + 385 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {E} _ {S D R} = 1 0 0 \\times \\operatorname {c l a m p} (\\mathbf {E}, 0, 0. 0 1), \\quad \\mathbf {E} \\in [ 0, 1 ] \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 117, + 393, + 468, + 411 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where 0.01 correspond to $100\\mathrm{nit}$ in normalized linear HDR $\\mathbf{E}$ (PQ container), and $\\times 100$ is to adapt the container discrepancy (nominal peak 1 means $10000\\mathrm{nit} \\rightarrow 100\\mathrm{nit}$ ).", + "bbox": [ + 75, + 420, + 468, + 465 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "So far, $\\mathbf{E}_{SDR}$ is still in BT.2020 gamut. Therefore, we append Gamut Mapping (+GM): First, Color Space Transform (CST) from BT.2020 RGB primaries to BT.709:", + "bbox": [ + 75, + 465, + 468, + 510 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {E} _ {S D R O O G} = \\mathbf {M} \\mathbf {E} _ {S D R}, \\mathbf {M} = \\left[ \\begin{array}{l l l} 1. 6 6 0 5 & 0. 5 8 7 6 & - 0. 0 7 2 8 \\\\ - 0. 1 2 4 6 & 1. 1 3 2 9 & 0. 0 0 8 3 \\\\ - 0. 0 1 8 2 & - 0. 1 0 0 6 & 1. 1 1 8 7 \\end{array} \\right] \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 86, + 518, + 468, + 546 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\exists \\mathbf{E}_{SDR_{OOG}} \\notin [0,1]$ i.e. out-of-gamut (OOG) pixels will fall outside valid range after CST to a narrower volume. Dealing OOG is the key of gamut mapping, instead of soft-mapping [63, 75] (used by [76]'s DM) preserving as much WCG information to narrow gamut, we use hard-clipping which clips all OOG pixels to BT.709 boundary (Fig.5):", + "bbox": [ + 75, + 553, + 468, + 645 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {E} _ {S D R _ {7 0 9}} = \\operatorname {c l a m p} \\left(\\mathbf {E} _ {S D R _ {O O G}}, 0, 1\\right) \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 150, + 656, + 468, + 672 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Then, $\\mathbf{E}_{SDR_{709}}$ from both (2) & (3) is converted to SDR encoded value $\\mathbf{E}_{SDR_{709}}^{\\prime}$ by BT.1886 [77] OETF (approximate gamma2.22) and 8bit JPEG compression with QF=80.", + "bbox": [ + 75, + 681, + 468, + 726 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Our philosophy lies in that most explicitly-defined operations can be streamlined to a clamp(\\cdot) function: Only when luminance and color volume is clipped, multiple-to-one mapping occurs in DM, the trained network can learn corresponding recovery ability. Also, ALL & ASL in Tab.6 show that these DMs produce SDR with reasonable style, ensuring that network will not learn style deterioration.", + "bbox": [ + 75, + 726, + 468, + 832 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 76, + 845, + 209, + 861 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Training detail. For each HDR frame randomly resized to $[0.25\\mathrm{x},1\\mathrm{x}]$ , 6 patches sized $600\\times 600$ are cropped from", + "bbox": [ + 75, + 869, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "random position. Then, each of $6 \\times 3878$ HDR patches is degraded to SDR by 1 of the 3 proposed DMs in equal probability, and again stored in JPEG with QF=75.", + "bbox": [ + 498, + 90, + 890, + 136 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "All LSN parameters are Kaiming initialized, optimized by $l_{1}$ loss and AdaM with learning rate starting from $2 \\times 10^{-4}$ and decaying to half every $10^{5}$ iter till $4 \\times 10^{5}$ .", + "bbox": [ + 498, + 136, + 890, + 181 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Criteria and configuration", + "text_level": 1, + "bbox": [ + 498, + 193, + 738, + 209 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Criteria. Since the destination of SDR-to-HDRTV upconversion is human perception, to provide a 'step-change' improvement [1] of HDRTV than SDRTV, methods should be assessed based on if: C1: result is visually-pleasuring, from both brightness (C1A) & color appearance (C1B) etc., C2: HDRTV's advance on HDR/WCG volume is properly recovered and utilized, C3: bright and dark areas are recovered or at least enhanced and C4: artifacts are avoided. Yet, as found by [18,19], current distance-based metrics e.g. PSNR fails, since the main contributor of an appealing score lies in the accuracy of learned numerical distribution, rather C1-4. Hence, we use new assessment criteria in Tab.7.", + "bbox": [ + 496, + 215, + 890, + 397 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/de2411227cb748559b3491c79e7a7e3facafbf896f34556348fda499cd79051e.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Visuals in Fig.1&6Metrics in Tab.8subj. exp. (\\$4.3)
CIA-ALL, HDRBQoverall rating & selection of attribution
CIBASL
C3yellow&blue boxes-
C4detailed visuals
C23D Yxy diagramFHL(WG)P, EHL(WG)-
", + "bbox": [ + 501, + 410, + 890, + 474 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 7. How each criteria C1-4 is assessed: via detailed visuals, fine-grained tailored metrics and subjective experiment.", + "bbox": [ + 498, + 486, + 888, + 513 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Competitors. As in Tab.8, our method is compared with 6 learning-based methods [4-9], and 2 commercial software DaVinci and Nuke. Results from joint-SR methods [4-6] are bilinear downscaled before comparison. Note that learning-based methods are not re-trained with our dataset since we treat dataset as a crucial attribution to performance.", + "bbox": [ + 496, + 532, + 890, + 623 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Test set. We select 12 4K SDR videos with $10s$ duration and $50fps$ . 3 of 12 are degraded from HDR-GT counterpart by YouTube DM (§2.3), the rest 9 are graded from HDR sequence No. 2, 13, 15, 20, 21, 22, 42, 44, 55 in [78]. Experiment fairness is guaranteed since both DMs (SDR-HDR relationship) in test set are 'unseen' by our network (this even gives a bonus to methods trained with YouTube DM [4, 5, 7-9]).", + "bbox": [ + 496, + 623, + 890, + 729 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Result", + "text_level": 1, + "bbox": [ + 500, + 739, + 586, + 753 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Results from all competitors are shown in Tab.8 & Fig.6.", + "bbox": [ + 517, + 763, + 890, + 777 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Metrics. From Tab.8 we know that methods trained with YouTube DM all tend to produce lower satiation (ASL) and brightness (ALL&HDRBQ), and their ASL is lower even than input SDR. Also, SR-ITM-GAN [6] recovers least HDR&WCG volume since its label HDR (from Zeng20 dataset) is of least extent of HDR/WCG (Tab.5). Note that current methods produce adequate FWGP, but from 3D Yxy chromaticity diagram in Fig.6 we known that their WCG", + "bbox": [ + 496, + 779, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "22236", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/ac8d6267e8330550aa160bdc21d7d677690f62a6d883098713a39ded588b16f6.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Method (network)how network is trained(recovery rate %, GT is 100%) how HDR/WCG volume is recoveredshift rate %, GT is 0%) overall-styleconventional metrics
dataset (GT)DMFHLPEHLFWGPEWGASLALLHDRBQPSNRSSIMΔEVDP3
Input SDR-00006.57010.76-23.92dB0.886144.976.571
Deep SR-ITM [4]KAISTYouTube(14.03)0.2323(9.70)0.372(175.2)1.0964(71.96)0.172(-20.33)5.485(-15.06)9.580(-72.80)1.42826.59dB0.811532.546.917
JSI-GAN [5](12.33)0.2041(3.55)0.136(213.1)1.3334(88.23)0.212(-16.62)5.741(-14.36)6.659(-79.20)1.09227.87dB0.842030.237.452
SR-ITM-GAN [6]Zeng20Reinhard(8.04)0.1332(14.33)0.550(00.00)0.0000(00.00)0.000(-7.04)6.400(-30.37)7.853(-57.12)2.25128.04dB0.883124.786.707
HDRTVNet [7]HDRTV1KYouTube(18.59)0.3078(16.29)0.625(388.8)2.4334(28.82)0.069(-15.51)5.817(-13.47)9.759(-69.65)1.59330.82dB0.881227.588.120
KPN-MFI [8]own(1.17)0.0193(0.02)0.001(392.9)2.4592(15.82)0.038(-21.74)5.388(-16.10)9.462(-82.47)0.92029.37dB0.874627.477.785
FMNet [9]HDRTV1KOurs × 3(13.67)0.2264(12.35)0.474(396.5)2.4813(91.29)0.220(-16.91)5.770(-13.48)9.758(-71.24)1.51030.91dB0.885527.168.069
LSN (ours)(256.6)4.2509(71.96)2.599(109.8)0.6873(150.0)0.361(+9.25)7.522(+81.12)20.42(-27.04)3.82924.47dB0.831037.848.130
DaVinci-(54.96)0.9103(43.16)1.655(310.0)1.9399(85.22)0.205(-21.36)5.414(-21.42)8.863(-39.23)3.19026.39dB0.891835.478.528
Nuke(112.1)1.8565(24.70)0.384(00.00)0.0000(00.00)0.000(-27.53)4.990(+17.93)13.30(-51.19)2.56220.87dB0.727364.297.479
HDR-GT (ref.)(100.0)1.6562(100.0)3.835(100.0)0.6258(100.0)0.240(0.00)6.885(0.00)11.28(0.00)5.248----
ablation studies ↓
LSN (ours)HDRTV4KYouTube(13.09)0.2168(6.63)0.254(401.3)2.5118(59.81)0.144(-14.68)5.874(-13.46)9.760(-76.89)1.21330.15dB0.885828.047.902
HDRTV4KReinhard(9.06)0.1501(19.14)0.734(00.00)0.0000(00.00)0.000(-5.74)6.490(-25.65)8.387(-52.84)2.47527.70dB0.843626.036.832
Zeng20ours × 3(109.6)1.8147(9.00)0.345(0.95)0.0593(0.76)0.002(+3.36)7.117(+66.64)18.79(-59.52)2.12425.17dB0.817928.587.874
HDRTV1Kours × 3(177.5)2.9405(52.49)2.013(279.5)1.7494(70.43)0.169(+6.56)7.337(+52.30)17.18(-34.83)3.42024.30dB0.835138.038.002
", + "bbox": [ + 78, + 88, + 895, + 229 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 8. Metrics. We use fine-grained tailored metrics (column 3-9, defined in Tab.4) to assess criteria C1&2 (§4.1): The more significant metrics in column 3-6 are, the better HDR&WCG volume is recovered (C2) by specific method. Also, column 8-10 closer with GT stands for similar brightness&color appearance. Note that we allow column 8-10 slightly bigger than GT, which means result HDR is reasonably more visual-pleasuring (C1) than GT. Based on this, we highlight those results unfavorable for viewing experience.", + "bbox": [ + 75, + 239, + 893, + 299 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5bdedd9f6f5df459fa30e7afadab773e681e4101cdb7cf659dc196437df6b97d.jpg", + "image_caption": [ + "Figure 6. Visuals. We provide comparisons on $3D$ Yxy chromaticity diagram to assess how HDR/WCG volume is recovered (C2), and detailed visuals on bright (yellow arrow, with luminance of same pixel indicated) and dark (blue arrow) areas to assess method's recover ability (C3). Note that conclusion 'SDR is more vivid than HDR' could not be drawn because HDR will appear dimmer than SDR in print version (explained in Fig.1). We hence turn to ASL & HDRBQ (column 8-10) and later subjective experiment to see if HDR is indeed dimmer (C1). Comparison on more scenes will be provided in supplementary material." + ], + "image_footnote": [], + "bbox": [ + 120, + 311, + 849, + 645 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "pixels mainly exists in low luminance range, which means they are of little help to viewing experience. Our method is able to recover adequate HDR/WCG volume, meanwhile reasonably enhance the brightness and saturation. $^{1}$", + "bbox": [ + 75, + 751, + 470, + 813 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Visuals. Methods' recover ability is illustrated by Fig.6: Current ones underperform in both bright (yellow) and dark (blue) areas. Specifically, methods trained with YouTube DM produce dim (HDBBQ) and desaturated (ASL) result, and even get lower luminance than input SDR at same position (arrow in yellow box). This confirms the finding in §2.3 that network will learn to darken and desaturate if DM tend to brighten and over-saturate. Also, our method recovers more detail in bright and dark areas with a better style.", + "bbox": [ + 496, + 751, + 890, + 888 + ], + "page_idx": 6 + }, + { + "type": "page_footnote", + "text": "1We also provide conventional PSNR, SSIM, $\\Delta E$ (color difference [79]) and VDP3 (HDR-VDP-3 [80]), but they mostly represent output's closer value with GT (For example, result both dimmer (e.g. Deep SR-ITM [4]) and more vivid (ours) than GT will have a similar low score.), thus are helpless for our assessment. This phenomenon was first found by [18, 19], and will be further explained in supplementary material.", + "bbox": [ + 75, + 825, + 470, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "22237", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Subjective experiment", + "text_level": 1, + "bbox": [ + 76, + 90, + 284, + 107 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Currently, few subjective studies [19, 43, 59, 81-83] are designed for SDR-to-HDR procedure (rather between different HDR). Similar to [19], we judge if output HDR is better than origin SDR. Specifically, as in Fig.7, we use 2 side-by-side SONY KD85X9000H display supporting both SDR and HDRTV, one is calibrated to 100nit/BT.709 SDR and another PQ1000nit/BT.2020 HDR. Each (input)SDR-outputHDR pair is displayed twice with $3s$ gray level interval and following $10s$ for rating: each participant is asked to continuously rate from -5(SDR much better) to 0(same) to 5(HDR much better), meanwhile select at least 1 attribution (bottom Fig.7) of his/her rating. Such process is repeated 9(#participant) $\\times$ 9(#competitor) $\\times$ 12(#clip) times.", + "bbox": [ + 75, + 114, + 473, + 311 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/244aa22540d7bfac2f34b05827bf7b13d5ee2b1b89f9bb907fa1d826fd65cbd0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 125, + 325, + 419, + 412 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4b7b15968c8cc53e6f00e2a98cfced6927c27e50076efedc7c066305ab1af67f.jpg", + "image_caption": [ + "Figure 7. Environment and result of subjective experiment. 2 displays are calibrated differently to ensure both SDR and HDR are correctly visualized. Results are provided in quartile chart." + ], + "image_footnote": [], + "bbox": [ + 127, + 412, + 419, + 580 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Result shows that only 2 methods (ours & DaVinci) are recognized better than input SDR. For methods [4-6], their main 'attribution' (bottom Fig.7) is artifact ( $C4$ , see Fig.6 blue box). For artifact-free 'YouTube-DM' methods [7-9], they are rated slightly worse mainly for lower saturation ( $C1B$ ) and incapability in information recovery, consisting with Tab.8 and Fig.6. Form 'attribution' we also notice that our method got better score mainly for viewing experience ( $C1$ ) and information recovery ( $C3$ ). Also, our 'bad cases' lies in recovered large dark area with intrinsic noise etc. amplified and uneliminated. This is why we got more checks on $C4(\\times 24)$ than commercial methods DaVinci and Nuke.", + "bbox": [ + 75, + 652, + 468, + 834 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.4. Ablation studies", + "text_level": 1, + "bbox": [ + 76, + 845, + 238, + 859 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "On DM. When DM is changed to YouTube, Tab.8 witnesses a significant decline on FHLP, EHL, ASL, ALL", + "bbox": [ + 76, + 869, + 468, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "and HDRBQ, while Fig.8 confirms a result similar to those 'YouTube-DM' methods, i.e. worse viewing experience and less recover ability. Also, when using Reinhard DM which contains no clipping, result's highlight area stay unlearned.", + "bbox": [ + 496, + 90, + 890, + 151 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "On dataset. Here, we use original DMs, but label HDR from other dataset. In Fig.8 yellow box, our DMs encourage the network to output higher luminance, but since Zeng20 is of least extent of HDR i.e. these highlight do not exist in label HDR, our LSN will not 'recognize' them and thus produce artifact. Since this dataset is also of least extent of WCG, FWGP&EWG in Tab.8 drop obviously. When using slightly-inferior HDRTV1K as label, difference is relatively less significant. Yet, in both cases, ASL&ALL are similar since DM i.e. network's style tendency is unaltered.", + "bbox": [ + 496, + 152, + 893, + 303 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/fc7cb3945dbe14230bd04c48397e2499e47c5247d60fdac778b88e7f67d240aa.jpg", + "image_caption": [ + "Figure 8. Result of ablation studies proves the importance of both high-quality label HDR and rational DMs. Specifically, absent of both label HDR's HDR/WCG volume (Zeng20) and DM's degradation (Reinhard) will impair LSN's recover ability, meanwhile YouTube DM's style will make our LSN commonplace as others." + ], + "image_footnote": [], + "bbox": [ + 511, + 315, + 880, + 599 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 709, + 617, + 724 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "There are 2 types of low level vision: 'sole-restoration' whose destination is only clean or GT e.g. denoising, and 'perceptual-motivated' aiming at better viewing experience e.g. image enhancement/retouching. SDR-to-HDRTV upconversion belongs to both. Yet, current methods only realize (it belongs to) the former and neglect the latter, leading their concentration only on network mechanism.", + "bbox": [ + 496, + 734, + 890, + 839 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To this end, our response is two-fold: (1) focusing on the impact of training set, and ameliorating its quality by proposing new dataset and DMs, (2) involving novel assessment criteria based on the 'perceptual' principal.", + "bbox": [ + 496, + 840, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "22238", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] ITU, Geneva, Switzerland, Report ITU-R BT.2381-0: Requirements for high dynamic range television (HDR-TV) systems, 0 ed., 7 2015. 1, 6", + "[2] ITU, Geneva, Switzerland, Recommendation ITU-R BT.2100-2: Image parameter values for high dynamic range television for use in production and international programme exchange, 2 ed., 07 2018. 1, 2", + "[3] ITU, Geneva, Switzerland, Recommendation ITU-R BT.2020-2: Parameter values for ultra-high definition television systems for production and international programme exchange, 2 ed., 10 2015. 1, 2", + "[4] S. Y. Kim, J. Oh, and M. Kim, \"Deep sr-item: Joint learning of super-resolution and inverse tone-mapping for 4k uhd hdr applications,\" in Proc. ICCV, pp. 3116-3125, 2019. 2, 3, 5, 6, 7, 8", + "[5] S. Y. Kim, J. Oh, and M. Kim, \"Jsi-gan: Gan-based joint super-resolution and inverse tone-mapping with pixel-wise task-specific filters for uhd hdr video,\" in Proc. AAAI, vol. 34, pp. 11287-11295, 2020. 2, 3, 6, 7, 8", + "[6] H. Zeng, X. Zhang, Z. Yu, and Y. Wang, “Sr-itm-gan: Learning 4k uhd hdr with a generative adversarial network,” IEEE Access, vol. 8, pp. 182815–182827, 2020. 2, 3, 5, 6, 7, 8", + "[7] X. Chen, Z. Zhang, J. S. Ren, L. Tian, Y. Qiao, and C. Dong, “A new journey from sdrtv to hdttv,” in Proc. ICCV, pp. 4500–4509, 2021. 2, 3, 5, 6, 7, 8", + "[8] G. Cao, F. Zhou, H. Yan, A. Wang, and L. Fan, \"Kpn-mfi: A kernel prediction network with multi-frame interaction for video inverse tone mapping,\" in Proc. IJCAI, pp. 806-812, 2022. 2, 3, 6, 7, 8", + "[9] G. Xu, Q. Hou, L. Zhang, and M.-M. Cheng, \"Fmnet: Frequency-aware modulation network for sdr-to-hdr translation,\" in Proc. ACMMM, pp. 6425-6435, 2022. 2, 3, 6, 7, 8", + "[10] Z. Liang, \"Cvpr2022-1st workshop on vision dataset understanding,\" https://sites.google.com/view/vdu-cvpr22, 2022.2", + "[11] K. Zhang, J. Liang, L. Van Gool, and R. Timofte, \"Designing a practical degradation model for deep blind image superresolution,\" in Proc. ICCV, pp. 4791-4800, 2021. 2, 3", + "[12] V. Dewil, A. Barral, G. Facciolo, and P. Arias, \"Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?\", in Proc. CVPR, pp. 4900-4910, 2022. 2, 3", + "[13] J. Jiang, K. Zhang, and R. Timofte, “Towards flexible blindJPEG artifacts removal,” in Proc. ICCV, pp. 4997–5006, 2021. 2, 3", + "[14] K. Jiang, Z. Wang, Z. Wang, C. Chen, P. Yi, T. Lu, and C.-W. Lin, \"Degrade is upgrade: Learning degradation for low-light image enhancement,\" in Proc. AAAI, vol. 36, pp. 1078-1086, 2022. 2, 3", + "[15] Y. Zhou, Y. Song, and X. Du, \"Modular degradation simulation and restoration for under-display camera,\" in Proc. ACCV, pp. 265-282, 2022. 2, 3" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[16] C. Guo and X. Jiang, “Lhdr: Hdr reconstruction for legacy content using a lightweight dnn,” in Proc. ACCV, pp. 3155–3171, 2022. 2, 3", + "[17] S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M.-H. Yang, \"Restormer: Efficient transformer for high-resolution image restoration,\" in Proc. CVPR, pp. 5728-5739, 2022. 2, 3, 4", + "[18] G. Eilertsen, S. Hajisharif, P. Hanji, A. Tsimirikoglou, R. K. Mantiuk, and J. Unger, \"How to cheat with metrics in single-image hdr reconstruction,\" in Proc. ICCV, pp. 3998-4007, 2021. 2, 3, 6, 7", + "[19] P. Hanji, R. Mantiuk, G. Eilertsen, S. Hajisharif, and J. Unger, “Comparison of single image hdr reconstruction methods—the caveats of quality assessment,” in Proc. SIGGRAPH, pp. 1–8, 2022. 2, 6, 7, 8", + "[20] L. Wang and K.-J. Yoon, \"Deep learning for hdr imaging: State-of-the-art and future trends,\" IEEE Trans. PAMI, 2021. 2", + "[21] P. Debevec, \"Image-based lighting,\" in ACM SIGGRAPH 2006 Courses, pp. 4-es, 2006. 2", + "[22] E. Reinhard, W. Heidrich, P. Debevec, et al., High dynamic range imaging: acquisition, display, and image-based lighting. Morgan Kaufmann, 2010. 2", + "[23] M. Gharbi, J. Chen, J. T. Barron, S. W. Hasinoff, and F. Durand, “Deep bilateral learning for real-time image enhancement,” ACM Trans. Graph., vol. 36, no. 4, pp. 1–12, 2017. 2", + "[24] X. Yang, K. Xu, Y. Song, Q. Zhang, X. Wei, and R. W. Lau, \"Image correction via deep reciprocating hdr transformation,\" in Proc. CVPR, pp. 1798-1807, 2018. 2", + "[25] Z. Zheng, W. Ren, X. Cao, T. Wang, and X. Jia, \"Ultra-high-definition image hdr reconstruction via collaborative bilateral learning,\" in Proc. ICCV, pp. 4449-4458, 2021. 2", + "[26] B. Mildenhall, P. Hedman, R. Martin-Brualla, P. P. Srinivasan, and J. T. Barron, “Nerf in the dark: High dynamic range view synthesis from noisy raw images,” in Proc. CVPR, pp. 16190–16199, 2022. 2", + "[27] N. K. Kalantari, R. Ramamoorthi, et al., \"Deep high dynamic range imaging of dynamic scenes,\" ACM Trans. Graph., vol. 36, no. 4, pp. 144-1, 2017. 2", + "[28] S. Wu, J. Xu, Y.-W. Tai, and C.-K. Tang, \"Deep high dynamic range imaging with large foreground motions,\" in Proc. ECCV, pp. 117-132, 2018. 2", + "[29] Q. Yan, D. Gong, Q. Shi, A. v. d. Hengel, C. Shen, I. Reid, and Y. Zhang, “Attention-guided network for ghost-free high dynamic range imaging,” in Proc. CVPR, pp. 1751–1760, 2019. 2", + "[30] G. Chen, C. Chen, S. Guo, Z. Liang, K.-Y. K. Wong, and L. Zhang, \"Hdr video reconstruction: A coarse-to-fine network and a real-world benchmark dataset,\" in Proc. ICCV, pp. 2502-2511, 2021. 2", + "[31] Y. Niu, J. Wu, W. Liu, W. Guo, and R. W. Lau, \"Hdr-gan: Hdr image reconstruction from multi-exposed ldr images with large motions,\" IEEE Transactions on Image Processing, vol. 30, pp. 3885-3896, 2021. 2" + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "22239", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[32] E. Pérez-Pellitero et al., “Ntire 2022 challenge on high dynamic range imaging: Methods and results,” in Proc. CVPR, pp. 1009–1023, 2022. 2", + "[33] G. Eilertsen, J. Kronander, et al., \"Hdr image reconstruction from a single exposure using deep cnns,\" ACM Trans. Graph., vol. 36, no. 6, pp. 1-15, 2017. 2, 3", + "[34] D. Marnerides, T. Bashford-Rogers, et al., \"Expandnet: A deep convolutional neural network for high dynamic range expansion from low dynamic range content,\" Comput. Graph. Forum, vol. 37, no. 2, pp. 37-49, 2018. 2", + "[35] Y.-L. Liu, W.-S. Lai, Y.-S. Chen, Y.-L. Kao, M.-H. Yang, Y.-Y. Chuang, and J.-B. Huang, \"Single-image hdr reconstruction by learning to reverse the camera pipeline,\" in Proc. CVPR, pp. 1651-1660, 2020. 2, 3", + "[36] M. S. Santos, T. I. Ren, and N. K. Kalantari, \"Single image hdr reconstruction using a cnn with masked features and perceptual loss,\" ACM Trans. Graph., vol. 39, no. 4, pp. 80-1, 2020. 2", + "[37] X. Chen, Y. Liu, et al., \"Hdrunet: Single image hdr reconstruction with denoising and dequantization,\" in Proc. CVPR, pp. 354-363, 2021. 2", + "[38] S. Ning, H. Xu, L. Song, R. Xie, and W. Zhang, “Learning an inverse tone mapping network with a generative adversarial regularizer,” in Proc. ICASSP, pp. 1383–1387, 2018. 2, 3", + "[39] K. Hirao, Z. Cheng, M. Takeuchi, and J. Katto, \"Convolutional neural network based inverse tone mapping for high dynamic range display using lucore,\" in 2019 IEEE International Conference on Consumer Electronics (ICCE), pp. 1-2, 2019. 2", + "[40] S. Y. Kim, D.-E. Kim, and M. Kim, \"Itm-cnn: Learning the inverse tone mapping from low dynamic range video to high dynamic range displays using convolutional neural networks,\" in Proc. ACCV, pp. 395-409, 2018. 2, 3", + "[41] Y. Xu, S. Ning, R. Xie, and L. Song, \"Gan based multiexposure inverse tone mapping,\" in Proc. ICIP, pp. 4365-4369, 2019. 2, 3", + "[42] Y. Xu, L. Song, R. Xie, and W. Zhang, \"Deep video inverse tone mapping,\" in 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp. 142-147, 2019. 2, 3", + "[43] D.-E. Kim and M. Kim, “Learning-based low-complexity reverse tone mapping with linear mapping,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 2, pp. 400–414, 2019. 2, 3, 8", + "[44] J. Zou, K. Mei, and S. Sun, \"Multi-scale video inverse tone mapping with deformable alignment,\" in 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), pp. 9-12, 2020. 2, 3", + "[45] T. Chen and P. Shi, \"An inverse tone mapping algorithm based on multi-scale dual-branch network,\" in 2021 International Conference on Culture-oriented Science & Technology (ICCST), pp. 187–191, 2021. 2, 3", + "[46] G. Xu, Y. Yang, J. Xu, L. Wang, X.-T. Zhen, and M.-M. Cheng, \"Joint super-resolution and inverse tone-mapping: A feature decomposition aggregation network and a new benchmark,\" 2022. 2, 3" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[47] G. He, K. Xu, L. Xu, C. Wu, M. Sun, X. Wen, and Y.-W. Tai, \"Sdrtv-to-hdrtv via hierarchical dynamic context feature mapping,\" in Proc. ACMM, pp. 2890-2898, 2022. 2, 3", + "[48] G. He, S. Long, L. Xu, C. Wu, J. Zhou, M. Sun, X. Wen, and Y. Dai, “Global priors guided modulation network for joint super-resolution and inverse tone-mapping,” 2022. 2, 3", + "[49] K. Xu, L. Xu, G. He, C. Wu, Z. Ma, M. Sun, and Y.-W. Tai, \"Sdrtv-to-hdrtv conversion via spatial-temporal feature fusion,\" 2022. 2, 3", + "[50] T. Shao, D. Zhai, J. Jiang, and X. Liu, \"Hybrid conditional deep inverse tone mapping,\" in Proc. ACMM, pp. 1016-1024, 2022. 2, 3", + "[51] A. Mustafa, P. Hanji, and R. K. Mantiuk, \"Distilling style from image pairs for global forward and inverse tone mapping,\" in The 19th ACM SIGGRAPH European Conference on Visual Media Production (CVMP), pp. 1-10, 2022. 2, 3, 4", + "[52] M. Yao, D. He, X. Li, Z. Pan, and Z. Xiong, \"Bidirectional translation between uhd-hdr and hd-sdr videos,\" IEEE Transactions on Multimedia, 2023. 2, 3", + "[53] R. Tang, F. Meng, and L. Bai, “Zoned mapping network from sdr video to hdr video,” in 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), vol. 5, pp. 1466–1472, 2022. 2, 3", + "[54] SMPTE, NY, USA, ST 2084:2014 - SMPTE Standard - High Dynamic Range Electro-Optical Transfer Function of Mastering Reference Displays, 2014. 2", + "[55] ITU, Geneva, Switzerland, Recommendation ITU-R BT.709-6: Parameter values for the HDTV standards for production and international programme exchange, 6 ed., 6 2015. 2, 5", + "[56] Z. Cheng, T. Wang, Y. Li, F. Song, C. Chen, and Z. Xiong, \"Towards real-world hdrtv reconstruction: A data synthesis-based approach,\" in Proc. ECCV, pp. 199-216, 2022. 3", + "[57] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, \"Photographic tone reproduction for digital images,\" in Proc. 29th SIGGRAPH, pp. 267-276, 2002. 3", + "[58] ITU, Geneva, Switzerland, Report ITU-R BT.2446-1: Methods for conversion of high dynamic range content to standard dynamic range content and vice-versa, 1 ed., 3 2021. 3, 5", + "[59] P. Mohammadi, M. T. Pourazad, and P. Nasiopoulos, \"A perception-based inverse tone mapping operator for high dynamic range video applications,\" IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 5, pp. 1711-1723, 2020. 3, 8", + "[60] D. Marnerides, T. Bashford-Rogers, and K. Debattista, \"Deep hdr hallucination for inverse tone mapping,\" Sensors, vol. 21, no. 12, p. 4032, 2021. 3", + "[61] Y. Zhang and T. Aydin, \"Deep hdr estimation with generative detail reconstruction,\" in Computer Graphics Forum, vol. 40, pp. 179-190, 2021. 3", + "[62] L. Bai, Y. Yang, and G. Fu, \"Analysis of high dynamic range and wide color gamut of uhdtv,\" in 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), vol. 5, pp. 1750-1755, 2021. 4" + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "22240", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[63] ITU, Geneva, Switzerland, Report ITU-R BT.2407-0: Colour gamut conversion from Recommendation ITU-R BT.2020 to Recommendation ITU-R BT.709, 0 ed., 10 2017. 4, 6", + "[64] ITU, Geneva, Switzerland, Recommendation ITU-R BT.500-14: Methodologies for the subjective assessment of the quality of television images, 14 ed., 10 2019. 4", + "[65] D. Hasler and S. E. Suesstrunk, “Measuring colorfulness in natural images,” in Human vision and electronic imaging VIII, vol. 5007, pp. 87–95, 2003. 4", + "[66] Dolby, \"What is ictcp? - introduction.\" https:// professional.dolby.com/siteassets/pdfs/ ictcp_dolbywhitepaper_v071.pdf.4", + "[67] S. Ploumis, R. Boitard, and P. Nasiopoulos, \"Image brightness quantification for hdr,\" in 2020 28th European Signal Processing Conference (EUSIPCO), pp. 640-644, 2021. 4", + "[68] Arri, \"Camera sample footage & reference image.\" https://www.arri.com/en/learn-help/ learn-help-camera-system/camera-sample-footage-reference-image, 2022.4", + "[69] Netflix, \"Netflix open content.\" https://opencontent.nsf1x.com/, 2020.4", + "[70] J. Froehlich, S. Grandinetti, B. Eberhardt, S. Walter, A. Schilling, and H. Brendel, “Creating cinematic wide gamut hdr-video for the evaluation of tone mapping operators and hdr-displays,” in Digital photography X, vol. 9023, pp. 279–288, 2014. 4", + "[71] Y. Wang, S. Inguva, and B. Adsumilli, \"YoutubeUGC dataset for video compression research,\" in 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), pp. 1-5, 2019. 4", + "[72] Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethuraman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in Proc. ICIP, pp. 786–790, 2022. 4", + "[73] L. Van der Maaten and G. Hinton, \"Visualizing data using t-sne,\" Journal of machine learning research, vol. 9, no. 11, 2008. 5", + "[74] D. Walker, C. Payne, P. Hodoul, and M. Dolan, \"Color management with opencolorio v2,\" in ACM SIGGRAPH 2021 Courses, pp. 1-226, 2021. 5", + "[75] J. Morovic, Color gamut mapping. John Wiley & Sons, 2008. 6", + "[76] H. Le, T. Jeong, A. Abdelhamed, H. J. Shin, and M. S. Brown, \"Gamutnet: Restoring wide-gamut colors for camera-captured images,\" in Color and Imaging Conference, vol. 2021, pp. 7-12, 2021. 6", + "[77] ITU, Geneva, Switzerland, Recommendation ITU-R BT.1886-0: Reference electro-optical transfer function for flat panel displays used in HDTV studio production, 0 ed., 3 2011. 6", + "[78] ITU, Geneva, Switzerland, Report ITU-R BT.2245-10: HDTV and UHDTV including HDR-TV test materials for assessment of picture quality, 10 ed., 9 2022. 6" + ], + "bbox": [ + 78, + 90, + 470, + 901 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[79] ITU, Geneva, Switzerland, Recommendation ITU-R BT.2124-0: Objective metric for the assessment of the potential visibility of colour differences in television, 0 ed., 1 2019. 7", + "[80] K. Wolski, D. Giunchi, et al., \"Dataset and metrics for predicting local visible differences,\" ACM Trans. Graph., vol. 37, no. 5, pp. 1-14, 2018. 7", + "[81] C. Bist, R. Cozot, G. Madec, and X. Ducloux, “Tone expansion using lighting style aesthetics,” Computers & Graphics, vol. 62, pp. 77–86, 2017. 8", + "[82] G. Luzardo, J. Aelterman, H. Luong, S. Rousseaux, D. Ochoa, and W. Philips, “Fully-automatic inverse tone mapping algorithm based on dynamic mid-level tone mapping,” APSIPA Transactions on Signal and Information Processing, vol. 9, p. e7, 2020. 8", + "[83] A. Stojkovic, J. Aelterman, H. Luong, H. Van Parys, and W. Philips, “Highlights analysis system (hans) for low dynamic range to high dynamic range conversion of cinematic low dynamic range content,” IEEE Access, vol. 9, pp. 43938–43969, 2021. 8" + ], + "bbox": [ + 501, + 90, + 890, + 388 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "22241", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models/ed51a5a5-a3b7-4784-b738-9ddfdcaf0202_model.json b/2023/Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models/ed51a5a5-a3b7-4784-b738-9ddfdcaf0202_model.json new file mode 100644 index 0000000000000000000000000000000000000000..d91b9ca2be95dbc2fe567244d1757a4d1cdaa172 --- /dev/null +++ b/2023/Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models/ed51a5a5-a3b7-4784-b738-9ddfdcaf0202_model.json @@ -0,0 +1,3038 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.0, + 0.812, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.102, + 0.13, + 0.87, + 0.175 + ], + "angle": 0, + "content": "Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models" + }, + { + "type": "text", + "bbox": [ + 0.245, + 0.203, + 0.748, + 0.222 + ], + "angle": 0, + "content": "Cheng Guo\\(^{1,2}\\), Leidong Fan\\(^{3,2}\\), Ziyu Xue\\(^{4,1}\\) and Xiuhua Jiang\\(^{2,1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.089, + 0.222, + 0.91, + 0.258 + ], + "angle": 0, + "content": "\\(^{1}\\)State Key Laboratory of Media Convergence and Communication, Communication University of China \\(^{2}\\)Peng Cheng Laboratory \\(^{3}\\)Peking University" + }, + { + "type": "text", + "bbox": [ + 0.171, + 0.257, + 0.825, + 0.275 + ], + "angle": 0, + "content": "\\(^{4}\\)Academy of Broadcasting Science, National Radio and Television Administration" + }, + { + "type": "text", + "bbox": [ + 0.148, + 0.278, + 0.845, + 0.293 + ], + "angle": 0, + "content": "{guocheng,jiangxiuhua}@cuc.edu.cn，fanleidong@stu.pku.edu.cn，xueziyu@abs.ac.cn" + }, + { + "type": "image", + "bbox": [ + 0.099, + 0.342, + 0.209, + 0.393 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.132, + 0.392, + 0.177, + 0.4 + ], + "angle": 0, + "content": "Input SDR" + }, + { + "type": "image", + "bbox": [ + 0.212, + 0.343, + 0.321, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.215, + 0.392, + 0.314, + 0.401 + ], + "angle": 0, + "content": "Deep SR-ITM (ICCV'19)" + }, + { + "type": "image", + "bbox": [ + 0.323, + 0.343, + 0.431, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.334, + 0.392, + 0.413, + 0.401 + ], + "angle": 0, + "content": "JSI-GAN (AAA1'20)" + }, + { + "type": "image", + "bbox": [ + 0.432, + 0.343, + 0.54, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.441, + 0.392, + 0.53, + 0.401 + ], + "angle": 0, + "content": "HDRTVNet (ICCV'21)" + }, + { + "type": "image", + "bbox": [ + 0.541, + 0.343, + 0.655, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.55, + 0.392, + 0.642, + 0.401 + ], + "angle": 0, + "content": "FMNet (ACM MM'22)" + }, + { + "type": "image", + "bbox": [ + 0.655, + 0.343, + 0.762, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.695, + 0.392, + 0.716, + 0.401 + ], + "angle": 0, + "content": "Ours" + }, + { + "type": "image", + "bbox": [ + 0.763, + 0.343, + 0.872, + 0.392 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.798, + 0.392, + 0.834, + 0.401 + ], + "angle": 0, + "content": "GT-HDR" + }, + { + "type": "image", + "bbox": [ + 0.099, + 0.401, + 0.21, + 0.482 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.127, + 0.482, + 0.169, + 0.488 + ], + "angle": 0, + "content": "A B A 0 0 0" + }, + { + "type": "image", + "bbox": [ + 0.212, + 0.401, + 0.321, + 0.482 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.229, + 0.482, + 0.279, + 0.488 + ], + "angle": 0, + "content": "1 0" + }, + { + "type": "image", + "bbox": [ + 0.323, + 0.401, + 0.431, + 0.481 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.345, + 0.482, + 0.385, + 0.488 + ], + "angle": 0, + "content": "28 0.8 0.8 0.7" + }, + { + "type": "image", + "bbox": [ + 0.432, + 0.401, + 0.54, + 0.481 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.441, + 0.482, + 0.498, + 0.488 + ], + "angle": 0, + "content": "A 0" + }, + { + "type": "image", + "bbox": [ + 0.541, + 0.401, + 0.653, + 0.481 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.55, + 0.482, + 0.589, + 0.488 + ], + "angle": 0, + "content": "15 10 \nY 10" + }, + { + "type": "image", + "bbox": [ + 0.655, + 0.402, + 0.762, + 0.481 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.674, + 0.482, + 0.729, + 0.488 + ], + "angle": 0, + "content": "0.8 0.8 0.7 0.8 0.8" + }, + { + "type": "image", + "bbox": [ + 0.763, + 0.402, + 0.872, + 0.481 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.798, + 0.482, + 0.834, + 0.488 + ], + "angle": 0, + "content": "0.8 0.7 0.6 0.5" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.5, + 0.893, + 0.57 + ], + "angle": 0, + "content": "Figure 1. Up-converting SDR content for HDR-WCG TV display. To ensure better viewing experience [1], we use (1) \\(3D\\) Yxy chromaticity diagram (vertical axis \\(Y\\) for HDR/lumiannce, \\(xy\\) plane for WCG/color) to see how HDRTV's advance on HDR&WCG volume is recovered, and (2) detailed visuals (yellow and blue boxes) to assess method's recover ability. Note that HDR is dimer here in print version since its large luminance&color container is interpreted by small SDR capacity, and will appear normal if correctly visualized (top Fig.7). Still, from (3) 'Brightness Index' & 'Saturation Level' we know that result from current methods is more dim and desaturated than GT." + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.586, + 0.314, + 0.601 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.629, + 0.473, + 0.902 + ], + "angle": 0, + "content": "In media industry, the demand of SDR-to-HDRTV upconversion arises when users possess HDR-WCG (high dynamic range-wide color gamut) TVs while most off-the-shelf footage is still in SDR (standard dynamic range). The research community has started tackling this low-level vision task by learning-based approaches. When applied to real SDR, yet, current methods tend to produce dim and desaturated result, making nearly no improvement on viewing experience. Different from other network-oriented methods, we attribute such deficiency to training set (HDR-SDR pair). Consequently, we propose new HDRTV dataset (dubbed HDRTV4K) and new HDR-to-SDR degradation models. Then, it's used to train a luminance-segmented network (LSN) consisting of a global mapping trunk, and two Transformer branches on bright and dark luminance range. We also update assessment criteria by tailored metrics and subjective experiment. Finally, ablation studies are conducted to prove the effectiveness. Our work is available" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.588, + 0.876, + 0.602 + ], + "angle": 0, + "content": "at: https://github.com/AndreGuo/HDRTVDM." + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.633, + 0.633, + 0.648 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.892, + 0.764 + ], + "angle": 0, + "content": "The dynamic range of image is defined as the maximum recorded luminance to the minimum. Larger luminance container endows high dynamic range (HDR) a better expressiveness of scene. In media and film industry, the superiority of HDR is further boosted by advanced electro-optical transfer function (EOTF) e.g. PQ/HLG [2], and wide color-gamut (WCG) RGB primaries e.g. BT.2020 [3]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.765, + 0.893, + 0.901 + ], + "angle": 0, + "content": "While WCG-HDR displays are becoming more readily available in consumer market, most commercial footage is still in standard dynamic range (SDR) since WCG-HDR version is yet scarce due to exorbitant production workflow. Hence, there raise the demand of converting vast existing SDR content for HDRTV service. Such SDR may carry irreproducible scenes, but more likely, imperfections brought by old imaging system and transmission. This indicates that SDR-to-HDRTV up-conversion is an ill-posed low-level vi" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22231" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.47, + 0.121 + ], + "angle": 0, + "content": "sion task, and research community has therefore begun involving learning-based methods ([4-9] etc.)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.123, + 0.47, + 0.304 + ], + "angle": 0, + "content": "Yet, versatile networks they use (§2.1), we find current methods' result dim and desaturated when feeding real SDR images (Fig.1), conflicting with the perceptual motive of SDR-to-HDRTV up-conversion. As reported by CVPR22-1st Workshop on Vision Dataset Understanding [10], most methods are network-oriented and understate the impact of training set. For restoration-like low-level vision, there are 2 ingredients of a training set: the quality of label GT itself, and the GT-to-LQ degradation model (DM) i.e. what the network learns to restore. Such neglect is getting remedied in other low-level vision tasks [11-16], but still pervasive in learning-based SDR-to-HDRTV up-conversion." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.305, + 0.469, + 0.531 + ], + "angle": 0, + "content": "Not serendipitously, we find dataset the reason why current methods underperform. We exploit several HDRTV-tailored metrics (Tab.4) to assess current training set:(1) by measuring label HDR's extent of \\( HDR/WCG \\) etc. (Tab.5), we notice that its quality and diversity are inadequate to incentive the network to produce appealing result, (2) via the statistics of degraded SDR, we find current HDR-to-SDR DMs' tendency to exaggeratedly alter the saturation and brightness (see Tab.6) thus network will learn a SDR-to-HDR deterioration. Hence, we propose HDRTV4K dataset (\\(\\S 3.2\\)) consisting of high-quality and diversified (Fig.4) HDRTV frames as label. Then exploit 3 new HDRTV-to-SDR DMs (\\(\\S 3.3\\)) avoiding above insufficiency, meanwhile possessing appropriate degradation capability (Tab.6) so the network can learn reasonable restoration ability." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.532, + 0.47, + 0.638 + ], + "angle": 0, + "content": "Afterwards, we formulate the task as the combination of global mapping on the full luminance range and recovery of low/high luminance range. Correspondingly, we propose Luminance Segmented Network (LSN, §3.1) where a global trunk and two Transformer-style UNet [17] branches are assigned to respectively execute divergent operations required in different segmented luminance ranges (areas)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.638, + 0.469, + 0.714 + ], + "angle": 0, + "content": "Lastly, as found by [18, 19], conventional distance-based metrics well-performed in solely-reconstruction task (e.g. denoising) fail for perceptual-motivated HDR reconstruction, we therefore update the assessment criteria with fine-grained metrics (§4.2) and subjective experiment (§4.3) etc." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.714, + 0.469, + 0.805 + ], + "angle": 0, + "content": "Our contributions are three-fold: (1) Emphasizing & verifying the impact of dataset on SDR-to-HDRTV task, which has long been understated. (2) Exploiting novel HDRTV dataset and HDR-to-SDR degradation models for network to learn. (3) Introducing new problem formulation, and accordingly proposing novel luminance segmented network." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.821, + 0.227, + 0.836 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.847, + 0.334, + 0.863 + ], + "angle": 0, + "content": "2.1. Nomenclature and our scope" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.469, + 0.902 + ], + "angle": 0, + "content": "Plenty of HDR-related learning-based methods [20] have been proposed, yet, they markedly differ in intended appli" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.273 + ], + "angle": 0, + "content": "cation. As in Tab.1, 'linear HDR' means scene-referred HDR images dedicating to record the linear radiance for graphics application e.g. image-based lighting [21,22], and 'HDRTV' stands for our display-referred WCG-HDR [2] format. Clearly, ① synthesize HDR view on SDR display, ② emphasize dealing with misalignment between multiple SDRs and is designed for new imaging pipeline, while ③ and ④ are all oriented for existing SDR, but for different application. Since many works are proposed before community's clarity on above nomenclature, we classify a method as SDR-to-HDRTV up-conversion (④, our task) if its target HDR is claimed in PQ/HLG EOTF and BT.2020 WCG." + }, + { + "type": "table", + "bbox": [ + 0.51, + 0.282, + 0.885, + 0.382 + ], + "angle": 0, + "content": "
Task nameFromTo singleMethods
①HDR-style enhancementsingle SDRenhanced SDRe.g. [23-26]
②multi-exposure HDR imagingmultiple SDRlinear HDRe.g. [27-32]
③SI-HDR*single SDRe.g. [33-37]
④ (ours)iTM* or up-conversionHDRTV frame[4-9] [38-53]
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.518, + 0.382, + 0.874, + 0.394 + ], + "angle": 0, + "content": "*: SI-HDR: Single-Image HDR reconstruction, iTM: inverse Tone-Mapping." + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.405, + 0.892, + 0.433 + ], + "angle": 0, + "content": "Table 1. Various learning-based HDR-related tasks. Green/cyan/magenta terms are respectively from [18]/our/ [7] nomenclature." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.448, + 0.892, + 0.569 + ], + "angle": 0, + "content": "In our scope (4), methods designed their networks with distinct philosophy: Except e.g. semantic-requiring highlight recovery, main process of our task belongs to global mapping resembling image retouching/enhancement. Following this, [7] formulates the task as 3 steps and use \\(1 \\times 1\\) convolution for global part, [43] learn the mapping between small sorted image patches, while [51] conducts explicitly-defined per-pixel mapping using the learned latent vector." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.569, + 0.892, + 0.629 + ], + "angle": 0, + "content": "Feature modulation is also popular to involve global prior information. Compared with [7], they change: prior's type [47, 49], modulation tensor's shape [48-50], modulation mechanism [9] and modulation's position [50]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.629, + 0.892, + 0.704 + ], + "angle": 0, + "content": "To coordinate consecutive video frames, [42] applies 3D convolution with extra temporal-D, [8] take 3 frames as input and deploy multi-frame interaction module at UNet bottleneck. Also, for alignment, [44,49] use deformable convolution whose offset map is predicted by different modules." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.892, + 0.78 + ], + "angle": 0, + "content": "Due to the resolution discrepancy between SDRTV and HDRTV, some methods [4-6, 46, 48, 52] jointly conduct super-resolution and up-conversion. Also, some methods are assisted by non-learning pre-processing [53] or bypass [43, 45], while [41] is trained for badly-exposed SDR." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.787, + 0.661, + 0.802 + ], + "angle": 0, + "content": "2.2. HDRTV dataset" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Diversified networks they use, there're currently only 3 open HDRTV training set (Tab.2). All datasets (including ours) contain HDRTV frames in (D65 white) BT.2020 [3] RGB primaries (gamut), PQ [54] EOTF and 1000nit peak luminance, and SDR counterpart in BT.709 [55] gamut, gamma EOTF and 100nit peak luminance." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22232" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.082, + 0.089, + 0.466, + 0.144 + ], + "angle": 0, + "content": "
Dataset (Usage)#pairResolutionHDR format
KAIST [4] ([5,48,52])39840160×160uint16 MATLAB .mat YUV
Zeng20 [6]23229UHDH.265 main10 YUV
HDRTV1K [7] ([9,47,50])123516bit .png RGB
HDRTV4K (ours new)3878HD&UHD16bit lossless .tif RGB
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.155, + 0.47, + 0.239 + ], + "angle": 0, + "content": "Table 2. Status of different HDRTV dataset: training set part. Our HDR frames sized both HD \\((1920\\times 1080)\\) and UHD \\((3840\\times 2160)\\) are manually chosen from \\(>220\\) different videos clips, and encapsulated in lossless (LZW or deflate) TIFF. Others are respectively extracted from only 7 [4]/18 [7] TV demos and 1 graded movie [6], their quality and diversity are quantified later in Tab.5 & Fig.4." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.267, + 0.388, + 0.283 + ], + "angle": 0, + "content": "2.3. HDRTV-to-SDR degradation model" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.291, + 0.469, + 0.366 + ], + "angle": 0, + "content": "Most methods follow the common practice to degrade label HDR(GT) to input SDR(LQ). Degradation model (DM) matters since it determined what network can learn, and is relatively paid more attention even in SI-HDR [16, 33, 35]. Yet, few discussion is made in SDR-to-HDRTV (Tab.3):" + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.38, + 0.466, + 0.415 + ], + "angle": 0, + "content": "
DMYouTubeReinhardother DMs
Usage[4,5,7-9,40,44,46-50,52][6,38,41-43]2446a [45]etc. [51,53,56]
DatasetKAIST & HDRTV1KZeng20
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.426, + 0.469, + 0.454 + ], + "angle": 0, + "content": "Table 3. Current HDRTV-to-SDR degradation models (DMs). 'Dataset' means SDR there is degraded from HDR using that DM." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.473, + 0.469, + 0.563 + ], + "angle": 0, + "content": "Youtube stands for the default conversion YouTube applied to BT.2020/PQ1000 HDR content to produce its SDR-applicable version, Reinhard/2446a means tonemapming HDR to SDR using Reinhard TMO [57]/BT.2446 [58]Method A. [51]/[53]/[56] respectively degrade HDR to SDR by grading/Habel TMO/another learned network." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.565, + 0.469, + 0.775 + ], + "angle": 0, + "content": "In other restoration tasks [11-16], DMs are designed to have proper extent and diversity of degradation so network can learn appropriate restore capability and good generalization. Accordingly, we argue that current DMs are not favorable for training. Specifically, the motive of YouTube is to synthesize HDR view for user possessing only SDR display, it tends to enhance/increase the brightness and saturation so network will, vise-versa, learn to deteriorate/decline them. Also, tone-mapping e.g. Reinhard and 2446a dedicate to preserve as much information from HDR, they have monotonically increasing mapping curves (Fig.5) without highlight clipping, therefore the trained network is not likely to recover much information in over-exposed areas. Above observations are later proven in Tab.6, Fig.1&6 etc." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.793, + 0.25, + 0.809 + ], + "angle": 0, + "content": "3. Proposed Method" + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.819, + 0.429, + 0.833 + ], + "angle": 0, + "content": "The overview of our method is illustrated in Fig.2." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.847, + 0.256, + 0.861 + ], + "angle": 0, + "content": "3.1. Network structure" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.469, + 0.9 + ], + "angle": 0, + "content": "Problem formulation helps the researcher to clarify what degradation the network should restore. Given \\( x \\) the" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.891, + 0.121 + ], + "angle": 0, + "content": "SDR, y the HDR. Previous methods [7, 47, 49] hypothesis that x and y are from single RAW file of same camera." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.122, + 0.892, + 0.258 + ], + "angle": 0, + "content": "This applies to SDR-HDR synchronous production where \\(\\mathbf{x}\\) is also of high quality, thus up-conversion \\((\\mathbf{y} = f(\\mathbf{x}))\\) is only supposed to follow specific style and recover less missing information. However, since our method is for existing SDR, we assume that SDR \\((\\mathbf{x})\\) and it imaginary HDR counterpart \\((\\mathbf{y})\\) were simultaneously shot by different camera and later pipeline, similar to [18]. The imperfection of SDR pipeline makes existing SDR susceptible to limited latitude, narrow gamut and other degradations \\((\\mathbf{x} = d(\\mathbf{y}))\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.258, + 0.892, + 0.422 + ], + "angle": 0, + "content": "Some works [7, 35] suppose \\( d(\\cdot) \\) are introduced orderly, and assign hierarchical sub-networks to for \\( f(\\cdot) \\). Since cascaded networks are bulky for real application, we instead formulate that specific degradation is more visible in different luminance range. Concretely, over-exposure occurs in high-luminance range in SDR \\( (\\mathbf{x}_h) \\), low-luminance range \\( (\\mathbf{x}_l) \\) is more susceptible to noise etc., while the quality of mid-luminance range \\( (\\mathbf{x}_m) \\) is relatively higher. Segmented luminance ranges are treated separately by some traditional up-conversion operators ([59] etc.), and here we introduce this idea to our deep neural network (DNN)—LSN:" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.424, + 0.892, + 0.469 + ], + "angle": 0, + "content": "Luminance Segmented Network (LSN) consists of an trunk on full-luminance range \\((\\mathbf{x})\\), and 2 branches respectively on \\(\\mathbf{x}_l\\) and \\(\\mathbf{x}_h\\) which are segmented by:" + }, + { + "type": "equation", + "bbox": [ + 0.523, + 0.474, + 0.891, + 0.503 + ], + "angle": 0, + "content": "\\[\n\\mathbf {x} _ {l} = \\max (0, \\frac {t - \\mathbf {x}}{t}), \\mathbf {x} _ {h} = \\max (0, \\frac {\\mathbf {x} - 1}{t} + 1) \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.508, + 0.892, + 0.554 + ], + "angle": 0, + "content": "where \\(\\mathbf{x} \\in [0,1]\\). That is, luminance range lower/higher than threshold \\(t\\) (empirically set to 0.05) is linearly mapped to more significant value [0, 1], as in top-right Fig.2." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.554, + 0.892, + 0.674 + ], + "angle": 0, + "content": "After segmentation, \\(\\mathbf{x}\\), \\(\\mathbf{x}_l\\) and \\(\\mathbf{x}_h\\) require distinct DNN operation. First, as found by [7] etc. (§2.1), the majority of \\(f(\\cdot)\\) belongs to global (pixel-independent) operation similar to image enhancement/retouching. Therefore, we assign 5 cascaded receptive-field-free \\(1 \\times 1\\) convolution layers on full luminance range \\((\\mathbf{x})\\) as the Global Mapping Trunk. Similar to [7, 9, 47-50], we append a modulation branch (Fig.3 left) to involve \\(\\mathbf{x}\\)'s global prior into DNN's decision." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.674, + 0.892, + 0.84 + ], + "angle": 0, + "content": "On the other hand, \\( f(\\cdot) \\) still involves non-global restoration where local semantic should be aggregated. Specifically, DNN's mission in \\( \\mathbf{x}_l \\) is similar to denoising and low-light enhancement, while that in \\( \\mathbf{x}_h \\) resembles image inpainting (claimed by [60, 61]) where lost content is hallucinated. These low-level vision tasks require larger receptive-filed, we hence choose Transformer blocks [17] specializing long-distance dependency, and arranged them as encoder-decoder with skip-connections (UNet). Transformer-style UNet branches on \\( \\mathbf{x}_l \\) and \\( \\mathbf{x}_h \\) share the same structure but different parameters. The detail of LSN is depicted in Fig.3." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.848, + 0.682, + 0.862 + ], + "angle": 0, + "content": "3.2. HDRTV4K dataset" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.891, + 0.9 + ], + "angle": 0, + "content": "After designing LSN, we need better training set to unleash its capability. We start with the quality of label HDR:" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22233" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.123, + 0.087, + 0.852, + 0.304 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.315, + 0.895, + 0.375 + ], + "angle": 0, + "content": "Figure 2. Overview of this work. Our Luminance Segmented Network (LSN, §3.1) is designed based on novel problem formulation, then supervisedly trained with label HDR from the proposed HDRTV4K dataset (§3.2), and input SDR degraded by novel degradation models (DMs, §3.3). The major concerns of our LSN, HDRTV4K dataset, and DMs are respectively: recovering dark&bright areas, improving the quality (Tab.5) and diversity (Fig.4) of label GT-HDR, and ensuring the LQ-SDR is with proper style and degradation (Tab.6 & Fig.5)." + }, + { + "type": "image", + "bbox": [ + 0.08, + 0.394, + 0.47, + 0.552 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.561, + 0.471, + 0.632 + ], + "angle": 0, + "content": "Figure 3. Detailed structure of LSN modules. 'k3s1n16' means convolution layer with \\(3 \\times 3\\) kernel, stride=1, out nc=16, and 'FC32' is fully-connected layer with out nc=32. We deploy only 1 Transformer block [17] at each en/decode level of UNet to lighten LSN (#param=325k) for its application on 4K resolution." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.659, + 0.47, + 0.704 + ], + "angle": 0, + "content": "Motivation. In §1, we argue the quality of label HDR in current datasets. Here, it is assessed from 3 aspects by 10 metrics in Tab.4, based on following considerations:" + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.705, + 0.47, + 0.81 + ], + "angle": 0, + "content": "First, greater extent of HDR/WCG stands more probability for network to learn pixel in advanced WCG-HDR volume beyond SDR's capability. Meanwhile, higher intra-frame diversity means better generalization the network can learn within small batch-size, i.e. bigger SI/CF/stdL indicate more diversified high-frequency patterns/richer color volume/greater luminance contrast for network to learn." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Also, style distillation [51] has become a major contributor to method's performance in similar task e.g. image enhancement/retouching, we therefore append 3 metrics quantifying the overall-style of label HDR. Note that network's learned style will be, substantially, affected more by degradation model (DM) which will be discussed later." + }, + { + "type": "table", + "bbox": [ + 0.503, + 0.394, + 0.892, + 0.653 + ], + "angle": 0, + "content": "
Metrics on the extent of HDR/WCG ↓
FHLPFraction of HighLight Pixel: Spatial ratio of ‘highlight’ pixel i.e. whose normalized luminance Y = 0.2627R + 0.6780G +0.0593B > 0.01 (100nit, SDR's peak luminance.)
EHLExtent of HighLight: Average pixel (i) distance between the luminance of HDR and its clip-to-100nit version1: 1/n ∑i=1n√[Yi - clip(Yi)]2, clip(x) = clamp(x, 0, 0.01)
FWGPFraction of Wide-Gamut Pixel: Spatial ratio of WCG pixel i.e. whose [x, y] coordinates fall inside BT.2020 but outside SDR's BT.709 gamut in Yxy chromaticity diagram.
EWGExtent of Wide-Gamut [62]: Average pixel-distance between WCG-HDR and its gamut-hard-clipped [63] version2: 1/n ∑i=1n||Si - HC(Si)||2, S = [X, Y, Z]T
Metrics on intra-frame diversity ↓ (all variance-based)
SISpatial Information: Standard deviation over the pixels of Sobel-filtered frame, defined in Annex 6 of [64].
CFColorFulness: Defined in [65].
stdLstandard deviation of Luminance, over all pixels of a frame.
Metrics on overall-style ↓
ASLAverage Saturation Level: Normalized pixel-average length of HDR chrominance component C = [Ct, Cp]T [66]: 1/n ∑i=1n||Ci||2, C ∈ [−0.5, 0.5]
ALLAverage Luminance Level: Pixel-average of Y in FHLP.
HDRBQHDR Brightness Quantification [67], visual salience involved.
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.511, + 0.653, + 0.875, + 0.661 + ], + "angle": 0, + "content": "1: EHL is to compensate cases e.g. an all-101 n it HDR frame with \\(100\\%\\) FHLP but less extent of highlight." + }, + { + "type": "table_footnote", + "bbox": [ + 0.512, + 0.662, + 0.763, + 0.671 + ], + "angle": 0, + "content": "2: You can find the formulation of gamut hard-clipping \\((HC(\\cdot))\\) at Eq.5." + }, + { + "type": "list", + "bbox": [ + 0.511, + 0.653, + 0.875, + 0.671 + ], + "angle": 0, + "content": null + }, + { + "type": "table_caption", + "bbox": [ + 0.5, + 0.684, + 0.892, + 0.727 + ], + "angle": 0, + "content": "Table 4. The quality and diversity of label HDR is measured from 3 aspects by 10 metrics above (both in positive correlation), results are in Tab.5. See supplementary material for full illustration." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.761, + 0.894, + 0.853 + ], + "angle": 0, + "content": "Our work. Statistics in Tab.5 confirms the deficiency of current datasets, i.e. lower intra-frame diversity, and most importantly, less extent of HDR/WCG preventing their network from producing true HDR-WCG volume. To this end, we propose a new dataset HDRTV4K consisting of 3878 BT.2020/PQ1000 (Tab.2) HDR frames with higher quality." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Specifically, these frames are manually extracted and aligned from various open content ([68-72] etc.) with greater extent of HDR/WCG, higher intra-frame diversity" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22234" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.095, + 0.089, + 0.878, + 0.178 + ], + "angle": 0, + "content": "
Metrics \nDatasetExtent of HDRExtent of WCGIntra-frame diversityOverall-style
FHLPEHLFWGPEWGSICFstdLASLALLHDRBQ
KAIST [4]1.52500.20255.47710.11041.93725.94850.95978.908717.28541.8597
Zeng20 [6]0.01970.00120.47920.00340.12314.20480.31463.80616.08050.3781
HDRTV1K [7]1.28430.19712.90890.16332.237811.07221.800610.941415.16262.7970
HDRTV4K (ours)5.30830.95952.63690.51233.550810.58823.48379.827421.19965.1593
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.187, + 0.895, + 0.232 + ], + "angle": 0, + "content": "Table 5. Quality of label HDR frames, manifested in the frame-average of 10 metrics from Tab.4. Greater extent of HDR/WCG encourages network to produce more pixels in non-SDR volume, while intra-frame diversity is helpful for network's generalization ability. All numbers are in percentage (\\%), and we highlight those the most favorable to training. Their diversity is further demonstrated in Fig.4." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.259, + 0.471, + 0.32 + ], + "angle": 0, + "content": "and reasonable style (Tab. 5). Note that we re-grade some original footage using DaVinci Resolve to add some perturbation on the diversity. The thumbnails of our dataset and its frame distribution comparison are visualized in Fig.4." + }, + { + "type": "image", + "bbox": [ + 0.082, + 0.332, + 0.209, + 0.389 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.211, + 0.332, + 0.338, + 0.389 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.34, + 0.332, + 0.469, + 0.389 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.082, + 0.39, + 0.209, + 0.446 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.211, + 0.39, + 0.338, + 0.446 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.34, + 0.39, + 0.469, + 0.446 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.081, + 0.45, + 0.468, + 0.61 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.081, + 0.612, + 0.21, + 0.669 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.211, + 0.612, + 0.338, + 0.669 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.34, + 0.612, + 0.468, + 0.669 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.68, + 0.47, + 0.777 + ], + "angle": 0, + "content": "Figure 4. Diversity comparison: our HDRTV4K dataset vs. others. Here, each 2D-coordinate is the projection of single frame's 10-D vector (containing 10 metrics from Tab.5) using t-SNE [73] \\((\\mathrm{exg} = 3,\\mathrm{prep} = 50)\\). We also depict thumbnail HDR frames from our dataset (1)-(9) with their corresponding 2D-coordinates highlighted with gray circle. As seen, our dataset provides wider frame distribution i.e. more diversified scenes for network to learn." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.802, + 0.304, + 0.818 + ], + "angle": 0, + "content": "3.3. New degradation models" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.471, + 0.903 + ], + "angle": 0, + "content": "After obtaining label HDR with higher quality, we focus on degradation model (DM) which determines the restoration network will learn. As claimed in §2.3, current DMs fail for undue saturation & brightness change and meager over-exposure degradation, which are verified by ALL," + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.258, + 0.892, + 0.335 + ], + "angle": 0, + "content": "ASL, FOEP in Tab.6. That is, YouTube/2446a tend to increase/decline both ASL & ALL so network will learn to simultaneously de/over-saturate and unduly-darken/brighten. Also, 2446a and Rienhard provide small FOEP which is adverse to network's over-exposure hallucination ability." + }, + { + "type": "table", + "bbox": [ + 0.504, + 0.346, + 0.891, + 0.444 + ], + "angle": 0, + "content": "
SDR Degraded byFOEP1ALLASL2SICF
CurrentDM2446a0.1815.8803.6246.0416.573
Reinhard1.26421.8877.44214.14712.568
YouTube5.43928.21914.64118.54525.225
OursDM2446c+GM1.73911.66910.18312.50319.391
HC+GM4.25214.06210.37715.09020.146
OCIO21.58018.8879.97713.57818.052
criteria↑: better whenkept3samllerkept--
Source HDR (Tab.5)45.30821.2009.8273.55110.588
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.513, + 0.444, + 0.856, + 0.453 + ], + "angle": 0, + "content": "1: FOEP: Fraction of Over-exposed Pixels: Spatial ratio of pixels whose normalized luminance \\( Y = 1 \\)." + }, + { + "type": "table_footnote", + "bbox": [ + 0.513, + 0.453, + 0.858, + 0.462 + ], + "angle": 0, + "content": "2: For SDR, ASL is also calculated as Tab.4, but with \\(\\mathbf{C} = [C_h, C_r]^{\\mathrm{T}}\\) [55] rather \\([C_t, C_p]^{\\mathrm{T}}\\)." + }, + { + "type": "table_footnote", + "bbox": [ + 0.513, + 0.462, + 0.851, + 0.472 + ], + "angle": 0, + "content": "3: Assuming that HDR's highlight \\((>100n it)\\) part should all be clipped to 1 (over-exposure) in SDR." + }, + { + "type": "table_footnote", + "bbox": [ + 0.513, + 0.472, + 0.876, + 0.482 + ], + "angle": 0, + "content": "4: Gray means container discrepancy i.e. HDR/SDR's metric should be different even for well-degraded SDR." + }, + { + "type": "list", + "bbox": [ + 0.513, + 0.444, + 0.876, + 0.482 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.495, + 0.892, + 0.594 + ], + "angle": 0, + "content": "Table 6. Given label HDR from HDRTV4K dataset, SDR's statistics alters when degraded by different DMs. Results are in percentage, we mark those unfavorable / neutral / beneficial for training based on the observation in §2.3 & 3.3. As seen, our DMs provide adequate over-exposure (FOEP) and no undue brightness (ALL) & saturation (ASL) change for network to learn. Example of different degraded SDR can be found in supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.61, + 0.892, + 0.639 + ], + "angle": 0, + "content": "This motivate us to utilize/exploit new DMs with proper extent of degradation and no deficiencies on style:" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.64, + 0.892, + 0.685 + ], + "angle": 0, + "content": "(1) OCIO2: OpenColorIO v2 [74], commercial method from BT.2020/PQ1000 HDR container to BT.709/gamma SDR, implemented by 3D look-up table (LUT) here." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.685, + 0.892, + 0.792 + ], + "angle": 0, + "content": "(2) \\( 2446c + GM \\): Modified Method \\( C \\) tone-mapping from Rec.2446 [58], followed by Gamut Mapping. Here, each HDR encoded value \\( \\mathbf{E}' = [R', G', B']^{\\mathrm{T}} \\) (we use superscript ' for non-linearity) is linearize to \\( \\mathbf{E} \\) by PQ EOTF, and then transferred to \\( Yxy \\). Then \\( Y \\) is globally mapped to \\( Y_{SDR} \\), while \\( xy \\) are kept and then recomposed with \\( Y_{SDR} \\) to form \\( \\mathbf{E}_{SDR} \\) and later \\( \\mathbf{E}_{SDR_{709}}' \\). Our modification lies in:" + }, + { + "type": "list", + "bbox": [ + 0.499, + 0.64, + 0.892, + 0.792 + ], + "angle": 0, + "content": null + }, + { + "type": "equation", + "bbox": [ + 0.557, + 0.8, + 0.891, + 0.816 + ], + "angle": 0, + "content": "\\[\nY _ {S D R} = \\operatorname {c l a m p} \\left(T M _ {2 4 4 6 c} (Y), 0, 1 0 0 \\text {n i t}\\right) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.892, + 0.901 + ], + "angle": 0, + "content": "where \\(TM_{2446c}(\\cdot)\\in [0,118\\text{nit} ]\\) is the original global mapping curve in [58] (Fig.5 dashed line). That is, we clip the original output range to 100nit to produce more FOEP, rather than linear-scale which share same shortcoming with DM 2446a. Gamut mapping will be introduced in Eq.4&5." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22235" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.08, + 0.088, + 0.473, + 0.221 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.23, + 0.47, + 0.3 + ], + "angle": 0, + "content": "Figure 5. One of the key ideas of the proposed degradation models (DMs, §3.3): HDR's luminance (left) and color (right) volume should be properly clipped, so that network will learn corresponding restoration. Another idea is keeping a sensible brightness & color/saturation style during degradation, please refer to Tab.6." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.325, + 0.471, + 0.386 + ], + "angle": 0, + "content": "(3) \\(HC + GM\\): Hard-Clipping and Gamut Mapping, a container conversion with SDR part unchanged and all luminance/color in HDR/WCG volume hard-clipped. For luminance part, we clip all \\(>100\\mathrm{nit}\\) pixels to \\(100\\mathrm{nit}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.118, + 0.395, + 0.47, + 0.412 + ], + "angle": 0, + "content": "\\[\n\\mathbf {E} _ {S D R} = 1 0 0 \\times \\operatorname {c l a m p} (\\mathbf {E}, 0, 0. 0 1), \\quad \\mathbf {E} \\in [ 0, 1 ] \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.421, + 0.469, + 0.466 + ], + "angle": 0, + "content": "where 0.01 correspond to \\(100\\mathrm{nit}\\) in normalized linear HDR \\(\\mathbf{E}\\) (PQ container), and \\(\\times 100\\) is to adapt the container discrepancy (nominal peak 1 means \\(10000\\mathrm{nit} \\rightarrow 100\\mathrm{nit}\\))." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.467, + 0.469, + 0.511 + ], + "angle": 0, + "content": "So far, \\(\\mathbf{E}_{SDR}\\) is still in BT.2020 gamut. Therefore, we append Gamut Mapping (+GM): First, Color Space Transform (CST) from BT.2020 RGB primaries to BT.709:" + }, + { + "type": "equation", + "bbox": [ + 0.088, + 0.52, + 0.469, + 0.547 + ], + "angle": 0, + "content": "\\[\n\\mathbf {E} _ {S D R O O G} = \\mathbf {M} \\mathbf {E} _ {S D R}, \\mathbf {M} = \\left[ \\begin{array}{l l l} 1. 6 6 0 5 & 0. 5 8 7 6 & - 0. 0 7 2 8 \\\\ - 0. 1 2 4 6 & 1. 1 3 2 9 & 0. 0 0 8 3 \\\\ - 0. 0 1 8 2 & - 0. 1 0 0 6 & 1. 1 1 8 7 \\end{array} \\right] \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.554, + 0.469, + 0.646 + ], + "angle": 0, + "content": "where \\(\\exists \\mathbf{E}_{SDR_{OOG}} \\notin [0,1]\\) i.e. out-of-gamut (OOG) pixels will fall outside valid range after CST to a narrower volume. Dealing OOG is the key of gamut mapping, instead of soft-mapping [63, 75] (used by [76]'s DM) preserving as much WCG information to narrow gamut, we use hard-clipping which clips all OOG pixels to BT.709 boundary (Fig.5):" + }, + { + "type": "equation", + "bbox": [ + 0.151, + 0.657, + 0.469, + 0.673 + ], + "angle": 0, + "content": "\\[\n\\mathbf {E} _ {S D R _ {7 0 9}} = \\operatorname {c l a m p} \\left(\\mathbf {E} _ {S D R _ {O O G}}, 0, 1\\right) \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.682, + 0.469, + 0.727 + ], + "angle": 0, + "content": "Then, \\(\\mathbf{E}_{SDR_{709}}\\) from both (2) & (3) is converted to SDR encoded value \\(\\mathbf{E}_{SDR_{709}}^{\\prime}\\) by BT.1886 [77] OETF (approximate gamma2.22) and 8bit JPEG compression with QF=80." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.727, + 0.469, + 0.833 + ], + "angle": 0, + "content": "Our philosophy lies in that most explicitly-defined operations can be streamlined to a clamp(\\cdot) function: Only when luminance and color volume is clipped, multiple-to-one mapping occurs in DM, the trained network can learn corresponding recovery ability. Also, ALL & ASL in Tab.6 show that these DMs produce SDR with reasonable style, ensuring that network will not learn style deterioration." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.846, + 0.21, + 0.862 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Training detail. For each HDR frame randomly resized to \\([0.25\\mathrm{x},1\\mathrm{x}]\\), 6 patches sized \\(600\\times 600\\) are cropped from" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.137 + ], + "angle": 0, + "content": "random position. Then, each of \\(6 \\times 3878\\) HDR patches is degraded to SDR by 1 of the 3 proposed DMs in equal probability, and again stored in JPEG with QF=75." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.137, + 0.892, + 0.183 + ], + "angle": 0, + "content": "All LSN parameters are Kaiming initialized, optimized by \\( l_{1} \\) loss and AdaM with learning rate starting from \\( 2 \\times 10^{-4} \\) and decaying to half every \\( 10^{5} \\) iter till \\( 4 \\times 10^{5} \\)." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.194, + 0.74, + 0.21 + ], + "angle": 0, + "content": "4.1. Criteria and configuration" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.217, + 0.892, + 0.398 + ], + "angle": 0, + "content": "Criteria. Since the destination of SDR-to-HDRTV upconversion is human perception, to provide a 'step-change' improvement [1] of HDRTV than SDRTV, methods should be assessed based on if: C1: result is visually-pleasuring, from both brightness (C1A) & color appearance (C1B) etc., C2: HDRTV's advance on HDR/WCG volume is properly recovered and utilized, C3: bright and dark areas are recovered or at least enhanced and C4: artifacts are avoided. Yet, as found by [18,19], current distance-based metrics e.g. PSNR fails, since the main contributor of an appealing score lies in the accuracy of learned numerical distribution, rather C1-4. Hence, we use new assessment criteria in Tab.7." + }, + { + "type": "table", + "bbox": [ + 0.502, + 0.411, + 0.892, + 0.476 + ], + "angle": 0, + "content": "
Visuals in Fig.1&6Metrics in Tab.8subj. exp. (\\$4.3)
CIA-ALL, HDRBQoverall rating & selection of attribution
CIBASL
C3yellow&blue boxes-
C4detailed visuals
C23D Yxy diagramFHL(WG)P, EHL(WG)-
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.487, + 0.89, + 0.515 + ], + "angle": 0, + "content": "Table 7. How each criteria C1-4 is assessed: via detailed visuals, fine-grained tailored metrics and subjective experiment." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.533, + 0.892, + 0.624 + ], + "angle": 0, + "content": "Competitors. As in Tab.8, our method is compared with 6 learning-based methods [4-9], and 2 commercial software DaVinci and Nuke. Results from joint-SR methods [4-6] are bilinear downscaled before comparison. Note that learning-based methods are not re-trained with our dataset since we treat dataset as a crucial attribution to performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.624, + 0.892, + 0.73 + ], + "angle": 0, + "content": "Test set. We select 12 4K SDR videos with \\(10s\\) duration and \\(50fps\\). 3 of 12 are degraded from HDR-GT counterpart by YouTube DM (§2.3), the rest 9 are graded from HDR sequence No. 2, 13, 15, 20, 21, 22, 42, 44, 55 in [78]. Experiment fairness is guaranteed since both DMs (SDR-HDR relationship) in test set are 'unseen' by our network (this even gives a bonus to methods trained with YouTube DM [4, 5, 7-9])." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.741, + 0.587, + 0.755 + ], + "angle": 0, + "content": "4.2. Result" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.765, + 0.892, + 0.779 + ], + "angle": 0, + "content": "Results from all competitors are shown in Tab.8 & Fig.6." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Metrics. From Tab.8 we know that methods trained with YouTube DM all tend to produce lower satiation (ASL) and brightness (ALL&HDRBQ), and their ASL is lower even than input SDR. Also, SR-ITM-GAN [6] recovers least HDR&WCG volume since its label HDR (from Zeng20 dataset) is of least extent of HDR/WCG (Tab.5). Note that current methods produce adequate FWGP, but from 3D Yxy chromaticity diagram in Fig.6 we known that their WCG" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22236" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.079, + 0.089, + 0.896, + 0.23 + ], + "angle": 0, + "content": "
Method (network)how network is trained(recovery rate %, GT is 100%) how HDR/WCG volume is recoveredshift rate %, GT is 0%) overall-styleconventional metrics
dataset (GT)DMFHLPEHLFWGPEWGASLALLHDRBQPSNRSSIMΔEVDP3
Input SDR-00006.57010.76-23.92dB0.886144.976.571
Deep SR-ITM [4]KAISTYouTube(14.03)0.2323(9.70)0.372(175.2)1.0964(71.96)0.172(-20.33)5.485(-15.06)9.580(-72.80)1.42826.59dB0.811532.546.917
JSI-GAN [5](12.33)0.2041(3.55)0.136(213.1)1.3334(88.23)0.212(-16.62)5.741(-14.36)6.659(-79.20)1.09227.87dB0.842030.237.452
SR-ITM-GAN [6]Zeng20Reinhard(8.04)0.1332(14.33)0.550(00.00)0.0000(00.00)0.000(-7.04)6.400(-30.37)7.853(-57.12)2.25128.04dB0.883124.786.707
HDRTVNet [7]HDRTV1KYouTube(18.59)0.3078(16.29)0.625(388.8)2.4334(28.82)0.069(-15.51)5.817(-13.47)9.759(-69.65)1.59330.82dB0.881227.588.120
KPN-MFI [8]own(1.17)0.0193(0.02)0.001(392.9)2.4592(15.82)0.038(-21.74)5.388(-16.10)9.462(-82.47)0.92029.37dB0.874627.477.785
FMNet [9]HDRTV1KOurs × 3(13.67)0.2264(12.35)0.474(396.5)2.4813(91.29)0.220(-16.91)5.770(-13.48)9.758(-71.24)1.51030.91dB0.885527.168.069
LSN (ours)(256.6)4.2509(71.96)2.599(109.8)0.6873(150.0)0.361(+9.25)7.522(+81.12)20.42(-27.04)3.82924.47dB0.831037.848.130
DaVinci-(54.96)0.9103(43.16)1.655(310.0)1.9399(85.22)0.205(-21.36)5.414(-21.42)8.863(-39.23)3.19026.39dB0.891835.478.528
Nuke(112.1)1.8565(24.70)0.384(00.00)0.0000(00.00)0.000(-27.53)4.990(+17.93)13.30(-51.19)2.56220.87dB0.727364.297.479
HDR-GT (ref.)(100.0)1.6562(100.0)3.835(100.0)0.6258(100.0)0.240(0.00)6.885(0.00)11.28(0.00)5.248----
ablation studies ↓
LSN (ours)HDRTV4KYouTube(13.09)0.2168(6.63)0.254(401.3)2.5118(59.81)0.144(-14.68)5.874(-13.46)9.760(-76.89)1.21330.15dB0.885828.047.902
HDRTV4KReinhard(9.06)0.1501(19.14)0.734(00.00)0.0000(00.00)0.000(-5.74)6.490(-25.65)8.387(-52.84)2.47527.70dB0.843626.036.832
Zeng20ours × 3(109.6)1.8147(9.00)0.345(0.95)0.0593(0.76)0.002(+3.36)7.117(+66.64)18.79(-59.52)2.12425.17dB0.817928.587.874
HDRTV1Kours × 3(177.5)2.9405(52.49)2.013(279.5)1.7494(70.43)0.169(+6.56)7.337(+52.30)17.18(-34.83)3.42024.30dB0.835138.038.002
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.24, + 0.895, + 0.3 + ], + "angle": 0, + "content": "Table 8. Metrics. We use fine-grained tailored metrics (column 3-9, defined in Tab.4) to assess criteria C1&2 (§4.1): The more significant metrics in column 3-6 are, the better HDR&WCG volume is recovered (C2) by specific method. Also, column 8-10 closer with GT stands for similar brightness&color appearance. Note that we allow column 8-10 slightly bigger than GT, which means result HDR is reasonably more visual-pleasuring (C1) than GT. Based on this, we highlight those results unfavorable for viewing experience." + }, + { + "type": "image", + "bbox": [ + 0.122, + 0.312, + 0.851, + 0.646 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.656, + 0.895, + 0.727 + ], + "angle": 0, + "content": "Figure 6. Visuals. We provide comparisons on \\(3D\\) Yxy chromaticity diagram to assess how HDR/WCG volume is recovered (C2), and detailed visuals on bright (yellow arrow, with luminance of same pixel indicated) and dark (blue arrow) areas to assess method's recover ability (C3). Note that conclusion 'SDR is more vivid than HDR' could not be drawn because HDR will appear dimmer than SDR in print version (explained in Fig.1). We hence turn to ASL & HDRBQ (column 8-10) and later subjective experiment to see if HDR is indeed dimmer (C1). Comparison on more scenes will be provided in supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.752, + 0.471, + 0.814 + ], + "angle": 0, + "content": "pixels mainly exists in low luminance range, which means they are of little help to viewing experience. Our method is able to recover adequate HDR/WCG volume, meanwhile reasonably enhance the brightness and saturation.\\(^{1}\\)" + }, + { + "type": "page_footnote", + "bbox": [ + 0.076, + 0.827, + 0.471, + 0.902 + ], + "angle": 0, + "content": "1We also provide conventional PSNR, SSIM, \\(\\Delta E\\) (color difference [79]) and VDP3 (HDR-VDP-3 [80]), but they mostly represent output's closer value with GT (For example, result both dimmer (e.g. Deep SR-ITM [4]) and more vivid (ours) than GT will have a similar low score.), thus are helpless for our assessment. This phenomenon was first found by [18, 19], and will be further explained in supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.752, + 0.892, + 0.89 + ], + "angle": 0, + "content": "Visuals. Methods' recover ability is illustrated by Fig.6: Current ones underperform in both bright (yellow) and dark (blue) areas. Specifically, methods trained with YouTube DM produce dim (HDBBQ) and desaturated (ASL) result, and even get lower luminance than input SDR at same position (arrow in yellow box). This confirms the finding in §2.3 that network will learn to darken and desaturate if DM tend to brighten and over-saturate. Also, our method recovers more detail in bright and dark areas with a better style." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22237" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.077, + 0.091, + 0.285, + 0.108 + ], + "angle": 0, + "content": "4.3. Subjective experiment" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.115, + 0.474, + 0.313 + ], + "angle": 0, + "content": "Currently, few subjective studies [19, 43, 59, 81-83] are designed for SDR-to-HDR procedure (rather between different HDR). Similar to [19], we judge if output HDR is better than origin SDR. Specifically, as in Fig.7, we use 2 side-by-side SONY KD85X9000H display supporting both SDR and HDRTV, one is calibrated to 100nit/BT.709 SDR and another PQ1000nit/BT.2020 HDR. Each (input)SDR-outputHDR pair is displayed twice with \\(3s\\) gray level interval and following \\(10s\\) for rating: each participant is asked to continuously rate from -5(SDR much better) to 0(same) to 5(HDR much better), meanwhile select at least 1 attribution (bottom Fig.7) of his/her rating. Such process is repeated 9(#participant) \\(\\times\\) 9(#competitor) \\(\\times\\) 12(#clip) times." + }, + { + "type": "image", + "bbox": [ + 0.127, + 0.326, + 0.421, + 0.414 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.128, + 0.414, + 0.42, + 0.582 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.593, + 0.47, + 0.636 + ], + "angle": 0, + "content": "Figure 7. Environment and result of subjective experiment. 2 displays are calibrated differently to ensure both SDR and HDR are correctly visualized. Results are provided in quartile chart." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.653, + 0.47, + 0.835 + ], + "angle": 0, + "content": "Result shows that only 2 methods (ours & DaVinci) are recognized better than input SDR. For methods [4-6], their main 'attribution' (bottom Fig.7) is artifact (\\(C4\\), see Fig.6 blue box). For artifact-free 'YouTube-DM' methods [7-9], they are rated slightly worse mainly for lower saturation (\\(C1B\\)) and incapability in information recovery, consisting with Tab.8 and Fig.6. Form 'attribution' we also notice that our method got better score mainly for viewing experience (\\(C1\\)) and information recovery (\\(C3\\)). Also, our 'bad cases' lies in recovered large dark area with intrinsic noise etc. amplified and uneliminated. This is why we got more checks on \\(C4(\\times 24)\\) than commercial methods DaVinci and Nuke." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.847, + 0.239, + 0.861 + ], + "angle": 0, + "content": "4.4. Ablation studies" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.47, + 0.901 + ], + "angle": 0, + "content": "On DM. When DM is changed to YouTube, Tab.8 witnesses a significant decline on FHLP, EHL, ASL, ALL" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.152 + ], + "angle": 0, + "content": "and HDRBQ, while Fig.8 confirms a result similar to those 'YouTube-DM' methods, i.e. worse viewing experience and less recover ability. Also, when using Reinhard DM which contains no clipping, result's highlight area stay unlearned." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.153, + 0.895, + 0.304 + ], + "angle": 0, + "content": "On dataset. Here, we use original DMs, but label HDR from other dataset. In Fig.8 yellow box, our DMs encourage the network to output higher luminance, but since Zeng20 is of least extent of HDR i.e. these highlight do not exist in label HDR, our LSN will not 'recognize' them and thus produce artifact. Since this dataset is also of least extent of WCG, FWGP&EWG in Tab.8 drop obviously. When using slightly-inferior HDRTV1K as label, difference is relatively less significant. Yet, in both cases, ASL&ALL are similar since DM i.e. network's style tendency is unaltered." + }, + { + "type": "image", + "bbox": [ + 0.513, + 0.316, + 0.882, + 0.601 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.61, + 0.892, + 0.68 + ], + "angle": 0, + "content": "Figure 8. Result of ablation studies proves the importance of both high-quality label HDR and rational DMs. Specifically, absent of both label HDR's HDR/WCG volume (Zeng20) and DM's degradation (Reinhard) will impair LSN's recover ability, meanwhile YouTube DM's style will make our LSN commonplace as others." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.71, + 0.619, + 0.725 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.892, + 0.84 + ], + "angle": 0, + "content": "There are 2 types of low level vision: 'sole-restoration' whose destination is only clean or GT e.g. denoising, and 'perceptual-motivated' aiming at better viewing experience e.g. image enhancement/retouching. SDR-to-HDRTV upconversion belongs to both. Yet, current methods only realize (it belongs to) the former and neglect the latter, leading their concentration only on network mechanism." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.841, + 0.892, + 0.901 + ], + "angle": 0, + "content": "To this end, our response is two-fold: (1) focusing on the impact of training set, and ameliorating its quality by proposing new dataset and DMs, (2) involving novel assessment criteria based on the 'perceptual' principal." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22238" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.175, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.157 + ], + "angle": 0, + "content": "[1] ITU, Geneva, Switzerland, Report ITU-R BT.2381-0: Requirements for high dynamic range television (HDR-TV) systems, 0 ed., 7 2015. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.16, + 0.472, + 0.216 + ], + "angle": 0, + "content": "[2] ITU, Geneva, Switzerland, Recommendation ITU-R BT.2100-2: Image parameter values for high dynamic range television for use in production and international programme exchange, 2 ed., 07 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.219, + 0.471, + 0.275 + ], + "angle": 0, + "content": "[3] ITU, Geneva, Switzerland, Recommendation ITU-R BT.2020-2: Parameter values for ultra-high definition television systems for production and international programme exchange, 2 ed., 10 2015. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.279, + 0.471, + 0.333 + ], + "angle": 0, + "content": "[4] S. Y. Kim, J. Oh, and M. Kim, \"Deep sr-item: Joint learning of super-resolution and inverse tone-mapping for 4k uhd hdr applications,\" in Proc. ICCV, pp. 3116-3125, 2019. 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.337, + 0.47, + 0.393 + ], + "angle": 0, + "content": "[5] S. Y. Kim, J. Oh, and M. Kim, \"Jsi-gan: Gan-based joint super-resolution and inverse tone-mapping with pixel-wise task-specific filters for uhd hdr video,\" in Proc. AAAI, vol. 34, pp. 11287-11295, 2020. 2, 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.397, + 0.47, + 0.439 + ], + "angle": 0, + "content": "[6] H. Zeng, X. Zhang, Z. Yu, and Y. Wang, “Sr-itm-gan: Learning 4k uhd hdr with a generative adversarial network,” IEEE Access, vol. 8, pp. 182815–182827, 2020. 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.442, + 0.47, + 0.485 + ], + "angle": 0, + "content": "[7] X. Chen, Z. Zhang, J. S. Ren, L. Tian, Y. Qiao, and C. Dong, “A new journey from sdrtv to hdttv,” in Proc. ICCV, pp. 4500–4509, 2021. 2, 3, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.487, + 0.47, + 0.543 + ], + "angle": 0, + "content": "[8] G. Cao, F. Zhou, H. Yan, A. Wang, and L. Fan, \"Kpn-mfi: A kernel prediction network with multi-frame interaction for video inverse tone mapping,\" in Proc. IJCAI, pp. 806-812, 2022. 2, 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.546, + 0.47, + 0.601 + ], + "angle": 0, + "content": "[9] G. Xu, Q. Hou, L. Zhang, and M.-M. Cheng, \"Fmnet: Frequency-aware modulation network for sdr-to-hdr translation,\" in Proc. ACMMM, pp. 6425-6435, 2022. 2, 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.605, + 0.47, + 0.647 + ], + "angle": 0, + "content": "[10] Z. Liang, \"Cvpr2022-1st workshop on vision dataset understanding,\" https://sites.google.com/view/vdu-cvpr22, 2022.2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.65, + 0.469, + 0.693 + ], + "angle": 0, + "content": "[11] K. Zhang, J. Liang, L. Van Gool, and R. Timofte, \"Designing a practical degradation model for deep blind image superresolution,\" in Proc. ICCV, pp. 4791-4800, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.696, + 0.47, + 0.751 + ], + "angle": 0, + "content": "[12] V. Dewil, A. Barral, G. Facciolo, and P. Arias, \"Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?\", in Proc. CVPR, pp. 4900-4910, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.755, + 0.469, + 0.796 + ], + "angle": 0, + "content": "[13] J. Jiang, K. Zhang, and R. Timofte, “Towards flexible blindJPEG artifacts removal,” in Proc. ICCV, pp. 4997–5006, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.8, + 0.47, + 0.855 + ], + "angle": 0, + "content": "[14] K. Jiang, Z. Wang, Z. Wang, C. Chen, P. Yi, T. Lu, and C.-W. Lin, \"Degrade is upgrade: Learning degradation for low-light image enhancement,\" in Proc. AAAI, vol. 36, pp. 1078-1086, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[15] Y. Zhou, Y. Song, and X. Du, \"Modular degradation simulation and restoration for under-display camera,\" in Proc. ACCV, pp. 265-282, 2022. 2, 3" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.115, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.134 + ], + "angle": 0, + "content": "[16] C. Guo and X. Jiang, “Lhdr: Hdr reconstruction for legacy content using a lightweight dnn,” in Proc. ACCV, pp. 3155–3171, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.137, + 0.892, + 0.192 + ], + "angle": 0, + "content": "[17] S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M.-H. Yang, \"Restormer: Efficient transformer for high-resolution image restoration,\" in Proc. CVPR, pp. 5728-5739, 2022. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.196, + 0.892, + 0.251 + ], + "angle": 0, + "content": "[18] G. Eilertsen, S. Hajisharif, P. Hanji, A. Tsimirikoglou, R. K. Mantiuk, and J. Unger, \"How to cheat with metrics in single-image hdr reconstruction,\" in Proc. ICCV, pp. 3998-4007, 2021. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.254, + 0.892, + 0.31 + ], + "angle": 0, + "content": "[19] P. Hanji, R. Mantiuk, G. Eilertsen, S. Hajisharif, and J. Unger, “Comparison of single image hdr reconstruction methods—the caveats of quality assessment,” in Proc. SIGGRAPH, pp. 1–8, 2022. 2, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.313, + 0.892, + 0.353 + ], + "angle": 0, + "content": "[20] L. Wang and K.-J. Yoon, \"Deep learning for hdr imaging: State-of-the-art and future trends,\" IEEE Trans. PAMI, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.357, + 0.892, + 0.386 + ], + "angle": 0, + "content": "[21] P. Debevec, \"Image-based lighting,\" in ACM SIGGRAPH 2006 Courses, pp. 4-es, 2006. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.388, + 0.892, + 0.43 + ], + "angle": 0, + "content": "[22] E. Reinhard, W. Heidrich, P. Debevec, et al., High dynamic range imaging: acquisition, display, and image-based lighting. Morgan Kaufmann, 2010. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.433, + 0.892, + 0.487 + ], + "angle": 0, + "content": "[23] M. Gharbi, J. Chen, J. T. Barron, S. W. Hasinoff, and F. Durand, “Deep bilateral learning for real-time image enhancement,” ACM Trans. Graph., vol. 36, no. 4, pp. 1–12, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.491, + 0.892, + 0.533 + ], + "angle": 0, + "content": "[24] X. Yang, K. Xu, Y. Song, Q. Zhang, X. Wei, and R. W. Lau, \"Image correction via deep reciprocating hdr transformation,\" in Proc. CVPR, pp. 1798-1807, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.536, + 0.892, + 0.578 + ], + "angle": 0, + "content": "[25] Z. Zheng, W. Ren, X. Cao, T. Wang, and X. Jia, \"Ultra-high-definition image hdr reconstruction via collaborative bilateral learning,\" in Proc. ICCV, pp. 4449-4458, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.581, + 0.892, + 0.636 + ], + "angle": 0, + "content": "[26] B. Mildenhall, P. Hedman, R. Martin-Brualla, P. P. Srinivasan, and J. T. Barron, “Nerf in the dark: High dynamic range view synthesis from noisy raw images,” in Proc. CVPR, pp. 16190–16199, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.639, + 0.892, + 0.681 + ], + "angle": 0, + "content": "[27] N. K. Kalantari, R. Ramamoorthi, et al., \"Deep high dynamic range imaging of dynamic scenes,\" ACM Trans. Graph., vol. 36, no. 4, pp. 144-1, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.684, + 0.892, + 0.725 + ], + "angle": 0, + "content": "[28] S. Wu, J. Xu, Y.-W. Tai, and C.-K. Tang, \"Deep high dynamic range imaging with large foreground motions,\" in Proc. ECCV, pp. 117-132, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.728, + 0.892, + 0.783 + ], + "angle": 0, + "content": "[29] Q. Yan, D. Gong, Q. Shi, A. v. d. Hengel, C. Shen, I. Reid, and Y. Zhang, “Attention-guided network for ghost-free high dynamic range imaging,” in Proc. CVPR, pp. 1751–1760, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.786, + 0.892, + 0.842 + ], + "angle": 0, + "content": "[30] G. Chen, C. Chen, S. Guo, Z. Liang, K.-Y. K. Wong, and L. Zhang, \"Hdr video reconstruction: A coarse-to-fine network and a real-world benchmark dataset,\" in Proc. ICCV, pp. 2502-2511, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.846, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[31] Y. Niu, J. Wu, W. Liu, W. Guo, and R. W. Lau, \"Hdr-gan: Hdr image reconstruction from multi-exposed ldr images with large motions,\" IEEE Transactions on Image Processing, vol. 30, pp. 3885-3896, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "22239" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.469, + 0.135 + ], + "angle": 0, + "content": "[32] E. Pérez-Pellitero et al., “Ntire 2022 challenge on high dynamic range imaging: Methods and results,” in Proc. CVPR, pp. 1009–1023, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.137, + 0.469, + 0.18 + ], + "angle": 0, + "content": "[33] G. Eilertsen, J. Kronander, et al., \"Hdr image reconstruction from a single exposure using deep cnns,\" ACM Trans. Graph., vol. 36, no. 6, pp. 1-15, 2017. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.183, + 0.469, + 0.238 + ], + "angle": 0, + "content": "[34] D. Marnerides, T. Bashford-Rogers, et al., \"Expandnet: A deep convolutional neural network for high dynamic range expansion from low dynamic range content,\" Comput. Graph. Forum, vol. 37, no. 2, pp. 37-49, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.241, + 0.468, + 0.297 + ], + "angle": 0, + "content": "[35] Y.-L. Liu, W.-S. Lai, Y.-S. Chen, Y.-L. Kao, M.-H. Yang, Y.-Y. Chuang, and J.-B. Huang, \"Single-image hdr reconstruction by learning to reverse the camera pipeline,\" in Proc. CVPR, pp. 1651-1660, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.3, + 0.468, + 0.355 + ], + "angle": 0, + "content": "[36] M. S. Santos, T. I. Ren, and N. K. Kalantari, \"Single image hdr reconstruction using a cnn with masked features and perceptual loss,\" ACM Trans. Graph., vol. 39, no. 4, pp. 80-1, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.359, + 0.468, + 0.401 + ], + "angle": 0, + "content": "[37] X. Chen, Y. Liu, et al., \"Hdrunet: Single image hdr reconstruction with denoising and dequantization,\" in Proc. CVPR, pp. 354-363, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.403, + 0.468, + 0.445 + ], + "angle": 0, + "content": "[38] S. Ning, H. Xu, L. Song, R. Xie, and W. Zhang, “Learning an inverse tone mapping network with a generative adversarial regularizer,” in Proc. ICASSP, pp. 1383–1387, 2018. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.448, + 0.468, + 0.516 + ], + "angle": 0, + "content": "[39] K. Hirao, Z. Cheng, M. Takeuchi, and J. Katto, \"Convolutional neural network based inverse tone mapping for high dynamic range display using lucore,\" in 2019 IEEE International Conference on Consumer Electronics (ICCE), pp. 1-2, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.521, + 0.468, + 0.577 + ], + "angle": 0, + "content": "[40] S. Y. Kim, D.-E. Kim, and M. Kim, \"Itm-cnn: Learning the inverse tone mapping from low dynamic range video to high dynamic range displays using convolutional neural networks,\" in Proc. ACCV, pp. 395-409, 2018. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.58, + 0.468, + 0.621 + ], + "angle": 0, + "content": "[41] Y. Xu, S. Ning, R. Xie, and L. Song, \"Gan based multiexposure inverse tone mapping,\" in Proc. ICIP, pp. 4365-4369, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.625, + 0.468, + 0.667 + ], + "angle": 0, + "content": "[42] Y. Xu, L. Song, R. Xie, and W. Zhang, \"Deep video inverse tone mapping,\" in 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp. 142-147, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.669, + 0.468, + 0.724 + ], + "angle": 0, + "content": "[43] D.-E. Kim and M. Kim, “Learning-based low-complexity reverse tone mapping with linear mapping,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 2, pp. 400–414, 2019. 2, 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.728, + 0.468, + 0.784 + ], + "angle": 0, + "content": "[44] J. Zou, K. Mei, and S. Sun, \"Multi-scale video inverse tone mapping with deformable alignment,\" in 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), pp. 9-12, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.787, + 0.468, + 0.843 + ], + "angle": 0, + "content": "[45] T. Chen and P. Shi, \"An inverse tone mapping algorithm based on multi-scale dual-branch network,\" in 2021 International Conference on Culture-oriented Science & Technology (ICCST), pp. 187–191, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.846, + 0.468, + 0.9 + ], + "angle": 0, + "content": "[46] G. Xu, Y. Yang, J. Xu, L. Wang, X.-T. Zhen, and M.-M. Cheng, \"Joint super-resolution and inverse tone-mapping: A feature decomposition aggregation network and a new benchmark,\" 2022. 2, 3" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.469, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.135 + ], + "angle": 0, + "content": "[47] G. He, K. Xu, L. Xu, C. Wu, M. Sun, X. Wen, and Y.-W. Tai, \"Sdrtv-to-hdrtv via hierarchical dynamic context feature mapping,\" in Proc. ACMM, pp. 2890-2898, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.137, + 0.892, + 0.18 + ], + "angle": 0, + "content": "[48] G. He, S. Long, L. Xu, C. Wu, J. Zhou, M. Sun, X. Wen, and Y. Dai, “Global priors guided modulation network for joint super-resolution and inverse tone-mapping,” 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.182, + 0.892, + 0.223 + ], + "angle": 0, + "content": "[49] K. Xu, L. Xu, G. He, C. Wu, Z. Ma, M. Sun, and Y.-W. Tai, \"Sdrtv-to-hdrtv conversion via spatial-temporal feature fusion,\" 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.226, + 0.892, + 0.268 + ], + "angle": 0, + "content": "[50] T. Shao, D. Zhai, J. Jiang, and X. Liu, \"Hybrid conditional deep inverse tone mapping,\" in Proc. ACMM, pp. 1016-1024, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.272, + 0.892, + 0.339 + ], + "angle": 0, + "content": "[51] A. Mustafa, P. Hanji, and R. K. Mantiuk, \"Distilling style from image pairs for global forward and inverse tone mapping,\" in The 19th ACM SIGGRAPH European Conference on Visual Media Production (CVMP), pp. 1-10, 2022. 2, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.343, + 0.892, + 0.385 + ], + "angle": 0, + "content": "[52] M. Yao, D. He, X. Li, Z. Pan, and Z. Xiong, \"Bidirectional translation between uhd-hdr and hd-sdr videos,\" IEEE Transactions on Multimedia, 2023. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.388, + 0.892, + 0.457 + ], + "angle": 0, + "content": "[53] R. Tang, F. Meng, and L. Bai, “Zoned mapping network from sdr video to hdr video,” in 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), vol. 5, pp. 1466–1472, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.46, + 0.892, + 0.503 + ], + "angle": 0, + "content": "[54] SMPTE, NY, USA, ST 2084:2014 - SMPTE Standard - High Dynamic Range Electro-Optical Transfer Function of Mastering Reference Displays, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.505, + 0.892, + 0.547 + ], + "angle": 0, + "content": "[55] ITU, Geneva, Switzerland, Recommendation ITU-R BT.709-6: Parameter values for the HDTV standards for production and international programme exchange, 6 ed., 6 2015. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.55, + 0.892, + 0.592 + ], + "angle": 0, + "content": "[56] Z. Cheng, T. Wang, Y. Li, F. Song, C. Chen, and Z. Xiong, \"Towards real-world hdrtv reconstruction: A data synthesis-based approach,\" in Proc. ECCV, pp. 199-216, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.595, + 0.892, + 0.637 + ], + "angle": 0, + "content": "[57] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, \"Photographic tone reproduction for digital images,\" in Proc. 29th SIGGRAPH, pp. 267-276, 2002. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.639, + 0.892, + 0.681 + ], + "angle": 0, + "content": "[58] ITU, Geneva, Switzerland, Report ITU-R BT.2446-1: Methods for conversion of high dynamic range content to standard dynamic range content and vice-versa, 1 ed., 3 2021. 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.684, + 0.892, + 0.753 + ], + "angle": 0, + "content": "[59] P. Mohammadi, M. T. Pourazad, and P. Nasiopoulos, \"A perception-based inverse tone mapping operator for high dynamic range video applications,\" IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 5, pp. 1711-1723, 2020. 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.756, + 0.892, + 0.798 + ], + "angle": 0, + "content": "[60] D. Marnerides, T. Bashford-Rogers, and K. Debattista, \"Deep hdr hallucination for inverse tone mapping,\" Sensors, vol. 21, no. 12, p. 4032, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.801, + 0.892, + 0.843 + ], + "angle": 0, + "content": "[61] Y. Zhang and T. Aydin, \"Deep hdr estimation with generative detail reconstruction,\" in Computer Graphics Forum, vol. 40, pp. 179-190, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.846, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[62] L. Bai, Y. Yang, and G. Fu, \"Analysis of high dynamic range and wide color gamut of uhdtv,\" in 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), vol. 5, pp. 1750-1755, 2021. 4" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "22240" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.134 + ], + "angle": 0, + "content": "[63] ITU, Geneva, Switzerland, Report ITU-R BT.2407-0: Colour gamut conversion from Recommendation ITU-R BT.2020 to Recommendation ITU-R BT.709, 0 ed., 10 2017. 4, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.138, + 0.471, + 0.182 + ], + "angle": 0, + "content": "[64] ITU, Geneva, Switzerland, Recommendation ITU-R BT.500-14: Methodologies for the subjective assessment of the quality of television images, 14 ed., 10 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.185, + 0.469, + 0.228 + ], + "angle": 0, + "content": "[65] D. Hasler and S. E. Suesstrunk, “Measuring colorfulness in natural images,” in Human vision and electronic imaging VIII, vol. 5007, pp. 87–95, 2003. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.232, + 0.468, + 0.275 + ], + "angle": 0, + "content": "[66] Dolby, \"What is ictcp? - introduction.\" https:// professional.dolby.com/siteassets/pdfs/ ictcp_dolbywhitepaper_v071.pdf.4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.279, + 0.469, + 0.322 + ], + "angle": 0, + "content": "[67] S. Ploumis, R. Boitard, and P. Nasiopoulos, \"Image brightness quantification for hdr,\" in 2020 28th European Signal Processing Conference (EUSIPCO), pp. 640-644, 2021. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.325, + 0.468, + 0.38 + ], + "angle": 0, + "content": "[68] Arri, \"Camera sample footage & reference image.\" https://www.arri.com/en/learn-help/ learn-help-camera-system/camera-sample-footage-reference-image, 2022.4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.385, + 0.468, + 0.413 + ], + "angle": 0, + "content": "[69] Netflix, \"Netflix open content.\" https://opencontent.nsf1x.com/, 2020.4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.418, + 0.469, + 0.488 + ], + "angle": 0, + "content": "[70] J. Froehlich, S. Grandinetti, B. Eberhardt, S. Walter, A. Schilling, and H. Brendel, “Creating cinematic wide gamut hdr-video for the evaluation of tone mapping operators and hdr-displays,” in Digital photography X, vol. 9023, pp. 279–288, 2014. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.492, + 0.469, + 0.548 + ], + "angle": 0, + "content": "[71] Y. Wang, S. Inguva, and B. Adsumilli, \"YoutubeUGC dataset for video compression research,\" in 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), pp. 1-5, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.552, + 0.469, + 0.608 + ], + "angle": 0, + "content": "[72] Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethuraman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in Proc. ICIP, pp. 786–790, 2022. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.612, + 0.469, + 0.653 + ], + "angle": 0, + "content": "[73] L. Van der Maaten and G. Hinton, \"Visualizing data using t-sne,\" Journal of machine learning research, vol. 9, no. 11, 2008. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.659, + 0.469, + 0.702 + ], + "angle": 0, + "content": "[74] D. Walker, C. Payne, P. Hodoul, and M. Dolan, \"Color management with opencolorio v2,\" in ACM SIGGRAPH 2021 Courses, pp. 1-226, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.706, + 0.469, + 0.733 + ], + "angle": 0, + "content": "[75] J. Morovic, Color gamut mapping. John Wiley & Sons, 2008. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.738, + 0.469, + 0.794 + ], + "angle": 0, + "content": "[76] H. Le, T. Jeong, A. Abdelhamed, H. J. Shin, and M. S. Brown, \"Gamutnet: Restoring wide-gamut colors for camera-captured images,\" in Color and Imaging Conference, vol. 2021, pp. 7-12, 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.799, + 0.469, + 0.854 + ], + "angle": 0, + "content": "[77] ITU, Geneva, Switzerland, Recommendation ITU-R BT.1886-0: Reference electro-optical transfer function for flat panel displays used in HDTV studio production, 0 ed., 3 2011. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.469, + 0.902 + ], + "angle": 0, + "content": "[78] ITU, Geneva, Switzerland, Report ITU-R BT.2245-10: HDTV and UHDTV including HDR-TV test materials for assessment of picture quality, 10 ed., 9 2022. 6" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.471, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.092, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[79] ITU, Geneva, Switzerland, Recommendation ITU-R BT.2124-0: Objective metric for the assessment of the potential visibility of colour differences in television, 0 ed., 1 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.153, + 0.892, + 0.195 + ], + "angle": 0, + "content": "[80] K. Wolski, D. Giunchi, et al., \"Dataset and metrics for predicting local visible differences,\" ACM Trans. Graph., vol. 37, no. 5, pp. 1-14, 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.2, + 0.892, + 0.242 + ], + "angle": 0, + "content": "[81] C. Bist, R. Cozot, G. Madec, and X. Ducloux, “Tone expansion using lighting style aesthetics,” Computers & Graphics, vol. 62, pp. 77–86, 2017. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.246, + 0.892, + 0.316 + ], + "angle": 0, + "content": "[82] G. Luzardo, J. Aelterman, H. Luong, S. Rousseaux, D. Ochoa, and W. Philips, “Fully-automatic inverse tone mapping algorithm based on dynamic mid-level tone mapping,” APSIPA Transactions on Signal and Information Processing, vol. 9, p. e7, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.32, + 0.892, + 0.389 + ], + "angle": 0, + "content": "[83] A. Stojkovic, J. Aelterman, H. Luong, H. Van Parys, and W. Philips, “Highlights analysis system (hans) for low dynamic range to high dynamic range conversion of cinematic low dynamic range content,” IEEE Access, vol. 9, pp. 43938–43969, 2021. 8" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.092, + 0.892, + 0.389 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22241" + } + ] +] \ No newline at end of file diff --git a/2023/Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models/ed51a5a5-a3b7-4784-b738-9ddfdcaf0202_origin.pdf b/2023/Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models/ed51a5a5-a3b7-4784-b738-9ddfdcaf0202_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..f5e1d220873c7f3b4d8e554bed122c004e5c322c --- /dev/null +++ b/2023/Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models/ed51a5a5-a3b7-4784-b738-9ddfdcaf0202_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d3dab3a697aac95420cc1ce3239a004439c3fd07e921246b0c33c252ffdc7811 +size 10179802 diff --git a/2023/Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models/full.md b/2023/Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models/full.md new file mode 100644 index 0000000000000000000000000000000000000000..770a6e9c8c5fc0a91671d5b35ab2d02db38fad00 --- /dev/null +++ b/2023/Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models/full.md @@ -0,0 +1,409 @@ +# Learning a Practical SDR-to-HDRTV Up-conversion using New Dataset and Degradation Models + +Cheng Guo $^{1,2}$ , Leidong Fan $^{3,2}$ , Ziyu Xue $^{4,1}$ and Xiuhua Jiang $^{2,1}$ + +$^{1}$ State Key Laboratory of Media Convergence and Communication, Communication University of China $^{2}$ Peng Cheng Laboratory $^{3}$ Peking University + +$^{4}$ Academy of Broadcasting Science, National Radio and Television Administration + +{guocheng,jiangxiuhua}@cuc.edu.cn，fanleidong@stu.pku.edu.cn，xueziyu@abs.ac.cn + +![](images/bbb856030678578f243f1f93e54d5b89fab49bafc2f52fc344a840180dd5a974.jpg) +Input SDR + +![](images/e75e06aa47d3cd877c2874190f7c626122f7612c484dd0401629d7d9bf967c35.jpg) +Deep SR-ITM (ICCV'19) + +![](images/ca864b7d1172bf0bbcdd51b58477c255a7a429946e4dece6180d7717bfe27c7d.jpg) +JSI-GAN (AAA1'20) + +![](images/f93f2abd8fc8a7e817bdcd7c7e43ef5388a90c8f97557172dfc216b1935f01d0.jpg) +HDRTVNet (ICCV'21) + +![](images/0fd757765cf7fc063cb362953dc2022f5728c4ba3dcdb39052f7230e719de895.jpg) +FMNet (ACM MM'22) + +![](images/046e7fd0d8a0f874cccf60cd5e9743ed57ab5a57f92a356a0d58c9e89526d6b7.jpg) +Ours + +![](images/e738b69c632d1cf2652b75d259d63b808ebdb677c15d03fdc60e02bbef5eae26.jpg) +GT-HDR + +![](images/48fe8c4509e93f14ff00469f6909018687bcdaaedcef6655c2131584ea9e74fd.jpg) +A B A 0 0 0 +Figure 1. Up-converting SDR content for HDR-WCG TV display. To ensure better viewing experience [1], we use (1) $3D$ Yxy chromaticity diagram (vertical axis $Y$ for HDR/lumiannce, $xy$ plane for WCG/color) to see how HDRTV's advance on HDR&WCG volume is recovered, and (2) detailed visuals (yellow and blue boxes) to assess method's recover ability. Note that HDR is dimer here in print version since its large luminance&color container is interpreted by small SDR capacity, and will appear normal if correctly visualized (top Fig.7). Still, from (3) 'Brightness Index' & 'Saturation Level' we know that result from current methods is more dim and desaturated than GT. + +![](images/48262841fb43cf3ed8d2494f736ef84a2c466e82584a4f58dc7d2d50579d6bc1.jpg) +1 0 + +![](images/2d79477cdfc769ca8d7570a879887ed21598a194be7b8bd25848689d1134d0ce.jpg) +28 0.8 0.8 0.7 + +![](images/b1f7d02ccc112727cc86e661ae0e556cffd5cadbe56ce77726a5f7badaf43f34.jpg) +A 0 + +![](images/54c7d365dfa578c8014a81fbf3a02deda0b9f37b4d1931a2937dbc2e2fad1f52.jpg) +15 10 +Y 10 + +![](images/d93747b1ceee6bec62d8ff5ecbbe8e6a69e37a9f9118cd62d033c7f6993fec0d.jpg) +0.8 0.8 0.7 0.8 0.8 + +![](images/38dcad6372fc6a25407a2d3f77cfb126feea422e33f7eb9540ce601729b7dcb2.jpg) +0.8 0.7 0.6 0.5 + +# Abstract + +In media industry, the demand of SDR-to-HDRTV upconversion arises when users possess HDR-WCG (high dynamic range-wide color gamut) TVs while most off-the-shelf footage is still in SDR (standard dynamic range). The research community has started tackling this low-level vision task by learning-based approaches. When applied to real SDR, yet, current methods tend to produce dim and desaturated result, making nearly no improvement on viewing experience. Different from other network-oriented methods, we attribute such deficiency to training set (HDR-SDR pair). Consequently, we propose new HDRTV dataset (dubbed HDRTV4K) and new HDR-to-SDR degradation models. Then, it's used to train a luminance-segmented network (LSN) consisting of a global mapping trunk, and two Transformer branches on bright and dark luminance range. We also update assessment criteria by tailored metrics and subjective experiment. Finally, ablation studies are conducted to prove the effectiveness. Our work is available + +at: https://github.com/AndreGuo/HDRTVDM. + +# 1. Introduction + +The dynamic range of image is defined as the maximum recorded luminance to the minimum. Larger luminance container endows high dynamic range (HDR) a better expressiveness of scene. In media and film industry, the superiority of HDR is further boosted by advanced electro-optical transfer function (EOTF) e.g. PQ/HLG [2], and wide color-gamut (WCG) RGB primaries e.g. BT.2020 [3]. + +While WCG-HDR displays are becoming more readily available in consumer market, most commercial footage is still in standard dynamic range (SDR) since WCG-HDR version is yet scarce due to exorbitant production workflow. Hence, there raise the demand of converting vast existing SDR content for HDRTV service. Such SDR may carry irreproducible scenes, but more likely, imperfections brought by old imaging system and transmission. This indicates that SDR-to-HDRTV up-conversion is an ill-posed low-level vi + +sion task, and research community has therefore begun involving learning-based methods ([4-9] etc.). + +Yet, versatile networks they use (§2.1), we find current methods' result dim and desaturated when feeding real SDR images (Fig.1), conflicting with the perceptual motive of SDR-to-HDRTV up-conversion. As reported by CVPR22-1st Workshop on Vision Dataset Understanding [10], most methods are network-oriented and understate the impact of training set. For restoration-like low-level vision, there are 2 ingredients of a training set: the quality of label GT itself, and the GT-to-LQ degradation model (DM) i.e. what the network learns to restore. Such neglect is getting remedied in other low-level vision tasks [11-16], but still pervasive in learning-based SDR-to-HDRTV up-conversion. + +Not serendipitously, we find dataset the reason why current methods underperform. We exploit several HDRTV-tailored metrics (Tab.4) to assess current training set:(1) by measuring label HDR's extent of $HDR/WCG$ etc. (Tab.5), we notice that its quality and diversity are inadequate to incentive the network to produce appealing result, (2) via the statistics of degraded SDR, we find current HDR-to-SDR DMs' tendency to exaggeratedly alter the saturation and brightness (see Tab.6) thus network will learn a SDR-to-HDR deterioration. Hence, we propose HDRTV4K dataset ( $\S 3.2$ ) consisting of high-quality and diversified (Fig.4) HDRTV frames as label. Then exploit 3 new HDRTV-to-SDR DMs ( $\S 3.3$ ) avoiding above insufficiency, meanwhile possessing appropriate degradation capability (Tab.6) so the network can learn reasonable restoration ability. + +Afterwards, we formulate the task as the combination of global mapping on the full luminance range and recovery of low/high luminance range. Correspondingly, we propose Luminance Segmented Network (LSN, §3.1) where a global trunk and two Transformer-style UNet [17] branches are assigned to respectively execute divergent operations required in different segmented luminance ranges (areas). + +Lastly, as found by [18, 19], conventional distance-based metrics well-performed in solely-reconstruction task (e.g. denoising) fail for perceptual-motivated HDR reconstruction, we therefore update the assessment criteria with fine-grained metrics (§4.2) and subjective experiment (§4.3) etc. + +Our contributions are three-fold: (1) Emphasizing & verifying the impact of dataset on SDR-to-HDRTV task, which has long been understated. (2) Exploiting novel HDRTV dataset and HDR-to-SDR degradation models for network to learn. (3) Introducing new problem formulation, and accordingly proposing novel luminance segmented network. + +# 2. Related Works + +# 2.1. Nomenclature and our scope + +Plenty of HDR-related learning-based methods [20] have been proposed, yet, they markedly differ in intended appli + +cation. As in Tab.1, 'linear HDR' means scene-referred HDR images dedicating to record the linear radiance for graphics application e.g. image-based lighting [21,22], and 'HDRTV' stands for our display-referred WCG-HDR [2] format. Clearly, ① synthesize HDR view on SDR display, ② emphasize dealing with misalignment between multiple SDRs and is designed for new imaging pipeline, while ③ and ④ are all oriented for existing SDR, but for different application. Since many works are proposed before community's clarity on above nomenclature, we classify a method as SDR-to-HDRTV up-conversion (④, our task) if its target HDR is claimed in PQ/HLG EOTF and BT.2020 WCG. + +
Task nameFromTo singleMethods
①HDR-style enhancementsingle SDRenhanced SDRe.g. [23-26]
②multi-exposure HDR imagingmultiple SDRlinear HDRe.g. [27-32]
③SI-HDR*single SDRe.g. [33-37]
④ (ours)iTM* or up-conversionHDRTV frame[4-9] [38-53]
+ +*: SI-HDR: Single-Image HDR reconstruction, iTM: inverse Tone-Mapping. + +Table 1. Various learning-based HDR-related tasks. Green/cyan/magenta terms are respectively from [18]/our/ [7] nomenclature. + +In our scope (4), methods designed their networks with distinct philosophy: Except e.g. semantic-requiring highlight recovery, main process of our task belongs to global mapping resembling image retouching/enhancement. Following this, [7] formulates the task as 3 steps and use $1 \times 1$ convolution for global part, [43] learn the mapping between small sorted image patches, while [51] conducts explicitly-defined per-pixel mapping using the learned latent vector. + +Feature modulation is also popular to involve global prior information. Compared with [7], they change: prior's type [47, 49], modulation tensor's shape [48-50], modulation mechanism [9] and modulation's position [50]. + +To coordinate consecutive video frames, [42] applies 3D convolution with extra temporal-D, [8] take 3 frames as input and deploy multi-frame interaction module at UNet bottleneck. Also, for alignment, [44,49] use deformable convolution whose offset map is predicted by different modules. + +Due to the resolution discrepancy between SDRTV and HDRTV, some methods [4-6, 46, 48, 52] jointly conduct super-resolution and up-conversion. Also, some methods are assisted by non-learning pre-processing [53] or bypass [43, 45], while [41] is trained for badly-exposed SDR. + +# 2.2. HDRTV dataset + +Diversified networks they use, there're currently only 3 open HDRTV training set (Tab.2). All datasets (including ours) contain HDRTV frames in (D65 white) BT.2020 [3] RGB primaries (gamut), PQ [54] EOTF and 1000nit peak luminance, and SDR counterpart in BT.709 [55] gamut, gamma EOTF and 100nit peak luminance. + +
Dataset (Usage)#pairResolutionHDR format
KAIST [4] ([5,48,52])39840160×160uint16 MATLAB .mat YUV
Zeng20 [6]23229UHDH.265 main10 YUV
HDRTV1K [7] ([9,47,50])123516bit .png RGB
HDRTV4K (ours new)3878HD&UHD16bit lossless .tif RGB
+ +# 2.3. HDRTV-to-SDR degradation model + +Most methods follow the common practice to degrade label HDR(GT) to input SDR(LQ). Degradation model (DM) matters since it determined what network can learn, and is relatively paid more attention even in SI-HDR [16, 33, 35]. Yet, few discussion is made in SDR-to-HDRTV (Tab.3): + +Table 2. Status of different HDRTV dataset: training set part. Our HDR frames sized both HD $(1920\times 1080)$ and UHD $(3840\times 2160)$ are manually chosen from $>220$ different videos clips, and encapsulated in lossless (LZW or deflate) TIFF. Others are respectively extracted from only 7 [4]/18 [7] TV demos and 1 graded movie [6], their quality and diversity are quantified later in Tab.5 & Fig.4. + +
DMYouTubeReinhardother DMs
Usage[4,5,7-9,40,44,46-50,52][6,38,41-43]2446a [45]etc. [51,53,56]
DatasetKAIST & HDRTV1KZeng20
+ +Table 3. Current HDRTV-to-SDR degradation models (DMs). 'Dataset' means SDR there is degraded from HDR using that DM. + +Youtube stands for the default conversion YouTube applied to BT.2020/PQ1000 HDR content to produce its SDR-applicable version, Reinhard/2446a means tonemapming HDR to SDR using Reinhard TMO [57]/BT.2446 [58]Method A. [51]/[53]/[56] respectively degrade HDR to SDR by grading/Habel TMO/another learned network. + +In other restoration tasks [11-16], DMs are designed to have proper extent and diversity of degradation so network can learn appropriate restore capability and good generalization. Accordingly, we argue that current DMs are not favorable for training. Specifically, the motive of YouTube is to synthesize HDR view for user possessing only SDR display, it tends to enhance/increase the brightness and saturation so network will, vise-versa, learn to deteriorate/decline them. Also, tone-mapping e.g. Reinhard and 2446a dedicate to preserve as much information from HDR, they have monotonically increasing mapping curves (Fig.5) without highlight clipping, therefore the trained network is not likely to recover much information in over-exposed areas. Above observations are later proven in Tab.6, Fig.1&6 etc. + +# 3. Proposed Method + +The overview of our method is illustrated in Fig.2. + +# 3.1. Network structure + +Problem formulation helps the researcher to clarify what degradation the network should restore. Given $x$ the + +SDR, y the HDR. Previous methods [7, 47, 49] hypothesis that x and y are from single RAW file of same camera. + +This applies to SDR-HDR synchronous production where $\mathbf{x}$ is also of high quality, thus up-conversion $(\mathbf{y} = f(\mathbf{x}))$ is only supposed to follow specific style and recover less missing information. However, since our method is for existing SDR, we assume that SDR $(\mathbf{x})$ and it imaginary HDR counterpart $(\mathbf{y})$ were simultaneously shot by different camera and later pipeline, similar to [18]. The imperfection of SDR pipeline makes existing SDR susceptible to limited latitude, narrow gamut and other degradations $(\mathbf{x} = d(\mathbf{y}))$ . + +Some works [7, 35] suppose $d(\cdot)$ are introduced orderly, and assign hierarchical sub-networks to for $f(\cdot)$ . Since cascaded networks are bulky for real application, we instead formulate that specific degradation is more visible in different luminance range. Concretely, over-exposure occurs in high-luminance range in SDR $(\mathbf{x}_h)$ , low-luminance range $(\mathbf{x}_l)$ is more susceptible to noise etc., while the quality of mid-luminance range $(\mathbf{x}_m)$ is relatively higher. Segmented luminance ranges are treated separately by some traditional up-conversion operators ([59] etc.), and here we introduce this idea to our deep neural network (DNN)—LSN: + +Luminance Segmented Network (LSN) consists of an trunk on full-luminance range $(\mathbf{x})$ , and 2 branches respectively on $\mathbf{x}_l$ and $\mathbf{x}_h$ which are segmented by: + +$$ +\mathbf {x} _ {l} = \max (0, \frac {t - \mathbf {x}}{t}), \mathbf {x} _ {h} = \max (0, \frac {\mathbf {x} - 1}{t} + 1) \tag {1} +$$ + +where $\mathbf{x} \in [0,1]$ . That is, luminance range lower/higher than threshold $t$ (empirically set to 0.05) is linearly mapped to more significant value [0, 1], as in top-right Fig.2. + +After segmentation, $\mathbf{x}$ , $\mathbf{x}_l$ and $\mathbf{x}_h$ require distinct DNN operation. First, as found by [7] etc. (§2.1), the majority of $f(\cdot)$ belongs to global (pixel-independent) operation similar to image enhancement/retouching. Therefore, we assign 5 cascaded receptive-field-free $1 \times 1$ convolution layers on full luminance range $(\mathbf{x})$ as the Global Mapping Trunk. Similar to [7, 9, 47-50], we append a modulation branch (Fig.3 left) to involve $\mathbf{x}$ 's global prior into DNN's decision. + +On the other hand, $f(\cdot)$ still involves non-global restoration where local semantic should be aggregated. Specifically, DNN's mission in $\mathbf{x}_l$ is similar to denoising and low-light enhancement, while that in $\mathbf{x}_h$ resembles image inpainting (claimed by [60, 61]) where lost content is hallucinated. These low-level vision tasks require larger receptive-filed, we hence choose Transformer blocks [17] specializing long-distance dependency, and arranged them as encoder-decoder with skip-connections (UNet). Transformer-style UNet branches on $\mathbf{x}_l$ and $\mathbf{x}_h$ share the same structure but different parameters. The detail of LSN is depicted in Fig.3. + +# 3.2. HDRTV4K dataset + +After designing LSN, we need better training set to unleash its capability. We start with the quality of label HDR: + +![](images/c20142dbb6852e2a501f5d763c1255840dfdff6d08db00ab4631e52264969431.jpg) +Figure 2. Overview of this work. Our Luminance Segmented Network (LSN, §3.1) is designed based on novel problem formulation, then supervisedly trained with label HDR from the proposed HDRTV4K dataset (§3.2), and input SDR degraded by novel degradation models (DMs, §3.3). The major concerns of our LSN, HDRTV4K dataset, and DMs are respectively: recovering dark&bright areas, improving the quality (Tab.5) and diversity (Fig.4) of label GT-HDR, and ensuring the LQ-SDR is with proper style and degradation (Tab.6 & Fig.5). + +![](images/5b640231ea36dc6f53047156974a794bcfefddbfa43f39bbe1036865df08dd0c.jpg) +Figure 3. Detailed structure of LSN modules. 'k3s1n16' means convolution layer with $3 \times 3$ kernel, stride=1, out nc=16, and 'FC32' is fully-connected layer with out nc=32. We deploy only 1 Transformer block [17] at each en/decode level of UNet to lighten LSN (#param=325k) for its application on 4K resolution. + +Motivation. In §1, we argue the quality of label HDR in current datasets. Here, it is assessed from 3 aspects by 10 metrics in Tab.4, based on following considerations: + +First, greater extent of HDR/WCG stands more probability for network to learn pixel in advanced WCG-HDR volume beyond SDR's capability. Meanwhile, higher intra-frame diversity means better generalization the network can learn within small batch-size, i.e. bigger SI/CF/stdL indicate more diversified high-frequency patterns/richer color volume/greater luminance contrast for network to learn. + +Also, style distillation [51] has become a major contributor to method's performance in similar task e.g. image enhancement/retouching, we therefore append 3 metrics quantifying the overall-style of label HDR. Note that network's learned style will be, substantially, affected more by degradation model (DM) which will be discussed later. + +
Metrics on the extent of HDR/WCG ↓
FHLPFraction of HighLight Pixel: Spatial ratio of ‘highlight’ pixel i.e. whose normalized luminance Y = 0.2627R + 0.6780G +0.0593B > 0.01 (100nit, SDR's peak luminance.)
EHLExtent of HighLight: Average pixel (i) distance between the luminance of HDR and its clip-to-100nit version1: 1/n ∑i=1n√[Yi - clip(Yi)]2, clip(x) = clamp(x, 0, 0.01)
FWGPFraction of Wide-Gamut Pixel: Spatial ratio of WCG pixel i.e. whose [x, y] coordinates fall inside BT.2020 but outside SDR's BT.709 gamut in Yxy chromaticity diagram.
EWGExtent of Wide-Gamut [62]: Average pixel-distance between WCG-HDR and its gamut-hard-clipped [63] version2: 1/n ∑i=1n||Si - HC(Si)||2, S = [X, Y, Z]T
Metrics on intra-frame diversity ↓ (all variance-based)
SISpatial Information: Standard deviation over the pixels of Sobel-filtered frame, defined in Annex 6 of [64].
CFColorFulness: Defined in [65].
stdLstandard deviation of Luminance, over all pixels of a frame.
Metrics on overall-style ↓
ASLAverage Saturation Level: Normalized pixel-average length of HDR chrominance component C = [Ct, Cp]T [66]: 1/n ∑i=1n||Ci||2, C ∈ [−0.5, 0.5]
ALLAverage Luminance Level: Pixel-average of Y in FHLP.
HDRBQHDR Brightness Quantification [67], visual salience involved.
+ +1: EHL is to compensate cases e.g. an all-101 n it HDR frame with $100\%$ FHLP but less extent of highlight. +2: You can find the formulation of gamut hard-clipping $(HC(\cdot))$ at Eq.5. + +Table 4. The quality and diversity of label HDR is measured from 3 aspects by 10 metrics above (both in positive correlation), results are in Tab.5. See supplementary material for full illustration. + +Our work. Statistics in Tab.5 confirms the deficiency of current datasets, i.e. lower intra-frame diversity, and most importantly, less extent of HDR/WCG preventing their network from producing true HDR-WCG volume. To this end, we propose a new dataset HDRTV4K consisting of 3878 BT.2020/PQ1000 (Tab.2) HDR frames with higher quality. + +Specifically, these frames are manually extracted and aligned from various open content ([68-72] etc.) with greater extent of HDR/WCG, higher intra-frame diversity + +
Metrics +DatasetExtent of HDRExtent of WCGIntra-frame diversityOverall-style
FHLPEHLFWGPEWGSICFstdLASLALLHDRBQ
KAIST [4]1.52500.20255.47710.11041.93725.94850.95978.908717.28541.8597
Zeng20 [6]0.01970.00120.47920.00340.12314.20480.31463.80616.08050.3781
HDRTV1K [7]1.28430.19712.90890.16332.237811.07221.800610.941415.16262.7970
HDRTV4K (ours)5.30830.95952.63690.51233.550810.58823.48379.827421.19965.1593
+ +and reasonable style (Tab. 5). Note that we re-grade some original footage using DaVinci Resolve to add some perturbation on the diversity. The thumbnails of our dataset and its frame distribution comparison are visualized in Fig.4. + +![](images/9631aecb115177faf01a396f7ed46936d73d2152aff7e94ae585c283bfd011c0.jpg) + +![](images/15e19d47db101494bc31b9a7b7927b4ceefcf491276f3a52d4139a0291074d6a.jpg) + +![](images/e326d4d45e45a1ebcad6839aa8d6e78dbd503cf9bef3ee84966bf8f7c813ba5e.jpg) + +![](images/e09ffe26fd4c0512b298d9c93d30156daa2f268d1c1737fa4d227caa0a741a71.jpg) + +![](images/13c9957352cd0340e6dfc3ec5700ff0a18d138bd895ef1ae4aa9fa68d2b3b77a.jpg) + +![](images/6e2507c9eabfb26de4419477ce8d581c615e9b8ba1b49ad20306260bc67c0046.jpg) + +![](images/766f292c0519f767e5c7346e87b981493f6652dc39fba0a56a0be88280dd28e1.jpg) + +![](images/09a9ba62bf1a4dd233389f8825951695bca6a5f2024aaa03e03b0832c0969522.jpg) +Figure 4. Diversity comparison: our HDRTV4K dataset vs. others. Here, each 2D-coordinate is the projection of single frame's 10-D vector (containing 10 metrics from Tab.5) using t-SNE [73] $(\mathrm{exg} = 3,\mathrm{prep} = 50)$ . We also depict thumbnail HDR frames from our dataset (1)-(9) with their corresponding 2D-coordinates highlighted with gray circle. As seen, our dataset provides wider frame distribution i.e. more diversified scenes for network to learn. + +![](images/0427cd22e78ba264d2a946b0eff5e427922b93fbf097087796be0b81a239f0f6.jpg) + +![](images/661d59522178f90cecde33e03b82d01fae034286b370d19d3d447646eae3f3df.jpg) + +# 3.3. New degradation models + +After obtaining label HDR with higher quality, we focus on degradation model (DM) which determines the restoration network will learn. As claimed in §2.3, current DMs fail for undue saturation & brightness change and meager over-exposure degradation, which are verified by ALL, + +ASL, FOEP in Tab.6. That is, YouTube/2446a tend to increase/decline both ASL & ALL so network will learn to simultaneously de/over-saturate and unduly-darken/brighten. Also, 2446a and Rienhard provide small FOEP which is adverse to network's over-exposure hallucination ability. + +Table 5. Quality of label HDR frames, manifested in the frame-average of 10 metrics from Tab.4. Greater extent of HDR/WCG encourages network to produce more pixels in non-SDR volume, while intra-frame diversity is helpful for network's generalization ability. All numbers are in percentage (\%), and we highlight those the most favorable to training. Their diversity is further demonstrated in Fig.4. + +
SDR Degraded byFOEP1ALLASL2SICF
CurrentDM2446a0.1815.8803.6246.0416.573
Reinhard1.26421.8877.44214.14712.568
YouTube5.43928.21914.64118.54525.225
OursDM2446c+GM1.73911.66910.18312.50319.391
HC+GM4.25214.06210.37715.09020.146
OCIO21.58018.8879.97713.57818.052
criteria↑: better whenkept3samllerkept--
Source HDR (Tab.5)45.30821.2009.8273.55110.588
+ +1: FOEP: Fraction of Over-exposed Pixels: Spatial ratio of pixels whose normalized luminance $Y = 1$ . +2: For SDR, ASL is also calculated as Tab.4, but with $\mathbf{C} = [C_h, C_r]^{\mathrm{T}}$ [55] rather $[C_t, C_p]^{\mathrm{T}}$ . +3: Assuming that HDR's highlight $(>100n it)$ part should all be clipped to 1 (over-exposure) in SDR. +4: Gray means container discrepancy i.e. HDR/SDR's metric should be different even for well-degraded SDR. + +Table 6. Given label HDR from HDRTV4K dataset, SDR's statistics alters when degraded by different DMs. Results are in percentage, we mark those unfavorable / neutral / beneficial for training based on the observation in §2.3 & 3.3. As seen, our DMs provide adequate over-exposure (FOEP) and no undue brightness (ALL) & saturation (ASL) change for network to learn. Example of different degraded SDR can be found in supplementary material. + +This motivate us to utilize/exploit new DMs with proper extent of degradation and no deficiencies on style: + +(1) OCIO2: OpenColorIO v2 [74], commercial method from BT.2020/PQ1000 HDR container to BT.709/gamma SDR, implemented by 3D look-up table (LUT) here. +(2) $2446c + GM$ : Modified Method $C$ tone-mapping from Rec.2446 [58], followed by Gamut Mapping. Here, each HDR encoded value $\mathbf{E}' = [R', G', B']^{\mathrm{T}}$ (we use superscript ' for non-linearity) is linearize to $\mathbf{E}$ by PQ EOTF, and then transferred to $Yxy$ . Then $Y$ is globally mapped to $Y_{SDR}$ , while $xy$ are kept and then recomposed with $Y_{SDR}$ to form $\mathbf{E}_{SDR}$ and later $\mathbf{E}_{SDR_{709}}'$ . Our modification lies in: + +$$ +Y _ {S D R} = \operatorname {c l a m p} \left(T M _ {2 4 4 6 c} (Y), 0, 1 0 0 \text {n i t}\right) \tag {2} +$$ + +where $TM_{2446c}(\cdot)\in [0,118\text{nit} ]$ is the original global mapping curve in [58] (Fig.5 dashed line). That is, we clip the original output range to 100nit to produce more FOEP, rather than linear-scale which share same shortcoming with DM 2446a. Gamut mapping will be introduced in Eq.4&5. + +![](images/6d83014d1cad1b53747f3913acb162ca84587da10788be6ba467824e474603e8.jpg) +Figure 5. One of the key ideas of the proposed degradation models (DMs, §3.3): HDR's luminance (left) and color (right) volume should be properly clipped, so that network will learn corresponding restoration. Another idea is keeping a sensible brightness & color/saturation style during degradation, please refer to Tab.6. + +(3) $HC + GM$ : Hard-Clipping and Gamut Mapping, a container conversion with SDR part unchanged and all luminance/color in HDR/WCG volume hard-clipped. For luminance part, we clip all $>100\mathrm{nit}$ pixels to $100\mathrm{nit}$ : + +$$ +\mathbf {E} _ {S D R} = 1 0 0 \times \operatorname {c l a m p} (\mathbf {E}, 0, 0. 0 1), \quad \mathbf {E} \in [ 0, 1 ] \tag {3} +$$ + +where 0.01 correspond to $100\mathrm{nit}$ in normalized linear HDR $\mathbf{E}$ (PQ container), and $\times 100$ is to adapt the container discrepancy (nominal peak 1 means $10000\mathrm{nit} \rightarrow 100\mathrm{nit}$ ). + +So far, $\mathbf{E}_{SDR}$ is still in BT.2020 gamut. Therefore, we append Gamut Mapping (+GM): First, Color Space Transform (CST) from BT.2020 RGB primaries to BT.709: + +$$ +\mathbf {E} _ {S D R O O G} = \mathbf {M} \mathbf {E} _ {S D R}, \mathbf {M} = \left[ \begin{array}{l l l} 1. 6 6 0 5 & 0. 5 8 7 6 & - 0. 0 7 2 8 \\ - 0. 1 2 4 6 & 1. 1 3 2 9 & 0. 0 0 8 3 \\ - 0. 0 1 8 2 & - 0. 1 0 0 6 & 1. 1 1 8 7 \end{array} \right] \tag {4} +$$ + +where $\exists \mathbf{E}_{SDR_{OOG}} \notin [0,1]$ i.e. out-of-gamut (OOG) pixels will fall outside valid range after CST to a narrower volume. Dealing OOG is the key of gamut mapping, instead of soft-mapping [63, 75] (used by [76]'s DM) preserving as much WCG information to narrow gamut, we use hard-clipping which clips all OOG pixels to BT.709 boundary (Fig.5): + +$$ +\mathbf {E} _ {S D R _ {7 0 9}} = \operatorname {c l a m p} \left(\mathbf {E} _ {S D R _ {O O G}}, 0, 1\right) \tag {5} +$$ + +Then, $\mathbf{E}_{SDR_{709}}$ from both (2) & (3) is converted to SDR encoded value $\mathbf{E}_{SDR_{709}}^{\prime}$ by BT.1886 [77] OETF (approximate gamma2.22) and 8bit JPEG compression with QF=80. + +Our philosophy lies in that most explicitly-defined operations can be streamlined to a clamp(\cdot) function: Only when luminance and color volume is clipped, multiple-to-one mapping occurs in DM, the trained network can learn corresponding recovery ability. Also, ALL & ASL in Tab.6 show that these DMs produce SDR with reasonable style, ensuring that network will not learn style deterioration. + +# 4. Experiments + +Training detail. For each HDR frame randomly resized to $[0.25\mathrm{x},1\mathrm{x}]$ , 6 patches sized $600\times 600$ are cropped from + +random position. Then, each of $6 \times 3878$ HDR patches is degraded to SDR by 1 of the 3 proposed DMs in equal probability, and again stored in JPEG with QF=75. + +All LSN parameters are Kaiming initialized, optimized by $l_{1}$ loss and AdaM with learning rate starting from $2 \times 10^{-4}$ and decaying to half every $10^{5}$ iter till $4 \times 10^{5}$ . + +# 4.1. Criteria and configuration + +Criteria. Since the destination of SDR-to-HDRTV upconversion is human perception, to provide a 'step-change' improvement [1] of HDRTV than SDRTV, methods should be assessed based on if: C1: result is visually-pleasuring, from both brightness (C1A) & color appearance (C1B) etc., C2: HDRTV's advance on HDR/WCG volume is properly recovered and utilized, C3: bright and dark areas are recovered or at least enhanced and C4: artifacts are avoided. Yet, as found by [18,19], current distance-based metrics e.g. PSNR fails, since the main contributor of an appealing score lies in the accuracy of learned numerical distribution, rather C1-4. Hence, we use new assessment criteria in Tab.7. + +
Visuals in Fig.1&6Metrics in Tab.8subj. exp. (\$4.3)
CIA-ALL, HDRBQoverall rating & selection of attribution
CIBASL
C3yellow&blue boxes-
C4detailed visuals
C23D Yxy diagramFHL(WG)P, EHL(WG)-
+ +Table 7. How each criteria C1-4 is assessed: via detailed visuals, fine-grained tailored metrics and subjective experiment. + +Competitors. As in Tab.8, our method is compared with 6 learning-based methods [4-9], and 2 commercial software DaVinci and Nuke. Results from joint-SR methods [4-6] are bilinear downscaled before comparison. Note that learning-based methods are not re-trained with our dataset since we treat dataset as a crucial attribution to performance. + +Test set. We select 12 4K SDR videos with $10s$ duration and $50fps$ . 3 of 12 are degraded from HDR-GT counterpart by YouTube DM (§2.3), the rest 9 are graded from HDR sequence No. 2, 13, 15, 20, 21, 22, 42, 44, 55 in [78]. Experiment fairness is guaranteed since both DMs (SDR-HDR relationship) in test set are 'unseen' by our network (this even gives a bonus to methods trained with YouTube DM [4, 5, 7-9]). + +# 4.2. Result + +Results from all competitors are shown in Tab.8 & Fig.6. + +Metrics. From Tab.8 we know that methods trained with YouTube DM all tend to produce lower satiation (ASL) and brightness (ALL&HDRBQ), and their ASL is lower even than input SDR. Also, SR-ITM-GAN [6] recovers least HDR&WCG volume since its label HDR (from Zeng20 dataset) is of least extent of HDR/WCG (Tab.5). Note that current methods produce adequate FWGP, but from 3D Yxy chromaticity diagram in Fig.6 we known that their WCG + +
Method (network)how network is trained(recovery rate %, GT is 100%) how HDR/WCG volume is recoveredshift rate %, GT is 0%) overall-styleconventional metrics
dataset (GT)DMFHLPEHLFWGPEWGASLALLHDRBQPSNRSSIMΔEVDP3
Input SDR-00006.57010.76-23.92dB0.886144.976.571
Deep SR-ITM [4]KAISTYouTube(14.03)0.2323(9.70)0.372(175.2)1.0964(71.96)0.172(-20.33)5.485(-15.06)9.580(-72.80)1.42826.59dB0.811532.546.917
JSI-GAN [5](12.33)0.2041(3.55)0.136(213.1)1.3334(88.23)0.212(-16.62)5.741(-14.36)6.659(-79.20)1.09227.87dB0.842030.237.452
SR-ITM-GAN [6]Zeng20Reinhard(8.04)0.1332(14.33)0.550(00.00)0.0000(00.00)0.000(-7.04)6.400(-30.37)7.853(-57.12)2.25128.04dB0.883124.786.707
HDRTVNet [7]HDRTV1KYouTube(18.59)0.3078(16.29)0.625(388.8)2.4334(28.82)0.069(-15.51)5.817(-13.47)9.759(-69.65)1.59330.82dB0.881227.588.120
KPN-MFI [8]own(1.17)0.0193(0.02)0.001(392.9)2.4592(15.82)0.038(-21.74)5.388(-16.10)9.462(-82.47)0.92029.37dB0.874627.477.785
FMNet [9]HDRTV1KOurs × 3(13.67)0.2264(12.35)0.474(396.5)2.4813(91.29)0.220(-16.91)5.770(-13.48)9.758(-71.24)1.51030.91dB0.885527.168.069
LSN (ours)(256.6)4.2509(71.96)2.599(109.8)0.6873(150.0)0.361(+9.25)7.522(+81.12)20.42(-27.04)3.82924.47dB0.831037.848.130
DaVinci-(54.96)0.9103(43.16)1.655(310.0)1.9399(85.22)0.205(-21.36)5.414(-21.42)8.863(-39.23)3.19026.39dB0.891835.478.528
Nuke(112.1)1.8565(24.70)0.384(00.00)0.0000(00.00)0.000(-27.53)4.990(+17.93)13.30(-51.19)2.56220.87dB0.727364.297.479
HDR-GT (ref.)(100.0)1.6562(100.0)3.835(100.0)0.6258(100.0)0.240(0.00)6.885(0.00)11.28(0.00)5.248----
ablation studies ↓
LSN (ours)HDRTV4KYouTube(13.09)0.2168(6.63)0.254(401.3)2.5118(59.81)0.144(-14.68)5.874(-13.46)9.760(-76.89)1.21330.15dB0.885828.047.902
HDRTV4KReinhard(9.06)0.1501(19.14)0.734(00.00)0.0000(00.00)0.000(-5.74)6.490(-25.65)8.387(-52.84)2.47527.70dB0.843626.036.832
Zeng20ours × 3(109.6)1.8147(9.00)0.345(0.95)0.0593(0.76)0.002(+3.36)7.117(+66.64)18.79(-59.52)2.12425.17dB0.817928.587.874
HDRTV1Kours × 3(177.5)2.9405(52.49)2.013(279.5)1.7494(70.43)0.169(+6.56)7.337(+52.30)17.18(-34.83)3.42024.30dB0.835138.038.002
+ +Table 8. Metrics. We use fine-grained tailored metrics (column 3-9, defined in Tab.4) to assess criteria C1&2 (§4.1): The more significant metrics in column 3-6 are, the better HDR&WCG volume is recovered (C2) by specific method. Also, column 8-10 closer with GT stands for similar brightness&color appearance. Note that we allow column 8-10 slightly bigger than GT, which means result HDR is reasonably more visual-pleasuring (C1) than GT. Based on this, we highlight those results unfavorable for viewing experience. + +![](images/5bdedd9f6f5df459fa30e7afadab773e681e4101cdb7cf659dc196437df6b97d.jpg) +Figure 6. Visuals. We provide comparisons on $3D$ Yxy chromaticity diagram to assess how HDR/WCG volume is recovered (C2), and detailed visuals on bright (yellow arrow, with luminance of same pixel indicated) and dark (blue arrow) areas to assess method's recover ability (C3). Note that conclusion 'SDR is more vivid than HDR' could not be drawn because HDR will appear dimmer than SDR in print version (explained in Fig.1). We hence turn to ASL & HDRBQ (column 8-10) and later subjective experiment to see if HDR is indeed dimmer (C1). Comparison on more scenes will be provided in supplementary material. + +pixels mainly exists in low luminance range, which means they are of little help to viewing experience. Our method is able to recover adequate HDR/WCG volume, meanwhile reasonably enhance the brightness and saturation. $^{1}$ + +Visuals. Methods' recover ability is illustrated by Fig.6: Current ones underperform in both bright (yellow) and dark (blue) areas. Specifically, methods trained with YouTube DM produce dim (HDBBQ) and desaturated (ASL) result, and even get lower luminance than input SDR at same position (arrow in yellow box). This confirms the finding in §2.3 that network will learn to darken and desaturate if DM tend to brighten and over-saturate. Also, our method recovers more detail in bright and dark areas with a better style. + +# 4.3. Subjective experiment + +Currently, few subjective studies [19, 43, 59, 81-83] are designed for SDR-to-HDR procedure (rather between different HDR). Similar to [19], we judge if output HDR is better than origin SDR. Specifically, as in Fig.7, we use 2 side-by-side SONY KD85X9000H display supporting both SDR and HDRTV, one is calibrated to 100nit/BT.709 SDR and another PQ1000nit/BT.2020 HDR. Each (input)SDR-outputHDR pair is displayed twice with $3s$ gray level interval and following $10s$ for rating: each participant is asked to continuously rate from -5(SDR much better) to 0(same) to 5(HDR much better), meanwhile select at least 1 attribution (bottom Fig.7) of his/her rating. Such process is repeated 9(#participant) $\times$ 9(#competitor) $\times$ 12(#clip) times. + +![](images/244aa22540d7bfac2f34b05827bf7b13d5ee2b1b89f9bb907fa1d826fd65cbd0.jpg) + +![](images/4b7b15968c8cc53e6f00e2a98cfced6927c27e50076efedc7c066305ab1af67f.jpg) +Figure 7. Environment and result of subjective experiment. 2 displays are calibrated differently to ensure both SDR and HDR are correctly visualized. Results are provided in quartile chart. + +Result shows that only 2 methods (ours & DaVinci) are recognized better than input SDR. For methods [4-6], their main 'attribution' (bottom Fig.7) is artifact ( $C4$ , see Fig.6 blue box). For artifact-free 'YouTube-DM' methods [7-9], they are rated slightly worse mainly for lower saturation ( $C1B$ ) and incapability in information recovery, consisting with Tab.8 and Fig.6. Form 'attribution' we also notice that our method got better score mainly for viewing experience ( $C1$ ) and information recovery ( $C3$ ). Also, our 'bad cases' lies in recovered large dark area with intrinsic noise etc. amplified and uneliminated. This is why we got more checks on $C4(\times 24)$ than commercial methods DaVinci and Nuke. + +# 4.4. Ablation studies + +On DM. When DM is changed to YouTube, Tab.8 witnesses a significant decline on FHLP, EHL, ASL, ALL + +and HDRBQ, while Fig.8 confirms a result similar to those 'YouTube-DM' methods, i.e. worse viewing experience and less recover ability. Also, when using Reinhard DM which contains no clipping, result's highlight area stay unlearned. + +On dataset. Here, we use original DMs, but label HDR from other dataset. In Fig.8 yellow box, our DMs encourage the network to output higher luminance, but since Zeng20 is of least extent of HDR i.e. these highlight do not exist in label HDR, our LSN will not 'recognize' them and thus produce artifact. Since this dataset is also of least extent of WCG, FWGP&EWG in Tab.8 drop obviously. When using slightly-inferior HDRTV1K as label, difference is relatively less significant. Yet, in both cases, ASL&ALL are similar since DM i.e. network's style tendency is unaltered. + +![](images/fc7cb3945dbe14230bd04c48397e2499e47c5247d60fdac778b88e7f67d240aa.jpg) +Figure 8. Result of ablation studies proves the importance of both high-quality label HDR and rational DMs. Specifically, absent of both label HDR's HDR/WCG volume (Zeng20) and DM's degradation (Reinhard) will impair LSN's recover ability, meanwhile YouTube DM's style will make our LSN commonplace as others. + +# 5. Conclusion + +There are 2 types of low level vision: 'sole-restoration' whose destination is only clean or GT e.g. denoising, and 'perceptual-motivated' aiming at better viewing experience e.g. image enhancement/retouching. SDR-to-HDRTV upconversion belongs to both. Yet, current methods only realize (it belongs to) the former and neglect the latter, leading their concentration only on network mechanism. + +To this end, our response is two-fold: (1) focusing on the impact of training set, and ameliorating its quality by proposing new dataset and DMs, (2) involving novel assessment criteria based on the 'perceptual' principal. + +# References + +[1] ITU, Geneva, Switzerland, Report ITU-R BT.2381-0: Requirements for high dynamic range television (HDR-TV) systems, 0 ed., 7 2015. 1, 6 +[2] ITU, Geneva, Switzerland, Recommendation ITU-R BT.2100-2: Image parameter values for high dynamic range television for use in production and international programme exchange, 2 ed., 07 2018. 1, 2 +[3] ITU, Geneva, Switzerland, Recommendation ITU-R BT.2020-2: Parameter values for ultra-high definition television systems for production and international programme exchange, 2 ed., 10 2015. 1, 2 +[4] S. Y. Kim, J. Oh, and M. Kim, "Deep sr-item: Joint learning of super-resolution and inverse tone-mapping for 4k uhd hdr applications," in Proc. ICCV, pp. 3116-3125, 2019. 2, 3, 5, 6, 7, 8 +[5] S. Y. Kim, J. Oh, and M. Kim, "Jsi-gan: Gan-based joint super-resolution and inverse tone-mapping with pixel-wise task-specific filters for uhd hdr video," in Proc. AAAI, vol. 34, pp. 11287-11295, 2020. 2, 3, 6, 7, 8 +[6] H. Zeng, X. Zhang, Z. Yu, and Y. Wang, “Sr-itm-gan: Learning 4k uhd hdr with a generative adversarial network,” IEEE Access, vol. 8, pp. 182815–182827, 2020. 2, 3, 5, 6, 7, 8 +[7] X. Chen, Z. Zhang, J. S. Ren, L. Tian, Y. Qiao, and C. Dong, “A new journey from sdrtv to hdttv,” in Proc. ICCV, pp. 4500–4509, 2021. 2, 3, 5, 6, 7, 8 +[8] G. Cao, F. Zhou, H. Yan, A. Wang, and L. Fan, "Kpn-mfi: A kernel prediction network with multi-frame interaction for video inverse tone mapping," in Proc. IJCAI, pp. 806-812, 2022. 2, 3, 6, 7, 8 +[9] G. Xu, Q. Hou, L. Zhang, and M.-M. Cheng, "Fmnet: Frequency-aware modulation network for sdr-to-hdr translation," in Proc. ACMMM, pp. 6425-6435, 2022. 2, 3, 6, 7, 8 +[10] Z. Liang, "Cvpr2022-1st workshop on vision dataset understanding," https://sites.google.com/view/vdu-cvpr22, 2022.2 +[11] K. Zhang, J. Liang, L. Van Gool, and R. Timofte, "Designing a practical degradation model for deep blind image superresolution," in Proc. ICCV, pp. 4791-4800, 2021. 2, 3 +[12] V. Dewil, A. Barral, G. Facciolo, and P. Arias, "Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?", in Proc. CVPR, pp. 4900-4910, 2022. 2, 3 +[13] J. Jiang, K. Zhang, and R. Timofte, “Towards flexible blindJPEG artifacts removal,” in Proc. ICCV, pp. 4997–5006, 2021. 2, 3 +[14] K. Jiang, Z. Wang, Z. Wang, C. Chen, P. Yi, T. Lu, and C.-W. Lin, "Degrade is upgrade: Learning degradation for low-light image enhancement," in Proc. AAAI, vol. 36, pp. 1078-1086, 2022. 2, 3 +[15] Y. Zhou, Y. Song, and X. Du, "Modular degradation simulation and restoration for under-display camera," in Proc. ACCV, pp. 265-282, 2022. 2, 3 + +[16] C. Guo and X. Jiang, “Lhdr: Hdr reconstruction for legacy content using a lightweight dnn,” in Proc. ACCV, pp. 3155–3171, 2022. 2, 3 +[17] S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M.-H. Yang, "Restormer: Efficient transformer for high-resolution image restoration," in Proc. CVPR, pp. 5728-5739, 2022. 2, 3, 4 +[18] G. Eilertsen, S. Hajisharif, P. Hanji, A. Tsimirikoglou, R. K. Mantiuk, and J. Unger, "How to cheat with metrics in single-image hdr reconstruction," in Proc. ICCV, pp. 3998-4007, 2021. 2, 3, 6, 7 +[19] P. Hanji, R. Mantiuk, G. Eilertsen, S. Hajisharif, and J. Unger, “Comparison of single image hdr reconstruction methods—the caveats of quality assessment,” in Proc. SIGGRAPH, pp. 1–8, 2022. 2, 6, 7, 8 +[20] L. Wang and K.-J. Yoon, "Deep learning for hdr imaging: State-of-the-art and future trends," IEEE Trans. PAMI, 2021. 2 +[21] P. Debevec, "Image-based lighting," in ACM SIGGRAPH 2006 Courses, pp. 4-es, 2006. 2 +[22] E. Reinhard, W. Heidrich, P. Debevec, et al., High dynamic range imaging: acquisition, display, and image-based lighting. Morgan Kaufmann, 2010. 2 +[23] M. Gharbi, J. Chen, J. T. Barron, S. W. Hasinoff, and F. Durand, “Deep bilateral learning for real-time image enhancement,” ACM Trans. Graph., vol. 36, no. 4, pp. 1–12, 2017. 2 +[24] X. Yang, K. Xu, Y. Song, Q. Zhang, X. Wei, and R. W. Lau, "Image correction via deep reciprocating hdr transformation," in Proc. CVPR, pp. 1798-1807, 2018. 2 +[25] Z. Zheng, W. Ren, X. Cao, T. Wang, and X. Jia, "Ultra-high-definition image hdr reconstruction via collaborative bilateral learning," in Proc. ICCV, pp. 4449-4458, 2021. 2 +[26] B. Mildenhall, P. Hedman, R. Martin-Brualla, P. P. Srinivasan, and J. T. Barron, “Nerf in the dark: High dynamic range view synthesis from noisy raw images,” in Proc. CVPR, pp. 16190–16199, 2022. 2 +[27] N. K. Kalantari, R. Ramamoorthi, et al., "Deep high dynamic range imaging of dynamic scenes," ACM Trans. Graph., vol. 36, no. 4, pp. 144-1, 2017. 2 +[28] S. Wu, J. Xu, Y.-W. Tai, and C.-K. Tang, "Deep high dynamic range imaging with large foreground motions," in Proc. ECCV, pp. 117-132, 2018. 2 +[29] Q. Yan, D. Gong, Q. Shi, A. v. d. Hengel, C. Shen, I. Reid, and Y. Zhang, “Attention-guided network for ghost-free high dynamic range imaging,” in Proc. CVPR, pp. 1751–1760, 2019. 2 +[30] G. Chen, C. Chen, S. Guo, Z. Liang, K.-Y. K. Wong, and L. Zhang, "Hdr video reconstruction: A coarse-to-fine network and a real-world benchmark dataset," in Proc. ICCV, pp. 2502-2511, 2021. 2 +[31] Y. Niu, J. Wu, W. Liu, W. Guo, and R. W. Lau, "Hdr-gan: Hdr image reconstruction from multi-exposed ldr images with large motions," IEEE Transactions on Image Processing, vol. 30, pp. 3885-3896, 2021. 2 + +[32] E. Pérez-Pellitero et al., “Ntire 2022 challenge on high dynamic range imaging: Methods and results,” in Proc. CVPR, pp. 1009–1023, 2022. 2 +[33] G. Eilertsen, J. Kronander, et al., "Hdr image reconstruction from a single exposure using deep cnns," ACM Trans. Graph., vol. 36, no. 6, pp. 1-15, 2017. 2, 3 +[34] D. Marnerides, T. Bashford-Rogers, et al., "Expandnet: A deep convolutional neural network for high dynamic range expansion from low dynamic range content," Comput. Graph. Forum, vol. 37, no. 2, pp. 37-49, 2018. 2 +[35] Y.-L. Liu, W.-S. Lai, Y.-S. Chen, Y.-L. Kao, M.-H. Yang, Y.-Y. Chuang, and J.-B. Huang, "Single-image hdr reconstruction by learning to reverse the camera pipeline," in Proc. CVPR, pp. 1651-1660, 2020. 2, 3 +[36] M. S. Santos, T. I. Ren, and N. K. Kalantari, "Single image hdr reconstruction using a cnn with masked features and perceptual loss," ACM Trans. Graph., vol. 39, no. 4, pp. 80-1, 2020. 2 +[37] X. Chen, Y. Liu, et al., "Hdrunet: Single image hdr reconstruction with denoising and dequantization," in Proc. CVPR, pp. 354-363, 2021. 2 +[38] S. Ning, H. Xu, L. Song, R. Xie, and W. Zhang, “Learning an inverse tone mapping network with a generative adversarial regularizer,” in Proc. ICASSP, pp. 1383–1387, 2018. 2, 3 +[39] K. Hirao, Z. Cheng, M. Takeuchi, and J. Katto, "Convolutional neural network based inverse tone mapping for high dynamic range display using lucore," in 2019 IEEE International Conference on Consumer Electronics (ICCE), pp. 1-2, 2019. 2 +[40] S. Y. Kim, D.-E. Kim, and M. Kim, "Itm-cnn: Learning the inverse tone mapping from low dynamic range video to high dynamic range displays using convolutional neural networks," in Proc. ACCV, pp. 395-409, 2018. 2, 3 +[41] Y. Xu, S. Ning, R. Xie, and L. Song, "Gan based multiexposure inverse tone mapping," in Proc. ICIP, pp. 4365-4369, 2019. 2, 3 +[42] Y. Xu, L. Song, R. Xie, and W. Zhang, "Deep video inverse tone mapping," in 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp. 142-147, 2019. 2, 3 +[43] D.-E. Kim and M. Kim, “Learning-based low-complexity reverse tone mapping with linear mapping,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 2, pp. 400–414, 2019. 2, 3, 8 +[44] J. Zou, K. Mei, and S. Sun, "Multi-scale video inverse tone mapping with deformable alignment," in 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), pp. 9-12, 2020. 2, 3 +[45] T. Chen and P. Shi, "An inverse tone mapping algorithm based on multi-scale dual-branch network," in 2021 International Conference on Culture-oriented Science & Technology (ICCST), pp. 187–191, 2021. 2, 3 +[46] G. Xu, Y. Yang, J. Xu, L. Wang, X.-T. Zhen, and M.-M. Cheng, "Joint super-resolution and inverse tone-mapping: A feature decomposition aggregation network and a new benchmark," 2022. 2, 3 + +[47] G. He, K. Xu, L. Xu, C. Wu, M. Sun, X. Wen, and Y.-W. Tai, "Sdrtv-to-hdrtv via hierarchical dynamic context feature mapping," in Proc. ACMM, pp. 2890-2898, 2022. 2, 3 +[48] G. He, S. Long, L. Xu, C. Wu, J. Zhou, M. Sun, X. Wen, and Y. Dai, “Global priors guided modulation network for joint super-resolution and inverse tone-mapping,” 2022. 2, 3 +[49] K. Xu, L. Xu, G. He, C. Wu, Z. Ma, M. Sun, and Y.-W. Tai, "Sdrtv-to-hdrtv conversion via spatial-temporal feature fusion," 2022. 2, 3 +[50] T. Shao, D. Zhai, J. Jiang, and X. Liu, "Hybrid conditional deep inverse tone mapping," in Proc. ACMM, pp. 1016-1024, 2022. 2, 3 +[51] A. Mustafa, P. Hanji, and R. K. Mantiuk, "Distilling style from image pairs for global forward and inverse tone mapping," in The 19th ACM SIGGRAPH European Conference on Visual Media Production (CVMP), pp. 1-10, 2022. 2, 3, 4 +[52] M. Yao, D. He, X. Li, Z. Pan, and Z. Xiong, "Bidirectional translation between uhd-hdr and hd-sdr videos," IEEE Transactions on Multimedia, 2023. 2, 3 +[53] R. Tang, F. Meng, and L. Bai, “Zoned mapping network from sdr video to hdr video,” in 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), vol. 5, pp. 1466–1472, 2022. 2, 3 +[54] SMPTE, NY, USA, ST 2084:2014 - SMPTE Standard - High Dynamic Range Electro-Optical Transfer Function of Mastering Reference Displays, 2014. 2 +[55] ITU, Geneva, Switzerland, Recommendation ITU-R BT.709-6: Parameter values for the HDTV standards for production and international programme exchange, 6 ed., 6 2015. 2, 5 +[56] Z. Cheng, T. Wang, Y. Li, F. Song, C. Chen, and Z. Xiong, "Towards real-world hdrtv reconstruction: A data synthesis-based approach," in Proc. ECCV, pp. 199-216, 2022. 3 +[57] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, "Photographic tone reproduction for digital images," in Proc. 29th SIGGRAPH, pp. 267-276, 2002. 3 +[58] ITU, Geneva, Switzerland, Report ITU-R BT.2446-1: Methods for conversion of high dynamic range content to standard dynamic range content and vice-versa, 1 ed., 3 2021. 3, 5 +[59] P. Mohammadi, M. T. Pourazad, and P. Nasiopoulos, "A perception-based inverse tone mapping operator for high dynamic range video applications," IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 5, pp. 1711-1723, 2020. 3, 8 +[60] D. Marnerides, T. Bashford-Rogers, and K. Debattista, "Deep hdr hallucination for inverse tone mapping," Sensors, vol. 21, no. 12, p. 4032, 2021. 3 +[61] Y. Zhang and T. Aydin, "Deep hdr estimation with generative detail reconstruction," in Computer Graphics Forum, vol. 40, pp. 179-190, 2021. 3 +[62] L. Bai, Y. Yang, and G. Fu, "Analysis of high dynamic range and wide color gamut of uhdtv," in 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), vol. 5, pp. 1750-1755, 2021. 4 + +[63] ITU, Geneva, Switzerland, Report ITU-R BT.2407-0: Colour gamut conversion from Recommendation ITU-R BT.2020 to Recommendation ITU-R BT.709, 0 ed., 10 2017. 4, 6 +[64] ITU, Geneva, Switzerland, Recommendation ITU-R BT.500-14: Methodologies for the subjective assessment of the quality of television images, 14 ed., 10 2019. 4 +[65] D. Hasler and S. E. Suesstrunk, “Measuring colorfulness in natural images,” in Human vision and electronic imaging VIII, vol. 5007, pp. 87–95, 2003. 4 +[66] Dolby, "What is ictcp? - introduction." https:// professional.dolby.com/siteassets/pdfs/ ictcp_dolbywhitepaper_v071.pdf.4 +[67] S. Ploumis, R. Boitard, and P. Nasiopoulos, "Image brightness quantification for hdr," in 2020 28th European Signal Processing Conference (EUSIPCO), pp. 640-644, 2021. 4 +[68] Arri, "Camera sample footage & reference image." https://www.arri.com/en/learn-help/ learn-help-camera-system/camera-sample-footage-reference-image, 2022.4 +[69] Netflix, "Netflix open content." https://opencontent.nsf1x.com/, 2020.4 +[70] J. Froehlich, S. Grandinetti, B. Eberhardt, S. Walter, A. Schilling, and H. Brendel, “Creating cinematic wide gamut hdr-video for the evaluation of tone mapping operators and hdr-displays,” in Digital photography X, vol. 9023, pp. 279–288, 2014. 4 +[71] Y. Wang, S. Inguva, and B. Adsumilli, "YoutubeUGC dataset for video compression research," in 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), pp. 1-5, 2019. 4 +[72] Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethuraman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in Proc. ICIP, pp. 786–790, 2022. 4 +[73] L. Van der Maaten and G. Hinton, "Visualizing data using t-sne," Journal of machine learning research, vol. 9, no. 11, 2008. 5 +[74] D. Walker, C. Payne, P. Hodoul, and M. Dolan, "Color management with opencolorio v2," in ACM SIGGRAPH 2021 Courses, pp. 1-226, 2021. 5 +[75] J. Morovic, Color gamut mapping. John Wiley & Sons, 2008. 6 +[76] H. Le, T. Jeong, A. Abdelhamed, H. J. Shin, and M. S. Brown, "Gamutnet: Restoring wide-gamut colors for camera-captured images," in Color and Imaging Conference, vol. 2021, pp. 7-12, 2021. 6 +[77] ITU, Geneva, Switzerland, Recommendation ITU-R BT.1886-0: Reference electro-optical transfer function for flat panel displays used in HDTV studio production, 0 ed., 3 2011. 6 +[78] ITU, Geneva, Switzerland, Report ITU-R BT.2245-10: HDTV and UHDTV including HDR-TV test materials for assessment of picture quality, 10 ed., 9 2022. 6 + +[79] ITU, Geneva, Switzerland, Recommendation ITU-R BT.2124-0: Objective metric for the assessment of the potential visibility of colour differences in television, 0 ed., 1 2019. 7 +[80] K. Wolski, D. Giunchi, et al., "Dataset and metrics for predicting local visible differences," ACM Trans. Graph., vol. 37, no. 5, pp. 1-14, 2018. 7 +[81] C. Bist, R. Cozot, G. Madec, and X. Ducloux, “Tone expansion using lighting style aesthetics,” Computers & Graphics, vol. 62, pp. 77–86, 2017. 8 +[82] G. Luzardo, J. Aelterman, H. Luong, S. Rousseaux, D. Ochoa, and W. Philips, “Fully-automatic inverse tone mapping algorithm based on dynamic mid-level tone mapping,” APSIPA Transactions on Signal and Information Processing, vol. 9, p. e7, 2020. 8 +[83] A. Stojkovic, J. Aelterman, H. Luong, H. Van Parys, and W. Philips, “Highlights analysis system (hans) for low dynamic range to high dynamic range conversion of cinematic low dynamic range content,” IEEE Access, vol. 9, pp. 43938–43969, 2021. 8 \ No newline at end of file diff --git a/2023/Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models/images.zip b/2023/Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..263f2d3f05fd11a5fa1bbd53158b02ec78d27fa8 --- /dev/null +++ b/2023/Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c6f3beb10981a279904631aacb332d93d0a741fd31bafa8c67d931a0d500eddb +size 1118171 diff --git a/2023/Learning a Practical SDR-to-HDRTV Up-Conversion Using New Dataset and Degradation Models/layout.json b/2023/Learning a Practical SDR-to-HDRTV Up-Conversion 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Up-converting SDR content for HDR-WCG TV display. To ensure better viewing experience [1], we use (1) " + }, + { + "bbox": [ + 46, + 396, + 546, + 451 + ], + "type": "inline_equation", + "content": "3D" + }, + { + "bbox": [ + 46, + 396, + 546, + 451 + ], + "type": "text", + "content": " Yxy chromaticity diagram (vertical axis " + }, + { + "bbox": [ + 46, + 396, + 546, + 451 + ], + "type": "inline_equation", + "content": "Y" + }, + { + "bbox": [ + 46, + 396, + 546, + 451 + ], + "type": "text", + "content": " for HDR/lumiannce, " + }, + { + "bbox": [ + 46, + 396, + 546, + 451 + ], + "type": "inline_equation", + "content": "xy" + }, + { + "bbox": [ + 46, + 396, + 546, + 451 + ], + "type": "text", + "content": " plane for WCG/color) to see how HDRTV's advance on HDR&WCG volume is recovered, and (2) detailed visuals (yellow and blue boxes) to assess method's recover ability. Note that HDR is dimer here in print version since its large luminance&color container is interpreted by small SDR capacity, and will appear normal if correctly visualized (top Fig.7). Still, from (3) 'Brightness Index' & 'Saturation Level' we know that result from current methods is more dim and desaturated than GT." + } + ] + } + ], + "index": 35, + "angle": 0, + "type": "image_caption" + } + ], + "index": 21 + }, + { + "type": "image", + "bbox": [ + 129, + 317, + 196, + 381 + ], + "blocks": [ + { + "bbox": [ + 129, + 317, + 196, + 381 + ], + "lines": [ + { + "bbox": [ + 129, + 317, + 196, + 381 + ], + "spans": [ + { + "bbox": [ + 129, + 317, + 196, + 381 + ], + "type": "image", + "image_path": "48262841fb43cf3ed8d2494f736ef84a2c466e82584a4f58dc7d2d50579d6bc1.jpg" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 140, + 381, + 170, + 386 + ], + "lines": [ + { + "bbox": [ + 140, + 381, + 170, + 386 + ], + "spans": [ + { + "bbox": [ + 140, + 381, + 170, + 386 + ], + "type": "text", + "content": "1 0" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_caption" + } + ], + "index": 23 + }, + { + "type": "image", + 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+ "bbox": [ + 45, + 498, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 498, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 45, + 498, + 289, + 714 + ], + "type": "text", + "content": "In media industry, the demand of SDR-to-HDRTV upconversion arises when users possess HDR-WCG (high dynamic range-wide color gamut) TVs while most off-the-shelf footage is still in SDR (standard dynamic range). The research community has started tackling this low-level vision task by learning-based approaches. When applied to real SDR, yet, current methods tend to produce dim and desaturated result, making nearly no improvement on viewing experience. Different from other network-oriented methods, we attribute such deficiency to training set (HDR-SDR pair). Consequently, we propose new HDRTV dataset (dubbed HDRTV4K) and new HDR-to-SDR degradation models. Then, it's used to train a luminance-segmented network (LSN) consisting of a global mapping trunk, and two Transformer branches on bright and dark luminance range. We also update assessment criteria by tailored metrics and subjective experiment. Finally, ablation studies are conducted to prove the effectiveness. Our work is available" + } + ] + } + ], + "index": 37 + }, + { + "bbox": [ + 306, + 465, + 536, + 476 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 465, + 536, + 476 + ], + "spans": [ + { + "bbox": [ + 306, + 465, + 536, + 476 + ], + "type": "text", + "content": "at: https://github.com/AndreGuo/HDRTVDM." + } + ] + } + ], + "index": 38 + }, + { + "bbox": [ + 306, + 501, + 387, + 513 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 501, + 387, + 513 + ], + "spans": [ + { + "bbox": [ + 306, + 501, + 387, + 513 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 39 + }, + { + "bbox": [ + 304, + 521, + 545, + 605 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 545, + 605 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 545, + 605 + ], + "type": "text", + "content": "The dynamic range of image is defined as the maximum recorded luminance to the minimum. Larger luminance container endows high dynamic range (HDR) a better expressiveness of scene. In media and film industry, the superiority of HDR is further boosted by advanced electro-optical transfer function (EOTF) e.g. PQ/HLG [2], and wide color-gamut (WCG) RGB primaries e.g. BT.2020 [3]." + } + ] + } + ], + "index": 40 + }, + { + "bbox": [ + 304, + 605, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 605, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 605, + 546, + 713 + ], + "type": "text", + "content": "While WCG-HDR displays are becoming more readily available in consumer market, most commercial footage is still in standard dynamic range (SDR) since WCG-HDR version is yet scarce due to exorbitant production workflow. Hence, there raise the demand of converting vast existing SDR content for HDRTV service. Such SDR may carry irreproducible scenes, but more likely, imperfections brought by old imaging system and transmission. This indicates that SDR-to-HDRTV up-conversion is an ill-posed low-level vi" + } + ] + } + ], + "index": 41 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22231" + } + ] + } + ], + "index": 42 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 95 + ], + "type": "text", + "content": "sion task, and research community has therefore begun involving learning-based methods ([4-9] etc.)." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 97, + 287, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 97, + 287, + 240 + ], + "spans": [ + { + "bbox": [ + 46, + 97, + 287, + 240 + ], + "type": "text", + "content": "Yet, versatile networks they use (§2.1), we find current methods' result dim and desaturated when feeding real SDR images (Fig.1), conflicting with the perceptual motive of SDR-to-HDRTV up-conversion. As reported by CVPR22-1st Workshop on Vision Dataset Understanding [10], most methods are network-oriented and understate the impact of training set. For restoration-like low-level vision, there are 2 ingredients of a training set: the quality of label GT itself, and the GT-to-LQ degradation model (DM) i.e. what the network learns to restore. Such neglect is getting remedied in other low-level vision tasks [11-16], but still pervasive in learning-based SDR-to-HDRTV up-conversion." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 241, + 287, + 420 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 241, + 287, + 420 + ], + "spans": [ + { + "bbox": [ + 46, + 241, + 287, + 420 + ], + "type": "text", + "content": "Not serendipitously, we find dataset the reason why current methods underperform. We exploit several HDRTV-tailored metrics (Tab.4) to assess current training set:(1) by measuring label HDR's extent of " + }, + { + "bbox": [ + 46, + 241, + 287, + 420 + ], + "type": "inline_equation", + "content": "HDR/WCG" + }, + { + "bbox": [ + 46, + 241, + 287, + 420 + ], + "type": "text", + "content": " etc. (Tab.5), we notice that its quality and diversity are inadequate to incentive the network to produce appealing result, (2) via the statistics of degraded SDR, we find current HDR-to-SDR DMs' tendency to exaggeratedly alter the saturation and brightness (see Tab.6) thus network will learn a SDR-to-HDR deterioration. Hence, we propose HDRTV4K dataset (" + }, + { + "bbox": [ + 46, + 241, + 287, + 420 + ], + "type": "inline_equation", + "content": "\\S 3.2" + }, + { + "bbox": [ + 46, + 241, + 287, + 420 + ], + "type": "text", + "content": ") consisting of high-quality and diversified (Fig.4) HDRTV frames as label. Then exploit 3 new HDRTV-to-SDR DMs (" + }, + { + "bbox": [ + 46, + 241, + 287, + 420 + ], + "type": "inline_equation", + "content": "\\S 3.3" + }, + { + "bbox": [ + 46, + 241, + 287, + 420 + ], + "type": "text", + "content": ") avoiding above insufficiency, meanwhile possessing appropriate degradation capability (Tab.6) so the network can learn reasonable restoration ability." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 421, + 287, + 505 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 421, + 287, + 505 + ], + "spans": [ + { + "bbox": [ + 46, + 421, + 287, + 505 + ], + "type": "text", + "content": "Afterwards, we formulate the task as the combination of global mapping on the full luminance range and recovery of low/high luminance range. Correspondingly, we propose Luminance Segmented Network (LSN, §3.1) where a global trunk and two Transformer-style UNet [17] branches are assigned to respectively execute divergent operations required in different segmented luminance ranges (areas)." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 505, + 287, + 565 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 505, + 287, + 565 + ], + "spans": [ + { + "bbox": [ + 46, + 505, + 287, + 565 + ], + "type": "text", + "content": "Lastly, as found by [18, 19], conventional distance-based metrics well-performed in solely-reconstruction task (e.g. denoising) fail for perceptual-motivated HDR reconstruction, we therefore update the assessment criteria with fine-grained metrics (§4.2) and subjective experiment (§4.3) etc." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 565, + 287, + 637 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 565, + 287, + 637 + ], + "spans": [ + { + "bbox": [ + 46, + 565, + 287, + 637 + ], + "type": "text", + "content": "Our contributions are three-fold: (1) Emphasizing & verifying the impact of dataset on SDR-to-HDRTV task, which has long been understated. (2) Exploiting novel HDRTV dataset and HDR-to-SDR degradation models for network to learn. (3) Introducing new problem formulation, and accordingly proposing novel luminance segmented network." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 650, + 138, + 662 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 650, + 138, + 662 + ], + "spans": [ + { + "bbox": [ + 47, + 650, + 138, + 662 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 670, + 204, + 683 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 670, + 204, + 683 + ], + "spans": [ + { + "bbox": [ + 47, + 670, + 204, + 683 + ], + "type": "text", + "content": "2.1. Nomenclature and our scope" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "text", + "content": "Plenty of HDR-related learning-based methods [20] have been proposed, yet, they markedly differ in intended appli" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 72, + 545, + 216 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 216 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 216 + ], + "type": "text", + "content": "cation. As in Tab.1, 'linear HDR' means scene-referred HDR images dedicating to record the linear radiance for graphics application e.g. image-based lighting [21,22], and 'HDRTV' stands for our display-referred WCG-HDR [2] format. Clearly, ① synthesize HDR view on SDR display, ② emphasize dealing with misalignment between multiple SDRs and is designed for new imaging pipeline, while ③ and ④ are all oriented for existing SDR, but for different application. Since many works are proposed before community's clarity on above nomenclature, we classify a method as SDR-to-HDRTV up-conversion (④, our task) if its target HDR is claimed in PQ/HLG EOTF and BT.2020 WCG." + } + ] + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 312, + 223, + 541, + 302 + ], + "blocks": [ + { + "bbox": [ + 312, + 223, + 541, + 302 + ], + "lines": [ + { + "bbox": [ + 312, + 223, + 541, + 302 + ], + "spans": [ + { + "bbox": [ + 312, + 223, + 541, + 302 + ], + "type": "table", + "html": "
Task nameFromTo singleMethods
①HDR-style enhancementsingle SDRenhanced SDRe.g. [23-26]
②multi-exposure HDR imagingmultiple SDRlinear HDRe.g. [27-32]
③SI-HDR*single SDRe.g. [33-37]
④ (ours)iTM* or up-conversionHDRTV frame[4-9] [38-53]
", + "image_path": "f68ccc343aeab65f916031d1d1a41b704338275168b45864a7e6a3966e4da74d.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 317, + 302, + 534, + 312 + ], + "lines": [ + { + "bbox": [ + 317, + 302, + 534, + 312 + ], + "spans": [ + { + "bbox": [ + 317, + 302, + 534, + 312 + ], + "type": "text", + "content": "*: SI-HDR: Single-Image HDR reconstruction, iTM: inverse Tone-Mapping." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 320, + 545, + 342 + ], + "lines": [ + { + "bbox": [ + 306, + 320, + 545, + 342 + ], + "spans": [ + { + "bbox": [ + 306, + 320, + 545, + 342 + ], + "type": "text", + "content": "Table 1. Various learning-based HDR-related tasks. Green/cyan/magenta terms are respectively from [18]/our/ [7] nomenclature." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 354, + 545, + 450 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 354, + 545, + 450 + ], + "spans": [ + { + "bbox": [ + 304, + 354, + 545, + 450 + ], + "type": "text", + "content": "In our scope (4), methods designed their networks with distinct philosophy: Except e.g. semantic-requiring highlight recovery, main process of our task belongs to global mapping resembling image retouching/enhancement. Following this, [7] formulates the task as 3 steps and use " + }, + { + "bbox": [ + 304, + 354, + 545, + 450 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 304, + 354, + 545, + 450 + ], + "type": "text", + "content": " convolution for global part, [43] learn the mapping between small sorted image patches, while [51] conducts explicitly-defined per-pixel mapping using the learned latent vector." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 450, + 545, + 498 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 450, + 545, + 498 + ], + "spans": [ + { + "bbox": [ + 304, + 450, + 545, + 498 + ], + "type": "text", + "content": "Feature modulation is also popular to involve global prior information. Compared with [7], they change: prior's type [47, 49], modulation tensor's shape [48-50], modulation mechanism [9] and modulation's position [50]." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 498, + 545, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 545, + 557 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 545, + 557 + ], + "type": "text", + "content": "To coordinate consecutive video frames, [42] applies 3D convolution with extra temporal-D, [8] take 3 frames as input and deploy multi-frame interaction module at UNet bottleneck. Also, for alignment, [44,49] use deformable convolution whose offset map is predicted by different modules." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 558, + 545, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 545, + 617 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 545, + 617 + ], + "type": "text", + "content": "Due to the resolution discrepancy between SDRTV and HDRTV, some methods [4-6, 46, 48, 52] jointly conduct super-resolution and up-conversion. Also, some methods are assisted by non-learning pre-processing [53] or bypass [43, 45], while [41] is trained for badly-exposed SDR." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 623, + 404, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 623, + 404, + 635 + ], + "spans": [ + { + "bbox": [ + 306, + 623, + 404, + 635 + ], + "type": "text", + "content": "2.2. HDRTV dataset" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": "Diversified networks they use, there're currently only 3 open HDRTV training set (Tab.2). All datasets (including ours) contain HDRTV frames in (D65 white) BT.2020 [3] RGB primaries (gamut), PQ [54] EOTF and 1000nit peak luminance, and SDR counterpart in BT.709 [55] gamut, gamma EOTF and 100nit peak luminance." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22232" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 50, + 70, + 285, + 114 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 285, + 114 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 285, + 114 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 285, + 114 + ], + "type": "table", + "html": "
Dataset (Usage)#pairResolutionHDR format
KAIST [4] ([5,48,52])39840160×160uint16 MATLAB .mat YUV
Zeng20 [6]23229UHDH.265 main10 YUV
HDRTV1K [7] ([9,47,50])123516bit .png RGB
HDRTV4K (ours new)3878HD&UHD16bit lossless .tif RGB
", + "image_path": "118c9e0019274aa44a9169f81d2b15651fd2245ff47716c3ffc0f352ad6f409b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 211, + 237, + 224 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 211, + 237, + 224 + ], + "spans": [ + { + "bbox": [ + 47, + 211, + 237, + 224 + ], + "type": "text", + "content": "2.3. HDRTV-to-SDR degradation model" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 230, + 287, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 230, + 287, + 289 + ], + "spans": [ + { + "bbox": [ + 46, + 230, + 287, + 289 + ], + "type": "text", + "content": "Most methods follow the common practice to degrade label HDR(GT) to input SDR(LQ). Degradation model (DM) matters since it determined what network can learn, and is relatively paid more attention even in SI-HDR [16, 33, 35]. Yet, few discussion is made in SDR-to-HDRTV (Tab.3):" + } + ] + } + ], + "index": 3 + }, + { + "type": "table", + "bbox": [ + 50, + 300, + 285, + 328 + ], + "blocks": [ + { + "bbox": [ + 46, + 122, + 287, + 189 + ], + "lines": [ + { + "bbox": [ + 46, + 122, + 287, + 189 + ], + "spans": [ + { + "bbox": [ + 46, + 122, + 287, + 189 + ], + "type": "text", + "content": "Table 2. Status of different HDRTV dataset: training set part. Our HDR frames sized both HD " + }, + { + "bbox": [ + 46, + 122, + 287, + 189 + ], + "type": "inline_equation", + "content": "(1920\\times 1080)" + }, + { + "bbox": [ + 46, + 122, + 287, + 189 + ], + "type": "text", + "content": " and UHD " + }, + { + "bbox": [ + 46, + 122, + 287, + 189 + ], + "type": "inline_equation", + "content": "(3840\\times 2160)" + }, + { + "bbox": [ + 46, + 122, + 287, + 189 + ], + "type": "text", + "content": " are manually chosen from " + }, + { + "bbox": [ + 46, + 122, + 287, + 189 + ], + "type": "inline_equation", + "content": ">220" + }, + { + "bbox": [ + 46, + 122, + 287, + 189 + ], + "type": "text", + "content": " different videos clips, and encapsulated in lossless (LZW or deflate) TIFF. Others are respectively extracted from only 7 [4]/18 [7] TV demos and 1 graded movie [6], their quality and diversity are quantified later in Tab.5 & Fig.4." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 300, + 285, + 328 + ], + "lines": [ + { + "bbox": [ + 50, + 300, + 285, + 328 + ], + "spans": [ + { + "bbox": [ + 50, + 300, + 285, + 328 + ], + "type": "table", + "html": "
DMYouTubeReinhardother DMs
Usage[4,5,7-9,40,44,46-50,52][6,38,41-43]2446a [45]etc. [51,53,56]
DatasetKAIST & HDRTV1KZeng20
", + "image_path": "fbedbc53198d6a83f4d53ca902111e76855a0e4702f3aad7e7a9174abc0aef0e.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 337, + 287, + 359 + ], + "lines": [ + { + "bbox": [ + 47, + 337, + 287, + 359 + ], + "spans": [ + { + "bbox": [ + 47, + 337, + 287, + 359 + ], + "type": "text", + "content": "Table 3. Current HDRTV-to-SDR degradation models (DMs). 'Dataset' means SDR there is degraded from HDR using that DM." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 374, + 287, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 374, + 287, + 445 + ], + "spans": [ + { + "bbox": [ + 46, + 374, + 287, + 445 + ], + "type": "text", + "content": "Youtube stands for the default conversion YouTube applied to BT.2020/PQ1000 HDR content to produce its SDR-applicable version, Reinhard/2446a means tonemapming HDR to SDR using Reinhard TMO [57]/BT.2446 [58]Method A. [51]/[53]/[56] respectively degrade HDR to SDR by grading/Habel TMO/another learned network." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 447, + 287, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 447, + 287, + 613 + ], + "spans": [ + { + "bbox": [ + 46, + 447, + 287, + 613 + ], + "type": "text", + "content": "In other restoration tasks [11-16], DMs are designed to have proper extent and diversity of degradation so network can learn appropriate restore capability and good generalization. Accordingly, we argue that current DMs are not favorable for training. Specifically, the motive of YouTube is to synthesize HDR view for user possessing only SDR display, it tends to enhance/increase the brightness and saturation so network will, vise-versa, learn to deteriorate/decline them. Also, tone-mapping e.g. Reinhard and 2446a dedicate to preserve as much information from HDR, they have monotonically increasing mapping curves (Fig.5) without highlight clipping, therefore the trained network is not likely to recover much information in over-exposed areas. Above observations are later proven in Tab.6, Fig.1&6 etc." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 628, + 153, + 640 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 628, + 153, + 640 + ], + "spans": [ + { + "bbox": [ + 47, + 628, + 153, + 640 + ], + "type": "text", + "content": "3. Proposed Method" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 59, + 648, + 262, + 659 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 648, + 262, + 659 + ], + "spans": [ + { + "bbox": [ + 59, + 648, + 262, + 659 + ], + "type": "text", + "content": "The overview of our method is illustrated in Fig.2." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 670, + 156, + 681 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 670, + 156, + 681 + ], + "spans": [ + { + "bbox": [ + 47, + 670, + 156, + 681 + ], + "type": "text", + "content": "3.1. Network structure" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 689, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 712 + ], + "type": "text", + "content": "Problem formulation helps the researcher to clarify what degradation the network should restore. Given " + }, + { + "bbox": [ + 47, + 689, + 287, + 712 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 47, + 689, + 287, + 712 + ], + "type": "text", + "content": " the" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 72, + 545, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 95 + ], + "type": "text", + "content": "SDR, y the HDR. Previous methods [7, 47, 49] hypothesis that x and y are from single RAW file of same camera." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 96, + 545, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 96, + 545, + 204 + ], + "spans": [ + { + "bbox": [ + 304, + 96, + 545, + 204 + ], + "type": "text", + "content": "This applies to SDR-HDR synchronous production where " + }, + { + "bbox": [ + 304, + 96, + 545, + 204 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 96, + 545, + 204 + ], + "type": "text", + "content": " is also of high quality, thus up-conversion " + }, + { + "bbox": [ + 304, + 96, + 545, + 204 + ], + "type": "inline_equation", + "content": "(\\mathbf{y} = f(\\mathbf{x}))" + }, + { + "bbox": [ + 304, + 96, + 545, + 204 + ], + "type": "text", + "content": " is only supposed to follow specific style and recover less missing information. However, since our method is for existing SDR, we assume that SDR " + }, + { + "bbox": [ + 304, + 96, + 545, + 204 + ], + "type": "inline_equation", + "content": "(\\mathbf{x})" + }, + { + "bbox": [ + 304, + 96, + 545, + 204 + ], + "type": "text", + "content": " and it imaginary HDR counterpart " + }, + { + "bbox": [ + 304, + 96, + 545, + 204 + ], + "type": "inline_equation", + "content": "(\\mathbf{y})" + }, + { + "bbox": [ + 304, + 96, + 545, + 204 + ], + "type": "text", + "content": " were simultaneously shot by different camera and later pipeline, similar to [18]. The imperfection of SDR pipeline makes existing SDR susceptible to limited latitude, narrow gamut and other degradations " + }, + { + "bbox": [ + 304, + 96, + 545, + 204 + ], + "type": "inline_equation", + "content": "(\\mathbf{x} = d(\\mathbf{y}))" + }, + { + "bbox": [ + 304, + 96, + 545, + 204 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "spans": [ + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": "Some works [7, 35] suppose " + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "inline_equation", + "content": "d(\\cdot)" + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": " are introduced orderly, and assign hierarchical sub-networks to for " + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "inline_equation", + "content": "f(\\cdot)" + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": ". Since cascaded networks are bulky for real application, we instead formulate that specific degradation is more visible in different luminance range. Concretely, over-exposure occurs in high-luminance range in SDR " + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "inline_equation", + "content": "(\\mathbf{x}_h)" + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": ", low-luminance range " + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "inline_equation", + "content": "(\\mathbf{x}_l)" + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": " is more susceptible to noise etc., while the quality of mid-luminance range " + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "inline_equation", + "content": "(\\mathbf{x}_m)" + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": " is relatively higher. Segmented luminance ranges are treated separately by some traditional up-conversion operators ([59] etc.), and here we introduce this idea to our deep neural network (DNN)—LSN:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 335, + 545, + 371 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 335, + 545, + 371 + ], + "spans": [ + { + "bbox": [ + 304, + 335, + 545, + 371 + ], + "type": "text", + "content": "Luminance Segmented Network (LSN) consists of an trunk on full-luminance range " + }, + { + "bbox": [ + 304, + 335, + 545, + 371 + ], + "type": "inline_equation", + "content": "(\\mathbf{x})" + }, + { + "bbox": [ + 304, + 335, + 545, + 371 + ], + "type": "text", + "content": ", and 2 branches respectively on " + }, + { + "bbox": [ + 304, + 335, + 545, + 371 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_l" + }, + { + "bbox": [ + 304, + 335, + 545, + 371 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 335, + 545, + 371 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_h" + }, + { + "bbox": [ + 304, + 335, + 545, + 371 + ], + "type": "text", + "content": " which are segmented by:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 320, + 375, + 545, + 398 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 375, + 545, + 398 + ], + "spans": [ + { + "bbox": [ + 320, + 375, + 545, + 398 + ], + "type": "interline_equation", + "content": "\\mathbf {x} _ {l} = \\max (0, \\frac {t - \\mathbf {x}}{t}), \\mathbf {x} _ {h} = \\max (0, \\frac {\\mathbf {x} - 1}{t} + 1) \\tag {1}", + "image_path": "fbfe692b49274e5746014d25ee40b4c40f07cb158eca7ead91b5a81492638f55.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 402, + 545, + 438 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 402, + 545, + 438 + ], + "spans": [ + { + "bbox": [ + 304, + 402, + 545, + 438 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 402, + 545, + 438 + ], + "type": "inline_equation", + "content": "\\mathbf{x} \\in [0,1]" + }, + { + "bbox": [ + 304, + 402, + 545, + 438 + ], + "type": "text", + "content": ". That is, luminance range lower/higher than threshold " + }, + { + "bbox": [ + 304, + 402, + 545, + 438 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 402, + 545, + 438 + ], + "type": "text", + "content": " (empirically set to 0.05) is linearly mapped to more significant value [0, 1], as in top-right Fig.2." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "text", + "content": "After segmentation, " + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_l" + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_h" + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "text", + "content": " require distinct DNN operation. First, as found by [7] etc. (§2.1), the majority of " + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "inline_equation", + "content": "f(\\cdot)" + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "text", + "content": " belongs to global (pixel-independent) operation similar to image enhancement/retouching. Therefore, we assign 5 cascaded receptive-field-free " + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "text", + "content": " convolution layers on full luminance range " + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "inline_equation", + "content": "(\\mathbf{x})" + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "text", + "content": " as the Global Mapping Trunk. Similar to [7, 9, 47-50], we append a modulation branch (Fig.3 left) to involve " + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "inline_equation", + "content": "\\mathbf{x}" + }, + { + "bbox": [ + 304, + 438, + 545, + 533 + ], + "type": "text", + "content": "'s global prior into DNN's decision." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 533, + 545, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 533, + 545, + 665 + ], + "spans": [ + { + "bbox": [ + 304, + 533, + 545, + 665 + ], + "type": "text", + "content": "On the other hand, " + }, + { + "bbox": [ + 304, + 533, + 545, + 665 + ], + "type": "inline_equation", + "content": "f(\\cdot)" + }, + { + "bbox": [ + 304, + 533, + 545, + 665 + ], + "type": "text", + "content": " still involves non-global restoration where local semantic should be aggregated. Specifically, DNN's mission in " + }, + { + "bbox": [ + 304, + 533, + 545, + 665 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_l" + }, + { + "bbox": [ + 304, + 533, + 545, + 665 + ], + "type": "text", + "content": " is similar to denoising and low-light enhancement, while that in " + }, + { + "bbox": [ + 304, + 533, + 545, + 665 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_h" + }, + { + "bbox": [ + 304, + 533, + 545, + 665 + ], + "type": "text", + "content": " resembles image inpainting (claimed by [60, 61]) where lost content is hallucinated. These low-level vision tasks require larger receptive-filed, we hence choose Transformer blocks [17] specializing long-distance dependency, and arranged them as encoder-decoder with skip-connections (UNet). Transformer-style UNet branches on " + }, + { + "bbox": [ + 304, + 533, + 545, + 665 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_l" + }, + { + "bbox": [ + 304, + 533, + 545, + 665 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 533, + 545, + 665 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_h" + }, + { + "bbox": [ + 304, + 533, + 545, + 665 + ], + "type": "text", + "content": " share the same structure but different parameters. The detail of LSN is depicted in Fig.3." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 671, + 417, + 682 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 671, + 417, + 682 + ], + "spans": [ + { + "bbox": [ + 306, + 671, + 417, + 682 + ], + "type": "text", + "content": "3.2. HDRTV4K dataset" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 306, + 689, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 545, + 712 + ], + "type": "text", + "content": "After designing LSN, we need better training set to unleash its capability. We start with the quality of label HDR:" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22233" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 75, + 68, + 521, + 240 + ], + "blocks": [ + { + "bbox": [ + 75, + 68, + 521, + 240 + ], + "lines": [ + { + "bbox": [ + 75, + 68, + 521, + 240 + ], + "spans": [ + { + "bbox": [ + 75, + 68, + 521, + 240 + ], + "type": "image", + "image_path": "c20142dbb6852e2a501f5d763c1255840dfdff6d08db00ab4631e52264969431.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 249, + 547, + 297 + ], + "lines": [ + { + "bbox": [ + 46, + 249, + 547, + 297 + ], + "spans": [ + { + "bbox": [ + 46, + 249, + 547, + 297 + ], + "type": "text", + "content": "Figure 2. Overview of this work. Our Luminance Segmented Network (LSN, §3.1) is designed based on novel problem formulation, then supervisedly trained with label HDR from the proposed HDRTV4K dataset (§3.2), and input SDR degraded by novel degradation models (DMs, §3.3). The major concerns of our LSN, HDRTV4K dataset, and DMs are respectively: recovering dark&bright areas, improving the quality (Tab.5) and diversity (Fig.4) of label GT-HDR, and ensuring the LQ-SDR is with proper style and degradation (Tab.6 & Fig.5)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 48, + 312, + 287, + 437 + ], + "blocks": [ + { + "bbox": [ + 48, + 312, + 287, + 437 + ], + "lines": [ + { + "bbox": [ + 48, + 312, + 287, + 437 + ], + "spans": [ + { + "bbox": [ + 48, + 312, + 287, + 437 + ], + "type": "image", + "image_path": "5b640231ea36dc6f53047156974a794bcfefddbfa43f39bbe1036865df08dd0c.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 444, + 288, + 500 + ], + "lines": [ + { + "bbox": [ + 46, + 444, + 288, + 500 + ], + "spans": [ + { + "bbox": [ + 46, + 444, + 288, + 500 + ], + "type": "text", + "content": "Figure 3. Detailed structure of LSN modules. 'k3s1n16' means convolution layer with " + }, + { + "bbox": [ + 46, + 444, + 288, + 500 + ], + "type": "inline_equation", + "content": "3 \\times 3" + }, + { + "bbox": [ + 46, + 444, + 288, + 500 + ], + "type": "text", + "content": " kernel, stride=1, out nc=16, and 'FC32' is fully-connected layer with out nc=32. We deploy only 1 Transformer block [17] at each en/decode level of UNet to lighten LSN (#param=325k) for its application on 4K resolution." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 521, + 287, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 521, + 287, + 557 + ], + "spans": [ + { + "bbox": [ + 46, + 521, + 287, + 557 + ], + "type": "text", + "content": "Motivation. In §1, we argue the quality of label HDR in current datasets. Here, it is assessed from 3 aspects by 10 metrics in Tab.4, based on following considerations:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 45, + 558, + 287, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 558, + 287, + 641 + ], + "spans": [ + { + "bbox": [ + 45, + 558, + 287, + 641 + ], + "type": "text", + "content": "First, greater extent of HDR/WCG stands more probability for network to learn pixel in advanced WCG-HDR volume beyond SDR's capability. Meanwhile, higher intra-frame diversity means better generalization the network can learn within small batch-size, i.e. bigger SI/CF/stdL indicate more diversified high-frequency patterns/richer color volume/greater luminance contrast for network to learn." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 642, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 288, + 715 + ], + "type": "text", + "content": "Also, style distillation [51] has become a major contributor to method's performance in similar task e.g. image enhancement/retouching, we therefore append 3 metrics quantifying the overall-style of label HDR. Note that network's learned style will be, substantially, affected more by degradation model (DM) which will be discussed later." + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 307, + 312, + 545, + 517 + ], + "blocks": [ + { + "bbox": [ + 307, + 312, + 545, + 517 + ], + "lines": [ + { + "bbox": [ + 307, + 312, + 545, + 517 + ], + "spans": [ + { + "bbox": [ + 307, + 312, + 545, + 517 + ], + "type": "table", + "html": "
Metrics on the extent of HDR/WCG ↓
FHLPFraction of HighLight Pixel: Spatial ratio of ‘highlight’ pixel i.e. whose normalized luminance Y = 0.2627R + 0.6780G +0.0593B > 0.01 (100nit, SDR's peak luminance.)
EHLExtent of HighLight: Average pixel (i) distance between the luminance of HDR and its clip-to-100nit version1: 1/n ∑i=1n√[Yi - clip(Yi)]2, clip(x) = clamp(x, 0, 0.01)
FWGPFraction of Wide-Gamut Pixel: Spatial ratio of WCG pixel i.e. whose [x, y] coordinates fall inside BT.2020 but outside SDR's BT.709 gamut in Yxy chromaticity diagram.
EWGExtent of Wide-Gamut [62]: Average pixel-distance between WCG-HDR and its gamut-hard-clipped [63] version2: 1/n ∑i=1n||Si - HC(Si)||2, S = [X, Y, Z]T
Metrics on intra-frame diversity ↓ (all variance-based)
SISpatial Information: Standard deviation over the pixels of Sobel-filtered frame, defined in Annex 6 of [64].
CFColorFulness: Defined in [65].
stdLstandard deviation of Luminance, over all pixels of a frame.
Metrics on overall-style ↓
ASLAverage Saturation Level: Normalized pixel-average length of HDR chrominance component C = [Ct, Cp]T [66]: 1/n ∑i=1n||Ci||2, C ∈ [−0.5, 0.5]
ALLAverage Luminance Level: Pixel-average of Y in FHLP.
HDRBQHDR Brightness Quantification [67], visual salience involved.
", + "image_path": "28ebdfdc556a000988b8326015d02dd28170e7765a289d1cdd5479294f0e440c.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 312, + 517, + 535, + 523 + ], + "lines": [ + { + "bbox": [ + 312, + 517, + 535, + 523 + ], + "spans": [ + { + "bbox": [ + 312, + 517, + 535, + 523 + ], + "type": "text", + "content": "1: EHL is to compensate cases e.g. an all-101 n it HDR frame with " + }, + { + "bbox": [ + 312, + 517, + 535, + 523 + ], + "type": "inline_equation", + "content": "100\\%" + }, + { + "bbox": [ + 312, + 517, + 535, + 523 + ], + "type": "text", + "content": " FHLP but less extent of highlight." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_footnote" + }, + { + "bbox": [ + 313, + 524, + 466, + 531 + ], + "lines": [ + { + "bbox": [ + 313, + 524, + 466, + 531 + ], + "spans": [ + { + "bbox": [ + 313, + 524, + 466, + 531 + ], + "type": "text", + "content": "2: You can find the formulation of gamut hard-clipping " + }, + { + "bbox": [ + 313, + 524, + 466, + 531 + ], + "type": "inline_equation", + "content": "(HC(\\cdot))" + }, + { + "bbox": [ + 313, + 524, + 466, + 531 + ], + "type": "text", + "content": " at Eq.5." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 541, + 545, + 575 + ], + "lines": [ + { + "bbox": [ + 306, + 541, + 545, + 575 + ], + "spans": [ + { + "bbox": [ + 306, + 541, + 545, + 575 + ], + "type": "text", + "content": "Table 4. The quality and diversity of label HDR is measured from 3 aspects by 10 metrics above (both in positive correlation), results are in Tab.5. See supplementary material for full illustration." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 602, + 547, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 602, + 547, + 675 + ], + "spans": [ + { + "bbox": [ + 304, + 602, + 547, + 675 + ], + "type": "text", + "content": "Our work. Statistics in Tab.5 confirms the deficiency of current datasets, i.e. lower intra-frame diversity, and most importantly, less extent of HDR/WCG preventing their network from producing true HDR-WCG volume. To this end, we propose a new dataset HDRTV4K consisting of 3878 BT.2020/PQ1000 (Tab.2) HDR frames with higher quality." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "type": "text", + "content": "Specifically, these frames are manually extracted and aligned from various open content ([68-72] etc.) with greater extent of HDR/WCG, higher intra-frame diversity" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22234" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 58, + 70, + 537, + 140 + ], + "blocks": [ + { + "bbox": [ + 58, + 70, + 537, + 140 + ], + "lines": [ + { + "bbox": [ + 58, + 70, + 537, + 140 + ], + "spans": [ + { + "bbox": [ + 58, + 70, + 537, + 140 + ], + "type": "table", + "html": "
Metrics \nDatasetExtent of HDRExtent of WCGIntra-frame diversityOverall-style
FHLPEHLFWGPEWGSICFstdLASLALLHDRBQ
KAIST [4]1.52500.20255.47710.11041.93725.94850.95978.908717.28541.8597
Zeng20 [6]0.01970.00120.47920.00340.12314.20480.31463.80616.08050.3781
HDRTV1K [7]1.28430.19712.90890.16332.237811.07221.800610.941415.16262.7970
HDRTV4K (ours)5.30830.95952.63690.51233.550810.58823.48379.827421.19965.1593
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The thumbnails of our dataset and its frame distribution comparison are visualized in Fig.4." + } + ] + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 50, + 262, + 127, + 308 + ], + "blocks": [ + { + "bbox": [ + 50, + 262, + 127, + 308 + ], + "lines": [ + { + "bbox": [ + 50, + 262, + 127, + 308 + ], + "spans": [ + { + "bbox": [ + 50, + 262, + 127, + 308 + ], + "type": "image", + "image_path": "9631aecb115177faf01a396f7ed46936d73d2152aff7e94ae585c283bfd011c0.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 129, + 262, + 206, + 308 + ], + "blocks": [ + { + "bbox": [ + 129, + 262, + 206, + 308 + ], + "lines": [ + { + "bbox": [ + 129, + 262, + 206, + 308 + ], + "spans": [ + { + "bbox": [ + 129, + 262, + 206, + 308 + ], + "type": "image", + "image_path": "15e19d47db101494bc31b9a7b7927b4ceefcf491276f3a52d4139a0291074d6a.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 208, + 262, + 287, + 308 + ], + "blocks": [ + { + "bbox": [ + 208, + 262, + 287, + 308 + ], + "lines": [ + { + "bbox": [ + 208, + 262, + 287, + 308 + ], + "spans": [ + { + "bbox": [ + 208, + 262, + 287, + 308 + ], + "type": "image", + "image_path": "e326d4d45e45a1ebcad6839aa8d6e78dbd503cf9bef3ee84966bf8f7c813ba5e.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 50, + 308, + 127, + 353 + ], + "blocks": [ + { + "bbox": [ + 50, + 308, + 127, + 353 + ], + "lines": [ + { + "bbox": [ + 50, + 308, + 127, + 353 + ], + "spans": [ + { + "bbox": [ + 50, + 308, + 127, + 353 + ], + "type": "image", + "image_path": "e09ffe26fd4c0512b298d9c93d30156daa2f268d1c1737fa4d227caa0a741a71.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 129, + 308, + 206, + 353 + ], + "blocks": [ + { + "bbox": [ + 129, + 308, + 206, + 353 + ], + "lines": [ + { + "bbox": [ + 129, + 308, + 206, + 353 + ], + "spans": [ + { + "bbox": [ + 129, + 308, + 206, + 353 + ], + "type": "image", + "image_path": "13c9957352cd0340e6dfc3ec5700ff0a18d138bd895ef1ae4aa9fa68d2b3b77a.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 208, + 308, + 287, + 353 + ], + "blocks": [ + { + "bbox": [ + 208, + 308, + 287, + 353 + ], + "lines": [ + { + "bbox": [ + 208, + 308, + 287, + 353 + ], + "spans": [ + { + "bbox": [ + 208, + 308, + 287, + 353 + ], + "type": "image", + "image_path": "6e2507c9eabfb26de4419477ce8d581c615e9b8ba1b49ad20306260bc67c0046.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 49, + 356, + 286, + 483 + ], + "blocks": [ + { + "bbox": [ + 49, + 356, + 286, + 483 + ], + "lines": [ + { + "bbox": [ + 49, + 356, + 286, + 483 + ], + "spans": [ + { + "bbox": [ + 49, + 356, + 286, + 483 + ], + "type": "image", + "image_path": "766f292c0519f767e5c7346e87b981493f6652dc39fba0a56a0be88280dd28e1.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 49, + 484, + 128, + 529 + ], + "blocks": [ + { + "bbox": [ + 49, + 484, + 128, + 529 + ], + "lines": [ + { + "bbox": [ + 49, + 484, + 128, + 529 + ], + "spans": [ + { + "bbox": [ + 49, + 484, + 128, + 529 + ], + "type": "image", + "image_path": "09a9ba62bf1a4dd233389f8825951695bca6a5f2024aaa03e03b0832c0969522.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 538, + 287, + 615 + ], + "lines": [ + { + "bbox": [ + 46, + 538, + 287, + 615 + ], + "spans": [ + { + "bbox": [ + 46, + 538, + 287, + 615 + ], + "type": "text", + "content": "Figure 4. Diversity comparison: our HDRTV4K dataset vs. others. Here, each 2D-coordinate is the projection of single frame's 10-D vector (containing 10 metrics from Tab.5) using t-SNE [73] " + }, + { + "bbox": [ + 46, + 538, + 287, + 615 + ], + "type": "inline_equation", + "content": "(\\mathrm{exg} = 3,\\mathrm{prep} = 50)" + }, + { + "bbox": [ + 46, + 538, + 287, + 615 + ], + "type": "text", + "content": ". We also depict thumbnail HDR frames from our dataset (1)-(9) with their corresponding 2D-coordinates highlighted with gray circle. As seen, our dataset provides wider frame distribution i.e. more diversified scenes for network to learn." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 129, + 484, + 206, + 529 + ], + "blocks": [ + { + "bbox": [ + 129, + 484, + 206, + 529 + ], + "lines": [ + { + "bbox": [ + 129, + 484, + 206, + 529 + ], + "spans": [ + { + "bbox": [ + 129, + 484, + 206, + 529 + ], + "type": "image", + "image_path": "0427cd22e78ba264d2a946b0eff5e427922b93fbf097087796be0b81a239f0f6.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 208, + 484, + 286, + 529 + ], + "blocks": [ + { + "bbox": [ + 208, + 484, + 286, + 529 + ], + "lines": [ + { + "bbox": [ + 208, + 484, + 286, + 529 + ], + "spans": [ + { + "bbox": [ + 208, + 484, + 286, + 529 + ], + "type": "image", + "image_path": "661d59522178f90cecde33e03b82d01fae034286b370d19d3d447646eae3f3df.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 47, + 635, + 186, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 635, + 186, + 647 + ], + "spans": [ + { + "bbox": [ + 47, + 635, + 186, + 647 + ], + "type": "text", + "content": "3.3. New degradation models" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "type": "text", + "content": "After obtaining label HDR with higher quality, we focus on degradation model (DM) which determines the restoration network will learn. As claimed in §2.3, current DMs fail for undue saturation & brightness change and meager over-exposure degradation, which are verified by ALL," + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 204, + 545, + 265 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 204, + 545, + 265 + ], + "spans": [ + { + "bbox": [ + 305, + 204, + 545, + 265 + ], + "type": "text", + "content": "ASL, FOEP in Tab.6. That is, YouTube/2446a tend to increase/decline both ASL & ALL so network will learn to simultaneously de/over-saturate and unduly-darken/brighten. Also, 2446a and Rienhard provide small FOEP which is adverse to network's over-exposure hallucination ability." + } + ] + } + ], + "index": 16 + }, + { + "type": "table", + "bbox": [ + 308, + 274, + 545, + 351 + ], + "blocks": [ + { + "bbox": [ + 46, + 148, + 547, + 183 + ], + "lines": [ + { + "bbox": [ + 46, + 148, + 547, + 183 + ], + "spans": [ + { + "bbox": [ + 46, + 148, + 547, + 183 + ], + "type": "text", + "content": "Table 5. Quality of label HDR frames, manifested in the frame-average of 10 metrics from Tab.4. Greater extent of HDR/WCG encourages network to produce more pixels in non-SDR volume, while intra-frame diversity is helpful for network's generalization ability. All numbers are in percentage (\\%), and we highlight those the most favorable to training. Their diversity is further demonstrated in Fig.4." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 308, + 274, + 545, + 351 + ], + "lines": [ + { + "bbox": [ + 308, + 274, + 545, + 351 + ], + "spans": [ + { + "bbox": [ + 308, + 274, + 545, + 351 + ], + "type": "table", + "html": "
SDR Degraded byFOEP1ALLASL2SICF
CurrentDM2446a0.1815.8803.6246.0416.573
Reinhard1.26421.8877.44214.14712.568
YouTube5.43928.21914.64118.54525.225
OursDM2446c+GM1.73911.66910.18312.50319.391
HC+GM4.25214.06210.37715.09020.146
OCIO21.58018.8879.97713.57818.052
criteria↑: better whenkept3samllerkept--
Source HDR (Tab.5)45.30821.2009.8273.55110.588
", + "image_path": "c7cc6ea70a41559553ac6c2adb8a2f1774c744f14bc4d1c7430387b8ecf5ca2f.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 313, + 351, + 523, + 358 + ], + "lines": [ + { + "bbox": [ + 313, + 351, + 523, + 358 + ], + "spans": [ + { + "bbox": [ + 313, + 351, + 523, + 358 + ], + "type": "text", + "content": "1: FOEP: Fraction of Over-exposed Pixels: Spatial ratio of pixels whose normalized luminance " + }, + { + "bbox": [ + 313, + 351, + 523, + 358 + ], + "type": "inline_equation", + "content": "Y = 1" + }, + { + "bbox": [ + 313, + 351, + 523, + 358 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "table_footnote" + }, + { + "bbox": [ + 313, + 358, + 525, + 365 + ], + "lines": [ + { + "bbox": [ + 313, + 358, + 525, + 365 + ], + "spans": [ + { + "bbox": [ + 313, + 358, + 525, + 365 + ], + "type": "text", + "content": "2: For SDR, ASL is also calculated as Tab.4, but with " + }, + { + "bbox": [ + 313, + 358, + 525, + 365 + ], + "type": "inline_equation", + "content": "\\mathbf{C} = [C_h, C_r]^{\\mathrm{T}}" + }, + { + "bbox": [ + 313, + 358, + 525, + 365 + ], + "type": "text", + "content": " [55] rather " + }, + { + "bbox": [ + 313, + 358, + 525, + 365 + ], + "type": "inline_equation", + "content": "[C_t, C_p]^{\\mathrm{T}}" + }, + { + "bbox": [ + 313, + 358, + 525, + 365 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "table_footnote" + }, + { + "bbox": [ + 313, + 365, + 520, + 373 + ], + "lines": [ + { + "bbox": [ + 313, + 365, + 520, + 373 + ], + "spans": [ + { + "bbox": [ + 313, + 365, + 520, + 373 + ], + "type": "text", + "content": "3: Assuming that HDR's highlight " + }, + { + "bbox": [ + 313, + 365, + 520, + 373 + ], + "type": "inline_equation", + "content": "(>100n it)" + }, + { + "bbox": [ + 313, + 365, + 520, + 373 + ], + "type": "text", + "content": " part should all be clipped to 1 (over-exposure) in SDR." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "table_footnote" + }, + { + "bbox": [ + 313, + 373, + 536, + 381 + ], + "lines": [ + { + "bbox": [ + 313, + 373, + 536, + 381 + ], + "spans": [ + { + "bbox": [ + 313, + 373, + 536, + 381 + ], + "type": "text", + "content": "4: Gray means container discrepancy i.e. HDR/SDR's metric should be different even for well-degraded SDR." + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 392, + 545, + 470 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 392, + 545, + 470 + ], + "spans": [ + { + "bbox": [ + 305, + 392, + 545, + 470 + ], + "type": "text", + "content": "Table 6. Given label HDR from HDRTV4K dataset, SDR's statistics alters when degraded by different DMs. Results are in percentage, we mark those unfavorable / neutral / beneficial for training based on the observation in §2.3 & 3.3. As seen, our DMs provide adequate over-exposure (FOEP) and no undue brightness (ALL) & saturation (ASL) change for network to learn. Example of different degraded SDR can be found in supplementary material." + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 305, + 483, + 545, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 483, + 545, + 506 + ], + "spans": [ + { + "bbox": [ + 305, + 483, + 545, + 506 + ], + "type": "text", + "content": "This motivate us to utilize/exploit new DMs with proper extent of degradation and no deficiencies on style:" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 305, + 506, + 545, + 627 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 305, + 506, + 545, + 542 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 506, + 545, + 542 + ], + "spans": [ + { + "bbox": [ + 305, + 506, + 545, + 542 + ], + "type": "text", + "content": "(1) OCIO2: OpenColorIO v2 [74], commercial method from BT.2020/PQ1000 HDR container to BT.709/gamma SDR, implemented by 3D look-up table (LUT) here." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "spans": [ + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "text", + "content": "(2) " + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "inline_equation", + "content": "2446c + GM" + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "text", + "content": ": Modified Method " + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "text", + "content": " tone-mapping from Rec.2446 [58], followed by Gamut Mapping. Here, each HDR encoded value " + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "inline_equation", + "content": "\\mathbf{E}' = [R', G', B']^{\\mathrm{T}}" + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "text", + "content": " (we use superscript ' for non-linearity) is linearize to " + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "inline_equation", + "content": "\\mathbf{E}" + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "text", + "content": " by PQ EOTF, and then transferred to " + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "inline_equation", + "content": "Yxy" + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "text", + "content": ". Then " + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "inline_equation", + "content": "Y" + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "text", + "content": " is globally mapped to " + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "inline_equation", + "content": "Y_{SDR}" + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "text", + "content": ", while " + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "inline_equation", + "content": "xy" + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "text", + "content": " are kept and then recomposed with " + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "inline_equation", + "content": "Y_{SDR}" + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "text", + "content": " to form " + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "inline_equation", + "content": "\\mathbf{E}_{SDR}" + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "text", + "content": " and later " + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "inline_equation", + "content": "\\mathbf{E}_{SDR_{709}}'" + }, + { + "bbox": [ + 305, + 542, + 545, + 627 + ], + "type": "text", + "content": ". Our modification lies in:" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 340, + 633, + 545, + 646 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 340, + 633, + 545, + 646 + ], + "spans": [ + { + "bbox": [ + 340, + 633, + 545, + 646 + ], + "type": "interline_equation", + "content": "Y _ {S D R} = \\operatorname {c l a m p} \\left(T M _ {2 4 4 6 c} (Y), 0, 1 0 0 \\text {n i t}\\right) \\tag {2}", + "image_path": "ab12bac80f65df1e94d1a2beca18aa12843768650f5865f4d13a5f148c71fb1b.jpg" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "inline_equation", + "content": "TM_{2446c}(\\cdot)\\in [0,118\\text{nit} ]" + }, + { + "bbox": [ + 304, + 653, + 545, + 713 + ], + "type": "text", + "content": " is the original global mapping curve in [58] (Fig.5 dashed line). That is, we clip the original output range to 100nit to produce more FOEP, rather than linear-scale which share same shortcoming with DM 2446a. Gamut mapping will be introduced in Eq.4&5." + } + ] + } + ], + "index": 29 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22235" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 69, + 289, + 175 + ], + "blocks": [ + { + "bbox": [ + 48, + 69, + 289, + 175 + ], + "lines": [ + { + "bbox": [ + 48, + 69, + 289, + 175 + ], + "spans": [ + { + "bbox": [ + 48, + 69, + 289, + 175 + ], + "type": "image", + "image_path": "6d83014d1cad1b53747f3913acb162ca84587da10788be6ba467824e474603e8.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 182, + 287, + 237 + ], + "lines": [ + { + "bbox": [ + 46, + 182, + 287, + 237 + ], + "spans": [ + { + "bbox": [ + 46, + 182, + 287, + 237 + ], + "type": "text", + "content": "Figure 5. One of the key ideas of the proposed degradation models (DMs, §3.3): HDR's luminance (left) and color (right) volume should be properly clipped, so that network will learn corresponding restoration. Another idea is keeping a sensible brightness & color/saturation style during degradation, please refer to Tab.6." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 257, + 288, + 305 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 257, + 288, + 305 + ], + "spans": [ + { + "bbox": [ + 46, + 257, + 288, + 305 + ], + "type": "text", + "content": "(3) " + }, + { + "bbox": [ + 46, + 257, + 288, + 305 + ], + "type": "inline_equation", + "content": "HC + GM" + }, + { + "bbox": [ + 46, + 257, + 288, + 305 + ], + "type": "text", + "content": ": Hard-Clipping and Gamut Mapping, a container conversion with SDR part unchanged and all luminance/color in HDR/WCG volume hard-clipped. For luminance part, we clip all " + }, + { + "bbox": [ + 46, + 257, + 288, + 305 + ], + "type": "inline_equation", + "content": ">100\\mathrm{nit}" + }, + { + "bbox": [ + 46, + 257, + 288, + 305 + ], + "type": "text", + "content": " pixels to " + }, + { + "bbox": [ + 46, + 257, + 288, + 305 + ], + "type": "inline_equation", + "content": "100\\mathrm{nit}" + }, + { + "bbox": [ + 46, + 257, + 288, + 305 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 72, + 312, + 287, + 326 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 72, + 312, + 287, + 326 + ], + "spans": [ + { + "bbox": [ + 72, + 312, + 287, + 326 + ], + "type": "interline_equation", + "content": "\\mathbf {E} _ {S D R} = 1 0 0 \\times \\operatorname {c l a m p} (\\mathbf {E}, 0, 0. 0 1), \\quad \\mathbf {E} \\in [ 0, 1 ] \\tag {3}", + "image_path": "5837193c83a074391c51b03a75ebd70d0f61ae7889f1ecbab4f019fab3e015df.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 333, + 287, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 333, + 287, + 369 + ], + "spans": [ + { + "bbox": [ + 46, + 333, + 287, + 369 + ], + "type": "text", + "content": "where 0.01 correspond to " + }, + { + "bbox": [ + 46, + 333, + 287, + 369 + ], + "type": "inline_equation", + "content": "100\\mathrm{nit}" + }, + { + "bbox": [ + 46, + 333, + 287, + 369 + ], + "type": "text", + "content": " in normalized linear HDR " + }, + { + "bbox": [ + 46, + 333, + 287, + 369 + ], + "type": "inline_equation", + "content": "\\mathbf{E}" + }, + { + "bbox": [ + 46, + 333, + 287, + 369 + ], + "type": "text", + "content": " (PQ container), and " + }, + { + "bbox": [ + 46, + 333, + 287, + 369 + ], + "type": "inline_equation", + "content": "\\times 100" + }, + { + "bbox": [ + 46, + 333, + 287, + 369 + ], + "type": "text", + "content": " is to adapt the container discrepancy (nominal peak 1 means " + }, + { + "bbox": [ + 46, + 333, + 287, + 369 + ], + "type": "inline_equation", + "content": "10000\\mathrm{nit} \\rightarrow 100\\mathrm{nit}" + }, + { + "bbox": [ + 46, + 333, + 287, + 369 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 369, + 287, + 404 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 369, + 287, + 404 + ], + "spans": [ + { + "bbox": [ + 46, + 369, + 287, + 404 + ], + "type": "text", + "content": "So far, " + }, + { + "bbox": [ + 46, + 369, + 287, + 404 + ], + "type": "inline_equation", + "content": "\\mathbf{E}_{SDR}" + }, + { + "bbox": [ + 46, + 369, + 287, + 404 + ], + "type": "text", + "content": " is still in BT.2020 gamut. Therefore, we append Gamut Mapping (+GM): First, Color Space Transform (CST) from BT.2020 RGB primaries to BT.709:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 411, + 287, + 433 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 411, + 287, + 433 + ], + "spans": [ + { + "bbox": [ + 53, + 411, + 287, + 433 + ], + "type": "interline_equation", + "content": "\\mathbf {E} _ {S D R O O G} = \\mathbf {M} \\mathbf {E} _ {S D R}, \\mathbf {M} = \\left[ \\begin{array}{l l l} 1. 6 6 0 5 & 0. 5 8 7 6 & - 0. 0 7 2 8 \\\\ - 0. 1 2 4 6 & 1. 1 3 2 9 & 0. 0 0 8 3 \\\\ - 0. 0 1 8 2 & - 0. 1 0 0 6 & 1. 1 1 8 7 \\end{array} \\right] \\tag {4}", + "image_path": "f572610b8f179af9bd7dfc31f948bf6483134add4d3b6a1c152e2da31be3650c.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 438, + 287, + 511 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 438, + 287, + 511 + ], + "spans": [ + { + "bbox": [ + 46, + 438, + 287, + 511 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 438, + 287, + 511 + ], + "type": "inline_equation", + "content": "\\exists \\mathbf{E}_{SDR_{OOG}} \\notin [0,1]" + }, + { + "bbox": [ + 46, + 438, + 287, + 511 + ], + "type": "text", + "content": " i.e. out-of-gamut (OOG) pixels will fall outside valid range after CST to a narrower volume. Dealing OOG is the key of gamut mapping, instead of soft-mapping [63, 75] (used by [76]'s DM) preserving as much WCG information to narrow gamut, we use hard-clipping which clips all OOG pixels to BT.709 boundary (Fig.5):" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 92, + 520, + 287, + 533 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 92, + 520, + 287, + 533 + ], + "spans": [ + { + "bbox": [ + 92, + 520, + 287, + 533 + ], + "type": "interline_equation", + "content": "\\mathbf {E} _ {S D R _ {7 0 9}} = \\operatorname {c l a m p} \\left(\\mathbf {E} _ {S D R _ {O O G}}, 0, 1\\right) \\tag {5}", + "image_path": "69bceeb2d169ad1baa740c425ca2b3c7af15e1060fb848321adb1aeaca213485.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 540, + 287, + 575 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 540, + 287, + 575 + ], + "spans": [ + { + "bbox": [ + 46, + 540, + 287, + 575 + ], + "type": "text", + "content": "Then, " + }, + { + "bbox": [ + 46, + 540, + 287, + 575 + ], + "type": "inline_equation", + "content": "\\mathbf{E}_{SDR_{709}}" + }, + { + "bbox": [ + 46, + 540, + 287, + 575 + ], + "type": "text", + "content": " from both (2) & (3) is converted to SDR encoded value " + }, + { + "bbox": [ + 46, + 540, + 287, + 575 + ], + "type": "inline_equation", + "content": "\\mathbf{E}_{SDR_{709}}^{\\prime}" + }, + { + "bbox": [ + 46, + 540, + 287, + 575 + ], + "type": "text", + "content": " by BT.1886 [77] OETF (approximate gamma2.22) and 8bit JPEG compression with QF=80." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 575, + 287, + 659 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 575, + 287, + 659 + ], + "spans": [ + { + "bbox": [ + 46, + 575, + 287, + 659 + ], + "type": "text", + "content": "Our philosophy lies in that most explicitly-defined operations can be streamlined to a clamp(\\cdot) function: Only when luminance and color volume is clipped, multiple-to-one mapping occurs in DM, the trained network can learn corresponding recovery ability. Also, ALL & ASL in Tab.6 show that these DMs produce SDR with reasonable style, ensuring that network will not learn style deterioration." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 670, + 128, + 682 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 670, + 128, + 682 + ], + "spans": [ + { + "bbox": [ + 47, + 670, + 128, + 682 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "text", + "content": "Training detail. For each HDR frame randomly resized to " + }, + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "inline_equation", + "content": "[0.25\\mathrm{x},1\\mathrm{x}]" + }, + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "text", + "content": ", 6 patches sized " + }, + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "inline_equation", + "content": "600\\times 600" + }, + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "text", + "content": " are cropped from" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "type": "text", + "content": "random position. Then, each of " + }, + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "type": "inline_equation", + "content": "6 \\times 3878" + }, + { + "bbox": [ + 305, + 72, + 545, + 108 + ], + "type": "text", + "content": " HDR patches is degraded to SDR by 1 of the 3 proposed DMs in equal probability, and again stored in JPEG with QF=75." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 108, + 545, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 108, + 545, + 144 + ], + "spans": [ + { + "bbox": [ + 305, + 108, + 545, + 144 + ], + "type": "text", + "content": "All LSN parameters are Kaiming initialized, optimized by " + }, + { + "bbox": [ + 305, + 108, + 545, + 144 + ], + "type": "inline_equation", + "content": "l_{1}" + }, + { + "bbox": [ + 305, + 108, + 545, + 144 + ], + "type": "text", + "content": " loss and AdaM with learning rate starting from " + }, + { + "bbox": [ + 305, + 108, + 545, + 144 + ], + "type": "inline_equation", + "content": "2 \\times 10^{-4}" + }, + { + "bbox": [ + 305, + 108, + 545, + 144 + ], + "type": "text", + "content": " and decaying to half every " + }, + { + "bbox": [ + 305, + 108, + 545, + 144 + ], + "type": "inline_equation", + "content": "10^{5}" + }, + { + "bbox": [ + 305, + 108, + 545, + 144 + ], + "type": "text", + "content": " iter till " + }, + { + "bbox": [ + 305, + 108, + 545, + 144 + ], + "type": "inline_equation", + "content": "4 \\times 10^{5}" + }, + { + "bbox": [ + 305, + 108, + 545, + 144 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 153, + 452, + 166 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 153, + 452, + 166 + ], + "spans": [ + { + "bbox": [ + 305, + 153, + 452, + 166 + ], + "type": "text", + "content": "4.1. Criteria and configuration" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 171, + 545, + 315 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 171, + 545, + 315 + ], + "spans": [ + { + "bbox": [ + 304, + 171, + 545, + 315 + ], + "type": "text", + "content": "Criteria. Since the destination of SDR-to-HDRTV upconversion is human perception, to provide a 'step-change' improvement [1] of HDRTV than SDRTV, methods should be assessed based on if: C1: result is visually-pleasuring, from both brightness (C1A) & color appearance (C1B) etc., C2: HDRTV's advance on HDR/WCG volume is properly recovered and utilized, C3: bright and dark areas are recovered or at least enhanced and C4: artifacts are avoided. Yet, as found by [18,19], current distance-based metrics e.g. PSNR fails, since the main contributor of an appealing score lies in the accuracy of learned numerical distribution, rather C1-4. Hence, we use new assessment criteria in Tab.7." + } + ] + } + ], + "index": 16 + }, + { + "type": "table", + "bbox": [ + 307, + 325, + 545, + 376 + ], + "blocks": [ + { + "bbox": [ + 307, + 325, + 545, + 376 + ], + "lines": [ + { + "bbox": [ + 307, + 325, + 545, + 376 + ], + "spans": [ + { + "bbox": [ + 307, + 325, + 545, + 376 + ], + "type": "table", + "html": "
Visuals in Fig.1&6Metrics in Tab.8subj. exp. (\\$4.3)
CIA-ALL, HDRBQoverall rating & selection of attribution
CIBASL
C3yellow&blue boxes-
C4detailed visuals
C23D Yxy diagramFHL(WG)P, EHL(WG)-
", + "image_path": "de2411227cb748559b3491c79e7a7e3facafbf896f34556348fda499cd79051e.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "table_body" + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 385, + 544, + 407 + ], + "lines": [ + { + "bbox": [ + 305, + 385, + 544, + 407 + ], + "spans": [ + { + "bbox": [ + 305, + 385, + 544, + 407 + ], + "type": "text", + "content": "Table 7. How each criteria C1-4 is assessed: via detailed visuals, fine-grained tailored metrics and subjective experiment." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 422, + 545, + 494 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 422, + 545, + 494 + ], + "spans": [ + { + "bbox": [ + 304, + 422, + 545, + 494 + ], + "type": "text", + "content": "Competitors. As in Tab.8, our method is compared with 6 learning-based methods [4-9], and 2 commercial software DaVinci and Nuke. Results from joint-SR methods [4-6] are bilinear downscaled before comparison. Note that learning-based methods are not re-trained with our dataset since we treat dataset as a crucial attribution to performance." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 494, + 545, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 494, + 545, + 578 + ], + "spans": [ + { + "bbox": [ + 304, + 494, + 545, + 578 + ], + "type": "text", + "content": "Test set. We select 12 4K SDR videos with " + }, + { + "bbox": [ + 304, + 494, + 545, + 578 + ], + "type": "inline_equation", + "content": "10s" + }, + { + "bbox": [ + 304, + 494, + 545, + 578 + ], + "type": "text", + "content": " duration and " + }, + { + "bbox": [ + 304, + 494, + 545, + 578 + ], + "type": "inline_equation", + "content": "50fps" + }, + { + "bbox": [ + 304, + 494, + 545, + 578 + ], + "type": "text", + "content": ". 3 of 12 are degraded from HDR-GT counterpart by YouTube DM (§2.3), the rest 9 are graded from HDR sequence No. 2, 13, 15, 20, 21, 22, 42, 44, 55 in [78]. Experiment fairness is guaranteed since both DMs (SDR-HDR relationship) in test set are 'unseen' by our network (this even gives a bonus to methods trained with YouTube DM [4, 5, 7-9])." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 306, + 586, + 359, + 597 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 586, + 359, + 597 + ], + "spans": [ + { + "bbox": [ + 306, + 586, + 359, + 597 + ], + "type": "text", + "content": "4.2. Result" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 317, + 605, + 545, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 605, + 545, + 616 + ], + "spans": [ + { + "bbox": [ + 317, + 605, + 545, + 616 + ], + "type": "text", + "content": "Results from all competitors are shown in Tab.8 & Fig.6." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": "Metrics. From Tab.8 we know that methods trained with YouTube DM all tend to produce lower satiation (ASL) and brightness (ALL&HDRBQ), and their ASL is lower even than input SDR. Also, SR-ITM-GAN [6] recovers least HDR&WCG volume since its label HDR (from Zeng20 dataset) is of least extent of HDR/WCG (Tab.5). Note that current methods produce adequate FWGP, but from 3D Yxy chromaticity diagram in Fig.6 we known that their WCG" + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22236" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 48, + 70, + 548, + 182 + ], + "blocks": [ + { + "bbox": [ + 48, + 70, + 548, + 182 + ], + "lines": [ + { + "bbox": [ + 48, + 70, + 548, + 182 + ], + "spans": [ + { + "bbox": [ + 48, + 70, + 548, + 182 + ], + "type": "table", + "html": "
Method (network)how network is trained(recovery rate %, GT is 100%) how HDR/WCG volume is recoveredshift rate %, GT is 0%) overall-styleconventional metrics
dataset (GT)DMFHLPEHLFWGPEWGASLALLHDRBQPSNRSSIMΔEVDP3
Input SDR-00006.57010.76-23.92dB0.886144.976.571
Deep SR-ITM [4]KAISTYouTube(14.03)0.2323(9.70)0.372(175.2)1.0964(71.96)0.172(-20.33)5.485(-15.06)9.580(-72.80)1.42826.59dB0.811532.546.917
JSI-GAN [5](12.33)0.2041(3.55)0.136(213.1)1.3334(88.23)0.212(-16.62)5.741(-14.36)6.659(-79.20)1.09227.87dB0.842030.237.452
SR-ITM-GAN [6]Zeng20Reinhard(8.04)0.1332(14.33)0.550(00.00)0.0000(00.00)0.000(-7.04)6.400(-30.37)7.853(-57.12)2.25128.04dB0.883124.786.707
HDRTVNet [7]HDRTV1KYouTube(18.59)0.3078(16.29)0.625(388.8)2.4334(28.82)0.069(-15.51)5.817(-13.47)9.759(-69.65)1.59330.82dB0.881227.588.120
KPN-MFI [8]own(1.17)0.0193(0.02)0.001(392.9)2.4592(15.82)0.038(-21.74)5.388(-16.10)9.462(-82.47)0.92029.37dB0.874627.477.785
FMNet [9]HDRTV1KOurs × 3(13.67)0.2264(12.35)0.474(396.5)2.4813(91.29)0.220(-16.91)5.770(-13.48)9.758(-71.24)1.51030.91dB0.885527.168.069
LSN (ours)(256.6)4.2509(71.96)2.599(109.8)0.6873(150.0)0.361(+9.25)7.522(+81.12)20.42(-27.04)3.82924.47dB0.831037.848.130
DaVinci-(54.96)0.9103(43.16)1.655(310.0)1.9399(85.22)0.205(-21.36)5.414(-21.42)8.863(-39.23)3.19026.39dB0.891835.478.528
Nuke(112.1)1.8565(24.70)0.384(00.00)0.0000(00.00)0.000(-27.53)4.990(+17.93)13.30(-51.19)2.56220.87dB0.727364.297.479
HDR-GT (ref.)(100.0)1.6562(100.0)3.835(100.0)0.6258(100.0)0.240(0.00)6.885(0.00)11.28(0.00)5.248----
ablation studies ↓
LSN (ours)HDRTV4KYouTube(13.09)0.2168(6.63)0.254(401.3)2.5118(59.81)0.144(-14.68)5.874(-13.46)9.760(-76.89)1.21330.15dB0.885828.047.902
HDRTV4KReinhard(9.06)0.1501(19.14)0.734(00.00)0.0000(00.00)0.000(-5.74)6.490(-25.65)8.387(-52.84)2.47527.70dB0.843626.036.832
Zeng20ours × 3(109.6)1.8147(9.00)0.345(0.95)0.0593(0.76)0.002(+3.36)7.117(+66.64)18.79(-59.52)2.12425.17dB0.817928.587.874
HDRTV1Kours × 3(177.5)2.9405(52.49)2.013(279.5)1.7494(70.43)0.169(+6.56)7.337(+52.30)17.18(-34.83)3.42024.30dB0.835138.038.002
", + "image_path": "ac8d6267e8330550aa160bdc21d7d677690f62a6d883098713a39ded588b16f6.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 190, + 547, + 237 + ], + "lines": [ + { + "bbox": [ + 46, + 190, + 547, + 237 + ], + "spans": [ + { + "bbox": [ + 46, + 190, + 547, + 237 + ], + "type": "text", + "content": "Table 8. Metrics. We use fine-grained tailored metrics (column 3-9, defined in Tab.4) to assess criteria C1&2 (§4.1): The more significant metrics in column 3-6 are, the better HDR&WCG volume is recovered (C2) by specific method. Also, column 8-10 closer with GT stands for similar brightness&color appearance. Note that we allow column 8-10 slightly bigger than GT, which means result HDR is reasonably more visual-pleasuring (C1) than GT. Based on this, we highlight those results unfavorable for viewing experience." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 74, + 247, + 520, + 511 + ], + "blocks": [ + { + "bbox": [ + 74, + 247, + 520, + 511 + ], + "lines": [ + { + "bbox": [ + 74, + 247, + 520, + 511 + ], + "spans": [ + { + "bbox": [ + 74, + 247, + 520, + 511 + ], + "type": "image", + "image_path": "5bdedd9f6f5df459fa30e7afadab773e681e4101cdb7cf659dc196437df6b97d.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 519, + 547, + 575 + ], + "lines": [ + { + "bbox": [ + 46, + 519, + 547, + 575 + ], + "spans": [ + { + "bbox": [ + 46, + 519, + 547, + 575 + ], + "type": "text", + "content": "Figure 6. Visuals. We provide comparisons on " + }, + { + "bbox": [ + 46, + 519, + 547, + 575 + ], + "type": "inline_equation", + "content": "3D" + }, + { + "bbox": [ + 46, + 519, + 547, + 575 + ], + "type": "text", + "content": " Yxy chromaticity diagram to assess how HDR/WCG volume is recovered (C2), and detailed visuals on bright (yellow arrow, with luminance of same pixel indicated) and dark (blue arrow) areas to assess method's recover ability (C3). Note that conclusion 'SDR is more vivid than HDR' could not be drawn because HDR will appear dimmer than SDR in print version (explained in Fig.1). We hence turn to ASL & HDRBQ (column 8-10) and later subjective experiment to see if HDR is indeed dimmer (C1). Comparison on more scenes will be provided in supplementary material." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 595, + 288, + 644 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 595, + 288, + 644 + ], + "spans": [ + { + "bbox": [ + 46, + 595, + 288, + 644 + ], + "type": "text", + "content": "pixels mainly exists in low luminance range, which means they are of little help to viewing experience. Our method is able to recover adequate HDR/WCG volume, meanwhile reasonably enhance the brightness and saturation." + }, + { + "bbox": [ + 46, + 595, + 288, + 644 + ], + "type": "inline_equation", + "content": "^{1}" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 595, + 545, + 704 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 595, + 545, + 704 + ], + "spans": [ + { + "bbox": [ + 304, + 595, + 545, + 704 + ], + "type": "text", + "content": "Visuals. Methods' recover ability is illustrated by Fig.6: Current ones underperform in both bright (yellow) and dark (blue) areas. Specifically, methods trained with YouTube DM produce dim (HDBBQ) and desaturated (ASL) result, and even get lower luminance than input SDR at same position (arrow in yellow box). This confirms the finding in §2.3 that network will learn to darken and desaturate if DM tend to brighten and over-saturate. Also, our method recovers more detail in bright and dark areas with a better style." + } + ] + } + ], + "index": 6 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 46, + 654, + 288, + 714 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 288, + 714 + ], + "type": "text", + "content": "1We also provide conventional PSNR, SSIM, " + }, + { + "bbox": [ + 46, + 654, + 288, + 714 + ], + "type": "inline_equation", + "content": "\\Delta E" + }, + { + "bbox": [ + 46, + 654, + 288, + 714 + ], + "type": "text", + "content": " (color difference [79]) and VDP3 (HDR-VDP-3 [80]), but they mostly represent output's closer value with GT (For example, result both dimmer (e.g. Deep SR-ITM [4]) and more vivid (ours) than GT will have a similar low score.), thus are helpless for our assessment. This phenomenon was first found by [18, 19], and will be further explained in supplementary material." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22237" + } + ] + } + ], + "index": 7 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 174, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 174, + 85 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 174, + 85 + ], + "type": "text", + "content": "4.3. Subjective experiment" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 91, + 290, + 247 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 91, + 290, + 247 + ], + "spans": [ + { + "bbox": [ + 46, + 91, + 290, + 247 + ], + "type": "text", + "content": "Currently, few subjective studies [19, 43, 59, 81-83] are designed for SDR-to-HDR procedure (rather between different HDR). Similar to [19], we judge if output HDR is better than origin SDR. Specifically, as in Fig.7, we use 2 side-by-side SONY KD85X9000H display supporting both SDR and HDRTV, one is calibrated to 100nit/BT.709 SDR and another PQ1000nit/BT.2020 HDR. Each (input)SDR-outputHDR pair is displayed twice with " + }, + { + "bbox": [ + 46, + 91, + 290, + 247 + ], + "type": "inline_equation", + "content": "3s" + }, + { + "bbox": [ + 46, + 91, + 290, + 247 + ], + "type": "text", + "content": " gray level interval and following " + }, + { + "bbox": [ + 46, + 91, + 290, + 247 + ], + "type": "inline_equation", + "content": "10s" + }, + { + "bbox": [ + 46, + 91, + 290, + 247 + ], + "type": "text", + "content": " for rating: each participant is asked to continuously rate from -5(SDR much better) to 0(same) to 5(HDR much better), meanwhile select at least 1 attribution (bottom Fig.7) of his/her rating. Such process is repeated 9(#participant) " + }, + { + "bbox": [ + 46, + 91, + 290, + 247 + ], + "type": "inline_equation", + "content": "\\times" + }, + { + "bbox": [ + 46, + 91, + 290, + 247 + ], + "type": "text", + "content": " 9(#competitor) " + }, + { + "bbox": [ + 46, + 91, + 290, + 247 + ], + "type": "inline_equation", + "content": "\\times" + }, + { + "bbox": [ + 46, + 91, + 290, + 247 + ], + "type": "text", + "content": " 12(#clip) times." + } + ] + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 77, + 258, + 257, + 327 + ], + "blocks": [ + { + "bbox": [ + 77, + 258, + 257, + 327 + ], + "lines": [ + { + "bbox": [ + 77, + 258, + 257, + 327 + ], + "spans": [ + { + "bbox": [ + 77, + 258, + 257, + 327 + ], + "type": "image", + "image_path": "244aa22540d7bfac2f34b05827bf7b13d5ee2b1b89f9bb907fa1d826fd65cbd0.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 78, + 327, + 257, + 460 + ], + "blocks": [ + { + "bbox": [ + 78, + 327, + 257, + 460 + ], + "lines": [ + { + "bbox": [ + 78, + 327, + 257, + 460 + ], + "spans": [ + { + "bbox": [ + 78, + 327, + 257, + 460 + ], + "type": "image", + "image_path": "4b7b15968c8cc53e6f00e2a98cfced6927c27e50076efedc7c066305ab1af67f.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 469, + 287, + 503 + ], + "lines": [ + { + "bbox": [ + 46, + 469, + 287, + 503 + ], + "spans": [ + { + "bbox": [ + 46, + 469, + 287, + 503 + ], + "type": "text", + "content": "Figure 7. Environment and result of subjective experiment. 2 displays are calibrated differently to ensure both SDR and HDR are correctly visualized. Results are provided in quartile chart." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 517, + 287, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 517, + 287, + 661 + ], + "spans": [ + { + "bbox": [ + 46, + 517, + 287, + 661 + ], + "type": "text", + "content": "Result shows that only 2 methods (ours & DaVinci) are recognized better than input SDR. For methods [4-6], their main 'attribution' (bottom Fig.7) is artifact (" + }, + { + "bbox": [ + 46, + 517, + 287, + 661 + ], + "type": "inline_equation", + "content": "C4" + }, + { + "bbox": [ + 46, + 517, + 287, + 661 + ], + "type": "text", + "content": ", see Fig.6 blue box). For artifact-free 'YouTube-DM' methods [7-9], they are rated slightly worse mainly for lower saturation (" + }, + { + "bbox": [ + 46, + 517, + 287, + 661 + ], + "type": "inline_equation", + "content": "C1B" + }, + { + "bbox": [ + 46, + 517, + 287, + 661 + ], + "type": "text", + "content": ") and incapability in information recovery, consisting with Tab.8 and Fig.6. Form 'attribution' we also notice that our method got better score mainly for viewing experience (" + }, + { + "bbox": [ + 46, + 517, + 287, + 661 + ], + "type": "inline_equation", + "content": "C1" + }, + { + "bbox": [ + 46, + 517, + 287, + 661 + ], + "type": "text", + "content": ") and information recovery (" + }, + { + "bbox": [ + 46, + 517, + 287, + 661 + ], + "type": "inline_equation", + "content": "C3" + }, + { + "bbox": [ + 46, + 517, + 287, + 661 + ], + "type": "text", + "content": "). Also, our 'bad cases' lies in recovered large dark area with intrinsic noise etc. amplified and uneliminated. This is why we got more checks on " + }, + { + "bbox": [ + 46, + 517, + 287, + 661 + ], + "type": "inline_equation", + "content": "C4(\\times 24)" + }, + { + "bbox": [ + 46, + 517, + 287, + 661 + ], + "type": "text", + "content": " than commercial methods DaVinci and Nuke." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 670, + 146, + 681 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 670, + 146, + 681 + ], + "spans": [ + { + "bbox": [ + 47, + 670, + 146, + 681 + ], + "type": "text", + "content": "4.4. Ablation studies" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": "On DM. When DM is changed to YouTube, Tab.8 witnesses a significant decline on FHLP, EHL, ASL, ALL" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "text", + "content": "and HDRBQ, while Fig.8 confirms a result similar to those 'YouTube-DM' methods, i.e. worse viewing experience and less recover ability. Also, when using Reinhard DM which contains no clipping, result's highlight area stay unlearned." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 121, + 547, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 121, + 547, + 240 + ], + "spans": [ + { + "bbox": [ + 304, + 121, + 547, + 240 + ], + "type": "text", + "content": "On dataset. Here, we use original DMs, but label HDR from other dataset. In Fig.8 yellow box, our DMs encourage the network to output higher luminance, but since Zeng20 is of least extent of HDR i.e. these highlight do not exist in label HDR, our LSN will not 'recognize' them and thus produce artifact. Since this dataset is also of least extent of WCG, FWGP&EWG in Tab.8 drop obviously. When using slightly-inferior HDRTV1K as label, difference is relatively less significant. Yet, in both cases, ASL&ALL are similar since DM i.e. network's style tendency is unaltered." + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 313, + 250, + 539, + 475 + ], + "blocks": [ + { + "bbox": [ + 313, + 250, + 539, + 475 + ], + "lines": [ + { + "bbox": [ + 313, + 250, + 539, + 475 + ], + "spans": [ + { + "bbox": [ + 313, + 250, + 539, + 475 + ], + "type": "image", + "image_path": "fc7cb3945dbe14230bd04c48397e2499e47c5247d60fdac778b88e7f67d240aa.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 483, + 545, + 538 + ], + "lines": [ + { + "bbox": [ + 304, + 483, + 545, + 538 + ], + "spans": [ + { + "bbox": [ + 304, + 483, + 545, + 538 + ], + "type": "text", + "content": "Figure 8. Result of ablation studies proves the importance of both high-quality label HDR and rational DMs. Specifically, absent of both label HDR's HDR/WCG volume (Zeng20) and DM's degradation (Reinhard) will impair LSN's recover ability, meanwhile YouTube DM's style will make our LSN commonplace as others." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 562, + 378, + 574 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 562, + 378, + 574 + ], + "spans": [ + { + "bbox": [ + 306, + 562, + 378, + 574 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 582, + 545, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 545, + 665 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 545, + 665 + ], + "type": "text", + "content": "There are 2 types of low level vision: 'sole-restoration' whose destination is only clean or GT e.g. denoising, and 'perceptual-motivated' aiming at better viewing experience e.g. image enhancement/retouching. SDR-to-HDRTV upconversion belongs to both. Yet, current methods only realize (it belongs to) the former and neglect the latter, leading their concentration only on network mechanism." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 666, + 545, + 713 + ], + "type": "text", + "content": "To this end, our response is two-fold: (1) focusing on the impact of training set, and ameliorating its quality by proposing new dataset and DMs, (2) involving novel assessment criteria based on the 'perceptual' principal." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22238" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 124 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 124 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 124 + ], + "type": "text", + "content": "[1] ITU, Geneva, Switzerland, Report ITU-R BT.2381-0: Requirements for high dynamic range television (HDR-TV) systems, 0 ed., 7 2015. 1, 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 126, + 288, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 126, + 288, + 171 + ], + "spans": [ + { + "bbox": [ + 53, + 126, + 288, + 171 + ], + "type": "text", + "content": "[2] ITU, Geneva, Switzerland, Recommendation ITU-R BT.2100-2: Image parameter values for high dynamic range television for use in production and international programme exchange, 2 ed., 07 2018. 1, 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 173, + 288, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 173, + 288, + 217 + ], + "spans": [ + { + "bbox": [ + 53, + 173, + 288, + 217 + ], + "type": "text", + "content": "[3] ITU, Geneva, Switzerland, Recommendation ITU-R BT.2020-2: Parameter values for ultra-high definition television systems for production and international programme exchange, 2 ed., 10 2015. 1, 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 220, + 288, + 263 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 220, + 288, + 263 + ], + "spans": [ + { + "bbox": [ + 53, + 220, + 288, + 263 + ], + "type": "text", + "content": "[4] S. Y. Kim, J. Oh, and M. Kim, \"Deep sr-item: Joint learning of super-resolution and inverse tone-mapping for 4k uhd hdr applications,\" in Proc. ICCV, pp. 3116-3125, 2019. 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 266, + 287, + 311 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 266, + 287, + 311 + ], + "spans": [ + { + "bbox": [ + 53, + 266, + 287, + 311 + ], + "type": "text", + "content": "[5] S. Y. Kim, J. Oh, and M. Kim, \"Jsi-gan: Gan-based joint super-resolution and inverse tone-mapping with pixel-wise task-specific filters for uhd hdr video,\" in Proc. AAAI, vol. 34, pp. 11287-11295, 2020. 2, 3, 6, 7, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 314, + 287, + 347 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 314, + 287, + 347 + ], + "spans": [ + { + "bbox": [ + 53, + 314, + 287, + 347 + ], + "type": "text", + "content": "[6] H. Zeng, X. Zhang, Z. Yu, and Y. Wang, “Sr-itm-gan: Learning 4k uhd hdr with a generative adversarial network,” IEEE Access, vol. 8, pp. 182815–182827, 2020. 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 350, + 287, + 384 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 350, + 287, + 384 + ], + "spans": [ + { + "bbox": [ + 53, + 350, + 287, + 384 + ], + "type": "text", + "content": "[7] X. Chen, Z. Zhang, J. S. Ren, L. Tian, Y. Qiao, and C. Dong, “A new journey from sdrtv to hdttv,” in Proc. ICCV, pp. 4500–4509, 2021. 2, 3, 5, 6, 7, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 385, + 287, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 385, + 287, + 430 + ], + "spans": [ + { + "bbox": [ + 53, + 385, + 287, + 430 + ], + "type": "text", + "content": "[8] G. Cao, F. Zhou, H. Yan, A. Wang, and L. Fan, \"Kpn-mfi: A kernel prediction network with multi-frame interaction for video inverse tone mapping,\" in Proc. IJCAI, pp. 806-812, 2022. 2, 3, 6, 7, 8" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 432, + 287, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 432, + 287, + 475 + ], + "spans": [ + { + "bbox": [ + 53, + 432, + 287, + 475 + ], + "type": "text", + "content": "[9] G. Xu, Q. Hou, L. Zhang, and M.-M. Cheng, \"Fmnet: Frequency-aware modulation network for sdr-to-hdr translation,\" in Proc. ACMMM, pp. 6425-6435, 2022. 2, 3, 6, 7, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 479, + 287, + 512 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 479, + 287, + 512 + ], + "spans": [ + { + "bbox": [ + 48, + 479, + 287, + 512 + ], + "type": "text", + "content": "[10] Z. Liang, \"Cvpr2022-1st workshop on vision dataset understanding,\" https://sites.google.com/view/vdu-cvpr22, 2022.2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 514, + 287, + 548 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 514, + 287, + 548 + ], + "spans": [ + { + "bbox": [ + 48, + 514, + 287, + 548 + ], + "type": "text", + "content": "[11] K. Zhang, J. Liang, L. Van Gool, and R. Timofte, \"Designing a practical degradation model for deep blind image superresolution,\" in Proc. ICCV, pp. 4791-4800, 2021. 2, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 551, + 287, + 594 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 551, + 287, + 594 + ], + "spans": [ + { + "bbox": [ + 48, + 551, + 287, + 594 + ], + "type": "text", + "content": "[12] V. Dewil, A. Barral, G. Facciolo, and P. Arias, \"Self-supervision versus synthetic datasets: which is the lesser evil in the context of video denoising?\", in Proc. CVPR, pp. 4900-4910, 2022. 2, 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 597, + 287, + 630 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 597, + 287, + 630 + ], + "spans": [ + { + "bbox": [ + 48, + 597, + 287, + 630 + ], + "type": "text", + "content": "[13] J. Jiang, K. Zhang, and R. Timofte, “Towards flexible blindJPEG artifacts removal,” in Proc. ICCV, pp. 4997–5006, 2021. 2, 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 633, + 287, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 633, + 287, + 677 + ], + "spans": [ + { + "bbox": [ + 48, + 633, + 287, + 677 + ], + "type": "text", + "content": "[14] K. Jiang, Z. Wang, Z. Wang, C. Chen, P. Yi, T. Lu, and C.-W. Lin, \"Degrade is upgrade: Learning degradation for low-light image enhancement,\" in Proc. AAAI, vol. 36, pp. 1078-1086, 2022. 2, 3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "text", + "content": "[15] Y. Zhou, Y. Song, and X. Du, \"Modular degradation simulation and restoration for under-display camera,\" in Proc. ACCV, pp. 265-282, 2022. 2, 3" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 33, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "text", + "content": "[16] C. Guo and X. Jiang, “Lhdr: Hdr reconstruction for legacy content using a lightweight dnn,” in Proc. ACCV, pp. 3155–3171, 2022. 2, 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 108, + 545, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 108, + 545, + 152 + ], + "spans": [ + { + "bbox": [ + 307, + 108, + 545, + 152 + ], + "type": "text", + "content": "[17] S. W. Zamir, A. Arora, S. Khan, M. Hayat, F. S. Khan, and M.-H. Yang, \"Restormer: Efficient transformer for high-resolution image restoration,\" in Proc. CVPR, pp. 5728-5739, 2022. 2, 3, 4" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 155, + 545, + 198 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 155, + 545, + 198 + ], + "spans": [ + { + "bbox": [ + 307, + 155, + 545, + 198 + ], + "type": "text", + "content": "[18] G. Eilertsen, S. Hajisharif, P. Hanji, A. Tsimirikoglou, R. K. Mantiuk, and J. Unger, \"How to cheat with metrics in single-image hdr reconstruction,\" in Proc. ICCV, pp. 3998-4007, 2021. 2, 3, 6, 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 201, + 545, + 245 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 201, + 545, + 245 + ], + "spans": [ + { + "bbox": [ + 307, + 201, + 545, + 245 + ], + "type": "text", + "content": "[19] P. Hanji, R. Mantiuk, G. Eilertsen, S. Hajisharif, and J. Unger, “Comparison of single image hdr reconstruction methods—the caveats of quality assessment,” in Proc. SIGGRAPH, pp. 1–8, 2022. 2, 6, 7, 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 247, + 545, + 279 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 247, + 545, + 279 + ], + "spans": [ + { + "bbox": [ + 307, + 247, + 545, + 279 + ], + "type": "text", + "content": "[20] L. Wang and K.-J. Yoon, \"Deep learning for hdr imaging: State-of-the-art and future trends,\" IEEE Trans. PAMI, 2021. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 282, + 545, + 305 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 282, + 545, + 305 + ], + "spans": [ + { + "bbox": [ + 307, + 282, + 545, + 305 + ], + "type": "text", + "content": "[21] P. Debevec, \"Image-based lighting,\" in ACM SIGGRAPH 2006 Courses, pp. 4-es, 2006. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 307, + 545, + 340 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 307, + 545, + 340 + ], + "spans": [ + { + "bbox": [ + 307, + 307, + 545, + 340 + ], + "type": "text", + "content": "[22] E. Reinhard, W. Heidrich, P. Debevec, et al., High dynamic range imaging: acquisition, display, and image-based lighting. Morgan Kaufmann, 2010. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 342, + 545, + 385 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 342, + 545, + 385 + ], + "spans": [ + { + "bbox": [ + 307, + 342, + 545, + 385 + ], + "type": "text", + "content": "[23] M. Gharbi, J. Chen, J. T. Barron, S. W. Hasinoff, and F. Durand, “Deep bilateral learning for real-time image enhancement,” ACM Trans. Graph., vol. 36, no. 4, pp. 1–12, 2017. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 388, + 545, + 422 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 388, + 545, + 422 + ], + "spans": [ + { + "bbox": [ + 307, + 388, + 545, + 422 + ], + "type": "text", + "content": "[24] X. Yang, K. Xu, Y. Song, Q. Zhang, X. Wei, and R. W. Lau, \"Image correction via deep reciprocating hdr transformation,\" in Proc. CVPR, pp. 1798-1807, 2018. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 424, + 545, + 457 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 424, + 545, + 457 + ], + "spans": [ + { + "bbox": [ + 307, + 424, + 545, + 457 + ], + "type": "text", + "content": "[25] Z. Zheng, W. Ren, X. Cao, T. Wang, and X. Jia, \"Ultra-high-definition image hdr reconstruction via collaborative bilateral learning,\" in Proc. ICCV, pp. 4449-4458, 2021. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 460, + 545, + 503 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 460, + 545, + 503 + ], + "spans": [ + { + "bbox": [ + 307, + 460, + 545, + 503 + ], + "type": "text", + "content": "[26] B. Mildenhall, P. Hedman, R. Martin-Brualla, P. P. Srinivasan, and J. T. Barron, “Nerf in the dark: High dynamic range view synthesis from noisy raw images,” in Proc. CVPR, pp. 16190–16199, 2022. 2" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 506, + 545, + 539 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 506, + 545, + 539 + ], + "spans": [ + { + "bbox": [ + 307, + 506, + 545, + 539 + ], + "type": "text", + "content": "[27] N. K. Kalantari, R. Ramamoorthi, et al., \"Deep high dynamic range imaging of dynamic scenes,\" ACM Trans. Graph., vol. 36, no. 4, pp. 144-1, 2017. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 541, + 545, + 574 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 541, + 545, + 574 + ], + "spans": [ + { + "bbox": [ + 307, + 541, + 545, + 574 + ], + "type": "text", + "content": "[28] S. Wu, J. Xu, Y.-W. Tai, and C.-K. Tang, \"Deep high dynamic range imaging with large foreground motions,\" in Proc. ECCV, pp. 117-132, 2018. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 307, + 576, + 545, + 620 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 576, + 545, + 620 + ], + "spans": [ + { + "bbox": [ + 307, + 576, + 545, + 620 + ], + "type": "text", + "content": "[29] Q. Yan, D. Gong, Q. Shi, A. v. d. Hengel, C. Shen, I. Reid, and Y. Zhang, “Attention-guided network for ghost-free high dynamic range imaging,” in Proc. CVPR, pp. 1751–1760, 2019. 2" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 307, + 622, + 545, + 666 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 622, + 545, + 666 + ], + "spans": [ + { + "bbox": [ + 307, + 622, + 545, + 666 + ], + "type": "text", + "content": "[30] G. Chen, C. Chen, S. Guo, Z. Liang, K.-Y. K. Wong, and L. Zhang, \"Hdr video reconstruction: A coarse-to-fine network and a real-world benchmark dataset,\" in Proc. ICCV, pp. 2502-2511, 2021. 2" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 307, + 670, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 670, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 670, + 545, + 713 + ], + "type": "text", + "content": "[31] Y. Niu, J. Wu, W. Liu, W. Guo, and R. W. Lau, \"Hdr-gan: Hdr image reconstruction from multi-exposed ldr images with large motions,\" IEEE Transactions on Image Processing, vol. 30, pp. 3885-3896, 2021. 2" + } + ] + } + ], + "index": 32 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "22239" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "type": "text", + "content": "[32] E. Pérez-Pellitero et al., “Ntire 2022 challenge on high dynamic range imaging: Methods and results,” in Proc. CVPR, pp. 1009–1023, 2022. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 108, + 287, + 142 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 108, + 287, + 142 + ], + "spans": [ + { + "bbox": [ + 49, + 108, + 287, + 142 + ], + "type": "text", + "content": "[33] G. Eilertsen, J. Kronander, et al., \"Hdr image reconstruction from a single exposure using deep cnns,\" ACM Trans. Graph., vol. 36, no. 6, pp. 1-15, 2017. 2, 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 144, + 287, + 188 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 144, + 287, + 188 + ], + "spans": [ + { + "bbox": [ + 49, + 144, + 287, + 188 + ], + "type": "text", + "content": "[34] D. Marnerides, T. Bashford-Rogers, et al., \"Expandnet: A deep convolutional neural network for high dynamic range expansion from low dynamic range content,\" Comput. Graph. Forum, vol. 37, no. 2, pp. 37-49, 2018. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 190, + 286, + 235 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 190, + 286, + 235 + ], + "spans": [ + { + "bbox": [ + 49, + 190, + 286, + 235 + ], + "type": "text", + "content": "[35] Y.-L. Liu, W.-S. Lai, Y.-S. Chen, Y.-L. Kao, M.-H. Yang, Y.-Y. Chuang, and J.-B. Huang, \"Single-image hdr reconstruction by learning to reverse the camera pipeline,\" in Proc. CVPR, pp. 1651-1660, 2020. 2, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 237, + 286, + 281 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 237, + 286, + 281 + ], + "spans": [ + { + "bbox": [ + 49, + 237, + 286, + 281 + ], + "type": "text", + "content": "[36] M. S. Santos, T. I. Ren, and N. K. Kalantari, \"Single image hdr reconstruction using a cnn with masked features and perceptual loss,\" ACM Trans. Graph., vol. 39, no. 4, pp. 80-1, 2020. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 284, + 286, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 284, + 286, + 317 + ], + "spans": [ + { + "bbox": [ + 49, + 284, + 286, + 317 + ], + "type": "text", + "content": "[37] X. Chen, Y. Liu, et al., \"Hdrunet: Single image hdr reconstruction with denoising and dequantization,\" in Proc. CVPR, pp. 354-363, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 319, + 286, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 319, + 286, + 352 + ], + "spans": [ + { + "bbox": [ + 49, + 319, + 286, + 352 + ], + "type": "text", + "content": "[38] S. Ning, H. Xu, L. Song, R. Xie, and W. Zhang, “Learning an inverse tone mapping network with a generative adversarial regularizer,” in Proc. ICASSP, pp. 1383–1387, 2018. 2, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 49, + 354, + 286, + 408 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 354, + 286, + 408 + ], + "spans": [ + { + "bbox": [ + 49, + 354, + 286, + 408 + ], + "type": "text", + "content": "[39] K. Hirao, Z. Cheng, M. Takeuchi, and J. Katto, \"Convolutional neural network based inverse tone mapping for high dynamic range display using lucore,\" in 2019 IEEE International Conference on Consumer Electronics (ICCE), pp. 1-2, 2019. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 49, + 412, + 286, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 412, + 286, + 456 + ], + "spans": [ + { + "bbox": [ + 49, + 412, + 286, + 456 + ], + "type": "text", + "content": "[40] S. Y. Kim, D.-E. Kim, and M. Kim, \"Itm-cnn: Learning the inverse tone mapping from low dynamic range video to high dynamic range displays using convolutional neural networks,\" in Proc. ACCV, pp. 395-409, 2018. 2, 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 49, + 459, + 286, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 459, + 286, + 491 + ], + "spans": [ + { + "bbox": [ + 49, + 459, + 286, + 491 + ], + "type": "text", + "content": "[41] Y. Xu, S. Ning, R. Xie, and L. Song, \"Gan based multiexposure inverse tone mapping,\" in Proc. ICIP, pp. 4365-4369, 2019. 2, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 49, + 495, + 286, + 528 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 495, + 286, + 528 + ], + "spans": [ + { + "bbox": [ + 49, + 495, + 286, + 528 + ], + "type": "text", + "content": "[42] Y. Xu, L. Song, R. Xie, and W. Zhang, \"Deep video inverse tone mapping,\" in 2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), pp. 142-147, 2019. 2, 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 49, + 529, + 286, + 573 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 529, + 286, + 573 + ], + "spans": [ + { + "bbox": [ + 49, + 529, + 286, + 573 + ], + "type": "text", + "content": "[43] D.-E. Kim and M. Kim, “Learning-based low-complexity reverse tone mapping with linear mapping,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 2, pp. 400–414, 2019. 2, 3, 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 49, + 576, + 286, + 620 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 576, + 286, + 620 + ], + "spans": [ + { + "bbox": [ + 49, + 576, + 286, + 620 + ], + "type": "text", + "content": "[44] J. Zou, K. Mei, and S. Sun, \"Multi-scale video inverse tone mapping with deformable alignment,\" in 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP), pp. 9-12, 2020. 2, 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 49, + 623, + 286, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 623, + 286, + 667 + ], + "spans": [ + { + "bbox": [ + 49, + 623, + 286, + 667 + ], + "type": "text", + "content": "[45] T. Chen and P. Shi, \"An inverse tone mapping algorithm based on multi-scale dual-branch network,\" in 2021 International Conference on Culture-oriented Science & Technology (ICCST), pp. 187–191, 2021. 2, 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 49, + 670, + 286, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 670, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 49, + 670, + 286, + 712 + ], + "type": "text", + "content": "[46] G. Xu, Y. Yang, J. Xu, L. Wang, X.-T. Zhen, and M.-M. Cheng, \"Joint super-resolution and inverse tone-mapping: A feature decomposition aggregation network and a new benchmark,\" 2022. 2, 3" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 32, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "text", + "content": "[47] G. He, K. Xu, L. Xu, C. Wu, M. Sun, X. Wen, and Y.-W. Tai, \"Sdrtv-to-hdrtv via hierarchical dynamic context feature mapping,\" in Proc. ACMM, pp. 2890-2898, 2022. 2, 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 108, + 545, + 142 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 108, + 545, + 142 + ], + "spans": [ + { + "bbox": [ + 308, + 108, + 545, + 142 + ], + "type": "text", + "content": "[48] G. He, S. Long, L. Xu, C. Wu, J. Zhou, M. Sun, X. Wen, and Y. Dai, “Global priors guided modulation network for joint super-resolution and inverse tone-mapping,” 2022. 2, 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 144, + 545, + 176 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 144, + 545, + 176 + ], + "spans": [ + { + "bbox": [ + 308, + 144, + 545, + 176 + ], + "type": "text", + "content": "[49] K. Xu, L. Xu, G. He, C. Wu, Z. Ma, M. Sun, and Y.-W. Tai, \"Sdrtv-to-hdrtv conversion via spatial-temporal feature fusion,\" 2022. 2, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 178, + 545, + 212 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 178, + 545, + 212 + ], + "spans": [ + { + "bbox": [ + 308, + 178, + 545, + 212 + ], + "type": "text", + "content": "[50] T. Shao, D. Zhai, J. Jiang, and X. Liu, \"Hybrid conditional deep inverse tone mapping,\" in Proc. ACMM, pp. 1016-1024, 2022. 2, 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 215, + 545, + 268 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 215, + 545, + 268 + ], + "spans": [ + { + "bbox": [ + 308, + 215, + 545, + 268 + ], + "type": "text", + "content": "[51] A. Mustafa, P. Hanji, and R. K. Mantiuk, \"Distilling style from image pairs for global forward and inverse tone mapping,\" in The 19th ACM SIGGRAPH European Conference on Visual Media Production (CVMP), pp. 1-10, 2022. 2, 3, 4" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 271, + 545, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 271, + 545, + 304 + ], + "spans": [ + { + "bbox": [ + 308, + 271, + 545, + 304 + ], + "type": "text", + "content": "[52] M. Yao, D. He, X. Li, Z. Pan, and Z. Xiong, \"Bidirectional translation between uhd-hdr and hd-sdr videos,\" IEEE Transactions on Multimedia, 2023. 2, 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 307, + 545, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 307, + 545, + 361 + ], + "spans": [ + { + "bbox": [ + 308, + 307, + 545, + 361 + ], + "type": "text", + "content": "[53] R. Tang, F. Meng, and L. Bai, “Zoned mapping network from sdr video to hdr video,” in 2022 IEEE 5th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), vol. 5, pp. 1466–1472, 2022. 2, 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 364, + 545, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 364, + 545, + 398 + ], + "spans": [ + { + "bbox": [ + 308, + 364, + 545, + 398 + ], + "type": "text", + "content": "[54] SMPTE, NY, USA, ST 2084:2014 - SMPTE Standard - High Dynamic Range Electro-Optical Transfer Function of Mastering Reference Displays, 2014. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 399, + 545, + 433 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 399, + 545, + 433 + ], + "spans": [ + { + "bbox": [ + 308, + 399, + 545, + 433 + ], + "type": "text", + "content": "[55] ITU, Geneva, Switzerland, Recommendation ITU-R BT.709-6: Parameter values for the HDTV standards for production and international programme exchange, 6 ed., 6 2015. 2, 5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 435, + 545, + 468 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 435, + 545, + 468 + ], + "spans": [ + { + "bbox": [ + 308, + 435, + 545, + 468 + ], + "type": "text", + "content": "[56] Z. Cheng, T. Wang, Y. Li, F. Song, C. Chen, and Z. Xiong, \"Towards real-world hdrtv reconstruction: A data synthesis-based approach,\" in Proc. ECCV, pp. 199-216, 2022. 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 471, + 545, + 504 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 471, + 545, + 504 + ], + "spans": [ + { + "bbox": [ + 308, + 471, + 545, + 504 + ], + "type": "text", + "content": "[57] E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, \"Photographic tone reproduction for digital images,\" in Proc. 29th SIGGRAPH, pp. 267-276, 2002. 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 506, + 545, + 539 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 506, + 545, + 539 + ], + "spans": [ + { + "bbox": [ + 308, + 506, + 545, + 539 + ], + "type": "text", + "content": "[58] ITU, Geneva, Switzerland, Report ITU-R BT.2446-1: Methods for conversion of high dynamic range content to standard dynamic range content and vice-versa, 1 ed., 3 2021. 3, 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 541, + 545, + 596 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 541, + 545, + 596 + ], + "spans": [ + { + "bbox": [ + 308, + 541, + 545, + 596 + ], + "type": "text", + "content": "[59] P. Mohammadi, M. T. Pourazad, and P. Nasiopoulos, \"A perception-based inverse tone mapping operator for high dynamic range video applications,\" IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 5, pp. 1711-1723, 2020. 3, 8" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 598, + 545, + 632 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 598, + 545, + 632 + ], + "spans": [ + { + "bbox": [ + 308, + 598, + 545, + 632 + ], + "type": "text", + "content": "[60] D. Marnerides, T. Bashford-Rogers, and K. Debattista, \"Deep hdr hallucination for inverse tone mapping,\" Sensors, vol. 21, no. 12, p. 4032, 2021. 3" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 634, + 545, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 634, + 545, + 667 + ], + "spans": [ + { + "bbox": [ + 308, + 634, + 545, + 667 + ], + "type": "text", + "content": "[61] Y. Zhang and T. Aydin, \"Deep hdr estimation with generative detail reconstruction,\" in Computer Graphics Forum, vol. 40, pp. 179-190, 2021. 3" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 670, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 670, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 308, + 670, + 545, + 712 + ], + "type": "text", + "content": "[62] L. Bai, Y. Yang, and G. Fu, \"Analysis of high dynamic range and wide color gamut of uhdtv,\" in 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), vol. 5, pp. 1750-1755, 2021. 4" + } + ] + } + ], + "index": 31 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "22240" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "type": "text", + "content": "[63] ITU, Geneva, Switzerland, Report ITU-R BT.2407-0: Colour gamut conversion from Recommendation ITU-R BT.2020 to Recommendation ITU-R BT.709, 0 ed., 10 2017. 4, 6" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 109, + 288, + 144 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 109, + 288, + 144 + ], + "spans": [ + { + "bbox": [ + 49, + 109, + 288, + 144 + ], + "type": "text", + "content": "[64] ITU, Geneva, Switzerland, Recommendation ITU-R BT.500-14: Methodologies for the subjective assessment of the quality of television images, 14 ed., 10 2019. 4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 146, + 287, + 180 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 146, + 287, + 180 + ], + "spans": [ + { + "bbox": [ + 48, + 146, + 287, + 180 + ], + "type": "text", + "content": "[65] D. Hasler and S. E. Suesstrunk, “Measuring colorfulness in natural images,” in Human vision and electronic imaging VIII, vol. 5007, pp. 87–95, 2003. 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 183, + 286, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 183, + 286, + 217 + ], + "spans": [ + { + "bbox": [ + 48, + 183, + 286, + 217 + ], + "type": "text", + "content": "[66] Dolby, \"What is ictcp? - introduction.\" https:// professional.dolby.com/siteassets/pdfs/ ictcp_dolbywhitepaper_v071.pdf.4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 220, + 287, + 255 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 220, + 287, + 255 + ], + "spans": [ + { + "bbox": [ + 48, + 220, + 287, + 255 + ], + "type": "text", + "content": "[67] S. Ploumis, R. Boitard, and P. Nasiopoulos, \"Image brightness quantification for hdr,\" in 2020 28th European Signal Processing Conference (EUSIPCO), pp. 640-644, 2021. 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 257, + 286, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 257, + 286, + 300 + ], + "spans": [ + { + "bbox": [ + 48, + 257, + 286, + 300 + ], + "type": "text", + "content": "[68] Arri, \"Camera sample footage & reference image.\" https://www.arri.com/en/learn-help/ learn-help-camera-system/camera-sample-footage-reference-image, 2022.4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 304, + 286, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 304, + 286, + 327 + ], + "spans": [ + { + "bbox": [ + 48, + 304, + 286, + 327 + ], + "type": "text", + "content": "[69] Netflix, \"Netflix open content.\" https://opencontent.nsf1x.com/, 2020.4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 331, + 287, + 386 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 331, + 287, + 386 + ], + "spans": [ + { + "bbox": [ + 48, + 331, + 287, + 386 + ], + "type": "text", + "content": "[70] J. Froehlich, S. Grandinetti, B. Eberhardt, S. Walter, A. Schilling, and H. Brendel, “Creating cinematic wide gamut hdr-video for the evaluation of tone mapping operators and hdr-displays,” in Digital photography X, vol. 9023, pp. 279–288, 2014. 4" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 389, + 287, + 434 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 389, + 287, + 434 + ], + "spans": [ + { + "bbox": [ + 48, + 389, + 287, + 434 + ], + "type": "text", + "content": "[71] Y. Wang, S. Inguva, and B. Adsumilli, \"YoutubeUGC dataset for video compression research,\" in 2019 IEEE 21st International Workshop on Multimedia Signal Processing (MMSP), pp. 1-5, 2019. 4" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 437, + 287, + 481 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 437, + 287, + 481 + ], + "spans": [ + { + "bbox": [ + 48, + 437, + 287, + 481 + ], + "type": "text", + "content": "[72] Z. Shang, J. P. Ebenezer, A. C. Bovik, Y. Wu, H. Wei, and S. Sethuraman, “Subjective assessment of high dynamic range videos under different ambient conditions,” in Proc. ICIP, pp. 786–790, 2022. 4" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 484, + 287, + 517 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 484, + 287, + 517 + ], + "spans": [ + { + "bbox": [ + 48, + 484, + 287, + 517 + ], + "type": "text", + "content": "[73] L. Van der Maaten and G. Hinton, \"Visualizing data using t-sne,\" Journal of machine learning research, vol. 9, no. 11, 2008. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 521, + 287, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 521, + 287, + 555 + ], + "spans": [ + { + "bbox": [ + 48, + 521, + 287, + 555 + ], + "type": "text", + "content": "[74] D. Walker, C. Payne, P. Hodoul, and M. Dolan, \"Color management with opencolorio v2,\" in ACM SIGGRAPH 2021 Courses, pp. 1-226, 2021. 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 559, + 287, + 580 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 559, + 287, + 580 + ], + "spans": [ + { + "bbox": [ + 48, + 559, + 287, + 580 + ], + "type": "text", + "content": "[75] J. Morovic, Color gamut mapping. John Wiley & Sons, 2008. 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 584, + 287, + 628 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 584, + 287, + 628 + ], + "spans": [ + { + "bbox": [ + 48, + 584, + 287, + 628 + ], + "type": "text", + "content": "[76] H. Le, T. Jeong, A. Abdelhamed, H. J. Shin, and M. S. Brown, \"Gamutnet: Restoring wide-gamut colors for camera-captured images,\" in Color and Imaging Conference, vol. 2021, pp. 7-12, 2021. 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 632, + 287, + 676 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 632, + 287, + 676 + ], + "spans": [ + { + "bbox": [ + 48, + 632, + 287, + 676 + ], + "type": "text", + "content": "[77] ITU, Geneva, Switzerland, Recommendation ITU-R BT.1886-0: Reference electro-optical transfer function for flat panel displays used in HDTV studio production, 0 ed., 3 2011. 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 680, + 287, + 714 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 287, + 714 + ], + "type": "text", + "content": "[78] ITU, Geneva, Switzerland, Report ITU-R BT.2245-10: HDTV and UHDTV including HDR-TV test materials for assessment of picture quality, 10 ed., 9 2022. 6" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 72, + 545, + 308 + ], + "type": "list", + "angle": 0, + "index": 22, + "blocks": [ + { + "bbox": [ + 307, + 72, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 72, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 545, + 116 + ], + "type": "text", + "content": "[79] ITU, Geneva, Switzerland, Recommendation ITU-R BT.2124-0: Objective metric for the assessment of the potential visibility of colour differences in television, 0 ed., 1 2019. 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 121, + 545, + 154 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 121, + 545, + 154 + ], + "spans": [ + { + "bbox": [ + 307, + 121, + 545, + 154 + ], + "type": "text", + "content": "[80] K. Wolski, D. Giunchi, et al., \"Dataset and metrics for predicting local visible differences,\" ACM Trans. Graph., vol. 37, no. 5, pp. 1-14, 2018. 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 158, + 545, + 191 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 158, + 545, + 191 + ], + "spans": [ + { + "bbox": [ + 307, + 158, + 545, + 191 + ], + "type": "text", + "content": "[81] C. Bist, R. Cozot, G. Madec, and X. Ducloux, “Tone expansion using lighting style aesthetics,” Computers & Graphics, vol. 62, pp. 77–86, 2017. 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 194, + 545, + 250 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 194, + 545, + 250 + ], + "spans": [ + { + "bbox": [ + 307, + 194, + 545, + 250 + ], + "type": "text", + "content": "[82] G. Luzardo, J. Aelterman, H. Luong, S. Rousseaux, D. Ochoa, and W. Philips, “Fully-automatic inverse tone mapping algorithm based on dynamic mid-level tone mapping,” APSIPA Transactions on Signal and Information Processing, vol. 9, p. e7, 2020. 8" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 253, + 545, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 253, + 545, + 308 + ], + "spans": [ + { + "bbox": [ + 307, + 253, + 545, + 308 + ], + "type": "text", + "content": "[83] A. Stojkovic, J. Aelterman, H. Luong, H. Van Parys, and W. Philips, “Highlights analysis system (hans) for low dynamic range to high dynamic range conversion of cinematic low dynamic range content,” IEEE Access, vol. 9, pp. 43938–43969, 2021. 8" + } + ] + } + ], + "index": 21 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 317, + 757 + ], + "type": "text", + "content": "22241" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances/443449b8-5cd8-436a-ab74-6d4bb5de9279_content_list.json b/2023/Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances/443449b8-5cd8-436a-ab74-6d4bb5de9279_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..b34b4843ff73fdea9da5b1113ff9bfe2ba349c6f --- /dev/null +++ b/2023/Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances/443449b8-5cd8-436a-ab74-6d4bb5de9279_content_list.json @@ -0,0 +1,1674 @@ +[ + { + "type": "text", + "text": "Learning a Simple Low-light Image Enhancer from Paired Low-light Instances", + "text_level": 1, + "bbox": [ + 86, + 130, + 880, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Zhenqi Fu $^{1}$ , Yan Yang $^{2}$ , Xiaotong Tu $^{1*}$ , Yue Huang $^{1}$ , Xinghao Ding $^{1}$ , Kai-Kuang Ma $^{3}$ $^{1}$ Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, School of Informatics, Xiamen University, China \n $^{2}$ School of Computer Science and Technology, Hangzhou Dianzi University, China \n $^{3}$ School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore \nfuzhenqi@stu.xmu.edu.cn, yangyanyy@hdu.edu.cn, {xttu, yhuang2010, dxh}@xmu.edu.cn, ekkma@ntu.edu.sg", + "bbox": [ + 104, + 179, + 866, + 305 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 339, + 313, + 354 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Low-light Image Enhancement (LIE) aims at improving contrast and restoring details for images captured in lowlight conditions. Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image details due to the limited information in a single image and the poor adaptability of handcrafted priors. To this end, we propose PairLIE, an unsupervised approach that learns adaptive priors from low-light image pairs. First, the network is expected to generate the same clean images as the two inputs share the same image content. To achieve this, we impose the network with the Retinex theory and make the two reflectance components consistent. Second, to assist the Retinex decomposition, we propose to remove inappropriate features in the raw image with a simple self-supervised mechanism. Extensive experiments on public datasets show that the proposed PairLIE achieves comparable performance against the state-of-the-art approaches with a simpler network and fewer handcrafted priors. Code is available at: https://github.com/zhengqifu/PairLIE.", + "bbox": [ + 75, + 369, + 473, + 690 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 715, + 209, + 732 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Images captured under low-light environments always suffer from multiple distortions, such as low contrast, poor visibility, and sensor noise. Those low-light images are unsatisfactory for information transmission because they incur challenges in human visualization and subsequent computer vision tasks [25]. To correct contrast, uncover textures, and remove sensor noise, great efforts have been made in developing Low-light Image Enhancement (LIE) algorithms in the past decades [1,5,6,8,28,35].", + "bbox": [ + 75, + 742, + 468, + 878 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f2137459d062c837db19e86fcc6289c31263630f9183091f381801e609a7b4fe.jpg", + "image_caption": [ + "Figure 1. Comparison between the previous solution (a) and the proposed method (b) from the aspect of Retinex theory. The key idea of our method is to learn adaptive priors from low-light image pairs. As a result, our solution needs fewer handcrafted priors and the network is more robust. Note that image pairs are only used in the training phase." + ], + "image_footnote": [], + "bbox": [ + 501, + 338, + 890, + 534 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Histogram-based and Retinex-based approaches are two well-known LIE techniques. The former enhances the contrast of an image by redistributing the luminous intensity on the histogram [3, 14]. The latter decomposes an observed image $I$ into illumination $L$ and reflectance $R$ via $I = L\\circ R$ , where $\\circ$ denotes the element-wise multiplication [6, 13, 17]. Specifically, the reflectance component $R$ is assumed to be consistent under different light conditions because $R$ represents the physical properties of the objects. As the Retinex theory can well model color perceptions of human vision, Retinex-based methods have attracted relatively more attention in the LIE community.", + "bbox": [ + 496, + 670, + 893, + 852 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recent years have witnessed great success in developing learning-based LIE algorithms. Among these approaches, most solutions rely on low-light and normal-light image", + "bbox": [ + 496, + 854, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author.", + "bbox": [ + 94, + 886, + 220, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "22252", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "pairs [33, 38]. However, collecting high-quality reference maps in real-world scenarios is time-consuming and expensive [32]. To eliminate the requirement for normal-light images, unsupervised and zero-shot LIE approaches are proposed. Concretely, the former trains a deep neural network using a set of collected low-light samples [7, 18], while the latter only employs the test image itself in the network optimization [40, 41]. Due to the absence of reference images, unsupervised and zero-shot LIE approaches depend on handcrafted priors to guide network training. Nevertheless, due to the complex natural scenes and the limited information in a single low-light image, it is difficult for those methods to attain a high-quality result.", + "bbox": [ + 75, + 90, + 472, + 287 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To tackle the issues of limited information in a single low-light image and the poor adaptability of handcrafted priors, we propose to leverage paired low-light instances to train the LIE network. The main difference between our solution and previous approaches is illustrated in Fig. 1. Note that acquiring paired low-light images will complicate the imaging process since it needs to cope with the misalignment between the two images. Nevertheless, compared with collecting low-light and normal-light image pairs, our solution is more practical. Additionally, twice-exposure images provide useful information for solving the LIE task. As a result, our solution can reduce the demand for handcrafted priors and improve the adaptability of the network.", + "bbox": [ + 75, + 287, + 472, + 484 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "With paired low-light instances, we propose a novel learning-based LIE method, termed PairLIE. The core insight of our approach is to sufficiently exploit priors from paired low-light images. Therefore, we consider employing the Retinex theory and deep learning to decompose low-light images into illumination and reflectance components. First, since the two low-light inputs share the same content, the estimated reflectance components are expected to be consistent. Second, instead of directly imposing the Retinex decomposition on original low-light images, we adopt a simple self-supervised mechanism to remove inappropriate features and implement the Retinex decomposition on the optimized image. This can avoid sub-optimal estimations because the Retinex model has limitations in low-light modeling. As a result, with fewer prior constraints and a simpler network, the proposed PairLIE achieves competitive performance in public LIE datasets. In summary, the contributions of this paper are as follows:", + "bbox": [ + 75, + 484, + 472, + 755 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a generic LIE solution using paired low-light images. The network is based on Retinex decomposition with several novel reference-free losses.", + "- To achieve an accurate decomposition, we first project the original image to remove inappropriate features.", + "- With fewer manually designed priors and a simpler network, the proposed solution achieves comparable performance against state-of-the-art methods." + ], + "bbox": [ + 94, + 766, + 467, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 89, + 640, + 106 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Over the decades, extensive LIE methods have been presented, which can be roughly categorized into conventional approaches and learning-based techniques.", + "bbox": [ + 498, + 116, + 890, + 161 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Conventional Methods", + "text_level": 1, + "bbox": [ + 500, + 172, + 710, + 188 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Histogram-based techniques perform light enhancement by expanding the dynamic range of an image. For example, Park et al. [23] separated the dynamic range of the histogram into several parts and resized the gray-scale range based on the area ratio. Arici et al. [1] introduced penalty terms to avoid the unnatural look and visual artifacts of the enhanced image. Lee et al. [14] applied the layered difference representation of 2D histograms to amplify the gray-level differences between adjacent pixels.", + "bbox": [ + 496, + 198, + 890, + 334 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Retinex-based methods first decompose the low-light image into reflectance and illumination components. Subsequently, those approaches either consider the reflectance as the enhanced image or adjust the illumination and then recompose it with reflectance to generate the enhanced result. Wang et al. [29] proposed a LIE algorithm to promote naturalness and enhance details for non-uniform illumination images. Fu et al. [6] used a weighted variational model to preserve the reflectance with more details. Guo et al. [8] first estimated the illumination by calculating the maximum value in R, G, and B channels. Then, they refined the illumination map by imposing a structure prior. Li et al. [17] improved the performance of LIE by introducing a noise map in the Retinex model. Xu et al. [34] proposed a texture-aware Retinex model solved by an alternating optimization algorithm. Hao et al. [9] presented a novel Retinex-based LIE method performed in a semi-decoupled way.", + "bbox": [ + 496, + 335, + 892, + 592 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Learning-based Methods", + "text_level": 1, + "bbox": [ + 500, + 604, + 728, + 619 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Commonly, learning-based LIE methods rely on paired low-light and normal-light images. Lore et al. [19] designed a stacked sparse denoising auto-encoder to enhance low-light images. The proposed model was trained on synthetic image pairs. Wei et al. [32] first built a real-world low-light image enhancement dataset including low-light and normal-light image pairs. Then, they trained an end-to-end network in a fully-supervised way. Wang et al. [28] introduced an intermediate illumination map in the network to associate the low-light input with the expected enhancement result. Chen et al. [4] collected a dataset of short-exposure low-light images, with corresponding long-exposure reference maps. Based on the dataset, the authors developed a fully-convolutional network for enhancing low-light images. Zhang et al. [38] proposed a supervised approach to decompose the low-light image into illumination and reflectance components. Wu et al. [33] proposed a Retinex-based deep unfolding network to promote adaptability and", + "bbox": [ + 496, + 628, + 893, + 901 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "22253", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/91b2602074189879adc07748938cd501978690d377742704b370c393a603f87b.jpg", + "image_caption": [ + "Figure 2. The architecture of PairLIE. In the training phase, P-Net is first adopted to remove inappropriate features of the original image. L-Net and R-Net are then employed to estimate the illumination and reflectance components. Three loss functions are used to guide the network optimization, including a self-supervised projection loss $\\mathcal{L}_P$ , a reflectance consistency loss $\\mathcal{L}_C$ , and a Retinex loss $\\mathcal{L}_R$ . In the testing stage, given a low-light image, P-Net, L-Net, and R-Net are used to decompose the input into reflectance and illumination. Subsequently, PairLIE adjusts the illumination and recomposes it with the reflectance to capture the enhanced image." + ], + "image_footnote": [], + "bbox": [ + 84, + 92, + 883, + 338 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "efficiency. Xu et al. [35] combined signal-to-noise-ratio-aware transformers and convolutional networks to enhance low-light images. Zhang et al. [39] proposed a color-consistent network to mitigate the color difference between the enhanced image and ground-truth.", + "bbox": [ + 75, + 445, + 468, + 521 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Recently, unsupervised (also known as self-supervised) networks are developed to eliminate the requirement for reference images. For example, Zhu et al. [41] proposed a zero-shot LIE approach that trained a deep network using the input image itself. Guo et al. [7] proposed a reference-free LIE approach based on curve estimation. Their network was optimized with a set of non-reference loss functions. Liu et al. [18] proposed a lightweight LIE network by integrating the unrolling techniques and prior architecture search strategies. Jiang et al. [12] proposed a LIE method based on the generative adversarial network and unpaired training data. Zhao et al. [40] designed a unified zero-reference network for enhancing low-light images based on Deep image Prior (DIP) [27]. Jiang et al. [11] presented a Retinex-based unsupervised decomposition and correction network for LIE. Ma et al. [22] established a cascaded illumination estimation process to achieve fast, flexible, and robust LIE in complex scenarios.", + "bbox": [ + 75, + 522, + 470, + 794 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Proposed Method", + "text_level": 1, + "bbox": [ + 76, + 813, + 250, + 830 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We first give the problem definition of using low-light image pairs for LIE. Then, we detail the pipeline and loss functions. Finally, we introduce the dataset for training the proposed network.", + "bbox": [ + 75, + 839, + 468, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Retinex Model with Paired Low-light Images", + "text_level": 1, + "bbox": [ + 498, + 445, + 879, + 462 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "According to the Retinex theory, low-light image $I$ can be decomposed into illumination $L$ and reflectance $R$ as:", + "bbox": [ + 498, + 468, + 892, + 498 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nI = L \\circ R, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 656, + 506, + 890, + 521 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\circ$ denotes the element-wise multiplication. Illumination $L$ describes the light intensity of objects. $L$ should be piece-wise continuous and textureless. Reflectance $R$ represents the physical properties of the objects. $R$ should contain textures and details in the observed image. The Retinex decomposition is highly ill-posed. Various methods have been proposed to handle this problem [6, 17, 29, 34]. A generic solution of the Retinex decomposition is to minimize the following energy function:", + "bbox": [ + 496, + 529, + 892, + 665 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\underset {L, R} {\\operatorname {a r g m i n}} \\| L \\circ R - I \\| _ {2} + \\lambda_ {L} f _ {L} (L) + \\lambda_ {R} f _ {R} (R), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 521, + 672, + 890, + 696 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $f_{L}$ and $f_{R}$ are prior constraints for $L$ and $R$ , respectively. $\\lambda_{L}$ and $\\lambda_{R}$ denote the weights. $\\| L \\circ R - I \\|_{2}$ is the data-fidelity term between the input and reconstructed image. To achieve a reasonable decomposition, most of the LIE approaches focus on introducing assorted prior constraints enforced on the two components, such as priors of structure [17], smooth [32], and bright channel [15, 26].", + "bbox": [ + 496, + 704, + 890, + 810 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "However, handcrafted priors are commonly not adaptive enough due to the diverse natural scenes and light conditions. In this paper, instead of exploiting handcrafted priors for $L$ and $R$ from a single image, we apply paired low-light images to automatically learn adaptive priors in a data-driven fashion. Those low-light image pairs share the same", + "bbox": [ + 496, + 810, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "22254", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "scene content but different illumination. Mathematically, Retinex decomposition with low-light image pairs can be expressed as:", + "bbox": [ + 75, + 90, + 468, + 135 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\left\\{ \\begin{array}{l} I _ {1} = L _ {1} \\circ R \\\\ I _ {2} = L _ {2} \\circ R \\end{array} , \\right. \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 215, + 138, + 468, + 170 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $I_{1}$ and $I_{2}$ are low-light image pairs, which share the reflectance component $R$ . Intuitively, Eq. 3 can address the decomposition better than Eq. 1 because more information and constraints are introduced.", + "bbox": [ + 75, + 178, + 468, + 238 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Network Structure", + "text_level": 1, + "bbox": [ + 76, + 250, + 258, + 265 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The whole pipeline of our method is illustrated in Fig. 2. We use L-Net and R-Net to estimate the illumination and reflectance components, respectively. L-Net and R-Net are very similar and simple, both of which contain five convolutional layers. The activation function of the first four convolutional layers is ReLU. L-Net and R-Net end with a sigmoid layer to normalize the output into [0, 1]. According to the Retinex theory, the three color channels are assumed to have the same illumination. Therefore, the output channel of L-Net is set as 1 while that of R-Net is set as 3. Note that, this paper does not focus on designing modernistic network structures. In contrast, we aim at providing a generic solution for LIE under paired low-light instances. In our experiments, we found those simple network already achieves comparable performance. Apart from L-Net and R-Net, we introduce P-Net to remove inappropriate features from the original image. Specifically, the structure of the P-Net is identical to the R-Net.", + "bbox": [ + 75, + 275, + 468, + 546 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In the training phase, the original low-light image pairs $I_{1}$ and $I_{2}$ are first taken into the P-Net, yielding two optimized versions $i_{1}$ and $i_{2}$ . Then, L-Net and R-Net are applied to estimate the latent illumination $(L_{1}$ and $L_{2})$ and reflectance $(R_{1}$ and $R_{2})$ . To optimize the network, three loss functions are designed in PairLIE. The first is the projection loss $\\mathcal{L}_P$ that measures the difference between $I$ and $i$ . The second is the reflectance consistency loss $\\mathcal{L}_C$ calculated based on $R_{1}$ and $R_{2}$ . The third is the Retinex loss $\\mathcal{L}_R$ that restricts the decomposed components to satisfy the Retinex theory. In the testing period, given a low-light image, P-Net, R-Net, and L-Net are applied to calculate the final enhanced image:", + "bbox": [ + 75, + 547, + 468, + 744 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nI _ {e n} = g (L) \\circ R = L ^ {\\lambda} \\circ R, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 181, + 757, + 468, + 773 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\lambda$ is the illumination correction factor, $I_{en}$ denotes the enhanced image.", + "bbox": [ + 75, + 787, + 468, + 819 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Projection Loss", + "text_level": 1, + "bbox": [ + 76, + 830, + 233, + 847 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Instead of performing the deep Retinex decomposition on the original low-light image, we propose to first remove inappropriate features to ensure the input can be accurately", + "bbox": [ + 75, + 854, + 468, + 900 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/6e822a5293d15f22e67c6f1c0f444bebd6cdc826d510b240e51c30fe38c09583.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 501, + 88, + 890, + 165 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/3663ec6808ac0ccf714bc920eb54740c8720bfab08987545570a4bbcc2b1dab4.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 503, + 165, + 890, + 238 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/89e27b4b7988345981b7a677e1f4d41e260e9569a775de9f0a37d2d4e145e79b.jpg", + "image_caption": [ + "Figure 3. Example of projected images and corresponding difference maps. A smaller value indicates a higher similarity between the projected and original images." + ], + "image_footnote": [], + "bbox": [ + 503, + 239, + 890, + 315 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "decomposed by an ideal Retinex model (i.e., Eq. 1). Specifically, we design the following loss to guide projection:", + "bbox": [ + 498, + 398, + 888, + 430 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {p} = \\left\\| I _ {1} - i _ {1} \\right\\| _ {2} ^ {2}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 633, + 440, + 890, + 459 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $i_1$ refers to the projected image. The loss function $\\mathcal{L}_p$ transforms the original image to a specific image that is more suitable for the Retinex decomposition. Concretely, this image is expected to be noise free since we do not consider the noise component in the Retinex model. Besides, some useless features are also discarded in this stage. Our projection loss can be interpreted from the view of error reallocation, which can be expressed as:", + "bbox": [ + 498, + 470, + 890, + 592 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\operatorname * {a r g m i n} _ {L, R} \\| I - L \\circ R - \\varepsilon \\| \\\\ = \\underset {L, R} {\\operatorname {a r g m i n}} \\| I - i + i - L \\circ R - \\varepsilon \\| \\tag {6} \\\\ \\leq \\left\\| I - i - \\varepsilon \\right\\| + \\operatorname * {a r g m i n} _ {L, R} \\left\\| i - L \\circ R \\right\\|, \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 604, + 890, + 686 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\varepsilon$ denotes the error. According to Eq. 6, the goal of $\\mathcal{L}_p$ is to move the error from the Retinex decomposition to the projection stage. Fig. 3 gives an example of the calculated projected image and corresponding difference map. As can be seen, the projected image is highly similar to the original one, and sensor noise is removed since it cannot be formulated by the Retinex model. Note that the projection loss needs to cooperate with the other constraints to avoid a trivial solution, i.e., $i_1 = I_1$ .", + "bbox": [ + 498, + 700, + 890, + 835 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Reflectance Consistency Loss", + "text_level": 1, + "bbox": [ + 500, + 845, + 759, + 863 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Reflectance consistency loss $\\mathcal{L}_C$ is calculated based on low-light image pairs and the Retinex theory. Compared", + "bbox": [ + 500, + 869, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "22255", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "with handcrafted priors, $\\mathcal{L}_C$ is more accurate and adaptive because it reveals the physical properties of the objects. Mathematically, $\\mathcal{L}_C$ is formulated as:", + "bbox": [ + 76, + 90, + 468, + 137 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {C} = \\left\\| R _ {1} - R _ {2} \\right\\| _ {2} ^ {2}, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 202, + 148, + 468, + 169 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $R_{1}$ and $R_{2}$ refer to the reflectance components of paired low-light images. $\\mathcal{L}_C$ enforces the network to predict the same reflectance components because low-light image pairs share the same objects.", + "bbox": [ + 76, + 181, + 468, + 243 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Since sensor noise hidden in dark regions will be amplified when the contrast is improved. To cope with the noise issue, existing approaches either add a smoothness term on the estimated reflectance or perform a denoise operation after the enhancement. In our method, the sensor noise can be implicitly removed by Eq. 7. This is because the two low-light images contain independent noise of the same scene. As discussed in [16], paired noisy images can be leveraged to train denoising models. This is because noise is random and different in two images, the deep network cannot fit the noise in one image to another. In our case, the two low-light images can help each other to remove noise during the Retinex decomposition. Therefore, PairLIE does not require additional handcrafted constraints of noise.", + "bbox": [ + 76, + 244, + 470, + 455 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.5. Retinex Loss", + "text_level": 1, + "bbox": [ + 76, + 470, + 212, + 484 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Since PairLIE employs the Retinex theory to decompose low-light images into illumination and reflectance components, some basic constraints of Retinex decomposition are introduced, which can be formulated as:", + "bbox": [ + 76, + 494, + 468, + 555 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {R} = \\| R \\circ L - i \\| _ {2} ^ {2} + \\| R - i / s t o p g r a d (L) \\| _ {2} ^ {2} \\tag {8} \\\\ + \\left\\| L - L _ {0} \\right\\| _ {2} ^ {2} + \\left\\| \\nabla L \\right\\| _ {1}, \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 114, + 566, + 468, + 609 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $i$ denotes the projected image, $L_0$ refers to the initial estimation of illumination, $\\nabla$ represents the horizontal and vertical gradients. First, the decomposed components should satisfy the requirement for reconstructing the input image. Therefore, a reconstruction term $\\| R\\circ L - i\\| _2^2$ is applied to ensure a reasonable decomposition. Once the illumination is estimated, the reflectance can be calculated through the pixel-wise division between the low-light image and its illumination map. Therefore, we additionally add a term $\\| R - i / stopgrad(L)\\| _2^2$ to guide the decomposition. Note that we stop the gradient of the illumination to make the training stable. Different from most existing methods that use a lot of handcrafted priors, PairLIE only imposes a smooth term and an initialization term on $L$ . Specifically, the initialized illumination $L_{0}$ is calculated via the maximum of the R, G, and B channels:", + "bbox": [ + 76, + 622, + 470, + 863 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL _ {0} = \\max _ {c \\in \\{R, G, B \\}} I ^ {c} (x). \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 191, + 878, + 468, + 902 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/0f20a8651124a02c15240234e8ccdbb19f7864bbf7b3c7a7c66259435e884c2b.jpg", + "image_caption": [ + "Figure 4. Statistics concerning sequence length of the collected data. Most sequences have 2 to 4 low-light images." + ], + "image_footnote": [], + "bbox": [ + 509, + 90, + 880, + 258 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.6. Overall Loss", + "text_level": 1, + "bbox": [ + 500, + 323, + 633, + 338 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The overall loss function for training our PairLIE is a liner combination of each loss:", + "bbox": [ + 498, + 345, + 890, + 376 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {A l l} = w _ {0} \\mathcal {L} _ {P} + w _ {1} \\mathcal {L} _ {C} + w _ {2} \\mathcal {L} _ {R}, \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 583, + 385, + 890, + 401 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $w_{0}, w_{1}$ , and $w_{2}$ denote the weights.", + "bbox": [ + 500, + 409, + 779, + 422 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.7. Training Data Collection", + "text_level": 1, + "bbox": [ + 500, + 431, + 725, + 446 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We collect low-light image pairs from SICE (part2) [2] and LOL (training set) [32], which contain multi-exposure images. These datasets adopt some specific operators to deal with the misalignment caused by camera shaking or object moving. Note that the SICE dataset consists of both under and over-exposed images. We only select under-exposure and well-aligned cases for constructing the training set. As a result, we collect 324 sequences (a total of 1000 low-light images). As shown in Fig. 4, each sequence includes 2 to 6 samples. In the training phase, we randomly select two images from each sequence to constitute a pair.", + "bbox": [ + 498, + 454, + 890, + 621 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 500, + 633, + 633, + 648 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We first describe implementation details, evaluation datasets, and performance criteria. Then, we present the quantitative and qualitative comparisons with state-of-the-art methods. Finally, we conduct ablative experiments to validate each component.", + "bbox": [ + 498, + 657, + 890, + 734 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Implementation Details", + "text_level": 1, + "bbox": [ + 500, + 741, + 717, + 757 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We implement PairLIE with PyTorch. In the training phase, we randomly crop images to the size of $128 \\times 128$ . A batch size of 1 is applied. We use ADAM with the initial learning rate of $1 \\times 10^{-4}$ to optimize the network. The number of training epochs is set to 400. The learning rate is half-decayed per 100 epochs. The default correction factor $\\lambda$ is 0.2. In extremely dark cases such as the LOL dataset, we set $\\lambda = 0.14$ . As for the hyper-parameter $w_0$ , $w_1$ , and $w_2$ in Eq. 10, we set $w_0 = 500$ , $w_1 = w_2 = 1$ , empirically.", + "bbox": [ + 498, + 763, + 890, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "22256", + "bbox": [ + 478, + 944, + 519, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/59e0b42a9d9a6984dc78bc0d3cce16a595e1e18cab01bac8502efa357afc1775.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodTypeLOLSICE
PSNR↑SSIM↑LPIPS↓DeltaE ↓PSNR↑SSIM↑LPIPS↓DeltaE ↓
SDD [9]T13.340.6370.74321.8315.350.7410.23216.08
STAR [34]T12.910.5180.36623.4615.170.7270.24616.35
MBLLEN [20]S17.860.7270.22513.6813.640.6320.29718.60
RetinexNet [32]S17.550.6480.37912.6919.890.7830.2768.715
GLADNet [30]S19.720.6800.32112.2819.980.8370.2038.947
KinD [38]S17.650.7750.17112.4921.100.8380.1958.009
DRBN [36]S16.290.5510.26013.4415.580.5220.28913.78
URetinexNet [33]S19.840.8260.12810.6521.640.8430.1927.728
ZeroDCE [7]U14.860.5590.33518.8118.690.8100.20711.93
RRDNet [41]U11.400.4570.36226.4313.280.6780.22119.64
RUAS [18]U16.400.5000.27016.8513.180.7340.36316.81
SCI [22]U14.780.5220.33919.5215.950.7870.23513.71
EnlightenGAN [12]U17.480.6510.32214.5018.730.8220.21610.42
PairLIE (Ours)U19.510.7360.24810.8021.320.8400.2167.835
", + "bbox": [ + 117, + 87, + 854, + 334 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 1. Quantitative comparisons with state-of-the-art methods on LOL and SICE datasets. \"T\", \"S\", and \"U\" represent \"Traditional\", \"Supervised\", and \"Unsupervised\" methods, respectively. The top three results are marked in bold.", + "bbox": [ + 75, + 345, + 890, + 375 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Datasets and Criteria", + "text_level": 1, + "bbox": [ + 76, + 398, + 277, + 414 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As described before, low-light image pairs collected from SICE and LOL are applied to train PairLIE. We select another 50 sequences (150 images) from SICE and use the official evaluation set (15 images) of LOL to measure the model performance. Since SICE and LOL contains reference images, we employ PSNR, SSIM [31], LPIPS [37], and DeltaE with CIE2000 standard [24] to objectively evaluate the performance of each method. A higher PSNR/SSIM score indicates the result is closer to the reference. A lower LPIPS/DeltaE value denotes better enhancement performance. Furthermore, we adopt the MEF dataset [21] for visual comparisons.", + "bbox": [ + 75, + 422, + 470, + 604 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Compared Methods", + "text_level": 1, + "bbox": [ + 76, + 614, + 266, + 631 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "PairLIE is compared with 13 state-of-the-art LIE methods, which can be divided into the following three categories: traditional methods (SDD [9], STAR [34]), supervised approaches (MBLLEN [20], RetinexNet [32], GALDNet [30], KinD [38], DRBN [36], URetinexNet [33]), and unsupervised methods (Zero-DCE [7], RRDNet [41], RUAS [18], SCI [22], and EnlightenGAN [12]). Note that the results of all those methods are reproduced by using the official codes with recommended parameters.", + "bbox": [ + 75, + 638, + 468, + 776 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.4. Quantitative Comparisons", + "text_level": 1, + "bbox": [ + 76, + 786, + 316, + 801 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Tab. 1 lists the quantitative performance of the LOL and SICE datasets. As can be observed, traditional and unsupervised methods obtain relatively poor results. This is reasonable because it is difficult to learn an accurate enhancement model without the reference image. Besides, the performance of those solutions is highly dependent on the used", + "bbox": [ + 75, + 809, + 470, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "prriors. However, hand-crafted features may not be adaptive enough for assorted light conditions. In Tab. 1, PairLIE achieves the best performance among the five unsupervised methods and gets competitive results compared with supervised approaches. Since paired low-light images provide sufficient information for solving the LIE task, PairLIE can reduce the demand for handcrafted priors. As a result, PairLIE achieves significant performance improvement.", + "bbox": [ + 496, + 400, + 890, + 521 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.5. Visual Comparisons", + "text_level": 1, + "bbox": [ + 498, + 529, + 691, + 545 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Fig. 5 illustrates the visual comparisons of different LIE methods. Our observations are as follows: 1) The proposed method achieves visually pleasing results in terms of brightness, color, contrast, and naturalness. While other methods fail to cope with the extreme black light in this case. 2) Although supervised approaches GLADNet, KinD, and URetinexNet show good performance on LOL and SICE datasets according to Tab. 1, their generalization ability may be limited as supervised learning is sensitive to the data distribution. We further show an example of noise suppression in Fig. 6. As can be seen, although PairLIE does not introduce any handcrafted priors about the noise, our method can successfully suppress sensor noise in dark regions and the result is clear and natural. In contrast, the competitors either amplify noise or fail in correcting color and contrast, leading to poor visual quality.", + "bbox": [ + 496, + 551, + 890, + 794 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.6. Decomposition Visualization", + "text_level": 1, + "bbox": [ + 498, + 801, + 754, + 816 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We visualize the reflectance and illumination components to validate the effectiveness of our method. As shown in Fig. 7, the reflectance contains rich textures and details while the illumination is piece-wise continuous and textureless, demonstrating PairLIE can accurately decompose the", + "bbox": [ + 496, + 825, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "22257", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/e478eeb719a15ed9541a875727ad1373f075c5df7492e51970097243ac61557a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 127, + 88, + 267, + 170 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f58b916d867ca2b6584205cdcdbb05324ef7fde23884a9bb955c59473a2f16f4.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 271, + 88, + 411, + 170 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f08909e98601022179e48aa43fc58df5266787990a4fc05178f69d7b5f20522a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 413, + 88, + 555, + 170 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/afb9b26901f098fedfb9da94fa3193b6f849f5c0c0e9a83f31344f4143daa97f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 558, + 88, + 697, + 170 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/390026e1e5c740607bc3a567a28653ff46d3ad2138469f0358821bc0de1ae2c1.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 700, + 88, + 841, + 170 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/49e699a4805c8e3acdee585cdeb4bb664d37c8b1226e5972172854e6fdb0831a.jpg", + "image_caption": [ + "Original", + "ZeroDCE" + ], + "image_footnote": [], + "bbox": [ + 127, + 181, + 267, + 265 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/eb3bfed158d033a1565c43bc80f84ee2fd9ae3d26aea9a145b306805430fdefa.jpg", + "image_caption": [ + "SDD", + "RUAS" + ], + "image_footnote": [], + "bbox": [ + 271, + 181, + 411, + 265 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/16faa83c64d59f9802da57a4a345b96230fd12c1ed01c702191e0f5fe0b483f5.jpg", + "image_caption": [ + "GLADNet", + "SCI" + ], + "image_footnote": [], + "bbox": [ + 413, + 181, + 553, + 265 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/311f9964d3dc69ee52e07ce6cf5273ed8064d27ce6dce998d8b94e20061abd9a.jpg", + "image_caption": [ + "KinD", + "EnlightenGAN" + ], + "image_footnote": [], + "bbox": [ + 558, + 181, + 697, + 265 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b4d57c479808cfd0acc442b1285d9548d966b0bebc2a777544e74e5d8fad38f9.jpg", + "image_caption": [ + "URetinext", + "PairLIE (Ours)" + ], + "image_footnote": [], + "bbox": [ + 700, + 181, + 841, + 265 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/1c1594e32d0bbb69084209fead842299ae3b9bf662c32524906a200fe15bd3a7.jpg", + "image_caption": [ + "Figure 5. Visual comparisons of different LIE methods. SDD [9], RetinexNet [32], GALDNet [30], KinD [38], URetinexNet [33], Zero-DCE [7], RRDNet [41], RUAS [18], SCI [22], EnlightenGAN [12], and PairLIE (Ours).", + "Original" + ], + "image_footnote": [], + "bbox": [ + 125, + 335, + 362, + 410 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/faf0cd3f5ca227a3b547aa34e576fb24fb8f9a8dc70e4fbce077afe049871d79.jpg", + "image_caption": [ + "ZeroDCE" + ], + "image_footnote": [], + "bbox": [ + 367, + 335, + 602, + 410 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5b40267e28a9c42d87bc026bc6e4451c83f39247040d337cf79221c629ec880b.jpg", + "image_caption": [ + "RUAS" + ], + "image_footnote": [], + "bbox": [ + 607, + 335, + 841, + 410 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/549dd9ca8351127766b0cbc6add0e0bcf96eafc4b19349739ee7300bbc3a0574.jpg", + "image_caption": [ + "SCI", + "Figure 6. Performance comparisons in noise removal. Zoom in for the best view. Zero-DCE [7], RUAS [18], SCI [22], EnlightenGAN [12], and PairLIE (Ours). Our result is visually satisfactory without obvious noise." + ], + "image_footnote": [], + "bbox": [ + 127, + 422, + 361, + 494 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/0e1d248fe20f45d9ea308d35de62012d74b323ff44028e1b8893e684147c5e6f.jpg", + "image_caption": [ + "EnlightenGAN" + ], + "image_footnote": [], + "bbox": [ + 367, + 422, + 602, + 494 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/3fb5d354e5c39cc8afe0976358f7d0d12a5a8d85a8265ad30774aba59aa00672.jpg", + "image_caption": [ + "PairLIE (Ours)" + ], + "image_footnote": [], + "bbox": [ + 607, + 422, + 841, + 494 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "low-light image. We further show the enhancement results with different correction factors. With the increase of $\\lambda$ , the image brightness decreases gradually. When $\\lambda$ is larger than 0.4 or smaller than 0.2, the enhanced images show obvious under/over-enhancement effects. In this work, the default $\\lambda$ is set as 0.2. Note that users can adjust $\\lambda$ in the testing phase according to their personal preferences.", + "bbox": [ + 75, + 577, + 468, + 683 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.7. Ablation Studies", + "text_level": 1, + "bbox": [ + 76, + 694, + 240, + 709 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We conduct ablation studies under different settings to understand how each component affects performance. We have tried the following three variations over the original PairLIE. 1) Setting A: without $\\mathcal{L}_C$ . 2) Setting B: without $\\mathcal{L}_P$ . 3) Setting C: without prior terms, i.e., removing $\\| L - L_0\\| _2^2$ and $\\| \\nabla L\\| _1$ from $\\mathcal{L}_R$ .", + "bbox": [ + 75, + 718, + 468, + 810 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Tab. 2 and Fig. 8 report the results of our ablation studies on the LOL dataset. We can observe that our approach significantly outperforms setting A, which demonstrates the effectiveness of learning adaptive priors using low-light image pairs. Compared with setting B, PairLIE achieves slight performance improvement. The results of setting B suggest", + "bbox": [ + 75, + 810, + 468, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "that P-Net can assist the Retinex decomposition, but it is not necessary for stably training the network. To further understand the role of P-Net, we show the reconstruction error $\\kappa$ in Fig. 9. The blue line denotes the decomposition using original low-light images, i.e., $\\kappa = |L\\circ R - I|$ , while the orange line refers to the decomposition using projected images, i.e., $\\kappa = |L\\circ R - i|$ . As can be seen, the proposed method obtains a smaller reconstruction error than the baseline. Therefore, P-Net can remove inappropriate features and the output is more suitable for the Retinex decomposition. The results of Setting C show that when priors terms are removed, the performance drops significantly. This is because the Rrtinex decomposition requires some basic priors to initialize and restrain the illumination.", + "bbox": [ + 496, + 577, + 890, + 787 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.8. Discussions", + "text_level": 1, + "bbox": [ + 500, + 800, + 624, + 814 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Acquiring low-light image pairs. Although PairLIE achieves promising performance with fewer handcrafted priors and a simpler network, collecting low-light image pairs is relatively expensive compared with recording a signal low-light image. This problem can be partly solved by", + "bbox": [ + 496, + 824, + 890, + 901 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "22258", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f6b650455f9efd1b5a6996d37e09348c40d038803f34e258e67f1a8418ca8489.jpg", + "image_caption": [ + "Figure 7. Visualization of the Retinex decomposition. The enhanced results are presented under different correction factors. We set the default $\\lambda$ as 0.2, but users can adjust the $\\lambda$ according to their preferences." + ], + "image_footnote": [], + "bbox": [ + 109, + 88, + 859, + 258 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/a55fae39d61d3c14216c482d03ef4d260a9fa3adb8360100c45b3cf6c724976f.jpg", + "image_caption": [ + "Figure 8. Visual comparisons of the ablation studies. Setting A: without $\\mathcal{L}_C$ . Setting B: without $\\mathcal{L}_P$ . Setting C: without prior terms. Our result is visually closer to the reference." + ], + "image_footnote": [], + "bbox": [ + 109, + 311, + 859, + 396 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/c835da82c141522df6ecee6f77ed387f29080a3d315d7461c688d0bced372150.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodPSNR↑SSIM↑
Setting A17.700.567
Setting B19.500.724
Setting C14.690.684
PairLIE19.510.736
", + "bbox": [ + 119, + 458, + 428, + 539 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 2. Quantitative results of ablation studies on the LOL dataset. The best results are marked in bold.", + "bbox": [ + 75, + 549, + 468, + 578 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/7bef45fabaceb3b263477697d38514f35d65282c9b5c69cc1cd89815da390c63.jpg", + "image_caption": [ + "Figure 9. Visualization of the reconstruction error. Baseline refers to the decomposition with original low-light images." + ], + "image_footnote": [], + "bbox": [ + 76, + 597, + 470, + 756 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "generating training pairs from a single low-light image. For example, one can use neighbor masks [10] to generate two similar low-light images from a degraded observation. Additionally, specific recorruption techniques can be leveraged to capture paired training instances.", + "bbox": [ + 75, + 825, + 470, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Illumination components. PairLIE solves the Retinex decomposition with paired low-light images. The basic idea is to generate the same reflectance components since the reflectance is independent of the light. However, the relationship between illumination components is not sufficiently exploited in this paper. Illumination plays a critical role in the Retinex model. Therefore, we believe that PairLIE can be further improved by introducing new constraints between two illumination maps.", + "bbox": [ + 496, + 462, + 893, + 599 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusions", + "text_level": 1, + "bbox": [ + 500, + 612, + 627, + 628 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This paper proposes PairLIE, a reference-free approach for low-light image enhancement that benefits from both Retinex-based and learning-based solutions. By learning adaptive constraints from low-light image pairs, PairLIE reduces the dependence on handcrafted priors and thus generalizes well on various scenes. To assist the decomposition, PairLIE first removes inappropriate features in the original image and then implements the decomposition on the optimized image. Extensive experiments on public benchmarks show that PairLIE outperforms the state-of-the-art unsupervised methods significantly. In future works, we will concentrate on exploiting effective priors of illumination in a data-driven fashion.", + "bbox": [ + 496, + 637, + 893, + 833 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements: The work was supported partly by the National Natural Science Foundation of China under Grants 82172033, U19B2031, 61971369, 52105126, 82272071, and 62271430.", + "bbox": [ + 496, + 839, + 893, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "22259", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Tarik Arici, Salih Dikbas, and Yucel Altunbasak. A histogram modification framework and its application for image contrast enhancement. IEEE TIP, 18(9):1921-1935, 2009. 1, 2", + "[2] Jianrui Cai, Shuhang Gu, and Lei Zhang. Learning a deep single image contrast enhancer from multi-exposure images. IEEE TIP, 27(4):2049-2062, 2018. 5", + "[3] Turgay Celik and Tardi Tjahadi. Contextual and variational contrast enhancement. IEEE TIP, 20(12):3431-3441, 2011. 1", + "[4] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. In CVPR, pages 3291-3300, 2018. 2", + "[5] Xueyang Fu, Delu Zeng, Yue Huang, Yinghao Liao, Xinghao Ding, and John Paisley. A fusion-based enhancing method for weakly illuminated images. Signal Processing, 129:82-96, 2016. 1", + "[6] Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, and Xinghao Ding. A weighted variational model for simultaneous reflectance and illumination estimation. In CVPR, pages 2782-2790, 2016. 1, 2, 3", + "[7] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In CVPR, pages 1777-1786, 2020. 2, 3, 6, 7", + "[8] Xiaojie Guo, Yu Li, and Haibin Ling. Lime: Low-light image enhancement via illumination map estimation. IEEE TIP, 26(2):982-993, 2017. 1, 2", + "[9] Shijie Hao, Xu Han, Yanrong Guo, Xin Xu, and Meng Wang. Low-light image enhancement with semi-decoupled decomposition. IEEE TMM, 22(12):3025-3038, 2020. 2, 6, 7", + "[10] Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, and Jianzhuang Liu. Neighbor2neighbor: Self-supervised denoising from single noisy images. In CVPR, pages 14781-14790, 2021. 8", + "[11] Qiuping Jiang, Yudong Mao, Runmin Cong, Wenqi Ren, Chao Huang, and Feng Shao. Unsupervised decomposition and correction network for low-light image enhancement. IEEE TITS, pages 1-16, 2022. 3", + "[12] Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang Wang. *Enlightengan: Deep light enhancement without paired supervision*. *IEEE TIP*, 30:2340-2349, 2021. 3, 6, 7", + "[13] Edwin H Land. The retina theory of color vision. Scientific American, 237(6):108-129, 1977. 1", + "[14] Chulwoo Lee, Chul Lee, and Chang-Su Kim. Contrast enhancement based on layered difference representation of 2d histograms. IEEE TIP, 22(12):5372-5384, 2013. 1, 2", + "[15] Hunsang Lee, Kwanghoon Sohn, and Dongbo Min. Unsupervised low-light image enhancement using bright channel prior. IEEE SPL, 27:251-255, 2020. 3", + "[16] Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila. Noise2Noise: Learning image restoration without clean data. In ICML, pages 2965-2974, 2018. 5" + ], + "bbox": [ + 78, + 114, + 468, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[17] Mading Li, Jiaying Liu, Wenhan Yang, Xiaoyan Sun, and Zongming Guo. Structure-revealing low-light image enhancement via robust retinae model. IEEE TIP, 27(6):2828-2841, 2018. 1, 2, 3", + "[18] Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In CVPR, pages 10561-10570, 2021. 2, 3, 6, 7", + "[19] Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. Linet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recognition, 61:650-662, 2017. 2", + "[20] Feifan Lv, Feng Lu, Jianhua Wu, and Chongsoon Lim. Mbllen: Low-light image/video enhancement using cnns. In BMVC, pages 1-13, 2018. 6", + "[21] Kede Ma, Kai Zeng, and Zhou Wang. Perceptual quality assessment for multi-exposure image fusion. IEEE TIP, 24(11):3345-3356, 2015. 6", + "[22] Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. Toward fast, flexible, and robust low-light image enhancement. In CVPR, pages 5637-5646, 2022. 3, 6, 7", + "[23] Gyu-Hee Park, Hwa-Hyun Cho, and Myung-Ryul Choi. A contrast enhancement method using dynamic range separate histogram equalization. IEEE TCE, 54(4):1981-1987, 2008. 2", + "[24] Gaurav Sharma, Wencheng Wu, and Edul N Dalal. The ciede2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research and Application, 30(1):21-30, 2005. 6", + "[25] Weitao Song, Masanori Suganuma, X. Liu, Norimasa Shimobayashi, D. Maruta, and Takayuki Okatani. Matching in the dark: A dataset for matching image pairs of low-light scenes. In ICCV, pages 6009-6018, 2021. 1", + "[26] Li Tao, Chuang Zhu, Jiawen Song, Tao Lu, Huizhu Jia, and Xiaodong Xie. Low-light image enhancement using cnn and bright channel prior. In ICIP, pages 3215-3219, 2017. 3", + "[27] Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. Deep image prior. In CVPR, pages 9446-9454, 2018. 3", + "[28] Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, and Jiaya Jia. Underexposed photo enhancement using deep illumination estimation. In CVPR, pages 6842-6850, 2019. 1, 2", + "[29] Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE TIP, 22(9):3538-3548, 2013. 2, 3", + "[30] Wenjing Wang, Chen Wei, Wenhan Yang, and Jiaying Liu. Gladnet: Low-light enhancement network with global awareness. In IEEE International Conference on Automatic Face and Gesture Recognition, pages 751-755, 2018. 6, 7", + "[31] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE TIP, 13(4):600-612, 2004. 6", + "[32] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retina decomposition for low-light enhancement. In BMVC, pages 1-12, 2018. 2, 3, 5, 6, 7", + "[33] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. Uretinex-net: Retinex-based" + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "22260", + "bbox": [ + 478, + 945, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "deep unfolding network for low-light image enhancement. In CVPR, pages 5901-5910, 2022. 2, 6, 7", + "[34] Jun Xu, Yingkun Hou, Dongwei Ren, Li Liu, Fan Zhu, Mengyang Yu, Haoqian Wang, and Ling Shao. Star: A structure and texture aware retina model. IEEE TIP, 29:5022-5037, 2020. 2, 3, 6", + "[35] Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. Snr-aware low-light image enhancement. In CVPR, pages 17714-17724, 2022. 1, 3", + "[36] Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In CVPR, pages 3060-3069, 2020. 6", + "[37] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 6", + "[38] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In ACM MM, pages 1632-1640, 2019. 2, 6, 7", + "[39] Zhao Zhang, Huan Zheng, Richang Hong, Mingliang Xu, Shuicheng Yan, and Meng Wang. Deep color consistent network for low-light image enhancement. In CVPR, pages 1899-1908, 2022. 3", + "[40] Zunjin Zhao, Bangshu Xiong, Lei Wang, Qiaofeng Ou, Lei Yu, and Fa Kuang. Retinexdip: A unified deep framework for low-light image enhancement. IEEE TCSVT, 32(3):1076-1088, 2022. 2, 3", + "[41] Anqi Zhu, Lin Zhang, Ying Shen, Yong Ma, Shengjie Zhao, and Yicong Zhou. Zero-shot restoration of underexposed images via robust retina decomposition. In ICME, pages 1-6, 2020. 2, 3, 6, 7" + ], + "bbox": [ + 78, + 90, + 468, + 542 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "22261", + "bbox": [ + 478, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances/443449b8-5cd8-436a-ab74-6d4bb5de9279_model.json b/2023/Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances/443449b8-5cd8-436a-ab74-6d4bb5de9279_model.json new file mode 100644 index 0000000000000000000000000000000000000000..1b293b8013e71b6fffe83358c736aed667740cc3 --- /dev/null +++ b/2023/Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances/443449b8-5cd8-436a-ab74-6d4bb5de9279_model.json @@ -0,0 +1,2277 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.044 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.088, + 0.131, + 0.882, + 0.153 + ], + "angle": 0, + "content": "Learning a Simple Low-light Image Enhancer from Paired Low-light Instances" + }, + { + "type": "text", + "bbox": [ + 0.106, + 0.18, + 0.867, + 0.306 + ], + "angle": 0, + "content": "Zhenqi Fu\\(^{1}\\), Yan Yang\\(^{2}\\), Xiaotong Tu\\(^{1*}\\), Yue Huang\\(^{1}\\), Xinghao Ding\\(^{1}\\), Kai-Kuang Ma\\(^{3}\\) \n\\(^{1}\\)Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, School of Informatics, Xiamen University, China \n\\(^{2}\\)School of Computer Science and Technology, Hangzhou Dianzi University, China \n\\(^{3}\\) School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore \nfuzhenqi@stu.xmu.edu.cn, yangyanyy@hdu.edu.cn, {xttu, yhuang2010, dxh}@xmu.edu.cn, ekkma@ntu.edu.sg" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.34, + 0.314, + 0.356 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.371, + 0.474, + 0.691 + ], + "angle": 0, + "content": "Low-light Image Enhancement (LIE) aims at improving contrast and restoring details for images captured in lowlight conditions. Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image details due to the limited information in a single image and the poor adaptability of handcrafted priors. To this end, we propose PairLIE, an unsupervised approach that learns adaptive priors from low-light image pairs. First, the network is expected to generate the same clean images as the two inputs share the same image content. To achieve this, we impose the network with the Retinex theory and make the two reflectance components consistent. Second, to assist the Retinex decomposition, we propose to remove inappropriate features in the raw image with a simple self-supervised mechanism. Extensive experiments on public datasets show that the proposed PairLIE achieves comparable performance against the state-of-the-art approaches with a simpler network and fewer handcrafted priors. Code is available at: https://github.com/zhengqifu/PairLIE." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.717, + 0.21, + 0.733 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.743, + 0.47, + 0.879 + ], + "angle": 0, + "content": "Images captured under low-light environments always suffer from multiple distortions, such as low contrast, poor visibility, and sensor noise. Those low-light images are unsatisfactory for information transmission because they incur challenges in human visualization and subsequent computer vision tasks [25]. To correct contrast, uncover textures, and remove sensor noise, great efforts have been made in developing Low-light Image Enhancement (LIE) algorithms in the past decades [1,5,6,8,28,35]." + }, + { + "type": "image", + "bbox": [ + 0.502, + 0.339, + 0.892, + 0.535 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.554, + 0.895, + 0.638 + ], + "angle": 0, + "content": "Figure 1. Comparison between the previous solution (a) and the proposed method (b) from the aspect of Retinex theory. The key idea of our method is to learn adaptive priors from low-light image pairs. As a result, our solution needs fewer handcrafted priors and the network is more robust. Note that image pairs are only used in the training phase." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.671, + 0.895, + 0.853 + ], + "angle": 0, + "content": "Histogram-based and Retinex-based approaches are two well-known LIE techniques. The former enhances the contrast of an image by redistributing the luminous intensity on the histogram [3, 14]. The latter decomposes an observed image \\(I\\) into illumination \\(L\\) and reflectance \\(R\\) via \\(I = L\\circ R\\), where \\(\\circ\\) denotes the element-wise multiplication [6, 13, 17]. Specifically, the reflectance component \\(R\\) is assumed to be consistent under different light conditions because \\(R\\) represents the physical properties of the objects. As the Retinex theory can well model color perceptions of human vision, Retinex-based methods have attracted relatively more attention in the LIE community." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Recent years have witnessed great success in developing learning-based LIE algorithms. Among these approaches, most solutions rely on low-light and normal-light image" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.222, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "22252" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.288 + ], + "angle": 0, + "content": "pairs [33, 38]. However, collecting high-quality reference maps in real-world scenarios is time-consuming and expensive [32]. To eliminate the requirement for normal-light images, unsupervised and zero-shot LIE approaches are proposed. Concretely, the former trains a deep neural network using a set of collected low-light samples [7, 18], while the latter only employs the test image itself in the network optimization [40, 41]. Due to the absence of reference images, unsupervised and zero-shot LIE approaches depend on handcrafted priors to guide network training. Nevertheless, due to the complex natural scenes and the limited information in a single low-light image, it is difficult for those methods to attain a high-quality result." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.288, + 0.473, + 0.485 + ], + "angle": 0, + "content": "To tackle the issues of limited information in a single low-light image and the poor adaptability of handcrafted priors, we propose to leverage paired low-light instances to train the LIE network. The main difference between our solution and previous approaches is illustrated in Fig. 1. Note that acquiring paired low-light images will complicate the imaging process since it needs to cope with the misalignment between the two images. Nevertheless, compared with collecting low-light and normal-light image pairs, our solution is more practical. Additionally, twice-exposure images provide useful information for solving the LIE task. As a result, our solution can reduce the demand for handcrafted priors and improve the adaptability of the network." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.485, + 0.473, + 0.756 + ], + "angle": 0, + "content": "With paired low-light instances, we propose a novel learning-based LIE method, termed PairLIE. The core insight of our approach is to sufficiently exploit priors from paired low-light images. Therefore, we consider employing the Retinex theory and deep learning to decompose low-light images into illumination and reflectance components. First, since the two low-light inputs share the same content, the estimated reflectance components are expected to be consistent. Second, instead of directly imposing the Retinex decomposition on original low-light images, we adopt a simple self-supervised mechanism to remove inappropriate features and implement the Retinex decomposition on the optimized image. This can avoid sub-optimal estimations because the Retinex model has limitations in low-light modeling. As a result, with fewer prior constraints and a simpler network, the proposed PairLIE achieves competitive performance in public LIE datasets. In summary, the contributions of this paper are as follows:" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.767, + 0.468, + 0.811 + ], + "angle": 0, + "content": "- We propose a generic LIE solution using paired low-light images. The network is based on Retinex decomposition with several novel reference-free losses." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.819, + 0.468, + 0.849 + ], + "angle": 0, + "content": "- To achieve an accurate decomposition, we first project the original image to remove inappropriate features." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.856, + 0.468, + 0.901 + ], + "angle": 0, + "content": "- With fewer manually designed priors and a simpler network, the proposed solution achieves comparable performance against state-of-the-art methods." + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.767, + 0.468, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.09, + 0.642, + 0.107 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.117, + 0.892, + 0.162 + ], + "angle": 0, + "content": "Over the decades, extensive LIE methods have been presented, which can be roughly categorized into conventional approaches and learning-based techniques." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.174, + 0.712, + 0.189 + ], + "angle": 0, + "content": "2.1. Conventional Methods" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.199, + 0.892, + 0.335 + ], + "angle": 0, + "content": "Histogram-based techniques perform light enhancement by expanding the dynamic range of an image. For example, Park et al. [23] separated the dynamic range of the histogram into several parts and resized the gray-scale range based on the area ratio. Arici et al. [1] introduced penalty terms to avoid the unnatural look and visual artifacts of the enhanced image. Lee et al. [14] applied the layered difference representation of 2D histograms to amplify the gray-level differences between adjacent pixels." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.336, + 0.893, + 0.593 + ], + "angle": 0, + "content": "Retinex-based methods first decompose the low-light image into reflectance and illumination components. Subsequently, those approaches either consider the reflectance as the enhanced image or adjust the illumination and then recompose it with reflectance to generate the enhanced result. Wang et al. [29] proposed a LIE algorithm to promote naturalness and enhance details for non-uniform illumination images. Fu et al. [6] used a weighted variational model to preserve the reflectance with more details. Guo et al. [8] first estimated the illumination by calculating the maximum value in R, G, and B channels. Then, they refined the illumination map by imposing a structure prior. Li et al. [17] improved the performance of LIE by introducing a noise map in the Retinex model. Xu et al. [34] proposed a texture-aware Retinex model solved by an alternating optimization algorithm. Hao et al. [9] presented a novel Retinex-based LIE method performed in a semi-decoupled way." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.605, + 0.729, + 0.62 + ], + "angle": 0, + "content": "2.2. Learning-based Methods" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.629, + 0.895, + 0.902 + ], + "angle": 0, + "content": "Commonly, learning-based LIE methods rely on paired low-light and normal-light images. Lore et al. [19] designed a stacked sparse denoising auto-encoder to enhance low-light images. The proposed model was trained on synthetic image pairs. Wei et al. [32] first built a real-world low-light image enhancement dataset including low-light and normal-light image pairs. Then, they trained an end-to-end network in a fully-supervised way. Wang et al. [28] introduced an intermediate illumination map in the network to associate the low-light input with the expected enhancement result. Chen et al. [4] collected a dataset of short-exposure low-light images, with corresponding long-exposure reference maps. Based on the dataset, the authors developed a fully-convolutional network for enhancing low-light images. Zhang et al. [38] proposed a supervised approach to decompose the low-light image into illumination and reflectance components. Wu et al. [33] proposed a Retinex-based deep unfolding network to promote adaptability and" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "22253" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.086, + 0.093, + 0.885, + 0.339 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.35, + 0.893, + 0.42 + ], + "angle": 0, + "content": "Figure 2. The architecture of PairLIE. In the training phase, P-Net is first adopted to remove inappropriate features of the original image. L-Net and R-Net are then employed to estimate the illumination and reflectance components. Three loss functions are used to guide the network optimization, including a self-supervised projection loss \\(\\mathcal{L}_P\\), a reflectance consistency loss \\(\\mathcal{L}_C\\), and a Retinex loss \\(\\mathcal{L}_R\\). In the testing stage, given a low-light image, P-Net, L-Net, and R-Net are used to decompose the input into reflectance and illumination. Subsequently, PairLIE adjusts the illumination and recomposes it with the reflectance to capture the enhanced image." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.446, + 0.47, + 0.522 + ], + "angle": 0, + "content": "efficiency. Xu et al. [35] combined signal-to-noise-ratio-aware transformers and convolutional networks to enhance low-light images. Zhang et al. [39] proposed a color-consistent network to mitigate the color difference between the enhanced image and ground-truth." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.523, + 0.471, + 0.795 + ], + "angle": 0, + "content": "Recently, unsupervised (also known as self-supervised) networks are developed to eliminate the requirement for reference images. For example, Zhu et al. [41] proposed a zero-shot LIE approach that trained a deep network using the input image itself. Guo et al. [7] proposed a reference-free LIE approach based on curve estimation. Their network was optimized with a set of non-reference loss functions. Liu et al. [18] proposed a lightweight LIE network by integrating the unrolling techniques and prior architecture search strategies. Jiang et al. [12] proposed a LIE method based on the generative adversarial network and unpaired training data. Zhao et al. [40] designed a unified zero-reference network for enhancing low-light images based on Deep image Prior (DIP) [27]. Jiang et al. [11] presented a Retinex-based unsupervised decomposition and correction network for LIE. Ma et al. [22] established a cascaded illumination estimation process to achieve fast, flexible, and robust LIE in complex scenarios." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.814, + 0.251, + 0.831 + ], + "angle": 0, + "content": "3. Proposed Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.47, + 0.902 + ], + "angle": 0, + "content": "We first give the problem definition of using low-light image pairs for LIE. Then, we detail the pipeline and loss functions. Finally, we introduce the dataset for training the proposed network." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.446, + 0.88, + 0.463 + ], + "angle": 0, + "content": "3.1. Retinex Model with Paired Low-light Images" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.469, + 0.893, + 0.499 + ], + "angle": 0, + "content": "According to the Retinex theory, low-light image \\(I\\) can be decomposed into illumination \\(L\\) and reflectance \\(R\\) as:" + }, + { + "type": "equation", + "bbox": [ + 0.657, + 0.507, + 0.891, + 0.522 + ], + "angle": 0, + "content": "\\[\nI = L \\circ R, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.53, + 0.893, + 0.666 + ], + "angle": 0, + "content": "where \\(\\circ\\) denotes the element-wise multiplication. Illumination \\(L\\) describes the light intensity of objects. \\(L\\) should be piece-wise continuous and textureless. Reflectance \\(R\\) represents the physical properties of the objects. \\(R\\) should contain textures and details in the observed image. The Retinex decomposition is highly ill-posed. Various methods have been proposed to handle this problem [6, 17, 29, 34]. A generic solution of the Retinex decomposition is to minimize the following energy function:" + }, + { + "type": "equation", + "bbox": [ + 0.522, + 0.673, + 0.891, + 0.698 + ], + "angle": 0, + "content": "\\[\n\\underset {L, R} {\\operatorname {a r g m i n}} \\| L \\circ R - I \\| _ {2} + \\lambda_ {L} f _ {L} (L) + \\lambda_ {R} f _ {R} (R), \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.892, + 0.811 + ], + "angle": 0, + "content": "where \\( f_{L} \\) and \\( f_{R} \\) are prior constraints for \\( L \\) and \\( R \\), respectively. \\( \\lambda_{L} \\) and \\( \\lambda_{R} \\) denote the weights. \\( \\| L \\circ R - I \\|_{2} \\) is the data-fidelity term between the input and reconstructed image. To achieve a reasonable decomposition, most of the LIE approaches focus on introducing assorted prior constraints enforced on the two components, such as priors of structure [17], smooth [32], and bright channel [15, 26]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.901 + ], + "angle": 0, + "content": "However, handcrafted priors are commonly not adaptive enough due to the diverse natural scenes and light conditions. In this paper, instead of exploiting handcrafted priors for \\( L \\) and \\( R \\) from a single image, we apply paired low-light images to automatically learn adaptive priors in a data-driven fashion. Those low-light image pairs share the same" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "22254" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.136 + ], + "angle": 0, + "content": "scene content but different illumination. Mathematically, Retinex decomposition with low-light image pairs can be expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.217, + 0.139, + 0.469, + 0.171 + ], + "angle": 0, + "content": "\\[\n\\left\\{ \\begin{array}{l} I _ {1} = L _ {1} \\circ R \\\\ I _ {2} = L _ {2} \\circ R \\end{array} , \\right. \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.179, + 0.469, + 0.239 + ], + "angle": 0, + "content": "where \\( I_{1} \\) and \\( I_{2} \\) are low-light image pairs, which share the reflectance component \\( R \\). Intuitively, Eq. 3 can address the decomposition better than Eq. 1 because more information and constraints are introduced." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.251, + 0.259, + 0.266 + ], + "angle": 0, + "content": "3.2. Network Structure" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.276, + 0.47, + 0.547 + ], + "angle": 0, + "content": "The whole pipeline of our method is illustrated in Fig. 2. We use L-Net and R-Net to estimate the illumination and reflectance components, respectively. L-Net and R-Net are very similar and simple, both of which contain five convolutional layers. The activation function of the first four convolutional layers is ReLU. L-Net and R-Net end with a sigmoid layer to normalize the output into [0, 1]. According to the Retinex theory, the three color channels are assumed to have the same illumination. Therefore, the output channel of L-Net is set as 1 while that of R-Net is set as 3. Note that, this paper does not focus on designing modernistic network structures. In contrast, we aim at providing a generic solution for LIE under paired low-light instances. In our experiments, we found those simple network already achieves comparable performance. Apart from L-Net and R-Net, we introduce P-Net to remove inappropriate features from the original image. Specifically, the structure of the P-Net is identical to the R-Net." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.549, + 0.47, + 0.745 + ], + "angle": 0, + "content": "In the training phase, the original low-light image pairs \\(I_{1}\\) and \\(I_{2}\\) are first taken into the P-Net, yielding two optimized versions \\(i_{1}\\) and \\(i_{2}\\). Then, L-Net and R-Net are applied to estimate the latent illumination \\((L_{1}\\) and \\(L_{2})\\) and reflectance \\((R_{1}\\) and \\(R_{2})\\). To optimize the network, three loss functions are designed in PairLIE. The first is the projection loss \\(\\mathcal{L}_P\\) that measures the difference between \\(I\\) and \\(i\\). The second is the reflectance consistency loss \\(\\mathcal{L}_C\\) calculated based on \\(R_{1}\\) and \\(R_{2}\\). The third is the Retinex loss \\(\\mathcal{L}_R\\) that restricts the decomposed components to satisfy the Retinex theory. In the testing period, given a low-light image, P-Net, R-Net, and L-Net are applied to calculate the final enhanced image:" + }, + { + "type": "equation", + "bbox": [ + 0.182, + 0.758, + 0.469, + 0.775 + ], + "angle": 0, + "content": "\\[\nI _ {e n} = g (L) \\circ R = L ^ {\\lambda} \\circ R, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.789, + 0.469, + 0.82 + ], + "angle": 0, + "content": "where \\(\\lambda\\) is the illumination correction factor, \\(I_{en}\\) denotes the enhanced image." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.831, + 0.234, + 0.848 + ], + "angle": 0, + "content": "3.3. Projection Loss" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Instead of performing the deep Retinex decomposition on the original low-light image, we propose to first remove inappropriate features to ensure the input can be accurately" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.089, + 0.892, + 0.166 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.166, + 0.891, + 0.239 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.24, + 0.891, + 0.316 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.331, + 0.892, + 0.373 + ], + "angle": 0, + "content": "Figure 3. Example of projected images and corresponding difference maps. A smaller value indicates a higher similarity between the projected and original images." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.4, + 0.89, + 0.431 + ], + "angle": 0, + "content": "decomposed by an ideal Retinex model (i.e., Eq. 1). Specifically, we design the following loss to guide projection:" + }, + { + "type": "equation", + "bbox": [ + 0.634, + 0.441, + 0.891, + 0.46 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {p} = \\left\\| I _ {1} - i _ {1} \\right\\| _ {2} ^ {2}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.472, + 0.892, + 0.593 + ], + "angle": 0, + "content": "where \\( i_1 \\) refers to the projected image. The loss function \\( \\mathcal{L}_p \\) transforms the original image to a specific image that is more suitable for the Retinex decomposition. Concretely, this image is expected to be noise free since we do not consider the noise component in the Retinex model. Besides, some useless features are also discarded in this stage. Our projection loss can be interpreted from the view of error reallocation, which can be expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.567, + 0.606, + 0.891, + 0.688 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\operatorname * {a r g m i n} _ {L, R} \\| I - L \\circ R - \\varepsilon \\| \\\\ = \\underset {L, R} {\\operatorname {a r g m i n}} \\| I - i + i - L \\circ R - \\varepsilon \\| \\tag {6} \\\\ \\leq \\left\\| I - i - \\varepsilon \\right\\| + \\operatorname * {a r g m i n} _ {L, R} \\left\\| i - L \\circ R \\right\\|, \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.701, + 0.892, + 0.836 + ], + "angle": 0, + "content": "where \\(\\varepsilon\\) denotes the error. According to Eq. 6, the goal of \\(\\mathcal{L}_p\\) is to move the error from the Retinex decomposition to the projection stage. Fig. 3 gives an example of the calculated projected image and corresponding difference map. As can be seen, the projected image is highly similar to the original one, and sensor noise is removed since it cannot be formulated by the Retinex model. Note that the projection loss needs to cooperate with the other constraints to avoid a trivial solution, i.e., \\(i_1 = I_1\\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.847, + 0.76, + 0.864 + ], + "angle": 0, + "content": "3.4. Reflectance Consistency Loss" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Reflectance consistency loss \\(\\mathcal{L}_C\\) is calculated based on low-light image pairs and the Retinex theory. Compared" + }, + { + "type": "page_number", + "bbox": [ + 0.479, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22255" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.47, + 0.138 + ], + "angle": 0, + "content": "with handcrafted priors, \\(\\mathcal{L}_C\\) is more accurate and adaptive because it reveals the physical properties of the objects. Mathematically, \\(\\mathcal{L}_C\\) is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.204, + 0.15, + 0.47, + 0.17 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {C} = \\left\\| R _ {1} - R _ {2} \\right\\| _ {2} ^ {2}, \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.183, + 0.47, + 0.244 + ], + "angle": 0, + "content": "where \\( R_{1} \\) and \\( R_{2} \\) refer to the reflectance components of paired low-light images. \\( \\mathcal{L}_C \\) enforces the network to predict the same reflectance components because low-light image pairs share the same objects." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.245, + 0.471, + 0.456 + ], + "angle": 0, + "content": "Since sensor noise hidden in dark regions will be amplified when the contrast is improved. To cope with the noise issue, existing approaches either add a smoothness term on the estimated reflectance or perform a denoise operation after the enhancement. In our method, the sensor noise can be implicitly removed by Eq. 7. This is because the two low-light images contain independent noise of the same scene. As discussed in [16], paired noisy images can be leveraged to train denoising models. This is because noise is random and different in two images, the deep network cannot fit the noise in one image to another. In our case, the two low-light images can help each other to remove noise during the Retinex decomposition. Therefore, PairLIE does not require additional handcrafted constraints of noise." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.471, + 0.214, + 0.485 + ], + "angle": 0, + "content": "3.5. Retinex Loss" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.495, + 0.47, + 0.556 + ], + "angle": 0, + "content": "Since PairLIE employs the Retinex theory to decompose low-light images into illumination and reflectance components, some basic constraints of Retinex decomposition are introduced, which can be formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.116, + 0.568, + 0.47, + 0.61 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {R} = \\| R \\circ L - i \\| _ {2} ^ {2} + \\| R - i / s t o p g r a d (L) \\| _ {2} ^ {2} \\tag {8} \\\\ + \\left\\| L - L _ {0} \\right\\| _ {2} ^ {2} + \\left\\| \\nabla L \\right\\| _ {1}, \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.623, + 0.471, + 0.864 + ], + "angle": 0, + "content": "where \\(i\\) denotes the projected image, \\(L_0\\) refers to the initial estimation of illumination, \\(\\nabla\\) represents the horizontal and vertical gradients. First, the decomposed components should satisfy the requirement for reconstructing the input image. Therefore, a reconstruction term \\(\\| R\\circ L - i\\| _2^2\\) is applied to ensure a reasonable decomposition. Once the illumination is estimated, the reflectance can be calculated through the pixel-wise division between the low-light image and its illumination map. Therefore, we additionally add a term \\(\\| R - i / stopgrad(L)\\| _2^2\\) to guide the decomposition. Note that we stop the gradient of the illumination to make the training stable. Different from most existing methods that use a lot of handcrafted priors, PairLIE only imposes a smooth term and an initialization term on \\(L\\). Specifically, the initialized illumination \\(L_{0}\\) is calculated via the maximum of the R, G, and B channels:" + }, + { + "type": "equation", + "bbox": [ + 0.192, + 0.879, + 0.47, + 0.904 + ], + "angle": 0, + "content": "\\[\nL _ {0} = \\max _ {c \\in \\{R, G, B \\}} I ^ {c} (x). \\tag {9}\n\\]" + }, + { + "type": "image", + "bbox": [ + 0.511, + 0.092, + 0.882, + 0.26 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.273, + 0.892, + 0.302 + ], + "angle": 0, + "content": "Figure 4. Statistics concerning sequence length of the collected data. Most sequences have 2 to 4 low-light images." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.324, + 0.635, + 0.339 + ], + "angle": 0, + "content": "3.6. Overall Loss" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.347, + 0.892, + 0.377 + ], + "angle": 0, + "content": "The overall loss function for training our PairLIE is a liner combination of each loss:" + }, + { + "type": "equation", + "bbox": [ + 0.584, + 0.386, + 0.892, + 0.402 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {A l l} = w _ {0} \\mathcal {L} _ {P} + w _ {1} \\mathcal {L} _ {C} + w _ {2} \\mathcal {L} _ {R}, \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.41, + 0.78, + 0.424 + ], + "angle": 0, + "content": "where \\(w_{0}, w_{1}\\), and \\(w_{2}\\) denote the weights." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.432, + 0.727, + 0.448 + ], + "angle": 0, + "content": "3.7. Training Data Collection" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.455, + 0.892, + 0.622 + ], + "angle": 0, + "content": "We collect low-light image pairs from SICE (part2) [2] and LOL (training set) [32], which contain multi-exposure images. These datasets adopt some specific operators to deal with the misalignment caused by camera shaking or object moving. Note that the SICE dataset consists of both under and over-exposed images. We only select under-exposure and well-aligned cases for constructing the training set. As a result, we collect 324 sequences (a total of 1000 low-light images). As shown in Fig. 4, each sequence includes 2 to 6 samples. In the training phase, we randomly select two images from each sequence to constitute a pair." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.634, + 0.634, + 0.65 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.659, + 0.892, + 0.735 + ], + "angle": 0, + "content": "We first describe implementation details, evaluation datasets, and performance criteria. Then, we present the quantitative and qualitative comparisons with state-of-the-art methods. Finally, we conduct ablative experiments to validate each component." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.742, + 0.718, + 0.758 + ], + "angle": 0, + "content": "4.1. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.765, + 0.892, + 0.902 + ], + "angle": 0, + "content": "We implement PairLIE with PyTorch. In the training phase, we randomly crop images to the size of \\(128 \\times 128\\). A batch size of 1 is applied. We use ADAM with the initial learning rate of \\(1 \\times 10^{-4}\\) to optimize the network. The number of training epochs is set to 400. The learning rate is half-decayed per 100 epochs. The default correction factor \\(\\lambda\\) is 0.2. In extremely dark cases such as the LOL dataset, we set \\(\\lambda = 0.14\\). As for the hyper-parameter \\(w_0\\), \\(w_1\\), and \\(w_2\\) in Eq. 10, we set \\(w_0 = 500\\), \\(w_1 = w_2 = 1\\), empirically." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "22256" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.118, + 0.088, + 0.855, + 0.335 + ], + "angle": 0, + "content": "
MethodTypeLOLSICE
PSNR↑SSIM↑LPIPS↓DeltaE ↓PSNR↑SSIM↑LPIPS↓DeltaE ↓
SDD [9]T13.340.6370.74321.8315.350.7410.23216.08
STAR [34]T12.910.5180.36623.4615.170.7270.24616.35
MBLLEN [20]S17.860.7270.22513.6813.640.6320.29718.60
RetinexNet [32]S17.550.6480.37912.6919.890.7830.2768.715
GLADNet [30]S19.720.6800.32112.2819.980.8370.2038.947
KinD [38]S17.650.7750.17112.4921.100.8380.1958.009
DRBN [36]S16.290.5510.26013.4415.580.5220.28913.78
URetinexNet [33]S19.840.8260.12810.6521.640.8430.1927.728
ZeroDCE [7]U14.860.5590.33518.8118.690.8100.20711.93
RRDNet [41]U11.400.4570.36226.4313.280.6780.22119.64
RUAS [18]U16.400.5000.27016.8513.180.7340.36316.81
SCI [22]U14.780.5220.33919.5215.950.7870.23513.71
EnlightenGAN [12]U17.480.6510.32214.5018.730.8220.21610.42
PairLIE (Ours)U19.510.7360.24810.8021.320.8400.2167.835
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.346, + 0.892, + 0.375 + ], + "angle": 0, + "content": "Table 1. Quantitative comparisons with state-of-the-art methods on LOL and SICE datasets. \"T\", \"S\", and \"U\" represent \"Traditional\", \"Supervised\", and \"Unsupervised\" methods, respectively. The top three results are marked in bold." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.4, + 0.279, + 0.415 + ], + "angle": 0, + "content": "4.2. Datasets and Criteria" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.424, + 0.471, + 0.606 + ], + "angle": 0, + "content": "As described before, low-light image pairs collected from SICE and LOL are applied to train PairLIE. We select another 50 sequences (150 images) from SICE and use the official evaluation set (15 images) of LOL to measure the model performance. Since SICE and LOL contains reference images, we employ PSNR, SSIM [31], LPIPS [37], and DeltaE with CIE2000 standard [24] to objectively evaluate the performance of each method. A higher PSNR/SSIM score indicates the result is closer to the reference. A lower LPIPS/DeltaE value denotes better enhancement performance. Furthermore, we adopt the MEF dataset [21] for visual comparisons." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.616, + 0.267, + 0.632 + ], + "angle": 0, + "content": "4.3. Compared Methods" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.64, + 0.47, + 0.777 + ], + "angle": 0, + "content": "PairLIE is compared with 13 state-of-the-art LIE methods, which can be divided into the following three categories: traditional methods (SDD [9], STAR [34]), supervised approaches (MBLLEN [20], RetinexNet [32], GALDNet [30], KinD [38], DRBN [36], URetinexNet [33]), and unsupervised methods (Zero-DCE [7], RRDNet [41], RUAS [18], SCI [22], and EnlightenGAN [12]). Note that the results of all those methods are reproduced by using the official codes with recommended parameters." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.787, + 0.317, + 0.803 + ], + "angle": 0, + "content": "4.4. Quantitative Comparisons" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Tab. 1 lists the quantitative performance of the LOL and SICE datasets. As can be observed, traditional and unsupervised methods obtain relatively poor results. This is reasonable because it is difficult to learn an accurate enhancement model without the reference image. Besides, the performance of those solutions is highly dependent on the used" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.401, + 0.892, + 0.522 + ], + "angle": 0, + "content": "prriors. However, hand-crafted features may not be adaptive enough for assorted light conditions. In Tab. 1, PairLIE achieves the best performance among the five unsupervised methods and gets competitive results compared with supervised approaches. Since paired low-light images provide sufficient information for solving the LIE task, PairLIE can reduce the demand for handcrafted priors. As a result, PairLIE achieves significant performance improvement." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.53, + 0.692, + 0.546 + ], + "angle": 0, + "content": "4.5. Visual Comparisons" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.553, + 0.892, + 0.795 + ], + "angle": 0, + "content": "Fig. 5 illustrates the visual comparisons of different LIE methods. Our observations are as follows: 1) The proposed method achieves visually pleasing results in terms of brightness, color, contrast, and naturalness. While other methods fail to cope with the extreme black light in this case. 2) Although supervised approaches GLADNet, KinD, and URetinexNet show good performance on LOL and SICE datasets according to Tab. 1, their generalization ability may be limited as supervised learning is sensitive to the data distribution. We further show an example of noise suppression in Fig. 6. As can be seen, although PairLIE does not introduce any handcrafted priors about the noise, our method can successfully suppress sensor noise in dark regions and the result is clear and natural. In contrast, the competitors either amplify noise or fail in correcting color and contrast, leading to poor visual quality." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.802, + 0.755, + 0.818 + ], + "angle": 0, + "content": "4.6. Decomposition Visualization" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.826, + 0.892, + 0.901 + ], + "angle": 0, + "content": "We visualize the reflectance and illumination components to validate the effectiveness of our method. As shown in Fig. 7, the reflectance contains rich textures and details while the illumination is piece-wise continuous and textureless, demonstrating PairLIE can accurately decompose the" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22257" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.128, + 0.089, + 0.269, + 0.171 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.178, + 0.172, + 0.22, + 0.183 + ], + "angle": 0, + "content": "Original" + }, + { + "type": "image", + "bbox": [ + 0.272, + 0.089, + 0.412, + 0.171 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.329, + 0.172, + 0.356, + 0.182 + ], + "angle": 0, + "content": "SDD" + }, + { + "type": "image", + "bbox": [ + 0.415, + 0.089, + 0.556, + 0.171 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.459, + 0.172, + 0.512, + 0.182 + ], + "angle": 0, + "content": "GLADNet" + }, + { + "type": "image", + "bbox": [ + 0.559, + 0.089, + 0.699, + 0.171 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.615, + 0.172, + 0.642, + 0.182 + ], + "angle": 0, + "content": "KinD" + }, + { + "type": "image", + "bbox": [ + 0.702, + 0.089, + 0.843, + 0.171 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.746, + 0.172, + 0.799, + 0.182 + ], + "angle": 0, + "content": "URetinext" + }, + { + "type": "image", + "bbox": [ + 0.128, + 0.183, + 0.269, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.173, + 0.267, + 0.225, + 0.278 + ], + "angle": 0, + "content": "ZeroDCE" + }, + { + "type": "image", + "bbox": [ + 0.272, + 0.183, + 0.412, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.325, + 0.267, + 0.359, + 0.278 + ], + "angle": 0, + "content": "RUAS" + }, + { + "type": "image", + "bbox": [ + 0.415, + 0.183, + 0.555, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.474, + 0.267, + 0.496, + 0.278 + ], + "angle": 0, + "content": "SCI" + }, + { + "type": "image", + "bbox": [ + 0.559, + 0.183, + 0.699, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.591, + 0.267, + 0.667, + 0.278 + ], + "angle": 0, + "content": "EnlightenGAN" + }, + { + "type": "image", + "bbox": [ + 0.702, + 0.183, + 0.842, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.734, + 0.267, + 0.81, + 0.278 + ], + "angle": 0, + "content": "PairLIE (Ours)" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.294, + 0.892, + 0.323 + ], + "angle": 0, + "content": "Figure 5. Visual comparisons of different LIE methods. SDD [9], RetinexNet [32], GALDNet [30], KinD [38], URetinexNet [33], Zero-DCE [7], RRDNet [41], RUAS [18], SCI [22], EnlightenGAN [12], and PairLIE (Ours)." + }, + { + "type": "image", + "bbox": [ + 0.127, + 0.336, + 0.363, + 0.411 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.224, + 0.412, + 0.267, + 0.422 + ], + "angle": 0, + "content": "Original" + }, + { + "type": "image", + "bbox": [ + 0.369, + 0.336, + 0.603, + 0.411 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.459, + 0.412, + 0.511, + 0.422 + ], + "angle": 0, + "content": "ZeroDCE" + }, + { + "type": "image", + "bbox": [ + 0.608, + 0.337, + 0.843, + 0.411 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.705, + 0.412, + 0.741, + 0.421 + ], + "angle": 0, + "content": "RUAS" + }, + { + "type": "image", + "bbox": [ + 0.128, + 0.423, + 0.362, + 0.496 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.188, + 0.497, + 0.211, + 0.507 + ], + "angle": 0, + "content": "SCI" + }, + { + "type": "image", + "bbox": [ + 0.368, + 0.423, + 0.603, + 0.496 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.446, + 0.497, + 0.525, + 0.509 + ], + "angle": 0, + "content": "EnlightenGAN" + }, + { + "type": "image", + "bbox": [ + 0.608, + 0.423, + 0.842, + 0.496 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.685, + 0.497, + 0.765, + 0.509 + ], + "angle": 0, + "content": "PairLIE (Ours)" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.523, + 0.89, + 0.551 + ], + "angle": 0, + "content": "Figure 6. Performance comparisons in noise removal. Zoom in for the best view. Zero-DCE [7], RUAS [18], SCI [22], EnlightenGAN [12], and PairLIE (Ours). Our result is visually satisfactory without obvious noise." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.578, + 0.469, + 0.684 + ], + "angle": 0, + "content": "low-light image. We further show the enhancement results with different correction factors. With the increase of \\(\\lambda\\), the image brightness decreases gradually. When \\(\\lambda\\) is larger than 0.4 or smaller than 0.2, the enhanced images show obvious under/over-enhancement effects. In this work, the default \\(\\lambda\\) is set as 0.2. Note that users can adjust \\(\\lambda\\) in the testing phase according to their personal preferences." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.695, + 0.241, + 0.71 + ], + "angle": 0, + "content": "4.7. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.719, + 0.469, + 0.811 + ], + "angle": 0, + "content": "We conduct ablation studies under different settings to understand how each component affects performance. We have tried the following three variations over the original PairLIE. 1) Setting A: without \\(\\mathcal{L}_C\\). 2) Setting B: without \\(\\mathcal{L}_P\\). 3) Setting C: without prior terms, i.e., removing \\(\\| L - L_0\\| _2^2\\) and \\(\\| \\nabla L\\| _1\\) from \\(\\mathcal{L}_R\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.469, + 0.902 + ], + "angle": 0, + "content": "Tab. 2 and Fig. 8 report the results of our ablation studies on the LOL dataset. We can observe that our approach significantly outperforms setting A, which demonstrates the effectiveness of learning adaptive priors using low-light image pairs. Compared with setting B, PairLIE achieves slight performance improvement. The results of setting B suggest" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.578, + 0.892, + 0.789 + ], + "angle": 0, + "content": "that P-Net can assist the Retinex decomposition, but it is not necessary for stably training the network. To further understand the role of P-Net, we show the reconstruction error \\(\\kappa\\) in Fig. 9. The blue line denotes the decomposition using original low-light images, i.e., \\(\\kappa = |L\\circ R - I|\\), while the orange line refers to the decomposition using projected images, i.e., \\(\\kappa = |L\\circ R - i|\\). As can be seen, the proposed method obtains a smaller reconstruction error than the baseline. Therefore, P-Net can remove inappropriate features and the output is more suitable for the Retinex decomposition. The results of Setting C show that when priors terms are removed, the performance drops significantly. This is because the Rrtinex decomposition requires some basic priors to initialize and restrain the illumination." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.801, + 0.625, + 0.815 + ], + "angle": 0, + "content": "4.8. Discussions" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.892, + 0.902 + ], + "angle": 0, + "content": "Acquiring low-light image pairs. Although PairLIE achieves promising performance with fewer handcrafted priors and a simpler network, collecting low-light image pairs is relatively expensive compared with recording a signal low-light image. This problem can be partly solved by" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "22258" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.111, + 0.089, + 0.861, + 0.259 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.27, + 0.895, + 0.3 + ], + "angle": 0, + "content": "Figure 7. Visualization of the Retinex decomposition. The enhanced results are presented under different correction factors. We set the default \\(\\lambda\\) as 0.2, but users can adjust the \\(\\lambda\\) according to their preferences." + }, + { + "type": "image", + "bbox": [ + 0.111, + 0.312, + 0.861, + 0.397 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.408, + 0.893, + 0.438 + ], + "angle": 0, + "content": "Figure 8. Visual comparisons of the ablation studies. Setting A: without \\(\\mathcal{L}_C\\). Setting B: without \\(\\mathcal{L}_P\\). Setting C: without prior terms. Our result is visually closer to the reference." + }, + { + "type": "table", + "bbox": [ + 0.12, + 0.459, + 0.429, + 0.54 + ], + "angle": 0, + "content": "
MethodPSNR↑SSIM↑
Setting A17.700.567
Setting B19.500.724
Setting C14.690.684
PairLIE19.510.736
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.55, + 0.47, + 0.579 + ], + "angle": 0, + "content": "Table 2. Quantitative results of ablation studies on the LOL dataset. The best results are marked in bold." + }, + { + "type": "image", + "bbox": [ + 0.078, + 0.598, + 0.472, + 0.757 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.768, + 0.47, + 0.798 + ], + "angle": 0, + "content": "Figure 9. Visualization of the reconstruction error. Baseline refers to the decomposition with original low-light images." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.471, + 0.903 + ], + "angle": 0, + "content": "generating training pairs from a single low-light image. For example, one can use neighbor masks [10] to generate two similar low-light images from a degraded observation. Additionally, specific recorruption techniques can be leveraged to capture paired training instances." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.463, + 0.895, + 0.6 + ], + "angle": 0, + "content": "Illumination components. PairLIE solves the Retinex decomposition with paired low-light images. The basic idea is to generate the same reflectance components since the reflectance is independent of the light. However, the relationship between illumination components is not sufficiently exploited in this paper. Illumination plays a critical role in the Retinex model. Therefore, we believe that PairLIE can be further improved by introducing new constraints between two illumination maps." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.613, + 0.628, + 0.629 + ], + "angle": 0, + "content": "5. Conclusions" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.638, + 0.895, + 0.834 + ], + "angle": 0, + "content": "This paper proposes PairLIE, a reference-free approach for low-light image enhancement that benefits from both Retinex-based and learning-based solutions. By learning adaptive constraints from low-light image pairs, PairLIE reduces the dependence on handcrafted priors and thus generalizes well on various scenes. To assist the decomposition, PairLIE first removes inappropriate features in the original image and then implements the decomposition on the optimized image. Extensive experiments on public benchmarks show that PairLIE outperforms the state-of-the-art unsupervised methods significantly. In future works, we will concentrate on exploiting effective priors of illumination in a data-driven fashion." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.895, + 0.9 + ], + "angle": 0, + "content": "Acknowledgements: The work was supported partly by the National Natural Science Foundation of China under Grants 82172033, U19B2031, 61971369, 52105126, 82272071, and 62271430." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "22259" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.175, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.169 + ], + "angle": 0, + "content": "[1] Tarik Arici, Salih Dikbas, and Yucel Altunbasak. A histogram modification framework and its application for image contrast enhancement. IEEE TIP, 18(9):1921-1935, 2009. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.172, + 0.468, + 0.211 + ], + "angle": 0, + "content": "[2] Jianrui Cai, Shuhang Gu, and Lei Zhang. Learning a deep single image contrast enhancer from multi-exposure images. IEEE TIP, 27(4):2049-2062, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.215, + 0.468, + 0.253 + ], + "angle": 0, + "content": "[3] Turgay Celik and Tardi Tjahadi. Contextual and variational contrast enhancement. IEEE TIP, 20(12):3431-3441, 2011. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.256, + 0.468, + 0.295 + ], + "angle": 0, + "content": "[4] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. In CVPR, pages 3291-3300, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.298, + 0.468, + 0.352 + ], + "angle": 0, + "content": "[5] Xueyang Fu, Delu Zeng, Yue Huang, Yinghao Liao, Xinghao Ding, and John Paisley. A fusion-based enhancing method for weakly illuminated images. Signal Processing, 129:82-96, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.355, + 0.468, + 0.408 + ], + "angle": 0, + "content": "[6] Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, and Xinghao Ding. A weighted variational model for simultaneous reflectance and illumination estimation. In CVPR, pages 2782-2790, 2016. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.411, + 0.468, + 0.464 + ], + "angle": 0, + "content": "[7] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In CVPR, pages 1777-1786, 2020. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.467, + 0.468, + 0.506 + ], + "angle": 0, + "content": "[8] Xiaojie Guo, Yu Li, and Haibin Ling. Lime: Low-light image enhancement via illumination map estimation. IEEE TIP, 26(2):982-993, 2017. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.509, + 0.468, + 0.55 + ], + "angle": 0, + "content": "[9] Shijie Hao, Xu Han, Yanrong Guo, Xin Xu, and Meng Wang. Low-light image enhancement with semi-decoupled decomposition. IEEE TMM, 22(12):3025-3038, 2020. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.552, + 0.468, + 0.605 + ], + "angle": 0, + "content": "[10] Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, and Jianzhuang Liu. Neighbor2neighbor: Self-supervised denoising from single noisy images. In CVPR, pages 14781-14790, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.607, + 0.468, + 0.661 + ], + "angle": 0, + "content": "[11] Qiuping Jiang, Yudong Mao, Runmin Cong, Wenqi Ren, Chao Huang, and Feng Shao. Unsupervised decomposition and correction network for low-light image enhancement. IEEE TITS, pages 1-16, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.664, + 0.468, + 0.73 + ], + "angle": 0, + "content": "[12] Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang Wang. *Enlightengan: Deep light enhancement without paired supervision*. *IEEE TIP*, 30:2340-2349, 2021. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.733, + 0.468, + 0.759 + ], + "angle": 0, + "content": "[13] Edwin H Land. The retina theory of color vision. Scientific American, 237(6):108-129, 1977. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.762, + 0.468, + 0.802 + ], + "angle": 0, + "content": "[14] Chulwoo Lee, Chul Lee, and Chang-Su Kim. Contrast enhancement based on layered difference representation of 2d histograms. IEEE TIP, 22(12):5372-5384, 2013. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.804, + 0.468, + 0.844 + ], + "angle": 0, + "content": "[15] Hunsang Lee, Kwanghoon Sohn, and Dongbo Min. Unsupervised low-light image enhancement using bright channel prior. IEEE SPL, 27:251-255, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.468, + 0.9 + ], + "angle": 0, + "content": "[16] Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila. Noise2Noise: Learning image restoration without clean data. In ICML, pages 2965-2974, 2018. 5" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[17] Mading Li, Jiaying Liu, Wenhan Yang, Xiaoyan Sun, and Zongming Guo. Structure-revealing low-light image enhancement via robust retinae model. IEEE TIP, 27(6):2828-2841, 2018. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.15, + 0.892, + 0.204 + ], + "angle": 0, + "content": "[18] Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In CVPR, pages 10561-10570, 2021. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.207, + 0.892, + 0.247 + ], + "angle": 0, + "content": "[19] Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. Linet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recognition, 61:650-662, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.249, + 0.892, + 0.289 + ], + "angle": 0, + "content": "[20] Feifan Lv, Feng Lu, Jianhua Wu, and Chongsoon Lim. Mbllen: Low-light image/video enhancement using cnns. In BMVC, pages 1-13, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.292, + 0.892, + 0.331 + ], + "angle": 0, + "content": "[21] Kede Ma, Kai Zeng, and Zhou Wang. Perceptual quality assessment for multi-exposure image fusion. IEEE TIP, 24(11):3345-3356, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.334, + 0.892, + 0.375 + ], + "angle": 0, + "content": "[22] Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. Toward fast, flexible, and robust low-light image enhancement. In CVPR, pages 5637-5646, 2022. 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.377, + 0.892, + 0.431 + ], + "angle": 0, + "content": "[23] Gyu-Hee Park, Hwa-Hyun Cho, and Myung-Ryul Choi. A contrast enhancement method using dynamic range separate histogram equalization. IEEE TCE, 54(4):1981-1987, 2008. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.434, + 0.892, + 0.489 + ], + "angle": 0, + "content": "[24] Gaurav Sharma, Wencheng Wu, and Edul N Dalal. The ciede2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research and Application, 30(1):21-30, 2005. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.491, + 0.892, + 0.545 + ], + "angle": 0, + "content": "[25] Weitao Song, Masanori Suganuma, X. Liu, Norimasa Shimobayashi, D. Maruta, and Takayuki Okatani. Matching in the dark: A dataset for matching image pairs of low-light scenes. In ICCV, pages 6009-6018, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.547, + 0.892, + 0.588 + ], + "angle": 0, + "content": "[26] Li Tao, Chuang Zhu, Jiawen Song, Tao Lu, Huizhu Jia, and Xiaodong Xie. Low-light image enhancement using cnn and bright channel prior. In ICIP, pages 3215-3219, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.59, + 0.892, + 0.617 + ], + "angle": 0, + "content": "[27] Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. Deep image prior. In CVPR, pages 9446-9454, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.619, + 0.892, + 0.672 + ], + "angle": 0, + "content": "[28] Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, and Jiaya Jia. Underexposed photo enhancement using deep illumination estimation. In CVPR, pages 6842-6850, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.675, + 0.892, + 0.728 + ], + "angle": 0, + "content": "[29] Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE TIP, 22(9):3538-3548, 2013. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.732, + 0.892, + 0.786 + ], + "angle": 0, + "content": "[30] Wenjing Wang, Chen Wei, Wenhan Yang, and Jiaying Liu. Gladnet: Low-light enhancement network with global awareness. In IEEE International Conference on Automatic Face and Gesture Recognition, pages 751-755, 2018. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.789, + 0.892, + 0.829 + ], + "angle": 0, + "content": "[31] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE TIP, 13(4):600-612, 2004. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.831, + 0.892, + 0.871 + ], + "angle": 0, + "content": "[32] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retina decomposition for low-light enhancement. In BMVC, pages 1-12, 2018. 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.874, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[33] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. Uretinex-net: Retinex-based" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "22260" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.092, + 0.468, + 0.12 + ], + "angle": 0, + "content": "deep unfolding network for low-light image enhancement. In CVPR, pages 5901-5910, 2022. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.469, + 0.176 + ], + "angle": 0, + "content": "[34] Jun Xu, Yingkun Hou, Dongwei Ren, Li Liu, Fan Zhu, Mengyang Yu, Haoqian Wang, and Ling Shao. Star: A structure and texture aware retina model. IEEE TIP, 29:5022-5037, 2020. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.179, + 0.469, + 0.219 + ], + "angle": 0, + "content": "[35] Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. Snr-aware low-light image enhancement. In CVPR, pages 17714-17724, 2022. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.221, + 0.469, + 0.276 + ], + "angle": 0, + "content": "[36] Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In CVPR, pages 3060-3069, 2020. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.278, + 0.469, + 0.331 + ], + "angle": 0, + "content": "[37] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.334, + 0.469, + 0.375 + ], + "angle": 0, + "content": "[38] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In ACM MM, pages 1632-1640, 2019. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.377, + 0.469, + 0.431 + ], + "angle": 0, + "content": "[39] Zhao Zhang, Huan Zheng, Richang Hong, Mingliang Xu, Shuicheng Yan, and Meng Wang. Deep color consistent network for low-light image enhancement. In CVPR, pages 1899-1908, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.434, + 0.469, + 0.487 + ], + "angle": 0, + "content": "[40] Zunjin Zhao, Bangshu Xiong, Lei Wang, Qiaofeng Ou, Lei Yu, and Fa Kuang. Retinexdip: A unified deep framework for low-light image enhancement. IEEE TCSVT, 32(3):1076-1088, 2022. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.49, + 0.469, + 0.544 + ], + "angle": 0, + "content": "[41] Anqi Zhu, Lin Zhang, Ying Shen, Yong Ma, Shengjie Zhao, and Yicong Zhou. Zero-shot restoration of underexposed images via robust retina decomposition. 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Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image details due to the limited information in a single image and the poor adaptability of handcrafted priors. To this end, we propose PairLIE, an unsupervised approach that learns adaptive priors from low-light image pairs. First, the network is expected to generate the same clean images as the two inputs share the same image content. To achieve this, we impose the network with the Retinex theory and make the two reflectance components consistent. Second, to assist the Retinex decomposition, we propose to remove inappropriate features in the raw image with a simple self-supervised mechanism. Extensive experiments on public datasets show that the proposed PairLIE achieves comparable performance against the state-of-the-art approaches with a simpler network and fewer handcrafted priors. Code is available at: https://github.com/zhengqifu/PairLIE. + +# 1. Introduction + +Images captured under low-light environments always suffer from multiple distortions, such as low contrast, poor visibility, and sensor noise. Those low-light images are unsatisfactory for information transmission because they incur challenges in human visualization and subsequent computer vision tasks [25]. To correct contrast, uncover textures, and remove sensor noise, great efforts have been made in developing Low-light Image Enhancement (LIE) algorithms in the past decades [1,5,6,8,28,35]. + +![](images/f2137459d062c837db19e86fcc6289c31263630f9183091f381801e609a7b4fe.jpg) +Figure 1. Comparison between the previous solution (a) and the proposed method (b) from the aspect of Retinex theory. The key idea of our method is to learn adaptive priors from low-light image pairs. As a result, our solution needs fewer handcrafted priors and the network is more robust. Note that image pairs are only used in the training phase. + +Histogram-based and Retinex-based approaches are two well-known LIE techniques. The former enhances the contrast of an image by redistributing the luminous intensity on the histogram [3, 14]. The latter decomposes an observed image $I$ into illumination $L$ and reflectance $R$ via $I = L\circ R$ , where $\circ$ denotes the element-wise multiplication [6, 13, 17]. Specifically, the reflectance component $R$ is assumed to be consistent under different light conditions because $R$ represents the physical properties of the objects. As the Retinex theory can well model color perceptions of human vision, Retinex-based methods have attracted relatively more attention in the LIE community. + +Recent years have witnessed great success in developing learning-based LIE algorithms. Among these approaches, most solutions rely on low-light and normal-light image + +pairs [33, 38]. However, collecting high-quality reference maps in real-world scenarios is time-consuming and expensive [32]. To eliminate the requirement for normal-light images, unsupervised and zero-shot LIE approaches are proposed. Concretely, the former trains a deep neural network using a set of collected low-light samples [7, 18], while the latter only employs the test image itself in the network optimization [40, 41]. Due to the absence of reference images, unsupervised and zero-shot LIE approaches depend on handcrafted priors to guide network training. Nevertheless, due to the complex natural scenes and the limited information in a single low-light image, it is difficult for those methods to attain a high-quality result. + +To tackle the issues of limited information in a single low-light image and the poor adaptability of handcrafted priors, we propose to leverage paired low-light instances to train the LIE network. The main difference between our solution and previous approaches is illustrated in Fig. 1. Note that acquiring paired low-light images will complicate the imaging process since it needs to cope with the misalignment between the two images. Nevertheless, compared with collecting low-light and normal-light image pairs, our solution is more practical. Additionally, twice-exposure images provide useful information for solving the LIE task. As a result, our solution can reduce the demand for handcrafted priors and improve the adaptability of the network. + +With paired low-light instances, we propose a novel learning-based LIE method, termed PairLIE. The core insight of our approach is to sufficiently exploit priors from paired low-light images. Therefore, we consider employing the Retinex theory and deep learning to decompose low-light images into illumination and reflectance components. First, since the two low-light inputs share the same content, the estimated reflectance components are expected to be consistent. Second, instead of directly imposing the Retinex decomposition on original low-light images, we adopt a simple self-supervised mechanism to remove inappropriate features and implement the Retinex decomposition on the optimized image. This can avoid sub-optimal estimations because the Retinex model has limitations in low-light modeling. As a result, with fewer prior constraints and a simpler network, the proposed PairLIE achieves competitive performance in public LIE datasets. In summary, the contributions of this paper are as follows: + +- We propose a generic LIE solution using paired low-light images. The network is based on Retinex decomposition with several novel reference-free losses. +- To achieve an accurate decomposition, we first project the original image to remove inappropriate features. +- With fewer manually designed priors and a simpler network, the proposed solution achieves comparable performance against state-of-the-art methods. + +# 2. Related Work + +Over the decades, extensive LIE methods have been presented, which can be roughly categorized into conventional approaches and learning-based techniques. + +# 2.1. Conventional Methods + +Histogram-based techniques perform light enhancement by expanding the dynamic range of an image. For example, Park et al. [23] separated the dynamic range of the histogram into several parts and resized the gray-scale range based on the area ratio. Arici et al. [1] introduced penalty terms to avoid the unnatural look and visual artifacts of the enhanced image. Lee et al. [14] applied the layered difference representation of 2D histograms to amplify the gray-level differences between adjacent pixels. + +Retinex-based methods first decompose the low-light image into reflectance and illumination components. Subsequently, those approaches either consider the reflectance as the enhanced image or adjust the illumination and then recompose it with reflectance to generate the enhanced result. Wang et al. [29] proposed a LIE algorithm to promote naturalness and enhance details for non-uniform illumination images. Fu et al. [6] used a weighted variational model to preserve the reflectance with more details. Guo et al. [8] first estimated the illumination by calculating the maximum value in R, G, and B channels. Then, they refined the illumination map by imposing a structure prior. Li et al. [17] improved the performance of LIE by introducing a noise map in the Retinex model. Xu et al. [34] proposed a texture-aware Retinex model solved by an alternating optimization algorithm. Hao et al. [9] presented a novel Retinex-based LIE method performed in a semi-decoupled way. + +# 2.2. Learning-based Methods + +Commonly, learning-based LIE methods rely on paired low-light and normal-light images. Lore et al. [19] designed a stacked sparse denoising auto-encoder to enhance low-light images. The proposed model was trained on synthetic image pairs. Wei et al. [32] first built a real-world low-light image enhancement dataset including low-light and normal-light image pairs. Then, they trained an end-to-end network in a fully-supervised way. Wang et al. [28] introduced an intermediate illumination map in the network to associate the low-light input with the expected enhancement result. Chen et al. [4] collected a dataset of short-exposure low-light images, with corresponding long-exposure reference maps. Based on the dataset, the authors developed a fully-convolutional network for enhancing low-light images. Zhang et al. [38] proposed a supervised approach to decompose the low-light image into illumination and reflectance components. Wu et al. [33] proposed a Retinex-based deep unfolding network to promote adaptability and + +![](images/91b2602074189879adc07748938cd501978690d377742704b370c393a603f87b.jpg) +Figure 2. The architecture of PairLIE. In the training phase, P-Net is first adopted to remove inappropriate features of the original image. L-Net and R-Net are then employed to estimate the illumination and reflectance components. Three loss functions are used to guide the network optimization, including a self-supervised projection loss $\mathcal{L}_P$ , a reflectance consistency loss $\mathcal{L}_C$ , and a Retinex loss $\mathcal{L}_R$ . In the testing stage, given a low-light image, P-Net, L-Net, and R-Net are used to decompose the input into reflectance and illumination. Subsequently, PairLIE adjusts the illumination and recomposes it with the reflectance to capture the enhanced image. + +efficiency. Xu et al. [35] combined signal-to-noise-ratio-aware transformers and convolutional networks to enhance low-light images. Zhang et al. [39] proposed a color-consistent network to mitigate the color difference between the enhanced image and ground-truth. + +Recently, unsupervised (also known as self-supervised) networks are developed to eliminate the requirement for reference images. For example, Zhu et al. [41] proposed a zero-shot LIE approach that trained a deep network using the input image itself. Guo et al. [7] proposed a reference-free LIE approach based on curve estimation. Their network was optimized with a set of non-reference loss functions. Liu et al. [18] proposed a lightweight LIE network by integrating the unrolling techniques and prior architecture search strategies. Jiang et al. [12] proposed a LIE method based on the generative adversarial network and unpaired training data. Zhao et al. [40] designed a unified zero-reference network for enhancing low-light images based on Deep image Prior (DIP) [27]. Jiang et al. [11] presented a Retinex-based unsupervised decomposition and correction network for LIE. Ma et al. [22] established a cascaded illumination estimation process to achieve fast, flexible, and robust LIE in complex scenarios. + +# 3. Proposed Method + +We first give the problem definition of using low-light image pairs for LIE. Then, we detail the pipeline and loss functions. Finally, we introduce the dataset for training the proposed network. + +# 3.1. Retinex Model with Paired Low-light Images + +According to the Retinex theory, low-light image $I$ can be decomposed into illumination $L$ and reflectance $R$ as: + +$$ +I = L \circ R, \tag {1} +$$ + +where $\circ$ denotes the element-wise multiplication. Illumination $L$ describes the light intensity of objects. $L$ should be piece-wise continuous and textureless. Reflectance $R$ represents the physical properties of the objects. $R$ should contain textures and details in the observed image. The Retinex decomposition is highly ill-posed. Various methods have been proposed to handle this problem [6, 17, 29, 34]. A generic solution of the Retinex decomposition is to minimize the following energy function: + +$$ +\underset {L, R} {\operatorname {a r g m i n}} \| L \circ R - I \| _ {2} + \lambda_ {L} f _ {L} (L) + \lambda_ {R} f _ {R} (R), \tag {2} +$$ + +where $f_{L}$ and $f_{R}$ are prior constraints for $L$ and $R$ , respectively. $\lambda_{L}$ and $\lambda_{R}$ denote the weights. $\| L \circ R - I \|_{2}$ is the data-fidelity term between the input and reconstructed image. To achieve a reasonable decomposition, most of the LIE approaches focus on introducing assorted prior constraints enforced on the two components, such as priors of structure [17], smooth [32], and bright channel [15, 26]. + +However, handcrafted priors are commonly not adaptive enough due to the diverse natural scenes and light conditions. In this paper, instead of exploiting handcrafted priors for $L$ and $R$ from a single image, we apply paired low-light images to automatically learn adaptive priors in a data-driven fashion. Those low-light image pairs share the same + +scene content but different illumination. Mathematically, Retinex decomposition with low-light image pairs can be expressed as: + +$$ +\left\{ \begin{array}{l} I _ {1} = L _ {1} \circ R \\ I _ {2} = L _ {2} \circ R \end{array} , \right. \tag {3} +$$ + +where $I_{1}$ and $I_{2}$ are low-light image pairs, which share the reflectance component $R$ . Intuitively, Eq. 3 can address the decomposition better than Eq. 1 because more information and constraints are introduced. + +# 3.2. Network Structure + +The whole pipeline of our method is illustrated in Fig. 2. We use L-Net and R-Net to estimate the illumination and reflectance components, respectively. L-Net and R-Net are very similar and simple, both of which contain five convolutional layers. The activation function of the first four convolutional layers is ReLU. L-Net and R-Net end with a sigmoid layer to normalize the output into [0, 1]. According to the Retinex theory, the three color channels are assumed to have the same illumination. Therefore, the output channel of L-Net is set as 1 while that of R-Net is set as 3. Note that, this paper does not focus on designing modernistic network structures. In contrast, we aim at providing a generic solution for LIE under paired low-light instances. In our experiments, we found those simple network already achieves comparable performance. Apart from L-Net and R-Net, we introduce P-Net to remove inappropriate features from the original image. Specifically, the structure of the P-Net is identical to the R-Net. + +In the training phase, the original low-light image pairs $I_{1}$ and $I_{2}$ are first taken into the P-Net, yielding two optimized versions $i_{1}$ and $i_{2}$ . Then, L-Net and R-Net are applied to estimate the latent illumination $(L_{1}$ and $L_{2})$ and reflectance $(R_{1}$ and $R_{2})$ . To optimize the network, three loss functions are designed in PairLIE. The first is the projection loss $\mathcal{L}_P$ that measures the difference between $I$ and $i$ . The second is the reflectance consistency loss $\mathcal{L}_C$ calculated based on $R_{1}$ and $R_{2}$ . The third is the Retinex loss $\mathcal{L}_R$ that restricts the decomposed components to satisfy the Retinex theory. In the testing period, given a low-light image, P-Net, R-Net, and L-Net are applied to calculate the final enhanced image: + +$$ +I _ {e n} = g (L) \circ R = L ^ {\lambda} \circ R, \tag {4} +$$ + +where $\lambda$ is the illumination correction factor, $I_{en}$ denotes the enhanced image. + +# 3.3. Projection Loss + +Instead of performing the deep Retinex decomposition on the original low-light image, we propose to first remove inappropriate features to ensure the input can be accurately + +![](images/6e822a5293d15f22e67c6f1c0f444bebd6cdc826d510b240e51c30fe38c09583.jpg) + +![](images/3663ec6808ac0ccf714bc920eb54740c8720bfab08987545570a4bbcc2b1dab4.jpg) + +![](images/89e27b4b7988345981b7a677e1f4d41e260e9569a775de9f0a37d2d4e145e79b.jpg) +Figure 3. Example of projected images and corresponding difference maps. A smaller value indicates a higher similarity between the projected and original images. + +decomposed by an ideal Retinex model (i.e., Eq. 1). Specifically, we design the following loss to guide projection: + +$$ +\mathcal {L} _ {p} = \left\| I _ {1} - i _ {1} \right\| _ {2} ^ {2}, \tag {5} +$$ + +where $i_1$ refers to the projected image. The loss function $\mathcal{L}_p$ transforms the original image to a specific image that is more suitable for the Retinex decomposition. Concretely, this image is expected to be noise free since we do not consider the noise component in the Retinex model. Besides, some useless features are also discarded in this stage. Our projection loss can be interpreted from the view of error reallocation, which can be expressed as: + +$$ +\begin{array}{l} \operatorname * {a r g m i n} _ {L, R} \| I - L \circ R - \varepsilon \| \\ = \underset {L, R} {\operatorname {a r g m i n}} \| I - i + i - L \circ R - \varepsilon \| \tag {6} \\ \leq \left\| I - i - \varepsilon \right\| + \operatorname * {a r g m i n} _ {L, R} \left\| i - L \circ R \right\|, \\ \end{array} +$$ + +where $\varepsilon$ denotes the error. According to Eq. 6, the goal of $\mathcal{L}_p$ is to move the error from the Retinex decomposition to the projection stage. Fig. 3 gives an example of the calculated projected image and corresponding difference map. As can be seen, the projected image is highly similar to the original one, and sensor noise is removed since it cannot be formulated by the Retinex model. Note that the projection loss needs to cooperate with the other constraints to avoid a trivial solution, i.e., $i_1 = I_1$ . + +# 3.4. Reflectance Consistency Loss + +Reflectance consistency loss $\mathcal{L}_C$ is calculated based on low-light image pairs and the Retinex theory. Compared + +with handcrafted priors, $\mathcal{L}_C$ is more accurate and adaptive because it reveals the physical properties of the objects. Mathematically, $\mathcal{L}_C$ is formulated as: + +$$ +\mathcal {L} _ {C} = \left\| R _ {1} - R _ {2} \right\| _ {2} ^ {2}, \tag {7} +$$ + +where $R_{1}$ and $R_{2}$ refer to the reflectance components of paired low-light images. $\mathcal{L}_C$ enforces the network to predict the same reflectance components because low-light image pairs share the same objects. + +Since sensor noise hidden in dark regions will be amplified when the contrast is improved. To cope with the noise issue, existing approaches either add a smoothness term on the estimated reflectance or perform a denoise operation after the enhancement. In our method, the sensor noise can be implicitly removed by Eq. 7. This is because the two low-light images contain independent noise of the same scene. As discussed in [16], paired noisy images can be leveraged to train denoising models. This is because noise is random and different in two images, the deep network cannot fit the noise in one image to another. In our case, the two low-light images can help each other to remove noise during the Retinex decomposition. Therefore, PairLIE does not require additional handcrafted constraints of noise. + +# 3.5. Retinex Loss + +Since PairLIE employs the Retinex theory to decompose low-light images into illumination and reflectance components, some basic constraints of Retinex decomposition are introduced, which can be formulated as: + +$$ +\begin{array}{l} \mathcal {L} _ {R} = \| R \circ L - i \| _ {2} ^ {2} + \| R - i / s t o p g r a d (L) \| _ {2} ^ {2} \tag {8} \\ + \left\| L - L _ {0} \right\| _ {2} ^ {2} + \left\| \nabla L \right\| _ {1}, \\ \end{array} +$$ + +where $i$ denotes the projected image, $L_0$ refers to the initial estimation of illumination, $\nabla$ represents the horizontal and vertical gradients. First, the decomposed components should satisfy the requirement for reconstructing the input image. Therefore, a reconstruction term $\| R\circ L - i\| _2^2$ is applied to ensure a reasonable decomposition. Once the illumination is estimated, the reflectance can be calculated through the pixel-wise division between the low-light image and its illumination map. Therefore, we additionally add a term $\| R - i / stopgrad(L)\| _2^2$ to guide the decomposition. Note that we stop the gradient of the illumination to make the training stable. Different from most existing methods that use a lot of handcrafted priors, PairLIE only imposes a smooth term and an initialization term on $L$ . Specifically, the initialized illumination $L_{0}$ is calculated via the maximum of the R, G, and B channels: + +$$ +L _ {0} = \max _ {c \in \{R, G, B \}} I ^ {c} (x). \tag {9} +$$ + +![](images/0f20a8651124a02c15240234e8ccdbb19f7864bbf7b3c7a7c66259435e884c2b.jpg) +Figure 4. Statistics concerning sequence length of the collected data. Most sequences have 2 to 4 low-light images. + +# 3.6. Overall Loss + +The overall loss function for training our PairLIE is a liner combination of each loss: + +$$ +\mathcal {L} _ {A l l} = w _ {0} \mathcal {L} _ {P} + w _ {1} \mathcal {L} _ {C} + w _ {2} \mathcal {L} _ {R}, \tag {10} +$$ + +where $w_{0}, w_{1}$ , and $w_{2}$ denote the weights. + +# 3.7. Training Data Collection + +We collect low-light image pairs from SICE (part2) [2] and LOL (training set) [32], which contain multi-exposure images. These datasets adopt some specific operators to deal with the misalignment caused by camera shaking or object moving. Note that the SICE dataset consists of both under and over-exposed images. We only select under-exposure and well-aligned cases for constructing the training set. As a result, we collect 324 sequences (a total of 1000 low-light images). As shown in Fig. 4, each sequence includes 2 to 6 samples. In the training phase, we randomly select two images from each sequence to constitute a pair. + +# 4. Experiments + +We first describe implementation details, evaluation datasets, and performance criteria. Then, we present the quantitative and qualitative comparisons with state-of-the-art methods. Finally, we conduct ablative experiments to validate each component. + +# 4.1. Implementation Details + +We implement PairLIE with PyTorch. In the training phase, we randomly crop images to the size of $128 \times 128$ . A batch size of 1 is applied. We use ADAM with the initial learning rate of $1 \times 10^{-4}$ to optimize the network. The number of training epochs is set to 400. The learning rate is half-decayed per 100 epochs. The default correction factor $\lambda$ is 0.2. In extremely dark cases such as the LOL dataset, we set $\lambda = 0.14$ . As for the hyper-parameter $w_0$ , $w_1$ , and $w_2$ in Eq. 10, we set $w_0 = 500$ , $w_1 = w_2 = 1$ , empirically. + +
MethodTypeLOLSICE
PSNR↑SSIM↑LPIPS↓DeltaE ↓PSNR↑SSIM↑LPIPS↓DeltaE ↓
SDD [9]T13.340.6370.74321.8315.350.7410.23216.08
STAR [34]T12.910.5180.36623.4615.170.7270.24616.35
MBLLEN [20]S17.860.7270.22513.6813.640.6320.29718.60
RetinexNet [32]S17.550.6480.37912.6919.890.7830.2768.715
GLADNet [30]S19.720.6800.32112.2819.980.8370.2038.947
KinD [38]S17.650.7750.17112.4921.100.8380.1958.009
DRBN [36]S16.290.5510.26013.4415.580.5220.28913.78
URetinexNet [33]S19.840.8260.12810.6521.640.8430.1927.728
ZeroDCE [7]U14.860.5590.33518.8118.690.8100.20711.93
RRDNet [41]U11.400.4570.36226.4313.280.6780.22119.64
RUAS [18]U16.400.5000.27016.8513.180.7340.36316.81
SCI [22]U14.780.5220.33919.5215.950.7870.23513.71
EnlightenGAN [12]U17.480.6510.32214.5018.730.8220.21610.42
PairLIE (Ours)U19.510.7360.24810.8021.320.8400.2167.835
+ +Table 1. Quantitative comparisons with state-of-the-art methods on LOL and SICE datasets. "T", "S", and "U" represent "Traditional", "Supervised", and "Unsupervised" methods, respectively. The top three results are marked in bold. + +# 4.2. Datasets and Criteria + +As described before, low-light image pairs collected from SICE and LOL are applied to train PairLIE. We select another 50 sequences (150 images) from SICE and use the official evaluation set (15 images) of LOL to measure the model performance. Since SICE and LOL contains reference images, we employ PSNR, SSIM [31], LPIPS [37], and DeltaE with CIE2000 standard [24] to objectively evaluate the performance of each method. A higher PSNR/SSIM score indicates the result is closer to the reference. A lower LPIPS/DeltaE value denotes better enhancement performance. Furthermore, we adopt the MEF dataset [21] for visual comparisons. + +# 4.3. Compared Methods + +PairLIE is compared with 13 state-of-the-art LIE methods, which can be divided into the following three categories: traditional methods (SDD [9], STAR [34]), supervised approaches (MBLLEN [20], RetinexNet [32], GALDNet [30], KinD [38], DRBN [36], URetinexNet [33]), and unsupervised methods (Zero-DCE [7], RRDNet [41], RUAS [18], SCI [22], and EnlightenGAN [12]). Note that the results of all those methods are reproduced by using the official codes with recommended parameters. + +# 4.4. Quantitative Comparisons + +Tab. 1 lists the quantitative performance of the LOL and SICE datasets. As can be observed, traditional and unsupervised methods obtain relatively poor results. This is reasonable because it is difficult to learn an accurate enhancement model without the reference image. Besides, the performance of those solutions is highly dependent on the used + +prriors. However, hand-crafted features may not be adaptive enough for assorted light conditions. In Tab. 1, PairLIE achieves the best performance among the five unsupervised methods and gets competitive results compared with supervised approaches. Since paired low-light images provide sufficient information for solving the LIE task, PairLIE can reduce the demand for handcrafted priors. As a result, PairLIE achieves significant performance improvement. + +# 4.5. Visual Comparisons + +Fig. 5 illustrates the visual comparisons of different LIE methods. Our observations are as follows: 1) The proposed method achieves visually pleasing results in terms of brightness, color, contrast, and naturalness. While other methods fail to cope with the extreme black light in this case. 2) Although supervised approaches GLADNet, KinD, and URetinexNet show good performance on LOL and SICE datasets according to Tab. 1, their generalization ability may be limited as supervised learning is sensitive to the data distribution. We further show an example of noise suppression in Fig. 6. As can be seen, although PairLIE does not introduce any handcrafted priors about the noise, our method can successfully suppress sensor noise in dark regions and the result is clear and natural. In contrast, the competitors either amplify noise or fail in correcting color and contrast, leading to poor visual quality. + +# 4.6. Decomposition Visualization + +We visualize the reflectance and illumination components to validate the effectiveness of our method. As shown in Fig. 7, the reflectance contains rich textures and details while the illumination is piece-wise continuous and textureless, demonstrating PairLIE can accurately decompose the + +![](images/e478eeb719a15ed9541a875727ad1373f075c5df7492e51970097243ac61557a.jpg) + +![](images/f58b916d867ca2b6584205cdcdbb05324ef7fde23884a9bb955c59473a2f16f4.jpg) + +![](images/f08909e98601022179e48aa43fc58df5266787990a4fc05178f69d7b5f20522a.jpg) + +![](images/afb9b26901f098fedfb9da94fa3193b6f849f5c0c0e9a83f31344f4143daa97f.jpg) + +![](images/390026e1e5c740607bc3a567a28653ff46d3ad2138469f0358821bc0de1ae2c1.jpg) + +![](images/49e699a4805c8e3acdee585cdeb4bb664d37c8b1226e5972172854e6fdb0831a.jpg) +Original +ZeroDCE + +![](images/eb3bfed158d033a1565c43bc80f84ee2fd9ae3d26aea9a145b306805430fdefa.jpg) +SDD +RUAS + +![](images/16faa83c64d59f9802da57a4a345b96230fd12c1ed01c702191e0f5fe0b483f5.jpg) +GLADNet +SCI + +![](images/311f9964d3dc69ee52e07ce6cf5273ed8064d27ce6dce998d8b94e20061abd9a.jpg) +KinD +EnlightenGAN + +![](images/b4d57c479808cfd0acc442b1285d9548d966b0bebc2a777544e74e5d8fad38f9.jpg) +URetinext +PairLIE (Ours) + +![](images/1c1594e32d0bbb69084209fead842299ae3b9bf662c32524906a200fe15bd3a7.jpg) +Figure 5. Visual comparisons of different LIE methods. SDD [9], RetinexNet [32], GALDNet [30], KinD [38], URetinexNet [33], Zero-DCE [7], RRDNet [41], RUAS [18], SCI [22], EnlightenGAN [12], and PairLIE (Ours). +Original + +![](images/faf0cd3f5ca227a3b547aa34e576fb24fb8f9a8dc70e4fbce077afe049871d79.jpg) +ZeroDCE + +![](images/5b40267e28a9c42d87bc026bc6e4451c83f39247040d337cf79221c629ec880b.jpg) +RUAS + +![](images/549dd9ca8351127766b0cbc6add0e0bcf96eafc4b19349739ee7300bbc3a0574.jpg) +SCI +Figure 6. Performance comparisons in noise removal. Zoom in for the best view. Zero-DCE [7], RUAS [18], SCI [22], EnlightenGAN [12], and PairLIE (Ours). Our result is visually satisfactory without obvious noise. + +![](images/0e1d248fe20f45d9ea308d35de62012d74b323ff44028e1b8893e684147c5e6f.jpg) +EnlightenGAN + +![](images/3fb5d354e5c39cc8afe0976358f7d0d12a5a8d85a8265ad30774aba59aa00672.jpg) +PairLIE (Ours) + +low-light image. We further show the enhancement results with different correction factors. With the increase of $\lambda$ , the image brightness decreases gradually. When $\lambda$ is larger than 0.4 or smaller than 0.2, the enhanced images show obvious under/over-enhancement effects. In this work, the default $\lambda$ is set as 0.2. Note that users can adjust $\lambda$ in the testing phase according to their personal preferences. + +# 4.7. Ablation Studies + +We conduct ablation studies under different settings to understand how each component affects performance. We have tried the following three variations over the original PairLIE. 1) Setting A: without $\mathcal{L}_C$ . 2) Setting B: without $\mathcal{L}_P$ . 3) Setting C: without prior terms, i.e., removing $\| L - L_0\| _2^2$ and $\| \nabla L\| _1$ from $\mathcal{L}_R$ . + +Tab. 2 and Fig. 8 report the results of our ablation studies on the LOL dataset. We can observe that our approach significantly outperforms setting A, which demonstrates the effectiveness of learning adaptive priors using low-light image pairs. Compared with setting B, PairLIE achieves slight performance improvement. The results of setting B suggest + +that P-Net can assist the Retinex decomposition, but it is not necessary for stably training the network. To further understand the role of P-Net, we show the reconstruction error $\kappa$ in Fig. 9. The blue line denotes the decomposition using original low-light images, i.e., $\kappa = |L\circ R - I|$ , while the orange line refers to the decomposition using projected images, i.e., $\kappa = |L\circ R - i|$ . As can be seen, the proposed method obtains a smaller reconstruction error than the baseline. Therefore, P-Net can remove inappropriate features and the output is more suitable for the Retinex decomposition. The results of Setting C show that when priors terms are removed, the performance drops significantly. This is because the Rrtinex decomposition requires some basic priors to initialize and restrain the illumination. + +# 4.8. Discussions + +Acquiring low-light image pairs. Although PairLIE achieves promising performance with fewer handcrafted priors and a simpler network, collecting low-light image pairs is relatively expensive compared with recording a signal low-light image. This problem can be partly solved by + +![](images/f6b650455f9efd1b5a6996d37e09348c40d038803f34e258e67f1a8418ca8489.jpg) +Figure 7. Visualization of the Retinex decomposition. The enhanced results are presented under different correction factors. We set the default $\lambda$ as 0.2, but users can adjust the $\lambda$ according to their preferences. + +![](images/a55fae39d61d3c14216c482d03ef4d260a9fa3adb8360100c45b3cf6c724976f.jpg) +Figure 8. Visual comparisons of the ablation studies. Setting A: without $\mathcal{L}_C$ . Setting B: without $\mathcal{L}_P$ . Setting C: without prior terms. Our result is visually closer to the reference. + +
MethodPSNR↑SSIM↑
Setting A17.700.567
Setting B19.500.724
Setting C14.690.684
PairLIE19.510.736
+ +Table 2. Quantitative results of ablation studies on the LOL dataset. The best results are marked in bold. + +![](images/7bef45fabaceb3b263477697d38514f35d65282c9b5c69cc1cd89815da390c63.jpg) +Figure 9. Visualization of the reconstruction error. Baseline refers to the decomposition with original low-light images. + +generating training pairs from a single low-light image. For example, one can use neighbor masks [10] to generate two similar low-light images from a degraded observation. Additionally, specific recorruption techniques can be leveraged to capture paired training instances. + +Illumination components. PairLIE solves the Retinex decomposition with paired low-light images. The basic idea is to generate the same reflectance components since the reflectance is independent of the light. However, the relationship between illumination components is not sufficiently exploited in this paper. Illumination plays a critical role in the Retinex model. Therefore, we believe that PairLIE can be further improved by introducing new constraints between two illumination maps. + +# 5. Conclusions + +This paper proposes PairLIE, a reference-free approach for low-light image enhancement that benefits from both Retinex-based and learning-based solutions. By learning adaptive constraints from low-light image pairs, PairLIE reduces the dependence on handcrafted priors and thus generalizes well on various scenes. To assist the decomposition, PairLIE first removes inappropriate features in the original image and then implements the decomposition on the optimized image. Extensive experiments on public benchmarks show that PairLIE outperforms the state-of-the-art unsupervised methods significantly. In future works, we will concentrate on exploiting effective priors of illumination in a data-driven fashion. + +Acknowledgements: The work was supported partly by the National Natural Science Foundation of China under Grants 82172033, U19B2031, 61971369, 52105126, 82272071, and 62271430. + +# References + +[1] Tarik Arici, Salih Dikbas, and Yucel Altunbasak. A histogram modification framework and its application for image contrast enhancement. IEEE TIP, 18(9):1921-1935, 2009. 1, 2 +[2] Jianrui Cai, Shuhang Gu, and Lei Zhang. Learning a deep single image contrast enhancer from multi-exposure images. IEEE TIP, 27(4):2049-2062, 2018. 5 +[3] Turgay Celik and Tardi Tjahadi. Contextual and variational contrast enhancement. IEEE TIP, 20(12):3431-3441, 2011. 1 +[4] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. In CVPR, pages 3291-3300, 2018. 2 +[5] Xueyang Fu, Delu Zeng, Yue Huang, Yinghao Liao, Xinghao Ding, and John Paisley. A fusion-based enhancing method for weakly illuminated images. Signal Processing, 129:82-96, 2016. 1 +[6] Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, and Xinghao Ding. A weighted variational model for simultaneous reflectance and illumination estimation. In CVPR, pages 2782-2790, 2016. 1, 2, 3 +[7] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In CVPR, pages 1777-1786, 2020. 2, 3, 6, 7 +[8] Xiaojie Guo, Yu Li, and Haibin Ling. Lime: Low-light image enhancement via illumination map estimation. IEEE TIP, 26(2):982-993, 2017. 1, 2 +[9] Shijie Hao, Xu Han, Yanrong Guo, Xin Xu, and Meng Wang. Low-light image enhancement with semi-decoupled decomposition. IEEE TMM, 22(12):3025-3038, 2020. 2, 6, 7 +[10] Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, and Jianzhuang Liu. Neighbor2neighbor: Self-supervised denoising from single noisy images. In CVPR, pages 14781-14790, 2021. 8 +[11] Qiuping Jiang, Yudong Mao, Runmin Cong, Wenqi Ren, Chao Huang, and Feng Shao. Unsupervised decomposition and correction network for low-light image enhancement. IEEE TITS, pages 1-16, 2022. 3 +[12] Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang Wang. *Enlightengan: Deep light enhancement without paired supervision*. *IEEE TIP*, 30:2340-2349, 2021. 3, 6, 7 +[13] Edwin H Land. The retina theory of color vision. Scientific American, 237(6):108-129, 1977. 1 +[14] Chulwoo Lee, Chul Lee, and Chang-Su Kim. Contrast enhancement based on layered difference representation of 2d histograms. IEEE TIP, 22(12):5372-5384, 2013. 1, 2 +[15] Hunsang Lee, Kwanghoon Sohn, and Dongbo Min. Unsupervised low-light image enhancement using bright channel prior. IEEE SPL, 27:251-255, 2020. 3 +[16] Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila. Noise2Noise: Learning image restoration without clean data. In ICML, pages 2965-2974, 2018. 5 + +[17] Mading Li, Jiaying Liu, Wenhan Yang, Xiaoyan Sun, and Zongming Guo. Structure-revealing low-light image enhancement via robust retinae model. IEEE TIP, 27(6):2828-2841, 2018. 1, 2, 3 +[18] Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In CVPR, pages 10561-10570, 2021. 2, 3, 6, 7 +[19] Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. Linet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recognition, 61:650-662, 2017. 2 +[20] Feifan Lv, Feng Lu, Jianhua Wu, and Chongsoon Lim. Mbllen: Low-light image/video enhancement using cnns. In BMVC, pages 1-13, 2018. 6 +[21] Kede Ma, Kai Zeng, and Zhou Wang. Perceptual quality assessment for multi-exposure image fusion. IEEE TIP, 24(11):3345-3356, 2015. 6 +[22] Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. Toward fast, flexible, and robust low-light image enhancement. In CVPR, pages 5637-5646, 2022. 3, 6, 7 +[23] Gyu-Hee Park, Hwa-Hyun Cho, and Myung-Ryul Choi. A contrast enhancement method using dynamic range separate histogram equalization. IEEE TCE, 54(4):1981-1987, 2008. 2 +[24] Gaurav Sharma, Wencheng Wu, and Edul N Dalal. The ciede2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research and Application, 30(1):21-30, 2005. 6 +[25] Weitao Song, Masanori Suganuma, X. Liu, Norimasa Shimobayashi, D. Maruta, and Takayuki Okatani. Matching in the dark: A dataset for matching image pairs of low-light scenes. In ICCV, pages 6009-6018, 2021. 1 +[26] Li Tao, Chuang Zhu, Jiawen Song, Tao Lu, Huizhu Jia, and Xiaodong Xie. Low-light image enhancement using cnn and bright channel prior. In ICIP, pages 3215-3219, 2017. 3 +[27] Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. Deep image prior. In CVPR, pages 9446-9454, 2018. 3 +[28] Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, and Jiaya Jia. Underexposed photo enhancement using deep illumination estimation. In CVPR, pages 6842-6850, 2019. 1, 2 +[29] Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE TIP, 22(9):3538-3548, 2013. 2, 3 +[30] Wenjing Wang, Chen Wei, Wenhan Yang, and Jiaying Liu. Gladnet: Low-light enhancement network with global awareness. In IEEE International Conference on Automatic Face and Gesture Recognition, pages 751-755, 2018. 6, 7 +[31] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE TIP, 13(4):600-612, 2004. 6 +[32] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retina decomposition for low-light enhancement. In BMVC, pages 1-12, 2018. 2, 3, 5, 6, 7 +[33] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. Uretinex-net: Retinex-based + +deep unfolding network for low-light image enhancement. In CVPR, pages 5901-5910, 2022. 2, 6, 7 +[34] Jun Xu, Yingkun Hou, Dongwei Ren, Li Liu, Fan Zhu, Mengyang Yu, Haoqian Wang, and Ling Shao. Star: A structure and texture aware retina model. IEEE TIP, 29:5022-5037, 2020. 2, 3, 6 +[35] Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. Snr-aware low-light image enhancement. In CVPR, pages 17714-17724, 2022. 1, 3 +[36] Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In CVPR, pages 3060-3069, 2020. 6 +[37] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 6 +[38] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In ACM MM, pages 1632-1640, 2019. 2, 6, 7 +[39] Zhao Zhang, Huan Zheng, Richang Hong, Mingliang Xu, Shuicheng Yan, and Meng Wang. Deep color consistent network for low-light image enhancement. In CVPR, pages 1899-1908, 2022. 3 +[40] Zunjin Zhao, Bangshu Xiong, Lei Wang, Qiaofeng Ou, Lei Yu, and Fa Kuang. Retinexdip: A unified deep framework for low-light image enhancement. IEEE TCSVT, 32(3):1076-1088, 2022. 2, 3 +[41] Anqi Zhu, Lin Zhang, Ying Shen, Yong Ma, Shengjie Zhao, and Yicong Zhou. Zero-shot restoration of underexposed images via robust retina decomposition. 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Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image details due to the limited information in a single image and the poor adaptability of handcrafted priors. To this end, we propose PairLIE, an unsupervised approach that learns adaptive priors from low-light image pairs. First, the network is expected to generate the same clean images as the two inputs share the same image content. To achieve this, we impose the network with the Retinex theory and make the two reflectance components consistent. Second, to assist the Retinex decomposition, we propose to remove inappropriate features in the raw image with a simple self-supervised mechanism. Extensive experiments on public datasets show that the proposed PairLIE achieves comparable performance against the state-of-the-art approaches with a simpler network and fewer handcrafted priors. Code is available at: https://github.com/zhengqifu/PairLIE." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 567, + 128, + 580 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 567, + 128, + 580 + ], + "spans": [ + { + "bbox": [ + 47, + 567, + 128, + 580 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 588, + 287, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 588, + 287, + 696 + ], + "spans": [ + { + "bbox": [ + 46, + 588, + 287, + 696 + ], + "type": "text", + "content": "Images captured under low-light environments always suffer from multiple distortions, such as low contrast, poor visibility, and sensor noise. Those low-light images are unsatisfactory for information transmission because they incur challenges in human visualization and subsequent computer vision tasks [25]. To correct contrast, uncover textures, and remove sensor noise, great efforts have been made in developing Low-light Image Enhancement (LIE) algorithms in the past decades [1,5,6,8,28,35]." + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 307, + 268, + 545, + 423 + ], + "blocks": [ + { + "bbox": [ + 307, + 268, + 545, + 423 + ], + "lines": [ + { + "bbox": [ + 307, + 268, + 545, + 423 + ], + "spans": [ + { + "bbox": [ + 307, + 268, + 545, + 423 + ], + "type": "image", + "image_path": "f2137459d062c837db19e86fcc6289c31263630f9183091f381801e609a7b4fe.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 438, + 547, + 505 + ], + "lines": [ + { + "bbox": [ + 304, + 438, + 547, + 505 + ], + "spans": [ + { + "bbox": [ + 304, + 438, + 547, + 505 + ], + "type": "text", + "content": "Figure 1. Comparison between the previous solution (a) and the proposed method (b) from the aspect of Retinex theory. The key idea of our method is to learn adaptive priors from low-light image pairs. As a result, our solution needs fewer handcrafted priors and the network is more robust. Note that image pairs are only used in the training phase." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 531, + 547, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 531, + 547, + 675 + ], + "spans": [ + { + "bbox": [ + 304, + 531, + 547, + 675 + ], + "type": "text", + "content": "Histogram-based and Retinex-based approaches are two well-known LIE techniques. The former enhances the contrast of an image by redistributing the luminous intensity on the histogram [3, 14]. The latter decomposes an observed image " + }, + { + "bbox": [ + 304, + 531, + 547, + 675 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 304, + 531, + 547, + 675 + ], + "type": "text", + "content": " into illumination " + }, + { + "bbox": [ + 304, + 531, + 547, + 675 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 304, + 531, + 547, + 675 + ], + "type": "text", + "content": " and reflectance " + }, + { + "bbox": [ + 304, + 531, + 547, + 675 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 304, + 531, + 547, + 675 + ], + "type": "text", + "content": " via " + }, + { + "bbox": [ + 304, + 531, + 547, + 675 + ], + "type": "inline_equation", + "content": "I = L\\circ R" + }, + { + "bbox": [ + 304, + 531, + 547, + 675 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 531, + 547, + 675 + ], + "type": "inline_equation", + "content": "\\circ" + }, + { + "bbox": [ + 304, + 531, + 547, + 675 + ], + "type": "text", + "content": " denotes the element-wise multiplication [6, 13, 17]. 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As the Retinex theory can well model color perceptions of human vision, Retinex-based methods have attracted relatively more attention in the LIE community." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "type": "text", + "content": "Recent years have witnessed great success in developing learning-based LIE algorithms. Among these approaches, most solutions rely on low-light and normal-light image" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "type": "text", + "content": "*Corresponding author." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22252" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "content": "pairs [33, 38]. However, collecting high-quality reference maps in real-world scenarios is time-consuming and expensive [32]. To eliminate the requirement for normal-light images, unsupervised and zero-shot LIE approaches are proposed. Concretely, the former trains a deep neural network using a set of collected low-light samples [7, 18], while the latter only employs the test image itself in the network optimization [40, 41]. Due to the absence of reference images, unsupervised and zero-shot LIE approaches depend on handcrafted priors to guide network training. Nevertheless, due to the complex natural scenes and the limited information in a single low-light image, it is difficult for those methods to attain a high-quality result." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 228, + 289, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 228, + 289, + 384 + ], + "spans": [ + { + "bbox": [ + 46, + 228, + 289, + 384 + ], + "type": "text", + "content": "To tackle the issues of limited information in a single low-light image and the poor adaptability of handcrafted priors, we propose to leverage paired low-light instances to train the LIE network. The main difference between our solution and previous approaches is illustrated in Fig. 1. Note that acquiring paired low-light images will complicate the imaging process since it needs to cope with the misalignment between the two images. Nevertheless, compared with collecting low-light and normal-light image pairs, our solution is more practical. Additionally, twice-exposure images provide useful information for solving the LIE task. As a result, our solution can reduce the demand for handcrafted priors and improve the adaptability of the network." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 384, + 289, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 384, + 289, + 598 + ], + "spans": [ + { + "bbox": [ + 46, + 384, + 289, + 598 + ], + "type": "text", + "content": "With paired low-light instances, we propose a novel learning-based LIE method, termed PairLIE. The core insight of our approach is to sufficiently exploit priors from paired low-light images. Therefore, we consider employing the Retinex theory and deep learning to decompose low-light images into illumination and reflectance components. First, since the two low-light inputs share the same content, the estimated reflectance components are expected to be consistent. Second, instead of directly imposing the Retinex decomposition on original low-light images, we adopt a simple self-supervised mechanism to remove inappropriate features and implement the Retinex decomposition on the optimized image. This can avoid sub-optimal estimations because the Retinex model has limitations in low-light modeling. As a result, with fewer prior constraints and a simpler network, the proposed PairLIE achieves competitive performance in public LIE datasets. In summary, the contributions of this paper are as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 58, + 607, + 286, + 713 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 58, + 607, + 286, + 642 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 607, + 286, + 642 + ], + "spans": [ + { + "bbox": [ + 58, + 607, + 286, + 642 + ], + "type": "text", + "content": "- We propose a generic LIE solution using paired low-light images. The network is based on Retinex decomposition with several novel reference-free losses." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 58, + 648, + 286, + 672 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 648, + 286, + 672 + ], + "spans": [ + { + "bbox": [ + 58, + 648, + 286, + 672 + ], + "type": "text", + "content": "- To achieve an accurate decomposition, we first project the original image to remove inappropriate features." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 58, + 677, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 677, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 58, + 677, + 286, + 713 + ], + "type": "text", + "content": "- With fewer manually designed priors and a simpler network, the proposed solution achieves comparable performance against state-of-the-art methods." + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 71, + 392, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 71, + 392, + 84 + ], + "spans": [ + { + "bbox": [ + 306, + 71, + 392, + 84 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 92, + 545, + 128 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 92, + 545, + 128 + ], + "spans": [ + { + "bbox": [ + 305, + 92, + 545, + 128 + ], + "type": "text", + "content": "Over the decades, extensive LIE methods have been presented, which can be roughly categorized into conventional approaches and learning-based techniques." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 137, + 435, + 149 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 137, + 435, + 149 + ], + "spans": [ + { + "bbox": [ + 306, + 137, + 435, + 149 + ], + "type": "text", + "content": "2.1. Conventional Methods" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 157, + 545, + 265 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 157, + 545, + 265 + ], + "spans": [ + { + "bbox": [ + 304, + 157, + 545, + 265 + ], + "type": "text", + "content": "Histogram-based techniques perform light enhancement by expanding the dynamic range of an image. For example, Park et al. [23] separated the dynamic range of the histogram into several parts and resized the gray-scale range based on the area ratio. Arici et al. [1] introduced penalty terms to avoid the unnatural look and visual artifacts of the enhanced image. Lee et al. [14] applied the layered difference representation of 2D histograms to amplify the gray-level differences between adjacent pixels." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 266, + 546, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 266, + 546, + 469 + ], + "spans": [ + { + "bbox": [ + 304, + 266, + 546, + 469 + ], + "type": "text", + "content": "Retinex-based methods first decompose the low-light image into reflectance and illumination components. Subsequently, those approaches either consider the reflectance as the enhanced image or adjust the illumination and then recompose it with reflectance to generate the enhanced result. Wang et al. [29] proposed a LIE algorithm to promote naturalness and enhance details for non-uniform illumination images. Fu et al. [6] used a weighted variational model to preserve the reflectance with more details. Guo et al. [8] first estimated the illumination by calculating the maximum value in R, G, and B channels. Then, they refined the illumination map by imposing a structure prior. Li et al. [17] improved the performance of LIE by introducing a noise map in the Retinex model. Xu et al. [34] proposed a texture-aware Retinex model solved by an alternating optimization algorithm. Hao et al. [9] presented a novel Retinex-based LIE method performed in a semi-decoupled way." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 479, + 446, + 491 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 479, + 446, + 491 + ], + "spans": [ + { + "bbox": [ + 306, + 479, + 446, + 491 + ], + "type": "text", + "content": "2.2. Learning-based Methods" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 498, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 547, + 714 + ], + "type": "text", + "content": "Commonly, learning-based LIE methods rely on paired low-light and normal-light images. Lore et al. [19] designed a stacked sparse denoising auto-encoder to enhance low-light images. The proposed model was trained on synthetic image pairs. Wei et al. [32] first built a real-world low-light image enhancement dataset including low-light and normal-light image pairs. Then, they trained an end-to-end network in a fully-supervised way. Wang et al. [28] introduced an intermediate illumination map in the network to associate the low-light input with the expected enhancement result. Chen et al. [4] collected a dataset of short-exposure low-light images, with corresponding long-exposure reference maps. Based on the dataset, the authors developed a fully-convolutional network for enhancing low-light images. Zhang et al. [38] proposed a supervised approach to decompose the low-light image into illumination and reflectance components. Wu et al. [33] proposed a Retinex-based deep unfolding network to promote adaptability and" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22253" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 52, + 73, + 541, + 268 + ], + "blocks": [ + { + "bbox": [ + 52, + 73, + 541, + 268 + ], + "lines": [ + { + "bbox": [ + 52, + 73, + 541, + 268 + ], + "spans": [ + { + "bbox": [ + 52, + 73, + 541, + 268 + ], + "type": "image", + "image_path": "91b2602074189879adc07748938cd501978690d377742704b370c393a603f87b.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 277, + 546, + 332 + ], + "lines": [ + { + "bbox": [ + 46, + 277, + 546, + 332 + ], + "spans": [ + { + "bbox": [ + 46, + 277, + 546, + 332 + ], + "type": "text", + "content": "Figure 2. The architecture of PairLIE. In the training phase, P-Net is first adopted to remove inappropriate features of the original image. L-Net and R-Net are then employed to estimate the illumination and reflectance components. Three loss functions are used to guide the network optimization, including a self-supervised projection loss " + }, + { + "bbox": [ + 46, + 277, + 546, + 332 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_P" + }, + { + "bbox": [ + 46, + 277, + 546, + 332 + ], + "type": "text", + "content": ", a reflectance consistency loss " + }, + { + "bbox": [ + 46, + 277, + 546, + 332 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + }, + { + "bbox": [ + 46, + 277, + 546, + 332 + ], + "type": "text", + "content": ", and a Retinex loss " + }, + { + "bbox": [ + 46, + 277, + 546, + 332 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_R" + }, + { + "bbox": [ + 46, + 277, + 546, + 332 + ], + "type": "text", + "content": ". In the testing stage, given a low-light image, P-Net, L-Net, and R-Net are used to decompose the input into reflectance and illumination. Subsequently, PairLIE adjusts the illumination and recomposes it with the reflectance to capture the enhanced image." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 353, + 287, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 353, + 287, + 413 + ], + "spans": [ + { + "bbox": [ + 46, + 353, + 287, + 413 + ], + "type": "text", + "content": "efficiency. Xu et al. [35] combined signal-to-noise-ratio-aware transformers and convolutional networks to enhance low-light images. Zhang et al. [39] proposed a color-consistent network to mitigate the color difference between the enhanced image and ground-truth." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 414, + 288, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 414, + 288, + 629 + ], + "spans": [ + { + "bbox": [ + 46, + 414, + 288, + 629 + ], + "type": "text", + "content": "Recently, unsupervised (also known as self-supervised) networks are developed to eliminate the requirement for reference images. For example, Zhu et al. [41] proposed a zero-shot LIE approach that trained a deep network using the input image itself. Guo et al. [7] proposed a reference-free LIE approach based on curve estimation. Their network was optimized with a set of non-reference loss functions. Liu et al. [18] proposed a lightweight LIE network by integrating the unrolling techniques and prior architecture search strategies. Jiang et al. [12] proposed a LIE method based on the generative adversarial network and unpaired training data. Zhao et al. [40] designed a unified zero-reference network for enhancing low-light images based on Deep image Prior (DIP) [27]. Jiang et al. [11] presented a Retinex-based unsupervised decomposition and correction network for LIE. Ma et al. [22] established a cascaded illumination estimation process to achieve fast, flexible, and robust LIE in complex scenarios." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 644, + 153, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 644, + 153, + 658 + ], + "spans": [ + { + "bbox": [ + 47, + 644, + 153, + 658 + ], + "type": "text", + "content": "3. Proposed Method" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": "We first give the problem definition of using low-light image pairs for LIE. Then, we detail the pipeline and loss functions. Finally, we introduce the dataset for training the proposed network." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 353, + 538, + 366 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 353, + 538, + 366 + ], + "spans": [ + { + "bbox": [ + 305, + 353, + 538, + 366 + ], + "type": "text", + "content": "3.1. Retinex Model with Paired Low-light Images" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 371, + 546, + 395 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 371, + 546, + 395 + ], + "spans": [ + { + "bbox": [ + 305, + 371, + 546, + 395 + ], + "type": "text", + "content": "According to the Retinex theory, low-light image " + }, + { + "bbox": [ + 305, + 371, + 546, + 395 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 305, + 371, + 546, + 395 + ], + "type": "text", + "content": " can be decomposed into illumination " + }, + { + "bbox": [ + 305, + 371, + 546, + 395 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 305, + 371, + 546, + 395 + ], + "type": "text", + "content": " and reflectance " + }, + { + "bbox": [ + 305, + 371, + 546, + 395 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 305, + 371, + 546, + 395 + ], + "type": "text", + "content": " as:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 402, + 401, + 545, + 413 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 402, + 401, + 545, + 413 + ], + "spans": [ + { + "bbox": [ + 402, + 401, + 545, + 413 + ], + "type": "interline_equation", + "content": "I = L \\circ R, \\tag {1}", + "image_path": "92e431e222a51c0ea0f8e749923a03cd5833673827df70a5b5a15077dd666b1b.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 419, + 546, + 527 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 419, + 546, + 527 + ], + "spans": [ + { + "bbox": [ + 304, + 419, + 546, + 527 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 419, + 546, + 527 + ], + "type": "inline_equation", + "content": "\\circ" + }, + { + "bbox": [ + 304, + 419, + 546, + 527 + ], + "type": "text", + "content": " denotes the element-wise multiplication. Illumination " + }, + { + "bbox": [ + 304, + 419, + 546, + 527 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 304, + 419, + 546, + 527 + ], + "type": "text", + "content": " describes the light intensity of objects. " + }, + { + "bbox": [ + 304, + 419, + 546, + 527 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 304, + 419, + 546, + 527 + ], + "type": "text", + "content": " should be piece-wise continuous and textureless. Reflectance " + }, + { + "bbox": [ + 304, + 419, + 546, + 527 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 304, + 419, + 546, + 527 + ], + "type": "text", + "content": " represents the physical properties of the objects. " + }, + { + "bbox": [ + 304, + 419, + 546, + 527 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 304, + 419, + 546, + 527 + ], + "type": "text", + "content": " should contain textures and details in the observed image. The Retinex decomposition is highly ill-posed. Various methods have been proposed to handle this problem [6, 17, 29, 34]. A generic solution of the Retinex decomposition is to minimize the following energy function:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 319, + 533, + 545, + 552 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 319, + 533, + 545, + 552 + ], + "spans": [ + { + "bbox": [ + 319, + 533, + 545, + 552 + ], + "type": "interline_equation", + "content": "\\underset {L, R} {\\operatorname {a r g m i n}} \\| L \\circ R - I \\| _ {2} + \\lambda_ {L} f _ {L} (L) + \\lambda_ {R} f _ {R} (R), \\tag {2}", + "image_path": "f98d9ff03cf11ece5a530a50de027bf1dfd44268d7ca9cbb2c58a414c76fec9b.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "inline_equation", + "content": "f_{L}" + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "inline_equation", + "content": "f_{R}" + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "text", + "content": " are prior constraints for " + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "text", + "content": ", respectively. " + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "inline_equation", + "content": "\\lambda_{L}" + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "inline_equation", + "content": "\\lambda_{R}" + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "text", + "content": " denote the weights. " + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "inline_equation", + "content": "\\| L \\circ R - I \\|_{2}" + }, + { + "bbox": [ + 304, + 558, + 545, + 642 + ], + "type": "text", + "content": " is the data-fidelity term between the input and reconstructed image. To achieve a reasonable decomposition, most of the LIE approaches focus on introducing assorted prior constraints enforced on the two components, such as priors of structure [17], smooth [32], and bright channel [15, 26]." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": "However, handcrafted priors are commonly not adaptive enough due to the diverse natural scenes and light conditions. In this paper, instead of exploiting handcrafted priors for " + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": " from a single image, we apply paired low-light images to automatically learn adaptive priors in a data-driven fashion. Those low-light image pairs share the same" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22254" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 107 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 107 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 107 + ], + "type": "text", + "content": "scene content but different illumination. Mathematically, Retinex decomposition with low-light image pairs can be expressed as:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 132, + 110, + 287, + 135 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 132, + 110, + 287, + 135 + ], + "spans": [ + { + "bbox": [ + 132, + 110, + 287, + 135 + ], + "type": "interline_equation", + "content": "\\left\\{ \\begin{array}{l} I _ {1} = L _ {1} \\circ R \\\\ I _ {2} = L _ {2} \\circ R \\end{array} , \\right. \\tag {3}", + "image_path": "aa6303631010bdf846382624ff07614324371449bf3b12aee0b3a3179391cfac.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 141, + 287, + 189 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 141, + 287, + 189 + ], + "spans": [ + { + "bbox": [ + 46, + 141, + 287, + 189 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 141, + 287, + 189 + ], + "type": "inline_equation", + "content": "I_{1}" + }, + { + "bbox": [ + 46, + 141, + 287, + 189 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 141, + 287, + 189 + ], + "type": "inline_equation", + "content": "I_{2}" + }, + { + "bbox": [ + 46, + 141, + 287, + 189 + ], + "type": "text", + "content": " are low-light image pairs, which share the reflectance component " + }, + { + "bbox": [ + 46, + 141, + 287, + 189 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 46, + 141, + 287, + 189 + ], + "type": "text", + "content": ". Intuitively, Eq. 3 can address the decomposition better than Eq. 1 because more information and constraints are introduced." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 198, + 158, + 210 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 198, + 158, + 210 + ], + "spans": [ + { + "bbox": [ + 47, + 198, + 158, + 210 + ], + "type": "text", + "content": "3.2. Network Structure" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 218, + 287, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 218, + 287, + 433 + ], + "spans": [ + { + "bbox": [ + 46, + 218, + 287, + 433 + ], + "type": "text", + "content": "The whole pipeline of our method is illustrated in Fig. 2. We use L-Net and R-Net to estimate the illumination and reflectance components, respectively. L-Net and R-Net are very similar and simple, both of which contain five convolutional layers. The activation function of the first four convolutional layers is ReLU. L-Net and R-Net end with a sigmoid layer to normalize the output into [0, 1]. According to the Retinex theory, the three color channels are assumed to have the same illumination. Therefore, the output channel of L-Net is set as 1 while that of R-Net is set as 3. Note that, this paper does not focus on designing modernistic network structures. In contrast, we aim at providing a generic solution for LIE under paired low-light instances. In our experiments, we found those simple network already achieves comparable performance. Apart from L-Net and R-Net, we introduce P-Net to remove inappropriate features from the original image. Specifically, the structure of the P-Net is identical to the R-Net." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "spans": [ + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": "In the training phase, the original low-light image pairs " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "I_{1}" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "I_{2}" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": " are first taken into the P-Net, yielding two optimized versions " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "i_{1}" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "i_{2}" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": ". Then, L-Net and R-Net are applied to estimate the latent illumination " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "(L_{1}" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "L_{2})" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": " and reflectance " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "(R_{1}" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "R_{2})" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": ". To optimize the network, three loss functions are designed in PairLIE. The first is the projection loss " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_P" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": " that measures the difference between " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": ". The second is the reflectance consistency loss " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": " calculated based on " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "R_{1}" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "R_{2}" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": ". The third is the Retinex loss " + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_R" + }, + { + "bbox": [ + 46, + 434, + 287, + 590 + ], + "type": "text", + "content": " that restricts the decomposed components to satisfy the Retinex theory. In the testing period, given a low-light image, P-Net, R-Net, and L-Net are applied to calculate the final enhanced image:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 111, + 600, + 287, + 613 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 600, + 287, + 613 + ], + "spans": [ + { + "bbox": [ + 111, + 600, + 287, + 613 + ], + "type": "interline_equation", + "content": "I _ {e n} = g (L) \\circ R = L ^ {\\lambda} \\circ R, \\tag {4}", + "image_path": "614c06e80a70afa7207dad67811b6c9b87c94967a0364d0d31983dd2a49c1aea.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 624, + 287, + 649 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 624, + 287, + 649 + ], + "spans": [ + { + "bbox": [ + 46, + 624, + 287, + 649 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 624, + 287, + 649 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 624, + 287, + 649 + ], + "type": "text", + "content": " is the illumination correction factor, " + }, + { + "bbox": [ + 46, + 624, + 287, + 649 + ], + "type": "inline_equation", + "content": "I_{en}" + }, + { + "bbox": [ + 46, + 624, + 287, + 649 + ], + "type": "text", + "content": " denotes the enhanced image." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 658, + 143, + 671 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 658, + 143, + 671 + ], + "spans": [ + { + "bbox": [ + 47, + 658, + 143, + 671 + ], + "type": "text", + "content": "3.3. Projection Loss" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 677, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 287, + 713 + ], + "type": "text", + "content": "Instead of performing the deep Retinex decomposition on the original low-light image, we propose to first remove inappropriate features to ensure the input can be accurately" + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 307, + 70, + 545, + 131 + ], + "blocks": [ + { + "bbox": [ + 307, + 70, + 545, + 131 + ], + "lines": [ + { + "bbox": [ + 307, + 70, + 545, + 131 + ], + "spans": [ + { + "bbox": [ + 307, + 70, + 545, + 131 + ], + "type": "image", + "image_path": "6e822a5293d15f22e67c6f1c0f444bebd6cdc826d510b240e51c30fe38c09583.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 308, + 131, + 545, + 189 + ], + "blocks": [ + { + "bbox": [ + 308, + 131, + 545, + 189 + ], + "lines": [ + { + "bbox": [ + 308, + 131, + 545, + 189 + ], + "spans": [ + { + "bbox": [ + 308, + 131, + 545, + 189 + ], + "type": "image", + "image_path": "3663ec6808ac0ccf714bc920eb54740c8720bfab08987545570a4bbcc2b1dab4.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 308, + 190, + 545, + 250 + ], + "blocks": [ + { + "bbox": [ + 308, + 190, + 545, + 250 + ], + "lines": [ + { + "bbox": [ + 308, + 190, + 545, + 250 + ], + "spans": [ + { + "bbox": [ + 308, + 190, + 545, + 250 + ], + "type": "image", + "image_path": "89e27b4b7988345981b7a677e1f4d41e260e9569a775de9f0a37d2d4e145e79b.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 262, + 545, + 295 + ], + "lines": [ + { + "bbox": [ + 305, + 262, + 545, + 295 + ], + "spans": [ + { + "bbox": [ + 305, + 262, + 545, + 295 + ], + "type": "text", + "content": "Figure 3. Example of projected images and corresponding difference maps. A smaller value indicates a higher similarity between the projected and original images." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 316, + 544, + 341 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 316, + 544, + 341 + ], + "spans": [ + { + "bbox": [ + 305, + 316, + 544, + 341 + ], + "type": "text", + "content": "decomposed by an ideal Retinex model (i.e., Eq. 1). Specifically, we design the following loss to guide projection:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 388, + 349, + 545, + 364 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 388, + 349, + 545, + 364 + ], + "spans": [ + { + "bbox": [ + 388, + 349, + 545, + 364 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {p} = \\left\\| I _ {1} - i _ {1} \\right\\| _ {2} ^ {2}, \\tag {5}", + "image_path": "13374d1b6b716481c51fbc920266e4f3e6986d5170a3d7d9a47bc405d566997a.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 373, + 545, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 373, + 545, + 469 + ], + "spans": [ + { + "bbox": [ + 305, + 373, + 545, + 469 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 373, + 545, + 469 + ], + "type": "inline_equation", + "content": "i_1" + }, + { + "bbox": [ + 305, + 373, + 545, + 469 + ], + "type": "text", + "content": " refers to the projected image. The loss function " + }, + { + "bbox": [ + 305, + 373, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_p" + }, + { + "bbox": [ + 305, + 373, + 545, + 469 + ], + "type": "text", + "content": " transforms the original image to a specific image that is more suitable for the Retinex decomposition. Concretely, this image is expected to be noise free since we do not consider the noise component in the Retinex model. Besides, some useless features are also discarded in this stage. Our projection loss can be interpreted from the view of error reallocation, which can be expressed as:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 347, + 479, + 545, + 544 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 479, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 347, + 479, + 545, + 544 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\operatorname * {a r g m i n} _ {L, R} \\| I - L \\circ R - \\varepsilon \\| \\\\ = \\underset {L, R} {\\operatorname {a r g m i n}} \\| I - i + i - L \\circ R - \\varepsilon \\| \\tag {6} \\\\ \\leq \\left\\| I - i - \\varepsilon \\right\\| + \\operatorname * {a r g m i n} _ {L, R} \\left\\| i - L \\circ R \\right\\|, \\\\ \\end{array}", + "image_path": "71d3956fec099179bbce63b90c6ffea6c07cd724d1bde001b03e3223efc9d743.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 555, + 545, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 555, + 545, + 662 + ], + "spans": [ + { + "bbox": [ + 305, + 555, + 545, + 662 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 555, + 545, + 662 + ], + "type": "inline_equation", + "content": "\\varepsilon" + }, + { + "bbox": [ + 305, + 555, + 545, + 662 + ], + "type": "text", + "content": " denotes the error. According to Eq. 6, the goal of " + }, + { + "bbox": [ + 305, + 555, + 545, + 662 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_p" + }, + { + "bbox": [ + 305, + 555, + 545, + 662 + ], + "type": "text", + "content": " is to move the error from the Retinex decomposition to the projection stage. Fig. 3 gives an example of the calculated projected image and corresponding difference map. As can be seen, the projected image is highly similar to the original one, and sensor noise is removed since it cannot be formulated by the Retinex model. Note that the projection loss needs to cooperate with the other constraints to avoid a trivial solution, i.e., " + }, + { + "bbox": [ + 305, + 555, + 545, + 662 + ], + "type": "inline_equation", + "content": "i_1 = I_1" + }, + { + "bbox": [ + 305, + 555, + 545, + 662 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 670, + 465, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 670, + 465, + 684 + ], + "spans": [ + { + "bbox": [ + 306, + 670, + 465, + 684 + ], + "type": "text", + "content": "3.4. Reflectance Consistency Loss" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "type": "text", + "content": "Reflectance consistency loss " + }, + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + }, + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "type": "text", + "content": " is calculated based on low-light image pairs and the Retinex theory. Compared" + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22255" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 109 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 109 + ], + "type": "text", + "content": "with handcrafted priors, " + }, + { + "bbox": [ + 47, + 72, + 287, + 109 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + }, + { + "bbox": [ + 47, + 72, + 287, + 109 + ], + "type": "text", + "content": " is more accurate and adaptive because it reveals the physical properties of the objects. Mathematically, " + }, + { + "bbox": [ + 47, + 72, + 287, + 109 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + }, + { + "bbox": [ + 47, + 72, + 287, + 109 + ], + "type": "text", + "content": " is formulated as:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 124, + 118, + 287, + 134 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 124, + 118, + 287, + 134 + ], + "spans": [ + { + "bbox": [ + 124, + 118, + 287, + 134 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {C} = \\left\\| R _ {1} - R _ {2} \\right\\| _ {2} ^ {2}, \\tag {7}", + "image_path": "f7ec6d8544b31af1f5bd4a3938954c39053b6eb34baf64a2dcb8fba1fb60b493.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 144, + 287, + 193 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 144, + 287, + 193 + ], + "spans": [ + { + "bbox": [ + 47, + 144, + 287, + 193 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 144, + 287, + 193 + ], + "type": "inline_equation", + "content": "R_{1}" + }, + { + "bbox": [ + 47, + 144, + 287, + 193 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 144, + 287, + 193 + ], + "type": "inline_equation", + "content": "R_{2}" + }, + { + "bbox": [ + 47, + 144, + 287, + 193 + ], + "type": "text", + "content": " refer to the reflectance components of paired low-light images. " + }, + { + "bbox": [ + 47, + 144, + 287, + 193 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + }, + { + "bbox": [ + 47, + 144, + 287, + 193 + ], + "type": "text", + "content": " enforces the network to predict the same reflectance components because low-light image pairs share the same objects." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 194, + 288, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 194, + 288, + 361 + ], + "spans": [ + { + "bbox": [ + 47, + 194, + 288, + 361 + ], + "type": "text", + "content": "Since sensor noise hidden in dark regions will be amplified when the contrast is improved. To cope with the noise issue, existing approaches either add a smoothness term on the estimated reflectance or perform a denoise operation after the enhancement. In our method, the sensor noise can be implicitly removed by Eq. 7. This is because the two low-light images contain independent noise of the same scene. As discussed in [16], paired noisy images can be leveraged to train denoising models. This is because noise is random and different in two images, the deep network cannot fit the noise in one image to another. In our case, the two low-light images can help each other to remove noise during the Retinex decomposition. Therefore, PairLIE does not require additional handcrafted constraints of noise." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 373, + 130, + 384 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 373, + 130, + 384 + ], + "spans": [ + { + "bbox": [ + 47, + 373, + 130, + 384 + ], + "type": "text", + "content": "3.5. Retinex Loss" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 392, + 287, + 440 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 392, + 287, + 440 + ], + "spans": [ + { + "bbox": [ + 47, + 392, + 287, + 440 + ], + "type": "text", + "content": "Since PairLIE employs the Retinex theory to decompose low-light images into illumination and reflectance components, some basic constraints of Retinex decomposition are introduced, which can be formulated as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 70, + 449, + 287, + 483 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 449, + 287, + 483 + ], + "spans": [ + { + "bbox": [ + 70, + 449, + 287, + 483 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {R} = \\| R \\circ L - i \\| _ {2} ^ {2} + \\| R - i / s t o p g r a d (L) \\| _ {2} ^ {2} \\tag {8} \\\\ + \\left\\| L - L _ {0} \\right\\| _ {2} ^ {2} + \\left\\| \\nabla L \\right\\| _ {1}, \\\\ \\end{array}", + "image_path": "802492f5de303bef438d75b64a89a5061f218987189a9628a376e80f9a141f47.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "spans": [ + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "text", + "content": " denotes the projected image, " + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "inline_equation", + "content": "L_0" + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "text", + "content": " refers to the initial estimation of illumination, " + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "inline_equation", + "content": "\\nabla" + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "text", + "content": " represents the horizontal and vertical gradients. First, the decomposed components should satisfy the requirement for reconstructing the input image. Therefore, a reconstruction term " + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "inline_equation", + "content": "\\| R\\circ L - i\\| _2^2" + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "text", + "content": " is applied to ensure a reasonable decomposition. Once the illumination is estimated, the reflectance can be calculated through the pixel-wise division between the low-light image and its illumination map. Therefore, we additionally add a term " + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "inline_equation", + "content": "\\| R - i / stopgrad(L)\\| _2^2" + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "text", + "content": " to guide the decomposition. Note that we stop the gradient of the illumination to make the training stable. Different from most existing methods that use a lot of handcrafted priors, PairLIE only imposes a smooth term and an initialization term on " + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "text", + "content": ". Specifically, the initialized illumination " + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "inline_equation", + "content": "L_{0}" + }, + { + "bbox": [ + 47, + 493, + 288, + 684 + ], + "type": "text", + "content": " is calculated via the maximum of the R, G, and B channels:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 117, + 696, + 287, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 117, + 696, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 117, + 696, + 287, + 715 + ], + "type": "interline_equation", + "content": "L _ {0} = \\max _ {c \\in \\{R, G, B \\}} I ^ {c} (x). \\tag {9}", + "image_path": "a784cf472198740253c6fbfd2f69b191bc6c27a3684923b2828399e15429b8fa.jpg" + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 312, + 72, + 539, + 205 + ], + "blocks": [ + { + "bbox": [ + 312, + 72, + 539, + 205 + ], + "lines": [ + { + "bbox": [ + 312, + 72, + 539, + 205 + ], + "spans": [ + { + "bbox": [ + 312, + 72, + 539, + 205 + ], + "type": "image", + "image_path": "0f20a8651124a02c15240234e8ccdbb19f7864bbf7b3c7a7c66259435e884c2b.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 216, + 545, + 239 + ], + "lines": [ + { + "bbox": [ + 305, + 216, + 545, + 239 + ], + "spans": [ + { + "bbox": [ + 305, + 216, + 545, + 239 + ], + "type": "text", + "content": "Figure 4. Statistics concerning sequence length of the collected data. Most sequences have 2 to 4 low-light images." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 256, + 388, + 268 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 256, + 388, + 268 + ], + "spans": [ + { + "bbox": [ + 306, + 256, + 388, + 268 + ], + "type": "text", + "content": "3.6. Overall Loss" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 274, + 545, + 298 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 274, + 545, + 298 + ], + "spans": [ + { + "bbox": [ + 305, + 274, + 545, + 298 + ], + "type": "text", + "content": "The overall loss function for training our PairLIE is a liner combination of each loss:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 357, + 305, + 545, + 318 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 357, + 305, + 545, + 318 + ], + "spans": [ + { + "bbox": [ + 357, + 305, + 545, + 318 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {A l l} = w _ {0} \\mathcal {L} _ {P} + w _ {1} \\mathcal {L} _ {C} + w _ {2} \\mathcal {L} _ {R}, \\tag {10}", + "image_path": "a4381aea1c6a67760a487ca31f9e94bad19164cbc20d24e457582c9db83ac2d4.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 324, + 477, + 335 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 324, + 477, + 335 + ], + "spans": [ + { + "bbox": [ + 306, + 324, + 477, + 335 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 306, + 324, + 477, + 335 + ], + "type": "inline_equation", + "content": "w_{0}, w_{1}" + }, + { + "bbox": [ + 306, + 324, + 477, + 335 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 306, + 324, + 477, + 335 + ], + "type": "inline_equation", + "content": "w_{2}" + }, + { + "bbox": [ + 306, + 324, + 477, + 335 + ], + "type": "text", + "content": " denote the weights." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 342, + 444, + 354 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 342, + 444, + 354 + ], + "spans": [ + { + "bbox": [ + 306, + 342, + 444, + 354 + ], + "type": "text", + "content": "3.7. Training Data Collection" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 360, + 545, + 492 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 360, + 545, + 492 + ], + "spans": [ + { + "bbox": [ + 305, + 360, + 545, + 492 + ], + "type": "text", + "content": "We collect low-light image pairs from SICE (part2) [2] and LOL (training set) [32], which contain multi-exposure images. These datasets adopt some specific operators to deal with the misalignment caused by camera shaking or object moving. Note that the SICE dataset consists of both under and over-exposed images. We only select under-exposure and well-aligned cases for constructing the training set. As a result, we collect 324 sequences (a total of 1000 low-light images). As shown in Fig. 4, each sequence includes 2 to 6 samples. In the training phase, we randomly select two images from each sequence to constitute a pair." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 502, + 388, + 514 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 502, + 388, + 514 + ], + "spans": [ + { + "bbox": [ + 306, + 502, + 388, + 514 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 521, + 545, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 521, + 545, + 582 + ], + "spans": [ + { + "bbox": [ + 305, + 521, + 545, + 582 + ], + "type": "text", + "content": "We first describe implementation details, evaluation datasets, and performance criteria. Then, we present the quantitative and qualitative comparisons with state-of-the-art methods. Finally, we conduct ablative experiments to validate each component." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 587, + 439, + 600 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 587, + 439, + 600 + ], + "spans": [ + { + "bbox": [ + 306, + 587, + 439, + 600 + ], + "type": "text", + "content": "4.1. Implementation Details" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "text", + "content": "We implement PairLIE with PyTorch. In the training phase, we randomly crop images to the size of " + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "inline_equation", + "content": "128 \\times 128" + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "text", + "content": ". A batch size of 1 is applied. We use ADAM with the initial learning rate of " + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "inline_equation", + "content": "1 \\times 10^{-4}" + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "text", + "content": " to optimize the network. The number of training epochs is set to 400. The learning rate is half-decayed per 100 epochs. The default correction factor " + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "text", + "content": " is 0.2. In extremely dark cases such as the LOL dataset, we set " + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "inline_equation", + "content": "\\lambda = 0.14" + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "text", + "content": ". As for the hyper-parameter " + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "inline_equation", + "content": "w_0" + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "inline_equation", + "content": "w_1" + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "inline_equation", + "content": "w_2" + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "text", + "content": " in Eq. 10, we set " + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "inline_equation", + "content": "w_0 = 500" + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "inline_equation", + "content": "w_1 = w_2 = 1" + }, + { + "bbox": [ + 305, + 605, + 545, + 714 + ], + "type": "text", + "content": ", empirically." + } + ] + } + ], + "index": 20 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 757 + ], + "type": "text", + "content": "22256" + } + ] + } + ], + "index": 21 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 72, + 69, + 523, + 265 + ], + "blocks": [ + { + "bbox": [ + 72, + 69, + 523, + 265 + ], + "lines": [ + { + "bbox": [ + 72, + 69, + 523, + 265 + ], + "spans": [ + { + "bbox": [ + 72, + 69, + 523, + 265 + ], + "type": "table", + "html": "
MethodTypeLOLSICE
PSNR↑SSIM↑LPIPS↓DeltaE ↓PSNR↑SSIM↑LPIPS↓DeltaE ↓
SDD [9]T13.340.6370.74321.8315.350.7410.23216.08
STAR [34]T12.910.5180.36623.4615.170.7270.24616.35
MBLLEN [20]S17.860.7270.22513.6813.640.6320.29718.60
RetinexNet [32]S17.550.6480.37912.6919.890.7830.2768.715
GLADNet [30]S19.720.6800.32112.2819.980.8370.2038.947
KinD [38]S17.650.7750.17112.4921.100.8380.1958.009
DRBN [36]S16.290.5510.26013.4415.580.5220.28913.78
URetinexNet [33]S19.840.8260.12810.6521.640.8430.1927.728
ZeroDCE [7]U14.860.5590.33518.8118.690.8100.20711.93
RRDNet [41]U11.400.4570.36226.4313.280.6780.22119.64
RUAS [18]U16.400.5000.27016.8513.180.7340.36316.81
SCI [22]U14.780.5220.33919.5215.950.7870.23513.71
EnlightenGAN [12]U17.480.6510.32214.5018.730.8220.21610.42
PairLIE (Ours)U19.510.7360.24810.8021.320.8400.2167.835
", + "image_path": "59e0b42a9d9a6984dc78bc0d3cce16a595e1e18cab01bac8502efa357afc1775.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 274, + 545, + 297 + ], + "lines": [ + { + "bbox": [ + 46, + 274, + 545, + 297 + ], + "spans": [ + { + "bbox": [ + 46, + 274, + 545, + 297 + ], + "type": "text", + "content": "Table 1. Quantitative comparisons with state-of-the-art methods on LOL and SICE datasets. \"T\", \"S\", and \"U\" represent \"Traditional\", \"Supervised\", and \"Unsupervised\" methods, respectively. The top three results are marked in bold." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 47, + 316, + 170, + 328 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 316, + 170, + 328 + ], + "spans": [ + { + "bbox": [ + 47, + 316, + 170, + 328 + ], + "type": "text", + "content": "4.2. Datasets and Criteria" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 335, + 288, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 335, + 288, + 479 + ], + "spans": [ + { + "bbox": [ + 46, + 335, + 288, + 479 + ], + "type": "text", + "content": "As described before, low-light image pairs collected from SICE and LOL are applied to train PairLIE. We select another 50 sequences (150 images) from SICE and use the official evaluation set (15 images) of LOL to measure the model performance. Since SICE and LOL contains reference images, we employ PSNR, SSIM [31], LPIPS [37], and DeltaE with CIE2000 standard [24] to objectively evaluate the performance of each method. A higher PSNR/SSIM score indicates the result is closer to the reference. A lower LPIPS/DeltaE value denotes better enhancement performance. Furthermore, we adopt the MEF dataset [21] for visual comparisons." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 487, + 163, + 500 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 487, + 163, + 500 + ], + "spans": [ + { + "bbox": [ + 47, + 487, + 163, + 500 + ], + "type": "text", + "content": "4.3. Compared Methods" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 506, + 287, + 615 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 506, + 287, + 615 + ], + "spans": [ + { + "bbox": [ + 46, + 506, + 287, + 615 + ], + "type": "text", + "content": "PairLIE is compared with 13 state-of-the-art LIE methods, which can be divided into the following three categories: traditional methods (SDD [9], STAR [34]), supervised approaches (MBLLEN [20], RetinexNet [32], GALDNet [30], KinD [38], DRBN [36], URetinexNet [33]), and unsupervised methods (Zero-DCE [7], RRDNet [41], RUAS [18], SCI [22], and EnlightenGAN [12]). Note that the results of all those methods are reproduced by using the official codes with recommended parameters." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 623, + 194, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 623, + 194, + 635 + ], + "spans": [ + { + "bbox": [ + 47, + 623, + 194, + 635 + ], + "type": "text", + "content": "4.4. Quantitative Comparisons" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 641, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 288, + 714 + ], + "type": "text", + "content": "Tab. 1 lists the quantitative performance of the LOL and SICE datasets. As can be observed, traditional and unsupervised methods obtain relatively poor results. This is reasonable because it is difficult to learn an accurate enhancement model without the reference image. Besides, the performance of those solutions is highly dependent on the used" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 317, + 545, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 317, + 545, + 413 + ], + "spans": [ + { + "bbox": [ + 304, + 317, + 545, + 413 + ], + "type": "text", + "content": "prriors. However, hand-crafted features may not be adaptive enough for assorted light conditions. In Tab. 1, PairLIE achieves the best performance among the five unsupervised methods and gets competitive results compared with supervised approaches. Since paired low-light images provide sufficient information for solving the LIE task, PairLIE can reduce the demand for handcrafted priors. As a result, PairLIE achieves significant performance improvement." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 419, + 423, + 432 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 419, + 423, + 432 + ], + "spans": [ + { + "bbox": [ + 305, + 419, + 423, + 432 + ], + "type": "text", + "content": "4.5. Visual Comparisons" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 437, + 545, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 437, + 545, + 629 + ], + "spans": [ + { + "bbox": [ + 304, + 437, + 545, + 629 + ], + "type": "text", + "content": "Fig. 5 illustrates the visual comparisons of different LIE methods. Our observations are as follows: 1) The proposed method achieves visually pleasing results in terms of brightness, color, contrast, and naturalness. While other methods fail to cope with the extreme black light in this case. 2) Although supervised approaches GLADNet, KinD, and URetinexNet show good performance on LOL and SICE datasets according to Tab. 1, their generalization ability may be limited as supervised learning is sensitive to the data distribution. We further show an example of noise suppression in Fig. 6. As can be seen, although PairLIE does not introduce any handcrafted priors about the noise, our method can successfully suppress sensor noise in dark regions and the result is clear and natural. In contrast, the competitors either amplify noise or fail in correcting color and contrast, leading to poor visual quality." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 635, + 462, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 635, + 462, + 647 + ], + "spans": [ + { + "bbox": [ + 305, + 635, + 462, + 647 + ], + "type": "text", + "content": "4.6. Decomposition Visualization" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 654, + 545, + 713 + ], + "type": "text", + "content": "We visualize the reflectance and illumination components to validate the effectiveness of our method. As shown in Fig. 7, the reflectance contains rich textures and details while the illumination is piece-wise continuous and textureless, demonstrating PairLIE can accurately decompose the" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22257" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 78, + 70, + 164, + 135 + ], + "blocks": [ + { + "bbox": [ + 78, + 70, + 164, + 135 + ], + "lines": [ + { + "bbox": [ + 78, + 70, + 164, + 135 + ], + "spans": [ + { + "bbox": [ + 78, + 70, + 164, + 135 + ], + "type": "image", + "image_path": "e478eeb719a15ed9541a875727ad1373f075c5df7492e51970097243ac61557a.jpg" + } + ] + } + ], + "index": 0, + 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Visual comparisons of different LIE methods. 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Performance comparisons in noise removal. Zoom in for the best view. Zero-DCE [7], RUAS [18], SCI [22], EnlightenGAN [12], and PairLIE (Ours). Our result is visually satisfactory without obvious noise." + } + ] + } + ], + "index": 33, + "angle": 0, + "type": "image_caption" + } + ], + "index": 27 + }, + { + "type": "image", + "bbox": [ + 225, + 335, + 369, + 392 + ], + "blocks": [ + { + "bbox": [ + 225, + 335, + 369, + 392 + ], + "lines": [ + { + "bbox": [ + 225, + 335, + 369, + 392 + ], + "spans": [ + { + "bbox": [ + 225, + 335, + 369, + 392 + ], + "type": "image", + "image_path": "0e1d248fe20f45d9ea308d35de62012d74b323ff44028e1b8893e684147c5e6f.jpg" + } + ] + } + ], + "index": 29, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 272, + 393, + 321, + 403 + ], + "lines": [ + { + "bbox": [ + 272, + 393, + 321, + 403 + ], + "spans": [ + { + "bbox": [ + 272, + 393, + 321, + 403 + ], + "type": "text", + "content": "EnlightenGAN" + } + ] + } + ], + "index": 30, + "angle": 0, + "type": "image_caption" + } + ], + "index": 29 + }, + { + "type": "image", + "bbox": [ + 372, + 335, + 515, + 392 + ], + "blocks": [ + { + "bbox": [ + 372, + 335, + 515, + 392 + ], + "lines": [ + { + "bbox": [ + 372, + 335, + 515, + 392 + ], + "spans": [ + { + "bbox": [ + 372, + 335, + 515, + 392 + ], + "type": "image", + "image_path": "3fb5d354e5c39cc8afe0976358f7d0d12a5a8d85a8265ad30774aba59aa00672.jpg" + } + ] + } + ], + "index": 31, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 419, + 393, + 468, + 403 + ], + "lines": [ + { + "bbox": [ + 419, + 393, + 468, + 403 + ], + "spans": [ + { + "bbox": [ + 419, + 393, + 468, + 403 + ], + "type": "text", + "content": "PairLIE (Ours)" + } + ] + } + ], + "index": 32, + "angle": 0, + "type": "image_caption" + } + ], + "index": 31 + }, + { + "bbox": [ + 46, + 457, + 287, + 541 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 457, + 287, + 541 + ], + "spans": [ + { + "bbox": [ + 46, + 457, + 287, + 541 + ], + "type": "text", + "content": "low-light image. We further show the enhancement results with different correction factors. With the increase of " + }, + { + "bbox": [ + 46, + 457, + 287, + 541 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 457, + 287, + 541 + ], + "type": "text", + "content": ", the image brightness decreases gradually. When " + }, + { + "bbox": [ + 46, + 457, + 287, + 541 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 457, + 287, + 541 + ], + "type": "text", + "content": " is larger than 0.4 or smaller than 0.2, the enhanced images show obvious under/over-enhancement effects. In this work, the default " + }, + { + "bbox": [ + 46, + 457, + 287, + 541 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 457, + 287, + 541 + ], + "type": "text", + "content": " is set as 0.2. Note that users can adjust " + }, + { + "bbox": [ + 46, + 457, + 287, + 541 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 457, + 287, + 541 + ], + "type": "text", + "content": " in the testing phase according to their personal preferences." + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 47, + 550, + 147, + 562 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 550, + 147, + 562 + ], + "spans": [ + { + "bbox": [ + 47, + 550, + 147, + 562 + ], + "type": "text", + "content": "4.7. Ablation Studies" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 46, + 569, + 287, + 642 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 569, + 287, + 642 + ], + "spans": [ + { + "bbox": [ + 46, + 569, + 287, + 642 + ], + "type": "text", + "content": "We conduct ablation studies under different settings to understand how each component affects performance. We have tried the following three variations over the original PairLIE. 1) Setting A: without " + }, + { + "bbox": [ + 46, + 569, + 287, + 642 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + }, + { + "bbox": [ + 46, + 569, + 287, + 642 + ], + "type": "text", + "content": ". 2) Setting B: without " + }, + { + "bbox": [ + 46, + 569, + 287, + 642 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_P" + }, + { + "bbox": [ + 46, + 569, + 287, + 642 + ], + "type": "text", + "content": ". 3) Setting C: without prior terms, i.e., removing " + }, + { + "bbox": [ + 46, + 569, + 287, + 642 + ], + "type": "inline_equation", + "content": "\\| L - L_0\\| _2^2" + }, + { + "bbox": [ + 46, + 569, + 287, + 642 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 569, + 287, + 642 + ], + "type": "inline_equation", + "content": "\\| \\nabla L\\| _1" + }, + { + "bbox": [ + 46, + 569, + 287, + 642 + ], + "type": "text", + "content": " from " + }, + { + "bbox": [ + 46, + 569, + 287, + 642 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_R" + }, + { + "bbox": [ + 46, + 569, + 287, + 642 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 36 + }, + { + "bbox": [ + 46, + 642, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 287, + 714 + ], + "type": "text", + "content": "Tab. 2 and Fig. 8 report the results of our ablation studies on the LOL dataset. We can observe that our approach significantly outperforms setting A, which demonstrates the effectiveness of learning adaptive priors using low-light image pairs. Compared with setting B, PairLIE achieves slight performance improvement. The results of setting B suggest" + } + ] + } + ], + "index": 37 + }, + { + "bbox": [ + 304, + 457, + 545, + 624 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 457, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 304, + 457, + 545, + 624 + ], + "type": "text", + "content": "that P-Net can assist the Retinex decomposition, but it is not necessary for stably training the network. To further understand the role of P-Net, we show the reconstruction error " + }, + { + "bbox": [ + 304, + 457, + 545, + 624 + ], + "type": "inline_equation", + "content": "\\kappa" + }, + { + "bbox": [ + 304, + 457, + 545, + 624 + ], + "type": "text", + "content": " in Fig. 9. The blue line denotes the decomposition using original low-light images, i.e., " + }, + { + "bbox": [ + 304, + 457, + 545, + 624 + ], + "type": "inline_equation", + "content": "\\kappa = |L\\circ R - I|" + }, + { + "bbox": [ + 304, + 457, + 545, + 624 + ], + "type": "text", + "content": ", while the orange line refers to the decomposition using projected images, i.e., " + }, + { + "bbox": [ + 304, + 457, + 545, + 624 + ], + "type": "inline_equation", + "content": "\\kappa = |L\\circ R - i|" + }, + { + "bbox": [ + 304, + 457, + 545, + 624 + ], + "type": "text", + "content": ". As can be seen, the proposed method obtains a smaller reconstruction error than the baseline. Therefore, P-Net can remove inappropriate features and the output is more suitable for the Retinex decomposition. The results of Setting C show that when priors terms are removed, the performance drops significantly. This is because the Rrtinex decomposition requires some basic priors to initialize and restrain the illumination." + } + ] + } + ], + "index": 38 + }, + { + "bbox": [ + 306, + 634, + 382, + 645 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 634, + 382, + 645 + ], + "spans": [ + { + "bbox": [ + 306, + 634, + 382, + 645 + ], + "type": "text", + "content": "4.8. Discussions" + } + ] + } + ], + "index": 39 + }, + { + "bbox": [ + 304, + 653, + 545, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 545, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 545, + 714 + ], + "type": "text", + "content": "Acquiring low-light image pairs. Although PairLIE achieves promising performance with fewer handcrafted priors and a simpler network, collecting low-light image pairs is relatively expensive compared with recording a signal low-light image. This problem can be partly solved by" + } + ] + } + ], + "index": 40 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "22258" + } + ] + } + ], + "index": 41 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 67, + 70, + 526, + 205 + ], + "blocks": [ + { + "bbox": [ + 67, + 70, + 526, + 205 + ], + "lines": [ + { + "bbox": [ + 67, + 70, + 526, + 205 + ], + "spans": [ + { + "bbox": [ + 67, + 70, + 526, + 205 + ], + "type": "image", + "image_path": "f6b650455f9efd1b5a6996d37e09348c40d038803f34e258e67f1a8418ca8489.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 213, + 547, + 237 + ], + "lines": [ + { + "bbox": [ + 46, + 213, + 547, + 237 + ], + "spans": [ + { + "bbox": [ + 46, + 213, + 547, + 237 + ], + "type": "text", + "content": "Figure 7. Visualization of the Retinex decomposition. The enhanced results are presented under different correction factors. We set the default " + }, + { + "bbox": [ + 46, + 213, + 547, + 237 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 213, + 547, + 237 + ], + "type": "text", + "content": " as 0.2, but users can adjust the " + }, + { + "bbox": [ + 46, + 213, + 547, + 237 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 213, + 547, + 237 + ], + "type": "text", + "content": " according to their preferences." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 67, + 247, + 526, + 314 + ], + "blocks": [ + { + "bbox": [ + 67, + 247, + 526, + 314 + ], + "lines": [ + { + "bbox": [ + 67, + 247, + 526, + 314 + ], + "spans": [ + { + "bbox": [ + 67, + 247, + 526, + 314 + ], + "type": "image", + "image_path": "a55fae39d61d3c14216c482d03ef4d260a9fa3adb8360100c45b3cf6c724976f.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 323, + 546, + 346 + ], + "lines": [ + { + "bbox": [ + 46, + 323, + 546, + 346 + ], + "spans": [ + { + "bbox": [ + 46, + 323, + 546, + 346 + ], + "type": "text", + "content": "Figure 8. Visual comparisons of the ablation studies. Setting A: without " + }, + { + "bbox": [ + 46, + 323, + 546, + 346 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + }, + { + "bbox": [ + 46, + 323, + 546, + 346 + ], + "type": "text", + "content": ". Setting B: without " + }, + { + "bbox": [ + 46, + 323, + 546, + 346 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_P" + }, + { + "bbox": [ + 46, + 323, + 546, + 346 + ], + "type": "text", + "content": ". Setting C: without prior terms. Our result is visually closer to the reference." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 73, + 363, + 262, + 427 + ], + "blocks": [ + { + "bbox": [ + 73, + 363, + 262, + 427 + ], + "lines": [ + { + "bbox": [ + 73, + 363, + 262, + 427 + ], + "spans": [ + { + "bbox": [ + 73, + 363, + 262, + 427 + ], + "type": "table", + "html": "
MethodPSNR↑SSIM↑
Setting A17.700.567
Setting B19.500.724
Setting C14.690.684
PairLIE19.510.736
", + "image_path": "c835da82c141522df6ecee6f77ed387f29080a3d315d7461c688d0bced372150.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 435, + 287, + 458 + ], + "lines": [ + { + "bbox": [ + 46, + 435, + 287, + 458 + ], + "spans": [ + { + "bbox": [ + 46, + 435, + 287, + 458 + ], + "type": "text", + "content": "Table 2. Quantitative results of ablation studies on the LOL dataset. The best results are marked in bold." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 47, + 473, + 288, + 599 + ], + "blocks": [ + { + "bbox": [ + 47, + 473, + 288, + 599 + ], + "lines": [ + { + "bbox": [ + 47, + 473, + 288, + 599 + ], + "spans": [ + { + "bbox": [ + 47, + 473, + 288, + 599 + ], + "type": "image", + "image_path": "7bef45fabaceb3b263477697d38514f35d65282c9b5c69cc1cd89815da390c63.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 608, + 287, + 632 + ], + "lines": [ + { + "bbox": [ + 46, + 608, + 287, + 632 + ], + "spans": [ + { + "bbox": [ + 46, + 608, + 287, + 632 + ], + "type": "text", + "content": "Figure 9. Visualization of the reconstruction error. Baseline refers to the decomposition with original low-light images." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 654, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 288, + 715 + ], + "type": "text", + "content": "generating training pairs from a single low-light image. For example, one can use neighbor masks [10] to generate two similar low-light images from a degraded observation. Additionally, specific recorruption techniques can be leveraged to capture paired training instances." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 366, + 547, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 366, + 547, + 475 + ], + "spans": [ + { + "bbox": [ + 304, + 366, + 547, + 475 + ], + "type": "text", + "content": "Illumination components. PairLIE solves the Retinex decomposition with paired low-light images. The basic idea is to generate the same reflectance components since the reflectance is independent of the light. However, the relationship between illumination components is not sufficiently exploited in this paper. Illumination plays a critical role in the Retinex model. Therefore, we believe that PairLIE can be further improved by introducing new constraints between two illumination maps." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 485, + 384, + 498 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 485, + 384, + 498 + ], + "spans": [ + { + "bbox": [ + 306, + 485, + 384, + 498 + ], + "type": "text", + "content": "5. Conclusions" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 505, + 547, + 660 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 505, + 547, + 660 + ], + "spans": [ + { + "bbox": [ + 304, + 505, + 547, + 660 + ], + "type": "text", + "content": "This paper proposes PairLIE, a reference-free approach for low-light image enhancement that benefits from both Retinex-based and learning-based solutions. By learning adaptive constraints from low-light image pairs, PairLIE reduces the dependence on handcrafted priors and thus generalizes well on various scenes. To assist the decomposition, PairLIE first removes inappropriate features in the original image and then implements the decomposition on the optimized image. Extensive experiments on public benchmarks show that PairLIE outperforms the state-of-the-art unsupervised methods significantly. In future works, we will concentrate on exploiting effective priors of illumination in a data-driven fashion." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 665, + 547, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 547, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 547, + 712 + ], + "type": "text", + "content": "Acknowledgements: The work was supported partly by the National Natural Science Foundation of China under Grants 82172033, U19B2031, 61971369, 52105126, 82272071, and 62271430." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "22259" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 133 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 133 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 133 + ], + "type": "text", + "content": "[1] Tarik Arici, Salih Dikbas, and Yucel Altunbasak. A histogram modification framework and its application for image contrast enhancement. IEEE TIP, 18(9):1921-1935, 2009. 1, 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 136, + 286, + 167 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 136, + 286, + 167 + ], + "spans": [ + { + "bbox": [ + 53, + 136, + 286, + 167 + ], + "type": "text", + "content": "[2] Jianrui Cai, Shuhang Gu, and Lei Zhang. Learning a deep single image contrast enhancer from multi-exposure images. IEEE TIP, 27(4):2049-2062, 2018. 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 170, + 286, + 200 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 170, + 286, + 200 + ], + "spans": [ + { + "bbox": [ + 53, + 170, + 286, + 200 + ], + "type": "text", + "content": "[3] Turgay Celik and Tardi Tjahadi. Contextual and variational contrast enhancement. IEEE TIP, 20(12):3431-3441, 2011. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 202, + 286, + 233 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 202, + 286, + 233 + ], + "spans": [ + { + "bbox": [ + 53, + 202, + 286, + 233 + ], + "type": "text", + "content": "[4] Chen Chen, Qifeng Chen, Jia Xu, and Vladlen Koltun. Learning to see in the dark. In CVPR, pages 3291-3300, 2018. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 236, + 286, + 278 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 236, + 286, + 278 + ], + "spans": [ + { + "bbox": [ + 53, + 236, + 286, + 278 + ], + "type": "text", + "content": "[5] Xueyang Fu, Delu Zeng, Yue Huang, Yinghao Liao, Xinghao Ding, and John Paisley. A fusion-based enhancing method for weakly illuminated images. Signal Processing, 129:82-96, 2016. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 281, + 286, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 281, + 286, + 323 + ], + "spans": [ + { + "bbox": [ + 53, + 281, + 286, + 323 + ], + "type": "text", + "content": "[6] Xueyang Fu, Delu Zeng, Yue Huang, Xiao-Ping Zhang, and Xinghao Ding. A weighted variational model for simultaneous reflectance and illumination estimation. In CVPR, pages 2782-2790, 2016. 1, 2, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 325, + 286, + 367 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 325, + 286, + 367 + ], + "spans": [ + { + "bbox": [ + 53, + 325, + 286, + 367 + ], + "type": "text", + "content": "[7] Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. Zero-reference deep curve estimation for low-light image enhancement. In CVPR, pages 1777-1786, 2020. 2, 3, 6, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 369, + 286, + 400 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 369, + 286, + 400 + ], + "spans": [ + { + "bbox": [ + 53, + 369, + 286, + 400 + ], + "type": "text", + "content": "[8] Xiaojie Guo, Yu Li, and Haibin Ling. Lime: Low-light image enhancement via illumination map estimation. IEEE TIP, 26(2):982-993, 2017. 1, 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 403, + 286, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 403, + 286, + 435 + ], + "spans": [ + { + "bbox": [ + 53, + 403, + 286, + 435 + ], + "type": "text", + "content": "[9] Shijie Hao, Xu Han, Yanrong Guo, Xin Xu, and Meng Wang. Low-light image enhancement with semi-decoupled decomposition. IEEE TMM, 22(12):3025-3038, 2020. 2, 6, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 437, + 286, + 479 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 437, + 286, + 479 + ], + "spans": [ + { + "bbox": [ + 48, + 437, + 286, + 479 + ], + "type": "text", + "content": "[10] Tao Huang, Songjiang Li, Xu Jia, Huchuan Lu, and Jianzhuang Liu. Neighbor2neighbor: Self-supervised denoising from single noisy images. In CVPR, pages 14781-14790, 2021. 8" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 480, + 286, + 523 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 480, + 286, + 523 + ], + "spans": [ + { + "bbox": [ + 48, + 480, + 286, + 523 + ], + "type": "text", + "content": "[11] Qiuping Jiang, Yudong Mao, Runmin Cong, Wenqi Ren, Chao Huang, and Feng Shao. Unsupervised decomposition and correction network for low-light image enhancement. IEEE TITS, pages 1-16, 2022. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 525, + 286, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 525, + 286, + 578 + ], + "spans": [ + { + "bbox": [ + 48, + 525, + 286, + 578 + ], + "type": "text", + "content": "[12] Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, and Zhangyang Wang. *Enlightengan: Deep light enhancement without paired supervision*. *IEEE TIP*, 30:2340-2349, 2021. 3, 6, 7" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 580, + 286, + 601 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 580, + 286, + 601 + ], + "spans": [ + { + "bbox": [ + 48, + 580, + 286, + 601 + ], + "type": "text", + "content": "[13] Edwin H Land. The retina theory of color vision. Scientific American, 237(6):108-129, 1977. 1" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 603, + 286, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 603, + 286, + 635 + ], + "spans": [ + { + "bbox": [ + 48, + 603, + 286, + 635 + ], + "type": "text", + "content": "[14] Chulwoo Lee, Chul Lee, and Chang-Su Kim. Contrast enhancement based on layered difference representation of 2d histograms. IEEE TIP, 22(12):5372-5384, 2013. 1, 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 636, + 286, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 636, + 286, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 636, + 286, + 668 + ], + "type": "text", + "content": "[15] Hunsang Lee, Kwanghoon Sohn, and Dongbo Min. Unsupervised low-light image enhancement using bright channel prior. IEEE SPL, 27:251-255, 2020. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 670, + 286, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 286, + 712 + ], + "type": "text", + "content": "[16] Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila. Noise2Noise: Learning image restoration without clean data. In ICML, pages 2965-2974, 2018. 5" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 35, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "text", + "content": "[17] Mading Li, Jiaying Liu, Wenhan Yang, Xiaoyan Sun, and Zongming Guo. Structure-revealing low-light image enhancement via robust retinae model. IEEE TIP, 27(6):2828-2841, 2018. 1, 2, 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 118, + 545, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 161 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 161 + ], + "type": "text", + "content": "[18] Risheng Liu, Long Ma, Jiaao Zhang, Xin Fan, and Zhongxuan Luo. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In CVPR, pages 10561-10570, 2021. 2, 3, 6, 7" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 163, + 545, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 163, + 545, + 195 + ], + "spans": [ + { + "bbox": [ + 307, + 163, + 545, + 195 + ], + "type": "text", + "content": "[19] Kin Gwn Lore, Adedotun Akintayo, and Soumik Sarkar. Linet: A deep autoencoder approach to natural low-light image enhancement. Pattern Recognition, 61:650-662, 2017. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 197, + 545, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 197, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 307, + 197, + 545, + 228 + ], + "type": "text", + "content": "[20] Feifan Lv, Feng Lu, Jianhua Wu, and Chongsoon Lim. Mbllen: Low-light image/video enhancement using cnns. In BMVC, pages 1-13, 2018. 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 231, + 545, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 231, + 545, + 262 + ], + "spans": [ + { + "bbox": [ + 307, + 231, + 545, + 262 + ], + "type": "text", + "content": "[21] Kede Ma, Kai Zeng, and Zhou Wang. Perceptual quality assessment for multi-exposure image fusion. IEEE TIP, 24(11):3345-3356, 2015. 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 264, + 545, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 264, + 545, + 297 + ], + "spans": [ + { + "bbox": [ + 307, + 264, + 545, + 297 + ], + "type": "text", + "content": "[22] Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. Toward fast, flexible, and robust low-light image enhancement. In CVPR, pages 5637-5646, 2022. 3, 6, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 298, + 545, + 341 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 298, + 545, + 341 + ], + "spans": [ + { + "bbox": [ + 307, + 298, + 545, + 341 + ], + "type": "text", + "content": "[23] Gyu-Hee Park, Hwa-Hyun Cho, and Myung-Ryul Choi. A contrast enhancement method using dynamic range separate histogram equalization. IEEE TCE, 54(4):1981-1987, 2008. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 343, + 545, + 387 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 343, + 545, + 387 + ], + "spans": [ + { + "bbox": [ + 307, + 343, + 545, + 387 + ], + "type": "text", + "content": "[24] Gaurav Sharma, Wencheng Wu, and Edul N Dalal. The ciede2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations. Color Research and Application, 30(1):21-30, 2005. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 388, + 545, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 388, + 545, + 431 + ], + "spans": [ + { + "bbox": [ + 307, + 388, + 545, + 431 + ], + "type": "text", + "content": "[25] Weitao Song, Masanori Suganuma, X. Liu, Norimasa Shimobayashi, D. Maruta, and Takayuki Okatani. Matching in the dark: A dataset for matching image pairs of low-light scenes. In ICCV, pages 6009-6018, 2021. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 433, + 545, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 433, + 545, + 465 + ], + "spans": [ + { + "bbox": [ + 307, + 433, + 545, + 465 + ], + "type": "text", + "content": "[26] Li Tao, Chuang Zhu, Jiawen Song, Tao Lu, Huizhu Jia, and Xiaodong Xie. Low-light image enhancement using cnn and bright channel prior. In ICIP, pages 3215-3219, 2017. 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 467, + 545, + 488 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 467, + 545, + 488 + ], + "spans": [ + { + "bbox": [ + 307, + 467, + 545, + 488 + ], + "type": "text", + "content": "[27] Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky. Deep image prior. In CVPR, pages 9446-9454, 2018. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 490, + 545, + 532 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 490, + 545, + 532 + ], + "spans": [ + { + "bbox": [ + 307, + 490, + 545, + 532 + ], + "type": "text", + "content": "[28] Ruixing Wang, Qing Zhang, Chi-Wing Fu, Xiaoyong Shen, Wei-Shi Zheng, and Jiaya Jia. Underexposed photo enhancement using deep illumination estimation. In CVPR, pages 6842-6850, 2019. 1, 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 307, + 534, + 545, + 576 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 534, + 545, + 576 + ], + "spans": [ + { + "bbox": [ + 307, + 534, + 545, + 576 + ], + "type": "text", + "content": "[29] Shuhang Wang, Jin Zheng, Hai-Miao Hu, and Bo Li. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE TIP, 22(9):3538-3548, 2013. 2, 3" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 307, + 579, + 545, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 579, + 545, + 622 + ], + "spans": [ + { + "bbox": [ + 307, + 579, + 545, + 622 + ], + "type": "text", + "content": "[30] Wenjing Wang, Chen Wei, Wenhan Yang, and Jiaying Liu. Gladnet: Low-light enhancement network with global awareness. In IEEE International Conference on Automatic Face and Gesture Recognition, pages 751-755, 2018. 6, 7" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 307, + 624, + 545, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 624, + 545, + 656 + ], + "spans": [ + { + "bbox": [ + 307, + 624, + 545, + 656 + ], + "type": "text", + "content": "[31] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE TIP, 13(4):600-612, 2004. 6" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 307, + 658, + 545, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 658, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 307, + 658, + 545, + 689 + ], + "type": "text", + "content": "[32] Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. Deep retina decomposition for low-light enhancement. In BMVC, pages 1-12, 2018. 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 307, + 692, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 692, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 692, + 545, + 713 + ], + "type": "text", + "content": "[33] Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. Uretinex-net: Retinex-based" + } + ] + } + ], + "index": 34 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 749, + 318, + 757 + ], + "type": "text", + "content": "22260" + } + ] + } + ], + "index": 36 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 430 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 66, + 72, + 286, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 72, + 286, + 95 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 286, + 95 + ], + "type": "text", + "content": "deep unfolding network for low-light image enhancement. In CVPR, pages 5901-5910, 2022. 2, 6, 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 287, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 287, + 139 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 287, + 139 + ], + "type": "text", + "content": "[34] Jun Xu, Yingkun Hou, Dongwei Ren, Li Liu, Fan Zhu, Mengyang Yu, Haoqian Wang, and Ling Shao. Star: A structure and texture aware retina model. IEEE TIP, 29:5022-5037, 2020. 2, 3, 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 141, + 287, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 141, + 287, + 173 + ], + "spans": [ + { + "bbox": [ + 48, + 141, + 287, + 173 + ], + "type": "text", + "content": "[35] Xiaogang Xu, Ruixing Wang, Chi-Wing Fu, and Jiaya Jia. Snr-aware low-light image enhancement. In CVPR, pages 17714-17724, 2022. 1, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 175, + 287, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 175, + 287, + 218 + ], + "spans": [ + { + "bbox": [ + 48, + 175, + 287, + 218 + ], + "type": "text", + "content": "[36] Wenhan Yang, Shiqi Wang, Yuming Fang, Yue Wang, and Jiaying Liu. From fidelity to perceptual quality: A semi-supervised approach for low-light image enhancement. In CVPR, pages 3060-3069, 2020. 6" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 220, + 287, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 220, + 287, + 262 + ], + "spans": [ + { + "bbox": [ + 48, + 220, + 287, + 262 + ], + "type": "text", + "content": "[37] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, pages 586-595, 2018. 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 264, + 287, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 264, + 287, + 297 + ], + "spans": [ + { + "bbox": [ + 48, + 264, + 287, + 297 + ], + "type": "text", + "content": "[38] Yonghua Zhang, Jiawan Zhang, and Xiaojie Guo. Kindling the darkness: A practical low-light image enhancer. In ACM MM, pages 1632-1640, 2019. 2, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 298, + 287, + 341 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 298, + 287, + 341 + ], + "spans": [ + { + "bbox": [ + 48, + 298, + 287, + 341 + ], + "type": "text", + "content": "[39] Zhao Zhang, Huan Zheng, Richang Hong, Mingliang Xu, Shuicheng Yan, and Meng Wang. Deep color consistent network for low-light image enhancement. In CVPR, pages 1899-1908, 2022. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 343, + 287, + 385 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 343, + 287, + 385 + ], + "spans": [ + { + "bbox": [ + 48, + 343, + 287, + 385 + ], + "type": "text", + "content": "[40] Zunjin Zhao, Bangshu Xiong, Lei Wang, Qiaofeng Ou, Lei Yu, and Fa Kuang. Retinexdip: A unified deep framework for low-light image enhancement. IEEE TCSVT, 32(3):1076-1088, 2022. 2, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 388, + 287, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 388, + 287, + 430 + ], + "spans": [ + { + "bbox": [ + 48, + 388, + 287, + 430 + ], + "type": "text", + "content": "[41] Anqi Zhu, Lin Zhang, Ying Shen, Yong Ma, Shengjie Zhao, and Yicong Zhou. Zero-shot restoration of underexposed images via robust retina decomposition. 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In this paper, we find that most existing Transformers usually use all similarities of the tokens from the query-key pairs for the feature aggregation. However, if the tokens from the query are different from those of the key, the self-attention values estimated from these tokens also involve in feature aggregation, which accordingly interferes with the clear image restoration. To overcome this problem, we propose an effective DeRaining network, Sparse Transformer (DRSformer) that can adaptively keep the most useful self-attention values for feature aggregation so that the aggregated features better facilitate high-quality image reconstruction. Specifically, we develop a learnable top-k selection operator to adaptively retain the most crucial attention scores from the keys for each query for better feature aggregation. Simultaneously, as the naive feed-forward network in Transformers does not model the multi-scale information that is important for latent clear image restoration, we develop an effective mixed-scale feed-forward network to generate better features for image deraining. To learn an enriched set of hybrid features, which combines local context from CNN operators, we equip our model with mixture of experts feature compensator to present a cooperation refinement deraining scheme. Extensive experimental results on the commonly used benchmarks demonstrate that the proposed method achieves favorable performance against state-of-the-art approaches. The source code and trained models are available at https://github.com/cschenxiang/DRSformer.", + "bbox": [ + 75, + 318, + 472, + 803 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 816, + 209, + 832 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Single image deraining is a typical low-level vision problem emerging in the last decade. It aims to recover the clean", + "bbox": [ + 76, + 842, + 468, + 873 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/81b2306b1e6ad80b002a771910517eecfa02fd5f91f604f86555e43e6508a567.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 506, + 284, + 630, + 352 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9319dbb7697948071921f1ff095663b8ebccadd39b27295eb885780a532d03dd.jpg", + "image_caption": [ + "(a) Rainy Input" + ], + "image_footnote": [], + "bbox": [ + 504, + 352, + 630, + 382 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/12e158fc2450523a74a45fcb3506c95253b3a8a9be5858dfc3b8776c9606d60c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 633, + 284, + 758, + 351 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e71a367e39a78581910fcc36fb86d1e37b95cf6b39737d51c415247c7cafc52d.jpg", + "image_caption": [ + "(b) Uformer [48]" + ], + "image_footnote": [], + "bbox": [ + 633, + 352, + 756, + 382 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/287e423fb8bcac29ef5f66af9578115b3a4a766e68765ccf0c8d0612fe1238f0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 759, + 285, + 885, + 351 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/ebc2333ba343693709de1ac80900c34f895dabd0305f4034b7792c968e89fc57.jpg", + "image_caption": [ + "(c) Restormer [58]" + ], + "image_footnote": [], + "bbox": [ + 759, + 352, + 885, + 382 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/282d2d6b058f81d52fe8a2913402cc5b4554173da3555d5986ba883e6fb4ada7.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 503, + 397, + 629, + 464 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/b3d11295a87a076c342b6ce31c84c7f03c288729ef5025f41883e06a45702bb4.jpg", + "image_caption": [ + "(d) IDT [50]" + ], + "image_footnote": [], + "bbox": [ + 504, + 464, + 627, + 494 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/3c83250c0dde52eca05c44120432223be024c7784c29a28875ac414c98d423bb.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 630, + 397, + 756, + 463 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/df8ab293b11227701d658f2bfe972e87b9db7f45035fe9abe1ad292d0b326200.jpg", + "image_caption": [ + "(e) Ours" + ], + "image_footnote": [], + "bbox": [ + 630, + 464, + 754, + 494 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f8b017499f691f635d84c6daca01d5074b78738b00d6bd9d926b76c2cf418cdb.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 759, + 397, + 885, + 463 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/f0a733dcb3205b45e0ad52a3f08506fb002c6145c2722a7997aa5346b4a60ba5.jpg", + "image_caption": [ + "(f) Ground Truth", + "Figure 1. Image deraining results between our method and recent Transformer-based methods [48,50,58]. Our method can generate high-quality image with more accurate detail and texture recovery." + ], + "image_footnote": [], + "bbox": [ + 759, + 464, + 883, + 494 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "image from the observed rainy one. As the clear image and rain streaks are unknown, it is an ill-posed inverse problem. To solve this problem, early approaches [20, 24, 60] usually impose various priors based on statistical properties of rain streaks and clear images. In fact, these handcrafted priors are not robust to complex and varying rainy scenarios, which limit the deraining performance.", + "bbox": [ + 496, + 568, + 890, + 672 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recently, numerous learning-based methods [4, 19, 23, 36, 52, 53, 56] have resorted to diverse CNN architectures as a preferable choice compared to traditional algorithms. However, the intrinsic characteristics of convolutional operation, i.e., local receptive fields and independence of input content, hinder the model's capacity to eliminate long-range rain degradation perturbation. To alleviate such limitations, Transformers [2, 26, 35, 50] have been applied to image de- raining and have achieved decent performance as they can better model the non-local information for high-quality image reconstruction. Nevertheless, the image details, which are local features of images, are not modeled well by these approaches when restoring clear images as shown in Figure 1. One main reason is that the self-attention in Transformers does not model the local invariant properties that", + "bbox": [ + 496, + 674, + 892, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author.", + "bbox": [ + 94, + 886, + 220, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "5896", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "CNNs do well. Since rain streaks tend to confuse with background details in local regions, recent studies [5, 18, 57] try to mitigate such drawbacks by combining CNN operations and Transformers for boosting image deraining, where the Transformers based on the standard formulations.", + "bbox": [ + 75, + 90, + 468, + 165 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We note that the standard Transformers [40] usually use all attention relations based on the query-key pairs to aggregate features. As the tokens from the key are not always relevant to those from the query, using the self-attention values estimated from these tokens in the feature aggregation interferes with the following latent clear image restoration. The root cause behind this deficiency lies in that, the native dense calculation pattern of self-attention amplifies relatively smaller similarity weights, making feature interaction and aggregation process susceptible to implicit noises. This also naturally leads to corresponding redundant or irrelevant representations are still taken into consideration when modeling global feature dependencies [44, 64]. Thus, these findings motivate us to explore the most useful self-attention values so that we can make full use of the features for better image restoration.", + "bbox": [ + 75, + 167, + 468, + 411 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To this end, we develop an effective sparse Transformer network for image deraining, named as DRSformer. Specifically, the key component of the proposed framework is the sparse Transformer block (STB) which contains a top- $k$ sparse attention (TKSA) that keeps the most useful self-attention values for feature aggregation and a mixed-scale feed-forward network (MSFN) that explores the multi-scale features for better image deraining. First, we design the top- $k$ attention mechanism to replace the vanilla self-attention [40]. The TKSA keeps the largest $K$ similarity scores between the queries and the keys for the self-attention computing, thereby facilitating better feature aggregation. Furthermore, the developed MSFN further explores the multi-scale information to better improve the aggregated features. Finally, based on the observation that rain distribution reveals the degradation location and degree, we also introduce mixture of experts feature compensator (MEFC) to provide collaborative refinement for STB. With the above-mentioned designs, our proposed method offers three-fold advantages: (1) it can enjoy natural robustness in terms of less sensitivity to useless feature interference, (2) it can not only enrich the locality but also empower the capability of global feature exploitation, and (3) it can co-expllore data (embodied in MEFC) and content (embodied in STB) sparsity for achieving deraining performance gains.", + "bbox": [ + 75, + 412, + 470, + 789 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The main contributions are summarized as follows:", + "bbox": [ + 96, + 792, + 434, + 806 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a sparse Transformer architecture to help generate high-quality deraining results with more accurate detail and texture recovery.", + "- We develop a simple yet effective learnable top- $k$ selection operator to adaptively maintain the most useful self-attention values for better feature aggregation." + ], + "bbox": [ + 94, + 810, + 467, + 900 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We design an effective feed-forward network based on mixed-scale fusion strategy to explore multi-scale representations for better facilitating image deraining.", + "- Extensive experimental results on various benchmarks demonstrate that our method achieves favorable performance against state-of-the-art (SOTA) approaches." + ], + "bbox": [ + 517, + 90, + 888, + 181 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 194, + 640, + 209 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Single image deraining. Since image deraining is an ill-posed problem, traditional methods [12, 20, 24, 30, 60] usually develop kinds of image priors to provide additional constraints. However, these handcrafted priors tend to rely on empirical observations and thus are not able to model the inherent properties of clear images. To overcome this problem, numerous CNN-based frameworks [53] have been developed to solve image deraining and achieved decent restoration performance. To better represent the rain distribution, several studies take rain characteristics such as rain direction [27], density [61], veiling effect [15] into account, and optimize the network structure via recursive computation [19, 23, 36] or transfer mechanism [16, 49, 54, 55]. Although these methods achieve better performance than the hand-crafted prior-based ones, they have difficulty capturing the long-range dependencies due to the intrinsic limitations of convolution. Different CNN-based deraining approaches, we utilize the Transformer as the network backbone to model non-local information for image deraining.", + "bbox": [ + 496, + 215, + 890, + 502 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Vision Transformers. Motivated by the great success of the Transformers [7] in natural language processing (NLP) [40] and high-level vision tasks [1, 28], Transformers have been applied to image restoration [2, 13, 48, 51, 58] and perform better than the previous CNN-based baselines as they are able to model non-local information. For the field of image rain removal, Jiang et al. [18] design a dynamic associated deraining network by incorporating self-attention in Transformer with a background recovery network. More recently, Xiao et al. [50] elaborately develop image deraining Transformer (IDT) with window-based and spatial-based dual Transformer to achieve excellent results. Note that, most existing methods rely on the dense dot-product self-attention as the heart of Transformers. However, one shortcoming of this computation manner is that redundant or irrelevant features with smaller weights may interfere with the attention map, which makes the output features contain potential noises. In this work, we propose sparse attention in Transformer to relieve the negligence of the most relevant information faced by vanilla self-attention.", + "bbox": [ + 496, + 505, + 892, + 806 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Sparse representation. With inspirations drawn from neural activity in biological brains, sparsity of hidden representation in deep neural networks as a tantalizing \"free lunch\" emerges for both vision and NLP tasks [44, 64]. Indeed, it is widely proven that sparse representation also plays a critical role in handling low-level vision problems, such as im", + "bbox": [ + 496, + 810, + 890, + 898 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "5897", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/8723a1773c75db6b092b47df82de7b6841b7f36984e27b0f3b907c5ea152ead0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 76, + 85, + 895, + 210 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/f7d3b9b6bc7c821cd110409d35fa63f7be2beaf7b193751bf226bc7cfccbfef8.jpg", + "image_caption": [ + "Figure 2. The overall architecture of the proposed sparse Transformer network for image deraining (DRSformer), which mainly contains sparse Transformer block (STB) with top- $k$ sparse attention (TKSA) and mixed-scale feed-forward network (MSFN), and mixture of experts feature compensator (MEFC). LN refers to the layer normalization and DW-Conv refers to the depth-wise convolution." + ], + "image_footnote": [], + "bbox": [ + 101, + 213, + 310, + 333 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/8d0bd17d78a0f4711ad5413c5e5b22f8131a59c830d59b2a282ec958b1b449e5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 318, + 213, + 625, + 333 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/98e7dd37366b46e86ef3d41966669cd5b5759f02181e9e7b84fa275e6912f602.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 630, + 213, + 872, + 333 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "age deraining [46] and super-resolution [31]. In principle, sparse attention can be categorized into data-based (fixed) sparse attention and content-based sparse attention [6, 38]. For data-based sparse attention, several local attention operations are introduced into CNN backbone, which mainly considers attending only to local window size. Recent studies [11, 42] have investigated enforcing sparsity to Transformer backbone. More recently, Zhang et al. [63] design an attention retractable Transformer to allow tokens from sparse areas to interact features, which is data-based sparsity. Different from it, we implement a simple but effective approximation for self-attention based on top- $k$ selection to achieve sparse attention, which is content-based sparsity.", + "bbox": [ + 75, + 391, + 472, + 589 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Top-k selection. Zhao et al. [64] first propose an explicit selection method based on top- $k$ mechanism in NLP tasks. Driven by their success, $k$ -NN attention [42, 44] is further introduced for boosting vision Transformers. Unlike performing top- $k$ selection in the spatial dimension [44], we design an efficient top- $k$ useful channel selection operator.", + "bbox": [ + 75, + 593, + 470, + 686 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Proposed Method", + "text_level": 1, + "bbox": [ + 76, + 694, + 250, + 710 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we first describe the overall pipeline and symmetrically hierarchical network architecture for image deraining. Afterward, we provide the details of the proposed sparse Transformer block (STB), as the fundamental building unit of our method, which mainly contains two key elements: top- $k$ sparse attention (TKSA) and mixed-scale feed-forward network (MSFN). Finally, we present the introduced mixture of experts feature compensator (MEFC).", + "bbox": [ + 75, + 715, + 468, + 838 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Overall pipeline", + "text_level": 1, + "bbox": [ + 76, + 847, + 236, + 863 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The overall pipeline of our proposed DRSformer, shown in Figure 2, is based on a hierarchical encoder-decoder", + "bbox": [ + 76, + 869, + 470, + 900 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "framework. Given a rainy image $I_{rain} \\in \\mathbb{R}^{H \\times W \\times 3}$ , where $H \\times W$ represents the spatial resolution of the feature map, we perform overlapped image patch embedding with $3 \\times 3$ convolution. In the network backbone, we stack $N_{i \\in [1,2,3,4]}$ STBs to extract rich features for spatially-varying rain distribution. To excavate the multi-scale representation from rain degeneration, each level of encoder-decoder pipeline covers its own specific spatial resolution and channel dimension. For feature down-sampling and up-sampling, we apply pixel-unshuffle and pixel-shuffle operations. Similar to [48,50,58], we also add skip-connections to bridge across continuous intermediate features for stable training. In each STB, unlike the standard self-attention [7] in Transformer, we develop TKSA to achieve feature sparsity, aiming to enforce the feature aggregation process more effectively. In addition, a MSFN is introduced into STB to enrich multiscale local information and help image restoration. At the early and final stages of the model learning, we equip our model with $N_0$ MEFCs to provide complementary feature refinement, so that high-quality clear outputs can be finally reconstructed. With this hybrid formulation, we allow DRSformer to exploit both the adaptive content and the intrinsic property of rainy images, facilitating the separation of undesired rain streaks and latent clear background, and experiments demonstrate that the above design choices yield quality improvements (see Sec. 4.3)", + "bbox": [ + 496, + 391, + 892, + 784 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The final reconstructed result is obtained by: $I_{derain} = \\mathcal{F}(I_{rain}) + I_{rain}$ , where $\\mathcal{F}(\\cdot)$ is the overall network and it is trained by minimizing the following loss function:", + "bbox": [ + 498, + 785, + 893, + 830 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\left\\| I _ {\\text {d e r a i n}} - I _ {\\text {g t}} \\right\\| _ {1}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 617, + 842, + 890, + 859 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $I_{gt}$ denotes the ground-truth image, and $\\| \\cdot \\| _1$ denotes the $L_{1}$ -norm.", + "bbox": [ + 498, + 869, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "5898", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Sparse Transformer block", + "text_level": 1, + "bbox": [ + 76, + 90, + 313, + 106 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "As the standard Transformers [7, 40, 58] take all the tokens to compute self-attention globally, which is unfriendly for image restoration due to it may involve noisy interactions between the irrelevant features. To solve such limitations, we develop a sparse Transformer block (STB) as the feature extraction unit by taking the advantages of sparsity that emerged in neural networks [64]. Formally, given the input features at the $(l - 1)$ -th block $\\mathbf{X}_{l - 1}$ , the encoding procedures of STB can be defined as:", + "bbox": [ + 75, + 113, + 468, + 248 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {X} _ {l} ^ {\\prime} = \\mathbf {X} _ {l - 1} + \\operatorname {T K S A} \\left(\\ln \\left(\\mathbf {X} _ {l - 1}\\right)\\right), \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 151, + 251, + 468, + 268 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {X} _ {l} = \\mathbf {X} _ {l} ^ {\\prime} + \\operatorname {M S F N} \\left(\\ln \\left(\\mathbf {X} _ {l} ^ {\\prime}\\right)\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 168, + 276, + 468, + 292 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where LN denotes the layer normalization; $\\mathbf{X}_l^{\\prime}$ and $\\mathbf{X}_l$ denote the outputs from the top- $k$ sparse attention (TKSA) and mixed-scale feed-forward network (MSFN), which are described below.", + "bbox": [ + 75, + 294, + 468, + 354 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Top-k sparse attention (TKSA). We revisit the standard self-attention in Transformer, which has become an empirical operation in most of the existing models. Given a query $Q$ , key $K$ and value $V$ with the dimension of $\\mathbb{R}^{L\\times d}$ , the output of dot-product attention is generally formulated as:", + "bbox": [ + 76, + 358, + 468, + 434 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {A t t} (\\mathbf {Q}, \\mathbf {K}, \\mathbf {V}) = \\operatorname {s o f t m a x} \\left(\\frac {\\mathbf {Q} \\mathbf {K} ^ {\\top}}{\\lambda}\\right) \\mathbf {V}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 138, + 436, + 468, + 472 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{Q}$ , $\\mathbf{K}$ , and $\\mathbf{V}$ denote the matrix forms of $Q$ , $K$ , and $V$ , respectively. $\\lambda$ is an optional temperature factor defined by $\\lambda = \\sqrt{d}$ . Generally, multi-head attention is implemented to each of the $k$ new $Q$ , $K$ and $V$ , yielding $d = C / k$ channel dimensional outputs which are concatenated and then got the final result for all heads via the linear projection. Noted that, this vanilla self-attention paradigm is based on densely fully-connected, which requires computing the attention map for all query-key pairs. In our work, we develop TKSA to replace it, thus avoiding the involvement of irrelevant information during the feature interaction process.", + "bbox": [ + 75, + 474, + 468, + 642 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Specifically, we first encode channel-wise context by applying $1 \\times 1$ convolutions followed by $3 \\times 3$ depth-wise convolutions. Inspired by [58], we apply self-attention across channels rather than the spatial dimension to reduce the time and memory complexity. Next, we calculate similarities of pixel pairs between all the reshaped queries and keys, and mask out the unnecessary elements assigned with lower attention weights in the transposed attention matrix $\\mathbf{M}$ of size $\\mathbb{R}^{\\hat{C} \\times \\hat{C}}$ . Unlike the dropout strategy of randomly abandoning the scores, an adaptive selection of the top- $k$ contributive scores is implemented upon $\\mathbf{M}$ , aiming to preserve the most significant components and remove the useless ones [3]. Here, $k$ is an adjustable parameter to dynamically control the magnitude of sparsity, which is formally obtained by weighted average of some proper fractions, such as $\\frac{2}{3}$ . Thus, only top- $k$ values within the range $[\\Delta_1, \\Delta_2]$ are normalized from each row of $\\mathbf{M}$ for softmax computing. For", + "bbox": [ + 75, + 643, + 468, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "other elements that are smaller than top- $k$ scores, we replace their probabilities with 0 at given indices using the scatter function. This dynamic selection makes the attention from dense to sparse, which is derived by:", + "bbox": [ + 496, + 90, + 890, + 151 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\operatorname {S p a r s e A t t} (\\mathbf {Q}, \\mathbf {K}, \\mathbf {V}) = \\operatorname {s o f t m a x} \\left(\\mathcal {T} _ {k} \\left(\\frac {\\mathbf {Q} \\mathbf {K} ^ {\\top}}{\\lambda}\\right)\\right) \\mathbf {V}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 514, + 157, + 890, + 191 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathcal{T}_k(\\cdot)$ is the learnable top- $k$ selection operator:", + "bbox": [ + 500, + 196, + 846, + 213 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\left[ \\mathcal {T} _ {k} (\\boldsymbol {S}) \\right] _ {i j} = \\left\\{ \\begin{array}{l l} S _ {i j} & S _ {i j} \\geq t _ {i} \\\\ 0 & \\text {o t h e r w i s e .} \\end{array} \\right. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 584, + 219, + 890, + 260 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $t_i$ is the $k$ -th largest value in the $j$ -th row of $\\frac{\\mathbf{Q}\\mathbf{K}^{\\top}}{\\lambda}$ . Here, we calculate $\\mathbf{Q}\\mathbf{K}^{\\top}$ in the same way as [58] to generate $\\mathbf{M}$ of size $\\mathbb{R}^{\\hat{C}\\times \\hat{C}}$ . As we use the multi-head strategy, we finally concatenate all the outputs of multi-head attention, and then get the final result by the linear projection.", + "bbox": [ + 496, + 267, + 890, + 348 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Mixed-scale feed-forward network (MSFN). Previous studies [48, 50, 58] usually introduce single-scale depth-wise convolutions into the regular feed-forward network to imporve locality. However, those exploitations all ignore the correlations of multi-scale rain streaks. In fact, rich multi-scale representation has fully demonstrated its effectiveness [19, 41] in better removing rain. Here, we design a MSFN by inserting two multi-scale depth-wise convolution paths in the transmission process, see Figure 2. Given an input tensor $\\mathbf{X}_{l-1} \\in \\mathbb{R}^{H \\times W \\times C}$ , after layer normalization, we first utilize $1 \\times 1$ convolution to expand the channel dimension in the ratio of $r$ , then feed it into two parallel branches. During the feature transformation, the $3 \\times 3$ and $5 \\times 5$ depth-wise convolutions are employed to enhance the multi-scale local information extraction. In this way, the entire feature fusion procedure of the developed MSFN is formulated as:", + "bbox": [ + 496, + 358, + 890, + 601 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\mathbf {X}} _ {l} = f _ {1 \\times 1} ^ {c} (\\operatorname {L N} \\left(\\mathbf {X} _ {l - 1}\\right)),\n$$\n", + "text_format": "latex", + "bbox": [ + 501, + 607, + 674, + 625 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {X} _ {l} ^ {p _ {1}} = \\sigma \\left(f _ {3 \\times 3} ^ {d w c} (\\hat {\\mathbf {X}} _ {l})\\right), \\mathbf {X} _ {l} ^ {s _ {1}} = \\sigma \\left(f _ {5 \\times 5} ^ {d w c} (\\hat {\\mathbf {X}} _ {l})\\right),\n$$\n", + "text_format": "latex", + "bbox": [ + 501, + 628, + 816, + 652 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {X} _ {l} ^ {p _ {2}} = \\sigma \\left(f _ {3 \\times 3} ^ {d w c} \\left[ \\mathbf {X} _ {l} ^ {p _ {1}}, \\mathbf {X} _ {l} ^ {s _ {1}} \\right]\\right), \\mathbf {X} _ {l} ^ {s _ {2}} = \\sigma \\left(f _ {5 \\times 5} ^ {d w c} \\left[ \\mathbf {X} _ {l} ^ {s _ {1}}, \\mathbf {X} _ {l} ^ {p _ {1}} \\right]\\right),\n$$\n", + "text_format": "latex", + "bbox": [ + 501, + 655, + 887, + 672 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {X} _ {l} = f _ {1 \\times 1} ^ {c} \\left[ \\mathbf {X} _ {l} ^ {p _ {2}}, \\mathbf {X} _ {l} ^ {s _ {2}} \\right] + \\mathbf {X} _ {l - 1}, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 501, + 675, + 890, + 703 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\sigma (\\cdot)$ is a ReLU activation, $f_{1\\times 1}^{c}$ represents $1\\times 1$ convolution, $f_{3\\times 3}^{dwc}$ and $f_{5\\times 5}^{dwc}$ denote $3\\times 3$ and $5\\times 5$ depth-wise convolutions, and $[\\cdot ]$ is the channel-wise concatenation.", + "bbox": [ + 496, + 703, + 890, + 748 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Mixture of experts feature compensator", + "text_level": 1, + "bbox": [ + 500, + 756, + 841, + 772 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To fill in the comprehensive faculty of integrating sparsity in the DRSformer, we further introduce MEFC to perform a unified co-exploration towards joint data and content sparsity. Recalling the classical design of effective CNN models [39], we elaborately select multiple sparse CNN operations to form parallel layers, dubbed as experts, which involve an average pooling with receptive field of $3 \\times 3$ , separable convolution layers with kernel sizes of $1 \\times 1$", + "bbox": [ + 496, + 779, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "5899", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/f598adf348929d247e4d359e44e69e24c630a91a6b6912142d31e55dc5ebefa0.jpg", + "table_caption": [ + "Table 1. Comparison of quantitative results on synthetic and real datasets. Bold and underline indicate the best and second-best results." + ], + "table_footnote": [], + "table_body": "
DatasetsRain200LRain200HDID-DataDDN-DataSPA-Data
MetricsPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIM
Prior-based methodsDSC [30]27.160.866314.730.381524.240.827927.310.837334.950.9416
GMM [24]28.660.865214.500.416425.810.834427.550.847934.300.9428
CNN-based methodsDDN [8]34.680.967126.050.805630.970.911630.000.904136.160.9457
RESCAN [23]36.090.969726.750.835333.380.941731.940.934538.110.9707
PReNet [36]37.800.981429.040.899133.170.948132.600.945940.160.9816
MSPFN [19]38.580.982729.360.903433.720.955032.990.933343.430.9843
RCDNet [43]39.170.988530.240.904834.080.953233.040.947243.360.9831
MPRNet [59]39.470.982530.670.911033.990.959033.100.934743.640.9844
DualGCN [9]40.730.988631.150.912534.370.962033.010.948944.180.9902
SPDNet [56]40.500.987531.280.920734.570.956033.150.945743.200.9871
Transformer-based methodsUformer [48]40.200.986030.800.910535.020.962133.950.954546.130.9913
Restormer [58]40.990.989032.000.932935.290.964134.200.957147.980.9921
IDT [50]40.740.988432.100.934434.890.962333.840.954947.350.9930
DRSformer41.230.989432.170.932635.350.964634.350.958848.540.9924
", + "bbox": [ + 91, + 104, + 875, + 309 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$3 \\times 3, 5 \\times 5, 7 \\times 7$ , and dilated convolution layers with kernel sizes of $3 \\times 3, 5 \\times 5, 7 \\times 7$ . Different from the conventional mixture of experts [17, 37], our MEFC does not attach an external gating network. Instead, we make the self-attention [14,21] become a switcher of different experts to adaptively select the importance of diverse representations depending on the inputs. Given an input feature map $\\mathbf{X}_{l-1} \\in \\mathbb{R}^{H \\times W \\times C}$ , we first apply the channel-wise average to generate $C$ -dimensional channel descriptor $\\mathbf{z}_c \\in \\mathbb{R}^C$ :", + "bbox": [ + 75, + 316, + 470, + 452 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {z} _ {c} = \\frac {1}{H \\times W} \\sum_ {i = 1} ^ {H} \\sum_ {j = 1} ^ {W} \\mathbf {X} _ {l - 1} (i, j), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 156, + 460, + 468, + 503 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathbf{X}_{l - 1}(i,j)$ is the $(y,x)$ position of the feature $\\mathbf{X}_{l - 1}$ . Then, the coefficient vector of each expert is allocated corresponding to the learnable weight matrices $\\mathbf{W}_1\\in \\mathbb{R}^{T\\times C}$ and $\\mathbf{W}_2\\in \\mathbb{R}^{O\\times T}$ . Here, $T$ is the dimension of the weight matrices. To avoid altering the sizes of its inputs and outputs, we zero pad the input feature maps computed by each expert. Finally, the output of the $l$ -th MEFC is calculated by:", + "bbox": [ + 75, + 512, + 468, + 632 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {T} _ {l} = \\mathbf {W} _ {2} \\sigma \\left(\\mathbf {W} _ {1} \\mathbf {z} _ {c}\\right),\n$$\n", + "text_format": "latex", + "bbox": [ + 135, + 628, + 281, + 646 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {X} _ {l} = f _ {1 \\times 1} ^ {c} \\left[ \\sum_ {i = 1} ^ {O} f _ {e x p} \\left(\\mathbf {X} _ {l}, \\mathbf {T} _ {l}\\right) \\right] + \\mathbf {X} _ {l - 1}, \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 135, + 648, + 468, + 691 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $f_{exp}$ and $O$ represent the expert operations and the number of experts, respectively. $f_{1\\times 1}^{c}$ represents $1\\times 1$ convolution, $\\sigma (\\cdot)$ is a ReLU function, and $[\\cdot ]$ is the channelwise concatenation. With this design, MEFC is now closely linked to the main STBs so that is able to adaptively remove the rainy effects of diverse appearances.", + "bbox": [ + 75, + 693, + 468, + 785 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments and Analysis", + "text_level": 1, + "bbox": [ + 76, + 795, + 318, + 811 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Experimental settings", + "text_level": 1, + "bbox": [ + 76, + 816, + 281, + 832 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. We implement deraining experiments on multiple public benchmarks, including Rain200L/H [52], DID-Data [61] and DDN-Data [8]. Rain200L and Rain200H contain 1,800 synthetic rainy images for training and 200 ones for", + "bbox": [ + 76, + 839, + 468, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "testing. DID-Data and DDN-Data consist of 12,000 and 12,600 synthetic images with different rain directions and density levels. There are 1,200 and 1,400 rainy images for testing. In addition, we also evaluate our method using a large-scale real-world dataset, i.e., SPA-Data [45], containing 638,492 image pairs for training and 1,000 testing ones.", + "bbox": [ + 496, + 316, + 890, + 407 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Comparison methods. We compare our DRSformer with two prior-based models (DSC [30] and GMM [24]), CNN-based methods (DDN [8], RESCAN [23], PReNet [36], MSPFN [19], RCDNet [43], MPRNet [59], DualGCN [9], and SPDNet [56]), and recent Transformer-based methods (Uformer [48], Restormer [58], and IDT [50]). For recent representative methods (DualGCN, SPDNet, Restormer and IDT), we retrain their models provided by the authors if no pretrained models are provided, otherwise we evaluate them with their online codes for fair comparisons. For other approaches, we refer to some reported results in [10, 50].", + "bbox": [ + 496, + 411, + 892, + 578 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation metrics. We adopt PSNR [34] and SSIM [47] as the evaluation metrics for the above benchmarks. Following previous deraining methods [10, 19], we calculate PSNR and SSIM metrics in Y channel of YCbCr space. For the rainy images without ground truth images, we use the non-reference metrics including NIQE [33] and BRISQUE [32].", + "bbox": [ + 496, + 579, + 890, + 670 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training details. In our model, $\\{N_0,N_1,N_2,N_3,N_4\\}$ are set to $\\{4,4,6,6,8\\}$ , and the number of attention heads for four STBs of the same level is set to $\\{1,2,4,8\\}$ . The initial channel $C$ is 48 and the expanding ratio is set to 2. In terms of MEFC, we set $O = 8$ for the number of experts and $T = 32$ for the weight matrix. Note that we do not use MEFC for training Rain200L and SPA-Data, because their rain streaks are less complex and easier to learn. In terms of STB, the sparseness $[\\Delta_1,\\Delta_2]$ in TKSA is set to $[\\frac{1}{2},\\frac{4}{5} ]$ , and the channel expansion factor $r$ in MSFN is set to 2.66. During training, we use AdamW optimizer with batch size of 8 and patch size of 128 for total 300K iterations. The initial learning rate is fixed as $1\\times 10^{-4}$ for 92K iterations, and then decreased to $1\\times 10^{-6}$ for 208K iterations with the cosine annealing scheme [29]. For data augmentation, vertical and", + "bbox": [ + 496, + 674, + 892, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "5900", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/536fa7d411ea2ca30e1ccaf0927498b1229fe805910fccabb0c93205623dbbd0.jpg", + "image_caption": [ + "Figure 3. Visual quality comparison on the Rain200H dataset. Zooming in the figures offers a better view at the deraining capability." + ], + "image_footnote": [], + "bbox": [ + 83, + 89, + 890, + 239 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/120d2f44fedd5e6fbe4351355f87b70d881f9949ba086eadc73a149bd788d0a0.jpg", + "image_caption": [ + "Figure 4. Visual quality comparison on the SPA-Data dataset. Zooming in the figures offers a better view at the deraining capability." + ], + "image_footnote": [], + "bbox": [ + 83, + 261, + 890, + 428 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "horizontal flips are randomly applied. The entire framework is performed on the PyTorch with 4 NVIDIA GeForce RTX 3090 GPUs, which works in an end-to-end learning fashion without costly large-scale pretraining [2].", + "bbox": [ + 75, + 453, + 468, + 515 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Comparisons with the state-of-the-arts", + "text_level": 1, + "bbox": [ + 76, + 518, + 410, + 534 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Synthetic datasets. The quantitative evaluations on different benchmark datasets are reported in Table 1. As shown, we can note that our proposed method outperforms all the other derainers, especially on PSNR, e.g., DRSformer surpasses the concurrent approach IDT by $0.4\\mathrm{dB}$ on average. Compared with previous CNN-based models, this progress can be much more obvious. The notable increasing scores on the DID-Data and DDN-Data benchmarks reveal that our method can properly handle diverse types of spatially-varying rain streaks. For convincing evidence, we show the visual quality comparison between samples generated by recent approaches in Figure 3. The pure CNN-based models, e.g., MPRNet and SPDNet, fail to restore clear images in heavy rainy scenarios. It can be seen that the results of all computing Transformer-based methods are flawed in terms of detail and texture recovery. Unfortunately, IDT even introduces considerable boundary artifacts. Thanks to the developed sparse attention with top- $k$ selection, our method can generate high-quality deraining results, which are more consistent with that of the ground truth.", + "bbox": [ + 75, + 537, + 468, + 840 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Real-world datasets. We further conduct experiments on the SPA-Data benchmark dataset, and corresponding results are provided in the last column of Table 1. As expected, our model continues to achieve the highest PSNR/SSIM", + "bbox": [ + 75, + 840, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "value, exhibiting the superior of DRSformer in terms of deraining performance and generalization. The visual quality comparison can be observed in Figure 4. In contrast, our method significantly competes with others in removing most rain streaks while preserving truthful image structures. In order to further validate the effectiveness of DRSformer, we also randomly choose 20 real rainy images without ground truths from Internet-Data [45] to perform another evaluation. As displayed in Table 2, our net gets the lower NIQE and BRISQUE values, which means a high-quality output with clearer content and better perceptual quality against other comparative models on the real rainy scenarios. Through qualitative quality comparison in Figure 5, most deep models are sensitive to spatially-long rain streaks and leave some apparent rain effects. On the contrary, our net successfully removes most rain perturbation and owns a visual pleasant recovery effect, which implies that it can well generalize to unseen real-world data types.", + "bbox": [ + 496, + 453, + 890, + 727 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Ablation studies", + "text_level": 1, + "bbox": [ + 500, + 733, + 661, + 747 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Effectiveness of Top- $k$ selection. To examine the effect of the top- $k$ selection in the TKSA, we present the deraining results of TKSA w/o top- $k$ in Figure 6. We can see that the PSNR values of the derained images by the method without using the top- $k$ selection are lower than those by the method using the top- $k$ selection. In addition, we also note that each element of the self-attention matrix $\\operatorname{softmax}\\left(\\frac{\\mathbf{QK}^\\top}{\\lambda}\\right)$ in Eq. (4) is non-negative and the summation of the elements from each row of $\\operatorname{softmax}\\left(\\frac{\\mathbf{QK}^\\top}{\\lambda}\\right)$ is 1. Thus, applying", + "bbox": [ + 496, + 752, + 893, + 904 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "5901", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/f357b853996f2c2d8499fa548cf819e79091e4d95c0fe340d97c07bd74c7a356.jpg", + "image_caption": [ + "Figure 5. Visual quality comparison on real-world rainy images. Zooming in the figures offers a better view at the deraining capability." + ], + "image_footnote": [], + "bbox": [ + 81, + 90, + 890, + 202 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/993d4c5df9ef87ca22b7b97d6bc516ac83cccdfb54e27f1818199fcbc6f1a58f.jpg", + "table_caption": [ + "Table 2. Comparison of quantitative results on real-world rainy images, and note that lower scores indicate better image quality." + ], + "table_footnote": [], + "table_body": "
MethodsRainy InputMPRNet [59]SPDNet [56]Uformer [48]Restormer [58]IDT [50]Ours
NIQE ↓ / BRISQUE ↓5.829 / 33.1294.740 / 32.0184.422 / 26.1734.833 / 28.1065.005 / 34.0364.238 / 25.5734.095 / 22.730
", + "bbox": [ + 78, + 238, + 890, + 268 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4e28ed1af5f5dd2bfe5a233539ceb121f8cb416ec9a89a41a61988934af98e2e.jpg", + "image_caption": [ + "Figure 6. Ablation analysis for top- $k$ selection on the benchmarks." + ], + "image_footnote": [], + "bbox": [ + 78, + 277, + 472, + 458 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/02564e6021a75b2b7ac9db6f3a1aaed2a1ed6a17a3c70802cbaae7f7bce85d61.jpg", + "image_caption": [ + "Figure 7. Ablation analysis for different number $k$ in the TKSA." + ], + "image_footnote": [], + "bbox": [ + 78, + 476, + 468, + 626 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "the self-attention matrix $\\mathrm{softmax}\\left(\\frac{\\mathbf{Q}\\mathbf{K}^{\\top}}{\\lambda}\\right)$ to $\\mathbf{V}$ will remove the high-frequency information of $\\mathbf{V}$ , which lead to oversmoothed results.", + "bbox": [ + 75, + 666, + 468, + 717 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To understand the effect of such top- $k$ selection, we further use high-pass filtering (HPF) to visualize learned features in Figure 8. Compared to standard self-attention operation (w/o top- $k$ ), our strategy can better help reconstruct finer-detail feature and improve potential restoration quality. As the nearby pixels tend to be more similar than others, top- $k$ selection operator helps to reduce the irrelevant context from long-range pixel dependency. This step of selection allows the smaller similarity weights (from a part of long-range feature interactions) to be discarded in the procedure of self-attention calculation, thus facilitating more accurate representation for achieving high-quality output.", + "bbox": [ + 75, + 719, + 472, + 902 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/1ada1dcb12e435ee63992c9444cd0836324784d3bb2c288f3baa6a1e6baa2a40.jpg", + "image_caption": [ + "(a) w/o Top-k", + "Figure 8. Visualization of feature maps. Our proposed top- $k$ selection can effectively leverage pixel-dependent properties of image structure and generate more precise high-frequency details." + ], + "image_footnote": [], + "bbox": [ + 501, + 277, + 596, + 353 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4c256991d37e73bb5585fad29c6b50e384fa8e523d4ab6c041dd3d4d21a99d03.jpg", + "image_caption": [ + "(a) w Top-k" + ], + "image_footnote": [], + "bbox": [ + 598, + 277, + 694, + 353 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c7e4ce87a8bf985027500380b891ab5ad48c72b5baa2dde8bbde2531f793821d.jpg", + "image_caption": [ + "(b) w/o Top-k" + ], + "image_footnote": [], + "bbox": [ + 696, + 277, + 792, + 353 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/74e98c58f8fba24b8728ec91eea1cf697f0879664f89594a8c141fa35eb77caf.jpg", + "image_caption": [ + "(b) w Top-k" + ], + "image_footnote": [], + "bbox": [ + 795, + 277, + 892, + 353 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effect of the number of $k$ . The key parameter for our proposed TKSA is $k$ , and its influence is investigated in Figure 7. We note that the optimal choice of $k$ determines the boundary control of the sparsity rate. If $k$ is manually set to a single value, such as $\\frac{1}{2}$ , we notice that its performance is sensitive to $k$ . To avoid an exhaustive search, we set a controllable interval range for $k$ to dynamically learn the most contribute score. When $k$ is too small, we find that the performance will undoubtedly decline sharply due to insufficient global information aggregation. The best result can achieve 32.18 dB when $[\\Delta_1, \\Delta_2]$ in the TKSA is assigned to $[\\frac{1}{2}, \\frac{4}{5}]$ . As $k$ continues to increase, the final deraining performance gradually decreases due to the introduction of irrelevant and useless features.", + "bbox": [ + 496, + 424, + 893, + 635 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effectiveness of MSFN. To evaluate the effectiveness of the proposed MSFN, we compare it with three baselines: (1) conventional feed-forward network (FN) [7], (2) Dconv feed-forward network (DFN) [25], and (3) gated-Dconv feed-forward network (GDFN) [58]. The quantitative analysis results on Rain200H are listed in Table 3. Although GDFN introduces a gating mechanism in two same-scale depth-wise convolution streams to bring performance advantages, it still neglects the multi-scale knowledge for deraining. By adding local feature extraction and fusion at different scales, the MSFN can indeed better boost the performance, and achieve PSNR gain of 0.21 dB over GDFN.", + "bbox": [ + 496, + 636, + 893, + 818 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effectiveness of MEFC. To evaluate the effectiveness of MEFC, we perform experiments based on different model variants in Table 4. Compared to the baseline model (a), MEFC provides additional performance benefits thanks to auxiliary data sparsity. In addition, we observe that MEFCs", + "bbox": [ + 496, + 824, + 893, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "5902", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/da8b77b7d856e53abec4db87595d7a5e4a738bf341648f602349ab97207b9041.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 81, + 90, + 196, + 152 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/fe173f9d7ba56970bd618cb089a40a8f1249b08a387616109e73f68256b0e1cd.jpg", + "image_caption": [ + "Rainy Input" + ], + "image_footnote": [], + "bbox": [ + 81, + 151, + 196, + 171 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c9516d4a1e73ad66e229bd8be6c54265b72dc2a2feee320ce87e44c44872a9cf.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 197, + 90, + 312, + 151 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/843dd603bd0c643cb5f1354fa4b3e024d4bc070d792bd63985a189d99b4fe60a.jpg", + "image_caption": [ + "Model (a)" + ], + "image_footnote": [], + "bbox": [ + 197, + 151, + 312, + 171 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/ae6895d20a4d31ee6e7b5d9a916df23d0b314f777d2693344412e6fd46466e2e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 313, + 90, + 426, + 151 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/909294225ade7097b701d091a868778ab5c55899af3b04ae6a07c0107704c143.jpg", + "image_caption": [ + "Model (b)" + ], + "image_footnote": [], + "bbox": [ + 313, + 151, + 426, + 171 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/a4de0c5a3116a8e924b7edea87a1261decd746b5bef6e361b04f2fffbf0c876a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 428, + 90, + 542, + 151 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/10b02929a4dd8e8085cd847458c1487f1831c385c254bea201e4e3d80afd1b86.jpg", + "image_caption": [ + "Model (c)" + ], + "image_footnote": [], + "bbox": [ + 428, + 151, + 542, + 171 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/7fc539e328d0580f193cb8b677ae4b1cce10b67299a9fa2c007c49b311711c9d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 542, + 90, + 658, + 151 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/13e18f84aac41635f450556868b27a826fd82a66c96f5018fea9bc4d2112795c.jpg", + "image_caption": [ + "Model (d)" + ], + "image_footnote": [], + "bbox": [ + 542, + 151, + 658, + 171 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/684e085c8258ffd91f62f9f2d3aad40b3f940702afda4bb3e402979f3743e74c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 658, + 90, + 774, + 151 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/ef7d6d06c99917e75d83a3d47b29c08f24a5bbd5dcd1c2bd9ad35ccb74c54780.jpg", + "image_caption": [ + "Model (e)" + ], + "image_footnote": [], + "bbox": [ + 658, + 151, + 774, + 171 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/8d9181af13bc303170bd580c76c8c7208b91ddd3e9d846cd15fc85bafd029c27.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 774, + 90, + 888, + 151 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/c896fee33aa2820e4dd05e5df75af62f1f87d753ee810c3434e534f536307953.jpg", + "image_caption": [ + "Model (f)" + ], + "image_footnote": [], + "bbox": [ + 774, + 151, + 888, + 171 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/947d0450f4d464662836d6462ef1f05dd148f31f2d63600438928826ad22e135.jpg", + "table_caption": [ + "Table 3. Ablation study for different feed-forward networks." + ], + "table_footnote": [], + "table_body": "
ModelsFN [7]DFN [25]GDFN [58]MSFN
PSNR / SSIM31.84 / 0.927531.88 / 0.927931.97 / 0.928632.18 / 0.9330
", + "bbox": [ + 81, + 222, + 467, + 246 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/f9183d91905817d307638146ea891c1e2846bab8fb155ea879a309f62eab7380.jpg", + "table_caption": [ + "Table 4. Ablation study for different variants of our DRSformer. MEFC-1 and MEFC-2 denote MEFC in early and final stages." + ], + "table_footnote": [], + "table_body": "
ModelsMEFC-1STBsMEFC-2ExpertsPSNR / SSIM
(a)✓032.03 / 0.9308
(b)✓✓832.01 / 0.9311
(c)✓✓832.07 / 0.9328
(d)✓✓✓132.06 / 0.9316
(e)✓✓✓432.14 / 0.9325
(f)✓✓✓832.18 / 0.9330
", + "bbox": [ + 83, + 281, + 465, + 372 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "at different locations of the network pipeline have specific impacts on the restoration performance. Indeed, we also analyze the effect of the different numbers of experts in each MEFC. When using single expert model (d), the performance is dramatically degraded compared with our multi-expert model (f). Unlike setting all experts to the same structure [21], our multi-expert formulation is more diverse, which brings its gains to the performance due to different receptive fields and disparate CNN operations. Through the zoomed boxes in Figure 9, the recovered results of the model with all the above components tend to be clearer since it enables more diverse features to be fully used during the restoration process. All in all, our model (f) performs better than the other possible configurations, which indicates that each design strategy that we consider has its own contribution to the final performance of DRSformer.", + "bbox": [ + 75, + 382, + 468, + 623 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.4. Closely-related methods", + "text_level": 1, + "bbox": [ + 76, + 633, + 299, + 648 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We note that the recent method [22] proposes a $k$ -NN image Transformer (KiT) to solve image restoration by aggregating $k$ similar patches with the pair-wise local attention. Compared with KiT that employs complex locality sensitive hashing that cannot ensure sufficient global interaction, our simple but effective top- $k$ selection mechanism not only enjoys the locality but also empowers the ability of global relation mining. As the code of KiT is not available, we refer to the results of their paper. Figure 10 shows qualitative comparisons trained on the Rain800 [62]. We can see that KiT tends to blur the contents and cause color distortion. In contrast, our method leads to better deraining results.", + "bbox": [ + 75, + 657, + 468, + 839 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In addition, we also note that [44] recently designs the $k$ -NN attention to enhance the representation ability of vision Transformers by selecting the top- $k$ similar tokens. Different from KVT [44], which implements top- $k$ selection", + "bbox": [ + 75, + 839, + 468, + 900 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/e6e7ff566ac4ad802c8cca6ed2bbc22fcb6c27034cf56baf4e13b8852ac0ad29.jpg", + "image_caption": [ + "(a) Rainy Input", + "Figure 10. Comparison results with closely-related methods." + ], + "image_footnote": [], + "bbox": [ + 504, + 205, + 594, + 300 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/df669920b55726d47d1fdec2001f70ae6dc02a6a9fbeff276d05b6130cf7159b.jpg", + "image_caption": [ + "(b) KiT [22]" + ], + "image_footnote": [], + "bbox": [ + 601, + 207, + 692, + 300 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/fc372621f5b08d1248ee83766e35bc202aaaea10d5d1bb91e80b73a821971a16.jpg", + "image_caption": [ + "(c) KVT [44]" + ], + "image_footnote": [], + "bbox": [ + 696, + 207, + 787, + 300 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/edd59521d86ebe0f2e40f7395a0bd31a86a0233a291d1b479b110588f0763cb6.jpg", + "image_caption": [ + "Figure 9. Ablation qualitative comparison for different variants of DRSformer. The models (a-f) are consistent with the settings in Table 4.", + "(d) Ours" + ], + "image_footnote": [], + "bbox": [ + 794, + 207, + 885, + 300 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "in the spatial dimension, our operator is more efficient in computing sparse attention across channels. Furthermore, the sparsity level of $k$ in our proposed TKSA is dynamically learnable, rather than the fixed setting in [44]. Here, we adopt $k$ -NN attention in KVT to replace our TKSA for comparison. To ensure the fair comparison, the same training settings are kept for model testing. As shown in Figure 10 (c) and (d), our method can generate a clearer image.", + "bbox": [ + 496, + 333, + 890, + 455 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Concluding Remarks", + "text_level": 1, + "bbox": [ + 500, + 463, + 700, + 479 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have presented an effective sparse Transformer network, called DRSformer, to solve image deraining. Based on the observation that vanilla self-attention in Transformer may suffer from the global interaction of irrelevant information, we develop the top- $k$ sparse attention to keep the most useful self-attention values for better feature aggregation. To facilitate the aggregated features for removing rain, we develop a mixed-scale feed-forward network to better explore multi-scale representations. Furthermore, the mixture of experts feature compensator is introduced to the model to provide collaborative refinement for the sparse Transformer backbone, so that the fine details of the reconstructed image is preserved. Experimental results show that our DRSformer performs favorably against state-of-the-art methods.", + "bbox": [ + 496, + 484, + 890, + 696 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations. Our proposed method aims to further boost image deraining performance, but there are limitations in the model efficiency. Specifically, our model requires 33.7 Million parameters and costs 242.9G FLOPs on one image with size of $256 \\times 256$ . We will apply the pruning or distillation scheme in our model to maintain the original deraining performance while achieving credible model compression.", + "bbox": [ + 496, + 702, + 890, + 808 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements. This work has been partly supported by the National Key R&D Program of China (No. 2018AAA0102001), the National Natural Science Foundation of China (Nos. U22B2049, U19B2040, 61922043, 61872421, 62272230), and the Fundamental Research Funds for the Central Universities (No. 30920041109).", + "bbox": [ + 496, + 810, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "5903", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In ECCV, pages 213-229, 2020. 2", + "[2] Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao. Pre-trained image processing transformer. In CVPR, pages 12299-12310, 2021. 1, 2, 6", + "[3] Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, and Zhangyang Wang. Chasing sparsity in vision transformers: An end-to-end exploration. Advances in Neural Information Processing Systems, 34:19974-19988, 2021. 4", + "[4] Xiang Chen, Jinshan Pan, Kui Jiang, Yufeng Li, Yufeng Huang, Caihua Kong, Longgang Dai, and Zhentao Fan. Unpaired deep image deraining using dual contrastive learning. In CVPR, pages 2017-2026, 2022. 1", + "[5] Xiang Chen, Jinshan Pan, Jiyang Lu, Zhentao Fan, and Hao Li. Hybrid cnn-transformer feature fusion for single image deraining. In AAAI, 2023. 2", + "[6] Gonçalo M Correia, Vlad Niculae, and André FT Martins. Adaptively sparse transformers. arXiv preprint arXiv:1909.00015, 2019.3", + "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 2, 3, 4, 7, 8", + "[8] Xueyang Fu, Jiabin Huang, Delu Zeng, Yue Huang, Xinghao Ding, and John Paisley. Removing rain from single images via a deep detail network. In CVPR, pages 3855-3863, 2017. 5", + "[9] Xueyang Fu, Qi Qi, Zheng-Jun Zha, Yurui Zhu, and Xing-hao Ding. Rain streak removal via dual graph convolutional network. In AAAI, pages 1352-1360, 2021. 5", + "[10] Xueyang Fu, Jie Xiao, Yurui Zhu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Continual image deraining with hypergraph convolutional networks. IEEE TPAMI, 2023. 5", + "[11] Zhihong Fu, Zehua Fu, Qingjie Liu, Wenrui Cai, and Yunhong Wang. Sparsett: Visual tracking with sparse transformers. arXiv preprint arXiv:2205.03776, 2022. 3", + "[12] Shuhang Gu, Deyu Meng, Wangmeng Zuo, and Lei Zhang. Joint convolutional analysis and synthesis: sparse representation for single image layer separation. In ICCV, 2017. 2", + "[13] Chun-Le Guo, Qixin Yan, Saeed Anwar, Runmin Cong, Wenqi Ren, and Chongyi Li. Image dehazing transformer with transmission-aware 3d position embedding. In CVPR, pages 5812-5820, 2022. 2", + "[14] Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In CVPR, pages 7132-7141, 2018. 5", + "[15] Xiaowei Hu, Chi-Wing Fu, Lei Zhu, and Pheng-Ann Heng. Depth-attentional features for single-image rain removal. In CVPR, pages 8022–8031, 2019. 2", + "[16] Huaibo Huang, Aijing Yu, and Ran He. Memory oriented transfer learning for semi-supervised image deraining. In CVPR, pages 7732-7741, 2021. 2" + ], + "bbox": [ + 78, + 114, + 468, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[17] Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. Adaptive mixtures of local experts. Neural computation, 3(1):79-87, 1991. 5", + "[18] Kui Jiang, Zhongyuan Wang, Chen Chen, Zheng Wang, Laizhong Cui, and Chia-Wen Lin. Magic elf: Image deraining meets association learning and transformer. In ACM MM, 2022. 2", + "[19] Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, and Junjun Jiang. Multi-scale progressive fusion network for single image deraining. In CVPR, pages 8346-8355, 2020. 1, 2, 4, 5", + "[20] Li-Wei Kang, Chia-Wen Lin, and Yu-Hsiang Fu. Automatic single-image-based rain streaks removal via image decomposition. IEEE TIP, 21(4):1742-1755, 2011. 1, 2", + "[21] Sijin Kim, Namhyuk Ahn, and Kyung-Ah Sohn. Restoring spatially-heterogeneous distortions using mixture of experts network. In ACCV, 2020. 5, 8", + "[22] Hunsang Lee, Hyesong Choi, Kwanghoon Sohn, and Dongbo Min. Knn local attention for image restoration. In CVPR, pages 2139-2149, 2022. 8", + "[23] Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, and Hongbin Zha. Recurrent squeeze-and-excitation context aggregation net for single image deraining. In ECCV, pages 254-269, 2018. 1, 2, 5", + "[24] Yu Li, Robby T Tan, Xiaojie Guo, Jiangbo Lu, and Michael S Brown. Rain streak removal using layer priors. In CVPR, pages 2736-2744, 2016. 1, 2, 5", + "[25] Yawei Li, Kai Zhang, Jiezhang Cao, Radu Timofte, and Luc Van Gool. Localvit: Bringing locality to vision transformers. arXiv preprint arXiv:2104.05707, 2021. 7, 8", + "[26] Yuanchu Liang, Saeed Anwar, and Yang Liu. Drt: A lightweight single image deraining recursive transformer. In CVPRW, pages 589-598, 2022. 1", + "[27] Yang Liu, Ziyu Yue, Jinshan Pan, and Zhixun Su. Unpaired learning for deep image deraining with rain direction regularizer. In ICCV, pages 4753-4761, 2021. 2", + "[28] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021. 2", + "[29] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. In *ICLR*, 2016. 5", + "[30] Yu Luo, Yong Xu, and Hui Ji. Removing rain from a single image via discriminative sparse coding. In ICCV, pages 3397-3405, 2015. 2, 5", + "[31] Yiqun Mei, Yuchen Fan, and Yuqian Zhou. Image superresolution with non-local sparse attention. In CVPR, pages 3517-3526, 2021. 3", + "[32] Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik. No-reference image quality assessment in the spatial domain. IEEE TIP, 21(12):4695-4708, 2012. 5", + "[33] Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a \"completely blind\" image quality analyzer. IEEE SPL, 20(3):209-212, 2012. 5", + "[34] Huynh-Thu Q. and Ghanbari M. Scope of validity of psnr in image/video quality assessment. *Electronics Letters*, 44(13):800-801, 2008. 5" + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "5904", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[35] Qin Qin, Jingke Yan, Qin Wang, Xin Wang, Minyao Li, and Yuqing Wang. Etdnet: An efficient transformer deraining model. IEEE Access, 9:119881-119893, 2021. 1", + "[36] Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng. Progressive image deraining networks: A better and simpler baseline. In CVPR, pages 3937-3946, 2019. 1, 2, 5", + "[37] Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. Gated fusion network for single image dehazing. In CVPR, pages 3253-3261, 2018. 5", + "[38] Aurko Roy, Mohammad Saffar, Ashish Vaswani, and David Grangier. Efficient content-based sparse attention with routing transformers. Transactions of the Association for Computational Linguistics, 9:53-68, 2021. 3", + "[39] Masanori Suganuma, Xing Liu, and Takayuki Okatani. Attention-based adaptive selection of operations for image restoration in the presence of unknown combined distortions. In CVPR, pages 9039-9048, 2019. 4", + "[40] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 2, 4", + "[41] Cong Wang, Xiaoying Xing, Yutong Wu, Zhixun Su, and Junyang Chen. Dcsfn: Deep cross-scale fusion network for single image rain removal. In ACM MM, pages 1643-1651, 2020. 4", + "[42] Haochen Wang, Jiayi Shen, Yongtuo Liu, Yan Gao, and Efstratos Gavves. Nformer: Robust person re-identification with neighbor transformer. In CVPR, pages 7297-7307, 2022. 3", + "[43] Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng. A model-driven deep neural network for single image rain removal. In CVPR, pages 3103-3112, 2020. 5", + "[44] Pichao Wang, Xue Wang, Fan Wang, Ming Lin, Shuning Chang, Wen Xie, Hao Li, and Rong Jin. Kvt: k-nn attention for boosting vision transformers. In ECCV, 2022. 2, 3, 8", + "[45] Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson WH Lau. Spatial attentive single-image deraining with a high quality real rain dataset. In CVPR, pages 12270-12279, 2019. 5, 6", + "[46] Yinglong Wang, Chao Ma, and Bing Zeng. Multi-decoding deraining network and quasi-sparsity based training. In CVPR, pages 13375–13384, 2021. 3", + "[47] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE TIP, 13(4):600-612, 2004. 5", + "[48] Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li. Uformer: A general u-shaped transformer for image restoration. In CVPR, pages 17683-17693, 2022. 1, 2, 3, 4, 5, 7", + "[49] Wei Wei, Deyu Meng, Qian Zhao, Zongben Xu, and Ying Wu. Semi-supervised transfer learning for image rain removal. In CVPR, pages 3877-3886, 2019. 2", + "[50] Jie Xiao, Xueyang Fu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Image de-raining transformer. IEEE TPAMI, 2022. 1, 2, 3, 4, 5, 7" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[51] Fuzhi Yang, Huan Yang, Jianlong Fu, Hongtao Lu, and Baining Guo. Learning texture transformer network for image super-resolution. In CVPR, 2020. 2", + "[52] Wenhan Yang, Robby T Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan. Deep joint rain detection and removal from a single image. In CVPR, pages 1357-1366, 2017. 1, 5", + "[53] Wenhan Yang, Robby T Tan, Shiqi Wang, Yuming Fang, and Jiaying Liu. Single image deraining: From model-based to data-driven and beyond. IEEE TPAMI, 2020. 1, 2", + "[54] Rajeev Yasarla, Vishwanath A Sindagi, and Vishal M Patel. Syn2real transfer learning for image deraining using gaussian processes. In CVPR, pages 2726-2736, 2020. 2", + "[55] Yuntong Ye, Yi Chang, Hanyu Zhou, and Luxin Yan. Closing the loop: Joint rain generation and removal via disentangled image translation. In CVPR, pages 2053-2062, 2021. 2", + "[56] Qiaosi Yi, Juncheng Li, Qinyan Dai, Faming Fang, Guixu Zhang, and Tieyong Zeng. Structure-preserving deraining with residue channel prior guidance. In ICCV, pages 4238-4247, 2021. 1, 5, 7", + "[57] Kun Yuan, Shaopeng Guo, Ziwei Liu, Aojun Zhou, Fengwei Yu, and Wei Wu. Incorporating convolution designs into visual transformers. In ICCV, pages 579-588, 2021. 2", + "[58] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, pages 5728-5739, 2022. 1, 2, 3, 4, 5, 7, 8", + "[59] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In CVPR, pages 14821-14831, 2021. 5, 7", + "[60] He Zhang and Vishal M Patel. Convolutional sparse and low-rank coding-based rain streak removal. In WACV, pages 1259-1267, 2017. 1, 2", + "[61] He Zhang and Vishal M Patel. Density-aware single image de-raining using a multi-stream dense network. In CVPR, pages 695-704, 2018. 2, 5", + "[62] He Zhang, Vishwanath Sindagi, and Vishal M Patel. Image de-raining using a conditional generative adversarial network. IEEE TCSVT, 30(11):3943-3956, 2019. 8", + "[63] Jiale Zhang, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, and Xin Yuan. Accurate image restoration with attention retractable transformer. *ICLR*, 2023. 3", + "[64] Guangxiang Zhao, Junyang Lin, Zhiyuan Zhang, Xuancheng Ren, Qi Su, and Xu Sun. Explicit sparse transformer: Concentrated attention through explicit selection. ICLR, 2020. 2, 3, 4" + ], + "bbox": [ + 501, + 92, + 890, + 771 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "5905", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning a Sparse Transformer Network for Effective Image Deraining/f486121e-86e3-4a45-bbf0-b8f552e4c49f_model.json b/2023/Learning a Sparse Transformer Network for Effective Image Deraining/f486121e-86e3-4a45-bbf0-b8f552e4c49f_model.json new file mode 100644 index 0000000000000000000000000000000000000000..3b35df58cdeda5b19674adeb1e20abea653bdc50 --- /dev/null +++ b/2023/Learning a Sparse Transformer Network for Effective Image Deraining/f486121e-86e3-4a45-bbf0-b8f552e4c49f_model.json @@ -0,0 +1,2805 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.0, + 0.812, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.123, + 0.131, + 0.849, + 0.154 + ], + "angle": 0, + "content": "Learning A Sparse Transformer Network for Effective Image Deraining" + }, + { + "type": "text", + "bbox": [ + 0.261, + 0.18, + 0.713, + 0.199 + ], + "angle": 0, + "content": "Xiang Chen\\(^{1}\\) Hao Li\\(^{1}\\) Mingqiang Li\\(^{2}\\) Jinshan Pan\\(^{1*}\\)" + }, + { + "type": "text", + "bbox": [ + 0.111, + 0.199, + 0.856, + 0.235 + ], + "angle": 0, + "content": "\\(^{1}\\)School of Computer Science and Engineering, Nanjing University of Science and Technology \\(^{2}\\)Information Science Academy, China Electronics Technology Group Corporation" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.287, + 0.314, + 0.303 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.319, + 0.473, + 0.804 + ], + "angle": 0, + "content": "Transformers-based methods have achieved significant performance in image deraining as they can model the non-local information which is vital for high-quality image reconstruction. In this paper, we find that most existing Transformers usually use all similarities of the tokens from the query-key pairs for the feature aggregation. However, if the tokens from the query are different from those of the key, the self-attention values estimated from these tokens also involve in feature aggregation, which accordingly interferes with the clear image restoration. To overcome this problem, we propose an effective DeRaining network, Sparse Transformer (DRSformer) that can adaptively keep the most useful self-attention values for feature aggregation so that the aggregated features better facilitate high-quality image reconstruction. Specifically, we develop a learnable top-k selection operator to adaptively retain the most crucial attention scores from the keys for each query for better feature aggregation. Simultaneously, as the naive feed-forward network in Transformers does not model the multi-scale information that is important for latent clear image restoration, we develop an effective mixed-scale feed-forward network to generate better features for image deraining. To learn an enriched set of hybrid features, which combines local context from CNN operators, we equip our model with mixture of experts feature compensator to present a cooperation refinement deraining scheme. Extensive experimental results on the commonly used benchmarks demonstrate that the proposed method achieves favorable performance against state-of-the-art approaches. The source code and trained models are available at https://github.com/cschenxiang/DRSformer." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.817, + 0.21, + 0.833 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.843, + 0.47, + 0.874 + ], + "angle": 0, + "content": "Single image deraining is a typical low-level vision problem emerging in the last decade. It aims to recover the clean" + }, + { + "type": "image", + "bbox": [ + 0.507, + 0.285, + 0.632, + 0.353 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.506, + 0.353, + 0.632, + 0.383 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.529, + 0.385, + 0.611, + 0.397 + ], + "angle": 0, + "content": "(a) Rainy Input" + }, + { + "type": "image", + "bbox": [ + 0.635, + 0.285, + 0.759, + 0.352 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.634, + 0.353, + 0.758, + 0.383 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.651, + 0.385, + 0.742, + 0.397 + ], + "angle": 0, + "content": "(b) Uformer [48]" + }, + { + "type": "image", + "bbox": [ + 0.761, + 0.286, + 0.887, + 0.352 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.761, + 0.353, + 0.887, + 0.383 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.776, + 0.385, + 0.874, + 0.397 + ], + "angle": 0, + "content": "(c) Restormer [58]" + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.398, + 0.63, + 0.465 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.465, + 0.629, + 0.495 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.534, + 0.497, + 0.601, + 0.509 + ], + "angle": 0, + "content": "(d) IDT [50]" + }, + { + "type": "image", + "bbox": [ + 0.632, + 0.398, + 0.757, + 0.464 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.632, + 0.465, + 0.756, + 0.495 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.673, + 0.497, + 0.718, + 0.509 + ], + "angle": 0, + "content": "(e) Ours" + }, + { + "type": "image", + "bbox": [ + 0.76, + 0.398, + 0.886, + 0.464 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.76, + 0.465, + 0.885, + 0.495 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.777, + 0.497, + 0.868, + 0.509 + ], + "angle": 0, + "content": "(f) Ground Truth" + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.515, + 0.892, + 0.557 + ], + "angle": 0, + "content": "Figure 1. Image deraining results between our method and recent Transformer-based methods [48,50,58]. Our method can generate high-quality image with more accurate detail and texture recovery." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.569, + 0.892, + 0.674 + ], + "angle": 0, + "content": "image from the observed rainy one. As the clear image and rain streaks are unknown, it is an ill-posed inverse problem. To solve this problem, early approaches [20, 24, 60] usually impose various priors based on statistical properties of rain streaks and clear images. In fact, these handcrafted priors are not robust to complex and varying rainy scenarios, which limit the deraining performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.675, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Recently, numerous learning-based methods [4, 19, 23, 36, 52, 53, 56] have resorted to diverse CNN architectures as a preferable choice compared to traditional algorithms. However, the intrinsic characteristics of convolutional operation, i.e., local receptive fields and independence of input content, hinder the model's capacity to eliminate long-range rain degradation perturbation. To alleviate such limitations, Transformers [2, 26, 35, 50] have been applied to image de- raining and have achieved decent performance as they can better model the non-local information for high-quality image reconstruction. Nevertheless, the image details, which are local features of images, are not modeled well by these approaches when restoring clear images as shown in Figure 1. One main reason is that the self-attention in Transformers does not model the local invariant properties that" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.222, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5896" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.166 + ], + "angle": 0, + "content": "CNNs do well. Since rain streaks tend to confuse with background details in local regions, recent studies [5, 18, 57] try to mitigate such drawbacks by combining CNN operations and Transformers for boosting image deraining, where the Transformers based on the standard formulations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.169, + 0.47, + 0.412 + ], + "angle": 0, + "content": "We note that the standard Transformers [40] usually use all attention relations based on the query-key pairs to aggregate features. As the tokens from the key are not always relevant to those from the query, using the self-attention values estimated from these tokens in the feature aggregation interferes with the following latent clear image restoration. The root cause behind this deficiency lies in that, the native dense calculation pattern of self-attention amplifies relatively smaller similarity weights, making feature interaction and aggregation process susceptible to implicit noises. This also naturally leads to corresponding redundant or irrelevant representations are still taken into consideration when modeling global feature dependencies [44, 64]. Thus, these findings motivate us to explore the most useful self-attention values so that we can make full use of the features for better image restoration." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.413, + 0.471, + 0.79 + ], + "angle": 0, + "content": "To this end, we develop an effective sparse Transformer network for image deraining, named as DRSformer. Specifically, the key component of the proposed framework is the sparse Transformer block (STB) which contains a top-\\(k\\) sparse attention (TKSA) that keeps the most useful self-attention values for feature aggregation and a mixed-scale feed-forward network (MSFN) that explores the multi-scale features for better image deraining. First, we design the top-\\(k\\) attention mechanism to replace the vanilla self-attention [40]. The TKSA keeps the largest \\(K\\) similarity scores between the queries and the keys for the self-attention computing, thereby facilitating better feature aggregation. Furthermore, the developed MSFN further explores the multi-scale information to better improve the aggregated features. Finally, based on the observation that rain distribution reveals the degradation location and degree, we also introduce mixture of experts feature compensator (MEFC) to provide collaborative refinement for STB. With the above-mentioned designs, our proposed method offers three-fold advantages: (1) it can enjoy natural robustness in terms of less sensitivity to useless feature interference, (2) it can not only enrich the locality but also empower the capability of global feature exploitation, and (3) it can co-expllore data (embodied in MEFC) and content (embodied in STB) sparsity for achieving deraining performance gains." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.794, + 0.436, + 0.807 + ], + "angle": 0, + "content": "The main contributions are summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.811, + 0.468, + 0.854 + ], + "angle": 0, + "content": "- We propose a sparse Transformer architecture to help generate high-quality deraining results with more accurate detail and texture recovery." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.856, + 0.468, + 0.901 + ], + "angle": 0, + "content": "- We develop a simple yet effective learnable top-\\(k\\) selection operator to adaptively maintain the most useful self-attention values for better feature aggregation." + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.811, + 0.468, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.092, + 0.89, + 0.136 + ], + "angle": 0, + "content": "- We design an effective feed-forward network based on mixed-scale fusion strategy to explore multi-scale representations for better facilitating image deraining." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.137, + 0.89, + 0.182 + ], + "angle": 0, + "content": "- Extensive experimental results on various benchmarks demonstrate that our method achieves favorable performance against state-of-the-art (SOTA) approaches." + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.092, + 0.89, + 0.182 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.195, + 0.642, + 0.21 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.216, + 0.892, + 0.503 + ], + "angle": 0, + "content": "Single image deraining. Since image deraining is an ill-posed problem, traditional methods [12, 20, 24, 30, 60] usually develop kinds of image priors to provide additional constraints. However, these handcrafted priors tend to rely on empirical observations and thus are not able to model the inherent properties of clear images. To overcome this problem, numerous CNN-based frameworks [53] have been developed to solve image deraining and achieved decent restoration performance. To better represent the rain distribution, several studies take rain characteristics such as rain direction [27], density [61], veiling effect [15] into account, and optimize the network structure via recursive computation [19, 23, 36] or transfer mechanism [16, 49, 54, 55]. Although these methods achieve better performance than the hand-crafted prior-based ones, they have difficulty capturing the long-range dependencies due to the intrinsic limitations of convolution. Different CNN-based deraining approaches, we utilize the Transformer as the network backbone to model non-local information for image deraining." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.506, + 0.893, + 0.807 + ], + "angle": 0, + "content": "Vision Transformers. Motivated by the great success of the Transformers [7] in natural language processing (NLP) [40] and high-level vision tasks [1, 28], Transformers have been applied to image restoration [2, 13, 48, 51, 58] and perform better than the previous CNN-based baselines as they are able to model non-local information. For the field of image rain removal, Jiang et al. [18] design a dynamic associated deraining network by incorporating self-attention in Transformer with a background recovery network. More recently, Xiao et al. [50] elaborately develop image deraining Transformer (IDT) with window-based and spatial-based dual Transformer to achieve excellent results. Note that, most existing methods rely on the dense dot-product self-attention as the heart of Transformers. However, one shortcoming of this computation manner is that redundant or irrelevant features with smaller weights may interfere with the attention map, which makes the output features contain potential noises. In this work, we propose sparse attention in Transformer to relieve the negligence of the most relevant information faced by vanilla self-attention." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Sparse representation. With inspirations drawn from neural activity in biological brains, sparsity of hidden representation in deep neural networks as a tantalizing \"free lunch\" emerges for both vision and NLP tasks [44, 64]. Indeed, it is widely proven that sparse representation also plays a critical role in handling low-level vision problems, such as im" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5897" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.078, + 0.087, + 0.896, + 0.212 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.102, + 0.214, + 0.311, + 0.334 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.319, + 0.214, + 0.626, + 0.334 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.632, + 0.214, + 0.874, + 0.334 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.075, + 0.342, + 0.895, + 0.385 + ], + "angle": 0, + "content": "Figure 2. The overall architecture of the proposed sparse Transformer network for image deraining (DRSformer), which mainly contains sparse Transformer block (STB) with top- \\(k\\) sparse attention (TKSA) and mixed-scale feed-forward network (MSFN), and mixture of experts feature compensator (MEFC). LN refers to the layer normalization and DW-Conv refers to the depth-wise convolution." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.392, + 0.473, + 0.59 + ], + "angle": 0, + "content": "age deraining [46] and super-resolution [31]. In principle, sparse attention can be categorized into data-based (fixed) sparse attention and content-based sparse attention [6, 38]. For data-based sparse attention, several local attention operations are introduced into CNN backbone, which mainly considers attending only to local window size. Recent studies [11, 42] have investigated enforcing sparsity to Transformer backbone. More recently, Zhang et al. [63] design an attention retractable Transformer to allow tokens from sparse areas to interact features, which is data-based sparsity. Different from it, we implement a simple but effective approximation for self-attention based on top-\\(k\\) selection to achieve sparse attention, which is content-based sparsity." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.594, + 0.471, + 0.687 + ], + "angle": 0, + "content": "Top-k selection. Zhao et al. [64] first propose an explicit selection method based on top-\\(k\\) mechanism in NLP tasks. Driven by their success, \\(k\\)-NN attention [42, 44] is further introduced for boosting vision Transformers. Unlike performing top-\\(k\\) selection in the spatial dimension [44], we design an efficient top-\\(k\\) useful channel selection operator." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.695, + 0.251, + 0.712 + ], + "angle": 0, + "content": "3. Proposed Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.717, + 0.47, + 0.839 + ], + "angle": 0, + "content": "In this section, we first describe the overall pipeline and symmetrically hierarchical network architecture for image deraining. Afterward, we provide the details of the proposed sparse Transformer block (STB), as the fundamental building unit of our method, which mainly contains two key elements: top-\\(k\\) sparse attention (TKSA) and mixed-scale feed-forward network (MSFN). Finally, we present the introduced mixture of experts feature compensator (MEFC)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.848, + 0.238, + 0.864 + ], + "angle": 0, + "content": "3.1. Overall pipeline" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.471, + 0.901 + ], + "angle": 0, + "content": "The overall pipeline of our proposed DRSformer, shown in Figure 2, is based on a hierarchical encoder-decoder" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.392, + 0.893, + 0.785 + ], + "angle": 0, + "content": "framework. Given a rainy image \\( I_{rain} \\in \\mathbb{R}^{H \\times W \\times 3} \\), where \\( H \\times W \\) represents the spatial resolution of the feature map, we perform overlapped image patch embedding with \\( 3 \\times 3 \\) convolution. In the network backbone, we stack \\( N_{i \\in [1,2,3,4]} \\) STBs to extract rich features for spatially-varying rain distribution. To excavate the multi-scale representation from rain degeneration, each level of encoder-decoder pipeline covers its own specific spatial resolution and channel dimension. For feature down-sampling and up-sampling, we apply pixel-unshuffle and pixel-shuffle operations. Similar to [48,50,58], we also add skip-connections to bridge across continuous intermediate features for stable training. In each STB, unlike the standard self-attention [7] in Transformer, we develop TKSA to achieve feature sparsity, aiming to enforce the feature aggregation process more effectively. In addition, a MSFN is introduced into STB to enrich multiscale local information and help image restoration. At the early and final stages of the model learning, we equip our model with \\( N_0 \\) MEFCs to provide complementary feature refinement, so that high-quality clear outputs can be finally reconstructed. With this hybrid formulation, we allow DRSformer to exploit both the adaptive content and the intrinsic property of rainy images, facilitating the separation of undesired rain streaks and latent clear background, and experiments demonstrate that the above design choices yield quality improvements (see Sec. 4.3)" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.786, + 0.894, + 0.832 + ], + "angle": 0, + "content": "The final reconstructed result is obtained by: \\( I_{derain} = \\mathcal{F}(I_{rain}) + I_{rain} \\), where \\( \\mathcal{F}(\\cdot) \\) is the overall network and it is trained by minimizing the following loss function:" + }, + { + "type": "equation", + "bbox": [ + 0.618, + 0.843, + 0.892, + 0.861 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\left\\| I _ {\\text {d e r a i n}} - I _ {\\text {g t}} \\right\\| _ {1}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "where \\(I_{gt}\\) denotes the ground-truth image, and \\(\\| \\cdot \\| _1\\) denotes the \\(L_{1}\\)-norm." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5898" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.078, + 0.091, + 0.314, + 0.107 + ], + "angle": 0, + "content": "3.2. Sparse Transformer block" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.114, + 0.47, + 0.249 + ], + "angle": 0, + "content": "As the standard Transformers [7, 40, 58] take all the tokens to compute self-attention globally, which is unfriendly for image restoration due to it may involve noisy interactions between the irrelevant features. To solve such limitations, we develop a sparse Transformer block (STB) as the feature extraction unit by taking the advantages of sparsity that emerged in neural networks [64]. Formally, given the input features at the \\((l - 1)\\)-th block \\(\\mathbf{X}_{l - 1}\\), the encoding procedures of STB can be defined as:" + }, + { + "type": "equation", + "bbox": [ + 0.152, + 0.252, + 0.469, + 0.27 + ], + "angle": 0, + "content": "\\[\n\\mathbf {X} _ {l} ^ {\\prime} = \\mathbf {X} _ {l - 1} + \\operatorname {T K S A} \\left(\\ln \\left(\\mathbf {X} _ {l - 1}\\right)\\right), \\tag {2}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.169, + 0.277, + 0.469, + 0.294 + ], + "angle": 0, + "content": "\\[\n\\mathbf {X} _ {l} = \\mathbf {X} _ {l} ^ {\\prime} + \\operatorname {M S F N} \\left(\\ln \\left(\\mathbf {X} _ {l} ^ {\\prime}\\right)\\right), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.295, + 0.469, + 0.355 + ], + "angle": 0, + "content": "where LN denotes the layer normalization; \\(\\mathbf{X}_l^{\\prime}\\) and \\(\\mathbf{X}_l\\) denote the outputs from the top- \\(k\\) sparse attention (TKSA) and mixed-scale feed-forward network (MSFN), which are described below." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.359, + 0.469, + 0.435 + ], + "angle": 0, + "content": "Top-k sparse attention (TKSA). We revisit the standard self-attention in Transformer, which has become an empirical operation in most of the existing models. Given a query \\( Q \\), key \\( K \\) and value \\( V \\) with the dimension of \\( \\mathbb{R}^{L\\times d} \\), the output of dot-product attention is generally formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.14, + 0.438, + 0.469, + 0.473 + ], + "angle": 0, + "content": "\\[\n\\operatorname {A t t} (\\mathbf {Q}, \\mathbf {K}, \\mathbf {V}) = \\operatorname {s o f t m a x} \\left(\\frac {\\mathbf {Q} \\mathbf {K} ^ {\\top}}{\\lambda}\\right) \\mathbf {V}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.476, + 0.469, + 0.643 + ], + "angle": 0, + "content": "where \\(\\mathbf{Q}\\), \\(\\mathbf{K}\\), and \\(\\mathbf{V}\\) denote the matrix forms of \\(Q\\), \\(K\\), and \\(V\\), respectively. \\(\\lambda\\) is an optional temperature factor defined by \\(\\lambda = \\sqrt{d}\\). Generally, multi-head attention is implemented to each of the \\(k\\) new \\(Q\\), \\(K\\) and \\(V\\), yielding \\(d = C / k\\) channel dimensional outputs which are concatenated and then got the final result for all heads via the linear projection. Noted that, this vanilla self-attention paradigm is based on densely fully-connected, which requires computing the attention map for all query-key pairs. In our work, we develop TKSA to replace it, thus avoiding the involvement of irrelevant information during the feature interaction process." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.644, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Specifically, we first encode channel-wise context by applying \\(1 \\times 1\\) convolutions followed by \\(3 \\times 3\\) depth-wise convolutions. Inspired by [58], we apply self-attention across channels rather than the spatial dimension to reduce the time and memory complexity. Next, we calculate similarities of pixel pairs between all the reshaped queries and keys, and mask out the unnecessary elements assigned with lower attention weights in the transposed attention matrix \\(\\mathbf{M}\\) of size \\(\\mathbb{R}^{\\hat{C} \\times \\hat{C}}\\). Unlike the dropout strategy of randomly abandoning the scores, an adaptive selection of the top-\\(k\\) contributive scores is implemented upon \\(\\mathbf{M}\\), aiming to preserve the most significant components and remove the useless ones [3]. Here, \\(k\\) is an adjustable parameter to dynamically control the magnitude of sparsity, which is formally obtained by weighted average of some proper fractions, such as \\(\\frac{2}{3}\\). Thus, only top-\\(k\\) values within the range \\([\\Delta_1, \\Delta_2]\\) are normalized from each row of \\(\\mathbf{M}\\) for softmax computing. For" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.152 + ], + "angle": 0, + "content": "other elements that are smaller than top-\\(k\\) scores, we replace their probabilities with 0 at given indices using the scatter function. This dynamic selection makes the attention from dense to sparse, which is derived by:" + }, + { + "type": "equation", + "bbox": [ + 0.516, + 0.158, + 0.892, + 0.193 + ], + "angle": 0, + "content": "\\[\n\\operatorname {S p a r s e A t t} (\\mathbf {Q}, \\mathbf {K}, \\mathbf {V}) = \\operatorname {s o f t m a x} \\left(\\mathcal {T} _ {k} \\left(\\frac {\\mathbf {Q} \\mathbf {K} ^ {\\top}}{\\lambda}\\right)\\right) \\mathbf {V}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.198, + 0.848, + 0.214 + ], + "angle": 0, + "content": "where \\(\\mathcal{T}_k(\\cdot)\\) is the learnable top- \\(k\\) selection operator:" + }, + { + "type": "equation", + "bbox": [ + 0.586, + 0.22, + 0.891, + 0.261 + ], + "angle": 0, + "content": "\\[\n\\left[ \\mathcal {T} _ {k} (\\boldsymbol {S}) \\right] _ {i j} = \\left\\{ \\begin{array}{l l} S _ {i j} & S _ {i j} \\geq t _ {i} \\\\ 0 & \\text {o t h e r w i s e .} \\end{array} \\right. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.268, + 0.892, + 0.349 + ], + "angle": 0, + "content": "where \\(t_i\\) is the \\(k\\)-th largest value in the \\(j\\)-th row of \\(\\frac{\\mathbf{Q}\\mathbf{K}^{\\top}}{\\lambda}\\). Here, we calculate \\(\\mathbf{Q}\\mathbf{K}^{\\top}\\) in the same way as [58] to generate \\(\\mathbf{M}\\) of size \\(\\mathbb{R}^{\\hat{C}\\times \\hat{C}}\\). As we use the multi-head strategy, we finally concatenate all the outputs of multi-head attention, and then get the final result by the linear projection." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.359, + 0.892, + 0.602 + ], + "angle": 0, + "content": "Mixed-scale feed-forward network (MSFN). Previous studies [48, 50, 58] usually introduce single-scale depth-wise convolutions into the regular feed-forward network to imporve locality. However, those exploitations all ignore the correlations of multi-scale rain streaks. In fact, rich multi-scale representation has fully demonstrated its effectiveness [19, 41] in better removing rain. Here, we design a MSFN by inserting two multi-scale depth-wise convolution paths in the transmission process, see Figure 2. Given an input tensor \\(\\mathbf{X}_{l-1} \\in \\mathbb{R}^{H \\times W \\times C}\\), after layer normalization, we first utilize \\(1 \\times 1\\) convolution to expand the channel dimension in the ratio of \\(r\\), then feed it into two parallel branches. During the feature transformation, the \\(3 \\times 3\\) and \\(5 \\times 5\\) depth-wise convolutions are employed to enhance the multi-scale local information extraction. In this way, the entire feature fusion procedure of the developed MSFN is formulated as:" + }, + { + "type": "equation", + "bbox": [ + 0.502, + 0.608, + 0.676, + 0.626 + ], + "angle": 0, + "content": "\\[\n\\hat {\\mathbf {X}} _ {l} = f _ {1 \\times 1} ^ {c} (\\operatorname {L N} \\left(\\mathbf {X} _ {l - 1}\\right)),\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.502, + 0.629, + 0.817, + 0.654 + ], + "angle": 0, + "content": "\\[\n\\mathbf {X} _ {l} ^ {p _ {1}} = \\sigma \\left(f _ {3 \\times 3} ^ {d w c} (\\hat {\\mathbf {X}} _ {l})\\right), \\mathbf {X} _ {l} ^ {s _ {1}} = \\sigma \\left(f _ {5 \\times 5} ^ {d w c} (\\hat {\\mathbf {X}} _ {l})\\right),\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.502, + 0.656, + 0.888, + 0.674 + ], + "angle": 0, + "content": "\\[\n\\mathbf {X} _ {l} ^ {p _ {2}} = \\sigma \\left(f _ {3 \\times 3} ^ {d w c} \\left[ \\mathbf {X} _ {l} ^ {p _ {1}}, \\mathbf {X} _ {l} ^ {s _ {1}} \\right]\\right), \\mathbf {X} _ {l} ^ {s _ {2}} = \\sigma \\left(f _ {5 \\times 5} ^ {d w c} \\left[ \\mathbf {X} _ {l} ^ {s _ {1}}, \\mathbf {X} _ {l} ^ {p _ {1}} \\right]\\right),\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.502, + 0.676, + 0.891, + 0.704 + ], + "angle": 0, + "content": "\\[\n\\mathbf {X} _ {l} = f _ {1 \\times 1} ^ {c} \\left[ \\mathbf {X} _ {l} ^ {p _ {2}}, \\mathbf {X} _ {l} ^ {s _ {2}} \\right] + \\mathbf {X} _ {l - 1}, \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.704, + 0.891, + 0.749 + ], + "angle": 0, + "content": "where \\(\\sigma (\\cdot)\\) is a ReLU activation, \\(f_{1\\times 1}^{c}\\) represents \\(1\\times 1\\) convolution, \\(f_{3\\times 3}^{dwc}\\) and \\(f_{5\\times 5}^{dwc}\\) denote \\(3\\times 3\\) and \\(5\\times 5\\) depth-wise convolutions, and \\([\\cdot ]\\) is the channel-wise concatenation." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.757, + 0.842, + 0.773 + ], + "angle": 0, + "content": "3.3. Mixture of experts feature compensator" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.892, + 0.901 + ], + "angle": 0, + "content": "To fill in the comprehensive faculty of integrating sparsity in the DRSformer, we further introduce MEFC to perform a unified co-exploration towards joint data and content sparsity. Recalling the classical design of effective CNN models [39], we elaborately select multiple sparse CNN operations to form parallel layers, dubbed as experts, which involve an average pooling with receptive field of \\( 3 \\times 3 \\), separable convolution layers with kernel sizes of \\( 1 \\times 1 \\)" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5899" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.087, + 0.09, + 0.882, + 0.104 + ], + "angle": 0, + "content": "Table 1. Comparison of quantitative results on synthetic and real datasets. Bold and underline indicate the best and second-best results." + }, + { + "type": "table", + "bbox": [ + 0.092, + 0.105, + 0.877, + 0.31 + ], + "angle": 0, + "content": "
DatasetsRain200LRain200HDID-DataDDN-DataSPA-Data
MetricsPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIM
Prior-based methodsDSC [30]27.160.866314.730.381524.240.827927.310.837334.950.9416
GMM [24]28.660.865214.500.416425.810.834427.550.847934.300.9428
CNN-based methodsDDN [8]34.680.967126.050.805630.970.911630.000.904136.160.9457
RESCAN [23]36.090.969726.750.835333.380.941731.940.934538.110.9707
PReNet [36]37.800.981429.040.899133.170.948132.600.945940.160.9816
MSPFN [19]38.580.982729.360.903433.720.955032.990.933343.430.9843
RCDNet [43]39.170.988530.240.904834.080.953233.040.947243.360.9831
MPRNet [59]39.470.982530.670.911033.990.959033.100.934743.640.9844
DualGCN [9]40.730.988631.150.912534.370.962033.010.948944.180.9902
SPDNet [56]40.500.987531.280.920734.570.956033.150.945743.200.9871
Transformer-based methodsUformer [48]40.200.986030.800.910535.020.962133.950.954546.130.9913
Restormer [58]40.990.989032.000.932935.290.964134.200.957147.980.9921
IDT [50]40.740.988432.100.934434.890.962333.840.954947.350.9930
DRSformer41.230.989432.170.932635.350.964634.350.958848.540.9924
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.318, + 0.471, + 0.453 + ], + "angle": 0, + "content": "\\(3 \\times 3, 5 \\times 5, 7 \\times 7\\), and dilated convolution layers with kernel sizes of \\(3 \\times 3, 5 \\times 5, 7 \\times 7\\). Different from the conventional mixture of experts [17, 37], our MEFC does not attach an external gating network. Instead, we make the self-attention [14,21] become a switcher of different experts to adaptively select the importance of diverse representations depending on the inputs. Given an input feature map \\(\\mathbf{X}_{l-1} \\in \\mathbb{R}^{H \\times W \\times C}\\), we first apply the channel-wise average to generate \\(C\\)-dimensional channel descriptor \\(\\mathbf{z}_c \\in \\mathbb{R}^C\\):" + }, + { + "type": "equation", + "bbox": [ + 0.158, + 0.462, + 0.469, + 0.504 + ], + "angle": 0, + "content": "\\[\n\\mathbf {z} _ {c} = \\frac {1}{H \\times W} \\sum_ {i = 1} ^ {H} \\sum_ {j = 1} ^ {W} \\mathbf {X} _ {l - 1} (i, j), \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.513, + 0.47, + 0.633 + ], + "angle": 0, + "content": "where \\(\\mathbf{X}_{l - 1}(i,j)\\) is the \\((y,x)\\) position of the feature \\(\\mathbf{X}_{l - 1}\\). Then, the coefficient vector of each expert is allocated corresponding to the learnable weight matrices \\(\\mathbf{W}_1\\in \\mathbb{R}^{T\\times C}\\) and \\(\\mathbf{W}_2\\in \\mathbb{R}^{O\\times T}\\). Here, \\(T\\) is the dimension of the weight matrices. To avoid altering the sizes of its inputs and outputs, we zero pad the input feature maps computed by each expert. Finally, the output of the \\(l\\)-th MEFC is calculated by:" + }, + { + "type": "equation", + "bbox": [ + 0.136, + 0.63, + 0.282, + 0.647 + ], + "angle": 0, + "content": "\\[\n\\mathbf {T} _ {l} = \\mathbf {W} _ {2} \\sigma \\left(\\mathbf {W} _ {1} \\mathbf {z} _ {c}\\right),\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.136, + 0.65, + 0.469, + 0.692 + ], + "angle": 0, + "content": "\\[\n\\mathbf {X} _ {l} = f _ {1 \\times 1} ^ {c} \\left[ \\sum_ {i = 1} ^ {O} f _ {e x p} \\left(\\mathbf {X} _ {l}, \\mathbf {T} _ {l}\\right) \\right] + \\mathbf {X} _ {l - 1}, \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.694, + 0.47, + 0.786 + ], + "angle": 0, + "content": "where \\( f_{exp} \\) and \\( O \\) represent the expert operations and the number of experts, respectively. \\( f_{1\\times 1}^{c} \\) represents \\( 1\\times 1 \\) convolution, \\( \\sigma (\\cdot) \\) is a ReLU function, and \\( [\\cdot ] \\) is the channelwise concatenation. With this design, MEFC is now closely linked to the main STBs so that is able to adaptively remove the rainy effects of diverse appearances." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.796, + 0.32, + 0.813 + ], + "angle": 0, + "content": "4. Experiments and Analysis" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.817, + 0.282, + 0.833 + ], + "angle": 0, + "content": "4.1. Experimental settings" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.84, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Datasets. We implement deraining experiments on multiple public benchmarks, including Rain200L/H [52], DID-Data [61] and DDN-Data [8]. Rain200L and Rain200H contain 1,800 synthetic rainy images for training and 200 ones for" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.318, + 0.892, + 0.409 + ], + "angle": 0, + "content": "testing. DID-Data and DDN-Data consist of 12,000 and 12,600 synthetic images with different rain directions and density levels. There are 1,200 and 1,400 rainy images for testing. In addition, we also evaluate our method using a large-scale real-world dataset, i.e., SPA-Data [45], containing 638,492 image pairs for training and 1,000 testing ones." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.412, + 0.893, + 0.579 + ], + "angle": 0, + "content": "Comparison methods. We compare our DRSformer with two prior-based models (DSC [30] and GMM [24]), CNN-based methods (DDN [8], RESCAN [23], PReNet [36], MSPFN [19], RCDNet [43], MPRNet [59], DualGCN [9], and SPDNet [56]), and recent Transformer-based methods (Uformer [48], Restormer [58], and IDT [50]). For recent representative methods (DualGCN, SPDNet, Restormer and IDT), we retrain their models provided by the authors if no pretrained models are provided, otherwise we evaluate them with their online codes for fair comparisons. For other approaches, we refer to some reported results in [10, 50]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.58, + 0.892, + 0.671 + ], + "angle": 0, + "content": "Evaluation metrics. We adopt PSNR [34] and SSIM [47] as the evaluation metrics for the above benchmarks. Following previous deraining methods [10, 19], we calculate PSNR and SSIM metrics in Y channel of YCbCr space. For the rainy images without ground truth images, we use the non-reference metrics including NIQE [33] and BRISQUE [32]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.675, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Training details. In our model, \\(\\{N_0,N_1,N_2,N_3,N_4\\}\\) are set to \\(\\{4,4,6,6,8\\}\\), and the number of attention heads for four STBs of the same level is set to \\(\\{1,2,4,8\\}\\). The initial channel \\(C\\) is 48 and the expanding ratio is set to 2. In terms of MEFC, we set \\(O = 8\\) for the number of experts and \\(T = 32\\) for the weight matrix. Note that we do not use MEFC for training Rain200L and SPA-Data, because their rain streaks are less complex and easier to learn. In terms of STB, the sparseness \\([\\Delta_1,\\Delta_2]\\) in TKSA is set to \\([\\frac{1}{2},\\frac{4}{5} ]\\), and the channel expansion factor \\(r\\) in MSFN is set to 2.66. During training, we use AdamW optimizer with batch size of 8 and patch size of 128 for total 300K iterations. The initial learning rate is fixed as \\(1\\times 10^{-4}\\) for 92K iterations, and then decreased to \\(1\\times 10^{-6}\\) for 208K iterations with the cosine annealing scheme [29]. For data augmentation, vertical and" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5900" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.09, + 0.891, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.093, + 0.243, + 0.877, + 0.258 + ], + "angle": 0, + "content": "Figure 3. Visual quality comparison on the Rain200H dataset. Zooming in the figures offers a better view at the deraining capability." + }, + { + "type": "image", + "bbox": [ + 0.084, + 0.262, + 0.891, + 0.429 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.093, + 0.432, + 0.877, + 0.446 + ], + "angle": 0, + "content": "Figure 4. Visual quality comparison on the SPA-Data dataset. Zooming in the figures offers a better view at the deraining capability." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.454, + 0.47, + 0.516 + ], + "angle": 0, + "content": "horizontal flips are randomly applied. The entire framework is performed on the PyTorch with 4 NVIDIA GeForce RTX 3090 GPUs, which works in an end-to-end learning fashion without costly large-scale pretraining [2]." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.52, + 0.411, + 0.535 + ], + "angle": 0, + "content": "4.2. Comparisons with the state-of-the-arts" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.539, + 0.47, + 0.841 + ], + "angle": 0, + "content": "Synthetic datasets. The quantitative evaluations on different benchmark datasets are reported in Table 1. As shown, we can note that our proposed method outperforms all the other derainers, especially on PSNR, e.g., DRSformer surpasses the concurrent approach IDT by \\(0.4\\mathrm{dB}\\) on average. Compared with previous CNN-based models, this progress can be much more obvious. The notable increasing scores on the DID-Data and DDN-Data benchmarks reveal that our method can properly handle diverse types of spatially-varying rain streaks. For convincing evidence, we show the visual quality comparison between samples generated by recent approaches in Figure 3. The pure CNN-based models, e.g., MPRNet and SPDNet, fail to restore clear images in heavy rainy scenarios. It can be seen that the results of all computing Transformer-based methods are flawed in terms of detail and texture recovery. Unfortunately, IDT even introduces considerable boundary artifacts. Thanks to the developed sparse attention with top-\\(k\\) selection, our method can generate high-quality deraining results, which are more consistent with that of the ground truth." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.841, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Real-world datasets. We further conduct experiments on the SPA-Data benchmark dataset, and corresponding results are provided in the last column of Table 1. As expected, our model continues to achieve the highest PSNR/SSIM" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.454, + 0.892, + 0.728 + ], + "angle": 0, + "content": "value, exhibiting the superior of DRSformer in terms of deraining performance and generalization. The visual quality comparison can be observed in Figure 4. In contrast, our method significantly competes with others in removing most rain streaks while preserving truthful image structures. In order to further validate the effectiveness of DRSformer, we also randomly choose 20 real rainy images without ground truths from Internet-Data [45] to perform another evaluation. As displayed in Table 2, our net gets the lower NIQE and BRISQUE values, which means a high-quality output with clearer content and better perceptual quality against other comparative models on the real rainy scenarios. Through qualitative quality comparison in Figure 5, most deep models are sensitive to spatially-long rain streaks and leave some apparent rain effects. On the contrary, our net successfully removes most rain perturbation and owns a visual pleasant recovery effect, which implies that it can well generalize to unseen real-world data types." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.734, + 0.663, + 0.748 + ], + "angle": 0, + "content": "4.3. Ablation studies" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.753, + 0.894, + 0.905 + ], + "angle": 0, + "content": "Effectiveness of Top-\\(k\\) selection. To examine the effect of the top-\\(k\\) selection in the TKSA, we present the deraining results of TKSA w/o top-\\(k\\) in Figure 6. We can see that the PSNR values of the derained images by the method without using the top-\\(k\\) selection are lower than those by the method using the top-\\(k\\) selection. In addition, we also note that each element of the self-attention matrix \\(\\operatorname{softmax}\\left(\\frac{\\mathbf{QK}^\\top}{\\lambda}\\right)\\) in Eq. (4) is non-negative and the summation of the elements from each row of \\(\\operatorname{softmax}\\left(\\frac{\\mathbf{QK}^\\top}{\\lambda}\\right)\\) is 1. Thus, applying" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "5901" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.091, + 0.891, + 0.203 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.086, + 0.206, + 0.882, + 0.221 + ], + "angle": 0, + "content": "Figure 5. Visual quality comparison on real-world rainy images. Zooming in the figures offers a better view at the deraining capability." + }, + { + "type": "table_caption", + "bbox": [ + 0.107, + 0.224, + 0.862, + 0.238 + ], + "angle": 0, + "content": "Table 2. Comparison of quantitative results on real-world rainy images, and note that lower scores indicate better image quality." + }, + { + "type": "table", + "bbox": [ + 0.08, + 0.239, + 0.891, + 0.269 + ], + "angle": 0, + "content": "
MethodsRainy InputMPRNet [59]SPDNet [56]Uformer [48]Restormer [58]IDT [50]Ours
NIQE ↓ / BRISQUE ↓5.829 / 33.1294.740 / 32.0184.422 / 26.1734.833 / 28.1065.005 / 34.0364.238 / 25.5734.095 / 22.730
" + }, + { + "type": "image", + "bbox": [ + 0.08, + 0.279, + 0.473, + 0.459 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.078, + 0.461, + 0.469, + 0.476 + ], + "angle": 0, + "content": "Figure 6. Ablation analysis for top-\\(k\\) selection on the benchmarks." + }, + { + "type": "image", + "bbox": [ + 0.079, + 0.477, + 0.47, + 0.627 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.082, + 0.631, + 0.465, + 0.645 + ], + "angle": 0, + "content": "Figure 7. Ablation analysis for different number \\( k \\) in the TKSA." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.667, + 0.47, + 0.718 + ], + "angle": 0, + "content": "the self-attention matrix \\(\\mathrm{softmax}\\left(\\frac{\\mathbf{Q}\\mathbf{K}^{\\top}}{\\lambda}\\right)\\) to \\(\\mathbf{V}\\) will remove the high-frequency information of \\(\\mathbf{V}\\), which lead to oversmoothed results." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.72, + 0.473, + 0.904 + ], + "angle": 0, + "content": "To understand the effect of such top-\\(k\\) selection, we further use high-pass filtering (HPF) to visualize learned features in Figure 8. Compared to standard self-attention operation (w/o top-\\(k\\)), our strategy can better help reconstruct finer-detail feature and improve potential restoration quality. As the nearby pixels tend to be more similar than others, top-\\(k\\) selection operator helps to reduce the irrelevant context from long-range pixel dependency. This step of selection allows the smaller similarity weights (from a part of long-range feature interactions) to be discarded in the procedure of self-attention calculation, thus facilitating more accurate representation for achieving high-quality output." + }, + { + "type": "image", + "bbox": [ + 0.502, + 0.279, + 0.598, + 0.354 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.518, + 0.354, + 0.582, + 0.364 + ], + "angle": 0, + "content": "(a) w/o Top-k" + }, + { + "type": "image", + "bbox": [ + 0.599, + 0.279, + 0.696, + 0.354 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.621, + 0.354, + 0.676, + 0.364 + ], + "angle": 0, + "content": "(a) w Top-k" + }, + { + "type": "image", + "bbox": [ + 0.697, + 0.279, + 0.794, + 0.354 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.714, + 0.354, + 0.779, + 0.364 + ], + "angle": 0, + "content": "(b) w/o Top-k" + }, + { + "type": "image", + "bbox": [ + 0.796, + 0.279, + 0.893, + 0.354 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.816, + 0.354, + 0.872, + 0.364 + ], + "angle": 0, + "content": "(b) w Top-k" + }, + { + "type": "image_caption", + "bbox": [ + 0.5, + 0.372, + 0.893, + 0.415 + ], + "angle": 0, + "content": "Figure 8. Visualization of feature maps. Our proposed top-\\(k\\) selection can effectively leverage pixel-dependent properties of image structure and generate more precise high-frequency details." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.425, + 0.895, + 0.636 + ], + "angle": 0, + "content": "Effect of the number of \\( k \\). The key parameter for our proposed TKSA is \\( k \\), and its influence is investigated in Figure 7. We note that the optimal choice of \\( k \\) determines the boundary control of the sparsity rate. If \\( k \\) is manually set to a single value, such as \\( \\frac{1}{2} \\), we notice that its performance is sensitive to \\( k \\). To avoid an exhaustive search, we set a controllable interval range for \\( k \\) to dynamically learn the most contribute score. When \\( k \\) is too small, we find that the performance will undoubtedly decline sharply due to insufficient global information aggregation. The best result can achieve 32.18 dB when \\( [\\Delta_1, \\Delta_2] \\) in the TKSA is assigned to \\( [\\frac{1}{2}, \\frac{4}{5}] \\). As \\( k \\) continues to increase, the final deraining performance gradually decreases due to the introduction of irrelevant and useless features." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.637, + 0.895, + 0.819 + ], + "angle": 0, + "content": "Effectiveness of MSFN. To evaluate the effectiveness of the proposed MSFN, we compare it with three baselines: (1) conventional feed-forward network (FN) [7], (2) Dconv feed-forward network (DFN) [25], and (3) gated-Dconv feed-forward network (GDFN) [58]. The quantitative analysis results on Rain200H are listed in Table 3. Although GDFN introduces a gating mechanism in two same-scale depth-wise convolution streams to bring performance advantages, it still neglects the multi-scale knowledge for deraining. By adding local feature extraction and fusion at different scales, the MSFN can indeed better boost the performance, and achieve PSNR gain of 0.21 dB over GDFN." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Effectiveness of MEFC. To evaluate the effectiveness of MEFC, we perform experiments based on different model variants in Table 4. Compared to the baseline model (a), MEFC provides additional performance benefits thanks to auxiliary data sparsity. In addition, we observe that MEFCs" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5902" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.091, + 0.197, + 0.153 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.082, + 0.152, + 0.197, + 0.172 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.11, + 0.172, + 0.168, + 0.182 + ], + "angle": 0, + "content": "Rainy Input" + }, + { + "type": "image", + "bbox": [ + 0.198, + 0.091, + 0.313, + 0.152 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.199, + 0.152, + 0.313, + 0.172 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.231, + 0.172, + 0.279, + 0.182 + ], + "angle": 0, + "content": "Model (a)" + }, + { + "type": "image", + "bbox": [ + 0.314, + 0.091, + 0.428, + 0.152 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.315, + 0.152, + 0.427, + 0.172 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.346, + 0.172, + 0.395, + 0.182 + ], + "angle": 0, + "content": "Model (b)" + }, + { + "type": "image", + "bbox": [ + 0.429, + 0.091, + 0.543, + 0.152 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.429, + 0.152, + 0.543, + 0.172 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.461, + 0.172, + 0.509, + 0.182 + ], + "angle": 0, + "content": "Model (c)" + }, + { + "type": "image", + "bbox": [ + 0.543, + 0.091, + 0.659, + 0.152 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.543, + 0.152, + 0.659, + 0.172 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.576, + 0.172, + 0.625, + 0.182 + ], + "angle": 0, + "content": "Model (d)" + }, + { + "type": "image", + "bbox": [ + 0.66, + 0.091, + 0.775, + 0.152 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.66, + 0.152, + 0.776, + 0.172 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.692, + 0.172, + 0.74, + 0.182 + ], + "angle": 0, + "content": "Model (e)" + }, + { + "type": "image", + "bbox": [ + 0.776, + 0.091, + 0.89, + 0.152 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.776, + 0.152, + 0.889, + 0.172 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.808, + 0.172, + 0.855, + 0.182 + ], + "angle": 0, + "content": "Model (f)" + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.186, + 0.89, + 0.2 + ], + "angle": 0, + "content": "Figure 9. Ablation qualitative comparison for different variants of DRSformer. The models (a-f) are consistent with the settings in Table 4." + }, + { + "type": "table_caption", + "bbox": [ + 0.093, + 0.208, + 0.452, + 0.221 + ], + "angle": 0, + "content": "Table 3. Ablation study for different feed-forward networks." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.223, + 0.468, + 0.247 + ], + "angle": 0, + "content": "
ModelsFN [7]DFN [25]GDFN [58]MSFN
PSNR / SSIM31.84 / 0.927531.88 / 0.927931.97 / 0.928632.18 / 0.9330
" + }, + { + "type": "table_caption", + "bbox": [ + 0.078, + 0.254, + 0.468, + 0.28 + ], + "angle": 0, + "content": "Table 4. Ablation study for different variants of our DRSformer. MEFC-1 and MEFC-2 denote MEFC in early and final stages." + }, + { + "type": "table", + "bbox": [ + 0.084, + 0.282, + 0.466, + 0.373 + ], + "angle": 0, + "content": "
ModelsMEFC-1STBsMEFC-2ExpertsPSNR / SSIM
(a)✓032.03 / 0.9308
(b)✓✓832.01 / 0.9311
(c)✓✓832.07 / 0.9328
(d)✓✓✓132.06 / 0.9316
(e)✓✓✓432.14 / 0.9325
(f)✓✓✓832.18 / 0.9330
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.383, + 0.47, + 0.624 + ], + "angle": 0, + "content": "at different locations of the network pipeline have specific impacts on the restoration performance. Indeed, we also analyze the effect of the different numbers of experts in each MEFC. When using single expert model (d), the performance is dramatically degraded compared with our multi-expert model (f). Unlike setting all experts to the same structure [21], our multi-expert formulation is more diverse, which brings its gains to the performance due to different receptive fields and disparate CNN operations. Through the zoomed boxes in Figure 9, the recovered results of the model with all the above components tend to be clearer since it enables more diverse features to be fully used during the restoration process. All in all, our model (f) performs better than the other possible configurations, which indicates that each design strategy that we consider has its own contribution to the final performance of DRSformer." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.635, + 0.3, + 0.65 + ], + "angle": 0, + "content": "4.4. Closely-related methods" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.659, + 0.469, + 0.84 + ], + "angle": 0, + "content": "We note that the recent method [22] proposes a \\(k\\)-NN image Transformer (KiT) to solve image restoration by aggregating \\(k\\) similar patches with the pair-wise local attention. Compared with KiT that employs complex locality sensitive hashing that cannot ensure sufficient global interaction, our simple but effective top-\\(k\\) selection mechanism not only enjoys the locality but also empowers the ability of global relation mining. As the code of KiT is not available, we refer to the results of their paper. Figure 10 shows qualitative comparisons trained on the Rain800 [62]. We can see that KiT tends to blur the contents and cause color distortion. In contrast, our method leads to better deraining results." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.469, + 0.901 + ], + "angle": 0, + "content": "In addition, we also note that [44] recently designs the \\(k\\)-NN attention to enhance the representation ability of vision Transformers by selecting the top-\\(k\\) similar tokens. Different from KVT [44], which implements top-\\(k\\) selection" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.207, + 0.596, + 0.301 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.509, + 0.305, + 0.592, + 0.316 + ], + "angle": 0, + "content": "(a) Rainy Input" + }, + { + "type": "image", + "bbox": [ + 0.602, + 0.208, + 0.693, + 0.301 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.614, + 0.304, + 0.68, + 0.316 + ], + "angle": 0, + "content": "(b) KiT [22]" + }, + { + "type": "image", + "bbox": [ + 0.697, + 0.208, + 0.789, + 0.301 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.707, + 0.304, + 0.779, + 0.316 + ], + "angle": 0, + "content": "(c) KVT [44]" + }, + { + "type": "image", + "bbox": [ + 0.795, + 0.208, + 0.886, + 0.301 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.817, + 0.304, + 0.863, + 0.315 + ], + "angle": 0, + "content": "(d) Ours" + }, + { + "type": "image_caption", + "bbox": [ + 0.515, + 0.318, + 0.875, + 0.332 + ], + "angle": 0, + "content": "Figure 10. Comparison results with closely-related methods." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.334, + 0.892, + 0.456 + ], + "angle": 0, + "content": "in the spatial dimension, our operator is more efficient in computing sparse attention across channels. Furthermore, the sparsity level of \\( k \\) in our proposed TKSA is dynamically learnable, rather than the fixed setting in [44]. Here, we adopt \\( k \\)-NN attention in KVT to replace our TKSA for comparison. To ensure the fair comparison, the same training settings are kept for model testing. As shown in Figure 10 (c) and (d), our method can generate a clearer image." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.464, + 0.702, + 0.48 + ], + "angle": 0, + "content": "5. Concluding Remarks" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.486, + 0.892, + 0.697 + ], + "angle": 0, + "content": "We have presented an effective sparse Transformer network, called DRSformer, to solve image deraining. Based on the observation that vanilla self-attention in Transformer may suffer from the global interaction of irrelevant information, we develop the top-\\(k\\) sparse attention to keep the most useful self-attention values for better feature aggregation. To facilitate the aggregated features for removing rain, we develop a mixed-scale feed-forward network to better explore multi-scale representations. Furthermore, the mixture of experts feature compensator is introduced to the model to provide collaborative refinement for the sparse Transformer backbone, so that the fine details of the reconstructed image is preserved. Experimental results show that our DRSformer performs favorably against state-of-the-art methods." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.703, + 0.892, + 0.809 + ], + "angle": 0, + "content": "Limitations. Our proposed method aims to further boost image deraining performance, but there are limitations in the model efficiency. Specifically, our model requires 33.7 Million parameters and costs 242.9G FLOPs on one image with size of \\(256 \\times 256\\). We will apply the pruning or distillation scheme in our model to maintain the original deraining performance while achieving credible model compression." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Acknowledgements. This work has been partly supported by the National Key R&D Program of China (No. 2018AAA0102001), the National Natural Science Foundation of China (Nos. U22B2049, U19B2040, 61922043, 61872421, 62272230), and the Fundamental Research Funds for the Central Universities (No. 30920041109)." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5903" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.169 + ], + "angle": 0, + "content": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In ECCV, pages 213-229, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.171, + 0.47, + 0.226 + ], + "angle": 0, + "content": "[2] Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao. Pre-trained image processing transformer. In CVPR, pages 12299-12310, 2021. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.228, + 0.47, + 0.282 + ], + "angle": 0, + "content": "[3] Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, and Zhangyang Wang. Chasing sparsity in vision transformers: An end-to-end exploration. Advances in Neural Information Processing Systems, 34:19974-19988, 2021. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.284, + 0.47, + 0.338 + ], + "angle": 0, + "content": "[4] Xiang Chen, Jinshan Pan, Kui Jiang, Yufeng Li, Yufeng Huang, Caihua Kong, Longgang Dai, and Zhentao Fan. Unpaired deep image deraining using dual contrastive learning. In CVPR, pages 2017-2026, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.34, + 0.47, + 0.381 + ], + "angle": 0, + "content": "[5] Xiang Chen, Jinshan Pan, Jiyang Lu, Zhentao Fan, and Hao Li. Hybrid cnn-transformer feature fusion for single image deraining. In AAAI, 2023. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.382, + 0.47, + 0.422 + ], + "angle": 0, + "content": "[6] Gonçalo M Correia, Vlad Niculae, and André FT Martins. Adaptively sparse transformers. arXiv preprint arXiv:1909.00015, 2019.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.424, + 0.47, + 0.506 + ], + "angle": 0, + "content": "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 2, 3, 4, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.508, + 0.47, + 0.561 + ], + "angle": 0, + "content": "[8] Xueyang Fu, Jiabin Huang, Delu Zeng, Yue Huang, Xinghao Ding, and John Paisley. Removing rain from single images via a deep detail network. In CVPR, pages 3855-3863, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.564, + 0.47, + 0.605 + ], + "angle": 0, + "content": "[9] Xueyang Fu, Qi Qi, Zheng-Jun Zha, Yurui Zhu, and Xing-hao Ding. Rain streak removal via dual graph convolutional network. In AAAI, pages 1352-1360, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.607, + 0.47, + 0.647 + ], + "angle": 0, + "content": "[10] Xueyang Fu, Jie Xiao, Yurui Zhu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Continual image deraining with hypergraph convolutional networks. IEEE TPAMI, 2023. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.649, + 0.47, + 0.69 + ], + "angle": 0, + "content": "[11] Zhihong Fu, Zehua Fu, Qingjie Liu, Wenrui Cai, and Yunhong Wang. Sparsett: Visual tracking with sparse transformers. arXiv preprint arXiv:2205.03776, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.691, + 0.47, + 0.732 + ], + "angle": 0, + "content": "[12] Shuhang Gu, Deyu Meng, Wangmeng Zuo, and Lei Zhang. Joint convolutional analysis and synthesis: sparse representation for single image layer separation. In ICCV, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.733, + 0.47, + 0.788 + ], + "angle": 0, + "content": "[13] Chun-Le Guo, Qixin Yan, Saeed Anwar, Runmin Cong, Wenqi Ren, and Chongyi Li. Image dehazing transformer with transmission-aware 3d position embedding. In CVPR, pages 5812-5820, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.789, + 0.47, + 0.816 + ], + "angle": 0, + "content": "[14] Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In CVPR, pages 7132-7141, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.818, + 0.47, + 0.858 + ], + "angle": 0, + "content": "[15] Xiaowei Hu, Chi-Wing Fu, Lei Zhu, and Pheng-Ann Heng. Depth-attentional features for single-image rain removal. In CVPR, pages 8022–8031, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.86, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[16] Huaibo Huang, Aijing Yu, and Ran He. Memory oriented transfer learning for semi-supervised image deraining. In CVPR, pages 7732-7741, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.115, + 0.47, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.134 + ], + "angle": 0, + "content": "[17] Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. Adaptive mixtures of local experts. Neural computation, 3(1):79-87, 1991. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.892, + 0.189 + ], + "angle": 0, + "content": "[18] Kui Jiang, Zhongyuan Wang, Chen Chen, Zheng Wang, Laizhong Cui, and Chia-Wen Lin. Magic elf: Image deraining meets association learning and transformer. In ACM MM, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.192, + 0.892, + 0.247 + ], + "angle": 0, + "content": "[19] Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, and Junjun Jiang. Multi-scale progressive fusion network for single image deraining. In CVPR, pages 8346-8355, 2020. 1, 2, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.249, + 0.892, + 0.289 + ], + "angle": 0, + "content": "[20] Li-Wei Kang, Chia-Wen Lin, and Yu-Hsiang Fu. Automatic single-image-based rain streaks removal via image decomposition. IEEE TIP, 21(4):1742-1755, 2011. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.291, + 0.892, + 0.332 + ], + "angle": 0, + "content": "[21] Sijin Kim, Namhyuk Ahn, and Kyung-Ah Sohn. Restoring spatially-heterogeneous distortions using mixture of experts network. In ACCV, 2020. 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.334, + 0.892, + 0.375 + ], + "angle": 0, + "content": "[22] Hunsang Lee, Hyesong Choi, Kwanghoon Sohn, and Dongbo Min. Knn local attention for image restoration. In CVPR, pages 2139-2149, 2022. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.377, + 0.892, + 0.431 + ], + "angle": 0, + "content": "[23] Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, and Hongbin Zha. Recurrent squeeze-and-excitation context aggregation net for single image deraining. In ECCV, pages 254-269, 2018. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.433, + 0.892, + 0.474 + ], + "angle": 0, + "content": "[24] Yu Li, Robby T Tan, Xiaojie Guo, Jiangbo Lu, and Michael S Brown. Rain streak removal using layer priors. In CVPR, pages 2736-2744, 2016. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.476, + 0.892, + 0.517 + ], + "angle": 0, + "content": "[25] Yawei Li, Kai Zhang, Jiezhang Cao, Radu Timofte, and Luc Van Gool. Localvit: Bringing locality to vision transformers. arXiv preprint arXiv:2104.05707, 2021. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.519, + 0.892, + 0.559 + ], + "angle": 0, + "content": "[26] Yuanchu Liang, Saeed Anwar, and Yang Liu. Drt: A lightweight single image deraining recursive transformer. In CVPRW, pages 589-598, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.561, + 0.892, + 0.602 + ], + "angle": 0, + "content": "[27] Yang Liu, Ziyu Yue, Jinshan Pan, and Zhixun Su. Unpaired learning for deep image deraining with rain direction regularizer. In ICCV, pages 4753-4761, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.604, + 0.892, + 0.658 + ], + "angle": 0, + "content": "[28] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.66, + 0.892, + 0.687 + ], + "angle": 0, + "content": "[29] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. In *ICLR*, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.689, + 0.892, + 0.73 + ], + "angle": 0, + "content": "[30] Yu Luo, Yong Xu, and Hui Ji. Removing rain from a single image via discriminative sparse coding. In ICCV, pages 3397-3405, 2015. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.732, + 0.892, + 0.772 + ], + "angle": 0, + "content": "[31] Yiqun Mei, Yuchen Fan, and Yuqian Zhou. Image superresolution with non-local sparse attention. In CVPR, pages 3517-3526, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.774, + 0.892, + 0.814 + ], + "angle": 0, + "content": "[32] Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik. No-reference image quality assessment in the spatial domain. IEEE TIP, 21(12):4695-4708, 2012. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.817, + 0.892, + 0.858 + ], + "angle": 0, + "content": "[33] Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a \"completely blind\" image quality analyzer. IEEE SPL, 20(3):209-212, 2012. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.86, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[34] Huynh-Thu Q. and Ghanbari M. Scope of validity of psnr in image/video quality assessment. *Electronics Letters*, 44(13):800-801, 2008. 5" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5904" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.134 + ], + "angle": 0, + "content": "[35] Qin Qin, Jingke Yan, Qin Wang, Xin Wang, Minyao Li, and Yuqing Wang. Etdnet: An efficient transformer deraining model. IEEE Access, 9:119881-119893, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.134, + 0.47, + 0.189 + ], + "angle": 0, + "content": "[36] Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng. Progressive image deraining networks: A better and simpler baseline. In CVPR, pages 3937-3946, 2019. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.19, + 0.469, + 0.244 + ], + "angle": 0, + "content": "[37] Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. Gated fusion network for single image dehazing. In CVPR, pages 3253-3261, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.246, + 0.469, + 0.302 + ], + "angle": 0, + "content": "[38] Aurko Roy, Mohammad Saffar, Ashish Vaswani, and David Grangier. Efficient content-based sparse attention with routing transformers. Transactions of the Association for Computational Linguistics, 9:53-68, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.302, + 0.469, + 0.357 + ], + "angle": 0, + "content": "[39] Masanori Suganuma, Xing Liu, and Takayuki Okatani. Attention-based adaptive selection of operations for image restoration in the presence of unknown combined distortions. In CVPR, pages 9039-9048, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.359, + 0.469, + 0.413 + ], + "angle": 0, + "content": "[40] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.414, + 0.469, + 0.467 + ], + "angle": 0, + "content": "[41] Cong Wang, Xiaoying Xing, Yutong Wu, Zhixun Su, and Junyang Chen. Dcsfn: Deep cross-scale fusion network for single image rain removal. In ACM MM, pages 1643-1651, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.469, + 0.469, + 0.523 + ], + "angle": 0, + "content": "[42] Haochen Wang, Jiayi Shen, Yongtuo Liu, Yan Gao, and Efstratos Gavves. Nformer: Robust person re-identification with neighbor transformer. In CVPR, pages 7297-7307, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.525, + 0.469, + 0.566 + ], + "angle": 0, + "content": "[43] Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng. A model-driven deep neural network for single image rain removal. In CVPR, pages 3103-3112, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.567, + 0.469, + 0.62 + ], + "angle": 0, + "content": "[44] Pichao Wang, Xue Wang, Fan Wang, Ming Lin, Shuning Chang, Wen Xie, Hao Li, and Rong Jin. Kvt: k-nn attention for boosting vision transformers. In ECCV, 2022. 2, 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.622, + 0.469, + 0.678 + ], + "angle": 0, + "content": "[45] Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson WH Lau. Spatial attentive single-image deraining with a high quality real rain dataset. In CVPR, pages 12270-12279, 2019. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.679, + 0.469, + 0.718 + ], + "angle": 0, + "content": "[46] Yinglong Wang, Chao Ma, and Bing Zeng. Multi-decoding deraining network and quasi-sparsity based training. In CVPR, pages 13375–13384, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.72, + 0.469, + 0.761 + ], + "angle": 0, + "content": "[47] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE TIP, 13(4):600-612, 2004. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.762, + 0.469, + 0.817 + ], + "angle": 0, + "content": "[48] Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li. Uformer: A general u-shaped transformer for image restoration. In CVPR, pages 17683-17693, 2022. 1, 2, 3, 4, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.818, + 0.469, + 0.859 + ], + "angle": 0, + "content": "[49] Wei Wei, Deyu Meng, Qian Zhao, Zongben Xu, and Ying Wu. Semi-supervised transfer learning for image rain removal. In CVPR, pages 3877-3886, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.86, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[50] Jie Xiao, Xueyang Fu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Image de-raining transformer. IEEE TPAMI, 2022. 1, 2, 3, 4, 5, 7" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.134 + ], + "angle": 0, + "content": "[51] Fuzhi Yang, Huan Yang, Jianlong Fu, Hongtao Lu, and Baining Guo. Learning texture transformer network for image super-resolution. In CVPR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.892, + 0.19 + ], + "angle": 0, + "content": "[52] Wenhan Yang, Robby T Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan. Deep joint rain detection and removal from a single image. In CVPR, pages 1357-1366, 2017. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.192, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[53] Wenhan Yang, Robby T Tan, Shiqi Wang, Yuming Fang, and Jiaying Liu. Single image deraining: From model-based to data-driven and beyond. IEEE TPAMI, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.235, + 0.892, + 0.277 + ], + "angle": 0, + "content": "[54] Rajeev Yasarla, Vishwanath A Sindagi, and Vishal M Patel. Syn2real transfer learning for image deraining using gaussian processes. In CVPR, pages 2726-2736, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.278, + 0.892, + 0.32 + ], + "angle": 0, + "content": "[55] Yuntong Ye, Yi Chang, Hanyu Zhou, and Luxin Yan. Closing the loop: Joint rain generation and removal via disentangled image translation. In CVPR, pages 2053-2062, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.321, + 0.892, + 0.375 + ], + "angle": 0, + "content": "[56] Qiaosi Yi, Juncheng Li, Qinyan Dai, Faming Fang, Guixu Zhang, and Tieyong Zeng. Structure-preserving deraining with residue channel prior guidance. In ICCV, pages 4238-4247, 2021. 1, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.377, + 0.892, + 0.418 + ], + "angle": 0, + "content": "[57] Kun Yuan, Shaopeng Guo, Ziwei Liu, Aojun Zhou, Fengwei Yu, and Wei Wu. Incorporating convolution designs into visual transformers. In ICCV, pages 579-588, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.42, + 0.892, + 0.488 + ], + "angle": 0, + "content": "[58] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, pages 5728-5739, 2022. 1, 2, 3, 4, 5, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.49, + 0.892, + 0.545 + ], + "angle": 0, + "content": "[59] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In CVPR, pages 14821-14831, 2021. 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.547, + 0.892, + 0.588 + ], + "angle": 0, + "content": "[60] He Zhang and Vishal M Patel. Convolutional sparse and low-rank coding-based rain streak removal. In WACV, pages 1259-1267, 2017. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.59, + 0.892, + 0.631 + ], + "angle": 0, + "content": "[61] He Zhang and Vishal M Patel. Density-aware single image de-raining using a multi-stream dense network. In CVPR, pages 695-704, 2018. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.633, + 0.892, + 0.674 + ], + "angle": 0, + "content": "[62] He Zhang, Vishwanath Sindagi, and Vishal M Patel. Image de-raining using a conditional generative adversarial network. IEEE TCSVT, 30(11):3943-3956, 2019. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.675, + 0.892, + 0.716 + ], + "angle": 0, + "content": "[63] Jiale Zhang, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, and Xin Yuan. Accurate image restoration with attention retractable transformer. *ICLR*, 2023. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.718, + 0.892, + 0.772 + ], + "angle": 0, + "content": "[64] Guangxiang Zhao, Junyang Lin, Zhiyuan Zhang, Xuancheng Ren, Qi Su, and Xu Sun. Explicit sparse transformer: Concentrated attention through explicit selection. ICLR, 2020. 2, 3, 4" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.772 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "5905" + } + ] +] \ No newline at end of file diff --git a/2023/Learning a Sparse Transformer Network for Effective Image Deraining/f486121e-86e3-4a45-bbf0-b8f552e4c49f_origin.pdf b/2023/Learning a Sparse Transformer Network for Effective Image Deraining/f486121e-86e3-4a45-bbf0-b8f552e4c49f_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..f56724a7bfa0a2087c759848bdb26225e77e8f4b --- /dev/null +++ b/2023/Learning a Sparse Transformer Network for Effective Image Deraining/f486121e-86e3-4a45-bbf0-b8f552e4c49f_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bd56c763cda489c01d697bf28fe9efece959ff71f67d88a49dcd193c7f68a346 +size 6027281 diff --git a/2023/Learning a Sparse Transformer Network for Effective Image Deraining/full.md b/2023/Learning a Sparse Transformer Network for Effective Image Deraining/full.md new file mode 100644 index 0000000000000000000000000000000000000000..c41d14ed27e80ebfbcd16376766d3a74fbf9a2e6 --- /dev/null +++ b/2023/Learning a Sparse Transformer Network for Effective Image Deraining/full.md @@ -0,0 +1,400 @@ +# Learning A Sparse Transformer Network for Effective Image Deraining + +Xiang Chen $^{1}$ Hao Li $^{1}$ Mingqiang Li $^{2}$ Jinshan Pan $^{1*}$ + +$^{1}$ School of Computer Science and Engineering, Nanjing University of Science and Technology $^{2}$ Information Science Academy, China Electronics Technology Group Corporation + +# Abstract + +Transformers-based methods have achieved significant performance in image deraining as they can model the non-local information which is vital for high-quality image reconstruction. In this paper, we find that most existing Transformers usually use all similarities of the tokens from the query-key pairs for the feature aggregation. However, if the tokens from the query are different from those of the key, the self-attention values estimated from these tokens also involve in feature aggregation, which accordingly interferes with the clear image restoration. To overcome this problem, we propose an effective DeRaining network, Sparse Transformer (DRSformer) that can adaptively keep the most useful self-attention values for feature aggregation so that the aggregated features better facilitate high-quality image reconstruction. Specifically, we develop a learnable top-k selection operator to adaptively retain the most crucial attention scores from the keys for each query for better feature aggregation. Simultaneously, as the naive feed-forward network in Transformers does not model the multi-scale information that is important for latent clear image restoration, we develop an effective mixed-scale feed-forward network to generate better features for image deraining. To learn an enriched set of hybrid features, which combines local context from CNN operators, we equip our model with mixture of experts feature compensator to present a cooperation refinement deraining scheme. Extensive experimental results on the commonly used benchmarks demonstrate that the proposed method achieves favorable performance against state-of-the-art approaches. The source code and trained models are available at https://github.com/cschenxiang/DRSformer. + +# 1. Introduction + +Single image deraining is a typical low-level vision problem emerging in the last decade. It aims to recover the clean + +![](images/81b2306b1e6ad80b002a771910517eecfa02fd5f91f604f86555e43e6508a567.jpg) + +![](images/9319dbb7697948071921f1ff095663b8ebccadd39b27295eb885780a532d03dd.jpg) +(a) Rainy Input + +![](images/12e158fc2450523a74a45fcb3506c95253b3a8a9be5858dfc3b8776c9606d60c.jpg) + +![](images/e71a367e39a78581910fcc36fb86d1e37b95cf6b39737d51c415247c7cafc52d.jpg) +(b) Uformer [48] + +![](images/287e423fb8bcac29ef5f66af9578115b3a4a766e68765ccf0c8d0612fe1238f0.jpg) + +![](images/ebc2333ba343693709de1ac80900c34f895dabd0305f4034b7792c968e89fc57.jpg) +(c) Restormer [58] + +![](images/282d2d6b058f81d52fe8a2913402cc5b4554173da3555d5986ba883e6fb4ada7.jpg) + +![](images/b3d11295a87a076c342b6ce31c84c7f03c288729ef5025f41883e06a45702bb4.jpg) +(d) IDT [50] + +![](images/3c83250c0dde52eca05c44120432223be024c7784c29a28875ac414c98d423bb.jpg) + +![](images/df8ab293b11227701d658f2bfe972e87b9db7f45035fe9abe1ad292d0b326200.jpg) +(e) Ours + +![](images/f8b017499f691f635d84c6daca01d5074b78738b00d6bd9d926b76c2cf418cdb.jpg) + +![](images/f0a733dcb3205b45e0ad52a3f08506fb002c6145c2722a7997aa5346b4a60ba5.jpg) +(f) Ground Truth +Figure 1. Image deraining results between our method and recent Transformer-based methods [48,50,58]. Our method can generate high-quality image with more accurate detail and texture recovery. + +image from the observed rainy one. As the clear image and rain streaks are unknown, it is an ill-posed inverse problem. To solve this problem, early approaches [20, 24, 60] usually impose various priors based on statistical properties of rain streaks and clear images. In fact, these handcrafted priors are not robust to complex and varying rainy scenarios, which limit the deraining performance. + +Recently, numerous learning-based methods [4, 19, 23, 36, 52, 53, 56] have resorted to diverse CNN architectures as a preferable choice compared to traditional algorithms. However, the intrinsic characteristics of convolutional operation, i.e., local receptive fields and independence of input content, hinder the model's capacity to eliminate long-range rain degradation perturbation. To alleviate such limitations, Transformers [2, 26, 35, 50] have been applied to image de- raining and have achieved decent performance as they can better model the non-local information for high-quality image reconstruction. Nevertheless, the image details, which are local features of images, are not modeled well by these approaches when restoring clear images as shown in Figure 1. One main reason is that the self-attention in Transformers does not model the local invariant properties that + +CNNs do well. Since rain streaks tend to confuse with background details in local regions, recent studies [5, 18, 57] try to mitigate such drawbacks by combining CNN operations and Transformers for boosting image deraining, where the Transformers based on the standard formulations. + +We note that the standard Transformers [40] usually use all attention relations based on the query-key pairs to aggregate features. As the tokens from the key are not always relevant to those from the query, using the self-attention values estimated from these tokens in the feature aggregation interferes with the following latent clear image restoration. The root cause behind this deficiency lies in that, the native dense calculation pattern of self-attention amplifies relatively smaller similarity weights, making feature interaction and aggregation process susceptible to implicit noises. This also naturally leads to corresponding redundant or irrelevant representations are still taken into consideration when modeling global feature dependencies [44, 64]. Thus, these findings motivate us to explore the most useful self-attention values so that we can make full use of the features for better image restoration. + +To this end, we develop an effective sparse Transformer network for image deraining, named as DRSformer. Specifically, the key component of the proposed framework is the sparse Transformer block (STB) which contains a top- $k$ sparse attention (TKSA) that keeps the most useful self-attention values for feature aggregation and a mixed-scale feed-forward network (MSFN) that explores the multi-scale features for better image deraining. First, we design the top- $k$ attention mechanism to replace the vanilla self-attention [40]. The TKSA keeps the largest $K$ similarity scores between the queries and the keys for the self-attention computing, thereby facilitating better feature aggregation. Furthermore, the developed MSFN further explores the multi-scale information to better improve the aggregated features. Finally, based on the observation that rain distribution reveals the degradation location and degree, we also introduce mixture of experts feature compensator (MEFC) to provide collaborative refinement for STB. With the above-mentioned designs, our proposed method offers three-fold advantages: (1) it can enjoy natural robustness in terms of less sensitivity to useless feature interference, (2) it can not only enrich the locality but also empower the capability of global feature exploitation, and (3) it can co-expllore data (embodied in MEFC) and content (embodied in STB) sparsity for achieving deraining performance gains. + +The main contributions are summarized as follows: + +- We propose a sparse Transformer architecture to help generate high-quality deraining results with more accurate detail and texture recovery. +- We develop a simple yet effective learnable top- $k$ selection operator to adaptively maintain the most useful self-attention values for better feature aggregation. + +- We design an effective feed-forward network based on mixed-scale fusion strategy to explore multi-scale representations for better facilitating image deraining. +- Extensive experimental results on various benchmarks demonstrate that our method achieves favorable performance against state-of-the-art (SOTA) approaches. + +# 2. Related Work + +Single image deraining. Since image deraining is an ill-posed problem, traditional methods [12, 20, 24, 30, 60] usually develop kinds of image priors to provide additional constraints. However, these handcrafted priors tend to rely on empirical observations and thus are not able to model the inherent properties of clear images. To overcome this problem, numerous CNN-based frameworks [53] have been developed to solve image deraining and achieved decent restoration performance. To better represent the rain distribution, several studies take rain characteristics such as rain direction [27], density [61], veiling effect [15] into account, and optimize the network structure via recursive computation [19, 23, 36] or transfer mechanism [16, 49, 54, 55]. Although these methods achieve better performance than the hand-crafted prior-based ones, they have difficulty capturing the long-range dependencies due to the intrinsic limitations of convolution. Different CNN-based deraining approaches, we utilize the Transformer as the network backbone to model non-local information for image deraining. + +Vision Transformers. Motivated by the great success of the Transformers [7] in natural language processing (NLP) [40] and high-level vision tasks [1, 28], Transformers have been applied to image restoration [2, 13, 48, 51, 58] and perform better than the previous CNN-based baselines as they are able to model non-local information. For the field of image rain removal, Jiang et al. [18] design a dynamic associated deraining network by incorporating self-attention in Transformer with a background recovery network. More recently, Xiao et al. [50] elaborately develop image deraining Transformer (IDT) with window-based and spatial-based dual Transformer to achieve excellent results. Note that, most existing methods rely on the dense dot-product self-attention as the heart of Transformers. However, one shortcoming of this computation manner is that redundant or irrelevant features with smaller weights may interfere with the attention map, which makes the output features contain potential noises. In this work, we propose sparse attention in Transformer to relieve the negligence of the most relevant information faced by vanilla self-attention. + +Sparse representation. With inspirations drawn from neural activity in biological brains, sparsity of hidden representation in deep neural networks as a tantalizing "free lunch" emerges for both vision and NLP tasks [44, 64]. Indeed, it is widely proven that sparse representation also plays a critical role in handling low-level vision problems, such as im + +![](images/8723a1773c75db6b092b47df82de7b6841b7f36984e27b0f3b907c5ea152ead0.jpg) + +![](images/f7d3b9b6bc7c821cd110409d35fa63f7be2beaf7b193751bf226bc7cfccbfef8.jpg) +Figure 2. The overall architecture of the proposed sparse Transformer network for image deraining (DRSformer), which mainly contains sparse Transformer block (STB) with top- $k$ sparse attention (TKSA) and mixed-scale feed-forward network (MSFN), and mixture of experts feature compensator (MEFC). LN refers to the layer normalization and DW-Conv refers to the depth-wise convolution. + +![](images/8d0bd17d78a0f4711ad5413c5e5b22f8131a59c830d59b2a282ec958b1b449e5.jpg) + +![](images/98e7dd37366b46e86ef3d41966669cd5b5759f02181e9e7b84fa275e6912f602.jpg) + +age deraining [46] and super-resolution [31]. In principle, sparse attention can be categorized into data-based (fixed) sparse attention and content-based sparse attention [6, 38]. For data-based sparse attention, several local attention operations are introduced into CNN backbone, which mainly considers attending only to local window size. Recent studies [11, 42] have investigated enforcing sparsity to Transformer backbone. More recently, Zhang et al. [63] design an attention retractable Transformer to allow tokens from sparse areas to interact features, which is data-based sparsity. Different from it, we implement a simple but effective approximation for self-attention based on top- $k$ selection to achieve sparse attention, which is content-based sparsity. + +Top-k selection. Zhao et al. [64] first propose an explicit selection method based on top- $k$ mechanism in NLP tasks. Driven by their success, $k$ -NN attention [42, 44] is further introduced for boosting vision Transformers. Unlike performing top- $k$ selection in the spatial dimension [44], we design an efficient top- $k$ useful channel selection operator. + +# 3. Proposed Method + +In this section, we first describe the overall pipeline and symmetrically hierarchical network architecture for image deraining. Afterward, we provide the details of the proposed sparse Transformer block (STB), as the fundamental building unit of our method, which mainly contains two key elements: top- $k$ sparse attention (TKSA) and mixed-scale feed-forward network (MSFN). Finally, we present the introduced mixture of experts feature compensator (MEFC). + +# 3.1. Overall pipeline + +The overall pipeline of our proposed DRSformer, shown in Figure 2, is based on a hierarchical encoder-decoder + +framework. Given a rainy image $I_{rain} \in \mathbb{R}^{H \times W \times 3}$ , where $H \times W$ represents the spatial resolution of the feature map, we perform overlapped image patch embedding with $3 \times 3$ convolution. In the network backbone, we stack $N_{i \in [1,2,3,4]}$ STBs to extract rich features for spatially-varying rain distribution. To excavate the multi-scale representation from rain degeneration, each level of encoder-decoder pipeline covers its own specific spatial resolution and channel dimension. For feature down-sampling and up-sampling, we apply pixel-unshuffle and pixel-shuffle operations. Similar to [48,50,58], we also add skip-connections to bridge across continuous intermediate features for stable training. In each STB, unlike the standard self-attention [7] in Transformer, we develop TKSA to achieve feature sparsity, aiming to enforce the feature aggregation process more effectively. In addition, a MSFN is introduced into STB to enrich multiscale local information and help image restoration. At the early and final stages of the model learning, we equip our model with $N_0$ MEFCs to provide complementary feature refinement, so that high-quality clear outputs can be finally reconstructed. With this hybrid formulation, we allow DRSformer to exploit both the adaptive content and the intrinsic property of rainy images, facilitating the separation of undesired rain streaks and latent clear background, and experiments demonstrate that the above design choices yield quality improvements (see Sec. 4.3) + +The final reconstructed result is obtained by: $I_{derain} = \mathcal{F}(I_{rain}) + I_{rain}$ , where $\mathcal{F}(\cdot)$ is the overall network and it is trained by minimizing the following loss function: + +$$ +\mathcal {L} = \left\| I _ {\text {d e r a i n}} - I _ {\text {g t}} \right\| _ {1}, \tag {1} +$$ + +where $I_{gt}$ denotes the ground-truth image, and $\| \cdot \| _1$ denotes the $L_{1}$ -norm. + +# 3.2. Sparse Transformer block + +As the standard Transformers [7, 40, 58] take all the tokens to compute self-attention globally, which is unfriendly for image restoration due to it may involve noisy interactions between the irrelevant features. To solve such limitations, we develop a sparse Transformer block (STB) as the feature extraction unit by taking the advantages of sparsity that emerged in neural networks [64]. Formally, given the input features at the $(l - 1)$ -th block $\mathbf{X}_{l - 1}$ , the encoding procedures of STB can be defined as: + +$$ +\mathbf {X} _ {l} ^ {\prime} = \mathbf {X} _ {l - 1} + \operatorname {T K S A} \left(\ln \left(\mathbf {X} _ {l - 1}\right)\right), \tag {2} +$$ + +$$ +\mathbf {X} _ {l} = \mathbf {X} _ {l} ^ {\prime} + \operatorname {M S F N} \left(\ln \left(\mathbf {X} _ {l} ^ {\prime}\right)\right), \tag {3} +$$ + +where LN denotes the layer normalization; $\mathbf{X}_l^{\prime}$ and $\mathbf{X}_l$ denote the outputs from the top- $k$ sparse attention (TKSA) and mixed-scale feed-forward network (MSFN), which are described below. + +Top-k sparse attention (TKSA). We revisit the standard self-attention in Transformer, which has become an empirical operation in most of the existing models. Given a query $Q$ , key $K$ and value $V$ with the dimension of $\mathbb{R}^{L\times d}$ , the output of dot-product attention is generally formulated as: + +$$ +\operatorname {A t t} (\mathbf {Q}, \mathbf {K}, \mathbf {V}) = \operatorname {s o f t m a x} \left(\frac {\mathbf {Q} \mathbf {K} ^ {\top}}{\lambda}\right) \mathbf {V}, \tag {4} +$$ + +where $\mathbf{Q}$ , $\mathbf{K}$ , and $\mathbf{V}$ denote the matrix forms of $Q$ , $K$ , and $V$ , respectively. $\lambda$ is an optional temperature factor defined by $\lambda = \sqrt{d}$ . Generally, multi-head attention is implemented to each of the $k$ new $Q$ , $K$ and $V$ , yielding $d = C / k$ channel dimensional outputs which are concatenated and then got the final result for all heads via the linear projection. Noted that, this vanilla self-attention paradigm is based on densely fully-connected, which requires computing the attention map for all query-key pairs. In our work, we develop TKSA to replace it, thus avoiding the involvement of irrelevant information during the feature interaction process. + +Specifically, we first encode channel-wise context by applying $1 \times 1$ convolutions followed by $3 \times 3$ depth-wise convolutions. Inspired by [58], we apply self-attention across channels rather than the spatial dimension to reduce the time and memory complexity. Next, we calculate similarities of pixel pairs between all the reshaped queries and keys, and mask out the unnecessary elements assigned with lower attention weights in the transposed attention matrix $\mathbf{M}$ of size $\mathbb{R}^{\hat{C} \times \hat{C}}$ . Unlike the dropout strategy of randomly abandoning the scores, an adaptive selection of the top- $k$ contributive scores is implemented upon $\mathbf{M}$ , aiming to preserve the most significant components and remove the useless ones [3]. Here, $k$ is an adjustable parameter to dynamically control the magnitude of sparsity, which is formally obtained by weighted average of some proper fractions, such as $\frac{2}{3}$ . Thus, only top- $k$ values within the range $[\Delta_1, \Delta_2]$ are normalized from each row of $\mathbf{M}$ for softmax computing. For + +other elements that are smaller than top- $k$ scores, we replace their probabilities with 0 at given indices using the scatter function. This dynamic selection makes the attention from dense to sparse, which is derived by: + +$$ +\operatorname {S p a r s e A t t} (\mathbf {Q}, \mathbf {K}, \mathbf {V}) = \operatorname {s o f t m a x} \left(\mathcal {T} _ {k} \left(\frac {\mathbf {Q} \mathbf {K} ^ {\top}}{\lambda}\right)\right) \mathbf {V}, \tag {5} +$$ + +where $\mathcal{T}_k(\cdot)$ is the learnable top- $k$ selection operator: + +$$ +\left[ \mathcal {T} _ {k} (\boldsymbol {S}) \right] _ {i j} = \left\{ \begin{array}{l l} S _ {i j} & S _ {i j} \geq t _ {i} \\ 0 & \text {o t h e r w i s e .} \end{array} \right. \tag {6} +$$ + +where $t_i$ is the $k$ -th largest value in the $j$ -th row of $\frac{\mathbf{Q}\mathbf{K}^{\top}}{\lambda}$ . Here, we calculate $\mathbf{Q}\mathbf{K}^{\top}$ in the same way as [58] to generate $\mathbf{M}$ of size $\mathbb{R}^{\hat{C}\times \hat{C}}$ . As we use the multi-head strategy, we finally concatenate all the outputs of multi-head attention, and then get the final result by the linear projection. + +Mixed-scale feed-forward network (MSFN). Previous studies [48, 50, 58] usually introduce single-scale depth-wise convolutions into the regular feed-forward network to imporve locality. However, those exploitations all ignore the correlations of multi-scale rain streaks. In fact, rich multi-scale representation has fully demonstrated its effectiveness [19, 41] in better removing rain. Here, we design a MSFN by inserting two multi-scale depth-wise convolution paths in the transmission process, see Figure 2. Given an input tensor $\mathbf{X}_{l-1} \in \mathbb{R}^{H \times W \times C}$ , after layer normalization, we first utilize $1 \times 1$ convolution to expand the channel dimension in the ratio of $r$ , then feed it into two parallel branches. During the feature transformation, the $3 \times 3$ and $5 \times 5$ depth-wise convolutions are employed to enhance the multi-scale local information extraction. In this way, the entire feature fusion procedure of the developed MSFN is formulated as: + +$$ +\hat {\mathbf {X}} _ {l} = f _ {1 \times 1} ^ {c} (\operatorname {L N} \left(\mathbf {X} _ {l - 1}\right)), +$$ + +$$ +\mathbf {X} _ {l} ^ {p _ {1}} = \sigma \left(f _ {3 \times 3} ^ {d w c} (\hat {\mathbf {X}} _ {l})\right), \mathbf {X} _ {l} ^ {s _ {1}} = \sigma \left(f _ {5 \times 5} ^ {d w c} (\hat {\mathbf {X}} _ {l})\right), +$$ + +$$ +\mathbf {X} _ {l} ^ {p _ {2}} = \sigma \left(f _ {3 \times 3} ^ {d w c} \left[ \mathbf {X} _ {l} ^ {p _ {1}}, \mathbf {X} _ {l} ^ {s _ {1}} \right]\right), \mathbf {X} _ {l} ^ {s _ {2}} = \sigma \left(f _ {5 \times 5} ^ {d w c} \left[ \mathbf {X} _ {l} ^ {s _ {1}}, \mathbf {X} _ {l} ^ {p _ {1}} \right]\right), +$$ + +$$ +\mathbf {X} _ {l} = f _ {1 \times 1} ^ {c} \left[ \mathbf {X} _ {l} ^ {p _ {2}}, \mathbf {X} _ {l} ^ {s _ {2}} \right] + \mathbf {X} _ {l - 1}, \tag {7} +$$ + +where $\sigma (\cdot)$ is a ReLU activation, $f_{1\times 1}^{c}$ represents $1\times 1$ convolution, $f_{3\times 3}^{dwc}$ and $f_{5\times 5}^{dwc}$ denote $3\times 3$ and $5\times 5$ depth-wise convolutions, and $[\cdot ]$ is the channel-wise concatenation. + +# 3.3. Mixture of experts feature compensator + +To fill in the comprehensive faculty of integrating sparsity in the DRSformer, we further introduce MEFC to perform a unified co-exploration towards joint data and content sparsity. Recalling the classical design of effective CNN models [39], we elaborately select multiple sparse CNN operations to form parallel layers, dubbed as experts, which involve an average pooling with receptive field of $3 \times 3$ , separable convolution layers with kernel sizes of $1 \times 1$ + +Table 1. Comparison of quantitative results on synthetic and real datasets. Bold and underline indicate the best and second-best results. + +
DatasetsRain200LRain200HDID-DataDDN-DataSPA-Data
MetricsPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIM
Prior-based methodsDSC [30]27.160.866314.730.381524.240.827927.310.837334.950.9416
GMM [24]28.660.865214.500.416425.810.834427.550.847934.300.9428
CNN-based methodsDDN [8]34.680.967126.050.805630.970.911630.000.904136.160.9457
RESCAN [23]36.090.969726.750.835333.380.941731.940.934538.110.9707
PReNet [36]37.800.981429.040.899133.170.948132.600.945940.160.9816
MSPFN [19]38.580.982729.360.903433.720.955032.990.933343.430.9843
RCDNet [43]39.170.988530.240.904834.080.953233.040.947243.360.9831
MPRNet [59]39.470.982530.670.911033.990.959033.100.934743.640.9844
DualGCN [9]40.730.988631.150.912534.370.962033.010.948944.180.9902
SPDNet [56]40.500.987531.280.920734.570.956033.150.945743.200.9871
Transformer-based methodsUformer [48]40.200.986030.800.910535.020.962133.950.954546.130.9913
Restormer [58]40.990.989032.000.932935.290.964134.200.957147.980.9921
IDT [50]40.740.988432.100.934434.890.962333.840.954947.350.9930
DRSformer41.230.989432.170.932635.350.964634.350.958848.540.9924
+ +$3 \times 3, 5 \times 5, 7 \times 7$ , and dilated convolution layers with kernel sizes of $3 \times 3, 5 \times 5, 7 \times 7$ . Different from the conventional mixture of experts [17, 37], our MEFC does not attach an external gating network. Instead, we make the self-attention [14,21] become a switcher of different experts to adaptively select the importance of diverse representations depending on the inputs. Given an input feature map $\mathbf{X}_{l-1} \in \mathbb{R}^{H \times W \times C}$ , we first apply the channel-wise average to generate $C$ -dimensional channel descriptor $\mathbf{z}_c \in \mathbb{R}^C$ : + +$$ +\mathbf {z} _ {c} = \frac {1}{H \times W} \sum_ {i = 1} ^ {H} \sum_ {j = 1} ^ {W} \mathbf {X} _ {l - 1} (i, j), \tag {8} +$$ + +where $\mathbf{X}_{l - 1}(i,j)$ is the $(y,x)$ position of the feature $\mathbf{X}_{l - 1}$ . Then, the coefficient vector of each expert is allocated corresponding to the learnable weight matrices $\mathbf{W}_1\in \mathbb{R}^{T\times C}$ and $\mathbf{W}_2\in \mathbb{R}^{O\times T}$ . Here, $T$ is the dimension of the weight matrices. To avoid altering the sizes of its inputs and outputs, we zero pad the input feature maps computed by each expert. Finally, the output of the $l$ -th MEFC is calculated by: + +$$ +\mathbf {T} _ {l} = \mathbf {W} _ {2} \sigma \left(\mathbf {W} _ {1} \mathbf {z} _ {c}\right), +$$ + +$$ +\mathbf {X} _ {l} = f _ {1 \times 1} ^ {c} \left[ \sum_ {i = 1} ^ {O} f _ {e x p} \left(\mathbf {X} _ {l}, \mathbf {T} _ {l}\right) \right] + \mathbf {X} _ {l - 1}, \tag {9} +$$ + +where $f_{exp}$ and $O$ represent the expert operations and the number of experts, respectively. $f_{1\times 1}^{c}$ represents $1\times 1$ convolution, $\sigma (\cdot)$ is a ReLU function, and $[\cdot ]$ is the channelwise concatenation. With this design, MEFC is now closely linked to the main STBs so that is able to adaptively remove the rainy effects of diverse appearances. + +# 4. Experiments and Analysis + +# 4.1. Experimental settings + +Datasets. We implement deraining experiments on multiple public benchmarks, including Rain200L/H [52], DID-Data [61] and DDN-Data [8]. Rain200L and Rain200H contain 1,800 synthetic rainy images for training and 200 ones for + +testing. DID-Data and DDN-Data consist of 12,000 and 12,600 synthetic images with different rain directions and density levels. There are 1,200 and 1,400 rainy images for testing. In addition, we also evaluate our method using a large-scale real-world dataset, i.e., SPA-Data [45], containing 638,492 image pairs for training and 1,000 testing ones. + +Comparison methods. We compare our DRSformer with two prior-based models (DSC [30] and GMM [24]), CNN-based methods (DDN [8], RESCAN [23], PReNet [36], MSPFN [19], RCDNet [43], MPRNet [59], DualGCN [9], and SPDNet [56]), and recent Transformer-based methods (Uformer [48], Restormer [58], and IDT [50]). For recent representative methods (DualGCN, SPDNet, Restormer and IDT), we retrain their models provided by the authors if no pretrained models are provided, otherwise we evaluate them with their online codes for fair comparisons. For other approaches, we refer to some reported results in [10, 50]. + +Evaluation metrics. We adopt PSNR [34] and SSIM [47] as the evaluation metrics for the above benchmarks. Following previous deraining methods [10, 19], we calculate PSNR and SSIM metrics in Y channel of YCbCr space. For the rainy images without ground truth images, we use the non-reference metrics including NIQE [33] and BRISQUE [32]. + +Training details. In our model, $\{N_0,N_1,N_2,N_3,N_4\}$ are set to $\{4,4,6,6,8\}$ , and the number of attention heads for four STBs of the same level is set to $\{1,2,4,8\}$ . The initial channel $C$ is 48 and the expanding ratio is set to 2. In terms of MEFC, we set $O = 8$ for the number of experts and $T = 32$ for the weight matrix. Note that we do not use MEFC for training Rain200L and SPA-Data, because their rain streaks are less complex and easier to learn. In terms of STB, the sparseness $[\Delta_1,\Delta_2]$ in TKSA is set to $[\frac{1}{2},\frac{4}{5} ]$ , and the channel expansion factor $r$ in MSFN is set to 2.66. During training, we use AdamW optimizer with batch size of 8 and patch size of 128 for total 300K iterations. The initial learning rate is fixed as $1\times 10^{-4}$ for 92K iterations, and then decreased to $1\times 10^{-6}$ for 208K iterations with the cosine annealing scheme [29]. For data augmentation, vertical and + +![](images/536fa7d411ea2ca30e1ccaf0927498b1229fe805910fccabb0c93205623dbbd0.jpg) +Figure 3. Visual quality comparison on the Rain200H dataset. Zooming in the figures offers a better view at the deraining capability. + +![](images/120d2f44fedd5e6fbe4351355f87b70d881f9949ba086eadc73a149bd788d0a0.jpg) +Figure 4. Visual quality comparison on the SPA-Data dataset. Zooming in the figures offers a better view at the deraining capability. + +horizontal flips are randomly applied. The entire framework is performed on the PyTorch with 4 NVIDIA GeForce RTX 3090 GPUs, which works in an end-to-end learning fashion without costly large-scale pretraining [2]. + +# 4.2. Comparisons with the state-of-the-arts + +Synthetic datasets. The quantitative evaluations on different benchmark datasets are reported in Table 1. As shown, we can note that our proposed method outperforms all the other derainers, especially on PSNR, e.g., DRSformer surpasses the concurrent approach IDT by $0.4\mathrm{dB}$ on average. Compared with previous CNN-based models, this progress can be much more obvious. The notable increasing scores on the DID-Data and DDN-Data benchmarks reveal that our method can properly handle diverse types of spatially-varying rain streaks. For convincing evidence, we show the visual quality comparison between samples generated by recent approaches in Figure 3. The pure CNN-based models, e.g., MPRNet and SPDNet, fail to restore clear images in heavy rainy scenarios. It can be seen that the results of all computing Transformer-based methods are flawed in terms of detail and texture recovery. Unfortunately, IDT even introduces considerable boundary artifacts. Thanks to the developed sparse attention with top- $k$ selection, our method can generate high-quality deraining results, which are more consistent with that of the ground truth. + +Real-world datasets. We further conduct experiments on the SPA-Data benchmark dataset, and corresponding results are provided in the last column of Table 1. As expected, our model continues to achieve the highest PSNR/SSIM + +value, exhibiting the superior of DRSformer in terms of deraining performance and generalization. The visual quality comparison can be observed in Figure 4. In contrast, our method significantly competes with others in removing most rain streaks while preserving truthful image structures. In order to further validate the effectiveness of DRSformer, we also randomly choose 20 real rainy images without ground truths from Internet-Data [45] to perform another evaluation. As displayed in Table 2, our net gets the lower NIQE and BRISQUE values, which means a high-quality output with clearer content and better perceptual quality against other comparative models on the real rainy scenarios. Through qualitative quality comparison in Figure 5, most deep models are sensitive to spatially-long rain streaks and leave some apparent rain effects. On the contrary, our net successfully removes most rain perturbation and owns a visual pleasant recovery effect, which implies that it can well generalize to unseen real-world data types. + +# 4.3. Ablation studies + +Effectiveness of Top- $k$ selection. To examine the effect of the top- $k$ selection in the TKSA, we present the deraining results of TKSA w/o top- $k$ in Figure 6. We can see that the PSNR values of the derained images by the method without using the top- $k$ selection are lower than those by the method using the top- $k$ selection. In addition, we also note that each element of the self-attention matrix $\operatorname{softmax}\left(\frac{\mathbf{QK}^\top}{\lambda}\right)$ in Eq. (4) is non-negative and the summation of the elements from each row of $\operatorname{softmax}\left(\frac{\mathbf{QK}^\top}{\lambda}\right)$ is 1. Thus, applying + +![](images/f357b853996f2c2d8499fa548cf819e79091e4d95c0fe340d97c07bd74c7a356.jpg) +Figure 5. Visual quality comparison on real-world rainy images. Zooming in the figures offers a better view at the deraining capability. + +Table 2. Comparison of quantitative results on real-world rainy images, and note that lower scores indicate better image quality. + +
MethodsRainy InputMPRNet [59]SPDNet [56]Uformer [48]Restormer [58]IDT [50]Ours
NIQE ↓ / BRISQUE ↓5.829 / 33.1294.740 / 32.0184.422 / 26.1734.833 / 28.1065.005 / 34.0364.238 / 25.5734.095 / 22.730
+ +![](images/4e28ed1af5f5dd2bfe5a233539ceb121f8cb416ec9a89a41a61988934af98e2e.jpg) +Figure 6. Ablation analysis for top- $k$ selection on the benchmarks. + +![](images/02564e6021a75b2b7ac9db6f3a1aaed2a1ed6a17a3c70802cbaae7f7bce85d61.jpg) +Figure 7. Ablation analysis for different number $k$ in the TKSA. + +the self-attention matrix $\mathrm{softmax}\left(\frac{\mathbf{Q}\mathbf{K}^{\top}}{\lambda}\right)$ to $\mathbf{V}$ will remove the high-frequency information of $\mathbf{V}$ , which lead to oversmoothed results. + +To understand the effect of such top- $k$ selection, we further use high-pass filtering (HPF) to visualize learned features in Figure 8. Compared to standard self-attention operation (w/o top- $k$ ), our strategy can better help reconstruct finer-detail feature and improve potential restoration quality. As the nearby pixels tend to be more similar than others, top- $k$ selection operator helps to reduce the irrelevant context from long-range pixel dependency. This step of selection allows the smaller similarity weights (from a part of long-range feature interactions) to be discarded in the procedure of self-attention calculation, thus facilitating more accurate representation for achieving high-quality output. + +![](images/1ada1dcb12e435ee63992c9444cd0836324784d3bb2c288f3baa6a1e6baa2a40.jpg) +(a) w/o Top-k +Figure 8. Visualization of feature maps. Our proposed top- $k$ selection can effectively leverage pixel-dependent properties of image structure and generate more precise high-frequency details. + +![](images/4c256991d37e73bb5585fad29c6b50e384fa8e523d4ab6c041dd3d4d21a99d03.jpg) +(a) w Top-k + +![](images/c7e4ce87a8bf985027500380b891ab5ad48c72b5baa2dde8bbde2531f793821d.jpg) +(b) w/o Top-k + +![](images/74e98c58f8fba24b8728ec91eea1cf697f0879664f89594a8c141fa35eb77caf.jpg) +(b) w Top-k + +Effect of the number of $k$ . The key parameter for our proposed TKSA is $k$ , and its influence is investigated in Figure 7. We note that the optimal choice of $k$ determines the boundary control of the sparsity rate. If $k$ is manually set to a single value, such as $\frac{1}{2}$ , we notice that its performance is sensitive to $k$ . To avoid an exhaustive search, we set a controllable interval range for $k$ to dynamically learn the most contribute score. When $k$ is too small, we find that the performance will undoubtedly decline sharply due to insufficient global information aggregation. The best result can achieve 32.18 dB when $[\Delta_1, \Delta_2]$ in the TKSA is assigned to $[\frac{1}{2}, \frac{4}{5}]$ . As $k$ continues to increase, the final deraining performance gradually decreases due to the introduction of irrelevant and useless features. + +Effectiveness of MSFN. To evaluate the effectiveness of the proposed MSFN, we compare it with three baselines: (1) conventional feed-forward network (FN) [7], (2) Dconv feed-forward network (DFN) [25], and (3) gated-Dconv feed-forward network (GDFN) [58]. The quantitative analysis results on Rain200H are listed in Table 3. Although GDFN introduces a gating mechanism in two same-scale depth-wise convolution streams to bring performance advantages, it still neglects the multi-scale knowledge for deraining. By adding local feature extraction and fusion at different scales, the MSFN can indeed better boost the performance, and achieve PSNR gain of 0.21 dB over GDFN. + +Effectiveness of MEFC. To evaluate the effectiveness of MEFC, we perform experiments based on different model variants in Table 4. Compared to the baseline model (a), MEFC provides additional performance benefits thanks to auxiliary data sparsity. In addition, we observe that MEFCs + +![](images/da8b77b7d856e53abec4db87595d7a5e4a738bf341648f602349ab97207b9041.jpg) + +![](images/fe173f9d7ba56970bd618cb089a40a8f1249b08a387616109e73f68256b0e1cd.jpg) +Rainy Input + +![](images/c9516d4a1e73ad66e229bd8be6c54265b72dc2a2feee320ce87e44c44872a9cf.jpg) + +![](images/843dd603bd0c643cb5f1354fa4b3e024d4bc070d792bd63985a189d99b4fe60a.jpg) +Model (a) + +![](images/ae6895d20a4d31ee6e7b5d9a916df23d0b314f777d2693344412e6fd46466e2e.jpg) + +![](images/909294225ade7097b701d091a868778ab5c55899af3b04ae6a07c0107704c143.jpg) +Model (b) + +![](images/a4de0c5a3116a8e924b7edea87a1261decd746b5bef6e361b04f2fffbf0c876a.jpg) + +![](images/10b02929a4dd8e8085cd847458c1487f1831c385c254bea201e4e3d80afd1b86.jpg) +Model (c) + +![](images/7fc539e328d0580f193cb8b677ae4b1cce10b67299a9fa2c007c49b311711c9d.jpg) + +![](images/13e18f84aac41635f450556868b27a826fd82a66c96f5018fea9bc4d2112795c.jpg) +Model (d) + +![](images/684e085c8258ffd91f62f9f2d3aad40b3f940702afda4bb3e402979f3743e74c.jpg) + +![](images/ef7d6d06c99917e75d83a3d47b29c08f24a5bbd5dcd1c2bd9ad35ccb74c54780.jpg) +Model (e) + +![](images/8d9181af13bc303170bd580c76c8c7208b91ddd3e9d846cd15fc85bafd029c27.jpg) + +![](images/c896fee33aa2820e4dd05e5df75af62f1f87d753ee810c3434e534f536307953.jpg) +Model (f) + +Table 3. Ablation study for different feed-forward networks. + +
ModelsFN [7]DFN [25]GDFN [58]MSFN
PSNR / SSIM31.84 / 0.927531.88 / 0.927931.97 / 0.928632.18 / 0.9330
+ +Table 4. Ablation study for different variants of our DRSformer. MEFC-1 and MEFC-2 denote MEFC in early and final stages. + +
ModelsMEFC-1STBsMEFC-2ExpertsPSNR / SSIM
(a)✓032.03 / 0.9308
(b)✓✓832.01 / 0.9311
(c)✓✓832.07 / 0.9328
(d)✓✓✓132.06 / 0.9316
(e)✓✓✓432.14 / 0.9325
(f)✓✓✓832.18 / 0.9330
+ +at different locations of the network pipeline have specific impacts on the restoration performance. Indeed, we also analyze the effect of the different numbers of experts in each MEFC. When using single expert model (d), the performance is dramatically degraded compared with our multi-expert model (f). Unlike setting all experts to the same structure [21], our multi-expert formulation is more diverse, which brings its gains to the performance due to different receptive fields and disparate CNN operations. Through the zoomed boxes in Figure 9, the recovered results of the model with all the above components tend to be clearer since it enables more diverse features to be fully used during the restoration process. All in all, our model (f) performs better than the other possible configurations, which indicates that each design strategy that we consider has its own contribution to the final performance of DRSformer. + +# 4.4. Closely-related methods + +We note that the recent method [22] proposes a $k$ -NN image Transformer (KiT) to solve image restoration by aggregating $k$ similar patches with the pair-wise local attention. Compared with KiT that employs complex locality sensitive hashing that cannot ensure sufficient global interaction, our simple but effective top- $k$ selection mechanism not only enjoys the locality but also empowers the ability of global relation mining. As the code of KiT is not available, we refer to the results of their paper. Figure 10 shows qualitative comparisons trained on the Rain800 [62]. We can see that KiT tends to blur the contents and cause color distortion. In contrast, our method leads to better deraining results. + +In addition, we also note that [44] recently designs the $k$ -NN attention to enhance the representation ability of vision Transformers by selecting the top- $k$ similar tokens. Different from KVT [44], which implements top- $k$ selection + +![](images/e6e7ff566ac4ad802c8cca6ed2bbc22fcb6c27034cf56baf4e13b8852ac0ad29.jpg) +(a) Rainy Input +Figure 10. Comparison results with closely-related methods. + +![](images/df669920b55726d47d1fdec2001f70ae6dc02a6a9fbeff276d05b6130cf7159b.jpg) +(b) KiT [22] + +![](images/fc372621f5b08d1248ee83766e35bc202aaaea10d5d1bb91e80b73a821971a16.jpg) +(c) KVT [44] + +![](images/edd59521d86ebe0f2e40f7395a0bd31a86a0233a291d1b479b110588f0763cb6.jpg) +Figure 9. Ablation qualitative comparison for different variants of DRSformer. The models (a-f) are consistent with the settings in Table 4. +(d) Ours + +in the spatial dimension, our operator is more efficient in computing sparse attention across channels. Furthermore, the sparsity level of $k$ in our proposed TKSA is dynamically learnable, rather than the fixed setting in [44]. Here, we adopt $k$ -NN attention in KVT to replace our TKSA for comparison. To ensure the fair comparison, the same training settings are kept for model testing. As shown in Figure 10 (c) and (d), our method can generate a clearer image. + +# 5. Concluding Remarks + +We have presented an effective sparse Transformer network, called DRSformer, to solve image deraining. Based on the observation that vanilla self-attention in Transformer may suffer from the global interaction of irrelevant information, we develop the top- $k$ sparse attention to keep the most useful self-attention values for better feature aggregation. To facilitate the aggregated features for removing rain, we develop a mixed-scale feed-forward network to better explore multi-scale representations. Furthermore, the mixture of experts feature compensator is introduced to the model to provide collaborative refinement for the sparse Transformer backbone, so that the fine details of the reconstructed image is preserved. Experimental results show that our DRSformer performs favorably against state-of-the-art methods. + +Limitations. Our proposed method aims to further boost image deraining performance, but there are limitations in the model efficiency. Specifically, our model requires 33.7 Million parameters and costs 242.9G FLOPs on one image with size of $256 \times 256$ . We will apply the pruning or distillation scheme in our model to maintain the original deraining performance while achieving credible model compression. + +Acknowledgements. This work has been partly supported by the National Key R&D Program of China (No. 2018AAA0102001), the National Natural Science Foundation of China (Nos. U22B2049, U19B2040, 61922043, 61872421, 62272230), and the Fundamental Research Funds for the Central Universities (No. 30920041109). + +# References + +[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In ECCV, pages 213-229, 2020. 2 +[2] Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao. Pre-trained image processing transformer. In CVPR, pages 12299-12310, 2021. 1, 2, 6 +[3] Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, and Zhangyang Wang. Chasing sparsity in vision transformers: An end-to-end exploration. Advances in Neural Information Processing Systems, 34:19974-19988, 2021. 4 +[4] Xiang Chen, Jinshan Pan, Kui Jiang, Yufeng Li, Yufeng Huang, Caihua Kong, Longgang Dai, and Zhentao Fan. Unpaired deep image deraining using dual contrastive learning. In CVPR, pages 2017-2026, 2022. 1 +[5] Xiang Chen, Jinshan Pan, Jiyang Lu, Zhentao Fan, and Hao Li. Hybrid cnn-transformer feature fusion for single image deraining. In AAAI, 2023. 2 +[6] Gonçalo M Correia, Vlad Niculae, and André FT Martins. Adaptively sparse transformers. arXiv preprint arXiv:1909.00015, 2019.3 +[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 2, 3, 4, 7, 8 +[8] Xueyang Fu, Jiabin Huang, Delu Zeng, Yue Huang, Xinghao Ding, and John Paisley. Removing rain from single images via a deep detail network. In CVPR, pages 3855-3863, 2017. 5 +[9] Xueyang Fu, Qi Qi, Zheng-Jun Zha, Yurui Zhu, and Xing-hao Ding. Rain streak removal via dual graph convolutional network. In AAAI, pages 1352-1360, 2021. 5 +[10] Xueyang Fu, Jie Xiao, Yurui Zhu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Continual image deraining with hypergraph convolutional networks. IEEE TPAMI, 2023. 5 +[11] Zhihong Fu, Zehua Fu, Qingjie Liu, Wenrui Cai, and Yunhong Wang. Sparsett: Visual tracking with sparse transformers. arXiv preprint arXiv:2205.03776, 2022. 3 +[12] Shuhang Gu, Deyu Meng, Wangmeng Zuo, and Lei Zhang. Joint convolutional analysis and synthesis: sparse representation for single image layer separation. In ICCV, 2017. 2 +[13] Chun-Le Guo, Qixin Yan, Saeed Anwar, Runmin Cong, Wenqi Ren, and Chongyi Li. Image dehazing transformer with transmission-aware 3d position embedding. In CVPR, pages 5812-5820, 2022. 2 +[14] Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In CVPR, pages 7132-7141, 2018. 5 +[15] Xiaowei Hu, Chi-Wing Fu, Lei Zhu, and Pheng-Ann Heng. Depth-attentional features for single-image rain removal. In CVPR, pages 8022–8031, 2019. 2 +[16] Huaibo Huang, Aijing Yu, and Ran He. Memory oriented transfer learning for semi-supervised image deraining. In CVPR, pages 7732-7741, 2021. 2 + +[17] Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. Adaptive mixtures of local experts. Neural computation, 3(1):79-87, 1991. 5 +[18] Kui Jiang, Zhongyuan Wang, Chen Chen, Zheng Wang, Laizhong Cui, and Chia-Wen Lin. Magic elf: Image deraining meets association learning and transformer. In ACM MM, 2022. 2 +[19] Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, and Junjun Jiang. Multi-scale progressive fusion network for single image deraining. In CVPR, pages 8346-8355, 2020. 1, 2, 4, 5 +[20] Li-Wei Kang, Chia-Wen Lin, and Yu-Hsiang Fu. Automatic single-image-based rain streaks removal via image decomposition. IEEE TIP, 21(4):1742-1755, 2011. 1, 2 +[21] Sijin Kim, Namhyuk Ahn, and Kyung-Ah Sohn. Restoring spatially-heterogeneous distortions using mixture of experts network. In ACCV, 2020. 5, 8 +[22] Hunsang Lee, Hyesong Choi, Kwanghoon Sohn, and Dongbo Min. Knn local attention for image restoration. In CVPR, pages 2139-2149, 2022. 8 +[23] Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, and Hongbin Zha. Recurrent squeeze-and-excitation context aggregation net for single image deraining. In ECCV, pages 254-269, 2018. 1, 2, 5 +[24] Yu Li, Robby T Tan, Xiaojie Guo, Jiangbo Lu, and Michael S Brown. Rain streak removal using layer priors. In CVPR, pages 2736-2744, 2016. 1, 2, 5 +[25] Yawei Li, Kai Zhang, Jiezhang Cao, Radu Timofte, and Luc Van Gool. Localvit: Bringing locality to vision transformers. arXiv preprint arXiv:2104.05707, 2021. 7, 8 +[26] Yuanchu Liang, Saeed Anwar, and Yang Liu. Drt: A lightweight single image deraining recursive transformer. In CVPRW, pages 589-598, 2022. 1 +[27] Yang Liu, Ziyu Yue, Jinshan Pan, and Zhixun Su. Unpaired learning for deep image deraining with rain direction regularizer. In ICCV, pages 4753-4761, 2021. 2 +[28] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021. 2 +[29] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. In *ICLR*, 2016. 5 +[30] Yu Luo, Yong Xu, and Hui Ji. Removing rain from a single image via discriminative sparse coding. In ICCV, pages 3397-3405, 2015. 2, 5 +[31] Yiqun Mei, Yuchen Fan, and Yuqian Zhou. Image superresolution with non-local sparse attention. In CVPR, pages 3517-3526, 2021. 3 +[32] Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik. No-reference image quality assessment in the spatial domain. IEEE TIP, 21(12):4695-4708, 2012. 5 +[33] Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a "completely blind" image quality analyzer. IEEE SPL, 20(3):209-212, 2012. 5 +[34] Huynh-Thu Q. and Ghanbari M. Scope of validity of psnr in image/video quality assessment. *Electronics Letters*, 44(13):800-801, 2008. 5 + +[35] Qin Qin, Jingke Yan, Qin Wang, Xin Wang, Minyao Li, and Yuqing Wang. Etdnet: An efficient transformer deraining model. IEEE Access, 9:119881-119893, 2021. 1 +[36] Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng. Progressive image deraining networks: A better and simpler baseline. In CVPR, pages 3937-3946, 2019. 1, 2, 5 +[37] Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. Gated fusion network for single image dehazing. In CVPR, pages 3253-3261, 2018. 5 +[38] Aurko Roy, Mohammad Saffar, Ashish Vaswani, and David Grangier. Efficient content-based sparse attention with routing transformers. Transactions of the Association for Computational Linguistics, 9:53-68, 2021. 3 +[39] Masanori Suganuma, Xing Liu, and Takayuki Okatani. Attention-based adaptive selection of operations for image restoration in the presence of unknown combined distortions. In CVPR, pages 9039-9048, 2019. 4 +[40] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 2, 4 +[41] Cong Wang, Xiaoying Xing, Yutong Wu, Zhixun Su, and Junyang Chen. Dcsfn: Deep cross-scale fusion network for single image rain removal. In ACM MM, pages 1643-1651, 2020. 4 +[42] Haochen Wang, Jiayi Shen, Yongtuo Liu, Yan Gao, and Efstratos Gavves. Nformer: Robust person re-identification with neighbor transformer. In CVPR, pages 7297-7307, 2022. 3 +[43] Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng. A model-driven deep neural network for single image rain removal. In CVPR, pages 3103-3112, 2020. 5 +[44] Pichao Wang, Xue Wang, Fan Wang, Ming Lin, Shuning Chang, Wen Xie, Hao Li, and Rong Jin. Kvt: k-nn attention for boosting vision transformers. In ECCV, 2022. 2, 3, 8 +[45] Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson WH Lau. Spatial attentive single-image deraining with a high quality real rain dataset. In CVPR, pages 12270-12279, 2019. 5, 6 +[46] Yinglong Wang, Chao Ma, and Bing Zeng. Multi-decoding deraining network and quasi-sparsity based training. In CVPR, pages 13375–13384, 2021. 3 +[47] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE TIP, 13(4):600-612, 2004. 5 +[48] Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li. Uformer: A general u-shaped transformer for image restoration. In CVPR, pages 17683-17693, 2022. 1, 2, 3, 4, 5, 7 +[49] Wei Wei, Deyu Meng, Qian Zhao, Zongben Xu, and Ying Wu. Semi-supervised transfer learning for image rain removal. In CVPR, pages 3877-3886, 2019. 2 +[50] Jie Xiao, Xueyang Fu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Image de-raining transformer. IEEE TPAMI, 2022. 1, 2, 3, 4, 5, 7 + +[51] Fuzhi Yang, Huan Yang, Jianlong Fu, Hongtao Lu, and Baining Guo. Learning texture transformer network for image super-resolution. In CVPR, 2020. 2 +[52] Wenhan Yang, Robby T Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan. Deep joint rain detection and removal from a single image. In CVPR, pages 1357-1366, 2017. 1, 5 +[53] Wenhan Yang, Robby T Tan, Shiqi Wang, Yuming Fang, and Jiaying Liu. Single image deraining: From model-based to data-driven and beyond. IEEE TPAMI, 2020. 1, 2 +[54] Rajeev Yasarla, Vishwanath A Sindagi, and Vishal M Patel. Syn2real transfer learning for image deraining using gaussian processes. In CVPR, pages 2726-2736, 2020. 2 +[55] Yuntong Ye, Yi Chang, Hanyu Zhou, and Luxin Yan. Closing the loop: Joint rain generation and removal via disentangled image translation. In CVPR, pages 2053-2062, 2021. 2 +[56] Qiaosi Yi, Juncheng Li, Qinyan Dai, Faming Fang, Guixu Zhang, and Tieyong Zeng. Structure-preserving deraining with residue channel prior guidance. In ICCV, pages 4238-4247, 2021. 1, 5, 7 +[57] Kun Yuan, Shaopeng Guo, Ziwei Liu, Aojun Zhou, Fengwei Yu, and Wei Wu. Incorporating convolution designs into visual transformers. In ICCV, pages 579-588, 2021. 2 +[58] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, pages 5728-5739, 2022. 1, 2, 3, 4, 5, 7, 8 +[59] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In CVPR, pages 14821-14831, 2021. 5, 7 +[60] He Zhang and Vishal M Patel. Convolutional sparse and low-rank coding-based rain streak removal. In WACV, pages 1259-1267, 2017. 1, 2 +[61] He Zhang and Vishal M Patel. Density-aware single image de-raining using a multi-stream dense network. In CVPR, pages 695-704, 2018. 2, 5 +[62] He Zhang, Vishwanath Sindagi, and Vishal M Patel. Image de-raining using a conditional generative adversarial network. IEEE TCSVT, 30(11):3943-3956, 2019. 8 +[63] Jiale Zhang, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, and Xin Yuan. Accurate image restoration with attention retractable transformer. *ICLR*, 2023. 3 +[64] Guangxiang Zhao, Junyang Lin, Zhiyuan Zhang, Xuancheng Ren, Qi Su, and Xu Sun. Explicit sparse transformer: Concentrated attention through explicit selection. 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In this paper, we find that most existing Transformers usually use all similarities of the tokens from the query-key pairs for the feature aggregation. However, if the tokens from the query are different from those of the key, the self-attention values estimated from these tokens also involve in feature aggregation, which accordingly interferes with the clear image restoration. To overcome this problem, we propose an effective DeRaining network, Sparse Transformer (DRSformer) that can adaptively keep the most useful self-attention values for feature aggregation so that the aggregated features better facilitate high-quality image reconstruction. Specifically, we develop a learnable top-k selection operator to adaptively retain the most crucial attention scores from the keys for each query for better feature aggregation. 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Image deraining results between our method and recent Transformer-based methods [48,50,58]. Our method can generate high-quality image with more accurate detail and texture recovery." + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_caption" + } + ], + "index": 25 + }, + { + "bbox": [ + 304, + 450, + 545, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 450, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 304, + 450, + 545, + 533 + ], + "type": "text", + "content": "image from the observed rainy one. As the clear image and rain streaks are unknown, it is an ill-posed inverse problem. To solve this problem, early approaches [20, 24, 60] usually impose various priors based on statistical properties of rain streaks and clear images. In fact, these handcrafted priors are not robust to complex and varying rainy scenarios, which limit the deraining performance." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "content": "Recently, numerous learning-based methods [4, 19, 23, 36, 52, 53, 56] have resorted to diverse CNN architectures as a preferable choice compared to traditional algorithms. However, the intrinsic characteristics of convolutional operation, i.e., local receptive fields and independence of input content, hinder the model's capacity to eliminate long-range rain degradation perturbation. To alleviate such limitations, Transformers [2, 26, 35, 50] have been applied to image de- raining and have achieved decent performance as they can better model the non-local information for high-quality image reconstruction. Nevertheless, the image details, which are local features of images, are not modeled well by these approaches when restoring clear images as shown in Figure 1. One main reason is that the self-attention in Transformers does not model the local invariant properties that" + } + ] + } + ], + "index": 29 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 35 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "type": "text", + "content": "*Corresponding author." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5896" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 131 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 131 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 131 + ], + "type": "text", + "content": "CNNs do well. Since rain streaks tend to confuse with background details in local regions, recent studies [5, 18, 57] try to mitigate such drawbacks by combining CNN operations and Transformers for boosting image deraining, where the Transformers based on the standard formulations." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 133, + 287, + 326 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 133, + 287, + 326 + ], + "spans": [ + { + "bbox": [ + 46, + 133, + 287, + 326 + ], + "type": "text", + "content": "We note that the standard Transformers [40] usually use all attention relations based on the query-key pairs to aggregate features. As the tokens from the key are not always relevant to those from the query, using the self-attention values estimated from these tokens in the feature aggregation interferes with the following latent clear image restoration. The root cause behind this deficiency lies in that, the native dense calculation pattern of self-attention amplifies relatively smaller similarity weights, making feature interaction and aggregation process susceptible to implicit noises. This also naturally leads to corresponding redundant or irrelevant representations are still taken into consideration when modeling global feature dependencies [44, 64]. Thus, these findings motivate us to explore the most useful self-attention values so that we can make full use of the features for better image restoration." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 327, + 288, + 625 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 327, + 288, + 625 + ], + "spans": [ + { + "bbox": [ + 46, + 327, + 288, + 625 + ], + "type": "text", + "content": "To this end, we develop an effective sparse Transformer network for image deraining, named as DRSformer. Specifically, the key component of the proposed framework is the sparse Transformer block (STB) which contains a top-" + }, + { + "bbox": [ + 46, + 327, + 288, + 625 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 327, + 288, + 625 + ], + "type": "text", + "content": " sparse attention (TKSA) that keeps the most useful self-attention values for feature aggregation and a mixed-scale feed-forward network (MSFN) that explores the multi-scale features for better image deraining. First, we design the top-" + }, + { + "bbox": [ + 46, + 327, + 288, + 625 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 327, + 288, + 625 + ], + "type": "text", + "content": " attention mechanism to replace the vanilla self-attention [40]. The TKSA keeps the largest " + }, + { + "bbox": [ + 46, + 327, + 288, + 625 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 327, + 288, + 625 + ], + "type": "text", + "content": " similarity scores between the queries and the keys for the self-attention computing, thereby facilitating better feature aggregation. Furthermore, the developed MSFN further explores the multi-scale information to better improve the aggregated features. Finally, based on the observation that rain distribution reveals the degradation location and degree, we also introduce mixture of experts feature compensator (MEFC) to provide collaborative refinement for STB. With the above-mentioned designs, our proposed method offers three-fold advantages: (1) it can enjoy natural robustness in terms of less sensitivity to useless feature interference, (2) it can not only enrich the locality but also empower the capability of global feature exploitation, and (3) it can co-expllore data (embodied in MEFC) and content (embodied in STB) sparsity for achieving deraining performance gains." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 59, + 628, + 266, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 628, + 266, + 639 + ], + "spans": [ + { + "bbox": [ + 59, + 628, + 266, + 639 + ], + "type": "text", + "content": "The main contributions are summarized as follows:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 58, + 642, + 286, + 713 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 58, + 642, + 286, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 642, + 286, + 676 + ], + "spans": [ + { + "bbox": [ + 58, + 642, + 286, + 676 + ], + "type": "text", + "content": "- We propose a sparse Transformer architecture to help generate high-quality deraining results with more accurate detail and texture recovery." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 58, + 677, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 677, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 58, + 677, + 286, + 713 + ], + "type": "text", + "content": "- We develop a simple yet effective learnable top-" + }, + { + "bbox": [ + 58, + 677, + 286, + 713 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 58, + 677, + 286, + 713 + ], + "type": "text", + "content": " selection operator to adaptively maintain the most useful self-attention values for better feature aggregation." + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 317, + 72, + 544, + 144 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 317, + 72, + 544, + 107 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 72, + 544, + 107 + ], + "spans": [ + { + "bbox": [ + 317, + 72, + 544, + 107 + ], + "type": "text", + "content": "- We design an effective feed-forward network based on mixed-scale fusion strategy to explore multi-scale representations for better facilitating image deraining." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 317, + 108, + 544, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 108, + 544, + 144 + ], + "spans": [ + { + "bbox": [ + 317, + 108, + 544, + 144 + ], + "type": "text", + "content": "- Extensive experimental results on various benchmarks demonstrate that our method achieves favorable performance against state-of-the-art (SOTA) approaches." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 154, + 392, + 166 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 154, + 392, + 166 + ], + "spans": [ + { + "bbox": [ + 306, + 154, + 392, + 166 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 171, + 545, + 398 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 171, + 545, + 398 + ], + "spans": [ + { + "bbox": [ + 304, + 171, + 545, + 398 + ], + "type": "text", + "content": "Single image deraining. Since image deraining is an ill-posed problem, traditional methods [12, 20, 24, 30, 60] usually develop kinds of image priors to provide additional constraints. However, these handcrafted priors tend to rely on empirical observations and thus are not able to model the inherent properties of clear images. To overcome this problem, numerous CNN-based frameworks [53] have been developed to solve image deraining and achieved decent restoration performance. To better represent the rain distribution, several studies take rain characteristics such as rain direction [27], density [61], veiling effect [15] into account, and optimize the network structure via recursive computation [19, 23, 36] or transfer mechanism [16, 49, 54, 55]. Although these methods achieve better performance than the hand-crafted prior-based ones, they have difficulty capturing the long-range dependencies due to the intrinsic limitations of convolution. Different CNN-based deraining approaches, we utilize the Transformer as the network backbone to model non-local information for image deraining." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 400, + 546, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 400, + 546, + 639 + ], + "spans": [ + { + "bbox": [ + 304, + 400, + 546, + 639 + ], + "type": "text", + "content": "Vision Transformers. Motivated by the great success of the Transformers [7] in natural language processing (NLP) [40] and high-level vision tasks [1, 28], Transformers have been applied to image restoration [2, 13, 48, 51, 58] and perform better than the previous CNN-based baselines as they are able to model non-local information. For the field of image rain removal, Jiang et al. [18] design a dynamic associated deraining network by incorporating self-attention in Transformer with a background recovery network. More recently, Xiao et al. [50] elaborately develop image deraining Transformer (IDT) with window-based and spatial-based dual Transformer to achieve excellent results. Note that, most existing methods rely on the dense dot-product self-attention as the heart of Transformers. However, one shortcoming of this computation manner is that redundant or irrelevant features with smaller weights may interfere with the attention map, which makes the output features contain potential noises. In this work, we propose sparse attention in Transformer to relieve the negligence of the most relevant information faced by vanilla self-attention." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "type": "text", + "content": "Sparse representation. With inspirations drawn from neural activity in biological brains, sparsity of hidden representation in deep neural networks as a tantalizing \"free lunch\" emerges for both vision and NLP tasks [44, 64]. Indeed, it is widely proven that sparse representation also plays a critical role in handling low-level vision problems, such as im" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5897" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 68, + 548, + 167 + ], + "blocks": [ + { + "bbox": [ + 47, + 68, + 548, + 167 + ], + "lines": [ + { + "bbox": [ + 47, + 68, + 548, + 167 + ], + "spans": [ + { + "bbox": [ + 47, + 68, + 548, + 167 + ], + "type": "image", + "image_path": "8723a1773c75db6b092b47df82de7b6841b7f36984e27b0f3b907c5ea152ead0.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 62, + 169, + 190, + 264 + ], + "blocks": [ + { + "bbox": [ + 62, + 169, + 190, + 264 + ], + "lines": [ + { + "bbox": [ + 62, + 169, + 190, + 264 + ], + "spans": [ + { + "bbox": [ + 62, + 169, + 190, + 264 + ], + "type": "image", + "image_path": "f7d3b9b6bc7c821cd110409d35fa63f7be2beaf7b193751bf226bc7cfccbfef8.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 270, + 547, + 304 + ], + "lines": [ + { + "bbox": [ + 45, + 270, + 547, + 304 + ], + "spans": [ + { + "bbox": [ + 45, + 270, + 547, + 304 + ], + "type": "text", + "content": "Figure 2. The overall architecture of the proposed sparse Transformer network for image deraining (DRSformer), which mainly contains sparse Transformer block (STB) with top- " + }, + { + "bbox": [ + 45, + 270, + 547, + 304 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 45, + 270, + 547, + 304 + ], + "type": "text", + "content": " sparse attention (TKSA) and mixed-scale feed-forward network (MSFN), and mixture of experts feature compensator (MEFC). LN refers to the layer normalization and DW-Conv refers to the depth-wise convolution." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 195, + 169, + 383, + 264 + ], + "blocks": [ + { + "bbox": [ + 195, + 169, + 383, + 264 + ], + "lines": [ + { + "bbox": [ + 195, + 169, + 383, + 264 + ], + "spans": [ + { + "bbox": [ + 195, + 169, + 383, + 264 + ], + "type": "image", + "image_path": "8d0bd17d78a0f4711ad5413c5e5b22f8131a59c830d59b2a282ec958b1b449e5.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 386, + 169, + 534, + 264 + ], + "blocks": [ + { + "bbox": [ + 386, + 169, + 534, + 264 + ], + "lines": [ + { + "bbox": [ + 386, + 169, + 534, + 264 + ], + "spans": [ + { + "bbox": [ + 386, + 169, + 534, + 264 + ], + "type": "image", + "image_path": "98e7dd37366b46e86ef3d41966669cd5b5759f02181e9e7b84fa275e6912f602.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 310, + 289, + 467 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 310, + 289, + 467 + ], + "spans": [ + { + "bbox": [ + 46, + 310, + 289, + 467 + ], + "type": "text", + "content": "age deraining [46] and super-resolution [31]. In principle, sparse attention can be categorized into data-based (fixed) sparse attention and content-based sparse attention [6, 38]. For data-based sparse attention, several local attention operations are introduced into CNN backbone, which mainly considers attending only to local window size. Recent studies [11, 42] have investigated enforcing sparsity to Transformer backbone. More recently, Zhang et al. [63] design an attention retractable Transformer to allow tokens from sparse areas to interact features, which is data-based sparsity. Different from it, we implement a simple but effective approximation for self-attention based on top-" + }, + { + "bbox": [ + 46, + 310, + 289, + 467 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 310, + 289, + 467 + ], + "type": "text", + "content": " selection to achieve sparse attention, which is content-based sparsity." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 470, + 288, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 470, + 288, + 544 + ], + "spans": [ + { + "bbox": [ + 46, + 470, + 288, + 544 + ], + "type": "text", + "content": "Top-k selection. Zhao et al. [64] first propose an explicit selection method based on top-" + }, + { + "bbox": [ + 46, + 470, + 288, + 544 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 470, + 288, + 544 + ], + "type": "text", + "content": " mechanism in NLP tasks. Driven by their success, " + }, + { + "bbox": [ + 46, + 470, + 288, + 544 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 470, + 288, + 544 + ], + "type": "text", + "content": "-NN attention [42, 44] is further introduced for boosting vision Transformers. Unlike performing top-" + }, + { + "bbox": [ + 46, + 470, + 288, + 544 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 470, + 288, + 544 + ], + "type": "text", + "content": " selection in the spatial dimension [44], we design an efficient top-" + }, + { + "bbox": [ + 46, + 470, + 288, + 544 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 470, + 288, + 544 + ], + "type": "text", + "content": " useful channel selection operator." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 550, + 153, + 563 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 550, + 153, + 563 + ], + "spans": [ + { + "bbox": [ + 47, + 550, + 153, + 563 + ], + "type": "text", + "content": "3. Proposed Method" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 567, + 287, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 567, + 287, + 664 + ], + "spans": [ + { + "bbox": [ + 46, + 567, + 287, + 664 + ], + "type": "text", + "content": "In this section, we first describe the overall pipeline and symmetrically hierarchical network architecture for image deraining. Afterward, we provide the details of the proposed sparse Transformer block (STB), as the fundamental building unit of our method, which mainly contains two key elements: top-" + }, + { + "bbox": [ + 46, + 567, + 287, + 664 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 567, + 287, + 664 + ], + "type": "text", + "content": " sparse attention (TKSA) and mixed-scale feed-forward network (MSFN). Finally, we present the introduced mixture of experts feature compensator (MEFC)." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 671, + 145, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 671, + 145, + 684 + ], + "spans": [ + { + "bbox": [ + 47, + 671, + 145, + 684 + ], + "type": "text", + "content": "3.1. Overall pipeline" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 689, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 288, + 713 + ], + "type": "text", + "content": "The overall pipeline of our proposed DRSformer, shown in Figure 2, is based on a hierarchical encoder-decoder" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 310, + 546, + 621 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 310, + 546, + 621 + ], + "spans": [ + { + "bbox": [ + 304, + 310, + 546, + 621 + ], + "type": "text", + "content": "framework. Given a rainy image " + }, + { + "bbox": [ + 304, + 310, + 546, + 621 + ], + "type": "inline_equation", + "content": "I_{rain} \\in \\mathbb{R}^{H \\times W \\times 3}" + }, + { + "bbox": [ + 304, + 310, + 546, + 621 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 310, + 546, + 621 + ], + "type": "inline_equation", + "content": "H \\times W" + }, + { + "bbox": [ + 304, + 310, + 546, + 621 + ], + "type": "text", + "content": " represents the spatial resolution of the feature map, we perform overlapped image patch embedding with " + }, + { + "bbox": [ + 304, + 310, + 546, + 621 + ], + "type": "inline_equation", + "content": "3 \\times 3" + }, + { + "bbox": [ + 304, + 310, + 546, + 621 + ], + "type": "text", + "content": " convolution. In the network backbone, we stack " + }, + { + "bbox": [ + 304, + 310, + 546, + 621 + ], + "type": "inline_equation", + "content": "N_{i \\in [1,2,3,4]}" + }, + { + "bbox": [ + 304, + 310, + 546, + 621 + ], + "type": "text", + "content": " STBs to extract rich features for spatially-varying rain distribution. To excavate the multi-scale representation from rain degeneration, each level of encoder-decoder pipeline covers its own specific spatial resolution and channel dimension. For feature down-sampling and up-sampling, we apply pixel-unshuffle and pixel-shuffle operations. Similar to [48,50,58], we also add skip-connections to bridge across continuous intermediate features for stable training. In each STB, unlike the standard self-attention [7] in Transformer, we develop TKSA to achieve feature sparsity, aiming to enforce the feature aggregation process more effectively. In addition, a MSFN is introduced into STB to enrich multiscale local information and help image restoration. At the early and final stages of the model learning, we equip our model with " + }, + { + "bbox": [ + 304, + 310, + 546, + 621 + ], + "type": "inline_equation", + "content": "N_0" + }, + { + "bbox": [ + 304, + 310, + 546, + 621 + ], + "type": "text", + "content": " MEFCs to provide complementary feature refinement, so that high-quality clear outputs can be finally reconstructed. With this hybrid formulation, we allow DRSformer to exploit both the adaptive content and the intrinsic property of rainy images, facilitating the separation of undesired rain streaks and latent clear background, and experiments demonstrate that the above design choices yield quality improvements (see Sec. 4.3)" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 622, + 547, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 622, + 547, + 658 + ], + "spans": [ + { + "bbox": [ + 305, + 622, + 547, + 658 + ], + "type": "text", + "content": "The final reconstructed result is obtained by: " + }, + { + "bbox": [ + 305, + 622, + 547, + 658 + ], + "type": "inline_equation", + "content": "I_{derain} = \\mathcal{F}(I_{rain}) + I_{rain}" + }, + { + "bbox": [ + 305, + 622, + 547, + 658 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 305, + 622, + 547, + 658 + ], + "type": "inline_equation", + "content": "\\mathcal{F}(\\cdot)" + }, + { + "bbox": [ + 305, + 622, + 547, + 658 + ], + "type": "text", + "content": " is the overall network and it is trained by minimizing the following loss function:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 378, + 667, + 545, + 681 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 378, + 667, + 545, + 681 + ], + "spans": [ + { + "bbox": [ + 378, + 667, + 545, + 681 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\left\\| I _ {\\text {d e r a i n}} - I _ {\\text {g t}} \\right\\| _ {1}, \\tag {1}", + "image_path": "176e5408a3f322d715886d9aedb1b693b083bd7eae6418dada1072cbae676895.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "I_{gt}" + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": " denotes the ground-truth image, and " + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\| \\cdot \\| _1" + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": " denotes the " + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "L_{1}" + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": "-norm." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5898" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 192, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 192, + 84 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 192, + 84 + ], + "type": "text", + "content": "3.2. Sparse Transformer block" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 90, + 287, + 197 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 90, + 287, + 197 + ], + "spans": [ + { + "bbox": [ + 46, + 90, + 287, + 197 + ], + "type": "text", + "content": "As the standard Transformers [7, 40, 58] take all the tokens to compute self-attention globally, which is unfriendly for image restoration due to it may involve noisy interactions between the irrelevant features. To solve such limitations, we develop a sparse Transformer block (STB) as the feature extraction unit by taking the advantages of sparsity that emerged in neural networks [64]. Formally, given the input features at the " + }, + { + "bbox": [ + 46, + 90, + 287, + 197 + ], + "type": "inline_equation", + "content": "(l - 1)" + }, + { + "bbox": [ + 46, + 90, + 287, + 197 + ], + "type": "text", + "content": "-th block " + }, + { + "bbox": [ + 46, + 90, + 287, + 197 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_{l - 1}" + }, + { + "bbox": [ + 46, + 90, + 287, + 197 + ], + "type": "text", + "content": ", the encoding procedures of STB can be defined as:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 93, + 199, + 287, + 213 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 93, + 199, + 287, + 213 + ], + "spans": [ + { + "bbox": [ + 93, + 199, + 287, + 213 + ], + "type": "interline_equation", + "content": "\\mathbf {X} _ {l} ^ {\\prime} = \\mathbf {X} _ {l - 1} + \\operatorname {T K S A} \\left(\\ln \\left(\\mathbf {X} _ {l - 1}\\right)\\right), \\tag {2}", + "image_path": "98e46705da057fe7f031f05699f7bfb3637bae09e579ec5520e3ec1fbd6e318b.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 103, + 219, + 287, + 232 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 103, + 219, + 287, + 232 + ], + "spans": [ + { + "bbox": [ + 103, + 219, + 287, + 232 + ], + "type": "interline_equation", + "content": "\\mathbf {X} _ {l} = \\mathbf {X} _ {l} ^ {\\prime} + \\operatorname {M S F N} \\left(\\ln \\left(\\mathbf {X} _ {l} ^ {\\prime}\\right)\\right), \\tag {3}", + "image_path": "f8c647a4aa7b50615b12651128c54a9db33a436ea8c808fb737539ef36a29043.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 233, + 287, + 281 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 233, + 287, + 281 + ], + "spans": [ + { + "bbox": [ + 46, + 233, + 287, + 281 + ], + "type": "text", + "content": "where LN denotes the layer normalization; " + }, + { + "bbox": [ + 46, + 233, + 287, + 281 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_l^{\\prime}" + }, + { + "bbox": [ + 46, + 233, + 287, + 281 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 233, + 287, + 281 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_l" + }, + { + "bbox": [ + 46, + 233, + 287, + 281 + ], + "type": "text", + "content": " denote the outputs from the top- " + }, + { + "bbox": [ + 46, + 233, + 287, + 281 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 233, + 287, + 281 + ], + "type": "text", + "content": " sparse attention (TKSA) and mixed-scale feed-forward network (MSFN), which are described below." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 284, + 287, + 344 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 284, + 287, + 344 + ], + "spans": [ + { + "bbox": [ + 47, + 284, + 287, + 344 + ], + "type": "text", + "content": "Top-k sparse attention (TKSA). We revisit the standard self-attention in Transformer, which has become an empirical operation in most of the existing models. Given a query " + }, + { + "bbox": [ + 47, + 284, + 287, + 344 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 47, + 284, + 287, + 344 + ], + "type": "text", + "content": ", key " + }, + { + "bbox": [ + 47, + 284, + 287, + 344 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 47, + 284, + 287, + 344 + ], + "type": "text", + "content": " and value " + }, + { + "bbox": [ + 47, + 284, + 287, + 344 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 47, + 284, + 287, + 344 + ], + "type": "text", + "content": " with the dimension of " + }, + { + "bbox": [ + 47, + 284, + 287, + 344 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{L\\times d}" + }, + { + "bbox": [ + 47, + 284, + 287, + 344 + ], + "type": "text", + "content": ", the output of dot-product attention is generally formulated as:" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 85, + 346, + 287, + 374 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 85, + 346, + 287, + 374 + ], + "spans": [ + { + "bbox": [ + 85, + 346, + 287, + 374 + ], + "type": "interline_equation", + "content": "\\operatorname {A t t} (\\mathbf {Q}, \\mathbf {K}, \\mathbf {V}) = \\operatorname {s o f t m a x} \\left(\\frac {\\mathbf {Q} \\mathbf {K} ^ {\\top}}{\\lambda}\\right) \\mathbf {V}, \\tag {4}", + "image_path": "203afd88d2a4a4611da435009d546ebcbdd4d0e5c7d8f14640bc7d0780d9494a.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "spans": [ + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "inline_equation", + "content": "\\mathbf{Q}" + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "inline_equation", + "content": "\\mathbf{K}" + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "inline_equation", + "content": "\\mathbf{V}" + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": " denote the matrix forms of " + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": ", respectively. " + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": " is an optional temperature factor defined by " + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "inline_equation", + "content": "\\lambda = \\sqrt{d}" + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": ". Generally, multi-head attention is implemented to each of the " + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": " new " + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": ", yielding " + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "inline_equation", + "content": "d = C / k" + }, + { + "bbox": [ + 46, + 376, + 287, + 509 + ], + "type": "text", + "content": " channel dimensional outputs which are concatenated and then got the final result for all heads via the linear projection. Noted that, this vanilla self-attention paradigm is based on densely fully-connected, which requires computing the attention map for all query-key pairs. In our work, we develop TKSA to replace it, thus avoiding the involvement of irrelevant information during the feature interaction process." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "text", + "content": "Specifically, we first encode channel-wise context by applying " + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "text", + "content": " convolutions followed by " + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "inline_equation", + "content": "3 \\times 3" + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "text", + "content": " depth-wise convolutions. Inspired by [58], we apply self-attention across channels rather than the spatial dimension to reduce the time and memory complexity. Next, we calculate similarities of pixel pairs between all the reshaped queries and keys, and mask out the unnecessary elements assigned with lower attention weights in the transposed attention matrix " + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{M}" + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "text", + "content": " of size " + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{\\hat{C} \\times \\hat{C}}" + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "text", + "content": ". Unlike the dropout strategy of randomly abandoning the scores, an adaptive selection of the top-" + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "text", + "content": " contributive scores is implemented upon " + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{M}" + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "text", + "content": ", aiming to preserve the most significant components and remove the useless ones [3]. Here, " + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "text", + "content": " is an adjustable parameter to dynamically control the magnitude of sparsity, which is formally obtained by weighted average of some proper fractions, such as " + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\frac{2}{3}" + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "text", + "content": ". Thus, only top-" + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "text", + "content": " values within the range " + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "inline_equation", + "content": "[\\Delta_1, \\Delta_2]" + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "text", + "content": " are normalized from each row of " + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{M}" + }, + { + "bbox": [ + 46, + 510, + 287, + 714 + ], + "type": "text", + "content": " for softmax computing. For" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "text", + "content": "other elements that are smaller than top-" + }, + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 72, + 545, + 120 + ], + "type": "text", + "content": " scores, we replace their probabilities with 0 at given indices using the scatter function. This dynamic selection makes the attention from dense to sparse, which is derived by:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 315, + 125, + 545, + 152 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 315, + 125, + 545, + 152 + ], + "spans": [ + { + "bbox": [ + 315, + 125, + 545, + 152 + ], + "type": "interline_equation", + "content": "\\operatorname {S p a r s e A t t} (\\mathbf {Q}, \\mathbf {K}, \\mathbf {V}) = \\operatorname {s o f t m a x} \\left(\\mathcal {T} _ {k} \\left(\\frac {\\mathbf {Q} \\mathbf {K} ^ {\\top}}{\\lambda}\\right)\\right) \\mathbf {V}, \\tag {5}", + "image_path": "80d90d23fbadeb0079ae9d8844f1f6ba191489e296efd12f56d8a0d1e836fd93.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 156, + 518, + 169 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 156, + 518, + 169 + ], + "spans": [ + { + "bbox": [ + 306, + 156, + 518, + 169 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 306, + 156, + 518, + 169 + ], + "type": "inline_equation", + "content": "\\mathcal{T}_k(\\cdot)" + }, + { + "bbox": [ + 306, + 156, + 518, + 169 + ], + "type": "text", + "content": " is the learnable top- " + }, + { + "bbox": [ + 306, + 156, + 518, + 169 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 306, + 156, + 518, + 169 + ], + "type": "text", + "content": " selection operator:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 358, + 174, + 545, + 206 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 358, + 174, + 545, + 206 + ], + "spans": [ + { + "bbox": [ + 358, + 174, + 545, + 206 + ], + "type": "interline_equation", + "content": "\\left[ \\mathcal {T} _ {k} (\\boldsymbol {S}) \\right] _ {i j} = \\left\\{ \\begin{array}{l l} S _ {i j} & S _ {i j} \\geq t _ {i} \\\\ 0 & \\text {o t h e r w i s e .} \\end{array} \\right. \\tag {6}", + "image_path": "02d895172b646ac7434121879ec0e1cf83be001fabc1f6a62172353e243a3dcd.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "spans": [ + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "inline_equation", + "content": "t_i" + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "text", + "content": " is the " + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "text", + "content": "-th largest value in the " + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "text", + "content": "-th row of " + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "inline_equation", + "content": "\\frac{\\mathbf{Q}\\mathbf{K}^{\\top}}{\\lambda}" + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "text", + "content": ". Here, we calculate " + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "inline_equation", + "content": "\\mathbf{Q}\\mathbf{K}^{\\top}" + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "text", + "content": " in the same way as [58] to generate " + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "inline_equation", + "content": "\\mathbf{M}" + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "text", + "content": " of size " + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^{\\hat{C}\\times \\hat{C}}" + }, + { + "bbox": [ + 304, + 212, + 545, + 276 + ], + "type": "text", + "content": ". As we use the multi-head strategy, we finally concatenate all the outputs of multi-head attention, and then get the final result by the linear projection." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 284, + 545, + 476 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 284, + 545, + 476 + ], + "spans": [ + { + "bbox": [ + 304, + 284, + 545, + 476 + ], + "type": "text", + "content": "Mixed-scale feed-forward network (MSFN). Previous studies [48, 50, 58] usually introduce single-scale depth-wise convolutions into the regular feed-forward network to imporve locality. However, those exploitations all ignore the correlations of multi-scale rain streaks. In fact, rich multi-scale representation has fully demonstrated its effectiveness [19, 41] in better removing rain. Here, we design a MSFN by inserting two multi-scale depth-wise convolution paths in the transmission process, see Figure 2. Given an input tensor " + }, + { + "bbox": [ + 304, + 284, + 545, + 476 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_{l-1} \\in \\mathbb{R}^{H \\times W \\times C}" + }, + { + "bbox": [ + 304, + 284, + 545, + 476 + ], + "type": "text", + "content": ", after layer normalization, we first utilize " + }, + { + "bbox": [ + 304, + 284, + 545, + 476 + ], + "type": "inline_equation", + "content": "1 \\times 1" + }, + { + "bbox": [ + 304, + 284, + 545, + 476 + ], + "type": "text", + "content": " convolution to expand the channel dimension in the ratio of " + }, + { + "bbox": [ + 304, + 284, + 545, + 476 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 304, + 284, + 545, + 476 + ], + "type": "text", + "content": ", then feed it into two parallel branches. During the feature transformation, the " + }, + { + "bbox": [ + 304, + 284, + 545, + 476 + ], + "type": "inline_equation", + "content": "3 \\times 3" + }, + { + "bbox": [ + 304, + 284, + 545, + 476 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 284, + 545, + 476 + ], + "type": "inline_equation", + "content": "5 \\times 5" + }, + { + "bbox": [ + 304, + 284, + 545, + 476 + ], + "type": "text", + "content": " depth-wise convolutions are employed to enhance the multi-scale local information extraction. In this way, the entire feature fusion procedure of the developed MSFN is formulated as:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 481, + 413, + 495 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 481, + 413, + 495 + ], + "spans": [ + { + "bbox": [ + 307, + 481, + 413, + 495 + ], + "type": "interline_equation", + "content": "\\hat {\\mathbf {X}} _ {l} = f _ {1 \\times 1} ^ {c} (\\operatorname {L N} \\left(\\mathbf {X} _ {l - 1}\\right)),", + "image_path": "b833de435156289d982f9a85fef82712f4cbac228b5eeda87a1343cc9dafe936.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 498, + 500, + 517 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 498, + 500, + 517 + ], + "spans": [ + { + "bbox": [ + 307, + 498, + 500, + 517 + ], + "type": "interline_equation", + "content": "\\mathbf {X} _ {l} ^ {p _ {1}} = \\sigma \\left(f _ {3 \\times 3} ^ {d w c} (\\hat {\\mathbf {X}} _ {l})\\right), \\mathbf {X} _ {l} ^ {s _ {1}} = \\sigma \\left(f _ {5 \\times 5} ^ {d w c} (\\hat {\\mathbf {X}} _ {l})\\right),", + "image_path": "2f8dea30194a0664a2c4d1c43bd57f32b1e89dd4b3aaa0476fc06cd56c7338dd.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 519, + 543, + 533 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 519, + 543, + 533 + ], + "spans": [ + { + "bbox": [ + 307, + 519, + 543, + 533 + ], + "type": "interline_equation", + "content": "\\mathbf {X} _ {l} ^ {p _ {2}} = \\sigma \\left(f _ {3 \\times 3} ^ {d w c} \\left[ \\mathbf {X} _ {l} ^ {p _ {1}}, \\mathbf {X} _ {l} ^ {s _ {1}} \\right]\\right), \\mathbf {X} _ {l} ^ {s _ {2}} = \\sigma \\left(f _ {5 \\times 5} ^ {d w c} \\left[ \\mathbf {X} _ {l} ^ {s _ {1}}, \\mathbf {X} _ {l} ^ {p _ {1}} \\right]\\right),", + "image_path": "22b5908d690fa646ebb740273242534da688c0b916bf67bd90cdbae4f21a59e6.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 535, + 545, + 557 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 535, + 545, + 557 + ], + "spans": [ + { + "bbox": [ + 307, + 535, + 545, + 557 + ], + "type": "interline_equation", + "content": "\\mathbf {X} _ {l} = f _ {1 \\times 1} ^ {c} \\left[ \\mathbf {X} _ {l} ^ {p _ {2}}, \\mathbf {X} _ {l} ^ {s _ {2}} \\right] + \\mathbf {X} _ {l - 1}, \\tag {7}", + "image_path": "34d9d8b51637761f3449379d57e4c603b57419b1c3fa84b60a69915bc851c776.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "spans": [ + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "inline_equation", + "content": "\\sigma (\\cdot)" + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "text", + "content": " is a ReLU activation, " + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "inline_equation", + "content": "f_{1\\times 1}^{c}" + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "text", + "content": " represents " + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "inline_equation", + "content": "1\\times 1" + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "text", + "content": " convolution, " + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "inline_equation", + "content": "f_{3\\times 3}^{dwc}" + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "inline_equation", + "content": "f_{5\\times 5}^{dwc}" + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "text", + "content": " denote " + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "inline_equation", + "content": "3\\times 3" + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "inline_equation", + "content": "5\\times 5" + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "text", + "content": " depth-wise convolutions, and " + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "inline_equation", + "content": "[\\cdot ]" + }, + { + "bbox": [ + 304, + 557, + 545, + 593 + ], + "type": "text", + "content": " is the channel-wise concatenation." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 599, + 515, + 612 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 599, + 515, + 612 + ], + "spans": [ + { + "bbox": [ + 306, + 599, + 515, + 612 + ], + "type": "text", + "content": "3.3. Mixture of experts feature compensator" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": "To fill in the comprehensive faculty of integrating sparsity in the DRSformer, we further introduce MEFC to perform a unified co-exploration towards joint data and content sparsity. Recalling the classical design of effective CNN models [39], we elaborately select multiple sparse CNN operations to form parallel layers, dubbed as experts, which involve an average pooling with receptive field of " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "3 \\times 3" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": ", separable convolution layers with kernel sizes of " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "1 \\times 1" + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5899" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 56, + 83, + 536, + 245 + ], + "blocks": [ + { + "bbox": [ + 53, + 71, + 539, + 82 + ], + "lines": [ + { + "bbox": [ + 53, + 71, + 539, + 82 + ], + "spans": [ + { + "bbox": [ + 53, + 71, + 539, + 82 + ], + "type": "text", + "content": "Table 1. Comparison of quantitative results on synthetic and real datasets. Bold and underline indicate the best and second-best results." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 56, + 83, + 536, + 245 + ], + "lines": [ + { + "bbox": [ + 56, + 83, + 536, + 245 + ], + "spans": [ + { + "bbox": [ + 56, + 83, + 536, + 245 + ], + "type": "table", + "html": "
DatasetsRain200LRain200HDID-DataDDN-DataSPA-Data
MetricsPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIMPSNRSSIM
Prior-based methodsDSC [30]27.160.866314.730.381524.240.827927.310.837334.950.9416
GMM [24]28.660.865214.500.416425.810.834427.550.847934.300.9428
CNN-based methodsDDN [8]34.680.967126.050.805630.970.911630.000.904136.160.9457
RESCAN [23]36.090.969726.750.835333.380.941731.940.934538.110.9707
PReNet [36]37.800.981429.040.899133.170.948132.600.945940.160.9816
MSPFN [19]38.580.982729.360.903433.720.955032.990.933343.430.9843
RCDNet [43]39.170.988530.240.904834.080.953233.040.947243.360.9831
MPRNet [59]39.470.982530.670.911033.990.959033.100.934743.640.9844
DualGCN [9]40.730.988631.150.912534.370.962033.010.948944.180.9902
SPDNet [56]40.500.987531.280.920734.570.956033.150.945743.200.9871
Transformer-based methodsUformer [48]40.200.986030.800.910535.020.962133.950.954546.130.9913
Restormer [58]40.990.989032.000.932935.290.964134.200.957147.980.9921
IDT [50]40.740.988432.100.934434.890.962333.840.954947.350.9930
DRSformer41.230.989432.170.932635.350.964634.350.958848.540.9924
", + "image_path": "f598adf348929d247e4d359e44e69e24c630a91a6b6912142d31e55dc5ebefa0.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 251, + 288, + 358 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 251, + 288, + 358 + ], + "spans": [ + { + "bbox": [ + 46, + 251, + 288, + 358 + ], + "type": "inline_equation", + "content": "3 \\times 3, 5 \\times 5, 7 \\times 7" + }, + { + "bbox": [ + 46, + 251, + 288, + 358 + ], + "type": "text", + "content": ", and dilated convolution layers with kernel sizes of " + }, + { + "bbox": [ + 46, + 251, + 288, + 358 + ], + "type": "inline_equation", + "content": "3 \\times 3, 5 \\times 5, 7 \\times 7" + }, + { + "bbox": [ + 46, + 251, + 288, + 358 + ], + "type": "text", + "content": ". Different from the conventional mixture of experts [17, 37], our MEFC does not attach an external gating network. Instead, we make the self-attention [14,21] become a switcher of different experts to adaptively select the importance of diverse representations depending on the inputs. Given an input feature map " + }, + { + "bbox": [ + 46, + 251, + 288, + 358 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_{l-1} \\in \\mathbb{R}^{H \\times W \\times C}" + }, + { + "bbox": [ + 46, + 251, + 288, + 358 + ], + "type": "text", + "content": ", we first apply the channel-wise average to generate " + }, + { + "bbox": [ + 46, + 251, + 288, + 358 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 46, + 251, + 288, + 358 + ], + "type": "text", + "content": "-dimensional channel descriptor " + }, + { + "bbox": [ + 46, + 251, + 288, + 358 + ], + "type": "inline_equation", + "content": "\\mathbf{z}_c \\in \\mathbb{R}^C" + }, + { + "bbox": [ + 46, + 251, + 288, + 358 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 96, + 365, + 287, + 399 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 96, + 365, + 287, + 399 + ], + "spans": [ + { + "bbox": [ + 96, + 365, + 287, + 399 + ], + "type": "interline_equation", + "content": "\\mathbf {z} _ {c} = \\frac {1}{H \\times W} \\sum_ {i = 1} ^ {H} \\sum_ {j = 1} ^ {W} \\mathbf {X} _ {l - 1} (i, j), \\tag {8}", + "image_path": "f61d1473b025d7d54a2906de964b02e2ad5346f0df70938ad6d16751495a23dd.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "spans": [ + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_{l - 1}(i,j)" + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "text", + "content": " is the " + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "inline_equation", + "content": "(y,x)" + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "text", + "content": " position of the feature " + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_{l - 1}" + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "text", + "content": ". Then, the coefficient vector of each expert is allocated corresponding to the learnable weight matrices " + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "inline_equation", + "content": "\\mathbf{W}_1\\in \\mathbb{R}^{T\\times C}" + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "inline_equation", + "content": "\\mathbf{W}_2\\in \\mathbb{R}^{O\\times T}" + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "text", + "content": ". Here, " + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "text", + "content": " is the dimension of the weight matrices. To avoid altering the sizes of its inputs and outputs, we zero pad the input feature maps computed by each expert. Finally, the output of the " + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 46, + 406, + 287, + 501 + ], + "type": "text", + "content": "-th MEFC is calculated by:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 83, + 498, + 172, + 512 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 498, + 172, + 512 + ], + "spans": [ + { + "bbox": [ + 83, + 498, + 172, + 512 + ], + "type": "interline_equation", + "content": "\\mathbf {T} _ {l} = \\mathbf {W} _ {2} \\sigma \\left(\\mathbf {W} _ {1} \\mathbf {z} _ {c}\\right),", + "image_path": "b27ad45e4c1966ea2bc6d37b7ba2b72b59b12adcc15f9e10380265c9cdd35a67.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 83, + 514, + 287, + 548 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 83, + 514, + 287, + 548 + ], + "spans": [ + { + "bbox": [ + 83, + 514, + 287, + 548 + ], + "type": "interline_equation", + "content": "\\mathbf {X} _ {l} = f _ {1 \\times 1} ^ {c} \\left[ \\sum_ {i = 1} ^ {O} f _ {e x p} \\left(\\mathbf {X} _ {l}, \\mathbf {T} _ {l}\\right) \\right] + \\mathbf {X} _ {l - 1}, \\tag {9}", + "image_path": "43a1070f00b29618cad4f497f5b0fff3a1354af6db9446b9fb891d58ee70526e.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "spans": [ + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "inline_equation", + "content": "f_{exp}" + }, + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "inline_equation", + "content": "O" + }, + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "text", + "content": " represent the expert operations and the number of experts, respectively. " + }, + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "inline_equation", + "content": "f_{1\\times 1}^{c}" + }, + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "text", + "content": " represents " + }, + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "inline_equation", + "content": "1\\times 1" + }, + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "text", + "content": " convolution, " + }, + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "inline_equation", + "content": "\\sigma (\\cdot)" + }, + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "text", + "content": " is a ReLU function, and " + }, + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "inline_equation", + "content": "[\\cdot ]" + }, + { + "bbox": [ + 46, + 549, + 287, + 622 + ], + "type": "text", + "content": " is the channelwise concatenation. With this design, MEFC is now closely linked to the main STBs so that is able to adaptively remove the rainy effects of diverse appearances." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 630, + 195, + 643 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 630, + 195, + 643 + ], + "spans": [ + { + "bbox": [ + 47, + 630, + 195, + 643 + ], + "type": "text", + "content": "4. Experiments and Analysis" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 647, + 172, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 647, + 172, + 659 + ], + "spans": [ + { + "bbox": [ + 47, + 647, + 172, + 659 + ], + "type": "text", + "content": "4.1. Experimental settings" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 665, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 665, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 665, + 287, + 714 + ], + "type": "text", + "content": "Datasets. We implement deraining experiments on multiple public benchmarks, including Rain200L/H [52], DID-Data [61] and DDN-Data [8]. Rain200L and Rain200H contain 1,800 synthetic rainy images for training and 200 ones for" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 251, + 545, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 251, + 545, + 323 + ], + "spans": [ + { + "bbox": [ + 304, + 251, + 545, + 323 + ], + "type": "text", + "content": "testing. DID-Data and DDN-Data consist of 12,000 and 12,600 synthetic images with different rain directions and density levels. There are 1,200 and 1,400 rainy images for testing. In addition, we also evaluate our method using a large-scale real-world dataset, i.e., SPA-Data [45], containing 638,492 image pairs for training and 1,000 testing ones." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 326, + 546, + 458 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 326, + 546, + 458 + ], + "spans": [ + { + "bbox": [ + 304, + 326, + 546, + 458 + ], + "type": "text", + "content": "Comparison methods. We compare our DRSformer with two prior-based models (DSC [30] and GMM [24]), CNN-based methods (DDN [8], RESCAN [23], PReNet [36], MSPFN [19], RCDNet [43], MPRNet [59], DualGCN [9], and SPDNet [56]), and recent Transformer-based methods (Uformer [48], Restormer [58], and IDT [50]). For recent representative methods (DualGCN, SPDNet, Restormer and IDT), we retrain their models provided by the authors if no pretrained models are provided, otherwise we evaluate them with their online codes for fair comparisons. For other approaches, we refer to some reported results in [10, 50]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 459, + 545, + 531 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 459, + 545, + 531 + ], + "spans": [ + { + "bbox": [ + 304, + 459, + 545, + 531 + ], + "type": "text", + "content": "Evaluation metrics. We adopt PSNR [34] and SSIM [47] as the evaluation metrics for the above benchmarks. Following previous deraining methods [10, 19], we calculate PSNR and SSIM metrics in Y channel of YCbCr space. For the rainy images without ground truth images, we use the non-reference metrics including NIQE [33] and BRISQUE [32]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "content": "Training details. In our model, " + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "inline_equation", + "content": "\\{N_0,N_1,N_2,N_3,N_4\\}" + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "content": " are set to " + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "inline_equation", + "content": "\\{4,4,6,6,8\\}" + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "content": ", and the number of attention heads for four STBs of the same level is set to " + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "inline_equation", + "content": "\\{1,2,4,8\\}" + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "content": ". The initial channel " + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "content": " is 48 and the expanding ratio is set to 2. In terms of MEFC, we set " + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "inline_equation", + "content": "O = 8" + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "content": " for the number of experts and " + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "inline_equation", + "content": "T = 32" + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "content": " for the weight matrix. Note that we do not use MEFC for training Rain200L and SPA-Data, because their rain streaks are less complex and easier to learn. In terms of STB, the sparseness " + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "inline_equation", + "content": "[\\Delta_1,\\Delta_2]" + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "content": " in TKSA is set to " + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "inline_equation", + "content": "[\\frac{1}{2},\\frac{4}{5} ]" + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "content": ", and the channel expansion factor " + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "content": " in MSFN is set to 2.66. During training, we use AdamW optimizer with batch size of 8 and patch size of 128 for total 300K iterations. The initial learning rate is fixed as " + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "inline_equation", + "content": "1\\times 10^{-4}" + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "content": " for 92K iterations, and then decreased to " + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "inline_equation", + "content": "1\\times 10^{-6}" + }, + { + "bbox": [ + 304, + 534, + 546, + 713 + ], + "type": "text", + "content": " for 208K iterations with the cosine annealing scheme [29]. For data augmentation, vertical and" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5900" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 51, + 71, + 545, + 190 + ], + "blocks": [ + { + "bbox": [ + 51, + 71, + 545, + 190 + ], + "lines": [ + { + "bbox": [ + 51, + 71, + 545, + 190 + ], + "spans": [ + { + "bbox": [ + 51, + 71, + 545, + 190 + ], + "type": "image", + "image_path": "536fa7d411ea2ca30e1ccaf0927498b1229fe805910fccabb0c93205623dbbd0.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 56, + 192, + 536, + 204 + ], + "lines": [ + { + "bbox": [ + 56, + 192, + 536, + 204 + ], + "spans": [ + { + "bbox": [ + 56, + 192, + 536, + 204 + ], + "type": "text", + "content": "Figure 3. Visual quality comparison on the Rain200H dataset. Zooming in the figures offers a better view at the deraining capability." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 51, + 207, + 545, + 339 + ], + "blocks": [ + { + "bbox": [ + 51, + 207, + 545, + 339 + ], + "lines": [ + { + "bbox": [ + 51, + 207, + 545, + 339 + ], + "spans": [ + { + "bbox": [ + 51, + 207, + 545, + 339 + ], + "type": "image", + "image_path": "120d2f44fedd5e6fbe4351355f87b70d881f9949ba086eadc73a149bd788d0a0.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 56, + 342, + 536, + 353 + ], + "lines": [ + { + "bbox": [ + 56, + 342, + 536, + 353 + ], + "spans": [ + { + "bbox": [ + 56, + 342, + 536, + 353 + ], + "type": "text", + "content": "Figure 4. Visual quality comparison on the SPA-Data dataset. Zooming in the figures offers a better view at the deraining capability." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 359, + 287, + 408 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 359, + 287, + 408 + ], + "spans": [ + { + "bbox": [ + 46, + 359, + 287, + 408 + ], + "type": "text", + "content": "horizontal flips are randomly applied. The entire framework is performed on the PyTorch with 4 NVIDIA GeForce RTX 3090 GPUs, which works in an end-to-end learning fashion without costly large-scale pretraining [2]." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 411, + 251, + 423 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 411, + 251, + 423 + ], + "spans": [ + { + "bbox": [ + 47, + 411, + 251, + 423 + ], + "type": "text", + "content": "4.2. Comparisons with the state-of-the-arts" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 426, + 287, + 666 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 426, + 287, + 666 + ], + "spans": [ + { + "bbox": [ + 46, + 426, + 287, + 666 + ], + "type": "text", + "content": "Synthetic datasets. The quantitative evaluations on different benchmark datasets are reported in Table 1. As shown, we can note that our proposed method outperforms all the other derainers, especially on PSNR, e.g., DRSformer surpasses the concurrent approach IDT by " + }, + { + "bbox": [ + 46, + 426, + 287, + 666 + ], + "type": "inline_equation", + "content": "0.4\\mathrm{dB}" + }, + { + "bbox": [ + 46, + 426, + 287, + 666 + ], + "type": "text", + "content": " on average. Compared with previous CNN-based models, this progress can be much more obvious. The notable increasing scores on the DID-Data and DDN-Data benchmarks reveal that our method can properly handle diverse types of spatially-varying rain streaks. For convincing evidence, we show the visual quality comparison between samples generated by recent approaches in Figure 3. The pure CNN-based models, e.g., MPRNet and SPDNet, fail to restore clear images in heavy rainy scenarios. It can be seen that the results of all computing Transformer-based methods are flawed in terms of detail and texture recovery. Unfortunately, IDT even introduces considerable boundary artifacts. Thanks to the developed sparse attention with top-" + }, + { + "bbox": [ + 46, + 426, + 287, + 666 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 426, + 287, + 666 + ], + "type": "text", + "content": " selection, our method can generate high-quality deraining results, which are more consistent with that of the ground truth." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 666, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 666, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 666, + 287, + 713 + ], + "type": "text", + "content": "Real-world datasets. We further conduct experiments on the SPA-Data benchmark dataset, and corresponding results are provided in the last column of Table 1. As expected, our model continues to achieve the highest PSNR/SSIM" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 359, + 545, + 576 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 359, + 545, + 576 + ], + "spans": [ + { + "bbox": [ + 304, + 359, + 545, + 576 + ], + "type": "text", + "content": "value, exhibiting the superior of DRSformer in terms of deraining performance and generalization. The visual quality comparison can be observed in Figure 4. In contrast, our method significantly competes with others in removing most rain streaks while preserving truthful image structures. In order to further validate the effectiveness of DRSformer, we also randomly choose 20 real rainy images without ground truths from Internet-Data [45] to perform another evaluation. As displayed in Table 2, our net gets the lower NIQE and BRISQUE values, which means a high-quality output with clearer content and better perceptual quality against other comparative models on the real rainy scenarios. Through qualitative quality comparison in Figure 5, most deep models are sensitive to spatially-long rain streaks and leave some apparent rain effects. On the contrary, our net successfully removes most rain perturbation and owns a visual pleasant recovery effect, which implies that it can well generalize to unseen real-world data types." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 581, + 405, + 592 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 581, + 405, + 592 + ], + "spans": [ + { + "bbox": [ + 306, + 581, + 405, + 592 + ], + "type": "text", + "content": "4.3. Ablation studies" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "spans": [ + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "text", + "content": "Effectiveness of Top-" + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "text", + "content": " selection. To examine the effect of the top-" + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "text", + "content": " selection in the TKSA, we present the deraining results of TKSA w/o top-" + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "text", + "content": " in Figure 6. We can see that the PSNR values of the derained images by the method without using the top-" + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "text", + "content": " selection are lower than those by the method using the top-" + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "text", + "content": " selection. In addition, we also note that each element of the self-attention matrix " + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "inline_equation", + "content": "\\operatorname{softmax}\\left(\\frac{\\mathbf{QK}^\\top}{\\lambda}\\right)" + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "text", + "content": " in Eq. (4) is non-negative and the summation of the elements from each row of " + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "inline_equation", + "content": "\\operatorname{softmax}\\left(\\frac{\\mathbf{QK}^\\top}{\\lambda}\\right)" + }, + { + "bbox": [ + 304, + 596, + 547, + 716 + ], + "type": "text", + "content": " is 1. Thus, applying" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 314, + 757 + ], + "type": "text", + "content": "5901" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 72, + 545, + 160 + ], + "blocks": [ + { + "bbox": [ + 50, + 72, + 545, + 160 + ], + "lines": [ + { + "bbox": [ + 50, + 72, + 545, + 160 + ], + "spans": [ + { + "bbox": [ + 50, + 72, + 545, + 160 + ], + "type": "image", + "image_path": "f357b853996f2c2d8499fa548cf819e79091e4d95c0fe340d97c07bd74c7a356.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 52, + 163, + 539, + 175 + ], + "lines": [ + { + "bbox": [ + 52, + 163, + 539, + 175 + ], + "spans": [ + { + "bbox": [ + 52, + 163, + 539, + 175 + ], + "type": "text", + "content": "Figure 5. Visual quality comparison on real-world rainy images. Zooming in the figures offers a better view at the deraining capability." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 48, + 189, + 545, + 213 + ], + "blocks": [ + { + "bbox": [ + 65, + 177, + 527, + 188 + ], + "lines": [ + { + "bbox": [ + 65, + 177, + 527, + 188 + ], + "spans": [ + { + "bbox": [ + 65, + 177, + 527, + 188 + ], + "type": "text", + "content": "Table 2. Comparison of quantitative results on real-world rainy images, and note that lower scores indicate better image quality." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 189, + 545, + 213 + ], + "lines": [ + { + "bbox": [ + 48, + 189, + 545, + 213 + ], + "spans": [ + { + "bbox": [ + 48, + 189, + 545, + 213 + ], + "type": "table", + "html": "
MethodsRainy InputMPRNet [59]SPDNet [56]Uformer [48]Restormer [58]IDT [50]Ours
NIQE ↓ / BRISQUE ↓5.829 / 33.1294.740 / 32.0184.422 / 26.1734.833 / 28.1065.005 / 34.0364.238 / 25.5734.095 / 22.730
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Ablation analysis for top-" + }, + { + "bbox": [ + 47, + 365, + 287, + 376 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 47, + 365, + 287, + 376 + ], + "type": "text", + "content": " selection on the benchmarks." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 48, + 377, + 287, + 496 + ], + "blocks": [ + { + "bbox": [ + 48, + 377, + 287, + 496 + ], + "lines": [ + { + "bbox": [ + 48, + 377, + 287, + 496 + ], + "spans": [ + { + "bbox": [ + 48, + 377, + 287, + 496 + ], + "type": "image", + "image_path": "02564e6021a75b2b7ac9db6f3a1aaed2a1ed6a17a3c70802cbaae7f7bce85d61.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 50, + 499, + 284, + 510 + ], + "lines": [ + { + "bbox": [ + 50, + 499, + 284, + 510 + ], + "spans": [ + { + "bbox": [ + 50, + 499, + 284, + 510 + ], + "type": "text", + "content": "Figure 7. Ablation analysis for different number " + }, + { + "bbox": [ + 50, + 499, + 284, + 510 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 50, + 499, + 284, + 510 + ], + "type": "text", + "content": " in the TKSA." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 528, + 287, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 528, + 287, + 568 + ], + "spans": [ + { + "bbox": [ + 46, + 528, + 287, + 568 + ], + "type": "text", + "content": "the self-attention matrix " + }, + { + "bbox": [ + 46, + 528, + 287, + 568 + ], + "type": "inline_equation", + "content": "\\mathrm{softmax}\\left(\\frac{\\mathbf{Q}\\mathbf{K}^{\\top}}{\\lambda}\\right)" + }, + { + "bbox": [ + 46, + 528, + 287, + 568 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 46, + 528, + 287, + 568 + ], + "type": "inline_equation", + "content": "\\mathbf{V}" + }, + { + "bbox": [ + 46, + 528, + 287, + 568 + ], + "type": "text", + "content": " will remove the high-frequency information of " + }, + { + "bbox": [ + 46, + 528, + 287, + 568 + ], + "type": "inline_equation", + "content": "\\mathbf{V}" + }, + { + "bbox": [ + 46, + 528, + 287, + 568 + ], + "type": "text", + "content": ", which lead to oversmoothed results." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 570, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 570, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 570, + 289, + 715 + ], + "type": "text", + "content": "To understand the effect of such top-" + }, + { + "bbox": [ + 46, + 570, + 289, + 715 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 570, + 289, + 715 + ], + "type": "text", + "content": " selection, we further use high-pass filtering (HPF) to visualize learned features in Figure 8. Compared to standard self-attention operation (w/o top-" + }, + { + "bbox": [ + 46, + 570, + 289, + 715 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 570, + 289, + 715 + ], + "type": "text", + "content": "), our strategy can better help reconstruct finer-detail feature and improve potential restoration quality. As the nearby pixels tend to be more similar than others, top-" + }, + { + "bbox": [ + 46, + 570, + 289, + 715 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 570, + 289, + 715 + ], + "type": "text", + "content": " selection operator helps to reduce the irrelevant context from long-range pixel dependency. This step of selection allows the smaller similarity weights (from a part of long-range feature interactions) to be discarded in the procedure of self-attention calculation, thus facilitating more accurate representation for achieving high-quality output." + } + ] + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 307, + 220, + 365, + 280 + ], + "blocks": [ + { + "bbox": [ + 307, + 220, + 365, + 280 + ], + "lines": [ + { + "bbox": [ + 307, + 220, + 365, + 280 + ], + "spans": [ + { + "bbox": [ + 307, + 220, + 365, + 280 + ], + "type": "image", + "image_path": "1ada1dcb12e435ee63992c9444cd0836324784d3bb2c288f3baa6a1e6baa2a40.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 317, + 280, + 356, + 288 + ], + "lines": [ + { + "bbox": [ + 317, + 280, + 356, + 288 + ], + "spans": [ + { + "bbox": [ + 317, + 280, + 356, + 288 + ], + "type": "text", + "content": "(a) w/o Top-k" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 306, + 294, + 546, + 328 + ], + "lines": [ + { + "bbox": [ + 306, + 294, + 546, + 328 + ], + "spans": [ + { + "bbox": [ + 306, + 294, + 546, + 328 + ], + "type": "text", + "content": "Figure 8. Visualization of feature maps. 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The key parameter for our proposed TKSA is " + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "text", + "content": ", and its influence is investigated in Figure 7. We note that the optimal choice of " + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "text", + "content": " determines the boundary control of the sparsity rate. If " + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "text", + "content": " is manually set to a single value, such as " + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "inline_equation", + "content": "\\frac{1}{2}" + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "text", + "content": ", we notice that its performance is sensitive to " + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "text", + "content": ". To avoid an exhaustive search, we set a controllable interval range for " + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "text", + "content": " to dynamically learn the most contribute score. When " + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "text", + "content": " is too small, we find that the performance will undoubtedly decline sharply due to insufficient global information aggregation. The best result can achieve 32.18 dB when " + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "inline_equation", + "content": "[\\Delta_1, \\Delta_2]" + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "text", + "content": " in the TKSA is assigned to " + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "inline_equation", + "content": "[\\frac{1}{2}, \\frac{4}{5}]" + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "text", + "content": ". As " + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 336, + 547, + 503 + ], + "type": "text", + "content": " continues to increase, the final deraining performance gradually decreases due to the introduction of irrelevant and useless features." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 504, + 547, + 648 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 504, + 547, + 648 + ], + "spans": [ + { + "bbox": [ + 304, + 504, + 547, + 648 + ], + "type": "text", + "content": "Effectiveness of MSFN. To evaluate the effectiveness of the proposed MSFN, we compare it with three baselines: (1) conventional feed-forward network (FN) [7], (2) Dconv feed-forward network (DFN) [25], and (3) gated-Dconv feed-forward network (GDFN) [58]. The quantitative analysis results on Rain200H are listed in Table 3. Although GDFN introduces a gating mechanism in two same-scale depth-wise convolution streams to bring performance advantages, it still neglects the multi-scale knowledge for deraining. By adding local feature extraction and fusion at different scales, the MSFN can indeed better boost the performance, and achieve PSNR gain of 0.21 dB over GDFN." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 653, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 547, + 713 + ], + "type": "text", + "content": "Effectiveness of MEFC. To evaluate the effectiveness of MEFC, we perform experiments based on different model variants in Table 4. Compared to the baseline model (a), MEFC provides additional performance benefits thanks to auxiliary data sparsity. 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Ablation study for different feed-forward networks." + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 176, + 286, + 195 + ], + "lines": [ + { + "bbox": [ + 50, + 176, + 286, + 195 + ], + "spans": [ + { + "bbox": [ + 50, + 176, + 286, + 195 + ], + "type": "table", + "html": "
ModelsFN [7]DFN [25]GDFN [58]MSFN
PSNR / SSIM31.84 / 0.927531.88 / 0.927931.97 / 0.928632.18 / 0.9330
", + "image_path": "947d0450f4d464662836d6462ef1f05dd148f31f2d63600438928826ad22e135.jpg" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "table_body" + } + ], + "index": 23 + }, + { + "type": "table", + "bbox": [ + 51, + 223, + 285, + 295 + ], + "blocks": [ + { + "bbox": [ + 47, + 201, + 286, + 221 + ], + "lines": [ + { + "bbox": [ + 47, + 201, + 286, + 221 + ], + "spans": [ + { + "bbox": [ + 47, + 201, + 286, + 221 + ], + "type": "text", + "content": "Table 4. Ablation study for different variants of our DRSformer. MEFC-1 and MEFC-2 denote MEFC in early and final stages." + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 51, + 223, + 285, + 295 + ], + "lines": [ + { + "bbox": [ + 51, + 223, + 285, + 295 + ], + "spans": [ + { + "bbox": [ + 51, + 223, + 285, + 295 + ], + "type": "table", + "html": "
ModelsMEFC-1STBsMEFC-2ExpertsPSNR / SSIM
(a)✓032.03 / 0.9308
(b)✓✓832.01 / 0.9311
(c)✓✓832.07 / 0.9328
(d)✓✓✓132.06 / 0.9316
(e)✓✓✓432.14 / 0.9325
(f)✓✓✓832.18 / 0.9330
", + "image_path": "f9183d91905817d307638146ea891c1e2846bab8fb155ea879a309f62eab7380.jpg" + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "table_body" + } + ], + "index": 25 + }, + { + "bbox": [ + 46, + 303, + 287, + 494 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 303, + 287, + 494 + ], + "spans": [ + { + "bbox": [ + 46, + 303, + 287, + 494 + ], + "type": "text", + "content": "at different locations of the network pipeline have specific impacts on the restoration performance. Indeed, we also analyze the effect of the different numbers of experts in each MEFC. When using single expert model (d), the performance is dramatically degraded compared with our multi-expert model (f). Unlike setting all experts to the same structure [21], our multi-expert formulation is more diverse, which brings its gains to the performance due to different receptive fields and disparate CNN operations. Through the zoomed boxes in Figure 9, the recovered results of the model with all the above components tend to be clearer since it enables more diverse features to be fully used during the restoration process. All in all, our model (f) performs better than the other possible configurations, which indicates that each design strategy that we consider has its own contribution to the final performance of DRSformer." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 47, + 502, + 183, + 514 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 502, + 183, + 514 + ], + "spans": [ + { + "bbox": [ + 47, + 502, + 183, + 514 + ], + "type": "text", + "content": "4.4. Closely-related methods" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "spans": [ + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "type": "text", + "content": "We note that the recent method [22] proposes a " + }, + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "type": "text", + "content": "-NN image Transformer (KiT) to solve image restoration by aggregating " + }, + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "type": "text", + "content": " similar patches with the pair-wise local attention. Compared with KiT that employs complex locality sensitive hashing that cannot ensure sufficient global interaction, our simple but effective top-" + }, + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 521, + 287, + 665 + ], + "type": "text", + "content": " selection mechanism not only enjoys the locality but also empowers the ability of global relation mining. As the code of KiT is not available, we refer to the results of their paper. Figure 10 shows qualitative comparisons trained on the Rain800 [62]. We can see that KiT tends to blur the contents and cause color distortion. In contrast, our method leads to better deraining results." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "type": "text", + "content": "In addition, we also note that [44] recently designs the " + }, + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "type": "text", + "content": "-NN attention to enhance the representation ability of vision Transformers by selecting the top-" + }, + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "type": "text", + "content": " similar tokens. Different from KVT [44], which implements top-" + }, + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 665, + 287, + 713 + ], + "type": "text", + "content": " selection" + } + ] + } + ], + "index": 29 + }, + { + "type": "image", + "bbox": [ + 309, + 163, + 364, + 238 + ], + "blocks": [ + { + "bbox": [ + 309, + 163, + 364, + 238 + ], + "lines": [ + { + "bbox": [ + 309, + 163, + 364, + 238 + ], + "spans": [ + { + "bbox": [ + 309, + 163, + 364, + 238 + ], + "type": "image", + "image_path": "e6e7ff566ac4ad802c8cca6ed2bbc22fcb6c27034cf56baf4e13b8852ac0ad29.jpg" + } + ] + } + ], + "index": 30, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 311, + 241, + 362, + 250 + ], + "lines": [ + { + "bbox": [ + 311, + 241, + 362, + 250 + ], + "spans": [ + { + "bbox": [ + 311, + 241, + 362, + 250 + ], + "type": "text", + "content": "(a) Rainy Input" + } + ] + } + ], + "index": 31, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 315, + 251, + 535, + 262 + ], + "lines": [ + { + "bbox": [ + 315, + 251, + 535, + 262 + ], + "spans": [ + { + "bbox": [ + 315, + 251, + 535, + 262 + ], + "type": "text", + "content": "Figure 10. Comparison results with closely-related methods." + } + ] + } + ], + "index": 38, + "angle": 0, + "type": "image_caption" + } + ], + "index": 30 + }, + { + "type": "image", + "bbox": [ + 368, + 164, + 424, + 238 + ], + "blocks": [ + { + "bbox": [ + 368, + 164, + 424, + 238 + ], + "lines": [ + { + "bbox": [ + 368, + 164, + 424, + 238 + ], + "spans": [ + { + "bbox": [ + 368, + 164, + 424, + 238 + ], + "type": "image", + "image_path": "df669920b55726d47d1fdec2001f70ae6dc02a6a9fbeff276d05b6130cf7159b.jpg" + } + ] + } + ], + "index": 32, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 375, + 240, + 416, + 250 + ], + "lines": [ + { + "bbox": [ + 375, + 240, + 416, + 250 + ], + "spans": [ + { + "bbox": [ + 375, + 240, + 416, + 250 + ], + "type": "text", + "content": "(b) KiT [22]" + } + ] + } + ], + "index": 33, + "angle": 0, + "type": "image_caption" + } + ], + "index": 32 + }, + { + "type": "image", + "bbox": [ + 426, + 164, + 482, + 238 + ], + "blocks": [ + { + "bbox": [ + 426, + 164, + 482, + 238 + ], + "lines": [ + { + "bbox": [ + 426, + 164, + 482, + 238 + ], + "spans": [ + { + "bbox": [ + 426, + 164, + 482, + 238 + ], + "type": "image", + "image_path": "fc372621f5b08d1248ee83766e35bc202aaaea10d5d1bb91e80b73a821971a16.jpg" + } + ] + } + ], + "index": 34, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 432, + 240, + 476, + 250 + ], + "lines": [ + { + "bbox": [ + 432, + 240, + 476, + 250 + ], + "spans": [ + { + "bbox": [ + 432, + 240, + 476, + 250 + ], + "type": "text", + "content": "(c) KVT [44]" + } + ] + } + ], + "index": 35, + "angle": 0, + "type": "image_caption" + } + ], + "index": 34 + }, + { + "type": "image", + "bbox": [ + 486, + 164, + 542, + 238 + ], + "blocks": [ + { + "bbox": [ + 47, + 147, + 544, + 158 + ], + "lines": [ + { + "bbox": [ + 47, + 147, + 544, + 158 + ], + "spans": [ + { + "bbox": [ + 47, + 147, + 544, + 158 + ], + "type": "text", + "content": "Figure 9. Ablation qualitative comparison for different variants of DRSformer. The models (a-f) are consistent with the settings in Table 4." + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 486, + 164, + 542, + 238 + ], + "lines": [ + { + "bbox": [ + 486, + 164, + 542, + 238 + ], + "spans": [ + { + "bbox": [ + 486, + 164, + 542, + 238 + ], + "type": "image", + "image_path": "edd59521d86ebe0f2e40f7395a0bd31a86a0233a291d1b479b110588f0763cb6.jpg" + } + ] + } + ], + "index": 36, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 500, + 240, + 528, + 249 + ], + "lines": [ + { + "bbox": [ + 500, + 240, + 528, + 249 + ], + "spans": [ + { + "bbox": [ + 500, + 240, + 528, + 249 + ], + "type": "text", + "content": "(d) Ours" + } + ] + } + ], + "index": 37, + "angle": 0, + "type": "image_caption" + } + ], + "index": 36 + }, + { + "bbox": [ + 304, + 264, + 545, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 264, + 545, + 361 + ], + "spans": [ + { + "bbox": [ + 304, + 264, + 545, + 361 + ], + "type": "text", + "content": "in the spatial dimension, our operator is more efficient in computing sparse attention across channels. Furthermore, the sparsity level of " + }, + { + "bbox": [ + 304, + 264, + 545, + 361 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 264, + 545, + 361 + ], + "type": "text", + "content": " in our proposed TKSA is dynamically learnable, rather than the fixed setting in [44]. Here, we adopt " + }, + { + "bbox": [ + 304, + 264, + 545, + 361 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 264, + 545, + 361 + ], + "type": "text", + "content": "-NN attention in KVT to replace our TKSA for comparison. To ensure the fair comparison, the same training settings are kept for model testing. As shown in Figure 10 (c) and (d), our method can generate a clearer image." + } + ] + } + ], + "index": 39 + }, + { + "bbox": [ + 306, + 367, + 429, + 380 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 367, + 429, + 380 + ], + "spans": [ + { + "bbox": [ + 306, + 367, + 429, + 380 + ], + "type": "text", + "content": "5. Concluding Remarks" + } + ] + } + ], + "index": 40 + }, + { + "bbox": [ + 304, + 384, + 545, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 384, + 545, + 552 + ], + "spans": [ + { + "bbox": [ + 304, + 384, + 545, + 552 + ], + "type": "text", + "content": "We have presented an effective sparse Transformer network, called DRSformer, to solve image deraining. Based on the observation that vanilla self-attention in Transformer may suffer from the global interaction of irrelevant information, we develop the top-" + }, + { + "bbox": [ + 304, + 384, + 545, + 552 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 384, + 545, + 552 + ], + "type": "text", + "content": " sparse attention to keep the most useful self-attention values for better feature aggregation. To facilitate the aggregated features for removing rain, we develop a mixed-scale feed-forward network to better explore multi-scale representations. Furthermore, the mixture of experts feature compensator is introduced to the model to provide collaborative refinement for the sparse Transformer backbone, so that the fine details of the reconstructed image is preserved. Experimental results show that our DRSformer performs favorably against state-of-the-art methods." + } + ] + } + ], + "index": 41 + }, + { + "bbox": [ + 304, + 556, + 545, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 556, + 545, + 640 + ], + "spans": [ + { + "bbox": [ + 304, + 556, + 545, + 640 + ], + "type": "text", + "content": "Limitations. Our proposed method aims to further boost image deraining performance, but there are limitations in the model efficiency. Specifically, our model requires 33.7 Million parameters and costs 242.9G FLOPs on one image with size of " + }, + { + "bbox": [ + 304, + 556, + 545, + 640 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 304, + 556, + 545, + 640 + ], + "type": "text", + "content": ". We will apply the pruning or distillation scheme in our model to maintain the original deraining performance while achieving credible model compression." + } + ] + } + ], + "index": 42 + }, + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 712 + ], + "type": "text", + "content": "Acknowledgements. This work has been partly supported by the National Key R&D Program of China (No. 2018AAA0102001), the National Natural Science Foundation of China (Nos. U22B2049, U19B2040, 61922043, 61872421, 62272230), and the Fundamental Research Funds for the Central Universities (No. 30920041109)." + } + ] + } + ], + "index": 43 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5903" + } + ] + } + ], + "index": 44 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 287, + 713 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 133 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 133 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 133 + ], + "type": "text", + "content": "[1] Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to-end object detection with transformers. In ECCV, pages 213-229, 2020. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 135, + 287, + 178 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 135, + 287, + 178 + ], + "spans": [ + { + "bbox": [ + 53, + 135, + 287, + 178 + ], + "type": "text", + "content": "[2] Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, and Wen Gao. Pre-trained image processing transformer. In CVPR, pages 12299-12310, 2021. 1, 2, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 180, + 287, + 223 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 180, + 287, + 223 + ], + "spans": [ + { + "bbox": [ + 53, + 180, + 287, + 223 + ], + "type": "text", + "content": "[3] Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, and Zhangyang Wang. Chasing sparsity in vision transformers: An end-to-end exploration. Advances in Neural Information Processing Systems, 34:19974-19988, 2021. 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 224, + 287, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 224, + 287, + 267 + ], + "spans": [ + { + "bbox": [ + 53, + 224, + 287, + 267 + ], + "type": "text", + "content": "[4] Xiang Chen, Jinshan Pan, Kui Jiang, Yufeng Li, Yufeng Huang, Caihua Kong, Longgang Dai, and Zhentao Fan. Unpaired deep image deraining using dual contrastive learning. In CVPR, pages 2017-2026, 2022. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 269, + 287, + 301 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 269, + 287, + 301 + ], + "spans": [ + { + "bbox": [ + 53, + 269, + 287, + 301 + ], + "type": "text", + "content": "[5] Xiang Chen, Jinshan Pan, Jiyang Lu, Zhentao Fan, and Hao Li. Hybrid cnn-transformer feature fusion for single image deraining. In AAAI, 2023. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 302, + 287, + 334 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 302, + 287, + 334 + ], + "spans": [ + { + "bbox": [ + 53, + 302, + 287, + 334 + ], + "type": "text", + "content": "[6] Gonçalo M Correia, Vlad Niculae, and André FT Martins. Adaptively sparse transformers. arXiv preprint arXiv:1909.00015, 2019.3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 335, + 287, + 400 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 335, + 287, + 400 + ], + "spans": [ + { + "bbox": [ + 53, + 335, + 287, + 400 + ], + "type": "text", + "content": "[7] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In ICLR, 2021. 2, 3, 4, 7, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 402, + 287, + 444 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 402, + 287, + 444 + ], + "spans": [ + { + "bbox": [ + 53, + 402, + 287, + 444 + ], + "type": "text", + "content": "[8] Xueyang Fu, Jiabin Huang, Delu Zeng, Yue Huang, Xinghao Ding, and John Paisley. Removing rain from single images via a deep detail network. In CVPR, pages 3855-3863, 2017. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 446, + 287, + 479 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 446, + 287, + 479 + ], + "spans": [ + { + "bbox": [ + 53, + 446, + 287, + 479 + ], + "type": "text", + "content": "[9] Xueyang Fu, Qi Qi, Zheng-Jun Zha, Yurui Zhu, and Xing-hao Ding. Rain streak removal via dual graph convolutional network. In AAAI, pages 1352-1360, 2021. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 480, + 287, + 512 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 480, + 287, + 512 + ], + "spans": [ + { + "bbox": [ + 48, + 480, + 287, + 512 + ], + "type": "text", + "content": "[10] Xueyang Fu, Jie Xiao, Yurui Zhu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Continual image deraining with hypergraph convolutional networks. IEEE TPAMI, 2023. 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 514, + 287, + 546 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 514, + 287, + 546 + ], + "spans": [ + { + "bbox": [ + 48, + 514, + 287, + 546 + ], + "type": "text", + "content": "[11] Zhihong Fu, Zehua Fu, Qingjie Liu, Wenrui Cai, and Yunhong Wang. Sparsett: Visual tracking with sparse transformers. arXiv preprint arXiv:2205.03776, 2022. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 547, + 287, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 547, + 287, + 579 + ], + "spans": [ + { + "bbox": [ + 48, + 547, + 287, + 579 + ], + "type": "text", + "content": "[12] Shuhang Gu, Deyu Meng, Wangmeng Zuo, and Lei Zhang. Joint convolutional analysis and synthesis: sparse representation for single image layer separation. In ICCV, 2017. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 580, + 287, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 580, + 287, + 624 + ], + "spans": [ + { + "bbox": [ + 48, + 580, + 287, + 624 + ], + "type": "text", + "content": "[13] Chun-Le Guo, Qixin Yan, Saeed Anwar, Runmin Cong, Wenqi Ren, and Chongyi Li. Image dehazing transformer with transmission-aware 3d position embedding. In CVPR, pages 5812-5820, 2022. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 624, + 287, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 287, + 646 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 287, + 646 + ], + "type": "text", + "content": "[14] Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. In CVPR, pages 7132-7141, 2018. 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 647, + 287, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 287, + 679 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 287, + 679 + ], + "type": "text", + "content": "[15] Xiaowei Hu, Chi-Wing Fu, Lei Zhu, and Pheng-Ann Heng. Depth-attentional features for single-image rain removal. In CVPR, pages 8022–8031, 2019. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 681, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 681, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 681, + 287, + 713 + ], + "type": "text", + "content": "[16] Huaibo Huang, Aijing Yu, and Ran He. Memory oriented transfer learning for semi-supervised image deraining. In CVPR, pages 7732-7741, 2021. 2" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 36, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "text", + "content": "[17] Robert A Jacobs, Michael I Jordan, Steven J Nowlan, and Geoffrey E Hinton. Adaptive mixtures of local experts. Neural computation, 3(1):79-87, 1991. 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 107, + 545, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 545, + 149 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 545, + 149 + ], + "type": "text", + "content": "[18] Kui Jiang, Zhongyuan Wang, Chen Chen, Zheng Wang, Laizhong Cui, and Chia-Wen Lin. Magic elf: Image deraining meets association learning and transformer. In ACM MM, 2022. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 195 + ], + "type": "text", + "content": "[19] Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, and Junjun Jiang. Multi-scale progressive fusion network for single image deraining. In CVPR, pages 8346-8355, 2020. 1, 2, 4, 5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 197, + 545, + 228 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 197, + 545, + 228 + ], + "spans": [ + { + "bbox": [ + 307, + 197, + 545, + 228 + ], + "type": "text", + "content": "[20] Li-Wei Kang, Chia-Wen Lin, and Yu-Hsiang Fu. Automatic single-image-based rain streaks removal via image decomposition. IEEE TIP, 21(4):1742-1755, 2011. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 230, + 545, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 230, + 545, + 262 + ], + "spans": [ + { + "bbox": [ + 307, + 230, + 545, + 262 + ], + "type": "text", + "content": "[21] Sijin Kim, Namhyuk Ahn, and Kyung-Ah Sohn. Restoring spatially-heterogeneous distortions using mixture of experts network. In ACCV, 2020. 5, 8" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 264, + 545, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 264, + 545, + 297 + ], + "spans": [ + { + "bbox": [ + 307, + 264, + 545, + 297 + ], + "type": "text", + "content": "[22] Hunsang Lee, Hyesong Choi, Kwanghoon Sohn, and Dongbo Min. Knn local attention for image restoration. In CVPR, pages 2139-2149, 2022. 8" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 298, + 545, + 341 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 298, + 545, + 341 + ], + "spans": [ + { + "bbox": [ + 307, + 298, + 545, + 341 + ], + "type": "text", + "content": "[23] Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, and Hongbin Zha. Recurrent squeeze-and-excitation context aggregation net for single image deraining. In ECCV, pages 254-269, 2018. 1, 2, 5" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 342, + 545, + 375 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 342, + 545, + 375 + ], + "spans": [ + { + "bbox": [ + 307, + 342, + 545, + 375 + ], + "type": "text", + "content": "[24] Yu Li, Robby T Tan, Xiaojie Guo, Jiangbo Lu, and Michael S Brown. Rain streak removal using layer priors. In CVPR, pages 2736-2744, 2016. 1, 2, 5" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 376, + 545, + 409 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 376, + 545, + 409 + ], + "spans": [ + { + "bbox": [ + 307, + 376, + 545, + 409 + ], + "type": "text", + "content": "[25] Yawei Li, Kai Zhang, Jiezhang Cao, Radu Timofte, and Luc Van Gool. Localvit: Bringing locality to vision transformers. arXiv preprint arXiv:2104.05707, 2021. 7, 8" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 411, + 545, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 411, + 545, + 442 + ], + "spans": [ + { + "bbox": [ + 307, + 411, + 545, + 442 + ], + "type": "text", + "content": "[26] Yuanchu Liang, Saeed Anwar, and Yang Liu. Drt: A lightweight single image deraining recursive transformer. In CVPRW, pages 589-598, 2022. 1" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 444, + 545, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 444, + 545, + 476 + ], + "spans": [ + { + "bbox": [ + 307, + 444, + 545, + 476 + ], + "type": "text", + "content": "[27] Yang Liu, Ziyu Yue, Jinshan Pan, and Zhixun Su. Unpaired learning for deep image deraining with rain direction regularizer. In ICCV, pages 4753-4761, 2021. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 478, + 545, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 478, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 307, + 478, + 545, + 521 + ], + "type": "text", + "content": "[28] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, pages 10012-10022, 2021. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 307, + 522, + 545, + 544 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 522, + 545, + 544 + ], + "spans": [ + { + "bbox": [ + 307, + 522, + 545, + 544 + ], + "type": "text", + "content": "[29] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. In *ICLR*, 2016. 5" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 307, + 545, + 545, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 545, + 545, + 578 + ], + "spans": [ + { + "bbox": [ + 307, + 545, + 545, + 578 + ], + "type": "text", + "content": "[30] Yu Luo, Yong Xu, and Hui Ji. Removing rain from a single image via discriminative sparse coding. In ICCV, pages 3397-3405, 2015. 2, 5" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 307, + 579, + 545, + 611 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 579, + 545, + 611 + ], + "spans": [ + { + "bbox": [ + 307, + 579, + 545, + 611 + ], + "type": "text", + "content": "[31] Yiqun Mei, Yuchen Fan, and Yuqian Zhou. Image superresolution with non-local sparse attention. In CVPR, pages 3517-3526, 2021. 3" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 307, + 613, + 545, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 613, + 545, + 644 + ], + "spans": [ + { + "bbox": [ + 307, + 613, + 545, + 644 + ], + "type": "text", + "content": "[32] Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik. No-reference image quality assessment in the spatial domain. IEEE TIP, 21(12):4695-4708, 2012. 5" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 307, + 647, + 545, + 679 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 647, + 545, + 679 + ], + "spans": [ + { + "bbox": [ + 307, + 647, + 545, + 679 + ], + "type": "text", + "content": "[33] Anish Mittal, Rajiv Soundararajan, and Alan C Bovik. Making a \"completely blind\" image quality analyzer. IEEE SPL, 20(3):209-212, 2012. 5" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 307, + 681, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 681, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 307, + 681, + 545, + 712 + ], + "type": "text", + "content": "[34] Huynh-Thu Q. and Ghanbari M. Scope of validity of psnr in image/video quality assessment. *Electronics Letters*, 44(13):800-801, 2008. 5" + } + ] + } + ], + "index": 35 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5904" + } + ] + } + ], + "index": 37 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 16, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 106 + ], + "type": "text", + "content": "[35] Qin Qin, Jingke Yan, Qin Wang, Xin Wang, Minyao Li, and Yuqing Wang. Etdnet: An efficient transformer deraining model. IEEE Access, 9:119881-119893, 2021. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 106, + 287, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 106, + 287, + 149 + ], + "spans": [ + { + "bbox": [ + 48, + 106, + 287, + 149 + ], + "type": "text", + "content": "[36] Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng. Progressive image deraining networks: A better and simpler baseline. In CVPR, pages 3937-3946, 2019. 1, 2, 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 150, + 287, + 193 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 150, + 287, + 193 + ], + "spans": [ + { + "bbox": [ + 48, + 150, + 287, + 193 + ], + "type": "text", + "content": "[37] Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. Gated fusion network for single image dehazing. In CVPR, pages 3253-3261, 2018. 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 194, + 287, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 194, + 287, + 239 + ], + "spans": [ + { + "bbox": [ + 48, + 194, + 287, + 239 + ], + "type": "text", + "content": "[38] Aurko Roy, Mohammad Saffar, Ashish Vaswani, and David Grangier. Efficient content-based sparse attention with routing transformers. Transactions of the Association for Computational Linguistics, 9:53-68, 2021. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 239, + 287, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 239, + 287, + 282 + ], + "spans": [ + { + "bbox": [ + 48, + 239, + 287, + 282 + ], + "type": "text", + "content": "[39] Masanori Suganuma, Xing Liu, and Takayuki Okatani. Attention-based adaptive selection of operations for image restoration in the presence of unknown combined distortions. In CVPR, pages 9039-9048, 2019. 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 284, + 287, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 284, + 287, + 327 + ], + "spans": [ + { + "bbox": [ + 48, + 284, + 287, + 327 + ], + "type": "text", + "content": "[40] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 2, 4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 327, + 287, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 327, + 287, + 369 + ], + "spans": [ + { + "bbox": [ + 48, + 327, + 287, + 369 + ], + "type": "text", + "content": "[41] Cong Wang, Xiaoying Xing, Yutong Wu, Zhixun Su, and Junyang Chen. Dcsfn: Deep cross-scale fusion network for single image rain removal. In ACM MM, pages 1643-1651, 2020. 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 371, + 287, + 414 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 371, + 287, + 414 + ], + "spans": [ + { + "bbox": [ + 48, + 371, + 287, + 414 + ], + "type": "text", + "content": "[42] Haochen Wang, Jiayi Shen, Yongtuo Liu, Yan Gao, and Efstratos Gavves. Nformer: Robust person re-identification with neighbor transformer. In CVPR, pages 7297-7307, 2022. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 415, + 287, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 415, + 287, + 448 + ], + "spans": [ + { + "bbox": [ + 48, + 415, + 287, + 448 + ], + "type": "text", + "content": "[43] Hong Wang, Qi Xie, Qian Zhao, and Deyu Meng. A model-driven deep neural network for single image rain removal. In CVPR, pages 3103-3112, 2020. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 449, + 287, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 449, + 287, + 491 + ], + "spans": [ + { + "bbox": [ + 48, + 449, + 287, + 491 + ], + "type": "text", + "content": "[44] Pichao Wang, Xue Wang, Fan Wang, Ming Lin, Shuning Chang, Wen Xie, Hao Li, and Rong Jin. Kvt: k-nn attention for boosting vision transformers. In ECCV, 2022. 2, 3, 8" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 492, + 287, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 492, + 287, + 536 + ], + "spans": [ + { + "bbox": [ + 48, + 492, + 287, + 536 + ], + "type": "text", + "content": "[45] Tianyu Wang, Xin Yang, Ke Xu, Shaozhe Chen, Qiang Zhang, and Rynson WH Lau. Spatial attentive single-image deraining with a high quality real rain dataset. In CVPR, pages 12270-12279, 2019. 5, 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 537, + 287, + 568 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 537, + 287, + 568 + ], + "spans": [ + { + "bbox": [ + 48, + 537, + 287, + 568 + ], + "type": "text", + "content": "[46] Yinglong Wang, Chao Ma, and Bing Zeng. Multi-decoding deraining network and quasi-sparsity based training. In CVPR, pages 13375–13384, 2021. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 570, + 287, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 570, + 287, + 602 + ], + "spans": [ + { + "bbox": [ + 48, + 570, + 287, + 602 + ], + "type": "text", + "content": "[47] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli. Image quality assessment: from error visibility to structural similarity. IEEE TIP, 13(4):600-612, 2004. 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 603, + 287, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 603, + 287, + 647 + ], + "spans": [ + { + "bbox": [ + 48, + 603, + 287, + 647 + ], + "type": "text", + "content": "[48] Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li. Uformer: A general u-shaped transformer for image restoration. In CVPR, pages 17683-17693, 2022. 1, 2, 3, 4, 5, 7" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 647, + 287, + 680 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 287, + 680 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 287, + 680 + ], + "type": "text", + "content": "[49] Wei Wei, Deyu Meng, Qian Zhao, Zongben Xu, and Ying Wu. Semi-supervised transfer learning for image rain removal. In CVPR, pages 3877-3886, 2019. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 681, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 681, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 681, + 287, + 712 + ], + "type": "text", + "content": "[50] Jie Xiao, Xueyang Fu, Aiping Liu, Feng Wu, and Zheng-Jun Zha. Image de-raining transformer. IEEE TPAMI, 2022. 1, 2, 3, 4, 5, 7" + } + ] + } + ], + "index": 15 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 611 + ], + "type": "list", + "angle": 0, + "index": 31, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 106 + ], + "type": "text", + "content": "[51] Fuzhi Yang, Huan Yang, Jianlong Fu, Hongtao Lu, and Baining Guo. Learning texture transformer network for image super-resolution. In CVPR, 2020. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 545, + 150 + ], + "type": "text", + "content": "[52] Wenhan Yang, Robby T Tan, Jiashi Feng, Jiaying Liu, Zongming Guo, and Shuicheng Yan. Deep joint rain detection and removal from a single image. In CVPR, pages 1357-1366, 2017. 1, 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 152, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 545, + 184 + ], + "type": "text", + "content": "[53] Wenhan Yang, Robby T Tan, Shiqi Wang, Yuming Fang, and Jiaying Liu. Single image deraining: From model-based to data-driven and beyond. IEEE TPAMI, 2020. 1, 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 186, + 545, + 219 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 545, + 219 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 545, + 219 + ], + "type": "text", + "content": "[54] Rajeev Yasarla, Vishwanath A Sindagi, and Vishal M Patel. Syn2real transfer learning for image deraining using gaussian processes. In CVPR, pages 2726-2736, 2020. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 220, + 545, + 253 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 220, + 545, + 253 + ], + "spans": [ + { + "bbox": [ + 307, + 220, + 545, + 253 + ], + "type": "text", + "content": "[55] Yuntong Ye, Yi Chang, Hanyu Zhou, and Luxin Yan. Closing the loop: Joint rain generation and removal via disentangled image translation. In CVPR, pages 2053-2062, 2021. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 254, + 545, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 254, + 545, + 297 + ], + "spans": [ + { + "bbox": [ + 307, + 254, + 545, + 297 + ], + "type": "text", + "content": "[56] Qiaosi Yi, Juncheng Li, Qinyan Dai, Faming Fang, Guixu Zhang, and Tieyong Zeng. Structure-preserving deraining with residue channel prior guidance. In ICCV, pages 4238-4247, 2021. 1, 5, 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 298, + 545, + 331 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 298, + 545, + 331 + ], + "spans": [ + { + "bbox": [ + 307, + 298, + 545, + 331 + ], + "type": "text", + "content": "[57] Kun Yuan, Shaopeng Guo, Ziwei Liu, Aojun Zhou, Fengwei Yu, and Wei Wu. Incorporating convolution designs into visual transformers. In ICCV, pages 579-588, 2021. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 332, + 545, + 386 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 332, + 545, + 386 + ], + "spans": [ + { + "bbox": [ + 307, + 332, + 545, + 386 + ], + "type": "text", + "content": "[58] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. Restormer: Efficient transformer for high-resolution image restoration. In CVPR, pages 5728-5739, 2022. 1, 2, 3, 4, 5, 7, 8" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 388, + 545, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 388, + 545, + 431 + ], + "spans": [ + { + "bbox": [ + 307, + 388, + 545, + 431 + ], + "type": "text", + "content": "[59] Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. Multi-stage progressive image restoration. In CVPR, pages 14821-14831, 2021. 5, 7" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 433, + 545, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 433, + 545, + 465 + ], + "spans": [ + { + "bbox": [ + 307, + 433, + 545, + 465 + ], + "type": "text", + "content": "[60] He Zhang and Vishal M Patel. Convolutional sparse and low-rank coding-based rain streak removal. In WACV, pages 1259-1267, 2017. 1, 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 467, + 545, + 499 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 467, + 545, + 499 + ], + "spans": [ + { + "bbox": [ + 307, + 467, + 545, + 499 + ], + "type": "text", + "content": "[61] He Zhang and Vishal M Patel. Density-aware single image de-raining using a multi-stream dense network. In CVPR, pages 695-704, 2018. 2, 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 501, + 545, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 501, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 307, + 501, + 545, + 533 + ], + "type": "text", + "content": "[62] He Zhang, Vishwanath Sindagi, and Vishal M Patel. Image de-raining using a conditional generative adversarial network. IEEE TCSVT, 30(11):3943-3956, 2019. 8" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 534, + 545, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 534, + 545, + 567 + ], + "spans": [ + { + "bbox": [ + 307, + 534, + 545, + 567 + ], + "type": "text", + "content": "[63] Jiale Zhang, Yulun Zhang, Jinjin Gu, Yongbing Zhang, Linghe Kong, and Xin Yuan. Accurate image restoration with attention retractable transformer. *ICLR*, 2023. 3" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 307, + 568, + 545, + 611 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 568, + 545, + 611 + ], + "spans": [ + { + "bbox": [ + 307, + 568, + 545, + 611 + ], + "type": "text", + "content": "[64] Guangxiang Zhao, Junyang Lin, Zhiyuan Zhang, Xuancheng Ren, Qi Su, and Xu Sun. Explicit sparse transformer: Concentrated attention through explicit selection. ICLR, 2020. 2, 3, 4" + } + ] + } + ], + "index": 30 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "5905" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning and Aggregating Lane Graphs for Urban Automated Driving/71072545-68f3-478a-8f5b-1abe54803639_content_list.json b/2023/Learning and Aggregating Lane Graphs for Urban Automated Driving/71072545-68f3-478a-8f5b-1abe54803639_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..7baaa06f2175973c1c2d12e4260199201ed28398 --- /dev/null +++ b/2023/Learning and Aggregating Lane Graphs for Urban Automated Driving/71072545-68f3-478a-8f5b-1abe54803639_content_list.json @@ -0,0 +1,1353 @@ +[ + { + "type": "text", + "text": "Learning and Aggregating Lane Graphs for Urban Automated Driving", + "text_level": 1, + "bbox": [ + 127, + 130, + 841, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Martin Büchner $^{1*}$ Jannik Zürn $^{1*}$ Ion-George Todoran $^{2}$ Abhinav Valada $^{1}$ Wolfram Burgard $^{3}$ $^{1}$ University of Freiburg $^{2}$ Woven by Toyota $^{3}$ University of Technology Nuremberg", + "bbox": [ + 99, + 179, + 867, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 253, + 313, + 271 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Lane graph estimation is an essential and highly challenging task in automated driving and HD map learning. Existing methods using either onboard or aerial imagery struggle with complex lane topologies, out-of-distribution scenarios, or significant occlusions in the image space. Moreover, merging overlapping lane graphs to obtain consistent large-scale graphs remains difficult. To overcome these challenges, we propose a novel bottom-up approach to lane graph estimation from aerial imagery that aggregates multiple overlapping graphs into a single consistent graph. Due to its modular design, our method allows us to address two complementary tasks: predicting ego-respective successor lane graphs from arbitrary vehicle positions using a graph neural network and aggregating these predictions into a consistent global lane graph. Extensive experiments on a large-scale lane graph dataset demonstrate that our approach yields highly accurate lane graphs, even in regions with severe occlusions. The presented approach to graph aggregation proves to eliminate inconsistent predictions while increasing the overall graph quality. We make our large-scale urban lane graph dataset and code publicly available at http://urbanlanegraph.cs.uni-freiburg.de.", + "bbox": [ + 76, + 287, + 473, + 619 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 651, + 209, + 667 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Most automated driving vehicles rely on the knowledge of their immediate surroundings to safely navigate urban environments. Onboard sensors including LiDARs and cameras provide perception inputs that are utilized in multiple tasks such as localization [7, 21, 27], tracking [4], or scene understanding [20, 24, 26, 37] to aggregate representations of the environment. However, robust planning and control typically require vastly more detailed and less noisy world models in the form of HD map data [12]. In particular, information on lane parametrization and connectivity is essential for both planning future driving maneuvers as well as high-level navigation tasks. Creating and maintaining HD maps in the form of lane graphs is a time-consuming and arduous", + "bbox": [ + 76, + 676, + 470, + 875 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/230389817c2eb7990cec05d3cf265dc55cc38aaaf2d903a95a1b38b9baa4102b.jpg", + "image_caption": [ + "Figure 1. Our approach predicts accurate lane graphs from aerial images of complex urban environments. We visualize the estimated lane graph in magenta and indicate model initialization points with yellow circles." + ], + "image_footnote": [], + "bbox": [ + 503, + 253, + 890, + 491 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "task due to the large amount of detail required in the annotation and the data curation process including map updates based on local environment changes such as construction sites.", + "bbox": [ + 496, + 563, + 893, + 625 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Previous approaches to lane graph estimation have shown shortcomings in predicting lane graphs due to multiple deficiencies: On the one hand, methods using onboard imagery typically degrade at complex real-world intersections and under significant occlusions, e.g., when following another vehicle [5, 6]. On the other hand, methods based on aerial imagery show reduced performance when confronted with occlusions in the bird's-eye-view (BEV) due to, e.g., vegetation or shadows, and suffer from catastrophic drift when unconstrained in out-of-distribution scenarios [30]. Previous works treat intersections and non-intersections inherently differently [15] and thus require elaborated heuristics and post-processing to merge single predictions into a consistent lane graph. Moreover, prior works do not focus on use cases where multiple predicted graphs must be merged into a single consistent solution, which is essential for enabling the automatic generation of highly detailed lane graphs of large contiguous regions.", + "bbox": [ + 496, + 628, + 895, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Equal contribution", + "bbox": [ + 94, + 886, + 202, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "13415", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Related to the aforementioned challenges, we propose a novel two-stage graph neural network (GNN) approach termed LaneGNN that operates on single aerial color images for lane graph prediction. Inspired by methods in the field of trajectory prediction [8], we formulate a bottom-up approach according to which we place a virtual agent into a local crop of the aerial image and predict reachable successor lane graphs from its positions. To transform multiple disjoint local solutions into a single global solution, we aggregate a global representation by iteratively inferring the lane graph from consecutive poses, ultimately imitating real-world driving behavior. This iterative approach not only increases the predicted area covered but also improves graph accuracy based on data association and rejection. Note that we do not require any human in the loop to perform the graph aggregation. We visualize the output of our graph aggregation procedure in Fig. 1, in which we superimpose the predicted graph on the aerial image input. Using this framework, we envision two applications: ego-centered successor path prediction and full lane graph estimation by aggregation.", + "bbox": [ + 76, + 90, + 472, + 393 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To summarize, the main contributions of this work are:", + "bbox": [ + 96, + 395, + 460, + 409 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- An innovative bottom-up approach to lane graph estimation in challenging environments that explicitly encodes graph-level lane topology from input aerial images in a scenario-agnostic manner.", + "- A novel graph aggregation scheme enabling robust and method-agnostic merging of graph-level predictions.", + "- The large-scale lane graph dataset UrbanLaneGraph comprising high-resolution aerial images aligned with dense lane graph annotations aggregated from the Argoverse2 dataset that we make publicly available.", + "- Extensive experiments and ablation studies demonstrating the significance of our findings." + ], + "bbox": [ + 96, + 412, + 468, + 592 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 76, + 612, + 225, + 628 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In recent years, the prediction of topological road features such as road graphs and lane graphs have been extensively studied. In our discussion, we differentiate between road graph learning and lane graph learning. While road graphs encode the topological connections between road segments, lane graphs describe the locations and connectivity between all lanes, resulting in a spatially much denser graph. Many prior works focus on vehicle trajectory prediction, conditioned on HD map features such as lane centerline and boundary positions [9, 10, 28]. These models do not aim at exclusively predicting the road or lane graph structure from onboard vehicle images or aerial images but instead at predicting future vehicle states such as position and orientation.", + "bbox": [ + 76, + 640, + 472, + 835 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Road Graph Learning: Prior works investigate estimating road graphs from both onboard sensors [18] and from aerial images [1, 22, 34] or focus on extracting pixel-level road segmentation from images and extracting graphical road rep", + "bbox": [ + 76, + 839, + 472, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "resentations, i.e., using morphological image operators or graph neural networks to extract the connectivity between different roads within the image [1, 19]. Other approaches investigate iterative methods and interpret road graph prediction as a sequential prediction task [2, 22].", + "bbox": [ + 496, + 90, + 893, + 167 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Lane Graph Learning from Vehicle Data: Some earlier works in lane graph learning from onboard vehicle sensors such as cameras and LiDAR formulate lane extraction as an image-based lane centerline regression task [16]. Homayounfar et al. [17] aggregate onboard LiDAR data on highways and leverage a recurrent neural network to generate highway lane graphs in an iterative manner. Zhou et al. [35] utilize the OpenStreetMap database and a semantic particle filter to accumulate projected semantic predictions from a vehicle ego-view into a map representation. Zhang et al. [31] propose an online road map extraction system for a sensor setup onboard a moving vehicle and construct a graph representation of the road network using a fully convolutional neural network. More recently, Can et al. proposed two different methods [5, 6] for lane connectivity learning in intersection scenarios from onboard camera images.", + "bbox": [ + 496, + 170, + 893, + 412 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Lane Graph Learning from Birds-Eye-View Data: Despite the advantages of leveraging readily available aerial data for training lane graphs, only a few works considered using aerial images as an input modality for the graph learning task. Zürn et al. [36] propose a lane centerline regression model jointly with a Graph R-CNN backbone to predict nodes and edges of the lane graph from a local aggregated bird's-eye-view image crop. More recently, He et al. [15] propose a two-stage graph estimation pipeline. They first extract lanes at non-intersection areas and subsequently predict the connectivity of each pair of lanes, and extract the valid turning lanes to complete the map at intersections.", + "bbox": [ + 496, + 415, + 893, + 597 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In contrast to existing works, we do not estimate the complete lane graph visible in a given crop but only the part of the graph that is reachable from a virtual agent pose located within the crop, simplifying the graph estimation problem based on reduced topological complexity. Furthermore, we leverage a GNN to explicitly model the relationships between graph nodes, allowing us to leverage recent developments in the field of geometric deep learning. This explicit graph encoding and prediction allows us to formulate a model that does not internally differentiate between intersection areas and non-intersection areas, in contrast to some related works. Additionally, we propose a novel large-scale dataset for lane graph estimation from aerial images, allowing the research community to easily evaluate and compare their approaches.", + "bbox": [ + 496, + 599, + 895, + 828 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "3. Dataset", + "text_level": 1, + "bbox": [ + 500, + 844, + 588, + 859 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To evaluate our approach on challenging real-world data, we compiled the UrbanLaneGraph dataset. It is a first-of-its", + "bbox": [ + 500, + 869, + 893, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "13416", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/d0ae2fedc68d6e8fd2c1fa3ebf83e3f7d526ceaee8c0134617e5ec2217d391d1.jpg", + "image_caption": [ + "Figure 2. Overview of our LaneGNN model $\\mathcal{M}$ predicting successor lane graphs. As a pre-training step, we train lane centerline and ego lane centerline regressor models. The ego lane regression $\\mathbf{S}_{\\text{lane}}^{\\text{ego}}$ is used as a prior for sampling proposal nodes $V$ in corridors that have a high likelihood of entailing the successor graph. The model learns a binary classification of node and edge scores while also predicting the probability of a node being an endpoint of a given lane segment." + ], + "image_footnote": [], + "bbox": [ + 76, + 85, + 893, + 200 + ], + "page_idx": 2 + }, + { + "type": "table", + "img_path": "images/fdddf529981cfbf4ba345bdc2719c0c8428c819ec606fe17b926fe977bf716fb.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetCityLane Splits/MergesTotal Length [km]
UrbanLaneGraph (from Argoverse2)Palo Alto4752796.4
Austin8495531.7
Miami7642850.8
Pittsburgh146101314.3
Washington D.C.2066739.6
Detroit5424990.5
LaneExtraction [15]Boston, Seattle, Phoenix, Miami2262398.6
NuScenes [11]Boston163076.9
", + "bbox": [ + 101, + 268, + 446, + 411 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Table 1. Key statistics of our UrbanLaneGraph dataset, aggregated from the Argoverse2 dataset [29], used for our experiments, compared with other recent datasets for lane graph estimation.", + "bbox": [ + 75, + 419, + 470, + 458 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "kind dataset for large-scale lane graph estimation from aerial images. The dataset contains aerial images from the cities of Austin, Miami, Pittsburgh, Palo Alto, Detroit, and Washington DC. The images have a resolution of $15\\mathrm{cm}$ per pixel. To obtain the corresponding lane annotations, we leverage the Argoverse2 dataset [29] which entails lane graphs for large sections of the respective cities. The lane graphs in the Argoverse2 dataset are provided on a per-scenario basis, covering only small local areas in one graph. Therefore, we collect all local lane graphs of each city and aggregate them into one globally consistent graph per city, thereby removing inconsistent or redundant nodes or edges. The annotated regions feature a diverse range of environments including urban, suburban, and rural regions with complex lane topologies. Accumulated over all cities, the overall length of all lanes spans over $5.000\\mathrm{km}$ . We split each city into disjoint training and testing regions. We list key dataset statistics in Tab. 1, indicating the scale of our generated dataset compared with other recently proposed datasets containing graph annotations. For more details on the dataset and exemplary visualizations, please refer to Sec. S.1 in the supplementary material.", + "bbox": [ + 75, + 476, + 472, + 792 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Technical Approach", + "text_level": 1, + "bbox": [ + 76, + 813, + 269, + 830 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our approach is divided into two stages: lane graph learning and lane graph aggregation. In the first stage (Sec. 4.1), we train our GNN model, denoted as LaneGNN, to predict the successor lane graph, entailing the nodes and edges", + "bbox": [ + 75, + 839, + 472, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "that can be logically visited from the pose of a virtual vehicle agent. In the second phase (Sec. 4.2), we use our trained LaneGNN model to traverse a large map area. This is achieved by selecting an initial starting pose and predicting the successor lane graph from this pose. Subsequently, we iteratively estimate the traversable lane graph from the current pose and move forward along the predicted graph while aggregating. In the following, we detail both stages of our approach.", + "bbox": [ + 496, + 271, + 893, + 409 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.1. Lane Graph Learning", + "text_level": 1, + "bbox": [ + 498, + 417, + 705, + 434 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We formulate the task as a supervised learning problem where a successor lane graph $\\hat{G}$ is estimated based on an aerial image $\\mathbf{I}_{bev}$ . First, a directed graph $G_{I} = (V,E)$ covering relevant regions is constructed by sampling from likely regions of $\\mathbf{I}_{bev}$ (see Fig. 2). For all our models and experiments we choose a spatial resolution of $256\\times 256$ pixels. The graph comprises a set of nodes $i\\in V$ that are connected via directed edges $E\\subseteq \\{(i,j)|(i,j)\\in V^2$ and $i\\neq j\\}$ that constitute potentially valid lane graph edges. The graph is attributed using both node features $\\mathbf{X}\\in \\mathbb{R}^{\\| V\\| \\times D}$ and edge features $\\mathbf{X}_e\\in \\mathbb{R}^{\\| E\\| \\times (D_{geo} + D_{bev})}$ , where $D_{geo}$ and $D_{bev}$ denote the dimensionality of the involved edge features (see Sec. 4.1). The overall model estimates an output graph $\\hat{G} = \\mathcal{M}(G_I|\\theta)$ , where $\\mathcal{M}$ is parameterized by network model weights $\\theta \\coloneqq (\\theta_{reg},\\theta_{GNN})$ .", + "bbox": [ + 496, + 441, + 893, + 669 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Lane Regression and Graph Construction: We train two regression networks: Firstly, a centerline regression network predicting the likelihood map of lane centerlines $\\mathbf{S}_{\\text {lane }}$ , and secondly, a segmentation network predicting all reachable lanes $\\mathbf{S}_{\\text {lane }}^{\\text {ego }}$ starting from the initial virtual agent pose at the bottom center of $\\mathbf{I}_{\\text {bev }}$ . We use identical PSPNet [33] architectures with a ResNet-152 feature extractor for this task. We sample equally-distributed node positions using Halton sequences [14] that are later filtered based on the obtained ego-lane segmentation mask $\\mathbf{S}_{\\text {lane }}^{\\text {ego }}$ , which serves as a region of interest for sampling (see Fig. 2). Directed edges $E$ among nodes are initialized for pairs of nodes with a Euclidean distance $d_{ij} \\in [d_{\\min}, d_{\\max}]$ . Initial node features $\\mathbf{X}$ are crafted solely based on their 2D positions $\\mathbf{x}_i = (x_i, y_i)$ , while geometric edge features are defined as", + "bbox": [ + 496, + 674, + 895, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "13417", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/407c9ff555e79867676abb0afa03327503510f77169a4b7371156d3e96eea80d.jpg", + "image_caption": [ + "Temporal evolution of local BEV crops" + ], + "image_footnote": [], + "bbox": [ + 80, + 88, + 318, + 243 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/7396d3f022676ae324cabe31a31342df11055cddd5769f1d6cb7fac054c7a1ab.jpg", + "image_caption": [ + "Figure 3. In our graph aggregation procedure, we iteratively obtain oriented image crops based on virtual agent poses along the currently predicted successor graph. For each crop, our LaneGNN model predicts a successor graph $G_{pred}$ , which is aggregated into a globally consistent lane graph $G_{agg}$ ." + ], + "image_footnote": [], + "bbox": [ + 377, + 88, + 617, + 243 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/1ba4dc55f165808e7614c9d49259db931ab428f1f8b032b4ef77db69a6ebcaee.jpg", + "image_caption": [ + "Temporal evolution of aggregated graph" + ], + "image_footnote": [], + "bbox": [ + 622, + 88, + 892, + 243 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {x} _ {i j, g e o} = \\left(\\tan^ {- 1} \\frac {\\Delta y _ {i j}}{\\Delta x _ {i j}}, \\sqrt {\\Delta x _ {i j} {} ^ {2} + \\Delta y _ {i j} {} ^ {2}}, \\bar {x} _ {i j}, \\bar {y} _ {i j}\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 96, + 327, + 470, + 357 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\Delta x_{ij}$ and $\\Delta y_{ij}$ represent node position differences and $\\overline{x}_{ij}$ , $\\overline{y}_{ij}$ are edge middle point coordinates. In addition to the geometric edge feature, we generate aerial edge features: Per edge, a small oriented region of $\\mathbf{I}_{bev}$ and the lane segmentation $\\mathbf{S}_{lane}$ is obtained based on the direction of the edge, which provides $\\mathbf{X}_{ij,bev} = [\\mathbf{I}_{bev}^{*},\\mathbf{S}_{lane}^{*}]$ with dimension $D_{bev} = 4\\times 32\\times 32$", + "bbox": [ + 75, + 364, + 468, + 470 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Feature Encoding and Message Passing: In our approach, we estimate edge probabilities, node probabilities, and whether a node is terminal. While the nodes themselves hold only unidirectional information, we encode the notion of direction using the proposed edge feature as outlined above. We utilize a causal variant of neural message passing as proposed by Brasó et al. [3]. By imposing a causality prior, our network encodes predecessor and successor features during message passing. This formulation of message passing renders our approach direction-aware. Initial node and geometric edge features are encoded using multi-layer perceptrons $f_{enc}^{\\square}$ (MLP) while the aerial edge feature is transformed using a ResNet-18 architecture $f_{enc}^{e,bev}$ . The geometric and aerial edge features are concatenated and fused subsequently to arrive at various node and edge embeddings $\\mathbf{H}_v^{(0)}$ and $\\mathbf{H}_e^{(0)}$ :", + "bbox": [ + 75, + 477, + 470, + 719 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nf _ {e n c} ^ {e, b e v} \\left(\\mathbf {X} _ {e, b e v}\\right) = \\mathbf {H} _ {e, b e v} ^ {(0)}, f _ {e n c} ^ {e, g e o} \\left(\\mathbf {X} _ {e, g e o}\\right) = \\mathbf {H} _ {e, g e o} ^ {(0)}, (2)\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 727, + 470, + 747 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nf _ {e n c} ^ {v} (\\mathbf {X}) = \\mathbf {H} _ {v} ^ {(0)}, f _ {f u s e} ^ {e} ([ \\mathbf {H} _ {e, g e o} ^ {(0)}, \\mathbf {H} _ {e, b e v} ^ {(0)} ]) = \\mathbf {H} _ {e} ^ {(0)}. (3)\n$$\n", + "text_format": "latex", + "bbox": [ + 114, + 766, + 468, + 785 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In the following, multiple message-passing steps are performed using various ReLU-activated MLPs denoted by $f_{\\square}$ as follows. The edge feature is updated based on the current neighboring node features $\\mathbf{h}_i^{(l - 1)}$ , $\\mathbf{h}_j^{(l - 1)}$ and the edge feature $\\mathbf{h}_{ij}^{(l - 1)}$ :", + "bbox": [ + 75, + 787, + 470, + 873 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {h} _ {i j} ^ {(l)} = f _ {e} \\left(\\left[ \\mathbf {h} _ {i} ^ {(l - 1)}, \\mathbf {h} _ {j} ^ {(l - 1)}, \\mathbf {h} _ {i j} ^ {(l - 1)} \\right]\\right). \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 880, + 470, + 906 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Messages $\\mathbf{m}_{ij}^{(l)}$ are crafted based on either predecessors $\\mathcal{N}_{pred}(i)$ or successors $\\mathcal{N}_{succ}(i)$ of a node $i$ and propagated based on the constructed adjacency. In the next step, all predecessors and successor messages, respectively, are aggregated using a permutation-invariant sum:", + "bbox": [ + 498, + 305, + 893, + 383 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {h} _ {i, p r e d} ^ {(l)} = \\sum_ {j \\in \\mathcal {N} _ {p r e d} (i)} \\underbrace {f _ {v} ^ {p r e d} \\left(\\left[ \\mathbf {h} _ {j} ^ {(l - 1)} , \\mathbf {h} _ {i j} ^ {(l)} , \\mathbf {h} _ {j} ^ {(0)} \\right]\\right)} _ {\\mathbf {m} _ {j i} ^ {(l)}}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 545, + 392, + 892, + 441 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {h} _ {i, s u c c} ^ {(l)} = \\sum_ {j \\in \\mathcal {N} _ {s u c c} (i)} \\underbrace {f _ {v} ^ {s u c c} \\left(\\left[ \\mathbf {h} _ {j} ^ {(l - 1)} , \\mathbf {h} _ {i j} ^ {(l)} , \\mathbf {h} _ {j} ^ {(0)} \\right]\\right)} _ {\\mathbf {m} _ {i j} ^ {(l)}}. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 545, + 455, + 892, + 503 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Note that the message crafting includes skip connections to initial node embeddings $\\mathbf{h}_j^{(0)}$ . Nodes are updated by combining the two features using concatenation:", + "bbox": [ + 498, + 508, + 893, + 555 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {h} _ {i} ^ {(l)} = f _ {v} \\left(\\left[ \\mathbf {h} _ {i, p r e d} ^ {(l)}, \\mathbf {h} _ {i, s u c c} ^ {(l)} \\right]\\right). \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 598, + 566, + 890, + 590 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, sigmoid-valued edge scores $\\hat{e}_{ij}$ and node score $\\hat{s}_i$ are predicted from the obtained edge and node embeddings. In a separate network head, we classify each node as being a terminal node or not, denoted as a scalar $\\hat{t}_i$ . Therefore, we optimize the following combined binary cross entropy:", + "bbox": [ + 498, + 599, + 892, + 676 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = - \\sum_ {| V |} s _ {i} \\log \\hat {s} _ {i} - \\sum_ {| V |} t _ {i} \\log \\hat {t} _ {i} - \\sum_ {| E |} e _ {i j} \\log \\hat {e} _ {i j}. \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 535, + 686, + 890, + 718 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The ground truth graph $G_{GT}$ as a learning target $(s_i, t_i, e_{ij})$ is generated based on the map annotations for the given cropped region and the corresponding closest nodes. This is further outlined in the supplementary material in Sec. S.3.", + "bbox": [ + 498, + 729, + 892, + 790 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Iterative Temporal Graph Aggregation", + "text_level": 1, + "bbox": [ + 498, + 800, + 834, + 816 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In the second stage of our approach, we aggregate local successor graphs into a globally consistent lane graph. First, we prune the predicted per-crop lane graph, and second, we iteratively aggregate the sparse graphs. In the following, we detail both components.", + "bbox": [ + 496, + 824, + 893, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "13418", + "bbox": [ + 478, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/e426750ea4d61fd31a189d749cf08d8b89d80c59b7a5a56333707835caf1506e.jpg", + "image_caption": [ + "Figure 4. Comparison of raw and pruned lane graph predictions. On the left-hand side, we visualize estimated edge scores $\\hat{e}_{ij}$ and node scores $\\hat{s}_i$ using the same color scaling. On the right-hand side, we visualize the pruned graph. The circled numbers denote the order of traversal based on estimated terminal node scores." + ], + "image_footnote": [], + "bbox": [ + 81, + 89, + 272, + 234 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/1de6e21e2b71f6f65ca648fa2f609566381fa98bdf54a6bd129c1a5d841316a4.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 277, + 88, + 467, + 234 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Pruning: The graph prediction obtained from the LaneGNN model follows the graph connectivity initially generated during sampling. Since the model prediction contains a number of redundant paths with high predicted node and edge scores, we prune the obtained solution to obtain sparse lane representations. We formulate the graph pruning problem as a search problem from a starting node to possibly multiple predicted terminal nodes (see Sec. 4.1). Predicted lane split points should coincide with actual split points. Thus, different branches should share the same set of edges up to a split point. We use Dijkstra's algorithm to iteratively find high-score paths between the initial pose and terminal nodes, ordered from high to low scores until all terminal nodes are reached. Edge scores contained in found paths are set to zero. In Fig. 4, we visualize the output of this step.", + "bbox": [ + 75, + 333, + 472, + 561 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Aggregation: We iteratively aggregate predicted successor lane graphs $G_{pred} = (V_{pred}, E_{pred})$ into a globally consistent and complete graph $G_{agg} = (V_{agg}, E_{agg})$ as depicted in Fig. 3. The predicted successor graph $G_{pred}^{t}$ is added to the current aggregated graph $G_{agg}^{t-1}$ at time step $t$ : $G_{agg}^{t} \\gets \\text{aggregate}(G_{pred}^{t}, G_{agg}^{t-1})$ . Plausible branches of $G_{pred}$ are merged into $G_{agg}$ and thus extend the aggregated graph with every iteration if new grounds are covered in the respective crops. The next virtual agent pose is extracted from the set of forward-facing edges of $G_{agg}$ given the current pose. Only edges with a significant weight due to previous aggregations are selected for this set. As the node positions differ slightly with each model forward pass we observe roughly similar paths in the lateral sense wrt. the ground-truth graph. However, along the longitudinal dimension of a branch, we observe deviations in node position, which must be circumvented when aggregating the graph. Based on this, we only take the lateral deviation wrt. $G_{agg}^{t}$ into account when merging $G_{pred}^{t}$ . Thus, $G_{agg}^{t}$ is only updated in a lateral sense while the longitudinal misalignment of the two sets of nodes is neglected (see also Sec. S.5 in the supplementary material). The nodes of $G_{pred}^{t}$ have a", + "bbox": [ + 75, + 566, + 472, + 904 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "weight of 1 while a node of $G_{agg}^{t}$ holds a weight equal to the number of merges it has observed so far. If a novel node $i \\in V_{pred}$ is not close to any other node in $k \\in V_{agg}$ it is added to the aggregated graph including its incident edges. This ultimately allows a weighting-based merging of arbitrary pairs of graphs, which is used for global lane graph estimation (see Sec. 5.4).", + "bbox": [ + 496, + 90, + 893, + 196 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We observe that the more graphs we aggregate, the more we are certain about the significance of particular graph branches. Following a weighting-based approach allows us to set certain thresholds that allow modification of $G_{agg}$ . Thus, implausible graph branches, semantically similar parallel paths, redundant edges, and isolated nodes are deleted based on confidence and distance as well as angle criteria (see Fig. 3). As a result, we are able to decrease the number of false positive split and merge points to obtain more consistent global graphs. We observe that this approach greatly improves lane graph prediction accuracy in difficult occluded and out-of-distribution scenarios since the sum of model predictions covering the same region shed light onto what potentially constitutes, e.g., a valid and an invalid branch.", + "bbox": [ + 496, + 198, + 893, + 407 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Multiple forward passes naturally lead to a multitude of lane split points (both true positive and false positive splits). We interpret each split point as an element of an exploration frontier. A queue of unexplored, high-probability graph branches is maintained, which is queried in a depth-first manner as soon as the currently traversed branch terminates. Following the weighting-based approach, a branch is only explored if its depth-limited oriented successor tree weight exceeds a certain level of confidence. Due to this flexible approach, we can essentially handle arbitrary lane graph topologies with a single holistic approach. For more details on the graph pruning and aggregation procedures, please refer to the supplementary material. Finally, we apply multiple iterations of Laplacian smoothing to $G_{pred}^{t}$ , which modifies the original node positions in order to even out position irregularities caused by sampling while keeping the adjacency represented by $E_{pred}^{t}$ constant.", + "bbox": [ + 496, + 407, + 895, + 667 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experimental Results", + "text_level": 1, + "bbox": [ + 500, + 676, + 705, + 695 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In the following, we present our experimental findings. We first define and illustrate three tasks on which we benchmark our method. Subsequently, we describe the evaluation metrics and compare against other methods as well as our own baselines. We provide extensive qualitative and quantitative evaluations on our UrbanLaneGraph dataset.", + "bbox": [ + 496, + 702, + 893, + 794 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1. Proposed Tasks", + "text_level": 1, + "bbox": [ + 500, + 801, + 656, + 816 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We propose three distinct and complementary tasks. In the first task, successor lane graph prediction (Successor-LGP, Sec. 5.3), we aim at predicting a feasible ego-reachable successor lane graphs from the current pose of the virtual agent. The purpose of the Successor-LGP task is to measure", + "bbox": [ + 496, + 825, + 893, + 902 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "13419", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/a01f617d4d18448cc5d04f5d46bce0f41555a99361e5c20bb78da7fa62947b34.jpg", + "table_caption": [ + "Table 2. Quantitative results of our model including ablation studies, in comparison with baseline models for the Successor-LGP task on our UrbanLaneGraph dataset. P/R denotes Precision/Recall. For our LaneGNN model variants, we denote CMP as causal message passing, aerial node features as AerN, aerial edge features as AerE, and the ego-lane regression with $\\mathbf{S}_{\\text{lane}}^{\\text{ego}}$ . For all metrics, higher values mean better results." + ], + "table_footnote": [], + "table_body": "
ModelCMPAerNAerES laneTOPO P/R ↑GEO P/R ↑APLS ↑SDA20 ↑SDA50 ↑Graph IoU ↑
Skeletonized Regression----0.597/0.6130.578/0.6010.3150.0200.1850.180
LaneGraphNet [36]----0.0/0.00.0/0.00.1790.00.00.063
LaneGNN (ours)XX✓✓0.549/0.6770.548/0.6710.1880.1680.3230.312
✓✓X✓0.562/0.6560.562/0.6550.1920.1510.2980.320
✓XX✓0.545/0.6930.545/0.6880.2000.1880.3110.336
✓X✓X0.578/0.6690.577/0.6590.1500.1320.2270.250
✓X✓✓0.600/0.6990.599/0.6950.2020.2270.3770.347
", + "bbox": [ + 99, + 136, + 867, + 241 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "the prediction quality of potential future driving paths when no HD map coverage is available. In the second task, full lane graph prediction (Full-LGP, Sec. 5.4), we evaluate the quality of regionally aggregated lane graphs in the context of HD map estimation. This task aims at measuring the predictive power of our full two-stage model performing lane graph inference and graph aggregation in conjunction. For such purposes, the full ground truth lane graph of a given map area is compared to the aggregated predictions of our model. Finally, we carry out a high-level path planning task (Sec. 5.5) on the predicted lane graphs, intended to analyze the fidelity of routes planned on the predicted graphs.", + "bbox": [ + 75, + 255, + 472, + 436 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Evaluation Metrics", + "text_level": 1, + "bbox": [ + 76, + 455, + 261, + 470 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We leverage multiple complementary metrics for performance evaluation as detailed below.", + "bbox": [ + 75, + 483, + 470, + 513 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Graph IoU: This metric measures the intersection-over-union (IoU) of two graphs rendered as a binary image [36], where pixels closer than $d = 5$ pixels are assigned the label 1 and the remaining pixels the label 0. Equivalent to the evaluation of semantic segmentation models, we determine the IoU values for the non-zero pixels.", + "bbox": [ + 75, + 513, + 470, + 604 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The APLS metric sums the differences in optimal path lengths between nodes in the ground truth graph $G$ and the proposal graph $G'$ [25]. The APLS metric scales from 0 (worst) to 1 (best). Formally, it is defined as", + "bbox": [ + 75, + 604, + 470, + 664 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathrm {A P L S} = 1 - \\frac {1}{N _ {p}} \\sum \\min \\left\\{1, \\frac {\\left| d \\left(v _ {1} , v _ {2}\\right) - d \\left(v _ {1} ^ {\\prime} , v _ {2} ^ {\\prime}\\right) \\right|}{d \\left(v _ {1} , v _ {2}\\right)} \\right\\}, \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 93, + 684, + 470, + 715 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $v_{i}$ and $v_{i}^{\\prime}$ are nodes in $G$ and $G^{\\prime}$ , respectively. $N_{p}$ denotes the number of paths in $G$ and $d(\\cdot, \\cdot)$ is the path length. For more details, please refer to [25].", + "bbox": [ + 75, + 734, + 468, + 779 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "TOPO / GEO metrics: Following previous works in road network extraction and lane graph estimation, we use the GEO metric and the TOPO metric. For definitions and details on these metrics, please refer to [15] and to the supplementary material.", + "bbox": [ + 75, + 779, + 470, + 854 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Split Detection Accuracy $(\\mathbf{SDA}_{\\mathbf{R}})$ : This metric evaluates how accurately a model predicts the lane split within a circle of radius $R$ pixels from a given ground truth lane split.", + "bbox": [ + 75, + 854, + 470, + 901 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.3. Successor Lane Graph Prediction", + "text_level": 1, + "bbox": [ + 498, + 253, + 794, + 270 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In the following, we evaluate LaneGNN by ablating and comparing it with two baselines on the Successor-LGP task: morphological skeletonization of the ego-lane regression as well as a modified LaneGraphNet [36] to be used for successor lane graph prediction. We list quantitative results in Tab. 2. Our results demonstrate that none of the baseline methods are capable of estimating accurate lane graphs given the challenging topology of the lane graphs in the dataset. The LaneGraphNet [36] model fails to model the graph for many samples, yielding low scores in all metrics. Despite its simplicity, the skeletonized regression model achieves the highest APLS score and comparably high GEO/TOPO scores. However, it fails to accurately predict lane split points, resulting in low SDA scores. Increasing the number of nodes of the skeleton leads to many more false positive splits and thus deteriorates further.", + "bbox": [ + 496, + 279, + 893, + 520 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Regarding different variants of LaneGNN, we find that, e.g., causal message passing (CMP) increases performance over standard message passing, which underlines the significance of the imposed causality prior for lane graph learning. In order to show the efficacy of computationally more demanding aerial image edge features (AerE) compared to uni-directional aerial image node features (AerN), we ablate on this in Tab. 2 as well. We observe stark increases across the TOPO, GEO and SDA metrics when utilizing aerial edge features. Lastly, we replace the ego-lane segmentation mask $\\mathbf{S}_{lane}^{ego}$ used for sampling with the standard lane segmentation mask. Our findings show that especially APLS, SDA, and Graph IoU drastically decrease as graph estimation becomes more difficult due to a generally enlarged sampling region (see Fig. 2).", + "bbox": [ + 496, + 521, + 893, + 748 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "These results are further illustrated in Fig. 5, where we show qualitative comparisons of predictions of our best-performing model with predictions from the two baselines. We find that the quality of predictions by LaneGraphNet [36] is generally low, rendering it unsuitable for the task. The skeletonized regression baseline is capable of following basic lane graph topologies, but lane split points cannot be resolved accurately. In contrast, our LaneGNN model is capable of modeling most graphs with high accuracy; both in intersection areas and in straight road sections. For more", + "bbox": [ + 496, + 750, + 895, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "13420", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/30ab3d07f5345b826dc09cd8a2372540a541ca72d53688146b68ffc68571618c.jpg", + "image_caption": [ + "Figure 5. Qualitative results on the Successor-LGP task. We compare predictions of our model with LaneGraphNet [36] and a morphological image skeletonization baseline. Predicted nodes are visualized with points while predicted edges are visualized as directed arrows. We illustrate failure cases in the two rightmost columns. Best viewed zoomed in." + ], + "image_footnote": [], + "bbox": [ + 98, + 88, + 867, + 409 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/72a724059a3ca173ac262118dbbfc84b67c9eff44bce3f6fa711d3e646f989c5.jpg", + "table_caption": [ + "Table 3. Quantitative evaluation for the Full-LGP task on the test-set of our UrbanLaneGraph dataset. We compare a baseline model with graphs aggregated with a naive aggregation scheme and our iterative temporal aggregation scheme. P/R denotes Precision/Recall. Higher values mean better results." + ], + "table_footnote": [], + "table_body": "
ModelTOPO P/R ↑GEO P/R ↑APLS ↑Graph IoU ↑
LaneExtraction [15]0.405/0.5070.491/0.4540.0720.213
Aggregation (naive)0.366/0.6540.523/0.7270.1010.376
Aggregation (ours)0.481/0.6700.649/0.6890.1030.384
", + "bbox": [ + 81, + 551, + 460, + 614 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "results, please refer to the supplementary material, Sec. S.6.", + "bbox": [ + 76, + 633, + 470, + 648 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.4. Full Lane Graph Prediction", + "text_level": 1, + "bbox": [ + 76, + 662, + 328, + 680 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "For the Full-LGP task, we compare our approach with LaneExtraction [15]. Since their used graph representation is incompatible with ours, we train it on their provided dataset. To allow for a fair comparison, we evaluate both our method and LaneExtraction only on scenes in the city of Miami, Florida, as it is contained in both of the datasets. We select a testing region that is not part of the training data for either of the models. To initialize our aggregation scheme, we select starting poses obtained from intermediate segmentation predictions including yaw angles of the LaneExtraction model. Tab. 3 lists the evaluation results for the Full-LGP task obtained with LaneExtraction [15] and the results obtained with our LaneGNN model in conjunction with two aggregation schemes: a naive aggregation scheme baseline", + "bbox": [ + 75, + 688, + 470, + 900 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "and our full aggregation scheme. The naïve aggregation scheme merges nodes in close proximity while not relying on unvalidated split/merge or parallel path removal as well as the lateral weighting-based merging (Sec. 4.2). Our experiments show that our aggregation scheme outperforms both the LaneExtraction model and the naïve aggregation scheme on nearly all evaluation metrics. We note that our method improves the TOPO/GEO precision metrics while maintaining similar recalls due to better handling of redundant nodes. Fig. 6 illustrates successive aggregations from our model while indicating the used initialization points from the LaneExtraction model. Since our aggregation approach does not differentiate between intersection and non-intersection regions, it does not deteriorate in regions that do not exactly fit this categorization. Furthermore, our model exhibits superior performance in reduced visibility settings introduced by stark illumination changes or road occlusions from vegetation, as illustrated in Fig. 6. One of the decisive assets of our bottom-up method is that it allows to explore regions that are missed by LaneExtraction [15] as they are entailed in their predicted segmentation masks. For more qualitative and quantitative results, please refer to the supplementary material, Sec. S.7.", + "bbox": [ + 496, + 482, + 893, + 830 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.5. Path Planning", + "text_level": 1, + "bbox": [ + 500, + 844, + 648, + 861 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To illustrate the efficacy of our aggregation scheme, we evaluate the quality of the lane graph on a planning task. We", + "bbox": [ + 500, + 869, + 893, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "13421", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/a74c0e589d82200af6d540f3a83108e19b1c443824711783a97cb7ce8cd88a93.jpg", + "image_caption": [ + "Figure 6. Qualitative results on the Full-LGP task. We visualize predictions of LaneExtraction [15] (top row) and aggregated LaneGNN predictions (bottom row). Our model is initialized at poses using predicted lane direction masks of LaneExtraction (indicated with yellow circles). Best viewed zoomed in." + ], + "image_footnote": [], + "bbox": [ + 137, + 88, + 830, + 393 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/48db9db948b60080501e9e42e7851b7d5ec6c2353a61e05829f7f3167466c16b.jpg", + "table_caption": [ + "Table 4. Quantitative evaluation for the planning task. MMD denotes mean minimum distance, MED denotes mean endpoint distance, and SR denotes the path planning success rate." + ], + "table_footnote": [], + "table_body": "
ModelMMD [m] ↓MED [m] ↓SR ↑
LaneExtraction [15]157.0339.40.47
Aggregation (ours)2.219.70.46
", + "bbox": [ + 133, + 496, + 411, + 550 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "generate 1000 randomly selected starting poses in the Miami graph test area from which a plan to a random goal within the graph must be found, using A* search. We place the points such that a maximal optimal route length of $200\\mathrm{m}$ is not exceeded. To evaluate the planned routes, we compare the mean minimum distance (MMD) and the mean route endpoint distance (MED) between the paths on the predicted graph and the ground truth graph, respectively. We also report the success rate (SR), indicating the number of cases in which a path between start and goal exists. We list the results in Tab. 4. While the SR for our aggregation scheme and the LaneExtraction predictions is similar, the low MMD and MED values of our aggregation scheme indicate that our generated lane graph entails shorter and more direct paths, compared to LaneExtraction. We show additional results in the supplementary material, Sec. S.8.", + "bbox": [ + 75, + 565, + 468, + 806 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.6. Limitations", + "text_level": 1, + "bbox": [ + 76, + 816, + 204, + 830 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Due to its bottom-up architecture, the proposed approach performs well for most evaluated scenes in urban and suburban surroundings but struggles with highly complex graph topologies such as multi-lane intersections or roundabouts.", + "bbox": [ + 75, + 839, + 470, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Moreover, due to the iterative formulation of our aggregation scheme, the inference time of our approach increases with the number of nodes and edges. To speed up inference time, future work might include adaptively changing the distance between consecutive virtual agent positions and leveraging efficient neighborhood lookup methods such as k-d trees. Parallel execution of multiple agents would additionally boost run time to match top-down approaches and is feasible in terms of the proposed aggregation scheme.", + "bbox": [ + 498, + 450, + 893, + 587 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 623, + 617, + 638 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we presented a novel lane graph estimation framework complemented with a novel dataset comprising aerial images. We showed that formulating the lane graph estimation problem as bottom-up graph neural network approach leveraging agent-centric views yields promising results. In addition, we presented a novel aggregation scheme to merge successive lane graphs to produce large-scale solutions. A first-of-its-kind dataset and benchmark for lane graph estimation from aerial images will enable further research in this field. Future work could address end-to-end training and exploiting further modalities such as onboard vehicle cameras for additional context information.", + "bbox": [ + 496, + 655, + 893, + 835 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements: This work was partly funded by the German Research Foundation (DFG) Emmy Noether Program grant number 468878300, DFG grant number BU 865/10-2, and a hardware grant from NVIDIA.", + "bbox": [ + 496, + 839, + 893, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "13422", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, and Vishal M Patel. Spin road mapper: Extracting roads from aerial images via spatial and interaction space graph reasoning for autonomous driving. In 2022 International Conference on Robotics and Automation (ICRA), pages 343-350. IEEE, 2022. 2", + "[2] Favyen Bastani, Songtao He, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, and David DeWitt. Roadtracer: Automatic extraction of road networks from aerial images. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 4720-4728, 2018. 2", + "[3] Guillem Brasó and Laura Leal-Taixe. Learning a neural solver for multiple object tracking. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 4", + "[4] Martin Büchner and Abhinav Valada. 3d multi-object tracking using graph neural networks with cross-edge modality attention. IEEE Robotics and Automation Letters, 7(4):9707-9714, 2022. 1", + "[5] Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, and Luc Van Gool. Structured bird's-eye-view traffic scene understanding from onboard images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15661-15670, 2021. 1, 2", + "[6] Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, and Luc Van Gool. Topology preserving local road network estimation from single onboard camera image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17263-17272, 2022. 1, 2", + "[7] Daniele Cattaneo, Matteo Vaghi, and Abhinav Valada. Lcdnet: Deep loop closure detection and point cloud registration for lidar slam. IEEE Transactions on Robotics, 38(4):2074-2093, 2022. 1", + "[8] Yuning Chai, Benjamin Sapp, Mayank Bansal, and Dragomir Anguelov. Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction. In Conference on Robot Learning, pages 86-99. PMLR, 2020. 2", + "[9] Dian Chen and Philipp Kähenbuhl. Learning from all vehicles. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17222-17231, 2022. 2", + "[10] Nemanja Djuric, Henggang Cui, Zhaoen Su, Shangxuan Wu, Huahua Wang, Fang-Chieh Chou, Luisa San Martin, Song Feng, Rui Hu, Yang Xu, et al. Multixnet: Multiclass multi-stage multimodal motion prediction. In 2021 IEEE Intelligent Vehicles Symposium (IV), pages 435-442. IEEE, 2021. 2", + "[11] Whye Kit Fong, Rohit Mohan, Juana Valeria Hurtado, Lubing Zhou, Holger Caesar, Oscar Beijbom, and Abhinav Valada. Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking. IEEE Robotics and Automation Letters, 7(2):3795-3802, 2022. 3", + "[12] Nikhil Gosala and Abhinav Valada. Bird's-eye-view panoptic segmentation using monocular frontal view images. IEEE Robotics and Automation Letters, 7(2):1968-1975, 2022. 1" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Aric Hagberg, Pieter Swart, and Daniel S Chult. Exploring network structure, dynamics, and function using networkx. Technical report, Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2008. 12", + "[14] J Halton and G Smith. Radical inverse quasi-random point sequence, algorithm 247. Commun. ACM, 7(12):701, 1964. 3", + "[15] Songtao He and Hari Balakrishnan. Lane-level street map extraction from aerial imagery. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2080-2089, 2022. 1, 2, 3, 6, 7, 8, 13, 21", + "[16] Namdar Homayounfar, Wei-Chiu Ma, Shrinidhi Kowshika Lakshmikanth, and Raquel Urtasun. Hierarchical recurrent attention networks for structured online maps. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 3417-3426, 2018. 2", + "[17] Namdar Homayounfar, Wei-Chiu Ma, Justin Liang, Xinyu Wu, Jack Fan, and Raquel Urtasun. Dagmapper: Learning to map by discovering lane topology. In Proceedings of the IEEE International Conference on Computer Vision, pages 2911-2920, 2019. 2", + "[18] Justin Liang, Namdar Homayounfar, Wei-Chiu Ma, Shenlong Wang, and Raquel Urtasun. Convolutional recurrent network for road boundary extraction. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 9512-9521, 2019. 2", + "[19] Gellert Mattyus, Wenjie Luo, and Raquel Urtasun. Deeproadmapper: Extracting road topology from aerial images. In Proceedings of the IEEE International Conference on Computer Vision, pages 3438-3446, 2017. 2", + "[20] Rohit Mohan and Abhinav Valada. Amodal panoptic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21023-21032, 2022. 1", + "[21] Kursat Petek, Kshitij Sirohi, Daniel Buscher, and Wolfram Burgard. Robust monocular localization in sparse hd maps leveraging multi-task uncertainty estimation. In 2022 International Conference on Robotics and Automation (ICRA), pages 4163-4169. IEEE, 2022. 1", + "[22] Yong-Qiang Tan, Shang-Hua Gao, Xuan-Yi Li, Ming-Ming Cheng, and Bo Ren. Vecroad: Point-based iterative graph exploration for road graphs extraction. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 8910-8918, 2020. 2", + "[23] Shinji Umeyama. Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(04):376-380, 1991. 11", + "[24] Abhinav Valada, Ankit Dhall, and Wolfram Burgard. Convoluted mixture of deep experts for robust semantic segmentation. In IEEE/RSJ International conference on intelligent robots and systems (IROS) workshop, state estimation and terrain perception for all terrain mobile robots, volume 2, 2016. 1", + "[25] Adam Van Etten, Dave Lindenbaum, and Todd M Bacastow. Spacenet: A remote sensing dataset and challenge series. arXiv preprint arXiv:1807.01232, 2018. 6", + "[26] Johan Vertens and Wolfram Burgard. Usegscene: Unsupervised learning of depth, optical flow and ego-motion with" + ], + "bbox": [ + 501, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "13423", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "semantic guidance and coupled networks. arXiv preprint arXiv:2207.07469, 2022. 1", + "[27] Niclas Vödisch, Daniele Cattaneo, Wolfram Burgard, and Abhinav Valada. Continual slam: Beyond lifelong simultaneous localization and mapping through continual learning. arXiv preprint arXiv:2203.01578, 2022. 1", + "[28] Jingke Wang, Tengju Ye, Ziqing Gu, and Junbo Chen. Ltp: Lane-based trajectory prediction for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17134-17142, 2022. 2", + "[29] Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, et al. Argoverse 2: Next generation datasets for self-driving perception and forecasting. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021. 3, 11", + "[30] Zhenhua Xu, Yuxuan Liu, Lu Gan, Yuxiang Sun, Xinyu Wu, Ming Liu, and Lujia Wang. Ringdet: Road network graph detection by transformer in aerial images. IEEE Transactions on Geoscience and Remote Sensing, 2022. 1", + "[31] Li Zhang, Faezeh Tafazzoli, Gunther Krehl, Runsheng Xu, Timo Rehfeld, Manuel Schier, and Arunava Seal. Hierarchical road topology learning for urban map-less driving. arXiv preprint arXiv:2104.00084, 2021. 2" + ], + "bbox": [ + 78, + 90, + 470, + 445 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[32] Tongjie Y Zhang and Ching Y. Suen. A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 27(3):236-239, 1984. 15", + "[33] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881-2890, 2017. 3, 16", + "[34] Lichen Zhou, Chuang Zhang, and Ming Wu. D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 182-186, 2018. 2", + "[35] Yiyang Zhou, Yuichi Takeda, Masayoshi Tomizuka, and Wei Zhan. Automatic construction of lane-level hd maps for urban scenes. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6649-6656. IEEE, 2021. 2", + "[36] Jannik Zürn, Johan Vertens, and Wolfram Burgard. Lane graph estimation for scene understanding in urban driving. IEEE Robotics and Automation Letters, 6(4):8615-8622, 2021. 2, 6, 7", + "[37] Jannik Zürn, Sebastian Weber, and Wolfram Burgard. Trackletmapper: Ground surface segmentation and mapping from traffic participant trajectories. In 6th Annual Conference on Robot Learning, 2022. 1" + ], + "bbox": [ + 501, + 92, + 893, + 445 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "13424", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning and Aggregating Lane Graphs for Urban Automated Driving/71072545-68f3-478a-8f5b-1abe54803639_model.json b/2023/Learning and Aggregating Lane Graphs for Urban Automated Driving/71072545-68f3-478a-8f5b-1abe54803639_model.json new file mode 100644 index 0000000000000000000000000000000000000000..cb2db02c0f730dde941b145c83d5d40f23fb9150 --- /dev/null +++ b/2023/Learning and Aggregating Lane Graphs for Urban Automated Driving/71072545-68f3-478a-8f5b-1abe54803639_model.json @@ -0,0 +1,1826 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.128, + 0.131, + 0.842, + 0.154 + ], + "angle": 0, + "content": "Learning and Aggregating Lane Graphs for Urban Automated Driving" + }, + { + "type": "text", + "bbox": [ + 0.1, + 0.18, + 0.868, + 0.222 + ], + "angle": 0, + "content": "Martin Büchner\\(^{1*}\\) Jannik Zürn\\(^{1*}\\) Ion-George Todoran\\(^{2}\\) Abhinav Valada\\(^{1}\\) Wolfram Burgard\\(^{3}\\) \n\\(^{1}\\)University of Freiburg \\(^{2}\\)Woven by Toyota \\(^{3}\\)University of Technology Nuremberg" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.255, + 0.314, + 0.272 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.288, + 0.474, + 0.621 + ], + "angle": 0, + "content": "Lane graph estimation is an essential and highly challenging task in automated driving and HD map learning. Existing methods using either onboard or aerial imagery struggle with complex lane topologies, out-of-distribution scenarios, or significant occlusions in the image space. Moreover, merging overlapping lane graphs to obtain consistent large-scale graphs remains difficult. To overcome these challenges, we propose a novel bottom-up approach to lane graph estimation from aerial imagery that aggregates multiple overlapping graphs into a single consistent graph. Due to its modular design, our method allows us to address two complementary tasks: predicting ego-respective successor lane graphs from arbitrary vehicle positions using a graph neural network and aggregating these predictions into a consistent global lane graph. Extensive experiments on a large-scale lane graph dataset demonstrate that our approach yields highly accurate lane graphs, even in regions with severe occlusions. The presented approach to graph aggregation proves to eliminate inconsistent predictions while increasing the overall graph quality. We make our large-scale urban lane graph dataset and code publicly available at http://urbanlanegraph.cs.uni-freiburg.de." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.652, + 0.21, + 0.668 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.678, + 0.471, + 0.875 + ], + "angle": 0, + "content": "Most automated driving vehicles rely on the knowledge of their immediate surroundings to safely navigate urban environments. Onboard sensors including LiDARs and cameras provide perception inputs that are utilized in multiple tasks such as localization [7, 21, 27], tracking [4], or scene understanding [20, 24, 26, 37] to aggregate representations of the environment. However, robust planning and control typically require vastly more detailed and less noisy world models in the form of HD map data [12]. In particular, information on lane parametrization and connectivity is essential for both planning future driving maneuvers as well as high-level navigation tasks. Creating and maintaining HD maps in the form of lane graphs is a time-consuming and arduous" + }, + { + "type": "image", + "bbox": [ + 0.504, + 0.254, + 0.891, + 0.492 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.501, + 0.893, + 0.54 + ], + "angle": 0, + "content": "Figure 1. Our approach predicts accurate lane graphs from aerial images of complex urban environments. We visualize the estimated lane graph in magenta and indicate model initialization points with yellow circles." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.564, + 0.895, + 0.625 + ], + "angle": 0, + "content": "task due to the large amount of detail required in the annotation and the data curation process including map updates based on local environment changes such as construction sites." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.629, + 0.897, + 0.902 + ], + "angle": 0, + "content": "Previous approaches to lane graph estimation have shown shortcomings in predicting lane graphs due to multiple deficiencies: On the one hand, methods using onboard imagery typically degrade at complex real-world intersections and under significant occlusions, e.g., when following another vehicle [5, 6]. On the other hand, methods based on aerial imagery show reduced performance when confronted with occlusions in the bird's-eye-view (BEV) due to, e.g., vegetation or shadows, and suffer from catastrophic drift when unconstrained in out-of-distribution scenarios [30]. Previous works treat intersections and non-intersections inherently differently [15] and thus require elaborated heuristics and post-processing to merge single predictions into a consistent lane graph. Moreover, prior works do not focus on use cases where multiple predicted graphs must be merged into a single consistent solution, which is essential for enabling the automatic generation of highly detailed lane graphs of large contiguous regions." + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.204, + 0.9 + ], + "angle": 0, + "content": "*Equal contribution" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13415" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.473, + 0.394 + ], + "angle": 0, + "content": "Related to the aforementioned challenges, we propose a novel two-stage graph neural network (GNN) approach termed LaneGNN that operates on single aerial color images for lane graph prediction. Inspired by methods in the field of trajectory prediction [8], we formulate a bottom-up approach according to which we place a virtual agent into a local crop of the aerial image and predict reachable successor lane graphs from its positions. To transform multiple disjoint local solutions into a single global solution, we aggregate a global representation by iteratively inferring the lane graph from consecutive poses, ultimately imitating real-world driving behavior. This iterative approach not only increases the predicted area covered but also improves graph accuracy based on data association and rejection. Note that we do not require any human in the loop to perform the graph aggregation. We visualize the output of our graph aggregation procedure in Fig. 1, in which we superimpose the predicted graph on the aerial image input. Using this framework, we envision two applications: ego-centered successor path prediction and full lane graph estimation by aggregation." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.396, + 0.461, + 0.41 + ], + "angle": 0, + "content": "To summarize, the main contributions of this work are:" + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.413, + 0.47, + 0.471 + ], + "angle": 0, + "content": "- An innovative bottom-up approach to lane graph estimation in challenging environments that explicitly encodes graph-level lane topology from input aerial images in a scenario-agnostic manner." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.474, + 0.469, + 0.503 + ], + "angle": 0, + "content": "- A novel graph aggregation scheme enabling robust and method-agnostic merging of graph-level predictions." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.504, + 0.47, + 0.563 + ], + "angle": 0, + "content": "- The large-scale lane graph dataset UrbanLaneGraph comprising high-resolution aerial images aligned with dense lane graph annotations aggregated from the Argoverse2 dataset that we make publicly available." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.565, + 0.47, + 0.593 + ], + "angle": 0, + "content": "- Extensive experiments and ablation studies demonstrating the significance of our findings." + }, + { + "type": "list", + "bbox": [ + 0.097, + 0.413, + 0.47, + 0.593 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.613, + 0.227, + 0.629 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.641, + 0.473, + 0.837 + ], + "angle": 0, + "content": "In recent years, the prediction of topological road features such as road graphs and lane graphs have been extensively studied. In our discussion, we differentiate between road graph learning and lane graph learning. While road graphs encode the topological connections between road segments, lane graphs describe the locations and connectivity between all lanes, resulting in a spatially much denser graph. Many prior works focus on vehicle trajectory prediction, conditioned on HD map features such as lane centerline and boundary positions [9, 10, 28]. These models do not aim at exclusively predicting the road or lane graph structure from onboard vehicle images or aerial images but instead at predicting future vehicle states such as position and orientation." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.84, + 0.473, + 0.902 + ], + "angle": 0, + "content": "Road Graph Learning: Prior works investigate estimating road graphs from both onboard sensors [18] and from aerial images [1, 22, 34] or focus on extracting pixel-level road segmentation from images and extracting graphical road rep" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.168 + ], + "angle": 0, + "content": "resentations, i.e., using morphological image operators or graph neural networks to extract the connectivity between different roads within the image [1, 19]. Other approaches investigate iterative methods and interpret road graph prediction as a sequential prediction task [2, 22]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.171, + 0.895, + 0.413 + ], + "angle": 0, + "content": "Lane Graph Learning from Vehicle Data: Some earlier works in lane graph learning from onboard vehicle sensors such as cameras and LiDAR formulate lane extraction as an image-based lane centerline regression task [16]. Homayounfar et al. [17] aggregate onboard LiDAR data on highways and leverage a recurrent neural network to generate highway lane graphs in an iterative manner. Zhou et al. [35] utilize the OpenStreetMap database and a semantic particle filter to accumulate projected semantic predictions from a vehicle ego-view into a map representation. Zhang et al. [31] propose an online road map extraction system for a sensor setup onboard a moving vehicle and construct a graph representation of the road network using a fully convolutional neural network. More recently, Can et al. proposed two different methods [5, 6] for lane connectivity learning in intersection scenarios from onboard camera images." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.416, + 0.895, + 0.598 + ], + "angle": 0, + "content": "Lane Graph Learning from Birds-Eye-View Data: Despite the advantages of leveraging readily available aerial data for training lane graphs, only a few works considered using aerial images as an input modality for the graph learning task. Zürn et al. [36] propose a lane centerline regression model jointly with a Graph R-CNN backbone to predict nodes and edges of the lane graph from a local aggregated bird's-eye-view image crop. More recently, He et al. [15] propose a two-stage graph estimation pipeline. They first extract lanes at non-intersection areas and subsequently predict the connectivity of each pair of lanes, and extract the valid turning lanes to complete the map at intersections." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.601, + 0.896, + 0.829 + ], + "angle": 0, + "content": "In contrast to existing works, we do not estimate the complete lane graph visible in a given crop but only the part of the graph that is reachable from a virtual agent pose located within the crop, simplifying the graph estimation problem based on reduced topological complexity. Furthermore, we leverage a GNN to explicitly model the relationships between graph nodes, allowing us to leverage recent developments in the field of geometric deep learning. This explicit graph encoding and prediction allows us to formulate a model that does not internally differentiate between intersection areas and non-intersection areas, in contrast to some related works. Additionally, we propose a novel large-scale dataset for lane graph estimation from aerial images, allowing the research community to easily evaluate and compare their approaches." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.845, + 0.589, + 0.86 + ], + "angle": 0, + "content": "3. Dataset" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.895, + 0.901 + ], + "angle": 0, + "content": "To evaluate our approach on challenging real-world data, we compiled the UrbanLaneGraph dataset. It is a first-of-its" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "13416" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.078, + 0.087, + 0.895, + 0.201 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.208, + 0.895, + 0.259 + ], + "angle": 0, + "content": "Figure 2. Overview of our LaneGNN model \\(\\mathcal{M}\\) predicting successor lane graphs. As a pre-training step, we train lane centerline and ego lane centerline regressor models. The ego lane regression \\(\\mathbf{S}_{\\text{lane}}^{\\text{ego}}\\) is used as a prior for sampling proposal nodes \\(V\\) in corridors that have a high likelihood of entailing the successor graph. The model learns a binary classification of node and edge scores while also predicting the probability of a node being an endpoint of a given lane segment." + }, + { + "type": "table", + "bbox": [ + 0.102, + 0.27, + 0.447, + 0.412 + ], + "angle": 0, + "content": "
DatasetCityLane Splits/MergesTotal Length [km]
UrbanLaneGraph (from Argoverse2)Palo Alto4752796.4
Austin8495531.7
Miami7642850.8
Pittsburgh146101314.3
Washington D.C.2066739.6
Detroit5424990.5
LaneExtraction [15]Boston, Seattle, Phoenix, Miami2262398.6
NuScenes [11]Boston163076.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.42, + 0.472, + 0.459 + ], + "angle": 0, + "content": "Table 1. Key statistics of our UrbanLaneGraph dataset, aggregated from the Argoverse2 dataset [29], used for our experiments, compared with other recent datasets for lane graph estimation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.477, + 0.473, + 0.794 + ], + "angle": 0, + "content": "kind dataset for large-scale lane graph estimation from aerial images. The dataset contains aerial images from the cities of Austin, Miami, Pittsburgh, Palo Alto, Detroit, and Washington DC. The images have a resolution of \\(15\\mathrm{cm}\\) per pixel. To obtain the corresponding lane annotations, we leverage the Argoverse2 dataset [29] which entails lane graphs for large sections of the respective cities. The lane graphs in the Argoverse2 dataset are provided on a per-scenario basis, covering only small local areas in one graph. Therefore, we collect all local lane graphs of each city and aggregate them into one globally consistent graph per city, thereby removing inconsistent or redundant nodes or edges. The annotated regions feature a diverse range of environments including urban, suburban, and rural regions with complex lane topologies. Accumulated over all cities, the overall length of all lanes spans over \\(5.000\\mathrm{km}\\). We split each city into disjoint training and testing regions. We list key dataset statistics in Tab. 1, indicating the scale of our generated dataset compared with other recently proposed datasets containing graph annotations. For more details on the dataset and exemplary visualizations, please refer to Sec. S.1 in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.814, + 0.27, + 0.831 + ], + "angle": 0, + "content": "4. Technical Approach" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.473, + 0.903 + ], + "angle": 0, + "content": "Our approach is divided into two stages: lane graph learning and lane graph aggregation. In the first stage (Sec. 4.1), we train our GNN model, denoted as LaneGNN, to predict the successor lane graph, entailing the nodes and edges" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.272, + 0.895, + 0.41 + ], + "angle": 0, + "content": "that can be logically visited from the pose of a virtual vehicle agent. In the second phase (Sec. 4.2), we use our trained LaneGNN model to traverse a large map area. This is achieved by selecting an initial starting pose and predicting the successor lane graph from this pose. Subsequently, we iteratively estimate the traversable lane graph from the current pose and move forward along the predicted graph while aggregating. In the following, we detail both stages of our approach." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.419, + 0.707, + 0.435 + ], + "angle": 0, + "content": "4.1. Lane Graph Learning" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.442, + 0.895, + 0.67 + ], + "angle": 0, + "content": "We formulate the task as a supervised learning problem where a successor lane graph \\(\\hat{G}\\) is estimated based on an aerial image \\(\\mathbf{I}_{bev}\\). First, a directed graph \\(G_{I} = (V,E)\\) covering relevant regions is constructed by sampling from likely regions of \\(\\mathbf{I}_{bev}\\) (see Fig. 2). For all our models and experiments we choose a spatial resolution of \\(256\\times 256\\) pixels. The graph comprises a set of nodes \\(i\\in V\\) that are connected via directed edges \\(E\\subseteq \\{(i,j)|(i,j)\\in V^2\\) and \\(i\\neq j\\}\\) that constitute potentially valid lane graph edges. The graph is attributed using both node features \\(\\mathbf{X}\\in \\mathbb{R}^{\\| V\\| \\times D}\\) and edge features \\(\\mathbf{X}_e\\in \\mathbb{R}^{\\| E\\| \\times (D_{geo} + D_{bev})}\\), where \\(D_{geo}\\) and \\(D_{bev}\\) denote the dimensionality of the involved edge features (see Sec. 4.1). The overall model estimates an output graph \\(\\hat{G} = \\mathcal{M}(G_I|\\theta)\\), where \\(\\mathcal{M}\\) is parameterized by network model weights \\(\\theta \\coloneqq (\\theta_{reg},\\theta_{GNN})\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.675, + 0.896, + 0.902 + ], + "angle": 0, + "content": "Lane Regression and Graph Construction: We train two regression networks: Firstly, a centerline regression network predicting the likelihood map of lane centerlines \\(\\mathbf{S}_{\\text {lane }}\\), and secondly, a segmentation network predicting all reachable lanes \\(\\mathbf{S}_{\\text {lane }}^{\\text {ego }}\\) starting from the initial virtual agent pose at the bottom center of \\(\\mathbf{I}_{\\text {bev }}\\). We use identical PSPNet [33] architectures with a ResNet-152 feature extractor for this task. We sample equally-distributed node positions using Halton sequences [14] that are later filtered based on the obtained ego-lane segmentation mask \\(\\mathbf{S}_{\\text {lane }}^{\\text {ego }}\\), which serves as a region of interest for sampling (see Fig. 2). Directed edges \\(E\\) among nodes are initialized for pairs of nodes with a Euclidean distance \\(d_{ij} \\in [d_{\\min}, d_{\\max}]\\). Initial node features \\(\\mathbf{X}\\) are crafted solely based on their 2D positions \\(\\mathbf{x}_i = (x_i, y_i)\\), while geometric edge features are defined as" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13417" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.089, + 0.32, + 0.244 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.093, + 0.245, + 0.306, + 0.257 + ], + "angle": 0, + "content": "Temporal evolution of local BEV crops" + }, + { + "type": "image", + "bbox": [ + 0.379, + 0.089, + 0.619, + 0.244 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.624, + 0.089, + 0.893, + 0.244 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.521, + 0.245, + 0.748, + 0.258 + ], + "angle": 0, + "content": "Temporal evolution of aggregated graph" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.266, + 0.893, + 0.293 + ], + "angle": 0, + "content": "Figure 3. In our graph aggregation procedure, we iteratively obtain oriented image crops based on virtual agent poses along the currently predicted successor graph. For each crop, our LaneGNN model predicts a successor graph \\( G_{pred} \\), which is aggregated into a globally consistent lane graph \\( G_{agg} \\)." + }, + { + "type": "equation", + "bbox": [ + 0.098, + 0.328, + 0.471, + 0.358 + ], + "angle": 0, + "content": "\\[\n\\mathbf {x} _ {i j, g e o} = \\left(\\tan^ {- 1} \\frac {\\Delta y _ {i j}}{\\Delta x _ {i j}}, \\sqrt {\\Delta x _ {i j} {} ^ {2} + \\Delta y _ {i j} {} ^ {2}}, \\bar {x} _ {i j}, \\bar {y} _ {i j}\\right), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.365, + 0.47, + 0.472 + ], + "angle": 0, + "content": "where \\(\\Delta x_{ij}\\) and \\(\\Delta y_{ij}\\) represent node position differences and \\(\\overline{x}_{ij}\\), \\(\\overline{y}_{ij}\\) are edge middle point coordinates. In addition to the geometric edge feature, we generate aerial edge features: Per edge, a small oriented region of \\(\\mathbf{I}_{bev}\\) and the lane segmentation \\(\\mathbf{S}_{lane}\\) is obtained based on the direction of the edge, which provides \\(\\mathbf{X}_{ij,bev} = [\\mathbf{I}_{bev}^{*},\\mathbf{S}_{lane}^{*}]\\) with dimension \\(D_{bev} = 4\\times 32\\times 32\\)" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.478, + 0.471, + 0.72 + ], + "angle": 0, + "content": "Feature Encoding and Message Passing: In our approach, we estimate edge probabilities, node probabilities, and whether a node is terminal. While the nodes themselves hold only unidirectional information, we encode the notion of direction using the proposed edge feature as outlined above. We utilize a causal variant of neural message passing as proposed by Brasó et al. [3]. By imposing a causality prior, our network encodes predecessor and successor features during message passing. This formulation of message passing renders our approach direction-aware. Initial node and geometric edge features are encoded using multi-layer perceptrons \\( f_{enc}^{\\square} \\) (MLP) while the aerial edge feature is transformed using a ResNet-18 architecture \\( f_{enc}^{e,bev} \\). The geometric and aerial edge features are concatenated and fused subsequently to arrive at various node and edge embeddings \\( \\mathbf{H}_v^{(0)} \\) and \\( \\mathbf{H}_e^{(0)} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.101, + 0.728, + 0.471, + 0.748 + ], + "angle": 0, + "content": "\\[\nf _ {e n c} ^ {e, b e v} \\left(\\mathbf {X} _ {e, b e v}\\right) = \\mathbf {H} _ {e, b e v} ^ {(0)}, f _ {e n c} ^ {e, g e o} \\left(\\mathbf {X} _ {e, g e o}\\right) = \\mathbf {H} _ {e, g e o} ^ {(0)}, (2)\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.115, + 0.767, + 0.47, + 0.786 + ], + "angle": 0, + "content": "\\[\nf _ {e n c} ^ {v} (\\mathbf {X}) = \\mathbf {H} _ {v} ^ {(0)}, f _ {f u s e} ^ {e} ([ \\mathbf {H} _ {e, g e o} ^ {(0)}, \\mathbf {H} _ {e, b e v} ^ {(0)} ]) = \\mathbf {H} _ {e} ^ {(0)}. (3)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.788, + 0.471, + 0.874 + ], + "angle": 0, + "content": "In the following, multiple message-passing steps are performed using various ReLU-activated MLPs denoted by \\( f_{\\square} \\) as follows. The edge feature is updated based on the current neighboring node features \\( \\mathbf{h}_i^{(l - 1)} \\), \\( \\mathbf{h}_j^{(l - 1)} \\) and the edge feature \\( \\mathbf{h}_{ij}^{(l - 1)} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.154, + 0.881, + 0.471, + 0.907 + ], + "angle": 0, + "content": "\\[\n\\mathbf {h} _ {i j} ^ {(l)} = f _ {e} \\left(\\left[ \\mathbf {h} _ {i} ^ {(l - 1)}, \\mathbf {h} _ {j} ^ {(l - 1)}, \\mathbf {h} _ {i j} ^ {(l - 1)} \\right]\\right). \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.306, + 0.895, + 0.384 + ], + "angle": 0, + "content": "Messages \\(\\mathbf{m}_{ij}^{(l)}\\) are crafted based on either predecessors \\(\\mathcal{N}_{pred}(i)\\) or successors \\(\\mathcal{N}_{succ}(i)\\) of a node \\(i\\) and propagated based on the constructed adjacency. In the next step, all predecessors and successor messages, respectively, are aggregated using a permutation-invariant sum:" + }, + { + "type": "equation", + "bbox": [ + 0.546, + 0.393, + 0.893, + 0.442 + ], + "angle": 0, + "content": "\\[\n\\mathbf {h} _ {i, p r e d} ^ {(l)} = \\sum_ {j \\in \\mathcal {N} _ {p r e d} (i)} \\underbrace {f _ {v} ^ {p r e d} \\left(\\left[ \\mathbf {h} _ {j} ^ {(l - 1)} , \\mathbf {h} _ {i j} ^ {(l)} , \\mathbf {h} _ {j} ^ {(0)} \\right]\\right)} _ {\\mathbf {m} _ {j i} ^ {(l)}}, \\tag {5}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.546, + 0.456, + 0.893, + 0.504 + ], + "angle": 0, + "content": "\\[\n\\mathbf {h} _ {i, s u c c} ^ {(l)} = \\sum_ {j \\in \\mathcal {N} _ {s u c c} (i)} \\underbrace {f _ {v} ^ {s u c c} \\left(\\left[ \\mathbf {h} _ {j} ^ {(l - 1)} , \\mathbf {h} _ {i j} ^ {(l)} , \\mathbf {h} _ {j} ^ {(0)} \\right]\\right)} _ {\\mathbf {m} _ {i j} ^ {(l)}}. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.509, + 0.894, + 0.556 + ], + "angle": 0, + "content": "Note that the message crafting includes skip connections to initial node embeddings \\(\\mathbf{h}_j^{(0)}\\). Nodes are updated by combining the two features using concatenation:" + }, + { + "type": "equation", + "bbox": [ + 0.599, + 0.567, + 0.892, + 0.592 + ], + "angle": 0, + "content": "\\[\n\\mathbf {h} _ {i} ^ {(l)} = f _ {v} \\left(\\left[ \\mathbf {h} _ {i, p r e d} ^ {(l)}, \\mathbf {h} _ {i, s u c c} ^ {(l)} \\right]\\right). \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.601, + 0.893, + 0.678 + ], + "angle": 0, + "content": "Finally, sigmoid-valued edge scores \\(\\hat{e}_{ij}\\) and node score \\(\\hat{s}_i\\) are predicted from the obtained edge and node embeddings. In a separate network head, we classify each node as being a terminal node or not, denoted as a scalar \\(\\hat{t}_i\\). Therefore, we optimize the following combined binary cross entropy:" + }, + { + "type": "equation", + "bbox": [ + 0.536, + 0.688, + 0.892, + 0.719 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = - \\sum_ {| V |} s _ {i} \\log \\hat {s} _ {i} - \\sum_ {| V |} t _ {i} \\log \\hat {t} _ {i} - \\sum_ {| E |} e _ {i j} \\log \\hat {e} _ {i j}. \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.73, + 0.893, + 0.791 + ], + "angle": 0, + "content": "The ground truth graph \\( G_{GT} \\) as a learning target \\( (s_i, t_i, e_{ij}) \\) is generated based on the map annotations for the given cropped region and the corresponding closest nodes. This is further outlined in the supplementary material in Sec. S.3." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.801, + 0.836, + 0.818 + ], + "angle": 0, + "content": "4.2. Iterative Temporal Graph Aggregation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.825, + 0.894, + 0.901 + ], + "angle": 0, + "content": "In the second stage of our approach, we aggregate local successor graphs into a globally consistent lane graph. First, we prune the predicted per-crop lane graph, and second, we iteratively aggregate the sparse graphs. In the following, we detail both components." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13418" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.09, + 0.273, + 0.236 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.278, + 0.089, + 0.468, + 0.236 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.246, + 0.47, + 0.307 + ], + "angle": 0, + "content": "Figure 4. Comparison of raw and pruned lane graph predictions. On the left-hand side, we visualize estimated edge scores \\(\\hat{e}_{ij}\\) and node scores \\(\\hat{s}_i\\) using the same color scaling. On the right-hand side, we visualize the pruned graph. The circled numbers denote the order of traversal based on estimated terminal node scores." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.334, + 0.473, + 0.562 + ], + "angle": 0, + "content": "Pruning: The graph prediction obtained from the LaneGNN model follows the graph connectivity initially generated during sampling. Since the model prediction contains a number of redundant paths with high predicted node and edge scores, we prune the obtained solution to obtain sparse lane representations. We formulate the graph pruning problem as a search problem from a starting node to possibly multiple predicted terminal nodes (see Sec. 4.1). Predicted lane split points should coincide with actual split points. Thus, different branches should share the same set of edges up to a split point. We use Dijkstra's algorithm to iteratively find high-score paths between the initial pose and terminal nodes, ordered from high to low scores until all terminal nodes are reached. Edge scores contained in found paths are set to zero. In Fig. 4, we visualize the output of this step." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.568, + 0.473, + 0.905 + ], + "angle": 0, + "content": "Aggregation: We iteratively aggregate predicted successor lane graphs \\( G_{pred} = (V_{pred}, E_{pred}) \\) into a globally consistent and complete graph \\( G_{agg} = (V_{agg}, E_{agg}) \\) as depicted in Fig. 3. The predicted successor graph \\( G_{pred}^{t} \\) is added to the current aggregated graph \\( G_{agg}^{t-1} \\) at time step \\( t \\): \\( G_{agg}^{t} \\gets \\text{aggregate}(G_{pred}^{t}, G_{agg}^{t-1}) \\). Plausible branches of \\( G_{pred} \\) are merged into \\( G_{agg} \\) and thus extend the aggregated graph with every iteration if new grounds are covered in the respective crops. The next virtual agent pose is extracted from the set of forward-facing edges of \\( G_{agg} \\) given the current pose. Only edges with a significant weight due to previous aggregations are selected for this set. As the node positions differ slightly with each model forward pass we observe roughly similar paths in the lateral sense wrt. the ground-truth graph. However, along the longitudinal dimension of a branch, we observe deviations in node position, which must be circumvented when aggregating the graph. Based on this, we only take the lateral deviation wrt. \\( G_{agg}^{t} \\) into account when merging \\( G_{pred}^{t} \\). Thus, \\( G_{agg}^{t} \\) is only updated in a lateral sense while the longitudinal misalignment of the two sets of nodes is neglected (see also Sec. S.5 in the supplementary material). The nodes of \\( G_{pred}^{t} \\) have a" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.198 + ], + "angle": 0, + "content": "weight of 1 while a node of \\( G_{agg}^{t} \\) holds a weight equal to the number of merges it has observed so far. If a novel node \\( i \\in V_{pred} \\) is not close to any other node in \\( k \\in V_{agg} \\) it is added to the aggregated graph including its incident edges. This ultimately allows a weighting-based merging of arbitrary pairs of graphs, which is used for global lane graph estimation (see Sec. 5.4)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.199, + 0.895, + 0.409 + ], + "angle": 0, + "content": "We observe that the more graphs we aggregate, the more we are certain about the significance of particular graph branches. Following a weighting-based approach allows us to set certain thresholds that allow modification of \\( G_{agg} \\). Thus, implausible graph branches, semantically similar parallel paths, redundant edges, and isolated nodes are deleted based on confidence and distance as well as angle criteria (see Fig. 3). As a result, we are able to decrease the number of false positive split and merge points to obtain more consistent global graphs. We observe that this approach greatly improves lane graph prediction accuracy in difficult occluded and out-of-distribution scenarios since the sum of model predictions covering the same region shed light onto what potentially constitutes, e.g., a valid and an invalid branch." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.409, + 0.897, + 0.669 + ], + "angle": 0, + "content": "Multiple forward passes naturally lead to a multitude of lane split points (both true positive and false positive splits). We interpret each split point as an element of an exploration frontier. A queue of unexplored, high-probability graph branches is maintained, which is queried in a depth-first manner as soon as the currently traversed branch terminates. Following the weighting-based approach, a branch is only explored if its depth-limited oriented successor tree weight exceeds a certain level of confidence. Due to this flexible approach, we can essentially handle arbitrary lane graph topologies with a single holistic approach. For more details on the graph pruning and aggregation procedures, please refer to the supplementary material. Finally, we apply multiple iterations of Laplacian smoothing to \\( G_{pred}^{t} \\), which modifies the original node positions in order to even out position irregularities caused by sampling while keeping the adjacency represented by \\( E_{pred}^{t} \\) constant." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.678, + 0.707, + 0.696 + ], + "angle": 0, + "content": "5. Experimental Results" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.703, + 0.895, + 0.795 + ], + "angle": 0, + "content": "In the following, we present our experimental findings. We first define and illustrate three tasks on which we benchmark our method. Subsequently, we describe the evaluation metrics and compare against other methods as well as our own baselines. We provide extensive qualitative and quantitative evaluations on our UrbanLaneGraph dataset." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.802, + 0.658, + 0.818 + ], + "angle": 0, + "content": "5.1. Proposed Tasks" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.826, + 0.895, + 0.903 + ], + "angle": 0, + "content": "We propose three distinct and complementary tasks. In the first task, successor lane graph prediction (Successor-LGP, Sec. 5.3), we aim at predicting a feasible ego-reachable successor lane graphs from the current pose of the virtual agent. The purpose of the Successor-LGP task is to measure" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "13419" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.089, + 0.895, + 0.129 + ], + "angle": 0, + "content": "Table 2. Quantitative results of our model including ablation studies, in comparison with baseline models for the Successor-LGP task on our UrbanLaneGraph dataset. P/R denotes Precision/Recall. For our LaneGNN model variants, we denote CMP as causal message passing, aerial node features as AerN, aerial edge features as AerE, and the ego-lane regression with \\( \\mathbf{S}_{\\text{lane}}^{\\text{ego}} \\). For all metrics, higher values mean better results." + }, + { + "type": "table", + "bbox": [ + 0.101, + 0.137, + 0.868, + 0.242 + ], + "angle": 0, + "content": "
ModelCMPAerNAerES laneTOPO P/R ↑GEO P/R ↑APLS ↑SDA20 ↑SDA50 ↑Graph IoU ↑
Skeletonized Regression----0.597/0.6130.578/0.6010.3150.0200.1850.180
LaneGraphNet [36]----0.0/0.00.0/0.00.1790.00.00.063
LaneGNN (ours)XX✓✓0.549/0.6770.548/0.6710.1880.1680.3230.312
✓✓X✓0.562/0.6560.562/0.6550.1920.1510.2980.320
✓XX✓0.545/0.6930.545/0.6880.2000.1880.3110.336
✓X✓X0.578/0.6690.577/0.6590.1500.1320.2270.250
✓X✓✓0.600/0.6990.599/0.6950.2020.2270.3770.347
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.256, + 0.473, + 0.438 + ], + "angle": 0, + "content": "the prediction quality of potential future driving paths when no HD map coverage is available. In the second task, full lane graph prediction (Full-LGP, Sec. 5.4), we evaluate the quality of regionally aggregated lane graphs in the context of HD map estimation. This task aims at measuring the predictive power of our full two-stage model performing lane graph inference and graph aggregation in conjunction. For such purposes, the full ground truth lane graph of a given map area is compared to the aggregated predictions of our model. Finally, we carry out a high-level path planning task (Sec. 5.5) on the predicted lane graphs, intended to analyze the fidelity of routes planned on the predicted graphs." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.457, + 0.262, + 0.472 + ], + "angle": 0, + "content": "5.2. Evaluation Metrics" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.484, + 0.471, + 0.514 + ], + "angle": 0, + "content": "We leverage multiple complementary metrics for performance evaluation as detailed below." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.514, + 0.471, + 0.605 + ], + "angle": 0, + "content": "Graph IoU: This metric measures the intersection-over-union (IoU) of two graphs rendered as a binary image [36], where pixels closer than \\( d = 5 \\) pixels are assigned the label 1 and the remaining pixels the label 0. Equivalent to the evaluation of semantic segmentation models, we determine the IoU values for the non-zero pixels." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.605, + 0.471, + 0.665 + ], + "angle": 0, + "content": "The APLS metric sums the differences in optimal path lengths between nodes in the ground truth graph \\( G \\) and the proposal graph \\( G' \\) [25]. The APLS metric scales from 0 (worst) to 1 (best). Formally, it is defined as" + }, + { + "type": "equation", + "bbox": [ + 0.094, + 0.685, + 0.471, + 0.716 + ], + "angle": 0, + "content": "\\[\n\\mathrm {A P L S} = 1 - \\frac {1}{N _ {p}} \\sum \\min \\left\\{1, \\frac {\\left| d \\left(v _ {1} , v _ {2}\\right) - d \\left(v _ {1} ^ {\\prime} , v _ {2} ^ {\\prime}\\right) \\right|}{d \\left(v _ {1} , v _ {2}\\right)} \\right\\}, \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.469, + 0.78 + ], + "angle": 0, + "content": "where \\( v_{i} \\) and \\( v_{i}^{\\prime} \\) are nodes in \\( G \\) and \\( G^{\\prime} \\), respectively. \\( N_{p} \\) denotes the number of paths in \\( G \\) and \\( d(\\cdot, \\cdot) \\) is the path length. For more details, please refer to [25]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.471, + 0.856 + ], + "angle": 0, + "content": "TOPO / GEO metrics: Following previous works in road network extraction and lane graph estimation, we use the GEO metric and the TOPO metric. For definitions and details on these metrics, please refer to [15] and to the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Split Detection Accuracy \\((\\mathbf{SDA}_{\\mathbf{R}})\\): This metric evaluates how accurately a model predicts the lane split within a circle of radius \\(R\\) pixels from a given ground truth lane split." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.255, + 0.795, + 0.271 + ], + "angle": 0, + "content": "5.3. Successor Lane Graph Prediction" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.28, + 0.895, + 0.521 + ], + "angle": 0, + "content": "In the following, we evaluate LaneGNN by ablating and comparing it with two baselines on the Successor-LGP task: morphological skeletonization of the ego-lane regression as well as a modified LaneGraphNet [36] to be used for successor lane graph prediction. We list quantitative results in Tab. 2. Our results demonstrate that none of the baseline methods are capable of estimating accurate lane graphs given the challenging topology of the lane graphs in the dataset. The LaneGraphNet [36] model fails to model the graph for many samples, yielding low scores in all metrics. Despite its simplicity, the skeletonized regression model achieves the highest APLS score and comparably high GEO/TOPO scores. However, it fails to accurately predict lane split points, resulting in low SDA scores. Increasing the number of nodes of the skeleton leads to many more false positive splits and thus deteriorates further." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.522, + 0.895, + 0.749 + ], + "angle": 0, + "content": "Regarding different variants of LaneGNN, we find that, e.g., causal message passing (CMP) increases performance over standard message passing, which underlines the significance of the imposed causality prior for lane graph learning. In order to show the efficacy of computationally more demanding aerial image edge features (AerE) compared to uni-directional aerial image node features (AerN), we ablate on this in Tab. 2 as well. We observe stark increases across the TOPO, GEO and SDA metrics when utilizing aerial edge features. Lastly, we replace the ego-lane segmentation mask \\(\\mathbf{S}_{lane}^{ego}\\) used for sampling with the standard lane segmentation mask. Our findings show that especially APLS, SDA, and Graph IoU drastically decrease as graph estimation becomes more difficult due to a generally enlarged sampling region (see Fig. 2)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.897, + 0.901 + ], + "angle": 0, + "content": "These results are further illustrated in Fig. 5, where we show qualitative comparisons of predictions of our best-performing model with predictions from the two baselines. We find that the quality of predictions by LaneGraphNet [36] is generally low, rendering it unsuitable for the task. The skeletonized regression baseline is capable of following basic lane graph topologies, but lane split points cannot be resolved accurately. In contrast, our LaneGNN model is capable of modeling most graphs with high accuracy; both in intersection areas and in straight road sections. For more" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "13420" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.099, + 0.089, + 0.868, + 0.41 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.419, + 0.895, + 0.459 + ], + "angle": 0, + "content": "Figure 5. Qualitative results on the Successor-LGP task. We compare predictions of our model with LaneGraphNet [36] and a morphological image skeletonization baseline. Predicted nodes are visualized with points while predicted edges are visualized as directed arrows. We illustrate failure cases in the two rightmost columns. Best viewed zoomed in." + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.481, + 0.472, + 0.542 + ], + "angle": 0, + "content": "Table 3. Quantitative evaluation for the Full-LGP task on the test-set of our UrbanLaneGraph dataset. We compare a baseline model with graphs aggregated with a naive aggregation scheme and our iterative temporal aggregation scheme. P/R denotes Precision/Recall. Higher values mean better results." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.553, + 0.462, + 0.616 + ], + "angle": 0, + "content": "
ModelTOPO P/R ↑GEO P/R ↑APLS ↑Graph IoU ↑
LaneExtraction [15]0.405/0.5070.491/0.4540.0720.213
Aggregation (naive)0.366/0.6540.523/0.7270.1010.376
Aggregation (ours)0.481/0.6700.649/0.6890.1030.384
" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.635, + 0.471, + 0.65 + ], + "angle": 0, + "content": "results, please refer to the supplementary material, Sec. S.6." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.664, + 0.329, + 0.681 + ], + "angle": 0, + "content": "5.4. Full Lane Graph Prediction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.689, + 0.472, + 0.901 + ], + "angle": 0, + "content": "For the Full-LGP task, we compare our approach with LaneExtraction [15]. Since their used graph representation is incompatible with ours, we train it on their provided dataset. To allow for a fair comparison, we evaluate both our method and LaneExtraction only on scenes in the city of Miami, Florida, as it is contained in both of the datasets. We select a testing region that is not part of the training data for either of the models. To initialize our aggregation scheme, we select starting poses obtained from intermediate segmentation predictions including yaw angles of the LaneExtraction model. Tab. 3 lists the evaluation results for the Full-LGP task obtained with LaneExtraction [15] and the results obtained with our LaneGNN model in conjunction with two aggregation schemes: a naive aggregation scheme baseline" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.483, + 0.895, + 0.832 + ], + "angle": 0, + "content": "and our full aggregation scheme. The naïve aggregation scheme merges nodes in close proximity while not relying on unvalidated split/merge or parallel path removal as well as the lateral weighting-based merging (Sec. 4.2). Our experiments show that our aggregation scheme outperforms both the LaneExtraction model and the naïve aggregation scheme on nearly all evaluation metrics. We note that our method improves the TOPO/GEO precision metrics while maintaining similar recalls due to better handling of redundant nodes. Fig. 6 illustrates successive aggregations from our model while indicating the used initialization points from the LaneExtraction model. Since our aggregation approach does not differentiate between intersection and non-intersection regions, it does not deteriorate in regions that do not exactly fit this categorization. Furthermore, our model exhibits superior performance in reduced visibility settings introduced by stark illumination changes or road occlusions from vegetation, as illustrated in Fig. 6. One of the decisive assets of our bottom-up method is that it allows to explore regions that are missed by LaneExtraction [15] as they are entailed in their predicted segmentation masks. For more qualitative and quantitative results, please refer to the supplementary material, Sec. S.7." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.845, + 0.649, + 0.862 + ], + "angle": 0, + "content": "5.5. Path Planning" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.894, + 0.903 + ], + "angle": 0, + "content": "To illustrate the efficacy of our aggregation scheme, we evaluate the quality of the lane graph on a planning task. We" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "13421" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.138, + 0.089, + 0.831, + 0.395 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.4, + 0.893, + 0.425 + ], + "angle": 0, + "content": "Figure 6. Qualitative results on the Full-LGP task. We visualize predictions of LaneExtraction [15] (top row) and aggregated LaneGNN predictions (bottom row). Our model is initialized at poses using predicted lane direction masks of LaneExtraction (indicated with yellow circles). Best viewed zoomed in." + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.449, + 0.47, + 0.486 + ], + "angle": 0, + "content": "Table 4. Quantitative evaluation for the planning task. MMD denotes mean minimum distance, MED denotes mean endpoint distance, and SR denotes the path planning success rate." + }, + { + "type": "table", + "bbox": [ + 0.134, + 0.497, + 0.413, + 0.551 + ], + "angle": 0, + "content": "
ModelMMD [m] ↓MED [m] ↓SR ↑
LaneExtraction [15]157.0339.40.47
Aggregation (ours)2.219.70.46
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.566, + 0.47, + 0.807 + ], + "angle": 0, + "content": "generate 1000 randomly selected starting poses in the Miami graph test area from which a plan to a random goal within the graph must be found, using A* search. We place the points such that a maximal optimal route length of \\(200\\mathrm{m}\\) is not exceeded. To evaluate the planned routes, we compare the mean minimum distance (MMD) and the mean route endpoint distance (MED) between the paths on the predicted graph and the ground truth graph, respectively. We also report the success rate (SR), indicating the number of cases in which a path between start and goal exists. We list the results in Tab. 4. While the SR for our aggregation scheme and the LaneExtraction predictions is similar, the low MMD and MED values of our aggregation scheme indicate that our generated lane graph entails shorter and more direct paths, compared to LaneExtraction. We show additional results in the supplementary material, Sec. S.8." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.817, + 0.205, + 0.832 + ], + "angle": 0, + "content": "5.6. Limitations" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Due to its bottom-up architecture, the proposed approach performs well for most evaluated scenes in urban and suburban surroundings but struggles with highly complex graph topologies such as multi-lane intersections or roundabouts." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.451, + 0.895, + 0.588 + ], + "angle": 0, + "content": "Moreover, due to the iterative formulation of our aggregation scheme, the inference time of our approach increases with the number of nodes and edges. To speed up inference time, future work might include adaptively changing the distance between consecutive virtual agent positions and leveraging efficient neighborhood lookup methods such as k-d trees. Parallel execution of multiple agents would additionally boost run time to match top-down approaches and is feasible in terms of the proposed aggregation scheme." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.624, + 0.619, + 0.639 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.656, + 0.895, + 0.837 + ], + "angle": 0, + "content": "In this work, we presented a novel lane graph estimation framework complemented with a novel dataset comprising aerial images. We showed that formulating the lane graph estimation problem as bottom-up graph neural network approach leveraging agent-centric views yields promising results. In addition, we presented a novel aggregation scheme to merge successive lane graphs to produce large-scale solutions. A first-of-its-kind dataset and benchmark for lane graph estimation from aerial images will enable further research in this field. Future work could address end-to-end training and exploiting further modalities such as onboard vehicle cameras for additional context information." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Acknowledgements: This work was partly funded by the German Research Foundation (DFG) Emmy Noether Program grant number 468878300, DFG grant number BU 865/10-2, and a hardware grant from NVIDIA." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13422" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.198 + ], + "angle": 0, + "content": "[1] Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, and Vishal M Patel. Spin road mapper: Extracting roads from aerial images via spatial and interaction space graph reasoning for autonomous driving. In 2022 International Conference on Robotics and Automation (ICRA), pages 343-350. IEEE, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.201, + 0.472, + 0.284 + ], + "angle": 0, + "content": "[2] Favyen Bastani, Songtao He, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, and David DeWitt. Roadtracer: Automatic extraction of road networks from aerial images. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 4720-4728, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.287, + 0.47, + 0.342 + ], + "angle": 0, + "content": "[3] Guillem Brasó and Laura Leal-Taixe. Learning a neural solver for multiple object tracking. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.344, + 0.47, + 0.399 + ], + "angle": 0, + "content": "[4] Martin Büchner and Abhinav Valada. 3d multi-object tracking using graph neural networks with cross-edge modality attention. IEEE Robotics and Automation Letters, 7(4):9707-9714, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.402, + 0.47, + 0.471 + ], + "angle": 0, + "content": "[5] Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, and Luc Van Gool. Structured bird's-eye-view traffic scene understanding from onboard images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15661-15670, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.473, + 0.47, + 0.543 + ], + "angle": 0, + "content": "[6] Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, and Luc Van Gool. Topology preserving local road network estimation from single onboard camera image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17263-17272, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.545, + 0.47, + 0.599 + ], + "angle": 0, + "content": "[7] Daniele Cattaneo, Matteo Vaghi, and Abhinav Valada. Lcdnet: Deep loop closure detection and point cloud registration for lidar slam. IEEE Transactions on Robotics, 38(4):2074-2093, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.602, + 0.47, + 0.657 + ], + "angle": 0, + "content": "[8] Yuning Chai, Benjamin Sapp, Mayank Bansal, and Dragomir Anguelov. Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction. In Conference on Robot Learning, pages 86-99. PMLR, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.66, + 0.47, + 0.713 + ], + "angle": 0, + "content": "[9] Dian Chen and Philipp Kähenbuhl. Learning from all vehicles. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17222-17231, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.716, + 0.47, + 0.786 + ], + "angle": 0, + "content": "[10] Nemanja Djuric, Henggang Cui, Zhaoen Su, Shangxuan Wu, Huahua Wang, Fang-Chieh Chou, Luisa San Martin, Song Feng, Rui Hu, Yang Xu, et al. Multixnet: Multiclass multi-stage multimodal motion prediction. In 2021 IEEE Intelligent Vehicles Symposium (IV), pages 435-442. IEEE, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.788, + 0.47, + 0.857 + ], + "angle": 0, + "content": "[11] Whye Kit Fong, Rohit Mohan, Juana Valeria Hurtado, Lubing Zhou, Holger Caesar, Oscar Beijbom, and Abhinav Valada. Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking. IEEE Robotics and Automation Letters, 7(2):3795-3802, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.859, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[12] Nikhil Gosala and Abhinav Valada. Bird's-eye-view panoptic segmentation using monocular frontal view images. IEEE Robotics and Automation Letters, 7(2):1968-1975, 2022. 1" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.147 + ], + "angle": 0, + "content": "[13] Aric Hagberg, Pieter Swart, and Daniel S Chult. Exploring network structure, dynamics, and function using networkx. Technical report, Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2008. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.894, + 0.176 + ], + "angle": 0, + "content": "[14] J Halton and G Smith. Radical inverse quasi-random point sequence, algorithm 247. Commun. ACM, 7(12):701, 1964. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.178, + 0.894, + 0.232 + ], + "angle": 0, + "content": "[15] Songtao He and Hari Balakrishnan. Lane-level street map extraction from aerial imagery. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2080-2089, 2022. 1, 2, 3, 6, 7, 8, 13, 21" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.234, + 0.894, + 0.302 + ], + "angle": 0, + "content": "[16] Namdar Homayounfar, Wei-Chiu Ma, Shrinidhi Kowshika Lakshmikanth, and Raquel Urtasun. Hierarchical recurrent attention networks for structured online maps. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 3417-3426, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.304, + 0.893, + 0.37 + ], + "angle": 0, + "content": "[17] Namdar Homayounfar, Wei-Chiu Ma, Justin Liang, Xinyu Wu, Jack Fan, and Raquel Urtasun. Dagmapper: Learning to map by discovering lane topology. In Proceedings of the IEEE International Conference on Computer Vision, pages 2911-2920, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.372, + 0.893, + 0.44 + ], + "angle": 0, + "content": "[18] Justin Liang, Namdar Homayounfar, Wei-Chiu Ma, Shenlong Wang, and Raquel Urtasun. Convolutional recurrent network for road boundary extraction. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 9512-9521, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.442, + 0.894, + 0.496 + ], + "angle": 0, + "content": "[19] Gellert Mattyus, Wenjie Luo, and Raquel Urtasun. Deeproadmapper: Extracting road topology from aerial images. In Proceedings of the IEEE International Conference on Computer Vision, pages 3438-3446, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.498, + 0.894, + 0.551 + ], + "angle": 0, + "content": "[20] Rohit Mohan and Abhinav Valada. Amodal panoptic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21023-21032, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.553, + 0.894, + 0.621 + ], + "angle": 0, + "content": "[21] Kursat Petek, Kshitij Sirohi, Daniel Buscher, and Wolfram Burgard. Robust monocular localization in sparse hd maps leveraging multi-task uncertainty estimation. In 2022 International Conference on Robotics and Automation (ICRA), pages 4163-4169. IEEE, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.623, + 0.894, + 0.692 + ], + "angle": 0, + "content": "[22] Yong-Qiang Tan, Shang-Hua Gao, Xuan-Yi Li, Ming-Ming Cheng, and Bo Ren. Vecroad: Point-based iterative graph exploration for road graphs extraction. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 8910-8918, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.693, + 0.893, + 0.747 + ], + "angle": 0, + "content": "[23] Shinji Umeyama. Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(04):376-380, 1991. 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.749, + 0.894, + 0.829 + ], + "angle": 0, + "content": "[24] Abhinav Valada, Ankit Dhall, and Wolfram Burgard. Convoluted mixture of deep experts for robust semantic segmentation. In IEEE/RSJ International conference on intelligent robots and systems (IROS) workshop, state estimation and terrain perception for all terrain mobile robots, volume 2, 2016. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.832, + 0.894, + 0.872 + ], + "angle": 0, + "content": "[25] Adam Van Etten, Dave Lindenbaum, and Todd M Bacastow. Spacenet: A remote sensing dataset and challenge series. arXiv preprint arXiv:1807.01232, 2018. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.874, + 0.894, + 0.901 + ], + "angle": 0, + "content": "[26] Johan Vertens and Wolfram Burgard. Usegscene: Unsupervised learning of depth, optical flow and ego-motion with" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13423" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.092, + 0.47, + 0.12 + ], + "angle": 0, + "content": "semantic guidance and coupled networks. arXiv preprint arXiv:2207.07469, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.472, + 0.177 + ], + "angle": 0, + "content": "[27] Niclas Vödisch, Daniele Cattaneo, Wolfram Burgard, and Abhinav Valada. Continual slam: Beyond lifelong simultaneous localization and mapping through continual learning. arXiv preprint arXiv:2203.01578, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.179, + 0.472, + 0.234 + ], + "angle": 0, + "content": "[28] Jingke Wang, Tengju Ye, Ziqing Gu, and Junbo Chen. Ltp: Lane-based trajectory prediction for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17134-17142, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.236, + 0.472, + 0.332 + ], + "angle": 0, + "content": "[29] Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, et al. Argoverse 2: Next generation datasets for self-driving perception and forecasting. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021. 3, 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.334, + 0.472, + 0.389 + ], + "angle": 0, + "content": "[30] Zhenhua Xu, Yuxuan Liu, Lu Gan, Yuxiang Sun, Xinyu Wu, Ming Liu, and Lujia Wang. Ringdet: Road network graph detection by transformer in aerial images. IEEE Transactions on Geoscience and Remote Sensing, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.391, + 0.472, + 0.446 + ], + "angle": 0, + "content": "[31] Li Zhang, Faezeh Tafazzoli, Gunther Krehl, Runsheng Xu, Timo Rehfeld, Manuel Schier, and Arunava Seal. Hierarchical road topology learning for urban map-less driving. arXiv preprint arXiv:2104.00084, 2021. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.446 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.893, + 0.134 + ], + "angle": 0, + "content": "[32] Tongjie Y Zhang and Ching Y. Suen. A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 27(3):236-239, 1984. 15" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.136, + 0.894, + 0.192 + ], + "angle": 0, + "content": "[33] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881-2890, 2017. 3, 16" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.193, + 0.894, + 0.262 + ], + "angle": 0, + "content": "[34] Lichen Zhou, Chuang Zhang, and Ming Wu. D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 182-186, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.263, + 0.894, + 0.331 + ], + "angle": 0, + "content": "[35] Yiyang Zhou, Yuichi Takeda, Masayoshi Tomizuka, and Wei Zhan. Automatic construction of lane-level hd maps for urban scenes. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6649-6656. IEEE, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.334, + 0.894, + 0.389 + ], + "angle": 0, + "content": "[36] Jannik Zürn, Johan Vertens, and Wolfram Burgard. Lane graph estimation for scene understanding in urban driving. IEEE Robotics and Automation Letters, 6(4):8615-8622, 2021. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.391, + 0.894, + 0.446 + ], + "angle": 0, + "content": "[37] Jannik Zürn, Sebastian Weber, and Wolfram Burgard. Trackletmapper: Ground surface segmentation and mapping from traffic participant trajectories. In 6th Annual Conference on Robot Learning, 2022. 1" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.446 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "13424" + } + ] +] \ No newline at end of file diff --git a/2023/Learning and Aggregating Lane Graphs for Urban Automated Driving/71072545-68f3-478a-8f5b-1abe54803639_origin.pdf b/2023/Learning and Aggregating Lane Graphs for Urban Automated Driving/71072545-68f3-478a-8f5b-1abe54803639_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ccabf75acf8b76a95532008d1388cb40dcd83df0 --- /dev/null +++ b/2023/Learning and Aggregating Lane Graphs for Urban Automated Driving/71072545-68f3-478a-8f5b-1abe54803639_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7e37bfb1fdb32fd23d923bf61eef4e15753edbfa1f4b5ba9924277fe4b514cf2 +size 7907059 diff --git a/2023/Learning and Aggregating Lane Graphs for Urban Automated Driving/full.md b/2023/Learning and Aggregating Lane Graphs for Urban Automated Driving/full.md new file mode 100644 index 0000000000000000000000000000000000000000..bd7d07f17c3bdaaeeb19ac32f86bc9efaacbe2ea --- /dev/null +++ b/2023/Learning and Aggregating Lane Graphs for Urban Automated Driving/full.md @@ -0,0 +1,264 @@ +# Learning and Aggregating Lane Graphs for Urban Automated Driving + +Martin Büchner $^{1*}$ Jannik Zürn $^{1*}$ Ion-George Todoran $^{2}$ Abhinav Valada $^{1}$ Wolfram Burgard $^{3}$ $^{1}$ University of Freiburg $^{2}$ Woven by Toyota $^{3}$ University of Technology Nuremberg + +# Abstract + +Lane graph estimation is an essential and highly challenging task in automated driving and HD map learning. Existing methods using either onboard or aerial imagery struggle with complex lane topologies, out-of-distribution scenarios, or significant occlusions in the image space. Moreover, merging overlapping lane graphs to obtain consistent large-scale graphs remains difficult. To overcome these challenges, we propose a novel bottom-up approach to lane graph estimation from aerial imagery that aggregates multiple overlapping graphs into a single consistent graph. Due to its modular design, our method allows us to address two complementary tasks: predicting ego-respective successor lane graphs from arbitrary vehicle positions using a graph neural network and aggregating these predictions into a consistent global lane graph. Extensive experiments on a large-scale lane graph dataset demonstrate that our approach yields highly accurate lane graphs, even in regions with severe occlusions. The presented approach to graph aggregation proves to eliminate inconsistent predictions while increasing the overall graph quality. We make our large-scale urban lane graph dataset and code publicly available at http://urbanlanegraph.cs.uni-freiburg.de. + +# 1. Introduction + +Most automated driving vehicles rely on the knowledge of their immediate surroundings to safely navigate urban environments. Onboard sensors including LiDARs and cameras provide perception inputs that are utilized in multiple tasks such as localization [7, 21, 27], tracking [4], or scene understanding [20, 24, 26, 37] to aggregate representations of the environment. However, robust planning and control typically require vastly more detailed and less noisy world models in the form of HD map data [12]. In particular, information on lane parametrization and connectivity is essential for both planning future driving maneuvers as well as high-level navigation tasks. Creating and maintaining HD maps in the form of lane graphs is a time-consuming and arduous + +![](images/230389817c2eb7990cec05d3cf265dc55cc38aaaf2d903a95a1b38b9baa4102b.jpg) +Figure 1. Our approach predicts accurate lane graphs from aerial images of complex urban environments. We visualize the estimated lane graph in magenta and indicate model initialization points with yellow circles. + +task due to the large amount of detail required in the annotation and the data curation process including map updates based on local environment changes such as construction sites. + +Previous approaches to lane graph estimation have shown shortcomings in predicting lane graphs due to multiple deficiencies: On the one hand, methods using onboard imagery typically degrade at complex real-world intersections and under significant occlusions, e.g., when following another vehicle [5, 6]. On the other hand, methods based on aerial imagery show reduced performance when confronted with occlusions in the bird's-eye-view (BEV) due to, e.g., vegetation or shadows, and suffer from catastrophic drift when unconstrained in out-of-distribution scenarios [30]. Previous works treat intersections and non-intersections inherently differently [15] and thus require elaborated heuristics and post-processing to merge single predictions into a consistent lane graph. Moreover, prior works do not focus on use cases where multiple predicted graphs must be merged into a single consistent solution, which is essential for enabling the automatic generation of highly detailed lane graphs of large contiguous regions. + +Related to the aforementioned challenges, we propose a novel two-stage graph neural network (GNN) approach termed LaneGNN that operates on single aerial color images for lane graph prediction. Inspired by methods in the field of trajectory prediction [8], we formulate a bottom-up approach according to which we place a virtual agent into a local crop of the aerial image and predict reachable successor lane graphs from its positions. To transform multiple disjoint local solutions into a single global solution, we aggregate a global representation by iteratively inferring the lane graph from consecutive poses, ultimately imitating real-world driving behavior. This iterative approach not only increases the predicted area covered but also improves graph accuracy based on data association and rejection. Note that we do not require any human in the loop to perform the graph aggregation. We visualize the output of our graph aggregation procedure in Fig. 1, in which we superimpose the predicted graph on the aerial image input. Using this framework, we envision two applications: ego-centered successor path prediction and full lane graph estimation by aggregation. + +To summarize, the main contributions of this work are: + +- An innovative bottom-up approach to lane graph estimation in challenging environments that explicitly encodes graph-level lane topology from input aerial images in a scenario-agnostic manner. +- A novel graph aggregation scheme enabling robust and method-agnostic merging of graph-level predictions. +- The large-scale lane graph dataset UrbanLaneGraph comprising high-resolution aerial images aligned with dense lane graph annotations aggregated from the Argoverse2 dataset that we make publicly available. +- Extensive experiments and ablation studies demonstrating the significance of our findings. + +# 2. Related Works + +In recent years, the prediction of topological road features such as road graphs and lane graphs have been extensively studied. In our discussion, we differentiate between road graph learning and lane graph learning. While road graphs encode the topological connections between road segments, lane graphs describe the locations and connectivity between all lanes, resulting in a spatially much denser graph. Many prior works focus on vehicle trajectory prediction, conditioned on HD map features such as lane centerline and boundary positions [9, 10, 28]. These models do not aim at exclusively predicting the road or lane graph structure from onboard vehicle images or aerial images but instead at predicting future vehicle states such as position and orientation. + +Road Graph Learning: Prior works investigate estimating road graphs from both onboard sensors [18] and from aerial images [1, 22, 34] or focus on extracting pixel-level road segmentation from images and extracting graphical road rep + +resentations, i.e., using morphological image operators or graph neural networks to extract the connectivity between different roads within the image [1, 19]. Other approaches investigate iterative methods and interpret road graph prediction as a sequential prediction task [2, 22]. + +Lane Graph Learning from Vehicle Data: Some earlier works in lane graph learning from onboard vehicle sensors such as cameras and LiDAR formulate lane extraction as an image-based lane centerline regression task [16]. Homayounfar et al. [17] aggregate onboard LiDAR data on highways and leverage a recurrent neural network to generate highway lane graphs in an iterative manner. Zhou et al. [35] utilize the OpenStreetMap database and a semantic particle filter to accumulate projected semantic predictions from a vehicle ego-view into a map representation. Zhang et al. [31] propose an online road map extraction system for a sensor setup onboard a moving vehicle and construct a graph representation of the road network using a fully convolutional neural network. More recently, Can et al. proposed two different methods [5, 6] for lane connectivity learning in intersection scenarios from onboard camera images. + +Lane Graph Learning from Birds-Eye-View Data: Despite the advantages of leveraging readily available aerial data for training lane graphs, only a few works considered using aerial images as an input modality for the graph learning task. Zürn et al. [36] propose a lane centerline regression model jointly with a Graph R-CNN backbone to predict nodes and edges of the lane graph from a local aggregated bird's-eye-view image crop. More recently, He et al. [15] propose a two-stage graph estimation pipeline. They first extract lanes at non-intersection areas and subsequently predict the connectivity of each pair of lanes, and extract the valid turning lanes to complete the map at intersections. + +In contrast to existing works, we do not estimate the complete lane graph visible in a given crop but only the part of the graph that is reachable from a virtual agent pose located within the crop, simplifying the graph estimation problem based on reduced topological complexity. Furthermore, we leverage a GNN to explicitly model the relationships between graph nodes, allowing us to leverage recent developments in the field of geometric deep learning. This explicit graph encoding and prediction allows us to formulate a model that does not internally differentiate between intersection areas and non-intersection areas, in contrast to some related works. Additionally, we propose a novel large-scale dataset for lane graph estimation from aerial images, allowing the research community to easily evaluate and compare their approaches. + +# 3. Dataset + +To evaluate our approach on challenging real-world data, we compiled the UrbanLaneGraph dataset. It is a first-of-its + +![](images/d0ae2fedc68d6e8fd2c1fa3ebf83e3f7d526ceaee8c0134617e5ec2217d391d1.jpg) +Figure 2. Overview of our LaneGNN model $\mathcal{M}$ predicting successor lane graphs. As a pre-training step, we train lane centerline and ego lane centerline regressor models. The ego lane regression $\mathbf{S}_{\text{lane}}^{\text{ego}}$ is used as a prior for sampling proposal nodes $V$ in corridors that have a high likelihood of entailing the successor graph. The model learns a binary classification of node and edge scores while also predicting the probability of a node being an endpoint of a given lane segment. + +
DatasetCityLane Splits/MergesTotal Length [km]
UrbanLaneGraph (from Argoverse2)Palo Alto4752796.4
Austin8495531.7
Miami7642850.8
Pittsburgh146101314.3
Washington D.C.2066739.6
Detroit5424990.5
LaneExtraction [15]Boston, Seattle, Phoenix, Miami2262398.6
NuScenes [11]Boston163076.9
+ +Table 1. Key statistics of our UrbanLaneGraph dataset, aggregated from the Argoverse2 dataset [29], used for our experiments, compared with other recent datasets for lane graph estimation. + +kind dataset for large-scale lane graph estimation from aerial images. The dataset contains aerial images from the cities of Austin, Miami, Pittsburgh, Palo Alto, Detroit, and Washington DC. The images have a resolution of $15\mathrm{cm}$ per pixel. To obtain the corresponding lane annotations, we leverage the Argoverse2 dataset [29] which entails lane graphs for large sections of the respective cities. The lane graphs in the Argoverse2 dataset are provided on a per-scenario basis, covering only small local areas in one graph. Therefore, we collect all local lane graphs of each city and aggregate them into one globally consistent graph per city, thereby removing inconsistent or redundant nodes or edges. The annotated regions feature a diverse range of environments including urban, suburban, and rural regions with complex lane topologies. Accumulated over all cities, the overall length of all lanes spans over $5.000\mathrm{km}$ . We split each city into disjoint training and testing regions. We list key dataset statistics in Tab. 1, indicating the scale of our generated dataset compared with other recently proposed datasets containing graph annotations. For more details on the dataset and exemplary visualizations, please refer to Sec. S.1 in the supplementary material. + +# 4. Technical Approach + +Our approach is divided into two stages: lane graph learning and lane graph aggregation. In the first stage (Sec. 4.1), we train our GNN model, denoted as LaneGNN, to predict the successor lane graph, entailing the nodes and edges + +that can be logically visited from the pose of a virtual vehicle agent. In the second phase (Sec. 4.2), we use our trained LaneGNN model to traverse a large map area. This is achieved by selecting an initial starting pose and predicting the successor lane graph from this pose. Subsequently, we iteratively estimate the traversable lane graph from the current pose and move forward along the predicted graph while aggregating. In the following, we detail both stages of our approach. + +# 4.1. Lane Graph Learning + +We formulate the task as a supervised learning problem where a successor lane graph $\hat{G}$ is estimated based on an aerial image $\mathbf{I}_{bev}$ . First, a directed graph $G_{I} = (V,E)$ covering relevant regions is constructed by sampling from likely regions of $\mathbf{I}_{bev}$ (see Fig. 2). For all our models and experiments we choose a spatial resolution of $256\times 256$ pixels. The graph comprises a set of nodes $i\in V$ that are connected via directed edges $E\subseteq \{(i,j)|(i,j)\in V^2$ and $i\neq j\}$ that constitute potentially valid lane graph edges. The graph is attributed using both node features $\mathbf{X}\in \mathbb{R}^{\| V\| \times D}$ and edge features $\mathbf{X}_e\in \mathbb{R}^{\| E\| \times (D_{geo} + D_{bev})}$ , where $D_{geo}$ and $D_{bev}$ denote the dimensionality of the involved edge features (see Sec. 4.1). The overall model estimates an output graph $\hat{G} = \mathcal{M}(G_I|\theta)$ , where $\mathcal{M}$ is parameterized by network model weights $\theta \coloneqq (\theta_{reg},\theta_{GNN})$ . + +Lane Regression and Graph Construction: We train two regression networks: Firstly, a centerline regression network predicting the likelihood map of lane centerlines $\mathbf{S}_{\text {lane }}$ , and secondly, a segmentation network predicting all reachable lanes $\mathbf{S}_{\text {lane }}^{\text {ego }}$ starting from the initial virtual agent pose at the bottom center of $\mathbf{I}_{\text {bev }}$ . We use identical PSPNet [33] architectures with a ResNet-152 feature extractor for this task. We sample equally-distributed node positions using Halton sequences [14] that are later filtered based on the obtained ego-lane segmentation mask $\mathbf{S}_{\text {lane }}^{\text {ego }}$ , which serves as a region of interest for sampling (see Fig. 2). Directed edges $E$ among nodes are initialized for pairs of nodes with a Euclidean distance $d_{ij} \in [d_{\min}, d_{\max}]$ . Initial node features $\mathbf{X}$ are crafted solely based on their 2D positions $\mathbf{x}_i = (x_i, y_i)$ , while geometric edge features are defined as + +![](images/407c9ff555e79867676abb0afa03327503510f77169a4b7371156d3e96eea80d.jpg) +Temporal evolution of local BEV crops + +![](images/7396d3f022676ae324cabe31a31342df11055cddd5769f1d6cb7fac054c7a1ab.jpg) +Figure 3. In our graph aggregation procedure, we iteratively obtain oriented image crops based on virtual agent poses along the currently predicted successor graph. For each crop, our LaneGNN model predicts a successor graph $G_{pred}$ , which is aggregated into a globally consistent lane graph $G_{agg}$ . + +![](images/1ba4dc55f165808e7614c9d49259db931ab428f1f8b032b4ef77db69a6ebcaee.jpg) +Temporal evolution of aggregated graph + +$$ +\mathbf {x} _ {i j, g e o} = \left(\tan^ {- 1} \frac {\Delta y _ {i j}}{\Delta x _ {i j}}, \sqrt {\Delta x _ {i j} {} ^ {2} + \Delta y _ {i j} {} ^ {2}}, \bar {x} _ {i j}, \bar {y} _ {i j}\right), \tag {1} +$$ + +where $\Delta x_{ij}$ and $\Delta y_{ij}$ represent node position differences and $\overline{x}_{ij}$ , $\overline{y}_{ij}$ are edge middle point coordinates. In addition to the geometric edge feature, we generate aerial edge features: Per edge, a small oriented region of $\mathbf{I}_{bev}$ and the lane segmentation $\mathbf{S}_{lane}$ is obtained based on the direction of the edge, which provides $\mathbf{X}_{ij,bev} = [\mathbf{I}_{bev}^{*},\mathbf{S}_{lane}^{*}]$ with dimension $D_{bev} = 4\times 32\times 32$ + +Feature Encoding and Message Passing: In our approach, we estimate edge probabilities, node probabilities, and whether a node is terminal. While the nodes themselves hold only unidirectional information, we encode the notion of direction using the proposed edge feature as outlined above. We utilize a causal variant of neural message passing as proposed by Brasó et al. [3]. By imposing a causality prior, our network encodes predecessor and successor features during message passing. This formulation of message passing renders our approach direction-aware. Initial node and geometric edge features are encoded using multi-layer perceptrons $f_{enc}^{\square}$ (MLP) while the aerial edge feature is transformed using a ResNet-18 architecture $f_{enc}^{e,bev}$ . The geometric and aerial edge features are concatenated and fused subsequently to arrive at various node and edge embeddings $\mathbf{H}_v^{(0)}$ and $\mathbf{H}_e^{(0)}$ : + +$$ +f _ {e n c} ^ {e, b e v} \left(\mathbf {X} _ {e, b e v}\right) = \mathbf {H} _ {e, b e v} ^ {(0)}, f _ {e n c} ^ {e, g e o} \left(\mathbf {X} _ {e, g e o}\right) = \mathbf {H} _ {e, g e o} ^ {(0)}, (2) +$$ + +$$ +f _ {e n c} ^ {v} (\mathbf {X}) = \mathbf {H} _ {v} ^ {(0)}, f _ {f u s e} ^ {e} ([ \mathbf {H} _ {e, g e o} ^ {(0)}, \mathbf {H} _ {e, b e v} ^ {(0)} ]) = \mathbf {H} _ {e} ^ {(0)}. (3) +$$ + +In the following, multiple message-passing steps are performed using various ReLU-activated MLPs denoted by $f_{\square}$ as follows. The edge feature is updated based on the current neighboring node features $\mathbf{h}_i^{(l - 1)}$ , $\mathbf{h}_j^{(l - 1)}$ and the edge feature $\mathbf{h}_{ij}^{(l - 1)}$ : + +$$ +\mathbf {h} _ {i j} ^ {(l)} = f _ {e} \left(\left[ \mathbf {h} _ {i} ^ {(l - 1)}, \mathbf {h} _ {j} ^ {(l - 1)}, \mathbf {h} _ {i j} ^ {(l - 1)} \right]\right). \tag {4} +$$ + +Messages $\mathbf{m}_{ij}^{(l)}$ are crafted based on either predecessors $\mathcal{N}_{pred}(i)$ or successors $\mathcal{N}_{succ}(i)$ of a node $i$ and propagated based on the constructed adjacency. In the next step, all predecessors and successor messages, respectively, are aggregated using a permutation-invariant sum: + +$$ +\mathbf {h} _ {i, p r e d} ^ {(l)} = \sum_ {j \in \mathcal {N} _ {p r e d} (i)} \underbrace {f _ {v} ^ {p r e d} \left(\left[ \mathbf {h} _ {j} ^ {(l - 1)} , \mathbf {h} _ {i j} ^ {(l)} , \mathbf {h} _ {j} ^ {(0)} \right]\right)} _ {\mathbf {m} _ {j i} ^ {(l)}}, \tag {5} +$$ + +$$ +\mathbf {h} _ {i, s u c c} ^ {(l)} = \sum_ {j \in \mathcal {N} _ {s u c c} (i)} \underbrace {f _ {v} ^ {s u c c} \left(\left[ \mathbf {h} _ {j} ^ {(l - 1)} , \mathbf {h} _ {i j} ^ {(l)} , \mathbf {h} _ {j} ^ {(0)} \right]\right)} _ {\mathbf {m} _ {i j} ^ {(l)}}. \tag {6} +$$ + +Note that the message crafting includes skip connections to initial node embeddings $\mathbf{h}_j^{(0)}$ . Nodes are updated by combining the two features using concatenation: + +$$ +\mathbf {h} _ {i} ^ {(l)} = f _ {v} \left(\left[ \mathbf {h} _ {i, p r e d} ^ {(l)}, \mathbf {h} _ {i, s u c c} ^ {(l)} \right]\right). \tag {7} +$$ + +Finally, sigmoid-valued edge scores $\hat{e}_{ij}$ and node score $\hat{s}_i$ are predicted from the obtained edge and node embeddings. In a separate network head, we classify each node as being a terminal node or not, denoted as a scalar $\hat{t}_i$ . Therefore, we optimize the following combined binary cross entropy: + +$$ +\mathcal {L} = - \sum_ {| V |} s _ {i} \log \hat {s} _ {i} - \sum_ {| V |} t _ {i} \log \hat {t} _ {i} - \sum_ {| E |} e _ {i j} \log \hat {e} _ {i j}. \tag {8} +$$ + +The ground truth graph $G_{GT}$ as a learning target $(s_i, t_i, e_{ij})$ is generated based on the map annotations for the given cropped region and the corresponding closest nodes. This is further outlined in the supplementary material in Sec. S.3. + +# 4.2. Iterative Temporal Graph Aggregation + +In the second stage of our approach, we aggregate local successor graphs into a globally consistent lane graph. First, we prune the predicted per-crop lane graph, and second, we iteratively aggregate the sparse graphs. In the following, we detail both components. + +![](images/e426750ea4d61fd31a189d749cf08d8b89d80c59b7a5a56333707835caf1506e.jpg) +Figure 4. Comparison of raw and pruned lane graph predictions. On the left-hand side, we visualize estimated edge scores $\hat{e}_{ij}$ and node scores $\hat{s}_i$ using the same color scaling. On the right-hand side, we visualize the pruned graph. The circled numbers denote the order of traversal based on estimated terminal node scores. + +![](images/1de6e21e2b71f6f65ca648fa2f609566381fa98bdf54a6bd129c1a5d841316a4.jpg) + +Pruning: The graph prediction obtained from the LaneGNN model follows the graph connectivity initially generated during sampling. Since the model prediction contains a number of redundant paths with high predicted node and edge scores, we prune the obtained solution to obtain sparse lane representations. We formulate the graph pruning problem as a search problem from a starting node to possibly multiple predicted terminal nodes (see Sec. 4.1). Predicted lane split points should coincide with actual split points. Thus, different branches should share the same set of edges up to a split point. We use Dijkstra's algorithm to iteratively find high-score paths between the initial pose and terminal nodes, ordered from high to low scores until all terminal nodes are reached. Edge scores contained in found paths are set to zero. In Fig. 4, we visualize the output of this step. + +Aggregation: We iteratively aggregate predicted successor lane graphs $G_{pred} = (V_{pred}, E_{pred})$ into a globally consistent and complete graph $G_{agg} = (V_{agg}, E_{agg})$ as depicted in Fig. 3. The predicted successor graph $G_{pred}^{t}$ is added to the current aggregated graph $G_{agg}^{t-1}$ at time step $t$ : $G_{agg}^{t} \gets \text{aggregate}(G_{pred}^{t}, G_{agg}^{t-1})$ . Plausible branches of $G_{pred}$ are merged into $G_{agg}$ and thus extend the aggregated graph with every iteration if new grounds are covered in the respective crops. The next virtual agent pose is extracted from the set of forward-facing edges of $G_{agg}$ given the current pose. Only edges with a significant weight due to previous aggregations are selected for this set. As the node positions differ slightly with each model forward pass we observe roughly similar paths in the lateral sense wrt. the ground-truth graph. However, along the longitudinal dimension of a branch, we observe deviations in node position, which must be circumvented when aggregating the graph. Based on this, we only take the lateral deviation wrt. $G_{agg}^{t}$ into account when merging $G_{pred}^{t}$ . Thus, $G_{agg}^{t}$ is only updated in a lateral sense while the longitudinal misalignment of the two sets of nodes is neglected (see also Sec. S.5 in the supplementary material). The nodes of $G_{pred}^{t}$ have a + +weight of 1 while a node of $G_{agg}^{t}$ holds a weight equal to the number of merges it has observed so far. If a novel node $i \in V_{pred}$ is not close to any other node in $k \in V_{agg}$ it is added to the aggregated graph including its incident edges. This ultimately allows a weighting-based merging of arbitrary pairs of graphs, which is used for global lane graph estimation (see Sec. 5.4). + +We observe that the more graphs we aggregate, the more we are certain about the significance of particular graph branches. Following a weighting-based approach allows us to set certain thresholds that allow modification of $G_{agg}$ . Thus, implausible graph branches, semantically similar parallel paths, redundant edges, and isolated nodes are deleted based on confidence and distance as well as angle criteria (see Fig. 3). As a result, we are able to decrease the number of false positive split and merge points to obtain more consistent global graphs. We observe that this approach greatly improves lane graph prediction accuracy in difficult occluded and out-of-distribution scenarios since the sum of model predictions covering the same region shed light onto what potentially constitutes, e.g., a valid and an invalid branch. + +Multiple forward passes naturally lead to a multitude of lane split points (both true positive and false positive splits). We interpret each split point as an element of an exploration frontier. A queue of unexplored, high-probability graph branches is maintained, which is queried in a depth-first manner as soon as the currently traversed branch terminates. Following the weighting-based approach, a branch is only explored if its depth-limited oriented successor tree weight exceeds a certain level of confidence. Due to this flexible approach, we can essentially handle arbitrary lane graph topologies with a single holistic approach. For more details on the graph pruning and aggregation procedures, please refer to the supplementary material. Finally, we apply multiple iterations of Laplacian smoothing to $G_{pred}^{t}$ , which modifies the original node positions in order to even out position irregularities caused by sampling while keeping the adjacency represented by $E_{pred}^{t}$ constant. + +# 5. Experimental Results + +In the following, we present our experimental findings. We first define and illustrate three tasks on which we benchmark our method. Subsequently, we describe the evaluation metrics and compare against other methods as well as our own baselines. We provide extensive qualitative and quantitative evaluations on our UrbanLaneGraph dataset. + +# 5.1. Proposed Tasks + +We propose three distinct and complementary tasks. In the first task, successor lane graph prediction (Successor-LGP, Sec. 5.3), we aim at predicting a feasible ego-reachable successor lane graphs from the current pose of the virtual agent. The purpose of the Successor-LGP task is to measure + +Table 2. Quantitative results of our model including ablation studies, in comparison with baseline models for the Successor-LGP task on our UrbanLaneGraph dataset. P/R denotes Precision/Recall. For our LaneGNN model variants, we denote CMP as causal message passing, aerial node features as AerN, aerial edge features as AerE, and the ego-lane regression with $\mathbf{S}_{\text{lane}}^{\text{ego}}$ . For all metrics, higher values mean better results. + +
ModelCMPAerNAerES laneTOPO P/R ↑GEO P/R ↑APLS ↑SDA20 ↑SDA50 ↑Graph IoU ↑
Skeletonized Regression----0.597/0.6130.578/0.6010.3150.0200.1850.180
LaneGraphNet [36]----0.0/0.00.0/0.00.1790.00.00.063
LaneGNN (ours)XX✓✓0.549/0.6770.548/0.6710.1880.1680.3230.312
✓✓X✓0.562/0.6560.562/0.6550.1920.1510.2980.320
✓XX✓0.545/0.6930.545/0.6880.2000.1880.3110.336
✓X✓X0.578/0.6690.577/0.6590.1500.1320.2270.250
✓X✓✓0.600/0.6990.599/0.6950.2020.2270.3770.347
+ +the prediction quality of potential future driving paths when no HD map coverage is available. In the second task, full lane graph prediction (Full-LGP, Sec. 5.4), we evaluate the quality of regionally aggregated lane graphs in the context of HD map estimation. This task aims at measuring the predictive power of our full two-stage model performing lane graph inference and graph aggregation in conjunction. For such purposes, the full ground truth lane graph of a given map area is compared to the aggregated predictions of our model. Finally, we carry out a high-level path planning task (Sec. 5.5) on the predicted lane graphs, intended to analyze the fidelity of routes planned on the predicted graphs. + +# 5.2. Evaluation Metrics + +We leverage multiple complementary metrics for performance evaluation as detailed below. + +Graph IoU: This metric measures the intersection-over-union (IoU) of two graphs rendered as a binary image [36], where pixels closer than $d = 5$ pixels are assigned the label 1 and the remaining pixels the label 0. Equivalent to the evaluation of semantic segmentation models, we determine the IoU values for the non-zero pixels. + +The APLS metric sums the differences in optimal path lengths between nodes in the ground truth graph $G$ and the proposal graph $G'$ [25]. The APLS metric scales from 0 (worst) to 1 (best). Formally, it is defined as + +$$ +\mathrm {A P L S} = 1 - \frac {1}{N _ {p}} \sum \min \left\{1, \frac {\left| d \left(v _ {1} , v _ {2}\right) - d \left(v _ {1} ^ {\prime} , v _ {2} ^ {\prime}\right) \right|}{d \left(v _ {1} , v _ {2}\right)} \right\}, \tag {9} +$$ + +where $v_{i}$ and $v_{i}^{\prime}$ are nodes in $G$ and $G^{\prime}$ , respectively. $N_{p}$ denotes the number of paths in $G$ and $d(\cdot, \cdot)$ is the path length. For more details, please refer to [25]. + +TOPO / GEO metrics: Following previous works in road network extraction and lane graph estimation, we use the GEO metric and the TOPO metric. For definitions and details on these metrics, please refer to [15] and to the supplementary material. + +Split Detection Accuracy $(\mathbf{SDA}_{\mathbf{R}})$ : This metric evaluates how accurately a model predicts the lane split within a circle of radius $R$ pixels from a given ground truth lane split. + +# 5.3. Successor Lane Graph Prediction + +In the following, we evaluate LaneGNN by ablating and comparing it with two baselines on the Successor-LGP task: morphological skeletonization of the ego-lane regression as well as a modified LaneGraphNet [36] to be used for successor lane graph prediction. We list quantitative results in Tab. 2. Our results demonstrate that none of the baseline methods are capable of estimating accurate lane graphs given the challenging topology of the lane graphs in the dataset. The LaneGraphNet [36] model fails to model the graph for many samples, yielding low scores in all metrics. Despite its simplicity, the skeletonized regression model achieves the highest APLS score and comparably high GEO/TOPO scores. However, it fails to accurately predict lane split points, resulting in low SDA scores. Increasing the number of nodes of the skeleton leads to many more false positive splits and thus deteriorates further. + +Regarding different variants of LaneGNN, we find that, e.g., causal message passing (CMP) increases performance over standard message passing, which underlines the significance of the imposed causality prior for lane graph learning. In order to show the efficacy of computationally more demanding aerial image edge features (AerE) compared to uni-directional aerial image node features (AerN), we ablate on this in Tab. 2 as well. We observe stark increases across the TOPO, GEO and SDA metrics when utilizing aerial edge features. Lastly, we replace the ego-lane segmentation mask $\mathbf{S}_{lane}^{ego}$ used for sampling with the standard lane segmentation mask. Our findings show that especially APLS, SDA, and Graph IoU drastically decrease as graph estimation becomes more difficult due to a generally enlarged sampling region (see Fig. 2). + +These results are further illustrated in Fig. 5, where we show qualitative comparisons of predictions of our best-performing model with predictions from the two baselines. We find that the quality of predictions by LaneGraphNet [36] is generally low, rendering it unsuitable for the task. The skeletonized regression baseline is capable of following basic lane graph topologies, but lane split points cannot be resolved accurately. In contrast, our LaneGNN model is capable of modeling most graphs with high accuracy; both in intersection areas and in straight road sections. For more + +![](images/30ab3d07f5345b826dc09cd8a2372540a541ca72d53688146b68ffc68571618c.jpg) +Figure 5. Qualitative results on the Successor-LGP task. We compare predictions of our model with LaneGraphNet [36] and a morphological image skeletonization baseline. Predicted nodes are visualized with points while predicted edges are visualized as directed arrows. We illustrate failure cases in the two rightmost columns. Best viewed zoomed in. + +Table 3. Quantitative evaluation for the Full-LGP task on the test-set of our UrbanLaneGraph dataset. We compare a baseline model with graphs aggregated with a naive aggregation scheme and our iterative temporal aggregation scheme. P/R denotes Precision/Recall. Higher values mean better results. + +
ModelTOPO P/R ↑GEO P/R ↑APLS ↑Graph IoU ↑
LaneExtraction [15]0.405/0.5070.491/0.4540.0720.213
Aggregation (naive)0.366/0.6540.523/0.7270.1010.376
Aggregation (ours)0.481/0.6700.649/0.6890.1030.384
+ +results, please refer to the supplementary material, Sec. S.6. + +# 5.4. Full Lane Graph Prediction + +For the Full-LGP task, we compare our approach with LaneExtraction [15]. Since their used graph representation is incompatible with ours, we train it on their provided dataset. To allow for a fair comparison, we evaluate both our method and LaneExtraction only on scenes in the city of Miami, Florida, as it is contained in both of the datasets. We select a testing region that is not part of the training data for either of the models. To initialize our aggregation scheme, we select starting poses obtained from intermediate segmentation predictions including yaw angles of the LaneExtraction model. Tab. 3 lists the evaluation results for the Full-LGP task obtained with LaneExtraction [15] and the results obtained with our LaneGNN model in conjunction with two aggregation schemes: a naive aggregation scheme baseline + +and our full aggregation scheme. The naïve aggregation scheme merges nodes in close proximity while not relying on unvalidated split/merge or parallel path removal as well as the lateral weighting-based merging (Sec. 4.2). Our experiments show that our aggregation scheme outperforms both the LaneExtraction model and the naïve aggregation scheme on nearly all evaluation metrics. We note that our method improves the TOPO/GEO precision metrics while maintaining similar recalls due to better handling of redundant nodes. Fig. 6 illustrates successive aggregations from our model while indicating the used initialization points from the LaneExtraction model. Since our aggregation approach does not differentiate between intersection and non-intersection regions, it does not deteriorate in regions that do not exactly fit this categorization. Furthermore, our model exhibits superior performance in reduced visibility settings introduced by stark illumination changes or road occlusions from vegetation, as illustrated in Fig. 6. One of the decisive assets of our bottom-up method is that it allows to explore regions that are missed by LaneExtraction [15] as they are entailed in their predicted segmentation masks. For more qualitative and quantitative results, please refer to the supplementary material, Sec. S.7. + +# 5.5. Path Planning + +To illustrate the efficacy of our aggregation scheme, we evaluate the quality of the lane graph on a planning task. We + +![](images/a74c0e589d82200af6d540f3a83108e19b1c443824711783a97cb7ce8cd88a93.jpg) +Figure 6. Qualitative results on the Full-LGP task. We visualize predictions of LaneExtraction [15] (top row) and aggregated LaneGNN predictions (bottom row). Our model is initialized at poses using predicted lane direction masks of LaneExtraction (indicated with yellow circles). Best viewed zoomed in. + +Table 4. Quantitative evaluation for the planning task. MMD denotes mean minimum distance, MED denotes mean endpoint distance, and SR denotes the path planning success rate. + +
ModelMMD [m] ↓MED [m] ↓SR ↑
LaneExtraction [15]157.0339.40.47
Aggregation (ours)2.219.70.46
+ +generate 1000 randomly selected starting poses in the Miami graph test area from which a plan to a random goal within the graph must be found, using A* search. We place the points such that a maximal optimal route length of $200\mathrm{m}$ is not exceeded. To evaluate the planned routes, we compare the mean minimum distance (MMD) and the mean route endpoint distance (MED) between the paths on the predicted graph and the ground truth graph, respectively. We also report the success rate (SR), indicating the number of cases in which a path between start and goal exists. We list the results in Tab. 4. While the SR for our aggregation scheme and the LaneExtraction predictions is similar, the low MMD and MED values of our aggregation scheme indicate that our generated lane graph entails shorter and more direct paths, compared to LaneExtraction. We show additional results in the supplementary material, Sec. S.8. + +# 5.6. Limitations + +Due to its bottom-up architecture, the proposed approach performs well for most evaluated scenes in urban and suburban surroundings but struggles with highly complex graph topologies such as multi-lane intersections or roundabouts. + +Moreover, due to the iterative formulation of our aggregation scheme, the inference time of our approach increases with the number of nodes and edges. To speed up inference time, future work might include adaptively changing the distance between consecutive virtual agent positions and leveraging efficient neighborhood lookup methods such as k-d trees. Parallel execution of multiple agents would additionally boost run time to match top-down approaches and is feasible in terms of the proposed aggregation scheme. + +# 6. Conclusion + +In this work, we presented a novel lane graph estimation framework complemented with a novel dataset comprising aerial images. We showed that formulating the lane graph estimation problem as bottom-up graph neural network approach leveraging agent-centric views yields promising results. In addition, we presented a novel aggregation scheme to merge successive lane graphs to produce large-scale solutions. A first-of-its-kind dataset and benchmark for lane graph estimation from aerial images will enable further research in this field. Future work could address end-to-end training and exploiting further modalities such as onboard vehicle cameras for additional context information. + +Acknowledgements: This work was partly funded by the German Research Foundation (DFG) Emmy Noether Program grant number 468878300, DFG grant number BU 865/10-2, and a hardware grant from NVIDIA. + +# References + +[1] Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, and Vishal M Patel. Spin road mapper: Extracting roads from aerial images via spatial and interaction space graph reasoning for autonomous driving. In 2022 International Conference on Robotics and Automation (ICRA), pages 343-350. IEEE, 2022. 2 +[2] Favyen Bastani, Songtao He, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, and David DeWitt. Roadtracer: Automatic extraction of road networks from aerial images. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 4720-4728, 2018. 2 +[3] Guillem Brasó and Laura Leal-Taixe. Learning a neural solver for multiple object tracking. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 4 +[4] Martin Büchner and Abhinav Valada. 3d multi-object tracking using graph neural networks with cross-edge modality attention. IEEE Robotics and Automation Letters, 7(4):9707-9714, 2022. 1 +[5] Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, and Luc Van Gool. Structured bird's-eye-view traffic scene understanding from onboard images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15661-15670, 2021. 1, 2 +[6] Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, and Luc Van Gool. Topology preserving local road network estimation from single onboard camera image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17263-17272, 2022. 1, 2 +[7] Daniele Cattaneo, Matteo Vaghi, and Abhinav Valada. Lcdnet: Deep loop closure detection and point cloud registration for lidar slam. IEEE Transactions on Robotics, 38(4):2074-2093, 2022. 1 +[8] Yuning Chai, Benjamin Sapp, Mayank Bansal, and Dragomir Anguelov. Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction. In Conference on Robot Learning, pages 86-99. PMLR, 2020. 2 +[9] Dian Chen and Philipp Kähenbuhl. Learning from all vehicles. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17222-17231, 2022. 2 +[10] Nemanja Djuric, Henggang Cui, Zhaoen Su, Shangxuan Wu, Huahua Wang, Fang-Chieh Chou, Luisa San Martin, Song Feng, Rui Hu, Yang Xu, et al. Multixnet: Multiclass multi-stage multimodal motion prediction. In 2021 IEEE Intelligent Vehicles Symposium (IV), pages 435-442. IEEE, 2021. 2 +[11] Whye Kit Fong, Rohit Mohan, Juana Valeria Hurtado, Lubing Zhou, Holger Caesar, Oscar Beijbom, and Abhinav Valada. Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking. IEEE Robotics and Automation Letters, 7(2):3795-3802, 2022. 3 +[12] Nikhil Gosala and Abhinav Valada. Bird's-eye-view panoptic segmentation using monocular frontal view images. IEEE Robotics and Automation Letters, 7(2):1968-1975, 2022. 1 + +[13] Aric Hagberg, Pieter Swart, and Daniel S Chult. Exploring network structure, dynamics, and function using networkx. Technical report, Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2008. 12 +[14] J Halton and G Smith. Radical inverse quasi-random point sequence, algorithm 247. Commun. ACM, 7(12):701, 1964. 3 +[15] Songtao He and Hari Balakrishnan. Lane-level street map extraction from aerial imagery. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2080-2089, 2022. 1, 2, 3, 6, 7, 8, 13, 21 +[16] Namdar Homayounfar, Wei-Chiu Ma, Shrinidhi Kowshika Lakshmikanth, and Raquel Urtasun. Hierarchical recurrent attention networks for structured online maps. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 3417-3426, 2018. 2 +[17] Namdar Homayounfar, Wei-Chiu Ma, Justin Liang, Xinyu Wu, Jack Fan, and Raquel Urtasun. Dagmapper: Learning to map by discovering lane topology. In Proceedings of the IEEE International Conference on Computer Vision, pages 2911-2920, 2019. 2 +[18] Justin Liang, Namdar Homayounfar, Wei-Chiu Ma, Shenlong Wang, and Raquel Urtasun. Convolutional recurrent network for road boundary extraction. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 9512-9521, 2019. 2 +[19] Gellert Mattyus, Wenjie Luo, and Raquel Urtasun. Deeproadmapper: Extracting road topology from aerial images. In Proceedings of the IEEE International Conference on Computer Vision, pages 3438-3446, 2017. 2 +[20] Rohit Mohan and Abhinav Valada. Amodal panoptic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21023-21032, 2022. 1 +[21] Kursat Petek, Kshitij Sirohi, Daniel Buscher, and Wolfram Burgard. Robust monocular localization in sparse hd maps leveraging multi-task uncertainty estimation. In 2022 International Conference on Robotics and Automation (ICRA), pages 4163-4169. IEEE, 2022. 1 +[22] Yong-Qiang Tan, Shang-Hua Gao, Xuan-Yi Li, Ming-Ming Cheng, and Bo Ren. Vecroad: Point-based iterative graph exploration for road graphs extraction. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 8910-8918, 2020. 2 +[23] Shinji Umeyama. Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(04):376-380, 1991. 11 +[24] Abhinav Valada, Ankit Dhall, and Wolfram Burgard. Convoluted mixture of deep experts for robust semantic segmentation. In IEEE/RSJ International conference on intelligent robots and systems (IROS) workshop, state estimation and terrain perception for all terrain mobile robots, volume 2, 2016. 1 +[25] Adam Van Etten, Dave Lindenbaum, and Todd M Bacastow. Spacenet: A remote sensing dataset and challenge series. arXiv preprint arXiv:1807.01232, 2018. 6 +[26] Johan Vertens and Wolfram Burgard. Usegscene: Unsupervised learning of depth, optical flow and ego-motion with + +semantic guidance and coupled networks. arXiv preprint arXiv:2207.07469, 2022. 1 +[27] Niclas Vödisch, Daniele Cattaneo, Wolfram Burgard, and Abhinav Valada. Continual slam: Beyond lifelong simultaneous localization and mapping through continual learning. arXiv preprint arXiv:2203.01578, 2022. 1 +[28] Jingke Wang, Tengju Ye, Ziqing Gu, and Junbo Chen. Ltp: Lane-based trajectory prediction for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17134-17142, 2022. 2 +[29] Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, et al. Argoverse 2: Next generation datasets for self-driving perception and forecasting. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021. 3, 11 +[30] Zhenhua Xu, Yuxuan Liu, Lu Gan, Yuxiang Sun, Xinyu Wu, Ming Liu, and Lujia Wang. Ringdet: Road network graph detection by transformer in aerial images. IEEE Transactions on Geoscience and Remote Sensing, 2022. 1 +[31] Li Zhang, Faezeh Tafazzoli, Gunther Krehl, Runsheng Xu, Timo Rehfeld, Manuel Schier, and Arunava Seal. Hierarchical road topology learning for urban map-less driving. arXiv preprint arXiv:2104.00084, 2021. 2 + +[32] Tongjie Y Zhang and Ching Y. Suen. A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 27(3):236-239, 1984. 15 +[33] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881-2890, 2017. 3, 16 +[34] Lichen Zhou, Chuang Zhang, and Ming Wu. D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 182-186, 2018. 2 +[35] Yiyang Zhou, Yuichi Takeda, Masayoshi Tomizuka, and Wei Zhan. Automatic construction of lane-level hd maps for urban scenes. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6649-6656. IEEE, 2021. 2 +[36] Jannik Zürn, Johan Vertens, and Wolfram Burgard. Lane graph estimation for scene understanding in urban driving. IEEE Robotics and Automation Letters, 6(4):8615-8622, 2021. 2, 6, 7 +[37] Jannik Zürn, Sebastian Weber, and Wolfram Burgard. Trackletmapper: Ground surface segmentation and mapping from traffic participant trajectories. 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Existing methods using either onboard or aerial imagery struggle with complex lane topologies, out-of-distribution scenarios, or significant occlusions in the image space. Moreover, merging overlapping lane graphs to obtain consistent large-scale graphs remains difficult. To overcome these challenges, we propose a novel bottom-up approach to lane graph estimation from aerial imagery that aggregates multiple overlapping graphs into a single consistent graph. Due to its modular design, our method allows us to address two complementary tasks: predicting ego-respective successor lane graphs from arbitrary vehicle positions using a graph neural network and aggregating these predictions into a consistent global lane graph. Extensive experiments on a large-scale lane graph dataset demonstrate that our approach yields highly accurate lane graphs, even in regions with severe occlusions. The presented approach to graph aggregation proves to eliminate inconsistent predictions while increasing the overall graph quality. We make our large-scale urban lane graph dataset and code publicly available at http://urbanlanegraph.cs.uni-freiburg.de." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 516, + 128, + 529 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 516, + 128, + 529 + ], + "spans": [ + { + "bbox": [ + 47, + 516, + 128, + 529 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 536, + 288, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 536, + 288, + 693 + ], + "spans": [ + { + "bbox": [ + 47, + 536, + 288, + 693 + ], + "type": "text", + "content": "Most automated driving vehicles rely on the knowledge of their immediate surroundings to safely navigate urban environments. Onboard sensors including LiDARs and cameras provide perception inputs that are utilized in multiple tasks such as localization [7, 21, 27], tracking [4], or scene understanding [20, 24, 26, 37] to aggregate representations of the environment. However, robust planning and control typically require vastly more detailed and less noisy world models in the form of HD map data [12]. In particular, information on lane parametrization and connectivity is essential for both planning future driving maneuvers as well as high-level navigation tasks. Creating and maintaining HD maps in the form of lane graphs is a time-consuming and arduous" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 308, + 201, + 545, + 389 + ], + "blocks": [ + { + "bbox": [ + 308, + 201, + 545, + 389 + ], + "lines": [ + { + "bbox": [ + 308, + 201, + 545, + 389 + ], + "spans": [ + { + "bbox": [ + 308, + 201, + 545, + 389 + ], + "type": "image", + "image_path": "230389817c2eb7990cec05d3cf265dc55cc38aaaf2d903a95a1b38b9baa4102b.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 396, + 546, + 427 + ], + "lines": [ + { + "bbox": [ + 305, + 396, + 546, + 427 + ], + "spans": [ + { + "bbox": [ + 305, + 396, + 546, + 427 + ], + "type": "text", + "content": "Figure 1. Our approach predicts accurate lane graphs from aerial images of complex urban environments. We visualize the estimated lane graph in magenta and indicate model initialization points with yellow circles." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 446, + 547, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 446, + 547, + 495 + ], + "spans": [ + { + "bbox": [ + 304, + 446, + 547, + 495 + ], + "type": "text", + "content": "task due to the large amount of detail required in the annotation and the data curation process including map updates based on local environment changes such as construction sites." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 498, + 548, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 498, + 548, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 498, + 548, + 714 + ], + "type": "text", + "content": "Previous approaches to lane graph estimation have shown shortcomings in predicting lane graphs due to multiple deficiencies: On the one hand, methods using onboard imagery typically degrade at complex real-world intersections and under significant occlusions, e.g., when following another vehicle [5, 6]. On the other hand, methods based on aerial imagery show reduced performance when confronted with occlusions in the bird's-eye-view (BEV) due to, e.g., vegetation or shadows, and suffer from catastrophic drift when unconstrained in out-of-distribution scenarios [30]. Previous works treat intersections and non-intersections inherently differently [15] and thus require elaborated heuristics and post-processing to merge single predictions into a consistent lane graph. Moreover, prior works do not focus on use cases where multiple predicted graphs must be merged into a single consistent solution, which is essential for enabling the automatic generation of highly detailed lane graphs of large contiguous regions." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 702, + 124, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 124, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 124, + 712 + ], + "type": "text", + "content": "*Equal contribution" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13415" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 289, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 289, + 312 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 289, + 312 + ], + "type": "text", + "content": "Related to the aforementioned challenges, we propose a novel two-stage graph neural network (GNN) approach termed LaneGNN that operates on single aerial color images for lane graph prediction. Inspired by methods in the field of trajectory prediction [8], we formulate a bottom-up approach according to which we place a virtual agent into a local crop of the aerial image and predict reachable successor lane graphs from its positions. To transform multiple disjoint local solutions into a single global solution, we aggregate a global representation by iteratively inferring the lane graph from consecutive poses, ultimately imitating real-world driving behavior. This iterative approach not only increases the predicted area covered but also improves graph accuracy based on data association and rejection. Note that we do not require any human in the loop to perform the graph aggregation. We visualize the output of our graph aggregation procedure in Fig. 1, in which we superimpose the predicted graph on the aerial image input. Using this framework, we envision two applications: ego-centered successor path prediction and full lane graph estimation by aggregation." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 59, + 313, + 282, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 313, + 282, + 324 + ], + "spans": [ + { + "bbox": [ + 59, + 313, + 282, + 324 + ], + "type": "text", + "content": "To summarize, the main contributions of this work are:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 59, + 327, + 287, + 469 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 59, + 327, + 287, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 327, + 287, + 373 + ], + "spans": [ + { + "bbox": [ + 59, + 327, + 287, + 373 + ], + "type": "text", + "content": "- An innovative bottom-up approach to lane graph estimation in challenging environments that explicitly encodes graph-level lane topology from input aerial images in a scenario-agnostic manner." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 59, + 375, + 287, + 398 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 375, + 287, + 398 + ], + "spans": [ + { + "bbox": [ + 59, + 375, + 287, + 398 + ], + "type": "text", + "content": "- A novel graph aggregation scheme enabling robust and method-agnostic merging of graph-level predictions." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 59, + 399, + 287, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 399, + 287, + 445 + ], + "spans": [ + { + "bbox": [ + 59, + 399, + 287, + 445 + ], + "type": "text", + "content": "- The large-scale lane graph dataset UrbanLaneGraph comprising high-resolution aerial images aligned with dense lane graph annotations aggregated from the Argoverse2 dataset that we make publicly available." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 59, + 447, + 287, + 469 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 447, + 287, + 469 + ], + "spans": [ + { + "bbox": [ + 59, + 447, + 287, + 469 + ], + "type": "text", + "content": "- Extensive experiments and ablation studies demonstrating the significance of our findings." + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 47, + 485, + 138, + 498 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 485, + 138, + 498 + ], + "spans": [ + { + "bbox": [ + 47, + 485, + 138, + 498 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 507, + 289, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 507, + 289, + 662 + ], + "spans": [ + { + "bbox": [ + 47, + 507, + 289, + 662 + ], + "type": "text", + "content": "In recent years, the prediction of topological road features such as road graphs and lane graphs have been extensively studied. In our discussion, we differentiate between road graph learning and lane graph learning. While road graphs encode the topological connections between road segments, lane graphs describe the locations and connectivity between all lanes, resulting in a spatially much denser graph. Many prior works focus on vehicle trajectory prediction, conditioned on HD map features such as lane centerline and boundary positions [9, 10, 28]. These models do not aim at exclusively predicting the road or lane graph structure from onboard vehicle images or aerial images but instead at predicting future vehicle states such as position and orientation." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 665, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 665, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 665, + 289, + 714 + ], + "type": "text", + "content": "Road Graph Learning: Prior works investigate estimating road graphs from both onboard sensors [18] and from aerial images [1, 22, 34] or focus on extracting pixel-level road segmentation from images and extracting graphical road rep" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "text", + "content": "resentations, i.e., using morphological image operators or graph neural networks to extract the connectivity between different roads within the image [1, 19]. Other approaches investigate iterative methods and interpret road graph prediction as a sequential prediction task [2, 22]." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 135, + 547, + 327 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 135, + 547, + 327 + ], + "spans": [ + { + "bbox": [ + 304, + 135, + 547, + 327 + ], + "type": "text", + "content": "Lane Graph Learning from Vehicle Data: Some earlier works in lane graph learning from onboard vehicle sensors such as cameras and LiDAR formulate lane extraction as an image-based lane centerline regression task [16]. Homayounfar et al. [17] aggregate onboard LiDAR data on highways and leverage a recurrent neural network to generate highway lane graphs in an iterative manner. Zhou et al. [35] utilize the OpenStreetMap database and a semantic particle filter to accumulate projected semantic predictions from a vehicle ego-view into a map representation. Zhang et al. [31] propose an online road map extraction system for a sensor setup onboard a moving vehicle and construct a graph representation of the road network using a fully convolutional neural network. More recently, Can et al. proposed two different methods [5, 6] for lane connectivity learning in intersection scenarios from onboard camera images." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 329, + 547, + 473 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 329, + 547, + 473 + ], + "spans": [ + { + "bbox": [ + 304, + 329, + 547, + 473 + ], + "type": "text", + "content": "Lane Graph Learning from Birds-Eye-View Data: Despite the advantages of leveraging readily available aerial data for training lane graphs, only a few works considered using aerial images as an input modality for the graph learning task. Zürn et al. [36] propose a lane centerline regression model jointly with a Graph R-CNN backbone to predict nodes and edges of the lane graph from a local aggregated bird's-eye-view image crop. More recently, He et al. [15] propose a two-stage graph estimation pipeline. They first extract lanes at non-intersection areas and subsequently predict the connectivity of each pair of lanes, and extract the valid turning lanes to complete the map at intersections." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 475, + 548, + 656 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 475, + 548, + 656 + ], + "spans": [ + { + "bbox": [ + 304, + 475, + 548, + 656 + ], + "type": "text", + "content": "In contrast to existing works, we do not estimate the complete lane graph visible in a given crop but only the part of the graph that is reachable from a virtual agent pose located within the crop, simplifying the graph estimation problem based on reduced topological complexity. Furthermore, we leverage a GNN to explicitly model the relationships between graph nodes, allowing us to leverage recent developments in the field of geometric deep learning. This explicit graph encoding and prediction allows us to formulate a model that does not internally differentiate between intersection areas and non-intersection areas, in contrast to some related works. Additionally, we propose a novel large-scale dataset for lane graph estimation from aerial images, allowing the research community to easily evaluate and compare their approaches." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 669, + 360, + 681 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 669, + 360, + 681 + ], + "spans": [ + { + "bbox": [ + 306, + 669, + 360, + 681 + ], + "type": "text", + "content": "3. Dataset" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 689, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 547, + 713 + ], + "type": "text", + "content": "To evaluate our approach on challenging real-world data, we compiled the UrbanLaneGraph dataset. It is a first-of-its" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "13416" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 68, + 547, + 159 + ], + "blocks": [ + { + "bbox": [ + 47, + 68, + 547, + 159 + ], + "lines": [ + { + "bbox": [ + 47, + 68, + 547, + 159 + ], + "spans": [ + { + "bbox": [ + 47, + 68, + 547, + 159 + ], + "type": "image", + "image_path": "d0ae2fedc68d6e8fd2c1fa3ebf83e3f7d526ceaee8c0134617e5ec2217d391d1.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 164, + 547, + 205 + ], + "lines": [ + { + "bbox": [ + 46, + 164, + 547, + 205 + ], + "spans": [ + { + "bbox": [ + 46, + 164, + 547, + 205 + ], + "type": "text", + "content": "Figure 2. Overview of our LaneGNN model " + }, + { + "bbox": [ + 46, + 164, + 547, + 205 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 46, + 164, + 547, + 205 + ], + "type": "text", + "content": " predicting successor lane graphs. As a pre-training step, we train lane centerline and ego lane centerline regressor models. The ego lane regression " + }, + { + "bbox": [ + 46, + 164, + 547, + 205 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_{\\text{lane}}^{\\text{ego}}" + }, + { + "bbox": [ + 46, + 164, + 547, + 205 + ], + "type": "text", + "content": " is used as a prior for sampling proposal nodes " + }, + { + "bbox": [ + 46, + 164, + 547, + 205 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 46, + 164, + 547, + 205 + ], + "type": "text", + "content": " in corridors that have a high likelihood of entailing the successor graph. The model learns a binary classification of node and edge scores while also predicting the probability of a node being an endpoint of a given lane segment." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 62, + 213, + 273, + 326 + ], + "blocks": [ + { + "bbox": [ + 62, + 213, + 273, + 326 + ], + "lines": [ + { + "bbox": [ + 62, + 213, + 273, + 326 + ], + "spans": [ + { + "bbox": [ + 62, + 213, + 273, + 326 + ], + "type": "table", + "html": "
DatasetCityLane Splits/MergesTotal Length [km]
UrbanLaneGraph (from Argoverse2)Palo Alto4752796.4
Austin8495531.7
Miami7642850.8
Pittsburgh146101314.3
Washington D.C.2066739.6
Detroit5424990.5
LaneExtraction [15]Boston, Seattle, Phoenix, Miami2262398.6
NuScenes [11]Boston163076.9
", + "image_path": "fdddf529981cfbf4ba345bdc2719c0c8428c819ec606fe17b926fe977bf716fb.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 332, + 288, + 363 + ], + "lines": [ + { + "bbox": [ + 46, + 332, + 288, + 363 + ], + "spans": [ + { + "bbox": [ + 46, + 332, + 288, + 363 + ], + "type": "text", + "content": "Table 1. Key statistics of our UrbanLaneGraph dataset, aggregated from the Argoverse2 dataset [29], used for our experiments, compared with other recent datasets for lane graph estimation." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 377, + 289, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 377, + 289, + 628 + ], + "spans": [ + { + "bbox": [ + 46, + 377, + 289, + 628 + ], + "type": "text", + "content": "kind dataset for large-scale lane graph estimation from aerial images. The dataset contains aerial images from the cities of Austin, Miami, Pittsburgh, Palo Alto, Detroit, and Washington DC. The images have a resolution of " + }, + { + "bbox": [ + 46, + 377, + 289, + 628 + ], + "type": "inline_equation", + "content": "15\\mathrm{cm}" + }, + { + "bbox": [ + 46, + 377, + 289, + 628 + ], + "type": "text", + "content": " per pixel. To obtain the corresponding lane annotations, we leverage the Argoverse2 dataset [29] which entails lane graphs for large sections of the respective cities. The lane graphs in the Argoverse2 dataset are provided on a per-scenario basis, covering only small local areas in one graph. Therefore, we collect all local lane graphs of each city and aggregate them into one globally consistent graph per city, thereby removing inconsistent or redundant nodes or edges. The annotated regions feature a diverse range of environments including urban, suburban, and rural regions with complex lane topologies. Accumulated over all cities, the overall length of all lanes spans over " + }, + { + "bbox": [ + 46, + 377, + 289, + 628 + ], + "type": "inline_equation", + "content": "5.000\\mathrm{km}" + }, + { + "bbox": [ + 46, + 377, + 289, + 628 + ], + "type": "text", + "content": ". We split each city into disjoint training and testing regions. We list key dataset statistics in Tab. 1, indicating the scale of our generated dataset compared with other recently proposed datasets containing graph annotations. For more details on the dataset and exemplary visualizations, please refer to Sec. S.1 in the supplementary material." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 644, + 165, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 644, + 165, + 658 + ], + "spans": [ + { + "bbox": [ + 47, + 644, + 165, + 658 + ], + "type": "text", + "content": "4. Technical Approach" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 665, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 289, + 715 + ], + "type": "text", + "content": "Our approach is divided into two stages: lane graph learning and lane graph aggregation. In the first stage (Sec. 4.1), we train our GNN model, denoted as LaneGNN, to predict the successor lane graph, entailing the nodes and edges" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 215, + 547, + 324 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 215, + 547, + 324 + ], + "spans": [ + { + "bbox": [ + 304, + 215, + 547, + 324 + ], + "type": "text", + "content": "that can be logically visited from the pose of a virtual vehicle agent. In the second phase (Sec. 4.2), we use our trained LaneGNN model to traverse a large map area. This is achieved by selecting an initial starting pose and predicting the successor lane graph from this pose. Subsequently, we iteratively estimate the traversable lane graph from the current pose and move forward along the predicted graph while aggregating. In the following, we detail both stages of our approach." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 331, + 432, + 344 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 331, + 432, + 344 + ], + "spans": [ + { + "bbox": [ + 305, + 331, + 432, + 344 + ], + "type": "text", + "content": "4.1. Lane Graph Learning" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "spans": [ + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": "We formulate the task as a supervised learning problem where a successor lane graph " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "\\hat{G}" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": " is estimated based on an aerial image " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{bev}" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": ". First, a directed graph " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "G_{I} = (V,E)" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": " covering relevant regions is constructed by sampling from likely regions of " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{bev}" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": " (see Fig. 2). For all our models and experiments we choose a spatial resolution of " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "256\\times 256" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": " pixels. The graph comprises a set of nodes " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "i\\in V" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": " that are connected via directed edges " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "E\\subseteq \\{(i,j)|(i,j)\\in V^2" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "i\\neq j\\}" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": " that constitute potentially valid lane graph edges. The graph is attributed using both node features " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "\\mathbf{X}\\in \\mathbb{R}^{\\| V\\| \\times D}" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": " and edge features " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_e\\in \\mathbb{R}^{\\| E\\| \\times (D_{geo} + D_{bev})}" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "D_{geo}" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "D_{bev}" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": " denote the dimensionality of the involved edge features (see Sec. 4.1). The overall model estimates an output graph " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "\\hat{G} = \\mathcal{M}(G_I|\\theta)" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": " is parameterized by network model weights " + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "inline_equation", + "content": "\\theta \\coloneqq (\\theta_{reg},\\theta_{GNN})" + }, + { + "bbox": [ + 304, + 350, + 547, + 530 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "text", + "content": "Lane Regression and Graph Construction: We train two regression networks: Firstly, a centerline regression network predicting the likelihood map of lane centerlines " + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_{\\text {lane }}" + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "text", + "content": ", and secondly, a segmentation network predicting all reachable lanes " + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_{\\text {lane }}^{\\text {ego }}" + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "text", + "content": " starting from the initial virtual agent pose at the bottom center of " + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{\\text {bev }}" + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "text", + "content": ". We use identical PSPNet [33] architectures with a ResNet-152 feature extractor for this task. We sample equally-distributed node positions using Halton sequences [14] that are later filtered based on the obtained ego-lane segmentation mask " + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_{\\text {lane }}^{\\text {ego }}" + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "text", + "content": ", which serves as a region of interest for sampling (see Fig. 2). Directed edges " + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "inline_equation", + "content": "E" + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "text", + "content": " among nodes are initialized for pairs of nodes with a Euclidean distance " + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "inline_equation", + "content": "d_{ij} \\in [d_{\\min}, d_{\\max}]" + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "text", + "content": ". Initial node features " + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{X}" + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "text", + "content": " are crafted solely based on their 2D positions " + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{x}_i = (x_i, y_i)" + }, + { + "bbox": [ + 304, + 534, + 548, + 714 + ], + "type": "text", + "content": ", while geometric edge features are defined as" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13417" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 49, + 70, + 195, + 193 + ], + "blocks": [ + { + "bbox": [ + 49, + 70, + 195, + 193 + ], + "lines": [ + { + "bbox": [ + 49, + 70, + 195, + 193 + ], + "spans": [ + { + "bbox": [ + 49, + 70, + 195, + 193 + ], + "type": "image", + "image_path": "407c9ff555e79867676abb0afa03327503510f77169a4b7371156d3e96eea80d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 56, + 194, + 187, + 203 + ], + "lines": [ + { + "bbox": [ + 56, + 194, + 187, + 203 + ], + "spans": [ + { + "bbox": [ + 56, + 194, + 187, + 203 + ], + "type": "text", + "content": "Temporal evolution of local BEV crops" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 231, + 70, + 378, + 193 + ], + "blocks": [ + { + "bbox": [ + 231, + 70, + 378, + 193 + ], + "lines": [ + { + "bbox": [ + 231, + 70, + 378, + 193 + ], + "spans": [ + { + "bbox": [ + 231, + 70, + 378, + 193 + ], + "type": "image", + "image_path": "7396d3f022676ae324cabe31a31342df11055cddd5769f1d6cb7fac054c7a1ab.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 210, + 546, + 232 + ], + "lines": [ + { + "bbox": [ + 46, + 210, + 546, + 232 + ], + "spans": [ + { + "bbox": [ + 46, + 210, + 546, + 232 + ], + "type": "text", + "content": "Figure 3. In our graph aggregation procedure, we iteratively obtain oriented image crops based on virtual agent poses along the currently predicted successor graph. For each crop, our LaneGNN model predicts a successor graph " + }, + { + "bbox": [ + 46, + 210, + 546, + 232 + ], + "type": "inline_equation", + "content": "G_{pred}" + }, + { + "bbox": [ + 46, + 210, + 546, + 232 + ], + "type": "text", + "content": ", which is aggregated into a globally consistent lane graph " + }, + { + "bbox": [ + 46, + 210, + 546, + 232 + ], + "type": "inline_equation", + "content": "G_{agg}" + }, + { + "bbox": [ + 46, + 210, + 546, + 232 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 381, + 70, + 546, + 193 + ], + "blocks": [ + { + "bbox": [ + 381, + 70, + 546, + 193 + ], + "lines": [ + { + "bbox": [ + 381, + 70, + 546, + 193 + ], + "spans": [ + { + "bbox": [ + 381, + 70, + 546, + 193 + ], + "type": "image", + "image_path": "1ba4dc55f165808e7614c9d49259db931ab428f1f8b032b4ef77db69a6ebcaee.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 318, + 194, + 457, + 204 + ], + "lines": [ + { + "bbox": [ + 318, + 194, + 457, + 204 + ], + "spans": [ + { + "bbox": [ + 318, + 194, + 457, + 204 + ], + "type": "text", + "content": "Temporal evolution of aggregated graph" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "bbox": [ + 59, + 259, + 288, + 283 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 259, + 288, + 283 + ], + "spans": [ + { + "bbox": [ + 59, + 259, + 288, + 283 + ], + "type": "interline_equation", + "content": "\\mathbf {x} _ {i j, g e o} = \\left(\\tan^ {- 1} \\frac {\\Delta y _ {i j}}{\\Delta x _ {i j}}, \\sqrt {\\Delta x _ {i j} {} ^ {2} + \\Delta y _ {i j} {} ^ {2}}, \\bar {x} _ {i j}, \\bar {y} _ {i j}\\right), \\tag {1}", + "image_path": "1da5e8862946675f7da8755d940c4665cde6e7e2e6de525cd50a30f04b59358c.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "spans": [ + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "inline_equation", + "content": "\\Delta x_{ij}" + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "inline_equation", + "content": "\\Delta y_{ij}" + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "text", + "content": " represent node position differences and " + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "inline_equation", + "content": "\\overline{x}_{ij}" + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "inline_equation", + "content": "\\overline{y}_{ij}" + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "text", + "content": " are edge middle point coordinates. In addition to the geometric edge feature, we generate aerial edge features: Per edge, a small oriented region of " + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "inline_equation", + "content": "\\mathbf{I}_{bev}" + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "text", + "content": " and the lane segmentation " + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_{lane}" + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "text", + "content": " is obtained based on the direction of the edge, which provides " + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_{ij,bev} = [\\mathbf{I}_{bev}^{*},\\mathbf{S}_{lane}^{*}]" + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "text", + "content": " with dimension " + }, + { + "bbox": [ + 46, + 289, + 287, + 373 + ], + "type": "inline_equation", + "content": "D_{bev} = 4\\times 32\\times 32" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 378, + 288, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 378, + 288, + 570 + ], + "spans": [ + { + "bbox": [ + 46, + 378, + 288, + 570 + ], + "type": "text", + "content": "Feature Encoding and Message Passing: In our approach, we estimate edge probabilities, node probabilities, and whether a node is terminal. While the nodes themselves hold only unidirectional information, we encode the notion of direction using the proposed edge feature as outlined above. We utilize a causal variant of neural message passing as proposed by Brasó et al. [3]. By imposing a causality prior, our network encodes predecessor and successor features during message passing. This formulation of message passing renders our approach direction-aware. Initial node and geometric edge features are encoded using multi-layer perceptrons " + }, + { + "bbox": [ + 46, + 378, + 288, + 570 + ], + "type": "inline_equation", + "content": "f_{enc}^{\\square}" + }, + { + "bbox": [ + 46, + 378, + 288, + 570 + ], + "type": "text", + "content": " (MLP) while the aerial edge feature is transformed using a ResNet-18 architecture " + }, + { + "bbox": [ + 46, + 378, + 288, + 570 + ], + "type": "inline_equation", + "content": "f_{enc}^{e,bev}" + }, + { + "bbox": [ + 46, + 378, + 288, + 570 + ], + "type": "text", + "content": ". The geometric and aerial edge features are concatenated and fused subsequently to arrive at various node and edge embeddings " + }, + { + "bbox": [ + 46, + 378, + 288, + 570 + ], + "type": "inline_equation", + "content": "\\mathbf{H}_v^{(0)}" + }, + { + "bbox": [ + 46, + 378, + 288, + 570 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 378, + 288, + 570 + ], + "type": "inline_equation", + "content": "\\mathbf{H}_e^{(0)}" + }, + { + "bbox": [ + 46, + 378, + 288, + 570 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 61, + 576, + 288, + 592 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 576, + 288, + 592 + ], + "spans": [ + { + "bbox": [ + 61, + 576, + 288, + 592 + ], + "type": "interline_equation", + "content": "f _ {e n c} ^ {e, b e v} \\left(\\mathbf {X} _ {e, b e v}\\right) = \\mathbf {H} _ {e, b e v} ^ {(0)}, f _ {e n c} ^ {e, g e o} \\left(\\mathbf {X} _ {e, g e o}\\right) = \\mathbf {H} _ {e, g e o} ^ {(0)}, (2)", + "image_path": "fc762a43e1740354f80ee21c7eeb4dd6e8111e9719ea6118eb95b4d7278f3abc.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 70, + 607, + 287, + 622 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 70, + 607, + 287, + 622 + ], + "spans": [ + { + "bbox": [ + 70, + 607, + 287, + 622 + ], + "type": "interline_equation", + "content": "f _ {e n c} ^ {v} (\\mathbf {X}) = \\mathbf {H} _ {v} ^ {(0)}, f _ {f u s e} ^ {e} ([ \\mathbf {H} _ {e, g e o} ^ {(0)}, \\mathbf {H} _ {e, b e v} ^ {(0)} ]) = \\mathbf {H} _ {e} ^ {(0)}. (3)", + "image_path": "6c704145988ff0fc4b48a248413c111e27b53df6c4efa0b154fdd1d3fe89154c.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 624, + 288, + 692 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 624, + 288, + 692 + ], + "spans": [ + { + "bbox": [ + 46, + 624, + 288, + 692 + ], + "type": "text", + "content": "In the following, multiple message-passing steps are performed using various ReLU-activated MLPs denoted by " + }, + { + "bbox": [ + 46, + 624, + 288, + 692 + ], + "type": "inline_equation", + "content": "f_{\\square}" + }, + { + "bbox": [ + 46, + 624, + 288, + 692 + ], + "type": "text", + "content": " as follows. The edge feature is updated based on the current neighboring node features " + }, + { + "bbox": [ + 46, + 624, + 288, + 692 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_i^{(l - 1)}" + }, + { + "bbox": [ + 46, + 624, + 288, + 692 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 624, + 288, + 692 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_j^{(l - 1)}" + }, + { + "bbox": [ + 46, + 624, + 288, + 692 + ], + "type": "text", + "content": " and the edge feature " + }, + { + "bbox": [ + 46, + 624, + 288, + 692 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{ij}^{(l - 1)}" + }, + { + "bbox": [ + 46, + 624, + 288, + 692 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 94, + 697, + 288, + 718 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 697, + 288, + 718 + ], + "spans": [ + { + "bbox": [ + 94, + 697, + 288, + 718 + ], + "type": "interline_equation", + "content": "\\mathbf {h} _ {i j} ^ {(l)} = f _ {e} \\left(\\left[ \\mathbf {h} _ {i} ^ {(l - 1)}, \\mathbf {h} _ {j} ^ {(l - 1)}, \\mathbf {h} _ {i j} ^ {(l - 1)} \\right]\\right). \\tag {4}", + "image_path": "35d622bff82e26dd8a53544229ffc88f42792a39581fc65492ee94881ee8088a.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 242, + 547, + 304 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 242, + 547, + 304 + ], + "spans": [ + { + "bbox": [ + 305, + 242, + 547, + 304 + ], + "type": "text", + "content": "Messages " + }, + { + "bbox": [ + 305, + 242, + 547, + 304 + ], + "type": "inline_equation", + "content": "\\mathbf{m}_{ij}^{(l)}" + }, + { + "bbox": [ + 305, + 242, + 547, + 304 + ], + "type": "text", + "content": " are crafted based on either predecessors " + }, + { + "bbox": [ + 305, + 242, + 547, + 304 + ], + "type": "inline_equation", + "content": "\\mathcal{N}_{pred}(i)" + }, + { + "bbox": [ + 305, + 242, + 547, + 304 + ], + "type": "text", + "content": " or successors " + }, + { + "bbox": [ + 305, + 242, + 547, + 304 + ], + "type": "inline_equation", + "content": "\\mathcal{N}_{succ}(i)" + }, + { + "bbox": [ + 305, + 242, + 547, + 304 + ], + "type": "text", + "content": " of a node " + }, + { + "bbox": [ + 305, + 242, + 547, + 304 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 305, + 242, + 547, + 304 + ], + "type": "text", + "content": " and propagated based on the constructed adjacency. In the next step, all predecessors and successor messages, respectively, are aggregated using a permutation-invariant sum:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 334, + 311, + 546, + 350 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 334, + 311, + 546, + 350 + ], + "spans": [ + { + "bbox": [ + 334, + 311, + 546, + 350 + ], + "type": "interline_equation", + "content": "\\mathbf {h} _ {i, p r e d} ^ {(l)} = \\sum_ {j \\in \\mathcal {N} _ {p r e d} (i)} \\underbrace {f _ {v} ^ {p r e d} \\left(\\left[ \\mathbf {h} _ {j} ^ {(l - 1)} , \\mathbf {h} _ {i j} ^ {(l)} , \\mathbf {h} _ {j} ^ {(0)} \\right]\\right)} _ {\\mathbf {m} _ {j i} ^ {(l)}}, \\tag {5}", + "image_path": "35fa9a3223bf80e489a2367944e54287f85a1dab54a73b16c57abf40b1ec8126.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 334, + 361, + 546, + 399 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 334, + 361, + 546, + 399 + ], + "spans": [ + { + "bbox": [ + 334, + 361, + 546, + 399 + ], + "type": "interline_equation", + "content": "\\mathbf {h} _ {i, s u c c} ^ {(l)} = \\sum_ {j \\in \\mathcal {N} _ {s u c c} (i)} \\underbrace {f _ {v} ^ {s u c c} \\left(\\left[ \\mathbf {h} _ {j} ^ {(l - 1)} , \\mathbf {h} _ {i j} ^ {(l)} , \\mathbf {h} _ {j} ^ {(0)} \\right]\\right)} _ {\\mathbf {m} _ {i j} ^ {(l)}}. \\tag {6}", + "image_path": "6fdd00ede97ea44a05323c383c5402a7985d56136b3f0d2d3d5a62e18fe87370.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 403, + 547, + 440 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 403, + 547, + 440 + ], + "spans": [ + { + "bbox": [ + 305, + 403, + 547, + 440 + ], + "type": "text", + "content": "Note that the message crafting includes skip connections to initial node embeddings " + }, + { + "bbox": [ + 305, + 403, + 547, + 440 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_j^{(0)}" + }, + { + "bbox": [ + 305, + 403, + 547, + 440 + ], + "type": "text", + "content": ". Nodes are updated by combining the two features using concatenation:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 366, + 449, + 545, + 468 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 366, + 449, + 545, + 468 + ], + "spans": [ + { + "bbox": [ + 366, + 449, + 545, + 468 + ], + "type": "interline_equation", + "content": "\\mathbf {h} _ {i} ^ {(l)} = f _ {v} \\left(\\left[ \\mathbf {h} _ {i, p r e d} ^ {(l)}, \\mathbf {h} _ {i, s u c c} ^ {(l)} \\right]\\right). \\tag {7}", + "image_path": "7c31a30910e9da8a876db9b2862cce25251d2151e3f7f3bc06a9a312cf9f0dda.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 475, + 546, + 536 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 475, + 546, + 536 + ], + "spans": [ + { + "bbox": [ + 305, + 475, + 546, + 536 + ], + "type": "text", + "content": "Finally, sigmoid-valued edge scores " + }, + { + "bbox": [ + 305, + 475, + 546, + 536 + ], + "type": "inline_equation", + "content": "\\hat{e}_{ij}" + }, + { + "bbox": [ + 305, + 475, + 546, + 536 + ], + "type": "text", + "content": " and node score " + }, + { + "bbox": [ + 305, + 475, + 546, + 536 + ], + "type": "inline_equation", + "content": "\\hat{s}_i" + }, + { + "bbox": [ + 305, + 475, + 546, + 536 + ], + "type": "text", + "content": " are predicted from the obtained edge and node embeddings. In a separate network head, we classify each node as being a terminal node or not, denoted as a scalar " + }, + { + "bbox": [ + 305, + 475, + 546, + 536 + ], + "type": "inline_equation", + "content": "\\hat{t}_i" + }, + { + "bbox": [ + 305, + 475, + 546, + 536 + ], + "type": "text", + "content": ". Therefore, we optimize the following combined binary cross entropy:" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 328, + 544, + 545, + 569 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 328, + 544, + 545, + 569 + ], + "spans": [ + { + "bbox": [ + 328, + 544, + 545, + 569 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = - \\sum_ {| V |} s _ {i} \\log \\hat {s} _ {i} - \\sum_ {| V |} t _ {i} \\log \\hat {t} _ {i} - \\sum_ {| E |} e _ {i j} \\log \\hat {e} _ {i j}. \\tag {8}", + "image_path": "cad4e3dcd1ba459e01907ab65c6c156d10ef3ddf6a5a42ce457be188a7875579.jpg" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 305, + 578, + 546, + 626 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 578, + 546, + 626 + ], + "spans": [ + { + "bbox": [ + 305, + 578, + 546, + 626 + ], + "type": "text", + "content": "The ground truth graph " + }, + { + "bbox": [ + 305, + 578, + 546, + 626 + ], + "type": "inline_equation", + "content": "G_{GT}" + }, + { + "bbox": [ + 305, + 578, + 546, + 626 + ], + "type": "text", + "content": " as a learning target " + }, + { + "bbox": [ + 305, + 578, + 546, + 626 + ], + "type": "inline_equation", + "content": "(s_i, t_i, e_{ij})" + }, + { + "bbox": [ + 305, + 578, + 546, + 626 + ], + "type": "text", + "content": " is generated based on the map annotations for the given cropped region and the corresponding closest nodes. This is further outlined in the supplementary material in Sec. S.3." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 305, + 634, + 511, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 634, + 511, + 647 + ], + "spans": [ + { + "bbox": [ + 305, + 634, + 511, + 647 + ], + "type": "text", + "content": "4.2. Iterative Temporal Graph Aggregation" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 653, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 653, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 653, + 547, + 713 + ], + "type": "text", + "content": "In the second stage of our approach, we aggregate local successor graphs into a globally consistent lane graph. First, we prune the predicted per-crop lane graph, and second, we iteratively aggregate the sparse graphs. In the following, we detail both components." + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 317, + 757 + ], + "type": "text", + "content": "13418" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 71, + 167, + 186 + ], + "blocks": [ + { + "bbox": [ + 50, + 71, + 167, + 186 + ], + "lines": [ + { + "bbox": [ + 50, + 71, + 167, + 186 + ], + "spans": [ + { + "bbox": [ + 50, + 71, + 167, + 186 + ], + "type": "image", + "image_path": "e426750ea4d61fd31a189d749cf08d8b89d80c59b7a5a56333707835caf1506e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 194, + 287, + 243 + ], + "lines": [ + { + "bbox": [ + 46, + 194, + 287, + 243 + ], + "spans": [ + { + "bbox": [ + 46, + 194, + 287, + 243 + ], + "type": "text", + "content": "Figure 4. Comparison of raw and pruned lane graph predictions. On the left-hand side, we visualize estimated edge scores " + }, + { + "bbox": [ + 46, + 194, + 287, + 243 + ], + "type": "inline_equation", + "content": "\\hat{e}_{ij}" + }, + { + "bbox": [ + 46, + 194, + 287, + 243 + ], + "type": "text", + "content": " and node scores " + }, + { + "bbox": [ + 46, + 194, + 287, + 243 + ], + "type": "inline_equation", + "content": "\\hat{s}_i" + }, + { + "bbox": [ + 46, + 194, + 287, + 243 + ], + "type": "text", + "content": " using the same color scaling. On the right-hand side, we visualize the pruned graph. The circled numbers denote the order of traversal based on estimated terminal node scores." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 170, + 70, + 286, + 186 + ], + "blocks": [ + { + "bbox": [ + 170, + 70, + 286, + 186 + ], + "lines": [ + { + "bbox": [ + 170, + 70, + 286, + 186 + ], + "spans": [ + { + "bbox": [ + 170, + 70, + 286, + 186 + ], + "type": "image", + "image_path": "1de6e21e2b71f6f65ca648fa2f609566381fa98bdf54a6bd129c1a5d841316a4.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 264, + 289, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 264, + 289, + 445 + ], + "spans": [ + { + "bbox": [ + 46, + 264, + 289, + 445 + ], + "type": "text", + "content": "Pruning: The graph prediction obtained from the LaneGNN model follows the graph connectivity initially generated during sampling. Since the model prediction contains a number of redundant paths with high predicted node and edge scores, we prune the obtained solution to obtain sparse lane representations. We formulate the graph pruning problem as a search problem from a starting node to possibly multiple predicted terminal nodes (see Sec. 4.1). Predicted lane split points should coincide with actual split points. Thus, different branches should share the same set of edges up to a split point. We use Dijkstra's algorithm to iteratively find high-score paths between the initial pose and terminal nodes, ordered from high to low scores until all terminal nodes are reached. Edge scores contained in found paths are set to zero. In Fig. 4, we visualize the output of this step." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "spans": [ + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": "Aggregation: We iteratively aggregate predicted successor lane graphs " + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "inline_equation", + "content": "G_{pred} = (V_{pred}, E_{pred})" + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": " into a globally consistent and complete graph " + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "inline_equation", + "content": "G_{agg} = (V_{agg}, E_{agg})" + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": " as depicted in Fig. 3. The predicted successor graph " + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "inline_equation", + "content": "G_{pred}^{t}" + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": " is added to the current aggregated graph " + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "inline_equation", + "content": "G_{agg}^{t-1}" + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": " at time step " + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": ": " + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "inline_equation", + "content": "G_{agg}^{t} \\gets \\text{aggregate}(G_{pred}^{t}, G_{agg}^{t-1})" + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": ". Plausible branches of " + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "inline_equation", + "content": "G_{pred}" + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": " are merged into " + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "inline_equation", + "content": "G_{agg}" + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": " and thus extend the aggregated graph with every iteration if new grounds are covered in the respective crops. The next virtual agent pose is extracted from the set of forward-facing edges of " + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "inline_equation", + "content": "G_{agg}" + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": " given the current pose. Only edges with a significant weight due to previous aggregations are selected for this set. As the node positions differ slightly with each model forward pass we observe roughly similar paths in the lateral sense wrt. the ground-truth graph. However, along the longitudinal dimension of a branch, we observe deviations in node position, which must be circumvented when aggregating the graph. Based on this, we only take the lateral deviation wrt. " + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "inline_equation", + "content": "G_{agg}^{t}" + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": " into account when merging " + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "inline_equation", + "content": "G_{pred}^{t}" + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": ". Thus, " + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "inline_equation", + "content": "G_{agg}^{t}" + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": " is only updated in a lateral sense while the longitudinal misalignment of the two sets of nodes is neglected (see also Sec. S.5 in the supplementary material). The nodes of " + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "inline_equation", + "content": "G_{pred}^{t}" + }, + { + "bbox": [ + 46, + 449, + 289, + 716 + ], + "type": "text", + "content": " have a" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "text", + "content": "weight of 1 while a node of " + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "inline_equation", + "content": "G_{agg}^{t}" + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "text", + "content": " holds a weight equal to the number of merges it has observed so far. If a novel node " + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "inline_equation", + "content": "i \\in V_{pred}" + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "text", + "content": " is not close to any other node in " + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "inline_equation", + "content": "k \\in V_{agg}" + }, + { + "bbox": [ + 304, + 72, + 547, + 156 + ], + "type": "text", + "content": " it is added to the aggregated graph including its incident edges. This ultimately allows a weighting-based merging of arbitrary pairs of graphs, which is used for global lane graph estimation (see Sec. 5.4)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 157, + 547, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 157, + 547, + 323 + ], + "spans": [ + { + "bbox": [ + 304, + 157, + 547, + 323 + ], + "type": "text", + "content": "We observe that the more graphs we aggregate, the more we are certain about the significance of particular graph branches. Following a weighting-based approach allows us to set certain thresholds that allow modification of " + }, + { + "bbox": [ + 304, + 157, + 547, + 323 + ], + "type": "inline_equation", + "content": "G_{agg}" + }, + { + "bbox": [ + 304, + 157, + 547, + 323 + ], + "type": "text", + "content": ". Thus, implausible graph branches, semantically similar parallel paths, redundant edges, and isolated nodes are deleted based on confidence and distance as well as angle criteria (see Fig. 3). As a result, we are able to decrease the number of false positive split and merge points to obtain more consistent global graphs. We observe that this approach greatly improves lane graph prediction accuracy in difficult occluded and out-of-distribution scenarios since the sum of model predictions covering the same region shed light onto what potentially constitutes, e.g., a valid and an invalid branch." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 323, + 548, + 529 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 323, + 548, + 529 + ], + "spans": [ + { + "bbox": [ + 304, + 323, + 548, + 529 + ], + "type": "text", + "content": "Multiple forward passes naturally lead to a multitude of lane split points (both true positive and false positive splits). We interpret each split point as an element of an exploration frontier. A queue of unexplored, high-probability graph branches is maintained, which is queried in a depth-first manner as soon as the currently traversed branch terminates. Following the weighting-based approach, a branch is only explored if its depth-limited oriented successor tree weight exceeds a certain level of confidence. Due to this flexible approach, we can essentially handle arbitrary lane graph topologies with a single holistic approach. For more details on the graph pruning and aggregation procedures, please refer to the supplementary material. Finally, we apply multiple iterations of Laplacian smoothing to " + }, + { + "bbox": [ + 304, + 323, + 548, + 529 + ], + "type": "inline_equation", + "content": "G_{pred}^{t}" + }, + { + "bbox": [ + 304, + 323, + 548, + 529 + ], + "type": "text", + "content": ", which modifies the original node positions in order to even out position irregularities caused by sampling while keeping the adjacency represented by " + }, + { + "bbox": [ + 304, + 323, + 548, + 529 + ], + "type": "inline_equation", + "content": "E_{pred}^{t}" + }, + { + "bbox": [ + 304, + 323, + 548, + 529 + ], + "type": "text", + "content": " constant." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 536, + 432, + 551 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 536, + 432, + 551 + ], + "spans": [ + { + "bbox": [ + 306, + 536, + 432, + 551 + ], + "type": "text", + "content": "5. Experimental Results" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 556, + 547, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 556, + 547, + 629 + ], + "spans": [ + { + "bbox": [ + 304, + 556, + 547, + 629 + ], + "type": "text", + "content": "In the following, we present our experimental findings. We first define and illustrate three tasks on which we benchmark our method. Subsequently, we describe the evaluation metrics and compare against other methods as well as our own baselines. We provide extensive qualitative and quantitative evaluations on our UrbanLaneGraph dataset." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 635, + 402, + 647 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 635, + 402, + 647 + ], + "spans": [ + { + "bbox": [ + 306, + 635, + 402, + 647 + ], + "type": "text", + "content": "5.1. Proposed Tasks" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 654, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 654, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 654, + 547, + 715 + ], + "type": "text", + "content": "We propose three distinct and complementary tasks. In the first task, successor lane graph prediction (Successor-LGP, Sec. 5.3), we aim at predicting a feasible ego-reachable successor lane graphs from the current pose of the virtual agent. The purpose of the Successor-LGP task is to measure" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "13419" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 61, + 108, + 531, + 191 + ], + "blocks": [ + { + "bbox": [ + 46, + 70, + 547, + 102 + ], + "lines": [ + { + "bbox": [ + 46, + 70, + 547, + 102 + ], + "spans": [ + { + "bbox": [ + 46, + 70, + 547, + 102 + ], + "type": "text", + "content": "Table 2. Quantitative results of our model including ablation studies, in comparison with baseline models for the Successor-LGP task on our UrbanLaneGraph dataset. P/R denotes Precision/Recall. For our LaneGNN model variants, we denote CMP as causal message passing, aerial node features as AerN, aerial edge features as AerE, and the ego-lane regression with " + }, + { + "bbox": [ + 46, + 70, + 547, + 102 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_{\\text{lane}}^{\\text{ego}}" + }, + { + "bbox": [ + 46, + 70, + 547, + 102 + ], + "type": "text", + "content": ". For all metrics, higher values mean better results." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 61, + 108, + 531, + 191 + ], + "lines": [ + { + "bbox": [ + 61, + 108, + 531, + 191 + ], + "spans": [ + { + "bbox": [ + 61, + 108, + 531, + 191 + ], + "type": "table", + "html": "
ModelCMPAerNAerES laneTOPO P/R ↑GEO P/R ↑APLS ↑SDA20 ↑SDA50 ↑Graph IoU ↑
Skeletonized Regression----0.597/0.6130.578/0.6010.3150.0200.1850.180
LaneGraphNet [36]----0.0/0.00.0/0.00.1790.00.00.063
LaneGNN (ours)XX✓✓0.549/0.6770.548/0.6710.1880.1680.3230.312
✓✓X✓0.562/0.6560.562/0.6550.1920.1510.2980.320
✓XX✓0.545/0.6930.545/0.6880.2000.1880.3110.336
✓X✓X0.578/0.6690.577/0.6590.1500.1320.2270.250
✓X✓✓0.600/0.6990.599/0.6950.2020.2270.3770.347
", + "image_path": "a01f617d4d18448cc5d04f5d46bce0f41555a99361e5c20bb78da7fa62947b34.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 202, + 289, + 346 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 202, + 289, + 346 + ], + "spans": [ + { + "bbox": [ + 46, + 202, + 289, + 346 + ], + "type": "text", + "content": "the prediction quality of potential future driving paths when no HD map coverage is available. In the second task, full lane graph prediction (Full-LGP, Sec. 5.4), we evaluate the quality of regionally aggregated lane graphs in the context of HD map estimation. This task aims at measuring the predictive power of our full two-stage model performing lane graph inference and graph aggregation in conjunction. For such purposes, the full ground truth lane graph of a given map area is compared to the aggregated predictions of our model. Finally, we carry out a high-level path planning task (Sec. 5.5) on the predicted lane graphs, intended to analyze the fidelity of routes planned on the predicted graphs." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 361, + 160, + 373 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 361, + 160, + 373 + ], + "spans": [ + { + "bbox": [ + 47, + 361, + 160, + 373 + ], + "type": "text", + "content": "5.2. Evaluation Metrics" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 383, + 288, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 383, + 288, + 407 + ], + "spans": [ + { + "bbox": [ + 46, + 383, + 288, + 407 + ], + "type": "text", + "content": "We leverage multiple complementary metrics for performance evaluation as detailed below." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 407, + 288, + 479 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 407, + 288, + 479 + ], + "spans": [ + { + "bbox": [ + 46, + 407, + 288, + 479 + ], + "type": "text", + "content": "Graph IoU: This metric measures the intersection-over-union (IoU) of two graphs rendered as a binary image [36], where pixels closer than " + }, + { + "bbox": [ + 46, + 407, + 288, + 479 + ], + "type": "inline_equation", + "content": "d = 5" + }, + { + "bbox": [ + 46, + 407, + 288, + 479 + ], + "type": "text", + "content": " pixels are assigned the label 1 and the remaining pixels the label 0. Equivalent to the evaluation of semantic segmentation models, we determine the IoU values for the non-zero pixels." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 479, + 288, + 526 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 479, + 288, + 526 + ], + "spans": [ + { + "bbox": [ + 46, + 479, + 288, + 526 + ], + "type": "text", + "content": "The APLS metric sums the differences in optimal path lengths between nodes in the ground truth graph " + }, + { + "bbox": [ + 46, + 479, + 288, + 526 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 46, + 479, + 288, + 526 + ], + "type": "text", + "content": " and the proposal graph " + }, + { + "bbox": [ + 46, + 479, + 288, + 526 + ], + "type": "inline_equation", + "content": "G'" + }, + { + "bbox": [ + 46, + 479, + 288, + 526 + ], + "type": "text", + "content": " [25]. The APLS metric scales from 0 (worst) to 1 (best). Formally, it is defined as" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 57, + 542, + 288, + 567 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 542, + 288, + 567 + ], + "spans": [ + { + "bbox": [ + 57, + 542, + 288, + 567 + ], + "type": "interline_equation", + "content": "\\mathrm {A P L S} = 1 - \\frac {1}{N _ {p}} \\sum \\min \\left\\{1, \\frac {\\left| d \\left(v _ {1} , v _ {2}\\right) - d \\left(v _ {1} ^ {\\prime} , v _ {2} ^ {\\prime}\\right) \\right|}{d \\left(v _ {1} , v _ {2}\\right)} \\right\\}, \\tag {9}", + "image_path": "6fcc540a020f3b2d80c74cf68d94386ec864cc6c0242280763d0b0e45ed72e9b.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "inline_equation", + "content": "v_{i}" + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "inline_equation", + "content": "v_{i}^{\\prime}" + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "text", + "content": " are nodes in " + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "inline_equation", + "content": "G^{\\prime}" + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "text", + "content": ", respectively. " + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "inline_equation", + "content": "N_{p}" + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "text", + "content": " denotes the number of paths in " + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "inline_equation", + "content": "d(\\cdot, \\cdot)" + }, + { + "bbox": [ + 46, + 582, + 287, + 617 + ], + "type": "text", + "content": " is the path length. For more details, please refer to [25]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 617, + 288, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 288, + 677 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 288, + 677 + ], + "type": "text", + "content": "TOPO / GEO metrics: Following previous works in road network extraction and lane graph estimation, we use the GEO metric and the TOPO metric. For definitions and details on these metrics, please refer to [15] and to the supplementary material." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "content": "Split Detection Accuracy " + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "inline_equation", + "content": "(\\mathbf{SDA}_{\\mathbf{R}})" + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "content": ": This metric evaluates how accurately a model predicts the lane split within a circle of radius " + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "content": " pixels from a given ground truth lane split." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 201, + 486, + 214 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 201, + 486, + 214 + ], + "spans": [ + { + "bbox": [ + 305, + 201, + 486, + 214 + ], + "type": "text", + "content": "5.3. Successor Lane Graph Prediction" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 221, + 547, + 412 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 221, + 547, + 412 + ], + "spans": [ + { + "bbox": [ + 304, + 221, + 547, + 412 + ], + "type": "text", + "content": "In the following, we evaluate LaneGNN by ablating and comparing it with two baselines on the Successor-LGP task: morphological skeletonization of the ego-lane regression as well as a modified LaneGraphNet [36] to be used for successor lane graph prediction. We list quantitative results in Tab. 2. Our results demonstrate that none of the baseline methods are capable of estimating accurate lane graphs given the challenging topology of the lane graphs in the dataset. The LaneGraphNet [36] model fails to model the graph for many samples, yielding low scores in all metrics. Despite its simplicity, the skeletonized regression model achieves the highest APLS score and comparably high GEO/TOPO scores. However, it fails to accurately predict lane split points, resulting in low SDA scores. Increasing the number of nodes of the skeleton leads to many more false positive splits and thus deteriorates further." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 413, + 547, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 413, + 547, + 593 + ], + "spans": [ + { + "bbox": [ + 304, + 413, + 547, + 593 + ], + "type": "text", + "content": "Regarding different variants of LaneGNN, we find that, e.g., causal message passing (CMP) increases performance over standard message passing, which underlines the significance of the imposed causality prior for lane graph learning. In order to show the efficacy of computationally more demanding aerial image edge features (AerE) compared to uni-directional aerial image node features (AerN), we ablate on this in Tab. 2 as well. We observe stark increases across the TOPO, GEO and SDA metrics when utilizing aerial edge features. Lastly, we replace the ego-lane segmentation mask " + }, + { + "bbox": [ + 304, + 413, + 547, + 593 + ], + "type": "inline_equation", + "content": "\\mathbf{S}_{lane}^{ego}" + }, + { + "bbox": [ + 304, + 413, + 547, + 593 + ], + "type": "text", + "content": " used for sampling with the standard lane segmentation mask. Our findings show that especially APLS, SDA, and Graph IoU drastically decrease as graph estimation becomes more difficult due to a generally enlarged sampling region (see Fig. 2)." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 594, + 548, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 548, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 548, + 713 + ], + "type": "text", + "content": "These results are further illustrated in Fig. 5, where we show qualitative comparisons of predictions of our best-performing model with predictions from the two baselines. We find that the quality of predictions by LaneGraphNet [36] is generally low, rendering it unsuitable for the task. The skeletonized regression baseline is capable of following basic lane graph topologies, but lane split points cannot be resolved accurately. In contrast, our LaneGNN model is capable of modeling most graphs with high accuracy; both in intersection areas and in straight road sections. For more" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "13420" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 60, + 70, + 531, + 324 + ], + "blocks": [ + { + "bbox": [ + 60, + 70, + 531, + 324 + ], + "lines": [ + { + "bbox": [ + 60, + 70, + 531, + 324 + ], + "spans": [ + { + "bbox": [ + 60, + 70, + 531, + 324 + ], + "type": "image", + "image_path": "30ab3d07f5345b826dc09cd8a2372540a541ca72d53688146b68ffc68571618c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 331, + 547, + 363 + ], + "lines": [ + { + "bbox": [ + 46, + 331, + 547, + 363 + ], + "spans": [ + { + "bbox": [ + 46, + 331, + 547, + 363 + ], + "type": "text", + "content": "Figure 5. Qualitative results on the Successor-LGP task. We compare predictions of our model with LaneGraphNet [36] and a morphological image skeletonization baseline. Predicted nodes are visualized with points while predicted edges are visualized as directed arrows. We illustrate failure cases in the two rightmost columns. Best viewed zoomed in." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 50, + 437, + 282, + 487 + ], + "blocks": [ + { + "bbox": [ + 46, + 380, + 288, + 429 + ], + "lines": [ + { + "bbox": [ + 46, + 380, + 288, + 429 + ], + "spans": [ + { + "bbox": [ + 46, + 380, + 288, + 429 + ], + "type": "text", + "content": "Table 3. Quantitative evaluation for the Full-LGP task on the test-set of our UrbanLaneGraph dataset. We compare a baseline model with graphs aggregated with a naive aggregation scheme and our iterative temporal aggregation scheme. P/R denotes Precision/Recall. Higher values mean better results." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 437, + 282, + 487 + ], + "lines": [ + { + "bbox": [ + 50, + 437, + 282, + 487 + ], + "spans": [ + { + "bbox": [ + 50, + 437, + 282, + 487 + ], + "type": "table", + "html": "
ModelTOPO P/R ↑GEO P/R ↑APLS ↑Graph IoU ↑
LaneExtraction [15]0.405/0.5070.491/0.4540.0720.213
Aggregation (naive)0.366/0.6540.523/0.7270.1010.376
Aggregation (ours)0.481/0.6700.649/0.6890.1030.384
", + "image_path": "72a724059a3ca173ac262118dbbfc84b67c9eff44bce3f6fa711d3e646f989c5.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 502, + 288, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 502, + 288, + 514 + ], + "spans": [ + { + "bbox": [ + 47, + 502, + 288, + 514 + ], + "type": "text", + "content": "results, please refer to the supplementary material, Sec. S.6." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 525, + 201, + 539 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 525, + 201, + 539 + ], + "spans": [ + { + "bbox": [ + 47, + 525, + 201, + 539 + ], + "type": "text", + "content": "5.4. Full Lane Graph Prediction" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 545, + 288, + 713 + ], + "type": "text", + "content": "For the Full-LGP task, we compare our approach with LaneExtraction [15]. Since their used graph representation is incompatible with ours, we train it on their provided dataset. To allow for a fair comparison, we evaluate both our method and LaneExtraction only on scenes in the city of Miami, Florida, as it is contained in both of the datasets. We select a testing region that is not part of the training data for either of the models. To initialize our aggregation scheme, we select starting poses obtained from intermediate segmentation predictions including yaw angles of the LaneExtraction model. Tab. 3 lists the evaluation results for the Full-LGP task obtained with LaneExtraction [15] and the results obtained with our LaneGNN model in conjunction with two aggregation schemes: a naive aggregation scheme baseline" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 382, + 547, + 658 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 382, + 547, + 658 + ], + "spans": [ + { + "bbox": [ + 304, + 382, + 547, + 658 + ], + "type": "text", + "content": "and our full aggregation scheme. The naïve aggregation scheme merges nodes in close proximity while not relying on unvalidated split/merge or parallel path removal as well as the lateral weighting-based merging (Sec. 4.2). Our experiments show that our aggregation scheme outperforms both the LaneExtraction model and the naïve aggregation scheme on nearly all evaluation metrics. We note that our method improves the TOPO/GEO precision metrics while maintaining similar recalls due to better handling of redundant nodes. Fig. 6 illustrates successive aggregations from our model while indicating the used initialization points from the LaneExtraction model. Since our aggregation approach does not differentiate between intersection and non-intersection regions, it does not deteriorate in regions that do not exactly fit this categorization. Furthermore, our model exhibits superior performance in reduced visibility settings introduced by stark illumination changes or road occlusions from vegetation, as illustrated in Fig. 6. One of the decisive assets of our bottom-up method is that it allows to explore regions that are missed by LaneExtraction [15] as they are entailed in their predicted segmentation masks. For more qualitative and quantitative results, please refer to the supplementary material, Sec. S.7." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 669, + 397, + 682 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 669, + 397, + 682 + ], + "spans": [ + { + "bbox": [ + 306, + 669, + 397, + 682 + ], + "type": "text", + "content": "5.5. Path Planning" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 689, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 547, + 715 + ], + "type": "text", + "content": "To illustrate the efficacy of our aggregation scheme, we evaluate the quality of the lane graph on a planning task. We" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13421" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 84, + 70, + 508, + 312 + ], + "blocks": [ + { + "bbox": [ + 84, + 70, + 508, + 312 + ], + "lines": [ + { + "bbox": [ + 84, + 70, + 508, + 312 + ], + "spans": [ + { + "bbox": [ + 84, + 70, + 508, + 312 + ], + "type": "image", + "image_path": "a74c0e589d82200af6d540f3a83108e19b1c443824711783a97cb7ce8cd88a93.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 316, + 546, + 336 + ], + "lines": [ + { + "bbox": [ + 46, + 316, + 546, + 336 + ], + "spans": [ + { + "bbox": [ + 46, + 316, + 546, + 336 + ], + "type": "text", + "content": "Figure 6. Qualitative results on the Full-LGP task. We visualize predictions of LaneExtraction [15] (top row) and aggregated LaneGNN predictions (bottom row). Our model is initialized at poses using predicted lane direction masks of LaneExtraction (indicated with yellow circles). Best viewed zoomed in." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 82, + 393, + 252, + 436 + ], + "blocks": [ + { + "bbox": [ + 46, + 355, + 287, + 384 + ], + "lines": [ + { + "bbox": [ + 46, + 355, + 287, + 384 + ], + "spans": [ + { + "bbox": [ + 46, + 355, + 287, + 384 + ], + "type": "text", + "content": "Table 4. Quantitative evaluation for the planning task. MMD denotes mean minimum distance, MED denotes mean endpoint distance, and SR denotes the path planning success rate." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 82, + 393, + 252, + 436 + ], + "lines": [ + { + "bbox": [ + 82, + 393, + 252, + 436 + ], + "spans": [ + { + "bbox": [ + 82, + 393, + 252, + 436 + ], + "type": "table", + "html": "
ModelMMD [m] ↓MED [m] ↓SR ↑
LaneExtraction [15]157.0339.40.47
Aggregation (ours)2.219.70.46
", + "image_path": "48db9db948b60080501e9e42e7851b7d5ec6c2353a61e05829f7f3167466c16b.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_body" + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 448, + 287, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 448, + 287, + 639 + ], + "spans": [ + { + "bbox": [ + 46, + 448, + 287, + 639 + ], + "type": "text", + "content": "generate 1000 randomly selected starting poses in the Miami graph test area from which a plan to a random goal within the graph must be found, using A* search. We place the points such that a maximal optimal route length of " + }, + { + "bbox": [ + 46, + 448, + 287, + 639 + ], + "type": "inline_equation", + "content": "200\\mathrm{m}" + }, + { + "bbox": [ + 46, + 448, + 287, + 639 + ], + "type": "text", + "content": " is not exceeded. To evaluate the planned routes, we compare the mean minimum distance (MMD) and the mean route endpoint distance (MED) between the paths on the predicted graph and the ground truth graph, respectively. We also report the success rate (SR), indicating the number of cases in which a path between start and goal exists. We list the results in Tab. 4. While the SR for our aggregation scheme and the LaneExtraction predictions is similar, the low MMD and MED values of our aggregation scheme indicate that our generated lane graph entails shorter and more direct paths, compared to LaneExtraction. We show additional results in the supplementary material, Sec. S.8." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 647, + 125, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 647, + 125, + 658 + ], + "spans": [ + { + "bbox": [ + 47, + 647, + 125, + 658 + ], + "type": "text", + "content": "5.6. Limitations" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 665, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 713 + ], + "type": "text", + "content": "Due to its bottom-up architecture, the proposed approach performs well for most evaluated scenes in urban and suburban surroundings but struggles with highly complex graph topologies such as multi-lane intersections or roundabouts." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 357, + 547, + 465 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 357, + 547, + 465 + ], + "spans": [ + { + "bbox": [ + 305, + 357, + 547, + 465 + ], + "type": "text", + "content": "Moreover, due to the iterative formulation of our aggregation scheme, the inference time of our approach increases with the number of nodes and edges. To speed up inference time, future work might include adaptively changing the distance between consecutive virtual agent positions and leveraging efficient neighborhood lookup methods such as k-d trees. Parallel execution of multiple agents would additionally boost run time to match top-down approaches and is feasible in terms of the proposed aggregation scheme." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 494, + 378, + 506 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 494, + 378, + 506 + ], + "spans": [ + { + "bbox": [ + 306, + 494, + 378, + 506 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 519, + 547, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 519, + 547, + 662 + ], + "spans": [ + { + "bbox": [ + 304, + 519, + 547, + 662 + ], + "type": "text", + "content": "In this work, we presented a novel lane graph estimation framework complemented with a novel dataset comprising aerial images. We showed that formulating the lane graph estimation problem as bottom-up graph neural network approach leveraging agent-centric views yields promising results. In addition, we presented a novel aggregation scheme to merge successive lane graphs to produce large-scale solutions. A first-of-its-kind dataset and benchmark for lane graph estimation from aerial images will enable further research in this field. Future work could address end-to-end training and exploiting further modalities such as onboard vehicle cameras for additional context information." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 665, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 547, + 713 + ], + "type": "text", + "content": "Acknowledgements: This work was partly funded by the German Research Foundation (DFG) Emmy Noether Program grant number 468878300, DFG grant number BU 865/10-2, and a hardware grant from NVIDIA." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13422" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 156 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 156 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 156 + ], + "type": "text", + "content": "[1] Wele Gedara Chaminda Bandara, Jeya Maria Jose Valanarasu, and Vishal M Patel. Spin road mapper: Extracting roads from aerial images via spatial and interaction space graph reasoning for autonomous driving. In 2022 International Conference on Robotics and Automation (ICRA), pages 343-350. IEEE, 2022. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 159, + 288, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 159, + 288, + 224 + ], + "spans": [ + { + "bbox": [ + 53, + 159, + 288, + 224 + ], + "type": "text", + "content": "[2] Favyen Bastani, Songtao He, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, and David DeWitt. Roadtracer: Automatic extraction of road networks from aerial images. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 4720-4728, 2018. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 227, + 287, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 227, + 287, + 270 + ], + "spans": [ + { + "bbox": [ + 53, + 227, + 287, + 270 + ], + "type": "text", + "content": "[3] Guillem Brasó and Laura Leal-Taixe. Learning a neural solver for multiple object tracking. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020. 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 272, + 287, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 272, + 287, + 316 + ], + "spans": [ + { + "bbox": [ + 53, + 272, + 287, + 316 + ], + "type": "text", + "content": "[4] Martin Büchner and Abhinav Valada. 3d multi-object tracking using graph neural networks with cross-edge modality attention. IEEE Robotics and Automation Letters, 7(4):9707-9714, 2022. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 318, + 287, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 318, + 287, + 373 + ], + "spans": [ + { + "bbox": [ + 53, + 318, + 287, + 373 + ], + "type": "text", + "content": "[5] Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, and Luc Van Gool. Structured bird's-eye-view traffic scene understanding from onboard images. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 15661-15670, 2021. 1, 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 374, + 287, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 374, + 287, + 430 + ], + "spans": [ + { + "bbox": [ + 53, + 374, + 287, + 430 + ], + "type": "text", + "content": "[6] Yigit Baran Can, Alexander Liniger, Danda Pani Paudel, and Luc Van Gool. Topology preserving local road network estimation from single onboard camera image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17263-17272, 2022. 1, 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 431, + 287, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 431, + 287, + 474 + ], + "spans": [ + { + "bbox": [ + 53, + 431, + 287, + 474 + ], + "type": "text", + "content": "[7] Daniele Cattaneo, Matteo Vaghi, and Abhinav Valada. Lcdnet: Deep loop closure detection and point cloud registration for lidar slam. IEEE Transactions on Robotics, 38(4):2074-2093, 2022. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 476, + 287, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 476, + 287, + 520 + ], + "spans": [ + { + "bbox": [ + 53, + 476, + 287, + 520 + ], + "type": "text", + "content": "[8] Yuning Chai, Benjamin Sapp, Mayank Bansal, and Dragomir Anguelov. Multipath: Multiple probabilistic anchor trajectory hypotheses for behavior prediction. In Conference on Robot Learning, pages 86-99. PMLR, 2020. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 522, + 287, + 564 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 522, + 287, + 564 + ], + "spans": [ + { + "bbox": [ + 53, + 522, + 287, + 564 + ], + "type": "text", + "content": "[9] Dian Chen and Philipp Kähenbuhl. Learning from all vehicles. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17222-17231, 2022. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 567, + 287, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 567, + 287, + 622 + ], + "spans": [ + { + "bbox": [ + 48, + 567, + 287, + 622 + ], + "type": "text", + "content": "[10] Nemanja Djuric, Henggang Cui, Zhaoen Su, Shangxuan Wu, Huahua Wang, Fang-Chieh Chou, Luisa San Martin, Song Feng, Rui Hu, Yang Xu, et al. Multixnet: Multiclass multi-stage multimodal motion prediction. In 2021 IEEE Intelligent Vehicles Symposium (IV), pages 435-442. IEEE, 2021. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 624, + 287, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 287, + 678 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 287, + 678 + ], + "type": "text", + "content": "[11] Whye Kit Fong, Rohit Mohan, Juana Valeria Hurtado, Lubing Zhou, Holger Caesar, Oscar Beijbom, and Abhinav Valada. Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking. IEEE Robotics and Automation Letters, 7(2):3795-3802, 2022. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 680, + 287, + 713 + ], + "type": "text", + "content": "[12] Nikhil Gosala and Abhinav Valada. Bird's-eye-view panoptic segmentation using monocular frontal view images. IEEE Robotics and Automation Letters, 7(2):1968-1975, 2022. 1" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 307, + 73, + 547, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 547, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 547, + 116 + ], + "type": "text", + "content": "[13] Aric Hagberg, Pieter Swart, and Daniel S Chult. Exploring network structure, dynamics, and function using networkx. Technical report, Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2008. 12" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 118, + 547, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 547, + 139 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 547, + 139 + ], + "type": "text", + "content": "[14] J Halton and G Smith. Radical inverse quasi-random point sequence, algorithm 247. Commun. ACM, 7(12):701, 1964. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 140, + 547, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 140, + 547, + 183 + ], + "spans": [ + { + "bbox": [ + 308, + 140, + 547, + 183 + ], + "type": "text", + "content": "[15] Songtao He and Hari Balakrishnan. Lane-level street map extraction from aerial imagery. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 2080-2089, 2022. 1, 2, 3, 6, 7, 8, 13, 21" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 185, + 547, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 185, + 547, + 239 + ], + "spans": [ + { + "bbox": [ + 308, + 185, + 547, + 239 + ], + "type": "text", + "content": "[16] Namdar Homayounfar, Wei-Chiu Ma, Shrinidhi Kowshika Lakshmikanth, and Raquel Urtasun. Hierarchical recurrent attention networks for structured online maps. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 3417-3426, 2018. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 240, + 546, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 240, + 546, + 293 + ], + "spans": [ + { + "bbox": [ + 308, + 240, + 546, + 293 + ], + "type": "text", + "content": "[17] Namdar Homayounfar, Wei-Chiu Ma, Justin Liang, Xinyu Wu, Jack Fan, and Raquel Urtasun. Dagmapper: Learning to map by discovering lane topology. In Proceedings of the IEEE International Conference on Computer Vision, pages 2911-2920, 2019. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 294, + 546, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 294, + 546, + 348 + ], + "spans": [ + { + "bbox": [ + 308, + 294, + 546, + 348 + ], + "type": "text", + "content": "[18] Justin Liang, Namdar Homayounfar, Wei-Chiu Ma, Shenlong Wang, and Raquel Urtasun. Convolutional recurrent network for road boundary extraction. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 9512-9521, 2019. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 350, + 547, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 350, + 547, + 392 + ], + "spans": [ + { + "bbox": [ + 308, + 350, + 547, + 392 + ], + "type": "text", + "content": "[19] Gellert Mattyus, Wenjie Luo, and Raquel Urtasun. Deeproadmapper: Extracting road topology from aerial images. In Proceedings of the IEEE International Conference on Computer Vision, pages 3438-3446, 2017. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 394, + 547, + 436 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 394, + 547, + 436 + ], + "spans": [ + { + "bbox": [ + 308, + 394, + 547, + 436 + ], + "type": "text", + "content": "[20] Rohit Mohan and Abhinav Valada. Amodal panoptic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21023-21032, 2022. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 437, + 547, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 437, + 547, + 491 + ], + "spans": [ + { + "bbox": [ + 308, + 437, + 547, + 491 + ], + "type": "text", + "content": "[21] Kursat Petek, Kshitij Sirohi, Daniel Buscher, and Wolfram Burgard. Robust monocular localization in sparse hd maps leveraging multi-task uncertainty estimation. In 2022 International Conference on Robotics and Automation (ICRA), pages 4163-4169. IEEE, 2022. 1" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 493, + 547, + 548 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 493, + 547, + 548 + ], + "spans": [ + { + "bbox": [ + 308, + 493, + 547, + 548 + ], + "type": "text", + "content": "[22] Yong-Qiang Tan, Shang-Hua Gao, Xuan-Yi Li, Ming-Ming Cheng, and Bo Ren. Vecroad: Point-based iterative graph exploration for road graphs extraction. In Proc. of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pages 8910-8918, 2020. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 548, + 546, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 548, + 546, + 591 + ], + "spans": [ + { + "bbox": [ + 308, + 548, + 546, + 591 + ], + "type": "text", + "content": "[23] Shinji Umeyama. Least-squares estimation of transformation parameters between two point patterns. IEEE Transactions on Pattern Analysis & Machine Intelligence, 13(04):376-380, 1991. 11" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 593, + 547, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 593, + 547, + 656 + ], + "spans": [ + { + "bbox": [ + 308, + 593, + 547, + 656 + ], + "type": "text", + "content": "[24] Abhinav Valada, Ankit Dhall, and Wolfram Burgard. Convoluted mixture of deep experts for robust semantic segmentation. In IEEE/RSJ International conference on intelligent robots and systems (IROS) workshop, state estimation and terrain perception for all terrain mobile robots, volume 2, 2016. 1" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 658, + 547, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 658, + 547, + 690 + ], + "spans": [ + { + "bbox": [ + 308, + 658, + 547, + 690 + ], + "type": "text", + "content": "[25] Adam Van Etten, Dave Lindenbaum, and Todd M Bacastow. Spacenet: A remote sensing dataset and challenge series. arXiv preprint arXiv:1807.01232, 2018. 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 692, + 547, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 692, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 692, + 547, + 713 + ], + "type": "text", + "content": "[26] Johan Vertens and Wolfram Burgard. Usegscene: Unsupervised learning of depth, optical flow and ego-motion with" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13423" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 353 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 66, + 72, + 287, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 72, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 287, + 95 + ], + "type": "text", + "content": "semantic guidance and coupled networks. arXiv preprint arXiv:2207.07469, 2022. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 288, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 288, + 140 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 288, + 140 + ], + "type": "text", + "content": "[27] Niclas Vödisch, Daniele Cattaneo, Wolfram Burgard, and Abhinav Valada. Continual slam: Beyond lifelong simultaneous localization and mapping through continual learning. arXiv preprint arXiv:2203.01578, 2022. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 141, + 288, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 141, + 288, + 185 + ], + "spans": [ + { + "bbox": [ + 48, + 141, + 288, + 185 + ], + "type": "text", + "content": "[28] Jingke Wang, Tengju Ye, Ziqing Gu, and Junbo Chen. Ltp: Lane-based trajectory prediction for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17134-17142, 2022. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 186, + 288, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 186, + 288, + 262 + ], + "spans": [ + { + "bbox": [ + 48, + 186, + 288, + 262 + ], + "type": "text", + "content": "[29] Benjamin Wilson, William Qi, Tanmay Agarwal, John Lambert, Jagjeet Singh, Siddhesh Khandelwal, Bowen Pan, Ratnesh Kumar, Andrew Hartnett, Jhony Kaesemodel Pontes, et al. Argoverse 2: Next generation datasets for self-driving perception and forecasting. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2), 2021. 3, 11" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 264, + 288, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 264, + 288, + 308 + ], + "spans": [ + { + "bbox": [ + 48, + 264, + 288, + 308 + ], + "type": "text", + "content": "[30] Zhenhua Xu, Yuxuan Liu, Lu Gan, Yuxiang Sun, Xinyu Wu, Ming Liu, and Lujia Wang. Ringdet: Road network graph detection by transformer in aerial images. IEEE Transactions on Geoscience and Remote Sensing, 2022. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 309, + 288, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 309, + 288, + 353 + ], + "spans": [ + { + "bbox": [ + 48, + 309, + 288, + 353 + ], + "type": "text", + "content": "[31] Li Zhang, Faezeh Tafazzoli, Gunther Krehl, Runsheng Xu, Timo Rehfeld, Manuel Schier, and Arunava Seal. Hierarchical road topology learning for urban map-less driving. arXiv preprint arXiv:2104.00084, 2021. 2" + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 353 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 307, + 73, + 546, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 546, + 106 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 546, + 106 + ], + "type": "text", + "content": "[32] Tongjie Y Zhang and Ching Y. Suen. A fast parallel algorithm for thinning digital patterns. Communications of the ACM, 27(3):236-239, 1984. 15" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 307, + 107, + 547, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 107, + 547, + 152 + ], + "spans": [ + { + "bbox": [ + 307, + 107, + 547, + 152 + ], + "type": "text", + "content": "[33] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881-2890, 2017. 3, 16" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 307, + 152, + 547, + 207 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 152, + 547, + 207 + ], + "spans": [ + { + "bbox": [ + 307, + 152, + 547, + 207 + ], + "type": "text", + "content": "[34] Lichen Zhou, Chuang Zhang, and Ming Wu. D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 182-186, 2018. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 308, + 208, + 547, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 208, + 547, + 262 + ], + "spans": [ + { + "bbox": [ + 308, + 208, + 547, + 262 + ], + "type": "text", + "content": "[35] Yiyang Zhou, Yuichi Takeda, Masayoshi Tomizuka, and Wei Zhan. Automatic construction of lane-level hd maps for urban scenes. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 6649-6656. IEEE, 2021. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 308, + 264, + 547, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 264, + 547, + 308 + ], + "spans": [ + { + "bbox": [ + 308, + 264, + 547, + 308 + ], + "type": "text", + "content": "[36] Jannik Zürn, Johan Vertens, and Wolfram Burgard. Lane graph estimation for scene understanding in urban driving. IEEE Robotics and Automation Letters, 6(4):8615-8622, 2021. 2, 6, 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 308, + 309, + 547, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 309, + 547, + 353 + ], + "spans": [ + { + "bbox": [ + 308, + 309, + 547, + 353 + ], + "type": "text", + "content": "[37] Jannik Zürn, Sebastian Weber, and Wolfram Burgard. Trackletmapper: Ground surface segmentation and mapping from traffic participant trajectories. In 6th Annual Conference on Robot Learning, 2022. 1" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "13424" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning on Gradients_ Generalized Artifacts Representation for GAN-Generated Images Detection/2c415e18-250d-42c6-9ecf-17946adf591a_content_list.json b/2023/Learning on Gradients_ Generalized Artifacts Representation for GAN-Generated Images Detection/2c415e18-250d-42c6-9ecf-17946adf591a_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..ae70fd86d53191bc524bc52ed1a5089aafa1d9ea --- /dev/null +++ b/2023/Learning on Gradients_ Generalized Artifacts Representation for GAN-Generated Images Detection/2c415e18-250d-42c6-9ecf-17946adf591a_content_list.json @@ -0,0 +1,1292 @@ +[ + { + "type": "text", + "text": "Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection", + "text_level": 1, + "bbox": [ + 156, + 130, + 816, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Chuangchuang Tan $^{1,2}$ , Yao Zhao $^{1,2*}$ , Shikui Wei $^{1,2}$ , Guanghua Gu $^{3,4}$ , Yunchao Wei $^{1,2}$", + "bbox": [ + 151, + 202, + 815, + 220 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Institute of Information Science, Beijing Jiaotong University", + "bbox": [ + 300, + 223, + 668, + 238 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{2}$ Beijing Key Laboratory of Advanced Information Science and Network Technology", + "bbox": [ + 230, + 239, + 736, + 256 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{3}$ School of Information Science and Engineering, Yanshan University", + "bbox": [ + 276, + 258, + 691, + 273 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "4Hebei Key Laboratory of Information Transmission and Signal Processing", + "bbox": [ + 261, + 276, + 707, + 291 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{21112002, yzhao, shkwei}@bjtu.edu.cn, guguanghua@ysu.edu.cn, wychaol987@gmail.com", + "bbox": [ + 119, + 294, + 843, + 309 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 344, + 313, + 359 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Recently, there has been a significant advancement in image generation technology, known as GAN. It can easily generate realistic fake images, leading to an increased risk of abuse. However, most image detectors suffer from sharp performance drops in unseen domains. The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models. In this work, we introduce a novel detection framework, named Learning on Gradients (LGrad), designed for identifying GAN-generated images, with the aim of constructing a generalized detector with cross-model and cross-data. Specifically, a pretrained CNN model is employed as a transformation model to convert images into gradients. Subsequently, we leverage these gradients to present the generalized artifacts, which are fed into the classifier to ascertain the authenticity of the images. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. To the best of our knowledge, this is the first study to utilize gradients as the representation of artifacts in GAN-generated images. Extensive experiments demonstrate the effectiveness and robustness of gradients as generalized artifact representations. Our detector achieves a new state-of-the-art performance with a remarkable gain of $11.4\\%$ . The code is released at https://github.com/chuangchuanqtan/LGrad.", + "bbox": [ + 75, + 377, + 473, + 753 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 786, + 209, + 801 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Over the past years, remarkable progress has been made in deep generative models, i.e. generative adversarial networks (GAN) [14], its variations [3, 21-23, 36], and VAE [25]. The generated media is highly realistic and indistin", + "bbox": [ + 76, + 813, + 468, + 875 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/329654e7c33c656d2bb7453466b597a02c75bc5733b031630980a8394e869b75.jpg", + "image_caption": [ + "Figure 1. Visualization of gradients of real images and GAN-generated images extracted from a pre-trained model. To fully understand the gradients, heatmaps for R, G, and B channels also are shown, where red is high. In the gradients, the content of images is filtered out, and only the discriminative pixels are retained for the pre-trained model's target task. We utilize the gradients as the generalized artifacts representation to develop a novel detection framework." + ], + "image_footnote": [], + "bbox": [ + 501, + 344, + 890, + 594 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "guishable from real to human eyes. Although it has the potential for many novel applications [8], it also brings new risks due to the abuse of fake information. The misuse of DeepFakes has been confirmed that some people swap the faces of women onto pornographic videos [8]. In addition, some individuals, even non-experts, can malevolently manipulate or create fake images or videos for political or economic purposes, leading to serious social problems [17]. Thus, it is extremely necessary to develop forgery detection techniques to help people determine the credibility of the", + "bbox": [ + 496, + 750, + 893, + 902 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 94, + 886, + 220, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "12105", + "bbox": [ + 480, + 944, + 517, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "media [17, 40].", + "bbox": [ + 76, + 90, + 179, + 104 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Various detectors [11-13, 16, 18, 19, 30, 42, 45] have been developed to detect GAN-generated images. Some studies [16, 30, 45] focus on human face images, while others [11, 18, 19, 42] handle various categories of images. They mainly depend on local regions artifacts [4, 44], blending boundary [26], global textures [30], and frequency-level artifacts [13, 18, 19, 45]. However, those methods heavily rely on the training settings, resulting in failure detection of images from unseen categories or GAN models. The test images in the actual scene are always from unknown sources [17], rendering it challenging to develop generalized detectors. There are some works [19, 42] exploiting pre-processing, data augmentation, and reducing the effects of frequency-level artifacts to develop a robust detector. Nevertheless, there still needs to be a more generalized representation of the clue produced by generation models, which is critical for robust fake image detection.", + "bbox": [ + 75, + 107, + 468, + 364 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To tackle this problem, we propose a novel and simple detection framework, referred to as Learning on Gradients (LGrad). A new generalized feature, Gradients, is developed to serve as a representation of the artifacts produced by GAN models. We believe that the gradients of a trained CNN model can highlight the important pixels in the target task, thereby serving as a valuable cue for detecting fake images. As shown in Figure 1, we adopt a pre-trained discriminator of ProGAN [21] to extract gradients of images produced by Celeba-HQ [21], ProGAN [21], StyleGAN [22], StyleGAN2 [23]. In these gradients, the content of images is filtered out, and only the discriminative pixels that are relevant to the pre-trained model's target task are retained. Therefore, the gradients are more dependent on the pre-trained model rather than on training sources, thereby enhancing the detector's performance with unseen data. In our framework, a pretrained model, called transformation model, is employed to convert images to gradients. These gradients serve as the generalized artifacts and are fed into the classifier to obtain a robust detector. Since the transformation model is indeterminate in our framework, targeted anti-detection cannot be effectively launched.", + "bbox": [ + 75, + 366, + 470, + 698 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To validate the performance of our LGrad, we only use images generated by ProGAN to train the detector and evaluate it with various sources, including cross-category, cross-model, and cross-model & category. Numerous experiments prove the effectiveness and robustness of gradients as generalized artifacts. Our detector achieves a new state-of-the-art performance in known and unseen settings.", + "bbox": [ + 75, + 700, + 468, + 806 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our paper makes the following contributions:", + "bbox": [ + 96, + 809, + 398, + 824 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- We develop a new detection framework, Learning on Gradient (LGrad), to detect GAN-generated images. Our detector achieves a new state-of-the-art performance.", + "bbox": [ + 94, + 839, + 468, + 898 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We introduce a new generalized artifact representation, Gradients, for GAN-generated image detection. Furthermore, we are the first to use gradients as the representation of artifacts.", + "- Our framework turns the data-driven problem into a transformation-model-driven problem. The robustness of the detector is improved with the introduction of the transformation model.", + "- We prove the great potential of the discriminator of GAN in detecting GAN-generated images." + ], + "bbox": [ + 517, + 90, + 890, + 266 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 280, + 640, + 296 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this section, we discuss recent approaches for generated image detection. The previous methods attempt to exploit spatial information or frequency artifacts as the representation of clues produced by the generation model for fake image detection.", + "bbox": [ + 496, + 306, + 890, + 382 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Image-based Fake Detection", + "text_level": 1, + "bbox": [ + 500, + 393, + 754, + 409 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Early studies employ spatial information from generated images to identify the fake images, such as color spaces [45] and global texture [30]. Rossler et al. [37] introduce a largescale manipulated face dataset FaceForensics++ and adopt Xception [7] as detector to identify the fake images with compression. Li et al. [27] detect fake face videos by analyzing eye blinking in the videos. ForensicTransfer [9] presents a new autoencoder-based architecture to learn a discriminative feature representation in the latent space for detection well on new domains. Yu et al. [44] and Marra et al. [31] believe that every GAN model has unique fingerprints after training, and extracting it from generated images to perform detection. Bayar et al. [2] design a new convolutional network architecture to suppress the image of the content and learn manipulation features for a generalized detector. Gram-Net [30] leverages the Gram matrix to extract global texture as the robust representation for fake face detection. Face X-ray [26] develops the blending boundary to present the artifacts for detecting manipulated face images. Chai et al. [4] design a patch-based classifier to limit the receptive field of the model for learning redundant artifacts in image patches. The patch-based predictions focus on local artifacts rather than global structure, improving the robustness of the detector. Yu et al. [45] use the channel difference image and spectrum image to mine the intrinsic clues of images from the view of the camera imaging process. Wang et al. [42] directly adopt a large number of real and fake images with data augmentation to train a binary classifier for improving unseen image performance. They use some post-processing operations to improve robustness during training, such as JPEG compression, blurring, and resizing. PCL [46] extracts the distinct source features of", + "bbox": [ + 496, + 417, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "12106", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/2950c4a6c6b633636272a0328972c34a486a8275aa477ec2a72ab0ddb83a1e2e.jpg", + "image_caption": [ + "Figure 2. The overall pipeline of the proposed framework. The method uses the same transformation model and the same classifier in the testing and training phases." + ], + "image_footnote": [], + "bbox": [ + 148, + 87, + 823, + 363 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "images by self-consistency learning for detecting deepfake. He et al. [16] design a re-synthesis procedure to extract visual cues for robust deepfake detection.", + "bbox": [ + 75, + 426, + 470, + 472 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Frequency-based Fake Detection", + "text_level": 1, + "bbox": [ + 76, + 484, + 364, + 500 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Since GAN architectures depend on up-scaling operations, the generated images contain unique frequency-level artifacts compared to neutral images [11]. Several works [1, 11, 12, 31, 32, 34] leverage the spectral distributions of images as the representation of artifacts for fake image detection. LOG [32] proposes a two-branch recurrent network to integrate information from the color domain and the frequency domain for detecting manipulated faces. $F^3$ -Net [34] designs a two-stream collaborative learning network to mine the forgery patterns in fake images, which exploits two frequency-aware clues, frequency-aware decomposed image components and local frequency statistics, to present subtle forgery patterns and high-level semantics, respectively. BiHPF [18] adopts two high-pass filters to amplify the magnitudes of the artifacts. FrePGAN [19] validates that the frequency-level artifacts of fake images are evident but uniquely vary by the type of GAN model or object category, which can cause overfitting issues for fake image detection. Thus, it generated the frequency-level perturbation maps to remove the effect of the frequency-level artifacts.", + "bbox": [ + 75, + 508, + 472, + 828 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Learning on Gradients framework", + "text_level": 1, + "bbox": [ + 76, + 844, + 382, + 861 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The key to the fake image detection task is developing a generalized representation of artifacts generated by", + "bbox": [ + 76, + 869, + 470, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "GAN models. It should be generalized and robust enough to span diverse categories and different GAN models. In order to achieve this, we design a novel detection framework to improve the cross-source performance in this work. The overview of our method is shown in Figure 2. Our framework employs gradients as the generalized representation to obtain the robust detector. We transform the images to the gradients by a pretrained CNN model named transformation model. In the process of transforming, most of the content of images is filtered out due to the pooling layer in the CNN model, and the essential pixels for the transformation model are highlighted. Figure 1 provides the visualization evidence for our explanation. Extensive experiments in Section 4 demonstrate the effectiveness of the gradients using as the artifact representation.", + "bbox": [ + 496, + 426, + 890, + 654 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Transformation to Gradients", + "text_level": 1, + "bbox": [ + 500, + 664, + 758, + 679 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The previous methods based on pixel and frequency-level artifacts highly rely on the training data, resulting in failure with unseen images. To solve this overfitting issue, we adopt any one pretrained CNN model as the transformation model to convert all images consisting of training images and test images to gradients. This approach effectively maps all data to the gradient domain, which is determined by the chosen transformation model. By relying on this more generalized representation of artifacts, we can achieve improved performance in detecting GAN-generated images across diverse categories and GAN models.", + "bbox": [ + 496, + 688, + 890, + 853 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We denote the images set as $I = \\{(I_i, y_i)\\}_{i=0}^{N-1}$ , where $y_i = \\{0,1\\}$ is the label of the images $I_i \\in \\mathbb{R}^{w \\times h \\times c}$ , which is real ( $y = 0$ ) or fake ( $y = 1$ ). The $w, h, c$ present width,", + "bbox": [ + 498, + 854, + 890, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "12107", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "height, and the number of channels. The transformation model is defined as $M(\\cdot)$ . We first feed images $I_{i}$ into $M$ model.", + "bbox": [ + 75, + 90, + 468, + 133 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nl = M \\left(I _ {i}\\right), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 232, + 137, + 468, + 152 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $l \\in \\mathbb{R}^{n \\times c}$ is the output vectors of the transformation model $M$ . Then, we calculate the gradients of $sum(l)$ with respect to the input $I_i$ ,", + "bbox": [ + 75, + 160, + 468, + 205 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nG = \\frac {\\partial \\operatorname {s u m} (l)}{\\partial I _ {i}}. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 218, + 217, + 468, + 250 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$G$ is the generalized artifacts representation of our method. Note that the transformation model $M$ is fixed, and its parameter would not update in our framework. The quality of gradients $G$ highly depends on the transformation model $M$ , which is helpful to reduce the reliance on training data and enhance the generalization of the representations. In our experiments, we adopt various popular CNN models to implement the transformation model, including the classification model, segmentation model, discriminator of GAN, contrastive learning model, and GAN Inversion.", + "bbox": [ + 75, + 258, + 468, + 410 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Detecting Fake Images", + "text_level": 1, + "bbox": [ + 76, + 421, + 287, + 436 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "During the training stage of our framework, we use normalized gradients ranging from 0 to 255 to train the binary classification network. We adopt the popular classifier to distinguish whether the input gradients correspond to GAN-generated images or not, and employ a cross-entropy loss function to optimize the network. During the inference time, the test images are first converted to gradients by the transformation model employed in the training stage. Then, we obtain the final result by feeding gradients into the trained classification network.", + "bbox": [ + 75, + 444, + 468, + 594 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Experimental Results", + "text_level": 1, + "bbox": [ + 76, + 609, + 281, + 626 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Dataset", + "text_level": 1, + "bbox": [ + 76, + 635, + 171, + 648 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To evaluate the proposed approach, we adopt the dataset provided by Wang et al. [42] to conduct experiments following the previous works [18, 19]. It contains various fake images generated by ProGAN [21], StyleGAN [22], StyleGAN2 [23], BigGAN [3], CycleGAN [49], StarGAN [6], GauGAN [33] and Deepfake [37]. The real images are sampled from LSUN [43], ImageNet [38], CelebA [29], CelebA-HQ [21], COCO [28], and FaceForensics++ [37]. During the training stage, we train the classifier with images generated by ProGAN.", + "bbox": [ + 75, + 657, + 468, + 808 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In addition, to verify the usefulness of our method for face data, we sample 20,000 real images from Celeba-HQ, and generate 20,000 face images by ProGAN as the training set. And the images generated by StyleGAN, StyleGAN2 trained by Celeba-HQ [21] are employed as the test set. All images are resized to $256 \\times 256$ resolution.", + "bbox": [ + 75, + 810, + 468, + 898 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Implementation Details", + "text_level": 1, + "bbox": [ + 500, + 90, + 715, + 106 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We apply the Resnet50 [15] model pre-trained with ImageNet [38] as the classifier, and train it by Adam [24] with learning rate of $5 \\times 10^{-4}$ . The learning rate is decreased by twenty percent after every ten epochs. The batch size is set to 16, and the number of epochs is 100. In this paper, the results of the last epoch are used as the final performance. In addition, we randomly crop the input images during the training phase and directly feed images into the classifier in the test stage.", + "bbox": [ + 496, + 113, + 890, + 250 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To fully understand the gradients as the generalized artifacts, we further employ various popular models to implement the transformation model, i.e. VGG16 [39], InceptionV3 [41], Resnet50 [15], DeeplabV3 [5], CLIP [35], ViT [10], discriminator of ProGAN [21], StyleGAN [22], StyleGAN2 [23], and GAN Idinvert [48]. All of those models in this paper are released by the official code. Following the baselines [18, 19], we utilize the average precision score (A.P.) and accuracy (Acc.) as evaluation metrics to evaluate the proposed method.", + "bbox": [ + 496, + 250, + 890, + 402 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.3. Detection Performance Evaluations", + "text_level": 1, + "bbox": [ + 500, + 412, + 805, + 426 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To assess the effectiveness of our detector, we conducted evaluations in four different settings: cross-model images, cross-category images, cross-model & category images, and perturbed images. Specifically, the detector is trained on a known GAN model, e.g. ProGAN-Horse [21] dataset, while evaluated with unseen images: 1) cross-category images, produced by the same GAN model trained on different datasets, e.g. ProGAN-Airplane and ProGAN-Diningtable; 2) cross-model images, generated by different GAN models trained on the same dataset, e.g. StyleGAN-Horse [22] and StyleGAN2-Horse [23]; and 3) cross-model & category images, where both training data and GAN model are different from the training setting, e.g. BigGAN-ImageNet [3,38] and GauGAN-COCO [28, 33]. Meanwhile, to fully understand the proposed LGrad framework in terms of different transformation models, we further implement the transformation model with various popular CNN networks.", + "bbox": [ + 496, + 436, + 890, + 694 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/a48e4af6e4027d90bbf46903af0ca8d0cb8660ef544f119adaad04f80887996c.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Trans. ModelsProGAN-CelebaHQStyleGAN-CelebaHQStyleGAN2-CelebaHQ
Acc.A.P.Acc.A.P.Acc.A.P.
Input Image99.6100.076.898.964.296.2
ProGAN-RandomInit91.497.357.466.352.456.9
VGG1696.999.879.696.368.291.6
CLIP-RN5099.5100.099.4100.099.2100.0
ProGAN-bedroom98.8100.098.499.996.599.6
ProGAN-bridge96.499.584.895.081.993.6
", + "bbox": [ + 501, + 708, + 890, + 871 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Table 1. Cross-model Performance on face images data.", + "bbox": [ + 527, + 882, + 861, + 896 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "12108", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/06e769ead091d613469ae273a4dd10289e7333d2131b7278d99ebae38ee6c474.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Test-categoriesCLIPTrans.Bedroom Trans.Bridge Trans.
ACCAPACCAPACCAPAP
airplane92.997.099.4100.099.4100.0
bicycle80.594.694.598.594.898.5
bird85.694.499.2100.097.599.7
boat90.696.699.6100.098.599.9
bottle94.798.297.7100.099.2100.0
bus81.594.998.1100.096.799.8
car84.095.799.7100.099.1100.0
cat90.296.199.7100.098.799.9
chair94.598.596.299.999.1100.0
cow85.595.099.5100.097.799.8
diningtable92.497.798.599.896.999.5
dog89.896.199.6100.098.499.9
horse89.096.3100.0100.099.8100.0
motorbike79.294.897.099.796.999.3
person93.697.198.9100.099.1100.0
pottedplant77.995.697.599.795.799.1
sheep83.294.299.2100.095.899.3
sofa94.198.699.1100.099.2100.0
train83.694.494.499.796.199.6
tvmonitor93.098.796.5100.099.2100.0
Mean87.896.298.299.997.999.7
", + "bbox": [ + 76, + 90, + 470, + 459 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 2. Cross-category Performance on the ProGAN models trained on different LSUN [43] object datasets.", + "bbox": [ + 75, + 468, + 468, + 498 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3.1 Cross-model Performance.", + "text_level": 1, + "bbox": [ + 76, + 530, + 316, + 542 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We employ face images to show the cross-model performance of our proposed LGrad framework. The training set contains 25,000 fake images generated by ProGAN [21] and 25,000 real images randomly sampled from Celeba-HQ [21]. The test set consists of 10,000 images, half of which are real images randomly sampled from Celeba-HQ [21], while the other half are fake images generated by StyleGAN [22] and StyleGAN2 [23]. We adopt popular CNN models such as VGG16 [39], CLIP-RN50 [35], and discriminators of ProGAN [21] as the transformation models, and also randomly initialize a discriminator of ProGAN to perform the transformation to demonstrate the effectiveness of pre-trained models. In addition, we train the detector directly on the images to compare it with the gradients-based detector.", + "bbox": [ + 75, + 551, + 468, + 777 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The experimental results are shown in Table 1. We can observe that image-based detectors suffer from sharp performance drops on StyleGAN [22] and StyleGAN2 [23]. It only achieves the Acc. of $64.2\\%$ on StyleGAN2, which is much lower than the performance on ProGAN. In contrast, all gradients-based detectors transformed by pre-trained models outperform the one with random initialization of ProGAN. The results demonstrate that the transformation", + "bbox": [ + 75, + 779, + 470, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "model eliminates the contents of images while retaining the discriminative regions in the gradients. Thus, the gradients can effectively present the generalized artifacts produced by GAN models. The detector based on CLIP-RestNet50, trained on various (image, text) pairs, achieves the best accuracy of $99.4\\%$ and $99.2\\%$ on StyleGAN and StyleGAN2, respectively. The discriminators of ProGAN trained on bedroom or bridge datasets yield comparable results with the CLIP-RestNet50 model. Our proposed framework shows remarkable performance on face data.", + "bbox": [ + 496, + 90, + 890, + 242 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3.2 Cross-category Performance.", + "text_level": 1, + "bbox": [ + 498, + 261, + 756, + 275 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We leverage the training set from Wang et al. [42] to validate the cross-category performance, which contains 20 object categories generated by ProGAN models. Each category comprises 18,000 fake images and an equal number of real images. We trained our detector using the horse dataset and evaluated its performance on the remaining 19 datasets. We implement the transformation model with two discriminators of ProGAN and Contrastive learning model, including ProGAN-bedroom, ProGAN-bridge, and CLIP-ResNet50. Table 2 reports the results of our detectors. The performance of the horse is calculated on the training data. When adopting the CLIP-RestNet50 model as the transformation model, the detector achieves the best result on cross-model performance compared to the other models. We also evaluate the performance of CLIP-RestNet50 on the cross-category case. In Table 2, the CLIP-RestNet50 achieves mean Acc. of $87.8\\%$ on 20 categories, while the ProGAN-bedroom and ProGAN-bridge obtain higher accuracies of $98.2\\%$ and $97.9\\%$ , respectively. It can be observed that discriminators are more suitable as the transformation model to detect cross-category images. In summary, we can confirm that our detectors show excellent performance in detecting unseen categories.", + "bbox": [ + 496, + 284, + 890, + 631 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3.3 Cross-model & category Performance.", + "text_level": 1, + "bbox": [ + 498, + 650, + 820, + 665 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We further expand the testing scope to evaluate the robustness of our proposed method. The gradients-based detector is performed on eight different models following baselines [18, 19] including ProGAN, StyleGAN, StyleGAN2, BigGAN, CycleGAN, StarGAN, GauGAN, and Deepfake. We adopt the discriminators of ProGAN and StyleGAN, trained using the LSUN bedroom dataset, as the transformation model. For fair comparisons, we use the same experimental setting employed in baselines [19] to evaluate the generality of the detector. Specifically, three training configurations are employed to train the detector, including 1-class (horse), 2-class (chair, horse), and 4-class (car, cat, chair, horse) settings.", + "bbox": [ + 496, + 674, + 890, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Table 3 presents the results of our proposed LGrad framework in comparison to various previous methods. We", + "bbox": [ + 498, + 869, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "12109", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/ce47825aac95d45841e49217a3ee4890563162132c99f94532f0e2eabcb2d140.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodsSettingsTest Models
Input #classProGANStyleGANStyleGAN2BigGAN CycleGANStarGANGauGAN DeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc. A.P.
Wang [42]Image150.463.850.479.368.294.750.261.350.052.950.048.250.367.650.151.5
Frank [12]Freq178.977.969.464.867.464.062.358.667.465.460.559.567.569.152.447.3
Durall [11]Freq185.179.559.255.270.463.857.053.966.761.499.899.658.754.853.051.9
BiHPF [18]Freq182.581.468.062.868.863.667.062.575.574.290.190.173.692.151.649.9
FrePGAN [19]Image195.599.480.690.677.493.063.560.559.459.999.6100.053.049.170.481.5
LGrad (ProGAN-bedroom)Grad198.499.982.695.683.398.476.281.882.390.699.7100.071.775.052.857.8
LGrad (StyleGAN-bedroom)Grad199.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.9
Wang [42]Image264.692.752.882.875.796.651.670.558.681.551.274.353.686.650.651.5
Frank [12]Freq285.781.373.168.575.070.976.970.886.580.885.077.067.365.350.155.3
Durall [11]Freq279.073.963.658.867.362.169.562.965.460.899.499.467.063.050.550.2
BiHPF [18]Freq287.487.471.674.177.081.182.680.686.086.693.880.875.388.253.754.0
FrePGANImage299.099.980.892.072.294.066.061.869.170.398.5100.053.151.062.280.6
LGrad (ProGAN-bedroom)Grad299.5100.085.899.383.599.478.987.778.889.099.6100.070.577.651.952.7
LGrad (StyleGAN-bedroom)Grad299.8100.094.899.792.499.682.592.485.994.799.799.973.783.260.667.8
Wang [42]Image491.499.463.891.476.497.552.973.372.788.663.890.863.992.251.762.3
Frank [12]Freq490.385.274.572.073.171.488.786.075.571.299.599.569.277.460.749.1
Durall [11]Freq481.174.454.452.666.862.060.156.369.064.098.198.161.957.450.250.0
BiHPF [18]Freq490.786.276.975.176.274.784.981.781.978.994.494.469.578.154.454.6
FrePGAN [19]Image499.099.980.789.684.198.669.271.171.174.499.9100.060.371.770.991.9
LGrad (ProGAN-bedroom)Grad499.7100.087.899.191.799.780.989.378.289.099.8100.073.578.653.155.0
LGrad (StyleGAN-bedroom)Grad499.9100.094.899.996.099.982.990.785.394.099.6100.072.479.358.067.9
", + "bbox": [ + 76, + 90, + 893, + 522 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/0875370b236eec01f4a8808c6e13d60a628524f754dffd807a4e596c3c8cc6f2.jpg", + "table_caption": [ + "Table 3. Classification accuracy with cross-model & category." + ], + "table_footnote": [], + "table_body": "
PerturbedProGANStyleGANStyleGAN2BigGANCycleGANStarGANGauGANDeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.
No99.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.986.392.7
blur90.196.590.695.787.593.471.176.370.673.693.698.063.769.561.262.678.583.2
cropping99.299.995.999.694.099.680.188.078.588.194.499.470.678.167.582.385.091.9
jpeg76.290.074.490.272.689.166.074.672.783.276.089.560.167.658.065.269.581.2
noise77.187.773.384.874.384.968.277.466.477.276.088.860.469.157.565.169.179.4
combined86.394.683.593.082.492.371.278.973.080.484.794.364.771.561.267.475.984.1
", + "bbox": [ + 78, + 561, + 890, + 695 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 4. Performance of common image perturbations.", + "bbox": [ + 320, + 705, + 645, + 718 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "can find that the proposed LGrad framework successfully surpasses the counterparts in the metric of average precision score and accuracy. Our detector shows significantly better generalization to other methods, except on GauGAN and Deepfake. Notably, our LGrad framework achieves higher accuracy values increase to $99.4\\%$ , $96.0\\%$ , $93.8\\%$ , $79.5\\%$ , $84.7\\%$ on the ProGAN, StyleGAN, StyleGAN2, BigGAN, CycleGAN models with the 1-class setting, respectively. We obtain comparable results on the GauGAN model and deepfake. Furthermore, our LGrad", + "bbox": [ + 75, + 744, + 470, + 896 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "with StyleGAN-bedroom model successfully achieves a new state-of-the-art accuracy, surpassing FrePGAN [19] by $11.4\\%$ and $13.4\\%$ in terms of mean accuracy and mean average precision score, respectively. We also compare to FingerprintNet [20] evaluated on six GAN models. When evaluated on StyleGAN, StyleGAN2, BigGAN, CycleGAN, StarGAN, and GauGAN, FingerprintNet and the proposed method achieve the mean Accuracy of $82.6\\%$ and $87.4\\%$ , respectively. We achieve a gain of $4.8\\%$ in terms of mean accuracy.", + "bbox": [ + 496, + 744, + 890, + 897 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "12110", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/988d3a0a18b536efd69fc6c705e6b0d1a5c86288a7865d3c0e60c54ee0ad2c07.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Trans. ModelProGANStyleGANStyleGAN2BigGANCycleGANStarGANGauGANDeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.
VGG16 [39]96.099.565.588.874.793.873.078.177.886.199.8100.060.763.160.467.876.084.7
InceptionV3 [41]64.974.458.866.965.473.850.952.159.168.452.658.554.056.549.850.156.962.6
Resnet50 [15]86.495.081.092.283.793.557.256.268.375.896.499.551.252.763.470.473.479.4
CLIP-Resnet50 [35]87.695.880.290.078.991.060.161.184.287.988.595.172.671.664.964.477.182.1
ViT [10]51.271.151.765.752.467.950.653.354.175.251.664.050.661.150.053.751.564.0
DeeplabV3 [5]81.691.668.780.470.684.554.555.766.271.087.994.751.753.159.358.967.673.7
Idinvert [48]97.499.871.695.371.295.486.694.878.785.797.499.772.082.160.172.179.490.6
ProGAN-bedroom [21]98.499.982.695.683.398.476.281.882.390.699.7100.071.775.052.857.880.987.4
ProGAN-bridge [21]97.899.786.497.585.797.372.578.776.887.594.199.962.575.853.261.378.687.2
StyleGAN-bedroom [22]99.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.986.392.7
StyleGAN-cats [22]97.499.783.497.377.496.469.874.679.390.297.899.868.077.465.972.979.988.5
StyleGAN2-church [23]99.1100.088.297.791.999.670.171.780.689.195.699.860.868.972.776.582.487.9
", + "bbox": [ + 76, + 89, + 893, + 315 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/e1f22c68d124f68b94b26a550ef39c21ebf07de0707355d030f7393508d47c3b.jpg", + "table_caption": [ + "Table 5. Performance of different transformation models." + ], + "table_footnote": [], + "table_body": "
MethodsNum. of Train. dataProGANStyleGANStyleGAN2BigGANCycleGANStarGANGauGANDeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.
FrePGAN [19]36k95.599.480.690.677.493.063.560.559.459.999.6100.053.049.170.481.574.979.3
LGrad4k95.999.588.998.091.799.059.155.258.758.487.899.859.266.265.983.575.982.4
LGrad9k98.399.992.499.492.299.471.774.573.076.898.1100.064.074.268.079.382.287.9
LGrad18k98.8100.095.199.891.499.776.887.179.890.799.2100.068.980.463.973.084.291.3
LGrad36k99.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.986.392.7
LGrad72k99.8100.094.899.792.499.682.592.485.994.799.799.973.783.260.667.886.292.2
", + "bbox": [ + 76, + 353, + 895, + 486 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 6. Results with variance in number of training data.", + "bbox": [ + 313, + 496, + 655, + 510 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In comparison to the performance of FrePGAN [19], our detector obtains the most significant improvement on StyleGAN, increasing accuracy by $15.4\\%$ , $14.0\\%$ , $14.1\\%$ with 1-class, 2-class, and 4-class settings, respectively. It is worth noting that the LGrad framework fails to perform well on the deepfake model, which is not a GAN model and is trained with MSE loss and SSIM loss, resulting in an accuracy value of only $66.7\\%$ . The results indicate that the proposed LGrad approach is less reliant on the amount of data, as it achieves similar results with 1-class, 2-class, and 4-class settings. As the number of training data increases, the deepfake performance decreases, resulting in a drop in the mean accuracy of all generative models.", + "bbox": [ + 75, + 537, + 468, + 733 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In addition, we leverage the high-frequency component of images as the artifacts representation to train the classifier with the same settings. High-frequency-based method obtains the mean accuracy of $75.1\\%$ , $75.0\\%$ , and $73.0\\%$ with 1-class, 2-class, and 4-class settings, respectively. Our LGrad increases accuracy by $11.2\\%$ , $11.2\\%$ , and $13.1\\%$ on the same setting, respectively. Gradients are different from high-frequency information. This suggests that gradients can be used as an effective representation of artifacts that are capable of detecting GAN-generated images across diverse categories and various GAN models.", + "bbox": [ + 75, + 734, + 470, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3.4 Robustness against Image Perturbations.", + "text_level": 1, + "bbox": [ + 498, + 537, + 838, + 551 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To evaluate the robustness of the proposed framework to image perturbations, we apply common image perturbations on the test images with a probability of $50\\%$ following [13]. These perturbations include blurring, cropping, compression, adding random noise, and a combination of all of them. In this subsection, the discriminator of StyleGAN-bedroom is used as the transformation model. The results are presented in Table 4. It can be observed that our detectors are robust with respect to cropping and blur, but obtain degraded performance onJPEG and noise. This could be due to the fact that the influence ofJPEG and noise for the transformation model results in the transformed gradients that do not work well. We could further improve the robustness of the transformation model to tackle this problem.", + "bbox": [ + 496, + 561, + 890, + 773 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Visualization", + "text_level": 1, + "bbox": [ + 500, + 786, + 635, + 800 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Figure 3 shows fake images generated by ProGAN and StyleGAN, real images sampled from CelebA-HQ, the gradients transformed from images, and Class Activate Map (CAM) [47] extracted from the detector. We adopt the discriminator of ProGAN-bedroom as the transformation model. It can be observed from gradients that most of the", + "bbox": [ + 496, + 809, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "12111", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/84aacbd73d666f4a9f4a115a92b9e99cbb93852faa2eff3386a3d3842ef30f4c.jpg", + "image_caption": [ + "Figure 3. The visualization of gradients and Class Activate Map (CAM) [47] extracted from detector on face images." + ], + "image_footnote": [], + "bbox": [ + 130, + 88, + 367, + 282 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/9f6efd423babf8b5e446ce8f253cb112f4e37fa4ca3f8c9705b71d1ce2b42106.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 372, + 88, + 607, + 282 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/32fbe059b34da30d882150ec8a0b767c6db1e36934bd78594a7dd299f15251d0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 614, + 88, + 841, + 282 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "content of images is filtered out, and the essential pixels for the transformation model are highlighted in the process of transforming. Although some texture information is preserved, its value is relatively low. Furthermore, the class activate map of the detector trained on gradients highlights the discriminative region for detection, and it can be seen that the maps of the real image highlight the face region, while most maps of the generated image are located in the background area. It reflects that the gradients, as the generalized representation of artifacts, contain high-level rather than low-level features such as texture.", + "bbox": [ + 75, + 335, + 472, + 501 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Ablation Study", + "text_level": 1, + "bbox": [ + 76, + 522, + 228, + 540 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effect of Transformation Model. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. The images are converted into the gradient, which serves as the generalized representation of artifacts. The performance of the detector highly relies on the quality of the transformation model. Now we discuss the influence of the transformation model on the detection performance, by employing ten pre-trained models. Table 5 shows the across-model & data results of different transformation models. We can observe that gradients generated by the discriminator of GAN perform better on unseen data compared to the classification model and contrastive learning model. In addition, discriminators with different structures or trained with different data exhibit varying performances. Thus, our framework turns the data-dependent problem into a transformation-model-dependent problem. In our experiment, the discriminator of StyleGAN-bedroom obtains the best performance.", + "bbox": [ + 75, + 550, + 468, + 823 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effect of the number of training data In order to test the influence of the number of training data for the detector, we sample 4,000, 18,000, and 36,000 images which contain equal numbers of real and fake images from the horse dataset as the training set, respectively. We also use $72\\mathrm{k}$", + "bbox": [ + 75, + 824, + 470, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "images of all the chairs and horses set to train the detector. In this subsection, the discriminator of StyleGAN-bedroom is also used as the transformation model. Table 6 compares the proposed framework with the different number of training data. Our detector achieves similar across-model & data performance with 18,000, 36,000, and 72,000 training images. In addition, the proposed LGrad with 4,000 training data still outperforms FrePGAN with 36,000 images, indicating the superiority of our method.", + "bbox": [ + 496, + 335, + 893, + 472 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 492, + 617, + 508 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this work, we propose a novel detection framework, called Learning on Gradients(LGrad), designed to identify GAN-generated images. In our method, the gradients are employed as a generalized representation to describe the artifacts produced by generation models. Specifically, we utilize a pre-trained CNN model as the transformation model to convert images into gradients effectively transforming the data-dependent problem into a transformation-model-dependent problem. Numerous experiments in our paper prove the great potential of the discriminator of GAN in detecting GAN-generated images. Moreover, we demonstrate the effectiveness and robustness of using gradients as generalized artifact representations in detecting GAN-generated images. Nonetheless, we acknowledge that the LGrad framework may not perform well in detecting non-GAN generative models, such as deepfake, and we aim to address this limitation in future research.", + "bbox": [ + 496, + 520, + 893, + 777 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments This work is supported in part by the National Key R&D Program of China (No.2021ZD0112100), National NSF of China (No.61972022, No.U1936212, No.62120106009, No.62072394), Natural Science Foundation of Hebei province (F2021203019).", + "bbox": [ + 496, + 810, + 893, + 902 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "12112", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Jawadul H Bappy, Cody Simons, Lakshmanan Nataraj, BS Manjunath, and Amit K Roy-Chowdhury. Hybrid lstm and encoder-decoder architecture for detection of image forgeries. IEEE Transactions on Image Processing, 28(7):3286-3300, 2019. 3", + "[2] Belhassen Bayar and Matthew C Stamm. A deep learning approach to universal image manipulation detection using a new convolutional layer. In Proceedings of the 4th ACM workshop on information hiding and multimedia security, pages 5-10, 2016. 2", + "[3] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. In International Conference on Learning Representations, 2018. 1, 4", + "[4] Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola. What makes fake images detectable? understanding properties that generalize. In European conference on computer vision, pages 103-120. Springer, 2020. 2", + "[5] Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017.4.7", + "[6] Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8789-8797, 2018. 4", + "[7] François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017. 2", + "[8] Samantha Cole. We are truly fucked: Everyone is making ai-generated fake porn now. blog post, 2018. Accessed April 17, 2020. 1", + "[9] Davide Cozzolino, Justus Thies, Andreas Rössler, Christian Riess, Matthias Nießner, and Luisa Verdoliva. Forensictransfer: Weakly-supervised domain adaptation for forgery detection. arXiv preprint arXiv:1812.02510, 2018. 2", + "[10] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale, 2020. 4, 7", + "[11] Ricard Durall, Margret Keuper, and Janis Keuper. Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7890-7899, 2020. 2, 3, 6", + "[12] Joel Frank, Thorsten Eisenhofer, Lea Schonherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. In International conference on machine learning, pages 3247-3258. PMLR, 2020. 2, 3, 6" + ], + "bbox": [ + 78, + 114, + 470, + 898 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Joel Frank, Thorsten Eisenhofer, Lea Schonherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. In International conference on machine learning, pages 3247-3258. PMLR, 2020. 2, 7", + "[14] Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C Courville, and Yoshua Bengio. Generative adversarial nets. In NIPS, 2014. 1", + "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 4, 7", + "[16] Yang He, Ning Yu, Margret Keuper, and Mario Fritz. Beyond the spectrum: Detecting deepfakes via re-synthesis. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 2534–2541. International Joint Conferences on Artificial Intelligence Organization, 2021. 2, 3", + "[17] Nils Hulzebosch, Sarah Ibrahimi, and Marcel Worring. Detecting cnn-generated facial images in real-world scenarios. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 642-643, 2020. 1, 2", + "[18] Yonghyun Jeong, Doyeon Kim, Seungjai Min, Seongho Joe, Youngjune Gwon, and Jongwon Choi. Bihpf: Bilateral high-pass filters for robust deepfake detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 48-57, 2022. 2, 3, 4, 5, 6", + "[19] Yonghyun Jeong, Doyeon Kim, Youngmin Ro, and Jongwon Choi. Frepgan: Robust deepfake detection using frequency-level perturbations. arXiv preprint arXiv:2202.03347, 2022. 2, 3, 4, 5, 6, 7", + "[20] Yonghyun Jeong, Doyeon Kim, Youngmin Ro, Pyounggeon Kim, and Jongwon Choi. Fingerprint: Synthesized fingerprints for generated image detection. In European Conference on Computer Vision, pages 76-94. Springer, 2022. 6", + "[21] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. In International Conference on Learning Representations, 2018. 1, 2, 4, 5, 7", + "[22] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401-4410, 2019. 1, 2, 4, 5, 7", + "[23] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110-8119, 2020. 1, 2, 4, 5, 7", + "[24] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR (Poster), 2015. 4", + "[25] Diederik P Kingma and Max Welling. Auto-encoding variational {Bayes}. In Int. Conf. on Learning Representations. 1" + ], + "bbox": [ + 501, + 92, + 893, + 898 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "12113", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[26] Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo. Face x-ray for more general face forgery detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5001-5010, 2020. 2", + "[27] Yuezun Li, Ming-Ching Chang, and Siwei Lyu. In ictu oculi: Exposing ai created fake videos by detecting eye blinking. In 2018 IEEE International workshop on information forensics and security (WIFS), pages 1-7. IEEE, 2018. 2", + "[28] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 4", + "[29] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, pages 3730-3738, 2015. 4", + "[30] Zhengzhe Liu, Xiaojuan Qi, and Philip HS Torr. Global texture enhancement for fake face detection in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8060-8069, 2020. 2", + "[31] Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi. Do gans leave artificial fingerprints? In 2019 IEEE conference on multimedia information processing and retrieval (MIPR), pages 506-511. IEEE, 2019. 2, 3", + "[32] Iacopo Masi, Aditya Killekar, Royston Marian Mascarenhas, Shenoy Pratik Gurudatt, and Wael AbdAlmageed. Two-branch recurrent network for isolating deepfakes in videos. In European conference on computer vision, pages 667–684. Springer, 2020. 3", + "[33] Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu. Semantic image synthesis with spatially-adaptive normalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2337-2346, 2019. 4", + "[34] Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European conference on computer vision, pages 86-103. Springer, 2020. 3", + "[35] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 4, 5, 7", + "[36] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015. 1", + "[37] Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: Learning to detect manipulated facial images. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1-11, 2019. 2, 4", + "[38] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy," + ], + "bbox": [ + 78, + 90, + 470, + 902 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252, 2015. 4", + "[39] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations, 2015. 4, 5, 7", + "[40] Ke Sun, Hong Liu, Qixiang Ye, Yue Gao, Jianzhuang Liu, Ling Shao, and Rongrong Ji. Domain general face forgery detection by learning to weight. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 2638-2646, 2021. 2", + "[41] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016. 4, 7", + "[42] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros. Cnn-generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8695-8704, 2020. 2, 4, 5, 6", + "[43] Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015. 4, 5", + "[44] Ning Yu, Larry S Davis, and Mario Fritz. Attributing fake images to gans: Learning and analyzing gan fingerprints. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7556-7566, 2019. 2", + "[45] Yang Yu, Rongrong Ni, and Yao Zhao. Mining generalized features for detecting ai-manipulated fake faces. arXiv preprint arXiv:2010.14129, 2020. 2", + "[46] Tianchen Zhao, Xiang Xu, Mingze Xu, Hui Ding, Yuanjun Xiong, and Wei Xia. Learning self-consistency for deepfake detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 15023-15033, 2021. 2", + "[47] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2921-2929, 2016. 7, 8", + "[48] Jiapeng Zhu, Yujun Shen, Deli Zhao, and Bolei Zhou. Indomain gan inversion for real image editing. In European conference on computer vision, pages 592-608. Springer, 2020. 4, 7", + "[49] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223-2232, 2017. 4" + ], + "bbox": [ + 503, + 92, + 893, + 810 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "12114", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning on Gradients_ Generalized Artifacts Representation for GAN-Generated Images Detection/2c415e18-250d-42c6-9ecf-17946adf591a_model.json b/2023/Learning on Gradients_ Generalized Artifacts Representation for GAN-Generated Images Detection/2c415e18-250d-42c6-9ecf-17946adf591a_model.json new file mode 100644 index 0000000000000000000000000000000000000000..914a2f4fe31779bb8d86732d8e4ad6f20d740576 --- /dev/null +++ b/2023/Learning on Gradients_ Generalized Artifacts Representation for GAN-Generated Images Detection/2c415e18-250d-42c6-9ecf-17946adf591a_model.json @@ -0,0 +1,1826 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.157, + 0.131, + 0.817, + 0.177 + ], + "angle": 0, + "content": "Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection" + }, + { + "type": "text", + "bbox": [ + 0.152, + 0.203, + 0.816, + 0.222 + ], + "angle": 0, + "content": "Chuangchuang Tan\\(^{1,2}\\), Yao Zhao\\(^{1,2*}\\), Shikui Wei\\(^{1,2}\\), Guanghua Gu\\(^{3,4}\\), Yunchao Wei\\(^{1,2}\\)" + }, + { + "type": "text", + "bbox": [ + 0.301, + 0.224, + 0.669, + 0.239 + ], + "angle": 0, + "content": "\\(^{1}\\)Institute of Information Science, Beijing Jiaotong University" + }, + { + "type": "text", + "bbox": [ + 0.232, + 0.241, + 0.737, + 0.257 + ], + "angle": 0, + "content": "\\(^{2}\\)Beijing Key Laboratory of Advanced Information Science and Network Technology" + }, + { + "type": "text", + "bbox": [ + 0.277, + 0.259, + 0.692, + 0.275 + ], + "angle": 0, + "content": "\\(^{3}\\)School of Information Science and Engineering, Yanshan University" + }, + { + "type": "text", + "bbox": [ + 0.262, + 0.277, + 0.709, + 0.292 + ], + "angle": 0, + "content": "4Hebei Key Laboratory of Information Transmission and Signal Processing" + }, + { + "type": "text", + "bbox": [ + 0.12, + 0.295, + 0.844, + 0.31 + ], + "angle": 0, + "content": "{21112002, yzhao, shkwei}@bjtu.edu.cn, guguanghua@ysu.edu.cn, wychaol987@gmail.com" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.345, + 0.314, + 0.361 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.378, + 0.474, + 0.754 + ], + "angle": 0, + "content": "Recently, there has been a significant advancement in image generation technology, known as GAN. It can easily generate realistic fake images, leading to an increased risk of abuse. However, most image detectors suffer from sharp performance drops in unseen domains. The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models. In this work, we introduce a novel detection framework, named Learning on Gradients (LGrad), designed for identifying GAN-generated images, with the aim of constructing a generalized detector with cross-model and cross-data. Specifically, a pretrained CNN model is employed as a transformation model to convert images into gradients. Subsequently, we leverage these gradients to present the generalized artifacts, which are fed into the classifier to ascertain the authenticity of the images. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. To the best of our knowledge, this is the first study to utilize gradients as the representation of artifacts in GAN-generated images. Extensive experiments demonstrate the effectiveness and robustness of gradients as generalized artifact representations. Our detector achieves a new state-of-the-art performance with a remarkable gain of \\(11.4\\%\\). The code is released at https://github.com/chuangchuanqtan/LGrad." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.787, + 0.21, + 0.803 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.814, + 0.47, + 0.875 + ], + "angle": 0, + "content": "Over the past years, remarkable progress has been made in deep generative models, i.e. generative adversarial networks (GAN) [14], its variations [3, 21-23, 36], and VAE [25]. The generated media is highly realistic and indistin" + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.345, + 0.891, + 0.595 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.606, + 0.895, + 0.716 + ], + "angle": 0, + "content": "Figure 1. Visualization of gradients of real images and GAN-generated images extracted from a pre-trained model. To fully understand the gradients, heatmaps for R, G, and B channels also are shown, where red is high. In the gradients, the content of images is filtered out, and only the discriminative pixels are retained for the pre-trained model's target task. We utilize the gradients as the generalized artifacts representation to develop a novel detection framework." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.895, + 0.903 + ], + "angle": 0, + "content": "guishable from real to human eyes. Although it has the potential for many novel applications [8], it also brings new risks due to the abuse of fake information. The misuse of DeepFakes has been confirmed that some people swap the faces of women onto pornographic videos [8]. In addition, some individuals, even non-experts, can malevolently manipulate or create fake images or videos for political or economic purposes, leading to serious social problems [17]. Thus, it is extremely necessary to develop forgery detection techniques to help people determine the credibility of the" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.221, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.958 + ], + "angle": 0, + "content": "12105" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.078, + 0.092, + 0.18, + 0.106 + ], + "angle": 0, + "content": "media [17, 40]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.108, + 0.47, + 0.365 + ], + "angle": 0, + "content": "Various detectors [11-13, 16, 18, 19, 30, 42, 45] have been developed to detect GAN-generated images. Some studies [16, 30, 45] focus on human face images, while others [11, 18, 19, 42] handle various categories of images. They mainly depend on local regions artifacts [4, 44], blending boundary [26], global textures [30], and frequency-level artifacts [13, 18, 19, 45]. However, those methods heavily rely on the training settings, resulting in failure detection of images from unseen categories or GAN models. The test images in the actual scene are always from unknown sources [17], rendering it challenging to develop generalized detectors. There are some works [19, 42] exploiting pre-processing, data augmentation, and reducing the effects of frequency-level artifacts to develop a robust detector. Nevertheless, there still needs to be a more generalized representation of the clue produced by generation models, which is critical for robust fake image detection." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.367, + 0.471, + 0.699 + ], + "angle": 0, + "content": "To tackle this problem, we propose a novel and simple detection framework, referred to as Learning on Gradients (LGrad). A new generalized feature, Gradients, is developed to serve as a representation of the artifacts produced by GAN models. We believe that the gradients of a trained CNN model can highlight the important pixels in the target task, thereby serving as a valuable cue for detecting fake images. As shown in Figure 1, we adopt a pre-trained discriminator of ProGAN [21] to extract gradients of images produced by Celeba-HQ [21], ProGAN [21], StyleGAN [22], StyleGAN2 [23]. In these gradients, the content of images is filtered out, and only the discriminative pixels that are relevant to the pre-trained model's target task are retained. Therefore, the gradients are more dependent on the pre-trained model rather than on training sources, thereby enhancing the detector's performance with unseen data. In our framework, a pretrained model, called transformation model, is employed to convert images to gradients. These gradients serve as the generalized artifacts and are fed into the classifier to obtain a robust detector. Since the transformation model is indeterminate in our framework, targeted anti-detection cannot be effectively launched." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.701, + 0.47, + 0.808 + ], + "angle": 0, + "content": "To validate the performance of our LGrad, we only use images generated by ProGAN to train the detector and evaluate it with various sources, including cross-category, cross-model, and cross-model & category. Numerous experiments prove the effectiveness and robustness of gradients as generalized artifacts. Our detector achieves a new state-of-the-art performance in known and unseen settings." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.81, + 0.399, + 0.825 + ], + "angle": 0, + "content": "Our paper makes the following contributions:" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.84, + 0.47, + 0.9 + ], + "angle": 0, + "content": "- We develop a new detection framework, Learning on Gradient (LGrad), to detect GAN-generated images. Our detector achieves a new state-of-the-art performance." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.092, + 0.892, + 0.15 + ], + "angle": 0, + "content": "- We introduce a new generalized artifact representation, Gradients, for GAN-generated image detection. Furthermore, we are the first to use gradients as the representation of artifacts." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.164, + 0.892, + 0.223 + ], + "angle": 0, + "content": "- Our framework turns the data-driven problem into a transformation-model-driven problem. The robustness of the detector is improved with the introduction of the transformation model." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.236, + 0.892, + 0.267 + ], + "angle": 0, + "content": "- We prove the great potential of the discriminator of GAN in detecting GAN-generated images." + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.092, + 0.892, + 0.267 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.281, + 0.642, + 0.297 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.308, + 0.892, + 0.383 + ], + "angle": 0, + "content": "In this section, we discuss recent approaches for generated image detection. The previous methods attempt to exploit spatial information or frequency artifacts as the representation of clues produced by the generation model for fake image detection." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.394, + 0.755, + 0.41 + ], + "angle": 0, + "content": "2.1. Image-based Fake Detection" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.418, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Early studies employ spatial information from generated images to identify the fake images, such as color spaces [45] and global texture [30]. Rossler et al. [37] introduce a largescale manipulated face dataset FaceForensics++ and adopt Xception [7] as detector to identify the fake images with compression. Li et al. [27] detect fake face videos by analyzing eye blinking in the videos. ForensicTransfer [9] presents a new autoencoder-based architecture to learn a discriminative feature representation in the latent space for detection well on new domains. Yu et al. [44] and Marra et al. [31] believe that every GAN model has unique fingerprints after training, and extracting it from generated images to perform detection. Bayar et al. [2] design a new convolutional network architecture to suppress the image of the content and learn manipulation features for a generalized detector. Gram-Net [30] leverages the Gram matrix to extract global texture as the robust representation for fake face detection. Face X-ray [26] develops the blending boundary to present the artifacts for detecting manipulated face images. Chai et al. [4] design a patch-based classifier to limit the receptive field of the model for learning redundant artifacts in image patches. The patch-based predictions focus on local artifacts rather than global structure, improving the robustness of the detector. Yu et al. [45] use the channel difference image and spectrum image to mine the intrinsic clues of images from the view of the camera imaging process. Wang et al. [42] directly adopt a large number of real and fake images with data augmentation to train a binary classifier for improving unseen image performance. They use some post-processing operations to improve robustness during training, such as JPEG compression, blurring, and resizing. PCL [46] extracts the distinct source features of" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "12106" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.149, + 0.088, + 0.825, + 0.364 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.372, + 0.895, + 0.403 + ], + "angle": 0, + "content": "Figure 2. The overall pipeline of the proposed framework. The method uses the same transformation model and the same classifier in the testing and training phases." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.428, + 0.471, + 0.473 + ], + "angle": 0, + "content": "images by self-consistency learning for detecting deepfake. He et al. [16] design a re-synthesis procedure to extract visual cues for robust deepfake detection." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.486, + 0.365, + 0.501 + ], + "angle": 0, + "content": "2.2. Frequency-based Fake Detection" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.51, + 0.473, + 0.829 + ], + "angle": 0, + "content": "Since GAN architectures depend on up-scaling operations, the generated images contain unique frequency-level artifacts compared to neutral images [11]. Several works [1, 11, 12, 31, 32, 34] leverage the spectral distributions of images as the representation of artifacts for fake image detection. LOG [32] proposes a two-branch recurrent network to integrate information from the color domain and the frequency domain for detecting manipulated faces. \\( F^3 \\)-Net [34] designs a two-stream collaborative learning network to mine the forgery patterns in fake images, which exploits two frequency-aware clues, frequency-aware decomposed image components and local frequency statistics, to present subtle forgery patterns and high-level semantics, respectively. BiHPF [18] adopts two high-pass filters to amplify the magnitudes of the artifacts. FrePGAN [19] validates that the frequency-level artifacts of fake images are evident but uniquely vary by the type of GAN model or object category, which can cause overfitting issues for fake image detection. Thus, it generated the frequency-level perturbation maps to remove the effect of the frequency-level artifacts." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.845, + 0.383, + 0.862 + ], + "angle": 0, + "content": "3. Learning on Gradients framework" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.471, + 0.903 + ], + "angle": 0, + "content": "The key to the fake image detection task is developing a generalized representation of artifacts generated by" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.428, + 0.892, + 0.655 + ], + "angle": 0, + "content": "GAN models. It should be generalized and robust enough to span diverse categories and different GAN models. In order to achieve this, we design a novel detection framework to improve the cross-source performance in this work. The overview of our method is shown in Figure 2. Our framework employs gradients as the generalized representation to obtain the robust detector. We transform the images to the gradients by a pretrained CNN model named transformation model. In the process of transforming, most of the content of images is filtered out due to the pooling layer in the CNN model, and the essential pixels for the transformation model are highlighted. Figure 1 provides the visualization evidence for our explanation. Extensive experiments in Section 4 demonstrate the effectiveness of the gradients using as the artifact representation." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.665, + 0.759, + 0.68 + ], + "angle": 0, + "content": "3.1. Transformation to Gradients" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.892, + 0.854 + ], + "angle": 0, + "content": "The previous methods based on pixel and frequency-level artifacts highly rely on the training data, resulting in failure with unseen images. To solve this overfitting issue, we adopt any one pretrained CNN model as the transformation model to convert all images consisting of training images and test images to gradients. This approach effectively maps all data to the gradient domain, which is determined by the chosen transformation model. By relying on this more generalized representation of artifacts, we can achieve improved performance in detecting GAN-generated images across diverse categories and GAN models." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.855, + 0.892, + 0.903 + ], + "angle": 0, + "content": "We denote the images set as \\( I = \\{(I_i, y_i)\\}_{i=0}^{N-1} \\), where \\( y_i = \\{0,1\\} \\) is the label of the images \\( I_i \\in \\mathbb{R}^{w \\times h \\times c} \\), which is real (\\( y = 0 \\)) or fake (\\( y = 1 \\)). The \\( w, h, c \\) present width," + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12107" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.135 + ], + "angle": 0, + "content": "height, and the number of channels. The transformation model is defined as \\( M(\\cdot) \\). We first feed images \\( I_{i} \\) into \\( M \\) model." + }, + { + "type": "equation", + "bbox": [ + 0.233, + 0.138, + 0.469, + 0.154 + ], + "angle": 0, + "content": "\\[\nl = M \\left(I _ {i}\\right), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.161, + 0.469, + 0.207 + ], + "angle": 0, + "content": "where \\(l \\in \\mathbb{R}^{n \\times c}\\) is the output vectors of the transformation model \\(M\\). Then, we calculate the gradients of \\(sum(l)\\) with respect to the input \\(I_i\\)," + }, + { + "type": "equation", + "bbox": [ + 0.219, + 0.218, + 0.469, + 0.25 + ], + "angle": 0, + "content": "\\[\nG = \\frac {\\partial \\operatorname {s u m} (l)}{\\partial I _ {i}}. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.26, + 0.469, + 0.411 + ], + "angle": 0, + "content": "\\(G\\) is the generalized artifacts representation of our method. Note that the transformation model \\(M\\) is fixed, and its parameter would not update in our framework. The quality of gradients \\(G\\) highly depends on the transformation model \\(M\\), which is helpful to reduce the reliance on training data and enhance the generalization of the representations. In our experiments, we adopt various popular CNN models to implement the transformation model, including the classification model, segmentation model, discriminator of GAN, contrastive learning model, and GAN Inversion." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.422, + 0.288, + 0.437 + ], + "angle": 0, + "content": "3.2. Detecting Fake Images" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.445, + 0.469, + 0.595 + ], + "angle": 0, + "content": "During the training stage of our framework, we use normalized gradients ranging from 0 to 255 to train the binary classification network. We adopt the popular classifier to distinguish whether the input gradients correspond to GAN-generated images or not, and employ a cross-entropy loss function to optimize the network. During the inference time, the test images are first converted to gradients by the transformation model employed in the training stage. Then, we obtain the final result by feeding gradients into the trained classification network." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.61, + 0.282, + 0.627 + ], + "angle": 0, + "content": "4. Experimental Results" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.636, + 0.173, + 0.65 + ], + "angle": 0, + "content": "4.1. Dataset" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.659, + 0.469, + 0.809 + ], + "angle": 0, + "content": "To evaluate the proposed approach, we adopt the dataset provided by Wang et al. [42] to conduct experiments following the previous works [18, 19]. It contains various fake images generated by ProGAN [21], StyleGAN [22], StyleGAN2 [23], BigGAN [3], CycleGAN [49], StarGAN [6], GauGAN [33] and Deepfake [37]. The real images are sampled from LSUN [43], ImageNet [38], CelebA [29], CelebA-HQ [21], COCO [28], and FaceForensics++ [37]. During the training stage, we train the classifier with images generated by ProGAN." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.811, + 0.469, + 0.9 + ], + "angle": 0, + "content": "In addition, to verify the usefulness of our method for face data, we sample 20,000 real images from Celeba-HQ, and generate 20,000 face images by ProGAN as the training set. And the images generated by StyleGAN, StyleGAN2 trained by Celeba-HQ [21] are employed as the test set. All images are resized to \\(256 \\times 256\\) resolution." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.091, + 0.717, + 0.107 + ], + "angle": 0, + "content": "4.2. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.114, + 0.892, + 0.25 + ], + "angle": 0, + "content": "We apply the Resnet50 [15] model pre-trained with ImageNet [38] as the classifier, and train it by Adam [24] with learning rate of \\( 5 \\times 10^{-4} \\). The learning rate is decreased by twenty percent after every ten epochs. The batch size is set to 16, and the number of epochs is 100. In this paper, the results of the last epoch are used as the final performance. In addition, we randomly crop the input images during the training phase and directly feed images into the classifier in the test stage." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.251, + 0.892, + 0.403 + ], + "angle": 0, + "content": "To fully understand the gradients as the generalized artifacts, we further employ various popular models to implement the transformation model, i.e. VGG16 [39], InceptionV3 [41], Resnet50 [15], DeeplabV3 [5], CLIP [35], ViT [10], discriminator of ProGAN [21], StyleGAN [22], StyleGAN2 [23], and GAN Idinvert [48]. All of those models in this paper are released by the official code. Following the baselines [18, 19], we utilize the average precision score (A.P.) and accuracy (Acc.) as evaluation metrics to evaluate the proposed method." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.414, + 0.807, + 0.428 + ], + "angle": 0, + "content": "4.3. Detection Performance Evaluations" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.437, + 0.892, + 0.695 + ], + "angle": 0, + "content": "To assess the effectiveness of our detector, we conducted evaluations in four different settings: cross-model images, cross-category images, cross-model & category images, and perturbed images. Specifically, the detector is trained on a known GAN model, e.g. ProGAN-Horse [21] dataset, while evaluated with unseen images: 1) cross-category images, produced by the same GAN model trained on different datasets, e.g. ProGAN-Airplane and ProGAN-Diningtable; 2) cross-model images, generated by different GAN models trained on the same dataset, e.g. StyleGAN-Horse [22] and StyleGAN2-Horse [23]; and 3) cross-model & category images, where both training data and GAN model are different from the training setting, e.g. BigGAN-ImageNet [3,38] and GauGAN-COCO [28, 33]. Meanwhile, to fully understand the proposed LGrad framework in terms of different transformation models, we further implement the transformation model with various popular CNN networks." + }, + { + "type": "table", + "bbox": [ + 0.502, + 0.709, + 0.892, + 0.872 + ], + "angle": 0, + "content": "
Trans. ModelsProGAN-CelebaHQStyleGAN-CelebaHQStyleGAN2-CelebaHQ
Acc.A.P.Acc.A.P.Acc.A.P.
Input Image99.6100.076.898.964.296.2
ProGAN-RandomInit91.497.357.466.352.456.9
VGG1696.999.879.696.368.291.6
CLIP-RN5099.5100.099.4100.099.2100.0
ProGAN-bedroom98.8100.098.499.996.599.6
ProGAN-bridge96.499.584.895.081.993.6
" + }, + { + "type": "table_caption", + "bbox": [ + 0.529, + 0.883, + 0.862, + 0.897 + ], + "angle": 0, + "content": "Table 1. Cross-model Performance on face images data." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12108" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.078, + 0.091, + 0.472, + 0.46 + ], + "angle": 0, + "content": "
Test-categoriesCLIPTrans.Bedroom Trans.Bridge Trans.
ACCAPACCAPACCAPAP
airplane92.997.099.4100.099.4100.0
bicycle80.594.694.598.594.898.5
bird85.694.499.2100.097.599.7
boat90.696.699.6100.098.599.9
bottle94.798.297.7100.099.2100.0
bus81.594.998.1100.096.799.8
car84.095.799.7100.099.1100.0
cat90.296.199.7100.098.799.9
chair94.598.596.299.999.1100.0
cow85.595.099.5100.097.799.8
diningtable92.497.798.599.896.999.5
dog89.896.199.6100.098.499.9
horse89.096.3100.0100.099.8100.0
motorbike79.294.897.099.796.999.3
person93.697.198.9100.099.1100.0
pottedplant77.995.697.599.795.799.1
sheep83.294.299.2100.095.899.3
sofa94.198.699.1100.099.2100.0
train83.694.494.499.796.199.6
tvmonitor93.098.796.5100.099.2100.0
Mean87.896.298.299.997.999.7
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.469, + 0.47, + 0.499 + ], + "angle": 0, + "content": "Table 2. Cross-category Performance on the ProGAN models trained on different LSUN [43] object datasets." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.531, + 0.317, + 0.544 + ], + "angle": 0, + "content": "4.3.1 Cross-model Performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.553, + 0.47, + 0.779 + ], + "angle": 0, + "content": "We employ face images to show the cross-model performance of our proposed LGrad framework. The training set contains 25,000 fake images generated by ProGAN [21] and 25,000 real images randomly sampled from Celeba-HQ [21]. The test set consists of 10,000 images, half of which are real images randomly sampled from Celeba-HQ [21], while the other half are fake images generated by StyleGAN [22] and StyleGAN2 [23]. We adopt popular CNN models such as VGG16 [39], CLIP-RN50 [35], and discriminators of ProGAN [21] as the transformation models, and also randomly initialize a discriminator of ProGAN to perform the transformation to demonstrate the effectiveness of pre-trained models. In addition, we train the detector directly on the images to compare it with the gradients-based detector." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.471, + 0.901 + ], + "angle": 0, + "content": "The experimental results are shown in Table 1. We can observe that image-based detectors suffer from sharp performance drops on StyleGAN [22] and StyleGAN2 [23]. It only achieves the Acc. of \\(64.2\\%\\) on StyleGAN2, which is much lower than the performance on ProGAN. In contrast, all gradients-based detectors transformed by pre-trained models outperform the one with random initialization of ProGAN. The results demonstrate that the transformation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.243 + ], + "angle": 0, + "content": "model eliminates the contents of images while retaining the discriminative regions in the gradients. Thus, the gradients can effectively present the generalized artifacts produced by GAN models. The detector based on CLIP-RestNet50, trained on various (image, text) pairs, achieves the best accuracy of \\(99.4\\%\\) and \\(99.2\\%\\) on StyleGAN and StyleGAN2, respectively. The discriminators of ProGAN trained on bedroom or bridge datasets yield comparable results with the CLIP-RestNet50 model. Our proposed framework shows remarkable performance on face data." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.262, + 0.757, + 0.276 + ], + "angle": 0, + "content": "4.3.2 Cross-category Performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.285, + 0.892, + 0.632 + ], + "angle": 0, + "content": "We leverage the training set from Wang et al. [42] to validate the cross-category performance, which contains 20 object categories generated by ProGAN models. Each category comprises 18,000 fake images and an equal number of real images. We trained our detector using the horse dataset and evaluated its performance on the remaining 19 datasets. We implement the transformation model with two discriminators of ProGAN and Contrastive learning model, including ProGAN-bedroom, ProGAN-bridge, and CLIP-ResNet50. Table 2 reports the results of our detectors. The performance of the horse is calculated on the training data. When adopting the CLIP-RestNet50 model as the transformation model, the detector achieves the best result on cross-model performance compared to the other models. We also evaluate the performance of CLIP-RestNet50 on the cross-category case. In Table 2, the CLIP-RestNet50 achieves mean Acc. of \\(87.8\\%\\) on 20 categories, while the ProGAN-bedroom and ProGAN-bridge obtain higher accuracies of \\(98.2\\%\\) and \\(97.9\\%\\), respectively. It can be observed that discriminators are more suitable as the transformation model to detect cross-category images. In summary, we can confirm that our detectors show excellent performance in detecting unseen categories." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.651, + 0.821, + 0.666 + ], + "angle": 0, + "content": "4.3.3 Cross-model & category Performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.675, + 0.892, + 0.87 + ], + "angle": 0, + "content": "We further expand the testing scope to evaluate the robustness of our proposed method. The gradients-based detector is performed on eight different models following baselines [18, 19] including ProGAN, StyleGAN, StyleGAN2, BigGAN, CycleGAN, StarGAN, GauGAN, and Deepfake. We adopt the discriminators of ProGAN and StyleGAN, trained using the LSUN bedroom dataset, as the transformation model. For fair comparisons, we use the same experimental setting employed in baselines [19] to evaluate the generality of the detector. Specifically, three training configurations are employed to train the detector, including 1-class (horse), 2-class (chair, horse), and 4-class (car, cat, chair, horse) settings." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Table 3 presents the results of our proposed LGrad framework in comparison to various previous methods. We" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "12109" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.078, + 0.091, + 0.895, + 0.523 + ], + "angle": 0, + "content": "
MethodsSettingsTest Models
Input #classProGANStyleGANStyleGAN2BigGAN CycleGANStarGANGauGAN DeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc. A.P.
Wang [42]Image150.463.850.479.368.294.750.261.350.052.950.048.250.367.650.151.5
Frank [12]Freq178.977.969.464.867.464.062.358.667.465.460.559.567.569.152.447.3
Durall [11]Freq185.179.559.255.270.463.857.053.966.761.499.899.658.754.853.051.9
BiHPF [18]Freq182.581.468.062.868.863.667.062.575.574.290.190.173.692.151.649.9
FrePGAN [19]Image195.599.480.690.677.493.063.560.559.459.999.6100.053.049.170.481.5
LGrad (ProGAN-bedroom)Grad198.499.982.695.683.398.476.281.882.390.699.7100.071.775.052.857.8
LGrad (StyleGAN-bedroom)Grad199.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.9
Wang [42]Image264.692.752.882.875.796.651.670.558.681.551.274.353.686.650.651.5
Frank [12]Freq285.781.373.168.575.070.976.970.886.580.885.077.067.365.350.155.3
Durall [11]Freq279.073.963.658.867.362.169.562.965.460.899.499.467.063.050.550.2
BiHPF [18]Freq287.487.471.674.177.081.182.680.686.086.693.880.875.388.253.754.0
FrePGANImage299.099.980.892.072.294.066.061.869.170.398.5100.053.151.062.280.6
LGrad (ProGAN-bedroom)Grad299.5100.085.899.383.599.478.987.778.889.099.6100.070.577.651.952.7
LGrad (StyleGAN-bedroom)Grad299.8100.094.899.792.499.682.592.485.994.799.799.973.783.260.667.8
Wang [42]Image491.499.463.891.476.497.552.973.372.788.663.890.863.992.251.762.3
Frank [12]Freq490.385.274.572.073.171.488.786.075.571.299.599.569.277.460.749.1
Durall [11]Freq481.174.454.452.666.862.060.156.369.064.098.198.161.957.450.250.0
BiHPF [18]Freq490.786.276.975.176.274.784.981.781.978.994.494.469.578.154.454.6
FrePGAN [19]Image499.099.980.789.684.198.669.271.171.174.499.9100.060.371.770.991.9
LGrad (ProGAN-bedroom)Grad499.7100.087.899.191.799.780.989.378.289.099.8100.073.578.653.155.0
LGrad (StyleGAN-bedroom)Grad499.9100.094.899.996.099.982.990.785.394.099.6100.072.479.358.067.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.3, + 0.533, + 0.669, + 0.547 + ], + "angle": 0, + "content": "Table 3. Classification accuracy with cross-model & category." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.563, + 0.892, + 0.696 + ], + "angle": 0, + "content": "
PerturbedProGANStyleGANStyleGAN2BigGANCycleGANStarGANGauGANDeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.
No99.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.986.392.7
blur90.196.590.695.787.593.471.176.370.673.693.698.063.769.561.262.678.583.2
cropping99.299.995.999.694.099.680.188.078.588.194.499.470.678.167.582.385.091.9
jpeg76.290.074.490.272.689.166.074.672.783.276.089.560.167.658.065.269.581.2
noise77.187.773.384.874.384.968.277.466.477.276.088.860.469.157.565.169.179.4
combined86.394.683.593.082.492.371.278.973.080.484.794.364.771.561.267.475.984.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.321, + 0.706, + 0.647, + 0.719 + ], + "angle": 0, + "content": "Table 4. Performance of common image perturbations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.746, + 0.471, + 0.897 + ], + "angle": 0, + "content": "can find that the proposed LGrad framework successfully surpasses the counterparts in the metric of average precision score and accuracy. Our detector shows significantly better generalization to other methods, except on GauGAN and Deepfake. Notably, our LGrad framework achieves higher accuracy values increase to \\(99.4\\%\\), \\(96.0\\%\\), \\(93.8\\%\\), \\(79.5\\%\\), \\(84.7\\%\\) on the ProGAN, StyleGAN, StyleGAN2, BigGAN, CycleGAN models with the 1-class setting, respectively. We obtain comparable results on the GauGAN model and deepfake. Furthermore, our LGrad" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.746, + 0.892, + 0.898 + ], + "angle": 0, + "content": "with StyleGAN-bedroom model successfully achieves a new state-of-the-art accuracy, surpassing FrePGAN [19] by \\(11.4\\%\\) and \\(13.4\\%\\) in terms of mean accuracy and mean average precision score, respectively. We also compare to FingerprintNet [20] evaluated on six GAN models. When evaluated on StyleGAN, StyleGAN2, BigGAN, CycleGAN, StarGAN, and GauGAN, FingerprintNet and the proposed method achieve the mean Accuracy of \\(82.6\\%\\) and \\(87.4\\%\\), respectively. We achieve a gain of \\(4.8\\%\\) in terms of mean accuracy." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "12110" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.078, + 0.09, + 0.895, + 0.316 + ], + "angle": 0, + "content": "
Trans. ModelProGANStyleGANStyleGAN2BigGANCycleGANStarGANGauGANDeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.
VGG16 [39]96.099.565.588.874.793.873.078.177.886.199.8100.060.763.160.467.876.084.7
InceptionV3 [41]64.974.458.866.965.473.850.952.159.168.452.658.554.056.549.850.156.962.6
Resnet50 [15]86.495.081.092.283.793.557.256.268.375.896.499.551.252.763.470.473.479.4
CLIP-Resnet50 [35]87.695.880.290.078.991.060.161.184.287.988.595.172.671.664.964.477.182.1
ViT [10]51.271.151.765.752.467.950.653.354.175.251.664.050.661.150.053.751.564.0
DeeplabV3 [5]81.691.668.780.470.684.554.555.766.271.087.994.751.753.159.358.967.673.7
Idinvert [48]97.499.871.695.371.295.486.694.878.785.797.499.772.082.160.172.179.490.6
ProGAN-bedroom [21]98.499.982.695.683.398.476.281.882.390.699.7100.071.775.052.857.880.987.4
ProGAN-bridge [21]97.899.786.497.585.797.372.578.776.887.594.199.962.575.853.261.378.687.2
StyleGAN-bedroom [22]99.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.986.392.7
StyleGAN-cats [22]97.499.783.497.377.496.469.874.679.390.297.899.868.077.465.972.979.988.5
StyleGAN2-church [23]99.1100.088.297.791.999.670.171.780.689.195.699.860.868.972.776.582.487.9
" + }, + { + "type": "table_caption", + "bbox": [ + 0.315, + 0.324, + 0.655, + 0.338 + ], + "angle": 0, + "content": "Table 5. Performance of different transformation models." + }, + { + "type": "table", + "bbox": [ + 0.078, + 0.354, + 0.897, + 0.487 + ], + "angle": 0, + "content": "
MethodsNum. of Train. dataProGANStyleGANStyleGAN2BigGANCycleGANStarGANGauGANDeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.
FrePGAN [19]36k95.599.480.690.677.493.063.560.559.459.999.6100.053.049.170.481.574.979.3
LGrad4k95.999.588.998.091.799.059.155.258.758.487.899.859.266.265.983.575.982.4
LGrad9k98.399.992.499.492.299.471.774.573.076.898.1100.064.074.268.079.382.287.9
LGrad18k98.8100.095.199.891.499.776.887.179.890.799.2100.068.980.463.973.084.291.3
LGrad36k99.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.986.392.7
LGrad72k99.8100.094.899.792.499.682.592.485.994.799.799.973.783.260.667.886.292.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.314, + 0.497, + 0.656, + 0.511 + ], + "angle": 0, + "content": "Table 6. Results with variance in number of training data." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.538, + 0.47, + 0.734 + ], + "angle": 0, + "content": "In comparison to the performance of FrePGAN [19], our detector obtains the most significant improvement on StyleGAN, increasing accuracy by \\(15.4\\%\\), \\(14.0\\%\\), \\(14.1\\%\\) with 1-class, 2-class, and 4-class settings, respectively. It is worth noting that the LGrad framework fails to perform well on the deepfake model, which is not a GAN model and is trained with MSE loss and SSIM loss, resulting in an accuracy value of only \\(66.7\\%\\). The results indicate that the proposed LGrad approach is less reliant on the amount of data, as it achieves similar results with 1-class, 2-class, and 4-class settings. As the number of training data increases, the deepfake performance decreases, resulting in a drop in the mean accuracy of all generative models." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.471, + 0.902 + ], + "angle": 0, + "content": "In addition, we leverage the high-frequency component of images as the artifacts representation to train the classifier with the same settings. High-frequency-based method obtains the mean accuracy of \\(75.1\\%\\), \\(75.0\\%\\), and \\(73.0\\%\\) with 1-class, 2-class, and 4-class settings, respectively. Our LGrad increases accuracy by \\(11.2\\%\\), \\(11.2\\%\\), and \\(13.1\\%\\) on the same setting, respectively. Gradients are different from high-frequency information. This suggests that gradients can be used as an effective representation of artifacts that are capable of detecting GAN-generated images across diverse categories and various GAN models." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.538, + 0.839, + 0.553 + ], + "angle": 0, + "content": "4.3.4 Robustness against Image Perturbations." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.563, + 0.892, + 0.775 + ], + "angle": 0, + "content": "To evaluate the robustness of the proposed framework to image perturbations, we apply common image perturbations on the test images with a probability of \\(50\\%\\) following [13]. These perturbations include blurring, cropping, compression, adding random noise, and a combination of all of them. In this subsection, the discriminator of StyleGAN-bedroom is used as the transformation model. The results are presented in Table 4. It can be observed that our detectors are robust with respect to cropping and blur, but obtain degraded performance onJPEG and noise. This could be due to the fact that the influence ofJPEG and noise for the transformation model results in the transformed gradients that do not work well. We could further improve the robustness of the transformation model to tackle this problem." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.787, + 0.637, + 0.801 + ], + "angle": 0, + "content": "4.4. Visualization" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Figure 3 shows fake images generated by ProGAN and StyleGAN, real images sampled from CelebA-HQ, the gradients transformed from images, and Class Activate Map (CAM) [47] extracted from the detector. We adopt the discriminator of ProGAN-bedroom as the transformation model. It can be observed from gradients that most of the" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.518, + 0.957 + ], + "angle": 0, + "content": "12111" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.132, + 0.089, + 0.368, + 0.284 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.373, + 0.089, + 0.609, + 0.284 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.615, + 0.089, + 0.842, + 0.284 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.138, + 0.295, + 0.83, + 0.31 + ], + "angle": 0, + "content": "Figure 3. The visualization of gradients and Class Activate Map (CAM) [47] extracted from detector on face images." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.336, + 0.473, + 0.502 + ], + "angle": 0, + "content": "content of images is filtered out, and the essential pixels for the transformation model are highlighted in the process of transforming. Although some texture information is preserved, its value is relatively low. Furthermore, the class activate map of the detector trained on gradients highlights the discriminative region for detection, and it can be seen that the maps of the real image highlight the face region, while most maps of the generated image are located in the background area. It reflects that the gradients, as the generalized representation of artifacts, contain high-level rather than low-level features such as texture." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.523, + 0.229, + 0.541 + ], + "angle": 0, + "content": "5. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.551, + 0.47, + 0.824 + ], + "angle": 0, + "content": "Effect of Transformation Model. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. The images are converted into the gradient, which serves as the generalized representation of artifacts. The performance of the detector highly relies on the quality of the transformation model. Now we discuss the influence of the transformation model on the detection performance, by employing ten pre-trained models. Table 5 shows the across-model & data results of different transformation models. We can observe that gradients generated by the discriminator of GAN perform better on unseen data compared to the classification model and contrastive learning model. In addition, discriminators with different structures or trained with different data exhibit varying performances. Thus, our framework turns the data-dependent problem into a transformation-model-dependent problem. In our experiment, the discriminator of StyleGAN-bedroom obtains the best performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.471, + 0.903 + ], + "angle": 0, + "content": "Effect of the number of training data In order to test the influence of the number of training data for the detector, we sample 4,000, 18,000, and 36,000 images which contain equal numbers of real and fake images from the horse dataset as the training set, respectively. We also use \\(72\\mathrm{k}\\)" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.336, + 0.895, + 0.473 + ], + "angle": 0, + "content": "images of all the chairs and horses set to train the detector. In this subsection, the discriminator of StyleGAN-bedroom is also used as the transformation model. Table 6 compares the proposed framework with the different number of training data. Our detector achieves similar across-model & data performance with 18,000, 36,000, and 72,000 training images. In addition, the proposed LGrad with 4,000 training data still outperforms FrePGAN with 36,000 images, indicating the superiority of our method." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.493, + 0.619, + 0.51 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.521, + 0.895, + 0.779 + ], + "angle": 0, + "content": "In this work, we propose a novel detection framework, called Learning on Gradients(LGrad), designed to identify GAN-generated images. In our method, the gradients are employed as a generalized representation to describe the artifacts produced by generation models. Specifically, we utilize a pre-trained CNN model as the transformation model to convert images into gradients effectively transforming the data-dependent problem into a transformation-model-dependent problem. Numerous experiments in our paper prove the great potential of the discriminator of GAN in detecting GAN-generated images. Moreover, we demonstrate the effectiveness and robustness of using gradients as generalized artifact representations in detecting GAN-generated images. Nonetheless, we acknowledge that the LGrad framework may not perform well in detecting non-GAN generative models, such as deepfake, and we aim to address this limitation in future research." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Acknowledgments This work is supported in part by the National Key R&D Program of China (No.2021ZD0112100), National NSF of China (No.61972022, No.U1936212, No.62120106009, No.62072394), Natural Science Foundation of Hebei province (F2021203019)." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "12112" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.176, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.185 + ], + "angle": 0, + "content": "[1] Jawadul H Bappy, Cody Simons, Lakshmanan Nataraj, BS Manjunath, and Amit K Roy-Chowdhury. Hybrid lstm and encoder-decoder architecture for detection of image forgeries. IEEE Transactions on Image Processing, 28(7):3286-3300, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.187, + 0.472, + 0.257 + ], + "angle": 0, + "content": "[2] Belhassen Bayar and Matthew C Stamm. A deep learning approach to universal image manipulation detection using a new convolutional layer. In Proceedings of the 4th ACM workshop on information hiding and multimedia security, pages 5-10, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.259, + 0.471, + 0.313 + ], + "angle": 0, + "content": "[3] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. In International Conference on Learning Representations, 2018. 1, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.316, + 0.471, + 0.372 + ], + "angle": 0, + "content": "[4] Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola. What makes fake images detectable? understanding properties that generalize. In European conference on computer vision, pages 103-120. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.374, + 0.471, + 0.429 + ], + "angle": 0, + "content": "[5] Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017.4.7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.431, + 0.47, + 0.514 + ], + "angle": 0, + "content": "[6] Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8789-8797, 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.517, + 0.47, + 0.571 + ], + "angle": 0, + "content": "[7] François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.574, + 0.47, + 0.614 + ], + "angle": 0, + "content": "[8] Samantha Cole. We are truly fucked: Everyone is making ai-generated fake porn now. blog post, 2018. Accessed April 17, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.618, + 0.47, + 0.673 + ], + "angle": 0, + "content": "[9] Davide Cozzolino, Justus Thies, Andreas Rössler, Christian Riess, Matthias Nießner, and Luisa Verdoliva. Forensictransfer: Weakly-supervised domain adaptation for forgery detection. arXiv preprint arXiv:1812.02510, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.675, + 0.47, + 0.758 + ], + "angle": 0, + "content": "[10] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale, 2020. 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.76, + 0.47, + 0.829 + ], + "angle": 0, + "content": "[11] Ricard Durall, Margret Keuper, and Janis Keuper. Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7890-7899, 2020. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.831, + 0.47, + 0.9 + ], + "angle": 0, + "content": "[12] Joel Frank, Thorsten Eisenhofer, Lea Schonherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. In International conference on machine learning, pages 3247-3258. PMLR, 2020. 2, 3, 6" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.116, + 0.472, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.161 + ], + "angle": 0, + "content": "[13] Joel Frank, Thorsten Eisenhofer, Lea Schonherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. In International conference on machine learning, pages 3247-3258. PMLR, 2020. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.164, + 0.892, + 0.218 + ], + "angle": 0, + "content": "[14] Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C Courville, and Yoshua Bengio. Generative adversarial nets. In NIPS, 2014. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.221, + 0.892, + 0.276 + ], + "angle": 0, + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.278, + 0.892, + 0.36 + ], + "angle": 0, + "content": "[16] Yang He, Ning Yu, Margret Keuper, and Mario Fritz. Beyond the spectrum: Detecting deepfakes via re-synthesis. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 2534–2541. International Joint Conferences on Artificial Intelligence Organization, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.362, + 0.892, + 0.431 + ], + "angle": 0, + "content": "[17] Nils Hulzebosch, Sarah Ibrahimi, and Marcel Worring. Detecting cnn-generated facial images in real-world scenarios. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 642-643, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.433, + 0.894, + 0.503 + ], + "angle": 0, + "content": "[18] Yonghyun Jeong, Doyeon Kim, Seungjai Min, Seongho Joe, Youngjune Gwon, and Jongwon Choi. Bihpf: Bilateral high-pass filters for robust deepfake detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 48-57, 2022. 2, 3, 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.504, + 0.892, + 0.559 + ], + "angle": 0, + "content": "[19] Yonghyun Jeong, Doyeon Kim, Youngmin Ro, and Jongwon Choi. Frepgan: Robust deepfake detection using frequency-level perturbations. arXiv preprint arXiv:2202.03347, 2022. 2, 3, 4, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.561, + 0.892, + 0.628 + ], + "angle": 0, + "content": "[20] Yonghyun Jeong, Doyeon Kim, Youngmin Ro, Pyounggeon Kim, and Jongwon Choi. Fingerprint: Synthesized fingerprints for generated image detection. In European Conference on Computer Vision, pages 76-94. Springer, 2022. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.632, + 0.892, + 0.686 + ], + "angle": 0, + "content": "[21] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. In International Conference on Learning Representations, 2018. 1, 2, 4, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.688, + 0.892, + 0.757 + ], + "angle": 0, + "content": "[22] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401-4410, 2019. 1, 2, 4, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.759, + 0.894, + 0.829 + ], + "angle": 0, + "content": "[23] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110-8119, 2020. 1, 2, 4, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.831, + 0.892, + 0.857 + ], + "angle": 0, + "content": "[24] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR (Poster), 2015. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.859, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[25] Diederik P Kingma and Max Welling. Auto-encoding variational {Bayes}. In Int. Conf. on Learning Representations. 1" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "12113" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.161 + ], + "angle": 0, + "content": "[26] Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo. Face x-ray for more general face forgery detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5001-5010, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.162, + 0.47, + 0.218 + ], + "angle": 0, + "content": "[27] Yuezun Li, Ming-Ching Chang, and Siwei Lyu. In ictu oculi: Exposing ai created fake videos by detecting eye blinking. In 2018 IEEE International workshop on information forensics and security (WIFS), pages 1-7. IEEE, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.218, + 0.47, + 0.287 + ], + "angle": 0, + "content": "[28] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.288, + 0.471, + 0.342 + ], + "angle": 0, + "content": "[29] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, pages 3730-3738, 2015. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.344, + 0.47, + 0.399 + ], + "angle": 0, + "content": "[30] Zhengzhe Liu, Xiaojuan Qi, and Philip HS Torr. Global texture enhancement for fake face detection in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8060-8069, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.4, + 0.47, + 0.467 + ], + "angle": 0, + "content": "[31] Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi. Do gans leave artificial fingerprints? In 2019 IEEE conference on multimedia information processing and retrieval (MIPR), pages 506-511. IEEE, 2019. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.469, + 0.469, + 0.538 + ], + "angle": 0, + "content": "[32] Iacopo Masi, Aditya Killekar, Royston Marian Mascarenhas, Shenoy Pratik Gurudatt, and Wael AbdAlmageed. Two-branch recurrent network for isolating deepfakes in videos. In European conference on computer vision, pages 667–684. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.539, + 0.469, + 0.607 + ], + "angle": 0, + "content": "[33] Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu. Semantic image synthesis with spatially-adaptive normalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2337-2346, 2019. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.608, + 0.469, + 0.663 + ], + "angle": 0, + "content": "[34] Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European conference on computer vision, pages 86-103. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.664, + 0.469, + 0.747 + ], + "angle": 0, + "content": "[35] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 4, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.748, + 0.469, + 0.802 + ], + "angle": 0, + "content": "[36] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.803, + 0.469, + 0.872 + ], + "angle": 0, + "content": "[37] Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: Learning to detect manipulated facial images. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1-11, 2019. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.469, + 0.903 + ], + "angle": 0, + "content": "[38] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy," + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.471, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.093, + 0.894, + 0.134 + ], + "angle": 0, + "content": "Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252, 2015. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.136, + 0.892, + 0.177 + ], + "angle": 0, + "content": "[39] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations, 2015. 4, 5, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.179, + 0.892, + 0.246 + ], + "angle": 0, + "content": "[40] Ke Sun, Hong Liu, Qixiang Ye, Yue Gao, Jianzhuang Liu, Ling Shao, and Rongrong Ji. Domain general face forgery detection by learning to weight. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 2638-2646, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.249, + 0.892, + 0.317 + ], + "angle": 0, + "content": "[41] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016. 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.319, + 0.892, + 0.388 + ], + "angle": 0, + "content": "[42] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros. Cnn-generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8695-8704, 2020. 2, 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.39, + 0.892, + 0.445 + ], + "angle": 0, + "content": "[43] Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015. 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.446, + 0.892, + 0.502 + ], + "angle": 0, + "content": "[44] Ning Yu, Larry S Davis, and Mario Fritz. Attributing fake images to gans: Learning and analyzing gan fingerprints. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7556-7566, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.503, + 0.892, + 0.544 + ], + "angle": 0, + "content": "[45] Yang Yu, Rongrong Ni, and Yao Zhao. Mining generalized features for detecting ai-manipulated fake faces. arXiv preprint arXiv:2010.14129, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.546, + 0.892, + 0.613 + ], + "angle": 0, + "content": "[46] Tianchen Zhao, Xiang Xu, Mingze Xu, Hui Ding, Yuanjun Xiong, and Wei Xia. Learning self-consistency for deepfake detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 15023-15033, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.616, + 0.892, + 0.684 + ], + "angle": 0, + "content": "[47] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2921-2929, 2016. 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.686, + 0.892, + 0.741 + ], + "angle": 0, + "content": "[48] Jiapeng Zhu, Yujun Shen, Deli Zhao, and Bolei Zhou. Indomain gan inversion for real image editing. In European conference on computer vision, pages 592-608. Springer, 2020. 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.742, + 0.892, + 0.811 + ], + "angle": 0, + "content": "[49] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223-2232, 2017. 4" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.093, + 0.894, + 0.811 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "12114" + } + ] +] \ No newline at end of file diff --git a/2023/Learning on Gradients_ Generalized Artifacts Representation for GAN-Generated Images Detection/2c415e18-250d-42c6-9ecf-17946adf591a_origin.pdf b/2023/Learning on Gradients_ Generalized Artifacts Representation for GAN-Generated Images Detection/2c415e18-250d-42c6-9ecf-17946adf591a_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..87c070fc29cbd46d669004830781a842f74fa779 --- /dev/null +++ b/2023/Learning on Gradients_ Generalized Artifacts Representation for GAN-Generated Images Detection/2c415e18-250d-42c6-9ecf-17946adf591a_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c4094340b44531f94cf58d47a89e88025225c6ae3a84e5a9e3a8dec089be663f +size 1276875 diff --git a/2023/Learning on Gradients_ Generalized Artifacts Representation for GAN-Generated Images Detection/full.md b/2023/Learning on Gradients_ Generalized Artifacts Representation for GAN-Generated Images Detection/full.md new file mode 100644 index 0000000000000000000000000000000000000000..6f4aef6366a6db71c7041a0d45a15360978f9d2f --- /dev/null +++ b/2023/Learning on Gradients_ Generalized Artifacts Representation for GAN-Generated Images Detection/full.md @@ -0,0 +1,244 @@ +# Learning on Gradients: Generalized Artifacts Representation for GAN-Generated Images Detection + +Chuangchuang Tan $^{1,2}$ , Yao Zhao $^{1,2*}$ , Shikui Wei $^{1,2}$ , Guanghua Gu $^{3,4}$ , Yunchao Wei $^{1,2}$ + +$^{1}$ Institute of Information Science, Beijing Jiaotong University + +$^{2}$ Beijing Key Laboratory of Advanced Information Science and Network Technology + +$^{3}$ School of Information Science and Engineering, Yanshan University + +4Hebei Key Laboratory of Information Transmission and Signal Processing + +{21112002, yzhao, shkwei}@bjtu.edu.cn, guguanghua@ysu.edu.cn, wychaol987@gmail.com + +# Abstract + +Recently, there has been a significant advancement in image generation technology, known as GAN. It can easily generate realistic fake images, leading to an increased risk of abuse. However, most image detectors suffer from sharp performance drops in unseen domains. The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models. In this work, we introduce a novel detection framework, named Learning on Gradients (LGrad), designed for identifying GAN-generated images, with the aim of constructing a generalized detector with cross-model and cross-data. Specifically, a pretrained CNN model is employed as a transformation model to convert images into gradients. Subsequently, we leverage these gradients to present the generalized artifacts, which are fed into the classifier to ascertain the authenticity of the images. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. To the best of our knowledge, this is the first study to utilize gradients as the representation of artifacts in GAN-generated images. Extensive experiments demonstrate the effectiveness and robustness of gradients as generalized artifact representations. Our detector achieves a new state-of-the-art performance with a remarkable gain of $11.4\%$ . The code is released at https://github.com/chuangchuanqtan/LGrad. + +# 1. Introduction + +Over the past years, remarkable progress has been made in deep generative models, i.e. generative adversarial networks (GAN) [14], its variations [3, 21-23, 36], and VAE [25]. The generated media is highly realistic and indistin + +![](images/329654e7c33c656d2bb7453466b597a02c75bc5733b031630980a8394e869b75.jpg) +Figure 1. Visualization of gradients of real images and GAN-generated images extracted from a pre-trained model. To fully understand the gradients, heatmaps for R, G, and B channels also are shown, where red is high. In the gradients, the content of images is filtered out, and only the discriminative pixels are retained for the pre-trained model's target task. We utilize the gradients as the generalized artifacts representation to develop a novel detection framework. + +guishable from real to human eyes. Although it has the potential for many novel applications [8], it also brings new risks due to the abuse of fake information. The misuse of DeepFakes has been confirmed that some people swap the faces of women onto pornographic videos [8]. In addition, some individuals, even non-experts, can malevolently manipulate or create fake images or videos for political or economic purposes, leading to serious social problems [17]. Thus, it is extremely necessary to develop forgery detection techniques to help people determine the credibility of the + +media [17, 40]. + +Various detectors [11-13, 16, 18, 19, 30, 42, 45] have been developed to detect GAN-generated images. Some studies [16, 30, 45] focus on human face images, while others [11, 18, 19, 42] handle various categories of images. They mainly depend on local regions artifacts [4, 44], blending boundary [26], global textures [30], and frequency-level artifacts [13, 18, 19, 45]. However, those methods heavily rely on the training settings, resulting in failure detection of images from unseen categories or GAN models. The test images in the actual scene are always from unknown sources [17], rendering it challenging to develop generalized detectors. There are some works [19, 42] exploiting pre-processing, data augmentation, and reducing the effects of frequency-level artifacts to develop a robust detector. Nevertheless, there still needs to be a more generalized representation of the clue produced by generation models, which is critical for robust fake image detection. + +To tackle this problem, we propose a novel and simple detection framework, referred to as Learning on Gradients (LGrad). A new generalized feature, Gradients, is developed to serve as a representation of the artifacts produced by GAN models. We believe that the gradients of a trained CNN model can highlight the important pixels in the target task, thereby serving as a valuable cue for detecting fake images. As shown in Figure 1, we adopt a pre-trained discriminator of ProGAN [21] to extract gradients of images produced by Celeba-HQ [21], ProGAN [21], StyleGAN [22], StyleGAN2 [23]. In these gradients, the content of images is filtered out, and only the discriminative pixels that are relevant to the pre-trained model's target task are retained. Therefore, the gradients are more dependent on the pre-trained model rather than on training sources, thereby enhancing the detector's performance with unseen data. In our framework, a pretrained model, called transformation model, is employed to convert images to gradients. These gradients serve as the generalized artifacts and are fed into the classifier to obtain a robust detector. Since the transformation model is indeterminate in our framework, targeted anti-detection cannot be effectively launched. + +To validate the performance of our LGrad, we only use images generated by ProGAN to train the detector and evaluate it with various sources, including cross-category, cross-model, and cross-model & category. Numerous experiments prove the effectiveness and robustness of gradients as generalized artifacts. Our detector achieves a new state-of-the-art performance in known and unseen settings. + +Our paper makes the following contributions: + +- We develop a new detection framework, Learning on Gradient (LGrad), to detect GAN-generated images. Our detector achieves a new state-of-the-art performance. + +- We introduce a new generalized artifact representation, Gradients, for GAN-generated image detection. Furthermore, we are the first to use gradients as the representation of artifacts. +- Our framework turns the data-driven problem into a transformation-model-driven problem. The robustness of the detector is improved with the introduction of the transformation model. +- We prove the great potential of the discriminator of GAN in detecting GAN-generated images. + +# 2. Related Work + +In this section, we discuss recent approaches for generated image detection. The previous methods attempt to exploit spatial information or frequency artifacts as the representation of clues produced by the generation model for fake image detection. + +# 2.1. Image-based Fake Detection + +Early studies employ spatial information from generated images to identify the fake images, such as color spaces [45] and global texture [30]. Rossler et al. [37] introduce a largescale manipulated face dataset FaceForensics++ and adopt Xception [7] as detector to identify the fake images with compression. Li et al. [27] detect fake face videos by analyzing eye blinking in the videos. ForensicTransfer [9] presents a new autoencoder-based architecture to learn a discriminative feature representation in the latent space for detection well on new domains. Yu et al. [44] and Marra et al. [31] believe that every GAN model has unique fingerprints after training, and extracting it from generated images to perform detection. Bayar et al. [2] design a new convolutional network architecture to suppress the image of the content and learn manipulation features for a generalized detector. Gram-Net [30] leverages the Gram matrix to extract global texture as the robust representation for fake face detection. Face X-ray [26] develops the blending boundary to present the artifacts for detecting manipulated face images. Chai et al. [4] design a patch-based classifier to limit the receptive field of the model for learning redundant artifacts in image patches. The patch-based predictions focus on local artifacts rather than global structure, improving the robustness of the detector. Yu et al. [45] use the channel difference image and spectrum image to mine the intrinsic clues of images from the view of the camera imaging process. Wang et al. [42] directly adopt a large number of real and fake images with data augmentation to train a binary classifier for improving unseen image performance. They use some post-processing operations to improve robustness during training, such as JPEG compression, blurring, and resizing. PCL [46] extracts the distinct source features of + +![](images/2950c4a6c6b633636272a0328972c34a486a8275aa477ec2a72ab0ddb83a1e2e.jpg) +Figure 2. The overall pipeline of the proposed framework. The method uses the same transformation model and the same classifier in the testing and training phases. + +images by self-consistency learning for detecting deepfake. He et al. [16] design a re-synthesis procedure to extract visual cues for robust deepfake detection. + +# 2.2. Frequency-based Fake Detection + +Since GAN architectures depend on up-scaling operations, the generated images contain unique frequency-level artifacts compared to neutral images [11]. Several works [1, 11, 12, 31, 32, 34] leverage the spectral distributions of images as the representation of artifacts for fake image detection. LOG [32] proposes a two-branch recurrent network to integrate information from the color domain and the frequency domain for detecting manipulated faces. $F^3$ -Net [34] designs a two-stream collaborative learning network to mine the forgery patterns in fake images, which exploits two frequency-aware clues, frequency-aware decomposed image components and local frequency statistics, to present subtle forgery patterns and high-level semantics, respectively. BiHPF [18] adopts two high-pass filters to amplify the magnitudes of the artifacts. FrePGAN [19] validates that the frequency-level artifacts of fake images are evident but uniquely vary by the type of GAN model or object category, which can cause overfitting issues for fake image detection. Thus, it generated the frequency-level perturbation maps to remove the effect of the frequency-level artifacts. + +# 3. Learning on Gradients framework + +The key to the fake image detection task is developing a generalized representation of artifacts generated by + +GAN models. It should be generalized and robust enough to span diverse categories and different GAN models. In order to achieve this, we design a novel detection framework to improve the cross-source performance in this work. The overview of our method is shown in Figure 2. Our framework employs gradients as the generalized representation to obtain the robust detector. We transform the images to the gradients by a pretrained CNN model named transformation model. In the process of transforming, most of the content of images is filtered out due to the pooling layer in the CNN model, and the essential pixels for the transformation model are highlighted. Figure 1 provides the visualization evidence for our explanation. Extensive experiments in Section 4 demonstrate the effectiveness of the gradients using as the artifact representation. + +# 3.1. Transformation to Gradients + +The previous methods based on pixel and frequency-level artifacts highly rely on the training data, resulting in failure with unseen images. To solve this overfitting issue, we adopt any one pretrained CNN model as the transformation model to convert all images consisting of training images and test images to gradients. This approach effectively maps all data to the gradient domain, which is determined by the chosen transformation model. By relying on this more generalized representation of artifacts, we can achieve improved performance in detecting GAN-generated images across diverse categories and GAN models. + +We denote the images set as $I = \{(I_i, y_i)\}_{i=0}^{N-1}$ , where $y_i = \{0,1\}$ is the label of the images $I_i \in \mathbb{R}^{w \times h \times c}$ , which is real ( $y = 0$ ) or fake ( $y = 1$ ). The $w, h, c$ present width, + +height, and the number of channels. The transformation model is defined as $M(\cdot)$ . We first feed images $I_{i}$ into $M$ model. + +$$ +l = M \left(I _ {i}\right), \tag {1} +$$ + +where $l \in \mathbb{R}^{n \times c}$ is the output vectors of the transformation model $M$ . Then, we calculate the gradients of $sum(l)$ with respect to the input $I_i$ , + +$$ +G = \frac {\partial \operatorname {s u m} (l)}{\partial I _ {i}}. \tag {2} +$$ + +$G$ is the generalized artifacts representation of our method. Note that the transformation model $M$ is fixed, and its parameter would not update in our framework. The quality of gradients $G$ highly depends on the transformation model $M$ , which is helpful to reduce the reliance on training data and enhance the generalization of the representations. In our experiments, we adopt various popular CNN models to implement the transformation model, including the classification model, segmentation model, discriminator of GAN, contrastive learning model, and GAN Inversion. + +# 3.2. Detecting Fake Images + +During the training stage of our framework, we use normalized gradients ranging from 0 to 255 to train the binary classification network. We adopt the popular classifier to distinguish whether the input gradients correspond to GAN-generated images or not, and employ a cross-entropy loss function to optimize the network. During the inference time, the test images are first converted to gradients by the transformation model employed in the training stage. Then, we obtain the final result by feeding gradients into the trained classification network. + +# 4. Experimental Results + +# 4.1. Dataset + +To evaluate the proposed approach, we adopt the dataset provided by Wang et al. [42] to conduct experiments following the previous works [18, 19]. It contains various fake images generated by ProGAN [21], StyleGAN [22], StyleGAN2 [23], BigGAN [3], CycleGAN [49], StarGAN [6], GauGAN [33] and Deepfake [37]. The real images are sampled from LSUN [43], ImageNet [38], CelebA [29], CelebA-HQ [21], COCO [28], and FaceForensics++ [37]. During the training stage, we train the classifier with images generated by ProGAN. + +In addition, to verify the usefulness of our method for face data, we sample 20,000 real images from Celeba-HQ, and generate 20,000 face images by ProGAN as the training set. And the images generated by StyleGAN, StyleGAN2 trained by Celeba-HQ [21] are employed as the test set. All images are resized to $256 \times 256$ resolution. + +# 4.2. Implementation Details + +We apply the Resnet50 [15] model pre-trained with ImageNet [38] as the classifier, and train it by Adam [24] with learning rate of $5 \times 10^{-4}$ . The learning rate is decreased by twenty percent after every ten epochs. The batch size is set to 16, and the number of epochs is 100. In this paper, the results of the last epoch are used as the final performance. In addition, we randomly crop the input images during the training phase and directly feed images into the classifier in the test stage. + +To fully understand the gradients as the generalized artifacts, we further employ various popular models to implement the transformation model, i.e. VGG16 [39], InceptionV3 [41], Resnet50 [15], DeeplabV3 [5], CLIP [35], ViT [10], discriminator of ProGAN [21], StyleGAN [22], StyleGAN2 [23], and GAN Idinvert [48]. All of those models in this paper are released by the official code. Following the baselines [18, 19], we utilize the average precision score (A.P.) and accuracy (Acc.) as evaluation metrics to evaluate the proposed method. + +# 4.3. Detection Performance Evaluations + +To assess the effectiveness of our detector, we conducted evaluations in four different settings: cross-model images, cross-category images, cross-model & category images, and perturbed images. Specifically, the detector is trained on a known GAN model, e.g. ProGAN-Horse [21] dataset, while evaluated with unseen images: 1) cross-category images, produced by the same GAN model trained on different datasets, e.g. ProGAN-Airplane and ProGAN-Diningtable; 2) cross-model images, generated by different GAN models trained on the same dataset, e.g. StyleGAN-Horse [22] and StyleGAN2-Horse [23]; and 3) cross-model & category images, where both training data and GAN model are different from the training setting, e.g. BigGAN-ImageNet [3,38] and GauGAN-COCO [28, 33]. Meanwhile, to fully understand the proposed LGrad framework in terms of different transformation models, we further implement the transformation model with various popular CNN networks. + +
Trans. ModelsProGAN-CelebaHQStyleGAN-CelebaHQStyleGAN2-CelebaHQ
Acc.A.P.Acc.A.P.Acc.A.P.
Input Image99.6100.076.898.964.296.2
ProGAN-RandomInit91.497.357.466.352.456.9
VGG1696.999.879.696.368.291.6
CLIP-RN5099.5100.099.4100.099.2100.0
ProGAN-bedroom98.8100.098.499.996.599.6
ProGAN-bridge96.499.584.895.081.993.6
+ +Table 1. Cross-model Performance on face images data. + +
Test-categoriesCLIPTrans.Bedroom Trans.Bridge Trans.
ACCAPACCAPACCAPAP
airplane92.997.099.4100.099.4100.0
bicycle80.594.694.598.594.898.5
bird85.694.499.2100.097.599.7
boat90.696.699.6100.098.599.9
bottle94.798.297.7100.099.2100.0
bus81.594.998.1100.096.799.8
car84.095.799.7100.099.1100.0
cat90.296.199.7100.098.799.9
chair94.598.596.299.999.1100.0
cow85.595.099.5100.097.799.8
diningtable92.497.798.599.896.999.5
dog89.896.199.6100.098.499.9
horse89.096.3100.0100.099.8100.0
motorbike79.294.897.099.796.999.3
person93.697.198.9100.099.1100.0
pottedplant77.995.697.599.795.799.1
sheep83.294.299.2100.095.899.3
sofa94.198.699.1100.099.2100.0
train83.694.494.499.796.199.6
tvmonitor93.098.796.5100.099.2100.0
Mean87.896.298.299.997.999.7
+ +Table 2. Cross-category Performance on the ProGAN models trained on different LSUN [43] object datasets. + +# 4.3.1 Cross-model Performance. + +We employ face images to show the cross-model performance of our proposed LGrad framework. The training set contains 25,000 fake images generated by ProGAN [21] and 25,000 real images randomly sampled from Celeba-HQ [21]. The test set consists of 10,000 images, half of which are real images randomly sampled from Celeba-HQ [21], while the other half are fake images generated by StyleGAN [22] and StyleGAN2 [23]. We adopt popular CNN models such as VGG16 [39], CLIP-RN50 [35], and discriminators of ProGAN [21] as the transformation models, and also randomly initialize a discriminator of ProGAN to perform the transformation to demonstrate the effectiveness of pre-trained models. In addition, we train the detector directly on the images to compare it with the gradients-based detector. + +The experimental results are shown in Table 1. We can observe that image-based detectors suffer from sharp performance drops on StyleGAN [22] and StyleGAN2 [23]. It only achieves the Acc. of $64.2\%$ on StyleGAN2, which is much lower than the performance on ProGAN. In contrast, all gradients-based detectors transformed by pre-trained models outperform the one with random initialization of ProGAN. The results demonstrate that the transformation + +model eliminates the contents of images while retaining the discriminative regions in the gradients. Thus, the gradients can effectively present the generalized artifacts produced by GAN models. The detector based on CLIP-RestNet50, trained on various (image, text) pairs, achieves the best accuracy of $99.4\%$ and $99.2\%$ on StyleGAN and StyleGAN2, respectively. The discriminators of ProGAN trained on bedroom or bridge datasets yield comparable results with the CLIP-RestNet50 model. Our proposed framework shows remarkable performance on face data. + +# 4.3.2 Cross-category Performance. + +We leverage the training set from Wang et al. [42] to validate the cross-category performance, which contains 20 object categories generated by ProGAN models. Each category comprises 18,000 fake images and an equal number of real images. We trained our detector using the horse dataset and evaluated its performance on the remaining 19 datasets. We implement the transformation model with two discriminators of ProGAN and Contrastive learning model, including ProGAN-bedroom, ProGAN-bridge, and CLIP-ResNet50. Table 2 reports the results of our detectors. The performance of the horse is calculated on the training data. When adopting the CLIP-RestNet50 model as the transformation model, the detector achieves the best result on cross-model performance compared to the other models. We also evaluate the performance of CLIP-RestNet50 on the cross-category case. In Table 2, the CLIP-RestNet50 achieves mean Acc. of $87.8\%$ on 20 categories, while the ProGAN-bedroom and ProGAN-bridge obtain higher accuracies of $98.2\%$ and $97.9\%$ , respectively. It can be observed that discriminators are more suitable as the transformation model to detect cross-category images. In summary, we can confirm that our detectors show excellent performance in detecting unseen categories. + +# 4.3.3 Cross-model & category Performance. + +We further expand the testing scope to evaluate the robustness of our proposed method. The gradients-based detector is performed on eight different models following baselines [18, 19] including ProGAN, StyleGAN, StyleGAN2, BigGAN, CycleGAN, StarGAN, GauGAN, and Deepfake. We adopt the discriminators of ProGAN and StyleGAN, trained using the LSUN bedroom dataset, as the transformation model. For fair comparisons, we use the same experimental setting employed in baselines [19] to evaluate the generality of the detector. Specifically, three training configurations are employed to train the detector, including 1-class (horse), 2-class (chair, horse), and 4-class (car, cat, chair, horse) settings. + +Table 3 presents the results of our proposed LGrad framework in comparison to various previous methods. We + +
MethodsSettingsTest Models
Input #classProGANStyleGANStyleGAN2BigGAN CycleGANStarGANGauGAN DeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc. A.P.
Wang [42]Image150.463.850.479.368.294.750.261.350.052.950.048.250.367.650.151.5
Frank [12]Freq178.977.969.464.867.464.062.358.667.465.460.559.567.569.152.447.3
Durall [11]Freq185.179.559.255.270.463.857.053.966.761.499.899.658.754.853.051.9
BiHPF [18]Freq182.581.468.062.868.863.667.062.575.574.290.190.173.692.151.649.9
FrePGAN [19]Image195.599.480.690.677.493.063.560.559.459.999.6100.053.049.170.481.5
LGrad (ProGAN-bedroom)Grad198.499.982.695.683.398.476.281.882.390.699.7100.071.775.052.857.8
LGrad (StyleGAN-bedroom)Grad199.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.9
Wang [42]Image264.692.752.882.875.796.651.670.558.681.551.274.353.686.650.651.5
Frank [12]Freq285.781.373.168.575.070.976.970.886.580.885.077.067.365.350.155.3
Durall [11]Freq279.073.963.658.867.362.169.562.965.460.899.499.467.063.050.550.2
BiHPF [18]Freq287.487.471.674.177.081.182.680.686.086.693.880.875.388.253.754.0
FrePGANImage299.099.980.892.072.294.066.061.869.170.398.5100.053.151.062.280.6
LGrad (ProGAN-bedroom)Grad299.5100.085.899.383.599.478.987.778.889.099.6100.070.577.651.952.7
LGrad (StyleGAN-bedroom)Grad299.8100.094.899.792.499.682.592.485.994.799.799.973.783.260.667.8
Wang [42]Image491.499.463.891.476.497.552.973.372.788.663.890.863.992.251.762.3
Frank [12]Freq490.385.274.572.073.171.488.786.075.571.299.599.569.277.460.749.1
Durall [11]Freq481.174.454.452.666.862.060.156.369.064.098.198.161.957.450.250.0
BiHPF [18]Freq490.786.276.975.176.274.784.981.781.978.994.494.469.578.154.454.6
FrePGAN [19]Image499.099.980.789.684.198.669.271.171.174.499.9100.060.371.770.991.9
LGrad (ProGAN-bedroom)Grad499.7100.087.899.191.799.780.989.378.289.099.8100.073.578.653.155.0
LGrad (StyleGAN-bedroom)Grad499.9100.094.899.996.099.982.990.785.394.099.6100.072.479.358.067.9
+ +Table 3. Classification accuracy with cross-model & category. + +
PerturbedProGANStyleGANStyleGAN2BigGANCycleGANStarGANGauGANDeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.
No99.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.986.392.7
blur90.196.590.695.787.593.471.176.370.673.693.698.063.769.561.262.678.583.2
cropping99.299.995.999.694.099.680.188.078.588.194.499.470.678.167.582.385.091.9
jpeg76.290.074.490.272.689.166.074.672.783.276.089.560.167.658.065.269.581.2
noise77.187.773.384.874.384.968.277.466.477.276.088.860.469.157.565.169.179.4
combined86.394.683.593.082.492.371.278.973.080.484.794.364.771.561.267.475.984.1
+ +Table 4. Performance of common image perturbations. + +can find that the proposed LGrad framework successfully surpasses the counterparts in the metric of average precision score and accuracy. Our detector shows significantly better generalization to other methods, except on GauGAN and Deepfake. Notably, our LGrad framework achieves higher accuracy values increase to $99.4\%$ , $96.0\%$ , $93.8\%$ , $79.5\%$ , $84.7\%$ on the ProGAN, StyleGAN, StyleGAN2, BigGAN, CycleGAN models with the 1-class setting, respectively. We obtain comparable results on the GauGAN model and deepfake. Furthermore, our LGrad + +with StyleGAN-bedroom model successfully achieves a new state-of-the-art accuracy, surpassing FrePGAN [19] by $11.4\%$ and $13.4\%$ in terms of mean accuracy and mean average precision score, respectively. We also compare to FingerprintNet [20] evaluated on six GAN models. When evaluated on StyleGAN, StyleGAN2, BigGAN, CycleGAN, StarGAN, and GauGAN, FingerprintNet and the proposed method achieve the mean Accuracy of $82.6\%$ and $87.4\%$ , respectively. We achieve a gain of $4.8\%$ in terms of mean accuracy. + +
Trans. ModelProGANStyleGANStyleGAN2BigGANCycleGANStarGANGauGANDeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.
VGG16 [39]96.099.565.588.874.793.873.078.177.886.199.8100.060.763.160.467.876.084.7
InceptionV3 [41]64.974.458.866.965.473.850.952.159.168.452.658.554.056.549.850.156.962.6
Resnet50 [15]86.495.081.092.283.793.557.256.268.375.896.499.551.252.763.470.473.479.4
CLIP-Resnet50 [35]87.695.880.290.078.991.060.161.184.287.988.595.172.671.664.964.477.182.1
ViT [10]51.271.151.765.752.467.950.653.354.175.251.664.050.661.150.053.751.564.0
DeeplabV3 [5]81.691.668.780.470.684.554.555.766.271.087.994.751.753.159.358.967.673.7
Idinvert [48]97.499.871.695.371.295.486.694.878.785.797.499.772.082.160.172.179.490.6
ProGAN-bedroom [21]98.499.982.695.683.398.476.281.882.390.699.7100.071.775.052.857.880.987.4
ProGAN-bridge [21]97.899.786.497.585.797.372.578.776.887.594.199.962.575.853.261.378.687.2
StyleGAN-bedroom [22]99.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.986.392.7
StyleGAN-cats [22]97.499.783.497.377.496.469.874.679.390.297.899.868.077.465.972.979.988.5
StyleGAN2-church [23]99.1100.088.297.791.999.670.171.780.689.195.699.860.868.972.776.582.487.9
+ +Table 5. Performance of different transformation models. + +
MethodsNum. of Train. dataProGANStyleGANStyleGAN2BigGANCycleGANStarGANGauGANDeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.
FrePGAN [19]36k95.599.480.690.677.493.063.560.559.459.999.6100.053.049.170.481.574.979.3
LGrad4k95.999.588.998.091.799.059.155.258.758.487.899.859.266.265.983.575.982.4
LGrad9k98.399.992.499.492.299.471.774.573.076.898.1100.064.074.268.079.382.287.9
LGrad18k98.8100.095.199.891.499.776.887.179.890.799.2100.068.980.463.973.084.291.3
LGrad36k99.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.986.392.7
LGrad72k99.8100.094.899.792.499.682.592.485.994.799.799.973.783.260.667.886.292.2
+ +Table 6. Results with variance in number of training data. + +In comparison to the performance of FrePGAN [19], our detector obtains the most significant improvement on StyleGAN, increasing accuracy by $15.4\%$ , $14.0\%$ , $14.1\%$ with 1-class, 2-class, and 4-class settings, respectively. It is worth noting that the LGrad framework fails to perform well on the deepfake model, which is not a GAN model and is trained with MSE loss and SSIM loss, resulting in an accuracy value of only $66.7\%$ . The results indicate that the proposed LGrad approach is less reliant on the amount of data, as it achieves similar results with 1-class, 2-class, and 4-class settings. As the number of training data increases, the deepfake performance decreases, resulting in a drop in the mean accuracy of all generative models. + +In addition, we leverage the high-frequency component of images as the artifacts representation to train the classifier with the same settings. High-frequency-based method obtains the mean accuracy of $75.1\%$ , $75.0\%$ , and $73.0\%$ with 1-class, 2-class, and 4-class settings, respectively. Our LGrad increases accuracy by $11.2\%$ , $11.2\%$ , and $13.1\%$ on the same setting, respectively. Gradients are different from high-frequency information. This suggests that gradients can be used as an effective representation of artifacts that are capable of detecting GAN-generated images across diverse categories and various GAN models. + +# 4.3.4 Robustness against Image Perturbations. + +To evaluate the robustness of the proposed framework to image perturbations, we apply common image perturbations on the test images with a probability of $50\%$ following [13]. These perturbations include blurring, cropping, compression, adding random noise, and a combination of all of them. In this subsection, the discriminator of StyleGAN-bedroom is used as the transformation model. The results are presented in Table 4. It can be observed that our detectors are robust with respect to cropping and blur, but obtain degraded performance onJPEG and noise. This could be due to the fact that the influence ofJPEG and noise for the transformation model results in the transformed gradients that do not work well. We could further improve the robustness of the transformation model to tackle this problem. + +# 4.4. Visualization + +Figure 3 shows fake images generated by ProGAN and StyleGAN, real images sampled from CelebA-HQ, the gradients transformed from images, and Class Activate Map (CAM) [47] extracted from the detector. We adopt the discriminator of ProGAN-bedroom as the transformation model. It can be observed from gradients that most of the + +![](images/84aacbd73d666f4a9f4a115a92b9e99cbb93852faa2eff3386a3d3842ef30f4c.jpg) +Figure 3. The visualization of gradients and Class Activate Map (CAM) [47] extracted from detector on face images. + +![](images/9f6efd423babf8b5e446ce8f253cb112f4e37fa4ca3f8c9705b71d1ce2b42106.jpg) + +![](images/32fbe059b34da30d882150ec8a0b767c6db1e36934bd78594a7dd299f15251d0.jpg) + +content of images is filtered out, and the essential pixels for the transformation model are highlighted in the process of transforming. Although some texture information is preserved, its value is relatively low. Furthermore, the class activate map of the detector trained on gradients highlights the discriminative region for detection, and it can be seen that the maps of the real image highlight the face region, while most maps of the generated image are located in the background area. It reflects that the gradients, as the generalized representation of artifacts, contain high-level rather than low-level features such as texture. + +# 5. Ablation Study + +Effect of Transformation Model. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. The images are converted into the gradient, which serves as the generalized representation of artifacts. The performance of the detector highly relies on the quality of the transformation model. Now we discuss the influence of the transformation model on the detection performance, by employing ten pre-trained models. Table 5 shows the across-model & data results of different transformation models. We can observe that gradients generated by the discriminator of GAN perform better on unseen data compared to the classification model and contrastive learning model. In addition, discriminators with different structures or trained with different data exhibit varying performances. Thus, our framework turns the data-dependent problem into a transformation-model-dependent problem. In our experiment, the discriminator of StyleGAN-bedroom obtains the best performance. + +Effect of the number of training data In order to test the influence of the number of training data for the detector, we sample 4,000, 18,000, and 36,000 images which contain equal numbers of real and fake images from the horse dataset as the training set, respectively. We also use $72\mathrm{k}$ + +images of all the chairs and horses set to train the detector. In this subsection, the discriminator of StyleGAN-bedroom is also used as the transformation model. Table 6 compares the proposed framework with the different number of training data. Our detector achieves similar across-model & data performance with 18,000, 36,000, and 72,000 training images. In addition, the proposed LGrad with 4,000 training data still outperforms FrePGAN with 36,000 images, indicating the superiority of our method. + +# 6. Conclusion + +In this work, we propose a novel detection framework, called Learning on Gradients(LGrad), designed to identify GAN-generated images. In our method, the gradients are employed as a generalized representation to describe the artifacts produced by generation models. Specifically, we utilize a pre-trained CNN model as the transformation model to convert images into gradients effectively transforming the data-dependent problem into a transformation-model-dependent problem. Numerous experiments in our paper prove the great potential of the discriminator of GAN in detecting GAN-generated images. Moreover, we demonstrate the effectiveness and robustness of using gradients as generalized artifact representations in detecting GAN-generated images. Nonetheless, we acknowledge that the LGrad framework may not perform well in detecting non-GAN generative models, such as deepfake, and we aim to address this limitation in future research. + +Acknowledgments This work is supported in part by the National Key R&D Program of China (No.2021ZD0112100), National NSF of China (No.61972022, No.U1936212, No.62120106009, No.62072394), Natural Science Foundation of Hebei province (F2021203019). + +# References + +[1] Jawadul H Bappy, Cody Simons, Lakshmanan Nataraj, BS Manjunath, and Amit K Roy-Chowdhury. Hybrid lstm and encoder-decoder architecture for detection of image forgeries. IEEE Transactions on Image Processing, 28(7):3286-3300, 2019. 3 +[2] Belhassen Bayar and Matthew C Stamm. A deep learning approach to universal image manipulation detection using a new convolutional layer. In Proceedings of the 4th ACM workshop on information hiding and multimedia security, pages 5-10, 2016. 2 +[3] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. In International Conference on Learning Representations, 2018. 1, 4 +[4] Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola. What makes fake images detectable? understanding properties that generalize. In European conference on computer vision, pages 103-120. Springer, 2020. 2 +[5] Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017.4.7 +[6] Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8789-8797, 2018. 4 +[7] François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017. 2 +[8] Samantha Cole. We are truly fucked: Everyone is making ai-generated fake porn now. blog post, 2018. Accessed April 17, 2020. 1 +[9] Davide Cozzolino, Justus Thies, Andreas Rössler, Christian Riess, Matthias Nießner, and Luisa Verdoliva. Forensictransfer: Weakly-supervised domain adaptation for forgery detection. arXiv preprint arXiv:1812.02510, 2018. 2 +[10] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale, 2020. 4, 7 +[11] Ricard Durall, Margret Keuper, and Janis Keuper. Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7890-7899, 2020. 2, 3, 6 +[12] Joel Frank, Thorsten Eisenhofer, Lea Schonherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. In International conference on machine learning, pages 3247-3258. PMLR, 2020. 2, 3, 6 + +[13] Joel Frank, Thorsten Eisenhofer, Lea Schonherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. In International conference on machine learning, pages 3247-3258. PMLR, 2020. 2, 7 +[14] Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C Courville, and Yoshua Bengio. Generative adversarial nets. In NIPS, 2014. 1 +[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 4, 7 +[16] Yang He, Ning Yu, Margret Keuper, and Mario Fritz. Beyond the spectrum: Detecting deepfakes via re-synthesis. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 2534–2541. International Joint Conferences on Artificial Intelligence Organization, 2021. 2, 3 +[17] Nils Hulzebosch, Sarah Ibrahimi, and Marcel Worring. Detecting cnn-generated facial images in real-world scenarios. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 642-643, 2020. 1, 2 +[18] Yonghyun Jeong, Doyeon Kim, Seungjai Min, Seongho Joe, Youngjune Gwon, and Jongwon Choi. Bihpf: Bilateral high-pass filters for robust deepfake detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 48-57, 2022. 2, 3, 4, 5, 6 +[19] Yonghyun Jeong, Doyeon Kim, Youngmin Ro, and Jongwon Choi. Frepgan: Robust deepfake detection using frequency-level perturbations. arXiv preprint arXiv:2202.03347, 2022. 2, 3, 4, 5, 6, 7 +[20] Yonghyun Jeong, Doyeon Kim, Youngmin Ro, Pyounggeon Kim, and Jongwon Choi. Fingerprint: Synthesized fingerprints for generated image detection. In European Conference on Computer Vision, pages 76-94. Springer, 2022. 6 +[21] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. In International Conference on Learning Representations, 2018. 1, 2, 4, 5, 7 +[22] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401-4410, 2019. 1, 2, 4, 5, 7 +[23] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110-8119, 2020. 1, 2, 4, 5, 7 +[24] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR (Poster), 2015. 4 +[25] Diederik P Kingma and Max Welling. Auto-encoding variational {Bayes}. In Int. Conf. on Learning Representations. 1 + +[26] Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo. Face x-ray for more general face forgery detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5001-5010, 2020. 2 +[27] Yuezun Li, Ming-Ching Chang, and Siwei Lyu. In ictu oculi: Exposing ai created fake videos by detecting eye blinking. In 2018 IEEE International workshop on information forensics and security (WIFS), pages 1-7. IEEE, 2018. 2 +[28] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 4 +[29] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, pages 3730-3738, 2015. 4 +[30] Zhengzhe Liu, Xiaojuan Qi, and Philip HS Torr. Global texture enhancement for fake face detection in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8060-8069, 2020. 2 +[31] Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi. Do gans leave artificial fingerprints? In 2019 IEEE conference on multimedia information processing and retrieval (MIPR), pages 506-511. IEEE, 2019. 2, 3 +[32] Iacopo Masi, Aditya Killekar, Royston Marian Mascarenhas, Shenoy Pratik Gurudatt, and Wael AbdAlmageed. Two-branch recurrent network for isolating deepfakes in videos. In European conference on computer vision, pages 667–684. Springer, 2020. 3 +[33] Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu. Semantic image synthesis with spatially-adaptive normalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2337-2346, 2019. 4 +[34] Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European conference on computer vision, pages 86-103. Springer, 2020. 3 +[35] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 4, 5, 7 +[36] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015. 1 +[37] Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: Learning to detect manipulated facial images. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1-11, 2019. 2, 4 +[38] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, + +Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252, 2015. 4 +[39] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations, 2015. 4, 5, 7 +[40] Ke Sun, Hong Liu, Qixiang Ye, Yue Gao, Jianzhuang Liu, Ling Shao, and Rongrong Ji. Domain general face forgery detection by learning to weight. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 2638-2646, 2021. 2 +[41] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016. 4, 7 +[42] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros. Cnn-generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8695-8704, 2020. 2, 4, 5, 6 +[43] Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015. 4, 5 +[44] Ning Yu, Larry S Davis, and Mario Fritz. Attributing fake images to gans: Learning and analyzing gan fingerprints. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7556-7566, 2019. 2 +[45] Yang Yu, Rongrong Ni, and Yao Zhao. Mining generalized features for detecting ai-manipulated fake faces. arXiv preprint arXiv:2010.14129, 2020. 2 +[46] Tianchen Zhao, Xiang Xu, Mingze Xu, Hui Ding, Yuanjun Xiong, and Wei Xia. Learning self-consistency for deepfake detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 15023-15033, 2021. 2 +[47] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2921-2929, 2016. 7, 8 +[48] Jiapeng Zhu, Yujun Shen, Deli Zhao, and Bolei Zhou. Indomain gan inversion for real image editing. In European conference on computer vision, pages 592-608. Springer, 2020. 4, 7 +[49] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. 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It can easily generate realistic fake images, leading to an increased risk of abuse. However, most image detectors suffer from sharp performance drops in unseen domains. The key of fake image detection is to develop a generalized representation to describe the artifacts produced by generation models. In this work, we introduce a novel detection framework, named Learning on Gradients (LGrad), designed for identifying GAN-generated images, with the aim of constructing a generalized detector with cross-model and cross-data. Specifically, a pretrained CNN model is employed as a transformation model to convert images into gradients. Subsequently, we leverage these gradients to present the generalized artifacts, which are fed into the classifier to ascertain the authenticity of the images. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. To the best of our knowledge, this is the first study to utilize gradients as the representation of artifacts in GAN-generated images. Extensive experiments demonstrate the effectiveness and robustness of gradients as generalized artifact representations. Our detector achieves a new state-of-the-art performance with a remarkable gain of " + }, + { + "bbox": [ + 46, + 299, + 290, + 597 + ], + "type": "inline_equation", + "content": "11.4\\%" + }, + { + "bbox": [ + 46, + 299, + 290, + 597 + ], + "type": "text", + "content": ". The code is released at https://github.com/chuangchuanqtan/LGrad." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 623, + 128, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 623, + 128, + 635 + ], + "spans": [ + { + "bbox": [ + 47, + 623, + 128, + 635 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 644, + 287, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 644, + 287, + 693 + ], + "spans": [ + { + "bbox": [ + 47, + 644, + 287, + 693 + ], + "type": "text", + "content": "Over the past years, remarkable progress has been made in deep generative models, i.e. generative adversarial networks (GAN) [14], its variations [3, 21-23, 36], and VAE [25]. The generated media is highly realistic and indistin" + } + ] + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 307, + 273, + 545, + 471 + ], + "blocks": [ + { + "bbox": [ + 307, + 273, + 545, + 471 + ], + "lines": [ + { + "bbox": [ + 307, + 273, + 545, + 471 + ], + "spans": [ + { + "bbox": [ + 307, + 273, + 545, + 471 + ], + "type": "image", + "image_path": "329654e7c33c656d2bb7453466b597a02c75bc5733b031630980a8394e869b75.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 479, + 547, + 567 + ], + "lines": [ + { + "bbox": [ + 304, + 479, + 547, + 567 + ], + "spans": [ + { + "bbox": [ + 304, + 479, + 547, + 567 + ], + "type": "text", + "content": "Figure 1. Visualization of gradients of real images and GAN-generated images extracted from a pre-trained model. To fully understand the gradients, heatmaps for R, G, and B channels also are shown, where red is high. In the gradients, the content of images is filtered out, and only the discriminative pixels are retained for the pre-trained model's target task. We utilize the gradients as the generalized artifacts representation to develop a novel detection framework." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 547, + 715 + ], + "type": "text", + "content": "guishable from real to human eyes. Although it has the potential for many novel applications [8], it also brings new risks due to the abuse of fake information. The misuse of DeepFakes has been confirmed that some people swap the faces of women onto pornographic videos [8]. In addition, some individuals, even non-experts, can malevolently manipulate or create fake images or videos for political or economic purposes, leading to serious social problems [17]. Thus, it is extremely necessary to develop forgery detection techniques to help people determine the credibility of the" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 135, + 712 + ], + "type": "text", + "content": "*Corresponding author" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 294, + 748, + 317, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 758 + ], + "type": "text", + "content": "12105" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 110, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 110, + 83 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 110, + 83 + ], + "type": "text", + "content": "media [17, 40]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 85, + 287, + 289 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 85, + 287, + 289 + ], + "spans": [ + { + "bbox": [ + 46, + 85, + 287, + 289 + ], + "type": "text", + "content": "Various detectors [11-13, 16, 18, 19, 30, 42, 45] have been developed to detect GAN-generated images. Some studies [16, 30, 45] focus on human face images, while others [11, 18, 19, 42] handle various categories of images. They mainly depend on local regions artifacts [4, 44], blending boundary [26], global textures [30], and frequency-level artifacts [13, 18, 19, 45]. However, those methods heavily rely on the training settings, resulting in failure detection of images from unseen categories or GAN models. The test images in the actual scene are always from unknown sources [17], rendering it challenging to develop generalized detectors. There are some works [19, 42] exploiting pre-processing, data augmentation, and reducing the effects of frequency-level artifacts to develop a robust detector. Nevertheless, there still needs to be a more generalized representation of the clue produced by generation models, which is critical for robust fake image detection." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 290, + 288, + 553 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 290, + 288, + 553 + ], + "spans": [ + { + "bbox": [ + 46, + 290, + 288, + 553 + ], + "type": "text", + "content": "To tackle this problem, we propose a novel and simple detection framework, referred to as Learning on Gradients (LGrad). A new generalized feature, Gradients, is developed to serve as a representation of the artifacts produced by GAN models. We believe that the gradients of a trained CNN model can highlight the important pixels in the target task, thereby serving as a valuable cue for detecting fake images. As shown in Figure 1, we adopt a pre-trained discriminator of ProGAN [21] to extract gradients of images produced by Celeba-HQ [21], ProGAN [21], StyleGAN [22], StyleGAN2 [23]. In these gradients, the content of images is filtered out, and only the discriminative pixels that are relevant to the pre-trained model's target task are retained. Therefore, the gradients are more dependent on the pre-trained model rather than on training sources, thereby enhancing the detector's performance with unseen data. In our framework, a pretrained model, called transformation model, is employed to convert images to gradients. These gradients serve as the generalized artifacts and are fed into the classifier to obtain a robust detector. Since the transformation model is indeterminate in our framework, targeted anti-detection cannot be effectively launched." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 555, + 287, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 555, + 287, + 639 + ], + "spans": [ + { + "bbox": [ + 46, + 555, + 287, + 639 + ], + "type": "text", + "content": "To validate the performance of our LGrad, we only use images generated by ProGAN to train the detector and evaluate it with various sources, including cross-category, cross-model, and cross-model & category. Numerous experiments prove the effectiveness and robustness of gradients as generalized artifacts. Our detector achieves a new state-of-the-art performance in known and unseen settings." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 59, + 641, + 244, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 641, + 244, + 653 + ], + "spans": [ + { + "bbox": [ + 59, + 641, + 244, + 653 + ], + "type": "text", + "content": "Our paper makes the following contributions:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 58, + 665, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 665, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 665, + 287, + 712 + ], + "type": "text", + "content": "- We develop a new detection framework, Learning on Gradient (LGrad), to detect GAN-generated images. Our detector achieves a new state-of-the-art performance." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 317, + 72, + 545, + 211 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 317, + 72, + 545, + 118 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 72, + 545, + 118 + ], + "spans": [ + { + "bbox": [ + 317, + 72, + 545, + 118 + ], + "type": "text", + "content": "- We introduce a new generalized artifact representation, Gradients, for GAN-generated image detection. Furthermore, we are the first to use gradients as the representation of artifacts." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 317, + 129, + 545, + 176 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 129, + 545, + 176 + ], + "spans": [ + { + "bbox": [ + 317, + 129, + 545, + 176 + ], + "type": "text", + "content": "- Our framework turns the data-driven problem into a transformation-model-driven problem. The robustness of the detector is improved with the introduction of the transformation model." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 317, + 186, + 545, + 211 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 186, + 545, + 211 + ], + "spans": [ + { + "bbox": [ + 317, + 186, + 545, + 211 + ], + "type": "text", + "content": "- We prove the great potential of the discriminator of GAN in detecting GAN-generated images." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 222, + 392, + 235 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 222, + 392, + 235 + ], + "spans": [ + { + "bbox": [ + 306, + 222, + 392, + 235 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 243, + 545, + 303 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 243, + 545, + 303 + ], + "spans": [ + { + "bbox": [ + 304, + 243, + 545, + 303 + ], + "type": "text", + "content": "In this section, we discuss recent approaches for generated image detection. The previous methods attempt to exploit spatial information or frequency artifacts as the representation of clues produced by the generation model for fake image detection." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 312, + 462, + 324 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 312, + 462, + 324 + ], + "spans": [ + { + "bbox": [ + 306, + 312, + 462, + 324 + ], + "type": "text", + "content": "2.1. Image-based Fake Detection" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 331, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 331, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 331, + 545, + 713 + ], + "type": "text", + "content": "Early studies employ spatial information from generated images to identify the fake images, such as color spaces [45] and global texture [30]. Rossler et al. [37] introduce a largescale manipulated face dataset FaceForensics++ and adopt Xception [7] as detector to identify the fake images with compression. Li et al. [27] detect fake face videos by analyzing eye blinking in the videos. ForensicTransfer [9] presents a new autoencoder-based architecture to learn a discriminative feature representation in the latent space for detection well on new domains. Yu et al. [44] and Marra et al. [31] believe that every GAN model has unique fingerprints after training, and extracting it from generated images to perform detection. Bayar et al. [2] design a new convolutional network architecture to suppress the image of the content and learn manipulation features for a generalized detector. Gram-Net [30] leverages the Gram matrix to extract global texture as the robust representation for fake face detection. Face X-ray [26] develops the blending boundary to present the artifacts for detecting manipulated face images. Chai et al. [4] design a patch-based classifier to limit the receptive field of the model for learning redundant artifacts in image patches. The patch-based predictions focus on local artifacts rather than global structure, improving the robustness of the detector. Yu et al. [45] use the channel difference image and spectrum image to mine the intrinsic clues of images from the view of the camera imaging process. Wang et al. [42] directly adopt a large number of real and fake images with data augmentation to train a binary classifier for improving unseen image performance. They use some post-processing operations to improve robustness during training, such as JPEG compression, blurring, and resizing. PCL [46] extracts the distinct source features of" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "12106" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 91, + 69, + 504, + 288 + ], + "blocks": [ + { + "bbox": [ + 91, + 69, + 504, + 288 + ], + "lines": [ + { + "bbox": [ + 91, + 69, + 504, + 288 + ], + "spans": [ + { + "bbox": [ + 91, + 69, + 504, + 288 + ], + "type": "image", + "image_path": "2950c4a6c6b633636272a0328972c34a486a8275aa477ec2a72ab0ddb83a1e2e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 294, + 547, + 319 + ], + "lines": [ + { + "bbox": [ + 46, + 294, + 547, + 319 + ], + "spans": [ + { + "bbox": [ + 46, + 294, + 547, + 319 + ], + "type": "text", + "content": "Figure 2. The overall pipeline of the proposed framework. The method uses the same transformation model and the same classifier in the testing and training phases." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 338, + 288, + 374 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 338, + 288, + 374 + ], + "spans": [ + { + "bbox": [ + 46, + 338, + 288, + 374 + ], + "type": "text", + "content": "images by self-consistency learning for detecting deepfake. He et al. [16] design a re-synthesis procedure to extract visual cues for robust deepfake detection." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 384, + 223, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 384, + 223, + 396 + ], + "spans": [ + { + "bbox": [ + 47, + 384, + 223, + 396 + ], + "type": "text", + "content": "2.2. Frequency-based Fake Detection" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 403, + 289, + 656 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 403, + 289, + 656 + ], + "spans": [ + { + "bbox": [ + 46, + 403, + 289, + 656 + ], + "type": "text", + "content": "Since GAN architectures depend on up-scaling operations, the generated images contain unique frequency-level artifacts compared to neutral images [11]. Several works [1, 11, 12, 31, 32, 34] leverage the spectral distributions of images as the representation of artifacts for fake image detection. LOG [32] proposes a two-branch recurrent network to integrate information from the color domain and the frequency domain for detecting manipulated faces. " + }, + { + "bbox": [ + 46, + 403, + 289, + 656 + ], + "type": "inline_equation", + "content": "F^3" + }, + { + "bbox": [ + 46, + 403, + 289, + 656 + ], + "type": "text", + "content": "-Net [34] designs a two-stream collaborative learning network to mine the forgery patterns in fake images, which exploits two frequency-aware clues, frequency-aware decomposed image components and local frequency statistics, to present subtle forgery patterns and high-level semantics, respectively. BiHPF [18] adopts two high-pass filters to amplify the magnitudes of the artifacts. FrePGAN [19] validates that the frequency-level artifacts of fake images are evident but uniquely vary by the type of GAN model or object category, which can cause overfitting issues for fake image detection. Thus, it generated the frequency-level perturbation maps to remove the effect of the frequency-level artifacts." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 669, + 234, + 682 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 669, + 234, + 682 + ], + "spans": [ + { + "bbox": [ + 47, + 669, + 234, + 682 + ], + "type": "text", + "content": "3. Learning on Gradients framework" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 689, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 288, + 715 + ], + "type": "text", + "content": "The key to the fake image detection task is developing a generalized representation of artifacts generated by" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 338, + 545, + 518 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 338, + 545, + 518 + ], + "spans": [ + { + "bbox": [ + 304, + 338, + 545, + 518 + ], + "type": "text", + "content": "GAN models. It should be generalized and robust enough to span diverse categories and different GAN models. In order to achieve this, we design a novel detection framework to improve the cross-source performance in this work. The overview of our method is shown in Figure 2. Our framework employs gradients as the generalized representation to obtain the robust detector. We transform the images to the gradients by a pretrained CNN model named transformation model. In the process of transforming, most of the content of images is filtered out due to the pooling layer in the CNN model, and the essential pixels for the transformation model are highlighted. Figure 1 provides the visualization evidence for our explanation. Extensive experiments in Section 4 demonstrate the effectiveness of the gradients using as the artifact representation." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 526, + 464, + 538 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 526, + 464, + 538 + ], + "spans": [ + { + "bbox": [ + 306, + 526, + 464, + 538 + ], + "type": "text", + "content": "3.1. Transformation to Gradients" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 545, + 545, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 545, + 676 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 545, + 676 + ], + "type": "text", + "content": "The previous methods based on pixel and frequency-level artifacts highly rely on the training data, resulting in failure with unseen images. To solve this overfitting issue, we adopt any one pretrained CNN model as the transformation model to convert all images consisting of training images and test images to gradients. This approach effectively maps all data to the gradient domain, which is determined by the chosen transformation model. By relying on this more generalized representation of artifacts, we can achieve improved performance in detecting GAN-generated images across diverse categories and GAN models." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "text", + "content": "We denote the images set as " + }, + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "inline_equation", + "content": "I = \\{(I_i, y_i)\\}_{i=0}^{N-1}" + }, + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "inline_equation", + "content": "y_i = \\{0,1\\}" + }, + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "text", + "content": " is the label of the images " + }, + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "inline_equation", + "content": "I_i \\in \\mathbb{R}^{w \\times h \\times c}" + }, + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "text", + "content": ", which is real (" + }, + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "inline_equation", + "content": "y = 0" + }, + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "text", + "content": ") or fake (" + }, + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "inline_equation", + "content": "y = 1" + }, + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "text", + "content": "). The " + }, + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "inline_equation", + "content": "w, h, c" + }, + { + "bbox": [ + 305, + 677, + 545, + 715 + ], + "type": "text", + "content": " present width," + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12107" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 106 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 106 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 106 + ], + "type": "text", + "content": "height, and the number of channels. The transformation model is defined as " + }, + { + "bbox": [ + 46, + 72, + 287, + 106 + ], + "type": "inline_equation", + "content": "M(\\cdot)" + }, + { + "bbox": [ + 46, + 72, + 287, + 106 + ], + "type": "text", + "content": ". We first feed images " + }, + { + "bbox": [ + 46, + 72, + 287, + 106 + ], + "type": "inline_equation", + "content": "I_{i}" + }, + { + "bbox": [ + 46, + 72, + 287, + 106 + ], + "type": "text", + "content": " into " + }, + { + "bbox": [ + 46, + 72, + 287, + 106 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 72, + 287, + 106 + ], + "type": "text", + "content": " model." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 142, + 109, + 287, + 121 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 142, + 109, + 287, + 121 + ], + "spans": [ + { + "bbox": [ + 142, + 109, + 287, + 121 + ], + "type": "interline_equation", + "content": "l = M \\left(I _ {i}\\right), \\tag {1}", + "image_path": "8327baa8308616a0093762652f4f718a40e54ed94005bdbb3be9ac880b9a6fd7.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 127, + 287, + 163 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 127, + 287, + 163 + ], + "spans": [ + { + "bbox": [ + 46, + 127, + 287, + 163 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 127, + 287, + 163 + ], + "type": "inline_equation", + "content": "l \\in \\mathbb{R}^{n \\times c}" + }, + { + "bbox": [ + 46, + 127, + 287, + 163 + ], + "type": "text", + "content": " is the output vectors of the transformation model " + }, + { + "bbox": [ + 46, + 127, + 287, + 163 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 127, + 287, + 163 + ], + "type": "text", + "content": ". Then, we calculate the gradients of " + }, + { + "bbox": [ + 46, + 127, + 287, + 163 + ], + "type": "inline_equation", + "content": "sum(l)" + }, + { + "bbox": [ + 46, + 127, + 287, + 163 + ], + "type": "text", + "content": " with respect to the input " + }, + { + "bbox": [ + 46, + 127, + 287, + 163 + ], + "type": "inline_equation", + "content": "I_i" + }, + { + "bbox": [ + 46, + 127, + 287, + 163 + ], + "type": "text", + "content": "," + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 134, + 172, + 287, + 198 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 134, + 172, + 287, + 198 + ], + "spans": [ + { + "bbox": [ + 134, + 172, + 287, + 198 + ], + "type": "interline_equation", + "content": "G = \\frac {\\partial \\operatorname {s u m} (l)}{\\partial I _ {i}}. \\tag {2}", + "image_path": "f3a4916c8df25b4f25c24522303cbda7b34f5f18a3f6f96bf873553928602cf8.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 205, + 287, + 325 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 205, + 287, + 325 + ], + "spans": [ + { + "bbox": [ + 46, + 205, + 287, + 325 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 46, + 205, + 287, + 325 + ], + "type": "text", + "content": " is the generalized artifacts representation of our method. Note that the transformation model " + }, + { + "bbox": [ + 46, + 205, + 287, + 325 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 205, + 287, + 325 + ], + "type": "text", + "content": " is fixed, and its parameter would not update in our framework. The quality of gradients " + }, + { + "bbox": [ + 46, + 205, + 287, + 325 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 46, + 205, + 287, + 325 + ], + "type": "text", + "content": " highly depends on the transformation model " + }, + { + "bbox": [ + 46, + 205, + 287, + 325 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 46, + 205, + 287, + 325 + ], + "type": "text", + "content": ", which is helpful to reduce the reliance on training data and enhance the generalization of the representations. In our experiments, we adopt various popular CNN models to implement the transformation model, including the classification model, segmentation model, discriminator of GAN, contrastive learning model, and GAN Inversion." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 334, + 176, + 346 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 334, + 176, + 346 + ], + "spans": [ + { + "bbox": [ + 47, + 334, + 176, + 346 + ], + "type": "text", + "content": "3.2. Detecting Fake Images" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 352, + 287, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 352, + 287, + 471 + ], + "spans": [ + { + "bbox": [ + 46, + 352, + 287, + 471 + ], + "type": "text", + "content": "During the training stage of our framework, we use normalized gradients ranging from 0 to 255 to train the binary classification network. We adopt the popular classifier to distinguish whether the input gradients correspond to GAN-generated images or not, and employ a cross-entropy loss function to optimize the network. During the inference time, the test images are first converted to gradients by the transformation model employed in the training stage. Then, we obtain the final result by feeding gradients into the trained classification network." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 483, + 172, + 496 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 483, + 172, + 496 + ], + "spans": [ + { + "bbox": [ + 47, + 483, + 172, + 496 + ], + "type": "text", + "content": "4. Experimental Results" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 503, + 105, + 514 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 503, + 105, + 514 + ], + "spans": [ + { + "bbox": [ + 47, + 503, + 105, + 514 + ], + "type": "text", + "content": "4.1. Dataset" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 521, + 287, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 521, + 287, + 640 + ], + "spans": [ + { + "bbox": [ + 46, + 521, + 287, + 640 + ], + "type": "text", + "content": "To evaluate the proposed approach, we adopt the dataset provided by Wang et al. [42] to conduct experiments following the previous works [18, 19]. It contains various fake images generated by ProGAN [21], StyleGAN [22], StyleGAN2 [23], BigGAN [3], CycleGAN [49], StarGAN [6], GauGAN [33] and Deepfake [37]. The real images are sampled from LSUN [43], ImageNet [38], CelebA [29], CelebA-HQ [21], COCO [28], and FaceForensics++ [37]. During the training stage, we train the classifier with images generated by ProGAN." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 642, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 642, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 642, + 287, + 712 + ], + "type": "text", + "content": "In addition, to verify the usefulness of our method for face data, we sample 20,000 real images from Celeba-HQ, and generate 20,000 face images by ProGAN as the training set. And the images generated by StyleGAN, StyleGAN2 trained by Celeba-HQ [21] are employed as the test set. All images are resized to " + }, + { + "bbox": [ + 46, + 642, + 287, + 712 + ], + "type": "inline_equation", + "content": "256 \\times 256" + }, + { + "bbox": [ + 46, + 642, + 287, + 712 + ], + "type": "text", + "content": " resolution." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 72, + 438, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 72, + 438, + 84 + ], + "spans": [ + { + "bbox": [ + 306, + 72, + 438, + 84 + ], + "type": "text", + "content": "4.2. Implementation Details" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 90, + 545, + 198 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 90, + 545, + 198 + ], + "spans": [ + { + "bbox": [ + 304, + 90, + 545, + 198 + ], + "type": "text", + "content": "We apply the Resnet50 [15] model pre-trained with ImageNet [38] as the classifier, and train it by Adam [24] with learning rate of " + }, + { + "bbox": [ + 304, + 90, + 545, + 198 + ], + "type": "inline_equation", + "content": "5 \\times 10^{-4}" + }, + { + "bbox": [ + 304, + 90, + 545, + 198 + ], + "type": "text", + "content": ". The learning rate is decreased by twenty percent after every ten epochs. The batch size is set to 16, and the number of epochs is 100. In this paper, the results of the last epoch are used as the final performance. In addition, we randomly crop the input images during the training phase and directly feed images into the classifier in the test stage." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 198, + 545, + 319 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 198, + 545, + 319 + ], + "spans": [ + { + "bbox": [ + 304, + 198, + 545, + 319 + ], + "type": "text", + "content": "To fully understand the gradients as the generalized artifacts, we further employ various popular models to implement the transformation model, i.e. VGG16 [39], InceptionV3 [41], Resnet50 [15], DeeplabV3 [5], CLIP [35], ViT [10], discriminator of ProGAN [21], StyleGAN [22], StyleGAN2 [23], and GAN Idinvert [48]. All of those models in this paper are released by the official code. Following the baselines [18, 19], we utilize the average precision score (A.P.) and accuracy (Acc.) as evaluation metrics to evaluate the proposed method." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 327, + 493, + 338 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 327, + 493, + 338 + ], + "spans": [ + { + "bbox": [ + 306, + 327, + 493, + 338 + ], + "type": "text", + "content": "4.3. Detection Performance Evaluations" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 346, + 545, + 550 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 346, + 545, + 550 + ], + "spans": [ + { + "bbox": [ + 304, + 346, + 545, + 550 + ], + "type": "text", + "content": "To assess the effectiveness of our detector, we conducted evaluations in four different settings: cross-model images, cross-category images, cross-model & category images, and perturbed images. Specifically, the detector is trained on a known GAN model, e.g. ProGAN-Horse [21] dataset, while evaluated with unseen images: 1) cross-category images, produced by the same GAN model trained on different datasets, e.g. ProGAN-Airplane and ProGAN-Diningtable; 2) cross-model images, generated by different GAN models trained on the same dataset, e.g. StyleGAN-Horse [22] and StyleGAN2-Horse [23]; and 3) cross-model & category images, where both training data and GAN model are different from the training setting, e.g. BigGAN-ImageNet [3,38] and GauGAN-COCO [28, 33]. Meanwhile, to fully understand the proposed LGrad framework in terms of different transformation models, we further implement the transformation model with various popular CNN networks." + } + ] + } + ], + "index": 15 + }, + { + "type": "table", + "bbox": [ + 307, + 561, + 545, + 690 + ], + "blocks": [ + { + "bbox": [ + 307, + 561, + 545, + 690 + ], + "lines": [ + { + "bbox": [ + 307, + 561, + 545, + 690 + ], + "spans": [ + { + "bbox": [ + 307, + 561, + 545, + 690 + ], + "type": "table", + "html": "
Trans. ModelsProGAN-CelebaHQStyleGAN-CelebaHQStyleGAN2-CelebaHQ
Acc.A.P.Acc.A.P.Acc.A.P.
Input Image99.6100.076.898.964.296.2
ProGAN-RandomInit91.497.357.466.352.456.9
VGG1696.999.879.696.368.291.6
CLIP-RN5099.5100.099.4100.099.2100.0
ProGAN-bedroom98.8100.098.499.996.599.6
ProGAN-bridge96.499.584.895.081.993.6
", + "image_path": "a48e4af6e4027d90bbf46903af0ca8d0cb8660ef544f119adaad04f80887996c.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "table_body" + } + ], + "index": 16 + }, + { + "bbox": [ + 323, + 699, + 527, + 710 + ], + "lines": [ + { + "bbox": [ + 323, + 699, + 527, + 710 + ], + "spans": [ + { + "bbox": [ + 323, + 699, + 527, + 710 + ], + "type": "text", + "content": "Table 1. Cross-model Performance on face images data." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12108" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 47, + 72, + 288, + 364 + ], + "blocks": [ + { + "bbox": [ + 47, + 72, + 288, + 364 + ], + "lines": [ + { + "bbox": [ + 47, + 72, + 288, + 364 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 288, + 364 + ], + "type": "table", + "html": "
Test-categoriesCLIPTrans.Bedroom Trans.Bridge Trans.
ACCAPACCAPACCAPAP
airplane92.997.099.4100.099.4100.0
bicycle80.594.694.598.594.898.5
bird85.694.499.2100.097.599.7
boat90.696.699.6100.098.599.9
bottle94.798.297.7100.099.2100.0
bus81.594.998.1100.096.799.8
car84.095.799.7100.099.1100.0
cat90.296.199.7100.098.799.9
chair94.598.596.299.999.1100.0
cow85.595.099.5100.097.799.8
diningtable92.497.798.599.896.999.5
dog89.896.199.6100.098.499.9
horse89.096.3100.0100.099.8100.0
motorbike79.294.897.099.796.999.3
person93.697.198.9100.099.1100.0
pottedplant77.995.697.599.795.799.1
sheep83.294.299.2100.095.899.3
sofa94.198.699.1100.099.2100.0
train83.694.494.499.796.199.6
tvmonitor93.098.796.5100.099.2100.0
Mean87.896.298.299.997.999.7
", + "image_path": "06e769ead091d613469ae273a4dd10289e7333d2131b7278d99ebae38ee6c474.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 371, + 287, + 395 + ], + "lines": [ + { + "bbox": [ + 46, + 371, + 287, + 395 + ], + "spans": [ + { + "bbox": [ + 46, + 371, + 287, + 395 + ], + "type": "text", + "content": "Table 2. Cross-category Performance on the ProGAN models trained on different LSUN [43] object datasets." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 47, + 420, + 194, + 430 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 420, + 194, + 430 + ], + "spans": [ + { + "bbox": [ + 47, + 420, + 194, + 430 + ], + "type": "text", + "content": "4.3.1 Cross-model Performance." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "spans": [ + { + "bbox": [ + 46, + 437, + 287, + 616 + ], + "type": "text", + "content": "We employ face images to show the cross-model performance of our proposed LGrad framework. The training set contains 25,000 fake images generated by ProGAN [21] and 25,000 real images randomly sampled from Celeba-HQ [21]. The test set consists of 10,000 images, half of which are real images randomly sampled from Celeba-HQ [21], while the other half are fake images generated by StyleGAN [22] and StyleGAN2 [23]. We adopt popular CNN models such as VGG16 [39], CLIP-RN50 [35], and discriminators of ProGAN [21] as the transformation models, and also randomly initialize a discriminator of ProGAN to perform the transformation to demonstrate the effectiveness of pre-trained models. In addition, we train the detector directly on the images to compare it with the gradients-based detector." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 617, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 288, + 713 + ], + "type": "text", + "content": "The experimental results are shown in Table 1. We can observe that image-based detectors suffer from sharp performance drops on StyleGAN [22] and StyleGAN2 [23]. It only achieves the Acc. of " + }, + { + "bbox": [ + 46, + 617, + 288, + 713 + ], + "type": "inline_equation", + "content": "64.2\\%" + }, + { + "bbox": [ + 46, + 617, + 288, + 713 + ], + "type": "text", + "content": " on StyleGAN2, which is much lower than the performance on ProGAN. In contrast, all gradients-based detectors transformed by pre-trained models outperform the one with random initialization of ProGAN. The results demonstrate that the transformation" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 72, + 545, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 192 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 192 + ], + "type": "text", + "content": "model eliminates the contents of images while retaining the discriminative regions in the gradients. Thus, the gradients can effectively present the generalized artifacts produced by GAN models. The detector based on CLIP-RestNet50, trained on various (image, text) pairs, achieves the best accuracy of " + }, + { + "bbox": [ + 304, + 72, + 545, + 192 + ], + "type": "inline_equation", + "content": "99.4\\%" + }, + { + "bbox": [ + 304, + 72, + 545, + 192 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 72, + 545, + 192 + ], + "type": "inline_equation", + "content": "99.2\\%" + }, + { + "bbox": [ + 304, + 72, + 545, + 192 + ], + "type": "text", + "content": " on StyleGAN and StyleGAN2, respectively. The discriminators of ProGAN trained on bedroom or bridge datasets yield comparable results with the CLIP-RestNet50 model. Our proposed framework shows remarkable performance on face data." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 207, + 463, + 218 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 207, + 463, + 218 + ], + "spans": [ + { + "bbox": [ + 305, + 207, + 463, + 218 + ], + "type": "text", + "content": "4.3.2 Cross-category Performance." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 225, + 545, + 500 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 225, + 545, + 500 + ], + "spans": [ + { + "bbox": [ + 304, + 225, + 545, + 500 + ], + "type": "text", + "content": "We leverage the training set from Wang et al. [42] to validate the cross-category performance, which contains 20 object categories generated by ProGAN models. Each category comprises 18,000 fake images and an equal number of real images. We trained our detector using the horse dataset and evaluated its performance on the remaining 19 datasets. We implement the transformation model with two discriminators of ProGAN and Contrastive learning model, including ProGAN-bedroom, ProGAN-bridge, and CLIP-ResNet50. Table 2 reports the results of our detectors. The performance of the horse is calculated on the training data. When adopting the CLIP-RestNet50 model as the transformation model, the detector achieves the best result on cross-model performance compared to the other models. We also evaluate the performance of CLIP-RestNet50 on the cross-category case. In Table 2, the CLIP-RestNet50 achieves mean Acc. of " + }, + { + "bbox": [ + 304, + 225, + 545, + 500 + ], + "type": "inline_equation", + "content": "87.8\\%" + }, + { + "bbox": [ + 304, + 225, + 545, + 500 + ], + "type": "text", + "content": " on 20 categories, while the ProGAN-bedroom and ProGAN-bridge obtain higher accuracies of " + }, + { + "bbox": [ + 304, + 225, + 545, + 500 + ], + "type": "inline_equation", + "content": "98.2\\%" + }, + { + "bbox": [ + 304, + 225, + 545, + 500 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 225, + 545, + 500 + ], + "type": "inline_equation", + "content": "97.9\\%" + }, + { + "bbox": [ + 304, + 225, + 545, + 500 + ], + "type": "text", + "content": ", respectively. It can be observed that discriminators are more suitable as the transformation model to detect cross-category images. In summary, we can confirm that our detectors show excellent performance in detecting unseen categories." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 515, + 502, + 527 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 515, + 502, + 527 + ], + "spans": [ + { + "bbox": [ + 305, + 515, + 502, + 527 + ], + "type": "text", + "content": "4.3.3 Cross-model & category Performance." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 534, + 545, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 534, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 304, + 534, + 545, + 689 + ], + "type": "text", + "content": "We further expand the testing scope to evaluate the robustness of our proposed method. The gradients-based detector is performed on eight different models following baselines [18, 19] including ProGAN, StyleGAN, StyleGAN2, BigGAN, CycleGAN, StarGAN, GauGAN, and Deepfake. We adopt the discriminators of ProGAN and StyleGAN, trained using the LSUN bedroom dataset, as the transformation model. For fair comparisons, we use the same experimental setting employed in baselines [19] to evaluate the generality of the detector. Specifically, three training configurations are employed to train the detector, including 1-class (horse), 2-class (chair, horse), and 4-class (car, cat, chair, horse) settings." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": "Table 3 presents the results of our proposed LGrad framework in comparison to various previous methods. We" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "12109" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 47, + 72, + 547, + 414 + ], + "blocks": [ + { + "bbox": [ + 47, + 72, + 547, + 414 + ], + "lines": [ + { + "bbox": [ + 47, + 72, + 547, + 414 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 547, + 414 + ], + "type": "table", + "html": "
MethodsSettingsTest Models
Input #classProGANStyleGANStyleGAN2BigGAN CycleGANStarGANGauGAN DeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc. A.P.
Wang [42]Image150.463.850.479.368.294.750.261.350.052.950.048.250.367.650.151.5
Frank [12]Freq178.977.969.464.867.464.062.358.667.465.460.559.567.569.152.447.3
Durall [11]Freq185.179.559.255.270.463.857.053.966.761.499.899.658.754.853.051.9
BiHPF [18]Freq182.581.468.062.868.863.667.062.575.574.290.190.173.692.151.649.9
FrePGAN [19]Image195.599.480.690.677.493.063.560.559.459.999.6100.053.049.170.481.5
LGrad (ProGAN-bedroom)Grad198.499.982.695.683.398.476.281.882.390.699.7100.071.775.052.857.8
LGrad (StyleGAN-bedroom)Grad199.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.9
Wang [42]Image264.692.752.882.875.796.651.670.558.681.551.274.353.686.650.651.5
Frank [12]Freq285.781.373.168.575.070.976.970.886.580.885.077.067.365.350.155.3
Durall [11]Freq279.073.963.658.867.362.169.562.965.460.899.499.467.063.050.550.2
BiHPF [18]Freq287.487.471.674.177.081.182.680.686.086.693.880.875.388.253.754.0
FrePGANImage299.099.980.892.072.294.066.061.869.170.398.5100.053.151.062.280.6
LGrad (ProGAN-bedroom)Grad299.5100.085.899.383.599.478.987.778.889.099.6100.070.577.651.952.7
LGrad (StyleGAN-bedroom)Grad299.8100.094.899.792.499.682.592.485.994.799.799.973.783.260.667.8
Wang [42]Image491.499.463.891.476.497.552.973.372.788.663.890.863.992.251.762.3
Frank [12]Freq490.385.274.572.073.171.488.786.075.571.299.599.569.277.460.749.1
Durall [11]Freq481.174.454.452.666.862.060.156.369.064.098.198.161.957.450.250.0
BiHPF [18]Freq490.786.276.975.176.274.784.981.781.978.994.494.469.578.154.454.6
FrePGAN [19]Image499.099.980.789.684.198.669.271.171.174.499.9100.060.371.770.991.9
LGrad (ProGAN-bedroom)Grad499.7100.087.899.191.799.780.989.378.289.099.8100.073.578.653.155.0
LGrad (StyleGAN-bedroom)Grad499.9100.094.899.996.099.982.990.785.394.099.6100.072.479.358.067.9
", + "image_path": "ce47825aac95d45841e49217a3ee4890563162132c99f94532f0e2eabcb2d140.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 48, + 445, + 545, + 551 + ], + "blocks": [ + { + "bbox": [ + 183, + 422, + 409, + 433 + ], + "lines": [ + { + "bbox": [ + 183, + 422, + 409, + 433 + ], + "spans": [ + { + "bbox": [ + 183, + 422, + 409, + 433 + ], + "type": "text", + "content": "Table 3. Classification accuracy with cross-model & category." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 445, + 545, + 551 + ], + "lines": [ + { + "bbox": [ + 48, + 445, + 545, + 551 + ], + "spans": [ + { + "bbox": [ + 48, + 445, + 545, + 551 + ], + "type": "table", + "html": "
PerturbedProGANStyleGANStyleGAN2BigGANCycleGANStarGANGauGANDeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.
No99.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.986.392.7
blur90.196.590.695.787.593.471.176.370.673.693.698.063.769.561.262.678.583.2
cropping99.299.995.999.694.099.680.188.078.588.194.499.470.678.167.582.385.091.9
jpeg76.290.074.490.272.689.166.074.672.783.276.089.560.167.658.065.269.581.2
noise77.187.773.384.874.384.968.277.466.477.276.088.860.469.157.565.169.179.4
combined86.394.683.593.082.492.371.278.973.080.484.794.364.771.561.267.475.984.1
", + "image_path": "0875370b236eec01f4a8808c6e13d60a628524f754dffd807a4e596c3c8cc6f2.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 196, + 559, + 395, + 569 + ], + "lines": [ + { + "bbox": [ + 196, + 559, + 395, + 569 + ], + "spans": [ + { + "bbox": [ + 196, + 559, + 395, + 569 + ], + "type": "text", + "content": "Table 4. Performance of common image perturbations." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 590, + 288, + 710 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 590, + 288, + 710 + ], + "spans": [ + { + "bbox": [ + 46, + 590, + 288, + 710 + ], + "type": "text", + "content": "can find that the proposed LGrad framework successfully surpasses the counterparts in the metric of average precision score and accuracy. Our detector shows significantly better generalization to other methods, except on GauGAN and Deepfake. Notably, our LGrad framework achieves higher accuracy values increase to " + }, + { + "bbox": [ + 46, + 590, + 288, + 710 + ], + "type": "inline_equation", + "content": "99.4\\%" + }, + { + "bbox": [ + 46, + 590, + 288, + 710 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 590, + 288, + 710 + ], + "type": "inline_equation", + "content": "96.0\\%" + }, + { + "bbox": [ + 46, + 590, + 288, + 710 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 590, + 288, + 710 + ], + "type": "inline_equation", + "content": "93.8\\%" + }, + { + "bbox": [ + 46, + 590, + 288, + 710 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 590, + 288, + 710 + ], + "type": "inline_equation", + "content": "79.5\\%" + }, + { + "bbox": [ + 46, + 590, + 288, + 710 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 590, + 288, + 710 + ], + "type": "inline_equation", + "content": "84.7\\%" + }, + { + "bbox": [ + 46, + 590, + 288, + 710 + ], + "type": "text", + "content": " on the ProGAN, StyleGAN, StyleGAN2, BigGAN, CycleGAN models with the 1-class setting, respectively. We obtain comparable results on the GauGAN model and deepfake. Furthermore, our LGrad" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 590, + 545, + 711 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 590, + 545, + 711 + ], + "spans": [ + { + "bbox": [ + 304, + 590, + 545, + 711 + ], + "type": "text", + "content": "with StyleGAN-bedroom model successfully achieves a new state-of-the-art accuracy, surpassing FrePGAN [19] by " + }, + { + "bbox": [ + 304, + 590, + 545, + 711 + ], + "type": "inline_equation", + "content": "11.4\\%" + }, + { + "bbox": [ + 304, + 590, + 545, + 711 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 590, + 545, + 711 + ], + "type": "inline_equation", + "content": "13.4\\%" + }, + { + "bbox": [ + 304, + 590, + 545, + 711 + ], + "type": "text", + "content": " in terms of mean accuracy and mean average precision score, respectively. We also compare to FingerprintNet [20] evaluated on six GAN models. When evaluated on StyleGAN, StyleGAN2, BigGAN, CycleGAN, StarGAN, and GauGAN, FingerprintNet and the proposed method achieve the mean Accuracy of " + }, + { + "bbox": [ + 304, + 590, + 545, + 711 + ], + "type": "inline_equation", + "content": "82.6\\%" + }, + { + "bbox": [ + 304, + 590, + 545, + 711 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 590, + 545, + 711 + ], + "type": "inline_equation", + "content": "87.4\\%" + }, + { + "bbox": [ + 304, + 590, + 545, + 711 + ], + "type": "text", + "content": ", respectively. We achieve a gain of " + }, + { + "bbox": [ + 304, + 590, + 545, + 711 + ], + "type": "inline_equation", + "content": "4.8\\%" + }, + { + "bbox": [ + 304, + 590, + 545, + 711 + ], + "type": "text", + "content": " in terms of mean accuracy." + } + ] + } + ], + "index": 5 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "12110" + } + ] + } + ], + "index": 6 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 47, + 71, + 547, + 250 + ], + "blocks": [ + { + "bbox": [ + 47, + 71, + 547, + 250 + ], + "lines": [ + { + "bbox": [ + 47, + 71, + 547, + 250 + ], + "spans": [ + { + "bbox": [ + 47, + 71, + 547, + 250 + ], + "type": "table", + "html": "
Trans. ModelProGANStyleGANStyleGAN2BigGANCycleGANStarGANGauGANDeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.
VGG16 [39]96.099.565.588.874.793.873.078.177.886.199.8100.060.763.160.467.876.084.7
InceptionV3 [41]64.974.458.866.965.473.850.952.159.168.452.658.554.056.549.850.156.962.6
Resnet50 [15]86.495.081.092.283.793.557.256.268.375.896.499.551.252.763.470.473.479.4
CLIP-Resnet50 [35]87.695.880.290.078.991.060.161.184.287.988.595.172.671.664.964.477.182.1
ViT [10]51.271.151.765.752.467.950.653.354.175.251.664.050.661.150.053.751.564.0
DeeplabV3 [5]81.691.668.780.470.684.554.555.766.271.087.994.751.753.159.358.967.673.7
Idinvert [48]97.499.871.695.371.295.486.694.878.785.797.499.772.082.160.172.179.490.6
ProGAN-bedroom [21]98.499.982.695.683.398.476.281.882.390.699.7100.071.775.052.857.880.987.4
ProGAN-bridge [21]97.899.786.497.585.797.372.578.776.887.594.199.962.575.853.261.378.687.2
StyleGAN-bedroom [22]99.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.986.392.7
StyleGAN-cats [22]97.499.783.497.377.496.469.874.679.390.297.899.868.077.465.972.979.988.5
StyleGAN2-church [23]99.1100.088.297.791.999.670.171.780.689.195.699.860.868.972.776.582.487.9
", + "image_path": "988d3a0a18b536efd69fc6c705e6b0d1a5c86288a7865d3c0e60c54ee0ad2c07.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 47, + 280, + 548, + 385 + ], + "blocks": [ + { + "bbox": [ + 192, + 256, + 400, + 267 + ], + "lines": [ + { + "bbox": [ + 192, + 256, + 400, + 267 + ], + "spans": [ + { + "bbox": [ + 192, + 256, + 400, + 267 + ], + "type": "text", + "content": "Table 5. Performance of different transformation models." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 47, + 280, + 548, + 385 + ], + "lines": [ + { + "bbox": [ + 47, + 280, + 548, + 385 + ], + "spans": [ + { + "bbox": [ + 47, + 280, + 548, + 385 + ], + "type": "table", + "html": "
MethodsNum. of Train. dataProGANStyleGANStyleGAN2BigGANCycleGANStarGANGauGANDeepfakeMean
Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.Acc.A.P.
FrePGAN [19]36k95.599.480.690.677.493.063.560.559.459.999.6100.053.049.170.481.574.979.3
LGrad4k95.999.588.998.091.799.059.155.258.758.487.899.859.266.265.983.575.982.4
LGrad9k98.399.992.499.492.299.471.774.573.076.898.1100.064.074.268.079.382.287.9
LGrad18k98.8100.095.199.891.499.776.887.179.890.799.2100.068.980.463.973.084.291.3
LGrad36k99.499.996.099.693.899.479.588.984.794.499.5100.070.981.866.777.986.392.7
LGrad72k99.8100.094.899.792.499.682.592.485.994.799.799.973.783.260.667.886.292.2
", + "image_path": "e1f22c68d124f68b94b26a550ef39c21ebf07de0707355d030f7393508d47c3b.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 192, + 393, + 401, + 404 + ], + "lines": [ + { + "bbox": [ + 192, + 393, + 401, + 404 + ], + "spans": [ + { + "bbox": [ + 192, + 393, + 401, + 404 + ], + "type": "text", + "content": "Table 6. Results with variance in number of training data." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 426, + 287, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 426, + 287, + 581 + ], + "spans": [ + { + "bbox": [ + 46, + 426, + 287, + 581 + ], + "type": "text", + "content": "In comparison to the performance of FrePGAN [19], our detector obtains the most significant improvement on StyleGAN, increasing accuracy by " + }, + { + "bbox": [ + 46, + 426, + 287, + 581 + ], + "type": "inline_equation", + "content": "15.4\\%" + }, + { + "bbox": [ + 46, + 426, + 287, + 581 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 426, + 287, + 581 + ], + "type": "inline_equation", + "content": "14.0\\%" + }, + { + "bbox": [ + 46, + 426, + 287, + 581 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 426, + 287, + 581 + ], + "type": "inline_equation", + "content": "14.1\\%" + }, + { + "bbox": [ + 46, + 426, + 287, + 581 + ], + "type": "text", + "content": " with 1-class, 2-class, and 4-class settings, respectively. It is worth noting that the LGrad framework fails to perform well on the deepfake model, which is not a GAN model and is trained with MSE loss and SSIM loss, resulting in an accuracy value of only " + }, + { + "bbox": [ + 46, + 426, + 287, + 581 + ], + "type": "inline_equation", + "content": "66.7\\%" + }, + { + "bbox": [ + 46, + 426, + 287, + 581 + ], + "type": "text", + "content": ". The results indicate that the proposed LGrad approach is less reliant on the amount of data, as it achieves similar results with 1-class, 2-class, and 4-class settings. As the number of training data increases, the deepfake performance decreases, resulting in a drop in the mean accuracy of all generative models." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "content": "In addition, we leverage the high-frequency component of images as the artifacts representation to train the classifier with the same settings. High-frequency-based method obtains the mean accuracy of " + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "inline_equation", + "content": "75.1\\%" + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "inline_equation", + "content": "75.0\\%" + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "inline_equation", + "content": "73.0\\%" + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "content": " with 1-class, 2-class, and 4-class settings, respectively. Our LGrad increases accuracy by " + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "inline_equation", + "content": "11.2\\%" + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "inline_equation", + "content": "11.2\\%" + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "inline_equation", + "content": "13.1\\%" + }, + { + "bbox": [ + 46, + 582, + 288, + 714 + ], + "type": "text", + "content": " on the same setting, respectively. Gradients are different from high-frequency information. This suggests that gradients can be used as an effective representation of artifacts that are capable of detecting GAN-generated images across diverse categories and various GAN models." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 426, + 513, + 437 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 426, + 513, + 437 + ], + "spans": [ + { + "bbox": [ + 305, + 426, + 513, + 437 + ], + "type": "text", + "content": "4.3.4 Robustness against Image Perturbations." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 445, + 545, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 445, + 545, + 613 + ], + "spans": [ + { + "bbox": [ + 304, + 445, + 545, + 613 + ], + "type": "text", + "content": "To evaluate the robustness of the proposed framework to image perturbations, we apply common image perturbations on the test images with a probability of " + }, + { + "bbox": [ + 304, + 445, + 545, + 613 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 304, + 445, + 545, + 613 + ], + "type": "text", + "content": " following [13]. These perturbations include blurring, cropping, compression, adding random noise, and a combination of all of them. In this subsection, the discriminator of StyleGAN-bedroom is used as the transformation model. The results are presented in Table 4. It can be observed that our detectors are robust with respect to cropping and blur, but obtain degraded performance onJPEG and noise. This could be due to the fact that the influence ofJPEG and noise for the transformation model results in the transformed gradients that do not work well. We could further improve the robustness of the transformation model to tackle this problem." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 623, + 389, + 634 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 623, + 389, + 634 + ], + "spans": [ + { + "bbox": [ + 306, + 623, + 389, + 634 + ], + "type": "text", + "content": "4.4. Visualization" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 641, + 545, + 713 + ], + "type": "text", + "content": "Figure 3 shows fake images generated by ProGAN and StyleGAN, real images sampled from CelebA-HQ, the gradients transformed from images, and Class Activate Map (CAM) [47] extracted from the detector. We adopt the discriminator of ProGAN-bedroom as the transformation model. It can be observed from gradients that most of the" + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "12111" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 80, + 70, + 225, + 224 + ], + "blocks": [ + { + "bbox": [ + 80, + 70, + 225, + 224 + ], + "lines": [ + { + "bbox": [ + 80, + 70, + 225, + 224 + ], + "spans": [ + { + "bbox": [ + 80, + 70, + 225, + 224 + ], + "type": "image", + "image_path": "84aacbd73d666f4a9f4a115a92b9e99cbb93852faa2eff3386a3d3842ef30f4c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 84, + 233, + 507, + 245 + ], + "lines": [ + { + "bbox": [ + 84, + 233, + 507, + 245 + ], + "spans": [ + { + "bbox": [ + 84, + 233, + 507, + 245 + ], + "type": "text", + "content": "Figure 3. The visualization of gradients and Class Activate Map (CAM) [47] extracted from detector on face images." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 228, + 70, + 372, + 224 + ], + "blocks": [ + { + "bbox": [ + 228, + 70, + 372, + 224 + ], + "lines": [ + { + "bbox": [ + 228, + 70, + 372, + 224 + ], + "spans": [ + { + "bbox": [ + 228, + 70, + 372, + 224 + ], + "type": "image", + "image_path": "9f6efd423babf8b5e446ce8f253cb112f4e37fa4ca3f8c9705b71d1ce2b42106.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 376, + 70, + 515, + 224 + ], + "blocks": [ + { + "bbox": [ + 376, + 70, + 515, + 224 + ], + "lines": [ + { + "bbox": [ + 376, + 70, + 515, + 224 + ], + "spans": [ + { + "bbox": [ + 376, + 70, + 515, + 224 + ], + "type": "image", + "image_path": "32fbe059b34da30d882150ec8a0b767c6db1e36934bd78594a7dd299f15251d0.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 266, + 289, + 397 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 266, + 289, + 397 + ], + "spans": [ + { + "bbox": [ + 46, + 266, + 289, + 397 + ], + "type": "text", + "content": "content of images is filtered out, and the essential pixels for the transformation model are highlighted in the process of transforming. Although some texture information is preserved, its value is relatively low. Furthermore, the class activate map of the detector trained on gradients highlights the discriminative region for detection, and it can be seen that the maps of the real image highlight the face region, while most maps of the generated image are located in the background area. It reflects that the gradients, as the generalized representation of artifacts, contain high-level rather than low-level features such as texture." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 414, + 140, + 428 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 414, + 140, + 428 + ], + "spans": [ + { + "bbox": [ + 47, + 414, + 140, + 428 + ], + "type": "text", + "content": "5. Ablation Study" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 436, + 287, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 436, + 287, + 652 + ], + "spans": [ + { + "bbox": [ + 46, + 436, + 287, + 652 + ], + "type": "text", + "content": "Effect of Transformation Model. In our framework, we turn the data-dependent problem into a transformation-model-dependent problem. The images are converted into the gradient, which serves as the generalized representation of artifacts. The performance of the detector highly relies on the quality of the transformation model. Now we discuss the influence of the transformation model on the detection performance, by employing ten pre-trained models. Table 5 shows the across-model & data results of different transformation models. We can observe that gradients generated by the discriminator of GAN perform better on unseen data compared to the classification model and contrastive learning model. In addition, discriminators with different structures or trained with different data exhibit varying performances. Thus, our framework turns the data-dependent problem into a transformation-model-dependent problem. In our experiment, the discriminator of StyleGAN-bedroom obtains the best performance." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "type": "text", + "content": "Effect of the number of training data In order to test the influence of the number of training data for the detector, we sample 4,000, 18,000, and 36,000 images which contain equal numbers of real and fake images from the horse dataset as the training set, respectively. We also use " + }, + { + "bbox": [ + 46, + 653, + 288, + 715 + ], + "type": "inline_equation", + "content": "72\\mathrm{k}" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 266, + 547, + 374 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 266, + 547, + 374 + ], + "spans": [ + { + "bbox": [ + 304, + 266, + 547, + 374 + ], + "type": "text", + "content": "images of all the chairs and horses set to train the detector. In this subsection, the discriminator of StyleGAN-bedroom is also used as the transformation model. Table 6 compares the proposed framework with the different number of training data. Our detector achieves similar across-model & data performance with 18,000, 36,000, and 72,000 training images. In addition, the proposed LGrad with 4,000 training data still outperforms FrePGAN with 36,000 images, indicating the superiority of our method." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 390, + 378, + 403 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 390, + 378, + 403 + ], + "spans": [ + { + "bbox": [ + 306, + 390, + 378, + 403 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 412, + 547, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 412, + 547, + 616 + ], + "spans": [ + { + "bbox": [ + 304, + 412, + 547, + 616 + ], + "type": "text", + "content": "In this work, we propose a novel detection framework, called Learning on Gradients(LGrad), designed to identify GAN-generated images. In our method, the gradients are employed as a generalized representation to describe the artifacts produced by generation models. Specifically, we utilize a pre-trained CNN model as the transformation model to convert images into gradients effectively transforming the data-dependent problem into a transformation-model-dependent problem. Numerous experiments in our paper prove the great potential of the discriminator of GAN in detecting GAN-generated images. Moreover, we demonstrate the effectiveness and robustness of using gradients as generalized artifact representations in detecting GAN-generated images. Nonetheless, we acknowledge that the LGrad framework may not perform well in detecting non-GAN generative models, such as deepfake, and we aim to address this limitation in future research." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 642, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 547, + 715 + ], + "type": "text", + "content": "Acknowledgments This work is supported in part by the National Key R&D Program of China (No.2021ZD0112100), National NSF of China (No.61972022, No.U1936212, No.62120106009, No.62072394), Natural Science Foundation of Hebei province (F2021203019)." + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "12112" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 712 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 146 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 146 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 146 + ], + "type": "text", + "content": "[1] Jawadul H Bappy, Cody Simons, Lakshmanan Nataraj, BS Manjunath, and Amit K Roy-Chowdhury. Hybrid lstm and encoder-decoder architecture for detection of image forgeries. IEEE Transactions on Image Processing, 28(7):3286-3300, 2019. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 148, + 288, + 203 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 148, + 288, + 203 + ], + "spans": [ + { + "bbox": [ + 53, + 148, + 288, + 203 + ], + "type": "text", + "content": "[2] Belhassen Bayar and Matthew C Stamm. A deep learning approach to universal image manipulation detection using a new convolutional layer. In Proceedings of the 4th ACM workshop on information hiding and multimedia security, pages 5-10, 2016. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 205, + 288, + 247 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 205, + 288, + 247 + ], + "spans": [ + { + "bbox": [ + 53, + 205, + 288, + 247 + ], + "type": "text", + "content": "[3] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. In International Conference on Learning Representations, 2018. 1, 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 250, + 288, + 294 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 250, + 288, + 294 + ], + "spans": [ + { + "bbox": [ + 53, + 250, + 288, + 294 + ], + "type": "text", + "content": "[4] Lucy Chai, David Bau, Ser-Nam Lim, and Phillip Isola. What makes fake images detectable? understanding properties that generalize. In European conference on computer vision, pages 103-120. Springer, 2020. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 296, + 288, + 339 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 296, + 288, + 339 + ], + "spans": [ + { + "bbox": [ + 53, + 296, + 288, + 339 + ], + "type": "text", + "content": "[5] Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587, 2017.4.7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 341, + 287, + 407 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 341, + 287, + 407 + ], + "spans": [ + { + "bbox": [ + 53, + 341, + 287, + 407 + ], + "type": "text", + "content": "[6] Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8789-8797, 2018. 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 409, + 287, + 452 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 409, + 287, + 452 + ], + "spans": [ + { + "bbox": [ + 53, + 409, + 287, + 452 + ], + "type": "text", + "content": "[7] François Chollet. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1251-1258, 2017. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 454, + 287, + 486 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 454, + 287, + 486 + ], + "spans": [ + { + "bbox": [ + 53, + 454, + 287, + 486 + ], + "type": "text", + "content": "[8] Samantha Cole. We are truly fucked: Everyone is making ai-generated fake porn now. blog post, 2018. Accessed April 17, 2020. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 489, + 287, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 489, + 287, + 533 + ], + "spans": [ + { + "bbox": [ + 53, + 489, + 287, + 533 + ], + "type": "text", + "content": "[9] Davide Cozzolino, Justus Thies, Andreas Rössler, Christian Riess, Matthias Nießner, and Luisa Verdoliva. Forensictransfer: Weakly-supervised domain adaptation for forgery detection. arXiv preprint arXiv:1812.02510, 2018. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 534, + 287, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 534, + 287, + 600 + ], + "spans": [ + { + "bbox": [ + 48, + 534, + 287, + 600 + ], + "type": "text", + "content": "[10] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale, 2020. 4, 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 601, + 287, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 601, + 287, + 656 + ], + "spans": [ + { + "bbox": [ + 48, + 601, + 287, + 656 + ], + "type": "text", + "content": "[11] Ricard Durall, Margret Keuper, and Janis Keuper. Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7890-7899, 2020. 2, 3, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 287, + 712 + ], + "type": "text", + "content": "[12] Joel Frank, Thorsten Eisenhofer, Lea Schonherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. In International conference on machine learning, pages 3247-3258. PMLR, 2020. 2, 3, 6" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 712 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 127 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 127 + ], + "type": "text", + "content": "[13] Joel Frank, Thorsten Eisenhofer, Lea Schonherr, Asja Fischer, Dorothea Kolossa, and Thorsten Holz. Leveraging frequency analysis for deep fake image recognition. In International conference on machine learning, pages 3247-3258. PMLR, 2020. 2, 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 129, + 545, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 545, + 172 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 545, + 172 + ], + "type": "text", + "content": "[14] Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C Courville, and Yoshua Bengio. Generative adversarial nets. In NIPS, 2014. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 175, + 545, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 175, + 545, + 218 + ], + "spans": [ + { + "bbox": [ + 307, + 175, + 545, + 218 + ], + "type": "text", + "content": "[15] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770-778, 2016. 4, 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 220, + 545, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 220, + 545, + 285 + ], + "spans": [ + { + "bbox": [ + 307, + 220, + 545, + 285 + ], + "type": "text", + "content": "[16] Yang He, Ning Yu, Margret Keuper, and Mario Fritz. Beyond the spectrum: Detecting deepfakes via re-synthesis. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pages 2534–2541. International Joint Conferences on Artificial Intelligence Organization, 2021. 2, 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 286, + 545, + 341 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 286, + 545, + 341 + ], + "spans": [ + { + "bbox": [ + 307, + 286, + 545, + 341 + ], + "type": "text", + "content": "[17] Nils Hulzebosch, Sarah Ibrahimi, and Marcel Worring. Detecting cnn-generated facial images in real-world scenarios. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 642-643, 2020. 1, 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 342, + 547, + 398 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 342, + 547, + 398 + ], + "spans": [ + { + "bbox": [ + 307, + 342, + 547, + 398 + ], + "type": "text", + "content": "[18] Yonghyun Jeong, Doyeon Kim, Seungjai Min, Seongho Joe, Youngjune Gwon, and Jongwon Choi. Bihpf: Bilateral high-pass filters for robust deepfake detection. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 48-57, 2022. 2, 3, 4, 5, 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 399, + 545, + 442 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 399, + 545, + 442 + ], + "spans": [ + { + "bbox": [ + 307, + 399, + 545, + 442 + ], + "type": "text", + "content": "[19] Yonghyun Jeong, Doyeon Kim, Youngmin Ro, and Jongwon Choi. Frepgan: Robust deepfake detection using frequency-level perturbations. arXiv preprint arXiv:2202.03347, 2022. 2, 3, 4, 5, 6, 7" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 444, + 545, + 497 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 444, + 545, + 497 + ], + "spans": [ + { + "bbox": [ + 307, + 444, + 545, + 497 + ], + "type": "text", + "content": "[20] Yonghyun Jeong, Doyeon Kim, Youngmin Ro, Pyounggeon Kim, and Jongwon Choi. Fingerprint: Synthesized fingerprints for generated image detection. In European Conference on Computer Vision, pages 76-94. Springer, 2022. 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 500, + 545, + 543 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 500, + 545, + 543 + ], + "spans": [ + { + "bbox": [ + 307, + 500, + 545, + 543 + ], + "type": "text", + "content": "[21] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. In International Conference on Learning Representations, 2018. 1, 2, 4, 5, 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 544, + 545, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 544, + 545, + 599 + ], + "spans": [ + { + "bbox": [ + 307, + 544, + 545, + 599 + ], + "type": "text", + "content": "[22] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4401-4410, 2019. 1, 2, 4, 5, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 601, + 547, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 601, + 547, + 656 + ], + "spans": [ + { + "bbox": [ + 307, + 601, + 547, + 656 + ], + "type": "text", + "content": "[23] Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8110-8119, 2020. 1, 2, 4, 5, 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 658, + 545, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 658, + 545, + 678 + ], + "spans": [ + { + "bbox": [ + 307, + 658, + 545, + 678 + ], + "type": "text", + "content": "[24] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In ICLR (Poster), 2015. 4" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 680, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 680, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 307, + 680, + 545, + 712 + ], + "type": "text", + "content": "[25] Diederik P Kingma and Max Welling. Auto-encoding variational {Bayes}. In Int. Conf. on Learning Representations. 1" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "12113" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 715 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "text", + "content": "[26] Lingzhi Li, Jianmin Bao, Ting Zhang, Hao Yang, Dong Chen, Fang Wen, and Baining Guo. Face x-ray for more general face forgery detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 5001-5010, 2020. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 128, + 287, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 128, + 287, + 172 + ], + "spans": [ + { + "bbox": [ + 48, + 128, + 287, + 172 + ], + "type": "text", + "content": "[27] Yuezun Li, Ming-Ching Chang, and Siwei Lyu. In ictu oculi: Exposing ai created fake videos by detecting eye blinking. In 2018 IEEE International workshop on information forensics and security (WIFS), pages 1-7. IEEE, 2018. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 172, + 287, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 172, + 287, + 227 + ], + "spans": [ + { + "bbox": [ + 48, + 172, + 287, + 227 + ], + "type": "text", + "content": "[28] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 4" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 228, + 288, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 228, + 288, + 270 + ], + "spans": [ + { + "bbox": [ + 48, + 228, + 288, + 270 + ], + "type": "text", + "content": "[29] Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaou Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision, pages 3730-3738, 2015. 4" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 272, + 287, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 272, + 287, + 316 + ], + "spans": [ + { + "bbox": [ + 48, + 272, + 287, + 316 + ], + "type": "text", + "content": "[30] Zhengzhe Liu, Xiaojuan Qi, and Philip HS Torr. Global texture enhancement for fake face detection in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8060-8069, 2020. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 316, + 287, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 316, + 287, + 369 + ], + "spans": [ + { + "bbox": [ + 48, + 316, + 287, + 369 + ], + "type": "text", + "content": "[31] Francesco Marra, Diego Gragnaniello, Luisa Verdoliva, and Giovanni Poggi. Do gans leave artificial fingerprints? In 2019 IEEE conference on multimedia information processing and retrieval (MIPR), pages 506-511. IEEE, 2019. 2, 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 371, + 287, + 426 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 371, + 287, + 426 + ], + "spans": [ + { + "bbox": [ + 48, + 371, + 287, + 426 + ], + "type": "text", + "content": "[32] Iacopo Masi, Aditya Killekar, Royston Marian Mascarenhas, Shenoy Pratik Gurudatt, and Wael AbdAlmageed. Two-branch recurrent network for isolating deepfakes in videos. In European conference on computer vision, pages 667–684. Springer, 2020. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 426, + 287, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 426, + 287, + 480 + ], + "spans": [ + { + "bbox": [ + 48, + 426, + 287, + 480 + ], + "type": "text", + "content": "[33] Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu. Semantic image synthesis with spatially-adaptive normalization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2337-2346, 2019. 4" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 481, + 287, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 481, + 287, + 525 + ], + "spans": [ + { + "bbox": [ + 48, + 481, + 287, + 525 + ], + "type": "text", + "content": "[34] Yuyang Qian, Guojun Yin, Lu Sheng, Zixuan Chen, and Jing Shao. Thinking in frequency: Face forgery detection by mining frequency-aware clues. In European conference on computer vision, pages 86-103. Springer, 2020. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 525, + 287, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 525, + 287, + 591 + ], + "spans": [ + { + "bbox": [ + 48, + 525, + 287, + 591 + ], + "type": "text", + "content": "[35] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 4, 5, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 592, + 287, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 592, + 287, + 635 + ], + "spans": [ + { + "bbox": [ + 48, + 592, + 287, + 635 + ], + "type": "text", + "content": "[36] Alec Radford, Luke Metz, and Soumith Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 635, + 287, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 635, + 287, + 690 + ], + "spans": [ + { + "bbox": [ + 48, + 635, + 287, + 690 + ], + "type": "text", + "content": "[37] Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. Faceforensics: Learning to detect manipulated facial images. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1-11, 2019. 2, 4" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 691, + 287, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 287, + 715 + ], + "type": "text", + "content": "[38] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy," + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 73, + 547, + 642 + ], + "type": "list", + "angle": 0, + "index": 26, + "blocks": [ + { + "bbox": [ + 327, + 73, + 547, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 73, + 547, + 106 + ], + "spans": [ + { + "bbox": [ + 327, + 73, + 547, + 106 + ], + "type": "text", + "content": "Aditya Khosla, Michael Bernstein, et al. Imagenet large scale visual recognition challenge. International journal of computer vision, 115(3):211-252, 2015. 4" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 308, + 107, + 545, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 107, + 545, + 140 + ], + "spans": [ + { + "bbox": [ + 308, + 107, + 545, + 140 + ], + "type": "text", + "content": "[39] K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations, 2015. 4, 5, 7" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 141, + 545, + 194 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 141, + 545, + 194 + ], + "spans": [ + { + "bbox": [ + 308, + 141, + 545, + 194 + ], + "type": "text", + "content": "[40] Ke Sun, Hong Liu, Qixiang Ye, Yue Gao, Jianzhuang Liu, Ling Shao, and Rongrong Ji. Domain general face forgery detection by learning to weight. In Proceedings of the AAAI conference on artificial intelligence, volume 35, pages 2638-2646, 2021. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 197, + 545, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 197, + 545, + 251 + ], + "spans": [ + { + "bbox": [ + 308, + 197, + 545, + 251 + ], + "type": "text", + "content": "[41] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826, 2016. 4, 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 252, + 545, + 307 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 252, + 545, + 307 + ], + "spans": [ + { + "bbox": [ + 308, + 252, + 545, + 307 + ], + "type": "text", + "content": "[42] Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros. Cnn-generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8695-8704, 2020. 2, 4, 5, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 308, + 545, + 352 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 308, + 545, + 352 + ], + "spans": [ + { + "bbox": [ + 308, + 308, + 545, + 352 + ], + "type": "text", + "content": "[43] Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015. 4, 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 353, + 545, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 353, + 545, + 397 + ], + "spans": [ + { + "bbox": [ + 308, + 353, + 545, + 397 + ], + "type": "text", + "content": "[44] Ning Yu, Larry S Davis, and Mario Fritz. Attributing fake images to gans: Learning and analyzing gan fingerprints. In Proceedings of the IEEE/CVF international conference on computer vision, pages 7556-7566, 2019. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 398, + 545, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 398, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 308, + 398, + 545, + 430 + ], + "type": "text", + "content": "[45] Yang Yu, Rongrong Ni, and Yao Zhao. Mining generalized features for detecting ai-manipulated fake faces. arXiv preprint arXiv:2010.14129, 2020. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 432, + 545, + 485 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 432, + 545, + 485 + ], + "spans": [ + { + "bbox": [ + 308, + 432, + 545, + 485 + ], + "type": "text", + "content": "[46] Tianchen Zhao, Xiang Xu, Mingze Xu, Hui Ding, Yuanjun Xiong, and Wei Xia. Learning self-consistency for deepfake detection. In Proceedings of the IEEE/CVF international conference on computer vision, pages 15023-15033, 2021. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 487, + 545, + 541 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 487, + 545, + 541 + ], + "spans": [ + { + "bbox": [ + 308, + 487, + 545, + 541 + ], + "type": "text", + "content": "[47] Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2921-2929, 2016. 7, 8" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 543, + 545, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 543, + 545, + 586 + ], + "spans": [ + { + "bbox": [ + 308, + 543, + 545, + 586 + ], + "type": "text", + "content": "[48] Jiapeng Zhu, Yujun Shen, Deli Zhao, and Bolei Zhou. Indomain gan inversion for real image editing. In European conference on computer vision, pages 592-608. Springer, 2020. 4, 7" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 587, + 545, + 642 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 587, + 545, + 642 + ], + "spans": [ + { + "bbox": [ + 308, + 587, + 545, + 642 + ], + "type": "text", + "content": "[49] Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, pages 2223-2232, 2017. 4" + } + ] + } + ], + "index": 25 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "12114" + } + ] + } + ], + "index": 27 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/52ba4497-4417-4913-a633-6839eab97318_content_list.json b/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/52ba4497-4417-4913-a633-6839eab97318_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..eb54d2094fd1e0c2c826cc94f4e29e4d76051945 --- /dev/null +++ b/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/52ba4497-4417-4913-a633-6839eab97318_content_list.json @@ -0,0 +1,1640 @@ +[ + { + "type": "text", + "text": "Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation", + "text_level": 1, + "bbox": [ + 207, + 130, + 767, + 176 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Liyan Chen Weihan Wang Filippos Mordohai Stevens Institute of Technology", + "bbox": [ + 264, + 203, + 705, + 239 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 273, + 313, + 289 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the observation that multi-task learning often leads to improved performance in all tasks. We show that this can be achieved by requiring the distribution of uncertainty to match the distribution of disparity errors via a KL divergence term in the network's loss function. A differentiable soft-histogramming technique is used to approximate the distributions so that they can be used in the loss. We experimentally assess the effectiveness of our approach and observe significant improvements in both disparity and uncertainty prediction on large datasets. Our code is available at https://github.com/11y00412/SEDNet.git.", + "bbox": [ + 75, + 305, + 473, + 517 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 542, + 209, + 558 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Many computer vision problems can be formulated as estimation tasks. Considering, however, that even high-performing estimators are not error-free, associating confidence or uncertainty with their estimates is of great importance, particularly in critical applications. In this paper, we focus on disparity estimation via stereo matching, but we are confident that our approach is applicable to other pixel-wise regression tasks after minor modifications.", + "bbox": [ + 75, + 568, + 470, + 688 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We distinguish between confidence and uncertainty: the former refers to a probability or likelihood of correctness, while the latter is related to the magnitude of the expected error of an estimate. Confidence can be used to reject estimates that are suspected to be incorrect, or to rank them from most to least reliable. We argue that uncertainty is more valuable because it can also be used for fusing multiple observations, e.g. in a Kalman filtering framework. Most research has focused on confidence estimation for stereo matching [12, 30]. Moreover, most methods estimate confidence for pre-computed disparities that are not further improved. Joint estimation of disparity and confidence, which benefits both due to multi-task learning, is addressed infrequently [19, 20, 26, 34].", + "bbox": [ + 75, + 688, + 470, + 901 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Our work is partially inspired by the joint treatment of epistemic and aleatoric uncertainty by Kendall and Gal [14], who propose novel loss functions that give rise to uncertainty estimates in pixel-wise vision tasks. Results on semantic segmentation and single-image depth estimation demonstrate how the primary task benefits from simultaneous uncertainty estimation. Kendall and Gal argue that \"in many big data regimes (such as the ones common to deep learning with image data), it is most effective to model aleatoric uncertainty,\" while epistemic uncertainty can be reduced when large amounts of data are available. Here, we restrict our attention to aleatoric uncertainty.", + "bbox": [ + 496, + 275, + 893, + 455 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Our motivation is that ideally we should be able to predict the magnitude of the estimator's error at each pixel. Of course, this is unrealistic, since if it was possible, we could drive all errors down to zero. A feasible objective is to train an uncertainty estimator whose outputs follow the same distribution as the true errors of the disparity estimator.", + "bbox": [ + 496, + 459, + 893, + 551 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we present an implementation of this concept via a deep network that jointly estimates disparity and its uncertainty from a pair of rectified images. We named the network SEDNet, for Stereo Error Distribution Network. SEDNet includes a novel, lightweight uncertainty estimation subnetwork that predicts the aleatoric uncertainty", + "bbox": [ + 496, + 553, + 893, + 645 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e362a72f5c3629ab4274193191678fe338b483a8b080ff40ab833442f96cb843.jpg", + "image_caption": [ + "Figure 1. Examples of left images and predicted disparity maps by SEDNet on DrivingStereo [36]. The first example is taken around sunset with over-exposure. The second example is taken on a rainy day with under-exposure. In both challenge cases, SEDNet predicts accurate disparity." + ], + "image_footnote": [], + "bbox": [ + 509, + 672, + 689, + 816 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/e99103418dc3b6934619a1a863d6f3c60d717e7f8652f6ca9f98b84846968259.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 702, + 672, + 883, + 813 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "17235", + "bbox": [ + 478, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "of stereo matching, and a new loss to match the distribution of uncertainties with that of disparity errors. To generate the inputs to this new loss, we approximate the distributions from the samples of disparity errors and uncertainty values in a differentiable way via a soft-histogramming technique.", + "bbox": [ + 75, + 90, + 467, + 166 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We present extensive experimental validation of SEDNet's performance in disparity estimation and uncertainty prediction on large datasets with ground truth. SEDNet is superior to baselines with similar, even identical, architecture, but without the proposed loss function. Our main contributions are:", + "bbox": [ + 75, + 167, + 467, + 255 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- a novel uncertainty estimation subnetwork that extracts information from the intermediate multi-resolution disparity maps generated by the disparity subnetwork,", + "- a differentiable soft-histogramming technique used to approximate the distributions of disparity errors and estimated uncertainties,", + "- a loss based on KL divergence applied on histograms obtained with the above technique." + ], + "bbox": [ + 94, + 257, + 467, + 378 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 76, + 392, + 217, + 407 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We refer readers to recent surveys on deep stereo matching [32] and on confidence estimation [30]. Here we summarize the most relevant publications to our work.", + "bbox": [ + 75, + 417, + 467, + 462 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Stereo matching networks operate on a cost volume, which aggregates 2D features at each potential disparity for every pixel, and can be constructed via correlation or concatenation. Correlation-based networks such as DispNetC [25], iResNet [24] and SegStereo [38], generate a single-channel correlation map between features extracted from the two views at each disparity level, favoring computation efficiency at the expense of losing the structural and semantic information in the feature representation. Concatenation-based networks, such as GCNet [16], PSMNet [2] and GANet [39], assemble features from both views at the disparity specified by the corresponding element of the cost volume. This promotes learning of contextual features but requires more parameters and a subsequent aggregation network.", + "bbox": [ + 75, + 463, + 467, + 686 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We select GwcNet [11] as the foundation of our network. GwcNet takes a hybrid approach by reducing the dimension of the unary feature channels before concatenation in the cost volume. This is accomplished by a Group Wise Correlation layer, which takes as input $N_{c}$ unary feature channels, divides them into $N_{g}$ groups, computes the correlation between channels in each group at all disparity levels, and uses the resulting correlation scores to form the cost volume. This reduces the size of the cost volume and the computational cost of 3D convolutions by a factor of $N_{c}:N_{g}$ , with $N_{g}$ much smaller than $N_{c}$ , but still provides rich similarity-measure features to the disparity estimator.", + "bbox": [ + 75, + 688, + 467, + 869 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Researchers have also focused on model reliability. In Bayesian Neural Networks (BNNs), different models are", + "bbox": [ + 76, + 869, + 467, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "sampled from the distribution of weights to estimate the mean and variance of the target distribution in an empirical manner, yielding estimates of uncertainty [7, 27]. Additional empirical strategies such as Bootstrapped Ensembles [23] and Monte Carlo Dropout [8] also sample from the distribution of weights. On the other hand, Graves [10] and Blundell et al. [1] proposed to replace the sampling with variational inference.", + "bbox": [ + 496, + 90, + 890, + 210 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Due to the high cost of training BNNs, methods for modeling the uncertainty or confidence in a predictive manner have also attracted interest. We distinguish between confidence and uncertainty: confidence is a binary variable trained with the BCE loss, while uncertainty is a continuous variable trained with L1 or L2 loss. Nix and Weigend [28] introduce NNs with one output for model prediction and one for data noise (aleatoric uncertainty). In addition to aleatoric uncertainty which captures the data noise of the observations, epistemic uncertainty, which accounts for the uncertainty of the model parameters, can also be modeled [3]. To capture both types, Kendall et al. [14, 15] proposed to combine empirical and predictive methods in a joint framework.", + "bbox": [ + 496, + 214, + 890, + 426 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "CNNs have been used to estimate confidence in stereo matching. The Confidence CNN (CCNN) [31], Patch Based Confidence Prediction (PBCP) [33], the Early Fusion Network (EFN) and the Late Fusion Network (LFN) [4] and Multi Modal CNN (MMC) [5] only use small patches of the disparity maps. Conversely, the Global Confidence Network (ConfNet) and the Local-Global Confidence Network (LGC) [35] introduce U-Net like architectures and take both the image and disparity map as inputs. As a baseline, we use the Locally Adaptive Fusion Network (LAF) [18] which predicts the confidence map based on tri-modal inputs: the cost and disparity maps and the color image. An extension based on knowledge distillation has also been published [17]. These strategies are effective and cheaper than the empirical ones, since they only require one forward pass.", + "bbox": [ + 496, + 429, + 890, + 655 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Only a subset of the confidence estimation literature has focused on joint disparity and confidence estimation. The Reflective Confidence Network (RCN) [34] is the first to combine a disparity and a confidence loss. The Unified Confidence Network (UCN) [19] and the Adversarial Confidence Network (ACN) [20] jointly estimate confidence and disparity from pre-computed cost volumes. UCN is self-supervised, while ACN combines a generative cost aggregation network and a discriminative confidence estimation network in an adversarial manner. Mehlretter [26] presents an approach that predicts both epistemic and aleatoric uncertainty using a Bayesian Neural Network, based on GCNet [16]. KL divergence is used to measure the distance between the approximation of the distribution of network parameters estimated by variational difference and the exact posterior distribution. (It should be noted that we use", + "bbox": [ + 496, + 659, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "17236", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "KL divergence for a completely different purpose on the distribution of disparity errors.)", + "bbox": [ + 76, + 90, + 468, + 119 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Relevant research in adjacent areas of computer vision includes the work of Poggi et al. [29] who comprehensively evaluate uncertainty estimation for self-supervised monocular depth estimation. Ilg et al. [13] study empirical ensembles, predictive models and predictive ensembles as uncertainty models for optical flow estimation.", + "bbox": [ + 75, + 121, + 467, + 210 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 76, + 223, + 168, + 238 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The objective of our work is to jointly estimate the disparity and its uncertainty. An important benefit of this joint formulation is that the multi-task network learns to predict more accurate disparities than the standalone disparity estimator when the uncertainty subnetwork is added. Given a stereo image pair $\\mathbf{X} = \\{\\mathbf{x}_l, \\mathbf{x}_r\\}$ , with image dimensions $H \\times W$ , and the corresponding ground truth disparity $\\hat{\\mathbf{d}}$ , the prediction $\\hat{\\mathbf{d}}$ of a stereo-matching network $f_{\\theta}$ can be represented as $\\hat{\\mathbf{d}} = f_{\\theta}(\\mathbf{x}_l, \\mathbf{x}_r)$ . For each pixel $i$ , the error $\\epsilon^{(i)}$ of the prediction is calculated using the L1 loss.", + "bbox": [ + 75, + 250, + 468, + 398 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Kendall and Gal [14] use the negative log-likelihood of the prediction model as the loss function to be minimized in pixel-wise tasks.", + "bbox": [ + 75, + 400, + 468, + 444 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We take the formulation a step further by requiring that the network generate a distribution of uncertainties that matches the distribution of errors. To this end, we propose to minimize the divergence $\\mathcal{D}$ between the distributions of predicted uncertainty and actual disparity error.", + "bbox": [ + 75, + 446, + 468, + 520 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In the following subsections, we present aleatoric uncertainty estimation (Section 3.1), the proposed KL divergence loss (Section 3.2), our network architecture (Section 3.3), and the combined loss function (Section 3.4).", + "bbox": [ + 75, + 521, + 468, + 580 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Aleatoric Uncertainty Estimation", + "text_level": 1, + "bbox": [ + 76, + 589, + 369, + 604 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In order to predict uncertainty and reduce the impact of noise, Kendall and Gal [14] minimize the pixel-wise negative log-likelihood of the prediction model, assuming that it follows a Gaussian distribution. The subsequent work of Ilg et al. [13] shows that the predicted distribution can be modeled as either Laplacian or Gaussian depending on whether the L1 or L2 loss is used for disparity estimation. Since we use the former, we can write the prediction model as:", + "bbox": [ + 75, + 612, + 468, + 733 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\np (\\mathbf {d} \\mid f _ {\\theta} (\\mathbf {X})) = \\mathcal {L} a p l a c e (f _ {\\theta} (\\mathbf {X}), \\sigma) \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 741, + 468, + 758 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where the mean is given by the model output and $\\sigma$ is the observation noise scalar.", + "bbox": [ + 75, + 766, + 468, + 795 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To model aleatoric uncertainty, Kendall and Gal [14] introduce pixel-specific noise parameters $\\sigma^{(i)}$ . We follow the approach of Ilg et al. [13], who do the same for a Laplacian model, and obtain the following pixel-wise loss function:", + "bbox": [ + 75, + 796, + 468, + 856 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {l o g} = \\frac {1}{n} \\sum_ {i = 1} ^ {n} \\frac {\\left| \\hat {\\mathbf {d}} ^ {(i)} - \\mathbf {d} ^ {(i)} \\right|}{\\exp (\\mathbf {s} ^ {(i)})} + \\frac {1}{n} \\sum_ {i = 1} ^ {n} \\mathbf {s} ^ {(i)} \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 137, + 864, + 468, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\hat{\\mathbf{d}}^{(i)}$ and $\\mathbf{d}^{(i)}$ are the predicted and ground truth disparity for pixel $i$ , $\\mathbf{s}^{(i)}$ is the log of the observation noise scalar $\\sigma^{(i)}$ , and $n$ is equal to the total number of the pixels. Equation (2) may be viewed as a robust loss function where the residual loss for a pixel is attenuated by its uncertainty, while the second term acts as a regularizer. We follow the authors' suggestion and train the network to predict the log of the observation noise scalar, $s$ , for numerical stability.", + "bbox": [ + 496, + 89, + 890, + 212 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Matching the Distribution of Errors", + "text_level": 1, + "bbox": [ + 500, + 223, + 810, + 239 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Training a model using Eq. (2) as the loss improves disparity estimation accuracy and favors uncertainty estimates correlated with the errors. Ideally, we would like each uncertainty estimate to be a precise predictor of the corresponding disparity error. Since this is infeasible, we would like the distribution of uncertainties to match the distribution of errors.", + "bbox": [ + 496, + 248, + 890, + 352 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The Kullback-Leibler (KL) divergence [22] is a natural choice for measuring the dissimilarity between the distribution of $\\epsilon$ and that of $\\sigma$ . Since the KL divergence is asymmetric, we choose the distribution of $\\epsilon$ as the reference. Therefore, our network should also minimize the following objective function:", + "bbox": [ + 496, + 354, + 890, + 445 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {d i v} = \\mathcal {D} \\left(P _ {\\sigma} (d)\\right) \\| Q _ {\\epsilon} (d)) = \\int_ {0} ^ {d _ {m a x}} P _ {\\sigma} (d) \\log \\frac {P _ {\\sigma} (d)}{Q _ {\\epsilon} (d)} d d \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 504, + 458, + 890, + 505 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $d$ spans the disparity range. Since the network regresses disparity, the continuous formulation of KL divergence is appropriate.", + "bbox": [ + 496, + 505, + 890, + 550 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Minimizing Eq. (3) directly requires closed form expressions for the two distributions, which are not available to us. They could be modeled as Laplace distributions, but the maximum likelihood estimator is not differentiable. Moreover, fitting models to the data may be imprecise at the tails of the distributions. Therefore we choose non-parametric representations in the form of histograms.", + "bbox": [ + 496, + 551, + 890, + 656 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Histogramming is also not a differentiable operation, leading us to soft-histogramming as a differentiable alternative. We specify a set of bins for the histograms based on the statistics of the errors $\\epsilon$ , since their distribution is the one that should be matched by the distribution of uncertainty estimates $\\sigma$ . Since the L1 loss, and our network in general, does not discriminate between positive and negative errors, we work with absolute values of $\\epsilon$ and $\\sigma$ .", + "bbox": [ + 496, + 657, + 890, + 777 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "For each batch during training, we compute the mean and standard deviation of the error, $\\mu_{\\epsilon}$ and $b_{\\epsilon}$ , set $C_0 = \\mu_{\\epsilon}$ as the center of the first bin, and $C_m = \\mu_{\\epsilon} + \\alpha_m b_{\\epsilon}$ as the center of the last bin. (We use $b_{\\epsilon}$ for the standard deviation to avoid overloading $\\sigma$ or $s$ . Also note that the last bin extends to the disparity range, which is also the maximum possible error.) We then define $m - 1$ centers evenly spaced in a linear or logarithmic scale between the first and last center.", + "bbox": [ + 496, + 779, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "17237", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/4862569d12ae2a05e4f942108be2f09f480e58666bb6b31d4cf884692d7ef05f.jpg", + "image_caption": [ + "Figure 2. An illustration of SEDNet. The stereo matching subnetwork consists of the feature extractor, cost volume generator and disparity estimator. It takes a rectified pair of images as input and predicts disparity maps at multiple resolutions. The uncertainty estimation subnetwork takes the predicted disparity maps as input and predicts corresponding uncertainty maps. The error maps, between the ground truth and predicted disparity, are used to supervise uncertainty estimation. During training, the network keeps the output at all resolutions, but returns only the highest resolution disparity and uncertainty maps during testing." + ], + "image_footnote": [], + "bbox": [ + 106, + 95, + 861, + 321 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Given the bin centers, we compute a soft-histogram for the errors and one for the uncertainties as follows, considering all pixels with ground truth disparity, and error values. We present the steps for $\\epsilon$ here. For each error $\\epsilon^{(i)}$ , we compute weights for every bin center which are inversely proportional to the distance.", + "bbox": [ + 75, + 436, + 468, + 526 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nw _ {j} \\left(\\epsilon^ {(i)}\\right) = \\lambda_ {1} \\cdot \\exp \\left(- \\frac {\\left(\\mu_ {\\epsilon} + \\alpha_ {j} b _ {\\epsilon} - \\epsilon^ {(i)}\\right) ^ {2}}{\\lambda_ {2}}\\right) \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 125, + 536, + 468, + 569 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\lambda_{1}$ and $\\lambda_{2}$ are hyper-parameters. Softmax is then applied to favor the nearest bins and the contributions of $n$ pixels are accumulated in the bins of the histogram $H_{\\epsilon}$ .", + "bbox": [ + 75, + 578, + 468, + 625 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nH _ {\\epsilon} (j) = \\frac {1}{n} \\sum_ {i = 1} ^ {n} \\frac {\\exp \\left(w _ {j} \\left(\\epsilon^ {(i)}\\right)\\right)}{\\sum_ {j = 0} ^ {m} \\exp \\left(w _ {j} \\left(\\epsilon^ {(i)}\\right)\\right)}, j \\in [ 0, m ] \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 99, + 633, + 468, + 671 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The histogram for $\\sigma$ , $H_{\\sigma}$ , is obtained similarly. (Note that the bins of both histograms are defined in terms of $b_{\\epsilon}$ .)", + "bbox": [ + 75, + 681, + 468, + 710 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The loss representing the discrete form of the KL divergence between the two histograms is given by:", + "bbox": [ + 75, + 710, + 467, + 742 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {d i v} = \\sum_ {j = 0} ^ {m} H _ {\\epsilon} (j) \\log \\frac {H _ {\\epsilon} (j)}{H _ {\\sigma} (j)} \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 174, + 751, + 468, + 792 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. SEDNet", + "text_level": 1, + "bbox": [ + 76, + 801, + 174, + 815 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our network architecture, named SEDNet, includes a disparity estimation subnetwork, an uncertainty estimation subnetwork, and is shown in Figure 2. In all experiments, we have adopted GwcNet [11] as the disparity estimation subnetwork, among other options, and we have designed", + "bbox": [ + 75, + 825, + 468, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "a novel uncertainty estimation subnetwork that interfaces with GwcNet. It is worth noting that the uncertainty subnetwork is extremely small.", + "bbox": [ + 498, + 436, + 890, + 481 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The GwcNet subnetwork extracts features from the images using a ResNet-like feature extractor, generates the cost volume, and assigns disparities to pixels using the soft-argmax operator [16]. The output module of the disparity predictor generates $K$ disparity maps at different resolutions.", + "bbox": [ + 498, + 484, + 890, + 574 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We propose a new uncertainty estimator integrated with the stereo matching network. The uncertainty estimation subnetwork learns to predict the log of the observation noise scalar, the error, at each pixel. The proposed subnetwork takes the multi-resolution disparity predictions as input, computes the pairwise differences vector (PDV) and passes it to a pixel-wise MLP to regress the uncertainty maps. Specifically, the disparity estimator outputs $K$ disparity maps at different resolutions $\\hat{\\mathbf{d}} = \\{\\hat{\\mathbf{d}}_1,\\dots,\\hat{\\mathbf{d}}_K\\}$ , which are first upsampled to full-resolution and then undergo pairwise differencing to form the PVD, which consists of $\\binom{K}{2}$ elements. The output set of uncertainty maps S also contains $K$ resolutions to match the disparity maps. We use $K = 4$ in all experiments.", + "bbox": [ + 498, + 580, + 890, + 791 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Loss Function", + "text_level": 1, + "bbox": [ + 500, + 813, + 643, + 827 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our loss function combines two parts: (1) the log-likelihood loss to optimize the error and uncertainty, (2) the KL divergence loss to match the distribution of uncertainty with the error. The total loss considers all disparity and un", + "bbox": [ + 498, + 840, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "17238", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "certainty maps upsampled to the highest resolution:", + "bbox": [ + 76, + 90, + 418, + 106 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\sum_ {k = 1} ^ {K} c _ {k} \\cdot \\left(\\mathcal {L} _ {\\text {l o g}, k} + \\mathcal {L} _ {\\text {d i v}, k}\\right) \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 169, + 118, + 468, + 160 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $c_k$ denotes the coefficients for the $k^{th}$ resolution level, $\\mathcal{L}_{\\log,k}$ and $\\mathcal{L}_{div,k}$ are computed by Eq. (2) and Eq. (6) on the prediction of the corresponding resolution level.", + "bbox": [ + 76, + 172, + 468, + 219 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiment Results", + "text_level": 1, + "bbox": [ + 76, + 234, + 266, + 250 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we present our experimental setup and results on within-domain and cross-domain experiments. Datasets, evaluation metrics and baselines are described in Section 4.1, implementation details in Section 4.2, and experimental results in Sections 4.3 and 4.4. Additionally, a synthetic-to-real transfer evaluation is presented in Section 4.5. We provide more quantitative and qualitative results, extending Section 4, in the Supplement, which also includes ablation studies on several aspects of SEDNet and the baselines, as well as disparity, error, and uncertainty maps of difficult examples.", + "bbox": [ + 75, + 260, + 468, + 425 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets and evaluation metrics", + "text_level": 1, + "bbox": [ + 76, + 436, + 356, + 450 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "SceneFlow [25] is a collection of three synthetic stereo datasets: FlyingThings3D, Driving, Monkaa. The datasets provide 35,454 training and 4,370 test stereo pairs in $960 \\times 540$ pixel resolution with dense ground-truth disparity maps. We use the finalpass versions of the rendered images which are more realistic because of the motion blur and depth of field effect.", + "bbox": [ + 75, + 460, + 468, + 565 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Virtual KITTI 2 (VK2) [6] is a synthetic clone of KITTI [9]. It consists of 21,260 synthetic $1,242 \\times 375$ stereo pairs from 6 driving scenes with 10 different imaging and weather conditions. Scene 006 (VK2-S6) is specified as the test set by the authors of the dataset. Since the car with the cameras is stopped for a long part of Scene 006 and only other cars move in the images, we split the last part of the scene where the car moves and denote it as VK2-S6-Moving. We report results separately on this subset. Since results on VK2-S6-Moving do not suffer from bias due to the almost constant background of the first part of the scene, evaluation on VK2-S6-Moving is more informative and fair.", + "bbox": [ + 75, + 566, + 468, + 747 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "DrivingStereo [37] is a large real-world autonomous driving dataset. It contains 174,437 training and 7,751 test stereo pairs at $881 \\times 400$ pixel resolution. The dataset provides sparse ground truth disparity as well as a challenging subset (DS-Weather) of 2,000 stereo pairs in 4 different weather conditions.", + "bbox": [ + 75, + 748, + 468, + 837 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For all datasets above, we exclude pixels with disparities $d > 192$ in training.", + "bbox": [ + 76, + 839, + 468, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Metrics. To evaluate disparity estimation, we compute the endpoint error (EPE) and the percentage of outliers (D1)", + "bbox": [ + 76, + 869, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "(i.e., the percentage of pixels with EPE $>3\\mathrm{px}$ or $\\geq 5\\%$ of the true depth).", + "bbox": [ + 498, + 90, + 890, + 121 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To evaluate uncertainty estimation, we use density-EPE ROC curves and the area under the curve (AUC) [12,18,30]. The ROC curves in our case measure EPE (not binary correctness) at increasing disparity map density by successively adding pixels in increasing order of uncertainty. The optimal AUC is obtained by adding pixels in order of increasing EPE and is therefore the lowest value any algorithm could achieve given the set of EPE values, while the estimated AUC is sorted by predicted uncertainty. To evaluate the precision of uncertainty estimation, we also introduce the absolute prediction error (APE), which is the average L1 distance between the error and the observation noise scalar, $\\sigma$ . We report the average and median APE over all pixels.", + "bbox": [ + 496, + 122, + 890, + 332 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Baselines. Since we use GwcNet [11] as the disparity estimation subnetwork of SEDNet, we compare SEDNet with three baselines: (1) the original GwcNet trained with smooth L1 loss. (2) LAF-Net [18] trained under the BCE loss on the left RGB images, the cost volumes and predicted disparity maps of GwcNet at the highest resolution. (3) SEDNet but only trained with the log-likelihood loss, therefore similar to Kendall and Gal's model [14]. We use $\\mathcal{L}_{\\log}$ in tables and figures for this baseline. We selected GwcNet because it is the backbone of SEDNet, and LAF-Net as the confidence estimation baseline due to its strong performance within the training domain according to [30].", + "bbox": [ + 496, + 334, + 890, + 515 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Implementation Details", + "text_level": 1, + "bbox": [ + 500, + 527, + 715, + 542 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We implemented all networks in PyTorch and used the Adam optimizer [21] with $\\beta_{1} = 0.9$ and $\\beta_{2} = 0.999$ for all experiments. Training of all models was stopped before overfitting occurred.", + "bbox": [ + 496, + 551, + 890, + 611 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Experiments on the VK2 dataset were performed on two NVIDIA RTX A6000 GPUs, each with 48 GB of RAM. For this dataset, we trained all models from scratch with an initial learning rate of 0.0001, down-scaled by 5 every 10 epochs. During training, we randomly cropped $512 \\times 256$ patches from the images. During testing, we evaluated at the full resolution of VK2.", + "bbox": [ + 496, + 612, + 890, + 717 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Experiments on the DrivingStereo dataset (DS) were also performed on two NVIDIA RTX A6000 GPUs. For this dataset, we did two experiments using the models pretrained on VK2: (1) we finetuned on the DS training set with a learning rate starting from 0.0001, down-scaled by 2 every 3 epochs after epoch 10, then performed in-domain evaluation on the DS test set; (2) we skipped the finetuning step and performed cross-domain evaluation on DS-Weather subset. During training, we randomly cropped the inputs to be the same size as in the VK2 experiments. During testing, we padded the test samples to be the same resolution as VK2.", + "bbox": [ + 496, + 719, + 890, + 898 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "17239", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/6750f2b5e2a8996e362567b254f9a16c4a59a21fe7c170bde9e23caddb132ed3.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetMethodLossInliersDisparity↓APE↓AUC↓
BCEL1LogKLBinsScaleDef.Pct(%)EPED1(%)Avg.MedianOpt.Est.
Scene FlowGwcNet-√------0.77584.127--10.9291-
+LAF√-------0.77584.127--10.929120.0813
+Llog-√√---EPE<596.960.76114.1310.69990.07285.744912.1121
+SEDNet-√√√11logEPE<μe+3bε98.420.67543.9630.57970.04324.91348.7195
VK2-S6GwcNet-√------0.41251.763--6.0962-
+Llog-√√---EPE<598.860.38991.5840.41360.17534.687212.5320
+SEDNet-√√√11logEPE<μe+3bε99.240.31091.3920.52340.14544.17269.7637
+SEDNet-√√√11logEPE<μe+5bε99.680.32361.4270.35610.10964.27679.9843
VK2-S6-MovingGwcNet-√------0.42531.689--5.9184-
+Llog-√√---EPE<598.910.42311.5370.45750.18904.366311.3532
+SEDNet-√√√11logEPE<μe+3bε99.620.35771.3890.59580.15733.90128.8339
+SEDNet-√√√11logEPE<μe+5bε99.760.38621.4200.40020.11644.04239.0631
DrivingStereo+Llog(FT)-√√-----0.53320.26410.34490.229721.700245.7096
+SEDNet(FT)-√√√11logEPE<μe+5bε99.860.52640.24390.33240.226721.285644.3297
DS-WeatherGwcNet-√------1.69628.313--44.4896-
+Llog-√√---EPE<595.782.39446.6662.14430.438341.190995.4264
+SEDNet-√√√11logEPE<μe+3bε98.951.56376.5082.34060.530938.487186.1118
+SEDNet-√√√11logEPE<μe+5bε99.411.70516.0571.58420.610439.805787.1882
", + "bbox": [ + 78, + 88, + 893, + 325 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/dfe5cb778e884384ff2bdff52b12ebe7f264c19eab785c6763a8a428a9f7368e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 86, + 388, + 344, + 450 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/9cd4bdcd8c13a17ef0a6f5087a3aa191db869c5a2d1824bb934d7fbd54d1d306.jpg", + "image_caption": [ + "Left Image", + "Right Image", + "Figure 3. Example from VK2-S6. In a rainy scene with poor illumination, recognizing objects far away from the camera is even difficult for human observers. $\\mathcal{L}_{log}$ fails to predict the disparity of objects, such as the traffic sign near the left street light. On the other hand, SEDNet accurately predicts the disparity of these challenging objects, while its uncertainty map also captures more information, such as the trees in the background, the traffic light near the trees and the street lights." + ], + "image_footnote": [], + "bbox": [ + 86, + 468, + 344, + 529 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/8e3ace30a511433ab10150f03eb6b1bfb265ce655540265334b81a5529045048.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 357, + 388, + 612, + 450 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/d3ae36d8ea80ff3bcfc3c6005483a9aff738f63ccd398c9a606a33b324edb7c9.jpg", + "image_caption": [ + "$\\mathcal{L}_{\\mathrm{log}}$ Disparity", + "SEDNet Disparity" + ], + "image_footnote": [], + "bbox": [ + 357, + 468, + 612, + 527 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/7198fbadf33c39ee5f21444a910572cbdfdf51f3dfdf468fd8673e8d7055cbba.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 625, + 388, + 882, + 450 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/7d3092f069ec2f646599c5086ea3825fe3c6a29ee3fa85c683e7872387ae4cfc.jpg", + "image_caption": [ + "$\\mathcal{L}_{\\mathrm{log}}$ Uncertainty", + "SEDNet Uncertainty" + ], + "image_footnote": [], + "bbox": [ + 627, + 468, + 880, + 529 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Experiments on the SceneFlow dataset were performed on an Nvidia Titan RTX GPU with 24 GB memory. We trained all models from scratch on $256 \\times 128$ patches cropped from half-resolution images to limit memory consumption. We set the initial learning rate to 0.001 and down-scaled it by 2 every 2 epochs after epoch 10.", + "bbox": [ + 75, + 637, + 468, + 728 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For all the experiments above, we applied an inlier filtering strategy during training, which only back-propagates from the inliers. See the last paragraph of Section 4.3 for", + "bbox": [ + 75, + 729, + 468, + 773 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/b00c8fdc14224877a240b5f4b142a129a891b7ed52bb7d3f8c0e3fff1a48ef2d.jpg", + "table_caption": [ + "Table 1. Quantitative results: (1) within-domain on SceneFlow, VK2-S6 and VK2-S6-Moving; (2) after finetuning (FT) on DrivingStereo; (3) cross-domain on DS-Weather. The best results in each category in each experiment are in bold typeface. SEDNet outperforms the baselines with respect to disparity and uncertainty metrics in the majority of experiments." + ], + "table_footnote": [], + "table_body": "
ArchitectureParamsMACs(G)
GwcNet6,909,7281075.82
SEDNet6,909,9181075.91
", + "bbox": [ + 130, + 782, + 415, + 835 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Comparison on number of parameters and computational complexity. MAC stands for multiply-accumulate operations. SEDNet only adds a 3-layer MLP, with 190 parameters, as an uncertainty decoder to GwcNet.", + "bbox": [ + 75, + 840, + 468, + 896 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "details. Table 2 shows the number of parameters and computational complexity of GwcNet and SEDNet.", + "bbox": [ + 496, + 637, + 890, + 669 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Qualitative and Quantitative Results", + "text_level": 1, + "bbox": [ + 498, + 679, + 816, + 695 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In Table 1, we present results: (1) within-domain on SceneFlow and VK2; (2) on the DrivingStereo test set after finetuning the VK2 models on the DrivingStereo training set; (3) cross-domain on the DS-Weather challenge test set by directly applying the model trained on VK2 without finetuning.", + "bbox": [ + 496, + 703, + 890, + 792 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Disparity Estimation. In all experiments, SEDNet achieves lower errors than all the baselines. See the EPE and D1 columns in Table 1. Even in extreme weather like fog and rain, SEDNet predicts good disparity unaffected by poor illumination and blur. See Figure 3.", + "bbox": [ + 496, + 794, + 890, + 869 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Uncertainty Estimation. Our method outperforms the baselines in all experiments according to the AUC metric,", + "bbox": [ + 498, + 869, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "17240", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/696f076058a8a225341a14fe4ac35c9e5c8f2e8dd8f6e70b6c94159e3d3fa96a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 86, + 87, + 344, + 179 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8736da3fd881d7cb680754f80b7df1892bdfccaab39e388af3c0a7fdcc39ed51.jpg", + "image_caption": [ + "Left Image", + "Right Image", + "Figure 4. Example from DS-Weather. Unlike the synthetic data, the rainy-day real images do not only suffer from poor illumination, but also pose challenges due to reflections in the water. In this example, the road is like a mirror, misleading the $\\mathcal{L}_{log}$ model. Recall that the LIDAR ground truth disparity is very sparse, and is even sparser in reflective regions. The disparity map of $\\mathcal{L}_{log}$ fails to distinguish the car and the reflection, but SEDNet is able to estimate the correct disparity and the uncertainty of the car." + ], + "image_footnote": [], + "bbox": [ + 86, + 195, + 344, + 286 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/69231d11834b0042b11ce0ecf710c14b2ed3afb6e0bbd3ed640aee69bd2f46b5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 357, + 87, + 614, + 179 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/6da5ba0360b890097ff4b7e87d8a9387069ca71b5987dddd61e03442b9019758.jpg", + "image_caption": [ + "$\\mathcal{L}_{\\text{log}}$ Disparity", + "SEDNet Disparity" + ], + "image_footnote": [], + "bbox": [ + 357, + 196, + 612, + 286 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/59b6d4d54e7523dbb6718abd3b3cda83b0f085e11d3c5ffea2de979041c4398a.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 625, + 87, + 882, + 179 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e5f3d4a0fb15f87d374c7464f56cf1c1aadc3cdd582f04dbf5b1a25d15487534.jpg", + "image_caption": [ + "$\\mathcal{L}_{\\mathrm{log}}$ Uncertainty", + "SEDNet Uncertainty" + ], + "image_footnote": [], + "bbox": [ + 625, + 196, + 880, + 286 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "as shown in the last two columns in Table 1. Compared to $\\mathcal{L}_{log}$ , SEDNet decreases the estimated AUC by $20\\% - 30\\%$ in the in-domain experiments, with a $10\\%$ decrease in optimal AUC, which depends on EPE. In the cross-domain evaluation, the advantage of SEDNet is even more evident. Figure 4 shows uncertainty maps for real data, on which our method captures details more faithfully. The ROC curves of the best $\\mathcal{L}_{log}$ and SEDNet models based on EPE on VK2-S6-Moving are presented in Figure 5.", + "bbox": [ + 75, + 393, + 472, + 532 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/473fec7f260e773882556db0f56a33e82bb545d37b0b7d06f51e0fd0d51138f8.jpg", + "image_caption": [ + "Figure 5. Comparison of density-EPE curves on VK2-S6-Moving. \"L.log (EPE<5)\" and \"SEDNet (3)\" represent the $\\mathcal{L}_{\\log}$ (with fixed inliers) and the SEDNet (with adaptive inliers of $\\mathrm{EPE} < \\mu_{\\epsilon} + 3b_{\\epsilon}$ ) of VK2-S6-Moving experiment in Table 1. While their corresponding optimal AUCs are almost equal, the advantage of SEDNet in estimated AUC is significant." + ], + "image_footnote": [], + "bbox": [ + 140, + 551, + 397, + 700 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effects of Back-propagation from Inliers. We apply two kinds of inlier filters: one with a fixed threshold that excludes all pixels that have an EPE larger than 5 from backpropagation; and one with adaptive threshold which excludes pixels that have EPE greater than a specified number of $b_{\\epsilon}$ from the mean error. Back-propagation from the", + "bbox": [ + 75, + 809, + 470, + 901 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "inliers only helps the network improve its performance on both disparity and uncertainty estimation. We attribute this to the suppression of harmful outliers that give rise to large gradients. Quantitative results for the baselines and the proposed method with different inlier settings are reported in the Supplement. The results show that using adaptive thresholds is better than fixed thresholds. Fixed thresholds exclude more pixels, especially at lower resolutions and in the early stages of training, preventing the network from learning how to correct them.", + "bbox": [ + 496, + 393, + 893, + 546 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Matching the Error Distribution.", + "text_level": 1, + "bbox": [ + 500, + 559, + 790, + 575 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Comparing the results of APE in all experiments in Table 1 and Figure 6, the difference between the errors and the predicted uncertainty is always close to the EPE, as expected. SEDNet, however, achieves lower APE, showing", + "bbox": [ + 496, + 584, + 893, + 645 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c10757329f3093edf814b6f4a5bf8bc5c44fbb74e47bdbc115136a4f8e13a640.jpg", + "image_caption": [ + "$\\mathcal{L}_{log}$", + "Figure 6. Distribution of error and predicted uncertainty. We pick the best model of $\\mathcal{L}_{\\text{log}}$ and SEDNet in VK2-S6-Moving, then randomly sample 5,000,000 points from the outputs of the two models. The distributions of error and $\\sigma$ of SEDNet are much closer than those of the $\\mathcal{L}_{\\text{log}}$ model, especially in the first 5 bins that contain more than $95\\%$ of the samples. The shorter tail of SEDNet also indicates fewer gross errors." + ], + "image_footnote": [], + "bbox": [ + 509, + 672, + 687, + 773 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/3a94bf511bc20a0763b3bcd8b6122696fe3976c58f24027ebac6c3f23b94bf23.jpg", + "image_caption": [ + "SEDNet" + ], + "image_footnote": [], + "bbox": [ + 702, + 672, + 880, + 773 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "17241", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/13539d97ef58846a8baf2d77672a3f9b49a20d73c6bf62046586c9b02ad5342c.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetMethodLossInliersDisparity↓APE↓AUC↓
BCEL1LogKLBinsScaleDef.Pct(%)EPED1(%)Avg.MedianOpt.Est.
VK2-S6-MorningGwcNet-√------0.46421.740--6.1845-
+Llog-√√---EPE<598.820.47741.6240.50670.18724.669812.5192
+SEDNet-√√√11logEPE<με+3bε99.620.40031.4420.61830.15534.18479.4063
VK2-S6-SunsetGwcNet-√------0.48101.825--6.6907
+Llog-√√---EPE<598.840.48631.6270.50600.18275.007513.7848
+SEDNet-√√√11logEPE<με+3bε99.610.41081.4750.61890.15094.584010.7946
VK2-S6-FogGwcNet-√------0.46601.812--6.8355-
+Llog-√√---EPE<598.980.44251.4480.46090.18654.898312.1305
+SEDNet-√√√11logEPE<με+3bε99.710.37311.2880.55170.15474.42009.9380
VK2-S6-RainGwcNet-√------0.46181.700--6.6774-
+Llog-√√---EPE<598.880.47071.5710.48990.18614.935113.3214
+SEDNet-√√√11logEPE<με+3bε99.690.38731.3560.66850.15374.401310.3362
DS-CloudyGwcNet-√------1.34135.229--37.4263-
+Llog-√√---EPE<597.481.47803.9481.26170.351334.448882.5380
+SEDNet-√√√11logEPE<με+3bε98.831.31834.4141.52600.402133.903773.6330
DS-SunnyGwcNet-√------1.54486.991--38.7386-
+Llog-√√---EPE<597.081.48374.6311.28060.383535.522685.8715
+SEDNet-√√√11logEPE<με+3bε98.641.55485.8783.00250.480835.652383.2573
DS-FoggyGwcNet-√------1.54768.859--51.4640-
+Llog-√√---EPE<594.892.95539.0152.69230.555648.7136101.7025
+SEDNet-√√√11logEPE<με+3bε99.271.53987.3572.41090.702347.793297.8627
DS-RainyGwcNet-√------3.191817.356--68.0346-
+Llog-√√---EPE<598.795.353912.5014.94800.575959.3952146.8906
+SEDNet-√√√11logEPE<με+3bε99.102.216511.0202.65990.672250.8103110.8360
", + "bbox": [ + 78, + 88, + 893, + 391 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 3. Quantitative results of synthetic to real evaluation. The top 4 subsets from VK2-S6-Moving are synthetic datasets, while the rest 4 subsets from DS-Weather are the real dataset. The best results in each category in each experiment are in bold typeface. SEDNet outperforms the baselines especially on the uncertainty estimation of the real data and under terrible weather (i.e., foggy and rainy).", + "bbox": [ + 75, + 401, + 892, + 444 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "that it matches the true distribution better.", + "bbox": [ + 76, + 470, + 354, + 484 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "4.5. Generalization from Synthetic to Real Data", + "text_level": 1, + "bbox": [ + 76, + 497, + 444, + 513 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Stereo-matching networks are typically trained on synthetic data and fine-tuned on small amounts of data from the target domain due to the cost and difficulty of acquiring real data with ground truth depth. In this section, we extend the experiments of VK2-S6 and DS-Weather in Table 1 to compare the generalization performance on unseen real domains of all methods trained only on synthetic data. We picked four synthetic subsets from VK2-S6, specifically Morning, Sunset, Fog and Rain, that have similar illumination conditions, visibility level and weather with the four real subsets of DS-Weather [37], i.e., Cloudy, Sunny, Foggy and Rainy. VK2-S6-Morning and VK2-S6-Sunset have similar illumination to DS-Cloudy and DS-Sunny, but the latter two are more challenging due to camera underexposure and overexposure. Even though they are acquired under the same weather, DS-Foggy and DS-Rainy are more difficult than VK2-S6-Fog and VK2-S6-Rain. This can be seen by comparing Figures 3 and 4. The synthetic examples only mimic the poor lighting conditions and challenges caused by the fog and rain, but ignore the Tyndall effect and reflections caused by the fog and stagnant water.", + "bbox": [ + 75, + 522, + 468, + 839 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "As mentioned above, all models are trained only on Scenes 001, 002, 018 and 020 of VK2. Quantitative results on synthetic to real transfer are reported in Table 3, where we only report the best model of each method. The top-", + "bbox": [ + 75, + 839, + 468, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "performing variant of SEDNet outperforms the baselines in the majority of experiments. An extended version of this table can be found in Table S.2 in the Supplement.", + "bbox": [ + 498, + 470, + 890, + 515 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 546, + 617, + 561 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have presented a novel approach for joint disparity and uncertainty estimation from stereo image pairs. The key idea is a unique loss function based on the KL divergence between the distributions of disparity errors and uncertainty estimates. This is made possible by a differentiable histogramming scheme that we also introduce here. To implement our approach, we extended the GwcNet architecture to include an uncertainty estimation subnetwork with only 190 parameters. Our experiments on multiple large datasets have demonstrated that our approach, named SEDNet, is effective in both disparity and uncertainty prediction. The success of our method is attributed to the novel loss function. SEDNet easily surpasses GwcNet in disparity estimation even though they have essentially the same capacity and almost identical architecture, up to the tiny uncertainty estimation subnetwork. We are optimistic that our approach will be similarly successful in other pixel-wise regression tasks, which we plan to address in future research.", + "bbox": [ + 496, + 577, + 890, + 849 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgment. This research has been supported in part by the National Science Foundation under award 2024653.", + "bbox": [ + 498, + 854, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "17242", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. Weight uncertainty in neural network. In ICML, pages 1613-1622, 2015. 2", + "[2] Jia-Ren Chang and Yong-Sheng Chen. Pyramid stereo matching network. In CVPR, pages 5410-5418, 2018. 2", + "[3] Armen Der Kiureghian and Ove Ditlevsen. Aleatory or epistemic? does it matter? Structural safety, 31(2):105-112, 2009. 2", + "[4] Zehua Fu, Mohsen Ardabilian, and Guillaume Stern. Stereo matching confidence learning based on multi-modal convolution neural networks. In International Workshop on Representations, Analysis and Recognition of Shape and Motion From Imaging Data, pages 69-81. Springer, 2017. 2", + "[5] Zehua Fu and Mohsen Ardabilian Fard. Learning confidence measures by multi-modal convolutional neural networks. In WACV, pages 1321-1330, 2018. 2", + "[6] Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig. Virtual worlds as proxy for multi-object tracking analysis. In CVPR, pages 4340-4349, 2016. 5", + "[7] Yarin Gal. Uncertainty in Deep Learning. PhD thesis, University of Cambridge, 2016. 2", + "[8] Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In ICML, pages 1050-1059, 2016. 2", + "[9] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In CVPR, 2012. 5", + "[10] Alex Graves. Practical variational inference for neural networks. In NeurIPS, volume 24, 2011. 2", + "[11] Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li. Group-wise correlation stereo network. In CVPR, 2019. 2, 4, 5", + "[12] Xiaoyan Hu and Filippos Mordohai. A quantitative evaluation of confidence measures for stereo vision. PAMI, 34(11):2121-2133, 2012. 1, 5", + "[13] Eddy Ilg, Ozgun Cicek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, and Thomas Brox. Uncertainty estimates and multi-hypotheses networks for optical flow. In ECCV, pages 652-667, 2018. 3", + "[14] Alex Kendall and Yarin Gal. What uncertainties do we need in Bayesian deep learning for computer vision? In NeurIPS, pages 5574-5584, 2017. 1, 2, 3, 5", + "[15] Alex Kendall, Yarin Gal, and Roberto Cipolla. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In CVPR, pages 7482-7491, 2018. 2", + "[16] Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, and Adam Bry. End-to-end learning of geometry and context for deep stereo regression. In ICCV, pages 66-75, 2017. 2, 4", + "[17] Sunok Kim, Seungryong Kim, Dongbo Min, Pascal Frossard, and Kwanghoon Sohn. Stereo confidence estimation via locally adaptive fusion and knowledge distillation. PAMI, 2022. 2", + "[18] Sunok Kim, Seungryong Kim, Dongbo Min, and Kwanghoon Sohn. LAF-Net: Locally adaptive fusion" + ], + "bbox": [ + 78, + 114, + 467, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "networks for stereo confidence estimation. In CVPR, 2019. 2,5", + "[19] Sunok Kim, Dongbo Min, Seungryong Kim, and Kwanghoon Sohn. Unified confidence estimation networks for robust stereo matching. TIP, 28(3):1299-1313, 2018. 1, 2", + "[20] Sunok Kim, Dongbo Min, Seungryong Kim, and Kwanghoon Sohn. Adversarial confidence estimation networks for robust stereo matching. TIST, 22(11):6875-6889, 2020. 1, 2", + "[21] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5", + "[22] S Kullback and R Leibler. On information and sufficiencyannals of mathematical statistics, 22, 79-86. MathSciNet MATH, 1951. 3", + "[23] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. In NeurIPS, volume 30, 2017. 2", + "[24] Zhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou, and Jianfeng Zhang. Learning for disparity estimation through feature constancy. In CVPR, pages 2811-2820, 2018. 2", + "[25] Nikolaus Mayer, Eddy Ig, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In CVPR, pages 4040-4048, 2016. 2, 5", + "[26] Max Mehltreter. Joint estimation of depth and its uncertainty from stereo images using bayesian deep learning. IS-PRS, 2:69-78, 2022. 1, 2", + "[27] Radford M Neal. Bayesian learning for neural networks, volume 118. Springer Science & Business Media, 2012. 2", + "[28] David A Nix and Andreas S Weigend. Estimating the mean and variance of the target probability distribution. In ICNN, volume 1, pages 55-60. IEEE, 1994. 2", + "[29] Matteo Poggi, Filippo Aleotti, Fabio Tosi, and Stefano Mattoccia. On the uncertainty of self-supervised monocular depth estimation. In CVPR, 2020. 3", + "[30] Matteo Poggi, Seungryong Kim, Fabio Tosi, Sunok Kim, Filippo Aleotti, Dongbo Min, Kwanghoon Sohn, and Stefano Mattoccia. On the confidence of stereo matching in a deep-learning era: a quantitative evaluation. PAMI, 2021. 1, 2, 5", + "[31] Matteo Poggi and Stefano Mattoccia. Learning from scratch: a confidence measure. In BMVC, 2016. 2", + "[32] Matteo Poggi, Fabio Tosi, Konstantinos Batsos, Philippos Mordohai, and Stefano Mattoccia. On the synergies between machine learning and binocular stereo for depth estimation from images: a survey. PAMI, 44(9):5314-5334, 2021. 2", + "[33] Akihito Seki and Marc Pollefeys. Patch based confidence prediction for dense disparity map. In BMVC, pages 23.1-23.13, 2016. 2", + "[34] Amit Shaked and Lior Wolf. Improved stereo matching with constant highway networks and reflective confidence learning. In CVPR, pages 4641-4650, 2017. 1, 2" + ], + "bbox": [ + 503, + 93, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "17243", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[35] Fabio Tosi, Matteo Poggi, Antonio Benincasa, and Stefano Mattoccia. Beyond local reasoning for stereo confidence estimation with deep learning. In ECCV, pages 319-334, 2018. 2", + "[36] Guorun Yang, Xiao Song, Chaoqin Huang, Zhidong Deng, Jianping Shi, and Bolei Zhou. DrivingStereo: A large-scale dataset for stereo matching in autonomous driving scenarios. In CVPR, 2019. 1", + "[37] Guorun Yang, Xiao Song, Chaoqin Huang, Zhidong Deng, Jianping Shi, and Bolei Zhou. Drivingstereo: A large-scale dataset for stereo matching in autonomous driving scenarios. In CVPR, 2019. 5, 8", + "[38] Guorun Yang, Hengshuang Zhao, Jianping Shi, Zhidong Deng, and Jiaya Jia. SegStereo: Exploiting semantic information for disparity estimation. In ECCV, pages 636-651, 2018. 2", + "[39] Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr. GA-Net: Guided aggregation net for end-to-end stereo matching. In CVPR, 2019. 2" + ], + "bbox": [ + 78, + 90, + 468, + 359 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "17244", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/52ba4497-4417-4913-a633-6839eab97318_model.json b/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/52ba4497-4417-4913-a633-6839eab97318_model.json new file mode 100644 index 0000000000000000000000000000000000000000..b360aa809ce9065d8d20a598f25a084559efd360 --- /dev/null +++ b/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/52ba4497-4417-4913-a633-6839eab97318_model.json @@ -0,0 +1,2211 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.208, + 0.131, + 0.768, + 0.177 + ], + "angle": 0, + "content": "Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation" + }, + { + "type": "text", + "bbox": [ + 0.265, + 0.204, + 0.706, + 0.24 + ], + "angle": 0, + "content": "Liyan Chen Weihan Wang Filippos Mordohai Stevens Institute of Technology" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.274, + 0.314, + 0.29 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.306, + 0.474, + 0.518 + ], + "angle": 0, + "content": "We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the observation that multi-task learning often leads to improved performance in all tasks. We show that this can be achieved by requiring the distribution of uncertainty to match the distribution of disparity errors via a KL divergence term in the network's loss function. A differentiable soft-histogramming technique is used to approximate the distributions so that they can be used in the loss. We experimentally assess the effectiveness of our approach and observe significant improvements in both disparity and uncertainty prediction on large datasets. Our code is available at https://github.com/11y00412/SEDNet.git." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.543, + 0.21, + 0.559 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.569, + 0.471, + 0.689 + ], + "angle": 0, + "content": "Many computer vision problems can be formulated as estimation tasks. Considering, however, that even high-performing estimators are not error-free, associating confidence or uncertainty with their estimates is of great importance, particularly in critical applications. In this paper, we focus on disparity estimation via stereo matching, but we are confident that our approach is applicable to other pixel-wise regression tasks after minor modifications." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.689, + 0.471, + 0.902 + ], + "angle": 0, + "content": "We distinguish between confidence and uncertainty: the former refers to a probability or likelihood of correctness, while the latter is related to the magnitude of the expected error of an estimate. Confidence can be used to reject estimates that are suspected to be incorrect, or to rank them from most to least reliable. We argue that uncertainty is more valuable because it can also be used for fusing multiple observations, e.g. in a Kalman filtering framework. Most research has focused on confidence estimation for stereo matching [12, 30]. Moreover, most methods estimate confidence for pre-computed disparities that are not further improved. Joint estimation of disparity and confidence, which benefits both due to multi-task learning, is addressed infrequently [19, 20, 26, 34]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.276, + 0.895, + 0.457 + ], + "angle": 0, + "content": "Our work is partially inspired by the joint treatment of epistemic and aleatoric uncertainty by Kendall and Gal [14], who propose novel loss functions that give rise to uncertainty estimates in pixel-wise vision tasks. Results on semantic segmentation and single-image depth estimation demonstrate how the primary task benefits from simultaneous uncertainty estimation. Kendall and Gal argue that \"in many big data regimes (such as the ones common to deep learning with image data), it is most effective to model aleatoric uncertainty,\" while epistemic uncertainty can be reduced when large amounts of data are available. Here, we restrict our attention to aleatoric uncertainty." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.46, + 0.895, + 0.552 + ], + "angle": 0, + "content": "Our motivation is that ideally we should be able to predict the magnitude of the estimator's error at each pixel. Of course, this is unrealistic, since if it was possible, we could drive all errors down to zero. A feasible objective is to train an uncertainty estimator whose outputs follow the same distribution as the true errors of the disparity estimator." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.554, + 0.895, + 0.646 + ], + "angle": 0, + "content": "In this paper, we present an implementation of this concept via a deep network that jointly estimates disparity and its uncertainty from a pair of rectified images. We named the network SEDNet, for Stereo Error Distribution Network. SEDNet includes a novel, lightweight uncertainty estimation subnetwork that predicts the aleatoric uncertainty" + }, + { + "type": "image", + "bbox": [ + 0.51, + 0.673, + 0.691, + 0.817 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.703, + 0.673, + 0.884, + 0.814 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.827, + 0.895, + 0.898 + ], + "angle": 0, + "content": "Figure 1. Examples of left images and predicted disparity maps by SEDNet on DrivingStereo [36]. The first example is taken around sunset with over-exposure. The second example is taken on a rainy day with under-exposure. In both challenge cases, SEDNet predicts accurate disparity." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "17235" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.468, + 0.167 + ], + "angle": 0, + "content": "of stereo matching, and a new loss to match the distribution of uncertainties with that of disparity errors. To generate the inputs to this new loss, we approximate the distributions from the samples of disparity errors and uncertainty values in a differentiable way via a soft-histogramming technique." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.168, + 0.468, + 0.256 + ], + "angle": 0, + "content": "We present extensive experimental validation of SEDNet's performance in disparity estimation and uncertainty prediction on large datasets with ground truth. SEDNet is superior to baselines with similar, even identical, architecture, but without the proposed loss function. Our main contributions are:" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.258, + 0.468, + 0.303 + ], + "angle": 0, + "content": "- a novel uncertainty estimation subnetwork that extracts information from the intermediate multi-resolution disparity maps generated by the disparity subnetwork," + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.304, + 0.468, + 0.347 + ], + "angle": 0, + "content": "- a differentiable soft-histogramming technique used to approximate the distributions of disparity errors and estimated uncertainties," + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.349, + 0.468, + 0.379 + ], + "angle": 0, + "content": "- a loss based on KL divergence applied on histograms obtained with the above technique." + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.258, + 0.468, + 0.379 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.393, + 0.218, + 0.408 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.418, + 0.468, + 0.463 + ], + "angle": 0, + "content": "We refer readers to recent surveys on deep stereo matching [32] and on confidence estimation [30]. Here we summarize the most relevant publications to our work." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.464, + 0.468, + 0.688 + ], + "angle": 0, + "content": "Stereo matching networks operate on a cost volume, which aggregates 2D features at each potential disparity for every pixel, and can be constructed via correlation or concatenation. Correlation-based networks such as DispNetC [25], iResNet [24] and SegStereo [38], generate a single-channel correlation map between features extracted from the two views at each disparity level, favoring computation efficiency at the expense of losing the structural and semantic information in the feature representation. Concatenation-based networks, such as GCNet [16], PSMNet [2] and GANet [39], assemble features from both views at the disparity specified by the corresponding element of the cost volume. This promotes learning of contextual features but requires more parameters and a subsequent aggregation network." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.689, + 0.468, + 0.87 + ], + "angle": 0, + "content": "We select GwcNet [11] as the foundation of our network. GwcNet takes a hybrid approach by reducing the dimension of the unary feature channels before concatenation in the cost volume. This is accomplished by a Group Wise Correlation layer, which takes as input \\( N_{c} \\) unary feature channels, divides them into \\( N_{g} \\) groups, computes the correlation between channels in each group at all disparity levels, and uses the resulting correlation scores to form the cost volume. This reduces the size of the cost volume and the computational cost of 3D convolutions by a factor of \\( N_{c}:N_{g} \\), with \\( N_{g} \\) much smaller than \\( N_{c} \\), but still provides rich similarity-measure features to the disparity estimator." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.468, + 0.901 + ], + "angle": 0, + "content": "Researchers have also focused on model reliability. In Bayesian Neural Networks (BNNs), different models are" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.212 + ], + "angle": 0, + "content": "sampled from the distribution of weights to estimate the mean and variance of the target distribution in an empirical manner, yielding estimates of uncertainty [7, 27]. Additional empirical strategies such as Bootstrapped Ensembles [23] and Monte Carlo Dropout [8] also sample from the distribution of weights. On the other hand, Graves [10] and Blundell et al. [1] proposed to replace the sampling with variational inference." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.215, + 0.892, + 0.427 + ], + "angle": 0, + "content": "Due to the high cost of training BNNs, methods for modeling the uncertainty or confidence in a predictive manner have also attracted interest. We distinguish between confidence and uncertainty: confidence is a binary variable trained with the BCE loss, while uncertainty is a continuous variable trained with L1 or L2 loss. Nix and Weigend [28] introduce NNs with one output for model prediction and one for data noise (aleatoric uncertainty). In addition to aleatoric uncertainty which captures the data noise of the observations, epistemic uncertainty, which accounts for the uncertainty of the model parameters, can also be modeled [3]. To capture both types, Kendall et al. [14, 15] proposed to combine empirical and predictive methods in a joint framework." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.43, + 0.892, + 0.656 + ], + "angle": 0, + "content": "CNNs have been used to estimate confidence in stereo matching. The Confidence CNN (CCNN) [31], Patch Based Confidence Prediction (PBCP) [33], the Early Fusion Network (EFN) and the Late Fusion Network (LFN) [4] and Multi Modal CNN (MMC) [5] only use small patches of the disparity maps. Conversely, the Global Confidence Network (ConfNet) and the Local-Global Confidence Network (LGC) [35] introduce U-Net like architectures and take both the image and disparity map as inputs. As a baseline, we use the Locally Adaptive Fusion Network (LAF) [18] which predicts the confidence map based on tri-modal inputs: the cost and disparity maps and the color image. An extension based on knowledge distillation has also been published [17]. These strategies are effective and cheaper than the empirical ones, since they only require one forward pass." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.66, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Only a subset of the confidence estimation literature has focused on joint disparity and confidence estimation. The Reflective Confidence Network (RCN) [34] is the first to combine a disparity and a confidence loss. The Unified Confidence Network (UCN) [19] and the Adversarial Confidence Network (ACN) [20] jointly estimate confidence and disparity from pre-computed cost volumes. UCN is self-supervised, while ACN combines a generative cost aggregation network and a discriminative confidence estimation network in an adversarial manner. Mehlretter [26] presents an approach that predicts both epistemic and aleatoric uncertainty using a Bayesian Neural Network, based on GCNet [16]. KL divergence is used to measure the distance between the approximation of the distribution of network parameters estimated by variational difference and the exact posterior distribution. (It should be noted that we use" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17236" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.47, + 0.121 + ], + "angle": 0, + "content": "KL divergence for a completely different purpose on the distribution of disparity errors.)" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.122, + 0.468, + 0.212 + ], + "angle": 0, + "content": "Relevant research in adjacent areas of computer vision includes the work of Poggi et al. [29] who comprehensively evaluate uncertainty estimation for self-supervised monocular depth estimation. Ilg et al. [13] study empirical ensembles, predictive models and predictive ensembles as uncertainty models for optical flow estimation." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.224, + 0.169, + 0.239 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.25, + 0.47, + 0.4 + ], + "angle": 0, + "content": "The objective of our work is to jointly estimate the disparity and its uncertainty. An important benefit of this joint formulation is that the multi-task network learns to predict more accurate disparities than the standalone disparity estimator when the uncertainty subnetwork is added. Given a stereo image pair \\(\\mathbf{X} = \\{\\mathbf{x}_l, \\mathbf{x}_r\\}\\), with image dimensions \\(H \\times W\\), and the corresponding ground truth disparity \\(\\hat{\\mathbf{d}}\\), the prediction \\(\\hat{\\mathbf{d}}\\) of a stereo-matching network \\(f_{\\theta}\\) can be represented as \\(\\hat{\\mathbf{d}} = f_{\\theta}(\\mathbf{x}_l, \\mathbf{x}_r)\\). For each pixel \\(i\\), the error \\(\\epsilon^{(i)}\\) of the prediction is calculated using the L1 loss." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.401, + 0.47, + 0.445 + ], + "angle": 0, + "content": "Kendall and Gal [14] use the negative log-likelihood of the prediction model as the loss function to be minimized in pixel-wise tasks." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.447, + 0.469, + 0.521 + ], + "angle": 0, + "content": "We take the formulation a step further by requiring that the network generate a distribution of uncertainties that matches the distribution of errors. To this end, we propose to minimize the divergence \\(\\mathcal{D}\\) between the distributions of predicted uncertainty and actual disparity error." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.522, + 0.469, + 0.581 + ], + "angle": 0, + "content": "In the following subsections, we present aleatoric uncertainty estimation (Section 3.1), the proposed KL divergence loss (Section 3.2), our network architecture (Section 3.3), and the combined loss function (Section 3.4)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.59, + 0.37, + 0.606 + ], + "angle": 0, + "content": "3.1. Aleatoric Uncertainty Estimation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.613, + 0.469, + 0.734 + ], + "angle": 0, + "content": "In order to predict uncertainty and reduce the impact of noise, Kendall and Gal [14] minimize the pixel-wise negative log-likelihood of the prediction model, assuming that it follows a Gaussian distribution. The subsequent work of Ilg et al. [13] shows that the predicted distribution can be modeled as either Laplacian or Gaussian depending on whether the L1 or L2 loss is used for disparity estimation. Since we use the former, we can write the prediction model as:" + }, + { + "type": "equation", + "bbox": [ + 0.155, + 0.742, + 0.469, + 0.759 + ], + "angle": 0, + "content": "\\[\np (\\mathbf {d} \\mid f _ {\\theta} (\\mathbf {X})) = \\mathcal {L} a p l a c e (f _ {\\theta} (\\mathbf {X}), \\sigma) \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.767, + 0.469, + 0.796 + ], + "angle": 0, + "content": "where the mean is given by the model output and \\(\\sigma\\) is the observation noise scalar." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.797, + 0.469, + 0.857 + ], + "angle": 0, + "content": "To model aleatoric uncertainty, Kendall and Gal [14] introduce pixel-specific noise parameters \\(\\sigma^{(i)}\\). We follow the approach of Ilg et al. [13], who do the same for a Laplacian model, and obtain the following pixel-wise loss function:" + }, + { + "type": "equation", + "bbox": [ + 0.138, + 0.865, + 0.469, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {l o g} = \\frac {1}{n} \\sum_ {i = 1} ^ {n} \\frac {\\left| \\hat {\\mathbf {d}} ^ {(i)} - \\mathbf {d} ^ {(i)} \\right|}{\\exp (\\mathbf {s} ^ {(i)})} + \\frac {1}{n} \\sum_ {i = 1} ^ {n} \\mathbf {s} ^ {(i)} \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.09, + 0.892, + 0.213 + ], + "angle": 0, + "content": "where \\(\\hat{\\mathbf{d}}^{(i)}\\) and \\(\\mathbf{d}^{(i)}\\) are the predicted and ground truth disparity for pixel \\(i\\), \\(\\mathbf{s}^{(i)}\\) is the log of the observation noise scalar \\(\\sigma^{(i)}\\), and \\(n\\) is equal to the total number of the pixels. Equation (2) may be viewed as a robust loss function where the residual loss for a pixel is attenuated by its uncertainty, while the second term acts as a regularizer. We follow the authors' suggestion and train the network to predict the log of the observation noise scalar, \\(s\\), for numerical stability." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.224, + 0.812, + 0.24 + ], + "angle": 0, + "content": "3.2. Matching the Distribution of Errors" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.249, + 0.892, + 0.353 + ], + "angle": 0, + "content": "Training a model using Eq. (2) as the loss improves disparity estimation accuracy and favors uncertainty estimates correlated with the errors. Ideally, we would like each uncertainty estimate to be a precise predictor of the corresponding disparity error. Since this is infeasible, we would like the distribution of uncertainties to match the distribution of errors." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.355, + 0.892, + 0.446 + ], + "angle": 0, + "content": "The Kullback-Leibler (KL) divergence [22] is a natural choice for measuring the dissimilarity between the distribution of \\(\\epsilon\\) and that of \\(\\sigma\\). Since the KL divergence is asymmetric, we choose the distribution of \\(\\epsilon\\) as the reference. Therefore, our network should also minimize the following objective function:" + }, + { + "type": "equation", + "bbox": [ + 0.505, + 0.459, + 0.891, + 0.506 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {d i v} = \\mathcal {D} \\left(P _ {\\sigma} (d)\\right) \\| Q _ {\\epsilon} (d)) = \\int_ {0} ^ {d _ {m a x}} P _ {\\sigma} (d) \\log \\frac {P _ {\\sigma} (d)}{Q _ {\\epsilon} (d)} d d \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.506, + 0.892, + 0.551 + ], + "angle": 0, + "content": "where \\(d\\) spans the disparity range. Since the network regresses disparity, the continuous formulation of KL divergence is appropriate." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.552, + 0.892, + 0.657 + ], + "angle": 0, + "content": "Minimizing Eq. (3) directly requires closed form expressions for the two distributions, which are not available to us. They could be modeled as Laplace distributions, but the maximum likelihood estimator is not differentiable. Moreover, fitting models to the data may be imprecise at the tails of the distributions. Therefore we choose non-parametric representations in the form of histograms." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.892, + 0.779 + ], + "angle": 0, + "content": "Histogramming is also not a differentiable operation, leading us to soft-histogramming as a differentiable alternative. We specify a set of bins for the histograms based on the statistics of the errors \\(\\epsilon\\), since their distribution is the one that should be matched by the distribution of uncertainty estimates \\(\\sigma\\). Since the L1 loss, and our network in general, does not discriminate between positive and negative errors, we work with absolute values of \\(\\epsilon\\) and \\(\\sigma\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.892, + 0.901 + ], + "angle": 0, + "content": "For each batch during training, we compute the mean and standard deviation of the error, \\(\\mu_{\\epsilon}\\) and \\(b_{\\epsilon}\\), set \\(C_0 = \\mu_{\\epsilon}\\) as the center of the first bin, and \\(C_m = \\mu_{\\epsilon} + \\alpha_m b_{\\epsilon}\\) as the center of the last bin. (We use \\(b_{\\epsilon}\\) for the standard deviation to avoid overloading \\(\\sigma\\) or \\(s\\). Also note that the last bin extends to the disparity range, which is also the maximum possible error.) We then define \\(m - 1\\) centers evenly spaced in a linear or logarithmic scale between the first and last center." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17237" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.107, + 0.096, + 0.862, + 0.322 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.34, + 0.892, + 0.412 + ], + "angle": 0, + "content": "Figure 2. An illustration of SEDNet. The stereo matching subnetwork consists of the feature extractor, cost volume generator and disparity estimator. It takes a rectified pair of images as input and predicts disparity maps at multiple resolutions. The uncertainty estimation subnetwork takes the predicted disparity maps as input and predicts corresponding uncertainty maps. The error maps, between the ground truth and predicted disparity, are used to supervise uncertainty estimation. During training, the network keeps the output at all resolutions, but returns only the highest resolution disparity and uncertainty maps during testing." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.437, + 0.47, + 0.527 + ], + "angle": 0, + "content": "Given the bin centers, we compute a soft-histogram for the errors and one for the uncertainties as follows, considering all pixels with ground truth disparity, and error values. We present the steps for \\(\\epsilon\\) here. For each error \\(\\epsilon^{(i)}\\), we compute weights for every bin center which are inversely proportional to the distance." + }, + { + "type": "equation", + "bbox": [ + 0.127, + 0.537, + 0.469, + 0.57 + ], + "angle": 0, + "content": "\\[\nw _ {j} \\left(\\epsilon^ {(i)}\\right) = \\lambda_ {1} \\cdot \\exp \\left(- \\frac {\\left(\\mu_ {\\epsilon} + \\alpha_ {j} b _ {\\epsilon} - \\epsilon^ {(i)}\\right) ^ {2}}{\\lambda_ {2}}\\right) \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.579, + 0.469, + 0.625 + ], + "angle": 0, + "content": "where \\(\\lambda_{1}\\) and \\(\\lambda_{2}\\) are hyper-parameters. Softmax is then applied to favor the nearest bins and the contributions of \\(n\\) pixels are accumulated in the bins of the histogram \\(H_{\\epsilon}\\)." + }, + { + "type": "equation", + "bbox": [ + 0.101, + 0.634, + 0.469, + 0.672 + ], + "angle": 0, + "content": "\\[\nH _ {\\epsilon} (j) = \\frac {1}{n} \\sum_ {i = 1} ^ {n} \\frac {\\exp \\left(w _ {j} \\left(\\epsilon^ {(i)}\\right)\\right)}{\\sum_ {j = 0} ^ {m} \\exp \\left(w _ {j} \\left(\\epsilon^ {(i)}\\right)\\right)}, j \\in [ 0, m ] \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.682, + 0.469, + 0.712 + ], + "angle": 0, + "content": "The histogram for \\(\\sigma\\), \\(H_{\\sigma}\\), is obtained similarly. (Note that the bins of both histograms are defined in terms of \\(b_{\\epsilon}\\).)" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.712, + 0.468, + 0.743 + ], + "angle": 0, + "content": "The loss representing the discrete form of the KL divergence between the two histograms is given by:" + }, + { + "type": "equation", + "bbox": [ + 0.175, + 0.752, + 0.469, + 0.793 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {d i v} = \\sum_ {j = 0} ^ {m} H _ {\\epsilon} (j) \\log \\frac {H _ {\\epsilon} (j)}{H _ {\\sigma} (j)} \\tag {6}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.802, + 0.176, + 0.816 + ], + "angle": 0, + "content": "3.3. SEDNet" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Our network architecture, named SEDNet, includes a disparity estimation subnetwork, an uncertainty estimation subnetwork, and is shown in Figure 2. In all experiments, we have adopted GwcNet [11] as the disparity estimation subnetwork, among other options, and we have designed" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.437, + 0.892, + 0.482 + ], + "angle": 0, + "content": "a novel uncertainty estimation subnetwork that interfaces with GwcNet. It is worth noting that the uncertainty subnetwork is extremely small." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.486, + 0.892, + 0.575 + ], + "angle": 0, + "content": "The GwcNet subnetwork extracts features from the images using a ResNet-like feature extractor, generates the cost volume, and assigns disparities to pixels using the soft-argmax operator [16]. The output module of the disparity predictor generates \\(K\\) disparity maps at different resolutions." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.581, + 0.892, + 0.792 + ], + "angle": 0, + "content": "We propose a new uncertainty estimator integrated with the stereo matching network. The uncertainty estimation subnetwork learns to predict the log of the observation noise scalar, the error, at each pixel. The proposed subnetwork takes the multi-resolution disparity predictions as input, computes the pairwise differences vector (PDV) and passes it to a pixel-wise MLP to regress the uncertainty maps. Specifically, the disparity estimator outputs \\(K\\) disparity maps at different resolutions \\(\\hat{\\mathbf{d}} = \\{\\hat{\\mathbf{d}}_1,\\dots,\\hat{\\mathbf{d}}_K\\}\\), which are first upsampled to full-resolution and then undergo pairwise differencing to form the PVD, which consists of \\(\\binom{K}{2}\\) elements. The output set of uncertainty maps S also contains \\(K\\) resolutions to match the disparity maps. We use \\(K = 4\\) in all experiments." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.814, + 0.645, + 0.828 + ], + "angle": 0, + "content": "3.4. Loss Function" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.841, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Our loss function combines two parts: (1) the log-likelihood loss to optimize the error and uncertainty, (2) the KL divergence loss to match the distribution of uncertainty with the error. The total loss considers all disparity and un" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17238" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.419, + 0.107 + ], + "angle": 0, + "content": "certainty maps upsampled to the highest resolution:" + }, + { + "type": "equation", + "bbox": [ + 0.17, + 0.119, + 0.47, + 0.161 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\sum_ {k = 1} ^ {K} c _ {k} \\cdot \\left(\\mathcal {L} _ {\\text {l o g}, k} + \\mathcal {L} _ {\\text {d i v}, k}\\right) \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.174, + 0.469, + 0.22 + ], + "angle": 0, + "content": "where \\( c_k \\) denotes the coefficients for the \\( k^{th} \\) resolution level, \\( \\mathcal{L}_{\\log,k} \\) and \\( \\mathcal{L}_{div,k} \\) are computed by Eq. (2) and Eq. (6) on the prediction of the corresponding resolution level." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.235, + 0.267, + 0.251 + ], + "angle": 0, + "content": "4. Experiment Results" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.261, + 0.47, + 0.426 + ], + "angle": 0, + "content": "In this section, we present our experimental setup and results on within-domain and cross-domain experiments. Datasets, evaluation metrics and baselines are described in Section 4.1, implementation details in Section 4.2, and experimental results in Sections 4.3 and 4.4. Additionally, a synthetic-to-real transfer evaluation is presented in Section 4.5. We provide more quantitative and qualitative results, extending Section 4, in the Supplement, which also includes ablation studies on several aspects of SEDNet and the baselines, as well as disparity, error, and uncertainty maps of difficult examples." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.438, + 0.357, + 0.452 + ], + "angle": 0, + "content": "4.1. Datasets and evaluation metrics" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.461, + 0.469, + 0.566 + ], + "angle": 0, + "content": "SceneFlow [25] is a collection of three synthetic stereo datasets: FlyingThings3D, Driving, Monkaa. The datasets provide 35,454 training and 4,370 test stereo pairs in \\(960 \\times 540\\) pixel resolution with dense ground-truth disparity maps. We use the finalpass versions of the rendered images which are more realistic because of the motion blur and depth of field effect." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.567, + 0.469, + 0.748 + ], + "angle": 0, + "content": "Virtual KITTI 2 (VK2) [6] is a synthetic clone of KITTI [9]. It consists of 21,260 synthetic \\(1,242 \\times 375\\) stereo pairs from 6 driving scenes with 10 different imaging and weather conditions. Scene 006 (VK2-S6) is specified as the test set by the authors of the dataset. Since the car with the cameras is stopped for a long part of Scene 006 and only other cars move in the images, we split the last part of the scene where the car moves and denote it as VK2-S6-Moving. We report results separately on this subset. Since results on VK2-S6-Moving do not suffer from bias due to the almost constant background of the first part of the scene, evaluation on VK2-S6-Moving is more informative and fair." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.749, + 0.469, + 0.838 + ], + "angle": 0, + "content": "DrivingStereo [37] is a large real-world autonomous driving dataset. It contains 174,437 training and 7,751 test stereo pairs at \\(881 \\times 400\\) pixel resolution. The dataset provides sparse ground truth disparity as well as a challenging subset (DS-Weather) of 2,000 stereo pairs in 4 different weather conditions." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.84, + 0.469, + 0.87 + ], + "angle": 0, + "content": "For all datasets above, we exclude pixels with disparities \\( d > 192 \\) in training." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Metrics. To evaluate disparity estimation, we compute the endpoint error (EPE) and the percentage of outliers (D1)" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.122 + ], + "angle": 0, + "content": "(i.e., the percentage of pixels with EPE \\(>3\\mathrm{px}\\) or \\(\\geq 5\\%\\) of the true depth)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.123, + 0.892, + 0.333 + ], + "angle": 0, + "content": "To evaluate uncertainty estimation, we use density-EPE ROC curves and the area under the curve (AUC) [12,18,30]. The ROC curves in our case measure EPE (not binary correctness) at increasing disparity map density by successively adding pixels in increasing order of uncertainty. The optimal AUC is obtained by adding pixels in order of increasing EPE and is therefore the lowest value any algorithm could achieve given the set of EPE values, while the estimated AUC is sorted by predicted uncertainty. To evaluate the precision of uncertainty estimation, we also introduce the absolute prediction error (APE), which is the average L1 distance between the error and the observation noise scalar, \\(\\sigma\\). We report the average and median APE over all pixels." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.335, + 0.892, + 0.516 + ], + "angle": 0, + "content": "Baselines. Since we use GwcNet [11] as the disparity estimation subnetwork of SEDNet, we compare SEDNet with three baselines: (1) the original GwcNet trained with smooth L1 loss. (2) LAF-Net [18] trained under the BCE loss on the left RGB images, the cost volumes and predicted disparity maps of GwcNet at the highest resolution. (3) SEDNet but only trained with the log-likelihood loss, therefore similar to Kendall and Gal's model [14]. We use \\(\\mathcal{L}_{\\log}\\) in tables and figures for this baseline. We selected GwcNet because it is the backbone of SEDNet, and LAF-Net as the confidence estimation baseline due to its strong performance within the training domain according to [30]." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.528, + 0.716, + 0.543 + ], + "angle": 0, + "content": "4.2. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.552, + 0.892, + 0.612 + ], + "angle": 0, + "content": "We implemented all networks in PyTorch and used the Adam optimizer [21] with \\(\\beta_{1} = 0.9\\) and \\(\\beta_{2} = 0.999\\) for all experiments. Training of all models was stopped before overfitting occurred." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.613, + 0.892, + 0.718 + ], + "angle": 0, + "content": "Experiments on the VK2 dataset were performed on two NVIDIA RTX A6000 GPUs, each with 48 GB of RAM. For this dataset, we trained all models from scratch with an initial learning rate of 0.0001, down-scaled by 5 every 10 epochs. During training, we randomly cropped \\(512 \\times 256\\) patches from the images. During testing, we evaluated at the full resolution of VK2." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Experiments on the DrivingStereo dataset (DS) were also performed on two NVIDIA RTX A6000 GPUs. For this dataset, we did two experiments using the models pretrained on VK2: (1) we finetuned on the DS training set with a learning rate starting from 0.0001, down-scaled by 2 every 3 epochs after epoch 10, then performed in-domain evaluation on the DS test set; (2) we skipped the finetuning step and performed cross-domain evaluation on DS-Weather subset. During training, we randomly cropped the inputs to be the same size as in the VK2 experiments. During testing, we padded the test samples to be the same resolution as VK2." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17239" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.079, + 0.089, + 0.894, + 0.326 + ], + "angle": 0, + "content": "
DatasetMethodLossInliersDisparity↓APE↓AUC↓
BCEL1LogKLBinsScaleDef.Pct(%)EPED1(%)Avg.MedianOpt.Est.
Scene FlowGwcNet-√------0.77584.127--10.9291-
+LAF√-------0.77584.127--10.929120.0813
+Llog-√√---EPE<596.960.76114.1310.69990.07285.744912.1121
+SEDNet-√√√11logEPE<μe+3bε98.420.67543.9630.57970.04324.91348.7195
VK2-S6GwcNet-√------0.41251.763--6.0962-
+Llog-√√---EPE<598.860.38991.5840.41360.17534.687212.5320
+SEDNet-√√√11logEPE<μe+3bε99.240.31091.3920.52340.14544.17269.7637
+SEDNet-√√√11logEPE<μe+5bε99.680.32361.4270.35610.10964.27679.9843
VK2-S6-MovingGwcNet-√------0.42531.689--5.9184-
+Llog-√√---EPE<598.910.42311.5370.45750.18904.366311.3532
+SEDNet-√√√11logEPE<μe+3bε99.620.35771.3890.59580.15733.90128.8339
+SEDNet-√√√11logEPE<μe+5bε99.760.38621.4200.40020.11644.04239.0631
DrivingStereo+Llog(FT)-√√-----0.53320.26410.34490.229721.700245.7096
+SEDNet(FT)-√√√11logEPE<μe+5bε99.860.52640.24390.33240.226721.285644.3297
DS-WeatherGwcNet-√------1.69628.313--44.4896-
+Llog-√√---EPE<595.782.39446.6662.14430.438341.190995.4264
+SEDNet-√√√11logEPE<μe+3bε98.951.56376.5082.34060.530938.487186.1118
+SEDNet-√√√11logEPE<μe+5bε99.411.70516.0571.58420.610439.805787.1882
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.334, + 0.893, + 0.378 + ], + "angle": 0, + "content": "Table 1. Quantitative results: (1) within-domain on SceneFlow, VK2-S6 and VK2-S6-Moving; (2) after finetuning (FT) on DrivingStereo; (3) cross-domain on DS-Weather. The best results in each category in each experiment are in bold typeface. SEDNet outperforms the baselines with respect to disparity and uncertainty metrics in the majority of experiments." + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.39, + 0.345, + 0.452 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.178, + 0.455, + 0.254, + 0.469 + ], + "angle": 0, + "content": "Left Image" + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.469, + 0.345, + 0.53 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.174, + 0.533, + 0.259, + 0.547 + ], + "angle": 0, + "content": "Right Image" + }, + { + "type": "image", + "bbox": [ + 0.358, + 0.39, + 0.613, + 0.452 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.437, + 0.455, + 0.534, + 0.469 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{\\mathrm{log}}\\) Disparity" + }, + { + "type": "image", + "bbox": [ + 0.358, + 0.469, + 0.613, + 0.529 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.425, + 0.533, + 0.545, + 0.547 + ], + "angle": 0, + "content": "SEDNet Disparity" + }, + { + "type": "image", + "bbox": [ + 0.627, + 0.39, + 0.883, + 0.452 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.699, + 0.455, + 0.811, + 0.469 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{\\mathrm{log}}\\) Uncertainty" + }, + { + "type": "image", + "bbox": [ + 0.628, + 0.469, + 0.882, + 0.53 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.686, + 0.532, + 0.824, + 0.547 + ], + "angle": 0, + "content": "SEDNet Uncertainty" + }, + { + "type": "image_caption", + "bbox": [ + 0.075, + 0.556, + 0.894, + 0.613 + ], + "angle": 0, + "content": "Figure 3. Example from VK2-S6. In a rainy scene with poor illumination, recognizing objects far away from the camera is even difficult for human observers. \\(\\mathcal{L}_{log}\\) fails to predict the disparity of objects, such as the traffic sign near the left street light. On the other hand, SEDNet accurately predicts the disparity of these challenging objects, while its uncertainty map also captures more information, such as the trees in the background, the traffic light near the trees and the street lights." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.638, + 0.47, + 0.729 + ], + "angle": 0, + "content": "Experiments on the SceneFlow dataset were performed on an Nvidia Titan RTX GPU with 24 GB memory. We trained all models from scratch on \\(256 \\times 128\\) patches cropped from half-resolution images to limit memory consumption. We set the initial learning rate to 0.001 and down-scaled it by 2 every 2 epochs after epoch 10." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.73, + 0.47, + 0.775 + ], + "angle": 0, + "content": "For all the experiments above, we applied an inlier filtering strategy during training, which only back-propagates from the inliers. See the last paragraph of Section 4.3 for" + }, + { + "type": "table", + "bbox": [ + 0.131, + 0.783, + 0.416, + 0.836 + ], + "angle": 0, + "content": "
ArchitectureParamsMACs(G)
GwcNet6,909,7281075.82
SEDNet6,909,9181075.91
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.842, + 0.47, + 0.897 + ], + "angle": 0, + "content": "Table 2. Comparison on number of parameters and computational complexity. MAC stands for multiply-accumulate operations. SEDNet only adds a 3-layer MLP, with 190 parameters, as an uncertainty decoder to GwcNet." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.638, + 0.892, + 0.67 + ], + "angle": 0, + "content": "details. Table 2 shows the number of parameters and computational complexity of GwcNet and SEDNet." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.68, + 0.818, + 0.696 + ], + "angle": 0, + "content": "4.3. Qualitative and Quantitative Results" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.704, + 0.892, + 0.794 + ], + "angle": 0, + "content": "In Table 1, we present results: (1) within-domain on SceneFlow and VK2; (2) on the DrivingStereo test set after finetuning the VK2 models on the DrivingStereo training set; (3) cross-domain on the DS-Weather challenge test set by directly applying the model trained on VK2 without finetuning." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.795, + 0.892, + 0.87 + ], + "angle": 0, + "content": "Disparity Estimation. In all experiments, SEDNet achieves lower errors than all the baselines. See the EPE and D1 columns in Table 1. Even in extreme weather like fog and rain, SEDNet predicts good disparity unaffected by poor illumination and blur. See Figure 3." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Uncertainty Estimation. Our method outperforms the baselines in all experiments according to the AUC metric," + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "17240" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.088, + 0.088, + 0.345, + 0.18 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.179, + 0.183, + 0.253, + 0.196 + ], + "angle": 0, + "content": "Left Image" + }, + { + "type": "image", + "bbox": [ + 0.087, + 0.196, + 0.345, + 0.287 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.174, + 0.29, + 0.258, + 0.304 + ], + "angle": 0, + "content": "Right Image" + }, + { + "type": "image", + "bbox": [ + 0.359, + 0.088, + 0.615, + 0.18 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.438, + 0.183, + 0.533, + 0.197 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{\\text{log}}\\) Disparity" + }, + { + "type": "image", + "bbox": [ + 0.359, + 0.197, + 0.613, + 0.287 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.425, + 0.29, + 0.545, + 0.305 + ], + "angle": 0, + "content": "SEDNet Disparity" + }, + { + "type": "image", + "bbox": [ + 0.627, + 0.088, + 0.883, + 0.18 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.699, + 0.183, + 0.811, + 0.197 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{\\mathrm{log}}\\) Uncertainty" + }, + { + "type": "image", + "bbox": [ + 0.627, + 0.197, + 0.882, + 0.287 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.686, + 0.29, + 0.824, + 0.305 + ], + "angle": 0, + "content": "SEDNet Uncertainty" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.313, + 0.894, + 0.37 + ], + "angle": 0, + "content": "Figure 4. Example from DS-Weather. Unlike the synthetic data, the rainy-day real images do not only suffer from poor illumination, but also pose challenges due to reflections in the water. In this example, the road is like a mirror, misleading the \\(\\mathcal{L}_{log}\\) model. Recall that the LIDAR ground truth disparity is very sparse, and is even sparser in reflective regions. The disparity map of \\(\\mathcal{L}_{log}\\) fails to distinguish the car and the reflection, but SEDNet is able to estimate the correct disparity and the uncertainty of the car." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.395, + 0.473, + 0.533 + ], + "angle": 0, + "content": "as shown in the last two columns in Table 1. Compared to \\(\\mathcal{L}_{log}\\), SEDNet decreases the estimated AUC by \\(20\\% - 30\\%\\) in the in-domain experiments, with a \\(10\\%\\) decrease in optimal AUC, which depends on EPE. In the cross-domain evaluation, the advantage of SEDNet is even more evident. Figure 4 shows uncertainty maps for real data, on which our method captures details more faithfully. The ROC curves of the best \\(\\mathcal{L}_{log}\\) and SEDNet models based on EPE on VK2-S6-Moving are presented in Figure 5." + }, + { + "type": "image", + "bbox": [ + 0.141, + 0.552, + 0.398, + 0.702 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.711, + 0.47, + 0.795 + ], + "angle": 0, + "content": "Figure 5. Comparison of density-EPE curves on VK2-S6-Moving. \"L.log (EPE<5)\" and \"SEDNet (3)\" represent the \\(\\mathcal{L}_{\\log}\\) (with fixed inliers) and the SEDNet (with adaptive inliers of \\(\\mathrm{EPE} < \\mu_{\\epsilon} + 3b_{\\epsilon}\\)) of VK2-S6-Moving experiment in Table 1. While their corresponding optimal AUCs are almost equal, the advantage of SEDNet in estimated AUC is significant." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Effects of Back-propagation from Inliers. We apply two kinds of inlier filters: one with a fixed threshold that excludes all pixels that have an EPE larger than 5 from backpropagation; and one with adaptive threshold which excludes pixels that have EPE greater than a specified number of \\( b_{\\epsilon} \\) from the mean error. Back-propagation from the" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.395, + 0.895, + 0.547 + ], + "angle": 0, + "content": "inliers only helps the network improve its performance on both disparity and uncertainty estimation. We attribute this to the suppression of harmful outliers that give rise to large gradients. Quantitative results for the baselines and the proposed method with different inlier settings are reported in the Supplement. The results show that using adaptive thresholds is better than fixed thresholds. Fixed thresholds exclude more pixels, especially at lower resolutions and in the early stages of training, preventing the network from learning how to correct them." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.56, + 0.791, + 0.577 + ], + "angle": 0, + "content": "4.4. Matching the Error Distribution." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.585, + 0.894, + 0.646 + ], + "angle": 0, + "content": "Comparing the results of APE in all experiments in Table 1 and Figure 6, the difference between the errors and the predicted uncertainty is always close to the EPE, as expected. SEDNet, however, achieves lower APE, showing" + }, + { + "type": "image", + "bbox": [ + 0.511, + 0.674, + 0.688, + 0.775 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.586, + 0.778, + 0.614, + 0.792 + ], + "angle": 0, + "content": "\\(\\mathcal{L}_{log}\\)" + }, + { + "type": "image", + "bbox": [ + 0.704, + 0.674, + 0.882, + 0.775 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.767, + 0.778, + 0.82, + 0.789 + ], + "angle": 0, + "content": "SEDNet" + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.8, + 0.894, + 0.898 + ], + "angle": 0, + "content": "Figure 6. Distribution of error and predicted uncertainty. We pick the best model of \\(\\mathcal{L}_{\\text{log}}\\) and SEDNet in VK2-S6-Moving, then randomly sample 5,000,000 points from the outputs of the two models. The distributions of error and \\(\\sigma\\) of SEDNet are much closer than those of the \\(\\mathcal{L}_{\\text{log}}\\) model, especially in the first 5 bins that contain more than \\(95\\%\\) of the samples. The shorter tail of SEDNet also indicates fewer gross errors." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "17241" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.079, + 0.089, + 0.894, + 0.392 + ], + "angle": 0, + "content": "
DatasetMethodLossInliersDisparity↓APE↓AUC↓
BCEL1LogKLBinsScaleDef.Pct(%)EPED1(%)Avg.MedianOpt.Est.
VK2-S6-MorningGwcNet-√------0.46421.740--6.1845-
+Llog-√√---EPE<598.820.47741.6240.50670.18724.669812.5192
+SEDNet-√√√11logEPE<με+3bε99.620.40031.4420.61830.15534.18479.4063
VK2-S6-SunsetGwcNet-√------0.48101.825--6.6907
+Llog-√√---EPE<598.840.48631.6270.50600.18275.007513.7848
+SEDNet-√√√11logEPE<με+3bε99.610.41081.4750.61890.15094.584010.7946
VK2-S6-FogGwcNet-√------0.46601.812--6.8355-
+Llog-√√---EPE<598.980.44251.4480.46090.18654.898312.1305
+SEDNet-√√√11logEPE<με+3bε99.710.37311.2880.55170.15474.42009.9380
VK2-S6-RainGwcNet-√------0.46181.700--6.6774-
+Llog-√√---EPE<598.880.47071.5710.48990.18614.935113.3214
+SEDNet-√√√11logEPE<με+3bε99.690.38731.3560.66850.15374.401310.3362
DS-CloudyGwcNet-√------1.34135.229--37.4263-
+Llog-√√---EPE<597.481.47803.9481.26170.351334.448882.5380
+SEDNet-√√√11logEPE<με+3bε98.831.31834.4141.52600.402133.903773.6330
DS-SunnyGwcNet-√------1.54486.991--38.7386-
+Llog-√√---EPE<597.081.48374.6311.28060.383535.522685.8715
+SEDNet-√√√11logEPE<με+3bε98.641.55485.8783.00250.480835.652383.2573
DS-FoggyGwcNet-√------1.54768.859--51.4640-
+Llog-√√---EPE<594.892.95539.0152.69230.555648.7136101.7025
+SEDNet-√√√11logEPE<με+3bε99.271.53987.3572.41090.702347.793297.8627
DS-RainyGwcNet-√------3.191817.356--68.0346-
+Llog-√√---EPE<598.795.353912.5014.94800.575959.3952146.8906
+SEDNet-√√√11logEPE<με+3bε99.102.216511.0202.65990.672250.8103110.8360
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.402, + 0.893, + 0.445 + ], + "angle": 0, + "content": "Table 3. Quantitative results of synthetic to real evaluation. The top 4 subsets from VK2-S6-Moving are synthetic datasets, while the rest 4 subsets from DS-Weather are the real dataset. The best results in each category in each experiment are in bold typeface. SEDNet outperforms the baselines especially on the uncertainty estimation of the real data and under terrible weather (i.e., foggy and rainy)." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.471, + 0.355, + 0.485 + ], + "angle": 0, + "content": "that it matches the true distribution better." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.498, + 0.446, + 0.515 + ], + "angle": 0, + "content": "4.5. Generalization from Synthetic to Real Data" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.523, + 0.47, + 0.84 + ], + "angle": 0, + "content": "Stereo-matching networks are typically trained on synthetic data and fine-tuned on small amounts of data from the target domain due to the cost and difficulty of acquiring real data with ground truth depth. In this section, we extend the experiments of VK2-S6 and DS-Weather in Table 1 to compare the generalization performance on unseen real domains of all methods trained only on synthetic data. We picked four synthetic subsets from VK2-S6, specifically Morning, Sunset, Fog and Rain, that have similar illumination conditions, visibility level and weather with the four real subsets of DS-Weather [37], i.e., Cloudy, Sunny, Foggy and Rainy. VK2-S6-Morning and VK2-S6-Sunset have similar illumination to DS-Cloudy and DS-Sunny, but the latter two are more challenging due to camera underexposure and overexposure. Even though they are acquired under the same weather, DS-Foggy and DS-Rainy are more difficult than VK2-S6-Fog and VK2-S6-Rain. This can be seen by comparing Figures 3 and 4. The synthetic examples only mimic the poor lighting conditions and challenges caused by the fog and rain, but ignore the Tyndall effect and reflections caused by the fog and stagnant water." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.47, + 0.903 + ], + "angle": 0, + "content": "As mentioned above, all models are trained only on Scenes 001, 002, 018 and 020 of VK2. Quantitative results on synthetic to real transfer are reported in Table 3, where we only report the best model of each method. The top-" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.471, + 0.892, + 0.516 + ], + "angle": 0, + "content": "performing variant of SEDNet outperforms the baselines in the majority of experiments. An extended version of this table can be found in Table S.2 in the Supplement." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.547, + 0.619, + 0.563 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.578, + 0.892, + 0.851 + ], + "angle": 0, + "content": "We have presented a novel approach for joint disparity and uncertainty estimation from stereo image pairs. The key idea is a unique loss function based on the KL divergence between the distributions of disparity errors and uncertainty estimates. This is made possible by a differentiable histogramming scheme that we also introduce here. To implement our approach, we extended the GwcNet architecture to include an uncertainty estimation subnetwork with only 190 parameters. Our experiments on multiple large datasets have demonstrated that our approach, named SEDNet, is effective in both disparity and uncertainty prediction. The success of our method is attributed to the novel loss function. SEDNet easily surpasses GwcNet in disparity estimation even though they have essentially the same capacity and almost identical architecture, up to the tiny uncertainty estimation subnetwork. We are optimistic that our approach will be similarly successful in other pixel-wise regression tasks, which we plan to address in future research." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Acknowledgment. This research has been supported in part by the National Science Foundation under award 2024653." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17242" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.468, + 0.157 + ], + "angle": 0, + "content": "[1] Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. Weight uncertainty in neural network. In ICML, pages 1613-1622, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.159, + 0.468, + 0.186 + ], + "angle": 0, + "content": "[2] Jia-Ren Chang and Yong-Sheng Chen. Pyramid stereo matching network. In CVPR, pages 5410-5418, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.188, + 0.468, + 0.227 + ], + "angle": 0, + "content": "[3] Armen Der Kiureghian and Ove Ditlevsen. Aleatory or epistemic? does it matter? Structural safety, 31(2):105-112, 2009. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.231, + 0.468, + 0.3 + ], + "angle": 0, + "content": "[4] Zehua Fu, Mohsen Ardabilian, and Guillaume Stern. Stereo matching confidence learning based on multi-modal convolution neural networks. In International Workshop on Representations, Analysis and Recognition of Shape and Motion From Imaging Data, pages 69-81. Springer, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.302, + 0.468, + 0.342 + ], + "angle": 0, + "content": "[5] Zehua Fu and Mohsen Ardabilian Fard. Learning confidence measures by multi-modal convolutional neural networks. In WACV, pages 1321-1330, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.345, + 0.468, + 0.385 + ], + "angle": 0, + "content": "[6] Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig. Virtual worlds as proxy for multi-object tracking analysis. In CVPR, pages 4340-4349, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.387, + 0.468, + 0.414 + ], + "angle": 0, + "content": "[7] Yarin Gal. Uncertainty in Deep Learning. PhD thesis, University of Cambridge, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.417, + 0.468, + 0.457 + ], + "angle": 0, + "content": "[8] Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In ICML, pages 1050-1059, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.46, + 0.468, + 0.5 + ], + "angle": 0, + "content": "[9] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In CVPR, 2012. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.503, + 0.468, + 0.528 + ], + "angle": 0, + "content": "[10] Alex Graves. Practical variational inference for neural networks. In NeurIPS, volume 24, 2011. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.532, + 0.468, + 0.572 + ], + "angle": 0, + "content": "[11] Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li. Group-wise correlation stereo network. In CVPR, 2019. 2, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.575, + 0.468, + 0.614 + ], + "angle": 0, + "content": "[12] Xiaoyan Hu and Filippos Mordohai. A quantitative evaluation of confidence measures for stereo vision. PAMI, 34(11):2121-2133, 2012. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.617, + 0.468, + 0.672 + ], + "angle": 0, + "content": "[13] Eddy Ilg, Ozgun Cicek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, and Thomas Brox. Uncertainty estimates and multi-hypotheses networks for optical flow. In ECCV, pages 652-667, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.675, + 0.468, + 0.715 + ], + "angle": 0, + "content": "[14] Alex Kendall and Yarin Gal. What uncertainties do we need in Bayesian deep learning for computer vision? In NeurIPS, pages 5574-5584, 2017. 1, 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.718, + 0.468, + 0.758 + ], + "angle": 0, + "content": "[15] Alex Kendall, Yarin Gal, and Roberto Cipolla. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In CVPR, pages 7482-7491, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.761, + 0.468, + 0.814 + ], + "angle": 0, + "content": "[16] Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, and Adam Bry. End-to-end learning of geometry and context for deep stereo regression. In ICCV, pages 66-75, 2017. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.817, + 0.468, + 0.87 + ], + "angle": 0, + "content": "[17] Sunok Kim, Seungryong Kim, Dongbo Min, Pascal Frossard, and Kwanghoon Sohn. Stereo confidence estimation via locally adaptive fusion and knowledge distillation. PAMI, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.874, + 0.468, + 0.901 + ], + "angle": 0, + "content": "[18] Sunok Kim, Seungryong Kim, Dongbo Min, and Kwanghoon Sohn. LAF-Net: Locally adaptive fusion" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.468, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.094, + 0.891, + 0.119 + ], + "angle": 0, + "content": "networks for stereo confidence estimation. In CVPR, 2019. 2,5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.122, + 0.892, + 0.176 + ], + "angle": 0, + "content": "[19] Sunok Kim, Dongbo Min, Seungryong Kim, and Kwanghoon Sohn. Unified confidence estimation networks for robust stereo matching. TIP, 28(3):1299-1313, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.179, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[20] Sunok Kim, Dongbo Min, Seungryong Kim, and Kwanghoon Sohn. Adversarial confidence estimation networks for robust stereo matching. TIST, 22(11):6875-6889, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.235, + 0.892, + 0.274 + ], + "angle": 0, + "content": "[21] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.278, + 0.892, + 0.318 + ], + "angle": 0, + "content": "[22] S Kullback and R Leibler. On information and sufficiencyannals of mathematical statistics, 22, 79-86. MathSciNet MATH, 1951. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.321, + 0.892, + 0.374 + ], + "angle": 0, + "content": "[23] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. In NeurIPS, volume 30, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.377, + 0.892, + 0.432 + ], + "angle": 0, + "content": "[24] Zhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou, and Jianfeng Zhang. Learning for disparity estimation through feature constancy. In CVPR, pages 2811-2820, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.434, + 0.892, + 0.501 + ], + "angle": 0, + "content": "[25] Nikolaus Mayer, Eddy Ig, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In CVPR, pages 4040-4048, 2016. 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.504, + 0.892, + 0.544 + ], + "angle": 0, + "content": "[26] Max Mehltreter. Joint estimation of depth and its uncertainty from stereo images using bayesian deep learning. IS-PRS, 2:69-78, 2022. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.547, + 0.892, + 0.573 + ], + "angle": 0, + "content": "[27] Radford M Neal. Bayesian learning for neural networks, volume 118. Springer Science & Business Media, 2012. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.576, + 0.892, + 0.616 + ], + "angle": 0, + "content": "[28] David A Nix and Andreas S Weigend. Estimating the mean and variance of the target probability distribution. In ICNN, volume 1, pages 55-60. IEEE, 1994. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.619, + 0.892, + 0.659 + ], + "angle": 0, + "content": "[29] Matteo Poggi, Filippo Aleotti, Fabio Tosi, and Stefano Mattoccia. On the uncertainty of self-supervised monocular depth estimation. In CVPR, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.661, + 0.892, + 0.728 + ], + "angle": 0, + "content": "[30] Matteo Poggi, Seungryong Kim, Fabio Tosi, Sunok Kim, Filippo Aleotti, Dongbo Min, Kwanghoon Sohn, and Stefano Mattoccia. On the confidence of stereo matching in a deep-learning era: a quantitative evaluation. PAMI, 2021. 1, 2, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.732, + 0.892, + 0.758 + ], + "angle": 0, + "content": "[31] Matteo Poggi and Stefano Mattoccia. Learning from scratch: a confidence measure. In BMVC, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.761, + 0.892, + 0.814 + ], + "angle": 0, + "content": "[32] Matteo Poggi, Fabio Tosi, Konstantinos Batsos, Philippos Mordohai, and Stefano Mattoccia. On the synergies between machine learning and binocular stereo for depth estimation from images: a survey. PAMI, 44(9):5314-5334, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.817, + 0.892, + 0.857 + ], + "angle": 0, + "content": "[33] Akihito Seki and Marc Pollefeys. Patch based confidence prediction for dense disparity map. In BMVC, pages 23.1-23.13, 2016. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.86, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[34] Amit Shaked and Lior Wolf. Improved stereo matching with constant highway networks and reflective confidence learning. In CVPR, pages 4641-4650, 2017. 1, 2" + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.094, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "17243" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.146 + ], + "angle": 0, + "content": "[35] Fabio Tosi, Matteo Poggi, Antonio Benincasa, and Stefano Mattoccia. Beyond local reasoning for stereo confidence estimation with deep learning. In ECCV, pages 319-334, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.149, + 0.469, + 0.204 + ], + "angle": 0, + "content": "[36] Guorun Yang, Xiao Song, Chaoqin Huang, Zhidong Deng, Jianping Shi, and Bolei Zhou. DrivingStereo: A large-scale dataset for stereo matching in autonomous driving scenarios. In CVPR, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.207, + 0.469, + 0.261 + ], + "angle": 0, + "content": "[37] Guorun Yang, Xiao Song, Chaoqin Huang, Zhidong Deng, Jianping Shi, and Bolei Zhou. Drivingstereo: A large-scale dataset for stereo matching in autonomous driving scenarios. In CVPR, 2019. 5, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.263, + 0.469, + 0.317 + ], + "angle": 0, + "content": "[38] Guorun Yang, Hengshuang Zhao, Jianping Shi, Zhidong Deng, and Jiaya Jia. SegStereo: Exploiting semantic information for disparity estimation. In ECCV, pages 636-651, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.32, + 0.469, + 0.361 + ], + "angle": 0, + "content": "[39] Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr. GA-Net: Guided aggregation net for end-to-end stereo matching. In CVPR, 2019. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.361 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.956 + ], + "angle": 0, + "content": "17244" + } + ] +] \ No newline at end of file diff --git a/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/52ba4497-4417-4913-a633-6839eab97318_origin.pdf b/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/52ba4497-4417-4913-a633-6839eab97318_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..171287db8ee2cffd0430975cc99e29fad36a5ede --- /dev/null +++ b/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/52ba4497-4417-4913-a633-6839eab97318_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:0b56fe81e5d0f81f5bb83f912821ab5c7b4b126cef6a3b5831cf35f184e5ab00 +size 4625109 diff --git a/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/full.md b/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..913f018d57e800880ed5b73c991b8b190fe504ae --- /dev/null +++ b/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/full.md @@ -0,0 +1,320 @@ +# Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation + +Liyan Chen Weihan Wang Filippos Mordohai Stevens Institute of Technology + +# Abstract + +We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the observation that multi-task learning often leads to improved performance in all tasks. We show that this can be achieved by requiring the distribution of uncertainty to match the distribution of disparity errors via a KL divergence term in the network's loss function. A differentiable soft-histogramming technique is used to approximate the distributions so that they can be used in the loss. We experimentally assess the effectiveness of our approach and observe significant improvements in both disparity and uncertainty prediction on large datasets. Our code is available at https://github.com/11y00412/SEDNet.git. + +# 1. Introduction + +Many computer vision problems can be formulated as estimation tasks. Considering, however, that even high-performing estimators are not error-free, associating confidence or uncertainty with their estimates is of great importance, particularly in critical applications. In this paper, we focus on disparity estimation via stereo matching, but we are confident that our approach is applicable to other pixel-wise regression tasks after minor modifications. + +We distinguish between confidence and uncertainty: the former refers to a probability or likelihood of correctness, while the latter is related to the magnitude of the expected error of an estimate. Confidence can be used to reject estimates that are suspected to be incorrect, or to rank them from most to least reliable. We argue that uncertainty is more valuable because it can also be used for fusing multiple observations, e.g. in a Kalman filtering framework. Most research has focused on confidence estimation for stereo matching [12, 30]. Moreover, most methods estimate confidence for pre-computed disparities that are not further improved. Joint estimation of disparity and confidence, which benefits both due to multi-task learning, is addressed infrequently [19, 20, 26, 34]. + +Our work is partially inspired by the joint treatment of epistemic and aleatoric uncertainty by Kendall and Gal [14], who propose novel loss functions that give rise to uncertainty estimates in pixel-wise vision tasks. Results on semantic segmentation and single-image depth estimation demonstrate how the primary task benefits from simultaneous uncertainty estimation. Kendall and Gal argue that "in many big data regimes (such as the ones common to deep learning with image data), it is most effective to model aleatoric uncertainty," while epistemic uncertainty can be reduced when large amounts of data are available. Here, we restrict our attention to aleatoric uncertainty. + +Our motivation is that ideally we should be able to predict the magnitude of the estimator's error at each pixel. Of course, this is unrealistic, since if it was possible, we could drive all errors down to zero. A feasible objective is to train an uncertainty estimator whose outputs follow the same distribution as the true errors of the disparity estimator. + +In this paper, we present an implementation of this concept via a deep network that jointly estimates disparity and its uncertainty from a pair of rectified images. We named the network SEDNet, for Stereo Error Distribution Network. SEDNet includes a novel, lightweight uncertainty estimation subnetwork that predicts the aleatoric uncertainty + +![](images/e362a72f5c3629ab4274193191678fe338b483a8b080ff40ab833442f96cb843.jpg) +Figure 1. Examples of left images and predicted disparity maps by SEDNet on DrivingStereo [36]. The first example is taken around sunset with over-exposure. The second example is taken on a rainy day with under-exposure. In both challenge cases, SEDNet predicts accurate disparity. + +![](images/e99103418dc3b6934619a1a863d6f3c60d717e7f8652f6ca9f98b84846968259.jpg) + +of stereo matching, and a new loss to match the distribution of uncertainties with that of disparity errors. To generate the inputs to this new loss, we approximate the distributions from the samples of disparity errors and uncertainty values in a differentiable way via a soft-histogramming technique. + +We present extensive experimental validation of SEDNet's performance in disparity estimation and uncertainty prediction on large datasets with ground truth. SEDNet is superior to baselines with similar, even identical, architecture, but without the proposed loss function. Our main contributions are: + +- a novel uncertainty estimation subnetwork that extracts information from the intermediate multi-resolution disparity maps generated by the disparity subnetwork, +- a differentiable soft-histogramming technique used to approximate the distributions of disparity errors and estimated uncertainties, +- a loss based on KL divergence applied on histograms obtained with the above technique. + +# 2. Related Work + +We refer readers to recent surveys on deep stereo matching [32] and on confidence estimation [30]. Here we summarize the most relevant publications to our work. + +Stereo matching networks operate on a cost volume, which aggregates 2D features at each potential disparity for every pixel, and can be constructed via correlation or concatenation. Correlation-based networks such as DispNetC [25], iResNet [24] and SegStereo [38], generate a single-channel correlation map between features extracted from the two views at each disparity level, favoring computation efficiency at the expense of losing the structural and semantic information in the feature representation. Concatenation-based networks, such as GCNet [16], PSMNet [2] and GANet [39], assemble features from both views at the disparity specified by the corresponding element of the cost volume. This promotes learning of contextual features but requires more parameters and a subsequent aggregation network. + +We select GwcNet [11] as the foundation of our network. GwcNet takes a hybrid approach by reducing the dimension of the unary feature channels before concatenation in the cost volume. This is accomplished by a Group Wise Correlation layer, which takes as input $N_{c}$ unary feature channels, divides them into $N_{g}$ groups, computes the correlation between channels in each group at all disparity levels, and uses the resulting correlation scores to form the cost volume. This reduces the size of the cost volume and the computational cost of 3D convolutions by a factor of $N_{c}:N_{g}$ , with $N_{g}$ much smaller than $N_{c}$ , but still provides rich similarity-measure features to the disparity estimator. + +Researchers have also focused on model reliability. In Bayesian Neural Networks (BNNs), different models are + +sampled from the distribution of weights to estimate the mean and variance of the target distribution in an empirical manner, yielding estimates of uncertainty [7, 27]. Additional empirical strategies such as Bootstrapped Ensembles [23] and Monte Carlo Dropout [8] also sample from the distribution of weights. On the other hand, Graves [10] and Blundell et al. [1] proposed to replace the sampling with variational inference. + +Due to the high cost of training BNNs, methods for modeling the uncertainty or confidence in a predictive manner have also attracted interest. We distinguish between confidence and uncertainty: confidence is a binary variable trained with the BCE loss, while uncertainty is a continuous variable trained with L1 or L2 loss. Nix and Weigend [28] introduce NNs with one output for model prediction and one for data noise (aleatoric uncertainty). In addition to aleatoric uncertainty which captures the data noise of the observations, epistemic uncertainty, which accounts for the uncertainty of the model parameters, can also be modeled [3]. To capture both types, Kendall et al. [14, 15] proposed to combine empirical and predictive methods in a joint framework. + +CNNs have been used to estimate confidence in stereo matching. The Confidence CNN (CCNN) [31], Patch Based Confidence Prediction (PBCP) [33], the Early Fusion Network (EFN) and the Late Fusion Network (LFN) [4] and Multi Modal CNN (MMC) [5] only use small patches of the disparity maps. Conversely, the Global Confidence Network (ConfNet) and the Local-Global Confidence Network (LGC) [35] introduce U-Net like architectures and take both the image and disparity map as inputs. As a baseline, we use the Locally Adaptive Fusion Network (LAF) [18] which predicts the confidence map based on tri-modal inputs: the cost and disparity maps and the color image. An extension based on knowledge distillation has also been published [17]. These strategies are effective and cheaper than the empirical ones, since they only require one forward pass. + +Only a subset of the confidence estimation literature has focused on joint disparity and confidence estimation. The Reflective Confidence Network (RCN) [34] is the first to combine a disparity and a confidence loss. The Unified Confidence Network (UCN) [19] and the Adversarial Confidence Network (ACN) [20] jointly estimate confidence and disparity from pre-computed cost volumes. UCN is self-supervised, while ACN combines a generative cost aggregation network and a discriminative confidence estimation network in an adversarial manner. Mehlretter [26] presents an approach that predicts both epistemic and aleatoric uncertainty using a Bayesian Neural Network, based on GCNet [16]. KL divergence is used to measure the distance between the approximation of the distribution of network parameters estimated by variational difference and the exact posterior distribution. (It should be noted that we use + +KL divergence for a completely different purpose on the distribution of disparity errors.) + +Relevant research in adjacent areas of computer vision includes the work of Poggi et al. [29] who comprehensively evaluate uncertainty estimation for self-supervised monocular depth estimation. Ilg et al. [13] study empirical ensembles, predictive models and predictive ensembles as uncertainty models for optical flow estimation. + +# 3. Method + +The objective of our work is to jointly estimate the disparity and its uncertainty. An important benefit of this joint formulation is that the multi-task network learns to predict more accurate disparities than the standalone disparity estimator when the uncertainty subnetwork is added. Given a stereo image pair $\mathbf{X} = \{\mathbf{x}_l, \mathbf{x}_r\}$ , with image dimensions $H \times W$ , and the corresponding ground truth disparity $\hat{\mathbf{d}}$ , the prediction $\hat{\mathbf{d}}$ of a stereo-matching network $f_{\theta}$ can be represented as $\hat{\mathbf{d}} = f_{\theta}(\mathbf{x}_l, \mathbf{x}_r)$ . For each pixel $i$ , the error $\epsilon^{(i)}$ of the prediction is calculated using the L1 loss. + +Kendall and Gal [14] use the negative log-likelihood of the prediction model as the loss function to be minimized in pixel-wise tasks. + +We take the formulation a step further by requiring that the network generate a distribution of uncertainties that matches the distribution of errors. To this end, we propose to minimize the divergence $\mathcal{D}$ between the distributions of predicted uncertainty and actual disparity error. + +In the following subsections, we present aleatoric uncertainty estimation (Section 3.1), the proposed KL divergence loss (Section 3.2), our network architecture (Section 3.3), and the combined loss function (Section 3.4). + +# 3.1. Aleatoric Uncertainty Estimation + +In order to predict uncertainty and reduce the impact of noise, Kendall and Gal [14] minimize the pixel-wise negative log-likelihood of the prediction model, assuming that it follows a Gaussian distribution. The subsequent work of Ilg et al. [13] shows that the predicted distribution can be modeled as either Laplacian or Gaussian depending on whether the L1 or L2 loss is used for disparity estimation. Since we use the former, we can write the prediction model as: + +$$ +p (\mathbf {d} \mid f _ {\theta} (\mathbf {X})) = \mathcal {L} a p l a c e (f _ {\theta} (\mathbf {X}), \sigma) \tag {1} +$$ + +where the mean is given by the model output and $\sigma$ is the observation noise scalar. + +To model aleatoric uncertainty, Kendall and Gal [14] introduce pixel-specific noise parameters $\sigma^{(i)}$ . We follow the approach of Ilg et al. [13], who do the same for a Laplacian model, and obtain the following pixel-wise loss function: + +$$ +\mathcal {L} _ {l o g} = \frac {1}{n} \sum_ {i = 1} ^ {n} \frac {\left| \hat {\mathbf {d}} ^ {(i)} - \mathbf {d} ^ {(i)} \right|}{\exp (\mathbf {s} ^ {(i)})} + \frac {1}{n} \sum_ {i = 1} ^ {n} \mathbf {s} ^ {(i)} \tag {2} +$$ + +where $\hat{\mathbf{d}}^{(i)}$ and $\mathbf{d}^{(i)}$ are the predicted and ground truth disparity for pixel $i$ , $\mathbf{s}^{(i)}$ is the log of the observation noise scalar $\sigma^{(i)}$ , and $n$ is equal to the total number of the pixels. Equation (2) may be viewed as a robust loss function where the residual loss for a pixel is attenuated by its uncertainty, while the second term acts as a regularizer. We follow the authors' suggestion and train the network to predict the log of the observation noise scalar, $s$ , for numerical stability. + +# 3.2. Matching the Distribution of Errors + +Training a model using Eq. (2) as the loss improves disparity estimation accuracy and favors uncertainty estimates correlated with the errors. Ideally, we would like each uncertainty estimate to be a precise predictor of the corresponding disparity error. Since this is infeasible, we would like the distribution of uncertainties to match the distribution of errors. + +The Kullback-Leibler (KL) divergence [22] is a natural choice for measuring the dissimilarity between the distribution of $\epsilon$ and that of $\sigma$ . Since the KL divergence is asymmetric, we choose the distribution of $\epsilon$ as the reference. Therefore, our network should also minimize the following objective function: + +$$ +\mathcal {L} _ {d i v} = \mathcal {D} \left(P _ {\sigma} (d)\right) \| Q _ {\epsilon} (d)) = \int_ {0} ^ {d _ {m a x}} P _ {\sigma} (d) \log \frac {P _ {\sigma} (d)}{Q _ {\epsilon} (d)} d d \tag {3} +$$ + +where $d$ spans the disparity range. Since the network regresses disparity, the continuous formulation of KL divergence is appropriate. + +Minimizing Eq. (3) directly requires closed form expressions for the two distributions, which are not available to us. They could be modeled as Laplace distributions, but the maximum likelihood estimator is not differentiable. Moreover, fitting models to the data may be imprecise at the tails of the distributions. Therefore we choose non-parametric representations in the form of histograms. + +Histogramming is also not a differentiable operation, leading us to soft-histogramming as a differentiable alternative. We specify a set of bins for the histograms based on the statistics of the errors $\epsilon$ , since their distribution is the one that should be matched by the distribution of uncertainty estimates $\sigma$ . Since the L1 loss, and our network in general, does not discriminate between positive and negative errors, we work with absolute values of $\epsilon$ and $\sigma$ . + +For each batch during training, we compute the mean and standard deviation of the error, $\mu_{\epsilon}$ and $b_{\epsilon}$ , set $C_0 = \mu_{\epsilon}$ as the center of the first bin, and $C_m = \mu_{\epsilon} + \alpha_m b_{\epsilon}$ as the center of the last bin. (We use $b_{\epsilon}$ for the standard deviation to avoid overloading $\sigma$ or $s$ . Also note that the last bin extends to the disparity range, which is also the maximum possible error.) We then define $m - 1$ centers evenly spaced in a linear or logarithmic scale between the first and last center. + +![](images/4862569d12ae2a05e4f942108be2f09f480e58666bb6b31d4cf884692d7ef05f.jpg) +Figure 2. An illustration of SEDNet. The stereo matching subnetwork consists of the feature extractor, cost volume generator and disparity estimator. It takes a rectified pair of images as input and predicts disparity maps at multiple resolutions. The uncertainty estimation subnetwork takes the predicted disparity maps as input and predicts corresponding uncertainty maps. The error maps, between the ground truth and predicted disparity, are used to supervise uncertainty estimation. During training, the network keeps the output at all resolutions, but returns only the highest resolution disparity and uncertainty maps during testing. + +Given the bin centers, we compute a soft-histogram for the errors and one for the uncertainties as follows, considering all pixels with ground truth disparity, and error values. We present the steps for $\epsilon$ here. For each error $\epsilon^{(i)}$ , we compute weights for every bin center which are inversely proportional to the distance. + +$$ +w _ {j} \left(\epsilon^ {(i)}\right) = \lambda_ {1} \cdot \exp \left(- \frac {\left(\mu_ {\epsilon} + \alpha_ {j} b _ {\epsilon} - \epsilon^ {(i)}\right) ^ {2}}{\lambda_ {2}}\right) \tag {4} +$$ + +where $\lambda_{1}$ and $\lambda_{2}$ are hyper-parameters. Softmax is then applied to favor the nearest bins and the contributions of $n$ pixels are accumulated in the bins of the histogram $H_{\epsilon}$ . + +$$ +H _ {\epsilon} (j) = \frac {1}{n} \sum_ {i = 1} ^ {n} \frac {\exp \left(w _ {j} \left(\epsilon^ {(i)}\right)\right)}{\sum_ {j = 0} ^ {m} \exp \left(w _ {j} \left(\epsilon^ {(i)}\right)\right)}, j \in [ 0, m ] \tag {5} +$$ + +The histogram for $\sigma$ , $H_{\sigma}$ , is obtained similarly. (Note that the bins of both histograms are defined in terms of $b_{\epsilon}$ .) + +The loss representing the discrete form of the KL divergence between the two histograms is given by: + +$$ +\mathcal {L} _ {d i v} = \sum_ {j = 0} ^ {m} H _ {\epsilon} (j) \log \frac {H _ {\epsilon} (j)}{H _ {\sigma} (j)} \tag {6} +$$ + +# 3.3. SEDNet + +Our network architecture, named SEDNet, includes a disparity estimation subnetwork, an uncertainty estimation subnetwork, and is shown in Figure 2. In all experiments, we have adopted GwcNet [11] as the disparity estimation subnetwork, among other options, and we have designed + +a novel uncertainty estimation subnetwork that interfaces with GwcNet. It is worth noting that the uncertainty subnetwork is extremely small. + +The GwcNet subnetwork extracts features from the images using a ResNet-like feature extractor, generates the cost volume, and assigns disparities to pixels using the soft-argmax operator [16]. The output module of the disparity predictor generates $K$ disparity maps at different resolutions. + +We propose a new uncertainty estimator integrated with the stereo matching network. The uncertainty estimation subnetwork learns to predict the log of the observation noise scalar, the error, at each pixel. The proposed subnetwork takes the multi-resolution disparity predictions as input, computes the pairwise differences vector (PDV) and passes it to a pixel-wise MLP to regress the uncertainty maps. Specifically, the disparity estimator outputs $K$ disparity maps at different resolutions $\hat{\mathbf{d}} = \{\hat{\mathbf{d}}_1,\dots,\hat{\mathbf{d}}_K\}$ , which are first upsampled to full-resolution and then undergo pairwise differencing to form the PVD, which consists of $\binom{K}{2}$ elements. The output set of uncertainty maps S also contains $K$ resolutions to match the disparity maps. We use $K = 4$ in all experiments. + +# 3.4. Loss Function + +Our loss function combines two parts: (1) the log-likelihood loss to optimize the error and uncertainty, (2) the KL divergence loss to match the distribution of uncertainty with the error. The total loss considers all disparity and un + +certainty maps upsampled to the highest resolution: + +$$ +\mathcal {L} = \sum_ {k = 1} ^ {K} c _ {k} \cdot \left(\mathcal {L} _ {\text {l o g}, k} + \mathcal {L} _ {\text {d i v}, k}\right) \tag {7} +$$ + +where $c_k$ denotes the coefficients for the $k^{th}$ resolution level, $\mathcal{L}_{\log,k}$ and $\mathcal{L}_{div,k}$ are computed by Eq. (2) and Eq. (6) on the prediction of the corresponding resolution level. + +# 4. Experiment Results + +In this section, we present our experimental setup and results on within-domain and cross-domain experiments. Datasets, evaluation metrics and baselines are described in Section 4.1, implementation details in Section 4.2, and experimental results in Sections 4.3 and 4.4. Additionally, a synthetic-to-real transfer evaluation is presented in Section 4.5. We provide more quantitative and qualitative results, extending Section 4, in the Supplement, which also includes ablation studies on several aspects of SEDNet and the baselines, as well as disparity, error, and uncertainty maps of difficult examples. + +# 4.1. Datasets and evaluation metrics + +SceneFlow [25] is a collection of three synthetic stereo datasets: FlyingThings3D, Driving, Monkaa. The datasets provide 35,454 training and 4,370 test stereo pairs in $960 \times 540$ pixel resolution with dense ground-truth disparity maps. We use the finalpass versions of the rendered images which are more realistic because of the motion blur and depth of field effect. + +Virtual KITTI 2 (VK2) [6] is a synthetic clone of KITTI [9]. It consists of 21,260 synthetic $1,242 \times 375$ stereo pairs from 6 driving scenes with 10 different imaging and weather conditions. Scene 006 (VK2-S6) is specified as the test set by the authors of the dataset. Since the car with the cameras is stopped for a long part of Scene 006 and only other cars move in the images, we split the last part of the scene where the car moves and denote it as VK2-S6-Moving. We report results separately on this subset. Since results on VK2-S6-Moving do not suffer from bias due to the almost constant background of the first part of the scene, evaluation on VK2-S6-Moving is more informative and fair. + +DrivingStereo [37] is a large real-world autonomous driving dataset. It contains 174,437 training and 7,751 test stereo pairs at $881 \times 400$ pixel resolution. The dataset provides sparse ground truth disparity as well as a challenging subset (DS-Weather) of 2,000 stereo pairs in 4 different weather conditions. + +For all datasets above, we exclude pixels with disparities $d > 192$ in training. + +Metrics. To evaluate disparity estimation, we compute the endpoint error (EPE) and the percentage of outliers (D1) + +(i.e., the percentage of pixels with EPE $>3\mathrm{px}$ or $\geq 5\%$ of the true depth). + +To evaluate uncertainty estimation, we use density-EPE ROC curves and the area under the curve (AUC) [12,18,30]. The ROC curves in our case measure EPE (not binary correctness) at increasing disparity map density by successively adding pixels in increasing order of uncertainty. The optimal AUC is obtained by adding pixels in order of increasing EPE and is therefore the lowest value any algorithm could achieve given the set of EPE values, while the estimated AUC is sorted by predicted uncertainty. To evaluate the precision of uncertainty estimation, we also introduce the absolute prediction error (APE), which is the average L1 distance between the error and the observation noise scalar, $\sigma$ . We report the average and median APE over all pixels. + +Baselines. Since we use GwcNet [11] as the disparity estimation subnetwork of SEDNet, we compare SEDNet with three baselines: (1) the original GwcNet trained with smooth L1 loss. (2) LAF-Net [18] trained under the BCE loss on the left RGB images, the cost volumes and predicted disparity maps of GwcNet at the highest resolution. (3) SEDNet but only trained with the log-likelihood loss, therefore similar to Kendall and Gal's model [14]. We use $\mathcal{L}_{\log}$ in tables and figures for this baseline. We selected GwcNet because it is the backbone of SEDNet, and LAF-Net as the confidence estimation baseline due to its strong performance within the training domain according to [30]. + +# 4.2. Implementation Details + +We implemented all networks in PyTorch and used the Adam optimizer [21] with $\beta_{1} = 0.9$ and $\beta_{2} = 0.999$ for all experiments. Training of all models was stopped before overfitting occurred. + +Experiments on the VK2 dataset were performed on two NVIDIA RTX A6000 GPUs, each with 48 GB of RAM. For this dataset, we trained all models from scratch with an initial learning rate of 0.0001, down-scaled by 5 every 10 epochs. During training, we randomly cropped $512 \times 256$ patches from the images. During testing, we evaluated at the full resolution of VK2. + +Experiments on the DrivingStereo dataset (DS) were also performed on two NVIDIA RTX A6000 GPUs. For this dataset, we did two experiments using the models pretrained on VK2: (1) we finetuned on the DS training set with a learning rate starting from 0.0001, down-scaled by 2 every 3 epochs after epoch 10, then performed in-domain evaluation on the DS test set; (2) we skipped the finetuning step and performed cross-domain evaluation on DS-Weather subset. During training, we randomly cropped the inputs to be the same size as in the VK2 experiments. During testing, we padded the test samples to be the same resolution as VK2. + +
DatasetMethodLossInliersDisparity↓APE↓AUC↓
BCEL1LogKLBinsScaleDef.Pct(%)EPED1(%)Avg.MedianOpt.Est.
Scene FlowGwcNet-√------0.77584.127--10.9291-
+LAF√-------0.77584.127--10.929120.0813
+Llog-√√---EPE<596.960.76114.1310.69990.07285.744912.1121
+SEDNet-√√√11logEPE<μe+3bε98.420.67543.9630.57970.04324.91348.7195
VK2-S6GwcNet-√------0.41251.763--6.0962-
+Llog-√√---EPE<598.860.38991.5840.41360.17534.687212.5320
+SEDNet-√√√11logEPE<μe+3bε99.240.31091.3920.52340.14544.17269.7637
+SEDNet-√√√11logEPE<μe+5bε99.680.32361.4270.35610.10964.27679.9843
VK2-S6-MovingGwcNet-√------0.42531.689--5.9184-
+Llog-√√---EPE<598.910.42311.5370.45750.18904.366311.3532
+SEDNet-√√√11logEPE<μe+3bε99.620.35771.3890.59580.15733.90128.8339
+SEDNet-√√√11logEPE<μe+5bε99.760.38621.4200.40020.11644.04239.0631
DrivingStereo+Llog(FT)-√√-----0.53320.26410.34490.229721.700245.7096
+SEDNet(FT)-√√√11logEPE<μe+5bε99.860.52640.24390.33240.226721.285644.3297
DS-WeatherGwcNet-√------1.69628.313--44.4896-
+Llog-√√---EPE<595.782.39446.6662.14430.438341.190995.4264
+SEDNet-√√√11logEPE<μe+3bε98.951.56376.5082.34060.530938.487186.1118
+SEDNet-√√√11logEPE<μe+5bε99.411.70516.0571.58420.610439.805787.1882
+ +![](images/dfe5cb778e884384ff2bdff52b12ebe7f264c19eab785c6763a8a428a9f7368e.jpg) + +![](images/9cd4bdcd8c13a17ef0a6f5087a3aa191db869c5a2d1824bb934d7fbd54d1d306.jpg) +Left Image +Right Image +Figure 3. Example from VK2-S6. In a rainy scene with poor illumination, recognizing objects far away from the camera is even difficult for human observers. $\mathcal{L}_{log}$ fails to predict the disparity of objects, such as the traffic sign near the left street light. On the other hand, SEDNet accurately predicts the disparity of these challenging objects, while its uncertainty map also captures more information, such as the trees in the background, the traffic light near the trees and the street lights. + +![](images/8e3ace30a511433ab10150f03eb6b1bfb265ce655540265334b81a5529045048.jpg) + +![](images/d3ae36d8ea80ff3bcfc3c6005483a9aff738f63ccd398c9a606a33b324edb7c9.jpg) +$\mathcal{L}_{\mathrm{log}}$ Disparity +SEDNet Disparity + +![](images/7198fbadf33c39ee5f21444a910572cbdfdf51f3dfdf468fd8673e8d7055cbba.jpg) + +![](images/7d3092f069ec2f646599c5086ea3825fe3c6a29ee3fa85c683e7872387ae4cfc.jpg) +$\mathcal{L}_{\mathrm{log}}$ Uncertainty +SEDNet Uncertainty + +Experiments on the SceneFlow dataset were performed on an Nvidia Titan RTX GPU with 24 GB memory. We trained all models from scratch on $256 \times 128$ patches cropped from half-resolution images to limit memory consumption. We set the initial learning rate to 0.001 and down-scaled it by 2 every 2 epochs after epoch 10. + +For all the experiments above, we applied an inlier filtering strategy during training, which only back-propagates from the inliers. See the last paragraph of Section 4.3 for + +Table 1. Quantitative results: (1) within-domain on SceneFlow, VK2-S6 and VK2-S6-Moving; (2) after finetuning (FT) on DrivingStereo; (3) cross-domain on DS-Weather. The best results in each category in each experiment are in bold typeface. SEDNet outperforms the baselines with respect to disparity and uncertainty metrics in the majority of experiments. + +
ArchitectureParamsMACs(G)
GwcNet6,909,7281075.82
SEDNet6,909,9181075.91
+ +Table 2. Comparison on number of parameters and computational complexity. MAC stands for multiply-accumulate operations. SEDNet only adds a 3-layer MLP, with 190 parameters, as an uncertainty decoder to GwcNet. + +details. Table 2 shows the number of parameters and computational complexity of GwcNet and SEDNet. + +# 4.3. Qualitative and Quantitative Results + +In Table 1, we present results: (1) within-domain on SceneFlow and VK2; (2) on the DrivingStereo test set after finetuning the VK2 models on the DrivingStereo training set; (3) cross-domain on the DS-Weather challenge test set by directly applying the model trained on VK2 without finetuning. + +Disparity Estimation. In all experiments, SEDNet achieves lower errors than all the baselines. See the EPE and D1 columns in Table 1. Even in extreme weather like fog and rain, SEDNet predicts good disparity unaffected by poor illumination and blur. See Figure 3. + +Uncertainty Estimation. Our method outperforms the baselines in all experiments according to the AUC metric, + +![](images/696f076058a8a225341a14fe4ac35c9e5c8f2e8dd8f6e70b6c94159e3d3fa96a.jpg) + +![](images/8736da3fd881d7cb680754f80b7df1892bdfccaab39e388af3c0a7fdcc39ed51.jpg) +Left Image +Right Image +Figure 4. Example from DS-Weather. Unlike the synthetic data, the rainy-day real images do not only suffer from poor illumination, but also pose challenges due to reflections in the water. In this example, the road is like a mirror, misleading the $\mathcal{L}_{log}$ model. Recall that the LIDAR ground truth disparity is very sparse, and is even sparser in reflective regions. The disparity map of $\mathcal{L}_{log}$ fails to distinguish the car and the reflection, but SEDNet is able to estimate the correct disparity and the uncertainty of the car. + +![](images/69231d11834b0042b11ce0ecf710c14b2ed3afb6e0bbd3ed640aee69bd2f46b5.jpg) + +![](images/6da5ba0360b890097ff4b7e87d8a9387069ca71b5987dddd61e03442b9019758.jpg) +$\mathcal{L}_{\text{log}}$ Disparity +SEDNet Disparity + +![](images/59b6d4d54e7523dbb6718abd3b3cda83b0f085e11d3c5ffea2de979041c4398a.jpg) + +![](images/e5f3d4a0fb15f87d374c7464f56cf1c1aadc3cdd582f04dbf5b1a25d15487534.jpg) +$\mathcal{L}_{\mathrm{log}}$ Uncertainty +SEDNet Uncertainty + +as shown in the last two columns in Table 1. Compared to $\mathcal{L}_{log}$ , SEDNet decreases the estimated AUC by $20\% - 30\%$ in the in-domain experiments, with a $10\%$ decrease in optimal AUC, which depends on EPE. In the cross-domain evaluation, the advantage of SEDNet is even more evident. Figure 4 shows uncertainty maps for real data, on which our method captures details more faithfully. The ROC curves of the best $\mathcal{L}_{log}$ and SEDNet models based on EPE on VK2-S6-Moving are presented in Figure 5. + +![](images/473fec7f260e773882556db0f56a33e82bb545d37b0b7d06f51e0fd0d51138f8.jpg) +Figure 5. Comparison of density-EPE curves on VK2-S6-Moving. "L.log (EPE<5)" and "SEDNet (3)" represent the $\mathcal{L}_{\log}$ (with fixed inliers) and the SEDNet (with adaptive inliers of $\mathrm{EPE} < \mu_{\epsilon} + 3b_{\epsilon}$ ) of VK2-S6-Moving experiment in Table 1. While their corresponding optimal AUCs are almost equal, the advantage of SEDNet in estimated AUC is significant. + +Effects of Back-propagation from Inliers. We apply two kinds of inlier filters: one with a fixed threshold that excludes all pixels that have an EPE larger than 5 from backpropagation; and one with adaptive threshold which excludes pixels that have EPE greater than a specified number of $b_{\epsilon}$ from the mean error. Back-propagation from the + +inliers only helps the network improve its performance on both disparity and uncertainty estimation. We attribute this to the suppression of harmful outliers that give rise to large gradients. Quantitative results for the baselines and the proposed method with different inlier settings are reported in the Supplement. The results show that using adaptive thresholds is better than fixed thresholds. Fixed thresholds exclude more pixels, especially at lower resolutions and in the early stages of training, preventing the network from learning how to correct them. + +# 4.4. Matching the Error Distribution. + +Comparing the results of APE in all experiments in Table 1 and Figure 6, the difference between the errors and the predicted uncertainty is always close to the EPE, as expected. SEDNet, however, achieves lower APE, showing + +![](images/c10757329f3093edf814b6f4a5bf8bc5c44fbb74e47bdbc115136a4f8e13a640.jpg) +$\mathcal{L}_{log}$ +Figure 6. Distribution of error and predicted uncertainty. We pick the best model of $\mathcal{L}_{\text{log}}$ and SEDNet in VK2-S6-Moving, then randomly sample 5,000,000 points from the outputs of the two models. The distributions of error and $\sigma$ of SEDNet are much closer than those of the $\mathcal{L}_{\text{log}}$ model, especially in the first 5 bins that contain more than $95\%$ of the samples. The shorter tail of SEDNet also indicates fewer gross errors. + +![](images/3a94bf511bc20a0763b3bcd8b6122696fe3976c58f24027ebac6c3f23b94bf23.jpg) +SEDNet + +
DatasetMethodLossInliersDisparity↓APE↓AUC↓
BCEL1LogKLBinsScaleDef.Pct(%)EPED1(%)Avg.MedianOpt.Est.
VK2-S6-MorningGwcNet-√------0.46421.740--6.1845-
+Llog-√√---EPE<598.820.47741.6240.50670.18724.669812.5192
+SEDNet-√√√11logEPE<με+3bε99.620.40031.4420.61830.15534.18479.4063
VK2-S6-SunsetGwcNet-√------0.48101.825--6.6907
+Llog-√√---EPE<598.840.48631.6270.50600.18275.007513.7848
+SEDNet-√√√11logEPE<με+3bε99.610.41081.4750.61890.15094.584010.7946
VK2-S6-FogGwcNet-√------0.46601.812--6.8355-
+Llog-√√---EPE<598.980.44251.4480.46090.18654.898312.1305
+SEDNet-√√√11logEPE<με+3bε99.710.37311.2880.55170.15474.42009.9380
VK2-S6-RainGwcNet-√------0.46181.700--6.6774-
+Llog-√√---EPE<598.880.47071.5710.48990.18614.935113.3214
+SEDNet-√√√11logEPE<με+3bε99.690.38731.3560.66850.15374.401310.3362
DS-CloudyGwcNet-√------1.34135.229--37.4263-
+Llog-√√---EPE<597.481.47803.9481.26170.351334.448882.5380
+SEDNet-√√√11logEPE<με+3bε98.831.31834.4141.52600.402133.903773.6330
DS-SunnyGwcNet-√------1.54486.991--38.7386-
+Llog-√√---EPE<597.081.48374.6311.28060.383535.522685.8715
+SEDNet-√√√11logEPE<με+3bε98.641.55485.8783.00250.480835.652383.2573
DS-FoggyGwcNet-√------1.54768.859--51.4640-
+Llog-√√---EPE<594.892.95539.0152.69230.555648.7136101.7025
+SEDNet-√√√11logEPE<με+3bε99.271.53987.3572.41090.702347.793297.8627
DS-RainyGwcNet-√------3.191817.356--68.0346-
+Llog-√√---EPE<598.795.353912.5014.94800.575959.3952146.8906
+SEDNet-√√√11logEPE<με+3bε99.102.216511.0202.65990.672250.8103110.8360
+ +Table 3. Quantitative results of synthetic to real evaluation. The top 4 subsets from VK2-S6-Moving are synthetic datasets, while the rest 4 subsets from DS-Weather are the real dataset. The best results in each category in each experiment are in bold typeface. SEDNet outperforms the baselines especially on the uncertainty estimation of the real data and under terrible weather (i.e., foggy and rainy). + +that it matches the true distribution better. + +# 4.5. Generalization from Synthetic to Real Data + +Stereo-matching networks are typically trained on synthetic data and fine-tuned on small amounts of data from the target domain due to the cost and difficulty of acquiring real data with ground truth depth. In this section, we extend the experiments of VK2-S6 and DS-Weather in Table 1 to compare the generalization performance on unseen real domains of all methods trained only on synthetic data. We picked four synthetic subsets from VK2-S6, specifically Morning, Sunset, Fog and Rain, that have similar illumination conditions, visibility level and weather with the four real subsets of DS-Weather [37], i.e., Cloudy, Sunny, Foggy and Rainy. VK2-S6-Morning and VK2-S6-Sunset have similar illumination to DS-Cloudy and DS-Sunny, but the latter two are more challenging due to camera underexposure and overexposure. Even though they are acquired under the same weather, DS-Foggy and DS-Rainy are more difficult than VK2-S6-Fog and VK2-S6-Rain. This can be seen by comparing Figures 3 and 4. The synthetic examples only mimic the poor lighting conditions and challenges caused by the fog and rain, but ignore the Tyndall effect and reflections caused by the fog and stagnant water. + +As mentioned above, all models are trained only on Scenes 001, 002, 018 and 020 of VK2. Quantitative results on synthetic to real transfer are reported in Table 3, where we only report the best model of each method. The top- + +performing variant of SEDNet outperforms the baselines in the majority of experiments. An extended version of this table can be found in Table S.2 in the Supplement. + +# 5. Conclusion + +We have presented a novel approach for joint disparity and uncertainty estimation from stereo image pairs. The key idea is a unique loss function based on the KL divergence between the distributions of disparity errors and uncertainty estimates. This is made possible by a differentiable histogramming scheme that we also introduce here. To implement our approach, we extended the GwcNet architecture to include an uncertainty estimation subnetwork with only 190 parameters. Our experiments on multiple large datasets have demonstrated that our approach, named SEDNet, is effective in both disparity and uncertainty prediction. The success of our method is attributed to the novel loss function. SEDNet easily surpasses GwcNet in disparity estimation even though they have essentially the same capacity and almost identical architecture, up to the tiny uncertainty estimation subnetwork. We are optimistic that our approach will be similarly successful in other pixel-wise regression tasks, which we plan to address in future research. + +Acknowledgment. This research has been supported in part by the National Science Foundation under award 2024653. + +# References + +[1] Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. Weight uncertainty in neural network. In ICML, pages 1613-1622, 2015. 2 +[2] Jia-Ren Chang and Yong-Sheng Chen. Pyramid stereo matching network. In CVPR, pages 5410-5418, 2018. 2 +[3] Armen Der Kiureghian and Ove Ditlevsen. Aleatory or epistemic? does it matter? Structural safety, 31(2):105-112, 2009. 2 +[4] Zehua Fu, Mohsen Ardabilian, and Guillaume Stern. Stereo matching confidence learning based on multi-modal convolution neural networks. In International Workshop on Representations, Analysis and Recognition of Shape and Motion From Imaging Data, pages 69-81. Springer, 2017. 2 +[5] Zehua Fu and Mohsen Ardabilian Fard. Learning confidence measures by multi-modal convolutional neural networks. In WACV, pages 1321-1330, 2018. 2 +[6] Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig. Virtual worlds as proxy for multi-object tracking analysis. In CVPR, pages 4340-4349, 2016. 5 +[7] Yarin Gal. Uncertainty in Deep Learning. PhD thesis, University of Cambridge, 2016. 2 +[8] Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In ICML, pages 1050-1059, 2016. 2 +[9] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In CVPR, 2012. 5 +[10] Alex Graves. Practical variational inference for neural networks. In NeurIPS, volume 24, 2011. 2 +[11] Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li. Group-wise correlation stereo network. In CVPR, 2019. 2, 4, 5 +[12] Xiaoyan Hu and Filippos Mordohai. A quantitative evaluation of confidence measures for stereo vision. PAMI, 34(11):2121-2133, 2012. 1, 5 +[13] Eddy Ilg, Ozgun Cicek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, and Thomas Brox. Uncertainty estimates and multi-hypotheses networks for optical flow. In ECCV, pages 652-667, 2018. 3 +[14] Alex Kendall and Yarin Gal. What uncertainties do we need in Bayesian deep learning for computer vision? In NeurIPS, pages 5574-5584, 2017. 1, 2, 3, 5 +[15] Alex Kendall, Yarin Gal, and Roberto Cipolla. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In CVPR, pages 7482-7491, 2018. 2 +[16] Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, and Adam Bry. End-to-end learning of geometry and context for deep stereo regression. In ICCV, pages 66-75, 2017. 2, 4 +[17] Sunok Kim, Seungryong Kim, Dongbo Min, Pascal Frossard, and Kwanghoon Sohn. Stereo confidence estimation via locally adaptive fusion and knowledge distillation. PAMI, 2022. 2 +[18] Sunok Kim, Seungryong Kim, Dongbo Min, and Kwanghoon Sohn. LAF-Net: Locally adaptive fusion + +networks for stereo confidence estimation. In CVPR, 2019. 2,5 +[19] Sunok Kim, Dongbo Min, Seungryong Kim, and Kwanghoon Sohn. Unified confidence estimation networks for robust stereo matching. TIP, 28(3):1299-1313, 2018. 1, 2 +[20] Sunok Kim, Dongbo Min, Seungryong Kim, and Kwanghoon Sohn. Adversarial confidence estimation networks for robust stereo matching. TIST, 22(11):6875-6889, 2020. 1, 2 +[21] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5 +[22] S Kullback and R Leibler. On information and sufficiencyannals of mathematical statistics, 22, 79-86. MathSciNet MATH, 1951. 3 +[23] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. In NeurIPS, volume 30, 2017. 2 +[24] Zhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou, and Jianfeng Zhang. Learning for disparity estimation through feature constancy. In CVPR, pages 2811-2820, 2018. 2 +[25] Nikolaus Mayer, Eddy Ig, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In CVPR, pages 4040-4048, 2016. 2, 5 +[26] Max Mehltreter. Joint estimation of depth and its uncertainty from stereo images using bayesian deep learning. IS-PRS, 2:69-78, 2022. 1, 2 +[27] Radford M Neal. Bayesian learning for neural networks, volume 118. Springer Science & Business Media, 2012. 2 +[28] David A Nix and Andreas S Weigend. Estimating the mean and variance of the target probability distribution. In ICNN, volume 1, pages 55-60. IEEE, 1994. 2 +[29] Matteo Poggi, Filippo Aleotti, Fabio Tosi, and Stefano Mattoccia. On the uncertainty of self-supervised monocular depth estimation. In CVPR, 2020. 3 +[30] Matteo Poggi, Seungryong Kim, Fabio Tosi, Sunok Kim, Filippo Aleotti, Dongbo Min, Kwanghoon Sohn, and Stefano Mattoccia. On the confidence of stereo matching in a deep-learning era: a quantitative evaluation. PAMI, 2021. 1, 2, 5 +[31] Matteo Poggi and Stefano Mattoccia. Learning from scratch: a confidence measure. In BMVC, 2016. 2 +[32] Matteo Poggi, Fabio Tosi, Konstantinos Batsos, Philippos Mordohai, and Stefano Mattoccia. On the synergies between machine learning and binocular stereo for depth estimation from images: a survey. PAMI, 44(9):5314-5334, 2021. 2 +[33] Akihito Seki and Marc Pollefeys. Patch based confidence prediction for dense disparity map. In BMVC, pages 23.1-23.13, 2016. 2 +[34] Amit Shaked and Lior Wolf. Improved stereo matching with constant highway networks and reflective confidence learning. In CVPR, pages 4641-4650, 2017. 1, 2 + +[35] Fabio Tosi, Matteo Poggi, Antonio Benincasa, and Stefano Mattoccia. Beyond local reasoning for stereo confidence estimation with deep learning. In ECCV, pages 319-334, 2018. 2 +[36] Guorun Yang, Xiao Song, Chaoqin Huang, Zhidong Deng, Jianping Shi, and Bolei Zhou. DrivingStereo: A large-scale dataset for stereo matching in autonomous driving scenarios. In CVPR, 2019. 1 +[37] Guorun Yang, Xiao Song, Chaoqin Huang, Zhidong Deng, Jianping Shi, and Bolei Zhou. Drivingstereo: A large-scale dataset for stereo matching in autonomous driving scenarios. In CVPR, 2019. 5, 8 +[38] Guorun Yang, Hengshuang Zhao, Jianping Shi, Zhidong Deng, and Jiaya Jia. SegStereo: Exploiting semantic information for disparity estimation. In ECCV, pages 636-651, 2018. 2 +[39] Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr. GA-Net: Guided aggregation net for end-to-end stereo matching. In CVPR, 2019. 2 \ No newline at end of file diff --git a/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/images.zip b/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..d93e3c0e9a63957d610060dcdaade65c2443c716 --- /dev/null +++ b/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7459c0a23ec3842965fd0450cc85823569e0a203da4b67f4ecac2a9e3f6079d8 +size 589443 diff --git a/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/layout.json b/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..8fbb2d8d5528691d056e65021e11a1ee8ffbed2b --- /dev/null +++ b/2023/Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation/layout.json @@ -0,0 +1,8589 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 127, + 103, + 470, + 140 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 127, + 103, + 470, + 140 + ], + "spans": [ + { + "bbox": [ + 127, + 103, + 470, + 140 + ], + "type": "text", + "content": "Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 162, + 161, + 432, + 190 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 162, + 161, + 432, + 190 + ], + "spans": [ + { + "bbox": [ + 162, + 161, + 432, + 190 + ], + "type": "text", + "content": "Liyan Chen Weihan Wang Filippos Mordohai Stevens Institute of Technology" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 143, + 217, + 192, + 229 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 143, + 217, + 192, + 229 + ], + "spans": [ + { + "bbox": [ + 143, + 217, + 192, + 229 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 242, + 290, + 410 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 242, + 290, + 410 + ], + "spans": [ + { + "bbox": [ + 46, + 242, + 290, + 410 + ], + "type": "text", + "content": "We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the observation that multi-task learning often leads to improved performance in all tasks. We show that this can be achieved by requiring the distribution of uncertainty to match the distribution of disparity errors via a KL divergence term in the network's loss function. A differentiable soft-histogramming technique is used to approximate the distributions so that they can be used in the loss. We experimentally assess the effectiveness of our approach and observe significant improvements in both disparity and uncertainty prediction on large datasets. Our code is available at https://github.com/11y00412/SEDNet.git." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 430, + 128, + 442 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 430, + 128, + 442 + ], + "spans": [ + { + "bbox": [ + 47, + 430, + 128, + 442 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 450, + 288, + 545 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 450, + 288, + 545 + ], + "spans": [ + { + "bbox": [ + 46, + 450, + 288, + 545 + ], + "type": "text", + "content": "Many computer vision problems can be formulated as estimation tasks. Considering, however, that even high-performing estimators are not error-free, associating confidence or uncertainty with their estimates is of great importance, particularly in critical applications. In this paper, we focus on disparity estimation via stereo matching, but we are confident that our approach is applicable to other pixel-wise regression tasks after minor modifications." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 545, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 545, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 545, + 288, + 714 + ], + "type": "text", + "content": "We distinguish between confidence and uncertainty: the former refers to a probability or likelihood of correctness, while the latter is related to the magnitude of the expected error of an estimate. Confidence can be used to reject estimates that are suspected to be incorrect, or to rank them from most to least reliable. We argue that uncertainty is more valuable because it can also be used for fusing multiple observations, e.g. in a Kalman filtering framework. Most research has focused on confidence estimation for stereo matching [12, 30]. Moreover, most methods estimate confidence for pre-computed disparities that are not further improved. Joint estimation of disparity and confidence, which benefits both due to multi-task learning, is addressed infrequently [19, 20, 26, 34]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 218, + 547, + 361 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 218, + 547, + 361 + ], + "spans": [ + { + "bbox": [ + 304, + 218, + 547, + 361 + ], + "type": "text", + "content": "Our work is partially inspired by the joint treatment of epistemic and aleatoric uncertainty by Kendall and Gal [14], who propose novel loss functions that give rise to uncertainty estimates in pixel-wise vision tasks. Results on semantic segmentation and single-image depth estimation demonstrate how the primary task benefits from simultaneous uncertainty estimation. Kendall and Gal argue that \"in many big data regimes (such as the ones common to deep learning with image data), it is most effective to model aleatoric uncertainty,\" while epistemic uncertainty can be reduced when large amounts of data are available. Here, we restrict our attention to aleatoric uncertainty." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 364, + 547, + 437 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 364, + 547, + 437 + ], + "spans": [ + { + "bbox": [ + 304, + 364, + 547, + 437 + ], + "type": "text", + "content": "Our motivation is that ideally we should be able to predict the magnitude of the estimator's error at each pixel. Of course, this is unrealistic, since if it was possible, we could drive all errors down to zero. A feasible objective is to train an uncertainty estimator whose outputs follow the same distribution as the true errors of the disparity estimator." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 438, + 547, + 511 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 438, + 547, + 511 + ], + "spans": [ + { + "bbox": [ + 304, + 438, + 547, + 511 + ], + "type": "text", + "content": "In this paper, we present an implementation of this concept via a deep network that jointly estimates disparity and its uncertainty from a pair of rectified images. We named the network SEDNet, for Stereo Error Distribution Network. SEDNet includes a novel, lightweight uncertainty estimation subnetwork that predicts the aleatoric uncertainty" + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 312, + 533, + 422, + 647 + ], + "blocks": [ + { + "bbox": [ + 312, + 533, + 422, + 647 + ], + "lines": [ + { + "bbox": [ + 312, + 533, + 422, + 647 + ], + "spans": [ + { + "bbox": [ + 312, + 533, + 422, + 647 + ], + "type": "image", + "image_path": "e362a72f5c3629ab4274193191678fe338b483a8b080ff40ab833442f96cb843.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 654, + 547, + 711 + ], + "lines": [ + { + "bbox": [ + 304, + 654, + 547, + 711 + ], + "spans": [ + { + "bbox": [ + 304, + 654, + 547, + 711 + ], + "type": "text", + "content": "Figure 1. Examples of left images and predicted disparity maps by SEDNet on DrivingStereo [36]. The first example is taken around sunset with over-exposure. The second example is taken on a rainy day with under-exposure. In both challenge cases, SEDNet predicts accurate disparity." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 430, + 533, + 541, + 644 + ], + "blocks": [ + { + "bbox": [ + 430, + 533, + 541, + 644 + ], + "lines": [ + { + "bbox": [ + 430, + 533, + 541, + 644 + ], + "spans": [ + { + "bbox": [ + 430, + 533, + 541, + 644 + ], + "type": "image", + "image_path": "e99103418dc3b6934619a1a863d6f3c60d717e7f8652f6ca9f98b84846968259.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 293, + 748, + 318, + 758 + ], + "type": "text", + "content": "17235" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 286, + 132 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 286, + 132 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 286, + 132 + ], + "type": "text", + "content": "of stereo matching, and a new loss to match the distribution of uncertainties with that of disparity errors. To generate the inputs to this new loss, we approximate the distributions from the samples of disparity errors and uncertainty values in a differentiable way via a soft-histogramming technique." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 133, + 286, + 202 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 133, + 286, + 202 + ], + "spans": [ + { + "bbox": [ + 46, + 133, + 286, + 202 + ], + "type": "text", + "content": "We present extensive experimental validation of SEDNet's performance in disparity estimation and uncertainty prediction on large datasets with ground truth. SEDNet is superior to baselines with similar, even identical, architecture, but without the proposed loss function. Our main contributions are:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 204, + 286, + 300 + ], + "type": "list", + "angle": 0, + "index": 5, + "blocks": [ + { + "bbox": [ + 58, + 204, + 286, + 239 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 204, + 286, + 239 + ], + "spans": [ + { + "bbox": [ + 58, + 204, + 286, + 239 + ], + "type": "text", + "content": "- a novel uncertainty estimation subnetwork that extracts information from the intermediate multi-resolution disparity maps generated by the disparity subnetwork," + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 58, + 240, + 286, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 240, + 286, + 274 + ], + "spans": [ + { + "bbox": [ + 58, + 240, + 286, + 274 + ], + "type": "text", + "content": "- a differentiable soft-histogramming technique used to approximate the distributions of disparity errors and estimated uncertainties," + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 58, + 276, + 286, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 276, + 286, + 300 + ], + "spans": [ + { + "bbox": [ + 58, + 276, + 286, + 300 + ], + "type": "text", + "content": "- a loss based on KL divergence applied on histograms obtained with the above technique." + } + ] + } + ], + "index": 4 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 47, + 311, + 133, + 323 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 311, + 133, + 323 + ], + "spans": [ + { + "bbox": [ + 47, + 311, + 133, + 323 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 331, + 286, + 366 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 331, + 286, + 366 + ], + "spans": [ + { + "bbox": [ + 46, + 331, + 286, + 366 + ], + "type": "text", + "content": "We refer readers to recent surveys on deep stereo matching [32] and on confidence estimation [30]. Here we summarize the most relevant publications to our work." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 367, + 286, + 544 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 367, + 286, + 544 + ], + "spans": [ + { + "bbox": [ + 46, + 367, + 286, + 544 + ], + "type": "text", + "content": "Stereo matching networks operate on a cost volume, which aggregates 2D features at each potential disparity for every pixel, and can be constructed via correlation or concatenation. Correlation-based networks such as DispNetC [25], iResNet [24] and SegStereo [38], generate a single-channel correlation map between features extracted from the two views at each disparity level, favoring computation efficiency at the expense of losing the structural and semantic information in the feature representation. Concatenation-based networks, such as GCNet [16], PSMNet [2] and GANet [39], assemble features from both views at the disparity specified by the corresponding element of the cost volume. This promotes learning of contextual features but requires more parameters and a subsequent aggregation network." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 545, + 286, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 545, + 286, + 689 + ], + "spans": [ + { + "bbox": [ + 46, + 545, + 286, + 689 + ], + "type": "text", + "content": "We select GwcNet [11] as the foundation of our network. GwcNet takes a hybrid approach by reducing the dimension of the unary feature channels before concatenation in the cost volume. This is accomplished by a Group Wise Correlation layer, which takes as input " + }, + { + "bbox": [ + 46, + 545, + 286, + 689 + ], + "type": "inline_equation", + "content": "N_{c}" + }, + { + "bbox": [ + 46, + 545, + 286, + 689 + ], + "type": "text", + "content": " unary feature channels, divides them into " + }, + { + "bbox": [ + 46, + 545, + 286, + 689 + ], + "type": "inline_equation", + "content": "N_{g}" + }, + { + "bbox": [ + 46, + 545, + 286, + 689 + ], + "type": "text", + "content": " groups, computes the correlation between channels in each group at all disparity levels, and uses the resulting correlation scores to form the cost volume. This reduces the size of the cost volume and the computational cost of 3D convolutions by a factor of " + }, + { + "bbox": [ + 46, + 545, + 286, + 689 + ], + "type": "inline_equation", + "content": "N_{c}:N_{g}" + }, + { + "bbox": [ + 46, + 545, + 286, + 689 + ], + "type": "text", + "content": ", with " + }, + { + "bbox": [ + 46, + 545, + 286, + 689 + ], + "type": "inline_equation", + "content": "N_{g}" + }, + { + "bbox": [ + 46, + 545, + 286, + 689 + ], + "type": "text", + "content": " much smaller than " + }, + { + "bbox": [ + 46, + 545, + 286, + 689 + ], + "type": "inline_equation", + "content": "N_{c}" + }, + { + "bbox": [ + 46, + 545, + 286, + 689 + ], + "type": "text", + "content": ", but still provides rich similarity-measure features to the disparity estimator." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 689, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 286, + 713 + ], + "type": "text", + "content": "Researchers have also focused on model reliability. In Bayesian Neural Networks (BNNs), different models are" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 72, + 545, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 167 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 167 + ], + "type": "text", + "content": "sampled from the distribution of weights to estimate the mean and variance of the target distribution in an empirical manner, yielding estimates of uncertainty [7, 27]. Additional empirical strategies such as Bootstrapped Ensembles [23] and Monte Carlo Dropout [8] also sample from the distribution of weights. On the other hand, Graves [10] and Blundell et al. [1] proposed to replace the sampling with variational inference." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 170, + 545, + 338 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 170, + 545, + 338 + ], + "spans": [ + { + "bbox": [ + 304, + 170, + 545, + 338 + ], + "type": "text", + "content": "Due to the high cost of training BNNs, methods for modeling the uncertainty or confidence in a predictive manner have also attracted interest. We distinguish between confidence and uncertainty: confidence is a binary variable trained with the BCE loss, while uncertainty is a continuous variable trained with L1 or L2 loss. Nix and Weigend [28] introduce NNs with one output for model prediction and one for data noise (aleatoric uncertainty). In addition to aleatoric uncertainty which captures the data noise of the observations, epistemic uncertainty, which accounts for the uncertainty of the model parameters, can also be modeled [3]. To capture both types, Kendall et al. [14, 15] proposed to combine empirical and predictive methods in a joint framework." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 340, + 545, + 519 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 340, + 545, + 519 + ], + "spans": [ + { + "bbox": [ + 304, + 340, + 545, + 519 + ], + "type": "text", + "content": "CNNs have been used to estimate confidence in stereo matching. The Confidence CNN (CCNN) [31], Patch Based Confidence Prediction (PBCP) [33], the Early Fusion Network (EFN) and the Late Fusion Network (LFN) [4] and Multi Modal CNN (MMC) [5] only use small patches of the disparity maps. Conversely, the Global Confidence Network (ConfNet) and the Local-Global Confidence Network (LGC) [35] introduce U-Net like architectures and take both the image and disparity map as inputs. As a baseline, we use the Locally Adaptive Fusion Network (LAF) [18] which predicts the confidence map based on tri-modal inputs: the cost and disparity maps and the color image. An extension based on knowledge distillation has also been published [17]. These strategies are effective and cheaper than the empirical ones, since they only require one forward pass." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 522, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 522, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 522, + 545, + 713 + ], + "type": "text", + "content": "Only a subset of the confidence estimation literature has focused on joint disparity and confidence estimation. The Reflective Confidence Network (RCN) [34] is the first to combine a disparity and a confidence loss. The Unified Confidence Network (UCN) [19] and the Adversarial Confidence Network (ACN) [20] jointly estimate confidence and disparity from pre-computed cost volumes. UCN is self-supervised, while ACN combines a generative cost aggregation network and a discriminative confidence estimation network in an adversarial manner. Mehlretter [26] presents an approach that predicts both epistemic and aleatoric uncertainty using a Bayesian Neural Network, based on GCNet [16]. KL divergence is used to measure the distance between the approximation of the distribution of network parameters estimated by variational difference and the exact posterior distribution. (It should be noted that we use" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17236" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 95 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 95 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 95 + ], + "type": "text", + "content": "KL divergence for a completely different purpose on the distribution of disparity errors.)" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 96, + 286, + 167 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 96, + 286, + 167 + ], + "spans": [ + { + "bbox": [ + 46, + 96, + 286, + 167 + ], + "type": "text", + "content": "Relevant research in adjacent areas of computer vision includes the work of Poggi et al. [29] who comprehensively evaluate uncertainty estimation for self-supervised monocular depth estimation. Ilg et al. [13] study empirical ensembles, predictive models and predictive ensembles as uncertainty models for optical flow estimation." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 177, + 103, + 189 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 177, + 103, + 189 + ], + "spans": [ + { + "bbox": [ + 47, + 177, + 103, + 189 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "spans": [ + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "text", + "content": "The objective of our work is to jointly estimate the disparity and its uncertainty. An important benefit of this joint formulation is that the multi-task network learns to predict more accurate disparities than the standalone disparity estimator when the uncertainty subnetwork is added. Given a stereo image pair " + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "inline_equation", + "content": "\\mathbf{X} = \\{\\mathbf{x}_l, \\mathbf{x}_r\\}" + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "text", + "content": ", with image dimensions " + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "inline_equation", + "content": "H \\times W" + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "text", + "content": ", and the corresponding ground truth disparity " + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{d}}" + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "text", + "content": ", the prediction " + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{d}}" + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "text", + "content": " of a stereo-matching network " + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "inline_equation", + "content": "f_{\\theta}" + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "text", + "content": " can be represented as " + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{d}} = f_{\\theta}(\\mathbf{x}_l, \\mathbf{x}_r)" + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "text", + "content": ". For each pixel " + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "text", + "content": ", the error " + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "inline_equation", + "content": "\\epsilon^{(i)}" + }, + { + "bbox": [ + 46, + 198, + 287, + 316 + ], + "type": "text", + "content": " of the prediction is calculated using the L1 loss." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 317, + 287, + 352 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 317, + 287, + 352 + ], + "spans": [ + { + "bbox": [ + 46, + 317, + 287, + 352 + ], + "type": "text", + "content": "Kendall and Gal [14] use the negative log-likelihood of the prediction model as the loss function to be minimized in pixel-wise tasks." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 354, + 287, + 412 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 354, + 287, + 412 + ], + "spans": [ + { + "bbox": [ + 46, + 354, + 287, + 412 + ], + "type": "text", + "content": "We take the formulation a step further by requiring that the network generate a distribution of uncertainties that matches the distribution of errors. To this end, we propose to minimize the divergence " + }, + { + "bbox": [ + 46, + 354, + 287, + 412 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 46, + 354, + 287, + 412 + ], + "type": "text", + "content": " between the distributions of predicted uncertainty and actual disparity error." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 413, + 287, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 413, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 46, + 413, + 287, + 460 + ], + "type": "text", + "content": "In the following subsections, we present aleatoric uncertainty estimation (Section 3.1), the proposed KL divergence loss (Section 3.2), our network architecture (Section 3.3), and the combined loss function (Section 3.4)." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 467, + 226, + 479 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 467, + 226, + 479 + ], + "spans": [ + { + "bbox": [ + 47, + 467, + 226, + 479 + ], + "type": "text", + "content": "3.1. Aleatoric Uncertainty Estimation" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 485, + 287, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 485, + 287, + 581 + ], + "spans": [ + { + "bbox": [ + 46, + 485, + 287, + 581 + ], + "type": "text", + "content": "In order to predict uncertainty and reduce the impact of noise, Kendall and Gal [14] minimize the pixel-wise negative log-likelihood of the prediction model, assuming that it follows a Gaussian distribution. The subsequent work of Ilg et al. [13] shows that the predicted distribution can be modeled as either Laplacian or Gaussian depending on whether the L1 or L2 loss is used for disparity estimation. Since we use the former, we can write the prediction model as:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 94, + 587, + 287, + 601 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 587, + 287, + 601 + ], + "spans": [ + { + "bbox": [ + 94, + 587, + 287, + 601 + ], + "type": "interline_equation", + "content": "p (\\mathbf {d} \\mid f _ {\\theta} (\\mathbf {X})) = \\mathcal {L} a p l a c e (f _ {\\theta} (\\mathbf {X}), \\sigma) \\tag {1}", + "image_path": "2e6a993bc3392080bca95e73b9686f8f1630795172d0c6e132fbef999ae4781c.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 607, + 287, + 630 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 607, + 287, + 630 + ], + "spans": [ + { + "bbox": [ + 46, + 607, + 287, + 630 + ], + "type": "text", + "content": "where the mean is given by the model output and " + }, + { + "bbox": [ + 46, + 607, + 287, + 630 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 46, + 607, + 287, + 630 + ], + "type": "text", + "content": " is the observation noise scalar." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 631, + 287, + 678 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 631, + 287, + 678 + ], + "spans": [ + { + "bbox": [ + 46, + 631, + 287, + 678 + ], + "type": "text", + "content": "To model aleatoric uncertainty, Kendall and Gal [14] introduce pixel-specific noise parameters " + }, + { + "bbox": [ + 46, + 631, + 287, + 678 + ], + "type": "inline_equation", + "content": "\\sigma^{(i)}" + }, + { + "bbox": [ + 46, + 631, + 287, + 678 + ], + "type": "text", + "content": ". We follow the approach of Ilg et al. [13], who do the same for a Laplacian model, and obtain the following pixel-wise loss function:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 84, + 685, + 287, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 84, + 685, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 84, + 685, + 287, + 715 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {l o g} = \\frac {1}{n} \\sum_ {i = 1} ^ {n} \\frac {\\left| \\hat {\\mathbf {d}} ^ {(i)} - \\mathbf {d} ^ {(i)} \\right|}{\\exp (\\mathbf {s} ^ {(i)})} + \\frac {1}{n} \\sum_ {i = 1} ^ {n} \\mathbf {s} ^ {(i)} \\tag {2}", + "image_path": "24440c24ef14cdf96a56743a103b2dbf1f1090fcc9ac77d618ce2e579ec9c68e.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "spans": [ + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{d}}^{(i)}" + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "inline_equation", + "content": "\\mathbf{d}^{(i)}" + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "text", + "content": " are the predicted and ground truth disparity for pixel " + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "inline_equation", + "content": "\\mathbf{s}^{(i)}" + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "text", + "content": " is the log of the observation noise scalar " + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "inline_equation", + "content": "\\sigma^{(i)}" + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "text", + "content": " is equal to the total number of the pixels. Equation (2) may be viewed as a robust loss function where the residual loss for a pixel is attenuated by its uncertainty, while the second term acts as a regularizer. We follow the authors' suggestion and train the network to predict the log of the observation noise scalar, " + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 71, + 545, + 168 + ], + "type": "text", + "content": ", for numerical stability." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 177, + 496, + 190 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 177, + 496, + 190 + ], + "spans": [ + { + "bbox": [ + 306, + 177, + 496, + 190 + ], + "type": "text", + "content": "3.2. Matching the Distribution of Errors" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 197, + 545, + 279 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 197, + 545, + 279 + ], + "spans": [ + { + "bbox": [ + 304, + 197, + 545, + 279 + ], + "type": "text", + "content": "Training a model using Eq. (2) as the loss improves disparity estimation accuracy and favors uncertainty estimates correlated with the errors. Ideally, we would like each uncertainty estimate to be a precise predictor of the corresponding disparity error. Since this is infeasible, we would like the distribution of uncertainties to match the distribution of errors." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 281, + 545, + 353 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 281, + 545, + 353 + ], + "spans": [ + { + "bbox": [ + 304, + 281, + 545, + 353 + ], + "type": "text", + "content": "The Kullback-Leibler (KL) divergence [22] is a natural choice for measuring the dissimilarity between the distribution of " + }, + { + "bbox": [ + 304, + 281, + 545, + 353 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 304, + 281, + 545, + 353 + ], + "type": "text", + "content": " and that of " + }, + { + "bbox": [ + 304, + 281, + 545, + 353 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 304, + 281, + 545, + 353 + ], + "type": "text", + "content": ". Since the KL divergence is asymmetric, we choose the distribution of " + }, + { + "bbox": [ + 304, + 281, + 545, + 353 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 304, + 281, + 545, + 353 + ], + "type": "text", + "content": " as the reference. Therefore, our network should also minimize the following objective function:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 309, + 363, + 545, + 400 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 309, + 363, + 545, + 400 + ], + "spans": [ + { + "bbox": [ + 309, + 363, + 545, + 400 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {d i v} = \\mathcal {D} \\left(P _ {\\sigma} (d)\\right) \\| Q _ {\\epsilon} (d)) = \\int_ {0} ^ {d _ {m a x}} P _ {\\sigma} (d) \\log \\frac {P _ {\\sigma} (d)}{Q _ {\\epsilon} (d)} d d \\tag {3}", + "image_path": "17a0ff280529973cf4516d1aa594de03c1a18aa806362f29502dd5df90788897.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 400, + 545, + 436 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 400, + 545, + 436 + ], + "spans": [ + { + "bbox": [ + 304, + 400, + 545, + 436 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 400, + 545, + 436 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 304, + 400, + 545, + 436 + ], + "type": "text", + "content": " spans the disparity range. Since the network regresses disparity, the continuous formulation of KL divergence is appropriate." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 437, + 545, + 520 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 437, + 545, + 520 + ], + "spans": [ + { + "bbox": [ + 304, + 437, + 545, + 520 + ], + "type": "text", + "content": "Minimizing Eq. (3) directly requires closed form expressions for the two distributions, which are not available to us. They could be modeled as Laplace distributions, but the maximum likelihood estimator is not differentiable. Moreover, fitting models to the data may be imprecise at the tails of the distributions. Therefore we choose non-parametric representations in the form of histograms." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 304, + 521, + 545, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 545, + 616 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 545, + 616 + ], + "type": "text", + "content": "Histogramming is also not a differentiable operation, leading us to soft-histogramming as a differentiable alternative. We specify a set of bins for the histograms based on the statistics of the errors " + }, + { + "bbox": [ + 304, + 521, + 545, + 616 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 304, + 521, + 545, + 616 + ], + "type": "text", + "content": ", since their distribution is the one that should be matched by the distribution of uncertainty estimates " + }, + { + "bbox": [ + 304, + 521, + 545, + 616 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 304, + 521, + 545, + 616 + ], + "type": "text", + "content": ". Since the L1 loss, and our network in general, does not discriminate between positive and negative errors, we work with absolute values of " + }, + { + "bbox": [ + 304, + 521, + 545, + 616 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 304, + 521, + 545, + 616 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 521, + 545, + 616 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 304, + 521, + 545, + 616 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": "For each batch during training, we compute the mean and standard deviation of the error, " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mu_{\\epsilon}" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "b_{\\epsilon}" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": ", set " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "C_0 = \\mu_{\\epsilon}" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": " as the center of the first bin, and " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "C_m = \\mu_{\\epsilon} + \\alpha_m b_{\\epsilon}" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": " as the center of the last bin. (We use " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "b_{\\epsilon}" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": " for the standard deviation to avoid overloading " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": " or " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": ". Also note that the last bin extends to the disparity range, which is also the maximum possible error.) We then define " + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "inline_equation", + "content": "m - 1" + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": " centers evenly spaced in a linear or logarithmic scale between the first and last center." + } + ] + } + ], + "index": 21 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17237" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 65, + 76, + 527, + 255 + ], + "blocks": [ + { + "bbox": [ + 65, + 76, + 527, + 255 + ], + "lines": [ + { + "bbox": [ + 65, + 76, + 527, + 255 + ], + "spans": [ + { + "bbox": [ + 65, + 76, + 527, + 255 + ], + "type": "image", + "image_path": "4862569d12ae2a05e4f942108be2f09f480e58666bb6b31d4cf884692d7ef05f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 269, + 545, + 326 + ], + "lines": [ + { + "bbox": [ + 46, + 269, + 545, + 326 + ], + "spans": [ + { + "bbox": [ + 46, + 269, + 545, + 326 + ], + "type": "text", + "content": "Figure 2. An illustration of SEDNet. The stereo matching subnetwork consists of the feature extractor, cost volume generator and disparity estimator. It takes a rectified pair of images as input and predicts disparity maps at multiple resolutions. The uncertainty estimation subnetwork takes the predicted disparity maps as input and predicts corresponding uncertainty maps. The error maps, between the ground truth and predicted disparity, are used to supervise uncertainty estimation. During training, the network keeps the output at all resolutions, but returns only the highest resolution disparity and uncertainty maps during testing." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 346, + 287, + 417 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 346, + 287, + 417 + ], + "spans": [ + { + "bbox": [ + 46, + 346, + 287, + 417 + ], + "type": "text", + "content": "Given the bin centers, we compute a soft-histogram for the errors and one for the uncertainties as follows, considering all pixels with ground truth disparity, and error values. We present the steps for " + }, + { + "bbox": [ + 46, + 346, + 287, + 417 + ], + "type": "inline_equation", + "content": "\\epsilon" + }, + { + "bbox": [ + 46, + 346, + 287, + 417 + ], + "type": "text", + "content": " here. For each error " + }, + { + "bbox": [ + 46, + 346, + 287, + 417 + ], + "type": "inline_equation", + "content": "\\epsilon^{(i)}" + }, + { + "bbox": [ + 46, + 346, + 287, + 417 + ], + "type": "text", + "content": ", we compute weights for every bin center which are inversely proportional to the distance." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 77, + 425, + 287, + 451 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 425, + 287, + 451 + ], + "spans": [ + { + "bbox": [ + 77, + 425, + 287, + 451 + ], + "type": "interline_equation", + "content": "w _ {j} \\left(\\epsilon^ {(i)}\\right) = \\lambda_ {1} \\cdot \\exp \\left(- \\frac {\\left(\\mu_ {\\epsilon} + \\alpha_ {j} b _ {\\epsilon} - \\epsilon^ {(i)}\\right) ^ {2}}{\\lambda_ {2}}\\right) \\tag {4}", + "image_path": "b74ef1e764b99c21abd7b21d5a337c3c77b92fbb14b8e07d43b7c6e897560dad.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 458, + 287, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 458, + 287, + 495 + ], + "spans": [ + { + "bbox": [ + 46, + 458, + 287, + 495 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 458, + 287, + 495 + ], + "type": "inline_equation", + "content": "\\lambda_{1}" + }, + { + "bbox": [ + 46, + 458, + 287, + 495 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 458, + 287, + 495 + ], + "type": "inline_equation", + "content": "\\lambda_{2}" + }, + { + "bbox": [ + 46, + 458, + 287, + 495 + ], + "type": "text", + "content": " are hyper-parameters. Softmax is then applied to favor the nearest bins and the contributions of " + }, + { + "bbox": [ + 46, + 458, + 287, + 495 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 46, + 458, + 287, + 495 + ], + "type": "text", + "content": " pixels are accumulated in the bins of the histogram " + }, + { + "bbox": [ + 46, + 458, + 287, + 495 + ], + "type": "inline_equation", + "content": "H_{\\epsilon}" + }, + { + "bbox": [ + 46, + 458, + 287, + 495 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 61, + 502, + 287, + 532 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 502, + 287, + 532 + ], + "spans": [ + { + "bbox": [ + 61, + 502, + 287, + 532 + ], + "type": "interline_equation", + "content": "H _ {\\epsilon} (j) = \\frac {1}{n} \\sum_ {i = 1} ^ {n} \\frac {\\exp \\left(w _ {j} \\left(\\epsilon^ {(i)}\\right)\\right)}{\\sum_ {j = 0} ^ {m} \\exp \\left(w _ {j} \\left(\\epsilon^ {(i)}\\right)\\right)}, j \\in [ 0, m ] \\tag {5}", + "image_path": "105bdabe8a5a21195fafc749814810884b5c263da02a295c703c4ffd9eb8510e.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 540, + 287, + 563 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 540, + 287, + 563 + ], + "spans": [ + { + "bbox": [ + 46, + 540, + 287, + 563 + ], + "type": "text", + "content": "The histogram for " + }, + { + "bbox": [ + 46, + 540, + 287, + 563 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 46, + 540, + 287, + 563 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 540, + 287, + 563 + ], + "type": "inline_equation", + "content": "H_{\\sigma}" + }, + { + "bbox": [ + 46, + 540, + 287, + 563 + ], + "type": "text", + "content": ", is obtained similarly. (Note that the bins of both histograms are defined in terms of " + }, + { + "bbox": [ + 46, + 540, + 287, + 563 + ], + "type": "inline_equation", + "content": "b_{\\epsilon}" + }, + { + "bbox": [ + 46, + 540, + 287, + 563 + ], + "type": "text", + "content": ".)" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 563, + 286, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 563, + 286, + 588 + ], + "spans": [ + { + "bbox": [ + 46, + 563, + 286, + 588 + ], + "type": "text", + "content": "The loss representing the discrete form of the KL divergence between the two histograms is given by:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 107, + 595, + 287, + 628 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 107, + 595, + 287, + 628 + ], + "spans": [ + { + "bbox": [ + 107, + 595, + 287, + 628 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {d i v} = \\sum_ {j = 0} ^ {m} H _ {\\epsilon} (j) \\log \\frac {H _ {\\epsilon} (j)}{H _ {\\sigma} (j)} \\tag {6}", + "image_path": "88842bfeb136b7b66081c3dc1ba9dfd1da051d4293e5d504b3402c7e2941b0ce.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 635, + 107, + 646 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 635, + 107, + 646 + ], + "spans": [ + { + "bbox": [ + 47, + 635, + 107, + 646 + ], + "type": "text", + "content": "3.3. SEDNet" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 654, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 287, + 713 + ], + "type": "text", + "content": "Our network architecture, named SEDNet, includes a disparity estimation subnetwork, an uncertainty estimation subnetwork, and is shown in Figure 2. In all experiments, we have adopted GwcNet [11] as the disparity estimation subnetwork, among other options, and we have designed" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 346, + 545, + 381 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 346, + 545, + 381 + ], + "spans": [ + { + "bbox": [ + 305, + 346, + 545, + 381 + ], + "type": "text", + "content": "a novel uncertainty estimation subnetwork that interfaces with GwcNet. It is worth noting that the uncertainty subnetwork is extremely small." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 384, + 545, + 455 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 384, + 545, + 455 + ], + "spans": [ + { + "bbox": [ + 305, + 384, + 545, + 455 + ], + "type": "text", + "content": "The GwcNet subnetwork extracts features from the images using a ResNet-like feature extractor, generates the cost volume, and assigns disparities to pixels using the soft-argmax operator [16]. The output module of the disparity predictor generates " + }, + { + "bbox": [ + 305, + 384, + 545, + 455 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 305, + 384, + 545, + 455 + ], + "type": "text", + "content": " disparity maps at different resolutions." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 460, + 545, + 627 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 460, + 545, + 627 + ], + "spans": [ + { + "bbox": [ + 305, + 460, + 545, + 627 + ], + "type": "text", + "content": "We propose a new uncertainty estimator integrated with the stereo matching network. The uncertainty estimation subnetwork learns to predict the log of the observation noise scalar, the error, at each pixel. The proposed subnetwork takes the multi-resolution disparity predictions as input, computes the pairwise differences vector (PDV) and passes it to a pixel-wise MLP to regress the uncertainty maps. Specifically, the disparity estimator outputs " + }, + { + "bbox": [ + 305, + 460, + 545, + 627 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 305, + 460, + 545, + 627 + ], + "type": "text", + "content": " disparity maps at different resolutions " + }, + { + "bbox": [ + 305, + 460, + 545, + 627 + ], + "type": "inline_equation", + "content": "\\hat{\\mathbf{d}} = \\{\\hat{\\mathbf{d}}_1,\\dots,\\hat{\\mathbf{d}}_K\\}" + }, + { + "bbox": [ + 305, + 460, + 545, + 627 + ], + "type": "text", + "content": ", which are first upsampled to full-resolution and then undergo pairwise differencing to form the PVD, which consists of " + }, + { + "bbox": [ + 305, + 460, + 545, + 627 + ], + "type": "inline_equation", + "content": "\\binom{K}{2}" + }, + { + "bbox": [ + 305, + 460, + 545, + 627 + ], + "type": "text", + "content": " elements. The output set of uncertainty maps S also contains " + }, + { + "bbox": [ + 305, + 460, + 545, + 627 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 305, + 460, + 545, + 627 + ], + "type": "text", + "content": " resolutions to match the disparity maps. We use " + }, + { + "bbox": [ + 305, + 460, + 545, + 627 + ], + "type": "inline_equation", + "content": "K = 4" + }, + { + "bbox": [ + 305, + 460, + 545, + 627 + ], + "type": "text", + "content": " in all experiments." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 644, + 394, + 655 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 644, + 394, + 655 + ], + "spans": [ + { + "bbox": [ + 306, + 644, + 394, + 655 + ], + "type": "text", + "content": "3.4. Loss Function" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 666, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 666, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 666, + 545, + 713 + ], + "type": "text", + "content": "Our loss function combines two parts: (1) the log-likelihood loss to optimize the error and uncertainty, (2) the KL divergence loss to match the distribution of uncertainty with the error. The total loss considers all disparity and un" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17238" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 256, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 256, + 84 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 256, + 84 + ], + "type": "text", + "content": "certainty maps upsampled to the highest resolution:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 104, + 94, + 287, + 127 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 94, + 287, + 127 + ], + "spans": [ + { + "bbox": [ + 104, + 94, + 287, + 127 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\sum_ {k = 1} ^ {K} c _ {k} \\cdot \\left(\\mathcal {L} _ {\\text {l o g}, k} + \\mathcal {L} _ {\\text {d i v}, k}\\right) \\tag {7}", + "image_path": "da8c9363913dab379faede65bb17e93dfa8c978cd281322331ccd1e8f6d2e77e.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 137, + 287, + 174 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 137, + 287, + 174 + ], + "spans": [ + { + "bbox": [ + 47, + 137, + 287, + 174 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 137, + 287, + 174 + ], + "type": "inline_equation", + "content": "c_k" + }, + { + "bbox": [ + 47, + 137, + 287, + 174 + ], + "type": "text", + "content": " denotes the coefficients for the " + }, + { + "bbox": [ + 47, + 137, + 287, + 174 + ], + "type": "inline_equation", + "content": "k^{th}" + }, + { + "bbox": [ + 47, + 137, + 287, + 174 + ], + "type": "text", + "content": " resolution level, " + }, + { + "bbox": [ + 47, + 137, + 287, + 174 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\log,k}" + }, + { + "bbox": [ + 47, + 137, + 287, + 174 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 137, + 287, + 174 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{div,k}" + }, + { + "bbox": [ + 47, + 137, + 287, + 174 + ], + "type": "text", + "content": " are computed by Eq. (2) and Eq. (6) on the prediction of the corresponding resolution level." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 186, + 163, + 198 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 186, + 163, + 198 + ], + "spans": [ + { + "bbox": [ + 47, + 186, + 163, + 198 + ], + "type": "text", + "content": "4. Experiment Results" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 206, + 287, + 337 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 206, + 287, + 337 + ], + "spans": [ + { + "bbox": [ + 46, + 206, + 287, + 337 + ], + "type": "text", + "content": "In this section, we present our experimental setup and results on within-domain and cross-domain experiments. Datasets, evaluation metrics and baselines are described in Section 4.1, implementation details in Section 4.2, and experimental results in Sections 4.3 and 4.4. Additionally, a synthetic-to-real transfer evaluation is presented in Section 4.5. We provide more quantitative and qualitative results, extending Section 4, in the Supplement, which also includes ablation studies on several aspects of SEDNet and the baselines, as well as disparity, error, and uncertainty maps of difficult examples." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 346, + 218, + 357 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 346, + 218, + 357 + ], + "spans": [ + { + "bbox": [ + 47, + 346, + 218, + 357 + ], + "type": "text", + "content": "4.1. Datasets and evaluation metrics" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 365, + 287, + 448 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 365, + 287, + 448 + ], + "spans": [ + { + "bbox": [ + 46, + 365, + 287, + 448 + ], + "type": "text", + "content": "SceneFlow [25] is a collection of three synthetic stereo datasets: FlyingThings3D, Driving, Monkaa. The datasets provide 35,454 training and 4,370 test stereo pairs in " + }, + { + "bbox": [ + 46, + 365, + 287, + 448 + ], + "type": "inline_equation", + "content": "960 \\times 540" + }, + { + "bbox": [ + 46, + 365, + 287, + 448 + ], + "type": "text", + "content": " pixel resolution with dense ground-truth disparity maps. We use the finalpass versions of the rendered images which are more realistic because of the motion blur and depth of field effect." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 449, + 287, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 449, + 287, + 592 + ], + "spans": [ + { + "bbox": [ + 46, + 449, + 287, + 592 + ], + "type": "text", + "content": "Virtual KITTI 2 (VK2) [6] is a synthetic clone of KITTI [9]. It consists of 21,260 synthetic " + }, + { + "bbox": [ + 46, + 449, + 287, + 592 + ], + "type": "inline_equation", + "content": "1,242 \\times 375" + }, + { + "bbox": [ + 46, + 449, + 287, + 592 + ], + "type": "text", + "content": " stereo pairs from 6 driving scenes with 10 different imaging and weather conditions. Scene 006 (VK2-S6) is specified as the test set by the authors of the dataset. Since the car with the cameras is stopped for a long part of Scene 006 and only other cars move in the images, we split the last part of the scene where the car moves and denote it as VK2-S6-Moving. We report results separately on this subset. Since results on VK2-S6-Moving do not suffer from bias due to the almost constant background of the first part of the scene, evaluation on VK2-S6-Moving is more informative and fair." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 593, + 287, + 663 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 593, + 287, + 663 + ], + "spans": [ + { + "bbox": [ + 46, + 593, + 287, + 663 + ], + "type": "text", + "content": "DrivingStereo [37] is a large real-world autonomous driving dataset. It contains 174,437 training and 7,751 test stereo pairs at " + }, + { + "bbox": [ + 46, + 593, + 287, + 663 + ], + "type": "inline_equation", + "content": "881 \\times 400" + }, + { + "bbox": [ + 46, + 593, + 287, + 663 + ], + "type": "text", + "content": " pixel resolution. The dataset provides sparse ground truth disparity as well as a challenging subset (DS-Weather) of 2,000 stereo pairs in 4 different weather conditions." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 665, + 287, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 665, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 47, + 665, + 287, + 689 + ], + "type": "text", + "content": "For all datasets above, we exclude pixels with disparities " + }, + { + "bbox": [ + 47, + 665, + 287, + 689 + ], + "type": "inline_equation", + "content": "d > 192" + }, + { + "bbox": [ + 47, + 665, + 287, + 689 + ], + "type": "text", + "content": " in training." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 713 + ], + "type": "text", + "content": "Metrics. To evaluate disparity estimation, we compute the endpoint error (EPE) and the percentage of outliers (D1)" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "text", + "content": "(i.e., the percentage of pixels with EPE " + }, + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "inline_equation", + "content": ">3\\mathrm{px}" + }, + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "text", + "content": " or " + }, + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "inline_equation", + "content": "\\geq 5\\%" + }, + { + "bbox": [ + 305, + 72, + 545, + 96 + ], + "type": "text", + "content": " of the true depth)." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 97, + 545, + 263 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 97, + 545, + 263 + ], + "spans": [ + { + "bbox": [ + 304, + 97, + 545, + 263 + ], + "type": "text", + "content": "To evaluate uncertainty estimation, we use density-EPE ROC curves and the area under the curve (AUC) [12,18,30]. The ROC curves in our case measure EPE (not binary correctness) at increasing disparity map density by successively adding pixels in increasing order of uncertainty. The optimal AUC is obtained by adding pixels in order of increasing EPE and is therefore the lowest value any algorithm could achieve given the set of EPE values, while the estimated AUC is sorted by predicted uncertainty. To evaluate the precision of uncertainty estimation, we also introduce the absolute prediction error (APE), which is the average L1 distance between the error and the observation noise scalar, " + }, + { + "bbox": [ + 304, + 97, + 545, + 263 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 304, + 97, + 545, + 263 + ], + "type": "text", + "content": ". We report the average and median APE over all pixels." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 265, + 545, + 408 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 265, + 545, + 408 + ], + "spans": [ + { + "bbox": [ + 304, + 265, + 545, + 408 + ], + "type": "text", + "content": "Baselines. Since we use GwcNet [11] as the disparity estimation subnetwork of SEDNet, we compare SEDNet with three baselines: (1) the original GwcNet trained with smooth L1 loss. (2) LAF-Net [18] trained under the BCE loss on the left RGB images, the cost volumes and predicted disparity maps of GwcNet at the highest resolution. (3) SEDNet but only trained with the log-likelihood loss, therefore similar to Kendall and Gal's model [14]. We use " + }, + { + "bbox": [ + 304, + 265, + 545, + 408 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\log}" + }, + { + "bbox": [ + 304, + 265, + 545, + 408 + ], + "type": "text", + "content": " in tables and figures for this baseline. We selected GwcNet because it is the backbone of SEDNet, and LAF-Net as the confidence estimation baseline due to its strong performance within the training domain according to [30]." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 418, + 438, + 430 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 418, + 438, + 430 + ], + "spans": [ + { + "bbox": [ + 306, + 418, + 438, + 430 + ], + "type": "text", + "content": "4.2. Implementation Details" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 437, + 545, + 484 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 437, + 545, + 484 + ], + "spans": [ + { + "bbox": [ + 304, + 437, + 545, + 484 + ], + "type": "text", + "content": "We implemented all networks in PyTorch and used the Adam optimizer [21] with " + }, + { + "bbox": [ + 304, + 437, + 545, + 484 + ], + "type": "inline_equation", + "content": "\\beta_{1} = 0.9" + }, + { + "bbox": [ + 304, + 437, + 545, + 484 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 437, + 545, + 484 + ], + "type": "inline_equation", + "content": "\\beta_{2} = 0.999" + }, + { + "bbox": [ + 304, + 437, + 545, + 484 + ], + "type": "text", + "content": " for all experiments. Training of all models was stopped before overfitting occurred." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 485, + 545, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 485, + 545, + 568 + ], + "spans": [ + { + "bbox": [ + 304, + 485, + 545, + 568 + ], + "type": "text", + "content": "Experiments on the VK2 dataset were performed on two NVIDIA RTX A6000 GPUs, each with 48 GB of RAM. For this dataset, we trained all models from scratch with an initial learning rate of 0.0001, down-scaled by 5 every 10 epochs. During training, we randomly cropped " + }, + { + "bbox": [ + 304, + 485, + 545, + 568 + ], + "type": "inline_equation", + "content": "512 \\times 256" + }, + { + "bbox": [ + 304, + 485, + 545, + 568 + ], + "type": "text", + "content": " patches from the images. During testing, we evaluated at the full resolution of VK2." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 570, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 545, + 712 + ], + "type": "text", + "content": "Experiments on the DrivingStereo dataset (DS) were also performed on two NVIDIA RTX A6000 GPUs. For this dataset, we did two experiments using the models pretrained on VK2: (1) we finetuned on the DS training set with a learning rate starting from 0.0001, down-scaled by 2 every 3 epochs after epoch 10, then performed in-domain evaluation on the DS test set; (2) we skipped the finetuning step and performed cross-domain evaluation on DS-Weather subset. During training, we randomly cropped the inputs to be the same size as in the VK2 experiments. During testing, we padded the test samples to be the same resolution as VK2." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17239" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 48, + 70, + 547, + 258 + ], + "blocks": [ + { + "bbox": [ + 48, + 70, + 547, + 258 + ], + "lines": [ + { + "bbox": [ + 48, + 70, + 547, + 258 + ], + "spans": [ + { + "bbox": [ + 48, + 70, + 547, + 258 + ], + "type": "table", + "html": "
DatasetMethodLossInliersDisparity↓APE↓AUC↓
BCEL1LogKLBinsScaleDef.Pct(%)EPED1(%)Avg.MedianOpt.Est.
Scene FlowGwcNet-√------0.77584.127--10.9291-
+LAF√-------0.77584.127--10.929120.0813
+Llog-√√---EPE<596.960.76114.1310.69990.07285.744912.1121
+SEDNet-√√√11logEPE<μe+3bε98.420.67543.9630.57970.04324.91348.7195
VK2-S6GwcNet-√------0.41251.763--6.0962-
+Llog-√√---EPE<598.860.38991.5840.41360.17534.687212.5320
+SEDNet-√√√11logEPE<μe+3bε99.240.31091.3920.52340.14544.17269.7637
+SEDNet-√√√11logEPE<μe+5bε99.680.32361.4270.35610.10964.27679.9843
VK2-S6-MovingGwcNet-√------0.42531.689--5.9184-
+Llog-√√---EPE<598.910.42311.5370.45750.18904.366311.3532
+SEDNet-√√√11logEPE<μe+3bε99.620.35771.3890.59580.15733.90128.8339
+SEDNet-√√√11logEPE<μe+5bε99.760.38621.4200.40020.11644.04239.0631
DrivingStereo+Llog(FT)-√√-----0.53320.26410.34490.229721.700245.7096
+SEDNet(FT)-√√√11logEPE<μe+5bε99.860.52640.24390.33240.226721.285644.3297
DS-WeatherGwcNet-√------1.69628.313--44.4896-
+Llog-√√---EPE<595.782.39446.6662.14430.438341.190995.4264
+SEDNet-√√√11logEPE<μe+3bε98.951.56376.5082.34060.530938.487186.1118
+SEDNet-√√√11logEPE<μe+5bε99.411.70516.0571.58420.610439.805787.1882
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Example from VK2-S6. In a rainy scene with poor illumination, recognizing objects far away from the camera is even difficult for human observers. " + }, + { + "bbox": [ + 45, + 440, + 547, + 485 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{log}" + }, + { + "bbox": [ + 45, + 440, + 547, + 485 + ], + "type": "text", + "content": " fails to predict the disparity of objects, such as the traffic sign near the left street light. On the other hand, SEDNet accurately predicts the disparity of these challenging objects, while its uncertainty map also captures more information, such as the trees in the background, the traffic light near the trees and the street lights." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 219, + 308, + 375, + 357 + ], + "blocks": [ + { + "bbox": [ + 219, + 308, + 375, + 357 + ], + "lines": [ + { + "bbox": [ + 219, + 308, + 375, + 357 + ], + "spans": [ + { + "bbox": [ + 219, + 308, + 375, + 357 + ], + "type": "image", + "image_path": "8e3ace30a511433ab10150f03eb6b1bfb265ce655540265334b81a5529045048.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 219, + 371, + 375, + 418 + ], + "blocks": [ + { + "bbox": [ + 267, + 360, + 326, + 371 + ], + "lines": [ + { + "bbox": [ + 267, + 360, + 326, + 371 + ], + "spans": [ + { + "bbox": [ + 267, + 360, + 326, + 371 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{log}}" + }, + { + "bbox": [ + 267, + 360, + 326, + 371 + ], + "type": "text", + "content": " Disparity" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 219, + 371, + 375, + 418 + ], + "lines": [ + { + "bbox": [ + 219, + 371, + 375, + 418 + ], + "spans": [ + { + "bbox": [ + 219, + 371, + 375, + 418 + ], + "type": "image", + "image_path": "d3ae36d8ea80ff3bcfc3c6005483a9aff738f63ccd398c9a606a33b324edb7c9.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 260, + 422, + 333, + 433 + ], + "lines": [ + { + "bbox": [ + 260, + 422, + 333, + 433 + ], + "spans": [ + { + "bbox": [ + 260, + 422, + 333, + 433 + ], + "type": "text", + "content": "SEDNet Disparity" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 383, + 308, + 540, + 357 + ], + "blocks": [ + { + "bbox": [ + 383, + 308, + 540, + 357 + ], + "lines": [ + { + "bbox": [ + 383, + 308, + 540, + 357 + ], + "spans": [ + { + "bbox": [ + 383, + 308, + 540, + 357 + ], + "type": "image", + "image_path": "7198fbadf33c39ee5f21444a910572cbdfdf51f3dfdf468fd8673e8d7055cbba.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + } + ], + "index": 10 + }, + { + "type": "image", + "bbox": [ + 384, + 371, + 539, + 419 + ], + "blocks": [ + { + "bbox": [ + 427, + 360, + 496, + 371 + ], + "lines": [ + { + "bbox": [ + 427, + 360, + 496, + 371 + ], + "spans": [ + { + "bbox": [ + 427, + 360, + 496, + 371 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{log}}" + }, + { + "bbox": [ + 427, + 360, + 496, + 371 + ], + "type": "text", + "content": " Uncertainty" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 384, + 371, + 539, + 419 + ], + "lines": [ + { + "bbox": [ + 384, + 371, + 539, + 419 + ], + "spans": [ + { + "bbox": [ + 384, + 371, + 539, + 419 + ], + "type": "image", + "image_path": "7d3092f069ec2f646599c5086ea3825fe3c6a29ee3fa85c683e7872387ae4cfc.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 419, + 421, + 504, + 433 + ], + "lines": [ + { + "bbox": [ + 419, + 421, + 504, + 433 + ], + "spans": [ + { + "bbox": [ + 419, + 421, + 504, + 433 + ], + "type": "text", + "content": "SEDNet Uncertainty" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 46, + 505, + 287, + 577 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 505, + 287, + 577 + ], + "spans": [ + { + "bbox": [ + 46, + 505, + 287, + 577 + ], + "type": "text", + "content": "Experiments on the SceneFlow dataset were performed on an Nvidia Titan RTX GPU with 24 GB memory. We trained all models from scratch on " + }, + { + "bbox": [ + 46, + 505, + 287, + 577 + ], + "type": "inline_equation", + "content": "256 \\times 128" + }, + { + "bbox": [ + 46, + 505, + 287, + 577 + ], + "type": "text", + "content": " patches cropped from half-resolution images to limit memory consumption. We set the initial learning rate to 0.001 and down-scaled it by 2 every 2 epochs after epoch 10." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 46, + 578, + 287, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 578, + 287, + 613 + ], + "spans": [ + { + "bbox": [ + 46, + 578, + 287, + 613 + ], + "type": "text", + "content": "For all the experiments above, we applied an inlier filtering strategy during training, which only back-propagates from the inliers. See the last paragraph of Section 4.3 for" + } + ] + } + ], + "index": 16 + }, + { + "type": "table", + "bbox": [ + 80, + 620, + 254, + 662 + ], + "blocks": [ + { + "bbox": [ + 46, + 264, + 546, + 299 + ], + "lines": [ + { + "bbox": [ + 46, + 264, + 546, + 299 + ], + "spans": [ + { + "bbox": [ + 46, + 264, + 546, + 299 + ], + "type": "text", + "content": "Table 1. Quantitative results: (1) within-domain on SceneFlow, VK2-S6 and VK2-S6-Moving; (2) after finetuning (FT) on DrivingStereo; (3) cross-domain on DS-Weather. The best results in each category in each experiment are in bold typeface. SEDNet outperforms the baselines with respect to disparity and uncertainty metrics in the majority of experiments." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 80, + 620, + 254, + 662 + ], + "lines": [ + { + "bbox": [ + 80, + 620, + 254, + 662 + ], + "spans": [ + { + "bbox": [ + 80, + 620, + 254, + 662 + ], + "type": "table", + "html": "
ArchitectureParamsMACs(G)
GwcNet6,909,7281075.82
SEDNet6,909,9181075.91
", + "image_path": "b00c8fdc14224877a240b5f4b142a129a891b7ed52bb7d3f8c0e3fff1a48ef2d.jpg" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "table_body" + } + ], + "index": 17 + }, + { + "bbox": [ + 46, + 666, + 287, + 710 + ], + "lines": [ + { + "bbox": [ + 46, + 666, + 287, + 710 + ], + "spans": [ + { + "bbox": [ + 46, + 666, + 287, + 710 + ], + "type": "text", + "content": "Table 2. Comparison on number of parameters and computational complexity. MAC stands for multiply-accumulate operations. SEDNet only adds a 3-layer MLP, with 190 parameters, as an uncertainty decoder to GwcNet." + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 505, + 545, + 530 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 505, + 545, + 530 + ], + "spans": [ + { + "bbox": [ + 304, + 505, + 545, + 530 + ], + "type": "text", + "content": "details. Table 2 shows the number of parameters and computational complexity of GwcNet and SEDNet." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 305, + 538, + 500, + 551 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 538, + 500, + 551 + ], + "spans": [ + { + "bbox": [ + 305, + 538, + 500, + 551 + ], + "type": "text", + "content": "4.3. Qualitative and Quantitative Results" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 557, + 545, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 557, + 545, + 628 + ], + "spans": [ + { + "bbox": [ + 304, + 557, + 545, + 628 + ], + "type": "text", + "content": "In Table 1, we present results: (1) within-domain on SceneFlow and VK2; (2) on the DrivingStereo test set after finetuning the VK2 models on the DrivingStereo training set; (3) cross-domain on the DS-Weather challenge test set by directly applying the model trained on VK2 without finetuning." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 629, + 545, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 629, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 304, + 629, + 545, + 689 + ], + "type": "text", + "content": "Disparity Estimation. In all experiments, SEDNet achieves lower errors than all the baselines. See the EPE and D1 columns in Table 1. Even in extreme weather like fog and rain, SEDNet predicts good disparity unaffected by poor illumination and blur. See Figure 3." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": "Uncertainty Estimation. Our method outperforms the baselines in all experiments according to the AUC metric," + } + ] + } + ], + "index": 23 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "17240" + } + ] + } + ], + "index": 24 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 69, + 211, + 142 + ], + "blocks": [ + { + "bbox": [ + 53, + 69, + 211, + 142 + ], + "lines": [ + { + "bbox": [ + 53, + 69, + 211, + 142 + ], + "spans": [ + { + "bbox": [ + 53, + 69, + 211, + 142 + ], + "type": "image", + "image_path": "696f076058a8a225341a14fe4ac35c9e5c8f2e8dd8f6e70b6c94159e3d3fa96a.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 53, + 155, + 211, + 227 + ], + "blocks": [ + { + "bbox": [ + 109, + 144, + 154, + 155 + ], + "lines": [ + { + "bbox": [ + 109, + 144, + 154, + 155 + ], + "spans": [ + { + "bbox": [ + 109, + 144, + 154, + 155 + ], + "type": "text", + "content": "Left Image" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 53, + 155, + 211, + 227 + ], + "lines": [ + { + "bbox": [ + 53, + 155, + 211, + 227 + ], + "spans": [ + { + "bbox": [ + 53, + 155, + 211, + 227 + ], + "type": "image", + "image_path": "8736da3fd881d7cb680754f80b7df1892bdfccaab39e388af3c0a7fdcc39ed51.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 106, + 229, + 157, + 240 + ], + "lines": [ + { + "bbox": [ + 106, + 229, + 157, + 240 + ], + "spans": [ + { + "bbox": [ + 106, + 229, + 157, + 240 + ], + "type": "text", + "content": "Right Image" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 46, + 247, + 547, + 293 + ], + "lines": [ + { + "bbox": [ + 46, + 247, + 547, + 293 + ], + "spans": [ + { + "bbox": [ + 46, + 247, + 547, + 293 + ], + "type": "text", + "content": "Figure 4. Example from DS-Weather. Unlike the synthetic data, the rainy-day real images do not only suffer from poor illumination, but also pose challenges due to reflections in the water. In this example, the road is like a mirror, misleading the " + }, + { + "bbox": [ + 46, + 247, + 547, + 293 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{log}" + }, + { + "bbox": [ + 46, + 247, + 547, + 293 + ], + "type": "text", + "content": " model. Recall that the LIDAR ground truth disparity is very sparse, and is even sparser in reflective regions. 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Compared to " + }, + { + "bbox": [ + 46, + 312, + 289, + 422 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{log}" + }, + { + "bbox": [ + 46, + 312, + 289, + 422 + ], + "type": "text", + "content": ", SEDNet decreases the estimated AUC by " + }, + { + "bbox": [ + 46, + 312, + 289, + 422 + ], + "type": "inline_equation", + "content": "20\\% - 30\\%" + }, + { + "bbox": [ + 46, + 312, + 289, + 422 + ], + "type": "text", + "content": " in the in-domain experiments, with a " + }, + { + "bbox": [ + 46, + 312, + 289, + 422 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 312, + 289, + 422 + ], + "type": "text", + "content": " decrease in optimal AUC, which depends on EPE. In the cross-domain evaluation, the advantage of SEDNet is even more evident. Figure 4 shows uncertainty maps for real data, on which our method captures details more faithfully. The ROC curves of the best " + }, + { + "bbox": [ + 46, + 312, + 289, + 422 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{log}" + }, + { + "bbox": [ + 46, + 312, + 289, + 422 + ], + "type": "text", + "content": " and SEDNet models based on EPE on VK2-S6-Moving are presented in Figure 5." + } + ] + } + ], + "index": 13 + }, + { + "type": "image", + "bbox": [ + 86, + 437, + 243, + 555 + ], + "blocks": [ + { + "bbox": [ + 86, + 437, + 243, + 555 + ], + "lines": [ + { + "bbox": [ + 86, + 437, + 243, + 555 + ], + "spans": [ + { + "bbox": [ + 86, + 437, + 243, + 555 + ], + "type": "image", + "image_path": "473fec7f260e773882556db0f56a33e82bb545d37b0b7d06f51e0fd0d51138f8.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 563, + 287, + 629 + ], + "lines": [ + { + "bbox": [ + 46, + 563, + 287, + 629 + ], + "spans": [ + { + "bbox": [ + 46, + 563, + 287, + 629 + ], + "type": "text", + "content": "Figure 5. Comparison of density-EPE curves on VK2-S6-Moving. \"L.log (EPE<5)\" and \"SEDNet (3)\" represent the " + }, + { + "bbox": [ + 46, + 563, + 287, + 629 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\log}" + }, + { + "bbox": [ + 46, + 563, + 287, + 629 + ], + "type": "text", + "content": " (with fixed inliers) and the SEDNet (with adaptive inliers of " + }, + { + "bbox": [ + 46, + 563, + 287, + 629 + ], + "type": "inline_equation", + "content": "\\mathrm{EPE} < \\mu_{\\epsilon} + 3b_{\\epsilon}" + }, + { + "bbox": [ + 46, + 563, + 287, + 629 + ], + "type": "text", + "content": ") of VK2-S6-Moving experiment in Table 1. While their corresponding optimal AUCs are almost equal, the advantage of SEDNet in estimated AUC is significant." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 641, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 288, + 714 + ], + "type": "text", + "content": "Effects of Back-propagation from Inliers. We apply two kinds of inlier filters: one with a fixed threshold that excludes all pixels that have an EPE larger than 5 from backpropagation; and one with adaptive threshold which excludes pixels that have EPE greater than a specified number of " + }, + { + "bbox": [ + 46, + 641, + 288, + 714 + ], + "type": "inline_equation", + "content": "b_{\\epsilon}" + }, + { + "bbox": [ + 46, + 641, + 288, + 714 + ], + "type": "text", + "content": " from the mean error. Back-propagation from the" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 312, + 547, + 433 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 312, + 547, + 433 + ], + "spans": [ + { + "bbox": [ + 304, + 312, + 547, + 433 + ], + "type": "text", + "content": "inliers only helps the network improve its performance on both disparity and uncertainty estimation. We attribute this to the suppression of harmful outliers that give rise to large gradients. Quantitative results for the baselines and the proposed method with different inlier settings are reported in the Supplement. The results show that using adaptive thresholds is better than fixed thresholds. Fixed thresholds exclude more pixels, especially at lower resolutions and in the early stages of training, preventing the network from learning how to correct them." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 306, + 443, + 484, + 456 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 443, + 484, + 456 + ], + "spans": [ + { + "bbox": [ + 306, + 443, + 484, + 456 + ], + "type": "text", + "content": "4.4. Matching the Error Distribution." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 463, + 547, + 511 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 463, + 547, + 511 + ], + "spans": [ + { + "bbox": [ + 304, + 463, + 547, + 511 + ], + "type": "text", + "content": "Comparing the results of APE in all experiments in Table 1 and Figure 6, the difference between the errors and the predicted uncertainty is always close to the EPE, as expected. SEDNet, however, achieves lower APE, showing" + } + ] + } + ], + "index": 19 + }, + { + "type": "image", + "bbox": [ + 312, + 533, + 421, + 613 + ], + "blocks": [ + { + "bbox": [ + 312, + 533, + 421, + 613 + ], + "lines": [ + { + "bbox": [ + 312, + 533, + 421, + 613 + ], + "spans": [ + { + "bbox": [ + 312, + 533, + 421, + 613 + ], + "type": "image", + "image_path": "c10757329f3093edf814b6f4a5bf8bc5c44fbb74e47bdbc115136a4f8e13a640.jpg" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 358, + 616, + 375, + 627 + ], + "lines": [ + { + "bbox": [ + 358, + 616, + 375, + 627 + ], + "spans": [ + { + "bbox": [ + 358, + 616, + 375, + 627 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{log}" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 304, + 633, + 547, + 711 + ], + "lines": [ + { + "bbox": [ + 304, + 633, + 547, + 711 + ], + "spans": [ + { + "bbox": [ + 304, + 633, + 547, + 711 + ], + "type": "text", + "content": "Figure 6. Distribution of error and predicted uncertainty. We pick the best model of " + }, + { + "bbox": [ + 304, + 633, + 547, + 711 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text{log}}" + }, + { + "bbox": [ + 304, + 633, + 547, + 711 + ], + "type": "text", + "content": " and SEDNet in VK2-S6-Moving, then randomly sample 5,000,000 points from the outputs of the two models. The distributions of error and " + }, + { + "bbox": [ + 304, + 633, + 547, + 711 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 304, + 633, + 547, + 711 + ], + "type": "text", + "content": " of SEDNet are much closer than those of the " + }, + { + "bbox": [ + 304, + 633, + 547, + 711 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\text{log}}" + }, + { + "bbox": [ + 304, + 633, + 547, + 711 + ], + "type": "text", + "content": " model, especially in the first 5 bins that contain more than " + }, + { + "bbox": [ + 304, + 633, + 547, + 711 + ], + "type": "inline_equation", + "content": "95\\%" + }, + { + "bbox": [ + 304, + 633, + 547, + 711 + ], + "type": "text", + "content": " of the samples. The shorter tail of SEDNet also indicates fewer gross errors." + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_caption" + } + ], + "index": 20 + }, + { + "type": "image", + "bbox": [ + 430, + 533, + 539, + 613 + ], + "blocks": [ + { + "bbox": [ + 430, + 533, + 539, + 613 + ], + "lines": [ + { + "bbox": [ + 430, + 533, + 539, + 613 + ], + "spans": [ + { + "bbox": [ + 430, + 533, + 539, + 613 + ], + "type": "image", + "image_path": "3a94bf511bc20a0763b3bcd8b6122696fe3976c58f24027ebac6c3f23b94bf23.jpg" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 469, + 616, + 501, + 624 + ], + "lines": [ + { + "bbox": [ + 469, + 616, + 501, + 624 + ], + "spans": [ + { + "bbox": [ + 469, + 616, + 501, + 624 + ], + "type": "text", + "content": "SEDNet" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_caption" + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17241" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 48, + 70, + 547, + 310 + ], + "blocks": [ + { + "bbox": [ + 48, + 70, + 547, + 310 + ], + "lines": [ + { + "bbox": [ + 48, + 70, + 547, + 310 + ], + "spans": [ + { + "bbox": [ + 48, + 70, + 547, + 310 + ], + "type": "table", + "html": "
DatasetMethodLossInliersDisparity↓APE↓AUC↓
BCEL1LogKLBinsScaleDef.Pct(%)EPED1(%)Avg.MedianOpt.Est.
VK2-S6-MorningGwcNet-√------0.46421.740--6.1845-
+Llog-√√---EPE<598.820.47741.6240.50670.18724.669812.5192
+SEDNet-√√√11logEPE<με+3bε99.620.40031.4420.61830.15534.18479.4063
VK2-S6-SunsetGwcNet-√------0.48101.825--6.6907
+Llog-√√---EPE<598.840.48631.6270.50600.18275.007513.7848
+SEDNet-√√√11logEPE<με+3bε99.610.41081.4750.61890.15094.584010.7946
VK2-S6-FogGwcNet-√------0.46601.812--6.8355-
+Llog-√√---EPE<598.980.44251.4480.46090.18654.898312.1305
+SEDNet-√√√11logEPE<με+3bε99.710.37311.2880.55170.15474.42009.9380
VK2-S6-RainGwcNet-√------0.46181.700--6.6774-
+Llog-√√---EPE<598.880.47071.5710.48990.18614.935113.3214
+SEDNet-√√√11logEPE<με+3bε99.690.38731.3560.66850.15374.401310.3362
DS-CloudyGwcNet-√------1.34135.229--37.4263-
+Llog-√√---EPE<597.481.47803.9481.26170.351334.448882.5380
+SEDNet-√√√11logEPE<με+3bε98.831.31834.4141.52600.402133.903773.6330
DS-SunnyGwcNet-√------1.54486.991--38.7386-
+Llog-√√---EPE<597.081.48374.6311.28060.383535.522685.8715
+SEDNet-√√√11logEPE<με+3bε98.641.55485.8783.00250.480835.652383.2573
DS-FoggyGwcNet-√------1.54768.859--51.4640-
+Llog-√√---EPE<594.892.95539.0152.69230.555648.7136101.7025
+SEDNet-√√√11logEPE<με+3bε99.271.53987.3572.41090.702347.793297.8627
DS-RainyGwcNet-√------3.191817.356--68.0346-
+Llog-√√---EPE<598.795.353912.5014.94800.575959.3952146.8906
+SEDNet-√√√11logEPE<με+3bε99.102.216511.0202.65990.672250.8103110.8360
", + "image_path": "13539d97ef58846a8baf2d77672a3f9b49a20d73c6bf62046586c9b02ad5342c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 318, + 546, + 352 + ], + "lines": [ + { + "bbox": [ + 46, + 318, + 546, + 352 + ], + "spans": [ + { + "bbox": [ + 46, + 318, + 546, + 352 + ], + "type": "text", + "content": "Table 3. Quantitative results of synthetic to real evaluation. The top 4 subsets from VK2-S6-Moving are synthetic datasets, while the rest 4 subsets from DS-Weather are the real dataset. The best results in each category in each experiment are in bold typeface. SEDNet outperforms the baselines especially on the uncertainty estimation of the real data and under terrible weather (i.e., foggy and rainy)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 47, + 373, + 217, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 373, + 217, + 384 + ], + "spans": [ + { + "bbox": [ + 47, + 373, + 217, + 384 + ], + "type": "text", + "content": "that it matches the true distribution better." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 394, + 272, + 407 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 394, + 272, + 407 + ], + "spans": [ + { + "bbox": [ + 47, + 394, + 272, + 407 + ], + "type": "text", + "content": "4.5. Generalization from Synthetic to Real Data" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 414, + 287, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 414, + 287, + 665 + ], + "spans": [ + { + "bbox": [ + 46, + 414, + 287, + 665 + ], + "type": "text", + "content": "Stereo-matching networks are typically trained on synthetic data and fine-tuned on small amounts of data from the target domain due to the cost and difficulty of acquiring real data with ground truth depth. In this section, we extend the experiments of VK2-S6 and DS-Weather in Table 1 to compare the generalization performance on unseen real domains of all methods trained only on synthetic data. We picked four synthetic subsets from VK2-S6, specifically Morning, Sunset, Fog and Rain, that have similar illumination conditions, visibility level and weather with the four real subsets of DS-Weather [37], i.e., Cloudy, Sunny, Foggy and Rainy. VK2-S6-Morning and VK2-S6-Sunset have similar illumination to DS-Cloudy and DS-Sunny, but the latter two are more challenging due to camera underexposure and overexposure. Even though they are acquired under the same weather, DS-Foggy and DS-Rainy are more difficult than VK2-S6-Fog and VK2-S6-Rain. This can be seen by comparing Figures 3 and 4. The synthetic examples only mimic the poor lighting conditions and challenges caused by the fog and rain, but ignore the Tyndall effect and reflections caused by the fog and stagnant water." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 665, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 715 + ], + "type": "text", + "content": "As mentioned above, all models are trained only on Scenes 001, 002, 018 and 020 of VK2. Quantitative results on synthetic to real transfer are reported in Table 3, where we only report the best model of each method. The top-" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 373, + 545, + 408 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 373, + 545, + 408 + ], + "spans": [ + { + "bbox": [ + 305, + 373, + 545, + 408 + ], + "type": "text", + "content": "performing variant of SEDNet outperforms the baselines in the majority of experiments. An extended version of this table can be found in Table S.2 in the Supplement." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 433, + 378, + 445 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 433, + 378, + 445 + ], + "spans": [ + { + "bbox": [ + 306, + 433, + 378, + 445 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 457, + 545, + 673 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 457, + 545, + 673 + ], + "spans": [ + { + "bbox": [ + 304, + 457, + 545, + 673 + ], + "type": "text", + "content": "We have presented a novel approach for joint disparity and uncertainty estimation from stereo image pairs. The key idea is a unique loss function based on the KL divergence between the distributions of disparity errors and uncertainty estimates. This is made possible by a differentiable histogramming scheme that we also introduce here. To implement our approach, we extended the GwcNet architecture to include an uncertainty estimation subnetwork with only 190 parameters. Our experiments on multiple large datasets have demonstrated that our approach, named SEDNet, is effective in both disparity and uncertainty prediction. The success of our method is attributed to the novel loss function. SEDNet easily surpasses GwcNet in disparity estimation even though they have essentially the same capacity and almost identical architecture, up to the tiny uncertainty estimation subnetwork. We are optimistic that our approach will be similarly successful in other pixel-wise regression tasks, which we plan to address in future research." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 677, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 712 + ], + "type": "text", + "content": "Acknowledgment. This research has been supported in part by the National Science Foundation under award 2024653." + } + ] + } + ], + "index": 9 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17242" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 286, + 713 + ], + "type": "list", + "angle": 0, + "index": 19, + "blocks": [ + { + "bbox": [ + 53, + 91, + 286, + 124 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 286, + 124 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 286, + 124 + ], + "type": "text", + "content": "[1] Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. Weight uncertainty in neural network. In ICML, pages 1613-1622, 2015. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 125, + 286, + 147 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 125, + 286, + 147 + ], + "spans": [ + { + "bbox": [ + 53, + 125, + 286, + 147 + ], + "type": "text", + "content": "[2] Jia-Ren Chang and Yong-Sheng Chen. Pyramid stereo matching network. In CVPR, pages 5410-5418, 2018. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 148, + 286, + 179 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 148, + 286, + 179 + ], + "spans": [ + { + "bbox": [ + 53, + 148, + 286, + 179 + ], + "type": "text", + "content": "[3] Armen Der Kiureghian and Ove Ditlevsen. Aleatory or epistemic? does it matter? Structural safety, 31(2):105-112, 2009. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 182, + 286, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 182, + 286, + 237 + ], + "spans": [ + { + "bbox": [ + 53, + 182, + 286, + 237 + ], + "type": "text", + "content": "[4] Zehua Fu, Mohsen Ardabilian, and Guillaume Stern. Stereo matching confidence learning based on multi-modal convolution neural networks. In International Workshop on Representations, Analysis and Recognition of Shape and Motion From Imaging Data, pages 69-81. Springer, 2017. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 239, + 286, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 239, + 286, + 270 + ], + "spans": [ + { + "bbox": [ + 53, + 239, + 286, + 270 + ], + "type": "text", + "content": "[5] Zehua Fu and Mohsen Ardabilian Fard. Learning confidence measures by multi-modal convolutional neural networks. In WACV, pages 1321-1330, 2018. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 273, + 286, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 273, + 286, + 304 + ], + "spans": [ + { + "bbox": [ + 53, + 273, + 286, + 304 + ], + "type": "text", + "content": "[6] Adrien Gaidon, Qiao Wang, Yohann Cabon, and Eleonora Vig. Virtual worlds as proxy for multi-object tracking analysis. In CVPR, pages 4340-4349, 2016. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 306, + 286, + 327 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 306, + 286, + 327 + ], + "spans": [ + { + "bbox": [ + 53, + 306, + 286, + 327 + ], + "type": "text", + "content": "[7] Yarin Gal. Uncertainty in Deep Learning. PhD thesis, University of Cambridge, 2016. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 330, + 286, + 361 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 330, + 286, + 361 + ], + "spans": [ + { + "bbox": [ + 53, + 330, + 286, + 361 + ], + "type": "text", + "content": "[8] Yarin Gal and Zoubin Ghahramani. Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In ICML, pages 1050-1059, 2016. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 364, + 286, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 364, + 286, + 396 + ], + "spans": [ + { + "bbox": [ + 53, + 364, + 286, + 396 + ], + "type": "text", + "content": "[9] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? The KITTI vision benchmark suite. In CVPR, 2012. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 398, + 286, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 398, + 286, + 418 + ], + "spans": [ + { + "bbox": [ + 48, + 398, + 286, + 418 + ], + "type": "text", + "content": "[10] Alex Graves. Practical variational inference for neural networks. In NeurIPS, volume 24, 2011. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 421, + 286, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 421, + 286, + 453 + ], + "spans": [ + { + "bbox": [ + 48, + 421, + 286, + 453 + ], + "type": "text", + "content": "[11] Xiaoyang Guo, Kai Yang, Wukui Yang, Xiaogang Wang, and Hongsheng Li. Group-wise correlation stereo network. In CVPR, 2019. 2, 4, 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 455, + 286, + 486 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 455, + 286, + 486 + ], + "spans": [ + { + "bbox": [ + 48, + 455, + 286, + 486 + ], + "type": "text", + "content": "[12] Xiaoyan Hu and Filippos Mordohai. A quantitative evaluation of confidence measures for stereo vision. PAMI, 34(11):2121-2133, 2012. 1, 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 488, + 286, + 532 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 488, + 286, + 532 + ], + "spans": [ + { + "bbox": [ + 48, + 488, + 286, + 532 + ], + "type": "text", + "content": "[13] Eddy Ilg, Ozgun Cicek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, and Thomas Brox. Uncertainty estimates and multi-hypotheses networks for optical flow. In ECCV, pages 652-667, 2018. 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 534, + 286, + 566 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 534, + 286, + 566 + ], + "spans": [ + { + "bbox": [ + 48, + 534, + 286, + 566 + ], + "type": "text", + "content": "[14] Alex Kendall and Yarin Gal. What uncertainties do we need in Bayesian deep learning for computer vision? In NeurIPS, pages 5574-5584, 2017. 1, 2, 3, 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 48, + 568, + 286, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 568, + 286, + 600 + ], + "spans": [ + { + "bbox": [ + 48, + 568, + 286, + 600 + ], + "type": "text", + "content": "[15] Alex Kendall, Yarin Gal, and Roberto Cipolla. Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In CVPR, pages 7482-7491, 2018. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 48, + 602, + 286, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 602, + 286, + 644 + ], + "spans": [ + { + "bbox": [ + 48, + 602, + 286, + 644 + ], + "type": "text", + "content": "[16] Alex Kendall, Hayk Martirosyan, Saumitro Dasgupta, Peter Henry, Ryan Kennedy, Abraham Bachrach, and Adam Bry. End-to-end learning of geometry and context for deep stereo regression. In ICCV, pages 66-75, 2017. 2, 4" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 48, + 647, + 286, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 286, + 689 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 286, + 689 + ], + "type": "text", + "content": "[17] Sunok Kim, Seungryong Kim, Dongbo Min, Pascal Frossard, and Kwanghoon Sohn. Stereo confidence estimation via locally adaptive fusion and knowledge distillation. PAMI, 2022. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 48, + 692, + 286, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 692, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 692, + 286, + 713 + ], + "type": "text", + "content": "[18] Sunok Kim, Seungryong Kim, Dongbo Min, and Kwanghoon Sohn. LAF-Net: Locally adaptive fusion" + } + ] + } + ], + "index": 18 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 74, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 37, + "blocks": [ + { + "bbox": [ + 327, + 74, + 545, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 327, + 74, + 545, + 94 + ], + "spans": [ + { + "bbox": [ + 327, + 74, + 545, + 94 + ], + "type": "text", + "content": "networks for stereo confidence estimation. In CVPR, 2019. 2,5" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 96, + 545, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 96, + 545, + 139 + ], + "spans": [ + { + "bbox": [ + 308, + 96, + 545, + 139 + ], + "type": "text", + "content": "[19] Sunok Kim, Dongbo Min, Seungryong Kim, and Kwanghoon Sohn. Unified confidence estimation networks for robust stereo matching. TIP, 28(3):1299-1313, 2018. 1, 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 141, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 141, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 308, + 141, + 545, + 184 + ], + "type": "text", + "content": "[20] Sunok Kim, Dongbo Min, Seungryong Kim, and Kwanghoon Sohn. Adversarial confidence estimation networks for robust stereo matching. TIST, 22(11):6875-6889, 2020. 1, 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 186, + 545, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 186, + 545, + 217 + ], + "spans": [ + { + "bbox": [ + 308, + 186, + 545, + 217 + ], + "type": "text", + "content": "[21] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 220, + 545, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 220, + 545, + 251 + ], + "spans": [ + { + "bbox": [ + 308, + 220, + 545, + 251 + ], + "type": "text", + "content": "[22] S Kullback and R Leibler. On information and sufficiencyannals of mathematical statistics, 22, 79-86. MathSciNet MATH, 1951. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 254, + 545, + 296 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 254, + 545, + 296 + ], + "spans": [ + { + "bbox": [ + 308, + 254, + 545, + 296 + ], + "type": "text", + "content": "[23] Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell. Simple and scalable predictive uncertainty estimation using deep ensembles. In NeurIPS, volume 30, 2017. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 298, + 545, + 342 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 298, + 545, + 342 + ], + "spans": [ + { + "bbox": [ + 308, + 298, + 545, + 342 + ], + "type": "text", + "content": "[24] Zhengfa Liang, Yiliu Feng, Yulan Guo, Hengzhu Liu, Wei Chen, Linbo Qiao, Li Zhou, and Jianfeng Zhang. Learning for disparity estimation through feature constancy. In CVPR, pages 2811-2820, 2018. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 343, + 545, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 343, + 545, + 396 + ], + "spans": [ + { + "bbox": [ + 308, + 343, + 545, + 396 + ], + "type": "text", + "content": "[25] Nikolaus Mayer, Eddy Ig, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In CVPR, pages 4040-4048, 2016. 2, 5" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 399, + 545, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 399, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 308, + 399, + 545, + 430 + ], + "type": "text", + "content": "[26] Max Mehltreter. Joint estimation of depth and its uncertainty from stereo images using bayesian deep learning. IS-PRS, 2:69-78, 2022. 1, 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 433, + 545, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 433, + 545, + 453 + ], + "spans": [ + { + "bbox": [ + 308, + 433, + 545, + 453 + ], + "type": "text", + "content": "[27] Radford M Neal. Bayesian learning for neural networks, volume 118. Springer Science & Business Media, 2012. 2" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 456, + 545, + 487 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 456, + 545, + 487 + ], + "spans": [ + { + "bbox": [ + 308, + 456, + 545, + 487 + ], + "type": "text", + "content": "[28] David A Nix and Andreas S Weigend. Estimating the mean and variance of the target probability distribution. In ICNN, volume 1, pages 55-60. IEEE, 1994. 2" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 490, + 545, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 490, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 308, + 490, + 545, + 521 + ], + "type": "text", + "content": "[29] Matteo Poggi, Filippo Aleotti, Fabio Tosi, and Stefano Mattoccia. On the uncertainty of self-supervised monocular depth estimation. In CVPR, 2020. 3" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 308, + 523, + 545, + 576 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 523, + 545, + 576 + ], + "spans": [ + { + "bbox": [ + 308, + 523, + 545, + 576 + ], + "type": "text", + "content": "[30] Matteo Poggi, Seungryong Kim, Fabio Tosi, Sunok Kim, Filippo Aleotti, Dongbo Min, Kwanghoon Sohn, and Stefano Mattoccia. On the confidence of stereo matching in a deep-learning era: a quantitative evaluation. PAMI, 2021. 1, 2, 5" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 308, + 579, + 545, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 579, + 545, + 600 + ], + "spans": [ + { + "bbox": [ + 308, + 579, + 545, + 600 + ], + "type": "text", + "content": "[31] Matteo Poggi and Stefano Mattoccia. Learning from scratch: a confidence measure. In BMVC, 2016. 2" + } + ] + } + ], + "index": 33 + }, + { + "bbox": [ + 308, + 602, + 545, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 602, + 545, + 644 + ], + "spans": [ + { + "bbox": [ + 308, + 602, + 545, + 644 + ], + "type": "text", + "content": "[32] Matteo Poggi, Fabio Tosi, Konstantinos Batsos, Philippos Mordohai, and Stefano Mattoccia. On the synergies between machine learning and binocular stereo for depth estimation from images: a survey. PAMI, 44(9):5314-5334, 2021. 2" + } + ] + } + ], + "index": 34 + }, + { + "bbox": [ + 308, + 647, + 545, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 647, + 545, + 678 + ], + "spans": [ + { + "bbox": [ + 308, + 647, + 545, + 678 + ], + "type": "text", + "content": "[33] Akihito Seki and Marc Pollefeys. Patch based confidence prediction for dense disparity map. In BMVC, pages 23.1-23.13, 2016. 2" + } + ] + } + ], + "index": 35 + }, + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "type": "text", + "content": "[34] Amit Shaked and Lior Wolf. Improved stereo matching with constant highway networks and reflective confidence learning. In CVPR, pages 4641-4650, 2017. 1, 2" + } + ] + } + ], + "index": 36 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "17243" + } + ] + } + ], + "index": 38 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 285 + ], + "type": "list", + "angle": 0, + "index": 5, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 115 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 115 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 115 + ], + "type": "text", + "content": "[35] Fabio Tosi, Matteo Poggi, Antonio Benincasa, and Stefano Mattoccia. Beyond local reasoning for stereo confidence estimation with deep learning. In ECCV, pages 319-334, 2018. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 118, + 287, + 161 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 118, + 287, + 161 + ], + "spans": [ + { + "bbox": [ + 49, + 118, + 287, + 161 + ], + "type": "text", + "content": "[36] Guorun Yang, Xiao Song, Chaoqin Huang, Zhidong Deng, Jianping Shi, and Bolei Zhou. DrivingStereo: A large-scale dataset for stereo matching in autonomous driving scenarios. In CVPR, 2019. 1" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 163, + 287, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 163, + 287, + 206 + ], + "spans": [ + { + "bbox": [ + 49, + 163, + 287, + 206 + ], + "type": "text", + "content": "[37] Guorun Yang, Xiao Song, Chaoqin Huang, Zhidong Deng, Jianping Shi, and Bolei Zhou. Drivingstereo: A large-scale dataset for stereo matching in autonomous driving scenarios. In CVPR, 2019. 5, 8" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 208, + 287, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 208, + 287, + 251 + ], + "spans": [ + { + "bbox": [ + 49, + 208, + 287, + 251 + ], + "type": "text", + "content": "[38] Guorun Yang, Hengshuang Zhao, Jianping Shi, Zhidong Deng, and Jiaya Jia. SegStereo: Exploiting semantic information for disparity estimation. In ECCV, pages 636-651, 2018. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 253, + 287, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 253, + 287, + 285 + ], + "spans": [ + { + "bbox": [ + 49, + 253, + 287, + 285 + ], + "type": "text", + "content": "[39] Feihu Zhang, Victor Prisacariu, Ruigang Yang, and Philip HS Torr. GA-Net: Guided aggregation net for end-to-end stereo matching. In CVPR, 2019. 2" + } + ] + } + ], + "index": 4 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "17244" + } + ] + } + ], + "index": 6 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Less Is More_ Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation/d3f4ed9a-b980-4e20-ba23-ec86d6475569_content_list.json b/2023/Less Is More_ Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation/d3f4ed9a-b980-4e20-ba23-ec86d6475569_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..0f1ca7d59e8885b06ebc7e8dbfe983dd787e0ce0 --- /dev/null +++ b/2023/Less Is More_ Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation/d3f4ed9a-b980-4e20-ba23-ec86d6475569_content_list.json @@ -0,0 +1,1686 @@ +[ + { + "type": "text", + "text": "Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation", + "text_level": 1, + "bbox": [ + 220, + 99, + 750, + 143 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Li Li", + "bbox": [ + 261, + 152, + 315, + 170 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Hubert P. H. Shum1", + "bbox": [ + 354, + 152, + 513, + 170 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Toby P. Breckon1,2", + "bbox": [ + 553, + 152, + 705, + 170 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Department of $\\{\\mathrm{Computer~Science}^1\\mid$ Engineering2}, Durham University, UK", + "bbox": [ + 179, + 171, + 789, + 190 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{li.li4, hubert.shum, toby.breckon}@durham.ac.uk", + "bbox": [ + 272, + 193, + 696, + 208 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 242, + 313, + 258 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervised semantic segmentation methods with application domains such as autonomous driving. Existing work very often employs relatively large segmentation backbone networks to improve segmentation accuracy, at the expense of computational costs. In addition, many use uniform sampling to reduce ground truth data requirements for learning needed, often resulting in sub-optimal performance. To address these issues, we propose a new pipeline that employs a smaller architecture, requiring fewer ground-truth annotations to achieve superior segmentation accuracy compared to contemporary approaches. This is facilitated via a novel Sparse Depthwise Separable Convolution module that significantly reduces the network parameter count while retaining overall task performance. To effectively sub-sample our training data, we propose a new Spatio-Temporal Redundant Frame Downsampling (ST-RFD) method that leverages knowledge of sensor motion within the environment to extract a more diverse subset of training data frame samples. To leverage the use of limited annotated data samples, we further propose a soft pseudo-label method informed by LiDAR reflectivity. Our method outperforms contemporary semi-supervised work in terms of mIoU, using less labeled data, on the SemanticKITTI (59.5@5%) and ScribbleKITTI (58.1@5%) benchmark datasets, based on a $2.3 \\times$ reduction in model parameters and $641 \\times$ fewer multiply-add operations whilst also demonstrating significant performance improvement on limited training data (i.e., Less is More).", + "bbox": [ + 75, + 268, + 473, + 722 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 726, + 209, + 742 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "3D semantic segmentation of LiDAR point clouds has played a key role in scene understanding, facilitating applications such as autonomous driving [6, 23, 26, 28, 46, 60, 63] and robotics [3, 38, 51, 52]. However, many contemporary methods require relatively large backbone architectures with millions of trainable parameters requiring many hundred gigabytes of annotated data for training at a significant computational cost. Considering the time-consuming and costly nature of 3D LiDAR annotation, such methods have become less feasible for practical deployment.", + "bbox": [ + 75, + 750, + 470, + 902 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d2109a11a3170aa183da17fbeefb710494f81a48e2d15fe15b9e1eb29dc62b4e.jpg", + "image_caption": [ + "Figure 1. mIoU performance $(\\%)$ against parameters and multiply-add operations on SemanticKITTI (fully annotated) and ScribbleKITTI (weakly annotated) under the $5\\%$ sampling protocol." + ], + "image_footnote": [], + "bbox": [ + 480, + 241, + 699, + 364 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/2515e6a36168911b2f6376032e62ad48a6d0413243f0fe8a492c75264318e6e8.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 704, + 241, + 913, + 364 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Existing supervised 3D semantic segmentation methods [13, 31, 38, 44, 51, 52, 56, 58, 63] primarily focus on designing network architectures for densely annotated data. To reduce the need for large-scale data annotation, and inspired by similar work in 2D [11, 47, 49], recent 3D work proposes efficient ways to learn from weak supervision [46]. However, such methods still suffer from high training costs and inferior on-task performance. To reduce computational costs, a 2D projection-based point cloud representation is often considered [3, 14, 31, 38, 51, 52, 56, 61], but again at the expense of significantly reduced on-task performance. As such, we observe a gap in the research literature for the design of semi or weakly supervised methodologies that employ a smaller-scale architectural backbone, hence facilitating improved training efficiency whilst also reducing their associated data annotation requirements.", + "bbox": [ + 496, + 441, + 895, + 683 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we propose a semi-supervised methodology for 3D LiDAR point cloud semantic segmentation. Facilitated by three novel design aspects, our Less is More (LiM) based methodologies require less training data and less training computation whilst offering (more) improved accuracy over contemporary state-of-the-art approaches (see Fig. 1).", + "bbox": [ + 496, + 686, + 893, + 777 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Firstly, from an architectural perspective, we propose a novel Sparse Depthwise Separable Convolution (SDSC) module, which substitutes traditional sparse 3D convolution into existing 3D semantic segmentation architectures, resulting in a significant reduction in trainable parameters and numerical computation whilst maintaining on-task performance (see Fig. 1). Depthwise Separable Convolution has shown to be very effective within image classification", + "bbox": [ + 496, + 779, + 895, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "9361", + "bbox": [ + 480, + 944, + 513, + 955 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "tasks [12]. Here, we tailor a sparse variant of 3D Depthwise Separable Convolution for 3D sparse data by first applying a single submanifold sparse convolutional filter [20, 21] to each input channel with a subsequent pointwise convolution to create a linear combination of the sparse depthwise convolution outputs. This work is the first to attempt to introduce depthwise convolution into the 3D point cloud segmentation field as a conduit to reduce model size. Our SDSC module facilitates a $50\\%$ reduction in trainable network parameters without any loss in segmentation performance.", + "bbox": [ + 75, + 90, + 472, + 242 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Secondly, from a training data perspective, we propose a novel Spatio-Temporal Redundant Frame Downsampling (ST-RFD) strategy that more effectively sub-samples a set of diverse frames from a continuously captured LiDAR sequence in order to maximize diversity within a minimal training set size. We observe that continuously captured LiDAR sequences often contain significant temporal redundancy, similar to that found in video [2], whereby temporally adjacent frames provide poor data variation. On this basis, we propose to compute the temporal correlation between adjacent frame pairs, and use this to select the most informative sub-set of LiDAR frames from a given sequence. Unlike passive sampling (e.g., uniform or random sampling), our active sampling approach samples frames from each sequence such that redundancy is minimized and hence training set diversity is maximal. When compared to commonplace passive random sampling approaches [29, 32, 46], ST-RFD explicitly focuses on extracting a diverse set of training frames that will hence maximize model generalization.", + "bbox": [ + 75, + 244, + 472, + 532 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Finally, in order to employ semi-supervised learning, we propose a soft pseudo-label method informed by the LiDAR reflectivity response, thus maximizing the use of any annotated data samples. Whilst directly using unreliable soft pseudo-labels generally results in performance deterioration [5], the voxels corresponding to the unreliable predictions can instead be effectively leveraged as negative samples of unlikely categories. Therefore, we use cross-entropy to separate all voxels into two groups, i.e., a reliable and an unreliable group with low and high-entropy voxels respectively. We utilize predictions from the reliable group to derive positive pseudo-labels, while the remaining voxels from the unreliable group are pushed into a FIFO category-wise memory bank of negative samples [4]. To further assist semantic segmentation of varying materials in the situation where we have weak/unreliable/no labels, we append the reflectivity response features onto the point cloud features, which again improve segmentation results.", + "bbox": [ + 75, + 534, + 472, + 806 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "We evaluate our method on the SemanticKITTI [7] and ScribbleKITTI [46] validation set. Our method outperforms contemporary state-of-the-art semi- [29,32] and weakly- [46] supervised methods and offers more in terms of performance on limited training data, whilst using less trainable parameters and less numerical operations (Less is More).", + "bbox": [ + 75, + 809, + 472, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Overall, our contributions can be summarized as follows:", + "bbox": [ + 517, + 90, + 893, + 104 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- A novel methodology for semi-supervised 3D LiDAR semantic segmentation that uses significantly less parameters and offers (more) superior accuracy.", + "- A novel Sparse Depthwise Separable Convolution (SDSC) module, to reduce trainable network parameters, and to both reduce the likelihood of over-fitting and facilitate a deeper network architecture.", + "- A novel Spatio-Temporal Redundant Frame Downsampling (ST-RFD) strategy, to extract a maximally diverse data subset for training by removing temporal redundancy and hence future annotation requirements.", + "- A novel soft pseudo-labeling method informed by Li-DAR reflectivity as a proxy to in-scene object material properties, facilitating effective use of limited data annotation." + ], + "bbox": [ + 516, + 108, + 893, + 337 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 353, + 640, + 369 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Semi-supervised learning (SSL) LiDAR semantic segmentation is a special instance of weak supervision that combines a small amount of labeled, with a large amount of unlabeled point cloud during training. Numerous approaches have been explored for LiDAR semantic segmentation. Projection-based approaches [31, 32, 36, 38, 51, 52, 56] make full use of 2D-convolution kernels by using range or other 2D image-based spherical coordinate representations of point clouds. Conversely, voxel-based approaches [32, 44, 46, 63] transform irregular point clouds to regular 3D grids and then apply 3D convolutional neural networks with a better balance of the efficiency and effectiveness. Pseudolabeling is generally applied to alleviate the side effect of intra-class negative pairs in feature learning from the teacher network [29, 32, 46, 57]. However, such methods only utilize samples with reliable predictions and thus ignore the valuable information that unreliable predictions carry. In our work, we combined a novel SSL framework with the mean teacher paradigm [45], demonstrating the utilization of unreliable pseudo-labels to improve segmentation performance.", + "bbox": [ + 496, + 378, + 895, + 680 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Depthwise separable convolution [41] is a depthwise convolution followed by a pointwise convolution, to reduce both model size and complexity. Being a more computationally efficient alternative than standard convolution, it is used for mobile applications [24, 25, 40] and hardware accelerators [37]. Furthermore, it is a building block of Xception [12], a deep convolutional neural network architecture that achieves state-of-the-art performance on the ImageNet [15] classification task, via more efficient use of model parameterization. In this work, we propose a novel sparse variant of depthwise separable convolution, which has both the efficiency advantages of depthwise separable convolution and those of sparse convolution for processing spatially-sparse data [20].", + "bbox": [ + 496, + 681, + 895, + 878 + ], + "page_idx": 1 + }, + { + "type": "page_footnote", + "text": "Full source code: https://github.com/l1997i/lim3d/.", + "bbox": [ + 517, + 886, + 875, + 898 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "9362", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/cb7efdaf02dbc34c6ee8777881c83f17f87d3d066f52549f8da8c0c1ef7c5cb1.jpg", + "image_caption": [ + "Figure 2. Our proposed architecture for unreliable pseudo-labels LiDAR semantic segmentation involves three stages: training, pseudolabeling, and distillation with unreliable learning. We apply ST-RFD sampling before training the Mean Teacher on available annotations." + ], + "image_footnote": [], + "bbox": [ + 81, + 88, + 890, + 244 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Temporal redundancy is highly prevalent within video [48, 62] and radar [35] sequences alike. Existing semi-supervised 3D LiDAR segmentation methods [32, 46] utilize a passive uniform sampling strategy to filter unlabeled points from a fully-labeled point cloud dataset. Active learning frameworks handle the redundancy to reduce annotation or training efforts by selecting informative and diverse subscenes for label acquisition [16, 27, 53]. We propose a novel temporal-redundancy-based sampling strategy with comparable time cost to uniform sampling, to reduce the inter-frame spatio-temporal redundancy and maximize data diversity.", + "bbox": [ + 75, + 299, + 473, + 465 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Methodology", + "text_level": 1, + "bbox": [ + 76, + 470, + 212, + 488 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We first present an overview of the mean teacher framework we employ (Sec. 3.1) and then explain our use of unreliable pseudo-labels informed by LiDAR reflectivity for semi-supervised learning (Sec. 3.2). Subsequently, we detail our ST-RFD strategy for dataset diversity (Sec. 3.4) and finally our parameter-reducing SDSC module (Sec. 3.5).", + "bbox": [ + 75, + 489, + 470, + 579 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Formally, given a LiDAR point cloud $P = \\{\\mathbf{p} \\mid \\mathbf{p} = (x,y,z,I,R) \\in \\mathbb{R}^5\\}$ where $(x,y,z)$ is a 3D coordinate, $I$ is intensity and $R$ is reflectivity, our goal is to train a semantic segmentation model by leveraging both a large amount of unlabeled $U = \\{\\mathbf{p}_i^u\\}_{i=1}^{N_u} \\subsetneq P$ and a smaller set of labeled data $V = \\{(\\mathbf{p}_i^v,\\mathbf{y}_i^v)\\}_{i=1}^{N_v} \\subsetneq P$ .", + "bbox": [ + 75, + 580, + 470, + 672 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Our overall architecture involves three stages (Fig. 2): (1) Training: we utilize reflectivity-prior descriptors and adapt the Mean Teacher framework to generate high-quality pseudo-labels; (2) Pseudo-labeling: we fix the trained teacher model prediction in a class-range-balanced [46] manner, expanding dataset with Reflectivity-based Test Time Augmentation (Reflec-TTA) during test time; (3) Distillation with unreliable predictions: we train on the generated pseudo-labels, and utilize unreliable pseudo-labels in a category-wise memory bank for improved discrimination.", + "bbox": [ + 75, + 672, + 470, + 823 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Mean Teacher Framework", + "text_level": 1, + "bbox": [ + 76, + 832, + 316, + 847 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We introduce weak supervision using the Mean Teacher framework [45], which avoids the prominent slow training issues associated with Temporal Ensembling [33]. This", + "bbox": [ + 75, + 854, + 470, + 901 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "framework consists of two models of the same architecture known as the student and teacher respectively, for which we utilize a Cylinder3D [63]-based segmentation head $f$ . The weights of the student model $\\theta$ are updated via standard backpropagation, while the weights of the teacher model $\\theta^{*}$ are updated by the student model through Exponential Moving Averaging (EMA):", + "bbox": [ + 496, + 299, + 893, + 405 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\theta_ {t + 1} ^ {*} = \\kappa \\theta_ {t} ^ {*} + (1 - \\kappa) \\theta_ {t + 1}, \\quad t \\in \\{0, 1, \\dots T - 1 \\}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 513, + 417, + 890, + 435 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\kappa$ denotes a smoothing coefficient to determine update speed, and $T$ is the maximum time step.", + "bbox": [ + 496, + 446, + 890, + 477 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "During training, we train a set of weakly-labeled point cloud frames with voxel-wise inputs generated via asymmetrical 3D convolution networks [63]. For every point cloud, our optimization target is to minimize the overall loss:", + "bbox": [ + 496, + 477, + 893, + 537 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\mathcal {L} _ {S} + \\lambda_ {U} \\mathcal {L} _ {U} + g \\lambda_ {C} \\mathcal {L} _ {C}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 550, + 890, + 566 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_S$ and $\\mathcal{L}_U$ denote the losses applied to the supervised and unsupervised set of points respectively, $\\mathcal{L}_C$ denotes the contrastive loss to make full use of unreliable pseudo-labels, $\\lambda_U$ is the weight coefficient of $\\mathcal{L}_U$ to balance the losses, and $g$ is the gated coefficient of $\\mathcal{L}_C$ . $g$ equals $\\lambda_C$ if and only if it is in the distillation stage. We use the consistency loss (implemented as a Kullback-Leibler divergence loss [23]), lovasz softmax loss [8], and the voxel-level InfoNCE [47] as $\\mathcal{L}_U$ , $\\mathcal{L}_S$ and $\\mathcal{L}_C$ respectively.", + "bbox": [ + 496, + 579, + 890, + 715 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We first generate our pseudo-labels for the unlabeled points via the teacher model. Subsequently, we generate reliable pseudo-labels in a class-range-balanced (CRB) [46] manner, and utilize the qualified unreliable pseudo-labels as negative samples in the distillation stage. Finally, we train the model with both reliable and qualified unreliable pseudo-labels to maximize the quality of the pseudo-labels.", + "bbox": [ + 496, + 715, + 890, + 821 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Learning from Unreliable Pseudo-Labels", + "text_level": 1, + "bbox": [ + 500, + 830, + 849, + 847 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Unreliable pseudo-labels are frequently eliminated from semi-supervised learning tasks or have their weights decreased to minimize performance loss [29, 39, 46, 55, 59, 65].", + "bbox": [ + 496, + 854, + 893, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "9363", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/efefc56c3c12de329886a22fd1f5a5a4c9474351589c7d194babad413fe2484c.jpg", + "image_caption": [ + "Figure 3. Illustration on unreliable pseudo-labels. Left: entropy predicted from an unlabeled point cloud, with lower entropy corresponding to greener color. Right: Category-wise probability of an unreliable prediction $\\mathsf{X}$ , only top-4 and last-4 probabilities shown." + ], + "image_footnote": [], + "bbox": [ + 78, + 89, + 468, + 161 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In line with this idea, we utilize CRB method [46] to first mask off unreliable pseudo-labels and then subsequently generate high-quality reliable pseudo-labels.", + "bbox": [ + 75, + 250, + 468, + 295 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "However, such a simplistic discarding of unreliable pseudo-labels may lead to valuable information loss as it is clear that unreliable pseudo-labels (i.e., the corresponding voxels with high entropy) can offer information that helps in discrimination. Voxels that correlate to unreliable predictions can alternatively be thought as negative samples for improbable categories [49], although performance would suffer if such unreliable predictions are used as pseudo-labels directly [5]. As shown in Fig. 3, the unreliable pseudo predictions show a similar level of confidence on car and truck classes, whilst being sure the voxel cannot be pole or road. Thus, together with the use of CRB for high-quality reliable pseudo-labels, we also ideally want to make full use of these remaining unreliable pseudo-labels rather than simply discarding them. Following [49], we propose a method to leverage such unreliable pseudo-labels for 3D voxels as negative samples. However, to maintain a stable amount of negative samples, we utilize a category-wise memory bank $\\mathcal{Q}_c$ (FIFO queue, [54]) to store all the negative samples for a given class $c$ . As negative candidates in some specific categories are severely limited in a mini-batch due to the long-tailed class distribution of many tasks (e.g. autonomous driving), without such an approach in place we may instead see the gradual dominance of large and simple-to-learn classes within our generated pseudo-labels.", + "bbox": [ + 75, + 296, + 470, + 672 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Following [22, 47], our method has three prerequisites, i.e., anchor voxels, positive candidates, and negative candidates. They are obtained by sampling from a particular subset, constructed via Eq. (3) and Eq. (4), in order to reduce overall computation. In particular, the set of features of all candidate anchor voxels for class $c$ is denoted as:", + "bbox": [ + 75, + 675, + 468, + 765 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {A} _ {c} = \\left\\{\\mathbf {E} _ {a, b} \\mid y _ {a, b} ^ {*} = c, p _ {a, b} (c) > \\delta_ {p} \\right\\}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 142, + 779, + 468, + 797 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathbf{E}_{a,b}$ is the feature embedding for the $a$ -th point cloud frame at voxel $b$ , $\\delta_p$ is the positive threshold of all classes, $p_{a,b}(c)$ is the softmax probability by the segmentation head at $c$ -th dimension. $y_{a,b}^*$ is set to the ground truth label $y_{a,b}^*$ if the ground truth is available, otherwise, $y_{a,b}^*$ is set to the pseudo label $\\hat{y}_{a,b}$ , due to the absence of ground truth.", + "bbox": [ + 75, + 809, + 468, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The positive sample is the common embedding center of all possible anchors, which is the same for all anchors from the same category, shown in Eq. (4).", + "bbox": [ + 498, + 90, + 892, + 137 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {E} _ {\\mathbf {c}} ^ {+} = \\frac {1}{| \\mathcal {A} _ {c} |} \\sum_ {\\mathbf {E} _ {c} \\in \\mathcal {A} _ {c}} \\mathbf {E} _ {c}. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 620, + 143, + 890, + 180 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Following [49], we similarly construct multiple negative samples $\\mathbf{E}_c^-$ for each anchor voxel.", + "bbox": [ + 498, + 186, + 890, + 217 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, for each anchor voxel containing one positive sample and $N - 1$ negative samples, we propose the voxel-level InfoNCE loss [47] (a variant of contrastive loss) $\\mathcal{L}_C$ in Eq. (5) to encourage maximal similarity between the anchor voxel and the positive sample, and the minimal similarity between the anchor voxel and multiple negative samples.", + "bbox": [ + 498, + 218, + 893, + 308 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\mathcal {L} _ {C} = - \\frac {1}{C} \\sum_ {c = 0} ^ {C - 1} \\underset {\\mathbf {E} _ {c}} {\\mathbb {E}} \\left[ \\log \\frac {f \\left(\\mathbf {e} _ {c} , \\mathbf {e} _ {c} ^ {+} , \\tau\\right)}{\\sum_ {\\mathbf {e} _ {c , j} ^ {-} \\in \\mathbf {E} _ {c} ^ {-}} f \\left(\\mathbf {e} _ {c} , \\mathbf {e} _ {c , j} ^ {-} , \\tau\\right)} \\right] \\\\ = - \\frac {1}{C} \\sum_ {c = 0} ^ {C - 1} \\mathbb {E} _ {\\mathbf {E} _ {c}} \\left[ \\log \\frac {\\exp \\left(\\left\\langle \\mathbf {e} _ {c} , \\mathbf {e} _ {c} ^ {+} \\right\\rangle / \\tau\\right)}{\\exp \\left(\\left\\langle \\mathbf {e} _ {c} , \\mathbf {e} _ {c} ^ {+} \\right\\rangle / \\tau\\right) + \\sum_ {j = 1} ^ {N - 1} \\exp \\left(\\left\\langle \\mathbf {e} _ {c} , \\mathbf {e} _ {c , j} ^ {-} \\right\\rangle / \\tau\\right)} \\right] \\tag {5} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 500, + 323, + 919, + 444 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\langle \\cdot, \\cdot \\rangle$ denotes cosine similarity. $\\mathbf{e_c}$ , $\\mathbf{e_c}^+$ and $\\mathbf{e}_{c,j}^-$ denote the embedding, positive sample of the current anchor voxel, and embedding of the $j$ -th negative sample of class $c$ .", + "bbox": [ + 498, + 448, + 893, + 494 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Reflectivity-Based Test Time Augmentation", + "text_level": 1, + "bbox": [ + 500, + 502, + 867, + 518 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To obtain minimal accuracy degradation despite very few weak labels, e.g., $1\\%$ weakly-labeled ScribbleKITTI [46] dataset, we propose a Test Time Augmentation (TTA) that does not depend on any label, but only relies on a feature of original LiDAR points themselves. Also included in almost every LiDAR benchmark dataset for autonomous driving [7, 10, 18, 30, 34, 46], is the intensity of light reflected from the surface of an object at each point. In the presence of limited data labels in the semi-supervised learning case, this property of the material surface, normalized by distance to obtain the surface reflectivity in Eq. (6), could readily act as auxiliary information to identify different semantic classes.", + "bbox": [ + 496, + 525, + 893, + 705 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our intuition is that reflectivity $R$ , as a point-wise distance-normalized intensity feature, offers consistency across lighting conditions and range as:", + "bbox": [ + 498, + 707, + 890, + 752 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nR = I r ^ {2} = \\frac {S}{4 \\pi r ^ {2}} \\cdot r ^ {2} \\propto S, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 602, + 758, + 890, + 789 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $S$ is the return strength of the LiDAR laser pulse, $I$ is the intensity and $r$ is the point distance from the source on the basis that scene objects with similar surface material, coating, and color characteristics will share similar $S$ returns.", + "bbox": [ + 496, + 794, + 893, + 854 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "On this basis, we define our novel reflectivity-based Test Time Augmentation (Reflec-TTA) technique, as a substitute for label-dependent Pyramid Local Semantic-context", + "bbox": [ + 498, + 854, + 893, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "9364", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/5693020f7cb1b08a21d6fa317de7d90291ddeeb78587668d9f881ce5e674b64e.jpg", + "image_caption": [ + "Figure 4. Coarse histograms of Reflec-TTA bins (not to scale)." + ], + "image_footnote": [], + "bbox": [ + 89, + 87, + 459, + 176 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "(PLS) augmentation [46] during test-time as ground truth is not available. We append our point-wise reflectivity to the existing point features in order to enhance performance in presence of false or non-existent pseudo-labels at the distillation stage. As shown in Fig. 4, and following [46], we apply various sizes $s$ of bins in cylindrical coordinates to analyze the intrinsic point distribution of the LiDAR sensor at varying resolutions (shown in red, green and blue in Fig. 4). For each bin $b_{i}$ , we compute a coarse histogram, $\\mathbf{h}_{i}$ :", + "bbox": [ + 75, + 222, + 472, + 358 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {h} _ {i} = \\left\\{h _ {i} ^ {(k)} \\mid k \\in [ 1, N _ {b} ] \\right\\} \\in \\mathbb {R} ^ {N _ {b}}, \\quad i \\in [ 1, s ],\n$$\n", + "text_format": "latex", + "bbox": [ + 96, + 369, + 426, + 395 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nh _ {i} ^ {(k)} = \\# \\left\\{R _ {j} \\in r _ {k}, \\forall j \\mid p _ {j} \\in b _ {i} \\right\\}, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 101, + 396, + 468, + 417 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nr _ {k} = \\left[ (k - 1) / N _ {b}, k / N _ {b}\\right), \\quad k \\in \\left[ 1, N _ {b} \\right].\n$$\n", + "text_format": "latex", + "bbox": [ + 101, + 419, + 410, + 435 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The Reflec-TTA features $R^{\\text{心}}$ of all points $p_j \\in b_j$ is further computed as the concatenation of the coarse histogram $\\mathbf{h}_i$ of the normalized histogram:", + "bbox": [ + 76, + 448, + 468, + 494 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nR ^ {\\circledast} = \\left\\{\\mathbf {h} _ {i} / \\operatorname * {m a x} \\left(\\mathbf {h} _ {i}\\right) \\mid i \\in [ 1, s ] \\right\\} \\in \\mathbb {R} ^ {s N _ {b}} \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 132, + 505, + 468, + 523 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In the distillation stage, we append $R^{\\text{※}}$ to the input features and redefine the input LiDAR point cloud as the augmented set of points $P^{\\text{※}} = \\{p \\mid (x,y,z,I,R^{\\text{※}}) \\in \\mathbb{R}^{sN_b + 4}\\}$ .", + "bbox": [ + 76, + 534, + 468, + 582 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.4. Spatio-Temporal Redundant Frame Downsampling", + "text_level": 1, + "bbox": [ + 76, + 590, + 468, + 606 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Due to the spatio-temporal correlation of LiDAR point cloud sequences often captured from vehicles in metropolitan locales, many large-scale point cloud datasets demonstrate significant redundancy. Common datasets employ a frame rate of $10\\mathrm{Hz}$ [7, 9, 10, 18, 30, 34, 42], and a number of concurrent laser channels (beams) of 32 [10], 64 [7, 9, 18, 30, 42] or 128 [34]. Faced with such large-scale, massively redundant training datasets, the popular practice of semi-supervised semantic segmentation approaches [11, 17, 32, 45, 64] is to uniformly sample $1\\%$ , $10\\%$ , $20\\%$ , or $50\\%$ of the available annotated training frames, without considering any redundancy attributable to temporary periods of stationary capture (e.g. due to traffic, Fig. 5) or multi-pass repetition (e.g. due to loop closure).", + "bbox": [ + 75, + 613, + 472, + 824 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To extract a diverse set of frames, we propose a novel algorithm called Spatio-Temporal Redundant Frame Downsampling (ST-RFD, Algorithm 1) that determines spatiotemporal redundancy by analyzing the spatial-overlap within time-continuous LiDAR frame sequences. The key idea", + "bbox": [ + 75, + 825, + 472, + 901 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/315a18dbfe42f30f71739715c59c75fba9e83e6d3d026081c0c59b608b9eec6e.jpg", + "image_caption": [ + "Figure 5. Illustration of LiDAR frame temporal correlation as [#frame ID] redundancy with $5\\%$ sampling on SemanticKITTI [7] (sequence 00) using uniform sampling (selected frames in $\\bullet$ ) and ST-RFD strategy ( $\\bullet$ )." + ], + "image_footnote": [], + "bbox": [ + 483, + 87, + 911, + 181 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "is that if spatial-overlap among some continuous frame sequence is high due to spatio-temporal redundancy, multiple representative frames can be sub-sampled for training, significantly reducing both training dataset size and redundant training computation.", + "bbox": [ + 496, + 261, + 893, + 335 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Fig. 6 shows an overview of ST-RFD. It is conducted inside each temporal continuous LiDAR sequence $e$ . First, we evenly divide $p$ point cloud frames in each sequence into $\\lceil p / q \\rceil$ subsets (containing $q$ frames). For each frame at time $t$ inside the subset, we find its corresponding RGB camera image in the dataset at time $t$ and $t + 1$ . To detect the spatio-temporal redundancy at time $t$ , the similarity $\\psi(t, t + 1)$ between temporally adjacent frames are then computed via the Structural Similarity Index Measure (SSIM, [50]). We utilize the mean value of similarity scores between all adjacent frames in the current subset as a proxy to estimate the spatio-temporal redundancy present. A sampling rate is then determined according to this mean similarity for frame selection within this subset. This is repeated for all subsets in every sequence to construct our final set of sub-sampled LiDAR frames for training.", + "bbox": [ + 496, + 337, + 893, + 578 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Concretely, as shown in Algorithm 1, we implement a ST-RFD supervisor that determines the most informative assignments (i.e., the key point cloud frames) that the teacher and student networks should train on respectively. The ST-RFD supervisor has an empirical supervisor function $v$ , which decides the amount of assignments, i.e., the sampling rate corresponding to the extent of spatio-temporal redundancy. Using SSIM [50] as the redundancy function $\\psi$ to measure the similarity between the RGB images associated with two adjacent point clouds, we define the empirical supervisor function $v$ with decay property $v(x) = \\exp(-\\beta x)$ , where $\\beta \\in (0, +\\infty)$ is the decay coefficient, and $x$ is the redundancy calculated from $\\psi$ . In this way, the higher the degree of spatio-temporal redundancy (as $\\psi \\to 1$ ), the lower the sampling rate our ST-RFD supervisor will allocate, hence reducing the training set requirements for teacher and student alike.", + "bbox": [ + 496, + 579, + 895, + 835 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "3.5. Sparse Depthwise Separable Convolution", + "text_level": 1, + "bbox": [ + 500, + 845, + 852, + 863 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Existing LiDAR point cloud semantic segmentation methods generally rely on a large-scale backbone architecture with", + "bbox": [ + 496, + 869, + 893, + 901 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "9365", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/4f10a0f9039838e59c95ab71ed37c7acfdbab630322376b1c9bbe79f4bb16629.jpg", + "image_caption": [ + "Figure 6. Overview of our proposed Spatio-Temporal Redundant Frame Downsampling approach." + ], + "image_footnote": [], + "bbox": [ + 83, + 87, + 883, + 164 + ], + "page_idx": 5 + }, + { + "type": "code", + "sub_type": "algorithm", + "code_caption": [ + "Algorithm 1: Spatio-Temporal Redundant Frame Downsampling." + ], + "code_body": "Input: Point cloud frames pool $P$ (size of $p$ ), subset size $q$ , redundancy function $\\psi \\in [0,1]$ and empirical supervisor function $v$ . \n1 Divide $P$ evenly into $\\lceil p / q\\rceil$ subsets $Q$ . \n2 $D \\leftarrow$ empty dictionary. \n3 for all $e \\leftarrow 0 : n_e - 1$ do \n4 // loop for all sequences \n5 for all $i \\leftarrow 0 : \\lceil p / q\\rceil - 1$ do \n6 // loop for subsets $Q$ \n7 $\\overline{M_i} \\leftarrow \\frac{1}{q} \\sum_{j=0}^{q-1} \\psi(Q_{i,j})$ . // redundancy \n8 $k_i \\leftarrow \\lceil v(\\overline{M_i}) \\cdot q \\rceil$ . \n9 $T_i \\leftarrow$ select $k_i$ frames in $Q_i$ with the smallest $M_i$ . \n10 $C_e \\leftarrow C_e \\cup T_i$ . \n11 Append key-value pair $(e, C_e)$ into $D$ . \nReturn: Dictionary $D$ .", + "bbox": [ + 81, + 241, + 470, + 465 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/1ce3f4649f7d55d53051888e4356abfba19f66195eacf23ff80906f8f455fbe2.jpg", + "image_caption": [ + "Figure 7. Illustration of the SDSC convolution module." + ], + "image_footnote": [], + "bbox": [ + 60, + 479, + 468, + 580 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "tens of millions of trainable parameters [23,26,28,46,60,63] due to the requirement for 3D (voxel-based) convolution operations, to operate on the voxelized topology of the otherwise unstructured LiDAR point cloud representation, which suffer from both high computational training demands and the risk of overfitting. Based on the observation that depthwise separable convolution has shown results comparable with regular convolution in tasks such as image classification but with significantly fewer trainable parameters [12,24,25,37,40,43], here we pursue the use of such an approach within 3D point cloud semantic segmentation.", + "bbox": [ + 75, + 612, + 468, + 779 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "As such we propose the first formulation of sparse variant depthwise separable convolution [25] applied to 3D point clouds, namely Sparse Depthwise Separable Convolution (SDSC). SDSC combines the established computational advantages of sparse convolution for point cloud segmentation [20], with the significant trainable parameter reduction offered by depthwise separable convolution [12].", + "bbox": [ + 75, + 779, + 470, + 885 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Our SDSC module, as outlined in Fig. 7, initially takes a", + "bbox": [ + 96, + 885, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "tensor $F \\in \\mathbb{R}^{H_F \\times W_F \\times L_F \\times M}$ as input, where $H_F$ , $W_F$ , $L_F$ and $M$ denote radius, azimuth, height in the cylinder coordinate [63] and channels respectively. Firstly, a sparse depthwise convolution $\\mathrm{SDC}(M, M, D_k, s = 1)$ is applied, with $M$ input and output feature planes, a kernel size of $D_k$ and stride $s$ in order to output a tensor $T \\in \\mathbb{R}^{H_F \\times W_F \\times L_F \\times M}$ . Inside our sparse depthwise convolution, $M$ sparse spatial convolutions are performed independently over each input channel using submanifold sparse convolution [20] due to its tensor shape preserving property at no computational or memory overhead. Secondly, the sparse pointwise convolution $\\mathrm{SPC}(M, N, 1, s = 1)$ projects the channels output $T$ by the sparse depthwise convolution onto a new channel space, to mix the information across different channels. As a result, the sparse depthwise separable convolution $\\mathrm{SDSC}(M, N, D_k, s = 1)$ is the compound of the sparse depthwise convolution and the sparse pointwise convolution, namely $\\mathrm{SDSC}(M, N, D_k, s = 1) = \\mathrm{SDC} \\circ \\mathrm{SPC}$ .", + "bbox": [ + 496, + 205, + 893, + 478 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Using a sparse voxelized input representation similar to [19], and a series of such SDSC sub-modules we construct the popular Cylinder3D [63] sub-architectures within our overall Mean Teacher architectural design (Fig. 2).", + "bbox": [ + 496, + 479, + 893, + 539 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4. Evaluation", + "text_level": 1, + "bbox": [ + 498, + 541, + 617, + 556 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We evaluate our proposed Less is More 3D (LiM3D) approach against state-of-the-art 3D point cloud semantic segmentation approaches using the SemanticKITTI [7] and ScribbleKITTI [46] benchmark datasets.", + "bbox": [ + 496, + 566, + 893, + 627 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.1. Experimental Setup", + "text_level": 1, + "bbox": [ + 498, + 635, + 689, + 652 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "SemanticKITTI [7] is a large-scale 3D point cloud dataset for semantic scene understanding with 20 semantic classes consisting of 22 sequences - [00 to 10 as training-split (of which 08 as validation-split) + 11 to 21 as test-split].", + "bbox": [ + 496, + 657, + 893, + 719 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "ScribbleKITTI [46] is the first scribble (i.e. sparsely) annotated dataset for LiDAR semantic segmentation providing sparse annotations for the training split of SemanticKITTI for 19 classes, with only $8.06\\%$ of points from the full SemanticKITTI dataset annotated.", + "bbox": [ + 496, + 719, + 893, + 794 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation Protocol: Following previous work [29,32,46, 63], we report performance on the SemanticKITTI and ScribbleKITTI training set for intermediate training steps, as this metric provides an indication of the pseudo-labeling quality, and on the validation set to assess the performance benefits of each individual component. Performance is reported using the mean Intersection over Union (mIoU, as $\\%$ ) metric.", + "bbox": [ + 496, + 795, + 893, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "9366", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/ee1d7490d9ce41eff9a03171306169b4ec72b41f251c5cb53a7c8e8326b1fc69.jpg", + "table_caption": [ + "Table 1. Comparative mIoU for Range- and Voxel-based methods using uniform sampling (U), sequential partition (P) and ST-RFD sampling (S): bold/underlined = best/2nd best; * denotes reproduced result; - denotes missing result due to unavailability from original authors." + ], + "table_footnote": [], + "table_body": "
Repr.Samp.MethodSemanticKITTI [7]ScribbleKITTI [46]
1%5%10%20%40%50%100%1%5%10%20%40%50%100%
RangeULaserMix [32](2022)43.4-58.859.4-61.4-38.3-54.455.6-58.7-
VoxelUCylinder3D [63](CVPR'21)-45.456.157.858.7-67.8-39.248.052.153.8-56.3
ULaserMix [32](2022)50.6-60.061.9-62.3-44.2-53.755.1-56.8-
PJiang et al. [29](ICCV'21)-41.849.958.859.9-65.8-------
UUnal et al. [46](CVPR'22)-49.9*58.7*59.1*60.9-68.2*-46.9*54.2*56.5*58.6*-61.3
SLiM3D+SDSC(ours)57.257.661.061.762.162.767.555.856.156.957.258.959.360.7
SLiM3D(ours)58.459.562.263.163.363.669.557.058.161.061.262.062.162.4
", + "bbox": [ + 80, + 119, + 893, + 247 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "For semi-supervised training, we report over both the benchmarks using the SemanticKITTI and ScribbleKITTI validation set under $5\\%$ , $10\\%$ , $20\\%$ , and $40\\%$ partitioning. We further report the relative performance of semi-supervised or scribble-supervised for ScribbleKITTI (SS) training to the fully supervised upper-bound (FS) in percentages (SS/FS) to further analyze semi-supervised performance and report the results for the fully-supervised training on both validation sets for reference. The trainable parameter count and number of multiply-adds (multi-adds) are additionally provided as a metric of computational cost.", + "bbox": [ + 75, + 273, + 470, + 438 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Implementation Details: Training is performed using $4 \\times$ NVIDIA A100 80GB GPU without pre-trained weights with a DDP shared training strategy [1] to maintain GPU scaling efficiency, whilst reducing memory overhead significantly. Specific hyper-parameters are set as follows - Mean Teacher: $\\kappa = 0.99$ ; unreliable pseudo-labeling: $\\lambda_{C} = 0.3$ , $\\tau = 0.5$ ; ST-RFD: $\\beta = \\{7.45, 5.72, 4.00, 2.28, 0\\}$ for sampling $\\{5\\%, 10\\%, 20\\%, 40\\%, 100\\}$ labeled training frames, assuming the remainder as unlabeled; Reflec-TTA: $N_{b} = 10$ , $s = 3$ various Reflec-TTA bin sizes, following [46], we set each bin $b_{i} = (\\rho, \\phi) \\in \\{(20, 40), (40, 80), (80, 120)\\}$ .", + "bbox": [ + 75, + 439, + 473, + 608 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d82192639287f5dbf30e7eb547e5c82753135d975c1a8a673bb720a2053b5d26.jpg", + "image_caption": [ + "Figure 8. Comparing the $10\\%$ sampling split of SemanticKITTI (SeK, first row) and ScribbleKITTI (ScK, second row) validation set with ground-truth (left), our approach (middle) and Unal et al. [46] (right) with areas of improvement highlighted." + ], + "image_footnote": [], + "bbox": [ + 76, + 619, + 472, + 767 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.2. Experimental Results", + "text_level": 1, + "bbox": [ + 76, + 838, + 279, + 854 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In Tab. 1, we present the performance of our Less is More 3D (LiM3D) point cloud semantic segmentation approach both with (LiM3D+SDSC) and without (LiM3D) SDSC in a side", + "bbox": [ + 75, + 854, + 472, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/dd1e94a3f42bf89e37b2d8ba28bade4def0db2e7f797d69da69c7376691b0dba.jpg", + "table_caption": [ + "Table 2. Component-wise ablation of LiM3D (mIoU as %, and #parameters in millions, M) on SemanticKITTI [7] training and validation sets where UP, RF, RT, ST, SD denote Unreliable Pseudolabeling, Reflectivity Feature, Reflec-TTA, ST-RFD, and SDSC module respectively." + ], + "table_footnote": [], + "table_body": "
UPRFRTSTSDTraining mIoU (%)Validation mIoU (%)#Params(M)
5%10%20%40%5%10%20%40%
82.887.587.888.254.858.159.360.849.6
✓----55.958.859.961.249.6
✓✓83.688.388.789.156.859.660.561.449.6
✓✓----57.559.861.262.649.6
✓✓✓----58.761.362.462.849.6
✓✓✓✓85.289.189.589.759.562.263.163.349.6
✓✓✓✓✓83.888.689.089.257.661.061.762.121.5
", + "bbox": [ + 503, + 342, + 893, + 464 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "by-side comparison with leading contemporary state-of-the-art approaches on the SemanticKITTI and ScribbleKITTI benchmark validation sets to illustrate our approach offers superior or comparable (within $1\\%$ mIoU) performance across all sampling ratios. Furthermore, we present supporting qualitative results in Fig. 8.", + "bbox": [ + 496, + 479, + 893, + 570 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "On SemanticKITTI, with a lack of available supervision, LiM3D shows a relative performance (SS/FS) from $85.6\\%$ (5%-fully-supervised) to $91.1\\%$ (40%-fully-supervised), and LiM3D+SDSC from $85.3\\%$ to $92.0\\%$ , compared to their respective fully supervised upper-bound. LiM3D/LiM3D+SDSC performance is also less sensitive to reduced labeled data sampling compared with other methods.", + "bbox": [ + 496, + 570, + 893, + 676 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Our model significantly outperforms on small ratio sampling splits, e.g., $5\\%$ and $10\\%$ . LiM3D shows up to $19.8\\%$ and $18.9\\%$ mIoU improvements whilst, with a smaller model size $\\mathrm{LiM3D + SDSC}$ again shows significant mIoU improvements by up to $16.4\\%$ and $15.5\\%$ when compared with other range and voxel-based methods respectively.", + "bbox": [ + 496, + 676, + 893, + 768 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.3. Ablation Studies", + "text_level": 1, + "bbox": [ + 500, + 773, + 663, + 789 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Effectiveness of Components. In Tab. 2 we ablate each component of LiM3D step by step and report the performance on the SemanticKITTI training set at the end of training as an overall indicator of pseudo-labeling quality in addition to the corresponding validation set.", + "bbox": [ + 496, + 792, + 893, + 869 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "As shown in Tab. 2, adding unreliable pseudo-labeling (UP) in the distillation stage, we can increase the valid mIoU", + "bbox": [ + 500, + 869, + 893, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "9367", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "by $+0.7\\%$ on average in validation set. Appending reflectivity features (RF) in the training stage, we further improve the mIoU on the training set by $+0.7\\%$ on average. Due to the improvements in training, the model generates a higher quality of pseudo-labels, which results to a $+0.5\\%$ increase in mIoU in the validation set. If we disable reflectivity features in the training stage, applying Reflec-TTA in the distillation stage alone, we then get an average improvement of $+1.3\\%$ compared with pseudo-labeling only. On the whole, enabling all reflectivity-based components (RF+RT) shows great improvements of up to $+2.8\\%$ in validation mIoU.", + "bbox": [ + 73, + 90, + 473, + 258 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/5c5eedf91d572b68d9cb72af62561d7931a6a61e64a24c02ceadada704a9297f.jpg", + "table_caption": [ + "Table 3. The computation cost and mIoU (in percentage) under $5\\%$ -labeled training results on SemanticKITTI (SeK) and ScribbleKITTI (ScK) validation set." + ], + "table_footnote": [], + "table_body": "
Method# Parameters# Mult-AddsSeK [7]ScK [46]
Cylinder3D [63]56.3476.9M45.439.2
Unal et al. [46]49.6420.2M49.946.9
2DPASS [58]26.5217.4M51.745.1
MinkowskiNet [13]21.7114.0G42.435.8
SPVNAS [44]12.573.8G45.138.9
LiM3D+SDSC (ours)21.5182.0M57.654.7
LiM3D (ours)49.6420.2M59.558.1
", + "bbox": [ + 78, + 310, + 470, + 412 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/a97af9feb49c63b45e85e37b7675a7755137b0c4614745e964a74763b04891fb.jpg", + "table_caption": [ + "Table 4. Effects of ST-RFD sampling on SemanticKITTI and ScribbleKITTI validation set (mIoU as %)." + ], + "table_footnote": [], + "table_body": "
SamplingSemanticKITTI [7]ScribbleKITTI [46]
5%10%20%40%5%10%20%40%
Random58.561.662.662.757.160.360.560.9
Uniform58.761.362.462.856.960.660.361.0
ST-RFD-R59.162.462.963.458.060.761.261.8
ST-RFD59.562.263.163.358.161.061.262.0
", + "bbox": [ + 78, + 455, + 473, + 534 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/28e7fac67e342a410269ada242abe6a934646eee82d04cc9f608a1137ec0960f.jpg", + "table_caption": [ + "Table 5. Effects of differing reliability using pseudo voxels on SemanticKITTI validation set, measured by the entropy of voxel-wise prediction. Unreliable and Reliable: selecting negative candidates with top $20\\%$ highest entropy scores and bottom $20\\%$ counterpart respectively. Random: sampling randomly regardless of entropy." + ], + "table_footnote": [], + "table_body": "
RatioUnreliableReliableRandom
mIoUSS/FFmIoUSS/FFmIoUSS/FF
5%59.585.657.282.356.481.2
10%62.289.560.887.559.785.9
20%63.190.861.488.360.587.1
40%63.391.162.890.461.388.2
", + "bbox": [ + 102, + 621, + 436, + 700 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/ca4de889a18c8f043a76596694f89937657028c6f75b9db717e0a04a8861477e.jpg", + "table_caption": [ + "Table 6. Reflectivity (Reflec-TTA) vs. Intensity (intensity-based TTA) on SemanticKITTI and ScribbleKITTI validation set (mIoU, %)." + ], + "table_footnote": [], + "table_body": "
TTASemanticKITTI [7]ScribbleKITTI [46]
5%10%20%40%5%10%20%40%
Intensity56.259.159.860.955.757.557.959.2
Reflectivity59.562.263.163.358.161.061.262.0
", + "bbox": [ + 78, + 744, + 478, + 801 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Substituting the uniform sampling with our ST-RFD strategy, we observe further average improvements of $+1.0\\%$ and $+0.8\\%$ on training and validation respectively (Tab. 2).", + "bbox": [ + 73, + 809, + 470, + 854 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Our SDSC module reduces the trainable parameters of our model by $57\\%$ , with a performance cost of $-0.7\\%$ and $-1.4\\%$ mIoU on training and validation respectively", + "bbox": [ + 75, + 854, + 470, + 900 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "(Tab. 2). Finally, we provide two models, one without SDSC (LiM3D) and one with (LiM3D+SDSC), corresponding to the bottom two rows of Tab. 2.", + "bbox": [ + 496, + 90, + 890, + 135 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effectiveness of SDSC module. In Tab. 3, we compare our LiM3D and LiM3D+SDSC with recent state-of-the-art methods under $5\\%$ -labeled semi-supervised training on the SemanticKITTI and ScribbleKITTI validation sets. LiM3D+SDSC outperforms the voxel-based methods [46, 63] with at least a $2.3\\times$ reduction in model size. Similarly, with comparable model size [13, 44, 58], LiM3D+SDSC has higher mIoU in both datasets and up to $641\\times$ fewer multiply-add operations.", + "bbox": [ + 496, + 136, + 893, + 271 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effectiveness of ST-RFD strategy. In Tab. 4, we illustrate the effectiveness of our ST-RFD strategy by comparing LiM3D with two widely-used strategies in semi-supervised training, i.e., random sampling and uniform sampling on SemanticKITTI [7] and ScribbleKITTI [46] validation set. Whilst uniform and random sampling have comparable results on both validation sets, simply applying our ST-RFD strategy improves the baseline by $+0.90\\%$ , $+0.75\\%$ , $+0.60\\%$ and $+0.55\\%$ on SemanticKITTI under $5\\%$ , $10\\%$ , $20\\%$ and $40\\%$ sampling protocol respectively. Furthermore, using corresponding range images of point cloud, rather than RGB images to compute the spatio-temporal redundancy within ST-RFD (see ST-RFD-R in Tab. 4), has no significant difference on the performance.", + "bbox": [ + 496, + 272, + 893, + 482 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effectiveness of Unreliable Pseudo-Labeling. In Tab. 5, we evaluate selecting negative candidates with different reliability to illustrate the improvements of using unreliable pseudo-labels in semi-supervised semantic segmentation. The \"Unreliable\" selecting of negative candidates outperforms other alternative methodologies, showing the positive performance impact of unreliable pseudo-labels.", + "bbox": [ + 496, + 483, + 893, + 589 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Effectiveness of Reflec-TTA. In Tab. 2, we compare LiM3D performance with and without Reflec-TTA and further experiment on the SemanticKITTI and ScribbleKITTI validation set in Tab. 6. This demonstrates that the LiDAR point-wise intensity feature $I^*$ , in place of the distance-normalized reflectivity feature $R^*$ , offers inferior on-task performance.", + "bbox": [ + 496, + 589, + 893, + 695 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 700, + 619, + 715 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This paper presents an efficient semi-supervised architecture for 3D point cloud semantic segmentation, which achieves more in terms of performance with less computational costs, less annotations, and less trainable model parameters (i.e., Less is More, LiM3D). Our architecture consists of three novel contributions: the SDSC convolution module, the ST-RFD sampling strategy, and the pseudo-labeling method informed by LiDAR reflectivity. These individual components can be applied to any 3D semantic segmentation architecture to reduce the gap between semi or weakly-supervised and fully-supervised learning on task performance, whilst managing model complexity and computation costs.", + "bbox": [ + 496, + 719, + 893, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "9368", + "bbox": [ + 480, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] FairScale. FairScale: A general purpose modular PyTorch library for high performance and large scale training, 2021. 7", + "[2] Shahriar Akramullah. Digital Video Concepts, Methods, and Metrics: Quality, Compression, Performance, and Power Trade-off Analysis. Apress, Berkeley, CA, 2014. 2", + "[3] Inigo Alonso, Luis Riazuelo, Luis Montesano, and Ana C. Murillo. 3D-MiniNet: Learning a 2D Representation From Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation. IEEE Robot. Autom. Lett., 5(4):5432-5439, Oct. 2020. 1", + "[4] Inigo Alonso, Alberto Sabater, David Ferstl, Luis Montesano, and Ana C. Murillo. Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank. In Int. Conf. Comput. Vis., pages 8199-8208, 2021. 2", + "[5] Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, and Kevin McGuinness. Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning. In Int. Jt. Conf. Neural Netw., 2020. 2, 4", + "[6] Amir Atapour-Abarghouei and Toby P. Breckon. Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3368–3379, 2019. 1", + "[7] Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyril Stachniss, and Jurgen Gall. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. In Int. Conf. Comput. Vis., pages 9296-9306, Seoul, Korea (South), Oct. 2019. 2, 4, 5, 6, 7, 8", + "[8] Maxim Berman, Amal Rannen Triki, and Matthew B. Blaschko. The Lovasz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 3", + "[9] Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, and Felix Heide. Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather. In IEEE Conf. Comput. Vis. Pattern Recog., pages 11679-11689, 2020. 5", + "[10] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. Nuscenes: A multimodal dataset for autonomous driving. IEEE Conf. Comput. Vis. Pattern Recog., pages 11618-11628, 2020. 4, 5", + "[11] Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang. Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2613-2622, June 2021. 1, 5", + "[12] François Chollet. Xception: Deep Learning with Depthwise Separable Convolutions. In IEEE Conf. Comput. Vis. Pattern Recog., Apr. 2017. 2, 6", + "[13] Christopher Choy, JunYoung Gwak, and Silvio Savarese. 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3070-3079, 2019. 1, 8" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] Tiago Cortinhal, George Tzelepis, and Eren Erdal Aksoy. SalsaNext: Fast, Uncertainty-Aware Semantic Segmentation of LiDAR Point Clouds. In Adv. Vis. Comput., pages 207-222, 2020. 1", + "[15] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In IEEE Conf. Comput. Vis. Pattern Recog., pages 248–255, 2009. 2", + "[16] Ngoc Phuong Anh Duong, Alexandre Almin, Léo Lemarie, and B Ravi Kiran. Reducing redundancy in Semantic-KITTI: Study on data augmentations within Active Learning. In Adv. Neural Inform. Process. Syst. Worksh., page 8, 2017. 3", + "[17] Geoff French. Semi-supervised semantic segmentation needs strong, varied perturbations. In Brit. Mach. Vis. Conf., page 21, 2020. 5", + "[18] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3354-3361, 2012. 4, 5", + "[19] Ben Graham. Sparse 3D convolutional neural networks. In Brit. Mach. Vis. Conf., pages 150.1-150.9, Swansea, 2015. 6", + "[20] Benjamin Graham, Martin Engelcke, and Laurens van der Maaten. 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 2, 6", + "[21] Benjamin Graham and Laurens van der Maaten. Submanifold Sparse Convolutional Networks, June 2017. 2", + "[22] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum Contrast for Unsupervised Visual Representation Learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9726-9735, 2020. 4", + "[23] Yuenan Hou, Xinge Zhu, Yuexin Ma, Chen Change Loy, and Yikang Li. Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., page 10, 2022. 1, 3, 6", + "[24] Andrew Howard, Mark Sandler, Bo Chen, Weijun Wang, Liang-Chieh Chen, Mingxing Tan, Grace Chu, Vijay Vasudevan, Yukun Zhu, Ruoming Pang, Hartwig Adam, and Quoc Le. Searching for MobileNetV3. In Int. Conf. Comput. Vis., pages 1314-1324, 2019. 2, 6", + "[25] Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andretto, and Hartwig Adam. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, Apr. 2017. 2, 6", + "[26] Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham. RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. In IEEE Conf. Comput. Vis. Pattern Recog., 2020. 1, 6", + "[27] Zeyu Hu, Xuyang Bai, Runze Zhang, Xin Wang, Guangyuan Sun, Hongbo Fu, and Chiew-Lan Tai. LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation. In Eur. Conf. Comput. Vis. Springer. 3", + "[28] Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Emilie Wirbel, and Patrick Pérez. xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation. IEEE Conf. Comput. Vis. Pattern Recog., 2021. 1, 6" + ], + "bbox": [ + 501, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "9369", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[29] Li Jiang, Shaoshuai Shi, Zhuotao Tian, Xin Lai, Shu Liu, ChiWing Fu, and Jiaya Jia. Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation. In Int. Conf. Comput. Vis., 2021. 2, 3, 6, 7", + "[30] R. Kesten, M. Usman, J. Houston, T. Pandya, K. Nadhamuni, A. Ferreira, M. Yuan, B. Low, A. Jain, P. Ondruska, S. Omari, S. Shah, A. Kulkarni, A. Kazakova, C. Tao, L. Platinsky, W. Jiang, and V. Shet. Lyft level 5 perception dataset 2020, 2019. 4, 5", + "[31] Deyvid Kochanov, Fatemeh Karimi Nejadasl, and Olaf Booij. KPRNet: Improving projection-based LiDAR semantic segmentation, Aug. 2020. 1, 2", + "[32] Lingdong Kong, Jiawei Ren, Liang Pan, and Ziwei Liu. LaserMix for Semi-Supervised LiDAR Semantic Segmentation, June 2022. 2, 3, 5, 6, 7", + "[33] Samuli Laine and Timo Aila. Temporal Ensembling for Semi-Supervised Learning. In Int. Conf. Learn. Represent., 2017. 3", + "[34] Li Li, Khalid N. Ismail, Hubert P. H. Shum, and Toby P. Breckon. DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications. In Int. Conf. 3D Vis., pages 1227-1237, London, United Kingdom, Dec. 2021. 4, 5", + "[35] Peizhao Li, Pu Wang, Karl Berntorp, and Hongfu Liu. Exploiting Temporal Relations on Radar Perception for Autonomous Driving. In IEEE Conf. Comput. Vis. Pattern Recog., pages 17071-17080, 2022. 3", + "[36] Venice Erin Liong, Thi Ngoc Tho Nguyen, Sergi Widjaja, Dhananjai Sharma, and Zhuang Jie Chong. AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation, Dec. 2020. 2", + "[37] Dominic Masters, Antoine Labatie, Zach Eaton-Rosen, and Carlo Luschi. Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution Training, Aug. 2021. 2, 6", + "[38] Andres Milioto, Ignacio Vizzo, Jens Behley, and Cyril Stachniss. RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation. In Int. Conf. Intell. Robots Syst., pages 4213-4220, 2019. 1, 2", + "[39] Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen. Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning. In Adv. Neural Inform. Process. Syst., volume 29, 2016. 3", + "[40] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zh-moginov, and Liang-Chieh Chen. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4510-4520, Salt Lake City, UT, 2018. IEEE. 2, 6", + "[41] Laurent Sifre. Rigid-Motion Scattering For Image Classification. PhD thesis, Ecole Polytechnique, 2014. 2", + "[42] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, and Dragomir Anguelov." + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Scalability in Perception for Autonomous Driving: Waymo Open Dataset. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2443-2451, Seattle, WA, USA, June 2020. 5", + "[43] Mingxing Tan and Quoc Le. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In Proc. 36th Int. Conf. Mach. Learn., pages 6105-6114. PMLR, May 2019. 6", + "[44] Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, and Song Han. Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution. In Eur. Conf. Comput. Vis., 2020. 1, 2, 8", + "[45] Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In Adv. Neural Inform. Process. Syst., 2017. 2, 3, 5", + "[46] Ozan Unal, Dengxin Dai, and Luc Van Gool. Scribblesupervised LiDAR semantic segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., 2022. 1, 2, 3, 4, 5, 6, 7, 8", + "[47] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation Learning with Contrastive Predictive Coding, Jan. 2019. 1, 3, 4", + "[48] Jue Wang, Gedas Bertasius, Du Tran, and Lorenzo Torresani. Long-Short Temporal Contrastive Learning of Video Transformers. In IEEE Conf. Comput. Vis. Pattern Recog., pages 13990–14000, 2022. 3", + "[49] Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, and Xinyi Le. Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels. IEEE Conf. Comput. Vis. Pattern Recog., Mar. 2022. 1, 4", + "[50] Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600-612, Apr. 2004. 5", + "[51] Bichen Wu, Alvin Wan, Xiangyu Yue, and Kurt Keutzer. SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud. In Int. Conf. Robot. Autom., pages 1887–1893, 2018. 1, 2", + "[52] Bichen Wu, Xuanyu Zhou, Sicheng Zhao, Xiangyu Yue, and Kurt Keutzer. SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud. In Int. Conf. Robot. Autom., pages 4376–4382, 2019. 1, 2", + "[53] Tsung-Han Wu, Yueh-Cheng Liu, Yu-Kai Huang, Hsin-Ying Lee, Hung-Ting Su, Ping-Chia Huang, and Winston H. Hsu. ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation. In Int. Conf. Comput. Vis. 3", + "[54] Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin. Unsupervised Feature Learning via Non-parametric Instance Discrimination. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3733-3742, 2018. 4", + "[55] Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V. Le. Self-Training With Noisy Student Improves ImageNet Classification. In IEEE Conf. Comput. Vis. Pattern Recog., pages 10687–10698, 2020. 3", + "[56] Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Va-jda, Kurt Keutzer, and Masayoshi Tomizuka. SqueezeSegV3:" + ], + "bbox": [ + 501, + 90, + 893, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "9370", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Eur. Conf. Comput. Vis., 2020. 1, 2", + "[57] Xu Yan, Jiantao Gao, Jie Li, Ruimao Zhang, Zhen Li, Rui Huang, and Shuguang Cui. Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion. In Conf. AAAI Artif. Intell., 2021. 2", + "[58] Xu Yan, Jiantao Gao, Chaoda Zheng, Chao Zheng, Ruimao Zhang, Shuguang Cui, and Zhen Li. 2DPASS: 2D priors assisted semantic segmentation on LiDAR point clouds. In Eur. Conf. Comput. Vis., 2022. 1, 8", + "[59] Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, and Yang Gao. ST++: Make Self-Training Work Better for Semi-Supervised Semantic Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4268–4277, 2022. 3", + "[60] Li Yi, Boqing Gong, and Thomas Funkhouser. Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds. In IEEE Conf. Comput. Vis. Pattern Recog., pages 15358-15368, Nashville, TN, USA, June 2021. 1, 6", + "[61] Yang Zhang, Zixiang Zhou, Philip David, Xiangyu Yue, Zerong Xi, Boqing Gong, and Hassan Foroosh. PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., 2020. 1", + "[62] Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, and Yichen Wei. Deep Feature Flow for Video Recognition. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4141-4150, Honolulu, HI, 2017. 3", + "[63] Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin. Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., 2021. 1, 2, 3, 6, 7, 8", + "[64] Yang Zou, Zhiding Yu, B. V. K. Vijaya Kumar, and Jinsong Wang. Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training. In Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and Yair Weiss, editors, Eur. Conf. Comput. Vis., volume 11207, pages 297-313, 2018. 5", + "[65] Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, and Tomas Pfister. PseudoSeg: Designing Pseudo Labels for Semantic Segmentation. In Int. Conf. Learn. Represent., 2021. 3" + ], + "bbox": [ + 78, + 90, + 468, + 739 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "9371", + "bbox": [ + 482, + 945, + 513, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Less Is More_ Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation/d3f4ed9a-b980-4e20-ba23-ec86d6475569_model.json b/2023/Less Is More_ Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation/d3f4ed9a-b980-4e20-ba23-ec86d6475569_model.json new file mode 100644 index 0000000000000000000000000000000000000000..c93d4a27bb4f6dfc3a74f108768802f33156754e --- /dev/null +++ b/2023/Less Is More_ Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation/d3f4ed9a-b980-4e20-ba23-ec86d6475569_model.json @@ -0,0 +1,2477 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.221, + 0.1, + 0.75, + 0.145 + ], + "angle": 0, + "content": "Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation" + }, + { + "type": "text", + "bbox": [ + 0.262, + 0.153, + 0.316, + 0.171 + ], + "angle": 0, + "content": "Li Li" + }, + { + "type": "text", + "bbox": [ + 0.356, + 0.154, + 0.514, + 0.171 + ], + "angle": 0, + "content": "Hubert P. H. Shum1" + }, + { + "type": "text", + "bbox": [ + 0.554, + 0.154, + 0.706, + 0.171 + ], + "angle": 0, + "content": "Toby P. Breckon1,2" + }, + { + "type": "text", + "bbox": [ + 0.18, + 0.172, + 0.79, + 0.191 + ], + "angle": 0, + "content": "Department of \\(\\{\\mathrm{Computer~Science}^1\\mid\\) Engineering2}, Durham University, UK" + }, + { + "type": "text", + "bbox": [ + 0.273, + 0.194, + 0.697, + 0.209 + ], + "angle": 0, + "content": "{li.li4, hubert.shum, toby.breckon}@durham.ac.uk" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.243, + 0.314, + 0.26 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.269, + 0.474, + 0.723 + ], + "angle": 0, + "content": "Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervised semantic segmentation methods with application domains such as autonomous driving. Existing work very often employs relatively large segmentation backbone networks to improve segmentation accuracy, at the expense of computational costs. In addition, many use uniform sampling to reduce ground truth data requirements for learning needed, often resulting in sub-optimal performance. To address these issues, we propose a new pipeline that employs a smaller architecture, requiring fewer ground-truth annotations to achieve superior segmentation accuracy compared to contemporary approaches. This is facilitated via a novel Sparse Depthwise Separable Convolution module that significantly reduces the network parameter count while retaining overall task performance. To effectively sub-sample our training data, we propose a new Spatio-Temporal Redundant Frame Downsampling (ST-RFD) method that leverages knowledge of sensor motion within the environment to extract a more diverse subset of training data frame samples. To leverage the use of limited annotated data samples, we further propose a soft pseudo-label method informed by LiDAR reflectivity. Our method outperforms contemporary semi-supervised work in terms of mIoU, using less labeled data, on the SemanticKITTI (59.5@5%) and ScribbleKITTI (58.1@5%) benchmark datasets, based on a \\(2.3 \\times\\) reduction in model parameters and \\(641 \\times\\) fewer multiply-add operations whilst also demonstrating significant performance improvement on limited training data (i.e., Less is More)." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.727, + 0.21, + 0.743 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.472, + 0.903 + ], + "angle": 0, + "content": "3D semantic segmentation of LiDAR point clouds has played a key role in scene understanding, facilitating applications such as autonomous driving [6, 23, 26, 28, 46, 60, 63] and robotics [3, 38, 51, 52]. However, many contemporary methods require relatively large backbone architectures with millions of trainable parameters requiring many hundred gigabytes of annotated data for training at a significant computational cost. Considering the time-consuming and costly nature of 3D LiDAR annotation, such methods have become less feasible for practical deployment." + }, + { + "type": "image", + "bbox": [ + 0.481, + 0.242, + 0.7, + 0.366 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.705, + 0.242, + 0.915, + 0.366 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.375, + 0.896, + 0.418 + ], + "angle": 0, + "content": "Figure 1. mIoU performance \\((\\%)\\) against parameters and multiply-add operations on SemanticKITTI (fully annotated) and ScribbleKITTI (weakly annotated) under the \\(5\\%\\) sampling protocol." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.442, + 0.896, + 0.684 + ], + "angle": 0, + "content": "Existing supervised 3D semantic segmentation methods [13, 31, 38, 44, 51, 52, 56, 58, 63] primarily focus on designing network architectures for densely annotated data. To reduce the need for large-scale data annotation, and inspired by similar work in 2D [11, 47, 49], recent 3D work proposes efficient ways to learn from weak supervision [46]. However, such methods still suffer from high training costs and inferior on-task performance. To reduce computational costs, a 2D projection-based point cloud representation is often considered [3, 14, 31, 38, 51, 52, 56, 61], but again at the expense of significantly reduced on-task performance. As such, we observe a gap in the research literature for the design of semi or weakly supervised methodologies that employ a smaller-scale architectural backbone, hence facilitating improved training efficiency whilst also reducing their associated data annotation requirements." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.687, + 0.895, + 0.779 + ], + "angle": 0, + "content": "In this paper, we propose a semi-supervised methodology for 3D LiDAR point cloud semantic segmentation. Facilitated by three novel design aspects, our Less is More (LiM) based methodologies require less training data and less training computation whilst offering (more) improved accuracy over contemporary state-of-the-art approaches (see Fig. 1)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.896, + 0.902 + ], + "angle": 0, + "content": "Firstly, from an architectural perspective, we propose a novel Sparse Depthwise Separable Convolution (SDSC) module, which substitutes traditional sparse 3D convolution into existing 3D semantic segmentation architectures, resulting in a significant reduction in trainable parameters and numerical computation whilst maintaining on-task performance (see Fig. 1). Depthwise Separable Convolution has shown to be very effective within image classification" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "9361" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.243 + ], + "angle": 0, + "content": "tasks [12]. Here, we tailor a sparse variant of 3D Depthwise Separable Convolution for 3D sparse data by first applying a single submanifold sparse convolutional filter [20, 21] to each input channel with a subsequent pointwise convolution to create a linear combination of the sparse depthwise convolution outputs. This work is the first to attempt to introduce depthwise convolution into the 3D point cloud segmentation field as a conduit to reduce model size. Our SDSC module facilitates a \\(50\\%\\) reduction in trainable network parameters without any loss in segmentation performance." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.246, + 0.473, + 0.533 + ], + "angle": 0, + "content": "Secondly, from a training data perspective, we propose a novel Spatio-Temporal Redundant Frame Downsampling (ST-RFD) strategy that more effectively sub-samples a set of diverse frames from a continuously captured LiDAR sequence in order to maximize diversity within a minimal training set size. We observe that continuously captured LiDAR sequences often contain significant temporal redundancy, similar to that found in video [2], whereby temporally adjacent frames provide poor data variation. On this basis, we propose to compute the temporal correlation between adjacent frame pairs, and use this to select the most informative sub-set of LiDAR frames from a given sequence. Unlike passive sampling (e.g., uniform or random sampling), our active sampling approach samples frames from each sequence such that redundancy is minimized and hence training set diversity is maximal. When compared to commonplace passive random sampling approaches [29, 32, 46], ST-RFD explicitly focuses on extracting a diverse set of training frames that will hence maximize model generalization." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.535, + 0.473, + 0.807 + ], + "angle": 0, + "content": "Finally, in order to employ semi-supervised learning, we propose a soft pseudo-label method informed by the LiDAR reflectivity response, thus maximizing the use of any annotated data samples. Whilst directly using unreliable soft pseudo-labels generally results in performance deterioration [5], the voxels corresponding to the unreliable predictions can instead be effectively leveraged as negative samples of unlikely categories. Therefore, we use cross-entropy to separate all voxels into two groups, i.e., a reliable and an unreliable group with low and high-entropy voxels respectively. We utilize predictions from the reliable group to derive positive pseudo-labels, while the remaining voxels from the unreliable group are pushed into a FIFO category-wise memory bank of negative samples [4]. To further assist semantic segmentation of varying materials in the situation where we have weak/unreliable/no labels, we append the reflectivity response features onto the point cloud features, which again improve segmentation results." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.473, + 0.902 + ], + "angle": 0, + "content": "We evaluate our method on the SemanticKITTI [7] and ScribbleKITTI [46] validation set. Our method outperforms contemporary state-of-the-art semi- [29,32] and weakly- [46] supervised methods and offers more in terms of performance on limited training data, whilst using less trainable parameters and less numerical operations (Less is More)." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.092, + 0.894, + 0.106 + ], + "angle": 0, + "content": "Overall, our contributions can be summarized as follows:" + }, + { + "type": "text", + "bbox": [ + 0.517, + 0.109, + 0.894, + 0.154 + ], + "angle": 0, + "content": "- A novel methodology for semi-supervised 3D LiDAR semantic segmentation that uses significantly less parameters and offers (more) superior accuracy." + }, + { + "type": "text", + "bbox": [ + 0.517, + 0.156, + 0.894, + 0.216 + ], + "angle": 0, + "content": "- A novel Sparse Depthwise Separable Convolution (SDSC) module, to reduce trainable network parameters, and to both reduce the likelihood of over-fitting and facilitate a deeper network architecture." + }, + { + "type": "text", + "bbox": [ + 0.517, + 0.218, + 0.894, + 0.277 + ], + "angle": 0, + "content": "- A novel Spatio-Temporal Redundant Frame Downsampling (ST-RFD) strategy, to extract a maximally diverse data subset for training by removing temporal redundancy and hence future annotation requirements." + }, + { + "type": "text", + "bbox": [ + 0.517, + 0.279, + 0.894, + 0.338 + ], + "angle": 0, + "content": "- A novel soft pseudo-labeling method informed by Li-DAR reflectivity as a proxy to in-scene object material properties, facilitating effective use of limited data annotation." + }, + { + "type": "list", + "bbox": [ + 0.517, + 0.109, + 0.894, + 0.338 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.354, + 0.642, + 0.37 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.379, + 0.896, + 0.681 + ], + "angle": 0, + "content": "Semi-supervised learning (SSL) LiDAR semantic segmentation is a special instance of weak supervision that combines a small amount of labeled, with a large amount of unlabeled point cloud during training. Numerous approaches have been explored for LiDAR semantic segmentation. Projection-based approaches [31, 32, 36, 38, 51, 52, 56] make full use of 2D-convolution kernels by using range or other 2D image-based spherical coordinate representations of point clouds. Conversely, voxel-based approaches [32, 44, 46, 63] transform irregular point clouds to regular 3D grids and then apply 3D convolutional neural networks with a better balance of the efficiency and effectiveness. Pseudolabeling is generally applied to alleviate the side effect of intra-class negative pairs in feature learning from the teacher network [29, 32, 46, 57]. However, such methods only utilize samples with reliable predictions and thus ignore the valuable information that unreliable predictions carry. In our work, we combined a novel SSL framework with the mean teacher paradigm [45], demonstrating the utilization of unreliable pseudo-labels to improve segmentation performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.682, + 0.897, + 0.879 + ], + "angle": 0, + "content": "Depthwise separable convolution [41] is a depthwise convolution followed by a pointwise convolution, to reduce both model size and complexity. Being a more computationally efficient alternative than standard convolution, it is used for mobile applications [24, 25, 40] and hardware accelerators [37]. Furthermore, it is a building block of Xception [12], a deep convolutional neural network architecture that achieves state-of-the-art performance on the ImageNet [15] classification task, via more efficient use of model parameterization. In this work, we propose a novel sparse variant of depthwise separable convolution, which has both the efficiency advantages of depthwise separable convolution and those of sparse convolution for processing spatially-sparse data [20]." + }, + { + "type": "page_footnote", + "bbox": [ + 0.518, + 0.887, + 0.876, + 0.9 + ], + "angle": 0, + "content": "Full source code: https://github.com/l1997i/lim3d/." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9362" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.089, + 0.891, + 0.245 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.255, + 0.894, + 0.285 + ], + "angle": 0, + "content": "Figure 2. Our proposed architecture for unreliable pseudo-labels LiDAR semantic segmentation involves three stages: training, pseudolabeling, and distillation with unreliable learning. We apply ST-RFD sampling before training the Mean Teacher on available annotations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.3, + 0.474, + 0.466 + ], + "angle": 0, + "content": "Temporal redundancy is highly prevalent within video [48, 62] and radar [35] sequences alike. Existing semi-supervised 3D LiDAR segmentation methods [32, 46] utilize a passive uniform sampling strategy to filter unlabeled points from a fully-labeled point cloud dataset. Active learning frameworks handle the redundancy to reduce annotation or training efforts by selecting informative and diverse subscenes for label acquisition [16, 27, 53]. We propose a novel temporal-redundancy-based sampling strategy with comparable time cost to uniform sampling, to reduce the inter-frame spatio-temporal redundancy and maximize data diversity." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.472, + 0.213, + 0.489 + ], + "angle": 0, + "content": "3. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.49, + 0.471, + 0.58 + ], + "angle": 0, + "content": "We first present an overview of the mean teacher framework we employ (Sec. 3.1) and then explain our use of unreliable pseudo-labels informed by LiDAR reflectivity for semi-supervised learning (Sec. 3.2). Subsequently, we detail our ST-RFD strategy for dataset diversity (Sec. 3.4) and finally our parameter-reducing SDSC module (Sec. 3.5)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.581, + 0.471, + 0.673 + ], + "angle": 0, + "content": "Formally, given a LiDAR point cloud \\( P = \\{\\mathbf{p} \\mid \\mathbf{p} = (x,y,z,I,R) \\in \\mathbb{R}^5\\} \\) where \\( (x,y,z) \\) is a 3D coordinate, \\( I \\) is intensity and \\( R \\) is reflectivity, our goal is to train a semantic segmentation model by leveraging both a large amount of unlabeled \\( U = \\{\\mathbf{p}_i^u\\}_{i=1}^{N_u} \\subsetneq P \\) and a smaller set of labeled data \\( V = \\{(\\mathbf{p}_i^v,\\mathbf{y}_i^v)\\}_{i=1}^{N_v} \\subsetneq P \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.673, + 0.472, + 0.824 + ], + "angle": 0, + "content": "Our overall architecture involves three stages (Fig. 2): (1) Training: we utilize reflectivity-prior descriptors and adapt the Mean Teacher framework to generate high-quality pseudo-labels; (2) Pseudo-labeling: we fix the trained teacher model prediction in a class-range-balanced [46] manner, expanding dataset with Reflectivity-based Test Time Augmentation (Reflec-TTA) during test time; (3) Distillation with unreliable predictions: we train on the generated pseudo-labels, and utilize unreliable pseudo-labels in a category-wise memory bank for improved discrimination." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.833, + 0.318, + 0.848 + ], + "angle": 0, + "content": "3.1. Mean Teacher Framework" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.471, + 0.902 + ], + "angle": 0, + "content": "We introduce weak supervision using the Mean Teacher framework [45], which avoids the prominent slow training issues associated with Temporal Ensembling [33]. This" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.3, + 0.895, + 0.406 + ], + "angle": 0, + "content": "framework consists of two models of the same architecture known as the student and teacher respectively, for which we utilize a Cylinder3D [63]-based segmentation head \\( f \\). The weights of the student model \\( \\theta \\) are updated via standard backpropagation, while the weights of the teacher model \\( \\theta^{*} \\) are updated by the student model through Exponential Moving Averaging (EMA):" + }, + { + "type": "equation", + "bbox": [ + 0.514, + 0.418, + 0.892, + 0.436 + ], + "angle": 0, + "content": "\\[\n\\theta_ {t + 1} ^ {*} = \\kappa \\theta_ {t} ^ {*} + (1 - \\kappa) \\theta_ {t + 1}, \\quad t \\in \\{0, 1, \\dots T - 1 \\}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.447, + 0.892, + 0.478 + ], + "angle": 0, + "content": "where \\(\\kappa\\) denotes a smoothing coefficient to determine update speed, and \\(T\\) is the maximum time step." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.478, + 0.894, + 0.538 + ], + "angle": 0, + "content": "During training, we train a set of weakly-labeled point cloud frames with voxel-wise inputs generated via asymmetrical 3D convolution networks [63]. For every point cloud, our optimization target is to minimize the overall loss:" + }, + { + "type": "equation", + "bbox": [ + 0.598, + 0.551, + 0.892, + 0.567 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\mathcal {L} _ {S} + \\lambda_ {U} \\mathcal {L} _ {U} + g \\lambda_ {C} \\mathcal {L} _ {C}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.58, + 0.892, + 0.716 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_S\\) and \\(\\mathcal{L}_U\\) denote the losses applied to the supervised and unsupervised set of points respectively, \\(\\mathcal{L}_C\\) denotes the contrastive loss to make full use of unreliable pseudo-labels, \\(\\lambda_U\\) is the weight coefficient of \\(\\mathcal{L}_U\\) to balance the losses, and \\(g\\) is the gated coefficient of \\(\\mathcal{L}_C\\). \\(g\\) equals \\(\\lambda_C\\) if and only if it is in the distillation stage. We use the consistency loss (implemented as a Kullback-Leibler divergence loss [23]), lovasz softmax loss [8], and the voxel-level InfoNCE [47] as \\(\\mathcal{L}_U\\), \\(\\mathcal{L}_S\\) and \\(\\mathcal{L}_C\\) respectively." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.716, + 0.892, + 0.822 + ], + "angle": 0, + "content": "We first generate our pseudo-labels for the unlabeled points via the teacher model. Subsequently, we generate reliable pseudo-labels in a class-range-balanced (CRB) [46] manner, and utilize the qualified unreliable pseudo-labels as negative samples in the distillation stage. Finally, we train the model with both reliable and qualified unreliable pseudo-labels to maximize the quality of the pseudo-labels." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.832, + 0.851, + 0.848 + ], + "angle": 0, + "content": "3.2. Learning from Unreliable Pseudo-Labels" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Unreliable pseudo-labels are frequently eliminated from semi-supervised learning tasks or have their weights decreased to minimize performance loss [29, 39, 46, 55, 59, 65]." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9363" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.08, + 0.09, + 0.47, + 0.162 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.174, + 0.471, + 0.233 + ], + "angle": 0, + "content": "Figure 3. Illustration on unreliable pseudo-labels. Left: entropy predicted from an unlabeled point cloud, with lower entropy corresponding to greener color. Right: Category-wise probability of an unreliable prediction \\(\\mathsf{X}\\), only top-4 and last-4 probabilities shown." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.25, + 0.47, + 0.296 + ], + "angle": 0, + "content": "In line with this idea, we utilize CRB method [46] to first mask off unreliable pseudo-labels and then subsequently generate high-quality reliable pseudo-labels." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.297, + 0.471, + 0.674 + ], + "angle": 0, + "content": "However, such a simplistic discarding of unreliable pseudo-labels may lead to valuable information loss as it is clear that unreliable pseudo-labels (i.e., the corresponding voxels with high entropy) can offer information that helps in discrimination. Voxels that correlate to unreliable predictions can alternatively be thought as negative samples for improbable categories [49], although performance would suffer if such unreliable predictions are used as pseudo-labels directly [5]. As shown in Fig. 3, the unreliable pseudo predictions show a similar level of confidence on car and truck classes, whilst being sure the voxel cannot be pole or road. Thus, together with the use of CRB for high-quality reliable pseudo-labels, we also ideally want to make full use of these remaining unreliable pseudo-labels rather than simply discarding them. Following [49], we propose a method to leverage such unreliable pseudo-labels for 3D voxels as negative samples. However, to maintain a stable amount of negative samples, we utilize a category-wise memory bank \\( \\mathcal{Q}_c \\) (FIFO queue, [54]) to store all the negative samples for a given class \\( c \\). As negative candidates in some specific categories are severely limited in a mini-batch due to the long-tailed class distribution of many tasks (e.g. autonomous driving), without such an approach in place we may instead see the gradual dominance of large and simple-to-learn classes within our generated pseudo-labels." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.676, + 0.47, + 0.766 + ], + "angle": 0, + "content": "Following [22, 47], our method has three prerequisites, i.e., anchor voxels, positive candidates, and negative candidates. They are obtained by sampling from a particular subset, constructed via Eq. (3) and Eq. (4), in order to reduce overall computation. In particular, the set of features of all candidate anchor voxels for class \\( c \\) is denoted as:" + }, + { + "type": "equation", + "bbox": [ + 0.143, + 0.78, + 0.469, + 0.798 + ], + "angle": 0, + "content": "\\[\n\\mathcal {A} _ {c} = \\left\\{\\mathbf {E} _ {a, b} \\mid y _ {a, b} ^ {*} = c, p _ {a, b} (c) > \\delta_ {p} \\right\\}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.47, + 0.902 + ], + "angle": 0, + "content": "where \\(\\mathbf{E}_{a,b}\\) is the feature embedding for the \\(a\\)-th point cloud frame at voxel \\(b\\), \\(\\delta_p\\) is the positive threshold of all classes, \\(p_{a,b}(c)\\) is the softmax probability by the segmentation head at \\(c\\)-th dimension. \\(y_{a,b}^*\\) is set to the ground truth label \\(y_{a,b}^*\\) if the ground truth is available, otherwise, \\(y_{a,b}^*\\) is set to the pseudo label \\(\\hat{y}_{a,b}\\), due to the absence of ground truth." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.893, + 0.138 + ], + "angle": 0, + "content": "The positive sample is the common embedding center of all possible anchors, which is the same for all anchors from the same category, shown in Eq. (4)." + }, + { + "type": "equation", + "bbox": [ + 0.621, + 0.144, + 0.892, + 0.181 + ], + "angle": 0, + "content": "\\[\n\\mathbf {E} _ {\\mathbf {c}} ^ {+} = \\frac {1}{| \\mathcal {A} _ {c} |} \\sum_ {\\mathbf {E} _ {c} \\in \\mathcal {A} _ {c}} \\mathbf {E} _ {c}. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.188, + 0.892, + 0.218 + ], + "angle": 0, + "content": "Following [49], we similarly construct multiple negative samples \\(\\mathbf{E}_c^-\\) for each anchor voxel." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.219, + 0.895, + 0.309 + ], + "angle": 0, + "content": "Finally, for each anchor voxel containing one positive sample and \\(N - 1\\) negative samples, we propose the voxel-level InfoNCE loss [47] (a variant of contrastive loss) \\(\\mathcal{L}_C\\) in Eq. (5) to encourage maximal similarity between the anchor voxel and the positive sample, and the minimal similarity between the anchor voxel and multiple negative samples." + }, + { + "type": "equation", + "bbox": [ + 0.5, + 0.324, + 0.921, + 0.445 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\mathcal {L} _ {C} = - \\frac {1}{C} \\sum_ {c = 0} ^ {C - 1} \\underset {\\mathbf {E} _ {c}} {\\mathbb {E}} \\left[ \\log \\frac {f \\left(\\mathbf {e} _ {c} , \\mathbf {e} _ {c} ^ {+} , \\tau\\right)}{\\sum_ {\\mathbf {e} _ {c , j} ^ {-} \\in \\mathbf {E} _ {c} ^ {-}} f \\left(\\mathbf {e} _ {c} , \\mathbf {e} _ {c , j} ^ {-} , \\tau\\right)} \\right] \\\\ = - \\frac {1}{C} \\sum_ {c = 0} ^ {C - 1} \\mathbb {E} _ {\\mathbf {E} _ {c}} \\left[ \\log \\frac {\\exp \\left(\\left\\langle \\mathbf {e} _ {c} , \\mathbf {e} _ {c} ^ {+} \\right\\rangle / \\tau\\right)}{\\exp \\left(\\left\\langle \\mathbf {e} _ {c} , \\mathbf {e} _ {c} ^ {+} \\right\\rangle / \\tau\\right) + \\sum_ {j = 1} ^ {N - 1} \\exp \\left(\\left\\langle \\mathbf {e} _ {c} , \\mathbf {e} _ {c , j} ^ {-} \\right\\rangle / \\tau\\right)} \\right] \\tag {5} \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.449, + 0.894, + 0.496 + ], + "angle": 0, + "content": "where \\(\\langle \\cdot, \\cdot \\rangle\\) denotes cosine similarity. \\(\\mathbf{e_c}\\), \\(\\mathbf{e_c}^+\\) and \\(\\mathbf{e}_{c,j}^-\\) denote the embedding, positive sample of the current anchor voxel, and embedding of the \\(j\\)-th negative sample of class \\(c\\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.503, + 0.868, + 0.519 + ], + "angle": 0, + "content": "3.3. Reflectivity-Based Test Time Augmentation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.526, + 0.894, + 0.707 + ], + "angle": 0, + "content": "To obtain minimal accuracy degradation despite very few weak labels, e.g., \\(1\\%\\) weakly-labeled ScribbleKITTI [46] dataset, we propose a Test Time Augmentation (TTA) that does not depend on any label, but only relies on a feature of original LiDAR points themselves. Also included in almost every LiDAR benchmark dataset for autonomous driving [7, 10, 18, 30, 34, 46], is the intensity of light reflected from the surface of an object at each point. In the presence of limited data labels in the semi-supervised learning case, this property of the material surface, normalized by distance to obtain the surface reflectivity in Eq. (6), could readily act as auxiliary information to identify different semantic classes." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.708, + 0.892, + 0.753 + ], + "angle": 0, + "content": "Our intuition is that reflectivity \\( R \\), as a point-wise distance-normalized intensity feature, offers consistency across lighting conditions and range as:" + }, + { + "type": "equation", + "bbox": [ + 0.604, + 0.759, + 0.892, + 0.79 + ], + "angle": 0, + "content": "\\[\nR = I r ^ {2} = \\frac {S}{4 \\pi r ^ {2}} \\cdot r ^ {2} \\propto S, \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.795, + 0.894, + 0.855 + ], + "angle": 0, + "content": "where \\( S \\) is the return strength of the LiDAR laser pulse, \\( I \\) is the intensity and \\( r \\) is the point distance from the source on the basis that scene objects with similar surface material, coating, and color characteristics will share similar \\( S \\) returns." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.894, + 0.901 + ], + "angle": 0, + "content": "On this basis, we define our novel reflectivity-based Test Time Augmentation (Reflec-TTA) technique, as a substitute for label-dependent Pyramid Local Semantic-context" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9364" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.09, + 0.088, + 0.46, + 0.178 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.087, + 0.188, + 0.46, + 0.203 + ], + "angle": 0, + "content": "Figure 4. Coarse histograms of Reflec-TTA bins (not to scale)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.223, + 0.473, + 0.359 + ], + "angle": 0, + "content": "(PLS) augmentation [46] during test-time as ground truth is not available. We append our point-wise reflectivity to the existing point features in order to enhance performance in presence of false or non-existent pseudo-labels at the distillation stage. As shown in Fig. 4, and following [46], we apply various sizes \\( s \\) of bins in cylindrical coordinates to analyze the intrinsic point distribution of the LiDAR sensor at varying resolutions (shown in red, green and blue in Fig. 4). For each bin \\( b_{i} \\), we compute a coarse histogram, \\( \\mathbf{h}_{i} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.098, + 0.37, + 0.428, + 0.396 + ], + "angle": 0, + "content": "\\[\n\\mathbf {h} _ {i} = \\left\\{h _ {i} ^ {(k)} \\mid k \\in [ 1, N _ {b} ] \\right\\} \\in \\mathbb {R} ^ {N _ {b}}, \\quad i \\in [ 1, s ],\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.102, + 0.397, + 0.469, + 0.418 + ], + "angle": 0, + "content": "\\[\nh _ {i} ^ {(k)} = \\# \\left\\{R _ {j} \\in r _ {k}, \\forall j \\mid p _ {j} \\in b _ {i} \\right\\}, \\tag {7}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.102, + 0.42, + 0.411, + 0.436 + ], + "angle": 0, + "content": "\\[\nr _ {k} = \\left[ (k - 1) / N _ {b}, k / N _ {b}\\right), \\quad k \\in \\left[ 1, N _ {b} \\right].\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.449, + 0.47, + 0.495 + ], + "angle": 0, + "content": "The Reflec-TTA features \\( R^{\\text{心}} \\) of all points \\( p_j \\in b_j \\) is further computed as the concatenation of the coarse histogram \\( \\mathbf{h}_i \\) of the normalized histogram:" + }, + { + "type": "equation", + "bbox": [ + 0.133, + 0.506, + 0.469, + 0.525 + ], + "angle": 0, + "content": "\\[\nR ^ {\\circledast} = \\left\\{\\mathbf {h} _ {i} / \\operatorname * {m a x} \\left(\\mathbf {h} _ {i}\\right) \\mid i \\in [ 1, s ] \\right\\} \\in \\mathbb {R} ^ {s N _ {b}} \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.535, + 0.47, + 0.583 + ], + "angle": 0, + "content": "In the distillation stage, we append \\( R^{\\text{※}} \\) to the input features and redefine the input LiDAR point cloud as the augmented set of points \\( P^{\\text{※}} = \\{p \\mid (x,y,z,I,R^{\\text{※}}) \\in \\mathbb{R}^{sN_b + 4}\\} \\)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.591, + 0.47, + 0.607 + ], + "angle": 0, + "content": "3.4. Spatio-Temporal Redundant Frame Downsampling" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.614, + 0.473, + 0.825 + ], + "angle": 0, + "content": "Due to the spatio-temporal correlation of LiDAR point cloud sequences often captured from vehicles in metropolitan locales, many large-scale point cloud datasets demonstrate significant redundancy. Common datasets employ a frame rate of \\(10\\mathrm{Hz}\\) [7, 9, 10, 18, 30, 34, 42], and a number of concurrent laser channels (beams) of 32 [10], 64 [7, 9, 18, 30, 42] or 128 [34]. Faced with such large-scale, massively redundant training datasets, the popular practice of semi-supervised semantic segmentation approaches [11, 17, 32, 45, 64] is to uniformly sample \\(1\\%\\), \\(10\\%\\), \\(20\\%\\), or \\(50\\%\\) of the available annotated training frames, without considering any redundancy attributable to temporary periods of stationary capture (e.g. due to traffic, Fig. 5) or multi-pass repetition (e.g. due to loop closure)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.473, + 0.902 + ], + "angle": 0, + "content": "To extract a diverse set of frames, we propose a novel algorithm called Spatio-Temporal Redundant Frame Downsampling (ST-RFD, Algorithm 1) that determines spatiotemporal redundancy by analyzing the spatial-overlap within time-continuous LiDAR frame sequences. The key idea" + }, + { + "type": "image", + "bbox": [ + 0.485, + 0.088, + 0.912, + 0.182 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.191, + 0.895, + 0.248 + ], + "angle": 0, + "content": "Figure 5. Illustration of LiDAR frame temporal correlation as [#frame ID] redundancy with \\(5\\%\\) sampling on SemanticKITTI [7] (sequence 00) using uniform sampling (selected frames in \\(\\bullet\\)) and ST-RFD strategy (\\(\\bullet\\))." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.262, + 0.895, + 0.337 + ], + "angle": 0, + "content": "is that if spatial-overlap among some continuous frame sequence is high due to spatio-temporal redundancy, multiple representative frames can be sub-sampled for training, significantly reducing both training dataset size and redundant training computation." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.338, + 0.895, + 0.579 + ], + "angle": 0, + "content": "Fig. 6 shows an overview of ST-RFD. It is conducted inside each temporal continuous LiDAR sequence \\( e \\). First, we evenly divide \\( p \\) point cloud frames in each sequence into \\( \\lceil p / q \\rceil \\) subsets (containing \\( q \\) frames). For each frame at time \\( t \\) inside the subset, we find its corresponding RGB camera image in the dataset at time \\( t \\) and \\( t + 1 \\). To detect the spatio-temporal redundancy at time \\( t \\), the similarity \\( \\psi(t, t + 1) \\) between temporally adjacent frames are then computed via the Structural Similarity Index Measure (SSIM, [50]). We utilize the mean value of similarity scores between all adjacent frames in the current subset as a proxy to estimate the spatio-temporal redundancy present. A sampling rate is then determined according to this mean similarity for frame selection within this subset. This is repeated for all subsets in every sequence to construct our final set of sub-sampled LiDAR frames for training." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.58, + 0.896, + 0.836 + ], + "angle": 0, + "content": "Concretely, as shown in Algorithm 1, we implement a ST-RFD supervisor that determines the most informative assignments (i.e., the key point cloud frames) that the teacher and student networks should train on respectively. The ST-RFD supervisor has an empirical supervisor function \\( v \\), which decides the amount of assignments, i.e., the sampling rate corresponding to the extent of spatio-temporal redundancy. Using SSIM [50] as the redundancy function \\( \\psi \\) to measure the similarity between the RGB images associated with two adjacent point clouds, we define the empirical supervisor function \\( v \\) with decay property \\( v(x) = \\exp(-\\beta x) \\), where \\( \\beta \\in (0, +\\infty) \\) is the decay coefficient, and \\( x \\) is the redundancy calculated from \\( \\psi \\). In this way, the higher the degree of spatio-temporal redundancy (as \\( \\psi \\to 1 \\)), the lower the sampling rate our ST-RFD supervisor will allocate, hence reducing the training set requirements for teacher and student alike." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.847, + 0.854, + 0.864 + ], + "angle": 0, + "content": "3.5. Sparse Depthwise Separable Convolution" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.871, + 0.894, + 0.902 + ], + "angle": 0, + "content": "Existing LiDAR point cloud semantic segmentation methods generally rely on a large-scale backbone architecture with" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9365" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.088, + 0.885, + 0.165 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.193, + 0.176, + 0.775, + 0.192 + ], + "angle": 0, + "content": "Figure 6. Overview of our proposed Spatio-Temporal Redundant Frame Downsampling approach." + }, + { + "type": "code_caption", + "bbox": [ + 0.086, + 0.211, + 0.427, + 0.24 + ], + "angle": 0, + "content": "Algorithm 1: Spatio-Temporal Redundant Frame Downsampling." + }, + { + "type": "algorithm", + "bbox": [ + 0.082, + 0.242, + 0.471, + 0.466 + ], + "angle": 0, + "content": "Input: Point cloud frames pool \\(P\\) (size of \\(p\\)), subset size \\(q\\), redundancy function \\(\\psi \\in [0,1]\\) and empirical supervisor function \\(v\\). \n1 Divide \\(P\\) evenly into \\(\\lceil p / q\\rceil\\) subsets \\(Q\\). \n2 \\(D \\leftarrow\\) empty dictionary. \n3 for all \\(e \\leftarrow 0 : n_e - 1\\) do \n4 // loop for all sequences \n5 for all \\(i \\leftarrow 0 : \\lceil p / q\\rceil - 1\\) do \n6 // loop for subsets \\(Q\\) \n7 \\(\\overline{M_i} \\leftarrow \\frac{1}{q} \\sum_{j=0}^{q-1} \\psi(Q_{i,j})\\). // redundancy \n8 \\(k_i \\leftarrow \\lceil v(\\overline{M_i}) \\cdot q \\rceil\\). \n9 \\(T_i \\leftarrow\\) select \\(k_i\\) frames in \\(Q_i\\) with the smallest \\(M_i\\). \n10 \\(C_e \\leftarrow C_e \\cup T_i\\). \n11 Append key-value pair \\((e, C_e)\\) into \\(D\\). \nReturn: Dictionary \\(D\\)." + }, + { + "type": "image", + "bbox": [ + 0.061, + 0.481, + 0.47, + 0.582 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.108, + 0.59, + 0.438, + 0.605 + ], + "angle": 0, + "content": "Figure 7. Illustration of the SDSC convolution module." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.613, + 0.47, + 0.78 + ], + "angle": 0, + "content": "tens of millions of trainable parameters [23,26,28,46,60,63] due to the requirement for 3D (voxel-based) convolution operations, to operate on the voxelized topology of the otherwise unstructured LiDAR point cloud representation, which suffer from both high computational training demands and the risk of overfitting. Based on the observation that depthwise separable convolution has shown results comparable with regular convolution in tasks such as image classification but with significantly fewer trainable parameters [12,24,25,37,40,43], here we pursue the use of such an approach within 3D point cloud semantic segmentation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.78, + 0.471, + 0.886 + ], + "angle": 0, + "content": "As such we propose the first formulation of sparse variant depthwise separable convolution [25] applied to 3D point clouds, namely Sparse Depthwise Separable Convolution (SDSC). SDSC combines the established computational advantages of sparse convolution for point cloud segmentation [20], with the significant trainable parameter reduction offered by depthwise separable convolution [12]." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.886, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Our SDSC module, as outlined in Fig. 7, initially takes a" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.206, + 0.895, + 0.479 + ], + "angle": 0, + "content": "tensor \\(F \\in \\mathbb{R}^{H_F \\times W_F \\times L_F \\times M}\\) as input, where \\(H_F\\), \\(W_F\\), \\(L_F\\) and \\(M\\) denote radius, azimuth, height in the cylinder coordinate [63] and channels respectively. Firstly, a sparse depthwise convolution \\(\\mathrm{SDC}(M, M, D_k, s = 1)\\) is applied, with \\(M\\) input and output feature planes, a kernel size of \\(D_k\\) and stride \\(s\\) in order to output a tensor \\(T \\in \\mathbb{R}^{H_F \\times W_F \\times L_F \\times M}\\). Inside our sparse depthwise convolution, \\(M\\) sparse spatial convolutions are performed independently over each input channel using submanifold sparse convolution [20] due to its tensor shape preserving property at no computational or memory overhead. Secondly, the sparse pointwise convolution \\(\\mathrm{SPC}(M, N, 1, s = 1)\\) projects the channels output \\(T\\) by the sparse depthwise convolution onto a new channel space, to mix the information across different channels. As a result, the sparse depthwise separable convolution \\(\\mathrm{SDSC}(M, N, D_k, s = 1)\\) is the compound of the sparse depthwise convolution and the sparse pointwise convolution, namely \\(\\mathrm{SDSC}(M, N, D_k, s = 1) = \\mathrm{SDC} \\circ \\mathrm{SPC}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.48, + 0.895, + 0.54 + ], + "angle": 0, + "content": "Using a sparse voxelized input representation similar to [19], and a series of such SDSC sub-modules we construct the popular Cylinder3D [63] sub-architectures within our overall Mean Teacher architectural design (Fig. 2)." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.542, + 0.618, + 0.558 + ], + "angle": 0, + "content": "4. Evaluation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.567, + 0.895, + 0.628 + ], + "angle": 0, + "content": "We evaluate our proposed Less is More 3D (LiM3D) approach against state-of-the-art 3D point cloud semantic segmentation approaches using the SemanticKITTI [7] and ScribbleKITTI [46] benchmark datasets." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.636, + 0.691, + 0.653 + ], + "angle": 0, + "content": "4.1. Experimental Setup" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.894, + 0.72 + ], + "angle": 0, + "content": "SemanticKITTI [7] is a large-scale 3D point cloud dataset for semantic scene understanding with 20 semantic classes consisting of 22 sequences - [00 to 10 as training-split (of which 08 as validation-split) + 11 to 21 as test-split]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.895, + 0.795 + ], + "angle": 0, + "content": "ScribbleKITTI [46] is the first scribble (i.e. sparsely) annotated dataset for LiDAR semantic segmentation providing sparse annotations for the training split of SemanticKITTI for 19 classes, with only \\(8.06\\%\\) of points from the full SemanticKITTI dataset annotated." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Evaluation Protocol: Following previous work [29,32,46, 63], we report performance on the SemanticKITTI and ScribbleKITTI training set for intermediate training steps, as this metric provides an indication of the pseudo-labeling quality, and on the validation set to assess the performance benefits of each individual component. Performance is reported using the mean Intersection over Union (mIoU, as \\(\\%\\) ) metric." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9366" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.089, + 0.892, + 0.118 + ], + "angle": 0, + "content": "Table 1. Comparative mIoU for Range- and Voxel-based methods using uniform sampling (U), sequential partition (P) and ST-RFD sampling (S): bold/underlined = best/2nd best; * denotes reproduced result; - denotes missing result due to unavailability from original authors." + }, + { + "type": "table", + "bbox": [ + 0.081, + 0.12, + 0.894, + 0.248 + ], + "angle": 0, + "content": "
Repr.Samp.MethodSemanticKITTI [7]ScribbleKITTI [46]
1%5%10%20%40%50%100%1%5%10%20%40%50%100%
RangeULaserMix [32](2022)43.4-58.859.4-61.4-38.3-54.455.6-58.7-
VoxelUCylinder3D [63](CVPR'21)-45.456.157.858.7-67.8-39.248.052.153.8-56.3
ULaserMix [32](2022)50.6-60.061.9-62.3-44.2-53.755.1-56.8-
PJiang et al. [29](ICCV'21)-41.849.958.859.9-65.8-------
UUnal et al. [46](CVPR'22)-49.9*58.7*59.1*60.9-68.2*-46.9*54.2*56.5*58.6*-61.3
SLiM3D+SDSC(ours)57.257.661.061.762.162.767.555.856.156.957.258.959.360.7
SLiM3D(ours)58.459.562.263.163.363.669.557.058.161.061.262.062.162.4
" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.274, + 0.471, + 0.439 + ], + "angle": 0, + "content": "For semi-supervised training, we report over both the benchmarks using the SemanticKITTI and ScribbleKITTI validation set under \\(5\\%\\), \\(10\\%\\), \\(20\\%\\), and \\(40\\%\\) partitioning. We further report the relative performance of semi-supervised or scribble-supervised for ScribbleKITTI (SS) training to the fully supervised upper-bound (FS) in percentages (SS/FS) to further analyze semi-supervised performance and report the results for the fully-supervised training on both validation sets for reference. The trainable parameter count and number of multiply-adds (multi-adds) are additionally provided as a metric of computational cost." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.44, + 0.474, + 0.609 + ], + "angle": 0, + "content": "Implementation Details: Training is performed using \\(4 \\times\\) NVIDIA A100 80GB GPU without pre-trained weights with a DDP shared training strategy [1] to maintain GPU scaling efficiency, whilst reducing memory overhead significantly. Specific hyper-parameters are set as follows - Mean Teacher: \\(\\kappa = 0.99\\); unreliable pseudo-labeling: \\(\\lambda_{C} = 0.3\\), \\(\\tau = 0.5\\); ST-RFD: \\(\\beta = \\{7.45, 5.72, 4.00, 2.28, 0\\}\\) for sampling \\(\\{5\\%, 10\\%, 20\\%, 40\\%, 100\\}\\) labeled training frames, assuming the remainder as unlabeled; Reflec-TTA: \\(N_{b} = 10\\), \\(s = 3\\) various Reflec-TTA bin sizes, following [46], we set each bin \\(b_{i} = (\\rho, \\phi) \\in \\{(20, 40), (40, 80), (80, 120)\\}\\)." + }, + { + "type": "image", + "bbox": [ + 0.078, + 0.62, + 0.473, + 0.768 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.776, + 0.471, + 0.833 + ], + "angle": 0, + "content": "Figure 8. Comparing the \\(10\\%\\) sampling split of SemanticKITTI (SeK, first row) and ScribbleKITTI (ScK, second row) validation set with ground-truth (left), our approach (middle) and Unal et al. [46] (right) with areas of improvement highlighted." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.839, + 0.281, + 0.855 + ], + "angle": 0, + "content": "4.2. Experimental Results" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.473, + 0.901 + ], + "angle": 0, + "content": "In Tab. 1, we present the performance of our Less is More 3D (LiM3D) point cloud semantic segmentation approach both with (LiM3D+SDSC) and without (LiM3D) SDSC in a side" + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.271, + 0.895, + 0.342 + ], + "angle": 0, + "content": "Table 2. Component-wise ablation of LiM3D (mIoU as %, and #parameters in millions, M) on SemanticKITTI [7] training and validation sets where UP, RF, RT, ST, SD denote Unreliable Pseudolabeling, Reflectivity Feature, Reflec-TTA, ST-RFD, and SDSC module respectively." + }, + { + "type": "table", + "bbox": [ + 0.504, + 0.343, + 0.895, + 0.465 + ], + "angle": 0, + "content": "
UPRFRTSTSDTraining mIoU (%)Validation mIoU (%)#Params(M)
5%10%20%40%5%10%20%40%
82.887.587.888.254.858.159.360.849.6
✓----55.958.859.961.249.6
✓✓83.688.388.789.156.859.660.561.449.6
✓✓----57.559.861.262.649.6
✓✓✓----58.761.362.462.849.6
✓✓✓✓85.289.189.589.759.562.263.163.349.6
✓✓✓✓✓83.888.689.089.257.661.061.762.121.5
" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.48, + 0.895, + 0.571 + ], + "angle": 0, + "content": "by-side comparison with leading contemporary state-of-the-art approaches on the SemanticKITTI and ScribbleKITTI benchmark validation sets to illustrate our approach offers superior or comparable (within \\(1\\%\\) mIoU) performance across all sampling ratios. Furthermore, we present supporting qualitative results in Fig. 8." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.571, + 0.895, + 0.678 + ], + "angle": 0, + "content": "On SemanticKITTI, with a lack of available supervision, LiM3D shows a relative performance (SS/FS) from \\(85.6\\%\\) (5%-fully-supervised) to \\(91.1\\%\\) (40%-fully-supervised), and LiM3D+SDSC from \\(85.3\\%\\) to \\(92.0\\%\\), compared to their respective fully supervised upper-bound. LiM3D/LiM3D+SDSC performance is also less sensitive to reduced labeled data sampling compared with other methods." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.678, + 0.895, + 0.77 + ], + "angle": 0, + "content": "Our model significantly outperforms on small ratio sampling splits, e.g., \\(5\\%\\) and \\(10\\%\\). LiM3D shows up to \\(19.8\\%\\) and \\(18.9\\%\\) mIoU improvements whilst, with a smaller model size \\(\\mathrm{LiM3D + SDSC}\\) again shows significant mIoU improvements by up to \\(16.4\\%\\) and \\(15.5\\%\\) when compared with other range and voxel-based methods respectively." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.775, + 0.665, + 0.79 + ], + "angle": 0, + "content": "4.3. Ablation Studies" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.794, + 0.894, + 0.87 + ], + "angle": 0, + "content": "Effectiveness of Components. In Tab. 2 we ablate each component of LiM3D step by step and report the performance on the SemanticKITTI training set at the end of training as an overall indicator of pseudo-labeling quality in addition to the corresponding validation set." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.894, + 0.901 + ], + "angle": 0, + "content": "As shown in Tab. 2, adding unreliable pseudo-labeling (UP) in the distillation stage, we can increase the valid mIoU" + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "9367" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.075, + 0.092, + 0.474, + 0.259 + ], + "angle": 0, + "content": "by \\(+0.7\\%\\) on average in validation set. Appending reflectivity features (RF) in the training stage, we further improve the mIoU on the training set by \\(+0.7\\%\\) on average. Due to the improvements in training, the model generates a higher quality of pseudo-labels, which results to a \\(+0.5\\%\\) increase in mIoU in the validation set. If we disable reflectivity features in the training stage, applying Reflec-TTA in the distillation stage alone, we then get an average improvement of \\(+1.3\\%\\) compared with pseudo-labeling only. On the whole, enabling all reflectivity-based components (RF+RT) shows great improvements of up to \\(+2.8\\%\\) in validation mIoU." + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.268, + 0.473, + 0.31 + ], + "angle": 0, + "content": "Table 3. The computation cost and mIoU (in percentage) under \\(5\\%\\) -labeled training results on SemanticKITTI (SeK) and ScribbleKITTI (ScK) validation set." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.311, + 0.472, + 0.413 + ], + "angle": 0, + "content": "
Method# Parameters# Mult-AddsSeK [7]ScK [46]
Cylinder3D [63]56.3476.9M45.439.2
Unal et al. [46]49.6420.2M49.946.9
2DPASS [58]26.5217.4M51.745.1
MinkowskiNet [13]21.7114.0G42.435.8
SPVNAS [44]12.573.8G45.138.9
LiM3D+SDSC (ours)21.5182.0M57.654.7
LiM3D (ours)49.6420.2M59.558.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.426, + 0.471, + 0.454 + ], + "angle": 0, + "content": "Table 4. Effects of ST-RFD sampling on SemanticKITTI and ScribbleKITTI validation set (mIoU as %)." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.456, + 0.475, + 0.535 + ], + "angle": 0, + "content": "
SamplingSemanticKITTI [7]ScribbleKITTI [46]
5%10%20%40%5%10%20%40%
Random58.561.662.662.757.160.360.560.9
Uniform58.761.362.462.856.960.660.361.0
ST-RFD-R59.162.462.963.458.060.761.261.8
ST-RFD59.562.263.163.358.161.061.262.0
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.551, + 0.472, + 0.621 + ], + "angle": 0, + "content": "Table 5. Effects of differing reliability using pseudo voxels on SemanticKITTI validation set, measured by the entropy of voxel-wise prediction. Unreliable and Reliable: selecting negative candidates with top \\(20\\%\\) highest entropy scores and bottom \\(20\\%\\) counterpart respectively. Random: sampling randomly regardless of entropy." + }, + { + "type": "table", + "bbox": [ + 0.104, + 0.622, + 0.437, + 0.702 + ], + "angle": 0, + "content": "
RatioUnreliableReliableRandom
mIoUSS/FFmIoUSS/FFmIoUSS/FF
5%59.585.657.282.356.481.2
10%62.289.560.887.559.785.9
20%63.190.861.488.360.587.1
40%63.391.162.890.461.388.2
" + }, + { + "type": "table_caption", + "bbox": [ + 0.077, + 0.715, + 0.47, + 0.743 + ], + "angle": 0, + "content": "Table 6. Reflectivity (Reflec-TTA) vs. Intensity (intensity-based TTA) on SemanticKITTI and ScribbleKITTI validation set (mIoU, %)." + }, + { + "type": "table", + "bbox": [ + 0.079, + 0.745, + 0.479, + 0.803 + ], + "angle": 0, + "content": "
TTASemanticKITTI [7]ScribbleKITTI [46]
5%10%20%40%5%10%20%40%
Intensity56.259.159.860.955.757.557.959.2
Reflectivity59.562.263.163.358.161.061.262.0
" + }, + { + "type": "text", + "bbox": [ + 0.075, + 0.81, + 0.471, + 0.856 + ], + "angle": 0, + "content": "Substituting the uniform sampling with our ST-RFD strategy, we observe further average improvements of \\(+1.0\\%\\) and \\(+0.8\\%\\) on training and validation respectively (Tab. 2)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Our SDSC module reduces the trainable parameters of our model by \\(57\\%\\), with a performance cost of \\(-0.7\\%\\) and \\(-1.4\\%\\) mIoU on training and validation respectively" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.136 + ], + "angle": 0, + "content": "(Tab. 2). Finally, we provide two models, one without SDSC (LiM3D) and one with (LiM3D+SDSC), corresponding to the bottom two rows of Tab. 2." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.137, + 0.895, + 0.272 + ], + "angle": 0, + "content": "Effectiveness of SDSC module. In Tab. 3, we compare our LiM3D and LiM3D+SDSC with recent state-of-the-art methods under \\(5\\%\\) -labeled semi-supervised training on the SemanticKITTI and ScribbleKITTI validation sets. LiM3D+SDSC outperforms the voxel-based methods [46, 63] with at least a \\(2.3\\times\\) reduction in model size. Similarly, with comparable model size [13, 44, 58], LiM3D+SDSC has higher mIoU in both datasets and up to \\(641\\times\\) fewer multiply-add operations." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.273, + 0.895, + 0.483 + ], + "angle": 0, + "content": "Effectiveness of ST-RFD strategy. In Tab. 4, we illustrate the effectiveness of our ST-RFD strategy by comparing LiM3D with two widely-used strategies in semi-supervised training, i.e., random sampling and uniform sampling on SemanticKITTI [7] and ScribbleKITTI [46] validation set. Whilst uniform and random sampling have comparable results on both validation sets, simply applying our ST-RFD strategy improves the baseline by \\(+0.90\\%\\), \\(+0.75\\%\\), \\(+0.60\\%\\) and \\(+0.55\\%\\) on SemanticKITTI under \\(5\\%\\), \\(10\\%\\), \\(20\\%\\) and \\(40\\%\\) sampling protocol respectively. Furthermore, using corresponding range images of point cloud, rather than RGB images to compute the spatio-temporal redundancy within ST-RFD (see ST-RFD-R in Tab. 4), has no significant difference on the performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.484, + 0.895, + 0.59 + ], + "angle": 0, + "content": "Effectiveness of Unreliable Pseudo-Labeling. In Tab. 5, we evaluate selecting negative candidates with different reliability to illustrate the improvements of using unreliable pseudo-labels in semi-supervised semantic segmentation. The \"Unreliable\" selecting of negative candidates outperforms other alternative methodologies, showing the positive performance impact of unreliable pseudo-labels." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.59, + 0.895, + 0.696 + ], + "angle": 0, + "content": "Effectiveness of Reflec-TTA. In Tab. 2, we compare LiM3D performance with and without Reflec-TTA and further experiment on the SemanticKITTI and ScribbleKITTI validation set in Tab. 6. This demonstrates that the LiDAR point-wise intensity feature \\( I^* \\), in place of the distance-normalized reflectivity feature \\( R^* \\), offers inferior on-task performance." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.702, + 0.62, + 0.717 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.895, + 0.901 + ], + "angle": 0, + "content": "This paper presents an efficient semi-supervised architecture for 3D point cloud semantic segmentation, which achieves more in terms of performance with less computational costs, less annotations, and less trainable model parameters (i.e., Less is More, LiM3D). Our architecture consists of three novel contributions: the SDSC convolution module, the ST-RFD sampling strategy, and the pseudo-labeling method informed by LiDAR reflectivity. These individual components can be applied to any 3D semantic segmentation architecture to reduce the gap between semi or weakly-supervised and fully-supervised learning on task performance, whilst managing model complexity and computation costs." + }, + { + "type": "page_number", + "bbox": [ + 0.482, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9368" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.144 + ], + "angle": 0, + "content": "[1] FairScale. FairScale: A general purpose modular PyTorch library for high performance and large scale training, 2021. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.146, + 0.472, + 0.187 + ], + "angle": 0, + "content": "[2] Shahriar Akramullah. Digital Video Concepts, Methods, and Metrics: Quality, Compression, Performance, and Power Trade-off Analysis. Apress, Berkeley, CA, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.189, + 0.472, + 0.258 + ], + "angle": 0, + "content": "[3] Inigo Alonso, Luis Riazuelo, Luis Montesano, and Ana C. Murillo. 3D-MiniNet: Learning a 2D Representation From Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation. IEEE Robot. Autom. Lett., 5(4):5432-5439, Oct. 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.261, + 0.472, + 0.328 + ], + "angle": 0, + "content": "[4] Inigo Alonso, Alberto Sabater, David Ferstl, Luis Montesano, and Ana C. Murillo. Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank. In Int. Conf. Comput. Vis., pages 8199-8208, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.332, + 0.472, + 0.386 + ], + "angle": 0, + "content": "[5] Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, and Kevin McGuinness. Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning. In Int. Jt. Conf. Neural Netw., 2020. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.389, + 0.472, + 0.459 + ], + "angle": 0, + "content": "[6] Amir Atapour-Abarghouei and Toby P. Breckon. Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3368–3379, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.461, + 0.472, + 0.529 + ], + "angle": 0, + "content": "[7] Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyril Stachniss, and Jurgen Gall. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. In Int. Conf. Comput. Vis., pages 9296-9306, Seoul, Korea (South), Oct. 2019. 2, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.531, + 0.472, + 0.599 + ], + "angle": 0, + "content": "[8] Maxim Berman, Amal Rannen Triki, and Matthew B. Blaschko. The Lovasz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.603, + 0.472, + 0.672 + ], + "angle": 0, + "content": "[9] Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, and Felix Heide. Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather. In IEEE Conf. Comput. Vis. Pattern Recog., pages 11679-11689, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.673, + 0.472, + 0.743 + ], + "angle": 0, + "content": "[10] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. Nuscenes: A multimodal dataset for autonomous driving. IEEE Conf. Comput. Vis. Pattern Recog., pages 11618-11628, 2020. 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.746, + 0.472, + 0.8 + ], + "angle": 0, + "content": "[11] Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang. Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2613-2622, June 2021. 1, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.802, + 0.472, + 0.844 + ], + "angle": 0, + "content": "[12] François Chollet. Xception: Deep Learning with Depthwise Separable Convolutions. In IEEE Conf. Comput. Vis. Pattern Recog., Apr. 2017. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[13] Christopher Choy, JunYoung Gwak, and Silvio Savarese. 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3070-3079, 2019. 1, 8" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.147 + ], + "angle": 0, + "content": "[14] Tiago Cortinhal, George Tzelepis, and Eren Erdal Aksoy. SalsaNext: Fast, Uncertainty-Aware Semantic Segmentation of LiDAR Point Clouds. In Adv. Vis. Comput., pages 207-222, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.894, + 0.203 + ], + "angle": 0, + "content": "[15] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In IEEE Conf. Comput. Vis. Pattern Recog., pages 248–255, 2009. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.205, + 0.894, + 0.26 + ], + "angle": 0, + "content": "[16] Ngoc Phuong Anh Duong, Alexandre Almin, Léo Lemarie, and B Ravi Kiran. Reducing redundancy in Semantic-KITTI: Study on data augmentations within Active Learning. In Adv. Neural Inform. Process. Syst. Worksh., page 8, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.261, + 0.894, + 0.3 + ], + "angle": 0, + "content": "[17] Geoff French. Semi-supervised semantic segmentation needs strong, varied perturbations. In Brit. Mach. Vis. Conf., page 21, 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.302, + 0.894, + 0.357 + ], + "angle": 0, + "content": "[18] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3354-3361, 2012. 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.358, + 0.894, + 0.385 + ], + "angle": 0, + "content": "[19] Ben Graham. Sparse 3D convolutional neural networks. In Brit. Mach. Vis. Conf., pages 150.1-150.9, Swansea, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.386, + 0.894, + 0.441 + ], + "angle": 0, + "content": "[20] Benjamin Graham, Martin Engelcke, and Laurens van der Maaten. 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.442, + 0.894, + 0.469 + ], + "angle": 0, + "content": "[21] Benjamin Graham and Laurens van der Maaten. Submanifold Sparse Convolutional Networks, June 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.47, + 0.894, + 0.525 + ], + "angle": 0, + "content": "[22] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum Contrast for Unsupervised Visual Representation Learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9726-9735, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.526, + 0.894, + 0.58 + ], + "angle": 0, + "content": "[23] Yuenan Hou, Xinge Zhu, Yuexin Ma, Chen Change Loy, and Yikang Li. Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., page 10, 2022. 1, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.581, + 0.894, + 0.65 + ], + "angle": 0, + "content": "[24] Andrew Howard, Mark Sandler, Bo Chen, Weijun Wang, Liang-Chieh Chen, Mingxing Tan, Grace Chu, Vijay Vasudevan, Yukun Zhu, Ruoming Pang, Hartwig Adam, and Quoc Le. Searching for MobileNetV3. In Int. Conf. Comput. Vis., pages 1314-1324, 2019. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.651, + 0.894, + 0.719 + ], + "angle": 0, + "content": "[25] Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andretto, and Hartwig Adam. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, Apr. 2017. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.721, + 0.894, + 0.788 + ], + "angle": 0, + "content": "[26] Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham. RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. In IEEE Conf. Comput. Vis. Pattern Recog., 2020. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.79, + 0.894, + 0.845 + ], + "angle": 0, + "content": "[27] Zeyu Hu, Xuyang Bai, Runze Zhang, Xin Wang, Guangyuan Sun, Hongbo Fu, and Chiew-Lan Tai. LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation. In Eur. Conf. Comput. Vis. Springer. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.846, + 0.894, + 0.901 + ], + "angle": 0, + "content": "[28] Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Emilie Wirbel, and Patrick Pérez. xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation. IEEE Conf. Comput. Vis. Pattern Recog., 2021. 1, 6" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9369" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "[29] Li Jiang, Shaoshuai Shi, Zhuotao Tian, Xin Lai, Shu Liu, ChiWing Fu, and Jiaya Jia. Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation. In Int. Conf. Comput. Vis., 2021. 2, 3, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.149, + 0.472, + 0.217 + ], + "angle": 0, + "content": "[30] R. Kesten, M. Usman, J. Houston, T. Pandya, K. Nadhamuni, A. Ferreira, M. Yuan, B. Low, A. Jain, P. Ondruska, S. Omari, S. Shah, A. Kulkarni, A. Kazakova, C. Tao, L. Platinsky, W. Jiang, and V. Shet. Lyft level 5 perception dataset 2020, 2019. 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.219, + 0.472, + 0.261 + ], + "angle": 0, + "content": "[31] Deyvid Kochanov, Fatemeh Karimi Nejadasl, and Olaf Booij. KPRNet: Improving projection-based LiDAR semantic segmentation, Aug. 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.263, + 0.472, + 0.304 + ], + "angle": 0, + "content": "[32] Lingdong Kong, Jiawei Ren, Liang Pan, and Ziwei Liu. LaserMix for Semi-Supervised LiDAR Semantic Segmentation, June 2022. 2, 3, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.305, + 0.472, + 0.346 + ], + "angle": 0, + "content": "[33] Samuli Laine and Timo Aila. Temporal Ensembling for Semi-Supervised Learning. In Int. Conf. Learn. Represent., 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.348, + 0.472, + 0.431 + ], + "angle": 0, + "content": "[34] Li Li, Khalid N. Ismail, Hubert P. H. Shum, and Toby P. Breckon. DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications. In Int. Conf. 3D Vis., pages 1227-1237, London, United Kingdom, Dec. 2021. 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.433, + 0.472, + 0.488 + ], + "angle": 0, + "content": "[35] Peizhao Li, Pu Wang, Karl Berntorp, and Hongfu Liu. Exploiting Temporal Relations on Radar Perception for Autonomous Driving. In IEEE Conf. Comput. Vis. Pattern Recog., pages 17071-17080, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.49, + 0.472, + 0.545 + ], + "angle": 0, + "content": "[36] Venice Erin Liong, Thi Ngoc Tho Nguyen, Sergi Widjaja, Dhananjai Sharma, and Zhuang Jie Chong. AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation, Dec. 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.547, + 0.472, + 0.603 + ], + "angle": 0, + "content": "[37] Dominic Masters, Antoine Labatie, Zach Eaton-Rosen, and Carlo Luschi. Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution Training, Aug. 2021. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.604, + 0.472, + 0.658 + ], + "angle": 0, + "content": "[38] Andres Milioto, Ignacio Vizzo, Jens Behley, and Cyril Stachniss. RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation. In Int. Conf. Intell. Robots Syst., pages 4213-4220, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.661, + 0.472, + 0.716 + ], + "angle": 0, + "content": "[39] Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen. Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning. In Adv. Neural Inform. Process. Syst., volume 29, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.718, + 0.472, + 0.786 + ], + "angle": 0, + "content": "[40] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zh-moginov, and Liang-Chieh Chen. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4510-4520, Salt Lake City, UT, 2018. IEEE. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.788, + 0.472, + 0.816 + ], + "angle": 0, + "content": "[41] Laurent Sifre. Rigid-Motion Scattering For Image Classification. PhD thesis, Ecole Polytechnique, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.818, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[42] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, and Dragomir Anguelov." + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.092, + 0.893, + 0.134 + ], + "angle": 0, + "content": "Scalability in Perception for Autonomous Driving: Waymo Open Dataset. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2443-2451, Seattle, WA, USA, June 2020. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.135, + 0.894, + 0.176 + ], + "angle": 0, + "content": "[43] Mingxing Tan and Quoc Le. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In Proc. 36th Int. Conf. Mach. Learn., pages 6105-6114. PMLR, May 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.177, + 0.894, + 0.231 + ], + "angle": 0, + "content": "[44] Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, and Song Han. Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution. In Eur. Conf. Comput. Vis., 2020. 1, 2, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.232, + 0.894, + 0.287 + ], + "angle": 0, + "content": "[45] Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In Adv. Neural Inform. Process. Syst., 2017. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.288, + 0.894, + 0.329 + ], + "angle": 0, + "content": "[46] Ozan Unal, Dengxin Dai, and Luc Van Gool. Scribblesupervised LiDAR semantic segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., 2022. 1, 2, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.33, + 0.894, + 0.37 + ], + "angle": 0, + "content": "[47] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation Learning with Contrastive Predictive Coding, Jan. 2019. 1, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.371, + 0.894, + 0.426 + ], + "angle": 0, + "content": "[48] Jue Wang, Gedas Bertasius, Du Tran, and Lorenzo Torresani. Long-Short Temporal Contrastive Learning of Video Transformers. In IEEE Conf. Comput. Vis. Pattern Recog., pages 13990–14000, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.427, + 0.894, + 0.495 + ], + "angle": 0, + "content": "[49] Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, and Xinyi Le. Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels. IEEE Conf. Comput. Vis. Pattern Recog., Mar. 2022. 1, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.497, + 0.894, + 0.551 + ], + "angle": 0, + "content": "[50] Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600-612, Apr. 2004. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.553, + 0.894, + 0.621 + ], + "angle": 0, + "content": "[51] Bichen Wu, Alvin Wan, Xiangyu Yue, and Kurt Keutzer. SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud. In Int. Conf. Robot. Autom., pages 1887–1893, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.622, + 0.894, + 0.691 + ], + "angle": 0, + "content": "[52] Bichen Wu, Xuanyu Zhou, Sicheng Zhao, Xiangyu Yue, and Kurt Keutzer. SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud. In Int. Conf. Robot. Autom., pages 4376–4382, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.692, + 0.894, + 0.761 + ], + "angle": 0, + "content": "[53] Tsung-Han Wu, Yueh-Cheng Liu, Yu-Kai Huang, Hsin-Ying Lee, Hung-Ting Su, Ping-Chia Huang, and Winston H. Hsu. ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation. In Int. Conf. Comput. Vis. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.761, + 0.894, + 0.816 + ], + "angle": 0, + "content": "[54] Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin. Unsupervised Feature Learning via Non-parametric Instance Discrimination. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3733-3742, 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.817, + 0.894, + 0.872 + ], + "angle": 0, + "content": "[55] Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V. Le. Self-Training With Noisy Student Improves ImageNet Classification. In IEEE Conf. Comput. Vis. Pattern Recog., pages 10687–10698, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.873, + 0.894, + 0.901 + ], + "angle": 0, + "content": "[56] Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Va-jda, Kurt Keutzer, and Masayoshi Tomizuka. SqueezeSegV3:" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.092, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "9370" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Eur. Conf. Comput. Vis., 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.149, + 0.47, + 0.217 + ], + "angle": 0, + "content": "[57] Xu Yan, Jiantao Gao, Jie Li, Ruimao Zhang, Zhen Li, Rui Huang, and Shuguang Cui. Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion. In Conf. AAAI Artif. Intell., 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.219, + 0.469, + 0.275 + ], + "angle": 0, + "content": "[58] Xu Yan, Jiantao Gao, Chaoda Zheng, Chao Zheng, Ruimao Zhang, Shuguang Cui, and Zhen Li. 2DPASS: 2D priors assisted semantic segmentation on LiDAR point clouds. In Eur. Conf. Comput. Vis., 2022. 1, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.277, + 0.469, + 0.331 + ], + "angle": 0, + "content": "[59] Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, and Yang Gao. ST++: Make Self-Training Work Better for Semi-Supervised Semantic Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4268–4277, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.333, + 0.469, + 0.401 + ], + "angle": 0, + "content": "[60] Li Yi, Boqing Gong, and Thomas Funkhouser. Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds. In IEEE Conf. Comput. Vis. Pattern Recog., pages 15358-15368, Nashville, TN, USA, June 2021. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.403, + 0.469, + 0.471 + ], + "angle": 0, + "content": "[61] Yang Zhang, Zixiang Zhou, Philip David, Xiangyu Yue, Zerong Xi, Boqing Gong, and Hassan Foroosh. PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.474, + 0.469, + 0.528 + ], + "angle": 0, + "content": "[62] Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, and Yichen Wei. Deep Feature Flow for Video Recognition. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4141-4150, Honolulu, HI, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.531, + 0.469, + 0.598 + ], + "angle": 0, + "content": "[63] Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin. Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., 2021. 1, 2, 3, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.601, + 0.469, + 0.683 + ], + "angle": 0, + "content": "[64] Yang Zou, Zhiding Yu, B. V. K. Vijaya Kumar, and Jinsong Wang. Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training. In Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and Yair Weiss, editors, Eur. Conf. Comput. Vis., volume 11207, pages 297-313, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.685, + 0.469, + 0.74 + ], + "angle": 0, + "content": "[65] Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, and Tomas Pfister. PseudoSeg: Designing Pseudo Labels for Semantic Segmentation. In Int. Conf. Learn. 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H. Shum1 + +Toby P. Breckon1,2 + +Department of $\{\mathrm{Computer~Science}^1\mid$ Engineering2}, Durham University, UK + +{li.li4, hubert.shum, toby.breckon}@durham.ac.uk + +# Abstract + +Whilst the availability of 3D LiDAR point cloud data has significantly grown in recent years, annotation remains expensive and time-consuming, leading to a demand for semi-supervised semantic segmentation methods with application domains such as autonomous driving. Existing work very often employs relatively large segmentation backbone networks to improve segmentation accuracy, at the expense of computational costs. In addition, many use uniform sampling to reduce ground truth data requirements for learning needed, often resulting in sub-optimal performance. To address these issues, we propose a new pipeline that employs a smaller architecture, requiring fewer ground-truth annotations to achieve superior segmentation accuracy compared to contemporary approaches. This is facilitated via a novel Sparse Depthwise Separable Convolution module that significantly reduces the network parameter count while retaining overall task performance. To effectively sub-sample our training data, we propose a new Spatio-Temporal Redundant Frame Downsampling (ST-RFD) method that leverages knowledge of sensor motion within the environment to extract a more diverse subset of training data frame samples. To leverage the use of limited annotated data samples, we further propose a soft pseudo-label method informed by LiDAR reflectivity. Our method outperforms contemporary semi-supervised work in terms of mIoU, using less labeled data, on the SemanticKITTI (59.5@5%) and ScribbleKITTI (58.1@5%) benchmark datasets, based on a $2.3 \times$ reduction in model parameters and $641 \times$ fewer multiply-add operations whilst also demonstrating significant performance improvement on limited training data (i.e., Less is More). + +# 1. Introduction + +3D semantic segmentation of LiDAR point clouds has played a key role in scene understanding, facilitating applications such as autonomous driving [6, 23, 26, 28, 46, 60, 63] and robotics [3, 38, 51, 52]. However, many contemporary methods require relatively large backbone architectures with millions of trainable parameters requiring many hundred gigabytes of annotated data for training at a significant computational cost. Considering the time-consuming and costly nature of 3D LiDAR annotation, such methods have become less feasible for practical deployment. + +![](images/d2109a11a3170aa183da17fbeefb710494f81a48e2d15fe15b9e1eb29dc62b4e.jpg) +Figure 1. mIoU performance $(\%)$ against parameters and multiply-add operations on SemanticKITTI (fully annotated) and ScribbleKITTI (weakly annotated) under the $5\%$ sampling protocol. + +![](images/2515e6a36168911b2f6376032e62ad48a6d0413243f0fe8a492c75264318e6e8.jpg) + +Existing supervised 3D semantic segmentation methods [13, 31, 38, 44, 51, 52, 56, 58, 63] primarily focus on designing network architectures for densely annotated data. To reduce the need for large-scale data annotation, and inspired by similar work in 2D [11, 47, 49], recent 3D work proposes efficient ways to learn from weak supervision [46]. However, such methods still suffer from high training costs and inferior on-task performance. To reduce computational costs, a 2D projection-based point cloud representation is often considered [3, 14, 31, 38, 51, 52, 56, 61], but again at the expense of significantly reduced on-task performance. As such, we observe a gap in the research literature for the design of semi or weakly supervised methodologies that employ a smaller-scale architectural backbone, hence facilitating improved training efficiency whilst also reducing their associated data annotation requirements. + +In this paper, we propose a semi-supervised methodology for 3D LiDAR point cloud semantic segmentation. Facilitated by three novel design aspects, our Less is More (LiM) based methodologies require less training data and less training computation whilst offering (more) improved accuracy over contemporary state-of-the-art approaches (see Fig. 1). + +Firstly, from an architectural perspective, we propose a novel Sparse Depthwise Separable Convolution (SDSC) module, which substitutes traditional sparse 3D convolution into existing 3D semantic segmentation architectures, resulting in a significant reduction in trainable parameters and numerical computation whilst maintaining on-task performance (see Fig. 1). Depthwise Separable Convolution has shown to be very effective within image classification + +tasks [12]. Here, we tailor a sparse variant of 3D Depthwise Separable Convolution for 3D sparse data by first applying a single submanifold sparse convolutional filter [20, 21] to each input channel with a subsequent pointwise convolution to create a linear combination of the sparse depthwise convolution outputs. This work is the first to attempt to introduce depthwise convolution into the 3D point cloud segmentation field as a conduit to reduce model size. Our SDSC module facilitates a $50\%$ reduction in trainable network parameters without any loss in segmentation performance. + +Secondly, from a training data perspective, we propose a novel Spatio-Temporal Redundant Frame Downsampling (ST-RFD) strategy that more effectively sub-samples a set of diverse frames from a continuously captured LiDAR sequence in order to maximize diversity within a minimal training set size. We observe that continuously captured LiDAR sequences often contain significant temporal redundancy, similar to that found in video [2], whereby temporally adjacent frames provide poor data variation. On this basis, we propose to compute the temporal correlation between adjacent frame pairs, and use this to select the most informative sub-set of LiDAR frames from a given sequence. Unlike passive sampling (e.g., uniform or random sampling), our active sampling approach samples frames from each sequence such that redundancy is minimized and hence training set diversity is maximal. When compared to commonplace passive random sampling approaches [29, 32, 46], ST-RFD explicitly focuses on extracting a diverse set of training frames that will hence maximize model generalization. + +Finally, in order to employ semi-supervised learning, we propose a soft pseudo-label method informed by the LiDAR reflectivity response, thus maximizing the use of any annotated data samples. Whilst directly using unreliable soft pseudo-labels generally results in performance deterioration [5], the voxels corresponding to the unreliable predictions can instead be effectively leveraged as negative samples of unlikely categories. Therefore, we use cross-entropy to separate all voxels into two groups, i.e., a reliable and an unreliable group with low and high-entropy voxels respectively. We utilize predictions from the reliable group to derive positive pseudo-labels, while the remaining voxels from the unreliable group are pushed into a FIFO category-wise memory bank of negative samples [4]. To further assist semantic segmentation of varying materials in the situation where we have weak/unreliable/no labels, we append the reflectivity response features onto the point cloud features, which again improve segmentation results. + +We evaluate our method on the SemanticKITTI [7] and ScribbleKITTI [46] validation set. Our method outperforms contemporary state-of-the-art semi- [29,32] and weakly- [46] supervised methods and offers more in terms of performance on limited training data, whilst using less trainable parameters and less numerical operations (Less is More). + +Overall, our contributions can be summarized as follows: + +- A novel methodology for semi-supervised 3D LiDAR semantic segmentation that uses significantly less parameters and offers (more) superior accuracy. +- A novel Sparse Depthwise Separable Convolution (SDSC) module, to reduce trainable network parameters, and to both reduce the likelihood of over-fitting and facilitate a deeper network architecture. +- A novel Spatio-Temporal Redundant Frame Downsampling (ST-RFD) strategy, to extract a maximally diverse data subset for training by removing temporal redundancy and hence future annotation requirements. +- A novel soft pseudo-labeling method informed by Li-DAR reflectivity as a proxy to in-scene object material properties, facilitating effective use of limited data annotation. + +# 2. Related Work + +Semi-supervised learning (SSL) LiDAR semantic segmentation is a special instance of weak supervision that combines a small amount of labeled, with a large amount of unlabeled point cloud during training. Numerous approaches have been explored for LiDAR semantic segmentation. Projection-based approaches [31, 32, 36, 38, 51, 52, 56] make full use of 2D-convolution kernels by using range or other 2D image-based spherical coordinate representations of point clouds. Conversely, voxel-based approaches [32, 44, 46, 63] transform irregular point clouds to regular 3D grids and then apply 3D convolutional neural networks with a better balance of the efficiency and effectiveness. Pseudolabeling is generally applied to alleviate the side effect of intra-class negative pairs in feature learning from the teacher network [29, 32, 46, 57]. However, such methods only utilize samples with reliable predictions and thus ignore the valuable information that unreliable predictions carry. In our work, we combined a novel SSL framework with the mean teacher paradigm [45], demonstrating the utilization of unreliable pseudo-labels to improve segmentation performance. + +Depthwise separable convolution [41] is a depthwise convolution followed by a pointwise convolution, to reduce both model size and complexity. Being a more computationally efficient alternative than standard convolution, it is used for mobile applications [24, 25, 40] and hardware accelerators [37]. Furthermore, it is a building block of Xception [12], a deep convolutional neural network architecture that achieves state-of-the-art performance on the ImageNet [15] classification task, via more efficient use of model parameterization. In this work, we propose a novel sparse variant of depthwise separable convolution, which has both the efficiency advantages of depthwise separable convolution and those of sparse convolution for processing spatially-sparse data [20]. + +![](images/cb7efdaf02dbc34c6ee8777881c83f17f87d3d066f52549f8da8c0c1ef7c5cb1.jpg) +Figure 2. Our proposed architecture for unreliable pseudo-labels LiDAR semantic segmentation involves three stages: training, pseudolabeling, and distillation with unreliable learning. We apply ST-RFD sampling before training the Mean Teacher on available annotations. + +Temporal redundancy is highly prevalent within video [48, 62] and radar [35] sequences alike. Existing semi-supervised 3D LiDAR segmentation methods [32, 46] utilize a passive uniform sampling strategy to filter unlabeled points from a fully-labeled point cloud dataset. Active learning frameworks handle the redundancy to reduce annotation or training efforts by selecting informative and diverse subscenes for label acquisition [16, 27, 53]. We propose a novel temporal-redundancy-based sampling strategy with comparable time cost to uniform sampling, to reduce the inter-frame spatio-temporal redundancy and maximize data diversity. + +# 3. Methodology + +We first present an overview of the mean teacher framework we employ (Sec. 3.1) and then explain our use of unreliable pseudo-labels informed by LiDAR reflectivity for semi-supervised learning (Sec. 3.2). Subsequently, we detail our ST-RFD strategy for dataset diversity (Sec. 3.4) and finally our parameter-reducing SDSC module (Sec. 3.5). + +Formally, given a LiDAR point cloud $P = \{\mathbf{p} \mid \mathbf{p} = (x,y,z,I,R) \in \mathbb{R}^5\}$ where $(x,y,z)$ is a 3D coordinate, $I$ is intensity and $R$ is reflectivity, our goal is to train a semantic segmentation model by leveraging both a large amount of unlabeled $U = \{\mathbf{p}_i^u\}_{i=1}^{N_u} \subsetneq P$ and a smaller set of labeled data $V = \{(\mathbf{p}_i^v,\mathbf{y}_i^v)\}_{i=1}^{N_v} \subsetneq P$ . + +Our overall architecture involves three stages (Fig. 2): (1) Training: we utilize reflectivity-prior descriptors and adapt the Mean Teacher framework to generate high-quality pseudo-labels; (2) Pseudo-labeling: we fix the trained teacher model prediction in a class-range-balanced [46] manner, expanding dataset with Reflectivity-based Test Time Augmentation (Reflec-TTA) during test time; (3) Distillation with unreliable predictions: we train on the generated pseudo-labels, and utilize unreliable pseudo-labels in a category-wise memory bank for improved discrimination. + +# 3.1. Mean Teacher Framework + +We introduce weak supervision using the Mean Teacher framework [45], which avoids the prominent slow training issues associated with Temporal Ensembling [33]. This + +framework consists of two models of the same architecture known as the student and teacher respectively, for which we utilize a Cylinder3D [63]-based segmentation head $f$ . The weights of the student model $\theta$ are updated via standard backpropagation, while the weights of the teacher model $\theta^{*}$ are updated by the student model through Exponential Moving Averaging (EMA): + +$$ +\theta_ {t + 1} ^ {*} = \kappa \theta_ {t} ^ {*} + (1 - \kappa) \theta_ {t + 1}, \quad t \in \{0, 1, \dots T - 1 \}, \tag {1} +$$ + +where $\kappa$ denotes a smoothing coefficient to determine update speed, and $T$ is the maximum time step. + +During training, we train a set of weakly-labeled point cloud frames with voxel-wise inputs generated via asymmetrical 3D convolution networks [63]. For every point cloud, our optimization target is to minimize the overall loss: + +$$ +\mathcal {L} = \mathcal {L} _ {S} + \lambda_ {U} \mathcal {L} _ {U} + g \lambda_ {C} \mathcal {L} _ {C}, \tag {2} +$$ + +where $\mathcal{L}_S$ and $\mathcal{L}_U$ denote the losses applied to the supervised and unsupervised set of points respectively, $\mathcal{L}_C$ denotes the contrastive loss to make full use of unreliable pseudo-labels, $\lambda_U$ is the weight coefficient of $\mathcal{L}_U$ to balance the losses, and $g$ is the gated coefficient of $\mathcal{L}_C$ . $g$ equals $\lambda_C$ if and only if it is in the distillation stage. We use the consistency loss (implemented as a Kullback-Leibler divergence loss [23]), lovasz softmax loss [8], and the voxel-level InfoNCE [47] as $\mathcal{L}_U$ , $\mathcal{L}_S$ and $\mathcal{L}_C$ respectively. + +We first generate our pseudo-labels for the unlabeled points via the teacher model. Subsequently, we generate reliable pseudo-labels in a class-range-balanced (CRB) [46] manner, and utilize the qualified unreliable pseudo-labels as negative samples in the distillation stage. Finally, we train the model with both reliable and qualified unreliable pseudo-labels to maximize the quality of the pseudo-labels. + +# 3.2. Learning from Unreliable Pseudo-Labels + +Unreliable pseudo-labels are frequently eliminated from semi-supervised learning tasks or have their weights decreased to minimize performance loss [29, 39, 46, 55, 59, 65]. + +![](images/efefc56c3c12de329886a22fd1f5a5a4c9474351589c7d194babad413fe2484c.jpg) +Figure 3. Illustration on unreliable pseudo-labels. Left: entropy predicted from an unlabeled point cloud, with lower entropy corresponding to greener color. Right: Category-wise probability of an unreliable prediction $\mathsf{X}$ , only top-4 and last-4 probabilities shown. + +In line with this idea, we utilize CRB method [46] to first mask off unreliable pseudo-labels and then subsequently generate high-quality reliable pseudo-labels. + +However, such a simplistic discarding of unreliable pseudo-labels may lead to valuable information loss as it is clear that unreliable pseudo-labels (i.e., the corresponding voxels with high entropy) can offer information that helps in discrimination. Voxels that correlate to unreliable predictions can alternatively be thought as negative samples for improbable categories [49], although performance would suffer if such unreliable predictions are used as pseudo-labels directly [5]. As shown in Fig. 3, the unreliable pseudo predictions show a similar level of confidence on car and truck classes, whilst being sure the voxel cannot be pole or road. Thus, together with the use of CRB for high-quality reliable pseudo-labels, we also ideally want to make full use of these remaining unreliable pseudo-labels rather than simply discarding them. Following [49], we propose a method to leverage such unreliable pseudo-labels for 3D voxels as negative samples. However, to maintain a stable amount of negative samples, we utilize a category-wise memory bank $\mathcal{Q}_c$ (FIFO queue, [54]) to store all the negative samples for a given class $c$ . As negative candidates in some specific categories are severely limited in a mini-batch due to the long-tailed class distribution of many tasks (e.g. autonomous driving), without such an approach in place we may instead see the gradual dominance of large and simple-to-learn classes within our generated pseudo-labels. + +Following [22, 47], our method has three prerequisites, i.e., anchor voxels, positive candidates, and negative candidates. They are obtained by sampling from a particular subset, constructed via Eq. (3) and Eq. (4), in order to reduce overall computation. In particular, the set of features of all candidate anchor voxels for class $c$ is denoted as: + +$$ +\mathcal {A} _ {c} = \left\{\mathbf {E} _ {a, b} \mid y _ {a, b} ^ {*} = c, p _ {a, b} (c) > \delta_ {p} \right\}, \tag {3} +$$ + +where $\mathbf{E}_{a,b}$ is the feature embedding for the $a$ -th point cloud frame at voxel $b$ , $\delta_p$ is the positive threshold of all classes, $p_{a,b}(c)$ is the softmax probability by the segmentation head at $c$ -th dimension. $y_{a,b}^*$ is set to the ground truth label $y_{a,b}^*$ if the ground truth is available, otherwise, $y_{a,b}^*$ is set to the pseudo label $\hat{y}_{a,b}$ , due to the absence of ground truth. + +The positive sample is the common embedding center of all possible anchors, which is the same for all anchors from the same category, shown in Eq. (4). + +$$ +\mathbf {E} _ {\mathbf {c}} ^ {+} = \frac {1}{| \mathcal {A} _ {c} |} \sum_ {\mathbf {E} _ {c} \in \mathcal {A} _ {c}} \mathbf {E} _ {c}. \tag {4} +$$ + +Following [49], we similarly construct multiple negative samples $\mathbf{E}_c^-$ for each anchor voxel. + +Finally, for each anchor voxel containing one positive sample and $N - 1$ negative samples, we propose the voxel-level InfoNCE loss [47] (a variant of contrastive loss) $\mathcal{L}_C$ in Eq. (5) to encourage maximal similarity between the anchor voxel and the positive sample, and the minimal similarity between the anchor voxel and multiple negative samples. + +$$ +\begin{array}{l} \mathcal {L} _ {C} = - \frac {1}{C} \sum_ {c = 0} ^ {C - 1} \underset {\mathbf {E} _ {c}} {\mathbb {E}} \left[ \log \frac {f \left(\mathbf {e} _ {c} , \mathbf {e} _ {c} ^ {+} , \tau\right)}{\sum_ {\mathbf {e} _ {c , j} ^ {-} \in \mathbf {E} _ {c} ^ {-}} f \left(\mathbf {e} _ {c} , \mathbf {e} _ {c , j} ^ {-} , \tau\right)} \right] \\ = - \frac {1}{C} \sum_ {c = 0} ^ {C - 1} \mathbb {E} _ {\mathbf {E} _ {c}} \left[ \log \frac {\exp \left(\left\langle \mathbf {e} _ {c} , \mathbf {e} _ {c} ^ {+} \right\rangle / \tau\right)}{\exp \left(\left\langle \mathbf {e} _ {c} , \mathbf {e} _ {c} ^ {+} \right\rangle / \tau\right) + \sum_ {j = 1} ^ {N - 1} \exp \left(\left\langle \mathbf {e} _ {c} , \mathbf {e} _ {c , j} ^ {-} \right\rangle / \tau\right)} \right] \tag {5} \\ \end{array} +$$ + +where $\langle \cdot, \cdot \rangle$ denotes cosine similarity. $\mathbf{e_c}$ , $\mathbf{e_c}^+$ and $\mathbf{e}_{c,j}^-$ denote the embedding, positive sample of the current anchor voxel, and embedding of the $j$ -th negative sample of class $c$ . + +# 3.3. Reflectivity-Based Test Time Augmentation + +To obtain minimal accuracy degradation despite very few weak labels, e.g., $1\%$ weakly-labeled ScribbleKITTI [46] dataset, we propose a Test Time Augmentation (TTA) that does not depend on any label, but only relies on a feature of original LiDAR points themselves. Also included in almost every LiDAR benchmark dataset for autonomous driving [7, 10, 18, 30, 34, 46], is the intensity of light reflected from the surface of an object at each point. In the presence of limited data labels in the semi-supervised learning case, this property of the material surface, normalized by distance to obtain the surface reflectivity in Eq. (6), could readily act as auxiliary information to identify different semantic classes. + +Our intuition is that reflectivity $R$ , as a point-wise distance-normalized intensity feature, offers consistency across lighting conditions and range as: + +$$ +R = I r ^ {2} = \frac {S}{4 \pi r ^ {2}} \cdot r ^ {2} \propto S, \tag {6} +$$ + +where $S$ is the return strength of the LiDAR laser pulse, $I$ is the intensity and $r$ is the point distance from the source on the basis that scene objects with similar surface material, coating, and color characteristics will share similar $S$ returns. + +On this basis, we define our novel reflectivity-based Test Time Augmentation (Reflec-TTA) technique, as a substitute for label-dependent Pyramid Local Semantic-context + +![](images/5693020f7cb1b08a21d6fa317de7d90291ddeeb78587668d9f881ce5e674b64e.jpg) +Figure 4. Coarse histograms of Reflec-TTA bins (not to scale). + +(PLS) augmentation [46] during test-time as ground truth is not available. We append our point-wise reflectivity to the existing point features in order to enhance performance in presence of false or non-existent pseudo-labels at the distillation stage. As shown in Fig. 4, and following [46], we apply various sizes $s$ of bins in cylindrical coordinates to analyze the intrinsic point distribution of the LiDAR sensor at varying resolutions (shown in red, green and blue in Fig. 4). For each bin $b_{i}$ , we compute a coarse histogram, $\mathbf{h}_{i}$ : + +$$ +\mathbf {h} _ {i} = \left\{h _ {i} ^ {(k)} \mid k \in [ 1, N _ {b} ] \right\} \in \mathbb {R} ^ {N _ {b}}, \quad i \in [ 1, s ], +$$ + +$$ +h _ {i} ^ {(k)} = \# \left\{R _ {j} \in r _ {k}, \forall j \mid p _ {j} \in b _ {i} \right\}, \tag {7} +$$ + +$$ +r _ {k} = \left[ (k - 1) / N _ {b}, k / N _ {b}\right), \quad k \in \left[ 1, N _ {b} \right]. +$$ + +The Reflec-TTA features $R^{\text{心}}$ of all points $p_j \in b_j$ is further computed as the concatenation of the coarse histogram $\mathbf{h}_i$ of the normalized histogram: + +$$ +R ^ {\circledast} = \left\{\mathbf {h} _ {i} / \operatorname * {m a x} \left(\mathbf {h} _ {i}\right) \mid i \in [ 1, s ] \right\} \in \mathbb {R} ^ {s N _ {b}} \tag {8} +$$ + +In the distillation stage, we append $R^{\text{※}}$ to the input features and redefine the input LiDAR point cloud as the augmented set of points $P^{\text{※}} = \{p \mid (x,y,z,I,R^{\text{※}}) \in \mathbb{R}^{sN_b + 4}\}$ . + +# 3.4. Spatio-Temporal Redundant Frame Downsampling + +Due to the spatio-temporal correlation of LiDAR point cloud sequences often captured from vehicles in metropolitan locales, many large-scale point cloud datasets demonstrate significant redundancy. Common datasets employ a frame rate of $10\mathrm{Hz}$ [7, 9, 10, 18, 30, 34, 42], and a number of concurrent laser channels (beams) of 32 [10], 64 [7, 9, 18, 30, 42] or 128 [34]. Faced with such large-scale, massively redundant training datasets, the popular practice of semi-supervised semantic segmentation approaches [11, 17, 32, 45, 64] is to uniformly sample $1\%$ , $10\%$ , $20\%$ , or $50\%$ of the available annotated training frames, without considering any redundancy attributable to temporary periods of stationary capture (e.g. due to traffic, Fig. 5) or multi-pass repetition (e.g. due to loop closure). + +To extract a diverse set of frames, we propose a novel algorithm called Spatio-Temporal Redundant Frame Downsampling (ST-RFD, Algorithm 1) that determines spatiotemporal redundancy by analyzing the spatial-overlap within time-continuous LiDAR frame sequences. The key idea + +![](images/315a18dbfe42f30f71739715c59c75fba9e83e6d3d026081c0c59b608b9eec6e.jpg) +Figure 5. Illustration of LiDAR frame temporal correlation as [#frame ID] redundancy with $5\%$ sampling on SemanticKITTI [7] (sequence 00) using uniform sampling (selected frames in $\bullet$ ) and ST-RFD strategy ( $\bullet$ ). + +is that if spatial-overlap among some continuous frame sequence is high due to spatio-temporal redundancy, multiple representative frames can be sub-sampled for training, significantly reducing both training dataset size and redundant training computation. + +Fig. 6 shows an overview of ST-RFD. It is conducted inside each temporal continuous LiDAR sequence $e$ . First, we evenly divide $p$ point cloud frames in each sequence into $\lceil p / q \rceil$ subsets (containing $q$ frames). For each frame at time $t$ inside the subset, we find its corresponding RGB camera image in the dataset at time $t$ and $t + 1$ . To detect the spatio-temporal redundancy at time $t$ , the similarity $\psi(t, t + 1)$ between temporally adjacent frames are then computed via the Structural Similarity Index Measure (SSIM, [50]). We utilize the mean value of similarity scores between all adjacent frames in the current subset as a proxy to estimate the spatio-temporal redundancy present. A sampling rate is then determined according to this mean similarity for frame selection within this subset. This is repeated for all subsets in every sequence to construct our final set of sub-sampled LiDAR frames for training. + +Concretely, as shown in Algorithm 1, we implement a ST-RFD supervisor that determines the most informative assignments (i.e., the key point cloud frames) that the teacher and student networks should train on respectively. The ST-RFD supervisor has an empirical supervisor function $v$ , which decides the amount of assignments, i.e., the sampling rate corresponding to the extent of spatio-temporal redundancy. Using SSIM [50] as the redundancy function $\psi$ to measure the similarity between the RGB images associated with two adjacent point clouds, we define the empirical supervisor function $v$ with decay property $v(x) = \exp(-\beta x)$ , where $\beta \in (0, +\infty)$ is the decay coefficient, and $x$ is the redundancy calculated from $\psi$ . In this way, the higher the degree of spatio-temporal redundancy (as $\psi \to 1$ ), the lower the sampling rate our ST-RFD supervisor will allocate, hence reducing the training set requirements for teacher and student alike. + +# 3.5. Sparse Depthwise Separable Convolution + +Existing LiDAR point cloud semantic segmentation methods generally rely on a large-scale backbone architecture with + +![](images/4f10a0f9039838e59c95ab71ed37c7acfdbab630322376b1c9bbe79f4bb16629.jpg) +Figure 6. Overview of our proposed Spatio-Temporal Redundant Frame Downsampling approach. + +Algorithm 1: Spatio-Temporal Redundant Frame Downsampling. +Input: Point cloud frames pool $P$ (size of $p$ ), subset size $q$ , redundancy function $\psi \in [0,1]$ and empirical supervisor function $v$ . +1 Divide $P$ evenly into $\lceil p / q\rceil$ subsets $Q$ . +2 $D \leftarrow$ empty dictionary. +3 for all $e \leftarrow 0 : n_e - 1$ do +4 // loop for all sequences +5 for all $i \leftarrow 0 : \lceil p / q\rceil - 1$ do +6 // loop for subsets $Q$ +7 $\overline{M_i} \leftarrow \frac{1}{q} \sum_{j=0}^{q-1} \psi(Q_{i,j})$ . // redundancy +8 $k_i \leftarrow \lceil v(\overline{M_i}) \cdot q \rceil$ . +9 $T_i \leftarrow$ select $k_i$ frames in $Q_i$ with the smallest $M_i$ . +10 $C_e \leftarrow C_e \cup T_i$ . +11 Append key-value pair $(e, C_e)$ into $D$ . +Return: Dictionary $D$ . + +![](images/1ce3f4649f7d55d53051888e4356abfba19f66195eacf23ff80906f8f455fbe2.jpg) +Figure 7. Illustration of the SDSC convolution module. + +tens of millions of trainable parameters [23,26,28,46,60,63] due to the requirement for 3D (voxel-based) convolution operations, to operate on the voxelized topology of the otherwise unstructured LiDAR point cloud representation, which suffer from both high computational training demands and the risk of overfitting. Based on the observation that depthwise separable convolution has shown results comparable with regular convolution in tasks such as image classification but with significantly fewer trainable parameters [12,24,25,37,40,43], here we pursue the use of such an approach within 3D point cloud semantic segmentation. + +As such we propose the first formulation of sparse variant depthwise separable convolution [25] applied to 3D point clouds, namely Sparse Depthwise Separable Convolution (SDSC). SDSC combines the established computational advantages of sparse convolution for point cloud segmentation [20], with the significant trainable parameter reduction offered by depthwise separable convolution [12]. + +Our SDSC module, as outlined in Fig. 7, initially takes a + +tensor $F \in \mathbb{R}^{H_F \times W_F \times L_F \times M}$ as input, where $H_F$ , $W_F$ , $L_F$ and $M$ denote radius, azimuth, height in the cylinder coordinate [63] and channels respectively. Firstly, a sparse depthwise convolution $\mathrm{SDC}(M, M, D_k, s = 1)$ is applied, with $M$ input and output feature planes, a kernel size of $D_k$ and stride $s$ in order to output a tensor $T \in \mathbb{R}^{H_F \times W_F \times L_F \times M}$ . Inside our sparse depthwise convolution, $M$ sparse spatial convolutions are performed independently over each input channel using submanifold sparse convolution [20] due to its tensor shape preserving property at no computational or memory overhead. Secondly, the sparse pointwise convolution $\mathrm{SPC}(M, N, 1, s = 1)$ projects the channels output $T$ by the sparse depthwise convolution onto a new channel space, to mix the information across different channels. As a result, the sparse depthwise separable convolution $\mathrm{SDSC}(M, N, D_k, s = 1)$ is the compound of the sparse depthwise convolution and the sparse pointwise convolution, namely $\mathrm{SDSC}(M, N, D_k, s = 1) = \mathrm{SDC} \circ \mathrm{SPC}$ . + +Using a sparse voxelized input representation similar to [19], and a series of such SDSC sub-modules we construct the popular Cylinder3D [63] sub-architectures within our overall Mean Teacher architectural design (Fig. 2). + +# 4. Evaluation + +We evaluate our proposed Less is More 3D (LiM3D) approach against state-of-the-art 3D point cloud semantic segmentation approaches using the SemanticKITTI [7] and ScribbleKITTI [46] benchmark datasets. + +# 4.1. Experimental Setup + +SemanticKITTI [7] is a large-scale 3D point cloud dataset for semantic scene understanding with 20 semantic classes consisting of 22 sequences - [00 to 10 as training-split (of which 08 as validation-split) + 11 to 21 as test-split]. + +ScribbleKITTI [46] is the first scribble (i.e. sparsely) annotated dataset for LiDAR semantic segmentation providing sparse annotations for the training split of SemanticKITTI for 19 classes, with only $8.06\%$ of points from the full SemanticKITTI dataset annotated. + +Evaluation Protocol: Following previous work [29,32,46, 63], we report performance on the SemanticKITTI and ScribbleKITTI training set for intermediate training steps, as this metric provides an indication of the pseudo-labeling quality, and on the validation set to assess the performance benefits of each individual component. Performance is reported using the mean Intersection over Union (mIoU, as $\%$ ) metric. + +Table 1. Comparative mIoU for Range- and Voxel-based methods using uniform sampling (U), sequential partition (P) and ST-RFD sampling (S): bold/underlined = best/2nd best; * denotes reproduced result; - denotes missing result due to unavailability from original authors. + +
Repr.Samp.MethodSemanticKITTI [7]ScribbleKITTI [46]
1%5%10%20%40%50%100%1%5%10%20%40%50%100%
RangeULaserMix [32](2022)43.4-58.859.4-61.4-38.3-54.455.6-58.7-
VoxelUCylinder3D [63](CVPR'21)-45.456.157.858.7-67.8-39.248.052.153.8-56.3
ULaserMix [32](2022)50.6-60.061.9-62.3-44.2-53.755.1-56.8-
PJiang et al. [29](ICCV'21)-41.849.958.859.9-65.8-------
UUnal et al. [46](CVPR'22)-49.9*58.7*59.1*60.9-68.2*-46.9*54.2*56.5*58.6*-61.3
SLiM3D+SDSC(ours)57.257.661.061.762.162.767.555.856.156.957.258.959.360.7
SLiM3D(ours)58.459.562.263.163.363.669.557.058.161.061.262.062.162.4
+ +For semi-supervised training, we report over both the benchmarks using the SemanticKITTI and ScribbleKITTI validation set under $5\%$ , $10\%$ , $20\%$ , and $40\%$ partitioning. We further report the relative performance of semi-supervised or scribble-supervised for ScribbleKITTI (SS) training to the fully supervised upper-bound (FS) in percentages (SS/FS) to further analyze semi-supervised performance and report the results for the fully-supervised training on both validation sets for reference. The trainable parameter count and number of multiply-adds (multi-adds) are additionally provided as a metric of computational cost. + +Implementation Details: Training is performed using $4 \times$ NVIDIA A100 80GB GPU without pre-trained weights with a DDP shared training strategy [1] to maintain GPU scaling efficiency, whilst reducing memory overhead significantly. Specific hyper-parameters are set as follows - Mean Teacher: $\kappa = 0.99$ ; unreliable pseudo-labeling: $\lambda_{C} = 0.3$ , $\tau = 0.5$ ; ST-RFD: $\beta = \{7.45, 5.72, 4.00, 2.28, 0\}$ for sampling $\{5\%, 10\%, 20\%, 40\%, 100\}$ labeled training frames, assuming the remainder as unlabeled; Reflec-TTA: $N_{b} = 10$ , $s = 3$ various Reflec-TTA bin sizes, following [46], we set each bin $b_{i} = (\rho, \phi) \in \{(20, 40), (40, 80), (80, 120)\}$ . + +![](images/d82192639287f5dbf30e7eb547e5c82753135d975c1a8a673bb720a2053b5d26.jpg) +Figure 8. Comparing the $10\%$ sampling split of SemanticKITTI (SeK, first row) and ScribbleKITTI (ScK, second row) validation set with ground-truth (left), our approach (middle) and Unal et al. [46] (right) with areas of improvement highlighted. + +# 4.2. Experimental Results + +In Tab. 1, we present the performance of our Less is More 3D (LiM3D) point cloud semantic segmentation approach both with (LiM3D+SDSC) and without (LiM3D) SDSC in a side + +Table 2. Component-wise ablation of LiM3D (mIoU as %, and #parameters in millions, M) on SemanticKITTI [7] training and validation sets where UP, RF, RT, ST, SD denote Unreliable Pseudolabeling, Reflectivity Feature, Reflec-TTA, ST-RFD, and SDSC module respectively. + +
UPRFRTSTSDTraining mIoU (%)Validation mIoU (%)#Params(M)
5%10%20%40%5%10%20%40%
82.887.587.888.254.858.159.360.849.6
✓----55.958.859.961.249.6
✓✓83.688.388.789.156.859.660.561.449.6
✓✓----57.559.861.262.649.6
✓✓✓----58.761.362.462.849.6
✓✓✓✓85.289.189.589.759.562.263.163.349.6
✓✓✓✓✓83.888.689.089.257.661.061.762.121.5
+ +by-side comparison with leading contemporary state-of-the-art approaches on the SemanticKITTI and ScribbleKITTI benchmark validation sets to illustrate our approach offers superior or comparable (within $1\%$ mIoU) performance across all sampling ratios. Furthermore, we present supporting qualitative results in Fig. 8. + +On SemanticKITTI, with a lack of available supervision, LiM3D shows a relative performance (SS/FS) from $85.6\%$ (5%-fully-supervised) to $91.1\%$ (40%-fully-supervised), and LiM3D+SDSC from $85.3\%$ to $92.0\%$ , compared to their respective fully supervised upper-bound. LiM3D/LiM3D+SDSC performance is also less sensitive to reduced labeled data sampling compared with other methods. + +Our model significantly outperforms on small ratio sampling splits, e.g., $5\%$ and $10\%$ . LiM3D shows up to $19.8\%$ and $18.9\%$ mIoU improvements whilst, with a smaller model size $\mathrm{LiM3D + SDSC}$ again shows significant mIoU improvements by up to $16.4\%$ and $15.5\%$ when compared with other range and voxel-based methods respectively. + +# 4.3. Ablation Studies + +Effectiveness of Components. In Tab. 2 we ablate each component of LiM3D step by step and report the performance on the SemanticKITTI training set at the end of training as an overall indicator of pseudo-labeling quality in addition to the corresponding validation set. + +As shown in Tab. 2, adding unreliable pseudo-labeling (UP) in the distillation stage, we can increase the valid mIoU + +by $+0.7\%$ on average in validation set. Appending reflectivity features (RF) in the training stage, we further improve the mIoU on the training set by $+0.7\%$ on average. Due to the improvements in training, the model generates a higher quality of pseudo-labels, which results to a $+0.5\%$ increase in mIoU in the validation set. If we disable reflectivity features in the training stage, applying Reflec-TTA in the distillation stage alone, we then get an average improvement of $+1.3\%$ compared with pseudo-labeling only. On the whole, enabling all reflectivity-based components (RF+RT) shows great improvements of up to $+2.8\%$ in validation mIoU. + +Table 3. The computation cost and mIoU (in percentage) under $5\%$ -labeled training results on SemanticKITTI (SeK) and ScribbleKITTI (ScK) validation set. + +
Method# Parameters# Mult-AddsSeK [7]ScK [46]
Cylinder3D [63]56.3476.9M45.439.2
Unal et al. [46]49.6420.2M49.946.9
2DPASS [58]26.5217.4M51.745.1
MinkowskiNet [13]21.7114.0G42.435.8
SPVNAS [44]12.573.8G45.138.9
LiM3D+SDSC (ours)21.5182.0M57.654.7
LiM3D (ours)49.6420.2M59.558.1
+ +Table 4. Effects of ST-RFD sampling on SemanticKITTI and ScribbleKITTI validation set (mIoU as %). + +
SamplingSemanticKITTI [7]ScribbleKITTI [46]
5%10%20%40%5%10%20%40%
Random58.561.662.662.757.160.360.560.9
Uniform58.761.362.462.856.960.660.361.0
ST-RFD-R59.162.462.963.458.060.761.261.8
ST-RFD59.562.263.163.358.161.061.262.0
+ +Table 5. Effects of differing reliability using pseudo voxels on SemanticKITTI validation set, measured by the entropy of voxel-wise prediction. Unreliable and Reliable: selecting negative candidates with top $20\%$ highest entropy scores and bottom $20\%$ counterpart respectively. Random: sampling randomly regardless of entropy. + +
RatioUnreliableReliableRandom
mIoUSS/FFmIoUSS/FFmIoUSS/FF
5%59.585.657.282.356.481.2
10%62.289.560.887.559.785.9
20%63.190.861.488.360.587.1
40%63.391.162.890.461.388.2
+ +Table 6. Reflectivity (Reflec-TTA) vs. Intensity (intensity-based TTA) on SemanticKITTI and ScribbleKITTI validation set (mIoU, %). + +
TTASemanticKITTI [7]ScribbleKITTI [46]
5%10%20%40%5%10%20%40%
Intensity56.259.159.860.955.757.557.959.2
Reflectivity59.562.263.163.358.161.061.262.0
+ +Substituting the uniform sampling with our ST-RFD strategy, we observe further average improvements of $+1.0\%$ and $+0.8\%$ on training and validation respectively (Tab. 2). + +Our SDSC module reduces the trainable parameters of our model by $57\%$ , with a performance cost of $-0.7\%$ and $-1.4\%$ mIoU on training and validation respectively + +(Tab. 2). Finally, we provide two models, one without SDSC (LiM3D) and one with (LiM3D+SDSC), corresponding to the bottom two rows of Tab. 2. + +Effectiveness of SDSC module. In Tab. 3, we compare our LiM3D and LiM3D+SDSC with recent state-of-the-art methods under $5\%$ -labeled semi-supervised training on the SemanticKITTI and ScribbleKITTI validation sets. LiM3D+SDSC outperforms the voxel-based methods [46, 63] with at least a $2.3\times$ reduction in model size. Similarly, with comparable model size [13, 44, 58], LiM3D+SDSC has higher mIoU in both datasets and up to $641\times$ fewer multiply-add operations. + +Effectiveness of ST-RFD strategy. In Tab. 4, we illustrate the effectiveness of our ST-RFD strategy by comparing LiM3D with two widely-used strategies in semi-supervised training, i.e., random sampling and uniform sampling on SemanticKITTI [7] and ScribbleKITTI [46] validation set. Whilst uniform and random sampling have comparable results on both validation sets, simply applying our ST-RFD strategy improves the baseline by $+0.90\%$ , $+0.75\%$ , $+0.60\%$ and $+0.55\%$ on SemanticKITTI under $5\%$ , $10\%$ , $20\%$ and $40\%$ sampling protocol respectively. Furthermore, using corresponding range images of point cloud, rather than RGB images to compute the spatio-temporal redundancy within ST-RFD (see ST-RFD-R in Tab. 4), has no significant difference on the performance. + +Effectiveness of Unreliable Pseudo-Labeling. In Tab. 5, we evaluate selecting negative candidates with different reliability to illustrate the improvements of using unreliable pseudo-labels in semi-supervised semantic segmentation. The "Unreliable" selecting of negative candidates outperforms other alternative methodologies, showing the positive performance impact of unreliable pseudo-labels. + +Effectiveness of Reflec-TTA. In Tab. 2, we compare LiM3D performance with and without Reflec-TTA and further experiment on the SemanticKITTI and ScribbleKITTI validation set in Tab. 6. This demonstrates that the LiDAR point-wise intensity feature $I^*$ , in place of the distance-normalized reflectivity feature $R^*$ , offers inferior on-task performance. + +# 5. Conclusion + +This paper presents an efficient semi-supervised architecture for 3D point cloud semantic segmentation, which achieves more in terms of performance with less computational costs, less annotations, and less trainable model parameters (i.e., Less is More, LiM3D). Our architecture consists of three novel contributions: the SDSC convolution module, the ST-RFD sampling strategy, and the pseudo-labeling method informed by LiDAR reflectivity. These individual components can be applied to any 3D semantic segmentation architecture to reduce the gap between semi or weakly-supervised and fully-supervised learning on task performance, whilst managing model complexity and computation costs. + +# References + +[1] FairScale. FairScale: A general purpose modular PyTorch library for high performance and large scale training, 2021. 7 +[2] Shahriar Akramullah. Digital Video Concepts, Methods, and Metrics: Quality, Compression, Performance, and Power Trade-off Analysis. Apress, Berkeley, CA, 2014. 2 +[3] Inigo Alonso, Luis Riazuelo, Luis Montesano, and Ana C. Murillo. 3D-MiniNet: Learning a 2D Representation From Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation. IEEE Robot. Autom. Lett., 5(4):5432-5439, Oct. 2020. 1 +[4] Inigo Alonso, Alberto Sabater, David Ferstl, Luis Montesano, and Ana C. Murillo. Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank. In Int. Conf. Comput. Vis., pages 8199-8208, 2021. 2 +[5] Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, and Kevin McGuinness. Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning. In Int. Jt. Conf. Neural Netw., 2020. 2, 4 +[6] Amir Atapour-Abarghouei and Toby P. Breckon. Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3368–3379, 2019. 1 +[7] Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyril Stachniss, and Jurgen Gall. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. In Int. Conf. Comput. Vis., pages 9296-9306, Seoul, Korea (South), Oct. 2019. 2, 4, 5, 6, 7, 8 +[8] Maxim Berman, Amal Rannen Triki, and Matthew B. Blaschko. The Lovasz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 3 +[9] Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, and Felix Heide. Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather. In IEEE Conf. Comput. Vis. Pattern Recog., pages 11679-11689, 2020. 5 +[10] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. Nuscenes: A multimodal dataset for autonomous driving. IEEE Conf. Comput. Vis. Pattern Recog., pages 11618-11628, 2020. 4, 5 +[11] Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang. Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2613-2622, June 2021. 1, 5 +[12] François Chollet. Xception: Deep Learning with Depthwise Separable Convolutions. In IEEE Conf. Comput. Vis. Pattern Recog., Apr. 2017. 2, 6 +[13] Christopher Choy, JunYoung Gwak, and Silvio Savarese. 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3070-3079, 2019. 1, 8 + +[14] Tiago Cortinhal, George Tzelepis, and Eren Erdal Aksoy. SalsaNext: Fast, Uncertainty-Aware Semantic Segmentation of LiDAR Point Clouds. In Adv. Vis. Comput., pages 207-222, 2020. 1 +[15] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In IEEE Conf. Comput. Vis. Pattern Recog., pages 248–255, 2009. 2 +[16] Ngoc Phuong Anh Duong, Alexandre Almin, Léo Lemarie, and B Ravi Kiran. Reducing redundancy in Semantic-KITTI: Study on data augmentations within Active Learning. In Adv. Neural Inform. Process. Syst. Worksh., page 8, 2017. 3 +[17] Geoff French. Semi-supervised semantic segmentation needs strong, varied perturbations. In Brit. Mach. Vis. Conf., page 21, 2020. 5 +[18] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3354-3361, 2012. 4, 5 +[19] Ben Graham. Sparse 3D convolutional neural networks. In Brit. Mach. Vis. Conf., pages 150.1-150.9, Swansea, 2015. 6 +[20] Benjamin Graham, Martin Engelcke, and Laurens van der Maaten. 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 2, 6 +[21] Benjamin Graham and Laurens van der Maaten. Submanifold Sparse Convolutional Networks, June 2017. 2 +[22] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum Contrast for Unsupervised Visual Representation Learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9726-9735, 2020. 4 +[23] Yuenan Hou, Xinge Zhu, Yuexin Ma, Chen Change Loy, and Yikang Li. Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., page 10, 2022. 1, 3, 6 +[24] Andrew Howard, Mark Sandler, Bo Chen, Weijun Wang, Liang-Chieh Chen, Mingxing Tan, Grace Chu, Vijay Vasudevan, Yukun Zhu, Ruoming Pang, Hartwig Adam, and Quoc Le. Searching for MobileNetV3. In Int. Conf. Comput. Vis., pages 1314-1324, 2019. 2, 6 +[25] Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andretto, and Hartwig Adam. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, Apr. 2017. 2, 6 +[26] Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham. RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. In IEEE Conf. Comput. Vis. Pattern Recog., 2020. 1, 6 +[27] Zeyu Hu, Xuyang Bai, Runze Zhang, Xin Wang, Guangyuan Sun, Hongbo Fu, and Chiew-Lan Tai. LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation. In Eur. Conf. Comput. Vis. Springer. 3 +[28] Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Emilie Wirbel, and Patrick Pérez. xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation. IEEE Conf. Comput. Vis. Pattern Recog., 2021. 1, 6 + +[29] Li Jiang, Shaoshuai Shi, Zhuotao Tian, Xin Lai, Shu Liu, ChiWing Fu, and Jiaya Jia. Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation. In Int. Conf. Comput. Vis., 2021. 2, 3, 6, 7 +[30] R. Kesten, M. Usman, J. Houston, T. Pandya, K. Nadhamuni, A. Ferreira, M. Yuan, B. Low, A. Jain, P. Ondruska, S. Omari, S. Shah, A. Kulkarni, A. Kazakova, C. Tao, L. Platinsky, W. Jiang, and V. Shet. Lyft level 5 perception dataset 2020, 2019. 4, 5 +[31] Deyvid Kochanov, Fatemeh Karimi Nejadasl, and Olaf Booij. KPRNet: Improving projection-based LiDAR semantic segmentation, Aug. 2020. 1, 2 +[32] Lingdong Kong, Jiawei Ren, Liang Pan, and Ziwei Liu. LaserMix for Semi-Supervised LiDAR Semantic Segmentation, June 2022. 2, 3, 5, 6, 7 +[33] Samuli Laine and Timo Aila. Temporal Ensembling for Semi-Supervised Learning. In Int. Conf. Learn. Represent., 2017. 3 +[34] Li Li, Khalid N. Ismail, Hubert P. H. Shum, and Toby P. Breckon. DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications. In Int. Conf. 3D Vis., pages 1227-1237, London, United Kingdom, Dec. 2021. 4, 5 +[35] Peizhao Li, Pu Wang, Karl Berntorp, and Hongfu Liu. Exploiting Temporal Relations on Radar Perception for Autonomous Driving. In IEEE Conf. Comput. Vis. Pattern Recog., pages 17071-17080, 2022. 3 +[36] Venice Erin Liong, Thi Ngoc Tho Nguyen, Sergi Widjaja, Dhananjai Sharma, and Zhuang Jie Chong. AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation, Dec. 2020. 2 +[37] Dominic Masters, Antoine Labatie, Zach Eaton-Rosen, and Carlo Luschi. Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution Training, Aug. 2021. 2, 6 +[38] Andres Milioto, Ignacio Vizzo, Jens Behley, and Cyril Stachniss. RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation. In Int. Conf. Intell. Robots Syst., pages 4213-4220, 2019. 1, 2 +[39] Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen. Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning. In Adv. Neural Inform. Process. Syst., volume 29, 2016. 3 +[40] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zh-moginov, and Liang-Chieh Chen. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4510-4520, Salt Lake City, UT, 2018. IEEE. 2, 6 +[41] Laurent Sifre. Rigid-Motion Scattering For Image Classification. PhD thesis, Ecole Polytechnique, 2014. 2 +[42] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, and Dragomir Anguelov. + +Scalability in Perception for Autonomous Driving: Waymo Open Dataset. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2443-2451, Seattle, WA, USA, June 2020. 5 +[43] Mingxing Tan and Quoc Le. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In Proc. 36th Int. Conf. Mach. Learn., pages 6105-6114. PMLR, May 2019. 6 +[44] Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, and Song Han. Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution. In Eur. Conf. Comput. Vis., 2020. 1, 2, 8 +[45] Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In Adv. Neural Inform. Process. Syst., 2017. 2, 3, 5 +[46] Ozan Unal, Dengxin Dai, and Luc Van Gool. Scribblesupervised LiDAR semantic segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., 2022. 1, 2, 3, 4, 5, 6, 7, 8 +[47] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation Learning with Contrastive Predictive Coding, Jan. 2019. 1, 3, 4 +[48] Jue Wang, Gedas Bertasius, Du Tran, and Lorenzo Torresani. Long-Short Temporal Contrastive Learning of Video Transformers. In IEEE Conf. Comput. Vis. Pattern Recog., pages 13990–14000, 2022. 3 +[49] Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, and Xinyi Le. Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels. IEEE Conf. Comput. Vis. Pattern Recog., Mar. 2022. 1, 4 +[50] Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600-612, Apr. 2004. 5 +[51] Bichen Wu, Alvin Wan, Xiangyu Yue, and Kurt Keutzer. SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud. In Int. Conf. Robot. Autom., pages 1887–1893, 2018. 1, 2 +[52] Bichen Wu, Xuanyu Zhou, Sicheng Zhao, Xiangyu Yue, and Kurt Keutzer. SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud. In Int. Conf. Robot. Autom., pages 4376–4382, 2019. 1, 2 +[53] Tsung-Han Wu, Yueh-Cheng Liu, Yu-Kai Huang, Hsin-Ying Lee, Hung-Ting Su, Ping-Chia Huang, and Winston H. Hsu. ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation. In Int. Conf. Comput. Vis. 3 +[54] Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin. Unsupervised Feature Learning via Non-parametric Instance Discrimination. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3733-3742, 2018. 4 +[55] Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V. Le. Self-Training With Noisy Student Improves ImageNet Classification. In IEEE Conf. Comput. Vis. Pattern Recog., pages 10687–10698, 2020. 3 +[56] Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Va-jda, Kurt Keutzer, and Masayoshi Tomizuka. SqueezeSegV3: + +Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Eur. Conf. Comput. Vis., 2020. 1, 2 +[57] Xu Yan, Jiantao Gao, Jie Li, Ruimao Zhang, Zhen Li, Rui Huang, and Shuguang Cui. Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion. In Conf. AAAI Artif. Intell., 2021. 2 +[58] Xu Yan, Jiantao Gao, Chaoda Zheng, Chao Zheng, Ruimao Zhang, Shuguang Cui, and Zhen Li. 2DPASS: 2D priors assisted semantic segmentation on LiDAR point clouds. In Eur. Conf. Comput. Vis., 2022. 1, 8 +[59] Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, and Yang Gao. ST++: Make Self-Training Work Better for Semi-Supervised Semantic Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4268–4277, 2022. 3 +[60] Li Yi, Boqing Gong, and Thomas Funkhouser. Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds. In IEEE Conf. Comput. Vis. Pattern Recog., pages 15358-15368, Nashville, TN, USA, June 2021. 1, 6 +[61] Yang Zhang, Zixiang Zhou, Philip David, Xiangyu Yue, Zerong Xi, Boqing Gong, and Hassan Foroosh. PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., 2020. 1 +[62] Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, and Yichen Wei. Deep Feature Flow for Video Recognition. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4141-4150, Honolulu, HI, 2017. 3 +[63] Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin. Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., 2021. 1, 2, 3, 6, 7, 8 +[64] Yang Zou, Zhiding Yu, B. V. K. Vijaya Kumar, and Jinsong Wang. Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training. In Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and Yair Weiss, editors, Eur. Conf. Comput. Vis., volume 11207, pages 297-313, 2018. 5 +[65] Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, and Tomas Pfister. PseudoSeg: Designing Pseudo Labels for Semantic Segmentation. In Int. Conf. Learn. Represent., 2021. 3 \ No newline at end of file diff --git a/2023/Less Is More_ Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation/images.zip b/2023/Less Is More_ Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..f1d83f53d165c30ae298936f38d24382ce77d73a --- /dev/null +++ b/2023/Less Is More_ Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4d328471e8bcfaa456815ca2458f147b1d624f9464743f18e361a7744cc60327 +size 566968 diff --git a/2023/Less Is More_ Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation/layout.json b/2023/Less Is More_ Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..0f9ad3d90e644b06b92a32e47ee178a11793f10d --- /dev/null +++ b/2023/Less Is More_ Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation/layout.json @@ -0,0 +1,11221 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 135, + 79, + 459, + 114 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 135, + 79, + 459, + 114 + ], + "spans": [ + { + "bbox": [ + 135, + 79, + 459, + 114 + ], + "type": "text", + "content": "Less is More: Reducing Task and Model Complexity for 3D Point Cloud Semantic Segmentation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 160, + 121, + 193, + 135 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 160, + 121, + 193, + 135 + ], + "spans": [ + { + "bbox": [ + 160, + 121, + 193, + 135 + ], + "type": "text", + "content": "Li Li" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 217, + 121, + 314, + 135 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 217, + 121, + 314, + 135 + ], + "spans": [ + { + "bbox": [ + 217, + 121, + 314, + 135 + ], + "type": "text", + "content": "Hubert P. 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Existing work very often employs relatively large segmentation backbone networks to improve segmentation accuracy, at the expense of computational costs. In addition, many use uniform sampling to reduce ground truth data requirements for learning needed, often resulting in sub-optimal performance. To address these issues, we propose a new pipeline that employs a smaller architecture, requiring fewer ground-truth annotations to achieve superior segmentation accuracy compared to contemporary approaches. This is facilitated via a novel Sparse Depthwise Separable Convolution module that significantly reduces the network parameter count while retaining overall task performance. To effectively sub-sample our training data, we propose a new Spatio-Temporal Redundant Frame Downsampling (ST-RFD) method that leverages knowledge of sensor motion within the environment to extract a more diverse subset of training data frame samples. To leverage the use of limited annotated data samples, we further propose a soft pseudo-label method informed by LiDAR reflectivity. 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Introduction" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 288, + 715 + ], + "type": "text", + "content": "3D semantic segmentation of LiDAR point clouds has played a key role in scene understanding, facilitating applications such as autonomous driving [6, 23, 26, 28, 46, 60, 63] and robotics [3, 38, 51, 52]. However, many contemporary methods require relatively large backbone architectures with millions of trainable parameters requiring many hundred gigabytes of annotated data for training at a significant computational cost. Considering the time-consuming and costly nature of 3D LiDAR annotation, such methods have become less feasible for practical deployment." + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 294, + 191, + 428, + 289 + ], + "blocks": [ + { + "bbox": [ + 294, + 191, + 428, + 289 + ], + "lines": [ + { + "bbox": [ + 294, + 191, + 428, + 289 + ], + "spans": [ + { + "bbox": [ + 294, + 191, + 428, + 289 + ], + "type": "image", + "image_path": "d2109a11a3170aa183da17fbeefb710494f81a48e2d15fe15b9e1eb29dc62b4e.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 297, + 548, + 331 + ], + "lines": [ + { + "bbox": [ + 305, + 297, + 548, + 331 + ], + "spans": [ + { + "bbox": [ + 305, + 297, + 548, + 331 + ], + "type": "text", + "content": "Figure 1. mIoU performance " + }, + { + "bbox": [ + 305, + 297, + 548, + 331 + ], + "type": "inline_equation", + "content": "(\\%)" + }, + { + "bbox": [ + 305, + 297, + 548, + 331 + ], + "type": "text", + "content": " against parameters and multiply-add operations on SemanticKITTI (fully annotated) and ScribbleKITTI (weakly annotated) under the " + }, + { + "bbox": [ + 305, + 297, + 548, + 331 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 305, + 297, + 548, + 331 + ], + "type": "text", + "content": " sampling protocol." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 431, + 191, + 559, + 289 + ], + "blocks": [ + { + "bbox": [ + 431, + 191, + 559, + 289 + ], + "lines": [ + { + "bbox": [ + 431, + 191, + 559, + 289 + ], + "spans": [ + { + "bbox": [ + 431, + 191, + 559, + 289 + ], + "type": "image", + "image_path": "2515e6a36168911b2f6376032e62ad48a6d0413243f0fe8a492c75264318e6e8.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 350, + 548, + 541 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 350, + 548, + 541 + ], + "spans": [ + { + "bbox": [ + 304, + 350, + 548, + 541 + ], + "type": "text", + "content": "Existing supervised 3D semantic segmentation methods [13, 31, 38, 44, 51, 52, 56, 58, 63] primarily focus on designing network architectures for densely annotated data. To reduce the need for large-scale data annotation, and inspired by similar work in 2D [11, 47, 49], recent 3D work proposes efficient ways to learn from weak supervision [46]. However, such methods still suffer from high training costs and inferior on-task performance. To reduce computational costs, a 2D projection-based point cloud representation is often considered [3, 14, 31, 38, 51, 52, 56, 61], but again at the expense of significantly reduced on-task performance. As such, we observe a gap in the research literature for the design of semi or weakly supervised methodologies that employ a smaller-scale architectural backbone, hence facilitating improved training efficiency whilst also reducing their associated data annotation requirements." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 544, + 547, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 544, + 547, + 616 + ], + "spans": [ + { + "bbox": [ + 304, + 544, + 547, + 616 + ], + "type": "text", + "content": "In this paper, we propose a semi-supervised methodology for 3D LiDAR point cloud semantic segmentation. Facilitated by three novel design aspects, our Less is More (LiM) based methodologies require less training data and less training computation whilst offering (more) improved accuracy over contemporary state-of-the-art approaches (see Fig. 1)." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 617, + 548, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 548, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 548, + 714 + ], + "type": "text", + "content": "Firstly, from an architectural perspective, we propose a novel Sparse Depthwise Separable Convolution (SDSC) module, which substitutes traditional sparse 3D convolution into existing 3D semantic segmentation architectures, resulting in a significant reduction in trainable parameters and numerical computation whilst maintaining on-task performance (see Fig. 1). Depthwise Separable Convolution has shown to be very effective within image classification" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 314, + 757 + ], + "type": "text", + "content": "9361" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 192 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 192 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 192 + ], + "type": "text", + "content": "tasks [12]. Here, we tailor a sparse variant of 3D Depthwise Separable Convolution for 3D sparse data by first applying a single submanifold sparse convolutional filter [20, 21] to each input channel with a subsequent pointwise convolution to create a linear combination of the sparse depthwise convolution outputs. This work is the first to attempt to introduce depthwise convolution into the 3D point cloud segmentation field as a conduit to reduce model size. Our SDSC module facilitates a " + }, + { + "bbox": [ + 46, + 72, + 289, + 192 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 46, + 72, + 289, + 192 + ], + "type": "text", + "content": " reduction in trainable network parameters without any loss in segmentation performance." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 194, + 289, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 194, + 289, + 422 + ], + "spans": [ + { + "bbox": [ + 46, + 194, + 289, + 422 + ], + "type": "text", + "content": "Secondly, from a training data perspective, we propose a novel Spatio-Temporal Redundant Frame Downsampling (ST-RFD) strategy that more effectively sub-samples a set of diverse frames from a continuously captured LiDAR sequence in order to maximize diversity within a minimal training set size. We observe that continuously captured LiDAR sequences often contain significant temporal redundancy, similar to that found in video [2], whereby temporally adjacent frames provide poor data variation. On this basis, we propose to compute the temporal correlation between adjacent frame pairs, and use this to select the most informative sub-set of LiDAR frames from a given sequence. Unlike passive sampling (e.g., uniform or random sampling), our active sampling approach samples frames from each sequence such that redundancy is minimized and hence training set diversity is maximal. When compared to commonplace passive random sampling approaches [29, 32, 46], ST-RFD explicitly focuses on extracting a diverse set of training frames that will hence maximize model generalization." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 423, + 289, + 639 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 423, + 289, + 639 + ], + "spans": [ + { + "bbox": [ + 46, + 423, + 289, + 639 + ], + "type": "text", + "content": "Finally, in order to employ semi-supervised learning, we propose a soft pseudo-label method informed by the LiDAR reflectivity response, thus maximizing the use of any annotated data samples. Whilst directly using unreliable soft pseudo-labels generally results in performance deterioration [5], the voxels corresponding to the unreliable predictions can instead be effectively leveraged as negative samples of unlikely categories. Therefore, we use cross-entropy to separate all voxels into two groups, i.e., a reliable and an unreliable group with low and high-entropy voxels respectively. We utilize predictions from the reliable group to derive positive pseudo-labels, while the remaining voxels from the unreliable group are pushed into a FIFO category-wise memory bank of negative samples [4]. To further assist semantic segmentation of varying materials in the situation where we have weak/unreliable/no labels, we append the reflectivity response features onto the point cloud features, which again improve segmentation results." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 641, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 289, + 714 + ], + "type": "text", + "content": "We evaluate our method on the SemanticKITTI [7] and ScribbleKITTI [46] validation set. Our method outperforms contemporary state-of-the-art semi- [29,32] and weakly- [46] supervised methods and offers more in terms of performance on limited training data, whilst using less trainable parameters and less numerical operations (Less is More)." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 317, + 72, + 547, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 72, + 547, + 83 + ], + "spans": [ + { + "bbox": [ + 317, + 72, + 547, + 83 + ], + "type": "text", + "content": "Overall, our contributions can be summarized as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 316, + 86, + 547, + 267 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 316, + 86, + 547, + 121 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 86, + 547, + 121 + ], + "spans": [ + { + "bbox": [ + 316, + 86, + 547, + 121 + ], + "type": "text", + "content": "- A novel methodology for semi-supervised 3D LiDAR semantic segmentation that uses significantly less parameters and offers (more) superior accuracy." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 316, + 123, + 547, + 171 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 123, + 547, + 171 + ], + "spans": [ + { + "bbox": [ + 316, + 123, + 547, + 171 + ], + "type": "text", + "content": "- A novel Sparse Depthwise Separable Convolution (SDSC) module, to reduce trainable network parameters, and to both reduce the likelihood of over-fitting and facilitate a deeper network architecture." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 316, + 172, + 547, + 219 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 172, + 547, + 219 + ], + "spans": [ + { + "bbox": [ + 316, + 172, + 547, + 219 + ], + "type": "text", + "content": "- A novel Spatio-Temporal Redundant Frame Downsampling (ST-RFD) strategy, to extract a maximally diverse data subset for training by removing temporal redundancy and hence future annotation requirements." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 316, + 220, + 547, + 267 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 316, + 220, + 547, + 267 + ], + "spans": [ + { + "bbox": [ + 316, + 220, + 547, + 267 + ], + "type": "text", + "content": "- A novel soft pseudo-labeling method informed by Li-DAR reflectivity as a proxy to in-scene object material properties, facilitating effective use of limited data annotation." + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 280, + 392, + 293 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 280, + 392, + 293 + ], + "spans": [ + { + "bbox": [ + 306, + 280, + 392, + 293 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 300, + 548, + 539 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 300, + 548, + 539 + ], + "spans": [ + { + "bbox": [ + 304, + 300, + 548, + 539 + ], + "type": "text", + "content": "Semi-supervised learning (SSL) LiDAR semantic segmentation is a special instance of weak supervision that combines a small amount of labeled, with a large amount of unlabeled point cloud during training. Numerous approaches have been explored for LiDAR semantic segmentation. Projection-based approaches [31, 32, 36, 38, 51, 52, 56] make full use of 2D-convolution kernels by using range or other 2D image-based spherical coordinate representations of point clouds. Conversely, voxel-based approaches [32, 44, 46, 63] transform irregular point clouds to regular 3D grids and then apply 3D convolutional neural networks with a better balance of the efficiency and effectiveness. Pseudolabeling is generally applied to alleviate the side effect of intra-class negative pairs in feature learning from the teacher network [29, 32, 46, 57]. However, such methods only utilize samples with reliable predictions and thus ignore the valuable information that unreliable predictions carry. In our work, we combined a novel SSL framework with the mean teacher paradigm [45], demonstrating the utilization of unreliable pseudo-labels to improve segmentation performance." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 540, + 548, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 540, + 548, + 696 + ], + "spans": [ + { + "bbox": [ + 304, + 540, + 548, + 696 + ], + "type": "text", + "content": "Depthwise separable convolution [41] is a depthwise convolution followed by a pointwise convolution, to reduce both model size and complexity. Being a more computationally efficient alternative than standard convolution, it is used for mobile applications [24, 25, 40] and hardware accelerators [37]. Furthermore, it is a building block of Xception [12], a deep convolutional neural network architecture that achieves state-of-the-art performance on the ImageNet [15] classification task, via more efficient use of model parameterization. In this work, we propose a novel sparse variant of depthwise separable convolution, which has both the efficiency advantages of depthwise separable convolution and those of sparse convolution for processing spatially-sparse data [20]." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 317, + 702, + 536, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 702, + 536, + 712 + ], + "spans": [ + { + "bbox": [ + 317, + 702, + 536, + 712 + ], + "type": "text", + "content": "Full source code: https://github.com/l1997i/lim3d/." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9362" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 70, + 545, + 194 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 545, + 194 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 545, + 194 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 545, + 194 + ], + "type": "image", + "image_path": "cb7efdaf02dbc34c6ee8777881c83f17f87d3d066f52549f8da8c0c1ef7c5cb1.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 201, + 547, + 225 + ], + "lines": [ + { + "bbox": [ + 46, + 201, + 547, + 225 + ], + "spans": [ + { + "bbox": [ + 46, + 201, + 547, + 225 + ], + "type": "text", + "content": "Figure 2. Our proposed architecture for unreliable pseudo-labels LiDAR semantic segmentation involves three stages: training, pseudolabeling, and distillation with unreliable learning. We apply ST-RFD sampling before training the Mean Teacher on available annotations." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 237, + 290, + 369 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 237, + 290, + 369 + ], + "spans": [ + { + "bbox": [ + 46, + 237, + 290, + 369 + ], + "type": "text", + "content": "Temporal redundancy is highly prevalent within video [48, 62] and radar [35] sequences alike. Existing semi-supervised 3D LiDAR segmentation methods [32, 46] utilize a passive uniform sampling strategy to filter unlabeled points from a fully-labeled point cloud dataset. Active learning frameworks handle the redundancy to reduce annotation or training efforts by selecting informative and diverse subscenes for label acquisition [16, 27, 53]. We propose a novel temporal-redundancy-based sampling strategy with comparable time cost to uniform sampling, to reduce the inter-frame spatio-temporal redundancy and maximize data diversity." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 373, + 130, + 387 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 373, + 130, + 387 + ], + "spans": [ + { + "bbox": [ + 47, + 373, + 130, + 387 + ], + "type": "text", + "content": "3. Methodology" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 388, + 288, + 459 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 388, + 288, + 459 + ], + "spans": [ + { + "bbox": [ + 46, + 388, + 288, + 459 + ], + "type": "text", + "content": "We first present an overview of the mean teacher framework we employ (Sec. 3.1) and then explain our use of unreliable pseudo-labels informed by LiDAR reflectivity for semi-supervised learning (Sec. 3.2). Subsequently, we detail our ST-RFD strategy for dataset diversity (Sec. 3.4) and finally our parameter-reducing SDSC module (Sec. 3.5)." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "spans": [ + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "text", + "content": "Formally, given a LiDAR point cloud " + }, + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "inline_equation", + "content": "P = \\{\\mathbf{p} \\mid \\mathbf{p} = (x,y,z,I,R) \\in \\mathbb{R}^5\\}" + }, + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "inline_equation", + "content": "(x,y,z)" + }, + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "text", + "content": " is a 3D coordinate, " + }, + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "text", + "content": " is intensity and " + }, + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "text", + "content": " is reflectivity, our goal is to train a semantic segmentation model by leveraging both a large amount of unlabeled " + }, + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "inline_equation", + "content": "U = \\{\\mathbf{p}_i^u\\}_{i=1}^{N_u} \\subsetneq P" + }, + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "text", + "content": " and a smaller set of labeled data " + }, + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "inline_equation", + "content": "V = \\{(\\mathbf{p}_i^v,\\mathbf{y}_i^v)\\}_{i=1}^{N_v} \\subsetneq P" + }, + { + "bbox": [ + 46, + 460, + 288, + 533 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 533, + 288, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 533, + 288, + 652 + ], + "spans": [ + { + "bbox": [ + 46, + 533, + 288, + 652 + ], + "type": "text", + "content": "Our overall architecture involves three stages (Fig. 2): (1) Training: we utilize reflectivity-prior descriptors and adapt the Mean Teacher framework to generate high-quality pseudo-labels; (2) Pseudo-labeling: we fix the trained teacher model prediction in a class-range-balanced [46] manner, expanding dataset with Reflectivity-based Test Time Augmentation (Reflec-TTA) during test time; (3) Distillation with unreliable predictions: we train on the generated pseudo-labels, and utilize unreliable pseudo-labels in a category-wise memory bank for improved discrimination." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 659, + 194, + 671 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 659, + 194, + 671 + ], + "spans": [ + { + "bbox": [ + 47, + 659, + 194, + 671 + ], + "type": "text", + "content": "3.1. Mean Teacher Framework" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 288, + 714 + ], + "type": "text", + "content": "We introduce weak supervision using the Mean Teacher framework [45], which avoids the prominent slow training issues associated with Temporal Ensembling [33]. This" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 237, + 547, + 321 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 237, + 547, + 321 + ], + "spans": [ + { + "bbox": [ + 304, + 237, + 547, + 321 + ], + "type": "text", + "content": "framework consists of two models of the same architecture known as the student and teacher respectively, for which we utilize a Cylinder3D [63]-based segmentation head " + }, + { + "bbox": [ + 304, + 237, + 547, + 321 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 304, + 237, + 547, + 321 + ], + "type": "text", + "content": ". The weights of the student model " + }, + { + "bbox": [ + 304, + 237, + 547, + 321 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 304, + 237, + 547, + 321 + ], + "type": "text", + "content": " are updated via standard backpropagation, while the weights of the teacher model " + }, + { + "bbox": [ + 304, + 237, + 547, + 321 + ], + "type": "inline_equation", + "content": "\\theta^{*}" + }, + { + "bbox": [ + 304, + 237, + 547, + 321 + ], + "type": "text", + "content": " are updated by the student model through Exponential Moving Averaging (EMA):" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 314, + 331, + 545, + 345 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 331, + 545, + 345 + ], + "spans": [ + { + "bbox": [ + 314, + 331, + 545, + 345 + ], + "type": "interline_equation", + "content": "\\theta_ {t + 1} ^ {*} = \\kappa \\theta_ {t} ^ {*} + (1 - \\kappa) \\theta_ {t + 1}, \\quad t \\in \\{0, 1, \\dots T - 1 \\}, \\tag {1}", + "image_path": "2a9d569190309d86dd0fc0602570bb5ba00b3a37c088ca02ef882b9a822ad82d.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 354, + 545, + 378 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 354, + 545, + 378 + ], + "spans": [ + { + "bbox": [ + 304, + 354, + 545, + 378 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 354, + 545, + 378 + ], + "type": "inline_equation", + "content": "\\kappa" + }, + { + "bbox": [ + 304, + 354, + 545, + 378 + ], + "type": "text", + "content": " denotes a smoothing coefficient to determine update speed, and " + }, + { + "bbox": [ + 304, + 354, + 545, + 378 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 354, + 545, + 378 + ], + "type": "text", + "content": " is the maximum time step." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 378, + 547, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 378, + 547, + 426 + ], + "spans": [ + { + "bbox": [ + 304, + 378, + 547, + 426 + ], + "type": "text", + "content": "During training, we train a set of weakly-labeled point cloud frames with voxel-wise inputs generated via asymmetrical 3D convolution networks [63]. For every point cloud, our optimization target is to minimize the overall loss:" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 365, + 436, + 545, + 449 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 436, + 545, + 449 + ], + "spans": [ + { + "bbox": [ + 365, + 436, + 545, + 449 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\mathcal {L} _ {S} + \\lambda_ {U} \\mathcal {L} _ {U} + g \\lambda_ {C} \\mathcal {L} _ {C}, \\tag {2}", + "image_path": "ad89e8086bfa0bf38dc3cfc42213353363f1c02545167474408e3f7987744fa5.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "spans": [ + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_S" + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_U" + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "content": " denote the losses applied to the supervised and unsupervised set of points respectively, " + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "content": " denotes the contrastive loss to make full use of unreliable pseudo-labels, " + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\lambda_U" + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "content": " is the weight coefficient of " + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_U" + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "content": " to balance the losses, and " + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "content": " is the gated coefficient of " + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "inline_equation", + "content": "g" + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "content": " equals " + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\lambda_C" + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "content": " if and only if it is in the distillation stage. We use the consistency loss (implemented as a Kullback-Leibler divergence loss [23]), lovasz softmax loss [8], and the voxel-level InfoNCE [47] as " + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_U" + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_S" + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + }, + { + "bbox": [ + 304, + 459, + 545, + 567 + ], + "type": "text", + "content": " respectively." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 567, + 545, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 567, + 545, + 651 + ], + "spans": [ + { + "bbox": [ + 304, + 567, + 545, + 651 + ], + "type": "text", + "content": "We first generate our pseudo-labels for the unlabeled points via the teacher model. Subsequently, we generate reliable pseudo-labels in a class-range-balanced (CRB) [46] manner, and utilize the qualified unreliable pseudo-labels as negative samples in the distillation stage. Finally, we train the model with both reliable and qualified unreliable pseudo-labels to maximize the quality of the pseudo-labels." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 658, + 520, + 671 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 658, + 520, + 671 + ], + "spans": [ + { + "bbox": [ + 306, + 658, + 520, + 671 + ], + "type": "text", + "content": "3.2. Learning from Unreliable Pseudo-Labels" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 547, + 713 + ], + "type": "text", + "content": "Unreliable pseudo-labels are frequently eliminated from semi-supervised learning tasks or have their weights decreased to minimize performance loss [29, 39, 46, 55, 59, 65]." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9363" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 71, + 287, + 128 + ], + "blocks": [ + { + "bbox": [ + 48, + 71, + 287, + 128 + ], + "lines": [ + { + "bbox": [ + 48, + 71, + 287, + 128 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 287, + 128 + ], + "type": "image", + "image_path": "efefc56c3c12de329886a22fd1f5a5a4c9474351589c7d194babad413fe2484c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 137, + 288, + 184 + ], + "lines": [ + { + "bbox": [ + 46, + 137, + 288, + 184 + ], + "spans": [ + { + "bbox": [ + 46, + 137, + 288, + 184 + ], + "type": "text", + "content": "Figure 3. Illustration on unreliable pseudo-labels. Left: entropy predicted from an unlabeled point cloud, with lower entropy corresponding to greener color. Right: Category-wise probability of an unreliable prediction " + }, + { + "bbox": [ + 46, + 137, + 288, + 184 + ], + "type": "inline_equation", + "content": "\\mathsf{X}" + }, + { + "bbox": [ + 46, + 137, + 288, + 184 + ], + "type": "text", + "content": ", only top-4 and last-4 probabilities shown." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 198, + 287, + 234 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 198, + 287, + 234 + ], + "spans": [ + { + "bbox": [ + 46, + 198, + 287, + 234 + ], + "type": "text", + "content": "In line with this idea, we utilize CRB method [46] to first mask off unreliable pseudo-labels and then subsequently generate high-quality reliable pseudo-labels." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 235, + 288, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 235, + 288, + 533 + ], + "spans": [ + { + "bbox": [ + 46, + 235, + 288, + 533 + ], + "type": "text", + "content": "However, such a simplistic discarding of unreliable pseudo-labels may lead to valuable information loss as it is clear that unreliable pseudo-labels (i.e., the corresponding voxels with high entropy) can offer information that helps in discrimination. Voxels that correlate to unreliable predictions can alternatively be thought as negative samples for improbable categories [49], although performance would suffer if such unreliable predictions are used as pseudo-labels directly [5]. As shown in Fig. 3, the unreliable pseudo predictions show a similar level of confidence on car and truck classes, whilst being sure the voxel cannot be pole or road. Thus, together with the use of CRB for high-quality reliable pseudo-labels, we also ideally want to make full use of these remaining unreliable pseudo-labels rather than simply discarding them. Following [49], we propose a method to leverage such unreliable pseudo-labels for 3D voxels as negative samples. However, to maintain a stable amount of negative samples, we utilize a category-wise memory bank " + }, + { + "bbox": [ + 46, + 235, + 288, + 533 + ], + "type": "inline_equation", + "content": "\\mathcal{Q}_c" + }, + { + "bbox": [ + 46, + 235, + 288, + 533 + ], + "type": "text", + "content": " (FIFO queue, [54]) to store all the negative samples for a given class " + }, + { + "bbox": [ + 46, + 235, + 288, + 533 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 46, + 235, + 288, + 533 + ], + "type": "text", + "content": ". As negative candidates in some specific categories are severely limited in a mini-batch due to the long-tailed class distribution of many tasks (e.g. autonomous driving), without such an approach in place we may instead see the gradual dominance of large and simple-to-learn classes within our generated pseudo-labels." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 535, + 287, + 606 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 535, + 287, + 606 + ], + "spans": [ + { + "bbox": [ + 46, + 535, + 287, + 606 + ], + "type": "text", + "content": "Following [22, 47], our method has three prerequisites, i.e., anchor voxels, positive candidates, and negative candidates. They are obtained by sampling from a particular subset, constructed via Eq. (3) and Eq. (4), in order to reduce overall computation. In particular, the set of features of all candidate anchor voxels for class " + }, + { + "bbox": [ + 46, + 535, + 287, + 606 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 46, + 535, + 287, + 606 + ], + "type": "text", + "content": " is denoted as:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 87, + 617, + 287, + 632 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 87, + 617, + 287, + 632 + ], + "spans": [ + { + "bbox": [ + 87, + 617, + 287, + 632 + ], + "type": "interline_equation", + "content": "\\mathcal {A} _ {c} = \\left\\{\\mathbf {E} _ {a, b} \\mid y _ {a, b} ^ {*} = c, p _ {a, b} (c) > \\delta_ {p} \\right\\}, \\tag {3}", + "image_path": "4295b371d1b72ddcd0c692ce75fc88e177e5839915b22bbcd8896a6c030392b0.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{E}_{a,b}" + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "content": " is the feature embedding for the " + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "content": "-th point cloud frame at voxel " + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\delta_p" + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "content": " is the positive threshold of all classes, " + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "inline_equation", + "content": "p_{a,b}(c)" + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "content": " is the softmax probability by the segmentation head at " + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "content": "-th dimension. " + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "inline_equation", + "content": "y_{a,b}^*" + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "content": " is set to the ground truth label " + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "inline_equation", + "content": "y_{a,b}^*" + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "content": " if the ground truth is available, otherwise, " + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "inline_equation", + "content": "y_{a,b}^*" + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "content": " is set to the pseudo label " + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\hat{y}_{a,b}" + }, + { + "bbox": [ + 46, + 641, + 287, + 714 + ], + "type": "text", + "content": ", due to the absence of ground truth." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 72, + 546, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 546, + 109 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 546, + 109 + ], + "type": "text", + "content": "The positive sample is the common embedding center of all possible anchors, which is the same for all anchors from the same category, shown in Eq. (4)." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 380, + 114, + 545, + 143 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 114, + 545, + 143 + ], + "spans": [ + { + "bbox": [ + 380, + 114, + 545, + 143 + ], + "type": "interline_equation", + "content": "\\mathbf {E} _ {\\mathbf {c}} ^ {+} = \\frac {1}{| \\mathcal {A} _ {c} |} \\sum_ {\\mathbf {E} _ {c} \\in \\mathcal {A} _ {c}} \\mathbf {E} _ {c}. \\tag {4}", + "image_path": "eb4d6db549ab6264d8f534659a707c133683fab6f33ed7906f99f8f9e7e6eb1b.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 148, + 545, + 172 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 148, + 545, + 172 + ], + "spans": [ + { + "bbox": [ + 305, + 148, + 545, + 172 + ], + "type": "text", + "content": "Following [49], we similarly construct multiple negative samples " + }, + { + "bbox": [ + 305, + 148, + 545, + 172 + ], + "type": "inline_equation", + "content": "\\mathbf{E}_c^-" + }, + { + "bbox": [ + 305, + 148, + 545, + 172 + ], + "type": "text", + "content": " for each anchor voxel." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 173, + 547, + 244 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 173, + 547, + 244 + ], + "spans": [ + { + "bbox": [ + 305, + 173, + 547, + 244 + ], + "type": "text", + "content": "Finally, for each anchor voxel containing one positive sample and " + }, + { + "bbox": [ + 305, + 173, + 547, + 244 + ], + "type": "inline_equation", + "content": "N - 1" + }, + { + "bbox": [ + 305, + 173, + 547, + 244 + ], + "type": "text", + "content": " negative samples, we propose the voxel-level InfoNCE loss [47] (a variant of contrastive loss) " + }, + { + "bbox": [ + 305, + 173, + 547, + 244 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_C" + }, + { + "bbox": [ + 305, + 173, + 547, + 244 + ], + "type": "text", + "content": " in Eq. (5) to encourage maximal similarity between the anchor voxel and the positive sample, and the minimal similarity between the anchor voxel and multiple negative samples." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 256, + 563, + 352 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 256, + 563, + 352 + ], + "spans": [ + { + "bbox": [ + 306, + 256, + 563, + 352 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\mathcal {L} _ {C} = - \\frac {1}{C} \\sum_ {c = 0} ^ {C - 1} \\underset {\\mathbf {E} _ {c}} {\\mathbb {E}} \\left[ \\log \\frac {f \\left(\\mathbf {e} _ {c} , \\mathbf {e} _ {c} ^ {+} , \\tau\\right)}{\\sum_ {\\mathbf {e} _ {c , j} ^ {-} \\in \\mathbf {E} _ {c} ^ {-}} f \\left(\\mathbf {e} _ {c} , \\mathbf {e} _ {c , j} ^ {-} , \\tau\\right)} \\right] \\\\ = - \\frac {1}{C} \\sum_ {c = 0} ^ {C - 1} \\mathbb {E} _ {\\mathbf {E} _ {c}} \\left[ \\log \\frac {\\exp \\left(\\left\\langle \\mathbf {e} _ {c} , \\mathbf {e} _ {c} ^ {+} \\right\\rangle / \\tau\\right)}{\\exp \\left(\\left\\langle \\mathbf {e} _ {c} , \\mathbf {e} _ {c} ^ {+} \\right\\rangle / \\tau\\right) + \\sum_ {j = 1} ^ {N - 1} \\exp \\left(\\left\\langle \\mathbf {e} _ {c} , \\mathbf {e} _ {c , j} ^ {-} \\right\\rangle / \\tau\\right)} \\right] \\tag {5} \\\\ \\end{array}", + "image_path": "a8e699535eed768a1c35f4db19fcc008197aed90e8d80bc448085b828c07bda8.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "spans": [ + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "inline_equation", + "content": "\\langle \\cdot, \\cdot \\rangle" + }, + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "text", + "content": " denotes cosine similarity. " + }, + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "inline_equation", + "content": "\\mathbf{e_c}" + }, + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "inline_equation", + "content": "\\mathbf{e_c}^+" + }, + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "inline_equation", + "content": "\\mathbf{e}_{c,j}^-" + }, + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "text", + "content": " denote the embedding, positive sample of the current anchor voxel, and embedding of the " + }, + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "text", + "content": "-th negative sample of class " + }, + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 305, + 355, + 547, + 392 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 398, + 531, + 411 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 398, + 531, + 411 + ], + "spans": [ + { + "bbox": [ + 306, + 398, + 531, + 411 + ], + "type": "text", + "content": "3.3. Reflectivity-Based Test Time Augmentation" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 416, + 547, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 416, + 547, + 559 + ], + "spans": [ + { + "bbox": [ + 304, + 416, + 547, + 559 + ], + "type": "text", + "content": "To obtain minimal accuracy degradation despite very few weak labels, e.g., " + }, + { + "bbox": [ + 304, + 416, + 547, + 559 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 304, + 416, + 547, + 559 + ], + "type": "text", + "content": " weakly-labeled ScribbleKITTI [46] dataset, we propose a Test Time Augmentation (TTA) that does not depend on any label, but only relies on a feature of original LiDAR points themselves. Also included in almost every LiDAR benchmark dataset for autonomous driving [7, 10, 18, 30, 34, 46], is the intensity of light reflected from the surface of an object at each point. In the presence of limited data labels in the semi-supervised learning case, this property of the material surface, normalized by distance to obtain the surface reflectivity in Eq. (6), could readily act as auxiliary information to identify different semantic classes." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 560, + 545, + 596 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 560, + 545, + 596 + ], + "spans": [ + { + "bbox": [ + 305, + 560, + 545, + 596 + ], + "type": "text", + "content": "Our intuition is that reflectivity " + }, + { + "bbox": [ + 305, + 560, + 545, + 596 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 305, + 560, + 545, + 596 + ], + "type": "text", + "content": ", as a point-wise distance-normalized intensity feature, offers consistency across lighting conditions and range as:" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 369, + 601, + 545, + 625 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 369, + 601, + 545, + 625 + ], + "spans": [ + { + "bbox": [ + 369, + 601, + 545, + 625 + ], + "type": "interline_equation", + "content": "R = I r ^ {2} = \\frac {S}{4 \\pi r ^ {2}} \\cdot r ^ {2} \\propto S, \\tag {6}", + "image_path": "89d091edfb0fd6e81ea62674d2ef22744952522c5a3e6f9f5381f529f98930ff.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 629, + 547, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 629, + 547, + 677 + ], + "spans": [ + { + "bbox": [ + 304, + 629, + 547, + 677 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 629, + 547, + 677 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 304, + 629, + 547, + 677 + ], + "type": "text", + "content": " is the return strength of the LiDAR laser pulse, " + }, + { + "bbox": [ + 304, + 629, + 547, + 677 + ], + "type": "inline_equation", + "content": "I" + }, + { + "bbox": [ + 304, + 629, + 547, + 677 + ], + "type": "text", + "content": " is the intensity and " + }, + { + "bbox": [ + 304, + 629, + 547, + 677 + ], + "type": "inline_equation", + "content": "r" + }, + { + "bbox": [ + 304, + 629, + 547, + 677 + ], + "type": "text", + "content": " is the point distance from the source on the basis that scene objects with similar surface material, coating, and color characteristics will share similar " + }, + { + "bbox": [ + 304, + 629, + 547, + 677 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 304, + 629, + 547, + 677 + ], + "type": "text", + "content": " returns." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 677, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 547, + 713 + ], + "type": "text", + "content": "On this basis, we define our novel reflectivity-based Test Time Augmentation (Reflec-TTA) technique, as a substitute for label-dependent Pyramid Local Semantic-context" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9364" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 55, + 69, + 281, + 140 + ], + "blocks": [ + { + "bbox": [ + 55, + 69, + 281, + 140 + ], + "lines": [ + { + "bbox": [ + 55, + 69, + 281, + 140 + ], + "spans": [ + { + "bbox": [ + 55, + 69, + 281, + 140 + ], + "type": "image", + "image_path": "5693020f7cb1b08a21d6fa317de7d90291ddeeb78587668d9f881ce5e674b64e.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 53, + 148, + 281, + 160 + ], + "lines": [ + { + "bbox": [ + 53, + 148, + 281, + 160 + ], + "spans": [ + { + "bbox": [ + 53, + 148, + 281, + 160 + ], + "type": "text", + "content": "Figure 4. Coarse histograms of Reflec-TTA bins (not to scale)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 176, + 289, + 284 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 176, + 289, + 284 + ], + "spans": [ + { + "bbox": [ + 46, + 176, + 289, + 284 + ], + "type": "text", + "content": "(PLS) augmentation [46] during test-time as ground truth is not available. We append our point-wise reflectivity to the existing point features in order to enhance performance in presence of false or non-existent pseudo-labels at the distillation stage. As shown in Fig. 4, and following [46], we apply various sizes " + }, + { + "bbox": [ + 46, + 176, + 289, + 284 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 46, + 176, + 289, + 284 + ], + "type": "text", + "content": " of bins in cylindrical coordinates to analyze the intrinsic point distribution of the LiDAR sensor at varying resolutions (shown in red, green and blue in Fig. 4). For each bin " + }, + { + "bbox": [ + 46, + 176, + 289, + 284 + ], + "type": "inline_equation", + "content": "b_{i}" + }, + { + "bbox": [ + 46, + 176, + 289, + 284 + ], + "type": "text", + "content": ", we compute a coarse histogram, " + }, + { + "bbox": [ + 46, + 176, + 289, + 284 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_{i}" + }, + { + "bbox": [ + 46, + 176, + 289, + 284 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 59, + 293, + 261, + 313 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 293, + 261, + 313 + ], + "spans": [ + { + "bbox": [ + 59, + 293, + 261, + 313 + ], + "type": "interline_equation", + "content": "\\mathbf {h} _ {i} = \\left\\{h _ {i} ^ {(k)} \\mid k \\in [ 1, N _ {b} ] \\right\\} \\in \\mathbb {R} ^ {N _ {b}}, \\quad i \\in [ 1, s ],", + "image_path": "a6c54c4eb2b230931a1fd94ccca73f2b2a3265c2aac999ce14d1c05e13a7e9fb.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 62, + 314, + 287, + 331 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 314, + 287, + 331 + ], + "spans": [ + { + "bbox": [ + 62, + 314, + 287, + 331 + ], + "type": "interline_equation", + "content": "h _ {i} ^ {(k)} = \\# \\left\\{R _ {j} \\in r _ {k}, \\forall j \\mid p _ {j} \\in b _ {i} \\right\\}, \\tag {7}", + "image_path": "2f6392d895fecc3fd4c63038548afadb0ff76a23ad1002208c272d2cf2c298ee.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 62, + 332, + 251, + 345 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 332, + 251, + 345 + ], + "spans": [ + { + "bbox": [ + 62, + 332, + 251, + 345 + ], + "type": "interline_equation", + "content": "r _ {k} = \\left[ (k - 1) / N _ {b}, k / N _ {b}\\right), \\quad k \\in \\left[ 1, N _ {b} \\right].", + "image_path": "4a0dd00fbedbe58a356afb64749c9d3021030c70114f3a671e599495925776e7.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 355, + 287, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 355, + 287, + 392 + ], + "spans": [ + { + "bbox": [ + 47, + 355, + 287, + 392 + ], + "type": "text", + "content": "The Reflec-TTA features " + }, + { + "bbox": [ + 47, + 355, + 287, + 392 + ], + "type": "inline_equation", + "content": "R^{\\text{心}}" + }, + { + "bbox": [ + 47, + 355, + 287, + 392 + ], + "type": "text", + "content": " of all points " + }, + { + "bbox": [ + 47, + 355, + 287, + 392 + ], + "type": "inline_equation", + "content": "p_j \\in b_j" + }, + { + "bbox": [ + 47, + 355, + 287, + 392 + ], + "type": "text", + "content": " is further computed as the concatenation of the coarse histogram " + }, + { + "bbox": [ + 47, + 355, + 287, + 392 + ], + "type": "inline_equation", + "content": "\\mathbf{h}_i" + }, + { + "bbox": [ + 47, + 355, + 287, + 392 + ], + "type": "text", + "content": " of the normalized histogram:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 81, + 400, + 287, + 415 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 400, + 287, + 415 + ], + "spans": [ + { + "bbox": [ + 81, + 400, + 287, + 415 + ], + "type": "interline_equation", + "content": "R ^ {\\circledast} = \\left\\{\\mathbf {h} _ {i} / \\operatorname * {m a x} \\left(\\mathbf {h} _ {i}\\right) \\mid i \\in [ 1, s ] \\right\\} \\in \\mathbb {R} ^ {s N _ {b}} \\tag {8}", + "image_path": "dcfcc225504129397f8525f7782bcb0b2b0e56d64d3a8b3bc402bf8809c610ae.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 423, + 287, + 461 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 423, + 287, + 461 + ], + "spans": [ + { + "bbox": [ + 47, + 423, + 287, + 461 + ], + "type": "text", + "content": "In the distillation stage, we append " + }, + { + "bbox": [ + 47, + 423, + 287, + 461 + ], + "type": "inline_equation", + "content": "R^{\\text{※}}" + }, + { + "bbox": [ + 47, + 423, + 287, + 461 + ], + "type": "text", + "content": " to the input features and redefine the input LiDAR point cloud as the augmented set of points " + }, + { + "bbox": [ + 47, + 423, + 287, + 461 + ], + "type": "inline_equation", + "content": "P^{\\text{※}} = \\{p \\mid (x,y,z,I,R^{\\text{※}}) \\in \\mathbb{R}^{sN_b + 4}\\}" + }, + { + "bbox": [ + 47, + 423, + 287, + 461 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 468, + 287, + 480 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 468, + 287, + 480 + ], + "spans": [ + { + "bbox": [ + 47, + 468, + 287, + 480 + ], + "type": "text", + "content": "3.4. Spatio-Temporal Redundant Frame Downsampling" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 486, + 289, + 653 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 486, + 289, + 653 + ], + "spans": [ + { + "bbox": [ + 46, + 486, + 289, + 653 + ], + "type": "text", + "content": "Due to the spatio-temporal correlation of LiDAR point cloud sequences often captured from vehicles in metropolitan locales, many large-scale point cloud datasets demonstrate significant redundancy. Common datasets employ a frame rate of " + }, + { + "bbox": [ + 46, + 486, + 289, + 653 + ], + "type": "inline_equation", + "content": "10\\mathrm{Hz}" + }, + { + "bbox": [ + 46, + 486, + 289, + 653 + ], + "type": "text", + "content": " [7, 9, 10, 18, 30, 34, 42], and a number of concurrent laser channels (beams) of 32 [10], 64 [7, 9, 18, 30, 42] or 128 [34]. Faced with such large-scale, massively redundant training datasets, the popular practice of semi-supervised semantic segmentation approaches [11, 17, 32, 45, 64] is to uniformly sample " + }, + { + "bbox": [ + 46, + 486, + 289, + 653 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 46, + 486, + 289, + 653 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 486, + 289, + 653 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 486, + 289, + 653 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 486, + 289, + 653 + ], + "type": "inline_equation", + "content": "20\\%" + }, + { + "bbox": [ + 46, + 486, + 289, + 653 + ], + "type": "text", + "content": ", or " + }, + { + "bbox": [ + 46, + 486, + 289, + 653 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 46, + 486, + 289, + 653 + ], + "type": "text", + "content": " of the available annotated training frames, without considering any redundancy attributable to temporary periods of stationary capture (e.g. due to traffic, Fig. 5) or multi-pass repetition (e.g. due to loop closure)." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 654, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 289, + 714 + ], + "type": "text", + "content": "To extract a diverse set of frames, we propose a novel algorithm called Spatio-Temporal Redundant Frame Downsampling (ST-RFD, Algorithm 1) that determines spatiotemporal redundancy by analyzing the spatial-overlap within time-continuous LiDAR frame sequences. The key idea" + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 296, + 69, + 558, + 144 + ], + "blocks": [ + { + "bbox": [ + 296, + 69, + 558, + 144 + ], + "lines": [ + { + "bbox": [ + 296, + 69, + 558, + 144 + ], + "spans": [ + { + "bbox": [ + 296, + 69, + 558, + 144 + ], + "type": "image", + "image_path": "315a18dbfe42f30f71739715c59c75fba9e83e6d3d026081c0c59b608b9eec6e.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 151, + 547, + 196 + ], + "lines": [ + { + "bbox": [ + 304, + 151, + 547, + 196 + ], + "spans": [ + { + "bbox": [ + 304, + 151, + 547, + 196 + ], + "type": "text", + "content": "Figure 5. Illustration of LiDAR frame temporal correlation as [#frame ID] redundancy with " + }, + { + "bbox": [ + 304, + 151, + 547, + 196 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 304, + 151, + 547, + 196 + ], + "type": "text", + "content": " sampling on SemanticKITTI [7] (sequence 00) using uniform sampling (selected frames in " + }, + { + "bbox": [ + 304, + 151, + 547, + 196 + ], + "type": "inline_equation", + "content": "\\bullet" + }, + { + "bbox": [ + 304, + 151, + 547, + 196 + ], + "type": "text", + "content": ") and ST-RFD strategy (" + }, + { + "bbox": [ + 304, + 151, + 547, + 196 + ], + "type": "inline_equation", + "content": "\\bullet" + }, + { + "bbox": [ + 304, + 151, + 547, + 196 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 207, + 547, + 266 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 207, + 547, + 266 + ], + "spans": [ + { + "bbox": [ + 304, + 207, + 547, + 266 + ], + "type": "text", + "content": "is that if spatial-overlap among some continuous frame sequence is high due to spatio-temporal redundancy, multiple representative frames can be sub-sampled for training, significantly reducing both training dataset size and redundant training computation." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "spans": [ + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "text", + "content": "Fig. 6 shows an overview of ST-RFD. It is conducted inside each temporal continuous LiDAR sequence " + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "inline_equation", + "content": "e" + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "text", + "content": ". First, we evenly divide " + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "text", + "content": " point cloud frames in each sequence into " + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "inline_equation", + "content": "\\lceil p / q \\rceil" + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "text", + "content": " subsets (containing " + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "text", + "content": " frames). For each frame at time " + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "text", + "content": " inside the subset, we find its corresponding RGB camera image in the dataset at time " + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "inline_equation", + "content": "t + 1" + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "text", + "content": ". To detect the spatio-temporal redundancy at time " + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "text", + "content": ", the similarity " + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "inline_equation", + "content": "\\psi(t, t + 1)" + }, + { + "bbox": [ + 304, + 267, + 547, + 458 + ], + "type": "text", + "content": " between temporally adjacent frames are then computed via the Structural Similarity Index Measure (SSIM, [50]). We utilize the mean value of similarity scores between all adjacent frames in the current subset as a proxy to estimate the spatio-temporal redundancy present. A sampling rate is then determined according to this mean similarity for frame selection within this subset. This is repeated for all subsets in every sequence to construct our final set of sub-sampled LiDAR frames for training." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "spans": [ + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "text", + "content": "Concretely, as shown in Algorithm 1, we implement a ST-RFD supervisor that determines the most informative assignments (i.e., the key point cloud frames) that the teacher and student networks should train on respectively. The ST-RFD supervisor has an empirical supervisor function " + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "inline_equation", + "content": "v" + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "text", + "content": ", which decides the amount of assignments, i.e., the sampling rate corresponding to the extent of spatio-temporal redundancy. Using SSIM [50] as the redundancy function " + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "inline_equation", + "content": "\\psi" + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "text", + "content": " to measure the similarity between the RGB images associated with two adjacent point clouds, we define the empirical supervisor function " + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "inline_equation", + "content": "v" + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "text", + "content": " with decay property " + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "inline_equation", + "content": "v(x) = \\exp(-\\beta x)" + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "inline_equation", + "content": "\\beta \\in (0, +\\infty)" + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "text", + "content": " is the decay coefficient, and " + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "text", + "content": " is the redundancy calculated from " + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "inline_equation", + "content": "\\psi" + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "text", + "content": ". In this way, the higher the degree of spatio-temporal redundancy (as " + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "inline_equation", + "content": "\\psi \\to 1" + }, + { + "bbox": [ + 304, + 459, + 548, + 662 + ], + "type": "text", + "content": "), the lower the sampling rate our ST-RFD supervisor will allocate, hence reducing the training set requirements for teacher and student alike." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 670, + 522, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 670, + 522, + 684 + ], + "spans": [ + { + "bbox": [ + 306, + 670, + 522, + 684 + ], + "type": "text", + "content": "3.5. Sparse Depthwise Separable Convolution" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 689, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 689, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 689, + 547, + 714 + ], + "type": "text", + "content": "Existing LiDAR point cloud semantic segmentation methods generally rely on a large-scale backbone architecture with" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9365" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 51, + 69, + 541, + 130 + ], + "blocks": [ + { + "bbox": [ + 51, + 69, + 541, + 130 + ], + "lines": [ + { + "bbox": [ + 51, + 69, + 541, + 130 + ], + "spans": [ + { + "bbox": [ + 51, + 69, + 541, + 130 + ], + "type": "image", + "image_path": "4f10a0f9039838e59c95ab71ed37c7acfdbab630322376b1c9bbe79f4bb16629.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 118, + 139, + 474, + 152 + ], + "lines": [ + { + "bbox": [ + 118, + 139, + 474, + 152 + ], + "spans": [ + { + "bbox": [ + 118, + 139, + 474, + 152 + ], + "type": "text", + "content": "Figure 6. Overview of our proposed Spatio-Temporal Redundant Frame Downsampling approach." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "code", + "bbox": [ + 50, + 191, + 288, + 369 + ], + "blocks": [ + { + "bbox": [ + 52, + 167, + 261, + 190 + ], + "lines": [ + { + "bbox": [ + 52, + 167, + 261, + 190 + ], + "spans": [ + { + "bbox": [ + 52, + 167, + 261, + 190 + ], + "type": "text", + "content": "Algorithm 1: Spatio-Temporal Redundant Frame Downsampling." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "code_caption" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "lines": [ + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "spans": [ + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": "Input: Point cloud frames pool " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": " (size of " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": "), subset size " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "q" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": ", redundancy function " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "\\psi \\in [0,1]" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": " and empirical supervisor function " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "v" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": ". \n1 Divide " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": " evenly into " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "\\lceil p / q\\rceil" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": " subsets " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": ". \n2 " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "D \\leftarrow" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": " empty dictionary. \n3 for all " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "e \\leftarrow 0 : n_e - 1" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": " do \n4 // loop for all sequences \n5 for all " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "i \\leftarrow 0 : \\lceil p / q\\rceil - 1" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": " do \n6 // loop for subsets " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "Q" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": " \n7 " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "\\overline{M_i} \\leftarrow \\frac{1}{q} \\sum_{j=0}^{q-1} \\psi(Q_{i,j})" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": ". // redundancy \n8 " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "k_i \\leftarrow \\lceil v(\\overline{M_i}) \\cdot q \\rceil" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": ". \n9 " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "T_i \\leftarrow" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": " select " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "k_i" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": " frames in " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "Q_i" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": " with the smallest " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "M_i" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": ". \n10 " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "C_e \\leftarrow C_e \\cup T_i" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": ". \n11 Append key-value pair " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "(e, C_e)" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": " into " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": ". \nReturn: Dictionary " + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 50, + 191, + 288, + 369 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "code_body" + } + ], + "index": 3, + "sub_type": "algorithm" + }, + { + "type": "image", + "bbox": [ + 37, + 380, + 287, + 460 + ], + "blocks": [ + { + "bbox": [ + 37, + 380, + 287, + 460 + ], + "lines": [ + { + "bbox": [ + 37, + 380, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 37, + 380, + 287, + 460 + ], + "type": "image", + "image_path": "1ce3f4649f7d55d53051888e4356abfba19f66195eacf23ff80906f8f455fbe2.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 66, + 467, + 268, + 479 + ], + "lines": [ + { + "bbox": [ + 66, + 467, + 268, + 479 + ], + "spans": [ + { + "bbox": [ + 66, + 467, + 268, + 479 + ], + "type": "text", + "content": "Figure 7. Illustration of the SDSC convolution module." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 485, + 287, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 485, + 287, + 617 + ], + "spans": [ + { + "bbox": [ + 46, + 485, + 287, + 617 + ], + "type": "text", + "content": "tens of millions of trainable parameters [23,26,28,46,60,63] due to the requirement for 3D (voxel-based) convolution operations, to operate on the voxelized topology of the otherwise unstructured LiDAR point cloud representation, which suffer from both high computational training demands and the risk of overfitting. Based on the observation that depthwise separable convolution has shown results comparable with regular convolution in tasks such as image classification but with significantly fewer trainable parameters [12,24,25,37,40,43], here we pursue the use of such an approach within 3D point cloud semantic segmentation." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 617, + 288, + 701 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 617, + 288, + 701 + ], + "spans": [ + { + "bbox": [ + 46, + 617, + 288, + 701 + ], + "type": "text", + "content": "As such we propose the first formulation of sparse variant depthwise separable convolution [25] applied to 3D point clouds, namely Sparse Depthwise Separable Convolution (SDSC). SDSC combines the established computational advantages of sparse convolution for point cloud segmentation [20], with the significant trainable parameter reduction offered by depthwise separable convolution [12]." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 59, + 701, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 701, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 59, + 701, + 287, + 713 + ], + "type": "text", + "content": "Our SDSC module, as outlined in Fig. 7, initially takes a" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "spans": [ + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": "tensor " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "F \\in \\mathbb{R}^{H_F \\times W_F \\times L_F \\times M}" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": " as input, where " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "H_F" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "W_F" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "L_F" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": " denote radius, azimuth, height in the cylinder coordinate [63] and channels respectively. Firstly, a sparse depthwise convolution " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "\\mathrm{SDC}(M, M, D_k, s = 1)" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": " is applied, with " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": " input and output feature planes, a kernel size of " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "D_k" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": " and stride " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": " in order to output a tensor " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "T \\in \\mathbb{R}^{H_F \\times W_F \\times L_F \\times M}" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": ". Inside our sparse depthwise convolution, " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": " sparse spatial convolutions are performed independently over each input channel using submanifold sparse convolution [20] due to its tensor shape preserving property at no computational or memory overhead. Secondly, the sparse pointwise convolution " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "\\mathrm{SPC}(M, N, 1, s = 1)" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": " projects the channels output " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": " by the sparse depthwise convolution onto a new channel space, to mix the information across different channels. As a result, the sparse depthwise separable convolution " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "\\mathrm{SDSC}(M, N, D_k, s = 1)" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": " is the compound of the sparse depthwise convolution and the sparse pointwise convolution, namely " + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "inline_equation", + "content": "\\mathrm{SDSC}(M, N, D_k, s = 1) = \\mathrm{SDC} \\circ \\mathrm{SPC}" + }, + { + "bbox": [ + 304, + 163, + 547, + 379 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 380, + 547, + 427 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 380, + 547, + 427 + ], + "spans": [ + { + "bbox": [ + 304, + 380, + 547, + 427 + ], + "type": "text", + "content": "Using a sparse voxelized input representation similar to [19], and a series of such SDSC sub-modules we construct the popular Cylinder3D [63] sub-architectures within our overall Mean Teacher architectural design (Fig. 2)." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 305, + 429, + 378, + 441 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 429, + 378, + 441 + ], + "spans": [ + { + "bbox": [ + 305, + 429, + 378, + 441 + ], + "type": "text", + "content": "4. Evaluation" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 449, + 547, + 497 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 449, + 547, + 497 + ], + "spans": [ + { + "bbox": [ + 304, + 449, + 547, + 497 + ], + "type": "text", + "content": "We evaluate our proposed Less is More 3D (LiM3D) approach against state-of-the-art 3D point cloud semantic segmentation approaches using the SemanticKITTI [7] and ScribbleKITTI [46] benchmark datasets." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 503, + 422, + 517 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 503, + 422, + 517 + ], + "spans": [ + { + "bbox": [ + 305, + 503, + 422, + 517 + ], + "type": "text", + "content": "4.1. Experimental Setup" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 521, + 547, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 547, + 570 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 547, + 570 + ], + "type": "text", + "content": "SemanticKITTI [7] is a large-scale 3D point cloud dataset for semantic scene understanding with 20 semantic classes consisting of 22 sequences - [00 to 10 as training-split (of which 08 as validation-split) + 11 to 21 as test-split]." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 570, + 547, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 547, + 629 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 547, + 629 + ], + "type": "text", + "content": "ScribbleKITTI [46] is the first scribble (i.e. sparsely) annotated dataset for LiDAR semantic segmentation providing sparse annotations for the training split of SemanticKITTI for 19 classes, with only " + }, + { + "bbox": [ + 304, + 570, + 547, + 629 + ], + "type": "inline_equation", + "content": "8.06\\%" + }, + { + "bbox": [ + 304, + 570, + 547, + 629 + ], + "type": "text", + "content": " of points from the full SemanticKITTI dataset annotated." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "type": "text", + "content": "Evaluation Protocol: Following previous work [29,32,46, 63], we report performance on the SemanticKITTI and ScribbleKITTI training set for intermediate training steps, as this metric provides an indication of the pseudo-labeling quality, and on the validation set to assess the performance benefits of each individual component. Performance is reported using the mean Intersection over Union (mIoU, as " + }, + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "type": "inline_equation", + "content": "\\%" + }, + { + "bbox": [ + 304, + 630, + 547, + 713 + ], + "type": "text", + "content": " ) metric." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9366" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 49, + 95, + 547, + 196 + ], + "blocks": [ + { + "bbox": [ + 47, + 70, + 545, + 93 + ], + "lines": [ + { + "bbox": [ + 47, + 70, + 545, + 93 + ], + "spans": [ + { + "bbox": [ + 47, + 70, + 545, + 93 + ], + "type": "text", + "content": "Table 1. Comparative mIoU for Range- and Voxel-based methods using uniform sampling (U), sequential partition (P) and ST-RFD sampling (S): bold/underlined = best/2nd best; * denotes reproduced result; - denotes missing result due to unavailability from original authors." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 49, + 95, + 547, + 196 + ], + "lines": [ + { + "bbox": [ + 49, + 95, + 547, + 196 + ], + "spans": [ + { + "bbox": [ + 49, + 95, + 547, + 196 + ], + "type": "table", + "html": "
Repr.Samp.MethodSemanticKITTI [7]ScribbleKITTI [46]
1%5%10%20%40%50%100%1%5%10%20%40%50%100%
RangeULaserMix [32](2022)43.4-58.859.4-61.4-38.3-54.455.6-58.7-
VoxelUCylinder3D [63](CVPR'21)-45.456.157.858.7-67.8-39.248.052.153.8-56.3
ULaserMix [32](2022)50.6-60.061.9-62.3-44.2-53.755.1-56.8-
PJiang et al. [29](ICCV'21)-41.849.958.859.9-65.8-------
UUnal et al. [46](CVPR'22)-49.9*58.7*59.1*60.9-68.2*-46.9*54.2*56.5*58.6*-61.3
SLiM3D+SDSC(ours)57.257.661.061.762.162.767.555.856.156.957.258.959.360.7
SLiM3D(ours)58.459.562.263.163.363.669.557.058.161.061.262.062.162.4
", + "image_path": "ee1d7490d9ce41eff9a03171306169b4ec72b41f251c5cb53a7c8e8326b1fc69.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 217, + 288, + 347 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 217, + 288, + 347 + ], + "spans": [ + { + "bbox": [ + 46, + 217, + 288, + 347 + ], + "type": "text", + "content": "For semi-supervised training, we report over both the benchmarks using the SemanticKITTI and ScribbleKITTI validation set under " + }, + { + "bbox": [ + 46, + 217, + 288, + 347 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 46, + 217, + 288, + 347 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 217, + 288, + 347 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 217, + 288, + 347 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 217, + 288, + 347 + ], + "type": "inline_equation", + "content": "20\\%" + }, + { + "bbox": [ + 46, + 217, + 288, + 347 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 46, + 217, + 288, + 347 + ], + "type": "inline_equation", + "content": "40\\%" + }, + { + "bbox": [ + 46, + 217, + 288, + 347 + ], + "type": "text", + "content": " partitioning. We further report the relative performance of semi-supervised or scribble-supervised for ScribbleKITTI (SS) training to the fully supervised upper-bound (FS) in percentages (SS/FS) to further analyze semi-supervised performance and report the results for the fully-supervised training on both validation sets for reference. The trainable parameter count and number of multiply-adds (multi-adds) are additionally provided as a metric of computational cost." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "spans": [ + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "text", + "content": "Implementation Details: Training is performed using " + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "inline_equation", + "content": "4 \\times" + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "text", + "content": " NVIDIA A100 80GB GPU without pre-trained weights with a DDP shared training strategy [1] to maintain GPU scaling efficiency, whilst reducing memory overhead significantly. Specific hyper-parameters are set as follows - Mean Teacher: " + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "inline_equation", + "content": "\\kappa = 0.99" + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "text", + "content": "; unreliable pseudo-labeling: " + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "inline_equation", + "content": "\\lambda_{C} = 0.3" + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "inline_equation", + "content": "\\tau = 0.5" + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "text", + "content": "; ST-RFD: " + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "inline_equation", + "content": "\\beta = \\{7.45, 5.72, 4.00, 2.28, 0\\}" + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "text", + "content": " for sampling " + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "inline_equation", + "content": "\\{5\\%, 10\\%, 20\\%, 40\\%, 100\\}" + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "text", + "content": " labeled training frames, assuming the remainder as unlabeled; Reflec-TTA: " + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "inline_equation", + "content": "N_{b} = 10" + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "inline_equation", + "content": "s = 3" + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "text", + "content": " various Reflec-TTA bin sizes, following [46], we set each bin " + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "inline_equation", + "content": "b_{i} = (\\rho, \\phi) \\in \\{(20, 40), (40, 80), (80, 120)\\}" + }, + { + "bbox": [ + 46, + 348, + 290, + 482 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 47, + 491, + 289, + 608 + ], + "blocks": [ + { + "bbox": [ + 47, + 491, + 289, + 608 + ], + "lines": [ + { + "bbox": [ + 47, + 491, + 289, + 608 + ], + "spans": [ + { + "bbox": [ + 47, + 491, + 289, + 608 + ], + "type": "image", + "image_path": "d82192639287f5dbf30e7eb547e5c82753135d975c1a8a673bb720a2053b5d26.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 614, + 288, + 659 + ], + "lines": [ + { + "bbox": [ + 46, + 614, + 288, + 659 + ], + "spans": [ + { + "bbox": [ + 46, + 614, + 288, + 659 + ], + "type": "text", + "content": "Figure 8. Comparing the " + }, + { + "bbox": [ + 46, + 614, + 288, + 659 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 46, + 614, + 288, + 659 + ], + "type": "text", + "content": " sampling split of SemanticKITTI (SeK, first row) and ScribbleKITTI (ScK, second row) validation set with ground-truth (left), our approach (middle) and Unal et al. [46] (right) with areas of improvement highlighted." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 664, + 171, + 677 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 664, + 171, + 677 + ], + "spans": [ + { + "bbox": [ + 47, + 664, + 171, + 677 + ], + "type": "text", + "content": "4.2. Experimental Results" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 677, + 289, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 289, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 289, + 713 + ], + "type": "text", + "content": "In Tab. 1, we present the performance of our Less is More 3D (LiM3D) point cloud semantic segmentation approach both with (LiM3D+SDSC) and without (LiM3D) SDSC in a side" + } + ] + } + ], + "index": 7 + }, + { + "type": "table", + "bbox": [ + 308, + 271, + 547, + 368 + ], + "blocks": [ + { + "bbox": [ + 304, + 214, + 547, + 270 + ], + "lines": [ + { + "bbox": [ + 304, + 214, + 547, + 270 + ], + "spans": [ + { + "bbox": [ + 304, + 214, + 547, + 270 + ], + "type": "text", + "content": "Table 2. Component-wise ablation of LiM3D (mIoU as %, and #parameters in millions, M) on SemanticKITTI [7] training and validation sets where UP, RF, RT, ST, SD denote Unreliable Pseudolabeling, Reflectivity Feature, Reflec-TTA, ST-RFD, and SDSC module respectively." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 308, + 271, + 547, + 368 + ], + "lines": [ + { + "bbox": [ + 308, + 271, + 547, + 368 + ], + "spans": [ + { + "bbox": [ + 308, + 271, + 547, + 368 + ], + "type": "table", + "html": "
UPRFRTSTSDTraining mIoU (%)Validation mIoU (%)#Params(M)
5%10%20%40%5%10%20%40%
82.887.587.888.254.858.159.360.849.6
✓----55.958.859.961.249.6
✓✓83.688.388.789.156.859.660.561.449.6
✓✓----57.559.861.262.649.6
✓✓✓----58.761.362.462.849.6
✓✓✓✓85.289.189.589.759.562.263.163.349.6
✓✓✓✓✓83.888.689.089.257.661.061.762.121.5
", + "image_path": "dd1e94a3f42bf89e37b2d8ba28bade4def0db2e7f797d69da69c7376691b0dba.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 380, + 547, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 380, + 547, + 452 + ], + "spans": [ + { + "bbox": [ + 304, + 380, + 547, + 452 + ], + "type": "text", + "content": "by-side comparison with leading contemporary state-of-the-art approaches on the SemanticKITTI and ScribbleKITTI benchmark validation sets to illustrate our approach offers superior or comparable (within " + }, + { + "bbox": [ + 304, + 380, + 547, + 452 + ], + "type": "inline_equation", + "content": "1\\%" + }, + { + "bbox": [ + 304, + 380, + 547, + 452 + ], + "type": "text", + "content": " mIoU) performance across all sampling ratios. Furthermore, we present supporting qualitative results in Fig. 8." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 452, + 547, + 536 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 452, + 547, + 536 + ], + "spans": [ + { + "bbox": [ + 304, + 452, + 547, + 536 + ], + "type": "text", + "content": "On SemanticKITTI, with a lack of available supervision, LiM3D shows a relative performance (SS/FS) from " + }, + { + "bbox": [ + 304, + 452, + 547, + 536 + ], + "type": "inline_equation", + "content": "85.6\\%" + }, + { + "bbox": [ + 304, + 452, + 547, + 536 + ], + "type": "text", + "content": " (5%-fully-supervised) to " + }, + { + "bbox": [ + 304, + 452, + 547, + 536 + ], + "type": "inline_equation", + "content": "91.1\\%" + }, + { + "bbox": [ + 304, + 452, + 547, + 536 + ], + "type": "text", + "content": " (40%-fully-supervised), and LiM3D+SDSC from " + }, + { + "bbox": [ + 304, + 452, + 547, + 536 + ], + "type": "inline_equation", + "content": "85.3\\%" + }, + { + "bbox": [ + 304, + 452, + 547, + 536 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 304, + 452, + 547, + 536 + ], + "type": "inline_equation", + "content": "92.0\\%" + }, + { + "bbox": [ + 304, + 452, + 547, + 536 + ], + "type": "text", + "content": ", compared to their respective fully supervised upper-bound. LiM3D/LiM3D+SDSC performance is also less sensitive to reduced labeled data sampling compared with other methods." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "spans": [ + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "text", + "content": "Our model significantly outperforms on small ratio sampling splits, e.g., " + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "text", + "content": ". LiM3D shows up to " + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "inline_equation", + "content": "19.8\\%" + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "inline_equation", + "content": "18.9\\%" + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "text", + "content": " mIoU improvements whilst, with a smaller model size " + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "inline_equation", + "content": "\\mathrm{LiM3D + SDSC}" + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "text", + "content": " again shows significant mIoU improvements by up to " + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "inline_equation", + "content": "16.4\\%" + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "inline_equation", + "content": "15.5\\%" + }, + { + "bbox": [ + 304, + 536, + 547, + 609 + ], + "type": "text", + "content": " when compared with other range and voxel-based methods respectively." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 613, + 406, + 625 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 613, + 406, + 625 + ], + "spans": [ + { + "bbox": [ + 306, + 613, + 406, + 625 + ], + "type": "text", + "content": "4.3. Ablation Studies" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 628, + 547, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 628, + 547, + 689 + ], + "spans": [ + { + "bbox": [ + 304, + 628, + 547, + 689 + ], + "type": "text", + "content": "Effectiveness of Components. In Tab. 2 we ablate each component of LiM3D step by step and report the performance on the SemanticKITTI training set at the end of training as an overall indicator of pseudo-labeling quality in addition to the corresponding validation set." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 689, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 547, + 713 + ], + "type": "text", + "content": "As shown in Tab. 2, adding unreliable pseudo-labeling (UP) in the distillation stage, we can increase the valid mIoU" + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9367" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "bbox": [ + 45, + 72, + 290, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 72, + 290, + 205 + ], + "spans": [ + { + "bbox": [ + 45, + 72, + 290, + 205 + ], + "type": "text", + "content": "by " + }, + { + "bbox": [ + 45, + 72, + 290, + 205 + ], + "type": "inline_equation", + "content": "+0.7\\%" + }, + { + "bbox": [ + 45, + 72, + 290, + 205 + ], + "type": "text", + "content": " on average in validation set. Appending reflectivity features (RF) in the training stage, we further improve the mIoU on the training set by " + }, + { + "bbox": [ + 45, + 72, + 290, + 205 + ], + "type": "inline_equation", + "content": "+0.7\\%" + }, + { + "bbox": [ + 45, + 72, + 290, + 205 + ], + "type": "text", + "content": " on average. Due to the improvements in training, the model generates a higher quality of pseudo-labels, which results to a " + }, + { + "bbox": [ + 45, + 72, + 290, + 205 + ], + "type": "inline_equation", + "content": "+0.5\\%" + }, + { + "bbox": [ + 45, + 72, + 290, + 205 + ], + "type": "text", + "content": " increase in mIoU in the validation set. If we disable reflectivity features in the training stage, applying Reflec-TTA in the distillation stage alone, we then get an average improvement of " + }, + { + "bbox": [ + 45, + 72, + 290, + 205 + ], + "type": "inline_equation", + "content": "+1.3\\%" + }, + { + "bbox": [ + 45, + 72, + 290, + 205 + ], + "type": "text", + "content": " compared with pseudo-labeling only. On the whole, enabling all reflectivity-based components (RF+RT) shows great improvements of up to " + }, + { + "bbox": [ + 45, + 72, + 290, + 205 + ], + "type": "inline_equation", + "content": "+2.8\\%" + }, + { + "bbox": [ + 45, + 72, + 290, + 205 + ], + "type": "text", + "content": " in validation mIoU." + } + ] + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 48, + 246, + 288, + 327 + ], + "blocks": [ + { + "bbox": [ + 47, + 212, + 289, + 245 + ], + "lines": [ + { + "bbox": [ + 47, + 212, + 289, + 245 + ], + "spans": [ + { + "bbox": [ + 47, + 212, + 289, + 245 + ], + "type": "text", + "content": "Table 3. The computation cost and mIoU (in percentage) under " + }, + { + "bbox": [ + 47, + 212, + 289, + 245 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 47, + 212, + 289, + 245 + ], + "type": "text", + "content": " -labeled training results on SemanticKITTI (SeK) and ScribbleKITTI (ScK) validation set." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 246, + 288, + 327 + ], + "lines": [ + { + "bbox": [ + 48, + 246, + 288, + 327 + ], + "spans": [ + { + "bbox": [ + 48, + 246, + 288, + 327 + ], + "type": "table", + "html": "
Method# Parameters# Mult-AddsSeK [7]ScK [46]
Cylinder3D [63]56.3476.9M45.439.2
Unal et al. [46]49.6420.2M49.946.9
2DPASS [58]26.5217.4M51.745.1
MinkowskiNet [13]21.7114.0G42.435.8
SPVNAS [44]12.573.8G45.138.9
LiM3D+SDSC (ours)21.5182.0M57.654.7
LiM3D (ours)49.6420.2M59.558.1
", + "image_path": "5c5eedf91d572b68d9cb72af62561d7931a6a61e64a24c02ceadada704a9297f.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 48, + 361, + 290, + 423 + ], + "blocks": [ + { + "bbox": [ + 47, + 337, + 288, + 359 + ], + "lines": [ + { + "bbox": [ + 47, + 337, + 288, + 359 + ], + "spans": [ + { + "bbox": [ + 47, + 337, + 288, + 359 + ], + "type": "text", + "content": "Table 4. Effects of ST-RFD sampling on SemanticKITTI and ScribbleKITTI validation set (mIoU as %)." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 361, + 290, + 423 + ], + "lines": [ + { + "bbox": [ + 48, + 361, + 290, + 423 + ], + "spans": [ + { + "bbox": [ + 48, + 361, + 290, + 423 + ], + "type": "table", + "html": "
SamplingSemanticKITTI [7]ScribbleKITTI [46]
5%10%20%40%5%10%20%40%
Random58.561.662.662.757.160.360.560.9
Uniform58.761.362.462.856.960.660.361.0
ST-RFD-R59.162.462.963.458.060.761.261.8
ST-RFD59.562.263.163.358.161.061.262.0
", + "image_path": "a97af9feb49c63b45e85e37b7675a7755137b0c4614745e964a74763b04891fb.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 63, + 492, + 267, + 555 + ], + "blocks": [ + { + "bbox": [ + 46, + 436, + 288, + 491 + ], + "lines": [ + { + "bbox": [ + 46, + 436, + 288, + 491 + ], + "spans": [ + { + "bbox": [ + 46, + 436, + 288, + 491 + ], + "type": "text", + "content": "Table 5. Effects of differing reliability using pseudo voxels on SemanticKITTI validation set, measured by the entropy of voxel-wise prediction. Unreliable and Reliable: selecting negative candidates with top " + }, + { + "bbox": [ + 46, + 436, + 288, + 491 + ], + "type": "inline_equation", + "content": "20\\%" + }, + { + "bbox": [ + 46, + 436, + 288, + 491 + ], + "type": "text", + "content": " highest entropy scores and bottom " + }, + { + "bbox": [ + 46, + 436, + 288, + 491 + ], + "type": "inline_equation", + "content": "20\\%" + }, + { + "bbox": [ + 46, + 436, + 288, + 491 + ], + "type": "text", + "content": " counterpart respectively. Random: sampling randomly regardless of entropy." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 63, + 492, + 267, + 555 + ], + "lines": [ + { + "bbox": [ + 63, + 492, + 267, + 555 + ], + "spans": [ + { + "bbox": [ + 63, + 492, + 267, + 555 + ], + "type": "table", + "html": "
RatioUnreliableReliableRandom
mIoUSS/FFmIoUSS/FFmIoUSS/FF
5%59.585.657.282.356.481.2
10%62.289.560.887.559.785.9
20%63.190.861.488.360.587.1
40%63.391.162.890.461.388.2
", + "image_path": "28e7fac67e342a410269ada242abe6a934646eee82d04cc9f608a1137ec0960f.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 48, + 590, + 293, + 635 + ], + "blocks": [ + { + "bbox": [ + 47, + 566, + 287, + 588 + ], + "lines": [ + { + "bbox": [ + 47, + 566, + 287, + 588 + ], + "spans": [ + { + "bbox": [ + 47, + 566, + 287, + 588 + ], + "type": "text", + "content": "Table 6. Reflectivity (Reflec-TTA) vs. Intensity (intensity-based TTA) on SemanticKITTI and ScribbleKITTI validation set (mIoU, %)." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 590, + 293, + 635 + ], + "lines": [ + { + "bbox": [ + 48, + 590, + 293, + 635 + ], + "spans": [ + { + "bbox": [ + 48, + 590, + 293, + 635 + ], + "type": "table", + "html": "
TTASemanticKITTI [7]ScribbleKITTI [46]
5%10%20%40%5%10%20%40%
Intensity56.259.159.860.955.757.557.959.2
Reflectivity59.562.263.163.358.161.061.262.0
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Finally, we provide two models, one without SDSC (LiM3D) and one with (LiM3D+SDSC), corresponding to the bottom two rows of Tab. 2." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 108, + 547, + 215 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 108, + 547, + 215 + ], + "spans": [ + { + "bbox": [ + 304, + 108, + 547, + 215 + ], + "type": "text", + "content": "Effectiveness of SDSC module. In Tab. 3, we compare our LiM3D and LiM3D+SDSC with recent state-of-the-art methods under " + }, + { + "bbox": [ + 304, + 108, + 547, + 215 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 304, + 108, + 547, + 215 + ], + "type": "text", + "content": " -labeled semi-supervised training on the SemanticKITTI and ScribbleKITTI validation sets. LiM3D+SDSC outperforms the voxel-based methods [46, 63] with at least a " + }, + { + "bbox": [ + 304, + 108, + 547, + 215 + ], + "type": "inline_equation", + "content": "2.3\\times" + }, + { + "bbox": [ + 304, + 108, + 547, + 215 + ], + "type": "text", + "content": " reduction in model size. Similarly, with comparable model size [13, 44, 58], LiM3D+SDSC has higher mIoU in both datasets and up to " + }, + { + "bbox": [ + 304, + 108, + 547, + 215 + ], + "type": "inline_equation", + "content": "641\\times" + }, + { + "bbox": [ + 304, + 108, + 547, + 215 + ], + "type": "text", + "content": " fewer multiply-add operations." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "spans": [ + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "text", + "content": "Effectiveness of ST-RFD strategy. In Tab. 4, we illustrate the effectiveness of our ST-RFD strategy by comparing LiM3D with two widely-used strategies in semi-supervised training, i.e., random sampling and uniform sampling on SemanticKITTI [7] and ScribbleKITTI [46] validation set. Whilst uniform and random sampling have comparable results on both validation sets, simply applying our ST-RFD strategy improves the baseline by " + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "inline_equation", + "content": "+0.90\\%" + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "inline_equation", + "content": "+0.75\\%" + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "inline_equation", + "content": "+0.60\\%" + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "inline_equation", + "content": "+0.55\\%" + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "text", + "content": " on SemanticKITTI under " + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "inline_equation", + "content": "10\\%" + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "inline_equation", + "content": "20\\%" + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "inline_equation", + "content": "40\\%" + }, + { + "bbox": [ + 304, + 216, + 547, + 382 + ], + "type": "text", + "content": " sampling protocol respectively. Furthermore, using corresponding range images of point cloud, rather than RGB images to compute the spatio-temporal redundancy within ST-RFD (see ST-RFD-R in Tab. 4), has no significant difference on the performance." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 383, + 547, + 467 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 383, + 547, + 467 + ], + "spans": [ + { + "bbox": [ + 304, + 383, + 547, + 467 + ], + "type": "text", + "content": "Effectiveness of Unreliable Pseudo-Labeling. In Tab. 5, we evaluate selecting negative candidates with different reliability to illustrate the improvements of using unreliable pseudo-labels in semi-supervised semantic segmentation. The \"Unreliable\" selecting of negative candidates outperforms other alternative methodologies, showing the positive performance impact of unreliable pseudo-labels." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 467, + 547, + 551 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 467, + 547, + 551 + ], + "spans": [ + { + "bbox": [ + 304, + 467, + 547, + 551 + ], + "type": "text", + "content": "Effectiveness of Reflec-TTA. In Tab. 2, we compare LiM3D performance with and without Reflec-TTA and further experiment on the SemanticKITTI and ScribbleKITTI validation set in Tab. 6. This demonstrates that the LiDAR point-wise intensity feature " + }, + { + "bbox": [ + 304, + 467, + 547, + 551 + ], + "type": "inline_equation", + "content": "I^*" + }, + { + "bbox": [ + 304, + 467, + 547, + 551 + ], + "type": "text", + "content": ", in place of the distance-normalized reflectivity feature " + }, + { + "bbox": [ + 304, + 467, + 547, + 551 + ], + "type": "inline_equation", + "content": "R^*" + }, + { + "bbox": [ + 304, + 467, + 547, + 551 + ], + "type": "text", + "content": ", offers inferior on-task performance." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 555, + 379, + 567 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 555, + 379, + 567 + ], + "spans": [ + { + "bbox": [ + 306, + 555, + 379, + 567 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 570, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 547, + 713 + ], + "type": "text", + "content": "This paper presents an efficient semi-supervised architecture for 3D point cloud semantic segmentation, which achieves more in terms of performance with less computational costs, less annotations, and less trainable model parameters (i.e., Less is More, LiM3D). Our architecture consists of three novel contributions: the SDSC convolution module, the ST-RFD sampling strategy, and the pseudo-labeling method informed by LiDAR reflectivity. These individual components can be applied to any 3D semantic segmentation architecture to reduce the gap between semi or weakly-supervised and fully-supervised learning on task performance, whilst managing model complexity and computation costs." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 315, + 757 + ], + "type": "text", + "content": "9368" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 114 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 114 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 114 + ], + "type": "text", + "content": "[1] FairScale. FairScale: A general purpose modular PyTorch library for high performance and large scale training, 2021. 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 115, + 288, + 148 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 115, + 288, + 148 + ], + "spans": [ + { + "bbox": [ + 53, + 115, + 288, + 148 + ], + "type": "text", + "content": "[2] Shahriar Akramullah. Digital Video Concepts, Methods, and Metrics: Quality, Compression, Performance, and Power Trade-off Analysis. Apress, Berkeley, CA, 2014. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 149, + 288, + 204 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 149, + 288, + 204 + ], + "spans": [ + { + "bbox": [ + 53, + 149, + 288, + 204 + ], + "type": "text", + "content": "[3] Inigo Alonso, Luis Riazuelo, Luis Montesano, and Ana C. Murillo. 3D-MiniNet: Learning a 2D Representation From Point Clouds for Fast and Efficient 3D LIDAR Semantic Segmentation. IEEE Robot. Autom. Lett., 5(4):5432-5439, Oct. 2020. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 206, + 288, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 206, + 288, + 259 + ], + "spans": [ + { + "bbox": [ + 53, + 206, + 288, + 259 + ], + "type": "text", + "content": "[4] Inigo Alonso, Alberto Sabater, David Ferstl, Luis Montesano, and Ana C. Murillo. Semi-Supervised Semantic Segmentation with Pixel-Level Contrastive Learning from a Class-wise Memory Bank. In Int. Conf. Comput. Vis., pages 8199-8208, 2021. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 262, + 288, + 305 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 262, + 288, + 305 + ], + "spans": [ + { + "bbox": [ + 53, + 262, + 288, + 305 + ], + "type": "text", + "content": "[5] Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor, and Kevin McGuinness. Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning. In Int. Jt. Conf. Neural Netw., 2020. 2, 4" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 308, + 288, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 308, + 288, + 363 + ], + "spans": [ + { + "bbox": [ + 53, + 308, + 288, + 363 + ], + "type": "text", + "content": "[6] Amir Atapour-Abarghouei and Toby P. Breckon. Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3368–3379, 2019. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 365, + 288, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 365, + 288, + 418 + ], + "spans": [ + { + "bbox": [ + 53, + 365, + 288, + 418 + ], + "type": "text", + "content": "[7] Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyril Stachniss, and Jurgen Gall. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. In Int. Conf. Comput. Vis., pages 9296-9306, Seoul, Korea (South), Oct. 2019. 2, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 420, + 288, + 474 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 420, + 288, + 474 + ], + "spans": [ + { + "bbox": [ + 53, + 420, + 288, + 474 + ], + "type": "text", + "content": "[8] Maxim Berman, Amal Rannen Triki, and Matthew B. Blaschko. The Lovasz-Softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 477, + 288, + 532 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 477, + 288, + 532 + ], + "spans": [ + { + "bbox": [ + 53, + 477, + 288, + 532 + ], + "type": "text", + "content": "[9] Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, and Felix Heide. Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather. In IEEE Conf. Comput. Vis. Pattern Recog., pages 11679-11689, 2020. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 533, + 288, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 533, + 288, + 588 + ], + "spans": [ + { + "bbox": [ + 48, + 533, + 288, + 588 + ], + "type": "text", + "content": "[10] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. Nuscenes: A multimodal dataset for autonomous driving. IEEE Conf. Comput. Vis. Pattern Recog., pages 11618-11628, 2020. 4, 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 590, + 288, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 590, + 288, + 633 + ], + "spans": [ + { + "bbox": [ + 48, + 590, + 288, + 633 + ], + "type": "text", + "content": "[11] Xiaokang Chen, Yuhui Yuan, Gang Zeng, and Jingdong Wang. Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2613-2622, June 2021. 1, 5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 635, + 288, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 635, + 288, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 635, + 288, + 668 + ], + "type": "text", + "content": "[12] François Chollet. Xception: Deep Learning with Depthwise Separable Convolutions. In IEEE Conf. Comput. Vis. Pattern Recog., Apr. 2017. 2, 6" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "type": "text", + "content": "[13] Christopher Choy, JunYoung Gwak, and Silvio Savarese. 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3070-3079, 2019. 1, 8" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 307, + 73, + 547, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 547, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 547, + 116 + ], + "type": "text", + "content": "[14] Tiago Cortinhal, George Tzelepis, and Eren Erdal Aksoy. SalsaNext: Fast, Uncertainty-Aware Semantic Segmentation of LiDAR Point Clouds. In Adv. Vis. Comput., pages 207-222, 2020. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 118, + 547, + 160 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 547, + 160 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 547, + 160 + ], + "type": "text", + "content": "[15] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In IEEE Conf. Comput. Vis. Pattern Recog., pages 248–255, 2009. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 162, + 547, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 162, + 547, + 205 + ], + "spans": [ + { + "bbox": [ + 308, + 162, + 547, + 205 + ], + "type": "text", + "content": "[16] Ngoc Phuong Anh Duong, Alexandre Almin, Léo Lemarie, and B Ravi Kiran. Reducing redundancy in Semantic-KITTI: Study on data augmentations within Active Learning. In Adv. Neural Inform. Process. Syst. Worksh., page 8, 2017. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 206, + 547, + 237 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 206, + 547, + 237 + ], + "spans": [ + { + "bbox": [ + 308, + 206, + 547, + 237 + ], + "type": "text", + "content": "[17] Geoff French. Semi-supervised semantic segmentation needs strong, varied perturbations. In Brit. Mach. Vis. Conf., page 21, 2020. 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 239, + 547, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 239, + 547, + 282 + ], + "spans": [ + { + "bbox": [ + 308, + 239, + 547, + 282 + ], + "type": "text", + "content": "[18] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3354-3361, 2012. 4, 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 283, + 547, + 304 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 283, + 547, + 304 + ], + "spans": [ + { + "bbox": [ + 308, + 283, + 547, + 304 + ], + "type": "text", + "content": "[19] Ben Graham. Sparse 3D convolutional neural networks. In Brit. Mach. Vis. Conf., pages 150.1-150.9, Swansea, 2015. 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 305, + 547, + 349 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 305, + 547, + 349 + ], + "spans": [ + { + "bbox": [ + 308, + 305, + 547, + 349 + ], + "type": "text", + "content": "[20] Benjamin Graham, Martin Engelcke, and Laurens van der Maaten. 3D Semantic Segmentation with Submanifold Sparse Convolutional Networks. In IEEE Conf. Comput. Vis. Pattern Recog., 2018. 2, 6" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 350, + 547, + 371 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 350, + 547, + 371 + ], + "spans": [ + { + "bbox": [ + 308, + 350, + 547, + 371 + ], + "type": "text", + "content": "[21] Benjamin Graham and Laurens van der Maaten. Submanifold Sparse Convolutional Networks, June 2017. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 372, + 547, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 372, + 547, + 415 + ], + "spans": [ + { + "bbox": [ + 308, + 372, + 547, + 415 + ], + "type": "text", + "content": "[22] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum Contrast for Unsupervised Visual Representation Learning. In IEEE Conf. Comput. Vis. Pattern Recog., pages 9726-9735, 2020. 4" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 416, + 547, + 459 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 416, + 547, + 459 + ], + "spans": [ + { + "bbox": [ + 308, + 416, + 547, + 459 + ], + "type": "text", + "content": "[23] Yuenan Hou, Xinge Zhu, Yuexin Ma, Chen Change Loy, and Yikang Li. Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., page 10, 2022. 1, 3, 6" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 460, + 547, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 460, + 547, + 514 + ], + "spans": [ + { + "bbox": [ + 308, + 460, + 547, + 514 + ], + "type": "text", + "content": "[24] Andrew Howard, Mark Sandler, Bo Chen, Weijun Wang, Liang-Chieh Chen, Mingxing Tan, Grace Chu, Vijay Vasudevan, Yukun Zhu, Ruoming Pang, Hartwig Adam, and Quoc Le. Searching for MobileNetV3. In Int. Conf. Comput. Vis., pages 1314-1324, 2019. 2, 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 515, + 547, + 569 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 515, + 547, + 569 + ], + "spans": [ + { + "bbox": [ + 308, + 515, + 547, + 569 + ], + "type": "text", + "content": "[25] Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andretto, and Hartwig Adam. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, Apr. 2017. 2, 6" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 571, + 547, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 571, + 547, + 624 + ], + "spans": [ + { + "bbox": [ + 308, + 571, + 547, + 624 + ], + "type": "text", + "content": "[26] Qingyong Hu, Bo Yang, Linhai Xie, Stefano Rosa, Yulan Guo, Zhihua Wang, Niki Trigoni, and Andrew Markham. RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds. In IEEE Conf. Comput. Vis. Pattern Recog., 2020. 1, 6" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 625, + 547, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 625, + 547, + 669 + ], + "spans": [ + { + "bbox": [ + 308, + 625, + 547, + 669 + ], + "type": "text", + "content": "[27] Zeyu Hu, Xuyang Bai, Runze Zhang, Xin Wang, Guangyuan Sun, Hongbo Fu, and Chiew-Lan Tai. LiDAL: Inter-frame Uncertainty Based Active Learning for 3D LiDAR Semantic Segmentation. In Eur. Conf. Comput. Vis. Springer. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 670, + 547, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 670, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 670, + 547, + 713 + ], + "type": "text", + "content": "[28] Maximilian Jaritz, Tuan-Hung Vu, Raoul de Charette, Emilie Wirbel, and Patrick Pérez. xMUDA: Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation. IEEE Conf. Comput. Vis. Pattern Recog., 2021. 1, 6" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "9369" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "text", + "content": "[29] Li Jiang, Shaoshuai Shi, Zhuotao Tian, Xin Lai, Shu Liu, ChiWing Fu, and Jiaya Jia. Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation. In Int. Conf. Comput. Vis., 2021. 2, 3, 6, 7" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 118, + 288, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 118, + 288, + 171 + ], + "spans": [ + { + "bbox": [ + 48, + 118, + 288, + 171 + ], + "type": "text", + "content": "[30] R. Kesten, M. Usman, J. Houston, T. Pandya, K. Nadhamuni, A. Ferreira, M. Yuan, B. Low, A. Jain, P. Ondruska, S. Omari, S. Shah, A. Kulkarni, A. Kazakova, C. Tao, L. Platinsky, W. Jiang, and V. Shet. Lyft level 5 perception dataset 2020, 2019. 4, 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 173, + 288, + 206 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 173, + 288, + 206 + ], + "spans": [ + { + "bbox": [ + 48, + 173, + 288, + 206 + ], + "type": "text", + "content": "[31] Deyvid Kochanov, Fatemeh Karimi Nejadasl, and Olaf Booij. KPRNet: Improving projection-based LiDAR semantic segmentation, Aug. 2020. 1, 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 208, + 288, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 208, + 288, + 240 + ], + "spans": [ + { + "bbox": [ + 48, + 208, + 288, + 240 + ], + "type": "text", + "content": "[32] Lingdong Kong, Jiawei Ren, Liang Pan, and Ziwei Liu. LaserMix for Semi-Supervised LiDAR Semantic Segmentation, June 2022. 2, 3, 5, 6, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 241, + 288, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 241, + 288, + 274 + ], + "spans": [ + { + "bbox": [ + 48, + 241, + 288, + 274 + ], + "type": "text", + "content": "[33] Samuli Laine and Timo Aila. Temporal Ensembling for Semi-Supervised Learning. In Int. Conf. Learn. Represent., 2017. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 275, + 288, + 341 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 275, + 288, + 341 + ], + "spans": [ + { + "bbox": [ + 48, + 275, + 288, + 341 + ], + "type": "text", + "content": "[34] Li Li, Khalid N. Ismail, Hubert P. H. Shum, and Toby P. Breckon. DurLAR: A High-Fidelity 128-Channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-Modal Autonomous Driving Applications. In Int. Conf. 3D Vis., pages 1227-1237, London, United Kingdom, Dec. 2021. 4, 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 342, + 288, + 386 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 342, + 288, + 386 + ], + "spans": [ + { + "bbox": [ + 48, + 342, + 288, + 386 + ], + "type": "text", + "content": "[35] Peizhao Li, Pu Wang, Karl Berntorp, and Hongfu Liu. Exploiting Temporal Relations on Radar Perception for Autonomous Driving. In IEEE Conf. Comput. Vis. Pattern Recog., pages 17071-17080, 2022. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 388, + 288, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 388, + 288, + 431 + ], + "spans": [ + { + "bbox": [ + 48, + 388, + 288, + 431 + ], + "type": "text", + "content": "[36] Venice Erin Liong, Thi Ngoc Tho Nguyen, Sergi Widjaja, Dhananjai Sharma, and Zhuang Jie Chong. AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation, Dec. 2020. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 433, + 288, + 477 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 433, + 288, + 477 + ], + "spans": [ + { + "bbox": [ + 48, + 433, + 288, + 477 + ], + "type": "text", + "content": "[37] Dominic Masters, Antoine Labatie, Zach Eaton-Rosen, and Carlo Luschi. Making EfficientNet More Efficient: Exploring Batch-Independent Normalization, Group Convolutions and Reduced Resolution Training, Aug. 2021. 2, 6" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 478, + 288, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 478, + 288, + 521 + ], + "spans": [ + { + "bbox": [ + 48, + 478, + 288, + 521 + ], + "type": "text", + "content": "[38] Andres Milioto, Ignacio Vizzo, Jens Behley, and Cyril Stachniss. RangeNet ++: Fast and Accurate LiDAR Semantic Segmentation. In Int. Conf. Intell. Robots Syst., pages 4213-4220, 2019. 1, 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 523, + 288, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 523, + 288, + 567 + ], + "spans": [ + { + "bbox": [ + 48, + 523, + 288, + 567 + ], + "type": "text", + "content": "[39] Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen. Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning. In Adv. Neural Inform. Process. Syst., volume 29, 2016. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 568, + 288, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 568, + 288, + 622 + ], + "spans": [ + { + "bbox": [ + 48, + 568, + 288, + 622 + ], + "type": "text", + "content": "[40] Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zh-moginov, and Liang-Chieh Chen. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4510-4520, Salt Lake City, UT, 2018. IEEE. 2, 6" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 624, + 288, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 624, + 288, + 646 + ], + "spans": [ + { + "bbox": [ + 48, + 624, + 288, + 646 + ], + "type": "text", + "content": "[41] Laurent Sifre. Rigid-Motion Scattering For Image Classification. PhD thesis, Ecole Polytechnique, 2014. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 647, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 288, + 713 + ], + "type": "text", + "content": "[42] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, and Dragomir Anguelov." + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 72, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 326, + 72, + 546, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 72, + 546, + 106 + ], + "spans": [ + { + "bbox": [ + 326, + 72, + 546, + 106 + ], + "type": "text", + "content": "Scalability in Perception for Autonomous Driving: Waymo Open Dataset. In IEEE Conf. Comput. Vis. Pattern Recog., pages 2443-2451, Seattle, WA, USA, June 2020. 5" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 106, + 547, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 106, + 547, + 139 + ], + "spans": [ + { + "bbox": [ + 307, + 106, + 547, + 139 + ], + "type": "text", + "content": "[43] Mingxing Tan and Quoc Le. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In Proc. 36th Int. Conf. Mach. Learn., pages 6105-6114. PMLR, May 2019. 6" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 140, + 547, + 182 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 140, + 547, + 182 + ], + "spans": [ + { + "bbox": [ + 307, + 140, + 547, + 182 + ], + "type": "text", + "content": "[44] Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, and Song Han. Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution. In Eur. Conf. Comput. Vis., 2020. 1, 2, 8" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 183, + 547, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 183, + 547, + 227 + ], + "spans": [ + { + "bbox": [ + 307, + 183, + 547, + 227 + ], + "type": "text", + "content": "[45] Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. In Adv. Neural Inform. Process. Syst., 2017. 2, 3, 5" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 228, + 547, + 260 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 228, + 547, + 260 + ], + "spans": [ + { + "bbox": [ + 307, + 228, + 547, + 260 + ], + "type": "text", + "content": "[46] Ozan Unal, Dengxin Dai, and Luc Van Gool. Scribblesupervised LiDAR semantic segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., 2022. 1, 2, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 261, + 547, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 261, + 547, + 293 + ], + "spans": [ + { + "bbox": [ + 307, + 261, + 547, + 293 + ], + "type": "text", + "content": "[47] Aaron van den Oord, Yazhe Li, and Oriol Vinyals. Representation Learning with Contrastive Predictive Coding, Jan. 2019. 1, 3, 4" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 293, + 547, + 337 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 293, + 547, + 337 + ], + "spans": [ + { + "bbox": [ + 307, + 293, + 547, + 337 + ], + "type": "text", + "content": "[48] Jue Wang, Gedas Bertasius, Du Tran, and Lorenzo Torresani. Long-Short Temporal Contrastive Learning of Video Transformers. In IEEE Conf. Comput. Vis. Pattern Recog., pages 13990–14000, 2022. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 338, + 547, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 338, + 547, + 392 + ], + "spans": [ + { + "bbox": [ + 307, + 338, + 547, + 392 + ], + "type": "text", + "content": "[49] Yuchao Wang, Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li, Guoqiang Jin, Liwei Wu, Rui Zhao, and Xinyi Le. Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels. IEEE Conf. Comput. Vis. Pattern Recog., Mar. 2022. 1, 4" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 393, + 547, + 436 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 393, + 547, + 436 + ], + "spans": [ + { + "bbox": [ + 307, + 393, + 547, + 436 + ], + "type": "text", + "content": "[50] Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process., 13(4):600-612, Apr. 2004. 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 437, + 547, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 437, + 547, + 491 + ], + "spans": [ + { + "bbox": [ + 307, + 437, + 547, + 491 + ], + "type": "text", + "content": "[51] Bichen Wu, Alvin Wan, Xiangyu Yue, and Kurt Keutzer. SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud. In Int. Conf. Robot. Autom., pages 1887–1893, 2018. 1, 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 492, + 547, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 492, + 547, + 547 + ], + "spans": [ + { + "bbox": [ + 307, + 492, + 547, + 547 + ], + "type": "text", + "content": "[52] Bichen Wu, Xuanyu Zhou, Sicheng Zhao, Xiangyu Yue, and Kurt Keutzer. SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud. In Int. Conf. Robot. Autom., pages 4376–4382, 2019. 1, 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 548, + 547, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 548, + 547, + 602 + ], + "spans": [ + { + "bbox": [ + 307, + 548, + 547, + 602 + ], + "type": "text", + "content": "[53] Tsung-Han Wu, Yueh-Cheng Liu, Yu-Kai Huang, Hsin-Ying Lee, Hung-Ting Su, Ping-Chia Huang, and Winston H. Hsu. ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic Segmentation. In Int. Conf. Comput. Vis. 3" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 602, + 547, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 602, + 547, + 646 + ], + "spans": [ + { + "bbox": [ + 307, + 602, + 547, + 646 + ], + "type": "text", + "content": "[54] Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin. Unsupervised Feature Learning via Non-parametric Instance Discrimination. In IEEE Conf. Comput. Vis. Pattern Recog., pages 3733-3742, 2018. 4" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 647, + 547, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 647, + 547, + 690 + ], + "spans": [ + { + "bbox": [ + 307, + 647, + 547, + 690 + ], + "type": "text", + "content": "[55] Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V. Le. Self-Training With Noisy Student Improves ImageNet Classification. In IEEE Conf. Comput. Vis. Pattern Recog., pages 10687–10698, 2020. 3" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 307, + 691, + 547, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 691, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 691, + 547, + 713 + ], + "type": "text", + "content": "[56] Chenfeng Xu, Bichen Wu, Zining Wang, Wei Zhan, Peter Va-jda, Kurt Keutzer, and Masayoshi Tomizuka. SqueezeSegV3:" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "9370" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 586 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 67, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 67, + 72, + 287, + 116 + ], + "type": "text", + "content": "Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation. In Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm, editors, Eur. Conf. Comput. Vis., 2020. 1, 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 118, + 287, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 118, + 287, + 171 + ], + "spans": [ + { + "bbox": [ + 48, + 118, + 287, + 171 + ], + "type": "text", + "content": "[57] Xu Yan, Jiantao Gao, Jie Li, Ruimao Zhang, Zhen Li, Rui Huang, and Shuguang Cui. Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion. In Conf. AAAI Artif. Intell., 2021. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 173, + 287, + 217 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 173, + 287, + 217 + ], + "spans": [ + { + "bbox": [ + 49, + 173, + 287, + 217 + ], + "type": "text", + "content": "[58] Xu Yan, Jiantao Gao, Chaoda Zheng, Chao Zheng, Ruimao Zhang, Shuguang Cui, and Zhen Li. 2DPASS: 2D priors assisted semantic segmentation on LiDAR point clouds. In Eur. Conf. Comput. Vis., 2022. 1, 8" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 219, + 287, + 262 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 219, + 287, + 262 + ], + "spans": [ + { + "bbox": [ + 49, + 219, + 287, + 262 + ], + "type": "text", + "content": "[59] Lihe Yang, Wei Zhuo, Lei Qi, Yinghuan Shi, and Yang Gao. ST++: Make Self-Training Work Better for Semi-Supervised Semantic Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4268–4277, 2022. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 263, + 287, + 317 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 263, + 287, + 317 + ], + "spans": [ + { + "bbox": [ + 49, + 263, + 287, + 317 + ], + "type": "text", + "content": "[60] Li Yi, Boqing Gong, and Thomas Funkhouser. Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds. In IEEE Conf. Comput. Vis. Pattern Recog., pages 15358-15368, Nashville, TN, USA, June 2021. 1, 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 319, + 287, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 319, + 287, + 373 + ], + "spans": [ + { + "bbox": [ + 49, + 319, + 287, + 373 + ], + "type": "text", + "content": "[61] Yang Zhang, Zixiang Zhou, Philip David, Xiangyu Yue, Zerong Xi, Boqing Gong, and Hassan Foroosh. PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., 2020. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 375, + 287, + 418 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 375, + 287, + 418 + ], + "spans": [ + { + "bbox": [ + 49, + 375, + 287, + 418 + ], + "type": "text", + "content": "[62] Xizhou Zhu, Yuwen Xiong, Jifeng Dai, Lu Yuan, and Yichen Wei. Deep Feature Flow for Video Recognition. In IEEE Conf. Comput. Vis. Pattern Recog., pages 4141-4150, Honolulu, HI, 2017. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 49, + 420, + 287, + 473 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 420, + 287, + 473 + ], + "spans": [ + { + "bbox": [ + 49, + 420, + 287, + 473 + ], + "type": "text", + "content": "[63] Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong, Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin. Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation. In IEEE Conf. Comput. Vis. Pattern Recog., 2021. 1, 2, 3, 6, 7, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 49, + 475, + 287, + 540 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 475, + 287, + 540 + ], + "spans": [ + { + "bbox": [ + 49, + 475, + 287, + 540 + ], + "type": "text", + "content": "[64] Yang Zou, Zhiding Yu, B. V. K. Vijaya Kumar, and Jinsong Wang. Unsupervised Domain Adaptation for Semantic Segmentation via Class-Balanced Self-training. In Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and Yair Weiss, editors, Eur. Conf. Comput. Vis., volume 11207, pages 297-313, 2018. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 49, + 542, + 287, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 542, + 287, + 586 + ], + "spans": [ + { + "bbox": [ + 49, + 542, + 287, + 586 + ], + "type": "text", + "content": "[65] Yuliang Zou, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, and Tomas Pfister. PseudoSeg: Designing Pseudo Labels for Semantic Segmentation. In Int. Conf. Learn. Represent., 2021. 3" + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 314, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 314, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 314, + 757 + ], + "type": "text", + "content": "9371" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Level-S$^2$fM_ Structure From Motion on Neural Level Set of Implicit Surfaces/b319b286-2e10-4d79-8a32-5ac089c156b9_content_list.json b/2023/Level-S$^2$fM_ Structure From Motion on Neural Level Set of Implicit Surfaces/b319b286-2e10-4d79-8a32-5ac089c156b9_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..c63821a03ccdf8abd6f9d3bcc4e2c9281cd61ece --- /dev/null +++ b/2023/Level-S$^2$fM_ Structure From Motion on Neural Level Set of Implicit Surfaces/b319b286-2e10-4d79-8a32-5ac089c156b9_content_list.json @@ -0,0 +1,1728 @@ +[ + { + "type": "text", + "text": "Level- $\\mathbf{S}^2$ fM: Structure from Motion on Neural Level Set of Implicit Surfaces", + "text_level": 1, + "bbox": [ + 99, + 128, + 869, + 151 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yuxi Xiao $^{1}$ Nan Xue $^{1*}$ Tianfu Wu $^{2}$ Gui-Song Xia $^{1}$", + "bbox": [ + 233, + 179, + 732, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ School of Computer Science, Wuhan University $^{2}$ Department of ECE, NC State University", + "bbox": [ + 98, + 198, + 869, + 217 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Project Page: https://henry123-boy.github.io/level-s2fm/", + "bbox": [ + 251, + 218, + 709, + 233 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 268, + 310, + 282 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "This paper presents a neural incremental Structure-from-Motion (SfM) approach, Level- $S^2fM$ , which estimates the camera poses and scene geometry from a set of uncalibrated images by learning coordinate MLPs for the implicit surfaces and the radiance fields from the established keypoint correspondences. Our novel formulation poses some new challenges due to inevitable two-view and few-view configurations in the incremental SfM pipeline, which complicates the optimization of coordinate MLPs for volumetric neural rendering with unknown camera poses. Nevertheless, we demonstrate that the strong inductive basis conveying in the 2D correspondences is promising to tackle those challenges by exploiting the relationship between the ray sampling schemes. Based on this, we revisit the pipeline of incremental SfM and renew the key components, including two-view geometry initialization, the camera poses registration, the 3D points triangulation, and Bundle Adjustment, with a fresh perspective based on neural implicit surfaces. By unifying the scene geometry in small MLP networks through coordinate MLPs, our Level- $S^2fM$ treats the zero-level set of the implicit surface as an informative top-down regularization to manage the reconstructed 3D points, reject the outliers in correspondences via querying SDF, and refine the estimated geometries by NBA (Neural BA). Not only does our Level- $S^2fM$ lead to promising results on camera pose estimation and scene geometry reconstruction, but it also shows a promising way for neural implicit rendering without knowing camera extrinsic beforehand.", + "bbox": [ + 75, + 301, + 472, + 724 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 741, + 207, + 756 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Structure-from-Motion (SfM) is a fundamental 3D vision problem that aims at reconstructing 3D scenes and estimating the camera motions from a set of uncalibrated images. As a long-standing problem, there have been a tremendous of studies that are mostly established on the keypoint correspondences across viewpoints and the theoretical findings of Multi-View Geometry (MVG) [11], and", + "bbox": [ + 75, + 767, + 468, + 875 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/9bf657d1192b28799055f99ee5dbe1ee89f776de6182e77ea37513a8076d1666.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 532, + 268, + 861, + 347 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/1ac1b7f8a99f579592434e639ae62d86d598aed024c2a0b0fe8f68a99adbebe9.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 532, + 348, + 861, + 404 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/98f2c3b924a4e3c3c9f2d3e130026408567fab6e9e0c2d26fd5f80fd9182e806.jpg", + "image_caption": [ + "Figure 1. SfM calculations on neural level sets. We learn to do geometric calculations including Triangulation, PnP, and Bundle Adjustment above neural level sets, which easily help to reject the outliers in the matches especially in the texture repeated scenes. Also, due to the continuous surface priors of neural level sets, we achieve better pose estimation accuracy and our reconstructed points are sticking on the surface which are painted with color in the figure. While, there are a lot of outlier 3d points reconstructed by COLMAP [32] which are painted by black." + ], + "image_footnote": [], + "bbox": [ + 529, + 405, + 857, + 540 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "have formed three representative pipelines of Incremental SfM [32], Global SfM [4,43], and Hybrid SfM [5].", + "bbox": [ + 496, + 685, + 890, + 715 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this paper, we focus on the incremental pipeline of SfM and we will use SfM to refer to the incremental SfM. Given an unordered image set, an SfM system initializes the computation by a pair of images that are with well-conditioned keypoint correspondences to yield an initial set of feature tracks, then incrementally adds new views one by one to estimate the camera pose from the 2D-3D point correspondences and update the feature tracks with new matches. Because the feature tracks are generated by grouping the putative 2D correspondences across viewpoints in bottom-up manners, they would be ineffective or inaccurate to represent holistic information of scenes. Accordingly,", + "bbox": [ + 496, + 719, + 892, + 900 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 44 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 94, + 886, + 218, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "17205", + "bbox": [ + 478, + 924, + 517, + 936 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Bundle Adjustment (BA) is necessary to jointly optimize the camera poses and 3D points in a top-down manner. The success of BA indicates that a global perspective is vital for accurate 3D reconstruction, however, their input feature tracks are the bottom-up cues without enough holistic constraints for the 3D scenes. To this end, we study to integrate the top-down information into the SfM system by proposing a novel Level- $\\mathbf{S}^2$ fM. Fig. 1 illustrates a representative case for the classic SfM systems that generate more flying 3D scene points, which can be addressed by our method.", + "bbox": [ + 75, + 90, + 472, + 241 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our Level- $\\mathrm{S}^2\\mathrm{fM}$ is inspired by the recently-emerged neural implicit surface that could manage all 3D scene points as the zero-level set of the signed distance function (SDF). Because the neural implicit surfaces can be parameterized by Multi-Layer Perceptrons (MLPs), it could be viewed as a kind of top-down information of 3D scenes and is of great potential for accurate 3D reconstruction. However, because both the 3D scene and camera poses are to be determined, it poses a challenging problem:", + "bbox": [ + 75, + 242, + 472, + 380 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "How can we optimize a neural SDF (or other neural fields such as NeRF) from only a set of uncalibrated images without any 3D information?", + "bbox": [ + 109, + 390, + 437, + 436 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Most recently, the above problem was partially answered in BARF [18] and NeRF-- [42] that relaxed the requirement of optimizing Neural Radiance Fields [24] without knowing accurate camera poses, but they can only handle the unknown pose configurations in small-scale face-forwarding scenes. Moreover, when we confine the problem in the incremental SfM pipelines, it would be more challenging as we need to optimize the neural fields with only two overlapped images at the initialization stage. To this end, we found that the optimization of neural SDF can be accomplished by the 2D matches at the initialization stage, and facilitate the management of feature tracks by querying the 3D points and tracing the 2D keypoints in a holistic way.", + "bbox": [ + 75, + 446, + 472, + 643 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "As shown in Fig. 1, we define a neural network that parameterizes an SDF as the unified representation for the underdetermined 3D scene and accomplishes the computations of PnP for camera pose intersection, the 3D points triangulation as well as the geometry refinement on the parameterized SDF. In the initialization stage with a pair of overlapped images, Level- $\\mathbf{S}^2$ fM uses the differentiable sphere tracing algorithm [19] to attain the corresponding 3d points of the keypoints and calculate the reprojection error to drive the joint optimization. For the traced 3d points with small SDF values and 2D reprojection errors for its feature track, they are added into a dynamic point set and take the point set with feature tracks as the Lagrangian representation for the level sets. Because the pose estimation and the scene points reconstruction are sequentially estimated, the estimation errors will be accumulated. To this end, we present an NBA (i.e., Neural Bundle Adjustment) that plays a similar", + "bbox": [ + 75, + 643, + 472, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "role as in Bundle Adjustment, but it optimizes the implicit surface and camera poses from the explicit flow of points by the energy function of the reprojection errors, which can be viewed as an evolutionary step between Lagrangian and Eulerian representations as discussed in [23].", + "bbox": [ + 496, + 90, + 893, + 167 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In the experiments, we evaluate our Level- $\\mathbf{S}^2$ fM on a variety of scenes from the BlendedMVS [45], DTU [14], and ETH3D [34] datasets. On the BlendedMVS dataset, our proposed Level- $\\mathbf{S}^2$ fM clearly outperforms the state-of-the-art COLMAP [32] by significant margins. On the DTU and ETH3D datasets [14, 34], our method also obtains on-par performance with COLMAP for both camera pose estimation and dense surface reconstruction, which are all computed in one stage.", + "bbox": [ + 496, + 167, + 893, + 303 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The contributions of this paper are in two folds:", + "bbox": [ + 519, + 303, + 834, + 318 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We present a novel neural SfM approach Level- $S^2$ fM, which formulates to optimize the coordinate MLP networks for implicit surface and radiance field and estimate the camera poses and scene geometry. To the best of our knowledge, our Level- $S^2$ fM is the first implicit neural SfM solution on the zero-level set of surfaces.", + "- From the perspective of neural implicit fields learning, we show that the challenging problems of two-view and few-view optimization of neural implicit fields can be addressed by exploiting the inductive biases conveyed in the 2D correspondences. Besides, our method presents a promising way for neural implicit rendering without knowing camera extrinsics beforehand." + ], + "bbox": [ + 519, + 328, + 890, + 536 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Works", + "text_level": 1, + "bbox": [ + 500, + 550, + 650, + 566 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Structure from Motion", + "text_level": 1, + "bbox": [ + 500, + 575, + 712, + 590 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "There has been a vast body of literature on Structure from Motion. Since an SfM system consists of many components, tremendous efforts have been devoted to improving the core components of SfM. In particular, the learning techniques were introduced in a variety of subproblems including image matching [7, 31], feature track mining and management [39], two-view 3D reconstruction [38,44], relative and absolute camera pose estimation [13] and Bundle Adjustment [3, 37]. Those studies indicated that the learning paradigms are promising to improve the quality of 3D reconstruction. However, to the best of our knowledge, the learning paradigms are not fully equipped in SfM systems. One possible reason for such a fact is that the many learning approaches are designed in a supervised learning fashion, which remains some risks on the out-of-distribution samples. The self-supervised learning approaches [9, 22] in 3D vision alleviated the requirement of data annotations, however, they have not been fully exploited in the whole pipeline of SfM. In contrast to the aforementioned studies, in this paper, we are interested in integrating the learning", + "bbox": [ + 496, + 598, + 893, + 902 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "17206", + "bbox": [ + 480, + 924, + 519, + 936 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "ability into the SfM system without incurring any external data annotations. From the perspective of system design in SfM, we verified that the strong inductive biases conveying in the 2D correspondences are promising and meaningful to drive the learning and optimization of SfM.", + "bbox": [ + 75, + 90, + 470, + 167 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Neural Implicit Representation for 3D Scene", + "text_level": 1, + "bbox": [ + 76, + 176, + 455, + 193 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Recently, the advent of neural implicit fields [24, 28, 29, 40, 41, 46] have greatly advanced many 3D vision problems such as novel-view synthesis [1, 24] and surface reconstruction [28, 29, 40, 46] by learning to optimize the coordinate MLPs from a set of posed RGB images of which the key to success is that the inductive biases of 3D are exploited by the neural networks. However, when the camera poses are invalid, it is hard to optimize the coordinate MLPs for neural implicit fields. To remedy this, the state-of-the-art SfM system, COLMAP [32], is extensively used to compute the camera poses as a preprocessing step.", + "bbox": [ + 75, + 199, + 468, + 366 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "To train the neural field from unknown poses directly, recently, BARF [18] and NeRF [42] explored to jointly optimize the camera poses and neural fields by the volumetric rendering with promising results obtained in forward-facing scenes. BARF can also work in some scenes of highly overlapped and dense image collections with the initialized poses as inputs. This problem was also studied in the RGB-D SLAM systems [2, 36, 47], however, their works mainly rely on the known depth information and focus on the camera pose tracking by the neural implicit fields. Therefore, how to optimize implicit neural fields from only a set of uncalibrated images without any 3D information input is still a challenging and open problem.", + "bbox": [ + 75, + 366, + 470, + 561 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this paper, we study the unknown-pose neural fields optimization and SfM together and present a unified solution that simultaneously learns the implicit surfaces and radiance fields alongside the camera pose estimation and scene reconstruction from a set of images.", + "bbox": [ + 75, + 563, + 468, + 638 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Preliminaries", + "text_level": 1, + "bbox": [ + 76, + 652, + 215, + 667 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we introduce the preliminaries on neural implicit surface rendering and the notations in SfM, which are all extensively used in our method.", + "bbox": [ + 75, + 676, + 468, + 723 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Neural Implicit Surface Rendering", + "text_level": 1, + "bbox": [ + 76, + 732, + 380, + 750 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The volumetric rendering of neural implicit surface [46] aims at learning a signed distance function $d_{\\Omega} : \\mathbb{R}^3 \\to \\mathbb{R}$ by the volumetric rendering from a set of posed images and then extracting the zero-level set of $\\phi$ as the reconstructed surface model of the image set. The state-of-the-art approach, VolSDF [46], integrates SDF representations with neural volume rendering via Laplacian distribution by", + "bbox": [ + 75, + 756, + 468, + 863 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\sigma (\\mathbf {x}) = \\frac {1}{\\beta} \\Psi_ {\\beta} (- d _ {\\Omega} (\\mathbf {x})), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 189, + 873, + 468, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\beta$ is a learnable parameter in VolSDF [46]. Based on Eq. (1), the volume rendering equation renders a ray $\\mathbf{x}(t)$ emanating from a camera position $\\mathbf{o} \\in \\mathbb{R}^3$ in unit direction $\\mathbf{v}$ , defined by $\\mathbf{x}(t) = \\mathbf{o} + t\\mathbf{v}$ by", + "bbox": [ + 498, + 90, + 890, + 151 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nI (\\mathbf {o}, \\mathbf {v}) = \\int_ {0} ^ {\\infty} L (\\mathbf {x} (t), \\mathbf {n} (t), \\mathbf {v}) \\sigma (\\mathbf {x} (t)) T (t) d t, \\quad (2)\n$$\n", + "text_format": "latex", + "bbox": [ + 537, + 160, + 890, + 193 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $L(\\mathbf{x},\\mathbf{n},\\mathbf{v})$ is the radiance field and $\\mathbf{n}(t)$ is the normal direction of the point $\\mathbf{x}(t)$ defined by $\\mathbf{n}(t) = \\nabla_{\\mathbf{x}}d_{\\Omega}(\\mathbf{x}(t))$ . In the learning of volume rendering, two coordinate MLP (Multi-Layer Perceptron) networks parameterize the SDF by $\\phi (\\mathbf{x}) = (d(\\mathbf{x}),\\mathbf{z}(\\mathbf{x}))\\in \\mathbb{R}^{1 + 256}$ and the radiance field by $L_{\\psi}(\\mathbf{x},\\mathbf{n},\\mathbf{v},\\mathbf{z})\\in \\mathbb{R}^3$ , and train them by the color loss $\\mathcal{L}_{\\mathrm{RGB}}(\\phi ,\\psi ,\\beta)$ and the Eikonal loss $\\mathcal{L}_{eik}(\\phi) = \\mathbb{E}_{\\mathbf{z}}(|\\nabla d(\\mathbf{z})|| - 1)$ .", + "bbox": [ + 496, + 200, + 890, + 321 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this paper, we use the equations (1) and (2) as the basic tools for Level- $\\mathrm{S}^2$ fM. To make the optimization of SDF and radiance networks easier, we set $\\beta$ as a small constant number and use the multi-resolution grid representations to avoid the potential of slow convergence and catastrophic forgetting since the scene scale is unknown and the original VolSDF [46] requires to normalize the known camera poses in a certain scale.", + "bbox": [ + 496, + 323, + 890, + 443 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Ray Sampling and Sphere Tracing", + "text_level": 1, + "bbox": [ + 498, + 452, + 800, + 468 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Iterative Ray Sampling. In the implementation, the continuous form of Eq. (2) is approximated in", + "bbox": [ + 498, + 474, + 890, + 506 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nI (\\mathbf {o}, \\mathbf {v}) \\approx \\sum_ {i = 1} ^ {m - 1} \\hat {\\tau} _ {i} L (\\mathbf {x} (t _ {i}), \\mathbf {n} (t _ {i}), \\mathbf {v}), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 575, + 513, + 890, + 555 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\{t_i\\}_{i=1}^m$ is the discrete samples, $0 = t_1 < t_2 < \\ldots < t_m = M$ , $M$ is some large constant. $\\hat{\\tau}_i \\approx \\tau(s_i) \\Delta s$ is the approximated PDF multiplied by the interval length. In VolSDF [46], $\\{t_i\\}_{i=1}^m$ is adaptively computed according to the opacity approximation error. Please move to [46] for its detail. In our method, we keep using this iterative sampling strategy when the rendering loss and the Eikonal loss is used. However, because the sampling set $\\{t_i\\}$ would be large, we do not use this strategy to compute the 3D points from 2D keypoints in our Level- $\\mathbf{S}^2$ fM and in turn to use the sphere tracing [10] as a faster way since our initial development of this work.", + "bbox": [ + 496, + 564, + 890, + 744 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Sphere Tracing. Sphere tracing is a geometric method to render the depth from a signed distance function. Different from iterative ray sampling, sphere tracing is designed to hit the surface point along the ray $\\mathbf{x}(t)$ with queries as few as possible. To make it clear, we use $s_i$ to denote the ray stamp of the queried point $\\mathbf{x}(s_i)$ . With the queried point $\\mathbf{x}(s_i)$ , the next ray stamp $s_{i + 1}$ is computed by $s_{i + 1} = \\phi (\\mathbf{x}(s_i))$ . In our study, we sample at most $N_{s} = 20$ points with the stop criterion $|\\phi (s_i)| < \\varepsilon$ , where $\\varepsilon$ is set to 0.002 in our experiment.", + "bbox": [ + 496, + 750, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "17207", + "bbox": [ + 478, + 925, + 517, + 936 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Remarks. Although both the iterative ray sampling [46] and sphere tracing [10] share the same target of computing the surface point along a ray, they have different behaviors in the neural implicit surface optimization. In detail, because VolSDF [46] aims at approximating the opacity by SDF, it updates the SDF network $\\phi (\\mathbf{x})$ by the rendering loss $\\mathcal{L}_{\\mathrm{color}}$ . As for sphere tracing, it is a geometric approach that only takes the SDF values into account for the computation. Such a difference is trivial to some extent, however, we found that their different focuses induce a loss function in our Level- $\\mathbf{S}^2$ fM to constraint the rendered depth values (or 3D points) for two-view initialization and 3D point triangulation.", + "bbox": [ + 75, + 90, + 472, + 287 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Notations in SfM", + "text_level": 1, + "bbox": [ + 76, + 297, + 243, + 313 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Correspondence Search. Given the image set $\\mathcal{I} = \\{I_i|i = 1\\ldots N_I\\}$ for reconstruction, the keypoint features of the image $I_{i}$ computed by SIFT [21] is denoted in $\\mathcal{F}_i = \\{(x_j,\\mathbf{f}_j)\\}$ , where $x_{j}\\in \\mathbb{R}^{2}$ is the 2D coordinate and $\\mathbf{f}_j\\in \\mathbb{R}^{128}$ is the feature descriptor of $x_{j}$ . Based on the SIFT features, we follow the schema in COLMAP [32] to establish the feature correspondences across views, in which we first do the exhaustive matching for all possible image pairs and then use the geometric verification to filter out the non-overlapped image pairs. After this, the potentially overlapped image pairs are denoted in $\\mathcal{C} = \\{(I_a,I_b)|I_a,I_b\\in \\mathcal{I}\\}$ , and the keypoint correspondences in the pair $(I_a,I_b)$ are denoted in the set $\\mathcal{M}_{ab} = \\{\\{(x_k,\\mathbf{f}_k),(x_l',\\mathbf{f}_l')\\} |(x_k,\\mathbf{f}_k\\in \\mathcal{F}_a,(x_l',f_l')\\in \\mathcal{F}_b\\}$ . Finally, all the prepared correspondences are organized as the scene graph [32, 35], which stores images as the graph nodes and the overlapped image pairs as the graph edges. In our Level- $\\mathrm{S^2fM}$ , we use the established correspondences to drive the learning of MLPs, estimate the camera poses, and reconstruct a sparse point set of correspondences.", + "bbox": [ + 75, + 321, + 472, + 625 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3D Scene Points and Feature Tracks. Because SfM is designated to simultaneously estimate the scene geometry from 2D correspondences, every successfully reconstructed 3D scene point is sourced from multiple 2D keypoint observations. To facilitate the representation, we denote the expected 3D point set in $\\mathcal{X} = \\{\\mathbf{X}_k\\in \\mathbb{R}^3 |k = 1,\\dots ,N_{3d}\\}$ . For each point $\\mathbf{X}_k\\in \\mathcal{X}$ , if it is reconstructed from the 2D keypoint $x_{j}\\in \\mathcal{F}_{i}$ , we denote such a relationship in a tuple $(k,i,j)$ . $\\mathcal{T} = \\{(k,i,j)\\}$ is the set of feature tracks.", + "bbox": [ + 75, + 632, + 468, + 770 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. The Proposed Level $\\mathbf{S}^2\\mathbf{f}\\mathbf{M}$", + "text_level": 1, + "bbox": [ + 76, + 782, + 316, + 800 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this section, we present the details of our Level- $S^2$ fM. As shown in Fig. 2, our method consists of three classical components including 1) the two-view geometry initialization, 2) the new frame registration, and 3) the new frame pose refinement, an implicit surface and a radiance field that are parameterized by neural networks. In what follows, we", + "bbox": [ + 75, + 809, + 470, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "will show how to solve the SfM problem by learning the implicit fields with 2D correspondences. We assume the intrinsic matrix $K$ is known and fixed.", + "bbox": [ + 496, + 90, + 890, + 135 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Two-view Initialization", + "text_level": 1, + "bbox": [ + 498, + 143, + 714, + 157 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We first select two good views $\\{I_a, I_b\\}$ for initialization from the scene graph and get their 2D matches $\\mathcal{M}_{ab} = \\{\\{(x_k, f_k), (x'_l, f'_l)\\} | (x_k, f_k) \\in \\mathcal{F}_a, (x'_l, f'_l) \\in \\mathcal{F}_b\\}$ . Based on the 2D matches $\\mathcal{M}_{ab}$ , we leverage the 5-point algorithm [27] and RANSAC to obtain the poses $P_a, P_b \\in SE(3)$ .", + "bbox": [ + 496, + 166, + 890, + 256 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "With the estimated camera poses $P_{a}, P_{b}$ , it is straightforward to optimize the SDF network $\\phi(\\mathbf{x})$ and the radiance field network $L_{\\psi}(\\mathbf{x}, \\mathbf{n}, \\mathbf{v}, \\mathbf{z})$ defined in Sec. 3 by minimizing the loss items $\\mathcal{L}_{\\mathrm{RGB}}$ and $\\mathcal{L}_{eik}$ as done in VolSDF [46]. However, it should be noted that the learning of volumetric surface rendering in such a way for the two-view inputs would trap into the local minimal by overfitting. To this end, we propose to use the differentiable sphere tracking [10, 19] for the corresponding rays in image $I_{a}$ and $I_{b}$ , which provides strong inductive biases for the optimization of networks.", + "bbox": [ + 496, + 257, + 890, + 421 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Specifically, denoted by a pair of feature match $(x_{k}, x_{l}^{\\prime})$ in the image pair $(I_{a}, I_{b})$ , the sphere tracing obtains the surface point $\\mathbf{X}_{a}^{k} = \\mathbf{o}_{a} + \\hat{t}_{a}\\mathbf{d}_{a}$ from the SDF and $\\mathbf{X}_{b}^{l} = \\mathbf{o}_{b} + \\hat{t}_{b}\\mathbf{d}_{b}$ , where $(\\mathbf{o}_{a}, \\mathbf{d}_{a})$ is the ray of $x_{k}$ , $(\\mathbf{o}_{b}, \\mathbf{d}_{b})$ is the ray of $x_{l}^{\\prime}$ . For the computation of $\\hat{t}_{a}$ and $\\hat{t}_{b}$ , please move to our supplementary materials. Ideally, the $\\mathbf{X}_{a}^{k}$ and $\\mathbf{X}_{b}^{l}$ should be as close as possible, therefore, we introduce a reprojection loss", + "bbox": [ + 496, + 422, + 890, + 542 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {r e p r o j}} = \\frac {1}{2 V} \\sum \\left(\\| \\hat {x} _ {k} - x _ {l} ^ {\\prime} \\| _ {2} + \\| \\hat {x} _ {l} ^ {\\prime} - x _ {k} \\| _ {2}\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 542, + 546, + 890, + 574 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $V$ is the number of correspondences, $\\hat{x}_k = \\Pi(\\mathbf{X}_a^k, K, P_b)$ and $\\hat{x}_l' = \\Pi(\\mathbf{X}_b^l, K, P_a)$ are the projected 2D coordinates of the traced 3D points by the projection $\\Pi$ .", + "bbox": [ + 496, + 579, + 890, + 625 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Considering the fact that the correspondences are sparse when the SDF network is not well optimized at some rays, the sparse sample points by sphere tracing on the SDF network may be either inaccurate or erroneous as shown in Fig. 3. Therefore, we use a depth consistency loss $\\mathcal{L}_{dc}$ to minimize the depth estimated by the sphere tracing and the volumetric rendering by", + "bbox": [ + 496, + 625, + 890, + 731 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {d c} = \\frac {1}{B} \\sum \\left\\| \\hat {t} _ {i} - \\int_ {0} ^ {\\infty} T (t) \\sigma (\\mathbf {x} (t)) d t \\right\\|, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 555, + 734, + 890, + 767 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where the rays $\\mathbf{x}(t)$ are randomly sampled from the images, and those rays are also used to compute the color loss $\\mathcal{L}_{\\mathrm{RGB}}$ . For the computation of Eikonal loss $\\mathcal{L}_{eik}$ , all the 3D points visited by sphere tracing and dense ray marching are used.", + "bbox": [ + 496, + 773, + 890, + 845 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In summary, our two-view initialization of Level- $\\mathbf{S}^2$ fM computes the total loss $\\mathcal{L}_{\\mathrm{total}}^{\\mathrm{init}}$ by", + "bbox": [ + 496, + 848, + 890, + 878 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {t o t a l}} ^ {\\text {i n i t}} = \\alpha_ {1} \\mathcal {L} _ {\\text {r e p r o j}} + \\alpha_ {2} \\mathcal {L} _ {e i k} + \\alpha_ {3} \\mathcal {L} _ {\\mathrm {R G B}} + \\alpha_ {4} \\mathcal {L} _ {\\mathrm {d c}}, \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 522, + 883, + 890, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "17208", + "bbox": [ + 478, + 924, + 517, + 936 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/aac95ac21d8fd5ca0be71fbfff5315a1e1fe2701de4bd12724fbcfc6b0a82058.jpg", + "image_caption": [ + "Figure 2. Overview of Level- $\\mathbf{S}^2$ fM." + ], + "image_footnote": [], + "bbox": [ + 140, + 88, + 831, + 281 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/27116119cd02475401ef4aab90037f5c492d2c020a068a7587df8e2c7e34d3cf.jpg", + "image_caption": [ + "3D points by sphere tracing (with depth consistency)" + ], + "image_footnote": [], + "bbox": [ + 125, + 328, + 267, + 435 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/99e2220062e8165c386ce513664a016a4008c2dac6e156ee5b4ae3b06c51f7de.jpg", + "image_caption": [ + "3D points by sphere tracing (without depth consistency)", + "Figure 3. An illustrative comparison for the depth consistency loss $\\mathcal{L}_{dc}$ in two-view initialization. As it is shown, when we remove $\\mathcal{L}_{dc}$ , the 3D points traced from all putative 2D keypoints correspondences will contain more flying 3D points." + ], + "image_footnote": [], + "bbox": [ + 282, + 328, + 421, + 436 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\alpha_{1},\\alpha_{2},\\alpha_{3}$ , and $\\alpha_{4}$ are the hyperparameters and use ADAM optimizer to optimize the networks.", + "bbox": [ + 76, + 536, + 468, + 566 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "When the initialization is finished, we compute the two 3D points $\\mathbf{X}$ and $\\mathbf{X}'$ for each correspondence by sphere tracing for image $I_{a}$ and $I_{b}$ . For an accurate correspondence, $\\| \\mathbf{X} - \\mathbf{X}'\\|$ and their SDF values should be all small enough, thus providing a good criterion to check the putative matches to initialize the 3D point set $\\mathcal{X}$ and the feature track set $\\mathcal{T}$ for all the verified two-view correspondences.", + "bbox": [ + 75, + 566, + 468, + 674 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. New Frame Registration", + "text_level": 1, + "bbox": [ + 76, + 683, + 302, + 699 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "For every newly added frame, we will first construct the 3D-2D correspondence from the existing pointset and its feature tracks. After that, we calculate a coarse pose of the new frame with the standard PnP algorithms [17], and then refine it with both the reprojection error and the rendering loss. The registration loss can be calculated as follow:", + "bbox": [ + 75, + 707, + 468, + 797 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {r e g i s t}} = \\beta_ {1} \\mathcal {L} _ {\\text {r e p r o j}} + \\beta_ {2} \\mathcal {L} _ {\\text {R G B}}, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 811, + 468, + 828 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where the $\\beta_{1},\\beta_{2}$ are two hyper-parameters, and the $\\mathcal{L}_{\\mathrm{reproj}}$ here are calculated by the 3D-2D correspondences.", + "bbox": [ + 76, + 839, + 468, + 869 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this optimization, the pose of the newly added frame, the SDF network, and the radiance field network are jointly", + "bbox": [ + 76, + 869, + 468, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "optimized. While during the changes in the pose and SDF, the original location in the pointset will maybe not be the right one on the surface. For this problem, we design a Neural Bundle Adjustment (NBA) strategy to dynamically update the pointset with respect to the SDF after the points triangulation and refinement in the next section. Therefore, we leave the details of NBA in Sec. 4.4.", + "bbox": [ + 496, + 332, + 890, + 436 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Points Triangulation and Refinement", + "text_level": 1, + "bbox": [ + 498, + 445, + 818, + 462 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Once the pose of the newly added frame is obtained, we step into the next procedure of refining the retrieved 2D points from the point set $\\mathcal{X}$ and triangulating new 2D points into 3D space to extend $\\mathcal{X}$ . This problem was formulated in the classical SfM frameworks, however, they are suffering from the following issues:", + "bbox": [ + 496, + 468, + 890, + 559 + ], + "page_idx": 4 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- The 2D Mismatches: This issue could be alleviated by geometric verifications like RANSAC [8] or better 2D keypoint matching approaches, however, when encountering the symmetry structures or repeated texture regions, those efforts are hard to work efficiently.", + "- Tiny Triangulation Angle: This issue will lead to an ill-conditioned problem for points triangulation [12]. Therefore, the classical SfM approaches will directly discard those points to avoid the ill-conditioned problem configuration." + ], + "bbox": [ + 517, + 565, + 890, + 724 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We address those issues by proposing an SDF-based triangulation. Similar to the two-view initialization in Sec. 4.1, we compute the 3D points for all the potential 2D keypoints in the first step. Then, for the 2D keypoints that have correspondences in the current feature track set $\\mathcal{T}$ , we use the tracing loss $\\mathcal{L}_{\\mathrm{tracing}}$", + "bbox": [ + 496, + 729, + 890, + 821 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {t r a c i n g}} = \\frac {1}{V ^ {\\prime}} \\sum_ {j} \\| \\mathbf {X} _ {j} ^ {s t} - \\mathbf {X} _ {j} \\|, \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 586, + 827, + 890, + 864 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathbf{X}_j\\in \\mathcal{X}$ is the retrieved 3D point of the 2D keypoint in the current frame, $V^{\\prime}$ is the number of retrieved 3D", + "bbox": [ + 496, + 869, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "17209", + "bbox": [ + 478, + 925, + 517, + 936 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "points. This loss function acts as the similar role of $\\mathcal{L}_{dc}$ in two-view initialization. Without it, similar phenomenon like Fig. 3 will happen.", + "bbox": [ + 75, + 90, + 468, + 136 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For the new 2D keypoints that are matched to the added images but without 3D information, both the reprojection loss similar to the two-view initialization and the tracing loss is used to yield the triangulation loss $\\mathcal{L}_{\\mathrm{tri}}$ by", + "bbox": [ + 75, + 136, + 468, + 198 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {t r i}} = \\mathcal {L} _ {\\text {r e p o r j}} ^ {\\text {m a s k}} + \\mathcal {L} _ {\\text {t r a c i n g}}, \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 184, + 204, + 468, + 223 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "where $\\mathcal{L}_{\\mathrm{report}}^{\\mathrm{mask}}$ only considers the 2D correspondences of which their distance between the 2D projections of the traced 3D points traced in different views are smaller than a loose threshold (45 pixels in our implementation).", + "bbox": [ + 75, + 231, + 468, + 292 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.4. Neural Bundle Adjustment on Surfaces", + "text_level": 1, + "bbox": [ + 76, + 300, + 413, + 316 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Because the camera pose estimation and the points triangulation are separated, which will involve accumulative errors for pose estimation and triangulation, as well as the implicit networks. Motivated by the Bundle Adjustment that is extensively used in classical approaches, we present a Neural Bundle Adjustment (NBA) that jointly optimizes the estimated camera points, the 3D point set, and the implicit networks as a refinement step. To avoid costly computation, our NBA step finds the closest surface points to dynamically update those variables.", + "bbox": [ + 75, + 323, + 468, + 473 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Denoted by the reconstructed 3D point set $\\mathcal{X}$ and the feature track $\\mathcal{T}$ , the camera poses $\\mathcal{P} = \\{P_1, \\dots, P_K\\}$ and the corresponding images $\\{I_1, \\dots, I_K\\}$ , as well as the networks $\\phi(\\mathbf{x})$ and $L_{\\psi}$ , in each step of NBA, we update the 3D point $\\mathbf{X} \\in \\mathcal{X}$ by", + "bbox": [ + 75, + 474, + 468, + 550 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {X} \\leftarrow \\mathbf {X} - \\phi (\\mathbf {X}) \\nabla \\phi (\\mathbf {X}), \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 187, + 559, + 468, + 574 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "and then compute the reprojection loss according to the feature track $\\mathcal{T}$ to jointly optimize the $\\phi$ the SDF network, $\\mathcal{P}$ the estimated camera poses, and $\\mathcal{X}$ the updated 3D point set. For the radiance network $L_{\\psi}$ , the rendering loss for randomly sampled rays is computed.", + "bbox": [ + 75, + 583, + 468, + 657 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In our implementation, we leverage our NBA by three times, which we call the 1-frame NBA, local NBA, and global NBA. Because the rendering loss involves more rays, we only use it for the 1-frame NBA after the camera registration and point triangulation. In terms of local NBA, for the newly added view, only the related views with correspondences are considered. After running the 1-frame and local NBA schemes, we globally update all reconstructed views and the point set. By leveraging the backpropagation, all the mentioned variables are updated as the refinement.", + "bbox": [ + 75, + 659, + 468, + 809 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 76, + 821, + 207, + 838 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Implementation Details, Datasets, and Metrics", + "text_level": 1, + "bbox": [ + 76, + 847, + 467, + 862 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation Details. In our implementation, we parameterize the SDF $\\phi (\\mathbf{x})$ by a multi-resolution features grid", + "bbox": [ + 75, + 869, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "and a two-layers MLP. To accelerate the computation, we follow InstantNGP [26] to use a hash table [25] for the feature grids. The radiance field $L_{\\psi}$ is also implemented in a multi-resolution feature grid and a three-layer MLP. Because our end task is the geometric 3D reconstruction, we use a high-resolution multi-scale feature grid for the SDF to ensure the accuracy of scene geometry but use a low-resolution feature grid to avoid the unnecessary computation cost for the radiance field. The specifications of the network architecture are given in supplementary material due to the limited space. All of these above are implemented in PyTorch [30], and we used the Adam [16] as the optimizer for the geometric calculations. For the 2D image matching and pose graph, we keep them the same with our baseline, COLMAP [32] for fair comparisons.", + "bbox": [ + 496, + 90, + 890, + 316 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Datasets. Three datasets are used for our evaluation. Firstly, we use 5 representative scenes including the LyingStatue, Stone, Fountain, Horse, and Statues from the BlendedMVS dataset [45] in our evaluation because it provides accurate ground truth of camera poses and contains a number of challenging scenes for SfM. Secondly, the DTU dataset for the MVS task is also used. The five representative scenes (scans of 24, 37, 65, 110 and 114) are used in our experiments. Finally, we evaluate our proposed method on the five scenes from the challenging ETH3D [34] dataset.", + "bbox": [ + 496, + 321, + 890, + 472 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation Metrics. In our evaluation, we use the Rotation error and ATE to quantitatively benchmark the pose accuracy, which simply depicts the difference between the ground truth and the aligned pose. During our evaluation, we used the provided API of Reconstruction Align in COLMAP [32] to do that. In terms of the reconstructed scene geometry, we use accuracy (Acc) and the precision (Prec) rate to evaluate the accuracy of our recovered 3D points and Chamfer-l1 distance to depict the accuracy of the reconstructed surface. Detailed definitions of these evaluation metrics are given in the supplementary material.", + "bbox": [ + 496, + 476, + 890, + 642 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Results on the BlendedMVS Dataset", + "text_level": 1, + "bbox": [ + 500, + 650, + 813, + 666 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Tab. 1 reports the quantitative evaluation results for the two versions of Level- $\\mathbf{S}^2$ fM and COLMAP [32]. The full version of Level- $\\mathbf{S}^2$ fM used all the mentioned components while the wo/render version removed the rendering loss for optimization. As it is reported, our Level- $\\mathbf{S}^2$ fM (full) consistently outperforms COLMAP [32] for camera pose estimation and sparse 3D point cloud reconstruction. It also reveals that rendering losses are required.", + "bbox": [ + 496, + 674, + 890, + 794 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In detail, our Level- $S^2$ fM (full) averagely reduced the estimation error from $1.54^\\circ$ by COLMAP [32] to $0.86^\\circ$ , obtaining a relative improvement of $55.84\\%$ . For the translation error, our Level- $S^2$ fM (full) decreases the error from $3.54 \\mathrm{~cm}$ to $3.36 \\mathrm{~cm}$ . For the sparse 3D point cloud reconstruction, the ACC metric is reduced from 3.16 to 2.25 for the full model and 2.63 for the wo/render version.", + "bbox": [ + 496, + 795, + 890, + 898 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "17210", + "bbox": [ + 478, + 925, + 517, + 936 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/9aaccc3504e32f4fe7a30b78e5c9d425122183ef734a01a2d012b99cd2e06e26.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ScenesCamera Pose EvaluationPoints Cloud Results Evaluation
Rotation (°) ↓Translation (cm) ↓Acc(cm)↓, Prec(≤3.5cm)↑
COLMAP [32]Level-S2fM (full)Level-S2fM (wo/render)COLMAP [32]Level-S2fM (full)Level-S2fM (wo/render)COLMAP [32]Level-S2fM (full)Level-S2fM (wo/render)
LyingStatue1.201.121.310.892.182.67Acc:0.83 Prec:0.99Acc:1.35 Prec:0.99Acc:1.76 Prec:0.98
Stone0.630.310.546.285.519.17Acc:5.44 Prec:0.66Acc:3.61 Prec:0.68Acc:5.04 Prec:0.64
Fountain4.341.652.327.412.874.11Acc:1.91 Prec:0.91Acc:1.13 Prec:0.98Acc:1.14 Prec:0.96
Horse0.330.940.921.185.717.58Acc:3.99 Prec:0.86Acc:4.18 Prec:0.71Acc:4.18 Prec:0.70
Statues1.210.360.441.980.560.62Acc:1.31 Prec:0.98Acc:0.95 Prec:0.99Acc:1.02 Prec:0.99
Mean1.540.861.113.543.364.83Acc:3.16 Prec:0.85Acc:2.25 Prec:0.87Acc:2.63 Prec:0.85
", + "bbox": [ + 120, + 88, + 851, + 203 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e84f16bff4968ec4cc8f153307d3e9c52372f8acc0ee3b9dd9184da810cb610b.jpg", + "image_caption": [ + "Figure 4. The reconstructed meshes, point clouds and camera poses for the Fountain and Horse scenes on the BlendedMVS dataset by our Level- $S^2$ fM (full). In the first column, the reconstructed scene geometry and the camera poses are shown together. For the 3D models, we show the different views of the sparse 3D points during the training and the textured meshes refused from the zero-level set surface." + ], + "image_footnote": [], + "bbox": [ + 112, + 250, + 428, + 421 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/4fe1ea47318e14430e09b907650909401676c4850fc469ab7cb3ef1c045efcfc.jpg", + "table_caption": [ + "Table 1. Quantitative results on the BlendedMVS dataset. For our Level- $S^2$ fM, we report the results by full version and an wo/render version that removes the rendering loss during optimization." + ], + "table_footnote": [], + "table_body": "
Pose SourceLyingStatueStoneFountainHorseStatuesMean
COLMAP29.128.425.423.529.227.12
Level-S2fM (Ours)29.528.927.123.630.227.86
GT29.529.427.424.131.128.3
", + "bbox": [ + 81, + 523, + 468, + 577 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Fig. 4 shows the reconstruction results by our method.", + "bbox": [ + 94, + 638, + 454, + 652 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Apart from the direct evaluation of the SfM results on the BlendedMVS dataset, we further compare the camera poses estimation results for different methods by training the NGP [26] (a fast version of NeRF [24]) to compare the performance of novel view synthesis in Tab. 2. As it is reported, the rendered images by our camera poses are consistently better than the ones by COLMAP poses.", + "bbox": [ + 75, + 654, + 468, + 758 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Results on the DTU Dataset", + "text_level": 1, + "bbox": [ + 76, + 770, + 326, + 786 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We conducted the evaluation on the DTU to illustrate the promising future of our Level- $\\mathbf{S}^2$ fM to unify the pose estimation, dense reconstruction, and novel view synthesis problems in one stage. For the comparison to COLMAP, we use their built-in PatchMatch MVS [33] functionality to obtain the dense surface points and then leverage its default surface reconstruction method (i.e., Poisson surface [15])", + "bbox": [ + 75, + 794, + 470, + 902 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/d0c26b2b205cf1a08d8b4ef8bfb004c3213f61457bbd26aa54adae21f6440ba3.jpg", + "table_caption": [ + "Table 2. Novel View Synthesis Comparison. The PSNR is used to compare the camera poses computed by COLMAP, Level- $\\mathbf{S}^2$ fM and the GT poses on the BlendedMVS dataset." + ], + "table_footnote": [], + "table_body": "
ScanCOLMAP [32]Level-S2fM (Ours)
Chamfer-ℓ1Rot. Err.Trans. Err.Chamfer-ℓ1Rot. Err.Trans. Err.
242.1760.382.872.4420.814.60
373.8370.414.863.0230.314.29
654.3940.454.233.1900.745.81
1103.3890.656.365.9020.826.82
1143.5770.353.582.0920.141.85
Mean3.3300.4484.383.4740.5644.67
", + "bbox": [ + 509, + 252, + 883, + 344 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/be8cdf4626d85caf933507dba7f4a29432174644211e241f443d362f115ef6bf.jpg", + "table_caption": [ + "Table 3. Quantitative results on DTU dataset. The Chamfer- $\\ell_1$ distance of the dense reconstruction results and as the errors of rotation and translation for camera pose estimation, are compared for COLMAP and our Level- $S^2$ fM. The unit of Chamfer- $\\ell_1$ and Translation errors are in millimetres." + ], + "table_footnote": [], + "table_body": "
SceneDetector & Matchercourtyardreliefdoorterrace2facade
COLMAP [32]SIFT [21]0.10°/0.016m0.10°/0.003m0.16°/0.002m0.14°/0.002m0.06°/0.016m
Level-SfM (Ours)0.21°/0.047m0.09°/0.003m0.19°/0.006m0.13°/0.003m0.12°/0.059m
COLMAP [32]SP [7]+SG [31]-0.36°/0.007m0.37°/0.003m0.16°/0.003m0.04°/0.014m
Level-SfM-0.42°/0.003m0.34°/0.007m0.12°/0.002m0.10°/0.051m
", + "bbox": [ + 501, + 425, + 890, + 465 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 4. Quantitative results of pose estimation on the five scenes in ETH3D dataset for COLMAP [32] and our proposed Level-S²fM by using different keypoint detector and matcher.", + "bbox": [ + 498, + 474, + 890, + 517 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "to obtain the mesh model. For our Level- $\\mathbf{S}^2$ fM, we use the MarchingCubes [20] to extract the mesh models from the zero-level set of the implicit surface. The quantitative evaluation results are shown in Tab. 3. In this dataset, our Level- $\\mathbf{S}^2$ fM obtains on-par performance with COLMAP.", + "bbox": [ + 498, + 544, + 890, + 619 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.4. Results on the ETH3D Dataset", + "text_level": 1, + "bbox": [ + 500, + 631, + 769, + 645 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We test our method on a more challenging dataset, ETH3D [34], which includes both sparse view collections for multi-scale outdoor and indoor scenes. To show the influence of different keypoint detection and matching algorithms for our method, we additionally make a comparison with SuperPoint (SP) [7] for detection and SuperGlue (SG) [31] for keypoint matching. As reported in Tab. 4, our method achieves comparable results with COLMAP [32]. However, we observe that our method gets slightly inferior results in some large-scale outdoor scenes, because of the limited representative capability of a single network for a large-scale scene.", + "bbox": [ + 496, + 655, + 890, + 835 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.5. Ablation Study", + "text_level": 1, + "bbox": [ + 500, + 845, + 653, + 863 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this section, we elaborate on why and how the SDF-based Triangulation (short in $SDF\\text{-Tri}$ ) and NBA work in", + "bbox": [ + 498, + 869, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "17211", + "bbox": [ + 478, + 926, + 517, + 936 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/4909671276579504bfea816eca2502fc9a5bc90e7dd74d5f1c6ec75e2e80406c.jpg", + "image_caption": [ + "Figure 5. Two-views Triangulated Point Clouds by Traditional triangulation and SDF-Based Triangulation, courtyard@ETH3D." + ], + "image_footnote": [], + "bbox": [ + 81, + 73, + 460, + 299 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/a215dc8cc13b84c37c2b3e178b7a7b662e93d01391b16722bc21488b1e8d0357.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
w/o SDF-Tri.w/o NBAw/o SDF-Tri & w/o NBAFull
1.23° / 0.498m0.46° / 0.113m3.48° / 0.846m0.21° / 0.047m
", + "bbox": [ + 81, + 300, + 460, + 324 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "our system. We first conduct the two-view triangulation with SDF-Tri and the traditional method respectively. Fig. 5 shows SDF-Tri can easily filter out the incorrect triangulation from wrong matches. One explanation is that the neural network distills inliers in a continuous zero-level set of surfaces (e.g., the planes in Fig. 5). Therefore, the triangulated outliers (the blue points flying off the planes) can be easily detected and filtered by their large SDF values. Similarly, NBA also benefits from the global level sets of surfaces, which average the errors among the inliers triangulated points and differs the outliers by their large SDF values. The quantitative results for SDF-Tri and NBA are shown at the bottom of Fig. 5.", + "bbox": [ + 75, + 361, + 468, + 556 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.6. Limitations of Level- $\\mathbf{S}^2$ fM", + "text_level": 1, + "bbox": [ + 76, + 571, + 313, + 585 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In order to explore the clear boundary of Level- $\\mathbf{S}^2$ fM and point out the potential future development, we discuss the limitation of Level- $\\mathbf{S}^2$ fM on the most typical indoor dataset, scannet [6]. In the Scannet [6], there are a lot of challenges including blurry images, and textureless areas. Because of the less texture, the SIFT-based keypoint correspondences may contain a large portion of outliers or insufficient matches. Meanwhile, the blur in images will also influence the accuracy of the 2d matches. Therefore, most SfM easily fails on this dataset. Our method is also limited by this because of SIFT matches.", + "bbox": [ + 75, + 595, + 468, + 762 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "To make the discussion clear, we run four scenes of Scannet [6] that were used for NICESLAM [47]. For the image sequence of each scene, the input image set for SfM is constructed by sampling for every 10 frames. Tab. 5 reports the pose accuracy of COLMAP and our Level- $S^2$ fM for results by adding 60 frames, 120 frames, and all the frames, which are concatenated by the \"slash\". As it is reported, our method usually performs well in the first 60 frames but its pose estimation accuracy suddenly decreases", + "bbox": [ + 75, + 763, + 470, + 900 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/a528a80b8c36907ab910f261fd780e7aa918f63e494d60f0602311c86f672aaa.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Scene ID0000005901690207
iMAP [36]rot°↓trans. (cm) ↓-197.1-18.9-96.4-28.7
NICESLAM [47]rot°↓trans. (cm) ↓-11.3-12.0-12.0-12.8
COLMAP [32]rot°↓trans. (cm) ↓1.47/2.71/2.718.20/16.8/14.11.40/2.52/2.667.20/10.8/11.52.64/1.84/2.5210.6/9.22/10.6failed-
Level-S2fMrot°↓1.44/2.23/1.986.50/11.8/26.71.50/2.59/3.728.20/32.9/35.72.94/9.09/14.36.35/14.3/44.8failed-
fulltrans. (cm) ↓
", + "bbox": [ + 503, + 88, + 890, + 176 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 5. Quantitative results of pose estimation on ScanNet [6]. For the COLMAP and Level- $S^2$ fM, we report their pose accuracy metrics when the 60/120/all frames are registered. For the last scene (i.e., 0207), both COLMAP and our method failed.", + "bbox": [ + 498, + 186, + 890, + 242 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "when adding some new frames. The textureless matches cause the matches very sparse and therefore hard to give a good registration of images. Meanwhile, since the radiance field learning is also challenging in the Scannet dataset, the bad initialized pose can not be refined well by the rendering loss. All of these limitations are basically from sparse 2D image matches. Besides, we observed that the ADAM optimizer will make the optimization of camera poses and scene points unstable, which would also affect the final results.", + "bbox": [ + 496, + 257, + 890, + 393 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 406, + 617, + 421 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This paper studies the longstanding problem of Structure-from-Motion by exploring and exploiting several important yet challenging issues including the two-view neural rendering in the initialization stage and few-view neural rendering in the early camera registration stage of incremental SfM for integrating the recent advances of neural implicit field learning into an SfM pipeline. We show that although the few-view neural rendering problem is challenging enough, it can be tackled by the 2D correspondences as they convey strong inductive biases for 3D scenes. Based on this, we present the first neural SfM solution that renews several key components of two-view geometry initialization, camera pose registration, and triangulation, as well as the Bundle Adjustment problem with neural implicit fields. In the experiments, we show that Level- $\\mathbf{S}^2$ fM outperforms the traditional SfM pipeline and set a new state-of-the-art for 3D reconstruction on the BlendedMVS dataset. We believe that our study will encourage the 3D vision community to rethink and reformulate Structure-from-Motion with learning-based new findings.", + "bbox": [ + 496, + 431, + 890, + 733 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments. This work was supported by the National Nature Science Foundation of China under grants 62101390 and U22B2011. This work was also supported by Wuhan University-Huawei Geoinformatics Innovation Laboratory. Y. Xiao would love to thank Sida Peng for the helpful discussions. T. Wu was supported by NSF IIS-1909644, IIS-1822477, CMMI-2024688, and IUSE-2013451. We are also grateful for the constructive comments by anonymous reviewers and area chairs. The views presented in this paper are those of the authors and should not be interpreted as representing any funding agencies.", + "bbox": [ + 496, + 750, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "17212", + "bbox": [ + 478, + 925, + 517, + 936 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In IEEE International Conference on Computer Vision (ICCV), pages 5835-5844, 2021. 3", + "[2] Ronald Clark. Volumetric bundle adjustment for online photorealistic scene capture. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6114-6122, 2022. 3", + "[3] Ronald Clark, Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, and Andrew J. Davison. Learning to solve nonlinear least squares for monocular stereo. In European Conference on Computer Vision (ECCV), volume 11212, pages 291-306, 2018. 2", + "[4] David J. Crandall, Andrew Owens, Noah Snavely, and Daniel P. Huttenlocher. Sfm with mfrs: Discrete-continuous optimization for large-scale structure from motion. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 35(12):2841–2853, 2013. 1", + "[5] Hainan Cui, Xiang Gao, Shuhan Shen, and Zhanyi Hu. Hsfm: Hybrid structure-from-motion. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2393-2402, 2017. 1", + "[6] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas A. Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2432-2443, 2017. 8", + "[7] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 224-236, 2018. 2, 7", + "[8] Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 5", + "[9] Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow. Digging into self-supervised monocular depth estimation. In IEEE International Conference on Computer Vision (ICCV), pages 3827-3837, 2019. 2", + "[10] John C. Hart. Sphere tracing: a geometric method for the antialiased ray tracing of implicit surfaces. The Visual Computer, 12(10):527-545, 1996. 3, 4", + "[11] Richard Hartley and Andrew Zisserman. Multiple view geometry in computer vision. Cambridge University Press, 2003. 1", + "[12] Richard I Hartley and Peter Sturm. Triangulation. Computer Vision and Image Understanding (CVIU), 68(2):146-157, 1997. 5", + "[13] Petr Hruby, Timothy Duff, Anton Leykin, and Tomás Pajdla. Learning to solve hard minimal problems. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5522-5532, 2022. 2", + "[14] Rasmus Ramsbøl Jensen, Anders Lindbjerg Dahl, George Vogiatzis, Engin Tola, and Henrik Aanæs. Large scale multi-" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "view stereopsis evaluation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 406-413, 2014. 2", + "[15] Michael M. Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Symposium on Geometry Processing, volume 256, pages 61-70, 2006. 7", + "[16] Diederik P Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations (ICLR), 2015. 6", + "[17] Vincent Lepetit, Francesc Moreno-Noguer, and Pascal Fua. Epnp: An accurate $O(n)$ solution to the pnp problem. International Journal of Computer Vision (IJCV), 81(2):155-166, 2009. 5", + "[18] Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey. BARF: bundle-adjusting neural radiance fields. In IEEE International Conference on Computer Vision (ICCV), pages 5721-5731, 2021. 2, 3", + "[19] Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui. DIST: rendering deep implicit signed distance function with differentiable sphere tracing. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2016-2025, 2020. 2, 4", + "[20] William E. Lorensen and Harvey E. Cline. Marching cubes: A high resolution 3d surface construction algorithm. In Proceedings of SIGGRAPH, pages 163-169, 1987. 7", + "[21] David G Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (IJCV), 60(2):91-110, 2004. 4, 7", + "[22] Reza Mahjourian, Martin Wicke, and Anelia Angelova. Unsupervised Learning of Depth and Ego-Motion From Monocular Video Using 3D Geometric Constraints. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5667-5675, 2018. 2", + "[23] Ishit Mehta, Manmohan Chandraker, and Ravi Ramamoorthi. A level set theory for neural implicit evolution under explicit flows. In European Conference on Computer Vision (ECCV), 2022. 2", + "[24] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision (ECCV), volume 12346, pages 405-421, 2020. 2, 3, 7", + "[25] Thomas Müller. tiny-cuda-nn, 4 2021. 6", + "[26] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. on Graphics (TOG), 41(4):102:1-102:15, 2022. 6, 7", + "[27] David Nister. An Efficient Solution to the Five-Point Relative Pose Problem. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 26(6):756-777, 2004. 4", + "[28] Michael Oechsle, Songyou Peng, and Andreas Geiger. UNISURF: unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In IEEE International Conference on Computer Vision (ICCV), pages 5569-5579, 2021. 3", + "[29] Jeong Joon Park, Peter Florence, Julian Straub, Richard A. Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation." + ], + "bbox": [ + 503, + 93, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "17213", + "bbox": [ + 478, + 926, + 519, + 936 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 165-174, 2019. 3", + "[30] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (NeurIPS), pages 8024-8035, 2019. 6", + "[31] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4937–4946, 2020. 2, 7", + "[32] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion Revisited. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4104-4113, 2016. 1, 2, 3, 4, 6, 7, 8", + "[33] Johannes L. Schonberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys. Pixelwise view selection for unstructured multi-view stereo. In European Conference on Computer Vision (ECCV), volume 9907, pages 501-518, 2016. 7", + "[34] Thomas Schöps, Torsten Sattler, and Marc Pollefeys. BAD SLAM: bundle adjusted direct RGB-D SLAM. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 134-144, 2019. 2, 6, 7", + "[35] Noah Snavely, Steven M. Seitz, and Richard Szeliski. Skeletal graphs for efficient structure from motion. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008. 4", + "[36] Edgar Sucar, Shikun Liu, Joseph Ortiz, and Andrew J. Davison. imap: Implicit mapping and positioning in real-time. In IEEE International Conference on Computer Vision (ICCV), pages 6209-6218, 2021. 3, 8", + "[37] Chengzhou Tang and Ping Tan. BA-Net: Dense Bundle Adjustment Networks. In International Conference on Learning Representations (ICLR), 2019. 2", + "[38] Jianyuan Wang, Yiran Zhong, Yuchao Dai, Stan Birchfield, Kaihao Zhang, Nikolai Smolyanskiy, and Hongdong Li. Deep two-view structure-from-motion revisited. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8953-8962, 2021. 2", + "[39] Lei Wang, Linlin Ge, Shan Luo, Zihan Yan, Zhaopeng Cui, and Jieqing Feng. Tc-sfm: Robust track-community-based structure-from-motion. CoRR, abs/2206.05866, 2022. 2", + "[40] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In Advances in Neural Information Processing Systems (NeurIPS), pages 27171-27183, 2021. 3", + "[41] Yiqun Wang, Ivan Skorokhodov, and Peter Wonka. Improved surface reconstruction using high-frequency details. CoRR, abs/2206.07850, 2022. 3", + "[42] Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor Adrian Prisacariu. Nerf-: Neural radiance fields without" + ], + "bbox": [ + 78, + 90, + 467, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "known camera parameters. CoRR, abs/2102.07064, 2021. 2, 3", + "[43] Kyle Wilson and Noah Snavely. Robust global translations with 1dsfm. In European Conference on Computer Vision (ECCV), volume 8691, pages 61-75, 2014. 1", + "[44] Yuxi Xiao, Li Li, Xiaodi Li, and Jian Yao. Deepmle: A robust deep maximum likelihood estimator for two-view structure from motion. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10643-10650, 2022. 2", + "[45] Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan. Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1787-1796, 2020. 2, 6", + "[46] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In Advances in Neural Information Processing Systems (NeurIPS), pages 4805-4815, 2021. 3, 4", + "[47] Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R. Oswald, and Marc Pollefeys. NICE-SLAM: neural implicit scalable encoding for SLAM. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 12776-12786, 2022. 3, 8" + ], + "bbox": [ + 501, + 92, + 890, + 430 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "17214", + "bbox": [ + 480, + 926, + 519, + 936 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Level-S$^2$fM_ Structure From Motion on Neural Level Set of Implicit Surfaces/b319b286-2e10-4d79-8a32-5ac089c156b9_model.json b/2023/Level-S$^2$fM_ Structure From Motion on Neural Level Set of Implicit Surfaces/b319b286-2e10-4d79-8a32-5ac089c156b9_model.json new file mode 100644 index 0000000000000000000000000000000000000000..b38b80ba87a372516e9d745a15b3ca9a28d5cadb --- /dev/null +++ b/2023/Level-S$^2$fM_ Structure From Motion on Neural Level Set of Implicit Surfaces/b319b286-2e10-4d79-8a32-5ac089c156b9_model.json @@ -0,0 +1,2299 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.045 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.101, + 0.13, + 0.87, + 0.152 + ], + "angle": 0, + "content": "Level-\\(\\mathbf{S}^2\\)fM: Structure from Motion on Neural Level Set of Implicit Surfaces" + }, + { + "type": "text", + "bbox": [ + 0.235, + 0.18, + 0.733, + 0.199 + ], + "angle": 0, + "content": "Yuxi Xiao\\(^{1}\\) Nan Xue\\(^{1*}\\) Tianfu Wu\\(^{2}\\) Gui-Song Xia\\(^{1}\\)" + }, + { + "type": "text", + "bbox": [ + 0.099, + 0.199, + 0.87, + 0.218 + ], + "angle": 0, + "content": "\\(^{1}\\) School of Computer Science, Wuhan University \\(^{2}\\) Department of ECE, NC State University" + }, + { + "type": "text", + "bbox": [ + 0.253, + 0.219, + 0.71, + 0.234 + ], + "angle": 0, + "content": "Project Page: https://henry123-boy.github.io/level-s2fm/" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.269, + 0.312, + 0.284 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.302, + 0.473, + 0.725 + ], + "angle": 0, + "content": "This paper presents a neural incremental Structure-from-Motion (SfM) approach, Level-\\( S^2fM \\), which estimates the camera poses and scene geometry from a set of uncalibrated images by learning coordinate MLPs for the implicit surfaces and the radiance fields from the established keypoint correspondences. Our novel formulation poses some new challenges due to inevitable two-view and few-view configurations in the incremental SfM pipeline, which complicates the optimization of coordinate MLPs for volumetric neural rendering with unknown camera poses. Nevertheless, we demonstrate that the strong inductive basis conveying in the 2D correspondences is promising to tackle those challenges by exploiting the relationship between the ray sampling schemes. Based on this, we revisit the pipeline of incremental SfM and renew the key components, including two-view geometry initialization, the camera poses registration, the 3D points triangulation, and Bundle Adjustment, with a fresh perspective based on neural implicit surfaces. By unifying the scene geometry in small MLP networks through coordinate MLPs, our Level-\\( S^2fM \\) treats the zero-level set of the implicit surface as an informative top-down regularization to manage the reconstructed 3D points, reject the outliers in correspondences via querying SDF, and refine the estimated geometries by NBA (Neural BA). Not only does our Level-\\( S^2fM \\) lead to promising results on camera pose estimation and scene geometry reconstruction, but it also shows a promising way for neural implicit rendering without knowing camera extrinsic beforehand." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.742, + 0.208, + 0.757 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.768, + 0.47, + 0.875 + ], + "angle": 0, + "content": "Structure-from-Motion (SfM) is a fundamental 3D vision problem that aims at reconstructing 3D scenes and estimating the camera motions from a set of uncalibrated images. As a long-standing problem, there have been a tremendous of studies that are mostly established on the keypoint correspondences across viewpoints and the theoretical findings of Multi-View Geometry (MVG) [11], and" + }, + { + "type": "image", + "bbox": [ + 0.533, + 0.269, + 0.862, + 0.348 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.533, + 0.349, + 0.862, + 0.405 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.531, + 0.406, + 0.859, + 0.541 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.548, + 0.892, + 0.674 + ], + "angle": 0, + "content": "Figure 1. SfM calculations on neural level sets. We learn to do geometric calculations including Triangulation, PnP, and Bundle Adjustment above neural level sets, which easily help to reject the outliers in the matches especially in the texture repeated scenes. Also, due to the continuous surface priors of neural level sets, we achieve better pose estimation accuracy and our reconstructed points are sticking on the surface which are painted with color in the figure. While, there are a lot of outlier 3d points reconstructed by COLMAP [32] which are painted by black." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.686, + 0.891, + 0.716 + ], + "angle": 0, + "content": "have formed three representative pipelines of Incremental SfM [32], Global SfM [4,43], and Hybrid SfM [5]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.893, + 0.901 + ], + "angle": 0, + "content": "In this paper, we focus on the incremental pipeline of SfM and we will use SfM to refer to the incremental SfM. Given an unordered image set, an SfM system initializes the computation by a pair of images that are with well-conditioned keypoint correspondences to yield an initial set of feature tracks, then incrementally adds new views one by one to estimate the camera pose from the 2D-3D point correspondences and update the feature tracks with new matches. Because the feature tracks are generated by grouping the putative 2D correspondences across viewpoints in bottom-up manners, they would be ineffective or inaccurate to represent holistic information of scenes. Accordingly," + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.22, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.925, + 0.519, + 0.937 + ], + "angle": 0, + "content": "17205" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.242 + ], + "angle": 0, + "content": "Bundle Adjustment (BA) is necessary to jointly optimize the camera poses and 3D points in a top-down manner. The success of BA indicates that a global perspective is vital for accurate 3D reconstruction, however, their input feature tracks are the bottom-up cues without enough holistic constraints for the 3D scenes. To this end, we study to integrate the top-down information into the SfM system by proposing a novel Level-\\(\\mathbf{S}^2\\)fM. Fig. 1 illustrates a representative case for the classic SfM systems that generate more flying 3D scene points, which can be addressed by our method." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.243, + 0.473, + 0.381 + ], + "angle": 0, + "content": "Our Level-\\(\\mathrm{S}^2\\mathrm{fM}\\) is inspired by the recently-emerged neural implicit surface that could manage all 3D scene points as the zero-level set of the signed distance function (SDF). Because the neural implicit surfaces can be parameterized by Multi-Layer Perceptrons (MLPs), it could be viewed as a kind of top-down information of 3D scenes and is of great potential for accurate 3D reconstruction. However, because both the 3D scene and camera poses are to be determined, it poses a challenging problem:" + }, + { + "type": "text", + "bbox": [ + 0.11, + 0.391, + 0.438, + 0.437 + ], + "angle": 0, + "content": "How can we optimize a neural SDF (or other neural fields such as NeRF) from only a set of uncalibrated images without any 3D information?" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.448, + 0.473, + 0.645 + ], + "angle": 0, + "content": "Most recently, the above problem was partially answered in BARF [18] and NeRF-- [42] that relaxed the requirement of optimizing Neural Radiance Fields [24] without knowing accurate camera poses, but they can only handle the unknown pose configurations in small-scale face-forwarding scenes. Moreover, when we confine the problem in the incremental SfM pipelines, it would be more challenging as we need to optimize the neural fields with only two overlapped images at the initialization stage. To this end, we found that the optimization of neural SDF can be accomplished by the 2D matches at the initialization stage, and facilitate the management of feature tracks by querying the 3D points and tracing the 2D keypoints in a holistic way." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.645, + 0.473, + 0.902 + ], + "angle": 0, + "content": "As shown in Fig. 1, we define a neural network that parameterizes an SDF as the unified representation for the underdetermined 3D scene and accomplishes the computations of PnP for camera pose intersection, the 3D points triangulation as well as the geometry refinement on the parameterized SDF. In the initialization stage with a pair of overlapped images, Level-\\(\\mathbf{S}^2\\)fM uses the differentiable sphere tracing algorithm [19] to attain the corresponding 3d points of the keypoints and calculate the reprojection error to drive the joint optimization. For the traced 3d points with small SDF values and 2D reprojection errors for its feature track, they are added into a dynamic point set and take the point set with feature tracks as the Lagrangian representation for the level sets. Because the pose estimation and the scene points reconstruction are sequentially estimated, the estimation errors will be accumulated. To this end, we present an NBA (i.e., Neural Bundle Adjustment) that plays a similar" + }, + { + "type": "text", + "bbox": [ + 0.497, + 0.092, + 0.895, + 0.168 + ], + "angle": 0, + "content": "role as in Bundle Adjustment, but it optimizes the implicit surface and camera poses from the explicit flow of points by the energy function of the reprojection errors, which can be viewed as an evolutionary step between Lagrangian and Eulerian representations as discussed in [23]." + }, + { + "type": "text", + "bbox": [ + 0.497, + 0.168, + 0.895, + 0.304 + ], + "angle": 0, + "content": "In the experiments, we evaluate our Level-\\(\\mathbf{S}^2\\)fM on a variety of scenes from the BlendedMVS [45], DTU [14], and ETH3D [34] datasets. On the BlendedMVS dataset, our proposed Level-\\(\\mathbf{S}^2\\)fM clearly outperforms the state-of-the-art COLMAP [32] by significant margins. On the DTU and ETH3D datasets [14, 34], our method also obtains on-par performance with COLMAP for both camera pose estimation and dense surface reconstruction, which are all computed in one stage." + }, + { + "type": "text", + "bbox": [ + 0.52, + 0.304, + 0.836, + 0.319 + ], + "angle": 0, + "content": "The contributions of this paper are in two folds:" + }, + { + "type": "text", + "bbox": [ + 0.52, + 0.329, + 0.892, + 0.42 + ], + "angle": 0, + "content": "- We present a novel neural SfM approach Level-\\(S^2\\)fM, which formulates to optimize the coordinate MLP networks for implicit surface and radiance field and estimate the camera poses and scene geometry. To the best of our knowledge, our Level-\\(S^2\\)fM is the first implicit neural SfM solution on the zero-level set of surfaces." + }, + { + "type": "text", + "bbox": [ + 0.52, + 0.431, + 0.892, + 0.537 + ], + "angle": 0, + "content": "- From the perspective of neural implicit fields learning, we show that the challenging problems of two-view and few-view optimization of neural implicit fields can be addressed by exploiting the inductive biases conveyed in the 2D correspondences. Besides, our method presents a promising way for neural implicit rendering without knowing camera extrinsics beforehand." + }, + { + "type": "list", + "bbox": [ + 0.52, + 0.329, + 0.892, + 0.537 + ], + "angle": 0, + "content": null + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.551, + 0.651, + 0.567 + ], + "angle": 0, + "content": "2. Related Works" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.576, + 0.714, + 0.591 + ], + "angle": 0, + "content": "2.1. Structure from Motion" + }, + { + "type": "text", + "bbox": [ + 0.497, + 0.599, + 0.895, + 0.903 + ], + "angle": 0, + "content": "There has been a vast body of literature on Structure from Motion. Since an SfM system consists of many components, tremendous efforts have been devoted to improving the core components of SfM. In particular, the learning techniques were introduced in a variety of subproblems including image matching [7, 31], feature track mining and management [39], two-view 3D reconstruction [38,44], relative and absolute camera pose estimation [13] and Bundle Adjustment [3, 37]. Those studies indicated that the learning paradigms are promising to improve the quality of 3D reconstruction. However, to the best of our knowledge, the learning paradigms are not fully equipped in SfM systems. One possible reason for such a fact is that the many learning approaches are designed in a supervised learning fashion, which remains some risks on the out-of-distribution samples. The self-supervised learning approaches [9, 22] in 3D vision alleviated the requirement of data annotations, however, they have not been fully exploited in the whole pipeline of SfM. In contrast to the aforementioned studies, in this paper, we are interested in integrating the learning" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.925, + 0.521, + 0.938 + ], + "angle": 0, + "content": "17206" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.471, + 0.168 + ], + "angle": 0, + "content": "ability into the SfM system without incurring any external data annotations. From the perspective of system design in SfM, we verified that the strong inductive biases conveying in the 2D correspondences are promising and meaningful to drive the learning and optimization of SfM." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.177, + 0.456, + 0.194 + ], + "angle": 0, + "content": "2.2. Neural Implicit Representation for 3D Scene" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.2, + 0.47, + 0.367 + ], + "angle": 0, + "content": "Recently, the advent of neural implicit fields [24, 28, 29, 40, 41, 46] have greatly advanced many 3D vision problems such as novel-view synthesis [1, 24] and surface reconstruction [28, 29, 40, 46] by learning to optimize the coordinate MLPs from a set of posed RGB images of which the key to success is that the inductive biases of 3D are exploited by the neural networks. However, when the camera poses are invalid, it is hard to optimize the coordinate MLPs for neural implicit fields. To remedy this, the state-of-the-art SfM system, COLMAP [32], is extensively used to compute the camera poses as a preprocessing step." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.367, + 0.471, + 0.563 + ], + "angle": 0, + "content": "To train the neural field from unknown poses directly, recently, BARF [18] and NeRF [42] explored to jointly optimize the camera poses and neural fields by the volumetric rendering with promising results obtained in forward-facing scenes. BARF can also work in some scenes of highly overlapped and dense image collections with the initialized poses as inputs. This problem was also studied in the RGB-D SLAM systems [2, 36, 47], however, their works mainly rely on the known depth information and focus on the camera pose tracking by the neural implicit fields. Therefore, how to optimize implicit neural fields from only a set of uncalibrated images without any 3D information input is still a challenging and open problem." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.564, + 0.47, + 0.639 + ], + "angle": 0, + "content": "In this paper, we study the unknown-pose neural fields optimization and SfM together and present a unified solution that simultaneously learns the implicit surfaces and radiance fields alongside the camera pose estimation and scene reconstruction from a set of images." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.653, + 0.216, + 0.669 + ], + "angle": 0, + "content": "3. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.678, + 0.47, + 0.724 + ], + "angle": 0, + "content": "In this section, we introduce the preliminaries on neural implicit surface rendering and the notations in SfM, which are all extensively used in our method." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.733, + 0.382, + 0.75 + ], + "angle": 0, + "content": "3.1. Neural Implicit Surface Rendering" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.757, + 0.469, + 0.864 + ], + "angle": 0, + "content": "The volumetric rendering of neural implicit surface [46] aims at learning a signed distance function \\( d_{\\Omega} : \\mathbb{R}^3 \\to \\mathbb{R} \\) by the volumetric rendering from a set of posed images and then extracting the zero-level set of \\( \\phi \\) as the reconstructed surface model of the image set. The state-of-the-art approach, VolSDF [46], integrates SDF representations with neural volume rendering via Laplacian distribution by" + }, + { + "type": "equation", + "bbox": [ + 0.19, + 0.874, + 0.47, + 0.904 + ], + "angle": 0, + "content": "\\[\n\\sigma (\\mathbf {x}) = \\frac {1}{\\beta} \\Psi_ {\\beta} (- d _ {\\Omega} (\\mathbf {x})), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.152 + ], + "angle": 0, + "content": "where \\(\\beta\\) is a learnable parameter in VolSDF [46]. Based on Eq. (1), the volume rendering equation renders a ray \\(\\mathbf{x}(t)\\) emanating from a camera position \\(\\mathbf{o} \\in \\mathbb{R}^3\\) in unit direction \\(\\mathbf{v}\\), defined by \\(\\mathbf{x}(t) = \\mathbf{o} + t\\mathbf{v}\\) by" + }, + { + "type": "equation", + "bbox": [ + 0.539, + 0.161, + 0.892, + 0.194 + ], + "angle": 0, + "content": "\\[\nI (\\mathbf {o}, \\mathbf {v}) = \\int_ {0} ^ {\\infty} L (\\mathbf {x} (t), \\mathbf {n} (t), \\mathbf {v}) \\sigma (\\mathbf {x} (t)) T (t) d t, \\quad (2)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.202, + 0.892, + 0.323 + ], + "angle": 0, + "content": "where \\(L(\\mathbf{x},\\mathbf{n},\\mathbf{v})\\) is the radiance field and \\(\\mathbf{n}(t)\\) is the normal direction of the point \\(\\mathbf{x}(t)\\) defined by \\(\\mathbf{n}(t) = \\nabla_{\\mathbf{x}}d_{\\Omega}(\\mathbf{x}(t))\\). In the learning of volume rendering, two coordinate MLP (Multi-Layer Perceptron) networks parameterize the SDF by \\(\\phi (\\mathbf{x}) = (d(\\mathbf{x}),\\mathbf{z}(\\mathbf{x}))\\in \\mathbb{R}^{1 + 256}\\) and the radiance field by \\(L_{\\psi}(\\mathbf{x},\\mathbf{n},\\mathbf{v},\\mathbf{z})\\in \\mathbb{R}^3\\), and train them by the color loss \\(\\mathcal{L}_{\\mathrm{RGB}}(\\phi ,\\psi ,\\beta)\\) and the Eikonal loss \\(\\mathcal{L}_{eik}(\\phi) = \\mathbb{E}_{\\mathbf{z}}(|\\nabla d(\\mathbf{z})|| - 1)\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.324, + 0.892, + 0.444 + ], + "angle": 0, + "content": "In this paper, we use the equations (1) and (2) as the basic tools for Level-\\(\\mathrm{S}^2\\)fM. To make the optimization of SDF and radiance networks easier, we set \\(\\beta\\) as a small constant number and use the multi-resolution grid representations to avoid the potential of slow convergence and catastrophic forgetting since the scene scale is unknown and the original VolSDF [46] requires to normalize the known camera poses in a certain scale." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.453, + 0.802, + 0.469 + ], + "angle": 0, + "content": "3.2. Ray Sampling and Sphere Tracing" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.476, + 0.892, + 0.507 + ], + "angle": 0, + "content": "Iterative Ray Sampling. In the implementation, the continuous form of Eq. (2) is approximated in" + }, + { + "type": "equation", + "bbox": [ + 0.576, + 0.515, + 0.892, + 0.556 + ], + "angle": 0, + "content": "\\[\nI (\\mathbf {o}, \\mathbf {v}) \\approx \\sum_ {i = 1} ^ {m - 1} \\hat {\\tau} _ {i} L (\\mathbf {x} (t _ {i}), \\mathbf {n} (t _ {i}), \\mathbf {v}), \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.565, + 0.892, + 0.746 + ], + "angle": 0, + "content": "where \\(\\{t_i\\}_{i=1}^m\\) is the discrete samples, \\(0 = t_1 < t_2 < \\ldots < t_m = M\\), \\(M\\) is some large constant. \\(\\hat{\\tau}_i \\approx \\tau(s_i) \\Delta s\\) is the approximated PDF multiplied by the interval length. In VolSDF [46], \\(\\{t_i\\}_{i=1}^m\\) is adaptively computed according to the opacity approximation error. Please move to [46] for its detail. In our method, we keep using this iterative sampling strategy when the rendering loss and the Eikonal loss is used. However, because the sampling set \\(\\{t_i\\}\\) would be large, we do not use this strategy to compute the 3D points from 2D keypoints in our Level-\\(\\mathbf{S}^2\\)fM and in turn to use the sphere tracing [10] as a faster way since our initial development of this work." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Sphere Tracing. Sphere tracing is a geometric method to render the depth from a signed distance function. Different from iterative ray sampling, sphere tracing is designed to hit the surface point along the ray \\(\\mathbf{x}(t)\\) with queries as few as possible. To make it clear, we use \\(s_i\\) to denote the ray stamp of the queried point \\(\\mathbf{x}(s_i)\\). With the queried point \\(\\mathbf{x}(s_i)\\), the next ray stamp \\(s_{i + 1}\\) is computed by \\(s_{i + 1} = \\phi (\\mathbf{x}(s_i))\\). In our study, we sample at most \\(N_{s} = 20\\) points with the stop criterion \\(|\\phi (s_i)| < \\varepsilon\\), where \\(\\varepsilon\\) is set to 0.002 in our experiment." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.926, + 0.519, + 0.938 + ], + "angle": 0, + "content": "17207" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.288 + ], + "angle": 0, + "content": "Remarks. Although both the iterative ray sampling [46] and sphere tracing [10] share the same target of computing the surface point along a ray, they have different behaviors in the neural implicit surface optimization. In detail, because VolSDF [46] aims at approximating the opacity by SDF, it updates the SDF network \\(\\phi (\\mathbf{x})\\) by the rendering loss \\(\\mathcal{L}_{\\mathrm{color}}\\). As for sphere tracing, it is a geometric approach that only takes the SDF values into account for the computation. Such a difference is trivial to some extent, however, we found that their different focuses induce a loss function in our Level-\\(\\mathbf{S}^2\\)fM to constraint the rendered depth values (or 3D points) for two-view initialization and 3D point triangulation." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.299, + 0.245, + 0.314 + ], + "angle": 0, + "content": "3.3. Notations in SfM" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.323, + 0.473, + 0.626 + ], + "angle": 0, + "content": "Correspondence Search. Given the image set \\(\\mathcal{I} = \\{I_i|i = 1\\ldots N_I\\}\\) for reconstruction, the keypoint features of the image \\(I_{i}\\) computed by SIFT [21] is denoted in \\(\\mathcal{F}_i = \\{(x_j,\\mathbf{f}_j)\\}\\), where \\(x_{j}\\in \\mathbb{R}^{2}\\) is the 2D coordinate and \\(\\mathbf{f}_j\\in \\mathbb{R}^{128}\\) is the feature descriptor of \\(x_{j}\\). Based on the SIFT features, we follow the schema in COLMAP [32] to establish the feature correspondences across views, in which we first do the exhaustive matching for all possible image pairs and then use the geometric verification to filter out the non-overlapped image pairs. After this, the potentially overlapped image pairs are denoted in \\(\\mathcal{C} = \\{(I_a,I_b)|I_a,I_b\\in \\mathcal{I}\\}\\), and the keypoint correspondences in the pair \\((I_a,I_b)\\) are denoted in the set \\(\\mathcal{M}_{ab} = \\{\\{(x_k,\\mathbf{f}_k),(x_l',\\mathbf{f}_l')\\} |(x_k,\\mathbf{f}_k\\in \\mathcal{F}_a,(x_l',f_l')\\in \\mathcal{F}_b\\}\\). Finally, all the prepared correspondences are organized as the scene graph [32, 35], which stores images as the graph nodes and the overlapped image pairs as the graph edges. In our Level-\\(\\mathrm{S^2fM}\\), we use the established correspondences to drive the learning of MLPs, estimate the camera poses, and reconstruct a sparse point set of correspondences." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.633, + 0.47, + 0.771 + ], + "angle": 0, + "content": "3D Scene Points and Feature Tracks. Because SfM is designated to simultaneously estimate the scene geometry from 2D correspondences, every successfully reconstructed 3D scene point is sourced from multiple 2D keypoint observations. To facilitate the representation, we denote the expected 3D point set in \\(\\mathcal{X} = \\{\\mathbf{X}_k\\in \\mathbb{R}^3 |k = 1,\\dots ,N_{3d}\\}\\). For each point \\(\\mathbf{X}_k\\in \\mathcal{X}\\), if it is reconstructed from the 2D keypoint \\(x_{j}\\in \\mathcal{F}_{i}\\), we denote such a relationship in a tuple \\((k,i,j)\\). \\(\\mathcal{T} = \\{(k,i,j)\\}\\) is the set of feature tracks." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.784, + 0.317, + 0.801 + ], + "angle": 0, + "content": "4. The Proposed Level \\(\\mathbf{S}^2\\mathbf{f}\\mathbf{M}\\)" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.471, + 0.902 + ], + "angle": 0, + "content": "In this section, we present the details of our Level-\\(S^2\\)fM. As shown in Fig. 2, our method consists of three classical components including 1) the two-view geometry initialization, 2) the new frame registration, and 3) the new frame pose refinement, an implicit surface and a radiance field that are parameterized by neural networks. In what follows, we" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.136 + ], + "angle": 0, + "content": "will show how to solve the SfM problem by learning the implicit fields with 2D correspondences. We assume the intrinsic matrix \\(K\\) is known and fixed." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.144, + 0.715, + 0.159 + ], + "angle": 0, + "content": "4.1. Two-view Initialization" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.167, + 0.892, + 0.257 + ], + "angle": 0, + "content": "We first select two good views \\(\\{I_a, I_b\\}\\) for initialization from the scene graph and get their 2D matches \\(\\mathcal{M}_{ab} = \\{\\{(x_k, f_k), (x'_l, f'_l)\\} | (x_k, f_k) \\in \\mathcal{F}_a, (x'_l, f'_l) \\in \\mathcal{F}_b\\}\\). Based on the 2D matches \\(\\mathcal{M}_{ab}\\), we leverage the 5-point algorithm [27] and RANSAC to obtain the poses \\(P_a, P_b \\in SE(3)\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.258, + 0.892, + 0.422 + ], + "angle": 0, + "content": "With the estimated camera poses \\( P_{a}, P_{b} \\), it is straightforward to optimize the SDF network \\( \\phi(\\mathbf{x}) \\) and the radiance field network \\( L_{\\psi}(\\mathbf{x}, \\mathbf{n}, \\mathbf{v}, \\mathbf{z}) \\) defined in Sec. 3 by minimizing the loss items \\( \\mathcal{L}_{\\mathrm{RGB}} \\) and \\( \\mathcal{L}_{eik} \\) as done in VolSDF [46]. However, it should be noted that the learning of volumetric surface rendering in such a way for the two-view inputs would trap into the local minimal by overfitting. To this end, we propose to use the differentiable sphere tracking [10, 19] for the corresponding rays in image \\( I_{a} \\) and \\( I_{b} \\), which provides strong inductive biases for the optimization of networks." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.424, + 0.892, + 0.543 + ], + "angle": 0, + "content": "Specifically, denoted by a pair of feature match \\((x_{k}, x_{l}^{\\prime})\\) in the image pair \\((I_{a}, I_{b})\\), the sphere tracing obtains the surface point \\(\\mathbf{X}_{a}^{k} = \\mathbf{o}_{a} + \\hat{t}_{a}\\mathbf{d}_{a}\\) from the SDF and \\(\\mathbf{X}_{b}^{l} = \\mathbf{o}_{b} + \\hat{t}_{b}\\mathbf{d}_{b}\\), where \\((\\mathbf{o}_{a}, \\mathbf{d}_{a})\\) is the ray of \\(x_{k}\\), \\((\\mathbf{o}_{b}, \\mathbf{d}_{b})\\) is the ray of \\(x_{l}^{\\prime}\\). For the computation of \\(\\hat{t}_{a}\\) and \\(\\hat{t}_{b}\\), please move to our supplementary materials. Ideally, the \\(\\mathbf{X}_{a}^{k}\\) and \\(\\mathbf{X}_{b}^{l}\\) should be as close as possible, therefore, we introduce a reprojection loss" + }, + { + "type": "equation", + "bbox": [ + 0.543, + 0.547, + 0.892, + 0.575 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {r e p r o j}} = \\frac {1}{2 V} \\sum \\left(\\| \\hat {x} _ {k} - x _ {l} ^ {\\prime} \\| _ {2} + \\| \\hat {x} _ {l} ^ {\\prime} - x _ {k} \\| _ {2}\\right), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.58, + 0.892, + 0.625 + ], + "angle": 0, + "content": "where \\(V\\) is the number of correspondences, \\(\\hat{x}_k = \\Pi(\\mathbf{X}_a^k, K, P_b)\\) and \\(\\hat{x}_l' = \\Pi(\\mathbf{X}_b^l, K, P_a)\\) are the projected 2D coordinates of the traced 3D points by the projection \\(\\Pi\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.626, + 0.892, + 0.732 + ], + "angle": 0, + "content": "Considering the fact that the correspondences are sparse when the SDF network is not well optimized at some rays, the sparse sample points by sphere tracing on the SDF network may be either inaccurate or erroneous as shown in Fig. 3. Therefore, we use a depth consistency loss \\(\\mathcal{L}_{dc}\\) to minimize the depth estimated by the sphere tracing and the volumetric rendering by" + }, + { + "type": "equation", + "bbox": [ + 0.557, + 0.736, + 0.892, + 0.768 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {d c} = \\frac {1}{B} \\sum \\left\\| \\hat {t} _ {i} - \\int_ {0} ^ {\\infty} T (t) \\sigma (\\mathbf {x} (t)) d t \\right\\|, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.774, + 0.892, + 0.847 + ], + "angle": 0, + "content": "where the rays \\(\\mathbf{x}(t)\\) are randomly sampled from the images, and those rays are also used to compute the color loss \\(\\mathcal{L}_{\\mathrm{RGB}}\\). For the computation of Eikonal loss \\(\\mathcal{L}_{eik}\\), all the 3D points visited by sphere tracing and dense ray marching are used." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.849, + 0.892, + 0.88 + ], + "angle": 0, + "content": "In summary, our two-view initialization of Level-\\(\\mathbf{S}^2\\)fM computes the total loss \\(\\mathcal{L}_{\\mathrm{total}}^{\\mathrm{init}}\\) by" + }, + { + "type": "equation", + "bbox": [ + 0.523, + 0.885, + 0.892, + 0.903 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {t o t a l}} ^ {\\text {i n i t}} = \\alpha_ {1} \\mathcal {L} _ {\\text {r e p r o j}} + \\alpha_ {2} \\mathcal {L} _ {e i k} + \\alpha_ {3} \\mathcal {L} _ {\\mathrm {R G B}} + \\alpha_ {4} \\mathcal {L} _ {\\mathrm {d c}}, \\tag {6}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.925, + 0.519, + 0.937 + ], + "angle": 0, + "content": "17208" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.141, + 0.089, + 0.833, + 0.282 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.375, + 0.292, + 0.596, + 0.307 + ], + "angle": 0, + "content": "Figure 2. Overview of Level-\\(\\mathbf{S}^2\\)fM." + }, + { + "type": "image", + "bbox": [ + 0.126, + 0.329, + 0.268, + 0.436 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.152, + 0.439, + 0.235, + 0.453 + ], + "angle": 0, + "content": "3D points by sphere tracing (with depth consistency)" + }, + { + "type": "image", + "bbox": [ + 0.283, + 0.329, + 0.422, + 0.437 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.31, + 0.439, + 0.394, + 0.454 + ], + "angle": 0, + "content": "3D points by sphere tracing (without depth consistency)" + }, + { + "type": "image_caption", + "bbox": [ + 0.077, + 0.464, + 0.47, + 0.52 + ], + "angle": 0, + "content": "Figure 3. An illustrative comparison for the depth consistency loss \\(\\mathcal{L}_{dc}\\) in two-view initialization. As it is shown, when we remove \\(\\mathcal{L}_{dc}\\), the 3D points traced from all putative 2D keypoints correspondences will contain more flying 3D points." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.537, + 0.47, + 0.567 + ], + "angle": 0, + "content": "where \\(\\alpha_{1},\\alpha_{2},\\alpha_{3}\\) , and \\(\\alpha_{4}\\) are the hyperparameters and use ADAM optimizer to optimize the networks." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.568, + 0.47, + 0.675 + ], + "angle": 0, + "content": "When the initialization is finished, we compute the two 3D points \\(\\mathbf{X}\\) and \\(\\mathbf{X}'\\) for each correspondence by sphere tracing for image \\(I_{a}\\) and \\(I_{b}\\). For an accurate correspondence, \\(\\| \\mathbf{X} - \\mathbf{X}'\\|\\) and their SDF values should be all small enough, thus providing a good criterion to check the putative matches to initialize the 3D point set \\(\\mathcal{X}\\) and the feature track set \\(\\mathcal{T}\\) for all the verified two-view correspondences." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.684, + 0.303, + 0.7 + ], + "angle": 0, + "content": "4.2. New Frame Registration" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.708, + 0.47, + 0.799 + ], + "angle": 0, + "content": "For every newly added frame, we will first construct the 3D-2D correspondence from the existing pointset and its feature tracks. After that, we calculate a coarse pose of the new frame with the standard PnP algorithms [17], and then refine it with both the reprojection error and the rendering loss. The registration loss can be calculated as follow:" + }, + { + "type": "equation", + "bbox": [ + 0.172, + 0.812, + 0.469, + 0.829 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {r e g i s t}} = \\beta_ {1} \\mathcal {L} _ {\\text {r e p r o j}} + \\beta_ {2} \\mathcal {L} _ {\\text {R G B}}, \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.84, + 0.469, + 0.871 + ], + "angle": 0, + "content": "where the \\(\\beta_{1},\\beta_{2}\\) are two hyper-parameters, and the \\(\\mathcal{L}_{\\mathrm{reproj}}\\) here are calculated by the 3D-2D correspondences." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.469, + 0.902 + ], + "angle": 0, + "content": "In this optimization, the pose of the newly added frame, the SDF network, and the radiance field network are jointly" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.333, + 0.892, + 0.438 + ], + "angle": 0, + "content": "optimized. While during the changes in the pose and SDF, the original location in the pointset will maybe not be the right one on the surface. For this problem, we design a Neural Bundle Adjustment (NBA) strategy to dynamically update the pointset with respect to the SDF after the points triangulation and refinement in the next section. Therefore, we leave the details of NBA in Sec. 4.4." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.446, + 0.82, + 0.463 + ], + "angle": 0, + "content": "4.3. Points Triangulation and Refinement" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.469, + 0.892, + 0.56 + ], + "angle": 0, + "content": "Once the pose of the newly added frame is obtained, we step into the next procedure of refining the retrieved 2D points from the point set \\(\\mathcal{X}\\) and triangulating new 2D points into 3D space to extend \\(\\mathcal{X}\\). This problem was formulated in the classical SfM frameworks, however, they are suffering from the following issues:" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.566, + 0.892, + 0.643 + ], + "angle": 0, + "content": "- The 2D Mismatches: This issue could be alleviated by geometric verifications like RANSAC [8] or better 2D keypoint matching approaches, however, when encountering the symmetry structures or repeated texture regions, those efforts are hard to work efficiently." + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.65, + 0.892, + 0.725 + ], + "angle": 0, + "content": "- Tiny Triangulation Angle: This issue will lead to an ill-conditioned problem for points triangulation [12]. Therefore, the classical SfM approaches will directly discard those points to avoid the ill-conditioned problem configuration." + }, + { + "type": "list", + "bbox": [ + 0.518, + 0.566, + 0.892, + 0.725 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.731, + 0.892, + 0.822 + ], + "angle": 0, + "content": "We address those issues by proposing an SDF-based triangulation. Similar to the two-view initialization in Sec. 4.1, we compute the 3D points for all the potential 2D keypoints in the first step. Then, for the 2D keypoints that have correspondences in the current feature track set \\(\\mathcal{T}\\), we use the tracing loss \\(\\mathcal{L}_{\\mathrm{tracing}}\\)" + }, + { + "type": "equation", + "bbox": [ + 0.587, + 0.828, + 0.892, + 0.865 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {t r a c i n g}} = \\frac {1}{V ^ {\\prime}} \\sum_ {j} \\| \\mathbf {X} _ {j} ^ {s t} - \\mathbf {X} _ {j} \\|, \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "where \\(\\mathbf{X}_j\\in \\mathcal{X}\\) is the retrieved 3D point of the 2D keypoint in the current frame, \\(V^{\\prime}\\) is the number of retrieved 3D" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.926, + 0.519, + 0.938 + ], + "angle": 0, + "content": "17209" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.137 + ], + "angle": 0, + "content": "points. This loss function acts as the similar role of \\(\\mathcal{L}_{dc}\\) in two-view initialization. Without it, similar phenomenon like Fig. 3 will happen." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.137, + 0.47, + 0.199 + ], + "angle": 0, + "content": "For the new 2D keypoints that are matched to the added images but without 3D information, both the reprojection loss similar to the two-view initialization and the tracing loss is used to yield the triangulation loss \\(\\mathcal{L}_{\\mathrm{tri}}\\) by" + }, + { + "type": "equation", + "bbox": [ + 0.185, + 0.205, + 0.469, + 0.224 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {t r i}} = \\mathcal {L} _ {\\text {r e p o r j}} ^ {\\text {m a s k}} + \\mathcal {L} _ {\\text {t r a c i n g}}, \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.232, + 0.47, + 0.293 + ], + "angle": 0, + "content": "where \\(\\mathcal{L}_{\\mathrm{report}}^{\\mathrm{mask}}\\) only considers the 2D correspondences of which their distance between the 2D projections of the traced 3D points traced in different views are smaller than a loose threshold (45 pixels in our implementation)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.301, + 0.415, + 0.317 + ], + "angle": 0, + "content": "4.4. Neural Bundle Adjustment on Surfaces" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.324, + 0.469, + 0.474 + ], + "angle": 0, + "content": "Because the camera pose estimation and the points triangulation are separated, which will involve accumulative errors for pose estimation and triangulation, as well as the implicit networks. Motivated by the Bundle Adjustment that is extensively used in classical approaches, we present a Neural Bundle Adjustment (NBA) that jointly optimizes the estimated camera points, the 3D point set, and the implicit networks as a refinement step. To avoid costly computation, our NBA step finds the closest surface points to dynamically update those variables." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.476, + 0.469, + 0.551 + ], + "angle": 0, + "content": "Denoted by the reconstructed 3D point set \\(\\mathcal{X}\\) and the feature track \\(\\mathcal{T}\\), the camera poses \\(\\mathcal{P} = \\{P_1, \\dots, P_K\\}\\) and the corresponding images \\(\\{I_1, \\dots, I_K\\}\\), as well as the networks \\(\\phi(\\mathbf{x})\\) and \\(L_{\\psi}\\), in each step of NBA, we update the 3D point \\(\\mathbf{X} \\in \\mathcal{X}\\) by" + }, + { + "type": "equation", + "bbox": [ + 0.188, + 0.56, + 0.469, + 0.575 + ], + "angle": 0, + "content": "\\[\n\\mathbf {X} \\leftarrow \\mathbf {X} - \\phi (\\mathbf {X}) \\nabla \\phi (\\mathbf {X}), \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.584, + 0.469, + 0.659 + ], + "angle": 0, + "content": "and then compute the reprojection loss according to the feature track \\(\\mathcal{T}\\) to jointly optimize the \\(\\phi\\) the SDF network, \\(\\mathcal{P}\\) the estimated camera poses, and \\(\\mathcal{X}\\) the updated 3D point set. For the radiance network \\(L_{\\psi}\\), the rendering loss for randomly sampled rays is computed." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.66, + 0.469, + 0.81 + ], + "angle": 0, + "content": "In our implementation, we leverage our NBA by three times, which we call the 1-frame NBA, local NBA, and global NBA. Because the rendering loss involves more rays, we only use it for the 1-frame NBA after the camera registration and point triangulation. In terms of local NBA, for the newly added view, only the related views with correspondences are considered. After running the 1-frame and local NBA schemes, we globally update all reconstructed views and the point set. By leveraging the backpropagation, all the mentioned variables are updated as the refinement." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.822, + 0.209, + 0.839 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.848, + 0.468, + 0.863 + ], + "angle": 0, + "content": "5.1. Implementation Details, Datasets, and Metrics" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.871, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Implementation Details. In our implementation, we parameterize the SDF \\(\\phi (\\mathbf{x})\\) by a multi-resolution features grid" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.318 + ], + "angle": 0, + "content": "and a two-layers MLP. To accelerate the computation, we follow InstantNGP [26] to use a hash table [25] for the feature grids. The radiance field \\( L_{\\psi} \\) is also implemented in a multi-resolution feature grid and a three-layer MLP. Because our end task is the geometric 3D reconstruction, we use a high-resolution multi-scale feature grid for the SDF to ensure the accuracy of scene geometry but use a low-resolution feature grid to avoid the unnecessary computation cost for the radiance field. The specifications of the network architecture are given in supplementary material due to the limited space. All of these above are implemented in PyTorch [30], and we used the Adam [16] as the optimizer for the geometric calculations. For the 2D image matching and pose graph, we keep them the same with our baseline, COLMAP [32] for fair comparisons." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.322, + 0.892, + 0.473 + ], + "angle": 0, + "content": "Datasets. Three datasets are used for our evaluation. Firstly, we use 5 representative scenes including the LyingStatue, Stone, Fountain, Horse, and Statues from the BlendedMVS dataset [45] in our evaluation because it provides accurate ground truth of camera poses and contains a number of challenging scenes for SfM. Secondly, the DTU dataset for the MVS task is also used. The five representative scenes (scans of 24, 37, 65, 110 and 114) are used in our experiments. Finally, we evaluate our proposed method on the five scenes from the challenging ETH3D [34] dataset." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.477, + 0.892, + 0.643 + ], + "angle": 0, + "content": "Evaluation Metrics. In our evaluation, we use the Rotation error and ATE to quantitatively benchmark the pose accuracy, which simply depicts the difference between the ground truth and the aligned pose. During our evaluation, we used the provided API of Reconstruction Align in COLMAP [32] to do that. In terms of the reconstructed scene geometry, we use accuracy (Acc) and the precision (Prec) rate to evaluate the accuracy of our recovered 3D points and Chamfer-l1 distance to depict the accuracy of the reconstructed surface. Detailed definitions of these evaluation metrics are given in the supplementary material." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.651, + 0.815, + 0.667 + ], + "angle": 0, + "content": "5.2. Results on the BlendedMVS Dataset" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.675, + 0.892, + 0.795 + ], + "angle": 0, + "content": "Tab. 1 reports the quantitative evaluation results for the two versions of Level-\\(\\mathbf{S}^2\\)fM and COLMAP [32]. The full version of Level-\\(\\mathbf{S}^2\\)fM used all the mentioned components while the wo/render version removed the rendering loss for optimization. As it is reported, our Level-\\(\\mathbf{S}^2\\)fM (full) consistently outperforms COLMAP [32] for camera pose estimation and sparse 3D point cloud reconstruction. It also reveals that rendering losses are required." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.892, + 0.9 + ], + "angle": 0, + "content": "In detail, our Level-\\(S^2\\)fM (full) averagely reduced the estimation error from \\(1.54^\\circ\\) by COLMAP [32] to \\(0.86^\\circ\\), obtaining a relative improvement of \\(55.84\\%\\). For the translation error, our Level-\\(S^2\\)fM (full) decreases the error from \\(3.54 \\mathrm{~cm}\\) to \\(3.36 \\mathrm{~cm}\\). For the sparse 3D point cloud reconstruction, the ACC metric is reduced from 3.16 to 2.25 for the full model and 2.63 for the wo/render version." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.926, + 0.519, + 0.938 + ], + "angle": 0, + "content": "17210" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.122, + 0.089, + 0.852, + 0.204 + ], + "angle": 0, + "content": "
ScenesCamera Pose EvaluationPoints Cloud Results Evaluation
Rotation (°) ↓Translation (cm) ↓Acc(cm)↓, Prec(≤3.5cm)↑
COLMAP [32]Level-S2fM (full)Level-S2fM (wo/render)COLMAP [32]Level-S2fM (full)Level-S2fM (wo/render)COLMAP [32]Level-S2fM (full)Level-S2fM (wo/render)
LyingStatue1.201.121.310.892.182.67Acc:0.83 Prec:0.99Acc:1.35 Prec:0.99Acc:1.76 Prec:0.98
Stone0.630.310.546.285.519.17Acc:5.44 Prec:0.66Acc:3.61 Prec:0.68Acc:5.04 Prec:0.64
Fountain4.341.652.327.412.874.11Acc:1.91 Prec:0.91Acc:1.13 Prec:0.98Acc:1.14 Prec:0.96
Horse0.330.940.921.185.717.58Acc:3.99 Prec:0.86Acc:4.18 Prec:0.71Acc:4.18 Prec:0.70
Statues1.210.360.441.980.560.62Acc:1.31 Prec:0.98Acc:0.95 Prec:0.99Acc:1.02 Prec:0.99
Mean1.540.861.113.543.364.83Acc:3.16 Prec:0.85Acc:2.25 Prec:0.87Acc:2.63 Prec:0.85
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.215, + 0.894, + 0.245 + ], + "angle": 0, + "content": "Table 1. Quantitative results on the BlendedMVS dataset. For our Level-\\(S^2\\)fM, we report the results by full version and an wo/render version that removes the rendering loss during optimization." + }, + { + "type": "image", + "bbox": [ + 0.114, + 0.251, + 0.429, + 0.422 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.43, + 0.473, + 0.526 + ], + "angle": 0, + "content": "Figure 4. The reconstructed meshes, point clouds and camera poses for the Fountain and Horse scenes on the BlendedMVS dataset by our Level-\\(S^2\\)fM (full). In the first column, the reconstructed scene geometry and the camera poses are shown together. For the 3D models, we show the different views of the sparse 3D points during the training and the textured meshes refused from the zero-level set surface." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.525, + 0.47, + 0.578 + ], + "angle": 0, + "content": "
Pose SourceLyingStatueStoneFountainHorseStatuesMean
COLMAP29.128.425.423.529.227.12
Level-S2fM (Ours)29.528.927.123.630.227.86
GT29.529.427.424.131.128.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.58, + 0.47, + 0.622 + ], + "angle": 0, + "content": "Table 2. Novel View Synthesis Comparison. The PSNR is used to compare the camera poses computed by COLMAP, Level-\\(\\mathbf{S}^2\\)fM and the GT poses on the BlendedMVS dataset." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.639, + 0.455, + 0.654 + ], + "angle": 0, + "content": "Fig. 4 shows the reconstruction results by our method." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.655, + 0.47, + 0.76 + ], + "angle": 0, + "content": "Apart from the direct evaluation of the SfM results on the BlendedMVS dataset, we further compare the camera poses estimation results for different methods by training the NGP [26] (a fast version of NeRF [24]) to compare the performance of novel view synthesis in Tab. 2. As it is reported, the rendered images by our camera poses are consistently better than the ones by COLMAP poses." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.771, + 0.327, + 0.787 + ], + "angle": 0, + "content": "5.3. Results on the DTU Dataset" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.795, + 0.471, + 0.903 + ], + "angle": 0, + "content": "We conducted the evaluation on the DTU to illustrate the promising future of our Level-\\(\\mathbf{S}^2\\)fM to unify the pose estimation, dense reconstruction, and novel view synthesis problems in one stage. For the comparison to COLMAP, we use their built-in PatchMatch MVS [33] functionality to obtain the dense surface points and then leverage its default surface reconstruction method (i.e., Poisson surface [15])" + }, + { + "type": "table", + "bbox": [ + 0.51, + 0.253, + 0.885, + 0.345 + ], + "angle": 0, + "content": "
ScanCOLMAP [32]Level-S2fM (Ours)
Chamfer-ℓ1Rot. Err.Trans. Err.Chamfer-ℓ1Rot. Err.Trans. Err.
242.1760.382.872.4420.814.60
373.8370.414.863.0230.314.29
654.3940.454.233.1900.745.81
1103.3890.656.365.9020.826.82
1143.5770.353.582.0920.141.85
Mean3.3300.4484.383.4740.5644.67
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.356, + 0.892, + 0.424 + ], + "angle": 0, + "content": "Table 3. Quantitative results on DTU dataset. The Chamfer- \\(\\ell_1\\) distance of the dense reconstruction results and as the errors of rotation and translation for camera pose estimation, are compared for COLMAP and our Level-\\(S^2\\)fM. The unit of Chamfer-\\(\\ell_1\\) and Translation errors are in millimetres." + }, + { + "type": "table", + "bbox": [ + 0.503, + 0.426, + 0.891, + 0.466 + ], + "angle": 0, + "content": "
SceneDetector & Matchercourtyardreliefdoorterrace2facade
COLMAP [32]SIFT [21]0.10°/0.016m0.10°/0.003m0.16°/0.002m0.14°/0.002m0.06°/0.016m
Level-SfM (Ours)0.21°/0.047m0.09°/0.003m0.19°/0.006m0.13°/0.003m0.12°/0.059m
COLMAP [32]SP [7]+SG [31]-0.36°/0.007m0.37°/0.003m0.16°/0.003m0.04°/0.014m
Level-SfM-0.42°/0.003m0.34°/0.007m0.12°/0.002m0.10°/0.051m
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.476, + 0.892, + 0.518 + ], + "angle": 0, + "content": "Table 4. Quantitative results of pose estimation on the five scenes in ETH3D dataset for COLMAP [32] and our proposed Level-S²fM by using different keypoint detector and matcher." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.545, + 0.892, + 0.621 + ], + "angle": 0, + "content": "to obtain the mesh model. For our Level-\\(\\mathbf{S}^2\\)fM, we use the MarchingCubes [20] to extract the mesh models from the zero-level set of the implicit surface. The quantitative evaluation results are shown in Tab. 3. In this dataset, our Level-\\(\\mathbf{S}^2\\)fM obtains on-par performance with COLMAP." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.632, + 0.771, + 0.646 + ], + "angle": 0, + "content": "5.4. Results on the ETH3D Dataset" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.656, + 0.892, + 0.837 + ], + "angle": 0, + "content": "We test our method on a more challenging dataset, ETH3D [34], which includes both sparse view collections for multi-scale outdoor and indoor scenes. To show the influence of different keypoint detection and matching algorithms for our method, we additionally make a comparison with SuperPoint (SP) [7] for detection and SuperGlue (SG) [31] for keypoint matching. As reported in Tab. 4, our method achieves comparable results with COLMAP [32]. However, we observe that our method gets slightly inferior results in some large-scale outdoor scenes, because of the limited representative capability of a single network for a large-scale scene." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.847, + 0.654, + 0.864 + ], + "angle": 0, + "content": "5.5. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "In this section, we elaborate on why and how the SDF-based Triangulation (short in \\(SDF\\text{-Tri}\\)) and NBA work in" + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.927, + 0.518, + 0.938 + ], + "angle": 0, + "content": "17211" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.074, + 0.462, + 0.3 + ], + "angle": 0, + "content": null + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.301, + 0.462, + 0.325 + ], + "angle": 0, + "content": "
w/o SDF-Tri.w/o NBAw/o SDF-Tri & w/o NBAFull
1.23° / 0.498m0.46° / 0.113m3.48° / 0.846m0.21° / 0.047m
" + }, + { + "type": "image_caption", + "bbox": [ + 0.078, + 0.328, + 0.468, + 0.353 + ], + "angle": 0, + "content": "Figure 5. Two-views Triangulated Point Clouds by Traditional triangulation and SDF-Based Triangulation, courtyard@ETH3D." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.362, + 0.47, + 0.557 + ], + "angle": 0, + "content": "our system. We first conduct the two-view triangulation with SDF-Tri and the traditional method respectively. Fig. 5 shows SDF-Tri can easily filter out the incorrect triangulation from wrong matches. One explanation is that the neural network distills inliers in a continuous zero-level set of surfaces (e.g., the planes in Fig. 5). Therefore, the triangulated outliers (the blue points flying off the planes) can be easily detected and filtered by their large SDF values. Similarly, NBA also benefits from the global level sets of surfaces, which average the errors among the inliers triangulated points and differs the outliers by their large SDF values. The quantitative results for SDF-Tri and NBA are shown at the bottom of Fig. 5." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.572, + 0.315, + 0.587 + ], + "angle": 0, + "content": "5.6. Limitations of Level-\\(\\mathbf{S}^2\\)fM" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.597, + 0.47, + 0.763 + ], + "angle": 0, + "content": "In order to explore the clear boundary of Level-\\(\\mathbf{S}^2\\)fM and point out the potential future development, we discuss the limitation of Level-\\(\\mathbf{S}^2\\)fM on the most typical indoor dataset, scannet [6]. In the Scannet [6], there are a lot of challenges including blurry images, and textureless areas. Because of the less texture, the SIFT-based keypoint correspondences may contain a large portion of outliers or insufficient matches. Meanwhile, the blur in images will also influence the accuracy of the 2d matches. Therefore, most SfM easily fails on this dataset. Our method is also limited by this because of SIFT matches." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.765, + 0.471, + 0.901 + ], + "angle": 0, + "content": "To make the discussion clear, we run four scenes of Scannet [6] that were used for NICESLAM [47]. For the image sequence of each scene, the input image set for SfM is constructed by sampling for every 10 frames. Tab. 5 reports the pose accuracy of COLMAP and our Level-\\(S^2\\)fM for results by adding 60 frames, 120 frames, and all the frames, which are concatenated by the \"slash\". As it is reported, our method usually performs well in the first 60 frames but its pose estimation accuracy suddenly decreases" + }, + { + "type": "table", + "bbox": [ + 0.504, + 0.089, + 0.891, + 0.177 + ], + "angle": 0, + "content": "
Scene ID0000005901690207
iMAP [36]rot°↓trans. (cm) ↓-197.1-18.9-96.4-28.7
NICESLAM [47]rot°↓trans. (cm) ↓-11.3-12.0-12.0-12.8
COLMAP [32]rot°↓trans. (cm) ↓1.47/2.71/2.718.20/16.8/14.11.40/2.52/2.667.20/10.8/11.52.64/1.84/2.5210.6/9.22/10.6failed-
Level-S2fMrot°↓1.44/2.23/1.986.50/11.8/26.71.50/2.59/3.728.20/32.9/35.72.94/9.09/14.36.35/14.3/44.8failed-
fulltrans. (cm) ↓
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.187, + 0.892, + 0.243 + ], + "angle": 0, + "content": "Table 5. Quantitative results of pose estimation on ScanNet [6]. For the COLMAP and Level-\\( S^2 \\)fM, we report their pose accuracy metrics when the 60/120/all frames are registered. For the last scene (i.e., 0207), both COLMAP and our method failed." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.258, + 0.892, + 0.394 + ], + "angle": 0, + "content": "when adding some new frames. The textureless matches cause the matches very sparse and therefore hard to give a good registration of images. Meanwhile, since the radiance field learning is also challenging in the Scannet dataset, the bad initialized pose can not be refined well by the rendering loss. All of these limitations are basically from sparse 2D image matches. Besides, we observed that the ADAM optimizer will make the optimization of camera poses and scene points unstable, which would also affect the final results." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.407, + 0.619, + 0.422 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.432, + 0.892, + 0.734 + ], + "angle": 0, + "content": "This paper studies the longstanding problem of Structure-from-Motion by exploring and exploiting several important yet challenging issues including the two-view neural rendering in the initialization stage and few-view neural rendering in the early camera registration stage of incremental SfM for integrating the recent advances of neural implicit field learning into an SfM pipeline. We show that although the few-view neural rendering problem is challenging enough, it can be tackled by the 2D correspondences as they convey strong inductive biases for 3D scenes. Based on this, we present the first neural SfM solution that renews several key components of two-view geometry initialization, camera pose registration, and triangulation, as well as the Bundle Adjustment problem with neural implicit fields. In the experiments, we show that Level-\\(\\mathbf{S}^2\\)fM outperforms the traditional SfM pipeline and set a new state-of-the-art for 3D reconstruction on the BlendedMVS dataset. We believe that our study will encourage the 3D vision community to rethink and reformulate Structure-from-Motion with learning-based new findings." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Acknowledgments. This work was supported by the National Nature Science Foundation of China under grants 62101390 and U22B2011. This work was also supported by Wuhan University-Huawei Geoinformatics Innovation Laboratory. Y. Xiao would love to thank Sida Peng for the helpful discussions. T. Wu was supported by NSF IIS-1909644, IIS-1822477, CMMI-2024688, and IUSE-2013451. We are also grateful for the constructive comments by anonymous reviewers and area chairs. The views presented in this paper are those of the authors and should not be interpreted as representing any funding agencies." + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.926, + 0.519, + 0.938 + ], + "angle": 0, + "content": "17212" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.184 + ], + "angle": 0, + "content": "[1] Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In IEEE International Conference on Computer Vision (ICCV), pages 5835-5844, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.186, + 0.472, + 0.24 + ], + "angle": 0, + "content": "[2] Ronald Clark. Volumetric bundle adjustment for online photorealistic scene capture. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6114-6122, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.242, + 0.471, + 0.31 + ], + "angle": 0, + "content": "[3] Ronald Clark, Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, and Andrew J. Davison. Learning to solve nonlinear least squares for monocular stereo. In European Conference on Computer Vision (ECCV), volume 11212, pages 291-306, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.312, + 0.471, + 0.38 + ], + "angle": 0, + "content": "[4] David J. Crandall, Andrew Owens, Noah Snavely, and Daniel P. Huttenlocher. Sfm with mfrs: Discrete-continuous optimization for large-scale structure from motion. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 35(12):2841–2853, 2013. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.382, + 0.47, + 0.436 + ], + "angle": 0, + "content": "[5] Hainan Cui, Xiang Gao, Shuhan Shen, and Zhanyi Hu. Hsfm: Hybrid structure-from-motion. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2393-2402, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.438, + 0.47, + 0.507 + ], + "angle": 0, + "content": "[6] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas A. Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2432-2443, 2017. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.509, + 0.47, + 0.576 + ], + "angle": 0, + "content": "[7] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 224-236, 2018. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.579, + 0.47, + 0.633 + ], + "angle": 0, + "content": "[8] Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.635, + 0.47, + 0.689 + ], + "angle": 0, + "content": "[9] Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow. Digging into self-supervised monocular depth estimation. In IEEE International Conference on Computer Vision (ICCV), pages 3827-3837, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.691, + 0.47, + 0.732 + ], + "angle": 0, + "content": "[10] John C. Hart. Sphere tracing: a geometric method for the antialiased ray tracing of implicit surfaces. The Visual Computer, 12(10):527-545, 1996. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.734, + 0.47, + 0.773 + ], + "angle": 0, + "content": "[11] Richard Hartley and Andrew Zisserman. Multiple view geometry in computer vision. Cambridge University Press, 2003. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.776, + 0.47, + 0.815 + ], + "angle": 0, + "content": "[12] Richard I Hartley and Peter Sturm. Triangulation. Computer Vision and Image Understanding (CVIU), 68(2):146-157, 1997. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.818, + 0.47, + 0.872 + ], + "angle": 0, + "content": "[13] Petr Hruby, Timothy Duff, Anton Leykin, and Tomás Pajdla. Learning to solve hard minimal problems. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5522-5532, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.874, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[14] Rasmus Ramsbøl Jensen, Anders Lindbjerg Dahl, George Vogiatzis, Engin Tola, and Henrik Aanæs. Large scale multi-" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.094, + 0.892, + 0.134 + ], + "angle": 0, + "content": "view stereopsis evaluation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 406-413, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.136, + 0.892, + 0.176 + ], + "angle": 0, + "content": "[15] Michael M. Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Symposium on Geometry Processing, volume 256, pages 61-70, 2006. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.178, + 0.892, + 0.218 + ], + "angle": 0, + "content": "[16] Diederik P Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations (ICLR), 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.22, + 0.892, + 0.272 + ], + "angle": 0, + "content": "[17] Vincent Lepetit, Francesc Moreno-Noguer, and Pascal Fua. Epnp: An accurate \\(O(n)\\) solution to the pnp problem. International Journal of Computer Vision (IJCV), 81(2):155-166, 2009. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.275, + 0.892, + 0.328 + ], + "angle": 0, + "content": "[18] Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey. BARF: bundle-adjusting neural radiance fields. In IEEE International Conference on Computer Vision (ICCV), pages 5721-5731, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.331, + 0.892, + 0.399 + ], + "angle": 0, + "content": "[19] Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui. DIST: rendering deep implicit signed distance function with differentiable sphere tracing. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2016-2025, 2020. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.401, + 0.892, + 0.44 + ], + "angle": 0, + "content": "[20] William E. Lorensen and Harvey E. Cline. Marching cubes: A high resolution 3d surface construction algorithm. In Proceedings of SIGGRAPH, pages 163-169, 1987. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.443, + 0.892, + 0.482 + ], + "angle": 0, + "content": "[21] David G Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (IJCV), 60(2):91-110, 2004. 4, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.485, + 0.892, + 0.552 + ], + "angle": 0, + "content": "[22] Reza Mahjourian, Martin Wicke, and Anelia Angelova. Unsupervised Learning of Depth and Ego-Motion From Monocular Video Using 3D Geometric Constraints. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5667-5675, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.554, + 0.892, + 0.607 + ], + "angle": 0, + "content": "[23] Ishit Mehta, Manmohan Chandraker, and Ravi Ramamoorthi. A level set theory for neural implicit evolution under explicit flows. In European Conference on Computer Vision (ECCV), 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.609, + 0.892, + 0.678 + ], + "angle": 0, + "content": "[24] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision (ECCV), volume 12346, pages 405-421, 2020. 2, 3, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.679, + 0.776, + 0.691 + ], + "angle": 0, + "content": "[25] Thomas Müller. tiny-cuda-nn, 4 2021. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.693, + 0.892, + 0.747 + ], + "angle": 0, + "content": "[26] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. on Graphics (TOG), 41(4):102:1-102:15, 2022. 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.749, + 0.892, + 0.789 + ], + "angle": 0, + "content": "[27] David Nister. An Efficient Solution to the Five-Point Relative Pose Problem. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 26(6):756-777, 2004. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.791, + 0.892, + 0.857 + ], + "angle": 0, + "content": "[28] Michael Oechsle, Songyou Peng, and Andreas Geiger. UNISURF: unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In IEEE International Conference on Computer Vision (ICCV), pages 5569-5579, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.86, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[29] Jeong Joon Park, Peter Florence, Julian Straub, Richard A. Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation." + }, + { + "type": "list", + "bbox": [ + 0.504, + 0.094, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.48, + 0.927, + 0.52, + 0.938 + ], + "angle": 0, + "content": "17213" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.092, + 0.468, + 0.12 + ], + "angle": 0, + "content": "In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 165-174, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.121, + 0.468, + 0.244 + ], + "angle": 0, + "content": "[30] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (NeurIPS), pages 8024-8035, 2019. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.246, + 0.468, + 0.314 + ], + "angle": 0, + "content": "[31] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4937–4946, 2020. 2, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.316, + 0.468, + 0.37 + ], + "angle": 0, + "content": "[32] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion Revisited. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4104-4113, 2016. 1, 2, 3, 4, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.371, + 0.468, + 0.438 + ], + "angle": 0, + "content": "[33] Johannes L. Schonberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys. Pixelwise view selection for unstructured multi-view stereo. In European Conference on Computer Vision (ECCV), volume 9907, pages 501-518, 2016. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.441, + 0.468, + 0.496 + ], + "angle": 0, + "content": "[34] Thomas Schöps, Torsten Sattler, and Marc Pollefeys. BAD SLAM: bundle adjusted direct RGB-D SLAM. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 134-144, 2019. 2, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.497, + 0.468, + 0.551 + ], + "angle": 0, + "content": "[35] Noah Snavely, Steven M. Seitz, and Richard Szeliski. Skeletal graphs for efficient structure from motion. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.553, + 0.468, + 0.607 + ], + "angle": 0, + "content": "[36] Edgar Sucar, Shikun Liu, Joseph Ortiz, and Andrew J. Davison. imap: Implicit mapping and positioning in real-time. In IEEE International Conference on Computer Vision (ICCV), pages 6209-6218, 2021. 3, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.608, + 0.468, + 0.649 + ], + "angle": 0, + "content": "[37] Chengzhou Tang and Ping Tan. BA-Net: Dense Bundle Adjustment Networks. In International Conference on Learning Representations (ICLR), 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.65, + 0.468, + 0.719 + ], + "angle": 0, + "content": "[38] Jianyuan Wang, Yiran Zhong, Yuchao Dai, Stan Birchfield, Kaihao Zhang, Nikolai Smolyanskiy, and Hongdong Li. Deep two-view structure-from-motion revisited. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8953-8962, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.72, + 0.468, + 0.761 + ], + "angle": 0, + "content": "[39] Lei Wang, Linlin Ge, Shan Luo, Zihan Yan, Zhaopeng Cui, and Jieqing Feng. Tc-sfm: Robust track-community-based structure-from-motion. CoRR, abs/2206.05866, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.762, + 0.468, + 0.831 + ], + "angle": 0, + "content": "[40] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In Advances in Neural Information Processing Systems (NeurIPS), pages 27171-27183, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.832, + 0.468, + 0.871 + ], + "angle": 0, + "content": "[41] Yiqun Wang, Ivan Skorokhodov, and Peter Wonka. Improved surface reconstruction using high-frequency details. CoRR, abs/2206.07850, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.468, + 0.9 + ], + "angle": 0, + "content": "[42] Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor Adrian Prisacariu. Nerf-: Neural radiance fields without" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.468, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.119 + ], + "angle": 0, + "content": "known camera parameters. CoRR, abs/2102.07064, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.121, + 0.892, + 0.163 + ], + "angle": 0, + "content": "[43] Kyle Wilson and Noah Snavely. Robust global translations with 1dsfm. In European Conference on Computer Vision (ECCV), volume 8691, pages 61-75, 2014. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.165, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[44] Yuxi Xiao, Li Li, Xiaodi Li, and Jian Yao. Deepmle: A robust deep maximum likelihood estimator for two-view structure from motion. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10643-10650, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.235, + 0.892, + 0.304 + ], + "angle": 0, + "content": "[45] Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan. Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1787-1796, 2020. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.305, + 0.892, + 0.36 + ], + "angle": 0, + "content": "[46] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In Advances in Neural Information Processing Systems (NeurIPS), pages 4805-4815, 2021. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.362, + 0.892, + 0.431 + ], + "angle": 0, + "content": "[47] Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R. Oswald, and Marc Pollefeys. NICE-SLAM: neural implicit scalable encoding for SLAM. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 12776-12786, 2022. 3, 8" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.431 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.927, + 0.52, + 0.938 + ], + "angle": 0, + "content": "17214" + } + ] +] \ No newline at end of file diff --git a/2023/Level-S$^2$fM_ Structure From Motion on Neural Level Set of Implicit Surfaces/b319b286-2e10-4d79-8a32-5ac089c156b9_origin.pdf b/2023/Level-S$^2$fM_ Structure From Motion on Neural Level Set of Implicit Surfaces/b319b286-2e10-4d79-8a32-5ac089c156b9_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..ba8c6bda6387cad0ea3a7a0c5417727fc2bcce59 --- /dev/null +++ b/2023/Level-S$^2$fM_ Structure From Motion on Neural Level Set of Implicit Surfaces/b319b286-2e10-4d79-8a32-5ac089c156b9_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:374d78231ea678082847597f52bc6b45639d308db3135a5ca614e07261854fba +size 7829523 diff --git a/2023/Level-S$^2$fM_ Structure From Motion on Neural Level Set of Implicit Surfaces/full.md b/2023/Level-S$^2$fM_ Structure From Motion on Neural Level Set of Implicit Surfaces/full.md new file mode 100644 index 0000000000000000000000000000000000000000..b3e888c671cd8415edc2f945b80bf6ae2671e494 --- /dev/null +++ b/2023/Level-S$^2$fM_ Structure From Motion on Neural Level Set of Implicit Surfaces/full.md @@ -0,0 +1,340 @@ +# Level- $\mathbf{S}^2$ fM: Structure from Motion on Neural Level Set of Implicit Surfaces + +Yuxi Xiao $^{1}$ Nan Xue $^{1*}$ Tianfu Wu $^{2}$ Gui-Song Xia $^{1}$ + +$^{1}$ School of Computer Science, Wuhan University $^{2}$ Department of ECE, NC State University + +Project Page: https://henry123-boy.github.io/level-s2fm/ + +# Abstract + +This paper presents a neural incremental Structure-from-Motion (SfM) approach, Level- $S^2fM$ , which estimates the camera poses and scene geometry from a set of uncalibrated images by learning coordinate MLPs for the implicit surfaces and the radiance fields from the established keypoint correspondences. Our novel formulation poses some new challenges due to inevitable two-view and few-view configurations in the incremental SfM pipeline, which complicates the optimization of coordinate MLPs for volumetric neural rendering with unknown camera poses. Nevertheless, we demonstrate that the strong inductive basis conveying in the 2D correspondences is promising to tackle those challenges by exploiting the relationship between the ray sampling schemes. Based on this, we revisit the pipeline of incremental SfM and renew the key components, including two-view geometry initialization, the camera poses registration, the 3D points triangulation, and Bundle Adjustment, with a fresh perspective based on neural implicit surfaces. By unifying the scene geometry in small MLP networks through coordinate MLPs, our Level- $S^2fM$ treats the zero-level set of the implicit surface as an informative top-down regularization to manage the reconstructed 3D points, reject the outliers in correspondences via querying SDF, and refine the estimated geometries by NBA (Neural BA). Not only does our Level- $S^2fM$ lead to promising results on camera pose estimation and scene geometry reconstruction, but it also shows a promising way for neural implicit rendering without knowing camera extrinsic beforehand. + +# 1. Introduction + +Structure-from-Motion (SfM) is a fundamental 3D vision problem that aims at reconstructing 3D scenes and estimating the camera motions from a set of uncalibrated images. As a long-standing problem, there have been a tremendous of studies that are mostly established on the keypoint correspondences across viewpoints and the theoretical findings of Multi-View Geometry (MVG) [11], and + +![](images/9bf657d1192b28799055f99ee5dbe1ee89f776de6182e77ea37513a8076d1666.jpg) + +![](images/1ac1b7f8a99f579592434e639ae62d86d598aed024c2a0b0fe8f68a99adbebe9.jpg) + +![](images/98f2c3b924a4e3c3c9f2d3e130026408567fab6e9e0c2d26fd5f80fd9182e806.jpg) +Figure 1. SfM calculations on neural level sets. We learn to do geometric calculations including Triangulation, PnP, and Bundle Adjustment above neural level sets, which easily help to reject the outliers in the matches especially in the texture repeated scenes. Also, due to the continuous surface priors of neural level sets, we achieve better pose estimation accuracy and our reconstructed points are sticking on the surface which are painted with color in the figure. While, there are a lot of outlier 3d points reconstructed by COLMAP [32] which are painted by black. + +have formed three representative pipelines of Incremental SfM [32], Global SfM [4,43], and Hybrid SfM [5]. + +In this paper, we focus on the incremental pipeline of SfM and we will use SfM to refer to the incremental SfM. Given an unordered image set, an SfM system initializes the computation by a pair of images that are with well-conditioned keypoint correspondences to yield an initial set of feature tracks, then incrementally adds new views one by one to estimate the camera pose from the 2D-3D point correspondences and update the feature tracks with new matches. Because the feature tracks are generated by grouping the putative 2D correspondences across viewpoints in bottom-up manners, they would be ineffective or inaccurate to represent holistic information of scenes. Accordingly, + +Bundle Adjustment (BA) is necessary to jointly optimize the camera poses and 3D points in a top-down manner. The success of BA indicates that a global perspective is vital for accurate 3D reconstruction, however, their input feature tracks are the bottom-up cues without enough holistic constraints for the 3D scenes. To this end, we study to integrate the top-down information into the SfM system by proposing a novel Level- $\mathbf{S}^2$ fM. Fig. 1 illustrates a representative case for the classic SfM systems that generate more flying 3D scene points, which can be addressed by our method. + +Our Level- $\mathrm{S}^2\mathrm{fM}$ is inspired by the recently-emerged neural implicit surface that could manage all 3D scene points as the zero-level set of the signed distance function (SDF). Because the neural implicit surfaces can be parameterized by Multi-Layer Perceptrons (MLPs), it could be viewed as a kind of top-down information of 3D scenes and is of great potential for accurate 3D reconstruction. However, because both the 3D scene and camera poses are to be determined, it poses a challenging problem: + +How can we optimize a neural SDF (or other neural fields such as NeRF) from only a set of uncalibrated images without any 3D information? + +Most recently, the above problem was partially answered in BARF [18] and NeRF-- [42] that relaxed the requirement of optimizing Neural Radiance Fields [24] without knowing accurate camera poses, but they can only handle the unknown pose configurations in small-scale face-forwarding scenes. Moreover, when we confine the problem in the incremental SfM pipelines, it would be more challenging as we need to optimize the neural fields with only two overlapped images at the initialization stage. To this end, we found that the optimization of neural SDF can be accomplished by the 2D matches at the initialization stage, and facilitate the management of feature tracks by querying the 3D points and tracing the 2D keypoints in a holistic way. + +As shown in Fig. 1, we define a neural network that parameterizes an SDF as the unified representation for the underdetermined 3D scene and accomplishes the computations of PnP for camera pose intersection, the 3D points triangulation as well as the geometry refinement on the parameterized SDF. In the initialization stage with a pair of overlapped images, Level- $\mathbf{S}^2$ fM uses the differentiable sphere tracing algorithm [19] to attain the corresponding 3d points of the keypoints and calculate the reprojection error to drive the joint optimization. For the traced 3d points with small SDF values and 2D reprojection errors for its feature track, they are added into a dynamic point set and take the point set with feature tracks as the Lagrangian representation for the level sets. Because the pose estimation and the scene points reconstruction are sequentially estimated, the estimation errors will be accumulated. To this end, we present an NBA (i.e., Neural Bundle Adjustment) that plays a similar + +role as in Bundle Adjustment, but it optimizes the implicit surface and camera poses from the explicit flow of points by the energy function of the reprojection errors, which can be viewed as an evolutionary step between Lagrangian and Eulerian representations as discussed in [23]. + +In the experiments, we evaluate our Level- $\mathbf{S}^2$ fM on a variety of scenes from the BlendedMVS [45], DTU [14], and ETH3D [34] datasets. On the BlendedMVS dataset, our proposed Level- $\mathbf{S}^2$ fM clearly outperforms the state-of-the-art COLMAP [32] by significant margins. On the DTU and ETH3D datasets [14, 34], our method also obtains on-par performance with COLMAP for both camera pose estimation and dense surface reconstruction, which are all computed in one stage. + +The contributions of this paper are in two folds: + +- We present a novel neural SfM approach Level- $S^2$ fM, which formulates to optimize the coordinate MLP networks for implicit surface and radiance field and estimate the camera poses and scene geometry. To the best of our knowledge, our Level- $S^2$ fM is the first implicit neural SfM solution on the zero-level set of surfaces. +- From the perspective of neural implicit fields learning, we show that the challenging problems of two-view and few-view optimization of neural implicit fields can be addressed by exploiting the inductive biases conveyed in the 2D correspondences. Besides, our method presents a promising way for neural implicit rendering without knowing camera extrinsics beforehand. + +# 2. Related Works + +# 2.1. Structure from Motion + +There has been a vast body of literature on Structure from Motion. Since an SfM system consists of many components, tremendous efforts have been devoted to improving the core components of SfM. In particular, the learning techniques were introduced in a variety of subproblems including image matching [7, 31], feature track mining and management [39], two-view 3D reconstruction [38,44], relative and absolute camera pose estimation [13] and Bundle Adjustment [3, 37]. Those studies indicated that the learning paradigms are promising to improve the quality of 3D reconstruction. However, to the best of our knowledge, the learning paradigms are not fully equipped in SfM systems. One possible reason for such a fact is that the many learning approaches are designed in a supervised learning fashion, which remains some risks on the out-of-distribution samples. The self-supervised learning approaches [9, 22] in 3D vision alleviated the requirement of data annotations, however, they have not been fully exploited in the whole pipeline of SfM. In contrast to the aforementioned studies, in this paper, we are interested in integrating the learning + +ability into the SfM system without incurring any external data annotations. From the perspective of system design in SfM, we verified that the strong inductive biases conveying in the 2D correspondences are promising and meaningful to drive the learning and optimization of SfM. + +# 2.2. Neural Implicit Representation for 3D Scene + +Recently, the advent of neural implicit fields [24, 28, 29, 40, 41, 46] have greatly advanced many 3D vision problems such as novel-view synthesis [1, 24] and surface reconstruction [28, 29, 40, 46] by learning to optimize the coordinate MLPs from a set of posed RGB images of which the key to success is that the inductive biases of 3D are exploited by the neural networks. However, when the camera poses are invalid, it is hard to optimize the coordinate MLPs for neural implicit fields. To remedy this, the state-of-the-art SfM system, COLMAP [32], is extensively used to compute the camera poses as a preprocessing step. + +To train the neural field from unknown poses directly, recently, BARF [18] and NeRF [42] explored to jointly optimize the camera poses and neural fields by the volumetric rendering with promising results obtained in forward-facing scenes. BARF can also work in some scenes of highly overlapped and dense image collections with the initialized poses as inputs. This problem was also studied in the RGB-D SLAM systems [2, 36, 47], however, their works mainly rely on the known depth information and focus on the camera pose tracking by the neural implicit fields. Therefore, how to optimize implicit neural fields from only a set of uncalibrated images without any 3D information input is still a challenging and open problem. + +In this paper, we study the unknown-pose neural fields optimization and SfM together and present a unified solution that simultaneously learns the implicit surfaces and radiance fields alongside the camera pose estimation and scene reconstruction from a set of images. + +# 3. Preliminaries + +In this section, we introduce the preliminaries on neural implicit surface rendering and the notations in SfM, which are all extensively used in our method. + +# 3.1. Neural Implicit Surface Rendering + +The volumetric rendering of neural implicit surface [46] aims at learning a signed distance function $d_{\Omega} : \mathbb{R}^3 \to \mathbb{R}$ by the volumetric rendering from a set of posed images and then extracting the zero-level set of $\phi$ as the reconstructed surface model of the image set. The state-of-the-art approach, VolSDF [46], integrates SDF representations with neural volume rendering via Laplacian distribution by + +$$ +\sigma (\mathbf {x}) = \frac {1}{\beta} \Psi_ {\beta} (- d _ {\Omega} (\mathbf {x})), \tag {1} +$$ + +where $\beta$ is a learnable parameter in VolSDF [46]. Based on Eq. (1), the volume rendering equation renders a ray $\mathbf{x}(t)$ emanating from a camera position $\mathbf{o} \in \mathbb{R}^3$ in unit direction $\mathbf{v}$ , defined by $\mathbf{x}(t) = \mathbf{o} + t\mathbf{v}$ by + +$$ +I (\mathbf {o}, \mathbf {v}) = \int_ {0} ^ {\infty} L (\mathbf {x} (t), \mathbf {n} (t), \mathbf {v}) \sigma (\mathbf {x} (t)) T (t) d t, \quad (2) +$$ + +where $L(\mathbf{x},\mathbf{n},\mathbf{v})$ is the radiance field and $\mathbf{n}(t)$ is the normal direction of the point $\mathbf{x}(t)$ defined by $\mathbf{n}(t) = \nabla_{\mathbf{x}}d_{\Omega}(\mathbf{x}(t))$ . In the learning of volume rendering, two coordinate MLP (Multi-Layer Perceptron) networks parameterize the SDF by $\phi (\mathbf{x}) = (d(\mathbf{x}),\mathbf{z}(\mathbf{x}))\in \mathbb{R}^{1 + 256}$ and the radiance field by $L_{\psi}(\mathbf{x},\mathbf{n},\mathbf{v},\mathbf{z})\in \mathbb{R}^3$ , and train them by the color loss $\mathcal{L}_{\mathrm{RGB}}(\phi ,\psi ,\beta)$ and the Eikonal loss $\mathcal{L}_{eik}(\phi) = \mathbb{E}_{\mathbf{z}}(|\nabla d(\mathbf{z})|| - 1)$ . + +In this paper, we use the equations (1) and (2) as the basic tools for Level- $\mathrm{S}^2$ fM. To make the optimization of SDF and radiance networks easier, we set $\beta$ as a small constant number and use the multi-resolution grid representations to avoid the potential of slow convergence and catastrophic forgetting since the scene scale is unknown and the original VolSDF [46] requires to normalize the known camera poses in a certain scale. + +# 3.2. Ray Sampling and Sphere Tracing + +Iterative Ray Sampling. In the implementation, the continuous form of Eq. (2) is approximated in + +$$ +I (\mathbf {o}, \mathbf {v}) \approx \sum_ {i = 1} ^ {m - 1} \hat {\tau} _ {i} L (\mathbf {x} (t _ {i}), \mathbf {n} (t _ {i}), \mathbf {v}), \tag {3} +$$ + +where $\{t_i\}_{i=1}^m$ is the discrete samples, $0 = t_1 < t_2 < \ldots < t_m = M$ , $M$ is some large constant. $\hat{\tau}_i \approx \tau(s_i) \Delta s$ is the approximated PDF multiplied by the interval length. In VolSDF [46], $\{t_i\}_{i=1}^m$ is adaptively computed according to the opacity approximation error. Please move to [46] for its detail. In our method, we keep using this iterative sampling strategy when the rendering loss and the Eikonal loss is used. However, because the sampling set $\{t_i\}$ would be large, we do not use this strategy to compute the 3D points from 2D keypoints in our Level- $\mathbf{S}^2$ fM and in turn to use the sphere tracing [10] as a faster way since our initial development of this work. + +Sphere Tracing. Sphere tracing is a geometric method to render the depth from a signed distance function. Different from iterative ray sampling, sphere tracing is designed to hit the surface point along the ray $\mathbf{x}(t)$ with queries as few as possible. To make it clear, we use $s_i$ to denote the ray stamp of the queried point $\mathbf{x}(s_i)$ . With the queried point $\mathbf{x}(s_i)$ , the next ray stamp $s_{i + 1}$ is computed by $s_{i + 1} = \phi (\mathbf{x}(s_i))$ . In our study, we sample at most $N_{s} = 20$ points with the stop criterion $|\phi (s_i)| < \varepsilon$ , where $\varepsilon$ is set to 0.002 in our experiment. + +Remarks. Although both the iterative ray sampling [46] and sphere tracing [10] share the same target of computing the surface point along a ray, they have different behaviors in the neural implicit surface optimization. In detail, because VolSDF [46] aims at approximating the opacity by SDF, it updates the SDF network $\phi (\mathbf{x})$ by the rendering loss $\mathcal{L}_{\mathrm{color}}$ . As for sphere tracing, it is a geometric approach that only takes the SDF values into account for the computation. Such a difference is trivial to some extent, however, we found that their different focuses induce a loss function in our Level- $\mathbf{S}^2$ fM to constraint the rendered depth values (or 3D points) for two-view initialization and 3D point triangulation. + +# 3.3. Notations in SfM + +Correspondence Search. Given the image set $\mathcal{I} = \{I_i|i = 1\ldots N_I\}$ for reconstruction, the keypoint features of the image $I_{i}$ computed by SIFT [21] is denoted in $\mathcal{F}_i = \{(x_j,\mathbf{f}_j)\}$ , where $x_{j}\in \mathbb{R}^{2}$ is the 2D coordinate and $\mathbf{f}_j\in \mathbb{R}^{128}$ is the feature descriptor of $x_{j}$ . Based on the SIFT features, we follow the schema in COLMAP [32] to establish the feature correspondences across views, in which we first do the exhaustive matching for all possible image pairs and then use the geometric verification to filter out the non-overlapped image pairs. After this, the potentially overlapped image pairs are denoted in $\mathcal{C} = \{(I_a,I_b)|I_a,I_b\in \mathcal{I}\}$ , and the keypoint correspondences in the pair $(I_a,I_b)$ are denoted in the set $\mathcal{M}_{ab} = \{\{(x_k,\mathbf{f}_k),(x_l',\mathbf{f}_l')\} |(x_k,\mathbf{f}_k\in \mathcal{F}_a,(x_l',f_l')\in \mathcal{F}_b\}$ . Finally, all the prepared correspondences are organized as the scene graph [32, 35], which stores images as the graph nodes and the overlapped image pairs as the graph edges. In our Level- $\mathrm{S^2fM}$ , we use the established correspondences to drive the learning of MLPs, estimate the camera poses, and reconstruct a sparse point set of correspondences. + +3D Scene Points and Feature Tracks. Because SfM is designated to simultaneously estimate the scene geometry from 2D correspondences, every successfully reconstructed 3D scene point is sourced from multiple 2D keypoint observations. To facilitate the representation, we denote the expected 3D point set in $\mathcal{X} = \{\mathbf{X}_k\in \mathbb{R}^3 |k = 1,\dots ,N_{3d}\}$ . For each point $\mathbf{X}_k\in \mathcal{X}$ , if it is reconstructed from the 2D keypoint $x_{j}\in \mathcal{F}_{i}$ , we denote such a relationship in a tuple $(k,i,j)$ . $\mathcal{T} = \{(k,i,j)\}$ is the set of feature tracks. + +# 4. The Proposed Level $\mathbf{S}^2\mathbf{f}\mathbf{M}$ + +In this section, we present the details of our Level- $S^2$ fM. As shown in Fig. 2, our method consists of three classical components including 1) the two-view geometry initialization, 2) the new frame registration, and 3) the new frame pose refinement, an implicit surface and a radiance field that are parameterized by neural networks. In what follows, we + +will show how to solve the SfM problem by learning the implicit fields with 2D correspondences. We assume the intrinsic matrix $K$ is known and fixed. + +# 4.1. Two-view Initialization + +We first select two good views $\{I_a, I_b\}$ for initialization from the scene graph and get their 2D matches $\mathcal{M}_{ab} = \{\{(x_k, f_k), (x'_l, f'_l)\} | (x_k, f_k) \in \mathcal{F}_a, (x'_l, f'_l) \in \mathcal{F}_b\}$ . Based on the 2D matches $\mathcal{M}_{ab}$ , we leverage the 5-point algorithm [27] and RANSAC to obtain the poses $P_a, P_b \in SE(3)$ . + +With the estimated camera poses $P_{a}, P_{b}$ , it is straightforward to optimize the SDF network $\phi(\mathbf{x})$ and the radiance field network $L_{\psi}(\mathbf{x}, \mathbf{n}, \mathbf{v}, \mathbf{z})$ defined in Sec. 3 by minimizing the loss items $\mathcal{L}_{\mathrm{RGB}}$ and $\mathcal{L}_{eik}$ as done in VolSDF [46]. However, it should be noted that the learning of volumetric surface rendering in such a way for the two-view inputs would trap into the local minimal by overfitting. To this end, we propose to use the differentiable sphere tracking [10, 19] for the corresponding rays in image $I_{a}$ and $I_{b}$ , which provides strong inductive biases for the optimization of networks. + +Specifically, denoted by a pair of feature match $(x_{k}, x_{l}^{\prime})$ in the image pair $(I_{a}, I_{b})$ , the sphere tracing obtains the surface point $\mathbf{X}_{a}^{k} = \mathbf{o}_{a} + \hat{t}_{a}\mathbf{d}_{a}$ from the SDF and $\mathbf{X}_{b}^{l} = \mathbf{o}_{b} + \hat{t}_{b}\mathbf{d}_{b}$ , where $(\mathbf{o}_{a}, \mathbf{d}_{a})$ is the ray of $x_{k}$ , $(\mathbf{o}_{b}, \mathbf{d}_{b})$ is the ray of $x_{l}^{\prime}$ . For the computation of $\hat{t}_{a}$ and $\hat{t}_{b}$ , please move to our supplementary materials. Ideally, the $\mathbf{X}_{a}^{k}$ and $\mathbf{X}_{b}^{l}$ should be as close as possible, therefore, we introduce a reprojection loss + +$$ +\mathcal {L} _ {\text {r e p r o j}} = \frac {1}{2 V} \sum \left(\| \hat {x} _ {k} - x _ {l} ^ {\prime} \| _ {2} + \| \hat {x} _ {l} ^ {\prime} - x _ {k} \| _ {2}\right), \tag {4} +$$ + +where $V$ is the number of correspondences, $\hat{x}_k = \Pi(\mathbf{X}_a^k, K, P_b)$ and $\hat{x}_l' = \Pi(\mathbf{X}_b^l, K, P_a)$ are the projected 2D coordinates of the traced 3D points by the projection $\Pi$ . + +Considering the fact that the correspondences are sparse when the SDF network is not well optimized at some rays, the sparse sample points by sphere tracing on the SDF network may be either inaccurate or erroneous as shown in Fig. 3. Therefore, we use a depth consistency loss $\mathcal{L}_{dc}$ to minimize the depth estimated by the sphere tracing and the volumetric rendering by + +$$ +\mathcal {L} _ {d c} = \frac {1}{B} \sum \left\| \hat {t} _ {i} - \int_ {0} ^ {\infty} T (t) \sigma (\mathbf {x} (t)) d t \right\|, \tag {5} +$$ + +where the rays $\mathbf{x}(t)$ are randomly sampled from the images, and those rays are also used to compute the color loss $\mathcal{L}_{\mathrm{RGB}}$ . For the computation of Eikonal loss $\mathcal{L}_{eik}$ , all the 3D points visited by sphere tracing and dense ray marching are used. + +In summary, our two-view initialization of Level- $\mathbf{S}^2$ fM computes the total loss $\mathcal{L}_{\mathrm{total}}^{\mathrm{init}}$ by + +$$ +\mathcal {L} _ {\text {t o t a l}} ^ {\text {i n i t}} = \alpha_ {1} \mathcal {L} _ {\text {r e p r o j}} + \alpha_ {2} \mathcal {L} _ {e i k} + \alpha_ {3} \mathcal {L} _ {\mathrm {R G B}} + \alpha_ {4} \mathcal {L} _ {\mathrm {d c}}, \tag {6} +$$ + +![](images/aac95ac21d8fd5ca0be71fbfff5315a1e1fe2701de4bd12724fbcfc6b0a82058.jpg) +Figure 2. Overview of Level- $\mathbf{S}^2$ fM. + +![](images/27116119cd02475401ef4aab90037f5c492d2c020a068a7587df8e2c7e34d3cf.jpg) +3D points by sphere tracing (with depth consistency) + +![](images/99e2220062e8165c386ce513664a016a4008c2dac6e156ee5b4ae3b06c51f7de.jpg) +3D points by sphere tracing (without depth consistency) +Figure 3. An illustrative comparison for the depth consistency loss $\mathcal{L}_{dc}$ in two-view initialization. As it is shown, when we remove $\mathcal{L}_{dc}$ , the 3D points traced from all putative 2D keypoints correspondences will contain more flying 3D points. + +where $\alpha_{1},\alpha_{2},\alpha_{3}$ , and $\alpha_{4}$ are the hyperparameters and use ADAM optimizer to optimize the networks. + +When the initialization is finished, we compute the two 3D points $\mathbf{X}$ and $\mathbf{X}'$ for each correspondence by sphere tracing for image $I_{a}$ and $I_{b}$ . For an accurate correspondence, $\| \mathbf{X} - \mathbf{X}'\|$ and their SDF values should be all small enough, thus providing a good criterion to check the putative matches to initialize the 3D point set $\mathcal{X}$ and the feature track set $\mathcal{T}$ for all the verified two-view correspondences. + +# 4.2. New Frame Registration + +For every newly added frame, we will first construct the 3D-2D correspondence from the existing pointset and its feature tracks. After that, we calculate a coarse pose of the new frame with the standard PnP algorithms [17], and then refine it with both the reprojection error and the rendering loss. The registration loss can be calculated as follow: + +$$ +\mathcal {L} _ {\text {r e g i s t}} = \beta_ {1} \mathcal {L} _ {\text {r e p r o j}} + \beta_ {2} \mathcal {L} _ {\text {R G B}}, \tag {7} +$$ + +where the $\beta_{1},\beta_{2}$ are two hyper-parameters, and the $\mathcal{L}_{\mathrm{reproj}}$ here are calculated by the 3D-2D correspondences. + +In this optimization, the pose of the newly added frame, the SDF network, and the radiance field network are jointly + +optimized. While during the changes in the pose and SDF, the original location in the pointset will maybe not be the right one on the surface. For this problem, we design a Neural Bundle Adjustment (NBA) strategy to dynamically update the pointset with respect to the SDF after the points triangulation and refinement in the next section. Therefore, we leave the details of NBA in Sec. 4.4. + +# 4.3. Points Triangulation and Refinement + +Once the pose of the newly added frame is obtained, we step into the next procedure of refining the retrieved 2D points from the point set $\mathcal{X}$ and triangulating new 2D points into 3D space to extend $\mathcal{X}$ . This problem was formulated in the classical SfM frameworks, however, they are suffering from the following issues: + +- The 2D Mismatches: This issue could be alleviated by geometric verifications like RANSAC [8] or better 2D keypoint matching approaches, however, when encountering the symmetry structures or repeated texture regions, those efforts are hard to work efficiently. +- Tiny Triangulation Angle: This issue will lead to an ill-conditioned problem for points triangulation [12]. Therefore, the classical SfM approaches will directly discard those points to avoid the ill-conditioned problem configuration. + +We address those issues by proposing an SDF-based triangulation. Similar to the two-view initialization in Sec. 4.1, we compute the 3D points for all the potential 2D keypoints in the first step. Then, for the 2D keypoints that have correspondences in the current feature track set $\mathcal{T}$ , we use the tracing loss $\mathcal{L}_{\mathrm{tracing}}$ + +$$ +\mathcal {L} _ {\text {t r a c i n g}} = \frac {1}{V ^ {\prime}} \sum_ {j} \| \mathbf {X} _ {j} ^ {s t} - \mathbf {X} _ {j} \|, \tag {8} +$$ + +where $\mathbf{X}_j\in \mathcal{X}$ is the retrieved 3D point of the 2D keypoint in the current frame, $V^{\prime}$ is the number of retrieved 3D + +points. This loss function acts as the similar role of $\mathcal{L}_{dc}$ in two-view initialization. Without it, similar phenomenon like Fig. 3 will happen. + +For the new 2D keypoints that are matched to the added images but without 3D information, both the reprojection loss similar to the two-view initialization and the tracing loss is used to yield the triangulation loss $\mathcal{L}_{\mathrm{tri}}$ by + +$$ +\mathcal {L} _ {\text {t r i}} = \mathcal {L} _ {\text {r e p o r j}} ^ {\text {m a s k}} + \mathcal {L} _ {\text {t r a c i n g}}, \tag {9} +$$ + +where $\mathcal{L}_{\mathrm{report}}^{\mathrm{mask}}$ only considers the 2D correspondences of which their distance between the 2D projections of the traced 3D points traced in different views are smaller than a loose threshold (45 pixels in our implementation). + +# 4.4. Neural Bundle Adjustment on Surfaces + +Because the camera pose estimation and the points triangulation are separated, which will involve accumulative errors for pose estimation and triangulation, as well as the implicit networks. Motivated by the Bundle Adjustment that is extensively used in classical approaches, we present a Neural Bundle Adjustment (NBA) that jointly optimizes the estimated camera points, the 3D point set, and the implicit networks as a refinement step. To avoid costly computation, our NBA step finds the closest surface points to dynamically update those variables. + +Denoted by the reconstructed 3D point set $\mathcal{X}$ and the feature track $\mathcal{T}$ , the camera poses $\mathcal{P} = \{P_1, \dots, P_K\}$ and the corresponding images $\{I_1, \dots, I_K\}$ , as well as the networks $\phi(\mathbf{x})$ and $L_{\psi}$ , in each step of NBA, we update the 3D point $\mathbf{X} \in \mathcal{X}$ by + +$$ +\mathbf {X} \leftarrow \mathbf {X} - \phi (\mathbf {X}) \nabla \phi (\mathbf {X}), \tag {10} +$$ + +and then compute the reprojection loss according to the feature track $\mathcal{T}$ to jointly optimize the $\phi$ the SDF network, $\mathcal{P}$ the estimated camera poses, and $\mathcal{X}$ the updated 3D point set. For the radiance network $L_{\psi}$ , the rendering loss for randomly sampled rays is computed. + +In our implementation, we leverage our NBA by three times, which we call the 1-frame NBA, local NBA, and global NBA. Because the rendering loss involves more rays, we only use it for the 1-frame NBA after the camera registration and point triangulation. In terms of local NBA, for the newly added view, only the related views with correspondences are considered. After running the 1-frame and local NBA schemes, we globally update all reconstructed views and the point set. By leveraging the backpropagation, all the mentioned variables are updated as the refinement. + +# 5. Experiments + +# 5.1. Implementation Details, Datasets, and Metrics + +Implementation Details. In our implementation, we parameterize the SDF $\phi (\mathbf{x})$ by a multi-resolution features grid + +and a two-layers MLP. To accelerate the computation, we follow InstantNGP [26] to use a hash table [25] for the feature grids. The radiance field $L_{\psi}$ is also implemented in a multi-resolution feature grid and a three-layer MLP. Because our end task is the geometric 3D reconstruction, we use a high-resolution multi-scale feature grid for the SDF to ensure the accuracy of scene geometry but use a low-resolution feature grid to avoid the unnecessary computation cost for the radiance field. The specifications of the network architecture are given in supplementary material due to the limited space. All of these above are implemented in PyTorch [30], and we used the Adam [16] as the optimizer for the geometric calculations. For the 2D image matching and pose graph, we keep them the same with our baseline, COLMAP [32] for fair comparisons. + +Datasets. Three datasets are used for our evaluation. Firstly, we use 5 representative scenes including the LyingStatue, Stone, Fountain, Horse, and Statues from the BlendedMVS dataset [45] in our evaluation because it provides accurate ground truth of camera poses and contains a number of challenging scenes for SfM. Secondly, the DTU dataset for the MVS task is also used. The five representative scenes (scans of 24, 37, 65, 110 and 114) are used in our experiments. Finally, we evaluate our proposed method on the five scenes from the challenging ETH3D [34] dataset. + +Evaluation Metrics. In our evaluation, we use the Rotation error and ATE to quantitatively benchmark the pose accuracy, which simply depicts the difference between the ground truth and the aligned pose. During our evaluation, we used the provided API of Reconstruction Align in COLMAP [32] to do that. In terms of the reconstructed scene geometry, we use accuracy (Acc) and the precision (Prec) rate to evaluate the accuracy of our recovered 3D points and Chamfer-l1 distance to depict the accuracy of the reconstructed surface. Detailed definitions of these evaluation metrics are given in the supplementary material. + +# 5.2. Results on the BlendedMVS Dataset + +Tab. 1 reports the quantitative evaluation results for the two versions of Level- $\mathbf{S}^2$ fM and COLMAP [32]. The full version of Level- $\mathbf{S}^2$ fM used all the mentioned components while the wo/render version removed the rendering loss for optimization. As it is reported, our Level- $\mathbf{S}^2$ fM (full) consistently outperforms COLMAP [32] for camera pose estimation and sparse 3D point cloud reconstruction. It also reveals that rendering losses are required. + +In detail, our Level- $S^2$ fM (full) averagely reduced the estimation error from $1.54^\circ$ by COLMAP [32] to $0.86^\circ$ , obtaining a relative improvement of $55.84\%$ . For the translation error, our Level- $S^2$ fM (full) decreases the error from $3.54 \mathrm{~cm}$ to $3.36 \mathrm{~cm}$ . For the sparse 3D point cloud reconstruction, the ACC metric is reduced from 3.16 to 2.25 for the full model and 2.63 for the wo/render version. + +
ScenesCamera Pose EvaluationPoints Cloud Results Evaluation
Rotation (°) ↓Translation (cm) ↓Acc(cm)↓, Prec(≤3.5cm)↑
COLMAP [32]Level-S2fM (full)Level-S2fM (wo/render)COLMAP [32]Level-S2fM (full)Level-S2fM (wo/render)COLMAP [32]Level-S2fM (full)Level-S2fM (wo/render)
LyingStatue1.201.121.310.892.182.67Acc:0.83 Prec:0.99Acc:1.35 Prec:0.99Acc:1.76 Prec:0.98
Stone0.630.310.546.285.519.17Acc:5.44 Prec:0.66Acc:3.61 Prec:0.68Acc:5.04 Prec:0.64
Fountain4.341.652.327.412.874.11Acc:1.91 Prec:0.91Acc:1.13 Prec:0.98Acc:1.14 Prec:0.96
Horse0.330.940.921.185.717.58Acc:3.99 Prec:0.86Acc:4.18 Prec:0.71Acc:4.18 Prec:0.70
Statues1.210.360.441.980.560.62Acc:1.31 Prec:0.98Acc:0.95 Prec:0.99Acc:1.02 Prec:0.99
Mean1.540.861.113.543.364.83Acc:3.16 Prec:0.85Acc:2.25 Prec:0.87Acc:2.63 Prec:0.85
+ +![](images/e84f16bff4968ec4cc8f153307d3e9c52372f8acc0ee3b9dd9184da810cb610b.jpg) +Figure 4. The reconstructed meshes, point clouds and camera poses for the Fountain and Horse scenes on the BlendedMVS dataset by our Level- $S^2$ fM (full). In the first column, the reconstructed scene geometry and the camera poses are shown together. For the 3D models, we show the different views of the sparse 3D points during the training and the textured meshes refused from the zero-level set surface. + +Table 1. Quantitative results on the BlendedMVS dataset. For our Level- $S^2$ fM, we report the results by full version and an wo/render version that removes the rendering loss during optimization. + +
Pose SourceLyingStatueStoneFountainHorseStatuesMean
COLMAP29.128.425.423.529.227.12
Level-S2fM (Ours)29.528.927.123.630.227.86
GT29.529.427.424.131.128.3
+ +Fig. 4 shows the reconstruction results by our method. + +Apart from the direct evaluation of the SfM results on the BlendedMVS dataset, we further compare the camera poses estimation results for different methods by training the NGP [26] (a fast version of NeRF [24]) to compare the performance of novel view synthesis in Tab. 2. As it is reported, the rendered images by our camera poses are consistently better than the ones by COLMAP poses. + +# 5.3. Results on the DTU Dataset + +We conducted the evaluation on the DTU to illustrate the promising future of our Level- $\mathbf{S}^2$ fM to unify the pose estimation, dense reconstruction, and novel view synthesis problems in one stage. For the comparison to COLMAP, we use their built-in PatchMatch MVS [33] functionality to obtain the dense surface points and then leverage its default surface reconstruction method (i.e., Poisson surface [15]) + +Table 2. Novel View Synthesis Comparison. The PSNR is used to compare the camera poses computed by COLMAP, Level- $\mathbf{S}^2$ fM and the GT poses on the BlendedMVS dataset. + +
ScanCOLMAP [32]Level-S2fM (Ours)
Chamfer-ℓ1Rot. Err.Trans. Err.Chamfer-ℓ1Rot. Err.Trans. Err.
242.1760.382.872.4420.814.60
373.8370.414.863.0230.314.29
654.3940.454.233.1900.745.81
1103.3890.656.365.9020.826.82
1143.5770.353.582.0920.141.85
Mean3.3300.4484.383.4740.5644.67
+ +Table 3. Quantitative results on DTU dataset. The Chamfer- $\ell_1$ distance of the dense reconstruction results and as the errors of rotation and translation for camera pose estimation, are compared for COLMAP and our Level- $S^2$ fM. The unit of Chamfer- $\ell_1$ and Translation errors are in millimetres. + +
SceneDetector & Matchercourtyardreliefdoorterrace2facade
COLMAP [32]SIFT [21]0.10°/0.016m0.10°/0.003m0.16°/0.002m0.14°/0.002m0.06°/0.016m
Level-SfM (Ours)0.21°/0.047m0.09°/0.003m0.19°/0.006m0.13°/0.003m0.12°/0.059m
COLMAP [32]SP [7]+SG [31]-0.36°/0.007m0.37°/0.003m0.16°/0.003m0.04°/0.014m
Level-SfM-0.42°/0.003m0.34°/0.007m0.12°/0.002m0.10°/0.051m
+ +Table 4. Quantitative results of pose estimation on the five scenes in ETH3D dataset for COLMAP [32] and our proposed Level-S²fM by using different keypoint detector and matcher. + +to obtain the mesh model. For our Level- $\mathbf{S}^2$ fM, we use the MarchingCubes [20] to extract the mesh models from the zero-level set of the implicit surface. The quantitative evaluation results are shown in Tab. 3. In this dataset, our Level- $\mathbf{S}^2$ fM obtains on-par performance with COLMAP. + +# 5.4. Results on the ETH3D Dataset + +We test our method on a more challenging dataset, ETH3D [34], which includes both sparse view collections for multi-scale outdoor and indoor scenes. To show the influence of different keypoint detection and matching algorithms for our method, we additionally make a comparison with SuperPoint (SP) [7] for detection and SuperGlue (SG) [31] for keypoint matching. As reported in Tab. 4, our method achieves comparable results with COLMAP [32]. However, we observe that our method gets slightly inferior results in some large-scale outdoor scenes, because of the limited representative capability of a single network for a large-scale scene. + +# 5.5. Ablation Study + +In this section, we elaborate on why and how the SDF-based Triangulation (short in $SDF\text{-Tri}$ ) and NBA work in + +![](images/4909671276579504bfea816eca2502fc9a5bc90e7dd74d5f1c6ec75e2e80406c.jpg) +Figure 5. Two-views Triangulated Point Clouds by Traditional triangulation and SDF-Based Triangulation, courtyard@ETH3D. + +
w/o SDF-Tri.w/o NBAw/o SDF-Tri & w/o NBAFull
1.23° / 0.498m0.46° / 0.113m3.48° / 0.846m0.21° / 0.047m
+ +our system. We first conduct the two-view triangulation with SDF-Tri and the traditional method respectively. Fig. 5 shows SDF-Tri can easily filter out the incorrect triangulation from wrong matches. One explanation is that the neural network distills inliers in a continuous zero-level set of surfaces (e.g., the planes in Fig. 5). Therefore, the triangulated outliers (the blue points flying off the planes) can be easily detected and filtered by their large SDF values. Similarly, NBA also benefits from the global level sets of surfaces, which average the errors among the inliers triangulated points and differs the outliers by their large SDF values. The quantitative results for SDF-Tri and NBA are shown at the bottom of Fig. 5. + +# 5.6. Limitations of Level- $\mathbf{S}^2$ fM + +In order to explore the clear boundary of Level- $\mathbf{S}^2$ fM and point out the potential future development, we discuss the limitation of Level- $\mathbf{S}^2$ fM on the most typical indoor dataset, scannet [6]. In the Scannet [6], there are a lot of challenges including blurry images, and textureless areas. Because of the less texture, the SIFT-based keypoint correspondences may contain a large portion of outliers or insufficient matches. Meanwhile, the blur in images will also influence the accuracy of the 2d matches. Therefore, most SfM easily fails on this dataset. Our method is also limited by this because of SIFT matches. + +To make the discussion clear, we run four scenes of Scannet [6] that were used for NICESLAM [47]. For the image sequence of each scene, the input image set for SfM is constructed by sampling for every 10 frames. Tab. 5 reports the pose accuracy of COLMAP and our Level- $S^2$ fM for results by adding 60 frames, 120 frames, and all the frames, which are concatenated by the "slash". As it is reported, our method usually performs well in the first 60 frames but its pose estimation accuracy suddenly decreases + +
Scene ID0000005901690207
iMAP [36]rot°↓trans. (cm) ↓-197.1-18.9-96.4-28.7
NICESLAM [47]rot°↓trans. (cm) ↓-11.3-12.0-12.0-12.8
COLMAP [32]rot°↓trans. (cm) ↓1.47/2.71/2.718.20/16.8/14.11.40/2.52/2.667.20/10.8/11.52.64/1.84/2.5210.6/9.22/10.6failed-
Level-S2fMrot°↓1.44/2.23/1.986.50/11.8/26.71.50/2.59/3.728.20/32.9/35.72.94/9.09/14.36.35/14.3/44.8failed-
fulltrans. (cm) ↓
+ +Table 5. Quantitative results of pose estimation on ScanNet [6]. For the COLMAP and Level- $S^2$ fM, we report their pose accuracy metrics when the 60/120/all frames are registered. For the last scene (i.e., 0207), both COLMAP and our method failed. + +when adding some new frames. The textureless matches cause the matches very sparse and therefore hard to give a good registration of images. Meanwhile, since the radiance field learning is also challenging in the Scannet dataset, the bad initialized pose can not be refined well by the rendering loss. All of these limitations are basically from sparse 2D image matches. Besides, we observed that the ADAM optimizer will make the optimization of camera poses and scene points unstable, which would also affect the final results. + +# 6. Conclusion + +This paper studies the longstanding problem of Structure-from-Motion by exploring and exploiting several important yet challenging issues including the two-view neural rendering in the initialization stage and few-view neural rendering in the early camera registration stage of incremental SfM for integrating the recent advances of neural implicit field learning into an SfM pipeline. We show that although the few-view neural rendering problem is challenging enough, it can be tackled by the 2D correspondences as they convey strong inductive biases for 3D scenes. Based on this, we present the first neural SfM solution that renews several key components of two-view geometry initialization, camera pose registration, and triangulation, as well as the Bundle Adjustment problem with neural implicit fields. In the experiments, we show that Level- $\mathbf{S}^2$ fM outperforms the traditional SfM pipeline and set a new state-of-the-art for 3D reconstruction on the BlendedMVS dataset. We believe that our study will encourage the 3D vision community to rethink and reformulate Structure-from-Motion with learning-based new findings. + +Acknowledgments. This work was supported by the National Nature Science Foundation of China under grants 62101390 and U22B2011. This work was also supported by Wuhan University-Huawei Geoinformatics Innovation Laboratory. Y. Xiao would love to thank Sida Peng for the helpful discussions. T. Wu was supported by NSF IIS-1909644, IIS-1822477, CMMI-2024688, and IUSE-2013451. We are also grateful for the constructive comments by anonymous reviewers and area chairs. The views presented in this paper are those of the authors and should not be interpreted as representing any funding agencies. + +# References + +[1] Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In IEEE International Conference on Computer Vision (ICCV), pages 5835-5844, 2021. 3 +[2] Ronald Clark. Volumetric bundle adjustment for online photorealistic scene capture. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6114-6122, 2022. 3 +[3] Ronald Clark, Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, and Andrew J. Davison. Learning to solve nonlinear least squares for monocular stereo. In European Conference on Computer Vision (ECCV), volume 11212, pages 291-306, 2018. 2 +[4] David J. Crandall, Andrew Owens, Noah Snavely, and Daniel P. Huttenlocher. Sfm with mfrs: Discrete-continuous optimization for large-scale structure from motion. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 35(12):2841–2853, 2013. 1 +[5] Hainan Cui, Xiang Gao, Shuhan Shen, and Zhanyi Hu. Hsfm: Hybrid structure-from-motion. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2393-2402, 2017. 1 +[6] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas A. Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2432-2443, 2017. 8 +[7] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 224-236, 2018. 2, 7 +[8] Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 5 +[9] Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow. Digging into self-supervised monocular depth estimation. In IEEE International Conference on Computer Vision (ICCV), pages 3827-3837, 2019. 2 +[10] John C. Hart. Sphere tracing: a geometric method for the antialiased ray tracing of implicit surfaces. The Visual Computer, 12(10):527-545, 1996. 3, 4 +[11] Richard Hartley and Andrew Zisserman. Multiple view geometry in computer vision. Cambridge University Press, 2003. 1 +[12] Richard I Hartley and Peter Sturm. Triangulation. Computer Vision and Image Understanding (CVIU), 68(2):146-157, 1997. 5 +[13] Petr Hruby, Timothy Duff, Anton Leykin, and Tomás Pajdla. Learning to solve hard minimal problems. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5522-5532, 2022. 2 +[14] Rasmus Ramsbøl Jensen, Anders Lindbjerg Dahl, George Vogiatzis, Engin Tola, and Henrik Aanæs. Large scale multi- + +view stereopsis evaluation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 406-413, 2014. 2 +[15] Michael M. Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Symposium on Geometry Processing, volume 256, pages 61-70, 2006. 7 +[16] Diederik P Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations (ICLR), 2015. 6 +[17] Vincent Lepetit, Francesc Moreno-Noguer, and Pascal Fua. Epnp: An accurate $O(n)$ solution to the pnp problem. International Journal of Computer Vision (IJCV), 81(2):155-166, 2009. 5 +[18] Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey. BARF: bundle-adjusting neural radiance fields. In IEEE International Conference on Computer Vision (ICCV), pages 5721-5731, 2021. 2, 3 +[19] Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui. DIST: rendering deep implicit signed distance function with differentiable sphere tracing. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2016-2025, 2020. 2, 4 +[20] William E. Lorensen and Harvey E. Cline. Marching cubes: A high resolution 3d surface construction algorithm. In Proceedings of SIGGRAPH, pages 163-169, 1987. 7 +[21] David G Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (IJCV), 60(2):91-110, 2004. 4, 7 +[22] Reza Mahjourian, Martin Wicke, and Anelia Angelova. Unsupervised Learning of Depth and Ego-Motion From Monocular Video Using 3D Geometric Constraints. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5667-5675, 2018. 2 +[23] Ishit Mehta, Manmohan Chandraker, and Ravi Ramamoorthi. A level set theory for neural implicit evolution under explicit flows. In European Conference on Computer Vision (ECCV), 2022. 2 +[24] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision (ECCV), volume 12346, pages 405-421, 2020. 2, 3, 7 +[25] Thomas Müller. tiny-cuda-nn, 4 2021. 6 +[26] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. on Graphics (TOG), 41(4):102:1-102:15, 2022. 6, 7 +[27] David Nister. An Efficient Solution to the Five-Point Relative Pose Problem. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 26(6):756-777, 2004. 4 +[28] Michael Oechsle, Songyou Peng, and Andreas Geiger. UNISURF: unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In IEEE International Conference on Computer Vision (ICCV), pages 5569-5579, 2021. 3 +[29] Jeong Joon Park, Peter Florence, Julian Straub, Richard A. Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. + +In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 165-174, 2019. 3 +[30] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (NeurIPS), pages 8024-8035, 2019. 6 +[31] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4937–4946, 2020. 2, 7 +[32] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion Revisited. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4104-4113, 2016. 1, 2, 3, 4, 6, 7, 8 +[33] Johannes L. Schonberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys. Pixelwise view selection for unstructured multi-view stereo. In European Conference on Computer Vision (ECCV), volume 9907, pages 501-518, 2016. 7 +[34] Thomas Schöps, Torsten Sattler, and Marc Pollefeys. BAD SLAM: bundle adjusted direct RGB-D SLAM. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 134-144, 2019. 2, 6, 7 +[35] Noah Snavely, Steven M. Seitz, and Richard Szeliski. Skeletal graphs for efficient structure from motion. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008. 4 +[36] Edgar Sucar, Shikun Liu, Joseph Ortiz, and Andrew J. Davison. imap: Implicit mapping and positioning in real-time. In IEEE International Conference on Computer Vision (ICCV), pages 6209-6218, 2021. 3, 8 +[37] Chengzhou Tang and Ping Tan. BA-Net: Dense Bundle Adjustment Networks. In International Conference on Learning Representations (ICLR), 2019. 2 +[38] Jianyuan Wang, Yiran Zhong, Yuchao Dai, Stan Birchfield, Kaihao Zhang, Nikolai Smolyanskiy, and Hongdong Li. Deep two-view structure-from-motion revisited. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8953-8962, 2021. 2 +[39] Lei Wang, Linlin Ge, Shan Luo, Zihan Yan, Zhaopeng Cui, and Jieqing Feng. Tc-sfm: Robust track-community-based structure-from-motion. CoRR, abs/2206.05866, 2022. 2 +[40] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In Advances in Neural Information Processing Systems (NeurIPS), pages 27171-27183, 2021. 3 +[41] Yiqun Wang, Ivan Skorokhodov, and Peter Wonka. Improved surface reconstruction using high-frequency details. CoRR, abs/2206.07850, 2022. 3 +[42] Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor Adrian Prisacariu. Nerf-: Neural radiance fields without + +known camera parameters. CoRR, abs/2102.07064, 2021. 2, 3 +[43] Kyle Wilson and Noah Snavely. Robust global translations with 1dsfm. In European Conference on Computer Vision (ECCV), volume 8691, pages 61-75, 2014. 1 +[44] Yuxi Xiao, Li Li, Xiaodi Li, and Jian Yao. Deepmle: A robust deep maximum likelihood estimator for two-view structure from motion. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10643-10650, 2022. 2 +[45] Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan. Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1787-1796, 2020. 2, 6 +[46] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In Advances in Neural Information Processing Systems (NeurIPS), pages 4805-4815, 2021. 3, 4 +[47] Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R. Oswald, and Marc Pollefeys. NICE-SLAM: neural implicit scalable encoding for SLAM. 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Introduction" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 608, + 287, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 608, + 287, + 693 + ], + "spans": [ + { + "bbox": [ + 46, + 608, + 287, + 693 + ], + "type": "text", + "content": "Structure-from-Motion (SfM) is a fundamental 3D vision problem that aims at reconstructing 3D scenes and estimating the camera motions from a set of uncalibrated images. 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SfM calculations on neural level sets. We learn to do geometric calculations including Triangulation, PnP, and Bundle Adjustment above neural level sets, which easily help to reject the outliers in the matches especially in the texture repeated scenes. Also, due to the continuous surface priors of neural level sets, we achieve better pose estimation accuracy and our reconstructed points are sticking on the surface which are painted with color in the figure. While, there are a lot of outlier 3d points reconstructed by COLMAP [32] which are painted by black." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 543, + 545, + 567 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 543, + 545, + 567 + ], + "spans": [ + { + "bbox": [ + 304, + 543, + 545, + 567 + ], + "type": "text", + "content": "have formed three representative pipelines of Incremental SfM [32], Global SfM [4,43], and Hybrid SfM [5]." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 570, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 546, + 713 + ], + "type": "text", + "content": "In this paper, we focus on the incremental pipeline of SfM and we will use SfM to refer to the incremental SfM. Given an unordered image set, an SfM system initializes the computation by a pair of images that are with well-conditioned keypoint correspondences to yield an initial set of feature tracks, then incrementally adds new views one by one to estimate the camera pose from the 2D-3D point correspondences and update the feature tracks with new matches. Because the feature tracks are generated by grouping the putative 2D correspondences across viewpoints in bottom-up manners, they would be ineffective or inaccurate to represent holistic information of scenes. 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Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 702, + 134, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 134, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 134, + 712 + ], + "type": "text", + "content": "*Corresponding author" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 293, + 732, + 317, + 742 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 732, + 317, + 742 + ], + "spans": [ + { + "bbox": [ + 293, + 732, + 317, + 742 + ], + "type": "text", + "content": "17205" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 191 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 191 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 191 + ], + "type": "text", + "content": "Bundle Adjustment (BA) is necessary to jointly optimize the camera poses and 3D points in a top-down manner. The success of BA indicates that a global perspective is vital for accurate 3D reconstruction, however, their input feature tracks are the bottom-up cues without enough holistic constraints for the 3D scenes. To this end, we study to integrate the top-down information into the SfM system by proposing a novel Level-" + }, + { + "bbox": [ + 46, + 72, + 289, + 191 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 46, + 72, + 289, + 191 + ], + "type": "text", + "content": "fM. Fig. 1 illustrates a representative case for the classic SfM systems that generate more flying 3D scene points, which can be addressed by our method." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 192, + 289, + 301 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 192, + 289, + 301 + ], + "spans": [ + { + "bbox": [ + 46, + 192, + 289, + 301 + ], + "type": "text", + "content": "Our Level-" + }, + { + "bbox": [ + 46, + 192, + 289, + 301 + ], + "type": "inline_equation", + "content": "\\mathrm{S}^2\\mathrm{fM}" + }, + { + "bbox": [ + 46, + 192, + 289, + 301 + ], + "type": "text", + "content": " is inspired by the recently-emerged neural implicit surface that could manage all 3D scene points as the zero-level set of the signed distance function (SDF). Because the neural implicit surfaces can be parameterized by Multi-Layer Perceptrons (MLPs), it could be viewed as a kind of top-down information of 3D scenes and is of great potential for accurate 3D reconstruction. However, because both the 3D scene and camera poses are to be determined, it poses a challenging problem:" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 67, + 309, + 268, + 346 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 309, + 268, + 346 + ], + "spans": [ + { + "bbox": [ + 67, + 309, + 268, + 346 + ], + "type": "text", + "content": "How can we optimize a neural SDF (or other neural fields such as NeRF) from only a set of uncalibrated images without any 3D information?" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 354, + 289, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 354, + 289, + 510 + ], + "spans": [ + { + "bbox": [ + 46, + 354, + 289, + 510 + ], + "type": "text", + "content": "Most recently, the above problem was partially answered in BARF [18] and NeRF-- [42] that relaxed the requirement of optimizing Neural Radiance Fields [24] without knowing accurate camera poses, but they can only handle the unknown pose configurations in small-scale face-forwarding scenes. Moreover, when we confine the problem in the incremental SfM pipelines, it would be more challenging as we need to optimize the neural fields with only two overlapped images at the initialization stage. To this end, we found that the optimization of neural SDF can be accomplished by the 2D matches at the initialization stage, and facilitate the management of feature tracks by querying the 3D points and tracing the 2D keypoints in a holistic way." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 510, + 289, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 510, + 289, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 510, + 289, + 714 + ], + "type": "text", + "content": "As shown in Fig. 1, we define a neural network that parameterizes an SDF as the unified representation for the underdetermined 3D scene and accomplishes the computations of PnP for camera pose intersection, the 3D points triangulation as well as the geometry refinement on the parameterized SDF. In the initialization stage with a pair of overlapped images, Level-" + }, + { + "bbox": [ + 46, + 510, + 289, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 46, + 510, + 289, + 714 + ], + "type": "text", + "content": "fM uses the differentiable sphere tracing algorithm [19] to attain the corresponding 3d points of the keypoints and calculate the reprojection error to drive the joint optimization. For the traced 3d points with small SDF values and 2D reprojection errors for its feature track, they are added into a dynamic point set and take the point set with feature tracks as the Lagrangian representation for the level sets. Because the pose estimation and the scene points reconstruction are sequentially estimated, the estimation errors will be accumulated. To this end, we present an NBA (i.e., Neural Bundle Adjustment) that plays a similar" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 133 + ], + "type": "text", + "content": "role as in Bundle Adjustment, but it optimizes the implicit surface and camera poses from the explicit flow of points by the energy function of the reprojection errors, which can be viewed as an evolutionary step between Lagrangian and Eulerian representations as discussed in [23]." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 133, + 547, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 133, + 547, + 240 + ], + "spans": [ + { + "bbox": [ + 304, + 133, + 547, + 240 + ], + "type": "text", + "content": "In the experiments, we evaluate our Level-" + }, + { + "bbox": [ + 304, + 133, + 547, + 240 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 304, + 133, + 547, + 240 + ], + "type": "text", + "content": "fM on a variety of scenes from the BlendedMVS [45], DTU [14], and ETH3D [34] datasets. On the BlendedMVS dataset, our proposed Level-" + }, + { + "bbox": [ + 304, + 133, + 547, + 240 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 304, + 133, + 547, + 240 + ], + "type": "text", + "content": "fM clearly outperforms the state-of-the-art COLMAP [32] by significant margins. On the DTU and ETH3D datasets [14, 34], our method also obtains on-par performance with COLMAP for both camera pose estimation and dense surface reconstruction, which are all computed in one stage." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 318, + 240, + 511, + 252 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 240, + 511, + 252 + ], + "spans": [ + { + "bbox": [ + 318, + 240, + 511, + 252 + ], + "type": "text", + "content": "The contributions of this paper are in two folds:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 318, + 260, + 545, + 425 + ], + "type": "list", + "angle": 0, + "index": 10, + "blocks": [ + { + "bbox": [ + 318, + 260, + 545, + 332 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 260, + 545, + 332 + ], + "spans": [ + { + "bbox": [ + 318, + 260, + 545, + 332 + ], + "type": "text", + "content": "- We present a novel neural SfM approach Level-" + }, + { + "bbox": [ + 318, + 260, + 545, + 332 + ], + "type": "inline_equation", + "content": "S^2" + }, + { + "bbox": [ + 318, + 260, + 545, + 332 + ], + "type": "text", + "content": "fM, which formulates to optimize the coordinate MLP networks for implicit surface and radiance field and estimate the camera poses and scene geometry. To the best of our knowledge, our Level-" + }, + { + "bbox": [ + 318, + 260, + 545, + 332 + ], + "type": "inline_equation", + "content": "S^2" + }, + { + "bbox": [ + 318, + 260, + 545, + 332 + ], + "type": "text", + "content": "fM is the first implicit neural SfM solution on the zero-level set of surfaces." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 318, + 341, + 545, + 425 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 341, + 545, + 425 + ], + "spans": [ + { + "bbox": [ + 318, + 341, + 545, + 425 + ], + "type": "text", + "content": "- From the perspective of neural implicit fields learning, we show that the challenging problems of two-view and few-view optimization of neural implicit fields can be addressed by exploiting the inductive biases conveyed in the 2D correspondences. Besides, our method presents a promising way for neural implicit rendering without knowing camera extrinsics beforehand." + } + ] + } + ], + "index": 9 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 306, + 436, + 398, + 449 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 436, + 398, + 449 + ], + "spans": [ + { + "bbox": [ + 306, + 436, + 398, + 449 + ], + "type": "text", + "content": "2. Related Works" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 456, + 436, + 468 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 456, + 436, + 468 + ], + "spans": [ + { + "bbox": [ + 306, + 456, + 436, + 468 + ], + "type": "text", + "content": "2.1. Structure from Motion" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 474, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 474, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 474, + 547, + 715 + ], + "type": "text", + "content": "There has been a vast body of literature on Structure from Motion. Since an SfM system consists of many components, tremendous efforts have been devoted to improving the core components of SfM. In particular, the learning techniques were introduced in a variety of subproblems including image matching [7, 31], feature track mining and management [39], two-view 3D reconstruction [38,44], relative and absolute camera pose estimation [13] and Bundle Adjustment [3, 37]. Those studies indicated that the learning paradigms are promising to improve the quality of 3D reconstruction. However, to the best of our knowledge, the learning paradigms are not fully equipped in SfM systems. One possible reason for such a fact is that the many learning approaches are designed in a supervised learning fashion, which remains some risks on the out-of-distribution samples. The self-supervised learning approaches [9, 22] in 3D vision alleviated the requirement of data annotations, however, they have not been fully exploited in the whole pipeline of SfM. In contrast to the aforementioned studies, in this paper, we are interested in integrating the learning" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 732, + 318, + 742 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 732, + 318, + 742 + ], + "spans": [ + { + "bbox": [ + 294, + 732, + 318, + 742 + ], + "type": "text", + "content": "17206" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 288, + 133 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 288, + 133 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 288, + 133 + ], + "type": "text", + "content": "ability into the SfM system without incurring any external data annotations. From the perspective of system design in SfM, we verified that the strong inductive biases conveying in the 2D correspondences are promising and meaningful to drive the learning and optimization of SfM." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 140, + 279, + 153 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 140, + 279, + 153 + ], + "spans": [ + { + "bbox": [ + 47, + 140, + 279, + 153 + ], + "type": "text", + "content": "2.2. Neural Implicit Representation for 3D Scene" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 158, + 287, + 290 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 158, + 287, + 290 + ], + "spans": [ + { + "bbox": [ + 46, + 158, + 287, + 290 + ], + "type": "text", + "content": "Recently, the advent of neural implicit fields [24, 28, 29, 40, 41, 46] have greatly advanced many 3D vision problems such as novel-view synthesis [1, 24] and surface reconstruction [28, 29, 40, 46] by learning to optimize the coordinate MLPs from a set of posed RGB images of which the key to success is that the inductive biases of 3D are exploited by the neural networks. However, when the camera poses are invalid, it is hard to optimize the coordinate MLPs for neural implicit fields. To remedy this, the state-of-the-art SfM system, COLMAP [32], is extensively used to compute the camera poses as a preprocessing step." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 290, + 288, + 445 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 290, + 288, + 445 + ], + "spans": [ + { + "bbox": [ + 46, + 290, + 288, + 445 + ], + "type": "text", + "content": "To train the neural field from unknown poses directly, recently, BARF [18] and NeRF [42] explored to jointly optimize the camera poses and neural fields by the volumetric rendering with promising results obtained in forward-facing scenes. BARF can also work in some scenes of highly overlapped and dense image collections with the initialized poses as inputs. This problem was also studied in the RGB-D SLAM systems [2, 36, 47], however, their works mainly rely on the known depth information and focus on the camera pose tracking by the neural implicit fields. Therefore, how to optimize implicit neural fields from only a set of uncalibrated images without any 3D information input is still a challenging and open problem." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 446, + 287, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 446, + 287, + 506 + ], + "spans": [ + { + "bbox": [ + 46, + 446, + 287, + 506 + ], + "type": "text", + "content": "In this paper, we study the unknown-pose neural fields optimization and SfM together and present a unified solution that simultaneously learns the implicit surfaces and radiance fields alongside the camera pose estimation and scene reconstruction from a set of images." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 517, + 132, + 529 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 517, + 132, + 529 + ], + "spans": [ + { + "bbox": [ + 47, + 517, + 132, + 529 + ], + "type": "text", + "content": "3. Preliminaries" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 536, + 287, + 573 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 536, + 287, + 573 + ], + "spans": [ + { + "bbox": [ + 46, + 536, + 287, + 573 + ], + "type": "text", + "content": "In this section, we introduce the preliminaries on neural implicit surface rendering and the notations in SfM, which are all extensively used in our method." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 580, + 233, + 594 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 580, + 233, + 594 + ], + "spans": [ + { + "bbox": [ + 47, + 580, + 233, + 594 + ], + "type": "text", + "content": "3.1. Neural Implicit Surface Rendering" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 599, + 287, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 599, + 287, + 684 + ], + "spans": [ + { + "bbox": [ + 46, + 599, + 287, + 684 + ], + "type": "text", + "content": "The volumetric rendering of neural implicit surface [46] aims at learning a signed distance function " + }, + { + "bbox": [ + 46, + 599, + 287, + 684 + ], + "type": "inline_equation", + "content": "d_{\\Omega} : \\mathbb{R}^3 \\to \\mathbb{R}" + }, + { + "bbox": [ + 46, + 599, + 287, + 684 + ], + "type": "text", + "content": " by the volumetric rendering from a set of posed images and then extracting the zero-level set of " + }, + { + "bbox": [ + 46, + 599, + 287, + 684 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 46, + 599, + 287, + 684 + ], + "type": "text", + "content": " as the reconstructed surface model of the image set. The state-of-the-art approach, VolSDF [46], integrates SDF representations with neural volume rendering via Laplacian distribution by" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 116, + 692, + 287, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 116, + 692, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 116, + 692, + 287, + 715 + ], + "type": "interline_equation", + "content": "\\sigma (\\mathbf {x}) = \\frac {1}{\\beta} \\Psi_ {\\beta} (- d _ {\\Omega} (\\mathbf {x})), \\tag {1}", + "image_path": "39881d73f2ed5476e8d31386c890dbea4297a62567c787620e1f99bee4f23b22.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " is a learnable parameter in VolSDF [46]. Based on Eq. (1), the volume rendering equation renders a ray " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "\\mathbf{x}(t)" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " emanating from a camera position " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "\\mathbf{o} \\in \\mathbb{R}^3" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " in unit direction " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "\\mathbf{v}" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": ", defined by " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "\\mathbf{x}(t) = \\mathbf{o} + t\\mathbf{v}" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " by" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 329, + 127, + 545, + 153 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 329, + 127, + 545, + 153 + ], + "spans": [ + { + "bbox": [ + 329, + 127, + 545, + 153 + ], + "type": "interline_equation", + "content": "I (\\mathbf {o}, \\mathbf {v}) = \\int_ {0} ^ {\\infty} L (\\mathbf {x} (t), \\mathbf {n} (t), \\mathbf {v}) \\sigma (\\mathbf {x} (t)) T (t) d t, \\quad (2)", + "image_path": "8ed2ec116e1c7f4bafdbbaef0639ad9765b95fba77ac3460e70b36cb75404b0a.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "spans": [ + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "inline_equation", + "content": "L(\\mathbf{x},\\mathbf{n},\\mathbf{v})" + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "text", + "content": " is the radiance field and " + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "inline_equation", + "content": "\\mathbf{n}(t)" + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "text", + "content": " is the normal direction of the point " + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "inline_equation", + "content": "\\mathbf{x}(t)" + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "text", + "content": " defined by " + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "inline_equation", + "content": "\\mathbf{n}(t) = \\nabla_{\\mathbf{x}}d_{\\Omega}(\\mathbf{x}(t))" + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "text", + "content": ". In the learning of volume rendering, two coordinate MLP (Multi-Layer Perceptron) networks parameterize the SDF by " + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "inline_equation", + "content": "\\phi (\\mathbf{x}) = (d(\\mathbf{x}),\\mathbf{z}(\\mathbf{x}))\\in \\mathbb{R}^{1 + 256}" + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "text", + "content": " and the radiance field by " + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "inline_equation", + "content": "L_{\\psi}(\\mathbf{x},\\mathbf{n},\\mathbf{v},\\mathbf{z})\\in \\mathbb{R}^3" + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "text", + "content": ", and train them by the color loss " + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{RGB}}(\\phi ,\\psi ,\\beta)" + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "text", + "content": " and the Eikonal loss " + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{eik}(\\phi) = \\mathbb{E}_{\\mathbf{z}}(|\\nabla d(\\mathbf{z})|| - 1)" + }, + { + "bbox": [ + 304, + 159, + 545, + 255 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 256, + 545, + 351 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 256, + 545, + 351 + ], + "spans": [ + { + "bbox": [ + 304, + 256, + 545, + 351 + ], + "type": "text", + "content": "In this paper, we use the equations (1) and (2) as the basic tools for Level-" + }, + { + "bbox": [ + 304, + 256, + 545, + 351 + ], + "type": "inline_equation", + "content": "\\mathrm{S}^2" + }, + { + "bbox": [ + 304, + 256, + 545, + 351 + ], + "type": "text", + "content": "fM. To make the optimization of SDF and radiance networks easier, we set " + }, + { + "bbox": [ + 304, + 256, + 545, + 351 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 304, + 256, + 545, + 351 + ], + "type": "text", + "content": " as a small constant number and use the multi-resolution grid representations to avoid the potential of slow convergence and catastrophic forgetting since the scene scale is unknown and the original VolSDF [46] requires to normalize the known camera poses in a certain scale." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 358, + 490, + 371 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 358, + 490, + 371 + ], + "spans": [ + { + "bbox": [ + 305, + 358, + 490, + 371 + ], + "type": "text", + "content": "3.2. Ray Sampling and Sphere Tracing" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 376, + 545, + 401 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 376, + 545, + 401 + ], + "spans": [ + { + "bbox": [ + 305, + 376, + 545, + 401 + ], + "type": "text", + "content": "Iterative Ray Sampling. In the implementation, the continuous form of Eq. (2) is approximated in" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 352, + 407, + 545, + 440 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 352, + 407, + 545, + 440 + ], + "spans": [ + { + "bbox": [ + 352, + 407, + 545, + 440 + ], + "type": "interline_equation", + "content": "I (\\mathbf {o}, \\mathbf {v}) \\approx \\sum_ {i = 1} ^ {m - 1} \\hat {\\tau} _ {i} L (\\mathbf {x} (t _ {i}), \\mathbf {n} (t _ {i}), \\mathbf {v}), \\tag {3}", + "image_path": "aab400f860b4906dbb132c4c0cd7e8a08fbca57187d58c028fbe13e26b6a14f1.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "spans": [ + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "inline_equation", + "content": "\\{t_i\\}_{i=1}^m" + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "text", + "content": " is the discrete samples, " + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "inline_equation", + "content": "0 = t_1 < t_2 < \\ldots < t_m = M" + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "text", + "content": " is some large constant. " + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "inline_equation", + "content": "\\hat{\\tau}_i \\approx \\tau(s_i) \\Delta s" + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "text", + "content": " is the approximated PDF multiplied by the interval length. In VolSDF [46], " + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "inline_equation", + "content": "\\{t_i\\}_{i=1}^m" + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "text", + "content": " is adaptively computed according to the opacity approximation error. Please move to [46] for its detail. In our method, we keep using this iterative sampling strategy when the rendering loss and the Eikonal loss is used. However, because the sampling set " + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "inline_equation", + "content": "\\{t_i\\}" + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "text", + "content": " would be large, we do not use this strategy to compute the 3D points from 2D keypoints in our Level-" + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 304, + 447, + 545, + 590 + ], + "type": "text", + "content": "fM and in turn to use the sphere tracing [10] as a faster way since our initial development of this work." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": "Sphere Tracing. Sphere tracing is a geometric method to render the depth from a signed distance function. Different from iterative ray sampling, sphere tracing is designed to hit the surface point along the ray " + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{x}(t)" + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": " with queries as few as possible. To make it clear, we use " + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "inline_equation", + "content": "s_i" + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": " to denote the ray stamp of the queried point " + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{x}(s_i)" + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": ". With the queried point " + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{x}(s_i)" + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": ", the next ray stamp " + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "inline_equation", + "content": "s_{i + 1}" + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": " is computed by " + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "inline_equation", + "content": "s_{i + 1} = \\phi (\\mathbf{x}(s_i))" + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": ". In our study, we sample at most " + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "inline_equation", + "content": "N_{s} = 20" + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": " points with the stop criterion " + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "inline_equation", + "content": "|\\phi (s_i)| < \\varepsilon" + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\varepsilon" + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": " is set to 0.002 in our experiment." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 733, + 317, + 742 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 733, + 317, + 742 + ], + "spans": [ + { + "bbox": [ + 293, + 733, + 317, + 742 + ], + "type": "text", + "content": "17207" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "content": "Remarks. Although both the iterative ray sampling [46] and sphere tracing [10] share the same target of computing the surface point along a ray, they have different behaviors in the neural implicit surface optimization. In detail, because VolSDF [46] aims at approximating the opacity by SDF, it updates the SDF network " + }, + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "inline_equation", + "content": "\\phi (\\mathbf{x})" + }, + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "content": " by the rendering loss " + }, + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{color}}" + }, + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "content": ". As for sphere tracing, it is a geometric approach that only takes the SDF values into account for the computation. Such a difference is trivial to some extent, however, we found that their different focuses induce a loss function in our Level-" + }, + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 46, + 72, + 289, + 228 + ], + "type": "text", + "content": "fM to constraint the rendered depth values (or 3D points) for two-view initialization and 3D point triangulation." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 236, + 149, + 248 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 236, + 149, + 248 + ], + "spans": [ + { + "bbox": [ + 47, + 236, + 149, + 248 + ], + "type": "text", + "content": "3.3. Notations in SfM" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "spans": [ + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "text", + "content": "Correspondence Search. Given the image set " + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "inline_equation", + "content": "\\mathcal{I} = \\{I_i|i = 1\\ldots N_I\\}" + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "text", + "content": " for reconstruction, the keypoint features of the image " + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "inline_equation", + "content": "I_{i}" + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "text", + "content": " computed by SIFT [21] is denoted in " + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "inline_equation", + "content": "\\mathcal{F}_i = \\{(x_j,\\mathbf{f}_j)\\}" + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "inline_equation", + "content": "x_{j}\\in \\mathbb{R}^{2}" + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "text", + "content": " is the 2D coordinate and " + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "inline_equation", + "content": "\\mathbf{f}_j\\in \\mathbb{R}^{128}" + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "text", + "content": " is the feature descriptor of " + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "inline_equation", + "content": "x_{j}" + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "text", + "content": ". Based on the SIFT features, we follow the schema in COLMAP [32] to establish the feature correspondences across views, in which we first do the exhaustive matching for all possible image pairs and then use the geometric verification to filter out the non-overlapped image pairs. After this, the potentially overlapped image pairs are denoted in " + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "inline_equation", + "content": "\\mathcal{C} = \\{(I_a,I_b)|I_a,I_b\\in \\mathcal{I}\\}" + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "text", + "content": ", and the keypoint correspondences in the pair " + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "inline_equation", + "content": "(I_a,I_b)" + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "text", + "content": " are denoted in the set " + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_{ab} = \\{\\{(x_k,\\mathbf{f}_k),(x_l',\\mathbf{f}_l')\\} |(x_k,\\mathbf{f}_k\\in \\mathcal{F}_a,(x_l',f_l')\\in \\mathcal{F}_b\\}" + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "text", + "content": ". Finally, all the prepared correspondences are organized as the scene graph [32, 35], which stores images as the graph nodes and the overlapped image pairs as the graph edges. In our Level-" + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "inline_equation", + "content": "\\mathrm{S^2fM}" + }, + { + "bbox": [ + 46, + 255, + 289, + 495 + ], + "type": "text", + "content": ", we use the established correspondences to drive the learning of MLPs, estimate the camera poses, and reconstruct a sparse point set of correspondences." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 501, + 287, + 610 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 501, + 287, + 610 + ], + "spans": [ + { + "bbox": [ + 46, + 501, + 287, + 610 + ], + "type": "text", + "content": "3D Scene Points and Feature Tracks. Because SfM is designated to simultaneously estimate the scene geometry from 2D correspondences, every successfully reconstructed 3D scene point is sourced from multiple 2D keypoint observations. To facilitate the representation, we denote the expected 3D point set in " + }, + { + "bbox": [ + 46, + 501, + 287, + 610 + ], + "type": "inline_equation", + "content": "\\mathcal{X} = \\{\\mathbf{X}_k\\in \\mathbb{R}^3 |k = 1,\\dots ,N_{3d}\\}" + }, + { + "bbox": [ + 46, + 501, + 287, + 610 + ], + "type": "text", + "content": ". For each point " + }, + { + "bbox": [ + 46, + 501, + 287, + 610 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_k\\in \\mathcal{X}" + }, + { + "bbox": [ + 46, + 501, + 287, + 610 + ], + "type": "text", + "content": ", if it is reconstructed from the 2D keypoint " + }, + { + "bbox": [ + 46, + 501, + 287, + 610 + ], + "type": "inline_equation", + "content": "x_{j}\\in \\mathcal{F}_{i}" + }, + { + "bbox": [ + 46, + 501, + 287, + 610 + ], + "type": "text", + "content": ", we denote such a relationship in a tuple " + }, + { + "bbox": [ + 46, + 501, + 287, + 610 + ], + "type": "inline_equation", + "content": "(k,i,j)" + }, + { + "bbox": [ + 46, + 501, + 287, + 610 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 46, + 501, + 287, + 610 + ], + "type": "inline_equation", + "content": "\\mathcal{T} = \\{(k,i,j)\\}" + }, + { + "bbox": [ + 46, + 501, + 287, + 610 + ], + "type": "text", + "content": " is the set of feature tracks." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 620, + 194, + 634 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 620, + 194, + 634 + ], + "spans": [ + { + "bbox": [ + 47, + 620, + 194, + 634 + ], + "type": "text", + "content": "4. The Proposed Level " + }, + { + "bbox": [ + 47, + 620, + 194, + 634 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2\\mathbf{f}\\mathbf{M}" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 641, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 288, + 714 + ], + "type": "text", + "content": "In this section, we present the details of our Level-" + }, + { + "bbox": [ + 46, + 641, + 288, + 714 + ], + "type": "inline_equation", + "content": "S^2" + }, + { + "bbox": [ + 46, + 641, + 288, + 714 + ], + "type": "text", + "content": "fM. As shown in Fig. 2, our method consists of three classical components including 1) the two-view geometry initialization, 2) the new frame registration, and 3) the new frame pose refinement, an implicit surface and a radiance field that are parameterized by neural networks. In what follows, we" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 72, + 545, + 107 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 107 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 107 + ], + "type": "text", + "content": "will show how to solve the SfM problem by learning the implicit fields with 2D correspondences. We assume the intrinsic matrix " + }, + { + "bbox": [ + 304, + 72, + 545, + 107 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 304, + 72, + 545, + 107 + ], + "type": "text", + "content": " is known and fixed." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 114, + 437, + 125 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 114, + 437, + 125 + ], + "spans": [ + { + "bbox": [ + 305, + 114, + 437, + 125 + ], + "type": "text", + "content": "4.1. Two-view Initialization" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 132, + 545, + 203 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 132, + 545, + 203 + ], + "spans": [ + { + "bbox": [ + 304, + 132, + 545, + 203 + ], + "type": "text", + "content": "We first select two good views " + }, + { + "bbox": [ + 304, + 132, + 545, + 203 + ], + "type": "inline_equation", + "content": "\\{I_a, I_b\\}" + }, + { + "bbox": [ + 304, + 132, + 545, + 203 + ], + "type": "text", + "content": " for initialization from the scene graph and get their 2D matches " + }, + { + "bbox": [ + 304, + 132, + 545, + 203 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_{ab} = \\{\\{(x_k, f_k), (x'_l, f'_l)\\} | (x_k, f_k) \\in \\mathcal{F}_a, (x'_l, f'_l) \\in \\mathcal{F}_b\\}" + }, + { + "bbox": [ + 304, + 132, + 545, + 203 + ], + "type": "text", + "content": ". Based on the 2D matches " + }, + { + "bbox": [ + 304, + 132, + 545, + 203 + ], + "type": "inline_equation", + "content": "\\mathcal{M}_{ab}" + }, + { + "bbox": [ + 304, + 132, + 545, + 203 + ], + "type": "text", + "content": ", we leverage the 5-point algorithm [27] and RANSAC to obtain the poses " + }, + { + "bbox": [ + 304, + 132, + 545, + 203 + ], + "type": "inline_equation", + "content": "P_a, P_b \\in SE(3)" + }, + { + "bbox": [ + 304, + 132, + 545, + 203 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "spans": [ + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": "With the estimated camera poses " + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "inline_equation", + "content": "P_{a}, P_{b}" + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": ", it is straightforward to optimize the SDF network " + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "inline_equation", + "content": "\\phi(\\mathbf{x})" + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": " and the radiance field network " + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "inline_equation", + "content": "L_{\\psi}(\\mathbf{x}, \\mathbf{n}, \\mathbf{v}, \\mathbf{z})" + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": " defined in Sec. 3 by minimizing the loss items " + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{RGB}}" + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{eik}" + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": " as done in VolSDF [46]. However, it should be noted that the learning of volumetric surface rendering in such a way for the two-view inputs would trap into the local minimal by overfitting. To this end, we propose to use the differentiable sphere tracking [10, 19] for the corresponding rays in image " + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "inline_equation", + "content": "I_{a}" + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "inline_equation", + "content": "I_{b}" + }, + { + "bbox": [ + 304, + 204, + 545, + 334 + ], + "type": "text", + "content": ", which provides strong inductive biases for the optimization of networks." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "content": "Specifically, denoted by a pair of feature match " + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "inline_equation", + "content": "(x_{k}, x_{l}^{\\prime})" + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "content": " in the image pair " + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "inline_equation", + "content": "(I_{a}, I_{b})" + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "content": ", the sphere tracing obtains the surface point " + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_{a}^{k} = \\mathbf{o}_{a} + \\hat{t}_{a}\\mathbf{d}_{a}" + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "content": " from the SDF and " + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_{b}^{l} = \\mathbf{o}_{b} + \\hat{t}_{b}\\mathbf{d}_{b}" + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "inline_equation", + "content": "(\\mathbf{o}_{a}, \\mathbf{d}_{a})" + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "content": " is the ray of " + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "inline_equation", + "content": "x_{k}" + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "inline_equation", + "content": "(\\mathbf{o}_{b}, \\mathbf{d}_{b})" + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "content": " is the ray of " + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "inline_equation", + "content": "x_{l}^{\\prime}" + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "content": ". For the computation of " + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "inline_equation", + "content": "\\hat{t}_{a}" + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "inline_equation", + "content": "\\hat{t}_{b}" + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "content": ", please move to our supplementary materials. Ideally, the " + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_{a}^{k}" + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_{b}^{l}" + }, + { + "bbox": [ + 304, + 335, + 545, + 430 + ], + "type": "text", + "content": " should be as close as possible, therefore, we introduce a reprojection loss" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 332, + 433, + 545, + 455 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 332, + 433, + 545, + 455 + ], + "spans": [ + { + "bbox": [ + 332, + 433, + 545, + 455 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {r e p r o j}} = \\frac {1}{2 V} \\sum \\left(\\| \\hat {x} _ {k} - x _ {l} ^ {\\prime} \\| _ {2} + \\| \\hat {x} _ {l} ^ {\\prime} - x _ {k} \\| _ {2}\\right), \\tag {4}", + "image_path": "89e47ccb9108e95dc86049691b00bb2bca969ee3ab8f7300b9d57be45ec20fec.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 459, + 545, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 459, + 545, + 495 + ], + "spans": [ + { + "bbox": [ + 304, + 459, + 545, + 495 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 459, + 545, + 495 + ], + "type": "inline_equation", + "content": "V" + }, + { + "bbox": [ + 304, + 459, + 545, + 495 + ], + "type": "text", + "content": " is the number of correspondences, " + }, + { + "bbox": [ + 304, + 459, + 545, + 495 + ], + "type": "inline_equation", + "content": "\\hat{x}_k = \\Pi(\\mathbf{X}_a^k, K, P_b)" + }, + { + "bbox": [ + 304, + 459, + 545, + 495 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 459, + 545, + 495 + ], + "type": "inline_equation", + "content": "\\hat{x}_l' = \\Pi(\\mathbf{X}_b^l, K, P_a)" + }, + { + "bbox": [ + 304, + 459, + 545, + 495 + ], + "type": "text", + "content": " are the projected 2D coordinates of the traced 3D points by the projection " + }, + { + "bbox": [ + 304, + 459, + 545, + 495 + ], + "type": "inline_equation", + "content": "\\Pi" + }, + { + "bbox": [ + 304, + 459, + 545, + 495 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 495, + 545, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 495, + 545, + 579 + ], + "spans": [ + { + "bbox": [ + 304, + 495, + 545, + 579 + ], + "type": "text", + "content": "Considering the fact that the correspondences are sparse when the SDF network is not well optimized at some rays, the sparse sample points by sphere tracing on the SDF network may be either inaccurate or erroneous as shown in Fig. 3. Therefore, we use a depth consistency loss " + }, + { + "bbox": [ + 304, + 495, + 545, + 579 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{dc}" + }, + { + "bbox": [ + 304, + 495, + 545, + 579 + ], + "type": "text", + "content": " to minimize the depth estimated by the sphere tracing and the volumetric rendering by" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 340, + 582, + 545, + 608 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 340, + 582, + 545, + 608 + ], + "spans": [ + { + "bbox": [ + 340, + 582, + 545, + 608 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {d c} = \\frac {1}{B} \\sum \\left\\| \\hat {t} _ {i} - \\int_ {0} ^ {\\infty} T (t) \\sigma (\\mathbf {x} (t)) d t \\right\\|, \\tag {5}", + "image_path": "166bfaccd3c4df14003796e2cb9719f9a5e9a2d46c8a1918fb76e667a9f16dce.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 613, + 545, + 670 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 613, + 545, + 670 + ], + "spans": [ + { + "bbox": [ + 304, + 613, + 545, + 670 + ], + "type": "text", + "content": "where the rays " + }, + { + "bbox": [ + 304, + 613, + 545, + 670 + ], + "type": "inline_equation", + "content": "\\mathbf{x}(t)" + }, + { + "bbox": [ + 304, + 613, + 545, + 670 + ], + "type": "text", + "content": " are randomly sampled from the images, and those rays are also used to compute the color loss " + }, + { + "bbox": [ + 304, + 613, + 545, + 670 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{RGB}}" + }, + { + "bbox": [ + 304, + 613, + 545, + 670 + ], + "type": "text", + "content": ". For the computation of Eikonal loss " + }, + { + "bbox": [ + 304, + 613, + 545, + 670 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{eik}" + }, + { + "bbox": [ + 304, + 613, + 545, + 670 + ], + "type": "text", + "content": ", all the 3D points visited by sphere tracing and dense ray marching are used." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 672, + 545, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 672, + 545, + 696 + ], + "spans": [ + { + "bbox": [ + 304, + 672, + 545, + 696 + ], + "type": "text", + "content": "In summary, our two-view initialization of Level-" + }, + { + "bbox": [ + 304, + 672, + 545, + 696 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 304, + 672, + 545, + 696 + ], + "type": "text", + "content": "fM computes the total loss " + }, + { + "bbox": [ + 304, + 672, + 545, + 696 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{total}}^{\\mathrm{init}}" + }, + { + "bbox": [ + 304, + 672, + 545, + 696 + ], + "type": "text", + "content": " by" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 320, + 700, + 545, + 715 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 700, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 320, + 700, + 545, + 715 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {t o t a l}} ^ {\\text {i n i t}} = \\alpha_ {1} \\mathcal {L} _ {\\text {r e p r o j}} + \\alpha_ {2} \\mathcal {L} _ {e i k} + \\alpha_ {3} \\mathcal {L} _ {\\mathrm {R G B}} + \\alpha_ {4} \\mathcal {L} _ {\\mathrm {d c}}, \\tag {6}", + "image_path": "f3dfa976771e73a3af35512498a1b6f1b53fb8a8511bc1f77d2aaffd2b4418b1.jpg" + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 732, + 317, + 742 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 732, + 317, + 742 + ], + "spans": [ + { + "bbox": [ + 293, + 732, + 317, + 742 + ], + "type": "text", + "content": "17208" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 86, + 70, + 509, + 223 + ], + "blocks": [ + { + "bbox": [ + 86, + 70, + 509, + 223 + ], + "lines": [ + { + "bbox": [ + 86, + 70, + 509, + 223 + ], + "spans": [ + { + "bbox": [ + 86, + 70, + 509, + 223 + ], + "type": "image", + "image_path": "aac95ac21d8fd5ca0be71fbfff5315a1e1fe2701de4bd12724fbcfc6b0a82058.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 229, + 231, + 364, + 243 + ], + "lines": [ + { + "bbox": [ + 229, + 231, + 364, + 243 + ], + "spans": [ + { + "bbox": [ + 229, + 231, + 364, + 243 + ], + "type": "text", + "content": "Figure 2. Overview of Level-" + }, + { + "bbox": [ + 229, + 231, + 364, + 243 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 229, + 231, + 364, + 243 + ], + "type": "text", + "content": "fM." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 77, + 260, + 164, + 345 + ], + "blocks": [ + { + "bbox": [ + 77, + 260, + 164, + 345 + ], + "lines": [ + { + "bbox": [ + 77, + 260, + 164, + 345 + ], + "spans": [ + { + "bbox": [ + 77, + 260, + 164, + 345 + ], + "type": "image", + "image_path": "27116119cd02475401ef4aab90037f5c492d2c020a068a7587df8e2c7e34d3cf.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 93, + 347, + 143, + 358 + ], + "lines": [ + { + "bbox": [ + 93, + 347, + 143, + 358 + ], + "spans": [ + { + "bbox": [ + 93, + 347, + 143, + 358 + ], + "type": "text", + "content": "3D points by sphere tracing (with depth consistency)" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 173, + 260, + 258, + 346 + ], + "blocks": [ + { + "bbox": [ + 173, + 260, + 258, + 346 + ], + "lines": [ + { + "bbox": [ + 173, + 260, + 258, + 346 + ], + "spans": [ + { + "bbox": [ + 173, + 260, + 258, + 346 + ], + "type": "image", + "image_path": "99e2220062e8165c386ce513664a016a4008c2dac6e156ee5b4ae3b06c51f7de.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 189, + 347, + 241, + 359 + ], + "lines": [ + { + "bbox": [ + 189, + 347, + 241, + 359 + ], + "spans": [ + { + "bbox": [ + 189, + 347, + 241, + 359 + ], + "type": "text", + "content": "3D points by sphere tracing (without depth consistency)" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 47, + 367, + 287, + 411 + ], + "lines": [ + { + "bbox": [ + 47, + 367, + 287, + 411 + ], + "spans": [ + { + "bbox": [ + 47, + 367, + 287, + 411 + ], + "type": "text", + "content": "Figure 3. An illustrative comparison for the depth consistency loss " + }, + { + "bbox": [ + 47, + 367, + 287, + 411 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{dc}" + }, + { + "bbox": [ + 47, + 367, + 287, + 411 + ], + "type": "text", + "content": " in two-view initialization. As it is shown, when we remove " + }, + { + "bbox": [ + 47, + 367, + 287, + 411 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{dc}" + }, + { + "bbox": [ + 47, + 367, + 287, + 411 + ], + "type": "text", + "content": ", the 3D points traced from all putative 2D keypoints correspondences will contain more flying 3D points." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 425, + 287, + 449 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 425, + 287, + 449 + ], + "spans": [ + { + "bbox": [ + 47, + 425, + 287, + 449 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 425, + 287, + 449 + ], + "type": "inline_equation", + "content": "\\alpha_{1},\\alpha_{2},\\alpha_{3}" + }, + { + "bbox": [ + 47, + 425, + 287, + 449 + ], + "type": "text", + "content": " , and " + }, + { + "bbox": [ + 47, + 425, + 287, + 449 + ], + "type": "inline_equation", + "content": "\\alpha_{4}" + }, + { + "bbox": [ + 47, + 425, + 287, + 449 + ], + "type": "text", + "content": " are the hyperparameters and use ADAM optimizer to optimize the networks." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "spans": [ + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": "When the initialization is finished, we compute the two 3D points " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "\\mathbf{X}" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "\\mathbf{X}'" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": " for each correspondence by sphere tracing for image " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "I_{a}" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "I_{b}" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": ". For an accurate correspondence, " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "\\| \\mathbf{X} - \\mathbf{X}'\\|" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": " and their SDF values should be all small enough, thus providing a good criterion to check the putative matches to initialize the 3D point set " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "\\mathcal{X}" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": " and the feature track set " + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 449, + 287, + 534 + ], + "type": "text", + "content": " for all the verified two-view correspondences." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 541, + 185, + 554 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 541, + 185, + 554 + ], + "spans": [ + { + "bbox": [ + 47, + 541, + 185, + 554 + ], + "type": "text", + "content": "4.2. New Frame Registration" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 560, + 287, + 632 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 560, + 287, + 632 + ], + "spans": [ + { + "bbox": [ + 46, + 560, + 287, + 632 + ], + "type": "text", + "content": "For every newly added frame, we will first construct the 3D-2D correspondence from the existing pointset and its feature tracks. After that, we calculate a coarse pose of the new frame with the standard PnP algorithms [17], and then refine it with both the reprojection error and the rendering loss. The registration loss can be calculated as follow:" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 643, + 287, + 656 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 643, + 287, + 656 + ], + "spans": [ + { + "bbox": [ + 105, + 643, + 287, + 656 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {r e g i s t}} = \\beta_ {1} \\mathcal {L} _ {\\text {r e p r o j}} + \\beta_ {2} \\mathcal {L} _ {\\text {R G B}}, \\tag {7}", + "image_path": "0c678874ca5cd6879fc3da7c3a815834362fce9b9b946a4e806a7a831d09c817.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 665, + 287, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 665, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 47, + 665, + 287, + 689 + ], + "type": "text", + "content": "where the " + }, + { + "bbox": [ + 47, + 665, + 287, + 689 + ], + "type": "inline_equation", + "content": "\\beta_{1},\\beta_{2}" + }, + { + "bbox": [ + 47, + 665, + 287, + 689 + ], + "type": "text", + "content": " are two hyper-parameters, and the " + }, + { + "bbox": [ + 47, + 665, + 287, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{reproj}}" + }, + { + "bbox": [ + 47, + 665, + 287, + 689 + ], + "type": "text", + "content": " here are calculated by the 3D-2D correspondences." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 714 + ], + "type": "text", + "content": "In this optimization, the pose of the newly added frame, the SDF network, and the radiance field network are jointly" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 263, + 545, + 346 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 263, + 545, + 346 + ], + "spans": [ + { + "bbox": [ + 304, + 263, + 545, + 346 + ], + "type": "text", + "content": "optimized. While during the changes in the pose and SDF, the original location in the pointset will maybe not be the right one on the surface. For this problem, we design a Neural Bundle Adjustment (NBA) strategy to dynamically update the pointset with respect to the SDF after the points triangulation and refinement in the next section. Therefore, we leave the details of NBA in Sec. 4.4." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 353, + 501, + 366 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 353, + 501, + 366 + ], + "spans": [ + { + "bbox": [ + 305, + 353, + 501, + 366 + ], + "type": "text", + "content": "4.3. Points Triangulation and Refinement" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 371, + 545, + 443 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 371, + 545, + 443 + ], + "spans": [ + { + "bbox": [ + 304, + 371, + 545, + 443 + ], + "type": "text", + "content": "Once the pose of the newly added frame is obtained, we step into the next procedure of refining the retrieved 2D points from the point set " + }, + { + "bbox": [ + 304, + 371, + 545, + 443 + ], + "type": "inline_equation", + "content": "\\mathcal{X}" + }, + { + "bbox": [ + 304, + 371, + 545, + 443 + ], + "type": "text", + "content": " and triangulating new 2D points into 3D space to extend " + }, + { + "bbox": [ + 304, + 371, + 545, + 443 + ], + "type": "inline_equation", + "content": "\\mathcal{X}" + }, + { + "bbox": [ + 304, + 371, + 545, + 443 + ], + "type": "text", + "content": ". This problem was formulated in the classical SfM frameworks, however, they are suffering from the following issues:" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 317, + 448, + 545, + 574 + ], + "type": "list", + "angle": 0, + "index": 19, + "blocks": [ + { + "bbox": [ + 317, + 448, + 545, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 448, + 545, + 509 + ], + "spans": [ + { + "bbox": [ + 317, + 448, + 545, + 509 + ], + "type": "text", + "content": "- The 2D Mismatches: This issue could be alleviated by geometric verifications like RANSAC [8] or better 2D keypoint matching approaches, however, when encountering the symmetry structures or repeated texture regions, those efforts are hard to work efficiently." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 317, + 514, + 545, + 574 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 514, + 545, + 574 + ], + "spans": [ + { + "bbox": [ + 317, + 514, + 545, + 574 + ], + "type": "text", + "content": "- Tiny Triangulation Angle: This issue will lead to an ill-conditioned problem for points triangulation [12]. Therefore, the classical SfM approaches will directly discard those points to avoid the ill-conditioned problem configuration." + } + ] + } + ], + "index": 18 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 304, + 578, + 545, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 578, + 545, + 651 + ], + "spans": [ + { + "bbox": [ + 304, + 578, + 545, + 651 + ], + "type": "text", + "content": "We address those issues by proposing an SDF-based triangulation. Similar to the two-view initialization in Sec. 4.1, we compute the 3D points for all the potential 2D keypoints in the first step. Then, for the 2D keypoints that have correspondences in the current feature track set " + }, + { + "bbox": [ + 304, + 578, + 545, + 651 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 304, + 578, + 545, + 651 + ], + "type": "text", + "content": ", we use the tracing loss " + }, + { + "bbox": [ + 304, + 578, + 545, + 651 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{tracing}}" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 359, + 655, + 545, + 685 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 359, + 655, + 545, + 685 + ], + "spans": [ + { + "bbox": [ + 359, + 655, + 545, + 685 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {t r a c i n g}} = \\frac {1}{V ^ {\\prime}} \\sum_ {j} \\| \\mathbf {X} _ {j} ^ {s t} - \\mathbf {X} _ {j} \\|, \\tag {8}", + "image_path": "e049a1ae1254372418da1948d4a7155e0ff9e834f571cc90d106b464d71b684f.jpg" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_j\\in \\mathcal{X}" + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "text", + "content": " is the retrieved 3D point of the 2D keypoint in the current frame, " + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "V^{\\prime}" + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "text", + "content": " is the number of retrieved 3D" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 733, + 317, + 742 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 733, + 317, + 742 + ], + "spans": [ + { + "bbox": [ + 293, + 733, + 317, + 742 + ], + "type": "text", + "content": "17209" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 108 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 108 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 108 + ], + "type": "text", + "content": "points. This loss function acts as the similar role of " + }, + { + "bbox": [ + 46, + 72, + 287, + 108 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{dc}" + }, + { + "bbox": [ + 46, + 72, + 287, + 108 + ], + "type": "text", + "content": " in two-view initialization. Without it, similar phenomenon like Fig. 3 will happen." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 108, + 287, + 157 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 108, + 287, + 157 + ], + "spans": [ + { + "bbox": [ + 46, + 108, + 287, + 157 + ], + "type": "text", + "content": "For the new 2D keypoints that are matched to the added images but without 3D information, both the reprojection loss similar to the two-view initialization and the tracing loss is used to yield the triangulation loss " + }, + { + "bbox": [ + 46, + 108, + 287, + 157 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{tri}}" + }, + { + "bbox": [ + 46, + 108, + 287, + 157 + ], + "type": "text", + "content": " by" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 113, + 162, + 287, + 177 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 113, + 162, + 287, + 177 + ], + "spans": [ + { + "bbox": [ + 113, + 162, + 287, + 177 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {t r i}} = \\mathcal {L} _ {\\text {r e p o r j}} ^ {\\text {m a s k}} + \\mathcal {L} _ {\\text {t r a c i n g}}, \\tag {9}", + "image_path": "2db6293d6451fd326cf843fd813095a39ea2715cbb2e247fb3a719aaaeab2735.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 183, + 287, + 232 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 183, + 287, + 232 + ], + "spans": [ + { + "bbox": [ + 46, + 183, + 287, + 232 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 183, + 287, + 232 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{report}}^{\\mathrm{mask}}" + }, + { + "bbox": [ + 46, + 183, + 287, + 232 + ], + "type": "text", + "content": " only considers the 2D correspondences of which their distance between the 2D projections of the traced 3D points traced in different views are smaller than a loose threshold (45 pixels in our implementation)." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 238, + 253, + 251 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 238, + 253, + 251 + ], + "spans": [ + { + "bbox": [ + 47, + 238, + 253, + 251 + ], + "type": "text", + "content": "4.4. Neural Bundle Adjustment on Surfaces" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 256, + 287, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 256, + 287, + 375 + ], + "spans": [ + { + "bbox": [ + 46, + 256, + 287, + 375 + ], + "type": "text", + "content": "Because the camera pose estimation and the points triangulation are separated, which will involve accumulative errors for pose estimation and triangulation, as well as the implicit networks. Motivated by the Bundle Adjustment that is extensively used in classical approaches, we present a Neural Bundle Adjustment (NBA) that jointly optimizes the estimated camera points, the 3D point set, and the implicit networks as a refinement step. To avoid costly computation, our NBA step finds the closest surface points to dynamically update those variables." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "spans": [ + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "text", + "content": "Denoted by the reconstructed 3D point set " + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "inline_equation", + "content": "\\mathcal{X}" + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "text", + "content": " and the feature track " + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "text", + "content": ", the camera poses " + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "inline_equation", + "content": "\\mathcal{P} = \\{P_1, \\dots, P_K\\}" + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "text", + "content": " and the corresponding images " + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "inline_equation", + "content": "\\{I_1, \\dots, I_K\\}" + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "text", + "content": ", as well as the networks " + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "inline_equation", + "content": "\\phi(\\mathbf{x})" + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "inline_equation", + "content": "L_{\\psi}" + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "text", + "content": ", in each step of NBA, we update the 3D point " + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "inline_equation", + "content": "\\mathbf{X} \\in \\mathcal{X}" + }, + { + "bbox": [ + 46, + 376, + 287, + 436 + ], + "type": "text", + "content": " by" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 115, + 443, + 287, + 455 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 115, + 443, + 287, + 455 + ], + "spans": [ + { + "bbox": [ + 115, + 443, + 287, + 455 + ], + "type": "interline_equation", + "content": "\\mathbf {X} \\leftarrow \\mathbf {X} - \\phi (\\mathbf {X}) \\nabla \\phi (\\mathbf {X}), \\tag {10}", + "image_path": "0564e4af84895dea71078d13bfc8076da0c5f4305c3553ce7a1610017f9da9e8.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 462, + 287, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 462, + 287, + 521 + ], + "spans": [ + { + "bbox": [ + 46, + 462, + 287, + 521 + ], + "type": "text", + "content": "and then compute the reprojection loss according to the feature track " + }, + { + "bbox": [ + 46, + 462, + 287, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 462, + 287, + 521 + ], + "type": "text", + "content": " to jointly optimize the " + }, + { + "bbox": [ + 46, + 462, + 287, + 521 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 46, + 462, + 287, + 521 + ], + "type": "text", + "content": " the SDF network, " + }, + { + "bbox": [ + 46, + 462, + 287, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{P}" + }, + { + "bbox": [ + 46, + 462, + 287, + 521 + ], + "type": "text", + "content": " the estimated camera poses, and " + }, + { + "bbox": [ + 46, + 462, + 287, + 521 + ], + "type": "inline_equation", + "content": "\\mathcal{X}" + }, + { + "bbox": [ + 46, + 462, + 287, + 521 + ], + "type": "text", + "content": " the updated 3D point set. For the radiance network " + }, + { + "bbox": [ + 46, + 462, + 287, + 521 + ], + "type": "inline_equation", + "content": "L_{\\psi}" + }, + { + "bbox": [ + 46, + 462, + 287, + 521 + ], + "type": "text", + "content": ", the rendering loss for randomly sampled rays is computed." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 522, + 287, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 522, + 287, + 641 + ], + "spans": [ + { + "bbox": [ + 46, + 522, + 287, + 641 + ], + "type": "text", + "content": "In our implementation, we leverage our NBA by three times, which we call the 1-frame NBA, local NBA, and global NBA. Because the rendering loss involves more rays, we only use it for the 1-frame NBA after the camera registration and point triangulation. In terms of local NBA, for the newly added view, only the related views with correspondences are considered. After running the 1-frame and local NBA schemes, we globally update all reconstructed views and the point set. By leveraging the backpropagation, all the mentioned variables are updated as the refinement." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 651, + 127, + 664 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 651, + 127, + 664 + ], + "spans": [ + { + "bbox": [ + 47, + 651, + 127, + 664 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 671, + 286, + 683 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 671, + 286, + 683 + ], + "spans": [ + { + "bbox": [ + 47, + 671, + 286, + 683 + ], + "type": "text", + "content": "5.1. Implementation Details, Datasets, and Metrics" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "text", + "content": "Implementation Details. In our implementation, we parameterize the SDF " + }, + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\phi (\\mathbf{x})" + }, + { + "bbox": [ + 46, + 689, + 287, + 713 + ], + "type": "text", + "content": " by a multi-resolution features grid" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 72, + 545, + 251 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 251 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 251 + ], + "type": "text", + "content": "and a two-layers MLP. To accelerate the computation, we follow InstantNGP [26] to use a hash table [25] for the feature grids. The radiance field " + }, + { + "bbox": [ + 304, + 72, + 545, + 251 + ], + "type": "inline_equation", + "content": "L_{\\psi}" + }, + { + "bbox": [ + 304, + 72, + 545, + 251 + ], + "type": "text", + "content": " is also implemented in a multi-resolution feature grid and a three-layer MLP. Because our end task is the geometric 3D reconstruction, we use a high-resolution multi-scale feature grid for the SDF to ensure the accuracy of scene geometry but use a low-resolution feature grid to avoid the unnecessary computation cost for the radiance field. The specifications of the network architecture are given in supplementary material due to the limited space. All of these above are implemented in PyTorch [30], and we used the Adam [16] as the optimizer for the geometric calculations. For the 2D image matching and pose graph, we keep them the same with our baseline, COLMAP [32] for fair comparisons." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 255, + 545, + 374 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 255, + 545, + 374 + ], + "spans": [ + { + "bbox": [ + 304, + 255, + 545, + 374 + ], + "type": "text", + "content": "Datasets. Three datasets are used for our evaluation. Firstly, we use 5 representative scenes including the LyingStatue, Stone, Fountain, Horse, and Statues from the BlendedMVS dataset [45] in our evaluation because it provides accurate ground truth of camera poses and contains a number of challenging scenes for SfM. Secondly, the DTU dataset for the MVS task is also used. The five representative scenes (scans of 24, 37, 65, 110 and 114) are used in our experiments. Finally, we evaluate our proposed method on the five scenes from the challenging ETH3D [34] dataset." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 377, + 545, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 377, + 545, + 509 + ], + "spans": [ + { + "bbox": [ + 304, + 377, + 545, + 509 + ], + "type": "text", + "content": "Evaluation Metrics. In our evaluation, we use the Rotation error and ATE to quantitatively benchmark the pose accuracy, which simply depicts the difference between the ground truth and the aligned pose. During our evaluation, we used the provided API of Reconstruction Align in COLMAP [32] to do that. In terms of the reconstructed scene geometry, we use accuracy (Acc) and the precision (Prec) rate to evaluate the accuracy of our recovered 3D points and Chamfer-l1 distance to depict the accuracy of the reconstructed surface. Detailed definitions of these evaluation metrics are given in the supplementary material." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 515, + 498, + 528 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 515, + 498, + 528 + ], + "spans": [ + { + "bbox": [ + 306, + 515, + 498, + 528 + ], + "type": "text", + "content": "5.2. Results on the BlendedMVS Dataset" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 534, + 545, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 534, + 545, + 629 + ], + "spans": [ + { + "bbox": [ + 304, + 534, + 545, + 629 + ], + "type": "text", + "content": "Tab. 1 reports the quantitative evaluation results for the two versions of Level-" + }, + { + "bbox": [ + 304, + 534, + 545, + 629 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 304, + 534, + 545, + 629 + ], + "type": "text", + "content": "fM and COLMAP [32]. The full version of Level-" + }, + { + "bbox": [ + 304, + 534, + 545, + 629 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 304, + 534, + 545, + 629 + ], + "type": "text", + "content": "fM used all the mentioned components while the wo/render version removed the rendering loss for optimization. As it is reported, our Level-" + }, + { + "bbox": [ + 304, + 534, + 545, + 629 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 304, + 534, + 545, + 629 + ], + "type": "text", + "content": "fM (full) consistently outperforms COLMAP [32] for camera pose estimation and sparse 3D point cloud reconstruction. It also reveals that rendering losses are required." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "text", + "content": "In detail, our Level-" + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "inline_equation", + "content": "S^2" + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "text", + "content": "fM (full) averagely reduced the estimation error from " + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "inline_equation", + "content": "1.54^\\circ" + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "text", + "content": " by COLMAP [32] to " + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "inline_equation", + "content": "0.86^\\circ" + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "text", + "content": ", obtaining a relative improvement of " + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "inline_equation", + "content": "55.84\\%" + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "text", + "content": ". For the translation error, our Level-" + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "inline_equation", + "content": "S^2" + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "text", + "content": "fM (full) decreases the error from " + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "inline_equation", + "content": "3.54 \\mathrm{~cm}" + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "inline_equation", + "content": "3.36 \\mathrm{~cm}" + }, + { + "bbox": [ + 304, + 630, + 545, + 712 + ], + "type": "text", + "content": ". For the sparse 3D point cloud reconstruction, the ACC metric is reduced from 3.16 to 2.25 for the full model and 2.63 for the wo/render version." + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 733, + 317, + 742 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 733, + 317, + 742 + ], + "spans": [ + { + "bbox": [ + 293, + 733, + 317, + 742 + ], + "type": "text", + "content": "17210" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 74, + 70, + 521, + 161 + ], + "blocks": [ + { + "bbox": [ + 74, + 70, + 521, + 161 + ], + "lines": [ + { + "bbox": [ + 74, + 70, + 521, + 161 + ], + "spans": [ + { + "bbox": [ + 74, + 70, + 521, + 161 + ], + "type": "table", + "html": "
ScenesCamera Pose EvaluationPoints Cloud Results Evaluation
Rotation (°) ↓Translation (cm) ↓Acc(cm)↓, Prec(≤3.5cm)↑
COLMAP [32]Level-S2fM (full)Level-S2fM (wo/render)COLMAP [32]Level-S2fM (full)Level-S2fM (wo/render)COLMAP [32]Level-S2fM (full)Level-S2fM (wo/render)
LyingStatue1.201.121.310.892.182.67Acc:0.83 Prec:0.99Acc:1.35 Prec:0.99Acc:1.76 Prec:0.98
Stone0.630.310.546.285.519.17Acc:5.44 Prec:0.66Acc:3.61 Prec:0.68Acc:5.04 Prec:0.64
Fountain4.341.652.327.412.874.11Acc:1.91 Prec:0.91Acc:1.13 Prec:0.98Acc:1.14 Prec:0.96
Horse0.330.940.921.185.717.58Acc:3.99 Prec:0.86Acc:4.18 Prec:0.71Acc:4.18 Prec:0.70
Statues1.210.360.441.980.560.62Acc:1.31 Prec:0.98Acc:0.95 Prec:0.99Acc:1.02 Prec:0.99
Mean1.540.861.113.543.364.83Acc:3.16 Prec:0.85Acc:2.25 Prec:0.87Acc:2.63 Prec:0.85
", + "image_path": "9aaccc3504e32f4fe7a30b78e5c9d425122183ef734a01a2d012b99cd2e06e26.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 69, + 198, + 262, + 334 + ], + "blocks": [ + { + "bbox": [ + 69, + 198, + 262, + 334 + ], + "lines": [ + { + "bbox": [ + 69, + 198, + 262, + 334 + ], + "spans": [ + { + "bbox": [ + 69, + 198, + 262, + 334 + ], + "type": "image", + "image_path": "e84f16bff4968ec4cc8f153307d3e9c52372f8acc0ee3b9dd9184da810cb610b.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 340, + 289, + 416 + ], + "lines": [ + { + "bbox": [ + 46, + 340, + 289, + 416 + ], + "spans": [ + { + "bbox": [ + 46, + 340, + 289, + 416 + ], + "type": "text", + "content": "Figure 4. The reconstructed meshes, point clouds and camera poses for the Fountain and Horse scenes on the BlendedMVS dataset by our Level-" + }, + { + "bbox": [ + 46, + 340, + 289, + 416 + ], + "type": "inline_equation", + "content": "S^2" + }, + { + "bbox": [ + 46, + 340, + 289, + 416 + ], + "type": "text", + "content": "fM (full). In the first column, the reconstructed scene geometry and the camera poses are shown together. For the 3D models, we show the different views of the sparse 3D points during the training and the textured meshes refused from the zero-level set surface." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "table", + "bbox": [ + 50, + 415, + 287, + 457 + ], + "blocks": [ + { + "bbox": [ + 46, + 170, + 547, + 194 + ], + "lines": [ + { + "bbox": [ + 46, + 170, + 547, + 194 + ], + "spans": [ + { + "bbox": [ + 46, + 170, + 547, + 194 + ], + "type": "text", + "content": "Table 1. Quantitative results on the BlendedMVS dataset. For our Level-" + }, + { + "bbox": [ + 46, + 170, + 547, + 194 + ], + "type": "inline_equation", + "content": "S^2" + }, + { + "bbox": [ + 46, + 170, + 547, + 194 + ], + "type": "text", + "content": "fM, we report the results by full version and an wo/render version that removes the rendering loss during optimization." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 50, + 415, + 287, + 457 + ], + "lines": [ + { + "bbox": [ + 50, + 415, + 287, + 457 + ], + "spans": [ + { + "bbox": [ + 50, + 415, + 287, + 457 + ], + "type": "table", + "html": "
Pose SourceLyingStatueStoneFountainHorseStatuesMean
COLMAP29.128.425.423.529.227.12
Level-S2fM (Ours)29.528.927.123.630.227.86
GT29.529.427.424.131.128.3
", + "image_path": "4fe1ea47318e14430e09b907650909401676c4850fc469ab7cb3ef1c045efcfc.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "table_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 58, + 506, + 278, + 517 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 506, + 278, + 517 + ], + "spans": [ + { + "bbox": [ + 58, + 506, + 278, + 517 + ], + "type": "text", + "content": "Fig. 4 shows the reconstruction results by our method." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 518, + 287, + 601 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 518, + 287, + 601 + ], + "spans": [ + { + "bbox": [ + 46, + 518, + 287, + 601 + ], + "type": "text", + "content": "Apart from the direct evaluation of the SfM results on the BlendedMVS dataset, we further compare the camera poses estimation results for different methods by training the NGP [26] (a fast version of NeRF [24]) to compare the performance of novel view synthesis in Tab. 2. As it is reported, the rendered images by our camera poses are consistently better than the ones by COLMAP poses." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 610, + 200, + 623 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 610, + 200, + 623 + ], + "spans": [ + { + "bbox": [ + 47, + 610, + 200, + 623 + ], + "type": "text", + "content": "5.3. Results on the DTU Dataset" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "type": "text", + "content": "We conducted the evaluation on the DTU to illustrate the promising future of our Level-" + }, + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 46, + 629, + 288, + 715 + ], + "type": "text", + "content": "fM to unify the pose estimation, dense reconstruction, and novel view synthesis problems in one stage. For the comparison to COLMAP, we use their built-in PatchMatch MVS [33] functionality to obtain the dense surface points and then leverage its default surface reconstruction method (i.e., Poisson surface [15])" + } + ] + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 312, + 200, + 541, + 273 + ], + "blocks": [ + { + "bbox": [ + 46, + 459, + 287, + 492 + ], + "lines": [ + { + "bbox": [ + 46, + 459, + 287, + 492 + ], + "spans": [ + { + "bbox": [ + 46, + 459, + 287, + 492 + ], + "type": "text", + "content": "Table 2. Novel View Synthesis Comparison. The PSNR is used to compare the camera poses computed by COLMAP, Level-" + }, + { + "bbox": [ + 46, + 459, + 287, + 492 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 46, + 459, + 287, + 492 + ], + "type": "text", + "content": "fM and the GT poses on the BlendedMVS dataset." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 312, + 200, + 541, + 273 + ], + "lines": [ + { + "bbox": [ + 312, + 200, + 541, + 273 + ], + "spans": [ + { + "bbox": [ + 312, + 200, + 541, + 273 + ], + "type": "table", + "html": "
ScanCOLMAP [32]Level-S2fM (Ours)
Chamfer-ℓ1Rot. Err.Trans. Err.Chamfer-ℓ1Rot. Err.Trans. Err.
242.1760.382.872.4420.814.60
373.8370.414.863.0230.314.29
654.3940.454.233.1900.745.81
1103.3890.656.365.9020.826.82
1143.5770.353.582.0920.141.85
Mean3.3300.4484.383.4740.5644.67
", + "image_path": "d0c26b2b205cf1a08d8b4ef8bfb004c3213f61457bbd26aa54adae21f6440ba3.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_body" + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 307, + 337, + 545, + 369 + ], + "blocks": [ + { + "bbox": [ + 305, + 281, + 545, + 335 + ], + "lines": [ + { + "bbox": [ + 305, + 281, + 545, + 335 + ], + "spans": [ + { + "bbox": [ + 305, + 281, + 545, + 335 + ], + "type": "text", + "content": "Table 3. Quantitative results on DTU dataset. The Chamfer- " + }, + { + "bbox": [ + 305, + 281, + 545, + 335 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 305, + 281, + 545, + 335 + ], + "type": "text", + "content": " distance of the dense reconstruction results and as the errors of rotation and translation for camera pose estimation, are compared for COLMAP and our Level-" + }, + { + "bbox": [ + 305, + 281, + 545, + 335 + ], + "type": "inline_equation", + "content": "S^2" + }, + { + "bbox": [ + 305, + 281, + 545, + 335 + ], + "type": "text", + "content": "fM. The unit of Chamfer-" + }, + { + "bbox": [ + 305, + 281, + 545, + 335 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 305, + 281, + 545, + 335 + ], + "type": "text", + "content": " and Translation errors are in millimetres." + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 307, + 337, + 545, + 369 + ], + "lines": [ + { + "bbox": [ + 307, + 337, + 545, + 369 + ], + "spans": [ + { + "bbox": [ + 307, + 337, + 545, + 369 + ], + "type": "table", + "html": "
SceneDetector & Matchercourtyardreliefdoorterrace2facade
COLMAP [32]SIFT [21]0.10°/0.016m0.10°/0.003m0.16°/0.002m0.14°/0.002m0.06°/0.016m
Level-SfM (Ours)0.21°/0.047m0.09°/0.003m0.19°/0.006m0.13°/0.003m0.12°/0.059m
COLMAP [32]SP [7]+SG [31]-0.36°/0.007m0.37°/0.003m0.16°/0.003m0.04°/0.014m
Level-SfM-0.42°/0.003m0.34°/0.007m0.12°/0.002m0.10°/0.051m
", + "image_path": "be8cdf4626d85caf933507dba7f4a29432174644211e241f443d362f115ef6bf.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "table_body" + } + ], + "index": 12 + }, + { + "bbox": [ + 305, + 376, + 545, + 410 + ], + "lines": [ + { + "bbox": [ + 305, + 376, + 545, + 410 + ], + "spans": [ + { + "bbox": [ + 305, + 376, + 545, + 410 + ], + "type": "text", + "content": "Table 4. Quantitative results of pose estimation on the five scenes in ETH3D dataset for COLMAP [32] and our proposed Level-S²fM by using different keypoint detector and matcher." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 305, + 431, + 545, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 431, + 545, + 491 + ], + "spans": [ + { + "bbox": [ + 305, + 431, + 545, + 491 + ], + "type": "text", + "content": "to obtain the mesh model. For our Level-" + }, + { + "bbox": [ + 305, + 431, + 545, + 491 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 305, + 431, + 545, + 491 + ], + "type": "text", + "content": "fM, we use the MarchingCubes [20] to extract the mesh models from the zero-level set of the implicit surface. The quantitative evaluation results are shown in Tab. 3. In this dataset, our Level-" + }, + { + "bbox": [ + 305, + 431, + 545, + 491 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 305, + 431, + 545, + 491 + ], + "type": "text", + "content": "fM obtains on-par performance with COLMAP." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 500, + 471, + 511 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 500, + 471, + 511 + ], + "spans": [ + { + "bbox": [ + 306, + 500, + 471, + 511 + ], + "type": "text", + "content": "5.4. Results on the ETH3D Dataset" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 519, + 545, + 662 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 519, + 545, + 662 + ], + "spans": [ + { + "bbox": [ + 304, + 519, + 545, + 662 + ], + "type": "text", + "content": "We test our method on a more challenging dataset, ETH3D [34], which includes both sparse view collections for multi-scale outdoor and indoor scenes. To show the influence of different keypoint detection and matching algorithms for our method, we additionally make a comparison with SuperPoint (SP) [7] for detection and SuperGlue (SG) [31] for keypoint matching. As reported in Tab. 4, our method achieves comparable results with COLMAP [32]. However, we observe that our method gets slightly inferior results in some large-scale outdoor scenes, because of the limited representative capability of a single network for a large-scale scene." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 306, + 670, + 400, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 670, + 400, + 684 + ], + "spans": [ + { + "bbox": [ + 306, + 670, + 400, + 684 + ], + "type": "text", + "content": "5.5. Ablation Study" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": "In this section, we elaborate on why and how the SDF-based Triangulation (short in " + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "SDF\\text{-Tri}" + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": ") and NBA work in" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 734, + 317, + 742 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 734, + 317, + 742 + ], + "spans": [ + { + "bbox": [ + 293, + 734, + 317, + 742 + ], + "type": "text", + "content": "17211" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 58, + 282, + 237 + ], + "blocks": [ + { + "bbox": [ + 50, + 58, + 282, + 237 + ], + "lines": [ + { + "bbox": [ + 50, + 58, + 282, + 237 + ], + "spans": [ + { + "bbox": [ + 50, + 58, + 282, + 237 + ], + "type": "image", + "image_path": "4909671276579504bfea816eca2502fc9a5bc90e7dd74d5f1c6ec75e2e80406c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 47, + 259, + 286, + 279 + ], + "lines": [ + { + "bbox": [ + 47, + 259, + 286, + 279 + ], + "spans": [ + { + "bbox": [ + 47, + 259, + 286, + 279 + ], + "type": "text", + "content": "Figure 5. Two-views Triangulated Point Clouds by Traditional triangulation and SDF-Based Triangulation, courtyard@ETH3D." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 50, + 238, + 282, + 257 + ], + "blocks": [ + { + "bbox": [ + 50, + 238, + 282, + 257 + ], + "lines": [ + { + "bbox": [ + 50, + 238, + 282, + 257 + ], + "spans": [ + { + "bbox": [ + 50, + 238, + 282, + 257 + ], + "type": "table", + "html": "
w/o SDF-Tri.w/o NBAw/o SDF-Tri & w/o NBAFull
1.23° / 0.498m0.46° / 0.113m3.48° / 0.846m0.21° / 0.047m
", + "image_path": "a215dc8cc13b84c37c2b3e178b7a7b662e93d01391b16722bc21488b1e8d0357.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 286, + 287, + 441 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 286, + 287, + 441 + ], + "spans": [ + { + "bbox": [ + 46, + 286, + 287, + 441 + ], + "type": "text", + "content": "our system. We first conduct the two-view triangulation with SDF-Tri and the traditional method respectively. Fig. 5 shows SDF-Tri can easily filter out the incorrect triangulation from wrong matches. One explanation is that the neural network distills inliers in a continuous zero-level set of surfaces (e.g., the planes in Fig. 5). Therefore, the triangulated outliers (the blue points flying off the planes) can be easily detected and filtered by their large SDF values. Similarly, NBA also benefits from the global level sets of surfaces, which average the errors among the inliers triangulated points and differs the outliers by their large SDF values. The quantitative results for SDF-Tri and NBA are shown at the bottom of Fig. 5." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 453, + 192, + 464 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 453, + 192, + 464 + ], + "spans": [ + { + "bbox": [ + 47, + 453, + 192, + 464 + ], + "type": "text", + "content": "5.6. Limitations of Level-" + }, + { + "bbox": [ + 47, + 453, + 192, + 464 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 47, + 453, + 192, + 464 + ], + "type": "text", + "content": "fM" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "spans": [ + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": "In order to explore the clear boundary of Level-" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": "fM and point out the potential future development, we discuss the limitation of Level-" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": "fM on the most typical indoor dataset, scannet [6]. In the Scannet [6], there are a lot of challenges including blurry images, and textureless areas. Because of the less texture, the SIFT-based keypoint correspondences may contain a large portion of outliers or insufficient matches. Meanwhile, the blur in images will also influence the accuracy of the 2d matches. Therefore, most SfM easily fails on this dataset. Our method is also limited by this because of SIFT matches." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 605, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 288, + 713 + ], + "type": "text", + "content": "To make the discussion clear, we run four scenes of Scannet [6] that were used for NICESLAM [47]. For the image sequence of each scene, the input image set for SfM is constructed by sampling for every 10 frames. Tab. 5 reports the pose accuracy of COLMAP and our Level-" + }, + { + "bbox": [ + 46, + 605, + 288, + 713 + ], + "type": "inline_equation", + "content": "S^2" + }, + { + "bbox": [ + 46, + 605, + 288, + 713 + ], + "type": "text", + "content": "fM for results by adding 60 frames, 120 frames, and all the frames, which are concatenated by the \"slash\". As it is reported, our method usually performs well in the first 60 frames but its pose estimation accuracy suddenly decreases" + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 308, + 70, + 545, + 140 + ], + "blocks": [ + { + "bbox": [ + 308, + 70, + 545, + 140 + ], + "lines": [ + { + "bbox": [ + 308, + 70, + 545, + 140 + ], + "spans": [ + { + "bbox": [ + 308, + 70, + 545, + 140 + ], + "type": "table", + "html": "
Scene ID0000005901690207
iMAP [36]rot°↓trans. (cm) ↓-197.1-18.9-96.4-28.7
NICESLAM [47]rot°↓trans. (cm) ↓-11.3-12.0-12.0-12.8
COLMAP [32]rot°↓trans. (cm) ↓1.47/2.71/2.718.20/16.8/14.11.40/2.52/2.667.20/10.8/11.52.64/1.84/2.5210.6/9.22/10.6failed-
Level-S2fMrot°↓1.44/2.23/1.986.50/11.8/26.71.50/2.59/3.728.20/32.9/35.72.94/9.09/14.36.35/14.3/44.8failed-
fulltrans. (cm) ↓
", + "image_path": "a528a80b8c36907ab910f261fd780e7aa918f63e494d60f0602311c86f672aaa.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 148, + 545, + 192 + ], + "lines": [ + { + "bbox": [ + 305, + 148, + 545, + 192 + ], + "spans": [ + { + "bbox": [ + 305, + 148, + 545, + 192 + ], + "type": "text", + "content": "Table 5. Quantitative results of pose estimation on ScanNet [6]. For the COLMAP and Level-" + }, + { + "bbox": [ + 305, + 148, + 545, + 192 + ], + "type": "inline_equation", + "content": "S^2" + }, + { + "bbox": [ + 305, + 148, + 545, + 192 + ], + "type": "text", + "content": "fM, we report their pose accuracy metrics when the 60/120/all frames are registered. For the last scene (i.e., 0207), both COLMAP and our method failed." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 204, + 545, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 204, + 545, + 312 + ], + "spans": [ + { + "bbox": [ + 304, + 204, + 545, + 312 + ], + "type": "text", + "content": "when adding some new frames. The textureless matches cause the matches very sparse and therefore hard to give a good registration of images. Meanwhile, since the radiance field learning is also challenging in the Scannet dataset, the bad initialized pose can not be refined well by the rendering loss. All of these limitations are basically from sparse 2D image matches. Besides, we observed that the ADAM optimizer will make the optimization of camera poses and scene points unstable, which would also affect the final results." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 322, + 378, + 334 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 322, + 378, + 334 + ], + "spans": [ + { + "bbox": [ + 306, + 322, + 378, + 334 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 342, + 545, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 342, + 545, + 581 + ], + "spans": [ + { + "bbox": [ + 304, + 342, + 545, + 581 + ], + "type": "text", + "content": "This paper studies the longstanding problem of Structure-from-Motion by exploring and exploiting several important yet challenging issues including the two-view neural rendering in the initialization stage and few-view neural rendering in the early camera registration stage of incremental SfM for integrating the recent advances of neural implicit field learning into an SfM pipeline. We show that although the few-view neural rendering problem is challenging enough, it can be tackled by the 2D correspondences as they convey strong inductive biases for 3D scenes. Based on this, we present the first neural SfM solution that renews several key components of two-view geometry initialization, camera pose registration, and triangulation, as well as the Bundle Adjustment problem with neural implicit fields. In the experiments, we show that Level-" + }, + { + "bbox": [ + 304, + 342, + 545, + 581 + ], + "type": "inline_equation", + "content": "\\mathbf{S}^2" + }, + { + "bbox": [ + 304, + 342, + 545, + 581 + ], + "type": "text", + "content": "fM outperforms the traditional SfM pipeline and set a new state-of-the-art for 3D reconstruction on the BlendedMVS dataset. We believe that our study will encourage the 3D vision community to rethink and reformulate Structure-from-Motion with learning-based new findings." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": "Acknowledgments. This work was supported by the National Nature Science Foundation of China under grants 62101390 and U22B2011. This work was also supported by Wuhan University-Huawei Geoinformatics Innovation Laboratory. Y. Xiao would love to thank Sida Peng for the helpful discussions. T. Wu was supported by NSF IIS-1909644, IIS-1822477, CMMI-2024688, and IUSE-2013451. We are also grateful for the constructive comments by anonymous reviewers and area chairs. The views presented in this paper are those of the authors and should not be interpreted as representing any funding agencies." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 733, + 317, + 742 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 733, + 317, + 742 + ], + "spans": [ + { + "bbox": [ + 293, + 733, + 317, + 742 + ], + "type": "text", + "content": "17212" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "text", + "content": "[1] Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In IEEE International Conference on Computer Vision (ICCV), pages 5835-5844, 2021. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 147, + 288, + 190 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 147, + 288, + 190 + ], + "spans": [ + { + "bbox": [ + 53, + 147, + 288, + 190 + ], + "type": "text", + "content": "[2] Ronald Clark. Volumetric bundle adjustment for online photorealistic scene capture. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6114-6122, 2022. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 191, + 288, + 245 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 191, + 288, + 245 + ], + "spans": [ + { + "bbox": [ + 53, + 191, + 288, + 245 + ], + "type": "text", + "content": "[3] Ronald Clark, Michael Bloesch, Jan Czarnowski, Stefan Leutenegger, and Andrew J. Davison. Learning to solve nonlinear least squares for monocular stereo. In European Conference on Computer Vision (ECCV), volume 11212, pages 291-306, 2018. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 247, + 288, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 247, + 288, + 300 + ], + "spans": [ + { + "bbox": [ + 53, + 247, + 288, + 300 + ], + "type": "text", + "content": "[4] David J. Crandall, Andrew Owens, Noah Snavely, and Daniel P. Huttenlocher. Sfm with mfrs: Discrete-continuous optimization for large-scale structure from motion. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 35(12):2841–2853, 2013. 1" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 302, + 287, + 345 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 302, + 287, + 345 + ], + "spans": [ + { + "bbox": [ + 53, + 302, + 287, + 345 + ], + "type": "text", + "content": "[5] Hainan Cui, Xiang Gao, Shuhan Shen, and Zhanyi Hu. Hsfm: Hybrid structure-from-motion. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2393-2402, 2017. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 346, + 287, + 401 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 346, + 287, + 401 + ], + "spans": [ + { + "bbox": [ + 53, + 346, + 287, + 401 + ], + "type": "text", + "content": "[6] Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas A. Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2432-2443, 2017. 8" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 403, + 287, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 403, + 287, + 456 + ], + "spans": [ + { + "bbox": [ + 53, + 403, + 287, + 456 + ], + "type": "text", + "content": "[7] Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superpoint: Self-supervised interest point detection and description. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pages 224-236, 2018. 2, 7" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 458, + 287, + 501 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 458, + 287, + 501 + ], + "spans": [ + { + "bbox": [ + 53, + 458, + 287, + 501 + ], + "type": "text", + "content": "[8] Martin A Fischler and Robert C Bolles. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM, 24(6):381-395, 1981. 5" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 502, + 287, + 545 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 502, + 287, + 545 + ], + "spans": [ + { + "bbox": [ + 53, + 502, + 287, + 545 + ], + "type": "text", + "content": "[9] Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow. Digging into self-supervised monocular depth estimation. In IEEE International Conference on Computer Vision (ICCV), pages 3827-3837, 2019. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 547, + 287, + 579 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 547, + 287, + 579 + ], + "spans": [ + { + "bbox": [ + 48, + 547, + 287, + 579 + ], + "type": "text", + "content": "[10] John C. Hart. Sphere tracing: a geometric method for the antialiased ray tracing of implicit surfaces. The Visual Computer, 12(10):527-545, 1996. 3, 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 581, + 287, + 612 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 581, + 287, + 612 + ], + "spans": [ + { + "bbox": [ + 48, + 581, + 287, + 612 + ], + "type": "text", + "content": "[11] Richard Hartley and Andrew Zisserman. Multiple view geometry in computer vision. Cambridge University Press, 2003. 1" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 614, + 287, + 645 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 614, + 287, + 645 + ], + "spans": [ + { + "bbox": [ + 48, + 614, + 287, + 645 + ], + "type": "text", + "content": "[12] Richard I Hartley and Peter Sturm. Triangulation. Computer Vision and Image Understanding (CVIU), 68(2):146-157, 1997. 5" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 647, + 287, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 647, + 287, + 690 + ], + "spans": [ + { + "bbox": [ + 48, + 647, + 287, + 690 + ], + "type": "text", + "content": "[13] Petr Hruby, Timothy Duff, Anton Leykin, and Tomás Pajdla. Learning to solve hard minimal problems. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5522-5532, 2022. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 692, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 692, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 692, + 287, + 713 + ], + "type": "text", + "content": "[14] Rasmus Ramsbøl Jensen, Anders Lindbjerg Dahl, George Vogiatzis, Engin Tola, and Henrik Aanæs. Large scale multi-" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 308, + 74, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 32, + "blocks": [ + { + "bbox": [ + 326, + 74, + 545, + 106 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 74, + 545, + 106 + ], + "spans": [ + { + "bbox": [ + 326, + 74, + 545, + 106 + ], + "type": "text", + "content": "view stereopsis evaluation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 406-413, 2014. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 107, + 545, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 107, + 545, + 139 + ], + "spans": [ + { + "bbox": [ + 308, + 107, + 545, + 139 + ], + "type": "text", + "content": "[15] Michael M. Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Symposium on Geometry Processing, volume 256, pages 61-70, 2006. 7" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 140, + 545, + 172 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 140, + 545, + 172 + ], + "spans": [ + { + "bbox": [ + 308, + 140, + 545, + 172 + ], + "type": "text", + "content": "[16] Diederik P Kingma and Jimmy Ba. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations (ICLR), 2015. 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 174, + 545, + 215 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 174, + 545, + 215 + ], + "spans": [ + { + "bbox": [ + 308, + 174, + 545, + 215 + ], + "type": "text", + "content": "[17] Vincent Lepetit, Francesc Moreno-Noguer, and Pascal Fua. Epnp: An accurate " + }, + { + "bbox": [ + 308, + 174, + 545, + 215 + ], + "type": "inline_equation", + "content": "O(n)" + }, + { + "bbox": [ + 308, + 174, + 545, + 215 + ], + "type": "text", + "content": " solution to the pnp problem. International Journal of Computer Vision (IJCV), 81(2):155-166, 2009. 5" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 217, + 545, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 217, + 545, + 259 + ], + "spans": [ + { + "bbox": [ + 308, + 217, + 545, + 259 + ], + "type": "text", + "content": "[18] Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey. BARF: bundle-adjusting neural radiance fields. In IEEE International Conference on Computer Vision (ICCV), pages 5721-5731, 2021. 2, 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 262, + 545, + 316 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 262, + 545, + 316 + ], + "spans": [ + { + "bbox": [ + 308, + 262, + 545, + 316 + ], + "type": "text", + "content": "[19] Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui. DIST: rendering deep implicit signed distance function with differentiable sphere tracing. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2016-2025, 2020. 2, 4" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 317, + 545, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 317, + 545, + 348 + ], + "spans": [ + { + "bbox": [ + 308, + 317, + 545, + 348 + ], + "type": "text", + "content": "[20] William E. Lorensen and Harvey E. Cline. Marching cubes: A high resolution 3d surface construction algorithm. In Proceedings of SIGGRAPH, pages 163-169, 1987. 7" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 350, + 545, + 381 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 350, + 545, + 381 + ], + "spans": [ + { + "bbox": [ + 308, + 350, + 545, + 381 + ], + "type": "text", + "content": "[21] David G Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (IJCV), 60(2):91-110, 2004. 4, 7" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 384, + 545, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 384, + 545, + 437 + ], + "spans": [ + { + "bbox": [ + 308, + 384, + 545, + 437 + ], + "type": "text", + "content": "[22] Reza Mahjourian, Martin Wicke, and Anelia Angelova. Unsupervised Learning of Depth and Ego-Motion From Monocular Video Using 3D Geometric Constraints. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 5667-5675, 2018. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 438, + 545, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 438, + 545, + 480 + ], + "spans": [ + { + "bbox": [ + 308, + 438, + 545, + 480 + ], + "type": "text", + "content": "[23] Ishit Mehta, Manmohan Chandraker, and Ravi Ramamoorthi. A level set theory for neural implicit evolution under explicit flows. In European Conference on Computer Vision (ECCV), 2022. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 482, + 545, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 482, + 545, + 536 + ], + "spans": [ + { + "bbox": [ + 308, + 482, + 545, + 536 + ], + "type": "text", + "content": "[24] Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision (ECCV), volume 12346, pages 405-421, 2020. 2, 3, 7" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 537, + 474, + 547 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 537, + 474, + 547 + ], + "spans": [ + { + "bbox": [ + 308, + 537, + 474, + 547 + ], + "type": "text", + "content": "[25] Thomas Müller. tiny-cuda-nn, 4 2021. 6" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 548, + 545, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 548, + 545, + 591 + ], + "spans": [ + { + "bbox": [ + 308, + 548, + 545, + 591 + ], + "type": "text", + "content": "[26] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. on Graphics (TOG), 41(4):102:1-102:15, 2022. 6, 7" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 593, + 545, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 593, + 545, + 624 + ], + "spans": [ + { + "bbox": [ + 308, + 593, + 545, + 624 + ], + "type": "text", + "content": "[27] David Nister. An Efficient Solution to the Five-Point Relative Pose Problem. IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), 26(6):756-777, 2004. 4" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 308, + 626, + 545, + 678 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 626, + 545, + 678 + ], + "spans": [ + { + "bbox": [ + 308, + 626, + 545, + 678 + ], + "type": "text", + "content": "[28] Michael Oechsle, Songyou Peng, and Andreas Geiger. UNISURF: unifying neural implicit surfaces and radiance fields for multi-view reconstruction. In IEEE International Conference on Computer Vision (ICCV), pages 5569-5579, 2021. 3" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 681, + 545, + 713 + ], + "type": "text", + "content": "[29] Jeong Joon Park, Peter Florence, Julian Straub, Richard A. Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation." + } + ] + } + ], + "index": 31 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 293, + 734, + 318, + 742 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 293, + 734, + 318, + 742 + ], + "spans": [ + { + "bbox": [ + 293, + 734, + 318, + 742 + ], + "type": "text", + "content": "17213" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 286, + 712 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 66, + 72, + 286, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 72, + 286, + 95 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 286, + 95 + ], + "type": "text", + "content": "In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 165-174, 2019. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 95, + 286, + 193 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 95, + 286, + 193 + ], + "spans": [ + { + "bbox": [ + 48, + 95, + 286, + 193 + ], + "type": "text", + "content": "[30] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. Pytorch: An imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems (NeurIPS), pages 8024-8035, 2019. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 194, + 286, + 248 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 194, + 286, + 248 + ], + "spans": [ + { + "bbox": [ + 48, + 194, + 286, + 248 + ], + "type": "text", + "content": "[31] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4937–4946, 2020. 2, 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 250, + 286, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 250, + 286, + 293 + ], + "spans": [ + { + "bbox": [ + 48, + 250, + 286, + 293 + ], + "type": "text", + "content": "[32] Johannes L Schonberger and Jan-Michael Frahm. Structure-from-motion Revisited. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 4104-4113, 2016. 1, 2, 3, 4, 6, 7, 8" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 293, + 286, + 346 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 293, + 286, + 346 + ], + "spans": [ + { + "bbox": [ + 48, + 293, + 286, + 346 + ], + "type": "text", + "content": "[33] Johannes L. Schonberger, Enliang Zheng, Jan-Michael Frahm, and Marc Pollefeys. Pixelwise view selection for unstructured multi-view stereo. In European Conference on Computer Vision (ECCV), volume 9907, pages 501-518, 2016. 7" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 349, + 286, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 349, + 286, + 392 + ], + "spans": [ + { + "bbox": [ + 48, + 349, + 286, + 392 + ], + "type": "text", + "content": "[34] Thomas Schöps, Torsten Sattler, and Marc Pollefeys. BAD SLAM: bundle adjusted direct RGB-D SLAM. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 134-144, 2019. 2, 6, 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 393, + 286, + 436 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 393, + 286, + 436 + ], + "spans": [ + { + "bbox": [ + 48, + 393, + 286, + 436 + ], + "type": "text", + "content": "[35] Noah Snavely, Steven M. Seitz, and Richard Szeliski. Skeletal graphs for efficient structure from motion. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008. 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 437, + 286, + 480 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 437, + 286, + 480 + ], + "spans": [ + { + "bbox": [ + 48, + 437, + 286, + 480 + ], + "type": "text", + "content": "[36] Edgar Sucar, Shikun Liu, Joseph Ortiz, and Andrew J. Davison. imap: Implicit mapping and positioning in real-time. In IEEE International Conference on Computer Vision (ICCV), pages 6209-6218, 2021. 3, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 481, + 286, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 481, + 286, + 514 + ], + "spans": [ + { + "bbox": [ + 48, + 481, + 286, + 514 + ], + "type": "text", + "content": "[37] Chengzhou Tang and Ping Tan. BA-Net: Dense Bundle Adjustment Networks. In International Conference on Learning Representations (ICLR), 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 514, + 286, + 569 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 514, + 286, + 569 + ], + "spans": [ + { + "bbox": [ + 48, + 514, + 286, + 569 + ], + "type": "text", + "content": "[38] Jianyuan Wang, Yiran Zhong, Yuchao Dai, Stan Birchfield, Kaihao Zhang, Nikolai Smolyanskiy, and Hongdong Li. Deep two-view structure-from-motion revisited. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 8953-8962, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 570, + 286, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 570, + 286, + 602 + ], + "spans": [ + { + "bbox": [ + 48, + 570, + 286, + 602 + ], + "type": "text", + "content": "[39] Lei Wang, Linlin Ge, Shan Luo, Zihan Yan, Zhaopeng Cui, and Jieqing Feng. Tc-sfm: Robust track-community-based structure-from-motion. CoRR, abs/2206.05866, 2022. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 603, + 286, + 658 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 603, + 286, + 658 + ], + "spans": [ + { + "bbox": [ + 48, + 603, + 286, + 658 + ], + "type": "text", + "content": "[40] Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. In Advances in Neural Information Processing Systems (NeurIPS), pages 27171-27183, 2021. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 658, + 286, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 286, + 689 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 286, + 689 + ], + "type": "text", + "content": "[41] Yiqun Wang, Ivan Skorokhodov, and Peter Wonka. Improved surface reconstruction using high-frequency details. CoRR, abs/2206.07850, 2022. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 691, + 286, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 286, + 712 + ], + "type": "text", + "content": "[42] Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor Adrian Prisacariu. Nerf-: Neural radiance fields without" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 341 + ], + "type": "list", + "angle": 0, + "index": 21, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 94 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 94 + ], + "type": "text", + "content": "known camera parameters. CoRR, abs/2102.07064, 2021. 2, 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 95, + 545, + 129 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 95, + 545, + 129 + ], + "spans": [ + { + "bbox": [ + 307, + 95, + 545, + 129 + ], + "type": "text", + "content": "[43] Kyle Wilson and Noah Snavely. Robust global translations with 1dsfm. In European Conference on Computer Vision (ECCV), volume 8691, pages 61-75, 2014. 1" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 130, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 130, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 130, + 545, + 184 + ], + "type": "text", + "content": "[44] Yuxi Xiao, Li Li, Xiaodi Li, and Jian Yao. Deepmle: A robust deep maximum likelihood estimator for two-view structure from motion. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 10643-10650, 2022. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 186, + 545, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 545, + 240 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 545, + 240 + ], + "type": "text", + "content": "[45] Yao Yao, Zixin Luo, Shiwei Li, Jingyang Zhang, Yufan Ren, Lei Zhou, Tian Fang, and Long Quan. Blendedmvs: A large-scale dataset for generalized multi-view stereo networks. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 1787-1796, 2020. 2, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 241, + 545, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 241, + 545, + 285 + ], + "spans": [ + { + "bbox": [ + 307, + 241, + 545, + 285 + ], + "type": "text", + "content": "[46] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In Advances in Neural Information Processing Systems (NeurIPS), pages 4805-4815, 2021. 3, 4" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 286, + 545, + 341 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 286, + 545, + 341 + ], + "spans": [ + { + "bbox": [ + 308, + 286, + 545, + 341 + ], + "type": "text", + "content": "[47] Zihan Zhu, Songyou Peng, Viktor Larsson, Weiwei Xu, Hujun Bao, Zhaopeng Cui, Martin R. Oswald, and Marc Pollefeys. NICE-SLAM: neural implicit scalable encoding for SLAM. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 12776-12786, 2022. 3, 8" + } + ] + } + ], + "index": 20 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 734, + 318, + 742 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 734, + 318, + 742 + ], + "spans": [ + { + "bbox": [ + 294, + 734, + 318, + 742 + ], + "type": "text", + "content": "17214" + } + ] + } + ], + "index": 22 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Leverage Interactive Affinity for Affordance Learning/65bdc10c-8ced-4506-a6bb-43e12b06ea29_content_list.json b/2023/Leverage Interactive Affinity for Affordance Learning/65bdc10c-8ced-4506-a6bb-43e12b06ea29_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..f2f156443e238e10a1e200a792334fda77bf937a --- /dev/null +++ b/2023/Leverage Interactive Affinity for Affordance Learning/65bdc10c-8ced-4506-a6bb-43e12b06ea29_content_list.json @@ -0,0 +1,1614 @@ +[ + { + "type": "text", + "text": "Leverage Interactive Affinity for Affordance Learning", + "text_level": 1, + "bbox": [ + 210, + 130, + 758, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Hongchen Luo $^{1\\ddagger}$ Wei Zhai $^{1\\ddagger}$ Jing Zhang $^{2}$ Yang Cao $^{1,4*}$ Dacheng Tao $^{3,2}$ $^{1}$ University of Science and Technology of China \n $^{2}$ The University of Sydney \n $^{3}$ JD Explore Academy \n $^{4}$ Institute of Artificial Intelligence, Hefei Comprehensive National Science Center \nlhc12@mail.ustc.edu.cn, jing.zhang1@sydney.edu.au, \n{wzhai056, forrest}@ustc.edu.cn, dacheng.tao@gmail.com", + "bbox": [ + 155, + 178, + 807, + 286 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 321, + 313, + 335 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Perceiving potential \"action possibilities\" (i.e., affordance) regions of images and learning interactive functionalities of objects from human demonstration is a challenging task due to the diversity of human-object interactions. Prevailing affordance learning algorithms often adopt the label assignment paradigm and presume that there is a unique relationship between functional region and affordance label, yielding poor performance when adapting to unseen environments with large appearance variations. In this paper, we propose to leverage interactive affinity for affordance learning, i.e. extracting interactive affinity from human-object interaction and transferring it to noninteractive objects. Interactive affinity, which represents the contacts between different parts of the human body and local regions of the target object, can provide inherent cues of interconnectivity between humans and objects, thereby reducing the ambiguity of the perceived action possibilities. Specifically, we propose a pose-aided interactive affinity learning framework that exploits human pose to guide the network to learn the interactive affinity from human-object interactions. Particularly, a keypoint heuristic perception (KHP) scheme is devised to exploit the keypoint association of human pose to alleviate the uncertainties due to interaction diversities and contact occlusions. Besides, a contact-driven affordance learning (CAL) dataset is constructed by collecting and labeling over 5,000 images. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. Code and dataset: github.com/lhc1224/PIAL-Net.", + "bbox": [ + 75, + 353, + 473, + 792 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 820, + 209, + 835 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The objective of affordance learning is to locate the \"action possibilities\" regions of an object [15, 18]. For an in-", + "bbox": [ + 76, + 845, + 468, + 876 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/98eb86f25abadbcd70c35499373d9b29964ba5c88f5492265af7eff56e51f3ed.jpg", + "image_caption": [ + "Figure 1. (a) Interaction affinity refers to the contact between different parts of the human body and the local regions of a target object. (b) The interactive affinity provides rich cues to guide the model to acquire invariant features of the object's local regions interacting with the body part, thus counteracting the multiple possibilities caused by diverse interactions." + ], + "image_footnote": [], + "bbox": [ + 532, + 316, + 856, + 468 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "telligent agent, it is vital to perceive not only the object semantics but also how to interact with various objects' local regions. Perceiving and reasoning about the object's interactable regions is a critical capability for embodied intelligent systems to interact with the environment actively, distinct from passive perception systems [3, 38, 39, 44]. Moreover, affordance learning has a wide range of applications in fields such as action recognition [13, 24, 43], scene understanding [9, 69], human-robot interaction [51, 63], autonomous driving [7] and VR/AR [50, 53].", + "bbox": [ + 496, + 582, + 892, + 733 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Affordance is a dynamic property closely related to humans and the environment [18]. Previous works [11, 37, 40, 46] focus on establishing mapping relationships between appearances and labels for affordance learning. However, they neglect the multiple possibilities of affordance brought about by changes in the environment and actors, leading to an incorrect perception. Recent studies [39, 48] utilize reinforcement learning to allow intelligent agents to perceive the environment through numerous interactions in simulated/actual scenarios. Such approaches are mainly limited by their high cost and struggle to generalize to", + "bbox": [ + 496, + 734, + 893, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author. ‡ Equal contributions.", + "bbox": [ + 94, + 886, + 339, + 898 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "6809", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/061d3118fa0f585873c713b1895f79252b44d411690656545452c6a7b6cb3fa5.jpg", + "image_caption": [ + "Figure 2. Motivation. (a) This paper explores the associations of interactable regions between diverse images by considering the context of contact regions with different body parts. (b) This paper considers leveraging the connection of human pose keypoints to alleviate the uncertainties due to interaction diversities and contact occlusions." + ], + "image_footnote": [], + "bbox": [ + 150, + 88, + 823, + 265 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "unseen scenarios [58]. To this end, researchers consider learning from human demonstration in an action-free manner [14, 29, 30, 38]. Nonetheless, they only roughly segment the whole object/interaction regions in a general way, which is still challenging to understand how the object is used. The multiple possibilities due to different local regions interacting with humans in various ways are not fully resolved. In this paper, we propose to leverage interactive affinity for affordance learning, i.e.extracting interactive affinity from human-object interaction and transferring it to non-interactive objects. The interactive affinity (Fig. 1 (a)) denotes the contacts between different human body parts and objects' local regions, which can provide inherent cues of interconnectivity between humans and objects, thereby reducing the ambiguity of the perceived action possibilities (Fig. 1 (b)).", + "bbox": [ + 75, + 340, + 472, + 582 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "However, it faces the challenges of interaction diversities and contact occlusions, leading to difficulties in acquiring a good interactive affinity representation. The human pose is independent of background, and the same interaction corresponds to approximately similar poses. Thus, it makes sense to use the association between pose keypoints to overcome the difficulty of obtaining interactive affinity representations. Moreover, it is challenging to transfer the interactive affinity to non-interactive object images due to variations in views, scales, and appearances. The context between the different body part contact regions (Fig. 2 (a)) provides the model with the possibility to explore the associations between the interactable regions of the various images to counteract transfer difficulties.", + "bbox": [ + 75, + 583, + 468, + 792 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In this paper, we present a pose-aided interactive affinity learning framework. First, an Interactive Feature Enhancement (IFE) module is introduced to explore the connections between different interactable regions of the images. Then, a Keypoint Heuristic Perception (KHP) scheme is devised to mine the interactive affinity representation from interaction and transfer it to non-interactive", + "bbox": [ + 75, + 795, + 470, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "objects. Specifically, the IFE module leverages the transformer to fully extract global contextual cues by exploiting the common relationships between their local interactable regions (Fig. 2 (a)). Then, they are used to establish associations between the object interactable regions in different images. Subsequently, the KHP scheme exploits the correlation between the human body keypoints and the contact region to guide the network to mine the object's local invariant features interacting with the body parts (Fig. 2 (b)).", + "bbox": [ + 496, + 340, + 893, + 476 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Although the numerous related datasets [9, 10, 19, 30, 36, 57, 67] that emerged during the development of affordance learning, there is still a lack of relevant datasets suited for leveraging interactive affinity. To carry out a thorough study, this paper constructs an Contact-driven Affordance Learning (CAL) dataset, consisting of 5,258 images from 23 affordance and 47 object categories. We conduct contrastive studies on the CAL dataset against six representative models in several related fields. Experimental results validate the effectiveness of our method in solving the multiple possibilities of affordance.", + "bbox": [ + 496, + 477, + 895, + 642 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Contributions: 1) We propose leveraging interactive affinity for affordance learning and establishing a CAL benchmark to facilitate the study of obtaining interactive affinity to counteract the multiple possibilities of affordance. 2) We propose a pose-aided interactive affinity learning framework that exploits pose data to guide the network to mine the interactive affinity of body parts and object regions from human-object interaction. 3) Experiments on the CAL dataset demonstrate that our model outperforms state-of-the-art methods and can serve as a strong baseline for future affordance learning research.", + "bbox": [ + 496, + 643, + 895, + 809 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 821, + 640, + 837 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Affordance Learning", + "text_level": 1, + "bbox": [ + 500, + 847, + 697, + 863 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Affordance learning has attracted extensive attention in recent years due to its immense value in many fields", + "bbox": [ + 498, + 869, + 893, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "6810", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "such as robotics, autonomous driving, embodied AI, etc [18,20,32,63]. Early works [10,11,34,35,37,40,46,47,55] mainly adopt the label assignment paradigm and presume that there is a unique relationship between functional region and affordance label. Nevertheless, it is hard to deal with the issue of the multiple possibilities due to environmental and operator changes. Recent studies consider using reinforcement learning by setting different reward functions to empower the agent to actively interact with the environment to acquire the ability to perceive affordance [26, 39, 48]. These methods involve extensive interactions and are costly in real scenarios, while there is still a large domain gap between the simulation and the real scenario. They also have the limitation of being poorly adaptive to unseen scenarios. Some other works consider learning the object's affordance from the human demonstration in an action-free manner [14, 29, 30, 38, 67], extracting interactions from the images/videos and transferring the human action intentions implied within them to the new unseen object, thus achieving perception and generalization. However, they only detect/segment the object as a whole or the interactable regions in a general way. They do not perceive how the object's local regions are used and have not fully resolved the multiple possibilities issue. In contrast to the above works, this paper considers using the inherent cues of interconnectivity between humans and objects to reduce the ambiguity of the perceived action possibilities.", + "bbox": [ + 76, + 90, + 472, + 500 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.2. Body Part Contact Learning", + "text_level": 1, + "bbox": [ + 76, + 507, + 333, + 523 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The contact between the body part and the object is an important clue for 3D reconstruction and human motion forecasting [2, 17, 31, 45, 49, 60, 65]. Independent estimation of human pose and object during 3D scene reconstruction can lead to incorrect body-scene interpolation and body floating. Human-scene contacts can provide reliable boundary conditions for improved 3D pose estimation and localization. Shimada et al. [49] use body-scene contacts to guide 3D human capture. Bhatnagar et al. [2] propose to jointly track humans, objects, and contacts along with collecting a large-scale BEHAVE dataset containing human models, objects, and contact annotations. Mao et al. [31] introduce distance-based contact maps as an explicit constraint for human motion forecasting. Yang et al. [65] propose to model hand-object interaction by explicitly representing the contact using the Contact Potential Field (CPF). In contrast to [65], this paper utilizes the interactive affinity to mine the interconnectivity between humans and objects, helping reduce the ambiguity in affordance learning.", + "bbox": [ + 75, + 530, + 472, + 819 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 76, + 829, + 166, + 844 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Given a human-object interaction image $I_{in}$ with a corresponding human pose $P$ , and a non-interactive image $I_{non}$ , we aim to extract the affordance affinity representation be", + "bbox": [ + 76, + 854, + 468, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "tween the human body part and the object local region from $I_{in}$ and transfer it to $I_{non}$ to predict the corresponding intertable region. The pose-aided interactive affinity learning framework is shown in Fig. 3. It first extracts features through a transformer [59] backbone to obtain $\\mathbb{X}_{in} = \\{X_{in_i}, i \\in [1,4]\\}$ and $\\mathbb{X}_{non} = \\{X_{non_i}, i \\in [1,4]\\}$ , respectively ( $i$ indexes the block of the backbone). Then, an interactive feature enhancement (IFE) module (Sec. 3.1) is introduced to establish a correlation between the intertable features from diverse images. Finally, a keypoint heuristic perception (KHP) scheme (Sec. 3.2) uses the association of human pose to mine the interactive affinity representation and then transfers it to the non-interactive image to predict the corresponding interactive region.", + "bbox": [ + 496, + 90, + 893, + 303 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Interactive Feature Enhancement Module", + "text_level": 1, + "bbox": [ + 498, + 311, + 857, + 327 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "The IFE module is shown in Fig. 3. Due to various views, scales, and appearances existing in the $I_{in}$ and $I_{non}$ , it is difficult to transfer the interactive affinity to the non-interactive branch. Therefore, we consider narrowing the discrepancy between the two branches by establishing the connection between the interactable regions before extracting the interactive affinity representation from human-object interaction. Inspired by the advantages of the transformer in modeling long-range global contextual information and its scalability of dealing with varied-length sequence [12, 22, 62], the IFE module adopts the transformer to associate cross-branch interactable regions. The cross transformer $(Z_1^{i + 1} = \\mathrm{CT}(Z_1^i, Z_2^i))$ is computed as:", + "bbox": [ + 496, + 335, + 890, + 532 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {Y} _ {1} ^ {i} = \\operatorname {M C A} \\left(\\operatorname {L N} \\left(\\mathbf {Z} _ {1} ^ {i}\\right), \\operatorname {L N} \\left(\\mathbf {Z} _ {2} ^ {i}\\right)\\right) + \\mathbf {Z} _ {1} ^ {i}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 576, + 541, + 890, + 558 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {Z} _ {1} ^ {i + 1} = \\operatorname {M L P} \\left(\\ln \\left(\\boldsymbol {Y} _ {1} ^ {i}\\right)\\right) + \\boldsymbol {Y} _ {1} ^ {i}, \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 599, + 566, + 890, + 583 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $\\mathrm{MCA}()$ denotes the dot-production attention [56], where query, key, and value go through different linear layers: $\\mathrm{MCA}(\\mathbf{X},\\mathbf{Y}) = \\mathrm{Attention}(\\mathbf{W}^{\\mathsf{Q}}\\mathbf{X},\\mathbf{W}^{\\mathsf{K}}\\mathbf{Y},\\mathbf{W}^{\\mathsf{V}}\\mathbf{Y})$ . The IFE module initializes a series of cross-branch tokens, which aim to aggregate the two branches' global contextual cues. Specifically, the cross-branch token $\\mathbf{X}_c^0\\in \\mathbb{R}^{l\\times c}$ ( $l$ and $c$ denote the number of tokens and channels, respectively) first extracts the contextual information of the two branches separately. It then extracts the contextual information common to $\\mathbf{X}_{in}$ and $\\mathbf{X}_{non}$ by mining the co-contextual features of their interactable regions. $\\mathbf{X}_c^i$ is updated as:", + "bbox": [ + 496, + 587, + 890, + 755 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {X} _ {c} ^ {i} = \\operatorname {C T} \\left(\\boldsymbol {X} _ {c} ^ {i}, \\left[ \\boldsymbol {X} _ {i n} ^ {i}, \\boldsymbol {X} _ {c} ^ {i} \\right]\\right), \\boldsymbol {X} _ {c} ^ {i} = \\operatorname {C T} \\left(\\boldsymbol {X} _ {c} ^ {i}, \\left[ \\boldsymbol {X} _ {\\text {n o n}} ^ {i}, \\boldsymbol {X} _ {c} ^ {i} \\right]\\right), \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 513, + 762, + 890, + 780 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {X} _ {c} ^ {i + 1} = \\operatorname {C T} \\left(\\boldsymbol {X} _ {c} ^ {i}, \\left[ \\boldsymbol {X} _ {i n} ^ {i}, \\boldsymbol {X} _ {\\text {n o n}} ^ {i}, \\boldsymbol {X} _ {c} ^ {i} \\right]\\right), \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 583, + 787, + 890, + 805 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $[\\cdot ,\\cdot ]$ represents the operation of concatenating two tensors. $X_{in}^{i}$ and $X_{non}^{i}$ $(i > 0)$ are the two branches feature sequences of the $i$ -th IFE block, respectively. $X_{in4}$ and $X_{non_4}$ are reshaped to $\\mathbb{R}^{wh\\times c}$ ( $w$ and $h$ are the width and height of the feature map, respectively) to obtain $X_{in}^{0}$ and $X_{non}^{0}$ , respectively. After updating the cross-branch", + "bbox": [ + 496, + 809, + 893, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "6811", + "bbox": [ + 482, + 944, + 513, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/c67c355c9ecf57af8bdacb9d2abe78c07fb52a7ad78eded03596f97e3512a88b.jpg", + "image_caption": [ + "Figure 3. Overview of the proposed pose-aided interactive affinity learning framework. Our model mainly consists of an interactive feature enhancement (IFE) module and a keypoint heuristic perception (KHP) scheme." + ], + "image_footnote": [], + "bbox": [ + 133, + 87, + 851, + 340 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "tokens, the IFE module then uses $X_{c}^{i + i}$ to update the interactive and non-interactive features, respectively, enabling the two branches to effectively strengthen the interaction-related features from a different branch and reduce the variation. The update procedure is as follows:", + "bbox": [ + 75, + 398, + 467, + 474 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {X} _ {i n} ^ {i + 1} = \\operatorname {C T} \\left(\\boldsymbol {X} _ {i n} ^ {i}, \\left[ \\boldsymbol {X} _ {i n} ^ {i}, \\boldsymbol {X} _ {c} ^ {i + 1} \\right]\\right), \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 169, + 481, + 468, + 500 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {X} _ {\\text {n o n}} ^ {i + 1} = \\operatorname {C T} \\left(\\boldsymbol {X} _ {\\text {n o n}} ^ {i}, \\left[ \\boldsymbol {X} _ {\\text {n o n}} ^ {i}, \\boldsymbol {X} _ {c} ^ {i + 1} \\right]\\right). \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 161, + 503, + 468, + 521 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "After passing through $m$ IFE blocks, we obtain the output features $X_{in}^{m}$ and $X_{non}^{m}$ , respectively, which integrate cross-branch interactable regions information.", + "bbox": [ + 76, + 523, + 467, + 570 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Keypoint Heuristic Perception Scheme", + "text_level": 1, + "bbox": [ + 76, + 578, + 408, + 594 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The KHP scheme is shown in Fig. 3. First, the IFE module's outputs $(X_{in}^{m} / X_{non}^{m})$ are sent to a multi-scale feature fusion layer to fuse with the shallow features extracted from the backbone, to obtain the higher resolution features [59]:", + "bbox": [ + 76, + 599, + 467, + 662 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {F} _ {i n _ {i}} = \\operatorname {U p s a m p l e} \\left(\\operatorname {L i n e a r} \\left(c, c ^ {\\prime}\\right) \\left(\\boldsymbol {X} _ {i n} ^ {m}\\right)\\right), \\quad i = 4, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 125, + 669, + 468, + 686 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {F} _ {i n _ {i}} = \\operatorname {U p s a m p l e} \\left(\\operatorname {L i n e a r} \\left(c _ {i}, c ^ {\\prime}\\right) \\left(\\boldsymbol {X} _ {i n _ {i}}\\right)\\right), \\quad i \\in [ 1, 3 ], \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 101, + 694, + 468, + 710 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\hat {\\boldsymbol {F}} _ {i n} = \\operatorname {L i n e a r} \\left(4 c ^ {\\prime}, c ^ {\\prime}\\right) \\left(\\operatorname {C o n c a t} \\left(\\boldsymbol {F} _ {i n _ {i}}\\right)\\right), \\quad i \\in [ 1, 4 ], \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 119, + 714, + 467, + 731 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathrm{Linear}(c_i, c_o)$ denotes a linear layer with inputs $C_i$ and outputs $c_o$ , respectively. $\\hat{\\pmb{F}}_{non}$ is calculated similar to $\\hat{\\pmb{F}}_{in}$ . The $\\hat{\\pmb{F}}_{in}$ is reshaped to 2D feature map and sent to a decoder $\\mathrm{Decoder}(\\cdot)$ to obtain the contact prediction: $D_{in} = \\mathrm{Decoder}(\\hat{\\pmb{F}}_{in})$ . $D_{in}$ contains $N_{cls}$ ( $N_{cls}$ is the number of body parts) channels, and the feature map at each channel represents the regions where different body parts interact with the object. The diversity of human body styles, clothing, interaction postures, and contact occlusion make it difficult to obtain an accurate interactive affinity representation of body parts and object regions. The KHP scheme", + "bbox": [ + 76, + 734, + 468, + 900 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "considers using the human pose to assist the network in suppressing irrelevant background regions and establishing a connection between body parts and contact regions. We first use a graph convolutional network (GCN) [1, 23, 64] to extract the features of pose $P$ :", + "bbox": [ + 496, + 400, + 890, + 476 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {X} _ {P} = \\boldsymbol {\\Lambda} ^ {- \\frac {1}{2}} (\\boldsymbol {A} \\otimes \\boldsymbol {M} + \\boldsymbol {I}) \\boldsymbol {\\Lambda} ^ {- \\frac {1}{2}} \\boldsymbol {P} \\boldsymbol {W} _ {g c n}, \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 568, + 483, + 890, + 502 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\pmb{P}$ indicates the coordinates of the keypoints' locations, $\\pmb{A}$ is the adjacency matrix and $\\pmb{I}$ is the identity matrix, $\\Lambda_{ii} = \\sum_{i}(\\pmb{A}_{ij} + \\pmb{I}_{ij})$ , $W_{gcn}$ is the weight matrix. $M$ is a learnable parameter matrix measuring the importance between edges, which has the same dimension as $\\pmb{A}$ and is initialized to all 1 [64]. Subsequently, we use the pose keypoint features corresponding to the body parts to guide the model to extract the corresponding interactive affinity representation from the contact region:", + "bbox": [ + 496, + 511, + 890, + 648 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {H} _ {j} = \\hat {\\boldsymbol {F}} _ {i n} \\otimes \\boldsymbol {P} _ {i n j}, \\quad \\bar {\\boldsymbol {H}} _ {j} = \\mathrm {C T} \\left(\\boldsymbol {H} _ {j}, \\boldsymbol {X} _ {P _ {j}}\\right) \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 562, + 655, + 890, + 672 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $X_{P_j}$ denotes the feature of the pose keypoint corresponding to the $j$ -th $(j \\in [1, N_{cls}])$ body part. Following this, we transfer the interactive affinity representation to the non-interactive object. We feed the interactive affinity features $\\bar{H}_j$ into the global average pooling layer (GAP) to obtain the feature representation $f_j: f_j = \\mathrm{GAP}(\\bar{H}_j)$ . Subsequently, $f_j$ is expanded to the size of $\\hat{F}_{non}$ , concatenated with $\\hat{F}_{non}$ and fed into a convolution layer to obtain the transferred features $K_j$ :", + "bbox": [ + 496, + 681, + 890, + 820 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {K} _ {j} = \\operatorname {C o n v} \\left(\\operatorname {C o n c a t} \\left(\\hat {\\boldsymbol {F}} _ {n o n}, \\operatorname {E x p a n d} \\left(\\boldsymbol {f} _ {j}\\right)\\right)\\right). \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 830, + 890, + 847 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Finally, $\\pmb{K}_j$ is concatenated together and fed into a decoder to obtain the prediction of the non-interactive branch: $D_{non} = \\mathrm{Decoder}(\\mathrm{Concat}(\\pmb{K}_j)), j \\in [1, N_{cls}]$ . During", + "bbox": [ + 496, + 854, + 890, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "6812", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/27ab41242fb44c244f1ba1bd7489e015ba794ac58a1880c8b612831d279d19b5.jpg", + "image_caption": [ + "Figure 4. Some examples and properties of Contact-driven Affordance Learning (CAL) dataset. (a) Statistics on the quantity of interactive and non-interactive images in each affordance category. (b) Confusion matrix for each affordance category interacting with body parts. (c) Some examples of interactive and non-interactive images and annotations in the dataset." + ], + "image_footnote": [], + "bbox": [ + 109, + 85, + 450, + 239 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/7945d9d84fd0380ba677323dba2a5dae84167dec24236c8e1be54d4b41aac9ce.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 460, + 88, + 851, + 239 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "training, the total loss is defined as: $L_{total} = \\lambda_1 L_{in} + \\lambda_2 L_{non}$ , where $L_{in}$ and $L_{non}$ are the binary cross-entropy losses for the two branches, respectively. $\\lambda_1$ and $\\lambda_2$ are the loss weight parameters and are both set to 1.", + "bbox": [ + 75, + 315, + 470, + 377 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiment", + "text_level": 1, + "bbox": [ + 76, + 393, + 200, + 411 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Dataset", + "text_level": 1, + "bbox": [ + 76, + 419, + 171, + 434 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Dataset collection. To fill the gap of lacking a suitable dataset, we select 5,265 images from the PAD/PADv2 [29, 67] and AGD20K [30] to compose the Contact-driven Affordance Learning (CAL) dataset. Some examples are shown in Fig. 4 (c). We refer to [6, 27] and choose the commonly used 23 affordance and 47 object categories. We retrieve images according to affordance categories, then manually filter and classify them. Finally, the dataset contains 2,689 interactive images and 2,569 non-interactive images.", + "bbox": [ + 75, + 444, + 468, + 580 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Dataset annotation. We consider the interaction of six different body parts: \"Hands\", \"Feet\", \"Mouth\", \"Hips\", \"Back\" and \"Outside\" (the \"Outside\" represents the region where the object contacts the outside world during the interaction, e.g., the wheel-ground contact region during riding). These categories cover almost all regions where humans interact with objects daily and thoroughly describe how various object regions are used. Since affordance learning recognizes \"action possibilities\" regions of the object, the heatmap is appropriate for describing the possibility of interactions. We refer to the previous annotation works [4, 5, 14, 21] and choose to annotate local regions of the image with different densities of points. Each point is first Gaussian blurred and normalized during generating the soft mask to obtain the final interaction correspondence map. Some annotated examples are shown in Fig. 4 (c).", + "bbox": [ + 75, + 582, + 468, + 823 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Statistical analysis. We conduct some statistical analysis to get a deeper insight into the dataset. Fig. 4 (a) shows the number of interactive and non-interactive images for each affordance category. The number of interactive and non-interactive images is comparable, but data imbalance", + "bbox": [ + 75, + 824, + 468, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "still exists between different categories. Fig. 4 (b) shows the confusion matrix for the affordance class with body part contact, where multiple contact regions exist for each category, implying the challenge of affordance learning.", + "bbox": [ + 498, + 315, + 890, + 377 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Benchmark Settings", + "text_level": 1, + "bbox": [ + 500, + 387, + 692, + 402 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To provide a comprehensive evaluation, three commonly used metrics Kullback-Leibler Divergence (KLD) [5], SIMiliarity (SIM) [54], and Normalized Scanpath Saliency (NSS) [42] are chosen, see supplementary material for more details. Our model is implemented in PyTorch and trained with the AdamW [28] optimizer. The input images are cropped to $224 \\times 224$ . We train the model for 60,000 iterations on a single NVIDIA 3090ti GPU with an initial learning rate of $6e-5$ and a batch size of 16. The hyperparameters $l$ and $m$ in the IFE module are set to 16 and 2, respectively. Furthermore, we provide two different settings to evaluate model learning and generalization capabilities: 1) Seen, i.e., the training set and the test set contain the same affordance/object categories, and 2) Unseen, i.e., the affordance/object categories in the training set and the test set do not overlap. The test set in Seen setting contains 4,484 interactive and non-interactive pairs, while the Unseen setting contains 3,297 pairs. The semantic segmentation method is chosen to compare the advantages of the methods in overcoming the multiple possibilities. We also choose the human pose estimation methods for comparing the interactive affinity for affordance learning approach. Besides, the few-shot segmentation model is also chosen for a fair comparison. In summary, we select 3 segmentation (DeepLabV3+ [8], PSPNet [68], Segformer [59]), 3 pose estimation (HRNet [52], ViTPose [61], HRFormer [66]), and one few-shot segmentation (HSNet [33]) methods from the relevant fields for comparison.", + "bbox": [ + 496, + 411, + 892, + 834 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Quantitative and Qualitative Comparisons", + "text_level": 1, + "bbox": [ + 500, + 845, + 862, + 863 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The experimental results of different methods on the CAL dataset are shown in Table 1. Our method outper", + "bbox": [ + 500, + 869, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "6813", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/26ad861af2bca52b2d4101c9b05f6307e13af7cc64eef17cf91bce4faa29203d.jpg", + "table_caption": [ + "Table 1. The results of different methods on the CAL dataset. The best results are in bold. Seen means that the training and test sets contain the same affordance/object categories, and Unseen means that the affordance/objects in the training and test sets do not overlap. “◇”, “♣”, and “♠” represent segmentation, human pose estimation, and few-shot segmentation models, respectively. The ◆ defines the relative improvement of our method over other methods." + ], + "table_footnote": [], + "table_body": "
MethodSeenUnseenparams (M)
KLD ↓SIM ↑NSS ↑KLD ↓SIM ↑NSS ↑
PSPNet [68]1.738◇44.5%0.332◇127.7%1.431◇160.2%9.491◇70.3%0.224◇92.0%0.960◇139.9%53.31
DLabV3+ [8]◇1.347◇28.4%0.683◇10.7%3.256◇14.3%5.632◇49.9%0.374◇15.0%1.993◇15.6%40.35
SegFormer [59]1.198◇19.4%0.741◇2.0%3.543◇5.1%5.957◇52.4%0.401◇3.6%2.167◇0.2%27.25
HRNet [52]14.897◇93.5%0.196◇285.7%1.859◇100.3%17.984◇84.3%0.045◇855.6%0.525◇338.7%28.54
ViTPose [61]♣4.303◇77.6%0.376◇101.1%1.456◇156.3%5.545◇49.1%0.246◇74.8%0.805◇186.1%89.99
HRFormer [66]1.259◇23.4%0.729◇3.7%3.479◇7.0%5.855◇51.8%0.393◇9.4%2.109◇9.2%10.10
HSNet [33]♠2.014◇52.1%0.431◇75.4%1.922◇93.7%3.016◇6.4%0.234◇83.8%1.007◇128.7%26.13
Ours0.9650.7563.7322.8230.4302.30336.32
", + "bbox": [ + 86, + 156, + 885, + 308 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/734b09abab0437f3c90c5d44b376121ef7dbb5cfdd74a813d6fe2640d2cba3f6.jpg", + "image_caption": [ + "Figure 5. Visualization of prediction results. We show the visualization results of our model, few-shot segmentation (HSNet [33]), the best human pose estimation model (HRFormer [66]) and the segmentation model (SegFormer [59])." + ], + "image_footnote": [], + "bbox": [ + 86, + 313, + 849, + 628 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "forms the segmentation, human pose estimation, and few-shot segmentation models in all metrics at both the Seen and Unseen settings. Taking KLD as the metric, our method exceeds the best segmentation method by $19.4\\%$ , surpasses the best human pose estimation model by $23.4\\%$ , and outperforms the few-shot model by $52.1\\%$ at the Seen setting. At the Unseen setting, our approach outperforms the best semantic segmentation method by $52.4\\%$ , the best human pose estimation model by $51.8\\%$ , and the few-shot segmentation model by $6.4\\%$ . Moreover, our method significantly exceeds HSNet [33] in both SIM and NSS metrics. The performance advantage of our model is even more pronounced at the Unseen setting. It validates that exploiting the intrinsic connection between the body part and the", + "bbox": [ + 75, + 688, + 472, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "object's local interactions using interactive affinity helps our model better generalize to unseen interactions and objects.", + "bbox": [ + 496, + 688, + 890, + 718 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Fig. 5 shows the visualization of affordance maps. Our method outperforms representative models at both Seen and Unseen settings. For the rugby ball and the punching bag, it is difficult to identify the corresponding interactions using the segmentation methods due to the multiple possibilities of affordance. It suggests that the interactive affinity can provide valuable hints about the interconnectedness of the human body part with the local region of the object, thereby helping reduce the ambiguity of the perceived action possibilities. For knives to be used for both \"Cut\" and \"Stick\", our model still recognizes the difference between interactions (\"Outside\"). It suggests that using pose", + "bbox": [ + 496, + 719, + 892, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "6814", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/36ea9bfa03ef8548d1c7950226f03a16bb1426cf998ce8d5a1fd1fc1bf4c96f1.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 86, + 88, + 176, + 138 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/d329ba3020863412c2dfea800bffbfd19f76624aa1fcb983229cf7bd295c262d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 179, + 88, + 267, + 138 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/09ff65746fc81693a036d221406157cea1eb666b496c3c566d61b2981fd6b706.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 272, + 88, + 362, + 138 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/9dd50001c1f697c9f1602adc6e725b590bb27080898a56737f5760a293ade5e3.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 367, + 88, + 454, + 138 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5e81d08fbb60ee1006c8c03eaca11f28d1484b8811119aa1620ce905b25650f0.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 86, + 138, + 176, + 188 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/f6563736ae521896156b051a86a38e7ff9307479118291f47c8e6edef3b94365.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 179, + 138, + 267, + 188 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/c9299e0aba8c07bb71774ed061509fbc5c144e91cf7aa8b762eb19eff1e2c975.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 274, + 138, + 362, + 188 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/15570717169525a3344450b456fce11b7b90c64ef28f002f37c8af6165fedfc8.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 367, + 138, + 454, + 188 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/e8f1197b8660ea4d92fccabfc2da399dd20aad72eff90df1982d6eecc685141c.jpg", + "image_caption": [ + "Figure 6. Different interactive images w.r.t Same non-interactive images. We show the results for predicting the back contact region under the interaction of \"Sit on\" and \"Lie on\"." + ], + "image_footnote": [], + "bbox": [ + 86, + 191, + 176, + 238 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5a50ce1ff7ae5273f498cef540d0401e5da1bb6234708331d13e95faf35ab9e6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 179, + 191, + 267, + 238 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/b9ced2e1178cd0b0cca4d477d7debed703f3d61f69742c1f54671dd5f3a6a217.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 274, + 191, + 362, + 238 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7595854db5d88d9c2599970b4e22a6cbb8429fdc2ab2fef25d3b3e2a4c7c588b.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 367, + 191, + 454, + 238 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/44345a6e4854285b931591f4df9de6d501936e6d682f32439badcf1c8d04b067.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 86, + 304, + 204, + 368 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/561e81411e9e39a0e9239bf4eec1f6216089e314e1b027cc881612288408a942.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 205, + 305, + 321, + 368 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/5c34cf239a6ccf3d227e9a33fa357007e31146457af573f758d9bdb3f8bab666.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 325, + 305, + 439, + 368 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/accfaf05d073c88b31249ddbb580d2983c6eabdd0a249875c3c9be7a0374983c.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 86, + 371, + 204, + 433 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8548cd0730254754ed78647da4e6de25f45dc43220b3acfcfbb318f447943705.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 207, + 371, + 321, + 434 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8967781888f3aec816cfeee733d70c69aaaf51d38a93664dc72c5a1fe791624e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 325, + 371, + 439, + 434 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/0d530658eed4c2c2a5659e72f31b7fc6b10c666ad29cc893b5909d9d83bdcd39.jpg", + "image_caption": [ + "Figure 7. Different interactive images w.r.t Same non-interactive images. We present the prediction results of the model for different non-interactive images of the same interactive image." + ], + "image_footnote": [], + "bbox": [ + 86, + 436, + 454, + 454 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "data as guidance can assist the network in identifying the region where the object is in contact with the outside world to complete the interaction. Fig. 6 shows the results of different interaction images for the same non-interactive image. Since \"Sit on\" and \"Lie on\" correspond to different contact regions on the back, we only visualize this part. Our model can perceive the variations between interactions and accurately transfer the interaction-related invariant features to the corresponding regions. Fig. 7 shows the prediction for the same interactive image corresponding to different non-interactive images. Although different object categories, various scales, and different views, our model can transfer the interactive affinity correctly for each part, which indicates that the IFE module can effectively establish the connection between the interactable regions from different branches and facilitate the transfer of interactive affinity.", + "bbox": [ + 75, + 529, + 468, + 772 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Ablation Study", + "text_level": 1, + "bbox": [ + 76, + 785, + 228, + 801 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "The ablation study results are shown in Table 2. Our framework shows a more pronounced improvement at the Unseen setting, suggesting that our method can generalize and transfer even for unseen interactions and objects with only a few interactive images. In particular, the KHP scheme shows a more evident improvement in the model's", + "bbox": [ + 75, + 809, + 468, + 900 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/be26bb26819a8a630eae5f303c88c7c9694bfb0d741e573abff0f7b30fe030ad.jpg", + "table_caption": [ + "Table 2. Ablation study. We investigate the influence of the IFE module and KHP scheme on model performance." + ], + "table_footnote": [], + "table_body": "
IFEKHPSeenUnseen
KLD ↓SIM ↑NSS ↑KLD ↓SIM ↑NSS ↑
✓1.2080.7283.4915.5930.3962.170
1.1260.7333.6254.5580.4042.143
✓1.1640.7463.6673.5760.4242.257
✓✓0.9650.7563.7322.8230.4302.303
", + "bbox": [ + 511, + 128, + 880, + 219 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/b41b40ce483d344a42c4425df680c3967b67fed318dd773f2300a45988c17c81.jpg", + "table_caption": [ + "Table 3. Ablation study of the pose guidance. We investigate the impact of different pose feature fusion methods in the KHP scheme. \"GAP\" means global average pooled pose node features. \"Conv\" means concatenation with the contact feature and then being fused by a convolutional layer." + ], + "table_footnote": [], + "table_body": "
ModeSeenUnseen
KLD ↓SIM ↑NSS ↑KLD ↓SIM ↑NSS ↑
GAP+Conv1.3170.7153.5174.0530.3831.962
Part+Conv1.1690.7333.6123.9330.4032.215
GAP+CT1.2530.7103.5784.2230.3861.937
Part+CT0.9650.7563.7322.8230.4302.303
", + "bbox": [ + 504, + 313, + 888, + 404 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "performance. It indicates that obtaining interactive affinity from human-object interaction can better exploit the connections between the body part and the local region of the object, thus suppressing the multiple possibilities of interaction diversity. Besides, we investigate the effect of the pose guidance on the results. As shown in Table 3, \"GAP\" loses the association between different part keypoints and performs worse than using the keypoints corresponding to different parts separately. \"Conv\" fusion is worse than the cross transformer, possibly due to the fact that the transformer explicitly computes the correlation between the contact region and the body keypoints, thus enabling better mine the interactive affinity representation.", + "bbox": [ + 496, + 429, + 890, + 625 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5. Performance Analysis", + "text_level": 1, + "bbox": [ + 498, + 635, + 705, + 651 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Different Classes. Fig. 8 shows the results of different models on each affordance category. Although our model does not outperform HSNet in some categories [33] at the Unseen setting (the scores are comparable), it still outperforms other methods, which indicates that our method has a good generalization ability when facing unseen objects. For the overlap of objects in \"Cut\", \"Stick\" and \"Lift\", a finer-grained interactive affinity representation can suppress the effects of multiple possibilities and achieve better results.", + "bbox": [ + 496, + 657, + 890, + 794 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Different body parts. Table 4 shows the results regarding different body parts. Our model achieves almost the best results at the Seen setting. At the Unseen setting, it also achieves excellent transfer performance in most interaction regions corresponding to the body parts. As most of the interaction is done through hand-object contact, the results of our model at the Unseen setting are much better", + "bbox": [ + 496, + 795, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "6815", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/37d2d6c475fb1d4d8ac41691d9ec8be7fe99e8e22ff7d836962c0dd84eacefa8.jpg", + "image_caption": [ + "Figure 8. Different classes. We measure the KLD metric for each affordance category, with darker colors representing higher performance. The left and right represent the results at the Seen and Unseen settings, respectively." + ], + "image_footnote": [], + "bbox": [ + 75, + 93, + 859, + 207 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4ece260a392754f1d58ce69556f55c6426c391076ac22690c430ce714ed9f083.jpg", + "image_caption": [ + "Figure 9. Different Hyper-parameters. We investigate the impact of the number of layers $m$ and tokens $l$ in the interactive enhancement module on the experimental results." + ], + "image_footnote": [], + "bbox": [ + 81, + 268, + 444, + 458 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "than all the other methods. It demonstrates that our method uses interactive affinity to explore the interconnections between body parts and local regions of objects, thus reducing the ambiguity in perceiving action possibilities and enabling accurate affordance knowledge transfer.", + "bbox": [ + 75, + 534, + 468, + 609 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Different hyper-parameters. Fig. 9 shows the impact of the hyper-parameters $l$ and $m$ in the IFE module on the model's performance. The influence at the Unseen setting is slightly more apparent, indicating that these parameters are more sensitive to the generalization and transfer of unseen objects. The number of cross-branch tokens greatly affects the model performance. Too small $l$ may lead to difficulties in fully expressing the contextual cues of the different branch interactable features, with the best results obtained at $l = 16$ . The model gives the best results with the number of layers $m = 2$ . The smaller $m$ makes it difficult to fully aggregate the contextual cues and establish associations in the interactable regions. As $m$ increases, it increases the parameters of the model and the complexity of the optimization, leading to negative results.", + "bbox": [ + 75, + 611, + 470, + 838 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations. As shown in Table 4, our method still has a large gap between \"Back\" and the other body parts. It is mainly due to the sample imbalance as fewer examples contain \"Back\". Besides, the sitting posture occludes most", + "bbox": [ + 75, + 839, + 470, + 901 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/dd192bf4227a6343f3fd0d6115bf7a47485f6eebb9d2cd401342b40038a65876.jpg", + "table_caption": [ + "Table 4. Different parts. We evaluate the predictions for each body part and object contact region. The Bold and Underline represent the best and sub-optimal results, respectively." + ], + "table_footnote": [], + "table_body": "
MethodHandFeetMouthHipsBackOutside
SeenPSPNet1.4711.2841.8601.4837.8181.337
DLabV3+0.7751.1953.0690.5767.1650.945
SegFormer0.5471.2932.1420.6067.5870.847
HRNet14.58317.20217.09514.19724.82313.208
HFormer0.6341.7412.5730.4307.1520.844
HSNet1.6401.5184.5611.2839.1411.218
Ours0.6871.1510.8160.3116.9750.570
UnseenPSPNet6.040-15.18711.32119.4798.043
DLabV3+4.393-11.3081.36611.3974.137
SegFormer3.989-13.0151.31011.1844.673
HRNet17.761-19.25717.22125.73717.830
HFormer4.477-11.5511.43912.1964.393
HSNet2.100-5.2591.0997.9842.356
Ours1.612-6.0840.8038.7371.855
", + "bbox": [ + 506, + 316, + 885, + 534 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "of the region where the object interacts with the back, resulting in an incomplete back contact region feature extraction. In future work, we will consider addressing the data imbalance [16, 25, 41] issue and exploiting the co-relation relationship between body parts.", + "bbox": [ + 498, + 559, + 890, + 635 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 651, + 617, + 666 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This paper proposes to leverage interactive affinity for effective affordance learning by counteracting the influence of multiple possibilities. To this end, a pose-aided interactive affinity learning framework is introduced, which is able to exploit pose data to guide the network to mine the interactive affinity representation of body parts and object local contact from human-object interaction. Furthermore, we constructed a contact-driven affordance learning (CAL) dataset by collecting and labeling over 5,000 images from 23 affordance categories. Our model outperforms six representative models in three related fields and can serve as a strong baseline for future affordance learning research.", + "bbox": [ + 496, + 676, + 890, + 857 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments. This work is supported by National Key R&D Program of China under Grant 2020AAA0105701 and ARC FL170100117 and IH180100002.", + "bbox": [ + 498, + 858, + 890, + 898 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "6816", + "bbox": [ + 482, + 944, + 514, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 106 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261, 2018.4", + "[2] Bharat Lal Bhatnagar, Xianghui Xie, Ilya A Petrov, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Behave: Dataset and method for tracking human object interactions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15935-15946, 2022. 3", + "[3] Jeannette Bohg, Karol Hausman, Bharath Sankaran, Oliver Brock, Danica Kragic, Stefan Schaal, and Gaurav S Sukhatme. Interactive perception: Leveraging action in perception and perception in action. IEEE Transactions on Robotics, 33(6):1273-1291, 2017. 1", + "[4] Zoya Bylinskii, Tilke Judd, Ali Borji, Laurent Itti, Frédo Durand, Aude Oliva, and Antonio Torralba. Mit saliency benchmark. 2015. 5", + "[5] Zoya Bylinskii, Tilke Judd, Aude Oliva, Antonio Torralba, and Frédo Durand. What do different evaluation metrics tell us about saliency models? IEEE transactions on pattern analysis and machine intelligence, 41(3):740-757, 2018. 5", + "[6] Yu-Wei Chao, Zhan Wang, Yugeng He, Jiaxuan Wang, and Jia Deng. HICO: A benchmark for recognizing human-object interactions in images. In Proceedings of the IEEE International Conference on Computer Vision, 2015. 5", + "[7] Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao. Deepdriving: Learning affordance for direct perception in autonomous driving. In Proceedings of the IEEE international conference on computer vision, pages 2722-2730, 2015. 1", + "[8] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), pages 801-818, 2018. 5, 6, 8", + "[9] Ching-Yao Chuang, Jiaman Li, Antonio Torralba, and Sanja Fidler. Learning to act properly: Predicting and explaining affordances from images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 975–983, 2018. 1, 2", + "[10] Shengheng Deng, Xun Xu, Chaozheng Wu, Ke Chen, and Kui Jia. 3d affordancenet: A benchmark for visual object affordance understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1778-1787, 2021. 2, 3", + "[11] Thanh-Toan Do, Anh Nguyen, and Ian Reid. Affordancenet: An end-to-end deep learning approach for object affordance detection. In 2018 IEEE international conference on robotics and automation (ICRA), pages 5882-5889. IEEE, 2018. 1, 3", + "[12] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Trans-" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "formers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.3", + "[13] Vibekananda Dutta and Teresa Zielinska. Action prediction based on physically grounded object affordances in human-object interactions. In 2017 11th International Workshop on Robot Motion and Control (RoMoCo), pages 47-52. IEEE, 2017. 1", + "[14] Kuan Fang, Te-Lin Wu, Daniel Yang, Silvio Savarese, and Joseph J Lim. Demo2vec: Reasoning object affordances from online videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2139-2147, 2018. 2, 3, 5", + "[15] James J Gibson. The theory of affordances. *Hilldale, USA*, 1(2):67-82, 1977. 1", + "[16] Khan Md Hasib, Md Iqbal, Faisal Muhammad Shah, Jubayer Al Mahmud, Mahmudul Hasan Popel, Md Showrov, Imran Hossain, Shakil Ahmed, Obaidur Rahman, et al. A survey of methods for managing the classification and solution of data imbalance problem. arXiv preprint arXiv:2012.11870, 2020. 8", + "[17] Mohamed Hassan, Partha Ghosh, Joachim Tesch, Dimitrios Tzionas, and Michael J Black. Populating 3d scenes by learning human-scene interaction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14708-14718, 2021. 3", + "[18] Mohammed Hassanin, Salman Khan, and Murat Tahtali. Visual affordance and function understanding: A survey. ACM Computing Surveys (CSUR), 54(3):1-35, 2021. 1, 3", + "[19] Tucker Hermans, James M Rehg, and Aaron Bobick. Affordance prediction via learned object attributes. In IEEE International Conference on Robotics and Automation (ICRA): Workshop on Semantic Perception, Mapping, and Exploration, pages 181-184. Citeseer, 2011. 2", + "[20] Lorenzo Jamone, Emre Ugur, Angelo Cangelosi, Luciano Fadiga, Alexandre Bernardino, Justus Piater, and José Santos-Victor. Affordances in psychology, neuroscience, and robotics: A survey. IEEE Transactions on Cognitive and Developmental Systems, 10(1):4-25, 2016. 3", + "[21] Tilke Judd, Frédo Durand, and Antonio Torralba. A benchmark of computational models of saliency to predict human fixations. 2012. 5", + "[22] Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah. Transformers in vision: A survey. ACM computing surveys (CSUR), 2021. 3", + "[23] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016. 4", + "[24] Hema S Koppula and Ashutosh Saxena. Anticipating human activities using object affordances for reactive robotic response. IEEE transactions on pattern analysis and machine intelligence, 38(1):14-29, 2015. 1", + "[25] Joffrey L Leevy, Taghi M Khoshgoftaar, Richard A Bauder, and Naeem Seliya. A survey on addressing high-class imbalance in big data. Journal of Big Data, 5(1):1-30, 2018. 8", + "[26] Yi-Chi Liao, Kashyap Todi, Aditya Acharya, Antti Keurulainen, Andrew Howes, and Antti Oulasvirta. Rediscovering" + ], + "bbox": [ + 501, + 93, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "6817", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "affordance: A reinforcement learning perspective. In CHI Conference on Human Factors in Computing Systems, pages 1-15, 2022. 3", + "[27] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 5", + "[28] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5", + "[29] Hongchen Luo, Wei Zhai, Jing Zhang, Yang Cao, and Dacheng Tao. One-shot affordance detection. arXiv preprint arXiv:2106.14747, 2021. 2, 3, 5", + "[30] Hongchen Luo, Wei Zhai, Jing Zhang, Yang Cao, and Dacheng Tao. Learning affordance grounding from exocentric images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2252-2261, 2022. 2, 3, 5", + "[31] Wei Mao, Miaomiao Liu, Richard Hartley, and Mathieu Salzmann. Contact-aware human motion forecasting. arXiv preprint arXiv:2210.03954, 2022. 3", + "[32] Huaqing Min, Ronghua Luo, Jinhui Zhu, Sheng Bi, et al. Affordance research in developmental robotics: A survey. IEEE Transactions on Cognitive and Developmental Systems, 8(4):237-255, 2016. 3", + "[33] Juhong Min, Dahiyun Kang, and Minsu Cho. Hypercorrelation squeeze for few-shot segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 5, 6, 7, 8", + "[34] Kaichun Mo, Leonidas J Guibas, Mustafa Mukadam, Abhinav Gupta, and Shubham Tulsiani. Where2act: From pixels to actions for articulated 3d objects. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6813-6823, 2021. 3", + "[35] Kaichun Mo, Yuzhe Qin, Fanbo Xiang, Hao Su, and Leonidas Guibas. O2o-afford: Annotation-free large-scale object-object affordance learning. In Conference on Robot Learning, pages 1666-1677. PMLR, 2022. 3", + "[36] Roozbeh Mottaghi, Connor Schenck, Dieter Fox, and Ali Farhadi. See the glass half full: Reasoning about liquid containers, their volume and content. In Proceedings of the IEEE International Conference on Computer Vision, pages 1871-1880, 2017. 2", + "[37] Austin Myers, Ching L Teo, Cornelia Fermüller, and Yiannis Aloimonos. Affordance detection of tool parts from geometric features. In 2015 IEEE International Conference on Robotics and Automation (ICRA), pages 1374-1381. IEEE, 2015. 1, 3", + "[38] Tushar Nagarajan, Christoph Feichtenhofer, and Kristen Grauman. Grounded human-object interaction hotspots from video. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8688-8697, 2019. 1, 2, 3", + "[39] Tushar Nagarajan and Kristen Grauman. Learning affordance landscapes for interaction exploration in 3d environments. Advances in Neural Information Processing Systems, 33:2005-2015, 2020. 1, 3" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[40] Anh Nguyen, Dimitrios Kanoulas, Darwin G Caldwell, and Nikos G Tsagarakis. Detecting object affordances with convolutional neural networks. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2765-2770. IEEE, 2016. 1, 3", + "[41] Minlong Peng, Qi Zhang, Xiaoyu Xing, Tao Gui, Xuanjing Huang, Yu-Gang Jiang, Keyu Ding, and Zhigang Chen. Trainable undersampling for class-imbalance learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4707-4714, 2019. 8", + "[42] Robert J Peters, Asha Iyer, Laurent Itti, and Christof Koch. Components of bottom-up gaze allocation in natural images. Vision research, 45(18):2397-2416, 2005. 5", + "[43] Siyuan Qi, Siyuan Huang, Ping Wei, and Song-Chun Zhu. Predicting human activities using stochastic grammar. In Proceedings of the IEEE International Conference on Computer Vision, pages 1164-1172, 2017. 1", + "[44] Santhosh K Ramakrishnan, Dinesh Jayaraman, and Kristen Grauman. An exploration of embodied visual exploration. International Journal of Computer Vision, 129(5):1616-1649, 2021. 1", + "[45] Manolis Savva, Angel X Chang, Pat Hanrahan, Matthew Fisher, and Matthias Nießner. Pigraphs: learning interaction snapshots from observations. ACM Transactions on Graphics (TOG), 35(4):1-12, 2016. 3", + "[46] Johann Sawatzky and Jurgen Gall. Adaptive binarization for weakly supervised affordance segmentation. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 1383-1391, 2017. 1, 3", + "[47] Johann Sawatzky, Abhilash Srikantha, and Juergen Gall. Weakly supervised affordance detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2795-2804, 2017. 3", + "[48] Giulio Schiavi, Paula Wulkop, Giuseppe Rizzi, Lionel Ott, Roland Siegwart, and Jen Jen Chung. Learning agent-aware affordances for closed-loop interaction with articulated objects. arXiv preprint arXiv:2209.05802, 2022. 1, 3", + "[49] Soshi Shimada, Vladislav Golyanik, Zhi Li, Patrick Pérez, Weipeng Xu, and Christian Theobalt. Hulc: 3d human motion capture with pose manifold sampling and dense contact guidance. In European Conference on Computer Vision, pages 516-533. Springer, 2022. 3", + "[50] Dong-Hee Shin. The role of affordance in the experience of virtual reality learning: Technological and affective affordances in virtual reality. Telematics and Informatics, 34(8):1826-1836, 2017. 1", + "[51] Tianmin Shu, Xiaofeng Gao, Michael S Ryoo, and Song-Chun Zhu. Learning social affordance grammar from videos: Transferring human interactions to human-robot interactions. In 2017 IEEE international conference on robotics and automation (ICRA), pages 1669–1676. IEEE, 2017. 1", + "[52] Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning for human pose estimation. In CVPR, 2019. 5, 6, 8", + "[53] Yuan Sun, Shuyue Fang, and Zuopeng Justin Zhang. Impression management strategies on enterprise social media platforms: An affordance perspective. International Journal of Information Management, 60:102359, 2021. 1" + ], + "bbox": [ + 501, + 92, + 890, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "6818", + "bbox": [ + 482, + 945, + 514, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[54] Michael J Swain and Dana H Ballard. Color indexing. International Journal of Computer Vision (IJCV), 7(1):11-32, 1991. 5", + "[55] Karthik Mahesh Varadarajan and Markus Vincze. Parallel deep learning with suggestive activation for object category recognition. In International Conference on Computer Vision Systems, pages 354-363. Springer, 2013. 3", + "[56] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3", + "[57] Xiaolong Wang, Rohit Girdhar, and Abhinav Gupta. Binge watching: Scaling affordance learning from sitcoms. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2596-2605, 2017. 2", + "[58] Yian Wang, Ruihai Wu, Kaichun Mo, Jiaqi Ke, Qingnan Fan, Leonidas J Guibas, and Hao Dong. Adaafford: Learning to adapt manipulation affordance for 3d articulated objects via few-shot interactions. In European Conference on Computer Vision, pages 90-107. Springer, 2022. 2", + "[59] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34:12077-12090, 2021. 3, 4, 5, 6, 8", + "[60] Xianghui Xie, Bharat Lal Bhatnagar, and Gerard Pons-Moll. Chore: Contact, human and object reconstruction from a single rgb image. arXiv preprint arXiv:2204.02445, 2022. 3", + "[61] Yufei Xu, Jing Zhang, Qiming Zhang, and Dacheng Tao. ViTPose: Simple vision transformer baselines for human pose estimation. In Advances in Neural Information Processing Systems, 2022. 5, 6", + "[62] Yufei Xu, Qiming Zhang, Jing Zhang, and Dacheng Tao. ViTAE: Vision transformer advanced by exploring intrinsic inductive bias. Advances in Neural Information Processing Systems, 34, 2021. 3", + "[63] Natsuki Yamanobe, Weiwei Wan, Ixchel G Ramirez-Alpizar, Damien Petit, Tokuo Tsuji, Shuichi Akizuki, Manabu Hashimoto, Kazuyuki Nagata, and Kensuke Harada. A brief review of affordance in robotic manipulation research. Advanced Robotics, 31(19-20):1086-1101, 2017. 1, 3", + "[64] Sijie Yan, Yuanjun Xiong, and Dahua Lin. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Thirty-second AAAI conference on artificial intelligence, 2018. 4", + "[65] Lixin Yang, Xinyu Zhan, Kailin Li, Wenqiang Xu, Jiefeng Li, and Cewu Lu. Cpf: Learning a contact potential field to model the hand-object interaction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11097-11106, 2021. 3", + "[66] Yuhui Yuan, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang, Xilin Chen, and Jingdong Wang. Hrformer: High-resolution transformer for dense prediction. 2021. 5, 6, 8", + "[67] Wei Zhai, Hongchen Luo, Jing Zhang, Yang Cao, and Dacheng Tao. One-shot object affordance detection in the wild. International Journal of Computer Vision, 130(10):2472-2500, 2022. 2, 3, 5" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[68] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881-2890, 2017. 5, 6, 8", + "[69] Yixin Zhu, Yibiao Zhao, and Song Chun Zhu. Understanding tools: Task-oriented object modeling, learning and recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2855-2864, 2015. 1" + ], + "bbox": [ + 501, + 90, + 893, + 204 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "6819", + "bbox": [ + 482, + 944, + 516, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Leverage Interactive Affinity for Affordance Learning/65bdc10c-8ced-4506-a6bb-43e12b06ea29_model.json b/2023/Leverage Interactive Affinity for Affordance Learning/65bdc10c-8ced-4506-a6bb-43e12b06ea29_model.json new file mode 100644 index 0000000000000000000000000000000000000000..1f36ab3f3fcb30ee909605e23e01e76aa218cc05 --- /dev/null +++ b/2023/Leverage Interactive Affinity for Affordance Learning/65bdc10c-8ced-4506-a6bb-43e12b06ea29_model.json @@ -0,0 +1,2356 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.212, + 0.131, + 0.759, + 0.154 + ], + "angle": 0, + "content": "Leverage Interactive Affinity for Affordance Learning" + }, + { + "type": "text", + "bbox": [ + 0.156, + 0.179, + 0.808, + 0.287 + ], + "angle": 0, + "content": "Hongchen Luo\\(^{1\\ddagger}\\) Wei Zhai\\(^{1\\ddagger}\\) Jing Zhang\\(^{2}\\) Yang Cao\\(^{1,4*}\\) Dacheng Tao\\(^{3,2}\\) \n\\(^{1}\\) University of Science and Technology of China \n\\(^{2}\\) The University of Sydney \n\\(^{3}\\) JD Explore Academy \n\\(^{4}\\) Institute of Artificial Intelligence, Hefei Comprehensive National Science Center \nlhc12@mail.ustc.edu.cn, jing.zhang1@sydney.edu.au, \n{wzhai056, forrest}@ustc.edu.cn, dacheng.tao@gmail.com" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.322, + 0.314, + 0.337 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.354, + 0.474, + 0.794 + ], + "angle": 0, + "content": "Perceiving potential \"action possibilities\" (i.e., affordance) regions of images and learning interactive functionalities of objects from human demonstration is a challenging task due to the diversity of human-object interactions. Prevailing affordance learning algorithms often adopt the label assignment paradigm and presume that there is a unique relationship between functional region and affordance label, yielding poor performance when adapting to unseen environments with large appearance variations. In this paper, we propose to leverage interactive affinity for affordance learning, i.e. extracting interactive affinity from human-object interaction and transferring it to noninteractive objects. Interactive affinity, which represents the contacts between different parts of the human body and local regions of the target object, can provide inherent cues of interconnectivity between humans and objects, thereby reducing the ambiguity of the perceived action possibilities. Specifically, we propose a pose-aided interactive affinity learning framework that exploits human pose to guide the network to learn the interactive affinity from human-object interactions. Particularly, a keypoint heuristic perception (KHP) scheme is devised to exploit the keypoint association of human pose to alleviate the uncertainties due to interaction diversities and contact occlusions. Besides, a contact-driven affordance learning (CAL) dataset is constructed by collecting and labeling over 5,000 images. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. Code and dataset: github.com/lhc1224/PIAL-Net." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.821, + 0.21, + 0.836 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.847, + 0.47, + 0.877 + ], + "angle": 0, + "content": "The objective of affordance learning is to locate the \"action possibilities\" regions of an object [15, 18]. For an in-" + }, + { + "type": "image", + "bbox": [ + 0.534, + 0.318, + 0.857, + 0.469 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.499, + 0.473, + 0.892, + 0.556 + ], + "angle": 0, + "content": "Figure 1. (a) Interaction affinity refers to the contact between different parts of the human body and the local regions of a target object. (b) The interactive affinity provides rich cues to guide the model to acquire invariant features of the object's local regions interacting with the body part, thus counteracting the multiple possibilities caused by diverse interactions." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.583, + 0.893, + 0.734 + ], + "angle": 0, + "content": "telligent agent, it is vital to perceive not only the object semantics but also how to interact with various objects' local regions. Perceiving and reasoning about the object's interactable regions is a critical capability for embodied intelligent systems to interact with the environment actively, distinct from passive perception systems [3, 38, 39, 44]. Moreover, affordance learning has a wide range of applications in fields such as action recognition [13, 24, 43], scene understanding [9, 69], human-robot interaction [51, 63], autonomous driving [7] and VR/AR [50, 53]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.895, + 0.902 + ], + "angle": 0, + "content": "Affordance is a dynamic property closely related to humans and the environment [18]. Previous works [11, 37, 40, 46] focus on establishing mapping relationships between appearances and labels for affordance learning. However, they neglect the multiple possibilities of affordance brought about by changes in the environment and actors, leading to an incorrect perception. Recent studies [39, 48] utilize reinforcement learning to allow intelligent agents to perceive the environment through numerous interactions in simulated/actual scenarios. Such approaches are mainly limited by their high cost and struggle to generalize to" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.887, + 0.341, + 0.9 + ], + "angle": 0, + "content": "*Corresponding author. ‡ Equal contributions." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6809" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.151, + 0.089, + 0.824, + 0.266 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.273, + 0.895, + 0.318 + ], + "angle": 0, + "content": "Figure 2. Motivation. (a) This paper explores the associations of interactable regions between diverse images by considering the context of contact regions with different body parts. (b) This paper considers leveraging the connection of human pose keypoints to alleviate the uncertainties due to interaction diversities and contact occlusions." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.341, + 0.473, + 0.583 + ], + "angle": 0, + "content": "unseen scenarios [58]. To this end, researchers consider learning from human demonstration in an action-free manner [14, 29, 30, 38]. Nonetheless, they only roughly segment the whole object/interaction regions in a general way, which is still challenging to understand how the object is used. The multiple possibilities due to different local regions interacting with humans in various ways are not fully resolved. In this paper, we propose to leverage interactive affinity for affordance learning, i.e.extracting interactive affinity from human-object interaction and transferring it to non-interactive objects. The interactive affinity (Fig. 1 (a)) denotes the contacts between different human body parts and objects' local regions, which can provide inherent cues of interconnectivity between humans and objects, thereby reducing the ambiguity of the perceived action possibilities (Fig. 1 (b))." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.584, + 0.47, + 0.794 + ], + "angle": 0, + "content": "However, it faces the challenges of interaction diversities and contact occlusions, leading to difficulties in acquiring a good interactive affinity representation. The human pose is independent of background, and the same interaction corresponds to approximately similar poses. Thus, it makes sense to use the association between pose keypoints to overcome the difficulty of obtaining interactive affinity representations. Moreover, it is challenging to transfer the interactive affinity to non-interactive object images due to variations in views, scales, and appearances. The context between the different body part contact regions (Fig. 2 (a)) provides the model with the possibility to explore the associations between the interactable regions of the various images to counteract transfer difficulties." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.796, + 0.471, + 0.901 + ], + "angle": 0, + "content": "In this paper, we present a pose-aided interactive affinity learning framework. First, an Interactive Feature Enhancement (IFE) module is introduced to explore the connections between different interactable regions of the images. Then, a Keypoint Heuristic Perception (KHP) scheme is devised to mine the interactive affinity representation from interaction and transfer it to non-interactive" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.341, + 0.895, + 0.477 + ], + "angle": 0, + "content": "objects. Specifically, the IFE module leverages the transformer to fully extract global contextual cues by exploiting the common relationships between their local interactable regions (Fig. 2 (a)). Then, they are used to establish associations between the object interactable regions in different images. Subsequently, the KHP scheme exploits the correlation between the human body keypoints and the contact region to guide the network to mine the object's local invariant features interacting with the body parts (Fig. 2 (b))." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.478, + 0.896, + 0.643 + ], + "angle": 0, + "content": "Although the numerous related datasets [9, 10, 19, 30, 36, 57, 67] that emerged during the development of affordance learning, there is still a lack of relevant datasets suited for leveraging interactive affinity. To carry out a thorough study, this paper constructs an Contact-driven Affordance Learning (CAL) dataset, consisting of 5,258 images from 23 affordance and 47 object categories. We conduct contrastive studies on the CAL dataset against six representative models in several related fields. Experimental results validate the effectiveness of our method in solving the multiple possibilities of affordance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.644, + 0.897, + 0.81 + ], + "angle": 0, + "content": "Contributions: 1) We propose leveraging interactive affinity for affordance learning and establishing a CAL benchmark to facilitate the study of obtaining interactive affinity to counteract the multiple possibilities of affordance. 2) We propose a pose-aided interactive affinity learning framework that exploits pose data to guide the network to mine the interactive affinity of body parts and object regions from human-object interaction. 3) Experiments on the CAL dataset demonstrate that our model outperforms state-of-the-art methods and can serve as a strong baseline for future affordance learning research." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.822, + 0.642, + 0.838 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.848, + 0.699, + 0.864 + ], + "angle": 0, + "content": "2.1. Affordance Learning" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Affordance learning has attracted extensive attention in recent years due to its immense value in many fields" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "6810" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.473, + 0.501 + ], + "angle": 0, + "content": "such as robotics, autonomous driving, embodied AI, etc [18,20,32,63]. Early works [10,11,34,35,37,40,46,47,55] mainly adopt the label assignment paradigm and presume that there is a unique relationship between functional region and affordance label. Nevertheless, it is hard to deal with the issue of the multiple possibilities due to environmental and operator changes. Recent studies consider using reinforcement learning by setting different reward functions to empower the agent to actively interact with the environment to acquire the ability to perceive affordance [26, 39, 48]. These methods involve extensive interactions and are costly in real scenarios, while there is still a large domain gap between the simulation and the real scenario. They also have the limitation of being poorly adaptive to unseen scenarios. Some other works consider learning the object's affordance from the human demonstration in an action-free manner [14, 29, 30, 38, 67], extracting interactions from the images/videos and transferring the human action intentions implied within them to the new unseen object, thus achieving perception and generalization. However, they only detect/segment the object as a whole or the interactable regions in a general way. They do not perceive how the object's local regions are used and have not fully resolved the multiple possibilities issue. In contrast to the above works, this paper considers using the inherent cues of interconnectivity between humans and objects to reduce the ambiguity of the perceived action possibilities." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.508, + 0.334, + 0.525 + ], + "angle": 0, + "content": "2.2. Body Part Contact Learning" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.531, + 0.473, + 0.82 + ], + "angle": 0, + "content": "The contact between the body part and the object is an important clue for 3D reconstruction and human motion forecasting [2, 17, 31, 45, 49, 60, 65]. Independent estimation of human pose and object during 3D scene reconstruction can lead to incorrect body-scene interpolation and body floating. Human-scene contacts can provide reliable boundary conditions for improved 3D pose estimation and localization. Shimada et al. [49] use body-scene contacts to guide 3D human capture. Bhatnagar et al. [2] propose to jointly track humans, objects, and contacts along with collecting a large-scale BEHAVE dataset containing human models, objects, and contact annotations. Mao et al. [31] introduce distance-based contact maps as an explicit constraint for human motion forecasting. Yang et al. [65] propose to model hand-object interaction by explicitly representing the contact using the Contact Potential Field (CPF). In contrast to [65], this paper utilizes the interactive affinity to mine the interconnectivity between humans and objects, helping reduce the ambiguity in affordance learning." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.83, + 0.168, + 0.845 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.856, + 0.47, + 0.903 + ], + "angle": 0, + "content": "Given a human-object interaction image \\( I_{in} \\) with a corresponding human pose \\( P \\), and a non-interactive image \\( I_{non} \\), we aim to extract the affordance affinity representation be" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.895, + 0.304 + ], + "angle": 0, + "content": "tween the human body part and the object local region from \\( I_{in} \\) and transfer it to \\( I_{non} \\) to predict the corresponding intertable region. The pose-aided interactive affinity learning framework is shown in Fig. 3. It first extracts features through a transformer [59] backbone to obtain \\( \\mathbb{X}_{in} = \\{X_{in_i}, i \\in [1,4]\\} \\) and \\( \\mathbb{X}_{non} = \\{X_{non_i}, i \\in [1,4]\\} \\), respectively (\\( i \\) indexes the block of the backbone). Then, an interactive feature enhancement (IFE) module (Sec. 3.1) is introduced to establish a correlation between the intertable features from diverse images. Finally, a keypoint heuristic perception (KHP) scheme (Sec. 3.2) uses the association of human pose to mine the interactive affinity representation and then transfers it to the non-interactive image to predict the corresponding interactive region." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.313, + 0.858, + 0.328 + ], + "angle": 0, + "content": "3.1. Interactive Feature Enhancement Module" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.336, + 0.892, + 0.534 + ], + "angle": 0, + "content": "The IFE module is shown in Fig. 3. Due to various views, scales, and appearances existing in the \\(I_{in}\\) and \\(I_{non}\\), it is difficult to transfer the interactive affinity to the non-interactive branch. Therefore, we consider narrowing the discrepancy between the two branches by establishing the connection between the interactable regions before extracting the interactive affinity representation from human-object interaction. Inspired by the advantages of the transformer in modeling long-range global contextual information and its scalability of dealing with varied-length sequence [12, 22, 62], the IFE module adopts the transformer to associate cross-branch interactable regions. The cross transformer \\((Z_1^{i + 1} = \\mathrm{CT}(Z_1^i, Z_2^i))\\) is computed as:" + }, + { + "type": "equation", + "bbox": [ + 0.577, + 0.542, + 0.891, + 0.559 + ], + "angle": 0, + "content": "\\[\n\\mathbf {Y} _ {1} ^ {i} = \\operatorname {M C A} \\left(\\operatorname {L N} \\left(\\mathbf {Z} _ {1} ^ {i}\\right), \\operatorname {L N} \\left(\\mathbf {Z} _ {2} ^ {i}\\right)\\right) + \\mathbf {Z} _ {1} ^ {i}, \\tag {1}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.601, + 0.567, + 0.891, + 0.584 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {Z} _ {1} ^ {i + 1} = \\operatorname {M L P} \\left(\\ln \\left(\\boldsymbol {Y} _ {1} ^ {i}\\right)\\right) + \\boldsymbol {Y} _ {1} ^ {i}, \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.588, + 0.892, + 0.756 + ], + "angle": 0, + "content": "where \\(\\mathrm{MCA}()\\) denotes the dot-production attention [56], where query, key, and value go through different linear layers: \\(\\mathrm{MCA}(\\mathbf{X},\\mathbf{Y}) = \\mathrm{Attention}(\\mathbf{W}^{\\mathsf{Q}}\\mathbf{X},\\mathbf{W}^{\\mathsf{K}}\\mathbf{Y},\\mathbf{W}^{\\mathsf{V}}\\mathbf{Y})\\). The IFE module initializes a series of cross-branch tokens, which aim to aggregate the two branches' global contextual cues. Specifically, the cross-branch token \\(\\mathbf{X}_c^0\\in \\mathbb{R}^{l\\times c}\\) (\\(l\\) and \\(c\\) denote the number of tokens and channels, respectively) first extracts the contextual information of the two branches separately. It then extracts the contextual information common to \\(\\mathbf{X}_{in}\\) and \\(\\mathbf{X}_{non}\\) by mining the co-contextual features of their interactable regions. \\(\\mathbf{X}_c^i\\) is updated as:" + }, + { + "type": "equation", + "bbox": [ + 0.514, + 0.763, + 0.892, + 0.781 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {X} _ {c} ^ {i} = \\operatorname {C T} \\left(\\boldsymbol {X} _ {c} ^ {i}, \\left[ \\boldsymbol {X} _ {i n} ^ {i}, \\boldsymbol {X} _ {c} ^ {i} \\right]\\right), \\boldsymbol {X} _ {c} ^ {i} = \\operatorname {C T} \\left(\\boldsymbol {X} _ {c} ^ {i}, \\left[ \\boldsymbol {X} _ {\\text {n o n}} ^ {i}, \\boldsymbol {X} _ {c} ^ {i} \\right]\\right), \\tag {3}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.584, + 0.789, + 0.891, + 0.806 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {X} _ {c} ^ {i + 1} = \\operatorname {C T} \\left(\\boldsymbol {X} _ {c} ^ {i}, \\left[ \\boldsymbol {X} _ {i n} ^ {i}, \\boldsymbol {X} _ {\\text {n o n}} ^ {i}, \\boldsymbol {X} _ {c} ^ {i} \\right]\\right), \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.81, + 0.895, + 0.903 + ], + "angle": 0, + "content": "where \\([\\cdot ,\\cdot ]\\) represents the operation of concatenating two tensors. \\(X_{in}^{i}\\) and \\(X_{non}^{i}\\) \\((i > 0)\\) are the two branches feature sequences of the \\(i\\)-th IFE block, respectively. \\(X_{in4}\\) and \\(X_{non_4}\\) are reshaped to \\(\\mathbb{R}^{wh\\times c}\\) (\\(w\\) and \\(h\\) are the width and height of the feature map, respectively) to obtain \\(X_{in}^{0}\\) and \\(X_{non}^{0}\\), respectively. After updating the cross-branch" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.514, + 0.957 + ], + "angle": 0, + "content": "6811" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.134, + 0.088, + 0.852, + 0.341 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.346, + 0.89, + 0.377 + ], + "angle": 0, + "content": "Figure 3. Overview of the proposed pose-aided interactive affinity learning framework. Our model mainly consists of an interactive feature enhancement (IFE) module and a keypoint heuristic perception (KHP) scheme." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.4, + 0.468, + 0.476 + ], + "angle": 0, + "content": "tokens, the IFE module then uses \\( X_{c}^{i + i} \\) to update the interactive and non-interactive features, respectively, enabling the two branches to effectively strengthen the interaction-related features from a different branch and reduce the variation. The update procedure is as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.17, + 0.482, + 0.469, + 0.5 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {X} _ {i n} ^ {i + 1} = \\operatorname {C T} \\left(\\boldsymbol {X} _ {i n} ^ {i}, \\left[ \\boldsymbol {X} _ {i n} ^ {i}, \\boldsymbol {X} _ {c} ^ {i + 1} \\right]\\right), \\tag {5}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.162, + 0.505, + 0.469, + 0.522 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {X} _ {\\text {n o n}} ^ {i + 1} = \\operatorname {C T} \\left(\\boldsymbol {X} _ {\\text {n o n}} ^ {i}, \\left[ \\boldsymbol {X} _ {\\text {n o n}} ^ {i}, \\boldsymbol {X} _ {c} ^ {i + 1} \\right]\\right). \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.525, + 0.468, + 0.571 + ], + "angle": 0, + "content": "After passing through \\( m \\) IFE blocks, we obtain the output features \\( X_{in}^{m} \\) and \\( X_{non}^{m} \\), respectively, which integrate cross-branch interactable regions information." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.579, + 0.409, + 0.595 + ], + "angle": 0, + "content": "3.2. Keypoint Heuristic Perception Scheme" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.601, + 0.468, + 0.663 + ], + "angle": 0, + "content": "The KHP scheme is shown in Fig. 3. First, the IFE module's outputs \\((X_{in}^{m} / X_{non}^{m})\\) are sent to a multi-scale feature fusion layer to fuse with the shallow features extracted from the backbone, to obtain the higher resolution features [59]:" + }, + { + "type": "equation", + "bbox": [ + 0.126, + 0.67, + 0.469, + 0.688 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {F} _ {i n _ {i}} = \\operatorname {U p s a m p l e} \\left(\\operatorname {L i n e a r} \\left(c, c ^ {\\prime}\\right) \\left(\\boldsymbol {X} _ {i n} ^ {m}\\right)\\right), \\quad i = 4, \\tag {7}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.102, + 0.695, + 0.469, + 0.712 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {F} _ {i n _ {i}} = \\operatorname {U p s a m p l e} \\left(\\operatorname {L i n e a r} \\left(c _ {i}, c ^ {\\prime}\\right) \\left(\\boldsymbol {X} _ {i n _ {i}}\\right)\\right), \\quad i \\in [ 1, 3 ], \\tag {8}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.12, + 0.715, + 0.468, + 0.732 + ], + "angle": 0, + "content": "\\[\n\\hat {\\boldsymbol {F}} _ {i n} = \\operatorname {L i n e a r} \\left(4 c ^ {\\prime}, c ^ {\\prime}\\right) \\left(\\operatorname {C o n c a t} \\left(\\boldsymbol {F} _ {i n _ {i}}\\right)\\right), \\quad i \\in [ 1, 4 ], \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.735, + 0.469, + 0.901 + ], + "angle": 0, + "content": "where \\(\\mathrm{Linear}(c_i, c_o)\\) denotes a linear layer with inputs \\(C_i\\) and outputs \\(c_o\\), respectively. \\(\\hat{\\pmb{F}}_{non}\\) is calculated similar to \\(\\hat{\\pmb{F}}_{in}\\). The \\(\\hat{\\pmb{F}}_{in}\\) is reshaped to 2D feature map and sent to a decoder \\(\\mathrm{Decoder}(\\cdot)\\) to obtain the contact prediction: \\(D_{in} = \\mathrm{Decoder}(\\hat{\\pmb{F}}_{in})\\). \\(D_{in}\\) contains \\(N_{cls}\\) (\\(N_{cls}\\) is the number of body parts) channels, and the feature map at each channel represents the regions where different body parts interact with the object. The diversity of human body styles, clothing, interaction postures, and contact occlusion make it difficult to obtain an accurate interactive affinity representation of body parts and object regions. The KHP scheme" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.401, + 0.892, + 0.477 + ], + "angle": 0, + "content": "considers using the human pose to assist the network in suppressing irrelevant background regions and establishing a connection between body parts and contact regions. We first use a graph convolutional network (GCN) [1, 23, 64] to extract the features of pose \\( P \\):" + }, + { + "type": "equation", + "bbox": [ + 0.569, + 0.484, + 0.891, + 0.503 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {X} _ {P} = \\boldsymbol {\\Lambda} ^ {- \\frac {1}{2}} (\\boldsymbol {A} \\otimes \\boldsymbol {M} + \\boldsymbol {I}) \\boldsymbol {\\Lambda} ^ {- \\frac {1}{2}} \\boldsymbol {P} \\boldsymbol {W} _ {g c n}, \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.512, + 0.892, + 0.649 + ], + "angle": 0, + "content": "where \\( \\pmb{P} \\) indicates the coordinates of the keypoints' locations, \\( \\pmb{A} \\) is the adjacency matrix and \\( \\pmb{I} \\) is the identity matrix, \\( \\Lambda_{ii} = \\sum_{i}(\\pmb{A}_{ij} + \\pmb{I}_{ij}) \\), \\( W_{gcn} \\) is the weight matrix. \\( M \\) is a learnable parameter matrix measuring the importance between edges, which has the same dimension as \\( \\pmb{A} \\) and is initialized to all 1 [64]. Subsequently, we use the pose keypoint features corresponding to the body parts to guide the model to extract the corresponding interactive affinity representation from the contact region:" + }, + { + "type": "equation", + "bbox": [ + 0.563, + 0.656, + 0.891, + 0.674 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {H} _ {j} = \\hat {\\boldsymbol {F}} _ {i n} \\otimes \\boldsymbol {P} _ {i n j}, \\quad \\bar {\\boldsymbol {H}} _ {j} = \\mathrm {C T} \\left(\\boldsymbol {H} _ {j}, \\boldsymbol {X} _ {P _ {j}}\\right) \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.682, + 0.892, + 0.821 + ], + "angle": 0, + "content": "where \\(X_{P_j}\\) denotes the feature of the pose keypoint corresponding to the \\(j\\)-th \\((j \\in [1, N_{cls}])\\) body part. Following this, we transfer the interactive affinity representation to the non-interactive object. We feed the interactive affinity features \\(\\bar{H}_j\\) into the global average pooling layer (GAP) to obtain the feature representation \\(f_j: f_j = \\mathrm{GAP}(\\bar{H}_j)\\). Subsequently, \\(f_j\\) is expanded to the size of \\(\\hat{F}_{non}\\), concatenated with \\(\\hat{F}_{non}\\) and fed into a convolution layer to obtain the transferred features \\(K_j\\):" + }, + { + "type": "equation", + "bbox": [ + 0.568, + 0.831, + 0.891, + 0.848 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {K} _ {j} = \\operatorname {C o n v} \\left(\\operatorname {C o n c a t} \\left(\\hat {\\boldsymbol {F}} _ {n o n}, \\operatorname {E x p a n d} \\left(\\boldsymbol {f} _ {j}\\right)\\right)\\right). \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.892, + 0.903 + ], + "angle": 0, + "content": "Finally, \\(\\pmb{K}_j\\) is concatenated together and fed into a decoder to obtain the prediction of the non-interactive branch: \\(D_{non} = \\mathrm{Decoder}(\\mathrm{Concat}(\\pmb{K}_j)), j \\in [1, N_{cls}]\\). During" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6812" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.111, + 0.086, + 0.452, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.461, + 0.089, + 0.852, + 0.24 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.247, + 0.894, + 0.29 + ], + "angle": 0, + "content": "Figure 4. Some examples and properties of Contact-driven Affordance Learning (CAL) dataset. (a) Statistics on the quantity of interactive and non-interactive images in each affordance category. (b) Confusion matrix for each affordance category interacting with body parts. (c) Some examples of interactive and non-interactive images and annotations in the dataset." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.316, + 0.471, + 0.378 + ], + "angle": 0, + "content": "training, the total loss is defined as: \\( L_{total} = \\lambda_1 L_{in} + \\lambda_2 L_{non} \\), where \\( L_{in} \\) and \\( L_{non} \\) are the binary cross-entropy losses for the two branches, respectively. \\( \\lambda_1 \\) and \\( \\lambda_2 \\) are the loss weight parameters and are both set to 1." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.395, + 0.202, + 0.412 + ], + "angle": 0, + "content": "4. Experiment" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.42, + 0.173, + 0.435 + ], + "angle": 0, + "content": "4.1. Dataset" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.445, + 0.47, + 0.581 + ], + "angle": 0, + "content": "Dataset collection. To fill the gap of lacking a suitable dataset, we select 5,265 images from the PAD/PADv2 [29, 67] and AGD20K [30] to compose the Contact-driven Affordance Learning (CAL) dataset. Some examples are shown in Fig. 4 (c). We refer to [6, 27] and choose the commonly used 23 affordance and 47 object categories. We retrieve images according to affordance categories, then manually filter and classify them. Finally, the dataset contains 2,689 interactive images and 2,569 non-interactive images." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.583, + 0.47, + 0.824 + ], + "angle": 0, + "content": "Dataset annotation. We consider the interaction of six different body parts: \"Hands\", \"Feet\", \"Mouth\", \"Hips\", \"Back\" and \"Outside\" (the \"Outside\" represents the region where the object contacts the outside world during the interaction, e.g., the wheel-ground contact region during riding). These categories cover almost all regions where humans interact with objects daily and thoroughly describe how various object regions are used. Since affordance learning recognizes \"action possibilities\" regions of the object, the heatmap is appropriate for describing the possibility of interactions. We refer to the previous annotation works [4, 5, 14, 21] and choose to annotate local regions of the image with different densities of points. Each point is first Gaussian blurred and normalized during generating the soft mask to obtain the final interaction correspondence map. Some annotated examples are shown in Fig. 4 (c)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.47, + 0.901 + ], + "angle": 0, + "content": "Statistical analysis. We conduct some statistical analysis to get a deeper insight into the dataset. Fig. 4 (a) shows the number of interactive and non-interactive images for each affordance category. The number of interactive and non-interactive images is comparable, but data imbalance" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.316, + 0.892, + 0.378 + ], + "angle": 0, + "content": "still exists between different categories. Fig. 4 (b) shows the confusion matrix for the affordance class with body part contact, where multiple contact regions exist for each category, implying the challenge of affordance learning." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.388, + 0.693, + 0.404 + ], + "angle": 0, + "content": "4.2. Benchmark Settings" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.412, + 0.893, + 0.835 + ], + "angle": 0, + "content": "To provide a comprehensive evaluation, three commonly used metrics Kullback-Leibler Divergence (KLD) [5], SIMiliarity (SIM) [54], and Normalized Scanpath Saliency (NSS) [42] are chosen, see supplementary material for more details. Our model is implemented in PyTorch and trained with the AdamW [28] optimizer. The input images are cropped to \\(224 \\times 224\\). We train the model for 60,000 iterations on a single NVIDIA 3090ti GPU with an initial learning rate of \\(6e-5\\) and a batch size of 16. The hyperparameters \\(l\\) and \\(m\\) in the IFE module are set to 16 and 2, respectively. Furthermore, we provide two different settings to evaluate model learning and generalization capabilities: 1) Seen, i.e., the training set and the test set contain the same affordance/object categories, and 2) Unseen, i.e., the affordance/object categories in the training set and the test set do not overlap. The test set in Seen setting contains 4,484 interactive and non-interactive pairs, while the Unseen setting contains 3,297 pairs. The semantic segmentation method is chosen to compare the advantages of the methods in overcoming the multiple possibilities. We also choose the human pose estimation methods for comparing the interactive affinity for affordance learning approach. Besides, the few-shot segmentation model is also chosen for a fair comparison. In summary, we select 3 segmentation (DeepLabV3+ [8], PSPNet [68], Segformer [59]), 3 pose estimation (HRNet [52], ViTPose [61], HRFormer [66]), and one few-shot segmentation (HSNet [33]) methods from the relevant fields for comparison." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.847, + 0.863, + 0.864 + ], + "angle": 0, + "content": "4.3. Quantitative and Qualitative Comparisons" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "The experimental results of different methods on the CAL dataset are shown in Table 1. Our method outper" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6813" + } + ], + [ + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.089, + 0.895, + 0.146 + ], + "angle": 0, + "content": "Table 1. The results of different methods on the CAL dataset. The best results are in bold. Seen means that the training and test sets contain the same affordance/object categories, and Unseen means that the affordance/objects in the training and test sets do not overlap. “◇”, “♣”, and “♠” represent segmentation, human pose estimation, and few-shot segmentation models, respectively. The ◆ defines the relative improvement of our method over other methods." + }, + { + "type": "table", + "bbox": [ + 0.087, + 0.157, + 0.887, + 0.309 + ], + "angle": 0, + "content": "
MethodSeenUnseenparams (M)
KLD ↓SIM ↑NSS ↑KLD ↓SIM ↑NSS ↑
PSPNet [68]1.738◇44.5%0.332◇127.7%1.431◇160.2%9.491◇70.3%0.224◇92.0%0.960◇139.9%53.31
DLabV3+ [8]◇1.347◇28.4%0.683◇10.7%3.256◇14.3%5.632◇49.9%0.374◇15.0%1.993◇15.6%40.35
SegFormer [59]1.198◇19.4%0.741◇2.0%3.543◇5.1%5.957◇52.4%0.401◇3.6%2.167◇0.2%27.25
HRNet [52]14.897◇93.5%0.196◇285.7%1.859◇100.3%17.984◇84.3%0.045◇855.6%0.525◇338.7%28.54
ViTPose [61]♣4.303◇77.6%0.376◇101.1%1.456◇156.3%5.545◇49.1%0.246◇74.8%0.805◇186.1%89.99
HRFormer [66]1.259◇23.4%0.729◇3.7%3.479◇7.0%5.855◇51.8%0.393◇9.4%2.109◇9.2%10.10
HSNet [33]♠2.014◇52.1%0.431◇75.4%1.922◇93.7%3.016◇6.4%0.234◇83.8%1.007◇128.7%26.13
Ours0.9650.7563.7322.8230.4302.30336.32
" + }, + { + "type": "image", + "bbox": [ + 0.087, + 0.314, + 0.851, + 0.629 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.634, + 0.893, + 0.664 + ], + "angle": 0, + "content": "Figure 5. Visualization of prediction results. We show the visualization results of our model, few-shot segmentation (HSNet [33]), the best human pose estimation model (HRFormer [66]) and the segmentation model (SegFormer [59])." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.689, + 0.473, + 0.901 + ], + "angle": 0, + "content": "forms the segmentation, human pose estimation, and few-shot segmentation models in all metrics at both the Seen and Unseen settings. Taking KLD as the metric, our method exceeds the best segmentation method by \\(19.4\\%\\), surpasses the best human pose estimation model by \\(23.4\\%\\), and outperforms the few-shot model by \\(52.1\\%\\) at the Seen setting. At the Unseen setting, our approach outperforms the best semantic segmentation method by \\(52.4\\%\\), the best human pose estimation model by \\(51.8\\%\\), and the few-shot segmentation model by \\(6.4\\%\\). Moreover, our method significantly exceeds HSNet [33] in both SIM and NSS metrics. The performance advantage of our model is even more pronounced at the Unseen setting. It validates that exploiting the intrinsic connection between the body part and the" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.892, + 0.719 + ], + "angle": 0, + "content": "object's local interactions using interactive affinity helps our model better generalize to unseen interactions and objects." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.72, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Fig. 5 shows the visualization of affordance maps. Our method outperforms representative models at both Seen and Unseen settings. For the rugby ball and the punching bag, it is difficult to identify the corresponding interactions using the segmentation methods due to the multiple possibilities of affordance. It suggests that the interactive affinity can provide valuable hints about the interconnectedness of the human body part with the local region of the object, thereby helping reduce the ambiguity of the perceived action possibilities. For knives to be used for both \"Cut\" and \"Stick\", our model still recognizes the difference between interactions (\"Outside\"). It suggests that using pose" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6814" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.088, + 0.089, + 0.177, + 0.139 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.181, + 0.089, + 0.269, + 0.139 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.274, + 0.089, + 0.364, + 0.139 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.368, + 0.089, + 0.455, + 0.139 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.14, + 0.177, + 0.189 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.181, + 0.14, + 0.269, + 0.189 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.275, + 0.14, + 0.364, + 0.189 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.368, + 0.14, + 0.455, + 0.189 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.192, + 0.177, + 0.239 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.181, + 0.192, + 0.269, + 0.239 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.275, + 0.192, + 0.364, + 0.239 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.368, + 0.192, + 0.455, + 0.239 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.246, + 0.47, + 0.287 + ], + "angle": 0, + "content": "Figure 6. Different interactive images w.r.t Same non-interactive images. We show the results for predicting the back contact region under the interaction of \"Sit on\" and \"Lie on\"." + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.305, + 0.205, + 0.369 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.207, + 0.306, + 0.322, + 0.369 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.326, + 0.306, + 0.441, + 0.369 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.372, + 0.205, + 0.434 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.208, + 0.372, + 0.323, + 0.435 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.326, + 0.372, + 0.441, + 0.435 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.088, + 0.438, + 0.455, + 0.455 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.459, + 0.47, + 0.502 + ], + "angle": 0, + "content": "Figure 7. Different interactive images w.r.t Same non-interactive images. We present the prediction results of the model for different non-interactive images of the same interactive image." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.53, + 0.47, + 0.773 + ], + "angle": 0, + "content": "data as guidance can assist the network in identifying the region where the object is in contact with the outside world to complete the interaction. Fig. 6 shows the results of different interaction images for the same non-interactive image. Since \"Sit on\" and \"Lie on\" correspond to different contact regions on the back, we only visualize this part. Our model can perceive the variations between interactions and accurately transfer the interaction-related invariant features to the corresponding regions. Fig. 7 shows the prediction for the same interactive image corresponding to different non-interactive images. Although different object categories, various scales, and different views, our model can transfer the interactive affinity correctly for each part, which indicates that the IFE module can effectively establish the connection between the interactable regions from different branches and facilitate the transfer of interactive affinity." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.786, + 0.23, + 0.802 + ], + "angle": 0, + "content": "4.4. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.81, + 0.47, + 0.901 + ], + "angle": 0, + "content": "The ablation study results are shown in Table 2. Our framework shows a more pronounced improvement at the Unseen setting, suggesting that our method can generalize and transfer even for unseen interactions and objects with only a few interactive images. In particular, the KHP scheme shows a more evident improvement in the model's" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.089, + 0.892, + 0.118 + ], + "angle": 0, + "content": "Table 2. Ablation study. We investigate the influence of the IFE module and KHP scheme on model performance." + }, + { + "type": "table", + "bbox": [ + 0.513, + 0.129, + 0.882, + 0.22 + ], + "angle": 0, + "content": "
IFEKHPSeenUnseen
KLD ↓SIM ↑NSS ↑KLD ↓SIM ↑NSS ↑
✓1.2080.7283.4915.5930.3962.170
1.1260.7333.6254.5580.4042.143
✓1.1640.7463.6673.5760.4242.257
✓✓0.9650.7563.7322.8230.4302.303
" + }, + { + "type": "table_caption", + "bbox": [ + 0.498, + 0.232, + 0.892, + 0.303 + ], + "angle": 0, + "content": "Table 3. Ablation study of the pose guidance. We investigate the impact of different pose feature fusion methods in the KHP scheme. \"GAP\" means global average pooled pose node features. \"Conv\" means concatenation with the contact feature and then being fused by a convolutional layer." + }, + { + "type": "table", + "bbox": [ + 0.506, + 0.314, + 0.889, + 0.405 + ], + "angle": 0, + "content": "
ModeSeenUnseen
KLD ↓SIM ↑NSS ↑KLD ↓SIM ↑NSS ↑
GAP+Conv1.3170.7153.5174.0530.3831.962
Part+Conv1.1690.7333.6123.9330.4032.215
GAP+CT1.2530.7103.5784.2230.3861.937
Part+CT0.9650.7563.7322.8230.4302.303
" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.43, + 0.892, + 0.626 + ], + "angle": 0, + "content": "performance. It indicates that obtaining interactive affinity from human-object interaction can better exploit the connections between the body part and the local region of the object, thus suppressing the multiple possibilities of interaction diversity. Besides, we investigate the effect of the pose guidance on the results. As shown in Table 3, \"GAP\" loses the association between different part keypoints and performs worse than using the keypoints corresponding to different parts separately. \"Conv\" fusion is worse than the cross transformer, possibly due to the fact that the transformer explicitly computes the correlation between the contact region and the body keypoints, thus enabling better mine the interactive affinity representation." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.636, + 0.707, + 0.652 + ], + "angle": 0, + "content": "4.5. Performance Analysis" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.659, + 0.892, + 0.795 + ], + "angle": 0, + "content": "Different Classes. Fig. 8 shows the results of different models on each affordance category. Although our model does not outperform HSNet in some categories [33] at the Unseen setting (the scores are comparable), it still outperforms other methods, which indicates that our method has a good generalization ability when facing unseen objects. For the overlap of objects in \"Cut\", \"Stick\" and \"Lift\", a finer-grained interactive affinity representation can suppress the effects of multiple possibilities and achieve better results." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.796, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Different body parts. Table 4 shows the results regarding different body parts. Our model achieves almost the best results at the Seen setting. At the Unseen setting, it also achieves excellent transfer performance in most interaction regions corresponding to the body parts. As most of the interaction is done through hand-object contact, the results of our model at the Unseen setting are much better" + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.957 + ], + "angle": 0, + "content": "6815" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.076, + 0.094, + 0.861, + 0.208 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.21, + 0.89, + 0.24 + ], + "angle": 0, + "content": "Figure 8. Different classes. We measure the KLD metric for each affordance category, with darker colors representing higher performance. The left and right represent the results at the Seen and Unseen settings, respectively." + }, + { + "type": "image", + "bbox": [ + 0.083, + 0.27, + 0.445, + 0.459 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.463, + 0.47, + 0.506 + ], + "angle": 0, + "content": "Figure 9. Different Hyper-parameters. We investigate the impact of the number of layers \\( m \\) and tokens \\( l \\) in the interactive enhancement module on the experimental results." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.535, + 0.47, + 0.61 + ], + "angle": 0, + "content": "than all the other methods. It demonstrates that our method uses interactive affinity to explore the interconnections between body parts and local regions of objects, thus reducing the ambiguity in perceiving action possibilities and enabling accurate affordance knowledge transfer." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.612, + 0.471, + 0.839 + ], + "angle": 0, + "content": "Different hyper-parameters. Fig. 9 shows the impact of the hyper-parameters \\( l \\) and \\( m \\) in the IFE module on the model's performance. The influence at the Unseen setting is slightly more apparent, indicating that these parameters are more sensitive to the generalization and transfer of unseen objects. The number of cross-branch tokens greatly affects the model performance. Too small \\( l \\) may lead to difficulties in fully expressing the contextual cues of the different branch interactable features, with the best results obtained at \\( l = 16 \\). The model gives the best results with the number of layers \\( m = 2 \\). The smaller \\( m \\) makes it difficult to fully aggregate the contextual cues and establish associations in the interactable regions. As \\( m \\) increases, it increases the parameters of the model and the complexity of the optimization, leading to negative results." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Limitations. As shown in Table 4, our method still has a large gap between \"Back\" and the other body parts. It is mainly due to the sample imbalance as fewer examples contain \"Back\". Besides, the sitting posture occludes most" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.263, + 0.893, + 0.306 + ], + "angle": 0, + "content": "Table 4. Different parts. We evaluate the predictions for each body part and object contact region. The Bold and Underline represent the best and sub-optimal results, respectively." + }, + { + "type": "table", + "bbox": [ + 0.508, + 0.317, + 0.887, + 0.535 + ], + "angle": 0, + "content": "
MethodHandFeetMouthHipsBackOutside
SeenPSPNet1.4711.2841.8601.4837.8181.337
DLabV3+0.7751.1953.0690.5767.1650.945
SegFormer0.5471.2932.1420.6067.5870.847
HRNet14.58317.20217.09514.19724.82313.208
HFormer0.6341.7412.5730.4307.1520.844
HSNet1.6401.5184.5611.2839.1411.218
Ours0.6871.1510.8160.3116.9750.570
UnseenPSPNet6.040-15.18711.32119.4798.043
DLabV3+4.393-11.3081.36611.3974.137
SegFormer3.989-13.0151.31011.1844.673
HRNet17.761-19.25717.22125.73717.830
HFormer4.477-11.5511.43912.1964.393
HSNet2.100-5.2591.0997.9842.356
Ours1.612-6.0840.8038.7371.855
" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.56, + 0.892, + 0.636 + ], + "angle": 0, + "content": "of the region where the object interacts with the back, resulting in an incomplete back contact region feature extraction. In future work, we will consider addressing the data imbalance [16, 25, 41] issue and exploiting the co-relation relationship between body parts." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.652, + 0.619, + 0.667 + ], + "angle": 0, + "content": "5. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.677, + 0.892, + 0.858 + ], + "angle": 0, + "content": "This paper proposes to leverage interactive affinity for effective affordance learning by counteracting the influence of multiple possibilities. To this end, a pose-aided interactive affinity learning framework is introduced, which is able to exploit pose data to guide the network to mine the interactive affinity representation of body parts and object local contact from human-object interaction. Furthermore, we constructed a contact-driven affordance learning (CAL) dataset by collecting and labeling over 5,000 images from 23 affordance categories. Our model outperforms six representative models in three related fields and can serve as a strong baseline for future affordance learning research." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.859, + 0.892, + 0.9 + ], + "angle": 0, + "content": "Acknowledgments. This work is supported by National Key R&D Program of China under Grant 2020AAA0105701 and ARC FL170100117 and IH180100002." + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.516, + 0.956 + ], + "angle": 0, + "content": "6816" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.079, + 0.09, + 0.176, + 0.107 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.197 + ], + "angle": 0, + "content": "[1] Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261, 2018.4" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.2, + 0.472, + 0.281 + ], + "angle": 0, + "content": "[2] Bharat Lal Bhatnagar, Xianghui Xie, Ilya A Petrov, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Behave: Dataset and method for tracking human object interactions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15935-15946, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.284, + 0.472, + 0.352 + ], + "angle": 0, + "content": "[3] Jeannette Bohg, Karol Hausman, Bharath Sankaran, Oliver Brock, Danica Kragic, Stefan Schaal, and Gaurav S Sukhatme. Interactive perception: Leveraging action in perception and perception in action. IEEE Transactions on Robotics, 33(6):1273-1291, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.354, + 0.472, + 0.394 + ], + "angle": 0, + "content": "[4] Zoya Bylinskii, Tilke Judd, Ali Borji, Laurent Itti, Frédo Durand, Aude Oliva, and Antonio Torralba. Mit saliency benchmark. 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.397, + 0.472, + 0.451 + ], + "angle": 0, + "content": "[5] Zoya Bylinskii, Tilke Judd, Aude Oliva, Antonio Torralba, and Frédo Durand. What do different evaluation metrics tell us about saliency models? IEEE transactions on pattern analysis and machine intelligence, 41(3):740-757, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.453, + 0.472, + 0.508 + ], + "angle": 0, + "content": "[6] Yu-Wei Chao, Zhan Wang, Yugeng He, Jiaxuan Wang, and Jia Deng. HICO: A benchmark for recognizing human-object interactions in images. In Proceedings of the IEEE International Conference on Computer Vision, 2015. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.51, + 0.472, + 0.577 + ], + "angle": 0, + "content": "[7] Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao. Deepdriving: Learning affordance for direct perception in autonomous driving. In Proceedings of the IEEE international conference on computer vision, pages 2722-2730, 2015. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.579, + 0.472, + 0.648 + ], + "angle": 0, + "content": "[8] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), pages 801-818, 2018. 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.65, + 0.472, + 0.717 + ], + "angle": 0, + "content": "[9] Ching-Yao Chuang, Jiaman Li, Antonio Torralba, and Sanja Fidler. Learning to act properly: Predicting and explaining affordances from images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 975–983, 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.72, + 0.472, + 0.788 + ], + "angle": 0, + "content": "[10] Shengheng Deng, Xun Xu, Chaozheng Wu, Ke Chen, and Kui Jia. 3d affordancenet: A benchmark for visual object affordance understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1778-1787, 2021. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.79, + 0.472, + 0.845 + ], + "angle": 0, + "content": "[11] Thanh-Toan Do, Anh Nguyen, and Ian Reid. Affordancenet: An end-to-end deep learning approach for object affordance detection. In 2018 IEEE international conference on robotics and automation (ICRA), pages 5882-5889. IEEE, 2018. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.079, + 0.847, + 0.472, + 0.901 + ], + "angle": 0, + "content": "[12] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Trans-" + }, + { + "type": "list", + "bbox": [ + 0.079, + 0.115, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.094, + 0.892, + 0.119 + ], + "angle": 0, + "content": "formers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.122, + 0.894, + 0.188 + ], + "angle": 0, + "content": "[13] Vibekananda Dutta and Teresa Zielinska. Action prediction based on physically grounded object affordances in human-object interactions. In 2017 11th International Workshop on Robot Motion and Control (RoMoCo), pages 47-52. IEEE, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.191, + 0.894, + 0.259 + ], + "angle": 0, + "content": "[14] Kuan Fang, Te-Lin Wu, Daniel Yang, Silvio Savarese, and Joseph J Lim. Demo2vec: Reasoning object affordances from online videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2139-2147, 2018. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.261, + 0.892, + 0.287 + ], + "angle": 0, + "content": "[15] James J Gibson. The theory of affordances. *Hilldale, USA*, 1(2):67-82, 1977. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.289, + 0.894, + 0.37 + ], + "angle": 0, + "content": "[16] Khan Md Hasib, Md Iqbal, Faisal Muhammad Shah, Jubayer Al Mahmud, Mahmudul Hasan Popel, Md Showrov, Imran Hossain, Shakil Ahmed, Obaidur Rahman, et al. A survey of methods for managing the classification and solution of data imbalance problem. arXiv preprint arXiv:2012.11870, 2020. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.372, + 0.894, + 0.441 + ], + "angle": 0, + "content": "[17] Mohamed Hassan, Partha Ghosh, Joachim Tesch, Dimitrios Tzionas, and Michael J Black. Populating 3d scenes by learning human-scene interaction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14708-14718, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.442, + 0.892, + 0.483 + ], + "angle": 0, + "content": "[18] Mohammed Hassanin, Salman Khan, and Murat Tahtali. Visual affordance and function understanding: A survey. ACM Computing Surveys (CSUR), 54(3):1-35, 2021. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.484, + 0.892, + 0.552 + ], + "angle": 0, + "content": "[19] Tucker Hermans, James M Rehg, and Aaron Bobick. Affordance prediction via learned object attributes. In IEEE International Conference on Robotics and Automation (ICRA): Workshop on Semantic Perception, Mapping, and Exploration, pages 181-184. Citeseer, 2011. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.554, + 0.892, + 0.621 + ], + "angle": 0, + "content": "[20] Lorenzo Jamone, Emre Ugur, Angelo Cangelosi, Luciano Fadiga, Alexandre Bernardino, Justus Piater, and José Santos-Victor. Affordances in psychology, neuroscience, and robotics: A survey. IEEE Transactions on Cognitive and Developmental Systems, 10(1):4-25, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.623, + 0.892, + 0.663 + ], + "angle": 0, + "content": "[21] Tilke Judd, Frédo Durand, and Antonio Torralba. A benchmark of computational models of saliency to predict human fixations. 2012. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.665, + 0.892, + 0.719 + ], + "angle": 0, + "content": "[22] Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah. Transformers in vision: A survey. ACM computing surveys (CSUR), 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.721, + 0.892, + 0.761 + ], + "angle": 0, + "content": "[23] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.763, + 0.892, + 0.817 + ], + "angle": 0, + "content": "[24] Hema S Koppula and Ashutosh Saxena. Anticipating human activities using object affordances for reactive robotic response. IEEE transactions on pattern analysis and machine intelligence, 38(1):14-29, 2015. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.818, + 0.892, + 0.871 + ], + "angle": 0, + "content": "[25] Joffrey L Leevy, Taghi M Khoshgoftaar, Richard A Bauder, and Naeem Seliya. A survey on addressing high-class imbalance in big data. Journal of Big Data, 5(1):1-30, 2018. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.873, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[26] Yi-Chi Liao, Kashyap Todi, Aditya Acharya, Antti Keurulainen, Andrew Howes, and Antti Oulasvirta. Rediscovering" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.094, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "6817" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.092, + 0.47, + 0.133 + ], + "angle": 0, + "content": "affordance: A reinforcement learning perspective. In CHI Conference on Human Factors in Computing Systems, pages 1-15, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.136, + 0.47, + 0.205 + ], + "angle": 0, + "content": "[27] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.207, + 0.469, + 0.234 + ], + "angle": 0, + "content": "[28] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.236, + 0.47, + 0.276 + ], + "angle": 0, + "content": "[29] Hongchen Luo, Wei Zhai, Jing Zhang, Yang Cao, and Dacheng Tao. One-shot affordance detection. arXiv preprint arXiv:2106.14747, 2021. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.279, + 0.47, + 0.347 + ], + "angle": 0, + "content": "[30] Hongchen Luo, Wei Zhai, Jing Zhang, Yang Cao, and Dacheng Tao. Learning affordance grounding from exocentric images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2252-2261, 2022. 2, 3, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.349, + 0.469, + 0.39 + ], + "angle": 0, + "content": "[31] Wei Mao, Miaomiao Liu, Richard Hartley, and Mathieu Salzmann. Contact-aware human motion forecasting. arXiv preprint arXiv:2210.03954, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.393, + 0.469, + 0.447 + ], + "angle": 0, + "content": "[32] Huaqing Min, Ronghua Luo, Jinhui Zhu, Sheng Bi, et al. Affordance research in developmental robotics: A survey. IEEE Transactions on Cognitive and Developmental Systems, 8(4):237-255, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.45, + 0.47, + 0.504 + ], + "angle": 0, + "content": "[33] Juhong Min, Dahiyun Kang, and Minsu Cho. Hypercorrelation squeeze for few-shot segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.507, + 0.469, + 0.575 + ], + "angle": 0, + "content": "[34] Kaichun Mo, Leonidas J Guibas, Mustafa Mukadam, Abhinav Gupta, and Shubham Tulsiani. Where2act: From pixels to actions for articulated 3d objects. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6813-6823, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.577, + 0.469, + 0.632 + ], + "angle": 0, + "content": "[35] Kaichun Mo, Yuzhe Qin, Fanbo Xiang, Hao Su, and Leonidas Guibas. O2o-afford: Annotation-free large-scale object-object affordance learning. In Conference on Robot Learning, pages 1666-1677. PMLR, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.634, + 0.469, + 0.701 + ], + "angle": 0, + "content": "[36] Roozbeh Mottaghi, Connor Schenck, Dieter Fox, and Ali Farhadi. See the glass half full: Reasoning about liquid containers, their volume and content. In Proceedings of the IEEE International Conference on Computer Vision, pages 1871-1880, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.705, + 0.469, + 0.771 + ], + "angle": 0, + "content": "[37] Austin Myers, Ching L Teo, Cornelia Fermüller, and Yiannis Aloimonos. Affordance detection of tool parts from geometric features. In 2015 IEEE International Conference on Robotics and Automation (ICRA), pages 1374-1381. IEEE, 2015. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.775, + 0.469, + 0.842 + ], + "angle": 0, + "content": "[38] Tushar Nagarajan, Christoph Feichtenhofer, and Kristen Grauman. Grounded human-object interaction hotspots from video. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8688-8697, 2019. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[39] Tushar Nagarajan and Kristen Grauman. Learning affordance landscapes for interaction exploration in 3d environments. Advances in Neural Information Processing Systems, 33:2005-2015, 2020. 1, 3" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.16 + ], + "angle": 0, + "content": "[40] Anh Nguyen, Dimitrios Kanoulas, Darwin G Caldwell, and Nikos G Tsagarakis. Detecting object affordances with convolutional neural networks. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2765-2770. IEEE, 2016. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.163, + 0.892, + 0.232 + ], + "angle": 0, + "content": "[41] Minlong Peng, Qi Zhang, Xiaoyu Xing, Tao Gui, Xuanjing Huang, Yu-Gang Jiang, Keyu Ding, and Zhigang Chen. Trainable undersampling for class-imbalance learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4707-4714, 2019. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.233, + 0.892, + 0.273 + ], + "angle": 0, + "content": "[42] Robert J Peters, Asha Iyer, Laurent Itti, and Christof Koch. Components of bottom-up gaze allocation in natural images. Vision research, 45(18):2397-2416, 2005. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.274, + 0.892, + 0.328 + ], + "angle": 0, + "content": "[43] Siyuan Qi, Siyuan Huang, Ping Wei, and Song-Chun Zhu. Predicting human activities using stochastic grammar. In Proceedings of the IEEE International Conference on Computer Vision, pages 1164-1172, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.33, + 0.892, + 0.383 + ], + "angle": 0, + "content": "[44] Santhosh K Ramakrishnan, Dinesh Jayaraman, and Kristen Grauman. An exploration of embodied visual exploration. International Journal of Computer Vision, 129(5):1616-1649, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.386, + 0.892, + 0.44 + ], + "angle": 0, + "content": "[45] Manolis Savva, Angel X Chang, Pat Hanrahan, Matthew Fisher, and Matthias Nießner. Pigraphs: learning interaction snapshots from observations. ACM Transactions on Graphics (TOG), 35(4):1-12, 2016. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.442, + 0.892, + 0.496 + ], + "angle": 0, + "content": "[46] Johann Sawatzky and Jurgen Gall. Adaptive binarization for weakly supervised affordance segmentation. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 1383-1391, 2017. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.498, + 0.892, + 0.552 + ], + "angle": 0, + "content": "[47] Johann Sawatzky, Abhilash Srikantha, and Juergen Gall. Weakly supervised affordance detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2795-2804, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.554, + 0.892, + 0.608 + ], + "angle": 0, + "content": "[48] Giulio Schiavi, Paula Wulkop, Giuseppe Rizzi, Lionel Ott, Roland Siegwart, and Jen Jen Chung. Learning agent-aware affordances for closed-loop interaction with articulated objects. arXiv preprint arXiv:2209.05802, 2022. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.609, + 0.892, + 0.678 + ], + "angle": 0, + "content": "[49] Soshi Shimada, Vladislav Golyanik, Zhi Li, Patrick Pérez, Weipeng Xu, and Christian Theobalt. Hulc: 3d human motion capture with pose manifold sampling and dense contact guidance. In European Conference on Computer Vision, pages 516-533. Springer, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.679, + 0.892, + 0.733 + ], + "angle": 0, + "content": "[50] Dong-Hee Shin. The role of affordance in the experience of virtual reality learning: Technological and affective affordances in virtual reality. Telematics and Informatics, 34(8):1826-1836, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.735, + 0.892, + 0.803 + ], + "angle": 0, + "content": "[51] Tianmin Shu, Xiaofeng Gao, Michael S Ryoo, and Song-Chun Zhu. Learning social affordance grammar from videos: Transferring human interactions to human-robot interactions. In 2017 IEEE international conference on robotics and automation (ICRA), pages 1669–1676. IEEE, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.804, + 0.892, + 0.844 + ], + "angle": 0, + "content": "[52] Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning for human pose estimation. In CVPR, 2019. 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.846, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[53] Yuan Sun, Shuyue Fang, and Zuopeng Justin Zhang. Impression management strategies on enterprise social media platforms: An affordance perspective. International Journal of Information Management, 60:102359, 2021. 1" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.946, + 0.516, + 0.956 + ], + "angle": 0, + "content": "6818" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.133 + ], + "angle": 0, + "content": "[54] Michael J Swain and Dana H Ballard. Color indexing. International Journal of Computer Vision (IJCV), 7(1):11-32, 1991. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.134, + 0.47, + 0.192 + ], + "angle": 0, + "content": "[55] Karthik Mahesh Varadarajan and Markus Vincze. Parallel deep learning with suggestive activation for object category recognition. In International Conference on Computer Vision Systems, pages 354-363. Springer, 2013. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.192, + 0.471, + 0.248 + ], + "angle": 0, + "content": "[56] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.249, + 0.471, + 0.305 + ], + "angle": 0, + "content": "[57] Xiaolong Wang, Rohit Girdhar, and Abhinav Gupta. Binge watching: Scaling affordance learning from sitcoms. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2596-2605, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.306, + 0.471, + 0.376 + ], + "angle": 0, + "content": "[58] Yian Wang, Ruihai Wu, Kaichun Mo, Jiaqi Ke, Qingnan Fan, Leonidas J Guibas, and Hao Dong. Adaafford: Learning to adapt manipulation affordance for 3d articulated objects via few-shot interactions. In European Conference on Computer Vision, pages 90-107. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.377, + 0.471, + 0.446 + ], + "angle": 0, + "content": "[59] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34:12077-12090, 2021. 3, 4, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.447, + 0.471, + 0.49 + ], + "angle": 0, + "content": "[60] Xianghui Xie, Bharat Lal Bhatnagar, and Gerard Pons-Moll. Chore: Contact, human and object reconstruction from a single rgb image. arXiv preprint arXiv:2204.02445, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.491, + 0.471, + 0.546 + ], + "angle": 0, + "content": "[61] Yufei Xu, Jing Zhang, Qiming Zhang, and Dacheng Tao. ViTPose: Simple vision transformer baselines for human pose estimation. In Advances in Neural Information Processing Systems, 2022. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.548, + 0.471, + 0.603 + ], + "angle": 0, + "content": "[62] Yufei Xu, Qiming Zhang, Jing Zhang, and Dacheng Tao. ViTAE: Vision transformer advanced by exploring intrinsic inductive bias. Advances in Neural Information Processing Systems, 34, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.604, + 0.471, + 0.673 + ], + "angle": 0, + "content": "[63] Natsuki Yamanobe, Weiwei Wan, Ixchel G Ramirez-Alpizar, Damien Petit, Tokuo Tsuji, Shuichi Akizuki, Manabu Hashimoto, Kazuyuki Nagata, and Kensuke Harada. A brief review of affordance in robotic manipulation research. Advanced Robotics, 31(19-20):1086-1101, 2017. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.675, + 0.471, + 0.731 + ], + "angle": 0, + "content": "[64] Sijie Yan, Yuanjun Xiong, and Dahua Lin. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Thirty-second AAAI conference on artificial intelligence, 2018. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.732, + 0.471, + 0.802 + ], + "angle": 0, + "content": "[65] Lixin Yang, Xinyu Zhan, Kailin Li, Wenqiang Xu, Jiefeng Li, and Cewu Lu. Cpf: Learning a contact potential field to model the hand-object interaction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11097-11106, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.803, + 0.471, + 0.845 + ], + "angle": 0, + "content": "[66] Yuhui Yuan, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang, Xilin Chen, and Jingdong Wang. Hrformer: High-resolution transformer for dense prediction. 2021. 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.471, + 0.901 + ], + "angle": 0, + "content": "[67] Wei Zhai, Hongchen Luo, Jing Zhang, Yang Cao, and Dacheng Tao. One-shot object affordance detection in the wild. International Journal of Computer Vision, 130(10):2472-2500, 2022. 2, 3, 5" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.471, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.092, + 0.893, + 0.149 + ], + "angle": 0, + "content": "[68] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881-2890, 2017. 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.149, + 0.894, + 0.205 + ], + "angle": 0, + "content": "[69] Yixin Zhu, Yibiao Zhao, and Song Chun Zhu. Understanding tools: Task-oriented object modeling, learning and recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2855-2864, 2015. 1" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.092, + 0.894, + 0.205 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.483, + 0.945, + 0.517, + 0.957 + ], + "angle": 0, + "content": "6819" + } + ] +] \ No newline at end of file diff --git a/2023/Leverage Interactive Affinity for Affordance Learning/65bdc10c-8ced-4506-a6bb-43e12b06ea29_origin.pdf b/2023/Leverage Interactive Affinity for Affordance Learning/65bdc10c-8ced-4506-a6bb-43e12b06ea29_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..6b4823333124e343bc21c214f9de7604a0f42b6a --- /dev/null +++ b/2023/Leverage Interactive Affinity for Affordance Learning/65bdc10c-8ced-4506-a6bb-43e12b06ea29_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a58d380a61f8ad466228e465720eec6fc439223b3f45675fa3858739d970307b +size 9261216 diff --git a/2023/Leverage Interactive Affinity for Affordance Learning/full.md b/2023/Leverage Interactive Affinity for Affordance Learning/full.md new file mode 100644 index 0000000000000000000000000000000000000000..f3f73d0a298913fae2b33df92864dcbb6c28da17 --- /dev/null +++ b/2023/Leverage Interactive Affinity for Affordance Learning/full.md @@ -0,0 +1,338 @@ +# Leverage Interactive Affinity for Affordance Learning + +Hongchen Luo $^{1\ddagger}$ Wei Zhai $^{1\ddagger}$ Jing Zhang $^{2}$ Yang Cao $^{1,4*}$ Dacheng Tao $^{3,2}$ $^{1}$ University of Science and Technology of China + $^{2}$ The University of Sydney + $^{3}$ JD Explore Academy + $^{4}$ Institute of Artificial Intelligence, Hefei Comprehensive National Science Center +lhc12@mail.ustc.edu.cn, jing.zhang1@sydney.edu.au, +{wzhai056, forrest}@ustc.edu.cn, dacheng.tao@gmail.com + +# Abstract + +Perceiving potential "action possibilities" (i.e., affordance) regions of images and learning interactive functionalities of objects from human demonstration is a challenging task due to the diversity of human-object interactions. Prevailing affordance learning algorithms often adopt the label assignment paradigm and presume that there is a unique relationship between functional region and affordance label, yielding poor performance when adapting to unseen environments with large appearance variations. In this paper, we propose to leverage interactive affinity for affordance learning, i.e. extracting interactive affinity from human-object interaction and transferring it to noninteractive objects. Interactive affinity, which represents the contacts between different parts of the human body and local regions of the target object, can provide inherent cues of interconnectivity between humans and objects, thereby reducing the ambiguity of the perceived action possibilities. Specifically, we propose a pose-aided interactive affinity learning framework that exploits human pose to guide the network to learn the interactive affinity from human-object interactions. Particularly, a keypoint heuristic perception (KHP) scheme is devised to exploit the keypoint association of human pose to alleviate the uncertainties due to interaction diversities and contact occlusions. Besides, a contact-driven affordance learning (CAL) dataset is constructed by collecting and labeling over 5,000 images. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. Code and dataset: github.com/lhc1224/PIAL-Net. + +# 1. Introduction + +The objective of affordance learning is to locate the "action possibilities" regions of an object [15, 18]. For an in- + +![](images/98eb86f25abadbcd70c35499373d9b29964ba5c88f5492265af7eff56e51f3ed.jpg) +Figure 1. (a) Interaction affinity refers to the contact between different parts of the human body and the local regions of a target object. (b) The interactive affinity provides rich cues to guide the model to acquire invariant features of the object's local regions interacting with the body part, thus counteracting the multiple possibilities caused by diverse interactions. + +telligent agent, it is vital to perceive not only the object semantics but also how to interact with various objects' local regions. Perceiving and reasoning about the object's interactable regions is a critical capability for embodied intelligent systems to interact with the environment actively, distinct from passive perception systems [3, 38, 39, 44]. Moreover, affordance learning has a wide range of applications in fields such as action recognition [13, 24, 43], scene understanding [9, 69], human-robot interaction [51, 63], autonomous driving [7] and VR/AR [50, 53]. + +Affordance is a dynamic property closely related to humans and the environment [18]. Previous works [11, 37, 40, 46] focus on establishing mapping relationships between appearances and labels for affordance learning. However, they neglect the multiple possibilities of affordance brought about by changes in the environment and actors, leading to an incorrect perception. Recent studies [39, 48] utilize reinforcement learning to allow intelligent agents to perceive the environment through numerous interactions in simulated/actual scenarios. Such approaches are mainly limited by their high cost and struggle to generalize to + +![](images/061d3118fa0f585873c713b1895f79252b44d411690656545452c6a7b6cb3fa5.jpg) +Figure 2. Motivation. (a) This paper explores the associations of interactable regions between diverse images by considering the context of contact regions with different body parts. (b) This paper considers leveraging the connection of human pose keypoints to alleviate the uncertainties due to interaction diversities and contact occlusions. + +unseen scenarios [58]. To this end, researchers consider learning from human demonstration in an action-free manner [14, 29, 30, 38]. Nonetheless, they only roughly segment the whole object/interaction regions in a general way, which is still challenging to understand how the object is used. The multiple possibilities due to different local regions interacting with humans in various ways are not fully resolved. In this paper, we propose to leverage interactive affinity for affordance learning, i.e.extracting interactive affinity from human-object interaction and transferring it to non-interactive objects. The interactive affinity (Fig. 1 (a)) denotes the contacts between different human body parts and objects' local regions, which can provide inherent cues of interconnectivity between humans and objects, thereby reducing the ambiguity of the perceived action possibilities (Fig. 1 (b)). + +However, it faces the challenges of interaction diversities and contact occlusions, leading to difficulties in acquiring a good interactive affinity representation. The human pose is independent of background, and the same interaction corresponds to approximately similar poses. Thus, it makes sense to use the association between pose keypoints to overcome the difficulty of obtaining interactive affinity representations. Moreover, it is challenging to transfer the interactive affinity to non-interactive object images due to variations in views, scales, and appearances. The context between the different body part contact regions (Fig. 2 (a)) provides the model with the possibility to explore the associations between the interactable regions of the various images to counteract transfer difficulties. + +In this paper, we present a pose-aided interactive affinity learning framework. First, an Interactive Feature Enhancement (IFE) module is introduced to explore the connections between different interactable regions of the images. Then, a Keypoint Heuristic Perception (KHP) scheme is devised to mine the interactive affinity representation from interaction and transfer it to non-interactive + +objects. Specifically, the IFE module leverages the transformer to fully extract global contextual cues by exploiting the common relationships between their local interactable regions (Fig. 2 (a)). Then, they are used to establish associations between the object interactable regions in different images. Subsequently, the KHP scheme exploits the correlation between the human body keypoints and the contact region to guide the network to mine the object's local invariant features interacting with the body parts (Fig. 2 (b)). + +Although the numerous related datasets [9, 10, 19, 30, 36, 57, 67] that emerged during the development of affordance learning, there is still a lack of relevant datasets suited for leveraging interactive affinity. To carry out a thorough study, this paper constructs an Contact-driven Affordance Learning (CAL) dataset, consisting of 5,258 images from 23 affordance and 47 object categories. We conduct contrastive studies on the CAL dataset against six representative models in several related fields. Experimental results validate the effectiveness of our method in solving the multiple possibilities of affordance. + +Contributions: 1) We propose leveraging interactive affinity for affordance learning and establishing a CAL benchmark to facilitate the study of obtaining interactive affinity to counteract the multiple possibilities of affordance. 2) We propose a pose-aided interactive affinity learning framework that exploits pose data to guide the network to mine the interactive affinity of body parts and object regions from human-object interaction. 3) Experiments on the CAL dataset demonstrate that our model outperforms state-of-the-art methods and can serve as a strong baseline for future affordance learning research. + +# 2. Related Work + +# 2.1. Affordance Learning + +Affordance learning has attracted extensive attention in recent years due to its immense value in many fields + +such as robotics, autonomous driving, embodied AI, etc [18,20,32,63]. Early works [10,11,34,35,37,40,46,47,55] mainly adopt the label assignment paradigm and presume that there is a unique relationship between functional region and affordance label. Nevertheless, it is hard to deal with the issue of the multiple possibilities due to environmental and operator changes. Recent studies consider using reinforcement learning by setting different reward functions to empower the agent to actively interact with the environment to acquire the ability to perceive affordance [26, 39, 48]. These methods involve extensive interactions and are costly in real scenarios, while there is still a large domain gap between the simulation and the real scenario. They also have the limitation of being poorly adaptive to unseen scenarios. Some other works consider learning the object's affordance from the human demonstration in an action-free manner [14, 29, 30, 38, 67], extracting interactions from the images/videos and transferring the human action intentions implied within them to the new unseen object, thus achieving perception and generalization. However, they only detect/segment the object as a whole or the interactable regions in a general way. They do not perceive how the object's local regions are used and have not fully resolved the multiple possibilities issue. In contrast to the above works, this paper considers using the inherent cues of interconnectivity between humans and objects to reduce the ambiguity of the perceived action possibilities. + +# 2.2. Body Part Contact Learning + +The contact between the body part and the object is an important clue for 3D reconstruction and human motion forecasting [2, 17, 31, 45, 49, 60, 65]. Independent estimation of human pose and object during 3D scene reconstruction can lead to incorrect body-scene interpolation and body floating. Human-scene contacts can provide reliable boundary conditions for improved 3D pose estimation and localization. Shimada et al. [49] use body-scene contacts to guide 3D human capture. Bhatnagar et al. [2] propose to jointly track humans, objects, and contacts along with collecting a large-scale BEHAVE dataset containing human models, objects, and contact annotations. Mao et al. [31] introduce distance-based contact maps as an explicit constraint for human motion forecasting. Yang et al. [65] propose to model hand-object interaction by explicitly representing the contact using the Contact Potential Field (CPF). In contrast to [65], this paper utilizes the interactive affinity to mine the interconnectivity between humans and objects, helping reduce the ambiguity in affordance learning. + +# 3. Method + +Given a human-object interaction image $I_{in}$ with a corresponding human pose $P$ , and a non-interactive image $I_{non}$ , we aim to extract the affordance affinity representation be + +tween the human body part and the object local region from $I_{in}$ and transfer it to $I_{non}$ to predict the corresponding intertable region. The pose-aided interactive affinity learning framework is shown in Fig. 3. It first extracts features through a transformer [59] backbone to obtain $\mathbb{X}_{in} = \{X_{in_i}, i \in [1,4]\}$ and $\mathbb{X}_{non} = \{X_{non_i}, i \in [1,4]\}$ , respectively ( $i$ indexes the block of the backbone). Then, an interactive feature enhancement (IFE) module (Sec. 3.1) is introduced to establish a correlation between the intertable features from diverse images. Finally, a keypoint heuristic perception (KHP) scheme (Sec. 3.2) uses the association of human pose to mine the interactive affinity representation and then transfers it to the non-interactive image to predict the corresponding interactive region. + +# 3.1. Interactive Feature Enhancement Module + +The IFE module is shown in Fig. 3. Due to various views, scales, and appearances existing in the $I_{in}$ and $I_{non}$ , it is difficult to transfer the interactive affinity to the non-interactive branch. Therefore, we consider narrowing the discrepancy between the two branches by establishing the connection between the interactable regions before extracting the interactive affinity representation from human-object interaction. Inspired by the advantages of the transformer in modeling long-range global contextual information and its scalability of dealing with varied-length sequence [12, 22, 62], the IFE module adopts the transformer to associate cross-branch interactable regions. The cross transformer $(Z_1^{i + 1} = \mathrm{CT}(Z_1^i, Z_2^i))$ is computed as: + +$$ +\mathbf {Y} _ {1} ^ {i} = \operatorname {M C A} \left(\operatorname {L N} \left(\mathbf {Z} _ {1} ^ {i}\right), \operatorname {L N} \left(\mathbf {Z} _ {2} ^ {i}\right)\right) + \mathbf {Z} _ {1} ^ {i}, \tag {1} +$$ + +$$ +\boldsymbol {Z} _ {1} ^ {i + 1} = \operatorname {M L P} \left(\ln \left(\boldsymbol {Y} _ {1} ^ {i}\right)\right) + \boldsymbol {Y} _ {1} ^ {i}, \tag {2} +$$ + +where $\mathrm{MCA}()$ denotes the dot-production attention [56], where query, key, and value go through different linear layers: $\mathrm{MCA}(\mathbf{X},\mathbf{Y}) = \mathrm{Attention}(\mathbf{W}^{\mathsf{Q}}\mathbf{X},\mathbf{W}^{\mathsf{K}}\mathbf{Y},\mathbf{W}^{\mathsf{V}}\mathbf{Y})$ . The IFE module initializes a series of cross-branch tokens, which aim to aggregate the two branches' global contextual cues. Specifically, the cross-branch token $\mathbf{X}_c^0\in \mathbb{R}^{l\times c}$ ( $l$ and $c$ denote the number of tokens and channels, respectively) first extracts the contextual information of the two branches separately. It then extracts the contextual information common to $\mathbf{X}_{in}$ and $\mathbf{X}_{non}$ by mining the co-contextual features of their interactable regions. $\mathbf{X}_c^i$ is updated as: + +$$ +\boldsymbol {X} _ {c} ^ {i} = \operatorname {C T} \left(\boldsymbol {X} _ {c} ^ {i}, \left[ \boldsymbol {X} _ {i n} ^ {i}, \boldsymbol {X} _ {c} ^ {i} \right]\right), \boldsymbol {X} _ {c} ^ {i} = \operatorname {C T} \left(\boldsymbol {X} _ {c} ^ {i}, \left[ \boldsymbol {X} _ {\text {n o n}} ^ {i}, \boldsymbol {X} _ {c} ^ {i} \right]\right), \tag {3} +$$ + +$$ +\boldsymbol {X} _ {c} ^ {i + 1} = \operatorname {C T} \left(\boldsymbol {X} _ {c} ^ {i}, \left[ \boldsymbol {X} _ {i n} ^ {i}, \boldsymbol {X} _ {\text {n o n}} ^ {i}, \boldsymbol {X} _ {c} ^ {i} \right]\right), \tag {4} +$$ + +where $[\cdot ,\cdot ]$ represents the operation of concatenating two tensors. $X_{in}^{i}$ and $X_{non}^{i}$ $(i > 0)$ are the two branches feature sequences of the $i$ -th IFE block, respectively. $X_{in4}$ and $X_{non_4}$ are reshaped to $\mathbb{R}^{wh\times c}$ ( $w$ and $h$ are the width and height of the feature map, respectively) to obtain $X_{in}^{0}$ and $X_{non}^{0}$ , respectively. After updating the cross-branch + +![](images/c67c355c9ecf57af8bdacb9d2abe78c07fb52a7ad78eded03596f97e3512a88b.jpg) +Figure 3. Overview of the proposed pose-aided interactive affinity learning framework. Our model mainly consists of an interactive feature enhancement (IFE) module and a keypoint heuristic perception (KHP) scheme. + +tokens, the IFE module then uses $X_{c}^{i + i}$ to update the interactive and non-interactive features, respectively, enabling the two branches to effectively strengthen the interaction-related features from a different branch and reduce the variation. The update procedure is as follows: + +$$ +\boldsymbol {X} _ {i n} ^ {i + 1} = \operatorname {C T} \left(\boldsymbol {X} _ {i n} ^ {i}, \left[ \boldsymbol {X} _ {i n} ^ {i}, \boldsymbol {X} _ {c} ^ {i + 1} \right]\right), \tag {5} +$$ + +$$ +\boldsymbol {X} _ {\text {n o n}} ^ {i + 1} = \operatorname {C T} \left(\boldsymbol {X} _ {\text {n o n}} ^ {i}, \left[ \boldsymbol {X} _ {\text {n o n}} ^ {i}, \boldsymbol {X} _ {c} ^ {i + 1} \right]\right). \tag {6} +$$ + +After passing through $m$ IFE blocks, we obtain the output features $X_{in}^{m}$ and $X_{non}^{m}$ , respectively, which integrate cross-branch interactable regions information. + +# 3.2. Keypoint Heuristic Perception Scheme + +The KHP scheme is shown in Fig. 3. First, the IFE module's outputs $(X_{in}^{m} / X_{non}^{m})$ are sent to a multi-scale feature fusion layer to fuse with the shallow features extracted from the backbone, to obtain the higher resolution features [59]: + +$$ +\boldsymbol {F} _ {i n _ {i}} = \operatorname {U p s a m p l e} \left(\operatorname {L i n e a r} \left(c, c ^ {\prime}\right) \left(\boldsymbol {X} _ {i n} ^ {m}\right)\right), \quad i = 4, \tag {7} +$$ + +$$ +\boldsymbol {F} _ {i n _ {i}} = \operatorname {U p s a m p l e} \left(\operatorname {L i n e a r} \left(c _ {i}, c ^ {\prime}\right) \left(\boldsymbol {X} _ {i n _ {i}}\right)\right), \quad i \in [ 1, 3 ], \tag {8} +$$ + +$$ +\hat {\boldsymbol {F}} _ {i n} = \operatorname {L i n e a r} \left(4 c ^ {\prime}, c ^ {\prime}\right) \left(\operatorname {C o n c a t} \left(\boldsymbol {F} _ {i n _ {i}}\right)\right), \quad i \in [ 1, 4 ], \tag {9} +$$ + +where $\mathrm{Linear}(c_i, c_o)$ denotes a linear layer with inputs $C_i$ and outputs $c_o$ , respectively. $\hat{\pmb{F}}_{non}$ is calculated similar to $\hat{\pmb{F}}_{in}$ . The $\hat{\pmb{F}}_{in}$ is reshaped to 2D feature map and sent to a decoder $\mathrm{Decoder}(\cdot)$ to obtain the contact prediction: $D_{in} = \mathrm{Decoder}(\hat{\pmb{F}}_{in})$ . $D_{in}$ contains $N_{cls}$ ( $N_{cls}$ is the number of body parts) channels, and the feature map at each channel represents the regions where different body parts interact with the object. The diversity of human body styles, clothing, interaction postures, and contact occlusion make it difficult to obtain an accurate interactive affinity representation of body parts and object regions. The KHP scheme + +considers using the human pose to assist the network in suppressing irrelevant background regions and establishing a connection between body parts and contact regions. We first use a graph convolutional network (GCN) [1, 23, 64] to extract the features of pose $P$ : + +$$ +\boldsymbol {X} _ {P} = \boldsymbol {\Lambda} ^ {- \frac {1}{2}} (\boldsymbol {A} \otimes \boldsymbol {M} + \boldsymbol {I}) \boldsymbol {\Lambda} ^ {- \frac {1}{2}} \boldsymbol {P} \boldsymbol {W} _ {g c n}, \tag {10} +$$ + +where $\pmb{P}$ indicates the coordinates of the keypoints' locations, $\pmb{A}$ is the adjacency matrix and $\pmb{I}$ is the identity matrix, $\Lambda_{ii} = \sum_{i}(\pmb{A}_{ij} + \pmb{I}_{ij})$ , $W_{gcn}$ is the weight matrix. $M$ is a learnable parameter matrix measuring the importance between edges, which has the same dimension as $\pmb{A}$ and is initialized to all 1 [64]. Subsequently, we use the pose keypoint features corresponding to the body parts to guide the model to extract the corresponding interactive affinity representation from the contact region: + +$$ +\boldsymbol {H} _ {j} = \hat {\boldsymbol {F}} _ {i n} \otimes \boldsymbol {P} _ {i n j}, \quad \bar {\boldsymbol {H}} _ {j} = \mathrm {C T} \left(\boldsymbol {H} _ {j}, \boldsymbol {X} _ {P _ {j}}\right) \tag {11} +$$ + +where $X_{P_j}$ denotes the feature of the pose keypoint corresponding to the $j$ -th $(j \in [1, N_{cls}])$ body part. Following this, we transfer the interactive affinity representation to the non-interactive object. We feed the interactive affinity features $\bar{H}_j$ into the global average pooling layer (GAP) to obtain the feature representation $f_j: f_j = \mathrm{GAP}(\bar{H}_j)$ . Subsequently, $f_j$ is expanded to the size of $\hat{F}_{non}$ , concatenated with $\hat{F}_{non}$ and fed into a convolution layer to obtain the transferred features $K_j$ : + +$$ +\boldsymbol {K} _ {j} = \operatorname {C o n v} \left(\operatorname {C o n c a t} \left(\hat {\boldsymbol {F}} _ {n o n}, \operatorname {E x p a n d} \left(\boldsymbol {f} _ {j}\right)\right)\right). \tag {12} +$$ + +Finally, $\pmb{K}_j$ is concatenated together and fed into a decoder to obtain the prediction of the non-interactive branch: $D_{non} = \mathrm{Decoder}(\mathrm{Concat}(\pmb{K}_j)), j \in [1, N_{cls}]$ . During + +![](images/27ab41242fb44c244f1ba1bd7489e015ba794ac58a1880c8b612831d279d19b5.jpg) +Figure 4. Some examples and properties of Contact-driven Affordance Learning (CAL) dataset. (a) Statistics on the quantity of interactive and non-interactive images in each affordance category. (b) Confusion matrix for each affordance category interacting with body parts. (c) Some examples of interactive and non-interactive images and annotations in the dataset. + +![](images/7945d9d84fd0380ba677323dba2a5dae84167dec24236c8e1be54d4b41aac9ce.jpg) + +training, the total loss is defined as: $L_{total} = \lambda_1 L_{in} + \lambda_2 L_{non}$ , where $L_{in}$ and $L_{non}$ are the binary cross-entropy losses for the two branches, respectively. $\lambda_1$ and $\lambda_2$ are the loss weight parameters and are both set to 1. + +# 4. Experiment + +# 4.1. Dataset + +Dataset collection. To fill the gap of lacking a suitable dataset, we select 5,265 images from the PAD/PADv2 [29, 67] and AGD20K [30] to compose the Contact-driven Affordance Learning (CAL) dataset. Some examples are shown in Fig. 4 (c). We refer to [6, 27] and choose the commonly used 23 affordance and 47 object categories. We retrieve images according to affordance categories, then manually filter and classify them. Finally, the dataset contains 2,689 interactive images and 2,569 non-interactive images. + +Dataset annotation. We consider the interaction of six different body parts: "Hands", "Feet", "Mouth", "Hips", "Back" and "Outside" (the "Outside" represents the region where the object contacts the outside world during the interaction, e.g., the wheel-ground contact region during riding). These categories cover almost all regions where humans interact with objects daily and thoroughly describe how various object regions are used. Since affordance learning recognizes "action possibilities" regions of the object, the heatmap is appropriate for describing the possibility of interactions. We refer to the previous annotation works [4, 5, 14, 21] and choose to annotate local regions of the image with different densities of points. Each point is first Gaussian blurred and normalized during generating the soft mask to obtain the final interaction correspondence map. Some annotated examples are shown in Fig. 4 (c). + +Statistical analysis. We conduct some statistical analysis to get a deeper insight into the dataset. Fig. 4 (a) shows the number of interactive and non-interactive images for each affordance category. The number of interactive and non-interactive images is comparable, but data imbalance + +still exists between different categories. Fig. 4 (b) shows the confusion matrix for the affordance class with body part contact, where multiple contact regions exist for each category, implying the challenge of affordance learning. + +# 4.2. Benchmark Settings + +To provide a comprehensive evaluation, three commonly used metrics Kullback-Leibler Divergence (KLD) [5], SIMiliarity (SIM) [54], and Normalized Scanpath Saliency (NSS) [42] are chosen, see supplementary material for more details. Our model is implemented in PyTorch and trained with the AdamW [28] optimizer. The input images are cropped to $224 \times 224$ . We train the model for 60,000 iterations on a single NVIDIA 3090ti GPU with an initial learning rate of $6e-5$ and a batch size of 16. The hyperparameters $l$ and $m$ in the IFE module are set to 16 and 2, respectively. Furthermore, we provide two different settings to evaluate model learning and generalization capabilities: 1) Seen, i.e., the training set and the test set contain the same affordance/object categories, and 2) Unseen, i.e., the affordance/object categories in the training set and the test set do not overlap. The test set in Seen setting contains 4,484 interactive and non-interactive pairs, while the Unseen setting contains 3,297 pairs. The semantic segmentation method is chosen to compare the advantages of the methods in overcoming the multiple possibilities. We also choose the human pose estimation methods for comparing the interactive affinity for affordance learning approach. Besides, the few-shot segmentation model is also chosen for a fair comparison. In summary, we select 3 segmentation (DeepLabV3+ [8], PSPNet [68], Segformer [59]), 3 pose estimation (HRNet [52], ViTPose [61], HRFormer [66]), and one few-shot segmentation (HSNet [33]) methods from the relevant fields for comparison. + +# 4.3. Quantitative and Qualitative Comparisons + +The experimental results of different methods on the CAL dataset are shown in Table 1. Our method outper + +Table 1. The results of different methods on the CAL dataset. The best results are in bold. Seen means that the training and test sets contain the same affordance/object categories, and Unseen means that the affordance/objects in the training and test sets do not overlap. “◇”, “♣”, and “♠” represent segmentation, human pose estimation, and few-shot segmentation models, respectively. The ◆ defines the relative improvement of our method over other methods. + +
MethodSeenUnseenparams (M)
KLD ↓SIM ↑NSS ↑KLD ↓SIM ↑NSS ↑
PSPNet [68]1.738◇44.5%0.332◇127.7%1.431◇160.2%9.491◇70.3%0.224◇92.0%0.960◇139.9%53.31
DLabV3+ [8]◇1.347◇28.4%0.683◇10.7%3.256◇14.3%5.632◇49.9%0.374◇15.0%1.993◇15.6%40.35
SegFormer [59]1.198◇19.4%0.741◇2.0%3.543◇5.1%5.957◇52.4%0.401◇3.6%2.167◇0.2%27.25
HRNet [52]14.897◇93.5%0.196◇285.7%1.859◇100.3%17.984◇84.3%0.045◇855.6%0.525◇338.7%28.54
ViTPose [61]♣4.303◇77.6%0.376◇101.1%1.456◇156.3%5.545◇49.1%0.246◇74.8%0.805◇186.1%89.99
HRFormer [66]1.259◇23.4%0.729◇3.7%3.479◇7.0%5.855◇51.8%0.393◇9.4%2.109◇9.2%10.10
HSNet [33]♠2.014◇52.1%0.431◇75.4%1.922◇93.7%3.016◇6.4%0.234◇83.8%1.007◇128.7%26.13
Ours0.9650.7563.7322.8230.4302.30336.32
+ +![](images/734b09abab0437f3c90c5d44b376121ef7dbb5cfdd74a813d6fe2640d2cba3f6.jpg) +Figure 5. Visualization of prediction results. We show the visualization results of our model, few-shot segmentation (HSNet [33]), the best human pose estimation model (HRFormer [66]) and the segmentation model (SegFormer [59]). + +forms the segmentation, human pose estimation, and few-shot segmentation models in all metrics at both the Seen and Unseen settings. Taking KLD as the metric, our method exceeds the best segmentation method by $19.4\%$ , surpasses the best human pose estimation model by $23.4\%$ , and outperforms the few-shot model by $52.1\%$ at the Seen setting. At the Unseen setting, our approach outperforms the best semantic segmentation method by $52.4\%$ , the best human pose estimation model by $51.8\%$ , and the few-shot segmentation model by $6.4\%$ . Moreover, our method significantly exceeds HSNet [33] in both SIM and NSS metrics. The performance advantage of our model is even more pronounced at the Unseen setting. It validates that exploiting the intrinsic connection between the body part and the + +object's local interactions using interactive affinity helps our model better generalize to unseen interactions and objects. + +Fig. 5 shows the visualization of affordance maps. Our method outperforms representative models at both Seen and Unseen settings. For the rugby ball and the punching bag, it is difficult to identify the corresponding interactions using the segmentation methods due to the multiple possibilities of affordance. It suggests that the interactive affinity can provide valuable hints about the interconnectedness of the human body part with the local region of the object, thereby helping reduce the ambiguity of the perceived action possibilities. For knives to be used for both "Cut" and "Stick", our model still recognizes the difference between interactions ("Outside"). It suggests that using pose + +![](images/36ea9bfa03ef8548d1c7950226f03a16bb1426cf998ce8d5a1fd1fc1bf4c96f1.jpg) + +![](images/d329ba3020863412c2dfea800bffbfd19f76624aa1fcb983229cf7bd295c262d.jpg) + +![](images/09ff65746fc81693a036d221406157cea1eb666b496c3c566d61b2981fd6b706.jpg) + +![](images/9dd50001c1f697c9f1602adc6e725b590bb27080898a56737f5760a293ade5e3.jpg) + +![](images/5e81d08fbb60ee1006c8c03eaca11f28d1484b8811119aa1620ce905b25650f0.jpg) + +![](images/f6563736ae521896156b051a86a38e7ff9307479118291f47c8e6edef3b94365.jpg) + +![](images/c9299e0aba8c07bb71774ed061509fbc5c144e91cf7aa8b762eb19eff1e2c975.jpg) + +![](images/15570717169525a3344450b456fce11b7b90c64ef28f002f37c8af6165fedfc8.jpg) + +![](images/e8f1197b8660ea4d92fccabfc2da399dd20aad72eff90df1982d6eecc685141c.jpg) +Figure 6. Different interactive images w.r.t Same non-interactive images. We show the results for predicting the back contact region under the interaction of "Sit on" and "Lie on". + +![](images/5a50ce1ff7ae5273f498cef540d0401e5da1bb6234708331d13e95faf35ab9e6.jpg) + +![](images/b9ced2e1178cd0b0cca4d477d7debed703f3d61f69742c1f54671dd5f3a6a217.jpg) + +![](images/7595854db5d88d9c2599970b4e22a6cbb8429fdc2ab2fef25d3b3e2a4c7c588b.jpg) + +![](images/44345a6e4854285b931591f4df9de6d501936e6d682f32439badcf1c8d04b067.jpg) + +![](images/561e81411e9e39a0e9239bf4eec1f6216089e314e1b027cc881612288408a942.jpg) + +![](images/5c34cf239a6ccf3d227e9a33fa357007e31146457af573f758d9bdb3f8bab666.jpg) + +![](images/accfaf05d073c88b31249ddbb580d2983c6eabdd0a249875c3c9be7a0374983c.jpg) + +![](images/8548cd0730254754ed78647da4e6de25f45dc43220b3acfcfbb318f447943705.jpg) + +![](images/8967781888f3aec816cfeee733d70c69aaaf51d38a93664dc72c5a1fe791624e.jpg) + +![](images/0d530658eed4c2c2a5659e72f31b7fc6b10c666ad29cc893b5909d9d83bdcd39.jpg) +Figure 7. Different interactive images w.r.t Same non-interactive images. We present the prediction results of the model for different non-interactive images of the same interactive image. + +data as guidance can assist the network in identifying the region where the object is in contact with the outside world to complete the interaction. Fig. 6 shows the results of different interaction images for the same non-interactive image. Since "Sit on" and "Lie on" correspond to different contact regions on the back, we only visualize this part. Our model can perceive the variations between interactions and accurately transfer the interaction-related invariant features to the corresponding regions. Fig. 7 shows the prediction for the same interactive image corresponding to different non-interactive images. Although different object categories, various scales, and different views, our model can transfer the interactive affinity correctly for each part, which indicates that the IFE module can effectively establish the connection between the interactable regions from different branches and facilitate the transfer of interactive affinity. + +# 4.4. Ablation Study + +The ablation study results are shown in Table 2. Our framework shows a more pronounced improvement at the Unseen setting, suggesting that our method can generalize and transfer even for unseen interactions and objects with only a few interactive images. In particular, the KHP scheme shows a more evident improvement in the model's + +Table 2. Ablation study. We investigate the influence of the IFE module and KHP scheme on model performance. + +
IFEKHPSeenUnseen
KLD ↓SIM ↑NSS ↑KLD ↓SIM ↑NSS ↑
✓1.2080.7283.4915.5930.3962.170
1.1260.7333.6254.5580.4042.143
✓1.1640.7463.6673.5760.4242.257
✓✓0.9650.7563.7322.8230.4302.303
+ +Table 3. Ablation study of the pose guidance. We investigate the impact of different pose feature fusion methods in the KHP scheme. "GAP" means global average pooled pose node features. "Conv" means concatenation with the contact feature and then being fused by a convolutional layer. + +
ModeSeenUnseen
KLD ↓SIM ↑NSS ↑KLD ↓SIM ↑NSS ↑
GAP+Conv1.3170.7153.5174.0530.3831.962
Part+Conv1.1690.7333.6123.9330.4032.215
GAP+CT1.2530.7103.5784.2230.3861.937
Part+CT0.9650.7563.7322.8230.4302.303
+ +performance. It indicates that obtaining interactive affinity from human-object interaction can better exploit the connections between the body part and the local region of the object, thus suppressing the multiple possibilities of interaction diversity. Besides, we investigate the effect of the pose guidance on the results. As shown in Table 3, "GAP" loses the association between different part keypoints and performs worse than using the keypoints corresponding to different parts separately. "Conv" fusion is worse than the cross transformer, possibly due to the fact that the transformer explicitly computes the correlation between the contact region and the body keypoints, thus enabling better mine the interactive affinity representation. + +# 4.5. Performance Analysis + +Different Classes. Fig. 8 shows the results of different models on each affordance category. Although our model does not outperform HSNet in some categories [33] at the Unseen setting (the scores are comparable), it still outperforms other methods, which indicates that our method has a good generalization ability when facing unseen objects. For the overlap of objects in "Cut", "Stick" and "Lift", a finer-grained interactive affinity representation can suppress the effects of multiple possibilities and achieve better results. + +Different body parts. Table 4 shows the results regarding different body parts. Our model achieves almost the best results at the Seen setting. At the Unseen setting, it also achieves excellent transfer performance in most interaction regions corresponding to the body parts. As most of the interaction is done through hand-object contact, the results of our model at the Unseen setting are much better + +![](images/37d2d6c475fb1d4d8ac41691d9ec8be7fe99e8e22ff7d836962c0dd84eacefa8.jpg) +Figure 8. Different classes. We measure the KLD metric for each affordance category, with darker colors representing higher performance. The left and right represent the results at the Seen and Unseen settings, respectively. + +![](images/4ece260a392754f1d58ce69556f55c6426c391076ac22690c430ce714ed9f083.jpg) +Figure 9. Different Hyper-parameters. We investigate the impact of the number of layers $m$ and tokens $l$ in the interactive enhancement module on the experimental results. + +than all the other methods. It demonstrates that our method uses interactive affinity to explore the interconnections between body parts and local regions of objects, thus reducing the ambiguity in perceiving action possibilities and enabling accurate affordance knowledge transfer. + +Different hyper-parameters. Fig. 9 shows the impact of the hyper-parameters $l$ and $m$ in the IFE module on the model's performance. The influence at the Unseen setting is slightly more apparent, indicating that these parameters are more sensitive to the generalization and transfer of unseen objects. The number of cross-branch tokens greatly affects the model performance. Too small $l$ may lead to difficulties in fully expressing the contextual cues of the different branch interactable features, with the best results obtained at $l = 16$ . The model gives the best results with the number of layers $m = 2$ . The smaller $m$ makes it difficult to fully aggregate the contextual cues and establish associations in the interactable regions. As $m$ increases, it increases the parameters of the model and the complexity of the optimization, leading to negative results. + +Limitations. As shown in Table 4, our method still has a large gap between "Back" and the other body parts. It is mainly due to the sample imbalance as fewer examples contain "Back". Besides, the sitting posture occludes most + +Table 4. Different parts. We evaluate the predictions for each body part and object contact region. The Bold and Underline represent the best and sub-optimal results, respectively. + +
MethodHandFeetMouthHipsBackOutside
SeenPSPNet1.4711.2841.8601.4837.8181.337
DLabV3+0.7751.1953.0690.5767.1650.945
SegFormer0.5471.2932.1420.6067.5870.847
HRNet14.58317.20217.09514.19724.82313.208
HFormer0.6341.7412.5730.4307.1520.844
HSNet1.6401.5184.5611.2839.1411.218
Ours0.6871.1510.8160.3116.9750.570
UnseenPSPNet6.040-15.18711.32119.4798.043
DLabV3+4.393-11.3081.36611.3974.137
SegFormer3.989-13.0151.31011.1844.673
HRNet17.761-19.25717.22125.73717.830
HFormer4.477-11.5511.43912.1964.393
HSNet2.100-5.2591.0997.9842.356
Ours1.612-6.0840.8038.7371.855
+ +of the region where the object interacts with the back, resulting in an incomplete back contact region feature extraction. In future work, we will consider addressing the data imbalance [16, 25, 41] issue and exploiting the co-relation relationship between body parts. + +# 5. Conclusion + +This paper proposes to leverage interactive affinity for effective affordance learning by counteracting the influence of multiple possibilities. To this end, a pose-aided interactive affinity learning framework is introduced, which is able to exploit pose data to guide the network to mine the interactive affinity representation of body parts and object local contact from human-object interaction. Furthermore, we constructed a contact-driven affordance learning (CAL) dataset by collecting and labeling over 5,000 images from 23 affordance categories. Our model outperforms six representative models in three related fields and can serve as a strong baseline for future affordance learning research. + +Acknowledgments. This work is supported by National Key R&D Program of China under Grant 2020AAA0105701 and ARC FL170100117 and IH180100002. + +# References + +[1] Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261, 2018.4 +[2] Bharat Lal Bhatnagar, Xianghui Xie, Ilya A Petrov, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Behave: Dataset and method for tracking human object interactions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15935-15946, 2022. 3 +[3] Jeannette Bohg, Karol Hausman, Bharath Sankaran, Oliver Brock, Danica Kragic, Stefan Schaal, and Gaurav S Sukhatme. Interactive perception: Leveraging action in perception and perception in action. IEEE Transactions on Robotics, 33(6):1273-1291, 2017. 1 +[4] Zoya Bylinskii, Tilke Judd, Ali Borji, Laurent Itti, Frédo Durand, Aude Oliva, and Antonio Torralba. Mit saliency benchmark. 2015. 5 +[5] Zoya Bylinskii, Tilke Judd, Aude Oliva, Antonio Torralba, and Frédo Durand. What do different evaluation metrics tell us about saliency models? IEEE transactions on pattern analysis and machine intelligence, 41(3):740-757, 2018. 5 +[6] Yu-Wei Chao, Zhan Wang, Yugeng He, Jiaxuan Wang, and Jia Deng. HICO: A benchmark for recognizing human-object interactions in images. In Proceedings of the IEEE International Conference on Computer Vision, 2015. 5 +[7] Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao. Deepdriving: Learning affordance for direct perception in autonomous driving. In Proceedings of the IEEE international conference on computer vision, pages 2722-2730, 2015. 1 +[8] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), pages 801-818, 2018. 5, 6, 8 +[9] Ching-Yao Chuang, Jiaman Li, Antonio Torralba, and Sanja Fidler. Learning to act properly: Predicting and explaining affordances from images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 975–983, 2018. 1, 2 +[10] Shengheng Deng, Xun Xu, Chaozheng Wu, Ke Chen, and Kui Jia. 3d affordancenet: A benchmark for visual object affordance understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1778-1787, 2021. 2, 3 +[11] Thanh-Toan Do, Anh Nguyen, and Ian Reid. Affordancenet: An end-to-end deep learning approach for object affordance detection. In 2018 IEEE international conference on robotics and automation (ICRA), pages 5882-5889. IEEE, 2018. 1, 3 +[12] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Trans- + +formers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.3 +[13] Vibekananda Dutta and Teresa Zielinska. Action prediction based on physically grounded object affordances in human-object interactions. In 2017 11th International Workshop on Robot Motion and Control (RoMoCo), pages 47-52. IEEE, 2017. 1 +[14] Kuan Fang, Te-Lin Wu, Daniel Yang, Silvio Savarese, and Joseph J Lim. Demo2vec: Reasoning object affordances from online videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2139-2147, 2018. 2, 3, 5 +[15] James J Gibson. The theory of affordances. *Hilldale, USA*, 1(2):67-82, 1977. 1 +[16] Khan Md Hasib, Md Iqbal, Faisal Muhammad Shah, Jubayer Al Mahmud, Mahmudul Hasan Popel, Md Showrov, Imran Hossain, Shakil Ahmed, Obaidur Rahman, et al. A survey of methods for managing the classification and solution of data imbalance problem. arXiv preprint arXiv:2012.11870, 2020. 8 +[17] Mohamed Hassan, Partha Ghosh, Joachim Tesch, Dimitrios Tzionas, and Michael J Black. Populating 3d scenes by learning human-scene interaction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14708-14718, 2021. 3 +[18] Mohammed Hassanin, Salman Khan, and Murat Tahtali. Visual affordance and function understanding: A survey. ACM Computing Surveys (CSUR), 54(3):1-35, 2021. 1, 3 +[19] Tucker Hermans, James M Rehg, and Aaron Bobick. Affordance prediction via learned object attributes. In IEEE International Conference on Robotics and Automation (ICRA): Workshop on Semantic Perception, Mapping, and Exploration, pages 181-184. Citeseer, 2011. 2 +[20] Lorenzo Jamone, Emre Ugur, Angelo Cangelosi, Luciano Fadiga, Alexandre Bernardino, Justus Piater, and José Santos-Victor. Affordances in psychology, neuroscience, and robotics: A survey. IEEE Transactions on Cognitive and Developmental Systems, 10(1):4-25, 2016. 3 +[21] Tilke Judd, Frédo Durand, and Antonio Torralba. A benchmark of computational models of saliency to predict human fixations. 2012. 5 +[22] Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah. Transformers in vision: A survey. ACM computing surveys (CSUR), 2021. 3 +[23] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016. 4 +[24] Hema S Koppula and Ashutosh Saxena. Anticipating human activities using object affordances for reactive robotic response. IEEE transactions on pattern analysis and machine intelligence, 38(1):14-29, 2015. 1 +[25] Joffrey L Leevy, Taghi M Khoshgoftaar, Richard A Bauder, and Naeem Seliya. A survey on addressing high-class imbalance in big data. Journal of Big Data, 5(1):1-30, 2018. 8 +[26] Yi-Chi Liao, Kashyap Todi, Aditya Acharya, Antti Keurulainen, Andrew Howes, and Antti Oulasvirta. Rediscovering + +affordance: A reinforcement learning perspective. In CHI Conference on Human Factors in Computing Systems, pages 1-15, 2022. 3 +[27] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 5 +[28] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5 +[29] Hongchen Luo, Wei Zhai, Jing Zhang, Yang Cao, and Dacheng Tao. One-shot affordance detection. arXiv preprint arXiv:2106.14747, 2021. 2, 3, 5 +[30] Hongchen Luo, Wei Zhai, Jing Zhang, Yang Cao, and Dacheng Tao. Learning affordance grounding from exocentric images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2252-2261, 2022. 2, 3, 5 +[31] Wei Mao, Miaomiao Liu, Richard Hartley, and Mathieu Salzmann. Contact-aware human motion forecasting. arXiv preprint arXiv:2210.03954, 2022. 3 +[32] Huaqing Min, Ronghua Luo, Jinhui Zhu, Sheng Bi, et al. Affordance research in developmental robotics: A survey. IEEE Transactions on Cognitive and Developmental Systems, 8(4):237-255, 2016. 3 +[33] Juhong Min, Dahiyun Kang, and Minsu Cho. Hypercorrelation squeeze for few-shot segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 5, 6, 7, 8 +[34] Kaichun Mo, Leonidas J Guibas, Mustafa Mukadam, Abhinav Gupta, and Shubham Tulsiani. Where2act: From pixels to actions for articulated 3d objects. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6813-6823, 2021. 3 +[35] Kaichun Mo, Yuzhe Qin, Fanbo Xiang, Hao Su, and Leonidas Guibas. O2o-afford: Annotation-free large-scale object-object affordance learning. In Conference on Robot Learning, pages 1666-1677. PMLR, 2022. 3 +[36] Roozbeh Mottaghi, Connor Schenck, Dieter Fox, and Ali Farhadi. See the glass half full: Reasoning about liquid containers, their volume and content. In Proceedings of the IEEE International Conference on Computer Vision, pages 1871-1880, 2017. 2 +[37] Austin Myers, Ching L Teo, Cornelia Fermüller, and Yiannis Aloimonos. Affordance detection of tool parts from geometric features. In 2015 IEEE International Conference on Robotics and Automation (ICRA), pages 1374-1381. IEEE, 2015. 1, 3 +[38] Tushar Nagarajan, Christoph Feichtenhofer, and Kristen Grauman. Grounded human-object interaction hotspots from video. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8688-8697, 2019. 1, 2, 3 +[39] Tushar Nagarajan and Kristen Grauman. Learning affordance landscapes for interaction exploration in 3d environments. Advances in Neural Information Processing Systems, 33:2005-2015, 2020. 1, 3 + +[40] Anh Nguyen, Dimitrios Kanoulas, Darwin G Caldwell, and Nikos G Tsagarakis. Detecting object affordances with convolutional neural networks. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2765-2770. IEEE, 2016. 1, 3 +[41] Minlong Peng, Qi Zhang, Xiaoyu Xing, Tao Gui, Xuanjing Huang, Yu-Gang Jiang, Keyu Ding, and Zhigang Chen. Trainable undersampling for class-imbalance learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4707-4714, 2019. 8 +[42] Robert J Peters, Asha Iyer, Laurent Itti, and Christof Koch. Components of bottom-up gaze allocation in natural images. Vision research, 45(18):2397-2416, 2005. 5 +[43] Siyuan Qi, Siyuan Huang, Ping Wei, and Song-Chun Zhu. Predicting human activities using stochastic grammar. In Proceedings of the IEEE International Conference on Computer Vision, pages 1164-1172, 2017. 1 +[44] Santhosh K Ramakrishnan, Dinesh Jayaraman, and Kristen Grauman. An exploration of embodied visual exploration. International Journal of Computer Vision, 129(5):1616-1649, 2021. 1 +[45] Manolis Savva, Angel X Chang, Pat Hanrahan, Matthew Fisher, and Matthias Nießner. Pigraphs: learning interaction snapshots from observations. ACM Transactions on Graphics (TOG), 35(4):1-12, 2016. 3 +[46] Johann Sawatzky and Jurgen Gall. Adaptive binarization for weakly supervised affordance segmentation. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 1383-1391, 2017. 1, 3 +[47] Johann Sawatzky, Abhilash Srikantha, and Juergen Gall. Weakly supervised affordance detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2795-2804, 2017. 3 +[48] Giulio Schiavi, Paula Wulkop, Giuseppe Rizzi, Lionel Ott, Roland Siegwart, and Jen Jen Chung. Learning agent-aware affordances for closed-loop interaction with articulated objects. arXiv preprint arXiv:2209.05802, 2022. 1, 3 +[49] Soshi Shimada, Vladislav Golyanik, Zhi Li, Patrick Pérez, Weipeng Xu, and Christian Theobalt. Hulc: 3d human motion capture with pose manifold sampling and dense contact guidance. In European Conference on Computer Vision, pages 516-533. Springer, 2022. 3 +[50] Dong-Hee Shin. The role of affordance in the experience of virtual reality learning: Technological and affective affordances in virtual reality. Telematics and Informatics, 34(8):1826-1836, 2017. 1 +[51] Tianmin Shu, Xiaofeng Gao, Michael S Ryoo, and Song-Chun Zhu. Learning social affordance grammar from videos: Transferring human interactions to human-robot interactions. In 2017 IEEE international conference on robotics and automation (ICRA), pages 1669–1676. IEEE, 2017. 1 +[52] Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning for human pose estimation. In CVPR, 2019. 5, 6, 8 +[53] Yuan Sun, Shuyue Fang, and Zuopeng Justin Zhang. Impression management strategies on enterprise social media platforms: An affordance perspective. International Journal of Information Management, 60:102359, 2021. 1 + +[54] Michael J Swain and Dana H Ballard. Color indexing. International Journal of Computer Vision (IJCV), 7(1):11-32, 1991. 5 +[55] Karthik Mahesh Varadarajan and Markus Vincze. Parallel deep learning with suggestive activation for object category recognition. In International Conference on Computer Vision Systems, pages 354-363. Springer, 2013. 3 +[56] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3 +[57] Xiaolong Wang, Rohit Girdhar, and Abhinav Gupta. Binge watching: Scaling affordance learning from sitcoms. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2596-2605, 2017. 2 +[58] Yian Wang, Ruihai Wu, Kaichun Mo, Jiaqi Ke, Qingnan Fan, Leonidas J Guibas, and Hao Dong. Adaafford: Learning to adapt manipulation affordance for 3d articulated objects via few-shot interactions. In European Conference on Computer Vision, pages 90-107. Springer, 2022. 2 +[59] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34:12077-12090, 2021. 3, 4, 5, 6, 8 +[60] Xianghui Xie, Bharat Lal Bhatnagar, and Gerard Pons-Moll. Chore: Contact, human and object reconstruction from a single rgb image. arXiv preprint arXiv:2204.02445, 2022. 3 +[61] Yufei Xu, Jing Zhang, Qiming Zhang, and Dacheng Tao. ViTPose: Simple vision transformer baselines for human pose estimation. In Advances in Neural Information Processing Systems, 2022. 5, 6 +[62] Yufei Xu, Qiming Zhang, Jing Zhang, and Dacheng Tao. ViTAE: Vision transformer advanced by exploring intrinsic inductive bias. Advances in Neural Information Processing Systems, 34, 2021. 3 +[63] Natsuki Yamanobe, Weiwei Wan, Ixchel G Ramirez-Alpizar, Damien Petit, Tokuo Tsuji, Shuichi Akizuki, Manabu Hashimoto, Kazuyuki Nagata, and Kensuke Harada. A brief review of affordance in robotic manipulation research. Advanced Robotics, 31(19-20):1086-1101, 2017. 1, 3 +[64] Sijie Yan, Yuanjun Xiong, and Dahua Lin. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Thirty-second AAAI conference on artificial intelligence, 2018. 4 +[65] Lixin Yang, Xinyu Zhan, Kailin Li, Wenqiang Xu, Jiefeng Li, and Cewu Lu. Cpf: Learning a contact potential field to model the hand-object interaction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11097-11106, 2021. 3 +[66] Yuhui Yuan, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang, Xilin Chen, and Jingdong Wang. Hrformer: High-resolution transformer for dense prediction. 2021. 5, 6, 8 +[67] Wei Zhai, Hongchen Luo, Jing Zhang, Yang Cao, and Dacheng Tao. One-shot object affordance detection in the wild. International Journal of Computer Vision, 130(10):2472-2500, 2022. 2, 3, 5 + +[68] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881-2890, 2017. 5, 6, 8 +[69] Yixin Zhu, Yibiao Zhao, and Song Chun Zhu. Understanding tools: Task-oriented object modeling, learning and recognition. 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Prevailing affordance learning algorithms often adopt the label assignment paradigm and presume that there is a unique relationship between functional region and affordance label, yielding poor performance when adapting to unseen environments with large appearance variations. In this paper, we propose to leverage interactive affinity for affordance learning, i.e. extracting interactive affinity from human-object interaction and transferring it to noninteractive objects. Interactive affinity, which represents the contacts between different parts of the human body and local regions of the target object, can provide inherent cues of interconnectivity between humans and objects, thereby reducing the ambiguity of the perceived action possibilities. Specifically, we propose a pose-aided interactive affinity learning framework that exploits human pose to guide the network to learn the interactive affinity from human-object interactions. Particularly, a keypoint heuristic perception (KHP) scheme is devised to exploit the keypoint association of human pose to alleviate the uncertainties due to interaction diversities and contact occlusions. Besides, a contact-driven affordance learning (CAL) dataset is constructed by collecting and labeling over 5,000 images. Experimental results demonstrate that our method outperforms the representative models regarding objective metrics and visual quality. Code and dataset: github.com/lhc1224/PIAL-Net." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 650, + 128, + 662 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 650, + 128, + 662 + ], + "spans": [ + { + "bbox": [ + 47, + 650, + 128, + 662 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 670, + 287, + 694 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 670, + 287, + 694 + ], + "spans": [ + { + "bbox": [ + 47, + 670, + 287, + 694 + ], + "type": "text", + "content": "The objective of affordance learning is to locate the \"action possibilities\" regions of an object [15, 18]. For an in-" + } + ] + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 326, + 251, + 524, + 371 + ], + "blocks": [ + { + "bbox": [ + 326, + 251, + 524, + 371 + ], + "lines": [ + { + "bbox": [ + 326, + 251, + 524, + 371 + ], + "spans": [ + { + "bbox": [ + 326, + 251, + 524, + 371 + ], + "type": "image", + "image_path": "98eb86f25abadbcd70c35499373d9b29964ba5c88f5492265af7eff56e51f3ed.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 305, + 374, + 545, + 440 + ], + "lines": [ + { + "bbox": [ + 305, + 374, + 545, + 440 + ], + "spans": [ + { + "bbox": [ + 305, + 374, + 545, + 440 + ], + "type": "text", + "content": "Figure 1. (a) Interaction affinity refers to the contact between different parts of the human body and the local regions of a target object. (b) The interactive affinity provides rich cues to guide the model to acquire invariant features of the object's local regions interacting with the body part, thus counteracting the multiple possibilities caused by diverse interactions." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 461, + 546, + 581 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 461, + 546, + 581 + ], + "spans": [ + { + "bbox": [ + 304, + 461, + 546, + 581 + ], + "type": "text", + "content": "telligent agent, it is vital to perceive not only the object semantics but also how to interact with various objects' local regions. Perceiving and reasoning about the object's interactable regions is a critical capability for embodied intelligent systems to interact with the environment actively, distinct from passive perception systems [3, 38, 39, 44]. Moreover, affordance learning has a wide range of applications in fields such as action recognition [13, 24, 43], scene understanding [9, 69], human-robot interaction [51, 63], autonomous driving [7] and VR/AR [50, 53]." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 582, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 547, + 714 + ], + "type": "text", + "content": "Affordance is a dynamic property closely related to humans and the environment [18]. Previous works [11, 37, 40, 46] focus on establishing mapping relationships between appearances and labels for affordance learning. However, they neglect the multiple possibilities of affordance brought about by changes in the environment and actors, leading to an incorrect perception. Recent studies [39, 48] utilize reinforcement learning to allow intelligent agents to perceive the environment through numerous interactions in simulated/actual scenarios. Such approaches are mainly limited by their high cost and struggle to generalize to" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 58, + 702, + 208, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 702, + 208, + 712 + ], + "spans": [ + { + "bbox": [ + 58, + 702, + 208, + 712 + ], + "type": "text", + "content": "*Corresponding author. ‡ Equal contributions." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6809" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 92, + 70, + 504, + 210 + ], + "blocks": [ + { + "bbox": [ + 92, + 70, + 504, + 210 + ], + "lines": [ + { + "bbox": [ + 92, + 70, + 504, + 210 + ], + "spans": [ + { + "bbox": [ + 92, + 70, + 504, + 210 + ], + "type": "image", + "image_path": "061d3118fa0f585873c713b1895f79252b44d411690656545452c6a7b6cb3fa5.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 216, + 547, + 251 + ], + "lines": [ + { + "bbox": [ + 46, + 216, + 547, + 251 + ], + "spans": [ + { + "bbox": [ + 46, + 216, + 547, + 251 + ], + "type": "text", + "content": "Figure 2. Motivation. (a) This paper explores the associations of interactable regions between diverse images by considering the context of contact regions with different body parts. (b) This paper considers leveraging the connection of human pose keypoints to alleviate the uncertainties due to interaction diversities and contact occlusions." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 270, + 289, + 461 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 270, + 289, + 461 + ], + "spans": [ + { + "bbox": [ + 46, + 270, + 289, + 461 + ], + "type": "text", + "content": "unseen scenarios [58]. To this end, researchers consider learning from human demonstration in an action-free manner [14, 29, 30, 38]. Nonetheless, they only roughly segment the whole object/interaction regions in a general way, which is still challenging to understand how the object is used. The multiple possibilities due to different local regions interacting with humans in various ways are not fully resolved. In this paper, we propose to leverage interactive affinity for affordance learning, i.e.extracting interactive affinity from human-object interaction and transferring it to non-interactive objects. The interactive affinity (Fig. 1 (a)) denotes the contacts between different human body parts and objects' local regions, which can provide inherent cues of interconnectivity between humans and objects, thereby reducing the ambiguity of the perceived action possibilities (Fig. 1 (b))." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 462, + 287, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 462, + 287, + 628 + ], + "spans": [ + { + "bbox": [ + 46, + 462, + 287, + 628 + ], + "type": "text", + "content": "However, it faces the challenges of interaction diversities and contact occlusions, leading to difficulties in acquiring a good interactive affinity representation. The human pose is independent of background, and the same interaction corresponds to approximately similar poses. Thus, it makes sense to use the association between pose keypoints to overcome the difficulty of obtaining interactive affinity representations. Moreover, it is challenging to transfer the interactive affinity to non-interactive object images due to variations in views, scales, and appearances. The context between the different body part contact regions (Fig. 2 (a)) provides the model with the possibility to explore the associations between the interactable regions of the various images to counteract transfer difficulties." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 630, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 630, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 630, + 288, + 713 + ], + "type": "text", + "content": "In this paper, we present a pose-aided interactive affinity learning framework. First, an Interactive Feature Enhancement (IFE) module is introduced to explore the connections between different interactable regions of the images. Then, a Keypoint Heuristic Perception (KHP) scheme is devised to mine the interactive affinity representation from interaction and transfer it to non-interactive" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 270, + 547, + 377 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 270, + 547, + 377 + ], + "spans": [ + { + "bbox": [ + 304, + 270, + 547, + 377 + ], + "type": "text", + "content": "objects. Specifically, the IFE module leverages the transformer to fully extract global contextual cues by exploiting the common relationships between their local interactable regions (Fig. 2 (a)). Then, they are used to establish associations between the object interactable regions in different images. Subsequently, the KHP scheme exploits the correlation between the human body keypoints and the contact region to guide the network to mine the object's local invariant features interacting with the body parts (Fig. 2 (b))." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 378, + 548, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 378, + 548, + 509 + ], + "spans": [ + { + "bbox": [ + 304, + 378, + 548, + 509 + ], + "type": "text", + "content": "Although the numerous related datasets [9, 10, 19, 30, 36, 57, 67] that emerged during the development of affordance learning, there is still a lack of relevant datasets suited for leveraging interactive affinity. To carry out a thorough study, this paper constructs an Contact-driven Affordance Learning (CAL) dataset, consisting of 5,258 images from 23 affordance and 47 object categories. We conduct contrastive studies on the CAL dataset against six representative models in several related fields. Experimental results validate the effectiveness of our method in solving the multiple possibilities of affordance." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 510, + 548, + 641 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 510, + 548, + 641 + ], + "spans": [ + { + "bbox": [ + 304, + 510, + 548, + 641 + ], + "type": "text", + "content": "Contributions: 1) We propose leveraging interactive affinity for affordance learning and establishing a CAL benchmark to facilitate the study of obtaining interactive affinity to counteract the multiple possibilities of affordance. 2) We propose a pose-aided interactive affinity learning framework that exploits pose data to guide the network to mine the interactive affinity of body parts and object regions from human-object interaction. 3) Experiments on the CAL dataset demonstrate that our model outperforms state-of-the-art methods and can serve as a strong baseline for future affordance learning research." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 651, + 392, + 663 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 651, + 392, + 663 + ], + "spans": [ + { + "bbox": [ + 306, + 651, + 392, + 663 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 306, + 671, + 427, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 671, + 427, + 684 + ], + "spans": [ + { + "bbox": [ + 306, + 671, + 427, + 684 + ], + "type": "text", + "content": "2.1. Affordance Learning" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 689, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 547, + 713 + ], + "type": "text", + "content": "Affordance learning has attracted extensive attention in recent years due to its immense value in many fields" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6810" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 289, + 396 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 289, + 396 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 289, + 396 + ], + "type": "text", + "content": "such as robotics, autonomous driving, embodied AI, etc [18,20,32,63]. Early works [10,11,34,35,37,40,46,47,55] mainly adopt the label assignment paradigm and presume that there is a unique relationship between functional region and affordance label. Nevertheless, it is hard to deal with the issue of the multiple possibilities due to environmental and operator changes. Recent studies consider using reinforcement learning by setting different reward functions to empower the agent to actively interact with the environment to acquire the ability to perceive affordance [26, 39, 48]. These methods involve extensive interactions and are costly in real scenarios, while there is still a large domain gap between the simulation and the real scenario. They also have the limitation of being poorly adaptive to unseen scenarios. Some other works consider learning the object's affordance from the human demonstration in an action-free manner [14, 29, 30, 38, 67], extracting interactions from the images/videos and transferring the human action intentions implied within them to the new unseen object, thus achieving perception and generalization. However, they only detect/segment the object as a whole or the interactable regions in a general way. They do not perceive how the object's local regions are used and have not fully resolved the multiple possibilities issue. In contrast to the above works, this paper considers using the inherent cues of interconnectivity between humans and objects to reduce the ambiguity of the perceived action possibilities." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 402, + 204, + 415 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 402, + 204, + 415 + ], + "spans": [ + { + "bbox": [ + 47, + 402, + 204, + 415 + ], + "type": "text", + "content": "2.2. Body Part Contact Learning" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 420, + 289, + 649 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 420, + 289, + 649 + ], + "spans": [ + { + "bbox": [ + 46, + 420, + 289, + 649 + ], + "type": "text", + "content": "The contact between the body part and the object is an important clue for 3D reconstruction and human motion forecasting [2, 17, 31, 45, 49, 60, 65]. Independent estimation of human pose and object during 3D scene reconstruction can lead to incorrect body-scene interpolation and body floating. Human-scene contacts can provide reliable boundary conditions for improved 3D pose estimation and localization. Shimada et al. [49] use body-scene contacts to guide 3D human capture. Bhatnagar et al. [2] propose to jointly track humans, objects, and contacts along with collecting a large-scale BEHAVE dataset containing human models, objects, and contact annotations. Mao et al. [31] introduce distance-based contact maps as an explicit constraint for human motion forecasting. Yang et al. [65] propose to model hand-object interaction by explicitly representing the contact using the Contact Potential Field (CPF). In contrast to [65], this paper utilizes the interactive affinity to mine the interconnectivity between humans and objects, helping reduce the ambiguity in affordance learning." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 657, + 102, + 669 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 657, + 102, + 669 + ], + "spans": [ + { + "bbox": [ + 47, + 657, + 102, + 669 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "text", + "content": "Given a human-object interaction image " + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "inline_equation", + "content": "I_{in}" + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "text", + "content": " with a corresponding human pose " + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "text", + "content": ", and a non-interactive image " + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "inline_equation", + "content": "I_{non}" + }, + { + "bbox": [ + 47, + 677, + 287, + 715 + ], + "type": "text", + "content": ", we aim to extract the affordance affinity representation be" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 72, + 547, + 240 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 547, + 240 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 547, + 240 + ], + "type": "text", + "content": "tween the human body part and the object local region from " + }, + { + "bbox": [ + 304, + 72, + 547, + 240 + ], + "type": "inline_equation", + "content": "I_{in}" + }, + { + "bbox": [ + 304, + 72, + 547, + 240 + ], + "type": "text", + "content": " and transfer it to " + }, + { + "bbox": [ + 304, + 72, + 547, + 240 + ], + "type": "inline_equation", + "content": "I_{non}" + }, + { + "bbox": [ + 304, + 72, + 547, + 240 + ], + "type": "text", + "content": " to predict the corresponding intertable region. The pose-aided interactive affinity learning framework is shown in Fig. 3. It first extracts features through a transformer [59] backbone to obtain " + }, + { + "bbox": [ + 304, + 72, + 547, + 240 + ], + "type": "inline_equation", + "content": "\\mathbb{X}_{in} = \\{X_{in_i}, i \\in [1,4]\\}" + }, + { + "bbox": [ + 304, + 72, + 547, + 240 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 72, + 547, + 240 + ], + "type": "inline_equation", + "content": "\\mathbb{X}_{non} = \\{X_{non_i}, i \\in [1,4]\\}" + }, + { + "bbox": [ + 304, + 72, + 547, + 240 + ], + "type": "text", + "content": ", respectively (" + }, + { + "bbox": [ + 304, + 72, + 547, + 240 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 72, + 547, + 240 + ], + "type": "text", + "content": " indexes the block of the backbone). Then, an interactive feature enhancement (IFE) module (Sec. 3.1) is introduced to establish a correlation between the intertable features from diverse images. Finally, a keypoint heuristic perception (KHP) scheme (Sec. 3.2) uses the association of human pose to mine the interactive affinity representation and then transfers it to the non-interactive image to predict the corresponding interactive region." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 305, + 247, + 525, + 259 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 247, + 525, + 259 + ], + "spans": [ + { + "bbox": [ + 305, + 247, + 525, + 259 + ], + "type": "text", + "content": "3.1. Interactive Feature Enhancement Module" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 266, + 545, + 422 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 266, + 545, + 422 + ], + "spans": [ + { + "bbox": [ + 304, + 266, + 545, + 422 + ], + "type": "text", + "content": "The IFE module is shown in Fig. 3. Due to various views, scales, and appearances existing in the " + }, + { + "bbox": [ + 304, + 266, + 545, + 422 + ], + "type": "inline_equation", + "content": "I_{in}" + }, + { + "bbox": [ + 304, + 266, + 545, + 422 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 266, + 545, + 422 + ], + "type": "inline_equation", + "content": "I_{non}" + }, + { + "bbox": [ + 304, + 266, + 545, + 422 + ], + "type": "text", + "content": ", it is difficult to transfer the interactive affinity to the non-interactive branch. Therefore, we consider narrowing the discrepancy between the two branches by establishing the connection between the interactable regions before extracting the interactive affinity representation from human-object interaction. Inspired by the advantages of the transformer in modeling long-range global contextual information and its scalability of dealing with varied-length sequence [12, 22, 62], the IFE module adopts the transformer to associate cross-branch interactable regions. The cross transformer " + }, + { + "bbox": [ + 304, + 266, + 545, + 422 + ], + "type": "inline_equation", + "content": "(Z_1^{i + 1} = \\mathrm{CT}(Z_1^i, Z_2^i))" + }, + { + "bbox": [ + 304, + 266, + 545, + 422 + ], + "type": "text", + "content": " is computed as:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 353, + 429, + 545, + 442 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 353, + 429, + 545, + 442 + ], + "spans": [ + { + "bbox": [ + 353, + 429, + 545, + 442 + ], + "type": "interline_equation", + "content": "\\mathbf {Y} _ {1} ^ {i} = \\operatorname {M C A} \\left(\\operatorname {L N} \\left(\\mathbf {Z} _ {1} ^ {i}\\right), \\operatorname {L N} \\left(\\mathbf {Z} _ {2} ^ {i}\\right)\\right) + \\mathbf {Z} _ {1} ^ {i}, \\tag {1}", + "image_path": "fc08424d7354f7a04c7455c2c3eb6ad252776b85f7eebe8212d7538b168b5e93.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 367, + 449, + 545, + 462 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 367, + 449, + 545, + 462 + ], + "spans": [ + { + "bbox": [ + 367, + 449, + 545, + 462 + ], + "type": "interline_equation", + "content": "\\boldsymbol {Z} _ {1} ^ {i + 1} = \\operatorname {M L P} \\left(\\ln \\left(\\boldsymbol {Y} _ {1} ^ {i}\\right)\\right) + \\boldsymbol {Y} _ {1} ^ {i}, \\tag {2}", + "image_path": "34ba6a7fb4a9441dcd1381d503fd412dbbfd6042c14492d3bf143f1b9eddbba8.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 465, + 545, + 598 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 465, + 545, + 598 + ], + "spans": [ + { + "bbox": [ + 304, + 465, + 545, + 598 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 465, + 545, + 598 + ], + "type": "inline_equation", + "content": "\\mathrm{MCA}()" + }, + { + "bbox": [ + 304, + 465, + 545, + 598 + ], + "type": "text", + "content": " denotes the dot-production attention [56], where query, key, and value go through different linear layers: " + }, + { + "bbox": [ + 304, + 465, + 545, + 598 + ], + "type": "inline_equation", + "content": "\\mathrm{MCA}(\\mathbf{X},\\mathbf{Y}) = \\mathrm{Attention}(\\mathbf{W}^{\\mathsf{Q}}\\mathbf{X},\\mathbf{W}^{\\mathsf{K}}\\mathbf{Y},\\mathbf{W}^{\\mathsf{V}}\\mathbf{Y})" + }, + { + "bbox": [ + 304, + 465, + 545, + 598 + ], + "type": "text", + "content": ". The IFE module initializes a series of cross-branch tokens, which aim to aggregate the two branches' global contextual cues. Specifically, the cross-branch token " + }, + { + "bbox": [ + 304, + 465, + 545, + 598 + ], + "type": "inline_equation", + "content": "\\mathbf{X}_c^0\\in \\mathbb{R}^{l\\times c}" + }, + { + "bbox": [ + 304, + 465, + 545, + 598 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 304, + 465, + 545, + 598 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 304, + 465, + 545, + 598 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 465, + 545, + 598 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 304, + 465, + 545, + 598 + ], + "type": "text", + "content": " denote the number of tokens and channels, respectively) first extracts the contextual information of the two branches separately. 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Overview of the proposed pose-aided interactive affinity learning framework. Our model mainly consists of an interactive feature enhancement (IFE) module and a keypoint heuristic perception (KHP) scheme." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 316, + 286, + 376 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 316, + 286, + 376 + ], + "spans": [ + { + "bbox": [ + 46, + 316, + 286, + 376 + ], + "type": "text", + "content": "tokens, the IFE module then uses " + }, + { + "bbox": [ + 46, + 316, + 286, + 376 + ], + "type": "inline_equation", + "content": "X_{c}^{i + i}" + }, + { + "bbox": [ + 46, + 316, + 286, + 376 + ], + "type": "text", + "content": " to update the interactive and non-interactive features, respectively, enabling the two branches to effectively strengthen the interaction-related features from a different branch and reduce the variation. The update procedure is as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 104, + 381, + 287, + 396 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 381, + 287, + 396 + ], + "spans": [ + { + "bbox": [ + 104, + 381, + 287, + 396 + ], + "type": "interline_equation", + "content": "\\boldsymbol {X} _ {i n} ^ {i + 1} = \\operatorname {C T} \\left(\\boldsymbol {X} _ {i n} ^ {i}, \\left[ \\boldsymbol {X} _ {i n} ^ {i}, \\boldsymbol {X} _ {c} ^ {i + 1} \\right]\\right), \\tag {5}", + "image_path": "1466639a7042825986b6c446f1acf4cc0374b4d56ef66fdb596d3c23aa8bde8a.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 99, + 399, + 287, + 413 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 99, + 399, + 287, + 413 + ], + "spans": [ + { + "bbox": [ + 99, + 399, + 287, + 413 + ], + "type": "interline_equation", + "content": "\\boldsymbol {X} _ {\\text {n o n}} ^ {i + 1} = \\operatorname {C T} \\left(\\boldsymbol {X} _ {\\text {n o n}} ^ {i}, \\left[ \\boldsymbol {X} _ {\\text {n o n}} ^ {i}, \\boldsymbol {X} _ {c} ^ {i + 1} \\right]\\right). \\tag {6}", + "image_path": "cb90e3783f04c9d414f20e70a89ecdb3b13a42a997a6ba014ed6b80c7ebe1f31.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 415, + 286, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 415, + 286, + 452 + ], + "spans": [ + { + "bbox": [ + 47, + 415, + 286, + 452 + ], + "type": "text", + "content": "After passing through " + }, + { + "bbox": [ + 47, + 415, + 286, + 452 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 47, + 415, + 286, + 452 + ], + "type": "text", + "content": " IFE blocks, we obtain the output features " + }, + { + "bbox": [ + 47, + 415, + 286, + 452 + ], + "type": "inline_equation", + "content": "X_{in}^{m}" + }, + { + "bbox": [ + 47, + 415, + 286, + 452 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 415, + 286, + 452 + ], + "type": "inline_equation", + "content": "X_{non}^{m}" + }, + { + "bbox": [ + 47, + 415, + 286, + 452 + ], + "type": "text", + "content": ", respectively, which integrate cross-branch interactable regions information." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 458, + 250, + 471 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 458, + 250, + 471 + ], + "spans": [ + { + "bbox": [ + 47, + 458, + 250, + 471 + ], + "type": "text", + "content": "3.2. Keypoint Heuristic Perception Scheme" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 475, + 286, + 525 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 475, + 286, + 525 + ], + "spans": [ + { + "bbox": [ + 47, + 475, + 286, + 525 + ], + "type": "text", + "content": "The KHP scheme is shown in Fig. 3. First, the IFE module's outputs " + }, + { + "bbox": [ + 47, + 475, + 286, + 525 + ], + "type": "inline_equation", + "content": "(X_{in}^{m} / X_{non}^{m})" + }, + { + "bbox": [ + 47, + 475, + 286, + 525 + ], + "type": "text", + "content": " are sent to a multi-scale feature fusion layer to fuse with the shallow features extracted from the backbone, to obtain the higher resolution features [59]:" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 77, + 530, + 287, + 544 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 530, + 287, + 544 + ], + "spans": [ + { + "bbox": [ + 77, + 530, + 287, + 544 + ], + "type": "interline_equation", + "content": "\\boldsymbol {F} _ {i n _ {i}} = \\operatorname {U p s a m p l e} \\left(\\operatorname {L i n e a r} \\left(c, c ^ {\\prime}\\right) \\left(\\boldsymbol {X} _ {i n} ^ {m}\\right)\\right), \\quad i = 4, \\tag {7}", + "image_path": "9ccdff4620857b6ed775ddfff51977ce286ea33c2a2315f527cae0c81b1492d5.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 62, + 550, + 287, + 563 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 550, + 287, + 563 + ], + "spans": [ + { + "bbox": [ + 62, + 550, + 287, + 563 + ], + "type": "interline_equation", + "content": "\\boldsymbol {F} _ {i n _ {i}} = \\operatorname {U p s a m p l e} \\left(\\operatorname {L i n e a r} \\left(c _ {i}, c ^ {\\prime}\\right) \\left(\\boldsymbol {X} _ {i n _ {i}}\\right)\\right), \\quad i \\in [ 1, 3 ], \\tag {8}", + "image_path": "09ffc563448e8b60b61dbc2b5173c1dbbc55108a178891b3dd2537310d6c733a.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 73, + 566, + 286, + 579 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 73, + 566, + 286, + 579 + ], + "spans": [ + { + "bbox": [ + 73, + 566, + 286, + 579 + ], + "type": "interline_equation", + "content": "\\hat {\\boldsymbol {F}} _ {i n} = \\operatorname {L i n e a r} \\left(4 c ^ {\\prime}, c ^ {\\prime}\\right) \\left(\\operatorname {C o n c a t} \\left(\\boldsymbol {F} _ {i n _ {i}}\\right)\\right), \\quad i \\in [ 1, 4 ], \\tag {9}", + "image_path": "7b2a2d7082fe0b6b475f7e4a5d7791bae8cb69990924e69672068c5590e44de5.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{Linear}(c_i, c_o)" + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "text", + "content": " denotes a linear layer with inputs " + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "inline_equation", + "content": "C_i" + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "text", + "content": " and outputs " + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "inline_equation", + "content": "c_o" + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "text", + "content": ", respectively. " + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\hat{\\pmb{F}}_{non}" + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "text", + "content": " is calculated similar to " + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\hat{\\pmb{F}}_{in}" + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "text", + "content": ". The " + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\hat{\\pmb{F}}_{in}" + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "text", + "content": " is reshaped to 2D feature map and sent to a decoder " + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\mathrm{Decoder}(\\cdot)" + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "text", + "content": " to obtain the contact prediction: " + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "inline_equation", + "content": "D_{in} = \\mathrm{Decoder}(\\hat{\\pmb{F}}_{in})" + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "inline_equation", + "content": "D_{in}" + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "text", + "content": " contains " + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "inline_equation", + "content": "N_{cls}" + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "text", + "content": " (" + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "inline_equation", + "content": "N_{cls}" + }, + { + "bbox": [ + 47, + 582, + 287, + 713 + ], + "type": "text", + "content": " is the number of body parts) channels, and the feature map at each channel represents the regions where different body parts interact with the object. The diversity of human body styles, clothing, interaction postures, and contact occlusion make it difficult to obtain an accurate interactive affinity representation of body parts and object regions. The KHP scheme" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 317, + 545, + 377 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 317, + 545, + 377 + ], + "spans": [ + { + "bbox": [ + 304, + 317, + 545, + 377 + ], + "type": "text", + "content": "considers using the human pose to assist the network in suppressing irrelevant background regions and establishing a connection between body parts and contact regions. We first use a graph convolutional network (GCN) [1, 23, 64] to extract the features of pose " + }, + { + "bbox": [ + 304, + 317, + 545, + 377 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 304, + 317, + 545, + 377 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 348, + 383, + 545, + 398 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 348, + 383, + 545, + 398 + ], + "spans": [ + { + "bbox": [ + 348, + 383, + 545, + 398 + ], + "type": "interline_equation", + "content": "\\boldsymbol {X} _ {P} = \\boldsymbol {\\Lambda} ^ {- \\frac {1}{2}} (\\boldsymbol {A} \\otimes \\boldsymbol {M} + \\boldsymbol {I}) \\boldsymbol {\\Lambda} ^ {- \\frac {1}{2}} \\boldsymbol {P} \\boldsymbol {W} _ {g c n}, \\tag {10}", + "image_path": "3ba4aeb0e24a0e37902262f7cd64940eaa55319f0cd6cc447051720e250e3ac8.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "spans": [ + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "inline_equation", + "content": "\\pmb{P}" + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "text", + "content": " indicates the coordinates of the keypoints' locations, " + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "inline_equation", + "content": "\\pmb{A}" + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "text", + "content": " is the adjacency matrix and " + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "inline_equation", + "content": "\\pmb{I}" + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "text", + "content": " is the identity matrix, " + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "inline_equation", + "content": "\\Lambda_{ii} = \\sum_{i}(\\pmb{A}_{ij} + \\pmb{I}_{ij})" + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "inline_equation", + "content": "W_{gcn}" + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "text", + "content": " is the weight matrix. " + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "text", + "content": " is a learnable parameter matrix measuring the importance between edges, which has the same dimension as " + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "inline_equation", + "content": "\\pmb{A}" + }, + { + "bbox": [ + 304, + 405, + 545, + 514 + ], + "type": "text", + "content": " and is initialized to all 1 [64]. Subsequently, we use the pose keypoint features corresponding to the body parts to guide the model to extract the corresponding interactive affinity representation from the contact region:" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 344, + 519, + 545, + 533 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 344, + 519, + 545, + 533 + ], + "spans": [ + { + "bbox": [ + 344, + 519, + 545, + 533 + ], + "type": "interline_equation", + "content": "\\boldsymbol {H} _ {j} = \\hat {\\boldsymbol {F}} _ {i n} \\otimes \\boldsymbol {P} _ {i n j}, \\quad \\bar {\\boldsymbol {H}} _ {j} = \\mathrm {C T} \\left(\\boldsymbol {H} _ {j}, \\boldsymbol {X} _ {P _ {j}}\\right) \\tag {11}", + "image_path": "a44f711683fd99b646b5c1816e5e38e60696425d3326d98cf71e4227d61be3cf.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "spans": [ + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "inline_equation", + "content": "X_{P_j}" + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "text", + "content": " denotes the feature of the pose keypoint corresponding to the " + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "text", + "content": "-th " + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "inline_equation", + "content": "(j \\in [1, N_{cls}])" + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "text", + "content": " body part. Following this, we transfer the interactive affinity representation to the non-interactive object. We feed the interactive affinity features " + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "inline_equation", + "content": "\\bar{H}_j" + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "text", + "content": " into the global average pooling layer (GAP) to obtain the feature representation " + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "inline_equation", + "content": "f_j: f_j = \\mathrm{GAP}(\\bar{H}_j)" + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "text", + "content": ". Subsequently, " + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "inline_equation", + "content": "f_j" + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "text", + "content": " is expanded to the size of " + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "inline_equation", + "content": "\\hat{F}_{non}" + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "text", + "content": ", concatenated with " + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "inline_equation", + "content": "\\hat{F}_{non}" + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "text", + "content": " and fed into a convolution layer to obtain the transferred features " + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "inline_equation", + "content": "K_j" + }, + { + "bbox": [ + 304, + 540, + 545, + 650 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 347, + 658, + 545, + 671 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 347, + 658, + 545, + 671 + ], + "spans": [ + { + "bbox": [ + 347, + 658, + 545, + 671 + ], + "type": "interline_equation", + "content": "\\boldsymbol {K} _ {j} = \\operatorname {C o n v} \\left(\\operatorname {C o n c a t} \\left(\\hat {\\boldsymbol {F}} _ {n o n}, \\operatorname {E x p a n d} \\left(\\boldsymbol {f} _ {j}\\right)\\right)\\right). \\tag {12}", + "image_path": "2fa325f4160951b82fd22e6d656ce3783ced24e785edab7982365b5c9b7fd5ed.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "text", + "content": "Finally, " + }, + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\pmb{K}_j" + }, + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "text", + "content": " is concatenated together and fed into a decoder to obtain the prediction of the non-interactive branch: " + }, + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "inline_equation", + "content": "D_{non} = \\mathrm{Decoder}(\\mathrm{Concat}(\\pmb{K}_j)), j \\in [1, N_{cls}]" + }, + { + "bbox": [ + 304, + 677, + 545, + 715 + ], + "type": "text", + "content": ". During" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6812" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 67, + 68, + 276, + 190 + ], + "blocks": [ + { + "bbox": [ + 67, + 68, + 276, + 190 + ], + "lines": [ + { + "bbox": [ + 67, + 68, + 276, + 190 + ], + "spans": [ + { + "bbox": [ + 67, + 68, + 276, + 190 + ], + "type": "image", + "image_path": "27ab41242fb44c244f1ba1bd7489e015ba794ac58a1880c8b612831d279d19b5.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 195, + 547, + 229 + ], + "lines": [ + { + "bbox": [ + 46, + 195, + 547, + 229 + ], + "spans": [ + { + "bbox": [ + 46, + 195, + 547, + 229 + ], + "type": "text", + "content": "Figure 4. Some examples and properties of Contact-driven Affordance Learning (CAL) dataset. (a) Statistics on the quantity of interactive and non-interactive images in each affordance category. (b) Confusion matrix for each affordance category interacting with body parts. (c) Some examples of interactive and non-interactive images and annotations in the dataset." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 282, + 70, + 521, + 190 + ], + "blocks": [ + { + "bbox": [ + 282, + 70, + 521, + 190 + ], + "lines": [ + { + "bbox": [ + 282, + 70, + 521, + 190 + ], + "spans": [ + { + "bbox": [ + 282, + 70, + 521, + 190 + ], + "type": "image", + "image_path": "7945d9d84fd0380ba677323dba2a5dae84167dec24236c8e1be54d4b41aac9ce.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 250, + 288, + 299 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 250, + 288, + 299 + ], + "spans": [ + { + "bbox": [ + 46, + 250, + 288, + 299 + ], + "type": "text", + "content": "training, the total loss is defined as: " + }, + { + "bbox": [ + 46, + 250, + 288, + 299 + ], + "type": "inline_equation", + "content": "L_{total} = \\lambda_1 L_{in} + \\lambda_2 L_{non}" + }, + { + "bbox": [ + 46, + 250, + 288, + 299 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 250, + 288, + 299 + ], + "type": "inline_equation", + "content": "L_{in}" + }, + { + "bbox": [ + 46, + 250, + 288, + 299 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 250, + 288, + 299 + ], + "type": "inline_equation", + "content": "L_{non}" + }, + { + "bbox": [ + 46, + 250, + 288, + 299 + ], + "type": "text", + "content": " are the binary cross-entropy losses for the two branches, respectively. " + }, + { + "bbox": [ + 46, + 250, + 288, + 299 + ], + "type": "inline_equation", + "content": "\\lambda_1" + }, + { + "bbox": [ + 46, + 250, + 288, + 299 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 250, + 288, + 299 + ], + "type": "inline_equation", + "content": "\\lambda_2" + }, + { + "bbox": [ + 46, + 250, + 288, + 299 + ], + "type": "text", + "content": " are the loss weight parameters and are both set to 1." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 312, + 123, + 326 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 312, + 123, + 326 + ], + "spans": [ + { + "bbox": [ + 47, + 312, + 123, + 326 + ], + "type": "text", + "content": "4. Experiment" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 332, + 105, + 344 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 332, + 105, + 344 + ], + "spans": [ + { + "bbox": [ + 47, + 332, + 105, + 344 + ], + "type": "text", + "content": "4.1. Dataset" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 352, + 287, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 352, + 287, + 460 + ], + "spans": [ + { + "bbox": [ + 46, + 352, + 287, + 460 + ], + "type": "text", + "content": "Dataset collection. To fill the gap of lacking a suitable dataset, we select 5,265 images from the PAD/PADv2 [29, 67] and AGD20K [30] to compose the Contact-driven Affordance Learning (CAL) dataset. Some examples are shown in Fig. 4 (c). We refer to [6, 27] and choose the commonly used 23 affordance and 47 object categories. We retrieve images according to affordance categories, then manually filter and classify them. Finally, the dataset contains 2,689 interactive images and 2,569 non-interactive images." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 461, + 287, + 652 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 461, + 287, + 652 + ], + "spans": [ + { + "bbox": [ + 46, + 461, + 287, + 652 + ], + "type": "text", + "content": "Dataset annotation. We consider the interaction of six different body parts: \"Hands\", \"Feet\", \"Mouth\", \"Hips\", \"Back\" and \"Outside\" (the \"Outside\" represents the region where the object contacts the outside world during the interaction, e.g., the wheel-ground contact region during riding). These categories cover almost all regions where humans interact with objects daily and thoroughly describe how various object regions are used. Since affordance learning recognizes \"action possibilities\" regions of the object, the heatmap is appropriate for describing the possibility of interactions. We refer to the previous annotation works [4, 5, 14, 21] and choose to annotate local regions of the image with different densities of points. Each point is first Gaussian blurred and normalized during generating the soft mask to obtain the final interaction correspondence map. Some annotated examples are shown in Fig. 4 (c)." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 653, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 287, + 713 + ], + "type": "text", + "content": "Statistical analysis. We conduct some statistical analysis to get a deeper insight into the dataset. Fig. 4 (a) shows the number of interactive and non-interactive images for each affordance category. The number of interactive and non-interactive images is comparable, but data imbalance" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 250, + 545, + 299 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 250, + 545, + 299 + ], + "spans": [ + { + "bbox": [ + 305, + 250, + 545, + 299 + ], + "type": "text", + "content": "still exists between different categories. Fig. 4 (b) shows the confusion matrix for the affordance class with body part contact, where multiple contact regions exist for each category, implying the challenge of affordance learning." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 307, + 424, + 319 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 307, + 424, + 319 + ], + "spans": [ + { + "bbox": [ + 306, + 307, + 424, + 319 + ], + "type": "text", + "content": "4.2. Benchmark Settings" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 326, + 546, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 326, + 546, + 661 + ], + "spans": [ + { + "bbox": [ + 304, + 326, + 546, + 661 + ], + "type": "text", + "content": "To provide a comprehensive evaluation, three commonly used metrics Kullback-Leibler Divergence (KLD) [5], SIMiliarity (SIM) [54], and Normalized Scanpath Saliency (NSS) [42] are chosen, see supplementary material for more details. Our model is implemented in PyTorch and trained with the AdamW [28] optimizer. The input images are cropped to " + }, + { + "bbox": [ + 304, + 326, + 546, + 661 + ], + "type": "inline_equation", + "content": "224 \\times 224" + }, + { + "bbox": [ + 304, + 326, + 546, + 661 + ], + "type": "text", + "content": ". We train the model for 60,000 iterations on a single NVIDIA 3090ti GPU with an initial learning rate of " + }, + { + "bbox": [ + 304, + 326, + 546, + 661 + ], + "type": "inline_equation", + "content": "6e-5" + }, + { + "bbox": [ + 304, + 326, + 546, + 661 + ], + "type": "text", + "content": " and a batch size of 16. The hyperparameters " + }, + { + "bbox": [ + 304, + 326, + 546, + 661 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 304, + 326, + 546, + 661 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 326, + 546, + 661 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 304, + 326, + 546, + 661 + ], + "type": "text", + "content": " in the IFE module are set to 16 and 2, respectively. Furthermore, we provide two different settings to evaluate model learning and generalization capabilities: 1) Seen, i.e., the training set and the test set contain the same affordance/object categories, and 2) Unseen, i.e., the affordance/object categories in the training set and the test set do not overlap. The test set in Seen setting contains 4,484 interactive and non-interactive pairs, while the Unseen setting contains 3,297 pairs. The semantic segmentation method is chosen to compare the advantages of the methods in overcoming the multiple possibilities. We also choose the human pose estimation methods for comparing the interactive affinity for affordance learning approach. Besides, the few-shot segmentation model is also chosen for a fair comparison. In summary, we select 3 segmentation (DeepLabV3+ [8], PSPNet [68], Segformer [59]), 3 pose estimation (HRNet [52], ViTPose [61], HRFormer [66]), and one few-shot segmentation (HSNet [33]) methods from the relevant fields for comparison." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 670, + 528, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 670, + 528, + 684 + ], + "spans": [ + { + "bbox": [ + 306, + 670, + 528, + 684 + ], + "type": "text", + "content": "4.3. Quantitative and Qualitative Comparisons" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 306, + 689, + 545, + 713 + ], + "type": "text", + "content": "The experimental results of different methods on the CAL dataset are shown in Table 1. Our method outper" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6813" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 53, + 124, + 542, + 244 + ], + "blocks": [ + { + "bbox": [ + 46, + 70, + 547, + 115 + ], + "lines": [ + { + "bbox": [ + 46, + 70, + 547, + 115 + ], + "spans": [ + { + "bbox": [ + 46, + 70, + 547, + 115 + ], + "type": "text", + "content": "Table 1. The results of different methods on the CAL dataset. The best results are in bold. Seen means that the training and test sets contain the same affordance/object categories, and Unseen means that the affordance/objects in the training and test sets do not overlap. “◇”, “♣”, and “♠” represent segmentation, human pose estimation, and few-shot segmentation models, respectively. The ◆ defines the relative improvement of our method over other methods." + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 53, + 124, + 542, + 244 + ], + "lines": [ + { + "bbox": [ + 53, + 124, + 542, + 244 + ], + "spans": [ + { + "bbox": [ + 53, + 124, + 542, + 244 + ], + "type": "table", + "html": "
MethodSeenUnseenparams (M)
KLD ↓SIM ↑NSS ↑KLD ↓SIM ↑NSS ↑
PSPNet [68]1.738◇44.5%0.332◇127.7%1.431◇160.2%9.491◇70.3%0.224◇92.0%0.960◇139.9%53.31
DLabV3+ [8]◇1.347◇28.4%0.683◇10.7%3.256◇14.3%5.632◇49.9%0.374◇15.0%1.993◇15.6%40.35
SegFormer [59]1.198◇19.4%0.741◇2.0%3.543◇5.1%5.957◇52.4%0.401◇3.6%2.167◇0.2%27.25
HRNet [52]14.897◇93.5%0.196◇285.7%1.859◇100.3%17.984◇84.3%0.045◇855.6%0.525◇338.7%28.54
ViTPose [61]♣4.303◇77.6%0.376◇101.1%1.456◇156.3%5.545◇49.1%0.246◇74.8%0.805◇186.1%89.99
HRFormer [66]1.259◇23.4%0.729◇3.7%3.479◇7.0%5.855◇51.8%0.393◇9.4%2.109◇9.2%10.10
HSNet [33]♠2.014◇52.1%0.431◇75.4%1.922◇93.7%3.016◇6.4%0.234◇83.8%1.007◇128.7%26.13
Ours0.9650.7563.7322.8230.4302.30336.32
", + "image_path": "26ad861af2bca52b2d4101c9b05f6307e13af7cc64eef17cf91bce4faa29203d.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 53, + 248, + 520, + 498 + ], + "blocks": [ + { + "bbox": [ + 53, + 248, + 520, + 498 + ], + "lines": [ + { + "bbox": [ + 53, + 248, + 520, + 498 + ], + "spans": [ + { + "bbox": [ + 53, + 248, + 520, + 498 + ], + "type": "image", + "image_path": "734b09abab0437f3c90c5d44b376121ef7dbb5cfdd74a813d6fe2640d2cba3f6.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 502, + 546, + 525 + ], + "lines": [ + { + "bbox": [ + 46, + 502, + 546, + 525 + ], + "spans": [ + { + "bbox": [ + 46, + 502, + 546, + 525 + ], + "type": "text", + "content": "Figure 5. Visualization of prediction results. We show the visualization results of our model, few-shot segmentation (HSNet [33]), the best human pose estimation model (HRFormer [66]) and the segmentation model (SegFormer [59])." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "text", + "content": "forms the segmentation, human pose estimation, and few-shot segmentation models in all metrics at both the Seen and Unseen settings. Taking KLD as the metric, our method exceeds the best segmentation method by " + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "inline_equation", + "content": "19.4\\%" + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "text", + "content": ", surpasses the best human pose estimation model by " + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "inline_equation", + "content": "23.4\\%" + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "text", + "content": ", and outperforms the few-shot model by " + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "inline_equation", + "content": "52.1\\%" + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "text", + "content": " at the Seen setting. At the Unseen setting, our approach outperforms the best semantic segmentation method by " + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "inline_equation", + "content": "52.4\\%" + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "text", + "content": ", the best human pose estimation model by " + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "inline_equation", + "content": "51.8\\%" + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "text", + "content": ", and the few-shot segmentation model by " + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "inline_equation", + "content": "6.4\\%" + }, + { + "bbox": [ + 46, + 545, + 289, + 713 + ], + "type": "text", + "content": ". Moreover, our method significantly exceeds HSNet [33] in both SIM and NSS metrics. The performance advantage of our model is even more pronounced at the Unseen setting. It validates that exploiting the intrinsic connection between the body part and the" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 304, + 545, + 545, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 545, + 569 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 545, + 569 + ], + "type": "text", + "content": "object's local interactions using interactive affinity helps our model better generalize to unseen interactions and objects." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 570, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 570, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 570, + 546, + 713 + ], + "type": "text", + "content": "Fig. 5 shows the visualization of affordance maps. Our method outperforms representative models at both Seen and Unseen settings. For the rugby ball and the punching bag, it is difficult to identify the corresponding interactions using the segmentation methods due to the multiple possibilities of affordance. It suggests that the interactive affinity can provide valuable hints about the interconnectedness of the human body part with the local region of the object, thereby helping reduce the ambiguity of the perceived action possibilities. For knives to be used for both \"Cut\" and \"Stick\", our model still recognizes the difference between interactions (\"Outside\"). It suggests that using pose" + } + ] + } + ], + "index": 6 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6814" + } + ] + } + ], + "index": 7 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 70, + 108, + 110 + ], + "blocks": [ + { + "bbox": [ + 53, + 70, + 108, + 110 + ], + "lines": [ + { + "bbox": [ + 53, + 70, + 108, + 110 + ], + "spans": [ + { + "bbox": [ + 53, + 70, + 108, + 110 + ], + "type": "image", + "image_path": "36ea9bfa03ef8548d1c7950226f03a16bb1426cf998ce8d5a1fd1fc1bf4c96f1.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 110, + 70, + 164, + 110 + ], + "blocks": [ + { + "bbox": [ + 110, + 70, + 164, + 110 + ], + "lines": [ + { + "bbox": [ + 110, + 70, + 164, + 110 + ], + "spans": [ + { + "bbox": [ + 110, + 70, + 164, + 110 + ], + "type": "image", + "image_path": "d329ba3020863412c2dfea800bffbfd19f76624aa1fcb983229cf7bd295c262d.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 167, + 70, + 222, + 110 + ], + "blocks": [ + { + "bbox": [ + 167, + 70, + 222, + 110 + ], + "lines": [ + { + "bbox": [ + 167, + 70, + 222, + 110 + ], + "spans": [ + { + "bbox": [ + 167, + 70, + 222, + 110 + ], + "type": "image", + "image_path": "09ff65746fc81693a036d221406157cea1eb666b496c3c566d61b2981fd6b706.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 225, + 70, + 278, + 110 + ], + "blocks": [ + { + "bbox": [ + 225, + 70, + 278, + 110 + ], + "lines": [ + { + "bbox": [ + 225, + 70, + 278, + 110 + ], + "spans": [ + { + "bbox": [ + 225, + 70, + 278, + 110 + ], + "type": "image", + "image_path": "9dd50001c1f697c9f1602adc6e725b590bb27080898a56737f5760a293ade5e3.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 53, + 110, + 108, + 149 + ], + "blocks": [ + { + "bbox": [ + 53, + 110, + 108, + 149 + ], + "lines": [ + { + "bbox": [ + 53, + 110, + 108, + 149 + ], + "spans": [ + { + "bbox": [ + 53, + 110, + 108, + 149 + ], + "type": "image", + "image_path": "5e81d08fbb60ee1006c8c03eaca11f28d1484b8811119aa1620ce905b25650f0.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "type": "image", + "bbox": [ + 110, + 110, + 164, + 149 + ], + "blocks": [ + { + "bbox": [ + 110, + 110, + 164, + 149 + ], + "lines": [ + { + "bbox": [ + 110, + 110, + 164, + 149 + ], + "spans": [ + { + "bbox": [ + 110, + 110, + 164, + 149 + ], + "type": "image", + "image_path": "f6563736ae521896156b051a86a38e7ff9307479118291f47c8e6edef3b94365.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 168, + 110, + 222, + 149 + ], + "blocks": [ + { + "bbox": [ + 168, + 110, + 222, + 149 + ], + "lines": [ + { + "bbox": [ + 168, + 110, + 222, + 149 + ], + "spans": [ + { + "bbox": [ + 168, + 110, + 222, + 149 + ], + "type": "image", + "image_path": "c9299e0aba8c07bb71774ed061509fbc5c144e91cf7aa8b762eb19eff1e2c975.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 225, + 110, + 278, + 149 + ], + "blocks": [ + { + "bbox": [ + 225, + 110, + 278, + 149 + ], + "lines": [ + { + "bbox": [ + 225, + 110, + 278, + 149 + ], + "spans": [ + { + "bbox": [ + 225, + 110, + 278, + 149 + ], + "type": "image", + "image_path": "15570717169525a3344450b456fce11b7b90c64ef28f002f37c8af6165fedfc8.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_body" + } + ], + "index": 7 + }, + { + "type": "image", + "bbox": [ + 53, + 152, + 108, + 189 + ], + "blocks": [ + { + "bbox": [ + 53, + 152, + 108, + 189 + ], + "lines": [ + { + "bbox": [ + 53, + 152, + 108, + 189 + ], + "spans": [ + { + "bbox": [ + 53, + 152, + 108, + 189 + ], + "type": "image", + "image_path": "e8f1197b8660ea4d92fccabfc2da399dd20aad72eff90df1982d6eecc685141c.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 194, + 287, + 227 + ], + "lines": [ + { + "bbox": [ + 46, + 194, + 287, + 227 + ], + "spans": [ + { + "bbox": [ + 46, + 194, + 287, + 227 + ], + "type": "text", + "content": "Figure 6. Different interactive images w.r.t Same non-interactive images. 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Different interactive images w.r.t Same non-interactive images. We present the prediction results of the model for different non-interactive images of the same interactive image." + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + } + ], + "index": 19 + }, + { + "bbox": [ + 46, + 419, + 287, + 612 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 419, + 287, + 612 + ], + "spans": [ + { + "bbox": [ + 46, + 419, + 287, + 612 + ], + "type": "text", + "content": "data as guidance can assist the network in identifying the region where the object is in contact with the outside world to complete the interaction. Fig. 6 shows the results of different interaction images for the same non-interactive image. Since \"Sit on\" and \"Lie on\" correspond to different contact regions on the back, we only visualize this part. Our model can perceive the variations between interactions and accurately transfer the interaction-related invariant features to the corresponding regions. Fig. 7 shows the prediction for the same interactive image corresponding to different non-interactive images. Although different object categories, various scales, and different views, our model can transfer the interactive affinity correctly for each part, which indicates that the IFE module can effectively establish the connection between the interactable regions from different branches and facilitate the transfer of interactive affinity." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 47, + 622, + 140, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 622, + 140, + 635 + ], + "spans": [ + { + "bbox": [ + 47, + 622, + 140, + 635 + ], + "type": "text", + "content": "4.4. Ablation Study" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 46, + 641, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 641, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 641, + 287, + 713 + ], + "type": "text", + "content": "The ablation study results are shown in Table 2. Our framework shows a more pronounced improvement at the Unseen setting, suggesting that our method can generalize and transfer even for unseen interactions and objects with only a few interactive images. In particular, the KHP scheme shows a more evident improvement in the model's" + } + ] + } + ], + "index": 23 + }, + { + "type": "table", + "bbox": [ + 313, + 102, + 539, + 174 + ], + "blocks": [ + { + "bbox": [ + 305, + 70, + 545, + 93 + ], + "lines": [ + { + "bbox": [ + 305, + 70, + 545, + 93 + ], + "spans": [ + { + "bbox": [ + 305, + 70, + 545, + 93 + ], + "type": "text", + "content": "Table 2. Ablation study. We investigate the influence of the IFE module and KHP scheme on model performance." + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 313, + 102, + 539, + 174 + ], + "lines": [ + { + "bbox": [ + 313, + 102, + 539, + 174 + ], + "spans": [ + { + "bbox": [ + 313, + 102, + 539, + 174 + ], + "type": "table", + "html": "
IFEKHPSeenUnseen
KLD ↓SIM ↑NSS ↑KLD ↓SIM ↑NSS ↑
✓1.2080.7283.4915.5930.3962.170
1.1260.7333.6254.5580.4042.143
✓1.1640.7463.6673.5760.4242.257
✓✓0.9650.7563.7322.8230.4302.303
", + "image_path": "be26bb26819a8a630eae5f303c88c7c9694bfb0d741e573abff0f7b30fe030ad.jpg" + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "table_body" + } + ], + "index": 25 + }, + { + "type": "table", + "bbox": [ + 309, + 248, + 544, + 320 + ], + "blocks": [ + { + "bbox": [ + 304, + 183, + 545, + 239 + ], + "lines": [ + { + "bbox": [ + 304, + 183, + 545, + 239 + ], + "spans": [ + { + "bbox": [ + 304, + 183, + 545, + 239 + ], + "type": "text", + "content": "Table 3. Ablation study of the pose guidance. We investigate the impact of different pose feature fusion methods in the KHP scheme. \"GAP\" means global average pooled pose node features. \"Conv\" means concatenation with the contact feature and then being fused by a convolutional layer." + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 309, + 248, + 544, + 320 + ], + "lines": [ + { + "bbox": [ + 309, + 248, + 544, + 320 + ], + "spans": [ + { + "bbox": [ + 309, + 248, + 544, + 320 + ], + "type": "table", + "html": "
ModeSeenUnseen
KLD ↓SIM ↑NSS ↑KLD ↓SIM ↑NSS ↑
GAP+Conv1.3170.7153.5174.0530.3831.962
Part+Conv1.1690.7333.6123.9330.4032.215
GAP+CT1.2530.7103.5784.2230.3861.937
Part+CT0.9650.7563.7322.8230.4302.303
", + "image_path": "b41b40ce483d344a42c4425df680c3967b67fed318dd773f2300a45988c17c81.jpg" + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "table_body" + } + ], + "index": 27 + }, + { + "bbox": [ + 304, + 340, + 545, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 340, + 545, + 495 + ], + "spans": [ + { + "bbox": [ + 304, + 340, + 545, + 495 + ], + "type": "text", + "content": "performance. It indicates that obtaining interactive affinity from human-object interaction can better exploit the connections between the body part and the local region of the object, thus suppressing the multiple possibilities of interaction diversity. Besides, we investigate the effect of the pose guidance on the results. As shown in Table 3, \"GAP\" loses the association between different part keypoints and performs worse than using the keypoints corresponding to different parts separately. \"Conv\" fusion is worse than the cross transformer, possibly due to the fact that the transformer explicitly computes the correlation between the contact region and the body keypoints, thus enabling better mine the interactive affinity representation." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 305, + 503, + 432, + 516 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 503, + 432, + 516 + ], + "spans": [ + { + "bbox": [ + 305, + 503, + 432, + 516 + ], + "type": "text", + "content": "4.5. Performance Analysis" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 304, + 521, + 545, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 521, + 545, + 629 + ], + "spans": [ + { + "bbox": [ + 304, + 521, + 545, + 629 + ], + "type": "text", + "content": "Different Classes. Fig. 8 shows the results of different models on each affordance category. Although our model does not outperform HSNet in some categories [33] at the Unseen setting (the scores are comparable), it still outperforms other methods, which indicates that our method has a good generalization ability when facing unseen objects. For the overlap of objects in \"Cut\", \"Stick\" and \"Lift\", a finer-grained interactive affinity representation can suppress the effects of multiple possibilities and achieve better results." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 630, + 545, + 713 + ], + "type": "text", + "content": "Different body parts. Table 4 shows the results regarding different body parts. Our model achieves almost the best results at the Seen setting. At the Unseen setting, it also achieves excellent transfer performance in most interaction regions corresponding to the body parts. As most of the interaction is done through hand-object contact, the results of our model at the Unseen setting are much better" + } + ] + } + ], + "index": 31 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6815" + } + ] + } + ], + "index": 32 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 46, + 74, + 526, + 164 + ], + "blocks": [ + { + "bbox": [ + 46, + 74, + 526, + 164 + ], + "lines": [ + { + "bbox": [ + 46, + 74, + 526, + 164 + ], + "spans": [ + { + "bbox": [ + 46, + 74, + 526, + 164 + ], + "type": "image", + "image_path": "37d2d6c475fb1d4d8ac41691d9ec8be7fe99e8e22ff7d836962c0dd84eacefa8.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 166, + 544, + 190 + ], + "lines": [ + { + "bbox": [ + 46, + 166, + 544, + 190 + ], + "spans": [ + { + "bbox": [ + 46, + 166, + 544, + 190 + ], + "type": "text", + "content": "Figure 8. Different classes. We measure the KLD metric for each affordance category, with darker colors representing higher performance. The left and right represent the results at the Seen and Unseen settings, respectively." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 50, + 213, + 272, + 363 + ], + "blocks": [ + { + "bbox": [ + 50, + 213, + 272, + 363 + ], + "lines": [ + { + "bbox": [ + 50, + 213, + 272, + 363 + ], + "spans": [ + { + "bbox": [ + 50, + 213, + 272, + 363 + ], + "type": "image", + "image_path": "4ece260a392754f1d58ce69556f55c6426c391076ac22690c430ce714ed9f083.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 366, + 287, + 400 + ], + "lines": [ + { + "bbox": [ + 46, + 366, + 287, + 400 + ], + "spans": [ + { + "bbox": [ + 46, + 366, + 287, + 400 + ], + "type": "text", + "content": "Figure 9. Different Hyper-parameters. We investigate the impact of the number of layers " + }, + { + "bbox": [ + 46, + 366, + 287, + 400 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 46, + 366, + 287, + 400 + ], + "type": "text", + "content": " and tokens " + }, + { + "bbox": [ + 46, + 366, + 287, + 400 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 46, + 366, + 287, + 400 + ], + "type": "text", + "content": " in the interactive enhancement module on the experimental results." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 423, + 287, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 423, + 287, + 483 + ], + "spans": [ + { + "bbox": [ + 46, + 423, + 287, + 483 + ], + "type": "text", + "content": "than all the other methods. It demonstrates that our method uses interactive affinity to explore the interconnections between body parts and local regions of objects, thus reducing the ambiguity in perceiving action possibilities and enabling accurate affordance knowledge transfer." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "spans": [ + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "text", + "content": "Different hyper-parameters. Fig. 9 shows the impact of the hyper-parameters " + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "text", + "content": " in the IFE module on the model's performance. The influence at the Unseen setting is slightly more apparent, indicating that these parameters are more sensitive to the generalization and transfer of unseen objects. The number of cross-branch tokens greatly affects the model performance. Too small " + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "text", + "content": " may lead to difficulties in fully expressing the contextual cues of the different branch interactable features, with the best results obtained at " + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "inline_equation", + "content": "l = 16" + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "text", + "content": ". The model gives the best results with the number of layers " + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "inline_equation", + "content": "m = 2" + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "text", + "content": ". The smaller " + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "text", + "content": " makes it difficult to fully aggregate the contextual cues and establish associations in the interactable regions. As " + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 46, + 484, + 288, + 664 + ], + "type": "text", + "content": " increases, it increases the parameters of the model and the complexity of the optimization, leading to negative results." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "text", + "content": "Limitations. As shown in Table 4, our method still has a large gap between \"Back\" and the other body parts. It is mainly due to the sample imbalance as fewer examples contain \"Back\". Besides, the sitting posture occludes most" + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 310, + 251, + 542, + 423 + ], + "blocks": [ + { + "bbox": [ + 305, + 208, + 546, + 242 + ], + "lines": [ + { + "bbox": [ + 305, + 208, + 546, + 242 + ], + "spans": [ + { + "bbox": [ + 305, + 208, + 546, + 242 + ], + "type": "text", + "content": "Table 4. Different parts. We evaluate the predictions for each body part and object contact region. The Bold and Underline represent the best and sub-optimal results, respectively." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 310, + 251, + 542, + 423 + ], + "lines": [ + { + "bbox": [ + 310, + 251, + 542, + 423 + ], + "spans": [ + { + "bbox": [ + 310, + 251, + 542, + 423 + ], + "type": "table", + "html": "
MethodHandFeetMouthHipsBackOutside
SeenPSPNet1.4711.2841.8601.4837.8181.337
DLabV3+0.7751.1953.0690.5767.1650.945
SegFormer0.5471.2932.1420.6067.5870.847
HRNet14.58317.20217.09514.19724.82313.208
HFormer0.6341.7412.5730.4307.1520.844
HSNet1.6401.5184.5611.2839.1411.218
Ours0.6871.1510.8160.3116.9750.570
UnseenPSPNet6.040-15.18711.32119.4798.043
DLabV3+4.393-11.3081.36611.3974.137
SegFormer3.989-13.0151.31011.1844.673
HRNet17.761-19.25717.22125.73717.830
HFormer4.477-11.5511.43912.1964.393
HSNet2.100-5.2591.0997.9842.356
Ours1.612-6.0840.8038.7371.855
", + "image_path": "dd192bf4227a6343f3fd0d6115bf7a47485f6eebb9d2cd401342b40038a65876.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_body" + } + ], + "index": 8 + }, + { + "bbox": [ + 305, + 443, + 545, + 503 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 443, + 545, + 503 + ], + "spans": [ + { + "bbox": [ + 305, + 443, + 545, + 503 + ], + "type": "text", + "content": "of the region where the object interacts with the back, resulting in an incomplete back contact region feature extraction. In future work, we will consider addressing the data imbalance [16, 25, 41] issue and exploiting the co-relation relationship between body parts." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 306, + 516, + 378, + 528 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 516, + 378, + 528 + ], + "spans": [ + { + "bbox": [ + 306, + 516, + 378, + 528 + ], + "type": "text", + "content": "5. Conclusion" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 536, + 545, + 679 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 536, + 545, + 679 + ], + "spans": [ + { + "bbox": [ + 304, + 536, + 545, + 679 + ], + "type": "text", + "content": "This paper proposes to leverage interactive affinity for effective affordance learning by counteracting the influence of multiple possibilities. To this end, a pose-aided interactive affinity learning framework is introduced, which is able to exploit pose data to guide the network to mine the interactive affinity representation of body parts and object local contact from human-object interaction. Furthermore, we constructed a contact-driven affordance learning (CAL) dataset by collecting and labeling over 5,000 images from 23 affordance categories. Our model outperforms six representative models in three related fields and can serve as a strong baseline for future affordance learning research." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 680, + 545, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 680, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 305, + 680, + 545, + 712 + ], + "type": "text", + "content": "Acknowledgments. This work is supported by National Key R&D Program of China under Grant 2020AAA0105701 and ARC FL170100117 and IH180100002." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 315, + 757 + ], + "type": "text", + "content": "6816" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 84 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 156 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 156 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 156 + ], + "type": "text", + "content": "[1] Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261, 2018.4" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 158, + 288, + 222 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 158, + 288, + 222 + ], + "spans": [ + { + "bbox": [ + 53, + 158, + 288, + 222 + ], + "type": "text", + "content": "[2] Bharat Lal Bhatnagar, Xianghui Xie, Ilya A Petrov, Cristian Sminchisescu, Christian Theobalt, and Gerard Pons-Moll. Behave: Dataset and method for tracking human object interactions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15935-15946, 2022. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 224, + 288, + 278 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 224, + 288, + 278 + ], + "spans": [ + { + "bbox": [ + 53, + 224, + 288, + 278 + ], + "type": "text", + "content": "[3] Jeannette Bohg, Karol Hausman, Bharath Sankaran, Oliver Brock, Danica Kragic, Stefan Schaal, and Gaurav S Sukhatme. Interactive perception: Leveraging action in perception and perception in action. IEEE Transactions on Robotics, 33(6):1273-1291, 2017. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 280, + 288, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 280, + 288, + 312 + ], + "spans": [ + { + "bbox": [ + 53, + 280, + 288, + 312 + ], + "type": "text", + "content": "[4] Zoya Bylinskii, Tilke Judd, Ali Borji, Laurent Itti, Frédo Durand, Aude Oliva, and Antonio Torralba. Mit saliency benchmark. 2015. 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 314, + 288, + 357 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 314, + 288, + 357 + ], + "spans": [ + { + "bbox": [ + 53, + 314, + 288, + 357 + ], + "type": "text", + "content": "[5] Zoya Bylinskii, Tilke Judd, Aude Oliva, Antonio Torralba, and Frédo Durand. What do different evaluation metrics tell us about saliency models? IEEE transactions on pattern analysis and machine intelligence, 41(3):740-757, 2018. 5" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 358, + 288, + 402 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 358, + 288, + 402 + ], + "spans": [ + { + "bbox": [ + 53, + 358, + 288, + 402 + ], + "type": "text", + "content": "[6] Yu-Wei Chao, Zhan Wang, Yugeng He, Jiaxuan Wang, and Jia Deng. HICO: A benchmark for recognizing human-object interactions in images. In Proceedings of the IEEE International Conference on Computer Vision, 2015. 5" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 403, + 288, + 456 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 403, + 288, + 456 + ], + "spans": [ + { + "bbox": [ + 53, + 403, + 288, + 456 + ], + "type": "text", + "content": "[7] Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao. Deepdriving: Learning affordance for direct perception in autonomous driving. In Proceedings of the IEEE international conference on computer vision, pages 2722-2730, 2015. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 458, + 288, + 513 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 458, + 288, + 513 + ], + "spans": [ + { + "bbox": [ + 53, + 458, + 288, + 513 + ], + "type": "text", + "content": "[8] Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European conference on computer vision (ECCV), pages 801-818, 2018. 5, 6, 8" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 514, + 288, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 514, + 288, + 567 + ], + "spans": [ + { + "bbox": [ + 53, + 514, + 288, + 567 + ], + "type": "text", + "content": "[9] Ching-Yao Chuang, Jiaman Li, Antonio Torralba, and Sanja Fidler. Learning to act properly: Predicting and explaining affordances from images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 975–983, 2018. 1, 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 570, + 288, + 624 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 570, + 288, + 624 + ], + "spans": [ + { + "bbox": [ + 48, + 570, + 288, + 624 + ], + "type": "text", + "content": "[10] Shengheng Deng, Xun Xu, Chaozheng Wu, Ke Chen, and Kui Jia. 3d affordancenet: A benchmark for visual object affordance understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1778-1787, 2021. 2, 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 625, + 288, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 625, + 288, + 669 + ], + "spans": [ + { + "bbox": [ + 48, + 625, + 288, + 669 + ], + "type": "text", + "content": "[11] Thanh-Toan Do, Anh Nguyen, and Ian Reid. Affordancenet: An end-to-end deep learning approach for object affordance detection. In 2018 IEEE international conference on robotics and automation (ICRA), pages 5882-5889. IEEE, 2018. 1, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "type": "text", + "content": "[12] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Trans-" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 74, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 326, + 74, + 545, + 94 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 74, + 545, + 94 + ], + "spans": [ + { + "bbox": [ + 326, + 74, + 545, + 94 + ], + "type": "text", + "content": "formers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.3" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 96, + 547, + 148 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 96, + 547, + 148 + ], + "spans": [ + { + "bbox": [ + 307, + 96, + 547, + 148 + ], + "type": "text", + "content": "[13] Vibekananda Dutta and Teresa Zielinska. Action prediction based on physically grounded object affordances in human-object interactions. In 2017 11th International Workshop on Robot Motion and Control (RoMoCo), pages 47-52. IEEE, 2017. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 151, + 547, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 151, + 547, + 205 + ], + "spans": [ + { + "bbox": [ + 307, + 151, + 547, + 205 + ], + "type": "text", + "content": "[14] Kuan Fang, Te-Lin Wu, Daniel Yang, Silvio Savarese, and Joseph J Lim. Demo2vec: Reasoning object affordances from online videos. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2139-2147, 2018. 2, 3, 5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 206, + 545, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 206, + 545, + 227 + ], + "spans": [ + { + "bbox": [ + 307, + 206, + 545, + 227 + ], + "type": "text", + "content": "[15] James J Gibson. The theory of affordances. *Hilldale, USA*, 1(2):67-82, 1977. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 228, + 547, + 293 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 228, + 547, + 293 + ], + "spans": [ + { + "bbox": [ + 307, + 228, + 547, + 293 + ], + "type": "text", + "content": "[16] Khan Md Hasib, Md Iqbal, Faisal Muhammad Shah, Jubayer Al Mahmud, Mahmudul Hasan Popel, Md Showrov, Imran Hossain, Shakil Ahmed, Obaidur Rahman, et al. A survey of methods for managing the classification and solution of data imbalance problem. arXiv preprint arXiv:2012.11870, 2020. 8" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 294, + 547, + 349 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 294, + 547, + 349 + ], + "spans": [ + { + "bbox": [ + 307, + 294, + 547, + 349 + ], + "type": "text", + "content": "[17] Mohamed Hassan, Partha Ghosh, Joachim Tesch, Dimitrios Tzionas, and Michael J Black. Populating 3d scenes by learning human-scene interaction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14708-14718, 2021. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 350, + 545, + 382 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 350, + 545, + 382 + ], + "spans": [ + { + "bbox": [ + 307, + 350, + 545, + 382 + ], + "type": "text", + "content": "[18] Mohammed Hassanin, Salman Khan, and Murat Tahtali. Visual affordance and function understanding: A survey. ACM Computing Surveys (CSUR), 54(3):1-35, 2021. 1, 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 383, + 545, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 383, + 545, + 437 + ], + "spans": [ + { + "bbox": [ + 307, + 383, + 545, + 437 + ], + "type": "text", + "content": "[19] Tucker Hermans, James M Rehg, and Aaron Bobick. Affordance prediction via learned object attributes. In IEEE International Conference on Robotics and Automation (ICRA): Workshop on Semantic Perception, Mapping, and Exploration, pages 181-184. Citeseer, 2011. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 438, + 545, + 491 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 438, + 545, + 491 + ], + "spans": [ + { + "bbox": [ + 307, + 438, + 545, + 491 + ], + "type": "text", + "content": "[20] Lorenzo Jamone, Emre Ugur, Angelo Cangelosi, Luciano Fadiga, Alexandre Bernardino, Justus Piater, and José Santos-Victor. Affordances in psychology, neuroscience, and robotics: A survey. IEEE Transactions on Cognitive and Developmental Systems, 10(1):4-25, 2016. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 493, + 545, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 493, + 545, + 525 + ], + "spans": [ + { + "bbox": [ + 307, + 493, + 545, + 525 + ], + "type": "text", + "content": "[21] Tilke Judd, Frédo Durand, and Antonio Torralba. A benchmark of computational models of saliency to predict human fixations. 2012. 5" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 526, + 545, + 569 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 526, + 545, + 569 + ], + "spans": [ + { + "bbox": [ + 307, + 526, + 545, + 569 + ], + "type": "text", + "content": "[22] Salman Khan, Muzammal Naseer, Munawar Hayat, Syed Waqas Zamir, Fahad Shahbaz Khan, and Mubarak Shah. Transformers in vision: A survey. ACM computing surveys (CSUR), 2021. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 571, + 545, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 571, + 545, + 602 + ], + "spans": [ + { + "bbox": [ + 307, + 571, + 545, + 602 + ], + "type": "text", + "content": "[23] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016. 4" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 604, + 545, + 647 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 604, + 545, + 647 + ], + "spans": [ + { + "bbox": [ + 307, + 604, + 545, + 647 + ], + "type": "text", + "content": "[24] Hema S Koppula and Ashutosh Saxena. Anticipating human activities using object affordances for reactive robotic response. IEEE transactions on pattern analysis and machine intelligence, 38(1):14-29, 2015. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 647, + 545, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 647, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 307, + 647, + 545, + 689 + ], + "type": "text", + "content": "[25] Joffrey L Leevy, Taghi M Khoshgoftaar, Richard A Bauder, and Naeem Seliya. A survey on addressing high-class imbalance in big data. Journal of Big Data, 5(1):1-30, 2018. 8" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 307, + 691, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 691, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 691, + 545, + 713 + ], + "type": "text", + "content": "[26] Yi-Chi Liao, Kashyap Todi, Aditya Acharya, Antti Keurulainen, Andrew Howes, and Antti Oulasvirta. Rediscovering" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "6817" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 67, + 72, + 287, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 72, + 287, + 105 + ], + "spans": [ + { + "bbox": [ + 67, + 72, + 287, + 105 + ], + "type": "text", + "content": "affordance: A reinforcement learning perspective. In CHI Conference on Human Factors in Computing Systems, pages 1-15, 2022. 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 107, + 287, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 107, + 287, + 162 + ], + "spans": [ + { + "bbox": [ + 48, + 107, + 287, + 162 + ], + "type": "text", + "content": "[27] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dálár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 163, + 287, + 185 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 163, + 287, + 185 + ], + "spans": [ + { + "bbox": [ + 48, + 163, + 287, + 185 + ], + "type": "text", + "content": "[28] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 186, + 287, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 186, + 287, + 218 + ], + "spans": [ + { + "bbox": [ + 48, + 186, + 287, + 218 + ], + "type": "text", + "content": "[29] Hongchen Luo, Wei Zhai, Jing Zhang, Yang Cao, and Dacheng Tao. One-shot affordance detection. arXiv preprint arXiv:2106.14747, 2021. 2, 3, 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 220, + 287, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 220, + 287, + 274 + ], + "spans": [ + { + "bbox": [ + 48, + 220, + 287, + 274 + ], + "type": "text", + "content": "[30] Hongchen Luo, Wei Zhai, Jing Zhang, Yang Cao, and Dacheng Tao. Learning affordance grounding from exocentric images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2252-2261, 2022. 2, 3, 5" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 276, + 287, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 276, + 287, + 308 + ], + "spans": [ + { + "bbox": [ + 48, + 276, + 287, + 308 + ], + "type": "text", + "content": "[31] Wei Mao, Miaomiao Liu, Richard Hartley, and Mathieu Salzmann. Contact-aware human motion forecasting. arXiv preprint arXiv:2210.03954, 2022. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 311, + 287, + 354 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 311, + 287, + 354 + ], + "spans": [ + { + "bbox": [ + 48, + 311, + 287, + 354 + ], + "type": "text", + "content": "[32] Huaqing Min, Ronghua Luo, Jinhui Zhu, Sheng Bi, et al. Affordance research in developmental robotics: A survey. IEEE Transactions on Cognitive and Developmental Systems, 8(4):237-255, 2016. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 356, + 287, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 356, + 287, + 399 + ], + "spans": [ + { + "bbox": [ + 48, + 356, + 287, + 399 + ], + "type": "text", + "content": "[33] Juhong Min, Dahiyun Kang, and Minsu Cho. Hypercorrelation squeeze for few-shot segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021. 5, 6, 7, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 401, + 287, + 455 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 401, + 287, + 455 + ], + "spans": [ + { + "bbox": [ + 48, + 401, + 287, + 455 + ], + "type": "text", + "content": "[34] Kaichun Mo, Leonidas J Guibas, Mustafa Mukadam, Abhinav Gupta, and Shubham Tulsiani. Where2act: From pixels to actions for articulated 3d objects. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6813-6823, 2021. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 456, + 287, + 500 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 456, + 287, + 500 + ], + "spans": [ + { + "bbox": [ + 48, + 456, + 287, + 500 + ], + "type": "text", + "content": "[35] Kaichun Mo, Yuzhe Qin, Fanbo Xiang, Hao Su, and Leonidas Guibas. O2o-afford: Annotation-free large-scale object-object affordance learning. In Conference on Robot Learning, pages 1666-1677. PMLR, 2022. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 502, + 287, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 502, + 287, + 555 + ], + "spans": [ + { + "bbox": [ + 48, + 502, + 287, + 555 + ], + "type": "text", + "content": "[36] Roozbeh Mottaghi, Connor Schenck, Dieter Fox, and Ali Farhadi. See the glass half full: Reasoning about liquid containers, their volume and content. In Proceedings of the IEEE International Conference on Computer Vision, pages 1871-1880, 2017. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 558, + 287, + 610 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 558, + 287, + 610 + ], + "spans": [ + { + "bbox": [ + 48, + 558, + 287, + 610 + ], + "type": "text", + "content": "[37] Austin Myers, Ching L Teo, Cornelia Fermüller, and Yiannis Aloimonos. Affordance detection of tool parts from geometric features. In 2015 IEEE International Conference on Robotics and Automation (ICRA), pages 1374-1381. IEEE, 2015. 1, 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 613, + 287, + 666 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 613, + 287, + 666 + ], + "spans": [ + { + "bbox": [ + 48, + 613, + 287, + 666 + ], + "type": "text", + "content": "[38] Tushar Nagarajan, Christoph Feichtenhofer, and Kristen Grauman. Grounded human-object interaction hotspots from video. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 8688-8697, 2019. 1, 2, 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "text", + "content": "[39] Tushar Nagarajan and Kristen Grauman. Learning affordance landscapes for interaction exploration in 3d environments. Advances in Neural Information Processing Systems, 33:2005-2015, 2020. 1, 3" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 29, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 126 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 126 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 126 + ], + "type": "text", + "content": "[40] Anh Nguyen, Dimitrios Kanoulas, Darwin G Caldwell, and Nikos G Tsagarakis. Detecting object affordances with convolutional neural networks. In 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 2765-2770. IEEE, 2016. 1, 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 129, + 545, + 183 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 545, + 183 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 545, + 183 + ], + "type": "text", + "content": "[41] Minlong Peng, Qi Zhang, Xiaoyu Xing, Tao Gui, Xuanjing Huang, Yu-Gang Jiang, Keyu Ding, and Zhigang Chen. Trainable undersampling for class-imbalance learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 33, pages 4707-4714, 2019. 8" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 184, + 545, + 216 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 184, + 545, + 216 + ], + "spans": [ + { + "bbox": [ + 308, + 184, + 545, + 216 + ], + "type": "text", + "content": "[42] Robert J Peters, Asha Iyer, Laurent Itti, and Christof Koch. Components of bottom-up gaze allocation in natural images. Vision research, 45(18):2397-2416, 2005. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 217, + 545, + 259 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 217, + 545, + 259 + ], + "spans": [ + { + "bbox": [ + 308, + 217, + 545, + 259 + ], + "type": "text", + "content": "[43] Siyuan Qi, Siyuan Huang, Ping Wei, and Song-Chun Zhu. Predicting human activities using stochastic grammar. In Proceedings of the IEEE International Conference on Computer Vision, pages 1164-1172, 2017. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 261, + 545, + 303 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 261, + 545, + 303 + ], + "spans": [ + { + "bbox": [ + 308, + 261, + 545, + 303 + ], + "type": "text", + "content": "[44] Santhosh K Ramakrishnan, Dinesh Jayaraman, and Kristen Grauman. An exploration of embodied visual exploration. International Journal of Computer Vision, 129(5):1616-1649, 2021. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 305, + 545, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 305, + 545, + 348 + ], + "spans": [ + { + "bbox": [ + 308, + 305, + 545, + 348 + ], + "type": "text", + "content": "[45] Manolis Savva, Angel X Chang, Pat Hanrahan, Matthew Fisher, and Matthias Nießner. Pigraphs: learning interaction snapshots from observations. ACM Transactions on Graphics (TOG), 35(4):1-12, 2016. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 350, + 545, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 350, + 545, + 392 + ], + "spans": [ + { + "bbox": [ + 308, + 350, + 545, + 392 + ], + "type": "text", + "content": "[46] Johann Sawatzky and Jurgen Gall. Adaptive binarization for weakly supervised affordance segmentation. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 1383-1391, 2017. 1, 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 394, + 545, + 437 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 394, + 545, + 437 + ], + "spans": [ + { + "bbox": [ + 308, + 394, + 545, + 437 + ], + "type": "text", + "content": "[47] Johann Sawatzky, Abhilash Srikantha, and Juergen Gall. Weakly supervised affordance detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2795-2804, 2017. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 438, + 545, + 481 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 438, + 545, + 481 + ], + "spans": [ + { + "bbox": [ + 308, + 438, + 545, + 481 + ], + "type": "text", + "content": "[48] Giulio Schiavi, Paula Wulkop, Giuseppe Rizzi, Lionel Ott, Roland Siegwart, and Jen Jen Chung. Learning agent-aware affordances for closed-loop interaction with articulated objects. arXiv preprint arXiv:2209.05802, 2022. 1, 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 482, + 545, + 536 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 482, + 545, + 536 + ], + "spans": [ + { + "bbox": [ + 308, + 482, + 545, + 536 + ], + "type": "text", + "content": "[49] Soshi Shimada, Vladislav Golyanik, Zhi Li, Patrick Pérez, Weipeng Xu, and Christian Theobalt. Hulc: 3d human motion capture with pose manifold sampling and dense contact guidance. In European Conference on Computer Vision, pages 516-533. Springer, 2022. 3" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 537, + 545, + 580 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 537, + 545, + 580 + ], + "spans": [ + { + "bbox": [ + 308, + 537, + 545, + 580 + ], + "type": "text", + "content": "[50] Dong-Hee Shin. The role of affordance in the experience of virtual reality learning: Technological and affective affordances in virtual reality. Telematics and Informatics, 34(8):1826-1836, 2017. 1" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 582, + 545, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 582, + 545, + 635 + ], + "spans": [ + { + "bbox": [ + 308, + 582, + 545, + 635 + ], + "type": "text", + "content": "[51] Tianmin Shu, Xiaofeng Gao, Michael S Ryoo, and Song-Chun Zhu. Learning social affordance grammar from videos: Transferring human interactions to human-robot interactions. In 2017 IEEE international conference on robotics and automation (ICRA), pages 1669–1676. IEEE, 2017. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 636, + 545, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 636, + 545, + 668 + ], + "spans": [ + { + "bbox": [ + 308, + 636, + 545, + 668 + ], + "type": "text", + "content": "[52] Ke Sun, Bin Xiao, Dong Liu, and Jingdong Wang. Deep high-resolution representation learning for human pose estimation. In CVPR, 2019. 5, 6, 8" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 670, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 670, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 308, + 670, + 545, + 712 + ], + "type": "text", + "content": "[53] Yuan Sun, Shuyue Fang, and Zuopeng Justin Zhang. Impression management strategies on enterprise social media platforms: An affordance perspective. International Journal of Information Management, 60:102359, 2021. 1" + } + ] + } + ], + "index": 28 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 749, + 315, + 757 + ], + "type": "text", + "content": "6818" + } + ] + } + ], + "index": 30 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 105 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 105 + ], + "type": "text", + "content": "[54] Michael J Swain and Dana H Ballard. Color indexing. International Journal of Computer Vision (IJCV), 7(1):11-32, 1991. 5" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 106, + 287, + 152 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 106, + 287, + 152 + ], + "spans": [ + { + "bbox": [ + 49, + 106, + 287, + 152 + ], + "type": "text", + "content": "[55] Karthik Mahesh Varadarajan and Markus Vincze. Parallel deep learning with suggestive activation for object category recognition. In International Conference on Computer Vision Systems, pages 354-363. Springer, 2013. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 152, + 288, + 196 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 152, + 288, + 196 + ], + "spans": [ + { + "bbox": [ + 48, + 152, + 288, + 196 + ], + "type": "text", + "content": "[56] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30, 2017. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 197, + 288, + 241 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 197, + 288, + 241 + ], + "spans": [ + { + "bbox": [ + 48, + 197, + 288, + 241 + ], + "type": "text", + "content": "[57] Xiaolong Wang, Rohit Girdhar, and Abhinav Gupta. Binge watching: Scaling affordance learning from sitcoms. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2596-2605, 2017. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 242, + 288, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 242, + 288, + 297 + ], + "spans": [ + { + "bbox": [ + 48, + 242, + 288, + 297 + ], + "type": "text", + "content": "[58] Yian Wang, Ruihai Wu, Kaichun Mo, Jiaqi Ke, Qingnan Fan, Leonidas J Guibas, and Hao Dong. Adaafford: Learning to adapt manipulation affordance for 3d articulated objects via few-shot interactions. In European Conference on Computer Vision, pages 90-107. Springer, 2022. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 298, + 288, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 298, + 288, + 353 + ], + "spans": [ + { + "bbox": [ + 48, + 298, + 288, + 353 + ], + "type": "text", + "content": "[59] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34:12077-12090, 2021. 3, 4, 5, 6, 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 354, + 288, + 388 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 354, + 288, + 388 + ], + "spans": [ + { + "bbox": [ + 48, + 354, + 288, + 388 + ], + "type": "text", + "content": "[60] Xianghui Xie, Bharat Lal Bhatnagar, and Gerard Pons-Moll. Chore: Contact, human and object reconstruction from a single rgb image. arXiv preprint arXiv:2204.02445, 2022. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 388, + 288, + 432 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 388, + 288, + 432 + ], + "spans": [ + { + "bbox": [ + 48, + 388, + 288, + 432 + ], + "type": "text", + "content": "[61] Yufei Xu, Jing Zhang, Qiming Zhang, and Dacheng Tao. ViTPose: Simple vision transformer baselines for human pose estimation. In Advances in Neural Information Processing Systems, 2022. 5, 6" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 434, + 288, + 477 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 434, + 288, + 477 + ], + "spans": [ + { + "bbox": [ + 48, + 434, + 288, + 477 + ], + "type": "text", + "content": "[62] Yufei Xu, Qiming Zhang, Jing Zhang, and Dacheng Tao. ViTAE: Vision transformer advanced by exploring intrinsic inductive bias. Advances in Neural Information Processing Systems, 34, 2021. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 478, + 288, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 478, + 288, + 533 + ], + "spans": [ + { + "bbox": [ + 48, + 478, + 288, + 533 + ], + "type": "text", + "content": "[63] Natsuki Yamanobe, Weiwei Wan, Ixchel G Ramirez-Alpizar, Damien Petit, Tokuo Tsuji, Shuichi Akizuki, Manabu Hashimoto, Kazuyuki Nagata, and Kensuke Harada. A brief review of affordance in robotic manipulation research. Advanced Robotics, 31(19-20):1086-1101, 2017. 1, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 534, + 288, + 578 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 534, + 288, + 578 + ], + "spans": [ + { + "bbox": [ + 48, + 534, + 288, + 578 + ], + "type": "text", + "content": "[64] Sijie Yan, Yuanjun Xiong, and Dahua Lin. Spatial temporal graph convolutional networks for skeleton-based action recognition. In Thirty-second AAAI conference on artificial intelligence, 2018. 4" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 579, + 288, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 579, + 288, + 635 + ], + "spans": [ + { + "bbox": [ + 48, + 579, + 288, + 635 + ], + "type": "text", + "content": "[65] Lixin Yang, Xinyu Zhan, Kailin Li, Wenqiang Xu, Jiefeng Li, and Cewu Lu. Cpf: Learning a contact potential field to model the hand-object interaction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 11097-11106, 2021. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 635, + 288, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 635, + 288, + 669 + ], + "spans": [ + { + "bbox": [ + 48, + 635, + 288, + 669 + ], + "type": "text", + "content": "[66] Yuhui Yuan, Rao Fu, Lang Huang, Weihong Lin, Chao Zhang, Xilin Chen, and Jingdong Wang. Hrformer: High-resolution transformer for dense prediction. 2021. 5, 6, 8" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 288, + 713 + ], + "type": "text", + "content": "[67] Wei Zhai, Hongchen Luo, Jing Zhang, Yang Cao, and Dacheng Tao. One-shot object affordance detection in the wild. International Journal of Computer Vision, 130(10):2472-2500, 2022. 2, 3, 5" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 72, + 547, + 162 + ], + "type": "list", + "angle": 0, + "index": 17, + "blocks": [ + { + "bbox": [ + 307, + 72, + 546, + 118 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 72, + 546, + 118 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 546, + 118 + ], + "type": "text", + "content": "[68] Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. Pyramid scene parsing network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2881-2890, 2017. 5, 6, 8" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 118, + 547, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 547, + 162 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 547, + 162 + ], + "type": "text", + "content": "[69] Yixin Zhu, Yibiao Zhao, and Song Chun Zhu. Understanding tools: Task-oriented object modeling, learning and recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2855-2864, 2015. 1" + } + ] + } + ], + "index": 16 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "spans": [ + { + "bbox": [ + 295, + 748, + 316, + 757 + ], + "type": "text", + "content": "6819" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Leveraging Hidden Positives for Unsupervised Semantic Segmentation/34b6701a-58b4-43b7-90c9-cd9aa3bd0efc_content_list.json b/2023/Leveraging Hidden Positives for Unsupervised Semantic Segmentation/34b6701a-58b4-43b7-90c9-cd9aa3bd0efc_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..ad0b18c6c2bfa9d7256fdece573c707dc8419b9b --- /dev/null +++ b/2023/Leveraging Hidden Positives for Unsupervised Semantic Segmentation/34b6701a-58b4-43b7-90c9-cd9aa3bd0efc_content_list.json @@ -0,0 +1,1571 @@ +[ + { + "type": "text", + "text": "Leveraging Hidden Positives for Unsupervised Semantic Segmentation", + "text_level": 1, + "bbox": [ + 129, + 161, + 838, + 182 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Hyun Seok Seong, WonJun Moon, SuBeen Lee, Jae-Pil Heo* Sungkyunkwan University", + "bbox": [ + 245, + 207, + 729, + 241 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{gustjrdl95，wjun0830，leesb7426，jaepilheo}@skku.edu", + "bbox": [ + 247, + 242, + 717, + 257 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 288, + 312, + 302 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Dramatic demand for manpower to label pixel-level annotations triggered the advent of unsupervised semantic segmentation. Although the recent work employing the vision transformer (ViT) backbone shows exceptional performance, there is still a lack of consideration for task-specific training guidance and local semantic consistency. To tackle these issues, we leverage contrastive learning by excavating hidden positives to learn rich semantic relationships and ensure semantic consistency in local regions. Specifically, we first discover two types of global hidden positives, task-agnostic and task-specific ones for each anchor based on the feature similarities defined by a fixed pre-trained backbone and a segmentation head-in-training, respectively. A gradual increase in the contribution of the latter induces the model to capture task-specific semantic features. In addition, we introduce a gradient propagation strategy to learn semantic consistency between adjacent patches, under the inherent premise that nearby patches are highly likely to possess the same semantics. Specifically, we add the loss propagating to local hidden positives, semantically similar nearby patches, in proportion to the predefined similarity scores. With these training schemes, our proposed method achieves new state-of-the-art (SOTA) results in COCO-stuff, Cityscapes, and Potsdam-3 datasets. Our code is available at: https://github.com/hynnsk/HP.", + "bbox": [ + 75, + 317, + 473, + 663 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 77, + 692, + 208, + 707 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Semantic segmentation is a major task for scene understanding and plays a crucial role in many applications including medical imaging and autonomous driving [5, 11, 28, 34, 38, 41]. However, existing supervised approaches demand large-scale pixel-level annotations which require huge labeling costs. It has triggered the advent of weakly-supervised [23, 33, 35, 36] and unsupervised semantic segmentation [8, 14, 19, 37] which are to learn without expensive pixel-level annotations.", + "bbox": [ + 75, + 716, + 468, + 840 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/d63481b8d520eb9a72d407603d99d0d4f248d0f715b86217ce3dfd95cd8d8e24.jpg", + "image_caption": [ + "(a) Visualization" + ], + "image_footnote": [], + "bbox": [ + 507, + 287, + 890, + 421 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/ed23234869160150eacb6ca895bdd02f4190bdb48d66734f2732c08a43af2660.jpg", + "image_caption": [ + "(b) Global hidden positive", + "Figure 1. Assuming a mini-batch comprising two images shown in (a), we describe two types of hidden positives, to leverage for contrastive learning. (a) With two types of hidden positives introduced in (b) and (c), we provide an example of how our training scheme provides more precise and consistent semantics. (b) (top) Semantically analogous patches throughout the mini-batch are selected as global hidden positives. (bottom) Data-driven criterion per anchor is designed for reliable positive collection. With the criterion, selected positives are illustrated in (b top). (c) (top) We define local hidden positives for each anchor to be the adjacent patches with high semantic consistency, i.e., blue boxes. (bottom) Average attention scores for adjacent patches from the pretrained transformer architecture. The blue line represents the attention score for local hidden positives while the red line for patches neighboring anchors but having low semantic consistency." + ], + "image_footnote": [], + "bbox": [ + 505, + 432, + 694, + 571 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/8db016c991b005fb583863305ebf471f3b7888405f28b072d2553f0648322498.jpg", + "image_caption": [ + "(c) Local hidden positive" + ], + "image_footnote": [], + "bbox": [ + 702, + 432, + 890, + 571 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Particularly, unsupervised semantic segmentation is one of the most challenging tasks, since it needs to capture pixel-level semantics from unlabeled data. In this context, clustering-based approaches have been proposed to learn", + "bbox": [ + 497, + 809, + 892, + 866 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 129, + 49, + 200, + 85 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 252, + 48, + 791, + 86 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*Corresponding author", + "bbox": [ + 95, + 853, + 220, + 866 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "19540", + "bbox": [ + 465, + 887, + 505, + 897 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "semantic-preserving clusters by attracting the augmented views in the pixel-level [8, 19]. They implemented the intuition of contrastive learning [3, 6, 7, 13, 15, 25] by ensuring the augmented pairs yield symmetric cluster assignments. More recently, as discovering pixel-level semantics from scratch is challenging, STEGO [14] broke down the problem into learning the representation and learning the segmentation head. With the learned patch-level representation from the seminal work in unsupervised learning [4, 39], they train the segmentation head with a distillation strategy. Although they have made great advancements, we point out their limitations in that they rely solely on a fixed backbone that is not specifically trained for the segmentation task and overlook the importance of semantic consistency along the adjacency that could be a crucial clue for segmentation.", + "bbox": [ + 75, + 125, + 472, + 332 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To take these into consideration, we leverage contrastive learning based on the mined hidden positives to ensure contextual consistency along the patches with analogous semantics, particularly the nearby patches, as described in Fig. 1. Specifically, we elaborately select the pseudopositive samples (i.e., global hidden positive, GHP) for contrastive learning to learn semantic consistency. Also, to ensure local consistency, we propagate the loss gradient to the adjacent patches (i.e., local hidden positive, LHP) in proportion to their equivalency. First, the GHP selection process is designed with two types of data reference pools, task-agnostic and task-specific, to collect the semantically consistent patch features throughout the mini-batch per anchor. For instance, the task-agnostic data reference pool is composed of features extracted by the unsupervised pretrained backbone. On the other hand, the task-specific reference pool is constructed with the features from the segmentation head-in-training to complement task relevance. Based on the two reference pools, two sets of GHP are selected each with generalized and task-specific perspectives. Second, to implement the property of locality and prevent the semantics from fluctuating, we propagate the loss gradient to adjacent patches (i.e., LHP) in proportion to the similarity scores built within the pretrained backbone. This enables the model to learn the relevance of the local context that nearby patches often belong to the same instance.", + "bbox": [ + 75, + 333, + 472, + 691 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our main contributions are summarized as:", + "bbox": [ + 95, + 692, + 383, + 704 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We propose a novel method to discover semantically similar pairs, called global hidden positives, to explicitly learn the semantic relationship among patches for unsupervised semantic segmentation.", + "- We utilize the task-specific features from a model-intraining and validate the effectiveness of progressive increase of their contribution.", + "- A gradient propagation to nearby similar patches, local hidden positives, is developed to learn local semantic consistency which is the nature of segmentation." + ], + "bbox": [ + 95, + 712, + 468, + 866 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "- Our approach outperforms existing state-of-the-art methods across extensive experiments.", + "bbox": [ + 517, + 125, + 890, + 153 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 499, + 167, + 640, + 182 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Unsupervised Semantic Segmentation", + "text_level": 1, + "bbox": [ + 499, + 190, + 823, + 205 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Semantic segmentation has been extensively studied for its wide applicability [5, 11, 29, 34, 38, 41], but collecting pixel-level annotations requires expensive costs. Therefore, many studies [8, 14, 17, 19, 30, 37] attempted to address semantic segmentation without any supervision. Earlier techniques tried to learn semantic correspondence at the pixel level. IIC [19] maximizes the mutual information between the features of two differently augmented images, and PiCIE [8] learns photometric and geometric invariances as an inductive bias. Yet, their training process highly depends on data augmentation, and learning semantic consistency without any prior knowledge is challenging. Therefore, recent methods [14, 37] adopted the ViT model trained in a self-supervised manner, i.e., DINO [4], as a backbone architecture. For instance, TransFGU [37] relocates the high-level semantic features from DINO into low-level pixel-wise features by generating pixel-wise pseudo labels. On the other hand, STEGO [14] utilizes knowledge distillation that learns correspondences between features extracted from DINO. Although STEGO shows a dramatic performance improvement compared to the prior works, it heavily relies on the pretrained backbone and overlooks the property of local consistency that the adjacent pixels are likely to belong to the same category. On the other hand, our training is driven by both the task-agnostic and task-specific pseudopositive features, and the gradients are conditionally propagated to the neighboring patches, thereby ensuring task-specificity and locality.", + "bbox": [ + 497, + 212, + 890, + 599 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Contrastive Learning", + "text_level": 1, + "bbox": [ + 499, + 609, + 700, + 624 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Self-supervised learning methods [2,3,6,7,12,13,15] aim to learn general representations without any annotations. Thanks to their good representation capability, they have been employed to yield remarkable performances in various downstream tasks [8, 16, 20, 26, 40]. Among them, contrastive learning approaches [3, 6, 7, 13, 15, 25] have shown unrivaled performances. In general, they learn representation by attracting a self-augmented set and repulsing other images [3, 6]. Other variants use additional memory [15] or only exploit the positive set for the attraction [7, 13]. This training scheme has also been utilized for unsupervised semantic segmentation [8, 19]. However, the aforementioned methods only considered augmented pairs for the positive which makes them very sensitive to the quality of the augmentation techniques. Our work differs in that our key idea is to collect and exploit reliable pseudo-positives throughout the mini-batch as described in Fig. 1 (b).", + "bbox": [ + 497, + 631, + 890, + 866 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "19541", + "bbox": [ + 465, + 887, + 504, + 897 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/c826c2f57f6882b466baa39d37e7de301aef35566216052464f0c49228288ad8.jpg", + "image_caption": [ + "Figure 2. Illustration of the global hidden positive (GHP) selection process. Our GHP can be divided into two sub-sets: task-agnostic and task-specific. An index set of task-agnostic GHP $P_{i}^{\\mathrm{ag}}$ comprises the indices of positives discovered within the task-agnostic reference pool $Q^{\\mathrm{ag}}$ . Note that, $Q^{\\mathrm{ag}}$ is composed of randomly sampled features extracted by the feature extractor $\\mathcal{F}$ . Once the anchor feature $f_{i}$ is projected to $z_{i}$ , other patches in the mini-batch are gathered as positives if their similarity with the anchor feature exceeds the similarity between the anchor and the most similar feature in $Q^{\\mathrm{ag}}$ . On the other hand, task-specific GHP is discovered in a similar manner but with task-specific reference pool $Q^{\\mathrm{sp}}$ which keeps being updated with the features from the momentum segmentation head $S'$ . Whereas the task-agnostic GHP set solely contributes to the initial training, the task-specific GHP set gradually replaces the portion of the task-agnostic set until the end of training." + ], + "image_footnote": [], + "bbox": [ + 90, + 123, + 887, + 345 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Method", + "text_level": 1, + "bbox": [ + 77, + 479, + 171, + 494 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we introduce the pseudo-positive selection strategy to discover hidden positives with analogous semantics in Sec. 3.2, the training objective with discovered positives in Sec. 3.3, and the gradient propagation scheme to preserve the property of locality in Sec. 3.4.", + "bbox": [ + 75, + 502, + 470, + 573 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Preliminary", + "text_level": 1, + "bbox": [ + 77, + 579, + 206, + 593 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In unsupervised semantic segmentation, the model utilizes unlabeled image set $X = \\{x_{b}\\}_{b = 1}^{B}$ where $B$ is the number of training data in the mini-batch. Given an image $x_{b}$ processed to the feature extractor $\\mathcal{F}$ , we have $H\\cdot W$ features of $f_{i}\\in \\mathbb{R}^{C}$ , where $i\\in [1,\\dots ,H\\cdot W]$ . Subsequently, the segmentation head $S$ maps a patch feature $f_{i}$ to the corresponding segmentation feature $s_i\\in \\mathbb{R}^K$ . And then, the projection head $\\mathcal{Z}$ produces a projected vector $z_{i}\\in \\mathbb{R}^{K}$ to formulate a contrastive loss function. In the inference stage, we use the segmentation feature $s_i$ .", + "bbox": [ + 75, + 600, + 468, + 738 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Based on the projected vector $z_{i}$ for the $i$ -th patch, let $j$ be the index of the augmented patch of $i$ -th one. Then, the conventional self-supervised contrastive loss [6] for $i$ -th patch in unsupervised semantic segmentation can be defined as follows:", + "bbox": [ + 75, + 739, + 468, + 808 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nL _ {i} ^ {\\text {s e l f}} = - \\log \\frac {\\exp \\left(\\sin \\left(z _ {i} , z _ {j} / \\tau\\right)\\right)}{\\sum_ {a \\in A} \\exp \\left(\\sin \\left(z _ {i} , z _ {a}\\right) / \\tau\\right)}, \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 137, + 813, + 468, + 845 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $A$ indicates a set of all indexes except $i$ , $\\tau$ denotes the", + "bbox": [ + 77, + 852, + 468, + 866 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "scalar temperature parameter, and $\\mathrm{sim}(\\cdot ,\\cdot)$ is cosine similarity between two vectors.", + "bbox": [ + 499, + 480, + 890, + 509 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Global Hidden Positives", + "text_level": 1, + "bbox": [ + 499, + 522, + 721, + 536 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Learning mutual information with augmented pixels only provides insufficient training signal in unsupervised semantic segmentation [14]. Therefore, it is important to discover hidden pseudo-positives to tailor the contrastive loss for unsupervised segmentation. To discover the hidden positives at the initial stage, we utilize the self-supervised pretrained backbone [4] as the task-agnostic criterion. Then, we gradually increase the contribution of hidden positives found in a task-specific way for the training. Fig. 2 provides the overview of the global hidden positive (GHP) selection process.", + "bbox": [ + 497, + 545, + 890, + 698 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Initially, the pretrained backbone is utilized to construct a task-agnostic reference pool to assess whether other features in the mini-batch are semantically-alike for each anchor feature. Specifically, task-agnostic reference pool, $Q^{\\mathrm{ag}} = \\{q_m\\}_{m=1}^M$ , is composed of $M$ randomly sampled features that are extracted by the unsupervised pretrained backbone $\\mathcal{F}$ . Note that, we only sample a single patch feature per image to ensure the semantic randomness of the reference pool. This task-agnostic reference pool is fixed as the pretrained backbone is frozen throughout the training.", + "bbox": [ + 497, + 698, + 890, + 837 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Once the reference pool is gathered, for each patch feature $f_{i}$ , we define an anchor-dependent similarity criterion", + "bbox": [ + 497, + 838, + 890, + 866 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "19542", + "bbox": [ + 465, + 887, + 505, + 897 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "$c_{i}$ to collect positives, as the distance to the closest feature within the reference pool $Q^{\\mathrm{ag}}$ by the cosine similarity:", + "bbox": [ + 75, + 125, + 468, + 153 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nc _ {i} = \\max _ {q _ {m} \\in Q ^ {\\mathrm {a g}}} \\operatorname {s i m} \\left(q _ {m}, f _ {i}\\right). \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 188, + 162, + 468, + 184 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For each anchor feature $f_{i}$ , we basically treat the other feature in the mini-batch $f_{j}$ as positive if the similarity between $f_{i}$ and $f_{j}$ is greater than $c_{i}$ . Still, although one patch feature might be the positive sample for the other, it may not hold mutually. This is because the criterion $c_{i}$ is anchor-dependent. To endow consistency in training, we make the GHP selection symmetric to prevent the relation between two patches from being ambiguous. Therefore, index set of $\\mathrm{GHP} P_{i}^{\\mathrm{ag}}$ for each $i$ -th anchor feature $f_{i}$ is defined as follows:", + "bbox": [ + 75, + 191, + 468, + 328 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nP _ {i} ^ {\\mathrm {a g}} = \\left\\{j \\mid \\operatorname {s i m} \\left(f _ {i}, f _ {j}\\right) > c _ {i} \\vee \\operatorname {s i m} \\left(f _ {i}, f _ {j}\\right) > c _ {j} \\right\\}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 95, + 338, + 468, + 356 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $j$ indicates the index for different patch features in the mini-batch. Accordingly, such a distribution-aware reference pool allows the discovery of globally analogous features in consideration of each anchor.", + "bbox": [ + 75, + 362, + 468, + 416 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "However, although the reference pool built upon the features from an unsupervised pretrained network can serve as an appropriate basis for positivity, it may be insufficient since it lacks task-specificity. We argue that features from the segmentation head are more task-specific than those from the pretrained backbone. Therefore, along with the GHP selected by $P^{\\mathrm{ag}}$ , we construct additional task-specific GHP utilizing the features from the segmentation head.", + "bbox": [ + 75, + 416, + 468, + 527 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Specifically, an index set of task-specific GHP $P_{i}^{\\mathrm{sp}}$ is formed similarly to Eq. 3 by comparing the features $s' = S'(f)$ and task-specific reference pool $Q^{\\mathrm{sp}}$ , where $S'$ indicates the momentum segmentation head, and the task-specific reference pool $Q^{\\mathrm{sp}}$ comprises $s'$ . Also, along with the update of the segmentation head, during training, the reference pool is periodically renewed. Formally, with $c_{i}'$ , calculated by substituting $Q^{\\mathrm{ag}}$ and $f_{i}$ in Eq. 2 with $Q^{\\mathrm{sp}}$ and $s_{i}'$ , respectively, the $P_{i}^{\\mathrm{sp}}$ is expressed as follows:", + "bbox": [ + 75, + 527, + 468, + 652 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nP _ {i} ^ {\\mathrm {s p}} = \\left\\{j \\mid \\operatorname {s i m} \\left(s _ {i} ^ {\\prime}, s _ {j} ^ {\\prime}\\right) > c _ {i} ^ {\\prime} \\vee \\operatorname {s i m} \\left(s _ {i} ^ {\\prime}, s _ {j} ^ {\\prime}\\right) > c _ {j} ^ {\\prime} \\right\\}. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 92, + 660, + 468, + 678 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Note that, the usage of the momentum segmentation head is for the stability of the reference pool [22,42].", + "bbox": [ + 75, + 685, + 468, + 712 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Objective Function", + "text_level": 1, + "bbox": [ + 77, + 720, + 260, + 733 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To formulate a contrastive objective with mined GHP in Sec. 3.2, we also need negative features. As we collected the positives throughout the mini-batch, the naive implementation of contrastive learning would utilize all features except the selected positives in the mini-batch as the negatives. However, since an immoderate increase in the size of the negative set may disturb the model training [32], we form a negative set $N_{i}$ by randomly choosing $\\rho \\%$ of the remaining patches for each $i$ -th anchor. Note that, the separate", + "bbox": [ + 75, + 741, + 468, + 866 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "index sets of negative samples $N_{i}^{\\mathrm{ag}}$ and $N_{i}^{\\mathrm{sp}}$ are defined for each $P_{i}^{\\mathrm{ag}}$ and $P_{i}^{\\mathrm{sp}}$ , correspondingly. Also, unlike Eq. 1, our contrastive loss for each $i$ -th anchor is more like a supervised objective [20] since we are given multiple positives:", + "bbox": [ + 497, + 125, + 890, + 181 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nL ^ {\\text {c o n t}} \\left(z _ {i}, P, N\\right) = \\frac {- 1}{| P |} \\sum_ {p \\in P} \\log \\frac {\\exp \\left(\\operatorname* {s i m} \\left(z _ {i} , z _ {p}\\right) / \\tau\\right)}{\\sum_ {n \\in (N \\cup P)} \\exp \\left(\\operatorname* {s i m} \\left(z _ {i} , z _ {n}\\right) / \\tau\\right)}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 499, + 188, + 927, + 229 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $z_{i}$ , $P$ , and $N$ are the projected anchor vector $\\mathcal{Z}(S(f_i))$ , positive index set, and negative index set, respectively. For simplicity, we use $\\Phi_i^{\\mathrm{ag}}$ and $\\Phi_i^{\\mathrm{sp}}$ to denote the objective functions with task-agnostic GHP $P_{i}^{\\mathrm{ag}}$ and task-specific GHP $P_{i}^{\\mathrm{sp}}$ for each $i$ -th anchor as follows:", + "bbox": [ + 497, + 237, + 890, + 307 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\Phi_ {i} ^ {\\mathrm {a g}} = L ^ {\\mathrm {c o n t}} (z _ {i}, P _ {i} ^ {\\mathrm {a g}}, N _ {i} ^ {\\mathrm {a g}}) \\\\ \\Phi_ {i} ^ {\\mathrm {s p}} = L ^ {\\text {c o n t}} \\left(z _ {i}, P _ {i} ^ {\\mathrm {s p}}, N _ {i} ^ {\\mathrm {s p}}\\right). \\tag {6} \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 606, + 315, + 890, + 349 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Gradient Propagation to Local Hidden Positives", + "text_level": 1, + "bbox": [ + 499, + 357, + 890, + 384 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Besides considering the semantically analogous features globally, it is a common hypothesis that nearby pixels are highly likely to belong to the same semantic class. To this end, we consider the property of locality by propagating the loss gradient to the surrounding features of the anchor. Still, the propagation should be cautiously designed since semantic labels of the adjacent patches are not given; semantic consistency between adjacent patches mostly holds, but sometimes does not (i.e., at object boundaries). Thus, to decide the semantically consistent patches nearby, we utilize the attention scores from the unsupervised pretrained ViT backbone $\\mathcal{F}$", + "bbox": [ + 497, + 391, + 890, + 555 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In detail, we first define the index set $I_{i}^{\\mathrm{surr}}$ for surrounding patches of the $i$ -th anchor including $i$ -th anchor itself. Also, given the spatial attention score for $i$ -th anchor $\\tilde{T}_i \\in \\mathbb{R}^{H \\cdot W}$ from the last self-attention layer in the backbone $\\mathcal{F}$ , we use the average value of $\\tilde{T}_i$ as a threshold to select an index set of LHP $I_{i}^{\\mathrm{local}}$ among $I_{i}^{\\mathrm{surr}}$ :", + "bbox": [ + 497, + 558, + 890, + 642 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nI _ {i} ^ {\\text {l o c a l}} = \\left\\{j \\mid j \\in I _ {i} ^ {\\text {s u r r}} \\wedge t _ {j} > \\operatorname {A v g} \\left(\\tilde {T} _ {i}\\right) \\right\\}, \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 559, + 648, + 890, + 666 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $t_j$ is $j$ -th element of $\\tilde{T}_i$ and $\\mathrm{Avg}(\\cdot)$ denotes the averaging function. The visualization of the attention scores for the adjacent patches is shown in Fig. 1 (b). By utilizing $I_i^{\\mathrm{local}}$ , we obtain the surrounding positive features $G_i$ and the corresponding attention score $T_i$ for LHP as follows:", + "bbox": [ + 497, + 675, + 890, + 745 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} G _ {i} = \\left\\{s _ {j} \\mid j \\in I _ {i} ^ {\\text {l o c a l}} \\right\\} \\\\ T _ {i} = \\left\\{t _ {j} \\mid j \\in I _ {i} ^ {\\text {l o c a l}} \\right\\}, \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 618, + 752, + 769, + 789 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $s_j$ is the feature of $j$ -th patch extracted by the segmentation head $S$ .", + "bbox": [ + 497, + 796, + 890, + 823 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "To propagate the loss gradient to the LHP, we mix the patch features in $G_{i}$ in proportion to the corresponding attention scores in $T_{i}$ . Formally, the mixed patch composed", + "bbox": [ + 497, + 824, + 890, + 866 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "19543", + "bbox": [ + 465, + 887, + 504, + 897 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/f13b47b3971214433afa6eb701b1a1a79f167cfb12a7f3d14997df322d2f5dbe.jpg", + "image_caption": [ + "Figure 3. Illustration of our gradient propagation strategy to preserve local semantic consistency. For each anchor, with its surrounding patches $I^{\\mathrm{surf}}$ and corresponding attention scores from feature extractor $\\mathcal{F}$ , local hidden positives (LHP) $I^{\\mathrm{local}}$ are appointed based on the threshold $\\mathrm{Avg}(\\hat{T})$ (Eq. 7). In a forward pass, the features of LHP $G$ (Eq. 8) are mixed by weighted average according to the attention scores $T$ to compute the objective function $\\Psi$ . In this way, the loss gradient propagates toward the LHP in proportion to $T$ in the backward pass." + ], + "image_footnote": [], + "bbox": [ + 80, + 122, + 890, + 290 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "of LHP $s_i^{\\mathrm{mix}}$ is expressed as:", + "bbox": [ + 75, + 376, + 265, + 391 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\ns _ {i} ^ {\\text {m i x}} = \\frac {1}{\\left| I _ {i} ^ {\\text {l o c a l}} \\right|} \\sum_ {j \\in I _ {i} ^ {\\text {l o c a l}}} \\sigma g _ {j} t _ {j}, \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 179, + 395, + 468, + 431 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\sigma$ is a scalar value to scale the attention score, and $g_{j}$ and $t_j$ indicate the $j$ -th element of $G_{i}$ and $T_{i}$ , respectively. Then, we define the objective functions $\\Psi_i^{\\mathrm{ag}}$ and $\\Psi_i^{\\mathrm{sp}}$ to learn the locality by inserting the projected mixed vector $z_{i}^{\\mathrm{mix}} = \\mathcal{Z}(s_{i}^{\\mathrm{mix}})$ into Eq. 5 as follows:", + "bbox": [ + 75, + 435, + 468, + 505 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\Psi_ {i} ^ {\\mathrm {a g}} = L ^ {\\text {c o n t}} \\left(z _ {i} ^ {\\text {m i x}}, P _ {i} ^ {\\mathrm {a g}}, N _ {i} ^ {\\mathrm {a g}}\\right) \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 176, + 510, + 468, + 533 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\Psi_ {i} ^ {\\mathrm {s p}} = L ^ {\\mathrm {c o n t}} (z _ {i} ^ {\\mathrm {m i x}}, P _ {i} ^ {\\mathrm {s p}}, N _ {i} ^ {\\mathrm {s p}}).\n$$\n", + "text_format": "latex", + "bbox": [ + 181, + 530, + 366, + 546 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Since these functions are calculated by utilizing the mixed vector $z_{i}^{\\mathrm{mix}}$ , the loss gradients are propagated to all features composing the $G_{i}$ , as described in Fig. 3. Therefore, the semantically-alike surrounding vectors in $G_{i}$ are updated in the same direction, thereby retaining semantic consistency within the neighboring patches.", + "bbox": [ + 75, + 549, + 468, + 633 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Overall, by combining all loss formulations with consistency regularizer $R_{i}$ that minimizes Euclidean distance between the projected vectors of two differently augmented patches, the final loss function is defined as follows:", + "bbox": [ + 75, + 633, + 468, + 688 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nL _ {i} = \\left(\\Phi_ {i} ^ {\\mathrm {a g}} + \\Psi_ {i} ^ {\\mathrm {a g}}\\right) + \\lambda \\left(\\Phi_ {i} ^ {\\mathrm {s p}} + \\Psi_ {i} ^ {\\mathrm {s p}}\\right) + \\alpha R _ {i}, \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 115, + 693, + 468, + 709 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\lambda$ and $\\alpha$ control the contribution of each loss. For instance, $\\lambda$ gradually increases from 0 to 1 during the training and $\\alpha$ remains constant at 0.05 throughout the training. Note that, the sample with zero positive is excluded from training although it rarely exists.", + "bbox": [ + 75, + 714, + 468, + 783 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Experiments", + "text_level": 1, + "bbox": [ + 75, + 794, + 208, + 809 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. Datasets and Experimental Settings", + "text_level": 1, + "bbox": [ + 75, + 815, + 386, + 832 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. We utilize COCO-stuff [1], Cityscapes [9], and Potsdam-3 datasets following the existing works [8, 14,", + "bbox": [ + 75, + 838, + 468, + 866 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "19]. COCO-stuff is a large-scale scene understanding dataset that consists of dense pixel-level annotations and Cityscapes is a more recently publicized dataset having street scenes across 50 different cities. Potsdam-3 dataset contains satellite images. Following the baselines [8,14,19], we choose the 27 classes for COCO-stuff and Cityscapes datasets, and 3 classes for Potsdam-3 dataset.", + "bbox": [ + 497, + 376, + 890, + 472 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Evaluation Protocols and Metrics. To evaluate our approach, we conduct two testing methods; clustering and linear probe [14]. Clustering is to measure how well the semantic-preserving clusters are formed. Once the unlabeled clusters are computed with the extracted representations, clusters are matched with the ground truth class labels using the Hungarian matching algorithm. On the other hand, the linear probe is a popular method to evaluate the quality of representations learned in unsupervised manners. Specifically, an additional linear layer is learned with representations extracted from the frozen unsupervised model for evaluation and ground truth labels. Inferring the representations of test data extracted by the frozen model with the learned linear layer, we can measure the quality of representations. With two types of protocols, the performance is measured by two common metrics; accuracy (Acc.) and mean Intersection Over Union (mIoU).", + "bbox": [ + 497, + 489, + 890, + 725 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Implementation Details. For fair comparisons with our baselines [14, 37], we follow them to mainly use DINO pretrained ViT models as a backbone network $\\mathcal{F}$ for the COCO-stuff dataset. In addition, we also test with the advanced backbone, SelfPatch [39], on the COCO-stuff dataset. The segmentation head $S$ is constructed with a two-layer RELU MLP as STEGO, and the projection head $\\mathcal{Z}$ is composed of a linear layer equipped with a normalization layer. The embedding dimension $K$ is set to 512 for ViT", + "bbox": [ + 497, + 741, + 890, + 866 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "19544", + "bbox": [ + 465, + 887, + 504, + 897 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/4ca9c7e270f5d2dc03f56aa4d7a7da0f6416212bf99fb6781ece81a9f12d7ad5.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodBackboneUnsupervisedLinear
Acc.mIoUAcc.mIoU
DC [2]R18+FPN19.9---
MDC [2]R18+FPN32.29.848.613.3
IIC [19]R18+FPN21.86.744.58.4
PiCIE [8]R18+FPN48.113.854.213.9
PiCIE+H [8]R18+FPN50.014.454.814.8
DINO [4]ViT-S/828.711.368.633.9
+ TransFGU [37]ViT-S/852.717.5--
+ STEGO [14]ViT-S/848.324.574.438.3
+ HP (Ours)ViT-S/857.224.675.642.7
DINO [4]ViT-S/1622.08.050.318.1
+ STEGO [14]ViT-S/1652.523.770.634.5
+ HP (Ours)ViT-S/1654.524.374.139.1
SelfPatch [39]ViT-S/1635.112.364.428.5
+ STEGO [14]ViT-S/1652.422.272.236.0
+ HP (Ours)ViT-S/1656.123.274.941.3
", + "bbox": [ + 85, + 123, + 460, + 345 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/063c1fa4d1a2018792c02b8f015242962b365642f5656792afa7e67caf63c1d6.jpg", + "table_caption": [ + "Table 1. Experimental results on COCO-stuff dataset with various backbones and pretrained models." + ], + "table_footnote": [], + "table_body": "
MethodBackboneUnsupervisedLinear
Acc.mIoUAcc.mIoU
MDC [2]R18+FPN40.77.1--
IIC [19]R18+FPN47.96.4--
PiCIE [8]R18+FPN65.512.3--
DINO [4]ViT-S/834.510.984.622.8
+ TransFGU [37]ViT-S/877.916.8--
+ HP (Ours)ViT-S/880.118.491.230.6
DINO [4]ViT-B/843.611.884.223.0
+ STEGO [14]ViT-B/873.221.090.326.8
+ HP (Ours)ViT-B/879.518.490.933.0
", + "bbox": [ + 85, + 394, + 460, + 540 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "S/8 and ViT-B/8 models, and 256 for ViT-S/16. We train the model for 3, 20, and 10 epochs for COCO-stuff, Cityscapes, and Potsdam-3 datasets, respectively, based on the AdamW optimizer with a learning rate of 0.0005 and weight decay of 0.1. The task-specific reference pool $Q^{\\mathrm{sp}}$ is renewed every 100 iterations throughout the training. The percentage of negative samples usage $\\rho$ is set to 2. In the last stage, we add a feature refinement step utilizing Conditional Random Field [21] as did in STEGO. Evaluation metrics, i.e., clustering and linear probe, are optimized with the Adam optimizer each with learning rates of 0.005 and 0.001.", + "bbox": [ + 75, + 587, + 467, + 739 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2. Experimental Results", + "text_level": 1, + "bbox": [ + 75, + 748, + 278, + 762 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We compare our proposed method against the prior techniques for the unsupervised segmentation [2, 8, 14, 19, 37]. Most of the results in the result tables are brought from the literature [8, 14]. In Tab. 1, it is observed that self-supervised models, i.e., DINO and SelfPatch, are already good segmentation predictors with the linear probe, which makes them a new baseline over the prior works for unsu", + "bbox": [ + 75, + 769, + 467, + 865 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/324d79940bb69c4ad024eca104f62b60702e44139581d30df255c0ae9135d22b.jpg", + "table_caption": [ + "Table 2. Experimental results on Cityscapes dataset." + ], + "table_footnote": [], + "table_body": "
MethodBackboneUnsup. Acc.
Random CNN [19]VGG1138.2
K-Means [27]VGG1145.7
SIFT [24]VGG1138.2
ContextPrediction [10]VGG1149.6
CC [18]VGG1163.9
DeepCluster [2]VGG1141.7
IIC [19]VGG1165.1
DINO [4]ViT-B/853.0
+ STEGO [14]ViT-B/877.0
+ HP (Ours)ViT-B/882.4
", + "bbox": [ + 532, + 123, + 858, + 268 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 3. Experimental results on Potsdam-3 dataset.", + "bbox": [ + 539, + 277, + 850, + 290 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "pervised segmentation. Furthermore, we utilize two pretrained backbones with two kinds of architectures to compare with STEGO, in detail. As reported, our proposed model provides consistent performance improvements over the previous SOTA model in almost all cases on the COCO-stuff dataset.", + "bbox": [ + 497, + 317, + 890, + 399 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Results on Cityscapes also show a similar tendency. As shown in Tab. 2, ours outperform previous methods except for the mIoU when clustering is used for the evaluation. For instance, we achieve $8.6\\%$ and $23\\%$ improvements in cluster accuracy and linear mIoU over STEGO, respectively. For the slight decrease in cluster mIoU with ViT-B/8 architecture, we argue that it is insignificant since the linear probe better describes the quality of representations. Specifically, clustering evaluation highly depends on the purpose of the dataset so that it is sensitive to the degree of class-specificity as different body parts can be either classified as human or as independent body parts. However, whereas it is more appropriate to detect each body part independently for unsupervised learning as ours do in Fig. 5, the annotations for general datasets, e.g., COCO-stuff and cityscape, treat these body parts as a human class. Such circumstances make the clustering evaluation vulnerable to the degree of the class hierarchy. In contrast, the linear probe projects these features to close proximity if the given label space considers them as a human body. Thus, we believe the linear probe is a more appropriate measure of representation quality.", + "bbox": [ + 497, + 401, + 890, + 705 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Also, we compare the cluster accuracy in Tab. 3 on Potsdam-3 dataset. We have achieved a $7\\%$ boost over STEGO, confirming that ours also performs well even in a completely new domain. Likewise, our superior results verify the effectiveness of our process of discovering global and local-hidden positive patches.", + "bbox": [ + 497, + 706, + 890, + 789 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Qualitative Results. In addition to the quantitative results, we report qualitative results in Fig. 4. In comparison to STEGO, we observe that our results include fewer mispredicted pixels throughout the images, while the strength", + "bbox": [ + 497, + 809, + 890, + 866 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "19545", + "bbox": [ + 465, + 887, + 504, + 897 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/65eef0aa688a80672d97938762d4abee4a90dc464ea940fc46d13095be9d08a1.jpg", + "image_caption": [ + "Figure 4. Qualitative comparison results of Ours and STEGO on the COCO-stuff dataset with DINO pretrained ViT-S/8 backbone." + ], + "image_footnote": [], + "bbox": [ + 80, + 124, + 892, + 289 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "of preserving semantic locality is also validated. For instance, whereas we find that the predicted label of the wheel is only partially correct in the 4th column, our results are consistent along the neighboring pixels. These results also demonstrate the superiority of our method.", + "bbox": [ + 75, + 337, + 470, + 407 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5. Ablation Study and Further Analysis", + "text_level": 1, + "bbox": [ + 75, + 420, + 413, + 437 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this section, we provide ablation studies and an analysis of our model. Particularly, we explore the contributions of main components and test with varying hyperparameters. Most experiments for ablation studies are conducted on the COCO-stuff dataset using the DINO pretrained ViTs/8 model, except for Sec. 5.2, which utilized the ViT-S/16.", + "bbox": [ + 75, + 442, + 468, + 527 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.1. Importance of the Main Components.", + "text_level": 1, + "bbox": [ + 75, + 536, + 401, + 552 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Tab. 4 reports the performances when an individual component or various combinations of them are not utilized. We found that task-specific GHP and LHP are essential in improving the performance of the unsupervised segmentation task. Compared to (d) where both the task-specific GHP and LHP are not used, the use of them each leads to $6.5\\%$ (b) and $11.6\\%$ (c) improvements. Also when used together, they bring $16\\%$ of performance boosts (a). Furthermore, the importance of preserving symmetry in selecting GHP can be found by comparing (a) to (e) as its usage boosts $5.9\\%$ , and consistency regularizer enhances performance by $2.5\\%$ by comparing (a) to (f). Lastly, (g) shows the performance of naively implemented contrastive learning (Eq. 1) with photometric perturbations used in PiCIE [8]. This also confirms the strengths of our proposed method as (a) enhances (g) by $51.3\\%$ .", + "bbox": [ + 75, + 558, + 470, + 779 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.2. Alternatives to Gradient Propagation Strategy", + "text_level": 1, + "bbox": [ + 75, + 789, + 468, + 804 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "There can be alternative ways to meet our goal of reflecting semantic consistency between adjacent patches. As an alternative method of gradient propagation, we simply apply the identical loss to the surrounding patches pro", + "bbox": [ + 75, + 809, + 468, + 866 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/4ae6ccbf3bc153a8e979c37f5a639fbf59b1c07cbd35da256ed246878129f94f.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
GHPLHPSARegUnsupervised Acc.mIoU
TATS
(a)✓✓✓✓✓57.224.6
(b)✓✓✓✓52.523.1
(c)✓✓✓✓55.019.1
(d)✓✓✓49.320.1
(e)✓✓✓✓54.023.6
(f)✓✓✓✓55.824.5
(g)37.810.4
", + "bbox": [ + 522, + 334, + 870, + 456 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/ca06ca78d0bedd300d2d780af087b8aa450946c7a80a5e451110506f1b0a5b06.jpg", + "table_caption": [ + "Table 4. Ablation study for each component. GHP, LHP, TA, TS, SA, and Reg denote Global Hidden Positive, Local Hidden Positive, task-agnostic, task-specific, symmetrical assignments, and consistency regularizer, respectively." + ], + "table_footnote": [], + "table_body": "
MethodUnsupervisedLinear
Acc.mIoUAcc.mIoU
DINO + GradProp54.524.374.139.1
DINO + LossProp54.723.274.340.5
SelfPatch + GradProp56.123.274.941.3
SelfPatch + LossProp54.522.275.141.4
", + "bbox": [ + 531, + 530, + 858, + 603 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 5. Comparison results between gradient propagation and loss propagation strategies.", + "bbox": [ + 497, + 612, + 890, + 638 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "portionally to their attention score (i.e., loss propagation). The results in Tab. 5 show that the loss propagation strategy performs comparably to the gradient propagation strategy. Nonetheless, we observed that the loss propagation approach incurs higher computational costs ( $1.2 \\times$ memory and $3 \\times$ time). Likewise, our method is implemented considering both effectiveness and efficiency.", + "bbox": [ + 497, + 665, + 890, + 762 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Visualization", + "text_level": 1, + "bbox": [ + 499, + 774, + 636, + 788 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "**Discovered GHP.** To demonstrate the effectiveness of our GHP selection process, we visualize the selected GHP sets for different anchors in a single image in Fig. 5. First, we observe that the corresponding reference point in the second column is semantically correlated with the anchor patch.", + "bbox": [ + 497, + 796, + 892, + 866 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "19546", + "bbox": [ + 465, + 887, + 505, + 897 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/66523cddee441d205398edf8b166148b8211598e22fde7ec67e93310bcb062a2.jpg", + "image_caption": [ + "Figure 5. Discovered patches by our GHP selection process. From the left to right columns, red boxes indicate the anchors, the closest patches within the task-agnostic reference pool (reference), and GHP sets chosen in the mini-batch, respectively." + ], + "image_footnote": [], + "bbox": [ + 80, + 123, + 470, + 315 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Also, the obtained GHP sets verify the appropriateness of the use of reference points as each anchor's criterion as they are capable of precisely discovering the semantically-similar positives. For example, we find that all the anchors, reference points, and GHP sets have the same semantic labels in the first and the second rows (solid (mountain) and ground (snow)). More intriguing results are in the third and fourth rows where we find that the GHP selection process distinguishes the body parts in a much more fine-grained manner than the given annotation, i.e., person. These results imply that the designed GHP selection is well-designed and capable of capturing detailed semantic contexts.", + "bbox": [ + 75, + 401, + 468, + 567 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.4. Robustness to Hyperparameters", + "text_level": 1, + "bbox": [ + 75, + 576, + 359, + 590 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this subsection, we enumerate our use of hyperparameters in Tab. 6 and conduct an ablation study. Overall, our proposed training scheme is robust to hyperparameters as Fig. 6 supports the claim. Below, we illustrate the influences of each parameter. Despite the slight drop in performance from time to time, it is insignificant since the change in performance is very marginal and results are consistent.", + "bbox": [ + 75, + 597, + 468, + 694 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The Number of Data in the Reference Pool. Fig. 6 (a) shows the performances with varying numbers of data $M$ in the reference pool. Generally, the reference pool is not very vulnerable to its size, unless the capacity is either too small or too large. When the reference pool is skinny, it may not be sufficient to represent all kinds of semantics present in the dataset. In other words, the small reference pool may induce a biased criterion for each anchor which may incur biased training. On the other hand, when the reference pool is too large, a tight threshold $(c_{i})$ could be derived, which can interfere with gathering GHP.", + "bbox": [ + 75, + 711, + 468, + 863 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/6497e8baaefc633f91edbd972097762ee4a3a4e325689274cf714942d90951e3.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetMτ
COCO-stuff20480.8
Cityscapes20480.6
Potsdam-310240.4
", + "bbox": [ + 605, + 123, + 786, + 179 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 6. Hyperparameter used for each dataset.", + "bbox": [ + 554, + 190, + 833, + 201 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/cab69db739c7d2d638c3402613fe399b6153c3954ed1b1be70d385bf17217044.jpg", + "image_caption": [ + "(a) Reference pool size $M$", + "Figure 6. Ablation studies on hyperparameters." + ], + "image_footnote": [], + "bbox": [ + 500, + 218, + 694, + 320 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/620f420fe7f663046824a44598d94b1663dda27caae407ee29208e75f5bf95e4.jpg", + "image_caption": [ + "(b) Temperature $\\tau$" + ], + "image_footnote": [], + "bbox": [ + 697, + 218, + 890, + 320 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Temperature Parameter. The temperature parameter $\\tau$ is the scaling parameter to manipulate the sensitivity of the contrastive loss. If $\\tau$ gets bigger, the objective function becomes robust to the difference between the similarity of the positive and negative samples. On the other hand, when $\\tau$ gets lower, the embedding distribution is likely to be more uniform [31]. Although the results do not fluctuate much, we observe that training uniformly distributed embedding space leads to a slight performance drop in Fig. 6 (b).", + "bbox": [ + 497, + 383, + 890, + 507 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 499, + 519, + 616, + 534 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper, we introduced a novel unsupervised semantic segmentation method by discovering and leveraging two types of hidden positives, global hidden positive (GHP) and local hidden positive (LHP), to learn rich semantic information with local consistency. First, anchor-dependent GHP comprises task-agnostic and task-specific positive sets which are used to tailor the contrastive learning for the unsupervised semantic segmentation task. Whereas the task-agnostic features are collected to guide the initial training, task-specific features are progressively engaged to learn the task-specific semantics information. Moreover, under the inherent premise that the adjacent patches are likely to be semantically similar, we propagate the loss gradient to the surrounding patches in proportion to their attention scores. This encourages the semantically similar peripheral patches to have the same objective as the anchor, resulting in semantic consistency between adjacent patches unless they belong to different objects. Finally, our proposed method achieves new state-of-the-art results in various datasets.", + "bbox": [ + 497, + 542, + 890, + 804 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements. This work was supported in part by MSIT/IITP (No. 2022-0-00680, 2019-0-00421, 2020-0-01821, 2021-0-02068), and MSIT&KNPA/KIPoT (Police Lab 2.0, No. 210121M06).", + "bbox": [ + 497, + 809, + 890, + 866 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "19547", + "bbox": [ + 465, + 887, + 504, + 897 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 124, + 171, + 138 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Holger Caesar, Jasper Uijlings, and Vittorio Ferrari. Cocostuff: Thing and stuff classes in context. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1209-1218, 2018. 5", + "[2] Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. Deep clustering for unsupervised learning of visual features. In Proceedings of the European conference on computer vision (ECCV), pages 132-149, 2018. 2, 6", + "[3] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems, 33:9912-9924, 2020. 2", + "[4] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9650-9660, 2021. 2, 3, 6", + "[5] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. *ICLR*, 2015. 1, 2", + "[6] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 2, 3", + "[7] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15750-15758, 2021. 2", + "[8] Jang Hyun Cho, Utkarsh Mall, Kavita Bala, and Bharath Hariharan. Picie: Unsupervised semantic segmentation using invariance and equivariance in clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16794-16804, 2021. 1, 2, 5, 6, 7", + "[9] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 5", + "[10] Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE international conference on computer vision, pages 1422-1430, 2015. 6", + "[11] Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3146-3154, 2019. 1, 2", + "[12] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In International Conference on Learning Representations, 2018. 2" + ], + "bbox": [ + 78, + 147, + 470, + 865 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020. 2", + "[14] Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, and William T. Freeman. Unsupervised semantic segmentation by distilling feature correspondences. In International Conference on Learning Representations, 2022. 1, 2, 3, 5, 6", + "[15] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 2", + "[16] Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song. Using self-supervised learning can improve model robustness and uncertainty. Advances in neural information processing systems, 32, 2019. 2", + "[17] Jyh-Jing Hwang, Stella X Yu, Jianbo Shi, Maxwell D Collins, Tien-Ju Yang, Xiao Zhang, and Liang-Chieh Chen. Segsort: Segmentation by discriminative sorting of segments. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7334-7344, 2019. 2", + "[18] Phillip Isola, Daniel Zoran, Dilip Krishnan, and Edward H Adelson. Learning visual groups from co-occurrences in space and time. arXiv preprint arXiv:1511.06811, 2015. 6", + "[19] Xu Ji, Joao F Henriques, and Andrea Vedaldi. Invariant information clustering for unsupervised image classification and segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9865-9874, 2019. 1, 2, 5, 6", + "[20] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. Advances in Neural Information Processing Systems, 33:18661-18673, 2020. 2, 4", + "[21] Philipp Krahenbihl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. Advances in neural information processing systems, 24, 2011. 6", + "[22] Samuli Laine and Timo Aila. Temporal ensembling for semi-supervised learning. ICLR, 2017. 4", + "[23] Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, and Sungroh Yoon. Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5267-5276, 2019. 1", + "[24] David G Lowe. Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision, volume 2, pages 1150-1157. IEEE, 1999. 6", + "[25] Ishan Misra and Laurens van der Maaten. Self-supervised learning of pretext-invariant representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6707-6717, 2020. 2" + ], + "bbox": [ + 502, + 125, + 890, + 865 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "19548", + "bbox": [ + 465, + 887, + 504, + 897 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[26] WonJun Moon, Ji-Hwan Kim, and Jae-Pil Heo. Tailoring self-supervision for supervised learning. In European Conference on Computer Vision, pages 346-364. Springer, 2022. 2", + "[27] Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. the Journal of machine learning research, 12:2825-2830, 2011. 6", + "[28] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234-241. Springer, 2015. 1", + "[29] Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7262-7272, 2021. 2", + "[30] Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc Van Gool. Unsupervised semantic segmentation by contrasting object mask proposals. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10052-10062, 2021. 2", + "[31] Feng Wang and Huaping Liu. Understanding the behaviour of contrastive loss. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2495-2504, 2021. 8", + "[32] Shujun Wang, Lequan Yu, Caizi Li, Chi-Wing Fu, and Pheng-Ann Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. In European Conference on Computer Vision, pages 159–176. Springer, 2020. 4", + "[33] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12275-12284, 2020. 1", + "[34] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34:12077-12090, 2021. 1, 2", + "[35] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, and Dan Xu. Multi-class token transformer for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4310-4319, 2022. 1", + "[36] Yazhou Yao, Tao Chen, Guo-Sen Xie, Chuanyi Zhang, Fumin Shen, Qi Wu, Zhenmin Tang, and Jian Zhang. Nonsalient region object mining for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2623-2632, 2021. 1", + "[37] Zhaoyuan Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li, and Rong Jin. Transfgu: a top-down approach to fine-grained unsupervised semantic segmentation. In European Conference on Computer Vision, pages 73-89. Springer, 2022. 1, 2, 5, 6" + ], + "bbox": [ + 78, + 125, + 468, + 866 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[38] Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object-contextual representations for semantic segmentation. In European conference on computer vision, pages 173-190. Springer, 2020. 1, 2", + "[39] Sukmin Yun, Hankook Lee, Jaehyung Kim, and Jinwoo Shin. Patch-level representation learning for self-supervised vision transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8354-8363, 2022. 2, 5, 6", + "[40] Xiangyun Zhao, Raviteja Vemulapalli, Philip Andrew Mansfield, Boqing Gong, Bradley Green, Lior Shapira, and Ying Wu. Contrastive learning for label efficient semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10623-10633, 2021. 2", + "[41] Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6881-6890, 2021. 1, 2", + "[42] Wu Zheng, Weiliang Tang, Li Jiang, and Chi-Wing Fu. Sessd: Self-ensembling single-stage object detector from point cloud. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14494–14503, 2021. 4" + ], + "bbox": [ + 502, + 125, + 890, + 471 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "19549", + "bbox": [ + 465, + 887, + 504, + 897 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Leveraging Hidden Positives for Unsupervised Semantic Segmentation/34b6701a-58b4-43b7-90c9-cd9aa3bd0efc_model.json b/2023/Leveraging Hidden Positives for Unsupervised Semantic Segmentation/34b6701a-58b4-43b7-90c9-cd9aa3bd0efc_model.json new file mode 100644 index 0000000000000000000000000000000000000000..05435b14985f4d987bc790940864ba96d0b1c6f9 --- /dev/null +++ b/2023/Leveraging Hidden Positives for Unsupervised Semantic Segmentation/34b6701a-58b4-43b7-90c9-cd9aa3bd0efc_model.json @@ -0,0 +1,2090 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.13, + 0.051, + 0.201, + 0.086 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.253, + 0.049, + 0.792, + 0.087 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.131, + 0.162, + 0.84, + 0.183 + ], + "angle": 0, + "content": "Leveraging Hidden Positives for Unsupervised Semantic Segmentation" + }, + { + "type": "text", + "bbox": [ + 0.246, + 0.209, + 0.731, + 0.242 + ], + "angle": 0, + "content": "Hyun Seok Seong, WonJun Moon, SuBeen Lee, Jae-Pil Heo* Sungkyunkwan University" + }, + { + "type": "text", + "bbox": [ + 0.248, + 0.243, + 0.719, + 0.258 + ], + "angle": 0, + "content": "{gustjrdl95，wjun0830，leesb7426，jaepilheo}@skku.edu" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.289, + 0.314, + 0.303 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.318, + 0.474, + 0.665 + ], + "angle": 0, + "content": "Dramatic demand for manpower to label pixel-level annotations triggered the advent of unsupervised semantic segmentation. Although the recent work employing the vision transformer (ViT) backbone shows exceptional performance, there is still a lack of consideration for task-specific training guidance and local semantic consistency. To tackle these issues, we leverage contrastive learning by excavating hidden positives to learn rich semantic relationships and ensure semantic consistency in local regions. Specifically, we first discover two types of global hidden positives, task-agnostic and task-specific ones for each anchor based on the feature similarities defined by a fixed pre-trained backbone and a segmentation head-in-training, respectively. A gradual increase in the contribution of the latter induces the model to capture task-specific semantic features. In addition, we introduce a gradient propagation strategy to learn semantic consistency between adjacent patches, under the inherent premise that nearby patches are highly likely to possess the same semantics. Specifically, we add the loss propagating to local hidden positives, semantically similar nearby patches, in proportion to the predefined similarity scores. With these training schemes, our proposed method achieves new state-of-the-art (SOTA) results in COCO-stuff, Cityscapes, and Potsdam-3 datasets. Our code is available at: https://github.com/hynnsk/HP." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.693, + 0.21, + 0.708 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.717, + 0.47, + 0.842 + ], + "angle": 0, + "content": "Semantic segmentation is a major task for scene understanding and plays a crucial role in many applications including medical imaging and autonomous driving [5, 11, 28, 34, 38, 41]. However, existing supervised approaches demand large-scale pixel-level annotations which require huge labeling costs. It has triggered the advent of weakly-supervised [23, 33, 35, 36] and unsupervised semantic segmentation [8, 14, 19, 37] which are to learn without expensive pixel-level annotations." + }, + { + "type": "image", + "bbox": [ + 0.508, + 0.288, + 0.891, + 0.422 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.647, + 0.422, + 0.741, + 0.432 + ], + "angle": 0, + "content": "(a) Visualization" + }, + { + "type": "image", + "bbox": [ + 0.506, + 0.433, + 0.695, + 0.572 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.525, + 0.573, + 0.674, + 0.585 + ], + "angle": 0, + "content": "(b) Global hidden positive" + }, + { + "type": "image", + "bbox": [ + 0.704, + 0.433, + 0.891, + 0.572 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.721, + 0.573, + 0.862, + 0.585 + ], + "angle": 0, + "content": "(c) Local hidden positive" + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.596, + 0.892, + 0.787 + ], + "angle": 0, + "content": "Figure 1. Assuming a mini-batch comprising two images shown in (a), we describe two types of hidden positives, to leverage for contrastive learning. (a) With two types of hidden positives introduced in (b) and (c), we provide an example of how our training scheme provides more precise and consistent semantics. (b) (top) Semantically analogous patches throughout the mini-batch are selected as global hidden positives. (bottom) Data-driven criterion per anchor is designed for reliable positive collection. With the criterion, selected positives are illustrated in (b top). (c) (top) We define local hidden positives for each anchor to be the adjacent patches with high semantic consistency, i.e., blue boxes. (bottom) Average attention scores for adjacent patches from the pretrained transformer architecture. The blue line represents the attention score for local hidden positives while the red line for patches neighboring anchors but having low semantic consistency." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.893, + 0.868 + ], + "angle": 0, + "content": "Particularly, unsupervised semantic segmentation is one of the most challenging tasks, since it needs to capture pixel-level semantics from unlabeled data. In this context, clustering-based approaches have been proposed to learn" + }, + { + "type": "page_footnote", + "bbox": [ + 0.096, + 0.854, + 0.221, + 0.867 + ], + "angle": 0, + "content": "*Corresponding author" + }, + { + "type": "page_number", + "bbox": [ + 0.466, + 0.888, + 0.506, + 0.899 + ], + "angle": 0, + "content": "19540" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.127, + 0.473, + 0.333 + ], + "angle": 0, + "content": "semantic-preserving clusters by attracting the augmented views in the pixel-level [8, 19]. They implemented the intuition of contrastive learning [3, 6, 7, 13, 15, 25] by ensuring the augmented pairs yield symmetric cluster assignments. More recently, as discovering pixel-level semantics from scratch is challenging, STEGO [14] broke down the problem into learning the representation and learning the segmentation head. With the learned patch-level representation from the seminal work in unsupervised learning [4, 39], they train the segmentation head with a distillation strategy. Although they have made great advancements, we point out their limitations in that they rely solely on a fixed backbone that is not specifically trained for the segmentation task and overlook the importance of semantic consistency along the adjacency that could be a crucial clue for segmentation." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.334, + 0.473, + 0.692 + ], + "angle": 0, + "content": "To take these into consideration, we leverage contrastive learning based on the mined hidden positives to ensure contextual consistency along the patches with analogous semantics, particularly the nearby patches, as described in Fig. 1. Specifically, we elaborately select the pseudopositive samples (i.e., global hidden positive, GHP) for contrastive learning to learn semantic consistency. Also, to ensure local consistency, we propagate the loss gradient to the adjacent patches (i.e., local hidden positive, LHP) in proportion to their equivalency. First, the GHP selection process is designed with two types of data reference pools, task-agnostic and task-specific, to collect the semantically consistent patch features throughout the mini-batch per anchor. For instance, the task-agnostic data reference pool is composed of features extracted by the unsupervised pretrained backbone. On the other hand, the task-specific reference pool is constructed with the features from the segmentation head-in-training to complement task relevance. Based on the two reference pools, two sets of GHP are selected each with generalized and task-specific perspectives. Second, to implement the property of locality and prevent the semantics from fluctuating, we propagate the loss gradient to adjacent patches (i.e., LHP) in proportion to the similarity scores built within the pretrained backbone. This enables the model to learn the relevance of the local context that nearby patches often belong to the same instance." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.693, + 0.384, + 0.705 + ], + "angle": 0, + "content": "Our main contributions are summarized as:" + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.713, + 0.469, + 0.769 + ], + "angle": 0, + "content": "- We propose a novel method to discover semantically similar pairs, called global hidden positives, to explicitly learn the semantic relationship among patches for unsupervised semantic segmentation." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.776, + 0.469, + 0.817 + ], + "angle": 0, + "content": "- We utilize the task-specific features from a model-intraining and validate the effectiveness of progressive increase of their contribution." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.826, + 0.469, + 0.867 + ], + "angle": 0, + "content": "- A gradient propagation to nearby similar patches, local hidden positives, is developed to learn local semantic consistency which is the nature of segmentation." + }, + { + "type": "list", + "bbox": [ + 0.097, + 0.713, + 0.469, + 0.867 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.127, + 0.892, + 0.154 + ], + "angle": 0, + "content": "- Our approach outperforms existing state-of-the-art methods across extensive experiments." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.168, + 0.642, + 0.183 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.191, + 0.825, + 0.206 + ], + "angle": 0, + "content": "2.1. Unsupervised Semantic Segmentation" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.213, + 0.892, + 0.6 + ], + "angle": 0, + "content": "Semantic segmentation has been extensively studied for its wide applicability [5, 11, 29, 34, 38, 41], but collecting pixel-level annotations requires expensive costs. Therefore, many studies [8, 14, 17, 19, 30, 37] attempted to address semantic segmentation without any supervision. Earlier techniques tried to learn semantic correspondence at the pixel level. IIC [19] maximizes the mutual information between the features of two differently augmented images, and PiCIE [8] learns photometric and geometric invariances as an inductive bias. Yet, their training process highly depends on data augmentation, and learning semantic consistency without any prior knowledge is challenging. Therefore, recent methods [14, 37] adopted the ViT model trained in a self-supervised manner, i.e., DINO [4], as a backbone architecture. For instance, TransFGU [37] relocates the high-level semantic features from DINO into low-level pixel-wise features by generating pixel-wise pseudo labels. On the other hand, STEGO [14] utilizes knowledge distillation that learns correspondences between features extracted from DINO. Although STEGO shows a dramatic performance improvement compared to the prior works, it heavily relies on the pretrained backbone and overlooks the property of local consistency that the adjacent pixels are likely to belong to the same category. On the other hand, our training is driven by both the task-agnostic and task-specific pseudopositive features, and the gradients are conditionally propagated to the neighboring patches, thereby ensuring task-specificity and locality." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.61, + 0.702, + 0.625 + ], + "angle": 0, + "content": "2.2. Contrastive Learning" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.632, + 0.892, + 0.867 + ], + "angle": 0, + "content": "Self-supervised learning methods [2,3,6,7,12,13,15] aim to learn general representations without any annotations. Thanks to their good representation capability, they have been employed to yield remarkable performances in various downstream tasks [8, 16, 20, 26, 40]. Among them, contrastive learning approaches [3, 6, 7, 13, 15, 25] have shown unrivaled performances. In general, they learn representation by attracting a self-augmented set and repulsing other images [3, 6]. Other variants use additional memory [15] or only exploit the positive set for the attraction [7, 13]. This training scheme has also been utilized for unsupervised semantic segmentation [8, 19]. However, the aforementioned methods only considered augmented pairs for the positive which makes them very sensitive to the quality of the augmentation techniques. Our work differs in that our key idea is to collect and exploit reliable pseudo-positives throughout the mini-batch as described in Fig. 1 (b)." + }, + { + "type": "page_number", + "bbox": [ + 0.466, + 0.888, + 0.505, + 0.899 + ], + "angle": 0, + "content": "19541" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.091, + 0.124, + 0.889, + 0.346 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.355, + 0.896, + 0.459 + ], + "angle": 0, + "content": "Figure 2. Illustration of the global hidden positive (GHP) selection process. Our GHP can be divided into two sub-sets: task-agnostic and task-specific. An index set of task-agnostic GHP \\( P_{i}^{\\mathrm{ag}} \\) comprises the indices of positives discovered within the task-agnostic reference pool \\( Q^{\\mathrm{ag}} \\). Note that, \\( Q^{\\mathrm{ag}} \\) is composed of randomly sampled features extracted by the feature extractor \\( \\mathcal{F} \\). Once the anchor feature \\( f_{i} \\) is projected to \\( z_{i} \\), other patches in the mini-batch are gathered as positives if their similarity with the anchor feature exceeds the similarity between the anchor and the most similar feature in \\( Q^{\\mathrm{ag}} \\). On the other hand, task-specific GHP is discovered in a similar manner but with task-specific reference pool \\( Q^{\\mathrm{sp}} \\) which keeps being updated with the features from the momentum segmentation head \\( S' \\). Whereas the task-agnostic GHP set solely contributes to the initial training, the task-specific GHP set gradually replaces the portion of the task-agnostic set until the end of training." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.48, + 0.172, + 0.495 + ], + "angle": 0, + "content": "3. Method" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.503, + 0.471, + 0.574 + ], + "angle": 0, + "content": "In this section, we introduce the pseudo-positive selection strategy to discover hidden positives with analogous semantics in Sec. 3.2, the training objective with discovered positives in Sec. 3.3, and the gradient propagation scheme to preserve the property of locality in Sec. 3.4." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.58, + 0.207, + 0.595 + ], + "angle": 0, + "content": "3.1. Preliminary" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.602, + 0.47, + 0.739 + ], + "angle": 0, + "content": "In unsupervised semantic segmentation, the model utilizes unlabeled image set \\( X = \\{x_{b}\\}_{b = 1}^{B} \\) where \\( B \\) is the number of training data in the mini-batch. Given an image \\( x_{b} \\) processed to the feature extractor \\( \\mathcal{F} \\), we have \\( H\\cdot W \\) features of \\( f_{i}\\in \\mathbb{R}^{C} \\), where \\( i\\in [1,\\dots ,H\\cdot W] \\). Subsequently, the segmentation head \\( S \\) maps a patch feature \\( f_{i} \\) to the corresponding segmentation feature \\( s_i\\in \\mathbb{R}^K \\). And then, the projection head \\( \\mathcal{Z} \\) produces a projected vector \\( z_{i}\\in \\mathbb{R}^{K} \\) to formulate a contrastive loss function. In the inference stage, we use the segmentation feature \\( s_i \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.74, + 0.47, + 0.809 + ], + "angle": 0, + "content": "Based on the projected vector \\( z_{i} \\) for the \\( i \\)-th patch, let \\( j \\) be the index of the augmented patch of \\( i \\)-th one. Then, the conventional self-supervised contrastive loss [6] for \\( i \\)-th patch in unsupervised semantic segmentation can be defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.139, + 0.814, + 0.47, + 0.846 + ], + "angle": 0, + "content": "\\[\nL _ {i} ^ {\\text {s e l f}} = - \\log \\frac {\\exp \\left(\\sin \\left(z _ {i} , z _ {j} / \\tau\\right)\\right)}{\\sum_ {a \\in A} \\exp \\left(\\sin \\left(z _ {i} , z _ {a}\\right) / \\tau\\right)}, \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.853, + 0.47, + 0.868 + ], + "angle": 0, + "content": "where \\(A\\) indicates a set of all indexes except \\(i\\), \\(\\tau\\) denotes the" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.481, + 0.892, + 0.51 + ], + "angle": 0, + "content": "scalar temperature parameter, and \\(\\mathrm{sim}(\\cdot ,\\cdot)\\) is cosine similarity between two vectors." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.523, + 0.722, + 0.537 + ], + "angle": 0, + "content": "3.2. Global Hidden Positives" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.546, + 0.892, + 0.699 + ], + "angle": 0, + "content": "Learning mutual information with augmented pixels only provides insufficient training signal in unsupervised semantic segmentation [14]. Therefore, it is important to discover hidden pseudo-positives to tailor the contrastive loss for unsupervised segmentation. To discover the hidden positives at the initial stage, we utilize the self-supervised pretrained backbone [4] as the task-agnostic criterion. Then, we gradually increase the contribution of hidden positives found in a task-specific way for the training. Fig. 2 provides the overview of the global hidden positive (GHP) selection process." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.699, + 0.892, + 0.838 + ], + "angle": 0, + "content": "Initially, the pretrained backbone is utilized to construct a task-agnostic reference pool to assess whether other features in the mini-batch are semantically-alike for each anchor feature. Specifically, task-agnostic reference pool, \\( Q^{\\mathrm{ag}} = \\{q_m\\}_{m=1}^M \\), is composed of \\( M \\) randomly sampled features that are extracted by the unsupervised pretrained backbone \\( \\mathcal{F} \\). Note that, we only sample a single patch feature per image to ensure the semantic randomness of the reference pool. This task-agnostic reference pool is fixed as the pretrained backbone is frozen throughout the training." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.839, + 0.892, + 0.868 + ], + "angle": 0, + "content": "Once the reference pool is gathered, for each patch feature \\( f_{i} \\), we define an anchor-dependent similarity criterion" + }, + { + "type": "page_number", + "bbox": [ + 0.466, + 0.888, + 0.506, + 0.899 + ], + "angle": 0, + "content": "19542" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.127, + 0.47, + 0.154 + ], + "angle": 0, + "content": "\\(c_{i}\\) to collect positives, as the distance to the closest feature within the reference pool \\(Q^{\\mathrm{ag}}\\) by the cosine similarity:" + }, + { + "type": "equation", + "bbox": [ + 0.189, + 0.163, + 0.469, + 0.185 + ], + "angle": 0, + "content": "\\[\nc _ {i} = \\max _ {q _ {m} \\in Q ^ {\\mathrm {a g}}} \\operatorname {s i m} \\left(q _ {m}, f _ {i}\\right). \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.192, + 0.469, + 0.33 + ], + "angle": 0, + "content": "For each anchor feature \\( f_{i} \\), we basically treat the other feature in the mini-batch \\( f_{j} \\) as positive if the similarity between \\( f_{i} \\) and \\( f_{j} \\) is greater than \\( c_{i} \\). Still, although one patch feature might be the positive sample for the other, it may not hold mutually. This is because the criterion \\( c_{i} \\) is anchor-dependent. To endow consistency in training, we make the GHP selection symmetric to prevent the relation between two patches from being ambiguous. Therefore, index set of \\( \\mathrm{GHP} P_{i}^{\\mathrm{ag}} \\) for each \\( i \\)-th anchor feature \\( f_{i} \\) is defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.097, + 0.339, + 0.469, + 0.357 + ], + "angle": 0, + "content": "\\[\nP _ {i} ^ {\\mathrm {a g}} = \\left\\{j \\mid \\operatorname {s i m} \\left(f _ {i}, f _ {j}\\right) > c _ {i} \\vee \\operatorname {s i m} \\left(f _ {i}, f _ {j}\\right) > c _ {j} \\right\\}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.363, + 0.469, + 0.417 + ], + "angle": 0, + "content": "where \\( j \\) indicates the index for different patch features in the mini-batch. Accordingly, such a distribution-aware reference pool allows the discovery of globally analogous features in consideration of each anchor." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.418, + 0.469, + 0.528 + ], + "angle": 0, + "content": "However, although the reference pool built upon the features from an unsupervised pretrained network can serve as an appropriate basis for positivity, it may be insufficient since it lacks task-specificity. We argue that features from the segmentation head are more task-specific than those from the pretrained backbone. Therefore, along with the GHP selected by \\( P^{\\mathrm{ag}} \\), we construct additional task-specific GHP utilizing the features from the segmentation head." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.528, + 0.469, + 0.653 + ], + "angle": 0, + "content": "Specifically, an index set of task-specific GHP \\( P_{i}^{\\mathrm{sp}} \\) is formed similarly to Eq. 3 by comparing the features \\( s' = S'(f) \\) and task-specific reference pool \\( Q^{\\mathrm{sp}} \\), where \\( S' \\) indicates the momentum segmentation head, and the task-specific reference pool \\( Q^{\\mathrm{sp}} \\) comprises \\( s' \\). Also, along with the update of the segmentation head, during training, the reference pool is periodically renewed. Formally, with \\( c_{i}' \\), calculated by substituting \\( Q^{\\mathrm{ag}} \\) and \\( f_{i} \\) in Eq. 2 with \\( Q^{\\mathrm{sp}} \\) and \\( s_{i}' \\), respectively, the \\( P_{i}^{\\mathrm{sp}} \\) is expressed as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.094, + 0.661, + 0.469, + 0.679 + ], + "angle": 0, + "content": "\\[\nP _ {i} ^ {\\mathrm {s p}} = \\left\\{j \\mid \\operatorname {s i m} \\left(s _ {i} ^ {\\prime}, s _ {j} ^ {\\prime}\\right) > c _ {i} ^ {\\prime} \\vee \\operatorname {s i m} \\left(s _ {i} ^ {\\prime}, s _ {j} ^ {\\prime}\\right) > c _ {j} ^ {\\prime} \\right\\}. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.686, + 0.469, + 0.713 + ], + "angle": 0, + "content": "Note that, the usage of the momentum segmentation head is for the stability of the reference pool [22,42]." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.721, + 0.262, + 0.735 + ], + "angle": 0, + "content": "3.3. Objective Function" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.742, + 0.469, + 0.867 + ], + "angle": 0, + "content": "To formulate a contrastive objective with mined GHP in Sec. 3.2, we also need negative features. As we collected the positives throughout the mini-batch, the naive implementation of contrastive learning would utilize all features except the selected positives in the mini-batch as the negatives. However, since an immoderate increase in the size of the negative set may disturb the model training [32], we form a negative set \\( N_{i} \\) by randomly choosing \\( \\rho \\% \\) of the remaining patches for each \\( i \\)-th anchor. Note that, the separate" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.126, + 0.892, + 0.182 + ], + "angle": 0, + "content": "index sets of negative samples \\( N_{i}^{\\mathrm{ag}} \\) and \\( N_{i}^{\\mathrm{sp}} \\) are defined for each \\( P_{i}^{\\mathrm{ag}} \\) and \\( P_{i}^{\\mathrm{sp}} \\), correspondingly. Also, unlike Eq. 1, our contrastive loss for each \\( i \\)-th anchor is more like a supervised objective [20] since we are given multiple positives:" + }, + { + "type": "equation", + "bbox": [ + 0.5, + 0.19, + 0.928, + 0.23 + ], + "angle": 0, + "content": "\\[\nL ^ {\\text {c o n t}} \\left(z _ {i}, P, N\\right) = \\frac {- 1}{| P |} \\sum_ {p \\in P} \\log \\frac {\\exp \\left(\\operatorname* {s i m} \\left(z _ {i} , z _ {p}\\right) / \\tau\\right)}{\\sum_ {n \\in (N \\cup P)} \\exp \\left(\\operatorname* {s i m} \\left(z _ {i} , z _ {n}\\right) / \\tau\\right)}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.238, + 0.892, + 0.308 + ], + "angle": 0, + "content": "where \\(z_{i}\\), \\(P\\), and \\(N\\) are the projected anchor vector \\(\\mathcal{Z}(S(f_i))\\), positive index set, and negative index set, respectively. For simplicity, we use \\(\\Phi_i^{\\mathrm{ag}}\\) and \\(\\Phi_i^{\\mathrm{sp}}\\) to denote the objective functions with task-agnostic GHP \\(P_{i}^{\\mathrm{ag}}\\) and task-specific GHP \\(P_{i}^{\\mathrm{sp}}\\) for each \\(i\\)-th anchor as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.608, + 0.316, + 0.891, + 0.35 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\Phi_ {i} ^ {\\mathrm {a g}} = L ^ {\\mathrm {c o n t}} (z _ {i}, P _ {i} ^ {\\mathrm {a g}}, N _ {i} ^ {\\mathrm {a g}}) \\\\ \\Phi_ {i} ^ {\\mathrm {s p}} = L ^ {\\text {c o n t}} \\left(z _ {i}, P _ {i} ^ {\\mathrm {s p}}, N _ {i} ^ {\\mathrm {s p}}\\right). \\tag {6} \\\\ \\end{array}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.358, + 0.891, + 0.385 + ], + "angle": 0, + "content": "3.4. Gradient Propagation to Local Hidden Positives" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.393, + 0.892, + 0.557 + ], + "angle": 0, + "content": "Besides considering the semantically analogous features globally, it is a common hypothesis that nearby pixels are highly likely to belong to the same semantic class. To this end, we consider the property of locality by propagating the loss gradient to the surrounding features of the anchor. Still, the propagation should be cautiously designed since semantic labels of the adjacent patches are not given; semantic consistency between adjacent patches mostly holds, but sometimes does not (i.e., at object boundaries). Thus, to decide the semantically consistent patches nearby, we utilize the attention scores from the unsupervised pretrained ViT backbone \\(\\mathcal{F}\\)" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.559, + 0.892, + 0.643 + ], + "angle": 0, + "content": "In detail, we first define the index set \\( I_{i}^{\\mathrm{surr}} \\) for surrounding patches of the \\( i \\)-th anchor including \\( i \\)-th anchor itself. Also, given the spatial attention score for \\( i \\)-th anchor \\( \\tilde{T}_i \\in \\mathbb{R}^{H \\cdot W} \\) from the last self-attention layer in the backbone \\( \\mathcal{F} \\), we use the average value of \\( \\tilde{T}_i \\) as a threshold to select an index set of LHP \\( I_{i}^{\\mathrm{local}} \\) among \\( I_{i}^{\\mathrm{surr}} \\):" + }, + { + "type": "equation", + "bbox": [ + 0.56, + 0.649, + 0.891, + 0.667 + ], + "angle": 0, + "content": "\\[\nI _ {i} ^ {\\text {l o c a l}} = \\left\\{j \\mid j \\in I _ {i} ^ {\\text {s u r r}} \\wedge t _ {j} > \\operatorname {A v g} \\left(\\tilde {T} _ {i}\\right) \\right\\}, \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.676, + 0.892, + 0.746 + ], + "angle": 0, + "content": "where \\( t_j \\) is \\( j \\)-th element of \\( \\tilde{T}_i \\) and \\( \\mathrm{Avg}(\\cdot) \\) denotes the averaging function. The visualization of the attention scores for the adjacent patches is shown in Fig. 1 (b). By utilizing \\( I_i^{\\mathrm{local}} \\), we obtain the surrounding positive features \\( G_i \\) and the corresponding attention score \\( T_i \\) for LHP as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.619, + 0.754, + 0.771, + 0.79 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} G _ {i} = \\left\\{s _ {j} \\mid j \\in I _ {i} ^ {\\text {l o c a l}} \\right\\} \\\\ T _ {i} = \\left\\{t _ {j} \\mid j \\in I _ {i} ^ {\\text {l o c a l}} \\right\\}, \\\\ \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.798, + 0.891, + 0.824 + ], + "angle": 0, + "content": "where \\( s_j \\) is the feature of \\( j \\)-th patch extracted by the segmentation head \\( S \\)." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.825, + 0.892, + 0.867 + ], + "angle": 0, + "content": "To propagate the loss gradient to the LHP, we mix the patch features in \\( G_{i} \\) in proportion to the corresponding attention scores in \\( T_{i} \\). Formally, the mixed patch composed" + }, + { + "type": "page_number", + "bbox": [ + 0.466, + 0.888, + 0.505, + 0.898 + ], + "angle": 0, + "content": "19543" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.123, + 0.891, + 0.291 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.301, + 0.893, + 0.355 + ], + "angle": 0, + "content": "Figure 3. Illustration of our gradient propagation strategy to preserve local semantic consistency. For each anchor, with its surrounding patches \\( I^{\\mathrm{surf}} \\) and corresponding attention scores from feature extractor \\( \\mathcal{F} \\), local hidden positives (LHP) \\( I^{\\mathrm{local}} \\) are appointed based on the threshold \\( \\mathrm{Avg}(\\hat{T}) \\) (Eq. 7). In a forward pass, the features of LHP \\( G \\) (Eq. 8) are mixed by weighted average according to the attention scores \\( T \\) to compute the objective function \\( \\Psi \\). In this way, the loss gradient propagates toward the LHP in proportion to \\( T \\) in the backward pass." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.377, + 0.267, + 0.392 + ], + "angle": 0, + "content": "of LHP \\( s_i^{\\mathrm{mix}} \\) is expressed as:" + }, + { + "type": "equation", + "bbox": [ + 0.181, + 0.396, + 0.47, + 0.432 + ], + "angle": 0, + "content": "\\[\ns _ {i} ^ {\\text {m i x}} = \\frac {1}{\\left| I _ {i} ^ {\\text {l o c a l}} \\right|} \\sum_ {j \\in I _ {i} ^ {\\text {l o c a l}}} \\sigma g _ {j} t _ {j}, \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.437, + 0.469, + 0.506 + ], + "angle": 0, + "content": "where \\(\\sigma\\) is a scalar value to scale the attention score, and \\(g_{j}\\) and \\(t_j\\) indicate the \\(j\\)-th element of \\(G_{i}\\) and \\(T_{i}\\), respectively. Then, we define the objective functions \\(\\Psi_i^{\\mathrm{ag}}\\) and \\(\\Psi_i^{\\mathrm{sp}}\\) to learn the locality by inserting the projected mixed vector \\(z_{i}^{\\mathrm{mix}} = \\mathcal{Z}(s_{i}^{\\mathrm{mix}})\\) into Eq. 5 as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.178, + 0.511, + 0.469, + 0.534 + ], + "angle": 0, + "content": "\\[\n\\Psi_ {i} ^ {\\mathrm {a g}} = L ^ {\\text {c o n t}} \\left(z _ {i} ^ {\\text {m i x}}, P _ {i} ^ {\\mathrm {a g}}, N _ {i} ^ {\\mathrm {a g}}\\right) \\tag {10}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.182, + 0.531, + 0.367, + 0.547 + ], + "angle": 0, + "content": "\\[\n\\Psi_ {i} ^ {\\mathrm {s p}} = L ^ {\\mathrm {c o n t}} (z _ {i} ^ {\\mathrm {m i x}}, P _ {i} ^ {\\mathrm {s p}}, N _ {i} ^ {\\mathrm {s p}}).\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.551, + 0.469, + 0.634 + ], + "angle": 0, + "content": "Since these functions are calculated by utilizing the mixed vector \\( z_{i}^{\\mathrm{mix}} \\), the loss gradients are propagated to all features composing the \\( G_{i} \\), as described in Fig. 3. Therefore, the semantically-alike surrounding vectors in \\( G_{i} \\) are updated in the same direction, thereby retaining semantic consistency within the neighboring patches." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.634, + 0.469, + 0.689 + ], + "angle": 0, + "content": "Overall, by combining all loss formulations with consistency regularizer \\( R_{i} \\) that minimizes Euclidean distance between the projected vectors of two differently augmented patches, the final loss function is defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.117, + 0.694, + 0.469, + 0.71 + ], + "angle": 0, + "content": "\\[\nL _ {i} = \\left(\\Phi_ {i} ^ {\\mathrm {a g}} + \\Psi_ {i} ^ {\\mathrm {a g}}\\right) + \\lambda \\left(\\Phi_ {i} ^ {\\mathrm {s p}} + \\Psi_ {i} ^ {\\mathrm {s p}}\\right) + \\alpha R _ {i}, \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.715, + 0.469, + 0.785 + ], + "angle": 0, + "content": "where \\(\\lambda\\) and \\(\\alpha\\) control the contribution of each loss. For instance, \\(\\lambda\\) gradually increases from 0 to 1 during the training and \\(\\alpha\\) remains constant at 0.05 throughout the training. Note that, the sample with zero positive is excluded from training although it rarely exists." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.795, + 0.21, + 0.81 + ], + "angle": 0, + "content": "4. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.817, + 0.387, + 0.833 + ], + "angle": 0, + "content": "4.1. Datasets and Experimental Settings" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.839, + 0.469, + 0.867 + ], + "angle": 0, + "content": "Datasets. We utilize COCO-stuff [1], Cityscapes [9], and Potsdam-3 datasets following the existing works [8, 14," + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.377, + 0.892, + 0.473 + ], + "angle": 0, + "content": "19]. COCO-stuff is a large-scale scene understanding dataset that consists of dense pixel-level annotations and Cityscapes is a more recently publicized dataset having street scenes across 50 different cities. Potsdam-3 dataset contains satellite images. Following the baselines [8,14,19], we choose the 27 classes for COCO-stuff and Cityscapes datasets, and 3 classes for Potsdam-3 dataset." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.49, + 0.892, + 0.726 + ], + "angle": 0, + "content": "Evaluation Protocols and Metrics. To evaluate our approach, we conduct two testing methods; clustering and linear probe [14]. Clustering is to measure how well the semantic-preserving clusters are formed. Once the unlabeled clusters are computed with the extracted representations, clusters are matched with the ground truth class labels using the Hungarian matching algorithm. On the other hand, the linear probe is a popular method to evaluate the quality of representations learned in unsupervised manners. Specifically, an additional linear layer is learned with representations extracted from the frozen unsupervised model for evaluation and ground truth labels. Inferring the representations of test data extracted by the frozen model with the learned linear layer, we can measure the quality of representations. With two types of protocols, the performance is measured by two common metrics; accuracy (Acc.) and mean Intersection Over Union (mIoU)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.742, + 0.892, + 0.867 + ], + "angle": 0, + "content": "Implementation Details. For fair comparisons with our baselines [14, 37], we follow them to mainly use DINO pretrained ViT models as a backbone network \\(\\mathcal{F}\\) for the COCO-stuff dataset. In addition, we also test with the advanced backbone, SelfPatch [39], on the COCO-stuff dataset. The segmentation head \\(S\\) is constructed with a two-layer RELU MLP as STEGO, and the projection head \\(\\mathcal{Z}\\) is composed of a linear layer equipped with a normalization layer. The embedding dimension \\(K\\) is set to 512 for ViT" + }, + { + "type": "page_number", + "bbox": [ + 0.466, + 0.888, + 0.505, + 0.898 + ], + "angle": 0, + "content": "19544" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.086, + 0.124, + 0.462, + 0.346 + ], + "angle": 0, + "content": "
MethodBackboneUnsupervisedLinear
Acc.mIoUAcc.mIoU
DC [2]R18+FPN19.9---
MDC [2]R18+FPN32.29.848.613.3
IIC [19]R18+FPN21.86.744.58.4
PiCIE [8]R18+FPN48.113.854.213.9
PiCIE+H [8]R18+FPN50.014.454.814.8
DINO [4]ViT-S/828.711.368.633.9
+ TransFGU [37]ViT-S/852.717.5--
+ STEGO [14]ViT-S/848.324.574.438.3
+ HP (Ours)ViT-S/857.224.675.642.7
DINO [4]ViT-S/1622.08.050.318.1
+ STEGO [14]ViT-S/1652.523.770.634.5
+ HP (Ours)ViT-S/1654.524.374.139.1
SelfPatch [39]ViT-S/1635.112.364.428.5
+ STEGO [14]ViT-S/1652.422.272.236.0
+ HP (Ours)ViT-S/1656.123.274.941.3
" + }, + { + "type": "table_caption", + "bbox": [ + 0.078, + 0.356, + 0.468, + 0.382 + ], + "angle": 0, + "content": "Table 1. Experimental results on COCO-stuff dataset with various backbones and pretrained models." + }, + { + "type": "table", + "bbox": [ + 0.086, + 0.395, + 0.462, + 0.541 + ], + "angle": 0, + "content": "
MethodBackboneUnsupervisedLinear
Acc.mIoUAcc.mIoU
MDC [2]R18+FPN40.77.1--
IIC [19]R18+FPN47.96.4--
PiCIE [8]R18+FPN65.512.3--
DINO [4]ViT-S/834.510.984.622.8
+ TransFGU [37]ViT-S/877.916.8--
+ HP (Ours)ViT-S/880.118.491.230.6
DINO [4]ViT-B/843.611.884.223.0
+ STEGO [14]ViT-B/873.221.090.326.8
+ HP (Ours)ViT-B/879.518.490.933.0
" + }, + { + "type": "table_caption", + "bbox": [ + 0.118, + 0.55, + 0.428, + 0.564 + ], + "angle": 0, + "content": "Table 2. Experimental results on Cityscapes dataset." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.588, + 0.468, + 0.74 + ], + "angle": 0, + "content": "S/8 and ViT-B/8 models, and 256 for ViT-S/16. We train the model for 3, 20, and 10 epochs for COCO-stuff, Cityscapes, and Potsdam-3 datasets, respectively, based on the AdamW optimizer with a learning rate of 0.0005 and weight decay of 0.1. The task-specific reference pool \\( Q^{\\mathrm{sp}} \\) is renewed every 100 iterations throughout the training. The percentage of negative samples usage \\( \\rho \\) is set to 2. In the last stage, we add a feature refinement step utilizing Conditional Random Field [21] as did in STEGO. Evaluation metrics, i.e., clustering and linear probe, are optimized with the Adam optimizer each with learning rates of 0.005 and 0.001." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.749, + 0.279, + 0.763 + ], + "angle": 0, + "content": "4.2. Experimental Results" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.77, + 0.468, + 0.866 + ], + "angle": 0, + "content": "We compare our proposed method against the prior techniques for the unsupervised segmentation [2, 8, 14, 19, 37]. Most of the results in the result tables are brought from the literature [8, 14]. In Tab. 1, it is observed that self-supervised models, i.e., DINO and SelfPatch, are already good segmentation predictors with the linear probe, which makes them a new baseline over the prior works for unsu" + }, + { + "type": "table", + "bbox": [ + 0.533, + 0.124, + 0.86, + 0.269 + ], + "angle": 0, + "content": "
MethodBackboneUnsup. Acc.
Random CNN [19]VGG1138.2
K-Means [27]VGG1145.7
SIFT [24]VGG1138.2
ContextPrediction [10]VGG1149.6
CC [18]VGG1163.9
DeepCluster [2]VGG1141.7
IIC [19]VGG1165.1
DINO [4]ViT-B/853.0
+ STEGO [14]ViT-B/877.0
+ HP (Ours)ViT-B/882.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.54, + 0.279, + 0.851, + 0.292 + ], + "angle": 0, + "content": "Table 3. Experimental results on Potsdam-3 dataset." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.318, + 0.892, + 0.4 + ], + "angle": 0, + "content": "pervised segmentation. Furthermore, we utilize two pretrained backbones with two kinds of architectures to compare with STEGO, in detail. As reported, our proposed model provides consistent performance improvements over the previous SOTA model in almost all cases on the COCO-stuff dataset." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.402, + 0.892, + 0.706 + ], + "angle": 0, + "content": "Results on Cityscapes also show a similar tendency. As shown in Tab. 2, ours outperform previous methods except for the mIoU when clustering is used for the evaluation. For instance, we achieve \\(8.6\\%\\) and \\(23\\%\\) improvements in cluster accuracy and linear mIoU over STEGO, respectively. For the slight decrease in cluster mIoU with ViT-B/8 architecture, we argue that it is insignificant since the linear probe better describes the quality of representations. Specifically, clustering evaluation highly depends on the purpose of the dataset so that it is sensitive to the degree of class-specificity as different body parts can be either classified as human or as independent body parts. However, whereas it is more appropriate to detect each body part independently for unsupervised learning as ours do in Fig. 5, the annotations for general datasets, e.g., COCO-stuff and cityscape, treat these body parts as a human class. Such circumstances make the clustering evaluation vulnerable to the degree of the class hierarchy. In contrast, the linear probe projects these features to close proximity if the given label space considers them as a human body. Thus, we believe the linear probe is a more appropriate measure of representation quality." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.707, + 0.892, + 0.79 + ], + "angle": 0, + "content": "Also, we compare the cluster accuracy in Tab. 3 on Potsdam-3 dataset. We have achieved a \\(7\\%\\) boost over STEGO, confirming that ours also performs well even in a completely new domain. Likewise, our superior results verify the effectiveness of our process of discovering global and local-hidden positive patches." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.867 + ], + "angle": 0, + "content": "Qualitative Results. In addition to the quantitative results, we report qualitative results in Fig. 4. In comparison to STEGO, we observe that our results include fewer mispredicted pixels throughout the images, while the strength" + }, + { + "type": "page_number", + "bbox": [ + 0.466, + 0.888, + 0.505, + 0.898 + ], + "angle": 0, + "content": "19545" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.125, + 0.894, + 0.29 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.098, + 0.3, + 0.871, + 0.314 + ], + "angle": 0, + "content": "Figure 4. Qualitative comparison results of Ours and STEGO on the COCO-stuff dataset with DINO pretrained ViT-S/8 backbone." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.338, + 0.471, + 0.408 + ], + "angle": 0, + "content": "of preserving semantic locality is also validated. For instance, whereas we find that the predicted label of the wheel is only partially correct in the 4th column, our results are consistent along the neighboring pixels. These results also demonstrate the superiority of our method." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.421, + 0.414, + 0.438 + ], + "angle": 0, + "content": "5. Ablation Study and Further Analysis" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.444, + 0.47, + 0.528 + ], + "angle": 0, + "content": "In this section, we provide ablation studies and an analysis of our model. Particularly, we explore the contributions of main components and test with varying hyperparameters. Most experiments for ablation studies are conducted on the COCO-stuff dataset using the DINO pretrained ViTs/8 model, except for Sec. 5.2, which utilized the ViT-S/16." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.537, + 0.403, + 0.553 + ], + "angle": 0, + "content": "5.1. Importance of the Main Components." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.559, + 0.471, + 0.78 + ], + "angle": 0, + "content": "Tab. 4 reports the performances when an individual component or various combinations of them are not utilized. We found that task-specific GHP and LHP are essential in improving the performance of the unsupervised segmentation task. Compared to (d) where both the task-specific GHP and LHP are not used, the use of them each leads to \\(6.5\\%\\) (b) and \\(11.6\\%\\) (c) improvements. Also when used together, they bring \\(16\\%\\) of performance boosts (a). Furthermore, the importance of preserving symmetry in selecting GHP can be found by comparing (a) to (e) as its usage boosts \\(5.9\\%\\), and consistency regularizer enhances performance by \\(2.5\\%\\) by comparing (a) to (f). Lastly, (g) shows the performance of naively implemented contrastive learning (Eq. 1) with photometric perturbations used in PiCIE [8]. This also confirms the strengths of our proposed method as (a) enhances (g) by \\(51.3\\%\\)." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.79, + 0.469, + 0.805 + ], + "angle": 0, + "content": "5.2. Alternatives to Gradient Propagation Strategy" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.811, + 0.47, + 0.868 + ], + "angle": 0, + "content": "There can be alternative ways to meet our goal of reflecting semantic consistency between adjacent patches. As an alternative method of gradient propagation, we simply apply the identical loss to the surrounding patches pro" + }, + { + "type": "table", + "bbox": [ + 0.523, + 0.335, + 0.872, + 0.457 + ], + "angle": 0, + "content": "
GHPLHPSARegUnsupervised Acc.mIoU
TATS
(a)✓✓✓✓✓57.224.6
(b)✓✓✓✓52.523.1
(c)✓✓✓✓55.019.1
(d)✓✓✓49.320.1
(e)✓✓✓✓54.023.6
(f)✓✓✓✓55.824.5
(g)37.810.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.465, + 0.893, + 0.517 + ], + "angle": 0, + "content": "Table 4. Ablation study for each component. GHP, LHP, TA, TS, SA, and Reg denote Global Hidden Positive, Local Hidden Positive, task-agnostic, task-specific, symmetrical assignments, and consistency regularizer, respectively." + }, + { + "type": "table", + "bbox": [ + 0.532, + 0.531, + 0.86, + 0.604 + ], + "angle": 0, + "content": "
MethodUnsupervisedLinear
Acc.mIoUAcc.mIoU
DINO + GradProp54.524.374.139.1
DINO + LossProp54.723.274.340.5
SelfPatch + GradProp56.123.274.941.3
SelfPatch + LossProp54.522.275.141.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.613, + 0.892, + 0.64 + ], + "angle": 0, + "content": "Table 5. Comparison results between gradient propagation and loss propagation strategies." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.666, + 0.892, + 0.763 + ], + "angle": 0, + "content": "portionally to their attention score (i.e., loss propagation). The results in Tab. 5 show that the loss propagation strategy performs comparably to the gradient propagation strategy. Nonetheless, we observed that the loss propagation approach incurs higher computational costs (\\(1.2 \\times\\) memory and \\(3 \\times\\) time). Likewise, our method is implemented considering both effectiveness and efficiency." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.775, + 0.637, + 0.789 + ], + "angle": 0, + "content": "5.3. Visualization" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.797, + 0.893, + 0.868 + ], + "angle": 0, + "content": "**Discovered GHP.** To demonstrate the effectiveness of our GHP selection process, we visualize the selected GHP sets for different anchors in a single image in Fig. 5. First, we observe that the corresponding reference point in the second column is semantically correlated with the anchor patch." + }, + { + "type": "page_number", + "bbox": [ + 0.466, + 0.888, + 0.506, + 0.899 + ], + "angle": 0, + "content": "19546" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.081, + 0.124, + 0.472, + 0.316 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.326, + 0.47, + 0.378 + ], + "angle": 0, + "content": "Figure 5. Discovered patches by our GHP selection process. From the left to right columns, red boxes indicate the anchors, the closest patches within the task-agnostic reference pool (reference), and GHP sets chosen in the mini-batch, respectively." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.402, + 0.47, + 0.568 + ], + "angle": 0, + "content": "Also, the obtained GHP sets verify the appropriateness of the use of reference points as each anchor's criterion as they are capable of precisely discovering the semantically-similar positives. For example, we find that all the anchors, reference points, and GHP sets have the same semantic labels in the first and the second rows (solid (mountain) and ground (snow)). More intriguing results are in the third and fourth rows where we find that the GHP selection process distinguishes the body parts in a much more fine-grained manner than the given annotation, i.e., person. These results imply that the designed GHP selection is well-designed and capable of capturing detailed semantic contexts." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.577, + 0.361, + 0.591 + ], + "angle": 0, + "content": "5.4. Robustness to Hyperparameters" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.598, + 0.469, + 0.695 + ], + "angle": 0, + "content": "In this subsection, we enumerate our use of hyperparameters in Tab. 6 and conduct an ablation study. Overall, our proposed training scheme is robust to hyperparameters as Fig. 6 supports the claim. Below, we illustrate the influences of each parameter. Despite the slight drop in performance from time to time, it is insignificant since the change in performance is very marginal and results are consistent." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.712, + 0.469, + 0.864 + ], + "angle": 0, + "content": "The Number of Data in the Reference Pool. Fig. 6 (a) shows the performances with varying numbers of data \\( M \\) in the reference pool. Generally, the reference pool is not very vulnerable to its size, unless the capacity is either too small or too large. When the reference pool is skinny, it may not be sufficient to represent all kinds of semantics present in the dataset. In other words, the small reference pool may induce a biased criterion for each anchor which may incur biased training. On the other hand, when the reference pool is too large, a tight threshold \\( (c_{i}) \\) could be derived, which can interfere with gathering GHP." + }, + { + "type": "table", + "bbox": [ + 0.606, + 0.124, + 0.788, + 0.18 + ], + "angle": 0, + "content": "
DatasetMτ
COCO-stuff20480.8
Cityscapes20480.6
Potsdam-310240.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.555, + 0.191, + 0.835, + 0.203 + ], + "angle": 0, + "content": "Table 6. Hyperparameter used for each dataset." + }, + { + "type": "image", + "bbox": [ + 0.501, + 0.219, + 0.695, + 0.321 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.529, + 0.325, + 0.674, + 0.336 + ], + "angle": 0, + "content": "(a) Reference pool size \\(M\\)" + }, + { + "type": "image", + "bbox": [ + 0.698, + 0.219, + 0.892, + 0.321 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.746, + 0.325, + 0.851, + 0.336 + ], + "angle": 0, + "content": "(b) Temperature \\(\\tau\\)" + }, + { + "type": "image_caption", + "bbox": [ + 0.555, + 0.346, + 0.835, + 0.359 + ], + "angle": 0, + "content": "Figure 6. Ablation studies on hyperparameters." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.384, + 0.892, + 0.508 + ], + "angle": 0, + "content": "Temperature Parameter. The temperature parameter \\(\\tau\\) is the scaling parameter to manipulate the sensitivity of the contrastive loss. If \\(\\tau\\) gets bigger, the objective function becomes robust to the difference between the similarity of the positive and negative samples. On the other hand, when \\(\\tau\\) gets lower, the embedding distribution is likely to be more uniform [31]. Although the results do not fluctuate much, we observe that training uniformly distributed embedding space leads to a slight performance drop in Fig. 6 (b)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.52, + 0.618, + 0.535 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.543, + 0.892, + 0.805 + ], + "angle": 0, + "content": "In this paper, we introduced a novel unsupervised semantic segmentation method by discovering and leveraging two types of hidden positives, global hidden positive (GHP) and local hidden positive (LHP), to learn rich semantic information with local consistency. First, anchor-dependent GHP comprises task-agnostic and task-specific positive sets which are used to tailor the contrastive learning for the unsupervised semantic segmentation task. Whereas the task-agnostic features are collected to guide the initial training, task-specific features are progressively engaged to learn the task-specific semantics information. Moreover, under the inherent premise that the adjacent patches are likely to be semantically similar, we propagate the loss gradient to the surrounding patches in proportion to their attention scores. This encourages the semantically similar peripheral patches to have the same objective as the anchor, resulting in semantic consistency between adjacent patches unless they belong to different objects. Finally, our proposed method achieves new state-of-the-art results in various datasets." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.867 + ], + "angle": 0, + "content": "Acknowledgements. This work was supported in part by MSIT/IITP (No. 2022-0-00680, 2019-0-00421, 2020-0-01821, 2021-0-02068), and MSIT&KNPA/KIPoT (Police Lab 2.0, No. 210121M06)." + }, + { + "type": "page_number", + "bbox": [ + 0.466, + 0.888, + 0.505, + 0.898 + ], + "angle": 0, + "content": "19547" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.125, + 0.173, + 0.139 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.148, + 0.47, + 0.2 + ], + "angle": 0, + "content": "[1] Holger Caesar, Jasper Uijlings, and Vittorio Ferrari. Cocostuff: Thing and stuff classes in context. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1209-1218, 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.201, + 0.472, + 0.263 + ], + "angle": 0, + "content": "[2] Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. Deep clustering for unsupervised learning of visual features. In Proceedings of the European conference on computer vision (ECCV), pages 132-149, 2018. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.266, + 0.471, + 0.329 + ], + "angle": 0, + "content": "[3] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems, 33:9912-9924, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.332, + 0.47, + 0.395 + ], + "angle": 0, + "content": "[4] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9650-9660, 2021. 2, 3, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.397, + 0.469, + 0.447 + ], + "angle": 0, + "content": "[5] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. *ICLR*, 2015. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.45, + 0.469, + 0.5 + ], + "angle": 0, + "content": "[6] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.502, + 0.469, + 0.552 + ], + "angle": 0, + "content": "[7] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15750-15758, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.554, + 0.469, + 0.618 + ], + "angle": 0, + "content": "[8] Jang Hyun Cho, Utkarsh Mall, Kavita Bala, and Bharath Hariharan. Picie: Unsupervised semantic segmentation using invariance and equivariance in clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16794-16804, 2021. 1, 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.62, + 0.469, + 0.696 + ], + "angle": 0, + "content": "[9] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.698, + 0.469, + 0.748 + ], + "angle": 0, + "content": "[10] Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE international conference on computer vision, pages 1422-1430, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.75, + 0.469, + 0.813 + ], + "angle": 0, + "content": "[11] Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3146-3154, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.814, + 0.469, + 0.866 + ], + "angle": 0, + "content": "[12] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In International Conference on Learning Representations, 2018. 2" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.148, + 0.472, + 0.866 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.127, + 0.892, + 0.203 + ], + "angle": 0, + "content": "[13] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.205, + 0.892, + 0.267 + ], + "angle": 0, + "content": "[14] Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, and William T. Freeman. Unsupervised semantic segmentation by distilling feature correspondences. In International Conference on Learning Representations, 2022. 1, 2, 3, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.269, + 0.892, + 0.331 + ], + "angle": 0, + "content": "[15] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.334, + 0.892, + 0.384 + ], + "angle": 0, + "content": "[16] Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song. Using self-supervised learning can improve model robustness and uncertainty. Advances in neural information processing systems, 32, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.386, + 0.892, + 0.45 + ], + "angle": 0, + "content": "[17] Jyh-Jing Hwang, Stella X Yu, Jianbo Shi, Maxwell D Collins, Tien-Ju Yang, Xiao Zhang, and Liang-Chieh Chen. Segsort: Segmentation by discriminative sorting of segments. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7334-7344, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.451, + 0.892, + 0.49 + ], + "angle": 0, + "content": "[18] Phillip Isola, Daniel Zoran, Dilip Krishnan, and Edward H Adelson. Learning visual groups from co-occurrences in space and time. arXiv preprint arXiv:1511.06811, 2015. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.491, + 0.892, + 0.553 + ], + "angle": 0, + "content": "[19] Xu Ji, Joao F Henriques, and Andrea Vedaldi. Invariant information clustering for unsupervised image classification and segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9865-9874, 2019. 1, 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.555, + 0.892, + 0.617 + ], + "angle": 0, + "content": "[20] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. Advances in Neural Information Processing Systems, 33:18661-18673, 2020. 2, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.62, + 0.892, + 0.669 + ], + "angle": 0, + "content": "[21] Philipp Krahenbihl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. Advances in neural information processing systems, 24, 2011. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.671, + 0.892, + 0.697 + ], + "angle": 0, + "content": "[22] Samuli Laine and Timo Aila. Temporal ensembling for semi-supervised learning. ICLR, 2017. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.699, + 0.892, + 0.762 + ], + "angle": 0, + "content": "[23] Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, and Sungroh Yoon. Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5267-5276, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.763, + 0.892, + 0.813 + ], + "angle": 0, + "content": "[24] David G Lowe. Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision, volume 2, pages 1150-1157. IEEE, 1999. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.814, + 0.892, + 0.866 + ], + "angle": 0, + "content": "[25] Ishan Misra and Laurens van der Maaten. Self-supervised learning of pretext-invariant representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6707-6717, 2020. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.127, + 0.892, + 0.866 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.467, + 0.888, + 0.505, + 0.898 + ], + "angle": 0, + "content": "19548" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.127, + 0.47, + 0.177 + ], + "angle": 0, + "content": "[26] WonJun Moon, Ji-Hwan Kim, and Jae-Pil Heo. Tailoring self-supervision for supervised learning. In European Conference on Computer Vision, pages 346-364. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.179, + 0.47, + 0.241 + ], + "angle": 0, + "content": "[27] Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. the Journal of machine learning research, 12:2825-2830, 2011. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.243, + 0.469, + 0.305 + ], + "angle": 0, + "content": "[28] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234-241. Springer, 2015. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.307, + 0.469, + 0.357 + ], + "angle": 0, + "content": "[29] Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7262-7272, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.358, + 0.469, + 0.42 + ], + "angle": 0, + "content": "[30] Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc Van Gool. Unsupervised semantic segmentation by contrasting object mask proposals. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10052-10062, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.421, + 0.469, + 0.47 + ], + "angle": 0, + "content": "[31] Feng Wang and Huaping Liu. Understanding the behaviour of contrastive loss. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2495-2504, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.472, + 0.469, + 0.533 + ], + "angle": 0, + "content": "[32] Shujun Wang, Lequan Yu, Caizi Li, Chi-Wing Fu, and Pheng-Ann Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. In European Conference on Computer Vision, pages 159–176. Springer, 2020. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.536, + 0.469, + 0.599 + ], + "angle": 0, + "content": "[33] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12275-12284, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.6, + 0.469, + 0.661 + ], + "angle": 0, + "content": "[34] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34:12077-12090, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.663, + 0.469, + 0.726 + ], + "angle": 0, + "content": "[35] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, and Dan Xu. Multi-class token transformer for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4310-4319, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.727, + 0.469, + 0.802 + ], + "angle": 0, + "content": "[36] Yazhou Yao, Tao Chen, Guo-Sen Xie, Chuanyi Zhang, Fumin Shen, Qi Wu, Zhenmin Tang, and Jian Zhang. Nonsalient region object mining for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2623-2632, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.804, + 0.469, + 0.867 + ], + "angle": 0, + "content": "[37] Zhaoyuan Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li, and Rong Jin. Transfgu: a top-down approach to fine-grained unsupervised semantic segmentation. In European Conference on Computer Vision, pages 73-89. Springer, 2022. 1, 2, 5, 6" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.127, + 0.47, + 0.867 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.127, + 0.892, + 0.177 + ], + "angle": 0, + "content": "[38] Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object-contextual representations for semantic segmentation. In European conference on computer vision, pages 173-190. Springer, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.179, + 0.892, + 0.241 + ], + "angle": 0, + "content": "[39] Sukmin Yun, Hankook Lee, Jaehyung Kim, and Jinwoo Shin. Patch-level representation learning for self-supervised vision transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8354-8363, 2022. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.243, + 0.892, + 0.318 + ], + "angle": 0, + "content": "[40] Xiangyun Zhao, Raviteja Vemulapalli, Philip Andrew Mansfield, Boqing Gong, Bradley Green, Lior Shapira, and Ying Wu. Contrastive learning for label efficient semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10623-10633, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.321, + 0.892, + 0.408 + ], + "angle": 0, + "content": "[41] Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6881-6890, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.41, + 0.892, + 0.472 + ], + "angle": 0, + "content": "[42] Wu Zheng, Weiliang Tang, Li Jiang, and Chi-Wing Fu. Sessd: Self-ensembling single-stage object detector from point cloud. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14494–14503, 2021. 4" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.127, + 0.892, + 0.472 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.467, + 0.888, + 0.505, + 0.898 + ], + "angle": 0, + "content": "19549" + } + ] +] \ No newline at end of file diff --git a/2023/Leveraging Hidden Positives for Unsupervised Semantic Segmentation/34b6701a-58b4-43b7-90c9-cd9aa3bd0efc_origin.pdf b/2023/Leveraging Hidden Positives for Unsupervised Semantic Segmentation/34b6701a-58b4-43b7-90c9-cd9aa3bd0efc_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..eaffa5d38e57b6d03ecc82688f7f4f76d80b0cf8 --- /dev/null +++ b/2023/Leveraging Hidden Positives for Unsupervised Semantic Segmentation/34b6701a-58b4-43b7-90c9-cd9aa3bd0efc_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:250e20da4276c1696ba204bda06f3f0fb34f0857d054681d1329c1d0d519f9a7 +size 1876792 diff --git a/2023/Leveraging Hidden Positives for Unsupervised Semantic Segmentation/full.md b/2023/Leveraging Hidden Positives for Unsupervised Semantic Segmentation/full.md new file mode 100644 index 0000000000000000000000000000000000000000..bceedc5e0dcb7d03f28801f5774d11cf5cd341a6 --- /dev/null +++ b/2023/Leveraging Hidden Positives for Unsupervised Semantic Segmentation/full.md @@ -0,0 +1,312 @@ +# Leveraging Hidden Positives for Unsupervised Semantic Segmentation + +Hyun Seok Seong, WonJun Moon, SuBeen Lee, Jae-Pil Heo* Sungkyunkwan University + +{gustjrdl95，wjun0830，leesb7426，jaepilheo}@skku.edu + +# Abstract + +Dramatic demand for manpower to label pixel-level annotations triggered the advent of unsupervised semantic segmentation. Although the recent work employing the vision transformer (ViT) backbone shows exceptional performance, there is still a lack of consideration for task-specific training guidance and local semantic consistency. To tackle these issues, we leverage contrastive learning by excavating hidden positives to learn rich semantic relationships and ensure semantic consistency in local regions. Specifically, we first discover two types of global hidden positives, task-agnostic and task-specific ones for each anchor based on the feature similarities defined by a fixed pre-trained backbone and a segmentation head-in-training, respectively. A gradual increase in the contribution of the latter induces the model to capture task-specific semantic features. In addition, we introduce a gradient propagation strategy to learn semantic consistency between adjacent patches, under the inherent premise that nearby patches are highly likely to possess the same semantics. Specifically, we add the loss propagating to local hidden positives, semantically similar nearby patches, in proportion to the predefined similarity scores. With these training schemes, our proposed method achieves new state-of-the-art (SOTA) results in COCO-stuff, Cityscapes, and Potsdam-3 datasets. Our code is available at: https://github.com/hynnsk/HP. + +# 1. Introduction + +Semantic segmentation is a major task for scene understanding and plays a crucial role in many applications including medical imaging and autonomous driving [5, 11, 28, 34, 38, 41]. However, existing supervised approaches demand large-scale pixel-level annotations which require huge labeling costs. It has triggered the advent of weakly-supervised [23, 33, 35, 36] and unsupervised semantic segmentation [8, 14, 19, 37] which are to learn without expensive pixel-level annotations. + +![](images/d63481b8d520eb9a72d407603d99d0d4f248d0f715b86217ce3dfd95cd8d8e24.jpg) +(a) Visualization + +![](images/ed23234869160150eacb6ca895bdd02f4190bdb48d66734f2732c08a43af2660.jpg) +(b) Global hidden positive +Figure 1. Assuming a mini-batch comprising two images shown in (a), we describe two types of hidden positives, to leverage for contrastive learning. (a) With two types of hidden positives introduced in (b) and (c), we provide an example of how our training scheme provides more precise and consistent semantics. (b) (top) Semantically analogous patches throughout the mini-batch are selected as global hidden positives. (bottom) Data-driven criterion per anchor is designed for reliable positive collection. With the criterion, selected positives are illustrated in (b top). (c) (top) We define local hidden positives for each anchor to be the adjacent patches with high semantic consistency, i.e., blue boxes. (bottom) Average attention scores for adjacent patches from the pretrained transformer architecture. The blue line represents the attention score for local hidden positives while the red line for patches neighboring anchors but having low semantic consistency. + +![](images/8db016c991b005fb583863305ebf471f3b7888405f28b072d2553f0648322498.jpg) +(c) Local hidden positive + +Particularly, unsupervised semantic segmentation is one of the most challenging tasks, since it needs to capture pixel-level semantics from unlabeled data. In this context, clustering-based approaches have been proposed to learn + +semantic-preserving clusters by attracting the augmented views in the pixel-level [8, 19]. They implemented the intuition of contrastive learning [3, 6, 7, 13, 15, 25] by ensuring the augmented pairs yield symmetric cluster assignments. More recently, as discovering pixel-level semantics from scratch is challenging, STEGO [14] broke down the problem into learning the representation and learning the segmentation head. With the learned patch-level representation from the seminal work in unsupervised learning [4, 39], they train the segmentation head with a distillation strategy. Although they have made great advancements, we point out their limitations in that they rely solely on a fixed backbone that is not specifically trained for the segmentation task and overlook the importance of semantic consistency along the adjacency that could be a crucial clue for segmentation. + +To take these into consideration, we leverage contrastive learning based on the mined hidden positives to ensure contextual consistency along the patches with analogous semantics, particularly the nearby patches, as described in Fig. 1. Specifically, we elaborately select the pseudopositive samples (i.e., global hidden positive, GHP) for contrastive learning to learn semantic consistency. Also, to ensure local consistency, we propagate the loss gradient to the adjacent patches (i.e., local hidden positive, LHP) in proportion to their equivalency. First, the GHP selection process is designed with two types of data reference pools, task-agnostic and task-specific, to collect the semantically consistent patch features throughout the mini-batch per anchor. For instance, the task-agnostic data reference pool is composed of features extracted by the unsupervised pretrained backbone. On the other hand, the task-specific reference pool is constructed with the features from the segmentation head-in-training to complement task relevance. Based on the two reference pools, two sets of GHP are selected each with generalized and task-specific perspectives. Second, to implement the property of locality and prevent the semantics from fluctuating, we propagate the loss gradient to adjacent patches (i.e., LHP) in proportion to the similarity scores built within the pretrained backbone. This enables the model to learn the relevance of the local context that nearby patches often belong to the same instance. + +Our main contributions are summarized as: + +- We propose a novel method to discover semantically similar pairs, called global hidden positives, to explicitly learn the semantic relationship among patches for unsupervised semantic segmentation. +- We utilize the task-specific features from a model-intraining and validate the effectiveness of progressive increase of their contribution. +- A gradient propagation to nearby similar patches, local hidden positives, is developed to learn local semantic consistency which is the nature of segmentation. + +- Our approach outperforms existing state-of-the-art methods across extensive experiments. + +# 2. Related Work + +# 2.1. Unsupervised Semantic Segmentation + +Semantic segmentation has been extensively studied for its wide applicability [5, 11, 29, 34, 38, 41], but collecting pixel-level annotations requires expensive costs. Therefore, many studies [8, 14, 17, 19, 30, 37] attempted to address semantic segmentation without any supervision. Earlier techniques tried to learn semantic correspondence at the pixel level. IIC [19] maximizes the mutual information between the features of two differently augmented images, and PiCIE [8] learns photometric and geometric invariances as an inductive bias. Yet, their training process highly depends on data augmentation, and learning semantic consistency without any prior knowledge is challenging. Therefore, recent methods [14, 37] adopted the ViT model trained in a self-supervised manner, i.e., DINO [4], as a backbone architecture. For instance, TransFGU [37] relocates the high-level semantic features from DINO into low-level pixel-wise features by generating pixel-wise pseudo labels. On the other hand, STEGO [14] utilizes knowledge distillation that learns correspondences between features extracted from DINO. Although STEGO shows a dramatic performance improvement compared to the prior works, it heavily relies on the pretrained backbone and overlooks the property of local consistency that the adjacent pixels are likely to belong to the same category. On the other hand, our training is driven by both the task-agnostic and task-specific pseudopositive features, and the gradients are conditionally propagated to the neighboring patches, thereby ensuring task-specificity and locality. + +# 2.2. Contrastive Learning + +Self-supervised learning methods [2,3,6,7,12,13,15] aim to learn general representations without any annotations. Thanks to their good representation capability, they have been employed to yield remarkable performances in various downstream tasks [8, 16, 20, 26, 40]. Among them, contrastive learning approaches [3, 6, 7, 13, 15, 25] have shown unrivaled performances. In general, they learn representation by attracting a self-augmented set and repulsing other images [3, 6]. Other variants use additional memory [15] or only exploit the positive set for the attraction [7, 13]. This training scheme has also been utilized for unsupervised semantic segmentation [8, 19]. However, the aforementioned methods only considered augmented pairs for the positive which makes them very sensitive to the quality of the augmentation techniques. Our work differs in that our key idea is to collect and exploit reliable pseudo-positives throughout the mini-batch as described in Fig. 1 (b). + +![](images/c826c2f57f6882b466baa39d37e7de301aef35566216052464f0c49228288ad8.jpg) +Figure 2. Illustration of the global hidden positive (GHP) selection process. Our GHP can be divided into two sub-sets: task-agnostic and task-specific. An index set of task-agnostic GHP $P_{i}^{\mathrm{ag}}$ comprises the indices of positives discovered within the task-agnostic reference pool $Q^{\mathrm{ag}}$ . Note that, $Q^{\mathrm{ag}}$ is composed of randomly sampled features extracted by the feature extractor $\mathcal{F}$ . Once the anchor feature $f_{i}$ is projected to $z_{i}$ , other patches in the mini-batch are gathered as positives if their similarity with the anchor feature exceeds the similarity between the anchor and the most similar feature in $Q^{\mathrm{ag}}$ . On the other hand, task-specific GHP is discovered in a similar manner but with task-specific reference pool $Q^{\mathrm{sp}}$ which keeps being updated with the features from the momentum segmentation head $S'$ . Whereas the task-agnostic GHP set solely contributes to the initial training, the task-specific GHP set gradually replaces the portion of the task-agnostic set until the end of training. + +# 3. Method + +In this section, we introduce the pseudo-positive selection strategy to discover hidden positives with analogous semantics in Sec. 3.2, the training objective with discovered positives in Sec. 3.3, and the gradient propagation scheme to preserve the property of locality in Sec. 3.4. + +# 3.1. Preliminary + +In unsupervised semantic segmentation, the model utilizes unlabeled image set $X = \{x_{b}\}_{b = 1}^{B}$ where $B$ is the number of training data in the mini-batch. Given an image $x_{b}$ processed to the feature extractor $\mathcal{F}$ , we have $H\cdot W$ features of $f_{i}\in \mathbb{R}^{C}$ , where $i\in [1,\dots ,H\cdot W]$ . Subsequently, the segmentation head $S$ maps a patch feature $f_{i}$ to the corresponding segmentation feature $s_i\in \mathbb{R}^K$ . And then, the projection head $\mathcal{Z}$ produces a projected vector $z_{i}\in \mathbb{R}^{K}$ to formulate a contrastive loss function. In the inference stage, we use the segmentation feature $s_i$ . + +Based on the projected vector $z_{i}$ for the $i$ -th patch, let $j$ be the index of the augmented patch of $i$ -th one. Then, the conventional self-supervised contrastive loss [6] for $i$ -th patch in unsupervised semantic segmentation can be defined as follows: + +$$ +L _ {i} ^ {\text {s e l f}} = - \log \frac {\exp \left(\sin \left(z _ {i} , z _ {j} / \tau\right)\right)}{\sum_ {a \in A} \exp \left(\sin \left(z _ {i} , z _ {a}\right) / \tau\right)}, \tag {1} +$$ + +where $A$ indicates a set of all indexes except $i$ , $\tau$ denotes the + +scalar temperature parameter, and $\mathrm{sim}(\cdot ,\cdot)$ is cosine similarity between two vectors. + +# 3.2. Global Hidden Positives + +Learning mutual information with augmented pixels only provides insufficient training signal in unsupervised semantic segmentation [14]. Therefore, it is important to discover hidden pseudo-positives to tailor the contrastive loss for unsupervised segmentation. To discover the hidden positives at the initial stage, we utilize the self-supervised pretrained backbone [4] as the task-agnostic criterion. Then, we gradually increase the contribution of hidden positives found in a task-specific way for the training. Fig. 2 provides the overview of the global hidden positive (GHP) selection process. + +Initially, the pretrained backbone is utilized to construct a task-agnostic reference pool to assess whether other features in the mini-batch are semantically-alike for each anchor feature. Specifically, task-agnostic reference pool, $Q^{\mathrm{ag}} = \{q_m\}_{m=1}^M$ , is composed of $M$ randomly sampled features that are extracted by the unsupervised pretrained backbone $\mathcal{F}$ . Note that, we only sample a single patch feature per image to ensure the semantic randomness of the reference pool. This task-agnostic reference pool is fixed as the pretrained backbone is frozen throughout the training. + +Once the reference pool is gathered, for each patch feature $f_{i}$ , we define an anchor-dependent similarity criterion + +$c_{i}$ to collect positives, as the distance to the closest feature within the reference pool $Q^{\mathrm{ag}}$ by the cosine similarity: + +$$ +c _ {i} = \max _ {q _ {m} \in Q ^ {\mathrm {a g}}} \operatorname {s i m} \left(q _ {m}, f _ {i}\right). \tag {2} +$$ + +For each anchor feature $f_{i}$ , we basically treat the other feature in the mini-batch $f_{j}$ as positive if the similarity between $f_{i}$ and $f_{j}$ is greater than $c_{i}$ . Still, although one patch feature might be the positive sample for the other, it may not hold mutually. This is because the criterion $c_{i}$ is anchor-dependent. To endow consistency in training, we make the GHP selection symmetric to prevent the relation between two patches from being ambiguous. Therefore, index set of $\mathrm{GHP} P_{i}^{\mathrm{ag}}$ for each $i$ -th anchor feature $f_{i}$ is defined as follows: + +$$ +P _ {i} ^ {\mathrm {a g}} = \left\{j \mid \operatorname {s i m} \left(f _ {i}, f _ {j}\right) > c _ {i} \vee \operatorname {s i m} \left(f _ {i}, f _ {j}\right) > c _ {j} \right\}, \tag {3} +$$ + +where $j$ indicates the index for different patch features in the mini-batch. Accordingly, such a distribution-aware reference pool allows the discovery of globally analogous features in consideration of each anchor. + +However, although the reference pool built upon the features from an unsupervised pretrained network can serve as an appropriate basis for positivity, it may be insufficient since it lacks task-specificity. We argue that features from the segmentation head are more task-specific than those from the pretrained backbone. Therefore, along with the GHP selected by $P^{\mathrm{ag}}$ , we construct additional task-specific GHP utilizing the features from the segmentation head. + +Specifically, an index set of task-specific GHP $P_{i}^{\mathrm{sp}}$ is formed similarly to Eq. 3 by comparing the features $s' = S'(f)$ and task-specific reference pool $Q^{\mathrm{sp}}$ , where $S'$ indicates the momentum segmentation head, and the task-specific reference pool $Q^{\mathrm{sp}}$ comprises $s'$ . Also, along with the update of the segmentation head, during training, the reference pool is periodically renewed. Formally, with $c_{i}'$ , calculated by substituting $Q^{\mathrm{ag}}$ and $f_{i}$ in Eq. 2 with $Q^{\mathrm{sp}}$ and $s_{i}'$ , respectively, the $P_{i}^{\mathrm{sp}}$ is expressed as follows: + +$$ +P _ {i} ^ {\mathrm {s p}} = \left\{j \mid \operatorname {s i m} \left(s _ {i} ^ {\prime}, s _ {j} ^ {\prime}\right) > c _ {i} ^ {\prime} \vee \operatorname {s i m} \left(s _ {i} ^ {\prime}, s _ {j} ^ {\prime}\right) > c _ {j} ^ {\prime} \right\}. \tag {4} +$$ + +Note that, the usage of the momentum segmentation head is for the stability of the reference pool [22,42]. + +# 3.3. Objective Function + +To formulate a contrastive objective with mined GHP in Sec. 3.2, we also need negative features. As we collected the positives throughout the mini-batch, the naive implementation of contrastive learning would utilize all features except the selected positives in the mini-batch as the negatives. However, since an immoderate increase in the size of the negative set may disturb the model training [32], we form a negative set $N_{i}$ by randomly choosing $\rho \%$ of the remaining patches for each $i$ -th anchor. Note that, the separate + +index sets of negative samples $N_{i}^{\mathrm{ag}}$ and $N_{i}^{\mathrm{sp}}$ are defined for each $P_{i}^{\mathrm{ag}}$ and $P_{i}^{\mathrm{sp}}$ , correspondingly. Also, unlike Eq. 1, our contrastive loss for each $i$ -th anchor is more like a supervised objective [20] since we are given multiple positives: + +$$ +L ^ {\text {c o n t}} \left(z _ {i}, P, N\right) = \frac {- 1}{| P |} \sum_ {p \in P} \log \frac {\exp \left(\operatorname* {s i m} \left(z _ {i} , z _ {p}\right) / \tau\right)}{\sum_ {n \in (N \cup P)} \exp \left(\operatorname* {s i m} \left(z _ {i} , z _ {n}\right) / \tau\right)}, \tag {5} +$$ + +where $z_{i}$ , $P$ , and $N$ are the projected anchor vector $\mathcal{Z}(S(f_i))$ , positive index set, and negative index set, respectively. For simplicity, we use $\Phi_i^{\mathrm{ag}}$ and $\Phi_i^{\mathrm{sp}}$ to denote the objective functions with task-agnostic GHP $P_{i}^{\mathrm{ag}}$ and task-specific GHP $P_{i}^{\mathrm{sp}}$ for each $i$ -th anchor as follows: + +$$ +\begin{array}{l} \Phi_ {i} ^ {\mathrm {a g}} = L ^ {\mathrm {c o n t}} (z _ {i}, P _ {i} ^ {\mathrm {a g}}, N _ {i} ^ {\mathrm {a g}}) \\ \Phi_ {i} ^ {\mathrm {s p}} = L ^ {\text {c o n t}} \left(z _ {i}, P _ {i} ^ {\mathrm {s p}}, N _ {i} ^ {\mathrm {s p}}\right). \tag {6} \\ \end{array} +$$ + +# 3.4. Gradient Propagation to Local Hidden Positives + +Besides considering the semantically analogous features globally, it is a common hypothesis that nearby pixels are highly likely to belong to the same semantic class. To this end, we consider the property of locality by propagating the loss gradient to the surrounding features of the anchor. Still, the propagation should be cautiously designed since semantic labels of the adjacent patches are not given; semantic consistency between adjacent patches mostly holds, but sometimes does not (i.e., at object boundaries). Thus, to decide the semantically consistent patches nearby, we utilize the attention scores from the unsupervised pretrained ViT backbone $\mathcal{F}$ + +In detail, we first define the index set $I_{i}^{\mathrm{surr}}$ for surrounding patches of the $i$ -th anchor including $i$ -th anchor itself. Also, given the spatial attention score for $i$ -th anchor $\tilde{T}_i \in \mathbb{R}^{H \cdot W}$ from the last self-attention layer in the backbone $\mathcal{F}$ , we use the average value of $\tilde{T}_i$ as a threshold to select an index set of LHP $I_{i}^{\mathrm{local}}$ among $I_{i}^{\mathrm{surr}}$ : + +$$ +I _ {i} ^ {\text {l o c a l}} = \left\{j \mid j \in I _ {i} ^ {\text {s u r r}} \wedge t _ {j} > \operatorname {A v g} \left(\tilde {T} _ {i}\right) \right\}, \tag {7} +$$ + +where $t_j$ is $j$ -th element of $\tilde{T}_i$ and $\mathrm{Avg}(\cdot)$ denotes the averaging function. The visualization of the attention scores for the adjacent patches is shown in Fig. 1 (b). By utilizing $I_i^{\mathrm{local}}$ , we obtain the surrounding positive features $G_i$ and the corresponding attention score $T_i$ for LHP as follows: + +$$ +\begin{array}{l} G _ {i} = \left\{s _ {j} \mid j \in I _ {i} ^ {\text {l o c a l}} \right\} \\ T _ {i} = \left\{t _ {j} \mid j \in I _ {i} ^ {\text {l o c a l}} \right\}, \\ \end{array} +$$ + +where $s_j$ is the feature of $j$ -th patch extracted by the segmentation head $S$ . + +To propagate the loss gradient to the LHP, we mix the patch features in $G_{i}$ in proportion to the corresponding attention scores in $T_{i}$ . Formally, the mixed patch composed + +![](images/f13b47b3971214433afa6eb701b1a1a79f167cfb12a7f3d14997df322d2f5dbe.jpg) +Figure 3. Illustration of our gradient propagation strategy to preserve local semantic consistency. For each anchor, with its surrounding patches $I^{\mathrm{surf}}$ and corresponding attention scores from feature extractor $\mathcal{F}$ , local hidden positives (LHP) $I^{\mathrm{local}}$ are appointed based on the threshold $\mathrm{Avg}(\hat{T})$ (Eq. 7). In a forward pass, the features of LHP $G$ (Eq. 8) are mixed by weighted average according to the attention scores $T$ to compute the objective function $\Psi$ . In this way, the loss gradient propagates toward the LHP in proportion to $T$ in the backward pass. + +of LHP $s_i^{\mathrm{mix}}$ is expressed as: + +$$ +s _ {i} ^ {\text {m i x}} = \frac {1}{\left| I _ {i} ^ {\text {l o c a l}} \right|} \sum_ {j \in I _ {i} ^ {\text {l o c a l}}} \sigma g _ {j} t _ {j}, \tag {9} +$$ + +where $\sigma$ is a scalar value to scale the attention score, and $g_{j}$ and $t_j$ indicate the $j$ -th element of $G_{i}$ and $T_{i}$ , respectively. Then, we define the objective functions $\Psi_i^{\mathrm{ag}}$ and $\Psi_i^{\mathrm{sp}}$ to learn the locality by inserting the projected mixed vector $z_{i}^{\mathrm{mix}} = \mathcal{Z}(s_{i}^{\mathrm{mix}})$ into Eq. 5 as follows: + +$$ +\Psi_ {i} ^ {\mathrm {a g}} = L ^ {\text {c o n t}} \left(z _ {i} ^ {\text {m i x}}, P _ {i} ^ {\mathrm {a g}}, N _ {i} ^ {\mathrm {a g}}\right) \tag {10} +$$ + +$$ +\Psi_ {i} ^ {\mathrm {s p}} = L ^ {\mathrm {c o n t}} (z _ {i} ^ {\mathrm {m i x}}, P _ {i} ^ {\mathrm {s p}}, N _ {i} ^ {\mathrm {s p}}). +$$ + +Since these functions are calculated by utilizing the mixed vector $z_{i}^{\mathrm{mix}}$ , the loss gradients are propagated to all features composing the $G_{i}$ , as described in Fig. 3. Therefore, the semantically-alike surrounding vectors in $G_{i}$ are updated in the same direction, thereby retaining semantic consistency within the neighboring patches. + +Overall, by combining all loss formulations with consistency regularizer $R_{i}$ that minimizes Euclidean distance between the projected vectors of two differently augmented patches, the final loss function is defined as follows: + +$$ +L _ {i} = \left(\Phi_ {i} ^ {\mathrm {a g}} + \Psi_ {i} ^ {\mathrm {a g}}\right) + \lambda \left(\Phi_ {i} ^ {\mathrm {s p}} + \Psi_ {i} ^ {\mathrm {s p}}\right) + \alpha R _ {i}, \tag {11} +$$ + +where $\lambda$ and $\alpha$ control the contribution of each loss. For instance, $\lambda$ gradually increases from 0 to 1 during the training and $\alpha$ remains constant at 0.05 throughout the training. Note that, the sample with zero positive is excluded from training although it rarely exists. + +# 4. Experiments + +# 4.1. Datasets and Experimental Settings + +Datasets. We utilize COCO-stuff [1], Cityscapes [9], and Potsdam-3 datasets following the existing works [8, 14, + +19]. COCO-stuff is a large-scale scene understanding dataset that consists of dense pixel-level annotations and Cityscapes is a more recently publicized dataset having street scenes across 50 different cities. Potsdam-3 dataset contains satellite images. Following the baselines [8,14,19], we choose the 27 classes for COCO-stuff and Cityscapes datasets, and 3 classes for Potsdam-3 dataset. + +Evaluation Protocols and Metrics. To evaluate our approach, we conduct two testing methods; clustering and linear probe [14]. Clustering is to measure how well the semantic-preserving clusters are formed. Once the unlabeled clusters are computed with the extracted representations, clusters are matched with the ground truth class labels using the Hungarian matching algorithm. On the other hand, the linear probe is a popular method to evaluate the quality of representations learned in unsupervised manners. Specifically, an additional linear layer is learned with representations extracted from the frozen unsupervised model for evaluation and ground truth labels. Inferring the representations of test data extracted by the frozen model with the learned linear layer, we can measure the quality of representations. With two types of protocols, the performance is measured by two common metrics; accuracy (Acc.) and mean Intersection Over Union (mIoU). + +Implementation Details. For fair comparisons with our baselines [14, 37], we follow them to mainly use DINO pretrained ViT models as a backbone network $\mathcal{F}$ for the COCO-stuff dataset. In addition, we also test with the advanced backbone, SelfPatch [39], on the COCO-stuff dataset. The segmentation head $S$ is constructed with a two-layer RELU MLP as STEGO, and the projection head $\mathcal{Z}$ is composed of a linear layer equipped with a normalization layer. The embedding dimension $K$ is set to 512 for ViT + +
MethodBackboneUnsupervisedLinear
Acc.mIoUAcc.mIoU
DC [2]R18+FPN19.9---
MDC [2]R18+FPN32.29.848.613.3
IIC [19]R18+FPN21.86.744.58.4
PiCIE [8]R18+FPN48.113.854.213.9
PiCIE+H [8]R18+FPN50.014.454.814.8
DINO [4]ViT-S/828.711.368.633.9
+ TransFGU [37]ViT-S/852.717.5--
+ STEGO [14]ViT-S/848.324.574.438.3
+ HP (Ours)ViT-S/857.224.675.642.7
DINO [4]ViT-S/1622.08.050.318.1
+ STEGO [14]ViT-S/1652.523.770.634.5
+ HP (Ours)ViT-S/1654.524.374.139.1
SelfPatch [39]ViT-S/1635.112.364.428.5
+ STEGO [14]ViT-S/1652.422.272.236.0
+ HP (Ours)ViT-S/1656.123.274.941.3
+ +Table 1. Experimental results on COCO-stuff dataset with various backbones and pretrained models. + +
MethodBackboneUnsupervisedLinear
Acc.mIoUAcc.mIoU
MDC [2]R18+FPN40.77.1--
IIC [19]R18+FPN47.96.4--
PiCIE [8]R18+FPN65.512.3--
DINO [4]ViT-S/834.510.984.622.8
+ TransFGU [37]ViT-S/877.916.8--
+ HP (Ours)ViT-S/880.118.491.230.6
DINO [4]ViT-B/843.611.884.223.0
+ STEGO [14]ViT-B/873.221.090.326.8
+ HP (Ours)ViT-B/879.518.490.933.0
+ +S/8 and ViT-B/8 models, and 256 for ViT-S/16. We train the model for 3, 20, and 10 epochs for COCO-stuff, Cityscapes, and Potsdam-3 datasets, respectively, based on the AdamW optimizer with a learning rate of 0.0005 and weight decay of 0.1. The task-specific reference pool $Q^{\mathrm{sp}}$ is renewed every 100 iterations throughout the training. The percentage of negative samples usage $\rho$ is set to 2. In the last stage, we add a feature refinement step utilizing Conditional Random Field [21] as did in STEGO. Evaluation metrics, i.e., clustering and linear probe, are optimized with the Adam optimizer each with learning rates of 0.005 and 0.001. + +# 4.2. Experimental Results + +We compare our proposed method against the prior techniques for the unsupervised segmentation [2, 8, 14, 19, 37]. Most of the results in the result tables are brought from the literature [8, 14]. In Tab. 1, it is observed that self-supervised models, i.e., DINO and SelfPatch, are already good segmentation predictors with the linear probe, which makes them a new baseline over the prior works for unsu + +Table 2. Experimental results on Cityscapes dataset. + +
MethodBackboneUnsup. Acc.
Random CNN [19]VGG1138.2
K-Means [27]VGG1145.7
SIFT [24]VGG1138.2
ContextPrediction [10]VGG1149.6
CC [18]VGG1163.9
DeepCluster [2]VGG1141.7
IIC [19]VGG1165.1
DINO [4]ViT-B/853.0
+ STEGO [14]ViT-B/877.0
+ HP (Ours)ViT-B/882.4
+ +Table 3. Experimental results on Potsdam-3 dataset. + +pervised segmentation. Furthermore, we utilize two pretrained backbones with two kinds of architectures to compare with STEGO, in detail. As reported, our proposed model provides consistent performance improvements over the previous SOTA model in almost all cases on the COCO-stuff dataset. + +Results on Cityscapes also show a similar tendency. As shown in Tab. 2, ours outperform previous methods except for the mIoU when clustering is used for the evaluation. For instance, we achieve $8.6\%$ and $23\%$ improvements in cluster accuracy and linear mIoU over STEGO, respectively. For the slight decrease in cluster mIoU with ViT-B/8 architecture, we argue that it is insignificant since the linear probe better describes the quality of representations. Specifically, clustering evaluation highly depends on the purpose of the dataset so that it is sensitive to the degree of class-specificity as different body parts can be either classified as human or as independent body parts. However, whereas it is more appropriate to detect each body part independently for unsupervised learning as ours do in Fig. 5, the annotations for general datasets, e.g., COCO-stuff and cityscape, treat these body parts as a human class. Such circumstances make the clustering evaluation vulnerable to the degree of the class hierarchy. In contrast, the linear probe projects these features to close proximity if the given label space considers them as a human body. Thus, we believe the linear probe is a more appropriate measure of representation quality. + +Also, we compare the cluster accuracy in Tab. 3 on Potsdam-3 dataset. We have achieved a $7\%$ boost over STEGO, confirming that ours also performs well even in a completely new domain. Likewise, our superior results verify the effectiveness of our process of discovering global and local-hidden positive patches. + +Qualitative Results. In addition to the quantitative results, we report qualitative results in Fig. 4. In comparison to STEGO, we observe that our results include fewer mispredicted pixels throughout the images, while the strength + +![](images/65eef0aa688a80672d97938762d4abee4a90dc464ea940fc46d13095be9d08a1.jpg) +Figure 4. Qualitative comparison results of Ours and STEGO on the COCO-stuff dataset with DINO pretrained ViT-S/8 backbone. + +of preserving semantic locality is also validated. For instance, whereas we find that the predicted label of the wheel is only partially correct in the 4th column, our results are consistent along the neighboring pixels. These results also demonstrate the superiority of our method. + +# 5. Ablation Study and Further Analysis + +In this section, we provide ablation studies and an analysis of our model. Particularly, we explore the contributions of main components and test with varying hyperparameters. Most experiments for ablation studies are conducted on the COCO-stuff dataset using the DINO pretrained ViTs/8 model, except for Sec. 5.2, which utilized the ViT-S/16. + +# 5.1. Importance of the Main Components. + +Tab. 4 reports the performances when an individual component or various combinations of them are not utilized. We found that task-specific GHP and LHP are essential in improving the performance of the unsupervised segmentation task. Compared to (d) where both the task-specific GHP and LHP are not used, the use of them each leads to $6.5\%$ (b) and $11.6\%$ (c) improvements. Also when used together, they bring $16\%$ of performance boosts (a). Furthermore, the importance of preserving symmetry in selecting GHP can be found by comparing (a) to (e) as its usage boosts $5.9\%$ , and consistency regularizer enhances performance by $2.5\%$ by comparing (a) to (f). Lastly, (g) shows the performance of naively implemented contrastive learning (Eq. 1) with photometric perturbations used in PiCIE [8]. This also confirms the strengths of our proposed method as (a) enhances (g) by $51.3\%$ . + +# 5.2. Alternatives to Gradient Propagation Strategy + +There can be alternative ways to meet our goal of reflecting semantic consistency between adjacent patches. As an alternative method of gradient propagation, we simply apply the identical loss to the surrounding patches pro + +
GHPLHPSARegUnsupervised Acc.mIoU
TATS
(a)✓✓✓✓✓57.224.6
(b)✓✓✓✓52.523.1
(c)✓✓✓✓55.019.1
(d)✓✓✓49.320.1
(e)✓✓✓✓54.023.6
(f)✓✓✓✓55.824.5
(g)37.810.4
+ +Table 4. Ablation study for each component. GHP, LHP, TA, TS, SA, and Reg denote Global Hidden Positive, Local Hidden Positive, task-agnostic, task-specific, symmetrical assignments, and consistency regularizer, respectively. + +
MethodUnsupervisedLinear
Acc.mIoUAcc.mIoU
DINO + GradProp54.524.374.139.1
DINO + LossProp54.723.274.340.5
SelfPatch + GradProp56.123.274.941.3
SelfPatch + LossProp54.522.275.141.4
+ +Table 5. Comparison results between gradient propagation and loss propagation strategies. + +portionally to their attention score (i.e., loss propagation). The results in Tab. 5 show that the loss propagation strategy performs comparably to the gradient propagation strategy. Nonetheless, we observed that the loss propagation approach incurs higher computational costs ( $1.2 \times$ memory and $3 \times$ time). Likewise, our method is implemented considering both effectiveness and efficiency. + +# 5.3. Visualization + +**Discovered GHP.** To demonstrate the effectiveness of our GHP selection process, we visualize the selected GHP sets for different anchors in a single image in Fig. 5. First, we observe that the corresponding reference point in the second column is semantically correlated with the anchor patch. + +![](images/66523cddee441d205398edf8b166148b8211598e22fde7ec67e93310bcb062a2.jpg) +Figure 5. Discovered patches by our GHP selection process. From the left to right columns, red boxes indicate the anchors, the closest patches within the task-agnostic reference pool (reference), and GHP sets chosen in the mini-batch, respectively. + +Also, the obtained GHP sets verify the appropriateness of the use of reference points as each anchor's criterion as they are capable of precisely discovering the semantically-similar positives. For example, we find that all the anchors, reference points, and GHP sets have the same semantic labels in the first and the second rows (solid (mountain) and ground (snow)). More intriguing results are in the third and fourth rows where we find that the GHP selection process distinguishes the body parts in a much more fine-grained manner than the given annotation, i.e., person. These results imply that the designed GHP selection is well-designed and capable of capturing detailed semantic contexts. + +# 5.4. Robustness to Hyperparameters + +In this subsection, we enumerate our use of hyperparameters in Tab. 6 and conduct an ablation study. Overall, our proposed training scheme is robust to hyperparameters as Fig. 6 supports the claim. Below, we illustrate the influences of each parameter. Despite the slight drop in performance from time to time, it is insignificant since the change in performance is very marginal and results are consistent. + +The Number of Data in the Reference Pool. Fig. 6 (a) shows the performances with varying numbers of data $M$ in the reference pool. Generally, the reference pool is not very vulnerable to its size, unless the capacity is either too small or too large. When the reference pool is skinny, it may not be sufficient to represent all kinds of semantics present in the dataset. In other words, the small reference pool may induce a biased criterion for each anchor which may incur biased training. On the other hand, when the reference pool is too large, a tight threshold $(c_{i})$ could be derived, which can interfere with gathering GHP. + +
DatasetMτ
COCO-stuff20480.8
Cityscapes20480.6
Potsdam-310240.4
+ +Table 6. Hyperparameter used for each dataset. + +![](images/cab69db739c7d2d638c3402613fe399b6153c3954ed1b1be70d385bf17217044.jpg) +(a) Reference pool size $M$ +Figure 6. Ablation studies on hyperparameters. + +![](images/620f420fe7f663046824a44598d94b1663dda27caae407ee29208e75f5bf95e4.jpg) +(b) Temperature $\tau$ + +Temperature Parameter. The temperature parameter $\tau$ is the scaling parameter to manipulate the sensitivity of the contrastive loss. If $\tau$ gets bigger, the objective function becomes robust to the difference between the similarity of the positive and negative samples. On the other hand, when $\tau$ gets lower, the embedding distribution is likely to be more uniform [31]. Although the results do not fluctuate much, we observe that training uniformly distributed embedding space leads to a slight performance drop in Fig. 6 (b). + +# 6. Conclusion + +In this paper, we introduced a novel unsupervised semantic segmentation method by discovering and leveraging two types of hidden positives, global hidden positive (GHP) and local hidden positive (LHP), to learn rich semantic information with local consistency. First, anchor-dependent GHP comprises task-agnostic and task-specific positive sets which are used to tailor the contrastive learning for the unsupervised semantic segmentation task. Whereas the task-agnostic features are collected to guide the initial training, task-specific features are progressively engaged to learn the task-specific semantics information. Moreover, under the inherent premise that the adjacent patches are likely to be semantically similar, we propagate the loss gradient to the surrounding patches in proportion to their attention scores. This encourages the semantically similar peripheral patches to have the same objective as the anchor, resulting in semantic consistency between adjacent patches unless they belong to different objects. Finally, our proposed method achieves new state-of-the-art results in various datasets. + +Acknowledgements. This work was supported in part by MSIT/IITP (No. 2022-0-00680, 2019-0-00421, 2020-0-01821, 2021-0-02068), and MSIT&KNPA/KIPoT (Police Lab 2.0, No. 210121M06). + +# References + +[1] Holger Caesar, Jasper Uijlings, and Vittorio Ferrari. Cocostuff: Thing and stuff classes in context. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1209-1218, 2018. 5 +[2] Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. Deep clustering for unsupervised learning of visual features. In Proceedings of the European conference on computer vision (ECCV), pages 132-149, 2018. 2, 6 +[3] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems, 33:9912-9924, 2020. 2 +[4] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9650-9660, 2021. 2, 3, 6 +[5] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. *ICLR*, 2015. 1, 2 +[6] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 2, 3 +[7] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15750-15758, 2021. 2 +[8] Jang Hyun Cho, Utkarsh Mall, Kavita Bala, and Bharath Hariharan. Picie: Unsupervised semantic segmentation using invariance and equivariance in clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16794-16804, 2021. 1, 2, 5, 6, 7 +[9] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 5 +[10] Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE international conference on computer vision, pages 1422-1430, 2015. 6 +[11] Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3146-3154, 2019. 1, 2 +[12] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In International Conference on Learning Representations, 2018. 2 + +[13] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020. 2 +[14] Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, and William T. Freeman. Unsupervised semantic segmentation by distilling feature correspondences. In International Conference on Learning Representations, 2022. 1, 2, 3, 5, 6 +[15] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 2 +[16] Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song. Using self-supervised learning can improve model robustness and uncertainty. Advances in neural information processing systems, 32, 2019. 2 +[17] Jyh-Jing Hwang, Stella X Yu, Jianbo Shi, Maxwell D Collins, Tien-Ju Yang, Xiao Zhang, and Liang-Chieh Chen. Segsort: Segmentation by discriminative sorting of segments. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7334-7344, 2019. 2 +[18] Phillip Isola, Daniel Zoran, Dilip Krishnan, and Edward H Adelson. Learning visual groups from co-occurrences in space and time. arXiv preprint arXiv:1511.06811, 2015. 6 +[19] Xu Ji, Joao F Henriques, and Andrea Vedaldi. Invariant information clustering for unsupervised image classification and segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9865-9874, 2019. 1, 2, 5, 6 +[20] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. Advances in Neural Information Processing Systems, 33:18661-18673, 2020. 2, 4 +[21] Philipp Krahenbihl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. Advances in neural information processing systems, 24, 2011. 6 +[22] Samuli Laine and Timo Aila. Temporal ensembling for semi-supervised learning. ICLR, 2017. 4 +[23] Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, and Sungroh Yoon. Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5267-5276, 2019. 1 +[24] David G Lowe. Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision, volume 2, pages 1150-1157. IEEE, 1999. 6 +[25] Ishan Misra and Laurens van der Maaten. Self-supervised learning of pretext-invariant representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6707-6717, 2020. 2 + +[26] WonJun Moon, Ji-Hwan Kim, and Jae-Pil Heo. Tailoring self-supervision for supervised learning. In European Conference on Computer Vision, pages 346-364. Springer, 2022. 2 +[27] Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. the Journal of machine learning research, 12:2825-2830, 2011. 6 +[28] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234-241. Springer, 2015. 1 +[29] Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7262-7272, 2021. 2 +[30] Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc Van Gool. Unsupervised semantic segmentation by contrasting object mask proposals. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10052-10062, 2021. 2 +[31] Feng Wang and Huaping Liu. Understanding the behaviour of contrastive loss. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2495-2504, 2021. 8 +[32] Shujun Wang, Lequan Yu, Caizi Li, Chi-Wing Fu, and Pheng-Ann Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. In European Conference on Computer Vision, pages 159–176. Springer, 2020. 4 +[33] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12275-12284, 2020. 1 +[34] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34:12077-12090, 2021. 1, 2 +[35] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, and Dan Xu. Multi-class token transformer for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4310-4319, 2022. 1 +[36] Yazhou Yao, Tao Chen, Guo-Sen Xie, Chuanyi Zhang, Fumin Shen, Qi Wu, Zhenmin Tang, and Jian Zhang. Nonsalient region object mining for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2623-2632, 2021. 1 +[37] Zhaoyuan Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li, and Rong Jin. Transfgu: a top-down approach to fine-grained unsupervised semantic segmentation. In European Conference on Computer Vision, pages 73-89. Springer, 2022. 1, 2, 5, 6 + +[38] Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object-contextual representations for semantic segmentation. In European conference on computer vision, pages 173-190. Springer, 2020. 1, 2 +[39] Sukmin Yun, Hankook Lee, Jaehyung Kim, and Jinwoo Shin. Patch-level representation learning for self-supervised vision transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8354-8363, 2022. 2, 5, 6 +[40] Xiangyun Zhao, Raviteja Vemulapalli, Philip Andrew Mansfield, Boqing Gong, Bradley Green, Lior Shapira, and Ying Wu. Contrastive learning for label efficient semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10623-10633, 2021. 2 +[41] Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6881-6890, 2021. 1, 2 +[42] Wu Zheng, Weiliang Tang, Li Jiang, and Chi-Wing Fu. Sessd: Self-ensembling single-stage object detector from point cloud. 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Although the recent work employing the vision transformer (ViT) backbone shows exceptional performance, there is still a lack of consideration for task-specific training guidance and local semantic consistency. To tackle these issues, we leverage contrastive learning by excavating hidden positives to learn rich semantic relationships and ensure semantic consistency in local regions. Specifically, we first discover two types of global hidden positives, task-agnostic and task-specific ones for each anchor based on the feature similarities defined by a fixed pre-trained backbone and a segmentation head-in-training, respectively. A gradual increase in the contribution of the latter induces the model to capture task-specific semantic features. In addition, we introduce a gradient propagation strategy to learn semantic consistency between adjacent patches, under the inherent premise that nearby patches are highly likely to possess the same semantics. Specifically, we add the loss propagating to local hidden positives, semantically similar nearby patches, in proportion to the predefined similarity scores. With these training schemes, our proposed method achieves new state-of-the-art (SOTA) results in COCO-stuff, Cityscapes, and Potsdam-3 datasets. Our code is available at: https://github.com/hynnsk/HP." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 583, + 124, + 596 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 583, + 124, + 596 + ], + "spans": [ + { + "bbox": [ + 46, + 583, + 124, + 596 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 45, + 603, + 279, + 708 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 603, + 279, + 708 + ], + "spans": [ + { + "bbox": [ + 45, + 603, + 279, + 708 + ], + "type": "text", + "content": "Semantic segmentation is a major task for scene understanding and plays a crucial role in many applications including medical imaging and autonomous driving [5, 11, 28, 34, 38, 41]. However, existing supervised approaches demand large-scale pixel-level annotations which require huge labeling costs. It has triggered the advent of weakly-supervised [23, 33, 35, 36] and unsupervised semantic segmentation [8, 14, 19, 37] which are to learn without expensive pixel-level annotations." + } + ] + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 302, + 242, + 530, + 355 + ], + "blocks": [ + { + "bbox": [ + 302, + 242, + 530, + 355 + ], + "lines": [ + { + "bbox": [ + 302, + 242, + 530, + 355 + ], + "spans": [ + { + "bbox": [ + 302, + 242, + 530, + 355 + ], + "type": "image", + "image_path": "d63481b8d520eb9a72d407603d99d0d4f248d0f715b86217ce3dfd95cd8d8e24.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 384, + 355, + 440, + 363 + ], + "lines": [ + { + "bbox": [ + 384, + 355, + 440, + 363 + ], + "spans": [ + { + "bbox": [ + 384, + 355, + 440, + 363 + ], + "type": "text", + "content": "(a) Visualization" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 9 + }, + { + "type": "image", + "bbox": [ + 301, + 364, + 413, + 481 + ], + "blocks": [ + { + "bbox": [ + 301, + 364, + 413, + 481 + ], + "lines": [ + { + "bbox": [ + 301, + 364, + 413, + 481 + ], + "spans": [ + { + "bbox": [ + 301, + 364, + 413, + 481 + ], + "type": "image", + "image_path": "ed23234869160150eacb6ca895bdd02f4190bdb48d66734f2732c08a43af2660.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 312, + 482, + 401, + 492 + ], + "lines": [ + { + "bbox": [ + 312, + 482, + 401, + 492 + ], + "spans": [ + { + "bbox": [ + 312, + 482, + 401, + 492 + ], + "type": "text", + "content": "(b) Global hidden positive" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 296, + 501, + 530, + 662 + ], + "lines": [ + { + "bbox": [ + 296, + 501, + 530, + 662 + ], + "spans": [ + { + "bbox": [ + 296, + 501, + 530, + 662 + ], + "type": "text", + "content": "Figure 1. Assuming a mini-batch comprising two images shown in (a), we describe two types of hidden positives, to leverage for contrastive learning. (a) With two types of hidden positives introduced in (b) and (c), we provide an example of how our training scheme provides more precise and consistent semantics. (b) (top) Semantically analogous patches throughout the mini-batch are selected as global hidden positives. (bottom) Data-driven criterion per anchor is designed for reliable positive collection. With the criterion, selected positives are illustrated in (b top). (c) (top) We define local hidden positives for each anchor to be the adjacent patches with high semantic consistency, i.e., blue boxes. (bottom) Average attention scores for adjacent patches from the pretrained transformer architecture. The blue line represents the attention score for local hidden positives while the red line for patches neighboring anchors but having low semantic consistency." + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 418, + 364, + 530, + 481 + ], + "blocks": [ + { + "bbox": [ + 418, + 364, + 530, + 481 + ], + "lines": [ + { + "bbox": [ + 418, + 364, + 530, + 481 + ], + "spans": [ + { + "bbox": [ + 418, + 364, + 530, + 481 + ], + "type": "image", + "image_path": "8db016c991b005fb583863305ebf471f3b7888405f28b072d2553f0648322498.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 428, + 482, + 512, + 492 + ], + "lines": [ + { + "bbox": [ + 428, + 482, + 512, + 492 + ], + "spans": [ + { + "bbox": [ + 428, + 482, + 512, + 492 + ], + "type": "text", + "content": "(c) Local hidden positive" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 13 + }, + { + "bbox": [ + 296, + 682, + 531, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 682, + 531, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 682, + 531, + 730 + ], + "type": "text", + "content": "Particularly, unsupervised semantic segmentation is one of the most challenging tasks, since it needs to capture pixel-level semantics from unlabeled data. In this context, clustering-based approaches have been proposed to learn" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 77, + 42, + 119, + 72 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 42, + 119, + 72 + ], + "spans": [ + { + "bbox": [ + 77, + 42, + 119, + 72 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 150, + 41, + 471, + 73 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 150, + 41, + 471, + 73 + ], + "spans": [ + { + "bbox": [ + 150, + 41, + 471, + 73 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 719, + 131, + 730 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 719, + 131, + 730 + ], + "spans": [ + { + "bbox": [ + 57, + 719, + 131, + 730 + ], + "type": "text", + "content": "*Corresponding author" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 277, + 747, + 301, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 277, + 747, + 301, + 756 + ], + "spans": [ + { + "bbox": [ + 277, + 747, + 301, + 756 + ], + "type": "text", + "content": "19540" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 45, + 106, + 281, + 280 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 106, + 281, + 280 + ], + "spans": [ + { + "bbox": [ + 45, + 106, + 281, + 280 + ], + "type": "text", + "content": "semantic-preserving clusters by attracting the augmented views in the pixel-level [8, 19]. They implemented the intuition of contrastive learning [3, 6, 7, 13, 15, 25] by ensuring the augmented pairs yield symmetric cluster assignments. More recently, as discovering pixel-level semantics from scratch is challenging, STEGO [14] broke down the problem into learning the representation and learning the segmentation head. With the learned patch-level representation from the seminal work in unsupervised learning [4, 39], they train the segmentation head with a distillation strategy. Although they have made great advancements, we point out their limitations in that they rely solely on a fixed backbone that is not specifically trained for the segmentation task and overlook the importance of semantic consistency along the adjacency that could be a crucial clue for segmentation." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 45, + 281, + 281, + 582 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 281, + 281, + 582 + ], + "spans": [ + { + "bbox": [ + 45, + 281, + 281, + 582 + ], + "type": "text", + "content": "To take these into consideration, we leverage contrastive learning based on the mined hidden positives to ensure contextual consistency along the patches with analogous semantics, particularly the nearby patches, as described in Fig. 1. Specifically, we elaborately select the pseudopositive samples (i.e., global hidden positive, GHP) for contrastive learning to learn semantic consistency. Also, to ensure local consistency, we propagate the loss gradient to the adjacent patches (i.e., local hidden positive, LHP) in proportion to their equivalency. First, the GHP selection process is designed with two types of data reference pools, task-agnostic and task-specific, to collect the semantically consistent patch features throughout the mini-batch per anchor. For instance, the task-agnostic data reference pool is composed of features extracted by the unsupervised pretrained backbone. On the other hand, the task-specific reference pool is constructed with the features from the segmentation head-in-training to complement task relevance. Based on the two reference pools, two sets of GHP are selected each with generalized and task-specific perspectives. Second, to implement the property of locality and prevent the semantics from fluctuating, we propagate the loss gradient to adjacent patches (i.e., LHP) in proportion to the similarity scores built within the pretrained backbone. This enables the model to learn the relevance of the local context that nearby patches often belong to the same instance." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 583, + 228, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 583, + 228, + 593 + ], + "spans": [ + { + "bbox": [ + 57, + 583, + 228, + 593 + ], + "type": "text", + "content": "Our main contributions are summarized as:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 600, + 279, + 730 + ], + "type": "list", + "angle": 0, + "index": 6, + "blocks": [ + { + "bbox": [ + 57, + 600, + 279, + 647 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 600, + 279, + 647 + ], + "spans": [ + { + "bbox": [ + 57, + 600, + 279, + 647 + ], + "type": "text", + "content": "- We propose a novel method to discover semantically similar pairs, called global hidden positives, to explicitly learn the semantic relationship among patches for unsupervised semantic segmentation." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 57, + 653, + 279, + 687 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 653, + 279, + 687 + ], + "spans": [ + { + "bbox": [ + 57, + 653, + 279, + 687 + ], + "type": "text", + "content": "- We utilize the task-specific features from a model-intraining and validate the effectiveness of progressive increase of their contribution." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 57, + 695, + 279, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 695, + 279, + 730 + ], + "spans": [ + { + "bbox": [ + 57, + 695, + 279, + 730 + ], + "type": "text", + "content": "- A gradient propagation to nearby similar patches, local hidden positives, is developed to learn local semantic consistency which is the nature of segmentation." + } + ] + } + ], + "index": 5 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 308, + 106, + 530, + 129 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 106, + 530, + 129 + ], + "spans": [ + { + "bbox": [ + 308, + 106, + 530, + 129 + ], + "type": "text", + "content": "- Our approach outperforms existing state-of-the-art methods across extensive experiments." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 297, + 141, + 381, + 154 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 141, + 381, + 154 + ], + "spans": [ + { + "bbox": [ + 297, + 141, + 381, + 154 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 297, + 160, + 490, + 173 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 160, + 490, + 173 + ], + "spans": [ + { + "bbox": [ + 297, + 160, + 490, + 173 + ], + "type": "text", + "content": "2.1. Unsupervised Semantic Segmentation" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 296, + 179, + 530, + 505 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 179, + 530, + 505 + ], + "spans": [ + { + "bbox": [ + 296, + 179, + 530, + 505 + ], + "type": "text", + "content": "Semantic segmentation has been extensively studied for its wide applicability [5, 11, 29, 34, 38, 41], but collecting pixel-level annotations requires expensive costs. Therefore, many studies [8, 14, 17, 19, 30, 37] attempted to address semantic segmentation without any supervision. Earlier techniques tried to learn semantic correspondence at the pixel level. IIC [19] maximizes the mutual information between the features of two differently augmented images, and PiCIE [8] learns photometric and geometric invariances as an inductive bias. Yet, their training process highly depends on data augmentation, and learning semantic consistency without any prior knowledge is challenging. Therefore, recent methods [14, 37] adopted the ViT model trained in a self-supervised manner, i.e., DINO [4], as a backbone architecture. For instance, TransFGU [37] relocates the high-level semantic features from DINO into low-level pixel-wise features by generating pixel-wise pseudo labels. On the other hand, STEGO [14] utilizes knowledge distillation that learns correspondences between features extracted from DINO. Although STEGO shows a dramatic performance improvement compared to the prior works, it heavily relies on the pretrained backbone and overlooks the property of local consistency that the adjacent pixels are likely to belong to the same category. On the other hand, our training is driven by both the task-agnostic and task-specific pseudopositive features, and the gradients are conditionally propagated to the neighboring patches, thereby ensuring task-specificity and locality." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 297, + 513, + 417, + 526 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 513, + 417, + 526 + ], + "spans": [ + { + "bbox": [ + 297, + 513, + 417, + 526 + ], + "type": "text", + "content": "2.2. Contrastive Learning" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 532, + 530, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 532, + 530, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 532, + 530, + 730 + ], + "type": "text", + "content": "Self-supervised learning methods [2,3,6,7,12,13,15] aim to learn general representations without any annotations. Thanks to their good representation capability, they have been employed to yield remarkable performances in various downstream tasks [8, 16, 20, 26, 40]. Among them, contrastive learning approaches [3, 6, 7, 13, 15, 25] have shown unrivaled performances. In general, they learn representation by attracting a self-augmented set and repulsing other images [3, 6]. Other variants use additional memory [15] or only exploit the positive set for the attraction [7, 13]. This training scheme has also been utilized for unsupervised semantic segmentation [8, 19]. However, the aforementioned methods only considered augmented pairs for the positive which makes them very sensitive to the quality of the augmentation techniques. Our work differs in that our key idea is to collect and exploit reliable pseudo-positives throughout the mini-batch as described in Fig. 1 (b)." + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "spans": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "text", + "content": "19541" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 54, + 104, + 528, + 291 + ], + "blocks": [ + { + "bbox": [ + 54, + 104, + 528, + 291 + ], + "lines": [ + { + "bbox": [ + 54, + 104, + 528, + 291 + ], + "spans": [ + { + "bbox": [ + 54, + 104, + 528, + 291 + ], + "type": "image", + "image_path": "c826c2f57f6882b466baa39d37e7de301aef35566216052464f0c49228288ad8.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "lines": [ + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "spans": [ + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "text", + "content": "Figure 2. Illustration of the global hidden positive (GHP) selection process. Our GHP can be divided into two sub-sets: task-agnostic and task-specific. An index set of task-agnostic GHP " + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "inline_equation", + "content": "P_{i}^{\\mathrm{ag}}" + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "text", + "content": " comprises the indices of positives discovered within the task-agnostic reference pool " + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "inline_equation", + "content": "Q^{\\mathrm{ag}}" + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "text", + "content": ". Note that, " + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "inline_equation", + "content": "Q^{\\mathrm{ag}}" + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "text", + "content": " is composed of randomly sampled features extracted by the feature extractor " + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "text", + "content": ". Once the anchor feature " + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "inline_equation", + "content": "f_{i}" + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "text", + "content": " is projected to " + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "inline_equation", + "content": "z_{i}" + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "text", + "content": ", other patches in the mini-batch are gathered as positives if their similarity with the anchor feature exceeds the similarity between the anchor and the most similar feature in " + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "inline_equation", + "content": "Q^{\\mathrm{ag}}" + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "text", + "content": ". On the other hand, task-specific GHP is discovered in a similar manner but with task-specific reference pool " + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "inline_equation", + "content": "Q^{\\mathrm{sp}}" + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "text", + "content": " which keeps being updated with the features from the momentum segmentation head " + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "inline_equation", + "content": "S'" + }, + { + "bbox": [ + 45, + 298, + 533, + 386 + ], + "type": "text", + "content": ". Whereas the task-agnostic GHP set solely contributes to the initial training, the task-specific GHP set gradually replaces the portion of the task-agnostic set until the end of training." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 404, + 102, + 416 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 404, + 102, + 416 + ], + "spans": [ + { + "bbox": [ + 46, + 404, + 102, + 416 + ], + "type": "text", + "content": "3. Method" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 45, + 423, + 280, + 483 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 423, + 280, + 483 + ], + "spans": [ + { + "bbox": [ + 45, + 423, + 280, + 483 + ], + "type": "text", + "content": "In this section, we introduce the pseudo-positive selection strategy to discover hidden positives with analogous semantics in Sec. 3.2, the training objective with discovered positives in Sec. 3.3, and the gradient propagation scheme to preserve the property of locality in Sec. 3.4." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 488, + 123, + 500 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 488, + 123, + 500 + ], + "spans": [ + { + "bbox": [ + 46, + 488, + 123, + 500 + ], + "type": "text", + "content": "3.1. Preliminary" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "spans": [ + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": "In unsupervised semantic segmentation, the model utilizes unlabeled image set " + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "inline_equation", + "content": "X = \\{x_{b}\\}_{b = 1}^{B}" + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "inline_equation", + "content": "B" + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": " is the number of training data in the mini-batch. Given an image " + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "inline_equation", + "content": "x_{b}" + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": " processed to the feature extractor " + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": ", we have " + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "inline_equation", + "content": "H\\cdot W" + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": " features of " + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "inline_equation", + "content": "f_{i}\\in \\mathbb{R}^{C}" + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "inline_equation", + "content": "i\\in [1,\\dots ,H\\cdot W]" + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": ". Subsequently, the segmentation head " + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": " maps a patch feature " + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "inline_equation", + "content": "f_{i}" + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": " to the corresponding segmentation feature " + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "inline_equation", + "content": "s_i\\in \\mathbb{R}^K" + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": ". And then, the projection head " + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "inline_equation", + "content": "\\mathcal{Z}" + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": " produces a projected vector " + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "inline_equation", + "content": "z_{i}\\in \\mathbb{R}^{K}" + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": " to formulate a contrastive loss function. In the inference stage, we use the segmentation feature " + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "inline_equation", + "content": "s_i" + }, + { + "bbox": [ + 45, + 506, + 279, + 622 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 45, + 623, + 279, + 681 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 623, + 279, + 681 + ], + "spans": [ + { + "bbox": [ + 45, + 623, + 279, + 681 + ], + "type": "text", + "content": "Based on the projected vector " + }, + { + "bbox": [ + 45, + 623, + 279, + 681 + ], + "type": "inline_equation", + "content": "z_{i}" + }, + { + "bbox": [ + 45, + 623, + 279, + 681 + ], + "type": "text", + "content": " for the " + }, + { + "bbox": [ + 45, + 623, + 279, + 681 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 45, + 623, + 279, + 681 + ], + "type": "text", + "content": "-th patch, let " + }, + { + "bbox": [ + 45, + 623, + 279, + 681 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 45, + 623, + 279, + 681 + ], + "type": "text", + "content": " be the index of the augmented patch of " + }, + { + "bbox": [ + 45, + 623, + 279, + 681 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 45, + 623, + 279, + 681 + ], + "type": "text", + "content": "-th one. Then, the conventional self-supervised contrastive loss [6] for " + }, + { + "bbox": [ + 45, + 623, + 279, + 681 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 45, + 623, + 279, + 681 + ], + "type": "text", + "content": "-th patch in unsupervised semantic segmentation can be defined as follows:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 82, + 685, + 279, + 712 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 82, + 685, + 279, + 712 + ], + "spans": [ + { + "bbox": [ + 82, + 685, + 279, + 712 + ], + "type": "interline_equation", + "content": "L _ {i} ^ {\\text {s e l f}} = - \\log \\frac {\\exp \\left(\\sin \\left(z _ {i} , z _ {j} / \\tau\\right)\\right)}{\\sum_ {a \\in A} \\exp \\left(\\sin \\left(z _ {i} , z _ {a}\\right) / \\tau\\right)}, \\tag {1}", + "image_path": "a44d16cea1586ae7443e98e5960078fa9d665c68774376b6a09015ab83d05cf3.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 718, + 279, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 718, + 279, + 730 + ], + "spans": [ + { + "bbox": [ + 46, + 718, + 279, + 730 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 46, + 718, + 279, + 730 + ], + "type": "inline_equation", + "content": "A" + }, + { + "bbox": [ + 46, + 718, + 279, + 730 + ], + "type": "text", + "content": " indicates a set of all indexes except " + }, + { + "bbox": [ + 46, + 718, + 279, + 730 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 46, + 718, + 279, + 730 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 46, + 718, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 46, + 718, + 279, + 730 + ], + "type": "text", + "content": " denotes the" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 297, + 405, + 530, + 429 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 405, + 530, + 429 + ], + "spans": [ + { + "bbox": [ + 297, + 405, + 530, + 429 + ], + "type": "text", + "content": "scalar temperature parameter, and " + }, + { + "bbox": [ + 297, + 405, + 530, + 429 + ], + "type": "inline_equation", + "content": "\\mathrm{sim}(\\cdot ,\\cdot)" + }, + { + "bbox": [ + 297, + 405, + 530, + 429 + ], + "type": "text", + "content": " is cosine similarity between two vectors." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 297, + 440, + 429, + 452 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 440, + 429, + 452 + ], + "spans": [ + { + "bbox": [ + 297, + 440, + 429, + 452 + ], + "type": "text", + "content": "3.2. Global Hidden Positives" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 459, + 530, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 459, + 530, + 588 + ], + "spans": [ + { + "bbox": [ + 296, + 459, + 530, + 588 + ], + "type": "text", + "content": "Learning mutual information with augmented pixels only provides insufficient training signal in unsupervised semantic segmentation [14]. Therefore, it is important to discover hidden pseudo-positives to tailor the contrastive loss for unsupervised segmentation. To discover the hidden positives at the initial stage, we utilize the self-supervised pretrained backbone [4] as the task-agnostic criterion. Then, we gradually increase the contribution of hidden positives found in a task-specific way for the training. Fig. 2 provides the overview of the global hidden positive (GHP) selection process." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 588, + 530, + 705 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 588, + 530, + 705 + ], + "spans": [ + { + "bbox": [ + 296, + 588, + 530, + 705 + ], + "type": "text", + "content": "Initially, the pretrained backbone is utilized to construct a task-agnostic reference pool to assess whether other features in the mini-batch are semantically-alike for each anchor feature. Specifically, task-agnostic reference pool, " + }, + { + "bbox": [ + 296, + 588, + 530, + 705 + ], + "type": "inline_equation", + "content": "Q^{\\mathrm{ag}} = \\{q_m\\}_{m=1}^M" + }, + { + "bbox": [ + 296, + 588, + 530, + 705 + ], + "type": "text", + "content": ", is composed of " + }, + { + "bbox": [ + 296, + 588, + 530, + 705 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 296, + 588, + 530, + 705 + ], + "type": "text", + "content": " randomly sampled features that are extracted by the unsupervised pretrained backbone " + }, + { + "bbox": [ + 296, + 588, + 530, + 705 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 296, + 588, + 530, + 705 + ], + "type": "text", + "content": ". Note that, we only sample a single patch feature per image to ensure the semantic randomness of the reference pool. This task-agnostic reference pool is fixed as the pretrained backbone is frozen throughout the training." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 706, + 530, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 706, + 530, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 706, + 530, + 730 + ], + "type": "text", + "content": "Once the reference pool is gathered, for each patch feature " + }, + { + "bbox": [ + 296, + 706, + 530, + 730 + ], + "type": "inline_equation", + "content": "f_{i}" + }, + { + "bbox": [ + 296, + 706, + 530, + 730 + ], + "type": "text", + "content": ", we define an anchor-dependent similarity criterion" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 277, + 747, + 301, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 277, + 747, + 301, + 756 + ], + "spans": [ + { + "bbox": [ + 277, + 747, + 301, + 756 + ], + "type": "text", + "content": "19542" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 45, + 106, + 279, + 129 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 106, + 279, + 129 + ], + "spans": [ + { + "bbox": [ + 45, + 106, + 279, + 129 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 45, + 106, + 279, + 129 + ], + "type": "text", + "content": " to collect positives, as the distance to the closest feature within the reference pool " + }, + { + "bbox": [ + 45, + 106, + 279, + 129 + ], + "type": "inline_equation", + "content": "Q^{\\mathrm{ag}}" + }, + { + "bbox": [ + 45, + 106, + 279, + 129 + ], + "type": "text", + "content": " by the cosine similarity:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 112, + 137, + 279, + 155 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 112, + 137, + 279, + 155 + ], + "spans": [ + { + "bbox": [ + 112, + 137, + 279, + 155 + ], + "type": "interline_equation", + "content": "c _ {i} = \\max _ {q _ {m} \\in Q ^ {\\mathrm {a g}}} \\operatorname {s i m} \\left(q _ {m}, f _ {i}\\right). \\tag {2}", + "image_path": "12bbd935ad97fe40887ecf64640a8160c129e11ca1bd928b94e21361fa8084f9.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "spans": [ + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "text", + "content": "For each anchor feature " + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "inline_equation", + "content": "f_{i}" + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "text", + "content": ", we basically treat the other feature in the mini-batch " + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "inline_equation", + "content": "f_{j}" + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "text", + "content": " as positive if the similarity between " + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "inline_equation", + "content": "f_{i}" + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "inline_equation", + "content": "f_{j}" + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "text", + "content": " is greater than " + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "text", + "content": ". Still, although one patch feature might be the positive sample for the other, it may not hold mutually. This is because the criterion " + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "inline_equation", + "content": "c_{i}" + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "text", + "content": " is anchor-dependent. To endow consistency in training, we make the GHP selection symmetric to prevent the relation between two patches from being ambiguous. Therefore, index set of " + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "inline_equation", + "content": "\\mathrm{GHP} P_{i}^{\\mathrm{ag}}" + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "text", + "content": " for each " + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "text", + "content": "-th anchor feature " + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "inline_equation", + "content": "f_{i}" + }, + { + "bbox": [ + 45, + 161, + 279, + 277 + ], + "type": "text", + "content": " is defined as follows:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 57, + 285, + 279, + 300 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 285, + 279, + 300 + ], + "spans": [ + { + "bbox": [ + 57, + 285, + 279, + 300 + ], + "type": "interline_equation", + "content": "P _ {i} ^ {\\mathrm {a g}} = \\left\\{j \\mid \\operatorname {s i m} \\left(f _ {i}, f _ {j}\\right) > c _ {i} \\vee \\operatorname {s i m} \\left(f _ {i}, f _ {j}\\right) > c _ {j} \\right\\}, \\tag {3}", + "image_path": "ea27f276d0db2a5c244eb424f3edf8665d5fbdd0bfe7804d20e1273bd9e10fc1.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 45, + 305, + 279, + 351 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 305, + 279, + 351 + ], + "spans": [ + { + "bbox": [ + 45, + 305, + 279, + 351 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 45, + 305, + 279, + 351 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 45, + 305, + 279, + 351 + ], + "type": "text", + "content": " indicates the index for different patch features in the mini-batch. Accordingly, such a distribution-aware reference pool allows the discovery of globally analogous features in consideration of each anchor." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 45, + 351, + 279, + 444 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 351, + 279, + 444 + ], + "spans": [ + { + "bbox": [ + 45, + 351, + 279, + 444 + ], + "type": "text", + "content": "However, although the reference pool built upon the features from an unsupervised pretrained network can serve as an appropriate basis for positivity, it may be insufficient since it lacks task-specificity. We argue that features from the segmentation head are more task-specific than those from the pretrained backbone. Therefore, along with the GHP selected by " + }, + { + "bbox": [ + 45, + 351, + 279, + 444 + ], + "type": "inline_equation", + "content": "P^{\\mathrm{ag}}" + }, + { + "bbox": [ + 45, + 351, + 279, + 444 + ], + "type": "text", + "content": ", we construct additional task-specific GHP utilizing the features from the segmentation head." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "spans": [ + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "content": "Specifically, an index set of task-specific GHP " + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "inline_equation", + "content": "P_{i}^{\\mathrm{sp}}" + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "content": " is formed similarly to Eq. 3 by comparing the features " + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "inline_equation", + "content": "s' = S'(f)" + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "content": " and task-specific reference pool " + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "inline_equation", + "content": "Q^{\\mathrm{sp}}" + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "inline_equation", + "content": "S'" + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "content": " indicates the momentum segmentation head, and the task-specific reference pool " + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "inline_equation", + "content": "Q^{\\mathrm{sp}}" + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "content": " comprises " + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "inline_equation", + "content": "s'" + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "content": ". Also, along with the update of the segmentation head, during training, the reference pool is periodically renewed. Formally, with " + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "inline_equation", + "content": "c_{i}'" + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "content": ", calculated by substituting " + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "inline_equation", + "content": "Q^{\\mathrm{ag}}" + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "inline_equation", + "content": "f_{i}" + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "content": " in Eq. 2 with " + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "inline_equation", + "content": "Q^{\\mathrm{sp}}" + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "inline_equation", + "content": "s_{i}'" + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "content": ", respectively, the " + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "inline_equation", + "content": "P_{i}^{\\mathrm{sp}}" + }, + { + "bbox": [ + 45, + 444, + 279, + 549 + ], + "type": "text", + "content": " is expressed as follows:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 55, + 556, + 279, + 571 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 556, + 279, + 571 + ], + "spans": [ + { + "bbox": [ + 55, + 556, + 279, + 571 + ], + "type": "interline_equation", + "content": "P _ {i} ^ {\\mathrm {s p}} = \\left\\{j \\mid \\operatorname {s i m} \\left(s _ {i} ^ {\\prime}, s _ {j} ^ {\\prime}\\right) > c _ {i} ^ {\\prime} \\vee \\operatorname {s i m} \\left(s _ {i} ^ {\\prime}, s _ {j} ^ {\\prime}\\right) > c _ {j} ^ {\\prime} \\right\\}. \\tag {4}", + "image_path": "b9f398d28d6fced2e0b37bd0f5105343da51e75cf3ae822914bda5e0f8df98be.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 45, + 577, + 279, + 600 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 577, + 279, + 600 + ], + "spans": [ + { + "bbox": [ + 45, + 577, + 279, + 600 + ], + "type": "text", + "content": "Note that, the usage of the momentum segmentation head is for the stability of the reference pool [22,42]." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 607, + 155, + 618 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 607, + 155, + 618 + ], + "spans": [ + { + "bbox": [ + 46, + 607, + 155, + 618 + ], + "type": "text", + "content": "3.3. Objective Function" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "spans": [ + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": "To formulate a contrastive objective with mined GHP in Sec. 3.2, we also need negative features. As we collected the positives throughout the mini-batch, the naive implementation of contrastive learning would utilize all features except the selected positives in the mini-batch as the negatives. However, since an immoderate increase in the size of the negative set may disturb the model training [32], we form a negative set " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "N_{i}" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": " by randomly choosing " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "\\rho \\%" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": " of the remaining patches for each " + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 45, + 624, + 279, + 730 + ], + "type": "text", + "content": "-th anchor. Note that, the separate" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 106, + 530, + 153 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 106, + 530, + 153 + ], + "spans": [ + { + "bbox": [ + 296, + 106, + 530, + 153 + ], + "type": "text", + "content": "index sets of negative samples " + }, + { + "bbox": [ + 296, + 106, + 530, + 153 + ], + "type": "inline_equation", + "content": "N_{i}^{\\mathrm{ag}}" + }, + { + "bbox": [ + 296, + 106, + 530, + 153 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 106, + 530, + 153 + ], + "type": "inline_equation", + "content": "N_{i}^{\\mathrm{sp}}" + }, + { + "bbox": [ + 296, + 106, + 530, + 153 + ], + "type": "text", + "content": " are defined for each " + }, + { + "bbox": [ + 296, + 106, + 530, + 153 + ], + "type": "inline_equation", + "content": "P_{i}^{\\mathrm{ag}}" + }, + { + "bbox": [ + 296, + 106, + 530, + 153 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 106, + 530, + 153 + ], + "type": "inline_equation", + "content": "P_{i}^{\\mathrm{sp}}" + }, + { + "bbox": [ + 296, + 106, + 530, + 153 + ], + "type": "text", + "content": ", correspondingly. Also, unlike Eq. 1, our contrastive loss for each " + }, + { + "bbox": [ + 296, + 106, + 530, + 153 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 296, + 106, + 530, + 153 + ], + "type": "text", + "content": "-th anchor is more like a supervised objective [20] since we are given multiple positives:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 297, + 159, + 552, + 193 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 159, + 552, + 193 + ], + "spans": [ + { + "bbox": [ + 297, + 159, + 552, + 193 + ], + "type": "interline_equation", + "content": "L ^ {\\text {c o n t}} \\left(z _ {i}, P, N\\right) = \\frac {- 1}{| P |} \\sum_ {p \\in P} \\log \\frac {\\exp \\left(\\operatorname* {s i m} \\left(z _ {i} , z _ {p}\\right) / \\tau\\right)}{\\sum_ {n \\in (N \\cup P)} \\exp \\left(\\operatorname* {s i m} \\left(z _ {i} , z _ {n}\\right) / \\tau\\right)}, \\tag {5}", + "image_path": "99cd270cb0bced5c7d241de013040b34874dafe45df690a261f1a99640d4fbce.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "spans": [ + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "inline_equation", + "content": "z_{i}" + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "inline_equation", + "content": "P" + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "text", + "content": ", and " + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "inline_equation", + "content": "N" + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "text", + "content": " are the projected anchor vector " + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "inline_equation", + "content": "\\mathcal{Z}(S(f_i))" + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "text", + "content": ", positive index set, and negative index set, respectively. For simplicity, we use " + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "inline_equation", + "content": "\\Phi_i^{\\mathrm{ag}}" + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "inline_equation", + "content": "\\Phi_i^{\\mathrm{sp}}" + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "text", + "content": " to denote the objective functions with task-agnostic GHP " + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "inline_equation", + "content": "P_{i}^{\\mathrm{ag}}" + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "text", + "content": " and task-specific GHP " + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "inline_equation", + "content": "P_{i}^{\\mathrm{sp}}" + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "text", + "content": " for each " + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 296, + 200, + 530, + 259 + ], + "type": "text", + "content": "-th anchor as follows:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 361, + 266, + 530, + 294 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 361, + 266, + 530, + 294 + ], + "spans": [ + { + "bbox": [ + 361, + 266, + 530, + 294 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\Phi_ {i} ^ {\\mathrm {a g}} = L ^ {\\mathrm {c o n t}} (z _ {i}, P _ {i} ^ {\\mathrm {a g}}, N _ {i} ^ {\\mathrm {a g}}) \\\\ \\Phi_ {i} ^ {\\mathrm {s p}} = L ^ {\\text {c o n t}} \\left(z _ {i}, P _ {i} ^ {\\mathrm {s p}}, N _ {i} ^ {\\mathrm {s p}}\\right). \\tag {6} \\\\ \\end{array}", + "image_path": "912ea3edc6d479688b0e0070815fdebe25b961e1b3fff75e38279a05e15dbe25.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 297, + 301, + 530, + 324 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 301, + 530, + 324 + ], + "spans": [ + { + "bbox": [ + 297, + 301, + 530, + 324 + ], + "type": "text", + "content": "3.4. Gradient Propagation to Local Hidden Positives" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 296, + 330, + 530, + 468 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 330, + 530, + 468 + ], + "spans": [ + { + "bbox": [ + 296, + 330, + 530, + 468 + ], + "type": "text", + "content": "Besides considering the semantically analogous features globally, it is a common hypothesis that nearby pixels are highly likely to belong to the same semantic class. To this end, we consider the property of locality by propagating the loss gradient to the surrounding features of the anchor. Still, the propagation should be cautiously designed since semantic labels of the adjacent patches are not given; semantic consistency between adjacent patches mostly holds, but sometimes does not (i.e., at object boundaries). Thus, to decide the semantically consistent patches nearby, we utilize the attention scores from the unsupervised pretrained ViT backbone " + }, + { + "bbox": [ + 296, + 330, + 530, + 468 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "spans": [ + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "text", + "content": "In detail, we first define the index set " + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "inline_equation", + "content": "I_{i}^{\\mathrm{surr}}" + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "text", + "content": " for surrounding patches of the " + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "text", + "content": "-th anchor including " + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "text", + "content": "-th anchor itself. Also, given the spatial attention score for " + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "text", + "content": "-th anchor " + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "inline_equation", + "content": "\\tilde{T}_i \\in \\mathbb{R}^{H \\cdot W}" + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "text", + "content": " from the last self-attention layer in the backbone " + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "text", + "content": ", we use the average value of " + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "inline_equation", + "content": "\\tilde{T}_i" + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "text", + "content": " as a threshold to select an index set of LHP " + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "inline_equation", + "content": "I_{i}^{\\mathrm{local}}" + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "text", + "content": " among " + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "inline_equation", + "content": "I_{i}^{\\mathrm{surr}}" + }, + { + "bbox": [ + 296, + 470, + 530, + 541 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 333, + 546, + 530, + 561 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 333, + 546, + 530, + 561 + ], + "spans": [ + { + "bbox": [ + 333, + 546, + 530, + 561 + ], + "type": "interline_equation", + "content": "I _ {i} ^ {\\text {l o c a l}} = \\left\\{j \\mid j \\in I _ {i} ^ {\\text {s u r r}} \\wedge t _ {j} > \\operatorname {A v g} \\left(\\tilde {T} _ {i}\\right) \\right\\}, \\tag {7}", + "image_path": "ec5d4d7bcffe85f2b1a13002ad223e39d05b0df070d1d3eab4ff0075868c75c5.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "spans": [ + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "inline_equation", + "content": "t_j" + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "text", + "content": " is " + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "text", + "content": "-th element of " + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "inline_equation", + "content": "\\tilde{T}_i" + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "inline_equation", + "content": "\\mathrm{Avg}(\\cdot)" + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "text", + "content": " denotes the averaging function. The visualization of the attention scores for the adjacent patches is shown in Fig. 1 (b). By utilizing " + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "inline_equation", + "content": "I_i^{\\mathrm{local}}" + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "text", + "content": ", we obtain the surrounding positive features " + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "inline_equation", + "content": "G_i" + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "text", + "content": " and the corresponding attention score " + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "inline_equation", + "content": "T_i" + }, + { + "bbox": [ + 296, + 569, + 530, + 628 + ], + "type": "text", + "content": " for LHP as follows:" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 368, + 634, + 458, + 665 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 368, + 634, + 458, + 665 + ], + "spans": [ + { + "bbox": [ + 368, + 634, + 458, + 665 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} G _ {i} = \\left\\{s _ {j} \\mid j \\in I _ {i} ^ {\\text {l o c a l}} \\right\\} \\\\ T _ {i} = \\left\\{t _ {j} \\mid j \\in I _ {i} ^ {\\text {l o c a l}} \\right\\}, \\\\ \\end{array}", + "image_path": "b1b989e0111ead0e0b54c2e0756c5085ea8b89d9c4fccb4e9e7e561dabe02e34.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 296, + 671, + 530, + 693 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 671, + 530, + 693 + ], + "spans": [ + { + "bbox": [ + 296, + 671, + 530, + 693 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 296, + 671, + 530, + 693 + ], + "type": "inline_equation", + "content": "s_j" + }, + { + "bbox": [ + 296, + 671, + 530, + 693 + ], + "type": "text", + "content": " is the feature of " + }, + { + "bbox": [ + 296, + 671, + 530, + 693 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 296, + 671, + 530, + 693 + ], + "type": "text", + "content": "-th patch extracted by the segmentation head " + }, + { + "bbox": [ + 296, + 671, + 530, + 693 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 296, + 671, + 530, + 693 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "text", + "content": "To propagate the loss gradient to the LHP, we mix the patch features in " + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "inline_equation", + "content": "G_{i}" + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "text", + "content": " in proportion to the corresponding attention scores in " + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "inline_equation", + "content": "T_{i}" + }, + { + "bbox": [ + 296, + 694, + 530, + 730 + ], + "type": "text", + "content": ". Formally, the mixed patch composed" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "spans": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "text", + "content": "19543" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 103, + 530, + 245 + ], + "blocks": [ + { + "bbox": [ + 48, + 103, + 530, + 245 + ], + "lines": [ + { + "bbox": [ + 48, + 103, + 530, + 245 + ], + "spans": [ + { + "bbox": [ + 48, + 103, + 530, + 245 + ], + "type": "image", + "image_path": "f13b47b3971214433afa6eb701b1a1a79f167cfb12a7f3d14997df322d2f5dbe.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "lines": [ + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "spans": [ + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "text", + "content": "Figure 3. Illustration of our gradient propagation strategy to preserve local semantic consistency. For each anchor, with its surrounding patches " + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "inline_equation", + "content": "I^{\\mathrm{surf}}" + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "text", + "content": " and corresponding attention scores from feature extractor " + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "text", + "content": ", local hidden positives (LHP) " + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "inline_equation", + "content": "I^{\\mathrm{local}}" + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "text", + "content": " are appointed based on the threshold " + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "inline_equation", + "content": "\\mathrm{Avg}(\\hat{T})" + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "text", + "content": " (Eq. 7). In a forward pass, the features of LHP " + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "inline_equation", + "content": "G" + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "text", + "content": " (Eq. 8) are mixed by weighted average according to the attention scores " + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "text", + "content": " to compute the objective function " + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "inline_equation", + "content": "\\Psi" + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "text", + "content": ". In this way, the loss gradient propagates toward the LHP in proportion to " + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 45, + 253, + 531, + 298 + ], + "type": "text", + "content": " in the backward pass." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 45, + 317, + 158, + 330 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 317, + 158, + 330 + ], + "spans": [ + { + "bbox": [ + 45, + 317, + 158, + 330 + ], + "type": "text", + "content": "of LHP " + }, + { + "bbox": [ + 45, + 317, + 158, + 330 + ], + "type": "inline_equation", + "content": "s_i^{\\mathrm{mix}}" + }, + { + "bbox": [ + 45, + 317, + 158, + 330 + ], + "type": "text", + "content": " is expressed as:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 107, + 333, + 279, + 363 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 107, + 333, + 279, + 363 + ], + "spans": [ + { + "bbox": [ + 107, + 333, + 279, + 363 + ], + "type": "interline_equation", + "content": "s _ {i} ^ {\\text {m i x}} = \\frac {1}{\\left| I _ {i} ^ {\\text {l o c a l}} \\right|} \\sum_ {j \\in I _ {i} ^ {\\text {l o c a l}}} \\sigma g _ {j} t _ {j}, \\tag {9}", + "image_path": "38465057923f879fcb9eb63574d3c87b5b9ae1fd3ccb27874c03d41355bc1657.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "spans": [ + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "text", + "content": " is a scalar value to scale the attention score, and " + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "inline_equation", + "content": "g_{j}" + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "inline_equation", + "content": "t_j" + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "text", + "content": " indicate the " + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "text", + "content": "-th element of " + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "inline_equation", + "content": "G_{i}" + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "inline_equation", + "content": "T_{i}" + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "text", + "content": ", respectively. Then, we define the objective functions " + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "inline_equation", + "content": "\\Psi_i^{\\mathrm{ag}}" + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "inline_equation", + "content": "\\Psi_i^{\\mathrm{sp}}" + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "text", + "content": " to learn the locality by inserting the projected mixed vector " + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "inline_equation", + "content": "z_{i}^{\\mathrm{mix}} = \\mathcal{Z}(s_{i}^{\\mathrm{mix}})" + }, + { + "bbox": [ + 45, + 367, + 279, + 426 + ], + "type": "text", + "content": " into Eq. 5 as follows:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 430, + 279, + 449 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 430, + 279, + 449 + ], + "spans": [ + { + "bbox": [ + 105, + 430, + 279, + 449 + ], + "type": "interline_equation", + "content": "\\Psi_ {i} ^ {\\mathrm {a g}} = L ^ {\\text {c o n t}} \\left(z _ {i} ^ {\\text {m i x}}, P _ {i} ^ {\\mathrm {a g}}, N _ {i} ^ {\\mathrm {a g}}\\right) \\tag {10}", + "image_path": "ecd627e3ed8d9bde24d30658ea771b2bb6437848ad6da6445cd81ce03fc1dfc4.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 108, + 447, + 218, + 460 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 108, + 447, + 218, + 460 + ], + "spans": [ + { + "bbox": [ + 108, + 447, + 218, + 460 + ], + "type": "interline_equation", + "content": "\\Psi_ {i} ^ {\\mathrm {s p}} = L ^ {\\mathrm {c o n t}} (z _ {i} ^ {\\mathrm {m i x}}, P _ {i} ^ {\\mathrm {s p}}, N _ {i} ^ {\\mathrm {s p}}).", + "image_path": "dcb25a30c5cff70419f7fb4f78b81e3f1b4f688b408c9d10dc67161d38c1565d.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 45, + 463, + 279, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 463, + 279, + 533 + ], + "spans": [ + { + "bbox": [ + 45, + 463, + 279, + 533 + ], + "type": "text", + "content": "Since these functions are calculated by utilizing the mixed vector " + }, + { + "bbox": [ + 45, + 463, + 279, + 533 + ], + "type": "inline_equation", + "content": "z_{i}^{\\mathrm{mix}}" + }, + { + "bbox": [ + 45, + 463, + 279, + 533 + ], + "type": "text", + "content": ", the loss gradients are propagated to all features composing the " + }, + { + "bbox": [ + 45, + 463, + 279, + 533 + ], + "type": "inline_equation", + "content": "G_{i}" + }, + { + "bbox": [ + 45, + 463, + 279, + 533 + ], + "type": "text", + "content": ", as described in Fig. 3. Therefore, the semantically-alike surrounding vectors in " + }, + { + "bbox": [ + 45, + 463, + 279, + 533 + ], + "type": "inline_equation", + "content": "G_{i}" + }, + { + "bbox": [ + 45, + 463, + 279, + 533 + ], + "type": "text", + "content": " are updated in the same direction, thereby retaining semantic consistency within the neighboring patches." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 45, + 533, + 279, + 580 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 533, + 279, + 580 + ], + "spans": [ + { + "bbox": [ + 45, + 533, + 279, + 580 + ], + "type": "text", + "content": "Overall, by combining all loss formulations with consistency regularizer " + }, + { + "bbox": [ + 45, + 533, + 279, + 580 + ], + "type": "inline_equation", + "content": "R_{i}" + }, + { + "bbox": [ + 45, + 533, + 279, + 580 + ], + "type": "text", + "content": " that minimizes Euclidean distance between the projected vectors of two differently augmented patches, the final loss function is defined as follows:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 69, + 584, + 279, + 597 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 69, + 584, + 279, + 597 + ], + "spans": [ + { + "bbox": [ + 69, + 584, + 279, + 597 + ], + "type": "interline_equation", + "content": "L _ {i} = \\left(\\Phi_ {i} ^ {\\mathrm {a g}} + \\Psi_ {i} ^ {\\mathrm {a g}}\\right) + \\lambda \\left(\\Phi_ {i} ^ {\\mathrm {s p}} + \\Psi_ {i} ^ {\\mathrm {s p}}\\right) + \\alpha R _ {i}, \\tag {11}", + "image_path": "88e159ec049e33cdb08feb1df4d67644995da0f98da3da3afc3c8e8fbdc441cb.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 45, + 602, + 279, + 660 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 602, + 279, + 660 + ], + "spans": [ + { + "bbox": [ + 45, + 602, + 279, + 660 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 45, + 602, + 279, + 660 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 45, + 602, + 279, + 660 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 45, + 602, + 279, + 660 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 45, + 602, + 279, + 660 + ], + "type": "text", + "content": " control the contribution of each loss. For instance, " + }, + { + "bbox": [ + 45, + 602, + 279, + 660 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 45, + 602, + 279, + 660 + ], + "type": "text", + "content": " gradually increases from 0 to 1 during the training and " + }, + { + "bbox": [ + 45, + 602, + 279, + 660 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 45, + 602, + 279, + 660 + ], + "type": "text", + "content": " remains constant at 0.05 throughout the training. Note that, the sample with zero positive is excluded from training although it rarely exists." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 45, + 669, + 124, + 682 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 669, + 124, + 682 + ], + "spans": [ + { + "bbox": [ + 45, + 669, + 124, + 682 + ], + "type": "text", + "content": "4. Experiments" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 45, + 687, + 230, + 701 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 687, + 230, + 701 + ], + "spans": [ + { + "bbox": [ + 45, + 687, + 230, + 701 + ], + "type": "text", + "content": "4.1. Datasets and Experimental Settings" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 45, + 706, + 279, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 706, + 279, + 730 + ], + "spans": [ + { + "bbox": [ + 45, + 706, + 279, + 730 + ], + "type": "text", + "content": "Datasets. We utilize COCO-stuff [1], Cityscapes [9], and Potsdam-3 datasets following the existing works [8, 14," + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 296, + 317, + 530, + 398 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 317, + 530, + 398 + ], + "spans": [ + { + "bbox": [ + 296, + 317, + 530, + 398 + ], + "type": "text", + "content": "19]. COCO-stuff is a large-scale scene understanding dataset that consists of dense pixel-level annotations and Cityscapes is a more recently publicized dataset having street scenes across 50 different cities. Potsdam-3 dataset contains satellite images. Following the baselines [8,14,19], we choose the 27 classes for COCO-stuff and Cityscapes datasets, and 3 classes for Potsdam-3 dataset." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 296, + 412, + 530, + 611 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 412, + 530, + 611 + ], + "spans": [ + { + "bbox": [ + 296, + 412, + 530, + 611 + ], + "type": "text", + "content": "Evaluation Protocols and Metrics. To evaluate our approach, we conduct two testing methods; clustering and linear probe [14]. Clustering is to measure how well the semantic-preserving clusters are formed. Once the unlabeled clusters are computed with the extracted representations, clusters are matched with the ground truth class labels using the Hungarian matching algorithm. On the other hand, the linear probe is a popular method to evaluate the quality of representations learned in unsupervised manners. Specifically, an additional linear layer is learned with representations extracted from the frozen unsupervised model for evaluation and ground truth labels. Inferring the representations of test data extracted by the frozen model with the learned linear layer, we can measure the quality of representations. With two types of protocols, the performance is measured by two common metrics; accuracy (Acc.) and mean Intersection Over Union (mIoU)." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 296, + 624, + 530, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 624, + 530, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 624, + 530, + 730 + ], + "type": "text", + "content": "Implementation Details. For fair comparisons with our baselines [14, 37], we follow them to mainly use DINO pretrained ViT models as a backbone network " + }, + { + "bbox": [ + 296, + 624, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\mathcal{F}" + }, + { + "bbox": [ + 296, + 624, + 530, + 730 + ], + "type": "text", + "content": " for the COCO-stuff dataset. In addition, we also test with the advanced backbone, SelfPatch [39], on the COCO-stuff dataset. The segmentation head " + }, + { + "bbox": [ + 296, + 624, + 530, + 730 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 296, + 624, + 530, + 730 + ], + "type": "text", + "content": " is constructed with a two-layer RELU MLP as STEGO, and the projection head " + }, + { + "bbox": [ + 296, + 624, + 530, + 730 + ], + "type": "inline_equation", + "content": "\\mathcal{Z}" + }, + { + "bbox": [ + 296, + 624, + 530, + 730 + ], + "type": "text", + "content": " is composed of a linear layer equipped with a normalization layer. The embedding dimension " + }, + { + "bbox": [ + 296, + 624, + 530, + 730 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 296, + 624, + 530, + 730 + ], + "type": "text", + "content": " is set to 512 for ViT" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "spans": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "text", + "content": "19544" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 51, + 104, + 274, + 291 + ], + "blocks": [ + { + "bbox": [ + 51, + 104, + 274, + 291 + ], + "lines": [ + { + "bbox": [ + 51, + 104, + 274, + 291 + ], + "spans": [ + { + "bbox": [ + 51, + 104, + 274, + 291 + ], + "type": "table", + "html": "
MethodBackboneUnsupervisedLinear
Acc.mIoUAcc.mIoU
DC [2]R18+FPN19.9---
MDC [2]R18+FPN32.29.848.613.3
IIC [19]R18+FPN21.86.744.58.4
PiCIE [8]R18+FPN48.113.854.213.9
PiCIE+H [8]R18+FPN50.014.454.814.8
DINO [4]ViT-S/828.711.368.633.9
+ TransFGU [37]ViT-S/852.717.5--
+ STEGO [14]ViT-S/848.324.574.438.3
+ HP (Ours)ViT-S/857.224.675.642.7
DINO [4]ViT-S/1622.08.050.318.1
+ STEGO [14]ViT-S/1652.523.770.634.5
+ HP (Ours)ViT-S/1654.524.374.139.1
SelfPatch [39]ViT-S/1635.112.364.428.5
+ STEGO [14]ViT-S/1652.422.272.236.0
+ HP (Ours)ViT-S/1656.123.274.941.3
", + "image_path": "4ca9c7e270f5d2dc03f56aa4d7a7da0f6416212bf99fb6781ece81a9f12d7ad5.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 51, + 332, + 274, + 455 + ], + "blocks": [ + { + "bbox": [ + 46, + 299, + 278, + 321 + ], + "lines": [ + { + "bbox": [ + 46, + 299, + 278, + 321 + ], + "spans": [ + { + "bbox": [ + 46, + 299, + 278, + 321 + ], + "type": "text", + "content": "Table 1. Experimental results on COCO-stuff dataset with various backbones and pretrained models." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 51, + 332, + 274, + 455 + ], + "lines": [ + { + "bbox": [ + 51, + 332, + 274, + 455 + ], + "spans": [ + { + "bbox": [ + 51, + 332, + 274, + 455 + ], + "type": "table", + "html": "
MethodBackboneUnsupervisedLinear
Acc.mIoUAcc.mIoU
MDC [2]R18+FPN40.77.1--
IIC [19]R18+FPN47.96.4--
PiCIE [8]R18+FPN65.512.3--
DINO [4]ViT-S/834.510.984.622.8
+ TransFGU [37]ViT-S/877.916.8--
+ HP (Ours)ViT-S/880.118.491.230.6
DINO [4]ViT-B/843.611.884.223.0
+ STEGO [14]ViT-B/873.221.090.326.8
+ HP (Ours)ViT-B/879.518.490.933.0
", + "image_path": "063c1fa4d1a2018792c02b8f015242962b365642f5656792afa7e67caf63c1d6.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 45, + 495, + 278, + 623 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 495, + 278, + 623 + ], + "spans": [ + { + "bbox": [ + 45, + 495, + 278, + 623 + ], + "type": "text", + "content": "S/8 and ViT-B/8 models, and 256 for ViT-S/16. We train the model for 3, 20, and 10 epochs for COCO-stuff, Cityscapes, and Potsdam-3 datasets, respectively, based on the AdamW optimizer with a learning rate of 0.0005 and weight decay of 0.1. The task-specific reference pool " + }, + { + "bbox": [ + 45, + 495, + 278, + 623 + ], + "type": "inline_equation", + "content": "Q^{\\mathrm{sp}}" + }, + { + "bbox": [ + 45, + 495, + 278, + 623 + ], + "type": "text", + "content": " is renewed every 100 iterations throughout the training. The percentage of negative samples usage " + }, + { + "bbox": [ + 45, + 495, + 278, + 623 + ], + "type": "inline_equation", + "content": "\\rho" + }, + { + "bbox": [ + 45, + 495, + 278, + 623 + ], + "type": "text", + "content": " is set to 2. In the last stage, we add a feature refinement step utilizing Conditional Random Field [21] as did in STEGO. Evaluation metrics, i.e., clustering and linear probe, are optimized with the Adam optimizer each with learning rates of 0.005 and 0.001." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 45, + 630, + 166, + 642 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 630, + 166, + 642 + ], + "spans": [ + { + "bbox": [ + 45, + 630, + 166, + 642 + ], + "type": "text", + "content": "4.2. Experimental Results" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 45, + 648, + 278, + 729 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 648, + 278, + 729 + ], + "spans": [ + { + "bbox": [ + 45, + 648, + 278, + 729 + ], + "type": "text", + "content": "We compare our proposed method against the prior techniques for the unsupervised segmentation [2, 8, 14, 19, 37]. Most of the results in the result tables are brought from the literature [8, 14]. In Tab. 1, it is observed that self-supervised models, i.e., DINO and SelfPatch, are already good segmentation predictors with the linear probe, which makes them a new baseline over the prior works for unsu" + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 317, + 104, + 511, + 226 + ], + "blocks": [ + { + "bbox": [ + 70, + 463, + 254, + 474 + ], + "lines": [ + { + "bbox": [ + 70, + 463, + 254, + 474 + ], + "spans": [ + { + "bbox": [ + 70, + 463, + 254, + 474 + ], + "type": "text", + "content": "Table 2. Experimental results on Cityscapes dataset." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 317, + 104, + 511, + 226 + ], + "lines": [ + { + "bbox": [ + 317, + 104, + 511, + 226 + ], + "spans": [ + { + "bbox": [ + 317, + 104, + 511, + 226 + ], + "type": "table", + "html": "
MethodBackboneUnsup. Acc.
Random CNN [19]VGG1138.2
K-Means [27]VGG1145.7
SIFT [24]VGG1138.2
ContextPrediction [10]VGG1149.6
CC [18]VGG1163.9
DeepCluster [2]VGG1141.7
IIC [19]VGG1165.1
DINO [4]ViT-B/853.0
+ STEGO [14]ViT-B/877.0
+ HP (Ours)ViT-B/882.4
", + "image_path": "324d79940bb69c4ad024eca104f62b60702e44139581d30df255c0ae9135d22b.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 321, + 234, + 506, + 245 + ], + "lines": [ + { + "bbox": [ + 321, + 234, + 506, + 245 + ], + "spans": [ + { + "bbox": [ + 321, + 234, + 506, + 245 + ], + "type": "text", + "content": "Table 3. Experimental results on Potsdam-3 dataset." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 296, + 267, + 530, + 336 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 267, + 530, + 336 + ], + "spans": [ + { + "bbox": [ + 296, + 267, + 530, + 336 + ], + "type": "text", + "content": "pervised segmentation. Furthermore, we utilize two pretrained backbones with two kinds of architectures to compare with STEGO, in detail. As reported, our proposed model provides consistent performance improvements over the previous SOTA model in almost all cases on the COCO-stuff dataset." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 296, + 338, + 530, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 338, + 530, + 594 + ], + "spans": [ + { + "bbox": [ + 296, + 338, + 530, + 594 + ], + "type": "text", + "content": "Results on Cityscapes also show a similar tendency. As shown in Tab. 2, ours outperform previous methods except for the mIoU when clustering is used for the evaluation. For instance, we achieve " + }, + { + "bbox": [ + 296, + 338, + 530, + 594 + ], + "type": "inline_equation", + "content": "8.6\\%" + }, + { + "bbox": [ + 296, + 338, + 530, + 594 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 338, + 530, + 594 + ], + "type": "inline_equation", + "content": "23\\%" + }, + { + "bbox": [ + 296, + 338, + 530, + 594 + ], + "type": "text", + "content": " improvements in cluster accuracy and linear mIoU over STEGO, respectively. For the slight decrease in cluster mIoU with ViT-B/8 architecture, we argue that it is insignificant since the linear probe better describes the quality of representations. Specifically, clustering evaluation highly depends on the purpose of the dataset so that it is sensitive to the degree of class-specificity as different body parts can be either classified as human or as independent body parts. However, whereas it is more appropriate to detect each body part independently for unsupervised learning as ours do in Fig. 5, the annotations for general datasets, e.g., COCO-stuff and cityscape, treat these body parts as a human class. Such circumstances make the clustering evaluation vulnerable to the degree of the class hierarchy. In contrast, the linear probe projects these features to close proximity if the given label space considers them as a human body. Thus, we believe the linear probe is a more appropriate measure of representation quality." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 595, + 530, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 595, + 530, + 665 + ], + "spans": [ + { + "bbox": [ + 296, + 595, + 530, + 665 + ], + "type": "text", + "content": "Also, we compare the cluster accuracy in Tab. 3 on Potsdam-3 dataset. We have achieved a " + }, + { + "bbox": [ + 296, + 595, + 530, + 665 + ], + "type": "inline_equation", + "content": "7\\%" + }, + { + "bbox": [ + 296, + 595, + 530, + 665 + ], + "type": "text", + "content": " boost over STEGO, confirming that ours also performs well even in a completely new domain. Likewise, our superior results verify the effectiveness of our process of discovering global and local-hidden positive patches." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 682, + 530, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 682, + 530, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 682, + 530, + 730 + ], + "type": "text", + "content": "Qualitative Results. In addition to the quantitative results, we report qualitative results in Fig. 4. In comparison to STEGO, we observe that our results include fewer mispredicted pixels throughout the images, while the strength" + } + ] + } + ], + "index": 12 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "spans": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "text", + "content": "19545" + } + ] + } + ], + "index": 13 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 105, + 531, + 244 + ], + "blocks": [ + { + "bbox": [ + 48, + 105, + 531, + 244 + ], + "lines": [ + { + "bbox": [ + 48, + 105, + 531, + 244 + ], + "spans": [ + { + "bbox": [ + 48, + 105, + 531, + 244 + ], + "type": "image", + "image_path": "65eef0aa688a80672d97938762d4abee4a90dc464ea940fc46d13095be9d08a1.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 58, + 252, + 518, + 264 + ], + "lines": [ + { + "bbox": [ + 58, + 252, + 518, + 264 + ], + "spans": [ + { + "bbox": [ + 58, + 252, + 518, + 264 + ], + "type": "text", + "content": "Figure 4. Qualitative comparison results of Ours and STEGO on the COCO-stuff dataset with DINO pretrained ViT-S/8 backbone." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 45, + 284, + 280, + 343 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 284, + 280, + 343 + ], + "spans": [ + { + "bbox": [ + 45, + 284, + 280, + 343 + ], + "type": "text", + "content": "of preserving semantic locality is also validated. For instance, whereas we find that the predicted label of the wheel is only partially correct in the 4th column, our results are consistent along the neighboring pixels. These results also demonstrate the superiority of our method." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 45, + 354, + 246, + 368 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 354, + 246, + 368 + ], + "spans": [ + { + "bbox": [ + 45, + 354, + 246, + 368 + ], + "type": "text", + "content": "5. Ablation Study and Further Analysis" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 45, + 373, + 279, + 444 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 373, + 279, + 444 + ], + "spans": [ + { + "bbox": [ + 45, + 373, + 279, + 444 + ], + "type": "text", + "content": "In this section, we provide ablation studies and an analysis of our model. Particularly, we explore the contributions of main components and test with varying hyperparameters. Most experiments for ablation studies are conducted on the COCO-stuff dataset using the DINO pretrained ViTs/8 model, except for Sec. 5.2, which utilized the ViT-S/16." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 45, + 452, + 239, + 465 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 452, + 239, + 465 + ], + "spans": [ + { + "bbox": [ + 45, + 452, + 239, + 465 + ], + "type": "text", + "content": "5.1. Importance of the Main Components." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "spans": [ + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "text", + "content": "Tab. 4 reports the performances when an individual component or various combinations of them are not utilized. We found that task-specific GHP and LHP are essential in improving the performance of the unsupervised segmentation task. Compared to (d) where both the task-specific GHP and LHP are not used, the use of them each leads to " + }, + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "inline_equation", + "content": "6.5\\%" + }, + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "text", + "content": " (b) and " + }, + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "inline_equation", + "content": "11.6\\%" + }, + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "text", + "content": " (c) improvements. Also when used together, they bring " + }, + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "inline_equation", + "content": "16\\%" + }, + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "text", + "content": " of performance boosts (a). Furthermore, the importance of preserving symmetry in selecting GHP can be found by comparing (a) to (e) as its usage boosts " + }, + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "inline_equation", + "content": "5.9\\%" + }, + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "text", + "content": ", and consistency regularizer enhances performance by " + }, + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "inline_equation", + "content": "2.5\\%" + }, + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "text", + "content": " by comparing (a) to (f). Lastly, (g) shows the performance of naively implemented contrastive learning (Eq. 1) with photometric perturbations used in PiCIE [8]. This also confirms the strengths of our proposed method as (a) enhances (g) by " + }, + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "inline_equation", + "content": "51.3\\%" + }, + { + "bbox": [ + 45, + 470, + 280, + 656 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 45, + 665, + 279, + 677 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 665, + 279, + 677 + ], + "spans": [ + { + "bbox": [ + 45, + 665, + 279, + 677 + ], + "type": "text", + "content": "5.2. Alternatives to Gradient Propagation Strategy" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 45, + 682, + 279, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 682, + 279, + 730 + ], + "spans": [ + { + "bbox": [ + 45, + 682, + 279, + 730 + ], + "type": "text", + "content": "There can be alternative ways to meet our goal of reflecting semantic consistency between adjacent patches. As an alternative method of gradient propagation, we simply apply the identical loss to the surrounding patches pro" + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 311, + 282, + 518, + 384 + ], + "blocks": [ + { + "bbox": [ + 311, + 282, + 518, + 384 + ], + "lines": [ + { + "bbox": [ + 311, + 282, + 518, + 384 + ], + "spans": [ + { + "bbox": [ + 311, + 282, + 518, + 384 + ], + "type": "table", + "html": "
GHPLHPSARegUnsupervised Acc.mIoU
TATS
(a)✓✓✓✓✓57.224.6
(b)✓✓✓✓52.523.1
(c)✓✓✓✓55.019.1
(d)✓✓✓49.320.1
(e)✓✓✓✓54.023.6
(f)✓✓✓✓55.824.5
(g)37.810.4
", + "image_path": "4ae6ccbf3bc153a8e979c37f5a639fbf59b1c07cbd35da256ed246878129f94f.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "type": "table", + "bbox": [ + 316, + 447, + 511, + 508 + ], + "blocks": [ + { + "bbox": [ + 296, + 391, + 531, + 435 + ], + "lines": [ + { + "bbox": [ + 296, + 391, + 531, + 435 + ], + "spans": [ + { + "bbox": [ + 296, + 391, + 531, + 435 + ], + "type": "text", + "content": "Table 4. Ablation study for each component. GHP, LHP, TA, TS, SA, and Reg denote Global Hidden Positive, Local Hidden Positive, task-agnostic, task-specific, symmetrical assignments, and consistency regularizer, respectively." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 316, + 447, + 511, + 508 + ], + "lines": [ + { + "bbox": [ + 316, + 447, + 511, + 508 + ], + "spans": [ + { + "bbox": [ + 316, + 447, + 511, + 508 + ], + "type": "table", + "html": "
MethodUnsupervisedLinear
Acc.mIoUAcc.mIoU
DINO + GradProp54.524.374.139.1
DINO + LossProp54.723.274.340.5
SelfPatch + GradProp56.123.274.941.3
SelfPatch + LossProp54.522.275.141.4
", + "image_path": "ca06ca78d0bedd300d2d780af087b8aa450946c7a80a5e451110506f1b0a5b06.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 516, + 530, + 538 + ], + "lines": [ + { + "bbox": [ + 296, + 516, + 530, + 538 + ], + "spans": [ + { + "bbox": [ + 296, + 516, + 530, + 538 + ], + "type": "text", + "content": "Table 5. Comparison results between gradient propagation and loss propagation strategies." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 296, + 560, + 530, + 642 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 560, + 530, + 642 + ], + "spans": [ + { + "bbox": [ + 296, + 560, + 530, + 642 + ], + "type": "text", + "content": "portionally to their attention score (i.e., loss propagation). The results in Tab. 5 show that the loss propagation strategy performs comparably to the gradient propagation strategy. Nonetheless, we observed that the loss propagation approach incurs higher computational costs (" + }, + { + "bbox": [ + 296, + 560, + 530, + 642 + ], + "type": "inline_equation", + "content": "1.2 \\times" + }, + { + "bbox": [ + 296, + 560, + 530, + 642 + ], + "type": "text", + "content": " memory and " + }, + { + "bbox": [ + 296, + 560, + 530, + 642 + ], + "type": "inline_equation", + "content": "3 \\times" + }, + { + "bbox": [ + 296, + 560, + 530, + 642 + ], + "type": "text", + "content": " time). Likewise, our method is implemented considering both effectiveness and efficiency." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 297, + 652, + 379, + 664 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 652, + 379, + 664 + ], + "spans": [ + { + "bbox": [ + 297, + 652, + 379, + 664 + ], + "type": "text", + "content": "5.3. Visualization" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 296, + 671, + 531, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 671, + 531, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 671, + 531, + 730 + ], + "type": "text", + "content": "**Discovered GHP.** To demonstrate the effectiveness of our GHP selection process, we visualize the selected GHP sets for different anchors in a single image in Fig. 5. First, we observe that the corresponding reference point in the second column is semantically correlated with the anchor patch." + } + ] + } + ], + "index": 15 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 277, + 747, + 301, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 277, + 747, + 301, + 756 + ], + "spans": [ + { + "bbox": [ + 277, + 747, + 301, + 756 + ], + "type": "text", + "content": "19546" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 48, + 104, + 280, + 266 + ], + "blocks": [ + { + "bbox": [ + 48, + 104, + 280, + 266 + ], + "lines": [ + { + "bbox": [ + 48, + 104, + 280, + 266 + ], + "spans": [ + { + "bbox": [ + 48, + 104, + 280, + 266 + ], + "type": "image", + "image_path": "66523cddee441d205398edf8b166148b8211598e22fde7ec67e93310bcb062a2.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 274, + 279, + 318 + ], + "lines": [ + { + "bbox": [ + 45, + 274, + 279, + 318 + ], + "spans": [ + { + "bbox": [ + 45, + 274, + 279, + 318 + ], + "type": "text", + "content": "Figure 5. Discovered patches by our GHP selection process. From the left to right columns, red boxes indicate the anchors, the closest patches within the task-agnostic reference pool (reference), and GHP sets chosen in the mini-batch, respectively." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 45, + 338, + 279, + 478 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 338, + 279, + 478 + ], + "spans": [ + { + "bbox": [ + 45, + 338, + 279, + 478 + ], + "type": "text", + "content": "Also, the obtained GHP sets verify the appropriateness of the use of reference points as each anchor's criterion as they are capable of precisely discovering the semantically-similar positives. For example, we find that all the anchors, reference points, and GHP sets have the same semantic labels in the first and the second rows (solid (mountain) and ground (snow)). More intriguing results are in the third and fourth rows where we find that the GHP selection process distinguishes the body parts in a much more fine-grained manner than the given annotation, i.e., person. These results imply that the designed GHP selection is well-designed and capable of capturing detailed semantic contexts." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 45, + 485, + 214, + 497 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 485, + 214, + 497 + ], + "spans": [ + { + "bbox": [ + 45, + 485, + 214, + 497 + ], + "type": "text", + "content": "5.4. Robustness to Hyperparameters" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 45, + 503, + 279, + 585 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 503, + 279, + 585 + ], + "spans": [ + { + "bbox": [ + 45, + 503, + 279, + 585 + ], + "type": "text", + "content": "In this subsection, we enumerate our use of hyperparameters in Tab. 6 and conduct an ablation study. Overall, our proposed training scheme is robust to hyperparameters as Fig. 6 supports the claim. Below, we illustrate the influences of each parameter. Despite the slight drop in performance from time to time, it is insignificant since the change in performance is very marginal and results are consistent." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 45, + 599, + 279, + 727 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 45, + 599, + 279, + 727 + ], + "spans": [ + { + "bbox": [ + 45, + 599, + 279, + 727 + ], + "type": "text", + "content": "The Number of Data in the Reference Pool. Fig. 6 (a) shows the performances with varying numbers of data " + }, + { + "bbox": [ + 45, + 599, + 279, + 727 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 45, + 599, + 279, + 727 + ], + "type": "text", + "content": " in the reference pool. Generally, the reference pool is not very vulnerable to its size, unless the capacity is either too small or too large. When the reference pool is skinny, it may not be sufficient to represent all kinds of semantics present in the dataset. In other words, the small reference pool may induce a biased criterion for each anchor which may incur biased training. On the other hand, when the reference pool is too large, a tight threshold " + }, + { + "bbox": [ + 45, + 599, + 279, + 727 + ], + "type": "inline_equation", + "content": "(c_{i})" + }, + { + "bbox": [ + 45, + 599, + 279, + 727 + ], + "type": "text", + "content": " could be derived, which can interfere with gathering GHP." + } + ] + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 360, + 104, + 468, + 151 + ], + "blocks": [ + { + "bbox": [ + 360, + 104, + 468, + 151 + ], + "lines": [ + { + "bbox": [ + 360, + 104, + 468, + 151 + ], + "spans": [ + { + "bbox": [ + 360, + 104, + 468, + 151 + ], + "type": "table", + "html": "
DatasetMτ
COCO-stuff20480.8
Cityscapes20480.6
Potsdam-310240.4
", + "image_path": "6497e8baaefc633f91edbd972097762ee4a3a4e325689274cf714942d90951e3.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_body" + } + ], + "index": 6 + }, + { + "bbox": [ + 330, + 160, + 496, + 170 + ], + "lines": [ + { + "bbox": [ + 330, + 160, + 496, + 170 + ], + "spans": [ + { + "bbox": [ + 330, + 160, + 496, + 170 + ], + "type": "text", + "content": "Table 6. Hyperparameter used for each dataset." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 298, + 184, + 413, + 270 + ], + "blocks": [ + { + "bbox": [ + 298, + 184, + 413, + 270 + ], + "lines": [ + { + "bbox": [ + 298, + 184, + 413, + 270 + ], + "spans": [ + { + "bbox": [ + 298, + 184, + 413, + 270 + ], + "type": "image", + "image_path": "cab69db739c7d2d638c3402613fe399b6153c3954ed1b1be70d385bf17217044.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 314, + 273, + 401, + 282 + ], + "lines": [ + { + "bbox": [ + 314, + 273, + 401, + 282 + ], + "spans": [ + { + "bbox": [ + 314, + 273, + 401, + 282 + ], + "type": "text", + "content": "(a) Reference pool size " + }, + { + "bbox": [ + 314, + 273, + 401, + 282 + ], + "type": "inline_equation", + "content": "M" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 330, + 291, + 496, + 302 + ], + "lines": [ + { + "bbox": [ + 330, + 291, + 496, + 302 + ], + "spans": [ + { + "bbox": [ + 330, + 291, + 496, + 302 + ], + "type": "text", + "content": "Figure 6. Ablation studies on hyperparameters." + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "type": "image", + "bbox": [ + 415, + 184, + 530, + 270 + ], + "blocks": [ + { + "bbox": [ + 415, + 184, + 530, + 270 + ], + "lines": [ + { + "bbox": [ + 415, + 184, + 530, + 270 + ], + "spans": [ + { + "bbox": [ + 415, + 184, + 530, + 270 + ], + "type": "image", + "image_path": "620f420fe7f663046824a44598d94b1663dda27caae407ee29208e75f5bf95e4.jpg" + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 443, + 273, + 506, + 282 + ], + "lines": [ + { + "bbox": [ + 443, + 273, + 506, + 282 + ], + "spans": [ + { + "bbox": [ + 443, + 273, + 506, + 282 + ], + "type": "text", + "content": "(b) Temperature " + }, + { + "bbox": [ + 443, + 273, + 506, + 282 + ], + "type": "inline_equation", + "content": "\\tau" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "image_caption" + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 323, + 530, + 427 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 323, + 530, + 427 + ], + "spans": [ + { + "bbox": [ + 296, + 323, + 530, + 427 + ], + "type": "text", + "content": "Temperature Parameter. The temperature parameter " + }, + { + "bbox": [ + 296, + 323, + 530, + 427 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 296, + 323, + 530, + 427 + ], + "type": "text", + "content": " is the scaling parameter to manipulate the sensitivity of the contrastive loss. If " + }, + { + "bbox": [ + 296, + 323, + 530, + 427 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 296, + 323, + 530, + 427 + ], + "type": "text", + "content": " gets bigger, the objective function becomes robust to the difference between the similarity of the positive and negative samples. On the other hand, when " + }, + { + "bbox": [ + 296, + 323, + 530, + 427 + ], + "type": "inline_equation", + "content": "\\tau" + }, + { + "bbox": [ + 296, + 323, + 530, + 427 + ], + "type": "text", + "content": " gets lower, the embedding distribution is likely to be more uniform [31]. Although the results do not fluctuate much, we observe that training uniformly distributed embedding space leads to a slight performance drop in Fig. 6 (b)." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 297, + 437, + 367, + 450 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 437, + 367, + 450 + ], + "spans": [ + { + "bbox": [ + 297, + 437, + 367, + 450 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 296, + 457, + 530, + 677 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 457, + 530, + 677 + ], + "spans": [ + { + "bbox": [ + 296, + 457, + 530, + 677 + ], + "type": "text", + "content": "In this paper, we introduced a novel unsupervised semantic segmentation method by discovering and leveraging two types of hidden positives, global hidden positive (GHP) and local hidden positive (LHP), to learn rich semantic information with local consistency. First, anchor-dependent GHP comprises task-agnostic and task-specific positive sets which are used to tailor the contrastive learning for the unsupervised semantic segmentation task. Whereas the task-agnostic features are collected to guide the initial training, task-specific features are progressively engaged to learn the task-specific semantics information. Moreover, under the inherent premise that the adjacent patches are likely to be semantically similar, we propagate the loss gradient to the surrounding patches in proportion to their attention scores. This encourages the semantically similar peripheral patches to have the same objective as the anchor, resulting in semantic consistency between adjacent patches unless they belong to different objects. Finally, our proposed method achieves new state-of-the-art results in various datasets." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 296, + 682, + 530, + 730 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 682, + 530, + 730 + ], + "spans": [ + { + "bbox": [ + 296, + 682, + 530, + 730 + ], + "type": "text", + "content": "Acknowledgements. This work was supported in part by MSIT/IITP (No. 2022-0-00680, 2019-0-00421, 2020-0-01821, 2021-0-02068), and MSIT&KNPA/KIPoT (Police Lab 2.0, No. 210121M06)." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "spans": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "text", + "content": "19547" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 105, + 102, + 117 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 105, + 102, + 117 + ], + "spans": [ + { + "bbox": [ + 47, + 105, + 102, + 117 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 124, + 280, + 729 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 51, + 124, + 279, + 168 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 124, + 279, + 168 + ], + "spans": [ + { + "bbox": [ + 51, + 124, + 279, + 168 + ], + "type": "text", + "content": "[1] Holger Caesar, Jasper Uijlings, and Vittorio Ferrari. Cocostuff: Thing and stuff classes in context. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1209-1218, 2018. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 51, + 169, + 280, + 221 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 169, + 280, + 221 + ], + "spans": [ + { + "bbox": [ + 51, + 169, + 280, + 221 + ], + "type": "text", + "content": "[2] Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. Deep clustering for unsupervised learning of visual features. In Proceedings of the European conference on computer vision (ECCV), pages 132-149, 2018. 2, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 51, + 223, + 280, + 277 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 223, + 280, + 277 + ], + "spans": [ + { + "bbox": [ + 51, + 223, + 280, + 277 + ], + "type": "text", + "content": "[3] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems, 33:9912-9924, 2020. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 51, + 279, + 279, + 332 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 279, + 279, + 332 + ], + "spans": [ + { + "bbox": [ + 51, + 279, + 279, + 332 + ], + "type": "text", + "content": "[4] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9650-9660, 2021. 2, 3, 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 51, + 334, + 279, + 376 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 334, + 279, + 376 + ], + "spans": [ + { + "bbox": [ + 51, + 334, + 279, + 376 + ], + "type": "text", + "content": "[5] Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. Semantic image segmentation with deep convolutional nets and fully connected crfs. *ICLR*, 2015. 1, 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 51, + 378, + 279, + 421 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 378, + 279, + 421 + ], + "spans": [ + { + "bbox": [ + 51, + 378, + 279, + 421 + ], + "type": "text", + "content": "[6] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International conference on machine learning, pages 1597-1607. PMLR, 2020. 2, 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 51, + 422, + 279, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 422, + 279, + 464 + ], + "spans": [ + { + "bbox": [ + 51, + 422, + 279, + 464 + ], + "type": "text", + "content": "[7] Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15750-15758, 2021. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 51, + 466, + 279, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 466, + 279, + 520 + ], + "spans": [ + { + "bbox": [ + 51, + 466, + 279, + 520 + ], + "type": "text", + "content": "[8] Jang Hyun Cho, Utkarsh Mall, Kavita Bala, and Bharath Hariharan. Picie: Unsupervised semantic segmentation using invariance and equivariance in clustering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16794-16804, 2021. 1, 2, 5, 6, 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 51, + 522, + 279, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 51, + 522, + 279, + 586 + ], + "spans": [ + { + "bbox": [ + 51, + 522, + 279, + 586 + ], + "type": "text", + "content": "[9] Marius Cordts, Mohamed Omran, Sebastian Ramos, Timo Rehfeld, Markus Enzweiler, Rodrigo Benenson, Uwe Franke, Stefan Roth, and Bernt Schiele. The cityscapes dataset for semantic urban scene understanding. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 3213-3223, 2016. 5" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 587, + 279, + 629 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 587, + 279, + 629 + ], + "spans": [ + { + "bbox": [ + 47, + 587, + 279, + 629 + ], + "type": "text", + "content": "[10] Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE international conference on computer vision, pages 1422-1430, 2015. 6" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 631, + 279, + 684 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 631, + 279, + 684 + ], + "spans": [ + { + "bbox": [ + 47, + 631, + 279, + 684 + ], + "type": "text", + "content": "[11] Jun Fu, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. Dual attention network for scene segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 3146-3154, 2019. 1, 2" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 685, + 279, + 729 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 685, + 279, + 729 + ], + "spans": [ + { + "bbox": [ + 47, + 685, + 279, + 729 + ], + "type": "text", + "content": "[12] Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. In International Conference on Learning Representations, 2018. 2" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 299, + 106, + 530, + 729 + ], + "type": "list", + "angle": 0, + "index": 27, + "blocks": [ + { + "bbox": [ + 299, + 106, + 530, + 170 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 106, + 530, + 170 + ], + "spans": [ + { + "bbox": [ + 299, + 106, + 530, + 170 + ], + "type": "text", + "content": "[13] Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent-a new approach to self-supervised learning. Advances in neural information processing systems, 33:21271-21284, 2020. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 299, + 172, + 530, + 224 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 172, + 530, + 224 + ], + "spans": [ + { + "bbox": [ + 299, + 172, + 530, + 224 + ], + "type": "text", + "content": "[14] Mark Hamilton, Zhoutong Zhang, Bharath Hariharan, Noah Snavely, and William T. Freeman. Unsupervised semantic segmentation by distilling feature correspondences. In International Conference on Learning Representations, 2022. 1, 2, 3, 5, 6" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 299, + 226, + 530, + 278 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 226, + 530, + 278 + ], + "spans": [ + { + "bbox": [ + 299, + 226, + 530, + 278 + ], + "type": "text", + "content": "[15] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9729-9738, 2020. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 299, + 281, + 530, + 323 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 281, + 530, + 323 + ], + "spans": [ + { + "bbox": [ + 299, + 281, + 530, + 323 + ], + "type": "text", + "content": "[16] Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song. Using self-supervised learning can improve model robustness and uncertainty. Advances in neural information processing systems, 32, 2019. 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 299, + 325, + 530, + 378 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 325, + 530, + 378 + ], + "spans": [ + { + "bbox": [ + 299, + 325, + 530, + 378 + ], + "type": "text", + "content": "[17] Jyh-Jing Hwang, Stella X Yu, Jianbo Shi, Maxwell D Collins, Tien-Ju Yang, Xiao Zhang, and Liang-Chieh Chen. Segsort: Segmentation by discriminative sorting of segments. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7334-7344, 2019. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 299, + 379, + 530, + 412 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 379, + 530, + 412 + ], + "spans": [ + { + "bbox": [ + 299, + 379, + 530, + 412 + ], + "type": "text", + "content": "[18] Phillip Isola, Daniel Zoran, Dilip Krishnan, and Edward H Adelson. Learning visual groups from co-occurrences in space and time. arXiv preprint arXiv:1511.06811, 2015. 6" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 299, + 413, + 530, + 465 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 413, + 530, + 465 + ], + "spans": [ + { + "bbox": [ + 299, + 413, + 530, + 465 + ], + "type": "text", + "content": "[19] Xu Ji, Joao F Henriques, and Andrea Vedaldi. Invariant information clustering for unsupervised image classification and segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 9865-9874, 2019. 1, 2, 5, 6" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 299, + 467, + 530, + 519 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 467, + 530, + 519 + ], + "spans": [ + { + "bbox": [ + 299, + 467, + 530, + 519 + ], + "type": "text", + "content": "[20] Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. Supervised contrastive learning. Advances in Neural Information Processing Systems, 33:18661-18673, 2020. 2, 4" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 299, + 522, + 530, + 563 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 522, + 530, + 563 + ], + "spans": [ + { + "bbox": [ + 299, + 522, + 530, + 563 + ], + "type": "text", + "content": "[21] Philipp Krahenbihl and Vladlen Koltun. Efficient inference in fully connected crfs with gaussian edge potentials. Advances in neural information processing systems, 24, 2011. 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 299, + 564, + 530, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 564, + 530, + 586 + ], + "spans": [ + { + "bbox": [ + 299, + 564, + 530, + 586 + ], + "type": "text", + "content": "[22] Samuli Laine and Timo Aila. Temporal ensembling for semi-supervised learning. ICLR, 2017. 4" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 299, + 588, + 530, + 641 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 588, + 530, + 641 + ], + "spans": [ + { + "bbox": [ + 299, + 588, + 530, + 641 + ], + "type": "text", + "content": "[23] Jungbeom Lee, Eunji Kim, Sungmin Lee, Jangho Lee, and Sungroh Yoon. Ficklenet: Weakly and semi-supervised semantic image segmentation using stochastic inference. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5267-5276, 2019. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 299, + 642, + 530, + 684 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 642, + 530, + 684 + ], + "spans": [ + { + "bbox": [ + 299, + 642, + 530, + 684 + ], + "type": "text", + "content": "[24] David G Lowe. Object recognition from local scale-invariant features. In Proceedings of the seventh IEEE international conference on computer vision, volume 2, pages 1150-1157. IEEE, 1999. 6" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 299, + 685, + 530, + 729 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 685, + 530, + 729 + ], + "spans": [ + { + "bbox": [ + 299, + 685, + 530, + 729 + ], + "type": "text", + "content": "[25] Ishan Misra and Laurens van der Maaten. Self-supervised learning of pretext-invariant representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6707-6717, 2020. 2" + } + ] + } + ], + "index": 26 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "spans": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "text", + "content": "19548" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 106, + 279, + 730 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 47, + 106, + 279, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 106, + 279, + 149 + ], + "spans": [ + { + "bbox": [ + 47, + 106, + 279, + 149 + ], + "type": "text", + "content": "[26] WonJun Moon, Ji-Hwan Kim, and Jae-Pil Heo. Tailoring self-supervision for supervised learning. In European Conference on Computer Vision, pages 346-364. Springer, 2022. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 150, + 279, + 202 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 150, + 279, + 202 + ], + "spans": [ + { + "bbox": [ + 47, + 150, + 279, + 202 + ], + "type": "text", + "content": "[27] Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. the Journal of machine learning research, 12:2825-2830, 2011. 6" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 204, + 279, + 256 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 204, + 279, + 256 + ], + "spans": [ + { + "bbox": [ + 47, + 204, + 279, + 256 + ], + "type": "text", + "content": "[28] Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical image computing and computer-assisted intervention, pages 234-241. Springer, 2015. 1" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 258, + 279, + 300 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 258, + 279, + 300 + ], + "spans": [ + { + "bbox": [ + 47, + 258, + 279, + 300 + ], + "type": "text", + "content": "[29] Robin Strudel, Ricardo Garcia, Ivan Laptev, and Cordelia Schmid. Segmenter: Transformer for semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7262-7272, 2021. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 301, + 279, + 353 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 301, + 279, + 353 + ], + "spans": [ + { + "bbox": [ + 47, + 301, + 279, + 353 + ], + "type": "text", + "content": "[30] Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, and Luc Van Gool. Unsupervised semantic segmentation by contrasting object mask proposals. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10052-10062, 2021. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 354, + 279, + 395 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 354, + 279, + 395 + ], + "spans": [ + { + "bbox": [ + 47, + 354, + 279, + 395 + ], + "type": "text", + "content": "[31] Feng Wang and Huaping Liu. Understanding the behaviour of contrastive loss. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2495-2504, 2021. 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 397, + 279, + 448 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 397, + 279, + 448 + ], + "spans": [ + { + "bbox": [ + 47, + 397, + 279, + 448 + ], + "type": "text", + "content": "[32] Shujun Wang, Lequan Yu, Caizi Li, Chi-Wing Fu, and Pheng-Ann Heng. Learning from extrinsic and intrinsic supervisions for domain generalization. In European Conference on Computer Vision, pages 159–176. Springer, 2020. 4" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 451, + 279, + 504 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 451, + 279, + 504 + ], + "spans": [ + { + "bbox": [ + 47, + 451, + 279, + 504 + ], + "type": "text", + "content": "[33] Yude Wang, Jie Zhang, Meina Kan, Shiguang Shan, and Xilin Chen. Self-supervised equivariant attention mechanism for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12275-12284, 2020. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 505, + 279, + 556 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 505, + 279, + 556 + ], + "spans": [ + { + "bbox": [ + 47, + 505, + 279, + 556 + ], + "type": "text", + "content": "[34] Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo. Segformer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34:12077-12090, 2021. 1, 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 558, + 279, + 611 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 558, + 279, + 611 + ], + "spans": [ + { + "bbox": [ + 47, + 558, + 279, + 611 + ], + "type": "text", + "content": "[35] Lian Xu, Wanli Ouyang, Mohammed Bennamoun, Farid Boussaid, and Dan Xu. Multi-class token transformer for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4310-4319, 2022. 1" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 612, + 279, + 675 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 612, + 279, + 675 + ], + "spans": [ + { + "bbox": [ + 47, + 612, + 279, + 675 + ], + "type": "text", + "content": "[36] Yazhou Yao, Tao Chen, Guo-Sen Xie, Chuanyi Zhang, Fumin Shen, Qi Wu, Zhenmin Tang, and Jian Zhang. Nonsalient region object mining for weakly supervised semantic segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2623-2632, 2021. 1" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 676, + 279, + 730 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 676, + 279, + 730 + ], + "spans": [ + { + "bbox": [ + 47, + 676, + 279, + 730 + ], + "type": "text", + "content": "[37] Zhaoyuan Yin, Pichao Wang, Fan Wang, Xianzhe Xu, Hanling Zhang, Hao Li, and Rong Jin. Transfgu: a top-down approach to fine-grained unsupervised semantic segmentation. In European Conference on Computer Vision, pages 73-89. Springer, 2022. 1, 2, 5, 6" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 299, + 106, + 530, + 397 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 299, + 106, + 530, + 149 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 106, + 530, + 149 + ], + "spans": [ + { + "bbox": [ + 299, + 106, + 530, + 149 + ], + "type": "text", + "content": "[38] Yuhui Yuan, Xilin Chen, and Jingdong Wang. Object-contextual representations for semantic segmentation. In European conference on computer vision, pages 173-190. Springer, 2020. 1, 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 299, + 150, + 530, + 202 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 150, + 530, + 202 + ], + "spans": [ + { + "bbox": [ + 299, + 150, + 530, + 202 + ], + "type": "text", + "content": "[39] Sukmin Yun, Hankook Lee, Jaehyung Kim, and Jinwoo Shin. Patch-level representation learning for self-supervised vision transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8354-8363, 2022. 2, 5, 6" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 299, + 204, + 530, + 267 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 204, + 530, + 267 + ], + "spans": [ + { + "bbox": [ + 299, + 204, + 530, + 267 + ], + "type": "text", + "content": "[40] Xiangyun Zhao, Raviteja Vemulapalli, Philip Andrew Mansfield, Boqing Gong, Bradley Green, Lior Shapira, and Ying Wu. Contrastive learning for label efficient semantic segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10623-10633, 2021. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 299, + 270, + 530, + 343 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 270, + 530, + 343 + ], + "spans": [ + { + "bbox": [ + 299, + 270, + 530, + 343 + ], + "type": "text", + "content": "[41] Sixiao Zheng, Jiachen Lu, Hengshuang Zhao, Xiatian Zhu, Zekun Luo, Yabiao Wang, Yanwei Fu, Jianfeng Feng, Tao Xiang, Philip HS Torr, et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6881-6890, 2021. 1, 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 299, + 345, + 530, + 397 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 345, + 530, + 397 + ], + "spans": [ + { + "bbox": [ + 299, + 345, + 530, + 397 + ], + "type": "text", + "content": "[42] Wu Zheng, Weiliang Tang, Li Jiang, and Chi-Wing Fu. Sessd: Self-ensembling single-stage object detector from point cloud. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14494–14503, 2021. 4" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "spans": [ + { + "bbox": [ + 277, + 747, + 300, + 756 + ], + "type": "text", + "content": "19549" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/5cf56994-80ce-462c-9dfe-e6178b31a06f_content_list.json b/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/5cf56994-80ce-462c-9dfe-e6178b31a06f_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..fee17846f75e4c43ef70d18e8570d0e19b5e233a --- /dev/null +++ b/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/5cf56994-80ce-462c-9dfe-e6178b31a06f_content_list.json @@ -0,0 +1,2124 @@ +[ + { + "type": "text", + "text": "Leveraging Inter-rater Agreement for Classification in the Presence of Noisy Labels", + "text_level": 1, + "bbox": [ + 124, + 181, + 845, + 219 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Maria Sofia Bucarelli $^{2}$ * Lucas Cassano $^{1}$ Federico Siciliano $^{2}$ * Amin Mantrach $^{1}$ Fabrizio Silvestri $^{2,3}$ $^{1}$ Amazon $^{2}$ Sapienza University of Rome $^{3}$ ISTI-CNR, Pisa, Italy", + "bbox": [ + 110, + 243, + 889, + 276 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{mariasofia.bucarelli, federico.siciliano}@uniromal.it", + "bbox": [ + 277, + 277, + 724, + 290 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{lcecasl, mantrach}@amazon.lu", + "bbox": [ + 381, + 293, + 620, + 305 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "fsilvestri@diag.uniroma1.it", + "bbox": [ + 381, + 309, + 608, + 320 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 250, + 350, + 322, + 364 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In practical settings, classification datasets are obtained through a labelling process that is usually done by humans. Labels can be noisy as they are obtained by aggregating the different individual labels assigned to the same sample by multiple, and possibly disagreeing, annotators. The interrater agreement on these datasets can be measured while the underlying noise distribution to which the labels are subject is assumed to be unknown. In this work, we: (i) show how to leverage the inter-annotator statistics to estimate the noise distribution to which labels are subject; (ii) introduce methods that use the estimate of the noise distribution to learn from the noisy dataset; and (iii) establish generalization bounds in the empirical risk minimization framework that depend on the estimated quantities. We conclude the paper by providing experiments that illustrate our findings.", + "bbox": [ + 100, + 378, + 473, + 586 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 102, + 610, + 225, + 624 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Supervised learning has seen enormous progress in the last decades, both theoretical and practical. Empirical risk minimization is used as a learning framework [23], which relies on the assumption that the model is trained with iid (independent and identically distributed) sampled data from the joint distribution between features and labels. As a consequence of generalization bounds, when this assumption is satisfied any desired performance can be achieved as long as enough training data is available. However in many real-world applications, due to flaws during the data collection and labeling process, the assumption that the training data is sampled from the true feature-label joint distribution does not hold. Training data is often annotated by human raters who have some non-zero probability of making mistakes. It", + "bbox": [ + 100, + 631, + 470, + 814 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "has been reported in [21] that the ratio of corrupted labels in some real-world datasets is between $8.0\\%$ and, $38.5\\%$ . As a consequence of the presence of incorrect labels in the training dataset, the aforementioned assumption is violated and hence performance guarantees based on generalization bounds no longer hold.", + "bbox": [ + 499, + 352, + 868, + 429 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "This gap between theory and practice raises the question whether it is possible to learn from datasets with noisy labels while still having performance guarantees. This question has received a lot of attention lately and has already been answered in the positive in some cases [15, 16]. Indeed multiple works have introduced learning algorithms that can cope with datasets with incorrect labels while guaranteeing desirable performance through provable generalization bounds. However, these solutions do not solve the entirety of the problem due to the fact that they rely on precise knowledge of the error rate to which the labels are subject, which is often unknown in practice. Several works [16, 26, 27] attempt to address this issue by introducing techniques to estimate such error rate. Some of these methods have the drawback of relying on assumptions that do not always hold in practice, such as the existence of anchor samples [16]. Ideally, it would be desirable to design learning algorithms that are both robust to noisy labels, and for which performance guarantees can be provided.", + "bbox": [ + 497, + 433, + 868, + 680 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "An approach, often used in industry to reduce the impact of errors made by human raters, is to label the same dataset multiple times by different annotators. Then the individual labels are combined to reduce the probability of erroneous labels in the dataset, two popular approaches are majority vote or soft labeling. In these cases inter-annotator agreement (IAA) scores (like Cohen's kappa [1] and Fleiss' kappa [5]) provide measurable metrics that are directly related to the probability of error present in the labels.", + "bbox": [ + 497, + 684, + 868, + 801 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Since the IAA holds a direct relationship with the error rate associated with the human raters, one could potentially estimate the error rate and leverage this estimate to modify", + "bbox": [ + 499, + 804, + 868, + 844 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 95, + 8, + 171, + 46 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 228, + 5, + 818, + 48 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "*This work was done during Maria Sofia Bucarelli's and Federico Siciliano's internship at Amazon.", + "bbox": [ + 100, + 821, + 470, + 843 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "3439", + "bbox": [ + 468, + 875, + 500, + 885 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "the learning algorithms with the objective of making them robust to the resulting noise in the labels. This is the main direction we explore in this work.", + "bbox": [ + 100, + 148, + 470, + 187 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Motivation and Contributions: This work is motivated by two main points: i- to the best of our knowledge there are no published results that indicate how to leverage the IAA statistics to estimate the label noise distribution; and ii- the generalization bounds of existing noise tolerant training methods often rely on unknown quantities (like the true noise distribution) instead of on quantities that can be measured (like the IAA statistics).", + "bbox": [ + 100, + 188, + 470, + 290 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our contributions are the following: (i) we provide a methodology to estimate the label noise distribution based on the IAA statistics; (ii) we show how to leverage this estimate to learn from the noisy dataset; and (iii) we provide generalization bounds for our methods that depend on known quantities.", + "bbox": [ + 100, + 292, + 470, + 371 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related works", + "text_level": 1, + "bbox": [ + 102, + 384, + 238, + 397 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Our work is related to literature on three main topics: (i) robust loss function design, (ii) label aggregating and (iii) noise rate estimation.", + "bbox": [ + 100, + 407, + 470, + 445 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Robust loss functions In classification tasks, the goal is to obtain the lowest probability of classification error. The $0 - 1$ loss counts how many errors a classifier makes on a given dataset and is often used in the evaluation of the classifier. However, it is rarely used in optimization procedures because it is non-differentiable and non-continuous. To overcome this, many learning strategies use some convex surrogates of the $0 - 1$ loss function (e.g. hinge loss, squared error loss, cross-entropy).", + "bbox": [ + 100, + 466, + 470, + 583 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "It was proved ([6], [7]) that symmetric loss functions, that are functions for which the sum of the risks over all categories is equivalent to a constant for each arbitrary example, are robust to label noise. Examples of symmetric loss functions include the $0 - 1$ loss, the Ramp Loss and (softmax) Mean Absolute Error (MAE). In [29] authors show that even if MAE is noise tolerant and cathegorical cross entropy (CCE) is not, MAE can perform poorly when used to train DNN in challenging domains. They also propose a loss function that can be seen as a generalization of MAE and CCE. Several other loss functions that do not strictly satisfy the symmetry condition have also been proposed to be robust against label noise when training deep neural networks [4, 13, 24].", + "bbox": [ + 100, + 584, + 470, + 764 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "[15] presents two methods to modify the surrogate loss in the presence of class-conditional random label noise. The first method introduces a new loss that is an unbiased estimator for a given surrogate loss, and the second method introduces a label-dependent loss. The paper provides generalization bounds for both methods, which depend on the", + "bbox": [ + 100, + 766, + 470, + 844 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "noise rate of the dataset and the complexity of the hypothesis space.", + "bbox": [ + 499, + 148, + 867, + 174 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Labels aggregation When constructing datasets for supervised learning, data is often not labeled by a single annotator, rather multiple imperfect annotators are asked to assign labels to documents. Typically, separate labels are aggregated into one before learning models are applied [3,20]. In our work, we propose to exploit a measure of the agreement between annotators to explicitly calculate the noise of the dataset. Recently some works revisited the choice of aggregating labels. In [19] authors explore how to train LETOR models with relevance judgments distributions instead of single-valued relevance labels. They interpret the output of a LETOR model as a probability value or distribution and define different KL divergence-based loss functions to train a model. The loss they proposed can be used to train any ranking model that relies on gradient-based learning (in particular they focused on transformer-based neural LETOR models and on the decision tree-based GBM model). However, the authors do not directly estimate the noise rates in the annotations or study how learning from these noisy labels affects the generalization error of the models trained with the methods they introduce. In [25] the authors analyze the performance of both label aggregation and non-aggregation approaches in the context of empirical risk minimization for a number of popular loss functions, including those designed specifically for the noisy label learning problem. They conclude that label separation is preferable to label aggregation when noise rates are high or the number of labelers/annotations is insufficient. [17] and [22] exploit the availability of multiple human annotations to construct soft labels and concludes that this increases performance in terms of generalization to out-of-training-distribution test datasets, and robustness to adversarial attacks. [2] focus on efficiently eliciting soft labels from individual annotators.", + "bbox": [ + 497, + 191, + 868, + 631 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Noise rate estimation A number of approaches have been proposed for estimating the noise transition matrix (i.e. the probabilities that correct labels are changed for incorrect ones) [12, 16, 31]. Usually these methods use a small number of anchor points (that are samples that belong to a specific class with probability one) [8]. In particular, [16] proposed a noise estimation method based on anchor points, with the intent to provide an 'end-to-end' noise-estimation-and-learning method. Due to the lack of anchor points in real data, some works focused on a way to detect anchor points in noisy data, [26, 27]. In [27] the authors propose to introduce an intermediate class to avoid directly estimating the noisy class posterior. [28] also propose an iterative noise estimation heuristic that aims to partly correct the error and pointed out that the methods introduced by [16]", + "bbox": [ + 497, + 649, + 868, + 845 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "3440", + "bbox": [ + 468, + 875, + 500, + 885 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "and [27] have an error in computing anchor points, and provide conditions on the noise under which the methods work or fail. [26] provides a solution that can infer the transition matrix without anchor points. Indeed they use the instances with the highest class posterior probabilities for noisy data as anchor points. Our work differs from the mentioned work that use anchor points because we do not need to assume the existence of anchor points or to have a validation set to learn the noise rate and we only use noisy data to train our model, moreover we neither aim to detect anchor points in the noisy data. Also most of these works do not study the generalization properties of the proposed models, while we also address this problem and find bound that depend on the estimated noise transition matrix.", + "bbox": [ + 100, + 148, + 472, + 330 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Another approach is based on the clusterability condition, that is an example belongs to the same true class of its nearest-neighbors representations. [30] presented a method that relies on statistics of high-order consensuses among the 2 nearest-neighbors noisy labels.", + "bbox": [ + 100, + 333, + 472, + 399 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Problem formulation", + "text_level": 1, + "bbox": [ + 102, + 420, + 290, + 434 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Notation", + "text_level": 1, + "bbox": [ + 102, + 445, + 200, + 458 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this paper we follow the following notation. Matrices and sets are denoted by upper-case and calligraphic letters, respectively. The space of $d$ -dimensional feature vectors is denoted by $\\mathcal{X} \\subset \\mathbb{R}^d$ .", + "bbox": [ + 100, + 467, + 472, + 521 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We denote by $C$ the number of classes and by $e_j$ the $j$ -th standard canonical vector in $\\mathbb{R}^C$ , namely the vector that has 1 in the $j$ -th position and zero in all the other positions. $\\mathcal{V} = \\{e_1,\\dots ,e_C\\} \\subset \\{0,1\\} ^C$ is the label set. Feature vectors and labels are denoted by $x$ and $y$ , respectively. $\\mathcal{D}$ is the joint distribution of the feature vectors and labels, i.e. $(x,y)\\sim \\mathcal{D}$ . The sampled dataset of size $n$ is denoted by $\\widehat{\\mathcal{D}} = \\{(x_i,y_i)\\}_{i = 1}^n$ . $f(x)$ denotes the output of the classifier $f$ for feature vector $x$ and is a $C$ dimensional vector. All vectors are column vectors.", + "bbox": [ + 100, + 524, + 472, + 655 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We denote by $\\ell(t, y)$ a generic loss function for the classification task that takes as input $C$ dimensional vectors $t$ and $y$ . In practice $t$ will contain the prediction of the model and $y$ will be the ground-truth label as a one-hot encoded vector. Namely $\\ell: [0,1]^C \\times \\mathcal{V} \\to \\mathbb{R}$ .", + "bbox": [ + 100, + 659, + 472, + 724 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2. Background", + "text_level": 1, + "bbox": [ + 102, + 742, + 226, + 756 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We consider the classification problem within the supervised learning framework, where the ultimate goal is to minimize the $\\ell$ -risk $R_{\\ell, \\mathcal{D}}(f) = \\mathbb{E}_{(x,y) \\sim \\mathcal{D}}[\\ell(f(x),y)]$ , for some loss function $\\ell$ . We denote by $\\mathcal{D}$ the joint distribution of feature vectors $x$ and labels $y$ . In practice, since the distribution is unknown instead of minimizing $R_{\\ell, \\mathcal{D}}(f)$ we minimize an", + "bbox": [ + 100, + 766, + 472, + 845 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "empirical risk over some sampled dataset $\\widehat{\\mathcal{D}}$ :", + "bbox": [ + 499, + 146, + 778, + 162 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\widehat {R} _ {\\ell , \\widehat {\\mathcal {D}}} (f) = \\frac {1}{n} \\sum_ {i = 1} ^ {n} \\ell \\left(f \\left(x _ {i}\\right), y _ {i}\\right) = \\mathbb {E} _ {(x, y) \\sim \\widehat {\\mathcal {D}}} [ \\ell (f (x), y) ]. \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 507, + 171, + 867, + 205 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this work we assume that the true labels $y_{i}$ are unknown and consider two scenarios, both of which rely on $H$ annotators.", + "bbox": [ + 497, + 213, + 868, + 252 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.2.1 Scenario I", + "text_level": 1, + "bbox": [ + 499, + 267, + 618, + 279 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this scenario we have access to the $H$ labels provided by the annotators for each sample, where $y_{i,a}$ refers to the label provided by the $a$ -th annotator for the $i$ -th sample. For a given feature vector $x_{i}$ the distribution of labels provided by annotator $a$ is given by its noise transition matrix $T_{a}$ , which is defined as follows:", + "bbox": [ + 497, + 287, + 868, + 365 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\left(T _ {a}\\right) _ {i, j} := \\mathbb {P} \\left(y _ {a} = j \\mid y = i\\right) \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 376, + 867, + 391 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Assumption 1. We assume that all annotators have the same noise transition matrix (i.e. $T_{a} = T$ for all $a$ ), that $T$ is symmetric and that its diagonal elements are larger than 0.5 (i.e. $\\mathbb{P}(y_a = i|y = i) > 0.5, \\forall i \\in \\{1,\\dots C\\}$ ).", + "bbox": [ + 497, + 400, + 868, + 454 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Note that by definition $T$ is right stochastic and hence also doubly stochastic. It is also strictly diagonally dominant and therefore non-singular.", + "bbox": [ + 497, + 460, + 868, + 500 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Proposition 3.0.1. $T$ is positive definite.", + "text_level": 1, + "bbox": [ + 499, + 507, + 754, + 522 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Proof. Since $T$ is symmetric it follows that all eigenvalues are real. Combining the fact that it is strictly diagonally dominant with Gershgorin's theorem we conclude that all eigenvalues lie in the range $(0,1]$ and hence $T$ is positive definite.", + "bbox": [ + 497, + 528, + 868, + 593 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Assumption 2. We assume that the annotators are conditionally independent on the true label $y$ :", + "bbox": [ + 499, + 603, + 867, + 629 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbb {P} \\left(y _ {a}, y _ {b} | y\\right) = \\mathbb {P} \\left(y _ {a} | y\\right) \\mathbb {P} \\left(y _ {b} | y\\right). \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 584, + 638, + 867, + 653 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We now define the IAA matrix $M_{ab}$ between annotators $a$ and $b$ as follows:", + "bbox": [ + 499, + 662, + 867, + 688 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\left(M _ {a b}\\right) _ {i, j} := \\mathbb {P} \\left(y _ {a} = i, y _ {b} = j\\right) \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 586, + 699, + 867, + 714 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Proposition 3.0.2. Leveraging Assumption 2 the agreement matrix $M_{a,b}$ can be written as follows:", + "bbox": [ + 499, + 723, + 868, + 749 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nM _ {a, b} = T _ {a} ^ {T} D T _ {b} \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 566, + 758, + 867, + 773 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\nD := \\operatorname {d i a g} \\left\\{\\nu \\right\\} \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 578, + 776, + 867, + 790 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\nu : = \\left[ \\mathbb {P} (y = 1), \\dots , \\mathbb {P} (y = C) \\right] ^ {T}. \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 581, + 793, + 867, + 808 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Due to Proposition 3.0.1 and the fact that $D$ is positive definite it follows that all matrices $M_{a,b}$ are invertible.", + "bbox": [ + 499, + 817, + 867, + 844 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "3441", + "bbox": [ + 468, + 875, + 500, + 885 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Assumption 3. We assume that the class probabilities (and hence $D$ ) are known.", + "bbox": [ + 100, + 148, + 472, + 173 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Due to Assumption 1 all annotators share the same noise transition matrix $T$ . Therefore $M_{ab}$ is independent of $a$ and $b$ and from now on we remove this dependency in the notation (i.e. we get $M = T^T DT$ ). Furthermore, since $T$ is invertible and $D$ diagonal and positive definite it follows that $M$ is also positive definite.", + "bbox": [ + 100, + 182, + 470, + 261 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Note that since we have access to all the labels provided by the $H$ annotators for all the samples we can obtain an estimate of $M$ which we denote $\\widehat{M}$ .", + "bbox": [ + 100, + 262, + 470, + 302 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Assumption 4. We assume that $\\widehat{M}$ is a consistent estimator.", + "bbox": [ + 102, + 309, + 470, + 325 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "For the case of two annotators, one possible consistent estimator $\\widehat{M_{a,b}}$ that exploits its symmetry condition is given by:", + "bbox": [ + 100, + 333, + 470, + 375 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\left(\\widehat {M _ {a , b}}\\right) _ {i, j} = \\sum_ {k = 1} ^ {n} \\frac {\\mathbb {1} \\left(y _ {a , k} = i , y _ {b , k} = j\\right) + \\mathbb {1} \\left(y _ {a , k} = j , y _ {b , k} = i\\right)}{2 n} \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 109, + 384, + 468, + 429 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "If the annotators have the same transition matrix, $M$ will be the same for all pairs of annotators. So we can estimate $M$ , in the case of $H \\geq 2$ by averaging the estimators $\\widehat{M}_{ab}$ obtain by Eq. (8) for all possible pairs of annotators. The estimator in this case can be written as", + "bbox": [ + 100, + 441, + 472, + 508 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n(\\widehat{M})_{i,j} = \\frac{1}{H(H - 1)}\\sum_{a = 1}^{H}\\sum_{\\substack{b = 1\\\\ b\\neq a}}^{H}\\sum_{h = 1}^{n}\\frac{\\mathbb{1}(y_{a,h} = i,y_{b,h} = j)}{n}. \\tag{9}\n$$\n", + "text_format": "latex", + "bbox": [ + 112, + 515, + 472, + 559 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2.2 Scenario II", + "text_level": 1, + "bbox": [ + 102, + 574, + 226, + 587 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In the second scenario, for each $i$ -th sample we are given a unique label $\\tilde{y}_i$ that is produced by aggregating the $H$ individual labels according to some known aggregating policy (like majority vote). In this case, since we do not have access to the individual annotations we assume that $\\widehat{M}$ is provided.", + "bbox": [ + 100, + 595, + 470, + 674 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The probability that label $y_{i}$ is corrupted to some other label $\\tilde{y}_{i}$ is given by the aggregated noise transition matrix $\\Gamma \\in [0,1]^{C\\times C}$ , where $\\Gamma_{ij} \\coloneqq \\mathbb{P}(\\tilde{y} = j|y = i)$ is the probability of the true label $i$ being flipped into a corrupted label $j$ and $C$ is the number of classes. Note that by definition $\\Gamma$ is a right stochastic matrix that is determined by $T$ , the amount of annotators $H$ and the aggregating policy. We will study both the case where $\\Gamma = T$ , and the case in which there exists a generic Lipschitz function $\\phi$ so that $\\Gamma^{-1} = \\phi (T)$ .", + "bbox": [ + 100, + 675, + 470, + 792 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "There are different policy choices to construct the dataset that lead to $\\Gamma = T$ . If we decide to use only one annotator, for instance $a$ , to build the final dataset, namely for each sample $\\tilde{y}^i = y_a^i$ we have $\\Gamma = T_a$ . Or if annotators are", + "bbox": [ + 100, + 793, + 470, + 845 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "homogeneous, i.e. they have the same noise transition matrix $T$ , and to build the final dataset we decide to randomly select the label of one of the annotators we have that $\\Gamma = T$ .", + "bbox": [ + 497, + 148, + 868, + 187 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Even restricting ourselves to the case of homogeneous annotators, depending on the rule with which we build the dataset we can have a more complex relationship between the matrix $T$ and $\\Gamma$ .", + "bbox": [ + 497, + 188, + 868, + 239 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We also obtain generalization bounds in the case where we have an estimate of the agreement matrix $M$ is not available and we only have access to a scalar representation of the inter-annotator agreement, in particular we consider the case where the Cohen's $\\kappa$ is given.", + "bbox": [ + 497, + 241, + 868, + 306 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2.3 Objective", + "text_level": 1, + "bbox": [ + 499, + 325, + 613, + 339 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The objective in both scenarios is to: i) use $\\widehat{M}$ to estimate the noise transition matrices ( $T$ and $\\Gamma$ ); ii) leverage these estimates to be able to learn from the noisy dataset in a more robust manner; and iii) obtain generalization bounds for the resulting learning methods.", + "bbox": [ + 497, + 347, + 868, + 414 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Main results", + "text_level": 1, + "bbox": [ + 499, + 427, + 621, + 441 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We divide the main contributions in three sections. In the first section we show how to estimate the noise matrices $T$ . Next we indicate how to leverage these estimates to learn for the datasets with noisy labels. Finally we obtain bounds, depending on the Rademacher complexity of the class of functions, on the generalization gap for a bounded and Lipschitz loss function", + "bbox": [ + 497, + 450, + 868, + 541 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Estimation of the noise transition matrices", + "text_level": 1, + "bbox": [ + 499, + 551, + 838, + 564 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We start stating the following Lemma that allows us to write the unknown matrix $T$ (and its inverse), as a function of $D$ and $M$ .", + "bbox": [ + 497, + 572, + 868, + 610 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Lemma 4.1. If $D^{\\frac{1}{2}}$ commutes with $T$ we have that:", + "bbox": [ + 499, + 621, + 821, + 635 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nT = U \\Lambda^ {\\frac {1}{2}} U ^ {T} \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 670, + 646, + 865, + 661 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nT ^ {- 1} = U \\Lambda^ {- \\frac {1}{2}} U ^ {T} \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 653, + 665, + 865, + 680 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nD ^ {- \\frac {1}{2}} M D ^ {- \\frac {1}{2}} = U \\Lambda U ^ {T} \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 600, + 684, + 867, + 700 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $U\\Lambda U^T$ is the eigenvalue decomposition of $D^{-\\frac{1}{2}}MD^{-\\frac{1}{2}}$ (i.e. $U$ is some orthogonal matrix and $\\Lambda$ is a diagonal positive definite matrix).", + "bbox": [ + 499, + 712, + 868, + 752 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "A detailed discussion of when the commutativity assumption is satisfied is included in Appendix B. The proof of the previous Lemma can be find in Appendix C.1.", + "bbox": [ + 497, + 763, + 868, + 801 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Note that we could use Lemma 4.1 to estimate $T$ as follows:", + "bbox": [ + 497, + 802, + 867, + 826 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\widehat {T} = \\widehat {U} \\widehat {\\Lambda} _ {M} ^ {\\frac {1}{2}} \\widehat {U} ^ {T} \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 636, + 827, + 867, + 845 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "3442", + "bbox": [ + 468, + 875, + 500, + 885 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\widehat{U}\\widehat{\\Lambda}_M\\widehat{U}^T$ is the eigenvalue decomposition of $D^{-\\frac{1}{2}}\\widehat{MD}^{-\\frac{1}{2}}$ . However such estimate can result in matrices that are not doubly stochastic, or diagonally dominant due to estimation errors. A more accurate estimate of $T$ could be obtained as $\\widehat{T} = \\pi (\\widehat{U}\\widehat{\\Lambda}_{M}^{\\frac{2}{3}}\\widehat{U}^{T})$ where $\\pi$ is a projection operator to the set of doubly stochastic, positive definite matrices with diagonal elements greater than 0.5 and non-negative entries (which is a convex set). We can obtain such projection by solving the following optimization problem:", + "bbox": [ + 100, + 146, + 473, + 268 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\widehat {T} = \\pi \\left(\\widehat {U} \\widehat {\\Lambda} _ {M} ^ {\\frac {1}{2}} \\widehat {U} ^ {T}\\right) = \\underset {B} {\\operatorname {a r g m i n}} | | B - \\widehat {U} \\widehat {\\Lambda} _ {M} ^ {\\frac {1}{2}} \\widehat {U} ^ {T} | | _ {2} ^ {2} \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 124, + 282, + 470, + 308 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} B = B ^ {T} \\\\ \\sum_ {j} B _ {i, j} = 1 \\quad \\forall i \\\\ \\text {s . t .} \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 186, + 315, + 386, + 364 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{c c} B _ {i, j} \\geq 0 & \\forall i, j \\\\ B _ {i, i} \\geq 0. 5 & \\forall i \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 186, + 366, + 374, + 396 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Note that this optimization problem is convex because the constraints are linear and for symmetric matrices it holds that $||\\widehat{T} - \\widehat{U}\\widehat{\\Lambda}_{M}^{\\frac{1}{2}}\\widehat{U}^{T}||_{2}^{2} = \\lambda_{\\max}(\\widehat{T} - \\widehat{U}\\widehat{\\Lambda}_{M}^{\\frac{1}{2}}\\widehat{U}^{T})$ , which is a convex function of $\\widehat{T}$ .", + "bbox": [ + 100, + 400, + 470, + 456 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To summarize, $T$ can be estimated as follows. First, obtain an estimate of $M$ . Then obtain the eigenvalue decomposition of $D^{-\\frac{1}{2}}\\widehat{MD}^{-\\frac{1}{2}} = \\widehat{U}\\widehat{\\Lambda}\\widehat{U}^T$ (note that this decomposition always exists because $D^{-\\frac{1}{2}}\\widehat{MD}^{-\\frac{1}{2}}$ is symmetric). Finally obtain the estimate as: $\\widehat{T} \\coloneqq \\pi (\\widehat{U}\\widehat{\\Lambda}^{\\frac{1}{2}}\\widehat{U}^{T})$ .", + "bbox": [ + 100, + 457, + 470, + 524 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Note that once the estimate of $\\widehat{T}$ is obtained, $\\widehat{\\Gamma}$ can be obtained since we assumed the label aggregating policy to be known.", + "bbox": [ + 100, + 524, + 470, + 564 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Lemma 4.2. Let $M_{a,b}$ be the agreement matrix for annotators $a$ and $b$ defined in Eq. (4) and $\\widehat{M_{a,b}}$ be the estimated agreement matrix defined in Eq. (8) and let $||.||_p$ be the matrix norm induced by the $p$ vector norm. For every $p \\in [1,\\infty]$ and for every $\\delta > 0$ , with probability at least $1 - \\delta$", + "bbox": [ + 100, + 571, + 470, + 649 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\left| \\left| M _ {a, b} - \\widehat {M _ {a , b}} \\right| \\right| _ {p} \\leq \\sqrt {\\frac {C ^ {2}}{2 n} \\ln \\frac {2 C ^ {2}}{\\delta}}. \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 174, + 650, + 470, + 678 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\mathbb{P}^n$ denotes the probability according to which the $n$ training samples are distributed, i.e. we are assuming that the samples are independently drawn according the probability $\\mathbb{P}$ .", + "bbox": [ + 100, + 680, + 470, + 733 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Proof. The proof can be found in Appendix C.2.", + "bbox": [ + 102, + 738, + 406, + 752 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "From Lemma 4.2 it follows that if $\\widehat{M}$ is estimated as in Eq. (9), since $\\widehat{M}$ is an average of $\\widehat{M_{ab}}$ it also holds that for every $p\\in [1,\\infty ]$ and for every $\\delta >0$ with probability at least $1 - \\delta$", + "bbox": [ + 100, + 760, + 470, + 812 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\left| \\left| M - \\widehat {M} \\right| \\right| _ {p} \\leq \\sqrt {\\frac {C ^ {2}}{2 n} \\ln \\frac {2 C ^ {2}}{\\delta}}. \\tag {16}\n$$\n", + "text_format": "latex", + "bbox": [ + 189, + 818, + 470, + 847 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Theorem 4.3. Let $T$ be the noise transition matrix defined as in Eq. (2) and $\\widehat{T}$ its estimate (defined as in Eq. (14)).", + "bbox": [ + 499, + 148, + 868, + 174 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "With probability at least $1 - \\delta$ :", + "bbox": [ + 517, + 174, + 712, + 187 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\left| \\left| T - \\widehat {T} \\right| \\right| _ {2} \\leq \\frac {C (\\sqrt {C} + 1) \\lambda_ {\\max } (D)}{\\lambda_ {\\min } (\\hat {T})} \\sqrt {\\frac {1}{2 n} \\ln \\frac {2 C ^ {2}}{\\delta}} \\tag {17a}\n$$\n", + "text_format": "latex", + "bbox": [ + 512, + 193, + 867, + 226 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\left| \\left| T ^ {- 1} - \\hat {T} ^ {- 1} \\right| \\right| _ {2} \\leq \\frac {9 C (\\sqrt {C} + 1) \\lambda_ {\\max } (D)}{\\lambda_ {\\min } (\\hat {T}) ^ {2}} \\sqrt {\\frac {1}{2 n} \\ln \\frac {2 C ^ {2}}{\\delta}} \\tag {17b}\n$$\n", + "text_format": "latex", + "bbox": [ + 507, + 239, + 867, + 286 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\nf o r n > \\frac {C ^ {2} (\\sqrt {C} + 1) ^ {2} (\\ln (2 C ^ {2}) ^ {2}}{2 \\lambda_ {\\operatorname* {m i n}} (\\hat {T}) ^ {2}}.\n$$\n", + "text_format": "latex", + "bbox": [ + 515, + 292, + 700, + 315 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Proof. The proof can be found in Appendix C.3.", + "bbox": [ + 499, + 319, + 801, + 332 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/0dd3992809e101651bd30e00d42486ec9be63260afaeacdee51b1eb171674e24.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 852, + 319, + 868, + 328 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "From the previous theorem we can notice that the error in estimation of $T$ decays as $\\frac{1}{\\sqrt{n}}$ as a function of $n$ .", + "bbox": [ + 499, + 340, + 867, + 370 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Learning from noisy labels", + "text_level": 1, + "bbox": [ + 499, + 375, + 727, + 389 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section we show how to leverage the estimates of the error rates to train the models.", + "bbox": [ + 499, + 394, + 868, + 420 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2.1 Posterior distribution of true labels as soft-labels", + "text_level": 1, + "bbox": [ + 499, + 435, + 868, + 448 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "It is noteworthy that if we have access to the labels provided by all annotators, the posterior probabilities of the true labels can be calculated leveraging $T$ and Bayes' Theorem as follows:", + "bbox": [ + 497, + 456, + 868, + 505 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\underbrace {\\mathbb {P} \\left(y _ {i} = c \\mid y _ {1 , i} , \\dots , y _ {H , i}\\right)} _ {:= p _ {c, i}} \\propto \\nu_ {c} \\prod_ {h = 1} ^ {H} \\underbrace {\\mathbb {P} \\left(y _ {h , i} \\mid y _ {i} = c\\right)} _ {= T _ {c, y _ {h, i}}} \\tag {18}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 511, + 867, + 555 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "we recall that $\\nu_{c} = \\mathbb{P}(y_{i} = c)$ and that the conditional probabilities on the r.h.s. are given by $T$ . In our case we can use our noisy transition estimates to estimate the posterior probabilities of the true labels, and afterwards we can use these posteriors to train the classifier.", + "bbox": [ + 497, + 562, + 868, + 627 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Lemma 4.4. For infinite annotators the posterior distribution over every sample calculated using the true $T$ converges to the dirac delta distribution centered on the true label almost surely (i.e. $\\lim_{H\\to \\infty}p_{c,i}\\stackrel {a.s}{=}\\mathbb{1}(y_i = c)$ ).", + "bbox": [ + 497, + 633, + 867, + 686 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Proof. See Appendix C.5.", + "bbox": [ + 499, + 691, + 665, + 704 + ], + "page_idx": 4 + }, + { + "type": "image", + "img_path": "images/c16bf70817f7d08dcf4da76a610fed959da3d85c645665178190710db7e3e64e.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 852, + 691, + 868, + 701 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We can use the posterior distributions as soft-labels defining the following loss for the i-th sample:", + "bbox": [ + 499, + 712, + 867, + 738 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\ell \\left(f \\left(x _ {i}\\right), y _ {1, i}, \\dots , y _ {H, i}\\right) = \\ell \\left(f \\left(x _ {i}\\right), \\bar {p} _ {i}\\right) \\tag {19}\n$$\n", + "text_format": "latex", + "bbox": [ + 563, + 744, + 867, + 760 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $\\bar{p}_i = [p_{1,i},\\dots ,p_{C,i}]^T$ . Or we can use the posterior distributions to weight the loss function at the $i$ -th sample evaluated at each of the possible labels:", + "bbox": [ + 497, + 764, + 868, + 805 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\ell \\left(f \\left(x _ {i}\\right), y _ {1, i}, \\dots , y _ {H, i}\\right) = \\sum_ {c = 1} ^ {C} p _ {c, i} \\ell \\left(f \\left(x _ {i}\\right), e _ {c}\\right) \\tag {20}\n$$\n", + "text_format": "latex", + "bbox": [ + 526, + 811, + 867, + 847 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "3443", + "bbox": [ + 468, + 875, + 500, + 885 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $e_c$ is the vector in $\\mathbb{R}^C$ with 1 in the $c$ -th position. Notice that for categorical cross entropy loss the two functions defined above correspond, but in general they define two different loss functions.", + "bbox": [ + 100, + 147, + 470, + 199 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Note that these soft-labels are different from the ones obtained by averaging the annotators labels as is done in [25]. The method using the posteriors exploits the $T$ matrix and thus more information than the simple mean of the values of the losses among annotators. We therefore expect this to yield better results than the aggregation using the mean proposed in [25]. These considerations are supported by the empirical results we obtained on synthetic datasets (see Sec. 6).", + "bbox": [ + 100, + 200, + 473, + 317 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.2.2 Robust loss functions", + "text_level": 1, + "bbox": [ + 102, + 332, + 292, + 345 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Another way to leverage the estimate of $T$ is to use robust loss functions, like the forward and backward loss functions presented in [15, 16]. Let $\\ell(t,y)$ be a generic loss function for the classification task, with a little abuse of notation we define $\\ell(t) = [\\ell(t,e_1),\\dots,\\ell(t,e_C)]^T$ . The backward and forward loss functions are defined in Eq. (21a) and Eq. (21b), respectively.", + "bbox": [ + 100, + 352, + 470, + 444 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nl _ {b} (t, y) = (\\widehat {\\Gamma} ^ {- 1} \\ell (t)) y \\tag {21a}\n$$\n", + "text_format": "latex", + "bbox": [ + 216, + 448, + 470, + 465 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nl _ {f} (t, y) = (\\ell (\\widehat {\\Gamma} ^ {T} t)) y \\tag {21b}\n$$\n", + "text_format": "latex", + "bbox": [ + 216, + 467, + 470, + 484 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To explain the notation in Eq. (21a) we are first doing the dot product between the matrix $\\Gamma^{-1}$ and the vector $\\ell(t)$ and then the dot product of the resulting vector with $y$ . These losses leverage aggregated labels and therefore different aggregating techniques can be used, like majority vote. Another possible aggregating technique that leverages the posterior probabilities is to assume that the true label is the one that corresponds to the class that has the highest posterior probability.", + "bbox": [ + 100, + 491, + 470, + 608 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "4.3. Generalizations gap bounds", + "text_level": 1, + "bbox": [ + 102, + 614, + 337, + 629 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In this section we derive generalization gap bounds for the backward loss that depend on the noise transition matrix estimated in Eq. (14). Since we are only addressing the problem for the backward loss, from now on we will denote the backward loss by $l$ .", + "bbox": [ + 100, + 634, + 470, + 700 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Remark 1. If $\\ell(t,y)$ is Lipschitz with constant $L$ , the loss function $l(t,y)$ is Lipschitz with Lipschitz constant $||\\Gamma^{-1}||_2 L$ .", + "bbox": [ + 100, + 706, + 472, + 745 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "We will prove the following theorem in the case of $\\Gamma = T$ . We emphasize that all the results apply also when $\\Gamma^{-1} = \\phi(T^{-1})$ and that the function that associate $\\Gamma^{-1}$ and $T^{-1}, \\phi$ is Lipschitz with respect to the norm $p$ , i.e. there exists a Lipschitz constant $L_{\\phi, p}$ s.t. $||\\phi(T^{-1}) - \\phi(\\hat{T}^{-1})||_p \\leq L_{\\phi, p}||T^{-1} - \\widehat{T}^{-1}||_p$ . The only difference is that in the bound we will have a factor $L_{\\phi, p}$ .", + "bbox": [ + 100, + 751, + 472, + 845 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "It has been proved, first in [15] (Lemma 1) for the binary classification task and then in general for the multiclass case in [16] (Theorem 1) that $l(t,y)$ is an unbiased estimator for $\\ell$ , i.e.", + "bbox": [ + 497, + 148, + 868, + 200 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbb {E} _ {\\tilde {y} | y} [ l (t, \\tilde {y}) ] = \\ell (t, y).\n$$\n", + "text_format": "latex", + "bbox": [ + 611, + 209, + 754, + 225 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Lemma 4.5. Let $\\ell$ be a bounded loss function, with $\\ell \\in [0,\\mu ]$ , s.t. there exists a Lipschitz function $\\alpha$ , with Lipschitz constant $L$ , so that $\\ell (f(x),y) = \\alpha |f(x) - y|$ . Let $\\widehat{R}_l(f)$ be the empirical risk for the loss $l$ and let $R_{l,\\mathcal{D}}$ be the risk for loss $l$ under the distribution $\\mathcal{D}$ , with $l$ unbiased estimator for the loss $\\ell$ . We denote by $\\hat{l}$ the backward loss obtained using $\\widehat{T}$ .", + "bbox": [ + 497, + 231, + 868, + 325 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} \\sup _ {f \\in \\mathcal {F}} | \\hat {R} _ {\\hat {l}} (f) - R _ {l, \\mathcal {D}} (f) | \\\\ \\leq \\left[ L \\lambda_ {\\min} (\\widehat {T} ^ {2}) + \\frac {\\mu \\lambda_ {\\min} (D)}{\\lambda_ {\\min} (\\widehat {T}) ^ {2}} \\sqrt {\\frac {1}{n} \\ln \\left(\\frac {4 C}{\\delta}\\right)} \\right] \\Re_ {n} (\\mathcal {F}) g (C). \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 502, + 344, + 867, + 406 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nw i t h g (C) = 6 C ^ {2} (\\sqrt {C} + 1)\n$$\n", + "text_format": "latex", + "bbox": [ + 500, + 413, + 677, + 429 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Theorem 4.6. Let $l$ be an unbiased estimator for $\\ell$ defined as in Eq. (21a), Denoting $\\hat{f} = \\operatorname*{argmin}_{f}(\\widehat{R}_{\\hat{l}}(f))$ . It holds that", + "bbox": [ + 497, + 435, + 868, + 471 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\begin{array}{l} R _ {\\ell , \\mathcal {D}} (\\hat {f}) - \\min _ {f \\in \\mathcal {F}} R _ {\\ell , \\mathcal {D}} (f) \\\\ \\leq \\left[ 2 L \\lambda_ {\\min } (\\widehat {T} ^ {2}) + \\frac {\\mu \\lambda_ {\\min } (D)}{\\lambda_ {\\min } (\\widehat {T}) ^ {2}} \\sqrt {\\frac {1}{n} \\ln \\left(\\frac {4 C}{\\delta}\\right)} \\right] \\Re_ {n} (\\mathcal {F}) g (C) \\\\ \\end{array}\n$$\n", + "text_format": "latex", + "bbox": [ + 500, + 482, + 867, + 542 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\nw i t h g (C) = 6 C ^ {2} (\\sqrt {C} + 1)\n$$\n", + "text_format": "latex", + "bbox": [ + 500, + 549, + 677, + 565 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The proofs of Lemma 4.5 and Theorem 4.6 can be found in Appendix C. We observe that in all the previous theorems, the bounds found are always decreasing as one over the square root of the number of samples. The above theorem gives us a performance bound for the classifier found minimizing the backward loss $l$ , i.e. the unbiased estimator of the loss $\\ell$ on the noisy dataset. The bounds found depend on, the Rademacher complexity of the function space and the Lipschitz constant of the loss function. The importance of these bounds lies in the fact that they allow us to obtain performance bounds for a model trained with noisy data that depends on values that we can estimate from the noisy dataset. In particular, there is no dependence on the true noise transition matrix of the annotators, as in other work [15] which is instead a quantity that cannot be known a priori having access only to the training data. More in detail the bound depends on the estimate noise transition matrix, the number of classes in the dataset, the Rademacher complexity and the Lipschitz constant, which we can take as known a priori and on the distribution of ground truth, which in many cases it makes sense to assume uniform.", + "bbox": [ + 497, + 571, + 868, + 845 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "3444", + "bbox": [ + 468, + 875, + 500, + 885 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Cohen's $\\kappa$", + "text_level": 1, + "bbox": [ + 102, + 147, + 203, + 160 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We can also consider the case where an estimate of the IAA matrix $M$ is not available and we only have access to a scalar representation of the inter-annotator agreement like Cohen's $\\kappa$ . In this case we can only estimate one parameter and hence the matrix $T$ has to be parameterized by a single parameter that can be estimated.", + "bbox": [ + 100, + 169, + 470, + 245 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "One particular example is the case where the noise is uniform among classes. Under these hypotheses, $T$ is a matrix with all values $1 - p$ on the diagonal and $\\frac{p}{C - 1}$ off the diagonal.", + "bbox": [ + 100, + 247, + 470, + 299 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Lemma 5.1 (Relationship between $p$ and $\\kappa$ ). In the case of classification with uniform noise for two homogeneous annotators with noise rate $p$ , i.e if $a$ is one annotator, $\\mathbb{P}(y_a = i|y = j) = p$ if $i \\neq j$ . If the distribution of the ground-truth labels is uniform, it holds that:", + "bbox": [ + 100, + 306, + 470, + 371 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\np = (1 - C ^ {- 1}) (1 - \\sqrt {\\kappa}) \\tag {22}\n$$\n", + "text_format": "latex", + "bbox": [ + 205, + 378, + 470, + 395 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "with $\\kappa$ the Cohen's kappa coefficient of the two annotators (see Appendix A).", + "bbox": [ + 100, + 403, + 470, + 429 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Proof. The proof can be found in Appendix C.6. $\\square$", + "bbox": [ + 102, + 435, + 470, + 450 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "If $T$ is assumed to be of the form described above (with all diagonal elements equal to $1 - p$ and all off-diagonal entries equal), it has one eigenvalue equal to 1 and all the rest are equal to $1 - pC(C - 1)^{-1}$ (this follows from the fact that in this case $T$ can be written as a weighted summation of the identity and a rank-one matrix). Hence using Eq. (22) we get that $\\lambda_{\\mathrm{min}}(T) = \\sqrt{\\kappa}$ . The bounds from Theorem 4.6 hold replacing $\\lambda_{\\mathrm{min}}(T)$ with $\\sqrt{\\kappa}$ . This allows us to obtain bound for the generalization gap of a classifier trained with backward loss even in the case where a single statistic on agreement between annotators is provided.", + "bbox": [ + 100, + 458, + 470, + 600 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "6. Experimental results", + "text_level": 1, + "bbox": [ + 102, + 612, + 289, + 625 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We performed experiments to validate the effectiveness of the method we propose for estimating $\\widehat{T}$ by studying the error in the estimation as a function of the number of samples. We also performed experiments to show how the estimated $T$ can be leveraged to train classifiers in the presence of noise labels. In particular, we performed experiments for a classification task on a synthetic dataset and on the CIFAR10-N dataset, comparing the performance of a classifier trained using labels obtained by some baseline aggregation method with the performance of a classifier trained using the distribution of posteriors obtained from the estimation of T (Eq. (18)) as soft-labels.", + "bbox": [ + 100, + 633, + 470, + 789 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Estimation of $T$ With these experiments we aim to validate the theoretical results of Sec. 4.1. We generate various matrices $T$ that are symmetric, stochastic and diagonally", + "bbox": [ + 100, + 804, + 470, + 844 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "dominant, the exact details about the generation of $T$ can be found in Appendix D.1. For each annotator we produce their prediction according to the matrix $T$ . We run experiments for the number of annotators $H = 10, 7, 3, 2$ . We report here the results for $H = 10$ , and 4 classes, all the other plots are in Appendix D.1. In Fig. 4 (as well as the plots in the Appendix) we can be observed that the error in the estimation decreases as $\\frac{1}{\\sqrt{n}}$ with $n$ number of samples, which is in agreement with the bound provided in Theorem 4.3. We also observed that, as expected, the estimation becomes more accurate as the number of annotators increases.", + "bbox": [ + 497, + 148, + 868, + 292 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/18bacc82ef382ef69447b40f5f97cb6766f85b03af34e2d2d01cbbe55eca8baf.jpg", + "image_caption": [ + "Figure 1. Error in the estimation of $T$ for 4 classes and 10 annotators. The plots are obtained by averaging different admissible matrices $T$ (see Appendix B) and averaged over matrices that have the same minimum eigenvalues rounded to the first decimal." + ], + "image_footnote": [ + "min eigenvalue T 0.2 0.3 0.4 0.6 1.0" + ], + "bbox": [ + 502, + 302, + 867, + 425 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Classification task with synthetic data We consider a classification task with a synthetic dataset. The features are generated uniformly in $[0,1]^2$ . The assignment of labels $(y)$ is done by following the label distribution established for each experiment, separating the space with lines parallel to the bisector of the first and third quadrants. More information on how the class distributions are generated can be found in Appendix D.2.", + "bbox": [ + 497, + 542, + 867, + 644 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "For each dataset annotations are generated according to the noise transition matrix $T$ . Various combinations of $T$ are tested that respect the assumptions of symmetry, stochasticity and diagonally dominance, as well as being commutative with D (more details can be found in Appendix B). The number of annotators is variable in the set $\\{3,5\\}$ . See Appendix D.2 for implementations details.", + "bbox": [ + 497, + 646, + 868, + 737 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Losses We use categorical cross entropy as loss function. We use both hard labels and soft labels to train the models.", + "bbox": [ + 497, + 752, + 867, + 777 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To train the models with hard labels an aggregation method is needed to obtain one final label from the annotators. We consider random and majority vote. In random aggregation the final label is randomly picked from the labels of the annotators. In majority vote the final label is the", + "bbox": [ + 497, + 777, + 868, + 843 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "3445", + "bbox": [ + 468, + 875, + 500, + 885 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "one with the most amount of votes (the mode), if the mode is not unique, we randomly choose one of the most voted classes. As soft-labels we consider the relative frequency among annotators and the posterior distribution according to Eq. (18). In the case of frequency for each sample we average the one-hot encoded annotations. Notice that random, majority vote and frequency soft labels do not leverage the estimate of $T$ while the posterior does. In Fig. 2 we report the results for 4 classes with distribution (0.4, 0.1, 0.4, 0.1) and 3 annotators.", + "bbox": [ + 100, + 148, + 473, + 277 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/a68d8b599e6489bb1baedb49f4caf8f03cba0124d0e1352b35561d181db89900.jpg", + "image_caption": [ + "Figure 2. Comparison between performance of Cross Entropy Loss using majority vote, random aggregation method or the posteriors (posterior) and relative frequency (average) as soft labels. On the y-axis the accuracy on a clean dataset and on the x-axis the values of the minimum on the diagonal of $T$ . Small values of the minimum diagonal value mean a noisy dataset, while the minimum is 1 in the noise-free case. The results are obtained for 3 annotators and 4 classes, by averaging on different amissible matrices $T$ (see Appendix B) that have the same minimum diagonal values rounded to the first decimal. The error bands show the maximum and minimum performance for each method." + ], + "image_footnote": [], + "bbox": [ + 102, + 288, + 468, + 440 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We use accuracy with respect to a clean dataset as performance metric. Our results show that using the posteriors distribution, as soft labels, allows for better performance than using the average of the labels assigned by annotators and than using majority vote or random aggregation.", + "bbox": [ + 100, + 598, + 470, + 663 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Our method is shown to be more robust to the noise and is also the one with less variance in the results. This confirms our hypotheses that by leveraging the matrix $\\hat{T}$ better classification accuracy can be achieved.", + "bbox": [ + 100, + 663, + 472, + 714 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Experiments on CIFAR10-N The CIFAR10-N dataset contains CIFAR-10 train images with noisy labels annotated by humans using Amazon Mechanical Turk. Each image is labelled by three independent annotators. Table 1 shows the accuracy achieved using the different aggregation methods. For this experiment we used Resnet34 [10] with and without pre-training. In both cases, our approach of aggrega", + "bbox": [ + 100, + 731, + 472, + 824 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "tion achieves the best performance. Note that in this dataset there are no guarantees that the assumptions we made on $T$ are satisfied, however the method is still applicable with positive results.", + "bbox": [ + 499, + 148, + 870, + 201 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/c06cd0f79b5b62943bc09dac22643f90cf8cc8746c4c79d50d1560cb321a588c.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
Aggregation MethodPretrainedNot-Pretrained
random0.718 ± 0.0350.579 ± 0.023
majority vote0.740 ± 0.0170.590 ± 0.006
average0.762 ± 0.0120.637 ± 0.016
posteriors (ours)0.794 ± 0.0050.652 ± 0.014
", + "bbox": [ + 500, + 217, + 885, + 296 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 1. Test Accuracy on CIFAR10-N with Resnet34", + "bbox": [ + 531, + 305, + 836, + 319 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "7. Concluding remarks", + "text_level": 1, + "bbox": [ + 499, + 355, + 685, + 371 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We have addressed the problem of learning from noisy labels in the case where the dataset is labeled by annotators that occasionally make mistakes. We have introduced a methodology to estimate the noise transition matrix $T$ of the annotators given the IAA. We further showed different techniques to leverage this estimate to learn from the noisy dataset in a robust manner. We have shown theoretically that the methods we introduce are sound. We supported our methodology with some experiments that confirms our estimation of the noise transition matrix is valid and that this can be leveraged in the learning process to obtain better performance.", + "bbox": [ + 497, + 378, + 870, + 536 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Limitations The main limitation of our current approach to estimate $T$ is that it only considers the case where $T$ is symmetric and $D$ assumed to be known and commute with $T$ . Extending the results to the case where $T$ might not be symmetric and different among annotators is one possible future research direction.", + "bbox": [ + 497, + 566, + 870, + 644 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 500, + 675, + 633, + 688 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We acknowledge financial support from NRRP MUR project PE0000013-FAIR. This research was partially supported by MIUR under the grant \"Dipartimento di eccellenza 2018-2022\" of the Department of Computer Science and the Department of Computer Engineering at Sapienza University of Rome. It was also partially supported by the ERC Advanced Grant 788893 \"AMDROMA\", the EC H2020RIA project \"SoBigData++\" (871042), the MIUR PRIN project \"ALGADIMAR\", and the project SERICS (PE00000014) under the NRRP MUR program funded by the EU-NGEU.", + "bbox": [ + 497, + 700, + 870, + 844 + ], + "page_idx": 7 + }, + { + "type": "page_footnote", + "text": "1http://www.noisylabels.com", + "bbox": [ + 119, + 831, + 321, + 844 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "3446", + "bbox": [ + 468, + 875, + 502, + 885 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 104, + 146, + 193, + 161 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] J. Cohen. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1):37-46, 1960. 1, 11", + "[2] K. M. Collins, U. Bhatt, and A. Weller. Eliciting and learning with soft labels from every annotator, 2022. 2", + "[3] A. P. Dawid and A. M. Skene. Maximum likelihood estimation of observer error-rates using the em algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1):20-28, 1979. 2", + "[4] L. Feng, S. Shu, Z. Lin, F. Lv, L. Li, and B. An. Can cross entropy loss be robust to label noise? In C. Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pages 2206-2212. International Joint Conferences on Artificial Intelligence Organization, 7 2020. Main track. 2", + "[5] J. Fleiss et al. Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5):378-382, 1971. 1", + "[6] A. Ghosh, H. Kumar, and P. S. Sastry. Robust loss functions under label noise for deep neural networks. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI'17, page 1919-1925. AAAI Press, 2017. 2", + "[7] A. Ghosh, N. Manwani, and P. Sastry. Making risk minimization tolerant to label noise. Neurocomputing, 160:93-107, 2015. 2", + "[8] D. Hendrycks, M. Mazeika, D. Wilson, and K. Gimpel. Using trusted data to train deep networks on labels corrupted by severe noise. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018. 2", + "[9] R. A. Horn and C. R. Johnson. Matrix Analysis. Cambridge University Press, USA, 2nd edition, 2012. 14", + "[10] A. Khetan, Z. C. Lipton, and A. Anandkumar. Learning from noisy singly-labeled data, 2017. 8", + "[11] R. Meir and T. Zhang. Generalization error bounds for bayesian mixture algorithms. J. Mach. Learn. Res., 4:839-860, dec 2003. 17", + "[12] A. Menon, B. V. Rooyen, C. S. Ong, and B. Williamson. Learning from corrupted binary labels via class-probability estimation. In F. Bach and" + ], + "bbox": [ + 104, + 168, + 470, + 844 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "D. Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, pages 125-134, Lille, France, 07-09 Jul 2015. PMLR. 2", + "[13] A. K. Menon, A. S. Rawat, S. J. Reddi, and S. Kumar. Can gradient clipping mitigate label noise? In International Conference on Learning Representations, 2020. 2", + "[14] M. Mohri, A. Rostamizadeh, and A. Talwalkar. Foundations of Machine Learning. The MIT Press, 2012. 17", + "[15] N. Natarajan, I. S. Dhillon, P. K. Ravikumar, and A. Tewari. Learning with noisy labels. In C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Weinberger, editors, Advances in Neural Information Processing Systems, volume 26. Curran Associates, Inc., 2013. 1, 2, 6", + "[16] G. Patrini, A. Rozza, A. Krishna Menon, R. Nock, and L. Qu. Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1944-1952, 2017. 1, 2, 6", + "[17] J. Peterson, R. Battleday, T. Griffiths, and O. Russakovsky. Human uncertainty makes classification more robust. In Proceedings - 2019 International Conference on Computer Vision, ICCV 2019, Proceedings of the IEEE International Conference on Computer Vision, pages 9616-9625, United States, Oct. 2019. Institute of Electrical and Electronics Engineers Inc. 2", + "[18] J. E. Potter. Matrix quadratic solutions. SIAM Journal on Applied Mathematics, 14(3):496-501, 1966. 12", + "[19] A. Purpura, G. Silvello, and G. A. Susto. Learning to rank from relevance judgments distributions, 2022. 2", + "[20] V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, and L. Moy. Learning from crowds. Journal of Machine Learning Research, 11(43):1297-1322, 2010. 2", + "[21] H. Song, M. Kim, D. Park, Y. Shin, and J.-G. Lee. Learning from noisy labels with deep neural networks: A survey, 2020. 1", + "[22] A. Uma, T. Fornaciari, D. Hovy, S. Paun, B. Plank, and M. Poesio. A case for soft loss functions. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 8(1):173-177, Oct. 2020. 2" + ], + "bbox": [ + 502, + 148, + 868, + 842 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "3447", + "bbox": [ + 468, + 875, + 500, + 885 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[23] V. N. Vapnik. Statistical Learning Theory. Wiley-Interscience, 1998. 1", + "[24] Y. Wang, X. Ma, Z. Chen, Y. Luo, J. Yi, and J. Bailey. Symmetric cross entropy for robust learning with noisy labels. In IEEE International Conference on Computer Vision, 2019. 2", + "[25] J. Wei, Z. Zhu, T. Luo, E. Amid, A. Kumar, and Y. Liu. To aggregate or not? learning with separate noisy labels, 2022. 2, 6", + "[26] X. Xia, T. Liu, N. Wang, B. Han, C. Gong, G. Niu, and M. Sugiyama. Are anchor points really indispensable in label-noise learning? In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. 1, 2, 3", + "[27] Y. Yao, T. Liu, B. Han, M. Gong, J. Deng, G. Niu, and M. Sugiyama. Dual t: Reducing estimation error for transition matrix in label-noise learning. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS'20, Red Hook, NY, USA, 2020. Curran Associates Inc. 1, 2, 3", + "[28] M. Zhang, J. Lee, and S. Agarwal. Learning from noisy labels with no change to the training process. In M. Meila and T. Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 12468-12478. PMLR, 18-24 Jul 2021. 2", + "[29] Z. Zhang and M. R. Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS'18, page 8792-8802, Red Hook, NY, USA, 2018. Curran Associates Inc. 2", + "[30] Z. Zhu, Y. Song, and Y. Liu. Clusterability as an alternative to anchor points when learning with noisy labels, 2021. 3", + "[31] Z. Zhu, J. Wang, and Y. Liu. Beyond images: Label noise transition matrix estimation for tasks with lower-quality features. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, and S. Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 27633-27653. PMLR, 17-23 Jul 2022. 2" + ], + "bbox": [ + 104, + 148, + 470, + 813 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "3448", + "bbox": [ + 468, + 875, + 500, + 885 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/5cf56994-80ce-462c-9dfe-e6178b31a06f_model.json b/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/5cf56994-80ce-462c-9dfe-e6178b31a06f_model.json new file mode 100644 index 0000000000000000000000000000000000000000..0575c61d338fd98257acfa382b86301e629a2d7b --- /dev/null +++ b/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/5cf56994-80ce-462c-9dfe-e6178b31a06f_model.json @@ -0,0 +1,2420 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.096, + 0.009, + 0.172, + 0.047 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.23, + 0.006, + 0.819, + 0.049 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.126, + 0.182, + 0.847, + 0.22 + ], + "angle": 0, + "content": "Leveraging Inter-rater Agreement for Classification in the Presence of Noisy Labels" + }, + { + "type": "text", + "bbox": [ + 0.112, + 0.244, + 0.89, + 0.277 + ], + "angle": 0, + "content": "Maria Sofia Bucarelli \\(^{2}\\) * Lucas Cassano \\(^{1}\\) Federico Siciliano \\(^{2}\\) * Amin Mantrach \\(^{1}\\) Fabrizio Silvestri \\(^{2,3}\\) \\(^{1}\\) Amazon \\(^{2}\\) Sapienza University of Rome \\(^{3}\\) ISTI-CNR, Pisa, Italy" + }, + { + "type": "text", + "bbox": [ + 0.278, + 0.278, + 0.726, + 0.291 + ], + "angle": 0, + "content": "{mariasofia.bucarelli, federico.siciliano}@uniromal.it" + }, + { + "type": "text", + "bbox": [ + 0.382, + 0.294, + 0.621, + 0.306 + ], + "angle": 0, + "content": "{lcecasl, mantrach}@amazon.lu" + }, + { + "type": "text", + "bbox": [ + 0.382, + 0.31, + 0.609, + 0.321 + ], + "angle": 0, + "content": "fsilvestri@diag.uniroma1.it" + }, + { + "type": "title", + "bbox": [ + 0.251, + 0.351, + 0.324, + 0.365 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.379, + 0.474, + 0.587 + ], + "angle": 0, + "content": "In practical settings, classification datasets are obtained through a labelling process that is usually done by humans. Labels can be noisy as they are obtained by aggregating the different individual labels assigned to the same sample by multiple, and possibly disagreeing, annotators. The interrater agreement on these datasets can be measured while the underlying noise distribution to which the labels are subject is assumed to be unknown. In this work, we: (i) show how to leverage the inter-annotator statistics to estimate the noise distribution to which labels are subject; (ii) introduce methods that use the estimate of the noise distribution to learn from the noisy dataset; and (iii) establish generalization bounds in the empirical risk minimization framework that depend on the estimated quantities. We conclude the paper by providing experiments that illustrate our findings." + }, + { + "type": "title", + "bbox": [ + 0.104, + 0.611, + 0.226, + 0.625 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.633, + 0.472, + 0.815 + ], + "angle": 0, + "content": "Supervised learning has seen enormous progress in the last decades, both theoretical and practical. Empirical risk minimization is used as a learning framework [23], which relies on the assumption that the model is trained with iid (independent and identically distributed) sampled data from the joint distribution between features and labels. As a consequence of generalization bounds, when this assumption is satisfied any desired performance can be achieved as long as enough training data is available. However in many real-world applications, due to flaws during the data collection and labeling process, the assumption that the training data is sampled from the true feature-label joint distribution does not hold. Training data is often annotated by human raters who have some non-zero probability of making mistakes. It" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.353, + 0.869, + 0.431 + ], + "angle": 0, + "content": "has been reported in [21] that the ratio of corrupted labels in some real-world datasets is between \\(8.0\\%\\) and, \\(38.5\\%\\). As a consequence of the presence of incorrect labels in the training dataset, the aforementioned assumption is violated and hence performance guarantees based on generalization bounds no longer hold." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.434, + 0.869, + 0.681 + ], + "angle": 0, + "content": "This gap between theory and practice raises the question whether it is possible to learn from datasets with noisy labels while still having performance guarantees. This question has received a lot of attention lately and has already been answered in the positive in some cases [15, 16]. Indeed multiple works have introduced learning algorithms that can cope with datasets with incorrect labels while guaranteeing desirable performance through provable generalization bounds. However, these solutions do not solve the entirety of the problem due to the fact that they rely on precise knowledge of the error rate to which the labels are subject, which is often unknown in practice. Several works [16, 26, 27] attempt to address this issue by introducing techniques to estimate such error rate. Some of these methods have the drawback of relying on assumptions that do not always hold in practice, such as the existence of anchor samples [16]. Ideally, it would be desirable to design learning algorithms that are both robust to noisy labels, and for which performance guarantees can be provided." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.685, + 0.869, + 0.802 + ], + "angle": 0, + "content": "An approach, often used in industry to reduce the impact of errors made by human raters, is to label the same dataset multiple times by different annotators. Then the individual labels are combined to reduce the probability of erroneous labels in the dataset, two popular approaches are majority vote or soft labeling. In these cases inter-annotator agreement (IAA) scores (like Cohen's kappa [1] and Fleiss' kappa [5]) provide measurable metrics that are directly related to the probability of error present in the labels." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.805, + 0.869, + 0.845 + ], + "angle": 0, + "content": "Since the IAA holds a direct relationship with the error rate associated with the human raters, one could potentially estimate the error rate and leverage this estimate to modify" + }, + { + "type": "page_footnote", + "bbox": [ + 0.102, + 0.822, + 0.471, + 0.844 + ], + "angle": 0, + "content": "*This work was done during Maria Sofia Bucarelli's and Federico Siciliano's internship at Amazon." + }, + { + "type": "page_number", + "bbox": [ + 0.469, + 0.876, + 0.501, + 0.887 + ], + "angle": 0, + "content": "3439" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.102, + 0.149, + 0.471, + 0.188 + ], + "angle": 0, + "content": "the learning algorithms with the objective of making them robust to the resulting noise in the labels. This is the main direction we explore in this work." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.189, + 0.472, + 0.292 + ], + "angle": 0, + "content": "Motivation and Contributions: This work is motivated by two main points: i- to the best of our knowledge there are no published results that indicate how to leverage the IAA statistics to estimate the label noise distribution; and ii- the generalization bounds of existing noise tolerant training methods often rely on unknown quantities (like the true noise distribution) instead of on quantities that can be measured (like the IAA statistics)." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.293, + 0.472, + 0.372 + ], + "angle": 0, + "content": "Our contributions are the following: (i) we provide a methodology to estimate the label noise distribution based on the IAA statistics; (ii) we show how to leverage this estimate to learn from the noisy dataset; and (iii) we provide generalization bounds for our methods that depend on known quantities." + }, + { + "type": "title", + "bbox": [ + 0.103, + 0.385, + 0.24, + 0.399 + ], + "angle": 0, + "content": "2. Related works" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.408, + 0.472, + 0.446 + ], + "angle": 0, + "content": "Our work is related to literature on three main topics: (i) robust loss function design, (ii) label aggregating and (iii) noise rate estimation." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.467, + 0.472, + 0.584 + ], + "angle": 0, + "content": "Robust loss functions In classification tasks, the goal is to obtain the lowest probability of classification error. The \\(0 - 1\\) loss counts how many errors a classifier makes on a given dataset and is often used in the evaluation of the classifier. However, it is rarely used in optimization procedures because it is non-differentiable and non-continuous. To overcome this, many learning strategies use some convex surrogates of the \\(0 - 1\\) loss function (e.g. hinge loss, squared error loss, cross-entropy)." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.585, + 0.472, + 0.766 + ], + "angle": 0, + "content": "It was proved ([6], [7]) that symmetric loss functions, that are functions for which the sum of the risks over all categories is equivalent to a constant for each arbitrary example, are robust to label noise. Examples of symmetric loss functions include the \\(0 - 1\\) loss, the Ramp Loss and (softmax) Mean Absolute Error (MAE). In [29] authors show that even if MAE is noise tolerant and cathegorical cross entropy (CCE) is not, MAE can perform poorly when used to train DNN in challenging domains. They also propose a loss function that can be seen as a generalization of MAE and CCE. Several other loss functions that do not strictly satisfy the symmetry condition have also been proposed to be robust against label noise when training deep neural networks [4, 13, 24]." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.767, + 0.472, + 0.845 + ], + "angle": 0, + "content": "[15] presents two methods to modify the surrogate loss in the presence of class-conditional random label noise. The first method introduces a new loss that is an unbiased estimator for a given surrogate loss, and the second method introduces a label-dependent loss. The paper provides generalization bounds for both methods, which depend on the" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.149, + 0.868, + 0.175 + ], + "angle": 0, + "content": "noise rate of the dataset and the complexity of the hypothesis space." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.192, + 0.87, + 0.633 + ], + "angle": 0, + "content": "Labels aggregation When constructing datasets for supervised learning, data is often not labeled by a single annotator, rather multiple imperfect annotators are asked to assign labels to documents. Typically, separate labels are aggregated into one before learning models are applied [3,20]. In our work, we propose to exploit a measure of the agreement between annotators to explicitly calculate the noise of the dataset. Recently some works revisited the choice of aggregating labels. In [19] authors explore how to train LETOR models with relevance judgments distributions instead of single-valued relevance labels. They interpret the output of a LETOR model as a probability value or distribution and define different KL divergence-based loss functions to train a model. The loss they proposed can be used to train any ranking model that relies on gradient-based learning (in particular they focused on transformer-based neural LETOR models and on the decision tree-based GBM model). However, the authors do not directly estimate the noise rates in the annotations or study how learning from these noisy labels affects the generalization error of the models trained with the methods they introduce. In [25] the authors analyze the performance of both label aggregation and non-aggregation approaches in the context of empirical risk minimization for a number of popular loss functions, including those designed specifically for the noisy label learning problem. They conclude that label separation is preferable to label aggregation when noise rates are high or the number of labelers/annotations is insufficient. [17] and [22] exploit the availability of multiple human annotations to construct soft labels and concludes that this increases performance in terms of generalization to out-of-training-distribution test datasets, and robustness to adversarial attacks. [2] focus on efficiently eliciting soft labels from individual annotators." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.65, + 0.87, + 0.846 + ], + "angle": 0, + "content": "Noise rate estimation A number of approaches have been proposed for estimating the noise transition matrix (i.e. the probabilities that correct labels are changed for incorrect ones) [12, 16, 31]. Usually these methods use a small number of anchor points (that are samples that belong to a specific class with probability one) [8]. In particular, [16] proposed a noise estimation method based on anchor points, with the intent to provide an 'end-to-end' noise-estimation-and-learning method. Due to the lack of anchor points in real data, some works focused on a way to detect anchor points in noisy data, [26, 27]. In [27] the authors propose to introduce an intermediate class to avoid directly estimating the noisy class posterior. [28] also propose an iterative noise estimation heuristic that aims to partly correct the error and pointed out that the methods introduced by [16]" + }, + { + "type": "page_number", + "bbox": [ + 0.469, + 0.876, + 0.502, + 0.887 + ], + "angle": 0, + "content": "3440" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.102, + 0.149, + 0.473, + 0.331 + ], + "angle": 0, + "content": "and [27] have an error in computing anchor points, and provide conditions on the noise under which the methods work or fail. [26] provides a solution that can infer the transition matrix without anchor points. Indeed they use the instances with the highest class posterior probabilities for noisy data as anchor points. Our work differs from the mentioned work that use anchor points because we do not need to assume the existence of anchor points or to have a validation set to learn the noise rate and we only use noisy data to train our model, moreover we neither aim to detect anchor points in the noisy data. Also most of these works do not study the generalization properties of the proposed models, while we also address this problem and find bound that depend on the estimated noise transition matrix." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.334, + 0.473, + 0.4 + ], + "angle": 0, + "content": "Another approach is based on the clusterability condition, that is an example belongs to the same true class of its nearest-neighbors representations. [30] presented a method that relies on statistics of high-order consensuses among the 2 nearest-neighbors noisy labels." + }, + { + "type": "title", + "bbox": [ + 0.103, + 0.421, + 0.292, + 0.435 + ], + "angle": 0, + "content": "3. Problem formulation" + }, + { + "type": "title", + "bbox": [ + 0.103, + 0.446, + 0.2, + 0.459 + ], + "angle": 0, + "content": "3.1. Notation" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.469, + 0.473, + 0.522 + ], + "angle": 0, + "content": "In this paper we follow the following notation. Matrices and sets are denoted by upper-case and calligraphic letters, respectively. The space of \\(d\\)-dimensional feature vectors is denoted by \\(\\mathcal{X} \\subset \\mathbb{R}^d\\)." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.525, + 0.473, + 0.656 + ], + "angle": 0, + "content": "We denote by \\(C\\) the number of classes and by \\(e_j\\) the \\(j\\)-th standard canonical vector in \\(\\mathbb{R}^C\\), namely the vector that has 1 in the \\(j\\)-th position and zero in all the other positions. \\(\\mathcal{V} = \\{e_1,\\dots ,e_C\\} \\subset \\{0,1\\} ^C\\) is the label set. Feature vectors and labels are denoted by \\(x\\) and \\(y\\), respectively. \\(\\mathcal{D}\\) is the joint distribution of the feature vectors and labels, i.e. \\((x,y)\\sim \\mathcal{D}\\). The sampled dataset of size \\(n\\) is denoted by \\(\\widehat{\\mathcal{D}} = \\{(x_i,y_i)\\}_{i = 1}^n\\). \\(f(x)\\) denotes the output of the classifier \\(f\\) for feature vector \\(x\\) and is a \\(C\\) dimensional vector. All vectors are column vectors." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.66, + 0.473, + 0.725 + ], + "angle": 0, + "content": "We denote by \\(\\ell(t, y)\\) a generic loss function for the classification task that takes as input \\(C\\) dimensional vectors \\(t\\) and \\(y\\). In practice \\(t\\) will contain the prediction of the model and \\(y\\) will be the ground-truth label as a one-hot encoded vector. Namely \\(\\ell: [0,1]^C \\times \\mathcal{V} \\to \\mathbb{R}\\)." + }, + { + "type": "title", + "bbox": [ + 0.103, + 0.743, + 0.227, + 0.757 + ], + "angle": 0, + "content": "3.2. Background" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.767, + 0.473, + 0.846 + ], + "angle": 0, + "content": "We consider the classification problem within the supervised learning framework, where the ultimate goal is to minimize the \\(\\ell\\)-risk \\(R_{\\ell, \\mathcal{D}}(f) = \\mathbb{E}_{(x,y) \\sim \\mathcal{D}}[\\ell(f(x),y)]\\), for some loss function \\(\\ell\\). We denote by \\(\\mathcal{D}\\) the joint distribution of feature vectors \\(x\\) and labels \\(y\\). In practice, since the distribution is unknown instead of minimizing \\(R_{\\ell, \\mathcal{D}}(f)\\) we minimize an" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.147, + 0.779, + 0.163 + ], + "angle": 0, + "content": "empirical risk over some sampled dataset \\(\\widehat{\\mathcal{D}}\\):" + }, + { + "type": "equation", + "bbox": [ + 0.508, + 0.172, + 0.868, + 0.206 + ], + "angle": 0, + "content": "\\[\n\\widehat {R} _ {\\ell , \\widehat {\\mathcal {D}}} (f) = \\frac {1}{n} \\sum_ {i = 1} ^ {n} \\ell \\left(f \\left(x _ {i}\\right), y _ {i}\\right) = \\mathbb {E} _ {(x, y) \\sim \\widehat {\\mathcal {D}}} [ \\ell (f (x), y) ]. \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.214, + 0.869, + 0.253 + ], + "angle": 0, + "content": "In this work we assume that the true labels \\( y_{i} \\) are unknown and consider two scenarios, both of which rely on \\( H \\) annotators." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.268, + 0.619, + 0.28 + ], + "angle": 0, + "content": "3.2.1 Scenario I" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.288, + 0.869, + 0.366 + ], + "angle": 0, + "content": "In this scenario we have access to the \\(H\\) labels provided by the annotators for each sample, where \\(y_{i,a}\\) refers to the label provided by the \\(a\\)-th annotator for the \\(i\\)-th sample. For a given feature vector \\(x_{i}\\) the distribution of labels provided by annotator \\(a\\) is given by its noise transition matrix \\(T_{a}\\), which is defined as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.598, + 0.377, + 0.868, + 0.392 + ], + "angle": 0, + "content": "\\[\n\\left(T _ {a}\\right) _ {i, j} := \\mathbb {P} \\left(y _ {a} = j \\mid y = i\\right) \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.401, + 0.869, + 0.455 + ], + "angle": 0, + "content": "Assumption 1. We assume that all annotators have the same noise transition matrix (i.e. \\( T_{a} = T \\) for all \\( a \\)), that \\( T \\) is symmetric and that its diagonal elements are larger than 0.5 (i.e. \\( \\mathbb{P}(y_a = i|y = i) > 0.5, \\forall i \\in \\{1,\\dots C\\} \\))." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.461, + 0.869, + 0.501 + ], + "angle": 0, + "content": "Note that by definition \\( T \\) is right stochastic and hence also doubly stochastic. It is also strictly diagonally dominant and therefore non-singular." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.508, + 0.755, + 0.523 + ], + "angle": 0, + "content": "Proposition 3.0.1. \\(T\\) is positive definite." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.529, + 0.869, + 0.594 + ], + "angle": 0, + "content": "Proof. Since \\( T \\) is symmetric it follows that all eigenvalues are real. Combining the fact that it is strictly diagonally dominant with Gershgorin's theorem we conclude that all eigenvalues lie in the range \\( (0,1] \\) and hence \\( T \\) is positive definite." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.604, + 0.868, + 0.63 + ], + "angle": 0, + "content": "Assumption 2. We assume that the annotators are conditionally independent on the true label \\( y \\):" + }, + { + "type": "equation", + "bbox": [ + 0.586, + 0.64, + 0.868, + 0.654 + ], + "angle": 0, + "content": "\\[\n\\mathbb {P} \\left(y _ {a}, y _ {b} | y\\right) = \\mathbb {P} \\left(y _ {a} | y\\right) \\mathbb {P} \\left(y _ {b} | y\\right). \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.663, + 0.868, + 0.689 + ], + "angle": 0, + "content": "We now define the IAA matrix \\(M_{ab}\\) between annotators \\(a\\) and \\(b\\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.588, + 0.7, + 0.868, + 0.715 + ], + "angle": 0, + "content": "\\[\n\\left(M _ {a b}\\right) _ {i, j} := \\mathbb {P} \\left(y _ {a} = i, y _ {b} = j\\right) \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.724, + 0.869, + 0.75 + ], + "angle": 0, + "content": "Proposition 3.0.2. Leveraging Assumption 2 the agreement matrix \\( M_{a,b} \\) can be written as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.568, + 0.759, + 0.868, + 0.774 + ], + "angle": 0, + "content": "\\[\nM _ {a, b} = T _ {a} ^ {T} D T _ {b} \\tag {5}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.579, + 0.777, + 0.868, + 0.791 + ], + "angle": 0, + "content": "\\[\nD := \\operatorname {d i a g} \\left\\{\\nu \\right\\} \\tag {6}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.582, + 0.794, + 0.868, + 0.809 + ], + "angle": 0, + "content": "\\[\n\\nu : = \\left[ \\mathbb {P} (y = 1), \\dots , \\mathbb {P} (y = C) \\right] ^ {T}. \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.818, + 0.868, + 0.845 + ], + "angle": 0, + "content": "Due to Proposition 3.0.1 and the fact that \\( D \\) is positive definite it follows that all matrices \\( M_{a,b} \\) are invertible." + }, + { + "type": "page_number", + "bbox": [ + 0.469, + 0.876, + 0.501, + 0.887 + ], + "angle": 0, + "content": "3441" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.102, + 0.149, + 0.473, + 0.174 + ], + "angle": 0, + "content": "Assumption 3. We assume that the class probabilities (and hence \\(D\\)) are known." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.184, + 0.472, + 0.262 + ], + "angle": 0, + "content": "Due to Assumption 1 all annotators share the same noise transition matrix \\( T \\). Therefore \\( M_{ab} \\) is independent of \\( a \\) and \\( b \\) and from now on we remove this dependency in the notation (i.e. we get \\( M = T^T DT \\)). Furthermore, since \\( T \\) is invertible and \\( D \\) diagonal and positive definite it follows that \\( M \\) is also positive definite." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.263, + 0.472, + 0.303 + ], + "angle": 0, + "content": "Note that since we have access to all the labels provided by the \\(H\\) annotators for all the samples we can obtain an estimate of \\(M\\) which we denote \\(\\widehat{M}\\)." + }, + { + "type": "text", + "bbox": [ + 0.103, + 0.311, + 0.471, + 0.326 + ], + "angle": 0, + "content": "Assumption 4. We assume that \\(\\widehat{M}\\) is a consistent estimator." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.334, + 0.472, + 0.376 + ], + "angle": 0, + "content": "For the case of two annotators, one possible consistent estimator \\(\\widehat{M_{a,b}}\\) that exploits its symmetry condition is given by:" + }, + { + "type": "equation", + "bbox": [ + 0.11, + 0.385, + 0.47, + 0.431 + ], + "angle": 0, + "content": "\\[\n\\left(\\widehat {M _ {a , b}}\\right) _ {i, j} = \\sum_ {k = 1} ^ {n} \\frac {\\mathbb {1} \\left(y _ {a , k} = i , y _ {b , k} = j\\right) + \\mathbb {1} \\left(y _ {a , k} = j , y _ {b , k} = i\\right)}{2 n} \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.442, + 0.473, + 0.509 + ], + "angle": 0, + "content": "If the annotators have the same transition matrix, \\(M\\) will be the same for all pairs of annotators. So we can estimate \\(M\\), in the case of \\(H \\geq 2\\) by averaging the estimators \\(\\widehat{M}_{ab}\\) obtain by Eq. (8) for all possible pairs of annotators. The estimator in this case can be written as" + }, + { + "type": "equation", + "bbox": [ + 0.114, + 0.516, + 0.473, + 0.56 + ], + "angle": 0, + "content": "\\[\n(\\widehat{M})_{i,j} = \\frac{1}{H(H - 1)}\\sum_{a = 1}^{H}\\sum_{\\substack{b = 1\\\\ b\\neq a}}^{H}\\sum_{h = 1}^{n}\\frac{\\mathbb{1}(y_{a,h} = i,y_{b,h} = j)}{n}. \\tag{9}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.103, + 0.575, + 0.228, + 0.588 + ], + "angle": 0, + "content": "3.2.2 Scenario II" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.596, + 0.472, + 0.675 + ], + "angle": 0, + "content": "In the second scenario, for each \\(i\\)-th sample we are given a unique label \\(\\tilde{y}_i\\) that is produced by aggregating the \\(H\\) individual labels according to some known aggregating policy (like majority vote). In this case, since we do not have access to the individual annotations we assume that \\(\\widehat{M}\\) is provided." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.676, + 0.472, + 0.793 + ], + "angle": 0, + "content": "The probability that label \\( y_{i} \\) is corrupted to some other label \\( \\tilde{y}_{i} \\) is given by the aggregated noise transition matrix \\( \\Gamma \\in [0,1]^{C\\times C} \\), where \\( \\Gamma_{ij} \\coloneqq \\mathbb{P}(\\tilde{y} = j|y = i) \\) is the probability of the true label \\( i \\) being flipped into a corrupted label \\( j \\) and \\( C \\) is the number of classes. Note that by definition \\( \\Gamma \\) is a right stochastic matrix that is determined by \\( T \\), the amount of annotators \\( H \\) and the aggregating policy. We will study both the case where \\( \\Gamma = T \\), and the case in which there exists a generic Lipschitz function \\( \\phi \\) so that \\( \\Gamma^{-1} = \\phi (T) \\)." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.794, + 0.472, + 0.846 + ], + "angle": 0, + "content": "There are different policy choices to construct the dataset that lead to \\(\\Gamma = T\\). If we decide to use only one annotator, for instance \\(a\\), to build the final dataset, namely for each sample \\(\\tilde{y}^i = y_a^i\\) we have \\(\\Gamma = T_a\\). Or if annotators are" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.149, + 0.869, + 0.188 + ], + "angle": 0, + "content": "homogeneous, i.e. they have the same noise transition matrix \\( T \\), and to build the final dataset we decide to randomly select the label of one of the annotators we have that \\( \\Gamma = T \\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.189, + 0.869, + 0.24 + ], + "angle": 0, + "content": "Even restricting ourselves to the case of homogeneous annotators, depending on the rule with which we build the dataset we can have a more complex relationship between the matrix \\( T \\) and \\( \\Gamma \\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.242, + 0.869, + 0.307 + ], + "angle": 0, + "content": "We also obtain generalization bounds in the case where we have an estimate of the agreement matrix \\(M\\) is not available and we only have access to a scalar representation of the inter-annotator agreement, in particular we consider the case where the Cohen's \\(\\kappa\\) is given." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.326, + 0.615, + 0.34 + ], + "angle": 0, + "content": "3.2.3 Objective" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.348, + 0.869, + 0.415 + ], + "angle": 0, + "content": "The objective in both scenarios is to: i) use \\(\\widehat{M}\\) to estimate the noise transition matrices (\\(T\\) and \\(\\Gamma\\)); ii) leverage these estimates to be able to learn from the noisy dataset in a more robust manner; and iii) obtain generalization bounds for the resulting learning methods." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.428, + 0.623, + 0.442 + ], + "angle": 0, + "content": "4. Main results" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.451, + 0.869, + 0.542 + ], + "angle": 0, + "content": "We divide the main contributions in three sections. In the first section we show how to estimate the noise matrices \\( T \\). Next we indicate how to leverage these estimates to learn for the datasets with noisy labels. Finally we obtain bounds, depending on the Rademacher complexity of the class of functions, on the generalization gap for a bounded and Lipschitz loss function" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.552, + 0.84, + 0.565 + ], + "angle": 0, + "content": "4.1. Estimation of the noise transition matrices" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.573, + 0.869, + 0.611 + ], + "angle": 0, + "content": "We start stating the following Lemma that allows us to write the unknown matrix \\( T \\) (and its inverse), as a function of \\( D \\) and \\( M \\)." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.622, + 0.822, + 0.636 + ], + "angle": 0, + "content": "Lemma 4.1. If \\(D^{\\frac{1}{2}}\\) commutes with \\(T\\) we have that:" + }, + { + "type": "equation", + "bbox": [ + 0.671, + 0.647, + 0.867, + 0.662 + ], + "angle": 0, + "content": "\\[\nT = U \\Lambda^ {\\frac {1}{2}} U ^ {T} \\tag {10}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.654, + 0.666, + 0.867, + 0.681 + ], + "angle": 0, + "content": "\\[\nT ^ {- 1} = U \\Lambda^ {- \\frac {1}{2}} U ^ {T} \\tag {11}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.601, + 0.685, + 0.868, + 0.701 + ], + "angle": 0, + "content": "\\[\nD ^ {- \\frac {1}{2}} M D ^ {- \\frac {1}{2}} = U \\Lambda U ^ {T} \\tag {12}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.713, + 0.87, + 0.753 + ], + "angle": 0, + "content": "where \\( U\\Lambda U^T \\) is the eigenvalue decomposition of \\( D^{-\\frac{1}{2}}MD^{-\\frac{1}{2}} \\) (i.e. \\( U \\) is some orthogonal matrix and \\( \\Lambda \\) is a diagonal positive definite matrix)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.764, + 0.869, + 0.802 + ], + "angle": 0, + "content": "A detailed discussion of when the commutativity assumption is satisfied is included in Appendix B. The proof of the previous Lemma can be find in Appendix C.1." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.803, + 0.868, + 0.827 + ], + "angle": 0, + "content": "Note that we could use Lemma 4.1 to estimate \\(T\\) as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.637, + 0.828, + 0.868, + 0.846 + ], + "angle": 0, + "content": "\\[\n\\widehat {T} = \\widehat {U} \\widehat {\\Lambda} _ {M} ^ {\\frac {1}{2}} \\widehat {U} ^ {T} \\tag {13}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.469, + 0.876, + 0.502, + 0.887 + ], + "angle": 0, + "content": "3442" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.102, + 0.147, + 0.474, + 0.269 + ], + "angle": 0, + "content": "where \\(\\widehat{U}\\widehat{\\Lambda}_M\\widehat{U}^T\\) is the eigenvalue decomposition of \\(D^{-\\frac{1}{2}}\\widehat{MD}^{-\\frac{1}{2}}\\). However such estimate can result in matrices that are not doubly stochastic, or diagonally dominant due to estimation errors. A more accurate estimate of \\(T\\) could be obtained as \\(\\widehat{T} = \\pi (\\widehat{U}\\widehat{\\Lambda}_{M}^{\\frac{2}{3}}\\widehat{U}^{T})\\) where \\(\\pi\\) is a projection operator to the set of doubly stochastic, positive definite matrices with diagonal elements greater than 0.5 and non-negative entries (which is a convex set). We can obtain such projection by solving the following optimization problem:" + }, + { + "type": "equation", + "bbox": [ + 0.126, + 0.283, + 0.472, + 0.309 + ], + "angle": 0, + "content": "\\[\n\\widehat {T} = \\pi \\left(\\widehat {U} \\widehat {\\Lambda} _ {M} ^ {\\frac {1}{2}} \\widehat {U} ^ {T}\\right) = \\underset {B} {\\operatorname {a r g m i n}} | | B - \\widehat {U} \\widehat {\\Lambda} _ {M} ^ {\\frac {1}{2}} \\widehat {U} ^ {T} | | _ {2} ^ {2} \\tag {14}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.187, + 0.317, + 0.388, + 0.365 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} B = B ^ {T} \\\\ \\sum_ {j} B _ {i, j} = 1 \\quad \\forall i \\\\ \\text {s . t .} \\end{array}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.187, + 0.367, + 0.375, + 0.397 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{c c} B _ {i, j} \\geq 0 & \\forall i, j \\\\ B _ {i, i} \\geq 0. 5 & \\forall i \\end{array}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.401, + 0.472, + 0.457 + ], + "angle": 0, + "content": "Note that this optimization problem is convex because the constraints are linear and for symmetric matrices it holds that \\(||\\widehat{T} - \\widehat{U}\\widehat{\\Lambda}_{M}^{\\frac{1}{2}}\\widehat{U}^{T}||_{2}^{2} = \\lambda_{\\max}(\\widehat{T} - \\widehat{U}\\widehat{\\Lambda}_{M}^{\\frac{1}{2}}\\widehat{U}^{T})\\), which is a convex function of \\(\\widehat{T}\\)." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.458, + 0.472, + 0.526 + ], + "angle": 0, + "content": "To summarize, \\(T\\) can be estimated as follows. First, obtain an estimate of \\(M\\). Then obtain the eigenvalue decomposition of \\(D^{-\\frac{1}{2}}\\widehat{MD}^{-\\frac{1}{2}} = \\widehat{U}\\widehat{\\Lambda}\\widehat{U}^T\\) (note that this decomposition always exists because \\(D^{-\\frac{1}{2}}\\widehat{MD}^{-\\frac{1}{2}}\\) is symmetric). Finally obtain the estimate as: \\(\\widehat{T} \\coloneqq \\pi (\\widehat{U}\\widehat{\\Lambda}^{\\frac{1}{2}}\\widehat{U}^{T})\\)." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.526, + 0.472, + 0.565 + ], + "angle": 0, + "content": "Note that once the estimate of \\(\\widehat{T}\\) is obtained, \\(\\widehat{\\Gamma}\\) can be obtained since we assumed the label aggregating policy to be known." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.572, + 0.472, + 0.65 + ], + "angle": 0, + "content": "Lemma 4.2. Let \\( M_{a,b} \\) be the agreement matrix for annotators \\( a \\) and \\( b \\) defined in Eq. (4) and \\( \\widehat{M_{a,b}} \\) be the estimated agreement matrix defined in Eq. (8) and let \\( ||.||_p \\) be the matrix norm induced by the \\( p \\) vector norm. For every \\( p \\in [1,\\infty] \\) and for every \\( \\delta > 0 \\), with probability at least \\( 1 - \\delta \\)" + }, + { + "type": "equation", + "bbox": [ + 0.176, + 0.651, + 0.472, + 0.679 + ], + "angle": 0, + "content": "\\[\n\\left| \\left| M _ {a, b} - \\widehat {M _ {a , b}} \\right| \\right| _ {p} \\leq \\sqrt {\\frac {C ^ {2}}{2 n} \\ln \\frac {2 C ^ {2}}{\\delta}}. \\tag {15}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.681, + 0.472, + 0.734 + ], + "angle": 0, + "content": "where \\(\\mathbb{P}^n\\) denotes the probability according to which the \\(n\\) training samples are distributed, i.e. we are assuming that the samples are independently drawn according the probability \\(\\mathbb{P}\\)." + }, + { + "type": "text", + "bbox": [ + 0.103, + 0.739, + 0.408, + 0.753 + ], + "angle": 0, + "content": "Proof. The proof can be found in Appendix C.2." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.761, + 0.472, + 0.813 + ], + "angle": 0, + "content": "From Lemma 4.2 it follows that if \\(\\widehat{M}\\) is estimated as in Eq. (9), since \\(\\widehat{M}\\) is an average of \\(\\widehat{M_{ab}}\\) it also holds that for every \\(p\\in [1,\\infty ]\\) and for every \\(\\delta >0\\) with probability at least \\(1 - \\delta\\)" + }, + { + "type": "equation", + "bbox": [ + 0.191, + 0.819, + 0.472, + 0.849 + ], + "angle": 0, + "content": "\\[\n\\left| \\left| M - \\widehat {M} \\right| \\right| _ {p} \\leq \\sqrt {\\frac {C ^ {2}}{2 n} \\ln \\frac {2 C ^ {2}}{\\delta}}. \\tag {16}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.149, + 0.869, + 0.175 + ], + "angle": 0, + "content": "Theorem 4.3. Let \\(T\\) be the noise transition matrix defined as in Eq. (2) and \\(\\widehat{T}\\) its estimate (defined as in Eq. (14))." + }, + { + "type": "text", + "bbox": [ + 0.519, + 0.175, + 0.714, + 0.188 + ], + "angle": 0, + "content": "With probability at least \\(1 - \\delta\\):" + }, + { + "type": "equation", + "bbox": [ + 0.513, + 0.194, + 0.868, + 0.227 + ], + "angle": 0, + "content": "\\[\n\\left| \\left| T - \\widehat {T} \\right| \\right| _ {2} \\leq \\frac {C (\\sqrt {C} + 1) \\lambda_ {\\max } (D)}{\\lambda_ {\\min } (\\hat {T})} \\sqrt {\\frac {1}{2 n} \\ln \\frac {2 C ^ {2}}{\\delta}} \\tag {17a}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.509, + 0.241, + 0.868, + 0.287 + ], + "angle": 0, + "content": "\\[\n\\left| \\left| T ^ {- 1} - \\hat {T} ^ {- 1} \\right| \\right| _ {2} \\leq \\frac {9 C (\\sqrt {C} + 1) \\lambda_ {\\max } (D)}{\\lambda_ {\\min } (\\hat {T}) ^ {2}} \\sqrt {\\frac {1}{2 n} \\ln \\frac {2 C ^ {2}}{\\delta}} \\tag {17b}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.516, + 0.293, + 0.701, + 0.317 + ], + "angle": 0, + "content": "\\[\nf o r n > \\frac {C ^ {2} (\\sqrt {C} + 1) ^ {2} (\\ln (2 C ^ {2}) ^ {2}}{2 \\lambda_ {\\operatorname* {m i n}} (\\hat {T}) ^ {2}}.\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.32, + 0.803, + 0.333 + ], + "angle": 0, + "content": "Proof. The proof can be found in Appendix C.3." + }, + { + "type": "image", + "bbox": [ + 0.853, + 0.32, + 0.869, + 0.33 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.341, + 0.868, + 0.371 + ], + "angle": 0, + "content": "From the previous theorem we can notice that the error in estimation of \\( T \\) decays as \\( \\frac{1}{\\sqrt{n}} \\) as a function of \\( n \\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.376, + 0.729, + 0.39 + ], + "angle": 0, + "content": "4.2. Learning from noisy labels" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.395, + 0.87, + 0.421 + ], + "angle": 0, + "content": "In this section we show how to leverage the estimates of the error rates to train the models." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.436, + 0.869, + 0.449 + ], + "angle": 0, + "content": "4.2.1 Posterior distribution of true labels as soft-labels" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.457, + 0.869, + 0.507 + ], + "angle": 0, + "content": "It is noteworthy that if we have access to the labels provided by all annotators, the posterior probabilities of the true labels can be calculated leveraging \\( T \\) and Bayes' Theorem as follows:" + }, + { + "type": "equation", + "bbox": [ + 0.518, + 0.513, + 0.868, + 0.556 + ], + "angle": 0, + "content": "\\[\n\\underbrace {\\mathbb {P} \\left(y _ {i} = c \\mid y _ {1 , i} , \\dots , y _ {H , i}\\right)} _ {:= p _ {c, i}} \\propto \\nu_ {c} \\prod_ {h = 1} ^ {H} \\underbrace {\\mathbb {P} \\left(y _ {h , i} \\mid y _ {i} = c\\right)} _ {= T _ {c, y _ {h, i}}} \\tag {18}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.563, + 0.869, + 0.628 + ], + "angle": 0, + "content": "we recall that \\(\\nu_{c} = \\mathbb{P}(y_{i} = c)\\) and that the conditional probabilities on the r.h.s. are given by \\(T\\). In our case we can use our noisy transition estimates to estimate the posterior probabilities of the true labels, and afterwards we can use these posteriors to train the classifier." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.634, + 0.868, + 0.687 + ], + "angle": 0, + "content": "Lemma 4.4. For infinite annotators the posterior distribution over every sample calculated using the true \\(T\\) converges to the dirac delta distribution centered on the true label almost surely (i.e. \\(\\lim_{H\\to \\infty}p_{c,i}\\stackrel {a.s}{=}\\mathbb{1}(y_i = c)\\))." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.692, + 0.667, + 0.705 + ], + "angle": 0, + "content": "Proof. See Appendix C.5." + }, + { + "type": "image", + "bbox": [ + 0.853, + 0.692, + 0.869, + 0.703 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.713, + 0.868, + 0.739 + ], + "angle": 0, + "content": "We can use the posterior distributions as soft-labels defining the following loss for the i-th sample:" + }, + { + "type": "equation", + "bbox": [ + 0.564, + 0.745, + 0.868, + 0.761 + ], + "angle": 0, + "content": "\\[\n\\ell \\left(f \\left(x _ {i}\\right), y _ {1, i}, \\dots , y _ {H, i}\\right) = \\ell \\left(f \\left(x _ {i}\\right), \\bar {p} _ {i}\\right) \\tag {19}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.766, + 0.869, + 0.806 + ], + "angle": 0, + "content": "where \\(\\bar{p}_i = [p_{1,i},\\dots ,p_{C,i}]^T\\) . Or we can use the posterior distributions to weight the loss function at the \\(i\\) -th sample evaluated at each of the possible labels:" + }, + { + "type": "equation", + "bbox": [ + 0.527, + 0.812, + 0.868, + 0.848 + ], + "angle": 0, + "content": "\\[\n\\ell \\left(f \\left(x _ {i}\\right), y _ {1, i}, \\dots , y _ {H, i}\\right) = \\sum_ {c = 1} ^ {C} p _ {c, i} \\ell \\left(f \\left(x _ {i}\\right), e _ {c}\\right) \\tag {20}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.469, + 0.876, + 0.502, + 0.887 + ], + "angle": 0, + "content": "3443" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.102, + 0.148, + 0.471, + 0.2 + ], + "angle": 0, + "content": "where \\(e_c\\) is the vector in \\(\\mathbb{R}^C\\) with 1 in the \\(c\\)-th position. Notice that for categorical cross entropy loss the two functions defined above correspond, but in general they define two different loss functions." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.201, + 0.474, + 0.318 + ], + "angle": 0, + "content": "Note that these soft-labels are different from the ones obtained by averaging the annotators labels as is done in [25]. The method using the posteriors exploits the \\(T\\) matrix and thus more information than the simple mean of the values of the losses among annotators. We therefore expect this to yield better results than the aggregation using the mean proposed in [25]. These considerations are supported by the empirical results we obtained on synthetic datasets (see Sec. 6)." + }, + { + "type": "title", + "bbox": [ + 0.103, + 0.333, + 0.294, + 0.346 + ], + "angle": 0, + "content": "4.2.2 Robust loss functions" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.353, + 0.472, + 0.445 + ], + "angle": 0, + "content": "Another way to leverage the estimate of \\( T \\) is to use robust loss functions, like the forward and backward loss functions presented in [15, 16]. Let \\( \\ell(t,y) \\) be a generic loss function for the classification task, with a little abuse of notation we define \\( \\ell(t) = [\\ell(t,e_1),\\dots,\\ell(t,e_C)]^T \\). The backward and forward loss functions are defined in Eq. (21a) and Eq. (21b), respectively." + }, + { + "type": "equation", + "bbox": [ + 0.218, + 0.45, + 0.471, + 0.466 + ], + "angle": 0, + "content": "\\[\nl _ {b} (t, y) = (\\widehat {\\Gamma} ^ {- 1} \\ell (t)) y \\tag {21a}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.218, + 0.469, + 0.471, + 0.485 + ], + "angle": 0, + "content": "\\[\nl _ {f} (t, y) = (\\ell (\\widehat {\\Gamma} ^ {T} t)) y \\tag {21b}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.492, + 0.472, + 0.609 + ], + "angle": 0, + "content": "To explain the notation in Eq. (21a) we are first doing the dot product between the matrix \\(\\Gamma^{-1}\\) and the vector \\(\\ell(t)\\) and then the dot product of the resulting vector with \\(y\\). These losses leverage aggregated labels and therefore different aggregating techniques can be used, like majority vote. Another possible aggregating technique that leverages the posterior probabilities is to assume that the true label is the one that corresponds to the class that has the highest posterior probability." + }, + { + "type": "title", + "bbox": [ + 0.103, + 0.615, + 0.338, + 0.63 + ], + "angle": 0, + "content": "4.3. Generalizations gap bounds" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.635, + 0.472, + 0.701 + ], + "angle": 0, + "content": "In this section we derive generalization gap bounds for the backward loss that depend on the noise transition matrix estimated in Eq. (14). Since we are only addressing the problem for the backward loss, from now on we will denote the backward loss by \\( l \\)." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.707, + 0.473, + 0.746 + ], + "angle": 0, + "content": "Remark 1. If \\(\\ell(t,y)\\) is Lipschitz with constant \\(L\\), the loss function \\(l(t,y)\\) is Lipschitz with Lipschitz constant \\(||\\Gamma^{-1}||_2 L\\)." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.752, + 0.473, + 0.846 + ], + "angle": 0, + "content": "We will prove the following theorem in the case of \\(\\Gamma = T\\). We emphasize that all the results apply also when \\(\\Gamma^{-1} = \\phi(T^{-1})\\) and that the function that associate \\(\\Gamma^{-1}\\) and \\(T^{-1}, \\phi\\) is Lipschitz with respect to the norm \\(p\\), i.e. there exists a Lipschitz constant \\(L_{\\phi, p}\\) s.t. \\(||\\phi(T^{-1}) - \\phi(\\hat{T}^{-1})||_p \\leq L_{\\phi, p}||T^{-1} - \\widehat{T}^{-1}||_p\\). The only difference is that in the bound we will have a factor \\(L_{\\phi, p}\\)." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.149, + 0.87, + 0.201 + ], + "angle": 0, + "content": "It has been proved, first in [15] (Lemma 1) for the binary classification task and then in general for the multiclass case in [16] (Theorem 1) that \\( l(t,y) \\) is an unbiased estimator for \\( \\ell \\), i.e." + }, + { + "type": "equation", + "bbox": [ + 0.613, + 0.21, + 0.756, + 0.226 + ], + "angle": 0, + "content": "\\[\n\\mathbb {E} _ {\\tilde {y} | y} [ l (t, \\tilde {y}) ] = \\ell (t, y).\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.232, + 0.87, + 0.326 + ], + "angle": 0, + "content": "Lemma 4.5. Let \\(\\ell\\) be a bounded loss function, with \\(\\ell \\in [0,\\mu ]\\), s.t. there exists a Lipschitz function \\(\\alpha\\), with Lipschitz constant \\(L\\), so that \\(\\ell (f(x),y) = \\alpha |f(x) - y|\\). Let \\(\\widehat{R}_l(f)\\) be the empirical risk for the loss \\(l\\) and let \\(R_{l,\\mathcal{D}}\\) be the risk for loss \\(l\\) under the distribution \\(\\mathcal{D}\\), with \\(l\\) unbiased estimator for the loss \\(\\ell\\). We denote by \\(\\hat{l}\\) the backward loss obtained using \\(\\widehat{T}\\)." + }, + { + "type": "equation", + "bbox": [ + 0.503, + 0.345, + 0.868, + 0.407 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} \\sup _ {f \\in \\mathcal {F}} | \\hat {R} _ {\\hat {l}} (f) - R _ {l, \\mathcal {D}} (f) | \\\\ \\leq \\left[ L \\lambda_ {\\min} (\\widehat {T} ^ {2}) + \\frac {\\mu \\lambda_ {\\min} (D)}{\\lambda_ {\\min} (\\widehat {T}) ^ {2}} \\sqrt {\\frac {1}{n} \\ln \\left(\\frac {4 C}{\\delta}\\right)} \\right] \\Re_ {n} (\\mathcal {F}) g (C). \\\\ \\end{array}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.501, + 0.414, + 0.678, + 0.43 + ], + "angle": 0, + "content": "\\[\nw i t h g (C) = 6 C ^ {2} (\\sqrt {C} + 1)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.436, + 0.87, + 0.472 + ], + "angle": 0, + "content": "Theorem 4.6. Let \\( l \\) be an unbiased estimator for \\( \\ell \\) defined as in Eq. (21a), Denoting \\( \\hat{f} = \\operatorname*{argmin}_{f}(\\widehat{R}_{\\hat{l}}(f)) \\). It holds that" + }, + { + "type": "equation", + "bbox": [ + 0.501, + 0.483, + 0.868, + 0.543 + ], + "angle": 0, + "content": "\\[\n\\begin{array}{l} R _ {\\ell , \\mathcal {D}} (\\hat {f}) - \\min _ {f \\in \\mathcal {F}} R _ {\\ell , \\mathcal {D}} (f) \\\\ \\leq \\left[ 2 L \\lambda_ {\\min } (\\widehat {T} ^ {2}) + \\frac {\\mu \\lambda_ {\\min } (D)}{\\lambda_ {\\min } (\\widehat {T}) ^ {2}} \\sqrt {\\frac {1}{n} \\ln \\left(\\frac {4 C}{\\delta}\\right)} \\right] \\Re_ {n} (\\mathcal {F}) g (C) \\\\ \\end{array}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.501, + 0.55, + 0.678, + 0.566 + ], + "angle": 0, + "content": "\\[\nw i t h g (C) = 6 C ^ {2} (\\sqrt {C} + 1)\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.572, + 0.87, + 0.846 + ], + "angle": 0, + "content": "The proofs of Lemma 4.5 and Theorem 4.6 can be found in Appendix C. We observe that in all the previous theorems, the bounds found are always decreasing as one over the square root of the number of samples. The above theorem gives us a performance bound for the classifier found minimizing the backward loss \\( l \\), i.e. the unbiased estimator of the loss \\( \\ell \\) on the noisy dataset. The bounds found depend on, the Rademacher complexity of the function space and the Lipschitz constant of the loss function. The importance of these bounds lies in the fact that they allow us to obtain performance bounds for a model trained with noisy data that depends on values that we can estimate from the noisy dataset. In particular, there is no dependence on the true noise transition matrix of the annotators, as in other work [15] which is instead a quantity that cannot be known a priori having access only to the training data. More in detail the bound depends on the estimate noise transition matrix, the number of classes in the dataset, the Rademacher complexity and the Lipschitz constant, which we can take as known a priori and on the distribution of ground truth, which in many cases it makes sense to assume uniform." + }, + { + "type": "page_number", + "bbox": [ + 0.469, + 0.876, + 0.502, + 0.887 + ], + "angle": 0, + "content": "3444" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.103, + 0.148, + 0.205, + 0.161 + ], + "angle": 0, + "content": "5. Cohen's \\(\\kappa\\)" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.17, + 0.471, + 0.247 + ], + "angle": 0, + "content": "We can also consider the case where an estimate of the IAA matrix \\(M\\) is not available and we only have access to a scalar representation of the inter-annotator agreement like Cohen's \\(\\kappa\\). In this case we can only estimate one parameter and hence the matrix \\(T\\) has to be parameterized by a single parameter that can be estimated." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.248, + 0.472, + 0.3 + ], + "angle": 0, + "content": "One particular example is the case where the noise is uniform among classes. Under these hypotheses, \\( T \\) is a matrix with all values \\( 1 - p \\) on the diagonal and \\( \\frac{p}{C - 1} \\) off the diagonal." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.307, + 0.472, + 0.372 + ], + "angle": 0, + "content": "Lemma 5.1 (Relationship between \\( p \\) and \\( \\kappa \\)). In the case of classification with uniform noise for two homogeneous annotators with noise rate \\( p \\), i.e if \\( a \\) is one annotator, \\( \\mathbb{P}(y_a = i|y = j) = p \\) if \\( i \\neq j \\). If the distribution of the ground-truth labels is uniform, it holds that:" + }, + { + "type": "equation", + "bbox": [ + 0.206, + 0.38, + 0.471, + 0.396 + ], + "angle": 0, + "content": "\\[\np = (1 - C ^ {- 1}) (1 - \\sqrt {\\kappa}) \\tag {22}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.404, + 0.471, + 0.43 + ], + "angle": 0, + "content": "with \\(\\kappa\\) the Cohen's kappa coefficient of the two annotators (see Appendix A)." + }, + { + "type": "text", + "bbox": [ + 0.103, + 0.437, + 0.472, + 0.451 + ], + "angle": 0, + "content": "Proof. The proof can be found in Appendix C.6. \\(\\square\\)" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.459, + 0.472, + 0.602 + ], + "angle": 0, + "content": "If \\( T \\) is assumed to be of the form described above (with all diagonal elements equal to \\( 1 - p \\) and all off-diagonal entries equal), it has one eigenvalue equal to 1 and all the rest are equal to \\( 1 - pC(C - 1)^{-1} \\) (this follows from the fact that in this case \\( T \\) can be written as a weighted summation of the identity and a rank-one matrix). Hence using Eq. (22) we get that \\( \\lambda_{\\mathrm{min}}(T) = \\sqrt{\\kappa} \\). The bounds from Theorem 4.6 hold replacing \\( \\lambda_{\\mathrm{min}}(T) \\) with \\( \\sqrt{\\kappa} \\). This allows us to obtain bound for the generalization gap of a classifier trained with backward loss even in the case where a single statistic on agreement between annotators is provided." + }, + { + "type": "title", + "bbox": [ + 0.103, + 0.613, + 0.29, + 0.627 + ], + "angle": 0, + "content": "6. Experimental results" + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.634, + 0.472, + 0.79 + ], + "angle": 0, + "content": "We performed experiments to validate the effectiveness of the method we propose for estimating \\(\\widehat{T}\\) by studying the error in the estimation as a function of the number of samples. We also performed experiments to show how the estimated \\(T\\) can be leveraged to train classifiers in the presence of noise labels. In particular, we performed experiments for a classification task on a synthetic dataset and on the CIFAR10-N dataset, comparing the performance of a classifier trained using labels obtained by some baseline aggregation method with the performance of a classifier trained using the distribution of posteriors obtained from the estimation of T (Eq. (18)) as soft-labels." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.805, + 0.471, + 0.845 + ], + "angle": 0, + "content": "Estimation of \\( T \\) With these experiments we aim to validate the theoretical results of Sec. 4.1. We generate various matrices \\( T \\) that are symmetric, stochastic and diagonally" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.149, + 0.869, + 0.293 + ], + "angle": 0, + "content": "dominant, the exact details about the generation of \\( T \\) can be found in Appendix D.1. For each annotator we produce their prediction according to the matrix \\( T \\). We run experiments for the number of annotators \\( H = 10, 7, 3, 2 \\). We report here the results for \\( H = 10 \\), and 4 classes, all the other plots are in Appendix D.1. In Fig. 4 (as well as the plots in the Appendix) we can be observed that the error in the estimation decreases as \\( \\frac{1}{\\sqrt{n}} \\) with \\( n \\) number of samples, which is in agreement with the bound provided in Theorem 4.3. We also observed that, as expected, the estimation becomes more accurate as the number of annotators increases." + }, + { + "type": "image", + "bbox": [ + 0.503, + 0.303, + 0.868, + 0.426 + ], + "angle": 0, + "content": null + }, + { + "type": "image_footnote", + "bbox": [ + 0.578, + 0.429, + 0.846, + 0.451 + ], + "angle": 0, + "content": "min eigenvalue T 0.2 0.3 0.4 0.6 1.0" + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.462, + 0.869, + 0.51 + ], + "angle": 0, + "content": "Figure 1. Error in the estimation of \\( T \\) for 4 classes and 10 annotators. The plots are obtained by averaging different admissible matrices \\( T \\) (see Appendix B) and averaged over matrices that have the same minimum eigenvalues rounded to the first decimal." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.543, + 0.868, + 0.646 + ], + "angle": 0, + "content": "Classification task with synthetic data We consider a classification task with a synthetic dataset. The features are generated uniformly in \\([0,1]^2\\). The assignment of labels \\((y)\\) is done by following the label distribution established for each experiment, separating the space with lines parallel to the bisector of the first and third quadrants. More information on how the class distributions are generated can be found in Appendix D.2." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.647, + 0.869, + 0.738 + ], + "angle": 0, + "content": "For each dataset annotations are generated according to the noise transition matrix \\( T \\). Various combinations of \\( T \\) are tested that respect the assumptions of symmetry, stochasticity and diagonally dominance, as well as being commutative with D (more details can be found in Appendix B). The number of annotators is variable in the set \\( \\{3,5\\} \\). See Appendix D.2 for implementations details." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.754, + 0.868, + 0.779 + ], + "angle": 0, + "content": "Losses We use categorical cross entropy as loss function. We use both hard labels and soft labels to train the models." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.779, + 0.869, + 0.844 + ], + "angle": 0, + "content": "To train the models with hard labels an aggregation method is needed to obtain one final label from the annotators. We consider random and majority vote. In random aggregation the final label is randomly picked from the labels of the annotators. In majority vote the final label is the" + }, + { + "type": "page_number", + "bbox": [ + 0.469, + 0.876, + 0.501, + 0.887 + ], + "angle": 0, + "content": "3445" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.102, + 0.149, + 0.474, + 0.278 + ], + "angle": 0, + "content": "one with the most amount of votes (the mode), if the mode is not unique, we randomly choose one of the most voted classes. As soft-labels we consider the relative frequency among annotators and the posterior distribution according to Eq. (18). In the case of frequency for each sample we average the one-hot encoded annotations. Notice that random, majority vote and frequency soft labels do not leverage the estimate of \\( T \\) while the posterior does. In Fig. 2 we report the results for 4 classes with distribution (0.4, 0.1, 0.4, 0.1) and 3 annotators." + }, + { + "type": "image", + "bbox": [ + 0.104, + 0.289, + 0.47, + 0.441 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.102, + 0.451, + 0.474, + 0.583 + ], + "angle": 0, + "content": "Figure 2. Comparison between performance of Cross Entropy Loss using majority vote, random aggregation method or the posteriors (posterior) and relative frequency (average) as soft labels. On the y-axis the accuracy on a clean dataset and on the x-axis the values of the minimum on the diagonal of \\( T \\). Small values of the minimum diagonal value mean a noisy dataset, while the minimum is 1 in the noise-free case. The results are obtained for 3 annotators and 4 classes, by averaging on different amissible matrices \\( T \\) (see Appendix B) that have the same minimum diagonal values rounded to the first decimal. The error bands show the maximum and minimum performance for each method." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.599, + 0.471, + 0.664 + ], + "angle": 0, + "content": "We use accuracy with respect to a clean dataset as performance metric. Our results show that using the posteriors distribution, as soft labels, allows for better performance than using the average of the labels assigned by annotators and than using majority vote or random aggregation." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.664, + 0.473, + 0.716 + ], + "angle": 0, + "content": "Our method is shown to be more robust to the noise and is also the one with less variance in the results. This confirms our hypotheses that by leveraging the matrix \\(\\hat{T}\\) better classification accuracy can be achieved." + }, + { + "type": "text", + "bbox": [ + 0.102, + 0.732, + 0.473, + 0.825 + ], + "angle": 0, + "content": "Experiments on CIFAR10-N The CIFAR10-N dataset contains CIFAR-10 train images with noisy labels annotated by humans using Amazon Mechanical Turk. Each image is labelled by three independent annotators. Table 1 shows the accuracy achieved using the different aggregation methods. For this experiment we used Resnet34 [10] with and without pre-training. In both cases, our approach of aggrega" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.149, + 0.871, + 0.202 + ], + "angle": 0, + "content": "tion achieves the best performance. Note that in this dataset there are no guarantees that the assumptions we made on \\( T \\) are satisfied, however the method is still applicable with positive results." + }, + { + "type": "table", + "bbox": [ + 0.502, + 0.218, + 0.887, + 0.298 + ], + "angle": 0, + "content": "
Aggregation MethodPretrainedNot-Pretrained
random0.718 ± 0.0350.579 ± 0.023
majority vote0.740 ± 0.0170.590 ± 0.006
average0.762 ± 0.0120.637 ± 0.016
posteriors (ours)0.794 ± 0.0050.652 ± 0.014
" + }, + { + "type": "table_caption", + "bbox": [ + 0.532, + 0.306, + 0.837, + 0.32 + ], + "angle": 0, + "content": "Table 1. Test Accuracy on CIFAR10-N with Resnet34" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.356, + 0.686, + 0.372 + ], + "angle": 0, + "content": "7. Concluding remarks" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.38, + 0.871, + 0.537 + ], + "angle": 0, + "content": "We have addressed the problem of learning from noisy labels in the case where the dataset is labeled by annotators that occasionally make mistakes. We have introduced a methodology to estimate the noise transition matrix \\( T \\) of the annotators given the IAA. We further showed different techniques to leverage this estimate to learn from the noisy dataset in a robust manner. We have shown theoretically that the methods we introduce are sound. We supported our methodology with some experiments that confirms our estimation of the noise transition matrix is valid and that this can be leveraged in the learning process to obtain better performance." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.567, + 0.871, + 0.646 + ], + "angle": 0, + "content": "Limitations The main limitation of our current approach to estimate \\( T \\) is that it only considers the case where \\( T \\) is symmetric and \\( D \\) assumed to be known and commute with \\( T \\). Extending the results to the case where \\( T \\) might not be symmetric and different among annotators is one possible future research direction." + }, + { + "type": "title", + "bbox": [ + 0.501, + 0.676, + 0.634, + 0.69 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.701, + 0.871, + 0.845 + ], + "angle": 0, + "content": "We acknowledge financial support from NRRP MUR project PE0000013-FAIR. This research was partially supported by MIUR under the grant \"Dipartimento di eccellenza 2018-2022\" of the Department of Computer Science and the Department of Computer Engineering at Sapienza University of Rome. It was also partially supported by the ERC Advanced Grant 788893 \"AMDROMA\", the EC H2020RIA project \"SoBigData++\" (871042), the MIUR PRIN project \"ALGADIMAR\", and the project SERICS (PE00000014) under the NRRP MUR program funded by the EU-NGEU." + }, + { + "type": "page_footnote", + "bbox": [ + 0.12, + 0.832, + 0.322, + 0.845 + ], + "angle": 0, + "content": "1http://www.noisylabels.com" + }, + { + "type": "page_number", + "bbox": [ + 0.469, + 0.876, + 0.503, + 0.887 + ], + "angle": 0, + "content": "3446" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.105, + 0.147, + 0.194, + 0.162 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.112, + 0.169, + 0.471, + 0.208 + ], + "angle": 0, + "content": "[1] J. Cohen. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1):37-46, 1960. 1, 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.112, + 0.217, + 0.472, + 0.255 + ], + "angle": 0, + "content": "[2] K. M. Collins, U. Bhatt, and A. Weller. Eliciting and learning with soft labels from every annotator, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.112, + 0.264, + 0.471, + 0.316 + ], + "angle": 0, + "content": "[3] A. P. Dawid and A. M. Skene. Maximum likelihood estimation of observer error-rates using the em algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1):20-28, 1979. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.112, + 0.324, + 0.471, + 0.414 + ], + "angle": 0, + "content": "[4] L. Feng, S. Shu, Z. Lin, F. Lv, L. Li, and B. An. Can cross entropy loss be robust to label noise? In C. Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pages 2206-2212. International Joint Conferences on Artificial Intelligence Organization, 7 2020. Main track. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.112, + 0.423, + 0.471, + 0.462 + ], + "angle": 0, + "content": "[5] J. Fleiss et al. Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5):378-382, 1971. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.112, + 0.47, + 0.471, + 0.535 + ], + "angle": 0, + "content": "[6] A. Ghosh, H. Kumar, and P. S. Sastry. Robust loss functions under label noise for deep neural networks. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI'17, page 1919-1925. AAAI Press, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.112, + 0.543, + 0.471, + 0.582 + ], + "angle": 0, + "content": "[7] A. Ghosh, N. Manwani, and P. Sastry. Making risk minimization tolerant to label noise. Neurocomputing, 160:93-107, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.112, + 0.591, + 0.471, + 0.681 + ], + "angle": 0, + "content": "[8] D. Hendrycks, M. Mazeika, D. Wilson, and K. Gimpel. Using trusted data to train deep networks on labels corrupted by severe noise. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.112, + 0.69, + 0.471, + 0.716 + ], + "angle": 0, + "content": "[9] R. A. Horn and C. R. Johnson. Matrix Analysis. Cambridge University Press, USA, 2nd edition, 2012. 14" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.724, + 0.471, + 0.75 + ], + "angle": 0, + "content": "[10] A. Khetan, Z. C. Lipton, and A. Anandkumar. Learning from noisy singly-labeled data, 2017. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.105, + 0.758, + 0.471, + 0.797 + ], + "angle": 0, + "content": "[11] R. Meir and T. Zhang. Generalization error bounds for bayesian mixture algorithms. J. Mach. Learn. Res., 4:839-860, dec 2003. 17" + }, + { + "type": "ref_text", + "bbox": [ + 0.105, + 0.806, + 0.471, + 0.845 + ], + "angle": 0, + "content": "[12] A. Menon, B. V. Rooyen, C. S. Ong, and B. Williamson. Learning from corrupted binary labels via class-probability estimation. In F. Bach and" + }, + { + "type": "list", + "bbox": [ + 0.105, + 0.169, + 0.472, + 0.845 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.535, + 0.149, + 0.869, + 0.201 + ], + "angle": 0, + "content": "D. Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, pages 125-134, Lille, France, 07-09 Jul 2015. PMLR. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.211, + 0.868, + 0.261 + ], + "angle": 0, + "content": "[13] A. K. Menon, A. S. Rawat, S. J. Reddi, and S. Kumar. Can gradient clipping mitigate label noise? In International Conference on Learning Representations, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.272, + 0.868, + 0.31 + ], + "angle": 0, + "content": "[14] M. Mohri, A. Rostamizadeh, and A. Talwalkar. Foundations of Machine Learning. The MIT Press, 2012. 17" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.321, + 0.868, + 0.398 + ], + "angle": 0, + "content": "[15] N. Natarajan, I. S. Dhillon, P. K. Ravikumar, and A. Tewari. Learning with noisy labels. In C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Weinberger, editors, Advances in Neural Information Processing Systems, volume 26. Curran Associates, Inc., 2013. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.409, + 0.869, + 0.474 + ], + "angle": 0, + "content": "[16] G. Patrini, A. Rozza, A. Krishna Menon, R. Nock, and L. Qu. Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1944-1952, 2017. 1, 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.483, + 0.869, + 0.588 + ], + "angle": 0, + "content": "[17] J. Peterson, R. Battleday, T. Griffiths, and O. Russakovsky. Human uncertainty makes classification more robust. In Proceedings - 2019 International Conference on Computer Vision, ICCV 2019, Proceedings of the IEEE International Conference on Computer Vision, pages 9616-9625, United States, Oct. 2019. Institute of Electrical and Electronics Engineers Inc. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.597, + 0.869, + 0.624 + ], + "angle": 0, + "content": "[18] J. E. Potter. Matrix quadratic solutions. SIAM Journal on Applied Mathematics, 14(3):496-501, 1966. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.633, + 0.868, + 0.659 + ], + "angle": 0, + "content": "[19] A. Purpura, G. Silvello, and G. A. Susto. Learning to rank from relevance judgments distributions, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.669, + 0.869, + 0.72 + ], + "angle": 0, + "content": "[20] V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, and L. Moy. Learning from crowds. Journal of Machine Learning Research, 11(43):1297-1322, 2010. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.73, + 0.868, + 0.77 + ], + "angle": 0, + "content": "[21] H. Song, M. Kim, D. Park, Y. Shin, and J.-G. Lee. Learning from noisy labels with deep neural networks: A survey, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.779, + 0.869, + 0.843 + ], + "angle": 0, + "content": "[22] A. Uma, T. Fornaciari, D. Hovy, S. Paun, B. Plank, and M. Poesio. A case for soft loss functions. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 8(1):173-177, Oct. 2020. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.149, + 0.869, + 0.843 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.469, + 0.876, + 0.501, + 0.887 + ], + "angle": 0, + "content": "3447" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.105, + 0.149, + 0.47, + 0.174 + ], + "angle": 0, + "content": "[23] V. N. Vapnik. Statistical Learning Theory. Wiley-Interscience, 1998. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.105, + 0.184, + 0.471, + 0.236 + ], + "angle": 0, + "content": "[24] Y. Wang, X. Ma, Z. Chen, Y. Luo, J. Yi, and J. Bailey. Symmetric cross entropy for robust learning with noisy labels. In IEEE International Conference on Computer Vision, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.244, + 0.471, + 0.283 + ], + "angle": 0, + "content": "[25] J. Wei, Z. Zhu, T. Luo, E. Amid, A. Kumar, and Y. Liu. To aggregate or not? learning with separate noisy labels, 2022. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.292, + 0.471, + 0.381 + ], + "angle": 0, + "content": "[26] X. Xia, T. Liu, N. Wang, B. Han, C. Gong, G. Niu, and M. Sugiyama. Are anchor points really indispensable in label-noise learning? In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.391, + 0.471, + 0.469 + ], + "angle": 0, + "content": "[27] Y. Yao, T. Liu, B. Han, M. Gong, J. Deng, G. Niu, and M. Sugiyama. Dual t: Reducing estimation error for transition matrix in label-noise learning. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS'20, Red Hook, NY, USA, 2020. Curran Associates Inc. 1, 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.478, + 0.471, + 0.566 + ], + "angle": 0, + "content": "[28] M. Zhang, J. Lee, and S. Agarwal. Learning from noisy labels with no change to the training process. In M. Meila and T. Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 12468-12478. PMLR, 18-24 Jul 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.577, + 0.471, + 0.654 + ], + "angle": 0, + "content": "[29] Z. Zhang and M. R. Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS'18, page 8792-8802, Red Hook, NY, USA, 2018. Curran Associates Inc. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.663, + 0.471, + 0.702 + ], + "angle": 0, + "content": "[30] Z. Zhu, Y. Song, and Y. Liu. Clusterability as an alternative to anchor points when learning with noisy labels, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.106, + 0.711, + 0.471, + 0.814 + ], + "angle": 0, + "content": "[31] Z. Zhu, J. Wang, and Y. Liu. Beyond images: Label noise transition matrix estimation for tasks with lower-quality features. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, and S. Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 27633-27653. PMLR, 17-23 Jul 2022. 2" + }, + { + "type": "list", + "bbox": [ + 0.105, + 0.149, + 0.471, + 0.814 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.469, + 0.876, + 0.501, + 0.887 + ], + "angle": 0, + "content": "3448" + } + ] +] \ No newline at end of file diff --git a/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/5cf56994-80ce-462c-9dfe-e6178b31a06f_origin.pdf b/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/5cf56994-80ce-462c-9dfe-e6178b31a06f_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..7a5e26c40a2b6f645cbe9df61e544cc66e75729a --- /dev/null +++ b/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/5cf56994-80ce-462c-9dfe-e6178b31a06f_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:cf568d28e8781ca6833d433c1ef2fef7ab14661392c7c2ff13de9f819f77151e +size 471702 diff --git a/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/full.md b/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/full.md new file mode 100644 index 0000000000000000000000000000000000000000..0f2fa324ed1bec1f53a930b755b276aad4233739 --- /dev/null +++ b/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/full.md @@ -0,0 +1,433 @@ +# Leveraging Inter-rater Agreement for Classification in the Presence of Noisy Labels + +Maria Sofia Bucarelli $^{2}$ * Lucas Cassano $^{1}$ Federico Siciliano $^{2}$ * Amin Mantrach $^{1}$ Fabrizio Silvestri $^{2,3}$ $^{1}$ Amazon $^{2}$ Sapienza University of Rome $^{3}$ ISTI-CNR, Pisa, Italy + +{mariasofia.bucarelli, federico.siciliano}@uniromal.it + +{lcecasl, mantrach}@amazon.lu + +fsilvestri@diag.uniroma1.it + +# Abstract + +In practical settings, classification datasets are obtained through a labelling process that is usually done by humans. Labels can be noisy as they are obtained by aggregating the different individual labels assigned to the same sample by multiple, and possibly disagreeing, annotators. The interrater agreement on these datasets can be measured while the underlying noise distribution to which the labels are subject is assumed to be unknown. In this work, we: (i) show how to leverage the inter-annotator statistics to estimate the noise distribution to which labels are subject; (ii) introduce methods that use the estimate of the noise distribution to learn from the noisy dataset; and (iii) establish generalization bounds in the empirical risk minimization framework that depend on the estimated quantities. We conclude the paper by providing experiments that illustrate our findings. + +# 1. Introduction + +Supervised learning has seen enormous progress in the last decades, both theoretical and practical. Empirical risk minimization is used as a learning framework [23], which relies on the assumption that the model is trained with iid (independent and identically distributed) sampled data from the joint distribution between features and labels. As a consequence of generalization bounds, when this assumption is satisfied any desired performance can be achieved as long as enough training data is available. However in many real-world applications, due to flaws during the data collection and labeling process, the assumption that the training data is sampled from the true feature-label joint distribution does not hold. Training data is often annotated by human raters who have some non-zero probability of making mistakes. It + +has been reported in [21] that the ratio of corrupted labels in some real-world datasets is between $8.0\%$ and, $38.5\%$ . As a consequence of the presence of incorrect labels in the training dataset, the aforementioned assumption is violated and hence performance guarantees based on generalization bounds no longer hold. + +This gap between theory and practice raises the question whether it is possible to learn from datasets with noisy labels while still having performance guarantees. This question has received a lot of attention lately and has already been answered in the positive in some cases [15, 16]. Indeed multiple works have introduced learning algorithms that can cope with datasets with incorrect labels while guaranteeing desirable performance through provable generalization bounds. However, these solutions do not solve the entirety of the problem due to the fact that they rely on precise knowledge of the error rate to which the labels are subject, which is often unknown in practice. Several works [16, 26, 27] attempt to address this issue by introducing techniques to estimate such error rate. Some of these methods have the drawback of relying on assumptions that do not always hold in practice, such as the existence of anchor samples [16]. Ideally, it would be desirable to design learning algorithms that are both robust to noisy labels, and for which performance guarantees can be provided. + +An approach, often used in industry to reduce the impact of errors made by human raters, is to label the same dataset multiple times by different annotators. Then the individual labels are combined to reduce the probability of erroneous labels in the dataset, two popular approaches are majority vote or soft labeling. In these cases inter-annotator agreement (IAA) scores (like Cohen's kappa [1] and Fleiss' kappa [5]) provide measurable metrics that are directly related to the probability of error present in the labels. + +Since the IAA holds a direct relationship with the error rate associated with the human raters, one could potentially estimate the error rate and leverage this estimate to modify + +the learning algorithms with the objective of making them robust to the resulting noise in the labels. This is the main direction we explore in this work. + +Motivation and Contributions: This work is motivated by two main points: i- to the best of our knowledge there are no published results that indicate how to leverage the IAA statistics to estimate the label noise distribution; and ii- the generalization bounds of existing noise tolerant training methods often rely on unknown quantities (like the true noise distribution) instead of on quantities that can be measured (like the IAA statistics). + +Our contributions are the following: (i) we provide a methodology to estimate the label noise distribution based on the IAA statistics; (ii) we show how to leverage this estimate to learn from the noisy dataset; and (iii) we provide generalization bounds for our methods that depend on known quantities. + +# 2. Related works + +Our work is related to literature on three main topics: (i) robust loss function design, (ii) label aggregating and (iii) noise rate estimation. + +Robust loss functions In classification tasks, the goal is to obtain the lowest probability of classification error. The $0 - 1$ loss counts how many errors a classifier makes on a given dataset and is often used in the evaluation of the classifier. However, it is rarely used in optimization procedures because it is non-differentiable and non-continuous. To overcome this, many learning strategies use some convex surrogates of the $0 - 1$ loss function (e.g. hinge loss, squared error loss, cross-entropy). + +It was proved ([6], [7]) that symmetric loss functions, that are functions for which the sum of the risks over all categories is equivalent to a constant for each arbitrary example, are robust to label noise. Examples of symmetric loss functions include the $0 - 1$ loss, the Ramp Loss and (softmax) Mean Absolute Error (MAE). In [29] authors show that even if MAE is noise tolerant and cathegorical cross entropy (CCE) is not, MAE can perform poorly when used to train DNN in challenging domains. They also propose a loss function that can be seen as a generalization of MAE and CCE. Several other loss functions that do not strictly satisfy the symmetry condition have also been proposed to be robust against label noise when training deep neural networks [4, 13, 24]. + +[15] presents two methods to modify the surrogate loss in the presence of class-conditional random label noise. The first method introduces a new loss that is an unbiased estimator for a given surrogate loss, and the second method introduces a label-dependent loss. The paper provides generalization bounds for both methods, which depend on the + +noise rate of the dataset and the complexity of the hypothesis space. + +Labels aggregation When constructing datasets for supervised learning, data is often not labeled by a single annotator, rather multiple imperfect annotators are asked to assign labels to documents. Typically, separate labels are aggregated into one before learning models are applied [3,20]. In our work, we propose to exploit a measure of the agreement between annotators to explicitly calculate the noise of the dataset. Recently some works revisited the choice of aggregating labels. In [19] authors explore how to train LETOR models with relevance judgments distributions instead of single-valued relevance labels. They interpret the output of a LETOR model as a probability value or distribution and define different KL divergence-based loss functions to train a model. The loss they proposed can be used to train any ranking model that relies on gradient-based learning (in particular they focused on transformer-based neural LETOR models and on the decision tree-based GBM model). However, the authors do not directly estimate the noise rates in the annotations or study how learning from these noisy labels affects the generalization error of the models trained with the methods they introduce. In [25] the authors analyze the performance of both label aggregation and non-aggregation approaches in the context of empirical risk minimization for a number of popular loss functions, including those designed specifically for the noisy label learning problem. They conclude that label separation is preferable to label aggregation when noise rates are high or the number of labelers/annotations is insufficient. [17] and [22] exploit the availability of multiple human annotations to construct soft labels and concludes that this increases performance in terms of generalization to out-of-training-distribution test datasets, and robustness to adversarial attacks. [2] focus on efficiently eliciting soft labels from individual annotators. + +Noise rate estimation A number of approaches have been proposed for estimating the noise transition matrix (i.e. the probabilities that correct labels are changed for incorrect ones) [12, 16, 31]. Usually these methods use a small number of anchor points (that are samples that belong to a specific class with probability one) [8]. In particular, [16] proposed a noise estimation method based on anchor points, with the intent to provide an 'end-to-end' noise-estimation-and-learning method. Due to the lack of anchor points in real data, some works focused on a way to detect anchor points in noisy data, [26, 27]. In [27] the authors propose to introduce an intermediate class to avoid directly estimating the noisy class posterior. [28] also propose an iterative noise estimation heuristic that aims to partly correct the error and pointed out that the methods introduced by [16] + +and [27] have an error in computing anchor points, and provide conditions on the noise under which the methods work or fail. [26] provides a solution that can infer the transition matrix without anchor points. Indeed they use the instances with the highest class posterior probabilities for noisy data as anchor points. Our work differs from the mentioned work that use anchor points because we do not need to assume the existence of anchor points or to have a validation set to learn the noise rate and we only use noisy data to train our model, moreover we neither aim to detect anchor points in the noisy data. Also most of these works do not study the generalization properties of the proposed models, while we also address this problem and find bound that depend on the estimated noise transition matrix. + +Another approach is based on the clusterability condition, that is an example belongs to the same true class of its nearest-neighbors representations. [30] presented a method that relies on statistics of high-order consensuses among the 2 nearest-neighbors noisy labels. + +# 3. Problem formulation + +# 3.1. Notation + +In this paper we follow the following notation. Matrices and sets are denoted by upper-case and calligraphic letters, respectively. The space of $d$ -dimensional feature vectors is denoted by $\mathcal{X} \subset \mathbb{R}^d$ . + +We denote by $C$ the number of classes and by $e_j$ the $j$ -th standard canonical vector in $\mathbb{R}^C$ , namely the vector that has 1 in the $j$ -th position and zero in all the other positions. $\mathcal{V} = \{e_1,\dots ,e_C\} \subset \{0,1\} ^C$ is the label set. Feature vectors and labels are denoted by $x$ and $y$ , respectively. $\mathcal{D}$ is the joint distribution of the feature vectors and labels, i.e. $(x,y)\sim \mathcal{D}$ . The sampled dataset of size $n$ is denoted by $\widehat{\mathcal{D}} = \{(x_i,y_i)\}_{i = 1}^n$ . $f(x)$ denotes the output of the classifier $f$ for feature vector $x$ and is a $C$ dimensional vector. All vectors are column vectors. + +We denote by $\ell(t, y)$ a generic loss function for the classification task that takes as input $C$ dimensional vectors $t$ and $y$ . In practice $t$ will contain the prediction of the model and $y$ will be the ground-truth label as a one-hot encoded vector. Namely $\ell: [0,1]^C \times \mathcal{V} \to \mathbb{R}$ . + +# 3.2. Background + +We consider the classification problem within the supervised learning framework, where the ultimate goal is to minimize the $\ell$ -risk $R_{\ell, \mathcal{D}}(f) = \mathbb{E}_{(x,y) \sim \mathcal{D}}[\ell(f(x),y)]$ , for some loss function $\ell$ . We denote by $\mathcal{D}$ the joint distribution of feature vectors $x$ and labels $y$ . In practice, since the distribution is unknown instead of minimizing $R_{\ell, \mathcal{D}}(f)$ we minimize an + +empirical risk over some sampled dataset $\widehat{\mathcal{D}}$ : + +$$ +\widehat {R} _ {\ell , \widehat {\mathcal {D}}} (f) = \frac {1}{n} \sum_ {i = 1} ^ {n} \ell \left(f \left(x _ {i}\right), y _ {i}\right) = \mathbb {E} _ {(x, y) \sim \widehat {\mathcal {D}}} [ \ell (f (x), y) ]. \tag {1} +$$ + +In this work we assume that the true labels $y_{i}$ are unknown and consider two scenarios, both of which rely on $H$ annotators. + +# 3.2.1 Scenario I + +In this scenario we have access to the $H$ labels provided by the annotators for each sample, where $y_{i,a}$ refers to the label provided by the $a$ -th annotator for the $i$ -th sample. For a given feature vector $x_{i}$ the distribution of labels provided by annotator $a$ is given by its noise transition matrix $T_{a}$ , which is defined as follows: + +$$ +\left(T _ {a}\right) _ {i, j} := \mathbb {P} \left(y _ {a} = j \mid y = i\right) \tag {2} +$$ + +Assumption 1. We assume that all annotators have the same noise transition matrix (i.e. $T_{a} = T$ for all $a$ ), that $T$ is symmetric and that its diagonal elements are larger than 0.5 (i.e. $\mathbb{P}(y_a = i|y = i) > 0.5, \forall i \in \{1,\dots C\}$ ). + +Note that by definition $T$ is right stochastic and hence also doubly stochastic. It is also strictly diagonally dominant and therefore non-singular. + +# Proposition 3.0.1. $T$ is positive definite. + +Proof. Since $T$ is symmetric it follows that all eigenvalues are real. Combining the fact that it is strictly diagonally dominant with Gershgorin's theorem we conclude that all eigenvalues lie in the range $(0,1]$ and hence $T$ is positive definite. + +Assumption 2. We assume that the annotators are conditionally independent on the true label $y$ : + +$$ +\mathbb {P} \left(y _ {a}, y _ {b} | y\right) = \mathbb {P} \left(y _ {a} | y\right) \mathbb {P} \left(y _ {b} | y\right). \tag {3} +$$ + +We now define the IAA matrix $M_{ab}$ between annotators $a$ and $b$ as follows: + +$$ +\left(M _ {a b}\right) _ {i, j} := \mathbb {P} \left(y _ {a} = i, y _ {b} = j\right) \tag {4} +$$ + +Proposition 3.0.2. Leveraging Assumption 2 the agreement matrix $M_{a,b}$ can be written as follows: + +$$ +M _ {a, b} = T _ {a} ^ {T} D T _ {b} \tag {5} +$$ + +$$ +D := \operatorname {d i a g} \left\{\nu \right\} \tag {6} +$$ + +$$ +\nu : = \left[ \mathbb {P} (y = 1), \dots , \mathbb {P} (y = C) \right] ^ {T}. \tag {7} +$$ + +Due to Proposition 3.0.1 and the fact that $D$ is positive definite it follows that all matrices $M_{a,b}$ are invertible. + +Assumption 3. We assume that the class probabilities (and hence $D$ ) are known. + +Due to Assumption 1 all annotators share the same noise transition matrix $T$ . Therefore $M_{ab}$ is independent of $a$ and $b$ and from now on we remove this dependency in the notation (i.e. we get $M = T^T DT$ ). Furthermore, since $T$ is invertible and $D$ diagonal and positive definite it follows that $M$ is also positive definite. + +Note that since we have access to all the labels provided by the $H$ annotators for all the samples we can obtain an estimate of $M$ which we denote $\widehat{M}$ . + +Assumption 4. We assume that $\widehat{M}$ is a consistent estimator. + +For the case of two annotators, one possible consistent estimator $\widehat{M_{a,b}}$ that exploits its symmetry condition is given by: + +$$ +\left(\widehat {M _ {a , b}}\right) _ {i, j} = \sum_ {k = 1} ^ {n} \frac {\mathbb {1} \left(y _ {a , k} = i , y _ {b , k} = j\right) + \mathbb {1} \left(y _ {a , k} = j , y _ {b , k} = i\right)}{2 n} \tag {8} +$$ + +If the annotators have the same transition matrix, $M$ will be the same for all pairs of annotators. So we can estimate $M$ , in the case of $H \geq 2$ by averaging the estimators $\widehat{M}_{ab}$ obtain by Eq. (8) for all possible pairs of annotators. The estimator in this case can be written as + +$$ +(\widehat{M})_{i,j} = \frac{1}{H(H - 1)}\sum_{a = 1}^{H}\sum_{\substack{b = 1\\ b\neq a}}^{H}\sum_{h = 1}^{n}\frac{\mathbb{1}(y_{a,h} = i,y_{b,h} = j)}{n}. \tag{9} +$$ + +# 3.2.2 Scenario II + +In the second scenario, for each $i$ -th sample we are given a unique label $\tilde{y}_i$ that is produced by aggregating the $H$ individual labels according to some known aggregating policy (like majority vote). In this case, since we do not have access to the individual annotations we assume that $\widehat{M}$ is provided. + +The probability that label $y_{i}$ is corrupted to some other label $\tilde{y}_{i}$ is given by the aggregated noise transition matrix $\Gamma \in [0,1]^{C\times C}$ , where $\Gamma_{ij} \coloneqq \mathbb{P}(\tilde{y} = j|y = i)$ is the probability of the true label $i$ being flipped into a corrupted label $j$ and $C$ is the number of classes. Note that by definition $\Gamma$ is a right stochastic matrix that is determined by $T$ , the amount of annotators $H$ and the aggregating policy. We will study both the case where $\Gamma = T$ , and the case in which there exists a generic Lipschitz function $\phi$ so that $\Gamma^{-1} = \phi (T)$ . + +There are different policy choices to construct the dataset that lead to $\Gamma = T$ . If we decide to use only one annotator, for instance $a$ , to build the final dataset, namely for each sample $\tilde{y}^i = y_a^i$ we have $\Gamma = T_a$ . Or if annotators are + +homogeneous, i.e. they have the same noise transition matrix $T$ , and to build the final dataset we decide to randomly select the label of one of the annotators we have that $\Gamma = T$ . + +Even restricting ourselves to the case of homogeneous annotators, depending on the rule with which we build the dataset we can have a more complex relationship between the matrix $T$ and $\Gamma$ . + +We also obtain generalization bounds in the case where we have an estimate of the agreement matrix $M$ is not available and we only have access to a scalar representation of the inter-annotator agreement, in particular we consider the case where the Cohen's $\kappa$ is given. + +# 3.2.3 Objective + +The objective in both scenarios is to: i) use $\widehat{M}$ to estimate the noise transition matrices ( $T$ and $\Gamma$ ); ii) leverage these estimates to be able to learn from the noisy dataset in a more robust manner; and iii) obtain generalization bounds for the resulting learning methods. + +# 4. Main results + +We divide the main contributions in three sections. In the first section we show how to estimate the noise matrices $T$ . Next we indicate how to leverage these estimates to learn for the datasets with noisy labels. Finally we obtain bounds, depending on the Rademacher complexity of the class of functions, on the generalization gap for a bounded and Lipschitz loss function + +# 4.1. Estimation of the noise transition matrices + +We start stating the following Lemma that allows us to write the unknown matrix $T$ (and its inverse), as a function of $D$ and $M$ . + +Lemma 4.1. If $D^{\frac{1}{2}}$ commutes with $T$ we have that: + +$$ +T = U \Lambda^ {\frac {1}{2}} U ^ {T} \tag {10} +$$ + +$$ +T ^ {- 1} = U \Lambda^ {- \frac {1}{2}} U ^ {T} \tag {11} +$$ + +$$ +D ^ {- \frac {1}{2}} M D ^ {- \frac {1}{2}} = U \Lambda U ^ {T} \tag {12} +$$ + +where $U\Lambda U^T$ is the eigenvalue decomposition of $D^{-\frac{1}{2}}MD^{-\frac{1}{2}}$ (i.e. $U$ is some orthogonal matrix and $\Lambda$ is a diagonal positive definite matrix). + +A detailed discussion of when the commutativity assumption is satisfied is included in Appendix B. The proof of the previous Lemma can be find in Appendix C.1. + +Note that we could use Lemma 4.1 to estimate $T$ as follows: + +$$ +\widehat {T} = \widehat {U} \widehat {\Lambda} _ {M} ^ {\frac {1}{2}} \widehat {U} ^ {T} \tag {13} +$$ + +where $\widehat{U}\widehat{\Lambda}_M\widehat{U}^T$ is the eigenvalue decomposition of $D^{-\frac{1}{2}}\widehat{MD}^{-\frac{1}{2}}$ . However such estimate can result in matrices that are not doubly stochastic, or diagonally dominant due to estimation errors. A more accurate estimate of $T$ could be obtained as $\widehat{T} = \pi (\widehat{U}\widehat{\Lambda}_{M}^{\frac{2}{3}}\widehat{U}^{T})$ where $\pi$ is a projection operator to the set of doubly stochastic, positive definite matrices with diagonal elements greater than 0.5 and non-negative entries (which is a convex set). We can obtain such projection by solving the following optimization problem: + +$$ +\widehat {T} = \pi \left(\widehat {U} \widehat {\Lambda} _ {M} ^ {\frac {1}{2}} \widehat {U} ^ {T}\right) = \underset {B} {\operatorname {a r g m i n}} | | B - \widehat {U} \widehat {\Lambda} _ {M} ^ {\frac {1}{2}} \widehat {U} ^ {T} | | _ {2} ^ {2} \tag {14} +$$ + +$$ +\begin{array}{l} B = B ^ {T} \\ \sum_ {j} B _ {i, j} = 1 \quad \forall i \\ \text {s . t .} \end{array} +$$ + +$$ +\begin{array}{c c} B _ {i, j} \geq 0 & \forall i, j \\ B _ {i, i} \geq 0. 5 & \forall i \end{array} +$$ + +Note that this optimization problem is convex because the constraints are linear and for symmetric matrices it holds that $||\widehat{T} - \widehat{U}\widehat{\Lambda}_{M}^{\frac{1}{2}}\widehat{U}^{T}||_{2}^{2} = \lambda_{\max}(\widehat{T} - \widehat{U}\widehat{\Lambda}_{M}^{\frac{1}{2}}\widehat{U}^{T})$ , which is a convex function of $\widehat{T}$ . + +To summarize, $T$ can be estimated as follows. First, obtain an estimate of $M$ . Then obtain the eigenvalue decomposition of $D^{-\frac{1}{2}}\widehat{MD}^{-\frac{1}{2}} = \widehat{U}\widehat{\Lambda}\widehat{U}^T$ (note that this decomposition always exists because $D^{-\frac{1}{2}}\widehat{MD}^{-\frac{1}{2}}$ is symmetric). Finally obtain the estimate as: $\widehat{T} \coloneqq \pi (\widehat{U}\widehat{\Lambda}^{\frac{1}{2}}\widehat{U}^{T})$ . + +Note that once the estimate of $\widehat{T}$ is obtained, $\widehat{\Gamma}$ can be obtained since we assumed the label aggregating policy to be known. + +Lemma 4.2. Let $M_{a,b}$ be the agreement matrix for annotators $a$ and $b$ defined in Eq. (4) and $\widehat{M_{a,b}}$ be the estimated agreement matrix defined in Eq. (8) and let $||.||_p$ be the matrix norm induced by the $p$ vector norm. For every $p \in [1,\infty]$ and for every $\delta > 0$ , with probability at least $1 - \delta$ + +$$ +\left| \left| M _ {a, b} - \widehat {M _ {a , b}} \right| \right| _ {p} \leq \sqrt {\frac {C ^ {2}}{2 n} \ln \frac {2 C ^ {2}}{\delta}}. \tag {15} +$$ + +where $\mathbb{P}^n$ denotes the probability according to which the $n$ training samples are distributed, i.e. we are assuming that the samples are independently drawn according the probability $\mathbb{P}$ . + +Proof. The proof can be found in Appendix C.2. + +From Lemma 4.2 it follows that if $\widehat{M}$ is estimated as in Eq. (9), since $\widehat{M}$ is an average of $\widehat{M_{ab}}$ it also holds that for every $p\in [1,\infty ]$ and for every $\delta >0$ with probability at least $1 - \delta$ + +$$ +\left| \left| M - \widehat {M} \right| \right| _ {p} \leq \sqrt {\frac {C ^ {2}}{2 n} \ln \frac {2 C ^ {2}}{\delta}}. \tag {16} +$$ + +Theorem 4.3. Let $T$ be the noise transition matrix defined as in Eq. (2) and $\widehat{T}$ its estimate (defined as in Eq. (14)). + +With probability at least $1 - \delta$ : + +$$ +\left| \left| T - \widehat {T} \right| \right| _ {2} \leq \frac {C (\sqrt {C} + 1) \lambda_ {\max } (D)}{\lambda_ {\min } (\hat {T})} \sqrt {\frac {1}{2 n} \ln \frac {2 C ^ {2}}{\delta}} \tag {17a} +$$ + +$$ +\left| \left| T ^ {- 1} - \hat {T} ^ {- 1} \right| \right| _ {2} \leq \frac {9 C (\sqrt {C} + 1) \lambda_ {\max } (D)}{\lambda_ {\min } (\hat {T}) ^ {2}} \sqrt {\frac {1}{2 n} \ln \frac {2 C ^ {2}}{\delta}} \tag {17b} +$$ + +$$ +f o r n > \frac {C ^ {2} (\sqrt {C} + 1) ^ {2} (\ln (2 C ^ {2}) ^ {2}}{2 \lambda_ {\operatorname* {m i n}} (\hat {T}) ^ {2}}. +$$ + +Proof. The proof can be found in Appendix C.3. + +![](images/0dd3992809e101651bd30e00d42486ec9be63260afaeacdee51b1eb171674e24.jpg) + +From the previous theorem we can notice that the error in estimation of $T$ decays as $\frac{1}{\sqrt{n}}$ as a function of $n$ . + +# 4.2. Learning from noisy labels + +In this section we show how to leverage the estimates of the error rates to train the models. + +# 4.2.1 Posterior distribution of true labels as soft-labels + +It is noteworthy that if we have access to the labels provided by all annotators, the posterior probabilities of the true labels can be calculated leveraging $T$ and Bayes' Theorem as follows: + +$$ +\underbrace {\mathbb {P} \left(y _ {i} = c \mid y _ {1 , i} , \dots , y _ {H , i}\right)} _ {:= p _ {c, i}} \propto \nu_ {c} \prod_ {h = 1} ^ {H} \underbrace {\mathbb {P} \left(y _ {h , i} \mid y _ {i} = c\right)} _ {= T _ {c, y _ {h, i}}} \tag {18} +$$ + +we recall that $\nu_{c} = \mathbb{P}(y_{i} = c)$ and that the conditional probabilities on the r.h.s. are given by $T$ . In our case we can use our noisy transition estimates to estimate the posterior probabilities of the true labels, and afterwards we can use these posteriors to train the classifier. + +Lemma 4.4. For infinite annotators the posterior distribution over every sample calculated using the true $T$ converges to the dirac delta distribution centered on the true label almost surely (i.e. $\lim_{H\to \infty}p_{c,i}\stackrel {a.s}{=}\mathbb{1}(y_i = c)$ ). + +Proof. See Appendix C.5. + +![](images/c16bf70817f7d08dcf4da76a610fed959da3d85c645665178190710db7e3e64e.jpg) + +We can use the posterior distributions as soft-labels defining the following loss for the i-th sample: + +$$ +\ell \left(f \left(x _ {i}\right), y _ {1, i}, \dots , y _ {H, i}\right) = \ell \left(f \left(x _ {i}\right), \bar {p} _ {i}\right) \tag {19} +$$ + +where $\bar{p}_i = [p_{1,i},\dots ,p_{C,i}]^T$ . Or we can use the posterior distributions to weight the loss function at the $i$ -th sample evaluated at each of the possible labels: + +$$ +\ell \left(f \left(x _ {i}\right), y _ {1, i}, \dots , y _ {H, i}\right) = \sum_ {c = 1} ^ {C} p _ {c, i} \ell \left(f \left(x _ {i}\right), e _ {c}\right) \tag {20} +$$ + +where $e_c$ is the vector in $\mathbb{R}^C$ with 1 in the $c$ -th position. Notice that for categorical cross entropy loss the two functions defined above correspond, but in general they define two different loss functions. + +Note that these soft-labels are different from the ones obtained by averaging the annotators labels as is done in [25]. The method using the posteriors exploits the $T$ matrix and thus more information than the simple mean of the values of the losses among annotators. We therefore expect this to yield better results than the aggregation using the mean proposed in [25]. These considerations are supported by the empirical results we obtained on synthetic datasets (see Sec. 6). + +# 4.2.2 Robust loss functions + +Another way to leverage the estimate of $T$ is to use robust loss functions, like the forward and backward loss functions presented in [15, 16]. Let $\ell(t,y)$ be a generic loss function for the classification task, with a little abuse of notation we define $\ell(t) = [\ell(t,e_1),\dots,\ell(t,e_C)]^T$ . The backward and forward loss functions are defined in Eq. (21a) and Eq. (21b), respectively. + +$$ +l _ {b} (t, y) = (\widehat {\Gamma} ^ {- 1} \ell (t)) y \tag {21a} +$$ + +$$ +l _ {f} (t, y) = (\ell (\widehat {\Gamma} ^ {T} t)) y \tag {21b} +$$ + +To explain the notation in Eq. (21a) we are first doing the dot product between the matrix $\Gamma^{-1}$ and the vector $\ell(t)$ and then the dot product of the resulting vector with $y$ . These losses leverage aggregated labels and therefore different aggregating techniques can be used, like majority vote. Another possible aggregating technique that leverages the posterior probabilities is to assume that the true label is the one that corresponds to the class that has the highest posterior probability. + +# 4.3. Generalizations gap bounds + +In this section we derive generalization gap bounds for the backward loss that depend on the noise transition matrix estimated in Eq. (14). Since we are only addressing the problem for the backward loss, from now on we will denote the backward loss by $l$ . + +Remark 1. If $\ell(t,y)$ is Lipschitz with constant $L$ , the loss function $l(t,y)$ is Lipschitz with Lipschitz constant $||\Gamma^{-1}||_2 L$ . + +We will prove the following theorem in the case of $\Gamma = T$ . We emphasize that all the results apply also when $\Gamma^{-1} = \phi(T^{-1})$ and that the function that associate $\Gamma^{-1}$ and $T^{-1}, \phi$ is Lipschitz with respect to the norm $p$ , i.e. there exists a Lipschitz constant $L_{\phi, p}$ s.t. $||\phi(T^{-1}) - \phi(\hat{T}^{-1})||_p \leq L_{\phi, p}||T^{-1} - \widehat{T}^{-1}||_p$ . The only difference is that in the bound we will have a factor $L_{\phi, p}$ . + +It has been proved, first in [15] (Lemma 1) for the binary classification task and then in general for the multiclass case in [16] (Theorem 1) that $l(t,y)$ is an unbiased estimator for $\ell$ , i.e. + +$$ +\mathbb {E} _ {\tilde {y} | y} [ l (t, \tilde {y}) ] = \ell (t, y). +$$ + +Lemma 4.5. Let $\ell$ be a bounded loss function, with $\ell \in [0,\mu ]$ , s.t. there exists a Lipschitz function $\alpha$ , with Lipschitz constant $L$ , so that $\ell (f(x),y) = \alpha |f(x) - y|$ . Let $\widehat{R}_l(f)$ be the empirical risk for the loss $l$ and let $R_{l,\mathcal{D}}$ be the risk for loss $l$ under the distribution $\mathcal{D}$ , with $l$ unbiased estimator for the loss $\ell$ . We denote by $\hat{l}$ the backward loss obtained using $\widehat{T}$ . + +$$ +\begin{array}{l} \sup _ {f \in \mathcal {F}} | \hat {R} _ {\hat {l}} (f) - R _ {l, \mathcal {D}} (f) | \\ \leq \left[ L \lambda_ {\min} (\widehat {T} ^ {2}) + \frac {\mu \lambda_ {\min} (D)}{\lambda_ {\min} (\widehat {T}) ^ {2}} \sqrt {\frac {1}{n} \ln \left(\frac {4 C}{\delta}\right)} \right] \Re_ {n} (\mathcal {F}) g (C). \\ \end{array} +$$ + +$$ +w i t h g (C) = 6 C ^ {2} (\sqrt {C} + 1) +$$ + +Theorem 4.6. Let $l$ be an unbiased estimator for $\ell$ defined as in Eq. (21a), Denoting $\hat{f} = \operatorname*{argmin}_{f}(\widehat{R}_{\hat{l}}(f))$ . It holds that + +$$ +\begin{array}{l} R _ {\ell , \mathcal {D}} (\hat {f}) - \min _ {f \in \mathcal {F}} R _ {\ell , \mathcal {D}} (f) \\ \leq \left[ 2 L \lambda_ {\min } (\widehat {T} ^ {2}) + \frac {\mu \lambda_ {\min } (D)}{\lambda_ {\min } (\widehat {T}) ^ {2}} \sqrt {\frac {1}{n} \ln \left(\frac {4 C}{\delta}\right)} \right] \Re_ {n} (\mathcal {F}) g (C) \\ \end{array} +$$ + +$$ +w i t h g (C) = 6 C ^ {2} (\sqrt {C} + 1) +$$ + +The proofs of Lemma 4.5 and Theorem 4.6 can be found in Appendix C. We observe that in all the previous theorems, the bounds found are always decreasing as one over the square root of the number of samples. The above theorem gives us a performance bound for the classifier found minimizing the backward loss $l$ , i.e. the unbiased estimator of the loss $\ell$ on the noisy dataset. The bounds found depend on, the Rademacher complexity of the function space and the Lipschitz constant of the loss function. The importance of these bounds lies in the fact that they allow us to obtain performance bounds for a model trained with noisy data that depends on values that we can estimate from the noisy dataset. In particular, there is no dependence on the true noise transition matrix of the annotators, as in other work [15] which is instead a quantity that cannot be known a priori having access only to the training data. More in detail the bound depends on the estimate noise transition matrix, the number of classes in the dataset, the Rademacher complexity and the Lipschitz constant, which we can take as known a priori and on the distribution of ground truth, which in many cases it makes sense to assume uniform. + +# 5. Cohen's $\kappa$ + +We can also consider the case where an estimate of the IAA matrix $M$ is not available and we only have access to a scalar representation of the inter-annotator agreement like Cohen's $\kappa$ . In this case we can only estimate one parameter and hence the matrix $T$ has to be parameterized by a single parameter that can be estimated. + +One particular example is the case where the noise is uniform among classes. Under these hypotheses, $T$ is a matrix with all values $1 - p$ on the diagonal and $\frac{p}{C - 1}$ off the diagonal. + +Lemma 5.1 (Relationship between $p$ and $\kappa$ ). In the case of classification with uniform noise for two homogeneous annotators with noise rate $p$ , i.e if $a$ is one annotator, $\mathbb{P}(y_a = i|y = j) = p$ if $i \neq j$ . If the distribution of the ground-truth labels is uniform, it holds that: + +$$ +p = (1 - C ^ {- 1}) (1 - \sqrt {\kappa}) \tag {22} +$$ + +with $\kappa$ the Cohen's kappa coefficient of the two annotators (see Appendix A). + +Proof. The proof can be found in Appendix C.6. $\square$ + +If $T$ is assumed to be of the form described above (with all diagonal elements equal to $1 - p$ and all off-diagonal entries equal), it has one eigenvalue equal to 1 and all the rest are equal to $1 - pC(C - 1)^{-1}$ (this follows from the fact that in this case $T$ can be written as a weighted summation of the identity and a rank-one matrix). Hence using Eq. (22) we get that $\lambda_{\mathrm{min}}(T) = \sqrt{\kappa}$ . The bounds from Theorem 4.6 hold replacing $\lambda_{\mathrm{min}}(T)$ with $\sqrt{\kappa}$ . This allows us to obtain bound for the generalization gap of a classifier trained with backward loss even in the case where a single statistic on agreement between annotators is provided. + +# 6. Experimental results + +We performed experiments to validate the effectiveness of the method we propose for estimating $\widehat{T}$ by studying the error in the estimation as a function of the number of samples. We also performed experiments to show how the estimated $T$ can be leveraged to train classifiers in the presence of noise labels. In particular, we performed experiments for a classification task on a synthetic dataset and on the CIFAR10-N dataset, comparing the performance of a classifier trained using labels obtained by some baseline aggregation method with the performance of a classifier trained using the distribution of posteriors obtained from the estimation of T (Eq. (18)) as soft-labels. + +Estimation of $T$ With these experiments we aim to validate the theoretical results of Sec. 4.1. We generate various matrices $T$ that are symmetric, stochastic and diagonally + +dominant, the exact details about the generation of $T$ can be found in Appendix D.1. For each annotator we produce their prediction according to the matrix $T$ . We run experiments for the number of annotators $H = 10, 7, 3, 2$ . We report here the results for $H = 10$ , and 4 classes, all the other plots are in Appendix D.1. In Fig. 4 (as well as the plots in the Appendix) we can be observed that the error in the estimation decreases as $\frac{1}{\sqrt{n}}$ with $n$ number of samples, which is in agreement with the bound provided in Theorem 4.3. We also observed that, as expected, the estimation becomes more accurate as the number of annotators increases. + +Figure 1. Error in the estimation of $T$ for 4 classes and 10 annotators. The plots are obtained by averaging different admissible matrices $T$ (see Appendix B) and averaged over matrices that have the same minimum eigenvalues rounded to the first decimal. +![](images/18bacc82ef382ef69447b40f5f97cb6766f85b03af34e2d2d01cbbe55eca8baf.jpg) +min eigenvalue T 0.2 0.3 0.4 0.6 1.0 + +Classification task with synthetic data We consider a classification task with a synthetic dataset. The features are generated uniformly in $[0,1]^2$ . The assignment of labels $(y)$ is done by following the label distribution established for each experiment, separating the space with lines parallel to the bisector of the first and third quadrants. More information on how the class distributions are generated can be found in Appendix D.2. + +For each dataset annotations are generated according to the noise transition matrix $T$ . Various combinations of $T$ are tested that respect the assumptions of symmetry, stochasticity and diagonally dominance, as well as being commutative with D (more details can be found in Appendix B). The number of annotators is variable in the set $\{3,5\}$ . See Appendix D.2 for implementations details. + +Losses We use categorical cross entropy as loss function. We use both hard labels and soft labels to train the models. + +To train the models with hard labels an aggregation method is needed to obtain one final label from the annotators. We consider random and majority vote. In random aggregation the final label is randomly picked from the labels of the annotators. In majority vote the final label is the + +one with the most amount of votes (the mode), if the mode is not unique, we randomly choose one of the most voted classes. As soft-labels we consider the relative frequency among annotators and the posterior distribution according to Eq. (18). In the case of frequency for each sample we average the one-hot encoded annotations. Notice that random, majority vote and frequency soft labels do not leverage the estimate of $T$ while the posterior does. In Fig. 2 we report the results for 4 classes with distribution (0.4, 0.1, 0.4, 0.1) and 3 annotators. + +![](images/a68d8b599e6489bb1baedb49f4caf8f03cba0124d0e1352b35561d181db89900.jpg) +Figure 2. Comparison between performance of Cross Entropy Loss using majority vote, random aggregation method or the posteriors (posterior) and relative frequency (average) as soft labels. On the y-axis the accuracy on a clean dataset and on the x-axis the values of the minimum on the diagonal of $T$ . Small values of the minimum diagonal value mean a noisy dataset, while the minimum is 1 in the noise-free case. The results are obtained for 3 annotators and 4 classes, by averaging on different amissible matrices $T$ (see Appendix B) that have the same minimum diagonal values rounded to the first decimal. The error bands show the maximum and minimum performance for each method. + +We use accuracy with respect to a clean dataset as performance metric. Our results show that using the posteriors distribution, as soft labels, allows for better performance than using the average of the labels assigned by annotators and than using majority vote or random aggregation. + +Our method is shown to be more robust to the noise and is also the one with less variance in the results. This confirms our hypotheses that by leveraging the matrix $\hat{T}$ better classification accuracy can be achieved. + +Experiments on CIFAR10-N The CIFAR10-N dataset contains CIFAR-10 train images with noisy labels annotated by humans using Amazon Mechanical Turk. Each image is labelled by three independent annotators. Table 1 shows the accuracy achieved using the different aggregation methods. For this experiment we used Resnet34 [10] with and without pre-training. In both cases, our approach of aggrega + +tion achieves the best performance. Note that in this dataset there are no guarantees that the assumptions we made on $T$ are satisfied, however the method is still applicable with positive results. + +
Aggregation MethodPretrainedNot-Pretrained
random0.718 ± 0.0350.579 ± 0.023
majority vote0.740 ± 0.0170.590 ± 0.006
average0.762 ± 0.0120.637 ± 0.016
posteriors (ours)0.794 ± 0.0050.652 ± 0.014
+ +Table 1. Test Accuracy on CIFAR10-N with Resnet34 + +# 7. Concluding remarks + +We have addressed the problem of learning from noisy labels in the case where the dataset is labeled by annotators that occasionally make mistakes. We have introduced a methodology to estimate the noise transition matrix $T$ of the annotators given the IAA. We further showed different techniques to leverage this estimate to learn from the noisy dataset in a robust manner. We have shown theoretically that the methods we introduce are sound. We supported our methodology with some experiments that confirms our estimation of the noise transition matrix is valid and that this can be leveraged in the learning process to obtain better performance. + +Limitations The main limitation of our current approach to estimate $T$ is that it only considers the case where $T$ is symmetric and $D$ assumed to be known and commute with $T$ . Extending the results to the case where $T$ might not be symmetric and different among annotators is one possible future research direction. + +# Acknowledgements + +We acknowledge financial support from NRRP MUR project PE0000013-FAIR. This research was partially supported by MIUR under the grant "Dipartimento di eccellenza 2018-2022" of the Department of Computer Science and the Department of Computer Engineering at Sapienza University of Rome. It was also partially supported by the ERC Advanced Grant 788893 "AMDROMA", the EC H2020RIA project "SoBigData++" (871042), the MIUR PRIN project "ALGADIMAR", and the project SERICS (PE00000014) under the NRRP MUR program funded by the EU-NGEU. + +# References + +[1] J. Cohen. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1):37-46, 1960. 1, 11 +[2] K. M. Collins, U. Bhatt, and A. Weller. Eliciting and learning with soft labels from every annotator, 2022. 2 +[3] A. P. Dawid and A. M. Skene. Maximum likelihood estimation of observer error-rates using the em algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1):20-28, 1979. 2 +[4] L. Feng, S. Shu, Z. Lin, F. Lv, L. Li, and B. An. Can cross entropy loss be robust to label noise? In C. Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pages 2206-2212. International Joint Conferences on Artificial Intelligence Organization, 7 2020. Main track. 2 +[5] J. Fleiss et al. Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5):378-382, 1971. 1 +[6] A. Ghosh, H. Kumar, and P. S. Sastry. Robust loss functions under label noise for deep neural networks. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI'17, page 1919-1925. AAAI Press, 2017. 2 +[7] A. Ghosh, N. Manwani, and P. Sastry. Making risk minimization tolerant to label noise. Neurocomputing, 160:93-107, 2015. 2 +[8] D. Hendrycks, M. Mazeika, D. Wilson, and K. Gimpel. Using trusted data to train deep networks on labels corrupted by severe noise. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018. 2 +[9] R. A. Horn and C. R. Johnson. Matrix Analysis. Cambridge University Press, USA, 2nd edition, 2012. 14 +[10] A. Khetan, Z. C. Lipton, and A. Anandkumar. Learning from noisy singly-labeled data, 2017. 8 +[11] R. Meir and T. Zhang. Generalization error bounds for bayesian mixture algorithms. J. Mach. Learn. Res., 4:839-860, dec 2003. 17 +[12] A. Menon, B. V. Rooyen, C. S. Ong, and B. Williamson. Learning from corrupted binary labels via class-probability estimation. In F. Bach and + +D. Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, pages 125-134, Lille, France, 07-09 Jul 2015. PMLR. 2 +[13] A. K. Menon, A. S. Rawat, S. J. Reddi, and S. Kumar. Can gradient clipping mitigate label noise? In International Conference on Learning Representations, 2020. 2 +[14] M. Mohri, A. Rostamizadeh, and A. Talwalkar. Foundations of Machine Learning. The MIT Press, 2012. 17 +[15] N. Natarajan, I. S. Dhillon, P. K. Ravikumar, and A. Tewari. Learning with noisy labels. In C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Weinberger, editors, Advances in Neural Information Processing Systems, volume 26. Curran Associates, Inc., 2013. 1, 2, 6 +[16] G. Patrini, A. Rozza, A. Krishna Menon, R. Nock, and L. Qu. Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1944-1952, 2017. 1, 2, 6 +[17] J. Peterson, R. Battleday, T. Griffiths, and O. Russakovsky. Human uncertainty makes classification more robust. In Proceedings - 2019 International Conference on Computer Vision, ICCV 2019, Proceedings of the IEEE International Conference on Computer Vision, pages 9616-9625, United States, Oct. 2019. Institute of Electrical and Electronics Engineers Inc. 2 +[18] J. E. Potter. Matrix quadratic solutions. SIAM Journal on Applied Mathematics, 14(3):496-501, 1966. 12 +[19] A. Purpura, G. Silvello, and G. A. Susto. Learning to rank from relevance judgments distributions, 2022. 2 +[20] V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, and L. Moy. Learning from crowds. Journal of Machine Learning Research, 11(43):1297-1322, 2010. 2 +[21] H. Song, M. Kim, D. Park, Y. Shin, and J.-G. Lee. Learning from noisy labels with deep neural networks: A survey, 2020. 1 +[22] A. Uma, T. Fornaciari, D. Hovy, S. Paun, B. Plank, and M. Poesio. A case for soft loss functions. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 8(1):173-177, Oct. 2020. 2 + +[23] V. N. Vapnik. Statistical Learning Theory. Wiley-Interscience, 1998. 1 +[24] Y. Wang, X. Ma, Z. Chen, Y. Luo, J. Yi, and J. Bailey. Symmetric cross entropy for robust learning with noisy labels. In IEEE International Conference on Computer Vision, 2019. 2 +[25] J. Wei, Z. Zhu, T. Luo, E. Amid, A. Kumar, and Y. Liu. To aggregate or not? learning with separate noisy labels, 2022. 2, 6 +[26] X. Xia, T. Liu, N. Wang, B. Han, C. Gong, G. Niu, and M. Sugiyama. Are anchor points really indispensable in label-noise learning? In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. 1, 2, 3 +[27] Y. Yao, T. Liu, B. Han, M. Gong, J. Deng, G. Niu, and M. Sugiyama. Dual t: Reducing estimation error for transition matrix in label-noise learning. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS'20, Red Hook, NY, USA, 2020. Curran Associates Inc. 1, 2, 3 +[28] M. Zhang, J. Lee, and S. Agarwal. Learning from noisy labels with no change to the training process. In M. Meila and T. Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 12468-12478. PMLR, 18-24 Jul 2021. 2 +[29] Z. Zhang and M. R. Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS'18, page 8792-8802, Red Hook, NY, USA, 2018. Curran Associates Inc. 2 +[30] Z. Zhu, Y. Song, and Y. Liu. Clusterability as an alternative to anchor points when learning with noisy labels, 2021. 3 +[31] Z. Zhu, J. Wang, and Y. Liu. Beyond images: Label noise transition matrix estimation for tasks with lower-quality features. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, and S. Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 27633-27653. PMLR, 17-23 Jul 2022. 2 \ No newline at end of file diff --git a/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/images.zip b/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/images.zip new file mode 100644 index 0000000000000000000000000000000000000000..4cbc823600d2af8f0deadf965bfe425e883fce4e --- /dev/null +++ b/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/images.zip @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:98f760057f518c13ea2d8c495a40aebafb4c01b0425e3136f7d392568dd8229d +size 240504 diff --git a/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/layout.json b/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/layout.json new file mode 100644 index 0000000000000000000000000000000000000000..9eb15796b63cb8c69371f9f408de64551b3ee834 --- /dev/null +++ b/2023/Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels/layout.json @@ -0,0 +1,12676 @@ +{ + "pdf_info": [ + { + "para_blocks": [ + { + "bbox": [ + 74, + 153, + 503, + 185 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 153, + 503, + 185 + ], + "spans": [ + { + "bbox": [ + 74, + 153, + 503, + 185 + ], + "type": "text", + "content": "Leveraging Inter-rater Agreement for Classification in the Presence of Noisy Labels" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "spans": [ + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "text", + "content": "Maria Sofia Bucarelli " + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "text", + "content": " * Lucas Cassano " + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "text", + "content": " Federico Siciliano " + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "text", + "content": " * Amin Mantrach " + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "text", + "content": " Fabrizio Silvestri " + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "inline_equation", + "content": "^{2,3}" + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "inline_equation", + "content": "^{1}" + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "text", + "content": " Amazon " + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "inline_equation", + "content": "^{2}" + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "text", + "content": " Sapienza University of Rome " + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "inline_equation", + "content": "^{3}" + }, + { + "bbox": [ + 66, + 205, + 529, + 233 + ], + "type": "text", + "content": " ISTI-CNR, Pisa, Italy" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 165, + 234, + 431, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 165, + 234, + 431, + 245 + ], + "spans": [ + { + "bbox": [ + 165, + 234, + 431, + 245 + ], + "type": "text", + "content": "{mariasofia.bucarelli, federico.siciliano}@uniromal.it" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 227, + 247, + 369, + 257 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 227, + 247, + 369, + 257 + ], + "spans": [ + { + "bbox": [ + 227, + 247, + 369, + 257 + ], + "type": "text", + "content": "{lcecasl, mantrach}@amazon.lu" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 227, + 261, + 362, + 270 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 227, + 261, + 362, + 270 + ], + "spans": [ + { + "bbox": [ + 227, + 261, + 362, + 270 + ], + "type": "text", + "content": "fsilvestri@diag.uniroma1.it" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 149, + 295, + 192, + 307 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 149, + 295, + 192, + 307 + ], + "spans": [ + { + "bbox": [ + 149, + 295, + 192, + 307 + ], + "type": "text", + "content": "Abstract" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 60, + 319, + 282, + 494 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 319, + 282, + 494 + ], + "spans": [ + { + "bbox": [ + 60, + 319, + 282, + 494 + ], + "type": "text", + "content": "In practical settings, classification datasets are obtained through a labelling process that is usually done by humans. Labels can be noisy as they are obtained by aggregating the different individual labels assigned to the same sample by multiple, and possibly disagreeing, annotators. The interrater agreement on these datasets can be measured while the underlying noise distribution to which the labels are subject is assumed to be unknown. In this work, we: (i) show how to leverage the inter-annotator statistics to estimate the noise distribution to which labels are subject; (ii) introduce methods that use the estimate of the noise distribution to learn from the noisy dataset; and (iii) establish generalization bounds in the empirical risk minimization framework that depend on the estimated quantities. We conclude the paper by providing experiments that illustrate our findings." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 61, + 514, + 134, + 526 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 514, + 134, + 526 + ], + "spans": [ + { + "bbox": [ + 61, + 514, + 134, + 526 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 60, + 532, + 280, + 686 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 532, + 280, + 686 + ], + "spans": [ + { + "bbox": [ + 60, + 532, + 280, + 686 + ], + "type": "text", + "content": "Supervised learning has seen enormous progress in the last decades, both theoretical and practical. Empirical risk minimization is used as a learning framework [23], which relies on the assumption that the model is trained with iid (independent and identically distributed) sampled data from the joint distribution between features and labels. As a consequence of generalization bounds, when this assumption is satisfied any desired performance can be achieved as long as enough training data is available. However in many real-world applications, due to flaws during the data collection and labeling process, the assumption that the training data is sampled from the true feature-label joint distribution does not hold. Training data is often annotated by human raters who have some non-zero probability of making mistakes. It" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 297, + 297, + 517, + 362 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 297, + 517, + 362 + ], + "spans": [ + { + "bbox": [ + 297, + 297, + 517, + 362 + ], + "type": "text", + "content": "has been reported in [21] that the ratio of corrupted labels in some real-world datasets is between " + }, + { + "bbox": [ + 297, + 297, + 517, + 362 + ], + "type": "inline_equation", + "content": "8.0\\%" + }, + { + "bbox": [ + 297, + 297, + 517, + 362 + ], + "type": "text", + "content": " and, " + }, + { + "bbox": [ + 297, + 297, + 517, + 362 + ], + "type": "inline_equation", + "content": "38.5\\%" + }, + { + "bbox": [ + 297, + 297, + 517, + 362 + ], + "type": "text", + "content": ". As a consequence of the presence of incorrect labels in the training dataset, the aforementioned assumption is violated and hence performance guarantees based on generalization bounds no longer hold." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 365, + 517, + 573 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 365, + 517, + 573 + ], + "spans": [ + { + "bbox": [ + 296, + 365, + 517, + 573 + ], + "type": "text", + "content": "This gap between theory and practice raises the question whether it is possible to learn from datasets with noisy labels while still having performance guarantees. This question has received a lot of attention lately and has already been answered in the positive in some cases [15, 16]. Indeed multiple works have introduced learning algorithms that can cope with datasets with incorrect labels while guaranteeing desirable performance through provable generalization bounds. However, these solutions do not solve the entirety of the problem due to the fact that they rely on precise knowledge of the error rate to which the labels are subject, which is often unknown in practice. Several works [16, 26, 27] attempt to address this issue by introducing techniques to estimate such error rate. Some of these methods have the drawback of relying on assumptions that do not always hold in practice, such as the existence of anchor samples [16]. Ideally, it would be desirable to design learning algorithms that are both robust to noisy labels, and for which performance guarantees can be provided." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 576, + 517, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 576, + 517, + 675 + ], + "spans": [ + { + "bbox": [ + 296, + 576, + 517, + 675 + ], + "type": "text", + "content": "An approach, often used in industry to reduce the impact of errors made by human raters, is to label the same dataset multiple times by different annotators. Then the individual labels are combined to reduce the probability of erroneous labels in the dataset, two popular approaches are majority vote or soft labeling. In these cases inter-annotator agreement (IAA) scores (like Cohen's kappa [1] and Fleiss' kappa [5]) provide measurable metrics that are directly related to the probability of error present in the labels." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 297, + 677, + 517, + 711 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 677, + 517, + 711 + ], + "spans": [ + { + "bbox": [ + 297, + 677, + 517, + 711 + ], + "type": "text", + "content": "Since the IAA holds a direct relationship with the error rate associated with the human raters, one could potentially estimate the error rate and leverage this estimate to modify" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 57, + 7, + 102, + 39 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 7, + 102, + 39 + ], + "spans": [ + { + "bbox": [ + 57, + 7, + 102, + 39 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 136, + 5, + 487, + 41 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 136, + 5, + 487, + 41 + ], + "spans": [ + { + "bbox": [ + 136, + 5, + 487, + 41 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 60, + 692, + 280, + 710 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 692, + 280, + 710 + ], + "spans": [ + { + "bbox": [ + 60, + 692, + 280, + 710 + ], + "type": "text", + "content": "*This work was done during Maria Sofia Bucarelli's and Federico Siciliano's internship at Amazon." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "spans": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "text", + "content": "3439" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "bbox": [ + 60, + 125, + 280, + 158 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 125, + 280, + 158 + ], + "spans": [ + { + "bbox": [ + 60, + 125, + 280, + 158 + ], + "type": "text", + "content": "the learning algorithms with the objective of making them robust to the resulting noise in the labels. This is the main direction we explore in this work." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 60, + 159, + 280, + 245 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 159, + 280, + 245 + ], + "spans": [ + { + "bbox": [ + 60, + 159, + 280, + 245 + ], + "type": "text", + "content": "Motivation and Contributions: This work is motivated by two main points: i- to the best of our knowledge there are no published results that indicate how to leverage the IAA statistics to estimate the label noise distribution; and ii- the generalization bounds of existing noise tolerant training methods often rely on unknown quantities (like the true noise distribution) instead of on quantities that can be measured (like the IAA statistics)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 60, + 246, + 280, + 313 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 246, + 280, + 313 + ], + "spans": [ + { + "bbox": [ + 60, + 246, + 280, + 313 + ], + "type": "text", + "content": "Our contributions are the following: (i) we provide a methodology to estimate the label noise distribution based on the IAA statistics; (ii) we show how to leverage this estimate to learn from the noisy dataset; and (iii) we provide generalization bounds for our methods that depend on known quantities." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 324, + 142, + 335 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 324, + 142, + 335 + ], + "spans": [ + { + "bbox": [ + 61, + 324, + 142, + 335 + ], + "type": "text", + "content": "2. Related works" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 60, + 343, + 280, + 375 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 343, + 280, + 375 + ], + "spans": [ + { + "bbox": [ + 60, + 343, + 280, + 375 + ], + "type": "text", + "content": "Our work is related to literature on three main topics: (i) robust loss function design, (ii) label aggregating and (iii) noise rate estimation." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 60, + 393, + 280, + 491 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 393, + 280, + 491 + ], + "spans": [ + { + "bbox": [ + 60, + 393, + 280, + 491 + ], + "type": "text", + "content": "Robust loss functions In classification tasks, the goal is to obtain the lowest probability of classification error. The " + }, + { + "bbox": [ + 60, + 393, + 280, + 491 + ], + "type": "inline_equation", + "content": "0 - 1" + }, + { + "bbox": [ + 60, + 393, + 280, + 491 + ], + "type": "text", + "content": " loss counts how many errors a classifier makes on a given dataset and is often used in the evaluation of the classifier. However, it is rarely used in optimization procedures because it is non-differentiable and non-continuous. To overcome this, many learning strategies use some convex surrogates of the " + }, + { + "bbox": [ + 60, + 393, + 280, + 491 + ], + "type": "inline_equation", + "content": "0 - 1" + }, + { + "bbox": [ + 60, + 393, + 280, + 491 + ], + "type": "text", + "content": " loss function (e.g. hinge loss, squared error loss, cross-entropy)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 60, + 492, + 280, + 644 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 492, + 280, + 644 + ], + "spans": [ + { + "bbox": [ + 60, + 492, + 280, + 644 + ], + "type": "text", + "content": "It was proved ([6], [7]) that symmetric loss functions, that are functions for which the sum of the risks over all categories is equivalent to a constant for each arbitrary example, are robust to label noise. Examples of symmetric loss functions include the " + }, + { + "bbox": [ + 60, + 492, + 280, + 644 + ], + "type": "inline_equation", + "content": "0 - 1" + }, + { + "bbox": [ + 60, + 492, + 280, + 644 + ], + "type": "text", + "content": " loss, the Ramp Loss and (softmax) Mean Absolute Error (MAE). In [29] authors show that even if MAE is noise tolerant and cathegorical cross entropy (CCE) is not, MAE can perform poorly when used to train DNN in challenging domains. They also propose a loss function that can be seen as a generalization of MAE and CCE. Several other loss functions that do not strictly satisfy the symmetry condition have also been proposed to be robust against label noise when training deep neural networks [4, 13, 24]." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 60, + 645, + 280, + 711 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 645, + 280, + 711 + ], + "spans": [ + { + "bbox": [ + 60, + 645, + 280, + 711 + ], + "type": "text", + "content": "[15] presents two methods to modify the surrogate loss in the presence of class-conditional random label noise. The first method introduces a new loss that is an unbiased estimator for a given surrogate loss, and the second method introduces a label-dependent loss. The paper provides generalization bounds for both methods, which depend on the" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 297, + 125, + 516, + 147 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 125, + 516, + 147 + ], + "spans": [ + { + "bbox": [ + 297, + 125, + 516, + 147 + ], + "type": "text", + "content": "noise rate of the dataset and the complexity of the hypothesis space." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 296, + 161, + 517, + 532 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 161, + 517, + 532 + ], + "spans": [ + { + "bbox": [ + 296, + 161, + 517, + 532 + ], + "type": "text", + "content": "Labels aggregation When constructing datasets for supervised learning, data is often not labeled by a single annotator, rather multiple imperfect annotators are asked to assign labels to documents. Typically, separate labels are aggregated into one before learning models are applied [3,20]. In our work, we propose to exploit a measure of the agreement between annotators to explicitly calculate the noise of the dataset. Recently some works revisited the choice of aggregating labels. In [19] authors explore how to train LETOR models with relevance judgments distributions instead of single-valued relevance labels. They interpret the output of a LETOR model as a probability value or distribution and define different KL divergence-based loss functions to train a model. The loss they proposed can be used to train any ranking model that relies on gradient-based learning (in particular they focused on transformer-based neural LETOR models and on the decision tree-based GBM model). However, the authors do not directly estimate the noise rates in the annotations or study how learning from these noisy labels affects the generalization error of the models trained with the methods they introduce. In [25] the authors analyze the performance of both label aggregation and non-aggregation approaches in the context of empirical risk minimization for a number of popular loss functions, including those designed specifically for the noisy label learning problem. They conclude that label separation is preferable to label aggregation when noise rates are high or the number of labelers/annotations is insufficient. [17] and [22] exploit the availability of multiple human annotations to construct soft labels and concludes that this increases performance in terms of generalization to out-of-training-distribution test datasets, and robustness to adversarial attacks. [2] focus on efficiently eliciting soft labels from individual annotators." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 296, + 547, + 517, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 547, + 517, + 712 + ], + "spans": [ + { + "bbox": [ + 296, + 547, + 517, + 712 + ], + "type": "text", + "content": "Noise rate estimation A number of approaches have been proposed for estimating the noise transition matrix (i.e. the probabilities that correct labels are changed for incorrect ones) [12, 16, 31]. Usually these methods use a small number of anchor points (that are samples that belong to a specific class with probability one) [8]. In particular, [16] proposed a noise estimation method based on anchor points, with the intent to provide an 'end-to-end' noise-estimation-and-learning method. Due to the lack of anchor points in real data, some works focused on a way to detect anchor points in noisy data, [26, 27]. In [27] the authors propose to introduce an intermediate class to avoid directly estimating the noisy class posterior. [28] also propose an iterative noise estimation heuristic that aims to partly correct the error and pointed out that the methods introduced by [16]" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "spans": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "text", + "content": "3440" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 60, + 125, + 281, + 278 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 125, + 281, + 278 + ], + "spans": [ + { + "bbox": [ + 60, + 125, + 281, + 278 + ], + "type": "text", + "content": "and [27] have an error in computing anchor points, and provide conditions on the noise under which the methods work or fail. [26] provides a solution that can infer the transition matrix without anchor points. Indeed they use the instances with the highest class posterior probabilities for noisy data as anchor points. Our work differs from the mentioned work that use anchor points because we do not need to assume the existence of anchor points or to have a validation set to learn the noise rate and we only use noisy data to train our model, moreover we neither aim to detect anchor points in the noisy data. Also most of these works do not study the generalization properties of the proposed models, while we also address this problem and find bound that depend on the estimated noise transition matrix." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 60, + 281, + 281, + 336 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 281, + 281, + 336 + ], + "spans": [ + { + "bbox": [ + 60, + 281, + 281, + 336 + ], + "type": "text", + "content": "Another approach is based on the clusterability condition, that is an example belongs to the same true class of its nearest-neighbors representations. [30] presented a method that relies on statistics of high-order consensuses among the 2 nearest-neighbors noisy labels." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 354, + 173, + 366 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 354, + 173, + 366 + ], + "spans": [ + { + "bbox": [ + 61, + 354, + 173, + 366 + ], + "type": "text", + "content": "3. Problem formulation" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 375, + 119, + 386 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 375, + 119, + 386 + ], + "spans": [ + { + "bbox": [ + 61, + 375, + 119, + 386 + ], + "type": "text", + "content": "3.1. Notation" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 60, + 394, + 281, + 439 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 394, + 281, + 439 + ], + "spans": [ + { + "bbox": [ + 60, + 394, + 281, + 439 + ], + "type": "text", + "content": "In this paper we follow the following notation. Matrices and sets are denoted by upper-case and calligraphic letters, respectively. The space of " + }, + { + "bbox": [ + 60, + 394, + 281, + 439 + ], + "type": "inline_equation", + "content": "d" + }, + { + "bbox": [ + 60, + 394, + 281, + 439 + ], + "type": "text", + "content": "-dimensional feature vectors is denoted by " + }, + { + "bbox": [ + 60, + 394, + 281, + 439 + ], + "type": "inline_equation", + "content": "\\mathcal{X} \\subset \\mathbb{R}^d" + }, + { + "bbox": [ + 60, + 394, + 281, + 439 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "spans": [ + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": "We denote by " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": " the number of classes and by " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "e_j" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": " the " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": "-th standard canonical vector in " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^C" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": ", namely the vector that has 1 in the " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": "-th position and zero in all the other positions. " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "\\mathcal{V} = \\{e_1,\\dots ,e_C\\} \\subset \\{0,1\\} ^C" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": " is the label set. Feature vectors and labels are denoted by " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": ", respectively. " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": " is the joint distribution of the feature vectors and labels, i.e. " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "(x,y)\\sim \\mathcal{D}" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": ". The sampled dataset of size " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": " is denoted by " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "\\widehat{\\mathcal{D}} = \\{(x_i,y_i)\\}_{i = 1}^n" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": ". " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "f(x)" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": " denotes the output of the classifier " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "f" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": " for feature vector " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": " and is a " + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 60, + 442, + 281, + 552 + ], + "type": "text", + "content": " dimensional vector. All vectors are column vectors." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "spans": [ + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "text", + "content": "We denote by " + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "inline_equation", + "content": "\\ell(t, y)" + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "text", + "content": " a generic loss function for the classification task that takes as input " + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "text", + "content": " dimensional vectors " + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "text", + "content": ". In practice " + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "text", + "content": " will contain the prediction of the model and " + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "text", + "content": " will be the ground-truth label as a one-hot encoded vector. Namely " + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "inline_equation", + "content": "\\ell: [0,1]^C \\times \\mathcal{V} \\to \\mathbb{R}" + }, + { + "bbox": [ + 60, + 555, + 281, + 610 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 625, + 135, + 637 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 625, + 135, + 637 + ], + "spans": [ + { + "bbox": [ + 61, + 625, + 135, + 637 + ], + "type": "text", + "content": "3.2. Background" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "spans": [ + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "text", + "content": "We consider the classification problem within the supervised learning framework, where the ultimate goal is to minimize the " + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "inline_equation", + "content": "\\ell" + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "text", + "content": "-risk " + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "inline_equation", + "content": "R_{\\ell, \\mathcal{D}}(f) = \\mathbb{E}_{(x,y) \\sim \\mathcal{D}}[\\ell(f(x),y)]" + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "text", + "content": ", for some loss function " + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "inline_equation", + "content": "\\ell" + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "text", + "content": ". We denote by " + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "text", + "content": " the joint distribution of feature vectors " + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "inline_equation", + "content": "x" + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "text", + "content": " and labels " + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "text", + "content": ". In practice, since the distribution is unknown instead of minimizing " + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "inline_equation", + "content": "R_{\\ell, \\mathcal{D}}(f)" + }, + { + "bbox": [ + 60, + 645, + 281, + 712 + ], + "type": "text", + "content": " we minimize an" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 297, + 123, + 463, + 137 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 123, + 463, + 137 + ], + "spans": [ + { + "bbox": [ + 297, + 123, + 463, + 137 + ], + "type": "text", + "content": "empirical risk over some sampled dataset " + }, + { + "bbox": [ + 297, + 123, + 463, + 137 + ], + "type": "inline_equation", + "content": "\\widehat{\\mathcal{D}}" + }, + { + "bbox": [ + 297, + 123, + 463, + 137 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 302, + 144, + 516, + 173 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 302, + 144, + 516, + 173 + ], + "spans": [ + { + "bbox": [ + 302, + 144, + 516, + 173 + ], + "type": "interline_equation", + "content": "\\widehat {R} _ {\\ell , \\widehat {\\mathcal {D}}} (f) = \\frac {1}{n} \\sum_ {i = 1} ^ {n} \\ell \\left(f \\left(x _ {i}\\right), y _ {i}\\right) = \\mathbb {E} _ {(x, y) \\sim \\widehat {\\mathcal {D}}} [ \\ell (f (x), y) ]. \\tag {1}", + "image_path": "42aca0f589eed1a8e850b6aad65b1319431ff9ee9b23ece2e97912fa3d8590e2.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 180, + 517, + 213 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 180, + 517, + 213 + ], + "spans": [ + { + "bbox": [ + 296, + 180, + 517, + 213 + ], + "type": "text", + "content": "In this work we assume that the true labels " + }, + { + "bbox": [ + 296, + 180, + 517, + 213 + ], + "type": "inline_equation", + "content": "y_{i}" + }, + { + "bbox": [ + 296, + 180, + 517, + 213 + ], + "type": "text", + "content": " are unknown and consider two scenarios, both of which rely on " + }, + { + "bbox": [ + 296, + 180, + 517, + 213 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 296, + 180, + 517, + 213 + ], + "type": "text", + "content": " annotators." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 297, + 225, + 368, + 235 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 225, + 368, + 235 + ], + "spans": [ + { + "bbox": [ + 297, + 225, + 368, + 235 + ], + "type": "text", + "content": "3.2.1 Scenario I" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "spans": [ + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "text", + "content": "In this scenario we have access to the " + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "text", + "content": " labels provided by the annotators for each sample, where " + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "inline_equation", + "content": "y_{i,a}" + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "text", + "content": " refers to the label provided by the " + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "text", + "content": "-th annotator for the " + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "text", + "content": "-th sample. For a given feature vector " + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "inline_equation", + "content": "x_{i}" + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "text", + "content": " the distribution of labels provided by annotator " + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "text", + "content": " is given by its noise transition matrix " + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "inline_equation", + "content": "T_{a}" + }, + { + "bbox": [ + 296, + 242, + 517, + 308 + ], + "type": "text", + "content": ", which is defined as follows:" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 355, + 317, + 516, + 330 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 355, + 317, + 516, + 330 + ], + "spans": [ + { + "bbox": [ + 355, + 317, + 516, + 330 + ], + "type": "interline_equation", + "content": "\\left(T _ {a}\\right) _ {i, j} := \\mathbb {P} \\left(y _ {a} = j \\mid y = i\\right) \\tag {2}", + "image_path": "362fdcddbba6ec94d574c576d6495afcab27e3a3a0f0d157fdf32e1c95e4a1f5.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 296, + 337, + 517, + 383 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 337, + 517, + 383 + ], + "spans": [ + { + "bbox": [ + 296, + 337, + 517, + 383 + ], + "type": "text", + "content": "Assumption 1. We assume that all annotators have the same noise transition matrix (i.e. " + }, + { + "bbox": [ + 296, + 337, + 517, + 383 + ], + "type": "inline_equation", + "content": "T_{a} = T" + }, + { + "bbox": [ + 296, + 337, + 517, + 383 + ], + "type": "text", + "content": " for all " + }, + { + "bbox": [ + 296, + 337, + 517, + 383 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 296, + 337, + 517, + 383 + ], + "type": "text", + "content": "), that " + }, + { + "bbox": [ + 296, + 337, + 517, + 383 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 337, + 517, + 383 + ], + "type": "text", + "content": " is symmetric and that its diagonal elements are larger than 0.5 (i.e. " + }, + { + "bbox": [ + 296, + 337, + 517, + 383 + ], + "type": "inline_equation", + "content": "\\mathbb{P}(y_a = i|y = i) > 0.5, \\forall i \\in \\{1,\\dots C\\}" + }, + { + "bbox": [ + 296, + 337, + 517, + 383 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 296, + 388, + 517, + 421 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 388, + 517, + 421 + ], + "spans": [ + { + "bbox": [ + 296, + 388, + 517, + 421 + ], + "type": "text", + "content": "Note that by definition " + }, + { + "bbox": [ + 296, + 388, + 517, + 421 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 388, + 517, + 421 + ], + "type": "text", + "content": " is right stochastic and hence also doubly stochastic. It is also strictly diagonally dominant and therefore non-singular." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 297, + 427, + 449, + 440 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 427, + 449, + 440 + ], + "spans": [ + { + "bbox": [ + 297, + 427, + 449, + 440 + ], + "type": "text", + "content": "Proposition 3.0.1. " + }, + { + "bbox": [ + 297, + 427, + 449, + 440 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 297, + 427, + 449, + 440 + ], + "type": "text", + "content": " is positive definite." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 296, + 445, + 517, + 500 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 445, + 517, + 500 + ], + "spans": [ + { + "bbox": [ + 296, + 445, + 517, + 500 + ], + "type": "text", + "content": "Proof. Since " + }, + { + "bbox": [ + 296, + 445, + 517, + 500 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 445, + 517, + 500 + ], + "type": "text", + "content": " is symmetric it follows that all eigenvalues are real. Combining the fact that it is strictly diagonally dominant with Gershgorin's theorem we conclude that all eigenvalues lie in the range " + }, + { + "bbox": [ + 296, + 445, + 517, + 500 + ], + "type": "inline_equation", + "content": "(0,1]" + }, + { + "bbox": [ + 296, + 445, + 517, + 500 + ], + "type": "text", + "content": " and hence " + }, + { + "bbox": [ + 296, + 445, + 517, + 500 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 445, + 517, + 500 + ], + "type": "text", + "content": " is positive definite." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 297, + 508, + 516, + 530 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 508, + 516, + 530 + ], + "spans": [ + { + "bbox": [ + 297, + 508, + 516, + 530 + ], + "type": "text", + "content": "Assumption 2. We assume that the annotators are conditionally independent on the true label " + }, + { + "bbox": [ + 297, + 508, + 516, + 530 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 297, + 508, + 516, + 530 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 348, + 538, + 516, + 550 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 348, + 538, + 516, + 550 + ], + "spans": [ + { + "bbox": [ + 348, + 538, + 516, + 550 + ], + "type": "interline_equation", + "content": "\\mathbb {P} \\left(y _ {a}, y _ {b} | y\\right) = \\mathbb {P} \\left(y _ {a} | y\\right) \\mathbb {P} \\left(y _ {b} | y\\right). \\tag {3}", + "image_path": "c0e5d43572325404652de3aeb87da86d217934e5d9490496ebaf403b65295155.jpg" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 297, + 558, + 516, + 580 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 558, + 516, + 580 + ], + "spans": [ + { + "bbox": [ + 297, + 558, + 516, + 580 + ], + "type": "text", + "content": "We now define the IAA matrix " + }, + { + "bbox": [ + 297, + 558, + 516, + 580 + ], + "type": "inline_equation", + "content": "M_{ab}" + }, + { + "bbox": [ + 297, + 558, + 516, + 580 + ], + "type": "text", + "content": " between annotators " + }, + { + "bbox": [ + 297, + 558, + 516, + 580 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 297, + 558, + 516, + 580 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 297, + 558, + 516, + 580 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 297, + 558, + 516, + 580 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 349, + 589, + 516, + 602 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 349, + 589, + 516, + 602 + ], + "spans": [ + { + "bbox": [ + 349, + 589, + 516, + 602 + ], + "type": "interline_equation", + "content": "\\left(M _ {a b}\\right) _ {i, j} := \\mathbb {P} \\left(y _ {a} = i, y _ {b} = j\\right) \\tag {4}", + "image_path": "5c1bfe8357c9d53f5d87144fb14993a7be09b3081dc6910b6395f103dd9d8fda.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 297, + 609, + 517, + 631 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 609, + 517, + 631 + ], + "spans": [ + { + "bbox": [ + 297, + 609, + 517, + 631 + ], + "type": "text", + "content": "Proposition 3.0.2. Leveraging Assumption 2 the agreement matrix " + }, + { + "bbox": [ + 297, + 609, + 517, + 631 + ], + "type": "inline_equation", + "content": "M_{a,b}" + }, + { + "bbox": [ + 297, + 609, + 517, + 631 + ], + "type": "text", + "content": " can be written as follows:" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 337, + 639, + 516, + 651 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 337, + 639, + 516, + 651 + ], + "spans": [ + { + "bbox": [ + 337, + 639, + 516, + 651 + ], + "type": "interline_equation", + "content": "M _ {a, b} = T _ {a} ^ {T} D T _ {b} \\tag {5}", + "image_path": "91dde3e3aaf2214cd20f3cb8bcdb464ecef562f00019bc3957b76bba37f1ae5d.jpg" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 344, + 654, + 516, + 666 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 344, + 654, + 516, + 666 + ], + "spans": [ + { + "bbox": [ + 344, + 654, + 516, + 666 + ], + "type": "interline_equation", + "content": "D := \\operatorname {d i a g} \\left\\{\\nu \\right\\} \\tag {6}", + "image_path": "7ba39c17ce34d6d1da6dd5708fcc92d3acfb9c7152f069e68d997f414f8fa9ff.jpg" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 346, + 668, + 516, + 681 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 346, + 668, + 516, + 681 + ], + "spans": [ + { + "bbox": [ + 346, + 668, + 516, + 681 + ], + "type": "interline_equation", + "content": "\\nu : = \\left[ \\mathbb {P} (y = 1), \\dots , \\mathbb {P} (y = C) \\right] ^ {T}. \\tag {7}", + "image_path": "3e393880f215608b5f1cda1ad478a47f8b4e8984a68dfc1af805f3d5adbd34d1.jpg" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 297, + 688, + 516, + 711 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 688, + 516, + 711 + ], + "spans": [ + { + "bbox": [ + 297, + 688, + 516, + 711 + ], + "type": "text", + "content": "Due to Proposition 3.0.1 and the fact that " + }, + { + "bbox": [ + 297, + 688, + 516, + 711 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 297, + 688, + 516, + 711 + ], + "type": "text", + "content": " is positive definite it follows that all matrices " + }, + { + "bbox": [ + 297, + 688, + 516, + 711 + ], + "type": "inline_equation", + "content": "M_{a,b}" + }, + { + "bbox": [ + 297, + 688, + 516, + 711 + ], + "type": "text", + "content": " are invertible." + } + ] + } + ], + "index": 27 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "spans": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "text", + "content": "3441" + } + ] + } + ], + "index": 28 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 60, + 125, + 281, + 146 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 125, + 281, + 146 + ], + "spans": [ + { + "bbox": [ + 60, + 125, + 281, + 146 + ], + "type": "text", + "content": "Assumption 3. We assume that the class probabilities (and hence " + }, + { + "bbox": [ + 60, + 125, + 281, + 146 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 60, + 125, + 281, + 146 + ], + "type": "text", + "content": ") are known." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "spans": [ + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "text", + "content": "Due to Assumption 1 all annotators share the same noise transition matrix " + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "text", + "content": ". Therefore " + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "inline_equation", + "content": "M_{ab}" + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "text", + "content": " is independent of " + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "text", + "content": " and from now on we remove this dependency in the notation (i.e. we get " + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "inline_equation", + "content": "M = T^T DT" + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "text", + "content": "). Furthermore, since " + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "text", + "content": " is invertible and " + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "text", + "content": " diagonal and positive definite it follows that " + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 60, + 154, + 280, + 220 + ], + "type": "text", + "content": " is also positive definite." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 60, + 221, + 280, + 255 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 221, + 280, + 255 + ], + "spans": [ + { + "bbox": [ + 60, + 221, + 280, + 255 + ], + "type": "text", + "content": "Note that since we have access to all the labels provided by the " + }, + { + "bbox": [ + 60, + 221, + 280, + 255 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 60, + 221, + 280, + 255 + ], + "type": "text", + "content": " annotators for all the samples we can obtain an estimate of " + }, + { + "bbox": [ + 60, + 221, + 280, + 255 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 60, + 221, + 280, + 255 + ], + "type": "text", + "content": " which we denote " + }, + { + "bbox": [ + 60, + 221, + 280, + 255 + ], + "type": "inline_equation", + "content": "\\widehat{M}" + }, + { + "bbox": [ + 60, + 221, + 280, + 255 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 61, + 261, + 280, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 261, + 280, + 274 + ], + "spans": [ + { + "bbox": [ + 61, + 261, + 280, + 274 + ], + "type": "text", + "content": "Assumption 4. We assume that " + }, + { + "bbox": [ + 61, + 261, + 280, + 274 + ], + "type": "inline_equation", + "content": "\\widehat{M}" + }, + { + "bbox": [ + 61, + 261, + 280, + 274 + ], + "type": "text", + "content": " is a consistent estimator." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 60, + 281, + 280, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 281, + 280, + 316 + ], + "spans": [ + { + "bbox": [ + 60, + 281, + 280, + 316 + ], + "type": "text", + "content": "For the case of two annotators, one possible consistent estimator " + }, + { + "bbox": [ + 60, + 281, + 280, + 316 + ], + "type": "inline_equation", + "content": "\\widehat{M_{a,b}}" + }, + { + "bbox": [ + 60, + 281, + 280, + 316 + ], + "type": "text", + "content": " that exploits its symmetry condition is given by:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 65, + 324, + 279, + 362 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 324, + 279, + 362 + ], + "spans": [ + { + "bbox": [ + 65, + 324, + 279, + 362 + ], + "type": "interline_equation", + "content": "\\left(\\widehat {M _ {a , b}}\\right) _ {i, j} = \\sum_ {k = 1} ^ {n} \\frac {\\mathbb {1} \\left(y _ {a , k} = i , y _ {b , k} = j\\right) + \\mathbb {1} \\left(y _ {a , k} = j , y _ {b , k} = i\\right)}{2 n} \\tag {8}", + "image_path": "0dd8f93c8a12f6ae5882e57c3c40b1f07508cdcc479068598bf9fcd9ed0bff52.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 60, + 372, + 281, + 428 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 372, + 281, + 428 + ], + "spans": [ + { + "bbox": [ + 60, + 372, + 281, + 428 + ], + "type": "text", + "content": "If the annotators have the same transition matrix, " + }, + { + "bbox": [ + 60, + 372, + 281, + 428 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 60, + 372, + 281, + 428 + ], + "type": "text", + "content": " will be the same for all pairs of annotators. So we can estimate " + }, + { + "bbox": [ + 60, + 372, + 281, + 428 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 60, + 372, + 281, + 428 + ], + "type": "text", + "content": ", in the case of " + }, + { + "bbox": [ + 60, + 372, + 281, + 428 + ], + "type": "inline_equation", + "content": "H \\geq 2" + }, + { + "bbox": [ + 60, + 372, + 281, + 428 + ], + "type": "text", + "content": " by averaging the estimators " + }, + { + "bbox": [ + 60, + 372, + 281, + 428 + ], + "type": "inline_equation", + "content": "\\widehat{M}_{ab}" + }, + { + "bbox": [ + 60, + 372, + 281, + 428 + ], + "type": "text", + "content": " obtain by Eq. (8) for all possible pairs of annotators. The estimator in this case can be written as" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 67, + 434, + 281, + 471 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 434, + 281, + 471 + ], + "spans": [ + { + "bbox": [ + 67, + 434, + 281, + 471 + ], + "type": "interline_equation", + "content": "(\\widehat{M})_{i,j} = \\frac{1}{H(H - 1)}\\sum_{a = 1}^{H}\\sum_{\\substack{b = 1\\\\ b\\neq a}}^{H}\\sum_{h = 1}^{n}\\frac{\\mathbb{1}(y_{a,h} = i,y_{b,h} = j)}{n}. \\tag{9}", + "image_path": "89953bd19a7209cda7fb958ceb5e8a200309b0d7d6170be93d479ed390ce1c06.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 61, + 484, + 135, + 495 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 484, + 135, + 495 + ], + "spans": [ + { + "bbox": [ + 61, + 484, + 135, + 495 + ], + "type": "text", + "content": "3.2.2 Scenario II" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 60, + 501, + 280, + 568 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 501, + 280, + 568 + ], + "spans": [ + { + "bbox": [ + 60, + 501, + 280, + 568 + ], + "type": "text", + "content": "In the second scenario, for each " + }, + { + "bbox": [ + 60, + 501, + 280, + 568 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 60, + 501, + 280, + 568 + ], + "type": "text", + "content": "-th sample we are given a unique label " + }, + { + "bbox": [ + 60, + 501, + 280, + 568 + ], + "type": "inline_equation", + "content": "\\tilde{y}_i" + }, + { + "bbox": [ + 60, + 501, + 280, + 568 + ], + "type": "text", + "content": " that is produced by aggregating the " + }, + { + "bbox": [ + 60, + 501, + 280, + 568 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 60, + 501, + 280, + 568 + ], + "type": "text", + "content": " individual labels according to some known aggregating policy (like majority vote). In this case, since we do not have access to the individual annotations we assume that " + }, + { + "bbox": [ + 60, + 501, + 280, + 568 + ], + "type": "inline_equation", + "content": "\\widehat{M}" + }, + { + "bbox": [ + 60, + 501, + 280, + 568 + ], + "type": "text", + "content": " is provided." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "spans": [ + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": "The probability that label " + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "inline_equation", + "content": "y_{i}" + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": " is corrupted to some other label " + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "inline_equation", + "content": "\\tilde{y}_{i}" + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": " is given by the aggregated noise transition matrix " + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "inline_equation", + "content": "\\Gamma \\in [0,1]^{C\\times C}" + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "inline_equation", + "content": "\\Gamma_{ij} \\coloneqq \\mathbb{P}(\\tilde{y} = j|y = i)" + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": " is the probability of the true label " + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": " being flipped into a corrupted label " + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": " is the number of classes. Note that by definition " + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "inline_equation", + "content": "\\Gamma" + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": " is a right stochastic matrix that is determined by " + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": ", the amount of annotators " + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": " and the aggregating policy. We will study both the case where " + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "inline_equation", + "content": "\\Gamma = T" + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": ", and the case in which there exists a generic Lipschitz function " + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "inline_equation", + "content": "\\phi" + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": " so that " + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "inline_equation", + "content": "\\Gamma^{-1} = \\phi (T)" + }, + { + "bbox": [ + 60, + 569, + 280, + 667 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 60, + 668, + 280, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 668, + 280, + 712 + ], + "spans": [ + { + "bbox": [ + 60, + 668, + 280, + 712 + ], + "type": "text", + "content": "There are different policy choices to construct the dataset that lead to " + }, + { + "bbox": [ + 60, + 668, + 280, + 712 + ], + "type": "inline_equation", + "content": "\\Gamma = T" + }, + { + "bbox": [ + 60, + 668, + 280, + 712 + ], + "type": "text", + "content": ". If we decide to use only one annotator, for instance " + }, + { + "bbox": [ + 60, + 668, + 280, + 712 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 60, + 668, + 280, + 712 + ], + "type": "text", + "content": ", to build the final dataset, namely for each sample " + }, + { + "bbox": [ + 60, + 668, + 280, + 712 + ], + "type": "inline_equation", + "content": "\\tilde{y}^i = y_a^i" + }, + { + "bbox": [ + 60, + 668, + 280, + 712 + ], + "type": "text", + "content": " we have " + }, + { + "bbox": [ + 60, + 668, + 280, + 712 + ], + "type": "inline_equation", + "content": "\\Gamma = T_a" + }, + { + "bbox": [ + 60, + 668, + 280, + 712 + ], + "type": "text", + "content": ". Or if annotators are" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 296, + 125, + 517, + 158 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 125, + 517, + 158 + ], + "spans": [ + { + "bbox": [ + 296, + 125, + 517, + 158 + ], + "type": "text", + "content": "homogeneous, i.e. they have the same noise transition matrix " + }, + { + "bbox": [ + 296, + 125, + 517, + 158 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 125, + 517, + 158 + ], + "type": "text", + "content": ", and to build the final dataset we decide to randomly select the label of one of the annotators we have that " + }, + { + "bbox": [ + 296, + 125, + 517, + 158 + ], + "type": "inline_equation", + "content": "\\Gamma = T" + }, + { + "bbox": [ + 296, + 125, + 517, + 158 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 159, + 517, + 202 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 159, + 517, + 202 + ], + "spans": [ + { + "bbox": [ + 296, + 159, + 517, + 202 + ], + "type": "text", + "content": "Even restricting ourselves to the case of homogeneous annotators, depending on the rule with which we build the dataset we can have a more complex relationship between the matrix " + }, + { + "bbox": [ + 296, + 159, + 517, + 202 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 159, + 517, + 202 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 159, + 517, + 202 + ], + "type": "inline_equation", + "content": "\\Gamma" + }, + { + "bbox": [ + 296, + 159, + 517, + 202 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 296, + 203, + 517, + 258 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 203, + 517, + 258 + ], + "spans": [ + { + "bbox": [ + 296, + 203, + 517, + 258 + ], + "type": "text", + "content": "We also obtain generalization bounds in the case where we have an estimate of the agreement matrix " + }, + { + "bbox": [ + 296, + 203, + 517, + 258 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 296, + 203, + 517, + 258 + ], + "type": "text", + "content": " is not available and we only have access to a scalar representation of the inter-annotator agreement, in particular we consider the case where the Cohen's " + }, + { + "bbox": [ + 296, + 203, + 517, + 258 + ], + "type": "inline_equation", + "content": "\\kappa" + }, + { + "bbox": [ + 296, + 203, + 517, + 258 + ], + "type": "text", + "content": " is given." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 297, + 274, + 365, + 286 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 274, + 365, + 286 + ], + "spans": [ + { + "bbox": [ + 297, + 274, + 365, + 286 + ], + "type": "text", + "content": "3.2.3 Objective" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 296, + 293, + 517, + 349 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 293, + 517, + 349 + ], + "spans": [ + { + "bbox": [ + 296, + 293, + 517, + 349 + ], + "type": "text", + "content": "The objective in both scenarios is to: i) use " + }, + { + "bbox": [ + 296, + 293, + 517, + 349 + ], + "type": "inline_equation", + "content": "\\widehat{M}" + }, + { + "bbox": [ + 296, + 293, + 517, + 349 + ], + "type": "text", + "content": " to estimate the noise transition matrices (" + }, + { + "bbox": [ + 296, + 293, + 517, + 349 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 293, + 517, + 349 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 293, + 517, + 349 + ], + "type": "inline_equation", + "content": "\\Gamma" + }, + { + "bbox": [ + 296, + 293, + 517, + 349 + ], + "type": "text", + "content": "); ii) leverage these estimates to be able to learn from the noisy dataset in a more robust manner; and iii) obtain generalization bounds for the resulting learning methods." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 297, + 360, + 370, + 372 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 360, + 370, + 372 + ], + "spans": [ + { + "bbox": [ + 297, + 360, + 370, + 372 + ], + "type": "text", + "content": "4. Main results" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 296, + 379, + 517, + 456 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 379, + 517, + 456 + ], + "spans": [ + { + "bbox": [ + 296, + 379, + 517, + 456 + ], + "type": "text", + "content": "We divide the main contributions in three sections. In the first section we show how to estimate the noise matrices " + }, + { + "bbox": [ + 296, + 379, + 517, + 456 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 379, + 517, + 456 + ], + "type": "text", + "content": ". Next we indicate how to leverage these estimates to learn for the datasets with noisy labels. Finally we obtain bounds, depending on the Rademacher complexity of the class of functions, on the generalization gap for a bounded and Lipschitz loss function" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 297, + 464, + 499, + 475 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 464, + 499, + 475 + ], + "spans": [ + { + "bbox": [ + 297, + 464, + 499, + 475 + ], + "type": "text", + "content": "4.1. Estimation of the noise transition matrices" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 296, + 482, + 517, + 514 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 482, + 517, + 514 + ], + "spans": [ + { + "bbox": [ + 296, + 482, + 517, + 514 + ], + "type": "text", + "content": "We start stating the following Lemma that allows us to write the unknown matrix " + }, + { + "bbox": [ + 296, + 482, + 517, + 514 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 482, + 517, + 514 + ], + "type": "text", + "content": " (and its inverse), as a function of " + }, + { + "bbox": [ + 296, + 482, + 517, + 514 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 296, + 482, + 517, + 514 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 296, + 482, + 517, + 514 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 296, + 482, + 517, + 514 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 297, + 523, + 489, + 535 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 523, + 489, + 535 + ], + "spans": [ + { + "bbox": [ + 297, + 523, + 489, + 535 + ], + "type": "text", + "content": "Lemma 4.1. If " + }, + { + "bbox": [ + 297, + 523, + 489, + 535 + ], + "type": "inline_equation", + "content": "D^{\\frac{1}{2}}" + }, + { + "bbox": [ + 297, + 523, + 489, + 535 + ], + "type": "text", + "content": " commutes with " + }, + { + "bbox": [ + 297, + 523, + 489, + 535 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 297, + 523, + 489, + 535 + ], + "type": "text", + "content": " we have that:" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 399, + 544, + 515, + 557 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 399, + 544, + 515, + 557 + ], + "spans": [ + { + "bbox": [ + 399, + 544, + 515, + 557 + ], + "type": "interline_equation", + "content": "T = U \\Lambda^ {\\frac {1}{2}} U ^ {T} \\tag {10}", + "image_path": "abcb9c4361fde198d64676449b88d2abbb23161c78c83521d5a72eceb182a3ac.jpg" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 389, + 560, + 515, + 573 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 389, + 560, + 515, + 573 + ], + "spans": [ + { + "bbox": [ + 389, + 560, + 515, + 573 + ], + "type": "interline_equation", + "content": "T ^ {- 1} = U \\Lambda^ {- \\frac {1}{2}} U ^ {T} \\tag {11}", + "image_path": "2c663850cefb8e48f72dc74037e5a24c8e4de5a6a168ca83d5f6528a5f3a9909.jpg" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 357, + 576, + 516, + 590 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 357, + 576, + 516, + 590 + ], + "spans": [ + { + "bbox": [ + 357, + 576, + 516, + 590 + ], + "type": "interline_equation", + "content": "D ^ {- \\frac {1}{2}} M D ^ {- \\frac {1}{2}} = U \\Lambda U ^ {T} \\tag {12}", + "image_path": "8e36bcdcde4fb90322c3aac74d95b4520a36493114ac50474715b8bc5c917257.jpg" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 297, + 600, + 517, + 634 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 600, + 517, + 634 + ], + "spans": [ + { + "bbox": [ + 297, + 600, + 517, + 634 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 297, + 600, + 517, + 634 + ], + "type": "inline_equation", + "content": "U\\Lambda U^T" + }, + { + "bbox": [ + 297, + 600, + 517, + 634 + ], + "type": "text", + "content": " is the eigenvalue decomposition of " + }, + { + "bbox": [ + 297, + 600, + 517, + 634 + ], + "type": "inline_equation", + "content": "D^{-\\frac{1}{2}}MD^{-\\frac{1}{2}}" + }, + { + "bbox": [ + 297, + 600, + 517, + 634 + ], + "type": "text", + "content": " (i.e. " + }, + { + "bbox": [ + 297, + 600, + 517, + 634 + ], + "type": "inline_equation", + "content": "U" + }, + { + "bbox": [ + 297, + 600, + 517, + 634 + ], + "type": "text", + "content": " is some orthogonal matrix and " + }, + { + "bbox": [ + 297, + 600, + 517, + 634 + ], + "type": "inline_equation", + "content": "\\Lambda" + }, + { + "bbox": [ + 297, + 600, + 517, + 634 + ], + "type": "text", + "content": " is a diagonal positive definite matrix)." + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 296, + 643, + 517, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 643, + 517, + 675 + ], + "spans": [ + { + "bbox": [ + 296, + 643, + 517, + 675 + ], + "type": "text", + "content": "A detailed discussion of when the commutativity assumption is satisfied is included in Appendix B. The proof of the previous Lemma can be find in Appendix C.1." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 296, + 676, + 516, + 696 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 676, + 516, + 696 + ], + "spans": [ + { + "bbox": [ + 296, + 676, + 516, + 696 + ], + "type": "text", + "content": "Note that we could use Lemma 4.1 to estimate " + }, + { + "bbox": [ + 296, + 676, + 516, + 696 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 676, + 516, + 696 + ], + "type": "text", + "content": " as follows:" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 379, + 697, + 516, + 712 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 379, + 697, + 516, + 712 + ], + "spans": [ + { + "bbox": [ + 379, + 697, + 516, + 712 + ], + "type": "interline_equation", + "content": "\\widehat {T} = \\widehat {U} \\widehat {\\Lambda} _ {M} ^ {\\frac {1}{2}} \\widehat {U} ^ {T} \\tag {13}", + "image_path": "76e7f4d46061b2a5effae09a0e24aaa7dd36068deed1f1ab724ee937462000d3.jpg" + } + ] + } + ], + "index": 28 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "spans": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "text", + "content": "3442" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 60, + 123, + 282, + 226 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 123, + 282, + 226 + ], + "spans": [ + { + "bbox": [ + 60, + 123, + 282, + 226 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 60, + 123, + 282, + 226 + ], + "type": "inline_equation", + "content": "\\widehat{U}\\widehat{\\Lambda}_M\\widehat{U}^T" + }, + { + "bbox": [ + 60, + 123, + 282, + 226 + ], + "type": "text", + "content": " is the eigenvalue decomposition of " + }, + { + "bbox": [ + 60, + 123, + 282, + 226 + ], + "type": "inline_equation", + "content": "D^{-\\frac{1}{2}}\\widehat{MD}^{-\\frac{1}{2}}" + }, + { + "bbox": [ + 60, + 123, + 282, + 226 + ], + "type": "text", + "content": ". However such estimate can result in matrices that are not doubly stochastic, or diagonally dominant due to estimation errors. A more accurate estimate of " + }, + { + "bbox": [ + 60, + 123, + 282, + 226 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 123, + 282, + 226 + ], + "type": "text", + "content": " could be obtained as " + }, + { + "bbox": [ + 60, + 123, + 282, + 226 + ], + "type": "inline_equation", + "content": "\\widehat{T} = \\pi (\\widehat{U}\\widehat{\\Lambda}_{M}^{\\frac{2}{3}}\\widehat{U}^{T})" + }, + { + "bbox": [ + 60, + 123, + 282, + 226 + ], + "type": "text", + "content": " where " + }, + { + "bbox": [ + 60, + 123, + 282, + 226 + ], + "type": "inline_equation", + "content": "\\pi" + }, + { + "bbox": [ + 60, + 123, + 282, + 226 + ], + "type": "text", + "content": " is a projection operator to the set of doubly stochastic, positive definite matrices with diagonal elements greater than 0.5 and non-negative entries (which is a convex set). We can obtain such projection by solving the following optimization problem:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 74, + 238, + 280, + 260 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 74, + 238, + 280, + 260 + ], + "spans": [ + { + "bbox": [ + 74, + 238, + 280, + 260 + ], + "type": "interline_equation", + "content": "\\widehat {T} = \\pi \\left(\\widehat {U} \\widehat {\\Lambda} _ {M} ^ {\\frac {1}{2}} \\widehat {U} ^ {T}\\right) = \\underset {B} {\\operatorname {a r g m i n}} | | B - \\widehat {U} \\widehat {\\Lambda} _ {M} ^ {\\frac {1}{2}} \\widehat {U} ^ {T} | | _ {2} ^ {2} \\tag {14}", + "image_path": "f7b5abc9619d5fc69f83c4edaa1e733ab6d381ca7d3b7abd5e47caf50621023b.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 111, + 266, + 230, + 307 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 266, + 230, + 307 + ], + "spans": [ + { + "bbox": [ + 111, + 266, + 230, + 307 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} B = B ^ {T} \\\\ \\sum_ {j} B _ {i, j} = 1 \\quad \\forall i \\\\ \\text {s . t .} \\end{array}", + "image_path": "59c22644012e37dc0ef43c8c6dff43b0dd36a854abc64acee617a396fbe17c03.jpg" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 111, + 309, + 223, + 334 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 111, + 309, + 223, + 334 + ], + "spans": [ + { + "bbox": [ + 111, + 309, + 223, + 334 + ], + "type": "interline_equation", + "content": "\\begin{array}{c c} B _ {i, j} \\geq 0 & \\forall i, j \\\\ B _ {i, i} \\geq 0. 5 & \\forall i \\end{array}", + "image_path": "67e1f58b6fd337c891d1ed970d332e351cf46cfb424042676c849cc1adc098eb.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 60, + 337, + 280, + 384 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 337, + 280, + 384 + ], + "spans": [ + { + "bbox": [ + 60, + 337, + 280, + 384 + ], + "type": "text", + "content": "Note that this optimization problem is convex because the constraints are linear and for symmetric matrices it holds that " + }, + { + "bbox": [ + 60, + 337, + 280, + 384 + ], + "type": "inline_equation", + "content": "||\\widehat{T} - \\widehat{U}\\widehat{\\Lambda}_{M}^{\\frac{1}{2}}\\widehat{U}^{T}||_{2}^{2} = \\lambda_{\\max}(\\widehat{T} - \\widehat{U}\\widehat{\\Lambda}_{M}^{\\frac{1}{2}}\\widehat{U}^{T})" + }, + { + "bbox": [ + 60, + 337, + 280, + 384 + ], + "type": "text", + "content": ", which is a convex function of " + }, + { + "bbox": [ + 60, + 337, + 280, + 384 + ], + "type": "inline_equation", + "content": "\\widehat{T}" + }, + { + "bbox": [ + 60, + 337, + 280, + 384 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 60, + 385, + 280, + 442 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 385, + 280, + 442 + ], + "spans": [ + { + "bbox": [ + 60, + 385, + 280, + 442 + ], + "type": "text", + "content": "To summarize, " + }, + { + "bbox": [ + 60, + 385, + 280, + 442 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 385, + 280, + 442 + ], + "type": "text", + "content": " can be estimated as follows. First, obtain an estimate of " + }, + { + "bbox": [ + 60, + 385, + 280, + 442 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 60, + 385, + 280, + 442 + ], + "type": "text", + "content": ". Then obtain the eigenvalue decomposition of " + }, + { + "bbox": [ + 60, + 385, + 280, + 442 + ], + "type": "inline_equation", + "content": "D^{-\\frac{1}{2}}\\widehat{MD}^{-\\frac{1}{2}} = \\widehat{U}\\widehat{\\Lambda}\\widehat{U}^T" + }, + { + "bbox": [ + 60, + 385, + 280, + 442 + ], + "type": "text", + "content": " (note that this decomposition always exists because " + }, + { + "bbox": [ + 60, + 385, + 280, + 442 + ], + "type": "inline_equation", + "content": "D^{-\\frac{1}{2}}\\widehat{MD}^{-\\frac{1}{2}}" + }, + { + "bbox": [ + 60, + 385, + 280, + 442 + ], + "type": "text", + "content": " is symmetric). Finally obtain the estimate as: " + }, + { + "bbox": [ + 60, + 385, + 280, + 442 + ], + "type": "inline_equation", + "content": "\\widehat{T} \\coloneqq \\pi (\\widehat{U}\\widehat{\\Lambda}^{\\frac{1}{2}}\\widehat{U}^{T})" + }, + { + "bbox": [ + 60, + 385, + 280, + 442 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 60, + 442, + 280, + 475 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 442, + 280, + 475 + ], + "spans": [ + { + "bbox": [ + 60, + 442, + 280, + 475 + ], + "type": "text", + "content": "Note that once the estimate of " + }, + { + "bbox": [ + 60, + 442, + 280, + 475 + ], + "type": "inline_equation", + "content": "\\widehat{T}" + }, + { + "bbox": [ + 60, + 442, + 280, + 475 + ], + "type": "text", + "content": " is obtained, " + }, + { + "bbox": [ + 60, + 442, + 280, + 475 + ], + "type": "inline_equation", + "content": "\\widehat{\\Gamma}" + }, + { + "bbox": [ + 60, + 442, + 280, + 475 + ], + "type": "text", + "content": " can be obtained since we assumed the label aggregating policy to be known." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "spans": [ + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "text", + "content": "Lemma 4.2. Let " + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "inline_equation", + "content": "M_{a,b}" + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "text", + "content": " be the agreement matrix for annotators " + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "inline_equation", + "content": "b" + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "text", + "content": " defined in Eq. (4) and " + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "inline_equation", + "content": "\\widehat{M_{a,b}}" + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "text", + "content": " be the estimated agreement matrix defined in Eq. (8) and let " + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "inline_equation", + "content": "||.||_p" + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "text", + "content": " be the matrix norm induced by the " + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "text", + "content": " vector norm. For every " + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "inline_equation", + "content": "p \\in [1,\\infty]" + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "text", + "content": " and for every " + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "inline_equation", + "content": "\\delta > 0" + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "text", + "content": ", with probability at least " + }, + { + "bbox": [ + 60, + 481, + 280, + 547 + ], + "type": "inline_equation", + "content": "1 - \\delta" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 104, + 548, + 280, + 571 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 104, + 548, + 280, + 571 + ], + "spans": [ + { + "bbox": [ + 104, + 548, + 280, + 571 + ], + "type": "interline_equation", + "content": "\\left| \\left| M _ {a, b} - \\widehat {M _ {a , b}} \\right| \\right| _ {p} \\leq \\sqrt {\\frac {C ^ {2}}{2 n} \\ln \\frac {2 C ^ {2}}{\\delta}}. \\tag {15}", + "image_path": "c087f90df6d6439fe25505de282aa737e984b0a40d505326cf6d977d8418cf4f.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 60, + 573, + 280, + 618 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 573, + 280, + 618 + ], + "spans": [ + { + "bbox": [ + 60, + 573, + 280, + 618 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 60, + 573, + 280, + 618 + ], + "type": "inline_equation", + "content": "\\mathbb{P}^n" + }, + { + "bbox": [ + 60, + 573, + 280, + 618 + ], + "type": "text", + "content": " denotes the probability according to which the " + }, + { + "bbox": [ + 60, + 573, + 280, + 618 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 60, + 573, + 280, + 618 + ], + "type": "text", + "content": " training samples are distributed, i.e. we are assuming that the samples are independently drawn according the probability " + }, + { + "bbox": [ + 60, + 573, + 280, + 618 + ], + "type": "inline_equation", + "content": "\\mathbb{P}" + }, + { + "bbox": [ + 60, + 573, + 280, + 618 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 61, + 622, + 242, + 634 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 622, + 242, + 634 + ], + "spans": [ + { + "bbox": [ + 61, + 622, + 242, + 634 + ], + "type": "text", + "content": "Proof. The proof can be found in Appendix C.2." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "spans": [ + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "type": "text", + "content": "From Lemma 4.2 it follows that if " + }, + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "type": "inline_equation", + "content": "\\widehat{M}" + }, + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "type": "text", + "content": " is estimated as in Eq. (9), since " + }, + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "type": "inline_equation", + "content": "\\widehat{M}" + }, + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "type": "text", + "content": " is an average of " + }, + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "type": "inline_equation", + "content": "\\widehat{M_{ab}}" + }, + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "type": "text", + "content": " it also holds that for every " + }, + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "type": "inline_equation", + "content": "p\\in [1,\\infty ]" + }, + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "type": "text", + "content": " and for every " + }, + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "type": "inline_equation", + "content": "\\delta >0" + }, + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "type": "text", + "content": " with probability at least " + }, + { + "bbox": [ + 60, + 640, + 280, + 684 + ], + "type": "inline_equation", + "content": "1 - \\delta" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 113, + 689, + 280, + 714 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 113, + 689, + 280, + 714 + ], + "spans": [ + { + "bbox": [ + 113, + 689, + 280, + 714 + ], + "type": "interline_equation", + "content": "\\left| \\left| M - \\widehat {M} \\right| \\right| _ {p} \\leq \\sqrt {\\frac {C ^ {2}}{2 n} \\ln \\frac {2 C ^ {2}}{\\delta}}. \\tag {16}", + "image_path": "16685c5ad607311950d20721adfc04b9c3abdb23446c6b1d10d68f5ca85e4e4c.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 297, + 125, + 517, + 147 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 125, + 517, + 147 + ], + "spans": [ + { + "bbox": [ + 297, + 125, + 517, + 147 + ], + "type": "text", + "content": "Theorem 4.3. Let " + }, + { + "bbox": [ + 297, + 125, + 517, + 147 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 297, + 125, + 517, + 147 + ], + "type": "text", + "content": " be the noise transition matrix defined as in Eq. (2) and " + }, + { + "bbox": [ + 297, + 125, + 517, + 147 + ], + "type": "inline_equation", + "content": "\\widehat{T}" + }, + { + "bbox": [ + 297, + 125, + 517, + 147 + ], + "type": "text", + "content": " its estimate (defined as in Eq. (14))." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 308, + 147, + 424, + 158 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 147, + 424, + 158 + ], + "spans": [ + { + "bbox": [ + 308, + 147, + 424, + 158 + ], + "type": "text", + "content": "With probability at least " + }, + { + "bbox": [ + 308, + 147, + 424, + 158 + ], + "type": "inline_equation", + "content": "1 - \\delta" + }, + { + "bbox": [ + 308, + 147, + 424, + 158 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 163, + 516, + 191 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 163, + 516, + 191 + ], + "spans": [ + { + "bbox": [ + 305, + 163, + 516, + 191 + ], + "type": "interline_equation", + "content": "\\left| \\left| T - \\widehat {T} \\right| \\right| _ {2} \\leq \\frac {C (\\sqrt {C} + 1) \\lambda_ {\\max } (D)}{\\lambda_ {\\min } (\\hat {T})} \\sqrt {\\frac {1}{2 n} \\ln \\frac {2 C ^ {2}}{\\delta}} \\tag {17a}", + "image_path": "0b20863d59218d447da1e718a0e121b87ec6ed3627c661c70cb2c771b1b6ddcb.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 302, + 202, + 516, + 241 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 302, + 202, + 516, + 241 + ], + "spans": [ + { + "bbox": [ + 302, + 202, + 516, + 241 + ], + "type": "interline_equation", + "content": "\\left| \\left| T ^ {- 1} - \\hat {T} ^ {- 1} \\right| \\right| _ {2} \\leq \\frac {9 C (\\sqrt {C} + 1) \\lambda_ {\\max } (D)}{\\lambda_ {\\min } (\\hat {T}) ^ {2}} \\sqrt {\\frac {1}{2 n} \\ln \\frac {2 C ^ {2}}{\\delta}} \\tag {17b}", + "image_path": "79d6178dca21576d53e8826bdd0d484aaaa683ce7a384c01268ade4ff7b46ebe.jpg" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 246, + 417, + 266 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 246, + 417, + 266 + ], + "spans": [ + { + "bbox": [ + 307, + 246, + 417, + 266 + ], + "type": "interline_equation", + "content": "f o r n > \\frac {C ^ {2} (\\sqrt {C} + 1) ^ {2} (\\ln (2 C ^ {2}) ^ {2}}{2 \\lambda_ {\\operatorname* {m i n}} (\\hat {T}) ^ {2}}.", + "image_path": "2b8cbd6e1219b5c16c343da4ecc9d4dfccd0507278bb71f489f9cffeb6aee37b.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 297, + 269, + 477, + 280 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 269, + 477, + 280 + ], + "spans": [ + { + "bbox": [ + 297, + 269, + 477, + 280 + ], + "type": "text", + "content": "Proof. The proof can be found in Appendix C.3." + } + ] + } + ], + "index": 18 + }, + { + "type": "image", + "bbox": [ + 507, + 269, + 517, + 277 + ], + "blocks": [ + { + "bbox": [ + 507, + 269, + 517, + 277 + ], + "lines": [ + { + "bbox": [ + 507, + 269, + 517, + 277 + ], + "spans": [ + { + "bbox": [ + 507, + 269, + 517, + 277 + ], + "type": "image", + "image_path": "0dd3992809e101651bd30e00d42486ec9be63260afaeacdee51b1eb171674e24.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + } + ], + "index": 19 + }, + { + "bbox": [ + 297, + 287, + 516, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 287, + 516, + 312 + ], + "spans": [ + { + "bbox": [ + 297, + 287, + 516, + 312 + ], + "type": "text", + "content": "From the previous theorem we can notice that the error in estimation of " + }, + { + "bbox": [ + 297, + 287, + 516, + 312 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 297, + 287, + 516, + 312 + ], + "type": "text", + "content": " decays as " + }, + { + "bbox": [ + 297, + 287, + 516, + 312 + ], + "type": "inline_equation", + "content": "\\frac{1}{\\sqrt{n}}" + }, + { + "bbox": [ + 297, + 287, + 516, + 312 + ], + "type": "text", + "content": " as a function of " + }, + { + "bbox": [ + 297, + 287, + 516, + 312 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 297, + 287, + 516, + 312 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 297, + 316, + 433, + 328 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 316, + 433, + 328 + ], + "spans": [ + { + "bbox": [ + 297, + 316, + 433, + 328 + ], + "type": "text", + "content": "4.2. Learning from noisy labels" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 297, + 332, + 517, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 332, + 517, + 354 + ], + "spans": [ + { + "bbox": [ + 297, + 332, + 517, + 354 + ], + "type": "text", + "content": "In this section we show how to leverage the estimates of the error rates to train the models." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 297, + 367, + 517, + 378 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 367, + 517, + 378 + ], + "spans": [ + { + "bbox": [ + 297, + 367, + 517, + 378 + ], + "type": "text", + "content": "4.2.1 Posterior distribution of true labels as soft-labels" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 296, + 384, + 517, + 426 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 384, + 517, + 426 + ], + "spans": [ + { + "bbox": [ + 296, + 384, + 517, + 426 + ], + "type": "text", + "content": "It is noteworthy that if we have access to the labels provided by all annotators, the posterior probabilities of the true labels can be calculated leveraging " + }, + { + "bbox": [ + 296, + 384, + 517, + 426 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 384, + 517, + 426 + ], + "type": "text", + "content": " and Bayes' Theorem as follows:" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 431, + 516, + 468 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 431, + 516, + 468 + ], + "spans": [ + { + "bbox": [ + 308, + 431, + 516, + 468 + ], + "type": "interline_equation", + "content": "\\underbrace {\\mathbb {P} \\left(y _ {i} = c \\mid y _ {1 , i} , \\dots , y _ {H , i}\\right)} _ {:= p _ {c, i}} \\propto \\nu_ {c} \\prod_ {h = 1} ^ {H} \\underbrace {\\mathbb {P} \\left(y _ {h , i} \\mid y _ {i} = c\\right)} _ {= T _ {c, y _ {h, i}}} \\tag {18}", + "image_path": "cd0f0d426d89ef78d98912f042ac2480cac89ae35c2e5f1216fd8690e685f132.jpg" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 296, + 474, + 517, + 528 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 474, + 517, + 528 + ], + "spans": [ + { + "bbox": [ + 296, + 474, + 517, + 528 + ], + "type": "text", + "content": "we recall that " + }, + { + "bbox": [ + 296, + 474, + 517, + 528 + ], + "type": "inline_equation", + "content": "\\nu_{c} = \\mathbb{P}(y_{i} = c)" + }, + { + "bbox": [ + 296, + 474, + 517, + 528 + ], + "type": "text", + "content": " and that the conditional probabilities on the r.h.s. are given by " + }, + { + "bbox": [ + 296, + 474, + 517, + 528 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 474, + 517, + 528 + ], + "type": "text", + "content": ". In our case we can use our noisy transition estimates to estimate the posterior probabilities of the true labels, and afterwards we can use these posteriors to train the classifier." + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 296, + 533, + 516, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 533, + 516, + 578 + ], + "spans": [ + { + "bbox": [ + 296, + 533, + 516, + 578 + ], + "type": "text", + "content": "Lemma 4.4. For infinite annotators the posterior distribution over every sample calculated using the true " + }, + { + "bbox": [ + 296, + 533, + 516, + 578 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 533, + 516, + 578 + ], + "type": "text", + "content": " converges to the dirac delta distribution centered on the true label almost surely (i.e. " + }, + { + "bbox": [ + 296, + 533, + 516, + 578 + ], + "type": "inline_equation", + "content": "\\lim_{H\\to \\infty}p_{c,i}\\stackrel {a.s}{=}\\mathbb{1}(y_i = c)" + }, + { + "bbox": [ + 296, + 533, + 516, + 578 + ], + "type": "text", + "content": ")." + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 297, + 582, + 396, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 582, + 396, + 593 + ], + "spans": [ + { + "bbox": [ + 297, + 582, + 396, + 593 + ], + "type": "text", + "content": "Proof. See Appendix C.5." + } + ] + } + ], + "index": 28 + }, + { + "type": "image", + "bbox": [ + 507, + 582, + 517, + 591 + ], + "blocks": [ + { + "bbox": [ + 507, + 582, + 517, + 591 + ], + "lines": [ + { + "bbox": [ + 507, + 582, + 517, + 591 + ], + "spans": [ + { + "bbox": [ + 507, + 582, + 517, + 591 + ], + "type": "image", + "image_path": "c16bf70817f7d08dcf4da76a610fed959da3d85c645665178190710db7e3e64e.jpg" + } + ] + } + ], + "index": 29, + "angle": 0, + "type": "image_body" + } + ], + "index": 29 + }, + { + "bbox": [ + 297, + 600, + 516, + 622 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 600, + 516, + 622 + ], + "spans": [ + { + "bbox": [ + 297, + 600, + 516, + 622 + ], + "type": "text", + "content": "We can use the posterior distributions as soft-labels defining the following loss for the i-th sample:" + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 335, + 627, + 516, + 640 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 335, + 627, + 516, + 640 + ], + "spans": [ + { + "bbox": [ + 335, + 627, + 516, + 640 + ], + "type": "interline_equation", + "content": "\\ell \\left(f \\left(x _ {i}\\right), y _ {1, i}, \\dots , y _ {H, i}\\right) = \\ell \\left(f \\left(x _ {i}\\right), \\bar {p} _ {i}\\right) \\tag {19}", + "image_path": "3c9e68533c33ac56598b809e5a1b8e574c50b4bec08f8614475834d6fcc60cc8.jpg" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 296, + 644, + 517, + 678 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 644, + 517, + 678 + ], + "spans": [ + { + "bbox": [ + 296, + 644, + 517, + 678 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 296, + 644, + 517, + 678 + ], + "type": "inline_equation", + "content": "\\bar{p}_i = [p_{1,i},\\dots ,p_{C,i}]^T" + }, + { + "bbox": [ + 296, + 644, + 517, + 678 + ], + "type": "text", + "content": " . Or we can use the posterior distributions to weight the loss function at the " + }, + { + "bbox": [ + 296, + 644, + 517, + 678 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 296, + 644, + 517, + 678 + ], + "type": "text", + "content": " -th sample evaluated at each of the possible labels:" + } + ] + } + ], + "index": 32 + }, + { + "bbox": [ + 313, + 683, + 516, + 714 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 313, + 683, + 516, + 714 + ], + "spans": [ + { + "bbox": [ + 313, + 683, + 516, + 714 + ], + "type": "interline_equation", + "content": "\\ell \\left(f \\left(x _ {i}\\right), y _ {1, i}, \\dots , y _ {H, i}\\right) = \\sum_ {c = 1} ^ {C} p _ {c, i} \\ell \\left(f \\left(x _ {i}\\right), e _ {c}\\right) \\tag {20}", + "image_path": "9bd0db5db73e8d8a300d8bedd6bca1cb813bacd8b7269c226d273a320023757e.jpg" + } + ] + } + ], + "index": 33 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "spans": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "text", + "content": "3443" + } + ] + } + ], + "index": 34 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 60, + 124, + 280, + 168 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 124, + 280, + 168 + ], + "spans": [ + { + "bbox": [ + 60, + 124, + 280, + 168 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 60, + 124, + 280, + 168 + ], + "type": "inline_equation", + "content": "e_c" + }, + { + "bbox": [ + 60, + 124, + 280, + 168 + ], + "type": "text", + "content": " is the vector in " + }, + { + "bbox": [ + 60, + 124, + 280, + 168 + ], + "type": "inline_equation", + "content": "\\mathbb{R}^C" + }, + { + "bbox": [ + 60, + 124, + 280, + 168 + ], + "type": "text", + "content": " with 1 in the " + }, + { + "bbox": [ + 60, + 124, + 280, + 168 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 60, + 124, + 280, + 168 + ], + "type": "text", + "content": "-th position. Notice that for categorical cross entropy loss the two functions defined above correspond, but in general they define two different loss functions." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 60, + 169, + 282, + 267 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 169, + 282, + 267 + ], + "spans": [ + { + "bbox": [ + 60, + 169, + 282, + 267 + ], + "type": "text", + "content": "Note that these soft-labels are different from the ones obtained by averaging the annotators labels as is done in [25]. The method using the posteriors exploits the " + }, + { + "bbox": [ + 60, + 169, + 282, + 267 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 169, + 282, + 267 + ], + "type": "text", + "content": " matrix and thus more information than the simple mean of the values of the losses among annotators. We therefore expect this to yield better results than the aggregation using the mean proposed in [25]. These considerations are supported by the empirical results we obtained on synthetic datasets (see Sec. 6)." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 61, + 280, + 174, + 291 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 280, + 174, + 291 + ], + "spans": [ + { + "bbox": [ + 61, + 280, + 174, + 291 + ], + "type": "text", + "content": "4.2.2 Robust loss functions" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 60, + 297, + 280, + 374 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 297, + 280, + 374 + ], + "spans": [ + { + "bbox": [ + 60, + 297, + 280, + 374 + ], + "type": "text", + "content": "Another way to leverage the estimate of " + }, + { + "bbox": [ + 60, + 297, + 280, + 374 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 297, + 280, + 374 + ], + "type": "text", + "content": " is to use robust loss functions, like the forward and backward loss functions presented in [15, 16]. Let " + }, + { + "bbox": [ + 60, + 297, + 280, + 374 + ], + "type": "inline_equation", + "content": "\\ell(t,y)" + }, + { + "bbox": [ + 60, + 297, + 280, + 374 + ], + "type": "text", + "content": " be a generic loss function for the classification task, with a little abuse of notation we define " + }, + { + "bbox": [ + 60, + 297, + 280, + 374 + ], + "type": "inline_equation", + "content": "\\ell(t) = [\\ell(t,e_1),\\dots,\\ell(t,e_C)]^T" + }, + { + "bbox": [ + 60, + 297, + 280, + 374 + ], + "type": "text", + "content": ". The backward and forward loss functions are defined in Eq. (21a) and Eq. (21b), respectively." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 129, + 378, + 280, + 392 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 129, + 378, + 280, + 392 + ], + "spans": [ + { + "bbox": [ + 129, + 378, + 280, + 392 + ], + "type": "interline_equation", + "content": "l _ {b} (t, y) = (\\widehat {\\Gamma} ^ {- 1} \\ell (t)) y \\tag {21a}", + "image_path": "4d3d824917dffc28a9a632fdb95daf35ac2e08a3232c8313e5e7662829d4af9b.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 129, + 394, + 280, + 408 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 129, + 394, + 280, + 408 + ], + "spans": [ + { + "bbox": [ + 129, + 394, + 280, + 408 + ], + "type": "interline_equation", + "content": "l _ {f} (t, y) = (\\ell (\\widehat {\\Gamma} ^ {T} t)) y \\tag {21b}", + "image_path": "8a39c312cfb5fb1925079c102e5a62b84654d36d122165838442de74bd3464fe.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 60, + 414, + 280, + 512 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 414, + 280, + 512 + ], + "spans": [ + { + "bbox": [ + 60, + 414, + 280, + 512 + ], + "type": "text", + "content": "To explain the notation in Eq. (21a) we are first doing the dot product between the matrix " + }, + { + "bbox": [ + 60, + 414, + 280, + 512 + ], + "type": "inline_equation", + "content": "\\Gamma^{-1}" + }, + { + "bbox": [ + 60, + 414, + 280, + 512 + ], + "type": "text", + "content": " and the vector " + }, + { + "bbox": [ + 60, + 414, + 280, + 512 + ], + "type": "inline_equation", + "content": "\\ell(t)" + }, + { + "bbox": [ + 60, + 414, + 280, + 512 + ], + "type": "text", + "content": " and then the dot product of the resulting vector with " + }, + { + "bbox": [ + 60, + 414, + 280, + 512 + ], + "type": "inline_equation", + "content": "y" + }, + { + "bbox": [ + 60, + 414, + 280, + 512 + ], + "type": "text", + "content": ". These losses leverage aggregated labels and therefore different aggregating techniques can be used, like majority vote. Another possible aggregating technique that leverages the posterior probabilities is to assume that the true label is the one that corresponds to the class that has the highest posterior probability." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 61, + 517, + 201, + 530 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 517, + 201, + 530 + ], + "spans": [ + { + "bbox": [ + 61, + 517, + 201, + 530 + ], + "type": "text", + "content": "4.3. Generalizations gap bounds" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 60, + 534, + 280, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 534, + 280, + 590 + ], + "spans": [ + { + "bbox": [ + 60, + 534, + 280, + 590 + ], + "type": "text", + "content": "In this section we derive generalization gap bounds for the backward loss that depend on the noise transition matrix estimated in Eq. (14). Since we are only addressing the problem for the backward loss, from now on we will denote the backward loss by " + }, + { + "bbox": [ + 60, + 534, + 280, + 590 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 60, + 534, + 280, + 590 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 60, + 595, + 281, + 628 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 595, + 281, + 628 + ], + "spans": [ + { + "bbox": [ + 60, + 595, + 281, + 628 + ], + "type": "text", + "content": "Remark 1. If " + }, + { + "bbox": [ + 60, + 595, + 281, + 628 + ], + "type": "inline_equation", + "content": "\\ell(t,y)" + }, + { + "bbox": [ + 60, + 595, + 281, + 628 + ], + "type": "text", + "content": " is Lipschitz with constant " + }, + { + "bbox": [ + 60, + 595, + 281, + 628 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 60, + 595, + 281, + 628 + ], + "type": "text", + "content": ", the loss function " + }, + { + "bbox": [ + 60, + 595, + 281, + 628 + ], + "type": "inline_equation", + "content": "l(t,y)" + }, + { + "bbox": [ + 60, + 595, + 281, + 628 + ], + "type": "text", + "content": " is Lipschitz with Lipschitz constant " + }, + { + "bbox": [ + 60, + 595, + 281, + 628 + ], + "type": "inline_equation", + "content": "||\\Gamma^{-1}||_2 L" + }, + { + "bbox": [ + 60, + 595, + 281, + 628 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "spans": [ + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "text", + "content": "We will prove the following theorem in the case of " + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "inline_equation", + "content": "\\Gamma = T" + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "text", + "content": ". We emphasize that all the results apply also when " + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "inline_equation", + "content": "\\Gamma^{-1} = \\phi(T^{-1})" + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "text", + "content": " and that the function that associate " + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "inline_equation", + "content": "\\Gamma^{-1}" + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "inline_equation", + "content": "T^{-1}, \\phi" + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "text", + "content": " is Lipschitz with respect to the norm " + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "text", + "content": ", i.e. there exists a Lipschitz constant " + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "inline_equation", + "content": "L_{\\phi, p}" + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "text", + "content": " s.t. " + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "inline_equation", + "content": "||\\phi(T^{-1}) - \\phi(\\hat{T}^{-1})||_p \\leq L_{\\phi, p}||T^{-1} - \\widehat{T}^{-1}||_p" + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "text", + "content": ". The only difference is that in the bound we will have a factor " + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "inline_equation", + "content": "L_{\\phi, p}" + }, + { + "bbox": [ + 60, + 633, + 281, + 712 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 125, + 517, + 169 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 125, + 517, + 169 + ], + "spans": [ + { + "bbox": [ + 296, + 125, + 517, + 169 + ], + "type": "text", + "content": "It has been proved, first in [15] (Lemma 1) for the binary classification task and then in general for the multiclass case in [16] (Theorem 1) that " + }, + { + "bbox": [ + 296, + 125, + 517, + 169 + ], + "type": "inline_equation", + "content": "l(t,y)" + }, + { + "bbox": [ + 296, + 125, + 517, + 169 + ], + "type": "text", + "content": " is an unbiased estimator for " + }, + { + "bbox": [ + 296, + 125, + 517, + 169 + ], + "type": "inline_equation", + "content": "\\ell" + }, + { + "bbox": [ + 296, + 125, + 517, + 169 + ], + "type": "text", + "content": ", i.e." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 364, + 176, + 449, + 190 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 364, + 176, + 449, + 190 + ], + "spans": [ + { + "bbox": [ + 364, + 176, + 449, + 190 + ], + "type": "interline_equation", + "content": "\\mathbb {E} _ {\\tilde {y} | y} [ l (t, \\tilde {y}) ] = \\ell (t, y).", + "image_path": "d7e8b2c9ca6d8505acb7d117c8e85bb7c27fe8b3a1d42a9988eaa91aeb6d63fe.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "spans": [ + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": "Lemma 4.5. Let " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "\\ell" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": " be a bounded loss function, with " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "\\ell \\in [0,\\mu ]" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": ", s.t. there exists a Lipschitz function " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": ", with Lipschitz constant " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "L" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": ", so that " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "\\ell (f(x),y) = \\alpha |f(x) - y|" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": ". Let " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "\\widehat{R}_l(f)" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": " be the empirical risk for the loss " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": " and let " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "R_{l,\\mathcal{D}}" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": " be the risk for loss " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": " under the distribution " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "\\mathcal{D}" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": ", with " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": " unbiased estimator for the loss " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "\\ell" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": ". We denote by " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "\\hat{l}" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": " the backward loss obtained using " + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "inline_equation", + "content": "\\widehat{T}" + }, + { + "bbox": [ + 296, + 195, + 517, + 274 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 299, + 290, + 516, + 342 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 290, + 516, + 342 + ], + "spans": [ + { + "bbox": [ + 299, + 290, + 516, + 342 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} \\sup _ {f \\in \\mathcal {F}} | \\hat {R} _ {\\hat {l}} (f) - R _ {l, \\mathcal {D}} (f) | \\\\ \\leq \\left[ L \\lambda_ {\\min} (\\widehat {T} ^ {2}) + \\frac {\\mu \\lambda_ {\\min} (D)}{\\lambda_ {\\min} (\\widehat {T}) ^ {2}} \\sqrt {\\frac {1}{n} \\ln \\left(\\frac {4 C}{\\delta}\\right)} \\right] \\Re_ {n} (\\mathcal {F}) g (C). \\\\ \\end{array}", + "image_path": "d8c5c1b269d795fbb9ae679c42f92b4c22c94ac5d8f36e61da5fa54f2aade3e4.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 298, + 348, + 403, + 362 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 348, + 403, + 362 + ], + "spans": [ + { + "bbox": [ + 298, + 348, + 403, + 362 + ], + "type": "interline_equation", + "content": "w i t h g (C) = 6 C ^ {2} (\\sqrt {C} + 1)", + "image_path": "159f56c8fdac56135084d0ac9e3e39124a8d6a4f8fbe98efd60a120971902a91.jpg" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 296, + 367, + 517, + 397 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 367, + 517, + 397 + ], + "spans": [ + { + "bbox": [ + 296, + 367, + 517, + 397 + ], + "type": "text", + "content": "Theorem 4.6. Let " + }, + { + "bbox": [ + 296, + 367, + 517, + 397 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 296, + 367, + 517, + 397 + ], + "type": "text", + "content": " be an unbiased estimator for " + }, + { + "bbox": [ + 296, + 367, + 517, + 397 + ], + "type": "inline_equation", + "content": "\\ell" + }, + { + "bbox": [ + 296, + 367, + 517, + 397 + ], + "type": "text", + "content": " defined as in Eq. (21a), Denoting " + }, + { + "bbox": [ + 296, + 367, + 517, + 397 + ], + "type": "inline_equation", + "content": "\\hat{f} = \\operatorname*{argmin}_{f}(\\widehat{R}_{\\hat{l}}(f))" + }, + { + "bbox": [ + 296, + 367, + 517, + 397 + ], + "type": "text", + "content": ". It holds that" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 298, + 406, + 516, + 457 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 406, + 516, + 457 + ], + "spans": [ + { + "bbox": [ + 298, + 406, + 516, + 457 + ], + "type": "interline_equation", + "content": "\\begin{array}{l} R _ {\\ell , \\mathcal {D}} (\\hat {f}) - \\min _ {f \\in \\mathcal {F}} R _ {\\ell , \\mathcal {D}} (f) \\\\ \\leq \\left[ 2 L \\lambda_ {\\min } (\\widehat {T} ^ {2}) + \\frac {\\mu \\lambda_ {\\min } (D)}{\\lambda_ {\\min } (\\widehat {T}) ^ {2}} \\sqrt {\\frac {1}{n} \\ln \\left(\\frac {4 C}{\\delta}\\right)} \\right] \\Re_ {n} (\\mathcal {F}) g (C) \\\\ \\end{array}", + "image_path": "06ec52ac6be7e8f69e506991f479fed009b4c5a3a0622b5e10f4e6955414430a.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 298, + 463, + 403, + 476 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 463, + 403, + 476 + ], + "spans": [ + { + "bbox": [ + 298, + 463, + 403, + 476 + ], + "type": "interline_equation", + "content": "w i t h g (C) = 6 C ^ {2} (\\sqrt {C} + 1)", + "image_path": "ef6057900b6acf617d815cd74ee5c69210fec3a1e9b92ea2c05bf13a562441f5.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 296, + 481, + 517, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 481, + 517, + 712 + ], + "spans": [ + { + "bbox": [ + 296, + 481, + 517, + 712 + ], + "type": "text", + "content": "The proofs of Lemma 4.5 and Theorem 4.6 can be found in Appendix C. We observe that in all the previous theorems, the bounds found are always decreasing as one over the square root of the number of samples. The above theorem gives us a performance bound for the classifier found minimizing the backward loss " + }, + { + "bbox": [ + 296, + 481, + 517, + 712 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 296, + 481, + 517, + 712 + ], + "type": "text", + "content": ", i.e. the unbiased estimator of the loss " + }, + { + "bbox": [ + 296, + 481, + 517, + 712 + ], + "type": "inline_equation", + "content": "\\ell" + }, + { + "bbox": [ + 296, + 481, + 517, + 712 + ], + "type": "text", + "content": " on the noisy dataset. The bounds found depend on, the Rademacher complexity of the function space and the Lipschitz constant of the loss function. The importance of these bounds lies in the fact that they allow us to obtain performance bounds for a model trained with noisy data that depends on values that we can estimate from the noisy dataset. In particular, there is no dependence on the true noise transition matrix of the annotators, as in other work [15] which is instead a quantity that cannot be known a priori having access only to the training data. More in detail the bound depends on the estimate noise transition matrix, the number of classes in the dataset, the Rademacher complexity and the Lipschitz constant, which we can take as known a priori and on the distribution of ground truth, which in many cases it makes sense to assume uniform." + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "spans": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "text", + "content": "3444" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "bbox": [ + 61, + 124, + 121, + 135 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 124, + 121, + 135 + ], + "spans": [ + { + "bbox": [ + 61, + 124, + 121, + 135 + ], + "type": "text", + "content": "5. Cohen's " + }, + { + "bbox": [ + 61, + 124, + 121, + 135 + ], + "type": "inline_equation", + "content": "\\kappa" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 60, + 143, + 280, + 207 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 143, + 280, + 207 + ], + "spans": [ + { + "bbox": [ + 60, + 143, + 280, + 207 + ], + "type": "text", + "content": "We can also consider the case where an estimate of the IAA matrix " + }, + { + "bbox": [ + 60, + 143, + 280, + 207 + ], + "type": "inline_equation", + "content": "M" + }, + { + "bbox": [ + 60, + 143, + 280, + 207 + ], + "type": "text", + "content": " is not available and we only have access to a scalar representation of the inter-annotator agreement like Cohen's " + }, + { + "bbox": [ + 60, + 143, + 280, + 207 + ], + "type": "inline_equation", + "content": "\\kappa" + }, + { + "bbox": [ + 60, + 143, + 280, + 207 + ], + "type": "text", + "content": ". In this case we can only estimate one parameter and hence the matrix " + }, + { + "bbox": [ + 60, + 143, + 280, + 207 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 143, + 280, + 207 + ], + "type": "text", + "content": " has to be parameterized by a single parameter that can be estimated." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 60, + 208, + 280, + 252 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 208, + 280, + 252 + ], + "spans": [ + { + "bbox": [ + 60, + 208, + 280, + 252 + ], + "type": "text", + "content": "One particular example is the case where the noise is uniform among classes. Under these hypotheses, " + }, + { + "bbox": [ + 60, + 208, + 280, + 252 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 208, + 280, + 252 + ], + "type": "text", + "content": " is a matrix with all values " + }, + { + "bbox": [ + 60, + 208, + 280, + 252 + ], + "type": "inline_equation", + "content": "1 - p" + }, + { + "bbox": [ + 60, + 208, + 280, + 252 + ], + "type": "text", + "content": " on the diagonal and " + }, + { + "bbox": [ + 60, + 208, + 280, + 252 + ], + "type": "inline_equation", + "content": "\\frac{p}{C - 1}" + }, + { + "bbox": [ + 60, + 208, + 280, + 252 + ], + "type": "text", + "content": " off the diagonal." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "spans": [ + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "text", + "content": "Lemma 5.1 (Relationship between " + }, + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "inline_equation", + "content": "\\kappa" + }, + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "text", + "content": "). In the case of classification with uniform noise for two homogeneous annotators with noise rate " + }, + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "inline_equation", + "content": "p" + }, + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "text", + "content": ", i.e if " + }, + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "text", + "content": " is one annotator, " + }, + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "inline_equation", + "content": "\\mathbb{P}(y_a = i|y = j) = p" + }, + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "text", + "content": " if " + }, + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "inline_equation", + "content": "i \\neq j" + }, + { + "bbox": [ + 60, + 258, + 280, + 313 + ], + "type": "text", + "content": ". If the distribution of the ground-truth labels is uniform, it holds that:" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 122, + 319, + 280, + 333 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 122, + 319, + 280, + 333 + ], + "spans": [ + { + "bbox": [ + 122, + 319, + 280, + 333 + ], + "type": "interline_equation", + "content": "p = (1 - C ^ {- 1}) (1 - \\sqrt {\\kappa}) \\tag {22}", + "image_path": "a5641b347f9e23659b9199e89638c02996be6cb283c55204c38a59db8d3e943b.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 60, + 340, + 280, + 362 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 340, + 280, + 362 + ], + "spans": [ + { + "bbox": [ + 60, + 340, + 280, + 362 + ], + "type": "text", + "content": "with " + }, + { + "bbox": [ + 60, + 340, + 280, + 362 + ], + "type": "inline_equation", + "content": "\\kappa" + }, + { + "bbox": [ + 60, + 340, + 280, + 362 + ], + "type": "text", + "content": " the Cohen's kappa coefficient of the two annotators (see Appendix A)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 61, + 367, + 280, + 379 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 367, + 280, + 379 + ], + "spans": [ + { + "bbox": [ + 61, + 367, + 280, + 379 + ], + "type": "text", + "content": "Proof. The proof can be found in Appendix C.6. " + }, + { + "bbox": [ + 61, + 367, + 280, + 379 + ], + "type": "inline_equation", + "content": "\\square" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "spans": [ + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "text", + "content": "If " + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "text", + "content": " is assumed to be of the form described above (with all diagonal elements equal to " + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "inline_equation", + "content": "1 - p" + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "text", + "content": " and all off-diagonal entries equal), it has one eigenvalue equal to 1 and all the rest are equal to " + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "inline_equation", + "content": "1 - pC(C - 1)^{-1}" + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "text", + "content": " (this follows from the fact that in this case " + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "text", + "content": " can be written as a weighted summation of the identity and a rank-one matrix). Hence using Eq. (22) we get that " + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{min}}(T) = \\sqrt{\\kappa}" + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "text", + "content": ". The bounds from Theorem 4.6 hold replacing " + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "inline_equation", + "content": "\\lambda_{\\mathrm{min}}(T)" + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "inline_equation", + "content": "\\sqrt{\\kappa}" + }, + { + "bbox": [ + 60, + 386, + 280, + 506 + ], + "type": "text", + "content": ". This allows us to obtain bound for the generalization gap of a classifier trained with backward loss even in the case where a single statistic on agreement between annotators is provided." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 61, + 516, + 172, + 527 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 61, + 516, + 172, + 527 + ], + "spans": [ + { + "bbox": [ + 61, + 516, + 172, + 527 + ], + "type": "text", + "content": "6. Experimental results" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 60, + 533, + 280, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 533, + 280, + 665 + ], + "spans": [ + { + "bbox": [ + 60, + 533, + 280, + 665 + ], + "type": "text", + "content": "We performed experiments to validate the effectiveness of the method we propose for estimating " + }, + { + "bbox": [ + 60, + 533, + 280, + 665 + ], + "type": "inline_equation", + "content": "\\widehat{T}" + }, + { + "bbox": [ + 60, + 533, + 280, + 665 + ], + "type": "text", + "content": " by studying the error in the estimation as a function of the number of samples. We also performed experiments to show how the estimated " + }, + { + "bbox": [ + 60, + 533, + 280, + 665 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 533, + 280, + 665 + ], + "type": "text", + "content": " can be leveraged to train classifiers in the presence of noise labels. In particular, we performed experiments for a classification task on a synthetic dataset and on the CIFAR10-N dataset, comparing the performance of a classifier trained using labels obtained by some baseline aggregation method with the performance of a classifier trained using the distribution of posteriors obtained from the estimation of T (Eq. (18)) as soft-labels." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 60, + 677, + 280, + 711 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 677, + 280, + 711 + ], + "spans": [ + { + "bbox": [ + 60, + 677, + 280, + 711 + ], + "type": "text", + "content": "Estimation of " + }, + { + "bbox": [ + 60, + 677, + 280, + 711 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 677, + 280, + 711 + ], + "type": "text", + "content": " With these experiments we aim to validate the theoretical results of Sec. 4.1. We generate various matrices " + }, + { + "bbox": [ + 60, + 677, + 280, + 711 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 677, + 280, + 711 + ], + "type": "text", + "content": " that are symmetric, stochastic and diagonally" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "spans": [ + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "text", + "content": "dominant, the exact details about the generation of " + }, + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "text", + "content": " can be found in Appendix D.1. For each annotator we produce their prediction according to the matrix " + }, + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "text", + "content": ". We run experiments for the number of annotators " + }, + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "inline_equation", + "content": "H = 10, 7, 3, 2" + }, + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "text", + "content": ". We report here the results for " + }, + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "inline_equation", + "content": "H = 10" + }, + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "text", + "content": ", and 4 classes, all the other plots are in Appendix D.1. In Fig. 4 (as well as the plots in the Appendix) we can be observed that the error in the estimation decreases as " + }, + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "inline_equation", + "content": "\\frac{1}{\\sqrt{n}}" + }, + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 296, + 125, + 517, + 246 + ], + "type": "text", + "content": " number of samples, which is in agreement with the bound provided in Theorem 4.3. We also observed that, as expected, the estimation becomes more accurate as the number of annotators increases." + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 299, + 255, + 516, + 358 + ], + "blocks": [ + { + "bbox": [ + 299, + 255, + 516, + 358 + ], + "lines": [ + { + "bbox": [ + 299, + 255, + 516, + 358 + ], + "spans": [ + { + "bbox": [ + 299, + 255, + 516, + 358 + ], + "type": "image", + "image_path": "18bacc82ef382ef69447b40f5f97cb6766f85b03af34e2d2d01cbbe55eca8baf.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 343, + 361, + 503, + 379 + ], + "lines": [ + { + "bbox": [ + 343, + 361, + 503, + 379 + ], + "spans": [ + { + "bbox": [ + 343, + 361, + 503, + 379 + ], + "type": "text", + "content": "min eigenvalue T 0.2 0.3 0.4 0.6 1.0" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_footnote" + }, + { + "bbox": [ + 296, + 389, + 517, + 429 + ], + "lines": [ + { + "bbox": [ + 296, + 389, + 517, + 429 + ], + "spans": [ + { + "bbox": [ + 296, + 389, + 517, + 429 + ], + "type": "text", + "content": "Figure 1. Error in the estimation of " + }, + { + "bbox": [ + 296, + 389, + 517, + 429 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 389, + 517, + 429 + ], + "type": "text", + "content": " for 4 classes and 10 annotators. The plots are obtained by averaging different admissible matrices " + }, + { + "bbox": [ + 296, + 389, + 517, + 429 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 389, + 517, + 429 + ], + "type": "text", + "content": " (see Appendix B) and averaged over matrices that have the same minimum eigenvalues rounded to the first decimal." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 457, + 516, + 543 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 457, + 516, + 543 + ], + "spans": [ + { + "bbox": [ + 296, + 457, + 516, + 543 + ], + "type": "text", + "content": "Classification task with synthetic data We consider a classification task with a synthetic dataset. The features are generated uniformly in " + }, + { + "bbox": [ + 296, + 457, + 516, + 543 + ], + "type": "inline_equation", + "content": "[0,1]^2" + }, + { + "bbox": [ + 296, + 457, + 516, + 543 + ], + "type": "text", + "content": ". The assignment of labels " + }, + { + "bbox": [ + 296, + 457, + 516, + 543 + ], + "type": "inline_equation", + "content": "(y)" + }, + { + "bbox": [ + 296, + 457, + 516, + 543 + ], + "type": "text", + "content": " is done by following the label distribution established for each experiment, separating the space with lines parallel to the bisector of the first and third quadrants. More information on how the class distributions are generated can be found in Appendix D.2." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 296, + 544, + 517, + 621 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 544, + 517, + 621 + ], + "spans": [ + { + "bbox": [ + 296, + 544, + 517, + 621 + ], + "type": "text", + "content": "For each dataset annotations are generated according to the noise transition matrix " + }, + { + "bbox": [ + 296, + 544, + 517, + 621 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 544, + 517, + 621 + ], + "type": "text", + "content": ". Various combinations of " + }, + { + "bbox": [ + 296, + 544, + 517, + 621 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 544, + 517, + 621 + ], + "type": "text", + "content": " are tested that respect the assumptions of symmetry, stochasticity and diagonally dominance, as well as being commutative with D (more details can be found in Appendix B). The number of annotators is variable in the set " + }, + { + "bbox": [ + 296, + 544, + 517, + 621 + ], + "type": "inline_equation", + "content": "\\{3,5\\}" + }, + { + "bbox": [ + 296, + 544, + 517, + 621 + ], + "type": "text", + "content": ". See Appendix D.2 for implementations details." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 296, + 634, + 516, + 655 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 634, + 516, + 655 + ], + "spans": [ + { + "bbox": [ + 296, + 634, + 516, + 655 + ], + "type": "text", + "content": "Losses We use categorical cross entropy as loss function. We use both hard labels and soft labels to train the models." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 296, + 655, + 517, + 710 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 655, + 517, + 710 + ], + "spans": [ + { + "bbox": [ + 296, + 655, + 517, + 710 + ], + "type": "text", + "content": "To train the models with hard labels an aggregation method is needed to obtain one final label from the annotators. We consider random and majority vote. In random aggregation the final label is randomly picked from the labels of the annotators. In majority vote the final label is the" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "spans": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "text", + "content": "3445" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "bbox": [ + 60, + 125, + 282, + 234 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 125, + 282, + 234 + ], + "spans": [ + { + "bbox": [ + 60, + 125, + 282, + 234 + ], + "type": "text", + "content": "one with the most amount of votes (the mode), if the mode is not unique, we randomly choose one of the most voted classes. As soft-labels we consider the relative frequency among annotators and the posterior distribution according to Eq. (18). In the case of frequency for each sample we average the one-hot encoded annotations. Notice that random, majority vote and frequency soft labels do not leverage the estimate of " + }, + { + "bbox": [ + 60, + 125, + 282, + 234 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 125, + 282, + 234 + ], + "type": "text", + "content": " while the posterior does. In Fig. 2 we report the results for 4 classes with distribution (0.4, 0.1, 0.4, 0.1) and 3 annotators." + } + ] + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 61, + 243, + 279, + 371 + ], + "blocks": [ + { + "bbox": [ + 61, + 243, + 279, + 371 + ], + "lines": [ + { + "bbox": [ + 61, + 243, + 279, + 371 + ], + "spans": [ + { + "bbox": [ + 61, + 243, + 279, + 371 + ], + "type": "image", + "image_path": "a68d8b599e6489bb1baedb49f4caf8f03cba0124d0e1352b35561d181db89900.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 60, + 379, + 282, + 490 + ], + "lines": [ + { + "bbox": [ + 60, + 379, + 282, + 490 + ], + "spans": [ + { + "bbox": [ + 60, + 379, + 282, + 490 + ], + "type": "text", + "content": "Figure 2. Comparison between performance of Cross Entropy Loss using majority vote, random aggregation method or the posteriors (posterior) and relative frequency (average) as soft labels. On the y-axis the accuracy on a clean dataset and on the x-axis the values of the minimum on the diagonal of " + }, + { + "bbox": [ + 60, + 379, + 282, + 490 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 379, + 282, + 490 + ], + "type": "text", + "content": ". Small values of the minimum diagonal value mean a noisy dataset, while the minimum is 1 in the noise-free case. The results are obtained for 3 annotators and 4 classes, by averaging on different amissible matrices " + }, + { + "bbox": [ + 60, + 379, + 282, + 490 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 60, + 379, + 282, + 490 + ], + "type": "text", + "content": " (see Appendix B) that have the same minimum diagonal values rounded to the first decimal. The error bands show the maximum and minimum performance for each method." + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "bbox": [ + 60, + 504, + 280, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 504, + 280, + 559 + ], + "spans": [ + { + "bbox": [ + 60, + 504, + 280, + 559 + ], + "type": "text", + "content": "We use accuracy with respect to a clean dataset as performance metric. Our results show that using the posteriors distribution, as soft labels, allows for better performance than using the average of the labels assigned by annotators and than using majority vote or random aggregation." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 60, + 559, + 281, + 602 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 559, + 281, + 602 + ], + "spans": [ + { + "bbox": [ + 60, + 559, + 281, + 602 + ], + "type": "text", + "content": "Our method is shown to be more robust to the noise and is also the one with less variance in the results. This confirms our hypotheses that by leveraging the matrix " + }, + { + "bbox": [ + 60, + 559, + 281, + 602 + ], + "type": "inline_equation", + "content": "\\hat{T}" + }, + { + "bbox": [ + 60, + 559, + 281, + 602 + ], + "type": "text", + "content": " better classification accuracy can be achieved." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 60, + 616, + 281, + 694 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 60, + 616, + 281, + 694 + ], + "spans": [ + { + "bbox": [ + 60, + 616, + 281, + 694 + ], + "type": "text", + "content": "Experiments on CIFAR10-N The CIFAR10-N dataset contains CIFAR-10 train images with noisy labels annotated by humans using Amazon Mechanical Turk. Each image is labelled by three independent annotators. Table 1 shows the accuracy achieved using the different aggregation methods. For this experiment we used Resnet34 [10] with and without pre-training. In both cases, our approach of aggrega" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 297, + 125, + 518, + 170 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 125, + 518, + 170 + ], + "spans": [ + { + "bbox": [ + 297, + 125, + 518, + 170 + ], + "type": "text", + "content": "tion achieves the best performance. Note that in this dataset there are no guarantees that the assumptions we made on " + }, + { + "bbox": [ + 297, + 125, + 518, + 170 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 297, + 125, + 518, + 170 + ], + "type": "text", + "content": " are satisfied, however the method is still applicable with positive results." + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 298, + 183, + 527, + 250 + ], + "blocks": [ + { + "bbox": [ + 298, + 183, + 527, + 250 + ], + "lines": [ + { + "bbox": [ + 298, + 183, + 527, + 250 + ], + "spans": [ + { + "bbox": [ + 298, + 183, + 527, + 250 + ], + "type": "table", + "html": "
Aggregation MethodPretrainedNot-Pretrained
random0.718 ± 0.0350.579 ± 0.023
majority vote0.740 ± 0.0170.590 ± 0.006
average0.762 ± 0.0120.637 ± 0.016
posteriors (ours)0.794 ± 0.0050.652 ± 0.014
", + "image_path": "c06cd0f79b5b62943bc09dac22643f90cf8cc8746c4c79d50d1560cb321a588c.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 316, + 257, + 498, + 269 + ], + "lines": [ + { + "bbox": [ + 316, + 257, + 498, + 269 + ], + "spans": [ + { + "bbox": [ + 316, + 257, + 498, + 269 + ], + "type": "text", + "content": "Table 1. Test Accuracy on CIFAR10-N with Resnet34" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 297, + 299, + 408, + 313 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 297, + 299, + 408, + 313 + ], + "spans": [ + { + "bbox": [ + 297, + 299, + 408, + 313 + ], + "type": "text", + "content": "7. Concluding remarks" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 296, + 319, + 518, + 452 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 319, + 518, + 452 + ], + "spans": [ + { + "bbox": [ + 296, + 319, + 518, + 452 + ], + "type": "text", + "content": "We have addressed the problem of learning from noisy labels in the case where the dataset is labeled by annotators that occasionally make mistakes. We have introduced a methodology to estimate the noise transition matrix " + }, + { + "bbox": [ + 296, + 319, + 518, + 452 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 319, + 518, + 452 + ], + "type": "text", + "content": " of the annotators given the IAA. We further showed different techniques to leverage this estimate to learn from the noisy dataset in a robust manner. We have shown theoretically that the methods we introduce are sound. We supported our methodology with some experiments that confirms our estimation of the noise transition matrix is valid and that this can be leveraged in the learning process to obtain better performance." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 296, + 477, + 518, + 543 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 477, + 518, + 543 + ], + "spans": [ + { + "bbox": [ + 296, + 477, + 518, + 543 + ], + "type": "text", + "content": "Limitations The main limitation of our current approach to estimate " + }, + { + "bbox": [ + 296, + 477, + 518, + 543 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 477, + 518, + 543 + ], + "type": "text", + "content": " is that it only considers the case where " + }, + { + "bbox": [ + 296, + 477, + 518, + 543 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 477, + 518, + 543 + ], + "type": "text", + "content": " is symmetric and " + }, + { + "bbox": [ + 296, + 477, + 518, + 543 + ], + "type": "inline_equation", + "content": "D" + }, + { + "bbox": [ + 296, + 477, + 518, + 543 + ], + "type": "text", + "content": " assumed to be known and commute with " + }, + { + "bbox": [ + 296, + 477, + 518, + 543 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 477, + 518, + 543 + ], + "type": "text", + "content": ". Extending the results to the case where " + }, + { + "bbox": [ + 296, + 477, + 518, + 543 + ], + "type": "inline_equation", + "content": "T" + }, + { + "bbox": [ + 296, + 477, + 518, + 543 + ], + "type": "text", + "content": " might not be symmetric and different among annotators is one possible future research direction." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 298, + 569, + 377, + 580 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 298, + 569, + 377, + 580 + ], + "spans": [ + { + "bbox": [ + 298, + 569, + 377, + 580 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 296, + 590, + 518, + 711 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 296, + 590, + 518, + 711 + ], + "spans": [ + { + "bbox": [ + 296, + 590, + 518, + 711 + ], + "type": "text", + "content": "We acknowledge financial support from NRRP MUR project PE0000013-FAIR. This research was partially supported by MIUR under the grant \"Dipartimento di eccellenza 2018-2022\" of the Department of Computer Science and the Department of Computer Engineering at Sapienza University of Rome. It was also partially supported by the ERC Advanced Grant 788893 \"AMDROMA\", the EC H2020RIA project \"SoBigData++\" (871042), the MIUR PRIN project \"ALGADIMAR\", and the project SERICS (PE00000014) under the NRRP MUR program funded by the EU-NGEU." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 71, + 700, + 191, + 711 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 71, + 700, + 191, + 711 + ], + "spans": [ + { + "bbox": [ + 71, + 700, + 191, + 711 + ], + "type": "text", + "content": "1http://www.noisylabels.com" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 279, + 737, + 299, + 746 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 279, + 737, + 299, + 746 + ], + "spans": [ + { + "bbox": [ + 279, + 737, + 299, + 746 + ], + "type": "text", + "content": "3446" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 62, + 123, + 115, + 136 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 123, + 115, + 136 + ], + "spans": [ + { + "bbox": [ + 62, + 123, + 115, + 136 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 62, + 142, + 280, + 711 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 66, + 142, + 280, + 175 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 142, + 280, + 175 + ], + "spans": [ + { + "bbox": [ + 66, + 142, + 280, + 175 + ], + "type": "text", + "content": "[1] J. Cohen. A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1):37-46, 1960. 1, 11" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 66, + 182, + 280, + 214 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 182, + 280, + 214 + ], + "spans": [ + { + "bbox": [ + 66, + 182, + 280, + 214 + ], + "type": "text", + "content": "[2] K. M. Collins, U. Bhatt, and A. Weller. Eliciting and learning with soft labels from every annotator, 2022. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 66, + 222, + 280, + 266 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 222, + 280, + 266 + ], + "spans": [ + { + "bbox": [ + 66, + 222, + 280, + 266 + ], + "type": "text", + "content": "[3] A. P. Dawid and A. M. Skene. Maximum likelihood estimation of observer error-rates using the em algorithm. Journal of the Royal Statistical Society. Series C (Applied Statistics), 28(1):20-28, 1979. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 66, + 272, + 280, + 348 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 272, + 280, + 348 + ], + "spans": [ + { + "bbox": [ + 66, + 272, + 280, + 348 + ], + "type": "text", + "content": "[4] L. Feng, S. Shu, Z. Lin, F. Lv, L. Li, and B. An. Can cross entropy loss be robust to label noise? In C. Bessiere, editor, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20, pages 2206-2212. International Joint Conferences on Artificial Intelligence Organization, 7 2020. Main track. 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 66, + 356, + 280, + 389 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 356, + 280, + 389 + ], + "spans": [ + { + "bbox": [ + 66, + 356, + 280, + 389 + ], + "type": "text", + "content": "[5] J. Fleiss et al. Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5):378-382, 1971. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 66, + 395, + 280, + 450 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 395, + 280, + 450 + ], + "spans": [ + { + "bbox": [ + 66, + 395, + 280, + 450 + ], + "type": "text", + "content": "[6] A. Ghosh, H. Kumar, and P. S. Sastry. Robust loss functions under label noise for deep neural networks. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, AAAI'17, page 1919-1925. AAAI Press, 2017. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 66, + 457, + 280, + 490 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 457, + 280, + 490 + ], + "spans": [ + { + "bbox": [ + 66, + 457, + 280, + 490 + ], + "type": "text", + "content": "[7] A. Ghosh, N. Manwani, and P. Sastry. Making risk minimization tolerant to label noise. Neurocomputing, 160:93-107, 2015. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 66, + 497, + 280, + 573 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 497, + 280, + 573 + ], + "spans": [ + { + "bbox": [ + 66, + 497, + 280, + 573 + ], + "type": "text", + "content": "[8] D. Hendrycks, M. Mazeika, D. Wilson, and K. Gimpel. Using trusted data to train deep networks on labels corrupted by severe noise. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 66, + 580, + 280, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 580, + 280, + 602 + ], + "spans": [ + { + "bbox": [ + 66, + 580, + 280, + 602 + ], + "type": "text", + "content": "[9] R. A. Horn and C. R. Johnson. Matrix Analysis. Cambridge University Press, USA, 2nd edition, 2012. 14" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 63, + 609, + 280, + 631 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 609, + 280, + 631 + ], + "spans": [ + { + "bbox": [ + 63, + 609, + 280, + 631 + ], + "type": "text", + "content": "[10] A. Khetan, Z. C. Lipton, and A. Anandkumar. Learning from noisy singly-labeled data, 2017. 8" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 62, + 638, + 280, + 671 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 638, + 280, + 671 + ], + "spans": [ + { + "bbox": [ + 62, + 638, + 280, + 671 + ], + "type": "text", + "content": "[11] R. Meir and T. Zhang. Generalization error bounds for bayesian mixture algorithms. J. Mach. Learn. Res., 4:839-860, dec 2003. 17" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 62, + 678, + 280, + 711 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 678, + 280, + 711 + ], + "spans": [ + { + "bbox": [ + 62, + 678, + 280, + 711 + ], + "type": "text", + "content": "[12] A. Menon, B. V. Rooyen, C. S. Ong, and B. Williamson. Learning from corrupted binary labels via class-probability estimation. In F. Bach and" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 299, + 125, + 517, + 709 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 318, + 125, + 517, + 169 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 125, + 517, + 169 + ], + "spans": [ + { + "bbox": [ + 318, + 125, + 517, + 169 + ], + "type": "text", + "content": "D. Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, pages 125-134, Lille, France, 07-09 Jul 2015. PMLR. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 299, + 177, + 516, + 219 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 177, + 516, + 219 + ], + "spans": [ + { + "bbox": [ + 299, + 177, + 516, + 219 + ], + "type": "text", + "content": "[13] A. K. Menon, A. S. Rawat, S. J. Reddi, and S. Kumar. Can gradient clipping mitigate label noise? In International Conference on Learning Representations, 2020. 2" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 299, + 229, + 516, + 261 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 229, + 516, + 261 + ], + "spans": [ + { + "bbox": [ + 299, + 229, + 516, + 261 + ], + "type": "text", + "content": "[14] M. Mohri, A. Rostamizadeh, and A. Talwalkar. Foundations of Machine Learning. The MIT Press, 2012. 17" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 299, + 270, + 516, + 335 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 270, + 516, + 335 + ], + "spans": [ + { + "bbox": [ + 299, + 270, + 516, + 335 + ], + "type": "text", + "content": "[15] N. Natarajan, I. S. Dhillon, P. K. Ravikumar, and A. Tewari. Learning with noisy labels. In C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and K. Weinberger, editors, Advances in Neural Information Processing Systems, volume 26. Curran Associates, Inc., 2013. 1, 2, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 299, + 344, + 517, + 399 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 344, + 517, + 399 + ], + "spans": [ + { + "bbox": [ + 299, + 344, + 517, + 399 + ], + "type": "text", + "content": "[16] G. Patrini, A. Rozza, A. Krishna Menon, R. Nock, and L. Qu. Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1944-1952, 2017. 1, 2, 6" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 299, + 406, + 517, + 495 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 406, + 517, + 495 + ], + "spans": [ + { + "bbox": [ + 299, + 406, + 517, + 495 + ], + "type": "text", + "content": "[17] J. Peterson, R. Battleday, T. Griffiths, and O. Russakovsky. Human uncertainty makes classification more robust. In Proceedings - 2019 International Conference on Computer Vision, ICCV 2019, Proceedings of the IEEE International Conference on Computer Vision, pages 9616-9625, United States, Oct. 2019. Institute of Electrical and Electronics Engineers Inc. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 299, + 502, + 517, + 525 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 502, + 517, + 525 + ], + "spans": [ + { + "bbox": [ + 299, + 502, + 517, + 525 + ], + "type": "text", + "content": "[18] J. E. Potter. Matrix quadratic solutions. SIAM Journal on Applied Mathematics, 14(3):496-501, 1966. 12" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 299, + 532, + 516, + 554 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 532, + 516, + 554 + ], + "spans": [ + { + "bbox": [ + 299, + 532, + 516, + 554 + ], + "type": "text", + "content": "[19] A. Purpura, G. Silvello, and G. A. Susto. Learning to rank from relevance judgments distributions, 2022. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 299, + 563, + 517, + 606 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 563, + 517, + 606 + ], + "spans": [ + { + "bbox": [ + 299, + 563, + 517, + 606 + ], + "type": "text", + "content": "[20] V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, and L. Moy. Learning from crowds. Journal of Machine Learning Research, 11(43):1297-1322, 2010. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 299, + 614, + 516, + 648 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 614, + 516, + 648 + ], + "spans": [ + { + "bbox": [ + 299, + 614, + 516, + 648 + ], + "type": "text", + "content": "[21] H. Song, M. Kim, D. Park, Y. Shin, and J.-G. Lee. Learning from noisy labels with deep neural networks: A survey, 2020. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 299, + 655, + 517, + 709 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 299, + 655, + 517, + 709 + ], + "spans": [ + { + "bbox": [ + 299, + 655, + 517, + 709 + ], + "type": "text", + "content": "[22] A. Uma, T. Fornaciari, D. Hovy, S. Paun, B. Plank, and M. Poesio. A case for soft loss functions. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 8(1):173-177, Oct. 2020. 2" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "spans": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "text", + "content": "3447" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 62, + 125, + 280, + 685 + ], + "type": "list", + "angle": 0, + "index": 9, + "blocks": [ + { + "bbox": [ + 62, + 125, + 279, + 146 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 125, + 279, + 146 + ], + "spans": [ + { + "bbox": [ + 62, + 125, + 279, + 146 + ], + "type": "text", + "content": "[23] V. N. Vapnik. Statistical Learning Theory. Wiley-Interscience, 1998. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 62, + 154, + 280, + 198 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 62, + 154, + 280, + 198 + ], + "spans": [ + { + "bbox": [ + 62, + 154, + 280, + 198 + ], + "type": "text", + "content": "[24] Y. Wang, X. Ma, Z. Chen, Y. Luo, J. Yi, and J. Bailey. Symmetric cross entropy for robust learning with noisy labels. In IEEE International Conference on Computer Vision, 2019. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 63, + 205, + 280, + 238 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 205, + 280, + 238 + ], + "spans": [ + { + "bbox": [ + 63, + 205, + 280, + 238 + ], + "type": "text", + "content": "[25] J. Wei, Z. Zhu, T. Luo, E. Amid, A. Kumar, and Y. Liu. To aggregate or not? learning with separate noisy labels, 2022. 2, 6" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 63, + 245, + 280, + 320 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 245, + 280, + 320 + ], + "spans": [ + { + "bbox": [ + 63, + 245, + 280, + 320 + ], + "type": "text", + "content": "[26] X. Xia, T. Liu, N. Wang, B. Han, C. Gong, G. Niu, and M. Sugiyama. Are anchor points really indispensable in label-noise learning? In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. 1, 2, 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 63, + 329, + 280, + 394 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 329, + 280, + 394 + ], + "spans": [ + { + "bbox": [ + 63, + 329, + 280, + 394 + ], + "type": "text", + "content": "[27] Y. Yao, T. Liu, B. Han, M. Gong, J. Deng, G. Niu, and M. Sugiyama. Dual t: Reducing estimation error for transition matrix in label-noise learning. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS'20, Red Hook, NY, USA, 2020. Curran Associates Inc. 1, 2, 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 63, + 402, + 280, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 402, + 280, + 476 + ], + "spans": [ + { + "bbox": [ + 63, + 402, + 280, + 476 + ], + "type": "text", + "content": "[28] M. Zhang, J. Lee, and S. Agarwal. Learning from noisy labels with no change to the training process. In M. Meila and T. Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 12468-12478. PMLR, 18-24 Jul 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 63, + 485, + 280, + 550 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 485, + 280, + 550 + ], + "spans": [ + { + "bbox": [ + 63, + 485, + 280, + 550 + ], + "type": "text", + "content": "[29] Z. Zhang and M. R. Sabuncu. Generalized cross entropy loss for training deep neural networks with noisy labels. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS'18, page 8792-8802, Red Hook, NY, USA, 2018. Curran Associates Inc. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 63, + 558, + 280, + 591 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 558, + 280, + 591 + ], + "spans": [ + { + "bbox": [ + 63, + 558, + 280, + 591 + ], + "type": "text", + "content": "[30] Z. Zhu, Y. Song, and Y. Liu. Clusterability as an alternative to anchor points when learning with noisy labels, 2021. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 63, + 598, + 280, + 685 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 63, + 598, + 280, + 685 + ], + "spans": [ + { + "bbox": [ + 63, + 598, + 280, + 685 + ], + "type": "text", + "content": "[31] Z. Zhu, J. Wang, and Y. Liu. Beyond images: Label noise transition matrix estimation for tasks with lower-quality features. In K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvari, G. Niu, and S. Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 27633-27653. PMLR, 17-23 Jul 2022. 2" + } + ] + } + ], + "index": 8 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "spans": [ + { + "bbox": [ + 279, + 737, + 298, + 746 + ], + "type": "text", + "content": "3448" + } + ] + } + ], + "index": 10 + } + ], + "page_size": [ + 595, + 842 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Leveraging Temporal Context in Low Representational Power Regimes/23cf52f5-5ab9-4d57-929a-b5407b0b21e2_content_list.json b/2023/Leveraging Temporal Context in Low Representational Power Regimes/23cf52f5-5ab9-4d57-929a-b5407b0b21e2_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..a897a24869d91c8917303c0f2a7fa03b0048f9ca --- /dev/null +++ b/2023/Leveraging Temporal Context in Low Representational Power Regimes/23cf52f5-5ab9-4d57-929a-b5407b0b21e2_content_list.json @@ -0,0 +1,1592 @@ +[ + { + "type": "text", + "text": "Leveraging Temporal Context in Low Representational Power Regimes", + "text_level": 1, + "bbox": [ + 125, + 130, + 843, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Camilo L. Fosco", + "bbox": [ + 187, + 190, + 325, + 207 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "SouYoung Jin", + "bbox": [ + 361, + 191, + 473, + 208 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Emilie Josephs", + "bbox": [ + 517, + 191, + 638, + 208 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Aude Oliva", + "bbox": [ + 678, + 191, + 772, + 207 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "camilolu@mit.edu", + "bbox": [ + 184, + 215, + 328, + 228 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "souyoung@mit.edu", + "bbox": [ + 344, + 215, + 488, + 228 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "eJosephs@mit.edu", + "bbox": [ + 504, + 215, + 650, + 228 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "oliva@mit.edu", + "bbox": [ + 666, + 215, + 784, + 228 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "MITCSAIL", + "bbox": [ + 431, + 233, + 532, + 250 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 285, + 313, + 301 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Computer vision models are excellent at identifying and exploiting regularities in the world. However, it is computationally costly to learn these regularities from scratch. This presents a challenge for low-parameter models, like those running on edge devices (e.g. smartphones). Can the performance of models with low representational power be improved by supplementing training with additional information about these statistical regularities? We explore this in the domains of action recognition and action anticipation, leveraging the fact that actions are typically embedded in stereotypical sequences. We introduce the Event Transition Matrix (ETM), computed from action labels in an untrimmed video dataset, which captures the temporal context of a given action, operationalized as the likelihood that it was preceded or followed by each other action in the set. We show that including information from the ETM during training improves action recognition and anticipation performance on various egocentric video datasets. Through ablation and control studies, we show that the coherent sequence of information captured by our ETM is key to this effect, and we find that the benefit of this explicit representation of temporal context is most pronounced for smaller models. Code, matrices and models are available in our project page: https://camilofosco.com/etm網站", + "bbox": [ + 75, + 316, + 473, + 694 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 723, + 209, + 739 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "A strength of computer vision models is their ability to identify and exploit statistical regularities in the world. Learning these regularities from scratch is computationally costly, which limits the accuracy of low-parameters models. It is critical to boost the performance of small models, since many devices lack the resources to host current state of the art models. One way to make the learning problem more tractable for small models may be to supplement them at training time with an explicit representation of the statistical regularities in the target domain. Here, we test whether", + "bbox": [ + 75, + 750, + 468, + 901 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/57d9fd47dcaca2b605eaa79cc1a203a8cd34d118308cc1905b9d61863e6cb697.jpg", + "image_caption": [ + "Figure 1. Action recognition performance difference between models trained with and without the proposed ETM approach. We train models with various model architectures, from small (left) to large (right): AVT-b [14], MoViNets [24] family, X3D [11] family, LambdaResNet-50 [4], and EfficientNets [57] family on EPIC-KITCHENS-100 [14]. We show that incorporating the ETM into training improves performance, and the impact is higher on smaller models." + ], + "image_footnote": [], + "bbox": [ + 517, + 289, + 870, + 468 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "video understanding models can be improved by providing them with information about typical event sequences during training.", + "bbox": [ + 496, + 626, + 892, + 670 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "We introduce the Event Transition Matrix (ETM), illustrated in Figure 2, which leverages the fact that events in real-world videos often occur in predictable sequences. Each row and column indexes an event. In the rows, the ETM captures the likelihood that the event was followed by each of the other events in the set. In the columns, it captures the likelihood that the event was preceded by each other event. To compute a cell's value, we combine information from all previous and subsequent events, weighted by their temporal distance from the queried action. Crucially, this breaks markovian assumptions and allows the matrix to capture long-range relationships. The ETM has two important properties. First, it acts as an explicit representation of the likelihood of event transitions, which provides additional pertinent information that a model can", + "bbox": [ + 496, + 674, + 893, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "10693", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/2fc50e3cafbfca93075d828895c973c5d00050251bc5bf7587a11a2d741b1825.jpg", + "image_caption": [ + "(a) Low-level descriptions in [10]" + ], + "image_footnote": [], + "bbox": [ + 81, + 88, + 568, + 284 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/109b46fcc27153da0e88c263fd0161409275ced2a14fabc7b122dcf5ba40c826.jpg", + "image_caption": [ + "(b) Event Transition Matrix", + "Figure 2. We construct a given Event Transition Matrix (ETM) from action labels drawn from a given dataset of untrimmed videos. (a) Each video depicts a continuous activity, and is paired with human annotations indicating the individual, low-level actions that compose the activity. (b) Using these action labels, we created the ETM by recording the frequency with which a given action label was preceded or followed by each other action label, accumulated across the videos in our set." + ], + "image_footnote": [], + "bbox": [ + 606, + 97, + 880, + 286 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "leverage without the cost of learning it for itself. Second, it augments representations of a given event with information about what came before and after it, providing additional target for the model to train on.", + "bbox": [ + 75, + 397, + 470, + 457 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "In the present paper, across multiple datasets and model architectures, we test the effectiveness of incorporating the ETM into training for low-parameter models. We test our approach on action recognition and action anticipation in egocentric video datasets, where actions occur in stereotypical sequences. However, this approach can apply to any kind of sequence in videos. We show that leveraging the ETM improves action recognition relative to baselines, and that this improvement relies on the coherence of the action sequence. We also show that action anticipation is improved with our ETM approach. In both cases, we show that the ETM approach can be incorporated into multiple different model architectures, and that the addition of the ETM has the highest impact on smaller models, as shown in Figure 1. Overall, this work demonstrates a potential path to more efficient models, based on supplementing small models at training time with explicit representations of regularities expected in the data.", + "bbox": [ + 75, + 458, + 468, + 731 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 76, + 744, + 218, + 760 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.1. Efficient AI", + "text_level": 1, + "bbox": [ + 76, + 770, + 204, + 786 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "State of the art neural networks for video understanding can achieve impressive classification results, but these models often have heavy computational requirements. Producing smaller, more efficient models that can perform comparable to larger ones is an active area of research, for deployment onto edge devices such as phones, wearables, drones or autonomous vehicles. Some approaches achieve effi", + "bbox": [ + 75, + 794, + 468, + 900 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "ciency by first training a large network, then using a smaller network to learn the mapping from input to output vectors with little loss on performance (i.e. knowledge distillation [3,15,20,23,27,32]), or by identifying and removing non-essential parameters (i.e. parameter pruning [18,19,26,29]). Others rely on adaptive policies to shorten or simplify the inference process, reducing the effective size of the architecture [5,6,33,45] or input [45,46]. Here, we propose an alternative approach, which involves intervening at training time to supplement low-parameter architectures with external, explicit representations of statistical regularities in the target domain.", + "bbox": [ + 496, + 397, + 890, + 580 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.2. Statistical Regularities and Bayesian Learning.", + "text_level": 1, + "bbox": [ + 500, + 589, + 890, + 606 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The environment is full of regularities. Objects classes have co-occurring attributes [34, 50], environments classes have distinctive combinations of objects, surfaces and textures [17, 51, 58, 60], and episodes are composed of predictable sequences of events [25, 63]. Having the ability to detect and leverage these regularities is a key feature of intelligence. Cognitive research in humans has established that representations of contextual regularities underlie most aspects of intelligence, spanning learning, generalization, prediction, compression, memory, language and more [7, 12, 54].", + "bbox": [ + 496, + 612, + 890, + 779 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Machine learning relies on identifying and making use of regularities. Typically, this happens in an emergent manner from the training regime. However, some approaches also rely on explicit representations of such regularities. For example, in natural language processing, word embeddings [37, 38, 47, 48, 59] acting as explicit representations of word transition probabilities have provided the basis for many downstream tasks.", + "bbox": [ + 496, + 779, + 890, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "10694", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2.3. Action Recognition and Anticipation", + "text_level": 1, + "bbox": [ + 76, + 90, + 395, + 107 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Action understanding is an enduring problem in video processing [8, 41, 43, 49, 64]. This can involve action recognition, which requires retrieving a label for a depicted action, and action anticipation, which requires retrieving a label for a future action based on a depicted action. Videos contain complex semantic relationships between objects, people and places, which evolve dynamically over time. Thus, action understanding models often incorporate elements of relational modeling, for example modeling the spatial relations among people or between people and objects [13, 56], between people and environment zones [44], or between the body parts of the person performing the actions [61]. More recently, multi-modal representation learning approaches have been introduced to learn the broader sensory context of actions [30, 36, 42].", + "bbox": [ + 75, + 114, + 472, + 340 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "2.4. Action Recognition and Anticipation with Temporal Context", + "text_level": 1, + "bbox": [ + 76, + 351, + 468, + 383 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Another kind of context that can be modeled in action understanding is the temporal relationships between the events that make up ongoing actions. Actions take place in sequences: you must first boil water and grind coffee beans, then pour water over the beans in order to successfully make coffee. Some models capitalize on such temporal regularities by modeling the dependence among individual video frames, either with RNNs [2, 56], relational reasoning networks [65] or more recently with attention-based methods and transformers [14, 52]. Other approaches model these sequences at a higher level, by learning the dependence among events (rather than individual frames), by using action co-occurrence matrices [22, 40], or by estimating the transitional probability between adjacent [35, 55] or more distant actions [21, 53].", + "bbox": [ + 75, + 390, + 472, + 618 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Building on this work, we show how leveraging explicit representations of typical action sequences in low-parameter models can improve their performance. Our motivation is that there is regularity in the temporal context of an action in both the past and the future, and leveraging this bi-directional regularity could bootstrap the model's learning. While some previous work [1] included a representation of the past in an action anticipation framework, our approach is distinct from it in two ways: (1) our representation of temporal context includes past, present and future, and extends over a larger time period, and (2) we pre-compute this representation, then use it to supplement learning.", + "bbox": [ + 75, + 618, + 472, + 801 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Proposed Approach", + "text_level": 1, + "bbox": [ + 76, + 814, + 267, + 832 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we introduce the Event Transition Matrix (ETM), which is an efficient approach to constructing a knowledge base about the temporal relation between events in video scenes (Section 3.1). We then propose a novel ap", + "bbox": [ + 75, + 839, + 470, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "proach to pre-training with this matrix, leveraging these relations to help recognize current events or predict past or future events in the input video snippet (Section 3.2).", + "bbox": [ + 496, + 90, + 892, + 137 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3.1. Event Transition Matrix (ETM)", + "text_level": 1, + "bbox": [ + 498, + 152, + 782, + 167 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Egocentric video datasets, such as EPIC-KITCHENS [9, 10] or EGO4D [16], contain long form videos that exhibit rich sequences of small actions or events. These sequences are densely annotated with labels for each event and timestamps indicating start and end points of each action. Suppose there are N event categories, $S = \\{s_1, s_2, \\dots, s_N\\}$ . Each video is annotated with a sequence of event instances, which can be denoted as $\\{y_1, y_2, \\dots, y_{t-1}, y_t, \\dots\\}$ . Using the timestamps, we can define a video snippet $x_k$ that contains the specific event, which is given the instance label $y_k$ , based on its category. Event descriptions, i.e. $x_k$ and $y_k$ , refer to the current ongoing action event, but do not convey the broader temporal context of that action, including what came before and after it. As in the example in Figure 2, when making coffee, \"add coffee beans\" happens earlier than \"brew coffee\", and these two events are temporally dependent on each other. To leverage the temporal context surrounding events, we compute the frequencies with which each event comes before/after each other event. More formally, we define a square matrix, $\\mathbf{M}_{N,N}$ , where each row and column corresponds to an event in $S$ and $\\mathbf{M}(i,j)$ corresponds to an estimated probability that an event $s_i$ happens before $s_j$ , and vice versa. This matrix is built with training set observations.", + "bbox": [ + 496, + 176, + 893, + 540 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "A naive approach for building the matrix is to update $\\mathbf{M}(y_l, y_m)$ for all possible pairs of events where event $l$ happens earlier than event $m$ . However, this approach does not incorporate the temporal distance between two events, since $(y_l, y_m)$ and $(y_l, y_n)$ contribute equally to constructing the matrix even when $l$ is much farther away from $m$ . In reality, actions separated by more time are less likely to be part of the same overall activity, and are therefore less likely to consistently co-vary. Therefore, we update the matrix with a decay function $\\delta(\\cdot)$ , as", + "bbox": [ + 496, + 542, + 893, + 694 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {M} \\left(y _ {l}, y _ {m}\\right) + = \\delta (m - l). \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 607, + 709, + 892, + 727 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "We normalize the matrix $\\mathbf{M}$ by the sum of each row and define a new matrix $\\mathbf{M}^R$ as", + "bbox": [ + 496, + 744, + 892, + 773 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\mathbf {M} ^ {R} (i, j) = \\frac {\\mathbf {M} (i , j)}{\\sum_ {k = 0} ^ {N} \\mathbf {M} (i , k)}. \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 596, + 787, + 890, + 825 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Each row in this matrix, $\\mathbf{M}^R (i,:)$ , can be seen as an empirical approximation of the distribution of possible events that can be transitioned to following event $s_i$ . Similarly, we can also define a matrix $\\mathbf{M}^C$ by column-wise normalization.", + "bbox": [ + 496, + 839, + 893, + 901 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "10695", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/11ef462ac7094adb0baeb2f626010bc56b77b7566829522d6e3a144ef140b8db.jpg", + "image_caption": [ + "Figure 3. Overview of the proposed approach for pre-training with ETM. The model takes a video snippet and encodes the input to predict the present, past, and future events. In particular, we use the column and the row of the ETM which correspond to the given event. These represent the distributions over past actions, and future actions respectively. More details are written in Section 3.2." + ], + "image_footnote": [], + "bbox": [ + 200, + 94, + 772, + 303 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Encoder Training with the Event Transition Matrix", + "text_level": 1, + "bbox": [ + 76, + 383, + 470, + 412 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "We propose a novel training protocol to enhance an arbitrary encoder by leveraging the ETM. A typical event recognition model is trained to take a video snippet, $x_{t}$ , transform it into a feature vector with an encoder $f(\\cdot)$ , and recognize an event in the input video by predicting a label from that vector. More formally, the model is trained to increase $p(y_{t}|g_{q}(f(x_{t}))$ , where $g_{q}$ represents a module that maps features into a probability distribution over labels. In our framework, we call $g_{q}$ the present module and train it with a typical cross-entropy loss function, $\\mathcal{L}_{q}$ .", + "bbox": [ + 75, + 422, + 468, + 571 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Our proposed method adds two additional modules to predict past and future event probabilities, as shown in Figure 3. A past module $g_{c}(\\cdot)$ receives $f(x_{t})$ and predicts a distribution of events occurring before the current action (which corresponds to a column of the column-normalized ETM, $\\mathbf{M}^{C}$ ). This module attempts to minimize the following loss:", + "bbox": [ + 75, + 573, + 468, + 679 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {c} = \\sum_ {k = 0} ^ {N} (C (k) - \\mathbf {M} ^ {C} (y _ {t}, k)) ^ {2}, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 160, + 686, + 468, + 729 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $C(k) = g_c(f(x_t))$ . Similarly, a future module $g_r(\\cdot)$ predicts a distribution of future events, and tries to minimize the distance between the prediction and a row of the row-normalized ETM, $\\mathbf{M}^R(:, y_t)$ , through the loss:", + "bbox": [ + 76, + 736, + 468, + 799 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {r} = \\sum_ {k = 0} ^ {N} (R (k) - \\mathbf {M} ^ {R} (k, y _ {t})) ^ {2}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 161, + 805, + 468, + 845 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $R(k) = g_r(f(x_t))$ . Altogether, our framework augments an arbitrary encoder with these past and future modules and minimizes the following objective during training:", + "bbox": [ + 76, + 854, + 470, + 902 + ], + "page_idx": 3 + }, + { + "type": "table", + "img_path": "images/86e49d55cece979af68e8e72958c26050d8fe9869190ba78f486148e78b7aff3.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetSegm.Actionsτa(s)Metrics (AR, AA)
EK100 [10]90k38071.0top-1, rec@5
EGO4D [16]39.2k35421.0top-1, rec@5
EGTEA [28]10.3k1060.5top-1, top-1
", + "bbox": [ + 504, + 381, + 895, + 455 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Table 1. Properties of the datasets used in our experiments. We showcase our three main datasets: EPIC-KITCHENS-100 (EK100), EGO4D LTA (EGO4D) and EGTEA Gaze+ (EGTEA). $\\tau_{a}$ corresponds to the time between input clips and target segments in action anticipation, and follows prior work [14]. The Metrics column shows the main performance metrics used in our action recognition and action anticipation experiments, respectively.", + "bbox": [ + 498, + 464, + 893, + 564 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} = \\omega_ {q} \\mathcal {L} _ {q} + \\omega_ {c} \\mathcal {L} _ {c} + \\omega_ {r} \\mathcal {L} _ {r}, \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 599, + 590, + 890, + 609 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "This training setup attempts to push the encoder towards outputting richer event representations that capture the regularities of the event's typical temporal context. Although simplistic, the framework is effective: we observe that instantiating $g_{c}$ and $g_{r}$ with simple multi-layer perceptrons is enough to achieve strong results on low-complexity models.", + "bbox": [ + 496, + 614, + 893, + 708 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4. Experimental Results", + "text_level": 1, + "bbox": [ + 500, + 720, + 705, + 738 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this section, we demonstrate the effectiveness of training with the Event Transition Matrix (ETM) on action recognition (Section 4.3) and action anticipation (Section 4.4). We describe the datasets used in Section 4.1 and give implementation details in Section 4.2.", + "bbox": [ + 496, + 746, + 893, + 821 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.1. Datasets", + "text_level": 1, + "bbox": [ + 500, + 830, + 602, + 845 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "EPIC-KITCHENS-100. [10] The EPIC-KITCHENS-100 dataset (EK100) contains 700 unscripted videos depicting cooking actions, totalling 100 hours. It presents verb", + "bbox": [ + 500, + 854, + 893, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "10696", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "and noun annotations over 90k segments of varying length. The dataset depicts 97 unique verbs, 300 unique nouns that combine to yield 3807 actions. We work with the provided training and validation sets, containing 67.2k and 9.7k segments respectively. Our ETM is constructed exclusively with training set data.", + "bbox": [ + 75, + 90, + 467, + 181 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "EGO4D. [16] This newer egocentric dataset contains 3670 hours of video from 71 different participants. We use the long term anticipation annotations provided by the authors [16], which reduces the set to a training split containing 493 unique videos with 23610 segments, and a validation set with 380 videos cut into 15587 segments. These annotations present 115 unique verbs, 477 unique nouns and 3542 unique actions. All segments are 240-frame long. To homogenize our setup and avoid generating ETMs with a biased diagonal, we merge contiguous segments together if they depict the same action. This yields 18896 segments for training and 12676 segments for validation, and we use this training set for ETM construction. We refer to this set as EGO4D LTA.", + "bbox": [ + 75, + 181, + 467, + 393 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "EGTEA Gaze+ [28] is another popular egocentric dataset with 10k segments annotated with 19 verbs, 51 nouns and 106 unique actions. We report performance for split 1 (provided by the authors), containing 8299 training segments and 2022 validation segments. Following previous work [14, 39], both action recognition and anticipation performance are measured with top-1 accuracy. The properties of each dataset are summarized in Table 1.", + "bbox": [ + 75, + 393, + 467, + 513 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Implementation Details", + "text_level": 1, + "bbox": [ + 76, + 527, + 294, + 542 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "ETM construction. We construct our ETM offline by observing all videos of our training set and adding a contribution to $M(y_{l},y_{m})$ modulated by a decay function $\\delta (v)$ , where $v$ is the temporal distance between the events, as in Equation 1. Importantly, to minimize sparsity, we only consider events that appear at least 4 times in the training set.", + "bbox": [ + 75, + 551, + 467, + 642 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Encoder training. With a given ETM, we train an encoder to generate embeddings that can be used to both recognize actions and predict ETM vectors from snippets of video. The encoder is tasked with producing an embedding that is fed to three modules: an action classification head (present module), a past vector regressor and a future vector regressor, as seen in Figure 3. We instantiate the past, present and future modules with simple fully-connected layers. We train with the loss in Eq. 5 and set $\\omega_{q} = \\omega_{c} = \\omega_{r} = \\frac{1}{3}$ . As the ETM is built with actions that appear at least four times in the training data, certain rare actions do not have target vectors. In those cases, the loss associated to regressing the past and future vectors is set to zero, and no gradients are back-propagated.", + "bbox": [ + 75, + 643, + 467, + 852 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We train our models with the Ranger21 optimizer [62] with a learning rate of 0.01 and cosine annealing with a 20 epochs half-cycle. We use a batch-size of 32, weight decay", + "bbox": [ + 75, + 854, + 467, + 900 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "of 0.0001 and dropout with $p = 0.5$ where applicable.", + "bbox": [ + 498, + 90, + 854, + 106 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Training setup for Action Anticipation. We follow the framework used in [14], where an encoder generates features from an input clip $c$ before feeding them to a decoder attempting to predict the future of $c$ , which is the label $y_{s}$ of an action segment $s$ happening $\\tau_{a}$ seconds after $c$ ends. In other words, for each action segment starting at time $\\tau_{s}$ , the decoder attempts to predict its action label $y_{s}$ using a clip $c$ that ends $\\tau_{a}$ seconds before $s$ . Crucially, we analyze how using an encoder trained with ETM supervision affects the performance of the system.", + "bbox": [ + 496, + 107, + 890, + 257 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We analyze two different setups: one were we freeze our pretrained encoder and only train the decoder, and another where we allow our encoder to be finetuned alongside the decoder. Following the AVT framework [14], we replace AVT-b (AVT's transformer-based encoder) with several variants of our own encoders. We keep AVT-h (AVT's transformer-based head) as a decoder and finetune it with our codes as input.", + "bbox": [ + 496, + 258, + 890, + 378 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We train until validation performance plateaus, and we operate with a batch size of 32, a learning rate of $10^{-4}$ with cosine annealed decay, and the Ranger21 optimizer. At test time, we employ 3-crop testing following [14], where we compute three 224px spatial crops from 248px input frames, and average the predictions of each segment.", + "bbox": [ + 496, + 380, + 890, + 470 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.3. Action Recognition Experiments", + "text_level": 1, + "bbox": [ + 500, + 482, + 785, + 500 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We first ask whether models trained with ETM supervision showed improved action recognition performance. To do that, we train a baseline MoViNet A0 on EK100. Following [10], our present head has a two-way output: one to predict verbs and one to predict nouns, with an average verb/noun loss. Accordingly, in Table 2, the Present tab shows the top-1 accuracies in verb and noun classification as well as Action accuracy, which corresponds to (verb, noun) pairs. We compare this to a MoViNet A0 trained on the same data but augmented with our ETM framework as described in Section 3. For this model, we also show the mean absolute error (MAE) on the past and the future predictions. Here, lower scores indicate better performance.", + "bbox": [ + 496, + 507, + 890, + 702 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To demonstrate that the performance gain is not merely due to the extra parameters gained from training with an external matrix, we compare against three more baselines, with versions of the matrix that disrupt the action sequence information to different degrees. In the Full shuffle baseline, cells of the matrix are randomly shuffled. This scrambles any sequence information in the matrix, while preserving the distribution of values across it. In the Columns shuffle, columns are kept intact, but their position in the matrix is randomly shuffled. This preserves individual distributions over past actions, but randomizes their correspondence with columns labels and scrambles the rows (similarly, the Row shuffle kept rows intact but scrambled their position in the", + "bbox": [ + 496, + 704, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "10697", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/c45bd569b9b313588e68e6460d4222ca67db4505d7eaec9501af81378f32dcf4.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
ModelPresentMAE on Past ↓MAE on Future ↓
Verb ↑Noun ↑Action ↑
Baseline64.847.436.8--
Full shuffle64.147.236.34.1174.012
Columns/rows shuffle64.747.636.73.2543.101
Co-occurrence65.349.037.91.2111.115
Only past vector65.749.338.20.901-
Only future vector65.549.838.3-0.898
ETM (Ours)67.951.240.20.8820.859
", + "bbox": [ + 209, + 88, + 763, + 265 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/23771f059f503111d2ca6899c2d64bb96f23c28a43d9bca32db1d2025e98c139.jpg", + "table_caption": [ + "Table 2. Action recognition results on various baseline models. We train the models on the EPIC-KITCHENS-100 dataset [14] with the MoViNet A0 [24]. For Present prediction, we show the verb and noun classification results and retrieval scores of (verb, noun). For Past and Future prediction, we show the mean absolute error (MAE) on the past and the future predicted vectors." + ], + "table_footnote": [], + "table_body": "
DatasetModelPresent
VerbNounAction
EK100 [14]Baseline64.847.436.8
ETM(Ours)67.951.240.2
EGO4DBaseline32.323.521.1
LTA [16]ETM(Ours)32.924.222.0
EGTEABaseline81.271.760.4
Gaze+ [28]ETM(Ours)83.472.962.5
", + "bbox": [ + 89, + 339, + 459, + 497 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "matrix). Finally, we also construct a matrix which does not consider the order of the events, instead using a symmetric matrix of co-occurrence for training the action recognition model. In Table 2, we see that our proposed ETM surpasses all other baselines. We show additional results in the supplementary material.", + "bbox": [ + 75, + 593, + 468, + 685 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Next, we further conduct ablation studies to probe whether it is necessary to use both the past and future information from the ETM to see these performance benefits. We train two additional models including only either the past or the future module. Interestingly, as shown in Table 2, training models with either side of the temporal information still improves performance to some extent, but we observe that training with the full matrix gives us the biggest improvement. These experiments serve as quantitative justifications for our framework's design choices.", + "bbox": [ + 75, + 686, + 468, + 838 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "To show that the proposed ETM approach is effective on multiple datasets, we also show in Table 3 the performance gains on two other egocentric activity datasets that contain untrimmed videos of actions annotated with verbs", + "bbox": [ + 75, + 839, + 470, + 900 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/297e7ddd0fe8bf08ce28628e561ce47ec2e477fccf3ed23989c1350aed121f23.jpg", + "table_caption": [ + "Table 3. Action recognition results on various datasets using MoViNet A0 [24] with and without ETM supervision. As can be seen, augmenting the model with our framework improves performance on all datasets." + ], + "table_footnote": [], + "table_body": "
Modelw/o ETMw/ ETM
MoviNet A0 [24]36.840.2
MoviNet A2 [24]41.243.4
X3D-XS [11]35.538.1
X3D-S [11]40.542.2
ConvNeXt-S 224 [31]20.132.4
LambdaResNet-50 [4]26.627.1
EfficientNet-B0 [57]25.326.3
EfficientNet-B4 [11]29.229.4
AVT-b [14]30.430.7
", + "bbox": [ + 532, + 339, + 859, + 534 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 4. Additional action recognition results on EPIC-KITCHENS-100 for models trained with and without ETMs. We show top-1 action classification results for several different architectures.", + "bbox": [ + 498, + 542, + 893, + 599 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "and nouns. Specifically, we train models on the EGO4D dataset [16] using the long term action anticipation annotations, as well as the Extended GTEA Gaze+ (EGTEA Gaze+) dataset [28].", + "bbox": [ + 498, + 657, + 890, + 719 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The ETM is model-agnostic and can be incorporated into any model architecture. We illustrate this by showing how this technique performs on a breadth of models: In Table 4, we show action recognition results for multiple model architectures with and without ETM training. Our framework benefits low-complexity models the most. Adding ETM supervision to an advanced encoder like AVT-b [14] produces slight improvements, but small models like EfficientNet-B0 achieve noticeable gains. We show qualitative performance results with correct and incorrect classifications in the supplementary material.", + "bbox": [ + 496, + 734, + 893, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "10698", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/cc5fdb18fa6cd21a8ed0d6d229a4bc05a856cbed07805e080351698cbec08df0.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
DatasetFrozen Encoder?BaselineETM (Ours)
Verb ↑Noun ↑Action ↑Verb ↑Noun ↑Action ↑
EK100✓19.920.47.221.520.58.1
20.821.38.022.422.79.1
EGO4D LTA✓17.116.610.318.117.811.4
18.217.511.119.919.112.9
EGTEA Gaze+✓42.137.628.943.438.931.3
43.538.530.346.540.734.1
", + "bbox": [ + 117, + 88, + 854, + 250 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/d5ccbb72b817c5e1e2d13c343802d6327b687db42d227442eb411ddebbc144b3.jpg", + "table_caption": [ + "Table 5. Action anticipation results using MoViNet A0 [24] as an encoder, trained with and without our ETM protocol. We show results on multiple datasets. Performance is measured with class-mean recall@5 at 1s for EK100 and EGO4D, and top-1 accuracy at 0.5s for EGTEA Gaze+, following previous work [14]" + ], + "table_footnote": [], + "table_body": "
Encoderw/o ETMwith ETM
MoViNet A0 [24]8.09.1
MoViNet A2 [24]10.210.8
X3D-XS [11]6.37.4
X3D-S [11]9.49.9
ConvNeXt-S 224 [31]4.15.0
EfficientNet B0 [57]7.28.0
EfficientNet B4 [57]9.410.1
AVT-b [14]13.413.5
", + "bbox": [ + 106, + 325, + 444, + 501 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 6. Additional action anticipation results on EPIC-KITCHCHENS-100 for encoders trained with and without ETMs. We show class-mean recall@5 results without and with ETM for a variety of encoders. All models are first pretrained on the action recognition task, with and without the ETM protocol.", + "bbox": [ + 75, + 511, + 470, + 583 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.4. Action Anticipation Experiments", + "text_level": 1, + "bbox": [ + 76, + 597, + 369, + 614 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We also demonstrate how an encoder pre-trained on our ETM can improve performance in downstream tasks, in this case action anticipation. We follow the anticipation model architecture and the evaluation protocol from [14] and report the accuracies in verb, noun, and action prediction. However, our key difference is that the video encoder was pre-trained on an action recognition task, supplemented with ETM at training time.", + "bbox": [ + 75, + 621, + 468, + 742 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 5 showcases performance differences between encoders with and without ETM pretraining. Low complexity models, like MoviNet A0 (showcased in Table 5) exhibit improved performance in all three datasets. More comparisons on different model architectures are shown in Table 6 and Figure 4.", + "bbox": [ + 75, + 743, + 468, + 834 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.5. ETM and Model Complexity", + "text_level": 1, + "bbox": [ + 76, + 845, + 334, + 863 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "To further investigate how model complexity interacts with the proposed ETM training protocol, we train pro", + "bbox": [ + 76, + 869, + 468, + 902 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "gressively more complex models in the same architecture families and analyze how task performance varies with model complexity. We perform this analysis with the MoViNet [24] and X3D [11] architecture families: MoViNets scale from A0 to A4 with increasing parameter count and GFLOPs (Billion of Floating Point Operations), while X3Ds scale from XS to XXL. We train MoViNets A0 to A4 and X3Ds XS to M for our experiments. We use the same training setup as Section 3.2, where one instance of each model is trained without the ETM and another with it.", + "bbox": [ + 496, + 329, + 893, + 479 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "For action recognition, we observe an increase in performance for low complexity models, i.e. A0 to A4, as shown in Figure 4 (a). The gain is larger for lower complexity models. We observe a similar effect for X3D models: the simpler the model is, the larger the improvement appears to be. This effect is visible on both EK100 and EGO4D.", + "bbox": [ + 496, + 482, + 893, + 571 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Figure 4 (b) shows that a similar effect occurs with action anticipation: using backbone models trained with our ETM protocol improves performance, especially if the backbone models are small. Once again, we use these models as encoders in the AVT [14] framework, replacing AVT-b (AVT's transformer-based encoder) with these variants and using AVT-h (AVT's transformer-based head) as a head. We only show GFLOPs of the encoders. We observe that on both datasets, encoders with lower representational power benefit more from the information about past and future regularities brought by the ETM.", + "bbox": [ + 496, + 574, + 893, + 739 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "4.6.ETM Alternatives", + "text_level": 1, + "bbox": [ + 500, + 753, + 676, + 768 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Our proposed ETM can take many forms. We analyzed the impact of three of the main properties of the matrix on action recognition performance: its size, the decay function used to build it, and the metric for temporal distance between events.", + "bbox": [ + 496, + 777, + 893, + 853 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Matrix Size. Computing the matrix with every unique event (verb-noun combination) appearing in the training set of EK100 yields a $13k \\times 13k$ matrix. Since single-instance", + "bbox": [ + 496, + 854, + 893, + 902 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "10699", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/fa1455f63c3f5ef2f30dd1453a174425855cda7a2a562e449dbb61a065042301.jpg", + "image_caption": [ + "(a) Action Recognition" + ], + "image_footnote": [], + "bbox": [ + 86, + 89, + 281, + 244 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/1de0d35c4c0ef810c01825011588c6c28c86be6503b15c91893777f5afd26726.jpg", + "image_caption": [ + "Figure 4. Performance for models on EK100 [14] and EGTEA Gaze+ [28] in the same architecture family. We see that for both MoViNets [24] and X3Ds [11], leveraging the ETM during training improves performance, and the effect is stronger with lower complexity models. MoViNet+ETM $(\\text{一} - \\triangle)$ ; MoViNet $(\\text{一} \\triangle)$ ; X3D+ETM $(\\text{一} - \\bullet)$ ; X3D $(\\text{一} \\bullet)$ ." + ], + "image_footnote": [], + "bbox": [ + 282, + 90, + 478, + 246 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/7594e132d97e1f5505f93385ad3bc7660325f45a0ca227444ab2e49b42310f8e.jpg", + "image_caption": [ + "(b) Action Anticipation" + ], + "image_footnote": [], + "bbox": [ + 486, + 90, + 681, + 244 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/525bda6059814220327b0fc0d13586f9611c20eb8802670cd581c7e030d1bab5.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 684, + 90, + 879, + 244 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "actions may be noisier, we reduced the dimensionality of the matrix by removing events with less than 4 occurrences in the training set, yielding a subset of 2562 actions and a matrix of size $2562 \\times 2562$ . We compare both alternatives in Table 7.", + "bbox": [ + 75, + 344, + 468, + 417 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Decay function alternatives. We compared linear decay, exponential decay and no decay. We show comparison results in Table 7.", + "bbox": [ + 75, + 419, + 468, + 463 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Measuring temporal distance. We considered the natural alternative of measuring temporal distance between events A and B in seconds, by looking at the time between the last frame of A and the first frame of B. We observed, however, that the durations of individual actions between A and B could vastly modulate the decay factor applied to $M(y_{A},y_{B})$ of our matrix, which might be counterproductive if A and B are causally related but simply separated by one long intermediate action. We therefore considered measuring distance through index difference in the sequence of actions for that video, which allows us to abstract away the durations of individual actions.", + "bbox": [ + 75, + 465, + 468, + 645 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We compared these alternatives based on their ability to boost performance on action recognition. We trained a ConvNeXt-S (224) [31] encoder with supervision from different ETM alternatives and evaluated action recognition performance (Table 7). Our results show that the choice of decay is generally not impactful unless no decay is used. We find our best performance on a matrix of reduced size, with index as a temporal distance metric, and with exponential decay.", + "bbox": [ + 75, + 647, + 468, + 782 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5. Conclusions", + "text_level": 1, + "bbox": [ + 76, + 799, + 204, + 814 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We test a novel training regime for video understanding, in which model performance can be increased by supervising models with external representations of temporal regularities. We show that using the ETM as a training target allows arbitrary models to learn about the bi-directional tem", + "bbox": [ + 75, + 824, + 468, + 898 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/d22039e3428630bdca568fe65e86ba5b564d383b0baa48dc1b4f5405c9c4795c.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
SizeDecayTemp. MetricPresent (top-1 accuracy)
VerbNounAction
13kLinearTime55.146.228.8
13kExponentialTime55.647.729.1
2.5kExponentialTime58.648.831.3
2.5kNo decay-58.148.030.5
2.5kLinearIndex60.149.331.9
2.5kExponentialIndex60.350.332.4
", + "bbox": [ + 501, + 340, + 893, + 503 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 7. Difference in action recognition performance between ETM alternatives. We train ConvNeXts on EPIC-KITCHENS-100 with different verions of our ETM to evaluate the ability of ETM to improve action recognition performance. We evaluate top-1 accuracy on action recognition over the validation set of EK100.", + "bbox": [ + 498, + 512, + 892, + 583 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "poral context of the action (i.e. the past and future), which improves action recognition and action anticipation performance. Finally, we tested the circumstances under which the ETM provides the largest benefit, and find significant performance boosts for low-complexity models. Amid ongoing efforts to increase the efficiency of computer vision models, we suggest that research which explores efficient architectures [11, 24] could be complemented by research that explores ways to leverage pre-learned representations of environmental regularities.", + "bbox": [ + 496, + 623, + 890, + 775 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "The biggest benefit of this data structure is in its flexibility and simplicity: it is model-agnostic, computationally inexpensive, and easy to incorporate into the learning regime of any action recognition or anticipation architecture.", + "bbox": [ + 498, + 784, + 890, + 845 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements. This work was partially funded by a grant from Meta. We thank Karl Ridgeway and the team at Meta Reality Labs Research for their helpful comments.", + "bbox": [ + 498, + 854, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "10700", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Yazan Abu Farha, Qiuhong Ke, Bernt Schiele, and Juergen Gall. Long-term anticipation of activities with cycle consistency. In DAGM German Conference on Pattern Recognition, pages 159-173. Springer, 2020. 3", + "[2] Yazan Abu Farha, Alexander Richard, and Juergen Gall. When will you do what?-anticipating temporal occurrences of activities. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5343-5352, 2018. 3", + "[3] Hande Alemdar, Vincent Leroy, Adrien Prost-Boucle, and Frédéric Pétrot. Ternary neural networks for resource-efficient ai applications. In 2017 international joint conference on neural networks (IJCNN), pages 2547-2554. IEEE, 2017. 2", + "[4] Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, and Quoc V Le. Attention augmented convolutional networks. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3286-3295, 2019. 1, 6", + "[5] Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, and Doina Precup. Conditional computation in neural networks for faster models. arXiv preprint arXiv:1511.06297, 2015. 2", + "[6] Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013. 2", + "[7] Michael R Brent. Speech segmentation and word discovery: A computational perspective. Trends in Cognitive Sciences, 3(8):294-301, 1999. 2", + "[8] João Carreira and Andrew Zisserman. Quo vadis, action recognition? A new model and the kinetics dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 4724-4733, 2017. 3", + "[9] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Scaling egocentric vision: The epic-kitchens dataset. In European Conference on Computer Vision (ECCV), 2018. 3", + "[10] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Jian Ma, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100. International Journal of Computer Vision (IJCV), 2021. 2, 3, 4, 5", + "[11] Christoph Feichtenhofer. X3d: Expanding architectures for efficient video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 203-213, 2020. 1, 6, 7, 8", + "[12] Vanessa E Ghosh and Asaf Gilboa. What is a memory schema? a historical perspective on current neuroscience literature. Neuropsychologia, 53:104-114, 2014. 2", + "[13] Rohit Girdhar, Joao Carreira, Carl Doersch, and Andrew Zisserman. Video action transformer network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 244-253, 2019. 3" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[14] Rohit Girdhar and Kristen Grauman. Anticipative video transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13505-13515, 2021. 1, 3, 4, 5, 6, 7, 8", + "[15] Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. Knowledge distillation: A survey. International Journal of Computer Vision, 129(6):1789-1819, 2021. 2", + "[16] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonio Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Iija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Christian Fuegen, Abraham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4d: Around the World in 3,000 Hours of Egocentric Video. CoRR, abs/2110.07058, 2021. 3, 4, 5, 6", + "[17] Michelle R Greene and Aude Oliva. Recognition of natural scenes from global properties: Seeing the forest without representing the trees. Cognitive psychology, 58(2):137-176, 2009. 2", + "[18] Song Han, Jeff Pool, John Tran, and William J. Dally. Learning both weights and connections for efficient neural networks. CoRR, abs/1506.02626, 2015. 2", + "[19] Babak Hassibi and David Stork. Second order derivatives for network pruning: Optimal brain surgeon. Advances in neural information processing systems, 5, 1992. 2", + "[20] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network (2015). arXiv preprint arXiv:1503.02531, 2, 2015. 2", + "[21] Qiuhong Ke, Mario Fritz, and Bernt Schiele. Time-conditioned action anticipation in one shot. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9925-9934, 2019. 3", + "[22] Dong-Jin Kim, Xiao Sun, Jinsoo Choi, Stephen Lin, and In So Kweon. Detecting human-object interactions with action co-occurrence priors. In European Conference on Computer Vision, pages 718–736. Springer, 2020. 3", + "[23] Yoon Kim and Alexander M Rush. Sequence-level knowledge distillation. arXiv preprint arXiv:1606.07947, 2016. 2" + ], + "bbox": [ + 501, + 92, + 890, + 902 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "10701", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[24] Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, and Boqing Gong. Movinets: Mobile video networks for efficient video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16020-16030, 2021. 1, 6, 7, 8", + "[25] Christopher A Kurby and Jeffrey M Zacks. Segmentation in the perception and memory of events. Trends in cognitive sciences, 12(2):72-79, 2008. 2", + "[26] Yann LeCun, John Denker, and Sara Solla. Optimal brain damage. Advances in neural information processing systems, 2, 1989. 2", + "[27] Jinyu Li, Rui Zhao, Jui-Ting Huang, and Yifan Gong. Learning small-size dnn with output-distribution-based criteria. In Fifteenth annual conference of the international speech communication association, 2014. 2", + "[28] Yin Li, Miao Liu, and James M. Rehg. In the eye of beholder: Joint learning of gaze and actions in first person video. In ECCV, 2018. 4, 5, 6, 8", + "[29] Tailin Liang, John Glossner, Lei Wang, Shaobo Shi, and Xiaotong Zhang. Pruning and quantization for deep neural network acceleration: A survey. Neurocomputing, 461:370-403, 2021. 2", + "[30] Alexander H Liu, SouYoung Jin, Cheng-I Jeff Lai, Andrew Rouditchenko, Aude Oliva, and James Glass. Cross-modal discrete representation learning. In Annual Meeting of the Association for Computational Linguistics (ACL), May 2022. 3", + "[31] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. arXiv preprint arXiv:2201.03545, 2022. 6, 7, 8", + "[32] Tambet Matiisen, Avital Oliver, Taco Cohen, and John Schulman. Teacher-student curriculum learning. IEEE transactions on neural networks and learning systems, 31(9):3732-3740, 2019. 2", + "[33] Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, and Rogerio Feris. Ar-net: Adaptive frame resolution for efficient action recognition. In European Conference on Computer Vision, pages 86-104. Springer, 2020. 2", + "[34] Carolyn B Mervis and Eleanor Rosch. Categorization of natural objects. Annual review of psychology, 32(1):89-115, 1981. 2", + "[35] Antoine Miech, Ivan Laptev, Josef Sivic, Heng Wang, Lorenzo Torresani, and Du Tran. Leveraging the present to anticipate the future in videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0-0, 2019. 3", + "[36] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic. Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In ICCV, pages 2630-2640, 2019. 3", + "[37] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013. 2", + "[38] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and" + ], + "bbox": [ + 78, + 90, + 468, + 900 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "phrases and their compositionality. Advances in neural information processing systems, 26, 2013. 2", + "[39] Kyle Min and Jason J Corso. Integrating human gaze into attention for egocentric activity recognition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1069-1078, 2021. 5", + "[40] Shayan Modiri Assari, Amir Roshan Zamir, and Mubarak Shah. Video classification using semantic concept co-occurrences. In CVPR, pages 2529–2536, 2014. 3", + "[41] Mathew Monfort, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa Brown, Quanfu Fan, Dan Gutfreund, Carl Vondrick, and Aude Oliva. Moments in time dataset: one million videos for event understanding. IEEE TPAMI, 42(2):502-508, 2019. 3", + "[42] Mathew Monfort, SouYoung Jin, Alexander Liu, David Harwath, Rogerio Feris, James Glass, and Aude Oliva. Spoken moments: Learning joint audio-visual representations from video descriptions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14871–14881, June 2021. 3", + "[43] Mathew Monfort, Bowen Pan, Kandan Ramakrishnan, Alex Andonian, Barry A Mcnamara, Alex Lascelles, Quanfu Fan, Dan Gutfreund, Rogerio Feris, and Aude Oliva. Multimoments in time: Learning and interpreting models for multi-action video understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-1, 2021. 3", + "[44] Tushar Nagarajan, Yanghao Li, Christoph Feichtenhofer, and Kristen Grauman. Ego-topo: Environment affordances from egocentric video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 163-172, 2020. 3", + "[45] Bowen Pan, Rameswar Panda, Camilo Fosco, Chung-Ching Lin, Alex Andonian, Yue Meng, Kate Saenko, Aude Oliva, and Rogerio Feris. Va-red2: Video adaptive redundancy reduction. arXiv preprint arXiv:2102.07887, 2021. 2", + "[46] Bowen Pan, Rameswar Panda, Yifan Jiang, Zhangyang Wang, Rogerio Feris, and Aude Oliva. Ia-red2: Interpretability-aware redundancy reduction for vision transformers. Advances in Neural Information Processing Systems, 34:24898-24911, 2021. 2", + "[47] Jeffrey Pennington, Richard Socher, and Christopher D Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532-1543, 2014. 2", + "[48] Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word representations, 2018. 2", + "[49] Ivan Rodin, Antonino Furnari, Dimitrios Mavroeidis, and Giovanni Maria Farinella. Untrimmed action anticipation. arXiv preprint arXiv:2202.04132, 2022. 3", + "[50] E Rosch, C B Mervis, W. D. Gray, D. M. Johnson, and P Boyes-Braem. Basic objects in natural categories. Cognitive psychology, 8(3):382-439, 1976. 2", + "[51] Zahra Sadeghi, James L McClelland, and Paul Hoffman. You shall know an object by the company it keeps: An investi" + ], + "bbox": [ + 501, + 92, + 893, + 900 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "10702", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "gation of semantic representations derived from object cooccurrence in visual scenes. Neuropsychologia, 76:52-61, 2015. 2", + "[52] Fadime Sener, Dipika Singhania, and Angela Yao. Temporal aggregate representations for long-range video understanding. In European Conference on Computer Vision, pages 154-171. Springer, 2020. 3", + "[53] Yang Shen, Bingbing Ni, Zefan Li, and Ning Zhuang. Ego-centric activity prediction via event modulated attention. In Proceedings of the European conference on computer vision (ECCV), pages 197-212, 2018. 3", + "[54] Brynn E Sherman, Kathryn N Graves, and Nicholas B Turk-Browne. The prevalence and importance of statistical learning in human cognition and behavior. Current opinion in behavioral sciences, 32:15-20, 2020. 2", + "[55] Bilge Soran, Ali Farhadi, and Linda Shapiro. Generating notifications for missing actions: Don't forget to turn the lights off! In Proceedings of the IEEE International Conference on Computer Vision, pages 4669-4677, 2015. 3", + "[56] Chen Sun, Abhinav Shrivastava, Carl Vondrick, Rahul Sukthankar, Kevin Murphy, and Cordelia Schmid. Relational action forecasting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 273-283, 2019. 3", + "[57] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019. 1, 6, 7", + "[58] Antonio Torralba and Aude Oliva. Statistics of natural image categories. Network: computation in neural systems, 14(3):391, 2003. 2", + "[59] Iulia Turc, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Well-read students learn better: On the importance of pre-training compact models. arXiv preprint arXiv:1908.08962, 2019. 2", + "[60] Melissa Le-Hoa Võ. The meaning and structure of scenes. Vision Research, 181:10-20, 2021. 2", + "[61] Daniel Weinland and Edmond Boyer. Action recognition using exemplar-based embedding. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pages 1-7. IEEE, 2008. 3", + "[62] Less Wright and Nestor Demeure. Ranger21: a synergistic deep learning optimizer. arXiv preprint arXiv:2106.13731, 2021.5", + "[63] Jeffrey M Zacks and Barbara Tversky. Event structure in perception and conception. Psychological bulletin, 127(1):3, 2001. 2", + "[64] Haotong Zhang, Fuhai Chen, and Angela Yao. Weakly-supervised dense action anticipation. arXiv preprint arXiv:2111.07593, 2021. 3", + "[65] Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba. Temporal relational reasoning in videos. European Conference on Computer Vision, 2018. 3" + ], + "bbox": [ + 78, + 90, + 467, + 842 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "10703", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/Leveraging Temporal Context in Low Representational Power Regimes/23cf52f5-5ab9-4d57-929a-b5407b0b21e2_model.json b/2023/Leveraging Temporal Context in Low Representational Power Regimes/23cf52f5-5ab9-4d57-929a-b5407b0b21e2_model.json new file mode 100644 index 0000000000000000000000000000000000000000..0c49b131e5472e6e41754a4278d8530682af59e2 --- /dev/null +++ b/2023/Leveraging Temporal Context in Low Representational Power Regimes/23cf52f5-5ab9-4d57-929a-b5407b0b21e2_model.json @@ -0,0 +1,2312 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.127, + 0.131, + 0.844, + 0.154 + ], + "angle": 0, + "content": "Leveraging Temporal Context in Low Representational Power Regimes" + }, + { + "type": "text", + "bbox": [ + 0.189, + 0.191, + 0.326, + 0.208 + ], + "angle": 0, + "content": "Camilo L. Fosco" + }, + { + "type": "text", + "bbox": [ + 0.362, + 0.192, + 0.475, + 0.209 + ], + "angle": 0, + "content": "SouYoung Jin" + }, + { + "type": "text", + "bbox": [ + 0.518, + 0.192, + 0.639, + 0.209 + ], + "angle": 0, + "content": "Emilie Josephs" + }, + { + "type": "text", + "bbox": [ + 0.679, + 0.192, + 0.773, + 0.208 + ], + "angle": 0, + "content": "Aude Oliva" + }, + { + "type": "text", + "bbox": [ + 0.186, + 0.216, + 0.33, + 0.229 + ], + "angle": 0, + "content": "camilolu@mit.edu" + }, + { + "type": "text", + "bbox": [ + 0.346, + 0.216, + 0.49, + 0.229 + ], + "angle": 0, + "content": "souyoung@mit.edu" + }, + { + "type": "text", + "bbox": [ + 0.506, + 0.216, + 0.651, + 0.229 + ], + "angle": 0, + "content": "eJosephs@mit.edu" + }, + { + "type": "text", + "bbox": [ + 0.667, + 0.216, + 0.785, + 0.229 + ], + "angle": 0, + "content": "oliva@mit.edu" + }, + { + "type": "text", + "bbox": [ + 0.432, + 0.234, + 0.534, + 0.25 + ], + "angle": 0, + "content": "MITCSAIL" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.286, + 0.314, + 0.303 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.318, + 0.474, + 0.695 + ], + "angle": 0, + "content": "Computer vision models are excellent at identifying and exploiting regularities in the world. However, it is computationally costly to learn these regularities from scratch. This presents a challenge for low-parameter models, like those running on edge devices (e.g. smartphones). Can the performance of models with low representational power be improved by supplementing training with additional information about these statistical regularities? We explore this in the domains of action recognition and action anticipation, leveraging the fact that actions are typically embedded in stereotypical sequences. We introduce the Event Transition Matrix (ETM), computed from action labels in an untrimmed video dataset, which captures the temporal context of a given action, operationalized as the likelihood that it was preceded or followed by each other action in the set. We show that including information from the ETM during training improves action recognition and anticipation performance on various egocentric video datasets. Through ablation and control studies, we show that the coherent sequence of information captured by our ETM is key to this effect, and we find that the benefit of this explicit representation of temporal context is most pronounced for smaller models. Code, matrices and models are available in our project page: https://camilofosco.com/etm網站" + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.724, + 0.21, + 0.74 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.75, + 0.47, + 0.902 + ], + "angle": 0, + "content": "A strength of computer vision models is their ability to identify and exploit statistical regularities in the world. Learning these regularities from scratch is computationally costly, which limits the accuracy of low-parameters models. It is critical to boost the performance of small models, since many devices lack the resources to host current state of the art models. One way to make the learning problem more tractable for small models may be to supplement them at training time with an explicit representation of the statistical regularities in the target domain. Here, we test whether" + }, + { + "type": "image", + "bbox": [ + 0.518, + 0.29, + 0.872, + 0.469 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.483, + 0.895, + 0.595 + ], + "angle": 0, + "content": "Figure 1. Action recognition performance difference between models trained with and without the proposed ETM approach. We train models with various model architectures, from small (left) to large (right): AVT-b [14], MoViNets [24] family, X3D [11] family, LambdaResNet-50 [4], and EfficientNets [57] family on EPIC-KITCHENS-100 [14]. We show that incorporating the ETM into training improves performance, and the impact is higher on smaller models." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.627, + 0.893, + 0.671 + ], + "angle": 0, + "content": "video understanding models can be improved by providing them with information about typical event sequences during training." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.675, + 0.895, + 0.902 + ], + "angle": 0, + "content": "We introduce the Event Transition Matrix (ETM), illustrated in Figure 2, which leverages the fact that events in real-world videos often occur in predictable sequences. Each row and column indexes an event. In the rows, the ETM captures the likelihood that the event was followed by each of the other events in the set. In the columns, it captures the likelihood that the event was preceded by each other event. To compute a cell's value, we combine information from all previous and subsequent events, weighted by their temporal distance from the queried action. Crucially, this breaks markovian assumptions and allows the matrix to capture long-range relationships. The ETM has two important properties. First, it acts as an explicit representation of the likelihood of event transitions, which provides additional pertinent information that a model can" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10693" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.082, + 0.089, + 0.57, + 0.285 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.235, + 0.291, + 0.412, + 0.304 + ], + "angle": 0, + "content": "(a) Low-level descriptions in [10]" + }, + { + "type": "image", + "bbox": [ + 0.607, + 0.098, + 0.882, + 0.287 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.675, + 0.291, + 0.822, + 0.303 + ], + "angle": 0, + "content": "(b) Event Transition Matrix" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.316, + 0.894, + 0.371 + ], + "angle": 0, + "content": "Figure 2. We construct a given Event Transition Matrix (ETM) from action labels drawn from a given dataset of untrimmed videos. (a) Each video depicts a continuous activity, and is paired with human annotations indicating the individual, low-level actions that compose the activity. (b) Using these action labels, we created the ETM by recording the frequency with which a given action label was preceded or followed by each other action label, accumulated across the videos in our set." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.398, + 0.471, + 0.458 + ], + "angle": 0, + "content": "leverage without the cost of learning it for itself. Second, it augments representations of a given event with information about what came before and after it, providing additional target for the model to train on." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.459, + 0.47, + 0.732 + ], + "angle": 0, + "content": "In the present paper, across multiple datasets and model architectures, we test the effectiveness of incorporating the ETM into training for low-parameter models. We test our approach on action recognition and action anticipation in egocentric video datasets, where actions occur in stereotypical sequences. However, this approach can apply to any kind of sequence in videos. We show that leveraging the ETM improves action recognition relative to baselines, and that this improvement relies on the coherence of the action sequence. We also show that action anticipation is improved with our ETM approach. In both cases, we show that the ETM approach can be incorporated into multiple different model architectures, and that the addition of the ETM has the highest impact on smaller models, as shown in Figure 1. Overall, this work demonstrates a potential path to more efficient models, based on supplementing small models at training time with explicit representations of regularities expected in the data." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.746, + 0.219, + 0.761 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.771, + 0.205, + 0.787 + ], + "angle": 0, + "content": "2.1. Efficient AI" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.795, + 0.469, + 0.901 + ], + "angle": 0, + "content": "State of the art neural networks for video understanding can achieve impressive classification results, but these models often have heavy computational requirements. Producing smaller, more efficient models that can perform comparable to larger ones is an active area of research, for deployment onto edge devices such as phones, wearables, drones or autonomous vehicles. Some approaches achieve effi" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.398, + 0.892, + 0.581 + ], + "angle": 0, + "content": "ciency by first training a large network, then using a smaller network to learn the mapping from input to output vectors with little loss on performance (i.e. knowledge distillation [3,15,20,23,27,32]), or by identifying and removing non-essential parameters (i.e. parameter pruning [18,19,26,29]). Others rely on adaptive policies to shorten or simplify the inference process, reducing the effective size of the architecture [5,6,33,45] or input [45,46]. Here, we propose an alternative approach, which involves intervening at training time to supplement low-parameter architectures with external, explicit representations of statistical regularities in the target domain." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.59, + 0.891, + 0.607 + ], + "angle": 0, + "content": "2.2. Statistical Regularities and Bayesian Learning." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.613, + 0.892, + 0.78 + ], + "angle": 0, + "content": "The environment is full of regularities. Objects classes have co-occurring attributes [34, 50], environments classes have distinctive combinations of objects, surfaces and textures [17, 51, 58, 60], and episodes are composed of predictable sequences of events [25, 63]. Having the ability to detect and leverage these regularities is a key feature of intelligence. Cognitive research in humans has established that representations of contextual regularities underlie most aspects of intelligence, spanning learning, generalization, prediction, compression, memory, language and more [7, 12, 54]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.78, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Machine learning relies on identifying and making use of regularities. Typically, this happens in an emergent manner from the training regime. However, some approaches also rely on explicit representations of such regularities. For example, in natural language processing, word embeddings [37, 38, 47, 48, 59] acting as explicit representations of word transition probabilities have provided the basis for many downstream tasks." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10694" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.077, + 0.091, + 0.396, + 0.108 + ], + "angle": 0, + "content": "2.3. Action Recognition and Anticipation" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.115, + 0.473, + 0.342 + ], + "angle": 0, + "content": "Action understanding is an enduring problem in video processing [8, 41, 43, 49, 64]. This can involve action recognition, which requires retrieving a label for a depicted action, and action anticipation, which requires retrieving a label for a future action based on a depicted action. Videos contain complex semantic relationships between objects, people and places, which evolve dynamically over time. Thus, action understanding models often incorporate elements of relational modeling, for example modeling the spatial relations among people or between people and objects [13, 56], between people and environment zones [44], or between the body parts of the person performing the actions [61]. More recently, multi-modal representation learning approaches have been introduced to learn the broader sensory context of actions [30, 36, 42]." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.352, + 0.47, + 0.385 + ], + "angle": 0, + "content": "2.4. Action Recognition and Anticipation with Temporal Context" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.391, + 0.473, + 0.619 + ], + "angle": 0, + "content": "Another kind of context that can be modeled in action understanding is the temporal relationships between the events that make up ongoing actions. Actions take place in sequences: you must first boil water and grind coffee beans, then pour water over the beans in order to successfully make coffee. Some models capitalize on such temporal regularities by modeling the dependence among individual video frames, either with RNNs [2, 56], relational reasoning networks [65] or more recently with attention-based methods and transformers [14, 52]. Other approaches model these sequences at a higher level, by learning the dependence among events (rather than individual frames), by using action co-occurrence matrices [22, 40], or by estimating the transitional probability between adjacent [35, 55] or more distant actions [21, 53]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.619, + 0.473, + 0.803 + ], + "angle": 0, + "content": "Building on this work, we show how leveraging explicit representations of typical action sequences in low-parameter models can improve their performance. Our motivation is that there is regularity in the temporal context of an action in both the past and the future, and leveraging this bi-directional regularity could bootstrap the model's learning. While some previous work [1] included a representation of the past in an action anticipation framework, our approach is distinct from it in two ways: (1) our representation of temporal context includes past, present and future, and extends over a larger time period, and (2) we pre-compute this representation, then use it to supplement learning." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.815, + 0.268, + 0.833 + ], + "angle": 0, + "content": "3. Proposed Approach" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.472, + 0.903 + ], + "angle": 0, + "content": "In this section, we introduce the Event Transition Matrix (ETM), which is an efficient approach to constructing a knowledge base about the temporal relation between events in video scenes (Section 3.1). We then propose a novel ap" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.893, + 0.138 + ], + "angle": 0, + "content": "proach to pre-training with this matrix, leveraging these relations to help recognize current events or predict past or future events in the input video snippet (Section 3.2)." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.153, + 0.783, + 0.169 + ], + "angle": 0, + "content": "3.1. Event Transition Matrix (ETM)" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.178, + 0.895, + 0.541 + ], + "angle": 0, + "content": "Egocentric video datasets, such as EPIC-KITCHENS [9, 10] or EGO4D [16], contain long form videos that exhibit rich sequences of small actions or events. These sequences are densely annotated with labels for each event and timestamps indicating start and end points of each action. Suppose there are N event categories, \\( S = \\{s_1, s_2, \\dots, s_N\\} \\). Each video is annotated with a sequence of event instances, which can be denoted as \\( \\{y_1, y_2, \\dots, y_{t-1}, y_t, \\dots\\} \\). Using the timestamps, we can define a video snippet \\( x_k \\) that contains the specific event, which is given the instance label \\( y_k \\), based on its category. Event descriptions, i.e. \\( x_k \\) and \\( y_k \\), refer to the current ongoing action event, but do not convey the broader temporal context of that action, including what came before and after it. As in the example in Figure 2, when making coffee, \"add coffee beans\" happens earlier than \"brew coffee\", and these two events are temporally dependent on each other. To leverage the temporal context surrounding events, we compute the frequencies with which each event comes before/after each other event. More formally, we define a square matrix, \\( \\mathbf{M}_{N,N} \\), where each row and column corresponds to an event in \\( S \\) and \\( \\mathbf{M}(i,j) \\) corresponds to an estimated probability that an event \\( s_i \\) happens before \\( s_j \\), and vice versa. This matrix is built with training set observations." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.543, + 0.895, + 0.695 + ], + "angle": 0, + "content": "A naive approach for building the matrix is to update \\(\\mathbf{M}(y_l, y_m)\\) for all possible pairs of events where event \\(l\\) happens earlier than event \\(m\\). However, this approach does not incorporate the temporal distance between two events, since \\((y_l, y_m)\\) and \\((y_l, y_n)\\) contribute equally to constructing the matrix even when \\(l\\) is much farther away from \\(m\\). In reality, actions separated by more time are less likely to be part of the same overall activity, and are therefore less likely to consistently co-vary. Therefore, we update the matrix with a decay function \\(\\delta(\\cdot)\\), as" + }, + { + "type": "equation", + "bbox": [ + 0.608, + 0.71, + 0.893, + 0.728 + ], + "angle": 0, + "content": "\\[\n\\mathbf {M} \\left(y _ {l}, y _ {m}\\right) + = \\delta (m - l). \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.745, + 0.893, + 0.775 + ], + "angle": 0, + "content": "We normalize the matrix \\(\\mathbf{M}\\) by the sum of each row and define a new matrix \\(\\mathbf{M}^R\\) as" + }, + { + "type": "equation", + "bbox": [ + 0.598, + 0.788, + 0.892, + 0.826 + ], + "angle": 0, + "content": "\\[\n\\mathbf {M} ^ {R} (i, j) = \\frac {\\mathbf {M} (i , j)}{\\sum_ {k = 0} ^ {N} \\mathbf {M} (i , k)}. \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.84, + 0.895, + 0.902 + ], + "angle": 0, + "content": "Each row in this matrix, \\(\\mathbf{M}^R (i,:)\\), can be seen as an empirical approximation of the distribution of possible events that can be transitioned to following event \\(s_i\\). Similarly, we can also define a matrix \\(\\mathbf{M}^C\\) by column-wise normalization." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10695" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.201, + 0.095, + 0.773, + 0.304 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.317, + 0.895, + 0.361 + ], + "angle": 0, + "content": "Figure 3. Overview of the proposed approach for pre-training with ETM. The model takes a video snippet and encodes the input to predict the present, past, and future events. In particular, we use the column and the row of the ETM which correspond to the given event. These represent the distributions over past actions, and future actions respectively. More details are written in Section 3.2." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.384, + 0.471, + 0.414 + ], + "angle": 0, + "content": "3.2. Encoder Training with the Event Transition Matrix" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.423, + 0.47, + 0.573 + ], + "angle": 0, + "content": "We propose a novel training protocol to enhance an arbitrary encoder by leveraging the ETM. A typical event recognition model is trained to take a video snippet, \\( x_{t} \\), transform it into a feature vector with an encoder \\( f(\\cdot) \\), and recognize an event in the input video by predicting a label from that vector. More formally, the model is trained to increase \\( p(y_{t}|g_{q}(f(x_{t})) \\), where \\( g_{q} \\) represents a module that maps features into a probability distribution over labels. In our framework, we call \\( g_{q} \\) the present module and train it with a typical cross-entropy loss function, \\( \\mathcal{L}_{q} \\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.574, + 0.47, + 0.68 + ], + "angle": 0, + "content": "Our proposed method adds two additional modules to predict past and future event probabilities, as shown in Figure 3. A past module \\( g_{c}(\\cdot) \\) receives \\( f(x_{t}) \\) and predicts a distribution of events occurring before the current action (which corresponds to a column of the column-normalized ETM, \\( \\mathbf{M}^{C} \\)). This module attempts to minimize the following loss:" + }, + { + "type": "equation", + "bbox": [ + 0.161, + 0.687, + 0.47, + 0.73 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {c} = \\sum_ {k = 0} ^ {N} (C (k) - \\mathbf {M} ^ {C} (y _ {t}, k)) ^ {2}, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.737, + 0.47, + 0.8 + ], + "angle": 0, + "content": "where \\(C(k) = g_c(f(x_t))\\). Similarly, a future module \\(g_r(\\cdot)\\) predicts a distribution of future events, and tries to minimize the distance between the prediction and a row of the row-normalized ETM, \\(\\mathbf{M}^R(:, y_t)\\), through the loss:" + }, + { + "type": "equation", + "bbox": [ + 0.162, + 0.806, + 0.469, + 0.847 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {r} = \\sum_ {k = 0} ^ {N} (R (k) - \\mathbf {M} ^ {R} (k, y _ {t})) ^ {2}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.856, + 0.471, + 0.903 + ], + "angle": 0, + "content": "where \\( R(k) = g_r(f(x_t)) \\). Altogether, our framework augments an arbitrary encoder with these past and future modules and minimizes the following objective during training:" + }, + { + "type": "table", + "bbox": [ + 0.505, + 0.382, + 0.897, + 0.456 + ], + "angle": 0, + "content": "
DatasetSegm.Actionsτa(s)Metrics (AR, AA)
EK100 [10]90k38071.0top-1, rec@5
EGO4D [16]39.2k35421.0top-1, rec@5
EGTEA [28]10.3k1060.5top-1, top-1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.465, + 0.895, + 0.565 + ], + "angle": 0, + "content": "Table 1. Properties of the datasets used in our experiments. We showcase our three main datasets: EPIC-KITCHENS-100 (EK100), EGO4D LTA (EGO4D) and EGTEA Gaze+ (EGTEA). \\(\\tau_{a}\\) corresponds to the time between input clips and target segments in action anticipation, and follows prior work [14]. The Metrics column shows the main performance metrics used in our action recognition and action anticipation experiments, respectively." + }, + { + "type": "equation", + "bbox": [ + 0.601, + 0.592, + 0.892, + 0.61 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} = \\omega_ {q} \\mathcal {L} _ {q} + \\omega_ {c} \\mathcal {L} _ {c} + \\omega_ {r} \\mathcal {L} _ {r}, \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.616, + 0.894, + 0.709 + ], + "angle": 0, + "content": "This training setup attempts to push the encoder towards outputting richer event representations that capture the regularities of the event's typical temporal context. Although simplistic, the framework is effective: we observe that instantiating \\( g_{c} \\) and \\( g_{r} \\) with simple multi-layer perceptrons is enough to achieve strong results on low-complexity models." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.721, + 0.707, + 0.739 + ], + "angle": 0, + "content": "4. Experimental Results" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.747, + 0.894, + 0.823 + ], + "angle": 0, + "content": "In this section, we demonstrate the effectiveness of training with the Event Transition Matrix (ETM) on action recognition (Section 4.3) and action anticipation (Section 4.4). We describe the datasets used in Section 4.1 and give implementation details in Section 4.2." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.832, + 0.603, + 0.847 + ], + "angle": 0, + "content": "4.1. Datasets" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.855, + 0.894, + 0.903 + ], + "angle": 0, + "content": "EPIC-KITCHENS-100. [10] The EPIC-KITCHENS-100 dataset (EK100) contains 700 unscripted videos depicting cooking actions, totalling 100 hours. It presents verb" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "10696" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.468, + 0.182 + ], + "angle": 0, + "content": "and noun annotations over 90k segments of varying length. The dataset depicts 97 unique verbs, 300 unique nouns that combine to yield 3807 actions. We work with the provided training and validation sets, containing 67.2k and 9.7k segments respectively. Our ETM is constructed exclusively with training set data." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.183, + 0.468, + 0.394 + ], + "angle": 0, + "content": "EGO4D. [16] This newer egocentric dataset contains 3670 hours of video from 71 different participants. We use the long term anticipation annotations provided by the authors [16], which reduces the set to a training split containing 493 unique videos with 23610 segments, and a validation set with 380 videos cut into 15587 segments. These annotations present 115 unique verbs, 477 unique nouns and 3542 unique actions. All segments are 240-frame long. To homogenize our setup and avoid generating ETMs with a biased diagonal, we merge contiguous segments together if they depict the same action. This yields 18896 segments for training and 12676 segments for validation, and we use this training set for ETM construction. We refer to this set as EGO4D LTA." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.395, + 0.468, + 0.515 + ], + "angle": 0, + "content": "EGTEA Gaze+ [28] is another popular egocentric dataset with 10k segments annotated with 19 verbs, 51 nouns and 106 unique actions. We report performance for split 1 (provided by the authors), containing 8299 training segments and 2022 validation segments. Following previous work [14, 39], both action recognition and anticipation performance are measured with top-1 accuracy. The properties of each dataset are summarized in Table 1." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.528, + 0.295, + 0.543 + ], + "angle": 0, + "content": "4.2. Implementation Details" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.552, + 0.468, + 0.643 + ], + "angle": 0, + "content": "ETM construction. We construct our ETM offline by observing all videos of our training set and adding a contribution to \\( M(y_{l},y_{m}) \\) modulated by a decay function \\( \\delta (v) \\), where \\( v \\) is the temporal distance between the events, as in Equation 1. Importantly, to minimize sparsity, we only consider events that appear at least 4 times in the training set." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.644, + 0.468, + 0.853 + ], + "angle": 0, + "content": "Encoder training. With a given ETM, we train an encoder to generate embeddings that can be used to both recognize actions and predict ETM vectors from snippets of video. The encoder is tasked with producing an embedding that is fed to three modules: an action classification head (present module), a past vector regressor and a future vector regressor, as seen in Figure 3. We instantiate the past, present and future modules with simple fully-connected layers. We train with the loss in Eq. 5 and set \\(\\omega_{q} = \\omega_{c} = \\omega_{r} = \\frac{1}{3}\\). As the ETM is built with actions that appear at least four times in the training data, certain rare actions do not have target vectors. In those cases, the loss associated to regressing the past and future vectors is set to zero, and no gradients are back-propagated." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.468, + 0.901 + ], + "angle": 0, + "content": "We train our models with the Ranger21 optimizer [62] with a learning rate of 0.01 and cosine annealing with a 20 epochs half-cycle. We use a batch-size of 32, weight decay" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.856, + 0.107 + ], + "angle": 0, + "content": "of 0.0001 and dropout with \\( p = 0.5 \\) where applicable." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.108, + 0.892, + 0.258 + ], + "angle": 0, + "content": "Training setup for Action Anticipation. We follow the framework used in [14], where an encoder generates features from an input clip \\( c \\) before feeding them to a decoder attempting to predict the future of \\( c \\), which is the label \\( y_{s} \\) of an action segment \\( s \\) happening \\( \\tau_{a} \\) seconds after \\( c \\) ends. In other words, for each action segment starting at time \\( \\tau_{s} \\), the decoder attempts to predict its action label \\( y_{s} \\) using a clip \\( c \\) that ends \\( \\tau_{a} \\) seconds before \\( s \\). Crucially, we analyze how using an encoder trained with ETM supervision affects the performance of the system." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.26, + 0.892, + 0.38 + ], + "angle": 0, + "content": "We analyze two different setups: one were we freeze our pretrained encoder and only train the decoder, and another where we allow our encoder to be finetuned alongside the decoder. Following the AVT framework [14], we replace AVT-b (AVT's transformer-based encoder) with several variants of our own encoders. We keep AVT-h (AVT's transformer-based head) as a decoder and finetune it with our codes as input." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.381, + 0.892, + 0.471 + ], + "angle": 0, + "content": "We train until validation performance plateaus, and we operate with a batch size of 32, a learning rate of \\(10^{-4}\\) with cosine annealed decay, and the Ranger21 optimizer. At test time, we employ 3-crop testing following [14], where we compute three 224px spatial crops from 248px input frames, and average the predictions of each segment." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.483, + 0.787, + 0.5 + ], + "angle": 0, + "content": "4.3. Action Recognition Experiments" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.508, + 0.892, + 0.703 + ], + "angle": 0, + "content": "We first ask whether models trained with ETM supervision showed improved action recognition performance. To do that, we train a baseline MoViNet A0 on EK100. Following [10], our present head has a two-way output: one to predict verbs and one to predict nouns, with an average verb/noun loss. Accordingly, in Table 2, the Present tab shows the top-1 accuracies in verb and noun classification as well as Action accuracy, which corresponds to (verb, noun) pairs. We compare this to a MoViNet A0 trained on the same data but augmented with our ETM framework as described in Section 3. For this model, we also show the mean absolute error (MAE) on the past and the future predictions. Here, lower scores indicate better performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.705, + 0.892, + 0.901 + ], + "angle": 0, + "content": "To demonstrate that the performance gain is not merely due to the extra parameters gained from training with an external matrix, we compare against three more baselines, with versions of the matrix that disrupt the action sequence information to different degrees. In the Full shuffle baseline, cells of the matrix are randomly shuffled. This scrambles any sequence information in the matrix, while preserving the distribution of values across it. In the Columns shuffle, columns are kept intact, but their position in the matrix is randomly shuffled. This preserves individual distributions over past actions, but randomizes their correspondence with columns labels and scrambles the rows (similarly, the Row shuffle kept rows intact but scrambled their position in the" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10697" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.21, + 0.089, + 0.764, + 0.266 + ], + "angle": 0, + "content": "
ModelPresentMAE on Past ↓MAE on Future ↓
Verb ↑Noun ↑Action ↑
Baseline64.847.436.8--
Full shuffle64.147.236.34.1174.012
Columns/rows shuffle64.747.636.73.2543.101
Co-occurrence65.349.037.91.2111.115
Only past vector65.749.338.20.901-
Only future vector65.549.838.3-0.898
ETM (Ours)67.951.240.20.8820.859
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.275, + 0.895, + 0.318 + ], + "angle": 0, + "content": "Table 2. Action recognition results on various baseline models. We train the models on the EPIC-KITCHENS-100 dataset [14] with the MoViNet A0 [24]. For Present prediction, we show the verb and noun classification results and retrieval scores of (verb, noun). For Past and Future prediction, we show the mean absolute error (MAE) on the past and the future predicted vectors." + }, + { + "type": "table", + "bbox": [ + 0.09, + 0.34, + 0.46, + 0.498 + ], + "angle": 0, + "content": "
DatasetModelPresent
VerbNounAction
EK100 [14]Baseline64.847.436.8
ETM(Ours)67.951.240.2
EGO4DBaseline32.323.521.1
LTA [16]ETM(Ours)32.924.222.0
EGTEABaseline81.271.760.4
Gaze+ [28]ETM(Ours)83.472.962.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.507, + 0.472, + 0.565 + ], + "angle": 0, + "content": "Table 3. Action recognition results on various datasets using MoViNet A0 [24] with and without ETM supervision. As can be seen, augmenting the model with our framework improves performance on all datasets." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.594, + 0.47, + 0.686 + ], + "angle": 0, + "content": "matrix). Finally, we also construct a matrix which does not consider the order of the events, instead using a symmetric matrix of co-occurrence for training the action recognition model. In Table 2, we see that our proposed ETM surpasses all other baselines. We show additional results in the supplementary material." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.687, + 0.47, + 0.839 + ], + "angle": 0, + "content": "Next, we further conduct ablation studies to probe whether it is necessary to use both the past and future information from the ETM to see these performance benefits. We train two additional models including only either the past or the future module. Interestingly, as shown in Table 2, training models with either side of the temporal information still improves performance to some extent, but we observe that training with the full matrix gives us the biggest improvement. These experiments serve as quantitative justifications for our framework's design choices." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.901 + ], + "angle": 0, + "content": "To show that the proposed ETM approach is effective on multiple datasets, we also show in Table 3 the performance gains on two other egocentric activity datasets that contain untrimmed videos of actions annotated with verbs" + }, + { + "type": "table", + "bbox": [ + 0.534, + 0.34, + 0.861, + 0.535 + ], + "angle": 0, + "content": "
Modelw/o ETMw/ ETM
MoviNet A0 [24]36.840.2
MoviNet A2 [24]41.243.4
X3D-XS [11]35.538.1
X3D-S [11]40.542.2
ConvNeXt-S 224 [31]20.132.4
LambdaResNet-50 [4]26.627.1
EfficientNet-B0 [57]25.326.3
EfficientNet-B4 [11]29.229.4
AVT-b [14]30.430.7
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.544, + 0.895, + 0.6 + ], + "angle": 0, + "content": "Table 4. Additional action recognition results on EPIC-KITCHENS-100 for models trained with and without ETMs. We show top-1 action classification results for several different architectures." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.658, + 0.892, + 0.72 + ], + "angle": 0, + "content": "and nouns. Specifically, we train models on the EGO4D dataset [16] using the long term action anticipation annotations, as well as the Extended GTEA Gaze+ (EGTEA Gaze+) dataset [28]." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.735, + 0.895, + 0.903 + ], + "angle": 0, + "content": "The ETM is model-agnostic and can be incorporated into any model architecture. We illustrate this by showing how this technique performs on a breadth of models: In Table 4, we show action recognition results for multiple model architectures with and without ETM training. Our framework benefits low-complexity models the most. Adding ETM supervision to an advanced encoder like AVT-b [14] produces slight improvements, but small models like EfficientNet-B0 achieve noticeable gains. We show qualitative performance results with correct and incorrect classifications in the supplementary material." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "10698" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.119, + 0.089, + 0.856, + 0.251 + ], + "angle": 0, + "content": "
DatasetFrozen Encoder?BaselineETM (Ours)
Verb ↑Noun ↑Action ↑Verb ↑Noun ↑Action ↑
EK100✓19.920.47.221.520.58.1
20.821.38.022.422.79.1
EGO4D LTA✓17.116.610.318.117.811.4
18.217.511.119.919.112.9
EGTEA Gaze+✓42.137.628.943.438.931.3
43.538.530.346.540.734.1
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.261, + 0.895, + 0.306 + ], + "angle": 0, + "content": "Table 5. Action anticipation results using MoViNet A0 [24] as an encoder, trained with and without our ETM protocol. We show results on multiple datasets. Performance is measured with class-mean recall@5 at 1s for EK100 and EGO4D, and top-1 accuracy at 0.5s for EGTEA Gaze+, following previous work [14]" + }, + { + "type": "table", + "bbox": [ + 0.107, + 0.327, + 0.446, + 0.502 + ], + "angle": 0, + "content": "
Encoderw/o ETMwith ETM
MoViNet A0 [24]8.09.1
MoViNet A2 [24]10.210.8
X3D-XS [11]6.37.4
X3D-S [11]9.49.9
ConvNeXt-S 224 [31]4.15.0
EfficientNet B0 [57]7.28.0
EfficientNet B4 [57]9.410.1
AVT-b [14]13.413.5
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.512, + 0.472, + 0.584 + ], + "angle": 0, + "content": "Table 6. Additional action anticipation results on EPIC-KITCHCHENS-100 for encoders trained with and without ETMs. We show class-mean recall@5 results without and with ETM for a variety of encoders. All models are first pretrained on the action recognition task, with and without the ETM protocol." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.598, + 0.37, + 0.615 + ], + "angle": 0, + "content": "4.4. Action Anticipation Experiments" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.622, + 0.47, + 0.743 + ], + "angle": 0, + "content": "We also demonstrate how an encoder pre-trained on our ETM can improve performance in downstream tasks, in this case action anticipation. We follow the anticipation model architecture and the evaluation protocol from [14] and report the accuracies in verb, noun, and action prediction. However, our key difference is that the video encoder was pre-trained on an action recognition task, supplemented with ETM at training time." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.744, + 0.47, + 0.835 + ], + "angle": 0, + "content": "Table 5 showcases performance differences between encoders with and without ETM pretraining. Low complexity models, like MoviNet A0 (showcased in Table 5) exhibit improved performance in all three datasets. More comparisons on different model architectures are shown in Table 6 and Figure 4." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.847, + 0.336, + 0.864 + ], + "angle": 0, + "content": "4.5. ETM and Model Complexity" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.871, + 0.47, + 0.903 + ], + "angle": 0, + "content": "To further investigate how model complexity interacts with the proposed ETM training protocol, we train pro" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.33, + 0.895, + 0.481 + ], + "angle": 0, + "content": "gressively more complex models in the same architecture families and analyze how task performance varies with model complexity. We perform this analysis with the MoViNet [24] and X3D [11] architecture families: MoViNets scale from A0 to A4 with increasing parameter count and GFLOPs (Billion of Floating Point Operations), while X3Ds scale from XS to XXL. We train MoViNets A0 to A4 and X3Ds XS to M for our experiments. We use the same training setup as Section 3.2, where one instance of each model is trained without the ETM and another with it." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.483, + 0.895, + 0.572 + ], + "angle": 0, + "content": "For action recognition, we observe an increase in performance for low complexity models, i.e. A0 to A4, as shown in Figure 4 (a). The gain is larger for lower complexity models. We observe a similar effect for X3D models: the simpler the model is, the larger the improvement appears to be. This effect is visible on both EK100 and EGO4D." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.575, + 0.895, + 0.741 + ], + "angle": 0, + "content": "Figure 4 (b) shows that a similar effect occurs with action anticipation: using backbone models trained with our ETM protocol improves performance, especially if the backbone models are small. Once again, we use these models as encoders in the AVT [14] framework, replacing AVT-b (AVT's transformer-based encoder) with these variants and using AVT-h (AVT's transformer-based head) as a head. We only show GFLOPs of the encoders. We observe that on both datasets, encoders with lower representational power benefit more from the information about past and future regularities brought by the ETM." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.755, + 0.677, + 0.77 + ], + "angle": 0, + "content": "4.6.ETM Alternatives" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.779, + 0.895, + 0.854 + ], + "angle": 0, + "content": "Our proposed ETM can take many forms. We analyzed the impact of three of the main properties of the matrix on action recognition performance: its size, the decay function used to build it, and the metric for temporal distance between events." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.856, + 0.895, + 0.903 + ], + "angle": 0, + "content": "Matrix Size. Computing the matrix with every unique event (verb-noun combination) appearing in the training set of EK100 yields a \\( 13k \\times 13k \\) matrix. Since single-instance" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "10699" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.087, + 0.09, + 0.282, + 0.246 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.284, + 0.091, + 0.48, + 0.247 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.223, + 0.251, + 0.347, + 0.264 + ], + "angle": 0, + "content": "(a) Action Recognition" + }, + { + "type": "image", + "bbox": [ + 0.487, + 0.091, + 0.682, + 0.246 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.685, + 0.092, + 0.88, + 0.246 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.623, + 0.251, + 0.748, + 0.264 + ], + "angle": 0, + "content": "(b) Action Anticipation" + }, + { + "type": "image_caption", + "bbox": [ + 0.075, + 0.276, + 0.893, + 0.318 + ], + "angle": 0, + "content": "Figure 4. Performance for models on EK100 [14] and EGTEA Gaze+ [28] in the same architecture family. We see that for both MoViNets [24] and X3Ds [11], leveraging the ETM during training improves performance, and the effect is stronger with lower complexity models. MoViNet+ETM \\((\\text{一} - \\triangle)\\); MoViNet \\((\\text{一} \\triangle)\\); X3D+ETM \\((\\text{一} - \\bullet)\\); X3D \\((\\text{一} \\bullet)\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.345, + 0.47, + 0.418 + ], + "angle": 0, + "content": "actions may be noisier, we reduced the dimensionality of the matrix by removing events with less than 4 occurrences in the training set, yielding a subset of 2562 actions and a matrix of size \\(2562 \\times 2562\\). We compare both alternatives in Table 7." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.42, + 0.469, + 0.464 + ], + "angle": 0, + "content": "Decay function alternatives. We compared linear decay, exponential decay and no decay. We show comparison results in Table 7." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.466, + 0.47, + 0.646 + ], + "angle": 0, + "content": "Measuring temporal distance. We considered the natural alternative of measuring temporal distance between events A and B in seconds, by looking at the time between the last frame of A and the first frame of B. We observed, however, that the durations of individual actions between A and B could vastly modulate the decay factor applied to \\( M(y_{A},y_{B}) \\) of our matrix, which might be counterproductive if A and B are causally related but simply separated by one long intermediate action. We therefore considered measuring distance through index difference in the sequence of actions for that video, which allows us to abstract away the durations of individual actions." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.648, + 0.47, + 0.784 + ], + "angle": 0, + "content": "We compared these alternatives based on their ability to boost performance on action recognition. We trained a ConvNeXt-S (224) [31] encoder with supervision from different ETM alternatives and evaluated action recognition performance (Table 7). Our results show that the choice of decay is generally not impactful unless no decay is used. We find our best performance on a matrix of reduced size, with index as a temporal distance metric, and with exponential decay." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.8, + 0.205, + 0.815 + ], + "angle": 0, + "content": "5. Conclusions" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.825, + 0.469, + 0.9 + ], + "angle": 0, + "content": "We test a novel training regime for video understanding, in which model performance can be increased by supervising models with external representations of temporal regularities. We show that using the ETM as a training target allows arbitrary models to learn about the bi-directional tem" + }, + { + "type": "table", + "bbox": [ + 0.502, + 0.342, + 0.895, + 0.504 + ], + "angle": 0, + "content": "
SizeDecayTemp. MetricPresent (top-1 accuracy)
VerbNounAction
13kLinearTime55.146.228.8
13kExponentialTime55.647.729.1
2.5kExponentialTime58.648.831.3
2.5kNo decay-58.148.030.5
2.5kLinearIndex60.149.331.9
2.5kExponentialIndex60.350.332.4
" + }, + { + "type": "table_caption", + "bbox": [ + 0.499, + 0.513, + 0.893, + 0.584 + ], + "angle": 0, + "content": "Table 7. Difference in action recognition performance between ETM alternatives. We train ConvNeXts on EPIC-KITCHENS-100 with different verions of our ETM to evaluate the ability of ETM to improve action recognition performance. We evaluate top-1 accuracy on action recognition over the validation set of EK100." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.624, + 0.892, + 0.776 + ], + "angle": 0, + "content": "poral context of the action (i.e. the past and future), which improves action recognition and action anticipation performance. Finally, we tested the circumstances under which the ETM provides the largest benefit, and find significant performance boosts for low-complexity models. Amid ongoing efforts to increase the efficiency of computer vision models, we suggest that research which explores efficient architectures [11, 24] could be complemented by research that explores ways to leverage pre-learned representations of environmental regularities." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.785, + 0.892, + 0.847 + ], + "angle": 0, + "content": "The biggest benefit of this data structure is in its flexibility and simplicity: it is model-agnostic, computationally inexpensive, and easy to incorporate into the learning regime of any action recognition or anticipation architecture." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Acknowledgements. This work was partially funded by a grant from Meta. We thank Karl Ridgeway and the team at Meta Reality Labs Research for their helpful comments." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "10700" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.171 + ], + "angle": 0, + "content": "[1] Yazan Abu Farha, Qiuhong Ke, Bernt Schiele, and Juergen Gall. Long-term anticipation of activities with cycle consistency. In DAGM German Conference on Pattern Recognition, pages 159-173. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.171, + 0.472, + 0.24 + ], + "angle": 0, + "content": "[2] Yazan Abu Farha, Alexander Richard, and Juergen Gall. When will you do what?-anticipating temporal occurrences of activities. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5343-5352, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.242, + 0.472, + 0.31 + ], + "angle": 0, + "content": "[3] Hande Alemdar, Vincent Leroy, Adrien Prost-Boucle, and Frédéric Pétrot. Ternary neural networks for resource-efficient ai applications. In 2017 international joint conference on neural networks (IJCNN), pages 2547-2554. IEEE, 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.311, + 0.472, + 0.368 + ], + "angle": 0, + "content": "[4] Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, and Quoc V Le. Attention augmented convolutional networks. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3286-3295, 2019. 1, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.368, + 0.472, + 0.41 + ], + "angle": 0, + "content": "[5] Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, and Doina Precup. Conditional computation in neural networks for faster models. arXiv preprint arXiv:1511.06297, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.411, + 0.472, + 0.465 + ], + "angle": 0, + "content": "[6] Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.466, + 0.472, + 0.508 + ], + "angle": 0, + "content": "[7] Michael R Brent. Speech segmentation and word discovery: A computational perspective. Trends in Cognitive Sciences, 3(8):294-301, 1999. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.509, + 0.472, + 0.579 + ], + "angle": 0, + "content": "[8] João Carreira and Andrew Zisserman. Quo vadis, action recognition? A new model and the kinetics dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 4724-4733, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.58, + 0.472, + 0.662 + ], + "angle": 0, + "content": "[9] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Scaling egocentric vision: The epic-kitchens dataset. In European Conference on Computer Vision (ECCV), 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.663, + 0.47, + 0.746 + ], + "angle": 0, + "content": "[10] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Jian Ma, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100. International Journal of Computer Vision (IJCV), 2021. 2, 3, 4, 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.747, + 0.47, + 0.803 + ], + "angle": 0, + "content": "[11] Christoph Feichtenhofer. X3d: Expanding architectures for efficient video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 203-213, 2020. 1, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.804, + 0.47, + 0.845 + ], + "angle": 0, + "content": "[12] Vanessa E Ghosh and Asaf Gilboa. What is a memory schema? a historical perspective on current neuroscience literature. Neuropsychologia, 53:104-114, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[13] Rohit Girdhar, Joao Carreira, Carl Doersch, and Andrew Zisserman. Video action transformer network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 244-253, 2019. 3" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[14] Rohit Girdhar and Kristen Grauman. Anticipative video transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13505-13515, 2021. 1, 3, 4, 5, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.15, + 0.892, + 0.205 + ], + "angle": 0, + "content": "[15] Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. Knowledge distillation: A survey. International Journal of Computer Vision, 129(6):1789-1819, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.208, + 0.892, + 0.568 + ], + "angle": 0, + "content": "[16] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonio Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Iija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Christian Fuegen, Abraham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4d: Around the World in 3,000 Hours of Egocentric Video. CoRR, abs/2110.07058, 2021. 3, 4, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.569, + 0.892, + 0.624 + ], + "angle": 0, + "content": "[17] Michelle R Greene and Aude Oliva. Recognition of natural scenes from global properties: Seeing the forest without representing the trees. Cognitive psychology, 58(2):137-176, 2009. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.627, + 0.892, + 0.668 + ], + "angle": 0, + "content": "[18] Song Han, Jeff Pool, John Tran, and William J. Dally. Learning both weights and connections for efficient neural networks. CoRR, abs/1506.02626, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.67, + 0.892, + 0.713 + ], + "angle": 0, + "content": "[19] Babak Hassibi and David Stork. Second order derivatives for network pruning: Optimal brain surgeon. Advances in neural information processing systems, 5, 1992. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.714, + 0.892, + 0.756 + ], + "angle": 0, + "content": "[20] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network (2015). arXiv preprint arXiv:1503.02531, 2, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.758, + 0.892, + 0.814 + ], + "angle": 0, + "content": "[21] Qiuhong Ke, Mario Fritz, and Bernt Schiele. Time-conditioned action anticipation in one shot. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9925-9934, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.816, + 0.892, + 0.872 + ], + "angle": 0, + "content": "[22] Dong-Jin Kim, Xiao Sun, Jinsoo Choi, Stephen Lin, and In So Kweon. Detecting human-object interactions with action co-occurrence priors. In European Conference on Computer Vision, pages 718–736. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.873, + 0.892, + 0.903 + ], + "angle": 0, + "content": "[23] Yoon Kim and Alexander M Rush. Sequence-level knowledge distillation. arXiv preprint arXiv:1606.07947, 2016. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.903 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "10701" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.174 + ], + "angle": 0, + "content": "[24] Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, and Boqing Gong. Movinets: Mobile video networks for efficient video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16020-16030, 2021. 1, 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.176, + 0.47, + 0.217 + ], + "angle": 0, + "content": "[25] Christopher A Kurby and Jeffrey M Zacks. Segmentation in the perception and memory of events. Trends in cognitive sciences, 12(2):72-79, 2008. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.218, + 0.469, + 0.258 + ], + "angle": 0, + "content": "[26] Yann LeCun, John Denker, and Sara Solla. Optimal brain damage. Advances in neural information processing systems, 2, 1989. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.26, + 0.469, + 0.314 + ], + "angle": 0, + "content": "[27] Jinyu Li, Rui Zhao, Jui-Ting Huang, and Yifan Gong. Learning small-size dnn with output-distribution-based criteria. In Fifteenth annual conference of the international speech communication association, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.316, + 0.469, + 0.357 + ], + "angle": 0, + "content": "[28] Yin Li, Miao Liu, and James M. Rehg. In the eye of beholder: Joint learning of gaze and actions in first person video. In ECCV, 2018. 4, 5, 6, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.358, + 0.469, + 0.412 + ], + "angle": 0, + "content": "[29] Tailin Liang, John Glossner, Lei Wang, Shaobo Shi, and Xiaotong Zhang. Pruning and quantization for deep neural network acceleration: A survey. Neurocomputing, 461:370-403, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.413, + 0.469, + 0.481 + ], + "angle": 0, + "content": "[30] Alexander H Liu, SouYoung Jin, Cheng-I Jeff Lai, Andrew Rouditchenko, Aude Oliva, and James Glass. Cross-modal discrete representation learning. In Annual Meeting of the Association for Computational Linguistics (ACL), May 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.483, + 0.469, + 0.524 + ], + "angle": 0, + "content": "[31] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. arXiv preprint arXiv:2201.03545, 2022. 6, 7, 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.525, + 0.469, + 0.579 + ], + "angle": 0, + "content": "[32] Tambet Matiisen, Avital Oliver, Taco Cohen, and John Schulman. Teacher-student curriculum learning. IEEE transactions on neural networks and learning systems, 31(9):3732-3740, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.581, + 0.469, + 0.65 + ], + "angle": 0, + "content": "[33] Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, and Rogerio Feris. Ar-net: Adaptive frame resolution for efficient action recognition. In European Conference on Computer Vision, pages 86-104. Springer, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.651, + 0.469, + 0.69 + ], + "angle": 0, + "content": "[34] Carolyn B Mervis and Eleanor Rosch. Categorization of natural objects. Annual review of psychology, 32(1):89-115, 1981. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.692, + 0.469, + 0.761 + ], + "angle": 0, + "content": "[35] Antoine Miech, Ivan Laptev, Josef Sivic, Heng Wang, Lorenzo Torresani, and Du Tran. Leveraging the present to anticipate the future in videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0-0, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.762, + 0.469, + 0.829 + ], + "angle": 0, + "content": "[36] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic. Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In ICCV, pages 2630-2640, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.831, + 0.469, + 0.872 + ], + "angle": 0, + "content": "[37] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[38] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.533, + 0.093, + 0.892, + 0.121 + ], + "angle": 0, + "content": "phrases and their compositionality. Advances in neural information processing systems, 26, 2013. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.122, + 0.894, + 0.178 + ], + "angle": 0, + "content": "[39] Kyle Min and Jason J Corso. Integrating human gaze into attention for egocentric activity recognition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1069-1078, 2021. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.179, + 0.892, + 0.221 + ], + "angle": 0, + "content": "[40] Shayan Modiri Assari, Amir Roshan Zamir, and Mubarak Shah. Video classification using semantic concept co-occurrences. In CVPR, pages 2529–2536, 2014. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.222, + 0.892, + 0.291 + ], + "angle": 0, + "content": "[41] Mathew Monfort, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa Brown, Quanfu Fan, Dan Gutfreund, Carl Vondrick, and Aude Oliva. Moments in time dataset: one million videos for event understanding. IEEE TPAMI, 42(2):502-508, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.293, + 0.892, + 0.375 + ], + "angle": 0, + "content": "[42] Mathew Monfort, SouYoung Jin, Alexander Liu, David Harwath, Rogerio Feris, James Glass, and Aude Oliva. Spoken moments: Learning joint audio-visual representations from video descriptions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14871–14881, June 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.377, + 0.892, + 0.472 + ], + "angle": 0, + "content": "[43] Mathew Monfort, Bowen Pan, Kandan Ramakrishnan, Alex Andonian, Barry A Mcnamara, Alex Lascelles, Quanfu Fan, Dan Gutfreund, Rogerio Feris, and Aude Oliva. Multimoments in time: Learning and interpreting models for multi-action video understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-1, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.475, + 0.892, + 0.544 + ], + "angle": 0, + "content": "[44] Tushar Nagarajan, Yanghao Li, Christoph Feichtenhofer, and Kristen Grauman. Ego-topo: Environment affordances from egocentric video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 163-172, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.546, + 0.892, + 0.602 + ], + "angle": 0, + "content": "[45] Bowen Pan, Rameswar Panda, Camilo Fosco, Chung-Ching Lin, Alex Andonian, Yue Meng, Kate Saenko, Aude Oliva, and Rogerio Feris. Va-red2: Video adaptive redundancy reduction. arXiv preprint arXiv:2102.07887, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.603, + 0.892, + 0.671 + ], + "angle": 0, + "content": "[46] Bowen Pan, Rameswar Panda, Yifan Jiang, Zhangyang Wang, Rogerio Feris, and Aude Oliva. Ia-red2: Interpretability-aware redundancy reduction for vision transformers. Advances in Neural Information Processing Systems, 34:24898-24911, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.673, + 0.892, + 0.741 + ], + "angle": 0, + "content": "[47] Jeffrey Pennington, Richard Socher, and Christopher D Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532-1543, 2014. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.743, + 0.892, + 0.786 + ], + "angle": 0, + "content": "[48] Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word representations, 2018. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.788, + 0.892, + 0.829 + ], + "angle": 0, + "content": "[49] Ivan Rodin, Antonino Furnari, Dimitrios Mavroeidis, and Giovanni Maria Farinella. Untrimmed action anticipation. arXiv preprint arXiv:2202.04132, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.831, + 0.892, + 0.872 + ], + "angle": 0, + "content": "[50] E Rosch, C B Mervis, W. D. Gray, D. M. Johnson, and P Boyes-Braem. Basic objects in natural categories. Cognitive psychology, 8(3):382-439, 1976. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.873, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[51] Zahra Sadeghi, James L McClelland, and Paul Hoffman. You shall know an object by the company it keeps: An investi" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "10702" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.108, + 0.092, + 0.468, + 0.133 + ], + "angle": 0, + "content": "gation of semantic representations derived from object cooccurrence in visual scenes. Neuropsychologia, 76:52-61, 2015. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.136, + 0.468, + 0.19 + ], + "angle": 0, + "content": "[52] Fadime Sener, Dipika Singhania, and Angela Yao. Temporal aggregate representations for long-range video understanding. In European Conference on Computer Vision, pages 154-171. Springer, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.192, + 0.468, + 0.247 + ], + "angle": 0, + "content": "[53] Yang Shen, Bingbing Ni, Zefan Li, and Ning Zhuang. Ego-centric activity prediction via event modulated attention. In Proceedings of the European conference on computer vision (ECCV), pages 197-212, 2018. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.249, + 0.468, + 0.303 + ], + "angle": 0, + "content": "[54] Brynn E Sherman, Kathryn N Graves, and Nicholas B Turk-Browne. The prevalence and importance of statistical learning in human cognition and behavior. Current opinion in behavioral sciences, 32:15-20, 2020. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.305, + 0.468, + 0.36 + ], + "angle": 0, + "content": "[55] Bilge Soran, Ali Farhadi, and Linda Shapiro. Generating notifications for missing actions: Don't forget to turn the lights off! In Proceedings of the IEEE International Conference on Computer Vision, pages 4669-4677, 2015. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.362, + 0.468, + 0.43 + ], + "angle": 0, + "content": "[56] Chen Sun, Abhinav Shrivastava, Carl Vondrick, Rahul Sukthankar, Kevin Murphy, and Cordelia Schmid. Relational action forecasting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 273-283, 2019. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.433, + 0.468, + 0.487 + ], + "angle": 0, + "content": "[57] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019. 1, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.489, + 0.468, + 0.53 + ], + "angle": 0, + "content": "[58] Antonio Torralba and Aude Oliva. Statistics of natural image categories. Network: computation in neural systems, 14(3):391, 2003. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.532, + 0.468, + 0.586 + ], + "angle": 0, + "content": "[59] Iulia Turc, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Well-read students learn better: On the importance of pre-training compact models. arXiv preprint arXiv:1908.08962, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.589, + 0.468, + 0.614 + ], + "angle": 0, + "content": "[60] Melissa Le-Hoa Võ. The meaning and structure of scenes. Vision Research, 181:10-20, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.618, + 0.468, + 0.671 + ], + "angle": 0, + "content": "[61] Daniel Weinland and Edmond Boyer. Action recognition using exemplar-based embedding. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pages 1-7. IEEE, 2008. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.674, + 0.468, + 0.714 + ], + "angle": 0, + "content": "[62] Less Wright and Nestor Demeure. Ranger21: a synergistic deep learning optimizer. arXiv preprint arXiv:2106.13731, 2021.5" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.717, + 0.468, + 0.757 + ], + "angle": 0, + "content": "[63] Jeffrey M Zacks and Barbara Tversky. Event structure in perception and conception. Psychological bulletin, 127(1):3, 2001. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.759, + 0.468, + 0.8 + ], + "angle": 0, + "content": "[64] Haotong Zhang, Fuhai Chen, and Angela Yao. Weakly-supervised dense action anticipation. arXiv preprint arXiv:2111.07593, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.081, + 0.802, + 0.468, + 0.843 + ], + "angle": 0, + "content": "[65] Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba. Temporal relational reasoning in videos. European Conference on Computer Vision, 2018. 3" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.468, + 0.843 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.956 + ], + "angle": 0, + "content": "10703" + } + ] +] \ No newline at end of file diff --git a/2023/Leveraging Temporal Context in Low Representational Power Regimes/23cf52f5-5ab9-4d57-929a-b5407b0b21e2_origin.pdf b/2023/Leveraging Temporal Context in Low Representational Power Regimes/23cf52f5-5ab9-4d57-929a-b5407b0b21e2_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..2c64d1bf0adc899594d94da92eb6b8c54b450694 --- /dev/null +++ b/2023/Leveraging Temporal Context in Low Representational Power Regimes/23cf52f5-5ab9-4d57-929a-b5407b0b21e2_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3a05e6c50f7285671d02b1f30835f9d11fbae2205bcbbc2182e44493007e73fa +size 3578272 diff --git a/2023/Leveraging Temporal Context in Low Representational Power Regimes/full.md b/2023/Leveraging Temporal Context in Low Representational Power Regimes/full.md new file mode 100644 index 0000000000000000000000000000000000000000..0d8f221dc26a6e0206abaa7e3f4a7407c22f58d3 --- /dev/null +++ b/2023/Leveraging Temporal Context in Low Representational Power Regimes/full.md @@ -0,0 +1,319 @@ +# Leveraging Temporal Context in Low Representational Power Regimes + +Camilo L. Fosco + +SouYoung Jin + +Emilie Josephs + +Aude Oliva + +camilolu@mit.edu + +souyoung@mit.edu + +eJosephs@mit.edu + +oliva@mit.edu + +MITCSAIL + +# Abstract + +Computer vision models are excellent at identifying and exploiting regularities in the world. However, it is computationally costly to learn these regularities from scratch. This presents a challenge for low-parameter models, like those running on edge devices (e.g. smartphones). Can the performance of models with low representational power be improved by supplementing training with additional information about these statistical regularities? We explore this in the domains of action recognition and action anticipation, leveraging the fact that actions are typically embedded in stereotypical sequences. We introduce the Event Transition Matrix (ETM), computed from action labels in an untrimmed video dataset, which captures the temporal context of a given action, operationalized as the likelihood that it was preceded or followed by each other action in the set. We show that including information from the ETM during training improves action recognition and anticipation performance on various egocentric video datasets. Through ablation and control studies, we show that the coherent sequence of information captured by our ETM is key to this effect, and we find that the benefit of this explicit representation of temporal context is most pronounced for smaller models. Code, matrices and models are available in our project page: https://camilofosco.com/etm網站 + +# 1. Introduction + +A strength of computer vision models is their ability to identify and exploit statistical regularities in the world. Learning these regularities from scratch is computationally costly, which limits the accuracy of low-parameters models. It is critical to boost the performance of small models, since many devices lack the resources to host current state of the art models. One way to make the learning problem more tractable for small models may be to supplement them at training time with an explicit representation of the statistical regularities in the target domain. Here, we test whether + +![](images/57d9fd47dcaca2b605eaa79cc1a203a8cd34d118308cc1905b9d61863e6cb697.jpg) +Figure 1. Action recognition performance difference between models trained with and without the proposed ETM approach. We train models with various model architectures, from small (left) to large (right): AVT-b [14], MoViNets [24] family, X3D [11] family, LambdaResNet-50 [4], and EfficientNets [57] family on EPIC-KITCHENS-100 [14]. We show that incorporating the ETM into training improves performance, and the impact is higher on smaller models. + +video understanding models can be improved by providing them with information about typical event sequences during training. + +We introduce the Event Transition Matrix (ETM), illustrated in Figure 2, which leverages the fact that events in real-world videos often occur in predictable sequences. Each row and column indexes an event. In the rows, the ETM captures the likelihood that the event was followed by each of the other events in the set. In the columns, it captures the likelihood that the event was preceded by each other event. To compute a cell's value, we combine information from all previous and subsequent events, weighted by their temporal distance from the queried action. Crucially, this breaks markovian assumptions and allows the matrix to capture long-range relationships. The ETM has two important properties. First, it acts as an explicit representation of the likelihood of event transitions, which provides additional pertinent information that a model can + +![](images/2fc50e3cafbfca93075d828895c973c5d00050251bc5bf7587a11a2d741b1825.jpg) +(a) Low-level descriptions in [10] + +![](images/109b46fcc27153da0e88c263fd0161409275ced2a14fabc7b122dcf5ba40c826.jpg) +(b) Event Transition Matrix +Figure 2. We construct a given Event Transition Matrix (ETM) from action labels drawn from a given dataset of untrimmed videos. (a) Each video depicts a continuous activity, and is paired with human annotations indicating the individual, low-level actions that compose the activity. (b) Using these action labels, we created the ETM by recording the frequency with which a given action label was preceded or followed by each other action label, accumulated across the videos in our set. + +leverage without the cost of learning it for itself. Second, it augments representations of a given event with information about what came before and after it, providing additional target for the model to train on. + +In the present paper, across multiple datasets and model architectures, we test the effectiveness of incorporating the ETM into training for low-parameter models. We test our approach on action recognition and action anticipation in egocentric video datasets, where actions occur in stereotypical sequences. However, this approach can apply to any kind of sequence in videos. We show that leveraging the ETM improves action recognition relative to baselines, and that this improvement relies on the coherence of the action sequence. We also show that action anticipation is improved with our ETM approach. In both cases, we show that the ETM approach can be incorporated into multiple different model architectures, and that the addition of the ETM has the highest impact on smaller models, as shown in Figure 1. Overall, this work demonstrates a potential path to more efficient models, based on supplementing small models at training time with explicit representations of regularities expected in the data. + +# 2. Related Work + +# 2.1. Efficient AI + +State of the art neural networks for video understanding can achieve impressive classification results, but these models often have heavy computational requirements. Producing smaller, more efficient models that can perform comparable to larger ones is an active area of research, for deployment onto edge devices such as phones, wearables, drones or autonomous vehicles. Some approaches achieve effi + +ciency by first training a large network, then using a smaller network to learn the mapping from input to output vectors with little loss on performance (i.e. knowledge distillation [3,15,20,23,27,32]), or by identifying and removing non-essential parameters (i.e. parameter pruning [18,19,26,29]). Others rely on adaptive policies to shorten or simplify the inference process, reducing the effective size of the architecture [5,6,33,45] or input [45,46]. Here, we propose an alternative approach, which involves intervening at training time to supplement low-parameter architectures with external, explicit representations of statistical regularities in the target domain. + +# 2.2. Statistical Regularities and Bayesian Learning. + +The environment is full of regularities. Objects classes have co-occurring attributes [34, 50], environments classes have distinctive combinations of objects, surfaces and textures [17, 51, 58, 60], and episodes are composed of predictable sequences of events [25, 63]. Having the ability to detect and leverage these regularities is a key feature of intelligence. Cognitive research in humans has established that representations of contextual regularities underlie most aspects of intelligence, spanning learning, generalization, prediction, compression, memory, language and more [7, 12, 54]. + +Machine learning relies on identifying and making use of regularities. Typically, this happens in an emergent manner from the training regime. However, some approaches also rely on explicit representations of such regularities. For example, in natural language processing, word embeddings [37, 38, 47, 48, 59] acting as explicit representations of word transition probabilities have provided the basis for many downstream tasks. + +# 2.3. Action Recognition and Anticipation + +Action understanding is an enduring problem in video processing [8, 41, 43, 49, 64]. This can involve action recognition, which requires retrieving a label for a depicted action, and action anticipation, which requires retrieving a label for a future action based on a depicted action. Videos contain complex semantic relationships between objects, people and places, which evolve dynamically over time. Thus, action understanding models often incorporate elements of relational modeling, for example modeling the spatial relations among people or between people and objects [13, 56], between people and environment zones [44], or between the body parts of the person performing the actions [61]. More recently, multi-modal representation learning approaches have been introduced to learn the broader sensory context of actions [30, 36, 42]. + +# 2.4. Action Recognition and Anticipation with Temporal Context + +Another kind of context that can be modeled in action understanding is the temporal relationships between the events that make up ongoing actions. Actions take place in sequences: you must first boil water and grind coffee beans, then pour water over the beans in order to successfully make coffee. Some models capitalize on such temporal regularities by modeling the dependence among individual video frames, either with RNNs [2, 56], relational reasoning networks [65] or more recently with attention-based methods and transformers [14, 52]. Other approaches model these sequences at a higher level, by learning the dependence among events (rather than individual frames), by using action co-occurrence matrices [22, 40], or by estimating the transitional probability between adjacent [35, 55] or more distant actions [21, 53]. + +Building on this work, we show how leveraging explicit representations of typical action sequences in low-parameter models can improve their performance. Our motivation is that there is regularity in the temporal context of an action in both the past and the future, and leveraging this bi-directional regularity could bootstrap the model's learning. While some previous work [1] included a representation of the past in an action anticipation framework, our approach is distinct from it in two ways: (1) our representation of temporal context includes past, present and future, and extends over a larger time period, and (2) we pre-compute this representation, then use it to supplement learning. + +# 3. Proposed Approach + +In this section, we introduce the Event Transition Matrix (ETM), which is an efficient approach to constructing a knowledge base about the temporal relation between events in video scenes (Section 3.1). We then propose a novel ap + +proach to pre-training with this matrix, leveraging these relations to help recognize current events or predict past or future events in the input video snippet (Section 3.2). + +# 3.1. Event Transition Matrix (ETM) + +Egocentric video datasets, such as EPIC-KITCHENS [9, 10] or EGO4D [16], contain long form videos that exhibit rich sequences of small actions or events. These sequences are densely annotated with labels for each event and timestamps indicating start and end points of each action. Suppose there are N event categories, $S = \{s_1, s_2, \dots, s_N\}$ . Each video is annotated with a sequence of event instances, which can be denoted as $\{y_1, y_2, \dots, y_{t-1}, y_t, \dots\}$ . Using the timestamps, we can define a video snippet $x_k$ that contains the specific event, which is given the instance label $y_k$ , based on its category. Event descriptions, i.e. $x_k$ and $y_k$ , refer to the current ongoing action event, but do not convey the broader temporal context of that action, including what came before and after it. As in the example in Figure 2, when making coffee, "add coffee beans" happens earlier than "brew coffee", and these two events are temporally dependent on each other. To leverage the temporal context surrounding events, we compute the frequencies with which each event comes before/after each other event. More formally, we define a square matrix, $\mathbf{M}_{N,N}$ , where each row and column corresponds to an event in $S$ and $\mathbf{M}(i,j)$ corresponds to an estimated probability that an event $s_i$ happens before $s_j$ , and vice versa. This matrix is built with training set observations. + +A naive approach for building the matrix is to update $\mathbf{M}(y_l, y_m)$ for all possible pairs of events where event $l$ happens earlier than event $m$ . However, this approach does not incorporate the temporal distance between two events, since $(y_l, y_m)$ and $(y_l, y_n)$ contribute equally to constructing the matrix even when $l$ is much farther away from $m$ . In reality, actions separated by more time are less likely to be part of the same overall activity, and are therefore less likely to consistently co-vary. Therefore, we update the matrix with a decay function $\delta(\cdot)$ , as + +$$ +\mathbf {M} \left(y _ {l}, y _ {m}\right) + = \delta (m - l). \tag {1} +$$ + +We normalize the matrix $\mathbf{M}$ by the sum of each row and define a new matrix $\mathbf{M}^R$ as + +$$ +\mathbf {M} ^ {R} (i, j) = \frac {\mathbf {M} (i , j)}{\sum_ {k = 0} ^ {N} \mathbf {M} (i , k)}. \tag {2} +$$ + +Each row in this matrix, $\mathbf{M}^R (i,:)$ , can be seen as an empirical approximation of the distribution of possible events that can be transitioned to following event $s_i$ . Similarly, we can also define a matrix $\mathbf{M}^C$ by column-wise normalization. + +![](images/11ef462ac7094adb0baeb2f626010bc56b77b7566829522d6e3a144ef140b8db.jpg) +Figure 3. Overview of the proposed approach for pre-training with ETM. The model takes a video snippet and encodes the input to predict the present, past, and future events. In particular, we use the column and the row of the ETM which correspond to the given event. These represent the distributions over past actions, and future actions respectively. More details are written in Section 3.2. + +# 3.2. Encoder Training with the Event Transition Matrix + +We propose a novel training protocol to enhance an arbitrary encoder by leveraging the ETM. A typical event recognition model is trained to take a video snippet, $x_{t}$ , transform it into a feature vector with an encoder $f(\cdot)$ , and recognize an event in the input video by predicting a label from that vector. More formally, the model is trained to increase $p(y_{t}|g_{q}(f(x_{t}))$ , where $g_{q}$ represents a module that maps features into a probability distribution over labels. In our framework, we call $g_{q}$ the present module and train it with a typical cross-entropy loss function, $\mathcal{L}_{q}$ . + +Our proposed method adds two additional modules to predict past and future event probabilities, as shown in Figure 3. A past module $g_{c}(\cdot)$ receives $f(x_{t})$ and predicts a distribution of events occurring before the current action (which corresponds to a column of the column-normalized ETM, $\mathbf{M}^{C}$ ). This module attempts to minimize the following loss: + +$$ +\mathcal {L} _ {c} = \sum_ {k = 0} ^ {N} (C (k) - \mathbf {M} ^ {C} (y _ {t}, k)) ^ {2}, \tag {3} +$$ + +where $C(k) = g_c(f(x_t))$ . Similarly, a future module $g_r(\cdot)$ predicts a distribution of future events, and tries to minimize the distance between the prediction and a row of the row-normalized ETM, $\mathbf{M}^R(:, y_t)$ , through the loss: + +$$ +\mathcal {L} _ {r} = \sum_ {k = 0} ^ {N} (R (k) - \mathbf {M} ^ {R} (k, y _ {t})) ^ {2}, \tag {4} +$$ + +where $R(k) = g_r(f(x_t))$ . Altogether, our framework augments an arbitrary encoder with these past and future modules and minimizes the following objective during training: + +
DatasetSegm.Actionsτa(s)Metrics (AR, AA)
EK100 [10]90k38071.0top-1, rec@5
EGO4D [16]39.2k35421.0top-1, rec@5
EGTEA [28]10.3k1060.5top-1, top-1
+ +Table 1. Properties of the datasets used in our experiments. We showcase our three main datasets: EPIC-KITCHENS-100 (EK100), EGO4D LTA (EGO4D) and EGTEA Gaze+ (EGTEA). $\tau_{a}$ corresponds to the time between input clips and target segments in action anticipation, and follows prior work [14]. The Metrics column shows the main performance metrics used in our action recognition and action anticipation experiments, respectively. + +$$ +\mathcal {L} = \omega_ {q} \mathcal {L} _ {q} + \omega_ {c} \mathcal {L} _ {c} + \omega_ {r} \mathcal {L} _ {r}, \tag {5} +$$ + +This training setup attempts to push the encoder towards outputting richer event representations that capture the regularities of the event's typical temporal context. Although simplistic, the framework is effective: we observe that instantiating $g_{c}$ and $g_{r}$ with simple multi-layer perceptrons is enough to achieve strong results on low-complexity models. + +# 4. Experimental Results + +In this section, we demonstrate the effectiveness of training with the Event Transition Matrix (ETM) on action recognition (Section 4.3) and action anticipation (Section 4.4). We describe the datasets used in Section 4.1 and give implementation details in Section 4.2. + +# 4.1. Datasets + +EPIC-KITCHENS-100. [10] The EPIC-KITCHENS-100 dataset (EK100) contains 700 unscripted videos depicting cooking actions, totalling 100 hours. It presents verb + +and noun annotations over 90k segments of varying length. The dataset depicts 97 unique verbs, 300 unique nouns that combine to yield 3807 actions. We work with the provided training and validation sets, containing 67.2k and 9.7k segments respectively. Our ETM is constructed exclusively with training set data. + +EGO4D. [16] This newer egocentric dataset contains 3670 hours of video from 71 different participants. We use the long term anticipation annotations provided by the authors [16], which reduces the set to a training split containing 493 unique videos with 23610 segments, and a validation set with 380 videos cut into 15587 segments. These annotations present 115 unique verbs, 477 unique nouns and 3542 unique actions. All segments are 240-frame long. To homogenize our setup and avoid generating ETMs with a biased diagonal, we merge contiguous segments together if they depict the same action. This yields 18896 segments for training and 12676 segments for validation, and we use this training set for ETM construction. We refer to this set as EGO4D LTA. + +EGTEA Gaze+ [28] is another popular egocentric dataset with 10k segments annotated with 19 verbs, 51 nouns and 106 unique actions. We report performance for split 1 (provided by the authors), containing 8299 training segments and 2022 validation segments. Following previous work [14, 39], both action recognition and anticipation performance are measured with top-1 accuracy. The properties of each dataset are summarized in Table 1. + +# 4.2. Implementation Details + +ETM construction. We construct our ETM offline by observing all videos of our training set and adding a contribution to $M(y_{l},y_{m})$ modulated by a decay function $\delta (v)$ , where $v$ is the temporal distance between the events, as in Equation 1. Importantly, to minimize sparsity, we only consider events that appear at least 4 times in the training set. + +Encoder training. With a given ETM, we train an encoder to generate embeddings that can be used to both recognize actions and predict ETM vectors from snippets of video. The encoder is tasked with producing an embedding that is fed to three modules: an action classification head (present module), a past vector regressor and a future vector regressor, as seen in Figure 3. We instantiate the past, present and future modules with simple fully-connected layers. We train with the loss in Eq. 5 and set $\omega_{q} = \omega_{c} = \omega_{r} = \frac{1}{3}$ . As the ETM is built with actions that appear at least four times in the training data, certain rare actions do not have target vectors. In those cases, the loss associated to regressing the past and future vectors is set to zero, and no gradients are back-propagated. + +We train our models with the Ranger21 optimizer [62] with a learning rate of 0.01 and cosine annealing with a 20 epochs half-cycle. We use a batch-size of 32, weight decay + +of 0.0001 and dropout with $p = 0.5$ where applicable. + +Training setup for Action Anticipation. We follow the framework used in [14], where an encoder generates features from an input clip $c$ before feeding them to a decoder attempting to predict the future of $c$ , which is the label $y_{s}$ of an action segment $s$ happening $\tau_{a}$ seconds after $c$ ends. In other words, for each action segment starting at time $\tau_{s}$ , the decoder attempts to predict its action label $y_{s}$ using a clip $c$ that ends $\tau_{a}$ seconds before $s$ . Crucially, we analyze how using an encoder trained with ETM supervision affects the performance of the system. + +We analyze two different setups: one were we freeze our pretrained encoder and only train the decoder, and another where we allow our encoder to be finetuned alongside the decoder. Following the AVT framework [14], we replace AVT-b (AVT's transformer-based encoder) with several variants of our own encoders. We keep AVT-h (AVT's transformer-based head) as a decoder and finetune it with our codes as input. + +We train until validation performance plateaus, and we operate with a batch size of 32, a learning rate of $10^{-4}$ with cosine annealed decay, and the Ranger21 optimizer. At test time, we employ 3-crop testing following [14], where we compute three 224px spatial crops from 248px input frames, and average the predictions of each segment. + +# 4.3. Action Recognition Experiments + +We first ask whether models trained with ETM supervision showed improved action recognition performance. To do that, we train a baseline MoViNet A0 on EK100. Following [10], our present head has a two-way output: one to predict verbs and one to predict nouns, with an average verb/noun loss. Accordingly, in Table 2, the Present tab shows the top-1 accuracies in verb and noun classification as well as Action accuracy, which corresponds to (verb, noun) pairs. We compare this to a MoViNet A0 trained on the same data but augmented with our ETM framework as described in Section 3. For this model, we also show the mean absolute error (MAE) on the past and the future predictions. Here, lower scores indicate better performance. + +To demonstrate that the performance gain is not merely due to the extra parameters gained from training with an external matrix, we compare against three more baselines, with versions of the matrix that disrupt the action sequence information to different degrees. In the Full shuffle baseline, cells of the matrix are randomly shuffled. This scrambles any sequence information in the matrix, while preserving the distribution of values across it. In the Columns shuffle, columns are kept intact, but their position in the matrix is randomly shuffled. This preserves individual distributions over past actions, but randomizes their correspondence with columns labels and scrambles the rows (similarly, the Row shuffle kept rows intact but scrambled their position in the + +
ModelPresentMAE on Past ↓MAE on Future ↓
Verb ↑Noun ↑Action ↑
Baseline64.847.436.8--
Full shuffle64.147.236.34.1174.012
Columns/rows shuffle64.747.636.73.2543.101
Co-occurrence65.349.037.91.2111.115
Only past vector65.749.338.20.901-
Only future vector65.549.838.3-0.898
ETM (Ours)67.951.240.20.8820.859
+ +Table 2. Action recognition results on various baseline models. We train the models on the EPIC-KITCHENS-100 dataset [14] with the MoViNet A0 [24]. For Present prediction, we show the verb and noun classification results and retrieval scores of (verb, noun). For Past and Future prediction, we show the mean absolute error (MAE) on the past and the future predicted vectors. + +
DatasetModelPresent
VerbNounAction
EK100 [14]Baseline64.847.436.8
ETM(Ours)67.951.240.2
EGO4DBaseline32.323.521.1
LTA [16]ETM(Ours)32.924.222.0
EGTEABaseline81.271.760.4
Gaze+ [28]ETM(Ours)83.472.962.5
+ +matrix). Finally, we also construct a matrix which does not consider the order of the events, instead using a symmetric matrix of co-occurrence for training the action recognition model. In Table 2, we see that our proposed ETM surpasses all other baselines. We show additional results in the supplementary material. + +Next, we further conduct ablation studies to probe whether it is necessary to use both the past and future information from the ETM to see these performance benefits. We train two additional models including only either the past or the future module. Interestingly, as shown in Table 2, training models with either side of the temporal information still improves performance to some extent, but we observe that training with the full matrix gives us the biggest improvement. These experiments serve as quantitative justifications for our framework's design choices. + +To show that the proposed ETM approach is effective on multiple datasets, we also show in Table 3 the performance gains on two other egocentric activity datasets that contain untrimmed videos of actions annotated with verbs + +Table 3. Action recognition results on various datasets using MoViNet A0 [24] with and without ETM supervision. As can be seen, augmenting the model with our framework improves performance on all datasets. + +
Modelw/o ETMw/ ETM
MoviNet A0 [24]36.840.2
MoviNet A2 [24]41.243.4
X3D-XS [11]35.538.1
X3D-S [11]40.542.2
ConvNeXt-S 224 [31]20.132.4
LambdaResNet-50 [4]26.627.1
EfficientNet-B0 [57]25.326.3
EfficientNet-B4 [11]29.229.4
AVT-b [14]30.430.7
+ +Table 4. Additional action recognition results on EPIC-KITCHENS-100 for models trained with and without ETMs. We show top-1 action classification results for several different architectures. + +and nouns. Specifically, we train models on the EGO4D dataset [16] using the long term action anticipation annotations, as well as the Extended GTEA Gaze+ (EGTEA Gaze+) dataset [28]. + +The ETM is model-agnostic and can be incorporated into any model architecture. We illustrate this by showing how this technique performs on a breadth of models: In Table 4, we show action recognition results for multiple model architectures with and without ETM training. Our framework benefits low-complexity models the most. Adding ETM supervision to an advanced encoder like AVT-b [14] produces slight improvements, but small models like EfficientNet-B0 achieve noticeable gains. We show qualitative performance results with correct and incorrect classifications in the supplementary material. + +
DatasetFrozen Encoder?BaselineETM (Ours)
Verb ↑Noun ↑Action ↑Verb ↑Noun ↑Action ↑
EK100✓19.920.47.221.520.58.1
20.821.38.022.422.79.1
EGO4D LTA✓17.116.610.318.117.811.4
18.217.511.119.919.112.9
EGTEA Gaze+✓42.137.628.943.438.931.3
43.538.530.346.540.734.1
+ +Table 5. Action anticipation results using MoViNet A0 [24] as an encoder, trained with and without our ETM protocol. We show results on multiple datasets. Performance is measured with class-mean recall@5 at 1s for EK100 and EGO4D, and top-1 accuracy at 0.5s for EGTEA Gaze+, following previous work [14] + +
Encoderw/o ETMwith ETM
MoViNet A0 [24]8.09.1
MoViNet A2 [24]10.210.8
X3D-XS [11]6.37.4
X3D-S [11]9.49.9
ConvNeXt-S 224 [31]4.15.0
EfficientNet B0 [57]7.28.0
EfficientNet B4 [57]9.410.1
AVT-b [14]13.413.5
+ +Table 6. Additional action anticipation results on EPIC-KITCHCHENS-100 for encoders trained with and without ETMs. We show class-mean recall@5 results without and with ETM for a variety of encoders. All models are first pretrained on the action recognition task, with and without the ETM protocol. + +# 4.4. Action Anticipation Experiments + +We also demonstrate how an encoder pre-trained on our ETM can improve performance in downstream tasks, in this case action anticipation. We follow the anticipation model architecture and the evaluation protocol from [14] and report the accuracies in verb, noun, and action prediction. However, our key difference is that the video encoder was pre-trained on an action recognition task, supplemented with ETM at training time. + +Table 5 showcases performance differences between encoders with and without ETM pretraining. Low complexity models, like MoviNet A0 (showcased in Table 5) exhibit improved performance in all three datasets. More comparisons on different model architectures are shown in Table 6 and Figure 4. + +# 4.5. ETM and Model Complexity + +To further investigate how model complexity interacts with the proposed ETM training protocol, we train pro + +gressively more complex models in the same architecture families and analyze how task performance varies with model complexity. We perform this analysis with the MoViNet [24] and X3D [11] architecture families: MoViNets scale from A0 to A4 with increasing parameter count and GFLOPs (Billion of Floating Point Operations), while X3Ds scale from XS to XXL. We train MoViNets A0 to A4 and X3Ds XS to M for our experiments. We use the same training setup as Section 3.2, where one instance of each model is trained without the ETM and another with it. + +For action recognition, we observe an increase in performance for low complexity models, i.e. A0 to A4, as shown in Figure 4 (a). The gain is larger for lower complexity models. We observe a similar effect for X3D models: the simpler the model is, the larger the improvement appears to be. This effect is visible on both EK100 and EGO4D. + +Figure 4 (b) shows that a similar effect occurs with action anticipation: using backbone models trained with our ETM protocol improves performance, especially if the backbone models are small. Once again, we use these models as encoders in the AVT [14] framework, replacing AVT-b (AVT's transformer-based encoder) with these variants and using AVT-h (AVT's transformer-based head) as a head. We only show GFLOPs of the encoders. We observe that on both datasets, encoders with lower representational power benefit more from the information about past and future regularities brought by the ETM. + +# 4.6.ETM Alternatives + +Our proposed ETM can take many forms. We analyzed the impact of three of the main properties of the matrix on action recognition performance: its size, the decay function used to build it, and the metric for temporal distance between events. + +Matrix Size. Computing the matrix with every unique event (verb-noun combination) appearing in the training set of EK100 yields a $13k \times 13k$ matrix. Since single-instance + +![](images/fa1455f63c3f5ef2f30dd1453a174425855cda7a2a562e449dbb61a065042301.jpg) +(a) Action Recognition + +![](images/1de0d35c4c0ef810c01825011588c6c28c86be6503b15c91893777f5afd26726.jpg) +Figure 4. Performance for models on EK100 [14] and EGTEA Gaze+ [28] in the same architecture family. We see that for both MoViNets [24] and X3Ds [11], leveraging the ETM during training improves performance, and the effect is stronger with lower complexity models. MoViNet+ETM $(\text{一} - \triangle)$ ; MoViNet $(\text{一} \triangle)$ ; X3D+ETM $(\text{一} - \bullet)$ ; X3D $(\text{一} \bullet)$ . + +![](images/7594e132d97e1f5505f93385ad3bc7660325f45a0ca227444ab2e49b42310f8e.jpg) +(b) Action Anticipation + +![](images/525bda6059814220327b0fc0d13586f9611c20eb8802670cd581c7e030d1bab5.jpg) + +actions may be noisier, we reduced the dimensionality of the matrix by removing events with less than 4 occurrences in the training set, yielding a subset of 2562 actions and a matrix of size $2562 \times 2562$ . We compare both alternatives in Table 7. + +Decay function alternatives. We compared linear decay, exponential decay and no decay. We show comparison results in Table 7. + +Measuring temporal distance. We considered the natural alternative of measuring temporal distance between events A and B in seconds, by looking at the time between the last frame of A and the first frame of B. We observed, however, that the durations of individual actions between A and B could vastly modulate the decay factor applied to $M(y_{A},y_{B})$ of our matrix, which might be counterproductive if A and B are causally related but simply separated by one long intermediate action. We therefore considered measuring distance through index difference in the sequence of actions for that video, which allows us to abstract away the durations of individual actions. + +We compared these alternatives based on their ability to boost performance on action recognition. We trained a ConvNeXt-S (224) [31] encoder with supervision from different ETM alternatives and evaluated action recognition performance (Table 7). Our results show that the choice of decay is generally not impactful unless no decay is used. We find our best performance on a matrix of reduced size, with index as a temporal distance metric, and with exponential decay. + +# 5. Conclusions + +We test a novel training regime for video understanding, in which model performance can be increased by supervising models with external representations of temporal regularities. We show that using the ETM as a training target allows arbitrary models to learn about the bi-directional tem + +
SizeDecayTemp. MetricPresent (top-1 accuracy)
VerbNounAction
13kLinearTime55.146.228.8
13kExponentialTime55.647.729.1
2.5kExponentialTime58.648.831.3
2.5kNo decay-58.148.030.5
2.5kLinearIndex60.149.331.9
2.5kExponentialIndex60.350.332.4
+ +Table 7. Difference in action recognition performance between ETM alternatives. We train ConvNeXts on EPIC-KITCHENS-100 with different verions of our ETM to evaluate the ability of ETM to improve action recognition performance. We evaluate top-1 accuracy on action recognition over the validation set of EK100. + +poral context of the action (i.e. the past and future), which improves action recognition and action anticipation performance. Finally, we tested the circumstances under which the ETM provides the largest benefit, and find significant performance boosts for low-complexity models. Amid ongoing efforts to increase the efficiency of computer vision models, we suggest that research which explores efficient architectures [11, 24] could be complemented by research that explores ways to leverage pre-learned representations of environmental regularities. + +The biggest benefit of this data structure is in its flexibility and simplicity: it is model-agnostic, computationally inexpensive, and easy to incorporate into the learning regime of any action recognition or anticipation architecture. + +Acknowledgements. This work was partially funded by a grant from Meta. We thank Karl Ridgeway and the team at Meta Reality Labs Research for their helpful comments. + +# References + +[1] Yazan Abu Farha, Qiuhong Ke, Bernt Schiele, and Juergen Gall. Long-term anticipation of activities with cycle consistency. In DAGM German Conference on Pattern Recognition, pages 159-173. Springer, 2020. 3 +[2] Yazan Abu Farha, Alexander Richard, and Juergen Gall. When will you do what?-anticipating temporal occurrences of activities. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5343-5352, 2018. 3 +[3] Hande Alemdar, Vincent Leroy, Adrien Prost-Boucle, and Frédéric Pétrot. Ternary neural networks for resource-efficient ai applications. In 2017 international joint conference on neural networks (IJCNN), pages 2547-2554. IEEE, 2017. 2 +[4] Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, and Quoc V Le. Attention augmented convolutional networks. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3286-3295, 2019. 1, 6 +[5] Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, and Doina Precup. Conditional computation in neural networks for faster models. arXiv preprint arXiv:1511.06297, 2015. 2 +[6] Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013. 2 +[7] Michael R Brent. Speech segmentation and word discovery: A computational perspective. Trends in Cognitive Sciences, 3(8):294-301, 1999. 2 +[8] João Carreira and Andrew Zisserman. Quo vadis, action recognition? A new model and the kinetics dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 4724-4733, 2017. 3 +[9] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Scaling egocentric vision: The epic-kitchens dataset. In European Conference on Computer Vision (ECCV), 2018. 3 +[10] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Jian Ma, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100. International Journal of Computer Vision (IJCV), 2021. 2, 3, 4, 5 +[11] Christoph Feichtenhofer. X3d: Expanding architectures for efficient video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 203-213, 2020. 1, 6, 7, 8 +[12] Vanessa E Ghosh and Asaf Gilboa. What is a memory schema? a historical perspective on current neuroscience literature. Neuropsychologia, 53:104-114, 2014. 2 +[13] Rohit Girdhar, Joao Carreira, Carl Doersch, and Andrew Zisserman. Video action transformer network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 244-253, 2019. 3 + +[14] Rohit Girdhar and Kristen Grauman. Anticipative video transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13505-13515, 2021. 1, 3, 4, 5, 6, 7, 8 +[15] Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. Knowledge distillation: A survey. International Journal of Computer Vision, 129(6):1789-1819, 2021. 2 +[16] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonio Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Iija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Christian Fuegen, Abraham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4d: Around the World in 3,000 Hours of Egocentric Video. CoRR, abs/2110.07058, 2021. 3, 4, 5, 6 +[17] Michelle R Greene and Aude Oliva. Recognition of natural scenes from global properties: Seeing the forest without representing the trees. Cognitive psychology, 58(2):137-176, 2009. 2 +[18] Song Han, Jeff Pool, John Tran, and William J. Dally. Learning both weights and connections for efficient neural networks. CoRR, abs/1506.02626, 2015. 2 +[19] Babak Hassibi and David Stork. Second order derivatives for network pruning: Optimal brain surgeon. Advances in neural information processing systems, 5, 1992. 2 +[20] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network (2015). arXiv preprint arXiv:1503.02531, 2, 2015. 2 +[21] Qiuhong Ke, Mario Fritz, and Bernt Schiele. Time-conditioned action anticipation in one shot. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9925-9934, 2019. 3 +[22] Dong-Jin Kim, Xiao Sun, Jinsoo Choi, Stephen Lin, and In So Kweon. Detecting human-object interactions with action co-occurrence priors. In European Conference on Computer Vision, pages 718–736. Springer, 2020. 3 +[23] Yoon Kim and Alexander M Rush. Sequence-level knowledge distillation. arXiv preprint arXiv:1606.07947, 2016. 2 + +[24] Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, and Boqing Gong. Movinets: Mobile video networks for efficient video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16020-16030, 2021. 1, 6, 7, 8 +[25] Christopher A Kurby and Jeffrey M Zacks. Segmentation in the perception and memory of events. Trends in cognitive sciences, 12(2):72-79, 2008. 2 +[26] Yann LeCun, John Denker, and Sara Solla. Optimal brain damage. Advances in neural information processing systems, 2, 1989. 2 +[27] Jinyu Li, Rui Zhao, Jui-Ting Huang, and Yifan Gong. Learning small-size dnn with output-distribution-based criteria. In Fifteenth annual conference of the international speech communication association, 2014. 2 +[28] Yin Li, Miao Liu, and James M. Rehg. In the eye of beholder: Joint learning of gaze and actions in first person video. In ECCV, 2018. 4, 5, 6, 8 +[29] Tailin Liang, John Glossner, Lei Wang, Shaobo Shi, and Xiaotong Zhang. Pruning and quantization for deep neural network acceleration: A survey. Neurocomputing, 461:370-403, 2021. 2 +[30] Alexander H Liu, SouYoung Jin, Cheng-I Jeff Lai, Andrew Rouditchenko, Aude Oliva, and James Glass. Cross-modal discrete representation learning. In Annual Meeting of the Association for Computational Linguistics (ACL), May 2022. 3 +[31] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. arXiv preprint arXiv:2201.03545, 2022. 6, 7, 8 +[32] Tambet Matiisen, Avital Oliver, Taco Cohen, and John Schulman. Teacher-student curriculum learning. IEEE transactions on neural networks and learning systems, 31(9):3732-3740, 2019. 2 +[33] Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, and Rogerio Feris. Ar-net: Adaptive frame resolution for efficient action recognition. In European Conference on Computer Vision, pages 86-104. Springer, 2020. 2 +[34] Carolyn B Mervis and Eleanor Rosch. Categorization of natural objects. Annual review of psychology, 32(1):89-115, 1981. 2 +[35] Antoine Miech, Ivan Laptev, Josef Sivic, Heng Wang, Lorenzo Torresani, and Du Tran. Leveraging the present to anticipate the future in videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0-0, 2019. 3 +[36] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic. Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In ICCV, pages 2630-2640, 2019. 3 +[37] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013. 2 +[38] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and + +phrases and their compositionality. Advances in neural information processing systems, 26, 2013. 2 +[39] Kyle Min and Jason J Corso. Integrating human gaze into attention for egocentric activity recognition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1069-1078, 2021. 5 +[40] Shayan Modiri Assari, Amir Roshan Zamir, and Mubarak Shah. Video classification using semantic concept co-occurrences. In CVPR, pages 2529–2536, 2014. 3 +[41] Mathew Monfort, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa Brown, Quanfu Fan, Dan Gutfreund, Carl Vondrick, and Aude Oliva. Moments in time dataset: one million videos for event understanding. IEEE TPAMI, 42(2):502-508, 2019. 3 +[42] Mathew Monfort, SouYoung Jin, Alexander Liu, David Harwath, Rogerio Feris, James Glass, and Aude Oliva. Spoken moments: Learning joint audio-visual representations from video descriptions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14871–14881, June 2021. 3 +[43] Mathew Monfort, Bowen Pan, Kandan Ramakrishnan, Alex Andonian, Barry A Mcnamara, Alex Lascelles, Quanfu Fan, Dan Gutfreund, Rogerio Feris, and Aude Oliva. Multimoments in time: Learning and interpreting models for multi-action video understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-1, 2021. 3 +[44] Tushar Nagarajan, Yanghao Li, Christoph Feichtenhofer, and Kristen Grauman. Ego-topo: Environment affordances from egocentric video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 163-172, 2020. 3 +[45] Bowen Pan, Rameswar Panda, Camilo Fosco, Chung-Ching Lin, Alex Andonian, Yue Meng, Kate Saenko, Aude Oliva, and Rogerio Feris. Va-red2: Video adaptive redundancy reduction. arXiv preprint arXiv:2102.07887, 2021. 2 +[46] Bowen Pan, Rameswar Panda, Yifan Jiang, Zhangyang Wang, Rogerio Feris, and Aude Oliva. Ia-red2: Interpretability-aware redundancy reduction for vision transformers. Advances in Neural Information Processing Systems, 34:24898-24911, 2021. 2 +[47] Jeffrey Pennington, Richard Socher, and Christopher D Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532-1543, 2014. 2 +[48] Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word representations, 2018. 2 +[49] Ivan Rodin, Antonino Furnari, Dimitrios Mavroeidis, and Giovanni Maria Farinella. Untrimmed action anticipation. arXiv preprint arXiv:2202.04132, 2022. 3 +[50] E Rosch, C B Mervis, W. D. Gray, D. M. Johnson, and P Boyes-Braem. Basic objects in natural categories. Cognitive psychology, 8(3):382-439, 1976. 2 +[51] Zahra Sadeghi, James L McClelland, and Paul Hoffman. You shall know an object by the company it keeps: An investi + +gation of semantic representations derived from object cooccurrence in visual scenes. Neuropsychologia, 76:52-61, 2015. 2 +[52] Fadime Sener, Dipika Singhania, and Angela Yao. Temporal aggregate representations for long-range video understanding. In European Conference on Computer Vision, pages 154-171. Springer, 2020. 3 +[53] Yang Shen, Bingbing Ni, Zefan Li, and Ning Zhuang. Ego-centric activity prediction via event modulated attention. In Proceedings of the European conference on computer vision (ECCV), pages 197-212, 2018. 3 +[54] Brynn E Sherman, Kathryn N Graves, and Nicholas B Turk-Browne. The prevalence and importance of statistical learning in human cognition and behavior. Current opinion in behavioral sciences, 32:15-20, 2020. 2 +[55] Bilge Soran, Ali Farhadi, and Linda Shapiro. Generating notifications for missing actions: Don't forget to turn the lights off! In Proceedings of the IEEE International Conference on Computer Vision, pages 4669-4677, 2015. 3 +[56] Chen Sun, Abhinav Shrivastava, Carl Vondrick, Rahul Sukthankar, Kevin Murphy, and Cordelia Schmid. Relational action forecasting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 273-283, 2019. 3 +[57] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019. 1, 6, 7 +[58] Antonio Torralba and Aude Oliva. Statistics of natural image categories. Network: computation in neural systems, 14(3):391, 2003. 2 +[59] Iulia Turc, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Well-read students learn better: On the importance of pre-training compact models. arXiv preprint arXiv:1908.08962, 2019. 2 +[60] Melissa Le-Hoa Võ. The meaning and structure of scenes. Vision Research, 181:10-20, 2021. 2 +[61] Daniel Weinland and Edmond Boyer. Action recognition using exemplar-based embedding. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pages 1-7. IEEE, 2008. 3 +[62] Less Wright and Nestor Demeure. Ranger21: a synergistic deep learning optimizer. arXiv preprint arXiv:2106.13731, 2021.5 +[63] Jeffrey M Zacks and Barbara Tversky. Event structure in perception and conception. Psychological bulletin, 127(1):3, 2001. 2 +[64] Haotong Zhang, Fuhai Chen, and Angela Yao. Weakly-supervised dense action anticipation. arXiv preprint arXiv:2111.07593, 2021. 3 +[65] Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba. Temporal relational reasoning in videos. 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However, it is computationally costly to learn these regularities from scratch. This presents a challenge for low-parameter models, like those running on edge devices (e.g. smartphones). Can the performance of models with low representational power be improved by supplementing training with additional information about these statistical regularities? We explore this in the domains of action recognition and action anticipation, leveraging the fact that actions are typically embedded in stereotypical sequences. We introduce the Event Transition Matrix (ETM), computed from action labels in an untrimmed video dataset, which captures the temporal context of a given action, operationalized as the likelihood that it was preceded or followed by each other action in the set. We show that including information from the ETM during training improves action recognition and anticipation performance on various egocentric video datasets. Through ablation and control studies, we show that the coherent sequence of information captured by our ETM is key to this effect, and we find that the benefit of this explicit representation of temporal context is most pronounced for smaller models. Code, matrices and models are available in our project page: https://camilofosco.com/etm網站" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 47, + 573, + 128, + 586 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 573, + 128, + 586 + ], + "spans": [ + { + "bbox": [ + 47, + 573, + 128, + 586 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 46, + 594, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 594, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 594, + 287, + 714 + ], + "type": "text", + "content": "A strength of computer vision models is their ability to identify and exploit statistical regularities in the world. Learning these regularities from scratch is computationally costly, which limits the accuracy of low-parameters models. It is critical to boost the performance of small models, since many devices lack the resources to host current state of the art models. One way to make the learning problem more tractable for small models may be to supplement them at training time with an explicit representation of the statistical regularities in the target domain. Here, we test whether" + } + ] + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 317, + 229, + 533, + 371 + ], + "blocks": [ + { + "bbox": [ + 317, + 229, + 533, + 371 + ], + "lines": [ + { + "bbox": [ + 317, + 229, + 533, + 371 + ], + "spans": [ + { + "bbox": [ + 317, + 229, + 533, + 371 + ], + "type": "image", + "image_path": "57d9fd47dcaca2b605eaa79cc1a203a8cd34d118308cc1905b9d61863e6cb697.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 382, + 547, + 471 + ], + "lines": [ + { + "bbox": [ + 304, + 382, + 547, + 471 + ], + "spans": [ + { + "bbox": [ + 304, + 382, + 547, + 471 + ], + "type": "text", + "content": "Figure 1. Action recognition performance difference between models trained with and without the proposed ETM approach. We train models with various model architectures, from small (left) to large (right): AVT-b [14], MoViNets [24] family, X3D [11] family, LambdaResNet-50 [4], and EfficientNets [57] family on EPIC-KITCHENS-100 [14]. We show that incorporating the ETM into training improves performance, and the impact is higher on smaller models." + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_caption" + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 496, + 546, + 531 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 496, + 546, + 531 + ], + "spans": [ + { + "bbox": [ + 304, + 496, + 546, + 531 + ], + "type": "text", + "content": "video understanding models can be improved by providing them with information about typical event sequences during training." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 534, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 534, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 534, + 547, + 714 + ], + "type": "text", + "content": "We introduce the Event Transition Matrix (ETM), illustrated in Figure 2, which leverages the fact that events in real-world videos often occur in predictable sequences. Each row and column indexes an event. In the rows, the ETM captures the likelihood that the event was followed by each of the other events in the set. In the columns, it captures the likelihood that the event was preceded by each other event. To compute a cell's value, we combine information from all previous and subsequent events, weighted by their temporal distance from the queried action. Crucially, this breaks markovian assumptions and allows the matrix to capture long-range relationships. The ETM has two important properties. First, it acts as an explicit representation of the likelihood of event transitions, which provides additional pertinent information that a model can" + } + ] + } + ], + "index": 19 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10693" + } + ] + } + ], + "index": 20 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 70, + 348, + 225 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 348, + 225 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 348, + 225 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 348, + 225 + ], + "type": "image", + "image_path": "2fc50e3cafbfca93075d828895c973c5d00050251bc5bf7587a11a2d741b1825.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 143, + 230, + 252, + 240 + ], + "lines": [ + { + "bbox": [ + 143, + 230, + 252, + 240 + ], + "spans": [ + { + "bbox": [ + 143, + 230, + 252, + 240 + ], + "type": "text", + "content": "(a) Low-level descriptions in [10]" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 371, + 77, + 539, + 227 + ], + "blocks": [ + { + "bbox": [ + 371, + 77, + 539, + 227 + ], + "lines": [ + { + "bbox": [ + 371, + 77, + 539, + 227 + ], + "spans": [ + { + "bbox": [ + 371, + 77, + 539, + 227 + ], + "type": "image", + "image_path": "109b46fcc27153da0e88c263fd0161409275ced2a14fabc7b122dcf5ba40c826.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 413, + 230, + 503, + 239 + ], + "lines": [ + { + "bbox": [ + 413, + 230, + 503, + 239 + ], + "spans": [ + { + "bbox": [ + 413, + 230, + 503, + 239 + ], + "type": "text", + "content": "(b) Event Transition Matrix" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 46, + 250, + 547, + 293 + ], + "lines": [ + { + "bbox": [ + 46, + 250, + 547, + 293 + ], + "spans": [ + { + "bbox": [ + 46, + 250, + 547, + 293 + ], + "type": "text", + "content": "Figure 2. We construct a given Event Transition Matrix (ETM) from action labels drawn from a given dataset of untrimmed videos. (a) Each video depicts a continuous activity, and is paired with human annotations indicating the individual, low-level actions that compose the activity. (b) Using these action labels, we created the ETM by recording the frequency with which a given action label was preceded or followed by each other action label, accumulated across the videos in our set." + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 315, + 288, + 362 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 315, + 288, + 362 + ], + "spans": [ + { + "bbox": [ + 46, + 315, + 288, + 362 + ], + "type": "text", + "content": "leverage without the cost of learning it for itself. Second, it augments representations of a given event with information about what came before and after it, providing additional target for the model to train on." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 363, + 287, + 579 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 363, + 287, + 579 + ], + "spans": [ + { + "bbox": [ + 46, + 363, + 287, + 579 + ], + "type": "text", + "content": "In the present paper, across multiple datasets and model architectures, we test the effectiveness of incorporating the ETM into training for low-parameter models. We test our approach on action recognition and action anticipation in egocentric video datasets, where actions occur in stereotypical sequences. However, this approach can apply to any kind of sequence in videos. We show that leveraging the ETM improves action recognition relative to baselines, and that this improvement relies on the coherence of the action sequence. We also show that action anticipation is improved with our ETM approach. In both cases, we show that the ETM approach can be incorporated into multiple different model architectures, and that the addition of the ETM has the highest impact on smaller models, as shown in Figure 1. Overall, this work demonstrates a potential path to more efficient models, based on supplementing small models at training time with explicit representations of regularities expected in the data." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 590, + 134, + 602 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 590, + 134, + 602 + ], + "spans": [ + { + "bbox": [ + 47, + 590, + 134, + 602 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 610, + 125, + 623 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 610, + 125, + 623 + ], + "spans": [ + { + "bbox": [ + 47, + 610, + 125, + 623 + ], + "type": "text", + "content": "2.1. Efficient AI" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 629, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 629, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 629, + 287, + 713 + ], + "type": "text", + "content": "State of the art neural networks for video understanding can achieve impressive classification results, but these models often have heavy computational requirements. Producing smaller, more efficient models that can perform comparable to larger ones is an active area of research, for deployment onto edge devices such as phones, wearables, drones or autonomous vehicles. Some approaches achieve effi" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 315, + 545, + 460 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 315, + 545, + 460 + ], + "spans": [ + { + "bbox": [ + 304, + 315, + 545, + 460 + ], + "type": "text", + "content": "ciency by first training a large network, then using a smaller network to learn the mapping from input to output vectors with little loss on performance (i.e. knowledge distillation [3,15,20,23,27,32]), or by identifying and removing non-essential parameters (i.e. parameter pruning [18,19,26,29]). Others rely on adaptive policies to shorten or simplify the inference process, reducing the effective size of the architecture [5,6,33,45] or input [45,46]. Here, we propose an alternative approach, which involves intervening at training time to supplement low-parameter architectures with external, explicit representations of statistical regularities in the target domain." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 467, + 545, + 480 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 467, + 545, + 480 + ], + "spans": [ + { + "bbox": [ + 306, + 467, + 545, + 480 + ], + "type": "text", + "content": "2.2. Statistical Regularities and Bayesian Learning." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 485, + 545, + 617 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 485, + 545, + 617 + ], + "spans": [ + { + "bbox": [ + 304, + 485, + 545, + 617 + ], + "type": "text", + "content": "The environment is full of regularities. Objects classes have co-occurring attributes [34, 50], environments classes have distinctive combinations of objects, surfaces and textures [17, 51, 58, 60], and episodes are composed of predictable sequences of events [25, 63]. Having the ability to detect and leverage these regularities is a key feature of intelligence. Cognitive research in humans has established that representations of contextual regularities underlie most aspects of intelligence, spanning learning, generalization, prediction, compression, memory, language and more [7, 12, 54]." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 617, + 545, + 713 + ], + "type": "text", + "content": "Machine learning relies on identifying and making use of regularities. Typically, this happens in an emergent manner from the training regime. However, some approaches also rely on explicit representations of such regularities. For example, in natural language processing, word embeddings [37, 38, 47, 48, 59] acting as explicit representations of word transition probabilities have provided the basis for many downstream tasks." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10694" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 242, + 85 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 242, + 85 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 242, + 85 + ], + "type": "text", + "content": "2.3. Action Recognition and Anticipation" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 91, + 289, + 270 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 91, + 289, + 270 + ], + "spans": [ + { + "bbox": [ + 46, + 91, + 289, + 270 + ], + "type": "text", + "content": "Action understanding is an enduring problem in video processing [8, 41, 43, 49, 64]. This can involve action recognition, which requires retrieving a label for a depicted action, and action anticipation, which requires retrieving a label for a future action based on a depicted action. Videos contain complex semantic relationships between objects, people and places, which evolve dynamically over time. Thus, action understanding models often incorporate elements of relational modeling, for example modeling the spatial relations among people or between people and objects [13, 56], between people and environment zones [44], or between the body parts of the person performing the actions [61]. More recently, multi-modal representation learning approaches have been introduced to learn the broader sensory context of actions [30, 36, 42]." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 278, + 287, + 304 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 278, + 287, + 304 + ], + "spans": [ + { + "bbox": [ + 47, + 278, + 287, + 304 + ], + "type": "text", + "content": "2.4. Action Recognition and Anticipation with Temporal Context" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 309, + 289, + 490 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 309, + 289, + 490 + ], + "spans": [ + { + "bbox": [ + 46, + 309, + 289, + 490 + ], + "type": "text", + "content": "Another kind of context that can be modeled in action understanding is the temporal relationships between the events that make up ongoing actions. Actions take place in sequences: you must first boil water and grind coffee beans, then pour water over the beans in order to successfully make coffee. Some models capitalize on such temporal regularities by modeling the dependence among individual video frames, either with RNNs [2, 56], relational reasoning networks [65] or more recently with attention-based methods and transformers [14, 52]. Other approaches model these sequences at a higher level, by learning the dependence among events (rather than individual frames), by using action co-occurrence matrices [22, 40], or by estimating the transitional probability between adjacent [35, 55] or more distant actions [21, 53]." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 490, + 289, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 490, + 289, + 635 + ], + "spans": [ + { + "bbox": [ + 46, + 490, + 289, + 635 + ], + "type": "text", + "content": "Building on this work, we show how leveraging explicit representations of typical action sequences in low-parameter models can improve their performance. Our motivation is that there is regularity in the temporal context of an action in both the past and the future, and leveraging this bi-directional regularity could bootstrap the model's learning. While some previous work [1] included a representation of the past in an action anticipation framework, our approach is distinct from it in two ways: (1) our representation of temporal context includes past, present and future, and extends over a larger time period, and (2) we pre-compute this representation, then use it to supplement learning." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 645, + 164, + 659 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 645, + 164, + 659 + ], + "spans": [ + { + "bbox": [ + 47, + 645, + 164, + 659 + ], + "type": "text", + "content": "3. Proposed Approach" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 715 + ], + "type": "text", + "content": "In this section, we introduce the Event Transition Matrix (ETM), which is an efficient approach to constructing a knowledge base about the temporal relation between events in video scenes (Section 3.1). We then propose a novel ap" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 72, + 546, + 109 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 546, + 109 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 546, + 109 + ], + "type": "text", + "content": "proach to pre-training with this matrix, leveraging these relations to help recognize current events or predict past or future events in the input video snippet (Section 3.2)." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 121, + 479, + 133 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 121, + 479, + 133 + ], + "spans": [ + { + "bbox": [ + 305, + 121, + 479, + 133 + ], + "type": "text", + "content": "3.1. Event Transition Matrix (ETM)" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "spans": [ + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "text", + "content": "Egocentric video datasets, such as EPIC-KITCHENS [9, 10] or EGO4D [16], contain long form videos that exhibit rich sequences of small actions or events. These sequences are densely annotated with labels for each event and timestamps indicating start and end points of each action. Suppose there are N event categories, " + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "inline_equation", + "content": "S = \\{s_1, s_2, \\dots, s_N\\}" + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "text", + "content": ". Each video is annotated with a sequence of event instances, which can be denoted as " + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "inline_equation", + "content": "\\{y_1, y_2, \\dots, y_{t-1}, y_t, \\dots\\}" + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "text", + "content": ". Using the timestamps, we can define a video snippet " + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "inline_equation", + "content": "x_k" + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "text", + "content": " that contains the specific event, which is given the instance label " + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "inline_equation", + "content": "y_k" + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "text", + "content": ", based on its category. Event descriptions, i.e. " + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "inline_equation", + "content": "x_k" + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "inline_equation", + "content": "y_k" + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "text", + "content": ", refer to the current ongoing action event, but do not convey the broader temporal context of that action, including what came before and after it. As in the example in Figure 2, when making coffee, \"add coffee beans\" happens earlier than \"brew coffee\", and these two events are temporally dependent on each other. To leverage the temporal context surrounding events, we compute the frequencies with which each event comes before/after each other event. More formally, we define a square matrix, " + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "inline_equation", + "content": "\\mathbf{M}_{N,N}" + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "text", + "content": ", where each row and column corresponds to an event in " + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "inline_equation", + "content": "S" + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "inline_equation", + "content": "\\mathbf{M}(i,j)" + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "text", + "content": " corresponds to an estimated probability that an event " + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "inline_equation", + "content": "s_i" + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "text", + "content": " happens before " + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "inline_equation", + "content": "s_j" + }, + { + "bbox": [ + 304, + 140, + 547, + 428 + ], + "type": "text", + "content": ", and vice versa. This matrix is built with training set observations." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "spans": [ + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "text", + "content": "A naive approach for building the matrix is to update " + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "inline_equation", + "content": "\\mathbf{M}(y_l, y_m)" + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "text", + "content": " for all possible pairs of events where event " + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "text", + "content": " happens earlier than event " + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "text", + "content": ". However, this approach does not incorporate the temporal distance between two events, since " + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "inline_equation", + "content": "(y_l, y_m)" + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "inline_equation", + "content": "(y_l, y_n)" + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "text", + "content": " contribute equally to constructing the matrix even when " + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "inline_equation", + "content": "l" + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "text", + "content": " is much farther away from " + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "text", + "content": ". In reality, actions separated by more time are less likely to be part of the same overall activity, and are therefore less likely to consistently co-vary. Therefore, we update the matrix with a decay function " + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "inline_equation", + "content": "\\delta(\\cdot)" + }, + { + "bbox": [ + 304, + 430, + 547, + 550 + ], + "type": "text", + "content": ", as" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 372, + 562, + 546, + 576 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 372, + 562, + 546, + 576 + ], + "spans": [ + { + "bbox": [ + 372, + 562, + 546, + 576 + ], + "type": "interline_equation", + "content": "\\mathbf {M} \\left(y _ {l}, y _ {m}\\right) + = \\delta (m - l). \\tag {1}", + "image_path": "46e37d7bf9f1db28193b241f43db79e07808e664cfb80f09ff28e8e860c11bde.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 590, + 546, + 613 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 590, + 546, + 613 + ], + "spans": [ + { + "bbox": [ + 304, + 590, + 546, + 613 + ], + "type": "text", + "content": "We normalize the matrix " + }, + { + "bbox": [ + 304, + 590, + 546, + 613 + ], + "type": "inline_equation", + "content": "\\mathbf{M}" + }, + { + "bbox": [ + 304, + 590, + 546, + 613 + ], + "type": "text", + "content": " by the sum of each row and define a new matrix " + }, + { + "bbox": [ + 304, + 590, + 546, + 613 + ], + "type": "inline_equation", + "content": "\\mathbf{M}^R" + }, + { + "bbox": [ + 304, + 590, + 546, + 613 + ], + "type": "text", + "content": " as" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 365, + 624, + 545, + 654 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 365, + 624, + 545, + 654 + ], + "spans": [ + { + "bbox": [ + 365, + 624, + 545, + 654 + ], + "type": "interline_equation", + "content": "\\mathbf {M} ^ {R} (i, j) = \\frac {\\mathbf {M} (i , j)}{\\sum_ {k = 0} ^ {N} \\mathbf {M} (i , k)}. \\tag {2}", + "image_path": "6edd0a9ebdb41e8749d46857f8597575dc99e162554af1be815ec35791e96e27.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "text", + "content": "Each row in this matrix, " + }, + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{M}^R (i,:)" + }, + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "text", + "content": ", can be seen as an empirical approximation of the distribution of possible events that can be transitioned to following event " + }, + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "inline_equation", + "content": "s_i" + }, + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "text", + "content": ". Similarly, we can also define a matrix " + }, + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "inline_equation", + "content": "\\mathbf{M}^C" + }, + { + "bbox": [ + 304, + 665, + 547, + 714 + ], + "type": "text", + "content": " by column-wise normalization." + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10695" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 123, + 75, + 473, + 240 + ], + "blocks": [ + { + "bbox": [ + 123, + 75, + 473, + 240 + ], + "lines": [ + { + "bbox": [ + 123, + 75, + 473, + 240 + ], + "spans": [ + { + "bbox": [ + 123, + 75, + 473, + 240 + ], + "type": "image", + "image_path": "11ef462ac7094adb0baeb2f626010bc56b77b7566829522d6e3a144ef140b8db.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 251, + 547, + 285 + ], + "lines": [ + { + "bbox": [ + 46, + 251, + 547, + 285 + ], + "spans": [ + { + "bbox": [ + 46, + 251, + 547, + 285 + ], + "type": "text", + "content": "Figure 3. Overview of the proposed approach for pre-training with ETM. The model takes a video snippet and encodes the input to predict the present, past, and future events. In particular, we use the column and the row of the ETM which correspond to the given event. These represent the distributions over past actions, and future actions respectively. More details are written in Section 3.2." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 304, + 288, + 327 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 304, + 288, + 327 + ], + "spans": [ + { + "bbox": [ + 47, + 304, + 288, + 327 + ], + "type": "text", + "content": "3.2. Encoder Training with the Event Transition Matrix" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "text", + "content": "We propose a novel training protocol to enhance an arbitrary encoder by leveraging the ETM. A typical event recognition model is trained to take a video snippet, " + }, + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "inline_equation", + "content": "x_{t}" + }, + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "text", + "content": ", transform it into a feature vector with an encoder " + }, + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "inline_equation", + "content": "f(\\cdot)" + }, + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "text", + "content": ", and recognize an event in the input video by predicting a label from that vector. More formally, the model is trained to increase " + }, + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "inline_equation", + "content": "p(y_{t}|g_{q}(f(x_{t}))" + }, + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "inline_equation", + "content": "g_{q}" + }, + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "text", + "content": " represents a module that maps features into a probability distribution over labels. In our framework, we call " + }, + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "inline_equation", + "content": "g_{q}" + }, + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "text", + "content": " the present module and train it with a typical cross-entropy loss function, " + }, + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{q}" + }, + { + "bbox": [ + 46, + 335, + 287, + 453 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 454, + 287, + 538 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 454, + 287, + 538 + ], + "spans": [ + { + "bbox": [ + 46, + 454, + 287, + 538 + ], + "type": "text", + "content": "Our proposed method adds two additional modules to predict past and future event probabilities, as shown in Figure 3. A past module " + }, + { + "bbox": [ + 46, + 454, + 287, + 538 + ], + "type": "inline_equation", + "content": "g_{c}(\\cdot)" + }, + { + "bbox": [ + 46, + 454, + 287, + 538 + ], + "type": "text", + "content": " receives " + }, + { + "bbox": [ + 46, + 454, + 287, + 538 + ], + "type": "inline_equation", + "content": "f(x_{t})" + }, + { + "bbox": [ + 46, + 454, + 287, + 538 + ], + "type": "text", + "content": " and predicts a distribution of events occurring before the current action (which corresponds to a column of the column-normalized ETM, " + }, + { + "bbox": [ + 46, + 454, + 287, + 538 + ], + "type": "inline_equation", + "content": "\\mathbf{M}^{C}" + }, + { + "bbox": [ + 46, + 454, + 287, + 538 + ], + "type": "text", + "content": "). This module attempts to minimize the following loss:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 98, + 544, + 287, + 578 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 98, + 544, + 287, + 578 + ], + "spans": [ + { + "bbox": [ + 98, + 544, + 287, + 578 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {c} = \\sum_ {k = 0} ^ {N} (C (k) - \\mathbf {M} ^ {C} (y _ {t}, k)) ^ {2}, \\tag {3}", + "image_path": "d2073cd586b360e74c7ccf5ad2d80151bcbbef0e37faf57265410309c442613a.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 583, + 287, + 633 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 583, + 287, + 633 + ], + "spans": [ + { + "bbox": [ + 47, + 583, + 287, + 633 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 583, + 287, + 633 + ], + "type": "inline_equation", + "content": "C(k) = g_c(f(x_t))" + }, + { + "bbox": [ + 47, + 583, + 287, + 633 + ], + "type": "text", + "content": ". Similarly, a future module " + }, + { + "bbox": [ + 47, + 583, + 287, + 633 + ], + "type": "inline_equation", + "content": "g_r(\\cdot)" + }, + { + "bbox": [ + 47, + 583, + 287, + 633 + ], + "type": "text", + "content": " predicts a distribution of future events, and tries to minimize the distance between the prediction and a row of the row-normalized ETM, " + }, + { + "bbox": [ + 47, + 583, + 287, + 633 + ], + "type": "inline_equation", + "content": "\\mathbf{M}^R(:, y_t)" + }, + { + "bbox": [ + 47, + 583, + 287, + 633 + ], + "type": "text", + "content": ", through the loss:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 99, + 638, + 287, + 670 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 99, + 638, + 287, + 670 + ], + "spans": [ + { + "bbox": [ + 99, + 638, + 287, + 670 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {r} = \\sum_ {k = 0} ^ {N} (R (k) - \\mathbf {M} ^ {R} (k, y _ {t})) ^ {2}, \\tag {4}", + "image_path": "4b13a749059ccb57766c803b2190d8040fceaca8e76d1d674c5321b87a66e801.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 677, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 677, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 677, + 288, + 715 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 677, + 288, + 715 + ], + "type": "inline_equation", + "content": "R(k) = g_r(f(x_t))" + }, + { + "bbox": [ + 47, + 677, + 288, + 715 + ], + "type": "text", + "content": ". Altogether, our framework augments an arbitrary encoder with these past and future modules and minimizes the following objective during training:" + } + ] + } + ], + "index": 8 + }, + { + "type": "table", + "bbox": [ + 309, + 302, + 548, + 361 + ], + "blocks": [ + { + "bbox": [ + 309, + 302, + 548, + 361 + ], + "lines": [ + { + "bbox": [ + 309, + 302, + 548, + 361 + ], + "spans": [ + { + "bbox": [ + 309, + 302, + 548, + 361 + ], + "type": "table", + "html": "
DatasetSegm.Actionsτa(s)Metrics (AR, AA)
EK100 [10]90k38071.0top-1, rec@5
EGO4D [16]39.2k35421.0top-1, rec@5
EGTEA [28]10.3k1060.5top-1, top-1
", + "image_path": "86e49d55cece979af68e8e72958c26050d8fe9869190ba78f486148e78b7aff3.jpg" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "table_body" + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 368, + 547, + 447 + ], + "lines": [ + { + "bbox": [ + 305, + 368, + 547, + 447 + ], + "spans": [ + { + "bbox": [ + 305, + 368, + 547, + 447 + ], + "type": "text", + "content": "Table 1. Properties of the datasets used in our experiments. We showcase our three main datasets: EPIC-KITCHENS-100 (EK100), EGO4D LTA (EGO4D) and EGTEA Gaze+ (EGTEA). " + }, + { + "bbox": [ + 305, + 368, + 547, + 447 + ], + "type": "inline_equation", + "content": "\\tau_{a}" + }, + { + "bbox": [ + 305, + 368, + 547, + 447 + ], + "type": "text", + "content": " corresponds to the time between input clips and target segments in action anticipation, and follows prior work [14]. The Metrics column shows the main performance metrics used in our action recognition and action anticipation experiments, respectively." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 367, + 468, + 545, + 483 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 367, + 468, + 545, + 483 + ], + "spans": [ + { + "bbox": [ + 367, + 468, + 545, + 483 + ], + "type": "interline_equation", + "content": "\\mathcal {L} = \\omega_ {q} \\mathcal {L} _ {q} + \\omega_ {c} \\mathcal {L} _ {c} + \\omega_ {r} \\mathcal {L} _ {r}, \\tag {5}", + "image_path": "db13ebaeeb8e4b67a364f2467e3aa27dfded8add8abf3809f2f8a10eedb9b4d0.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 487, + 547, + 561 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 487, + 547, + 561 + ], + "spans": [ + { + "bbox": [ + 304, + 487, + 547, + 561 + ], + "type": "text", + "content": "This training setup attempts to push the encoder towards outputting richer event representations that capture the regularities of the event's typical temporal context. Although simplistic, the framework is effective: we observe that instantiating " + }, + { + "bbox": [ + 304, + 487, + 547, + 561 + ], + "type": "inline_equation", + "content": "g_{c}" + }, + { + "bbox": [ + 304, + 487, + 547, + 561 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 487, + 547, + 561 + ], + "type": "inline_equation", + "content": "g_{r}" + }, + { + "bbox": [ + 304, + 487, + 547, + 561 + ], + "type": "text", + "content": " with simple multi-layer perceptrons is enough to achieve strong results on low-complexity models." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 571, + 432, + 585 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 571, + 432, + 585 + ], + "spans": [ + { + "bbox": [ + 306, + 571, + 432, + 585 + ], + "type": "text", + "content": "4. Experimental Results" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 591, + 547, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 591, + 547, + 651 + ], + "spans": [ + { + "bbox": [ + 304, + 591, + 547, + 651 + ], + "type": "text", + "content": "In this section, we demonstrate the effectiveness of training with the Event Transition Matrix (ETM) on action recognition (Section 4.3) and action anticipation (Section 4.4). We describe the datasets used in Section 4.1 and give implementation details in Section 4.2." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 658, + 369, + 670 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 658, + 369, + 670 + ], + "spans": [ + { + "bbox": [ + 306, + 658, + 369, + 670 + ], + "type": "text", + "content": "4.1. Datasets" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 677, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 677, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 306, + 677, + 547, + 715 + ], + "type": "text", + "content": "EPIC-KITCHENS-100. [10] The EPIC-KITCHENS-100 dataset (EK100) contains 700 unscripted videos depicting cooking actions, totalling 100 hours. It presents verb" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "10696" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 286, + 144 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 286, + 144 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 286, + 144 + ], + "type": "text", + "content": "and noun annotations over 90k segments of varying length. The dataset depicts 97 unique verbs, 300 unique nouns that combine to yield 3807 actions. We work with the provided training and validation sets, containing 67.2k and 9.7k segments respectively. Our ETM is constructed exclusively with training set data." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 144, + 286, + 312 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 144, + 286, + 312 + ], + "spans": [ + { + "bbox": [ + 46, + 144, + 286, + 312 + ], + "type": "text", + "content": "EGO4D. [16] This newer egocentric dataset contains 3670 hours of video from 71 different participants. We use the long term anticipation annotations provided by the authors [16], which reduces the set to a training split containing 493 unique videos with 23610 segments, and a validation set with 380 videos cut into 15587 segments. These annotations present 115 unique verbs, 477 unique nouns and 3542 unique actions. All segments are 240-frame long. To homogenize our setup and avoid generating ETMs with a biased diagonal, we merge contiguous segments together if they depict the same action. This yields 18896 segments for training and 12676 segments for validation, and we use this training set for ETM construction. We refer to this set as EGO4D LTA." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 312, + 286, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 312, + 286, + 407 + ], + "spans": [ + { + "bbox": [ + 46, + 312, + 286, + 407 + ], + "type": "text", + "content": "EGTEA Gaze+ [28] is another popular egocentric dataset with 10k segments annotated with 19 verbs, 51 nouns and 106 unique actions. We report performance for split 1 (provided by the authors), containing 8299 training segments and 2022 validation segments. Following previous work [14, 39], both action recognition and anticipation performance are measured with top-1 accuracy. The properties of each dataset are summarized in Table 1." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 418, + 180, + 430 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 418, + 180, + 430 + ], + "spans": [ + { + "bbox": [ + 47, + 418, + 180, + 430 + ], + "type": "text", + "content": "4.2. Implementation Details" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 437, + 286, + 509 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 437, + 286, + 509 + ], + "spans": [ + { + "bbox": [ + 46, + 437, + 286, + 509 + ], + "type": "text", + "content": "ETM construction. We construct our ETM offline by observing all videos of our training set and adding a contribution to " + }, + { + "bbox": [ + 46, + 437, + 286, + 509 + ], + "type": "inline_equation", + "content": "M(y_{l},y_{m})" + }, + { + "bbox": [ + 46, + 437, + 286, + 509 + ], + "type": "text", + "content": " modulated by a decay function " + }, + { + "bbox": [ + 46, + 437, + 286, + 509 + ], + "type": "inline_equation", + "content": "\\delta (v)" + }, + { + "bbox": [ + 46, + 437, + 286, + 509 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 46, + 437, + 286, + 509 + ], + "type": "inline_equation", + "content": "v" + }, + { + "bbox": [ + 46, + 437, + 286, + 509 + ], + "type": "text", + "content": " is the temporal distance between the events, as in Equation 1. Importantly, to minimize sparsity, we only consider events that appear at least 4 times in the training set." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 510, + 286, + 675 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 510, + 286, + 675 + ], + "spans": [ + { + "bbox": [ + 46, + 510, + 286, + 675 + ], + "type": "text", + "content": "Encoder training. With a given ETM, we train an encoder to generate embeddings that can be used to both recognize actions and predict ETM vectors from snippets of video. The encoder is tasked with producing an embedding that is fed to three modules: an action classification head (present module), a past vector regressor and a future vector regressor, as seen in Figure 3. We instantiate the past, present and future modules with simple fully-connected layers. We train with the loss in Eq. 5 and set " + }, + { + "bbox": [ + 46, + 510, + 286, + 675 + ], + "type": "inline_equation", + "content": "\\omega_{q} = \\omega_{c} = \\omega_{r} = \\frac{1}{3}" + }, + { + "bbox": [ + 46, + 510, + 286, + 675 + ], + "type": "text", + "content": ". As the ETM is built with actions that appear at least four times in the training data, certain rare actions do not have target vectors. In those cases, the loss associated to regressing the past and future vectors is set to zero, and no gradients are back-propagated." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 677, + 286, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 286, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 286, + 713 + ], + "type": "text", + "content": "We train our models with the Ranger21 optimizer [62] with a learning rate of 0.01 and cosine annealing with a 20 epochs half-cycle. We use a batch-size of 32, weight decay" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 305, + 72, + 523, + 84 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 523, + 84 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 523, + 84 + ], + "type": "text", + "content": "of 0.0001 and dropout with " + }, + { + "bbox": [ + 305, + 72, + 523, + 84 + ], + "type": "inline_equation", + "content": "p = 0.5" + }, + { + "bbox": [ + 305, + 72, + 523, + 84 + ], + "type": "text", + "content": " where applicable." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "spans": [ + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "text", + "content": "Training setup for Action Anticipation. We follow the framework used in [14], where an encoder generates features from an input clip " + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "text", + "content": " before feeding them to a decoder attempting to predict the future of " + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "text", + "content": ", which is the label " + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "inline_equation", + "content": "y_{s}" + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "text", + "content": " of an action segment " + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "text", + "content": " happening " + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "inline_equation", + "content": "\\tau_{a}" + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "text", + "content": " seconds after " + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "text", + "content": " ends. In other words, for each action segment starting at time " + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "inline_equation", + "content": "\\tau_{s}" + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "text", + "content": ", the decoder attempts to predict its action label " + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "inline_equation", + "content": "y_{s}" + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "text", + "content": " using a clip " + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "inline_equation", + "content": "c" + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "text", + "content": " that ends " + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "inline_equation", + "content": "\\tau_{a}" + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "text", + "content": " seconds before " + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "inline_equation", + "content": "s" + }, + { + "bbox": [ + 304, + 85, + 545, + 204 + ], + "type": "text", + "content": ". Crucially, we analyze how using an encoder trained with ETM supervision affects the performance of the system." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 205, + 545, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 205, + 545, + 300 + ], + "spans": [ + { + "bbox": [ + 304, + 205, + 545, + 300 + ], + "type": "text", + "content": "We analyze two different setups: one were we freeze our pretrained encoder and only train the decoder, and another where we allow our encoder to be finetuned alongside the decoder. Following the AVT framework [14], we replace AVT-b (AVT's transformer-based encoder) with several variants of our own encoders. We keep AVT-h (AVT's transformer-based head) as a decoder and finetune it with our codes as input." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 301, + 545, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 301, + 545, + 373 + ], + "spans": [ + { + "bbox": [ + 304, + 301, + 545, + 373 + ], + "type": "text", + "content": "We train until validation performance plateaus, and we operate with a batch size of 32, a learning rate of " + }, + { + "bbox": [ + 304, + 301, + 545, + 373 + ], + "type": "inline_equation", + "content": "10^{-4}" + }, + { + "bbox": [ + 304, + 301, + 545, + 373 + ], + "type": "text", + "content": " with cosine annealed decay, and the Ranger21 optimizer. At test time, we employ 3-crop testing following [14], where we compute three 224px spatial crops from 248px input frames, and average the predictions of each segment." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 382, + 481, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 382, + 481, + 396 + ], + "spans": [ + { + "bbox": [ + 306, + 382, + 481, + 396 + ], + "type": "text", + "content": "4.3. Action Recognition Experiments" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 402, + 545, + 556 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 402, + 545, + 556 + ], + "spans": [ + { + "bbox": [ + 304, + 402, + 545, + 556 + ], + "type": "text", + "content": "We first ask whether models trained with ETM supervision showed improved action recognition performance. To do that, we train a baseline MoViNet A0 on EK100. Following [10], our present head has a two-way output: one to predict verbs and one to predict nouns, with an average verb/noun loss. Accordingly, in Table 2, the Present tab shows the top-1 accuracies in verb and noun classification as well as Action accuracy, which corresponds to (verb, noun) pairs. We compare this to a MoViNet A0 trained on the same data but augmented with our ETM framework as described in Section 3. For this model, we also show the mean absolute error (MAE) on the past and the future predictions. Here, lower scores indicate better performance." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 558, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 558, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 558, + 545, + 713 + ], + "type": "text", + "content": "To demonstrate that the performance gain is not merely due to the extra parameters gained from training with an external matrix, we compare against three more baselines, with versions of the matrix that disrupt the action sequence information to different degrees. In the Full shuffle baseline, cells of the matrix are randomly shuffled. This scrambles any sequence information in the matrix, while preserving the distribution of values across it. In the Columns shuffle, columns are kept intact, but their position in the matrix is randomly shuffled. This preserves individual distributions over past actions, but randomizes their correspondence with columns labels and scrambles the rows (similarly, the Row shuffle kept rows intact but scrambled their position in the" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10697" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 128, + 70, + 467, + 210 + ], + "blocks": [ + { + "bbox": [ + 128, + 70, + 467, + 210 + ], + "lines": [ + { + "bbox": [ + 128, + 70, + 467, + 210 + ], + "spans": [ + { + "bbox": [ + 128, + 70, + 467, + 210 + ], + "type": "table", + "html": "
ModelPresentMAE on Past ↓MAE on Future ↓
Verb ↑Noun ↑Action ↑
Baseline64.847.436.8--
Full shuffle64.147.236.34.1174.012
Columns/rows shuffle64.747.636.73.2543.101
Co-occurrence65.349.037.91.2111.115
Only past vector65.749.338.20.901-
Only future vector65.549.838.3-0.898
ETM (Ours)67.951.240.20.8820.859
", + "image_path": "c45bd569b9b313588e68e6460d4222ca67db4505d7eaec9501af81378f32dcf4.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 55, + 269, + 281, + 394 + ], + "blocks": [ + { + "bbox": [ + 46, + 217, + 547, + 251 + ], + "lines": [ + { + "bbox": [ + 46, + 217, + 547, + 251 + ], + "spans": [ + { + "bbox": [ + 46, + 217, + 547, + 251 + ], + "type": "text", + "content": "Table 2. Action recognition results on various baseline models. We train the models on the EPIC-KITCHENS-100 dataset [14] with the MoViNet A0 [24]. For Present prediction, we show the verb and noun classification results and retrieval scores of (verb, noun). For Past and Future prediction, we show the mean absolute error (MAE) on the past and the future predicted vectors." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 55, + 269, + 281, + 394 + ], + "lines": [ + { + "bbox": [ + 55, + 269, + 281, + 394 + ], + "spans": [ + { + "bbox": [ + 55, + 269, + 281, + 394 + ], + "type": "table", + "html": "
DatasetModelPresent
VerbNounAction
EK100 [14]Baseline64.847.436.8
ETM(Ours)67.951.240.2
EGO4DBaseline32.323.521.1
LTA [16]ETM(Ours)32.924.222.0
EGTEABaseline81.271.760.4
Gaze+ [28]ETM(Ours)83.472.962.5
", + "image_path": "23771f059f503111d2ca6899c2d64bb96f23c28a43d9bca32db1d2025e98c139.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 470, + 287, + 543 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 470, + 287, + 543 + ], + "spans": [ + { + "bbox": [ + 46, + 470, + 287, + 543 + ], + "type": "text", + "content": "matrix). Finally, we also construct a matrix which does not consider the order of the events, instead using a symmetric matrix of co-occurrence for training the action recognition model. In Table 2, we see that our proposed ETM surpasses all other baselines. We show additional results in the supplementary material." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 544, + 287, + 664 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 544, + 287, + 664 + ], + "spans": [ + { + "bbox": [ + 46, + 544, + 287, + 664 + ], + "type": "text", + "content": "Next, we further conduct ablation studies to probe whether it is necessary to use both the past and future information from the ETM to see these performance benefits. We train two additional models including only either the past or the future module. Interestingly, as shown in Table 2, training models with either side of the temporal information still improves performance to some extent, but we observe that training with the full matrix gives us the biggest improvement. These experiments serve as quantitative justifications for our framework's design choices." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 665, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 713 + ], + "type": "text", + "content": "To show that the proposed ETM approach is effective on multiple datasets, we also show in Table 3 the performance gains on two other egocentric activity datasets that contain untrimmed videos of actions annotated with verbs" + } + ] + } + ], + "index": 6 + }, + { + "type": "table", + "bbox": [ + 326, + 269, + 526, + 423 + ], + "blocks": [ + { + "bbox": [ + 46, + 401, + 288, + 447 + ], + "lines": [ + { + "bbox": [ + 46, + 401, + 288, + 447 + ], + "spans": [ + { + "bbox": [ + 46, + 401, + 288, + 447 + ], + "type": "text", + "content": "Table 3. Action recognition results on various datasets using MoViNet A0 [24] with and without ETM supervision. As can be seen, augmenting the model with our framework improves performance on all datasets." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 326, + 269, + 526, + 423 + ], + "lines": [ + { + "bbox": [ + 326, + 269, + 526, + 423 + ], + "spans": [ + { + "bbox": [ + 326, + 269, + 526, + 423 + ], + "type": "table", + "html": "
Modelw/o ETMw/ ETM
MoviNet A0 [24]36.840.2
MoviNet A2 [24]41.243.4
X3D-XS [11]35.538.1
X3D-S [11]40.542.2
ConvNeXt-S 224 [31]20.132.4
LambdaResNet-50 [4]26.627.1
EfficientNet-B0 [57]25.326.3
EfficientNet-B4 [11]29.229.4
AVT-b [14]30.430.7
", + "image_path": "297e7ddd0fe8bf08ce28628e561ce47ec2e477fccf3ed23989c1350aed121f23.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 430, + 547, + 475 + ], + "lines": [ + { + "bbox": [ + 305, + 430, + 547, + 475 + ], + "spans": [ + { + "bbox": [ + 305, + 430, + 547, + 475 + ], + "type": "text", + "content": "Table 4. Additional action recognition results on EPIC-KITCHENS-100 for models trained with and without ETMs. We show top-1 action classification results for several different architectures." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 305, + 521, + 545, + 570 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 521, + 545, + 570 + ], + "spans": [ + { + "bbox": [ + 305, + 521, + 545, + 570 + ], + "type": "text", + "content": "and nouns. Specifically, we train models on the EGO4D dataset [16] using the long term action anticipation annotations, as well as the Extended GTEA Gaze+ (EGTEA Gaze+) dataset [28]." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 582, + 547, + 715 + ], + "type": "text", + "content": "The ETM is model-agnostic and can be incorporated into any model architecture. We illustrate this by showing how this technique performs on a breadth of models: In Table 4, we show action recognition results for multiple model architectures with and without ETM training. Our framework benefits low-complexity models the most. Adding ETM supervision to an advanced encoder like AVT-b [14] produces slight improvements, but small models like EfficientNet-B0 achieve noticeable gains. We show qualitative performance results with correct and incorrect classifications in the supplementary material." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "10698" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 72, + 70, + 523, + 198 + ], + "blocks": [ + { + "bbox": [ + 72, + 70, + 523, + 198 + ], + "lines": [ + { + "bbox": [ + 72, + 70, + 523, + 198 + ], + "spans": [ + { + "bbox": [ + 72, + 70, + 523, + 198 + ], + "type": "table", + "html": "
DatasetFrozen Encoder?BaselineETM (Ours)
Verb ↑Noun ↑Action ↑Verb ↑Noun ↑Action ↑
EK100✓19.920.47.221.520.58.1
20.821.38.022.422.79.1
EGO4D LTA✓17.116.610.318.117.811.4
18.217.511.119.919.112.9
EGTEA Gaze+✓42.137.628.943.438.931.3
43.538.530.346.540.734.1
", + "image_path": "cc5fdb18fa6cd21a8ed0d6d229a4bc05a856cbed07805e080351698cbec08df0.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 65, + 258, + 272, + 397 + ], + "blocks": [ + { + "bbox": [ + 46, + 206, + 547, + 242 + ], + "lines": [ + { + "bbox": [ + 46, + 206, + 547, + 242 + ], + "spans": [ + { + "bbox": [ + 46, + 206, + 547, + 242 + ], + "type": "text", + "content": "Table 5. Action anticipation results using MoViNet A0 [24] as an encoder, trained with and without our ETM protocol. We show results on multiple datasets. Performance is measured with class-mean recall@5 at 1s for EK100 and EGO4D, and top-1 accuracy at 0.5s for EGTEA Gaze+, following previous work [14]" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 65, + 258, + 272, + 397 + ], + "lines": [ + { + "bbox": [ + 65, + 258, + 272, + 397 + ], + "spans": [ + { + "bbox": [ + 65, + 258, + 272, + 397 + ], + "type": "table", + "html": "
Encoderw/o ETMwith ETM
MoViNet A0 [24]8.09.1
MoViNet A2 [24]10.210.8
X3D-XS [11]6.37.4
X3D-S [11]9.49.9
ConvNeXt-S 224 [31]4.15.0
EfficientNet B0 [57]7.28.0
EfficientNet B4 [57]9.410.1
AVT-b [14]13.413.5
", + "image_path": "d5ccbb72b817c5e1e2d13c343802d6327b687db42d227442eb411ddebbc144b3.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 405, + 288, + 462 + ], + "lines": [ + { + "bbox": [ + 46, + 405, + 288, + 462 + ], + "spans": [ + { + "bbox": [ + 46, + 405, + 288, + 462 + ], + "type": "text", + "content": "Table 6. Additional action anticipation results on EPIC-KITCHCHENS-100 for encoders trained with and without ETMs. We show class-mean recall@5 results without and with ETM for a variety of encoders. All models are first pretrained on the action recognition task, with and without the ETM protocol." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 47, + 473, + 226, + 487 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 473, + 226, + 487 + ], + "spans": [ + { + "bbox": [ + 47, + 473, + 226, + 487 + ], + "type": "text", + "content": "4.4. Action Anticipation Experiments" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 492, + 287, + 588 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 492, + 287, + 588 + ], + "spans": [ + { + "bbox": [ + 46, + 492, + 287, + 588 + ], + "type": "text", + "content": "We also demonstrate how an encoder pre-trained on our ETM can improve performance in downstream tasks, in this case action anticipation. We follow the anticipation model architecture and the evaluation protocol from [14] and report the accuracies in verb, noun, and action prediction. However, our key difference is that the video encoder was pre-trained on an action recognition task, supplemented with ETM at training time." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 589, + 287, + 661 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 589, + 287, + 661 + ], + "spans": [ + { + "bbox": [ + 46, + 589, + 287, + 661 + ], + "type": "text", + "content": "Table 5 showcases performance differences between encoders with and without ETM pretraining. Low complexity models, like MoviNet A0 (showcased in Table 5) exhibit improved performance in all three datasets. More comparisons on different model architectures are shown in Table 6 and Figure 4." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 670, + 205, + 684 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 670, + 205, + 684 + ], + "spans": [ + { + "bbox": [ + 47, + 670, + 205, + 684 + ], + "type": "text", + "content": "4.5. ETM and Model Complexity" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 689, + 287, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 689, + 287, + 715 + ], + "spans": [ + { + "bbox": [ + 47, + 689, + 287, + 715 + ], + "type": "text", + "content": "To further investigate how model complexity interacts with the proposed ETM training protocol, we train pro" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 261, + 547, + 380 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 261, + 547, + 380 + ], + "spans": [ + { + "bbox": [ + 304, + 261, + 547, + 380 + ], + "type": "text", + "content": "gressively more complex models in the same architecture families and analyze how task performance varies with model complexity. We perform this analysis with the MoViNet [24] and X3D [11] architecture families: MoViNets scale from A0 to A4 with increasing parameter count and GFLOPs (Billion of Floating Point Operations), while X3Ds scale from XS to XXL. We train MoViNets A0 to A4 and X3Ds XS to M for our experiments. We use the same training setup as Section 3.2, where one instance of each model is trained without the ETM and another with it." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 382, + 547, + 453 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 382, + 547, + 453 + ], + "spans": [ + { + "bbox": [ + 304, + 382, + 547, + 453 + ], + "type": "text", + "content": "For action recognition, we observe an increase in performance for low complexity models, i.e. A0 to A4, as shown in Figure 4 (a). The gain is larger for lower complexity models. We observe a similar effect for X3D models: the simpler the model is, the larger the improvement appears to be. This effect is visible on both EK100 and EGO4D." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 455, + 547, + 586 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 455, + 547, + 586 + ], + "spans": [ + { + "bbox": [ + 304, + 455, + 547, + 586 + ], + "type": "text", + "content": "Figure 4 (b) shows that a similar effect occurs with action anticipation: using backbone models trained with our ETM protocol improves performance, especially if the backbone models are small. Once again, we use these models as encoders in the AVT [14] framework, replacing AVT-b (AVT's transformer-based encoder) with these variants and using AVT-h (AVT's transformer-based head) as a head. We only show GFLOPs of the encoders. We observe that on both datasets, encoders with lower representational power benefit more from the information about past and future regularities brought by the ETM." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 597, + 414, + 609 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 597, + 414, + 609 + ], + "spans": [ + { + "bbox": [ + 306, + 597, + 414, + 609 + ], + "type": "text", + "content": "4.6.ETM Alternatives" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 616, + 547, + 676 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 616, + 547, + 676 + ], + "spans": [ + { + "bbox": [ + 304, + 616, + 547, + 676 + ], + "type": "text", + "content": "Our proposed ETM can take many forms. We analyzed the impact of three of the main properties of the matrix on action recognition performance: its size, the decay function used to build it, and the metric for temporal distance between events." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "type": "text", + "content": "Matrix Size. Computing the matrix with every unique event (verb-noun combination) appearing in the training set of EK100 yields a " + }, + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "type": "inline_equation", + "content": "13k \\times 13k" + }, + { + "bbox": [ + 304, + 677, + 547, + 715 + ], + "type": "text", + "content": " matrix. Since single-instance" + } + ] + } + ], + "index": 14 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "10699" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 53, + 71, + 172, + 194 + ], + "blocks": [ + { + "bbox": [ + 53, + 71, + 172, + 194 + ], + "lines": [ + { + "bbox": [ + 53, + 71, + 172, + 194 + ], + "spans": [ + { + "bbox": [ + 53, + 71, + 172, + 194 + ], + "type": "image", + "image_path": "fa1455f63c3f5ef2f30dd1453a174425855cda7a2a562e449dbb61a065042301.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 136, + 198, + 212, + 209 + ], + "lines": [ + { + "bbox": [ + 136, + 198, + 212, + 209 + ], + "spans": [ + { + "bbox": [ + 136, + 198, + 212, + 209 + ], + "type": "text", + "content": "(a) Action Recognition" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 173, + 72, + 293, + 195 + ], + "blocks": [ + { + "bbox": [ + 173, + 72, + 293, + 195 + ], + "lines": [ + { + "bbox": [ + 173, + 72, + 293, + 195 + ], + "spans": [ + { + "bbox": [ + 173, + 72, + 293, + 195 + ], + "type": "image", + "image_path": "1de0d35c4c0ef810c01825011588c6c28c86be6503b15c91893777f5afd26726.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 45, + 218, + 546, + 251 + ], + "lines": [ + { + "bbox": [ + 45, + 218, + 546, + 251 + ], + "spans": [ + { + "bbox": [ + 45, + 218, + 546, + 251 + ], + "type": "text", + "content": "Figure 4. Performance for models on EK100 [14] and EGTEA Gaze+ [28] in the same architecture family. We see that for both MoViNets [24] and X3Ds [11], leveraging the ETM during training improves performance, and the effect is stronger with lower complexity models. MoViNet+ETM " + }, + { + "bbox": [ + 45, + 218, + 546, + 251 + ], + "type": "inline_equation", + "content": "(\\text{一} - \\triangle)" + }, + { + "bbox": [ + 45, + 218, + 546, + 251 + ], + "type": "text", + "content": "; MoViNet " + }, + { + "bbox": [ + 45, + 218, + 546, + 251 + ], + "type": "inline_equation", + "content": "(\\text{一} \\triangle)" + }, + { + "bbox": [ + 45, + 218, + 546, + 251 + ], + "type": "text", + "content": "; X3D+ETM " + }, + { + "bbox": [ + 45, + 218, + 546, + 251 + ], + "type": "inline_equation", + "content": "(\\text{一} - \\bullet)" + }, + { + "bbox": [ + 45, + 218, + 546, + 251 + ], + "type": "text", + "content": "; X3D " + }, + { + "bbox": [ + 45, + 218, + 546, + 251 + ], + "type": "inline_equation", + "content": "(\\text{一} \\bullet)" + }, + { + "bbox": [ + 45, + 218, + 546, + 251 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 298, + 72, + 417, + 194 + ], + "blocks": [ + { + "bbox": [ + 298, + 72, + 417, + 194 + ], + "lines": [ + { + "bbox": [ + 298, + 72, + 417, + 194 + ], + "spans": [ + { + "bbox": [ + 298, + 72, + 417, + 194 + ], + "type": "image", + "image_path": "7594e132d97e1f5505f93385ad3bc7660325f45a0ca227444ab2e49b42310f8e.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 381, + 198, + 457, + 209 + ], + "lines": [ + { + "bbox": [ + 381, + 198, + 457, + 209 + ], + "spans": [ + { + "bbox": [ + 381, + 198, + 457, + 209 + ], + "type": "text", + "content": "(b) Action Anticipation" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 419, + 72, + 538, + 194 + ], + "blocks": [ + { + "bbox": [ + 419, + 72, + 538, + 194 + ], + "lines": [ + { + "bbox": [ + 419, + 72, + 538, + 194 + ], + "spans": [ + { + "bbox": [ + 419, + 72, + 538, + 194 + ], + "type": "image", + "image_path": "525bda6059814220327b0fc0d13586f9611c20eb8802670cd581c7e030d1bab5.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 273, + 287, + 331 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 273, + 287, + 331 + ], + "spans": [ + { + "bbox": [ + 46, + 273, + 287, + 331 + ], + "type": "text", + "content": "actions may be noisier, we reduced the dimensionality of the matrix by removing events with less than 4 occurrences in the training set, yielding a subset of 2562 actions and a matrix of size " + }, + { + "bbox": [ + 46, + 273, + 287, + 331 + ], + "type": "inline_equation", + "content": "2562 \\times 2562" + }, + { + "bbox": [ + 46, + 273, + 287, + 331 + ], + "type": "text", + "content": ". We compare both alternatives in Table 7." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 332, + 287, + 367 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 332, + 287, + 367 + ], + "spans": [ + { + "bbox": [ + 46, + 332, + 287, + 367 + ], + "type": "text", + "content": "Decay function alternatives. We compared linear decay, exponential decay and no decay. We show comparison results in Table 7." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 369, + 287, + 511 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 369, + 287, + 511 + ], + "spans": [ + { + "bbox": [ + 46, + 369, + 287, + 511 + ], + "type": "text", + "content": "Measuring temporal distance. We considered the natural alternative of measuring temporal distance between events A and B in seconds, by looking at the time between the last frame of A and the first frame of B. We observed, however, that the durations of individual actions between A and B could vastly modulate the decay factor applied to " + }, + { + "bbox": [ + 46, + 369, + 287, + 511 + ], + "type": "inline_equation", + "content": "M(y_{A},y_{B})" + }, + { + "bbox": [ + 46, + 369, + 287, + 511 + ], + "type": "text", + "content": " of our matrix, which might be counterproductive if A and B are causally related but simply separated by one long intermediate action. We therefore considered measuring distance through index difference in the sequence of actions for that video, which allows us to abstract away the durations of individual actions." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 513, + 287, + 620 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 513, + 287, + 620 + ], + "spans": [ + { + "bbox": [ + 46, + 513, + 287, + 620 + ], + "type": "text", + "content": "We compared these alternatives based on their ability to boost performance on action recognition. We trained a ConvNeXt-S (224) [31] encoder with supervision from different ETM alternatives and evaluated action recognition performance (Table 7). Our results show that the choice of decay is generally not impactful unless no decay is used. We find our best performance on a matrix of reduced size, with index as a temporal distance metric, and with exponential decay." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 633, + 125, + 645 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 633, + 125, + 645 + ], + "spans": [ + { + "bbox": [ + 47, + 633, + 125, + 645 + ], + "type": "text", + "content": "5. Conclusions" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 46, + 653, + 287, + 712 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 653, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 46, + 653, + 287, + 712 + ], + "type": "text", + "content": "We test a novel training regime for video understanding, in which model performance can be increased by supervising models with external representations of temporal regularities. We show that using the ETM as a training target allows arbitrary models to learn about the bi-directional tem" + } + ] + } + ], + "index": 12 + }, + { + "type": "table", + "bbox": [ + 307, + 270, + 547, + 399 + ], + "blocks": [ + { + "bbox": [ + 307, + 270, + 547, + 399 + ], + "lines": [ + { + "bbox": [ + 307, + 270, + 547, + 399 + ], + "spans": [ + { + "bbox": [ + 307, + 270, + 547, + 399 + ], + "type": "table", + "html": "
SizeDecayTemp. MetricPresent (top-1 accuracy)
VerbNounAction
13kLinearTime55.146.228.8
13kExponentialTime55.647.729.1
2.5kExponentialTime58.648.831.3
2.5kNo decay-58.148.030.5
2.5kLinearIndex60.149.331.9
2.5kExponentialIndex60.350.332.4
", + "image_path": "d22039e3428630bdca568fe65e86ba5b564d383b0baa48dc1b4f5405c9c4795c.jpg" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "table_body" + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 406, + 546, + 462 + ], + "lines": [ + { + "bbox": [ + 305, + 406, + 546, + 462 + ], + "spans": [ + { + "bbox": [ + 305, + 406, + 546, + 462 + ], + "type": "text", + "content": "Table 7. Difference in action recognition performance between ETM alternatives. We train ConvNeXts on EPIC-KITCHENS-100 with different verions of our ETM to evaluate the ability of ETM to improve action recognition performance. We evaluate top-1 accuracy on action recognition over the validation set of EK100." + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 304, + 494, + 545, + 614 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 494, + 545, + 614 + ], + "spans": [ + { + "bbox": [ + 304, + 494, + 545, + 614 + ], + "type": "text", + "content": "poral context of the action (i.e. the past and future), which improves action recognition and action anticipation performance. Finally, we tested the circumstances under which the ETM provides the largest benefit, and find significant performance boosts for low-complexity models. Amid ongoing efforts to increase the efficiency of computer vision models, we suggest that research which explores efficient architectures [11, 24] could be complemented by research that explores ways to leverage pre-learned representations of environmental regularities." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 621, + 545, + 670 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 621, + 545, + 670 + ], + "spans": [ + { + "bbox": [ + 305, + 621, + 545, + 670 + ], + "type": "text", + "content": "The biggest benefit of this data structure is in its flexibility and simplicity: it is model-agnostic, computationally inexpensive, and easy to incorporate into the learning regime of any action recognition or anticipation architecture." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": "Acknowledgements. This work was partially funded by a grant from Meta. We thank Karl Ridgeway and the team at Meta Reality Labs Research for their helpful comments." + } + ] + } + ], + "index": 17 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10700" + } + ] + } + ], + "index": 18 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 14, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 135 + ], + "type": "text", + "content": "[1] Yazan Abu Farha, Qiuhong Ke, Bernt Schiele, and Juergen Gall. Long-term anticipation of activities with cycle consistency. In DAGM German Conference on Pattern Recognition, pages 159-173. Springer, 2020. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 135, + 288, + 190 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 135, + 288, + 190 + ], + "spans": [ + { + "bbox": [ + 53, + 135, + 288, + 190 + ], + "type": "text", + "content": "[2] Yazan Abu Farha, Alexander Richard, and Juergen Gall. When will you do what?-anticipating temporal occurrences of activities. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5343-5352, 2018. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 191, + 288, + 245 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 191, + 288, + 245 + ], + "spans": [ + { + "bbox": [ + 53, + 191, + 288, + 245 + ], + "type": "text", + "content": "[3] Hande Alemdar, Vincent Leroy, Adrien Prost-Boucle, and Frédéric Pétrot. Ternary neural networks for resource-efficient ai applications. In 2017 international joint conference on neural networks (IJCNN), pages 2547-2554. IEEE, 2017. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 246, + 288, + 291 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 246, + 288, + 291 + ], + "spans": [ + { + "bbox": [ + 53, + 246, + 288, + 291 + ], + "type": "text", + "content": "[4] Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, and Quoc V Le. Attention augmented convolutional networks. In Proceedings of the IEEE/CVF international conference on computer vision, pages 3286-3295, 2019. 1, 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 291, + 288, + 324 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 291, + 288, + 324 + ], + "spans": [ + { + "bbox": [ + 53, + 291, + 288, + 324 + ], + "type": "text", + "content": "[5] Emmanuel Bengio, Pierre-Luc Bacon, Joelle Pineau, and Doina Precup. Conditional computation in neural networks for faster models. arXiv preprint arXiv:1511.06297, 2015. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 325, + 288, + 368 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 325, + 288, + 368 + ], + "spans": [ + { + "bbox": [ + 53, + 325, + 288, + 368 + ], + "type": "text", + "content": "[6] Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013. 2" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 369, + 288, + 402 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 369, + 288, + 402 + ], + "spans": [ + { + "bbox": [ + 53, + 369, + 288, + 402 + ], + "type": "text", + "content": "[7] Michael R Brent. Speech segmentation and word discovery: A computational perspective. Trends in Cognitive Sciences, 3(8):294-301, 1999. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 403, + 288, + 458 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 403, + 288, + 458 + ], + "spans": [ + { + "bbox": [ + 53, + 403, + 288, + 458 + ], + "type": "text", + "content": "[8] João Carreira and Andrew Zisserman. Quo vadis, action recognition? A new model and the kinetics dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017, pages 4724-4733, 2017. 3" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 459, + 288, + 524 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 459, + 288, + 524 + ], + "spans": [ + { + "bbox": [ + 53, + 459, + 288, + 524 + ], + "type": "text", + "content": "[9] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Sanja Fidler, Antonino Furnari, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Scaling egocentric vision: The epic-kitchens dataset. In European Conference on Computer Vision (ECCV), 2018. 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 525, + 287, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 525, + 287, + 590 + ], + "spans": [ + { + "bbox": [ + 48, + 525, + 287, + 590 + ], + "type": "text", + "content": "[10] Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Jian Ma, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray. Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100. International Journal of Computer Vision (IJCV), 2021. 2, 3, 4, 5" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 591, + 287, + 635 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 591, + 287, + 635 + ], + "spans": [ + { + "bbox": [ + 48, + 591, + 287, + 635 + ], + "type": "text", + "content": "[11] Christoph Feichtenhofer. X3d: Expanding architectures for efficient video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 203-213, 2020. 1, 6, 7, 8" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 636, + 287, + 669 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 636, + 287, + 669 + ], + "spans": [ + { + "bbox": [ + 48, + 636, + 287, + 669 + ], + "type": "text", + "content": "[12] Vanessa E Ghosh and Asaf Gilboa. What is a memory schema? a historical perspective on current neuroscience literature. Neuropsychologia, 53:104-114, 2014. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "type": "text", + "content": "[13] Rohit Girdhar, Joao Carreira, Carl Doersch, and Andrew Zisserman. Video action transformer network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 244-253, 2019. 3" + } + ] + } + ], + "index": 13 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 715 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "text", + "content": "[14] Rohit Girdhar and Kristen Grauman. Anticipative video transformer. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 13505-13515, 2021. 1, 3, 4, 5, 6, 7, 8" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 118, + 545, + 162 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 162 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 162 + ], + "type": "text", + "content": "[15] Jianping Gou, Baosheng Yu, Stephen J Maybank, and Dacheng Tao. Knowledge distillation: A survey. International Journal of Computer Vision, 129(6):1789-1819, 2021. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 164, + 545, + 449 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 164, + 545, + 449 + ], + "spans": [ + { + "bbox": [ + 307, + 164, + 545, + 449 + ], + "type": "text", + "content": "[16] Kristen Grauman, Andrew Westbury, Eugene Byrne, Zachary Chavis, Antonio Furnari, Rohit Girdhar, Jackson Hamburger, Hao Jiang, Miao Liu, Xingyu Liu, Miguel Martin, Tushar Nagarajan, Iija Radosavovic, Santhosh Kumar Ramakrishnan, Fiona Ryan, Jayant Sharma, Michael Wray, Mengmeng Xu, Eric Zhongcong Xu, Chen Zhao, Siddhant Bansal, Dhruv Batra, Vincent Cartillier, Sean Crane, Tien Do, Morrie Doulaty, Akshay Erapalli, Christoph Feichtenhofer, Adriano Fragomeni, Qichen Fu, Christian Fuegen, Abraham Gebreselasie, Cristina Gonzalez, James Hillis, Xuhua Huang, Yifei Huang, Wenqi Jia, Weslie Khoo, Jachym Kolar, Satwik Kottur, Anurag Kumar, Federico Landini, Chao Li, Yanghao Li, Zhenqiang Li, Karttikeya Mangalam, Raghava Modhugu, Jonathan Munro, Tullie Murrell, Takumi Nishiyasu, Will Price, Paola Ruiz Puentes, Merey Ramazanova, Leda Sari, Kiran Somasundaram, Audrey Southerland, Yusuke Sugano, Ruijie Tao, Minh Vo, Yuchen Wang, Xindi Wu, Takuma Yagi, Yunyi Zhu, Pablo Arbelaez, David Crandall, Dima Damen, Giovanni Maria Farinella, Bernard Ghanem, Vamsi Krishna Ithapu, C. V. Jawahar, Hanbyul Joo, Kris Kitani, Haizhou Li, Richard Newcombe, Aude Oliva, Hyun Soo Park, James M. Rehg, Yoichi Sato, Jianbo Shi, Mike Zheng Shou, Antonio Torralba, Lorenzo Torresani, Mingfei Yan, and Jitendra Malik. Ego4d: Around the World in 3,000 Hours of Egocentric Video. CoRR, abs/2110.07058, 2021. 3, 4, 5, 6" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 450, + 545, + 494 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 450, + 545, + 494 + ], + "spans": [ + { + "bbox": [ + 307, + 450, + 545, + 494 + ], + "type": "text", + "content": "[17] Michelle R Greene and Aude Oliva. Recognition of natural scenes from global properties: Seeing the forest without representing the trees. Cognitive psychology, 58(2):137-176, 2009. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 496, + 545, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 496, + 545, + 529 + ], + "spans": [ + { + "bbox": [ + 307, + 496, + 545, + 529 + ], + "type": "text", + "content": "[18] Song Han, Jeff Pool, John Tran, and William J. Dally. Learning both weights and connections for efficient neural networks. CoRR, abs/1506.02626, 2015. 2" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 530, + 545, + 564 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 530, + 545, + 564 + ], + "spans": [ + { + "bbox": [ + 307, + 530, + 545, + 564 + ], + "type": "text", + "content": "[19] Babak Hassibi and David Stork. Second order derivatives for network pruning: Optimal brain surgeon. Advances in neural information processing systems, 5, 1992. 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 565, + 545, + 598 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 565, + 545, + 598 + ], + "spans": [ + { + "bbox": [ + 307, + 565, + 545, + 598 + ], + "type": "text", + "content": "[20] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network (2015). arXiv preprint arXiv:1503.02531, 2, 2015. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 600, + 545, + 644 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 600, + 545, + 644 + ], + "spans": [ + { + "bbox": [ + 307, + 600, + 545, + 644 + ], + "type": "text", + "content": "[21] Qiuhong Ke, Mario Fritz, and Bernt Schiele. Time-conditioned action anticipation in one shot. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9925-9934, 2019. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 646, + 545, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 646, + 545, + 690 + ], + "spans": [ + { + "bbox": [ + 307, + 646, + 545, + 690 + ], + "type": "text", + "content": "[22] Dong-Jin Kim, Xiao Sun, Jinsoo Choi, Stephen Lin, and In So Kweon. Detecting human-object interactions with action co-occurrence priors. In European Conference on Computer Vision, pages 718–736. Springer, 2020. 3" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 691, + 545, + 715 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 691, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 307, + 691, + 545, + 715 + ], + "type": "text", + "content": "[23] Yoon Kim and Alexander M Rush. Sequence-level knowledge distillation. arXiv preprint arXiv:1606.07947, 2016. 2" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10701" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 713 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 137 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 137 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 137 + ], + "type": "text", + "content": "[24] Dan Kondratyuk, Liangzhe Yuan, Yandong Li, Li Zhang, Mingxing Tan, Matthew Brown, and Boqing Gong. Movinets: Mobile video networks for efficient video recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16020-16030, 2021. 1, 6, 7, 8" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 49, + 139, + 287, + 171 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 139, + 287, + 171 + ], + "spans": [ + { + "bbox": [ + 49, + 139, + 287, + 171 + ], + "type": "text", + "content": "[25] Christopher A Kurby and Jeffrey M Zacks. Segmentation in the perception and memory of events. Trends in cognitive sciences, 12(2):72-79, 2008. 2" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 172, + 287, + 204 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 172, + 287, + 204 + ], + "spans": [ + { + "bbox": [ + 48, + 172, + 287, + 204 + ], + "type": "text", + "content": "[26] Yann LeCun, John Denker, and Sara Solla. Optimal brain damage. Advances in neural information processing systems, 2, 1989. 2" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 205, + 287, + 248 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 205, + 287, + 248 + ], + "spans": [ + { + "bbox": [ + 48, + 205, + 287, + 248 + ], + "type": "text", + "content": "[27] Jinyu Li, Rui Zhao, Jui-Ting Huang, and Yifan Gong. Learning small-size dnn with output-distribution-based criteria. In Fifteenth annual conference of the international speech communication association, 2014. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 250, + 287, + 282 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 250, + 287, + 282 + ], + "spans": [ + { + "bbox": [ + 48, + 250, + 287, + 282 + ], + "type": "text", + "content": "[28] Yin Li, Miao Liu, and James M. Rehg. In the eye of beholder: Joint learning of gaze and actions in first person video. In ECCV, 2018. 4, 5, 6, 8" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 283, + 287, + 326 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 283, + 287, + 326 + ], + "spans": [ + { + "bbox": [ + 48, + 283, + 287, + 326 + ], + "type": "text", + "content": "[29] Tailin Liang, John Glossner, Lei Wang, Shaobo Shi, and Xiaotong Zhang. Pruning and quantization for deep neural network acceleration: A survey. Neurocomputing, 461:370-403, 2021. 2" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 327, + 287, + 380 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 327, + 287, + 380 + ], + "spans": [ + { + "bbox": [ + 48, + 327, + 287, + 380 + ], + "type": "text", + "content": "[30] Alexander H Liu, SouYoung Jin, Cheng-I Jeff Lai, Andrew Rouditchenko, Aude Oliva, and James Glass. Cross-modal discrete representation learning. In Annual Meeting of the Association for Computational Linguistics (ACL), May 2022. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 382, + 287, + 415 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 382, + 287, + 415 + ], + "spans": [ + { + "bbox": [ + 48, + 382, + 287, + 415 + ], + "type": "text", + "content": "[31] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. arXiv preprint arXiv:2201.03545, 2022. 6, 7, 8" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 415, + 287, + 458 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 415, + 287, + 458 + ], + "spans": [ + { + "bbox": [ + 48, + 415, + 287, + 458 + ], + "type": "text", + "content": "[32] Tambet Matiisen, Avital Oliver, Taco Cohen, and John Schulman. Teacher-student curriculum learning. IEEE transactions on neural networks and learning systems, 31(9):3732-3740, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 460, + 287, + 514 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 460, + 287, + 514 + ], + "spans": [ + { + "bbox": [ + 48, + 460, + 287, + 514 + ], + "type": "text", + "content": "[33] Yue Meng, Chung-Ching Lin, Rameswar Panda, Prasanna Sattigeri, Leonid Karlinsky, Aude Oliva, Kate Saenko, and Rogerio Feris. Ar-net: Adaptive frame resolution for efficient action recognition. In European Conference on Computer Vision, pages 86-104. Springer, 2020. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 515, + 287, + 546 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 515, + 287, + 546 + ], + "spans": [ + { + "bbox": [ + 48, + 515, + 287, + 546 + ], + "type": "text", + "content": "[34] Carolyn B Mervis and Eleanor Rosch. Categorization of natural objects. Annual review of psychology, 32(1):89-115, 1981. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 548, + 287, + 602 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 548, + 287, + 602 + ], + "spans": [ + { + "bbox": [ + 48, + 548, + 287, + 602 + ], + "type": "text", + "content": "[35] Antoine Miech, Ivan Laptev, Josef Sivic, Heng Wang, Lorenzo Torresani, and Du Tran. Leveraging the present to anticipate the future in videos. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 0-0, 2019. 3" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 603, + 287, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 603, + 287, + 656 + ], + "spans": [ + { + "bbox": [ + 48, + 603, + 287, + 656 + ], + "type": "text", + "content": "[36] Antoine Miech, Dimitri Zhukov, Jean-Baptiste Alayrac, Makarand Tapaswi, Ivan Laptev, and Josef Sivic. Howto100m: Learning a text-video embedding by watching hundred million narrated video clips. In ICCV, pages 2630-2640, 2019. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 48, + 658, + 287, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 287, + 690 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 287, + 690 + ], + "type": "text", + "content": "[37] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013. 2" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "text", + "content": "[38] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 30, + "blocks": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "spans": [ + { + "bbox": [ + 326, + 73, + 545, + 95 + ], + "type": "text", + "content": "phrases and their compositionality. Advances in neural information processing systems, 26, 2013. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 96, + 547, + 140 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 96, + 547, + 140 + ], + "spans": [ + { + "bbox": [ + 307, + 96, + 547, + 140 + ], + "type": "text", + "content": "[39] Kyle Min and Jason J Corso. Integrating human gaze into attention for egocentric activity recognition. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1069-1078, 2021. 5" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 141, + 545, + 175 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 141, + 545, + 175 + ], + "spans": [ + { + "bbox": [ + 307, + 141, + 545, + 175 + ], + "type": "text", + "content": "[40] Shayan Modiri Assari, Amir Roshan Zamir, and Mubarak Shah. Video classification using semantic concept co-occurrences. In CVPR, pages 2529–2536, 2014. 3" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 175, + 545, + 230 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 175, + 545, + 230 + ], + "spans": [ + { + "bbox": [ + 307, + 175, + 545, + 230 + ], + "type": "text", + "content": "[41] Mathew Monfort, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa Brown, Quanfu Fan, Dan Gutfreund, Carl Vondrick, and Aude Oliva. Moments in time dataset: one million videos for event understanding. IEEE TPAMI, 42(2):502-508, 2019. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 232, + 545, + 297 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 232, + 545, + 297 + ], + "spans": [ + { + "bbox": [ + 308, + 232, + 545, + 297 + ], + "type": "text", + "content": "[42] Mathew Monfort, SouYoung Jin, Alexander Liu, David Harwath, Rogerio Feris, James Glass, and Aude Oliva. Spoken moments: Learning joint audio-visual representations from video descriptions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 14871–14881, June 2021. 3" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 298, + 545, + 373 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 298, + 545, + 373 + ], + "spans": [ + { + "bbox": [ + 308, + 298, + 545, + 373 + ], + "type": "text", + "content": "[43] Mathew Monfort, Bowen Pan, Kandan Ramakrishnan, Alex Andonian, Barry A Mcnamara, Alex Lascelles, Quanfu Fan, Dan Gutfreund, Rogerio Feris, and Aude Oliva. Multimoments in time: Learning and interpreting models for multi-action video understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-1, 2021. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 376, + 545, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 376, + 545, + 430 + ], + "spans": [ + { + "bbox": [ + 308, + 376, + 545, + 430 + ], + "type": "text", + "content": "[44] Tushar Nagarajan, Yanghao Li, Christoph Feichtenhofer, and Kristen Grauman. Ego-topo: Environment affordances from egocentric video. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 163-172, 2020. 3" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 432, + 545, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 432, + 545, + 476 + ], + "spans": [ + { + "bbox": [ + 308, + 432, + 545, + 476 + ], + "type": "text", + "content": "[45] Bowen Pan, Rameswar Panda, Camilo Fosco, Chung-Ching Lin, Alex Andonian, Yue Meng, Kate Saenko, Aude Oliva, and Rogerio Feris. Va-red2: Video adaptive redundancy reduction. arXiv preprint arXiv:2102.07887, 2021. 2" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 477, + 545, + 531 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 477, + 545, + 531 + ], + "spans": [ + { + "bbox": [ + 308, + 477, + 545, + 531 + ], + "type": "text", + "content": "[46] Bowen Pan, Rameswar Panda, Yifan Jiang, Zhangyang Wang, Rogerio Feris, and Aude Oliva. Ia-red2: Interpretability-aware redundancy reduction for vision transformers. Advances in Neural Information Processing Systems, 34:24898-24911, 2021. 2" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 308, + 533, + 545, + 586 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 533, + 545, + 586 + ], + "spans": [ + { + "bbox": [ + 308, + 533, + 545, + 586 + ], + "type": "text", + "content": "[47] Jeffrey Pennington, Richard Socher, and Christopher D Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532-1543, 2014. 2" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 308, + 588, + 545, + 622 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 588, + 545, + 622 + ], + "spans": [ + { + "bbox": [ + 308, + 588, + 545, + 622 + ], + "type": "text", + "content": "[48] Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word representations, 2018. 2" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 308, + 624, + 545, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 624, + 545, + 656 + ], + "spans": [ + { + "bbox": [ + 308, + 624, + 545, + 656 + ], + "type": "text", + "content": "[49] Ivan Rodin, Antonino Furnari, Dimitrios Mavroeidis, and Giovanni Maria Farinella. Untrimmed action anticipation. arXiv preprint arXiv:2202.04132, 2022. 3" + } + ] + } + ], + "index": 27 + }, + { + "bbox": [ + 308, + 658, + 545, + 690 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 658, + 545, + 690 + ], + "spans": [ + { + "bbox": [ + 308, + 658, + 545, + 690 + ], + "type": "text", + "content": "[50] E Rosch, C B Mervis, W. D. Gray, D. M. Johnson, and P Boyes-Braem. Basic objects in natural categories. Cognitive psychology, 8(3):382-439, 1976. 2" + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 308, + 691, + 545, + 713 + ], + "type": "text", + "content": "[51] Zahra Sadeghi, James L McClelland, and Paul Hoffman. You shall know an object by the company it keeps: An investi" + } + ] + } + ], + "index": 29 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "10702" + } + ] + } + ], + "index": 31 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 286, + 667 + ], + "type": "list", + "angle": 0, + "index": 15, + "blocks": [ + { + "bbox": [ + 66, + 72, + 286, + 105 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 66, + 72, + 286, + 105 + ], + "spans": [ + { + "bbox": [ + 66, + 72, + 286, + 105 + ], + "type": "text", + "content": "gation of semantic representations derived from object cooccurrence in visual scenes. Neuropsychologia, 76:52-61, 2015. 2" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 107, + 286, + 150 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 107, + 286, + 150 + ], + "spans": [ + { + "bbox": [ + 48, + 107, + 286, + 150 + ], + "type": "text", + "content": "[52] Fadime Sener, Dipika Singhania, and Angela Yao. Temporal aggregate representations for long-range video understanding. In European Conference on Computer Vision, pages 154-171. Springer, 2020. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 49, + 152, + 286, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 152, + 286, + 195 + ], + "spans": [ + { + "bbox": [ + 49, + 152, + 286, + 195 + ], + "type": "text", + "content": "[53] Yang Shen, Bingbing Ni, Zefan Li, and Ning Zhuang. Ego-centric activity prediction via event modulated attention. In Proceedings of the European conference on computer vision (ECCV), pages 197-212, 2018. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 49, + 197, + 286, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 197, + 286, + 239 + ], + "spans": [ + { + "bbox": [ + 49, + 197, + 286, + 239 + ], + "type": "text", + "content": "[54] Brynn E Sherman, Kathryn N Graves, and Nicholas B Turk-Browne. The prevalence and importance of statistical learning in human cognition and behavior. Current opinion in behavioral sciences, 32:15-20, 2020. 2" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 49, + 241, + 286, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 241, + 286, + 285 + ], + "spans": [ + { + "bbox": [ + 49, + 241, + 286, + 285 + ], + "type": "text", + "content": "[55] Bilge Soran, Ali Farhadi, and Linda Shapiro. Generating notifications for missing actions: Don't forget to turn the lights off! In Proceedings of the IEEE International Conference on Computer Vision, pages 4669-4677, 2015. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 49, + 286, + 286, + 340 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 286, + 286, + 340 + ], + "spans": [ + { + "bbox": [ + 49, + 286, + 286, + 340 + ], + "type": "text", + "content": "[56] Chen Sun, Abhinav Shrivastava, Carl Vondrick, Rahul Sukthankar, Kevin Murphy, and Cordelia Schmid. Relational action forecasting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 273-283, 2019. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 49, + 342, + 286, + 385 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 342, + 286, + 385 + ], + "spans": [ + { + "bbox": [ + 49, + 342, + 286, + 385 + ], + "type": "text", + "content": "[57] Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105-6114. PMLR, 2019. 1, 6, 7" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 49, + 387, + 286, + 419 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 387, + 286, + 419 + ], + "spans": [ + { + "bbox": [ + 49, + 387, + 286, + 419 + ], + "type": "text", + "content": "[58] Antonio Torralba and Aude Oliva. Statistics of natural image categories. Network: computation in neural systems, 14(3):391, 2003. 2" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 49, + 421, + 286, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 421, + 286, + 464 + ], + "spans": [ + { + "bbox": [ + 49, + 421, + 286, + 464 + ], + "type": "text", + "content": "[59] Iulia Turc, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Well-read students learn better: On the importance of pre-training compact models. arXiv preprint arXiv:1908.08962, 2019. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 49, + 466, + 286, + 486 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 466, + 286, + 486 + ], + "spans": [ + { + "bbox": [ + 49, + 466, + 286, + 486 + ], + "type": "text", + "content": "[60] Melissa Le-Hoa Võ. The meaning and structure of scenes. Vision Research, 181:10-20, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 49, + 489, + 286, + 531 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 489, + 286, + 531 + ], + "spans": [ + { + "bbox": [ + 49, + 489, + 286, + 531 + ], + "type": "text", + "content": "[61] Daniel Weinland and Edmond Boyer. Action recognition using exemplar-based embedding. In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pages 1-7. IEEE, 2008. 3" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 49, + 533, + 286, + 565 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 533, + 286, + 565 + ], + "spans": [ + { + "bbox": [ + 49, + 533, + 286, + 565 + ], + "type": "text", + "content": "[62] Less Wright and Nestor Demeure. Ranger21: a synergistic deep learning optimizer. arXiv preprint arXiv:2106.13731, 2021.5" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 49, + 567, + 286, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 567, + 286, + 599 + ], + "spans": [ + { + "bbox": [ + 49, + 567, + 286, + 599 + ], + "type": "text", + "content": "[63] Jeffrey M Zacks and Barbara Tversky. Event structure in perception and conception. Psychological bulletin, 127(1):3, 2001. 2" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 49, + 601, + 286, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 601, + 286, + 633 + ], + "spans": [ + { + "bbox": [ + 49, + 601, + 286, + 633 + ], + "type": "text", + "content": "[64] Haotong Zhang, Fuhai Chen, and Angela Yao. Weakly-supervised dense action anticipation. arXiv preprint arXiv:2111.07593, 2021. 3" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 49, + 635, + 286, + 667 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 635, + 286, + 667 + ], + "spans": [ + { + "bbox": [ + 49, + 635, + 286, + 667 + ], + "type": "text", + "content": "[65] Bolei Zhou, Alex Andonian, Aude Oliva, and Antonio Torralba. Temporal relational reasoning in videos. European Conference on Computer Vision, 2018. 3" + } + ] + } + ], + "index": 14 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "10703" + } + ] + } + ], + "index": 16 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/Leveraging per Image-Token Consistency for Vision-Language Pre-Training/59c7554d-450f-4872-aa6d-2245cedbd11c_content_list.json b/2023/Leveraging per Image-Token Consistency for Vision-Language Pre-Training/59c7554d-450f-4872-aa6d-2245cedbd11c_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..26fc1ee8fc925db269a00d562e0c846442c413b2 --- /dev/null +++ b/2023/Leveraging per Image-Token Consistency for Vision-Language Pre-Training/59c7554d-450f-4872-aa6d-2245cedbd11c_content_list.json @@ -0,0 +1,1734 @@ +[ + { + "type": "text", + "text": "Leveraging per Image-Token Consistency for Vision-Language Pre-training", + "text_level": 1, + "bbox": [ + 104, + 130, + 867, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yunhao Gou $^{1,2*}$", + "bbox": [ + 140, + 179, + 269, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Tom Ko3", + "bbox": [ + 305, + 180, + 380, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Hansi Yang2", + "bbox": [ + 426, + 180, + 527, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "James Kwok", + "bbox": [ + 571, + 180, + 684, + 196 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Yu Zhang $^{1,4\\dagger}$", + "bbox": [ + 728, + 180, + 834, + 198 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Mingxuan Wang3", + "bbox": [ + 413, + 198, + 555, + 217 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "$^{1}$ Southern University of Science and Technology, $^{2}$ Hong Kong University of Science and Technology $^{3}$ ByteDance AI Lab, $^{4}$ Peng Cheng Laboratory", + "bbox": [ + 86, + 232, + 883, + 268 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{ygou, hyangbw}@connect.ust.hk jamesk@cse.ust.hk", + "bbox": [ + 266, + 271, + 705, + 286 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "{tom.ko, wangmingxuan.89}@bytedance.com yu.zhang.ust@gmail.com", + "bbox": [ + 205, + 289, + 767, + 304 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 339, + 313, + 354 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked language modeling (CMLM) to learn vision-language associations. However, we find that CMLM is insufficient for this purpose according to our observations: (1) Modality bias: a considerable amount of masked tokens in CMLM can be recovered with only the language information, ignoring the visual inputs. (2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations. To handle those limitations, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). In EPIC, for each image-sentence pair, we mask tokens that are salient to the image (i.e., Saliency-based Masking Strategy) and replace them with alternatives sampled from a language model (i.e., Inconsistent Token Generation Procedure), and then the model is required to determine for each token in the sentence whether it is consistent with the image (i.e., Image-Token Consistency Task). The proposed EPIC method is easily combined with pre-training methods. Extensive experiments show that the combination of the EPIC method and state-of-the-art pre-training approaches, including ViLT, ALBEF, METER, and X-VLM, leads to significant improvements on downstream tasks. Our coude is released at https://github.com/gyhdog99/epic", + "bbox": [ + 76, + 371, + 472, + 763 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 792, + 209, + 808 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Vision-language pre-training (VLP) [5,12,21,29,30,33, 37] aims to learn multi-modal representations from largescale image-text pairs. A pre-trained vision-language model", + "bbox": [ + 76, + 818, + 468, + 864 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "(VLM) fine-tuned with only a small amount of labeled data has shown state-of-the-art performance in many downstream tasks such as visual question answering and imag-text retrieval.", + "bbox": [ + 500, + 340, + 890, + 398 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "A primary concern in developing pre-training objectives for VLP models is how to learn better vision-language associations. In addition to coarse-grained approaches such as image-text matching/contrasting [10, 14, 27] that align concepts from two modalities at the sample level, fine-grained approaches such as cross-modal masked language/image modeling (CMLM/CMIM) [16, 19, 32] learn vision-language associations at the token-object level. For example, Fig. 1 shows a picture paired with the sentence \"Blue and yellow hydrant on the grass\". When the word \"hydrant\" is masked, in order to correctly recover the token, the model has to find the actual object in the image and associate it with the word \"hydrant\".", + "bbox": [ + 496, + 402, + 892, + 598 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "While effective, CMLM is insufficient for learning vision-language associations because of (1) modality bias; and (2) under-utilization of unmasked tokens. In vision-language understanding, modality bias refers to leveraging only one modality for training/inference and so cross-modal knowledge is not well explored [23]. We argue that modality bias exists in CMLM, and prevents the model from learning sufficient vision-language associations. Specifically, in the CMLM task, we expect to mask salient1 tokens (such as \"blue\", \"yellow\", \"fire-hydrant\", and \"grass\") as shown in the left of Fig. 1. These tokens are informative for learning vision-language association because masking them enforces the model to find the answer from the visual modality. However, in practice, whether a token is salient is unknown as we only have access to image-sentence level annotations. Given a fixed and relatively small masking ratio (typically $15\\%$ in CMLM), we might end up masking tokens that are less informative. For example, as shown", + "bbox": [ + 496, + 599, + 893, + 872 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 810, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "* Work was done when the author interned at ByteDance AI Lab.", + "bbox": [ + 93, + 875, + 441, + 887 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "The corresponding author.", + "bbox": [ + 94, + 887, + 248, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_footnote", + "text": "1The definition of \"saliency\" is given in Sec. 4.4.", + "bbox": [ + 517, + 886, + 779, + 900 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "19155", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/4581cca1be1dd1935d0125da4c9ada248a52b53056022090f839f9ab247f725c.jpg", + "image_caption": [ + "Ideal case" + ], + "image_footnote": [], + "bbox": [ + 81, + 90, + 294, + 243 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/d95c84a36ae24307d7befea56df46b915ffceb1c4e952637664073bdaff70359.jpg", + "image_caption": [ + "CMLM" + ], + "image_footnote": [], + "bbox": [ + 308, + 90, + 609, + 242 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/e028ceba64d908f5995a284668686a3e00b45bc9a772b0d30f5193719c0619f6.jpg", + "image_caption": [ + "EPIC (consistent or not?)", + "Figure 1. Illustrations of vision-language association learning. Ideal case: Fine-grained annotations (image regions and corresponding text tokens) are given, we can learn explicit associations (solid lines); CMLM: Without fine-grained annotations, we create supervision by masking, but this can be insufficient due to limited masking ratios and modality bias. EPIC: We find salient tokens and corrupt them to learn more associations. Both CMLM and EPIC learn implicit associations due to lack of region annotations." + ], + "image_footnote": [], + "bbox": [ + 617, + 90, + 880, + 242 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "in Fig. 1 (center), when \"the\" and \"and\" are masked, the model can predict these masked tokens with only language information. This thus is a form of modality bias as it circumvents using vision-language reasoning. Therefore, the modality bias can make CMLM insufficient to learn vision-language associations.", + "bbox": [ + 75, + 362, + 468, + 452 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Another source of insufficiency in CMLM comes from the under-utilization of unmasked tokens. Similar to Masked Language Modeling (MLM) [6] in language pretraining, the CMLM loss is computed over masked tokens rather than all tokens in the sentence. As a result, learning of cross-modal association is possible only for the masked tokens but not for the remaining unmasked ones. For example, in Fig. 1, ideally, there are four associations (shown in black arrows) between text tokens and the corresponding regions, while there is only one association for CMLM. Therefore, CMLM cannot leverage all tokens (including the unmasked ones) for learning vision-language associations.", + "bbox": [ + 75, + 454, + 468, + 635 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "To expedite the learning of cross-modal associations in VLP, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). For each image-sentence pair, we mask tokens that are salient to the image (Saliency-based Masking Strategy) and make them \"inconsistent\" with the image by replacing them with alternatives from a BERT-like language model (Inconsistent Token Generation Procedure). The model is then required to determine whether each token in the sentence is consistent with the image (Image-Token Consistency (ITC) Task). As this masks salient tokens and applies a language model to generate inconsistent tokens from them, the model has to refer to the visual modality to determine whether a token is inconsistent. Therefore, the modality bias problem can be alleviated. Moreover, we can make better use of the un", + "bbox": [ + 75, + 636, + 470, + 863 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "masked tokens for learning vision-language association as the ITC task requires the model to determine whether each token is consistent with the image.", + "bbox": [ + 504, + 362, + 892, + 407 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "The proposed EPIC method is easy to implement and widely applicable to a lot of vision-language model architectures. We demonstrate the effectiveness of our approach on various pre-training approaches, including ViLT [21], ALBEF [14], METER [8], and X-VLM [39], and observe significant improvements on downstream tasks. For example, on MSCOCO image-text retrieval, the proposed EPIC method achieves an absolute gain of $2.5\\%$ and $4.7\\%$ over METER and ViLT, respectively, in terms of the Recall@1 score. On visual reasoning tasks (e.g., NLVR2), the proposed method improves over ALBEF by $1.8\\%$ and X-VLM (the state-of-the-art within its model scale) by $1.3\\%$ . The proposed method also allows better generalization of pretraining models. For example, in zero-shot image-text retrieval, we improve X-VLM by $3.9\\%$ (COCO) and ViLT by $9.9\\%$ (Flickr30k).", + "bbox": [ + 500, + 409, + 892, + 650 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 662, + 640, + 679 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Learning Vision-Language Associations. In vision-language pre-training, a contrastive objective is often used to learn the coarse-grained associations between sentences and images [10, 14, 17, 27, 38]. However, models pretrained with this objective give unsatisfactory results on vision-language reasoning tasks (such as VQA [1] and NLVR2 [31]) which require understanding fine-grained vision-language associations. Cross-Modal Masked Language/Image Modeling (CMLM/CMIM) [2, 16, 19, 20] and its variants are applied to learn fine-grained vision-language associations in a self-supervised manner. Besides CMLM/CMIM, one can further leverage annotated region-phrase (e.g., bounding boxes) image-text data to enable the model with more sophisticated vision-language reasoning", + "bbox": [ + 496, + 688, + 892, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_footnote", + "text": "2A formal definition of (in)consistency tokens will be provided in Sec. 4.2.", + "bbox": [ + 76, + 875, + 467, + 898 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "19156", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "abilities [7, 11, 15, 39].", + "bbox": [ + 76, + 90, + 227, + 104 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Cross-Modal Masked Language/Image Modeling. CMLM and CMIM are widely adopted as pre-training objectives in vision-language pre-training. They corrupt a token/patch of a sentence/image and train the model to reconstruct the original data with information from both modalities. LXMERT [32] found that loading BERT into the text encoder harms pre-training because the pre-trained BERT can have high CMLM accuracy and thereby circumventing the cross-modal reasoning process. Recently, similar to our observations, Bitton et al. [2] showed that roughly $50\\%$ of the masked tokens in CMLM are punctuation or stop-words, leading to sample inefficiency of CMLM. They propose a rule-based masking strategy to mask object words, content words, or words with high concreteness [3] in CMLM according to different downstream tasks, expecting such words to be more related to the visual input. Such a strategy lacks generalization ability and cannot scale to more downstream tasks.", + "bbox": [ + 75, + 106, + 468, + 377 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Modality Bias in Vision-Language Understanding. In CMLM, predicting the masked token without referring to the visual modality can be seen as a consequence of modality bias towards language in vision-language understanding. Due to the existence of modality bias, the learning of vision-language associations is weakened. Similarly, modality bias also happens in visual question answering (VQA) [1] where the model is required to answer a question given a visual input. For example, simply answering \"tennis\" to the sport-related questions can achieve approximately $40\\%$ accuracy [23] on the VQA v1.0 dataset. To reduce such a bias, CF-VQA [23] proposes a counterfactual framework which directly subtracts the language-based predictions from the answers. In the proposed EPIC method, we mitigate the negative influence of language bias by allowing the model to learn more vision-language associations over a wider range of tokens.", + "bbox": [ + 75, + 378, + 470, + 633 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Empirical Analysis", + "text_level": 1, + "bbox": [ + 76, + 648, + 261, + 666 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "In this section, we empirically analyze the existence of modality bias and under-utilization of unmasked tokens in CMLM. Experimental details are in Appendix A.", + "bbox": [ + 75, + 674, + 468, + 718 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Identifying Modality Bias. If there is a strong modality bias towards language in Cross-Modal Language Modeling (CMLM), a pure language model (LM) that is blind to the visual inputs can achieve comparable accuracy on the Masked Language Modeling (MLM) task. Otherwise, the LM will be outperformed by the vision-language model (VLM) by a large margin. Based on this, we run CMLM on a VLM and MLM on a LM, respectively. They share the same input sentences and masked tokens, while the former additionally receives visual inputs. As shown in Fig. 2a, though using visual modality is helpful to recover the original token (80% Acc.), a pure LM can already achieve", + "bbox": [ + 75, + 719, + 470, + 901 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/0eda780c8fa8a47b77718b82ed9c3ff8699edfcb598c2ade7128c36f2fe522c2.jpg", + "image_caption": [ + "(a) Accuracy gaps." + ], + "image_footnote": [], + "bbox": [ + 504, + 90, + 687, + 196 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/2a65b82f5657f240cc48596296bcd5bf81805fb8558c01ed7807b19b02df0c65.jpg", + "image_caption": [ + "(b) Accuracy drops.", + "Figure 2. Left: CMLM/MLM accuracy of VLM/LM (y-axis) as the training process proceeds (x-axis). Right: relative accuracy drop of CMLM/MLM (y-xais) as we corrupt more unmasked tokens (x-axis). The colored bands indicate the variance of the results as each configuration is repeated for 5 times." + ], + "image_footnote": [], + "bbox": [ + 700, + 95, + 882, + 196 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "an accuracy of $70\\%$ . Therefore, a modality bias towards language does exist in the training of CMLM.", + "bbox": [ + 496, + 324, + 890, + 353 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Under-utilization of Unmasked Tokens. If the CMLM is weak at reasoning associations between images and un-masked tokens, replacing the unmasked ones with alternatives based on the language context should not prevent the model from recovering the masked tokens; otherwise, the performance of CMLM will drop significantly because the language context is inconsistent with the image. Therefore, we use a pre-trained VLM and LM to perform inference on CMLM and MLM, respectively, under a corrupted context in which we randomly replace unmasked tokens by sampling from the MLM head of a BERT. As illustrated in Fig. 2b, both the VLM and LM suffer performance deterioration under different corruption ratios. However, even though the text contexts are inconsistent with the image after corruption, the performance drop is less severe for the VLM as compared to the LM. In fact, the performance curves for CMLM and MLM overlap in terms of relative performance drop. Hence, we conclude that the CMLM is weak at learning associations between unmasked tokens and images.", + "bbox": [ + 496, + 354, + 892, + 643 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4. Methodology", + "text_level": 1, + "bbox": [ + 500, + 657, + 635, + 674 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Sec. 4.1 first introduces preliminaries of the proposed method. We then introduce the three components of EPIC. We formulate the ITC task and introduce the concept of token consistency w.r.t. an image in Sec. 4.2. We design the inconsistent token generation procedure in Sec. 4.3, and the saliency-based masking strategy in Sec. 4.4. Fig. 3 gives an overview of the proposed method. The complete EPIC algorithm is shown in Appendix B.", + "bbox": [ + 496, + 683, + 890, + 805 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "4.1. Preliminaries", + "text_level": 1, + "bbox": [ + 500, + 816, + 638, + 830 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Pre-training Framework. In vision-language pretraining, we have access to parallel image-text data $\\mathcal{D} = \\{(w_i,v_i)\\}_{i = 1}^N\\sim P_{\\mathcal{W},\\mathcal{V}}$ . Specifically, for any sentence $\\pmb {w} = [w_1,\\dots ,w_n]$ , there is a corresponding image $\\pmb {v} =$", + "bbox": [ + 500, + 839, + 892, + 902 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "19157", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 2 + }, + { + "type": "image", + "img_path": "images/37825ea61febc230cb59711e702d02c85421c1452b5ea7571a7812624257f78f.jpg", + "image_caption": [ + "Figure 3. An overview of EPIC. The input image and sentence are input to a teacher VLM to obtain the saliency for each text token w.r.t the image. Then we mask accordingly and generate tokens inconsistent with the image though a language model (fine-tuning during pre-training). Finally we train the VLM to determine for each token whether it is inconsistent with the image (ITC)." + ], + "image_footnote": [], + "bbox": [ + 83, + 90, + 890, + 262 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "$[v_{1}, \\ldots, v_{m}]$ in the form of grid-based or region-based features [8]. Without loss of generality, we assume a vision-language model $f_{\\mathrm{VL}}(\\cdot)$ in METER [8] of the following form: Given a pair of sentence $\\boldsymbol{w}$ and image $\\boldsymbol{v}$ , it first extracts text features and visual features via a text encoder and vision encoder, respectively. The text and visual features are then fed into a multi-modal fusion module, consisting of several layers of self-attention followed by cross-attention, to produce cross-modal representations for text and image. Note that the proposed method is also applicable to other architectures such as ViLT [12], ALBEF [14], and X-VLM [39].", + "bbox": [ + 75, + 342, + 472, + 525 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.2. Image-Token Consistency", + "text_level": 1, + "bbox": [ + 76, + 537, + 310, + 555 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "First, we introduce the concept of consistency of a text token with respect to an image.", + "bbox": [ + 75, + 563, + 468, + 594 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Consistency of Text Tokens w.r.t Images. In vision-language pre-training, we assume that the dataset is clean (without noise) and that we have access to the marginal distribution of the text corpus $P_{\\mathcal{W}}$ . For a pair of text $\\boldsymbol{w}$ and image $\\boldsymbol{v}$ , a text token $w_i$ in $\\boldsymbol{w}$ is \"consistent\" with the image given the remaining tokens, i.e., $P_{\\mathcal{W},\\mathcal{V}}(\\boldsymbol{W} = \\boldsymbol{w},\\boldsymbol{V} = \\boldsymbol{v})$ is large. If we replace a token $w_j$ with $w_j'$ such that the new sentence $\\bar{w}$ (1) does not match the semantic content of the image, i.e., $P_{\\mathcal{W},\\mathcal{V}}(\\boldsymbol{W} = \\bar{\\boldsymbol{w}},\\boldsymbol{V} = \\boldsymbol{v})$ is small and (2) is linguistically proper, i.e., $P_{\\mathcal{W}}(\\boldsymbol{W} = \\bar{\\boldsymbol{w}})$ is large, then $w_j'$ is inconsistent with the image given the remaining tokens.", + "bbox": [ + 75, + 595, + 468, + 762 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "An example is shown in Fig. 3. In the sentence \"Yellow and blue hydrant on the grass\", the word \"hydrant\" is consistent with the image given the remaining words. If we replace \"yellow\" with \"green\", it produces a sentence that does not match the image, and so \"green\" is inconsistent.", + "bbox": [ + 75, + 763, + 468, + 839 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Task Formulation. Given an image $v$ and a sentence $\\bar{w}$ with inconsistent tokens at positions $\\mathcal{T}$ , the model has to determine whether each token is consistent with the image. We will introduce the generation process of $\\bar{w}$ and $\\mathcal{T}$ in", + "bbox": [ + 75, + 839, + 468, + 901 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Sec. 4.3.", + "text_level": 1, + "bbox": [ + 498, + 343, + 560, + 356 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The $\\bar{\\boldsymbol{w}}$ and $\\pmb{v}$ are fed into VLM to obtain the last-layer hidden representations $\\left\\{\\bar{h}_i^{\\mathrm{VL}}\\right\\}_{i = 1}^n$ of $\\bar{w}$ . Let $\\beta$ be the weight vector for decision. The probability that each token (from position $i = 1$ to $n$ ) is consistent with the image is:", + "bbox": [ + 496, + 358, + 890, + 438 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nD _ {\\mathrm {I T C}} ^ {i} = \\operatorname {s i g m o i d} \\left(\\boldsymbol {\\beta} ^ {\\intercal} \\bar {\\boldsymbol {h}} _ {i} ^ {\\mathrm {V L}}\\right). \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 599, + 438, + 890, + 462 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Therefore, the EPIC model minimizes the following binary classification loss:", + "bbox": [ + 496, + 467, + 890, + 496 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {I T C}} = - \\sum_ {i \\notin \\mathcal {T}} \\log D _ {\\mathrm {I T C}} ^ {i} - \\sum_ {i \\in \\mathcal {T}} \\log \\left(1 - D _ {\\mathrm {I T C}} ^ {i}\\right). \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 522, + 506, + 890, + 539 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Leveraging Unmasked Tokens. It is easy to see that in Eq. (2), the EPIC model is tasked to determine for all the tokens whether they are consistent with the image or not. Therefore, the ITC task leverages more tokens for learning vision-language associations.", + "bbox": [ + 496, + 550, + 890, + 625 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Mitigating Modality Bias. In the second condition for inconsistent tokens, we require them to be linguistically proper for the sentence. We argue that this is essential for the ITC task to alleviate the modality bias problem because if the sentence after replacement $\\bar{w}$ is not linguistically proper, then the VLM can identify the replaced tokens simply by using the language context only. As a result, the VLM does not learn vision-language associations by decreasing $\\mathcal{L}_{\\mathrm{ITC}}$ . Instead, it simply conducts language modeling.", + "bbox": [ + 496, + 626, + 890, + 777 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "4.3. Generating Inconsistent Tokens", + "text_level": 1, + "bbox": [ + 498, + 786, + 779, + 801 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "In this section, we discuss how to generate sentences $\\bar{w}$ with tokens that fulfill the two conditions of inconsistent tokens in Sec. 4.2. Strictly speaking, we have to model the distributions $P_{\\mathcal{W},\\mathcal{V}}$ and $P_{\\mathcal{W}}$ for generation, but this is hard. Instead, we propose to approximately generate inconsistent tokens with a BERT-like language model.", + "bbox": [ + 496, + 810, + 890, + 900 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "19158", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Assume that we have a set $\\mathcal{M}$ of masking positions (we will discuss how to obtain $\\mathcal{M}$ in Sec. 4.4). Given the original text sequence $\\boldsymbol{w}$ , we first mask the corresponding tokens by replacing them with the [MASK] symbol:", + "bbox": [ + 76, + 90, + 468, + 151 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {w} ^ {\\text {m a s k}} = \\operatorname {M A S K} (\\boldsymbol {w}, \\mathcal {M}). \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 183, + 162, + 468, + 180 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The resulting sentence $\\pmb{w}^{\\mathrm{mask}}$ is fed to an auxiliary BERT-like language model $f_{L}(\\cdot)$ to obtain a sequence of contextual representations $\\pmb{H}^{\\mathrm{L}} = \\left\\{\\pmb{h}_{i}^{\\mathrm{L}}\\right\\}_{i=1}^{n}$ . We then obtain the probability of predicting a particular token $w_{k}$ as:", + "bbox": [ + 76, + 191, + 468, + 253 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\np _ {\\mathrm {M L M}} \\left(w _ {k} \\mid \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right) = \\frac {\\exp \\left(\\boldsymbol {e} \\left(w _ {k}\\right) ^ {\\top} \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right)}{\\sum_ {w ^ {\\prime}} \\exp \\left(\\boldsymbol {e} \\left(w ^ {\\prime}\\right) ^ {\\top} \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right)}, \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 125, + 263, + 468, + 303 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "where $e(\\cdot)$ denotes the token embedding. The new sentence, with the inconsistent tokens, is $\\bar{w} = [\\bar{w}_1, \\dots, \\bar{w}_n]$ :", + "bbox": [ + 76, + 313, + 468, + 344 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\bar {w} _ {i} \\sim p _ {\\mathrm {M L M}} \\left(w \\mid \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right). \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 194, + 356, + 468, + 376 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "The positions of the inconsistent tokens $\\mathcal{T}$ are obtained by:", + "bbox": [ + 76, + 381, + 464, + 396 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {T} = \\{t \\mid \\bar {w} _ {t} \\neq w _ {t}, \\forall t \\in \\mathcal {M} \\}. \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 171, + 407, + 468, + 426 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We also train the language model to fit to the sentence during pre-training by a MLM objective:", + "bbox": [ + 76, + 436, + 468, + 467 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {G E N}} = - \\sum_ {i \\in \\mathcal {M}} \\log p _ {\\mathrm {M L M}} \\left(w _ {i} \\mid \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right). \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 145, + 478, + 468, + 508 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We provide justification for using a language model to approximately fulfill the two conditions of inconsistent tokens. First, the language model has no access to the visual inputs. $P_{\\mathcal{W},\\nu}(\\boldsymbol {W} = \\bar{\\boldsymbol{w}},\\boldsymbol {V} = \\boldsymbol{v})$ is likely to be small, and so it generates samples approximating the first condition. Second, since the language model is fine-tuned during pretraining, $P_{\\mathcal{W}}(\\boldsymbol {W} = \\bar{\\boldsymbol{w}})$ is likely to be large. Thus, it generates samples that can approximate the second condition.", + "bbox": [ + 76, + 521, + 468, + 641 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.4. Saliency-based Masking", + "text_level": 1, + "bbox": [ + 76, + 650, + 299, + 667 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In this section, we discuss how to obtain masking positions $\\mathcal{M}$ . First, we introduce the concept of saliency.", + "bbox": [ + 76, + 674, + 468, + 703 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Saliency of Text Tokens w.r.t. Images. For a pair of sentence $\\mathbf{w}$ and image $\\mathbf{v}$ , a text token $w_{i}$ is salient w.r.t. to the image if the meaning of the token is strongly related to the content in the image, otherwise, it is not salient. In Fig. 3, words \"yellow\", \"blue\", \"hydrant\" and \"grass\" are salient w.r.t. the image, while \"and\", \"the\" are not.", + "bbox": [ + 76, + 704, + 468, + 794 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Masking Salient Tokens. As discussed in Sec. 4.3, we first mask the original sentence based on $\\mathcal{M}$ , and obtain the positions $\\mathcal{T}$ for inconsistent tokens by comparing the generated tokens with the original tokens (Eq. (6)). Since the language model is fine-tuned on the corpus, if we mask non-salient tokens w.r.t. an image, the language model is likely to recover the original tokens by attending to the remaining", + "bbox": [ + 75, + 795, + 468, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "language context. In this case, no inconsistent tokens will be generated, and the model cannot learn vision-language associations. However, if we mask salient tokens w.r.t. an image, the language model is incapable of recovering such tokens because it has no access to the visual input. Therefore, it is expected to mask salient tokens for generating inconsistent tokens.", + "bbox": [ + 496, + 90, + 890, + 195 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "In practice, we find salient tokens by selecting the tokens/positions with higher attention scores to the image. The cross-modal representation of the [CLS] token of the image $\\pmb{v}$ is query $\\pmb{q}_k^V\\in \\mathbb{R}^{d_h}$ , and that of all the elements in $\\pmb{w}$ are keys $\\pmb{K}_k^W\\in \\mathbb{R}^{n\\times d_h}$ at head $k$ , where $n$ is the sequence length of the sentence, $d_{h} = d / h$ is the dimension of a single-head output. For simplicity, we only consider the representations from the penultimate layer of the fusion module. The image-token saliency $\\alpha \\in \\mathbb{R}^d$ can then be written as", + "bbox": [ + 496, + 196, + 890, + 348 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\alpha} = \\operatorname {s o f t m a x} \\left(\\frac {1}{h} \\sum_ {k = 1} ^ {h} \\boldsymbol {q} _ {k} ^ {V} \\left(\\boldsymbol {K} _ {k} ^ {W}\\right) ^ {\\intercal}\\right). \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 570, + 359, + 890, + 401 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Finally, the masking positions $\\mathcal{M}$ are sampled without replacement from the categorical distribution $p(t = i; \\alpha) = \\alpha_i$ such that $|\\mathcal{M}| = m$ , where $m$ is the expected number of inconsistent tokens. In this way, tokens with high saliencies are more likely to be masked. Notice that we use a pre-trained vision-language model to obtain $\\boldsymbol{q}_k^V$ and $\\boldsymbol{K}_k^W$ . Details are in Appendix C.", + "bbox": [ + 496, + 412, + 890, + 518 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 500, + 534, + 633, + 551 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "5.1. Pre-training Settings", + "text_level": 1, + "bbox": [ + 500, + 559, + 694, + 575 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Baselines. The proposed method has no constraints on the network architectures, training objectives, and visual representations. It supports architectures conducting either cross-attention or self-attention for multi-modal fusion. In this study, we plug-in our method into four recent approaches with diversified cross-modal learning techniques: (i) METER, [8] which adopts modality-specific encoders with cross-attention, (ii) ALBEF [14], which utilizes cross-modal contrastive learning with cross-attention, (iii) X-VLM [39], which performs cross-modal alignments from multiple granularities with cross-attention, and (iv) ViLT [12], which fuses images and text in a single encoder via self-attention. We reproduce the pre-training for all the baselines with the settings where they achieve their best results.", + "bbox": [ + 496, + 583, + 890, + 808 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Datasets. There are four widely adopted datasets for vision-language pre-training: (i) COCO [18], (ii) Visual Genome (VG) [13], (iii) SBU Captions [24] and (iv) Conceptual Captions 3M (CC) [4]. We refer the combination of these datasets as 4M because there are 4 million unique images in them. We also experiment with a large-scale web dataset", + "bbox": [ + 496, + 810, + 890, + 900 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "19159", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "table", + "img_path": "images/18be0eeed49df7d50393a468091c3b9230b4f04cfce9e023e39ce65bb4a9b221.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
MethodDataEPICMSCOCO (5K test set)Flickr30K (1K test set)Flickr30K ZS(1K test set)
TRR@1/R@5/R@10IRR@1/R@5/R@10TRR@1/R@5/R@10IRR@1/R@5/R@10TRR@1/R@5/R@10IRR@1/R@5/R@10
METER4MX77.2/93.7/97.159.2/84.0/90.894.2/99.6/99.983.8/97.3/98.692.2/99.3/99.778.8/94.5/96.9
✓79.0/94.5/97.561.2/85.2/91.695.8/99.3/99.685.1/97.4/98.793.1/99.1/99.879.0/94.5/97.1
16MX78.2/93.8/96.959.8/84.2/90.894.6/99.7/99.885.2/97.4/98.893.1/99.4/99.881.1/96.0/97.8
✓79.7/94.8/97.562.5/85.4/91.996.5/99.9/99.986.7/97.8/99.094.7/99.5/99.882.6/95.9/99.8
ALBEF4MX73.3/92.4/96.456.4/81.8/88.994.4/99.4/99.882.1/95.7/97.991.1/98.7/99.476.1/93.0/95.9
✓75.1/92.9/96.458.6/82.7/89.395.6/99.7/99.983.7/96.7/98.491.7/99.2/99.878.1/93.8/96.4
16MX78.3/93.9/96.861.3/84.3/90.695.9/99.8/10085.7/97.2/98.893.6/99.5/99.883.2/95.9/97.7
✓79.2/94.7/97.562.9/85.4/91.396.4/100/10087.1/97.3/98.994.8/99.7/99.984.1/96.5/97.9
X-VLM4M+X79.8/95.1/97.762.7/85.6/91.496.7/99.9/10085.3/97.4/98.783.1/97.8/99.470.5/92.8/96.4
✓81.0/95.3/97.964.1/86.1/91.697.2/99.9/10087.0/97.6/98.786.2/98.6/99.874.4/94.3/97.0
16M+X79.5/95.4/97.863.3/85.6/91.496.8/100/10086.7/97.5/98.786.4/99.2/99.676.1/94.1/96.7
✓80.7/95.6/98.064.1/85.9/91.897.4/100/10087.3/97.6/98.889.0/99.0/99.775.4/94.2/96.8
ViLT4MX60.4/85.8/92.241.3/71.4/82.380.8/95.9/98.761.2/88.0/93.572.6/93.0/96.853.4/80.8/88.7
✓65.0/87.5/93.746.0/74.8/84.685.2/97.3/99.366.9/90.0/94.479.8/95.8/97.963.3/86.3/92.0
", + "bbox": [ + 81, + 88, + 890, + 320 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/2a4331b110eb719870e95d0ab75aafe2488446a2306a081977af63c5ab0a392d.jpg", + "table_caption": [ + "Table 1. Image-text retrieval results on the MSCOCO (fine-tuned) and Flickr30K (fine-tuned and zero-shot) datasets. IR: Image Retrieval and TR: Text Retrieval. Recall@ $K$ with $K = 1,5$ ,and 10 is used as the evaluation metric. Better results under the same baseline are marked in bold." + ], + "table_footnote": [], + "table_body": "
MethodDataEPICMSCOCO (5K test set)
TRIR
R@1R@5R@10R@1R@5R@10
X-VLM4M+X69.291.996.555.382.589.7
✓72.093.497.357.383.490.2
16M+X73.192.897.056.983.089.9
✓73.293.696.957.583.390.0
ViLT4MX54.681.489.138.669.080.2
✓64.186.592.547.174.884.6
", + "bbox": [ + 78, + 397, + 465, + 536 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Table 2. Zero-shot image-text retrieval results on MSCOCO.", + "bbox": [ + 91, + 537, + 452, + 551 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "CC12M [4]. We consider the combination of 4M and 12M datasets as 16M. Additionally, the X-VLM requires fine-grained annotations (e.g., object and region descriptions) for both the 4M and 16M settings. Therefore, the resulting dataset for X-VLM is called $4\\mathrm{M}^{+}$ and $16\\mathrm{M}^{+}$ , respectively. A more detailed description of the data statistics is in Appendix E.", + "bbox": [ + 75, + 566, + 468, + 672 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Implementation Details. For the pre-training baselines, we follow the official implementations provided by the authors of METER, X-VLM, ALBEF and ViLT. More details on their implementations are in Appendix F. For the proposed method, the auxiliary language model is chosen to be identical to the text encoder of the vision-language model. For ViLT without modality-specific encoders, we use BERT-base [6]. All the auxiliary language models are loaded directly from HuggingFace repository [34] with pretrained checkpoints. For every sentence, the number of masked tokens $m$ is calculated as $m = \\text{ceil}(\\text{mask\\_ratio} \\times \\text{len}(\\text{sentence}))$ . The mask ratio is set to 0.35 for all baselines (based on the hyper-parameter search results in Sec. 5.3). The loss weight $\\lambda$ of the Image-Token Consistency task is set to 8.", + "bbox": [ + 75, + 674, + 470, + 900 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2. Results on Downstream Tasks", + "text_level": 1, + "bbox": [ + 500, + 398, + 764, + 414 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Evaluation is performed on the following downstream tasks: (i) Image-Text Retrieval, (ii) Visual Question Answering (VQA), (iii) Natural Language for Visual Reasoning (NLVR2), and (iv) Visual Entailment (VE). Details on these tasks are in Appendix D.", + "bbox": [ + 496, + 422, + 890, + 501 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.2.1 Results for Image-Text Retrieval", + "text_level": 1, + "bbox": [ + 500, + 525, + 781, + 540 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Tables 1 and 2 show that EPIC is universally effective over the different baselines/datasets, since we achieve non-trivial improvement nearly for all the settings. In addition, compared with all the baselines, EPIC brings more significant improvement to ViLT on different datasets. For example, on the fine-tuned retrieval tasks, we achieve an absolute improvement of around $5\\%$ in terms of the TR/IR @1 metric on MSCOCO and Flickr30K. Furthermore, for zero-shot tasks, the improvement on the Flickr30K dataset is $7.2\\%$ in terms of TR@1, and $9.9\\%$ in terms of IR@1. The gap between ViLT and the other baselines can be explained by the fact that ViLT adopts a simplified architecture for vision-language pre-training and it does not incorporate more sophisticated tasks to learn vision-language associations. Nevertheless, the results on ViLT can be seen as an indicator for the effectiveness of the proposed method on a \"clean\" baseline.", + "bbox": [ + 496, + 551, + 890, + 808 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "For the other baselines with advanced strategies to learn vision-language associations (such as cross-modal contrasting in ALBEF, fine-grained reasoning in X-VLM, and powerful pre-trained encoders in METER), EPIC still demonstrates significant improvements. For example, compared with METER on the MSCOCO dataset (fine-tuned), EPIC", + "bbox": [ + 496, + 810, + 893, + 902 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "19160", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 5 + }, + { + "type": "table", + "img_path": "images/11270a595fe394003a24d14f1b97a4e77e98e54d491d9532582ecec8a08c549d.jpg", + "table_caption": [], + "table_footnote": [ + "Table 3. Evaluation results on downstream vision-language tasks: VQA, NLVR2, and SNLI-VE. “/” indicates that the original baseline does not conduct experiment on this task." + ], + "table_body": "
MethodDataEPICVQANLVR2SNLI-VE
devstddevstddevstd
METER4M+X77.777.981.882.581.481.0
✓77.978.083.583.581.681.8
16M+X78.378.482.784.381.781.8
✓78.678.785.085.282.182.3
ALBEF4M+X74.674.679.580.080.180.1
✓75.175.281.382.280.680.7
16M+X75.876.082.682.580.880.9
✓76.776.784.184.081.381.7
X-VLM4M+X78.178.283.384.1//
✓78.578.584.684.5//
16M+X78.078.284.384.5//
✓78.378.385.285.5//
ViLT4MX71.371.475.075.2//
✓71.871.877.277.1//
", + "bbox": [ + 86, + 88, + 455, + 345 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "achieves an absolute IR@1 improvement of $2\\%$ and $2.7\\%$ with 4M and 16M data, respectively. When using the XVLM as baseline, we observe an absolute improvement of $3.9\\%$ and $3.1\\%$ in terms of IR@1 and TR@1, respectively, on Flickr30K (zero-shot).", + "bbox": [ + 76, + 421, + 468, + 494 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.2.2 Results for VQA, NLVR2, and VE", + "text_level": 1, + "bbox": [ + 76, + 532, + 369, + 547 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 3 shows that EPIC is effective among all the vision-language tasks. We improve over METER in terms of the VQA dev accuracy by $0.3\\%$ under the 16M setting and XVLM by $0.4\\%$ under the $4\\mathrm{M}^{+}$ setting. Such improvement is non-trivial given the fact that these two baselines are quite competitive on this task. We also notice that EPIC improves ALBEF on VQA by $0.9\\%$ in dev accuracy. Further, we observe significant improvements over all baselines on NLVR2 (e.g., $+2.3\\%$ dev accuracy on METER 16M; $+2.2\\%$ std accuracy on ALBEF 4M). The proposed method is also effective on the SNLI-VE dataset. It brings an absolute improvement of $0.8\\%$ over ALBEF under the 16M setting.", + "bbox": [ + 75, + 561, + 468, + 744 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/bff118948d1123b97b3c639a1dcbfe766caafdcd1f28aa6d8ce92329585b67f8.jpg", + "table_caption": [], + "table_footnote": [ + "Table 4. Ablation studies on the effect of the ITC task, negative samples generation and the saliency-based masking strategy." + ], + "table_body": "
MethodNLVR2 devFlickr30K-ftFlickr30K-zsMSCOCO-ft
TR1IR1TR1IR1TR1IR1
vanilla METER79.689.276.683.267.771.052.5
ITC (rand.)79.991.577.883.269.070.853.5
ITC+LM80.992.178.983.871.573.455.6
EPIC81.092.979.084.372.574.155.6
", + "bbox": [ + 78, + 762, + 465, + 861 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/6440d8967772cb63f79b1cf1fe083f13f9ac23c818584d7fed177b92774d3c42.jpg", + "table_caption": [], + "table_footnote": [ + "Table 5. Ablation study on different negative sample generators." + ], + "table_body": "
GeneratorNLVR2 devFlickr30K-ftFlickr30K-zsMSCOCO-ft
TR1IR1TR1IR1TR1IR1
LM (cond.)79.992.478.784.071.872.554.5
VLM (SAS)80.591.079.283.270.472.454.9
LM (trained)80.791.379.584.872.173.655.1
LM (fixed)80.791.979.083.871.172.454.8
LM (fine-tune)81.092.979.084.372.574.155.6
", + "bbox": [ + 501, + 88, + 888, + 200 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Ablation Study", + "text_level": 1, + "bbox": [ + 500, + 242, + 651, + 258 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We conduct ablation studies based on the METER model due to its superior performance with basic training objectives (i.e., image-text matching and cross-modal masked language modeling). However, training a full-version of METER is expensive (roughly 4 days with 32 A100 80G). Hence, we conduct ablations on a smaller scale that is still representative. Specifically, the model is still pre-trained on the 4M dataset, but the input image is resized to $224 \\times 224$ (instead of $288 \\times 288$ in the normal METER setting). To speed up the training process, we replace the image encoder CLIP-16 [26] with CLIP-32 for less memory usage, and also shorten the training schedule to 50k steps.", + "bbox": [ + 496, + 266, + 890, + 446 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "We evaluate the pre-trained model on the NLVR2 task and retrieval task (Flickr30K fine-tuned/zero-shot, MSCOCO fine-tuned). For the NLVR2 task, we report the development accuracy. For the retrieval tasks, we report the IR1/TR1 accuracies on the corresponding validation sets.", + "bbox": [ + 496, + 446, + 890, + 522 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Component Analysis. As shown in Table 4, on top of vanilla METER, we first add an ITC task (ITC (rand.)) with inconsistent tokens sampled uniformly from the vocabulary (selecting $35\\%$ of positions for inconsistent tokens). In this case, though some of the sampled tokens do not satisfy the conditions for inconsistent tokens in Sec. 4.2, the pretrained model can still benefit from the ITC task. When we replace the random strategy with a language model, $ITC + LM$ , we generate inconsistent tokens approximating the conditions in Sec. 4.2. Finally, we choose to mask the tokens that are salient w.r.t. the image to generate inconsistent tokens, $ITC + saliency + LM$ , and this further improves the performance on downstream tasks. Therefore, each component of EPIC is effective to improve the performance of the pre-trained model.", + "bbox": [ + 496, + 523, + 890, + 748 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Conditions of Inconsistent Tokens. In this ablation experiment, we study the importance of the two conditions of inconsistent tokens in Sec. 4.2. LM (fine-tune) is proposed in EPIC and it produces inconsistent tokens approximating the two conditions. First, we drop the first condition, that is, $P_{\\mathcal{W},\\mathcal{V}}(\\boldsymbol {W} = \\bar{\\boldsymbol{w}},\\boldsymbol {V} = \\boldsymbol{v})$ is no longer small. We achieve this in two ways. (i) LM (cond.): When generating inconsistent samples and fine-tuning the LM, we replace the class token (first token) in the text with the one from the image encoder of the VLM (with gradient propagation canceled).", + "bbox": [ + 496, + 750, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "19161", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/8c0b25e9017ef6323faaad25592102cb19dbf1048b66b0e8fa8ee96b2a0f94db.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 83, + 93, + 197, + 186 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/0ad5ae239cea93712dd0a2b8d468b82d9c074dbd9eb349248fda5f3dffd23bc6.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 204, + 97, + 470, + 186 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/b0910e8ad4645f744fb7f14c7f3d103eafeee64b296ab589ebc2a69f9180164e.jpg", + "image_caption": [ + "(a)", + "(b)", + "Figure 4. Visualization on the token saliency distribution." + ], + "image_footnote": [], + "bbox": [ + 84, + 200, + 197, + 294 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/081aad5fcefe2fb518bc012f1bdec27ed9912ff65f697f0daa2f500c4133a6e8.jpg", + "image_caption": [ + "Figure 5. Grad-CAM visualization on the cross-attention maps of the image with respect to the ITC task." + ], + "image_footnote": [], + "bbox": [ + 205, + 203, + 470, + 292 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "(ii) VLM (SAS): We adapt SAS [36] from the text modality to the multi-modality setting. An auxiliary VLM (previous checkpoints of the VLM, detailed in Appendix G) is used to produce inconsistent tokens. As shown in Table 5, we observe that both $LM$ (cond.) and $VLM$ (SAS) suffer performance deterioration compared to $LM$ (fine-tune). This indicates the importance of the first condition.", + "bbox": [ + 75, + 354, + 468, + 460 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We then demonstrate the importance of the second condition, that is $P_{\\mathcal{W}}(\\boldsymbol{W} = \\bar{\\boldsymbol{w}})$ is large. Intuitively, we fail to satisfy this condition when we stop fine-tuning the LM on the text corpus, i.e., $LM$ (fixed). Further, for $LM$ (fine-tune), we experiment its possible alternatives, $LM$ (trained), where we fit the LM on the text corpus before instead of during pre-training. We observe that $LM$ (fixed) clearly decreases the performance (especially in MSCOCO-ft). This validates the effectiveness of the second condition. Note that $LM$ (fine-tune) outperforms $LM$ (trained) by a small margin. This gap can be attributed to the training dynamics brought by fine-tuning [22]. As the LM fits the text corpus gradually better during finetuning, it becomes increasingly hard for a model to identify whether a token is replaced. This dynamics encourages the model to perform curriculum learning, which improves performance.", + "bbox": [ + 75, + 460, + 470, + 703 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "5.4. Visualization", + "text_level": 1, + "bbox": [ + 76, + 710, + 215, + 724 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Token Saliency. Fig. 4 shows the saliency distribution of the tokens w.r.t. the image for an image-sentence pair. We can see that salient tokens have higher densities in the distribution. For example, in Fig. 4a, \"blue\" and \"yellow\" are highlighted. In Fig. 4b, the teacher model gives more mass on \"white\", \"stick\" and \"ball\". These confirm that salient tokens can be detected by EPIC.", + "bbox": [ + 75, + 734, + 468, + 839 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Grad-CAM of ITC. Fig. 5 visualizes the cross-attention maps of the image w.r.t. the inconsistent/consistent tokens using Grad-CAM [28]. In each row, we show the original image (left), the attention map w.r.t. the inconsistent tokens", + "bbox": [ + 75, + 839, + 470, + 901 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/8d9542d8b40272e0f31119a4b0f921ba3cae6a7f38aa3b0afc95de09896c03b8.jpg", + "image_caption": [ + "(a)" + ], + "image_footnote": [ + "Positive Sample: \"Two kids are playing with a magnet.\"" + ], + "bbox": [ + 496, + 90, + 617, + 167 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/4963f8fc40cf56ceaf9ef3128f1bc0036ae63fc81fc3057cfd4b86de87f44648.jpg", + "image_caption": [ + "\"pet\"" + ], + "image_footnote": [ + "Negative Sample: \"Two kids are playing with a" + ], + "bbox": [ + 624, + 90, + 746, + 167 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/95ea166556d65985bf4b8d74e9eb851a7a2de5e7030c637e5fe514344a56712d.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 751, + 90, + 872, + 167 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/d2dc28130810996989c1673edf78b62f7724c5f5c1d3908e95f34ac5e1a92e6d.jpg", + "image_caption": [ + "(b)" + ], + "image_footnote": [ + "Positive Sample: \"A young woman poses with a large knife and a sandwich.\"" + ], + "bbox": [ + 493, + 195, + 616, + 277 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/5ae4df8c3d2f682321edfff1f1d191aed2e8b1f8d930fc871f24a4b92894fb00.jpg", + "image_caption": [ + "\"cake\"/\"sandwich\"" + ], + "image_footnote": [ + "Negative Sample: \"A young women poses with a large cake and a ball." + ], + "bbox": [ + 622, + 196, + 746, + 277 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/7b4a6ce7b791e13bb6366a6402501147d2abb4c0c6dddc9d2f0d7d98ab39a521.jpg", + "image_caption": [ + "\"cake\"/\"ball\"" + ], + "image_footnote": [], + "bbox": [ + 751, + 196, + 874, + 277 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "(middle), and that w.r.t. the consistent ones (right). For example, in the first row, when we input the model with the original sentence, the model predicts that the token \"magnet\" is consistent and attends to the actual magnet in the image. However, when we replace \"magnet\" with \"pet\", the model predicts the token \"pet\" as \"inconsistent\" and still attends to the magnet in the image. This means the model is aware that the object in the image is actually a magnet instead of a pet. Similar observation exists for the second row. These demonstrate the model's ability to reason on cross-modal relationship.", + "bbox": [ + 496, + 354, + 890, + 521 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 542, + 617, + 559 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "In this paper we propose EPIC, a pre-training approach that leverage more text tokens for learning vision-language associations. It is less affected by the modality bias problem compared with CMLM. Specifically, we propose an ITC task to identify inconsistent tokens generated by a language model coupled with a saliency-based masking strategy. The task formulation of the ITC task and the design of inconsistent samples address the problems of under-utilization of unmasked tokens and modality bias. We perform extensive experiments and show that EPIC brings consistent performance gains over several baselines on a wide range of downstream tasks. Possible directions for future research can be: (i) approaching the conditions of inconsistent tokens in Sec. 4.2 more precisely; (ii) finding salient tokens without using a pre-trained teacher VLM.", + "bbox": [ + 496, + 573, + 890, + 800 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgements", + "text_level": 1, + "bbox": [ + 500, + 825, + 666, + 843 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "This work is supported by NSFC key grant 62136005, NSFC general grant 62076118, and Shenzhen fundamental research program JCYJ20210324105000003.", + "bbox": [ + 498, + 854, + 890, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "19162", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 173, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015. 2, 3, 11", + "[2] Yonatan Bitton, Michael Elhadad, Gabriel Stanovsky, and Roy Schwartz. Data efficient masked language modeling for vision and language. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3013-3028, Punta Cana, Dominican Republic, Nov. 2021. Association for Computational Linguistics. 2, 3", + "[3] Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman. Concreteness ratings for 40 thousand generally known english word lemmas. Behavior Research Methods, 46:904-911, 2014. 3", + "[4] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3558-3568, 2021. 5, 6", + "[5] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 1", + "[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 2, 6", + "[7] Zi-Yi Dou, Aishwarya Kamath, Zhe Gan, Pengchuan Zhang, Jianfeng Wang, Linjie Li, Zicheng Liu, Ce Liu, Yann LeCun, Nanyun Peng, et al. Coarse-to-fine vision-language pre-training with fusion in the backbone. arXiv preprint arXiv:2206.07643, 2022. 3", + "[8] Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, Zicheng Liu, and Michael Zeng. An empirical study of training end-to-end vision-and-language transformers. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 4, 5, 11, 12", + "[9] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Bartra, and Devi Parikh. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6325-6334, 2017. 11", + "[10] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 2", + "[11] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790, 2021. 3" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[12] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning, pages 5583-5594. PMLR, 2021. 1, 4, 5, 12", + "[13] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. International journal of computer vision, 123(1):32-73, 2017. 5", + "[14] Junnan Li, Ramprasaath R. Selvaraju, Akhilesh Deepak Gotmare, Shafiq R. Joty, Caiming Xiong, and Steven C. H. Hoi. Align before fuse: Vision and language representation learning with momentum distillation. In NeurIPS, 2021. 1, 2, 4, 5, 12", + "[15] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10965-10975, 2022. 3", + "[16] Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, et al. Oscar: Object-semantics aligned pre-training for vision-language tasks. In European Conference on Computer Vision, pages 121-137. Springer, 2020. 1, 2", + "[17] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208, 2021. 2", + "[18] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 5, 11", + "[19] Yongfei Liu, Chenfei Wu, Shao-yen Tseng, Vasudev Lal, Xuming He, and Nan Duan. Kd-vlp: Improving end-to-end vision-and-language pretraining with object knowledge distillation. arXiv preprint arXiv:2109.10504, 2021. 1, 2", + "[20] Haoyu Lu, Nanyi Fei, Yuqi Huo, Yizhao Gao, Zhiwu Lu, and Ji-Rong Wen. Cots: Collaborative two-stream vision-language pre-training model for cross-modal retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15692-15701, 2022. 2", + "[21] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems, 32, 2019. 1, 2", + "[22] Yu Meng, Chenyan Xiong, Payal Bajaj, Paul Bennett, Jiawei Han, Xia Song, et al. Coco-lm: Correcting and contrasting text sequences for language model pretraining. Advances in Neural Information Processing Systems, 34:23102-23114, 2021. 8", + "[23] Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong Wen. Counterfactual vqa: A cause-effect look at language bias. In Proceedings of the IEEE/CVF" + ], + "bbox": [ + 501, + 92, + 890, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "19163", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "Conference on Computer Vision and Pattern Recognition, pages 12700-12710, 2021. 1, 3", + "[24] Vicente Ordonez, Girish Kulkarni, and Tamara Berg. Im2text: Describing images using 1 million captioned photographs. Advances in neural information processing systems, 24, 2011. 5", + "[25] Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In Proceedings of the IEEE international conference on computer vision, pages 2641–2649, 2015. 11", + "[26] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 7, 11", + "[27] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 8748-8763. PMLR, 18-24 Jul 2021. 1, 2", + "[28] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618-626, 2017. 8", + "[29] Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela. Flava: A foundational language and vision alignment model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15638-15650, 2022. 1", + "[30] Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Vl-bert: Pre-training of generic visual-linguistic representations. arXiv preprint arXiv:1908.08530, 2019. 1", + "[31] Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Hua-jun Bai, and Yoav Artzi. A corpus for reasoning about natural language grounded in photographs. arXiv preprint arXiv:1811.00491, 2018. 2, 11", + "[32] Hao Tan and Mohit Bansal. LXMERT: Learning cross-modality encoder representations from transformers. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5100-5111, Hong Kong, China, Nov. 2019. Association for Computational Linguistics. 1, 3", + "[33] Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao. Simvlm: Simple visual language model pretraining with weak supervision. arXiv preprint arXiv:2108.10904, 2021. 1" + ], + "bbox": [ + 78, + 92, + 467, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[34] Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chau-mond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online, Oct. 2020. Association for Computational Linguistics. 6, 11", + "[35] Ning Xie, Farley Lai, Derek Doran, and Asim Kadav. Visual entailment: A novel task for fine-grained image understanding. arXiv preprint arXiv:1901.06706, 2019. 12", + "[36] Yifei Xu, Jingqiao Zhang, Ru He, Liangzhu Ge, Chao Yang, Cheng Yang, and Ying Nian Wu. Sas: Self-augmented strategy for language model pre-training. arXiv preprint arXiv:2106.07176, 2021. 8", + "[37] Jinyu Yang, Jiali Duan, Son Tran, Yi Xu, Sampath Chanda, Liquun Chen, Belinda Zeng, Trishul Chilimbi, and Junzhou Huang. Vision-language pre-training with triple contrastive learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15671-15680, 2022. 1", + "[38] Haoxuan You, Luowei Zhou, Bin Xiao, Noel Codella, Yu Cheng, Ruochen Xu, Shih-Fu Chang, and Lu Yuan. Learning visual representation from modality-shared contrastive language-image pre-training. In European Conference on Computer Vision, pages 69-87. Springer, 2022. 2", + "[39] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. arXiv preprint arXiv:2111.08276, 2021. 2, 3, 4, 5, 12" + ], + "bbox": [ + 501, + 92, + 890, + 553 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "19164", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 9 + } +] \ No newline at end of file diff --git a/2023/Leveraging per Image-Token Consistency for Vision-Language Pre-Training/59c7554d-450f-4872-aa6d-2245cedbd11c_model.json b/2023/Leveraging per Image-Token Consistency for Vision-Language Pre-Training/59c7554d-450f-4872-aa6d-2245cedbd11c_model.json new file mode 100644 index 0000000000000000000000000000000000000000..c8a1505380db2e7d980a8ffb9b2e925463bf265f --- /dev/null +++ b/2023/Leveraging per Image-Token Consistency for Vision-Language Pre-Training/59c7554d-450f-4872-aa6d-2245cedbd11c_model.json @@ -0,0 +1,2310 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.812, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.105, + 0.131, + 0.868, + 0.154 + ], + "angle": 0, + "content": "Leveraging per Image-Token Consistency for Vision-Language Pre-training" + }, + { + "type": "text", + "bbox": [ + 0.141, + 0.18, + 0.27, + 0.198 + ], + "angle": 0, + "content": "Yunhao Gou\\(^{1,2*}\\)" + }, + { + "type": "text", + "bbox": [ + 0.307, + 0.181, + 0.382, + 0.198 + ], + "angle": 0, + "content": "Tom Ko3" + }, + { + "type": "text", + "bbox": [ + 0.427, + 0.181, + 0.529, + 0.199 + ], + "angle": 0, + "content": "Hansi Yang2" + }, + { + "type": "text", + "bbox": [ + 0.572, + 0.181, + 0.685, + 0.198 + ], + "angle": 0, + "content": "James Kwok" + }, + { + "type": "text", + "bbox": [ + 0.729, + 0.181, + 0.835, + 0.199 + ], + "angle": 0, + "content": "Yu Zhang\\(^{1,4\\dagger}\\)" + }, + { + "type": "text", + "bbox": [ + 0.414, + 0.199, + 0.556, + 0.218 + ], + "angle": 0, + "content": "Mingxuan Wang3" + }, + { + "type": "text", + "bbox": [ + 0.088, + 0.233, + 0.885, + 0.27 + ], + "angle": 0, + "content": "\\(^{1}\\)Southern University of Science and Technology, \\(^{2}\\)Hong Kong University of Science and Technology \\(^{3}\\)ByteDance AI Lab, \\(^{4}\\)Peng Cheng Laboratory" + }, + { + "type": "text", + "bbox": [ + 0.267, + 0.272, + 0.707, + 0.287 + ], + "angle": 0, + "content": "{ygou, hyangbw}@connect.ust.hk jamesk@cse.ust.hk" + }, + { + "type": "text", + "bbox": [ + 0.207, + 0.29, + 0.769, + 0.305 + ], + "angle": 0, + "content": "{tom.ko, wangmingxuan.89}@bytedance.com yu.zhang.ust@gmail.com" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.34, + 0.314, + 0.356 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.372, + 0.473, + 0.765 + ], + "angle": 0, + "content": "Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked language modeling (CMLM) to learn vision-language associations. However, we find that CMLM is insufficient for this purpose according to our observations: (1) Modality bias: a considerable amount of masked tokens in CMLM can be recovered with only the language information, ignoring the visual inputs. (2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations. To handle those limitations, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). In EPIC, for each image-sentence pair, we mask tokens that are salient to the image (i.e., Saliency-based Masking Strategy) and replace them with alternatives sampled from a language model (i.e., Inconsistent Token Generation Procedure), and then the model is required to determine for each token in the sentence whether it is consistent with the image (i.e., Image-Token Consistency Task). The proposed EPIC method is easily combined with pre-training methods. Extensive experiments show that the combination of the EPIC method and state-of-the-art pre-training approaches, including ViLT, ALBEF, METER, and X-VLM, leads to significant improvements on downstream tasks. Our coude is released at https://github.com/gyhdog99/epic" + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.794, + 0.21, + 0.809 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.819, + 0.47, + 0.866 + ], + "angle": 0, + "content": "Vision-language pre-training (VLP) [5,12,21,29,30,33, 37] aims to learn multi-modal representations from largescale image-text pairs. A pre-trained vision-language model" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.341, + 0.892, + 0.4 + ], + "angle": 0, + "content": "(VLM) fine-tuned with only a small amount of labeled data has shown state-of-the-art performance in many downstream tasks such as visual question answering and imag-text retrieval." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.403, + 0.893, + 0.599 + ], + "angle": 0, + "content": "A primary concern in developing pre-training objectives for VLP models is how to learn better vision-language associations. In addition to coarse-grained approaches such as image-text matching/contrasting [10, 14, 27] that align concepts from two modalities at the sample level, fine-grained approaches such as cross-modal masked language/image modeling (CMLM/CMIM) [16, 19, 32] learn vision-language associations at the token-object level. For example, Fig. 1 shows a picture paired with the sentence \"Blue and yellow hydrant on the grass\". When the word \"hydrant\" is masked, in order to correctly recover the token, the model has to find the actual object in the image and associate it with the word \"hydrant\"." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.6, + 0.895, + 0.873 + ], + "angle": 0, + "content": "While effective, CMLM is insufficient for learning vision-language associations because of (1) modality bias; and (2) under-utilization of unmasked tokens. In vision-language understanding, modality bias refers to leveraging only one modality for training/inference and so cross-modal knowledge is not well explored [23]. We argue that modality bias exists in CMLM, and prevents the model from learning sufficient vision-language associations. Specifically, in the CMLM task, we expect to mask salient1 tokens (such as \"blue\", \"yellow\", \"fire-hydrant\", and \"grass\") as shown in the left of Fig. 1. These tokens are informative for learning vision-language association because masking them enforces the model to find the answer from the visual modality. However, in practice, whether a token is salient is unknown as we only have access to image-sentence level annotations. Given a fixed and relatively small masking ratio (typically \\(15\\%\\) in CMLM), we might end up masking tokens that are less informative. For example, as shown" + }, + { + "type": "page_footnote", + "bbox": [ + 0.094, + 0.875, + 0.442, + 0.888 + ], + "angle": 0, + "content": "* Work was done when the author interned at ByteDance AI Lab." + }, + { + "type": "page_footnote", + "bbox": [ + 0.095, + 0.888, + 0.249, + 0.901 + ], + "angle": 0, + "content": "The corresponding author." + }, + { + "type": "list", + "bbox": [ + 0.094, + 0.875, + 0.442, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_footnote", + "bbox": [ + 0.518, + 0.887, + 0.78, + 0.901 + ], + "angle": 0, + "content": "1The definition of \"saliency\" is given in Sec. 4.4." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "19155" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.083, + 0.092, + 0.295, + 0.244 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.162, + 0.251, + 0.226, + 0.264 + ], + "angle": 0, + "content": "Ideal case" + }, + { + "type": "image", + "bbox": [ + 0.309, + 0.092, + 0.61, + 0.243 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.438, + 0.251, + 0.482, + 0.264 + ], + "angle": 0, + "content": "CMLM" + }, + { + "type": "image", + "bbox": [ + 0.619, + 0.092, + 0.882, + 0.243 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.695, + 0.25, + 0.848, + 0.265 + ], + "angle": 0, + "content": "EPIC (consistent or not?)" + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.28, + 0.895, + 0.337 + ], + "angle": 0, + "content": "Figure 1. Illustrations of vision-language association learning. Ideal case: Fine-grained annotations (image regions and corresponding text tokens) are given, we can learn explicit associations (solid lines); CMLM: Without fine-grained annotations, we create supervision by masking, but this can be insufficient due to limited masking ratios and modality bias. EPIC: We find salient tokens and corrupt them to learn more associations. Both CMLM and EPIC learn implicit associations due to lack of region annotations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.363, + 0.47, + 0.453 + ], + "angle": 0, + "content": "in Fig. 1 (center), when \"the\" and \"and\" are masked, the model can predict these masked tokens with only language information. This thus is a form of modality bias as it circumvents using vision-language reasoning. Therefore, the modality bias can make CMLM insufficient to learn vision-language associations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.455, + 0.47, + 0.636 + ], + "angle": 0, + "content": "Another source of insufficiency in CMLM comes from the under-utilization of unmasked tokens. Similar to Masked Language Modeling (MLM) [6] in language pretraining, the CMLM loss is computed over masked tokens rather than all tokens in the sentence. As a result, learning of cross-modal association is possible only for the masked tokens but not for the remaining unmasked ones. For example, in Fig. 1, ideally, there are four associations (shown in black arrows) between text tokens and the corresponding regions, while there is only one association for CMLM. Therefore, CMLM cannot leverage all tokens (including the unmasked ones) for learning vision-language associations." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.637, + 0.471, + 0.864 + ], + "angle": 0, + "content": "To expedite the learning of cross-modal associations in VLP, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). For each image-sentence pair, we mask tokens that are salient to the image (Saliency-based Masking Strategy) and make them \"inconsistent\" with the image by replacing them with alternatives from a BERT-like language model (Inconsistent Token Generation Procedure). The model is then required to determine whether each token in the sentence is consistent with the image (Image-Token Consistency (ITC) Task). As this masks salient tokens and applies a language model to generate inconsistent tokens from them, the model has to refer to the visual modality to determine whether a token is inconsistent. Therefore, the modality bias problem can be alleviated. Moreover, we can make better use of the un" + }, + { + "type": "text", + "bbox": [ + 0.506, + 0.363, + 0.893, + 0.409 + ], + "angle": 0, + "content": "masked tokens for learning vision-language association as the ITC task requires the model to determine whether each token is consistent with the image." + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.41, + 0.893, + 0.651 + ], + "angle": 0, + "content": "The proposed EPIC method is easy to implement and widely applicable to a lot of vision-language model architectures. We demonstrate the effectiveness of our approach on various pre-training approaches, including ViLT [21], ALBEF [14], METER [8], and X-VLM [39], and observe significant improvements on downstream tasks. For example, on MSCOCO image-text retrieval, the proposed EPIC method achieves an absolute gain of \\(2.5\\%\\) and \\(4.7\\%\\) over METER and ViLT, respectively, in terms of the Recall@1 score. On visual reasoning tasks (e.g., NLVR2), the proposed method improves over ALBEF by \\(1.8\\%\\) and X-VLM (the state-of-the-art within its model scale) by \\(1.3\\%\\). The proposed method also allows better generalization of pretraining models. For example, in zero-shot image-text retrieval, we improve X-VLM by \\(3.9\\%\\) (COCO) and ViLT by \\(9.9\\%\\) (Flickr30k)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.664, + 0.642, + 0.68 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.689, + 0.893, + 0.901 + ], + "angle": 0, + "content": "Learning Vision-Language Associations. In vision-language pre-training, a contrastive objective is often used to learn the coarse-grained associations between sentences and images [10, 14, 17, 27, 38]. However, models pretrained with this objective give unsatisfactory results on vision-language reasoning tasks (such as VQA [1] and NLVR2 [31]) which require understanding fine-grained vision-language associations. Cross-Modal Masked Language/Image Modeling (CMLM/CMIM) [2, 16, 19, 20] and its variants are applied to learn fine-grained vision-language associations in a self-supervised manner. Besides CMLM/CMIM, one can further leverage annotated region-phrase (e.g., bounding boxes) image-text data to enable the model with more sophisticated vision-language reasoning" + }, + { + "type": "page_footnote", + "bbox": [ + 0.078, + 0.875, + 0.468, + 0.9 + ], + "angle": 0, + "content": "2A formal definition of (in)consistency tokens will be provided in Sec. 4.2." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "19156" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.228, + 0.106 + ], + "angle": 0, + "content": "abilities [7, 11, 15, 39]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.107, + 0.47, + 0.378 + ], + "angle": 0, + "content": "Cross-Modal Masked Language/Image Modeling. CMLM and CMIM are widely adopted as pre-training objectives in vision-language pre-training. They corrupt a token/patch of a sentence/image and train the model to reconstruct the original data with information from both modalities. LXMERT [32] found that loading BERT into the text encoder harms pre-training because the pre-trained BERT can have high CMLM accuracy and thereby circumventing the cross-modal reasoning process. Recently, similar to our observations, Bitton et al. [2] showed that roughly \\(50\\%\\) of the masked tokens in CMLM are punctuation or stop-words, leading to sample inefficiency of CMLM. They propose a rule-based masking strategy to mask object words, content words, or words with high concreteness [3] in CMLM according to different downstream tasks, expecting such words to be more related to the visual input. Such a strategy lacks generalization ability and cannot scale to more downstream tasks." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.379, + 0.471, + 0.635 + ], + "angle": 0, + "content": "Modality Bias in Vision-Language Understanding. In CMLM, predicting the masked token without referring to the visual modality can be seen as a consequence of modality bias towards language in vision-language understanding. Due to the existence of modality bias, the learning of vision-language associations is weakened. Similarly, modality bias also happens in visual question answering (VQA) [1] where the model is required to answer a question given a visual input. For example, simply answering \"tennis\" to the sport-related questions can achieve approximately \\(40\\%\\) accuracy [23] on the VQA v1.0 dataset. To reduce such a bias, CF-VQA [23] proposes a counterfactual framework which directly subtracts the language-based predictions from the answers. In the proposed EPIC method, we mitigate the negative influence of language bias by allowing the model to learn more vision-language associations over a wider range of tokens." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.649, + 0.262, + 0.667 + ], + "angle": 0, + "content": "3. Empirical Analysis" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.675, + 0.47, + 0.719 + ], + "angle": 0, + "content": "In this section, we empirically analyze the existence of modality bias and under-utilization of unmasked tokens in CMLM. Experimental details are in Appendix A." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.72, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Identifying Modality Bias. If there is a strong modality bias towards language in Cross-Modal Language Modeling (CMLM), a pure language model (LM) that is blind to the visual inputs can achieve comparable accuracy on the Masked Language Modeling (MLM) task. Otherwise, the LM will be outperformed by the vision-language model (VLM) by a large margin. Based on this, we run CMLM on a VLM and MLM on a LM, respectively. They share the same input sentences and masked tokens, while the former additionally receives visual inputs. As shown in Fig. 2a, though using visual modality is helpful to recover the original token (80% Acc.), a pure LM can already achieve" + }, + { + "type": "image", + "bbox": [ + 0.505, + 0.092, + 0.688, + 0.197 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.545, + 0.202, + 0.646, + 0.215 + ], + "angle": 0, + "content": "(a) Accuracy gaps." + }, + { + "type": "image", + "bbox": [ + 0.702, + 0.096, + 0.883, + 0.197 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.739, + 0.202, + 0.846, + 0.215 + ], + "angle": 0, + "content": "(b) Accuracy drops." + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.226, + 0.892, + 0.296 + ], + "angle": 0, + "content": "Figure 2. Left: CMLM/MLM accuracy of VLM/LM (y-axis) as the training process proceeds (x-axis). Right: relative accuracy drop of CMLM/MLM (y-xais) as we corrupt more unmasked tokens (x-axis). The colored bands indicate the variance of the results as each configuration is repeated for 5 times." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.325, + 0.892, + 0.354 + ], + "angle": 0, + "content": "an accuracy of \\(70\\%\\). Therefore, a modality bias towards language does exist in the training of CMLM." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.355, + 0.893, + 0.644 + ], + "angle": 0, + "content": "Under-utilization of Unmasked Tokens. If the CMLM is weak at reasoning associations between images and un-masked tokens, replacing the unmasked ones with alternatives based on the language context should not prevent the model from recovering the masked tokens; otherwise, the performance of CMLM will drop significantly because the language context is inconsistent with the image. Therefore, we use a pre-trained VLM and LM to perform inference on CMLM and MLM, respectively, under a corrupted context in which we randomly replace unmasked tokens by sampling from the MLM head of a BERT. As illustrated in Fig. 2b, both the VLM and LM suffer performance deterioration under different corruption ratios. However, even though the text contexts are inconsistent with the image after corruption, the performance drop is less severe for the VLM as compared to the LM. In fact, the performance curves for CMLM and MLM overlap in terms of relative performance drop. Hence, we conclude that the CMLM is weak at learning associations between unmasked tokens and images." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.658, + 0.636, + 0.675 + ], + "angle": 0, + "content": "4. Methodology" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.684, + 0.892, + 0.806 + ], + "angle": 0, + "content": "Sec. 4.1 first introduces preliminaries of the proposed method. We then introduce the three components of EPIC. We formulate the ITC task and introduce the concept of token consistency w.r.t. an image in Sec. 4.2. We design the inconsistent token generation procedure in Sec. 4.3, and the saliency-based masking strategy in Sec. 4.4. Fig. 3 gives an overview of the proposed method. The complete EPIC algorithm is shown in Appendix B." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.817, + 0.64, + 0.832 + ], + "angle": 0, + "content": "4.1. Preliminaries" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.84, + 0.893, + 0.903 + ], + "angle": 0, + "content": "Pre-training Framework. In vision-language pretraining, we have access to parallel image-text data \\(\\mathcal{D} = \\{(w_i,v_i)\\}_{i = 1}^N\\sim P_{\\mathcal{W},\\mathcal{V}}\\) . Specifically, for any sentence \\(\\pmb {w} = [w_1,\\dots ,w_n]\\) , there is a corresponding image \\(\\pmb {v} =\\)" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19157" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.092, + 0.891, + 0.263 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.275, + 0.893, + 0.318 + ], + "angle": 0, + "content": "Figure 3. An overview of EPIC. The input image and sentence are input to a teacher VLM to obtain the saliency for each text token w.r.t the image. Then we mask accordingly and generate tokens inconsistent with the image though a language model (fine-tuning during pre-training). Finally we train the VLM to determine for each token whether it is inconsistent with the image (ITC)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.343, + 0.473, + 0.526 + ], + "angle": 0, + "content": "\\([v_{1}, \\ldots, v_{m}]\\) in the form of grid-based or region-based features [8]. Without loss of generality, we assume a vision-language model \\(f_{\\mathrm{VL}}(\\cdot)\\) in METER [8] of the following form: Given a pair of sentence \\(\\boldsymbol{w}\\) and image \\(\\boldsymbol{v}\\), it first extracts text features and visual features via a text encoder and vision encoder, respectively. The text and visual features are then fed into a multi-modal fusion module, consisting of several layers of self-attention followed by cross-attention, to produce cross-modal representations for text and image. Note that the proposed method is also applicable to other architectures such as ViLT [12], ALBEF [14], and X-VLM [39]." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.539, + 0.312, + 0.556 + ], + "angle": 0, + "content": "4.2. Image-Token Consistency" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.564, + 0.47, + 0.595 + ], + "angle": 0, + "content": "First, we introduce the concept of consistency of a text token with respect to an image." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.596, + 0.47, + 0.763 + ], + "angle": 0, + "content": "Consistency of Text Tokens w.r.t Images. In vision-language pre-training, we assume that the dataset is clean (without noise) and that we have access to the marginal distribution of the text corpus \\( P_{\\mathcal{W}} \\). For a pair of text \\( \\boldsymbol{w} \\) and image \\( \\boldsymbol{v} \\), a text token \\( w_i \\) in \\( \\boldsymbol{w} \\) is \"consistent\" with the image given the remaining tokens, i.e., \\( P_{\\mathcal{W},\\mathcal{V}}(\\boldsymbol{W} = \\boldsymbol{w},\\boldsymbol{V} = \\boldsymbol{v}) \\) is large. If we replace a token \\( w_j \\) with \\( w_j' \\) such that the new sentence \\( \\bar{w} \\) (1) does not match the semantic content of the image, i.e., \\( P_{\\mathcal{W},\\mathcal{V}}(\\boldsymbol{W} = \\bar{\\boldsymbol{w}},\\boldsymbol{V} = \\boldsymbol{v}) \\) is small and (2) is linguistically proper, i.e., \\( P_{\\mathcal{W}}(\\boldsymbol{W} = \\bar{\\boldsymbol{w}}) \\) is large, then \\( w_j' \\) is inconsistent with the image given the remaining tokens." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.764, + 0.47, + 0.84 + ], + "angle": 0, + "content": "An example is shown in Fig. 3. In the sentence \"Yellow and blue hydrant on the grass\", the word \"hydrant\" is consistent with the image given the remaining words. If we replace \"yellow\" with \"green\", it produces a sentence that does not match the image, and so \"green\" is inconsistent." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Task Formulation. Given an image \\( v \\) and a sentence \\( \\bar{w} \\) with inconsistent tokens at positions \\( \\mathcal{T} \\), the model has to determine whether each token is consistent with the image. We will introduce the generation process of \\( \\bar{w} \\) and \\( \\mathcal{T} \\) in" + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.344, + 0.561, + 0.357 + ], + "angle": 0, + "content": "Sec. 4.3." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.359, + 0.892, + 0.439 + ], + "angle": 0, + "content": "The \\(\\bar{\\boldsymbol{w}}\\) and \\(\\pmb{v}\\) are fed into VLM to obtain the last-layer hidden representations \\(\\left\\{\\bar{h}_i^{\\mathrm{VL}}\\right\\}_{i = 1}^n\\) of \\(\\bar{w}\\). Let \\(\\beta\\) be the weight vector for decision. The probability that each token (from position \\(i = 1\\) to \\(n\\)) is consistent with the image is:" + }, + { + "type": "equation", + "bbox": [ + 0.601, + 0.439, + 0.891, + 0.463 + ], + "angle": 0, + "content": "\\[\nD _ {\\mathrm {I T C}} ^ {i} = \\operatorname {s i g m o i d} \\left(\\boldsymbol {\\beta} ^ {\\intercal} \\bar {\\boldsymbol {h}} _ {i} ^ {\\mathrm {V L}}\\right). \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.468, + 0.891, + 0.497 + ], + "angle": 0, + "content": "Therefore, the EPIC model minimizes the following binary classification loss:" + }, + { + "type": "equation", + "bbox": [ + 0.524, + 0.507, + 0.891, + 0.54 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {I T C}} = - \\sum_ {i \\notin \\mathcal {T}} \\log D _ {\\mathrm {I T C}} ^ {i} - \\sum_ {i \\in \\mathcal {T}} \\log \\left(1 - D _ {\\mathrm {I T C}} ^ {i}\\right). \\tag {2}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.551, + 0.892, + 0.626 + ], + "angle": 0, + "content": "Leveraging Unmasked Tokens. It is easy to see that in Eq. (2), the EPIC model is tasked to determine for all the tokens whether they are consistent with the image or not. Therefore, the ITC task leverages more tokens for learning vision-language associations." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.627, + 0.892, + 0.779 + ], + "angle": 0, + "content": "Mitigating Modality Bias. In the second condition for inconsistent tokens, we require them to be linguistically proper for the sentence. We argue that this is essential for the ITC task to alleviate the modality bias problem because if the sentence after replacement \\(\\bar{w}\\) is not linguistically proper, then the VLM can identify the replaced tokens simply by using the language context only. As a result, the VLM does not learn vision-language associations by decreasing \\(\\mathcal{L}_{\\mathrm{ITC}}\\). Instead, it simply conducts language modeling." + }, + { + "type": "title", + "bbox": [ + 0.499, + 0.787, + 0.78, + 0.803 + ], + "angle": 0, + "content": "4.3. Generating Inconsistent Tokens" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.901 + ], + "angle": 0, + "content": "In this section, we discuss how to generate sentences \\(\\bar{w}\\) with tokens that fulfill the two conditions of inconsistent tokens in Sec. 4.2. Strictly speaking, we have to model the distributions \\(P_{\\mathcal{W},\\mathcal{V}}\\) and \\(P_{\\mathcal{W}}\\) for generation, but this is hard. Instead, we propose to approximately generate inconsistent tokens with a BERT-like language model." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19158" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.077, + 0.092, + 0.47, + 0.152 + ], + "angle": 0, + "content": "Assume that we have a set \\(\\mathcal{M}\\) of masking positions (we will discuss how to obtain \\(\\mathcal{M}\\) in Sec. 4.4). Given the original text sequence \\(\\boldsymbol{w}\\), we first mask the corresponding tokens by replacing them with the [MASK] symbol:" + }, + { + "type": "equation", + "bbox": [ + 0.184, + 0.163, + 0.469, + 0.181 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {w} ^ {\\text {m a s k}} = \\operatorname {M A S K} (\\boldsymbol {w}, \\mathcal {M}). \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.192, + 0.469, + 0.255 + ], + "angle": 0, + "content": "The resulting sentence \\(\\pmb{w}^{\\mathrm{mask}}\\) is fed to an auxiliary BERT-like language model \\(f_{L}(\\cdot)\\) to obtain a sequence of contextual representations \\(\\pmb{H}^{\\mathrm{L}} = \\left\\{\\pmb{h}_{i}^{\\mathrm{L}}\\right\\}_{i=1}^{n}\\). We then obtain the probability of predicting a particular token \\(w_{k}\\) as:" + }, + { + "type": "equation", + "bbox": [ + 0.126, + 0.265, + 0.469, + 0.304 + ], + "angle": 0, + "content": "\\[\np _ {\\mathrm {M L M}} \\left(w _ {k} \\mid \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right) = \\frac {\\exp \\left(\\boldsymbol {e} \\left(w _ {k}\\right) ^ {\\top} \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right)}{\\sum_ {w ^ {\\prime}} \\exp \\left(\\boldsymbol {e} \\left(w ^ {\\prime}\\right) ^ {\\top} \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right)}, \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.314, + 0.469, + 0.345 + ], + "angle": 0, + "content": "where \\( e(\\cdot) \\) denotes the token embedding. The new sentence, with the inconsistent tokens, is \\( \\bar{w} = [\\bar{w}_1, \\dots, \\bar{w}_n] \\):" + }, + { + "type": "equation", + "bbox": [ + 0.196, + 0.357, + 0.469, + 0.377 + ], + "angle": 0, + "content": "\\[\n\\bar {w} _ {i} \\sim p _ {\\mathrm {M L M}} \\left(w \\mid \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right). \\tag {5}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.078, + 0.382, + 0.465, + 0.397 + ], + "angle": 0, + "content": "The positions of the inconsistent tokens \\(\\mathcal{T}\\) are obtained by:" + }, + { + "type": "equation", + "bbox": [ + 0.172, + 0.409, + 0.469, + 0.427 + ], + "angle": 0, + "content": "\\[\n\\mathcal {T} = \\{t \\mid \\bar {w} _ {t} \\neq w _ {t}, \\forall t \\in \\mathcal {M} \\}. \\tag {6}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.438, + 0.469, + 0.468 + ], + "angle": 0, + "content": "We also train the language model to fit to the sentence during pre-training by a MLM objective:" + }, + { + "type": "equation", + "bbox": [ + 0.147, + 0.479, + 0.469, + 0.51 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {G E N}} = - \\sum_ {i \\in \\mathcal {M}} \\log p _ {\\mathrm {M L M}} \\left(w _ {i} \\mid \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right). \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.522, + 0.469, + 0.642 + ], + "angle": 0, + "content": "We provide justification for using a language model to approximately fulfill the two conditions of inconsistent tokens. First, the language model has no access to the visual inputs. \\( P_{\\mathcal{W},\\nu}(\\boldsymbol {W} = \\bar{\\boldsymbol{w}},\\boldsymbol {V} = \\boldsymbol{v}) \\) is likely to be small, and so it generates samples approximating the first condition. Second, since the language model is fine-tuned during pretraining, \\( P_{\\mathcal{W}}(\\boldsymbol {W} = \\bar{\\boldsymbol{w}}) \\) is likely to be large. Thus, it generates samples that can approximate the second condition." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.651, + 0.3, + 0.668 + ], + "angle": 0, + "content": "4.4. Saliency-based Masking" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.675, + 0.469, + 0.704 + ], + "angle": 0, + "content": "In this section, we discuss how to obtain masking positions \\(\\mathcal{M}\\). First, we introduce the concept of saliency." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.705, + 0.469, + 0.795 + ], + "angle": 0, + "content": "Saliency of Text Tokens w.r.t. Images. For a pair of sentence \\( \\mathbf{w} \\) and image \\( \\mathbf{v} \\), a text token \\( w_{i} \\) is salient w.r.t. to the image if the meaning of the token is strongly related to the content in the image, otherwise, it is not salient. In Fig. 3, words \"yellow\", \"blue\", \"hydrant\" and \"grass\" are salient w.r.t. the image, while \"and\", \"the\" are not." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.796, + 0.469, + 0.902 + ], + "angle": 0, + "content": "Masking Salient Tokens. As discussed in Sec. 4.3, we first mask the original sentence based on \\(\\mathcal{M}\\), and obtain the positions \\(\\mathcal{T}\\) for inconsistent tokens by comparing the generated tokens with the original tokens (Eq. (6)). Since the language model is fine-tuned on the corpus, if we mask non-salient tokens w.r.t. an image, the language model is likely to recover the original tokens by attending to the remaining" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.196 + ], + "angle": 0, + "content": "language context. In this case, no inconsistent tokens will be generated, and the model cannot learn vision-language associations. However, if we mask salient tokens w.r.t. an image, the language model is incapable of recovering such tokens because it has no access to the visual input. Therefore, it is expected to mask salient tokens for generating inconsistent tokens." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.198, + 0.892, + 0.349 + ], + "angle": 0, + "content": "In practice, we find salient tokens by selecting the tokens/positions with higher attention scores to the image. The cross-modal representation of the [CLS] token of the image \\(\\pmb{v}\\) is query \\(\\pmb{q}_k^V\\in \\mathbb{R}^{d_h}\\), and that of all the elements in \\(\\pmb{w}\\) are keys \\(\\pmb{K}_k^W\\in \\mathbb{R}^{n\\times d_h}\\) at head \\(k\\), where \\(n\\) is the sequence length of the sentence, \\(d_{h} = d / h\\) is the dimension of a single-head output. For simplicity, we only consider the representations from the penultimate layer of the fusion module. The image-token saliency \\(\\alpha \\in \\mathbb{R}^d\\) can then be written as" + }, + { + "type": "equation", + "bbox": [ + 0.571, + 0.36, + 0.892, + 0.402 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\alpha} = \\operatorname {s o f t m a x} \\left(\\frac {1}{h} \\sum_ {k = 1} ^ {h} \\boldsymbol {q} _ {k} ^ {V} \\left(\\boldsymbol {K} _ {k} ^ {W}\\right) ^ {\\intercal}\\right). \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.414, + 0.892, + 0.52 + ], + "angle": 0, + "content": "Finally, the masking positions \\(\\mathcal{M}\\) are sampled without replacement from the categorical distribution \\(p(t = i; \\alpha) = \\alpha_i\\) such that \\(|\\mathcal{M}| = m\\), where \\(m\\) is the expected number of inconsistent tokens. In this way, tokens with high saliencies are more likely to be masked. Notice that we use a pre-trained vision-language model to obtain \\(\\boldsymbol{q}_k^V\\) and \\(\\boldsymbol{K}_k^W\\). Details are in Appendix C." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.535, + 0.634, + 0.552 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.56, + 0.696, + 0.576 + ], + "angle": 0, + "content": "5.1. Pre-training Settings" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.584, + 0.892, + 0.809 + ], + "angle": 0, + "content": "Baselines. The proposed method has no constraints on the network architectures, training objectives, and visual representations. It supports architectures conducting either cross-attention or self-attention for multi-modal fusion. In this study, we plug-in our method into four recent approaches with diversified cross-modal learning techniques: (i) METER, [8] which adopts modality-specific encoders with cross-attention, (ii) ALBEF [14], which utilizes cross-modal contrastive learning with cross-attention, (iii) X-VLM [39], which performs cross-modal alignments from multiple granularities with cross-attention, and (iv) ViLT [12], which fuses images and text in a single encoder via self-attention. We reproduce the pre-training for all the baselines with the settings where they achieve their best results." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Datasets. There are four widely adopted datasets for vision-language pre-training: (i) COCO [18], (ii) Visual Genome (VG) [13], (iii) SBU Captions [24] and (iv) Conceptual Captions 3M (CC) [4]. We refer the combination of these datasets as 4M because there are 4 million unique images in them. We also experiment with a large-scale web dataset" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19159" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.082, + 0.089, + 0.891, + 0.321 + ], + "angle": 0, + "content": "
MethodDataEPICMSCOCO (5K test set)Flickr30K (1K test set)Flickr30K ZS(1K test set)
TRR@1/R@5/R@10IRR@1/R@5/R@10TRR@1/R@5/R@10IRR@1/R@5/R@10TRR@1/R@5/R@10IRR@1/R@5/R@10
METER4MX77.2/93.7/97.159.2/84.0/90.894.2/99.6/99.983.8/97.3/98.692.2/99.3/99.778.8/94.5/96.9
✓79.0/94.5/97.561.2/85.2/91.695.8/99.3/99.685.1/97.4/98.793.1/99.1/99.879.0/94.5/97.1
16MX78.2/93.8/96.959.8/84.2/90.894.6/99.7/99.885.2/97.4/98.893.1/99.4/99.881.1/96.0/97.8
✓79.7/94.8/97.562.5/85.4/91.996.5/99.9/99.986.7/97.8/99.094.7/99.5/99.882.6/95.9/99.8
ALBEF4MX73.3/92.4/96.456.4/81.8/88.994.4/99.4/99.882.1/95.7/97.991.1/98.7/99.476.1/93.0/95.9
✓75.1/92.9/96.458.6/82.7/89.395.6/99.7/99.983.7/96.7/98.491.7/99.2/99.878.1/93.8/96.4
16MX78.3/93.9/96.861.3/84.3/90.695.9/99.8/10085.7/97.2/98.893.6/99.5/99.883.2/95.9/97.7
✓79.2/94.7/97.562.9/85.4/91.396.4/100/10087.1/97.3/98.994.8/99.7/99.984.1/96.5/97.9
X-VLM4M+X79.8/95.1/97.762.7/85.6/91.496.7/99.9/10085.3/97.4/98.783.1/97.8/99.470.5/92.8/96.4
✓81.0/95.3/97.964.1/86.1/91.697.2/99.9/10087.0/97.6/98.786.2/98.6/99.874.4/94.3/97.0
16M+X79.5/95.4/97.863.3/85.6/91.496.8/100/10086.7/97.5/98.786.4/99.2/99.676.1/94.1/96.7
✓80.7/95.6/98.064.1/85.9/91.897.4/100/10087.3/97.6/98.889.0/99.0/99.775.4/94.2/96.8
ViLT4MX60.4/85.8/92.241.3/71.4/82.380.8/95.9/98.761.2/88.0/93.572.6/93.0/96.853.4/80.8/88.7
✓65.0/87.5/93.746.0/74.8/84.685.2/97.3/99.366.9/90.0/94.479.8/95.8/97.963.3/86.3/92.0
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.331, + 0.893, + 0.375 + ], + "angle": 0, + "content": "Table 1. Image-text retrieval results on the MSCOCO (fine-tuned) and Flickr30K (fine-tuned and zero-shot) datasets. IR: Image Retrieval and TR: Text Retrieval. Recall@ \\( K \\) with \\( K = 1,5 \\) ,and 10 is used as the evaluation metric. Better results under the same baseline are marked in bold." + }, + { + "type": "table", + "bbox": [ + 0.08, + 0.398, + 0.466, + 0.537 + ], + "angle": 0, + "content": "
MethodDataEPICMSCOCO (5K test set)
TRIR
R@1R@5R@10R@1R@5R@10
X-VLM4M+X69.291.996.555.382.589.7
✓72.093.497.357.383.490.2
16M+X73.192.897.056.983.089.9
✓73.293.696.957.583.390.0
ViLT4MX54.681.489.138.669.080.2
✓64.186.592.547.174.884.6
" + }, + { + "type": "table_caption", + "bbox": [ + 0.092, + 0.539, + 0.454, + 0.553 + ], + "angle": 0, + "content": "Table 2. Zero-shot image-text retrieval results on MSCOCO." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.568, + 0.47, + 0.674 + ], + "angle": 0, + "content": "CC12M [4]. We consider the combination of 4M and 12M datasets as 16M. Additionally, the X-VLM requires fine-grained annotations (e.g., object and region descriptions) for both the 4M and 16M settings. Therefore, the resulting dataset for X-VLM is called \\(4\\mathrm{M}^{+}\\)and \\(16\\mathrm{M}^{+}\\), respectively. A more detailed description of the data statistics is in Appendix E." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.675, + 0.471, + 0.901 + ], + "angle": 0, + "content": "Implementation Details. For the pre-training baselines, we follow the official implementations provided by the authors of METER, X-VLM, ALBEF and ViLT. More details on their implementations are in Appendix F. For the proposed method, the auxiliary language model is chosen to be identical to the text encoder of the vision-language model. For ViLT without modality-specific encoders, we use BERT-base [6]. All the auxiliary language models are loaded directly from HuggingFace repository [34] with pretrained checkpoints. For every sentence, the number of masked tokens \\( m \\) is calculated as \\( m = \\text{ceil}(\\text{mask\\_ratio} \\times \\text{len}(\\text{sentence})) \\). The mask ratio is set to 0.35 for all baselines (based on the hyper-parameter search results in Sec. 5.3). The loss weight \\( \\lambda \\) of the Image-Token Consistency task is set to 8." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.399, + 0.766, + 0.415 + ], + "angle": 0, + "content": "5.2. Results on Downstream Tasks" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.424, + 0.892, + 0.502 + ], + "angle": 0, + "content": "Evaluation is performed on the following downstream tasks: (i) Image-Text Retrieval, (ii) Visual Question Answering (VQA), (iii) Natural Language for Visual Reasoning (NLVR2), and (iv) Visual Entailment (VE). Details on these tasks are in Appendix D." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.526, + 0.782, + 0.541 + ], + "angle": 0, + "content": "5.2.1 Results for Image-Text Retrieval" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.552, + 0.892, + 0.809 + ], + "angle": 0, + "content": "Tables 1 and 2 show that EPIC is universally effective over the different baselines/datasets, since we achieve non-trivial improvement nearly for all the settings. In addition, compared with all the baselines, EPIC brings more significant improvement to ViLT on different datasets. For example, on the fine-tuned retrieval tasks, we achieve an absolute improvement of around \\(5\\%\\) in terms of the TR/IR @1 metric on MSCOCO and Flickr30K. Furthermore, for zero-shot tasks, the improvement on the Flickr30K dataset is \\(7.2\\%\\) in terms of TR@1, and \\(9.9\\%\\) in terms of IR@1. The gap between ViLT and the other baselines can be explained by the fact that ViLT adopts a simplified architecture for vision-language pre-training and it does not incorporate more sophisticated tasks to learn vision-language associations. Nevertheless, the results on ViLT can be seen as an indicator for the effectiveness of the proposed method on a \"clean\" baseline." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.811, + 0.895, + 0.903 + ], + "angle": 0, + "content": "For the other baselines with advanced strategies to learn vision-language associations (such as cross-modal contrasting in ALBEF, fine-grained reasoning in X-VLM, and powerful pre-trained encoders in METER), EPIC still demonstrates significant improvements. For example, compared with METER on the MSCOCO dataset (fine-tuned), EPIC" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "19160" + } + ], + [ + { + "type": "table", + "bbox": [ + 0.088, + 0.089, + 0.457, + 0.346 + ], + "angle": 0, + "content": "
MethodDataEPICVQANLVR2SNLI-VE
devstddevstddevstd
METER4M+X77.777.981.882.581.481.0
✓77.978.083.583.581.681.8
16M+X78.378.482.784.381.781.8
✓78.678.785.085.282.182.3
ALBEF4M+X74.674.679.580.080.180.1
✓75.175.281.382.280.680.7
16M+X75.876.082.682.580.880.9
✓76.776.784.184.081.381.7
X-VLM4M+X78.178.283.384.1//
✓78.578.584.684.5//
16M+X78.078.284.384.5//
✓78.378.385.285.5//
ViLT4MX71.371.475.075.2//
✓71.871.877.277.1//
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.077, + 0.347, + 0.47, + 0.388 + ], + "angle": 0, + "content": "Table 3. Evaluation results on downstream vision-language tasks: VQA, NLVR2, and SNLI-VE. “/” indicates that the original baseline does not conduct experiment on this task." + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.422, + 0.47, + 0.496 + ], + "angle": 0, + "content": "achieves an absolute IR@1 improvement of \\(2\\%\\) and \\(2.7\\%\\) with 4M and 16M data, respectively. When using the XVLM as baseline, we observe an absolute improvement of \\(3.9\\%\\) and \\(3.1\\%\\) in terms of IR@1 and TR@1, respectively, on Flickr30K (zero-shot)." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.533, + 0.37, + 0.548 + ], + "angle": 0, + "content": "5.2.2 Results for VQA, NLVR2, and VE" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.563, + 0.47, + 0.745 + ], + "angle": 0, + "content": "Table 3 shows that EPIC is effective among all the vision-language tasks. We improve over METER in terms of the VQA dev accuracy by \\(0.3\\%\\) under the 16M setting and XVLM by \\(0.4\\%\\) under the \\(4\\mathrm{M}^{+}\\) setting. Such improvement is non-trivial given the fact that these two baselines are quite competitive on this task. We also notice that EPIC improves ALBEF on VQA by \\(0.9\\%\\) in dev accuracy. Further, we observe significant improvements over all baselines on NLVR2 (e.g., \\(+2.3\\%\\) dev accuracy on METER 16M; \\(+2.2\\%\\) std accuracy on ALBEF 4M). The proposed method is also effective on the SNLI-VE dataset. It brings an absolute improvement of \\(0.8\\%\\) over ALBEF under the 16M setting." + }, + { + "type": "table", + "bbox": [ + 0.08, + 0.763, + 0.466, + 0.862 + ], + "angle": 0, + "content": "
MethodNLVR2 devFlickr30K-ftFlickr30K-zsMSCOCO-ft
TR1IR1TR1IR1TR1IR1
vanilla METER79.689.276.683.267.771.052.5
ITC (rand.)79.991.577.883.269.070.853.5
ITC+LM80.992.178.983.871.573.455.6
EPIC81.092.979.084.372.574.155.6
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.077, + 0.863, + 0.469, + 0.892 + ], + "angle": 0, + "content": "Table 4. Ablation studies on the effect of the ITC task, negative samples generation and the saliency-based masking strategy." + }, + { + "type": "table", + "bbox": [ + 0.503, + 0.089, + 0.889, + 0.201 + ], + "angle": 0, + "content": "
GeneratorNLVR2 devFlickr30K-ftFlickr30K-zsMSCOCO-ft
TR1IR1TR1IR1TR1IR1
LM (cond.)79.992.478.784.071.872.554.5
VLM (SAS)80.591.079.283.270.472.454.9
LM (trained)80.791.379.584.872.173.655.1
LM (fixed)80.791.979.083.871.172.454.8
LM (fine-tune)81.092.979.084.372.574.155.6
" + }, + { + "type": "table_footnote", + "bbox": [ + 0.504, + 0.203, + 0.886, + 0.217 + ], + "angle": 0, + "content": "Table 5. Ablation study on different negative sample generators." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.243, + 0.653, + 0.26 + ], + "angle": 0, + "content": "5.3. Ablation Study" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.267, + 0.892, + 0.447 + ], + "angle": 0, + "content": "We conduct ablation studies based on the METER model due to its superior performance with basic training objectives (i.e., image-text matching and cross-modal masked language modeling). However, training a full-version of METER is expensive (roughly 4 days with 32 A100 80G). Hence, we conduct ablations on a smaller scale that is still representative. Specifically, the model is still pre-trained on the 4M dataset, but the input image is resized to \\(224 \\times 224\\) (instead of \\(288 \\times 288\\) in the normal METER setting). To speed up the training process, we replace the image encoder CLIP-16 [26] with CLIP-32 for less memory usage, and also shorten the training schedule to 50k steps." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.448, + 0.892, + 0.523 + ], + "angle": 0, + "content": "We evaluate the pre-trained model on the NLVR2 task and retrieval task (Flickr30K fine-tuned/zero-shot, MSCOCO fine-tuned). For the NLVR2 task, we report the development accuracy. For the retrieval tasks, we report the IR1/TR1 accuracies on the corresponding validation sets." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.524, + 0.892, + 0.749 + ], + "angle": 0, + "content": "Component Analysis. As shown in Table 4, on top of vanilla METER, we first add an ITC task (ITC (rand.)) with inconsistent tokens sampled uniformly from the vocabulary (selecting \\(35\\%\\) of positions for inconsistent tokens). In this case, though some of the sampled tokens do not satisfy the conditions for inconsistent tokens in Sec. 4.2, the pretrained model can still benefit from the ITC task. When we replace the random strategy with a language model, \\(ITC + LM\\), we generate inconsistent tokens approximating the conditions in Sec. 4.2. Finally, we choose to mask the tokens that are salient w.r.t. the image to generate inconsistent tokens, \\(ITC + saliency + LM\\), and this further improves the performance on downstream tasks. Therefore, each component of EPIC is effective to improve the performance of the pre-trained model." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Conditions of Inconsistent Tokens. In this ablation experiment, we study the importance of the two conditions of inconsistent tokens in Sec. 4.2. LM (fine-tune) is proposed in EPIC and it produces inconsistent tokens approximating the two conditions. First, we drop the first condition, that is, \\( P_{\\mathcal{W},\\mathcal{V}}(\\boldsymbol {W} = \\bar{\\boldsymbol{w}},\\boldsymbol {V} = \\boldsymbol{v}) \\) is no longer small. We achieve this in two ways. (i) LM (cond.): When generating inconsistent samples and fine-tuning the LM, we replace the class token (first token) in the text with the one from the image encoder of the VLM (with gradient propagation canceled)." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "19161" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.084, + 0.094, + 0.198, + 0.188 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.133, + 0.19, + 0.15, + 0.201 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.205, + 0.098, + 0.471, + 0.187 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.086, + 0.202, + 0.198, + 0.295 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.133, + 0.298, + 0.151, + 0.308 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.206, + 0.204, + 0.471, + 0.294 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.11, + 0.321, + 0.451, + 0.336 + ], + "angle": 0, + "content": "Figure 4. Visualization on the token saliency distribution." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.355, + 0.47, + 0.461 + ], + "angle": 0, + "content": "(ii) VLM (SAS): We adapt SAS [36] from the text modality to the multi-modality setting. An auxiliary VLM (previous checkpoints of the VLM, detailed in Appendix G) is used to produce inconsistent tokens. As shown in Table 5, we observe that both \\( LM \\) (cond.) and \\( VLM \\) (SAS) suffer performance deterioration compared to \\( LM \\) (fine-tune). This indicates the importance of the first condition." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.462, + 0.471, + 0.704 + ], + "angle": 0, + "content": "We then demonstrate the importance of the second condition, that is \\( P_{\\mathcal{W}}(\\boldsymbol{W} = \\bar{\\boldsymbol{w}}) \\) is large. Intuitively, we fail to satisfy this condition when we stop fine-tuning the LM on the text corpus, i.e., \\( LM \\) (fixed). Further, for \\( LM \\) (fine-tune), we experiment its possible alternatives, \\( LM \\) (trained), where we fit the LM on the text corpus before instead of during pre-training. We observe that \\( LM \\) (fixed) clearly decreases the performance (especially in MSCOCO-ft). This validates the effectiveness of the second condition. Note that \\( LM \\) (fine-tune) outperforms \\( LM \\) (trained) by a small margin. This gap can be attributed to the training dynamics brought by fine-tuning [22]. As the LM fits the text corpus gradually better during finetuning, it becomes increasingly hard for a model to identify whether a token is replaced. This dynamics encourages the model to perform curriculum learning, which improves performance." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.712, + 0.216, + 0.726 + ], + "angle": 0, + "content": "5.4. Visualization" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.735, + 0.47, + 0.84 + ], + "angle": 0, + "content": "Token Saliency. Fig. 4 shows the saliency distribution of the tokens w.r.t. the image for an image-sentence pair. We can see that salient tokens have higher densities in the distribution. For example, in Fig. 4a, \"blue\" and \"yellow\" are highlighted. In Fig. 4b, the teacher model gives more mass on \"white\", \"stick\" and \"ball\". These confirm that salient tokens can be detected by EPIC." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.84, + 0.471, + 0.902 + ], + "angle": 0, + "content": "Grad-CAM of ITC. Fig. 5 visualizes the cross-attention maps of the image w.r.t. the inconsistent/consistent tokens using Grad-CAM [28]. In each row, we show the original image (left), the attention map w.r.t. the inconsistent tokens" + }, + { + "type": "image", + "bbox": [ + 0.497, + 0.091, + 0.619, + 0.169 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.551, + 0.169, + 0.564, + 0.175 + ], + "angle": 0, + "content": "(a)" + }, + { + "type": "image", + "bbox": [ + 0.625, + 0.092, + 0.747, + 0.169 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.683, + 0.17, + 0.704, + 0.175 + ], + "angle": 0, + "content": "\"pet\"" + }, + { + "type": "image", + "bbox": [ + 0.752, + 0.092, + 0.874, + 0.169 + ], + "angle": 0, + "content": null + }, + { + "type": "image_footnote", + "bbox": [ + 0.58, + 0.176, + 0.805, + 0.184 + ], + "angle": 0, + "content": "Positive Sample: \"Two kids are playing with a magnet.\"" + }, + { + "type": "image_footnote", + "bbox": [ + 0.58, + 0.184, + 0.76, + 0.19 + ], + "angle": 0, + "content": "Negative Sample: \"Two kids are playing with a" + }, + { + "type": "image", + "bbox": [ + 0.495, + 0.196, + 0.617, + 0.278 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.55, + 0.279, + 0.564, + 0.284 + ], + "angle": 0, + "content": "(b)" + }, + { + "type": "image", + "bbox": [ + 0.624, + 0.197, + 0.747, + 0.278 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.652, + 0.278, + 0.728, + 0.284 + ], + "angle": 0, + "content": "\"cake\"/\"sandwich\"" + }, + { + "type": "image", + "bbox": [ + 0.752, + 0.197, + 0.875, + 0.278 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.791, + 0.278, + 0.847, + 0.284 + ], + "angle": 0, + "content": "\"cake\"/\"ball\"" + }, + { + "type": "image_footnote", + "bbox": [ + 0.53, + 0.285, + 0.839, + 0.291 + ], + "angle": 0, + "content": "Positive Sample: \"A young woman poses with a large knife and a sandwich.\"" + }, + { + "type": "image_footnote", + "bbox": [ + 0.532, + 0.291, + 0.812, + 0.3 + ], + "angle": 0, + "content": "Negative Sample: \"A young women poses with a large cake and a ball." + }, + { + "type": "image_caption", + "bbox": [ + 0.486, + 0.313, + 0.883, + 0.341 + ], + "angle": 0, + "content": "Figure 5. Grad-CAM visualization on the cross-attention maps of the image with respect to the ITC task." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.356, + 0.892, + 0.522 + ], + "angle": 0, + "content": "(middle), and that w.r.t. the consistent ones (right). For example, in the first row, when we input the model with the original sentence, the model predicts that the token \"magnet\" is consistent and attends to the actual magnet in the image. However, when we replace \"magnet\" with \"pet\", the model predicts the token \"pet\" as \"inconsistent\" and still attends to the magnet in the image. This means the model is aware that the object in the image is actually a magnet instead of a pet. Similar observation exists for the second row. These demonstrate the model's ability to reason on cross-modal relationship." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.544, + 0.619, + 0.56 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.574, + 0.892, + 0.801 + ], + "angle": 0, + "content": "In this paper we propose EPIC, a pre-training approach that leverage more text tokens for learning vision-language associations. It is less affected by the modality bias problem compared with CMLM. Specifically, we propose an ITC task to identify inconsistent tokens generated by a language model coupled with a saliency-based masking strategy. The task formulation of the ITC task and the design of inconsistent samples address the problems of under-utilization of unmasked tokens and modality bias. We perform extensive experiments and show that EPIC brings consistent performance gains over several baselines on a wide range of downstream tasks. Possible directions for future research can be: (i) approaching the conditions of inconsistent tokens in Sec. 4.2 more precisely; (ii) finding salient tokens without using a pre-trained teacher VLM." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.827, + 0.667, + 0.844 + ], + "angle": 0, + "content": "Acknowledgements" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "This work is supported by NSFC key grant 62136005, NSFC general grant 62076118, and Shenzhen fundamental research program JCYJ20210324105000003." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19162" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.174, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.115, + 0.47, + 0.184 + ], + "angle": 0, + "content": "[1] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015. 2, 3, 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.186, + 0.472, + 0.269 + ], + "angle": 0, + "content": "[2] Yonatan Bitton, Michael Elhadad, Gabriel Stanovsky, and Roy Schwartz. Data efficient masked language modeling for vision and language. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3013-3028, Punta Cana, Dominican Republic, Nov. 2021. Association for Computational Linguistics. 2, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.271, + 0.47, + 0.324 + ], + "angle": 0, + "content": "[3] Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman. Concreteness ratings for 40 thousand generally known english word lemmas. Behavior Research Methods, 46:904-911, 2014. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.326, + 0.47, + 0.395 + ], + "angle": 0, + "content": "[4] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3558-3568, 2021. 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.397, + 0.47, + 0.464 + ], + "angle": 0, + "content": "[5] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.467, + 0.47, + 0.521 + ], + "angle": 0, + "content": "[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.524, + 0.47, + 0.591 + ], + "angle": 0, + "content": "[7] Zi-Yi Dou, Aishwarya Kamath, Zhe Gan, Pengchuan Zhang, Jianfeng Wang, Linjie Li, Zicheng Liu, Ce Liu, Yann LeCun, Nanyun Peng, et al. Coarse-to-fine vision-language pre-training with fusion in the backbone. arXiv preprint arXiv:2206.07643, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.594, + 0.47, + 0.675 + ], + "angle": 0, + "content": "[8] Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, Zicheng Liu, and Michael Zeng. An empirical study of training end-to-end vision-and-language transformers. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 4, 5, 11, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.678, + 0.47, + 0.746 + ], + "angle": 0, + "content": "[9] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Bartra, and Devi Parikh. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6325-6334, 2017. 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.748, + 0.47, + 0.829 + ], + "angle": 0, + "content": "[10] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.832, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[11] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790, 2021. 3" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.115, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.147 + ], + "angle": 0, + "content": "[12] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning, pages 5583-5594. PMLR, 2021. 1, 4, 5, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.15, + 0.892, + 0.233 + ], + "angle": 0, + "content": "[13] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. International journal of computer vision, 123(1):32-73, 2017. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.235, + 0.892, + 0.303 + ], + "angle": 0, + "content": "[14] Junnan Li, Ramprasaath R. Selvaraju, Akhilesh Deepak Gotmare, Shafiq R. Joty, Caiming Xiong, and Steven C. H. Hoi. Align before fuse: Vision and language representation learning with momentum distillation. In NeurIPS, 2021. 1, 2, 4, 5, 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.306, + 0.892, + 0.388 + ], + "angle": 0, + "content": "[15] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10965-10975, 2022. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.39, + 0.892, + 0.459 + ], + "angle": 0, + "content": "[16] Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, et al. Oscar: Object-semantics aligned pre-training for vision-language tasks. In European Conference on Computer Vision, pages 121-137. Springer, 2020. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.462, + 0.892, + 0.53 + ], + "angle": 0, + "content": "[17] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.533, + 0.892, + 0.601 + ], + "angle": 0, + "content": "[18] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 5, 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.604, + 0.892, + 0.658 + ], + "angle": 0, + "content": "[19] Yongfei Liu, Chenfei Wu, Shao-yen Tseng, Vasudev Lal, Xuming He, and Nan Duan. Kd-vlp: Improving end-to-end vision-and-language pretraining with object knowledge distillation. arXiv preprint arXiv:2109.10504, 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.661, + 0.892, + 0.729 + ], + "angle": 0, + "content": "[20] Haoyu Lu, Nanyi Fei, Yuqi Huo, Yizhao Gao, Zhiwu Lu, and Ji-Rong Wen. Cots: Collaborative two-stream vision-language pre-training model for cross-modal retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15692-15701, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.732, + 0.892, + 0.787 + ], + "angle": 0, + "content": "[21] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems, 32, 2019. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.789, + 0.892, + 0.856 + ], + "angle": 0, + "content": "[22] Yu Meng, Chenyan Xiong, Payal Bajaj, Paul Bennett, Jiawei Han, Xia Song, et al. Coco-lm: Correcting and contrasting text sequences for language model pretraining. Advances in Neural Information Processing Systems, 34:23102-23114, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.859, + 0.892, + 0.901 + ], + "angle": 0, + "content": "[23] Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong Wen. Counterfactual vqa: A cause-effect look at language bias. In Proceedings of the IEEE/CVF" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "19163" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.11, + 0.093, + 0.468, + 0.12 + ], + "angle": 0, + "content": "Conference on Computer Vision and Pattern Recognition, pages 12700-12710, 2021. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.122, + 0.468, + 0.175 + ], + "angle": 0, + "content": "[24] Vicente Ordonez, Girish Kulkarni, and Tamara Berg. Im2text: Describing images using 1 million captioned photographs. Advances in neural information processing systems, 24, 2011. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.178, + 0.468, + 0.259 + ], + "angle": 0, + "content": "[25] Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In Proceedings of the IEEE international conference on computer vision, pages 2641–2649, 2015. 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.262, + 0.468, + 0.342 + ], + "angle": 0, + "content": "[26] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 7, 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.345, + 0.468, + 0.467 + ], + "angle": 0, + "content": "[27] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 8748-8763. PMLR, 18-24 Jul 2021. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.47, + 0.468, + 0.55 + ], + "angle": 0, + "content": "[28] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618-626, 2017. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.553, + 0.468, + 0.634 + ], + "angle": 0, + "content": "[29] Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela. Flava: A foundational language and vision alignment model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15638-15650, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.637, + 0.468, + 0.69 + ], + "angle": 0, + "content": "[30] Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Vl-bert: Pre-training of generic visual-linguistic representations. arXiv preprint arXiv:1908.08530, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.693, + 0.468, + 0.746 + ], + "angle": 0, + "content": "[31] Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Hua-jun Bai, and Yoav Artzi. A corpus for reasoning about natural language grounded in photographs. arXiv preprint arXiv:1811.00491, 2018. 2, 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.749, + 0.468, + 0.844 + ], + "angle": 0, + "content": "[32] Hao Tan and Mohit Bansal. LXMERT: Learning cross-modality encoder representations from transformers. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5100-5111, Hong Kong, China, Nov. 2019. Association for Computational Linguistics. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.468, + 0.899 + ], + "angle": 0, + "content": "[33] Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao. Simvlm: Simple visual language model pretraining with weak supervision. arXiv preprint arXiv:2108.10904, 2021. 1" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.093, + 0.468, + 0.899 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.244 + ], + "angle": 0, + "content": "[34] Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chau-mond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online, Oct. 2020. Association for Computational Linguistics. 6, 11" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.247, + 0.892, + 0.287 + ], + "angle": 0, + "content": "[35] Ning Xie, Farley Lai, Derek Doran, and Asim Kadav. Visual entailment: A novel task for fine-grained image understanding. arXiv preprint arXiv:1901.06706, 2019. 12" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.289, + 0.892, + 0.343 + ], + "angle": 0, + "content": "[36] Yifei Xu, Jingqiao Zhang, Ru He, Liangzhu Ge, Chao Yang, Cheng Yang, and Ying Nian Wu. Sas: Self-augmented strategy for language model pre-training. arXiv preprint arXiv:2106.07176, 2021. 8" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.346, + 0.892, + 0.428 + ], + "angle": 0, + "content": "[37] Jinyu Yang, Jiali Duan, Son Tran, Yi Xu, Sampath Chanda, Liquun Chen, Belinda Zeng, Trishul Chilimbi, and Junzhou Huang. Vision-language pre-training with triple contrastive learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15671-15680, 2022. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.431, + 0.892, + 0.498 + ], + "angle": 0, + "content": "[38] Haoxuan You, Luowei Zhou, Bin Xiao, Noel Codella, Yu Cheng, Ruochen Xu, Shih-Fu Chang, and Lu Yuan. Learning visual representation from modality-shared contrastive language-image pre-training. In European Conference on Computer Vision, pages 69-87. Springer, 2022. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.501, + 0.892, + 0.554 + ], + "angle": 0, + "content": "[39] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. arXiv preprint arXiv:2111.08276, 2021. 2, 3, 4, 5, 12" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.892, + 0.554 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "19164" + } + ] +] \ No newline at end of file diff --git a/2023/Leveraging per Image-Token Consistency for Vision-Language Pre-Training/59c7554d-450f-4872-aa6d-2245cedbd11c_origin.pdf b/2023/Leveraging per Image-Token Consistency for Vision-Language Pre-Training/59c7554d-450f-4872-aa6d-2245cedbd11c_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..f7607087af22e7ee25265d0bbb30b957ad06e174 --- /dev/null +++ b/2023/Leveraging per Image-Token Consistency for Vision-Language Pre-Training/59c7554d-450f-4872-aa6d-2245cedbd11c_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:576519d562633a82d109872d5468b364e060cb17385a86281ca5d6c440534ab8 +size 2064246 diff --git a/2023/Leveraging per Image-Token Consistency for Vision-Language Pre-Training/full.md b/2023/Leveraging per Image-Token Consistency for Vision-Language Pre-Training/full.md new file mode 100644 index 0000000000000000000000000000000000000000..e5f33795fd35d8d463080e0860f53eab1982d1fc --- /dev/null +++ b/2023/Leveraging per Image-Token Consistency for Vision-Language Pre-Training/full.md @@ -0,0 +1,332 @@ +# Leveraging per Image-Token Consistency for Vision-Language Pre-training + +Yunhao Gou $^{1,2*}$ + +Tom Ko3 + +Hansi Yang2 + +James Kwok + +Yu Zhang $^{1,4\dagger}$ + +Mingxuan Wang3 + +$^{1}$ Southern University of Science and Technology, $^{2}$ Hong Kong University of Science and Technology $^{3}$ ByteDance AI Lab, $^{4}$ Peng Cheng Laboratory + +{ygou, hyangbw}@connect.ust.hk jamesk@cse.ust.hk + +{tom.ko, wangmingxuan.89}@bytedance.com yu.zhang.ust@gmail.com + +# Abstract + +Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked language modeling (CMLM) to learn vision-language associations. However, we find that CMLM is insufficient for this purpose according to our observations: (1) Modality bias: a considerable amount of masked tokens in CMLM can be recovered with only the language information, ignoring the visual inputs. (2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations. To handle those limitations, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). In EPIC, for each image-sentence pair, we mask tokens that are salient to the image (i.e., Saliency-based Masking Strategy) and replace them with alternatives sampled from a language model (i.e., Inconsistent Token Generation Procedure), and then the model is required to determine for each token in the sentence whether it is consistent with the image (i.e., Image-Token Consistency Task). The proposed EPIC method is easily combined with pre-training methods. Extensive experiments show that the combination of the EPIC method and state-of-the-art pre-training approaches, including ViLT, ALBEF, METER, and X-VLM, leads to significant improvements on downstream tasks. Our coude is released at https://github.com/gyhdog99/epic + +# 1. Introduction + +Vision-language pre-training (VLP) [5,12,21,29,30,33, 37] aims to learn multi-modal representations from largescale image-text pairs. A pre-trained vision-language model + +(VLM) fine-tuned with only a small amount of labeled data has shown state-of-the-art performance in many downstream tasks such as visual question answering and imag-text retrieval. + +A primary concern in developing pre-training objectives for VLP models is how to learn better vision-language associations. In addition to coarse-grained approaches such as image-text matching/contrasting [10, 14, 27] that align concepts from two modalities at the sample level, fine-grained approaches such as cross-modal masked language/image modeling (CMLM/CMIM) [16, 19, 32] learn vision-language associations at the token-object level. For example, Fig. 1 shows a picture paired with the sentence "Blue and yellow hydrant on the grass". When the word "hydrant" is masked, in order to correctly recover the token, the model has to find the actual object in the image and associate it with the word "hydrant". + +While effective, CMLM is insufficient for learning vision-language associations because of (1) modality bias; and (2) under-utilization of unmasked tokens. In vision-language understanding, modality bias refers to leveraging only one modality for training/inference and so cross-modal knowledge is not well explored [23]. We argue that modality bias exists in CMLM, and prevents the model from learning sufficient vision-language associations. Specifically, in the CMLM task, we expect to mask salient1 tokens (such as "blue", "yellow", "fire-hydrant", and "grass") as shown in the left of Fig. 1. These tokens are informative for learning vision-language association because masking them enforces the model to find the answer from the visual modality. However, in practice, whether a token is salient is unknown as we only have access to image-sentence level annotations. Given a fixed and relatively small masking ratio (typically $15\%$ in CMLM), we might end up masking tokens that are less informative. For example, as shown + +![](images/4581cca1be1dd1935d0125da4c9ada248a52b53056022090f839f9ab247f725c.jpg) +Ideal case + +![](images/d95c84a36ae24307d7befea56df46b915ffceb1c4e952637664073bdaff70359.jpg) +CMLM + +![](images/e028ceba64d908f5995a284668686a3e00b45bc9a772b0d30f5193719c0619f6.jpg) +EPIC (consistent or not?) +Figure 1. Illustrations of vision-language association learning. Ideal case: Fine-grained annotations (image regions and corresponding text tokens) are given, we can learn explicit associations (solid lines); CMLM: Without fine-grained annotations, we create supervision by masking, but this can be insufficient due to limited masking ratios and modality bias. EPIC: We find salient tokens and corrupt them to learn more associations. Both CMLM and EPIC learn implicit associations due to lack of region annotations. + +in Fig. 1 (center), when "the" and "and" are masked, the model can predict these masked tokens with only language information. This thus is a form of modality bias as it circumvents using vision-language reasoning. Therefore, the modality bias can make CMLM insufficient to learn vision-language associations. + +Another source of insufficiency in CMLM comes from the under-utilization of unmasked tokens. Similar to Masked Language Modeling (MLM) [6] in language pretraining, the CMLM loss is computed over masked tokens rather than all tokens in the sentence. As a result, learning of cross-modal association is possible only for the masked tokens but not for the remaining unmasked ones. For example, in Fig. 1, ideally, there are four associations (shown in black arrows) between text tokens and the corresponding regions, while there is only one association for CMLM. Therefore, CMLM cannot leverage all tokens (including the unmasked ones) for learning vision-language associations. + +To expedite the learning of cross-modal associations in VLP, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). For each image-sentence pair, we mask tokens that are salient to the image (Saliency-based Masking Strategy) and make them "inconsistent" with the image by replacing them with alternatives from a BERT-like language model (Inconsistent Token Generation Procedure). The model is then required to determine whether each token in the sentence is consistent with the image (Image-Token Consistency (ITC) Task). As this masks salient tokens and applies a language model to generate inconsistent tokens from them, the model has to refer to the visual modality to determine whether a token is inconsistent. Therefore, the modality bias problem can be alleviated. Moreover, we can make better use of the un + +masked tokens for learning vision-language association as the ITC task requires the model to determine whether each token is consistent with the image. + +The proposed EPIC method is easy to implement and widely applicable to a lot of vision-language model architectures. We demonstrate the effectiveness of our approach on various pre-training approaches, including ViLT [21], ALBEF [14], METER [8], and X-VLM [39], and observe significant improvements on downstream tasks. For example, on MSCOCO image-text retrieval, the proposed EPIC method achieves an absolute gain of $2.5\%$ and $4.7\%$ over METER and ViLT, respectively, in terms of the Recall@1 score. On visual reasoning tasks (e.g., NLVR2), the proposed method improves over ALBEF by $1.8\%$ and X-VLM (the state-of-the-art within its model scale) by $1.3\%$ . The proposed method also allows better generalization of pretraining models. For example, in zero-shot image-text retrieval, we improve X-VLM by $3.9\%$ (COCO) and ViLT by $9.9\%$ (Flickr30k). + +# 2. Related Work + +Learning Vision-Language Associations. In vision-language pre-training, a contrastive objective is often used to learn the coarse-grained associations between sentences and images [10, 14, 17, 27, 38]. However, models pretrained with this objective give unsatisfactory results on vision-language reasoning tasks (such as VQA [1] and NLVR2 [31]) which require understanding fine-grained vision-language associations. Cross-Modal Masked Language/Image Modeling (CMLM/CMIM) [2, 16, 19, 20] and its variants are applied to learn fine-grained vision-language associations in a self-supervised manner. Besides CMLM/CMIM, one can further leverage annotated region-phrase (e.g., bounding boxes) image-text data to enable the model with more sophisticated vision-language reasoning + +abilities [7, 11, 15, 39]. + +Cross-Modal Masked Language/Image Modeling. CMLM and CMIM are widely adopted as pre-training objectives in vision-language pre-training. They corrupt a token/patch of a sentence/image and train the model to reconstruct the original data with information from both modalities. LXMERT [32] found that loading BERT into the text encoder harms pre-training because the pre-trained BERT can have high CMLM accuracy and thereby circumventing the cross-modal reasoning process. Recently, similar to our observations, Bitton et al. [2] showed that roughly $50\%$ of the masked tokens in CMLM are punctuation or stop-words, leading to sample inefficiency of CMLM. They propose a rule-based masking strategy to mask object words, content words, or words with high concreteness [3] in CMLM according to different downstream tasks, expecting such words to be more related to the visual input. Such a strategy lacks generalization ability and cannot scale to more downstream tasks. + +Modality Bias in Vision-Language Understanding. In CMLM, predicting the masked token without referring to the visual modality can be seen as a consequence of modality bias towards language in vision-language understanding. Due to the existence of modality bias, the learning of vision-language associations is weakened. Similarly, modality bias also happens in visual question answering (VQA) [1] where the model is required to answer a question given a visual input. For example, simply answering "tennis" to the sport-related questions can achieve approximately $40\%$ accuracy [23] on the VQA v1.0 dataset. To reduce such a bias, CF-VQA [23] proposes a counterfactual framework which directly subtracts the language-based predictions from the answers. In the proposed EPIC method, we mitigate the negative influence of language bias by allowing the model to learn more vision-language associations over a wider range of tokens. + +# 3. Empirical Analysis + +In this section, we empirically analyze the existence of modality bias and under-utilization of unmasked tokens in CMLM. Experimental details are in Appendix A. + +Identifying Modality Bias. If there is a strong modality bias towards language in Cross-Modal Language Modeling (CMLM), a pure language model (LM) that is blind to the visual inputs can achieve comparable accuracy on the Masked Language Modeling (MLM) task. Otherwise, the LM will be outperformed by the vision-language model (VLM) by a large margin. Based on this, we run CMLM on a VLM and MLM on a LM, respectively. They share the same input sentences and masked tokens, while the former additionally receives visual inputs. As shown in Fig. 2a, though using visual modality is helpful to recover the original token (80% Acc.), a pure LM can already achieve + +![](images/0eda780c8fa8a47b77718b82ed9c3ff8699edfcb598c2ade7128c36f2fe522c2.jpg) +(a) Accuracy gaps. + +![](images/2a65b82f5657f240cc48596296bcd5bf81805fb8558c01ed7807b19b02df0c65.jpg) +(b) Accuracy drops. +Figure 2. Left: CMLM/MLM accuracy of VLM/LM (y-axis) as the training process proceeds (x-axis). Right: relative accuracy drop of CMLM/MLM (y-xais) as we corrupt more unmasked tokens (x-axis). The colored bands indicate the variance of the results as each configuration is repeated for 5 times. + +an accuracy of $70\%$ . Therefore, a modality bias towards language does exist in the training of CMLM. + +Under-utilization of Unmasked Tokens. If the CMLM is weak at reasoning associations between images and un-masked tokens, replacing the unmasked ones with alternatives based on the language context should not prevent the model from recovering the masked tokens; otherwise, the performance of CMLM will drop significantly because the language context is inconsistent with the image. Therefore, we use a pre-trained VLM and LM to perform inference on CMLM and MLM, respectively, under a corrupted context in which we randomly replace unmasked tokens by sampling from the MLM head of a BERT. As illustrated in Fig. 2b, both the VLM and LM suffer performance deterioration under different corruption ratios. However, even though the text contexts are inconsistent with the image after corruption, the performance drop is less severe for the VLM as compared to the LM. In fact, the performance curves for CMLM and MLM overlap in terms of relative performance drop. Hence, we conclude that the CMLM is weak at learning associations between unmasked tokens and images. + +# 4. Methodology + +Sec. 4.1 first introduces preliminaries of the proposed method. We then introduce the three components of EPIC. We formulate the ITC task and introduce the concept of token consistency w.r.t. an image in Sec. 4.2. We design the inconsistent token generation procedure in Sec. 4.3, and the saliency-based masking strategy in Sec. 4.4. Fig. 3 gives an overview of the proposed method. The complete EPIC algorithm is shown in Appendix B. + +# 4.1. Preliminaries + +Pre-training Framework. In vision-language pretraining, we have access to parallel image-text data $\mathcal{D} = \{(w_i,v_i)\}_{i = 1}^N\sim P_{\mathcal{W},\mathcal{V}}$ . Specifically, for any sentence $\pmb {w} = [w_1,\dots ,w_n]$ , there is a corresponding image $\pmb {v} =$ + +![](images/37825ea61febc230cb59711e702d02c85421c1452b5ea7571a7812624257f78f.jpg) +Figure 3. An overview of EPIC. The input image and sentence are input to a teacher VLM to obtain the saliency for each text token w.r.t the image. Then we mask accordingly and generate tokens inconsistent with the image though a language model (fine-tuning during pre-training). Finally we train the VLM to determine for each token whether it is inconsistent with the image (ITC). + +$[v_{1}, \ldots, v_{m}]$ in the form of grid-based or region-based features [8]. Without loss of generality, we assume a vision-language model $f_{\mathrm{VL}}(\cdot)$ in METER [8] of the following form: Given a pair of sentence $\boldsymbol{w}$ and image $\boldsymbol{v}$ , it first extracts text features and visual features via a text encoder and vision encoder, respectively. The text and visual features are then fed into a multi-modal fusion module, consisting of several layers of self-attention followed by cross-attention, to produce cross-modal representations for text and image. Note that the proposed method is also applicable to other architectures such as ViLT [12], ALBEF [14], and X-VLM [39]. + +# 4.2. Image-Token Consistency + +First, we introduce the concept of consistency of a text token with respect to an image. + +Consistency of Text Tokens w.r.t Images. In vision-language pre-training, we assume that the dataset is clean (without noise) and that we have access to the marginal distribution of the text corpus $P_{\mathcal{W}}$ . For a pair of text $\boldsymbol{w}$ and image $\boldsymbol{v}$ , a text token $w_i$ in $\boldsymbol{w}$ is "consistent" with the image given the remaining tokens, i.e., $P_{\mathcal{W},\mathcal{V}}(\boldsymbol{W} = \boldsymbol{w},\boldsymbol{V} = \boldsymbol{v})$ is large. If we replace a token $w_j$ with $w_j'$ such that the new sentence $\bar{w}$ (1) does not match the semantic content of the image, i.e., $P_{\mathcal{W},\mathcal{V}}(\boldsymbol{W} = \bar{\boldsymbol{w}},\boldsymbol{V} = \boldsymbol{v})$ is small and (2) is linguistically proper, i.e., $P_{\mathcal{W}}(\boldsymbol{W} = \bar{\boldsymbol{w}})$ is large, then $w_j'$ is inconsistent with the image given the remaining tokens. + +An example is shown in Fig. 3. In the sentence "Yellow and blue hydrant on the grass", the word "hydrant" is consistent with the image given the remaining words. If we replace "yellow" with "green", it produces a sentence that does not match the image, and so "green" is inconsistent. + +Task Formulation. Given an image $v$ and a sentence $\bar{w}$ with inconsistent tokens at positions $\mathcal{T}$ , the model has to determine whether each token is consistent with the image. We will introduce the generation process of $\bar{w}$ and $\mathcal{T}$ in + +# Sec. 4.3. + +The $\bar{\boldsymbol{w}}$ and $\pmb{v}$ are fed into VLM to obtain the last-layer hidden representations $\left\{\bar{h}_i^{\mathrm{VL}}\right\}_{i = 1}^n$ of $\bar{w}$ . Let $\beta$ be the weight vector for decision. The probability that each token (from position $i = 1$ to $n$ ) is consistent with the image is: + +$$ +D _ {\mathrm {I T C}} ^ {i} = \operatorname {s i g m o i d} \left(\boldsymbol {\beta} ^ {\intercal} \bar {\boldsymbol {h}} _ {i} ^ {\mathrm {V L}}\right). \tag {1} +$$ + +Therefore, the EPIC model minimizes the following binary classification loss: + +$$ +\mathcal {L} _ {\mathrm {I T C}} = - \sum_ {i \notin \mathcal {T}} \log D _ {\mathrm {I T C}} ^ {i} - \sum_ {i \in \mathcal {T}} \log \left(1 - D _ {\mathrm {I T C}} ^ {i}\right). \tag {2} +$$ + +Leveraging Unmasked Tokens. It is easy to see that in Eq. (2), the EPIC model is tasked to determine for all the tokens whether they are consistent with the image or not. Therefore, the ITC task leverages more tokens for learning vision-language associations. + +Mitigating Modality Bias. In the second condition for inconsistent tokens, we require them to be linguistically proper for the sentence. We argue that this is essential for the ITC task to alleviate the modality bias problem because if the sentence after replacement $\bar{w}$ is not linguistically proper, then the VLM can identify the replaced tokens simply by using the language context only. As a result, the VLM does not learn vision-language associations by decreasing $\mathcal{L}_{\mathrm{ITC}}$ . Instead, it simply conducts language modeling. + +# 4.3. Generating Inconsistent Tokens + +In this section, we discuss how to generate sentences $\bar{w}$ with tokens that fulfill the two conditions of inconsistent tokens in Sec. 4.2. Strictly speaking, we have to model the distributions $P_{\mathcal{W},\mathcal{V}}$ and $P_{\mathcal{W}}$ for generation, but this is hard. Instead, we propose to approximately generate inconsistent tokens with a BERT-like language model. + +Assume that we have a set $\mathcal{M}$ of masking positions (we will discuss how to obtain $\mathcal{M}$ in Sec. 4.4). Given the original text sequence $\boldsymbol{w}$ , we first mask the corresponding tokens by replacing them with the [MASK] symbol: + +$$ +\boldsymbol {w} ^ {\text {m a s k}} = \operatorname {M A S K} (\boldsymbol {w}, \mathcal {M}). \tag {3} +$$ + +The resulting sentence $\pmb{w}^{\mathrm{mask}}$ is fed to an auxiliary BERT-like language model $f_{L}(\cdot)$ to obtain a sequence of contextual representations $\pmb{H}^{\mathrm{L}} = \left\{\pmb{h}_{i}^{\mathrm{L}}\right\}_{i=1}^{n}$ . We then obtain the probability of predicting a particular token $w_{k}$ as: + +$$ +p _ {\mathrm {M L M}} \left(w _ {k} \mid \boldsymbol {h} _ {i} ^ {\mathrm {L}}\right) = \frac {\exp \left(\boldsymbol {e} \left(w _ {k}\right) ^ {\top} \boldsymbol {h} _ {i} ^ {\mathrm {L}}\right)}{\sum_ {w ^ {\prime}} \exp \left(\boldsymbol {e} \left(w ^ {\prime}\right) ^ {\top} \boldsymbol {h} _ {i} ^ {\mathrm {L}}\right)}, \tag {4} +$$ + +where $e(\cdot)$ denotes the token embedding. The new sentence, with the inconsistent tokens, is $\bar{w} = [\bar{w}_1, \dots, \bar{w}_n]$ : + +$$ +\bar {w} _ {i} \sim p _ {\mathrm {M L M}} \left(w \mid \boldsymbol {h} _ {i} ^ {\mathrm {L}}\right). \tag {5} +$$ + +The positions of the inconsistent tokens $\mathcal{T}$ are obtained by: + +$$ +\mathcal {T} = \{t \mid \bar {w} _ {t} \neq w _ {t}, \forall t \in \mathcal {M} \}. \tag {6} +$$ + +We also train the language model to fit to the sentence during pre-training by a MLM objective: + +$$ +\mathcal {L} _ {\text {G E N}} = - \sum_ {i \in \mathcal {M}} \log p _ {\mathrm {M L M}} \left(w _ {i} \mid \boldsymbol {h} _ {i} ^ {\mathrm {L}}\right). \tag {7} +$$ + +We provide justification for using a language model to approximately fulfill the two conditions of inconsistent tokens. First, the language model has no access to the visual inputs. $P_{\mathcal{W},\nu}(\boldsymbol {W} = \bar{\boldsymbol{w}},\boldsymbol {V} = \boldsymbol{v})$ is likely to be small, and so it generates samples approximating the first condition. Second, since the language model is fine-tuned during pretraining, $P_{\mathcal{W}}(\boldsymbol {W} = \bar{\boldsymbol{w}})$ is likely to be large. Thus, it generates samples that can approximate the second condition. + +# 4.4. Saliency-based Masking + +In this section, we discuss how to obtain masking positions $\mathcal{M}$ . First, we introduce the concept of saliency. + +Saliency of Text Tokens w.r.t. Images. For a pair of sentence $\mathbf{w}$ and image $\mathbf{v}$ , a text token $w_{i}$ is salient w.r.t. to the image if the meaning of the token is strongly related to the content in the image, otherwise, it is not salient. In Fig. 3, words "yellow", "blue", "hydrant" and "grass" are salient w.r.t. the image, while "and", "the" are not. + +Masking Salient Tokens. As discussed in Sec. 4.3, we first mask the original sentence based on $\mathcal{M}$ , and obtain the positions $\mathcal{T}$ for inconsistent tokens by comparing the generated tokens with the original tokens (Eq. (6)). Since the language model is fine-tuned on the corpus, if we mask non-salient tokens w.r.t. an image, the language model is likely to recover the original tokens by attending to the remaining + +language context. In this case, no inconsistent tokens will be generated, and the model cannot learn vision-language associations. However, if we mask salient tokens w.r.t. an image, the language model is incapable of recovering such tokens because it has no access to the visual input. Therefore, it is expected to mask salient tokens for generating inconsistent tokens. + +In practice, we find salient tokens by selecting the tokens/positions with higher attention scores to the image. The cross-modal representation of the [CLS] token of the image $\pmb{v}$ is query $\pmb{q}_k^V\in \mathbb{R}^{d_h}$ , and that of all the elements in $\pmb{w}$ are keys $\pmb{K}_k^W\in \mathbb{R}^{n\times d_h}$ at head $k$ , where $n$ is the sequence length of the sentence, $d_{h} = d / h$ is the dimension of a single-head output. For simplicity, we only consider the representations from the penultimate layer of the fusion module. The image-token saliency $\alpha \in \mathbb{R}^d$ can then be written as + +$$ +\boldsymbol {\alpha} = \operatorname {s o f t m a x} \left(\frac {1}{h} \sum_ {k = 1} ^ {h} \boldsymbol {q} _ {k} ^ {V} \left(\boldsymbol {K} _ {k} ^ {W}\right) ^ {\intercal}\right). \tag {8} +$$ + +Finally, the masking positions $\mathcal{M}$ are sampled without replacement from the categorical distribution $p(t = i; \alpha) = \alpha_i$ such that $|\mathcal{M}| = m$ , where $m$ is the expected number of inconsistent tokens. In this way, tokens with high saliencies are more likely to be masked. Notice that we use a pre-trained vision-language model to obtain $\boldsymbol{q}_k^V$ and $\boldsymbol{K}_k^W$ . Details are in Appendix C. + +# 5. Experiments + +# 5.1. Pre-training Settings + +Baselines. The proposed method has no constraints on the network architectures, training objectives, and visual representations. It supports architectures conducting either cross-attention or self-attention for multi-modal fusion. In this study, we plug-in our method into four recent approaches with diversified cross-modal learning techniques: (i) METER, [8] which adopts modality-specific encoders with cross-attention, (ii) ALBEF [14], which utilizes cross-modal contrastive learning with cross-attention, (iii) X-VLM [39], which performs cross-modal alignments from multiple granularities with cross-attention, and (iv) ViLT [12], which fuses images and text in a single encoder via self-attention. We reproduce the pre-training for all the baselines with the settings where they achieve their best results. + +Datasets. There are four widely adopted datasets for vision-language pre-training: (i) COCO [18], (ii) Visual Genome (VG) [13], (iii) SBU Captions [24] and (iv) Conceptual Captions 3M (CC) [4]. We refer the combination of these datasets as 4M because there are 4 million unique images in them. We also experiment with a large-scale web dataset + +
MethodDataEPICMSCOCO (5K test set)Flickr30K (1K test set)Flickr30K ZS(1K test set)
TRR@1/R@5/R@10IRR@1/R@5/R@10TRR@1/R@5/R@10IRR@1/R@5/R@10TRR@1/R@5/R@10IRR@1/R@5/R@10
METER4MX77.2/93.7/97.159.2/84.0/90.894.2/99.6/99.983.8/97.3/98.692.2/99.3/99.778.8/94.5/96.9
✓79.0/94.5/97.561.2/85.2/91.695.8/99.3/99.685.1/97.4/98.793.1/99.1/99.879.0/94.5/97.1
16MX78.2/93.8/96.959.8/84.2/90.894.6/99.7/99.885.2/97.4/98.893.1/99.4/99.881.1/96.0/97.8
✓79.7/94.8/97.562.5/85.4/91.996.5/99.9/99.986.7/97.8/99.094.7/99.5/99.882.6/95.9/99.8
ALBEF4MX73.3/92.4/96.456.4/81.8/88.994.4/99.4/99.882.1/95.7/97.991.1/98.7/99.476.1/93.0/95.9
✓75.1/92.9/96.458.6/82.7/89.395.6/99.7/99.983.7/96.7/98.491.7/99.2/99.878.1/93.8/96.4
16MX78.3/93.9/96.861.3/84.3/90.695.9/99.8/10085.7/97.2/98.893.6/99.5/99.883.2/95.9/97.7
✓79.2/94.7/97.562.9/85.4/91.396.4/100/10087.1/97.3/98.994.8/99.7/99.984.1/96.5/97.9
X-VLM4M+X79.8/95.1/97.762.7/85.6/91.496.7/99.9/10085.3/97.4/98.783.1/97.8/99.470.5/92.8/96.4
✓81.0/95.3/97.964.1/86.1/91.697.2/99.9/10087.0/97.6/98.786.2/98.6/99.874.4/94.3/97.0
16M+X79.5/95.4/97.863.3/85.6/91.496.8/100/10086.7/97.5/98.786.4/99.2/99.676.1/94.1/96.7
✓80.7/95.6/98.064.1/85.9/91.897.4/100/10087.3/97.6/98.889.0/99.0/99.775.4/94.2/96.8
ViLT4MX60.4/85.8/92.241.3/71.4/82.380.8/95.9/98.761.2/88.0/93.572.6/93.0/96.853.4/80.8/88.7
✓65.0/87.5/93.746.0/74.8/84.685.2/97.3/99.366.9/90.0/94.479.8/95.8/97.963.3/86.3/92.0
+ +Table 1. Image-text retrieval results on the MSCOCO (fine-tuned) and Flickr30K (fine-tuned and zero-shot) datasets. IR: Image Retrieval and TR: Text Retrieval. Recall@ $K$ with $K = 1,5$ ,and 10 is used as the evaluation metric. Better results under the same baseline are marked in bold. + +
MethodDataEPICMSCOCO (5K test set)
TRIR
R@1R@5R@10R@1R@5R@10
X-VLM4M+X69.291.996.555.382.589.7
✓72.093.497.357.383.490.2
16M+X73.192.897.056.983.089.9
✓73.293.696.957.583.390.0
ViLT4MX54.681.489.138.669.080.2
✓64.186.592.547.174.884.6
+ +Table 2. Zero-shot image-text retrieval results on MSCOCO. + +CC12M [4]. We consider the combination of 4M and 12M datasets as 16M. Additionally, the X-VLM requires fine-grained annotations (e.g., object and region descriptions) for both the 4M and 16M settings. Therefore, the resulting dataset for X-VLM is called $4\mathrm{M}^{+}$ and $16\mathrm{M}^{+}$ , respectively. A more detailed description of the data statistics is in Appendix E. + +Implementation Details. For the pre-training baselines, we follow the official implementations provided by the authors of METER, X-VLM, ALBEF and ViLT. More details on their implementations are in Appendix F. For the proposed method, the auxiliary language model is chosen to be identical to the text encoder of the vision-language model. For ViLT without modality-specific encoders, we use BERT-base [6]. All the auxiliary language models are loaded directly from HuggingFace repository [34] with pretrained checkpoints. For every sentence, the number of masked tokens $m$ is calculated as $m = \text{ceil}(\text{mask\_ratio} \times \text{len}(\text{sentence}))$ . The mask ratio is set to 0.35 for all baselines (based on the hyper-parameter search results in Sec. 5.3). The loss weight $\lambda$ of the Image-Token Consistency task is set to 8. + +# 5.2. Results on Downstream Tasks + +Evaluation is performed on the following downstream tasks: (i) Image-Text Retrieval, (ii) Visual Question Answering (VQA), (iii) Natural Language for Visual Reasoning (NLVR2), and (iv) Visual Entailment (VE). Details on these tasks are in Appendix D. + +# 5.2.1 Results for Image-Text Retrieval + +Tables 1 and 2 show that EPIC is universally effective over the different baselines/datasets, since we achieve non-trivial improvement nearly for all the settings. In addition, compared with all the baselines, EPIC brings more significant improvement to ViLT on different datasets. For example, on the fine-tuned retrieval tasks, we achieve an absolute improvement of around $5\%$ in terms of the TR/IR @1 metric on MSCOCO and Flickr30K. Furthermore, for zero-shot tasks, the improvement on the Flickr30K dataset is $7.2\%$ in terms of TR@1, and $9.9\%$ in terms of IR@1. The gap between ViLT and the other baselines can be explained by the fact that ViLT adopts a simplified architecture for vision-language pre-training and it does not incorporate more sophisticated tasks to learn vision-language associations. Nevertheless, the results on ViLT can be seen as an indicator for the effectiveness of the proposed method on a "clean" baseline. + +For the other baselines with advanced strategies to learn vision-language associations (such as cross-modal contrasting in ALBEF, fine-grained reasoning in X-VLM, and powerful pre-trained encoders in METER), EPIC still demonstrates significant improvements. For example, compared with METER on the MSCOCO dataset (fine-tuned), EPIC + +
MethodDataEPICVQANLVR2SNLI-VE
devstddevstddevstd
METER4M+X77.777.981.882.581.481.0
✓77.978.083.583.581.681.8
16M+X78.378.482.784.381.781.8
✓78.678.785.085.282.182.3
ALBEF4M+X74.674.679.580.080.180.1
✓75.175.281.382.280.680.7
16M+X75.876.082.682.580.880.9
✓76.776.784.184.081.381.7
X-VLM4M+X78.178.283.384.1//
✓78.578.584.684.5//
16M+X78.078.284.384.5//
✓78.378.385.285.5//
ViLT4MX71.371.475.075.2//
✓71.871.877.277.1//
+ +Table 3. Evaluation results on downstream vision-language tasks: VQA, NLVR2, and SNLI-VE. “/” indicates that the original baseline does not conduct experiment on this task. + +achieves an absolute IR@1 improvement of $2\%$ and $2.7\%$ with 4M and 16M data, respectively. When using the XVLM as baseline, we observe an absolute improvement of $3.9\%$ and $3.1\%$ in terms of IR@1 and TR@1, respectively, on Flickr30K (zero-shot). + +# 5.2.2 Results for VQA, NLVR2, and VE + +Table 3 shows that EPIC is effective among all the vision-language tasks. We improve over METER in terms of the VQA dev accuracy by $0.3\%$ under the 16M setting and XVLM by $0.4\%$ under the $4\mathrm{M}^{+}$ setting. Such improvement is non-trivial given the fact that these two baselines are quite competitive on this task. We also notice that EPIC improves ALBEF on VQA by $0.9\%$ in dev accuracy. Further, we observe significant improvements over all baselines on NLVR2 (e.g., $+2.3\%$ dev accuracy on METER 16M; $+2.2\%$ std accuracy on ALBEF 4M). The proposed method is also effective on the SNLI-VE dataset. It brings an absolute improvement of $0.8\%$ over ALBEF under the 16M setting. + +
MethodNLVR2 devFlickr30K-ftFlickr30K-zsMSCOCO-ft
TR1IR1TR1IR1TR1IR1
vanilla METER79.689.276.683.267.771.052.5
ITC (rand.)79.991.577.883.269.070.853.5
ITC+LM80.992.178.983.871.573.455.6
EPIC81.092.979.084.372.574.155.6
+ +Table 4. Ablation studies on the effect of the ITC task, negative samples generation and the saliency-based masking strategy. + +
GeneratorNLVR2 devFlickr30K-ftFlickr30K-zsMSCOCO-ft
TR1IR1TR1IR1TR1IR1
LM (cond.)79.992.478.784.071.872.554.5
VLM (SAS)80.591.079.283.270.472.454.9
LM (trained)80.791.379.584.872.173.655.1
LM (fixed)80.791.979.083.871.172.454.8
LM (fine-tune)81.092.979.084.372.574.155.6
+ +Table 5. Ablation study on different negative sample generators. + +# 5.3. Ablation Study + +We conduct ablation studies based on the METER model due to its superior performance with basic training objectives (i.e., image-text matching and cross-modal masked language modeling). However, training a full-version of METER is expensive (roughly 4 days with 32 A100 80G). Hence, we conduct ablations on a smaller scale that is still representative. Specifically, the model is still pre-trained on the 4M dataset, but the input image is resized to $224 \times 224$ (instead of $288 \times 288$ in the normal METER setting). To speed up the training process, we replace the image encoder CLIP-16 [26] with CLIP-32 for less memory usage, and also shorten the training schedule to 50k steps. + +We evaluate the pre-trained model on the NLVR2 task and retrieval task (Flickr30K fine-tuned/zero-shot, MSCOCO fine-tuned). For the NLVR2 task, we report the development accuracy. For the retrieval tasks, we report the IR1/TR1 accuracies on the corresponding validation sets. + +Component Analysis. As shown in Table 4, on top of vanilla METER, we first add an ITC task (ITC (rand.)) with inconsistent tokens sampled uniformly from the vocabulary (selecting $35\%$ of positions for inconsistent tokens). In this case, though some of the sampled tokens do not satisfy the conditions for inconsistent tokens in Sec. 4.2, the pretrained model can still benefit from the ITC task. When we replace the random strategy with a language model, $ITC + LM$ , we generate inconsistent tokens approximating the conditions in Sec. 4.2. Finally, we choose to mask the tokens that are salient w.r.t. the image to generate inconsistent tokens, $ITC + saliency + LM$ , and this further improves the performance on downstream tasks. Therefore, each component of EPIC is effective to improve the performance of the pre-trained model. + +Conditions of Inconsistent Tokens. In this ablation experiment, we study the importance of the two conditions of inconsistent tokens in Sec. 4.2. LM (fine-tune) is proposed in EPIC and it produces inconsistent tokens approximating the two conditions. First, we drop the first condition, that is, $P_{\mathcal{W},\mathcal{V}}(\boldsymbol {W} = \bar{\boldsymbol{w}},\boldsymbol {V} = \boldsymbol{v})$ is no longer small. We achieve this in two ways. (i) LM (cond.): When generating inconsistent samples and fine-tuning the LM, we replace the class token (first token) in the text with the one from the image encoder of the VLM (with gradient propagation canceled). + +![](images/8c0b25e9017ef6323faaad25592102cb19dbf1048b66b0e8fa8ee96b2a0f94db.jpg) + +![](images/0ad5ae239cea93712dd0a2b8d468b82d9c074dbd9eb349248fda5f3dffd23bc6.jpg) + +![](images/b0910e8ad4645f744fb7f14c7f3d103eafeee64b296ab589ebc2a69f9180164e.jpg) +(a) +(b) +Figure 4. Visualization on the token saliency distribution. + +![](images/081aad5fcefe2fb518bc012f1bdec27ed9912ff65f697f0daa2f500c4133a6e8.jpg) +Figure 5. Grad-CAM visualization on the cross-attention maps of the image with respect to the ITC task. + +(ii) VLM (SAS): We adapt SAS [36] from the text modality to the multi-modality setting. An auxiliary VLM (previous checkpoints of the VLM, detailed in Appendix G) is used to produce inconsistent tokens. As shown in Table 5, we observe that both $LM$ (cond.) and $VLM$ (SAS) suffer performance deterioration compared to $LM$ (fine-tune). This indicates the importance of the first condition. + +We then demonstrate the importance of the second condition, that is $P_{\mathcal{W}}(\boldsymbol{W} = \bar{\boldsymbol{w}})$ is large. Intuitively, we fail to satisfy this condition when we stop fine-tuning the LM on the text corpus, i.e., $LM$ (fixed). Further, for $LM$ (fine-tune), we experiment its possible alternatives, $LM$ (trained), where we fit the LM on the text corpus before instead of during pre-training. We observe that $LM$ (fixed) clearly decreases the performance (especially in MSCOCO-ft). This validates the effectiveness of the second condition. Note that $LM$ (fine-tune) outperforms $LM$ (trained) by a small margin. This gap can be attributed to the training dynamics brought by fine-tuning [22]. As the LM fits the text corpus gradually better during finetuning, it becomes increasingly hard for a model to identify whether a token is replaced. This dynamics encourages the model to perform curriculum learning, which improves performance. + +# 5.4. Visualization + +Token Saliency. Fig. 4 shows the saliency distribution of the tokens w.r.t. the image for an image-sentence pair. We can see that salient tokens have higher densities in the distribution. For example, in Fig. 4a, "blue" and "yellow" are highlighted. In Fig. 4b, the teacher model gives more mass on "white", "stick" and "ball". These confirm that salient tokens can be detected by EPIC. + +Grad-CAM of ITC. Fig. 5 visualizes the cross-attention maps of the image w.r.t. the inconsistent/consistent tokens using Grad-CAM [28]. In each row, we show the original image (left), the attention map w.r.t. the inconsistent tokens + +(a) +![](images/8d9542d8b40272e0f31119a4b0f921ba3cae6a7f38aa3b0afc95de09896c03b8.jpg) +Positive Sample: "Two kids are playing with a magnet." + +"pet" +![](images/4963f8fc40cf56ceaf9ef3128f1bc0036ae63fc81fc3057cfd4b86de87f44648.jpg) +Negative Sample: "Two kids are playing with a + +![](images/95ea166556d65985bf4b8d74e9eb851a7a2de5e7030c637e5fe514344a56712d.jpg) + +(b) +![](images/d2dc28130810996989c1673edf78b62f7724c5f5c1d3908e95f34ac5e1a92e6d.jpg) +Positive Sample: "A young woman poses with a large knife and a sandwich." + +"cake"/"sandwich" +![](images/5ae4df8c3d2f682321edfff1f1d191aed2e8b1f8d930fc871f24a4b92894fb00.jpg) +Negative Sample: "A young women poses with a large cake and a ball. + +![](images/7b4a6ce7b791e13bb6366a6402501147d2abb4c0c6dddc9d2f0d7d98ab39a521.jpg) +"cake"/"ball" + +(middle), and that w.r.t. the consistent ones (right). For example, in the first row, when we input the model with the original sentence, the model predicts that the token "magnet" is consistent and attends to the actual magnet in the image. However, when we replace "magnet" with "pet", the model predicts the token "pet" as "inconsistent" and still attends to the magnet in the image. This means the model is aware that the object in the image is actually a magnet instead of a pet. Similar observation exists for the second row. These demonstrate the model's ability to reason on cross-modal relationship. + +# 6. Conclusion + +In this paper we propose EPIC, a pre-training approach that leverage more text tokens for learning vision-language associations. It is less affected by the modality bias problem compared with CMLM. Specifically, we propose an ITC task to identify inconsistent tokens generated by a language model coupled with a saliency-based masking strategy. The task formulation of the ITC task and the design of inconsistent samples address the problems of under-utilization of unmasked tokens and modality bias. We perform extensive experiments and show that EPIC brings consistent performance gains over several baselines on a wide range of downstream tasks. Possible directions for future research can be: (i) approaching the conditions of inconsistent tokens in Sec. 4.2 more precisely; (ii) finding salient tokens without using a pre-trained teacher VLM. + +# Acknowledgements + +This work is supported by NSFC key grant 62136005, NSFC general grant 62076118, and Shenzhen fundamental research program JCYJ20210324105000003. + +# References + +[1] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015. 2, 3, 11 +[2] Yonatan Bitton, Michael Elhadad, Gabriel Stanovsky, and Roy Schwartz. Data efficient masked language modeling for vision and language. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3013-3028, Punta Cana, Dominican Republic, Nov. 2021. Association for Computational Linguistics. 2, 3 +[3] Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman. Concreteness ratings for 40 thousand generally known english word lemmas. Behavior Research Methods, 46:904-911, 2014. 3 +[4] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3558-3568, 2021. 5, 6 +[5] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 1 +[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 2, 6 +[7] Zi-Yi Dou, Aishwarya Kamath, Zhe Gan, Pengchuan Zhang, Jianfeng Wang, Linjie Li, Zicheng Liu, Ce Liu, Yann LeCun, Nanyun Peng, et al. Coarse-to-fine vision-language pre-training with fusion in the backbone. arXiv preprint arXiv:2206.07643, 2022. 3 +[8] Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, Zicheng Liu, and Michael Zeng. An empirical study of training end-to-end vision-and-language transformers. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 4, 5, 11, 12 +[9] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Bartra, and Devi Parikh. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6325-6334, 2017. 11 +[10] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 2 +[11] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790, 2021. 3 + +[12] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning, pages 5583-5594. PMLR, 2021. 1, 4, 5, 12 +[13] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. International journal of computer vision, 123(1):32-73, 2017. 5 +[14] Junnan Li, Ramprasaath R. Selvaraju, Akhilesh Deepak Gotmare, Shafiq R. Joty, Caiming Xiong, and Steven C. H. Hoi. Align before fuse: Vision and language representation learning with momentum distillation. In NeurIPS, 2021. 1, 2, 4, 5, 12 +[15] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10965-10975, 2022. 3 +[16] Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, et al. Oscar: Object-semantics aligned pre-training for vision-language tasks. In European Conference on Computer Vision, pages 121-137. Springer, 2020. 1, 2 +[17] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208, 2021. 2 +[18] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 5, 11 +[19] Yongfei Liu, Chenfei Wu, Shao-yen Tseng, Vasudev Lal, Xuming He, and Nan Duan. Kd-vlp: Improving end-to-end vision-and-language pretraining with object knowledge distillation. arXiv preprint arXiv:2109.10504, 2021. 1, 2 +[20] Haoyu Lu, Nanyi Fei, Yuqi Huo, Yizhao Gao, Zhiwu Lu, and Ji-Rong Wen. Cots: Collaborative two-stream vision-language pre-training model for cross-modal retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15692-15701, 2022. 2 +[21] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems, 32, 2019. 1, 2 +[22] Yu Meng, Chenyan Xiong, Payal Bajaj, Paul Bennett, Jiawei Han, Xia Song, et al. Coco-lm: Correcting and contrasting text sequences for language model pretraining. Advances in Neural Information Processing Systems, 34:23102-23114, 2021. 8 +[23] Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong Wen. Counterfactual vqa: A cause-effect look at language bias. In Proceedings of the IEEE/CVF + +Conference on Computer Vision and Pattern Recognition, pages 12700-12710, 2021. 1, 3 +[24] Vicente Ordonez, Girish Kulkarni, and Tamara Berg. Im2text: Describing images using 1 million captioned photographs. Advances in neural information processing systems, 24, 2011. 5 +[25] Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In Proceedings of the IEEE international conference on computer vision, pages 2641–2649, 2015. 11 +[26] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 7, 11 +[27] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 8748-8763. PMLR, 18-24 Jul 2021. 1, 2 +[28] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618-626, 2017. 8 +[29] Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela. Flava: A foundational language and vision alignment model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15638-15650, 2022. 1 +[30] Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Vl-bert: Pre-training of generic visual-linguistic representations. arXiv preprint arXiv:1908.08530, 2019. 1 +[31] Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Hua-jun Bai, and Yoav Artzi. A corpus for reasoning about natural language grounded in photographs. arXiv preprint arXiv:1811.00491, 2018. 2, 11 +[32] Hao Tan and Mohit Bansal. LXMERT: Learning cross-modality encoder representations from transformers. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5100-5111, Hong Kong, China, Nov. 2019. Association for Computational Linguistics. 1, 3 +[33] Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao. Simvlm: Simple visual language model pretraining with weak supervision. arXiv preprint arXiv:2108.10904, 2021. 1 + +[34] Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chau-mond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online, Oct. 2020. Association for Computational Linguistics. 6, 11 +[35] Ning Xie, Farley Lai, Derek Doran, and Asim Kadav. Visual entailment: A novel task for fine-grained image understanding. arXiv preprint arXiv:1901.06706, 2019. 12 +[36] Yifei Xu, Jingqiao Zhang, Ru He, Liangzhu Ge, Chao Yang, Cheng Yang, and Ying Nian Wu. Sas: Self-augmented strategy for language model pre-training. arXiv preprint arXiv:2106.07176, 2021. 8 +[37] Jinyu Yang, Jiali Duan, Son Tran, Yi Xu, Sampath Chanda, Liquun Chen, Belinda Zeng, Trishul Chilimbi, and Junzhou Huang. Vision-language pre-training with triple contrastive learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15671-15680, 2022. 1 +[38] Haoxuan You, Luowei Zhou, Bin Xiao, Noel Codella, Yu Cheng, Ruochen Xu, Shih-Fu Chang, and Lu Yuan. Learning visual representation from modality-shared contrastive language-image pre-training. In European Conference on Computer Vision, pages 69-87. Springer, 2022. 2 +[39] Yan Zeng, Xinsong Zhang, and Hang Li. 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However, we find that CMLM is insufficient for this purpose according to our observations: (1) Modality bias: a considerable amount of masked tokens in CMLM can be recovered with only the language information, ignoring the visual inputs. (2) Under-utilization of the unmasked tokens: CMLM primarily focuses on the masked token but it cannot simultaneously leverage other tokens to learn vision-language associations. To handle those limitations, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). In EPIC, for each image-sentence pair, we mask tokens that are salient to the image (i.e., Saliency-based Masking Strategy) and replace them with alternatives sampled from a language model (i.e., Inconsistent Token Generation Procedure), and then the model is required to determine for each token in the sentence whether it is consistent with the image (i.e., Image-Token Consistency Task). The proposed EPIC method is easily combined with pre-training methods. Extensive experiments show that the combination of the EPIC method and state-of-the-art pre-training approaches, including ViLT, ALBEF, METER, and X-VLM, leads to significant improvements on downstream tasks. Our coude is released at https://github.com/gyhdog99/epic" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 47, + 628, + 128, + 640 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 628, + 128, + 640 + ], + "spans": [ + { + "bbox": [ + 47, + 628, + 128, + 640 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 47, + 648, + 287, + 685 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 648, + 287, + 685 + ], + "spans": [ + { + "bbox": [ + 47, + 648, + 287, + 685 + ], + "type": "text", + "content": "Vision-language pre-training (VLP) [5,12,21,29,30,33, 37] aims to learn multi-modal representations from largescale image-text pairs. A pre-trained vision-language model" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 270, + 545, + 316 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 270, + 545, + 316 + ], + "spans": [ + { + "bbox": [ + 306, + 270, + 545, + 316 + ], + "type": "text", + "content": "(VLM) fine-tuned with only a small amount of labeled data has shown state-of-the-art performance in many downstream tasks such as visual question answering and imag-text retrieval." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 319, + 546, + 474 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 319, + 546, + 474 + ], + "spans": [ + { + "bbox": [ + 304, + 319, + 546, + 474 + ], + "type": "text", + "content": "A primary concern in developing pre-training objectives for VLP models is how to learn better vision-language associations. In addition to coarse-grained approaches such as image-text matching/contrasting [10, 14, 27] that align concepts from two modalities at the sample level, fine-grained approaches such as cross-modal masked language/image modeling (CMLM/CMIM) [16, 19, 32] learn vision-language associations at the token-object level. For example, Fig. 1 shows a picture paired with the sentence \"Blue and yellow hydrant on the grass\". When the word \"hydrant\" is masked, in order to correctly recover the token, the model has to find the actual object in the image and associate it with the word \"hydrant\"." + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 475, + 547, + 691 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 475, + 547, + 691 + ], + "spans": [ + { + "bbox": [ + 304, + 475, + 547, + 691 + ], + "type": "text", + "content": "While effective, CMLM is insufficient for learning vision-language associations because of (1) modality bias; and (2) under-utilization of unmasked tokens. In vision-language understanding, modality bias refers to leveraging only one modality for training/inference and so cross-modal knowledge is not well explored [23]. We argue that modality bias exists in CMLM, and prevents the model from learning sufficient vision-language associations. Specifically, in the CMLM task, we expect to mask salient1 tokens (such as \"blue\", \"yellow\", \"fire-hydrant\", and \"grass\") as shown in the left of Fig. 1. These tokens are informative for learning vision-language association because masking them enforces the model to find the answer from the visual modality. However, in practice, whether a token is salient is unknown as we only have access to image-sentence level annotations. Given a fixed and relatively small masking ratio (typically " + }, + { + "bbox": [ + 304, + 475, + 547, + 691 + ], + "type": "inline_equation", + "content": "15\\%" + }, + { + "bbox": [ + 304, + 475, + 547, + 691 + ], + "type": "text", + "content": " in CMLM), we might end up masking tokens that are less informative. For example, as shown" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 496, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 57, + 693, + 270, + 703 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 57, + 693, + 270, + 703 + ], + "spans": [ + { + "bbox": [ + 57, + 693, + 270, + 703 + ], + "type": "text", + "content": "* Work was done when the author interned at ByteDance AI Lab." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 58, + 703, + 152, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 703, + 152, + 713 + ], + "spans": [ + { + "bbox": [ + 58, + 703, + 152, + 713 + ], + "type": "text", + "content": "The corresponding author." + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 317, + 702, + 477, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 702, + 477, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 702, + 477, + 713 + ], + "type": "text", + "content": "1The definition of \"saliency\" is given in Sec. 4.4." + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "19155" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 50, + 72, + 180, + 193 + ], + "blocks": [ + { + "bbox": [ + 50, + 72, + 180, + 193 + ], + "lines": [ + { + "bbox": [ + 50, + 72, + 180, + 193 + ], + "spans": [ + { + "bbox": [ + 50, + 72, + 180, + 193 + ], + "type": "image", + "image_path": "4581cca1be1dd1935d0125da4c9ada248a52b53056022090f839f9ab247f725c.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 99, + 198, + 138, + 209 + ], + "lines": [ + { + "bbox": [ + 99, + 198, + 138, + 209 + ], + "spans": [ + { + "bbox": [ + 99, + 198, + 138, + 209 + ], + "type": "text", + "content": "Ideal case" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 189, + 72, + 373, + 192 + ], + "blocks": [ + { + "bbox": [ + 189, + 72, + 373, + 192 + ], + "lines": [ + { + "bbox": [ + 189, + 72, + 373, + 192 + ], + "spans": [ + { + "bbox": [ + 189, + 72, + 373, + 192 + ], + "type": "image", + "image_path": "d95c84a36ae24307d7befea56df46b915ffceb1c4e952637664073bdaff70359.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 268, + 198, + 294, + 209 + ], + "lines": [ + { + "bbox": [ + 268, + 198, + 294, + 209 + ], + "spans": [ + { + "bbox": [ + 268, + 198, + 294, + 209 + ], + "type": "text", + "content": "CMLM" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 378, + 72, + 539, + 192 + ], + "blocks": [ + { + "bbox": [ + 378, + 72, + 539, + 192 + ], + "lines": [ + { + "bbox": [ + 378, + 72, + 539, + 192 + ], + "spans": [ + { + "bbox": [ + 378, + 72, + 539, + 192 + ], + "type": "image", + "image_path": "e028ceba64d908f5995a284668686a3e00b45bc9a772b0d30f5193719c0619f6.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 425, + 198, + 518, + 209 + ], + "lines": [ + { + "bbox": [ + 425, + 198, + 518, + 209 + ], + "spans": [ + { + "bbox": [ + 425, + 198, + 518, + 209 + ], + "type": "text", + "content": "EPIC (consistent or not?)" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 46, + 221, + 547, + 266 + ], + "lines": [ + { + "bbox": [ + 46, + 221, + 547, + 266 + ], + "spans": [ + { + "bbox": [ + 46, + 221, + 547, + 266 + ], + "type": "text", + "content": "Figure 1. Illustrations of vision-language association learning. Ideal case: Fine-grained annotations (image regions and corresponding text tokens) are given, we can learn explicit associations (solid lines); CMLM: Without fine-grained annotations, we create supervision by masking, but this can be insufficient due to limited masking ratios and modality bias. EPIC: We find salient tokens and corrupt them to learn more associations. Both CMLM and EPIC learn implicit associations due to lack of region annotations." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 287, + 287, + 358 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 287, + 287, + 358 + ], + "spans": [ + { + "bbox": [ + 46, + 287, + 287, + 358 + ], + "type": "text", + "content": "in Fig. 1 (center), when \"the\" and \"and\" are masked, the model can predict these masked tokens with only language information. This thus is a form of modality bias as it circumvents using vision-language reasoning. Therefore, the modality bias can make CMLM insufficient to learn vision-language associations." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 360, + 287, + 503 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 360, + 287, + 503 + ], + "spans": [ + { + "bbox": [ + 46, + 360, + 287, + 503 + ], + "type": "text", + "content": "Another source of insufficiency in CMLM comes from the under-utilization of unmasked tokens. Similar to Masked Language Modeling (MLM) [6] in language pretraining, the CMLM loss is computed over masked tokens rather than all tokens in the sentence. As a result, learning of cross-modal association is possible only for the masked tokens but not for the remaining unmasked ones. For example, in Fig. 1, ideally, there are four associations (shown in black arrows) between text tokens and the corresponding regions, while there is only one association for CMLM. Therefore, CMLM cannot leverage all tokens (including the unmasked ones) for learning vision-language associations." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 504, + 288, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 504, + 288, + 684 + ], + "spans": [ + { + "bbox": [ + 46, + 504, + 288, + 684 + ], + "type": "text", + "content": "To expedite the learning of cross-modal associations in VLP, we propose EPIC (lEveraging Per Image-Token Consistency for vision-language pre-training). For each image-sentence pair, we mask tokens that are salient to the image (Saliency-based Masking Strategy) and make them \"inconsistent\" with the image by replacing them with alternatives from a BERT-like language model (Inconsistent Token Generation Procedure). The model is then required to determine whether each token in the sentence is consistent with the image (Image-Token Consistency (ITC) Task). As this masks salient tokens and applies a language model to generate inconsistent tokens from them, the model has to refer to the visual modality to determine whether a token is inconsistent. Therefore, the modality bias problem can be alleviated. Moreover, we can make better use of the un" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 309, + 287, + 546, + 323 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 309, + 287, + 546, + 323 + ], + "spans": [ + { + "bbox": [ + 309, + 287, + 546, + 323 + ], + "type": "text", + "content": "masked tokens for learning vision-language association as the ITC task requires the model to determine whether each token is consistent with the image." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "spans": [ + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "text", + "content": "The proposed EPIC method is easy to implement and widely applicable to a lot of vision-language model architectures. We demonstrate the effectiveness of our approach on various pre-training approaches, including ViLT [21], ALBEF [14], METER [8], and X-VLM [39], and observe significant improvements on downstream tasks. For example, on MSCOCO image-text retrieval, the proposed EPIC method achieves an absolute gain of " + }, + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "inline_equation", + "content": "2.5\\%" + }, + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "inline_equation", + "content": "4.7\\%" + }, + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "text", + "content": " over METER and ViLT, respectively, in terms of the Recall@1 score. On visual reasoning tasks (e.g., NLVR2), the proposed method improves over ALBEF by " + }, + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "inline_equation", + "content": "1.8\\%" + }, + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "text", + "content": " and X-VLM (the state-of-the-art within its model scale) by " + }, + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "inline_equation", + "content": "1.3\\%" + }, + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "text", + "content": ". The proposed method also allows better generalization of pretraining models. For example, in zero-shot image-text retrieval, we improve X-VLM by " + }, + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "inline_equation", + "content": "3.9\\%" + }, + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "text", + "content": " (COCO) and ViLT by " + }, + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "inline_equation", + "content": "9.9\\%" + }, + { + "bbox": [ + 306, + 324, + 546, + 515 + ], + "type": "text", + "content": " (Flickr30k)." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 525, + 392, + 538 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 525, + 392, + 538 + ], + "spans": [ + { + "bbox": [ + 306, + 525, + 392, + 538 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 545, + 546, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 545, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 545, + 546, + 713 + ], + "type": "text", + "content": "Learning Vision-Language Associations. In vision-language pre-training, a contrastive objective is often used to learn the coarse-grained associations between sentences and images [10, 14, 17, 27, 38]. However, models pretrained with this objective give unsatisfactory results on vision-language reasoning tasks (such as VQA [1] and NLVR2 [31]) which require understanding fine-grained vision-language associations. Cross-Modal Masked Language/Image Modeling (CMLM/CMIM) [2, 16, 19, 20] and its variants are applied to learn fine-grained vision-language associations in a self-supervised manner. Besides CMLM/CMIM, one can further leverage annotated region-phrase (e.g., bounding boxes) image-text data to enable the model with more sophisticated vision-language reasoning" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 47, + 693, + 286, + 712 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 693, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 47, + 693, + 286, + 712 + ], + "type": "text", + "content": "2A formal definition of (in)consistency tokens will be provided in Sec. 4.2." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "19156" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 139, + 83 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 139, + 83 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 139, + 83 + ], + "type": "text", + "content": "abilities [7, 11, 15, 39]." + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 84, + 287, + 299 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 84, + 287, + 299 + ], + "spans": [ + { + "bbox": [ + 46, + 84, + 287, + 299 + ], + "type": "text", + "content": "Cross-Modal Masked Language/Image Modeling. CMLM and CMIM are widely adopted as pre-training objectives in vision-language pre-training. They corrupt a token/patch of a sentence/image and train the model to reconstruct the original data with information from both modalities. LXMERT [32] found that loading BERT into the text encoder harms pre-training because the pre-trained BERT can have high CMLM accuracy and thereby circumventing the cross-modal reasoning process. Recently, similar to our observations, Bitton et al. [2] showed that roughly " + }, + { + "bbox": [ + 46, + 84, + 287, + 299 + ], + "type": "inline_equation", + "content": "50\\%" + }, + { + "bbox": [ + 46, + 84, + 287, + 299 + ], + "type": "text", + "content": " of the masked tokens in CMLM are punctuation or stop-words, leading to sample inefficiency of CMLM. They propose a rule-based masking strategy to mask object words, content words, or words with high concreteness [3] in CMLM according to different downstream tasks, expecting such words to be more related to the visual input. Such a strategy lacks generalization ability and cannot scale to more downstream tasks." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 300, + 288, + 502 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 300, + 288, + 502 + ], + "spans": [ + { + "bbox": [ + 46, + 300, + 288, + 502 + ], + "type": "text", + "content": "Modality Bias in Vision-Language Understanding. In CMLM, predicting the masked token without referring to the visual modality can be seen as a consequence of modality bias towards language in vision-language understanding. Due to the existence of modality bias, the learning of vision-language associations is weakened. Similarly, modality bias also happens in visual question answering (VQA) [1] where the model is required to answer a question given a visual input. For example, simply answering \"tennis\" to the sport-related questions can achieve approximately " + }, + { + "bbox": [ + 46, + 300, + 288, + 502 + ], + "type": "inline_equation", + "content": "40\\%" + }, + { + "bbox": [ + 46, + 300, + 288, + 502 + ], + "type": "text", + "content": " accuracy [23] on the VQA v1.0 dataset. To reduce such a bias, CF-VQA [23] proposes a counterfactual framework which directly subtracts the language-based predictions from the answers. In the proposed EPIC method, we mitigate the negative influence of language bias by allowing the model to learn more vision-language associations over a wider range of tokens." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 514, + 160, + 528 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 514, + 160, + 528 + ], + "spans": [ + { + "bbox": [ + 47, + 514, + 160, + 528 + ], + "type": "text", + "content": "3. Empirical Analysis" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 534, + 287, + 569 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 534, + 287, + 569 + ], + "spans": [ + { + "bbox": [ + 46, + 534, + 287, + 569 + ], + "type": "text", + "content": "In this section, we empirically analyze the existence of modality bias and under-utilization of unmasked tokens in CMLM. Experimental details are in Appendix A." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 570, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 570, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 570, + 288, + 714 + ], + "type": "text", + "content": "Identifying Modality Bias. If there is a strong modality bias towards language in Cross-Modal Language Modeling (CMLM), a pure language model (LM) that is blind to the visual inputs can achieve comparable accuracy on the Masked Language Modeling (MLM) task. Otherwise, the LM will be outperformed by the vision-language model (VLM) by a large margin. Based on this, we run CMLM on a VLM and MLM on a LM, respectively. They share the same input sentences and masked tokens, while the former additionally receives visual inputs. As shown in Fig. 2a, though using visual modality is helpful to recover the original token (80% Acc.), a pure LM can already achieve" + } + ] + } + ], + "index": 5 + }, + { + "type": "image", + "bbox": [ + 309, + 72, + 421, + 156 + ], + "blocks": [ + { + "bbox": [ + 309, + 72, + 421, + 156 + ], + "lines": [ + { + "bbox": [ + 309, + 72, + 421, + 156 + ], + "spans": [ + { + "bbox": [ + 309, + 72, + 421, + 156 + ], + "type": "image", + "image_path": "0eda780c8fa8a47b77718b82ed9c3ff8699edfcb598c2ade7128c36f2fe522c2.jpg" + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 333, + 159, + 395, + 170 + ], + "lines": [ + { + "bbox": [ + 333, + 159, + 395, + 170 + ], + "spans": [ + { + "bbox": [ + 333, + 159, + 395, + 170 + ], + "type": "text", + "content": "(a) Accuracy gaps." + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "image_caption" + } + ], + "index": 6 + }, + { + "type": "image", + "bbox": [ + 429, + 76, + 540, + 156 + ], + "blocks": [ + { + "bbox": [ + 429, + 76, + 540, + 156 + ], + "lines": [ + { + "bbox": [ + 429, + 76, + 540, + 156 + ], + "spans": [ + { + "bbox": [ + 429, + 76, + 540, + 156 + ], + "type": "image", + "image_path": "2a65b82f5657f240cc48596296bcd5bf81805fb8558c01ed7807b19b02df0c65.jpg" + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 452, + 159, + 517, + 170 + ], + "lines": [ + { + "bbox": [ + 452, + 159, + 517, + 170 + ], + "spans": [ + { + "bbox": [ + 452, + 159, + 517, + 170 + ], + "type": "text", + "content": "(b) Accuracy drops." + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 304, + 178, + 545, + 234 + ], + "lines": [ + { + "bbox": [ + 304, + 178, + 545, + 234 + ], + "spans": [ + { + "bbox": [ + 304, + 178, + 545, + 234 + ], + "type": "text", + "content": "Figure 2. Left: CMLM/MLM accuracy of VLM/LM (y-axis) as the training process proceeds (x-axis). Right: relative accuracy drop of CMLM/MLM (y-xais) as we corrupt more unmasked tokens (x-axis). The colored bands indicate the variance of the results as each configuration is repeated for 5 times." + } + ] + } + ], + "index": 10, + "angle": 0, + "type": "image_caption" + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 257, + 545, + 280 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 257, + 545, + 280 + ], + "spans": [ + { + "bbox": [ + 304, + 257, + 545, + 280 + ], + "type": "text", + "content": "an accuracy of " + }, + { + "bbox": [ + 304, + 257, + 545, + 280 + ], + "type": "inline_equation", + "content": "70\\%" + }, + { + "bbox": [ + 304, + 257, + 545, + 280 + ], + "type": "text", + "content": ". Therefore, a modality bias towards language does exist in the training of CMLM." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 281, + 546, + 510 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 281, + 546, + 510 + ], + "spans": [ + { + "bbox": [ + 304, + 281, + 546, + 510 + ], + "type": "text", + "content": "Under-utilization of Unmasked Tokens. If the CMLM is weak at reasoning associations between images and un-masked tokens, replacing the unmasked ones with alternatives based on the language context should not prevent the model from recovering the masked tokens; otherwise, the performance of CMLM will drop significantly because the language context is inconsistent with the image. Therefore, we use a pre-trained VLM and LM to perform inference on CMLM and MLM, respectively, under a corrupted context in which we randomly replace unmasked tokens by sampling from the MLM head of a BERT. As illustrated in Fig. 2b, both the VLM and LM suffer performance deterioration under different corruption ratios. However, even though the text contexts are inconsistent with the image after corruption, the performance drop is less severe for the VLM as compared to the LM. In fact, the performance curves for CMLM and MLM overlap in terms of relative performance drop. Hence, we conclude that the CMLM is weak at learning associations between unmasked tokens and images." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 306, + 521, + 389, + 534 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 521, + 389, + 534 + ], + "spans": [ + { + "bbox": [ + 306, + 521, + 389, + 534 + ], + "type": "text", + "content": "4. Methodology" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 541, + 545, + 638 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 541, + 545, + 638 + ], + "spans": [ + { + "bbox": [ + 304, + 541, + 545, + 638 + ], + "type": "text", + "content": "Sec. 4.1 first introduces preliminaries of the proposed method. We then introduce the three components of EPIC. We formulate the ITC task and introduce the concept of token consistency w.r.t. an image in Sec. 4.2. We design the inconsistent token generation procedure in Sec. 4.3, and the saliency-based masking strategy in Sec. 4.4. Fig. 3 gives an overview of the proposed method. The complete EPIC algorithm is shown in Appendix B." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 647, + 391, + 658 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 647, + 391, + 658 + ], + "spans": [ + { + "bbox": [ + 306, + 647, + 391, + 658 + ], + "type": "text", + "content": "4.1. Preliminaries" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 665, + 546, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 665, + 546, + 715 + ], + "spans": [ + { + "bbox": [ + 306, + 665, + 546, + 715 + ], + "type": "text", + "content": "Pre-training Framework. In vision-language pretraining, we have access to parallel image-text data " + }, + { + "bbox": [ + 306, + 665, + 546, + 715 + ], + "type": "inline_equation", + "content": "\\mathcal{D} = \\{(w_i,v_i)\\}_{i = 1}^N\\sim P_{\\mathcal{W},\\mathcal{V}}" + }, + { + "bbox": [ + 306, + 665, + 546, + 715 + ], + "type": "text", + "content": " . Specifically, for any sentence " + }, + { + "bbox": [ + 306, + 665, + 546, + 715 + ], + "type": "inline_equation", + "content": "\\pmb {w} = [w_1,\\dots ,w_n]" + }, + { + "bbox": [ + 306, + 665, + 546, + 715 + ], + "type": "text", + "content": " , there is a corresponding image " + }, + { + "bbox": [ + 306, + 665, + 546, + 715 + ], + "type": "inline_equation", + "content": "\\pmb {v} =" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19157" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 51, + 72, + 545, + 208 + ], + "blocks": [ + { + "bbox": [ + 51, + 72, + 545, + 208 + ], + "lines": [ + { + "bbox": [ + 51, + 72, + 545, + 208 + ], + "spans": [ + { + "bbox": [ + 51, + 72, + 545, + 208 + ], + "type": "image", + "image_path": "37825ea61febc230cb59711e702d02c85421c1452b5ea7571a7812624257f78f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 217, + 546, + 251 + ], + "lines": [ + { + "bbox": [ + 46, + 217, + 546, + 251 + ], + "spans": [ + { + "bbox": [ + 46, + 217, + 546, + 251 + ], + "type": "text", + "content": "Figure 3. An overview of EPIC. The input image and sentence are input to a teacher VLM to obtain the saliency for each text token w.r.t the image. Then we mask accordingly and generate tokens inconsistent with the image though a language model (fine-tuning during pre-training). Finally we train the VLM to determine for each token whether it is inconsistent with the image (ITC)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 271, + 289, + 416 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 271, + 289, + 416 + ], + "spans": [ + { + "bbox": [ + 46, + 271, + 289, + 416 + ], + "type": "inline_equation", + "content": "[v_{1}, \\ldots, v_{m}]" + }, + { + "bbox": [ + 46, + 271, + 289, + 416 + ], + "type": "text", + "content": " in the form of grid-based or region-based features [8]. Without loss of generality, we assume a vision-language model " + }, + { + "bbox": [ + 46, + 271, + 289, + 416 + ], + "type": "inline_equation", + "content": "f_{\\mathrm{VL}}(\\cdot)" + }, + { + "bbox": [ + 46, + 271, + 289, + 416 + ], + "type": "text", + "content": " in METER [8] of the following form: Given a pair of sentence " + }, + { + "bbox": [ + 46, + 271, + 289, + 416 + ], + "type": "inline_equation", + "content": "\\boldsymbol{w}" + }, + { + "bbox": [ + 46, + 271, + 289, + 416 + ], + "type": "text", + "content": " and image " + }, + { + "bbox": [ + 46, + 271, + 289, + 416 + ], + "type": "inline_equation", + "content": "\\boldsymbol{v}" + }, + { + "bbox": [ + 46, + 271, + 289, + 416 + ], + "type": "text", + "content": ", it first extracts text features and visual features via a text encoder and vision encoder, respectively. The text and visual features are then fed into a multi-modal fusion module, consisting of several layers of self-attention followed by cross-attention, to produce cross-modal representations for text and image. Note that the proposed method is also applicable to other architectures such as ViLT [12], ALBEF [14], and X-VLM [39]." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 426, + 190, + 440 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 426, + 190, + 440 + ], + "spans": [ + { + "bbox": [ + 47, + 426, + 190, + 440 + ], + "type": "text", + "content": "4.2. Image-Token Consistency" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 446, + 287, + 471 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 446, + 287, + 471 + ], + "spans": [ + { + "bbox": [ + 46, + 446, + 287, + 471 + ], + "type": "text", + "content": "First, we introduce the concept of consistency of a text token with respect to an image." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "spans": [ + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": "Consistency of Text Tokens w.r.t Images. In vision-language pre-training, we assume that the dataset is clean (without noise) and that we have access to the marginal distribution of the text corpus " + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "P_{\\mathcal{W}}" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": ". For a pair of text " + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "\\boldsymbol{w}" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": " and image " + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "\\boldsymbol{v}" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": ", a text token " + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "w_i" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": " in " + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "\\boldsymbol{w}" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": " is \"consistent\" with the image given the remaining tokens, i.e., " + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "P_{\\mathcal{W},\\mathcal{V}}(\\boldsymbol{W} = \\boldsymbol{w},\\boldsymbol{V} = \\boldsymbol{v})" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": " is large. If we replace a token " + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "w_j" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "w_j'" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": " such that the new sentence " + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "\\bar{w}" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": " (1) does not match the semantic content of the image, i.e., " + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "P_{\\mathcal{W},\\mathcal{V}}(\\boldsymbol{W} = \\bar{\\boldsymbol{w}},\\boldsymbol{V} = \\boldsymbol{v})" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": " is small and (2) is linguistically proper, i.e., " + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "P_{\\mathcal{W}}(\\boldsymbol{W} = \\bar{\\boldsymbol{w}})" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": " is large, then " + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "inline_equation", + "content": "w_j'" + }, + { + "bbox": [ + 46, + 472, + 287, + 604 + ], + "type": "text", + "content": " is inconsistent with the image given the remaining tokens." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 605, + 287, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 605, + 287, + 665 + ], + "spans": [ + { + "bbox": [ + 46, + 605, + 287, + 665 + ], + "type": "text", + "content": "An example is shown in Fig. 3. In the sentence \"Yellow and blue hydrant on the grass\", the word \"hydrant\" is consistent with the image given the remaining words. If we replace \"yellow\" with \"green\", it produces a sentence that does not match the image, and so \"green\" is inconsistent." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": "Task Formulation. Given an image " + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "inline_equation", + "content": "v" + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": " and a sentence " + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\bar{w}" + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": " with inconsistent tokens at positions " + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": ", the model has to determine whether each token is consistent with the image. We will introduce the generation process of " + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\bar{w}" + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 665, + 287, + 714 + ], + "type": "text", + "content": " in" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 272, + 343, + 282 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 272, + 343, + 282 + ], + "spans": [ + { + "bbox": [ + 305, + 272, + 343, + 282 + ], + "type": "text", + "content": "Sec. 4.3." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "spans": [ + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "text", + "content": "The " + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "inline_equation", + "content": "\\bar{\\boldsymbol{w}}" + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "inline_equation", + "content": "\\pmb{v}" + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "text", + "content": " are fed into VLM to obtain the last-layer hidden representations " + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "inline_equation", + "content": "\\left\\{\\bar{h}_i^{\\mathrm{VL}}\\right\\}_{i = 1}^n" + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "inline_equation", + "content": "\\bar{w}" + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "text", + "content": ". Let " + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "inline_equation", + "content": "\\beta" + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "text", + "content": " be the weight vector for decision. The probability that each token (from position " + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "inline_equation", + "content": "i = 1" + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "text", + "content": " to " + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 304, + 284, + 545, + 347 + ], + "type": "text", + "content": ") is consistent with the image is:" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 367, + 347, + 545, + 366 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 367, + 347, + 545, + 366 + ], + "spans": [ + { + "bbox": [ + 367, + 347, + 545, + 366 + ], + "type": "interline_equation", + "content": "D _ {\\mathrm {I T C}} ^ {i} = \\operatorname {s i g m o i d} \\left(\\boldsymbol {\\beta} ^ {\\intercal} \\bar {\\boldsymbol {h}} _ {i} ^ {\\mathrm {V L}}\\right). \\tag {1}", + "image_path": "5f766e3b69931b0d25cdb942facce7cd2ff572a9584617279eac4e1724043f47.jpg" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 370, + 545, + 393 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 370, + 545, + 393 + ], + "spans": [ + { + "bbox": [ + 304, + 370, + 545, + 393 + ], + "type": "text", + "content": "Therefore, the EPIC model minimizes the following binary classification loss:" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 320, + 401, + 545, + 427 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 320, + 401, + 545, + 427 + ], + "spans": [ + { + "bbox": [ + 320, + 401, + 545, + 427 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {I T C}} = - \\sum_ {i \\notin \\mathcal {T}} \\log D _ {\\mathrm {I T C}} ^ {i} - \\sum_ {i \\in \\mathcal {T}} \\log \\left(1 - D _ {\\mathrm {I T C}} ^ {i}\\right). \\tag {2}", + "image_path": "708b68caa02509f9384324ebc9d946649214d19a000cc723028369ab242c79fc.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 436, + 545, + 495 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 436, + 545, + 495 + ], + "spans": [ + { + "bbox": [ + 304, + 436, + 545, + 495 + ], + "type": "text", + "content": "Leveraging Unmasked Tokens. It is easy to see that in Eq. (2), the EPIC model is tasked to determine for all the tokens whether they are consistent with the image or not. Therefore, the ITC task leverages more tokens for learning vision-language associations." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 304, + 496, + 545, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 496, + 545, + 616 + ], + "spans": [ + { + "bbox": [ + 304, + 496, + 545, + 616 + ], + "type": "text", + "content": "Mitigating Modality Bias. In the second condition for inconsistent tokens, we require them to be linguistically proper for the sentence. We argue that this is essential for the ITC task to alleviate the modality bias problem because if the sentence after replacement " + }, + { + "bbox": [ + 304, + 496, + 545, + 616 + ], + "type": "inline_equation", + "content": "\\bar{w}" + }, + { + "bbox": [ + 304, + 496, + 545, + 616 + ], + "type": "text", + "content": " is not linguistically proper, then the VLM can identify the replaced tokens simply by using the language context only. As a result, the VLM does not learn vision-language associations by decreasing " + }, + { + "bbox": [ + 304, + 496, + 545, + 616 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{ITC}}" + }, + { + "bbox": [ + 304, + 496, + 545, + 616 + ], + "type": "text", + "content": ". Instead, it simply conducts language modeling." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 305, + 623, + 477, + 635 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 623, + 477, + 635 + ], + "spans": [ + { + "bbox": [ + 305, + 623, + 477, + 635 + ], + "type": "text", + "content": "4.3. Generating Inconsistent Tokens" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": "In this section, we discuss how to generate sentences " + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "inline_equation", + "content": "\\bar{w}" + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": " with tokens that fulfill the two conditions of inconsistent tokens in Sec. 4.2. Strictly speaking, we have to model the distributions " + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "inline_equation", + "content": "P_{\\mathcal{W},\\mathcal{V}}" + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "inline_equation", + "content": "P_{\\mathcal{W}}" + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": " for generation, but this is hard. Instead, we propose to approximately generate inconsistent tokens with a BERT-like language model." + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19158" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "bbox": [ + 47, + 72, + 287, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 72, + 287, + 120 + ], + "spans": [ + { + "bbox": [ + 47, + 72, + 287, + 120 + ], + "type": "text", + "content": "Assume that we have a set " + }, + { + "bbox": [ + 47, + 72, + 287, + 120 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 47, + 72, + 287, + 120 + ], + "type": "text", + "content": " of masking positions (we will discuss how to obtain " + }, + { + "bbox": [ + 47, + 72, + 287, + 120 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 47, + 72, + 287, + 120 + ], + "type": "text", + "content": " in Sec. 4.4). Given the original text sequence " + }, + { + "bbox": [ + 47, + 72, + 287, + 120 + ], + "type": "inline_equation", + "content": "\\boldsymbol{w}" + }, + { + "bbox": [ + 47, + 72, + 287, + 120 + ], + "type": "text", + "content": ", we first mask the corresponding tokens by replacing them with the [MASK] symbol:" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 112, + 129, + 287, + 143 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 112, + 129, + 287, + 143 + ], + "spans": [ + { + "bbox": [ + 112, + 129, + 287, + 143 + ], + "type": "interline_equation", + "content": "\\boldsymbol {w} ^ {\\text {m a s k}} = \\operatorname {M A S K} (\\boldsymbol {w}, \\mathcal {M}). \\tag {3}", + "image_path": "da9f34a802685748784c70ab58cd92252f4436c471d5687f9aa85d75a0749489.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 152, + 287, + 201 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 152, + 287, + 201 + ], + "spans": [ + { + "bbox": [ + 47, + 152, + 287, + 201 + ], + "type": "text", + "content": "The resulting sentence " + }, + { + "bbox": [ + 47, + 152, + 287, + 201 + ], + "type": "inline_equation", + "content": "\\pmb{w}^{\\mathrm{mask}}" + }, + { + "bbox": [ + 47, + 152, + 287, + 201 + ], + "type": "text", + "content": " is fed to an auxiliary BERT-like language model " + }, + { + "bbox": [ + 47, + 152, + 287, + 201 + ], + "type": "inline_equation", + "content": "f_{L}(\\cdot)" + }, + { + "bbox": [ + 47, + 152, + 287, + 201 + ], + "type": "text", + "content": " to obtain a sequence of contextual representations " + }, + { + "bbox": [ + 47, + 152, + 287, + 201 + ], + "type": "inline_equation", + "content": "\\pmb{H}^{\\mathrm{L}} = \\left\\{\\pmb{h}_{i}^{\\mathrm{L}}\\right\\}_{i=1}^{n}" + }, + { + "bbox": [ + 47, + 152, + 287, + 201 + ], + "type": "text", + "content": ". We then obtain the probability of predicting a particular token " + }, + { + "bbox": [ + 47, + 152, + 287, + 201 + ], + "type": "inline_equation", + "content": "w_{k}" + }, + { + "bbox": [ + 47, + 152, + 287, + 201 + ], + "type": "text", + "content": " as:" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 77, + 209, + 287, + 240 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 77, + 209, + 287, + 240 + ], + "spans": [ + { + "bbox": [ + 77, + 209, + 287, + 240 + ], + "type": "interline_equation", + "content": "p _ {\\mathrm {M L M}} \\left(w _ {k} \\mid \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right) = \\frac {\\exp \\left(\\boldsymbol {e} \\left(w _ {k}\\right) ^ {\\top} \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right)}{\\sum_ {w ^ {\\prime}} \\exp \\left(\\boldsymbol {e} \\left(w ^ {\\prime}\\right) ^ {\\top} \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right)}, \\tag {4}", + "image_path": "cd576a7ab62e69678bc24eabe64f225578765ee40fef6ec790b04840f35296d4.jpg" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 248, + 287, + 273 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 248, + 287, + 273 + ], + "spans": [ + { + "bbox": [ + 47, + 248, + 287, + 273 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 248, + 287, + 273 + ], + "type": "inline_equation", + "content": "e(\\cdot)" + }, + { + "bbox": [ + 47, + 248, + 287, + 273 + ], + "type": "text", + "content": " denotes the token embedding. The new sentence, with the inconsistent tokens, is " + }, + { + "bbox": [ + 47, + 248, + 287, + 273 + ], + "type": "inline_equation", + "content": "\\bar{w} = [\\bar{w}_1, \\dots, \\bar{w}_n]" + }, + { + "bbox": [ + 47, + 248, + 287, + 273 + ], + "type": "text", + "content": ":" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 119, + 282, + 287, + 298 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 119, + 282, + 287, + 298 + ], + "spans": [ + { + "bbox": [ + 119, + 282, + 287, + 298 + ], + "type": "interline_equation", + "content": "\\bar {w} _ {i} \\sim p _ {\\mathrm {M L M}} \\left(w \\mid \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right). \\tag {5}", + "image_path": "5edd11d1a4a6fb784fd59798ec1ffbafc05f8ffae3e7feeab958257d6847f238.jpg" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 302, + 284, + 314 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 302, + 284, + 314 + ], + "spans": [ + { + "bbox": [ + 47, + 302, + 284, + 314 + ], + "type": "text", + "content": "The positions of the inconsistent tokens " + }, + { + "bbox": [ + 47, + 302, + 284, + 314 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 47, + 302, + 284, + 314 + ], + "type": "text", + "content": " are obtained by:" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 323, + 287, + 338 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 105, + 323, + 287, + 338 + ], + "spans": [ + { + "bbox": [ + 105, + 323, + 287, + 338 + ], + "type": "interline_equation", + "content": "\\mathcal {T} = \\{t \\mid \\bar {w} _ {t} \\neq w _ {t}, \\forall t \\in \\mathcal {M} \\}. \\tag {6}", + "image_path": "b106ece25d789b51c4927bcf134dc2a7088237a15dc2de6f488a4d1ff37d74aa.jpg" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 47, + 346, + 287, + 370 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 346, + 287, + 370 + ], + "spans": [ + { + "bbox": [ + 47, + 346, + 287, + 370 + ], + "type": "text", + "content": "We also train the language model to fit to the sentence during pre-training by a MLM objective:" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 89, + 379, + 287, + 403 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 89, + 379, + 287, + 403 + ], + "spans": [ + { + "bbox": [ + 89, + 379, + 287, + 403 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {G E N}} = - \\sum_ {i \\in \\mathcal {M}} \\log p _ {\\mathrm {M L M}} \\left(w _ {i} \\mid \\boldsymbol {h} _ {i} ^ {\\mathrm {L}}\\right). \\tag {7}", + "image_path": "f0536c35a976ed871926265b6b639a03aa8d348d2f19f9663ab5eceb3764472e.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 413, + 287, + 508 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 413, + 287, + 508 + ], + "spans": [ + { + "bbox": [ + 47, + 413, + 287, + 508 + ], + "type": "text", + "content": "We provide justification for using a language model to approximately fulfill the two conditions of inconsistent tokens. First, the language model has no access to the visual inputs. " + }, + { + "bbox": [ + 47, + 413, + 287, + 508 + ], + "type": "inline_equation", + "content": "P_{\\mathcal{W},\\nu}(\\boldsymbol {W} = \\bar{\\boldsymbol{w}},\\boldsymbol {V} = \\boldsymbol{v})" + }, + { + "bbox": [ + 47, + 413, + 287, + 508 + ], + "type": "text", + "content": " is likely to be small, and so it generates samples approximating the first condition. Second, since the language model is fine-tuned during pretraining, " + }, + { + "bbox": [ + 47, + 413, + 287, + 508 + ], + "type": "inline_equation", + "content": "P_{\\mathcal{W}}(\\boldsymbol {W} = \\bar{\\boldsymbol{w}})" + }, + { + "bbox": [ + 47, + 413, + 287, + 508 + ], + "type": "text", + "content": " is likely to be large. Thus, it generates samples that can approximate the second condition." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 515, + 183, + 529 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 515, + 183, + 529 + ], + "spans": [ + { + "bbox": [ + 47, + 515, + 183, + 529 + ], + "type": "text", + "content": "4.4. Saliency-based Masking" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 47, + 534, + 287, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 534, + 287, + 557 + ], + "spans": [ + { + "bbox": [ + 47, + 534, + 287, + 557 + ], + "type": "text", + "content": "In this section, we discuss how to obtain masking positions " + }, + { + "bbox": [ + 47, + 534, + 287, + 557 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 47, + 534, + 287, + 557 + ], + "type": "text", + "content": ". First, we introduce the concept of saliency." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 47, + 558, + 287, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 558, + 287, + 629 + ], + "spans": [ + { + "bbox": [ + 47, + 558, + 287, + 629 + ], + "type": "text", + "content": "Saliency of Text Tokens w.r.t. Images. For a pair of sentence " + }, + { + "bbox": [ + 47, + 558, + 287, + 629 + ], + "type": "inline_equation", + "content": "\\mathbf{w}" + }, + { + "bbox": [ + 47, + 558, + 287, + 629 + ], + "type": "text", + "content": " and image " + }, + { + "bbox": [ + 47, + 558, + 287, + 629 + ], + "type": "inline_equation", + "content": "\\mathbf{v}" + }, + { + "bbox": [ + 47, + 558, + 287, + 629 + ], + "type": "text", + "content": ", a text token " + }, + { + "bbox": [ + 47, + 558, + 287, + 629 + ], + "type": "inline_equation", + "content": "w_{i}" + }, + { + "bbox": [ + 47, + 558, + 287, + 629 + ], + "type": "text", + "content": " is salient w.r.t. to the image if the meaning of the token is strongly related to the content in the image, otherwise, it is not salient. In Fig. 3, words \"yellow\", \"blue\", \"hydrant\" and \"grass\" are salient w.r.t. the image, while \"and\", \"the\" are not." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 46, + 630, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 630, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 630, + 287, + 714 + ], + "type": "text", + "content": "Masking Salient Tokens. As discussed in Sec. 4.3, we first mask the original sentence based on " + }, + { + "bbox": [ + 46, + 630, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 46, + 630, + 287, + 714 + ], + "type": "text", + "content": ", and obtain the positions " + }, + { + "bbox": [ + 46, + 630, + 287, + 714 + ], + "type": "inline_equation", + "content": "\\mathcal{T}" + }, + { + "bbox": [ + 46, + 630, + 287, + 714 + ], + "type": "text", + "content": " for inconsistent tokens by comparing the generated tokens with the original tokens (Eq. (6)). Since the language model is fine-tuned on the corpus, if we mask non-salient tokens w.r.t. an image, the language model is likely to recover the original tokens by attending to the remaining" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 72, + 545, + 155 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 155 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 155 + ], + "type": "text", + "content": "language context. In this case, no inconsistent tokens will be generated, and the model cannot learn vision-language associations. However, if we mask salient tokens w.r.t. an image, the language model is incapable of recovering such tokens because it has no access to the visual input. Therefore, it is expected to mask salient tokens for generating inconsistent tokens." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "spans": [ + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "text", + "content": "In practice, we find salient tokens by selecting the tokens/positions with higher attention scores to the image. The cross-modal representation of the [CLS] token of the image " + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "inline_equation", + "content": "\\pmb{v}" + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "text", + "content": " is query " + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "inline_equation", + "content": "\\pmb{q}_k^V\\in \\mathbb{R}^{d_h}" + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "text", + "content": ", and that of all the elements in " + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "inline_equation", + "content": "\\pmb{w}" + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "text", + "content": " are keys " + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "inline_equation", + "content": "\\pmb{K}_k^W\\in \\mathbb{R}^{n\\times d_h}" + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "text", + "content": " at head " + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "text", + "content": " is the sequence length of the sentence, " + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "inline_equation", + "content": "d_{h} = d / h" + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "text", + "content": " is the dimension of a single-head output. For simplicity, we only consider the representations from the penultimate layer of the fusion module. The image-token saliency " + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "inline_equation", + "content": "\\alpha \\in \\mathbb{R}^d" + }, + { + "bbox": [ + 304, + 156, + 545, + 276 + ], + "type": "text", + "content": " can then be written as" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 349, + 285, + 545, + 318 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 349, + 285, + 545, + 318 + ], + "spans": [ + { + "bbox": [ + 349, + 285, + 545, + 318 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\alpha} = \\operatorname {s o f t m a x} \\left(\\frac {1}{h} \\sum_ {k = 1} ^ {h} \\boldsymbol {q} _ {k} ^ {V} \\left(\\boldsymbol {K} _ {k} ^ {W}\\right) ^ {\\intercal}\\right). \\tag {8}", + "image_path": "683a23ef0fe10439c98880b68fd9b215b25faf97a3664955fec4c496881db5b7.jpg" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "spans": [ + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "text", + "content": "Finally, the masking positions " + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "inline_equation", + "content": "\\mathcal{M}" + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "text", + "content": " are sampled without replacement from the categorical distribution " + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "inline_equation", + "content": "p(t = i; \\alpha) = \\alpha_i" + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "text", + "content": " such that " + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "inline_equation", + "content": "|\\mathcal{M}| = m" + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "text", + "content": " is the expected number of inconsistent tokens. In this way, tokens with high saliencies are more likely to be masked. Notice that we use a pre-trained vision-language model to obtain " + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "inline_equation", + "content": "\\boldsymbol{q}_k^V" + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "inline_equation", + "content": "\\boldsymbol{K}_k^W" + }, + { + "bbox": [ + 304, + 327, + 545, + 411 + ], + "type": "text", + "content": ". Details are in Appendix C." + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 306, + 423, + 388, + 437 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 423, + 388, + 437 + ], + "spans": [ + { + "bbox": [ + 306, + 423, + 388, + 437 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 443, + 425, + 456 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 443, + 425, + 456 + ], + "spans": [ + { + "bbox": [ + 306, + 443, + 425, + 456 + ], + "type": "text", + "content": "5.1. Pre-training Settings" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 462, + 545, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 462, + 545, + 640 + ], + "spans": [ + { + "bbox": [ + 304, + 462, + 545, + 640 + ], + "type": "text", + "content": "Baselines. The proposed method has no constraints on the network architectures, training objectives, and visual representations. It supports architectures conducting either cross-attention or self-attention for multi-modal fusion. In this study, we plug-in our method into four recent approaches with diversified cross-modal learning techniques: (i) METER, [8] which adopts modality-specific encoders with cross-attention, (ii) ALBEF [14], which utilizes cross-modal contrastive learning with cross-attention, (iii) X-VLM [39], which performs cross-modal alignments from multiple granularities with cross-attention, and (iv) ViLT [12], which fuses images and text in a single encoder via self-attention. We reproduce the pre-training for all the baselines with the settings where they achieve their best results." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 545, + 713 + ], + "type": "text", + "content": "Datasets. There are four widely adopted datasets for vision-language pre-training: (i) COCO [18], (ii) Visual Genome (VG) [13], (iii) SBU Captions [24] and (iv) Conceptual Captions 3M (CC) [4]. We refer the combination of these datasets as 4M because there are 4 million unique images in them. We also experiment with a large-scale web dataset" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19159" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 50, + 70, + 545, + 254 + ], + "blocks": [ + { + "bbox": [ + 50, + 70, + 545, + 254 + ], + "lines": [ + { + "bbox": [ + 50, + 70, + 545, + 254 + ], + "spans": [ + { + "bbox": [ + 50, + 70, + 545, + 254 + ], + "type": "table", + "html": "
MethodDataEPICMSCOCO (5K test set)Flickr30K (1K test set)Flickr30K ZS(1K test set)
TRR@1/R@5/R@10IRR@1/R@5/R@10TRR@1/R@5/R@10IRR@1/R@5/R@10TRR@1/R@5/R@10IRR@1/R@5/R@10
METER4MX77.2/93.7/97.159.2/84.0/90.894.2/99.6/99.983.8/97.3/98.692.2/99.3/99.778.8/94.5/96.9
✓79.0/94.5/97.561.2/85.2/91.695.8/99.3/99.685.1/97.4/98.793.1/99.1/99.879.0/94.5/97.1
16MX78.2/93.8/96.959.8/84.2/90.894.6/99.7/99.885.2/97.4/98.893.1/99.4/99.881.1/96.0/97.8
✓79.7/94.8/97.562.5/85.4/91.996.5/99.9/99.986.7/97.8/99.094.7/99.5/99.882.6/95.9/99.8
ALBEF4MX73.3/92.4/96.456.4/81.8/88.994.4/99.4/99.882.1/95.7/97.991.1/98.7/99.476.1/93.0/95.9
✓75.1/92.9/96.458.6/82.7/89.395.6/99.7/99.983.7/96.7/98.491.7/99.2/99.878.1/93.8/96.4
16MX78.3/93.9/96.861.3/84.3/90.695.9/99.8/10085.7/97.2/98.893.6/99.5/99.883.2/95.9/97.7
✓79.2/94.7/97.562.9/85.4/91.396.4/100/10087.1/97.3/98.994.8/99.7/99.984.1/96.5/97.9
X-VLM4M+X79.8/95.1/97.762.7/85.6/91.496.7/99.9/10085.3/97.4/98.783.1/97.8/99.470.5/92.8/96.4
✓81.0/95.3/97.964.1/86.1/91.697.2/99.9/10087.0/97.6/98.786.2/98.6/99.874.4/94.3/97.0
16M+X79.5/95.4/97.863.3/85.6/91.496.8/100/10086.7/97.5/98.786.4/99.2/99.676.1/94.1/96.7
✓80.7/95.6/98.064.1/85.9/91.897.4/100/10087.3/97.6/98.889.0/99.0/99.775.4/94.2/96.8
ViLT4MX60.4/85.8/92.241.3/71.4/82.380.8/95.9/98.761.2/88.0/93.572.6/93.0/96.853.4/80.8/88.7
✓65.0/87.5/93.746.0/74.8/84.685.2/97.3/99.366.9/90.0/94.479.8/95.8/97.963.3/86.3/92.0
", + "image_path": "18be0eeed49df7d50393a468091c3b9230b4f04cfce9e023e39ce65bb4a9b221.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 48, + 315, + 285, + 425 + ], + "blocks": [ + { + "bbox": [ + 46, + 262, + 546, + 297 + ], + "lines": [ + { + "bbox": [ + 46, + 262, + 546, + 297 + ], + "spans": [ + { + "bbox": [ + 46, + 262, + 546, + 297 + ], + "type": "text", + "content": "Table 1. Image-text retrieval results on the MSCOCO (fine-tuned) and Flickr30K (fine-tuned and zero-shot) datasets. IR: Image Retrieval and TR: Text Retrieval. Recall@ " + }, + { + "bbox": [ + 46, + 262, + 546, + 297 + ], + "type": "inline_equation", + "content": "K" + }, + { + "bbox": [ + 46, + 262, + 546, + 297 + ], + "type": "text", + "content": " with " + }, + { + "bbox": [ + 46, + 262, + 546, + 297 + ], + "type": "inline_equation", + "content": "K = 1,5" + }, + { + "bbox": [ + 46, + 262, + 546, + 297 + ], + "type": "text", + "content": " ,and 10 is used as the evaluation metric. Better results under the same baseline are marked in bold." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_caption" + }, + { + "bbox": [ + 48, + 315, + 285, + 425 + ], + "lines": [ + { + "bbox": [ + 48, + 315, + 285, + 425 + ], + "spans": [ + { + "bbox": [ + 48, + 315, + 285, + 425 + ], + "type": "table", + "html": "
MethodDataEPICMSCOCO (5K test set)
TRIR
R@1R@5R@10R@1R@5R@10
X-VLM4M+X69.291.996.555.382.589.7
✓72.093.497.357.383.490.2
16M+X73.192.897.056.983.089.9
✓73.293.696.957.583.390.0
ViLT4MX54.681.489.138.669.080.2
✓64.186.592.547.174.884.6
", + "image_path": "2a4331b110eb719870e95d0ab75aafe2488446a2306a081977af63c5ab0a392d.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 56, + 426, + 277, + 437 + ], + "lines": [ + { + "bbox": [ + 56, + 426, + 277, + 437 + ], + "spans": [ + { + "bbox": [ + 56, + 426, + 277, + 437 + ], + "type": "text", + "content": "Table 2. Zero-shot image-text retrieval results on MSCOCO." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "bbox": [ + 46, + 449, + 287, + 533 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 449, + 287, + 533 + ], + "spans": [ + { + "bbox": [ + 46, + 449, + 287, + 533 + ], + "type": "text", + "content": "CC12M [4]. We consider the combination of 4M and 12M datasets as 16M. Additionally, the X-VLM requires fine-grained annotations (e.g., object and region descriptions) for both the 4M and 16M settings. Therefore, the resulting dataset for X-VLM is called " + }, + { + "bbox": [ + 46, + 449, + 287, + 533 + ], + "type": "inline_equation", + "content": "4\\mathrm{M}^{+}" + }, + { + "bbox": [ + 46, + 449, + 287, + 533 + ], + "type": "text", + "content": "and " + }, + { + "bbox": [ + 46, + 449, + 287, + 533 + ], + "type": "inline_equation", + "content": "16\\mathrm{M}^{+}" + }, + { + "bbox": [ + 46, + 449, + 287, + 533 + ], + "type": "text", + "content": ", respectively. A more detailed description of the data statistics is in Appendix E." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 534, + 288, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 534, + 288, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 534, + 288, + 713 + ], + "type": "text", + "content": "Implementation Details. For the pre-training baselines, we follow the official implementations provided by the authors of METER, X-VLM, ALBEF and ViLT. More details on their implementations are in Appendix F. For the proposed method, the auxiliary language model is chosen to be identical to the text encoder of the vision-language model. For ViLT without modality-specific encoders, we use BERT-base [6]. All the auxiliary language models are loaded directly from HuggingFace repository [34] with pretrained checkpoints. For every sentence, the number of masked tokens " + }, + { + "bbox": [ + 46, + 534, + 288, + 713 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 46, + 534, + 288, + 713 + ], + "type": "text", + "content": " is calculated as " + }, + { + "bbox": [ + 46, + 534, + 288, + 713 + ], + "type": "inline_equation", + "content": "m = \\text{ceil}(\\text{mask\\_ratio} \\times \\text{len}(\\text{sentence}))" + }, + { + "bbox": [ + 46, + 534, + 288, + 713 + ], + "type": "text", + "content": ". The mask ratio is set to 0.35 for all baselines (based on the hyper-parameter search results in Sec. 5.3). The loss weight " + }, + { + "bbox": [ + 46, + 534, + 288, + 713 + ], + "type": "inline_equation", + "content": "\\lambda" + }, + { + "bbox": [ + 46, + 534, + 288, + 713 + ], + "type": "text", + "content": " of the Image-Token Consistency task is set to 8." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 306, + 316, + 468, + 328 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 316, + 468, + 328 + ], + "spans": [ + { + "bbox": [ + 306, + 316, + 468, + 328 + ], + "type": "text", + "content": "5.2. Results on Downstream Tasks" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 335, + 545, + 397 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 335, + 545, + 397 + ], + "spans": [ + { + "bbox": [ + 304, + 335, + 545, + 397 + ], + "type": "text", + "content": "Evaluation is performed on the following downstream tasks: (i) Image-Text Retrieval, (ii) Visual Question Answering (VQA), (iii) Natural Language for Visual Reasoning (NLVR2), and (iv) Visual Entailment (VE). Details on these tasks are in Appendix D." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 416, + 478, + 428 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 416, + 478, + 428 + ], + "spans": [ + { + "bbox": [ + 306, + 416, + 478, + 428 + ], + "type": "text", + "content": "5.2.1 Results for Image-Text Retrieval" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 437, + 545, + 640 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 437, + 545, + 640 + ], + "spans": [ + { + "bbox": [ + 304, + 437, + 545, + 640 + ], + "type": "text", + "content": "Tables 1 and 2 show that EPIC is universally effective over the different baselines/datasets, since we achieve non-trivial improvement nearly for all the settings. In addition, compared with all the baselines, EPIC brings more significant improvement to ViLT on different datasets. For example, on the fine-tuned retrieval tasks, we achieve an absolute improvement of around " + }, + { + "bbox": [ + 304, + 437, + 545, + 640 + ], + "type": "inline_equation", + "content": "5\\%" + }, + { + "bbox": [ + 304, + 437, + 545, + 640 + ], + "type": "text", + "content": " in terms of the TR/IR @1 metric on MSCOCO and Flickr30K. Furthermore, for zero-shot tasks, the improvement on the Flickr30K dataset is " + }, + { + "bbox": [ + 304, + 437, + 545, + 640 + ], + "type": "inline_equation", + "content": "7.2\\%" + }, + { + "bbox": [ + 304, + 437, + 545, + 640 + ], + "type": "text", + "content": " in terms of TR@1, and " + }, + { + "bbox": [ + 304, + 437, + 545, + 640 + ], + "type": "inline_equation", + "content": "9.9\\%" + }, + { + "bbox": [ + 304, + 437, + 545, + 640 + ], + "type": "text", + "content": " in terms of IR@1. The gap between ViLT and the other baselines can be explained by the fact that ViLT adopts a simplified architecture for vision-language pre-training and it does not incorporate more sophisticated tasks to learn vision-language associations. Nevertheless, the results on ViLT can be seen as an indicator for the effectiveness of the proposed method on a \"clean\" baseline." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 642, + 547, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 642, + 547, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 642, + 547, + 715 + ], + "type": "text", + "content": "For the other baselines with advanced strategies to learn vision-language associations (such as cross-modal contrasting in ALBEF, fine-grained reasoning in X-VLM, and powerful pre-trained encoders in METER), EPIC still demonstrates significant improvements. For example, compared with METER on the MSCOCO dataset (fine-tuned), EPIC" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "19160" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "table", + "bbox": [ + 53, + 70, + 279, + 274 + ], + "blocks": [ + { + "bbox": [ + 53, + 70, + 279, + 274 + ], + "lines": [ + { + "bbox": [ + 53, + 70, + 279, + 274 + ], + "spans": [ + { + "bbox": [ + 53, + 70, + 279, + 274 + ], + "type": "table", + "html": "
MethodDataEPICVQANLVR2SNLI-VE
devstddevstddevstd
METER4M+X77.777.981.882.581.481.0
✓77.978.083.583.581.681.8
16M+X78.378.482.784.381.781.8
✓78.678.785.085.282.182.3
ALBEF4M+X74.674.679.580.080.180.1
✓75.175.281.382.280.680.7
16M+X75.876.082.682.580.880.9
✓76.776.784.184.081.381.7
X-VLM4M+X78.178.283.384.1//
✓78.578.584.684.5//
16M+X78.078.284.384.5//
✓78.378.385.285.5//
ViLT4MX71.371.475.075.2//
✓71.871.877.277.1//
", + "image_path": "11270a595fe394003a24d14f1b97a4e77e98e54d491d9532582ecec8a08c549d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 47, + 274, + 287, + 307 + ], + "lines": [ + { + "bbox": [ + 47, + 274, + 287, + 307 + ], + "spans": [ + { + "bbox": [ + 47, + 274, + 287, + 307 + ], + "type": "text", + "content": "Table 3. Evaluation results on downstream vision-language tasks: VQA, NLVR2, and SNLI-VE. “/” indicates that the original baseline does not conduct experiment on this task." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 0 + }, + { + "bbox": [ + 47, + 334, + 287, + 392 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 334, + 287, + 392 + ], + "spans": [ + { + "bbox": [ + 47, + 334, + 287, + 392 + ], + "type": "text", + "content": "achieves an absolute IR@1 improvement of " + }, + { + "bbox": [ + 47, + 334, + 287, + 392 + ], + "type": "inline_equation", + "content": "2\\%" + }, + { + "bbox": [ + 47, + 334, + 287, + 392 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 334, + 287, + 392 + ], + "type": "inline_equation", + "content": "2.7\\%" + }, + { + "bbox": [ + 47, + 334, + 287, + 392 + ], + "type": "text", + "content": " with 4M and 16M data, respectively. When using the XVLM as baseline, we observe an absolute improvement of " + }, + { + "bbox": [ + 47, + 334, + 287, + 392 + ], + "type": "inline_equation", + "content": "3.9\\%" + }, + { + "bbox": [ + 47, + 334, + 287, + 392 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 334, + 287, + 392 + ], + "type": "inline_equation", + "content": "3.1\\%" + }, + { + "bbox": [ + 47, + 334, + 287, + 392 + ], + "type": "text", + "content": " in terms of IR@1 and TR@1, respectively, on Flickr30K (zero-shot)." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 422, + 226, + 434 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 422, + 226, + 434 + ], + "spans": [ + { + "bbox": [ + 47, + 422, + 226, + 434 + ], + "type": "text", + "content": "5.2.2 Results for VQA, NLVR2, and VE" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "spans": [ + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "text", + "content": "Table 3 shows that EPIC is effective among all the vision-language tasks. We improve over METER in terms of the VQA dev accuracy by " + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "inline_equation", + "content": "0.3\\%" + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "text", + "content": " under the 16M setting and XVLM by " + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "inline_equation", + "content": "0.4\\%" + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "text", + "content": " under the " + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "inline_equation", + "content": "4\\mathrm{M}^{+}" + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "text", + "content": " setting. Such improvement is non-trivial given the fact that these two baselines are quite competitive on this task. We also notice that EPIC improves ALBEF on VQA by " + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "inline_equation", + "content": "0.9\\%" + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "text", + "content": " in dev accuracy. Further, we observe significant improvements over all baselines on NLVR2 (e.g., " + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "inline_equation", + "content": "+2.3\\%" + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "text", + "content": " dev accuracy on METER 16M; " + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "inline_equation", + "content": "+2.2\\%" + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "text", + "content": " std accuracy on ALBEF 4M). The proposed method is also effective on the SNLI-VE dataset. It brings an absolute improvement of " + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "inline_equation", + "content": "0.8\\%" + }, + { + "bbox": [ + 46, + 445, + 287, + 590 + ], + "type": "text", + "content": " over ALBEF under the 16M setting." + } + ] + } + ], + "index": 4 + }, + { + "type": "table", + "bbox": [ + 48, + 604, + 285, + 682 + ], + "blocks": [ + { + "bbox": [ + 48, + 604, + 285, + 682 + ], + "lines": [ + { + "bbox": [ + 48, + 604, + 285, + 682 + ], + "spans": [ + { + "bbox": [ + 48, + 604, + 285, + 682 + ], + "type": "table", + "html": "
MethodNLVR2 devFlickr30K-ftFlickr30K-zsMSCOCO-ft
TR1IR1TR1IR1TR1IR1
vanilla METER79.689.276.683.267.771.052.5
ITC (rand.)79.991.577.883.269.070.853.5
ITC+LM80.992.178.983.871.573.455.6
EPIC81.092.979.084.372.574.155.6
", + "image_path": "bff118948d1123b97b3c639a1dcbfe766caafdcd1f28aa6d8ce92329585b67f8.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 47, + 683, + 287, + 706 + ], + "lines": [ + { + "bbox": [ + 47, + 683, + 287, + 706 + ], + "spans": [ + { + "bbox": [ + 47, + 683, + 287, + 706 + ], + "type": "text", + "content": "Table 4. Ablation studies on the effect of the ITC task, negative samples generation and the saliency-based masking strategy." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 5 + }, + { + "type": "table", + "bbox": [ + 307, + 70, + 544, + 159 + ], + "blocks": [ + { + "bbox": [ + 307, + 70, + 544, + 159 + ], + "lines": [ + { + "bbox": [ + 307, + 70, + 544, + 159 + ], + "spans": [ + { + "bbox": [ + 307, + 70, + 544, + 159 + ], + "type": "table", + "html": "
GeneratorNLVR2 devFlickr30K-ftFlickr30K-zsMSCOCO-ft
TR1IR1TR1IR1TR1IR1
LM (cond.)79.992.478.784.071.872.554.5
VLM (SAS)80.591.079.283.270.472.454.9
LM (trained)80.791.379.584.872.173.655.1
LM (fixed)80.791.979.083.871.172.454.8
LM (fine-tune)81.092.979.084.372.574.155.6
", + "image_path": "6440d8967772cb63f79b1cf1fe083f13f9ac23c818584d7fed177b92774d3c42.jpg" + } + ] + } + ], + "index": 7, + "angle": 0, + "type": "table_body" + }, + { + "bbox": [ + 308, + 160, + 542, + 171 + ], + "lines": [ + { + "bbox": [ + 308, + 160, + 542, + 171 + ], + "spans": [ + { + "bbox": [ + 308, + 160, + 542, + 171 + ], + "type": "text", + "content": "Table 5. Ablation study on different negative sample generators." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "table_footnote" + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 192, + 399, + 205 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 192, + 399, + 205 + ], + "spans": [ + { + "bbox": [ + 306, + 192, + 399, + 205 + ], + "type": "text", + "content": "5.3. Ablation Study" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 211, + 545, + 354 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 211, + 545, + 354 + ], + "spans": [ + { + "bbox": [ + 304, + 211, + 545, + 354 + ], + "type": "text", + "content": "We conduct ablation studies based on the METER model due to its superior performance with basic training objectives (i.e., image-text matching and cross-modal masked language modeling). However, training a full-version of METER is expensive (roughly 4 days with 32 A100 80G). Hence, we conduct ablations on a smaller scale that is still representative. Specifically, the model is still pre-trained on the 4M dataset, but the input image is resized to " + }, + { + "bbox": [ + 304, + 211, + 545, + 354 + ], + "type": "inline_equation", + "content": "224 \\times 224" + }, + { + "bbox": [ + 304, + 211, + 545, + 354 + ], + "type": "text", + "content": " (instead of " + }, + { + "bbox": [ + 304, + 211, + 545, + 354 + ], + "type": "inline_equation", + "content": "288 \\times 288" + }, + { + "bbox": [ + 304, + 211, + 545, + 354 + ], + "type": "text", + "content": " in the normal METER setting). To speed up the training process, we replace the image encoder CLIP-16 [26] with CLIP-32 for less memory usage, and also shorten the training schedule to 50k steps." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 354, + 545, + 414 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 354, + 545, + 414 + ], + "spans": [ + { + "bbox": [ + 304, + 354, + 545, + 414 + ], + "type": "text", + "content": "We evaluate the pre-trained model on the NLVR2 task and retrieval task (Flickr30K fine-tuned/zero-shot, MSCOCO fine-tuned). For the NLVR2 task, we report the development accuracy. For the retrieval tasks, we report the IR1/TR1 accuracies on the corresponding validation sets." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 415, + 545, + 593 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 415, + 545, + 593 + ], + "spans": [ + { + "bbox": [ + 304, + 415, + 545, + 593 + ], + "type": "text", + "content": "Component Analysis. As shown in Table 4, on top of vanilla METER, we first add an ITC task (ITC (rand.)) with inconsistent tokens sampled uniformly from the vocabulary (selecting " + }, + { + "bbox": [ + 304, + 415, + 545, + 593 + ], + "type": "inline_equation", + "content": "35\\%" + }, + { + "bbox": [ + 304, + 415, + 545, + 593 + ], + "type": "text", + "content": " of positions for inconsistent tokens). In this case, though some of the sampled tokens do not satisfy the conditions for inconsistent tokens in Sec. 4.2, the pretrained model can still benefit from the ITC task. When we replace the random strategy with a language model, " + }, + { + "bbox": [ + 304, + 415, + 545, + 593 + ], + "type": "inline_equation", + "content": "ITC + LM" + }, + { + "bbox": [ + 304, + 415, + 545, + 593 + ], + "type": "text", + "content": ", we generate inconsistent tokens approximating the conditions in Sec. 4.2. Finally, we choose to mask the tokens that are salient w.r.t. the image to generate inconsistent tokens, " + }, + { + "bbox": [ + 304, + 415, + 545, + 593 + ], + "type": "inline_equation", + "content": "ITC + saliency + LM" + }, + { + "bbox": [ + 304, + 415, + 545, + 593 + ], + "type": "text", + "content": ", and this further improves the performance on downstream tasks. Therefore, each component of EPIC is effective to improve the performance of the pre-trained model." + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": "Conditions of Inconsistent Tokens. In this ablation experiment, we study the importance of the two conditions of inconsistent tokens in Sec. 4.2. LM (fine-tune) is proposed in EPIC and it produces inconsistent tokens approximating the two conditions. First, we drop the first condition, that is, " + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "inline_equation", + "content": "P_{\\mathcal{W},\\mathcal{V}}(\\boldsymbol {W} = \\bar{\\boldsymbol{w}},\\boldsymbol {V} = \\boldsymbol{v})" + }, + { + "bbox": [ + 304, + 594, + 545, + 713 + ], + "type": "text", + "content": " is no longer small. We achieve this in two ways. (i) LM (cond.): When generating inconsistent samples and fine-tuning the LM, we replace the class token (first token) in the text with the one from the image encoder of the VLM (with gradient propagation canceled)." + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19161" + } + ] + } + ], + "index": 14 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 51, + 74, + 121, + 148 + ], + "blocks": [ + { + "bbox": [ + 51, + 74, + 121, + 148 + ], + "lines": [ + { + "bbox": [ + 51, + 74, + 121, + 148 + ], + "spans": [ + { + "bbox": [ + 51, + 74, + 121, + 148 + ], + "type": "image", + "image_path": "8c0b25e9017ef6323faaad25592102cb19dbf1048b66b0e8fa8ee96b2a0f94db.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 125, + 77, + 288, + 148 + ], + "blocks": [ + { + "bbox": [ + 125, + 77, + 288, + 148 + ], + "lines": [ + { + "bbox": [ + 125, + 77, + 288, + 148 + ], + "spans": [ + { + "bbox": [ + 125, + 77, + 288, + 148 + ], + "type": "image", + "image_path": "0ad5ae239cea93712dd0a2b8d468b82d9c074dbd9eb349248fda5f3dffd23bc6.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "type": "image", + "bbox": [ + 52, + 159, + 121, + 233 + ], + "blocks": [ + { + "bbox": [ + 81, + 150, + 91, + 159 + ], + "lines": [ + { + "bbox": [ + 81, + 150, + 91, + 159 + ], + "spans": [ + { + "bbox": [ + 81, + 150, + 91, + 159 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 52, + 159, + 121, + 233 + ], + "lines": [ + { + "bbox": [ + 52, + 159, + 121, + 233 + ], + "spans": [ + { + "bbox": [ + 52, + 159, + 121, + 233 + ], + "type": "image", + "image_path": "b0910e8ad4645f744fb7f14c7f3d103eafeee64b296ab589ebc2a69f9180164e.jpg" + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 81, + 236, + 92, + 243 + ], + "lines": [ + { + "bbox": [ + 81, + 236, + 92, + 243 + ], + "spans": [ + { + "bbox": [ + 81, + 236, + 92, + 243 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 67, + 254, + 276, + 266 + ], + "lines": [ + { + "bbox": [ + 67, + 254, + 276, + 266 + ], + "spans": [ + { + "bbox": [ + 67, + 254, + 276, + 266 + ], + "type": "text", + "content": "Figure 4. Visualization on the token saliency distribution." + } + ] + } + ], + "index": 6, + "angle": 0, + "type": "image_caption" + } + ], + "index": 3 + }, + { + "type": "image", + "bbox": [ + 126, + 161, + 288, + 232 + ], + "blocks": [ + { + "bbox": [ + 126, + 161, + 288, + 232 + ], + "lines": [ + { + "bbox": [ + 126, + 161, + 288, + 232 + ], + "spans": [ + { + "bbox": [ + 126, + 161, + 288, + 232 + ], + "type": "image", + "image_path": "081aad5fcefe2fb518bc012f1bdec27ed9912ff65f697f0daa2f500c4133a6e8.jpg" + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 297, + 247, + 540, + 270 + ], + "lines": [ + { + "bbox": [ + 297, + 247, + 540, + 270 + ], + "spans": [ + { + "bbox": [ + 297, + 247, + 540, + 270 + ], + "type": "text", + "content": "Figure 5. Grad-CAM visualization on the cross-attention maps of the image with respect to the ITC task." + } + ] + } + ], + "index": 27, + "angle": 0, + "type": "image_caption" + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 281, + 287, + 365 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 281, + 287, + 365 + ], + "spans": [ + { + "bbox": [ + 46, + 281, + 287, + 365 + ], + "type": "text", + "content": "(ii) VLM (SAS): We adapt SAS [36] from the text modality to the multi-modality setting. An auxiliary VLM (previous checkpoints of the VLM, detailed in Appendix G) is used to produce inconsistent tokens. As shown in Table 5, we observe that both " + }, + { + "bbox": [ + 46, + 281, + 287, + 365 + ], + "type": "inline_equation", + "content": "LM" + }, + { + "bbox": [ + 46, + 281, + 287, + 365 + ], + "type": "text", + "content": " (cond.) and " + }, + { + "bbox": [ + 46, + 281, + 287, + 365 + ], + "type": "inline_equation", + "content": "VLM" + }, + { + "bbox": [ + 46, + 281, + 287, + 365 + ], + "type": "text", + "content": " (SAS) suffer performance deterioration compared to " + }, + { + "bbox": [ + 46, + 281, + 287, + 365 + ], + "type": "inline_equation", + "content": "LM" + }, + { + "bbox": [ + 46, + 281, + 287, + 365 + ], + "type": "text", + "content": " (fine-tune). This indicates the importance of the first condition." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "spans": [ + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "text", + "content": "We then demonstrate the importance of the second condition, that is " + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "inline_equation", + "content": "P_{\\mathcal{W}}(\\boldsymbol{W} = \\bar{\\boldsymbol{w}})" + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "text", + "content": " is large. Intuitively, we fail to satisfy this condition when we stop fine-tuning the LM on the text corpus, i.e., " + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "inline_equation", + "content": "LM" + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "text", + "content": " (fixed). Further, for " + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "inline_equation", + "content": "LM" + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "text", + "content": " (fine-tune), we experiment its possible alternatives, " + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "inline_equation", + "content": "LM" + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "text", + "content": " (trained), where we fit the LM on the text corpus before instead of during pre-training. We observe that " + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "inline_equation", + "content": "LM" + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "text", + "content": " (fixed) clearly decreases the performance (especially in MSCOCO-ft). This validates the effectiveness of the second condition. Note that " + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "inline_equation", + "content": "LM" + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "text", + "content": " (fine-tune) outperforms " + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "inline_equation", + "content": "LM" + }, + { + "bbox": [ + 46, + 365, + 288, + 557 + ], + "type": "text", + "content": " (trained) by a small margin. This gap can be attributed to the training dynamics brought by fine-tuning [22]. As the LM fits the text corpus gradually better during finetuning, it becomes increasingly hard for a model to identify whether a token is replaced. This dynamics encourages the model to perform curriculum learning, which improves performance." + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 47, + 563, + 132, + 574 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 563, + 132, + 574 + ], + "spans": [ + { + "bbox": [ + 47, + 563, + 132, + 574 + ], + "type": "text", + "content": "5.4. Visualization" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 582, + 287, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 582, + 287, + 665 + ], + "spans": [ + { + "bbox": [ + 46, + 582, + 287, + 665 + ], + "type": "text", + "content": "Token Saliency. Fig. 4 shows the saliency distribution of the tokens w.r.t. the image for an image-sentence pair. We can see that salient tokens have higher densities in the distribution. For example, in Fig. 4a, \"blue\" and \"yellow\" are highlighted. In Fig. 4b, the teacher model gives more mass on \"white\", \"stick\" and \"ball\". These confirm that salient tokens can be detected by EPIC." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 665, + 288, + 714 + ], + "type": "text", + "content": "Grad-CAM of ITC. Fig. 5 visualizes the cross-attention maps of the image w.r.t. the inconsistent/consistent tokens using Grad-CAM [28]. In each row, we show the original image (left), the attention map w.r.t. the inconsistent tokens" + } + ] + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 304, + 72, + 378, + 133 + ], + "blocks": [ + { + "bbox": [ + 304, + 72, + 378, + 133 + ], + "lines": [ + { + "bbox": [ + 304, + 72, + 378, + 133 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 378, + 133 + ], + "type": "image", + "image_path": "8d9542d8b40272e0f31119a4b0f921ba3cae6a7f38aa3b0afc95de09896c03b8.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 337, + 133, + 345, + 138 + ], + "lines": [ + { + "bbox": [ + 337, + 133, + 345, + 138 + ], + "spans": [ + { + "bbox": [ + 337, + 133, + 345, + 138 + ], + "type": "text", + "content": "(a)" + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 354, + 139, + 492, + 145 + ], + "lines": [ + { + "bbox": [ + 354, + 139, + 492, + 145 + ], + "spans": [ + { + "bbox": [ + 354, + 139, + 492, + 145 + ], + "type": "text", + "content": "Positive Sample: \"Two kids are playing with a magnet.\"" + } + ] + } + ], + "index": 17, + "angle": 0, + "type": "image_footnote" + } + ], + "index": 12 + }, + { + "type": "image", + "bbox": [ + 382, + 72, + 457, + 133 + ], + "blocks": [ + { + "bbox": [ + 382, + 72, + 457, + 133 + ], + "lines": [ + { + "bbox": [ + 382, + 72, + 457, + 133 + ], + "spans": [ + { + "bbox": [ + 382, + 72, + 457, + 133 + ], + "type": "image", + "image_path": "4963f8fc40cf56ceaf9ef3128f1bc0036ae63fc81fc3057cfd4b86de87f44648.jpg" + } + ] + } + ], + "index": 14, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 417, + 134, + 430, + 138 + ], + "lines": [ + { + "bbox": [ + 417, + 134, + 430, + 138 + ], + "spans": [ + { + "bbox": [ + 417, + 134, + 430, + 138 + ], + "type": "text", + "content": "\"pet\"" + } + ] + } + ], + "index": 15, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 354, + 145, + 465, + 150 + ], + "lines": [ + { + "bbox": [ + 354, + 145, + 465, + 150 + ], + "spans": [ + { + "bbox": [ + 354, + 145, + 465, + 150 + ], + "type": "text", + "content": "Negative Sample: \"Two kids are playing with a" + } + ] + } + ], + "index": 18, + "angle": 0, + "type": "image_footnote" + } + ], + "index": 14 + }, + { + "type": "image", + "bbox": [ + 460, + 72, + 534, + 133 + ], + "blocks": [ + { + "bbox": [ + 460, + 72, + 534, + 133 + ], + "lines": [ + { + "bbox": [ + 460, + 72, + 534, + 133 + ], + "spans": [ + { + "bbox": [ + 460, + 72, + 534, + 133 + ], + "type": "image", + "image_path": "95ea166556d65985bf4b8d74e9eb851a7a2de5e7030c637e5fe514344a56712d.jpg" + } + ] + } + ], + "index": 16, + "angle": 0, + "type": "image_body" + } + ], + "index": 16 + }, + { + "type": "image", + "bbox": [ + 302, + 155, + 377, + 220 + ], + "blocks": [ + { + "bbox": [ + 302, + 155, + 377, + 220 + ], + "lines": [ + { + "bbox": [ + 302, + 155, + 377, + 220 + ], + "spans": [ + { + "bbox": [ + 302, + 155, + 377, + 220 + ], + "type": "image", + "image_path": "d2dc28130810996989c1673edf78b62f7724c5f5c1d3908e95f34ac5e1a92e6d.jpg" + } + ] + } + ], + "index": 19, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 336, + 220, + 345, + 224 + ], + "lines": [ + { + "bbox": [ + 336, + 220, + 345, + 224 + ], + "spans": [ + { + "bbox": [ + 336, + 220, + 345, + 224 + ], + "type": "text", + "content": "(b)" + } + ] + } + ], + "index": 20, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 324, + 225, + 513, + 230 + ], + "lines": [ + { + "bbox": [ + 324, + 225, + 513, + 230 + ], + "spans": [ + { + "bbox": [ + 324, + 225, + 513, + 230 + ], + "type": "text", + "content": "Positive Sample: \"A young woman poses with a large knife and a sandwich.\"" + } + ] + } + ], + "index": 25, + "angle": 0, + "type": "image_footnote" + } + ], + "index": 19 + }, + { + "type": "image", + "bbox": [ + 381, + 156, + 457, + 220 + ], + "blocks": [ + { + "bbox": [ + 381, + 156, + 457, + 220 + ], + "lines": [ + { + "bbox": [ + 381, + 156, + 457, + 220 + ], + "spans": [ + { + "bbox": [ + 381, + 156, + 457, + 220 + ], + "type": "image", + "image_path": "5ae4df8c3d2f682321edfff1f1d191aed2e8b1f8d930fc871f24a4b92894fb00.jpg" + } + ] + } + ], + "index": 21, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 399, + 220, + 445, + 224 + ], + "lines": [ + { + "bbox": [ + 399, + 220, + 445, + 224 + ], + "spans": [ + { + "bbox": [ + 399, + 220, + 445, + 224 + ], + "type": "text", + "content": "\"cake\"/\"sandwich\"" + } + ] + } + ], + "index": 22, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 325, + 230, + 496, + 237 + ], + "lines": [ + { + "bbox": [ + 325, + 230, + 496, + 237 + ], + "spans": [ + { + "bbox": [ + 325, + 230, + 496, + 237 + ], + "type": "text", + "content": "Negative Sample: \"A young women poses with a large cake and a ball." + } + ] + } + ], + "index": 26, + "angle": 0, + "type": "image_footnote" + } + ], + "index": 21 + }, + { + "type": "image", + "bbox": [ + 460, + 156, + 535, + 220 + ], + "blocks": [ + { + "bbox": [ + 460, + 156, + 535, + 220 + ], + "lines": [ + { + "bbox": [ + 460, + 156, + 535, + 220 + ], + "spans": [ + { + "bbox": [ + 460, + 156, + 535, + 220 + ], + "type": "image", + "image_path": "7b4a6ce7b791e13bb6366a6402501147d2abb4c0c6dddc9d2f0d7d98ab39a521.jpg" + } + ] + } + ], + "index": 23, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 484, + 220, + 518, + 224 + ], + "lines": [ + { + "bbox": [ + 484, + 220, + 518, + 224 + ], + "spans": [ + { + "bbox": [ + 484, + 220, + 518, + 224 + ], + "type": "text", + "content": "\"cake\"/\"ball\"" + } + ] + } + ], + "index": 24, + "angle": 0, + "type": "image_caption" + } + ], + "index": 23 + }, + { + "bbox": [ + 304, + 281, + 545, + 413 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 281, + 545, + 413 + ], + "spans": [ + { + "bbox": [ + 304, + 281, + 545, + 413 + ], + "type": "text", + "content": "(middle), and that w.r.t. the consistent ones (right). For example, in the first row, when we input the model with the original sentence, the model predicts that the token \"magnet\" is consistent and attends to the actual magnet in the image. However, when we replace \"magnet\" with \"pet\", the model predicts the token \"pet\" as \"inconsistent\" and still attends to the magnet in the image. This means the model is aware that the object in the image is actually a magnet instead of a pet. Similar observation exists for the second row. These demonstrate the model's ability to reason on cross-modal relationship." + } + ] + } + ], + "index": 28 + }, + { + "bbox": [ + 306, + 430, + 378, + 443 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 430, + 378, + 443 + ], + "spans": [ + { + "bbox": [ + 306, + 430, + 378, + 443 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 29 + }, + { + "bbox": [ + 304, + 454, + 545, + 634 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 454, + 545, + 634 + ], + "spans": [ + { + "bbox": [ + 304, + 454, + 545, + 634 + ], + "type": "text", + "content": "In this paper we propose EPIC, a pre-training approach that leverage more text tokens for learning vision-language associations. It is less affected by the modality bias problem compared with CMLM. Specifically, we propose an ITC task to identify inconsistent tokens generated by a language model coupled with a saliency-based masking strategy. The task formulation of the ITC task and the design of inconsistent samples address the problems of under-utilization of unmasked tokens and modality bias. We perform extensive experiments and show that EPIC brings consistent performance gains over several baselines on a wide range of downstream tasks. Possible directions for future research can be: (i) approaching the conditions of inconsistent tokens in Sec. 4.2 more precisely; (ii) finding salient tokens without using a pre-trained teacher VLM." + } + ] + } + ], + "index": 30 + }, + { + "bbox": [ + 306, + 654, + 408, + 668 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 654, + 408, + 668 + ], + "spans": [ + { + "bbox": [ + 306, + 654, + 408, + 668 + ], + "type": "text", + "content": "Acknowledgements" + } + ] + } + ], + "index": 31 + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": "This work is supported by NSFC key grant 62136005, NSFC general grant 62076118, and Shenzhen fundamental research program JCYJ20210324105000003." + } + ] + } + ], + "index": 32 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19162" + } + ] + } + ], + "index": 33 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 106, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "text", + "content": "[1] Stanislaw Antol, Aishwarya Agrawal, Jiasen Lu, Margaret Mitchell, Dhruv Batra, C Lawrence Zitnick, and Devi Parikh. Vqa: Visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 2425-2433, 2015. 2, 3, 11" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 147, + 288, + 213 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 147, + 288, + 213 + ], + "spans": [ + { + "bbox": [ + 53, + 147, + 288, + 213 + ], + "type": "text", + "content": "[2] Yonatan Bitton, Michael Elhadad, Gabriel Stanovsky, and Roy Schwartz. Data efficient masked language modeling for vision and language. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3013-3028, Punta Cana, Dominican Republic, Nov. 2021. Association for Computational Linguistics. 2, 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 214, + 287, + 256 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 214, + 287, + 256 + ], + "spans": [ + { + "bbox": [ + 53, + 214, + 287, + 256 + ], + "type": "text", + "content": "[3] Marc Brysbaert, Amy Beth Warriner, and Victor Kuperman. Concreteness ratings for 40 thousand generally known english word lemmas. Behavior Research Methods, 46:904-911, 2014. 3" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 258, + 287, + 312 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 258, + 287, + 312 + ], + "spans": [ + { + "bbox": [ + 53, + 258, + 287, + 312 + ], + "type": "text", + "content": "[4] Soravit Changpinyo, Piyush Sharma, Nan Ding, and Radu Soricut. Conceptual 12m: Pushing web-scale image-text pretraining to recognize long-tail visual concepts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3558-3568, 2021. 5, 6" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 314, + 287, + 367 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 314, + 287, + 367 + ], + "spans": [ + { + "bbox": [ + 53, + 314, + 287, + 367 + ], + "type": "text", + "content": "[5] Yen-Chun Chen, Linjie Li, Licheng Yu, Ahmed El Kholy, Faisal Ahmed, Zhe Gan, Yu Cheng, and Jingjing Liu. Uniter: Universal image-text representation learning. In European conference on computer vision, pages 104-120. Springer, 2020. 1" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 369, + 287, + 412 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 369, + 287, + 412 + ], + "spans": [ + { + "bbox": [ + 53, + 369, + 287, + 412 + ], + "type": "text", + "content": "[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018. 2, 6" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 415, + 287, + 468 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 415, + 287, + 468 + ], + "spans": [ + { + "bbox": [ + 53, + 415, + 287, + 468 + ], + "type": "text", + "content": "[7] Zi-Yi Dou, Aishwarya Kamath, Zhe Gan, Pengchuan Zhang, Jianfeng Wang, Linjie Li, Zicheng Liu, Ce Liu, Yann LeCun, Nanyun Peng, et al. Coarse-to-fine vision-language pre-training with fusion in the backbone. arXiv preprint arXiv:2206.07643, 2022. 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 470, + 287, + 534 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 470, + 287, + 534 + ], + "spans": [ + { + "bbox": [ + 53, + 470, + 287, + 534 + ], + "type": "text", + "content": "[8] Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, Zicheng Liu, and Michael Zeng. An empirical study of training end-to-end vision-and-language transformers. In Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 2, 4, 5, 11, 12" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 536, + 287, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 536, + 287, + 590 + ], + "spans": [ + { + "bbox": [ + 53, + 536, + 287, + 590 + ], + "type": "text", + "content": "[9] Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Bartra, and Devi Parikh. Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6325-6334, 2017. 11" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 592, + 287, + 656 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 592, + 287, + 656 + ], + "spans": [ + { + "bbox": [ + 48, + 592, + 287, + 656 + ], + "type": "text", + "content": "[10] Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. Scaling up visual and vision-language representation learning with noisy text supervision. In International Conference on Machine Learning, pages 4904-4916. PMLR, 2021. 1, 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 658, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 658, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 658, + 287, + 713 + ], + "type": "text", + "content": "[11] Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, and Nicolas Carion. Mdetr-modulated detection for end-to-end multi-modal understanding. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1780-1790, 2021. 3" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 713 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 116 + ], + "type": "text", + "content": "[12] Wonjae Kim, Bokyung Son, and Ildoo Kim. Vilt: Vision-and-language transformer without convolution or region supervision. In International Conference on Machine Learning, pages 5583-5594. PMLR, 2021. 1, 4, 5, 12" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 307, + 118, + 545, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 118, + 545, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 118, + 545, + 184 + ], + "type": "text", + "content": "[13] Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji Hata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A Shamma, et al. Visual genome: Connecting language and vision using crowdsourced dense image annotations. International journal of computer vision, 123(1):32-73, 2017. 5" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 186, + 545, + 239 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 545, + 239 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 545, + 239 + ], + "type": "text", + "content": "[14] Junnan Li, Ramprasaath R. Selvaraju, Akhilesh Deepak Gotmare, Shafiq R. Joty, Caiming Xiong, and Steven C. H. Hoi. Align before fuse: Vision and language representation learning with momentum distillation. In NeurIPS, 2021. 1, 2, 4, 5, 12" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 242, + 545, + 307 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 242, + 545, + 307 + ], + "spans": [ + { + "bbox": [ + 307, + 242, + 545, + 307 + ], + "type": "text", + "content": "[15] Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10965-10975, 2022. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 308, + 545, + 363 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 308, + 545, + 363 + ], + "spans": [ + { + "bbox": [ + 307, + 308, + 545, + 363 + ], + "type": "text", + "content": "[16] Xiujun Li, Xi Yin, Chunyuan Li, Pengchuan Zhang, Xiaowei Hu, Lei Zhang, Lijuan Wang, Houdong Hu, Li Dong, Furu Wei, et al. Oscar: Object-semantics aligned pre-training for vision-language tasks. In European Conference on Computer Vision, pages 121-137. Springer, 2020. 1, 2" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 365, + 545, + 419 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 365, + 545, + 419 + ], + "spans": [ + { + "bbox": [ + 307, + 365, + 545, + 419 + ], + "type": "text", + "content": "[17] Yangguang Li, Feng Liang, Lichen Zhao, Yufeng Cui, Wanli Ouyang, Jing Shao, Fengwei Yu, and Junjie Yan. Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm. arXiv preprint arXiv:2110.05208, 2021. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 422, + 545, + 475 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 422, + 545, + 475 + ], + "spans": [ + { + "bbox": [ + 307, + 422, + 545, + 475 + ], + "type": "text", + "content": "[18] Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dólar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages 740-755. Springer, 2014. 5, 11" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 478, + 545, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 478, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 307, + 478, + 545, + 521 + ], + "type": "text", + "content": "[19] Yongfei Liu, Chenfei Wu, Shao-yen Tseng, Vasudev Lal, Xuming He, and Nan Duan. Kd-vlp: Improving end-to-end vision-and-language pretraining with object knowledge distillation. arXiv preprint arXiv:2109.10504, 2021. 1, 2" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 523, + 545, + 577 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 523, + 545, + 577 + ], + "spans": [ + { + "bbox": [ + 307, + 523, + 545, + 577 + ], + "type": "text", + "content": "[20] Haoyu Lu, Nanyi Fei, Yuqi Huo, Yizhao Gao, Zhiwu Lu, and Ji-Rong Wen. Cots: Collaborative two-stream vision-language pre-training model for cross-modal retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15692-15701, 2022. 2" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 579, + 545, + 623 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 579, + 545, + 623 + ], + "spans": [ + { + "bbox": [ + 307, + 579, + 545, + 623 + ], + "type": "text", + "content": "[21] Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. Advances in neural information processing systems, 32, 2019. 1, 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 624, + 545, + 677 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 624, + 545, + 677 + ], + "spans": [ + { + "bbox": [ + 307, + 624, + 545, + 677 + ], + "type": "text", + "content": "[22] Yu Meng, Chenyan Xiong, Payal Bajaj, Paul Bennett, Jiawei Han, Xia Song, et al. Coco-lm: Correcting and contrasting text sequences for language model pretraining. Advances in Neural Information Processing Systems, 34:23102-23114, 2021. 8" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 680, + 545, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 680, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 680, + 545, + 713 + ], + "type": "text", + "content": "[23] Yulei Niu, Kaihua Tang, Hanwang Zhang, Zhiwu Lu, Xian-Sheng Hua, and Ji-Rong Wen. Counterfactual vqa: A cause-effect look at language bias. In Proceedings of the IEEE/CVF" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "19163" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 73, + 286, + 712 + ], + "type": "list", + "angle": 0, + "index": 11, + "blocks": [ + { + "bbox": [ + 67, + 73, + 286, + 95 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 67, + 73, + 286, + 95 + ], + "spans": [ + { + "bbox": [ + 67, + 73, + 286, + 95 + ], + "type": "text", + "content": "Conference on Computer Vision and Pattern Recognition, pages 12700-12710, 2021. 1, 3" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 96, + 286, + 138 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 96, + 286, + 138 + ], + "spans": [ + { + "bbox": [ + 48, + 96, + 286, + 138 + ], + "type": "text", + "content": "[24] Vicente Ordonez, Girish Kulkarni, and Tamara Berg. Im2text: Describing images using 1 million captioned photographs. Advances in neural information processing systems, 24, 2011. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 140, + 286, + 205 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 140, + 286, + 205 + ], + "spans": [ + { + "bbox": [ + 48, + 140, + 286, + 205 + ], + "type": "text", + "content": "[25] Bryan A Plummer, Liwei Wang, Chris M Cervantes, Juan C Caicedo, Julia Hockenmaier, and Svetlana Lazebnik. Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models. In Proceedings of the IEEE international conference on computer vision, pages 2641–2649, 2015. 11" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 207, + 286, + 270 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 207, + 286, + 270 + ], + "spans": [ + { + "bbox": [ + 48, + 207, + 286, + 270 + ], + "type": "text", + "content": "[26] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pages 8748-8763. PMLR, 2021. 7, 11" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 273, + 286, + 369 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 273, + 286, + 369 + ], + "spans": [ + { + "bbox": [ + 48, + 273, + 286, + 369 + ], + "type": "text", + "content": "[27] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. Learning transferable visual models from natural language supervision. In Marina Meila and Tong Zhang, editors, Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research, pages 8748-8763. PMLR, 18-24 Jul 2021. 1, 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 372, + 286, + 435 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 372, + 286, + 435 + ], + "spans": [ + { + "bbox": [ + 48, + 372, + 286, + 435 + ], + "type": "text", + "content": "[28] Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, pages 618-626, 2017. 8" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 437, + 286, + 502 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 437, + 286, + 502 + ], + "spans": [ + { + "bbox": [ + 48, + 437, + 286, + 502 + ], + "type": "text", + "content": "[29] Amanpreet Singh, Ronghang Hu, Vedanuj Goswami, Guillaume Couairon, Wojciech Galuba, Marcus Rohrbach, and Douwe Kiela. Flava: A foundational language and vision alignment model. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15638-15650, 2022. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 504, + 286, + 546 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 504, + 286, + 546 + ], + "spans": [ + { + "bbox": [ + 48, + 504, + 286, + 546 + ], + "type": "text", + "content": "[30] Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, and Jifeng Dai. Vl-bert: Pre-training of generic visual-linguistic representations. arXiv preprint arXiv:1908.08530, 2019. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 548, + 286, + 590 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 548, + 286, + 590 + ], + "spans": [ + { + "bbox": [ + 48, + 548, + 286, + 590 + ], + "type": "text", + "content": "[31] Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Hua-jun Bai, and Yoav Artzi. A corpus for reasoning about natural language grounded in photographs. arXiv preprint arXiv:1811.00491, 2018. 2, 11" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 593, + 286, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 593, + 286, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 593, + 286, + 668 + ], + "type": "text", + "content": "[32] Hao Tan and Mohit Bansal. LXMERT: Learning cross-modality encoder representations from transformers. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5100-5111, Hong Kong, China, Nov. 2019. Association for Computational Linguistics. 1, 3" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 670, + 286, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 286, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 286, + 712 + ], + "type": "text", + "content": "[33] Zirui Wang, Jiahui Yu, Adams Wei Yu, Zihang Dai, Yulia Tsvetkov, and Yuan Cao. Simvlm: Simple visual language model pretraining with weak supervision. arXiv preprint arXiv:2108.10904, 2021. 1" + } + ] + } + ], + "index": 10 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 545, + 438 + ], + "type": "list", + "angle": 0, + "index": 18, + "blocks": [ + { + "bbox": [ + 307, + 73, + 545, + 193 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 545, + 193 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 545, + 193 + ], + "type": "text", + "content": "[34] Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chau-mond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. Transformers: State-of-the-art natural language processing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online, Oct. 2020. Association for Computational Linguistics. 6, 11" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 307, + 195, + 545, + 227 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 195, + 545, + 227 + ], + "spans": [ + { + "bbox": [ + 307, + 195, + 545, + 227 + ], + "type": "text", + "content": "[35] Ning Xie, Farley Lai, Derek Doran, and Asim Kadav. Visual entailment: A novel task for fine-grained image understanding. arXiv preprint arXiv:1901.06706, 2019. 12" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 307, + 228, + 545, + 271 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 228, + 545, + 271 + ], + "spans": [ + { + "bbox": [ + 307, + 228, + 545, + 271 + ], + "type": "text", + "content": "[36] Yifei Xu, Jingqiao Zhang, Ru He, Liangzhu Ge, Chao Yang, Cheng Yang, and Ying Nian Wu. Sas: Self-augmented strategy for language model pre-training. arXiv preprint arXiv:2106.07176, 2021. 8" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 274, + 545, + 338 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 274, + 545, + 338 + ], + "spans": [ + { + "bbox": [ + 307, + 274, + 545, + 338 + ], + "type": "text", + "content": "[37] Jinyu Yang, Jiali Duan, Son Tran, Yi Xu, Sampath Chanda, Liquun Chen, Belinda Zeng, Trishul Chilimbi, and Junzhou Huang. Vision-language pre-training with triple contrastive learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15671-15680, 2022. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 341, + 545, + 394 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 341, + 545, + 394 + ], + "spans": [ + { + "bbox": [ + 307, + 341, + 545, + 394 + ], + "type": "text", + "content": "[38] Haoxuan You, Luowei Zhou, Bin Xiao, Noel Codella, Yu Cheng, Ruochen Xu, Shih-Fu Chang, and Lu Yuan. Learning visual representation from modality-shared contrastive language-image pre-training. In European Conference on Computer Vision, pages 69-87. Springer, 2022. 2" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 396, + 545, + 438 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 396, + 545, + 438 + ], + "spans": [ + { + "bbox": [ + 307, + 396, + 545, + 438 + ], + "type": "text", + "content": "[39] Yan Zeng, Xinsong Zhang, and Hang Li. Multi-grained vision language pre-training: Aligning texts with visual concepts. arXiv preprint arXiv:2111.08276, 2021. 2, 3, 4, 5, 12" + } + ] + } + ], + "index": 17 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "19164" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file diff --git a/2023/LiDAR-in-the-Loop Hyperparameter Optimization/9dfdd95b-2191-44f3-8949-8eb946acc7a7_content_list.json b/2023/LiDAR-in-the-Loop Hyperparameter Optimization/9dfdd95b-2191-44f3-8949-8eb946acc7a7_content_list.json new file mode 100644 index 0000000000000000000000000000000000000000..68274dd0b8e04e759a5905f1c39c16728fd9d23c --- /dev/null +++ b/2023/LiDAR-in-the-Loop Hyperparameter Optimization/9dfdd95b-2191-44f3-8949-8eb946acc7a7_content_list.json @@ -0,0 +1,1620 @@ +[ + { + "type": "text", + "text": "LiDAR-in-the-loop Hyperparameter Optimization", + "text_level": 1, + "bbox": [ + 230, + 130, + 740, + 152 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Félix Goudreault $^{1}$ Dominik Scheuble $^{2}$ Mario Bijelic $^{2,3}$ Nicolas Robidoux $^{1}$ Felix Heide $^{1,3}$ $^{1}$ Algolux $^{2}$ Mercedes-Benz $^{3}$ Princeton University", + "bbox": [ + 94, + 174, + 875, + 215 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Abstract", + "text_level": 1, + "bbox": [ + 233, + 250, + 313, + 266 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "LiDAR has become a cornerstone sensing modality for 3D vision. LiDAR systems emit pulses of light into the scene, take measurements of the returned signal, and rely on hardware digital signal processing (DSP) pipelines to construct 3D point clouds from these measurements. The resulting point clouds output by these DSPs are input to downstream 3D vision models – both, in the form of training datasets or as input at inference time. Existing LiDAR DSPs are composed of cascades of parameterized operations; modifying configuration parameters results in significant changes in the point clouds and consequently the output of downstream methods. Existing methods treat LiDAR systems as fixed black boxes and construct downstream task networks more robust with respect to measurement fluctuations. Departing from this approach, the proposed method directly optimizes LiDAR sensing and DSP parameters for downstream tasks. To investigate the optimization of LiDAR system parameters, we devise a realistic LiDAR simulation method that generates raw waveforms as input to a LiDAR DSP pipeline. We optimize LiDAR parameters for both 3D object detection IoU losses and depth error metrics by solving a nonlinear multi-objective optimization problem with a 0th-order stochastic algorithm. For automotive 3D object detection models, the proposed method outperforms manual expert tuning by $39.5\\%$ mean Average Precision (mAP).", + "bbox": [ + 76, + 272, + 473, + 650 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "1. Introduction", + "text_level": 1, + "bbox": [ + 76, + 667, + 209, + 684 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Environment perception for autonomous drones [12, 53] and vehicles [72] requires precise depth sensing for safety-critical control decisions. Scanning LiDAR sensors have been broadly adopted in autonomous driving [3, 7, 56] as they provide high temporal and spatial resolution, and recent advances in MEMS scanning [67] and photodiode technology [64] have reduced their cost and form factor.", + "bbox": [ + 75, + 688, + 468, + 794 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "The 3D LiDAR point cloud (PC) data that existing 3D detection methods take as input is produced by a LiDAR and digital signal processor (DSP) pipeline with many measurement and processing steps. Typical LiDAR sensors operate by sending out a laser pulse and measuring the temporal response through an Avalanche Photo Diode (APD). This temporal wavefront signal is fed to a DSP that extracts", + "bbox": [ + 75, + 795, + 470, + 901 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "peaks corresponding to echos from candidate targets within the scene [70]. As such, DSP processing results in a 1000-fold data reduction for a single emitted beam, producing single or multiple 3D points per beam. Compressing the waveform into points in 3D space with minimal information loss is challenging because of object discontinuities, sub-surface scattering, multipath reflections, and scattering media, see Fig. 1. In particular, significant scattering occurs in adverse weather conditions like fog [3,4,6,20,23,25,66], rain [6,18,66] and snow [19,25,27,34]. LiDAR manufacturers currently handle such complications by manually adjusting internal sensing and DSP parameters in controlled environments and restricted real-world scenarios using a combination of visual inspection and depth quality metrics.", + "bbox": [ + 496, + 251, + 893, + 462 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "Generally, LiDAR production units are black boxes with configuration parameters hidden from the user. To account for noisy point cloud measurements with spurious artifacts, existing work has explored methods that add simulated adverse effects and point cloud degradations that model rain [18, 26, 58], fog [3, 20, 50] and snow [19] to real black-box LiDAR datasets. Augmented point clouds in hand, downstream vision models are retrained for predictions more robust to point cloud data corruption. Another approach consists of generating synthetic measurements from 3D scenes using rendering engines [24, 57, 69]. Such existing methods typically avoid simulating transient light propagation and signal processing by converting 3D scene depth directly into a point cloud, thus lack physically realistic modeling of fluctuations arising from multipath effects or measurement noise. Notably, existing simulation methods that alter measurements or generate synthetic point clouds do not optimize sensing or DSP parameters for downstream vision performance.", + "bbox": [ + 496, + 462, + 893, + 750 + ], + "page_idx": 0 + }, + { + "type": "text", + "text": "In this work, we directly optimize LiDAR pulse configuration and DSP hyperparameters for end-to-end downstream 3D object detector losses and PC depth quality metrics, a challenging task because hyperparameter space involves tens to hundreds of categorical, discrete and effectively continuous parameters affecting downstream tasks in complex nonlinear ways via the intermediate point cloud. Examples of categorical hyperparameters are Velodyne LiDAR sensor return modes that configure internal wavefront peak selection algorithms for point cloud formation;", + "bbox": [ + 496, + 750, + 895, + 901 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "CVF", + "bbox": [ + 106, + 2, + 181, + 42 + ], + "page_idx": 0 + }, + { + "type": "header", + "text": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore.", + "bbox": [ + 236, + 0, + 807, + 46 + ], + "page_idx": 0 + }, + { + "type": "page_number", + "text": "13404", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 0 + }, + { + "type": "image", + "img_path": "images/b1bf9cf31d8ad4d0bf90895dde4162bb8e5972285926f5bf991492a6971a3467.jpg", + "image_caption": [ + "Figure 1. LiDAR Point Cloud Formation. Typical LiDAR sensor PC measurements are produced by a multi-stage measurement and signal processing chain: The LiDAR emits a laser pulse. This signal travels through the scene and returns to the detector after single or multiple reflections. Cluttered surfaces (a), strong retroreflectors (b) and ambient light are introduced (c) in the returned signals. Thus, the full transient waveform read by sensors is the superposition of multiple return paths. The DSP, itself a chain of processing blocks, processes all temporal waveforms and extracts a continuous stream of 3D points that forms the final point cloud (bottom)." + ], + "image_footnote": [], + "bbox": [ + 91, + 89, + 877, + 234 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "rotation velocity, which impacts angular resolution, is an example of a continuous hyperparameter [4].", + "bbox": [ + 75, + 321, + 468, + 352 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Grid search optimization is impractical here because of combinatorial explosion. Orthogonally to LiDAR, it was recently shown that 0th order solvers can find camera DSP hyperparameters that improve downstream 2D object detectors [36, 48]. We propose an optimization method for LiDAR sensing and DSP hyperparameters that minimizes end-to-end domain-specific losses such as RMSE of the measured depth against ground truth and IoU measured on downstream 3D object detection. To assess the proposed method, we devised a LiDAR simulation method based on the CARLA engine [13] that models a LiDAR DSP as well as the full transient noisy waveform formed by multiple laser echoes. We optimize sensing and DSP hyperparameters by solving a Multi-Objective black-box Optimization (MOO) problem with a novel CMA-ES (Covariance Matrix Adaptation-Evolution Strategy [21]) that relies on a max-rank multi-objective scalarization loss [36] to dynamically improve scale matching between different loss components. In combination with a novel champion selection method, it finds a balanced Pareto-optimal solution for which no loss component has a comparatively poor value for LiDAR optimization with multiple objectives. We validate the proposed optimization method for 3D object detection and point cloud depth estimation, both in simulation and using an off-the-shelf experimental LiDAR sensor.", + "bbox": [ + 75, + 353, + 472, + 729 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Specifically, we make the following contributions:", + "bbox": [ + 96, + 731, + 431, + 747 + ], + "page_idx": 1 + }, + { + "type": "list", + "sub_type": "text", + "list_items": [ + "- We introduce a LiDAR wavefront simulation for the CARLA simulation environment that models realistic transient scene responses.", + "- We devise a novel multi-objective optimization method for balanced MOO of LiDAR parameters.", + "- We validate end-to-end LiDAR sensing and DSP optimization for 3D object detection and depth estimation through simulation and with a real system. For all ap" + ], + "bbox": [ + 94, + 758, + 470, + 901 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "plications, our approach outperforms existing state-of-the-art methods, including expert tuning.", + "bbox": [ + 531, + 321, + 890, + 352 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Simulator code and DSP models are published here1.", + "bbox": [ + 498, + 362, + 849, + 378 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "Limitations Because commercial LiDAR units are IP-protected black boxes, interfacing their DSP hyperparameters is not straightforward. While the off-the-shelf LiDAR system used in this work makes some DSP hyperparameters accessible, most LiDAR systems are completely closed. We hope that these findings spur LiDAR vendors to follow the lead of digital camera and ISP (Image Signal Processor) vendors and open their processing pipelines.", + "bbox": [ + 496, + 378, + 890, + 501 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "2. Related Work", + "text_level": 1, + "bbox": [ + 500, + 508, + 640, + 523 + ], + "page_idx": 1 + }, + { + "type": "text", + "text": "DSP and Sensor Hyperparameter Optimization. A growing body of work explores the optimization of sensors and DSPs for downstream vision tasks. Existing methods target camera ISPs and optics [36, 48, 59, 60, 71]. Departing from manual hyperparameter tuning by experts, these approaches optimize them automatically, driven by one or more downstream performance metric. As DSP and sensor hyperparameters can be categorical and losses are often non-convex and noisy, diverse optimization methods have been proposed. Early methods targeted specific processing blocks [35, 40, 44] as differentiable programs or in a reduced parameter space. Recent methods rely on differentiable pipeline proxies [60] or 0th-order optimization [36], alone or in combination with block coordinate descent [48]. One advantage of 0th-order optimizers is that they handle black box hardware and DSPs. 0th-order solvers used to optimize camera systems include MOEA/D [40, 73] and CMA-ES [21, 36, 48]. These approaches successfully tackle camera pipeline optimization, from the optics [59] to downstream detectors [36, 48]. To our knowledge, however, no loss-driven method has been proposed for end-to-end LiDAR system optimization: existing LiDAR sensors are designed in isolation from downstream depth and detection", + "bbox": [ + 496, + 527, + 892, + 876 + ], + "page_idx": 1 + }, + { + "type": "page_footnote", + "text": "1https://light.princeton.edu/LITL-Optimization/", + "bbox": [ + 517, + 887, + 844, + 900 + ], + "page_idx": 1 + }, + { + "type": "page_number", + "text": "13405", + "bbox": [ + 480, + 944, + 519, + 957 + ], + "page_idx": 1 + }, + { + "type": "image", + "img_path": "images/d9a1dc6d8f2b96dac806d8559e099dcc7afbf79091925b6812cffa763359be1f.jpg", + "image_caption": [ + "Figure 2. LiDAR Simulation Method. We propose a parameterizable LiDAR simulation model that generates full transient waveforms by extracting scene response $H$ , ambient light $a$ and object reflectances $s, d, \\alpha$ from CARLA. End-to-end loss functions drive an MOO solver toward an optimal vector $\\Theta$ that includes both pulse and DSP hyperparameters: Wavefronts are processed by the DSP, resulting in a point cloud $\\mathbf{O}$ . The \"loop is closed\" by feeding $\\mathbf{O}$ to 3D object detection and depth estimation methods, the output of which is \"rated\" by loss functions $\\mathcal{L}$ . Validation datasets cycle through the loop until $\\Theta$ converges." + ], + "image_footnote": [], + "bbox": [ + 76, + 85, + 893, + 303 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "tasks. Aiming to close this gap, this work proposes automated LiDAR hyperparameter optimization.", + "bbox": [ + 75, + 391, + 468, + 421 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "LiDAR Sensing and Point Cloud Generation. LiDAR sensors produce point clouds by emitting pulses of light into a scene and measuring the round trip time of sensor returns. Extracting a point cloud from time-of-flight measurements is a complex process that depends on measurement procedure specifics like beam formation, scanning, pulse/continuous wave generation and peak finding within the acquired temporal laser response [1,46]. LiDAR sensors differ in their scanning pattern, beam steering technology, wavelength, pulse profile, coherence of the measurement step, detector technology and DSP capabilities to process the measurement echos [1,31].", + "bbox": [ + 75, + 425, + 470, + 606 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Existing LiDAR sensors can extract single or multiple peaks resulting from multi-path interreflections in the scene. Assuming a single Lambertian reflector in the scene, the temporal resolution and signal-to-noise ratio (SNR) of the measurement are tied to laser power. Accordingly, some existing optimization methods [43] use automated runtime laser power adjustment to maximize SNR while preventing oversaturation. Other approaches have been proposed for adaptive beam steering [38, 45]. Recently, Vodisch et al. [65] proposed beam configuration optimization via reinforcement learning methods driven by a downstream 3D detection loss. This method is the most similar to the proposed approach; however, it only predicts beam patterns, i.e., where to place sparse samples. In contrast, we optimize DSP hyperparameters in addition to sensing ones, pulse power and scanning parameters included.", + "bbox": [ + 75, + 609, + 468, + 851 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "LiDAR Simulation. To assess and validate the proposed method, we introduce a new LiDAR simulation method that plugs directly into the CARLA simulator. Existing", + "bbox": [ + 75, + 854, + 470, + 902 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "work [5, 14, 37, 47, 52] proposes several simulation environments; the open-source CARLA [14] simulator is broadly adopted in academia. Simulation frameworks have enabled the creation of multimodal synthetic datasets used successfully in academia and industry, e.g., PreSIL [24], SHIFT [57], AIODrive [32, 69] and SYNTHIA [49]. However, underlying simulation methods employ heuristic forward models; in addition, none of the datasets include full waveform returns that would allow simulating LiDAR point cloud generation. Even the AIODrive dataset [69], in which multiple peaks are returned via depth image convolution and Single Photon Avalanche Diode (SPAD) quantization, bakes transients into SPAD image formation, hence falls short of enabling realistic transient simulation. Similarly, real PC datasets augmentation methods have been employed to tackle rare occurring events like obstacles [16, 33], traffic [68], rain [20], fog [20] or snow [19, 26]. However, since the underlying datasets do not include the raw wavefronts, none of these approach facilitate modeling the DSP pipeline. We address these challenges with our simulation method that simulates full wavefront signals. Combined with a realistic DSP model, this produces PC data representative of real systems.", + "bbox": [ + 496, + 390, + 890, + 738 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "3. Transient LiDAR Forward Model", + "text_level": 1, + "bbox": [ + 500, + 742, + 807, + 757 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "Consider a single laser pulse emitted by a LiDAR unit into a 3D scene, from which a returned signal is detected by a SPAD detector which sends temporal histograms to the sensor DSP. For channel $n$ at time $t$ , we model the sensor-incident temporal photon flux $\\psi$ [22, 41, 54] as", + "bbox": [ + 496, + 762, + 890, + 839 + ], + "page_idx": 2 + }, + { + "type": "equation", + "text": "\n$$\n\\psi^ {(n)} (t) = \\left(H * g ^ {(n)}\\right) (t) + a (t), \\tag {1}\n$$\n", + "text_format": "latex", + "bbox": [ + 586, + 843, + 890, + 862 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "where $g^{(n)}$ is the temporally varying photon flux emitted by the laser for channel $n$ , $H$ is the transient response from the", + "bbox": [ + 496, + 869, + 890, + 900 + ], + "page_idx": 2 + }, + { + "type": "page_number", + "text": "13406", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 2 + }, + { + "type": "text", + "text": "scene, $a(t)$ is the ambient photon flux, and $*$ is the temporal convolution operator. By definition, the transient scene response $H$ includes multipath returns from scene interreflections and scattering. The detector measures the returned signal and digitizes the temporal measurement into temporal wavefronts processed by the DSP. For low photon counts or path lengths above a few meters typical in automotive scenes, the binning process can be modeled as a Poisson random process [22, 41, 43, 54]. We consequently model the wavefront's number of photons $r^{(n)}$ detected within the integration time $\\Delta$ in channel $n$ 's time bin $k$ as", + "bbox": [ + 75, + 90, + 472, + 256 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\nr ^ {(n)} [ k ] \\sim \\text {P o i s s o n} \\left(\\int_ {k \\Delta} ^ {(k + 1) \\Delta} \\psi^ {(n)} (t) d t\\right). \\tag {2}\n$$\n", + "text_format": "latex", + "bbox": [ + 130, + 258, + 468, + 299 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.1. Transient Scene and Pulse Model", + "text_level": 1, + "bbox": [ + 76, + 305, + 367, + 321 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Rasshofer et al. [46] introduced a linear model for direct laser reflections in the LiDAR context for the incident transient response $H * g^{(n)}$ of Eq. (1). With this model, we formalize the transient response as", + "bbox": [ + 76, + 328, + 468, + 388 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n(H * g ^ {(n)}) (R) = C \\int_ {0} ^ {2 \\tau^ {(n)}} g ^ {(n)} (t) H \\left(R - \\frac {\\mathrm {c t}}{2}\\right) d t, \\tag {3}\n$$\n", + "text_format": "latex", + "bbox": [ + 89, + 390, + 468, + 426 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $R$ is the distance between the sensor and the observed point, $\\mathbf{c}$ is the speed of light, $C$ is a proportionality constant, independent of $t$ and $R$ , describing the system, and $2\\tau^{(n)}$ is the total pulse duration for channel $n$ . We convert path length to time with $t = R / c$ . Following Hahner et al. [19] and Rasshofer et al. [46], we adopt the following pulse shape", + "bbox": [ + 76, + 430, + 468, + 535 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\ng ^ {(n)} (t) = \\left\\{ \\begin{array}{l l} P _ {0} ^ {(n)} \\sin^ {2} \\left(\\frac {\\pi t}{2 \\tau^ {(n)}}\\right) & \\text {i f} 0 \\leq t \\leq 2 \\tau^ {(n)} \\\\ 0 & \\text {o t h e r w i s e} \\end{array} , \\right. \\tag {4}\n$$\n", + "text_format": "latex", + "bbox": [ + 96, + 532, + 468, + 573 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $P_0^{(n)}$ is channel $n$ 's pulse power magnitude. The transient scene response $H$ embedded in Eq. (3) consists of the geometric attenuation of the light, proportional to $1 / (2R)^2$ , and the scene response. For a single opaque point object $i$ , the latter is proportional to its reflectance $\\rho_i$ and a Dirac function $\\delta(R - R_i)$ , $R_i$ being the object distance to the sensor. Reformulating Eq. (3) for a single echo from $i$ yields", + "bbox": [ + 76, + 575, + 468, + 696 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n(H * g ^ {(n)}) _ {i} (R) = \\left\\{ \\begin{array}{l l} f _ {i} ^ {(n)} (R) & \\text {i f} R _ {i} \\leq R \\leq R _ {i} + c \\tau^ {(n)} \\\\ 0 & \\text {o t h e r w i s e} \\end{array} \\right. \\tag {5}\n$$\n", + "text_format": "latex", + "bbox": [ + 81, + 694, + 464, + 734 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\text {w i t h} f _ {i} ^ {(n)} (R) = \\frac {C P _ {0} ^ {(n)} \\rho_ {i}}{4 R _ {i} ^ {2}} \\sin^ {2} \\left(\\frac {\\pi}{c \\tau^ {(n)}} (R - R _ {i})\\right). \\tag {6}\n$$\n", + "text_format": "latex", + "bbox": [ + 80, + 737, + 462, + 772 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.2. Object Reflectance Model", + "text_level": 1, + "bbox": [ + 76, + 781, + 310, + 797 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The object reflectance $\\rho_{i}$ depends on the material BRDF and the angle of incidence $\\theta$ . We model reflectance using specular and diffuse components of the retroreflected portion of the Cook-Terrance model [9], that is,", + "bbox": [ + 76, + 804, + 468, + 863 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\rho_ {i} = \\frac {\\alpha^ {4} s \\cos \\theta}{4 \\left[ \\cos^ {2} \\theta \\left(\\alpha^ {4} - 1\\right) + 1 \\right] ^ {2} \\left[ \\cos \\theta (1 - k) + k \\right] ^ {2}} + d \\cos \\theta , \\tag {7}\n$$\n", + "text_format": "latex", + "bbox": [ + 78, + 864, + 468, + 898 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $s, d$ and $\\alpha \\in [0,1]$ refer to the specular, diffuse and roughness properties of a surface material and $k = (\\alpha + 1)^2 / 8$ , see Supplemental Material. To render realistic textures without a large texture database, we approximate $s, d, \\alpha$ through CARLA's Phong-like parameters $s, d$ and $\\alpha$ . These parameters are not directly accessible, so we extract them by projecting targeted hit points onto custom camera images encoding their values, encoding illustrated at the bottom right of Fig. 2. Specifically, we define a function $\\mathcal{P}_i$ that, for a rendered image $I_{sd\\alpha}$ , returns pixel information at the location of projected point $i$ , that is, $[s, d, \\alpha] = \\mathcal{P}_i(I_{sd\\alpha})$ .", + "bbox": [ + 496, + 90, + 890, + 258 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.3. Ambient Illumination", + "text_level": 1, + "bbox": [ + 500, + 261, + 702, + 275 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "The ambient light $a(t)$ in Eq. (1) is modeled following Weng et al. [69]. A location $i$ is projected on the red channel of a rendered RGB camera image in which shadows and reflections are properly accounted for; denote this image by $I_{\\mathrm{red}}$ . We approximate $a(t)$ as a constant over waveform time bins, that is, $a(t) \\equiv a_i = \\mathcal{P}_i(I_{\\mathrm{red}})$ , where $a_i$ is independent of $t$ . See Supplemental Material for details.", + "bbox": [ + 496, + 280, + 890, + 386 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.4. Multipath Transients", + "text_level": 1, + "bbox": [ + 500, + 391, + 699, + 407 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "Multipath transients for laser beams hitting object discontinuities are taken as primary artifact sources in automotive scenarios. Multipath transients are modeled as linear combinations of neighboring waveforms. Specifically, a supersampled collection of $\\{R_i\\}$ and channels is computed using direct illumination only; then, for each LiDAR channel and each horizontal angle, a downsampled waveform $\\psi_j^{(m)}$ is obtained:", + "bbox": [ + 496, + 410, + 890, + 535 + ], + "page_idx": 3 + }, + { + "type": "equation", + "text": "\n$$\n\\psi_ {j} ^ {(m)} (R) = \\sum_ {i \\in \\mathcal {N} (j), n \\in \\mathcal {N} (m)} \\left(K _ {i} ^ {(n)} \\left(H * g ^ {(n)}\\right) _ {i} (R) + a _ {i}\\right), \\tag {8}\n$$\n", + "text_format": "latex", + "bbox": [ + 513, + 536, + 890, + 571 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "where $\\mathcal{N}(j)$ and $\\mathcal{N}(m)$ define respectively the spatial neighborhood of the target point $j$ and the channel $m$ , and the $K_{i}^{(n)}$ s are normalized weights that can be interpreted as a beam spatial intensity profile, see Supplemental Material.", + "bbox": [ + 496, + 574, + 890, + 637 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "3.5. LiDAR Sensing and DSP Model", + "text_level": 1, + "bbox": [ + 500, + 641, + 779, + 656 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "A sensor-incident waveform $\\psi_j^{(m)}$ is measured according to Eq. (2) and, along with sensor noise, subjected to clipping at a saturation level that depends on detector type. The DSP converts noisy and saturated waveforms $r_j^{(m)}$ into a point cloud $\\mathbf{O}$ that includes range and intensity information. The entire process, from the emission of a laser pulse $g^{(m)}$ to the output of a processed point cloud $\\mathbf{O}$ is shown in Fig. 3.", + "bbox": [ + 496, + 662, + 890, + 770 + ], + "page_idx": 3 + }, + { + "type": "text", + "text": "With compressed notation, we model LiDAR sensing as a function $\\Phi (\\Theta)$ with hyperparameters $\\Theta = (P_0^{(m)},\\tau^{(m)},V^{(m)})$ . Laser power $P_0^{(m)}$ and pulse duration $\\tau^{(m)}$ are functions of the channel $m$ ; they determine the emitted pulse $g^{(m)}$ . First, the DSP denoises by convolving the measured waveform $r_j^{(m)}$ with the emitted pulse $g^{(m)}$ , see [61]. Next, ambient light is estimated by removing the waveform's median from $r_j^{(m)}$ . This allows the DSP", + "bbox": [ + 496, + 771, + 890, + 902 + ], + "page_idx": 3 + }, + { + "type": "page_number", + "text": "13407", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 3 + }, + { + "type": "image", + "img_path": "images/71674520a79b0bf56f19aeed2673c034ebd7970c7a9e2de6f5888bfb88eb606d.jpg", + "image_caption": [ + "Figure 3. LiDAR Sensing and DSP Model $\\Phi$ . The LiDAR sensing and DSP hyperparameters $\\Theta$ , including pulse power, duration, and parameters for rising edge detection in a transient wavefront, affect the quality of the generated point cloud $\\mathbf{O}$ ." + ], + "image_footnote": [], + "bbox": [ + 143, + 87, + 401, + 255 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "to find adequate noise thresholds $V^{(m)}$ since ambient light varies strongly throughout the scene. Typical DSPs use a rising edge detector [30] that finds peaks along the denoised waveform by identifying intersections with $V^{(m)}$ . Multiple peaks may arise; the peak with the highest intensity is added to the point cloud O. Finally, the maximum intensity is compensated for the emitted laser pulse: The pulse half width $\\tau^{(m)} / 2$ and power level $P_0^{(m)}$ are used as scaling factors to recover the true intensities, see Eq. 3.", + "bbox": [ + 75, + 321, + 468, + 459 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Modern consumer LiDAR sensors have upward of 128 channels. Optimizing every channel hyperparameter individually is prohibitive. The Velodyne HDL-64 bundles lasers in two groups [62]; our LiDAR model likewise groups lower and upper lasers. Within each group, hyperparameter modulation is an affine function of the channel number with tuneable slope and bias. The edge threshold is modeled as a continuous parameter $V^{(m)} \\in [0,2]$ . In contrast, power levels $P_0^{(m)}$ can only take one of 11 values, the lowest making the peak almost indistinguishable from ambient noise and the highest likely to saturate at close range. Pulse duration $\\tau^{(m)}$ takes discrete values ranging from 3 to 15 ns in 1 ns increments. See Supplemental Material.", + "bbox": [ + 75, + 460, + 468, + 657 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4. Optimization", + "text_level": 1, + "bbox": [ + 76, + 661, + 212, + 678 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We propose a multi-objective optimization (MOO) method that finds Pareto-optimal (see the Supplemental Material for a definition) LiDAR hyperparameters. Specifically, optimization returns high-quality point clouds when optimized for depth precision and optimal mAP when point clouds are fed to a downstream object detection module. We first formulate the optimization problem, then we present the proposed optimization method.", + "bbox": [ + 75, + 681, + 468, + 801 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.1. LiDAR Hyperparameter Optimization", + "text_level": 1, + "bbox": [ + 76, + 805, + 408, + 821 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "We optimize hyperparameters with loss-driven end-to-end optimization. Like Robidoux et al. [48], we optimize the system as a whole, bundling hardware functionality, for example the laser beam power of each channel, with DSP functionality. The operation of the LiDAR imaging pipeline", + "bbox": [ + 75, + 825, + 468, + 901 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$\\Phi$ is modulated by $\\mathrm{P}$ hyperparameters $\\Theta = (\\Theta_{1},\\dots ,\\Theta_{\\mathrm{P}})$ with ranges of values normalized to the unit interval [0, 1]. With $\\mathrm{T}\\gg 2\\tau$ we model each of the J channels $\\Phi_j$ of $\\Phi = (\\Phi_1,\\ldots ,\\Phi_{\\mathrm{J}})$ as", + "bbox": [ + 498, + 90, + 890, + 151 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\Phi_ {j}: [ - \\mathrm {T}, 0 ] ^ {[ 0, \\infty)} \\times [ 0, 1 ] ^ {\\mathrm {P}} \\rightarrow \\mathbb {S} ^ {[ 0, \\infty)}, (\\mathrm {r} _ {j}, \\Theta) \\mapsto \\mathrm {O} _ {j}, \\tag {9}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 152, + 890, + 172 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "$\\mathrm{O}_j$ being a mapping from the unit sphere $\\mathbb{S}$ (proxy for projective geometry) to nonnegative distances with 0 understood as \"undefined\" so that each $\\Phi_j$ reconstructs a portion $\\mathrm{O}_j$ of the overall point cloud $\\mathbf{O}$ from a waveform $r_j$ truncated to the time interval $[-T,0]$ . The overall $\\Theta$ -modulated LiDAR pipeline", + "bbox": [ + 498, + 176, + 890, + 267 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\Phi : (\\mathbf {r} (H, \\Theta), \\Theta) \\rightarrow \\mathbf {O} \\tag {10}\n$$\n", + "text_format": "latex", + "bbox": [ + 617, + 268, + 890, + 284 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "maps the set of truncated wave forms $\\mathbf{r} = (\\mathrm{r}_1,\\dots ,\\mathrm{r}_{\\mathrm{J}})$ to the point cloud $\\mathbf{O}$ that contains the compressed information available to downstream detectors about the changing scene $H$ . We seek solutions of", + "bbox": [ + 498, + 287, + 890, + 347 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\boldsymbol {\\Theta} ^ {*} = \\underset {\\boldsymbol {\\Theta} \\in [ 0, 1 ] ^ {\\mathrm {P}}} {\\arg \\min } \\mathcal {L} (\\boldsymbol {\\Theta}), \\tag {11}\n$$\n", + "text_format": "latex", + "bbox": [ + 620, + 349, + 890, + 377 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "which are Pareto-optimal with respect to the MOO loss vector $\\mathcal{L} = (\\mathcal{L}_1, \\dots, \\mathcal{L}_{\\mathrm{L}})$ . Loss components may not directly use O; for example, $\\mathcal{L}_l$ may use data tapped out of the pipeline (e.g., a channel's waveform $\\mathbf{r}_j$ ) or the output of a downstream detector (e.g., a deep CNN) which ingests O (e.g., mAP). The Pareto front [28], which contains Pareto-optimal compromises between losses, is the solution set of Eq. (11), from which a single \"champion\" is selected using additional criteria.", + "bbox": [ + 498, + 378, + 890, + 513 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "4.2. Optimization Algorithm", + "text_level": 1, + "bbox": [ + 500, + 522, + 720, + 537 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "To solve the MOO problem, we propose a variant of the Covariance Matrix Adaptation-Evolution Strategy methods of Mosleh et al. [36] and Robidoux et al. [48] that differs from the state of the art in a number of ways. It is listed in Algorithm 1. We use centroid weights that change between generations; to obtain better transients, we greedily override the CMA-ES centroid whenever a new Best So Far (BSF) is found, isolating the computation of CMA-ES statistics from the resulting \"jump\" as well as from the novel inclusion of centroids as BSF candidates; and we formulate a stable dynamic max-rank loss scalarization which we use to drive optimization and as champion selection criterion.", + "bbox": [ + 496, + 542, + 890, + 722 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "Stable Dynamic Max-Rank Loss Scalarization Scalarizations like the well-known convex combination $\\sum_{l=1}^{L} \\mathrm{w}_l \\mathcal{L}_l$ which boils down to the $\\ell_1$ -norm of the loss vector $\\mathcal{L}$ with unit weights $\\mathrm{w}_l$ , are used to combine multiple objectives so that a single objective optimizer yield MOO solutions [15]. Scalarization weights are difficult to choose when loss variations are not commensurate. The max-rank loss addresses this issue. In the context of a generation-based algorithm like Algorithm 1, Mosleh et al. [36] define a max-rank loss as follows. Let", + "bbox": [ + 496, + 724, + 890, + 873 + ], + "page_idx": 4 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {R} _ {l} ^ {q, m, n} = \\operatorname {r a n k} \\left. \\mathcal {L} _ {l} ^ {q, m} \\text {w i t h i n} \\left\\{\\mathcal {L} _ {l} ^ {r, o} \\right\\} _ {r \\in \\{0, \\dots , 4 \\mathrm {P} \\}} ^ {o \\in \\{1, \\dots , n \\}} \\right. \\tag {12}\n$$\n", + "text_format": "latex", + "bbox": [ + 519, + 875, + 890, + 897 + ], + "page_idx": 4 + }, + { + "type": "page_number", + "text": "13408", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 4 + }, + { + "type": "text", + "text": "with ranks counted from 0 and loss component value ties resolved by left bisection. Then, the weighted (left-bisection) max-rank loss $\\mathcal{M}^{q,m,n}$ of the hyperparameter vector $\\Theta^{q,m}$ at the end of generation $n$ is", + "bbox": [ + 75, + 90, + 468, + 151 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {M} ^ {q, m, n} = \\max _ {l \\in \\{1, \\dots , \\mathrm {L} \\}} \\left(\\mathrm {w} _ {l} \\cdot \\mathcal {R} _ {l} ^ {q, m, n}\\right). \\tag {13}\n$$\n", + "text_format": "latex", + "bbox": [ + 153, + 152, + 468, + 176 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "The max-rank loss is dynamic. For a given $\\Theta^{q,m}$ its values are monotone non-decreasing with respect to the addition of data. Because weights multiply ranks, they are non-dimensional, hence dial in the relative importance of loss components. While Mosleh et al. [36] scale each $\\mathbf{w}_l$ by the (damped) running proportion of individuals that \"fail\" to pass a user-defined threshold, we find that such adaptive weigths break monotonicity. Instead, we keep $\\mathbf{w}_l$ s fixed. We also improve on Mosleh et al. in that we average left and right bisection ranks (exception: if the left bisection rank is 0, the \"average\" is set to 0) to stabilize $\\mathcal{M}^{q,m,n}$ with respect to loss value tie breaking (from, e.g., noise) and creation (quantization); this defines our stable (dynamically monotone) max-rank loss scalarization.", + "bbox": [ + 75, + 179, + 468, + 388 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Dual-Weight CMA-ES Besides more refined seatbelting (see Supplemental Material), Algorithm 1 differs from earlier CMA-ES in its use of non-constant (hyperparameter, not loss) centroid weights. Although variable CMA-ES generation sizes are common [39], the formula used to derive centroid weights is invariably kept fixed; in contrast, the proposed CMA-ES alternates between gradient-seeking centroid weights [36], which assign zero weight to the worst quartile of each generation instead of the usual half to exploit the symmetry of the second and third quartiles of Gaussian distributions to get a more accurate gradient approximation, and boundary-stabilizing centroid weights [48] with no discard so that further exploration not go in the wrong direction near generic local minima.", + "bbox": [ + 75, + 391, + 468, + 602 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Other novel components are that the loss of the weighted centroid of every generation is evaluated (standard CMA-ES only generate Gaussian clouds with them) and the greedy branch in Algorithm 1 (Lines 18-20): If any individual of the generation, weighted centroid included, turns out to be a strict minimizer, it becomes the next generation Gaussian cloud center.", + "bbox": [ + 75, + 602, + 468, + 705 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5. Experiments", + "text_level": 1, + "bbox": [ + 76, + 710, + 209, + 727 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "In the following, we first validate the proposed approach with the LiDAR simulation model, jointly optimizing depth estimation and downstream 3D object detection within simulated scenes. Then, we compare the proposed optimization algorithm to recent 0th-order solvers. Finally, we present experiments with an off-the-shelf hardware LiDAR unit.", + "bbox": [ + 75, + 729, + 468, + 820 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Experimental Setup As described in Sec. (3.5), hyperparameters affect the wavefront and DSP. We optimize the DSP rising edge threshold $V^{(m)}$ and 10 LiDAR hyperparameters that control low-level sensing parameters including the laser power $P_0^{(m)}$ and laser pulse width $\\tau^{(m)}$ for", + "bbox": [ + 75, + 821, + 468, + 900 + ], + "page_idx": 5 + }, + { + "type": "code", + "sub_type": "algorithm", + "code_caption": [ + "Algorithm 1 LiDAR Hyperparameter Optimization." + ], + "code_body": "Require: LiDAR $\\Phi$ , $\\Theta \\in [0,1]^{\\mathrm{P}}$ (initial hyperparameter vector), $\\mathbb{N} \\in \\mathbb{N}^*$ (number of generations), $\\varepsilon \\in \\left(0,\\frac{1}{3}\\right)$ (small bound), $\\mathbf{C} \\in \\mathbb{R}^{\\mathrm{P} \\times \\mathrm{P}}$ (CMA-ES \"directional\" covariance matrix factor), $\\sigma \\in [\\varepsilon ,\\frac{1}{3} ]$ (square root of covariance matrix \"scale\" factor) \n1: $\\mathbf{p} \\gets 0$ , $\\mathbf{c} \\gets 0$ (CMA-ES path vectors), $\\Theta_{\\mathrm{center}} \\gets \\Theta$ \n2: for $n = 1$ to N do \n3: $\\Theta^{0,n} \\gets \\Theta$ \n4: $\\mathcal{L}^{0,n} \\gets$ losses for LiDAR $\\Phi$ modulated by $\\Theta^{0,n}$ \n5: for $p = 1$ to 4P do \n6: $\\Theta^{p,n} \\gets$ random draw from Gaussian distribution with covariance matrix $\\sigma^2\\mathbf{C}$ centered at $\\Theta_{\\mathrm{center}}$ \n7: $\\Theta^{p,n} \\gets \\Theta^{p,n} +$ Gaussian distribution with diagonal cov. matrix proportional to square of quantization grain [48] \n8: $\\Theta^{p,n} \\gets \\Theta^{p,n}$ reflected back into $[0,1]^{\\mathrm{P}}$ \n9: $\\mathcal{L}^{p,n} \\gets$ losses for LiDAR $\\Phi$ modulated by $\\Theta^{p,n}$ \n10: end for \n11: Compute $\\{\\mathcal{M}^{q,m,n}\\}_{q\\in \\{0,\\dots,4\\mathrm{P}\\},m\\in \\{1,\\dots,n\\}}$ by including $\\{\\mathcal{L}^{p,n}\\}_{p\\in \\{0,\\dots,4\\mathrm{P}\\}}$ in rank computations \n12: Use \"eager\" [36] centroid weights with $\\lambda = 4\\mathrm{P}$ , $\\mu = 3\\mathrm{P}$ \n13: if $n$ is odd then \n14: Use \"stable\" [48] centroid weights with $\\lambda = \\mu = 4\\mathrm{P}$ \n15: end if \n16: Standard CMA-ES update [21] of $\\Theta$ , $\\sigma$ , C, p, c based on $\\{\\Theta^{p,n}\\}_{p\\in \\{1,\\dots,4\\mathrm{P}\\}}$ and $\\{\\mathcal{M}^{p,n,n}\\}_{p\\in \\{1,\\dots,4\\mathrm{P}\\}}$ \n17: $\\Theta_{\\mathrm{center}} \\gets \\Theta$ \n18: if min $p\\in \\{0,\\dots,4\\mathrm{P}\\}$ $M^{p,n,n} < \\min_{q\\in \\{0,\\dots,4\\mathrm{P}\\},m\\in \\{1,\\dots,n\\}} M^{q,m,n}$ then \n19: $\\Theta_{\\mathrm{center}} \\gets$ minimizer closest to centroid of minimizers \n20: end if \n21: end for \n22: return $\\Theta^{p,n}$ in the (guaranteed nonempty) intersection of the Pareto front and the set of minimizers of $\\mathcal{M}^{q,m,N}$ , with ties resolved by choosing the one closest to their centroid and remaining ties resolved by maximizing $n$ , then $p$", + "bbox": [ + 500, + 109, + 890, + 592 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "each of the 128 channels.", + "bbox": [ + 500, + 601, + 669, + 614 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "5.1. Optimization for Depth and Intensity", + "text_level": 1, + "bbox": [ + 500, + 625, + 821, + 642 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Next, we assess the method by optimizing point clouds for depth and intensity with the proposed optimizer. We minimize the average RMSE of the depth and the average RMSE of the intensity, specifically", + "bbox": [ + 496, + 646, + 890, + 707 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {d e p t h}} (\\Theta) = \\frac {1}{F} \\sum_ {f = 1} ^ {F} \\operatorname {R M S E} \\left(R _ {f}, \\Phi_ {f} ^ {(R)} (\\Theta)\\right), \\text {a n d} \\tag {14}\n$$\n", + "text_format": "latex", + "bbox": [ + 517, + 708, + 890, + 733 + ], + "page_idx": 5 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\text {i n t .}} (\\boldsymbol {\\Theta}) = \\frac {1}{F} \\sum_ {f = 1} ^ {F} \\operatorname {R M S E} \\left(I _ {f}, \\Phi_ {f} ^ {(I)} (\\boldsymbol {\\Theta})\\right), \\tag {15}\n$$\n", + "text_format": "latex", + "bbox": [ + 527, + 734, + 890, + 760 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "with $\\mathrm{F}$ the number of frames in the validation set, see Supplemental Material. The depth loss $\\mathcal{L}_{\\mathrm{depth}}$ rewards accurate point cloud depth estimates over the full range, whereas $\\mathcal{L}_{\\mathrm{int}}$ ensures that accurate intensities are measured with the pulse power $P_0^{(j)}$ . High output power generally results in more accurate point clouds at farther distances but also leads to excessive saturation; $\\mathcal{L}_{\\mathrm{int}}$ penalizes saturation.", + "bbox": [ + 496, + 761, + 890, + 869 + ], + "page_idx": 5 + }, + { + "type": "text", + "text": "Fig. 4 shows a comparison between ground-truth, expert-tuned and optimized PCs for a Pareto point that mini-", + "bbox": [ + 498, + 869, + 890, + 900 + ], + "page_idx": 5 + }, + { + "type": "page_number", + "text": "13409", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 5 + }, + { + "type": "image", + "img_path": "images/31999305e4163684ccd35bc2e46577a7b76fbe53fd257d8742a00d1fc0263e6f.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 76, + 85, + 370, + 220 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/90436da306de4d7dce667f8107c017be969ed52715e9c494ae27bdd791fc3d71.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 383, + 85, + 648, + 220 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/7b5958e900abf8005e67f73a04d0c27ea44825882bb55e2dc30d7076c12bd106.jpg", + "image_caption": [], + "image_footnote": [], + "bbox": [ + 656, + 88, + 890, + 220 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "mizes the depth error. Compared to the optimized point cloud measurement, the expert-tuned point cloud suffers from clutter, and its depth error increases with distance. The results reported in Table 1 validate the optimization method: The expert-tuned configuration has a loss vector $\\mathcal{L} = (\\mathcal{L}_{\\mathrm{depth}},\\mathcal{L}_{\\mathrm{int.}})$ equal to (12.195, 1.971); after 3000 evaluations of the objectives, the champion of the proposed method has a loss vector of (10.754, 0.216), improving depth and intensity metrics by $12\\%$ and $89\\%$ , respectively. We note that biasing optimization toward reducing $\\mathrm{RMSE}_{\\mathrm{depth}}$ more than $\\mathrm{RMSE}_{\\mathrm{int.}}$ can be done by changing Eq. (13) loss weights $\\mathrm{w}_l$ . In this work, loss component weights were kept at their default 1.", + "bbox": [ + 75, + 275, + 468, + 470 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 1 also shows a comparison of the proposed algorithm with other state-of-the-art MOO optimizers. With both champion selection criteria—the one proposed by Mosleh et al. [36], namely \"last Pareto point of the run\", and our own, \"Pareto point that minimizes the stable (weighted) max-rank loss\"—Algorithm (1) is the best performer.", + "bbox": [ + 75, + 472, + 468, + 564 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.2. Optimization for Object Detection", + "text_level": 1, + "bbox": [ + 76, + 571, + 375, + 588 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Next, we investigate optimization for object detection and classification. We use Average Precision (AP) as additional optimization objective. AP is maximized for cars and pedestrians at 40 recall positions [55] over an optimization set with $\\mathrm{F} = 100$ frames. With standard KITTI IoU thresholds [17], the CV loss is", + "bbox": [ + 75, + 590, + 468, + 681 + ], + "page_idx": 6 + }, + { + "type": "equation", + "text": "\n$$\n\\mathcal {L} _ {\\mathrm {o b j}, \\{\\text {c a r , p e d .} \\}} (\\Theta) = - \\mathrm {A P} _ {\\{\\text {c a r , p e d .} \\}} (\\Phi (\\Theta)), \\tag {16}\n$$\n", + "text_format": "latex", + "bbox": [ + 132, + 684, + 468, + 702 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "evaluated over a $0 - 80\\mathrm{m}$ range. We train a PV-RCNN [53] for 3D object detection on 5900 full range point clouds collected from the simulation environment with 8 different expert-tuned parameterizations $\\Theta$ , see training and dataset details in the Supplemental Material.", + "bbox": [ + 75, + 703, + 468, + 777 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "Table 2 contains a quantitative comparison between the proposed method and expert tuning. Compared to expert tuning, the proposed method increases AP by $52.9\\%$ and $27.2\\%$ for cars and pedestrians, respectively, on the full $0 - 80\\mathrm{m}$ range, with large increases predominately at close range. Lower AP values in the $0 - 30\\mathrm{m}$ range compared to $30 - 50\\mathrm{m}$ are due to a lower object occurrence (3.2 per frame vs. 3.6) and a less varied distribution of object yaw", + "bbox": [ + 75, + 780, + 468, + 901 + ], + "page_idx": 6 + }, + { + "type": "table", + "img_path": "images/1ee88b98c19a6151158d9c63327f033d9d01d1e108838c4067826b5ff865d6b1.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
SOLVERl1-norm (↓) last Pareto point [36]l1-norm (↓) proposed champion
Expert-Tuned14.09114.091
Proposed11.08910.995
NSGA-III [10,11]11.40811.574
Mosleh et al. [36]11.49711.497
AGE-MOEA [42]11.54711.427
C-TAEA [29]11.62211.461
RVEA [8]11.85111.437
R-NSGA-III [63]12.30611.619
U-NSGA-III [51]13.42311.512
SMS-EMOA [2]13.65811.750
", + "bbox": [ + 498, + 262, + 736, + 412 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/bccc9bf9d2737dce330692c4fd054570031c8a6c8d524850c06a48fdb046eac8.jpg", + "image_caption": [ + "Figure 4. Comparison between expert-tuned and optimized point clouds. Colors encode the individual depth error of each point (clipped at $2\\mathrm{m}$ ). Convergence plot shows the loss components $\\mathcal{L}_{\\mathrm{depth}}$ and $\\mathcal{L}_{\\mathrm{int}}$ (Eq. (14)-(15)) against optimization step (3000 loss evaluations total), the evolution of the champion and the final Pareto front, champion included.", + "Table 1. LiDAR Depth and Intensity MOO. The Pareto front (see figure) is approximately a straight line with slope -1. So, the variations of $\\mathcal{L}_{\\mathrm{depth}}$ and $\\mathcal{L}_{\\mathrm{int.}}$ are approximately balanced and the $\\ell_1$ -norm of the loss vector is a reasonable evaluation metric. For all solvers, the first column shows the $\\ell_1$ -norm of the last Pareto point of the run (champion per Mosleh et al. [36]); the last column instead uses the proposed champion selection method (Line 22 of Algorithm (1)). The proposed solver performs favorably; in addition, the proposed champion selection method improves results for all but two solvers (there is one tie). Median of three runs with 3000 loss evaluations; see the Supplemental Material for details." + ], + "image_footnote": [], + "bbox": [ + 736, + 260, + 887, + 417 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "angles (std. dev. of 0.63 rad vs. 1.25 rad) within the training set. Consequently, the detector can better adapt within the $30 - 50\\mathrm{m}$ bin. The quantitative findings agree with the qualitative results of Fig. 5, where optimized and expert-tuned point clouds are compared to the ground truth. Here, the clutter in the expert-tuned point cloud results in false positives and missed detections. In these examples, the proposed method misses only one pedestrian, whereas on the expert-tuned PC the detector misses them all. We also compare the average depth RMSE before and after filtering suppressed points; the low loss of the filtered PC suggests that clutter has been removed and that the detector prefers an accurate if sparser point cloud. Note however that a DSP optimized for the removal of clutter, like the proposed method, also suppresses ground points, with a small impact on object detection results, see Table 2 last column.", + "bbox": [ + 496, + 579, + 890, + 821 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "5.3. Off-the-shelf LiDAR Optimization", + "text_level": 1, + "bbox": [ + 500, + 833, + 802, + 849 + ], + "page_idx": 6 + }, + { + "type": "text", + "text": "In this section, we adapt the proposed optimization algorithm to an off-the-shelf Baraja Spectrum LiDAR sensor. Only a handful of hyperparameters are user-accessible: the", + "bbox": [ + 498, + 854, + 890, + 900 + ], + "page_idx": 6 + }, + { + "type": "page_number", + "text": "13410", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 6 + }, + { + "type": "image", + "img_path": "images/68dff62b3540dfd5abaf3b90e16181f91e9f0dda2815bbee501783de09539d32.jpg", + "image_caption": [ + "Figure 5. Comparison between ground-truth, expert-tuned and optimized point cloud for 3D object detection. Optimization results in clutter-free point clouds which helps reduce false positive detections. For visualization purposes only the camera field-of-view is shown." + ], + "image_footnote": [], + "bbox": [ + 76, + 85, + 890, + 255 + ], + "page_idx": 7 + }, + { + "type": "table", + "img_path": "images/0e1686c936204d311e3d9f5d7b7f1d4a6ffe04e401edc2e76f0a0bc5f8cd0cf3.jpg", + "table_caption": [], + "table_footnote": [], + "table_body": "
METHODmAP (↑)APcar (↑)APped. (↑)RMSEdepth (↓) filtered
0-80 m0-30 m30-50 m50-80 m0-80 m0-30 m30-50 m50-80 m0-80 m0-30 m30-50 m50-80 m
Expert7.3310.8810.181.2814.5821.7520.192.530.070.020.170.049.12 6.17
Proposed46.8652.1458.7027.0866.4775.0684.8641.2027.2529.2232.5312.9512.87 5.13
Margin+39.53+41.26+48.52+25.80+51.89+53.31+64.67+38.67+27.18+29.20+32.36+12.91+3.75 -1.04
", + "bbox": [ + 81, + 287, + 883, + 361 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Table 2. Optimization for 3D Object Detection. We report mAP and AP in different distance range bins, and unfiltered and filtered RMSE, for expert-tuned and proposed. Filtered $\\mathrm{RMSE}_{\\mathrm{depth}}$ excludes all points suppressed by the DSP. Best in bold.", + "bbox": [ + 75, + 366, + 892, + 395 + ], + "page_idx": 7 + }, + { + "type": "image", + "img_path": "images/bf3bcd69bf01ae9475471da7ffe8b9773bbbce2dc70b382697cded61ca990234.jpg", + "image_caption": [ + "Figure 6. Left: ground truth 3D histogram used to compute losses, see text, red bounding boxes denoting cars, with experimental setup zoom-ins: captured static scene (top) and LiDAR installation (bottom); the LiDAR detector is the blue unit. Middle: loss contributions for the expert-tuned parameters and Right: for optimized parameters. Brighter color means a smaller loss." + ], + "image_footnote": [], + "bbox": [ + 76, + 407, + 468, + 529 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "return mode that selects the waveform peaks used to generate the point cloud, the sensor heads scanning pattern, and the two sensor head motor frequencies which determine the PC angular resolution vs. sensor noise balance. Because raw wavefronts are inaccessible, we optimize 3D point cloud histograms taken over an (intermittently) static scene such as a workplace parking lot. Fig. 6 shows the experimental setup along with a photograph of the scene over which optimization was performed. The ground truth histogram is shown on the left of Fig. 6; it was generated by averaging 100 captures with a uniform angular resolution scanning pattern. Expert-tuned and optimized histogram contributions to the fitness function (described in the Supplemental Material) are respectively shown in the middle and right of Fig. 6; brighter bins indicate larger contributions. Optimized hyperparameters achieved better results by focusing laser scans in regions of interests (ROIs) where the ground truth was denser; expert-tuned ones produced", + "bbox": [ + 75, + 628, + 472, + 902 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "more uniform point clouds. Using the RMSE of PC histograms weighted by distance and number of points per bin, we estimate a $10.1\\mathrm{cm}$ error for the expert-tuned configuration vs. $3.0\\mathrm{cm}$ with optimized hyperparameters.", + "bbox": [ + 496, + 421, + 890, + 482 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "6. Conclusion", + "text_level": 1, + "bbox": [ + 500, + 484, + 619, + 500 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "We propose an in-the-loop black box 0th-order optimization method for LiDAR sensing pipelines that finds optimal parameters for depth and intensity estimation and 3D object detection and classification. To assess our method, we propose a novel LiDAR simulation method integrated into the CARLA simulator. We find that optimizing the LiDAR sensing pipeline can significantly improve depth, intensity and object detection and classification compared to manual expert tuning. Specifically, for 3D object detection our optimization method results in a major increase of $51.9\\%$ AP for cars and $27.2\\%$ AP for pedestrians, compared to fine-tuning a detector on expert-optimized LiDAR vendor parameters. An interesting direction for future research is real-time scene-dependent optimization of LiDAR scanning parameters, potentially leading to adaptive sensing in adverse weather in urban and highway scenarios.", + "bbox": [ + 496, + 505, + 892, + 746 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "Acknowledgments This work was supported by the AI-SEE project with funding from the FFG, BMBF, and NRCIRA. We thank the Federal Ministry for Economic Affairs and Energy for support through the PEGASUS-family project VVM-Verification and Validation Methods for Automated Vehicles Level 4 and 5. Felix Heide was supported by an NSF CAREER Award (2047359), a Packard Foundation Fellowship, a Sloan Research Fellowship, a Sony Young Faculty Award, a Project X Innovation Award, and an Amazon Science Research Award.", + "bbox": [ + 496, + 750, + 893, + 900 + ], + "page_idx": 7 + }, + { + "type": "page_number", + "text": "13411", + "bbox": [ + 480, + 944, + 517, + 955 + ], + "page_idx": 7 + }, + { + "type": "text", + "text": "References", + "text_level": 1, + "bbox": [ + 78, + 89, + 174, + 104 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[1] Behnam Behroozpour, Phillip A. M. Sandborn, Ming C. Wu, and Bernhard E. Boser. Lidar System Architectures and Circuits. IEEE Communications Magazine, 55(10):135-142, Oct. 2017. Conference Name: IEEE Communications Magazine. 3", + "[2] Nicola Beume, Boris Naujoks, and Michael Emmerich. SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3):1653–1669, Sept. 2007. 7", + "[3] Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, and Felix Heide. Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1", + "[4] Mario Bijelic, Tobias Gruber, and Werner Ritter. A benchmark for LiDAR sensors in fog: Is detection breaking down? In IEEE Intelligent Vehicles Symposium (IV), 2018. 1, 2", + "[5] Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, and Karol Zieba. End to End Learning for Self-Driving Cars, Apr. 2016. arXiv:1604.07316 [cs]. 3", + "[6] Alexander Carballo, Jacob Lambert, Abraham Monrroy Cano, David Robert Wong, Patiphon Narksri, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, and Kazuya Takeda. LIBRE: The multiple 3D LiDAR dataset. In IEEE Intelligent Vehicles Symposium (IV), 2020. 1", + "[7] M. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, and J. Hays. Argoverse: 3D tracking and forecasting with rich maps. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 1", + "[8] Ran Cheng, Yaochu Jin, Markus Olhofer, and Bernhard Sendhoff. A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 20(5):773-791, Oct. 2016. Conference Name: IEEE Transactions on Evolutionary Computation. 7", + "[9] Robert L. Cook and Kenneth E. Torrance. A reflectance model for computer graphics. ACM SIGGRAPH Computer Graphics, 15(3):307-316, Aug. 1981. 4", + "[10] K. Deb and J. Sundar. Reference point based multi-objective optimization using evolutionary algorithms | Proceedings of the 8th annual conference on Genetic and evolutionary computation. 7", + "[11] Kalyanmoy Deb and J. Sundar. Reference point based multi-objective optimization using evolutionary algorithms. In Proceedings of the 8th annual conference on Genetic and evolutionary computation, GECCO '06, pages 635-642, New York, NY, USA, July 2006. Association for Computing Machinery. 7", + "[12] Ayush Dewan, Tim Caselitz, Gian Diego Tipaldi, and Wolfram Burgard. Motion-based detection and tracking in 3D LiDAR scans. In IEEE International Conference on Robotics and Automation (ICRA), 2016. 1" + ], + "bbox": [ + 78, + 114, + 470, + 900 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[13] Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In Sergey Levine, Vincent Vanhoucke, and Ken Goldberg, editors, Proceedings of the 1st Annual Conference on Robot Learning, volume 78 of Proceedings of Machine Learning Research, pages 1-16. PMLR, 13-15 Nov 2017. 2", + "[14] Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In 1st Annual Conference on Robot Learning, 2017. 3", + "[15] Michael T. M. Emmerich and André H. Deutz. A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Natural Computing, 17(3):585-609, Sept. 2018. 5", + "[16] Jin Fang, Dingfu Zhou, Feilong Yan, Tongtong Zhao, Feihu Zhang, Yu Ma, Liang Wang, and Ruigang Yang. Augmented LiDAR Simulator for Autonomous Driving. IEEE Robotics and Automation Letters, 5(2):1931-1938, Apr. 2020. arXiv:1811.07112 [cs]. 3", + "[17] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2012. 7", + "[18] Christopher Goodin, Daniel Carruth, Matthew Doude, and Christopher Hudson. Predicting the influence of rain on LiDAR in ADAS. *Electronics*, 8, 2019. 1", + "[19] Martin Hahner, Christos Sakaridis, Mario Bijelic, Felix Heide, Fisher Yu, Dengxin Dai, and Luc Van Gool. LiDAR Snowfall Simulation for Robust 3D Object Detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 1, 3, 4", + "[20] Martin Hahner, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Fog simulation on real LiDAR point clouds for 3D object detection in adverse weather. In IEEE International Conference on Computer Vision (ICCV), 2021. 1, 3", + "[21] Nikolaus Hansen. The CMA evolution strategy: A tutorial, 2016. 2, 6", + "[22] Felix Heide, Steven Diamond, David B Lindell, and Gordon Wetzstein. Sub-picosecond photon-efficient 3d imaging using single-photon sensors. Scientific reports, 8(1):1-8, 2018. 3,4", + "[23] Robin Heinzler, Philipp Schindler, Jürgen Seekircher, Werner Ritter, and Wilhelm Stork. Weather influence and classification with automotive LiDAR sensors. In IEEE Intelligent Vehicles Symposium (IV), 2019. 1", + "[24] Braden Hurl, Krzysztof Czarnecki, and Steven Waslander. Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception, May 2019. arXiv:1905.00160 [cs]. 1, 3", + "[25] Maria Jokela, Matti Kutila, and Pasi Pyykonen. Testing and validation of automotive point-cloud sensors in adverse weather conditions. Applied Sciences, 9, 2019. 1", + "[26] Velat Kilic, Deepti Hegde, Vishwanath Sindagi, A. Brinton Cooper, Mark Foster, and Vishal Patel. LiDAR light scattering augmentation (LISA): physics-based simulation of adverse weather conditions for 3D object detection. arXiv preprint 2107.07004, 2021. 1, 3" + ], + "bbox": [ + 501, + 92, + 893, + 900 + ], + "page_idx": 8 + }, + { + "type": "page_number", + "text": "13412", + "bbox": [ + 480, + 944, + 519, + 955 + ], + "page_idx": 8 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[27] Matti Kutila, Pasi Pyykönen, Maria Jokela, Tobias Gruber, Mario Bijelic, and Werner Ritter. Benchmarking automotive LiDAR performance in arctic conditions. In IEEE International Conference on Intelligent Transportation Systems (ITSC), 2020. 1", + "[28] Marco Laumanns, Lothar Thiele, Kalyanmoy Deb, and Eckart Zitzler. Combining convergence and diversity in evolutionary multiobjective optimization. Evolutionary Computation, 10(3):263-282, 2002. 5", + "[29] Ke Li, Renzhi Chen, Guangtao Fu, and Xin Yao. Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 23(2):303-315, Apr. 2019. Conference Name: IEEE Transactions on Evolutionary Computation. 7", + "[30] Xiaolu Li, Bingwei Yang, Xinhao Xie, Duan Li, and Lijun Xu. Influence of waveform characteristics on lidar ranging accuracy and precision. Sensors, 18(4), 2018. 5", + "[31] Ievgeniia Maksymova, Christian Steger, and Norbert Druml. Review of LiDAR Sensor Data Acquisition and Compression for Automotive Applications. Proceedings, 2(13):852, 2018. Number: 13 Publisher: Multidisciplinary Digital Publishing Institute. 3", + "[32] Yunze Man, Xinshuo Weng, Prasanna Kumar Sivakumar, Matthew O'Toole, and Kris Kitani. Multi-echo lidar for 3d object detection. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 3743-3752, 2021. 3", + "[33] Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong, Wenyuan Zeng, Mikita Sazanovich, Shuhan Tan, Bin Yang, Wei-Chiu Ma, and Raquel Urtasun. Lidarsim: Realistic lidar simulation by leveraging the real world. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11164-11173, 2020. 3", + "[34] Sebastien Michaud, Jean-François Lalonde, and Philippe Giguère. Towards characterizing the behavior of LiDARs in snowy conditions. In Workshop on Planning, Perception and Navigation for Intelligent Vehicles, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015. 1", + "[35] Anish Mittal, Anush K. Moorthy, and Alan C. Bovik. Automatic parameter prediction for image denoising algorithms using perceptual quality features. In Human Vision and Electronic Imaging XVII, volume 8291, pages 110-116. SPIE, Feb. 2012. 2", + "[36] Ali Mosleh, Avinash Sharma, Emmanuel Onzon, Fahim Mannan, Nicolas Robidoux, and Felix Heide. Hardware-in-the-Loop End-to-End Optimization of Camera Image Processing Pipelines. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7526–7535, Seattle, WA, USA, June 2020. IEEE. 2, 5, 6, 7", + "[37] Matthias Müller, Vincent Casser, Jean Lahoud, Neil Smith, and Bernard Ghanem. Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications, Mar. 2018. arXiv:1708.05869 [cs]. 3" + ], + "bbox": [ + 78, + 90, + 468, + 898 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[38] Kazuki Nakamura, Kenji Narumi, Kohei Kikuchi, and Yasuhisa Inada. Liquid crystal-tunable optical phased array for LiDAR applications. In Smart Photonic and Optoelectronic Integrated Circuits XXIII, volume 11690, pages 94-99. SPIE, Mar. 2021. 3", + "[39] Kouhei Nishida and Youhei Akimoto. Population Size Adaptation for the CMA-ES Based on the Estimation Accuracy of the Natural Gradient. In Proceedings of the Genetic and Evolutionary Computation Conference 2016, GECCO '16, pages 237–244, New York, NY, USA, July 2016. Association for Computing Machinery. 6", + "[40] Jun Nishimura, Timo Gerasimow, Sushma Rao, Aleksandar Sutic, Chyuan-Tyng Wu, and Gilad Michael. Automatic ISP image quality tuning using non-linear optimization, Feb. 2019. arXiv:1902.09023 [cs]. 2", + "[41] Matthew O'Toole, Felix Heide, David B. Lindell, Kai Zang, Steven Diamond, and Gordon Wetzstein. Reconstructing Transient Images from Single-Photon Sensors. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2289–2297, Honolulu, HI, July 2017. IEEE. 3, 4", + "[42] Annibale Panichella. An adaptive evolutionary algorithm based on non-euclidean geometry for many-objective optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '19, pages 595-603, New York, NY, USA, July 2019. Association for Computing Machinery. 7", + "[43] Adithya K. Pediredla, Aswin C. Sankaranarayanan, Mauro Buttafava, Alberto Tosi, and Ashok Veeraraghavan. Signal Processing Based Pile-up Compensation for Gated Single-Photon Avalanche Diodes, June 2018. arXiv:1806.07437 [physics]. 3, 4", + "[44] Luke Pfister and Yoram Bresler. Learning Filter Bank Sparsifying Transforms. IEEE Transactions on Signal Processing, 67(2):504-519, Jan. 2019. Conference Name: IEEE Transactions on Signal Processing. 2", + "[45] Francesco Pittaluga, Zaid Tasneem, Justin Folden, Brevin Tilmon, Ayan Chakrabarti, and Sanjeev J. Koppal. Towards a MEMS-based Adaptive LIDAR, Oct. 2020. arXiv:2003.09545 [cs, eess]. 3", + "[46] Ralph Rasshofer, Martin Spies, and Hans Spies. Influences of weather phenomena on automotive laser radar systems. Advances in Radio Science, 9, 2011. 3, 4", + "[47] Stephan R. Richter, Zeeshan Hayden, and Vladlen Koltun. Playing for Benchmarks, Sept. 2017. arXiv:1709.07322 [cs]. 3", + "[48] Nicolas Robidoux, Luis E. Garcia Capel, Dong-eun Seo, Avinash Sharma, Federico Ariza, and Felix Heide. End-to-End High Dynamic Range Camera Pipeline Optimization. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6297–6307, 2021. 2, 5, 6", + "[49] German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M. Lopez. The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3234-3243, June 2016. ISSN: 1063-6919. 3" + ], + "bbox": [ + 501, + 90, + 890, + 898 + ], + "page_idx": 9 + }, + { + "type": "page_number", + "text": "13413", + "bbox": [ + 480, + 945, + 517, + 955 + ], + "page_idx": 9 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[50] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126(9):973-992, 2018. 1", + "[51] Haitham Seada and Kalyanmoy Deb. A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives. IEEE Transactions on Evolutionary Computation, 20(3):358-369, June 2016. Conference Name: IEEE Transactions on Evolutionary Computation. 7", + "[52] Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles, July 2017. arXiv:1705.05065 [cs]. 3", + "[53] Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, and Hongsheng Li. PV-RCNN: Point-voxel feature set abstraction for 3D object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1, 7", + "[54] Dongeek Shin. Computational imaging with small numbers of photons. Thesis, Massachusetts Institute of Technology, 2016. Accepted: 2016-07-18T20:05:44Z. 3, 4", + "[55] Andrea Simonelli, Samuel Rota Rota Bulò, Lorenzo Porzi, Manuel López-Antequera, and Peter Kontschieder. Disentangling monocular 3D object detection. arXiv preprint arXiv:1905.12365, 2019. 7", + "[56] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, and Dragomir Anguelov. Scalability in perception for autonomous driving: Waymo open dataset. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1", + "[57] Tao Sun, Mattia Segu, Janis Postels, Yuxuan Wang, Luc Van Gool, Bernt Schiele, Federico Tombari, and Fisher Yu. SHIFT: a synthetic driving dataset for continuous multi-task domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 21371-21382, June 2022. 1, 3", + "[58] Maxime Tremblay, Shirsendu S. Halder, Raoul de Charette, and Jean-François Lalonde. Rain rendering for evaluating and improving robustness to bad weather. International Journal of Computer Vision (IJCV), 126, 2021. 1", + "[59] Ethan Tseng, Ali Mosleh, Fahim Mannan, Karl St-Arnaud, Avinash Sharma, Yifan Peng, Alexander Braun, Derek Nowrouzezahrai, Jean-Francois Lalonde, and Felix Heide. Differentiable compound optics and processing pipeline optimization for end-to-end camera design. ACM Transactions on Graphics (TOG), 40(2):1-19, 2021. 2", + "[60] Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl ST. Arnaud, Derek Nowrouzezahrai, Jean-François Lalonde, and Felix Heide. Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Transactions on Graphics, 38(4):1-14, Aug. 2019. 2", + "[61] G. Turin. An introduction to matched filters. IRE Transactions on Information Theory, 6(3):311-329, 1960. 4" + ], + "bbox": [ + 78, + 90, + 470, + 900 + ], + "page_idx": 10 + }, + { + "type": "list", + "sub_type": "ref_text", + "list_items": [ + "[62] Velodyne. Hdl-64e user's manual, 2007. 5", + "[63] Yash Vesikar, Kalyanmoy Deb, and Julian Blank. Reference Point Based NSGA-III for Preferred Solutions. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1587–1594, Nov. 2018. 7", + "[64] F Villa, B Markovic, S Bellisai, D Bronzi, A Tosi, F Zappa, S Tisa, D Durini, S Weyers, U Paschen, et al. SPAD smart pixel for time-of-flight and time-correlated single-photon counting measurements. IEEE Photonics Journal, 4(3):795-804, 2012. 1", + "[65] Niclas Vödisch, Ozan Unal, Ke Li, Luc Van Gool, and Dengxin Dai. End-to-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization. IEEE Robotics and Automation Letters, 7(2):2242-2249, Apr. 2022. Conference Name: IEEE Robotics and Automation Letters. 3", + "[66] Andrew Wallace, Abderrahim Halimi, and Gerald Buller. Full waveform LiDAR for adverse weather conditions. IEEE Transactions on Vehicular Technology, 69, 2020. 1", + "[67] Dingkang Wang, Connor Watkins, and Huikai Xie. MEMS mirrors for LiDAR: A review. Micromachines, 11(5), 2020. 1", + "[68] Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas, Mengye Ren, and Raquel Urtasun. AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles, Jan. 2022. arXiv:2101.06549 [cs]. 3", + "[69] Xinshuo Weng, Yunze Man, Jinhyung Park, Ye Yuan, Dazhi Cheng, Matthew O'Toole, and Kris Kitani. All-In-One Drive: A Large-Scale Comprehensive Perception Dataset with High-Density Long-Range Point Clouds. arXiv, 2021. 1, 3, 4", + "[70] George Williams. Optimization of eyesafe avalanche photodiode LiDAR for automobile safety and autonomous navigation systems. Optical Engineering, 56(3):1-9, 2017. 1", + "[71] Chyuan-Tyng Wu, Leo F. Isikdogan, Sushma Rao, Bhavin Nayak, Timo Gerasimow, Aleksandar Sutic, Liron Ainkedem, and Gilad Michael. VisionISP: Repurposing the image signal processor for computer vision applications. In IEEE International Conference on Image Processing (ICIP), pages 4624-4628, 2019. 2", + "[72] Ji Zhang and Sanjiv Singh. LOAM: LiDAR odometry and mapping in real-time. Robotics: Science and Systems Conference (RSS), 2014. 1", + "[73] Qingfu Zhang and Hui Li. Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11:712-731, 2007. 2" + ], + "bbox": [ + 501, + 92, + 892, + 756 + ], + "page_idx": 10 + }, + { + "type": "page_number", + "text": "13414", + "bbox": [ + 480, + 945, + 519, + 955 + ], + "page_idx": 10 + } +] \ No newline at end of file diff --git a/2023/LiDAR-in-the-Loop Hyperparameter Optimization/9dfdd95b-2191-44f3-8949-8eb946acc7a7_model.json b/2023/LiDAR-in-the-Loop Hyperparameter Optimization/9dfdd95b-2191-44f3-8949-8eb946acc7a7_model.json new file mode 100644 index 0000000000000000000000000000000000000000..4cc58458130aadde94fd92a644f6215abec4aa34 --- /dev/null +++ b/2023/LiDAR-in-the-Loop Hyperparameter Optimization/9dfdd95b-2191-44f3-8949-8eb946acc7a7_model.json @@ -0,0 +1,2400 @@ +[ + [ + { + "type": "header", + "bbox": [ + 0.107, + 0.003, + 0.182, + 0.043 + ], + "angle": 0, + "content": "CVF" + }, + { + "type": "header", + "bbox": [ + 0.237, + 0.001, + 0.808, + 0.047 + ], + "angle": 0, + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + }, + { + "type": "title", + "bbox": [ + 0.232, + 0.131, + 0.741, + 0.154 + ], + "angle": 0, + "content": "LiDAR-in-the-loop Hyperparameter Optimization" + }, + { + "type": "text", + "bbox": [ + 0.095, + 0.175, + 0.877, + 0.217 + ], + "angle": 0, + "content": "Félix Goudreault\\(^{1}\\) Dominik Scheuble\\(^{2}\\) Mario Bijelic\\(^{2,3}\\) Nicolas Robidoux\\(^{1}\\) Felix Heide\\(^{1,3}\\) \n\\(^{1}\\)Algolux \\(^{2}\\)Mercedes-Benz \\(^{3}\\)Princeton University" + }, + { + "type": "title", + "bbox": [ + 0.235, + 0.251, + 0.314, + 0.267 + ], + "angle": 0, + "content": "Abstract" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.273, + 0.474, + 0.651 + ], + "angle": 0, + "content": "LiDAR has become a cornerstone sensing modality for 3D vision. LiDAR systems emit pulses of light into the scene, take measurements of the returned signal, and rely on hardware digital signal processing (DSP) pipelines to construct 3D point clouds from these measurements. The resulting point clouds output by these DSPs are input to downstream 3D vision models – both, in the form of training datasets or as input at inference time. Existing LiDAR DSPs are composed of cascades of parameterized operations; modifying configuration parameters results in significant changes in the point clouds and consequently the output of downstream methods. Existing methods treat LiDAR systems as fixed black boxes and construct downstream task networks more robust with respect to measurement fluctuations. Departing from this approach, the proposed method directly optimizes LiDAR sensing and DSP parameters for downstream tasks. To investigate the optimization of LiDAR system parameters, we devise a realistic LiDAR simulation method that generates raw waveforms as input to a LiDAR DSP pipeline. We optimize LiDAR parameters for both 3D object detection IoU losses and depth error metrics by solving a nonlinear multi-objective optimization problem with a 0th-order stochastic algorithm. For automotive 3D object detection models, the proposed method outperforms manual expert tuning by \\(39.5\\%\\) mean Average Precision (mAP)." + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.669, + 0.21, + 0.685 + ], + "angle": 0, + "content": "1. Introduction" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.689, + 0.47, + 0.795 + ], + "angle": 0, + "content": "Environment perception for autonomous drones [12, 53] and vehicles [72] requires precise depth sensing for safety-critical control decisions. Scanning LiDAR sensors have been broadly adopted in autonomous driving [3, 7, 56] as they provide high temporal and spatial resolution, and recent advances in MEMS scanning [67] and photodiode technology [64] have reduced their cost and form factor." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.796, + 0.471, + 0.902 + ], + "angle": 0, + "content": "The 3D LiDAR point cloud (PC) data that existing 3D detection methods take as input is produced by a LiDAR and digital signal processor (DSP) pipeline with many measurement and processing steps. Typical LiDAR sensors operate by sending out a laser pulse and measuring the temporal response through an Avalanche Photo Diode (APD). This temporal wavefront signal is fed to a DSP that extracts" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.252, + 0.895, + 0.463 + ], + "angle": 0, + "content": "peaks corresponding to echos from candidate targets within the scene [70]. As such, DSP processing results in a 1000-fold data reduction for a single emitted beam, producing single or multiple 3D points per beam. Compressing the waveform into points in 3D space with minimal information loss is challenging because of object discontinuities, sub-surface scattering, multipath reflections, and scattering media, see Fig. 1. In particular, significant scattering occurs in adverse weather conditions like fog [3,4,6,20,23,25,66], rain [6,18,66] and snow [19,25,27,34]. LiDAR manufacturers currently handle such complications by manually adjusting internal sensing and DSP parameters in controlled environments and restricted real-world scenarios using a combination of visual inspection and depth quality metrics." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.463, + 0.895, + 0.75 + ], + "angle": 0, + "content": "Generally, LiDAR production units are black boxes with configuration parameters hidden from the user. To account for noisy point cloud measurements with spurious artifacts, existing work has explored methods that add simulated adverse effects and point cloud degradations that model rain [18, 26, 58], fog [3, 20, 50] and snow [19] to real black-box LiDAR datasets. Augmented point clouds in hand, downstream vision models are retrained for predictions more robust to point cloud data corruption. Another approach consists of generating synthetic measurements from 3D scenes using rendering engines [24, 57, 69]. Such existing methods typically avoid simulating transient light propagation and signal processing by converting 3D scene depth directly into a point cloud, thus lack physically realistic modeling of fluctuations arising from multipath effects or measurement noise. Notably, existing simulation methods that alter measurements or generate synthetic point clouds do not optimize sensing or DSP parameters for downstream vision performance." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.896, + 0.902 + ], + "angle": 0, + "content": "In this work, we directly optimize LiDAR pulse configuration and DSP hyperparameters for end-to-end downstream 3D object detector losses and PC depth quality metrics, a challenging task because hyperparameter space involves tens to hundreds of categorical, discrete and effectively continuous parameters affecting downstream tasks in complex nonlinear ways via the intermediate point cloud. Examples of categorical hyperparameters are Velodyne LiDAR sensor return modes that configure internal wavefront peak selection algorithms for point cloud formation;" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.521, + 0.958 + ], + "angle": 0, + "content": "13404" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.092, + 0.09, + 0.878, + 0.235 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.238, + 0.895, + 0.31 + ], + "angle": 0, + "content": "Figure 1. LiDAR Point Cloud Formation. Typical LiDAR sensor PC measurements are produced by a multi-stage measurement and signal processing chain: The LiDAR emits a laser pulse. This signal travels through the scene and returns to the detector after single or multiple reflections. Cluttered surfaces (a), strong retroreflectors (b) and ambient light are introduced (c) in the returned signals. Thus, the full transient waveform read by sensors is the superposition of multiple return paths. The DSP, itself a chain of processing blocks, processes all temporal waveforms and extracts a continuous stream of 3D points that forms the final point cloud (bottom)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.322, + 0.47, + 0.353 + ], + "angle": 0, + "content": "rotation velocity, which impacts angular resolution, is an example of a continuous hyperparameter [4]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.354, + 0.473, + 0.73 + ], + "angle": 0, + "content": "Grid search optimization is impractical here because of combinatorial explosion. Orthogonally to LiDAR, it was recently shown that 0th order solvers can find camera DSP hyperparameters that improve downstream 2D object detectors [36, 48]. We propose an optimization method for LiDAR sensing and DSP hyperparameters that minimizes end-to-end domain-specific losses such as RMSE of the measured depth against ground truth and IoU measured on downstream 3D object detection. To assess the proposed method, we devised a LiDAR simulation method based on the CARLA engine [13] that models a LiDAR DSP as well as the full transient noisy waveform formed by multiple laser echoes. We optimize sensing and DSP hyperparameters by solving a Multi-Objective black-box Optimization (MOO) problem with a novel CMA-ES (Covariance Matrix Adaptation-Evolution Strategy [21]) that relies on a max-rank multi-objective scalarization loss [36] to dynamically improve scale matching between different loss components. In combination with a novel champion selection method, it finds a balanced Pareto-optimal solution for which no loss component has a comparatively poor value for LiDAR optimization with multiple objectives. We validate the proposed optimization method for 3D object detection and point cloud depth estimation, both in simulation and using an off-the-shelf experimental LiDAR sensor." + }, + { + "type": "text", + "bbox": [ + 0.097, + 0.732, + 0.433, + 0.748 + ], + "angle": 0, + "content": "Specifically, we make the following contributions:" + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.76, + 0.47, + 0.806 + ], + "angle": 0, + "content": "- We introduce a LiDAR wavefront simulation for the CARLA simulation environment that models realistic transient scene responses." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.815, + 0.47, + 0.847 + ], + "angle": 0, + "content": "- We devise a novel multi-objective optimization method for balanced MOO of LiDAR parameters." + }, + { + "type": "text", + "bbox": [ + 0.096, + 0.856, + 0.471, + 0.902 + ], + "angle": 0, + "content": "- We validate end-to-end LiDAR sensing and DSP optimization for 3D object detection and depth estimation through simulation and with a real system. For all ap" + }, + { + "type": "list", + "bbox": [ + 0.096, + 0.76, + 0.471, + 0.902 + ], + "angle": 0, + "content": null + }, + { + "type": "text", + "bbox": [ + 0.532, + 0.322, + 0.892, + 0.353 + ], + "angle": 0, + "content": "plications, our approach outperforms existing state-of-the-art methods, including expert tuning." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.363, + 0.851, + 0.379 + ], + "angle": 0, + "content": "Simulator code and DSP models are published here1." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.38, + 0.892, + 0.502 + ], + "angle": 0, + "content": "Limitations Because commercial LiDAR units are IP-protected black boxes, interfacing their DSP hyperparameters is not straightforward. While the off-the-shelf LiDAR system used in this work makes some DSP hyperparameters accessible, most LiDAR systems are completely closed. We hope that these findings spur LiDAR vendors to follow the lead of digital camera and ISP (Image Signal Processor) vendors and open their processing pipelines." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.509, + 0.642, + 0.525 + ], + "angle": 0, + "content": "2. Related Work" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.529, + 0.893, + 0.877 + ], + "angle": 0, + "content": "DSP and Sensor Hyperparameter Optimization. A growing body of work explores the optimization of sensors and DSPs for downstream vision tasks. Existing methods target camera ISPs and optics [36, 48, 59, 60, 71]. Departing from manual hyperparameter tuning by experts, these approaches optimize them automatically, driven by one or more downstream performance metric. As DSP and sensor hyperparameters can be categorical and losses are often non-convex and noisy, diverse optimization methods have been proposed. Early methods targeted specific processing blocks [35, 40, 44] as differentiable programs or in a reduced parameter space. Recent methods rely on differentiable pipeline proxies [60] or 0th-order optimization [36], alone or in combination with block coordinate descent [48]. One advantage of 0th-order optimizers is that they handle black box hardware and DSPs. 0th-order solvers used to optimize camera systems include MOEA/D [40, 73] and CMA-ES [21, 36, 48]. These approaches successfully tackle camera pipeline optimization, from the optics [59] to downstream detectors [36, 48]. To our knowledge, however, no loss-driven method has been proposed for end-to-end LiDAR system optimization: existing LiDAR sensors are designed in isolation from downstream depth and detection" + }, + { + "type": "page_footnote", + "bbox": [ + 0.518, + 0.888, + 0.846, + 0.901 + ], + "angle": 0, + "content": "1https://light.princeton.edu/LITL-Optimization/" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.958 + ], + "angle": 0, + "content": "13405" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.078, + 0.087, + 0.895, + 0.304 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.307, + 0.895, + 0.379 + ], + "angle": 0, + "content": "Figure 2. LiDAR Simulation Method. We propose a parameterizable LiDAR simulation model that generates full transient waveforms by extracting scene response \\( H \\), ambient light \\( a \\) and object reflectances \\( s, d, \\alpha \\) from CARLA. End-to-end loss functions drive an MOO solver toward an optimal vector \\( \\Theta \\) that includes both pulse and DSP hyperparameters: Wavefronts are processed by the DSP, resulting in a point cloud \\( \\mathbf{O} \\). The \"loop is closed\" by feeding \\( \\mathbf{O} \\) to 3D object detection and depth estimation methods, the output of which is \"rated\" by loss functions \\( \\mathcal{L} \\). Validation datasets cycle through the loop until \\( \\Theta \\) converges." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.392, + 0.47, + 0.422 + ], + "angle": 0, + "content": "tasks. Aiming to close this gap, this work proposes automated LiDAR hyperparameter optimization." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.426, + 0.471, + 0.607 + ], + "angle": 0, + "content": "LiDAR Sensing and Point Cloud Generation. LiDAR sensors produce point clouds by emitting pulses of light into a scene and measuring the round trip time of sensor returns. Extracting a point cloud from time-of-flight measurements is a complex process that depends on measurement procedure specifics like beam formation, scanning, pulse/continuous wave generation and peak finding within the acquired temporal laser response [1,46]. LiDAR sensors differ in their scanning pattern, beam steering technology, wavelength, pulse profile, coherence of the measurement step, detector technology and DSP capabilities to process the measurement echos [1,31]." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.61, + 0.47, + 0.852 + ], + "angle": 0, + "content": "Existing LiDAR sensors can extract single or multiple peaks resulting from multi-path interreflections in the scene. Assuming a single Lambertian reflector in the scene, the temporal resolution and signal-to-noise ratio (SNR) of the measurement are tied to laser power. Accordingly, some existing optimization methods [43] use automated runtime laser power adjustment to maximize SNR while preventing oversaturation. Other approaches have been proposed for adaptive beam steering [38, 45]. Recently, Vodisch et al. [65] proposed beam configuration optimization via reinforcement learning methods driven by a downstream 3D detection loss. This method is the most similar to the proposed approach; however, it only predicts beam patterns, i.e., where to place sparse samples. In contrast, we optimize DSP hyperparameters in addition to sensing ones, pulse power and scanning parameters included." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.856, + 0.471, + 0.903 + ], + "angle": 0, + "content": "LiDAR Simulation. To assess and validate the proposed method, we introduce a new LiDAR simulation method that plugs directly into the CARLA simulator. Existing" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.391, + 0.892, + 0.739 + ], + "angle": 0, + "content": "work [5, 14, 37, 47, 52] proposes several simulation environments; the open-source CARLA [14] simulator is broadly adopted in academia. Simulation frameworks have enabled the creation of multimodal synthetic datasets used successfully in academia and industry, e.g., PreSIL [24], SHIFT [57], AIODrive [32, 69] and SYNTHIA [49]. However, underlying simulation methods employ heuristic forward models; in addition, none of the datasets include full waveform returns that would allow simulating LiDAR point cloud generation. Even the AIODrive dataset [69], in which multiple peaks are returned via depth image convolution and Single Photon Avalanche Diode (SPAD) quantization, bakes transients into SPAD image formation, hence falls short of enabling realistic transient simulation. Similarly, real PC datasets augmentation methods have been employed to tackle rare occurring events like obstacles [16, 33], traffic [68], rain [20], fog [20] or snow [19, 26]. However, since the underlying datasets do not include the raw wavefronts, none of these approach facilitate modeling the DSP pipeline. We address these challenges with our simulation method that simulates full wavefront signals. Combined with a realistic DSP model, this produces PC data representative of real systems." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.743, + 0.808, + 0.758 + ], + "angle": 0, + "content": "3. Transient LiDAR Forward Model" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.763, + 0.892, + 0.84 + ], + "angle": 0, + "content": "Consider a single laser pulse emitted by a LiDAR unit into a 3D scene, from which a returned signal is detected by a SPAD detector which sends temporal histograms to the sensor DSP. For channel \\( n \\) at time \\( t \\), we model the sensor-incident temporal photon flux \\( \\psi \\) [22, 41, 54] as" + }, + { + "type": "equation", + "bbox": [ + 0.587, + 0.844, + 0.892, + 0.863 + ], + "angle": 0, + "content": "\\[\n\\psi^ {(n)} (t) = \\left(H * g ^ {(n)}\\right) (t) + a (t), \\tag {1}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.87, + 0.892, + 0.901 + ], + "angle": 0, + "content": "where \\(g^{(n)}\\) is the temporally varying photon flux emitted by the laser for channel \\(n\\), \\(H\\) is the transient response from the" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "13406" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.473, + 0.257 + ], + "angle": 0, + "content": "scene, \\( a(t) \\) is the ambient photon flux, and \\( * \\) is the temporal convolution operator. By definition, the transient scene response \\( H \\) includes multipath returns from scene interreflections and scattering. The detector measures the returned signal and digitizes the temporal measurement into temporal wavefronts processed by the DSP. For low photon counts or path lengths above a few meters typical in automotive scenes, the binning process can be modeled as a Poisson random process [22, 41, 43, 54]. We consequently model the wavefront's number of photons \\( r^{(n)} \\) detected within the integration time \\( \\Delta \\) in channel \\( n \\)'s time bin \\( k \\) as" + }, + { + "type": "equation", + "bbox": [ + 0.131, + 0.259, + 0.47, + 0.3 + ], + "angle": 0, + "content": "\\[\nr ^ {(n)} [ k ] \\sim \\text {P o i s s o n} \\left(\\int_ {k \\Delta} ^ {(k + 1) \\Delta} \\psi^ {(n)} (t) d t\\right). \\tag {2}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.306, + 0.369, + 0.322 + ], + "angle": 0, + "content": "3.1. Transient Scene and Pulse Model" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.329, + 0.47, + 0.389 + ], + "angle": 0, + "content": "Rasshofer et al. [46] introduced a linear model for direct laser reflections in the LiDAR context for the incident transient response \\( H * g^{(n)} \\) of Eq. (1). With this model, we formalize the transient response as" + }, + { + "type": "equation", + "bbox": [ + 0.091, + 0.391, + 0.47, + 0.428 + ], + "angle": 0, + "content": "\\[\n(H * g ^ {(n)}) (R) = C \\int_ {0} ^ {2 \\tau^ {(n)}} g ^ {(n)} (t) H \\left(R - \\frac {\\mathrm {c t}}{2}\\right) d t, \\tag {3}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.431, + 0.47, + 0.536 + ], + "angle": 0, + "content": "where \\(R\\) is the distance between the sensor and the observed point, \\(\\mathbf{c}\\) is the speed of light, \\(C\\) is a proportionality constant, independent of \\(t\\) and \\(R\\), describing the system, and \\(2\\tau^{(n)}\\) is the total pulse duration for channel \\(n\\). We convert path length to time with \\(t = R / c\\). Following Hahner et al. [19] and Rasshofer et al. [46], we adopt the following pulse shape" + }, + { + "type": "equation", + "bbox": [ + 0.097, + 0.534, + 0.469, + 0.574 + ], + "angle": 0, + "content": "\\[\ng ^ {(n)} (t) = \\left\\{ \\begin{array}{l l} P _ {0} ^ {(n)} \\sin^ {2} \\left(\\frac {\\pi t}{2 \\tau^ {(n)}}\\right) & \\text {i f} 0 \\leq t \\leq 2 \\tau^ {(n)} \\\\ 0 & \\text {o t h e r w i s e} \\end{array} , \\right. \\tag {4}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.576, + 0.469, + 0.697 + ], + "angle": 0, + "content": "where \\( P_0^{(n)} \\) is channel \\( n \\)'s pulse power magnitude. The transient scene response \\( H \\) embedded in Eq. (3) consists of the geometric attenuation of the light, proportional to \\( 1 / (2R)^2 \\), and the scene response. For a single opaque point object \\( i \\), the latter is proportional to its reflectance \\( \\rho_i \\) and a Dirac function \\( \\delta(R - R_i) \\), \\( R_i \\) being the object distance to the sensor. Reformulating Eq. (3) for a single echo from \\( i \\) yields" + }, + { + "type": "equation", + "bbox": [ + 0.082, + 0.695, + 0.465, + 0.735 + ], + "angle": 0, + "content": "\\[\n(H * g ^ {(n)}) _ {i} (R) = \\left\\{ \\begin{array}{l l} f _ {i} ^ {(n)} (R) & \\text {i f} R _ {i} \\leq R \\leq R _ {i} + c \\tau^ {(n)} \\\\ 0 & \\text {o t h e r w i s e} \\end{array} \\right. \\tag {5}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.081, + 0.738, + 0.464, + 0.773 + ], + "angle": 0, + "content": "\\[\n\\text {w i t h} f _ {i} ^ {(n)} (R) = \\frac {C P _ {0} ^ {(n)} \\rho_ {i}}{4 R _ {i} ^ {2}} \\sin^ {2} \\left(\\frac {\\pi}{c \\tau^ {(n)}} (R - R _ {i})\\right). \\tag {6}\n\\]" + }, + { + "type": "title", + "bbox": [ + 0.078, + 0.782, + 0.312, + 0.798 + ], + "angle": 0, + "content": "3.2. Object Reflectance Model" + }, + { + "type": "text", + "bbox": [ + 0.077, + 0.805, + 0.469, + 0.864 + ], + "angle": 0, + "content": "The object reflectance \\(\\rho_{i}\\) depends on the material BRDF and the angle of incidence \\(\\theta\\). We model reflectance using specular and diffuse components of the retroreflected portion of the Cook-Terrance model [9], that is," + }, + { + "type": "equation", + "bbox": [ + 0.079, + 0.865, + 0.469, + 0.899 + ], + "angle": 0, + "content": "\\[\n\\rho_ {i} = \\frac {\\alpha^ {4} s \\cos \\theta}{4 \\left[ \\cos^ {2} \\theta \\left(\\alpha^ {4} - 1\\right) + 1 \\right] ^ {2} \\left[ \\cos \\theta (1 - k) + k \\right] ^ {2}} + d \\cos \\theta , \\tag {7}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.092, + 0.892, + 0.259 + ], + "angle": 0, + "content": "where \\( s, d \\) and \\( \\alpha \\in [0,1] \\) refer to the specular, diffuse and roughness properties of a surface material and \\( k = (\\alpha + 1)^2 / 8 \\), see Supplemental Material. To render realistic textures without a large texture database, we approximate \\( s, d, \\alpha \\) through CARLA's Phong-like parameters \\( s, d \\) and \\( \\alpha \\). These parameters are not directly accessible, so we extract them by projecting targeted hit points onto custom camera images encoding their values, encoding illustrated at the bottom right of Fig. 2. Specifically, we define a function \\( \\mathcal{P}_i \\) that, for a rendered image \\( I_{sd\\alpha} \\), returns pixel information at the location of projected point \\( i \\), that is, \\( [s, d, \\alpha] = \\mathcal{P}_i(I_{sd\\alpha}) \\)." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.262, + 0.704, + 0.276 + ], + "angle": 0, + "content": "3.3. Ambient Illumination" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.281, + 0.892, + 0.387 + ], + "angle": 0, + "content": "The ambient light \\( a(t) \\) in Eq. (1) is modeled following Weng et al. [69]. A location \\( i \\) is projected on the red channel of a rendered RGB camera image in which shadows and reflections are properly accounted for; denote this image by \\( I_{\\mathrm{red}} \\). We approximate \\( a(t) \\) as a constant over waveform time bins, that is, \\( a(t) \\equiv a_i = \\mathcal{P}_i(I_{\\mathrm{red}}) \\), where \\( a_i \\) is independent of \\( t \\). See Supplemental Material for details." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.392, + 0.7, + 0.408 + ], + "angle": 0, + "content": "3.4. Multipath Transients" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.411, + 0.892, + 0.536 + ], + "angle": 0, + "content": "Multipath transients for laser beams hitting object discontinuities are taken as primary artifact sources in automotive scenarios. Multipath transients are modeled as linear combinations of neighboring waveforms. Specifically, a supersampled collection of \\(\\{R_i\\}\\) and channels is computed using direct illumination only; then, for each LiDAR channel and each horizontal angle, a downsampled waveform \\(\\psi_j^{(m)}\\) is obtained:" + }, + { + "type": "equation", + "bbox": [ + 0.514, + 0.537, + 0.891, + 0.572 + ], + "angle": 0, + "content": "\\[\n\\psi_ {j} ^ {(m)} (R) = \\sum_ {i \\in \\mathcal {N} (j), n \\in \\mathcal {N} (m)} \\left(K _ {i} ^ {(n)} \\left(H * g ^ {(n)}\\right) _ {i} (R) + a _ {i}\\right), \\tag {8}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.575, + 0.892, + 0.638 + ], + "angle": 0, + "content": "where \\(\\mathcal{N}(j)\\) and \\(\\mathcal{N}(m)\\) define respectively the spatial neighborhood of the target point \\(j\\) and the channel \\(m\\), and the \\(K_{i}^{(n)}\\)s are normalized weights that can be interpreted as a beam spatial intensity profile, see Supplemental Material." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.642, + 0.78, + 0.657 + ], + "angle": 0, + "content": "3.5. LiDAR Sensing and DSP Model" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.663, + 0.892, + 0.771 + ], + "angle": 0, + "content": "A sensor-incident waveform \\(\\psi_j^{(m)}\\) is measured according to Eq. (2) and, along with sensor noise, subjected to clipping at a saturation level that depends on detector type. The DSP converts noisy and saturated waveforms \\(r_j^{(m)}\\) into a point cloud \\(\\mathbf{O}\\) that includes range and intensity information. The entire process, from the emission of a laser pulse \\(g^{(m)}\\) to the output of a processed point cloud \\(\\mathbf{O}\\) is shown in Fig. 3." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.772, + 0.892, + 0.903 + ], + "angle": 0, + "content": "With compressed notation, we model LiDAR sensing as a function \\(\\Phi (\\Theta)\\) with hyperparameters \\(\\Theta = (P_0^{(m)},\\tau^{(m)},V^{(m)})\\). Laser power \\(P_0^{(m)}\\) and pulse duration \\(\\tau^{(m)}\\) are functions of the channel \\(m\\); they determine the emitted pulse \\(g^{(m)}\\). First, the DSP denoises by convolving the measured waveform \\(r_j^{(m)}\\) with the emitted pulse \\(g^{(m)}\\), see [61]. Next, ambient light is estimated by removing the waveform's median from \\(r_j^{(m)}\\). This allows the DSP" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13407" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.144, + 0.088, + 0.403, + 0.256 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.26, + 0.47, + 0.318 + ], + "angle": 0, + "content": "Figure 3. LiDAR Sensing and DSP Model \\(\\Phi\\). The LiDAR sensing and DSP hyperparameters \\(\\Theta\\), including pulse power, duration, and parameters for rising edge detection in a transient wavefront, affect the quality of the generated point cloud \\(\\mathbf{O}\\)." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.322, + 0.469, + 0.46 + ], + "angle": 0, + "content": "to find adequate noise thresholds \\( V^{(m)} \\) since ambient light varies strongly throughout the scene. Typical DSPs use a rising edge detector [30] that finds peaks along the denoised waveform by identifying intersections with \\( V^{(m)} \\). Multiple peaks may arise; the peak with the highest intensity is added to the point cloud O. Finally, the maximum intensity is compensated for the emitted laser pulse: The pulse half width \\( \\tau^{(m)} / 2 \\) and power level \\( P_0^{(m)} \\) are used as scaling factors to recover the true intensities, see Eq. 3." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.461, + 0.47, + 0.659 + ], + "angle": 0, + "content": "Modern consumer LiDAR sensors have upward of 128 channels. Optimizing every channel hyperparameter individually is prohibitive. The Velodyne HDL-64 bundles lasers in two groups [62]; our LiDAR model likewise groups lower and upper lasers. Within each group, hyperparameter modulation is an affine function of the channel number with tuneable slope and bias. The edge threshold is modeled as a continuous parameter \\( V^{(m)} \\in [0,2] \\). In contrast, power levels \\( P_0^{(m)} \\) can only take one of 11 values, the lowest making the peak almost indistinguishable from ambient noise and the highest likely to saturate at close range. Pulse duration \\( \\tau^{(m)} \\) takes discrete values ranging from 3 to 15 ns in 1 ns increments. See Supplemental Material." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.662, + 0.214, + 0.679 + ], + "angle": 0, + "content": "4. Optimization" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.682, + 0.469, + 0.803 + ], + "angle": 0, + "content": "We propose a multi-objective optimization (MOO) method that finds Pareto-optimal (see the Supplemental Material for a definition) LiDAR hyperparameters. Specifically, optimization returns high-quality point clouds when optimized for depth precision and optimal mAP when point clouds are fed to a downstream object detection module. We first formulate the optimization problem, then we present the proposed optimization method." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.806, + 0.409, + 0.823 + ], + "angle": 0, + "content": "4.1. LiDAR Hyperparameter Optimization" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.826, + 0.469, + 0.902 + ], + "angle": 0, + "content": "We optimize hyperparameters with loss-driven end-to-end optimization. Like Robidoux et al. [48], we optimize the system as a whole, bundling hardware functionality, for example the laser beam power of each channel, with DSP functionality. The operation of the LiDAR imaging pipeline" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.092, + 0.892, + 0.152 + ], + "angle": 0, + "content": "\\(\\Phi\\) is modulated by \\(\\mathrm{P}\\) hyperparameters \\(\\Theta = (\\Theta_{1},\\dots ,\\Theta_{\\mathrm{P}})\\) with ranges of values normalized to the unit interval [0, 1]. With \\(\\mathrm{T}\\gg 2\\tau\\) we model each of the J channels \\(\\Phi_j\\) of \\(\\Phi = (\\Phi_1,\\ldots ,\\Phi_{\\mathrm{J}})\\) as" + }, + { + "type": "equation", + "bbox": [ + 0.518, + 0.154, + 0.892, + 0.173 + ], + "angle": 0, + "content": "\\[\n\\Phi_ {j}: [ - \\mathrm {T}, 0 ] ^ {[ 0, \\infty)} \\times [ 0, 1 ] ^ {\\mathrm {P}} \\rightarrow \\mathbb {S} ^ {[ 0, \\infty)}, (\\mathrm {r} _ {j}, \\Theta) \\mapsto \\mathrm {O} _ {j}, \\tag {9}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.178, + 0.892, + 0.268 + ], + "angle": 0, + "content": "\\(\\mathrm{O}_j\\) being a mapping from the unit sphere \\(\\mathbb{S}\\) (proxy for projective geometry) to nonnegative distances with 0 understood as \"undefined\" so that each \\(\\Phi_j\\) reconstructs a portion \\(\\mathrm{O}_j\\) of the overall point cloud \\(\\mathbf{O}\\) from a waveform \\(r_j\\) truncated to the time interval \\([-T,0]\\). The overall \\(\\Theta\\)-modulated LiDAR pipeline" + }, + { + "type": "equation", + "bbox": [ + 0.618, + 0.269, + 0.891, + 0.285 + ], + "angle": 0, + "content": "\\[\n\\Phi : (\\mathbf {r} (H, \\Theta), \\Theta) \\rightarrow \\mathbf {O} \\tag {10}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.289, + 0.892, + 0.348 + ], + "angle": 0, + "content": "maps the set of truncated wave forms \\(\\mathbf{r} = (\\mathrm{r}_1,\\dots ,\\mathrm{r}_{\\mathrm{J}})\\) to the point cloud \\(\\mathbf{O}\\) that contains the compressed information available to downstream detectors about the changing scene \\(H\\). We seek solutions of" + }, + { + "type": "equation", + "bbox": [ + 0.621, + 0.351, + 0.891, + 0.378 + ], + "angle": 0, + "content": "\\[\n\\boldsymbol {\\Theta} ^ {*} = \\underset {\\boldsymbol {\\Theta} \\in [ 0, 1 ] ^ {\\mathrm {P}}} {\\arg \\min } \\mathcal {L} (\\boldsymbol {\\Theta}), \\tag {11}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.38, + 0.892, + 0.515 + ], + "angle": 0, + "content": "which are Pareto-optimal with respect to the MOO loss vector \\(\\mathcal{L} = (\\mathcal{L}_1, \\dots, \\mathcal{L}_{\\mathrm{L}})\\). Loss components may not directly use O; for example, \\(\\mathcal{L}_l\\) may use data tapped out of the pipeline (e.g., a channel's waveform \\(\\mathbf{r}_j\\)) or the output of a downstream detector (e.g., a deep CNN) which ingests O (e.g., mAP). The Pareto front [28], which contains Pareto-optimal compromises between losses, is the solution set of Eq. (11), from which a single \"champion\" is selected using additional criteria." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.523, + 0.722, + 0.539 + ], + "angle": 0, + "content": "4.2. Optimization Algorithm" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.543, + 0.892, + 0.723 + ], + "angle": 0, + "content": "To solve the MOO problem, we propose a variant of the Covariance Matrix Adaptation-Evolution Strategy methods of Mosleh et al. [36] and Robidoux et al. [48] that differs from the state of the art in a number of ways. It is listed in Algorithm 1. We use centroid weights that change between generations; to obtain better transients, we greedily override the CMA-ES centroid whenever a new Best So Far (BSF) is found, isolating the computation of CMA-ES statistics from the resulting \"jump\" as well as from the novel inclusion of centroids as BSF candidates; and we formulate a stable dynamic max-rank loss scalarization which we use to drive optimization and as champion selection criterion." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.725, + 0.892, + 0.874 + ], + "angle": 0, + "content": "Stable Dynamic Max-Rank Loss Scalarization Scalarizations like the well-known convex combination \\(\\sum_{l=1}^{L} \\mathrm{w}_l \\mathcal{L}_l\\) which boils down to the \\(\\ell_1\\)-norm of the loss vector \\(\\mathcal{L}\\) with unit weights \\(\\mathrm{w}_l\\), are used to combine multiple objectives so that a single objective optimizer yield MOO solutions [15]. Scalarization weights are difficult to choose when loss variations are not commensurate. The max-rank loss addresses this issue. In the context of a generation-based algorithm like Algorithm 1, Mosleh et al. [36] define a max-rank loss as follows. Let" + }, + { + "type": "equation", + "bbox": [ + 0.521, + 0.876, + 0.891, + 0.898 + ], + "angle": 0, + "content": "\\[\n\\mathcal {R} _ {l} ^ {q, m, n} = \\operatorname {r a n k} \\left. \\mathcal {L} _ {l} ^ {q, m} \\text {w i t h i n} \\left\\{\\mathcal {L} _ {l} ^ {r, o} \\right\\} _ {r \\in \\{0, \\dots , 4 \\mathrm {P} \\}} ^ {o \\in \\{1, \\dots , n \\}} \\right. \\tag {12}\n\\]" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13408" + } + ], + [ + { + "type": "text", + "bbox": [ + 0.076, + 0.092, + 0.47, + 0.152 + ], + "angle": 0, + "content": "with ranks counted from 0 and loss component value ties resolved by left bisection. Then, the weighted (left-bisection) max-rank loss \\(\\mathcal{M}^{q,m,n}\\) of the hyperparameter vector \\(\\Theta^{q,m}\\) at the end of generation \\(n\\) is" + }, + { + "type": "equation", + "bbox": [ + 0.154, + 0.153, + 0.469, + 0.178 + ], + "angle": 0, + "content": "\\[\n\\mathcal {M} ^ {q, m, n} = \\max _ {l \\in \\{1, \\dots , \\mathrm {L} \\}} \\left(\\mathrm {w} _ {l} \\cdot \\mathcal {R} _ {l} ^ {q, m, n}\\right). \\tag {13}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.18, + 0.47, + 0.39 + ], + "angle": 0, + "content": "The max-rank loss is dynamic. For a given \\(\\Theta^{q,m}\\) its values are monotone non-decreasing with respect to the addition of data. Because weights multiply ranks, they are non-dimensional, hence dial in the relative importance of loss components. While Mosleh et al. [36] scale each \\(\\mathbf{w}_l\\) by the (damped) running proportion of individuals that \"fail\" to pass a user-defined threshold, we find that such adaptive weigths break monotonicity. Instead, we keep \\(\\mathbf{w}_l\\) s fixed. We also improve on Mosleh et al. in that we average left and right bisection ranks (exception: if the left bisection rank is 0, the \"average\" is set to 0) to stabilize \\(\\mathcal{M}^{q,m,n}\\) with respect to loss value tie breaking (from, e.g., noise) and creation (quantization); this defines our stable (dynamically monotone) max-rank loss scalarization." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.392, + 0.469, + 0.603 + ], + "angle": 0, + "content": "Dual-Weight CMA-ES Besides more refined seatbelting (see Supplemental Material), Algorithm 1 differs from earlier CMA-ES in its use of non-constant (hyperparameter, not loss) centroid weights. Although variable CMA-ES generation sizes are common [39], the formula used to derive centroid weights is invariably kept fixed; in contrast, the proposed CMA-ES alternates between gradient-seeking centroid weights [36], which assign zero weight to the worst quartile of each generation instead of the usual half to exploit the symmetry of the second and third quartiles of Gaussian distributions to get a more accurate gradient approximation, and boundary-stabilizing centroid weights [48] with no discard so that further exploration not go in the wrong direction near generic local minima." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.603, + 0.469, + 0.707 + ], + "angle": 0, + "content": "Other novel components are that the loss of the weighted centroid of every generation is evaluated (standard CMA-ES only generate Gaussian clouds with them) and the greedy branch in Algorithm 1 (Lines 18-20): If any individual of the generation, weighted centroid included, turns out to be a strict minimizer, it becomes the next generation Gaussian cloud center." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.712, + 0.21, + 0.728 + ], + "angle": 0, + "content": "5. Experiments" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.731, + 0.469, + 0.821 + ], + "angle": 0, + "content": "In the following, we first validate the proposed approach with the LiDAR simulation model, jointly optimizing depth estimation and downstream 3D object detection within simulated scenes. Then, we compare the proposed optimization algorithm to recent 0th-order solvers. Finally, we present experiments with an off-the-shelf hardware LiDAR unit." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.823, + 0.469, + 0.901 + ], + "angle": 0, + "content": "Experimental Setup As described in Sec. (3.5), hyperparameters affect the wavefront and DSP. We optimize the DSP rising edge threshold \\( V^{(m)} \\) and 10 LiDAR hyperparameters that control low-level sensing parameters including the laser power \\( P_0^{(m)} \\) and laser pulse width \\( \\tau^{(m)} \\) for" + }, + { + "type": "code_caption", + "bbox": [ + 0.502, + 0.092, + 0.845, + 0.107 + ], + "angle": 0, + "content": "Algorithm 1 LiDAR Hyperparameter Optimization." + }, + { + "type": "algorithm", + "bbox": [ + 0.501, + 0.111, + 0.892, + 0.593 + ], + "angle": 0, + "content": "Require: LiDAR \\(\\Phi\\), \\(\\Theta \\in [0,1]^{\\mathrm{P}}\\) (initial hyperparameter vector), \\(\\mathbb{N} \\in \\mathbb{N}^*\\) (number of generations), \\(\\varepsilon \\in \\left(0,\\frac{1}{3}\\right)\\) (small bound), \\(\\mathbf{C} \\in \\mathbb{R}^{\\mathrm{P} \\times \\mathrm{P}}\\) (CMA-ES \"directional\" covariance matrix factor), \\(\\sigma \\in [\\varepsilon ,\\frac{1}{3} ]\\) (square root of covariance matrix \"scale\" factor) \n1: \\(\\mathbf{p} \\gets 0\\), \\(\\mathbf{c} \\gets 0\\) (CMA-ES path vectors), \\(\\Theta_{\\mathrm{center}} \\gets \\Theta\\) \n2: for \\(n = 1\\) to N do \n3: \\(\\Theta^{0,n} \\gets \\Theta\\) \n4: \\(\\mathcal{L}^{0,n} \\gets\\) losses for LiDAR \\(\\Phi\\) modulated by \\(\\Theta^{0,n}\\) \n5: for \\(p = 1\\) to 4P do \n6: \\(\\Theta^{p,n} \\gets\\) random draw from Gaussian distribution with covariance matrix \\(\\sigma^2\\mathbf{C}\\) centered at \\(\\Theta_{\\mathrm{center}}\\) \n7: \\(\\Theta^{p,n} \\gets \\Theta^{p,n} +\\) Gaussian distribution with diagonal cov. matrix proportional to square of quantization grain [48] \n8: \\(\\Theta^{p,n} \\gets \\Theta^{p,n}\\) reflected back into \\([0,1]^{\\mathrm{P}}\\) \n9: \\(\\mathcal{L}^{p,n} \\gets\\) losses for LiDAR \\(\\Phi\\) modulated by \\(\\Theta^{p,n}\\) \n10: end for \n11: Compute \\(\\{\\mathcal{M}^{q,m,n}\\}_{q\\in \\{0,\\dots,4\\mathrm{P}\\},m\\in \\{1,\\dots,n\\}}\\) by including \\(\\{\\mathcal{L}^{p,n}\\}_{p\\in \\{0,\\dots,4\\mathrm{P}\\}}\\) in rank computations \n12: Use \"eager\" [36] centroid weights with \\(\\lambda = 4\\mathrm{P}\\), \\(\\mu = 3\\mathrm{P}\\) \n13: if \\(n\\) is odd then \n14: Use \"stable\" [48] centroid weights with \\(\\lambda = \\mu = 4\\mathrm{P}\\) \n15: end if \n16: Standard CMA-ES update [21] of \\(\\Theta\\), \\(\\sigma\\), C, p, c based on \\(\\{\\Theta^{p,n}\\}_{p\\in \\{1,\\dots,4\\mathrm{P}\\}}\\) and \\(\\{\\mathcal{M}^{p,n,n}\\}_{p\\in \\{1,\\dots,4\\mathrm{P}\\}}\\) \n17: \\(\\Theta_{\\mathrm{center}} \\gets \\Theta\\) \n18: if min \\(p\\in \\{0,\\dots,4\\mathrm{P}\\}\\) \\(M^{p,n,n} < \\min_{q\\in \\{0,\\dots,4\\mathrm{P}\\},m\\in \\{1,\\dots,n\\}} M^{q,m,n}\\) then \n19: \\(\\Theta_{\\mathrm{center}} \\gets\\) minimizer closest to centroid of minimizers \n20: end if \n21: end for \n22: return \\(\\Theta^{p,n}\\) in the (guaranteed nonempty) intersection of the Pareto front and the set of minimizers of \\(\\mathcal{M}^{q,m,N}\\), with ties resolved by choosing the one closest to their centroid and remaining ties resolved by maximizing \\(n\\), then \\(p\\)" + }, + { + "type": "text", + "bbox": [ + 0.5, + 0.602, + 0.67, + 0.616 + ], + "angle": 0, + "content": "each of the 128 channels." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.626, + 0.822, + 0.643 + ], + "angle": 0, + "content": "5.1. Optimization for Depth and Intensity" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.647, + 0.892, + 0.708 + ], + "angle": 0, + "content": "Next, we assess the method by optimizing point clouds for depth and intensity with the proposed optimizer. We minimize the average RMSE of the depth and the average RMSE of the intensity, specifically" + }, + { + "type": "equation", + "bbox": [ + 0.518, + 0.709, + 0.892, + 0.734 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {d e p t h}} (\\Theta) = \\frac {1}{F} \\sum_ {f = 1} ^ {F} \\operatorname {R M S E} \\left(R _ {f}, \\Phi_ {f} ^ {(R)} (\\Theta)\\right), \\text {a n d} \\tag {14}\n\\]" + }, + { + "type": "equation", + "bbox": [ + 0.529, + 0.736, + 0.892, + 0.761 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\text {i n t .}} (\\boldsymbol {\\Theta}) = \\frac {1}{F} \\sum_ {f = 1} ^ {F} \\operatorname {R M S E} \\left(I _ {f}, \\Phi_ {f} ^ {(I)} (\\boldsymbol {\\Theta})\\right), \\tag {15}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.762, + 0.892, + 0.87 + ], + "angle": 0, + "content": "with \\( \\mathrm{F} \\) the number of frames in the validation set, see Supplemental Material. The depth loss \\( \\mathcal{L}_{\\mathrm{depth}} \\) rewards accurate point cloud depth estimates over the full range, whereas \\( \\mathcal{L}_{\\mathrm{int}} \\) ensures that accurate intensities are measured with the pulse power \\( P_0^{(j)} \\). High output power generally results in more accurate point clouds at farther distances but also leads to excessive saturation; \\( \\mathcal{L}_{\\mathrm{int}} \\) penalizes saturation." + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.871, + 0.892, + 0.901 + ], + "angle": 0, + "content": "Fig. 4 shows a comparison between ground-truth, expert-tuned and optimized PCs for a Pareto point that mini-" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13409" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.078, + 0.087, + 0.371, + 0.222 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.384, + 0.087, + 0.649, + 0.221 + ], + "angle": 0, + "content": null + }, + { + "type": "image", + "bbox": [ + 0.658, + 0.089, + 0.892, + 0.222 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.226, + 0.894, + 0.268 + ], + "angle": 0, + "content": "Figure 4. Comparison between expert-tuned and optimized point clouds. Colors encode the individual depth error of each point (clipped at \\(2\\mathrm{m}\\)). Convergence plot shows the loss components \\(\\mathcal{L}_{\\mathrm{depth}}\\) and \\(\\mathcal{L}_{\\mathrm{int}}\\) (Eq. (14)-(15)) against optimization step (3000 loss evaluations total), the evolution of the champion and the final Pareto front, champion included." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.276, + 0.47, + 0.472 + ], + "angle": 0, + "content": "mizes the depth error. Compared to the optimized point cloud measurement, the expert-tuned point cloud suffers from clutter, and its depth error increases with distance. The results reported in Table 1 validate the optimization method: The expert-tuned configuration has a loss vector \\(\\mathcal{L} = (\\mathcal{L}_{\\mathrm{depth}},\\mathcal{L}_{\\mathrm{int.}})\\) equal to (12.195, 1.971); after 3000 evaluations of the objectives, the champion of the proposed method has a loss vector of (10.754, 0.216), improving depth and intensity metrics by \\(12\\%\\) and \\(89\\%\\), respectively. We note that biasing optimization toward reducing \\(\\mathrm{RMSE}_{\\mathrm{depth}}\\) more than \\(\\mathrm{RMSE}_{\\mathrm{int.}}\\) can be done by changing Eq. (13) loss weights \\(\\mathrm{w}_l\\). In this work, loss component weights were kept at their default 1." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.473, + 0.47, + 0.565 + ], + "angle": 0, + "content": "Table 1 also shows a comparison of the proposed algorithm with other state-of-the-art MOO optimizers. With both champion selection criteria—the one proposed by Mosleh et al. [36], namely \"last Pareto point of the run\", and our own, \"Pareto point that minimizes the stable (weighted) max-rank loss\"—Algorithm (1) is the best performer." + }, + { + "type": "title", + "bbox": [ + 0.077, + 0.572, + 0.377, + 0.589 + ], + "angle": 0, + "content": "5.2. Optimization for Object Detection" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.592, + 0.469, + 0.682 + ], + "angle": 0, + "content": "Next, we investigate optimization for object detection and classification. We use Average Precision (AP) as additional optimization objective. AP is maximized for cars and pedestrians at 40 recall positions [55] over an optimization set with \\( \\mathrm{F} = 100 \\) frames. With standard KITTI IoU thresholds [17], the CV loss is" + }, + { + "type": "equation", + "bbox": [ + 0.133, + 0.685, + 0.469, + 0.703 + ], + "angle": 0, + "content": "\\[\n\\mathcal {L} _ {\\mathrm {o b j}, \\{\\text {c a r , p e d .} \\}} (\\Theta) = - \\mathrm {A P} _ {\\{\\text {c a r , p e d .} \\}} (\\Phi (\\Theta)), \\tag {16}\n\\]" + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.704, + 0.469, + 0.779 + ], + "angle": 0, + "content": "evaluated over a \\(0 - 80\\mathrm{m}\\) range. We train a PV-RCNN [53] for 3D object detection on 5900 full range point clouds collected from the simulation environment with 8 different expert-tuned parameterizations \\(\\Theta\\), see training and dataset details in the Supplemental Material." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.781, + 0.47, + 0.902 + ], + "angle": 0, + "content": "Table 2 contains a quantitative comparison between the proposed method and expert tuning. Compared to expert tuning, the proposed method increases AP by \\(52.9\\%\\) and \\(27.2\\%\\) for cars and pedestrians, respectively, on the full \\(0 - 80\\mathrm{m}\\) range, with large increases predominately at close range. Lower AP values in the \\(0 - 30\\mathrm{m}\\) range compared to \\(30 - 50\\mathrm{m}\\) are due to a lower object occurrence (3.2 per frame vs. 3.6) and a less varied distribution of object yaw" + }, + { + "type": "table", + "bbox": [ + 0.499, + 0.263, + 0.737, + 0.414 + ], + "angle": 0, + "content": "
SOLVERl1-norm (↓) last Pareto point [36]l1-norm (↓) proposed champion
Expert-Tuned14.09114.091
Proposed11.08910.995
NSGA-III [10,11]11.40811.574
Mosleh et al. [36]11.49711.497
AGE-MOEA [42]11.54711.427
C-TAEA [29]11.62211.461
RVEA [8]11.85111.437
R-NSGA-III [63]12.30611.619
U-NSGA-III [51]13.42311.512
SMS-EMOA [2]13.65811.750
" + }, + { + "type": "image", + "bbox": [ + 0.737, + 0.261, + 0.888, + 0.419 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.498, + 0.421, + 0.892, + 0.573 + ], + "angle": 0, + "content": "Table 1. LiDAR Depth and Intensity MOO. The Pareto front (see figure) is approximately a straight line with slope -1. So, the variations of \\(\\mathcal{L}_{\\mathrm{depth}}\\) and \\(\\mathcal{L}_{\\mathrm{int.}}\\) are approximately balanced and the \\(\\ell_1\\)-norm of the loss vector is a reasonable evaluation metric. For all solvers, the first column shows the \\(\\ell_1\\)-norm of the last Pareto point of the run (champion per Mosleh et al. [36]); the last column instead uses the proposed champion selection method (Line 22 of Algorithm (1)). The proposed solver performs favorably; in addition, the proposed champion selection method improves results for all but two solvers (there is one tie). Median of three runs with 3000 loss evaluations; see the Supplemental Material for details." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.58, + 0.892, + 0.822 + ], + "angle": 0, + "content": "angles (std. dev. of 0.63 rad vs. 1.25 rad) within the training set. Consequently, the detector can better adapt within the \\(30 - 50\\mathrm{m}\\) bin. The quantitative findings agree with the qualitative results of Fig. 5, where optimized and expert-tuned point clouds are compared to the ground truth. Here, the clutter in the expert-tuned point cloud results in false positives and missed detections. In these examples, the proposed method misses only one pedestrian, whereas on the expert-tuned PC the detector misses them all. We also compare the average depth RMSE before and after filtering suppressed points; the low loss of the filtered PC suggests that clutter has been removed and that the detector prefers an accurate if sparser point cloud. Note however that a DSP optimized for the removal of clutter, like the proposed method, also suppresses ground points, with a small impact on object detection results, see Table 2 last column." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.834, + 0.803, + 0.851 + ], + "angle": 0, + "content": "5.3. Off-the-shelf LiDAR Optimization" + }, + { + "type": "text", + "bbox": [ + 0.499, + 0.856, + 0.892, + 0.901 + ], + "angle": 0, + "content": "In this section, we adapt the proposed optimization algorithm to an off-the-shelf Baraja Spectrum LiDAR sensor. Only a handful of hyperparameters are user-accessible: the" + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13410" + } + ], + [ + { + "type": "image", + "bbox": [ + 0.078, + 0.087, + 0.892, + 0.256 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.259, + 0.893, + 0.287 + ], + "angle": 0, + "content": "Figure 5. Comparison between ground-truth, expert-tuned and optimized point cloud for 3D object detection. Optimization results in clutter-free point clouds which helps reduce false positive detections. For visualization purposes only the camera field-of-view is shown." + }, + { + "type": "table", + "bbox": [ + 0.082, + 0.289, + 0.885, + 0.362 + ], + "angle": 0, + "content": "
METHODmAP (↑)APcar (↑)APped. (↑)RMSEdepth (↓) filtered
0-80 m0-30 m30-50 m50-80 m0-80 m0-30 m30-50 m50-80 m0-80 m0-30 m30-50 m50-80 m
Expert7.3310.8810.181.2814.5821.7520.192.530.070.020.170.049.12 6.17
Proposed46.8652.1458.7027.0866.4775.0684.8641.2027.2529.2232.5312.9512.87 5.13
Margin+39.53+41.26+48.52+25.80+51.89+53.31+64.67+38.67+27.18+29.20+32.36+12.91+3.75 -1.04
" + }, + { + "type": "table_caption", + "bbox": [ + 0.076, + 0.367, + 0.893, + 0.396 + ], + "angle": 0, + "content": "Table 2. Optimization for 3D Object Detection. We report mAP and AP in different distance range bins, and unfiltered and filtered RMSE, for expert-tuned and proposed. Filtered \\(\\mathrm{RMSE}_{\\mathrm{depth}}\\) excludes all points suppressed by the DSP. Best in bold." + }, + { + "type": "image", + "bbox": [ + 0.078, + 0.409, + 0.47, + 0.53 + ], + "angle": 0, + "content": null + }, + { + "type": "image_caption", + "bbox": [ + 0.076, + 0.539, + 0.47, + 0.622 + ], + "angle": 0, + "content": "Figure 6. Left: ground truth 3D histogram used to compute losses, see text, red bounding boxes denoting cars, with experimental setup zoom-ins: captured static scene (top) and LiDAR installation (bottom); the LiDAR detector is the blue unit. Middle: loss contributions for the expert-tuned parameters and Right: for optimized parameters. Brighter color means a smaller loss." + }, + { + "type": "text", + "bbox": [ + 0.076, + 0.629, + 0.473, + 0.903 + ], + "angle": 0, + "content": "return mode that selects the waveform peaks used to generate the point cloud, the sensor heads scanning pattern, and the two sensor head motor frequencies which determine the PC angular resolution vs. sensor noise balance. Because raw wavefronts are inaccessible, we optimize 3D point cloud histograms taken over an (intermittently) static scene such as a workplace parking lot. Fig. 6 shows the experimental setup along with a photograph of the scene over which optimization was performed. The ground truth histogram is shown on the left of Fig. 6; it was generated by averaging 100 captures with a uniform angular resolution scanning pattern. Expert-tuned and optimized histogram contributions to the fitness function (described in the Supplemental Material) are respectively shown in the middle and right of Fig. 6; brighter bins indicate larger contributions. Optimized hyperparameters achieved better results by focusing laser scans in regions of interests (ROIs) where the ground truth was denser; expert-tuned ones produced" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.422, + 0.892, + 0.483 + ], + "angle": 0, + "content": "more uniform point clouds. Using the RMSE of PC histograms weighted by distance and number of points per bin, we estimate a \\(10.1\\mathrm{cm}\\) error for the expert-tuned configuration vs. \\(3.0\\mathrm{cm}\\) with optimized hyperparameters." + }, + { + "type": "title", + "bbox": [ + 0.5, + 0.486, + 0.62, + 0.5 + ], + "angle": 0, + "content": "6. Conclusion" + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.506, + 0.893, + 0.747 + ], + "angle": 0, + "content": "We propose an in-the-loop black box 0th-order optimization method for LiDAR sensing pipelines that finds optimal parameters for depth and intensity estimation and 3D object detection and classification. To assess our method, we propose a novel LiDAR simulation method integrated into the CARLA simulator. We find that optimizing the LiDAR sensing pipeline can significantly improve depth, intensity and object detection and classification compared to manual expert tuning. Specifically, for 3D object detection our optimization method results in a major increase of \\(51.9\\%\\) AP for cars and \\(27.2\\%\\) AP for pedestrians, compared to fine-tuning a detector on expert-optimized LiDAR vendor parameters. An interesting direction for future research is real-time scene-dependent optimization of LiDAR scanning parameters, potentially leading to adaptive sensing in adverse weather in urban and highway scenarios." + }, + { + "type": "text", + "bbox": [ + 0.498, + 0.75, + 0.895, + 0.901 + ], + "angle": 0, + "content": "Acknowledgments This work was supported by the AI-SEE project with funding from the FFG, BMBF, and NRCIRA. We thank the Federal Ministry for Economic Affairs and Energy for support through the PEGASUS-family project VVM-Verification and Validation Methods for Automated Vehicles Level 4 and 5. Felix Heide was supported by an NSF CAREER Award (2047359), a Packard Foundation Fellowship, a Sloan Research Fellowship, a Sony Young Faculty Award, a Project X Innovation Award, and an Amazon Science Research Award." + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.518, + 0.957 + ], + "angle": 0, + "content": "13411" + } + ], + [ + { + "type": "title", + "bbox": [ + 0.08, + 0.09, + 0.175, + 0.106 + ], + "angle": 0, + "content": "References" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.116, + 0.47, + 0.184 + ], + "angle": 0, + "content": "[1] Behnam Behroozpour, Phillip A. M. Sandborn, Ming C. Wu, and Bernhard E. Boser. Lidar System Architectures and Circuits. IEEE Communications Magazine, 55(10):135-142, Oct. 2017. Conference Name: IEEE Communications Magazine. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.187, + 0.472, + 0.243 + ], + "angle": 0, + "content": "[2] Nicola Beume, Boris Naujoks, and Michael Emmerich. SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3):1653–1669, Sept. 2007. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.246, + 0.47, + 0.315 + ], + "angle": 0, + "content": "[3] Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, and Felix Heide. Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.317, + 0.47, + 0.358 + ], + "angle": 0, + "content": "[4] Mario Bijelic, Tobias Gruber, and Werner Ritter. A benchmark for LiDAR sensors in fog: Is detection breaking down? In IEEE Intelligent Vehicles Symposium (IV), 2018. 1, 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.361, + 0.47, + 0.43 + ], + "angle": 0, + "content": "[5] Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, and Karol Zieba. End to End Learning for Self-Driving Cars, Apr. 2016. arXiv:1604.07316 [cs]. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.432, + 0.47, + 0.5 + ], + "angle": 0, + "content": "[6] Alexander Carballo, Jacob Lambert, Abraham Monrroy Cano, David Robert Wong, Patiphon Narksri, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, and Kazuya Takeda. LIBRE: The multiple 3D LiDAR dataset. In IEEE Intelligent Vehicles Symposium (IV), 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.503, + 0.47, + 0.572 + ], + "angle": 0, + "content": "[7] M. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, and J. Hays. Argoverse: 3D tracking and forecasting with rich maps. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.574, + 0.47, + 0.657 + ], + "angle": 0, + "content": "[8] Ran Cheng, Yaochu Jin, Markus Olhofer, and Bernhard Sendhoff. A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 20(5):773-791, Oct. 2016. Conference Name: IEEE Transactions on Evolutionary Computation. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.087, + 0.66, + 0.47, + 0.701 + ], + "angle": 0, + "content": "[9] Robert L. Cook and Kenneth E. Torrance. A reflectance model for computer graphics. ACM SIGGRAPH Computer Graphics, 15(3):307-316, Aug. 1981. 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.703, + 0.47, + 0.758 + ], + "angle": 0, + "content": "[10] K. Deb and J. Sundar. Reference point based multi-objective optimization using evolutionary algorithms | Proceedings of the 8th annual conference on Genetic and evolutionary computation. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.761, + 0.47, + 0.844 + ], + "angle": 0, + "content": "[11] Kalyanmoy Deb and J. Sundar. Reference point based multi-objective optimization using evolutionary algorithms. In Proceedings of the 8th annual conference on Genetic and evolutionary computation, GECCO '06, pages 635-642, New York, NY, USA, July 2006. Association for Computing Machinery. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.47, + 0.901 + ], + "angle": 0, + "content": "[12] Ayush Dewan, Tim Caselitz, Gian Diego Tipaldi, and Wolfram Burgard. Motion-based detection and tracking in 3D LiDAR scans. In IEEE International Conference on Robotics and Automation (ICRA), 2016. 1" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.116, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.176 + ], + "angle": 0, + "content": "[13] Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In Sergey Levine, Vincent Vanhoucke, and Ken Goldberg, editors, Proceedings of the 1st Annual Conference on Robot Learning, volume 78 of Proceedings of Machine Learning Research, pages 1-16. PMLR, 13-15 Nov 2017. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.179, + 0.894, + 0.233 + ], + "angle": 0, + "content": "[14] Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In 1st Annual Conference on Robot Learning, 2017. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.236, + 0.894, + 0.278 + ], + "angle": 0, + "content": "[15] Michael T. M. Emmerich and André H. Deutz. A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Natural Computing, 17(3):585-609, Sept. 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.281, + 0.893, + 0.349 + ], + "angle": 0, + "content": "[16] Jin Fang, Dingfu Zhou, Feilong Yan, Tongtong Zhao, Feihu Zhang, Yu Ma, Liang Wang, and Ruigang Yang. Augmented LiDAR Simulator for Autonomous Driving. IEEE Robotics and Automation Letters, 5(2):1931-1938, Apr. 2020. arXiv:1811.07112 [cs]. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.352, + 0.893, + 0.407 + ], + "angle": 0, + "content": "[17] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2012. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.41, + 0.893, + 0.451 + ], + "angle": 0, + "content": "[18] Christopher Goodin, Daniel Carruth, Matthew Doude, and Christopher Hudson. Predicting the influence of rain on LiDAR in ADAS. *Electronics*, 8, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.454, + 0.894, + 0.523 + ], + "angle": 0, + "content": "[19] Martin Hahner, Christos Sakaridis, Mario Bijelic, Felix Heide, Fisher Yu, Dengxin Dai, and Luc Van Gool. LiDAR Snowfall Simulation for Robust 3D Object Detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 1, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.526, + 0.894, + 0.581 + ], + "angle": 0, + "content": "[20] Martin Hahner, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Fog simulation on real LiDAR point clouds for 3D object detection in adverse weather. In IEEE International Conference on Computer Vision (ICCV), 2021. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.584, + 0.893, + 0.611 + ], + "angle": 0, + "content": "[21] Nikolaus Hansen. The CMA evolution strategy: A tutorial, 2016. 2, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.614, + 0.893, + 0.668 + ], + "angle": 0, + "content": "[22] Felix Heide, Steven Diamond, David B Lindell, and Gordon Wetzstein. Sub-picosecond photon-efficient 3d imaging using single-photon sensors. Scientific reports, 8(1):1-8, 2018. 3,4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.672, + 0.893, + 0.728 + ], + "angle": 0, + "content": "[23] Robin Heinzler, Philipp Schindler, Jürgen Seekircher, Werner Ritter, and Wilhelm Stork. Weather influence and classification with automotive LiDAR sensors. In IEEE Intelligent Vehicles Symposium (IV), 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.73, + 0.893, + 0.785 + ], + "angle": 0, + "content": "[24] Braden Hurl, Krzysztof Czarnecki, and Steven Waslander. Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception, May 2019. arXiv:1905.00160 [cs]. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.788, + 0.893, + 0.83 + ], + "angle": 0, + "content": "[25] Maria Jokela, Matti Kutila, and Pasi Pyykonen. Testing and validation of automotive point-cloud sensors in adverse weather conditions. Applied Sciences, 9, 2019. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.832, + 0.893, + 0.901 + ], + "angle": 0, + "content": "[26] Velat Kilic, Deepti Hegde, Vishwanath Sindagi, A. Brinton Cooper, Mark Foster, and Vishal Patel. LiDAR light scattering augmentation (LISA): physics-based simulation of adverse weather conditions for 3D object detection. arXiv preprint 2107.07004, 2021. 1, 3" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.894, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.945, + 0.52, + 0.957 + ], + "angle": 0, + "content": "13412" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.161 + ], + "angle": 0, + "content": "[27] Matti Kutila, Pasi Pyykönen, Maria Jokela, Tobias Gruber, Mario Bijelic, and Werner Ritter. Benchmarking automotive LiDAR performance in arctic conditions. In IEEE International Conference on Intelligent Transportation Systems (ITSC), 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.167, + 0.47, + 0.223 + ], + "angle": 0, + "content": "[28] Marco Laumanns, Lothar Thiele, Kalyanmoy Deb, and Eckart Zitzler. Combining convergence and diversity in evolutionary multiobjective optimization. Evolutionary Computation, 10(3):263-282, 2002. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.228, + 0.469, + 0.299 + ], + "angle": 0, + "content": "[29] Ke Li, Renzhi Chen, Guangtao Fu, and Xin Yao. Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 23(2):303-315, Apr. 2019. Conference Name: IEEE Transactions on Evolutionary Computation. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.304, + 0.469, + 0.347 + ], + "angle": 0, + "content": "[30] Xiaolu Li, Bingwei Yang, Xinhao Xie, Duan Li, and Lijun Xu. Influence of waveform characteristics on lidar ranging accuracy and precision. Sensors, 18(4), 2018. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.352, + 0.469, + 0.421 + ], + "angle": 0, + "content": "[31] Ievgeniia Maksymova, Christian Steger, and Norbert Druml. Review of LiDAR Sensor Data Acquisition and Compression for Automotive Applications. Proceedings, 2(13):852, 2018. Number: 13 Publisher: Multidisciplinary Digital Publishing Institute. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.427, + 0.469, + 0.495 + ], + "angle": 0, + "content": "[32] Yunze Man, Xinshuo Weng, Prasanna Kumar Sivakumar, Matthew O'Toole, and Kris Kitani. Multi-echo lidar for 3d object detection. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 3743-3752, 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.502, + 0.469, + 0.586 + ], + "angle": 0, + "content": "[33] Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong, Wenyuan Zeng, Mikita Sazanovich, Shuhan Tan, Bin Yang, Wei-Chiu Ma, and Raquel Urtasun. Lidarsim: Realistic lidar simulation by leveraging the real world. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11164-11173, 2020. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.592, + 0.469, + 0.674 + ], + "angle": 0, + "content": "[34] Sebastien Michaud, Jean-François Lalonde, and Philippe Giguère. Towards characterizing the behavior of LiDARs in snowy conditions. In Workshop on Planning, Perception and Navigation for Intelligent Vehicles, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.681, + 0.469, + 0.749 + ], + "angle": 0, + "content": "[35] Anish Mittal, Anush K. Moorthy, and Alan C. Bovik. Automatic parameter prediction for image denoising algorithms using perceptual quality features. In Human Vision and Electronic Imaging XVII, volume 8291, pages 110-116. SPIE, Feb. 2012. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.756, + 0.469, + 0.839 + ], + "angle": 0, + "content": "[36] Ali Mosleh, Avinash Sharma, Emmanuel Onzon, Fahim Mannan, Nicolas Robidoux, and Felix Heide. Hardware-in-the-Loop End-to-End Optimization of Camera Image Processing Pipelines. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7526–7535, Seattle, WA, USA, June 2020. IEEE. 2, 5, 6, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.846, + 0.469, + 0.9 + ], + "angle": 0, + "content": "[37] Matthias Müller, Vincent Casser, Jean Lahoud, Neil Smith, and Bernard Ghanem. Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications, Mar. 2018. arXiv:1708.05869 [cs]. 3" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.092, + 0.892, + 0.161 + ], + "angle": 0, + "content": "[38] Kazuki Nakamura, Kenji Narumi, Kohei Kikuchi, and Yasuhisa Inada. Liquid crystal-tunable optical phased array for LiDAR applications. In Smart Photonic and Optoelectronic Integrated Circuits XXIII, volume 11690, pages 94-99. SPIE, Mar. 2021. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.164, + 0.892, + 0.247 + ], + "angle": 0, + "content": "[39] Kouhei Nishida and Youhei Akimoto. Population Size Adaptation for the CMA-ES Based on the Estimation Accuracy of the Natural Gradient. In Proceedings of the Genetic and Evolutionary Computation Conference 2016, GECCO '16, pages 237–244, New York, NY, USA, July 2016. Association for Computing Machinery. 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.249, + 0.892, + 0.304 + ], + "angle": 0, + "content": "[40] Jun Nishimura, Timo Gerasimow, Sushma Rao, Aleksandar Sutic, Chyuan-Tyng Wu, and Gilad Michael. Automatic ISP image quality tuning using non-linear optimization, Feb. 2019. arXiv:1902.09023 [cs]. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.305, + 0.892, + 0.387 + ], + "angle": 0, + "content": "[41] Matthew O'Toole, Felix Heide, David B. Lindell, Kai Zang, Steven Diamond, and Gordon Wetzstein. Reconstructing Transient Images from Single-Photon Sensors. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2289–2297, Honolulu, HI, July 2017. IEEE. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.39, + 0.892, + 0.473 + ], + "angle": 0, + "content": "[42] Annibale Panichella. An adaptive evolutionary algorithm based on non-euclidean geometry for many-objective optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '19, pages 595-603, New York, NY, USA, July 2019. Association for Computing Machinery. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.475, + 0.892, + 0.545 + ], + "angle": 0, + "content": "[43] Adithya K. Pediredla, Aswin C. Sankaranarayanan, Mauro Buttafava, Alberto Tosi, and Ashok Veeraraghavan. Signal Processing Based Pile-up Compensation for Gated Single-Photon Avalanche Diodes, June 2018. arXiv:1806.07437 [physics]. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.547, + 0.892, + 0.602 + ], + "angle": 0, + "content": "[44] Luke Pfister and Yoram Bresler. Learning Filter Bank Sparsifying Transforms. IEEE Transactions on Signal Processing, 67(2):504-519, Jan. 2019. Conference Name: IEEE Transactions on Signal Processing. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.604, + 0.892, + 0.658 + ], + "angle": 0, + "content": "[45] Francesco Pittaluga, Zaid Tasneem, Justin Folden, Brevin Tilmon, Ayan Chakrabarti, and Sanjeev J. Koppal. Towards a MEMS-based Adaptive LIDAR, Oct. 2020. arXiv:2003.09545 [cs, eess]. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.66, + 0.892, + 0.702 + ], + "angle": 0, + "content": "[46] Ralph Rasshofer, Martin Spies, and Hans Spies. Influences of weather phenomena on automotive laser radar systems. Advances in Radio Science, 9, 2011. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.704, + 0.892, + 0.743 + ], + "angle": 0, + "content": "[47] Stephan R. Richter, Zeeshan Hayden, and Vladlen Koltun. Playing for Benchmarks, Sept. 2017. arXiv:1709.07322 [cs]. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.747, + 0.892, + 0.816 + ], + "angle": 0, + "content": "[48] Nicolas Robidoux, Luis E. Garcia Capel, Dong-eun Seo, Avinash Sharma, Federico Ariza, and Felix Heide. End-to-End High Dynamic Range Camera Pipeline Optimization. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6297–6307, 2021. 2, 5, 6" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.818, + 0.892, + 0.9 + ], + "angle": 0, + "content": "[49] German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M. Lopez. The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3234-3243, June 2016. ISSN: 1063-6919. 3" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.092, + 0.892, + 0.9 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.519, + 0.957 + ], + "angle": 0, + "content": "13413" + } + ], + [ + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.092, + 0.47, + 0.147 + ], + "angle": 0, + "content": "[50] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126(9):973-992, 2018. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.149, + 0.472, + 0.219 + ], + "angle": 0, + "content": "[51] Haitham Seada and Kalyanmoy Deb. A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives. IEEE Transactions on Evolutionary Computation, 20(3):358-369, June 2016. Conference Name: IEEE Transactions on Evolutionary Computation. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.221, + 0.47, + 0.276 + ], + "angle": 0, + "content": "[52] Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles, July 2017. arXiv:1705.05065 [cs]. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.278, + 0.47, + 0.348 + ], + "angle": 0, + "content": "[53] Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, and Hongsheng Li. PV-RCNN: Point-voxel feature set abstraction for 3D object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1, 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.349, + 0.47, + 0.39 + ], + "angle": 0, + "content": "[54] Dongeek Shin. Computational imaging with small numbers of photons. Thesis, Massachusetts Institute of Technology, 2016. Accepted: 2016-07-18T20:05:44Z. 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.392, + 0.47, + 0.447 + ], + "angle": 0, + "content": "[55] Andrea Simonelli, Samuel Rota Rota Bulò, Lorenzo Porzi, Manuel López-Antequera, and Peter Kontschieder. Disentangling monocular 3D object detection. arXiv preprint arXiv:1905.12365, 2019. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.449, + 0.47, + 0.573 + ], + "angle": 0, + "content": "[56] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, and Dragomir Anguelov. Scalability in perception for autonomous driving: Waymo open dataset. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.575, + 0.47, + 0.659 + ], + "angle": 0, + "content": "[57] Tao Sun, Mattia Segu, Janis Postels, Yuxuan Wang, Luc Van Gool, Bernt Schiele, Federico Tombari, and Fisher Yu. SHIFT: a synthetic driving dataset for continuous multi-task domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 21371-21382, June 2022. 1, 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.66, + 0.47, + 0.716 + ], + "angle": 0, + "content": "[58] Maxime Tremblay, Shirsendu S. Halder, Raoul de Charette, and Jean-François Lalonde. Rain rendering for evaluating and improving robustness to bad weather. International Journal of Computer Vision (IJCV), 126, 2021. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.718, + 0.47, + 0.8 + ], + "angle": 0, + "content": "[59] Ethan Tseng, Ali Mosleh, Fahim Mannan, Karl St-Arnaud, Avinash Sharma, Yifan Peng, Alexander Braun, Derek Nowrouzezahrai, Jean-Francois Lalonde, and Felix Heide. Differentiable compound optics and processing pipeline optimization for end-to-end camera design. ACM Transactions on Graphics (TOG), 40(2):1-19, 2021. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.802, + 0.47, + 0.871 + ], + "angle": 0, + "content": "[60] Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl ST. Arnaud, Derek Nowrouzezahrai, Jean-François Lalonde, and Felix Heide. Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Transactions on Graphics, 38(4):1-14, Aug. 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.08, + 0.873, + 0.469, + 0.901 + ], + "angle": 0, + "content": "[61] G. Turin. An introduction to matched filters. IRE Transactions on Information Theory, 6(3):311-329, 1960. 4" + }, + { + "type": "list", + "bbox": [ + 0.08, + 0.092, + 0.472, + 0.901 + ], + "angle": 0, + "content": null + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.093, + 0.788, + 0.107 + ], + "angle": 0, + "content": "[62] Velodyne. Hdl-64e user's manual, 2007. 5" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.109, + 0.893, + 0.163 + ], + "angle": 0, + "content": "[63] Yash Vesikar, Kalyanmoy Deb, and Julian Blank. Reference Point Based NSGA-III for Preferred Solutions. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1587–1594, Nov. 2018. 7" + }, + { + "type": "ref_text", + "bbox": [ + 0.503, + 0.165, + 0.893, + 0.233 + ], + "angle": 0, + "content": "[64] F Villa, B Markovic, S Bellisai, D Bronzi, A Tosi, F Zappa, S Tisa, D Durini, S Weyers, U Paschen, et al. SPAD smart pixel for time-of-flight and time-correlated single-photon counting measurements. IEEE Photonics Journal, 4(3):795-804, 2012. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.235, + 0.893, + 0.317 + ], + "angle": 0, + "content": "[65] Niclas Vödisch, Ozan Unal, Ke Li, Luc Van Gool, and Dengxin Dai. End-to-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization. IEEE Robotics and Automation Letters, 7(2):2242-2249, Apr. 2022. Conference Name: IEEE Robotics and Automation Letters. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.319, + 0.893, + 0.36 + ], + "angle": 0, + "content": "[66] Andrew Wallace, Abderrahim Halimi, and Gerald Buller. Full waveform LiDAR for adverse weather conditions. IEEE Transactions on Vehicular Technology, 69, 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.362, + 0.893, + 0.402 + ], + "angle": 0, + "content": "[67] Dingkang Wang, Connor Watkins, and Huikai Xie. MEMS mirrors for LiDAR: A review. Micromachines, 11(5), 2020. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.405, + 0.893, + 0.473 + ], + "angle": 0, + "content": "[68] Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas, Mengye Ren, and Raquel Urtasun. AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles, Jan. 2022. arXiv:2101.06549 [cs]. 3" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.476, + 0.893, + 0.544 + ], + "angle": 0, + "content": "[69] Xinshuo Weng, Yunze Man, Jinhyung Park, Ye Yuan, Dazhi Cheng, Matthew O'Toole, and Kris Kitani. All-In-One Drive: A Large-Scale Comprehensive Perception Dataset with High-Density Long-Range Point Clouds. arXiv, 2021. 1, 3, 4" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.546, + 0.893, + 0.587 + ], + "angle": 0, + "content": "[70] George Williams. Optimization of eyesafe avalanche photodiode LiDAR for automobile safety and autonomous navigation systems. Optical Engineering, 56(3):1-9, 2017. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.589, + 0.893, + 0.671 + ], + "angle": 0, + "content": "[71] Chyuan-Tyng Wu, Leo F. Isikdogan, Sushma Rao, Bhavin Nayak, Timo Gerasimow, Aleksandar Sutic, Liron Ainkedem, and Gilad Michael. VisionISP: Repurposing the image signal processor for computer vision applications. In IEEE International Conference on Image Processing (ICIP), pages 4624-4628, 2019. 2" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.673, + 0.893, + 0.713 + ], + "angle": 0, + "content": "[72] Ji Zhang and Sanjiv Singh. LOAM: LiDAR odometry and mapping in real-time. Robotics: Science and Systems Conference (RSS), 2014. 1" + }, + { + "type": "ref_text", + "bbox": [ + 0.504, + 0.716, + 0.893, + 0.757 + ], + "angle": 0, + "content": "[73] Qingfu Zhang and Hui Li. Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11:712-731, 2007. 2" + }, + { + "type": "list", + "bbox": [ + 0.503, + 0.093, + 0.893, + 0.757 + ], + "angle": 0, + "content": null + }, + { + "type": "page_number", + "bbox": [ + 0.481, + 0.946, + 0.52, + 0.957 + ], + "angle": 0, + "content": "13414" + } + ] +] \ No newline at end of file diff --git a/2023/LiDAR-in-the-Loop Hyperparameter Optimization/9dfdd95b-2191-44f3-8949-8eb946acc7a7_origin.pdf b/2023/LiDAR-in-the-Loop Hyperparameter Optimization/9dfdd95b-2191-44f3-8949-8eb946acc7a7_origin.pdf new file mode 100644 index 0000000000000000000000000000000000000000..854abd31d96859dd33ea5634636fb371cb449b2a --- /dev/null +++ b/2023/LiDAR-in-the-Loop Hyperparameter Optimization/9dfdd95b-2191-44f3-8949-8eb946acc7a7_origin.pdf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4e8662b0ab25d82eed9a0aa331e98fd26384cf18da2c583ef703e03c182b6fcc +size 8457945 diff --git a/2023/LiDAR-in-the-Loop Hyperparameter Optimization/full.md b/2023/LiDAR-in-the-Loop Hyperparameter Optimization/full.md new file mode 100644 index 0000000000000000000000000000000000000000..da5944eeff346ad9a2933b3804a0fd152c92527d --- /dev/null +++ b/2023/LiDAR-in-the-Loop Hyperparameter Optimization/full.md @@ -0,0 +1,366 @@ +# LiDAR-in-the-loop Hyperparameter Optimization + +Félix Goudreault $^{1}$ Dominik Scheuble $^{2}$ Mario Bijelic $^{2,3}$ Nicolas Robidoux $^{1}$ Felix Heide $^{1,3}$ $^{1}$ Algolux $^{2}$ Mercedes-Benz $^{3}$ Princeton University + +# Abstract + +LiDAR has become a cornerstone sensing modality for 3D vision. LiDAR systems emit pulses of light into the scene, take measurements of the returned signal, and rely on hardware digital signal processing (DSP) pipelines to construct 3D point clouds from these measurements. The resulting point clouds output by these DSPs are input to downstream 3D vision models – both, in the form of training datasets or as input at inference time. Existing LiDAR DSPs are composed of cascades of parameterized operations; modifying configuration parameters results in significant changes in the point clouds and consequently the output of downstream methods. Existing methods treat LiDAR systems as fixed black boxes and construct downstream task networks more robust with respect to measurement fluctuations. Departing from this approach, the proposed method directly optimizes LiDAR sensing and DSP parameters for downstream tasks. To investigate the optimization of LiDAR system parameters, we devise a realistic LiDAR simulation method that generates raw waveforms as input to a LiDAR DSP pipeline. We optimize LiDAR parameters for both 3D object detection IoU losses and depth error metrics by solving a nonlinear multi-objective optimization problem with a 0th-order stochastic algorithm. For automotive 3D object detection models, the proposed method outperforms manual expert tuning by $39.5\%$ mean Average Precision (mAP). + +# 1. Introduction + +Environment perception for autonomous drones [12, 53] and vehicles [72] requires precise depth sensing for safety-critical control decisions. Scanning LiDAR sensors have been broadly adopted in autonomous driving [3, 7, 56] as they provide high temporal and spatial resolution, and recent advances in MEMS scanning [67] and photodiode technology [64] have reduced their cost and form factor. + +The 3D LiDAR point cloud (PC) data that existing 3D detection methods take as input is produced by a LiDAR and digital signal processor (DSP) pipeline with many measurement and processing steps. Typical LiDAR sensors operate by sending out a laser pulse and measuring the temporal response through an Avalanche Photo Diode (APD). This temporal wavefront signal is fed to a DSP that extracts + +peaks corresponding to echos from candidate targets within the scene [70]. As such, DSP processing results in a 1000-fold data reduction for a single emitted beam, producing single or multiple 3D points per beam. Compressing the waveform into points in 3D space with minimal information loss is challenging because of object discontinuities, sub-surface scattering, multipath reflections, and scattering media, see Fig. 1. In particular, significant scattering occurs in adverse weather conditions like fog [3,4,6,20,23,25,66], rain [6,18,66] and snow [19,25,27,34]. LiDAR manufacturers currently handle such complications by manually adjusting internal sensing and DSP parameters in controlled environments and restricted real-world scenarios using a combination of visual inspection and depth quality metrics. + +Generally, LiDAR production units are black boxes with configuration parameters hidden from the user. To account for noisy point cloud measurements with spurious artifacts, existing work has explored methods that add simulated adverse effects and point cloud degradations that model rain [18, 26, 58], fog [3, 20, 50] and snow [19] to real black-box LiDAR datasets. Augmented point clouds in hand, downstream vision models are retrained for predictions more robust to point cloud data corruption. Another approach consists of generating synthetic measurements from 3D scenes using rendering engines [24, 57, 69]. Such existing methods typically avoid simulating transient light propagation and signal processing by converting 3D scene depth directly into a point cloud, thus lack physically realistic modeling of fluctuations arising from multipath effects or measurement noise. Notably, existing simulation methods that alter measurements or generate synthetic point clouds do not optimize sensing or DSP parameters for downstream vision performance. + +In this work, we directly optimize LiDAR pulse configuration and DSP hyperparameters for end-to-end downstream 3D object detector losses and PC depth quality metrics, a challenging task because hyperparameter space involves tens to hundreds of categorical, discrete and effectively continuous parameters affecting downstream tasks in complex nonlinear ways via the intermediate point cloud. Examples of categorical hyperparameters are Velodyne LiDAR sensor return modes that configure internal wavefront peak selection algorithms for point cloud formation; + +![](images/b1bf9cf31d8ad4d0bf90895dde4162bb8e5972285926f5bf991492a6971a3467.jpg) +Figure 1. LiDAR Point Cloud Formation. Typical LiDAR sensor PC measurements are produced by a multi-stage measurement and signal processing chain: The LiDAR emits a laser pulse. This signal travels through the scene and returns to the detector after single or multiple reflections. Cluttered surfaces (a), strong retroreflectors (b) and ambient light are introduced (c) in the returned signals. Thus, the full transient waveform read by sensors is the superposition of multiple return paths. The DSP, itself a chain of processing blocks, processes all temporal waveforms and extracts a continuous stream of 3D points that forms the final point cloud (bottom). + +rotation velocity, which impacts angular resolution, is an example of a continuous hyperparameter [4]. + +Grid search optimization is impractical here because of combinatorial explosion. Orthogonally to LiDAR, it was recently shown that 0th order solvers can find camera DSP hyperparameters that improve downstream 2D object detectors [36, 48]. We propose an optimization method for LiDAR sensing and DSP hyperparameters that minimizes end-to-end domain-specific losses such as RMSE of the measured depth against ground truth and IoU measured on downstream 3D object detection. To assess the proposed method, we devised a LiDAR simulation method based on the CARLA engine [13] that models a LiDAR DSP as well as the full transient noisy waveform formed by multiple laser echoes. We optimize sensing and DSP hyperparameters by solving a Multi-Objective black-box Optimization (MOO) problem with a novel CMA-ES (Covariance Matrix Adaptation-Evolution Strategy [21]) that relies on a max-rank multi-objective scalarization loss [36] to dynamically improve scale matching between different loss components. In combination with a novel champion selection method, it finds a balanced Pareto-optimal solution for which no loss component has a comparatively poor value for LiDAR optimization with multiple objectives. We validate the proposed optimization method for 3D object detection and point cloud depth estimation, both in simulation and using an off-the-shelf experimental LiDAR sensor. + +Specifically, we make the following contributions: + +- We introduce a LiDAR wavefront simulation for the CARLA simulation environment that models realistic transient scene responses. +- We devise a novel multi-objective optimization method for balanced MOO of LiDAR parameters. +- We validate end-to-end LiDAR sensing and DSP optimization for 3D object detection and depth estimation through simulation and with a real system. For all ap + +plications, our approach outperforms existing state-of-the-art methods, including expert tuning. + +Simulator code and DSP models are published here1. + +Limitations Because commercial LiDAR units are IP-protected black boxes, interfacing their DSP hyperparameters is not straightforward. While the off-the-shelf LiDAR system used in this work makes some DSP hyperparameters accessible, most LiDAR systems are completely closed. We hope that these findings spur LiDAR vendors to follow the lead of digital camera and ISP (Image Signal Processor) vendors and open their processing pipelines. + +# 2. Related Work + +DSP and Sensor Hyperparameter Optimization. A growing body of work explores the optimization of sensors and DSPs for downstream vision tasks. Existing methods target camera ISPs and optics [36, 48, 59, 60, 71]. Departing from manual hyperparameter tuning by experts, these approaches optimize them automatically, driven by one or more downstream performance metric. As DSP and sensor hyperparameters can be categorical and losses are often non-convex and noisy, diverse optimization methods have been proposed. Early methods targeted specific processing blocks [35, 40, 44] as differentiable programs or in a reduced parameter space. Recent methods rely on differentiable pipeline proxies [60] or 0th-order optimization [36], alone or in combination with block coordinate descent [48]. One advantage of 0th-order optimizers is that they handle black box hardware and DSPs. 0th-order solvers used to optimize camera systems include MOEA/D [40, 73] and CMA-ES [21, 36, 48]. These approaches successfully tackle camera pipeline optimization, from the optics [59] to downstream detectors [36, 48]. To our knowledge, however, no loss-driven method has been proposed for end-to-end LiDAR system optimization: existing LiDAR sensors are designed in isolation from downstream depth and detection + +![](images/d9a1dc6d8f2b96dac806d8559e099dcc7afbf79091925b6812cffa763359be1f.jpg) +Figure 2. LiDAR Simulation Method. We propose a parameterizable LiDAR simulation model that generates full transient waveforms by extracting scene response $H$ , ambient light $a$ and object reflectances $s, d, \alpha$ from CARLA. End-to-end loss functions drive an MOO solver toward an optimal vector $\Theta$ that includes both pulse and DSP hyperparameters: Wavefronts are processed by the DSP, resulting in a point cloud $\mathbf{O}$ . The "loop is closed" by feeding $\mathbf{O}$ to 3D object detection and depth estimation methods, the output of which is "rated" by loss functions $\mathcal{L}$ . Validation datasets cycle through the loop until $\Theta$ converges. + +tasks. Aiming to close this gap, this work proposes automated LiDAR hyperparameter optimization. + +LiDAR Sensing and Point Cloud Generation. LiDAR sensors produce point clouds by emitting pulses of light into a scene and measuring the round trip time of sensor returns. Extracting a point cloud from time-of-flight measurements is a complex process that depends on measurement procedure specifics like beam formation, scanning, pulse/continuous wave generation and peak finding within the acquired temporal laser response [1,46]. LiDAR sensors differ in their scanning pattern, beam steering technology, wavelength, pulse profile, coherence of the measurement step, detector technology and DSP capabilities to process the measurement echos [1,31]. + +Existing LiDAR sensors can extract single or multiple peaks resulting from multi-path interreflections in the scene. Assuming a single Lambertian reflector in the scene, the temporal resolution and signal-to-noise ratio (SNR) of the measurement are tied to laser power. Accordingly, some existing optimization methods [43] use automated runtime laser power adjustment to maximize SNR while preventing oversaturation. Other approaches have been proposed for adaptive beam steering [38, 45]. Recently, Vodisch et al. [65] proposed beam configuration optimization via reinforcement learning methods driven by a downstream 3D detection loss. This method is the most similar to the proposed approach; however, it only predicts beam patterns, i.e., where to place sparse samples. In contrast, we optimize DSP hyperparameters in addition to sensing ones, pulse power and scanning parameters included. + +LiDAR Simulation. To assess and validate the proposed method, we introduce a new LiDAR simulation method that plugs directly into the CARLA simulator. Existing + +work [5, 14, 37, 47, 52] proposes several simulation environments; the open-source CARLA [14] simulator is broadly adopted in academia. Simulation frameworks have enabled the creation of multimodal synthetic datasets used successfully in academia and industry, e.g., PreSIL [24], SHIFT [57], AIODrive [32, 69] and SYNTHIA [49]. However, underlying simulation methods employ heuristic forward models; in addition, none of the datasets include full waveform returns that would allow simulating LiDAR point cloud generation. Even the AIODrive dataset [69], in which multiple peaks are returned via depth image convolution and Single Photon Avalanche Diode (SPAD) quantization, bakes transients into SPAD image formation, hence falls short of enabling realistic transient simulation. Similarly, real PC datasets augmentation methods have been employed to tackle rare occurring events like obstacles [16, 33], traffic [68], rain [20], fog [20] or snow [19, 26]. However, since the underlying datasets do not include the raw wavefronts, none of these approach facilitate modeling the DSP pipeline. We address these challenges with our simulation method that simulates full wavefront signals. Combined with a realistic DSP model, this produces PC data representative of real systems. + +# 3. Transient LiDAR Forward Model + +Consider a single laser pulse emitted by a LiDAR unit into a 3D scene, from which a returned signal is detected by a SPAD detector which sends temporal histograms to the sensor DSP. For channel $n$ at time $t$ , we model the sensor-incident temporal photon flux $\psi$ [22, 41, 54] as + +$$ +\psi^ {(n)} (t) = \left(H * g ^ {(n)}\right) (t) + a (t), \tag {1} +$$ + +where $g^{(n)}$ is the temporally varying photon flux emitted by the laser for channel $n$ , $H$ is the transient response from the + +scene, $a(t)$ is the ambient photon flux, and $*$ is the temporal convolution operator. By definition, the transient scene response $H$ includes multipath returns from scene interreflections and scattering. The detector measures the returned signal and digitizes the temporal measurement into temporal wavefronts processed by the DSP. For low photon counts or path lengths above a few meters typical in automotive scenes, the binning process can be modeled as a Poisson random process [22, 41, 43, 54]. We consequently model the wavefront's number of photons $r^{(n)}$ detected within the integration time $\Delta$ in channel $n$ 's time bin $k$ as + +$$ +r ^ {(n)} [ k ] \sim \text {P o i s s o n} \left(\int_ {k \Delta} ^ {(k + 1) \Delta} \psi^ {(n)} (t) d t\right). \tag {2} +$$ + +# 3.1. Transient Scene and Pulse Model + +Rasshofer et al. [46] introduced a linear model for direct laser reflections in the LiDAR context for the incident transient response $H * g^{(n)}$ of Eq. (1). With this model, we formalize the transient response as + +$$ +(H * g ^ {(n)}) (R) = C \int_ {0} ^ {2 \tau^ {(n)}} g ^ {(n)} (t) H \left(R - \frac {\mathrm {c t}}{2}\right) d t, \tag {3} +$$ + +where $R$ is the distance between the sensor and the observed point, $\mathbf{c}$ is the speed of light, $C$ is a proportionality constant, independent of $t$ and $R$ , describing the system, and $2\tau^{(n)}$ is the total pulse duration for channel $n$ . We convert path length to time with $t = R / c$ . Following Hahner et al. [19] and Rasshofer et al. [46], we adopt the following pulse shape + +$$ +g ^ {(n)} (t) = \left\{ \begin{array}{l l} P _ {0} ^ {(n)} \sin^ {2} \left(\frac {\pi t}{2 \tau^ {(n)}}\right) & \text {i f} 0 \leq t \leq 2 \tau^ {(n)} \\ 0 & \text {o t h e r w i s e} \end{array} , \right. \tag {4} +$$ + +where $P_0^{(n)}$ is channel $n$ 's pulse power magnitude. The transient scene response $H$ embedded in Eq. (3) consists of the geometric attenuation of the light, proportional to $1 / (2R)^2$ , and the scene response. For a single opaque point object $i$ , the latter is proportional to its reflectance $\rho_i$ and a Dirac function $\delta(R - R_i)$ , $R_i$ being the object distance to the sensor. Reformulating Eq. (3) for a single echo from $i$ yields + +$$ +(H * g ^ {(n)}) _ {i} (R) = \left\{ \begin{array}{l l} f _ {i} ^ {(n)} (R) & \text {i f} R _ {i} \leq R \leq R _ {i} + c \tau^ {(n)} \\ 0 & \text {o t h e r w i s e} \end{array} \right. \tag {5} +$$ + +$$ +\text {w i t h} f _ {i} ^ {(n)} (R) = \frac {C P _ {0} ^ {(n)} \rho_ {i}}{4 R _ {i} ^ {2}} \sin^ {2} \left(\frac {\pi}{c \tau^ {(n)}} (R - R _ {i})\right). \tag {6} +$$ + +# 3.2. Object Reflectance Model + +The object reflectance $\rho_{i}$ depends on the material BRDF and the angle of incidence $\theta$ . We model reflectance using specular and diffuse components of the retroreflected portion of the Cook-Terrance model [9], that is, + +$$ +\rho_ {i} = \frac {\alpha^ {4} s \cos \theta}{4 \left[ \cos^ {2} \theta \left(\alpha^ {4} - 1\right) + 1 \right] ^ {2} \left[ \cos \theta (1 - k) + k \right] ^ {2}} + d \cos \theta , \tag {7} +$$ + +where $s, d$ and $\alpha \in [0,1]$ refer to the specular, diffuse and roughness properties of a surface material and $k = (\alpha + 1)^2 / 8$ , see Supplemental Material. To render realistic textures without a large texture database, we approximate $s, d, \alpha$ through CARLA's Phong-like parameters $s, d$ and $\alpha$ . These parameters are not directly accessible, so we extract them by projecting targeted hit points onto custom camera images encoding their values, encoding illustrated at the bottom right of Fig. 2. Specifically, we define a function $\mathcal{P}_i$ that, for a rendered image $I_{sd\alpha}$ , returns pixel information at the location of projected point $i$ , that is, $[s, d, \alpha] = \mathcal{P}_i(I_{sd\alpha})$ . + +# 3.3. Ambient Illumination + +The ambient light $a(t)$ in Eq. (1) is modeled following Weng et al. [69]. A location $i$ is projected on the red channel of a rendered RGB camera image in which shadows and reflections are properly accounted for; denote this image by $I_{\mathrm{red}}$ . We approximate $a(t)$ as a constant over waveform time bins, that is, $a(t) \equiv a_i = \mathcal{P}_i(I_{\mathrm{red}})$ , where $a_i$ is independent of $t$ . See Supplemental Material for details. + +# 3.4. Multipath Transients + +Multipath transients for laser beams hitting object discontinuities are taken as primary artifact sources in automotive scenarios. Multipath transients are modeled as linear combinations of neighboring waveforms. Specifically, a supersampled collection of $\{R_i\}$ and channels is computed using direct illumination only; then, for each LiDAR channel and each horizontal angle, a downsampled waveform $\psi_j^{(m)}$ is obtained: + +$$ +\psi_ {j} ^ {(m)} (R) = \sum_ {i \in \mathcal {N} (j), n \in \mathcal {N} (m)} \left(K _ {i} ^ {(n)} \left(H * g ^ {(n)}\right) _ {i} (R) + a _ {i}\right), \tag {8} +$$ + +where $\mathcal{N}(j)$ and $\mathcal{N}(m)$ define respectively the spatial neighborhood of the target point $j$ and the channel $m$ , and the $K_{i}^{(n)}$ s are normalized weights that can be interpreted as a beam spatial intensity profile, see Supplemental Material. + +# 3.5. LiDAR Sensing and DSP Model + +A sensor-incident waveform $\psi_j^{(m)}$ is measured according to Eq. (2) and, along with sensor noise, subjected to clipping at a saturation level that depends on detector type. The DSP converts noisy and saturated waveforms $r_j^{(m)}$ into a point cloud $\mathbf{O}$ that includes range and intensity information. The entire process, from the emission of a laser pulse $g^{(m)}$ to the output of a processed point cloud $\mathbf{O}$ is shown in Fig. 3. + +With compressed notation, we model LiDAR sensing as a function $\Phi (\Theta)$ with hyperparameters $\Theta = (P_0^{(m)},\tau^{(m)},V^{(m)})$ . Laser power $P_0^{(m)}$ and pulse duration $\tau^{(m)}$ are functions of the channel $m$ ; they determine the emitted pulse $g^{(m)}$ . First, the DSP denoises by convolving the measured waveform $r_j^{(m)}$ with the emitted pulse $g^{(m)}$ , see [61]. Next, ambient light is estimated by removing the waveform's median from $r_j^{(m)}$ . This allows the DSP + +![](images/71674520a79b0bf56f19aeed2673c034ebd7970c7a9e2de6f5888bfb88eb606d.jpg) +Figure 3. LiDAR Sensing and DSP Model $\Phi$ . The LiDAR sensing and DSP hyperparameters $\Theta$ , including pulse power, duration, and parameters for rising edge detection in a transient wavefront, affect the quality of the generated point cloud $\mathbf{O}$ . + +to find adequate noise thresholds $V^{(m)}$ since ambient light varies strongly throughout the scene. Typical DSPs use a rising edge detector [30] that finds peaks along the denoised waveform by identifying intersections with $V^{(m)}$ . Multiple peaks may arise; the peak with the highest intensity is added to the point cloud O. Finally, the maximum intensity is compensated for the emitted laser pulse: The pulse half width $\tau^{(m)} / 2$ and power level $P_0^{(m)}$ are used as scaling factors to recover the true intensities, see Eq. 3. + +Modern consumer LiDAR sensors have upward of 128 channels. Optimizing every channel hyperparameter individually is prohibitive. The Velodyne HDL-64 bundles lasers in two groups [62]; our LiDAR model likewise groups lower and upper lasers. Within each group, hyperparameter modulation is an affine function of the channel number with tuneable slope and bias. The edge threshold is modeled as a continuous parameter $V^{(m)} \in [0,2]$ . In contrast, power levels $P_0^{(m)}$ can only take one of 11 values, the lowest making the peak almost indistinguishable from ambient noise and the highest likely to saturate at close range. Pulse duration $\tau^{(m)}$ takes discrete values ranging from 3 to 15 ns in 1 ns increments. See Supplemental Material. + +# 4. Optimization + +We propose a multi-objective optimization (MOO) method that finds Pareto-optimal (see the Supplemental Material for a definition) LiDAR hyperparameters. Specifically, optimization returns high-quality point clouds when optimized for depth precision and optimal mAP when point clouds are fed to a downstream object detection module. We first formulate the optimization problem, then we present the proposed optimization method. + +# 4.1. LiDAR Hyperparameter Optimization + +We optimize hyperparameters with loss-driven end-to-end optimization. Like Robidoux et al. [48], we optimize the system as a whole, bundling hardware functionality, for example the laser beam power of each channel, with DSP functionality. The operation of the LiDAR imaging pipeline + +$\Phi$ is modulated by $\mathrm{P}$ hyperparameters $\Theta = (\Theta_{1},\dots ,\Theta_{\mathrm{P}})$ with ranges of values normalized to the unit interval [0, 1]. With $\mathrm{T}\gg 2\tau$ we model each of the J channels $\Phi_j$ of $\Phi = (\Phi_1,\ldots ,\Phi_{\mathrm{J}})$ as + +$$ +\Phi_ {j}: [ - \mathrm {T}, 0 ] ^ {[ 0, \infty)} \times [ 0, 1 ] ^ {\mathrm {P}} \rightarrow \mathbb {S} ^ {[ 0, \infty)}, (\mathrm {r} _ {j}, \Theta) \mapsto \mathrm {O} _ {j}, \tag {9} +$$ + +$\mathrm{O}_j$ being a mapping from the unit sphere $\mathbb{S}$ (proxy for projective geometry) to nonnegative distances with 0 understood as "undefined" so that each $\Phi_j$ reconstructs a portion $\mathrm{O}_j$ of the overall point cloud $\mathbf{O}$ from a waveform $r_j$ truncated to the time interval $[-T,0]$ . The overall $\Theta$ -modulated LiDAR pipeline + +$$ +\Phi : (\mathbf {r} (H, \Theta), \Theta) \rightarrow \mathbf {O} \tag {10} +$$ + +maps the set of truncated wave forms $\mathbf{r} = (\mathrm{r}_1,\dots ,\mathrm{r}_{\mathrm{J}})$ to the point cloud $\mathbf{O}$ that contains the compressed information available to downstream detectors about the changing scene $H$ . We seek solutions of + +$$ +\boldsymbol {\Theta} ^ {*} = \underset {\boldsymbol {\Theta} \in [ 0, 1 ] ^ {\mathrm {P}}} {\arg \min } \mathcal {L} (\boldsymbol {\Theta}), \tag {11} +$$ + +which are Pareto-optimal with respect to the MOO loss vector $\mathcal{L} = (\mathcal{L}_1, \dots, \mathcal{L}_{\mathrm{L}})$ . Loss components may not directly use O; for example, $\mathcal{L}_l$ may use data tapped out of the pipeline (e.g., a channel's waveform $\mathbf{r}_j$ ) or the output of a downstream detector (e.g., a deep CNN) which ingests O (e.g., mAP). The Pareto front [28], which contains Pareto-optimal compromises between losses, is the solution set of Eq. (11), from which a single "champion" is selected using additional criteria. + +# 4.2. Optimization Algorithm + +To solve the MOO problem, we propose a variant of the Covariance Matrix Adaptation-Evolution Strategy methods of Mosleh et al. [36] and Robidoux et al. [48] that differs from the state of the art in a number of ways. It is listed in Algorithm 1. We use centroid weights that change between generations; to obtain better transients, we greedily override the CMA-ES centroid whenever a new Best So Far (BSF) is found, isolating the computation of CMA-ES statistics from the resulting "jump" as well as from the novel inclusion of centroids as BSF candidates; and we formulate a stable dynamic max-rank loss scalarization which we use to drive optimization and as champion selection criterion. + +Stable Dynamic Max-Rank Loss Scalarization Scalarizations like the well-known convex combination $\sum_{l=1}^{L} \mathrm{w}_l \mathcal{L}_l$ which boils down to the $\ell_1$ -norm of the loss vector $\mathcal{L}$ with unit weights $\mathrm{w}_l$ , are used to combine multiple objectives so that a single objective optimizer yield MOO solutions [15]. Scalarization weights are difficult to choose when loss variations are not commensurate. The max-rank loss addresses this issue. In the context of a generation-based algorithm like Algorithm 1, Mosleh et al. [36] define a max-rank loss as follows. Let + +$$ +\mathcal {R} _ {l} ^ {q, m, n} = \operatorname {r a n k} \left. \mathcal {L} _ {l} ^ {q, m} \text {w i t h i n} \left\{\mathcal {L} _ {l} ^ {r, o} \right\} _ {r \in \{0, \dots , 4 \mathrm {P} \}} ^ {o \in \{1, \dots , n \}} \right. \tag {12} +$$ + +with ranks counted from 0 and loss component value ties resolved by left bisection. Then, the weighted (left-bisection) max-rank loss $\mathcal{M}^{q,m,n}$ of the hyperparameter vector $\Theta^{q,m}$ at the end of generation $n$ is + +$$ +\mathcal {M} ^ {q, m, n} = \max _ {l \in \{1, \dots , \mathrm {L} \}} \left(\mathrm {w} _ {l} \cdot \mathcal {R} _ {l} ^ {q, m, n}\right). \tag {13} +$$ + +The max-rank loss is dynamic. For a given $\Theta^{q,m}$ its values are monotone non-decreasing with respect to the addition of data. Because weights multiply ranks, they are non-dimensional, hence dial in the relative importance of loss components. While Mosleh et al. [36] scale each $\mathbf{w}_l$ by the (damped) running proportion of individuals that "fail" to pass a user-defined threshold, we find that such adaptive weigths break monotonicity. Instead, we keep $\mathbf{w}_l$ s fixed. We also improve on Mosleh et al. in that we average left and right bisection ranks (exception: if the left bisection rank is 0, the "average" is set to 0) to stabilize $\mathcal{M}^{q,m,n}$ with respect to loss value tie breaking (from, e.g., noise) and creation (quantization); this defines our stable (dynamically monotone) max-rank loss scalarization. + +Dual-Weight CMA-ES Besides more refined seatbelting (see Supplemental Material), Algorithm 1 differs from earlier CMA-ES in its use of non-constant (hyperparameter, not loss) centroid weights. Although variable CMA-ES generation sizes are common [39], the formula used to derive centroid weights is invariably kept fixed; in contrast, the proposed CMA-ES alternates between gradient-seeking centroid weights [36], which assign zero weight to the worst quartile of each generation instead of the usual half to exploit the symmetry of the second and third quartiles of Gaussian distributions to get a more accurate gradient approximation, and boundary-stabilizing centroid weights [48] with no discard so that further exploration not go in the wrong direction near generic local minima. + +Other novel components are that the loss of the weighted centroid of every generation is evaluated (standard CMA-ES only generate Gaussian clouds with them) and the greedy branch in Algorithm 1 (Lines 18-20): If any individual of the generation, weighted centroid included, turns out to be a strict minimizer, it becomes the next generation Gaussian cloud center. + +# 5. Experiments + +In the following, we first validate the proposed approach with the LiDAR simulation model, jointly optimizing depth estimation and downstream 3D object detection within simulated scenes. Then, we compare the proposed optimization algorithm to recent 0th-order solvers. Finally, we present experiments with an off-the-shelf hardware LiDAR unit. + +Experimental Setup As described in Sec. (3.5), hyperparameters affect the wavefront and DSP. We optimize the DSP rising edge threshold $V^{(m)}$ and 10 LiDAR hyperparameters that control low-level sensing parameters including the laser power $P_0^{(m)}$ and laser pulse width $\tau^{(m)}$ for + +Algorithm 1 LiDAR Hyperparameter Optimization. +Require: LiDAR $\Phi$ , $\Theta \in [0,1]^{\mathrm{P}}$ (initial hyperparameter vector), $\mathbb{N} \in \mathbb{N}^*$ (number of generations), $\varepsilon \in \left(0,\frac{1}{3}\right)$ (small bound), $\mathbf{C} \in \mathbb{R}^{\mathrm{P} \times \mathrm{P}}$ (CMA-ES "directional" covariance matrix factor), $\sigma \in [\varepsilon ,\frac{1}{3} ]$ (square root of covariance matrix "scale" factor) +1: $\mathbf{p} \gets 0$ , $\mathbf{c} \gets 0$ (CMA-ES path vectors), $\Theta_{\mathrm{center}} \gets \Theta$ +2: for $n = 1$ to N do +3: $\Theta^{0,n} \gets \Theta$ +4: $\mathcal{L}^{0,n} \gets$ losses for LiDAR $\Phi$ modulated by $\Theta^{0,n}$ +5: for $p = 1$ to 4P do +6: $\Theta^{p,n} \gets$ random draw from Gaussian distribution with covariance matrix $\sigma^2\mathbf{C}$ centered at $\Theta_{\mathrm{center}}$ +7: $\Theta^{p,n} \gets \Theta^{p,n} +$ Gaussian distribution with diagonal cov. matrix proportional to square of quantization grain [48] +8: $\Theta^{p,n} \gets \Theta^{p,n}$ reflected back into $[0,1]^{\mathrm{P}}$ +9: $\mathcal{L}^{p,n} \gets$ losses for LiDAR $\Phi$ modulated by $\Theta^{p,n}$ +10: end for +11: Compute $\{\mathcal{M}^{q,m,n}\}_{q\in \{0,\dots,4\mathrm{P}\},m\in \{1,\dots,n\}}$ by including $\{\mathcal{L}^{p,n}\}_{p\in \{0,\dots,4\mathrm{P}\}}$ in rank computations +12: Use "eager" [36] centroid weights with $\lambda = 4\mathrm{P}$ , $\mu = 3\mathrm{P}$ +13: if $n$ is odd then +14: Use "stable" [48] centroid weights with $\lambda = \mu = 4\mathrm{P}$ +15: end if +16: Standard CMA-ES update [21] of $\Theta$ , $\sigma$ , C, p, c based on $\{\Theta^{p,n}\}_{p\in \{1,\dots,4\mathrm{P}\}}$ and $\{\mathcal{M}^{p,n,n}\}_{p\in \{1,\dots,4\mathrm{P}\}}$ +17: $\Theta_{\mathrm{center}} \gets \Theta$ +18: if min $p\in \{0,\dots,4\mathrm{P}\}$ $M^{p,n,n} < \min_{q\in \{0,\dots,4\mathrm{P}\},m\in \{1,\dots,n\}} M^{q,m,n}$ then +19: $\Theta_{\mathrm{center}} \gets$ minimizer closest to centroid of minimizers +20: end if +21: end for +22: return $\Theta^{p,n}$ in the (guaranteed nonempty) intersection of the Pareto front and the set of minimizers of $\mathcal{M}^{q,m,N}$ , with ties resolved by choosing the one closest to their centroid and remaining ties resolved by maximizing $n$ , then $p$ + +each of the 128 channels. + +# 5.1. Optimization for Depth and Intensity + +Next, we assess the method by optimizing point clouds for depth and intensity with the proposed optimizer. We minimize the average RMSE of the depth and the average RMSE of the intensity, specifically + +$$ +\mathcal {L} _ {\text {d e p t h}} (\Theta) = \frac {1}{F} \sum_ {f = 1} ^ {F} \operatorname {R M S E} \left(R _ {f}, \Phi_ {f} ^ {(R)} (\Theta)\right), \text {a n d} \tag {14} +$$ + +$$ +\mathcal {L} _ {\text {i n t .}} (\boldsymbol {\Theta}) = \frac {1}{F} \sum_ {f = 1} ^ {F} \operatorname {R M S E} \left(I _ {f}, \Phi_ {f} ^ {(I)} (\boldsymbol {\Theta})\right), \tag {15} +$$ + +with $\mathrm{F}$ the number of frames in the validation set, see Supplemental Material. The depth loss $\mathcal{L}_{\mathrm{depth}}$ rewards accurate point cloud depth estimates over the full range, whereas $\mathcal{L}_{\mathrm{int}}$ ensures that accurate intensities are measured with the pulse power $P_0^{(j)}$ . High output power generally results in more accurate point clouds at farther distances but also leads to excessive saturation; $\mathcal{L}_{\mathrm{int}}$ penalizes saturation. + +Fig. 4 shows a comparison between ground-truth, expert-tuned and optimized PCs for a Pareto point that mini- + +![](images/31999305e4163684ccd35bc2e46577a7b76fbe53fd257d8742a00d1fc0263e6f.jpg) + +![](images/90436da306de4d7dce667f8107c017be969ed52715e9c494ae27bdd791fc3d71.jpg) + +![](images/7b5958e900abf8005e67f73a04d0c27ea44825882bb55e2dc30d7076c12bd106.jpg) + +mizes the depth error. Compared to the optimized point cloud measurement, the expert-tuned point cloud suffers from clutter, and its depth error increases with distance. The results reported in Table 1 validate the optimization method: The expert-tuned configuration has a loss vector $\mathcal{L} = (\mathcal{L}_{\mathrm{depth}},\mathcal{L}_{\mathrm{int.}})$ equal to (12.195, 1.971); after 3000 evaluations of the objectives, the champion of the proposed method has a loss vector of (10.754, 0.216), improving depth and intensity metrics by $12\%$ and $89\%$ , respectively. We note that biasing optimization toward reducing $\mathrm{RMSE}_{\mathrm{depth}}$ more than $\mathrm{RMSE}_{\mathrm{int.}}$ can be done by changing Eq. (13) loss weights $\mathrm{w}_l$ . In this work, loss component weights were kept at their default 1. + +Table 1 also shows a comparison of the proposed algorithm with other state-of-the-art MOO optimizers. With both champion selection criteria—the one proposed by Mosleh et al. [36], namely "last Pareto point of the run", and our own, "Pareto point that minimizes the stable (weighted) max-rank loss"—Algorithm (1) is the best performer. + +# 5.2. Optimization for Object Detection + +Next, we investigate optimization for object detection and classification. We use Average Precision (AP) as additional optimization objective. AP is maximized for cars and pedestrians at 40 recall positions [55] over an optimization set with $\mathrm{F} = 100$ frames. With standard KITTI IoU thresholds [17], the CV loss is + +$$ +\mathcal {L} _ {\mathrm {o b j}, \{\text {c a r , p e d .} \}} (\Theta) = - \mathrm {A P} _ {\{\text {c a r , p e d .} \}} (\Phi (\Theta)), \tag {16} +$$ + +evaluated over a $0 - 80\mathrm{m}$ range. We train a PV-RCNN [53] for 3D object detection on 5900 full range point clouds collected from the simulation environment with 8 different expert-tuned parameterizations $\Theta$ , see training and dataset details in the Supplemental Material. + +Table 2 contains a quantitative comparison between the proposed method and expert tuning. Compared to expert tuning, the proposed method increases AP by $52.9\%$ and $27.2\%$ for cars and pedestrians, respectively, on the full $0 - 80\mathrm{m}$ range, with large increases predominately at close range. Lower AP values in the $0 - 30\mathrm{m}$ range compared to $30 - 50\mathrm{m}$ are due to a lower object occurrence (3.2 per frame vs. 3.6) and a less varied distribution of object yaw + +
SOLVERl1-norm (↓) last Pareto point [36]l1-norm (↓) proposed champion
Expert-Tuned14.09114.091
Proposed11.08910.995
NSGA-III [10,11]11.40811.574
Mosleh et al. [36]11.49711.497
AGE-MOEA [42]11.54711.427
C-TAEA [29]11.62211.461
RVEA [8]11.85111.437
R-NSGA-III [63]12.30611.619
U-NSGA-III [51]13.42311.512
SMS-EMOA [2]13.65811.750
+ +![](images/bccc9bf9d2737dce330692c4fd054570031c8a6c8d524850c06a48fdb046eac8.jpg) +Figure 4. Comparison between expert-tuned and optimized point clouds. Colors encode the individual depth error of each point (clipped at $2\mathrm{m}$ ). Convergence plot shows the loss components $\mathcal{L}_{\mathrm{depth}}$ and $\mathcal{L}_{\mathrm{int}}$ (Eq. (14)-(15)) against optimization step (3000 loss evaluations total), the evolution of the champion and the final Pareto front, champion included. +Table 1. LiDAR Depth and Intensity MOO. The Pareto front (see figure) is approximately a straight line with slope -1. So, the variations of $\mathcal{L}_{\mathrm{depth}}$ and $\mathcal{L}_{\mathrm{int.}}$ are approximately balanced and the $\ell_1$ -norm of the loss vector is a reasonable evaluation metric. For all solvers, the first column shows the $\ell_1$ -norm of the last Pareto point of the run (champion per Mosleh et al. [36]); the last column instead uses the proposed champion selection method (Line 22 of Algorithm (1)). The proposed solver performs favorably; in addition, the proposed champion selection method improves results for all but two solvers (there is one tie). Median of three runs with 3000 loss evaluations; see the Supplemental Material for details. + +angles (std. dev. of 0.63 rad vs. 1.25 rad) within the training set. Consequently, the detector can better adapt within the $30 - 50\mathrm{m}$ bin. The quantitative findings agree with the qualitative results of Fig. 5, where optimized and expert-tuned point clouds are compared to the ground truth. Here, the clutter in the expert-tuned point cloud results in false positives and missed detections. In these examples, the proposed method misses only one pedestrian, whereas on the expert-tuned PC the detector misses them all. We also compare the average depth RMSE before and after filtering suppressed points; the low loss of the filtered PC suggests that clutter has been removed and that the detector prefers an accurate if sparser point cloud. Note however that a DSP optimized for the removal of clutter, like the proposed method, also suppresses ground points, with a small impact on object detection results, see Table 2 last column. + +# 5.3. Off-the-shelf LiDAR Optimization + +In this section, we adapt the proposed optimization algorithm to an off-the-shelf Baraja Spectrum LiDAR sensor. Only a handful of hyperparameters are user-accessible: the + +![](images/68dff62b3540dfd5abaf3b90e16181f91e9f0dda2815bbee501783de09539d32.jpg) +Figure 5. Comparison between ground-truth, expert-tuned and optimized point cloud for 3D object detection. Optimization results in clutter-free point clouds which helps reduce false positive detections. For visualization purposes only the camera field-of-view is shown. + +
METHODmAP (↑)APcar (↑)APped. (↑)RMSEdepth (↓) filtered
0-80 m0-30 m30-50 m50-80 m0-80 m0-30 m30-50 m50-80 m0-80 m0-30 m30-50 m50-80 m
Expert7.3310.8810.181.2814.5821.7520.192.530.070.020.170.049.12 6.17
Proposed46.8652.1458.7027.0866.4775.0684.8641.2027.2529.2232.5312.9512.87 5.13
Margin+39.53+41.26+48.52+25.80+51.89+53.31+64.67+38.67+27.18+29.20+32.36+12.91+3.75 -1.04
+ +Table 2. Optimization for 3D Object Detection. We report mAP and AP in different distance range bins, and unfiltered and filtered RMSE, for expert-tuned and proposed. Filtered $\mathrm{RMSE}_{\mathrm{depth}}$ excludes all points suppressed by the DSP. Best in bold. + +![](images/bf3bcd69bf01ae9475471da7ffe8b9773bbbce2dc70b382697cded61ca990234.jpg) +Figure 6. Left: ground truth 3D histogram used to compute losses, see text, red bounding boxes denoting cars, with experimental setup zoom-ins: captured static scene (top) and LiDAR installation (bottom); the LiDAR detector is the blue unit. Middle: loss contributions for the expert-tuned parameters and Right: for optimized parameters. Brighter color means a smaller loss. + +return mode that selects the waveform peaks used to generate the point cloud, the sensor heads scanning pattern, and the two sensor head motor frequencies which determine the PC angular resolution vs. sensor noise balance. Because raw wavefronts are inaccessible, we optimize 3D point cloud histograms taken over an (intermittently) static scene such as a workplace parking lot. Fig. 6 shows the experimental setup along with a photograph of the scene over which optimization was performed. The ground truth histogram is shown on the left of Fig. 6; it was generated by averaging 100 captures with a uniform angular resolution scanning pattern. Expert-tuned and optimized histogram contributions to the fitness function (described in the Supplemental Material) are respectively shown in the middle and right of Fig. 6; brighter bins indicate larger contributions. Optimized hyperparameters achieved better results by focusing laser scans in regions of interests (ROIs) where the ground truth was denser; expert-tuned ones produced + +more uniform point clouds. Using the RMSE of PC histograms weighted by distance and number of points per bin, we estimate a $10.1\mathrm{cm}$ error for the expert-tuned configuration vs. $3.0\mathrm{cm}$ with optimized hyperparameters. + +# 6. Conclusion + +We propose an in-the-loop black box 0th-order optimization method for LiDAR sensing pipelines that finds optimal parameters for depth and intensity estimation and 3D object detection and classification. To assess our method, we propose a novel LiDAR simulation method integrated into the CARLA simulator. We find that optimizing the LiDAR sensing pipeline can significantly improve depth, intensity and object detection and classification compared to manual expert tuning. Specifically, for 3D object detection our optimization method results in a major increase of $51.9\%$ AP for cars and $27.2\%$ AP for pedestrians, compared to fine-tuning a detector on expert-optimized LiDAR vendor parameters. An interesting direction for future research is real-time scene-dependent optimization of LiDAR scanning parameters, potentially leading to adaptive sensing in adverse weather in urban and highway scenarios. + +Acknowledgments This work was supported by the AI-SEE project with funding from the FFG, BMBF, and NRCIRA. We thank the Federal Ministry for Economic Affairs and Energy for support through the PEGASUS-family project VVM-Verification and Validation Methods for Automated Vehicles Level 4 and 5. Felix Heide was supported by an NSF CAREER Award (2047359), a Packard Foundation Fellowship, a Sloan Research Fellowship, a Sony Young Faculty Award, a Project X Innovation Award, and an Amazon Science Research Award. + +# References + +[1] Behnam Behroozpour, Phillip A. M. Sandborn, Ming C. Wu, and Bernhard E. Boser. Lidar System Architectures and Circuits. IEEE Communications Magazine, 55(10):135-142, Oct. 2017. Conference Name: IEEE Communications Magazine. 3 +[2] Nicola Beume, Boris Naujoks, and Michael Emmerich. SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3):1653–1669, Sept. 2007. 7 +[3] Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, and Felix Heide. Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1 +[4] Mario Bijelic, Tobias Gruber, and Werner Ritter. A benchmark for LiDAR sensors in fog: Is detection breaking down? In IEEE Intelligent Vehicles Symposium (IV), 2018. 1, 2 +[5] Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, and Karol Zieba. End to End Learning for Self-Driving Cars, Apr. 2016. arXiv:1604.07316 [cs]. 3 +[6] Alexander Carballo, Jacob Lambert, Abraham Monrroy Cano, David Robert Wong, Patiphon Narksri, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, and Kazuya Takeda. LIBRE: The multiple 3D LiDAR dataset. In IEEE Intelligent Vehicles Symposium (IV), 2020. 1 +[7] M. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, and J. Hays. Argoverse: 3D tracking and forecasting with rich maps. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 1 +[8] Ran Cheng, Yaochu Jin, Markus Olhofer, and Bernhard Sendhoff. A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 20(5):773-791, Oct. 2016. Conference Name: IEEE Transactions on Evolutionary Computation. 7 +[9] Robert L. Cook and Kenneth E. Torrance. A reflectance model for computer graphics. ACM SIGGRAPH Computer Graphics, 15(3):307-316, Aug. 1981. 4 +[10] K. Deb and J. Sundar. Reference point based multi-objective optimization using evolutionary algorithms | Proceedings of the 8th annual conference on Genetic and evolutionary computation. 7 +[11] Kalyanmoy Deb and J. Sundar. Reference point based multi-objective optimization using evolutionary algorithms. In Proceedings of the 8th annual conference on Genetic and evolutionary computation, GECCO '06, pages 635-642, New York, NY, USA, July 2006. Association for Computing Machinery. 7 +[12] Ayush Dewan, Tim Caselitz, Gian Diego Tipaldi, and Wolfram Burgard. Motion-based detection and tracking in 3D LiDAR scans. In IEEE International Conference on Robotics and Automation (ICRA), 2016. 1 + +[13] Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In Sergey Levine, Vincent Vanhoucke, and Ken Goldberg, editors, Proceedings of the 1st Annual Conference on Robot Learning, volume 78 of Proceedings of Machine Learning Research, pages 1-16. PMLR, 13-15 Nov 2017. 2 +[14] Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In 1st Annual Conference on Robot Learning, 2017. 3 +[15] Michael T. M. Emmerich and André H. Deutz. A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Natural Computing, 17(3):585-609, Sept. 2018. 5 +[16] Jin Fang, Dingfu Zhou, Feilong Yan, Tongtong Zhao, Feihu Zhang, Yu Ma, Liang Wang, and Ruigang Yang. Augmented LiDAR Simulator for Autonomous Driving. IEEE Robotics and Automation Letters, 5(2):1931-1938, Apr. 2020. arXiv:1811.07112 [cs]. 3 +[17] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2012. 7 +[18] Christopher Goodin, Daniel Carruth, Matthew Doude, and Christopher Hudson. Predicting the influence of rain on LiDAR in ADAS. *Electronics*, 8, 2019. 1 +[19] Martin Hahner, Christos Sakaridis, Mario Bijelic, Felix Heide, Fisher Yu, Dengxin Dai, and Luc Van Gool. LiDAR Snowfall Simulation for Robust 3D Object Detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 1, 3, 4 +[20] Martin Hahner, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Fog simulation on real LiDAR point clouds for 3D object detection in adverse weather. In IEEE International Conference on Computer Vision (ICCV), 2021. 1, 3 +[21] Nikolaus Hansen. The CMA evolution strategy: A tutorial, 2016. 2, 6 +[22] Felix Heide, Steven Diamond, David B Lindell, and Gordon Wetzstein. Sub-picosecond photon-efficient 3d imaging using single-photon sensors. Scientific reports, 8(1):1-8, 2018. 3,4 +[23] Robin Heinzler, Philipp Schindler, Jürgen Seekircher, Werner Ritter, and Wilhelm Stork. Weather influence and classification with automotive LiDAR sensors. In IEEE Intelligent Vehicles Symposium (IV), 2019. 1 +[24] Braden Hurl, Krzysztof Czarnecki, and Steven Waslander. Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception, May 2019. arXiv:1905.00160 [cs]. 1, 3 +[25] Maria Jokela, Matti Kutila, and Pasi Pyykonen. Testing and validation of automotive point-cloud sensors in adverse weather conditions. Applied Sciences, 9, 2019. 1 +[26] Velat Kilic, Deepti Hegde, Vishwanath Sindagi, A. Brinton Cooper, Mark Foster, and Vishal Patel. LiDAR light scattering augmentation (LISA): physics-based simulation of adverse weather conditions for 3D object detection. arXiv preprint 2107.07004, 2021. 1, 3 + +[27] Matti Kutila, Pasi Pyykönen, Maria Jokela, Tobias Gruber, Mario Bijelic, and Werner Ritter. Benchmarking automotive LiDAR performance in arctic conditions. In IEEE International Conference on Intelligent Transportation Systems (ITSC), 2020. 1 +[28] Marco Laumanns, Lothar Thiele, Kalyanmoy Deb, and Eckart Zitzler. Combining convergence and diversity in evolutionary multiobjective optimization. Evolutionary Computation, 10(3):263-282, 2002. 5 +[29] Ke Li, Renzhi Chen, Guangtao Fu, and Xin Yao. Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 23(2):303-315, Apr. 2019. Conference Name: IEEE Transactions on Evolutionary Computation. 7 +[30] Xiaolu Li, Bingwei Yang, Xinhao Xie, Duan Li, and Lijun Xu. Influence of waveform characteristics on lidar ranging accuracy and precision. Sensors, 18(4), 2018. 5 +[31] Ievgeniia Maksymova, Christian Steger, and Norbert Druml. Review of LiDAR Sensor Data Acquisition and Compression for Automotive Applications. Proceedings, 2(13):852, 2018. Number: 13 Publisher: Multidisciplinary Digital Publishing Institute. 3 +[32] Yunze Man, Xinshuo Weng, Prasanna Kumar Sivakumar, Matthew O'Toole, and Kris Kitani. Multi-echo lidar for 3d object detection. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 3743-3752, 2021. 3 +[33] Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong, Wenyuan Zeng, Mikita Sazanovich, Shuhan Tan, Bin Yang, Wei-Chiu Ma, and Raquel Urtasun. Lidarsim: Realistic lidar simulation by leveraging the real world. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11164-11173, 2020. 3 +[34] Sebastien Michaud, Jean-François Lalonde, and Philippe Giguère. Towards characterizing the behavior of LiDARs in snowy conditions. In Workshop on Planning, Perception and Navigation for Intelligent Vehicles, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015. 1 +[35] Anish Mittal, Anush K. Moorthy, and Alan C. Bovik. Automatic parameter prediction for image denoising algorithms using perceptual quality features. In Human Vision and Electronic Imaging XVII, volume 8291, pages 110-116. SPIE, Feb. 2012. 2 +[36] Ali Mosleh, Avinash Sharma, Emmanuel Onzon, Fahim Mannan, Nicolas Robidoux, and Felix Heide. Hardware-in-the-Loop End-to-End Optimization of Camera Image Processing Pipelines. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7526–7535, Seattle, WA, USA, June 2020. IEEE. 2, 5, 6, 7 +[37] Matthias Müller, Vincent Casser, Jean Lahoud, Neil Smith, and Bernard Ghanem. Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications, Mar. 2018. arXiv:1708.05869 [cs]. 3 + +[38] Kazuki Nakamura, Kenji Narumi, Kohei Kikuchi, and Yasuhisa Inada. Liquid crystal-tunable optical phased array for LiDAR applications. In Smart Photonic and Optoelectronic Integrated Circuits XXIII, volume 11690, pages 94-99. SPIE, Mar. 2021. 3 +[39] Kouhei Nishida and Youhei Akimoto. Population Size Adaptation for the CMA-ES Based on the Estimation Accuracy of the Natural Gradient. In Proceedings of the Genetic and Evolutionary Computation Conference 2016, GECCO '16, pages 237–244, New York, NY, USA, July 2016. Association for Computing Machinery. 6 +[40] Jun Nishimura, Timo Gerasimow, Sushma Rao, Aleksandar Sutic, Chyuan-Tyng Wu, and Gilad Michael. Automatic ISP image quality tuning using non-linear optimization, Feb. 2019. arXiv:1902.09023 [cs]. 2 +[41] Matthew O'Toole, Felix Heide, David B. Lindell, Kai Zang, Steven Diamond, and Gordon Wetzstein. Reconstructing Transient Images from Single-Photon Sensors. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2289–2297, Honolulu, HI, July 2017. IEEE. 3, 4 +[42] Annibale Panichella. An adaptive evolutionary algorithm based on non-euclidean geometry for many-objective optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '19, pages 595-603, New York, NY, USA, July 2019. Association for Computing Machinery. 7 +[43] Adithya K. Pediredla, Aswin C. Sankaranarayanan, Mauro Buttafava, Alberto Tosi, and Ashok Veeraraghavan. Signal Processing Based Pile-up Compensation for Gated Single-Photon Avalanche Diodes, June 2018. arXiv:1806.07437 [physics]. 3, 4 +[44] Luke Pfister and Yoram Bresler. Learning Filter Bank Sparsifying Transforms. IEEE Transactions on Signal Processing, 67(2):504-519, Jan. 2019. Conference Name: IEEE Transactions on Signal Processing. 2 +[45] Francesco Pittaluga, Zaid Tasneem, Justin Folden, Brevin Tilmon, Ayan Chakrabarti, and Sanjeev J. Koppal. Towards a MEMS-based Adaptive LIDAR, Oct. 2020. arXiv:2003.09545 [cs, eess]. 3 +[46] Ralph Rasshofer, Martin Spies, and Hans Spies. Influences of weather phenomena on automotive laser radar systems. Advances in Radio Science, 9, 2011. 3, 4 +[47] Stephan R. Richter, Zeeshan Hayden, and Vladlen Koltun. Playing for Benchmarks, Sept. 2017. arXiv:1709.07322 [cs]. 3 +[48] Nicolas Robidoux, Luis E. Garcia Capel, Dong-eun Seo, Avinash Sharma, Federico Ariza, and Felix Heide. End-to-End High Dynamic Range Camera Pipeline Optimization. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6297–6307, 2021. 2, 5, 6 +[49] German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M. Lopez. The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3234-3243, June 2016. ISSN: 1063-6919. 3 + +[50] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126(9):973-992, 2018. 1 +[51] Haitham Seada and Kalyanmoy Deb. A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives. IEEE Transactions on Evolutionary Computation, 20(3):358-369, June 2016. Conference Name: IEEE Transactions on Evolutionary Computation. 7 +[52] Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles, July 2017. arXiv:1705.05065 [cs]. 3 +[53] Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, and Hongsheng Li. PV-RCNN: Point-voxel feature set abstraction for 3D object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1, 7 +[54] Dongeek Shin. Computational imaging with small numbers of photons. Thesis, Massachusetts Institute of Technology, 2016. Accepted: 2016-07-18T20:05:44Z. 3, 4 +[55] Andrea Simonelli, Samuel Rota Rota Bulò, Lorenzo Porzi, Manuel López-Antequera, and Peter Kontschieder. Disentangling monocular 3D object detection. arXiv preprint arXiv:1905.12365, 2019. 7 +[56] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, and Dragomir Anguelov. Scalability in perception for autonomous driving: Waymo open dataset. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1 +[57] Tao Sun, Mattia Segu, Janis Postels, Yuxuan Wang, Luc Van Gool, Bernt Schiele, Federico Tombari, and Fisher Yu. SHIFT: a synthetic driving dataset for continuous multi-task domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 21371-21382, June 2022. 1, 3 +[58] Maxime Tremblay, Shirsendu S. Halder, Raoul de Charette, and Jean-François Lalonde. Rain rendering for evaluating and improving robustness to bad weather. International Journal of Computer Vision (IJCV), 126, 2021. 1 +[59] Ethan Tseng, Ali Mosleh, Fahim Mannan, Karl St-Arnaud, Avinash Sharma, Yifan Peng, Alexander Braun, Derek Nowrouzezahrai, Jean-Francois Lalonde, and Felix Heide. Differentiable compound optics and processing pipeline optimization for end-to-end camera design. ACM Transactions on Graphics (TOG), 40(2):1-19, 2021. 2 +[60] Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl ST. Arnaud, Derek Nowrouzezahrai, Jean-François Lalonde, and Felix Heide. Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Transactions on Graphics, 38(4):1-14, Aug. 2019. 2 +[61] G. Turin. An introduction to matched filters. IRE Transactions on Information Theory, 6(3):311-329, 1960. 4 + +[62] Velodyne. Hdl-64e user's manual, 2007. 5 +[63] Yash Vesikar, Kalyanmoy Deb, and Julian Blank. Reference Point Based NSGA-III for Preferred Solutions. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1587–1594, Nov. 2018. 7 +[64] F Villa, B Markovic, S Bellisai, D Bronzi, A Tosi, F Zappa, S Tisa, D Durini, S Weyers, U Paschen, et al. SPAD smart pixel for time-of-flight and time-correlated single-photon counting measurements. IEEE Photonics Journal, 4(3):795-804, 2012. 1 +[65] Niclas Vödisch, Ozan Unal, Ke Li, Luc Van Gool, and Dengxin Dai. End-to-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization. IEEE Robotics and Automation Letters, 7(2):2242-2249, Apr. 2022. Conference Name: IEEE Robotics and Automation Letters. 3 +[66] Andrew Wallace, Abderrahim Halimi, and Gerald Buller. Full waveform LiDAR for adverse weather conditions. IEEE Transactions on Vehicular Technology, 69, 2020. 1 +[67] Dingkang Wang, Connor Watkins, and Huikai Xie. MEMS mirrors for LiDAR: A review. Micromachines, 11(5), 2020. 1 +[68] Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas, Mengye Ren, and Raquel Urtasun. AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles, Jan. 2022. arXiv:2101.06549 [cs]. 3 +[69] Xinshuo Weng, Yunze Man, Jinhyung Park, Ye Yuan, Dazhi Cheng, Matthew O'Toole, and Kris Kitani. All-In-One Drive: A Large-Scale Comprehensive Perception Dataset with High-Density Long-Range Point Clouds. arXiv, 2021. 1, 3, 4 +[70] George Williams. Optimization of eyesafe avalanche photodiode LiDAR for automobile safety and autonomous navigation systems. Optical Engineering, 56(3):1-9, 2017. 1 +[71] Chyuan-Tyng Wu, Leo F. Isikdogan, Sushma Rao, Bhavin Nayak, Timo Gerasimow, Aleksandar Sutic, Liron Ainkedem, and Gilad Michael. VisionISP: Repurposing the image signal processor for computer vision applications. In IEEE International Conference on Image Processing (ICIP), pages 4624-4628, 2019. 2 +[72] Ji Zhang and Sanjiv Singh. LOAM: LiDAR odometry and mapping in real-time. Robotics: Science and Systems Conference (RSS), 2014. 1 +[73] Qingfu Zhang and Hui Li. Moea/d: A multiobjective evolutionary algorithm based on decomposition. 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LiDAR systems emit pulses of light into the scene, take measurements of the returned signal, and rely on hardware digital signal processing (DSP) pipelines to construct 3D point clouds from these measurements. The resulting point clouds output by these DSPs are input to downstream 3D vision models – both, in the form of training datasets or as input at inference time. Existing LiDAR DSPs are composed of cascades of parameterized operations; modifying configuration parameters results in significant changes in the point clouds and consequently the output of downstream methods. Existing methods treat LiDAR systems as fixed black boxes and construct downstream task networks more robust with respect to measurement fluctuations. Departing from this approach, the proposed method directly optimizes LiDAR sensing and DSP parameters for downstream tasks. To investigate the optimization of LiDAR system parameters, we devise a realistic LiDAR simulation method that generates raw waveforms as input to a LiDAR DSP pipeline. We optimize LiDAR parameters for both 3D object detection IoU losses and depth error metrics by solving a nonlinear multi-objective optimization problem with a 0th-order stochastic algorithm. For automotive 3D object detection models, the proposed method outperforms manual expert tuning by " + }, + { + "bbox": [ + 47, + 216, + 290, + 515 + ], + "type": "inline_equation", + "content": "39.5\\%" + }, + { + "bbox": [ + 47, + 216, + 290, + 515 + ], + "type": "text", + "content": " mean Average Precision (mAP)." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 529, + 128, + 542 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 529, + 128, + 542 + ], + "spans": [ + { + "bbox": [ + 47, + 529, + 128, + 542 + ], + "type": "text", + "content": "1. Introduction" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 545, + 287, + 629 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 545, + 287, + 629 + ], + "spans": [ + { + "bbox": [ + 46, + 545, + 287, + 629 + ], + "type": "text", + "content": "Environment perception for autonomous drones [12, 53] and vehicles [72] requires precise depth sensing for safety-critical control decisions. Scanning LiDAR sensors have been broadly adopted in autonomous driving [3, 7, 56] as they provide high temporal and spatial resolution, and recent advances in MEMS scanning [67] and photodiode technology [64] have reduced their cost and form factor." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 46, + 630, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 630, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 630, + 288, + 714 + ], + "type": "text", + "content": "The 3D LiDAR point cloud (PC) data that existing 3D detection methods take as input is produced by a LiDAR and digital signal processor (DSP) pipeline with many measurement and processing steps. Typical LiDAR sensors operate by sending out a laser pulse and measuring the temporal response through an Avalanche Photo Diode (APD). This temporal wavefront signal is fed to a DSP that extracts" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 199, + 547, + 366 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 199, + 547, + 366 + ], + "spans": [ + { + "bbox": [ + 304, + 199, + 547, + 366 + ], + "type": "text", + "content": "peaks corresponding to echos from candidate targets within the scene [70]. As such, DSP processing results in a 1000-fold data reduction for a single emitted beam, producing single or multiple 3D points per beam. Compressing the waveform into points in 3D space with minimal information loss is challenging because of object discontinuities, sub-surface scattering, multipath reflections, and scattering media, see Fig. 1. In particular, significant scattering occurs in adverse weather conditions like fog [3,4,6,20,23,25,66], rain [6,18,66] and snow [19,25,27,34]. LiDAR manufacturers currently handle such complications by manually adjusting internal sensing and DSP parameters in controlled environments and restricted real-world scenarios using a combination of visual inspection and depth quality metrics." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 366, + 547, + 594 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 366, + 547, + 594 + ], + "spans": [ + { + "bbox": [ + 304, + 366, + 547, + 594 + ], + "type": "text", + "content": "Generally, LiDAR production units are black boxes with configuration parameters hidden from the user. To account for noisy point cloud measurements with spurious artifacts, existing work has explored methods that add simulated adverse effects and point cloud degradations that model rain [18, 26, 58], fog [3, 20, 50] and snow [19] to real black-box LiDAR datasets. Augmented point clouds in hand, downstream vision models are retrained for predictions more robust to point cloud data corruption. Another approach consists of generating synthetic measurements from 3D scenes using rendering engines [24, 57, 69]. Such existing methods typically avoid simulating transient light propagation and signal processing by converting 3D scene depth directly into a point cloud, thus lack physically realistic modeling of fluctuations arising from multipath effects or measurement noise. Notably, existing simulation methods that alter measurements or generate synthetic point clouds do not optimize sensing or DSP parameters for downstream vision performance." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 594, + 548, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 548, + 714 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 548, + 714 + ], + "type": "text", + "content": "In this work, we directly optimize LiDAR pulse configuration and DSP hyperparameters for end-to-end downstream 3D object detector losses and PC depth quality metrics, a challenging task because hyperparameter space involves tens to hundreds of categorical, discrete and effectively continuous parameters affecting downstream tasks in complex nonlinear ways via the intermediate point cloud. Examples of categorical hyperparameters are Velodyne LiDAR sensor return modes that configure internal wavefront peak selection algorithms for point cloud formation;" + } + ] + } + ], + "index": 11 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "spans": [ + { + "bbox": [ + 65, + 2, + 111, + 34 + ], + "type": "text", + "content": "CVF" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "header", + "angle": 0, + "lines": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "spans": [ + { + "bbox": [ + 145, + 0, + 494, + 37 + ], + "type": "text", + "content": "This CVPR paper is the Open Access version, provided by the Computer Vision Foundation. Except for this watermark, it is identical to the accepted version; the final published version of the proceedings is available on IEEE Xplore." + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "13404" + } + ] + } + ], + "index": 12 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 0 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 56, + 71, + 537, + 186 + ], + "blocks": [ + { + "bbox": [ + 56, + 71, + 537, + 186 + ], + "lines": [ + { + "bbox": [ + 56, + 71, + 537, + 186 + ], + "spans": [ + { + "bbox": [ + 56, + 71, + 537, + 186 + ], + "type": "image", + "image_path": "b1bf9cf31d8ad4d0bf90895dde4162bb8e5972285926f5bf991492a6971a3467.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 188, + 547, + 245 + ], + "lines": [ + { + "bbox": [ + 46, + 188, + 547, + 245 + ], + "spans": [ + { + "bbox": [ + 46, + 188, + 547, + 245 + ], + "type": "text", + "content": "Figure 1. LiDAR Point Cloud Formation. Typical LiDAR sensor PC measurements are produced by a multi-stage measurement and signal processing chain: The LiDAR emits a laser pulse. This signal travels through the scene and returns to the detector after single or multiple reflections. Cluttered surfaces (a), strong retroreflectors (b) and ambient light are introduced (c) in the returned signals. Thus, the full transient waveform read by sensors is the superposition of multiple return paths. The DSP, itself a chain of processing blocks, processes all temporal waveforms and extracts a continuous stream of 3D points that forms the final point cloud (bottom)." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 255, + 287, + 279 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 255, + 287, + 279 + ], + "spans": [ + { + "bbox": [ + 46, + 255, + 287, + 279 + ], + "type": "text", + "content": "rotation velocity, which impacts angular resolution, is an example of a continuous hyperparameter [4]." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 280, + 289, + 578 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 280, + 289, + 578 + ], + "spans": [ + { + "bbox": [ + 46, + 280, + 289, + 578 + ], + "type": "text", + "content": "Grid search optimization is impractical here because of combinatorial explosion. Orthogonally to LiDAR, it was recently shown that 0th order solvers can find camera DSP hyperparameters that improve downstream 2D object detectors [36, 48]. We propose an optimization method for LiDAR sensing and DSP hyperparameters that minimizes end-to-end domain-specific losses such as RMSE of the measured depth against ground truth and IoU measured on downstream 3D object detection. To assess the proposed method, we devised a LiDAR simulation method based on the CARLA engine [13] that models a LiDAR DSP as well as the full transient noisy waveform formed by multiple laser echoes. We optimize sensing and DSP hyperparameters by solving a Multi-Objective black-box Optimization (MOO) problem with a novel CMA-ES (Covariance Matrix Adaptation-Evolution Strategy [21]) that relies on a max-rank multi-objective scalarization loss [36] to dynamically improve scale matching between different loss components. In combination with a novel champion selection method, it finds a balanced Pareto-optimal solution for which no loss component has a comparatively poor value for LiDAR optimization with multiple objectives. We validate the proposed optimization method for 3D object detection and point cloud depth estimation, both in simulation and using an off-the-shelf experimental LiDAR sensor." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 59, + 579, + 264, + 592 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 579, + 264, + 592 + ], + "spans": [ + { + "bbox": [ + 59, + 579, + 264, + 592 + ], + "type": "text", + "content": "Specifically, we make the following contributions:" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 58, + 601, + 288, + 714 + ], + "type": "list", + "angle": 0, + "index": 8, + "blocks": [ + { + "bbox": [ + 58, + 601, + 287, + 638 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 601, + 287, + 638 + ], + "spans": [ + { + "bbox": [ + 58, + 601, + 287, + 638 + ], + "type": "text", + "content": "- We introduce a LiDAR wavefront simulation for the CARLA simulation environment that models realistic transient scene responses." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 58, + 645, + 287, + 670 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 645, + 287, + 670 + ], + "spans": [ + { + "bbox": [ + 58, + 645, + 287, + 670 + ], + "type": "text", + "content": "- We devise a novel multi-objective optimization method for balanced MOO of LiDAR parameters." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 58, + 677, + 288, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 58, + 677, + 288, + 714 + ], + "spans": [ + { + "bbox": [ + 58, + 677, + 288, + 714 + ], + "type": "text", + "content": "- We validate end-to-end LiDAR sensing and DSP optimization for 3D object detection and depth estimation through simulation and with a real system. For all ap" + } + ] + } + ], + "index": 7 + } + ], + "sub_type": "text" + }, + { + "bbox": [ + 325, + 255, + 545, + 279 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 325, + 255, + 545, + 279 + ], + "spans": [ + { + "bbox": [ + 325, + 255, + 545, + 279 + ], + "type": "text", + "content": "plications, our approach outperforms existing state-of-the-art methods, including expert tuning." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 287, + 520, + 300 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 287, + 520, + 300 + ], + "spans": [ + { + "bbox": [ + 305, + 287, + 520, + 300 + ], + "type": "text", + "content": "Simulator code and DSP models are published here1." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 304, + 300, + 545, + 397 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 300, + 545, + 397 + ], + "spans": [ + { + "bbox": [ + 304, + 300, + 545, + 397 + ], + "type": "text", + "content": "Limitations Because commercial LiDAR units are IP-protected black boxes, interfacing their DSP hyperparameters is not straightforward. While the off-the-shelf LiDAR system used in this work makes some DSP hyperparameters accessible, most LiDAR systems are completely closed. We hope that these findings spur LiDAR vendors to follow the lead of digital camera and ISP (Image Signal Processor) vendors and open their processing pipelines." + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 306, + 403, + 392, + 415 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 403, + 392, + 415 + ], + "spans": [ + { + "bbox": [ + 306, + 403, + 392, + 415 + ], + "type": "text", + "content": "2. Related Work" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 418, + 546, + 694 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 418, + 546, + 694 + ], + "spans": [ + { + "bbox": [ + 304, + 418, + 546, + 694 + ], + "type": "text", + "content": "DSP and Sensor Hyperparameter Optimization. A growing body of work explores the optimization of sensors and DSPs for downstream vision tasks. Existing methods target camera ISPs and optics [36, 48, 59, 60, 71]. Departing from manual hyperparameter tuning by experts, these approaches optimize them automatically, driven by one or more downstream performance metric. As DSP and sensor hyperparameters can be categorical and losses are often non-convex and noisy, diverse optimization methods have been proposed. Early methods targeted specific processing blocks [35, 40, 44] as differentiable programs or in a reduced parameter space. Recent methods rely on differentiable pipeline proxies [60] or 0th-order optimization [36], alone or in combination with block coordinate descent [48]. One advantage of 0th-order optimizers is that they handle black box hardware and DSPs. 0th-order solvers used to optimize camera systems include MOEA/D [40, 73] and CMA-ES [21, 36, 48]. These approaches successfully tackle camera pipeline optimization, from the optics [59] to downstream detectors [36, 48]. To our knowledge, however, no loss-driven method has been proposed for end-to-end LiDAR system optimization: existing LiDAR sensors are designed in isolation from downstream depth and detection" + } + ] + } + ], + "index": 13 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 317, + 703, + 517, + 713 + ], + "type": "page_footnote", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 703, + 517, + 713 + ], + "spans": [ + { + "bbox": [ + 317, + 703, + 517, + 713 + ], + "type": "text", + "content": "1https://light.princeton.edu/LITL-Optimization/" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 758 + ], + "type": "text", + "content": "13405" + } + ] + } + ], + "index": 15 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 1 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 68, + 547, + 240 + ], + "blocks": [ + { + "bbox": [ + 47, + 68, + 547, + 240 + ], + "lines": [ + { + "bbox": [ + 47, + 68, + 547, + 240 + ], + "spans": [ + { + "bbox": [ + 47, + 68, + 547, + 240 + ], + "type": "image", + "image_path": "d9a1dc6d8f2b96dac806d8559e099dcc7afbf79091925b6812cffa763359be1f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "lines": [ + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "spans": [ + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "text", + "content": "Figure 2. LiDAR Simulation Method. We propose a parameterizable LiDAR simulation model that generates full transient waveforms by extracting scene response " + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "text", + "content": ", ambient light " + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "inline_equation", + "content": "a" + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "text", + "content": " and object reflectances " + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "inline_equation", + "content": "s, d, \\alpha" + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "text", + "content": " from CARLA. End-to-end loss functions drive an MOO solver toward an optimal vector " + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "text", + "content": " that includes both pulse and DSP hyperparameters: Wavefronts are processed by the DSP, resulting in a point cloud " + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "inline_equation", + "content": "\\mathbf{O}" + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "text", + "content": ". The \"loop is closed\" by feeding " + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "inline_equation", + "content": "\\mathbf{O}" + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "text", + "content": " to 3D object detection and depth estimation methods, the output of which is \"rated\" by loss functions " + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "text", + "content": ". Validation datasets cycle through the loop until " + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 46, + 243, + 547, + 300 + ], + "type": "text", + "content": " converges." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 310, + 287, + 334 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 310, + 287, + 334 + ], + "spans": [ + { + "bbox": [ + 46, + 310, + 287, + 334 + ], + "type": "text", + "content": "tasks. Aiming to close this gap, this work proposes automated LiDAR hyperparameter optimization." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 337, + 288, + 480 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 337, + 288, + 480 + ], + "spans": [ + { + "bbox": [ + 46, + 337, + 288, + 480 + ], + "type": "text", + "content": "LiDAR Sensing and Point Cloud Generation. LiDAR sensors produce point clouds by emitting pulses of light into a scene and measuring the round trip time of sensor returns. Extracting a point cloud from time-of-flight measurements is a complex process that depends on measurement procedure specifics like beam formation, scanning, pulse/continuous wave generation and peak finding within the acquired temporal laser response [1,46]. LiDAR sensors differ in their scanning pattern, beam steering technology, wavelength, pulse profile, coherence of the measurement step, detector technology and DSP capabilities to process the measurement echos [1,31]." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 483, + 287, + 674 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 483, + 287, + 674 + ], + "spans": [ + { + "bbox": [ + 46, + 483, + 287, + 674 + ], + "type": "text", + "content": "Existing LiDAR sensors can extract single or multiple peaks resulting from multi-path interreflections in the scene. Assuming a single Lambertian reflector in the scene, the temporal resolution and signal-to-noise ratio (SNR) of the measurement are tied to laser power. Accordingly, some existing optimization methods [43] use automated runtime laser power adjustment to maximize SNR while preventing oversaturation. Other approaches have been proposed for adaptive beam steering [38, 45]. Recently, Vodisch et al. [65] proposed beam configuration optimization via reinforcement learning methods driven by a downstream 3D detection loss. This method is the most similar to the proposed approach; however, it only predicts beam patterns, i.e., where to place sparse samples. In contrast, we optimize DSP hyperparameters in addition to sensing ones, pulse power and scanning parameters included." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 677, + 288, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 677, + 288, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 677, + 288, + 715 + ], + "type": "text", + "content": "LiDAR Simulation. To assess and validate the proposed method, we introduce a new LiDAR simulation method that plugs directly into the CARLA simulator. Existing" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 304, + 309, + 545, + 585 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 309, + 545, + 585 + ], + "spans": [ + { + "bbox": [ + 304, + 309, + 545, + 585 + ], + "type": "text", + "content": "work [5, 14, 37, 47, 52] proposes several simulation environments; the open-source CARLA [14] simulator is broadly adopted in academia. Simulation frameworks have enabled the creation of multimodal synthetic datasets used successfully in academia and industry, e.g., PreSIL [24], SHIFT [57], AIODrive [32, 69] and SYNTHIA [49]. However, underlying simulation methods employ heuristic forward models; in addition, none of the datasets include full waveform returns that would allow simulating LiDAR point cloud generation. Even the AIODrive dataset [69], in which multiple peaks are returned via depth image convolution and Single Photon Avalanche Diode (SPAD) quantization, bakes transients into SPAD image formation, hence falls short of enabling realistic transient simulation. Similarly, real PC datasets augmentation methods have been employed to tackle rare occurring events like obstacles [16, 33], traffic [68], rain [20], fog [20] or snow [19, 26]. However, since the underlying datasets do not include the raw wavefronts, none of these approach facilitate modeling the DSP pipeline. We address these challenges with our simulation method that simulates full wavefront signals. Combined with a realistic DSP model, this produces PC data representative of real systems." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 306, + 588, + 494, + 600 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 588, + 494, + 600 + ], + "spans": [ + { + "bbox": [ + 306, + 588, + 494, + 600 + ], + "type": "text", + "content": "3. Transient LiDAR Forward Model" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 304, + 604, + 545, + 665 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 604, + 545, + 665 + ], + "spans": [ + { + "bbox": [ + 304, + 604, + 545, + 665 + ], + "type": "text", + "content": "Consider a single laser pulse emitted by a LiDAR unit into a 3D scene, from which a returned signal is detected by a SPAD detector which sends temporal histograms to the sensor DSP. For channel " + }, + { + "bbox": [ + 304, + 604, + 545, + 665 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 304, + 604, + 545, + 665 + ], + "type": "text", + "content": " at time " + }, + { + "bbox": [ + 304, + 604, + 545, + 665 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 604, + 545, + 665 + ], + "type": "text", + "content": ", we model the sensor-incident temporal photon flux " + }, + { + "bbox": [ + 304, + 604, + 545, + 665 + ], + "type": "inline_equation", + "content": "\\psi" + }, + { + "bbox": [ + 304, + 604, + 545, + 665 + ], + "type": "text", + "content": " [22, 41, 54] as" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 359, + 668, + 545, + 683 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 359, + 668, + 545, + 683 + ], + "spans": [ + { + "bbox": [ + 359, + 668, + 545, + 683 + ], + "type": "interline_equation", + "content": "\\psi^ {(n)} (t) = \\left(H * g ^ {(n)}\\right) (t) + a (t), \\tag {1}", + "image_path": "920999ef295497cb6466d0e08c0a88ff1204e26c91cd3c5a542f6bcadd157be5.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "g^{(n)}" + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "text", + "content": " is the temporally varying photon flux emitted by the laser for channel " + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 304, + 689, + 545, + 713 + ], + "type": "text", + "content": " is the transient response from the" + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "13406" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 2 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "text", + "content": "scene, " + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "inline_equation", + "content": "a(t)" + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "text", + "content": " is the ambient photon flux, and " + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "inline_equation", + "content": "*" + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "text", + "content": " is the temporal convolution operator. By definition, the transient scene response " + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "text", + "content": " includes multipath returns from scene interreflections and scattering. The detector measures the returned signal and digitizes the temporal measurement into temporal wavefronts processed by the DSP. For low photon counts or path lengths above a few meters typical in automotive scenes, the binning process can be modeled as a Poisson random process [22, 41, 43, 54]. We consequently model the wavefront's number of photons " + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "inline_equation", + "content": "r^{(n)}" + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "text", + "content": " detected within the integration time " + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "inline_equation", + "content": "\\Delta" + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "text", + "content": " in channel " + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "text", + "content": "'s time bin " + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "inline_equation", + "content": "k" + }, + { + "bbox": [ + 46, + 72, + 289, + 203 + ], + "type": "text", + "content": " as" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 80, + 205, + 287, + 237 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 80, + 205, + 287, + 237 + ], + "spans": [ + { + "bbox": [ + 80, + 205, + 287, + 237 + ], + "type": "interline_equation", + "content": "r ^ {(n)} [ k ] \\sim \\text {P o i s s o n} \\left(\\int_ {k \\Delta} ^ {(k + 1) \\Delta} \\psi^ {(n)} (t) d t\\right). \\tag {2}", + "image_path": "8bdd91bc70573ad2a2f8c91b33a3d5b2a9fdb7892b75e739275497f93277d220.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 47, + 242, + 225, + 255 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 242, + 225, + 255 + ], + "spans": [ + { + "bbox": [ + 47, + 242, + 225, + 255 + ], + "type": "text", + "content": "3.1. Transient Scene and Pulse Model" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 47, + 260, + 287, + 308 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 260, + 287, + 308 + ], + "spans": [ + { + "bbox": [ + 47, + 260, + 287, + 308 + ], + "type": "text", + "content": "Rasshofer et al. [46] introduced a linear model for direct laser reflections in the LiDAR context for the incident transient response " + }, + { + "bbox": [ + 47, + 260, + 287, + 308 + ], + "type": "inline_equation", + "content": "H * g^{(n)}" + }, + { + "bbox": [ + 47, + 260, + 287, + 308 + ], + "type": "text", + "content": " of Eq. (1). With this model, we formalize the transient response as" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 55, + 309, + 287, + 338 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 55, + 309, + 287, + 338 + ], + "spans": [ + { + "bbox": [ + 55, + 309, + 287, + 338 + ], + "type": "interline_equation", + "content": "(H * g ^ {(n)}) (R) = C \\int_ {0} ^ {2 \\tau^ {(n)}} g ^ {(n)} (t) H \\left(R - \\frac {\\mathrm {c t}}{2}\\right) d t, \\tag {3}", + "image_path": "6b3f6d067f38d6df1f25cddf4df581ef3a3848885b3302b3fcf17a22e0b4862d.jpg" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "spans": [ + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "text", + "content": " is the distance between the sensor and the observed point, " + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "inline_equation", + "content": "\\mathbf{c}" + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "text", + "content": " is the speed of light, " + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "inline_equation", + "content": "C" + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "text", + "content": " is a proportionality constant, independent of " + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "inline_equation", + "content": "R" + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "text", + "content": ", describing the system, and " + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "inline_equation", + "content": "2\\tau^{(n)}" + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "text", + "content": " is the total pulse duration for channel " + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "text", + "content": ". We convert path length to time with " + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "inline_equation", + "content": "t = R / c" + }, + { + "bbox": [ + 47, + 341, + 287, + 424 + ], + "type": "text", + "content": ". Following Hahner et al. [19] and Rasshofer et al. [46], we adopt the following pulse shape" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 59, + 422, + 287, + 454 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 59, + 422, + 287, + 454 + ], + "spans": [ + { + "bbox": [ + 59, + 422, + 287, + 454 + ], + "type": "interline_equation", + "content": "g ^ {(n)} (t) = \\left\\{ \\begin{array}{l l} P _ {0} ^ {(n)} \\sin^ {2} \\left(\\frac {\\pi t}{2 \\tau^ {(n)}}\\right) & \\text {i f} 0 \\leq t \\leq 2 \\tau^ {(n)} \\\\ 0 & \\text {o t h e r w i s e} \\end{array} , \\right. \\tag {4}", + "image_path": "f088e5adf489c75a507dd09f98f1fa7c9ad48b2a093a195250690ec794f968cf.jpg" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "spans": [ + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "inline_equation", + "content": "P_0^{(n)}" + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "text", + "content": " is channel " + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "text", + "content": "'s pulse power magnitude. The transient scene response " + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "text", + "content": " embedded in Eq. (3) consists of the geometric attenuation of the light, proportional to " + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "inline_equation", + "content": "1 / (2R)^2" + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "text", + "content": ", and the scene response. For a single opaque point object " + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "text", + "content": ", the latter is proportional to its reflectance " + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "inline_equation", + "content": "\\rho_i" + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "text", + "content": " and a Dirac function " + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "inline_equation", + "content": "\\delta(R - R_i)" + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "inline_equation", + "content": "R_i" + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "text", + "content": " being the object distance to the sensor. Reformulating Eq. (3) for a single echo from " + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 47, + 456, + 287, + 552 + ], + "type": "text", + "content": " yields" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 50, + 550, + 284, + 582 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 50, + 550, + 284, + 582 + ], + "spans": [ + { + "bbox": [ + 50, + 550, + 284, + 582 + ], + "type": "interline_equation", + "content": "(H * g ^ {(n)}) _ {i} (R) = \\left\\{ \\begin{array}{l l} f _ {i} ^ {(n)} (R) & \\text {i f} R _ {i} \\leq R \\leq R _ {i} + c \\tau^ {(n)} \\\\ 0 & \\text {o t h e r w i s e} \\end{array} \\right. \\tag {5}", + "image_path": "6c8e0dd4fd1776cbb56db6ae67a2275acf80129910ebd8cca83292ecadd5e084.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 49, + 584, + 283, + 612 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 49, + 584, + 283, + 612 + ], + "spans": [ + { + "bbox": [ + 49, + 584, + 283, + 612 + ], + "type": "interline_equation", + "content": "\\text {w i t h} f _ {i} ^ {(n)} (R) = \\frac {C P _ {0} ^ {(n)} \\rho_ {i}}{4 R _ {i} ^ {2}} \\sin^ {2} \\left(\\frac {\\pi}{c \\tau^ {(n)}} (R - R _ {i})\\right). \\tag {6}", + "image_path": "35b28ddd4d92a1d78de913c2a8cb41a9a406985335c963edc56e50b622cf40c8.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 47, + 619, + 190, + 632 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 619, + 190, + 632 + ], + "spans": [ + { + "bbox": [ + 47, + 619, + 190, + 632 + ], + "type": "text", + "content": "3.2. Object Reflectance Model" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 47, + 637, + 287, + 684 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 637, + 287, + 684 + ], + "spans": [ + { + "bbox": [ + 47, + 637, + 287, + 684 + ], + "type": "text", + "content": "The object reflectance " + }, + { + "bbox": [ + 47, + 637, + 287, + 684 + ], + "type": "inline_equation", + "content": "\\rho_{i}" + }, + { + "bbox": [ + 47, + 637, + 287, + 684 + ], + "type": "text", + "content": " depends on the material BRDF and the angle of incidence " + }, + { + "bbox": [ + 47, + 637, + 287, + 684 + ], + "type": "inline_equation", + "content": "\\theta" + }, + { + "bbox": [ + 47, + 637, + 287, + 684 + ], + "type": "text", + "content": ". We model reflectance using specular and diffuse components of the retroreflected portion of the Cook-Terrance model [9], that is," + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 685, + 287, + 712 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 685, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 685, + 287, + 712 + ], + "type": "interline_equation", + "content": "\\rho_ {i} = \\frac {\\alpha^ {4} s \\cos \\theta}{4 \\left[ \\cos^ {2} \\theta \\left(\\alpha^ {4} - 1\\right) + 1 \\right] ^ {2} \\left[ \\cos \\theta (1 - k) + k \\right] ^ {2}} + d \\cos \\theta , \\tag {7}", + "image_path": "bf238f67b2a69545addae7f3e83a32d682891a9f30b56a54bd9640ed1e9a72e6.jpg" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "spans": [ + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "inline_equation", + "content": "s, d" + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "inline_equation", + "content": "\\alpha \\in [0,1]" + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "text", + "content": " refer to the specular, diffuse and roughness properties of a surface material and " + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "inline_equation", + "content": "k = (\\alpha + 1)^2 / 8" + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "text", + "content": ", see Supplemental Material. To render realistic textures without a large texture database, we approximate " + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "inline_equation", + "content": "s, d, \\alpha" + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "text", + "content": " through CARLA's Phong-like parameters " + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "inline_equation", + "content": "s, d" + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "inline_equation", + "content": "\\alpha" + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "text", + "content": ". These parameters are not directly accessible, so we extract them by projecting targeted hit points onto custom camera images encoding their values, encoding illustrated at the bottom right of Fig. 2. Specifically, we define a function " + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "inline_equation", + "content": "\\mathcal{P}_i" + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "text", + "content": " that, for a rendered image " + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "inline_equation", + "content": "I_{sd\\alpha}" + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "text", + "content": ", returns pixel information at the location of projected point " + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "text", + "content": ", that is, " + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "inline_equation", + "content": "[s, d, \\alpha] = \\mathcal{P}_i(I_{sd\\alpha})" + }, + { + "bbox": [ + 304, + 72, + 545, + 205 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 306, + 207, + 430, + 218 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 207, + 430, + 218 + ], + "spans": [ + { + "bbox": [ + 306, + 207, + 430, + 218 + ], + "type": "text", + "content": "3.3. Ambient Illumination" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "spans": [ + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "text", + "content": "The ambient light " + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "inline_equation", + "content": "a(t)" + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "text", + "content": " in Eq. (1) is modeled following Weng et al. [69]. A location " + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "inline_equation", + "content": "i" + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "text", + "content": " is projected on the red channel of a rendered RGB camera image in which shadows and reflections are properly accounted for; denote this image by " + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "inline_equation", + "content": "I_{\\mathrm{red}}" + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "text", + "content": ". We approximate " + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "inline_equation", + "content": "a(t)" + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "text", + "content": " as a constant over waveform time bins, that is, " + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "inline_equation", + "content": "a(t) \\equiv a_i = \\mathcal{P}_i(I_{\\mathrm{red}})" + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "text", + "content": ", where " + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "inline_equation", + "content": "a_i" + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "text", + "content": " is independent of " + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "inline_equation", + "content": "t" + }, + { + "bbox": [ + 304, + 222, + 545, + 306 + ], + "type": "text", + "content": ". See Supplemental Material for details." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 306, + 310, + 428, + 323 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 310, + 428, + 323 + ], + "spans": [ + { + "bbox": [ + 306, + 310, + 428, + 323 + ], + "type": "text", + "content": "3.4. Multipath Transients" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 325, + 545, + 424 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 325, + 545, + 424 + ], + "spans": [ + { + "bbox": [ + 304, + 325, + 545, + 424 + ], + "type": "text", + "content": "Multipath transients for laser beams hitting object discontinuities are taken as primary artifact sources in automotive scenarios. Multipath transients are modeled as linear combinations of neighboring waveforms. Specifically, a supersampled collection of " + }, + { + "bbox": [ + 304, + 325, + 545, + 424 + ], + "type": "inline_equation", + "content": "\\{R_i\\}" + }, + { + "bbox": [ + 304, + 325, + 545, + 424 + ], + "type": "text", + "content": " and channels is computed using direct illumination only; then, for each LiDAR channel and each horizontal angle, a downsampled waveform " + }, + { + "bbox": [ + 304, + 325, + 545, + 424 + ], + "type": "inline_equation", + "content": "\\psi_j^{(m)}" + }, + { + "bbox": [ + 304, + 325, + 545, + 424 + ], + "type": "text", + "content": " is obtained:" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 314, + 425, + 545, + 453 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 314, + 425, + 545, + 453 + ], + "spans": [ + { + "bbox": [ + 314, + 425, + 545, + 453 + ], + "type": "interline_equation", + "content": "\\psi_ {j} ^ {(m)} (R) = \\sum_ {i \\in \\mathcal {N} (j), n \\in \\mathcal {N} (m)} \\left(K _ {i} ^ {(n)} \\left(H * g ^ {(n)}\\right) _ {i} (R) + a _ {i}\\right), \\tag {8}", + "image_path": "96d6307bbcce40eb0d583bd430049f08261e4d7fecdd8eac9522de905b53d68f.jpg" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 304, + 455, + 545, + 505 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 455, + 545, + 505 + ], + "spans": [ + { + "bbox": [ + 304, + 455, + 545, + 505 + ], + "type": "text", + "content": "where " + }, + { + "bbox": [ + 304, + 455, + 545, + 505 + ], + "type": "inline_equation", + "content": "\\mathcal{N}(j)" + }, + { + "bbox": [ + 304, + 455, + 545, + 505 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 455, + 545, + 505 + ], + "type": "inline_equation", + "content": "\\mathcal{N}(m)" + }, + { + "bbox": [ + 304, + 455, + 545, + 505 + ], + "type": "text", + "content": " define respectively the spatial neighborhood of the target point " + }, + { + "bbox": [ + 304, + 455, + 545, + 505 + ], + "type": "inline_equation", + "content": "j" + }, + { + "bbox": [ + 304, + 455, + 545, + 505 + ], + "type": "text", + "content": " and the channel " + }, + { + "bbox": [ + 304, + 455, + 545, + 505 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 304, + 455, + 545, + 505 + ], + "type": "text", + "content": ", and the " + }, + { + "bbox": [ + 304, + 455, + 545, + 505 + ], + "type": "inline_equation", + "content": "K_{i}^{(n)}" + }, + { + "bbox": [ + 304, + 455, + 545, + 505 + ], + "type": "text", + "content": "s are normalized weights that can be interpreted as a beam spatial intensity profile, see Supplemental Material." + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 306, + 508, + 477, + 520 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 508, + 477, + 520 + ], + "spans": [ + { + "bbox": [ + 306, + 508, + 477, + 520 + ], + "type": "text", + "content": "3.5. LiDAR Sensing and DSP Model" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 304, + 525, + 545, + 610 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 525, + 545, + 610 + ], + "spans": [ + { + "bbox": [ + 304, + 525, + 545, + 610 + ], + "type": "text", + "content": "A sensor-incident waveform " + }, + { + "bbox": [ + 304, + 525, + 545, + 610 + ], + "type": "inline_equation", + "content": "\\psi_j^{(m)}" + }, + { + "bbox": [ + 304, + 525, + 545, + 610 + ], + "type": "text", + "content": " is measured according to Eq. (2) and, along with sensor noise, subjected to clipping at a saturation level that depends on detector type. The DSP converts noisy and saturated waveforms " + }, + { + "bbox": [ + 304, + 525, + 545, + 610 + ], + "type": "inline_equation", + "content": "r_j^{(m)}" + }, + { + "bbox": [ + 304, + 525, + 545, + 610 + ], + "type": "text", + "content": " into a point cloud " + }, + { + "bbox": [ + 304, + 525, + 545, + 610 + ], + "type": "inline_equation", + "content": "\\mathbf{O}" + }, + { + "bbox": [ + 304, + 525, + 545, + 610 + ], + "type": "text", + "content": " that includes range and intensity information. The entire process, from the emission of a laser pulse " + }, + { + "bbox": [ + 304, + 525, + 545, + 610 + ], + "type": "inline_equation", + "content": "g^{(m)}" + }, + { + "bbox": [ + 304, + 525, + 545, + 610 + ], + "type": "text", + "content": " to the output of a processed point cloud " + }, + { + "bbox": [ + 304, + 525, + 545, + 610 + ], + "type": "inline_equation", + "content": "\\mathbf{O}" + }, + { + "bbox": [ + 304, + 525, + 545, + 610 + ], + "type": "text", + "content": " is shown in Fig. 3." + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "spans": [ + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "text", + "content": "With compressed notation, we model LiDAR sensing as a function " + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\Phi (\\Theta)" + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "text", + "content": " with hyperparameters " + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\Theta = (P_0^{(m)},\\tau^{(m)},V^{(m)})" + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "text", + "content": ". Laser power " + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "inline_equation", + "content": "P_0^{(m)}" + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "text", + "content": " and pulse duration " + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "inline_equation", + "content": "\\tau^{(m)}" + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "text", + "content": " are functions of the channel " + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "inline_equation", + "content": "m" + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "text", + "content": "; they determine the emitted pulse " + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "inline_equation", + "content": "g^{(m)}" + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "text", + "content": ". First, the DSP denoises by convolving the measured waveform " + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "inline_equation", + "content": "r_j^{(m)}" + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "text", + "content": " with the emitted pulse " + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "inline_equation", + "content": "g^{(m)}" + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "text", + "content": ", see [61]. Next, ambient light is estimated by removing the waveform's median from " + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "inline_equation", + "content": "r_j^{(m)}" + }, + { + "bbox": [ + 304, + 611, + 545, + 715 + ], + "type": "text", + "content": ". This allows the DSP" + } + ] + } + ], + "index": 22 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13407" + } + ] + } + ], + "index": 23 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 3 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 88, + 69, + 246, + 202 + ], + "blocks": [ + { + "bbox": [ + 88, + 69, + 246, + 202 + ], + "lines": [ + { + "bbox": [ + 88, + 69, + 246, + 202 + ], + "spans": [ + { + "bbox": [ + 88, + 69, + 246, + 202 + ], + "type": "image", + "image_path": "71674520a79b0bf56f19aeed2673c034ebd7970c7a9e2de6f5888bfb88eb606d.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 205, + 287, + 251 + ], + "lines": [ + { + "bbox": [ + 46, + 205, + 287, + 251 + ], + "spans": [ + { + "bbox": [ + 46, + 205, + 287, + 251 + ], + "type": "text", + "content": "Figure 3. LiDAR Sensing and DSP Model " + }, + { + "bbox": [ + 46, + 205, + 287, + 251 + ], + "type": "inline_equation", + "content": "\\Phi" + }, + { + "bbox": [ + 46, + 205, + 287, + 251 + ], + "type": "text", + "content": ". The LiDAR sensing and DSP hyperparameters " + }, + { + "bbox": [ + 46, + 205, + 287, + 251 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 46, + 205, + 287, + 251 + ], + "type": "text", + "content": ", including pulse power, duration, and parameters for rising edge detection in a transient wavefront, affect the quality of the generated point cloud " + }, + { + "bbox": [ + 46, + 205, + 287, + 251 + ], + "type": "inline_equation", + "content": "\\mathbf{O}" + }, + { + "bbox": [ + 46, + 205, + 287, + 251 + ], + "type": "text", + "content": "." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "bbox": [ + 46, + 255, + 287, + 364 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 255, + 287, + 364 + ], + "spans": [ + { + "bbox": [ + 46, + 255, + 287, + 364 + ], + "type": "text", + "content": "to find adequate noise thresholds " + }, + { + "bbox": [ + 46, + 255, + 287, + 364 + ], + "type": "inline_equation", + "content": "V^{(m)}" + }, + { + "bbox": [ + 46, + 255, + 287, + 364 + ], + "type": "text", + "content": " since ambient light varies strongly throughout the scene. Typical DSPs use a rising edge detector [30] that finds peaks along the denoised waveform by identifying intersections with " + }, + { + "bbox": [ + 46, + 255, + 287, + 364 + ], + "type": "inline_equation", + "content": "V^{(m)}" + }, + { + "bbox": [ + 46, + 255, + 287, + 364 + ], + "type": "text", + "content": ". Multiple peaks may arise; the peak with the highest intensity is added to the point cloud O. Finally, the maximum intensity is compensated for the emitted laser pulse: The pulse half width " + }, + { + "bbox": [ + 46, + 255, + 287, + 364 + ], + "type": "inline_equation", + "content": "\\tau^{(m)} / 2" + }, + { + "bbox": [ + 46, + 255, + 287, + 364 + ], + "type": "text", + "content": " and power level " + }, + { + "bbox": [ + 46, + 255, + 287, + 364 + ], + "type": "inline_equation", + "content": "P_0^{(m)}" + }, + { + "bbox": [ + 46, + 255, + 287, + 364 + ], + "type": "text", + "content": " are used as scaling factors to recover the true intensities, see Eq. 3." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 365, + 287, + 521 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 365, + 287, + 521 + ], + "spans": [ + { + "bbox": [ + 46, + 365, + 287, + 521 + ], + "type": "text", + "content": "Modern consumer LiDAR sensors have upward of 128 channels. Optimizing every channel hyperparameter individually is prohibitive. The Velodyne HDL-64 bundles lasers in two groups [62]; our LiDAR model likewise groups lower and upper lasers. Within each group, hyperparameter modulation is an affine function of the channel number with tuneable slope and bias. The edge threshold is modeled as a continuous parameter " + }, + { + "bbox": [ + 46, + 365, + 287, + 521 + ], + "type": "inline_equation", + "content": "V^{(m)} \\in [0,2]" + }, + { + "bbox": [ + 46, + 365, + 287, + 521 + ], + "type": "text", + "content": ". In contrast, power levels " + }, + { + "bbox": [ + 46, + 365, + 287, + 521 + ], + "type": "inline_equation", + "content": "P_0^{(m)}" + }, + { + "bbox": [ + 46, + 365, + 287, + 521 + ], + "type": "text", + "content": " can only take one of 11 values, the lowest making the peak almost indistinguishable from ambient noise and the highest likely to saturate at close range. Pulse duration " + }, + { + "bbox": [ + 46, + 365, + 287, + 521 + ], + "type": "inline_equation", + "content": "\\tau^{(m)}" + }, + { + "bbox": [ + 46, + 365, + 287, + 521 + ], + "type": "text", + "content": " takes discrete values ranging from 3 to 15 ns in 1 ns increments. See Supplemental Material." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 47, + 524, + 130, + 537 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 524, + 130, + 537 + ], + "spans": [ + { + "bbox": [ + 47, + 524, + 130, + 537 + ], + "type": "text", + "content": "4. Optimization" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 540, + 287, + 635 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 540, + 287, + 635 + ], + "spans": [ + { + "bbox": [ + 46, + 540, + 287, + 635 + ], + "type": "text", + "content": "We propose a multi-objective optimization (MOO) method that finds Pareto-optimal (see the Supplemental Material for a definition) LiDAR hyperparameters. Specifically, optimization returns high-quality point clouds when optimized for depth precision and optimal mAP when point clouds are fed to a downstream object detection module. We first formulate the optimization problem, then we present the proposed optimization method." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 638, + 250, + 651 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 638, + 250, + 651 + ], + "spans": [ + { + "bbox": [ + 47, + 638, + 250, + 651 + ], + "type": "text", + "content": "4.1. LiDAR Hyperparameter Optimization" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 654, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 654, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 654, + 287, + 714 + ], + "type": "text", + "content": "We optimize hyperparameters with loss-driven end-to-end optimization. Like Robidoux et al. [48], we optimize the system as a whole, bundling hardware functionality, for example the laser beam power of each channel, with DSP functionality. The operation of the LiDAR imaging pipeline" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "spans": [ + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "\\Phi" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " is modulated by " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "\\mathrm{P}" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " hyperparameters " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "\\Theta = (\\Theta_{1},\\dots ,\\Theta_{\\mathrm{P}})" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " with ranges of values normalized to the unit interval [0, 1]. With " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "\\mathrm{T}\\gg 2\\tau" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " we model each of the J channels " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "\\Phi_j" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " of " + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "inline_equation", + "content": "\\Phi = (\\Phi_1,\\ldots ,\\Phi_{\\mathrm{J}})" + }, + { + "bbox": [ + 305, + 72, + 545, + 120 + ], + "type": "text", + "content": " as" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 317, + 121, + 545, + 137 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 121, + 545, + 137 + ], + "spans": [ + { + "bbox": [ + 317, + 121, + 545, + 137 + ], + "type": "interline_equation", + "content": "\\Phi_ {j}: [ - \\mathrm {T}, 0 ] ^ {[ 0, \\infty)} \\times [ 0, 1 ] ^ {\\mathrm {P}} \\rightarrow \\mathbb {S} ^ {[ 0, \\infty)}, (\\mathrm {r} _ {j}, \\Theta) \\mapsto \\mathrm {O} _ {j}, \\tag {9}", + "image_path": "4829acbe60b5452ad27bd32501c38f745ea02e56ccb20c178f827ca9c87ec711.jpg" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "spans": [ + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "inline_equation", + "content": "\\mathrm{O}_j" + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "text", + "content": " being a mapping from the unit sphere " + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "inline_equation", + "content": "\\mathbb{S}" + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "text", + "content": " (proxy for projective geometry) to nonnegative distances with 0 understood as \"undefined\" so that each " + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "inline_equation", + "content": "\\Phi_j" + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "text", + "content": " reconstructs a portion " + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "inline_equation", + "content": "\\mathrm{O}_j" + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "text", + "content": " of the overall point cloud " + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "inline_equation", + "content": "\\mathbf{O}" + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "text", + "content": " from a waveform " + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "inline_equation", + "content": "r_j" + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "text", + "content": " truncated to the time interval " + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "inline_equation", + "content": "[-T,0]" + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "text", + "content": ". The overall " + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 305, + 140, + 545, + 212 + ], + "type": "text", + "content": "-modulated LiDAR pipeline" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 378, + 213, + 545, + 225 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 378, + 213, + 545, + 225 + ], + "spans": [ + { + "bbox": [ + 378, + 213, + 545, + 225 + ], + "type": "interline_equation", + "content": "\\Phi : (\\mathbf {r} (H, \\Theta), \\Theta) \\rightarrow \\mathbf {O} \\tag {10}", + "image_path": "84cb84f5f1db85acfdc103eb45543834e429cf63be823d342d839a5755954d4c.jpg" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 305, + 228, + 545, + 275 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 228, + 545, + 275 + ], + "spans": [ + { + "bbox": [ + 305, + 228, + 545, + 275 + ], + "type": "text", + "content": "maps the set of truncated wave forms " + }, + { + "bbox": [ + 305, + 228, + 545, + 275 + ], + "type": "inline_equation", + "content": "\\mathbf{r} = (\\mathrm{r}_1,\\dots ,\\mathrm{r}_{\\mathrm{J}})" + }, + { + "bbox": [ + 305, + 228, + 545, + 275 + ], + "type": "text", + "content": " to the point cloud " + }, + { + "bbox": [ + 305, + 228, + 545, + 275 + ], + "type": "inline_equation", + "content": "\\mathbf{O}" + }, + { + "bbox": [ + 305, + 228, + 545, + 275 + ], + "type": "text", + "content": " that contains the compressed information available to downstream detectors about the changing scene " + }, + { + "bbox": [ + 305, + 228, + 545, + 275 + ], + "type": "inline_equation", + "content": "H" + }, + { + "bbox": [ + 305, + 228, + 545, + 275 + ], + "type": "text", + "content": ". We seek solutions of" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 380, + 277, + 545, + 299 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 380, + 277, + 545, + 299 + ], + "spans": [ + { + "bbox": [ + 380, + 277, + 545, + 299 + ], + "type": "interline_equation", + "content": "\\boldsymbol {\\Theta} ^ {*} = \\underset {\\boldsymbol {\\Theta} \\in [ 0, 1 ] ^ {\\mathrm {P}}} {\\arg \\min } \\mathcal {L} (\\boldsymbol {\\Theta}), \\tag {11}", + "image_path": "0f96e774331b9196a9d91eddea2012b8059e290f79959dfd937ac30662e55a29.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 305, + 300, + 545, + 407 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 300, + 545, + 407 + ], + "spans": [ + { + "bbox": [ + 305, + 300, + 545, + 407 + ], + "type": "text", + "content": "which are Pareto-optimal with respect to the MOO loss vector " + }, + { + "bbox": [ + 305, + 300, + 545, + 407 + ], + "type": "inline_equation", + "content": "\\mathcal{L} = (\\mathcal{L}_1, \\dots, \\mathcal{L}_{\\mathrm{L}})" + }, + { + "bbox": [ + 305, + 300, + 545, + 407 + ], + "type": "text", + "content": ". Loss components may not directly use O; for example, " + }, + { + "bbox": [ + 305, + 300, + 545, + 407 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_l" + }, + { + "bbox": [ + 305, + 300, + 545, + 407 + ], + "type": "text", + "content": " may use data tapped out of the pipeline (e.g., a channel's waveform " + }, + { + "bbox": [ + 305, + 300, + 545, + 407 + ], + "type": "inline_equation", + "content": "\\mathbf{r}_j" + }, + { + "bbox": [ + 305, + 300, + 545, + 407 + ], + "type": "text", + "content": ") or the output of a downstream detector (e.g., a deep CNN) which ingests O (e.g., mAP). The Pareto front [28], which contains Pareto-optimal compromises between losses, is the solution set of Eq. (11), from which a single \"champion\" is selected using additional criteria." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 414, + 441, + 426 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 414, + 441, + 426 + ], + "spans": [ + { + "bbox": [ + 306, + 414, + 441, + 426 + ], + "type": "text", + "content": "4.2. Optimization Algorithm" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 304, + 430, + 545, + 572 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 430, + 545, + 572 + ], + "spans": [ + { + "bbox": [ + 304, + 430, + 545, + 572 + ], + "type": "text", + "content": "To solve the MOO problem, we propose a variant of the Covariance Matrix Adaptation-Evolution Strategy methods of Mosleh et al. [36] and Robidoux et al. [48] that differs from the state of the art in a number of ways. It is listed in Algorithm 1. We use centroid weights that change between generations; to obtain better transients, we greedily override the CMA-ES centroid whenever a new Best So Far (BSF) is found, isolating the computation of CMA-ES statistics from the resulting \"jump\" as well as from the novel inclusion of centroids as BSF candidates; and we formulate a stable dynamic max-rank loss scalarization which we use to drive optimization and as champion selection criterion." + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 304, + 574, + 545, + 692 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 574, + 545, + 692 + ], + "spans": [ + { + "bbox": [ + 304, + 574, + 545, + 692 + ], + "type": "text", + "content": "Stable Dynamic Max-Rank Loss Scalarization Scalarizations like the well-known convex combination " + }, + { + "bbox": [ + 304, + 574, + 545, + 692 + ], + "type": "inline_equation", + "content": "\\sum_{l=1}^{L} \\mathrm{w}_l \\mathcal{L}_l" + }, + { + "bbox": [ + 304, + 574, + 545, + 692 + ], + "type": "text", + "content": " which boils down to the " + }, + { + "bbox": [ + 304, + 574, + 545, + 692 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 304, + 574, + 545, + 692 + ], + "type": "text", + "content": "-norm of the loss vector " + }, + { + "bbox": [ + 304, + 574, + 545, + 692 + ], + "type": "inline_equation", + "content": "\\mathcal{L}" + }, + { + "bbox": [ + 304, + 574, + 545, + 692 + ], + "type": "text", + "content": " with unit weights " + }, + { + "bbox": [ + 304, + 574, + 545, + 692 + ], + "type": "inline_equation", + "content": "\\mathrm{w}_l" + }, + { + "bbox": [ + 304, + 574, + 545, + 692 + ], + "type": "text", + "content": ", are used to combine multiple objectives so that a single objective optimizer yield MOO solutions [15]. Scalarization weights are difficult to choose when loss variations are not commensurate. The max-rank loss addresses this issue. In the context of a generation-based algorithm like Algorithm 1, Mosleh et al. [36] define a max-rank loss as follows. Let" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 318, + 693, + 545, + 711 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 318, + 693, + 545, + 711 + ], + "spans": [ + { + "bbox": [ + 318, + 693, + 545, + 711 + ], + "type": "interline_equation", + "content": "\\mathcal {R} _ {l} ^ {q, m, n} = \\operatorname {r a n k} \\left. \\mathcal {L} _ {l} ^ {q, m} \\text {w i t h i n} \\left\\{\\mathcal {L} _ {l} ^ {r, o} \\right\\} _ {r \\in \\{0, \\dots , 4 \\mathrm {P} \\}} ^ {o \\in \\{1, \\dots , n \\}} \\right. \\tag {12}", + "image_path": "ae719192110fe3891f5f49ee865b8863fbe86ebc456074e4c1eec77a71e35fe6.jpg" + } + ] + } + ], + "index": 18 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13408" + } + ] + } + ], + "index": 19 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 4 + }, + { + "para_blocks": [ + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "spans": [ + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "type": "text", + "content": "with ranks counted from 0 and loss component value ties resolved by left bisection. Then, the weighted (left-bisection) max-rank loss " + }, + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "type": "inline_equation", + "content": "\\mathcal{M}^{q,m,n}" + }, + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "type": "text", + "content": " of the hyperparameter vector " + }, + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "type": "inline_equation", + "content": "\\Theta^{q,m}" + }, + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "type": "text", + "content": " at the end of generation " + }, + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 46, + 72, + 287, + 120 + ], + "type": "text", + "content": " is" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 94, + 121, + 287, + 140 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 94, + 121, + 287, + 140 + ], + "spans": [ + { + "bbox": [ + 94, + 121, + 287, + 140 + ], + "type": "interline_equation", + "content": "\\mathcal {M} ^ {q, m, n} = \\max _ {l \\in \\{1, \\dots , \\mathrm {L} \\}} \\left(\\mathrm {w} _ {l} \\cdot \\mathcal {R} _ {l} ^ {q, m, n}\\right). \\tag {13}", + "image_path": "02c91e2be94a79d22f143bba215a66a03ee3b4509814724a2bb5a89ee793092c.jpg" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 46, + 142, + 287, + 308 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 142, + 287, + 308 + ], + "spans": [ + { + "bbox": [ + 46, + 142, + 287, + 308 + ], + "type": "text", + "content": "The max-rank loss is dynamic. For a given " + }, + { + "bbox": [ + 46, + 142, + 287, + 308 + ], + "type": "inline_equation", + "content": "\\Theta^{q,m}" + }, + { + "bbox": [ + 46, + 142, + 287, + 308 + ], + "type": "text", + "content": " its values are monotone non-decreasing with respect to the addition of data. Because weights multiply ranks, they are non-dimensional, hence dial in the relative importance of loss components. While Mosleh et al. [36] scale each " + }, + { + "bbox": [ + 46, + 142, + 287, + 308 + ], + "type": "inline_equation", + "content": "\\mathbf{w}_l" + }, + { + "bbox": [ + 46, + 142, + 287, + 308 + ], + "type": "text", + "content": " by the (damped) running proportion of individuals that \"fail\" to pass a user-defined threshold, we find that such adaptive weigths break monotonicity. Instead, we keep " + }, + { + "bbox": [ + 46, + 142, + 287, + 308 + ], + "type": "inline_equation", + "content": "\\mathbf{w}_l" + }, + { + "bbox": [ + 46, + 142, + 287, + 308 + ], + "type": "text", + "content": " s fixed. We also improve on Mosleh et al. in that we average left and right bisection ranks (exception: if the left bisection rank is 0, the \"average\" is set to 0) to stabilize " + }, + { + "bbox": [ + 46, + 142, + 287, + 308 + ], + "type": "inline_equation", + "content": "\\mathcal{M}^{q,m,n}" + }, + { + "bbox": [ + 46, + 142, + 287, + 308 + ], + "type": "text", + "content": " with respect to loss value tie breaking (from, e.g., noise) and creation (quantization); this defines our stable (dynamically monotone) max-rank loss scalarization." + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 310, + 287, + 477 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 310, + 287, + 477 + ], + "spans": [ + { + "bbox": [ + 46, + 310, + 287, + 477 + ], + "type": "text", + "content": "Dual-Weight CMA-ES Besides more refined seatbelting (see Supplemental Material), Algorithm 1 differs from earlier CMA-ES in its use of non-constant (hyperparameter, not loss) centroid weights. Although variable CMA-ES generation sizes are common [39], the formula used to derive centroid weights is invariably kept fixed; in contrast, the proposed CMA-ES alternates between gradient-seeking centroid weights [36], which assign zero weight to the worst quartile of each generation instead of the usual half to exploit the symmetry of the second and third quartiles of Gaussian distributions to get a more accurate gradient approximation, and boundary-stabilizing centroid weights [48] with no discard so that further exploration not go in the wrong direction near generic local minima." + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 46, + 477, + 287, + 559 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 477, + 287, + 559 + ], + "spans": [ + { + "bbox": [ + 46, + 477, + 287, + 559 + ], + "type": "text", + "content": "Other novel components are that the loss of the weighted centroid of every generation is evaluated (standard CMA-ES only generate Gaussian clouds with them) and the greedy branch in Algorithm 1 (Lines 18-20): If any individual of the generation, weighted centroid included, turns out to be a strict minimizer, it becomes the next generation Gaussian cloud center." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 47, + 563, + 128, + 576 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 563, + 128, + 576 + ], + "spans": [ + { + "bbox": [ + 47, + 563, + 128, + 576 + ], + "type": "text", + "content": "5. Experiments" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 46, + 578, + 287, + 650 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 578, + 287, + 650 + ], + "spans": [ + { + "bbox": [ + 46, + 578, + 287, + 650 + ], + "type": "text", + "content": "In the following, we first validate the proposed approach with the LiDAR simulation model, jointly optimizing depth estimation and downstream 3D object detection within simulated scenes. Then, we compare the proposed optimization algorithm to recent 0th-order solvers. Finally, we present experiments with an off-the-shelf hardware LiDAR unit." + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 651, + 287, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 651, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 46, + 651, + 287, + 713 + ], + "type": "text", + "content": "Experimental Setup As described in Sec. (3.5), hyperparameters affect the wavefront and DSP. We optimize the DSP rising edge threshold " + }, + { + "bbox": [ + 46, + 651, + 287, + 713 + ], + "type": "inline_equation", + "content": "V^{(m)}" + }, + { + "bbox": [ + 46, + 651, + 287, + 713 + ], + "type": "text", + "content": " and 10 LiDAR hyperparameters that control low-level sensing parameters including the laser power " + }, + { + "bbox": [ + 46, + 651, + 287, + 713 + ], + "type": "inline_equation", + "content": "P_0^{(m)}" + }, + { + "bbox": [ + 46, + 651, + 287, + 713 + ], + "type": "text", + "content": " and laser pulse width " + }, + { + "bbox": [ + 46, + 651, + 287, + 713 + ], + "type": "inline_equation", + "content": "\\tau^{(m)}" + }, + { + "bbox": [ + 46, + 651, + 287, + 713 + ], + "type": "text", + "content": " for" + } + ] + } + ], + "index": 7 + }, + { + "type": "code", + "bbox": [ + 306, + 87, + 545, + 469 + ], + "blocks": [ + { + "bbox": [ + 307, + 72, + 517, + 84 + ], + "lines": [ + { + "bbox": [ + 307, + 72, + 517, + 84 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 517, + 84 + ], + "type": "text", + "content": "Algorithm 1 LiDAR Hyperparameter Optimization." + } + ] + } + ], + "index": 8, + "angle": 0, + "type": "code_caption" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "lines": [ + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "spans": [ + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": "Require: LiDAR " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Phi" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Theta \\in [0,1]^{\\mathrm{P}}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " (initial hyperparameter vector), " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\mathbb{N} \\in \\mathbb{N}^*" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " (number of generations), " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\varepsilon \\in \\left(0,\\frac{1}{3}\\right)" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " (small bound), " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\mathbf{C} \\in \\mathbb{R}^{\\mathrm{P} \\times \\mathrm{P}}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " (CMA-ES \"directional\" covariance matrix factor), " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\sigma \\in [\\varepsilon ,\\frac{1}{3} ]" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " (square root of covariance matrix \"scale\" factor) \n1: " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\mathbf{p} \\gets 0" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\mathbf{c} \\gets 0" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " (CMA-ES path vectors), " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Theta_{\\mathrm{center}} \\gets \\Theta" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " \n2: for " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "n = 1" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " to N do \n3: " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Theta^{0,n} \\gets \\Theta" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " \n4: " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\mathcal{L}^{0,n} \\gets" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " losses for LiDAR " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Phi" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " modulated by " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Theta^{0,n}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " \n5: for " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "p = 1" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " to 4P do \n6: " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Theta^{p,n} \\gets" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " random draw from Gaussian distribution with covariance matrix " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\sigma^2\\mathbf{C}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " centered at " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Theta_{\\mathrm{center}}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " \n7: " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Theta^{p,n} \\gets \\Theta^{p,n} +" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " Gaussian distribution with diagonal cov. matrix proportional to square of quantization grain [48] \n8: " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Theta^{p,n} \\gets \\Theta^{p,n}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " reflected back into " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "[0,1]^{\\mathrm{P}}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " \n9: " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\mathcal{L}^{p,n} \\gets" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " losses for LiDAR " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Phi" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " modulated by " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Theta^{p,n}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " \n10: end for \n11: Compute " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\{\\mathcal{M}^{q,m,n}\\}_{q\\in \\{0,\\dots,4\\mathrm{P}\\},m\\in \\{1,\\dots,n\\}}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " by including " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\{\\mathcal{L}^{p,n}\\}_{p\\in \\{0,\\dots,4\\mathrm{P}\\}}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " in rank computations \n12: Use \"eager\" [36] centroid weights with " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\lambda = 4\\mathrm{P}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\mu = 3\\mathrm{P}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " \n13: if " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " is odd then \n14: Use \"stable\" [48] centroid weights with " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\lambda = \\mu = 4\\mathrm{P}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " \n15: end if \n16: Standard CMA-ES update [21] of " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": ", " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\sigma" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": ", C, p, c based on " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\{\\Theta^{p,n}\\}_{p\\in \\{1,\\dots,4\\mathrm{P}\\}}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\{\\mathcal{M}^{p,n,n}\\}_{p\\in \\{1,\\dots,4\\mathrm{P}\\}}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " \n17: " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Theta_{\\mathrm{center}} \\gets \\Theta" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " \n18: if min " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "p\\in \\{0,\\dots,4\\mathrm{P}\\}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "M^{p,n,n} < \\min_{q\\in \\{0,\\dots,4\\mathrm{P}\\},m\\in \\{1,\\dots,n\\}} M^{q,m,n}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " then \n19: " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Theta_{\\mathrm{center}} \\gets" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " minimizer closest to centroid of minimizers \n20: end if \n21: end for \n22: return " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\Theta^{p,n}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": " in the (guaranteed nonempty) intersection of the Pareto front and the set of minimizers of " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "\\mathcal{M}^{q,m,N}" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": ", with ties resolved by choosing the one closest to their centroid and remaining ties resolved by maximizing " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "n" + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "text", + "content": ", then " + }, + { + "bbox": [ + 306, + 87, + 545, + 469 + ], + "type": "inline_equation", + "content": "p" + } + ] + } + ], + "index": 9, + "angle": 0, + "type": "code_body" + } + ], + "index": 9, + "sub_type": "algorithm" + }, + { + "bbox": [ + 306, + 476, + 410, + 487 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 476, + 410, + 487 + ], + "spans": [ + { + "bbox": [ + 306, + 476, + 410, + 487 + ], + "type": "text", + "content": "each of the 128 channels." + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 306, + 495, + 503, + 509 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 495, + 503, + 509 + ], + "spans": [ + { + "bbox": [ + 306, + 495, + 503, + 509 + ], + "type": "text", + "content": "5.1. Optimization for Depth and Intensity" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 304, + 512, + 545, + 560 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 512, + 545, + 560 + ], + "spans": [ + { + "bbox": [ + 304, + 512, + 545, + 560 + ], + "type": "text", + "content": "Next, we assess the method by optimizing point clouds for depth and intensity with the proposed optimizer. We minimize the average RMSE of the depth and the average RMSE of the intensity, specifically" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 317, + 561, + 545, + 581 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 317, + 561, + 545, + 581 + ], + "spans": [ + { + "bbox": [ + 317, + 561, + 545, + 581 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {d e p t h}} (\\Theta) = \\frac {1}{F} \\sum_ {f = 1} ^ {F} \\operatorname {R M S E} \\left(R _ {f}, \\Phi_ {f} ^ {(R)} (\\Theta)\\right), \\text {a n d} \\tag {14}", + "image_path": "1e359e0c1ed8a555f1b2cb51b1350d749480fd2daf468df298921f9da51408f0.jpg" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 323, + 582, + 545, + 602 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 323, + 582, + 545, + 602 + ], + "spans": [ + { + "bbox": [ + 323, + 582, + 545, + 602 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\text {i n t .}} (\\boldsymbol {\\Theta}) = \\frac {1}{F} \\sum_ {f = 1} ^ {F} \\operatorname {R M S E} \\left(I _ {f}, \\Phi_ {f} ^ {(I)} (\\boldsymbol {\\Theta})\\right), \\tag {15}", + "image_path": "36e9b62fc1dd15c07305f5896d5c17b3f281f6658709a174f4b670dbb27df2e7.jpg" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 304, + 603, + 545, + 689 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 603, + 545, + 689 + ], + "spans": [ + { + "bbox": [ + 304, + 603, + 545, + 689 + ], + "type": "text", + "content": "with " + }, + { + "bbox": [ + 304, + 603, + 545, + 689 + ], + "type": "inline_equation", + "content": "\\mathrm{F}" + }, + { + "bbox": [ + 304, + 603, + 545, + 689 + ], + "type": "text", + "content": " the number of frames in the validation set, see Supplemental Material. The depth loss " + }, + { + "bbox": [ + 304, + 603, + 545, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{depth}}" + }, + { + "bbox": [ + 304, + 603, + 545, + 689 + ], + "type": "text", + "content": " rewards accurate point cloud depth estimates over the full range, whereas " + }, + { + "bbox": [ + 304, + 603, + 545, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{int}}" + }, + { + "bbox": [ + 304, + 603, + 545, + 689 + ], + "type": "text", + "content": " ensures that accurate intensities are measured with the pulse power " + }, + { + "bbox": [ + 304, + 603, + 545, + 689 + ], + "type": "inline_equation", + "content": "P_0^{(j)}" + }, + { + "bbox": [ + 304, + 603, + 545, + 689 + ], + "type": "text", + "content": ". High output power generally results in more accurate point clouds at farther distances but also leads to excessive saturation; " + }, + { + "bbox": [ + 304, + 603, + 545, + 689 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{int}}" + }, + { + "bbox": [ + 304, + 603, + 545, + 689 + ], + "type": "text", + "content": " penalizes saturation." + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 689, + 545, + 713 + ], + "type": "text", + "content": "Fig. 4 shows a comparison between ground-truth, expert-tuned and optimized PCs for a Pareto point that mini-" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13409" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 5 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 68, + 227, + 175 + ], + "blocks": [ + { + "bbox": [ + 47, + 68, + 227, + 175 + ], + "lines": [ + { + "bbox": [ + 47, + 68, + 227, + 175 + ], + "spans": [ + { + "bbox": [ + 47, + 68, + 227, + 175 + ], + "type": "image", + "image_path": "31999305e4163684ccd35bc2e46577a7b76fbe53fd257d8742a00d1fc0263e6f.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + } + ], + "index": 0 + }, + { + "type": "image", + "bbox": [ + 235, + 68, + 397, + 175 + ], + "blocks": [ + { + "bbox": [ + 235, + 68, + 397, + 175 + ], + "lines": [ + { + "bbox": [ + 235, + 68, + 397, + 175 + ], + "spans": [ + { + "bbox": [ + 235, + 68, + 397, + 175 + ], + "type": "image", + "image_path": "90436da306de4d7dce667f8107c017be969ed52715e9c494ae27bdd791fc3d71.jpg" + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_body" + } + ], + "index": 1 + }, + { + "type": "image", + "bbox": [ + 402, + 70, + 545, + 175 + ], + "blocks": [ + { + "bbox": [ + 402, + 70, + 545, + 175 + ], + "lines": [ + { + "bbox": [ + 402, + 70, + 545, + 175 + ], + "spans": [ + { + "bbox": [ + 402, + 70, + 545, + 175 + ], + "type": "image", + "image_path": "7b5958e900abf8005e67f73a04d0c27ea44825882bb55e2dc30d7076c12bd106.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "image_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "spans": [ + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "text", + "content": "mizes the depth error. Compared to the optimized point cloud measurement, the expert-tuned point cloud suffers from clutter, and its depth error increases with distance. The results reported in Table 1 validate the optimization method: The expert-tuned configuration has a loss vector " + }, + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "inline_equation", + "content": "\\mathcal{L} = (\\mathcal{L}_{\\mathrm{depth}},\\mathcal{L}_{\\mathrm{int.}})" + }, + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "text", + "content": " equal to (12.195, 1.971); after 3000 evaluations of the objectives, the champion of the proposed method has a loss vector of (10.754, 0.216), improving depth and intensity metrics by " + }, + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "inline_equation", + "content": "12\\%" + }, + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "inline_equation", + "content": "89\\%" + }, + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "text", + "content": ", respectively. We note that biasing optimization toward reducing " + }, + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "inline_equation", + "content": "\\mathrm{RMSE}_{\\mathrm{depth}}" + }, + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "text", + "content": " more than " + }, + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "inline_equation", + "content": "\\mathrm{RMSE}_{\\mathrm{int.}}" + }, + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "text", + "content": " can be done by changing Eq. (13) loss weights " + }, + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "inline_equation", + "content": "\\mathrm{w}_l" + }, + { + "bbox": [ + 46, + 218, + 287, + 373 + ], + "type": "text", + "content": ". In this work, loss component weights were kept at their default 1." + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 374, + 287, + 447 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 374, + 287, + 447 + ], + "spans": [ + { + "bbox": [ + 46, + 374, + 287, + 447 + ], + "type": "text", + "content": "Table 1 also shows a comparison of the proposed algorithm with other state-of-the-art MOO optimizers. With both champion selection criteria—the one proposed by Mosleh et al. [36], namely \"last Pareto point of the run\", and our own, \"Pareto point that minimizes the stable (weighted) max-rank loss\"—Algorithm (1) is the best performer." + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 47, + 453, + 230, + 466 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 47, + 453, + 230, + 466 + ], + "spans": [ + { + "bbox": [ + 47, + 453, + 230, + 466 + ], + "type": "text", + "content": "5.2. Optimization for Object Detection" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 46, + 468, + 287, + 540 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 468, + 287, + 540 + ], + "spans": [ + { + "bbox": [ + 46, + 468, + 287, + 540 + ], + "type": "text", + "content": "Next, we investigate optimization for object detection and classification. We use Average Precision (AP) as additional optimization objective. AP is maximized for cars and pedestrians at 40 recall positions [55] over an optimization set with " + }, + { + "bbox": [ + 46, + 468, + 287, + 540 + ], + "type": "inline_equation", + "content": "\\mathrm{F} = 100" + }, + { + "bbox": [ + 46, + 468, + 287, + 540 + ], + "type": "text", + "content": " frames. With standard KITTI IoU thresholds [17], the CV loss is" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 81, + 542, + 287, + 556 + ], + "type": "interline_equation", + "angle": 0, + "lines": [ + { + "bbox": [ + 81, + 542, + 287, + 556 + ], + "spans": [ + { + "bbox": [ + 81, + 542, + 287, + 556 + ], + "type": "interline_equation", + "content": "\\mathcal {L} _ {\\mathrm {o b j}, \\{\\text {c a r , p e d .} \\}} (\\Theta) = - \\mathrm {A P} _ {\\{\\text {c a r , p e d .} \\}} (\\Phi (\\Theta)), \\tag {16}", + "image_path": "3a4c82a32cfffdcc629082b8f01af9b19baf60afc29bb8a0a209a7bbd5ee8ee8.jpg" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 46, + 557, + 287, + 616 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 557, + 287, + 616 + ], + "spans": [ + { + "bbox": [ + 46, + 557, + 287, + 616 + ], + "type": "text", + "content": "evaluated over a " + }, + { + "bbox": [ + 46, + 557, + 287, + 616 + ], + "type": "inline_equation", + "content": "0 - 80\\mathrm{m}" + }, + { + "bbox": [ + 46, + 557, + 287, + 616 + ], + "type": "text", + "content": " range. We train a PV-RCNN [53] for 3D object detection on 5900 full range point clouds collected from the simulation environment with 8 different expert-tuned parameterizations " + }, + { + "bbox": [ + 46, + 557, + 287, + 616 + ], + "type": "inline_equation", + "content": "\\Theta" + }, + { + "bbox": [ + 46, + 557, + 287, + 616 + ], + "type": "text", + "content": ", see training and dataset details in the Supplemental Material." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "spans": [ + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "text", + "content": "Table 2 contains a quantitative comparison between the proposed method and expert tuning. Compared to expert tuning, the proposed method increases AP by " + }, + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "inline_equation", + "content": "52.9\\%" + }, + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "inline_equation", + "content": "27.2\\%" + }, + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "text", + "content": " for cars and pedestrians, respectively, on the full " + }, + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "inline_equation", + "content": "0 - 80\\mathrm{m}" + }, + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "text", + "content": " range, with large increases predominately at close range. Lower AP values in the " + }, + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "inline_equation", + "content": "0 - 30\\mathrm{m}" + }, + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "text", + "content": " range compared to " + }, + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "inline_equation", + "content": "30 - 50\\mathrm{m}" + }, + { + "bbox": [ + 46, + 618, + 287, + 714 + ], + "type": "text", + "content": " are due to a lower object occurrence (3.2 per frame vs. 3.6) and a less varied distribution of object yaw" + } + ] + } + ], + "index": 10 + }, + { + "type": "table", + "bbox": [ + 305, + 208, + 451, + 327 + ], + "blocks": [ + { + "bbox": [ + 305, + 208, + 451, + 327 + ], + "lines": [ + { + "bbox": [ + 305, + 208, + 451, + 327 + ], + "spans": [ + { + "bbox": [ + 305, + 208, + 451, + 327 + ], + "type": "table", + "html": "
SOLVERl1-norm (↓) last Pareto point [36]l1-norm (↓) proposed champion
Expert-Tuned14.09114.091
Proposed11.08910.995
NSGA-III [10,11]11.40811.574
Mosleh et al. [36]11.49711.497
AGE-MOEA [42]11.54711.427
C-TAEA [29]11.62211.461
RVEA [8]11.85111.437
R-NSGA-III [63]12.30611.619
U-NSGA-III [51]13.42311.512
SMS-EMOA [2]13.65811.750
", + "image_path": "1ee88b98c19a6151158d9c63327f033d9d01d1e108838c4067826b5ff865d6b1.jpg" + } + ] + } + ], + "index": 11, + "angle": 0, + "type": "table_body" + } + ], + "index": 11 + }, + { + "type": "image", + "bbox": [ + 451, + 206, + 543, + 331 + ], + "blocks": [ + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "lines": [ + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "spans": [ + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "text", + "content": "Figure 4. Comparison between expert-tuned and optimized point clouds. Colors encode the individual depth error of each point (clipped at " + }, + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "inline_equation", + "content": "2\\mathrm{m}" + }, + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "text", + "content": "). Convergence plot shows the loss components " + }, + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{depth}}" + }, + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{int}}" + }, + { + "bbox": [ + 46, + 178, + 547, + 212 + ], + "type": "text", + "content": " (Eq. (14)-(15)) against optimization step (3000 loss evaluations total), the evolution of the champion and the final Pareto front, champion included." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "image_caption" + }, + { + "bbox": [ + 451, + 206, + 543, + 331 + ], + "lines": [ + { + "bbox": [ + 451, + 206, + 543, + 331 + ], + "spans": [ + { + "bbox": [ + 451, + 206, + 543, + 331 + ], + "type": "image", + "image_path": "bccc9bf9d2737dce330692c4fd054570031c8a6c8d524850c06a48fdb046eac8.jpg" + } + ] + } + ], + "index": 12, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 304, + 333, + 545, + 453 + ], + "lines": [ + { + "bbox": [ + 304, + 333, + 545, + 453 + ], + "spans": [ + { + "bbox": [ + 304, + 333, + 545, + 453 + ], + "type": "text", + "content": "Table 1. LiDAR Depth and Intensity MOO. The Pareto front (see figure) is approximately a straight line with slope -1. So, the variations of " + }, + { + "bbox": [ + 304, + 333, + 545, + 453 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{depth}}" + }, + { + "bbox": [ + 304, + 333, + 545, + 453 + ], + "type": "text", + "content": " and " + }, + { + "bbox": [ + 304, + 333, + 545, + 453 + ], + "type": "inline_equation", + "content": "\\mathcal{L}_{\\mathrm{int.}}" + }, + { + "bbox": [ + 304, + 333, + 545, + 453 + ], + "type": "text", + "content": " are approximately balanced and the " + }, + { + "bbox": [ + 304, + 333, + 545, + 453 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 304, + 333, + 545, + 453 + ], + "type": "text", + "content": "-norm of the loss vector is a reasonable evaluation metric. For all solvers, the first column shows the " + }, + { + "bbox": [ + 304, + 333, + 545, + 453 + ], + "type": "inline_equation", + "content": "\\ell_1" + }, + { + "bbox": [ + 304, + 333, + 545, + 453 + ], + "type": "text", + "content": "-norm of the last Pareto point of the run (champion per Mosleh et al. [36]); the last column instead uses the proposed champion selection method (Line 22 of Algorithm (1)). The proposed solver performs favorably; in addition, the proposed champion selection method improves results for all but two solvers (there is one tie). Median of three runs with 3000 loss evaluations; see the Supplemental Material for details." + } + ] + } + ], + "index": 13, + "angle": 0, + "type": "image_caption" + } + ], + "index": 12 + }, + { + "bbox": [ + 304, + 459, + 545, + 651 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 459, + 545, + 651 + ], + "spans": [ + { + "bbox": [ + 304, + 459, + 545, + 651 + ], + "type": "text", + "content": "angles (std. dev. of 0.63 rad vs. 1.25 rad) within the training set. Consequently, the detector can better adapt within the " + }, + { + "bbox": [ + 304, + 459, + 545, + 651 + ], + "type": "inline_equation", + "content": "30 - 50\\mathrm{m}" + }, + { + "bbox": [ + 304, + 459, + 545, + 651 + ], + "type": "text", + "content": " bin. The quantitative findings agree with the qualitative results of Fig. 5, where optimized and expert-tuned point clouds are compared to the ground truth. Here, the clutter in the expert-tuned point cloud results in false positives and missed detections. In these examples, the proposed method misses only one pedestrian, whereas on the expert-tuned PC the detector misses them all. We also compare the average depth RMSE before and after filtering suppressed points; the low loss of the filtered PC suggests that clutter has been removed and that the detector prefers an accurate if sparser point cloud. Note however that a DSP optimized for the removal of clutter, like the proposed method, also suppresses ground points, with a small impact on object detection results, see Table 2 last column." + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 306, + 660, + 491, + 673 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 660, + 491, + 673 + ], + "spans": [ + { + "bbox": [ + 306, + 660, + 491, + 673 + ], + "type": "text", + "content": "5.3. Off-the-shelf LiDAR Optimization" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "spans": [ + { + "bbox": [ + 305, + 677, + 545, + 713 + ], + "type": "text", + "content": "In this section, we adapt the proposed optimization algorithm to an off-the-shelf Baraja Spectrum LiDAR sensor. Only a handful of hyperparameters are user-accessible: the" + } + ] + } + ], + "index": 16 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13410" + } + ] + } + ], + "index": 17 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 6 + }, + { + "para_blocks": [ + { + "type": "image", + "bbox": [ + 47, + 68, + 545, + 202 + ], + "blocks": [ + { + "bbox": [ + 47, + 68, + 545, + 202 + ], + "lines": [ + { + "bbox": [ + 47, + 68, + 545, + 202 + ], + "spans": [ + { + "bbox": [ + 47, + 68, + 545, + 202 + ], + "type": "image", + "image_path": "68dff62b3540dfd5abaf3b90e16181f91e9f0dda2815bbee501783de09539d32.jpg" + } + ] + } + ], + "index": 0, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 205, + 546, + 227 + ], + "lines": [ + { + "bbox": [ + 46, + 205, + 546, + 227 + ], + "spans": [ + { + "bbox": [ + 46, + 205, + 546, + 227 + ], + "type": "text", + "content": "Figure 5. Comparison between ground-truth, expert-tuned and optimized point cloud for 3D object detection. Optimization results in clutter-free point clouds which helps reduce false positive detections. For visualization purposes only the camera field-of-view is shown." + } + ] + } + ], + "index": 1, + "angle": 0, + "type": "image_caption" + } + ], + "index": 0 + }, + { + "type": "table", + "bbox": [ + 50, + 228, + 541, + 286 + ], + "blocks": [ + { + "bbox": [ + 50, + 228, + 541, + 286 + ], + "lines": [ + { + "bbox": [ + 50, + 228, + 541, + 286 + ], + "spans": [ + { + "bbox": [ + 50, + 228, + 541, + 286 + ], + "type": "table", + "html": "
METHODmAP (↑)APcar (↑)APped. (↑)RMSEdepth (↓) filtered
0-80 m0-30 m30-50 m50-80 m0-80 m0-30 m30-50 m50-80 m0-80 m0-30 m30-50 m50-80 m
Expert7.3310.8810.181.2814.5821.7520.192.530.070.020.170.049.12 6.17
Proposed46.8652.1458.7027.0866.4775.0684.8641.2027.2529.2232.5312.9512.87 5.13
Margin+39.53+41.26+48.52+25.80+51.89+53.31+64.67+38.67+27.18+29.20+32.36+12.91+3.75 -1.04
", + "image_path": "0e1686c936204d311e3d9f5d7b7f1d4a6ffe04e401edc2e76f0a0bc5f8cd0cf3.jpg" + } + ] + } + ], + "index": 2, + "angle": 0, + "type": "table_body" + } + ], + "index": 2 + }, + { + "bbox": [ + 46, + 290, + 546, + 313 + ], + "lines": [ + { + "bbox": [ + 46, + 290, + 546, + 313 + ], + "spans": [ + { + "bbox": [ + 46, + 290, + 546, + 313 + ], + "type": "text", + "content": "Table 2. Optimization for 3D Object Detection. We report mAP and AP in different distance range bins, and unfiltered and filtered RMSE, for expert-tuned and proposed. Filtered " + }, + { + "bbox": [ + 46, + 290, + 546, + 313 + ], + "type": "inline_equation", + "content": "\\mathrm{RMSE}_{\\mathrm{depth}}" + }, + { + "bbox": [ + 46, + 290, + 546, + 313 + ], + "type": "text", + "content": " excludes all points suppressed by the DSP. Best in bold." + } + ] + } + ], + "index": 3, + "angle": 0, + "type": "text" + }, + { + "type": "image", + "bbox": [ + 47, + 323, + 287, + 419 + ], + "blocks": [ + { + "bbox": [ + 47, + 323, + 287, + 419 + ], + "lines": [ + { + "bbox": [ + 47, + 323, + 287, + 419 + ], + "spans": [ + { + "bbox": [ + 47, + 323, + 287, + 419 + ], + "type": "image", + "image_path": "bf3bcd69bf01ae9475471da7ffe8b9773bbbce2dc70b382697cded61ca990234.jpg" + } + ] + } + ], + "index": 4, + "angle": 0, + "type": "image_body" + }, + { + "bbox": [ + 46, + 426, + 287, + 492 + ], + "lines": [ + { + "bbox": [ + 46, + 426, + 287, + 492 + ], + "spans": [ + { + "bbox": [ + 46, + 426, + 287, + 492 + ], + "type": "text", + "content": "Figure 6. Left: ground truth 3D histogram used to compute losses, see text, red bounding boxes denoting cars, with experimental setup zoom-ins: captured static scene (top) and LiDAR installation (bottom); the LiDAR detector is the blue unit. Middle: loss contributions for the expert-tuned parameters and Right: for optimized parameters. Brighter color means a smaller loss." + } + ] + } + ], + "index": 5, + "angle": 0, + "type": "image_caption" + } + ], + "index": 4 + }, + { + "bbox": [ + 46, + 498, + 289, + 715 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 46, + 498, + 289, + 715 + ], + "spans": [ + { + "bbox": [ + 46, + 498, + 289, + 715 + ], + "type": "text", + "content": "return mode that selects the waveform peaks used to generate the point cloud, the sensor heads scanning pattern, and the two sensor head motor frequencies which determine the PC angular resolution vs. sensor noise balance. Because raw wavefronts are inaccessible, we optimize 3D point cloud histograms taken over an (intermittently) static scene such as a workplace parking lot. Fig. 6 shows the experimental setup along with a photograph of the scene over which optimization was performed. The ground truth histogram is shown on the left of Fig. 6; it was generated by averaging 100 captures with a uniform angular resolution scanning pattern. Expert-tuned and optimized histogram contributions to the fitness function (described in the Supplemental Material) are respectively shown in the middle and right of Fig. 6; brighter bins indicate larger contributions. Optimized hyperparameters achieved better results by focusing laser scans in regions of interests (ROIs) where the ground truth was denser; expert-tuned ones produced" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 304, + 334, + 545, + 382 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 334, + 545, + 382 + ], + "spans": [ + { + "bbox": [ + 304, + 334, + 545, + 382 + ], + "type": "text", + "content": "more uniform point clouds. Using the RMSE of PC histograms weighted by distance and number of points per bin, we estimate a " + }, + { + "bbox": [ + 304, + 334, + 545, + 382 + ], + "type": "inline_equation", + "content": "10.1\\mathrm{cm}" + }, + { + "bbox": [ + 304, + 334, + 545, + 382 + ], + "type": "text", + "content": " error for the expert-tuned configuration vs. " + }, + { + "bbox": [ + 304, + 334, + 545, + 382 + ], + "type": "inline_equation", + "content": "3.0\\mathrm{cm}" + }, + { + "bbox": [ + 304, + 334, + 545, + 382 + ], + "type": "text", + "content": " with optimized hyperparameters." + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 306, + 384, + 379, + 396 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 306, + 384, + 379, + 396 + ], + "spans": [ + { + "bbox": [ + 306, + 384, + 379, + 396 + ], + "type": "text", + "content": "6. Conclusion" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 304, + 400, + 546, + 591 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 400, + 546, + 591 + ], + "spans": [ + { + "bbox": [ + 304, + 400, + 546, + 591 + ], + "type": "text", + "content": "We propose an in-the-loop black box 0th-order optimization method for LiDAR sensing pipelines that finds optimal parameters for depth and intensity estimation and 3D object detection and classification. To assess our method, we propose a novel LiDAR simulation method integrated into the CARLA simulator. We find that optimizing the LiDAR sensing pipeline can significantly improve depth, intensity and object detection and classification compared to manual expert tuning. Specifically, for 3D object detection our optimization method results in a major increase of " + }, + { + "bbox": [ + 304, + 400, + 546, + 591 + ], + "type": "inline_equation", + "content": "51.9\\%" + }, + { + "bbox": [ + 304, + 400, + 546, + 591 + ], + "type": "text", + "content": " AP for cars and " + }, + { + "bbox": [ + 304, + 400, + 546, + 591 + ], + "type": "inline_equation", + "content": "27.2\\%" + }, + { + "bbox": [ + 304, + 400, + 546, + 591 + ], + "type": "text", + "content": " AP for pedestrians, compared to fine-tuning a detector on expert-optimized LiDAR vendor parameters. An interesting direction for future research is real-time scene-dependent optimization of LiDAR scanning parameters, potentially leading to adaptive sensing in adverse weather in urban and highway scenarios." + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 304, + 594, + 547, + 713 + ], + "type": "text", + "angle": 0, + "lines": [ + { + "bbox": [ + 304, + 594, + 547, + 713 + ], + "spans": [ + { + "bbox": [ + 304, + 594, + 547, + 713 + ], + "type": "text", + "content": "Acknowledgments This work was supported by the AI-SEE project with funding from the FFG, BMBF, and NRCIRA. We thank the Federal Ministry for Economic Affairs and Energy for support through the PEGASUS-family project VVM-Verification and Validation Methods for Automated Vehicles Level 4 and 5. Felix Heide was supported by an NSF CAREER Award (2047359), a Packard Foundation Fellowship, a Sloan Research Fellowship, a Sony Young Faculty Award, a Project X Innovation Award, and an Amazon Science Research Award." + } + ] + } + ], + "index": 10 + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 317, + 757 + ], + "type": "text", + "content": "13411" + } + ] + } + ], + "index": 11 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 7 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "title", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "spans": [ + { + "bbox": [ + 48, + 71, + 107, + 83 + ], + "type": "text", + "content": "References" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 91, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 13, + "blocks": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "spans": [ + { + "bbox": [ + 53, + 91, + 287, + 145 + ], + "type": "text", + "content": "[1] Behnam Behroozpour, Phillip A. M. Sandborn, Ming C. Wu, and Bernhard E. Boser. Lidar System Architectures and Circuits. IEEE Communications Magazine, 55(10):135-142, Oct. 2017. Conference Name: IEEE Communications Magazine. 3" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 53, + 148, + 288, + 192 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 148, + 288, + 192 + ], + "spans": [ + { + "bbox": [ + 53, + 148, + 288, + 192 + ], + "type": "text", + "content": "[2] Nicola Beume, Boris Naujoks, and Michael Emmerich. SMS-EMOA: Multiobjective selection based on dominated hypervolume. European Journal of Operational Research, 181(3):1653–1669, Sept. 2007. 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 53, + 194, + 287, + 249 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 194, + 287, + 249 + ], + "spans": [ + { + "bbox": [ + 53, + 194, + 287, + 249 + ], + "type": "text", + "content": "[3] Mario Bijelic, Tobias Gruber, Fahim Mannan, Florian Kraus, Werner Ritter, Klaus Dietmayer, and Felix Heide. Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 53, + 251, + 287, + 283 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 251, + 287, + 283 + ], + "spans": [ + { + "bbox": [ + 53, + 251, + 287, + 283 + ], + "type": "text", + "content": "[4] Mario Bijelic, Tobias Gruber, and Werner Ritter. A benchmark for LiDAR sensors in fog: Is detection breaking down? In IEEE Intelligent Vehicles Symposium (IV), 2018. 1, 2" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 53, + 285, + 287, + 340 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 285, + 287, + 340 + ], + "spans": [ + { + "bbox": [ + 53, + 285, + 287, + 340 + ], + "type": "text", + "content": "[5] Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D. Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, Xin Zhang, Jake Zhao, and Karol Zieba. End to End Learning for Self-Driving Cars, Apr. 2016. arXiv:1604.07316 [cs]. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 53, + 342, + 287, + 396 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 342, + 287, + 396 + ], + "spans": [ + { + "bbox": [ + 53, + 342, + 287, + 396 + ], + "type": "text", + "content": "[6] Alexander Carballo, Jacob Lambert, Abraham Monrroy Cano, David Robert Wong, Patiphon Narksri, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, and Kazuya Takeda. LIBRE: The multiple 3D LiDAR dataset. In IEEE Intelligent Vehicles Symposium (IV), 2020. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 53, + 398, + 287, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 398, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 53, + 398, + 287, + 453 + ], + "type": "text", + "content": "[7] M. Chang, J. Lambert, P. Sangkloy, J. Singh, S. Bak, A. Hartnett, D. Wang, P. Carr, S. Lucey, D. Ramanan, and J. Hays. Argoverse: 3D tracking and forecasting with rich maps. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 53, + 454, + 287, + 520 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 454, + 287, + 520 + ], + "spans": [ + { + "bbox": [ + 53, + 454, + 287, + 520 + ], + "type": "text", + "content": "[8] Ran Cheng, Yaochu Jin, Markus Olhofer, and Bernhard Sendhoff. A Reference Vector Guided Evolutionary Algorithm for Many-Objective Optimization. IEEE Transactions on Evolutionary Computation, 20(5):773-791, Oct. 2016. Conference Name: IEEE Transactions on Evolutionary Computation. 7" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 53, + 522, + 287, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 53, + 522, + 287, + 555 + ], + "spans": [ + { + "bbox": [ + 53, + 522, + 287, + 555 + ], + "type": "text", + "content": "[9] Robert L. Cook and Kenneth E. Torrance. A reflectance model for computer graphics. ACM SIGGRAPH Computer Graphics, 15(3):307-316, Aug. 1981. 4" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 556, + 287, + 600 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 556, + 287, + 600 + ], + "spans": [ + { + "bbox": [ + 48, + 556, + 287, + 600 + ], + "type": "text", + "content": "[10] K. Deb and J. Sundar. Reference point based multi-objective optimization using evolutionary algorithms | Proceedings of the 8th annual conference on Genetic and evolutionary computation. 7" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 602, + 287, + 668 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 602, + 287, + 668 + ], + "spans": [ + { + "bbox": [ + 48, + 602, + 287, + 668 + ], + "type": "text", + "content": "[11] Kalyanmoy Deb and J. Sundar. Reference point based multi-objective optimization using evolutionary algorithms. In Proceedings of the 8th annual conference on Genetic and evolutionary computation, GECCO '06, pages 635-642, New York, NY, USA, July 2006. Association for Computing Machinery. 7" + } + ] + } + ], + "index": 11 + }, + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 287, + 713 + ], + "type": "text", + "content": "[12] Ayush Dewan, Tim Caselitz, Gian Diego Tipaldi, and Wolfram Burgard. Motion-based detection and tracking in 3D LiDAR scans. In IEEE International Conference on Robotics and Automation (ICRA), 2016. 1" + } + ] + } + ], + "index": 12 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 547, + 713 + ], + "type": "list", + "angle": 0, + "index": 28, + "blocks": [ + { + "bbox": [ + 307, + 73, + 547, + 139 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 547, + 139 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 547, + 139 + ], + "type": "text", + "content": "[13] Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In Sergey Levine, Vincent Vanhoucke, and Ken Goldberg, editors, Proceedings of the 1st Annual Conference on Robot Learning, volume 78 of Proceedings of Machine Learning Research, pages 1-16. PMLR, 13-15 Nov 2017. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 141, + 547, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 141, + 547, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 141, + 547, + 184 + ], + "type": "text", + "content": "[14] Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. CARLA: An open urban driving simulator. In 1st Annual Conference on Robot Learning, 2017. 3" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 186, + 547, + 220 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 186, + 547, + 220 + ], + "spans": [ + { + "bbox": [ + 307, + 186, + 547, + 220 + ], + "type": "text", + "content": "[15] Michael T. M. Emmerich and André H. Deutz. A tutorial on multiobjective optimization: fundamentals and evolutionary methods. Natural Computing, 17(3):585-609, Sept. 2018. 5" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 222, + 546, + 276 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 222, + 546, + 276 + ], + "spans": [ + { + "bbox": [ + 307, + 222, + 546, + 276 + ], + "type": "text", + "content": "[16] Jin Fang, Dingfu Zhou, Feilong Yan, Tongtong Zhao, Feihu Zhang, Yu Ma, Liang Wang, and Ruigang Yang. Augmented LiDAR Simulator for Autonomous Driving. IEEE Robotics and Automation Letters, 5(2):1931-1938, Apr. 2020. arXiv:1811.07112 [cs]. 3" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 278, + 546, + 322 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 278, + 546, + 322 + ], + "spans": [ + { + "bbox": [ + 307, + 278, + 546, + 322 + ], + "type": "text", + "content": "[17] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the KITTI vision benchmark suite. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2012. 7" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 324, + 546, + 357 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 324, + 546, + 357 + ], + "spans": [ + { + "bbox": [ + 307, + 324, + 546, + 357 + ], + "type": "text", + "content": "[18] Christopher Goodin, Daniel Carruth, Matthew Doude, and Christopher Hudson. Predicting the influence of rain on LiDAR in ADAS. *Electronics*, 8, 2019. 1" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 359, + 547, + 414 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 359, + 547, + 414 + ], + "spans": [ + { + "bbox": [ + 307, + 359, + 547, + 414 + ], + "type": "text", + "content": "[19] Martin Hahner, Christos Sakaridis, Mario Bijelic, Felix Heide, Fisher Yu, Dengxin Dai, and Luc Van Gool. LiDAR Snowfall Simulation for Robust 3D Object Detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022. 1, 3, 4" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 416, + 547, + 460 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 416, + 547, + 460 + ], + "spans": [ + { + "bbox": [ + 307, + 416, + 547, + 460 + ], + "type": "text", + "content": "[20] Martin Hahner, Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Fog simulation on real LiDAR point clouds for 3D object detection in adverse weather. In IEEE International Conference on Computer Vision (ICCV), 2021. 1, 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 462, + 546, + 483 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 462, + 546, + 483 + ], + "spans": [ + { + "bbox": [ + 307, + 462, + 546, + 483 + ], + "type": "text", + "content": "[21] Nikolaus Hansen. The CMA evolution strategy: A tutorial, 2016. 2, 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 486, + 546, + 529 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 486, + 546, + 529 + ], + "spans": [ + { + "bbox": [ + 307, + 486, + 546, + 529 + ], + "type": "text", + "content": "[22] Felix Heide, Steven Diamond, David B Lindell, and Gordon Wetzstein. Sub-picosecond photon-efficient 3d imaging using single-photon sensors. Scientific reports, 8(1):1-8, 2018. 3,4" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 307, + 532, + 546, + 576 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 532, + 546, + 576 + ], + "spans": [ + { + "bbox": [ + 307, + 532, + 546, + 576 + ], + "type": "text", + "content": "[23] Robin Heinzler, Philipp Schindler, Jürgen Seekircher, Werner Ritter, and Wilhelm Stork. Weather influence and classification with automotive LiDAR sensors. In IEEE Intelligent Vehicles Symposium (IV), 2019. 1" + } + ] + } + ], + "index": 24 + }, + { + "bbox": [ + 307, + 578, + 546, + 621 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 578, + 546, + 621 + ], + "spans": [ + { + "bbox": [ + 307, + 578, + 546, + 621 + ], + "type": "text", + "content": "[24] Braden Hurl, Krzysztof Czarnecki, and Steven Waslander. Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception, May 2019. arXiv:1905.00160 [cs]. 1, 3" + } + ] + } + ], + "index": 25 + }, + { + "bbox": [ + 307, + 624, + 546, + 657 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 624, + 546, + 657 + ], + "spans": [ + { + "bbox": [ + 307, + 624, + 546, + 657 + ], + "type": "text", + "content": "[25] Maria Jokela, Matti Kutila, and Pasi Pyykonen. Testing and validation of automotive point-cloud sensors in adverse weather conditions. Applied Sciences, 9, 2019. 1" + } + ] + } + ], + "index": 26 + }, + { + "bbox": [ + 307, + 658, + 546, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 658, + 546, + 713 + ], + "spans": [ + { + "bbox": [ + 307, + 658, + 546, + 713 + ], + "type": "text", + "content": "[26] Velat Kilic, Deepti Hegde, Vishwanath Sindagi, A. Brinton Cooper, Mark Foster, and Vishal Patel. LiDAR light scattering augmentation (LISA): physics-based simulation of adverse weather conditions for 3D object detection. arXiv preprint 2107.07004, 2021. 1, 3" + } + ] + } + ], + "index": 27 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 748, + 318, + 757 + ], + "type": "text", + "content": "13412" + } + ] + } + ], + "index": 29 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 8 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 712 + ], + "type": "list", + "angle": 0, + "index": 11, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 127 + ], + "type": "text", + "content": "[27] Matti Kutila, Pasi Pyykönen, Maria Jokela, Tobias Gruber, Mario Bijelic, and Werner Ritter. Benchmarking automotive LiDAR performance in arctic conditions. In IEEE International Conference on Intelligent Transportation Systems (ITSC), 2020. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 132, + 287, + 176 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 132, + 287, + 176 + ], + "spans": [ + { + "bbox": [ + 48, + 132, + 287, + 176 + ], + "type": "text", + "content": "[28] Marco Laumanns, Lothar Thiele, Kalyanmoy Deb, and Eckart Zitzler. Combining convergence and diversity in evolutionary multiobjective optimization. Evolutionary Computation, 10(3):263-282, 2002. 5" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 180, + 287, + 236 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 180, + 287, + 236 + ], + "spans": [ + { + "bbox": [ + 48, + 180, + 287, + 236 + ], + "type": "text", + "content": "[29] Ke Li, Renzhi Chen, Guangtao Fu, and Xin Yao. Two-Archive Evolutionary Algorithm for Constrained Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 23(2):303-315, Apr. 2019. Conference Name: IEEE Transactions on Evolutionary Computation. 7" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 240, + 287, + 274 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 240, + 287, + 274 + ], + "spans": [ + { + "bbox": [ + 48, + 240, + 287, + 274 + ], + "type": "text", + "content": "[30] Xiaolu Li, Bingwei Yang, Xinhao Xie, Duan Li, and Lijun Xu. Influence of waveform characteristics on lidar ranging accuracy and precision. Sensors, 18(4), 2018. 5" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 278, + 287, + 333 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 278, + 287, + 333 + ], + "spans": [ + { + "bbox": [ + 48, + 278, + 287, + 333 + ], + "type": "text", + "content": "[31] Ievgeniia Maksymova, Christian Steger, and Norbert Druml. Review of LiDAR Sensor Data Acquisition and Compression for Automotive Applications. Proceedings, 2(13):852, 2018. Number: 13 Publisher: Multidisciplinary Digital Publishing Institute. 3" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 338, + 287, + 392 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 338, + 287, + 392 + ], + "spans": [ + { + "bbox": [ + 48, + 338, + 287, + 392 + ], + "type": "text", + "content": "[32] Yunze Man, Xinshuo Weng, Prasanna Kumar Sivakumar, Matthew O'Toole, and Kris Kitani. Multi-echo lidar for 3d object detection. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pages 3743-3752, 2021. 3" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 397, + 287, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 397, + 287, + 464 + ], + "spans": [ + { + "bbox": [ + 48, + 397, + 287, + 464 + ], + "type": "text", + "content": "[33] Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong, Wenyuan Zeng, Mikita Sazanovich, Shuhan Tan, Bin Yang, Wei-Chiu Ma, and Raquel Urtasun. Lidarsim: Realistic lidar simulation by leveraging the real world. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 11164-11173, 2020. 3" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 468, + 287, + 533 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 468, + 287, + 533 + ], + "spans": [ + { + "bbox": [ + 48, + 468, + 287, + 533 + ], + "type": "text", + "content": "[34] Sebastien Michaud, Jean-François Lalonde, and Philippe Giguère. Towards characterizing the behavior of LiDARs in snowy conditions. In Workshop on Planning, Perception and Navigation for Intelligent Vehicles, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015. 1" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 539, + 287, + 593 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 539, + 287, + 593 + ], + "spans": [ + { + "bbox": [ + 48, + 539, + 287, + 593 + ], + "type": "text", + "content": "[35] Anish Mittal, Anush K. Moorthy, and Alan C. Bovik. Automatic parameter prediction for image denoising algorithms using perceptual quality features. In Human Vision and Electronic Imaging XVII, volume 8291, pages 110-116. SPIE, Feb. 2012. 2" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 598, + 287, + 664 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 598, + 287, + 664 + ], + "spans": [ + { + "bbox": [ + 48, + 598, + 287, + 664 + ], + "type": "text", + "content": "[36] Ali Mosleh, Avinash Sharma, Emmanuel Onzon, Fahim Mannan, Nicolas Robidoux, and Felix Heide. Hardware-in-the-Loop End-to-End Optimization of Camera Image Processing Pipelines. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 7526–7535, Seattle, WA, USA, June 2020. IEEE. 2, 5, 6, 7" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "spans": [ + { + "bbox": [ + 48, + 670, + 287, + 712 + ], + "type": "text", + "content": "[37] Matthias Müller, Vincent Casser, Jean Lahoud, Neil Smith, and Bernard Ghanem. Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications, Mar. 2018. arXiv:1708.05869 [cs]. 3" + } + ] + } + ], + "index": 10 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 72, + 545, + 712 + ], + "type": "list", + "angle": 0, + "index": 24, + "blocks": [ + { + "bbox": [ + 307, + 72, + 545, + 127 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 72, + 545, + 127 + ], + "spans": [ + { + "bbox": [ + 307, + 72, + 545, + 127 + ], + "type": "text", + "content": "[38] Kazuki Nakamura, Kenji Narumi, Kohei Kikuchi, and Yasuhisa Inada. Liquid crystal-tunable optical phased array for LiDAR applications. In Smart Photonic and Optoelectronic Integrated Circuits XXIII, volume 11690, pages 94-99. SPIE, Mar. 2021. 3" + } + ] + } + ], + "index": 12 + }, + { + "bbox": [ + 307, + 129, + 545, + 195 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 129, + 545, + 195 + ], + "spans": [ + { + "bbox": [ + 307, + 129, + 545, + 195 + ], + "type": "text", + "content": "[39] Kouhei Nishida and Youhei Akimoto. Population Size Adaptation for the CMA-ES Based on the Estimation Accuracy of the Natural Gradient. In Proceedings of the Genetic and Evolutionary Computation Conference 2016, GECCO '16, pages 237–244, New York, NY, USA, July 2016. Association for Computing Machinery. 6" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 307, + 197, + 545, + 240 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 197, + 545, + 240 + ], + "spans": [ + { + "bbox": [ + 307, + 197, + 545, + 240 + ], + "type": "text", + "content": "[40] Jun Nishimura, Timo Gerasimow, Sushma Rao, Aleksandar Sutic, Chyuan-Tyng Wu, and Gilad Michael. Automatic ISP image quality tuning using non-linear optimization, Feb. 2019. arXiv:1902.09023 [cs]. 2" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 241, + 545, + 306 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 241, + 545, + 306 + ], + "spans": [ + { + "bbox": [ + 307, + 241, + 545, + 306 + ], + "type": "text", + "content": "[41] Matthew O'Toole, Felix Heide, David B. Lindell, Kai Zang, Steven Diamond, and Gordon Wetzstein. Reconstructing Transient Images from Single-Photon Sensors. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 2289–2297, Honolulu, HI, July 2017. IEEE. 3, 4" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 307, + 308, + 545, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 308, + 545, + 374 + ], + "spans": [ + { + "bbox": [ + 307, + 308, + 545, + 374 + ], + "type": "text", + "content": "[42] Annibale Panichella. An adaptive evolutionary algorithm based on non-euclidean geometry for many-objective optimization. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO '19, pages 595-603, New York, NY, USA, July 2019. Association for Computing Machinery. 7" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 307, + 376, + 545, + 431 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 376, + 545, + 431 + ], + "spans": [ + { + "bbox": [ + 307, + 376, + 545, + 431 + ], + "type": "text", + "content": "[43] Adithya K. Pediredla, Aswin C. Sankaranarayanan, Mauro Buttafava, Alberto Tosi, and Ashok Veeraraghavan. Signal Processing Based Pile-up Compensation for Gated Single-Photon Avalanche Diodes, June 2018. arXiv:1806.07437 [physics]. 3, 4" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 307, + 433, + 545, + 476 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 433, + 545, + 476 + ], + "spans": [ + { + "bbox": [ + 307, + 433, + 545, + 476 + ], + "type": "text", + "content": "[44] Luke Pfister and Yoram Bresler. Learning Filter Bank Sparsifying Transforms. IEEE Transactions on Signal Processing, 67(2):504-519, Jan. 2019. Conference Name: IEEE Transactions on Signal Processing. 2" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 307, + 478, + 545, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 478, + 545, + 521 + ], + "spans": [ + { + "bbox": [ + 307, + 478, + 545, + 521 + ], + "type": "text", + "content": "[45] Francesco Pittaluga, Zaid Tasneem, Justin Folden, Brevin Tilmon, Ayan Chakrabarti, and Sanjeev J. Koppal. Towards a MEMS-based Adaptive LIDAR, Oct. 2020. arXiv:2003.09545 [cs, eess]. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 307, + 522, + 545, + 555 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 522, + 545, + 555 + ], + "spans": [ + { + "bbox": [ + 307, + 522, + 545, + 555 + ], + "type": "text", + "content": "[46] Ralph Rasshofer, Martin Spies, and Hans Spies. Influences of weather phenomena on automotive laser radar systems. Advances in Radio Science, 9, 2011. 3, 4" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 307, + 557, + 545, + 588 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 557, + 545, + 588 + ], + "spans": [ + { + "bbox": [ + 307, + 557, + 545, + 588 + ], + "type": "text", + "content": "[47] Stephan R. Richter, Zeeshan Hayden, and Vladlen Koltun. Playing for Benchmarks, Sept. 2017. arXiv:1709.07322 [cs]. 3" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 307, + 591, + 545, + 646 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 591, + 545, + 646 + ], + "spans": [ + { + "bbox": [ + 307, + 591, + 545, + 646 + ], + "type": "text", + "content": "[48] Nicolas Robidoux, Luis E. Garcia Capel, Dong-eun Seo, Avinash Sharma, Federico Ariza, and Felix Heide. End-to-End High Dynamic Range Camera Pipeline Optimization. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 6297–6307, 2021. 2, 5, 6" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 307, + 647, + 545, + 712 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 647, + 545, + 712 + ], + "spans": [ + { + "bbox": [ + 307, + 647, + 545, + 712 + ], + "type": "text", + "content": "[49] German Ros, Laura Sellart, Joanna Materzynska, David Vazquez, and Antonio M. Lopez. The SYNTHIA Dataset: A Large Collection of Synthetic Images for Semantic Segmentation of Urban Scenes. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 3234-3243, June 2016. ISSN: 1063-6919. 3" + } + ] + } + ], + "index": 23 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 317, + 757 + ], + "type": "text", + "content": "13413" + } + ] + } + ], + "index": 25 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 9 + }, + { + "para_blocks": [ + { + "bbox": [ + 48, + 72, + 288, + 713 + ], + "type": "list", + "angle": 0, + "index": 12, + "blocks": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "spans": [ + { + "bbox": [ + 48, + 72, + 287, + 116 + ], + "type": "text", + "content": "[50] Christos Sakaridis, Dengxin Dai, and Luc Van Gool. Semantic foggy scene understanding with synthetic data. International Journal of Computer Vision, 126(9):973-992, 2018. 1" + } + ] + } + ], + "index": 0 + }, + { + "bbox": [ + 48, + 118, + 288, + 173 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 118, + 288, + 173 + ], + "spans": [ + { + "bbox": [ + 48, + 118, + 288, + 173 + ], + "type": "text", + "content": "[51] Haitham Seada and Kalyanmoy Deb. A Unified Evolutionary Optimization Procedure for Single, Multiple, and Many Objectives. IEEE Transactions on Evolutionary Computation, 20(3):358-369, June 2016. Conference Name: IEEE Transactions on Evolutionary Computation. 7" + } + ] + } + ], + "index": 1 + }, + { + "bbox": [ + 48, + 175, + 287, + 218 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 175, + 287, + 218 + ], + "spans": [ + { + "bbox": [ + 48, + 175, + 287, + 218 + ], + "type": "text", + "content": "[52] Shital Shah, Debadeepta Dey, Chris Lovett, and Ashish Kapoor. AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles, July 2017. arXiv:1705.05065 [cs]. 3" + } + ] + } + ], + "index": 2 + }, + { + "bbox": [ + 48, + 220, + 287, + 275 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 220, + 287, + 275 + ], + "spans": [ + { + "bbox": [ + 48, + 220, + 287, + 275 + ], + "type": "text", + "content": "[53] Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, and Hongsheng Li. PV-RCNN: Point-voxel feature set abstraction for 3D object detection. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1, 7" + } + ] + } + ], + "index": 3 + }, + { + "bbox": [ + 48, + 276, + 287, + 308 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 276, + 287, + 308 + ], + "spans": [ + { + "bbox": [ + 48, + 276, + 287, + 308 + ], + "type": "text", + "content": "[54] Dongeek Shin. Computational imaging with small numbers of photons. Thesis, Massachusetts Institute of Technology, 2016. Accepted: 2016-07-18T20:05:44Z. 3, 4" + } + ] + } + ], + "index": 4 + }, + { + "bbox": [ + 48, + 310, + 287, + 354 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 310, + 287, + 354 + ], + "spans": [ + { + "bbox": [ + 48, + 310, + 287, + 354 + ], + "type": "text", + "content": "[55] Andrea Simonelli, Samuel Rota Rota Bulò, Lorenzo Porzi, Manuel López-Antequera, and Peter Kontschieder. Disentangling monocular 3D object detection. arXiv preprint arXiv:1905.12365, 2019. 7" + } + ] + } + ], + "index": 5 + }, + { + "bbox": [ + 48, + 355, + 287, + 453 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 355, + 287, + 453 + ], + "spans": [ + { + "bbox": [ + 48, + 355, + 287, + 453 + ], + "type": "text", + "content": "[56] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Aleksei Timofeev, Scott Ettinger, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, and Dragomir Anguelov. Scalability in perception for autonomous driving: Waymo open dataset. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020. 1" + } + ] + } + ], + "index": 6 + }, + { + "bbox": [ + 48, + 455, + 287, + 521 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 455, + 287, + 521 + ], + "spans": [ + { + "bbox": [ + 48, + 455, + 287, + 521 + ], + "type": "text", + "content": "[57] Tao Sun, Mattia Segu, Janis Postels, Yuxuan Wang, Luc Van Gool, Bernt Schiele, Federico Tombari, and Fisher Yu. SHIFT: a synthetic driving dataset for continuous multi-task domain adaptation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 21371-21382, June 2022. 1, 3" + } + ] + } + ], + "index": 7 + }, + { + "bbox": [ + 48, + 522, + 287, + 567 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 522, + 287, + 567 + ], + "spans": [ + { + "bbox": [ + 48, + 522, + 287, + 567 + ], + "type": "text", + "content": "[58] Maxime Tremblay, Shirsendu S. Halder, Raoul de Charette, and Jean-François Lalonde. Rain rendering for evaluating and improving robustness to bad weather. International Journal of Computer Vision (IJCV), 126, 2021. 1" + } + ] + } + ], + "index": 8 + }, + { + "bbox": [ + 48, + 568, + 287, + 633 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 568, + 287, + 633 + ], + "spans": [ + { + "bbox": [ + 48, + 568, + 287, + 633 + ], + "type": "text", + "content": "[59] Ethan Tseng, Ali Mosleh, Fahim Mannan, Karl St-Arnaud, Avinash Sharma, Yifan Peng, Alexander Braun, Derek Nowrouzezahrai, Jean-Francois Lalonde, and Felix Heide. Differentiable compound optics and processing pipeline optimization for end-to-end camera design. ACM Transactions on Graphics (TOG), 40(2):1-19, 2021. 2" + } + ] + } + ], + "index": 9 + }, + { + "bbox": [ + 48, + 635, + 287, + 689 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 635, + 287, + 689 + ], + "spans": [ + { + "bbox": [ + 48, + 635, + 287, + 689 + ], + "type": "text", + "content": "[60] Ethan Tseng, Felix Yu, Yuting Yang, Fahim Mannan, Karl ST. Arnaud, Derek Nowrouzezahrai, Jean-François Lalonde, and Felix Heide. Hyperparameter optimization in black-box image processing using differentiable proxies. ACM Transactions on Graphics, 38(4):1-14, Aug. 2019. 2" + } + ] + } + ], + "index": 10 + }, + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "spans": [ + { + "bbox": [ + 48, + 691, + 287, + 713 + ], + "type": "text", + "content": "[61] G. Turin. An introduction to matched filters. IRE Transactions on Information Theory, 6(3):311-329, 1960. 4" + } + ] + } + ], + "index": 11 + } + ], + "sub_type": "ref_text" + }, + { + "bbox": [ + 307, + 73, + 546, + 599 + ], + "type": "list", + "angle": 0, + "index": 25, + "blocks": [ + { + "bbox": [ + 307, + 73, + 482, + 84 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 73, + 482, + 84 + ], + "spans": [ + { + "bbox": [ + 307, + 73, + 482, + 84 + ], + "type": "text", + "content": "[62] Velodyne. Hdl-64e user's manual, 2007. 5" + } + ] + } + ], + "index": 13 + }, + { + "bbox": [ + 307, + 86, + 546, + 129 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 86, + 546, + 129 + ], + "spans": [ + { + "bbox": [ + 307, + 86, + 546, + 129 + ], + "type": "text", + "content": "[63] Yash Vesikar, Kalyanmoy Deb, and Julian Blank. Reference Point Based NSGA-III for Preferred Solutions. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1587–1594, Nov. 2018. 7" + } + ] + } + ], + "index": 14 + }, + { + "bbox": [ + 307, + 130, + 546, + 184 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 307, + 130, + 546, + 184 + ], + "spans": [ + { + "bbox": [ + 307, + 130, + 546, + 184 + ], + "type": "text", + "content": "[64] F Villa, B Markovic, S Bellisai, D Bronzi, A Tosi, F Zappa, S Tisa, D Durini, S Weyers, U Paschen, et al. SPAD smart pixel for time-of-flight and time-correlated single-photon counting measurements. IEEE Photonics Journal, 4(3):795-804, 2012. 1" + } + ] + } + ], + "index": 15 + }, + { + "bbox": [ + 308, + 186, + 546, + 251 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 186, + 546, + 251 + ], + "spans": [ + { + "bbox": [ + 308, + 186, + 546, + 251 + ], + "type": "text", + "content": "[65] Niclas Vödisch, Ozan Unal, Ke Li, Luc Van Gool, and Dengxin Dai. End-to-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization. IEEE Robotics and Automation Letters, 7(2):2242-2249, Apr. 2022. Conference Name: IEEE Robotics and Automation Letters. 3" + } + ] + } + ], + "index": 16 + }, + { + "bbox": [ + 308, + 252, + 546, + 285 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 252, + 546, + 285 + ], + "spans": [ + { + "bbox": [ + 308, + 252, + 546, + 285 + ], + "type": "text", + "content": "[66] Andrew Wallace, Abderrahim Halimi, and Gerald Buller. Full waveform LiDAR for adverse weather conditions. IEEE Transactions on Vehicular Technology, 69, 2020. 1" + } + ] + } + ], + "index": 17 + }, + { + "bbox": [ + 308, + 286, + 546, + 318 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 286, + 546, + 318 + ], + "spans": [ + { + "bbox": [ + 308, + 286, + 546, + 318 + ], + "type": "text", + "content": "[67] Dingkang Wang, Connor Watkins, and Huikai Xie. MEMS mirrors for LiDAR: A review. Micromachines, 11(5), 2020. 1" + } + ] + } + ], + "index": 18 + }, + { + "bbox": [ + 308, + 320, + 546, + 374 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 320, + 546, + 374 + ], + "spans": [ + { + "bbox": [ + 308, + 320, + 546, + 374 + ], + "type": "text", + "content": "[68] Jingkang Wang, Ava Pun, James Tu, Sivabalan Manivasagam, Abbas Sadat, Sergio Casas, Mengye Ren, and Raquel Urtasun. AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles, Jan. 2022. arXiv:2101.06549 [cs]. 3" + } + ] + } + ], + "index": 19 + }, + { + "bbox": [ + 308, + 376, + 546, + 430 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 376, + 546, + 430 + ], + "spans": [ + { + "bbox": [ + 308, + 376, + 546, + 430 + ], + "type": "text", + "content": "[69] Xinshuo Weng, Yunze Man, Jinhyung Park, Ye Yuan, Dazhi Cheng, Matthew O'Toole, and Kris Kitani. All-In-One Drive: A Large-Scale Comprehensive Perception Dataset with High-Density Long-Range Point Clouds. arXiv, 2021. 1, 3, 4" + } + ] + } + ], + "index": 20 + }, + { + "bbox": [ + 308, + 432, + 546, + 464 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 432, + 546, + 464 + ], + "spans": [ + { + "bbox": [ + 308, + 432, + 546, + 464 + ], + "type": "text", + "content": "[70] George Williams. Optimization of eyesafe avalanche photodiode LiDAR for automobile safety and autonomous navigation systems. Optical Engineering, 56(3):1-9, 2017. 1" + } + ] + } + ], + "index": 21 + }, + { + "bbox": [ + 308, + 466, + 546, + 531 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 466, + 546, + 531 + ], + "spans": [ + { + "bbox": [ + 308, + 466, + 546, + 531 + ], + "type": "text", + "content": "[71] Chyuan-Tyng Wu, Leo F. Isikdogan, Sushma Rao, Bhavin Nayak, Timo Gerasimow, Aleksandar Sutic, Liron Ainkedem, and Gilad Michael. VisionISP: Repurposing the image signal processor for computer vision applications. In IEEE International Conference on Image Processing (ICIP), pages 4624-4628, 2019. 2" + } + ] + } + ], + "index": 22 + }, + { + "bbox": [ + 308, + 533, + 546, + 564 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 533, + 546, + 564 + ], + "spans": [ + { + "bbox": [ + 308, + 533, + 546, + 564 + ], + "type": "text", + "content": "[72] Ji Zhang and Sanjiv Singh. LOAM: LiDAR odometry and mapping in real-time. Robotics: Science and Systems Conference (RSS), 2014. 1" + } + ] + } + ], + "index": 23 + }, + { + "bbox": [ + 308, + 567, + 546, + 599 + ], + "type": "ref_text", + "angle": 0, + "lines": [ + { + "bbox": [ + 308, + 567, + 546, + 599 + ], + "spans": [ + { + "bbox": [ + 308, + 567, + 546, + 599 + ], + "type": "text", + "content": "[73] Qingfu Zhang and Hui Li. Moea/d: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11:712-731, 2007. 2" + } + ] + } + ], + "index": 24 + } + ], + "sub_type": "ref_text" + } + ], + "discarded_blocks": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "page_number", + "angle": 0, + "lines": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "spans": [ + { + "bbox": [ + 294, + 749, + 318, + 757 + ], + "type": "text", + "content": "13414" + } + ] + } + ], + "index": 26 + } + ], + "page_size": [ + 612, + 792 + ], + "page_idx": 10 + } + ], + "_backend": "vlm", + "_version_name": "2.6.4" +} \ No newline at end of file